= len(movables)):\\n self.gm.reverseMove(self.currentState.requiredMovable)\\n self.currentState = self.currentState.parent\\n\\n return False\",\n \"def winningMove():\\r\\n\\tglobal turn, tile1, tile2, tile3, tile4, tile5, tile6, tile7, tile8, tile9\\r\\n\\r\\n\\tnoWin=True\\r\\n\\tmove=False\\r\\n\\tif turn==\\\"Player1\\\":\\r\\n\\t\\tif validMove(1):\\r\\n\\t\\t\\ttile1+=1\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove=1\\t\\r\\n\\t\\t\\ttile1+=-1\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\r\\n\\t\\tif validMove(2):\\r\\n\\t\\t\\ttile2+=1\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 2\\r\\n\\t\\t\\ttile2+=-1\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\r\\n\\t\\tif validMove(3):\\r\\n\\t\\t\\ttile3+=1\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 3\\r\\n\\t\\t\\ttile3+=-1\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\t\\t\\t\\r\\n\\t\\tif validMove(4):\\r\\n\\t\\t\\ttile4+=1\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 4\\t\\r\\n\\t\\t\\ttile4+=-1\\t\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\t\\t\\r\\n\\t\\tif validMove(5):\\r\\n\\t\\t\\ttile5+=1\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 5\\t\\t\\r\\n\\t\\t\\ttile5+=-1\\t\\t\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\t\\r\\n\\t\\tif validMove(6):\\r\\n\\t\\t\\ttile6+=1\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 6\\t\\r\\n\\t\\t\\ttile6+=-1\\t\\t\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\t\\r\\n\\t\\tif validMove(7):\\r\\n\\t\\t\\ttile7+=1\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 7\\t\\r\\n\\t\\t\\ttile7+=-1\\t\\t\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\t\\r\\n\\t\\tif validMove(8):\\r\\n\\t\\t\\ttile8+=1\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 8\\t\\r\\n\\t\\t\\ttile8+=-1\\t\\t\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\t\\r\\n\\t\\tif validMove(9):\\r\\n\\t\\t\\ttile9+=1\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 9\\t\\t\\r\\n\\t\\t\\ttile9+=-1\\t\\t\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\r\\n\\r\\n\\telif turn==\\\"Player2\\\":\\r\\n\\t\\tif validMove(1):\\r\\n\\t\\t\\ttile1+=2\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 1\\t\\t\\t\\t\\r\\n\\t\\t\\ttile1+=-2\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\r\\n\\t\\tif validMove(2):\\r\\n\\t\\t\\ttile2+=2\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 2\\r\\n\\t\\t\\ttile2+=-2\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\r\\n\\t\\tif validMove(3):\\r\\n\\t\\t\\ttile3+=2\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 3\\r\\n\\t\\t\\ttile3+=-2\\t\\t\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\t\\r\\n\\t\\tif validMove(4):\\r\\n\\t\\t\\ttile4+=2\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 4\\t\\r\\n\\t\\t\\ttile4+=-2\\t\\t\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\t\\r\\n\\t\\tif validMove(5):\\r\\n\\t\\t\\ttile5+=2\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 5\\t\\r\\n\\t\\t\\ttile5+=-2\\t\\t\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\t\\r\\n\\t\\tif validMove(6):\\r\\n\\t\\t\\ttile6+=2\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 6\\t\\r\\n\\t\\t\\ttile6+=-2\\t\\t\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\t\\r\\n\\t\\tif validMove(7):\\r\\n\\t\\t\\ttile7+=2\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 7\\t\\r\\n\\t\\t\\ttile7+=-2\\t\\t\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\t\\r\\n\\t\\tif validMove(8):\\r\\n\\t\\t\\ttile8+=2\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 8\\t\\r\\n\\t\\t\\ttile8+=-2\\t\\t\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\t\\r\\n\\t\\tif validMove(9):\\r\\n\\t\\t\\ttile9+=2\\r\\n\\t\\t\\tif win():\\r\\n\\t\\t\\t\\tnoWin=False\\r\\n\\t\\t\\t\\tmove= 9\\r\\n\\t\\t\\ttile9+=-2\\t\\t\\r\\n\\t\\t\\tif move:\\r\\n\\t\\t\\t\\treturn move\\t\\r\\n\\tif noWin:\\r\\n\\t\\treturn False\",\n \"def solveOneStep(self):\\n ### Student code goes here\\n # Mark this move as explored\\n self.visited[self.currentState] = True\\n self.visited_states.append(self.currentState.state)\\n\\n # Get move to make\\n movables = self.gm.getMovables()\\n # print(\\\"EXPLORING GAME STATE \\\" + str(self.gm.getGameState()) + \\\"---------------------------------------------------------\\\")\\n to_move = self.currentState.nextChildToVisit # movables index\\n # print(\\\"depth \\\", self.currentState.depth)\\n\\n # Return if done\\n if self.currentState.state == self.victoryCondition:\\n # print(\\\"DONE\\\")\\n return True\\n\\n # If current state has no children, make children\\n if not self.currentState.children:\\n for movable_statement in movables:\\n # Make the move\\n # print(\\\"implementing move \\\", movable_statement)\\n self.gm.makeMove(movable_statement)\\n\\n # Create a new state with this move made\\n new_state = self.gm.getGameState()\\n # print (\\\"new state \\\", new_state)\\n\\n # If the new state hasn't been visited and isn't in the queue then add it as a child and to the queue\\n if (new_state not in self.visited_states):\\n new_gs = GameState(new_state, self.currentState.depth + 1, movable_statement)\\n new_gs.parent = self.currentState\\n self.currentState.children.append(new_gs)\\n self.currentState.nextChildToVisit = to_move + 1\\n self.visited[new_gs] = True\\n self.visited_states.append(new_state)\\n self.gs_queue.append(new_gs)\\n\\n self.gm.reverseMove(movable_statement)\\n\\n # Return false if no more to explore\\n if not self.gs_queue:\\n return False\\n\\n # Revert to state at when current and next start to change\\n root_curr = self.currentState\\n self.currentState = self.gs_queue.popleft()\\n root_new = self.currentState\\n\\n # Backtrack to when current node and new node start to diverge\\n if root_new.depth == root_curr.depth:\\n while root_curr.state != root_new.state:\\n self.gm.reverseMove(root_curr.requiredMovable)\\n root_curr = root_curr.parent\\n root_new = root_new.parent\\n else:\\n while root_curr.requiredMovable:\\n self.gm.reverseMove(root_curr.requiredMovable)\\n root_curr = root_curr.parent\\n\\n # Return game master to state that we are exploring\\n # Find path between root and current state\\n path = []\\n currNode = self.currentState\\n while currNode != root_curr:\\n path.append(currNode.requiredMovable)\\n currNode = currNode.parent\\n\\n # Created backwards path, now make moves from root to current state\\n path.reverse()\\n for movable_statement in path:\\n self.gm.makeMove(movable_statement)\\n\\n return False\",\n \"def make_move(self):\\n\\n # get relavent information\\n affinity = self.get_affinity()\\n sample_space = self.get_game_space()\\n depth_limit = self.__search_depth\\n\\n # run a minimax search and get the best value\\n bestval = MinimaxTree.minimax(self, sample_space, affinity, depth_limit, True)\\n if bestval[0] is None: bestval = ((0,6),'x', 0)\\n\\n # print the number of nodes expanded \\n print(self.nodes_expanded)\\n\\n # make the move found by the search \\n self.get_game_space().set_tile(bestval[0][0], bestval[0][1], affinity)\",\n \"def action(new_board, move, player):\\r\\n \\r\\n global nodes_generated \\r\\n global min_prune\\r\\n global max_prune\\r\\n global max_depth\\r\\n \\r\\n if player == 1:\\r\\n for i in range(0,len(new_board.white)):\\r\\n if new_board.white[i] == move[0]:\\r\\n new_board.white[i] = move[1]\\r\\n if len(move) == 3:\\r\\n new_board.black.remove(move[2])\\r\\n elif player == -1:\\r\\n for i in range(0,len(new_board.black)):\\r\\n if new_board.black[i] == move[0]:\\r\\n new_board.black[i] = move[1]\\r\\n if len(move) == 3:\\r\\n new_board.white.remove(move[2])\\r\\n return new_board\",\n \"def undo_move(self):\\r\\n if len(self.moveLog) != 0:\\r\\n move = self.moveLog.pop()\\r\\n self.board[move.sr][move.sc] = move.pieceMoved\\r\\n self.board[move.er][move.ec] = move.pieceCaptured\\r\\n self.turn_white = not self.turn_white\\r\\n\\r\\n # king pos\\r\\n if move.pieceMoved == 'wk':\\r\\n self.wKingPos = (move.sr, move.sc)\\r\\n elif move.pieceMoved == 'bk':\\r\\n self.bKingPos = (move.sr, move.sc)\\r\\n\\r\\n # enpassant\\r\\n if move.isEnpassantMove:\\r\\n self.board[move.er][move.ec] = \\\"--\\\"\\r\\n self.board[move.sr][move.ec] = move.pieceCaptured\\r\\n self.enpas_pos = (move.er, move.ec)\\r\\n\\r\\n # pawn x2\\r\\n if move.pieceMoved[1] == \\\"p\\\" and abs(move.sr - move.er) == 2:\\r\\n self.enpas_pos = ()\\r\\n\\r\\n # castle rights\\r\\n self.castleRightsLog.pop()\\r\\n self.cr_castle_r = self.castleRightsLog[-1]\\r\\n\\r\\n # castle moves\\r\\n if move.castle:\\r\\n if move.ec - move.sc == 2:\\r\\n self.board[move.er][move.ec + 1] = self.board[move.er][move.ec - 1]\\r\\n self.board[move.er][move.ec - 1] = '--'\\r\\n else:\\r\\n self.board[move.er][move.ec - 2] = self.board[move.er][move.ec + 1]\\r\\n self.board[move.er][move.ec + 1] = '--'\",\n \"def black_wins(self):\\n possible_dirs = [[1, 0], [0, 1], [-1, 0], [0, -1], [1, 1], [-1, 1], [-1, -1], [1, -1]]\\n toMoves = []\\n king_spot = None\\n for row in range(8):\\n for col in range(8):\\n if self.board.squares[row][col] == '♔': # White king symbol\\n king_spot = (row, col)\\n break\\n if king_spot is not None:\\n break\\n\\n # Black doesn't win if it isn't checking the king\\n if king_spot not in self.blackThreat.keys():\\n return False\\n\\n for direction in possible_dirs:\\n toRow = king_spot[0] + direction[0]\\n toCol = king_spot[1] + direction[1]\\n\\n if ((0 <= toRow <= 7) and (0 <= toCol <= 7)):\\n toMoves.append((toRow, toCol))\\n\\n # check if we can move out of the mate\\n for move in toMoves:\\n piece = self.board.squares[move[0]][move[1]]\\n piece_color = self.pieces.piece_color(piece)\\n if piece_color != \\\"white\\\" and (move[0], move[1]) not in self.blackThreat.keys():\\n return False\\n\\n # check can I take the attacker\\n attack_spot = None\\n\\n for move in self.blackMoves:\\n if move[1] == king_spot:\\n attack_spot = move[0]\\n break\\n\\n # If there is a piece attacking, check the possible moves for black to see if one of them is a kill move\\n if attack_spot is not None:\\n attack_dir = (attack_spot[0] - king_spot[0], attack_spot[1] - king_spot[1])\\n for move in self.whiteMoves:\\n if move[1] == attack_spot:\\n # Check if the attacker can be taken. It can be taken by a non-king piece, or by the king if\\n # the spot is not protected\\n if (self.board.squares[move[0][0]][move[0][1]] == ChessPiece.W_KING and \\\\\\n (move[1]) not in self.blackThreat.keys()) or \\\\\\n (self.board.squares[move[0][0]][move[0][1]] != ChessPiece.W_KING):\\n return False\\n\\n piece = self.board.squares[attack_spot[0]][attack_spot[1]]\\n if piece == '♛' or piece == '♜' or piece == '♝':\\n h_dir = attack_dir[0]\\n v_dir = attack_dir[1]\\n check_squares = []\\n\\n if h_dir == 0:\\n for c in range(0, v_dir, int(v_dir / abs(v_dir))):\\n check_squares.append((king_spot[0], king_spot[1] + c))\\n elif v_dir == 0:\\n for r in range(0, h_dir, int(h_dir / abs(h_dir))):\\n check_squares.append((r + king_spot[0], king_spot[1]))\\n else:\\n rows = []\\n cols = []\\n for r in range(0, h_dir, int(h_dir / abs(h_dir))):\\n rows.append(r)\\n for c in range(0, v_dir, int(v_dir / abs(v_dir))):\\n cols.append(c)\\n for n in range(len(cols)):\\n check_squares.append((rows[n] + king_spot[0], cols[n] + king_spot[1]))\\n\\n for move in self.whiteMoves:\\n if move[1] in check_squares and move[0] != king_spot:\\n # print(move[0], move[1], king_spot, check_squares)\\n return False\\n\\n return True\",\n \"def solve(self):\\n while self.character.path[-1] != 88:\\n n = self.next_move()\\n if n is None:\\n self.character.path += ['Error: Could not find full path (budget does not suffice or unreachable).']\\n break\\n self.character.path += [n]\\n self.updated_occupied_locations()\\n self.currentTurn += 1\",\n \"def white_wins(self):\\n possible_dirs = [[1, 0], [0, 1], [-1, 0], [0, -1], [1, 1], [-1, 1], [-1, -1], [1, -1]]\\n toMoves = []\\n king_spot = None\\n for row in range(8):\\n for col in range(8):\\n if self.board.squares[row][col] == ChessPiece.B_KING: # Black king symbol\\n king_spot = (row, col)\\n break\\n if king_spot is not None:\\n break\\n\\n # White doesn't win if it isn't threatening the king\\n if king_spot not in self.whiteThreat.keys():\\n return False\\n\\n for direction in possible_dirs:\\n toRow = king_spot[0] + direction[0]\\n toCol = king_spot[1] + direction[1]\\n\\n if ((0 <= toRow <= 7) and (0 <= toCol <= 7)):\\n toMoves.append((toRow, toCol))\\n\\n # check if we can move out of the mate\\n for move in toMoves:\\n piece = self.board.squares[move[0]][move[1]]\\n piece_color = self.pieces.piece_color(piece)\\n if piece_color != \\\"black\\\" and (move[0], move[1]) not in self.whiteThreat.keys():\\n return False\\n\\n # check can I take the attacker\\n attack_spot = None\\n\\n for move in self.whiteMoves:\\n if move[1] == king_spot:\\n attack_spot = move[0]\\n break\\n\\n # If there is a piece attacking, check the possible moves for black to see if one of them is a kill move\\n if attack_spot is not None:\\n attack_dir = (attack_spot[0] - king_spot[0], attack_spot[1] - king_spot[1])\\n for move in self.blackMoves:\\n if move[1] == attack_spot:\\n # Check if the attacker can be taken. It can be taken by a non-king piece, or by the king if\\n # the spot is not protected\\n if (self.board.squares[move[0][0]][move[0][1]] == ChessPiece.B_KING and \\\\\\n (move[1]) not in self.whiteThreat.keys()) or \\\\\\n (self.board.squares[move[0][0]][move[0][1]] != ChessPiece.B_KING):\\n return False\\n\\n # Identify the attacking piece\\n piece = self.board.squares[attack_spot[0]][attack_spot[1]]\\n if piece == '♕' or piece == '♖' or piece == '♗':\\n h_dir = attack_dir[0]\\n v_dir = attack_dir[1]\\n check_squares = []\\n\\n if h_dir == 0:\\n for c in range(0, v_dir, int(v_dir / abs(v_dir))):\\n check_squares.append((king_spot[0], king_spot[1] + c))\\n elif v_dir == 0:\\n for r in range(0, h_dir, int(h_dir / abs(h_dir))):\\n check_squares.append((r + king_spot[0], king_spot[1]))\\n else:\\n rows = []\\n cols = []\\n for r in range(0, h_dir, int(h_dir / abs(h_dir))):\\n rows.append(r)\\n for c in range(0, v_dir, int(v_dir / abs(v_dir))):\\n cols.append(c)\\n for n in range(len(cols)):\\n check_squares.append((rows[n] + king_spot[0], cols[n] + king_spot[1]))\\n\\n for move in self.blackMoves:\\n if move[1] in check_squares and move[0] != king_spot:\\n return False\\n\\n return True\",\n \"def test_move_over_terrain(self):\\n # move over Water (0 extra)\\n b1 = board.Board(self.small_ter)\\n start = np.array((0, 1), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 1\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 1)\\n #\\n # move over Lava (0 extra)\\n start = np.array((5, 4), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 6\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 1)\\n #\\n # move over Barrier (illegal)\\n start = np.array((2, 3), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 0\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertFalse(isvalid)\\n #\\n # move over Rock (0 extra)\\n start = np.array((2, 3), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 2\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 1)\",\n \"def getPossibleMoves(self): # called to get possible positions this piece can go\\r\\n \\r\\n moves = {}\\r\\n\\r\\n ids = []\\r\\n\\r\\n for piece in self.board.pieces.values():\\r\\n if piece.name == \\\"empty\\\":\\r\\n piece.glow = False\\r\\n piece.ready = False\\r\\n\\r\\n self.piece = self\\r\\n\\r\\n def check(direction=\\\"left\\\", heading=\\\"north\\\", x=None, y=None):\\r\\n piece = self.piece\\r\\n if direction == \\\"left\\\": x -= 50\\r\\n else: x += 50\\r\\n\\r\\n if heading == \\\"north\\\": y -= 50\\r\\n else: y += 50\\r\\n\\r\\n if (x, y) in self.board.pieces: # position is empty\\r\\n empty = self.board.getPiece((x, y))\\r\\n empty.glow = True\\r\\n old, new, obj = (direction, heading), (x, y), piece\\r\\n identity = self.getRandomID(ids) # get an ID for the move\\r\\n moves[identity] = old, new, obj\\r\\n\\r\\n if piece.isKing: # piece is a king, so go on\\r\\n check(direction, heading, x, y)\\r\\n else: # its not empty, so check if its comrade\\r\\n x1, y1 = x+25, y+25\\r\\n piece2 = self.board.getPiece((x1, y1))\\r\\n try:\\r\\n if piece.isComrade(piece2):# piece is comrade so return\\r\\n return\\r\\n else: # piece is not comrade, so check empty\\r\\n if direction == \\\"left\\\": x2 = x1-25-50\\r\\n else: x2 = x1-25+50\\r\\n\\r\\n if heading == \\\"north\\\": y2 = y1-25-50\\r\\n else: y2 = y1-25+50\\r\\n\\r\\n if (x2, y2) in self.board.pieces: # its empty, so notify player\\r\\n empty = self.board.getPiece((x2, y2))\\r\\n empty.glow = True\\r\\n empty.ready = True\\r\\n\\r\\n old, new, obj = (direction, heading), (x2, y2), piece2\\r\\n identity = self.getRandomID(ids)\\r\\n moves[identity] = old, new, obj\\r\\n\\r\\n check(direction, heading, piece2.x-25, piece2.y-25)\\r\\n check(direction, heading, x2, y2)\\r\\n \\r\\n # check empty or comrade again\\r\\n if direction == \\\"left\\\": x3 = x2-50\\r\\n else: x3 = x2+50\\r\\n\\r\\n if heading == \\\"north\\\": y3 = y2-50\\r\\n else: y3 = y2+50\\r\\n\\r\\n if (x3, y3) in self.board.pieces: # positon(address) is empty\\r\\n return\\r\\n else: # there is a piece, so check if comrade, stop, if not comrade continue\\r\\n x3+=25\\r\\n y3+= 25\\r\\n\\r\\n piece3 = self.board.getPiece((x3, y3))\\r\\n if piece3.isComrade(piece2): # comrades, so stop\\r\\n return\\r\\n else: # not comrades, so continue\\r\\n self.piece = piece3\\r\\n check(direction, heading, x, y)\\r\\n\\r\\n #self.piece = piece2\\r\\n \\r\\n #check(direction, heading, x2, y2) # keep searching\\r\\n else: # its not empty, so return\\r\\n return\\r\\n except:\\r\\n pass\\r\\n\\r\\n if self.piece.name == \\\"white\\\": direction = \\\"north\\\"\\r\\n else: direction = \\\"south\\\"\\r\\n \\r\\n check(\\\"left\\\", direction, self.piece.x-25, self.piece.y-25)\\r\\n check(\\\"right\\\", direction, self.piece.x-25, self.piece.y-25)\\r\\n \\r\\n if self.piece.isKing:\\r\\n if self.piece.name == \\\"white\\\": heading = \\\"south\\\"\\r\\n else: heading = \\\"north\\\"\\r\\n \\r\\n check(\\\"left\\\", heading, self.piece.x-25, self.piece.y-25)\\r\\n check(\\\"right\\\", heading, self.piece.x-25, self.piece.y-25)\\r\\n\\r\\n if self.piece.name == \\\"white\\\":\\r\\n eatMoves = self.board.game.thinkEatMoves(moves, \\\"person\\\")\\r\\n if eatMoves is not None:\\r\\n return eatMoves\\r\\n\\r\\n return moves\",\n \"def move(self, i, j):\\n board_cpy = copy.deepcopy(self.board)\\n piece = self.board[self.selected[0]][self.selected[1]]\\n if piece == 0:\\n return False\\n elif piece.player != self.active_player:\\n print(\\\"it's not your turn\\\", piece.player, self.active_player)\\n return False\\n elif self.board[i][j] != 0:\\n if piece.player == self.board[i][j].player:\\n print(\\\"can't capture your own pieces\\\")\\n return False\\n if i==self.selected[0] and piece.direction == 0:\\n if piece.master == False:\\n if (i + j - sum(self.selected))%2 == 0:\\n print(\\\"must choose a different color\\\")\\n return False\\n if j < self.selected[1]:\\n spaces_between = [self.board[i][y] for y in range(j+1, self.selected[1])]\\n else:\\n spaces_between = [self.board[i][y] for y in range(self.selected[1]+1, j)]\\n print(spaces_between)\\n for p in spaces_between:\\n if p != 0:\\n print(\\\"can't jump pieces\\\")\\n return False\\n piece.direction = 1\\n self.board[i][j] = piece\\n self.board[self.selected[0]][self.selected[1]] = 0\\n elif j==self.selected[1] and piece.direction == 1:\\n if piece.master == False:\\n if (i + j - sum(self.selected))%2 == 0:\\n print(\\\"must choose a different color\\\")\\n return False\\n if i < self.selected[0]:\\n spaces_between = [self.board[x][j] for x in range(i+1, self.selected[0])]\\n else:\\n spaces_between = [self.board[x][j] for x in range(self.selected[0]+1, i)] \\n print(spaces_between)\\n for p in spaces_between:\\n if p != 0:\\n print(\\\"can't jump pieces\\\")\\n return False\\n piece.direction = 0\\n self.board[i][j] = piece\\n self.board[self.selected[0]][self.selected[1]] = 0\\n else:\\n print(\\\"Invalid movement\\\")\\n return False\\n if self.board[i][j] == 0:\\n print(\\\"Something wrong happened\\\")\\n return False\\n elif (i==0 or i==self.board_size-1) and (j==0 or j==self.board_size-1):\\n self.board[i][j].master = True\\n self.active_player = (self.active_player+1)%2\\n self.sequence += [board_cpy]\\n return True\",\n \"def make_move(self):\\n\\n # get relavent information\\n affinity = self.get_affinity()\\n sample_space = self.get_game_space()\\n depth_limit = self.__search_depth\\n\\n # run a minimax search and get the best value\\n bestval = MinimaxTree.alphabeta(self, sample_space, affinity, depth_limit, -10000, 10001, True)\\n if bestval[0] is None: bestval = ((0,6),'x', 0)\\n\\n # print the number of nodes expanded \\n print(self.nodes_expanded)\\n\\n # make the move found by the search \\n self.get_game_space().set_tile(bestval[0][0], bestval[0][1], affinity)\",\n \"def move(self): # AH note. Swich move with extra_steps?\\n if self.adjustment < 0:\\n self.position += self.extra_steps\\n super().move()\\n self.no_moves += 1\\n # Do the regular move\",\n \"def _move_forward(self):\\n\\t\\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\tif(self.y<=798):\\n\\t\\t\\tself.y = self.y+1\\n\\t\\t\\tif Board.board[self.x][self.y]=='0':\\n\\t\\t\\t\\tMario.score += 1\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='P':\\n\\t\\t\\t\\tMario.lives+=1\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_1-up.wav\\\"])\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='A':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\t\\t\\t\\tMario.attack = 1\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_powerup.wav\\\"])\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='@':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tMario.lives-=1\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_mariodie.wav\\\"])\\n\\t\\t\\t\\tif Mario.lives<=0:\\n\\t\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_gameover.wav\\\"])\\n\\t\\t\\t\\t\\treturn \\\"exit\\\"\\n\\t\\t\\t\\tos.system('clear')\\n\\t\\t\\t\\tprint(\\\"\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tNumber of Mario left\\\",Mario.lives)\\n\\t\\t\\t\\tMario.respawn(self.x,self.y)\\n\\t\\t\\t\\ttime.sleep(2)\\n\\t\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\n\\t\\t\\telif(Board.board[self.x][self.y]=='/'):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x-1][self.y]='M'\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='I':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_stage_clear.wav\\\"])\\n\\t\\t\\t\\tBoard.bonus_round()\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='K':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_stage_clear.wav\\\"])\\n\\t\\t\\t\\tenemy.boss_round()\\n\\n\\t\\t\\telif(Board.board[self.x][self.y] in obstacles):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y-1]='M'\\n\\n\\t\\t\\telif((Board.board[self.x+1][self.y-1]=='/' or Board.board[self.x+1][self.y-1]=='T') and Board.board[self.x+1][self.y]==' '):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y+1]='M'\\n\\t\\t\\t\\tMario.go_down(self)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\n\\t\\tif( self.y-1 >= ((Board.prev_j+Board.prev_k)/2) ):\\n\\t\\t\\tos.system('clear')\\n\\t\\t\\tBoard.prev_j += 1 \\n\\t\\t\\tBoard.prev_k += 1\\n\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\telse:\\n\\t\\t\\tos.system('clear')\\n\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\",\n \"def make_move(self, move, check_valid=True):\\r\\n self.board[move.sr][move.sc] = \\\"--\\\"\\r\\n self.board[move.er][move.ec] = move.pieceMoved\\r\\n self.moveLog.append(move)\\r\\n self.turn_white = not self.turn_white\\r\\n if move.pieceMoved == 'wk':\\r\\n self.wKingPos = (move.er, move.ec)\\r\\n elif move.pieceMoved == 'bk':\\r\\n self.bKingPos = (move.er, move.ec)\\r\\n\\r\\n if move.isEnpassantMove:\\r\\n self.board[move.sr][move.ec] = \\\"--\\\"\\r\\n\\r\\n if move.pieceMoved[1] == 'p' and abs(move.sr - move.er) == 2:\\r\\n self.enpas_pos = ((move.er + move.sr) // 2, move.ec)\\r\\n else:\\r\\n self.enpas_pos = ()\\r\\n\\r\\n if move.isPawnPromotion and not check_valid:\\r\\n promoted_piece = \\\"a\\\"\\r\\n while promoted_piece not in ('q', 'r', 'b', 'n'):\\r\\n promoted_piece = input(\\\"Promote to q, r, b, or n: \\\")\\r\\n self.board[move.er][move.ec] = move.pieceMoved[0] + promoted_piece\\r\\n\\r\\n # castle\\r\\n if move.castle:\\r\\n if move.ec - move.sc == 2:\\r\\n self.board[move.er][move.ec - 1] = self.board[move.er][move.ec + 1]\\r\\n self.board[move.er][move.ec + 1] = '--'\\r\\n else:\\r\\n self.board[move.er][move.ec + 1] = self.board[move.er][move.ec - 2]\\r\\n self.board[move.er][move.ec - 2] = '--'\\r\\n\\r\\n # castle rights on rook, king move\\r\\n self.update_castle_rights(move)\\r\\n self.castleRightsLog.append(CastleRights(self.cr_castle_r.wks, self.cr_castle_r.bks,\\r\\n self.cr_castle_r.wqs, self.cr_castle_r.bqs))\",\n \"def get_ai_move_minimax(self, gamestate, depth, current_player):\\n multiplier = 1 if current_player else -1\\n # Base case\\n if depth == 0:\\n return (\\n self.evaluate_board(\\n gamestate.board,\\n gamestate.white_checkmate,\\n gamestate.black_checkmate,\\n gamestate.stalemate,\\n )\\n * multiplier\\n )\\n\\n best_score = self.BLACK_CHECKMATE\\n for move in gamestate.get_valid_moves():\\n # Execute the move\\n (current_row, current_column), (new_row, new_column) = move\\n current_piece = gamestate.board.board[current_row][current_column]\\n piece_at_new_square = gamestate.board.board[new_row][new_column]\\n\\n gamestate.board.board[current_row][current_column] = None\\n gamestate.board.board[new_row][new_column] = current_piece\\n\\n # Update King's location\\n if isinstance(current_piece, King):\\n if current_player:\\n gamestate.white_king_location = (new_row, new_column)\\n else:\\n gamestate.black_king_location = (new_row, new_column)\\n\\n # Execute pawn promotion\\n elif isinstance(current_piece, Pawn):\\n if current_piece.colour and new_row == 0:\\n new_piece = Queen(new_row, new_column, True)\\n gamestate.board.white_pieces.remove(current_piece)\\n gamestate.board.white_pieces.append(new_piece)\\n gamestate.board.board[new_row][new_column] = new_piece\\n\\n elif not current_piece.colour and new_row == 7:\\n new_piece = Queen(new_row, new_column, False)\\n gamestate.board.black_pieces.remove(current_piece)\\n gamestate.board.black_pieces.append(new_piece)\\n gamestate.board.board[new_row][new_column] = new_piece\\n\\n # Switch player\\n gamestate.current_player_colour = not gamestate.current_player_colour\\n\\n if current_piece is None:\\n return self.BLACK_CHECKMATE\\n\\n current_piece.row = new_row\\n current_piece.column = new_column\\n\\n if piece_at_new_square:\\n if piece_at_new_square.colour:\\n gamestate.board.white_pieces.remove(piece_at_new_square)\\n else:\\n gamestate.board.black_pieces.remove(piece_at_new_square)\\n\\n gamestate.is_checkmate_or_stalemate()\\n gamestate.check_draw()\\n\\n # Evaluate score for gamestate recursively\\n score = -1 * self.get_ai_move_minimax(\\n gamestate, depth - 1, not current_player\\n )\\n\\n # Undo the move\\n gamestate.board.board[current_row][current_column] = current_piece\\n gamestate.board.board[new_row][new_column] = piece_at_new_square\\n\\n if isinstance(current_piece, King):\\n if current_player:\\n gamestate.white_king_location = (current_row, current_column)\\n else:\\n gamestate.black_king_location = (current_row, current_column)\\n\\n # Undo pawn promotion\\n elif isinstance(current_piece, Pawn):\\n if current_piece.colour and new_row == 0:\\n gamestate.board.white_pieces.append(current_piece)\\n gamestate.board.white_pieces.remove(new_piece)\\n elif not current_piece.colour and new_row == 7:\\n gamestate.board.black_pieces.append(current_piece)\\n gamestate.board.black_pieces.remove(new_piece)\\n\\n # Switch player back\\n gamestate.current_player_colour = not gamestate.current_player_colour\\n\\n current_piece.row = current_row\\n current_piece.column = current_column\\n\\n if piece_at_new_square:\\n if piece_at_new_square.colour:\\n gamestate.board.white_pieces.append(piece_at_new_square)\\n else:\\n gamestate.board.black_pieces.append(piece_at_new_square)\\n\\n gamestate.white_checkmate = False\\n gamestate.black_checkmate = False\\n gamestate.stalemate = False\\n\\n # Check if best score and update list of best moves\\n if score > best_score:\\n best_score = score\\n if depth == self.DEPTH:\\n self.minimax_best_moves = [move]\\n\\n elif score == best_score and depth == self.DEPTH:\\n self.minimax_best_moves.append(move)\\n\\n return best_score\",\n \"def get_next_moves(board, player):\\r\\n\\r\\n if player == 'hare':\\r\\n moves = []\\r\\n next_moves = []\\r\\n\\r\\n (row_from, col_from) = get_hare_positions(board)\\r\\n moves = possible_moves_list(row_from, col_from)\\r\\n\\r\\n for move in moves:\\r\\n row_to = move[0]\\r\\n col_to = move[1]\\r\\n\\r\\n if is_legal_move(player, row_from, col_from, row_to, col_to):\\r\\n \\\"\\\"\\\" if move is allowed then add to list of next moves\\\"\\\"\\\"\\r\\n next_moves.append(move)\\r\\n\\r\\n return next_moves\\r\\n\\r\\n else:\\r\\n \\\"\\\"\\\" for individual hounds\\r\\n get next moves\\\"\\\"\\\"\\r\\n moves = []\\r\\n next_moves_hound1 = []\\r\\n next_moves_hound2 = []\\r\\n next_moves_hound3 = []\\r\\n\\r\\n (row_hound_1, col_hound_1), (row_hound_2, col_hound_2), (row_hound_3, col_hound_3) = get_hound_positions(board)\\r\\n moves_hound1 = possible_moves_list(row_hound_1, col_hound_1)\\r\\n moves_hound2 = possible_moves_list(row_hound_2, col_hound_2)\\r\\n moves_hound3 = possible_moves_list(row_hound_3, col_hound_3)\\r\\n\\r\\n for move in moves_hound1:\\r\\n row_to = move[0]\\r\\n col_to = move[1]\\r\\n\\r\\n if is_legal_move(player, row_hound_1, col_hound_1, row_to, col_to):\\r\\n next_moves_hound1.append(move)\\r\\n\\r\\n for move in moves_hound2:\\r\\n row_to = move[0]\\r\\n col_to = move[1]\\r\\n\\r\\n if is_legal_move(player, row_hound_2, col_hound_2, row_to, col_to):\\r\\n next_moves_hound2.append(move)\\r\\n\\r\\n for move in moves_hound3:\\r\\n row_to = move[0]\\r\\n col_to = move[1]\\r\\n\\r\\n if is_legal_move(player, row_hound_3, col_hound_3, row_to, col_to):\\r\\n next_moves_hound3.append(move)\\r\\n\\r\\n return (next_moves_hound1, next_moves_hound2, next_moves_hound3)\",\n \"def _update_board(self, start: (int, int), dest: (int, int), extra_info=''):\\n\\n piece = self.board[start[0]][start[1]]\\n\\n # Move the piece itself\\n self.board[dest[0]][dest[1]] = piece\\n self.board[start[0]][start[1]] = EMPTY_SPACE\\n\\n # Special moves\\n if extra_info:\\n\\n # Castling kingside\\n if extra_info == CASTLE_KINGSIDE:\\n row = self._get_castling_row()\\n\\n # We already moved the king, so we just need to move the rook\\n self.board[row][5] = self.board[row][7]\\n self.board[row][7] = EMPTY_SPACE\\n\\n elif extra_info == CASTLE_QUEENSIDE:\\n row = self._get_castling_row()\\n\\n # King already moved, so just update the rook\\n self.board[row][3] = self.board[row][0]\\n self.board[row][0] = EMPTY_SPACE\\n\\n else: # Pawn promotion\\n self.board[dest[0]][dest[1]] = extra_info\\n\\n # en passant\\n self._update_en_passant(start, dest, piece)\",\n \"def move_up(self):\\n kya = self.board.board[self.player.y-1][self.player.x]\\n if self.player.y > 0 and kya != 'X' and kya != 'G':\\n self.board.board[self.player.y][self.player.x] = '.'\\n self.coin_taken(0, -1)\\n self.board.board[self.player.y-1][self.player.x] = 'P'\\n self.player.y -= 1\\n else:\\n print \\\"Can't move up\\\"\\n self.dont_move_ghosts = 1\",\n \"def move_buildings(self):\",\n \"def mark_moves(self, player):\\n [self.mark_move(player, p, d)\\n for p in self.pieces\\n for d in DIRECTIONS\\n if p.get_state() == player]\",\n \"def calculate_next_move(self, visit):\\n self.depth += 1\\n new_boards = []\\n for vehicle_id in range(len(self.vehicles)):\\n vehicle = self.vehicles[vehicle_id]\\n state = self.get_board()\\n if vehicle.orientation == 0: #horizontal\\n if vehicle.x > 0: #left\\n if state[vehicle.y][vehicle.x-1] == \\\"..\\\":\\n self.vehicles[vehicle_id].x -=1\\n if not self.get_board().tostring() in visit:\\n if not self.get_board().all in new_boards:\\n new_board = deepcopy(self)\\n self.vehicles[vehicle_id].x += 1\\n new_board.parent = self\\n new_boards.append(new_board)\\n else:\\n self.vehicles[vehicle_id].x += 1\\n\\n if vehicle.x + vehicle.length <= (len(state)-1): #right\\n if state[vehicle.y][vehicle.x+vehicle.length] == \\\"..\\\":\\n self.vehicles[vehicle_id].x += 1\\n if not self.get_board().tostring() in visit:\\n if not self.get_board().all in new_boards:\\n new_board = deepcopy(self)\\n self.vehicles[vehicle_id].x -= 1\\n new_board.parent = self\\n new_boards.append(new_board)\\n else:\\n self.vehicles[vehicle_id].x -= 1\\n\\n else: #vertical\\n if vehicle.y - 1 >= 0: #up\\n if state[vehicle.y-1][vehicle.x] == \\\"..\\\":\\n self.vehicles[vehicle_id].y -= 1\\n if not self.get_board().tostring() in visit:\\n if not self.get_board().all in new_boards:\\n new_board = deepcopy(self)\\n self.vehicles[vehicle_id].y += 1\\n new_board.parent = self\\n new_boards.append(new_board)\\n else:\\n self.vehicles[vehicle_id].y += 1\\n\\n if vehicle.y + vehicle.length <= (len(state)-1):\\n if state[vehicle.y + vehicle.length][vehicle.x] == \\\"..\\\":#down\\n self.vehicles[vehicle_id].y += 1\\n if not self.get_board().tostring() in visit:\\n if not self.get_board().all in new_boards:\\n new_board = deepcopy(self)\\n self.vehicles[vehicle_id].y -= 1\\n new_board.parent = self\\n new_boards.append(new_board)\\n else:\\n self.vehicles[vehicle_id].y -= 1\\n self.depth -= 1\\n return new_boards\",\n \"def new_move(self, grid_x, grid_y, player):\\n #duplication /!\\\\\\n if player == self.X:\\n self.draw_X(grid_x, grid_y)\\n self.board[grid_y][grid_x] = self.X\\n\\n elif player == self.O:\\n self.draw_O(grid_x, grid_y)\\n self.board[grid_y][grid_x] = self.O\",\n \"def possible_moves(self, piece):\\n def _index(orig, off):\\n \\\"\\\"\\\"Helper function to find the new index.\\\"\\\"\\\"\\n orig_x, orig_y = orig\\n off_x, off_y = off\\n return (orig_y - off_y) * self.ncols + (orig_x - off_x)\\n\\n p_x, p_y = piece\\n p_i = _index(piece, (0, 0))\\n\\n # pass a list of the four corners first for basic possibles\\n move_land = [((p_x + i, p_y + j), self.squares[_index(piece, (i, j))])\\\\\\n for i in [-1, 1] for j in [-1, 1]]\\n possibles = self.squares[p_i].can_move(piece, move_land)\\n\\n # next append the new list from jumps\\n jump_land = [((p_x + i, p_y + j), self.squares[_index(piece, (i, j))])\\\\\\n for j in [-2, 2] for i in [-2, 2]]\\n possibles += self.squares[p_i].can_jump(piece, move_land, jump_land)\\n\\n # clean out the list of duplicates, although there should be none\\n return [m for i, m in enumerate(possibles) if m not in possibles[:i]]\",\n \"def moveup(self, test_pylos):\\n possibilities = []\\n if test_pylos._state['visible']['turn'] is 0:\\n for layer in range(4):\\n for row in range(4 - layer):\\n for column in range(4 - layer):\\n value = test_pylos.get(layer, row, column)\\n if value is 0:\\n try:\\n test_pylos.canMove(layer, row,column)\\n except game.InvalidMoveException:\\n pass\\n # When we know which ball is moving, we can remove some possibilities from the\\n # availableholes list\\n availableholes = self.holes(test_pylos)\\n for i in availableholes:\\n if i[0] <= layer:\\n availableholes.remove(i)\\n elif i[0] == layer + 1 and i[1] == row and i[2] == column:\\n availableholes.remove(i)\\n elif i[0] == layer + 1 and i[1] == row + 1 and i[0] == column:\\n availableholes.remove(i)\\n elif i[0] == layer + 1 and i[1] == row + 1 and i[0] == column + 1:\\n availableholes.remove(i)\\n elif i[0] == layer + 1 and i[1] == row and i[0] == column + 1:\\n availableholes.remove(i)\\n\\n for i in availableholes:\\n move = {\\n 'move': 'move',\\n 'from': [layer, row, column],\\n 'to': i\\n }\\n possibilities.append(move)\\n\\n if test_pylos.createSquare((layer, row, column)) is True:\\n removableballs0 = self.removableballs0(test_pylos)\\n for i in removableballs0:\\n if i[0] == layer - 1 and i[1] == row and i[2] == column:\\n removableballs0.remove(i)\\n elif i[0] == layer - 1 and i[1] == row + 1 and i[2] == column:\\n removableballs0.remove(i)\\n elif i[0] == layer - 1 and i[1] == row + 1 and i[2] == column + 1:\\n removableballs0.remove(i)\\n elif i[0] == layer - 1 and i[1] == row and i[2] == column + 1:\\n removableballs0.remove(i)\\n elif i[0] == layer and i[1] == row and i[2] == column:\\n removableballs0.remove(i)\\n removableballs0.append(i)\\n removableballs01 = copy.deepcopy(removableballs0)\\n for i in removableballs0:\\n for j in removableballs01:\\n if i != j:\\n removewhat = []\\n removewhat.append([i, j])\\n move['remove'] = removewhat\\n else:\\n pass\\n possibilities.append(move)\\n\\n else:\\n for layer in range(4):\\n for row in range(4 - layer):\\n for column in range(4 - layer):\\n value = test_pylos.get(layer, row, column)\\n if value is 1:\\n try:\\n test_pylos.canMove(layer, row, column)\\n except game.InvalidMoveException:\\n pass\\n availableholes = self.holes(test_pylos)\\n for i in availableholes:\\n if i[0] <= layer:\\n availableholes.remove(i)\\n elif i[0] == layer + 1 and i[1] == row and i[2] == column:\\n availableholes.remove(i)\\n elif i[0] == layer + 1 and i[1] == row + 1 and i[0] == column:\\n availableholes.remove(i)\\n elif i[0] == layer + 1 and i[1] == row + 1 and i[0] == column + 1:\\n availableholes.remove(i)\\n elif i[0] == layer + 1 and i[1] == row and i[0] == column + 1:\\n availableholes.remove(i)\\n\\n for i in availableholes:\\n move = {\\n 'move': 'move',\\n 'from': [layer, row, column],\\n 'to': i\\n }\\n if test_pylos.createSquare((layer, row, column)) is True:\\n removableballs1 = self.removableballs1(test_pylos)\\n for i in removableballs1:\\n if i[0] == layer - 1 and i[1] == row and i[2] == column:\\n removableballs1.remove(i)\\n elif i[0] == layer - 1 and i[1] == row + 1 and i[2] == column:\\n removableballs1.remove(i)\\n elif i[0] == layer - 1 and i[1] == row + 1 and i[2] == column + 1:\\n removableballs1.remove(i)\\n elif i[0] == layer - 1 and i[1] == row and i[2] == column + 1:\\n removableballs1.remove(i)\\n elif i[0] == layer and i[1] == row and i[2] == column:\\n removableballs1.remove(i)\\n removableballs1.append(i)\\n removableballs11 = copy.deepcopy(removableballs1)\\n for i in removableballs1:\\n for j in removableballs11:\\n if i != j:\\n removewhat = []\\n removewhat.append([i, j])\\n move['remove'] = removewhat\\n else:\\n pass\\n possibilities.append(move)\\n return possibilities\",\n \"def update_position(self, canvas):\\n if self.x <= 0:\\n if self.direction == \\\"SW\\\":\\n self.direction = \\\"SE\\\"\\n if self.direction == \\\"W\\\":\\n self.direction = \\\"E\\\"\\n if self.direction == \\\"NW\\\":\\n self.direction = \\\"NE\\\"\\n if self.x >= canvas.width:\\n if self.direction == \\\"SE\\\":\\n self.direction = \\\"SW\\\"\\n if self.direction == \\\"E\\\":\\n self.direction = \\\"W\\\"\\n if self.direction == \\\"NE\\\":\\n self.direction = \\\"NW\\\"\\n if self.y <= 0:\\n if self.direction == \\\"NW\\\":\\n self.direction = \\\"SW\\\"\\n if self.direction == \\\"N\\\":\\n self.direction = \\\"S\\\"\\n if self.direction == \\\"NE\\\":\\n self.direction = \\\"SE\\\"\\n if self.y >= canvas.height:\\n if self.direction == \\\"SW\\\":\\n self.direction = \\\"NW\\\"\\n if self.direction == \\\"S\\\":\\n self.direction = \\\"N\\\"\\n if self.direction == \\\"SE\\\":\\n self.direction = \\\"NE\\\"\\n if self.direction == \\\"N\\\":\\n self.y -= 1\\n if self.direction == \\\"NE\\\":\\n self.y -= 1\\n self.x += 1\\n if self.direction == \\\"E\\\":\\n self.x += 1\\n if self.direction == \\\"SE\\\":\\n self.x += 1\\n self.y += 1\\n if self.direction == \\\"S\\\":\\n self.y += 1\\n if self.direction == \\\"SW\\\":\\n self.x -= 1\\n self.y += 1\\n if self.direction == \\\"W\\\":\\n self.x -= 1\\n if self.direction == \\\"NW\\\":\\n self.y -= 1\\n self.x -= 1\",\n \"def _get_piece_moves(self, x, y):\\n\\n piece = self.get_piece(x, y)\\n moves = []\\n\\n if not piece:\\n return moves\\n\\n if piece.name == 'rook' or piece.name == 'queen':\\n direcs = ['up', 'down', 'left', 'right']\\n moves = [self._get_moves_indirection(x, y, direc) for direc in\\n direcs]\\n\\n elif piece.name == 'bishop' or piece.name == 'queen':\\n direcs = ['d1', 'd2', 'd3', 'd4']\\n for direc in direcs:\\n moves += self._get_moves_indirection(x, y, direc)\\n\\n elif piece.name == 'king':\\n moves = [(x-1, y-1), (x-1, y), (x-1, y+1), (x, y-1),\\n (x, y+1), (x+1, y-1), (x+1, y), (x+1, y+1)]\\n\\n elif piece.name == 'knight':\\n moves = [(x-1, y-2), (x-2, y-1), (x-2, y+1), (x-1, y+2),\\n (x+1, y+2), (x+2, y+1), (x+1, y-2), (x+2, y-1)]\\n\\n elif piece.name == 'pawn':\\n if piece.color == ChessGame.BLACK:\\n moves = [(x-1, y), (x-1, y-1), (x-1, y+1)]\\n else:\\n moves = [(x+1, y), (x+1, y-1), (x+1, y+1)]\\n\\n tmp = list(moves)\\n for u, v in tmp:\\n if v != y and not self.is_enemy(u, v, piece.color):\\n moves.remove((u, v))\\n\\n if v == y and self.is_enemy(u, v, piece.color):\\n moves.remove((u, v))\\n\\n mycolor = piece.color\\n valid = set()\\n for (u, v) in moves:\\n if not self.in_bounds(u, v):\\n continue\\n\\n if not self.get_piece(u, v): # board is blank\\n valid.add((u, v))\\n\\n if self.is_enemy(u, v, mycolor):\\n valid.add((u, v))\\n\\n return valid\",\n \"def update_spaces_threatened(self):\\n # The threatened spaces will always be it's corners\\n current_row = self.position[0]\\n current_column = self.position[1]\\n corner1 = (current_row + 1 * self.direction, current_column - 1)\\n corner2 = (current_row + 1 * self.direction, current_column + 1)\\n current_spaces_threatened = [corner1, corner2]\\n self.spaces_threatened = current_spaces_threatened\\n update_threatening_king(self)\",\n \"def get_legal_moves(self, player: int) -> np.ndarray:\\n stage2 = self.is_stage2()\\n action_mask = np.zeros((24, 5, 25), dtype=bool)\\n # if stage 1 add set options\\n array_board = np.array(self.board)\\n if not stage2:\\n legal_pos = np.where(array_board == 0)[0]\\n for pos in legal_pos:\\n if self.is_mill(player, pos, self.board): # current selection completes a mill\\n opp_pos = np.where(array_board == -player)[0]\\n opp_pos = [opp_p for opp_p in opp_pos if\\n not self.is_mill(-player, opp_p, self.board)] # can't remove opponent in mill\\n if len(opp_pos) == 0: # exception for the case where all opponents pieces are in mills\\n opp_pos = np.where(array_board == -player)[0]\\n action_mask[pos, -1, opp_pos] = True\\n else:\\n action_mask[pos, -1, -1] = True # place piece on board\\n else:\\n from_pos_cands = np.where(array_board == player)[0]\\n for from_pos in from_pos_cands:\\n mill_cands = [(orient, adj) for orient, adj in enumerate(self.adjacent[from_pos]) if\\n adj is not None and self.board[adj] == 0] # TODO added not, need to validate\\n if_played_board = self.board.copy()\\n if_played_board[from_pos] = 0\\n for (orient, adj) in mill_cands:\\n if self.is_mill(player, adj, if_played_board):\\n opp_pos = np.where(array_board == -player)[0]\\n opp_pos = [opp_p for opp_p in opp_pos if not self.is_mill(-player, opp_p, if_played_board)]\\n if len(opp_pos) == 0: # exception for the case where all opponents pieces are in mills\\n opp_pos = np.where(array_board == -player)[0]\\n action_mask[from_pos, orient, opp_pos] = True\\n else:\\n action_mask[from_pos, orient, -1] = True\\n\\n return action_mask\",\n \"def make_move(move):\\n global manatee_pos\\n global hyacinths\\n global hyacinth_pos\\n\\n # Ends the program if movement is out of bounds\\n if move == (0, 0):\\n return None\\n new_pos = (manatee_pos[0] + move[0], manatee_pos[1] + move[1])\\n if new_pos[0] < 0 or new_pos[0] >= len(map):\\n return None\\n if new_pos[1] < 0 or new_pos[1] >= len(map[new_pos[0]]):\\n return None\\n\\n entity = map[new_pos[0]][new_pos[1]]\\n if entity == \\\"#\\\" or entity == \\\"G\\\":\\n # Runs if movement is impossible\\n return None\\n if entity == \\\" \\\" or entity == \\\".\\\":\\n # Runs if normal movement is possible\\n map[new_pos[0]][new_pos[1]] = \\\"M\\\"\\n map[manatee_pos[0]][manatee_pos[1]] = \\\" \\\"\\n manatee_pos = new_pos\\n return None\\n if entity == \\\"O\\\":\\n # Runs if manatee wins game\\n map[new_pos[0]][new_pos[1]] = \\\"M\\\"\\n map[manatee_pos[0]][manatee_pos[1]] = \\\" \\\"\\n manatee_pos = new_pos\\n return \\\"win\\\"\\n if entity == \\\"\\\\\\\\\\\":\\n # Runs if manatee eats hyacinth\\n map[new_pos[0]][new_pos[1]] = \\\"M\\\"\\n map[manatee_pos[0]][manatee_pos[1]] = \\\" \\\"\\n manatee_pos = new_pos\\n hyacinths += 1\\n if len(hyacinth_pos) == hyacinths:\\n map[grate_pos[0]][grate_pos[1]] = \\\"O\\\"\\n return None\\n if entity == \\\"*\\\":\\n # Checks if manatee can push boat\\n if move[0] == 0:\\n new_boat_pos = (new_pos[0] + move[0], new_pos[1] + move[1])\\n if new_boat_pos[0] < 0 or new_boat_pos[0] >= len(map):\\n return None\\n if new_boat_pos[1] < 0 \\\\\\n or new_boat_pos[1] >= len(map[new_boat_pos[0]]):\\n return None\\n if map[new_boat_pos[0]][new_boat_pos[1]] == \\\" \\\":\\n map[new_boat_pos[0]][new_boat_pos[1]] = \\\"*\\\"\\n map[new_pos[0]][new_pos[1]] = \\\"M\\\"\\n map[manatee_pos[0]][manatee_pos[1]] = \\\" \\\"\\n manatee_pos = new_pos\\n return None\\n return None\",\n \"def go_up(self):\\n\\t\\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\tMario._pass(self.x,self.y)\\n\\t\\ttemp = self.x\\n\\t\\tflag = 0\\n\\t\\twhile(temp>=self.x-8):\\n\\t\\t\\tif(Board.board[temp][self.y] in obstacles):\\n\\t\\t\\t\\tflag = 1\\n\\t\\t\\t\\ttemp_x = temp+1\\n\\t\\t\\t\\tbreak\\n\\t\\t\\ttemp = temp-1\\n\\n\\t\\tif(not flag):\\n\\t\\t\\ttemp_x = self.x-8\\n\\n\\t\\tif Board.board[temp_x-1][self.y]=='B':\\n\\t\\t\\tnew = self.y\\n\\t\\t\\tfor i in range(new-4,new+5):\\n\\t\\t\\t\\tif Board.board[temp_x-1][i]=='B':\\n\\t\\t\\t\\t\\tBoard.board[temp_x-1][i]='T'\\n\\t\\t\\tMario.bonus+=50\\n\\t\\t\\tif self.y==229 or self.y ==230 or self.y==231:\\n\\t\\t\\t\\tBoard.board[23][230]='P'\\n\\n\\n\\t\\tBoard.board[temp_x][self.y] = 'M'\\t\\t\\n\\t\\tos.system('clear')\\n\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\",\n \"def update(self):\\n if(self.game_on is True and\\n (self.player_turn is True or self.computer_turn is True)):\\n self.board.display()\\n if(self.board.tiles.count == self.SPOT * self.SPOT or\\n self.no_more_move()):\\n self.player_turn = False\\n self.computer_turn = False\\n if(self.no_more_move()):\\n print(\\\"No legal move for both!\\\")\\n print(\\\"Game Over!\\\")\\n if(len(self.board.tiles.blacks) ==\\n len(self.board.tiles.whites)):\\n self.tie = True\\n elif(len(self.board.tiles.blacks) >\\n len(self.board.tiles.whites)):\\n self.black_wins = True\\n elif(len(self.board.tiles.whites) >\\n len(self.board.tiles.blacks)):\\n self.white_wins = True\\n self.winner(len(self.board.tiles.blacks),\\n len(self.board.tiles.whites))\\n elif(self.game_on is False):\\n return\\n elif(self.player_turn is False and self.computer_turn is False):\\n self.make_record()\",\n \"def update_castle_rights(self, move):\\r\\n #deleting the right to castle on moving k/r\\r\\n #King\\r\\n if move.pieceMoved == 'wk':\\r\\n self.cr_castle_r.wks = False\\r\\n self.cr_castle_r.wqs = False\\r\\n elif move.pieceMoved == 'bk':\\r\\n self.cr_castle_r.bks = False\\r\\n self.cr_castle_r.bqs = False\\r\\n\\r\\n #Rook\\r\\n elif move.pieceMoved == 'wR':\\r\\n if move.sr == 7:\\r\\n if move.sc == 0:\\r\\n self.cr_castle_r.wqs = False\\r\\n elif move.sc == 7:\\r\\n self.cr_castle_r.wks = False\\r\\n elif move.pieceMoved == 'bR':\\r\\n if move.sr == 0:\\r\\n if move.sc == 0:\\r\\n self.cr_castle_r.bqs = False\\r\\n elif move.sc == 7:\\r\\n self.cr_castle_r.bks = False\",\n \"def MoveBishop(ChessPiece, NewSquare, AllPieces, Board):\\n\\n\\tfor Bishop in AllPieces[ChessPiece]:\\n\\t\\tX_BishopSquare = int(Bishop['CurrentSquare'][1])-1 # Integer Coordinate\\n\\t\\tY_BishopSquare = int(RowToNumber(Bishop['CurrentSquare'][0])) # Integer Coordinate\\n\\n\\t\\t# Diagonal Up Right..\\n\\t\\tfor i in range(1, 8):\\n\\t\\t\\tif X_BishopSquare+i <= 7 and Y_BishopSquare+i <= 7:\\n\\t\\t\\t\\tif NewSquare == (NumberToRow(str(Y_BishopSquare+i))+str(X_BishopSquare+i+1)):\\n\\t\\t\\t\\t\\tif Board[X_BishopSquare+i][Y_BishopSquare+i] is not ChessPiece[:5]:\\n\\t\\t\\t\\t\\t\\tBoard[X_BishopSquare][Y_BishopSquare] = 'Free'\\n\\t\\t\\t\\t\\t\\tBoard[X_BishopSquare+i][Y_BishopSquare+i] = ChessPiece[:5]\\n\\t\\t\\t\\t\\t\\treturn Bishop['CurrentSquare']\\n\\t\\t\\telse:\\n\\t\\t\\t\\tbreak\\n\\n\\t\\t# Diagonal Up Left..\\n\\t\\tfor i in range(1, 8): \\n\\t\\t\\tif X_BishopSquare+i <= 7 and Y_BishopSquare-i >= 0:\\n\\t\\t\\t\\tif NewSquare == (NumberToRow(str(Y_BishopSquare-i))+str(X_BishopSquare+i+1)):\\n\\t\\t\\t\\t\\tif Board[X_BishopSquare+i][Y_BishopSquare-i] is not ChessPiece[:5]:\\n\\t\\t\\t\\t\\t\\tBoard[X_BishopSquare][Y_BishopSquare] = 'Free'\\n\\t\\t\\t\\t\\t\\tBoard[X_BishopSquare+i][Y_BishopSquare-i] = ChessPiece[:5]\\n\\t\\t\\t\\t\\t\\treturn Bishop['CurrentSquare']\\n\\t\\t\\telse:\\n\\t\\t\\t\\tbreak\\n\\n\\t\\t# Diagonal Down Right..\\n\\t\\tfor i in range(1, 8): \\n\\t\\t\\tif X_BishopSquare-i >= 0 and Y_BishopSquare+i <= 7:\\n\\t\\t\\t\\tif NewSquare == (NumberToRow(str(Y_BishopSquare+i))+str(X_BishopSquare-i+1)):\\n\\t\\t\\t\\t\\tif Board[X_BishopSquare-i][Y_BishopSquare+i] is not ChessPiece[:5]:\\n\\t\\t\\t\\t\\t\\tBoard[X_BishopSquare][Y_BishopSquare] = 'Free'\\n\\t\\t\\t\\t\\t\\tBoard[X_BishopSquare-i][Y_BishopSquare+i] = ChessPiece[:5]\\n\\t\\t\\t\\t\\t\\treturn Bishop['CurrentSquare']\\n\\t\\t\\telse:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\n\\t\\t# Diagonal Down Left..\\n\\t\\tfor i in range(1, 8):\\n\\t\\t\\tif X_BishopSquare-i >= 0 and Y_BishopSquare-i >= 0:\\n\\t\\t\\t\\tif NewSquare == (NumberToRow(str(Y_BishopSquare-i))+str(X_BishopSquare-i+1)):\\n\\t\\t\\t\\t\\tif Board[X_BishopSquare-i][Y_BishopSquare-i] is not ChessPiece[:5]:\\n\\t\\t\\t\\t\\t\\tBoard[X_BishopSquare][Y_BishopSquare] = 'Free'\\n\\t\\t\\t\\t\\t\\tBoard[X_BishopSquare-i][Y_BishopSquare-i] = ChessPiece[:5]\\n\\t\\t\\t\\t\\t\\treturn Bishop['CurrentSquare']\\n\\t\\t\\telse:\\n\\t\\t\\t\\tbreak\\n\\n\\t# If Both Bishops Don't Match..\\n\\treturn None\",\n \"def castling_implement(self, turn, select, move):\\n \\n if select == self.coords.index(piece_class.KING_LOCATION[turn]):\\n if self.board[self.coords.index(piece_class.KING_LOCATION[turn])].move_track == False:\\n if move in [2, 58]:\\n self.board[move+1] = self.board[move-2]\\n self.board[move-2] = self.empty\\n if move in [6, 62]:\\n self.board[move-1] = self.board[move+1]\\n self.board[move+1] = self.empty\",\n \"def get_knight_moves(self, i, j, moves):\\r\\n directions = ((2, 1), (2, -1), (1, 2), (1, -2), (-2, 1), (-2, -1), (-1, 2), (-1, -2))\\r\\n if self.turn_white:\\r\\n opon = 'b'\\r\\n else:\\r\\n opon = 'w'\\r\\n for d in directions:\\r\\n cri = i + d[0]\\r\\n crj = j + d[1]\\r\\n if 0 <= cri <= 7 and 0 <= crj <= 7:\\r\\n # empty\\r\\n if self.board[cri][crj][0] == '-':\\r\\n moves.append(Move((i, j), (cri, crj), self.board))\\r\\n\\r\\n # oponent\\r\\n elif self.board[cri][crj][0] == opon:\\r\\n moves.append(Move((i, j), (cri, crj), self.board))\",\n \"def test_move_onto_terrain(self):\\n # move onto Water (1 extra)\\n b1 = board.Board(self.small_ter)\\n start = np.array((0, 3), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 2\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 2)\\n\\n # move onto Lava (4 extra)\\n start = np.array((3, 4), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 0\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertTrue(isvalid)\\n self.assertEqual(cost, 5)\\n\\n # move onto Barrier (illegal)\\n start = np.array((1, 4), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 1\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertFalse(isvalid)\\n\\n # move onto Rock (illegal)\\n start = np.array((1, 0), dtype='int')\\n k1 = knight.Knight(b1, start)\\n # set move choice\\n move_choice = 7\\n # determine move validity and cost\\n (cost, isvalid) = k1.validate_move(move_choice)\\n self.assertFalse(isvalid)\",\n \"def make_move(self, move, player, board):\\r\\n #nBoard = board.copy()\\r\\n board[move] = player\\r\\n for d in core.DIRECTIONS:\\r\\n if self.find_bracket(move, player, board, d)!=None:\\r\\n self.make_flips(move, player, board, d)\\r\\n return board\",\n \"def agent(obs_dict, config_dict):\\n global last_move\\n observation = Observation(obs_dict)\\n configuration = Configuration(config_dict)\\n player_index = observation.index\\n player_goose = observation.geese[player_index]\\n player_head = player_goose[0]\\n player_row, player_column = row_col(player_head, configuration.columns)\\n possible_moves = [Action.SOUTH.name, Action.NORTH.name, Action.WEST.name, Action.EAST.name]\\n possible_move = None\\n oldFoodDistanceToMe = 9999\\n \\n def getFoodsIndex(): \\n foods = []\\n for i in range(len(observation.food)):\\n food = observation.food[i]\\n foods.append(row_col(food, configuration.columns))\\n return foods\\n\\n def getGoose(index):\\n goose = []\\n for i in range(len(observation.geese[index])):\\n goose.append(row_col(observation.geese[index][i], configuration.columns))\\n return goose\\n\\n def getGeesesIndex():\\n geeses = []\\n for geese in observation.geese:\\n geeses.append(row_col(geese[0], configuration.columns))\\n return geeses\\n\\n def randomMove(moves):\\n if len(moves) > 1:\\n return moves[randint(0, len(moves) - 1)]\\n return moves[0]\\n\\n def countMoves(x1, y1, x2, y2):\\n return abs((x1-x2)+(y1-y2)) \\n\\n def willCollide(x, y, action):\\n #print(f'Goose[{player_index} in player_row: {player_row}, player_column: {player_column} move to {action} and can collide with x: {x}, y: {y}')\\n if action == Action.WEST.name:\\n if player_column - 1 == y and x == player_row:\\n return True \\n if player_column == 0 and y == 10 and x == player_row:\\n return True\\n if action == Action.EAST.name:\\n if player_column + 1 == y and x == player_row:\\n return True\\n if player_column == 10 and y == 0 and x == player_row:\\n return True\\n if action == Action.SOUTH.name:\\n if player_row + 1 == x and y == player_column:\\n return True\\n if player_row == 6 and x == 0 and y == player_column:\\n return True\\n if action == Action.NORTH.name:\\n if player_row - 1 == x and y == player_column:\\n return True\\n if player_row == 0 and x == 6 and y == player_column:\\n return True\\n return False\\n\\n def opposite(action):\\n if action == Action.NORTH.name:\\n return Action.SOUTH.name\\n if action == Action.SOUTH.name:\\n return Action.NORTH.name\\n if action == Action.EAST.name:\\n return Action.WEST.name\\n if action == Action.WEST.name:\\n return Action.EAST.name\\n\\n possible_moves = [Action.SOUTH.name, Action.NORTH.name, Action.WEST.name, Action.EAST.name]\\n\\n if last_move[player_index] is not None:\\n print(f'Geese {player_index} removes opposite last move: {opposite(last_move[player_index])}')\\n possible_moves.remove(opposite(last_move[player_index]))\\n\\n for geese_row, geese_column in getGeesesIndex():\\n for food_row, food_column in getFoodsIndex():\\n foodDistanceToMe = countMoves(food_row, food_column, player_row, player_column)\\n if oldFoodDistanceToMe > foodDistanceToMe:\\n oldFoodDistanceToMe = foodDistanceToMe\\n else:\\n continue\\n \\n geeseDistanceToFood = countMoves(geese_row, geese_column, food_row, food_column)\\n if foodDistanceToMe < geeseDistanceToFood:\\n if food_row > player_row:\\n possible_move = Action.SOUTH.name\\n\\n if food_row < player_row:\\n possible_move = Action.NORTH.name\\n\\n if food_column > player_column:\\n possible_move = Action.EAST.name\\n\\n if food_column < player_column:\\n possible_move = Action.WEST.name\\n \\n print(f'Geese {player_index} based by food, possible move: {possible_move}')\\n if possible_move not in possible_moves:\\n print(f'Geese {player_index}, possible move: {possible_move} is banned.')\\n possible_move = randomMove(possible_moves)\\n\\n for i in range(len(observation.geese)):\\n geese_index = getGoose(i)\\n j = 0\\n while j < len(geese_index):\\n collision = False\\n x, y = geese_index[j]\\n if i != player_index:\\n print(f'geese {player_index} -> geese {i} part {j}')\\n #print(f'goose[{player_index}] in {getGoose(player_index)[0]} with possible move: {possible_move} can colide with goose[{i}] in x: {x} and y: {y}')\\n while willCollide(x, y, possible_move):\\n collision = True\\n print(f'goose[{player_index}] in {getGoose(player_index)[0]} with possible move: {possible_move} will colide with goose[{i}] in x: {x} and y: {y}')\\n possible_moves.remove(possible_move)\\n possible_move = randomMove(possible_moves)\\n print(f'now goose[{player_index}] will to {possible_move}')\\n j = 0\\n if not collision:\\n j += 1\\n\\n for x, y in getGoose(player_index):\\n while willCollide(x, y, possible_move):\\n print(f'BODY HIT!!!')\\n possible_moves.remove(possible_move)\\n possible_move = randomMove(possible_moves)\\n\\n if len(possible_moves) == 1:\\n print(f'Geese {player_index} just remain this move: {possible_moves[0]}')\\n possible_move = possible_moves[0]\\n\\n last_move[player_index] = possible_move\\n\\n print(f'Geese {player_index}: {getGoose(player_index)} move to {possible_move}')\\n return possible_move\",\n \"def __update_valid_moves(self):\\r\\n\\r\\n\\t\\tmoves = ['Stay']\\r\\n\\r\\n\\t\\t# case for 21\\r\\n\\t\\tfor value in self.values:\\r\\n\\t\\t\\tif value > 21:\\r\\n\\t\\t\\t\\tself.valid_moves = 'Bust'\\r\\n\\t\\t\\t\\treturn\\r\\n\\t\\t\\tif value == 21:\\r\\n\\t\\t\\t\\tif len(self.cards) == 2:\\r\\n\\t\\t\\t\\t\\tself.valid_moves = 'Blackjack'\\r\\n\\t\\t\\t\\t\\treturn\\r\\n\\t\\t\\t\\tself.valid_moves = '21'\\r\\n\\t\\t\\t\\treturn\\r\\n\\t\\tmoves.append('Hit')\\r\\n\\r\\n\\t\\tif len(self.cards) <= 2:\\r\\n\\t\\t\\tmoves.append('Double')\\r\\n\\r\\n\\t\\tif len(self.cards) == 2:\\r\\n\\t\\t\\tif self.cards[0].rank == self.cards[1].rank:\\r\\n\\t\\t\\t\\tmoves.append('Split')\\r\\n\\r\\n\\t\\tself.valid_moves = moves\",\n \"def _get_valid_moves(self, piece):\\n moves = {}\\n left = piece.col - 1 # Left position\\n right = piece.col + 1 # Right position\\n row = piece.row # Current row\\n\\n if piece.get_player() == Player.white or piece.is_king():\\n # Checks the movements from the bottom to the top\\n moves.update(self._traverse_left(row - 1, max(row - 3, -1), -1, piece.get_player(), left))\\n moves.update(self._traverse_right(row - 1, max(row - 3, -1), -1, piece.get_player(), right))\\n\\n if piece.get_player() == Player.black or piece.is_king():\\n # Checks the movements from the top to the bottom\\n moves.update(self._traverse_left(row + 1, min(row + 3, constant.BOARD_DIMENSION),\\n 1, piece.get_player(), left))\\n moves.update(self._traverse_right(row + 1, min(row + 3, constant.BOARD_DIMENSION),\\n 1, piece.get_player(), right))\\n\\n return moves\",\n \"def move_length_3(self, move, new_state):\\n # First consider the move on 6 corners\\n for i in [[\\\"A\\\", 1, 2, 2, 5, 0, 3, 7, 11, 11],\\n [\\\"B\\\", 0, 2, 4, 8, 8, 3, 6, 9, 1],\\n [\\\"F\\\", 9, 9, 2, 0, 0, 6, 7, 8, 6],\\n [\\\"J\\\", 5, 9, 10, 11, 7, 6, 3, 1, 1],\\n [\\\"I\\\", 11, 5, 4, 1, 8, 5, 6, 7, 6],\\n [\\\"L\\\", 8, 5, 9, 10, 7, 3, 0, 7, 11]]:\\n if move == i[0]:\\n if new_state.letters[i[1]].isalpha():\\n new_state.claim[i[2]] \\\\\\n = new_state.get_current_player_name()[1]\\n if new_state.get_current_player_name()[1] \\\\\\n == new_state.letters[i[3]] \\\\\\n or new_state.get_current_player_name()[1] \\\\\\n == new_state.letters[i[4]]:\\n new_state.claim[i[5]] \\\\\\n = new_state.get_current_player_name()[1]\\n if (new_state.get_current_player_name()[1]\\n == new_state.letters[i[6]]\\n or new_state.get_current_player_name()[1]\\n == new_state.letters[i[7]]\\n or new_state.get_current_player_name()[1]\\n == new_state.letters[i[8]]) \\\\\\n and new_state.claim[i[9]] == \\\"@\\\":\\n new_state.claim[i[9]] \\\\\\n = new_state.get_current_player_name()[1]\\n\\n # then consider the move on the middle of each side\\n for i in [[\\\"C\\\", 0, 5, 0, 3, 4, 4, 6, 10, 10],\\n [\\\"E\\\", 1, 8, 8, 2, 3, 4, 7, 10, 3],\\n [\\\"K\\\", 9, 11, 7, 2, 6, 10, 7, 4, 3]]:\\n if move == i[0]:\\n if (new_state.get_current_player_name()[1]\\n == new_state.letters[i[1]]\\n or new_state.get_current_player_name()[1]\\n == new_state.letters[i[2]]) \\\\\\n and new_state.claim[i[3]] == \\\"@\\\":\\n new_state.claim[i[3]] \\\\\\n = new_state.get_current_player_name()[1]\\n if (new_state.get_current_player_name()[1]\\n == new_state.letters[i[4]]\\n or new_state.get_current_player_name()[1]\\n == new_state.letters[i[5]]) \\\\\\n and new_state.claim[i[6]] == \\\"@\\\":\\n new_state.claim[i[6]] \\\\\\n = new_state.get_current_player_name()[1]\\n\\n if (new_state.get_current_player_name()[1]\\n == new_state.letters[i[7]]\\n or new_state.get_current_player_name()[1]\\n == new_state.letters[i[8]]) \\\\\\n and new_state.claim[i[9]] == \\\"@\\\":\\n new_state.claim[i[9]] \\\\\\n = new_state.get_current_player_name()[1]\\n # Finally consider the internal move\\n internal = [[\\\"D\\\", 2, 4, 4, 1, 6, 9, 1, 0, 7, 11, 11],\\n [\\\"G\\\", 2, 10, 10, 9, 3, 1, 1, 5, 7, 8, 6],\\n [\\\"H\\\", 4, 10, 3, 0, 3, 11, 11, 5, 6, 8, 6]]\\n for i in internal:\\n if move == i[0]:\\n if (new_state.get_current_player_name()[1]\\n == new_state.letters[i[1]]\\n or new_state.get_current_player_name()[1]\\n == new_state.letters[i[2]]) \\\\\\n and new_state.claim[i[3]] == \\\"@\\\":\\n new_state.claim[i[3]] \\\\\\n = new_state.get_current_player_name()[1]\\n if (new_state.get_current_player_name()[1]\\n == new_state.letters[i[4]]\\n or new_state.get_current_player_name()[1]\\n == new_state.letters[i[5]]\\n or new_state.get_current_player_name()[1]\\n == new_state.letters[i[6]]) \\\\\\n and new_state.claim[i[7]] == \\\"@\\\":\\n new_state.claim[i[7]] \\\\\\n = new_state.get_current_player_name()[1]\\n\\n if (new_state.get_current_player_name()[1]\\n == new_state.letters[i[8]]\\n or new_state.get_current_player_name()[1]\\n == new_state.letters[i[9]]\\n or new_state.get_current_player_name()[1]\\n == new_state.letters[i[10]]) \\\\\\n and new_state.claim[i[11]] == \\\"@\\\":\\n new_state.claim[i[11]] \\\\\\n = new_state.get_current_player_name()[1]\\n\\n new_state.letters = [self.get_current_player_name()[1]\\n if i == move else i for i in self.letters]\\n\\n return StonehengeState(not self.p1_turn, new_state.length,\\n new_state.letters, new_state.claim)\",\n \"def play(self):\\n\\n value = 0 #player dictionary key\\n player = {0: 'O', 1: 'X'}\\n\\n moveCount = 0 #how many moves have occurred. also doubles as the self.order index.\\n turn = \\\"\\\"\\n while moveCount < self.n**2 and self.go == \\\"Tie\\\":\\n value = not value\\n turn = player[value] #X starts\\n key = self.order[moveCount]\\n i = key[0]\\n j = key[1]\\n\\n\\n# self.rows[i][0] == homogenous?\\n# self.rows[i][1] == X/O?\\n# self.rows[i][2] == count of X's/O's?\\n\\n# Check to see if row i is 'homogenous' (contains only X's or O's):\\n if self.rows[i][0]:\\n\\n# Check to see if any square in row i has been played. If it has been played,\\n# check to see if it was the same person who's current turn it is.\\n if self.rows[i][1] == \\\"\\\" or player[value] == self.rows[i][1]:\\n\\n# Mark the column with the current person's token (X or O).\\n# Admittedly, this could be improved to not update every time.\\n self.rows[i][1] = turn\\n\\n# Update the count by one.\\n self.rows[i][2] += 1\\n\\n# If the count is equal to the board size, end the game and return who won and how.\\n if self.rows[i][2] == self.n:\\n self.go = (turn, 'row ' + str(i))\\n\\n# If the current person who's turn it is,\\n# is not the same as the previous player who played this row,\\n# set this row's 'homogenous' attribute to false.\\n else:\\n self.rows[i][0] = False\\n\\n if self.cols[j][0]:\\n if self.cols[j][1] == \\\"\\\" or player[value] == self.cols[j][1]:\\n self.cols[j][1] = turn\\n self.cols[j][2] += 1\\n if self.cols[j][2] == self.n:\\n self.go = (turn, 'column ' + str(j))\\n else:\\n self.cols[j][0] = False\\n\\n# On boards of odd-sized 'n' (n = 3,5,7,etc...)\\n# the middle square is part of both diagonals: 'step' and 'same':\\n if i == j:\\n if self.diags['same'][0]:\\n if self.diags['same'][1] == \\\"\\\" or player[value] == self.diags['same'][1]:\\n self.diags['same'][1] = turn\\n self.diags['same'][2] += 1\\n if self.diags['same'][2] == self.n:\\n self.go = (turn, 'diagonal from 0,0 to n-1,n-1')\\n else:\\n self.diags['same'][0] = False\\n\\n if i + j + 1 == self.n:\\n if self.diags['step'][0]:\\n if self.diags['step'][1] == \\\"\\\" or player[value] == self.diags['step'][1]:\\n self.diags['step'][1] = turn\\n self.diags['step'][2] += 1\\n if self.diags['step'][2] == self.n:\\n self.go = (turn, 'diagonal from n-1,0 to 0,n-1')\\n else:\\n self.diags['step'][0] = False\\n\\n moveCount += 1\\n print(turn, key)\\n else:\\n return self.go\",\n \"def test_move_onto_past(self):\\n b1 = board.Board(self.small_plain)\\n start = np.array((3, 3), dtype='int')\\n k1 = knight.Knight(b1,start)\\n # set move choice 0\\n move_choice = 0\\n # change the board layout to reflect the move\\n k1.execute_move(move_choice)\\n self.assertTrue((k1.position == np.array((5, 4), dtype='int')).all())\\n # try to go back in strict mode (fail)\\n move_choice = 4\\n k1.execute_move(move_choice, strict=True)\\n # confirm lack of movement\\n self.assertTrue((k1.position == np.array((5, 4), dtype='int')).all())\\n # confirm state of board\\n with capture_output() as (out, _):\\n b1.display()\\n my_out = out.getvalue().strip()\\n out.close()\\n out_list = [ each.strip() for each in\\n \\\"\\\"\\\". . . . . . . .\\n . . . . . . . .\\n . . . . . . . .\\n . . . S . . . .\\n . . . x . . . .\\n . . . x K . . .\\n . . . . . . . .\\n . . . . . . . .\\\"\\\"\\\".split('\\\\n')]\\n expected_out = '\\\\n'.join(out_list)\\n self.assertEqual(my_out, expected_out)\\n # try to go back without strict mode\\n move_choice = 4\\n k1.execute_move(move_choice)\\n # confirm lack of movement\\n self.assertTrue((k1.position == np.array((3, 3), dtype='int')).all())\\n # confirm state of board\\n with capture_output() as (out, _):\\n b1.display()\\n my_out = out.getvalue().strip()\\n out.close()\\n out_list = [ each.strip() for each in\\n \\\"\\\"\\\". . . . . . . .\\n . . . . . . . .\\n . . . . . . . .\\n . . . K x . . .\\n . . . x x . . .\\n . . . x S . . .\\n . . . . . . . .\\n . . . . . . . .\\\"\\\"\\\".split('\\\\n')]\\n expected_out = '\\\\n'.join(out_list)\\n self.assertEqual(my_out, expected_out)\",\n \"def get_possible_moves(self, board: np.ndarray):\\n board_size = board.shape[0]\\n moves = []\\n if abs(self.value) == 1:\\n if self.start_row <= 2:\\n directions = [np.array((1, -1)), np.array((1, 1))]\\n else:\\n directions = [np.array((-1, 1)), np.array((-1, -1))]\\n else:\\n directions = [np.array((-1, 1)), np.array((1, 1)), np.array((-1, -1)), np.array((1, -1))]\\n for direction in directions:\\n within_board = True\\n i = 1\\n while within_board:\\n coord = self.coord + direction * i\\n within_board = _check_if_position_on_board(coord, board_size)\\n # break if first step is already out of board\\n if not within_board:\\n break\\n value_board = board[coord[0], coord[1]]\\n # break if there is a stone of them same player in the way\\n if value_board < 0 and self.value < 0 or value_board > 0 and self.value > 0:\\n break\\n # if there is no stone, than add this to move list.\\n if value_board == 0:\\n moves += [{\\\"old_coord\\\": self.coord, \\\"new_coord\\\": coord, \\\"jumped_stones\\\": [], \\\"jumped_values\\\": 0,\\n \\\"move_coords\\\": [coord]}]\\n # if there is a stone of the enemy\\n if (value_board < 0 < self.value) or (self.value < 0 < value_board):\\n # check if it can be jumped\\n coord_jump = coord + direction\\n move_coords = [coord_jump.copy()]\\n within_board_after_jump = _check_if_position_on_board(coord_jump, board_size)\\n # break if place behind stone is out of border\\n if not within_board_after_jump:\\n break\\n value_board_jump = board[coord_jump[0], coord_jump[1]]\\n jumped_stones = []\\n # break if there is no free place\\n if value_board_jump != 0:\\n break\\n jumped_stones += [coord]\\n moves_tmp = self.jump_chain(directions, board, coord_jump, value_board, jumped_stones, move_coords)\\n if len(moves_tmp) > 0:\\n moves += moves_tmp\\n else:\\n moves += [{\\\"old_coord\\\": self.coord, \\\"new_coord\\\": coord_jump, \\\"jumped_stones\\\": jumped_stones,\\n \\\"jumped_values\\\": abs(value_board), \\\"move_coords\\\": [coord_jump]}]\\n i += 1\\n # break if normal stone, because they can only move one field\\n if abs(self.value) == 1:\\n break\\n return moves\",\n \"def enumerate_moves(self):\\n add_ew = lambda x: [x+'e', x+'w']\\n allowed_catches = add_ew(self._directions[0])\\n moves = []\\n # First add the one/two step forward moves\\n new_slot = self._board.get_dir(self._current_space, self._directions[0])\\n if new_slot and new_slot.is_free():\\n moves.append(ChessMove(self._current_space, new_slot))\\n if (self._side == BLACK and new_slot.row == self._board.size - 1) or \\\\\\n (self._side == WHITE and new_slot.row == 0):\\n moves[-1].add_promotion()\\n if (self._side == BLACK and self._current_space.row == 1) or \\\\\\n (self._side == WHITE and self._current_space.row == self._board.size -2):\\n new_slot = self._board.get_dir(new_slot, self._directions[0])\\n if new_slot and new_slot.is_free():\\n moves.append(ChessMove(self._current_space, new_slot))\\n\\n # Now add all the captures.\\n for direction in allowed_catches:\\n new_slot = self._board.get_dir(self._current_space, direction)\\n if new_slot and new_slot.has_opponent(self._side):\\n moves.append(ChessMove(self._current_space, new_slot, [new_slot]))\\n if (self._side == BLACK and new_slot.row == self._board.size - 1) or \\\\\\n (self._side == WHITE and new_slot.row == 0):\\n moves[-1].add_promotion()\\n return moves\",\n \"def apply_move(self, move, state):\\n x, y , heading, grid_data = state\\n map_data = [row[:] for row in grid_data]\\n if move == self.MOVE_FORWARD:\\n # get coordinates for next cell\\n if heading == self.UP:\\n next_y = y - 1\\n next_x = x\\n elif heading == self.DOWN:\\n next_y = y + 1\\n next_x = x\\n elif heading == self.LEFT:\\n next_y = y\\n next_x = x - 1\\n else:\\n next_y = y\\n next_x = x + 1\\n\\n # handle special tile types\\n if map_data[next_y][next_x] == self.ICE_SYMBOL:\\n # handle ice tile - slide until first non-ice tile or blocked\\n if heading == self.UP:\\n for i in range(next_y, -1, -1):\\n if map_data[i][next_x] != self.ICE_SYMBOL:\\n if map_data[i][next_x] == self.WATER_SYMBOL:\\n # slide into water - game over\\n return self.GAME_OVER\\n elif self.cell_is_blocked(i, next_x, map_data):\\n # if blocked, stop on last ice cell\\n next_y = i + 1\\n break\\n else:\\n next_y = i\\n break\\n elif heading == self.DOWN:\\n for i in range(next_y, self.y_size):\\n if map_data[i][next_x] != self.ICE_SYMBOL:\\n if map_data[i][next_x] == self.WATER_SYMBOL:\\n # slide into water - game over\\n return self.GAME_OVER\\n elif self.cell_is_blocked(i, next_x, map_data):\\n # if blocked, stop on last ice cell\\n next_y = i - 1\\n break\\n else:\\n next_y = i\\n break\\n elif heading == self.LEFT:\\n for i in range(next_x, -1, -1):\\n if map_data[next_y][i] != self.ICE_SYMBOL:\\n if map_data[next_y][i] == self.WATER_SYMBOL:\\n # slide into water - game over\\n return self.GAME_OVER\\n elif self.cell_is_blocked(next_y, i, map_data):\\n # if blocked, stop on last ice cell\\n next_x = i + 1\\n break\\n else:\\n next_x = i\\n break\\n else:\\n for i in range(next_x, self.x_size):\\n if map_data[next_y][i] != self.ICE_SYMBOL:\\n if map_data[next_y][i] == self.WATER_SYMBOL:\\n # slide into water - game over\\n return self.GAME_OVER\\n elif self.cell_is_blocked(next_y, i, map_data):\\n # if blocked, stop on last ice cell\\n next_x = i - 1\\n break\\n else:\\n next_x = i\\n break\\n if map_data[next_y][next_x] == self.TELEPORT_SYMBOL:\\n # handle teleport - find the other teleporter\\n tpy, tpx = (None, None)\\n for i in range(self.y_size):\\n for j in range(self.x_size):\\n if map_data[i][j] == self.TELEPORT_SYMBOL and (i != next_y or j != next_x):\\n tpy, tpx = (i, j)\\n break\\n if tpy is not None:\\n break\\n if tpy is None:\\n raise Exception(\\\"LaserTank Map Error: Unmatched teleport symbol\\\")\\n next_y, next_x = (tpy, tpx)\\n else:\\n # if not ice or teleport, perform collision check\\n if self.cell_is_blocked(next_y, next_x, map_data):\\n return self.COLLISION\\n\\n # check for game over conditions\\n if self.cell_is_game_over(next_y, next_x, map_data):\\n return self.GAME_OVER\\n\\n # no collision and no game over - update player position\\n y = next_y\\n x = next_x\\n return (x, y, heading, map_data)\\n\\n elif move == self.TURN_LEFT:\\n # no collision or game over possible\\n if heading == self.UP:\\n heading = self.LEFT\\n elif heading == self.DOWN:\\n heading = self.RIGHT\\n elif heading == self.LEFT:\\n heading = self.DOWN\\n else:\\n heading = self.UP\\n return (x, y, heading, map_data)\\n\\n elif move == self.TURN_RIGHT:\\n # no collision or game over possible\\n if heading == self.UP:\\n heading = self.RIGHT\\n elif heading == self.DOWN:\\n heading = self.LEFT\\n elif heading == self.LEFT:\\n heading = self.UP\\n else:\\n heading = self.DOWN\\n return (x, y, heading, map_data)\\n\\n elif move == self.SHOOT_LASER:\\n # set laser direction\\n if heading == self.UP:\\n laserheading = self.UP\\n dy, dx = (-1, 0)\\n elif heading == self.DOWN:\\n laserheading = self.DOWN\\n dy, dx = (1, 0)\\n elif heading == self.LEFT:\\n laserheading = self.LEFT\\n dy, dx = (0, -1)\\n else:\\n laserheading = self.RIGHT\\n dy, dx = (0, 1)\\n\\n # loop until laser blocking object reached\\n ly, lx = (y, x)\\n while True:\\n ly += dy\\n lx += dx\\n\\n # handle boundary and immovable obstacles\\n if ly < 0 or ly >= self.y_size or \\\\\\n lx < 0 or lx >= self.x_size or \\\\\\n map_data[ly][lx] == self.OBSTACLE_SYMBOL:\\n # laser stopped without effect\\n return self.COLLISION\\n\\n # handle movable objects\\n elif self.cell_is_laser_movable(ly, lx, laserheading, map_data):\\n # check if tile can be moved without collision\\n if self.cell_is_blocked(ly + dy, lx + dx, map_data) or \\\\\\n map_data[ly + dy][lx + dx] == self.ICE_SYMBOL or \\\\\\n map_data[ly + dy][lx + dx] == self.TELEPORT_SYMBOL or \\\\\\n map_data[ly + dy][lx + dx] == self.FLAG_SYMBOL or \\\\\\n (ly + dy == y and lx + dx == x):\\n # tile cannot be moved\\n return self.COLLISION\\n else:\\n old_symbol = map_data[ly][lx]\\n map_data[ly][lx] = self.LAND_SYMBOL\\n if map_data[ly + dy][lx + dx] == self.WATER_SYMBOL:\\n # if new bridge position is water, convert to land tile\\n if old_symbol == self.BRIDGE_SYMBOL:\\n map_data[ly + dy][lx + dx] = self.LAND_SYMBOL\\n # otherwise, do not replace the old symbol\\n else:\\n # otherwise, move the tile forward\\n map_data[ly + dy][lx + dx] = old_symbol\\n break\\n\\n # handle bricks\\n elif map_data[ly][lx] == self.BRICK_SYMBOL:\\n # remove brick, replace with land\\n map_data[ly][lx] = self.LAND_SYMBOL\\n break\\n\\n # handle facing anti-tanks\\n elif (map_data[ly][lx] == self.ANTI_TANK_UP_SYMBOL and laserheading == self.DOWN) or \\\\\\n (map_data[ly][lx] == self.ANTI_TANK_DOWN_SYMBOL and laserheading == self.UP) or \\\\\\n (map_data[ly][lx] == self.ANTI_TANK_LEFT_SYMBOL and laserheading == self.RIGHT) or \\\\\\n (map_data[ly][lx] == self.ANTI_TANK_RIGHT_SYMBOL and laserheading == self.LEFT):\\n # mark anti-tank as destroyed\\n map_data[ly][lx] = self.ANTI_TANK_DESTROYED_SYMBOL\\n break\\n\\n # handle player laser collision\\n elif ly == y and lx == x:\\n return self.GAME_OVER\\n\\n # handle facing mirrors\\n elif (map_data[ly][lx] == self.MIRROR_UL_SYMBOL and laserheading == self.RIGHT) or \\\\\\n (map_data[ly][lx] == self.MIRROR_UR_SYMBOL and laserheading == self.LEFT):\\n # new direction is up\\n dy, dx = (-1, 0)\\n laserheading = self.UP\\n elif (map_data[ly][lx] == self.MIRROR_DL_SYMBOL and laserheading == self.RIGHT) or \\\\\\n (self.grid_data[ly][lx] == self.MIRROR_DR_SYMBOL and laserheading == self.LEFT):\\n # new direction is down\\n dy, dx = (1, 0)\\n laserheading = self.DOWN\\n elif (map_data[ly][lx] == self.MIRROR_UL_SYMBOL and laserheading == self.DOWN) or \\\\\\n (map_data[ly][lx] == self.MIRROR_DL_SYMBOL and laserheading == self.UP):\\n # new direction is left\\n dy, dx = (0, -1)\\n laserheading = self.LEFT\\n elif (map_data[ly][lx] == self.MIRROR_UR_SYMBOL and laserheading == self.DOWN) or \\\\\\n (map_data[ly][lx] == self.MIRROR_DR_SYMBOL and laserheading == self.UP):\\n # new direction is right\\n dy, dx = (0, 1)\\n laserheading = self.RIGHT\\n # do not terminate laser on facing mirror - keep looping\\n\\n # check for game over condition after effect of laser\\n if self.cell_is_game_over(y, x, map_data):\\n return self.GAME_OVER\\n return (x, y, heading, map_data)\\n return self.SUCCESS\",\n \"def op_jump_postconditions(self,oldPieceCoords,newPieceCoords):\\n\\n # Start of new state constrution\\n next_gs_board = Board.from_binary_matrix(self.board)\\n next_gs_board.set_element(newPieceCoords[0], newPieceCoords[1], self.curr_player)\\n next_gs_board.remove_element(oldPieceCoords[0], oldPieceCoords[1])\\n next_gs_next_player = self.curr_player\\n next_gs_next_move = self.FREE\\n next_gs_next_pieces = set()\\n\\n\\n new_gs = Eximo(next_gs_next_player,next_gs_next_move,next_gs_next_pieces,next_gs_board)\\n\\n # Check if moved piece has reached opposite side\\n if(new_gs.reach_otherside(newPieceCoords)):\\n new_gs.board.remove_element(newPieceCoords[0], newPieceCoords[1])\\n new_gs.next_move = self.ADDPIECE_2\\n new_gs.next_pieces = new_gs.addition_viable_tiles()\\n new_gs.perform_checkup()\\n\\n # Check if the next move must also be a jump by the same player\\n elif(new_gs.can_jump(newPieceCoords)):\\n new_gs.next_move = self.JUMP\\n new_gs.next_pieces = {newPieceCoords}\\n\\n else:\\n new_gs.curr_player = self.get_enemy(self.curr_player)\\n\\n # Check if the next_piece checkup needs to be made\\n if new_gs.curr_player == self.get_enemy(self.curr_player):\\n new_gs.perform_checkup()\\n\\n new_gs.last_piece = newPieceCoords\\n\\n return new_gs\",\n \"def solveOneStep(self):\\n ### Student code goes here\\n if (self.currentState.state == self.victoryCondition) or (self.currentState not in self.visited):\\n self.visited[self.currentState] = True\\n win_or_not = self.currentState.state == self.victoryCondition\\n return win_or_not\\n\\n if not self.currentState.nextChildToVisit: \\n its = 0\\n for movable in self.gm.getMovables():\\n its += 1\\n # time test\\n # too long \\n if its == \\\"too long\\\":\\n return \\\"too long\\\"\\n #make every move in movable\\n self.gm.makeMove(movable)\\n new = self.gm.getGameState()\\n new_gs = GameState(new, self.currentState.depth+1, movable)\\n \\n if new_gs not in self.visited:\\n new_gs.parent = self.currentState\\n self.currentState.children.append(new_gs)\\n self.gm.reverseMove(movable) \\n \\n num_children = len(self.currentState.children)\\n if self.currentState.nextChildToVisit < num_children:\\n new = self.currentState.children[self.currentState.nextChildToVisit]\\n self.currentState.nextChildToVisit = self.currentState.nextChildToVisit + 1\\n self.gm.makeMove(new.requiredMovable)\\n self.currentState = new\\n #recurse\\n return self.solveOneStep()\\n else:\\n self.currentState.nextChildToVisit = self.currentState.nextChildToVisit + 1\\n self.gm.reverseMove(self.currentState.requiredMovable)\\n self.currentState = self.currentState.parent\\n #recurse\\n return self.solveOneStep()\",\n \"def make_move(self, state):\\r\\n # intially set drop phase to true\\r\\n drop_phase = True\\r\\n move = [] # list to make moves with to return\\r\\n succ = self.succ(state) # get the successor of this state\\r\\n # intial postion of board to set up most advantagous spot if its empty\\r\\n if sum(x.count(self.my_piece) for x in self.board) == 0 and self.board[2][2] == ' ':\\r\\n move.insert(0, (2, 2))\\r\\n return move\\r\\n \\r\\n # check the number of 'r' and 'b' on board if theres 4 of each drop phase is false\\r\\n if sum(x.count('r') for x in self.board) == 4 and sum(x.count('b') for x in self.board) == 4:\\r\\n drop_phase = False\\r\\n\\r\\n # if not during drop phase use minimax to make next move from one postion to next\\r\\n if not drop_phase:\\r\\n move = []\\r\\n d = self.Max_value(state, 0)\\r\\n val = d['val']\\r\\n m = d['move']\\r\\n p = d['pos']\\r\\n f = d['from']\\r\\n s = sorted(succ, key=lambda e: e['f'])\\r\\n moveto = s[-1]\\r\\n move.insert(1, (moveto['from'][0], moveto['from'][1]))\\r\\n move.insert(0, (moveto['pos'][0], moveto['pos'][1]))\\r\\n return move # return the from, to move\\r\\n\\r\\n else: #else use minimax and to make move during drop phase selecting spot to place AI piece\\r\\n d = self.Max_value(state, 0)\\r\\n val = d['val']\\r\\n m = d['move']\\r\\n p = d['pos']\\r\\n hold = []\\r\\n move = []\\r\\n n = None\\r\\n hold = []\\r\\n for s in succ:\\r\\n p = s['pos'][0]\\r\\n p1 = s['pos'][1]\\r\\n if s['f'] == val and state[p][p1] == ' ':\\r\\n hold.append(s)\\r\\n if len(hold) == 1:\\r\\n row = hold[0]['pos'][0]\\r\\n col = hold[0]['pos'][1]\\r\\n else:\\r\\n f = sorted(hold, key=lambda e: e['pos'])\\r\\n row = f[0]['pos'][0]\\r\\n col = f[0]['pos'][1]\\r\\n\\r\\n move.insert(0, (row, col)) # return the move \\r\\n return move\",\n \"def move(self, row: int, col: int, player: int):\\n def addup(dict_name, invalid_set, another_invalid, locx, locy):\\n if locx == locy:\\n diag_name = (1,1)\\n if diag_name not in invalid_set:\\n dict_name[diag_name] += 1\\n if dict_name[diag_name] == self.tar:\\n return player\\n another_invalid.add(diag_name)\\n if locx == self.tar-1-locy:\\n diag_name = (-1, -1)\\n if diag_name not in invalid_set:\\n dict_name[diag_name] += 1\\n if dict_name[diag_name] == self.tar:\\n return player\\n another_invalid.add(diag_name)\\n curcol = (locy, None)\\n currow = (None, locx)\\n if curcol not in invalid_set:\\n dict_name[curcol] += 1\\n if dict_name[curcol] == self.tar:\\n return player\\n another_invalid.add(curcol)\\n if currow not in invalid_set:\\n dict_name[currow] += 1\\n if dict_name[currow] == self.tar:\\n return player\\n another_invalid.add(currow)\\n return 0\\n res = 0\\n if (row, col) not in self.walked:\\n if player == 1:\\n res = addup(self.p1, self.invalid_1, self.invalid_2, row, col)\\n if player == 2:\\n res = addup(self.p2, self.invalid_2, self.invalid_1, row, col)\\n self.walked.add((row, col))\\n return res\",\n \"def solveOneStep(self):\\n ### Student code goes here\\n\\n if self.currentState.state == self.victoryCondition:\\n return True\\n\\n current_depth = self.currentState.depth\\n found_move = False\\n while self.currentState.parent:\\n self.gm.reverseMove(self.currentState.requiredMovable)\\n self.currentState = self.currentState.parent\\n count = self.currentState.nextChildToVisit\\n if len(self.currentState.children) > count:\\n found_move = True\\n break\\n if not found_move:\\n for all_visited in self.visited.keys():\\n all_visited.nextChildToVisit = 0\\n current_depth += 1\\n if len(self.visited) == 1:\\n all_possible_moves = self.gm.getMovables()\\n for every_move in all_possible_moves:\\n self.gm.makeMove(every_move)\\n new_game_state = GameState(self.gm.getGameState(), current_depth, every_move)\\n new_game_state.parent = self.currentState\\n self.visited[new_game_state] = False\\n self.currentState.children.append(new_game_state)\\n self.gm.reverseMove(every_move)\\n while current_depth != self.currentState.depth:\\n count = self.currentState.nextChildToVisit\\n self.currentState.nextChildToVisit += 1\\n if len(self.currentState.children) > count:\\n self.currentState = self.currentState.children[count]\\n next_move = self.currentState.requiredMovable\\n self.gm.makeMove(next_move)\\n else:\\n found_move = False\\n while self.currentState.parent:\\n self.gm.reverseMove(self.currentState.requiredMovable)\\n self.currentState = self.currentState.parent\\n if len(self.currentState.children) > self.currentState.nextChildToVisit:\\n found_move = True\\n break\\n if not found_move:\\n return False\\n\\n if self.currentState.state != self.victoryCondition:\\n self.visited[self.currentState] = True\\n all_possible_moves = self.gm.getMovables()\\n next_depth = current_depth + 1\\n for every_move in all_possible_moves:\\n self.gm.makeMove(every_move)\\n new_game_state = GameState(self.gm.getGameState(), next_depth, every_move)\\n if new_game_state not in self.visited:\\n self.visited[new_game_state] = False\\n new_game_state.parent = self.currentState\\n self.currentState.children.append(new_game_state)\\n self.gm.reverseMove(every_move)\\n return False\\n else:\\n return True\",\n \"def exec_all_moves(self,level=0):\\n\\n capts = [self.op_capture_north, self.op_capture_nwest, self.op_capture_neast, self.op_capture_east, self.op_capture_west]\\n jmps = [self.op_jump_north, self.op_jump_nwest, self.op_jump_neast]\\n moves = [self.op_move_north,self.op_move_nwest, self.op_move_neast]\\n result = []\\n ops = []\\n\\n # Pre-select all operations that may be executed\\n if self.next_move == self.FREE:\\n capturingStarts, otherStarts = self.possible_capture()\\n\\n # Check for pieces that may capture\\n if len(capturingStarts) > 0:\\n self.next_move = self.CAPT\\n self.next_pieces = capturingStarts\\n else:\\n self.next_move = self.FREE\\n jmps.extend(moves)\\n ops = jmps\\n self.next_pieces = otherStarts\\n\\n elif self.next_move == self.CAPT:\\n ops = capts\\n elif self.next_move == self.JUMP:\\n ops = jmps\\n elif self.next_move == self.FREE:\\n jmps.extend(moves)\\n ops = jmps\\n elif self.next_move == self.ADDPIECE_2:\\n return self.op_add_piece_bot(self.next_pieces)\\n\\n # Execute possible operations for all viable pieces\\n for pos in self.next_pieces:\\n for op in ops:\\n newState = op(pos)\\n\\n # Check if op succeeded\\n if newState:\\n\\n # If the next player is the current player than the function is called recursevely, this is done so that outcomes account for successive plays by the same\\n # player ( successive jumps and captures or piece addition)\\n if newState.curr_player != self.curr_player:\\n result.append(newState)\\n else:\\n result.extend(newState.exec_all_moves(level+1))\\n\\n\\n return result\",\n \"def make_move(self, move):\\n # Unpack the information from the move\\n piece = self.get_piece(move[0])\\n row = move[1][0]\\n col = move[1][1]\\n eliminated_pieces = move[2]\\n\\n # Move the piece and eliminate the captured pieces\\n self.board[piece.row][piece.col] = None\\n self.board[row][col] = piece\\n piece.row = row\\n piece.col = col\\n self.remove(eliminated_pieces)\\n\\n # Checks if the piece has been promoted\\n if piece.get_player() == Player.black and \\\\\\n piece.row == constant.BOARD_DIMENSION - 1 \\\\\\n and not piece.is_king():\\n piece.make_king()\\n self.num_black_kings += 1\\n\\n elif piece.get_player() == Player.white and \\\\\\n piece.row == 0 \\\\\\n and not piece.is_king():\\n piece.make_king()\\n self.num_white_kings += 1\\n\\n # Add the move performed to the history of the game\\n self.moves.append(move)\",\n \"def update(self):\\n keys = pygame.key.get_pressed() # Checks for an input by the user\\n if keys[pygame.K_RIGHT]:\\n king.move_right() # Moves right if the user presses the right key\\n\\n if keys[pygame.K_LEFT]:\\n king.move_left() # Moves left if the user presses the left key\",\n \"def updateGameState(self):\\n boardArray = self._board.get_board()\\n \\n if self.state is self._BEGIN:\\n\\n for i in [1, self._board.get_board_size() - 2]:\\n for j in range(1, self._board.get_board_size() - 1):\\n\\n if boardArray[i][j] != self._board._EMPTY:\\n self.state = self._MIDDLE\\n return\\n\\n if boardArray[j][i] != self._board._EMPTY:\\n self.state = self._MIDDLE\\n return\\n\\n\\n elif self.state is self._MIDDLE:\\n nbPieces = self._board.get_total_pieces()\\n\\n if nbPieces >= self._board.get_board_size()**2 - ENDGAME:\\n self.state = self._END\\n return\",\n \"def _advance_turn(self):\\n\\n self.__turn_info['turn'] = ChessGame.BLACK if\\n self.__turn_info['turn'] == ChessGame.WHITE else ChessGame.WHITE\",\n \"def test_valid_moves(self):\\n b1 = board.Board(self.small_plain)\\n start = np.array((3, 3), dtype='int')\\n k1 = knight.Knight(b1,start)\\n # set move choice 0\\n move_choice = 0\\n # change the board layout to reflect the move\\n k1.execute_move(move_choice)\\n self.assertTrue((k1.position == np.array((5, 4), dtype='int')).all())\\n # confirm state of board\\n with capture_output() as (out, _):\\n b1.display()\\n my_out = out.getvalue().strip()\\n out.close()\\n out_list = [ each.strip() for each in\\n \\\"\\\"\\\". . . . . . . .\\n . . . . . . . .\\n . . . . . . . .\\n . . . S . . . .\\n . . . x . . . .\\n . . . x K . . .\\n . . . . . . . .\\n . . . . . . . .\\\"\\\"\\\".split('\\\\n')]\\n expected_out = '\\\\n'.join(out_list)\\n self.assertEqual(my_out, expected_out)\\n #\\n # set move choice 5\\n move_choice = 5\\n # change the board layout to reflect the move\\n k1.execute_move(move_choice)\\n self.assertTrue((k1.position == np.array((3, 5), dtype='int')).all())\\n # confirm state of board\\n with capture_output() as (out, _):\\n b1.display()\\n my_out = out.getvalue().strip()\\n out.close()\\n out_list = [ each.strip() for each in\\n \\\"\\\"\\\". . . . . . . .\\n . . . . . . . .\\n . . . . . . . .\\n . . . S x K . .\\n . . . x x . . .\\n . . . x S . . .\\n . . . . . . . .\\n . . . . . . . .\\\"\\\"\\\".split('\\\\n')]\\n expected_out = '\\\\n'.join(out_list)\\n self.assertEqual(my_out, expected_out)\\n #\\n # set move choice 6\\n move_choice = 6\\n # change the board layout to reflect the move\\n k1.execute_move(move_choice)\\n self.assertTrue((k1.position == np.array((2, 7), dtype='int')).all())\\n # confirm state of board\\n with capture_output() as (out, _):\\n b1.display()\\n my_out = out.getvalue().strip()\\n out.close()\\n out_list = [ each.strip() for each in\\n \\\"\\\"\\\". . . . . . . .\\n . . . . . . . .\\n . . . . . . . K\\n . . . S x S x x\\n . . . x x . . .\\n . . . x S . . .\\n . . . . . . . .\\n . . . . . . . .\\\"\\\"\\\".split('\\\\n')]\\n expected_out = '\\\\n'.join(out_list)\\n self.assertEqual(my_out, expected_out)\\n #\\n # set move choice 3\\n move_choice = 3\\n # change the board layout to reflect the move\\n k1.execute_move(move_choice)\\n self.assertTrue((k1.position == np.array((1, 5), dtype='int')).all())\\n # confirm state of board\\n with capture_output() as (out, _):\\n b1.display()\\n my_out = out.getvalue().strip()\\n out.close()\\n out_list = [ each.strip() for each in\\n \\\"\\\"\\\". . . . . . . .\\n . . . . . K . .\\n . . . . . x x S\\n . . . S x S x x\\n . . . x x . . .\\n . . . x S . . .\\n . . . . . . . .\\n . . . . . . . .\\\"\\\"\\\".split('\\\\n')]\\n expected_out = '\\\\n'.join(out_list)\\n self.assertEqual(my_out, expected_out)\\n #\\n # set move choice 2\\n move_choice = 2\\n # change the board layout to reflect the move\\n k1.execute_move(move_choice)\\n self.assertTrue((k1.position == np.array((2, 3), dtype='int')).all())\\n # confirm state of board\\n with capture_output() as (out, _):\\n b1.display()\\n my_out = out.getvalue().strip()\\n out.close()\\n out_list = [ each.strip() for each in\\n \\\"\\\"\\\". . . . . . . .\\n . . . x x S . .\\n . . . K . x x S\\n . . . S x S x x\\n . . . x x . . .\\n . . . x S . . .\\n . . . . . . . .\\n . . . . . . . .\\\"\\\"\\\".split('\\\\n')]\\n expected_out = '\\\\n'.join(out_list)\\n self.assertEqual(my_out, expected_out)\\n #\\n # reset board\\n b1 = board.Board(self.small_plain)\\n start = np.array((3, 3), dtype='int')\\n k1 = knight.Knight(b1,start)\\n # set move choice 7\\n move_choice = 7\\n # change the board layout to reflect the move\\n k1.execute_move(move_choice)\\n self.assertTrue((k1.position == np.array((4, 5), dtype='int')).all())\\n # confirm state of board\\n with capture_output() as (out, _):\\n b1.display()\\n my_out = out.getvalue().strip()\\n out.close()\\n out_list = [ each.strip() for each in\\n \\\"\\\"\\\". . . . . . . .\\n . . . . . . . .\\n . . . . . . . .\\n . . . S x x . .\\n . . . . . K . .\\n . . . . . . . .\\n . . . . . . . .\\n . . . . . . . .\\\"\\\"\\\".split('\\\\n')]\\n expected_out = '\\\\n'.join(out_list)\\n self.assertEqual(my_out, expected_out)\\n #\\n # set move choice 1\\n move_choice = 1\\n # change the board layout to reflect the move\\n k1.execute_move(move_choice)\\n self.assertTrue((k1.position == np.array((6, 4), dtype='int')).all())\\n # confirm state of board\\n with capture_output() as (out, _):\\n b1.display()\\n my_out = out.getvalue().strip()\\n out.close()\\n out_list = [ each.strip() for each in\\n \\\"\\\"\\\". . . . . . . .\\n . . . . . . . .\\n . . . . . . . .\\n . . . S x x . .\\n . . . . . S . .\\n . . . . . x . .\\n . . . . K x . .\\n . . . . . . . .\\\"\\\"\\\".split('\\\\n')]\\n expected_out = '\\\\n'.join(out_list)\\n self.assertEqual(my_out, expected_out)\\n #\\n # set move choice 4\\n move_choice = 4\\n # change the board layout to reflect the move\\n k1.execute_move(move_choice)\\n self.assertTrue((k1.position == np.array((4, 3), dtype='int')).all())\\n # confirm state of board\\n with capture_output() as (out, _):\\n b1.display()\\n my_out = out.getvalue().strip()\\n out.close()\\n out_list = [ each.strip() for each in\\n \\\"\\\"\\\". . . . . . . .\\n . . . . . . . .\\n . . . . . . . .\\n . . . S x x . .\\n . . . K x S . .\\n . . . . x x . .\\n . . . . S x . .\\n . . . . . . . .\\\"\\\"\\\".split('\\\\n')]\\n expected_out = '\\\\n'.join(out_list)\\n self.assertEqual(my_out, expected_out)\",\n \"def get_pawn_moves(self, i, j, moves):\\r\\n # white pawn\\r\\n if self.turn_white:\\r\\n # one&two squares moves\\r\\n if self.board[i - 1][j] == \\\"--\\\":\\r\\n moves.append(Move((i, j), (i - 1, j), self.board))\\r\\n if i == 6 and self.board[i - 2][j] == \\\"--\\\":\\r\\n moves.append(Move((i, j), (i - 2, j), self.board))\\r\\n # capturing\\r\\n # left\\r\\n if j >= 1:\\r\\n if self.board[i - 1][j - 1][0] == \\\"b\\\":\\r\\n moves.append(Move((i, j), (i - 1, j - 1), self.board))\\r\\n elif (i - 1, j - 1) == self.enpas_pos:\\r\\n moves.append(Move((i, j), (i - 1, j - 1), self.board, enpassant=True))\\r\\n\\r\\n # right\\r\\n if j <= 6:\\r\\n if self.board[i - 1][j + 1][0] == \\\"b\\\":\\r\\n moves.append(Move((i, j), (i - 1, j + 1), self.board))\\r\\n elif (i - 1, j + 1) == self.enpas_pos:\\r\\n moves.append(Move((i, j), (i - 1, j + 1), self.board, enpassant=True))\\r\\n # black\\r\\n else:\\r\\n # one&two squares moves\\r\\n if self.board[i + 1][j] == \\\"--\\\":\\r\\n moves.append(Move((i, j), (i + 1, j), self.board))\\r\\n if i == 1 and self.board[i + 2][j] == \\\"--\\\":\\r\\n moves.append(Move((i, j), (i + 2, j), self.board))\\r\\n # capturing\\r\\n # left\\r\\n if j >= 1:\\r\\n if self.board[i + 1][j - 1][0] == \\\"w\\\":\\r\\n moves.append(Move((i, j), (i + 1, j - 1), self.board))\\r\\n elif (i + 1, j - 1) == self.enpas_pos:\\r\\n moves.append(Move((i, j), (i + 1, j - 1), self.board, enpassant=True))\\r\\n # right\\r\\n if j <= 6:\\r\\n if self.board[i + 1][j + 1][0] == \\\"w\\\":\\r\\n moves.append(Move((i, j), (i + 1, j + 1), self.board))\\r\\n elif (i + 1, j + 1) == self.enpas_pos:\\r\\n moves.append(Move((i, j), (i + 1, j + 1), self.board, enpassant=True))\",\n \"def player_place_units(self):\\n # If player is \\\"losing\\\" to someone next to them, funnel units towards that square\\n places_needing_units = []\\n left_to_place = self.max_units\\n for node in self.nodes:\\n old_units = self.board.nodes[node]['old_units']\\n num_needed_here = 0\\n for neighbor in self.board[node]:\\n neighbor = self.board.nodes[neighbor]\\n if(neighbor['owner'] != self.player_num):\\n num_needed_here += neighbor['old_units']\\n self.front_line.append(node)\\n places_needing_units.append((node, num_needed_here, self.find_dist_to_enemy_node(src_node=node)))\\n places_needing_units = sorted(filter(lambda x: x[1] > 0,places_needing_units), key = lambda x: x[1])\\n\\n places_needing_units = sorted(places_needing_units, key=lambda x: x[2])\\n\\n for (node,amount) in places_needing_units:\\n amount_to_place_here = min(left_to_place,amount+1)\\n self.place_unit(node, amount_to_place_here)\\n left_to_place -= amount_to_place_here\\n if(left_to_place <= 0):\\n break\\n\\n nodes_added_to = [a[0] for a in places_needing_units]\\n\\n # add one to front_line nodes\\n if(left_to_place > 0):\\n for node in self.front_line:\\n self.place_unit(node,1)\\n left_to_place -= 1\\n if(left_to_place <= 0):\\n break\\n\\n return self.dict_moves #Returns moves built up over the phase. Do not modify!\",\n \"def make_move(self, move: Any) -> \\\"StonehengeState\\\":\\n new_board = deepcopy(self.current_board)\\n for index in range(len(self.current_board)):\\n if self.current_board[index] == move:\\n if self.p1_turn:\\n new_board = new_board.replace(\\n self.current_board[index], '1')\\n else:\\n new_board = new_board.replace(\\n self.current_board[index], '2')\\n new_ley_lines = deepcopy(self.current_ley_lines)\\n for item in new_ley_lines:\\n for key in item:\\n for index in range(len(key[1])):\\n if key[1][index] == move:\\n if self.p1_turn:\\n key[1][index] = '1'\\n else:\\n key[1][index] = '2'\\n change_dict = {}\\n for item in new_ley_lines:\\n for key in item:\\n p1_count = 0\\n p2_count = 0\\n for string in key[1]:\\n if string == '1':\\n p1_count += 1\\n if string == '2':\\n p2_count += 1\\n\\n\\n if p1_count >= len(key[1])/2 and p1_count > p2_count:\\n\\n change_dict[key[0]] = '1'\\n if p2_count >= len(key[1])/2 and p2_count > p1_count:\\n\\n change_dict[key[0]] = '2'\\n for key in change_dict:\\n if not (key == '1' or key == '2'):\\n if str(key) in new_board:\\n new_board = new_board.replace(str(key), change_dict[key])\\n for item in new_ley_lines:\\n for key1 in item:\\n if key == key1[0]:\\n key1[0] = change_dict[key]\\n\\n new_state = StonehengeState(not self.p1_turn, self.side_length,\\n new_board, new_ley_lines)\\n return new_state\",\n \"def get_pawn_moves(self, state):\\n pawn_moves = []\\n\\n if self.color == cc.WHITE_ACTIVE:\\n forward_1 = add_vectors(self.coord, cc.V_UP)\\n forward_2 = add_vectors(self.coord, cc.V_UP_2)\\n attacks = get_crawler_moves(self.coord, cc.W_PAWN_CAPTURE_VECTORS)\\n starting_rank = cc.RANK_2\\n promo_rank = cc.RANK_8\\n promo_pieces = cc.WHITE_PROMO\\n enemy_set = cc.BLACK_PIECES\\n elif self.color == cc.BLACK_ACTIVE:\\n forward_1 = add_vectors(self.coord, cc.V_DOWN)\\n forward_2 = add_vectors(self.coord, cc.V_DOWN_2)\\n attacks = get_crawler_moves(self.coord, cc.B_PAWN_CAPTURE_VECTORS)\\n starting_rank = cc.RANK_7\\n promo_rank = cc.RANK_1\\n promo_pieces = cc.BLACK_PROMO\\n enemy_set = cc.WHITE_PIECES\\n else:\\n raise Exception(\\\"get_pawn_moves: Invalid Piece Color\\\")\\n\\n if validate_move(forward_1) and state.board[forward_1] == cc.NO_PIECE:\\n if forward_1[0] == promo_rank:\\n for p in promo_pieces:\\n pawn_moves.append(cc.Action(self.string, self.coord, forward_1, promo=p))\\n else:\\n pawn_moves.append(cc.Action(self.string, self.coord, forward_1))\\n if self.coord[0] == starting_rank and validate_move(forward_2) and state.board[forward_2] == cc.NO_PIECE:\\n pawn_moves.append(cc.Action(self.string, self.coord, forward_2, en_p=forward_1))\\n\\n for attack in attacks:\\n if state.board[attack] in enemy_set:\\n if attack[0] == promo_rank:\\n for p in promo_pieces:\\n pawn_moves.append(cc.Action(self.string, self.coord, attack, capture=True, promo=p))\\n else:\\n pawn_moves.append(cc.Action(self.string, self.coord, attack, capture=True))\\n # Make sure Pawns can attack en_passant squares\\n elif attack == state.en_passant:\\n pawn_moves.append(cc.Action(self.string, self.coord, attack, capture=True))\\n\\n return pawn_moves\",\n \"def sideways_moves(queens, threshold=100):\\n side_counter = 0\\n steps = 0\\n queens.print_board()\\n print(\\\"conflicts: {}\\\".format(queens.heuristic()))\\n\\n while True:\\n steps += 1\\n current_h = queens.heuristic()\\n neighbors, conflicts = queens.neighbors()\\n\\n # Find the neighbor with the smallest amount of conflicts (h) \\n min_idx = random_minimum_neighbor(conflicts)\\n next_state = queens.coords_to_board(neighbors[min_idx])\\n next_state_h = conflicts[min_idx]\\n\\n # Increase or reset side was moves base on equality\\n if next_state_h == current_h:\\n side_counter += 1\\n elif next_state_h < current_h:\\n side_counter = 0\\n\\n queens.board = next_state\\n queens.print_board()\\n print(\\\"conflicts: {}\\\".format(next_state_h))\\n print(\\\"step: {}\\\".format(steps))\\n print(\\\"sideways move: {}\\\".format(side_counter))\\n \\n # Success check\\n if next_state_h == 0:\\n print(\\\"SUCCESS!\\\")\\n return True, steps\\n # Failure check\\n if next_state_h > current_h :\\n print(\\\"FAILED!\\\")\\n return False, steps\\n # Sideways move check\\n if side_counter >= threshold:\\n print(\\\"FAILED!\\\")\\n return False, steps\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.58154815\",\n \"0.5813604\",\n \"0.5812575\",\n \"0.5805229\",\n \"0.5805039\",\n \"0.58043665\",\n \"0.5798948\",\n \"0.57957274\",\n \"0.57901627\",\n \"0.57858264\",\n \"0.57828104\",\n \"0.57746303\",\n \"0.5773591\",\n \"0.5769014\",\n \"0.57688516\",\n \"0.57661015\",\n \"0.57558846\",\n \"0.57532567\",\n \"0.57523715\",\n \"0.5748808\",\n \"0.57469076\",\n \"0.5733973\",\n \"0.57285523\",\n \"0.57285225\",\n \"0.57248145\",\n \"0.57117933\",\n \"0.56955504\",\n \"0.5694392\",\n \"0.56922156\",\n \"0.5689399\",\n \"0.5674782\",\n \"0.5674613\",\n \"0.5666573\",\n \"0.56643534\",\n \"0.566352\",\n \"0.56628424\",\n \"0.5645118\",\n \"0.56437546\",\n \"0.5635512\",\n \"0.5634545\",\n \"0.56264126\",\n \"0.56226104\",\n \"0.56194776\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.85099286"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":84989,"cells":{"query":{"kind":"string","value":"Appends the possible move of the king if castling is possible"},"document":{"kind":"string","value":"def castling(self, turn, ai):\n\n if self.board[self.coords.index(piece_class.KING_LOCATION[turn])].move_track == True:\n return None\n \n \n castling_queenside = [self.coords.index(piece_class.KING_LOCATION[turn]), self.coords.index(piece_class.KING_LOCATION[turn]) - 1,\n self.coords.index(piece_class.KING_LOCATION[turn]) - 2, self.coords.index(piece_class.KING_LOCATION[turn]) - 3,\n self.coords.index(piece_class.KING_LOCATION[turn]) - 4]\n castling_kingside = [self.coords.index(piece_class.KING_LOCATION[turn]), self.coords.index(piece_class.KING_LOCATION[turn]) + 1,\n self.coords.index(piece_class.KING_LOCATION[turn]) + 2, self.coords.index(piece_class.KING_LOCATION[turn]) + 3]\n \n if self.castling_valid(turn, castling_kingside):\n self.board[self.coords.index(piece_class.KING_LOCATION[turn])].possible_moves.append(self.coords[castling_kingside[2]])\n \n if self.castling_valid(turn, castling_queenside):\n self.board[self.coords.index(piece_class.KING_LOCATION[turn])].possible_moves.append(self.coords[castling_queenside[2]])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def king_adjust(self, turn):\n\n opposite_turn = next_turn(turn)\n\n original_location_index = (piece_class.KING_LOCATION[turn][0] + piece_class.KING_LOCATION[turn][1] * 8)\n \n# if self.board[original_location_index] == self.empty:\n# print(\"yo\")\n \n self.board[original_location_index].possible_moves = [i for i in self.board[original_location_index].possible_moves if i not in self.long_dict[opposite_turn]]\n \n\n\n #king_path = [i for i in self.board[original_location_index].possible_moves if i not in self.path_dict[opposite_turn]]\n\n #removes moving into check from king path. Can remove this functionality from the move method now.\n #self.board[original_location_index].possible_moves = king_path\n #king_path_index = [(i[0] + i[1]*8) for i in self.board[original_location_index].possible_moves]\n\n \n #for i in king_path:\n # if i in self.long_dict[opposite_turn] or self.check_dict[opposite_turn]:\n # print(\"king path is: \", king_path)\n # king_path.remove(i)\n # print(\"king path is now: \", king_path)\n \n\n\n\n\n #for i in king_path_index:\n # enemy_piece = self.board[i]\n # self.board[i] = self.board[original_location_index]\n # self.board[original_location_index] = self.empty\n # self.loads_pathways(turn)\n # if self.coords[i] in self.path_dict[opposite_turn]:\n # print(\"yo\")\n \n #self.board[original_location_index] = self.board[i]\n #self.board[i] = enemy_piece","def castling_implement(self, turn, select, move):\n \n if select == self.coords.index(piece_class.KING_LOCATION[turn]):\n if self.board[self.coords.index(piece_class.KING_LOCATION[turn])].move_track == False:\n if move in [2, 58]:\n self.board[move+1] = self.board[move-2]\n self.board[move-2] = self.empty\n if move in [6, 62]:\n self.board[move-1] = self.board[move+1]\n self.board[move+1] = self.empty","def get_valid_moves(self):\r\n # castling and en-passant rights are stored, because move affects these values\r\n temp_enpassant_possible = self.enpas_pos\r\n temp_castle = CastleRights(self.cr_castle_r.wks, self.cr_castle_r.bks,\r\n self.cr_castle_r.wqs, self.cr_castle_r.bqs)\r\n\r\n # for validating a possible move\r\n #1 all possibile moves are generated\r\n #2 each pos moves are made\r\n #3 generate opponent move\r\n #4 check if any of those moves let the king attacked\r\n #5 moves which let the king in chess are eliminated\r\n #6 the moves are undone\r\n moves = self.get_all_possible_moves() # 1\r\n\r\n # castle moves are directly introduced in valid moves\r\n if not self.turn_white:\r\n self.get_castle_moves(self.bKingPos[0], self.bKingPos[1], moves)\r\n else:\r\n self.get_castle_moves(self.wKingPos[0], self.wKingPos[1], moves)\r\n\r\n for i in range(len(moves) - 1, -1, -1): # 2\r\n self.make_move(moves[i])\r\n # 3 #4\r\n self.turn_white = not self.turn_white\r\n if self.in_check():\r\n moves.remove(moves[i]) # 5\r\n self.turn_white = not self.turn_white\r\n self.undo_move()\r\n\r\n # game ending possibilities\r\n if len(moves) == 0:\r\n if self.in_check():\r\n self.checkMate = True\r\n print(\"Checkmate !\")\r\n else:\r\n self.staleMate = True\r\n print(\"Stalemate !\")\r\n else:\r\n self.checkMate = False\r\n self.staleMate = False\r\n\r\n # the rigths are restored, and the values are not affected\r\n self.enpas_pos = temp_enpassant_possible\r\n self.cr_castle_r = temp_castle\r\n\r\n return moves","def make_move(self, move, check_valid=True):\r\n self.board[move.sr][move.sc] = \"--\"\r\n self.board[move.er][move.ec] = move.pieceMoved\r\n self.moveLog.append(move)\r\n self.turn_white = not self.turn_white\r\n if move.pieceMoved == 'wk':\r\n self.wKingPos = (move.er, move.ec)\r\n elif move.pieceMoved == 'bk':\r\n self.bKingPos = (move.er, move.ec)\r\n\r\n if move.isEnpassantMove:\r\n self.board[move.sr][move.ec] = \"--\"\r\n\r\n if move.pieceMoved[1] == 'p' and abs(move.sr - move.er) == 2:\r\n self.enpas_pos = ((move.er + move.sr) // 2, move.ec)\r\n else:\r\n self.enpas_pos = ()\r\n\r\n if move.isPawnPromotion and not check_valid:\r\n promoted_piece = \"a\"\r\n while promoted_piece not in ('q', 'r', 'b', 'n'):\r\n promoted_piece = input(\"Promote to q, r, b, or n: \")\r\n self.board[move.er][move.ec] = move.pieceMoved[0] + promoted_piece\r\n\r\n # castle\r\n if move.castle:\r\n if move.ec - move.sc == 2:\r\n self.board[move.er][move.ec - 1] = self.board[move.er][move.ec + 1]\r\n self.board[move.er][move.ec + 1] = '--'\r\n else:\r\n self.board[move.er][move.ec + 1] = self.board[move.er][move.ec - 2]\r\n self.board[move.er][move.ec - 2] = '--'\r\n\r\n # castle rights on rook, king move\r\n self.update_castle_rights(move)\r\n self.castleRightsLog.append(CastleRights(self.cr_castle_r.wks, self.cr_castle_r.bks,\r\n self.cr_castle_r.wqs, self.cr_castle_r.bqs))","def _get_possible_castles(self, piece):\n\n valid_moves = set()\n\n # Cant castle while under check\n if self._check_for_check():\n return valid_moves\n\n row = self._get_castling_row(piece)\n king = 'K' if self.white_to_move else 'k'\n queen = 'Q' if self.white_to_move else 'q'\n\n # If there is nothing in the way, we can castle kingside\n if king in self.available_castles: # Does not check if f file is in check\n if self.board[row][5] == self.board[row][6] == EMPTY_SPACE:\n valid_moves.add(CASTLE_KINGSIDE)\n\n if queen in self.available_castles: # Does not check if d file is in check\n if self.board[row][1] == self.board[row][2] == self.board[row][3] == EMPTY_SPACE:\n valid_moves.add(CASTLE_QUEENSIDE)\n\n return valid_moves","def get_next_moves(board, player):\r\n\r\n if player == 'hare':\r\n moves = []\r\n next_moves = []\r\n\r\n (row_from, col_from) = get_hare_positions(board)\r\n moves = possible_moves_list(row_from, col_from)\r\n\r\n for move in moves:\r\n row_to = move[0]\r\n col_to = move[1]\r\n\r\n if is_legal_move(player, row_from, col_from, row_to, col_to):\r\n \"\"\" if move is allowed then add to list of next moves\"\"\"\r\n next_moves.append(move)\r\n\r\n return next_moves\r\n\r\n else:\r\n \"\"\" for individual hounds\r\n get next moves\"\"\"\r\n moves = []\r\n next_moves_hound1 = []\r\n next_moves_hound2 = []\r\n next_moves_hound3 = []\r\n\r\n (row_hound_1, col_hound_1), (row_hound_2, col_hound_2), (row_hound_3, col_hound_3) = get_hound_positions(board)\r\n moves_hound1 = possible_moves_list(row_hound_1, col_hound_1)\r\n moves_hound2 = possible_moves_list(row_hound_2, col_hound_2)\r\n moves_hound3 = possible_moves_list(row_hound_3, col_hound_3)\r\n\r\n for move in moves_hound1:\r\n row_to = move[0]\r\n col_to = move[1]\r\n\r\n if is_legal_move(player, row_hound_1, col_hound_1, row_to, col_to):\r\n next_moves_hound1.append(move)\r\n\r\n for move in moves_hound2:\r\n row_to = move[0]\r\n col_to = move[1]\r\n\r\n if is_legal_move(player, row_hound_2, col_hound_2, row_to, col_to):\r\n next_moves_hound2.append(move)\r\n\r\n for move in moves_hound3:\r\n row_to = move[0]\r\n col_to = move[1]\r\n\r\n if is_legal_move(player, row_hound_3, col_hound_3, row_to, col_to):\r\n next_moves_hound3.append(move)\r\n\r\n return (next_moves_hound1, next_moves_hound2, next_moves_hound3)","def get_legal_moves(self):\n # for each square in the castle figure out if an moves can occur from it.\n moves = []\n allowed = [self.turn]\n if self.turn == DEFENDER:\n allowed.extend((KING, CASTLE_OCCUPIED))\n it = np.nditer(self.board_state, flags=['multi_index'])\n while not it.finished:\n index = it.multi_index\n curr_loc = it[0]\n if curr_loc in allowed:\n moves.extend(self.get_legal_move_piece(curr_loc, index))\n it.iternext()\n return moves","def make_move(move):\n global manatee_pos\n global hyacinths\n global hyacinth_pos\n\n # Ends the program if movement is out of bounds\n if move == (0, 0):\n return None\n new_pos = (manatee_pos[0] + move[0], manatee_pos[1] + move[1])\n if new_pos[0] < 0 or new_pos[0] >= len(map):\n return None\n if new_pos[1] < 0 or new_pos[1] >= len(map[new_pos[0]]):\n return None\n\n entity = map[new_pos[0]][new_pos[1]]\n if entity == \"#\" or entity == \"G\":\n # Runs if movement is impossible\n return None\n if entity == \" \" or entity == \".\":\n # Runs if normal movement is possible\n map[new_pos[0]][new_pos[1]] = \"M\"\n map[manatee_pos[0]][manatee_pos[1]] = \" \"\n manatee_pos = new_pos\n return None\n if entity == \"O\":\n # Runs if manatee wins game\n map[new_pos[0]][new_pos[1]] = \"M\"\n map[manatee_pos[0]][manatee_pos[1]] = \" \"\n manatee_pos = new_pos\n return \"win\"\n if entity == \"\\\\\":\n # Runs if manatee eats hyacinth\n map[new_pos[0]][new_pos[1]] = \"M\"\n map[manatee_pos[0]][manatee_pos[1]] = \" \"\n manatee_pos = new_pos\n hyacinths += 1\n if len(hyacinth_pos) == hyacinths:\n map[grate_pos[0]][grate_pos[1]] = \"O\"\n return None\n if entity == \"*\":\n # Checks if manatee can push boat\n if move[0] == 0:\n new_boat_pos = (new_pos[0] + move[0], new_pos[1] + move[1])\n if new_boat_pos[0] < 0 or new_boat_pos[0] >= len(map):\n return None\n if new_boat_pos[1] < 0 \\\n or new_boat_pos[1] >= len(map[new_boat_pos[0]]):\n return None\n if map[new_boat_pos[0]][new_boat_pos[1]] == \" \":\n map[new_boat_pos[0]][new_boat_pos[1]] = \"*\"\n map[new_pos[0]][new_pos[1]] = \"M\"\n map[manatee_pos[0]][manatee_pos[1]] = \" \"\n manatee_pos = new_pos\n return None\n return None","def get_all_moves(self, castling_allowed=True):\n\n can_move = str.isupper if self.white_to_move else str.islower\n\n valid_moves = set()\n\n for row_num, row in enumerate(self.board):\n for col_num, piece in enumerate(row):\n if piece != EMPTY_SPACE and can_move(piece):\n\n location = (row_num, col_num)\n\n # Everything except the pawn movement\n if piece.lower() in NAME_TO_PIECE:\n valid_moves = valid_moves.union(self._get_standard_moves_for_piece(location, piece))\n\n # Pawn moves\n if piece.lower() == PAWN:\n valid_moves = valid_moves.union(self._get_pawn_moves(location, piece))\n\n # Castling\n if castling_allowed and piece.lower() == KING:\n valid_moves = valid_moves.union(self._get_possible_castles(piece))\n\n return valid_moves","def __update_valid_moves(self):\r\n\r\n\t\tmoves = ['Stay']\r\n\r\n\t\t# case for 21\r\n\t\tfor value in self.values:\r\n\t\t\tif value > 21:\r\n\t\t\t\tself.valid_moves = 'Bust'\r\n\t\t\t\treturn\r\n\t\t\tif value == 21:\r\n\t\t\t\tif len(self.cards) == 2:\r\n\t\t\t\t\tself.valid_moves = 'Blackjack'\r\n\t\t\t\t\treturn\r\n\t\t\t\tself.valid_moves = '21'\r\n\t\t\t\treturn\r\n\t\tmoves.append('Hit')\r\n\r\n\t\tif len(self.cards) <= 2:\r\n\t\t\tmoves.append('Double')\r\n\r\n\t\tif len(self.cards) == 2:\r\n\t\t\tif self.cards[0].rank == self.cards[1].rank:\r\n\t\t\t\tmoves.append('Split')\r\n\r\n\t\tself.valid_moves = moves","def legal_moves():\n\tlegal_moves = (\"r\", \"p\", \"s\")\n\treturn legal_moves","def get_legal_moves(self, fish_index, player):\n moves = []\n if fish_index == -1:\n return moves\n for direction in [\"left\", \"right\", \"top_left\", \"bottom_right\", \"top_right\", \"bottom_left\"]:\n moves += self.get_moves_direction(fish_index, direction, player)\n if len(moves) == 0:\n # penguin cant move - can kill itself - JK\n pass\n #moves.append(FishMove(fish_index, fish_index, player=player))\n return moves","def getPossibleMoves(self): # called to get possible positions this piece can go\r\n \r\n moves = {}\r\n\r\n ids = []\r\n\r\n for piece in self.board.pieces.values():\r\n if piece.name == \"empty\":\r\n piece.glow = False\r\n piece.ready = False\r\n\r\n self.piece = self\r\n\r\n def check(direction=\"left\", heading=\"north\", x=None, y=None):\r\n piece = self.piece\r\n if direction == \"left\": x -= 50\r\n else: x += 50\r\n\r\n if heading == \"north\": y -= 50\r\n else: y += 50\r\n\r\n if (x, y) in self.board.pieces: # position is empty\r\n empty = self.board.getPiece((x, y))\r\n empty.glow = True\r\n old, new, obj = (direction, heading), (x, y), piece\r\n identity = self.getRandomID(ids) # get an ID for the move\r\n moves[identity] = old, new, obj\r\n\r\n if piece.isKing: # piece is a king, so go on\r\n check(direction, heading, x, y)\r\n else: # its not empty, so check if its comrade\r\n x1, y1 = x+25, y+25\r\n piece2 = self.board.getPiece((x1, y1))\r\n try:\r\n if piece.isComrade(piece2):# piece is comrade so return\r\n return\r\n else: # piece is not comrade, so check empty\r\n if direction == \"left\": x2 = x1-25-50\r\n else: x2 = x1-25+50\r\n\r\n if heading == \"north\": y2 = y1-25-50\r\n else: y2 = y1-25+50\r\n\r\n if (x2, y2) in self.board.pieces: # its empty, so notify player\r\n empty = self.board.getPiece((x2, y2))\r\n empty.glow = True\r\n empty.ready = True\r\n\r\n old, new, obj = (direction, heading), (x2, y2), piece2\r\n identity = self.getRandomID(ids)\r\n moves[identity] = old, new, obj\r\n\r\n check(direction, heading, piece2.x-25, piece2.y-25)\r\n check(direction, heading, x2, y2)\r\n \r\n # check empty or comrade again\r\n if direction == \"left\": x3 = x2-50\r\n else: x3 = x2+50\r\n\r\n if heading == \"north\": y3 = y2-50\r\n else: y3 = y2+50\r\n\r\n if (x3, y3) in self.board.pieces: # positon(address) is empty\r\n return\r\n else: # there is a piece, so check if comrade, stop, if not comrade continue\r\n x3+=25\r\n y3+= 25\r\n\r\n piece3 = self.board.getPiece((x3, y3))\r\n if piece3.isComrade(piece2): # comrades, so stop\r\n return\r\n else: # not comrades, so continue\r\n self.piece = piece3\r\n check(direction, heading, x, y)\r\n\r\n #self.piece = piece2\r\n \r\n #check(direction, heading, x2, y2) # keep searching\r\n else: # its not empty, so return\r\n return\r\n except:\r\n pass\r\n\r\n if self.piece.name == \"white\": direction = \"north\"\r\n else: direction = \"south\"\r\n \r\n check(\"left\", direction, self.piece.x-25, self.piece.y-25)\r\n check(\"right\", direction, self.piece.x-25, self.piece.y-25)\r\n \r\n if self.piece.isKing:\r\n if self.piece.name == \"white\": heading = \"south\"\r\n else: heading = \"north\"\r\n \r\n check(\"left\", heading, self.piece.x-25, self.piece.y-25)\r\n check(\"right\", heading, self.piece.x-25, self.piece.y-25)\r\n\r\n if self.piece.name == \"white\":\r\n eatMoves = self.board.game.thinkEatMoves(moves, \"person\")\r\n if eatMoves is not None:\r\n return eatMoves\r\n\r\n return moves","def spaces_threatened_towards_king(self, list_of_directional_spaces_threatened):\n for lst in list_of_directional_spaces_threatened:\n for move in lst:\n if isinstance(THE_BOARD.coords_to_piece(move), King):\n return(lst)\n \n raise Exception(f\"{self.symbol} not threatening the enemy king\")","def spaces_threatened_towards_king(self, list_of_directional_spaces_threatened):\n for lst in list_of_directional_spaces_threatened:\n for move in lst:\n if isinstance(THE_BOARD.coords_to_piece(move), King):\n return(lst)\n \n raise Exception(f\"{self.symbol} not threatening the enemy king\")","def spaces_threatened_towards_king(self, list_of_directional_spaces_threatened):\n for lst in list_of_directional_spaces_threatened:\n for move in lst:\n if isinstance(THE_BOARD.coords_to_piece(move), King):\n return(lst)\n \n raise Exception(f\"{self.symbol} not threatening the enemy king\")","def action(self):\r\n\r\n\r\n #have we just started?\r\n if self.player_information[\"us\"][\"nTokens\"] == 0:\r\n move = generate_starting_move(self.player_information[\"us\"][\"player_side\"], self.board_array)\r\n return move\r\n\r\n #otherwise do minimax \r\n \r\n #start off with some shallow depth:\r\n if self.turn_no < 5:\r\n depth = 3\r\n else:\r\n depth = 2\r\n \r\n #set a constraint for search depth\r\n if self.total_tokens_on_board < 6:\r\n depth = 3\r\n elif self.total_tokens_on_board < 10:\r\n depth = 2\r\n else:\r\n depth = 1\r\n \r\n #have a time reference\r\n print(f'nthrows: {self.player_information[\"us\"][\"nThrowsRemaining\"]}')\r\n starting_time = int(round(time.time(), 0))\r\n #salvage result from minimax\r\n result = minimax(self.board_dict.copy(), self.player_tokens.copy(), self.co_existance_dict.copy(),\r\n None, None, None, depth, True, -math.inf, math.inf,\r\n (-5, -5), self.player_information.copy(), self.board_array, self.board_edge, \r\n starting_time, True, self.turn_no)\r\n\r\n #clean it up a bit \r\n print(self.board_dict)\r\n #tidy it up\r\n result = result[0]\r\n print(f'pre: {result}')\r\n #in case we get a bad move redo but make it very shallow\r\n if len(result) == 1 or result == (-5, -5):\r\n #force it to return a usable move\r\n counter = 0\r\n while (len(result) == 1) or (result == (-5, -5)):\r\n result = minimax(self.board_dict.copy(), self.player_tokens.copy(), self.co_existance_dict.copy(),\r\n None, None, None, 1, True, -math.inf, math.inf,\r\n (-5, -5), self.player_information.copy(), self.board_array, self.board_edge, \r\n starting_time, False, self.turn_no)\r\n result = result[0]\r\n counter += 1\r\n \r\n #if its taking too long\r\n if counter > 2: \r\n #generate one random possible move to use \r\n allied_tokens = [token for token in self.player_tokens if self.player_tokens[token] == \"us\"]\r\n move_list = generate_moves(self.board_dict, self.player_tokens, self.co_existance_dict, allied_tokens,\r\n self.player_information, self.board_array, True, \"all\")\r\n \r\n \r\n #if there are no moves\r\n if len(move_list) == 0:\r\n if self.player_information['us']['nThrowsRemaining'] > 0:\r\n throws = generate_possible_throws(self.board_dict, self.player_tokens, self.co_existance_dict, self.player_information, \"us\",\r\n self.player_information[\"us\"][\"player_side\"], self.board_array, \"all\" )\r\n result = random.choice(throws)\r\n \r\n else:\r\n result = random.choice(move_list)\r\n print(f'random: {result}')\r\n break\r\n\r\n print(f' inside: {result}')\r\n\r\n print(result)\r\n #otherwise clean it up\r\n if result[0] == 'throw':\r\n final_result = (result[0].upper(), result[1], result[2])\r\n else:\r\n final_result = (result[0].upper(), result[2], result[3])\r\n # return final result \r\n return final_result","def get_king_moves(self, i, j, moves):\r\n directions = ((1, 1), (1, -1), (1, 0), (0, -1), (0, 1), (-1, -1), (-1, 1), (-1, 0))\r\n if self.turn_white:\r\n oponent = 'b'\r\n else:\r\n oponent = 'w'\r\n for d in directions:\r\n cri = i + d[0]\r\n crj = j + d[1]\r\n if 0 <= cri <= 7 and 0 <= crj <= 7:\r\n # empty\r\n if self.board[cri][crj][0] == '-':\r\n moves.append(Move((i, j), (cri, crj), self.board))\r\n\r\n # oponent\r\n elif self.board[cri][crj][0] == oponent:\r\n moves.append(Move((i, j), (cri, crj), self.board))","def actions(self, state):\n\n actions = []\n \n # if its player 1's turn\n if state.maxs_turn==True:\n # look through all the squares on the board\n for coords in state.gameState:\n # if its a rebel append allowable move and attack actions\n if state.gameState[coords]=='R':\n if state.gameState[(coords[0]-1, coords[1])]== ' ':\n actions.append(\"Move: Rebel @ {} --> {}\".format(coords, (coords[0]-1, coords[1])))\n if ((coords[0]-1, coords[1]+1) in state.gameState) and (state.gameState[(coords[0]-1, coords[1]+1)]== 'S'):\n actions.append(\"Attack: Rebel @ {} --> Sith @ {}\".format(coords, (coords[0]-1, coords[1]+1)))\n if ((coords[0]-1, coords[1]-1) in state.gameState) and (state.gameState[(coords[0]-1, coords[1]-1)]== 'S'):\n actions.append(\"Attack: Rebel @ {} --> Sith @ {}\".format(coords, (coords[0]-1, coords[1]-1)))\n \n # if its a jedi append allowable move and attack actions\n elif state.gameState[coords]=='J':\n for direction in [(-1, 0),(-1,1),(0,1),(1,1),(1,0),(1,-1),(0,-1),(-1,-1)]:\n coord = (coords[0]+direction[0], coords[1]+direction[1])\n # walk in each direction until reaching the edge of board, or a player\n while (coord in state.gameState) and (state.gameState[coord] == ' '):\n actions.append(\"Move: Jedi @ {} --> {}\".format(coords, coord))\n coord = (coord[0]+direction[0], coord[1]+direction[1])\n # if we ran into a sith we can attack\n if (coord in state.gameState) and (state.gameState[coord] == 'S'):\n actions.append(\"Attack: Jedi @ {} --> Sith @ {}\".format(coords, coord))\n \n else:\n for coords in state.gameState:\n if state.gameState[coords]=='S':\n for direction in [(-1, 0),(-1,1),(0,1),(1,1),(1,0),(1,-1),(0,-1),(-1,-1)]:\n coord = (coords[0]+direction[0], coords[1]+direction[1])\n if (coord in state.gameState) and (state.gameState[coord] == ' '):\n actions.append(\"Move: Sith @ {} --> {}\".format(coords, coord))\n elif (coord in state.gameState) and (state.gameState[coord] == 'R'):\n actions.append(\"Attack: Sith @ {} --> Rebel @ {}\".format(coords, coord))\n elif (coord in state.gameState) and (state.gameState[coord] == 'J'):\n actions.append(\"Attack: Sith @ {} --> Jedi @ {}\".format(coords, coord))\n \n\n\n if len(actions)==0:\n actions.append(\"Pass\")\n \n actions.sort()\n \n return actions","def get_king_moves(self, state):\n #king_moves = []\n possible_moves = []\n if self.color == cc.WHITE_ACTIVE:\n enemy_color = cc.BLACK_ACTIVE\n enemy_pieces = cc.BLACK_PIECES\n elif self.color == cc.BLACK_ACTIVE:\n enemy_color = cc.WHITE_ACTIVE\n enemy_pieces = cc.WHITE_PIECES\n else:\n raise Exception(\"GameState: Invalid Active Color\")\n\n for vector in cc.KING_VECTORS:\n rank = self.coord[0] + vector[0]\n column = self.coord[1] + vector[1]\n if rank in cc.VALID_RANKS and column in cc.VALID_RANKS:\n if state.board[rank, column] == cc.NO_PIECE:\n possible_moves.append(cc.Action(self.string, self.coord, (rank, column)))\n elif state.board[rank, column] in enemy_pieces:\n possible_moves.append(cc.Action(self.string, self.coord, (rank, column), capture=True))\n \n # # Iterate over list of king moves, removing ones that are under attack\n # for move in possible_moves:\n # if not check.space_under_attack(state, move.end, enemy_color):\n # king_moves.append(move)\n\n return possible_moves","def next_turn(self): \n if (self.moves):\n self.board = self.select_move() \n self.moves = []\n self.roll = self.roll_dice()\n self.player = not self.player\n self.generate_valid_moves()","def enumerate_moves(self):\n add_ew = lambda x: [x+'e', x+'w']\n allowed_catches = add_ew(self._directions[0])\n moves = []\n # First add the one/two step forward moves\n new_slot = self._board.get_dir(self._current_space, self._directions[0])\n if new_slot and new_slot.is_free():\n moves.append(ChessMove(self._current_space, new_slot))\n if (self._side == BLACK and new_slot.row == self._board.size - 1) or \\\n (self._side == WHITE and new_slot.row == 0):\n moves[-1].add_promotion()\n if (self._side == BLACK and self._current_space.row == 1) or \\\n (self._side == WHITE and self._current_space.row == self._board.size -2):\n new_slot = self._board.get_dir(new_slot, self._directions[0])\n if new_slot and new_slot.is_free():\n moves.append(ChessMove(self._current_space, new_slot))\n\n # Now add all the captures.\n for direction in allowed_catches:\n new_slot = self._board.get_dir(self._current_space, direction)\n if new_slot and new_slot.has_opponent(self._side):\n moves.append(ChessMove(self._current_space, new_slot, [new_slot]))\n if (self._side == BLACK and new_slot.row == self._board.size - 1) or \\\n (self._side == WHITE and new_slot.row == 0):\n moves[-1].add_promotion()\n return moves","def make_move(self, move):\n # Unpack the information from the move\n piece = self.get_piece(move[0])\n row = move[1][0]\n col = move[1][1]\n eliminated_pieces = move[2]\n\n # Move the piece and eliminate the captured pieces\n self.board[piece.row][piece.col] = None\n self.board[row][col] = piece\n piece.row = row\n piece.col = col\n self.remove(eliminated_pieces)\n\n # Checks if the piece has been promoted\n if piece.get_player() == Player.black and \\\n piece.row == constant.BOARD_DIMENSION - 1 \\\n and not piece.is_king():\n piece.make_king()\n self.num_black_kings += 1\n\n elif piece.get_player() == Player.white and \\\n piece.row == 0 \\\n and not piece.is_king():\n piece.make_king()\n self.num_white_kings += 1\n\n # Add the move performed to the history of the game\n self.moves.append(move)","def make_turn(self):\n # if play first, start in the middle\n if np.count_nonzero(self.board) == 0:\n self.place_disc(self.board.shape[1] / 2)\n return 1\n\n\n # win if possible\n for try_column in range(0,self.board.shape[1]):\n if 0 == self.board[0, try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, self.id())\n if dhw.did_he_win(new_board, self.id(), try_column):\n self.place_disc(try_column)\n return 1\n\n # don't loose if in danger\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id())\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\n self.place_disc(try_column)\n return 1\n\n # don't fall in trap!\n forbidden_columns = []\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, self.id()) # my move\n new_board = self.simulate_place_disc(new_board, try_column, 3 - self.id()) # enemy move\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\n\n # don't ruin my trap\n for try_column in range(0, self.board.shape[1]):\n if 0 == self.board[0,try_column]:\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id()) # 'my' move\n new_board = self.simulate_place_disc(new_board, try_column, self.id()) # my move\n if dhw.did_he_win(new_board, self.id(), try_column):\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\n\n # allow forbidden_columns if no other choice\n if np.count_nonzero(self.board[0, :]) == self.board.shape[1] - len(forbidden_columns):\n forbidden_columns = []\n\n # otherwise, play randomly\n rannum = random.randrange(7)\n while 0 != self.board[0, rannum] or rannum in forbidden_columns:\n rannum = random.randrange(7)\n self.place_disc(rannum)\n return 1","def castle(self, is_white, is_kingside):\n if is_white and is_kingside:\n # Validate castling is possible\n if self.get_square('e', 1) != \"K\" or self.get_square('f', 1) != \" \" or self.get_square('g', 1) != \" \" \\\n or self.get_square('h', 1) != 'R':\n raise RuntimeError(\"White can not castle kingside\")\n # Make the move\n self.set_square('e', 1, ' ')\n self.set_square('f', 1, 'R')\n self.set_square('g', 1, 'K')\n self.set_square('h', 1, ' ')\n\n elif is_white and not is_kingside:\n # Validate castling is possible\n if self.get_square('e', 1) != \"K\" or self.get_square('d', 1) != \" \" or self.get_square('c', 1) != \" \" \\\n or self.get_square('b', 1) != \" \" or self.get_square('a', 1) != 'R':\n raise RuntimeError(\"White can not castle queenside\")\n # Make the move\n self.set_square('e', 1, ' ')\n self.set_square('d', 1, 'R')\n self.set_square('c', 1, 'K')\n self.set_square('a', 1, ' ')\n\n elif is_kingside: # for black\n # Validate castling is possible\n if self.get_square('e', 8) != \"k\" or self.get_square('f', 8) != \" \" or self.get_square('g', 8) != \" \" \\\n or self.get_square('h', 8) != 'r':\n raise RuntimeError(\"Black can not castle kingside\")\n # Make the move\n self.set_square('e', 8, ' ')\n self.set_square('f', 8, 'r')\n self.set_square('g', 8, 'k')\n self.set_square('h', 8, ' ')\n\n else: # black, queenside\n # Validate castling is possible\n if self.get_square('e', 8) != \"k\" or self.get_square('d', 8) != \" \" or self.get_square('c', 8) != \" \" \\\n or self.get_square('b', 8) != \" \" or self.get_square('a', 8) != 'r':\n raise RuntimeError(\"Black can not castle queenside\")\n # Make the move\n self.set_square('e', 8, ' ')\n self.set_square('d', 8, 'r')\n self.set_square('c', 8, 'k')\n self.set_square('a', 8, ' ')","def decide_next_move(self):\n pass","def decide(self):\n self.maybe_shoot()\n next(self.move_cycle)\n\n pass","def apply_move(self, move):\n if self.check_move(move=move):\n self.board_list[move] = self.current_player.marker # changes value in the board to player which is either X or O\n self.moves_made += str(move) # keeps track of all moves\n return True\n else:\n return False","def make_move(self, state):\r\n # intially set drop phase to true\r\n drop_phase = True\r\n move = [] # list to make moves with to return\r\n succ = self.succ(state) # get the successor of this state\r\n # intial postion of board to set up most advantagous spot if its empty\r\n if sum(x.count(self.my_piece) for x in self.board) == 0 and self.board[2][2] == ' ':\r\n move.insert(0, (2, 2))\r\n return move\r\n \r\n # check the number of 'r' and 'b' on board if theres 4 of each drop phase is false\r\n if sum(x.count('r') for x in self.board) == 4 and sum(x.count('b') for x in self.board) == 4:\r\n drop_phase = False\r\n\r\n # if not during drop phase use minimax to make next move from one postion to next\r\n if not drop_phase:\r\n move = []\r\n d = self.Max_value(state, 0)\r\n val = d['val']\r\n m = d['move']\r\n p = d['pos']\r\n f = d['from']\r\n s = sorted(succ, key=lambda e: e['f'])\r\n moveto = s[-1]\r\n move.insert(1, (moveto['from'][0], moveto['from'][1]))\r\n move.insert(0, (moveto['pos'][0], moveto['pos'][1]))\r\n return move # return the from, to move\r\n\r\n else: #else use minimax and to make move during drop phase selecting spot to place AI piece\r\n d = self.Max_value(state, 0)\r\n val = d['val']\r\n m = d['move']\r\n p = d['pos']\r\n hold = []\r\n move = []\r\n n = None\r\n hold = []\r\n for s in succ:\r\n p = s['pos'][0]\r\n p1 = s['pos'][1]\r\n if s['f'] == val and state[p][p1] == ' ':\r\n hold.append(s)\r\n if len(hold) == 1:\r\n row = hold[0]['pos'][0]\r\n col = hold[0]['pos'][1]\r\n else:\r\n f = sorted(hold, key=lambda e: e['pos'])\r\n row = f[0]['pos'][0]\r\n col = f[0]['pos'][1]\r\n\r\n move.insert(0, (row, col)) # return the move \r\n return move","def moves(self, rank, file, mask_black=None, mask_white=None, castling=set(), *args, **kwargs):\n # Initialise result\n result = np.zeros((self.n_ranks, self.n_files), dtype=bool)\n\n # Get mask of other pieces\n other_pieces = np.zeros((self.n_ranks, self.n_files), dtype=bool)\n if mask_black is not None:\n other_pieces = np.logical_or(other_pieces, mask_black)\n if mask_white is not None:\n other_pieces = np.logical_or(other_pieces, mask_white)\n\n # Set squares surrounding king to True\n result[\n max(0, rank-1) : rank+2,\n max(0, file-1) : file+2,\n ] = True\n\n # Add castling moves\n if 'K' in castling and self.color == Color.WHITE:\n assert rank == self.n_ranks-1 and file == 4, \"Cannot castle, king has moved!\"\n if not other_pieces[rank, file+1]:\n result[rank, file+2] = True\n if 'Q' in castling and self.color == Color.WHITE:\n assert rank == self.n_ranks-1 and file == 4, \"Cannot castle, king has moved!\"\n if not other_pieces[rank, file-1]:\n result[rank, file-2] = True\n if 'k' in castling and self.color == Color.BLACK:\n assert rank == 0 and file == 4, \"Cannot castle, king has moved!\"\n if not other_pieces[rank, file+1]:\n result[rank, file+2] = True\n if 'q' in castling and self.color == Color.BLACK:\n assert rank == 0 and file == 4, \"Cannot castle, king has moved!\"\n if not other_pieces[rank, file-1]:\n result[rank, file-2] = True\n\n # Ensure king does not capture own pieces\n if mask_white is not None and self.color == Color.WHITE:\n result = np.logical_and(result, ~mask_white)\n if mask_black is not None and self.color == Color.BLACK:\n result = np.logical_and(result, ~mask_black)\n\n # Remove own rank and file as moves\n result[rank, file] = False\n\n # Return result\n return result","def make_legal_move(move, board, start, end, move_number, en_passant_square, king_w, king_b, rook_w_l, rook_w_r,\n rook_b_l,\n rook_b_r, promotion_piece):\n # this function should have essentially what i had in main at first without making the move\n test_board = board.copy()\n valid_move = False\n piece = find_piece(board, start)\n end_piece = find_piece(board, end)\n\n if switch_player(move_number): # for whites move\n if (65 <= ord(piece) <= 90) and (validate_move(board, end, move_number)):\n if piece == \"P\":\n if pawn(board, start, end, move_number):\n if end[0] == 8:\n promotion(test_board, start, end, promotion_piece)\n if not check(test_board, move_number):\n retract_move(test_board, start, end, end_piece)\n promotion(board, start, end, promotion_piece)\n return True\n else:\n valid_move = False\n else:\n valid_move = True\n else:\n if can_en_passant(board, en_passant_square, end, start, move_number):\n execute_enpassant(test_board, start, end, move_number)\n if not check(test_board, move_number):\n retract_move(test_board, start, end, end_piece)\n execute_enpassant(board, start, end, move_number)\n return True\n else:\n return False\n elif piece == \"K\":\n if king(start, end):\n if controlled_squares(board, move_number, end):\n valid_move = True\n elif piece == \"N\":\n if knight(start, end):\n valid_move = True\n elif piece == \"B\":\n if bishop(board, start, end):\n valid_move = True\n elif piece == \"Q\":\n if queen(board, start, end):\n valid_move = True\n elif piece == \"R\":\n if rook(board, start, end):\n valid_move = True\n else:\n valid_move = False\n if valid_move:\n update_board(test_board, start, end)\n if not check(test_board, move_number):\n retract_move(test_board, start, end, end_piece)\n update_board(board, start, end)\n return True\n else:\n retract_move(test_board, start, end, end_piece)\n print(\"Illegal Move\")\n return False\n if not switch_player(move_number):\n if (97 <= ord(piece) <= 122) and validate_move(board, end, move_number):\n if piece == \"p\":\n if pawn(board, start, end, move_number):\n if end[0] == 1:\n promotion(test_board, start, end, promotion_piece)\n if not check(test_board, move_number):\n retract_move(test_board, start, end, end_piece)\n promotion(board, start, end, promotion_piece)\n return True\n else:\n valid_move = False\n else:\n valid_move = True\n else:\n if can_en_passant(board, en_passant_square, end, start, move_number):\n execute_enpassant(test_board, start, end, move_number)\n if not check(test_board, move_number):\n retract_move(test_board, start, end, end_piece)\n execute_enpassant(board, start, end, move_number)\n return True\n else:\n valid_move = False\n elif piece == \"k\":\n if king(start, end):\n if controlled_squares(board, move_number, end):\n valid_move = True\n elif piece == \"n\":\n if knight(start, end):\n valid_move = True\n elif piece == \"b\":\n if bishop(board, start, end):\n valid_move = True\n elif piece == \"q\":\n if queen(board, start, end):\n valid_move = True\n elif piece == \"r\":\n if rook(board, start, end):\n valid_move = True\n else:\n valid_move = False\n if valid_move:\n update_board(test_board, start, end)\n if not check(test_board, move_number):\n retract_move(test_board, start, end, end_piece)\n update_board(board, start, end)\n return True\n else:\n retract_move(test_board, start, end, end_piece)\n print(\"Illegal Move\")\n return False","def move_buildings(self):","def move(self, player, cell):\n \n if self.playable(cell):\n self.cells[cell] = player\n self.moves.append(\n (player, cell))\n \n if self.is_winner(player, cell) and self.winner is None:\n self.winner = player\n elif len(self.moves) >= 9 and self.winner is None:\n self.winner = \"cats\"\n return True\n return False","def makeMove(self, move, player):","def test_move_knight_illegally(self):\n self.c.board = [[(0, 0) for i in range(8)] for i in range(8)]\n for piece in [('N', True), ('N', False)]:\n self.c.turn = piece[1]\n self.c.board[4][4] = piece\n dests = [col + row for col in 'abcdefgh' for row in '12345678']\n for dest in dests:\n if dest in ['d6', 'f6', 'c5', 'g5', 'c3', 'g3', 'd2', 'f2']:\n continue\n self.groups['dest'] = dest\n self.assertRaises(\n MoveNotLegalError, self.c._knight_evaluator, self.groups)","def next_move(self):\n self.branchize()\n try:\n _, new_branch = self.score_scheme.pop(0)\n except IndexError:\n return False\n\n move_list = [new_branch[\"move\"]]\n parent = new_branch[\"parent\"]\n\n while True:\n try:\n move_list.append(parent[\"move\"]) \n parent = parent[\"parent\"]\n\n except KeyError:\n break\n\n self.__originate__()\n for i in move_list[::-1]:\n self.change(i)\n\n self.current_branch = new_branch\n self.output += str(self)\n return True","def possible_moves(self, piece):\n def _index(orig, off):\n \"\"\"Helper function to find the new index.\"\"\"\n orig_x, orig_y = orig\n off_x, off_y = off\n return (orig_y - off_y) * self.ncols + (orig_x - off_x)\n\n p_x, p_y = piece\n p_i = _index(piece, (0, 0))\n\n # pass a list of the four corners first for basic possibles\n move_land = [((p_x + i, p_y + j), self.squares[_index(piece, (i, j))])\\\n for i in [-1, 1] for j in [-1, 1]]\n possibles = self.squares[p_i].can_move(piece, move_land)\n\n # next append the new list from jumps\n jump_land = [((p_x + i, p_y + j), self.squares[_index(piece, (i, j))])\\\n for j in [-2, 2] for i in [-2, 2]]\n possibles += self.squares[p_i].can_jump(piece, move_land, jump_land)\n\n # clean out the list of duplicates, although there should be none\n return [m for i, m in enumerate(possibles) if m not in possibles[:i]]","def move():\n\tdirections = [\"up\", \"down\", \"left\", \"right\"]\n\tdata = bottle.request.json\n\t#print(\"MOVE:\", json.dumps(data))\n\t# THE BEST MOVE IS CALUCLATED USING A FLOODFILL. HIGHEST AREA WIN. ME SPEEL GOOD\n\tmove = \"\"\n\n\tupC = floodFill(getNextPosition(\"up\", data), data, arrayify(\"up\", data, not largestSnake(data)), 0)\n\tdownC = floodFill(getNextPosition(\"down\", data), data, arrayify(\"down\", data, not largestSnake(data)), 0)\n\trightC = floodFill(getNextPosition(\"right\", data), data, arrayify(\"right\", data, not largestSnake(data)), 0)\n\tleftC = floodFill(getNextPosition(\"left\", data), data, arrayify(\"left\", data, not largestSnake(data)), 0)\n\tmoveC = [upC, downC, rightC, leftC]\n\t#if moveC cannot find a viable move with ghostheads, it disables them so the snake doesn't kill itself\n\n\tmove = goto(moveC, findFood(data), data)\n\tprint(\"moveC before max = \" + str(moveC))\n\tif max(moveC) == 0:\n\t\tprint(\"ghosthead disabled\")\n\t\tupC = floodFill(getNextPosition(\"up\", data), data, arrayify(\"up\", data, False), 0)\n\t\tdownC = floodFill(getNextPosition(\"down\", data), data, arrayify(\"down\", data, False), 0)\n\t\trightC = floodFill(getNextPosition(\"right\", data), data, arrayify(\"right\", data, False), 0)\n\t\tleftC = floodFill(getNextPosition(\"left\", data), data, arrayify(\"left\", data, False), 0)\n\t\tmoveC = [upC, downC, rightC, leftC]\n\n\tprint(\"move after goto: \" + move)\n\tprint(\"movC after if max 0 block: \" + str(moveC))\n\tif move == \"\":\n\t\tgoodMoves = []\n\t\tif upC == max(moveC):\n\t\t\tgoodMoves.append(\"up\")\n\t\tif downC == max(moveC):\n\t\t\tgoodMoves.append(\"down\")\n\t\tif leftC == max(moveC):\n\t\t\tgoodMoves.append(\"left\")\n\t\tif rightC == max(moveC):\n\t\t\tgoodMoves.append(\"right\")\n\t\tmove = random.choice(goodMoves)\n\n\tprint(\"Turn: \" + str(data[\"turn\"]))\n\tprint(\"Move: \" + move)\n\n\tresponse = {\"move\": move, \"shout\": \"yeet\"}\n\treturn HTTPResponse(\n\t\tstatus=200,\n\t\theaders={\"Content-Type\": \"application/json\"},\n\t\tbody=json.dumps(response),\n\t)","def move_boats():\n hit_manatee = False\n for i in range(len(map)-1, -1, -1):\n for j in range(len(map[i])-1, -1, -1):\n if map[i][j] == \"*\":\n # Only runs if the entity is a boat\n if i + 1 >= len(map):\n continue\n if map[i+1][j] == \" \":\n # Moves boat downward if possible\n if i + 2 < len(map) and map[i+2][j] == \"M\":\n hit_manatee = True\n map[i+2][j] = \"W\"\n map[i+1][j] = \"*\"\n map[i][j] = \" \"\n elif map[i+1][j] == \"*\":\n # Boats colliding with each other\n new_boat_pos = (i, j)\n if j + 1 < len(map[i]) and map[i][j+1] == \" \" \\\n and map[i+1][j+1] == \" \":\n new_boat_pos = (i+1, j+1)\n elif j - 1 >= 0 and map[i][j-1] == \" \" \\\n and map[i+1][j-1] == \" \":\n new_boat_pos = (i+1, j-1)\n else:\n continue\n\n # Moves boat down to new position\n map[i][j] = \" \"\n map[new_boat_pos[0]][new_boat_pos[1]] = \"*\"\n if new_boat_pos[0] + 1 < len(map) and \\\n map[new_boat_pos[0] + 1][new_boat_pos[1]] == \"M\":\n hit_manatee = True\n map[new_boat_pos[0] + 1][new_boat_pos[1]] = \"W\"\n return \"injured\" if hit_manatee else None","def actions_turn(positions, player1, player2, creatures):\n\n attacking = []\n moving = []\n\n # Split the orders within attacks and movements\n for character in positions:\n if character in creatures:\n if positions[character]['order'] == 'attack':\n attacking += character\n else:\n moving += character\n elif character in player1:\n if positions[character]['order'] == 'attack':\n attacking += character\n else:\n moving += character\n\n else:\n if positions[character]['order'] == 'attack':\n attacking += character\n else:\n moving += character\n\n # Execute the attacks\n for character in attacking:\n # First attacking : the creatures\n if character in creatures:\n hero_name = character\n name_attack = positions[character]['name_attack']\n attack_coord = positions[character]['where']\n attack(positions, hero_name, name_attack, (0, 0), attack_coord, player1, player2, creatures)\n # Then the heroes of the first player\n elif character in player1:\n hero_name = character\n name_attack = positions[character]['name_attack']\n attack_coord = positions[character]['where']\n attack(positions, hero_name, name_attack, (0, 0), attack_coord, player1, player2, creatures)\n # Finally the ones of the second player\n else:\n hero_name = character\n name_attack = positions[character]['name_attack']\n attack_coord = positions[character]['where']\n attack(positions, hero_name, name_attack, (0, 0), attack_coord, player1, player2, creatures)\n\n for character in moving:\n print(character, moving)\n # First moving : the creatures\n if character in creatures:\n hero_name = character\n movement_coord = positions[character]['where']\n move(hero_name, positions, movement_coord)\n\n # Then the heroes of the first player\n elif character in player1:\n hero_name = character\n movement_coord = positions[character]['where']\n move(hero_name, positions, movement_coord)\n\n # Finally the ones of the second player\n else:\n hero_name = character\n movement_coord = positions[character]['where']\n move(hero_name, positions, movement_coord)\n\n return player1, player2, positions, creatures","def test_move_knight_legally(self):\n self.c.board[6] = [(0, 0) for i in range(8)]\n for piece in [('N', True), ('N', False)]:\n self.c.turn = piece[1]\n self.c.board[4][4] = piece\n for dest in ['d6', 'f6', 'c5', 'g5', 'c3', 'g3', 'd2', 'f2']:\n self.groups['dest'] = dest\n self.assertEqual(self.c._knight_evaluator(self.groups), (4, 4))","def move(self):\r\n their_move = self.last_moves[\"their_move\"]\r\n return (their_move == \"\" and random.choice(moves) or their_move)","def _check_for_check(self):\n\n # Since castling needs to check whether the current king is in check\n # Leaving this to be true would cause infinite recursion\n possible_moves = self.get_all_moves(castling_allowed=False)\n\n # Find location of opposing king\n king_piece = 'k' if self.white_to_move else 'K'\n for i in range(8):\n for j in range(8):\n if self.board[i][j] == king_piece:\n location = (i, j)\n\n # Check if there is a piece that can capture the king,\n # By checking if there is something that can move into a king's square\n king_location = State._coord_to_EAN(location)\n for move in possible_moves:\n if king_location in move:\n return True\n\n return False","def toAN(board, move, short=False, castleNotation=CASTLE_SAN):\n\n fcord = (move >> 6) & 63\n tcord = move & 63\n flag = move >> 12\n\n if flag in (KING_CASTLE, QUEEN_CASTLE):\n if castleNotation == CASTLE_SAN:\n return flag == KING_CASTLE and \"O-O\" or \"O-O-O\"\n elif castleNotation == CASTLE_KR:\n rooks = board.ini_rooks[board.color]\n tcord = rooks[flag == KING_CASTLE and 1 or 0]\n # No treatment needed for CASTLE_KK\n\n if flag == DROP:\n if board.variant == SITTUYINCHESS:\n s = \"%s@%s\" % (reprSignSittuyin[fcord], reprCord[tcord])\n else:\n s = \"%s@%s\" % (reprSign[fcord], reprCord[tcord])\n else:\n s = reprCord[fcord] + reprCord[tcord]\n\n if flag in PROMOTIONS:\n if short:\n if board.variant in (CAMBODIANCHESS, MAKRUKCHESS):\n s += reprSignMakruk[PROMOTE_PIECE(flag)].lower()\n elif board.variant == SITTUYINCHESS:\n s += reprSignSittuyin[PROMOTE_PIECE(flag)].lower()\n else:\n s += reprSign[PROMOTE_PIECE(flag)].lower()\n else:\n if board.variant in (CAMBODIANCHESS, MAKRUKCHESS):\n s += \"=\" + reprSignMakruk[PROMOTE_PIECE(flag)]\n elif board.variant == SITTUYINCHESS:\n s += \"=\" + reprSignSittuyin[PROMOTE_PIECE(flag)]\n else:\n s += \"=\" + reprSign[PROMOTE_PIECE(flag)]\n return s","def _get_piece_moves(self, x, y):\n\n piece = self.get_piece(x, y)\n moves = []\n\n if not piece:\n return moves\n\n if piece.name == 'rook' or piece.name == 'queen':\n direcs = ['up', 'down', 'left', 'right']\n moves = [self._get_moves_indirection(x, y, direc) for direc in\n direcs]\n\n elif piece.name == 'bishop' or piece.name == 'queen':\n direcs = ['d1', 'd2', 'd3', 'd4']\n for direc in direcs:\n moves += self._get_moves_indirection(x, y, direc)\n\n elif piece.name == 'king':\n moves = [(x-1, y-1), (x-1, y), (x-1, y+1), (x, y-1),\n (x, y+1), (x+1, y-1), (x+1, y), (x+1, y+1)]\n\n elif piece.name == 'knight':\n moves = [(x-1, y-2), (x-2, y-1), (x-2, y+1), (x-1, y+2),\n (x+1, y+2), (x+2, y+1), (x+1, y-2), (x+2, y-1)]\n\n elif piece.name == 'pawn':\n if piece.color == ChessGame.BLACK:\n moves = [(x-1, y), (x-1, y-1), (x-1, y+1)]\n else:\n moves = [(x+1, y), (x+1, y-1), (x+1, y+1)]\n\n tmp = list(moves)\n for u, v in tmp:\n if v != y and not self.is_enemy(u, v, piece.color):\n moves.remove((u, v))\n\n if v == y and self.is_enemy(u, v, piece.color):\n moves.remove((u, v))\n\n mycolor = piece.color\n valid = set()\n for (u, v) in moves:\n if not self.in_bounds(u, v):\n continue\n\n if not self.get_piece(u, v): # board is blank\n valid.add((u, v))\n\n if self.is_enemy(u, v, mycolor):\n valid.add((u, v))\n\n return valid","def legal_moves(board,player=None):\r\n \r\n possible_moves = []\r\n moves = []\r\n if player == None:\r\n moves += board.white + board.black\r\n elif player == -1:\r\n moves += board.black\r\n elif player == 1:\r\n moves += board.white\r\n \r\n captured = False\r\n for pos in moves:\r\n if pos[0] == 'A':\r\n m = [-8,-7,1,8,9]\r\n elif pos[0] == 'H':\r\n m = [-9,-8,-1,7,8]\r\n else:\r\n m = [-9,-8,-7,-1,1,7,8,9]\r\n loc = decode(pos)\r\n for i in m:\r\n captured = capture(board, player, possible_moves, pos, loc, i)\r\n canter(board, player, possible_moves, pos, loc, i)\r\n plain(board, player, possible_moves, pos, loc, i)\r\n \r\n if captured:\r\n enemy_list = []\r\n for capturing_move in possible_moves:\r\n if len(capturing_move) == 3:\r\n enemy_list.append(capturing_move)\r\n possible_moves = list(enemy_list)\r\n\r\n return possible_moves","def undo_move(self):\r\n if len(self.moveLog) != 0:\r\n move = self.moveLog.pop()\r\n self.board[move.sr][move.sc] = move.pieceMoved\r\n self.board[move.er][move.ec] = move.pieceCaptured\r\n self.turn_white = not self.turn_white\r\n\r\n # king pos\r\n if move.pieceMoved == 'wk':\r\n self.wKingPos = (move.sr, move.sc)\r\n elif move.pieceMoved == 'bk':\r\n self.bKingPos = (move.sr, move.sc)\r\n\r\n # enpassant\r\n if move.isEnpassantMove:\r\n self.board[move.er][move.ec] = \"--\"\r\n self.board[move.sr][move.ec] = move.pieceCaptured\r\n self.enpas_pos = (move.er, move.ec)\r\n\r\n # pawn x2\r\n if move.pieceMoved[1] == \"p\" and abs(move.sr - move.er) == 2:\r\n self.enpas_pos = ()\r\n\r\n # castle rights\r\n self.castleRightsLog.pop()\r\n self.cr_castle_r = self.castleRightsLog[-1]\r\n\r\n # castle moves\r\n if move.castle:\r\n if move.ec - move.sc == 2:\r\n self.board[move.er][move.ec + 1] = self.board[move.er][move.ec - 1]\r\n self.board[move.er][move.ec - 1] = '--'\r\n else:\r\n self.board[move.er][move.ec - 2] = self.board[move.er][move.ec + 1]\r\n self.board[move.er][move.ec + 1] = '--'","def legal_moves(self):\n moves = \"\"\n swappable = self.swappable_positions\n empty_position = self.get_position(0)\n\n for s in swappable:\n pos_diff = empty_position[0] - s[0], empty_position[1] - s[1]\n if pos_diff[0] > 0:\n moves += \"U\"\n elif pos_diff[0] < 0:\n moves += \"D\"\n elif pos_diff[1] > 0:\n moves += \"L\"\n elif pos_diff[1] < 0:\n moves += \"R\"\n\n return moves","def get_castle_moves(self, r, c, moves):\r\n if self.square_under_attack(r, c):\r\n return\r\n\r\n #king side\r\n #checking previous rights and player turn\r\n if (self.cr_castle_r.wks and self.turn_white) or (self.cr_castle_r.bks and not self.turn_white):\r\n if self.board[r][c + 2] == '--' and self.board[r][c + 1] == '--':\r\n if self.square_under_attack(r, c + 2) is False and self.square_under_attack(r, c + 1) is False:\r\n moves.append(Move((r, c), (r, c + 2), self.board, castle=True))\r\n\r\n #queen side\r\n if (self.turn_white and self.cr_castle_r.wqs) or (not self.turn_white and self.cr_castle_r.bqs):\r\n if self.board[r][c - 2]== '--' and self.board[r][c - 1] == '--' and self.board[r][c - 3] == '--':\r\n if self.square_under_attack(r, c - 1) is False and self.square_under_attack(r, c - 2) is False:\r\n moves.append(Move((r, c), (r, c - 2), self.board, castle=True))","def crowned(self): # called when this piece has become a 'King'\r\n \r\n self.isKing = True","def move(self, action):\n self.time += 1\n\n # If ship is destroyed ship can only contemplate sadness and despair\n if not action or not self.is_playable():\n return None\n\n self.actualise = False\n\n if self.leroy_time == 1:\n self.back_to_normal()\n if self.leroy_time > 0:\n self.leroy_time -= 1\n\n # there is a chance that the ia enter in leroy mode\n # the ia goes mad for some time, acting randomly\n # added to allow the ships to explore the possible actions and not stay passive\n if not self.player and self.leroy_time == 0 and self.agent.behavior == \"network\" and random() < LEROY_RATE:\n self.leroy_jenkins()\n\n # training reward depending on position\n # self.agent.reward = self.go_bottom_reward()\n\n if isinstance(action, ActionOneHot):\n if action.pointing:\n self.pointing = Point(randint(0, DEFAULT_WIDTH-1), randint(0, DEFAULT_HEIGHT-1))\n elif isinstance(action, Action):\n if action.pointing:\n self.pointing = action.pointing\n # print(\"action.pointing\", action.pointing)\n # print(\"turn \", self.direction)\n\n if action.thrust:\n self.thrust()\n if action.shoot:\n self.shoot()","def canter(board,player,possible_moves, pos, loc, i):\r\n \r\n next_piece = encode(loc+i) \r\n new_pos = loc + (i*2)\r\n \r\n if player == 1 and next_piece in board.white:\r\n if not((pos[0] == 'B') and ((next_piece)[0] == 'A')) and not((pos[0] == 'G') and ((next_piece)[0] == 'H')):\r\n new_enc = encode(new_pos)\r\n if new_pos in range(0,112) and (new_enc not in board.white) and (new_enc not in board.black) and (new_enc not in board.empty):\r\n possible_moves.append([pos,new_enc])\r\n if player == -1 and next_piece in board.black:\r\n if not((pos[0] == 'B') and ((next_piece)[0] == 'A')) and not((pos[0] == 'G') and ((next_piece)[0] == 'H')):\r\n new_enc = encode(new_pos)\r\n if new_pos in range(0,112) and (new_enc not in board.white) and (new_enc not in board.black) and (new_enc not in board.empty):\r\n possible_moves.append([pos,new_enc])","def finish_game(self, z):\n for move in self.moves: # add this game winner result to all past moves.\n move += [z]","def _finish_all_moves(self, current_time):\n\t\tfor piece_id in self.all_piece_ids:\n\t\t\tstate = getattr(self, piece_id)\n\t\t\tif len(state) == 0:\n\t\t\t\tcontinue\n\t\t\tpiece = Piece(state)\n\t\t\tif piece.moving:\n\t\t\t\tif piece.end_time <= current_time:\n\t\t\t\t\tself._finish_move(piece_id, piece, current_time)","def game_on(cls):\n sommet = cls()\n\n moves = []\n\n while not sommet.map.game_over():\n print(sommet.map)\n print(\"{} joue\".format(sommet.is_vamp))\n\n moves += [sommet.next_move()]\n\n sommet = cls(is_vamp=not sommet.is_vamp)\n sommet.map.add_moves(moves)\n\n print(sommet.map)\n print(\"Vainqueur : {}\".format(sommet.map.winner()))\n\n print(\"{} sommets ont été créés pour les besoins de cette simulation.\".format(\n cls.nb_vertices_created()))","def update_spaces_threatened(self):\n # Spaces threatened are all the spaces it can move to + guarding an ally at the end of a path\n self.spaces_threatened = self.all_possible_moves(return_spaces_threatened=True)\n update_threatening_king(self)","def update_spaces_threatened(self):\n # Spaces threatened are all the spaces it can move to + guarding an ally at the end of a path\n self.spaces_threatened = self.all_possible_moves(return_spaces_threatened=True)\n update_threatening_king(self)","def update_spaces_threatened(self):\n # Spaces threatened are all the spaces it can move to + guarding an ally at the end of a path\n self.spaces_threatened = self.all_possible_moves(return_spaces_threatened=True)\n update_threatening_king(self)","def turn():\r\n global turn_no\r\n turn_no += 1\r\n dlog('Starting Turn!')\r\n board_size = get_board_size()\r\n\r\n team = get_team()\r\n opp_team = Team.WHITE if team == Team.BLACK else team.BLACK\r\n dlog('Team: ' + str(team))\r\n\r\n robottype = get_type()\r\n dlog('Type: ' + str(robottype))\r\n\r\n if robottype == RobotType.PAWN:\r\n dlog('Human')\r\n\r\n r, c = get_location()\r\n dlog('My location is: ' + str(r) + ' ' + str(c))\r\n\r\n if team == Team.WHITE:\r\n forward = 1\r\n scan = 2\r\n else:\r\n forward = -1\r\n scan = -2\r\n \r\n # try capturing pieces\r\n if check_space_wrapper(r + forward, c + 1, board_size) == opp_team: # up and right\r\n capture(r + forward, c + 1)\r\n dlog('Captured at: (' + str(r + forward) + ', ' + str(c + 1) + ')') \r\n\r\n elif check_space_wrapper(r + forward, c - 1, board_size) == opp_team: # up and left\r\n capture(r + forward, c - 1)\r\n dlog('Captured at: (' + str(r + forward) + ', ' + str(c - 1) + ')')\r\n \r\n elif r + forward != -1 and r + forward != board_size and not check_space_wrapper(r + forward, c, board_size):\r\n try:\r\n if check_space(r, c-1) == get_team() or check_space(r-1, c-1) == get_team() or check_space(r, c+1) == get_team() or check_space(r-1, c+1) == get_team():\r\n move_forward()\r\n dlog('Moved forward!')\r\n except:\r\n pass\r\n\r\n \r\n else:\r\n board = get_board()\r\n dlog(str(board))\r\n if team == Team.WHITE:\r\n forward = 1\r\n index = 0\r\n\r\n else:\r\n forward = -1\r\n index = board_size - 1\r\n \r\n deep_accum = []\r\n c_indexes = []\r\n heuristic_accum = []\r\n heuristic = 0\r\n for c in range(board_size):\r\n close = []\r\n for r in range(board_size):\r\n dlog(str(check_space(r, c)))\r\n if check_space(r, c) == opp_team:\r\n if team == Team.WHITE:\r\n close.append(r)\r\n elif team == Team.BLACK:\r\n close.append(board_size-r-1)\r\n heuristic -= 1\r\n elif check_space(r, c) == team:\r\n heuristic += 1\r\n else:\r\n continue\r\n heuristic_accum.append([heuristic, c])\r\n if close != []:\r\n c_indexes.append(c)\r\n deep = sorted(close)\r\n deep_accum.append(deep[0])\r\n heuristic = 0\r\n close_index = sorted(list(zip(deep_accum, c_indexes)))\r\n for c in close_index:\r\n if c[0] == 0:\r\n continue \r\n col = c[1]\r\n weighted_val = heuristic_accum[col][0]-15\r\n heuristic_accum[col][0] = weighted_val\r\n break\r\n\r\n heuristic_accum = sorted(heuristic_accum)\r\n\r\n for heur in heuristic_accum:\r\n col = heur[1]\r\n if not check_space_wrapper(index, col, board_size) and not check_space_wrapper(index+forward, col+1, board_size) == opp_team and not check_space_wrapper(index+forward, col-1, board_size) == opp_team:\r\n spawn(index, col)\r\n dlog('Spawned unit at: (' + str(index) + ', ' + str(col) + ')')\r\n break\r\n\r\n bytecode = get_bytecode()\r\n dlog('Done! Bytecode left: ' + str(bytecode))","def mark_moves(self, player):\n [self.mark_move(player, p, d)\n for p in self.pieces\n for d in DIRECTIONS\n if p.get_state() == player]","def makeMove(self, movable_statement):\n ### Student code goes here\n\n # make statement of (on disk firstpeg) and (on disk secondpeg) from movable_statement\n # retract (on disk firstpeg)\n # add (on disk secondpeg)\n # that should be it...\n\n if type(movable_statement) != Statement or not self.kb.kb_ask(Fact(movable_statement, [])):\n # print(\"current gamestate: \", self.getGameState())\n # print(\"~~~~~~~Error: \", movable_statement, \"is not a valid movable statement~~~~~~~\")\n return None\n\n # print(\"current gamestate: \", self.getGameState())\n # if self.kb.kb_ask(parse_input(\"fact: (movable ?X ?A ?B)\")):\n # for binding in self.kb.kb_ask(parse_input(\"fact: (movable ?X ?A ?B)\")):\n # print(\"movable: \", binding.bindings[0].constant.element)\n # if self.kb.kb_ask(parse_input(\"fact: (empty ?X)\")):\n # for binding in self.kb.kb_ask(parse_input(\"fact: (empty ?X)\")):\n # print(\"empty: \", binding.bindings[0].constant.element)\n # if self.kb.kb_ask(parse_input(\"fact: (top ?X ?A)\")):\n # for binding in self.kb.kb_ask(parse_input(\"fact: (top ?X ?A)\")):\n # print(\"top fact: \", binding.bindings[0].constant.element)\n # print(\"moving: \", movable_statement)\n\n\n\n # remove old \"on\", add new \"on\"\n statement1_list = [\"on\"]\n statement2_list = [\"on\"]\n\n statement1_list.append(movable_statement.terms[0])\n statement1_list.append(movable_statement.terms[1])\n\n statement2_list.append(movable_statement.terms[0])\n statement2_list.append(movable_statement.terms[2])\n\n curr_fact = Fact(Statement(statement1_list), [])\n new_fact = Fact(Statement(statement2_list), [])\n\n\n # print(\"retracted: fact: (top \" + movable_statement.terms[0].term.element + \" \" + movable_statement.terms[1].term.element + \")\")\n\n\n # (movable disk1 peg1 peg2)\n # retract (fact: on disk1 peg1)\n\n # DISK We're moving from:\n # Retract previous on statement\n # Retract previous top statement\n # If empty, say empty.\n # If not empty, make the new top statement using ontopof\n\n # STEP 1\n self.kb.kb_retract(curr_fact)\n moved_disk= movable_statement.terms[0].term.element\n prev_peg= movable_statement.terms[1].term.element\n new_peg=movable_statement.terms[2].term.element\n\n # STEP 2\n top_ask = parse_input(\"fact: (top \" + moved_disk + \" \" + prev_peg + \")\")\n if self.kb.kb_ask(top_ask):\n self.kb.kb_retract(top_ask)\n else:\n print(\"something went wrong with top...\")\n\n # STEP 3\n prev_top_ask = parse_input(\"fact: (onTopOf \" + moved_disk + \" ?X)\")\n if self.kb.kb_ask(prev_top_ask):\n # then it's not empty, make this one the top.\n for binding in self.kb.kb_ask(prev_top_ask):\n # there should only be one binding...\n disk_on_bottom = binding.bindings[0].constant.element\n # make the disk on bottom the new top of the previous peg\n # then remove the \"ontopof\" fact because it's no longer true.\n self.kb.kb_assert(parse_input(\"fact: (top \" + disk_on_bottom + \" \" + prev_peg + \")\"))\n self.kb.kb_remove(parse_input(\"fact: (onTopOf \" + moved_disk + \" \" + disk_on_bottom + \")\"))\n else:\n self.kb.kb_assert(parse_input(\"fact: (empty \" + prev_peg + \")\"))\n\n\n\n # DISK WE'RE MOVING TO: DEALING WITH TOP, EMPTY, and ONTOPOF.\n # Add new on statement\n # if empty, then remove empty statement. if not, add new ontopof statement and remove old top statement.\n # Add new top statement\n\n # STEP 1\n self.kb.kb_assert(new_fact)\n\n # STEP 2\n empty_ask = parse_input(\"fact: (empty \" + new_peg + \")\")\n top_ask = parse_input(\"fact: (top ?X \" + new_peg + \")\")\n if self.kb.kb_ask(empty_ask):\n self.kb.kb_retract(empty_ask)\n else:\n top_disk = self.kb.kb_ask(top_ask).list_of_bindings[0][0].bindings[0].constant.element\n oldTopFact = parse_input(\"fact: (top \" + top_disk + \" \" + new_peg + \")\")\n ontopof_fact = parse_input(\"fact: (onTopOf \" + moved_disk + \" \" + top_disk + \")\")\n\n self.kb.kb_retract(oldTopFact)\n self.kb.kb_add(ontopof_fact)\n\n newTopFact = parse_input(\"fact: (top \" + moved_disk + \" \" + new_peg + \")\")\n self.kb.kb_add(newTopFact)\n\n # if self.kb.kb_ask(top_ask):\n # self.kb.kb_retract(parse_input(\n # \"fact: (top \" + self.kb.kb_ask(top_ask)[0].bindings[0].constant.element + \" \" + movable_statement.terms[\n # 1].term.element + \")\"))\n\n\n\n\n\n\n # if self.kb.kb_ask(empty_ask):\n # for binding in self.kb.kb_ask(empty_ask):\n # empty_peg = binding.bindings[0].constant.element\n # remove_empty = parse_input(\"fact: (empty \" + empty_peg + \")\")\n # if self.kb.kb_ask(parse_input(\"fact: (on ?X \" + empty_peg + \")\")):\n # print(\"removing: \", remove_empty)\n # self.kb.kb_retract(remove_empty)\n # # print(\"retracting: (empty \" + empty_peg + \")\")\n\n\n\n # DISK WE'RE MOVING FROM: DEALING WITH TOP AND EMPTY.\n # First, we retract the previous top statement.\n # then, we must check: if the disk is now empty, we must say it's empty.\n # if it's not, we must now add the new top statement.\n\n\n\n\n # empty check\n\n #\n # for top_statement in self.kb.kb_ask(parse_input(\"fact: (top ?X ?A)\")):\n # print(top_statement)","def prepare_next_turn(grid):\n empties = get_empty_cells(grid)\n y,x = random.choice(empties)\n grid[y][x] = 2 if random.random() < 0.9 else 4\n return any_possible_moves(grid)","def calculate_next_move(self, visit):\n self.depth += 1\n new_boards = []\n for vehicle_id in range(len(self.vehicles)):\n vehicle = self.vehicles[vehicle_id]\n state = self.get_board()\n if vehicle.orientation == 0: #horizontal\n if vehicle.x > 0: #left\n if state[vehicle.y][vehicle.x-1] == \"..\":\n self.vehicles[vehicle_id].x -=1\n if not self.get_board().tostring() in visit:\n if not self.get_board().all in new_boards:\n new_board = deepcopy(self)\n self.vehicles[vehicle_id].x += 1\n new_board.parent = self\n new_boards.append(new_board)\n else:\n self.vehicles[vehicle_id].x += 1\n\n if vehicle.x + vehicle.length <= (len(state)-1): #right\n if state[vehicle.y][vehicle.x+vehicle.length] == \"..\":\n self.vehicles[vehicle_id].x += 1\n if not self.get_board().tostring() in visit:\n if not self.get_board().all in new_boards:\n new_board = deepcopy(self)\n self.vehicles[vehicle_id].x -= 1\n new_board.parent = self\n new_boards.append(new_board)\n else:\n self.vehicles[vehicle_id].x -= 1\n\n else: #vertical\n if vehicle.y - 1 >= 0: #up\n if state[vehicle.y-1][vehicle.x] == \"..\":\n self.vehicles[vehicle_id].y -= 1\n if not self.get_board().tostring() in visit:\n if not self.get_board().all in new_boards:\n new_board = deepcopy(self)\n self.vehicles[vehicle_id].y += 1\n new_board.parent = self\n new_boards.append(new_board)\n else:\n self.vehicles[vehicle_id].y += 1\n\n if vehicle.y + vehicle.length <= (len(state)-1):\n if state[vehicle.y + vehicle.length][vehicle.x] == \"..\":#down\n self.vehicles[vehicle_id].y += 1\n if not self.get_board().tostring() in visit:\n if not self.get_board().all in new_boards:\n new_board = deepcopy(self)\n self.vehicles[vehicle_id].y -= 1\n new_board.parent = self\n new_boards.append(new_board)\n else:\n self.vehicles[vehicle_id].y -= 1\n self.depth -= 1\n return new_boards","def get_possible_moves(self) -> list:\n if self.p1_turn:\n name = '2'\n else:\n name = '1'\n\n count = 0\n for i in self.claim:\n if i == name:\n count += 1\n over = count >= 0.5 * len(self.claim)\n\n moves = []\n if not over:\n for i in self.letters:\n if i.isalpha():\n moves.append(i)\n return moves","def exec_all_moves(self,level=0):\n\n capts = [self.op_capture_north, self.op_capture_nwest, self.op_capture_neast, self.op_capture_east, self.op_capture_west]\n jmps = [self.op_jump_north, self.op_jump_nwest, self.op_jump_neast]\n moves = [self.op_move_north,self.op_move_nwest, self.op_move_neast]\n result = []\n ops = []\n\n # Pre-select all operations that may be executed\n if self.next_move == self.FREE:\n capturingStarts, otherStarts = self.possible_capture()\n\n # Check for pieces that may capture\n if len(capturingStarts) > 0:\n self.next_move = self.CAPT\n self.next_pieces = capturingStarts\n else:\n self.next_move = self.FREE\n jmps.extend(moves)\n ops = jmps\n self.next_pieces = otherStarts\n\n elif self.next_move == self.CAPT:\n ops = capts\n elif self.next_move == self.JUMP:\n ops = jmps\n elif self.next_move == self.FREE:\n jmps.extend(moves)\n ops = jmps\n elif self.next_move == self.ADDPIECE_2:\n return self.op_add_piece_bot(self.next_pieces)\n\n # Execute possible operations for all viable pieces\n for pos in self.next_pieces:\n for op in ops:\n newState = op(pos)\n\n # Check if op succeeded\n if newState:\n\n # If the next player is the current player than the function is called recursevely, this is done so that outcomes account for successive plays by the same\n # player ( successive jumps and captures or piece addition)\n if newState.curr_player != self.curr_player:\n result.append(newState)\n else:\n result.extend(newState.exec_all_moves(level+1))\n\n\n return result","def move_enemy(self, hero, pad, graph, msg):\r\n hr,hc = hero.position\r\n\r\n er, ec = self.position\r\n\r\n distance = math.sqrt((math.pow(hc - ec, 2)) + math.pow(hr - er, 2))\r\n\r\n if distance <= 12 and distance > 1:\r\n if not line_of_sight(pad, hero.position, self.position, graph):\r\n return\r\n\r\n if hr > er:\r\n temp_er = er + 1\r\n elif hr < er:\r\n temp_er = er - 1\r\n else:\r\n temp_er = er\r\n\r\n if hc < ec:\r\n temp_ec = ec - 1\r\n elif hc > ec:\r\n temp_ec = ec + 1\r\n else:\r\n temp_ec = ec\r\n\r\n if math.sqrt((math.pow(hc - temp_ec, 2)) + math.pow(hr - er, 2)) <= math.sqrt((math.pow(hc - ec, 2)) + math.pow(hr - temp_er, 2)):\r\n if pad.instr(er, temp_ec, 1) in p.valid_moves:\r\n temp_tile = self.tile\r\n self.tile = pad.instr(er, temp_ec, 1) #update tile under enemy\r\n pad.addstr(er, temp_ec, self.character)\r\n pad.addstr(er, ec, temp_tile)\r\n self.position = (er, temp_ec)\r\n elif pad.instr(temp_er, ec, 1) in p.valid_moves:\r\n temp_tile = self.tile\r\n self.tile = pad.instr(temp_er, ec, 1)\r\n pad.addstr(temp_er, ec, self.character)\r\n pad.addstr(er, ec, temp_tile)\r\n self.position = (temp_er, ec)\r\n else:\r\n if pad.instr(temp_er, ec, 1) in p.valid_moves:\r\n temp_tile = self.tile\r\n self.tile = pad.instr(temp_er, ec, 1)\r\n pad.addstr(temp_er, ec, self.character)\r\n pad.addstr(er, ec, temp_tile)\r\n self.position = (temp_er, ec)\r\n elif pad.instr(er, temp_ec, 1) in p.valid_moves:\r\n temp_tile = self.tile\r\n self.tile = pad.instr(er, temp_ec, 1)\r\n pad.addstr(er, temp_ec, self.character)\r\n pad.addstr(er, ec, self.tile)\r\n self.position = (er, temp_ec)\r\n\r\n er, ec = self.position\r\n\r\n distance = math.sqrt((math.pow(hc - ec, 2)) + math.pow(hr - er, 2))\r\n\r\n if distance == 1:\r\n hero.defend_attack(self.attack, msg, self.name)","def __move(self):\n if self.goal is None:\n if self.tasks:\n self.goal = self.tasks.pop()\n self.goal_history.append(self.goal)\n self.logger.log(\n f\"Crewmate {self.agent_id} set as goal: {self.goal.name} in\" +\n f\" {self.game_map.room_names[self.goal.room_id]}\",\n Logger.LOG | Logger.PRINT_VISUAL)\n else:\n self.room = self.game_map.move_random(self)\n self.location_history.append(self.room)\n return\n\n if self.room is not self.goal.room_id:\n self.room = self.game_map.next_toward(self, self.goal.room_id)\n\n # Log the current room we are in: Either the room we moved to, or the room that happens to be the goal room\n self.location_history.append(self.room)","def play_move(self, move_data, all_english_words):\r\n \r\n #first, make copy of board and try to apply move there.\r\n board_cpy = copy.deepcopy(self.myboard)\r\n \r\n rack_cpy = copy.deepcopy(self.rack) #***need to remove appropriate words from rack after we've made a move. \r\n\r\n i = 0 \r\n for cur_char in move_data[0]:\r\n new_tile = Tile(cur_char, self.game_bag.letter_freq_and_val[cur_char][1]) #create a new tile.\r\n if move_data[3] == \"horizontal\":\r\n print(\"adding cur_char {} at {} {}\".format(cur_char, move_data[1], move_data[2]+i))\r\n board_cpy.place_tile(move_data[1], move_data[2]+ i, new_tile)\r\n elif move_data[3] == \"vertical\":\r\n board_cpy.place_tile(move_data[1] + i, move_data[2], new_tile)\r\n\r\n i = i + 1\r\n #print(\"New i value is {}\".format(i))\r\n board_cpy.print_board()\r\n \r\n\r\n #once we're done placing the tiles, check for validity of entire board.\r\n cur_board_words = board_cpy.find_words_on_board()\r\n move_valid = True #assume move is valid, until proven otherwise.\r\n\r\n for word_data in cur_board_words:\r\n word = word_data[0]\r\n print(word)\r\n if word not in all_english_words:\r\n return False #do nothing else\r\n \r\n #print(\"Getting here; all words valid\")\r\n \r\n #getting here means that the move is actually valid, with no conflicts.\r\n main_board = self.myboard\r\n #In this case, add to the real board. \r\n i = 0 \r\n for cur_char in move_data[0]:\r\n new_tile = Tile(cur_char, self.game_bag.letter_freq_and_val[cur_char][1]) #create a new tile.\r\n if move_data[3] == \"horizontal\":\r\n main_board.place_tile(move_data[1], move_data[2]+ i, new_tile)\r\n elif move_data[3] == \"vertical\":\r\n main_board.place_tile(move_data[1] + i, move_data[2], new_tile)\r\n\r\n i = i + 1\r\n return True","def moveToNext(self):\n\t\tif self.G.debug:\n\t\t\ttic=time.clock()\n\t\tself.debugPrint('looks for new spot')\n\t\texceeds=self.m.exceedsAngleLim\t#function\n\t\tinside=self.m.isWithinPlantingBorders\t#function\n\t\tcart=self.m.getCartesian\n\t\tauto=self.m.automatic\n\t\tt=self.m.times\n\t\tcommands=[]\n\t\tif self.autoMoved:\n\t\t\topt=self.pos\n\t\t\tself.autoMoved=False #if this search is unsuccessfull, automove is enabled to next ideal pos.\n\t\telse:\n\t\t\topt=self.getNextOptimal()\n\t\tmoveTo=opt #for so long..\n\t\trTemp=0.1\n\t\tthTemp=0\n\t\tb=0.05 #constant for the spiral\n\t\ta=0.1\n\t\tplant=True #we will plant in this step...\n\t\td2=self.m.plantMinDist**2 #dist^2\n\t\tpossible = False #for so long\n\t\twhile not possible:\n\t\t\ttic=time.clock()\n\t\t\tpossible=True\n\t\t\tobstList=self.G.terrain.GetVisibleObstacles(moveTo, R=self.radius)\n\t\t\ttreeList=self.G.terrain.GetTrees(moveTo, R=self.radius+self.m.plantMinDist)\n\t\t\tobstList+=[tr for tr in treeList if not tr in obstList] #this procedure minimizes R in Getobst\n\t\t\t#[p1, p2]=self.getPHCoord(moveTo)\n\t\t\tphPos=self.getPHCoord(moveTo)\n\t\t\tplantSpots=self.getPlantingCoord(moveTo)\n\t\t\t#[f1,f2]=self.getPlantingCoord(moveTo)\n\t\t\tif self.otherDevice is not None:\n\t\t\t\totherDevPlantCor=self.otherDevice.getPlantingCoord(self.otherDevice.pos)\n\t\t\t\t#check for colissions and similar related to other device\n\t\t\t\tif collide(self, self.otherDevice, o1pos=moveTo): \n\t\t\t\t\tpossible=False\n\t\t\t\telse:\n\t\t\t\t\tfor o in otherDevPlantCor:\n\t\t\t\t\t\tfor f in plantSpots:\n\t\t\t\t\t\t\t#if getDistanceSq(f1, o)<d2 or getDistanceSq(f2, o)<d2:\n\t\t\t\t\t\t\tif getDistanceSq(f,o)<d2:#plantingspot of device is closer than allowed to other Device's plantingspot\n\t\t\t\t\t\t\t\tpossible=False\n\t\t\t\t\t\t\t\tbreak\t\t\n\t\t\tif possible:\t#either 1a or angle OK and above check OK\n\t\t\t\tfor obst in obstList:\n\t\t\t\t\t#tic=time.clock()\n\t\t\t\t\tif isinstance(obst, Tree):\n\t\t\t\t\t\t#other demands, more than 1.5 m from plantingspot.\n\t\t\t \t\t\tfor f in plantSpots:\n\t\t\t\t\t\t\t#if getDistanceSq(f1, o)<d2 or getDistanceSq(f2, o)<d2:\n\t\t\t\t\t\t\tif getDistanceSq(f, obst.pos)<d2 or collide(self, obst, o1pos=moveTo):\n\t\t\t\t\t\t\t\tpossible=False\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\telif isinstance(obst, Hole): #hole can be in beetween plantheads... Plantpos can be in hole.\n\t\t\t\t\t\tif len(self.plantHeads)==1: #bracke\n\t\t\t\t\t\t\tpossible=False\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telif collide(self.plantHeads[0], obst, o1pos=phPos[0]) or collide(self.plantHeads[1], obst, o1pos=phPos[1]):\n\t\t\t\t\t\t\tpossible=False\n\t\t\t\t\t\t\t#PlantingDevice.timesProf[0]+=time.clock()-tic\t\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\telif collide(self, obst, o1pos=moveTo):\n\t\t\t\t\t\tpossible=False\n\t\t\t\t\t\t#PlantingDevice.timesProf[0]+=time.clock()-tic\t\n\t\t\t\t\t\tbreak\n\t\t\t\tif possible and self.otherDevice is not None and exceeds(self, moveTo, self.otherDevice):\n\t\t\t\t\tpossible=False\t#angle is too big to the other device\n\t\t\t#at this point, all test for \"possibility\" are performed.\n\t\t\tPlantingDevice.timesProf[0]+=time.clock()-tic\n\t\t\tdthini=pi/50.\n\t\t\tif not possible:\n\t\t\t\t#move in a spiral outwards\n\t\t\t\trTemp=a+b*thTemp\n\t\t\t\tdth=(pi/25.)/(rTemp/2.)\n\t\t\t\tthTemp+=dth\n\t\t\t\tthInit=thTemp #used to avoid infinite loop\n\t\t\t\tmoveTo=cart([rTemp,thTemp],opt)\n\t\t\t\twhile not inside(moveTo) or (self.otherDevice is not None and exceeds(self, moveTo, self.otherDevice)):\n\t\t\t\t\t#outside borders or too big angle.. make above expression shorter..\n\t\t\t\t\t#self.pSpots.append(self.m.getCartesian([rTemp,thTemp], opt))\n\t\t\t\t\trTemp=a+b*thTemp\n\t\t\t\t\tthTemp+=(pi/25.)/(rTemp/2.)\t\t\t\t\t\n\t\t\t\t\t#if abs(thTemp-thInit)>2*pi: #if radius is too big..\n\t\t\t\t\tif abs(thInit-thTemp)>2*pi:\n\t\t\t\t\t\tplant=False #we will not plant this time.\n\t\t\t\t\t\t#move to make it easier for the other head:\n\t\t\t\t\t\tif self.otherDevice is not None and self.lastPos==self.pos and self.struckLastTime:\t\t\t\t\t\t\n\t\t\t\t\t\t\tthIni=self.posCyl[1]-dthini\n\t\t\t\t\t\t\tthTemp=thIni\n\t\t\t\t\t\t\t\"\"\"if exceeds(self, cart([self.posCyl[0],thTemp]), self.otherDevice):\n\t\t\t\t\t\t\t\tnp=cart([self.posCyl[0],thTemp])\"\"\" #old stuff... should be removed, right?\n\t\t\t\t\t\t\twhile inside(cart([self.posCyl[0],thTemp])) and not exceeds(self, cart([self.posCyl[0],thTemp]), self.otherDevice):\n\t\t\t\t\t\t\t\tthTemp-=dthini #moves in order to make more space\n\t\t\t\t\t\t\tif thTemp==thIni: #it wasnt inside or exceeded\n\t\t\t\t\t\t\t\tcommands.extend(self.releaseDriver()) #releases driver, if he is used\n\t\t\t\t\t\t\t\tif exceeds(self, cart([self.posCyl[0],thTemp]), self.otherDevice):\n\t\t\t\t\t\t\t\t\t#we are struck! Wait for other device to move.\n\t\t\t\t\t\t\t\t\tself.m.stopControl() #we could have reached the end here.\n\t\t\t\t\t\t\t\t\tcommands.append((waitevent, self, self.otherDevice.moveEvent))\n\t\t\t\t\t\t\t\telse: #not inside, we have reached the end of the half circle\n\t\t\t\t\t\t\t\t\tself.debugPrint(\"end of pattern reached, passivates %s device\"%self.mountPoint)\n\t\t\t\t\t\t\t\t\tself.noMoreSpots=True\n\t\t\t\t\t\t\t\t\tself.m.stopControl() #we could have reached the end here.\n\t\t\t\t\t\t\t\t\tcommands.append((passivate, self))\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tmoveTo=cart([self.posCyl[0],thTemp+dthini])\n\t\t\t\t\t\t\t\ttraveltime=self.setPos(moveTo)\n\t\t\t\t\t\t\t\tself.debugPrint('clears for other head')\n\t\t\t\t\t\t\t\tcommands=self.cmnd(commands, traveltime,auto=auto['clearForOtherHead'])\n\t\t\t\t\t\tif plant:\n\t\t\t\t\t\t\tcommands=self.cmnd(commands, t['searchTime'],auto=auto['micrositeSelection'])\n\t\t\t\t\t\t\tself.m.timeConsumption['searchTime']+=t['searchTime']\n\t\t\t\t\t\treturn (commands,plant)\n\t\t\t\t\tmoveTo=cart([rTemp,thTemp],opt)\n\t\ttravelTime=self.setPos(moveTo)\n\t\tself.debugPrint('traveltime: %f'%travelTime)\n\t\tif plant: #this timeconsumption is only for succesfull...\n\t\t\tcommands=self.cmnd(commands, t['searchTime'],auto=auto['micrositeSelection'])\n\t\t\tself.m.timeConsumption['searchTime']+=t['searchTime']\t\t\n\t\tcommands=self.cmnd(commands, travelTime,auto=auto['moveToMicro'])\n\t\treturn (commands,plant)","def legal_move_on(draw, board):\n start, _ = draw(strategies.sampled_from(sorted(board.pieces)))\n end = draw(strategies.sampled_from(sorted(board.movable_from(start))))\n return start, end","def get_possible_moves(self, board: np.ndarray):\n board_size = board.shape[0]\n moves = []\n if abs(self.value) == 1:\n if self.start_row <= 2:\n directions = [np.array((1, -1)), np.array((1, 1))]\n else:\n directions = [np.array((-1, 1)), np.array((-1, -1))]\n else:\n directions = [np.array((-1, 1)), np.array((1, 1)), np.array((-1, -1)), np.array((1, -1))]\n for direction in directions:\n within_board = True\n i = 1\n while within_board:\n coord = self.coord + direction * i\n within_board = _check_if_position_on_board(coord, board_size)\n # break if first step is already out of board\n if not within_board:\n break\n value_board = board[coord[0], coord[1]]\n # break if there is a stone of them same player in the way\n if value_board < 0 and self.value < 0 or value_board > 0 and self.value > 0:\n break\n # if there is no stone, than add this to move list.\n if value_board == 0:\n moves += [{\"old_coord\": self.coord, \"new_coord\": coord, \"jumped_stones\": [], \"jumped_values\": 0,\n \"move_coords\": [coord]}]\n # if there is a stone of the enemy\n if (value_board < 0 < self.value) or (self.value < 0 < value_board):\n # check if it can be jumped\n coord_jump = coord + direction\n move_coords = [coord_jump.copy()]\n within_board_after_jump = _check_if_position_on_board(coord_jump, board_size)\n # break if place behind stone is out of border\n if not within_board_after_jump:\n break\n value_board_jump = board[coord_jump[0], coord_jump[1]]\n jumped_stones = []\n # break if there is no free place\n if value_board_jump != 0:\n break\n jumped_stones += [coord]\n moves_tmp = self.jump_chain(directions, board, coord_jump, value_board, jumped_stones, move_coords)\n if len(moves_tmp) > 0:\n moves += moves_tmp\n else:\n moves += [{\"old_coord\": self.coord, \"new_coord\": coord_jump, \"jumped_stones\": jumped_stones,\n \"jumped_values\": abs(value_board), \"move_coords\": [coord_jump]}]\n i += 1\n # break if normal stone, because they can only move one field\n if abs(self.value) == 1:\n break\n return moves","def _invalidate_castles(self):\n\n if self.board[0][0] != 'r': # Black queenside\n self.available_castles = self.available_castles.replace('q', '')\n\n if self.board[0][7] != 'r': # Black kingside\n self.available_castles = self.available_castles.replace('k', '')\n\n if self.board[7][0] != 'R': # White queenside\n self.available_castles = self.available_castles.replace('Q', '')\n\n if self.board[7][7] != 'R': # White kingside\n self.available_castles = self.available_castles.replace('K', '')\n\n if self.board[0][4] != 'k':\n self.available_castles = self.available_castles.replace('k', '')\n self.available_castles = self.available_castles.replace('q', '')\n\n if self.board[7][4] != 'K':\n self.available_castles = self.available_castles.replace('K', '')\n self.available_castles = self.available_castles.replace('Q', '')","def is_king_move_valid(self, from_row, from_col, to_row, to_col):\n\n piece = self.board.squares[from_row][from_col]\n piece_color = self.piece_color(piece)\n\n if abs(to_row - from_row) <= 1 and abs(to_col - from_col) <= 1:\n if piece_color == \"white\":\n self.whiteCanCastleKside = False\n self.whiteCanCastleQside = False\n else:\n self.blackCanCastleKside = False\n self.blackCanCastleQside = False\n return True\n\n # TODO Castling implementation\n # if king and rook have not been moved yet this game, and no space between\n # the king and the rook are occupied or threatened, then the king can\n # move 2 spaces towards the rook, and the rook will be placed adjacent to the\n # king on the side closer to the center column.\n\n # TODO need function which returns squares being threatened which takes a piece position and board as a param\n\n if (piece_color == \"white\"):\n if self.whiteCanCastleKside and (from_row == 7 and from_col == 4) and (to_row == from_row) and (to_col == 6):\n # White kingside Castle\n if (self.board.squares[7][5] == None and self.board.squares[7][6] == None):\n if not self.testing:\n self.whiteCanCastleKside = False\n self.whiteCanCastleQside = False\n self.board.move_piece(7, 7, 7, 5)\n return True\n\n if self.whiteCanCastleQside and (from_row == 7 and from_col == 4) and (to_row == from_row) and (to_col == 2):\n # White queenside Castle\n if (self.board.squares[7][3] == None and self.board.squares[7][2] == None and self.board.squares[7][1] == None):\n\n if not self.testing:\n self.whiteCanCastleKside = False\n self.whiteCanCastleQside = False\n self.board.move_piece(7, 0, 7, 3)\n return True\n\n elif piece_color == \"black\":\n if self.blackCanCastleKside and (from_row == 0 and from_col == 4) and (to_row == from_row) and (to_col == 6):\n # black kingside Castle\n if (self.board.squares[0][5] == None and self.board.squares[0][6] == None):\n if not self.testing:\n self.blackCanCastleKside = False\n self.blackCanCastleQside = False\n self.board.move_piece(0, 7, 0, 5)\n return True\n\n if self.blackCanCastleQside and (from_row == 0 and from_col == 4) and (to_row == from_row) and (to_col == 2):\n # black queenside Castle\n if (self.board.squares[0][3] == None and self.board.squares[0][2] == None and self.board.squares[0][1] == None):\n if not self.testing:\n self.blackCanCastleKside = False\n self.blackCanCastleQside = False\n self.board.move_piece(0, 0, 0, 3)\n return True\n\n return False","def action(self, colony):\n \n if not self.is_true_king:\n self.reduce_armor(self.armor)\n else:\n super().action(colony)\n check_place = self.place.exit\n while check_place is not None:\n dragon = check_place.dragon\n if dragon is not None and dragon not in self.buffed_dragons:\n check_place.dragon.damage *=2\n self.buffed_dragons.append(check_place.dragon)\n if dragon is not None and dragon.is_container:\n contained_dragon = dragon.contained_dragon\n if contained_dragon is not None and contained_dragon not in self.buffed_dragons:\n check_place.dragon.contained_dragon.damage *=2\n self.buffed_dragons.append(check_place.dragon.contained_dragon)\n check_place = check_place.exit","def make_move(self):\n\n # get relavent information\n affinity = self.get_affinity()\n sample_space = self.get_game_space()\n depth_limit = self.__search_depth\n\n # run a minimax search and get the best value\n bestval = MinimaxTree.minimax(self, sample_space, affinity, depth_limit, True)\n if bestval[0] is None: bestval = ((0,6),'x', 0)\n\n # print the number of nodes expanded \n print(self.nodes_expanded)\n\n # make the move found by the search \n self.get_game_space().set_tile(bestval[0][0], bestval[0][1], affinity)","def black_wins(self):\n possible_dirs = [[1, 0], [0, 1], [-1, 0], [0, -1], [1, 1], [-1, 1], [-1, -1], [1, -1]]\n toMoves = []\n king_spot = None\n for row in range(8):\n for col in range(8):\n if self.board.squares[row][col] == '♔': # White king symbol\n king_spot = (row, col)\n break\n if king_spot is not None:\n break\n\n # Black doesn't win if it isn't checking the king\n if king_spot not in self.blackThreat.keys():\n return False\n\n for direction in possible_dirs:\n toRow = king_spot[0] + direction[0]\n toCol = king_spot[1] + direction[1]\n\n if ((0 <= toRow <= 7) and (0 <= toCol <= 7)):\n toMoves.append((toRow, toCol))\n\n # check if we can move out of the mate\n for move in toMoves:\n piece = self.board.squares[move[0]][move[1]]\n piece_color = self.pieces.piece_color(piece)\n if piece_color != \"white\" and (move[0], move[1]) not in self.blackThreat.keys():\n return False\n\n # check can I take the attacker\n attack_spot = None\n\n for move in self.blackMoves:\n if move[1] == king_spot:\n attack_spot = move[0]\n break\n\n # If there is a piece attacking, check the possible moves for black to see if one of them is a kill move\n if attack_spot is not None:\n attack_dir = (attack_spot[0] - king_spot[0], attack_spot[1] - king_spot[1])\n for move in self.whiteMoves:\n if move[1] == attack_spot:\n # Check if the attacker can be taken. It can be taken by a non-king piece, or by the king if\n # the spot is not protected\n if (self.board.squares[move[0][0]][move[0][1]] == ChessPiece.W_KING and \\\n (move[1]) not in self.blackThreat.keys()) or \\\n (self.board.squares[move[0][0]][move[0][1]] != ChessPiece.W_KING):\n return False\n\n piece = self.board.squares[attack_spot[0]][attack_spot[1]]\n if piece == '♛' or piece == '♜' or piece == '♝':\n h_dir = attack_dir[0]\n v_dir = attack_dir[1]\n check_squares = []\n\n if h_dir == 0:\n for c in range(0, v_dir, int(v_dir / abs(v_dir))):\n check_squares.append((king_spot[0], king_spot[1] + c))\n elif v_dir == 0:\n for r in range(0, h_dir, int(h_dir / abs(h_dir))):\n check_squares.append((r + king_spot[0], king_spot[1]))\n else:\n rows = []\n cols = []\n for r in range(0, h_dir, int(h_dir / abs(h_dir))):\n rows.append(r)\n for c in range(0, v_dir, int(v_dir / abs(v_dir))):\n cols.append(c)\n for n in range(len(cols)):\n check_squares.append((rows[n] + king_spot[0], cols[n] + king_spot[1]))\n\n for move in self.whiteMoves:\n if move[1] in check_squares and move[0] != king_spot:\n # print(move[0], move[1], king_spot, check_squares)\n return False\n\n return True","def _get_easy_move(self):\n # Two lists keep track of regular moves and paths of possible jumps\n moves = []\n required_jumps = []\n for row in range(self._checkers.dimension):\n for col in range(self._checkers.dimension):\n # Check same color pieces as player to see if they can jump.\n if self._checkers.get(row, col) == self._player:\n path_made = False\n found_jumps = []\n # i represents an option of direction to check\n for i in range(3):\n current_path = []\n # Builds a path of jumps by checking for a jump each\n # move\n while (not path_made):\n jumps = self.check_for_jump(self._player, row, col)\n if jumps == []:\n path_made = True\n break\n current_path += (jumps)\n incrow = 0\n inccol = 0\n # South west and east\n if i == 0:\n incrow = 2\n inccol = 2\n elif i == 1:\n incrow = 2\n incrow = -2\n # North west and east\n elif i == 2:\n incrow = -2\n inccol = -2\n elif i == 3:\n incrow = -2\n incrow = 2\n row += incrow\n col += inccol\n found_jumps.append(current_path)\n if len(found_jumps) > 0:\n # If there is at least one path then we append it\n # to the list of jumps\n required_jumps += found_jumps\n else:\n # Checks if a move can be made in each direction\n north_west = self._checkers.get(row - 1, col - 1)\n north_east = self._checkers.get(row - 1, col + 1)\n south_west = self._checkers.get(row + 1, col + 1)\n south_east = self._checkers.get(row + 1, col - 1)\n if north_west == CheckersBoard.empty:\n moves.append(Move(row, col, -1, -1))\n if north_east == CheckersBoard.empty:\n moves.append(Move(row, col, -1, 1))\n if south_west == CheckersBoard.empty:\n moves.append(Move(row, col, 1, 1))\n if south_east == CheckersBoard.empty:\n moves.append(Move(row, col, 1, -1))\n # A random move is calculated for the lists of moves\n # If a move can be made we prioritize the list with possible moves\n random_index = 0\n if len(required_jumps) != 0:\n random_index = random.randint(0, len(required_jumps))\n move_path = required_jumps[random_index]\n return move_path\n else:\n random_index = random.randint(0, len(moves))\n move = moves[random_index]\n return [move]","def prepare_next_turn(grid):\n\tempties = get_empty_cells(grid)\n\ty,x = random.choice(empties)\n\tgrid[y][x] = 2 if random.random() < prob_2 else 4\n\treturn any_possible_moves(grid)","def make_move(self):\n\n # get relavent information\n affinity = self.get_affinity()\n sample_space = self.get_game_space()\n depth_limit = self.__search_depth\n\n # run a minimax search and get the best value\n bestval = MinimaxTree.alphabeta(self, sample_space, affinity, depth_limit, -10000, 10001, True)\n if bestval[0] is None: bestval = ((0,6),'x', 0)\n\n # print the number of nodes expanded \n print(self.nodes_expanded)\n\n # make the move found by the search \n self.get_game_space().set_tile(bestval[0][0], bestval[0][1], affinity)","def generate_possible_moves_single_gp(self, game):\n for color in game.paths:\n path = game.paths[color]\n if path.is_complete():\n continue\n gp1, gp2 = path.get_grow_points()\n adj2gp1 = utils.get_adjacent_points(gp1)\n for possible in adj2gp1:\n # print \"Attempting\"\n # print game\n # print possible, path.can_be_added_to_path(possible, 1)\n if path.can_be_added_to_path(possible, 1):\n copy_game = deepcopy(game)\n \"\"\":type: Flow\"\"\"\n copy_game.paths[color].add_to_path(possible, 1)\n self.queue.append(copy_game)\n # print \"after attempt\"\n # print copy_game","def ai_move():\n\tinitial_state = map(get_filled_edges, rects)\n\tpossible_moves = []\n\tfor index, filled_edges in enumerate(initial_state):\n\t\tif filled_edges == 0:\n\t\t\tpossible_moves.extend([(index, i) for i in 'ltrb'])\n\t\telif filled_edges == 1:\n\t\t\tpossible_moves.extend(one_filled_edge(index))\n\t\telif filled_edges == 2:\n\t\t\tpossible_moves.extend(two_filled_edge(index))\n\t\telif filled_edges == 3:\n\t\t\tpossible_moves.extend(three_filled_edge(index))\n\tprint possible_moves\n\tpossible_decisions = []\n\tfor move in possible_moves:\n\t\tfinal_state = apply_move(move)\n\t\tpossible_decisions.append(is_feasible(initial_state, final_state))\n\tprint possible_decisions\n\t# randomizing when some decisions have the same weight\n\tmax_weight = max(possible_decisions)\n\t# list of indices which have the same weight\n\tmax_indices = []\n\tfor index, weight in enumerate(possible_decisions):\n\t\tif weight == max_weight:\n\t\t\tmax_indices.append(index)\n\tx = choice(max_indices)\n\tprint x\n\treturn possible_moves[x]\n\t# return possible_moves[possible_decisions.index(max(possible_decisions))]","def test_move_knight_legally_blocked(self):\n for piece in [('N', True), ('N', False)]:\n self.c.board = \\\n [[('K', piece[1]) for i in range(8)] for i in range(8)]\n self.c.turn = piece[1]\n self.c.board[4][4] = piece\n for dest in ['d6', 'f6', 'c5', 'g5', 'c3', 'g3', 'd2', 'f2']:\n self.groups['dest'] = dest\n self.assertRaises(\n MoveNotLegalError, self.c._knight_evaluator, self.groups)","def get_available_moves(self):\n available = []\n row, col = tuple(self.current_pos)\n if row - 1 >= 0 and self.maze[row - 1][col] != 'x':\n available.append('n')\n if row + 1 < len(self.maze) and self.maze[row + 1][col] != 'x':\n available.append('s')\n if col - 1 >= 0 and self.maze[row][col - 1] != 'x':\n available.append('w')\n if col + 1 < len(self.maze[row]) and self.maze[row][col + 1] != 'x':\n available.append('e')\n return available","def mappossiblemoves(self,nodeStart):\n destinations = [set() for ix in range(game.DICEMAX+1)]\n destinations[0] = set([nodeStart])\n routes = [[nodeStart]]\n #\n # TODO: Not convinced this adds anything more than it harms in this fn.\n #if self.hasEnteredRoomYet:\n # GAMEBOARD = game.TRIMMEDNODES\n #else:\n GAMEBOARD = game.BOARDNODES\n #\n for diceRoll in range(1,game.DICEMAX+1):\n newRoutes = []\n for route in routes:\n for nextLoc in GAMEBOARD[route[-1]]:\n if not nextLoc in route:\n # This location has not yet been visited by this route\n if nextLoc in game.ALLOWEDROOMNODES:\n # Location is a room - can move into it regardless\n # of population, but not move further. We can also\n # use any longer roll to move here by this route\n for thisRoll in range(diceRoll,game.DICEMAX+1):\n destinations[thisRoll].add(nextLoc)\n else:\n # Location is a square - can only move into it if\n # unoccupied, but can move further once there.\n if not nextLoc in self.charLocations:\n # Unoccupied (could check against \n # charLocations[1:] since we're the piece moving\n # but a nextLoc = charLocations[0] would be\n # rejected by being already in the route anyway)\n newRoute = list(route)\n newRoute.append(nextLoc)\n newRoutes.append(newRoute)\n destinations[diceRoll].add(nextLoc)\n routes = newRoutes\n #\n return [list(destSet) for destSet in destinations]","def capture(board,player,possible_moves, pos, loc, i):\r\n \r\n if player == 1 and encode(loc+i) in board.black:\r\n next_piece = encode(loc+i) \r\n new_pos = int(loc + (i*2)) \r\n if not((pos[0] == 'B') and ((next_piece)[0] == 'A')) and not((pos[0] == 'G') and ((next_piece)[0] == 'H')):\r\n new_enc = encode(new_pos)\r\n if new_pos in range(0,112) and (new_enc not in board.white) and (new_enc not in board.black) and (new_enc not in board.empty):\r\n possible_moves.append([pos,new_enc,next_piece])\r\n return True\r\n \r\n if player == -1 and encode(loc+i) in board.white: \r\n next_piece = encode(loc+i) \r\n new_pos = int(loc + (i*2))\r\n if not((pos[0] == 'B') and ((next_piece)[0] == 'A')) and not((pos[0] == 'G') and ((next_piece)[0] == 'H')):\r\n new_enc = encode(new_pos)\r\n if new_pos in range(0,112) and (new_enc not in board.white) and (new_enc not in board.black) and (new_enc not in board.empty):\r\n possible_moves.append([pos,new_enc,next_piece])\r\n return True\r\n \r\n return False","def move2(self):\n\n options = self.location.exits.keys()\n for key in options:\n if self.location.exits[key] == p.location:\n self.location.objects.remove(a)\n self.location = p.location\n self.location.objects.append(a)\n print('fred entered the room')\n self.attack(['attack', str(p.name)])\n break\n else:\n self.move1()","def find_moveable_pieces(self, die, p1): \n moveable = []\n if (p1):\n #must we re-enter?\n if (self.p1vec[0] > 0):\n if (self.free_spot(0, die, p1)):\n b = Board(self.p1vec[:],self.p2vec[:])\n b.move(0, die, p1)\n moveable.append(b)\n #no? ok then generate the moves\n else:\n for i in range(1, 25):\n if (self.p1vec[i] > 0):\n if (self.free_spot(i, die, p1)):\n b = Board(self.p1vec[:],self.p2vec[:])\n b.move(i, die, p1)\n moveable.append(b)\n else:\n #must we re-enter?\n if (self.p2vec[0] > 0):\n if (self.free_spot(0, die, p1)):\n b = Board(self.p1vec[:],self.p2vec[:])\n b.move(0, die, p1)\n moveable.append(b)\n #no? ok then generate the moves\n else:\n for i in range(1, 25):\n if (self.p2vec[i] > 0):\n if (self.free_spot(i, die, p1)):\n b = Board(self.p1vec[:],self.p2vec[:])\n b.move(i, die, p1)\n moveable.append(b)\n return moveable","def move(self):\n for agent in self.agents:\n if not agent.fidelity:\n options = agent.get_move_options(agent.hex, self.kernel_size, None, extend=True)\n target = random36.choices(population=options,weights=[x.quality**2 for x in options])\n agent.move(target[0])","def move(player, direction):\r\n \r\n curRoom = world.maps.World.mapGrid[player.room]\r\n door = curRoom.dirs[direction]\r\n roomid = world.maps.World.doors[door].getExitRoom(curRoom.id)\r\n newRoom = world.maps.World.mapGrid[roomid]\r\n brokesneak = False\r\n \r\n if player.getAttr(SNEAKING) and not utils.gameutils.stealthRoll(player):\r\n player.setAttr(SNEAKING, False)\r\n sendToRoomNotPlayer(player, \"{0}You notice {1} sneaking out to the {2}.\".format(LRED, player, DIRS[direction])) \r\n sendToPlayer(player, \"{0}You make a sound\".format(LRED))\r\n brokesneak = True\r\n elif not player.getAttr(SNEAKING): \r\n sendToRoomNotPlayer( player, \"{0} left to the {1}.\".format(player, DIRS[direction])) \r\n \r\n del curRoom.players[player.name]\r\n newRoom.players[player.name] = player\r\n player.room = newRoom.id\r\n\r\n if brokesneak:\r\n sendToRoomNotPlayer(player, \"{0}You notice {1} sneaking into the room from the {2}.\".format(LRED, player, DIRS[OPPOSITEDIRS[direction]])) \r\n elif not player.getAttr(SNEAKING):\r\n sendToRoomNotPlayer(player, \"{0} entered from the {1}.\".format(player, DIRS[OPPOSITEDIRS[direction]])) \r\n \r\n displayRoom(player, player.room)\r\n player.stats[MOVING] = False","def domove(self, depart, arrivee, promote):\n\n # Debugging tests\n # if(self.cases[depart].isEmpty()):\n # print('domove() ERROR : asked for an empty square move : ',depart,arrivee,promote)\n # return \n # if(int(depart)<0 or int(depart)>63):\n # print('domove() ERROR : incorrect FROM square number : ',depart)\n # return \n # if(int(arrivee)<0 or int(arrivee)>63):\n # print('domove() ERROR : incorrect TO square number : ',arrivee)\n # return\n # if(not(promote=='' or promote=='q' or promote=='r' or promote=='n' or promote=='b')):\n # print('domove() ERROR : incorrect promote : ',promote)\n # return\n\n # Informations to save in the history moves\n pieceDeplacee = self.cases[depart] # moved piece\n piecePrise = self.cases[arrivee] # taken piece, can be null : Piece()\n isEp = False # will be used to undo a ep move\n histEp = self.ep # saving the actual ep square (-1 or square number TO)\n hist_roque_56 = self.white_can_castle_56\n hist_roque_63 = self.white_can_castle_63\n hist_roque_0 = self.black_can_castle_0\n hist_roque_7 = self.black_can_castle_7\n flagViderEp = True # flag to erase ep or not : if the pawn moved is not taken directly, it can't be taken later\n\n # Moving piece\n self.cases[arrivee] = self.cases[depart]\n self.cases[depart] = Piece()\n\n self.ply += 1\n\n # a PAWN has been moved -------------------------------------\n if (pieceDeplacee.nom == 'PION'):\n\n # White PAWN\n if (pieceDeplacee.couleur == 'blanc'):\n\n # If the move is \"en passant\"\n if (self.ep == arrivee):\n piecePrise = self.cases[arrivee + 8] # take black pawn\n self.cases[arrivee + 8] = Piece()\n isEp = True\n\n # The white pawn moves 2 squares from starting square\n # then blacks can take \"en passant\" next move\n elif (self.ROW(depart) == 6 and self.ROW(arrivee) == 4):\n self.ep = arrivee + 8\n flagViderEp = False\n\n # Black PAWN\n else:\n\n if (self.ep == arrivee):\n piecePrise = self.cases[arrivee - 8]\n self.cases[arrivee - 8] = Piece()\n isEp = True\n\n elif (self.ROW(depart) == 1 and self.ROW(arrivee) == 3):\n self.ep = arrivee - 8\n flagViderEp = False\n\n # a ROOK has been moved--------------------------------------\n # update castle rights\n\n elif (pieceDeplacee.nom == 'TOUR'):\n\n # White ROOK\n if (pieceDeplacee.couleur == 'blanc'):\n if (depart == 56):\n self.white_can_castle_56 = False\n elif (depart == 63):\n self.white_can_castle_63 = False\n\n # Black ROOK\n else:\n if (depart == 0):\n self.black_can_castle_0 = False\n elif (depart == 7):\n self.black_can_castle_7 = False\n\n # a KING has been moved-----------------------------------------\n\n elif (pieceDeplacee.nom == 'ROI'):\n\n # White KING\n if (pieceDeplacee.couleur == 'blanc'):\n\n # moving from starting square\n if (depart == 60):\n # update castle rights\n self.white_can_castle_56 = False\n self.white_can_castle_63 = False\n\n # If castling, move the rook\n if (arrivee == 58):\n self.cases[56] = Piece()\n self.cases[59] = Piece('TOUR', 'blanc')\n\n elif (arrivee == 62):\n self.cases[63] = Piece()\n self.cases[61] = Piece('TOUR', 'blanc')\n\n # Black KING\n else:\n\n if (depart == 4):\n self.black_can_castle_0 = False\n self.black_can_castle_7 = False\n\n if (arrivee == 6):\n self.cases[7] = Piece()\n self.cases[5] = Piece('TOUR', 'noir')\n\n elif (arrivee == 2):\n self.cases[0] = Piece()\n self.cases[3] = Piece('TOUR', 'noir')\n\n # End pieces cases-----------------------------------------------\n\n # Any move cancels the ep move\n if (flagViderEp == True):\n self.ep = -1\n\n # Promote : the pawn is changed to requested piece\n if (promote != ''):\n if (promote == 'q'):\n self.cases[arrivee] = Piece('DAME', self.side2move)\n elif (promote == 'r'):\n self.cases[arrivee] = Piece('TOUR', self.side2move)\n elif (promote == 'n'):\n self.cases[arrivee] = Piece('CAVALIER', self.side2move)\n elif (promote == 'b'):\n self.cases[arrivee] = Piece('FOU', self.side2move)\n\n # Change side to move\n self.changeTrait()\n\n # Save move to the history list\n self.history.append((depart, \\\n arrivee, \\\n pieceDeplacee, \\\n piecePrise, \\\n isEp, \\\n histEp, \\\n promote, \\\n hist_roque_56, \\\n hist_roque_63, \\\n hist_roque_0, \\\n hist_roque_7))\n\n # If the move lets king in check, undo it and return false\n if (self.in_check(self.oppColor(self.side2move))):\n self.undomove()\n return False\n\n return True","def get_pawn_moves(self, state):\n pawn_moves = []\n\n if self.color == cc.WHITE_ACTIVE:\n forward_1 = add_vectors(self.coord, cc.V_UP)\n forward_2 = add_vectors(self.coord, cc.V_UP_2)\n attacks = get_crawler_moves(self.coord, cc.W_PAWN_CAPTURE_VECTORS)\n starting_rank = cc.RANK_2\n promo_rank = cc.RANK_8\n promo_pieces = cc.WHITE_PROMO\n enemy_set = cc.BLACK_PIECES\n elif self.color == cc.BLACK_ACTIVE:\n forward_1 = add_vectors(self.coord, cc.V_DOWN)\n forward_2 = add_vectors(self.coord, cc.V_DOWN_2)\n attacks = get_crawler_moves(self.coord, cc.B_PAWN_CAPTURE_VECTORS)\n starting_rank = cc.RANK_7\n promo_rank = cc.RANK_1\n promo_pieces = cc.BLACK_PROMO\n enemy_set = cc.WHITE_PIECES\n else:\n raise Exception(\"get_pawn_moves: Invalid Piece Color\")\n\n if validate_move(forward_1) and state.board[forward_1] == cc.NO_PIECE:\n if forward_1[0] == promo_rank:\n for p in promo_pieces:\n pawn_moves.append(cc.Action(self.string, self.coord, forward_1, promo=p))\n else:\n pawn_moves.append(cc.Action(self.string, self.coord, forward_1))\n if self.coord[0] == starting_rank and validate_move(forward_2) and state.board[forward_2] == cc.NO_PIECE:\n pawn_moves.append(cc.Action(self.string, self.coord, forward_2, en_p=forward_1))\n\n for attack in attacks:\n if state.board[attack] in enemy_set:\n if attack[0] == promo_rank:\n for p in promo_pieces:\n pawn_moves.append(cc.Action(self.string, self.coord, attack, capture=True, promo=p))\n else:\n pawn_moves.append(cc.Action(self.string, self.coord, attack, capture=True))\n # Make sure Pawns can attack en_passant squares\n elif attack == state.en_passant:\n pawn_moves.append(cc.Action(self.string, self.coord, attack, capture=True))\n\n return pawn_moves","def move(self, i, j):\n board_cpy = copy.deepcopy(self.board)\n piece = self.board[self.selected[0]][self.selected[1]]\n if piece == 0:\n return False\n elif piece.player != self.active_player:\n print(\"it's not your turn\", piece.player, self.active_player)\n return False\n elif self.board[i][j] != 0:\n if piece.player == self.board[i][j].player:\n print(\"can't capture your own pieces\")\n return False\n if i==self.selected[0] and piece.direction == 0:\n if piece.master == False:\n if (i + j - sum(self.selected))%2 == 0:\n print(\"must choose a different color\")\n return False\n if j < self.selected[1]:\n spaces_between = [self.board[i][y] for y in range(j+1, self.selected[1])]\n else:\n spaces_between = [self.board[i][y] for y in range(self.selected[1]+1, j)]\n print(spaces_between)\n for p in spaces_between:\n if p != 0:\n print(\"can't jump pieces\")\n return False\n piece.direction = 1\n self.board[i][j] = piece\n self.board[self.selected[0]][self.selected[1]] = 0\n elif j==self.selected[1] and piece.direction == 1:\n if piece.master == False:\n if (i + j - sum(self.selected))%2 == 0:\n print(\"must choose a different color\")\n return False\n if i < self.selected[0]:\n spaces_between = [self.board[x][j] for x in range(i+1, self.selected[0])]\n else:\n spaces_between = [self.board[x][j] for x in range(self.selected[0]+1, i)] \n print(spaces_between)\n for p in spaces_between:\n if p != 0:\n print(\"can't jump pieces\")\n return False\n piece.direction = 0\n self.board[i][j] = piece\n self.board[self.selected[0]][self.selected[1]] = 0\n else:\n print(\"Invalid movement\")\n return False\n if self.board[i][j] == 0:\n print(\"Something wrong happened\")\n return False\n elif (i==0 or i==self.board_size-1) and (j==0 or j==self.board_size-1):\n self.board[i][j].master = True\n self.active_player = (self.active_player+1)%2\n self.sequence += [board_cpy]\n return True","def get_legal_moves(self, player: int) -> np.ndarray:\n stage2 = self.is_stage2()\n action_mask = np.zeros((24, 5, 25), dtype=bool)\n # if stage 1 add set options\n array_board = np.array(self.board)\n if not stage2:\n legal_pos = np.where(array_board == 0)[0]\n for pos in legal_pos:\n if self.is_mill(player, pos, self.board): # current selection completes a mill\n opp_pos = np.where(array_board == -player)[0]\n opp_pos = [opp_p for opp_p in opp_pos if\n not self.is_mill(-player, opp_p, self.board)] # can't remove opponent in mill\n if len(opp_pos) == 0: # exception for the case where all opponents pieces are in mills\n opp_pos = np.where(array_board == -player)[0]\n action_mask[pos, -1, opp_pos] = True\n else:\n action_mask[pos, -1, -1] = True # place piece on board\n else:\n from_pos_cands = np.where(array_board == player)[0]\n for from_pos in from_pos_cands:\n mill_cands = [(orient, adj) for orient, adj in enumerate(self.adjacent[from_pos]) if\n adj is not None and self.board[adj] == 0] # TODO added not, need to validate\n if_played_board = self.board.copy()\n if_played_board[from_pos] = 0\n for (orient, adj) in mill_cands:\n if self.is_mill(player, adj, if_played_board):\n opp_pos = np.where(array_board == -player)[0]\n opp_pos = [opp_p for opp_p in opp_pos if not self.is_mill(-player, opp_p, if_played_board)]\n if len(opp_pos) == 0: # exception for the case where all opponents pieces are in mills\n opp_pos = np.where(array_board == -player)[0]\n action_mask[from_pos, orient, opp_pos] = True\n else:\n action_mask[from_pos, orient, -1] = True\n\n return action_mask","def determine_best_move(self, board):\r\n\r\n # Check if the AI is smart, run a smart version of the AI\r\n if self.is_smart:\r\n\r\n # Iterate through every column\r\n # keep track of any restricted moves (moves that may cause the challenger to win)\r\n # and keep track of the final result of where the AI should move\r\n restricted = []\r\n ai_move = -1\r\n for column in range(board.width):\r\n\r\n # Create two copies of the board to emulate AI moves and player moves\r\n ai_copy = board.copy()\r\n player_copy = board.copy()\r\n\r\n # Check if the current column is full, move onto the next column\r\n if board.is_column_full(column):\r\n continue\r\n\r\n # Column is not full; Emulate AI and player moves at this column\r\n ai_copy.add_piece(column, is_challenger = False) # AI move\r\n player_copy.add_piece(column, is_challenger = True) # Player move\r\n\r\n # Check if either the ai_copy or player_copy has a win in it\r\n ai_win_check = ai_copy.check_for_winner()\r\n player_win_check = player_copy.check_for_winner()\r\n\r\n # If either board has a win in it, make that the AI move\r\n # if the player would go to this current column in their next move\r\n # they would win, the AI should try to stop it\r\n # if the ai would go to this current column in its next move\r\n # they would win, the AI should immediately go here\r\n if ai_win_check == False or player_win_check == True:\r\n ai_move = column\r\n break\r\n\r\n # Neither of the moves would win in either board,\r\n # emulate the next moves on the same column\r\n else:\r\n\r\n # Check if the column is full, move onto the next column\r\n if ai_copy.is_column_full(column):\r\n continue\r\n\r\n # Column is not full, emulate the player move on the AI copy\r\n ai_copy.add_piece(column, is_challenger = True)\r\n\r\n # Check if the player would win; If so, do not let the AI go to this column\r\n player_win_check = ai_copy.check_for_winner()\r\n if player_win_check == True:\r\n restricted.append(column)\r\n\r\n # There has been no ai_move generated yet\r\n # generate a random column\r\n if ai_move == -1:\r\n\r\n # Check if the board is full, there must be a draw\r\n if board.is_board_full():\r\n return False\r\n\r\n # The board is not full, generate a random column that is not full\r\n while True:\r\n ai_move = randint(0, board.width - 1)\r\n\r\n # Check if the column is full, continue generating a random column\r\n if board.is_column_full(ai_move):\r\n continue\r\n\r\n # Check to see if this is the only available column to go to\r\n # or check to see if this column is a restricted move\r\n elif board.board[0].count(None) == 1 or len(restricted) == board.board[0].count(None):\r\n break\r\n\r\n # Check to see if the move is not a restricted move\r\n elif ai_move not in restricted:\r\n break\r\n\r\n # The AI is not smart, choose a random place\r\n else:\r\n ai_move = randint(0, board.width - 1)\r\n while board.is_column_full(ai_move):\r\n ai_move = randint(0, board.width - 1)\r\n\r\n # Make the AI go to its chosen move\r\n board.add_piece(ai_move, is_challenger = False)\r\n return True","def execution(move,legal,board,player):\r\n \r\n if player == 1:\r\n if move in legal:\r\n for i in range(0,len(board.white)):\r\n if board.white[i] == move[0]:\r\n board.white[i] = move[1]\r\n if len(move) == 3:\r\n board.black.remove(move[-1])\r\n\r\n else:\r\n print(\"Illegal move, please input a legal move\")\r\n human_move(board,player)\r\n else:\r\n if move in legal:\r\n if len(move) == 3:\r\n board.white.remove(move[-1])\r\n for i in range(0,len(board.black)):\r\n if board.black[i] == move[0]:\r\n board.black[i] = move[1]\r\n else:\r\n print(\"Illegal move, please input a legal move\")\r\n human_move(board,player)","def _move_forward(self):\n\t\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\n\t\tif(self.y<=798):\n\t\t\tself.y = self.y+1\n\t\t\tif Board.board[self.x][self.y]=='0':\n\t\t\t\tMario.score += 1\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\n\t\t\telif Board.board[self.x][self.y]=='P':\n\t\t\t\tMario.lives+=1\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_1-up.wav\"])\n\n\t\t\telif Board.board[self.x][self.y]=='A':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\t\t\t\tMario.attack = 1\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_powerup.wav\"])\n\n\t\t\telif Board.board[self.x][self.y]=='@':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tMario.lives-=1\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_mariodie.wav\"])\n\t\t\t\tif Mario.lives<=0:\n\t\t\t\t\tcall([\"aplay\",\"-q\",\"smb_gameover.wav\"])\n\t\t\t\t\treturn \"exit\"\n\t\t\t\tos.system('clear')\n\t\t\t\tprint(\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\t\\t\\t\\t\\t\\tNumber of Mario left\",Mario.lives)\n\t\t\t\tMario.respawn(self.x,self.y)\n\t\t\t\ttime.sleep(2)\n\t\t\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\n\n\t\t\telif(Board.board[self.x][self.y]=='/'):\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x-1][self.y]='M'\n\n\t\t\telif Board.board[self.x][self.y]=='I':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_stage_clear.wav\"])\n\t\t\t\tBoard.bonus_round()\n\n\t\t\telif Board.board[self.x][self.y]=='K':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_stage_clear.wav\"])\n\t\t\t\tenemy.boss_round()\n\n\t\t\telif(Board.board[self.x][self.y] in obstacles):\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y-1]='M'\n\n\t\t\telif((Board.board[self.x+1][self.y-1]=='/' or Board.board[self.x+1][self.y-1]=='T') and Board.board[self.x+1][self.y]==' '):\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y+1]='M'\n\t\t\t\tMario.go_down(self)\n\t\t\telse:\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\n\t\tif( self.y-1 >= ((Board.prev_j+Board.prev_k)/2) ):\n\t\t\tos.system('clear')\n\t\t\tBoard.prev_j += 1 \n\t\t\tBoard.prev_k += 1\n\t\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\n\t\telse:\n\t\t\tos.system('clear')\n\t\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)","def update(self, draw):\n for direction in draw.directions_enclosing:\n pos = draw.position\n done = False\n while not done:\n next_stone_in_line = pos.step(direction)\n #next_stone_in_line = tuple(x+y for x, y in zip(pos, direction))\n if self.board[next_stone_in_line] == opponent(draw.player):\n self.board[next_stone_in_line] = draw.player\n pos = next_stone_in_line\n else:\n done = True","def move_draught_end(event):\n global red_draughts, white_draughts\n global board_array\n global old_point\n global die_1_num, die_2_num, doubles\n draught = board.find_withtag(CURRENT)[0]\n #Figure out which point they want to put it on\n bottom = (event.y-click_offset[1] >= board_height//2)\n point_left_edges = [board_divisions*i for i in xrange(0,15) if i != 7]\n is_red = draught in red_draughts\n if bottom == False:\n new_point = 12+point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0])))\n else:\n new_point = 13-point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0]))) \n #Check legality\n if(board_array[new_point][1] > 1 and is_red) or (board_array[new_point][0] > 1 and not is_red): #if too many opposite color on square\n draw_draughts()\n return\n if(board_array[0][0] > 0 and is_red and old_point != 0)or(board_array[25][1] > 0 and not is_red and old_point != 25):#Obligated to move off bar first\n draw_draughts()\n return\n if(new_point == 0 and not is_red): #if white trying to bear off\n for i in xrange(7,26):\n if(board_array[i][1] > 0): #If white has a piece outside home, can't bear off\n draw_draughts()\n return\n if(new_point == 25 and is_red): #if red trying to bear off\n for i in xrange(0,18):\n if(board_array[i][0] > 0): #If red has a piece outside home, can't bear off\n draw_draughts()\n return \n \n if(new_point-old_point == die_1_num and is_red) or (old_point-new_point == die_1_num and not is_red):\n if(doubles == False) or (die_2_num != 0):\n die_1_num = 0\n else: \n die_2_num = die_1_num\n doubles = False\n elif(new_point-old_point == die_2_num and is_red) or (old_point-new_point == die_2_num and not is_red):\n if(doubles == False) or (die_1_num != 0):\n die_2_num = 0\n else: \n die_1_num = die_2_num\n doubles = False\n else: #Can't move there on this roll\n draw_draughts()\n return\n update_dice()\n #Update board_array\n if is_red:\n board_array[old_point][0] -= 1\n board_array[new_point][0] += 1\n if(board_array[new_point][1] == 1): #Handle hits\n board_array[new_point][1] -= 1\n board_array[25][1] += 1\n else:\n board_array[old_point][1] -= 1\n board_array[new_point][1] += 1\n if(board_array[new_point][0] == 1): #Handle hits\n board_array[new_point][0] -= 1\n board_array[0][0] += 1\n\n draw_draughts()\n if(die_1_num == 0 and die_2_num == 0):\n comp_turn()","def result(state, action):\n # Faster than deepcopy\n new_state = pickle.loads(pickle.dumps((state)))\n\n if action.castle == None:\n # Attacking En passant pawn\n if new_state.en_passant == action.end:\n new_state.board[action.start] = cc.NO_PIECE\n new_state.board[action.end] = action.piece\n # Delete black pawn\n if new_state.en_passant[0] == cc.RANK_6:\n down = (action.end[0]+1, action.end[1])\n new_state.board[down] = cc.NO_PIECE\n # Delete white pawn\n elif new_state.en_passant[0] == cc.RANK_3:\n up = (action.end[0]-1, action.end[1])\n new_state.board[up] = cc.NO_PIECE\n # Default Case\n else:\n # Delete piece from the start\n new_state.board[action.start] = \" \"\n # Place piece at the end\n new_state.board[action.end] = action.piece\n\n # Set en_passant space for a pawn moving 2\n if action.en_p:\n new_state.en_passant = cc.coord_to_alg(action.en_p)\n else:\n new_state.en_passant = cc.NO_C_EP\n\n # Remove castle availability if the rook or king move\n if action.piece == cc.W_ROOK:\n if action.start == (cc.RANK_1, cc.FILE_H):\n new_state.castles_avail = new_state.castles_avail.replace('K', '')\n elif action.start == (cc.RANK_1, cc.FILE_A):\n new_state.castles_avail = new_state.castles_avail.replace('Q', '')\n elif action.piece == cc.W_KING:\n if action.start == (cc.RANK_1, cc.FILE_E):\n new_state.castles_avail = new_state.castles_avail.replace('K', '')\n new_state.castles_avail = new_state.castles_avail.replace('Q', '')\n elif action.piece == cc.B_ROOK:\n if action.start == (cc.RANK_8, cc.FILE_H):\n new_state.castles_avail = new_state.castles_avail.replace('k', '')\n elif action.start == (cc.RANK_8, cc.FILE_A):\n new_state.castles_avail = new_state.castles_avail.replace('q', '')\n elif action.piece == cc.B_KING:\n if action.start == (cc.RANK_8, cc.FILE_E):\n new_state.castles_avail = new_state.castles_avail.replace('k', '')\n new_state.castles_avail = new_state.castles_avail.replace('q', '')\n\n # Update the halfmove count\n if action.capture or action.piece == cc.W_PAWN or action.piece == cc.B_PAWN:\n new_state.halfmove = 0\n else:\n new_state.halfmove += 1\n \n else: # Castle Time\n if action.castle == cc.CASTLE_QUEENSIDE:\n if state.active_color == cc.WHITE_ACTIVE:\n # Delete and Place Rook\n new_state.board[cc.RANK_1, cc.FILE_A] = \" \"\n new_state.board[cc.RANK_1, cc.FILE_D] = cc.W_ROOK\n # Delete and Place King\n new_state.board[cc.RANK_1, cc.FILE_E] = \" \"\n new_state.board[cc.RANK_1, cc.FILE_C] = cc.W_KING\n # Remove all White castling availability\n new_state.castles_avail = new_state.castles_avail.replace('K','')\n new_state.castles_avail = new_state.castles_avail.replace('Q','')\n # If the string is empty, replace with a dash\n if not new_state.castles_avail:\n new_state.castles_avail = cc.NO_C_EP\n elif state.active_color == cc.BLACK_ACTIVE:\n # Delete and Place Rook\n new_state.board[cc.RANK_8, cc.FILE_A] = \" \"\n new_state.board[cc.RANK_8, cc.FILE_D] = cc.B_ROOK\n # Delete and Place King\n new_state.board[cc.RANK_8, cc.FILE_E] = \" \"\n new_state.board[cc.RANK_8, cc.FILE_C] = cc.B_KING\n # Remove all Black castling availability\n new_state.castles_avail = new_state.castles_avail.replace('k','')\n new_state.castles_avail = new_state.castles_avail.replace('q','')\n # If the string is empty, replace with a dash\n if not new_state.castles_avail:\n new_state.castles_avail = cc.NO_C_EP\n\n elif action.castle == cc.CASTLE_KINGSIDE:\n if state.active_color == cc.WHITE_ACTIVE:\n # Delete and Place Rook\n new_state.board[cc.RANK_1, cc.FILE_H] = \" \"\n new_state.board[cc.RANK_1, cc.FILE_F] = cc.W_ROOK\n # Delete and Place King\n new_state.board[cc.RANK_1, cc.FILE_E] = \" \"\n new_state.board[cc.RANK_1, cc.FILE_G] = cc.W_KING\n # Remove all White castling availability\n new_state.castles_avail = new_state.castles_avail.replace('K','')\n new_state.castles_avail = new_state.castles_avail.replace('Q','')\n # If the string is empty, replace with a dash\n if not new_state.castles_avail:\n new_state.castles_avail = cc.NO_C_EP\n elif state.active_color == cc.BLACK_ACTIVE:\n # Delete and Place Rook\n new_state.board[cc.RANK_8, cc.FILE_H] = \" \"\n new_state.board[cc.RANK_8, cc.FILE_F] = cc.B_ROOK\n # Delete and Place King\n new_state.board[cc.RANK_8, cc.FILE_E] = \" \"\n new_state.board[cc.RANK_8, cc.FILE_G] = cc.B_KING\n # Remove all Black castling availability\n new_state.castles_avail = new_state.castles_avail.replace('k','')\n new_state.castles_avail = new_state.castles_avail.replace('q','')\n # If the string is empty, replace with a dash\n if not new_state.castles_avail:\n new_state.castles_avail = cc.NO_C_EP\n \n # Update fullmove count\n if new_state.active_color == cc.WHITE_ACTIVE:\n new_state.active_color = cc.BLACK_ACTIVE\n new_state.opp_color = cc.WHITE_ACTIVE\n else:\n new_state.active_color = cc.WHITE_ACTIVE\n new_state.opp_color = cc.BLACK_ACTIVE\n new_state.fullmove += 1\n\n new_state.active_king = new_state.find_king(new_state.active_color)\n new_state.inactive_king = new_state.find_king(new_state.opp_color)\n\n return new_state","def white_kingside_castling(self):\n return (self.castling[0] and self.empty((10, 7))\n and self.empty((10, 8)) and not self.attacked((10, 6), BLACK)\n and not self.attacked((10, 7), BLACK))"],"string":"[\n \"def king_adjust(self, turn):\\n\\n opposite_turn = next_turn(turn)\\n\\n original_location_index = (piece_class.KING_LOCATION[turn][0] + piece_class.KING_LOCATION[turn][1] * 8)\\n \\n# if self.board[original_location_index] == self.empty:\\n# print(\\\"yo\\\")\\n \\n self.board[original_location_index].possible_moves = [i for i in self.board[original_location_index].possible_moves if i not in self.long_dict[opposite_turn]]\\n \\n\\n\\n #king_path = [i for i in self.board[original_location_index].possible_moves if i not in self.path_dict[opposite_turn]]\\n\\n #removes moving into check from king path. Can remove this functionality from the move method now.\\n #self.board[original_location_index].possible_moves = king_path\\n #king_path_index = [(i[0] + i[1]*8) for i in self.board[original_location_index].possible_moves]\\n\\n \\n #for i in king_path:\\n # if i in self.long_dict[opposite_turn] or self.check_dict[opposite_turn]:\\n # print(\\\"king path is: \\\", king_path)\\n # king_path.remove(i)\\n # print(\\\"king path is now: \\\", king_path)\\n \\n\\n\\n\\n\\n #for i in king_path_index:\\n # enemy_piece = self.board[i]\\n # self.board[i] = self.board[original_location_index]\\n # self.board[original_location_index] = self.empty\\n # self.loads_pathways(turn)\\n # if self.coords[i] in self.path_dict[opposite_turn]:\\n # print(\\\"yo\\\")\\n \\n #self.board[original_location_index] = self.board[i]\\n #self.board[i] = enemy_piece\",\n \"def castling_implement(self, turn, select, move):\\n \\n if select == self.coords.index(piece_class.KING_LOCATION[turn]):\\n if self.board[self.coords.index(piece_class.KING_LOCATION[turn])].move_track == False:\\n if move in [2, 58]:\\n self.board[move+1] = self.board[move-2]\\n self.board[move-2] = self.empty\\n if move in [6, 62]:\\n self.board[move-1] = self.board[move+1]\\n self.board[move+1] = self.empty\",\n \"def get_valid_moves(self):\\r\\n # castling and en-passant rights are stored, because move affects these values\\r\\n temp_enpassant_possible = self.enpas_pos\\r\\n temp_castle = CastleRights(self.cr_castle_r.wks, self.cr_castle_r.bks,\\r\\n self.cr_castle_r.wqs, self.cr_castle_r.bqs)\\r\\n\\r\\n # for validating a possible move\\r\\n #1 all possibile moves are generated\\r\\n #2 each pos moves are made\\r\\n #3 generate opponent move\\r\\n #4 check if any of those moves let the king attacked\\r\\n #5 moves which let the king in chess are eliminated\\r\\n #6 the moves are undone\\r\\n moves = self.get_all_possible_moves() # 1\\r\\n\\r\\n # castle moves are directly introduced in valid moves\\r\\n if not self.turn_white:\\r\\n self.get_castle_moves(self.bKingPos[0], self.bKingPos[1], moves)\\r\\n else:\\r\\n self.get_castle_moves(self.wKingPos[0], self.wKingPos[1], moves)\\r\\n\\r\\n for i in range(len(moves) - 1, -1, -1): # 2\\r\\n self.make_move(moves[i])\\r\\n # 3 #4\\r\\n self.turn_white = not self.turn_white\\r\\n if self.in_check():\\r\\n moves.remove(moves[i]) # 5\\r\\n self.turn_white = not self.turn_white\\r\\n self.undo_move()\\r\\n\\r\\n # game ending possibilities\\r\\n if len(moves) == 0:\\r\\n if self.in_check():\\r\\n self.checkMate = True\\r\\n print(\\\"Checkmate !\\\")\\r\\n else:\\r\\n self.staleMate = True\\r\\n print(\\\"Stalemate !\\\")\\r\\n else:\\r\\n self.checkMate = False\\r\\n self.staleMate = False\\r\\n\\r\\n # the rigths are restored, and the values are not affected\\r\\n self.enpas_pos = temp_enpassant_possible\\r\\n self.cr_castle_r = temp_castle\\r\\n\\r\\n return moves\",\n \"def make_move(self, move, check_valid=True):\\r\\n self.board[move.sr][move.sc] = \\\"--\\\"\\r\\n self.board[move.er][move.ec] = move.pieceMoved\\r\\n self.moveLog.append(move)\\r\\n self.turn_white = not self.turn_white\\r\\n if move.pieceMoved == 'wk':\\r\\n self.wKingPos = (move.er, move.ec)\\r\\n elif move.pieceMoved == 'bk':\\r\\n self.bKingPos = (move.er, move.ec)\\r\\n\\r\\n if move.isEnpassantMove:\\r\\n self.board[move.sr][move.ec] = \\\"--\\\"\\r\\n\\r\\n if move.pieceMoved[1] == 'p' and abs(move.sr - move.er) == 2:\\r\\n self.enpas_pos = ((move.er + move.sr) // 2, move.ec)\\r\\n else:\\r\\n self.enpas_pos = ()\\r\\n\\r\\n if move.isPawnPromotion and not check_valid:\\r\\n promoted_piece = \\\"a\\\"\\r\\n while promoted_piece not in ('q', 'r', 'b', 'n'):\\r\\n promoted_piece = input(\\\"Promote to q, r, b, or n: \\\")\\r\\n self.board[move.er][move.ec] = move.pieceMoved[0] + promoted_piece\\r\\n\\r\\n # castle\\r\\n if move.castle:\\r\\n if move.ec - move.sc == 2:\\r\\n self.board[move.er][move.ec - 1] = self.board[move.er][move.ec + 1]\\r\\n self.board[move.er][move.ec + 1] = '--'\\r\\n else:\\r\\n self.board[move.er][move.ec + 1] = self.board[move.er][move.ec - 2]\\r\\n self.board[move.er][move.ec - 2] = '--'\\r\\n\\r\\n # castle rights on rook, king move\\r\\n self.update_castle_rights(move)\\r\\n self.castleRightsLog.append(CastleRights(self.cr_castle_r.wks, self.cr_castle_r.bks,\\r\\n self.cr_castle_r.wqs, self.cr_castle_r.bqs))\",\n \"def _get_possible_castles(self, piece):\\n\\n valid_moves = set()\\n\\n # Cant castle while under check\\n if self._check_for_check():\\n return valid_moves\\n\\n row = self._get_castling_row(piece)\\n king = 'K' if self.white_to_move else 'k'\\n queen = 'Q' if self.white_to_move else 'q'\\n\\n # If there is nothing in the way, we can castle kingside\\n if king in self.available_castles: # Does not check if f file is in check\\n if self.board[row][5] == self.board[row][6] == EMPTY_SPACE:\\n valid_moves.add(CASTLE_KINGSIDE)\\n\\n if queen in self.available_castles: # Does not check if d file is in check\\n if self.board[row][1] == self.board[row][2] == self.board[row][3] == EMPTY_SPACE:\\n valid_moves.add(CASTLE_QUEENSIDE)\\n\\n return valid_moves\",\n \"def get_next_moves(board, player):\\r\\n\\r\\n if player == 'hare':\\r\\n moves = []\\r\\n next_moves = []\\r\\n\\r\\n (row_from, col_from) = get_hare_positions(board)\\r\\n moves = possible_moves_list(row_from, col_from)\\r\\n\\r\\n for move in moves:\\r\\n row_to = move[0]\\r\\n col_to = move[1]\\r\\n\\r\\n if is_legal_move(player, row_from, col_from, row_to, col_to):\\r\\n \\\"\\\"\\\" if move is allowed then add to list of next moves\\\"\\\"\\\"\\r\\n next_moves.append(move)\\r\\n\\r\\n return next_moves\\r\\n\\r\\n else:\\r\\n \\\"\\\"\\\" for individual hounds\\r\\n get next moves\\\"\\\"\\\"\\r\\n moves = []\\r\\n next_moves_hound1 = []\\r\\n next_moves_hound2 = []\\r\\n next_moves_hound3 = []\\r\\n\\r\\n (row_hound_1, col_hound_1), (row_hound_2, col_hound_2), (row_hound_3, col_hound_3) = get_hound_positions(board)\\r\\n moves_hound1 = possible_moves_list(row_hound_1, col_hound_1)\\r\\n moves_hound2 = possible_moves_list(row_hound_2, col_hound_2)\\r\\n moves_hound3 = possible_moves_list(row_hound_3, col_hound_3)\\r\\n\\r\\n for move in moves_hound1:\\r\\n row_to = move[0]\\r\\n col_to = move[1]\\r\\n\\r\\n if is_legal_move(player, row_hound_1, col_hound_1, row_to, col_to):\\r\\n next_moves_hound1.append(move)\\r\\n\\r\\n for move in moves_hound2:\\r\\n row_to = move[0]\\r\\n col_to = move[1]\\r\\n\\r\\n if is_legal_move(player, row_hound_2, col_hound_2, row_to, col_to):\\r\\n next_moves_hound2.append(move)\\r\\n\\r\\n for move in moves_hound3:\\r\\n row_to = move[0]\\r\\n col_to = move[1]\\r\\n\\r\\n if is_legal_move(player, row_hound_3, col_hound_3, row_to, col_to):\\r\\n next_moves_hound3.append(move)\\r\\n\\r\\n return (next_moves_hound1, next_moves_hound2, next_moves_hound3)\",\n \"def get_legal_moves(self):\\n # for each square in the castle figure out if an moves can occur from it.\\n moves = []\\n allowed = [self.turn]\\n if self.turn == DEFENDER:\\n allowed.extend((KING, CASTLE_OCCUPIED))\\n it = np.nditer(self.board_state, flags=['multi_index'])\\n while not it.finished:\\n index = it.multi_index\\n curr_loc = it[0]\\n if curr_loc in allowed:\\n moves.extend(self.get_legal_move_piece(curr_loc, index))\\n it.iternext()\\n return moves\",\n \"def make_move(move):\\n global manatee_pos\\n global hyacinths\\n global hyacinth_pos\\n\\n # Ends the program if movement is out of bounds\\n if move == (0, 0):\\n return None\\n new_pos = (manatee_pos[0] + move[0], manatee_pos[1] + move[1])\\n if new_pos[0] < 0 or new_pos[0] >= len(map):\\n return None\\n if new_pos[1] < 0 or new_pos[1] >= len(map[new_pos[0]]):\\n return None\\n\\n entity = map[new_pos[0]][new_pos[1]]\\n if entity == \\\"#\\\" or entity == \\\"G\\\":\\n # Runs if movement is impossible\\n return None\\n if entity == \\\" \\\" or entity == \\\".\\\":\\n # Runs if normal movement is possible\\n map[new_pos[0]][new_pos[1]] = \\\"M\\\"\\n map[manatee_pos[0]][manatee_pos[1]] = \\\" \\\"\\n manatee_pos = new_pos\\n return None\\n if entity == \\\"O\\\":\\n # Runs if manatee wins game\\n map[new_pos[0]][new_pos[1]] = \\\"M\\\"\\n map[manatee_pos[0]][manatee_pos[1]] = \\\" \\\"\\n manatee_pos = new_pos\\n return \\\"win\\\"\\n if entity == \\\"\\\\\\\\\\\":\\n # Runs if manatee eats hyacinth\\n map[new_pos[0]][new_pos[1]] = \\\"M\\\"\\n map[manatee_pos[0]][manatee_pos[1]] = \\\" \\\"\\n manatee_pos = new_pos\\n hyacinths += 1\\n if len(hyacinth_pos) == hyacinths:\\n map[grate_pos[0]][grate_pos[1]] = \\\"O\\\"\\n return None\\n if entity == \\\"*\\\":\\n # Checks if manatee can push boat\\n if move[0] == 0:\\n new_boat_pos = (new_pos[0] + move[0], new_pos[1] + move[1])\\n if new_boat_pos[0] < 0 or new_boat_pos[0] >= len(map):\\n return None\\n if new_boat_pos[1] < 0 \\\\\\n or new_boat_pos[1] >= len(map[new_boat_pos[0]]):\\n return None\\n if map[new_boat_pos[0]][new_boat_pos[1]] == \\\" \\\":\\n map[new_boat_pos[0]][new_boat_pos[1]] = \\\"*\\\"\\n map[new_pos[0]][new_pos[1]] = \\\"M\\\"\\n map[manatee_pos[0]][manatee_pos[1]] = \\\" \\\"\\n manatee_pos = new_pos\\n return None\\n return None\",\n \"def get_all_moves(self, castling_allowed=True):\\n\\n can_move = str.isupper if self.white_to_move else str.islower\\n\\n valid_moves = set()\\n\\n for row_num, row in enumerate(self.board):\\n for col_num, piece in enumerate(row):\\n if piece != EMPTY_SPACE and can_move(piece):\\n\\n location = (row_num, col_num)\\n\\n # Everything except the pawn movement\\n if piece.lower() in NAME_TO_PIECE:\\n valid_moves = valid_moves.union(self._get_standard_moves_for_piece(location, piece))\\n\\n # Pawn moves\\n if piece.lower() == PAWN:\\n valid_moves = valid_moves.union(self._get_pawn_moves(location, piece))\\n\\n # Castling\\n if castling_allowed and piece.lower() == KING:\\n valid_moves = valid_moves.union(self._get_possible_castles(piece))\\n\\n return valid_moves\",\n \"def __update_valid_moves(self):\\r\\n\\r\\n\\t\\tmoves = ['Stay']\\r\\n\\r\\n\\t\\t# case for 21\\r\\n\\t\\tfor value in self.values:\\r\\n\\t\\t\\tif value > 21:\\r\\n\\t\\t\\t\\tself.valid_moves = 'Bust'\\r\\n\\t\\t\\t\\treturn\\r\\n\\t\\t\\tif value == 21:\\r\\n\\t\\t\\t\\tif len(self.cards) == 2:\\r\\n\\t\\t\\t\\t\\tself.valid_moves = 'Blackjack'\\r\\n\\t\\t\\t\\t\\treturn\\r\\n\\t\\t\\t\\tself.valid_moves = '21'\\r\\n\\t\\t\\t\\treturn\\r\\n\\t\\tmoves.append('Hit')\\r\\n\\r\\n\\t\\tif len(self.cards) <= 2:\\r\\n\\t\\t\\tmoves.append('Double')\\r\\n\\r\\n\\t\\tif len(self.cards) == 2:\\r\\n\\t\\t\\tif self.cards[0].rank == self.cards[1].rank:\\r\\n\\t\\t\\t\\tmoves.append('Split')\\r\\n\\r\\n\\t\\tself.valid_moves = moves\",\n \"def legal_moves():\\n\\tlegal_moves = (\\\"r\\\", \\\"p\\\", \\\"s\\\")\\n\\treturn legal_moves\",\n \"def get_legal_moves(self, fish_index, player):\\n moves = []\\n if fish_index == -1:\\n return moves\\n for direction in [\\\"left\\\", \\\"right\\\", \\\"top_left\\\", \\\"bottom_right\\\", \\\"top_right\\\", \\\"bottom_left\\\"]:\\n moves += self.get_moves_direction(fish_index, direction, player)\\n if len(moves) == 0:\\n # penguin cant move - can kill itself - JK\\n pass\\n #moves.append(FishMove(fish_index, fish_index, player=player))\\n return moves\",\n \"def getPossibleMoves(self): # called to get possible positions this piece can go\\r\\n \\r\\n moves = {}\\r\\n\\r\\n ids = []\\r\\n\\r\\n for piece in self.board.pieces.values():\\r\\n if piece.name == \\\"empty\\\":\\r\\n piece.glow = False\\r\\n piece.ready = False\\r\\n\\r\\n self.piece = self\\r\\n\\r\\n def check(direction=\\\"left\\\", heading=\\\"north\\\", x=None, y=None):\\r\\n piece = self.piece\\r\\n if direction == \\\"left\\\": x -= 50\\r\\n else: x += 50\\r\\n\\r\\n if heading == \\\"north\\\": y -= 50\\r\\n else: y += 50\\r\\n\\r\\n if (x, y) in self.board.pieces: # position is empty\\r\\n empty = self.board.getPiece((x, y))\\r\\n empty.glow = True\\r\\n old, new, obj = (direction, heading), (x, y), piece\\r\\n identity = self.getRandomID(ids) # get an ID for the move\\r\\n moves[identity] = old, new, obj\\r\\n\\r\\n if piece.isKing: # piece is a king, so go on\\r\\n check(direction, heading, x, y)\\r\\n else: # its not empty, so check if its comrade\\r\\n x1, y1 = x+25, y+25\\r\\n piece2 = self.board.getPiece((x1, y1))\\r\\n try:\\r\\n if piece.isComrade(piece2):# piece is comrade so return\\r\\n return\\r\\n else: # piece is not comrade, so check empty\\r\\n if direction == \\\"left\\\": x2 = x1-25-50\\r\\n else: x2 = x1-25+50\\r\\n\\r\\n if heading == \\\"north\\\": y2 = y1-25-50\\r\\n else: y2 = y1-25+50\\r\\n\\r\\n if (x2, y2) in self.board.pieces: # its empty, so notify player\\r\\n empty = self.board.getPiece((x2, y2))\\r\\n empty.glow = True\\r\\n empty.ready = True\\r\\n\\r\\n old, new, obj = (direction, heading), (x2, y2), piece2\\r\\n identity = self.getRandomID(ids)\\r\\n moves[identity] = old, new, obj\\r\\n\\r\\n check(direction, heading, piece2.x-25, piece2.y-25)\\r\\n check(direction, heading, x2, y2)\\r\\n \\r\\n # check empty or comrade again\\r\\n if direction == \\\"left\\\": x3 = x2-50\\r\\n else: x3 = x2+50\\r\\n\\r\\n if heading == \\\"north\\\": y3 = y2-50\\r\\n else: y3 = y2+50\\r\\n\\r\\n if (x3, y3) in self.board.pieces: # positon(address) is empty\\r\\n return\\r\\n else: # there is a piece, so check if comrade, stop, if not comrade continue\\r\\n x3+=25\\r\\n y3+= 25\\r\\n\\r\\n piece3 = self.board.getPiece((x3, y3))\\r\\n if piece3.isComrade(piece2): # comrades, so stop\\r\\n return\\r\\n else: # not comrades, so continue\\r\\n self.piece = piece3\\r\\n check(direction, heading, x, y)\\r\\n\\r\\n #self.piece = piece2\\r\\n \\r\\n #check(direction, heading, x2, y2) # keep searching\\r\\n else: # its not empty, so return\\r\\n return\\r\\n except:\\r\\n pass\\r\\n\\r\\n if self.piece.name == \\\"white\\\": direction = \\\"north\\\"\\r\\n else: direction = \\\"south\\\"\\r\\n \\r\\n check(\\\"left\\\", direction, self.piece.x-25, self.piece.y-25)\\r\\n check(\\\"right\\\", direction, self.piece.x-25, self.piece.y-25)\\r\\n \\r\\n if self.piece.isKing:\\r\\n if self.piece.name == \\\"white\\\": heading = \\\"south\\\"\\r\\n else: heading = \\\"north\\\"\\r\\n \\r\\n check(\\\"left\\\", heading, self.piece.x-25, self.piece.y-25)\\r\\n check(\\\"right\\\", heading, self.piece.x-25, self.piece.y-25)\\r\\n\\r\\n if self.piece.name == \\\"white\\\":\\r\\n eatMoves = self.board.game.thinkEatMoves(moves, \\\"person\\\")\\r\\n if eatMoves is not None:\\r\\n return eatMoves\\r\\n\\r\\n return moves\",\n \"def spaces_threatened_towards_king(self, list_of_directional_spaces_threatened):\\n for lst in list_of_directional_spaces_threatened:\\n for move in lst:\\n if isinstance(THE_BOARD.coords_to_piece(move), King):\\n return(lst)\\n \\n raise Exception(f\\\"{self.symbol} not threatening the enemy king\\\")\",\n \"def spaces_threatened_towards_king(self, list_of_directional_spaces_threatened):\\n for lst in list_of_directional_spaces_threatened:\\n for move in lst:\\n if isinstance(THE_BOARD.coords_to_piece(move), King):\\n return(lst)\\n \\n raise Exception(f\\\"{self.symbol} not threatening the enemy king\\\")\",\n \"def spaces_threatened_towards_king(self, list_of_directional_spaces_threatened):\\n for lst in list_of_directional_spaces_threatened:\\n for move in lst:\\n if isinstance(THE_BOARD.coords_to_piece(move), King):\\n return(lst)\\n \\n raise Exception(f\\\"{self.symbol} not threatening the enemy king\\\")\",\n \"def action(self):\\r\\n\\r\\n\\r\\n #have we just started?\\r\\n if self.player_information[\\\"us\\\"][\\\"nTokens\\\"] == 0:\\r\\n move = generate_starting_move(self.player_information[\\\"us\\\"][\\\"player_side\\\"], self.board_array)\\r\\n return move\\r\\n\\r\\n #otherwise do minimax \\r\\n \\r\\n #start off with some shallow depth:\\r\\n if self.turn_no < 5:\\r\\n depth = 3\\r\\n else:\\r\\n depth = 2\\r\\n \\r\\n #set a constraint for search depth\\r\\n if self.total_tokens_on_board < 6:\\r\\n depth = 3\\r\\n elif self.total_tokens_on_board < 10:\\r\\n depth = 2\\r\\n else:\\r\\n depth = 1\\r\\n \\r\\n #have a time reference\\r\\n print(f'nthrows: {self.player_information[\\\"us\\\"][\\\"nThrowsRemaining\\\"]}')\\r\\n starting_time = int(round(time.time(), 0))\\r\\n #salvage result from minimax\\r\\n result = minimax(self.board_dict.copy(), self.player_tokens.copy(), self.co_existance_dict.copy(),\\r\\n None, None, None, depth, True, -math.inf, math.inf,\\r\\n (-5, -5), self.player_information.copy(), self.board_array, self.board_edge, \\r\\n starting_time, True, self.turn_no)\\r\\n\\r\\n #clean it up a bit \\r\\n print(self.board_dict)\\r\\n #tidy it up\\r\\n result = result[0]\\r\\n print(f'pre: {result}')\\r\\n #in case we get a bad move redo but make it very shallow\\r\\n if len(result) == 1 or result == (-5, -5):\\r\\n #force it to return a usable move\\r\\n counter = 0\\r\\n while (len(result) == 1) or (result == (-5, -5)):\\r\\n result = minimax(self.board_dict.copy(), self.player_tokens.copy(), self.co_existance_dict.copy(),\\r\\n None, None, None, 1, True, -math.inf, math.inf,\\r\\n (-5, -5), self.player_information.copy(), self.board_array, self.board_edge, \\r\\n starting_time, False, self.turn_no)\\r\\n result = result[0]\\r\\n counter += 1\\r\\n \\r\\n #if its taking too long\\r\\n if counter > 2: \\r\\n #generate one random possible move to use \\r\\n allied_tokens = [token for token in self.player_tokens if self.player_tokens[token] == \\\"us\\\"]\\r\\n move_list = generate_moves(self.board_dict, self.player_tokens, self.co_existance_dict, allied_tokens,\\r\\n self.player_information, self.board_array, True, \\\"all\\\")\\r\\n \\r\\n \\r\\n #if there are no moves\\r\\n if len(move_list) == 0:\\r\\n if self.player_information['us']['nThrowsRemaining'] > 0:\\r\\n throws = generate_possible_throws(self.board_dict, self.player_tokens, self.co_existance_dict, self.player_information, \\\"us\\\",\\r\\n self.player_information[\\\"us\\\"][\\\"player_side\\\"], self.board_array, \\\"all\\\" )\\r\\n result = random.choice(throws)\\r\\n \\r\\n else:\\r\\n result = random.choice(move_list)\\r\\n print(f'random: {result}')\\r\\n break\\r\\n\\r\\n print(f' inside: {result}')\\r\\n\\r\\n print(result)\\r\\n #otherwise clean it up\\r\\n if result[0] == 'throw':\\r\\n final_result = (result[0].upper(), result[1], result[2])\\r\\n else:\\r\\n final_result = (result[0].upper(), result[2], result[3])\\r\\n # return final result \\r\\n return final_result\",\n \"def get_king_moves(self, i, j, moves):\\r\\n directions = ((1, 1), (1, -1), (1, 0), (0, -1), (0, 1), (-1, -1), (-1, 1), (-1, 0))\\r\\n if self.turn_white:\\r\\n oponent = 'b'\\r\\n else:\\r\\n oponent = 'w'\\r\\n for d in directions:\\r\\n cri = i + d[0]\\r\\n crj = j + d[1]\\r\\n if 0 <= cri <= 7 and 0 <= crj <= 7:\\r\\n # empty\\r\\n if self.board[cri][crj][0] == '-':\\r\\n moves.append(Move((i, j), (cri, crj), self.board))\\r\\n\\r\\n # oponent\\r\\n elif self.board[cri][crj][0] == oponent:\\r\\n moves.append(Move((i, j), (cri, crj), self.board))\",\n \"def actions(self, state):\\n\\n actions = []\\n \\n # if its player 1's turn\\n if state.maxs_turn==True:\\n # look through all the squares on the board\\n for coords in state.gameState:\\n # if its a rebel append allowable move and attack actions\\n if state.gameState[coords]=='R':\\n if state.gameState[(coords[0]-1, coords[1])]== ' ':\\n actions.append(\\\"Move: Rebel @ {} --> {}\\\".format(coords, (coords[0]-1, coords[1])))\\n if ((coords[0]-1, coords[1]+1) in state.gameState) and (state.gameState[(coords[0]-1, coords[1]+1)]== 'S'):\\n actions.append(\\\"Attack: Rebel @ {} --> Sith @ {}\\\".format(coords, (coords[0]-1, coords[1]+1)))\\n if ((coords[0]-1, coords[1]-1) in state.gameState) and (state.gameState[(coords[0]-1, coords[1]-1)]== 'S'):\\n actions.append(\\\"Attack: Rebel @ {} --> Sith @ {}\\\".format(coords, (coords[0]-1, coords[1]-1)))\\n \\n # if its a jedi append allowable move and attack actions\\n elif state.gameState[coords]=='J':\\n for direction in [(-1, 0),(-1,1),(0,1),(1,1),(1,0),(1,-1),(0,-1),(-1,-1)]:\\n coord = (coords[0]+direction[0], coords[1]+direction[1])\\n # walk in each direction until reaching the edge of board, or a player\\n while (coord in state.gameState) and (state.gameState[coord] == ' '):\\n actions.append(\\\"Move: Jedi @ {} --> {}\\\".format(coords, coord))\\n coord = (coord[0]+direction[0], coord[1]+direction[1])\\n # if we ran into a sith we can attack\\n if (coord in state.gameState) and (state.gameState[coord] == 'S'):\\n actions.append(\\\"Attack: Jedi @ {} --> Sith @ {}\\\".format(coords, coord))\\n \\n else:\\n for coords in state.gameState:\\n if state.gameState[coords]=='S':\\n for direction in [(-1, 0),(-1,1),(0,1),(1,1),(1,0),(1,-1),(0,-1),(-1,-1)]:\\n coord = (coords[0]+direction[0], coords[1]+direction[1])\\n if (coord in state.gameState) and (state.gameState[coord] == ' '):\\n actions.append(\\\"Move: Sith @ {} --> {}\\\".format(coords, coord))\\n elif (coord in state.gameState) and (state.gameState[coord] == 'R'):\\n actions.append(\\\"Attack: Sith @ {} --> Rebel @ {}\\\".format(coords, coord))\\n elif (coord in state.gameState) and (state.gameState[coord] == 'J'):\\n actions.append(\\\"Attack: Sith @ {} --> Jedi @ {}\\\".format(coords, coord))\\n \\n\\n\\n if len(actions)==0:\\n actions.append(\\\"Pass\\\")\\n \\n actions.sort()\\n \\n return actions\",\n \"def get_king_moves(self, state):\\n #king_moves = []\\n possible_moves = []\\n if self.color == cc.WHITE_ACTIVE:\\n enemy_color = cc.BLACK_ACTIVE\\n enemy_pieces = cc.BLACK_PIECES\\n elif self.color == cc.BLACK_ACTIVE:\\n enemy_color = cc.WHITE_ACTIVE\\n enemy_pieces = cc.WHITE_PIECES\\n else:\\n raise Exception(\\\"GameState: Invalid Active Color\\\")\\n\\n for vector in cc.KING_VECTORS:\\n rank = self.coord[0] + vector[0]\\n column = self.coord[1] + vector[1]\\n if rank in cc.VALID_RANKS and column in cc.VALID_RANKS:\\n if state.board[rank, column] == cc.NO_PIECE:\\n possible_moves.append(cc.Action(self.string, self.coord, (rank, column)))\\n elif state.board[rank, column] in enemy_pieces:\\n possible_moves.append(cc.Action(self.string, self.coord, (rank, column), capture=True))\\n \\n # # Iterate over list of king moves, removing ones that are under attack\\n # for move in possible_moves:\\n # if not check.space_under_attack(state, move.end, enemy_color):\\n # king_moves.append(move)\\n\\n return possible_moves\",\n \"def next_turn(self): \\n if (self.moves):\\n self.board = self.select_move() \\n self.moves = []\\n self.roll = self.roll_dice()\\n self.player = not self.player\\n self.generate_valid_moves()\",\n \"def enumerate_moves(self):\\n add_ew = lambda x: [x+'e', x+'w']\\n allowed_catches = add_ew(self._directions[0])\\n moves = []\\n # First add the one/two step forward moves\\n new_slot = self._board.get_dir(self._current_space, self._directions[0])\\n if new_slot and new_slot.is_free():\\n moves.append(ChessMove(self._current_space, new_slot))\\n if (self._side == BLACK and new_slot.row == self._board.size - 1) or \\\\\\n (self._side == WHITE and new_slot.row == 0):\\n moves[-1].add_promotion()\\n if (self._side == BLACK and self._current_space.row == 1) or \\\\\\n (self._side == WHITE and self._current_space.row == self._board.size -2):\\n new_slot = self._board.get_dir(new_slot, self._directions[0])\\n if new_slot and new_slot.is_free():\\n moves.append(ChessMove(self._current_space, new_slot))\\n\\n # Now add all the captures.\\n for direction in allowed_catches:\\n new_slot = self._board.get_dir(self._current_space, direction)\\n if new_slot and new_slot.has_opponent(self._side):\\n moves.append(ChessMove(self._current_space, new_slot, [new_slot]))\\n if (self._side == BLACK and new_slot.row == self._board.size - 1) or \\\\\\n (self._side == WHITE and new_slot.row == 0):\\n moves[-1].add_promotion()\\n return moves\",\n \"def make_move(self, move):\\n # Unpack the information from the move\\n piece = self.get_piece(move[0])\\n row = move[1][0]\\n col = move[1][1]\\n eliminated_pieces = move[2]\\n\\n # Move the piece and eliminate the captured pieces\\n self.board[piece.row][piece.col] = None\\n self.board[row][col] = piece\\n piece.row = row\\n piece.col = col\\n self.remove(eliminated_pieces)\\n\\n # Checks if the piece has been promoted\\n if piece.get_player() == Player.black and \\\\\\n piece.row == constant.BOARD_DIMENSION - 1 \\\\\\n and not piece.is_king():\\n piece.make_king()\\n self.num_black_kings += 1\\n\\n elif piece.get_player() == Player.white and \\\\\\n piece.row == 0 \\\\\\n and not piece.is_king():\\n piece.make_king()\\n self.num_white_kings += 1\\n\\n # Add the move performed to the history of the game\\n self.moves.append(move)\",\n \"def make_turn(self):\\n # if play first, start in the middle\\n if np.count_nonzero(self.board) == 0:\\n self.place_disc(self.board.shape[1] / 2)\\n return 1\\n\\n\\n # win if possible\\n for try_column in range(0,self.board.shape[1]):\\n if 0 == self.board[0, try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, self.id())\\n if dhw.did_he_win(new_board, self.id(), try_column):\\n self.place_disc(try_column)\\n return 1\\n\\n # don't loose if in danger\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id())\\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\\n self.place_disc(try_column)\\n return 1\\n\\n # don't fall in trap!\\n forbidden_columns = []\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, self.id()) # my move\\n new_board = self.simulate_place_disc(new_board, try_column, 3 - self.id()) # enemy move\\n if dhw.did_he_win(new_board, 3 - self.id(), try_column):\\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\\n\\n # don't ruin my trap\\n for try_column in range(0, self.board.shape[1]):\\n if 0 == self.board[0,try_column]:\\n new_board = self.simulate_place_disc(self.board, try_column, 3 - self.id()) # 'my' move\\n new_board = self.simulate_place_disc(new_board, try_column, self.id()) # my move\\n if dhw.did_he_win(new_board, self.id(), try_column):\\n if try_column not in forbidden_columns: forbidden_columns.append(try_column)\\n\\n # allow forbidden_columns if no other choice\\n if np.count_nonzero(self.board[0, :]) == self.board.shape[1] - len(forbidden_columns):\\n forbidden_columns = []\\n\\n # otherwise, play randomly\\n rannum = random.randrange(7)\\n while 0 != self.board[0, rannum] or rannum in forbidden_columns:\\n rannum = random.randrange(7)\\n self.place_disc(rannum)\\n return 1\",\n \"def castle(self, is_white, is_kingside):\\n if is_white and is_kingside:\\n # Validate castling is possible\\n if self.get_square('e', 1) != \\\"K\\\" or self.get_square('f', 1) != \\\" \\\" or self.get_square('g', 1) != \\\" \\\" \\\\\\n or self.get_square('h', 1) != 'R':\\n raise RuntimeError(\\\"White can not castle kingside\\\")\\n # Make the move\\n self.set_square('e', 1, ' ')\\n self.set_square('f', 1, 'R')\\n self.set_square('g', 1, 'K')\\n self.set_square('h', 1, ' ')\\n\\n elif is_white and not is_kingside:\\n # Validate castling is possible\\n if self.get_square('e', 1) != \\\"K\\\" or self.get_square('d', 1) != \\\" \\\" or self.get_square('c', 1) != \\\" \\\" \\\\\\n or self.get_square('b', 1) != \\\" \\\" or self.get_square('a', 1) != 'R':\\n raise RuntimeError(\\\"White can not castle queenside\\\")\\n # Make the move\\n self.set_square('e', 1, ' ')\\n self.set_square('d', 1, 'R')\\n self.set_square('c', 1, 'K')\\n self.set_square('a', 1, ' ')\\n\\n elif is_kingside: # for black\\n # Validate castling is possible\\n if self.get_square('e', 8) != \\\"k\\\" or self.get_square('f', 8) != \\\" \\\" or self.get_square('g', 8) != \\\" \\\" \\\\\\n or self.get_square('h', 8) != 'r':\\n raise RuntimeError(\\\"Black can not castle kingside\\\")\\n # Make the move\\n self.set_square('e', 8, ' ')\\n self.set_square('f', 8, 'r')\\n self.set_square('g', 8, 'k')\\n self.set_square('h', 8, ' ')\\n\\n else: # black, queenside\\n # Validate castling is possible\\n if self.get_square('e', 8) != \\\"k\\\" or self.get_square('d', 8) != \\\" \\\" or self.get_square('c', 8) != \\\" \\\" \\\\\\n or self.get_square('b', 8) != \\\" \\\" or self.get_square('a', 8) != 'r':\\n raise RuntimeError(\\\"Black can not castle queenside\\\")\\n # Make the move\\n self.set_square('e', 8, ' ')\\n self.set_square('d', 8, 'r')\\n self.set_square('c', 8, 'k')\\n self.set_square('a', 8, ' ')\",\n \"def decide_next_move(self):\\n pass\",\n \"def decide(self):\\n self.maybe_shoot()\\n next(self.move_cycle)\\n\\n pass\",\n \"def apply_move(self, move):\\n if self.check_move(move=move):\\n self.board_list[move] = self.current_player.marker # changes value in the board to player which is either X or O\\n self.moves_made += str(move) # keeps track of all moves\\n return True\\n else:\\n return False\",\n \"def make_move(self, state):\\r\\n # intially set drop phase to true\\r\\n drop_phase = True\\r\\n move = [] # list to make moves with to return\\r\\n succ = self.succ(state) # get the successor of this state\\r\\n # intial postion of board to set up most advantagous spot if its empty\\r\\n if sum(x.count(self.my_piece) for x in self.board) == 0 and self.board[2][2] == ' ':\\r\\n move.insert(0, (2, 2))\\r\\n return move\\r\\n \\r\\n # check the number of 'r' and 'b' on board if theres 4 of each drop phase is false\\r\\n if sum(x.count('r') for x in self.board) == 4 and sum(x.count('b') for x in self.board) == 4:\\r\\n drop_phase = False\\r\\n\\r\\n # if not during drop phase use minimax to make next move from one postion to next\\r\\n if not drop_phase:\\r\\n move = []\\r\\n d = self.Max_value(state, 0)\\r\\n val = d['val']\\r\\n m = d['move']\\r\\n p = d['pos']\\r\\n f = d['from']\\r\\n s = sorted(succ, key=lambda e: e['f'])\\r\\n moveto = s[-1]\\r\\n move.insert(1, (moveto['from'][0], moveto['from'][1]))\\r\\n move.insert(0, (moveto['pos'][0], moveto['pos'][1]))\\r\\n return move # return the from, to move\\r\\n\\r\\n else: #else use minimax and to make move during drop phase selecting spot to place AI piece\\r\\n d = self.Max_value(state, 0)\\r\\n val = d['val']\\r\\n m = d['move']\\r\\n p = d['pos']\\r\\n hold = []\\r\\n move = []\\r\\n n = None\\r\\n hold = []\\r\\n for s in succ:\\r\\n p = s['pos'][0]\\r\\n p1 = s['pos'][1]\\r\\n if s['f'] == val and state[p][p1] == ' ':\\r\\n hold.append(s)\\r\\n if len(hold) == 1:\\r\\n row = hold[0]['pos'][0]\\r\\n col = hold[0]['pos'][1]\\r\\n else:\\r\\n f = sorted(hold, key=lambda e: e['pos'])\\r\\n row = f[0]['pos'][0]\\r\\n col = f[0]['pos'][1]\\r\\n\\r\\n move.insert(0, (row, col)) # return the move \\r\\n return move\",\n \"def moves(self, rank, file, mask_black=None, mask_white=None, castling=set(), *args, **kwargs):\\n # Initialise result\\n result = np.zeros((self.n_ranks, self.n_files), dtype=bool)\\n\\n # Get mask of other pieces\\n other_pieces = np.zeros((self.n_ranks, self.n_files), dtype=bool)\\n if mask_black is not None:\\n other_pieces = np.logical_or(other_pieces, mask_black)\\n if mask_white is not None:\\n other_pieces = np.logical_or(other_pieces, mask_white)\\n\\n # Set squares surrounding king to True\\n result[\\n max(0, rank-1) : rank+2,\\n max(0, file-1) : file+2,\\n ] = True\\n\\n # Add castling moves\\n if 'K' in castling and self.color == Color.WHITE:\\n assert rank == self.n_ranks-1 and file == 4, \\\"Cannot castle, king has moved!\\\"\\n if not other_pieces[rank, file+1]:\\n result[rank, file+2] = True\\n if 'Q' in castling and self.color == Color.WHITE:\\n assert rank == self.n_ranks-1 and file == 4, \\\"Cannot castle, king has moved!\\\"\\n if not other_pieces[rank, file-1]:\\n result[rank, file-2] = True\\n if 'k' in castling and self.color == Color.BLACK:\\n assert rank == 0 and file == 4, \\\"Cannot castle, king has moved!\\\"\\n if not other_pieces[rank, file+1]:\\n result[rank, file+2] = True\\n if 'q' in castling and self.color == Color.BLACK:\\n assert rank == 0 and file == 4, \\\"Cannot castle, king has moved!\\\"\\n if not other_pieces[rank, file-1]:\\n result[rank, file-2] = True\\n\\n # Ensure king does not capture own pieces\\n if mask_white is not None and self.color == Color.WHITE:\\n result = np.logical_and(result, ~mask_white)\\n if mask_black is not None and self.color == Color.BLACK:\\n result = np.logical_and(result, ~mask_black)\\n\\n # Remove own rank and file as moves\\n result[rank, file] = False\\n\\n # Return result\\n return result\",\n \"def make_legal_move(move, board, start, end, move_number, en_passant_square, king_w, king_b, rook_w_l, rook_w_r,\\n rook_b_l,\\n rook_b_r, promotion_piece):\\n # this function should have essentially what i had in main at first without making the move\\n test_board = board.copy()\\n valid_move = False\\n piece = find_piece(board, start)\\n end_piece = find_piece(board, end)\\n\\n if switch_player(move_number): # for whites move\\n if (65 <= ord(piece) <= 90) and (validate_move(board, end, move_number)):\\n if piece == \\\"P\\\":\\n if pawn(board, start, end, move_number):\\n if end[0] == 8:\\n promotion(test_board, start, end, promotion_piece)\\n if not check(test_board, move_number):\\n retract_move(test_board, start, end, end_piece)\\n promotion(board, start, end, promotion_piece)\\n return True\\n else:\\n valid_move = False\\n else:\\n valid_move = True\\n else:\\n if can_en_passant(board, en_passant_square, end, start, move_number):\\n execute_enpassant(test_board, start, end, move_number)\\n if not check(test_board, move_number):\\n retract_move(test_board, start, end, end_piece)\\n execute_enpassant(board, start, end, move_number)\\n return True\\n else:\\n return False\\n elif piece == \\\"K\\\":\\n if king(start, end):\\n if controlled_squares(board, move_number, end):\\n valid_move = True\\n elif piece == \\\"N\\\":\\n if knight(start, end):\\n valid_move = True\\n elif piece == \\\"B\\\":\\n if bishop(board, start, end):\\n valid_move = True\\n elif piece == \\\"Q\\\":\\n if queen(board, start, end):\\n valid_move = True\\n elif piece == \\\"R\\\":\\n if rook(board, start, end):\\n valid_move = True\\n else:\\n valid_move = False\\n if valid_move:\\n update_board(test_board, start, end)\\n if not check(test_board, move_number):\\n retract_move(test_board, start, end, end_piece)\\n update_board(board, start, end)\\n return True\\n else:\\n retract_move(test_board, start, end, end_piece)\\n print(\\\"Illegal Move\\\")\\n return False\\n if not switch_player(move_number):\\n if (97 <= ord(piece) <= 122) and validate_move(board, end, move_number):\\n if piece == \\\"p\\\":\\n if pawn(board, start, end, move_number):\\n if end[0] == 1:\\n promotion(test_board, start, end, promotion_piece)\\n if not check(test_board, move_number):\\n retract_move(test_board, start, end, end_piece)\\n promotion(board, start, end, promotion_piece)\\n return True\\n else:\\n valid_move = False\\n else:\\n valid_move = True\\n else:\\n if can_en_passant(board, en_passant_square, end, start, move_number):\\n execute_enpassant(test_board, start, end, move_number)\\n if not check(test_board, move_number):\\n retract_move(test_board, start, end, end_piece)\\n execute_enpassant(board, start, end, move_number)\\n return True\\n else:\\n valid_move = False\\n elif piece == \\\"k\\\":\\n if king(start, end):\\n if controlled_squares(board, move_number, end):\\n valid_move = True\\n elif piece == \\\"n\\\":\\n if knight(start, end):\\n valid_move = True\\n elif piece == \\\"b\\\":\\n if bishop(board, start, end):\\n valid_move = True\\n elif piece == \\\"q\\\":\\n if queen(board, start, end):\\n valid_move = True\\n elif piece == \\\"r\\\":\\n if rook(board, start, end):\\n valid_move = True\\n else:\\n valid_move = False\\n if valid_move:\\n update_board(test_board, start, end)\\n if not check(test_board, move_number):\\n retract_move(test_board, start, end, end_piece)\\n update_board(board, start, end)\\n return True\\n else:\\n retract_move(test_board, start, end, end_piece)\\n print(\\\"Illegal Move\\\")\\n return False\",\n \"def move_buildings(self):\",\n \"def move(self, player, cell):\\n \\n if self.playable(cell):\\n self.cells[cell] = player\\n self.moves.append(\\n (player, cell))\\n \\n if self.is_winner(player, cell) and self.winner is None:\\n self.winner = player\\n elif len(self.moves) >= 9 and self.winner is None:\\n self.winner = \\\"cats\\\"\\n return True\\n return False\",\n \"def makeMove(self, move, player):\",\n \"def test_move_knight_illegally(self):\\n self.c.board = [[(0, 0) for i in range(8)] for i in range(8)]\\n for piece in [('N', True), ('N', False)]:\\n self.c.turn = piece[1]\\n self.c.board[4][4] = piece\\n dests = [col + row for col in 'abcdefgh' for row in '12345678']\\n for dest in dests:\\n if dest in ['d6', 'f6', 'c5', 'g5', 'c3', 'g3', 'd2', 'f2']:\\n continue\\n self.groups['dest'] = dest\\n self.assertRaises(\\n MoveNotLegalError, self.c._knight_evaluator, self.groups)\",\n \"def next_move(self):\\n self.branchize()\\n try:\\n _, new_branch = self.score_scheme.pop(0)\\n except IndexError:\\n return False\\n\\n move_list = [new_branch[\\\"move\\\"]]\\n parent = new_branch[\\\"parent\\\"]\\n\\n while True:\\n try:\\n move_list.append(parent[\\\"move\\\"]) \\n parent = parent[\\\"parent\\\"]\\n\\n except KeyError:\\n break\\n\\n self.__originate__()\\n for i in move_list[::-1]:\\n self.change(i)\\n\\n self.current_branch = new_branch\\n self.output += str(self)\\n return True\",\n \"def possible_moves(self, piece):\\n def _index(orig, off):\\n \\\"\\\"\\\"Helper function to find the new index.\\\"\\\"\\\"\\n orig_x, orig_y = orig\\n off_x, off_y = off\\n return (orig_y - off_y) * self.ncols + (orig_x - off_x)\\n\\n p_x, p_y = piece\\n p_i = _index(piece, (0, 0))\\n\\n # pass a list of the four corners first for basic possibles\\n move_land = [((p_x + i, p_y + j), self.squares[_index(piece, (i, j))])\\\\\\n for i in [-1, 1] for j in [-1, 1]]\\n possibles = self.squares[p_i].can_move(piece, move_land)\\n\\n # next append the new list from jumps\\n jump_land = [((p_x + i, p_y + j), self.squares[_index(piece, (i, j))])\\\\\\n for j in [-2, 2] for i in [-2, 2]]\\n possibles += self.squares[p_i].can_jump(piece, move_land, jump_land)\\n\\n # clean out the list of duplicates, although there should be none\\n return [m for i, m in enumerate(possibles) if m not in possibles[:i]]\",\n \"def move():\\n\\tdirections = [\\\"up\\\", \\\"down\\\", \\\"left\\\", \\\"right\\\"]\\n\\tdata = bottle.request.json\\n\\t#print(\\\"MOVE:\\\", json.dumps(data))\\n\\t# THE BEST MOVE IS CALUCLATED USING A FLOODFILL. HIGHEST AREA WIN. ME SPEEL GOOD\\n\\tmove = \\\"\\\"\\n\\n\\tupC = floodFill(getNextPosition(\\\"up\\\", data), data, arrayify(\\\"up\\\", data, not largestSnake(data)), 0)\\n\\tdownC = floodFill(getNextPosition(\\\"down\\\", data), data, arrayify(\\\"down\\\", data, not largestSnake(data)), 0)\\n\\trightC = floodFill(getNextPosition(\\\"right\\\", data), data, arrayify(\\\"right\\\", data, not largestSnake(data)), 0)\\n\\tleftC = floodFill(getNextPosition(\\\"left\\\", data), data, arrayify(\\\"left\\\", data, not largestSnake(data)), 0)\\n\\tmoveC = [upC, downC, rightC, leftC]\\n\\t#if moveC cannot find a viable move with ghostheads, it disables them so the snake doesn't kill itself\\n\\n\\tmove = goto(moveC, findFood(data), data)\\n\\tprint(\\\"moveC before max = \\\" + str(moveC))\\n\\tif max(moveC) == 0:\\n\\t\\tprint(\\\"ghosthead disabled\\\")\\n\\t\\tupC = floodFill(getNextPosition(\\\"up\\\", data), data, arrayify(\\\"up\\\", data, False), 0)\\n\\t\\tdownC = floodFill(getNextPosition(\\\"down\\\", data), data, arrayify(\\\"down\\\", data, False), 0)\\n\\t\\trightC = floodFill(getNextPosition(\\\"right\\\", data), data, arrayify(\\\"right\\\", data, False), 0)\\n\\t\\tleftC = floodFill(getNextPosition(\\\"left\\\", data), data, arrayify(\\\"left\\\", data, False), 0)\\n\\t\\tmoveC = [upC, downC, rightC, leftC]\\n\\n\\tprint(\\\"move after goto: \\\" + move)\\n\\tprint(\\\"movC after if max 0 block: \\\" + str(moveC))\\n\\tif move == \\\"\\\":\\n\\t\\tgoodMoves = []\\n\\t\\tif upC == max(moveC):\\n\\t\\t\\tgoodMoves.append(\\\"up\\\")\\n\\t\\tif downC == max(moveC):\\n\\t\\t\\tgoodMoves.append(\\\"down\\\")\\n\\t\\tif leftC == max(moveC):\\n\\t\\t\\tgoodMoves.append(\\\"left\\\")\\n\\t\\tif rightC == max(moveC):\\n\\t\\t\\tgoodMoves.append(\\\"right\\\")\\n\\t\\tmove = random.choice(goodMoves)\\n\\n\\tprint(\\\"Turn: \\\" + str(data[\\\"turn\\\"]))\\n\\tprint(\\\"Move: \\\" + move)\\n\\n\\tresponse = {\\\"move\\\": move, \\\"shout\\\": \\\"yeet\\\"}\\n\\treturn HTTPResponse(\\n\\t\\tstatus=200,\\n\\t\\theaders={\\\"Content-Type\\\": \\\"application/json\\\"},\\n\\t\\tbody=json.dumps(response),\\n\\t)\",\n \"def move_boats():\\n hit_manatee = False\\n for i in range(len(map)-1, -1, -1):\\n for j in range(len(map[i])-1, -1, -1):\\n if map[i][j] == \\\"*\\\":\\n # Only runs if the entity is a boat\\n if i + 1 >= len(map):\\n continue\\n if map[i+1][j] == \\\" \\\":\\n # Moves boat downward if possible\\n if i + 2 < len(map) and map[i+2][j] == \\\"M\\\":\\n hit_manatee = True\\n map[i+2][j] = \\\"W\\\"\\n map[i+1][j] = \\\"*\\\"\\n map[i][j] = \\\" \\\"\\n elif map[i+1][j] == \\\"*\\\":\\n # Boats colliding with each other\\n new_boat_pos = (i, j)\\n if j + 1 < len(map[i]) and map[i][j+1] == \\\" \\\" \\\\\\n and map[i+1][j+1] == \\\" \\\":\\n new_boat_pos = (i+1, j+1)\\n elif j - 1 >= 0 and map[i][j-1] == \\\" \\\" \\\\\\n and map[i+1][j-1] == \\\" \\\":\\n new_boat_pos = (i+1, j-1)\\n else:\\n continue\\n\\n # Moves boat down to new position\\n map[i][j] = \\\" \\\"\\n map[new_boat_pos[0]][new_boat_pos[1]] = \\\"*\\\"\\n if new_boat_pos[0] + 1 < len(map) and \\\\\\n map[new_boat_pos[0] + 1][new_boat_pos[1]] == \\\"M\\\":\\n hit_manatee = True\\n map[new_boat_pos[0] + 1][new_boat_pos[1]] = \\\"W\\\"\\n return \\\"injured\\\" if hit_manatee else None\",\n \"def actions_turn(positions, player1, player2, creatures):\\n\\n attacking = []\\n moving = []\\n\\n # Split the orders within attacks and movements\\n for character in positions:\\n if character in creatures:\\n if positions[character]['order'] == 'attack':\\n attacking += character\\n else:\\n moving += character\\n elif character in player1:\\n if positions[character]['order'] == 'attack':\\n attacking += character\\n else:\\n moving += character\\n\\n else:\\n if positions[character]['order'] == 'attack':\\n attacking += character\\n else:\\n moving += character\\n\\n # Execute the attacks\\n for character in attacking:\\n # First attacking : the creatures\\n if character in creatures:\\n hero_name = character\\n name_attack = positions[character]['name_attack']\\n attack_coord = positions[character]['where']\\n attack(positions, hero_name, name_attack, (0, 0), attack_coord, player1, player2, creatures)\\n # Then the heroes of the first player\\n elif character in player1:\\n hero_name = character\\n name_attack = positions[character]['name_attack']\\n attack_coord = positions[character]['where']\\n attack(positions, hero_name, name_attack, (0, 0), attack_coord, player1, player2, creatures)\\n # Finally the ones of the second player\\n else:\\n hero_name = character\\n name_attack = positions[character]['name_attack']\\n attack_coord = positions[character]['where']\\n attack(positions, hero_name, name_attack, (0, 0), attack_coord, player1, player2, creatures)\\n\\n for character in moving:\\n print(character, moving)\\n # First moving : the creatures\\n if character in creatures:\\n hero_name = character\\n movement_coord = positions[character]['where']\\n move(hero_name, positions, movement_coord)\\n\\n # Then the heroes of the first player\\n elif character in player1:\\n hero_name = character\\n movement_coord = positions[character]['where']\\n move(hero_name, positions, movement_coord)\\n\\n # Finally the ones of the second player\\n else:\\n hero_name = character\\n movement_coord = positions[character]['where']\\n move(hero_name, positions, movement_coord)\\n\\n return player1, player2, positions, creatures\",\n \"def test_move_knight_legally(self):\\n self.c.board[6] = [(0, 0) for i in range(8)]\\n for piece in [('N', True), ('N', False)]:\\n self.c.turn = piece[1]\\n self.c.board[4][4] = piece\\n for dest in ['d6', 'f6', 'c5', 'g5', 'c3', 'g3', 'd2', 'f2']:\\n self.groups['dest'] = dest\\n self.assertEqual(self.c._knight_evaluator(self.groups), (4, 4))\",\n \"def move(self):\\r\\n their_move = self.last_moves[\\\"their_move\\\"]\\r\\n return (their_move == \\\"\\\" and random.choice(moves) or their_move)\",\n \"def _check_for_check(self):\\n\\n # Since castling needs to check whether the current king is in check\\n # Leaving this to be true would cause infinite recursion\\n possible_moves = self.get_all_moves(castling_allowed=False)\\n\\n # Find location of opposing king\\n king_piece = 'k' if self.white_to_move else 'K'\\n for i in range(8):\\n for j in range(8):\\n if self.board[i][j] == king_piece:\\n location = (i, j)\\n\\n # Check if there is a piece that can capture the king,\\n # By checking if there is something that can move into a king's square\\n king_location = State._coord_to_EAN(location)\\n for move in possible_moves:\\n if king_location in move:\\n return True\\n\\n return False\",\n \"def toAN(board, move, short=False, castleNotation=CASTLE_SAN):\\n\\n fcord = (move >> 6) & 63\\n tcord = move & 63\\n flag = move >> 12\\n\\n if flag in (KING_CASTLE, QUEEN_CASTLE):\\n if castleNotation == CASTLE_SAN:\\n return flag == KING_CASTLE and \\\"O-O\\\" or \\\"O-O-O\\\"\\n elif castleNotation == CASTLE_KR:\\n rooks = board.ini_rooks[board.color]\\n tcord = rooks[flag == KING_CASTLE and 1 or 0]\\n # No treatment needed for CASTLE_KK\\n\\n if flag == DROP:\\n if board.variant == SITTUYINCHESS:\\n s = \\\"%s@%s\\\" % (reprSignSittuyin[fcord], reprCord[tcord])\\n else:\\n s = \\\"%s@%s\\\" % (reprSign[fcord], reprCord[tcord])\\n else:\\n s = reprCord[fcord] + reprCord[tcord]\\n\\n if flag in PROMOTIONS:\\n if short:\\n if board.variant in (CAMBODIANCHESS, MAKRUKCHESS):\\n s += reprSignMakruk[PROMOTE_PIECE(flag)].lower()\\n elif board.variant == SITTUYINCHESS:\\n s += reprSignSittuyin[PROMOTE_PIECE(flag)].lower()\\n else:\\n s += reprSign[PROMOTE_PIECE(flag)].lower()\\n else:\\n if board.variant in (CAMBODIANCHESS, MAKRUKCHESS):\\n s += \\\"=\\\" + reprSignMakruk[PROMOTE_PIECE(flag)]\\n elif board.variant == SITTUYINCHESS:\\n s += \\\"=\\\" + reprSignSittuyin[PROMOTE_PIECE(flag)]\\n else:\\n s += \\\"=\\\" + reprSign[PROMOTE_PIECE(flag)]\\n return s\",\n \"def _get_piece_moves(self, x, y):\\n\\n piece = self.get_piece(x, y)\\n moves = []\\n\\n if not piece:\\n return moves\\n\\n if piece.name == 'rook' or piece.name == 'queen':\\n direcs = ['up', 'down', 'left', 'right']\\n moves = [self._get_moves_indirection(x, y, direc) for direc in\\n direcs]\\n\\n elif piece.name == 'bishop' or piece.name == 'queen':\\n direcs = ['d1', 'd2', 'd3', 'd4']\\n for direc in direcs:\\n moves += self._get_moves_indirection(x, y, direc)\\n\\n elif piece.name == 'king':\\n moves = [(x-1, y-1), (x-1, y), (x-1, y+1), (x, y-1),\\n (x, y+1), (x+1, y-1), (x+1, y), (x+1, y+1)]\\n\\n elif piece.name == 'knight':\\n moves = [(x-1, y-2), (x-2, y-1), (x-2, y+1), (x-1, y+2),\\n (x+1, y+2), (x+2, y+1), (x+1, y-2), (x+2, y-1)]\\n\\n elif piece.name == 'pawn':\\n if piece.color == ChessGame.BLACK:\\n moves = [(x-1, y), (x-1, y-1), (x-1, y+1)]\\n else:\\n moves = [(x+1, y), (x+1, y-1), (x+1, y+1)]\\n\\n tmp = list(moves)\\n for u, v in tmp:\\n if v != y and not self.is_enemy(u, v, piece.color):\\n moves.remove((u, v))\\n\\n if v == y and self.is_enemy(u, v, piece.color):\\n moves.remove((u, v))\\n\\n mycolor = piece.color\\n valid = set()\\n for (u, v) in moves:\\n if not self.in_bounds(u, v):\\n continue\\n\\n if not self.get_piece(u, v): # board is blank\\n valid.add((u, v))\\n\\n if self.is_enemy(u, v, mycolor):\\n valid.add((u, v))\\n\\n return valid\",\n \"def legal_moves(board,player=None):\\r\\n \\r\\n possible_moves = []\\r\\n moves = []\\r\\n if player == None:\\r\\n moves += board.white + board.black\\r\\n elif player == -1:\\r\\n moves += board.black\\r\\n elif player == 1:\\r\\n moves += board.white\\r\\n \\r\\n captured = False\\r\\n for pos in moves:\\r\\n if pos[0] == 'A':\\r\\n m = [-8,-7,1,8,9]\\r\\n elif pos[0] == 'H':\\r\\n m = [-9,-8,-1,7,8]\\r\\n else:\\r\\n m = [-9,-8,-7,-1,1,7,8,9]\\r\\n loc = decode(pos)\\r\\n for i in m:\\r\\n captured = capture(board, player, possible_moves, pos, loc, i)\\r\\n canter(board, player, possible_moves, pos, loc, i)\\r\\n plain(board, player, possible_moves, pos, loc, i)\\r\\n \\r\\n if captured:\\r\\n enemy_list = []\\r\\n for capturing_move in possible_moves:\\r\\n if len(capturing_move) == 3:\\r\\n enemy_list.append(capturing_move)\\r\\n possible_moves = list(enemy_list)\\r\\n\\r\\n return possible_moves\",\n \"def undo_move(self):\\r\\n if len(self.moveLog) != 0:\\r\\n move = self.moveLog.pop()\\r\\n self.board[move.sr][move.sc] = move.pieceMoved\\r\\n self.board[move.er][move.ec] = move.pieceCaptured\\r\\n self.turn_white = not self.turn_white\\r\\n\\r\\n # king pos\\r\\n if move.pieceMoved == 'wk':\\r\\n self.wKingPos = (move.sr, move.sc)\\r\\n elif move.pieceMoved == 'bk':\\r\\n self.bKingPos = (move.sr, move.sc)\\r\\n\\r\\n # enpassant\\r\\n if move.isEnpassantMove:\\r\\n self.board[move.er][move.ec] = \\\"--\\\"\\r\\n self.board[move.sr][move.ec] = move.pieceCaptured\\r\\n self.enpas_pos = (move.er, move.ec)\\r\\n\\r\\n # pawn x2\\r\\n if move.pieceMoved[1] == \\\"p\\\" and abs(move.sr - move.er) == 2:\\r\\n self.enpas_pos = ()\\r\\n\\r\\n # castle rights\\r\\n self.castleRightsLog.pop()\\r\\n self.cr_castle_r = self.castleRightsLog[-1]\\r\\n\\r\\n # castle moves\\r\\n if move.castle:\\r\\n if move.ec - move.sc == 2:\\r\\n self.board[move.er][move.ec + 1] = self.board[move.er][move.ec - 1]\\r\\n self.board[move.er][move.ec - 1] = '--'\\r\\n else:\\r\\n self.board[move.er][move.ec - 2] = self.board[move.er][move.ec + 1]\\r\\n self.board[move.er][move.ec + 1] = '--'\",\n \"def legal_moves(self):\\n moves = \\\"\\\"\\n swappable = self.swappable_positions\\n empty_position = self.get_position(0)\\n\\n for s in swappable:\\n pos_diff = empty_position[0] - s[0], empty_position[1] - s[1]\\n if pos_diff[0] > 0:\\n moves += \\\"U\\\"\\n elif pos_diff[0] < 0:\\n moves += \\\"D\\\"\\n elif pos_diff[1] > 0:\\n moves += \\\"L\\\"\\n elif pos_diff[1] < 0:\\n moves += \\\"R\\\"\\n\\n return moves\",\n \"def get_castle_moves(self, r, c, moves):\\r\\n if self.square_under_attack(r, c):\\r\\n return\\r\\n\\r\\n #king side\\r\\n #checking previous rights and player turn\\r\\n if (self.cr_castle_r.wks and self.turn_white) or (self.cr_castle_r.bks and not self.turn_white):\\r\\n if self.board[r][c + 2] == '--' and self.board[r][c + 1] == '--':\\r\\n if self.square_under_attack(r, c + 2) is False and self.square_under_attack(r, c + 1) is False:\\r\\n moves.append(Move((r, c), (r, c + 2), self.board, castle=True))\\r\\n\\r\\n #queen side\\r\\n if (self.turn_white and self.cr_castle_r.wqs) or (not self.turn_white and self.cr_castle_r.bqs):\\r\\n if self.board[r][c - 2]== '--' and self.board[r][c - 1] == '--' and self.board[r][c - 3] == '--':\\r\\n if self.square_under_attack(r, c - 1) is False and self.square_under_attack(r, c - 2) is False:\\r\\n moves.append(Move((r, c), (r, c - 2), self.board, castle=True))\",\n \"def crowned(self): # called when this piece has become a 'King'\\r\\n \\r\\n self.isKing = True\",\n \"def move(self, action):\\n self.time += 1\\n\\n # If ship is destroyed ship can only contemplate sadness and despair\\n if not action or not self.is_playable():\\n return None\\n\\n self.actualise = False\\n\\n if self.leroy_time == 1:\\n self.back_to_normal()\\n if self.leroy_time > 0:\\n self.leroy_time -= 1\\n\\n # there is a chance that the ia enter in leroy mode\\n # the ia goes mad for some time, acting randomly\\n # added to allow the ships to explore the possible actions and not stay passive\\n if not self.player and self.leroy_time == 0 and self.agent.behavior == \\\"network\\\" and random() < LEROY_RATE:\\n self.leroy_jenkins()\\n\\n # training reward depending on position\\n # self.agent.reward = self.go_bottom_reward()\\n\\n if isinstance(action, ActionOneHot):\\n if action.pointing:\\n self.pointing = Point(randint(0, DEFAULT_WIDTH-1), randint(0, DEFAULT_HEIGHT-1))\\n elif isinstance(action, Action):\\n if action.pointing:\\n self.pointing = action.pointing\\n # print(\\\"action.pointing\\\", action.pointing)\\n # print(\\\"turn \\\", self.direction)\\n\\n if action.thrust:\\n self.thrust()\\n if action.shoot:\\n self.shoot()\",\n \"def canter(board,player,possible_moves, pos, loc, i):\\r\\n \\r\\n next_piece = encode(loc+i) \\r\\n new_pos = loc + (i*2)\\r\\n \\r\\n if player == 1 and next_piece in board.white:\\r\\n if not((pos[0] == 'B') and ((next_piece)[0] == 'A')) and not((pos[0] == 'G') and ((next_piece)[0] == 'H')):\\r\\n new_enc = encode(new_pos)\\r\\n if new_pos in range(0,112) and (new_enc not in board.white) and (new_enc not in board.black) and (new_enc not in board.empty):\\r\\n possible_moves.append([pos,new_enc])\\r\\n if player == -1 and next_piece in board.black:\\r\\n if not((pos[0] == 'B') and ((next_piece)[0] == 'A')) and not((pos[0] == 'G') and ((next_piece)[0] == 'H')):\\r\\n new_enc = encode(new_pos)\\r\\n if new_pos in range(0,112) and (new_enc not in board.white) and (new_enc not in board.black) and (new_enc not in board.empty):\\r\\n possible_moves.append([pos,new_enc])\",\n \"def finish_game(self, z):\\n for move in self.moves: # add this game winner result to all past moves.\\n move += [z]\",\n \"def _finish_all_moves(self, current_time):\\n\\t\\tfor piece_id in self.all_piece_ids:\\n\\t\\t\\tstate = getattr(self, piece_id)\\n\\t\\t\\tif len(state) == 0:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tpiece = Piece(state)\\n\\t\\t\\tif piece.moving:\\n\\t\\t\\t\\tif piece.end_time <= current_time:\\n\\t\\t\\t\\t\\tself._finish_move(piece_id, piece, current_time)\",\n \"def game_on(cls):\\n sommet = cls()\\n\\n moves = []\\n\\n while not sommet.map.game_over():\\n print(sommet.map)\\n print(\\\"{} joue\\\".format(sommet.is_vamp))\\n\\n moves += [sommet.next_move()]\\n\\n sommet = cls(is_vamp=not sommet.is_vamp)\\n sommet.map.add_moves(moves)\\n\\n print(sommet.map)\\n print(\\\"Vainqueur : {}\\\".format(sommet.map.winner()))\\n\\n print(\\\"{} sommets ont été créés pour les besoins de cette simulation.\\\".format(\\n cls.nb_vertices_created()))\",\n \"def update_spaces_threatened(self):\\n # Spaces threatened are all the spaces it can move to + guarding an ally at the end of a path\\n self.spaces_threatened = self.all_possible_moves(return_spaces_threatened=True)\\n update_threatening_king(self)\",\n \"def update_spaces_threatened(self):\\n # Spaces threatened are all the spaces it can move to + guarding an ally at the end of a path\\n self.spaces_threatened = self.all_possible_moves(return_spaces_threatened=True)\\n update_threatening_king(self)\",\n \"def update_spaces_threatened(self):\\n # Spaces threatened are all the spaces it can move to + guarding an ally at the end of a path\\n self.spaces_threatened = self.all_possible_moves(return_spaces_threatened=True)\\n update_threatening_king(self)\",\n \"def turn():\\r\\n global turn_no\\r\\n turn_no += 1\\r\\n dlog('Starting Turn!')\\r\\n board_size = get_board_size()\\r\\n\\r\\n team = get_team()\\r\\n opp_team = Team.WHITE if team == Team.BLACK else team.BLACK\\r\\n dlog('Team: ' + str(team))\\r\\n\\r\\n robottype = get_type()\\r\\n dlog('Type: ' + str(robottype))\\r\\n\\r\\n if robottype == RobotType.PAWN:\\r\\n dlog('Human')\\r\\n\\r\\n r, c = get_location()\\r\\n dlog('My location is: ' + str(r) + ' ' + str(c))\\r\\n\\r\\n if team == Team.WHITE:\\r\\n forward = 1\\r\\n scan = 2\\r\\n else:\\r\\n forward = -1\\r\\n scan = -2\\r\\n \\r\\n # try capturing pieces\\r\\n if check_space_wrapper(r + forward, c + 1, board_size) == opp_team: # up and right\\r\\n capture(r + forward, c + 1)\\r\\n dlog('Captured at: (' + str(r + forward) + ', ' + str(c + 1) + ')') \\r\\n\\r\\n elif check_space_wrapper(r + forward, c - 1, board_size) == opp_team: # up and left\\r\\n capture(r + forward, c - 1)\\r\\n dlog('Captured at: (' + str(r + forward) + ', ' + str(c - 1) + ')')\\r\\n \\r\\n elif r + forward != -1 and r + forward != board_size and not check_space_wrapper(r + forward, c, board_size):\\r\\n try:\\r\\n if check_space(r, c-1) == get_team() or check_space(r-1, c-1) == get_team() or check_space(r, c+1) == get_team() or check_space(r-1, c+1) == get_team():\\r\\n move_forward()\\r\\n dlog('Moved forward!')\\r\\n except:\\r\\n pass\\r\\n\\r\\n \\r\\n else:\\r\\n board = get_board()\\r\\n dlog(str(board))\\r\\n if team == Team.WHITE:\\r\\n forward = 1\\r\\n index = 0\\r\\n\\r\\n else:\\r\\n forward = -1\\r\\n index = board_size - 1\\r\\n \\r\\n deep_accum = []\\r\\n c_indexes = []\\r\\n heuristic_accum = []\\r\\n heuristic = 0\\r\\n for c in range(board_size):\\r\\n close = []\\r\\n for r in range(board_size):\\r\\n dlog(str(check_space(r, c)))\\r\\n if check_space(r, c) == opp_team:\\r\\n if team == Team.WHITE:\\r\\n close.append(r)\\r\\n elif team == Team.BLACK:\\r\\n close.append(board_size-r-1)\\r\\n heuristic -= 1\\r\\n elif check_space(r, c) == team:\\r\\n heuristic += 1\\r\\n else:\\r\\n continue\\r\\n heuristic_accum.append([heuristic, c])\\r\\n if close != []:\\r\\n c_indexes.append(c)\\r\\n deep = sorted(close)\\r\\n deep_accum.append(deep[0])\\r\\n heuristic = 0\\r\\n close_index = sorted(list(zip(deep_accum, c_indexes)))\\r\\n for c in close_index:\\r\\n if c[0] == 0:\\r\\n continue \\r\\n col = c[1]\\r\\n weighted_val = heuristic_accum[col][0]-15\\r\\n heuristic_accum[col][0] = weighted_val\\r\\n break\\r\\n\\r\\n heuristic_accum = sorted(heuristic_accum)\\r\\n\\r\\n for heur in heuristic_accum:\\r\\n col = heur[1]\\r\\n if not check_space_wrapper(index, col, board_size) and not check_space_wrapper(index+forward, col+1, board_size) == opp_team and not check_space_wrapper(index+forward, col-1, board_size) == opp_team:\\r\\n spawn(index, col)\\r\\n dlog('Spawned unit at: (' + str(index) + ', ' + str(col) + ')')\\r\\n break\\r\\n\\r\\n bytecode = get_bytecode()\\r\\n dlog('Done! Bytecode left: ' + str(bytecode))\",\n \"def mark_moves(self, player):\\n [self.mark_move(player, p, d)\\n for p in self.pieces\\n for d in DIRECTIONS\\n if p.get_state() == player]\",\n \"def makeMove(self, movable_statement):\\n ### Student code goes here\\n\\n # make statement of (on disk firstpeg) and (on disk secondpeg) from movable_statement\\n # retract (on disk firstpeg)\\n # add (on disk secondpeg)\\n # that should be it...\\n\\n if type(movable_statement) != Statement or not self.kb.kb_ask(Fact(movable_statement, [])):\\n # print(\\\"current gamestate: \\\", self.getGameState())\\n # print(\\\"~~~~~~~Error: \\\", movable_statement, \\\"is not a valid movable statement~~~~~~~\\\")\\n return None\\n\\n # print(\\\"current gamestate: \\\", self.getGameState())\\n # if self.kb.kb_ask(parse_input(\\\"fact: (movable ?X ?A ?B)\\\")):\\n # for binding in self.kb.kb_ask(parse_input(\\\"fact: (movable ?X ?A ?B)\\\")):\\n # print(\\\"movable: \\\", binding.bindings[0].constant.element)\\n # if self.kb.kb_ask(parse_input(\\\"fact: (empty ?X)\\\")):\\n # for binding in self.kb.kb_ask(parse_input(\\\"fact: (empty ?X)\\\")):\\n # print(\\\"empty: \\\", binding.bindings[0].constant.element)\\n # if self.kb.kb_ask(parse_input(\\\"fact: (top ?X ?A)\\\")):\\n # for binding in self.kb.kb_ask(parse_input(\\\"fact: (top ?X ?A)\\\")):\\n # print(\\\"top fact: \\\", binding.bindings[0].constant.element)\\n # print(\\\"moving: \\\", movable_statement)\\n\\n\\n\\n # remove old \\\"on\\\", add new \\\"on\\\"\\n statement1_list = [\\\"on\\\"]\\n statement2_list = [\\\"on\\\"]\\n\\n statement1_list.append(movable_statement.terms[0])\\n statement1_list.append(movable_statement.terms[1])\\n\\n statement2_list.append(movable_statement.terms[0])\\n statement2_list.append(movable_statement.terms[2])\\n\\n curr_fact = Fact(Statement(statement1_list), [])\\n new_fact = Fact(Statement(statement2_list), [])\\n\\n\\n # print(\\\"retracted: fact: (top \\\" + movable_statement.terms[0].term.element + \\\" \\\" + movable_statement.terms[1].term.element + \\\")\\\")\\n\\n\\n # (movable disk1 peg1 peg2)\\n # retract (fact: on disk1 peg1)\\n\\n # DISK We're moving from:\\n # Retract previous on statement\\n # Retract previous top statement\\n # If empty, say empty.\\n # If not empty, make the new top statement using ontopof\\n\\n # STEP 1\\n self.kb.kb_retract(curr_fact)\\n moved_disk= movable_statement.terms[0].term.element\\n prev_peg= movable_statement.terms[1].term.element\\n new_peg=movable_statement.terms[2].term.element\\n\\n # STEP 2\\n top_ask = parse_input(\\\"fact: (top \\\" + moved_disk + \\\" \\\" + prev_peg + \\\")\\\")\\n if self.kb.kb_ask(top_ask):\\n self.kb.kb_retract(top_ask)\\n else:\\n print(\\\"something went wrong with top...\\\")\\n\\n # STEP 3\\n prev_top_ask = parse_input(\\\"fact: (onTopOf \\\" + moved_disk + \\\" ?X)\\\")\\n if self.kb.kb_ask(prev_top_ask):\\n # then it's not empty, make this one the top.\\n for binding in self.kb.kb_ask(prev_top_ask):\\n # there should only be one binding...\\n disk_on_bottom = binding.bindings[0].constant.element\\n # make the disk on bottom the new top of the previous peg\\n # then remove the \\\"ontopof\\\" fact because it's no longer true.\\n self.kb.kb_assert(parse_input(\\\"fact: (top \\\" + disk_on_bottom + \\\" \\\" + prev_peg + \\\")\\\"))\\n self.kb.kb_remove(parse_input(\\\"fact: (onTopOf \\\" + moved_disk + \\\" \\\" + disk_on_bottom + \\\")\\\"))\\n else:\\n self.kb.kb_assert(parse_input(\\\"fact: (empty \\\" + prev_peg + \\\")\\\"))\\n\\n\\n\\n # DISK WE'RE MOVING TO: DEALING WITH TOP, EMPTY, and ONTOPOF.\\n # Add new on statement\\n # if empty, then remove empty statement. if not, add new ontopof statement and remove old top statement.\\n # Add new top statement\\n\\n # STEP 1\\n self.kb.kb_assert(new_fact)\\n\\n # STEP 2\\n empty_ask = parse_input(\\\"fact: (empty \\\" + new_peg + \\\")\\\")\\n top_ask = parse_input(\\\"fact: (top ?X \\\" + new_peg + \\\")\\\")\\n if self.kb.kb_ask(empty_ask):\\n self.kb.kb_retract(empty_ask)\\n else:\\n top_disk = self.kb.kb_ask(top_ask).list_of_bindings[0][0].bindings[0].constant.element\\n oldTopFact = parse_input(\\\"fact: (top \\\" + top_disk + \\\" \\\" + new_peg + \\\")\\\")\\n ontopof_fact = parse_input(\\\"fact: (onTopOf \\\" + moved_disk + \\\" \\\" + top_disk + \\\")\\\")\\n\\n self.kb.kb_retract(oldTopFact)\\n self.kb.kb_add(ontopof_fact)\\n\\n newTopFact = parse_input(\\\"fact: (top \\\" + moved_disk + \\\" \\\" + new_peg + \\\")\\\")\\n self.kb.kb_add(newTopFact)\\n\\n # if self.kb.kb_ask(top_ask):\\n # self.kb.kb_retract(parse_input(\\n # \\\"fact: (top \\\" + self.kb.kb_ask(top_ask)[0].bindings[0].constant.element + \\\" \\\" + movable_statement.terms[\\n # 1].term.element + \\\")\\\"))\\n\\n\\n\\n\\n\\n\\n # if self.kb.kb_ask(empty_ask):\\n # for binding in self.kb.kb_ask(empty_ask):\\n # empty_peg = binding.bindings[0].constant.element\\n # remove_empty = parse_input(\\\"fact: (empty \\\" + empty_peg + \\\")\\\")\\n # if self.kb.kb_ask(parse_input(\\\"fact: (on ?X \\\" + empty_peg + \\\")\\\")):\\n # print(\\\"removing: \\\", remove_empty)\\n # self.kb.kb_retract(remove_empty)\\n # # print(\\\"retracting: (empty \\\" + empty_peg + \\\")\\\")\\n\\n\\n\\n # DISK WE'RE MOVING FROM: DEALING WITH TOP AND EMPTY.\\n # First, we retract the previous top statement.\\n # then, we must check: if the disk is now empty, we must say it's empty.\\n # if it's not, we must now add the new top statement.\\n\\n\\n\\n\\n # empty check\\n\\n #\\n # for top_statement in self.kb.kb_ask(parse_input(\\\"fact: (top ?X ?A)\\\")):\\n # print(top_statement)\",\n \"def prepare_next_turn(grid):\\n empties = get_empty_cells(grid)\\n y,x = random.choice(empties)\\n grid[y][x] = 2 if random.random() < 0.9 else 4\\n return any_possible_moves(grid)\",\n \"def calculate_next_move(self, visit):\\n self.depth += 1\\n new_boards = []\\n for vehicle_id in range(len(self.vehicles)):\\n vehicle = self.vehicles[vehicle_id]\\n state = self.get_board()\\n if vehicle.orientation == 0: #horizontal\\n if vehicle.x > 0: #left\\n if state[vehicle.y][vehicle.x-1] == \\\"..\\\":\\n self.vehicles[vehicle_id].x -=1\\n if not self.get_board().tostring() in visit:\\n if not self.get_board().all in new_boards:\\n new_board = deepcopy(self)\\n self.vehicles[vehicle_id].x += 1\\n new_board.parent = self\\n new_boards.append(new_board)\\n else:\\n self.vehicles[vehicle_id].x += 1\\n\\n if vehicle.x + vehicle.length <= (len(state)-1): #right\\n if state[vehicle.y][vehicle.x+vehicle.length] == \\\"..\\\":\\n self.vehicles[vehicle_id].x += 1\\n if not self.get_board().tostring() in visit:\\n if not self.get_board().all in new_boards:\\n new_board = deepcopy(self)\\n self.vehicles[vehicle_id].x -= 1\\n new_board.parent = self\\n new_boards.append(new_board)\\n else:\\n self.vehicles[vehicle_id].x -= 1\\n\\n else: #vertical\\n if vehicle.y - 1 >= 0: #up\\n if state[vehicle.y-1][vehicle.x] == \\\"..\\\":\\n self.vehicles[vehicle_id].y -= 1\\n if not self.get_board().tostring() in visit:\\n if not self.get_board().all in new_boards:\\n new_board = deepcopy(self)\\n self.vehicles[vehicle_id].y += 1\\n new_board.parent = self\\n new_boards.append(new_board)\\n else:\\n self.vehicles[vehicle_id].y += 1\\n\\n if vehicle.y + vehicle.length <= (len(state)-1):\\n if state[vehicle.y + vehicle.length][vehicle.x] == \\\"..\\\":#down\\n self.vehicles[vehicle_id].y += 1\\n if not self.get_board().tostring() in visit:\\n if not self.get_board().all in new_boards:\\n new_board = deepcopy(self)\\n self.vehicles[vehicle_id].y -= 1\\n new_board.parent = self\\n new_boards.append(new_board)\\n else:\\n self.vehicles[vehicle_id].y -= 1\\n self.depth -= 1\\n return new_boards\",\n \"def get_possible_moves(self) -> list:\\n if self.p1_turn:\\n name = '2'\\n else:\\n name = '1'\\n\\n count = 0\\n for i in self.claim:\\n if i == name:\\n count += 1\\n over = count >= 0.5 * len(self.claim)\\n\\n moves = []\\n if not over:\\n for i in self.letters:\\n if i.isalpha():\\n moves.append(i)\\n return moves\",\n \"def exec_all_moves(self,level=0):\\n\\n capts = [self.op_capture_north, self.op_capture_nwest, self.op_capture_neast, self.op_capture_east, self.op_capture_west]\\n jmps = [self.op_jump_north, self.op_jump_nwest, self.op_jump_neast]\\n moves = [self.op_move_north,self.op_move_nwest, self.op_move_neast]\\n result = []\\n ops = []\\n\\n # Pre-select all operations that may be executed\\n if self.next_move == self.FREE:\\n capturingStarts, otherStarts = self.possible_capture()\\n\\n # Check for pieces that may capture\\n if len(capturingStarts) > 0:\\n self.next_move = self.CAPT\\n self.next_pieces = capturingStarts\\n else:\\n self.next_move = self.FREE\\n jmps.extend(moves)\\n ops = jmps\\n self.next_pieces = otherStarts\\n\\n elif self.next_move == self.CAPT:\\n ops = capts\\n elif self.next_move == self.JUMP:\\n ops = jmps\\n elif self.next_move == self.FREE:\\n jmps.extend(moves)\\n ops = jmps\\n elif self.next_move == self.ADDPIECE_2:\\n return self.op_add_piece_bot(self.next_pieces)\\n\\n # Execute possible operations for all viable pieces\\n for pos in self.next_pieces:\\n for op in ops:\\n newState = op(pos)\\n\\n # Check if op succeeded\\n if newState:\\n\\n # If the next player is the current player than the function is called recursevely, this is done so that outcomes account for successive plays by the same\\n # player ( successive jumps and captures or piece addition)\\n if newState.curr_player != self.curr_player:\\n result.append(newState)\\n else:\\n result.extend(newState.exec_all_moves(level+1))\\n\\n\\n return result\",\n \"def move_enemy(self, hero, pad, graph, msg):\\r\\n hr,hc = hero.position\\r\\n\\r\\n er, ec = self.position\\r\\n\\r\\n distance = math.sqrt((math.pow(hc - ec, 2)) + math.pow(hr - er, 2))\\r\\n\\r\\n if distance <= 12 and distance > 1:\\r\\n if not line_of_sight(pad, hero.position, self.position, graph):\\r\\n return\\r\\n\\r\\n if hr > er:\\r\\n temp_er = er + 1\\r\\n elif hr < er:\\r\\n temp_er = er - 1\\r\\n else:\\r\\n temp_er = er\\r\\n\\r\\n if hc < ec:\\r\\n temp_ec = ec - 1\\r\\n elif hc > ec:\\r\\n temp_ec = ec + 1\\r\\n else:\\r\\n temp_ec = ec\\r\\n\\r\\n if math.sqrt((math.pow(hc - temp_ec, 2)) + math.pow(hr - er, 2)) <= math.sqrt((math.pow(hc - ec, 2)) + math.pow(hr - temp_er, 2)):\\r\\n if pad.instr(er, temp_ec, 1) in p.valid_moves:\\r\\n temp_tile = self.tile\\r\\n self.tile = pad.instr(er, temp_ec, 1) #update tile under enemy\\r\\n pad.addstr(er, temp_ec, self.character)\\r\\n pad.addstr(er, ec, temp_tile)\\r\\n self.position = (er, temp_ec)\\r\\n elif pad.instr(temp_er, ec, 1) in p.valid_moves:\\r\\n temp_tile = self.tile\\r\\n self.tile = pad.instr(temp_er, ec, 1)\\r\\n pad.addstr(temp_er, ec, self.character)\\r\\n pad.addstr(er, ec, temp_tile)\\r\\n self.position = (temp_er, ec)\\r\\n else:\\r\\n if pad.instr(temp_er, ec, 1) in p.valid_moves:\\r\\n temp_tile = self.tile\\r\\n self.tile = pad.instr(temp_er, ec, 1)\\r\\n pad.addstr(temp_er, ec, self.character)\\r\\n pad.addstr(er, ec, temp_tile)\\r\\n self.position = (temp_er, ec)\\r\\n elif pad.instr(er, temp_ec, 1) in p.valid_moves:\\r\\n temp_tile = self.tile\\r\\n self.tile = pad.instr(er, temp_ec, 1)\\r\\n pad.addstr(er, temp_ec, self.character)\\r\\n pad.addstr(er, ec, self.tile)\\r\\n self.position = (er, temp_ec)\\r\\n\\r\\n er, ec = self.position\\r\\n\\r\\n distance = math.sqrt((math.pow(hc - ec, 2)) + math.pow(hr - er, 2))\\r\\n\\r\\n if distance == 1:\\r\\n hero.defend_attack(self.attack, msg, self.name)\",\n \"def __move(self):\\n if self.goal is None:\\n if self.tasks:\\n self.goal = self.tasks.pop()\\n self.goal_history.append(self.goal)\\n self.logger.log(\\n f\\\"Crewmate {self.agent_id} set as goal: {self.goal.name} in\\\" +\\n f\\\" {self.game_map.room_names[self.goal.room_id]}\\\",\\n Logger.LOG | Logger.PRINT_VISUAL)\\n else:\\n self.room = self.game_map.move_random(self)\\n self.location_history.append(self.room)\\n return\\n\\n if self.room is not self.goal.room_id:\\n self.room = self.game_map.next_toward(self, self.goal.room_id)\\n\\n # Log the current room we are in: Either the room we moved to, or the room that happens to be the goal room\\n self.location_history.append(self.room)\",\n \"def play_move(self, move_data, all_english_words):\\r\\n \\r\\n #first, make copy of board and try to apply move there.\\r\\n board_cpy = copy.deepcopy(self.myboard)\\r\\n \\r\\n rack_cpy = copy.deepcopy(self.rack) #***need to remove appropriate words from rack after we've made a move. \\r\\n\\r\\n i = 0 \\r\\n for cur_char in move_data[0]:\\r\\n new_tile = Tile(cur_char, self.game_bag.letter_freq_and_val[cur_char][1]) #create a new tile.\\r\\n if move_data[3] == \\\"horizontal\\\":\\r\\n print(\\\"adding cur_char {} at {} {}\\\".format(cur_char, move_data[1], move_data[2]+i))\\r\\n board_cpy.place_tile(move_data[1], move_data[2]+ i, new_tile)\\r\\n elif move_data[3] == \\\"vertical\\\":\\r\\n board_cpy.place_tile(move_data[1] + i, move_data[2], new_tile)\\r\\n\\r\\n i = i + 1\\r\\n #print(\\\"New i value is {}\\\".format(i))\\r\\n board_cpy.print_board()\\r\\n \\r\\n\\r\\n #once we're done placing the tiles, check for validity of entire board.\\r\\n cur_board_words = board_cpy.find_words_on_board()\\r\\n move_valid = True #assume move is valid, until proven otherwise.\\r\\n\\r\\n for word_data in cur_board_words:\\r\\n word = word_data[0]\\r\\n print(word)\\r\\n if word not in all_english_words:\\r\\n return False #do nothing else\\r\\n \\r\\n #print(\\\"Getting here; all words valid\\\")\\r\\n \\r\\n #getting here means that the move is actually valid, with no conflicts.\\r\\n main_board = self.myboard\\r\\n #In this case, add to the real board. \\r\\n i = 0 \\r\\n for cur_char in move_data[0]:\\r\\n new_tile = Tile(cur_char, self.game_bag.letter_freq_and_val[cur_char][1]) #create a new tile.\\r\\n if move_data[3] == \\\"horizontal\\\":\\r\\n main_board.place_tile(move_data[1], move_data[2]+ i, new_tile)\\r\\n elif move_data[3] == \\\"vertical\\\":\\r\\n main_board.place_tile(move_data[1] + i, move_data[2], new_tile)\\r\\n\\r\\n i = i + 1\\r\\n return True\",\n \"def moveToNext(self):\\n\\t\\tif self.G.debug:\\n\\t\\t\\ttic=time.clock()\\n\\t\\tself.debugPrint('looks for new spot')\\n\\t\\texceeds=self.m.exceedsAngleLim\\t#function\\n\\t\\tinside=self.m.isWithinPlantingBorders\\t#function\\n\\t\\tcart=self.m.getCartesian\\n\\t\\tauto=self.m.automatic\\n\\t\\tt=self.m.times\\n\\t\\tcommands=[]\\n\\t\\tif self.autoMoved:\\n\\t\\t\\topt=self.pos\\n\\t\\t\\tself.autoMoved=False #if this search is unsuccessfull, automove is enabled to next ideal pos.\\n\\t\\telse:\\n\\t\\t\\topt=self.getNextOptimal()\\n\\t\\tmoveTo=opt #for so long..\\n\\t\\trTemp=0.1\\n\\t\\tthTemp=0\\n\\t\\tb=0.05 #constant for the spiral\\n\\t\\ta=0.1\\n\\t\\tplant=True #we will plant in this step...\\n\\t\\td2=self.m.plantMinDist**2 #dist^2\\n\\t\\tpossible = False #for so long\\n\\t\\twhile not possible:\\n\\t\\t\\ttic=time.clock()\\n\\t\\t\\tpossible=True\\n\\t\\t\\tobstList=self.G.terrain.GetVisibleObstacles(moveTo, R=self.radius)\\n\\t\\t\\ttreeList=self.G.terrain.GetTrees(moveTo, R=self.radius+self.m.plantMinDist)\\n\\t\\t\\tobstList+=[tr for tr in treeList if not tr in obstList] #this procedure minimizes R in Getobst\\n\\t\\t\\t#[p1, p2]=self.getPHCoord(moveTo)\\n\\t\\t\\tphPos=self.getPHCoord(moveTo)\\n\\t\\t\\tplantSpots=self.getPlantingCoord(moveTo)\\n\\t\\t\\t#[f1,f2]=self.getPlantingCoord(moveTo)\\n\\t\\t\\tif self.otherDevice is not None:\\n\\t\\t\\t\\totherDevPlantCor=self.otherDevice.getPlantingCoord(self.otherDevice.pos)\\n\\t\\t\\t\\t#check for colissions and similar related to other device\\n\\t\\t\\t\\tif collide(self, self.otherDevice, o1pos=moveTo): \\n\\t\\t\\t\\t\\tpossible=False\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tfor o in otherDevPlantCor:\\n\\t\\t\\t\\t\\t\\tfor f in plantSpots:\\n\\t\\t\\t\\t\\t\\t\\t#if getDistanceSq(f1, o)<d2 or getDistanceSq(f2, o)<d2:\\n\\t\\t\\t\\t\\t\\t\\tif getDistanceSq(f,o)<d2:#plantingspot of device is closer than allowed to other Device's plantingspot\\n\\t\\t\\t\\t\\t\\t\\t\\tpossible=False\\n\\t\\t\\t\\t\\t\\t\\t\\tbreak\\t\\t\\n\\t\\t\\tif possible:\\t#either 1a or angle OK and above check OK\\n\\t\\t\\t\\tfor obst in obstList:\\n\\t\\t\\t\\t\\t#tic=time.clock()\\n\\t\\t\\t\\t\\tif isinstance(obst, Tree):\\n\\t\\t\\t\\t\\t\\t#other demands, more than 1.5 m from plantingspot.\\n\\t\\t\\t \\t\\t\\tfor f in plantSpots:\\n\\t\\t\\t\\t\\t\\t\\t#if getDistanceSq(f1, o)<d2 or getDistanceSq(f2, o)<d2:\\n\\t\\t\\t\\t\\t\\t\\tif getDistanceSq(f, obst.pos)<d2 or collide(self, obst, o1pos=moveTo):\\n\\t\\t\\t\\t\\t\\t\\t\\tpossible=False\\n\\t\\t\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\t\\telif isinstance(obst, Hole): #hole can be in beetween plantheads... Plantpos can be in hole.\\n\\t\\t\\t\\t\\t\\tif len(self.plantHeads)==1: #bracke\\n\\t\\t\\t\\t\\t\\t\\tpossible=False\\n\\t\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\t\\t\\telif collide(self.plantHeads[0], obst, o1pos=phPos[0]) or collide(self.plantHeads[1], obst, o1pos=phPos[1]):\\n\\t\\t\\t\\t\\t\\t\\tpossible=False\\n\\t\\t\\t\\t\\t\\t\\t#PlantingDevice.timesProf[0]+=time.clock()-tic\\t\\n\\t\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\t\\telif collide(self, obst, o1pos=moveTo):\\n\\t\\t\\t\\t\\t\\tpossible=False\\n\\t\\t\\t\\t\\t\\t#PlantingDevice.timesProf[0]+=time.clock()-tic\\t\\n\\t\\t\\t\\t\\t\\tbreak\\n\\t\\t\\t\\tif possible and self.otherDevice is not None and exceeds(self, moveTo, self.otherDevice):\\n\\t\\t\\t\\t\\tpossible=False\\t#angle is too big to the other device\\n\\t\\t\\t#at this point, all test for \\\"possibility\\\" are performed.\\n\\t\\t\\tPlantingDevice.timesProf[0]+=time.clock()-tic\\n\\t\\t\\tdthini=pi/50.\\n\\t\\t\\tif not possible:\\n\\t\\t\\t\\t#move in a spiral outwards\\n\\t\\t\\t\\trTemp=a+b*thTemp\\n\\t\\t\\t\\tdth=(pi/25.)/(rTemp/2.)\\n\\t\\t\\t\\tthTemp+=dth\\n\\t\\t\\t\\tthInit=thTemp #used to avoid infinite loop\\n\\t\\t\\t\\tmoveTo=cart([rTemp,thTemp],opt)\\n\\t\\t\\t\\twhile not inside(moveTo) or (self.otherDevice is not None and exceeds(self, moveTo, self.otherDevice)):\\n\\t\\t\\t\\t\\t#outside borders or too big angle.. make above expression shorter..\\n\\t\\t\\t\\t\\t#self.pSpots.append(self.m.getCartesian([rTemp,thTemp], opt))\\n\\t\\t\\t\\t\\trTemp=a+b*thTemp\\n\\t\\t\\t\\t\\tthTemp+=(pi/25.)/(rTemp/2.)\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t#if abs(thTemp-thInit)>2*pi: #if radius is too big..\\n\\t\\t\\t\\t\\tif abs(thInit-thTemp)>2*pi:\\n\\t\\t\\t\\t\\t\\tplant=False #we will not plant this time.\\n\\t\\t\\t\\t\\t\\t#move to make it easier for the other head:\\n\\t\\t\\t\\t\\t\\tif self.otherDevice is not None and self.lastPos==self.pos and self.struckLastTime:\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\tthIni=self.posCyl[1]-dthini\\n\\t\\t\\t\\t\\t\\t\\tthTemp=thIni\\n\\t\\t\\t\\t\\t\\t\\t\\\"\\\"\\\"if exceeds(self, cart([self.posCyl[0],thTemp]), self.otherDevice):\\n\\t\\t\\t\\t\\t\\t\\t\\tnp=cart([self.posCyl[0],thTemp])\\\"\\\"\\\" #old stuff... should be removed, right?\\n\\t\\t\\t\\t\\t\\t\\twhile inside(cart([self.posCyl[0],thTemp])) and not exceeds(self, cart([self.posCyl[0],thTemp]), self.otherDevice):\\n\\t\\t\\t\\t\\t\\t\\t\\tthTemp-=dthini #moves in order to make more space\\n\\t\\t\\t\\t\\t\\t\\tif thTemp==thIni: #it wasnt inside or exceeded\\n\\t\\t\\t\\t\\t\\t\\t\\tcommands.extend(self.releaseDriver()) #releases driver, if he is used\\n\\t\\t\\t\\t\\t\\t\\t\\tif exceeds(self, cart([self.posCyl[0],thTemp]), self.otherDevice):\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t#we are struck! Wait for other device to move.\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tself.m.stopControl() #we could have reached the end here.\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tcommands.append((waitevent, self, self.otherDevice.moveEvent))\\n\\t\\t\\t\\t\\t\\t\\t\\telse: #not inside, we have reached the end of the half circle\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tself.debugPrint(\\\"end of pattern reached, passivates %s device\\\"%self.mountPoint)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tself.noMoreSpots=True\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tself.m.stopControl() #we could have reached the end here.\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tcommands.append((passivate, self))\\n\\t\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\t\\tmoveTo=cart([self.posCyl[0],thTemp+dthini])\\n\\t\\t\\t\\t\\t\\t\\t\\ttraveltime=self.setPos(moveTo)\\n\\t\\t\\t\\t\\t\\t\\t\\tself.debugPrint('clears for other head')\\n\\t\\t\\t\\t\\t\\t\\t\\tcommands=self.cmnd(commands, traveltime,auto=auto['clearForOtherHead'])\\n\\t\\t\\t\\t\\t\\tif plant:\\n\\t\\t\\t\\t\\t\\t\\tcommands=self.cmnd(commands, t['searchTime'],auto=auto['micrositeSelection'])\\n\\t\\t\\t\\t\\t\\t\\tself.m.timeConsumption['searchTime']+=t['searchTime']\\n\\t\\t\\t\\t\\t\\treturn (commands,plant)\\n\\t\\t\\t\\t\\tmoveTo=cart([rTemp,thTemp],opt)\\n\\t\\ttravelTime=self.setPos(moveTo)\\n\\t\\tself.debugPrint('traveltime: %f'%travelTime)\\n\\t\\tif plant: #this timeconsumption is only for succesfull...\\n\\t\\t\\tcommands=self.cmnd(commands, t['searchTime'],auto=auto['micrositeSelection'])\\n\\t\\t\\tself.m.timeConsumption['searchTime']+=t['searchTime']\\t\\t\\n\\t\\tcommands=self.cmnd(commands, travelTime,auto=auto['moveToMicro'])\\n\\t\\treturn (commands,plant)\",\n \"def legal_move_on(draw, board):\\n start, _ = draw(strategies.sampled_from(sorted(board.pieces)))\\n end = draw(strategies.sampled_from(sorted(board.movable_from(start))))\\n return start, end\",\n \"def get_possible_moves(self, board: np.ndarray):\\n board_size = board.shape[0]\\n moves = []\\n if abs(self.value) == 1:\\n if self.start_row <= 2:\\n directions = [np.array((1, -1)), np.array((1, 1))]\\n else:\\n directions = [np.array((-1, 1)), np.array((-1, -1))]\\n else:\\n directions = [np.array((-1, 1)), np.array((1, 1)), np.array((-1, -1)), np.array((1, -1))]\\n for direction in directions:\\n within_board = True\\n i = 1\\n while within_board:\\n coord = self.coord + direction * i\\n within_board = _check_if_position_on_board(coord, board_size)\\n # break if first step is already out of board\\n if not within_board:\\n break\\n value_board = board[coord[0], coord[1]]\\n # break if there is a stone of them same player in the way\\n if value_board < 0 and self.value < 0 or value_board > 0 and self.value > 0:\\n break\\n # if there is no stone, than add this to move list.\\n if value_board == 0:\\n moves += [{\\\"old_coord\\\": self.coord, \\\"new_coord\\\": coord, \\\"jumped_stones\\\": [], \\\"jumped_values\\\": 0,\\n \\\"move_coords\\\": [coord]}]\\n # if there is a stone of the enemy\\n if (value_board < 0 < self.value) or (self.value < 0 < value_board):\\n # check if it can be jumped\\n coord_jump = coord + direction\\n move_coords = [coord_jump.copy()]\\n within_board_after_jump = _check_if_position_on_board(coord_jump, board_size)\\n # break if place behind stone is out of border\\n if not within_board_after_jump:\\n break\\n value_board_jump = board[coord_jump[0], coord_jump[1]]\\n jumped_stones = []\\n # break if there is no free place\\n if value_board_jump != 0:\\n break\\n jumped_stones += [coord]\\n moves_tmp = self.jump_chain(directions, board, coord_jump, value_board, jumped_stones, move_coords)\\n if len(moves_tmp) > 0:\\n moves += moves_tmp\\n else:\\n moves += [{\\\"old_coord\\\": self.coord, \\\"new_coord\\\": coord_jump, \\\"jumped_stones\\\": jumped_stones,\\n \\\"jumped_values\\\": abs(value_board), \\\"move_coords\\\": [coord_jump]}]\\n i += 1\\n # break if normal stone, because they can only move one field\\n if abs(self.value) == 1:\\n break\\n return moves\",\n \"def _invalidate_castles(self):\\n\\n if self.board[0][0] != 'r': # Black queenside\\n self.available_castles = self.available_castles.replace('q', '')\\n\\n if self.board[0][7] != 'r': # Black kingside\\n self.available_castles = self.available_castles.replace('k', '')\\n\\n if self.board[7][0] != 'R': # White queenside\\n self.available_castles = self.available_castles.replace('Q', '')\\n\\n if self.board[7][7] != 'R': # White kingside\\n self.available_castles = self.available_castles.replace('K', '')\\n\\n if self.board[0][4] != 'k':\\n self.available_castles = self.available_castles.replace('k', '')\\n self.available_castles = self.available_castles.replace('q', '')\\n\\n if self.board[7][4] != 'K':\\n self.available_castles = self.available_castles.replace('K', '')\\n self.available_castles = self.available_castles.replace('Q', '')\",\n \"def is_king_move_valid(self, from_row, from_col, to_row, to_col):\\n\\n piece = self.board.squares[from_row][from_col]\\n piece_color = self.piece_color(piece)\\n\\n if abs(to_row - from_row) <= 1 and abs(to_col - from_col) <= 1:\\n if piece_color == \\\"white\\\":\\n self.whiteCanCastleKside = False\\n self.whiteCanCastleQside = False\\n else:\\n self.blackCanCastleKside = False\\n self.blackCanCastleQside = False\\n return True\\n\\n # TODO Castling implementation\\n # if king and rook have not been moved yet this game, and no space between\\n # the king and the rook are occupied or threatened, then the king can\\n # move 2 spaces towards the rook, and the rook will be placed adjacent to the\\n # king on the side closer to the center column.\\n\\n # TODO need function which returns squares being threatened which takes a piece position and board as a param\\n\\n if (piece_color == \\\"white\\\"):\\n if self.whiteCanCastleKside and (from_row == 7 and from_col == 4) and (to_row == from_row) and (to_col == 6):\\n # White kingside Castle\\n if (self.board.squares[7][5] == None and self.board.squares[7][6] == None):\\n if not self.testing:\\n self.whiteCanCastleKside = False\\n self.whiteCanCastleQside = False\\n self.board.move_piece(7, 7, 7, 5)\\n return True\\n\\n if self.whiteCanCastleQside and (from_row == 7 and from_col == 4) and (to_row == from_row) and (to_col == 2):\\n # White queenside Castle\\n if (self.board.squares[7][3] == None and self.board.squares[7][2] == None and self.board.squares[7][1] == None):\\n\\n if not self.testing:\\n self.whiteCanCastleKside = False\\n self.whiteCanCastleQside = False\\n self.board.move_piece(7, 0, 7, 3)\\n return True\\n\\n elif piece_color == \\\"black\\\":\\n if self.blackCanCastleKside and (from_row == 0 and from_col == 4) and (to_row == from_row) and (to_col == 6):\\n # black kingside Castle\\n if (self.board.squares[0][5] == None and self.board.squares[0][6] == None):\\n if not self.testing:\\n self.blackCanCastleKside = False\\n self.blackCanCastleQside = False\\n self.board.move_piece(0, 7, 0, 5)\\n return True\\n\\n if self.blackCanCastleQside and (from_row == 0 and from_col == 4) and (to_row == from_row) and (to_col == 2):\\n # black queenside Castle\\n if (self.board.squares[0][3] == None and self.board.squares[0][2] == None and self.board.squares[0][1] == None):\\n if not self.testing:\\n self.blackCanCastleKside = False\\n self.blackCanCastleQside = False\\n self.board.move_piece(0, 0, 0, 3)\\n return True\\n\\n return False\",\n \"def action(self, colony):\\n \\n if not self.is_true_king:\\n self.reduce_armor(self.armor)\\n else:\\n super().action(colony)\\n check_place = self.place.exit\\n while check_place is not None:\\n dragon = check_place.dragon\\n if dragon is not None and dragon not in self.buffed_dragons:\\n check_place.dragon.damage *=2\\n self.buffed_dragons.append(check_place.dragon)\\n if dragon is not None and dragon.is_container:\\n contained_dragon = dragon.contained_dragon\\n if contained_dragon is not None and contained_dragon not in self.buffed_dragons:\\n check_place.dragon.contained_dragon.damage *=2\\n self.buffed_dragons.append(check_place.dragon.contained_dragon)\\n check_place = check_place.exit\",\n \"def make_move(self):\\n\\n # get relavent information\\n affinity = self.get_affinity()\\n sample_space = self.get_game_space()\\n depth_limit = self.__search_depth\\n\\n # run a minimax search and get the best value\\n bestval = MinimaxTree.minimax(self, sample_space, affinity, depth_limit, True)\\n if bestval[0] is None: bestval = ((0,6),'x', 0)\\n\\n # print the number of nodes expanded \\n print(self.nodes_expanded)\\n\\n # make the move found by the search \\n self.get_game_space().set_tile(bestval[0][0], bestval[0][1], affinity)\",\n \"def black_wins(self):\\n possible_dirs = [[1, 0], [0, 1], [-1, 0], [0, -1], [1, 1], [-1, 1], [-1, -1], [1, -1]]\\n toMoves = []\\n king_spot = None\\n for row in range(8):\\n for col in range(8):\\n if self.board.squares[row][col] == '♔': # White king symbol\\n king_spot = (row, col)\\n break\\n if king_spot is not None:\\n break\\n\\n # Black doesn't win if it isn't checking the king\\n if king_spot not in self.blackThreat.keys():\\n return False\\n\\n for direction in possible_dirs:\\n toRow = king_spot[0] + direction[0]\\n toCol = king_spot[1] + direction[1]\\n\\n if ((0 <= toRow <= 7) and (0 <= toCol <= 7)):\\n toMoves.append((toRow, toCol))\\n\\n # check if we can move out of the mate\\n for move in toMoves:\\n piece = self.board.squares[move[0]][move[1]]\\n piece_color = self.pieces.piece_color(piece)\\n if piece_color != \\\"white\\\" and (move[0], move[1]) not in self.blackThreat.keys():\\n return False\\n\\n # check can I take the attacker\\n attack_spot = None\\n\\n for move in self.blackMoves:\\n if move[1] == king_spot:\\n attack_spot = move[0]\\n break\\n\\n # If there is a piece attacking, check the possible moves for black to see if one of them is a kill move\\n if attack_spot is not None:\\n attack_dir = (attack_spot[0] - king_spot[0], attack_spot[1] - king_spot[1])\\n for move in self.whiteMoves:\\n if move[1] == attack_spot:\\n # Check if the attacker can be taken. It can be taken by a non-king piece, or by the king if\\n # the spot is not protected\\n if (self.board.squares[move[0][0]][move[0][1]] == ChessPiece.W_KING and \\\\\\n (move[1]) not in self.blackThreat.keys()) or \\\\\\n (self.board.squares[move[0][0]][move[0][1]] != ChessPiece.W_KING):\\n return False\\n\\n piece = self.board.squares[attack_spot[0]][attack_spot[1]]\\n if piece == '♛' or piece == '♜' or piece == '♝':\\n h_dir = attack_dir[0]\\n v_dir = attack_dir[1]\\n check_squares = []\\n\\n if h_dir == 0:\\n for c in range(0, v_dir, int(v_dir / abs(v_dir))):\\n check_squares.append((king_spot[0], king_spot[1] + c))\\n elif v_dir == 0:\\n for r in range(0, h_dir, int(h_dir / abs(h_dir))):\\n check_squares.append((r + king_spot[0], king_spot[1]))\\n else:\\n rows = []\\n cols = []\\n for r in range(0, h_dir, int(h_dir / abs(h_dir))):\\n rows.append(r)\\n for c in range(0, v_dir, int(v_dir / abs(v_dir))):\\n cols.append(c)\\n for n in range(len(cols)):\\n check_squares.append((rows[n] + king_spot[0], cols[n] + king_spot[1]))\\n\\n for move in self.whiteMoves:\\n if move[1] in check_squares and move[0] != king_spot:\\n # print(move[0], move[1], king_spot, check_squares)\\n return False\\n\\n return True\",\n \"def _get_easy_move(self):\\n # Two lists keep track of regular moves and paths of possible jumps\\n moves = []\\n required_jumps = []\\n for row in range(self._checkers.dimension):\\n for col in range(self._checkers.dimension):\\n # Check same color pieces as player to see if they can jump.\\n if self._checkers.get(row, col) == self._player:\\n path_made = False\\n found_jumps = []\\n # i represents an option of direction to check\\n for i in range(3):\\n current_path = []\\n # Builds a path of jumps by checking for a jump each\\n # move\\n while (not path_made):\\n jumps = self.check_for_jump(self._player, row, col)\\n if jumps == []:\\n path_made = True\\n break\\n current_path += (jumps)\\n incrow = 0\\n inccol = 0\\n # South west and east\\n if i == 0:\\n incrow = 2\\n inccol = 2\\n elif i == 1:\\n incrow = 2\\n incrow = -2\\n # North west and east\\n elif i == 2:\\n incrow = -2\\n inccol = -2\\n elif i == 3:\\n incrow = -2\\n incrow = 2\\n row += incrow\\n col += inccol\\n found_jumps.append(current_path)\\n if len(found_jumps) > 0:\\n # If there is at least one path then we append it\\n # to the list of jumps\\n required_jumps += found_jumps\\n else:\\n # Checks if a move can be made in each direction\\n north_west = self._checkers.get(row - 1, col - 1)\\n north_east = self._checkers.get(row - 1, col + 1)\\n south_west = self._checkers.get(row + 1, col + 1)\\n south_east = self._checkers.get(row + 1, col - 1)\\n if north_west == CheckersBoard.empty:\\n moves.append(Move(row, col, -1, -1))\\n if north_east == CheckersBoard.empty:\\n moves.append(Move(row, col, -1, 1))\\n if south_west == CheckersBoard.empty:\\n moves.append(Move(row, col, 1, 1))\\n if south_east == CheckersBoard.empty:\\n moves.append(Move(row, col, 1, -1))\\n # A random move is calculated for the lists of moves\\n # If a move can be made we prioritize the list with possible moves\\n random_index = 0\\n if len(required_jumps) != 0:\\n random_index = random.randint(0, len(required_jumps))\\n move_path = required_jumps[random_index]\\n return move_path\\n else:\\n random_index = random.randint(0, len(moves))\\n move = moves[random_index]\\n return [move]\",\n \"def prepare_next_turn(grid):\\n\\tempties = get_empty_cells(grid)\\n\\ty,x = random.choice(empties)\\n\\tgrid[y][x] = 2 if random.random() < prob_2 else 4\\n\\treturn any_possible_moves(grid)\",\n \"def make_move(self):\\n\\n # get relavent information\\n affinity = self.get_affinity()\\n sample_space = self.get_game_space()\\n depth_limit = self.__search_depth\\n\\n # run a minimax search and get the best value\\n bestval = MinimaxTree.alphabeta(self, sample_space, affinity, depth_limit, -10000, 10001, True)\\n if bestval[0] is None: bestval = ((0,6),'x', 0)\\n\\n # print the number of nodes expanded \\n print(self.nodes_expanded)\\n\\n # make the move found by the search \\n self.get_game_space().set_tile(bestval[0][0], bestval[0][1], affinity)\",\n \"def generate_possible_moves_single_gp(self, game):\\n for color in game.paths:\\n path = game.paths[color]\\n if path.is_complete():\\n continue\\n gp1, gp2 = path.get_grow_points()\\n adj2gp1 = utils.get_adjacent_points(gp1)\\n for possible in adj2gp1:\\n # print \\\"Attempting\\\"\\n # print game\\n # print possible, path.can_be_added_to_path(possible, 1)\\n if path.can_be_added_to_path(possible, 1):\\n copy_game = deepcopy(game)\\n \\\"\\\"\\\":type: Flow\\\"\\\"\\\"\\n copy_game.paths[color].add_to_path(possible, 1)\\n self.queue.append(copy_game)\\n # print \\\"after attempt\\\"\\n # print copy_game\",\n \"def ai_move():\\n\\tinitial_state = map(get_filled_edges, rects)\\n\\tpossible_moves = []\\n\\tfor index, filled_edges in enumerate(initial_state):\\n\\t\\tif filled_edges == 0:\\n\\t\\t\\tpossible_moves.extend([(index, i) for i in 'ltrb'])\\n\\t\\telif filled_edges == 1:\\n\\t\\t\\tpossible_moves.extend(one_filled_edge(index))\\n\\t\\telif filled_edges == 2:\\n\\t\\t\\tpossible_moves.extend(two_filled_edge(index))\\n\\t\\telif filled_edges == 3:\\n\\t\\t\\tpossible_moves.extend(three_filled_edge(index))\\n\\tprint possible_moves\\n\\tpossible_decisions = []\\n\\tfor move in possible_moves:\\n\\t\\tfinal_state = apply_move(move)\\n\\t\\tpossible_decisions.append(is_feasible(initial_state, final_state))\\n\\tprint possible_decisions\\n\\t# randomizing when some decisions have the same weight\\n\\tmax_weight = max(possible_decisions)\\n\\t# list of indices which have the same weight\\n\\tmax_indices = []\\n\\tfor index, weight in enumerate(possible_decisions):\\n\\t\\tif weight == max_weight:\\n\\t\\t\\tmax_indices.append(index)\\n\\tx = choice(max_indices)\\n\\tprint x\\n\\treturn possible_moves[x]\\n\\t# return possible_moves[possible_decisions.index(max(possible_decisions))]\",\n \"def test_move_knight_legally_blocked(self):\\n for piece in [('N', True), ('N', False)]:\\n self.c.board = \\\\\\n [[('K', piece[1]) for i in range(8)] for i in range(8)]\\n self.c.turn = piece[1]\\n self.c.board[4][4] = piece\\n for dest in ['d6', 'f6', 'c5', 'g5', 'c3', 'g3', 'd2', 'f2']:\\n self.groups['dest'] = dest\\n self.assertRaises(\\n MoveNotLegalError, self.c._knight_evaluator, self.groups)\",\n \"def get_available_moves(self):\\n available = []\\n row, col = tuple(self.current_pos)\\n if row - 1 >= 0 and self.maze[row - 1][col] != 'x':\\n available.append('n')\\n if row + 1 < len(self.maze) and self.maze[row + 1][col] != 'x':\\n available.append('s')\\n if col - 1 >= 0 and self.maze[row][col - 1] != 'x':\\n available.append('w')\\n if col + 1 < len(self.maze[row]) and self.maze[row][col + 1] != 'x':\\n available.append('e')\\n return available\",\n \"def mappossiblemoves(self,nodeStart):\\n destinations = [set() for ix in range(game.DICEMAX+1)]\\n destinations[0] = set([nodeStart])\\n routes = [[nodeStart]]\\n #\\n # TODO: Not convinced this adds anything more than it harms in this fn.\\n #if self.hasEnteredRoomYet:\\n # GAMEBOARD = game.TRIMMEDNODES\\n #else:\\n GAMEBOARD = game.BOARDNODES\\n #\\n for diceRoll in range(1,game.DICEMAX+1):\\n newRoutes = []\\n for route in routes:\\n for nextLoc in GAMEBOARD[route[-1]]:\\n if not nextLoc in route:\\n # This location has not yet been visited by this route\\n if nextLoc in game.ALLOWEDROOMNODES:\\n # Location is a room - can move into it regardless\\n # of population, but not move further. We can also\\n # use any longer roll to move here by this route\\n for thisRoll in range(diceRoll,game.DICEMAX+1):\\n destinations[thisRoll].add(nextLoc)\\n else:\\n # Location is a square - can only move into it if\\n # unoccupied, but can move further once there.\\n if not nextLoc in self.charLocations:\\n # Unoccupied (could check against \\n # charLocations[1:] since we're the piece moving\\n # but a nextLoc = charLocations[0] would be\\n # rejected by being already in the route anyway)\\n newRoute = list(route)\\n newRoute.append(nextLoc)\\n newRoutes.append(newRoute)\\n destinations[diceRoll].add(nextLoc)\\n routes = newRoutes\\n #\\n return [list(destSet) for destSet in destinations]\",\n \"def capture(board,player,possible_moves, pos, loc, i):\\r\\n \\r\\n if player == 1 and encode(loc+i) in board.black:\\r\\n next_piece = encode(loc+i) \\r\\n new_pos = int(loc + (i*2)) \\r\\n if not((pos[0] == 'B') and ((next_piece)[0] == 'A')) and not((pos[0] == 'G') and ((next_piece)[0] == 'H')):\\r\\n new_enc = encode(new_pos)\\r\\n if new_pos in range(0,112) and (new_enc not in board.white) and (new_enc not in board.black) and (new_enc not in board.empty):\\r\\n possible_moves.append([pos,new_enc,next_piece])\\r\\n return True\\r\\n \\r\\n if player == -1 and encode(loc+i) in board.white: \\r\\n next_piece = encode(loc+i) \\r\\n new_pos = int(loc + (i*2))\\r\\n if not((pos[0] == 'B') and ((next_piece)[0] == 'A')) and not((pos[0] == 'G') and ((next_piece)[0] == 'H')):\\r\\n new_enc = encode(new_pos)\\r\\n if new_pos in range(0,112) and (new_enc not in board.white) and (new_enc not in board.black) and (new_enc not in board.empty):\\r\\n possible_moves.append([pos,new_enc,next_piece])\\r\\n return True\\r\\n \\r\\n return False\",\n \"def move2(self):\\n\\n options = self.location.exits.keys()\\n for key in options:\\n if self.location.exits[key] == p.location:\\n self.location.objects.remove(a)\\n self.location = p.location\\n self.location.objects.append(a)\\n print('fred entered the room')\\n self.attack(['attack', str(p.name)])\\n break\\n else:\\n self.move1()\",\n \"def find_moveable_pieces(self, die, p1): \\n moveable = []\\n if (p1):\\n #must we re-enter?\\n if (self.p1vec[0] > 0):\\n if (self.free_spot(0, die, p1)):\\n b = Board(self.p1vec[:],self.p2vec[:])\\n b.move(0, die, p1)\\n moveable.append(b)\\n #no? ok then generate the moves\\n else:\\n for i in range(1, 25):\\n if (self.p1vec[i] > 0):\\n if (self.free_spot(i, die, p1)):\\n b = Board(self.p1vec[:],self.p2vec[:])\\n b.move(i, die, p1)\\n moveable.append(b)\\n else:\\n #must we re-enter?\\n if (self.p2vec[0] > 0):\\n if (self.free_spot(0, die, p1)):\\n b = Board(self.p1vec[:],self.p2vec[:])\\n b.move(0, die, p1)\\n moveable.append(b)\\n #no? ok then generate the moves\\n else:\\n for i in range(1, 25):\\n if (self.p2vec[i] > 0):\\n if (self.free_spot(i, die, p1)):\\n b = Board(self.p1vec[:],self.p2vec[:])\\n b.move(i, die, p1)\\n moveable.append(b)\\n return moveable\",\n \"def move(self):\\n for agent in self.agents:\\n if not agent.fidelity:\\n options = agent.get_move_options(agent.hex, self.kernel_size, None, extend=True)\\n target = random36.choices(population=options,weights=[x.quality**2 for x in options])\\n agent.move(target[0])\",\n \"def move(player, direction):\\r\\n \\r\\n curRoom = world.maps.World.mapGrid[player.room]\\r\\n door = curRoom.dirs[direction]\\r\\n roomid = world.maps.World.doors[door].getExitRoom(curRoom.id)\\r\\n newRoom = world.maps.World.mapGrid[roomid]\\r\\n brokesneak = False\\r\\n \\r\\n if player.getAttr(SNEAKING) and not utils.gameutils.stealthRoll(player):\\r\\n player.setAttr(SNEAKING, False)\\r\\n sendToRoomNotPlayer(player, \\\"{0}You notice {1} sneaking out to the {2}.\\\".format(LRED, player, DIRS[direction])) \\r\\n sendToPlayer(player, \\\"{0}You make a sound\\\".format(LRED))\\r\\n brokesneak = True\\r\\n elif not player.getAttr(SNEAKING): \\r\\n sendToRoomNotPlayer( player, \\\"{0} left to the {1}.\\\".format(player, DIRS[direction])) \\r\\n \\r\\n del curRoom.players[player.name]\\r\\n newRoom.players[player.name] = player\\r\\n player.room = newRoom.id\\r\\n\\r\\n if brokesneak:\\r\\n sendToRoomNotPlayer(player, \\\"{0}You notice {1} sneaking into the room from the {2}.\\\".format(LRED, player, DIRS[OPPOSITEDIRS[direction]])) \\r\\n elif not player.getAttr(SNEAKING):\\r\\n sendToRoomNotPlayer(player, \\\"{0} entered from the {1}.\\\".format(player, DIRS[OPPOSITEDIRS[direction]])) \\r\\n \\r\\n displayRoom(player, player.room)\\r\\n player.stats[MOVING] = False\",\n \"def domove(self, depart, arrivee, promote):\\n\\n # Debugging tests\\n # if(self.cases[depart].isEmpty()):\\n # print('domove() ERROR : asked for an empty square move : ',depart,arrivee,promote)\\n # return \\n # if(int(depart)<0 or int(depart)>63):\\n # print('domove() ERROR : incorrect FROM square number : ',depart)\\n # return \\n # if(int(arrivee)<0 or int(arrivee)>63):\\n # print('domove() ERROR : incorrect TO square number : ',arrivee)\\n # return\\n # if(not(promote=='' or promote=='q' or promote=='r' or promote=='n' or promote=='b')):\\n # print('domove() ERROR : incorrect promote : ',promote)\\n # return\\n\\n # Informations to save in the history moves\\n pieceDeplacee = self.cases[depart] # moved piece\\n piecePrise = self.cases[arrivee] # taken piece, can be null : Piece()\\n isEp = False # will be used to undo a ep move\\n histEp = self.ep # saving the actual ep square (-1 or square number TO)\\n hist_roque_56 = self.white_can_castle_56\\n hist_roque_63 = self.white_can_castle_63\\n hist_roque_0 = self.black_can_castle_0\\n hist_roque_7 = self.black_can_castle_7\\n flagViderEp = True # flag to erase ep or not : if the pawn moved is not taken directly, it can't be taken later\\n\\n # Moving piece\\n self.cases[arrivee] = self.cases[depart]\\n self.cases[depart] = Piece()\\n\\n self.ply += 1\\n\\n # a PAWN has been moved -------------------------------------\\n if (pieceDeplacee.nom == 'PION'):\\n\\n # White PAWN\\n if (pieceDeplacee.couleur == 'blanc'):\\n\\n # If the move is \\\"en passant\\\"\\n if (self.ep == arrivee):\\n piecePrise = self.cases[arrivee + 8] # take black pawn\\n self.cases[arrivee + 8] = Piece()\\n isEp = True\\n\\n # The white pawn moves 2 squares from starting square\\n # then blacks can take \\\"en passant\\\" next move\\n elif (self.ROW(depart) == 6 and self.ROW(arrivee) == 4):\\n self.ep = arrivee + 8\\n flagViderEp = False\\n\\n # Black PAWN\\n else:\\n\\n if (self.ep == arrivee):\\n piecePrise = self.cases[arrivee - 8]\\n self.cases[arrivee - 8] = Piece()\\n isEp = True\\n\\n elif (self.ROW(depart) == 1 and self.ROW(arrivee) == 3):\\n self.ep = arrivee - 8\\n flagViderEp = False\\n\\n # a ROOK has been moved--------------------------------------\\n # update castle rights\\n\\n elif (pieceDeplacee.nom == 'TOUR'):\\n\\n # White ROOK\\n if (pieceDeplacee.couleur == 'blanc'):\\n if (depart == 56):\\n self.white_can_castle_56 = False\\n elif (depart == 63):\\n self.white_can_castle_63 = False\\n\\n # Black ROOK\\n else:\\n if (depart == 0):\\n self.black_can_castle_0 = False\\n elif (depart == 7):\\n self.black_can_castle_7 = False\\n\\n # a KING has been moved-----------------------------------------\\n\\n elif (pieceDeplacee.nom == 'ROI'):\\n\\n # White KING\\n if (pieceDeplacee.couleur == 'blanc'):\\n\\n # moving from starting square\\n if (depart == 60):\\n # update castle rights\\n self.white_can_castle_56 = False\\n self.white_can_castle_63 = False\\n\\n # If castling, move the rook\\n if (arrivee == 58):\\n self.cases[56] = Piece()\\n self.cases[59] = Piece('TOUR', 'blanc')\\n\\n elif (arrivee == 62):\\n self.cases[63] = Piece()\\n self.cases[61] = Piece('TOUR', 'blanc')\\n\\n # Black KING\\n else:\\n\\n if (depart == 4):\\n self.black_can_castle_0 = False\\n self.black_can_castle_7 = False\\n\\n if (arrivee == 6):\\n self.cases[7] = Piece()\\n self.cases[5] = Piece('TOUR', 'noir')\\n\\n elif (arrivee == 2):\\n self.cases[0] = Piece()\\n self.cases[3] = Piece('TOUR', 'noir')\\n\\n # End pieces cases-----------------------------------------------\\n\\n # Any move cancels the ep move\\n if (flagViderEp == True):\\n self.ep = -1\\n\\n # Promote : the pawn is changed to requested piece\\n if (promote != ''):\\n if (promote == 'q'):\\n self.cases[arrivee] = Piece('DAME', self.side2move)\\n elif (promote == 'r'):\\n self.cases[arrivee] = Piece('TOUR', self.side2move)\\n elif (promote == 'n'):\\n self.cases[arrivee] = Piece('CAVALIER', self.side2move)\\n elif (promote == 'b'):\\n self.cases[arrivee] = Piece('FOU', self.side2move)\\n\\n # Change side to move\\n self.changeTrait()\\n\\n # Save move to the history list\\n self.history.append((depart, \\\\\\n arrivee, \\\\\\n pieceDeplacee, \\\\\\n piecePrise, \\\\\\n isEp, \\\\\\n histEp, \\\\\\n promote, \\\\\\n hist_roque_56, \\\\\\n hist_roque_63, \\\\\\n hist_roque_0, \\\\\\n hist_roque_7))\\n\\n # If the move lets king in check, undo it and return false\\n if (self.in_check(self.oppColor(self.side2move))):\\n self.undomove()\\n return False\\n\\n return True\",\n \"def get_pawn_moves(self, state):\\n pawn_moves = []\\n\\n if self.color == cc.WHITE_ACTIVE:\\n forward_1 = add_vectors(self.coord, cc.V_UP)\\n forward_2 = add_vectors(self.coord, cc.V_UP_2)\\n attacks = get_crawler_moves(self.coord, cc.W_PAWN_CAPTURE_VECTORS)\\n starting_rank = cc.RANK_2\\n promo_rank = cc.RANK_8\\n promo_pieces = cc.WHITE_PROMO\\n enemy_set = cc.BLACK_PIECES\\n elif self.color == cc.BLACK_ACTIVE:\\n forward_1 = add_vectors(self.coord, cc.V_DOWN)\\n forward_2 = add_vectors(self.coord, cc.V_DOWN_2)\\n attacks = get_crawler_moves(self.coord, cc.B_PAWN_CAPTURE_VECTORS)\\n starting_rank = cc.RANK_7\\n promo_rank = cc.RANK_1\\n promo_pieces = cc.BLACK_PROMO\\n enemy_set = cc.WHITE_PIECES\\n else:\\n raise Exception(\\\"get_pawn_moves: Invalid Piece Color\\\")\\n\\n if validate_move(forward_1) and state.board[forward_1] == cc.NO_PIECE:\\n if forward_1[0] == promo_rank:\\n for p in promo_pieces:\\n pawn_moves.append(cc.Action(self.string, self.coord, forward_1, promo=p))\\n else:\\n pawn_moves.append(cc.Action(self.string, self.coord, forward_1))\\n if self.coord[0] == starting_rank and validate_move(forward_2) and state.board[forward_2] == cc.NO_PIECE:\\n pawn_moves.append(cc.Action(self.string, self.coord, forward_2, en_p=forward_1))\\n\\n for attack in attacks:\\n if state.board[attack] in enemy_set:\\n if attack[0] == promo_rank:\\n for p in promo_pieces:\\n pawn_moves.append(cc.Action(self.string, self.coord, attack, capture=True, promo=p))\\n else:\\n pawn_moves.append(cc.Action(self.string, self.coord, attack, capture=True))\\n # Make sure Pawns can attack en_passant squares\\n elif attack == state.en_passant:\\n pawn_moves.append(cc.Action(self.string, self.coord, attack, capture=True))\\n\\n return pawn_moves\",\n \"def move(self, i, j):\\n board_cpy = copy.deepcopy(self.board)\\n piece = self.board[self.selected[0]][self.selected[1]]\\n if piece == 0:\\n return False\\n elif piece.player != self.active_player:\\n print(\\\"it's not your turn\\\", piece.player, self.active_player)\\n return False\\n elif self.board[i][j] != 0:\\n if piece.player == self.board[i][j].player:\\n print(\\\"can't capture your own pieces\\\")\\n return False\\n if i==self.selected[0] and piece.direction == 0:\\n if piece.master == False:\\n if (i + j - sum(self.selected))%2 == 0:\\n print(\\\"must choose a different color\\\")\\n return False\\n if j < self.selected[1]:\\n spaces_between = [self.board[i][y] for y in range(j+1, self.selected[1])]\\n else:\\n spaces_between = [self.board[i][y] for y in range(self.selected[1]+1, j)]\\n print(spaces_between)\\n for p in spaces_between:\\n if p != 0:\\n print(\\\"can't jump pieces\\\")\\n return False\\n piece.direction = 1\\n self.board[i][j] = piece\\n self.board[self.selected[0]][self.selected[1]] = 0\\n elif j==self.selected[1] and piece.direction == 1:\\n if piece.master == False:\\n if (i + j - sum(self.selected))%2 == 0:\\n print(\\\"must choose a different color\\\")\\n return False\\n if i < self.selected[0]:\\n spaces_between = [self.board[x][j] for x in range(i+1, self.selected[0])]\\n else:\\n spaces_between = [self.board[x][j] for x in range(self.selected[0]+1, i)] \\n print(spaces_between)\\n for p in spaces_between:\\n if p != 0:\\n print(\\\"can't jump pieces\\\")\\n return False\\n piece.direction = 0\\n self.board[i][j] = piece\\n self.board[self.selected[0]][self.selected[1]] = 0\\n else:\\n print(\\\"Invalid movement\\\")\\n return False\\n if self.board[i][j] == 0:\\n print(\\\"Something wrong happened\\\")\\n return False\\n elif (i==0 or i==self.board_size-1) and (j==0 or j==self.board_size-1):\\n self.board[i][j].master = True\\n self.active_player = (self.active_player+1)%2\\n self.sequence += [board_cpy]\\n return True\",\n \"def get_legal_moves(self, player: int) -> np.ndarray:\\n stage2 = self.is_stage2()\\n action_mask = np.zeros((24, 5, 25), dtype=bool)\\n # if stage 1 add set options\\n array_board = np.array(self.board)\\n if not stage2:\\n legal_pos = np.where(array_board == 0)[0]\\n for pos in legal_pos:\\n if self.is_mill(player, pos, self.board): # current selection completes a mill\\n opp_pos = np.where(array_board == -player)[0]\\n opp_pos = [opp_p for opp_p in opp_pos if\\n not self.is_mill(-player, opp_p, self.board)] # can't remove opponent in mill\\n if len(opp_pos) == 0: # exception for the case where all opponents pieces are in mills\\n opp_pos = np.where(array_board == -player)[0]\\n action_mask[pos, -1, opp_pos] = True\\n else:\\n action_mask[pos, -1, -1] = True # place piece on board\\n else:\\n from_pos_cands = np.where(array_board == player)[0]\\n for from_pos in from_pos_cands:\\n mill_cands = [(orient, adj) for orient, adj in enumerate(self.adjacent[from_pos]) if\\n adj is not None and self.board[adj] == 0] # TODO added not, need to validate\\n if_played_board = self.board.copy()\\n if_played_board[from_pos] = 0\\n for (orient, adj) in mill_cands:\\n if self.is_mill(player, adj, if_played_board):\\n opp_pos = np.where(array_board == -player)[0]\\n opp_pos = [opp_p for opp_p in opp_pos if not self.is_mill(-player, opp_p, if_played_board)]\\n if len(opp_pos) == 0: # exception for the case where all opponents pieces are in mills\\n opp_pos = np.where(array_board == -player)[0]\\n action_mask[from_pos, orient, opp_pos] = True\\n else:\\n action_mask[from_pos, orient, -1] = True\\n\\n return action_mask\",\n \"def determine_best_move(self, board):\\r\\n\\r\\n # Check if the AI is smart, run a smart version of the AI\\r\\n if self.is_smart:\\r\\n\\r\\n # Iterate through every column\\r\\n # keep track of any restricted moves (moves that may cause the challenger to win)\\r\\n # and keep track of the final result of where the AI should move\\r\\n restricted = []\\r\\n ai_move = -1\\r\\n for column in range(board.width):\\r\\n\\r\\n # Create two copies of the board to emulate AI moves and player moves\\r\\n ai_copy = board.copy()\\r\\n player_copy = board.copy()\\r\\n\\r\\n # Check if the current column is full, move onto the next column\\r\\n if board.is_column_full(column):\\r\\n continue\\r\\n\\r\\n # Column is not full; Emulate AI and player moves at this column\\r\\n ai_copy.add_piece(column, is_challenger = False) # AI move\\r\\n player_copy.add_piece(column, is_challenger = True) # Player move\\r\\n\\r\\n # Check if either the ai_copy or player_copy has a win in it\\r\\n ai_win_check = ai_copy.check_for_winner()\\r\\n player_win_check = player_copy.check_for_winner()\\r\\n\\r\\n # If either board has a win in it, make that the AI move\\r\\n # if the player would go to this current column in their next move\\r\\n # they would win, the AI should try to stop it\\r\\n # if the ai would go to this current column in its next move\\r\\n # they would win, the AI should immediately go here\\r\\n if ai_win_check == False or player_win_check == True:\\r\\n ai_move = column\\r\\n break\\r\\n\\r\\n # Neither of the moves would win in either board,\\r\\n # emulate the next moves on the same column\\r\\n else:\\r\\n\\r\\n # Check if the column is full, move onto the next column\\r\\n if ai_copy.is_column_full(column):\\r\\n continue\\r\\n\\r\\n # Column is not full, emulate the player move on the AI copy\\r\\n ai_copy.add_piece(column, is_challenger = True)\\r\\n\\r\\n # Check if the player would win; If so, do not let the AI go to this column\\r\\n player_win_check = ai_copy.check_for_winner()\\r\\n if player_win_check == True:\\r\\n restricted.append(column)\\r\\n\\r\\n # There has been no ai_move generated yet\\r\\n # generate a random column\\r\\n if ai_move == -1:\\r\\n\\r\\n # Check if the board is full, there must be a draw\\r\\n if board.is_board_full():\\r\\n return False\\r\\n\\r\\n # The board is not full, generate a random column that is not full\\r\\n while True:\\r\\n ai_move = randint(0, board.width - 1)\\r\\n\\r\\n # Check if the column is full, continue generating a random column\\r\\n if board.is_column_full(ai_move):\\r\\n continue\\r\\n\\r\\n # Check to see if this is the only available column to go to\\r\\n # or check to see if this column is a restricted move\\r\\n elif board.board[0].count(None) == 1 or len(restricted) == board.board[0].count(None):\\r\\n break\\r\\n\\r\\n # Check to see if the move is not a restricted move\\r\\n elif ai_move not in restricted:\\r\\n break\\r\\n\\r\\n # The AI is not smart, choose a random place\\r\\n else:\\r\\n ai_move = randint(0, board.width - 1)\\r\\n while board.is_column_full(ai_move):\\r\\n ai_move = randint(0, board.width - 1)\\r\\n\\r\\n # Make the AI go to its chosen move\\r\\n board.add_piece(ai_move, is_challenger = False)\\r\\n return True\",\n \"def execution(move,legal,board,player):\\r\\n \\r\\n if player == 1:\\r\\n if move in legal:\\r\\n for i in range(0,len(board.white)):\\r\\n if board.white[i] == move[0]:\\r\\n board.white[i] = move[1]\\r\\n if len(move) == 3:\\r\\n board.black.remove(move[-1])\\r\\n\\r\\n else:\\r\\n print(\\\"Illegal move, please input a legal move\\\")\\r\\n human_move(board,player)\\r\\n else:\\r\\n if move in legal:\\r\\n if len(move) == 3:\\r\\n board.white.remove(move[-1])\\r\\n for i in range(0,len(board.black)):\\r\\n if board.black[i] == move[0]:\\r\\n board.black[i] = move[1]\\r\\n else:\\r\\n print(\\\"Illegal move, please input a legal move\\\")\\r\\n human_move(board,player)\",\n \"def _move_forward(self):\\n\\t\\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\tif(self.y<=798):\\n\\t\\t\\tself.y = self.y+1\\n\\t\\t\\tif Board.board[self.x][self.y]=='0':\\n\\t\\t\\t\\tMario.score += 1\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='P':\\n\\t\\t\\t\\tMario.lives+=1\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_1-up.wav\\\"])\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='A':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\t\\t\\t\\tMario.attack = 1\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_powerup.wav\\\"])\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='@':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tMario.lives-=1\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_mariodie.wav\\\"])\\n\\t\\t\\t\\tif Mario.lives<=0:\\n\\t\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_gameover.wav\\\"])\\n\\t\\t\\t\\t\\treturn \\\"exit\\\"\\n\\t\\t\\t\\tos.system('clear')\\n\\t\\t\\t\\tprint(\\\"\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tNumber of Mario left\\\",Mario.lives)\\n\\t\\t\\t\\tMario.respawn(self.x,self.y)\\n\\t\\t\\t\\ttime.sleep(2)\\n\\t\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\n\\t\\t\\telif(Board.board[self.x][self.y]=='/'):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x-1][self.y]='M'\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='I':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_stage_clear.wav\\\"])\\n\\t\\t\\t\\tBoard.bonus_round()\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='K':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_stage_clear.wav\\\"])\\n\\t\\t\\t\\tenemy.boss_round()\\n\\n\\t\\t\\telif(Board.board[self.x][self.y] in obstacles):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y-1]='M'\\n\\n\\t\\t\\telif((Board.board[self.x+1][self.y-1]=='/' or Board.board[self.x+1][self.y-1]=='T') and Board.board[self.x+1][self.y]==' '):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y+1]='M'\\n\\t\\t\\t\\tMario.go_down(self)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\n\\t\\tif( self.y-1 >= ((Board.prev_j+Board.prev_k)/2) ):\\n\\t\\t\\tos.system('clear')\\n\\t\\t\\tBoard.prev_j += 1 \\n\\t\\t\\tBoard.prev_k += 1\\n\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\telse:\\n\\t\\t\\tos.system('clear')\\n\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\",\n \"def update(self, draw):\\n for direction in draw.directions_enclosing:\\n pos = draw.position\\n done = False\\n while not done:\\n next_stone_in_line = pos.step(direction)\\n #next_stone_in_line = tuple(x+y for x, y in zip(pos, direction))\\n if self.board[next_stone_in_line] == opponent(draw.player):\\n self.board[next_stone_in_line] = draw.player\\n pos = next_stone_in_line\\n else:\\n done = True\",\n \"def move_draught_end(event):\\n global red_draughts, white_draughts\\n global board_array\\n global old_point\\n global die_1_num, die_2_num, doubles\\n draught = board.find_withtag(CURRENT)[0]\\n #Figure out which point they want to put it on\\n bottom = (event.y-click_offset[1] >= board_height//2)\\n point_left_edges = [board_divisions*i for i in xrange(0,15) if i != 7]\\n is_red = draught in red_draughts\\n if bottom == False:\\n new_point = 12+point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0])))\\n else:\\n new_point = 13-point_left_edges.index(min(point_left_edges, key=lambda x:abs(x-event.x+click_offset[0]))) \\n #Check legality\\n if(board_array[new_point][1] > 1 and is_red) or (board_array[new_point][0] > 1 and not is_red): #if too many opposite color on square\\n draw_draughts()\\n return\\n if(board_array[0][0] > 0 and is_red and old_point != 0)or(board_array[25][1] > 0 and not is_red and old_point != 25):#Obligated to move off bar first\\n draw_draughts()\\n return\\n if(new_point == 0 and not is_red): #if white trying to bear off\\n for i in xrange(7,26):\\n if(board_array[i][1] > 0): #If white has a piece outside home, can't bear off\\n draw_draughts()\\n return\\n if(new_point == 25 and is_red): #if red trying to bear off\\n for i in xrange(0,18):\\n if(board_array[i][0] > 0): #If red has a piece outside home, can't bear off\\n draw_draughts()\\n return \\n \\n if(new_point-old_point == die_1_num and is_red) or (old_point-new_point == die_1_num and not is_red):\\n if(doubles == False) or (die_2_num != 0):\\n die_1_num = 0\\n else: \\n die_2_num = die_1_num\\n doubles = False\\n elif(new_point-old_point == die_2_num and is_red) or (old_point-new_point == die_2_num and not is_red):\\n if(doubles == False) or (die_1_num != 0):\\n die_2_num = 0\\n else: \\n die_1_num = die_2_num\\n doubles = False\\n else: #Can't move there on this roll\\n draw_draughts()\\n return\\n update_dice()\\n #Update board_array\\n if is_red:\\n board_array[old_point][0] -= 1\\n board_array[new_point][0] += 1\\n if(board_array[new_point][1] == 1): #Handle hits\\n board_array[new_point][1] -= 1\\n board_array[25][1] += 1\\n else:\\n board_array[old_point][1] -= 1\\n board_array[new_point][1] += 1\\n if(board_array[new_point][0] == 1): #Handle hits\\n board_array[new_point][0] -= 1\\n board_array[0][0] += 1\\n\\n draw_draughts()\\n if(die_1_num == 0 and die_2_num == 0):\\n comp_turn()\",\n \"def result(state, action):\\n # Faster than deepcopy\\n new_state = pickle.loads(pickle.dumps((state)))\\n\\n if action.castle == None:\\n # Attacking En passant pawn\\n if new_state.en_passant == action.end:\\n new_state.board[action.start] = cc.NO_PIECE\\n new_state.board[action.end] = action.piece\\n # Delete black pawn\\n if new_state.en_passant[0] == cc.RANK_6:\\n down = (action.end[0]+1, action.end[1])\\n new_state.board[down] = cc.NO_PIECE\\n # Delete white pawn\\n elif new_state.en_passant[0] == cc.RANK_3:\\n up = (action.end[0]-1, action.end[1])\\n new_state.board[up] = cc.NO_PIECE\\n # Default Case\\n else:\\n # Delete piece from the start\\n new_state.board[action.start] = \\\" \\\"\\n # Place piece at the end\\n new_state.board[action.end] = action.piece\\n\\n # Set en_passant space for a pawn moving 2\\n if action.en_p:\\n new_state.en_passant = cc.coord_to_alg(action.en_p)\\n else:\\n new_state.en_passant = cc.NO_C_EP\\n\\n # Remove castle availability if the rook or king move\\n if action.piece == cc.W_ROOK:\\n if action.start == (cc.RANK_1, cc.FILE_H):\\n new_state.castles_avail = new_state.castles_avail.replace('K', '')\\n elif action.start == (cc.RANK_1, cc.FILE_A):\\n new_state.castles_avail = new_state.castles_avail.replace('Q', '')\\n elif action.piece == cc.W_KING:\\n if action.start == (cc.RANK_1, cc.FILE_E):\\n new_state.castles_avail = new_state.castles_avail.replace('K', '')\\n new_state.castles_avail = new_state.castles_avail.replace('Q', '')\\n elif action.piece == cc.B_ROOK:\\n if action.start == (cc.RANK_8, cc.FILE_H):\\n new_state.castles_avail = new_state.castles_avail.replace('k', '')\\n elif action.start == (cc.RANK_8, cc.FILE_A):\\n new_state.castles_avail = new_state.castles_avail.replace('q', '')\\n elif action.piece == cc.B_KING:\\n if action.start == (cc.RANK_8, cc.FILE_E):\\n new_state.castles_avail = new_state.castles_avail.replace('k', '')\\n new_state.castles_avail = new_state.castles_avail.replace('q', '')\\n\\n # Update the halfmove count\\n if action.capture or action.piece == cc.W_PAWN or action.piece == cc.B_PAWN:\\n new_state.halfmove = 0\\n else:\\n new_state.halfmove += 1\\n \\n else: # Castle Time\\n if action.castle == cc.CASTLE_QUEENSIDE:\\n if state.active_color == cc.WHITE_ACTIVE:\\n # Delete and Place Rook\\n new_state.board[cc.RANK_1, cc.FILE_A] = \\\" \\\"\\n new_state.board[cc.RANK_1, cc.FILE_D] = cc.W_ROOK\\n # Delete and Place King\\n new_state.board[cc.RANK_1, cc.FILE_E] = \\\" \\\"\\n new_state.board[cc.RANK_1, cc.FILE_C] = cc.W_KING\\n # Remove all White castling availability\\n new_state.castles_avail = new_state.castles_avail.replace('K','')\\n new_state.castles_avail = new_state.castles_avail.replace('Q','')\\n # If the string is empty, replace with a dash\\n if not new_state.castles_avail:\\n new_state.castles_avail = cc.NO_C_EP\\n elif state.active_color == cc.BLACK_ACTIVE:\\n # Delete and Place Rook\\n new_state.board[cc.RANK_8, cc.FILE_A] = \\\" \\\"\\n new_state.board[cc.RANK_8, cc.FILE_D] = cc.B_ROOK\\n # Delete and Place King\\n new_state.board[cc.RANK_8, cc.FILE_E] = \\\" \\\"\\n new_state.board[cc.RANK_8, cc.FILE_C] = cc.B_KING\\n # Remove all Black castling availability\\n new_state.castles_avail = new_state.castles_avail.replace('k','')\\n new_state.castles_avail = new_state.castles_avail.replace('q','')\\n # If the string is empty, replace with a dash\\n if not new_state.castles_avail:\\n new_state.castles_avail = cc.NO_C_EP\\n\\n elif action.castle == cc.CASTLE_KINGSIDE:\\n if state.active_color == cc.WHITE_ACTIVE:\\n # Delete and Place Rook\\n new_state.board[cc.RANK_1, cc.FILE_H] = \\\" \\\"\\n new_state.board[cc.RANK_1, cc.FILE_F] = cc.W_ROOK\\n # Delete and Place King\\n new_state.board[cc.RANK_1, cc.FILE_E] = \\\" \\\"\\n new_state.board[cc.RANK_1, cc.FILE_G] = cc.W_KING\\n # Remove all White castling availability\\n new_state.castles_avail = new_state.castles_avail.replace('K','')\\n new_state.castles_avail = new_state.castles_avail.replace('Q','')\\n # If the string is empty, replace with a dash\\n if not new_state.castles_avail:\\n new_state.castles_avail = cc.NO_C_EP\\n elif state.active_color == cc.BLACK_ACTIVE:\\n # Delete and Place Rook\\n new_state.board[cc.RANK_8, cc.FILE_H] = \\\" \\\"\\n new_state.board[cc.RANK_8, cc.FILE_F] = cc.B_ROOK\\n # Delete and Place King\\n new_state.board[cc.RANK_8, cc.FILE_E] = \\\" \\\"\\n new_state.board[cc.RANK_8, cc.FILE_G] = cc.B_KING\\n # Remove all Black castling availability\\n new_state.castles_avail = new_state.castles_avail.replace('k','')\\n new_state.castles_avail = new_state.castles_avail.replace('q','')\\n # If the string is empty, replace with a dash\\n if not new_state.castles_avail:\\n new_state.castles_avail = cc.NO_C_EP\\n \\n # Update fullmove count\\n if new_state.active_color == cc.WHITE_ACTIVE:\\n new_state.active_color = cc.BLACK_ACTIVE\\n new_state.opp_color = cc.WHITE_ACTIVE\\n else:\\n new_state.active_color = cc.WHITE_ACTIVE\\n new_state.opp_color = cc.BLACK_ACTIVE\\n new_state.fullmove += 1\\n\\n new_state.active_king = new_state.find_king(new_state.active_color)\\n new_state.inactive_king = new_state.find_king(new_state.opp_color)\\n\\n return new_state\",\n \"def white_kingside_castling(self):\\n return (self.castling[0] and self.empty((10, 7))\\n and self.empty((10, 8)) and not self.attacked((10, 6), BLACK)\\n and not self.attacked((10, 7), BLACK))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7224072","0.6627195","0.65229857","0.6470024","0.6182009","0.6177418","0.61430883","0.6139011","0.61076343","0.6091473","0.6086108","0.60822123","0.6072754","0.6068784","0.6068784","0.6068784","0.60567504","0.60416263","0.6029341","0.6026139","0.6018981","0.6018434","0.601502","0.6001996","0.599956","0.59889746","0.5972622","0.59405476","0.593119","0.59306324","0.59238833","0.5910745","0.59104824","0.590638","0.5905136","0.5885825","0.5882176","0.58640695","0.58511627","0.5822712","0.58200246","0.5818379","0.5814889","0.5811587","0.58059233","0.58018017","0.5792937","0.5788382","0.57869714","0.5770713","0.5755354","0.57406324","0.57375056","0.57365113","0.5720468","0.5712972","0.5712972","0.5712972","0.5712586","0.57095313","0.5705547","0.5698541","0.56908506","0.5689668","0.5687584","0.5686171","0.56835115","0.56767786","0.5673007","0.56724006","0.5668914","0.56678665","0.56666636","0.5666544","0.5666303","0.5661606","0.56608886","0.5656728","0.56503415","0.5644531","0.5635861","0.5634779","0.56276464","0.5626298","0.5625897","0.5623328","0.56118023","0.56107384","0.559759","0.5596694","0.55965894","0.5591443","0.5587947","0.558781","0.55864483","0.55859125","0.55838215","0.55832386","0.55737334","0.55734175"],"string":"[\n \"0.7224072\",\n \"0.6627195\",\n \"0.65229857\",\n \"0.6470024\",\n \"0.6182009\",\n \"0.6177418\",\n \"0.61430883\",\n \"0.6139011\",\n \"0.61076343\",\n \"0.6091473\",\n \"0.6086108\",\n \"0.60822123\",\n \"0.6072754\",\n \"0.6068784\",\n \"0.6068784\",\n \"0.6068784\",\n \"0.60567504\",\n \"0.60416263\",\n \"0.6029341\",\n \"0.6026139\",\n \"0.6018981\",\n \"0.6018434\",\n \"0.601502\",\n \"0.6001996\",\n \"0.599956\",\n \"0.59889746\",\n \"0.5972622\",\n \"0.59405476\",\n \"0.593119\",\n \"0.59306324\",\n \"0.59238833\",\n \"0.5910745\",\n \"0.59104824\",\n \"0.590638\",\n \"0.5905136\",\n \"0.5885825\",\n \"0.5882176\",\n \"0.58640695\",\n \"0.58511627\",\n \"0.5822712\",\n \"0.58200246\",\n \"0.5818379\",\n \"0.5814889\",\n \"0.5811587\",\n \"0.58059233\",\n \"0.58018017\",\n \"0.5792937\",\n \"0.5788382\",\n \"0.57869714\",\n \"0.5770713\",\n \"0.5755354\",\n \"0.57406324\",\n \"0.57375056\",\n \"0.57365113\",\n \"0.5720468\",\n \"0.5712972\",\n \"0.5712972\",\n \"0.5712972\",\n \"0.5712586\",\n \"0.57095313\",\n \"0.5705547\",\n \"0.5698541\",\n \"0.56908506\",\n \"0.5689668\",\n \"0.5687584\",\n \"0.5686171\",\n \"0.56835115\",\n \"0.56767786\",\n \"0.5673007\",\n \"0.56724006\",\n \"0.5668914\",\n \"0.56678665\",\n \"0.56666636\",\n \"0.5666544\",\n \"0.5666303\",\n \"0.5661606\",\n \"0.56608886\",\n \"0.5656728\",\n \"0.56503415\",\n \"0.5644531\",\n \"0.5635861\",\n \"0.5634779\",\n \"0.56276464\",\n \"0.5626298\",\n \"0.5625897\",\n \"0.5623328\",\n \"0.56118023\",\n \"0.56107384\",\n \"0.559759\",\n \"0.5596694\",\n \"0.55965894\",\n \"0.5591443\",\n \"0.5587947\",\n \"0.558781\",\n \"0.55864483\",\n \"0.55859125\",\n \"0.55838215\",\n \"0.55832386\",\n \"0.55737334\",\n \"0.55734175\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72807145"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":84990,"cells":{"query":{"kind":"string","value":"Checks if castling is possible"},"document":{"kind":"string","value":"def castling_valid(self, turn, direction):\n \n opposite_colour = next_turn(turn)\n\n \n if self.board[direction[0]] and self.board[direction[-1]] != self.empty:\n if ((self.board[direction[0]].graphic) == piece_class.PIECEDICT[turn][piece_class.King] and \n (self.board[direction[-1]].graphic) == piece_class.PIECEDICT[turn][piece_class.Rook]):\n if self.board[direction[0]].move_track == False and self.board[direction[-1]].move_track == False:\n for i in self.path_dict[opposite_colour]:\n if i in self.coords:\n if self.coords.index(i) == direction[0]:\n \n return False\n \n if self.coords.index(i) == direction[1]:\n \n return False\n \n if self.coords.index(i) == direction[2]:\n \n return False\n \n if len(direction) == 4:\n if self.board[direction[1]] == self.empty:\n if self.board[direction[2]] == self.empty:\n \n return True\n \n if len(direction) == 5:\n if self.board[direction[1]] == self.empty:\n if self.board[direction[2]] == self.empty:\n if self.board[direction[3]] == self.empty:\n \n return True\n \n return False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _can_cast_to(self, value, cast_type):\n try:\n _ = cast_type(value)\n return True\n except ValueError:\n return False","def is_casting(self):\n # type: () -> bool\n return self._is_casting","def test_canConvert(string, cast, expected):\n assert canConvert(string, cast) == expected","def is_castxml(self):\n return self._is_castxml","def is_castxml1(self):\n return self._is_castxml1","def _assert_valid_value_and_cast(self, value):\n raise NotImplementedError()","def _can_cast(from_dtype, to_dtype):\n if cudf.utils.utils.is_na_like(from_dtype):\n return True\n if isinstance(from_dtype, type):\n from_dtype = cudf.dtype(from_dtype)\n if isinstance(to_dtype, type):\n to_dtype = cudf.dtype(to_dtype)\n\n # TODO : Add precision & scale checking for\n # decimal types in future\n\n if isinstance(from_dtype, cudf.core.dtypes.DecimalDtype):\n if isinstance(to_dtype, cudf.core.dtypes.DecimalDtype):\n return True\n elif isinstance(to_dtype, np.dtype):\n if to_dtype.kind in {\"i\", \"f\", \"u\", \"U\", \"O\"}:\n return True\n else:\n return False\n elif isinstance(from_dtype, np.dtype):\n if isinstance(to_dtype, np.dtype):\n return np.can_cast(from_dtype, to_dtype)\n elif isinstance(to_dtype, cudf.core.dtypes.DecimalDtype):\n if from_dtype.kind in {\"i\", \"f\", \"u\", \"U\", \"O\"}:\n return True\n else:\n return False\n elif isinstance(to_dtype, cudf.core.types.CategoricalDtype):\n return True\n else:\n return False\n elif isinstance(from_dtype, cudf.core.dtypes.ListDtype):\n # TODO: Add level based checks too once casting of\n # list columns is supported\n if isinstance(to_dtype, cudf.core.dtypes.ListDtype):\n return np.can_cast(from_dtype.leaf_type, to_dtype.leaf_type)\n else:\n return False\n elif isinstance(from_dtype, cudf.core.dtypes.CategoricalDtype):\n if isinstance(to_dtype, cudf.core.dtypes.CategoricalDtype):\n return True\n elif isinstance(to_dtype, np.dtype):\n return np.can_cast(from_dtype._categories.dtype, to_dtype)\n else:\n return False\n else:\n return np.can_cast(from_dtype, to_dtype)","def is_casting(self, is_casting):\n # type: (bool) -> None\n\n if is_casting is not None:\n if not isinstance(is_casting, bool):\n raise TypeError(\"Invalid type for `is_casting`, type has to be `bool`\")\n\n self._is_casting = is_casting","def test_cast(self):\n dim = Fidelity(\"epoch\", 1, 10)\n with pytest.raises(NotImplementedError):\n dim.cast()","def is_convertible(source, target):\n return __is_convertible_t(source, target).is_convertible()","def test_cast(self, option, value, expected):\n actual = option.cast(value)\n assert actual == expected","def backcast(self) -> bool:\n return self.__backcast","def check_type(self):\n return True","def test_cast_fail(self, option, value):\n with pytest.raises(errors.BisonError):\n option.cast(value)","def type_check(self):\n self.link_all_refs()\n self.check_ast()","def canAdapt(self, stru):\n return isinstance(stru, crystal)","def CheckType(self, *args, **kwargs):\n pass","def check_type_compat(input_a, input_b):\n return return_family_type(input_a) is return_family_type(input_b)","def checkType(self, value):\n pass","def test_casting_without_iterable(test_fixture, test_input, expected):\n test_fixture.cast_prop = test_input\n assert test_input == test_fixture.cast_prop == expected\n assert type(test_fixture.cast_prop) == type(expected)","def check_type(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\n\tassert type(X) == xr.DataArray, 'XCast requires a dataset to be of type \"Xarray.DataArray\"'","def test_as_type_invalid_cast(self, wrapped_unit: MyType) -> None:\n # Arrange.\n # Create a new fake UnitType.\n other_type = mock.MagicMock()\n # Give it a name for easier debugging.\n other_type.__name__ = \"OtherType\"\n\n # A fake unit to convert.\n to_convert = MyUnit(wrapped_unit, 10)\n\n # Act and assert.\n # Conversion should fail since no cast is registered.\n with pytest.raises(CastError):\n wrapped_unit.as_type(to_convert, other_type)","def can_convert(self, from_type, to_type):\n #\n # Test if the glpsol executable is available\n #\n if not pyomo.common.Executable(\"pico_convert\"):\n return False\n #\n # Return True for specific from/to pairs\n #\n if from_type == ProblemFormat.nl and to_type == ProblemFormat.cpxlp:\n return True\n if from_type == ProblemFormat.nl and to_type == ProblemFormat.mps:\n return True\n if from_type == ProblemFormat.mps and to_type == ProblemFormat.cpxlp:\n return True\n if from_type == ProblemFormat.cpxlp and to_type == ProblemFormat.mps:\n return True\n return False","def accepts(cls, value: Any) -> bool:\n try:\n cls.convert(value)\n return True\n except ValueError:\n return False","def CanConvert(*args, **kwargs):\n return _gdi_.EncodingConverter_CanConvert(*args, **kwargs)","def test_upcast(self):\r\n if config.cast_policy == 'custom':\r\n assert arange(iscalar()).dtype == iscalar().dtype\r\n assert arange(fscalar()).dtype == fscalar().dtype\r\n assert arange(dscalar()).dtype == dscalar().dtype\r\n\r\n # int32 + float32 -> float64\r\n assert arange(iscalar(), fscalar()).dtype == dscalar().dtype\r\n assert arange(iscalar(), dscalar()).dtype == dscalar().dtype\r\n assert arange(fscalar(), dscalar()).dtype == dscalar().dtype\r\n\r\n assert arange(iscalar(), fscalar(), dscalar()).dtype == \\\r\n dscalar().dtype\r\n elif config.cast_policy in ('numpy', 'numpy+floatX'):\r\n for dtype in get_numeric_types():\r\n # Test with a single argument.\r\n arange_dtype = arange(scalar(dtype=str(dtype))).dtype\r\n numpy_dtype = numpy.arange(numpy.array(1, dtype=dtype)).dtype\r\n if (dtype != 'float64' and\r\n numpy_dtype == 'float64' and\r\n config.cast_policy == 'numpy+floatX' and\r\n config.floatX == 'float32'):\r\n # We want a float32 arange.\r\n assert arange_dtype == 'float32'\r\n else:\r\n # Follow numpy.\r\n assert arange_dtype == numpy_dtype\r\n\r\n # Test with two arguments.\r\n for stop_dtype in get_numeric_types():\r\n arange_dtype = arange(\r\n start=scalar(dtype=str(dtype)),\r\n stop=scalar(dtype=str(stop_dtype))).dtype\r\n numpy_dtype = numpy.arange(\r\n start=numpy.array(0, dtype=dtype),\r\n stop=numpy.array(1, dtype=stop_dtype)).dtype\r\n if (dtype != 'float64' and\r\n stop_dtype != 'float64' and\r\n numpy_dtype == 'float64' and\r\n config.cast_policy == 'numpy+floatX' and\r\n config.floatX == 'float32'):\r\n # We want a float32 arange.\r\n assert arange_dtype == 'float32'\r\n else:\r\n # Follow numpy.\r\n assert arange_dtype == numpy_dtype\r\n\r\n # Test with three arguments.\r\n for step_dtype in get_numeric_types():\r\n arange_dtype = arange(\r\n start=scalar(dtype=str(dtype)),\r\n stop=scalar(dtype=str(stop_dtype)),\r\n step=scalar(dtype=str(step_dtype))).dtype\r\n numpy_dtype = numpy.arange(\r\n start=numpy.array(0, dtype=dtype),\r\n stop=numpy.array(1, dtype=stop_dtype),\r\n step=numpy.array(1, dtype=step_dtype)).dtype\r\n if (dtype != 'float64' and\r\n stop_dtype != 'float64' and\r\n step_dtype != 'float64' and\r\n numpy_dtype == 'float64' and\r\n config.cast_policy == 'numpy+floatX' and\r\n config.floatX == 'float32'):\r\n # We want a float32 arange.\r\n assert arange_dtype == 'float32'\r\n else:\r\n # Follow numpy.\r\n assert arange_dtype == numpy_dtype\r\n else:\r\n raise NotImplementedError(config.cast_policy)","def applies(cls, obj):\n return type(obj) in cls.types","def __contains__(self, a):\n try:\n self.convert(a)\n except CoercionFailed:\n return False\n\n return True","def check_force_cast(FROM, TO, operations, value):\n import re\n r = re.compile('(\\w+) \\%i\\d, \\$(-?\\d+)')\n r2 = re.compile('(\\w+) \\%i\\d')\n #\n value = rffi.cast(FROM, value)\n value = rffi.cast(lltype.Signed, value)\n #\n expected_value = rffi.cast(TO, value)\n expected_value = rffi.cast(lltype.Signed, expected_value)\n #\n for op in operations:\n match = r.match(op)\n if match is None:\n match = r2.match(op)\n assert match, \"line %r does not match regexp\" % (op,)\n opname = match.group(1)\n if opname == 'int_and':\n value &= int(match.group(2))\n elif opname == 'int_signext':\n numbytes = int(match.group(2))\n value = int_signext(value, numbytes)\n elif opname == 'int_is_true':\n value = bool(value)\n else:\n assert 0, opname\n #\n assert rffi.cast(lltype.Signed, value) == expected_value","def EncodingConverter_CanConvert(*args, **kwargs):\n return _gdi_.EncodingConverter_CanConvert(*args, **kwargs)","def test_evaluate_cast_expression(self):\n value = self.evaluate_common(\"cast(2D,'Edm.Double')\")\n self.assertTrue(\n value.type_code == edm.SimpleType.Double, \"Expected Double\")\n self.assertTrue(value.value == 2.0, \"Expected 2.0\")\n value = self.evaluate_common(\"cast(2L,'Edm.Single')\")\n self.assertTrue(\n value.type_code == edm.SimpleType.Single, \"Expected Single\")\n self.assertTrue(value.value == 2.0, \"Expected 2.0\")\n value = self.evaluate_common(\"cast(2,'Edm.Int64')\")\n self.assertTrue(\n value.type_code == edm.SimpleType.Int64, \"Expected Int64\")\n self.assertTrue(value.value == 2, \"Expected 2\")\n try:\n value = self.evaluate_common(\"cast(2.0D,'Edm.Single')\")\n self.fail(\"Double cast to Single\")\n except:\n pass\n value = self.evaluate_common(\"cast('x','Edm.String')\")\n self.assertTrue(value.value == 'x', \"Expected 'x'\")\n try:\n value = self.evaluate_common(\"cast(X'DEAD','Edm.String')\")\n self.fail(\"Binary cast to String\")\n except:\n pass\n try:\n value = self.evaluate_common(\"cast(1,'Edm.Boolean')\")\n self.fail(\"1 cast to Boolean\")\n except:\n pass\n value = self.evaluate_common(\"cast(null,'Edm.String')\")\n self.assertTrue(\n value.type_code == edm.SimpleType.String, \"Expected String\")\n self.assertTrue(value.value is None, \"Expected None\")\n value = self.evaluate_common(\"cast('Edm.Int16')\")\n self.assertTrue(\n value.type_code == edm.SimpleType.Int16, \"Expected Int16\")\n self.assertTrue(value.value is None, \"Expected None\")","def test_init_same_type(self, config: HandlerConfig) -> None:\n # Arrange.\n # Make it look like both units are the same type.\n config.mock_from_unit.is_compatible.return_value = True\n config.mock_to_unit.is_compatible.return_value = True\n\n # Act and assert.\n # Creating the wrapper should fail when both units are the same\n # type.\n with pytest.raises(CastError):\n CastHandler(config.mock_from_unit, config.mock_to_unit)","def verify_type(self, obj):\n return isinstance(obj, self.type_)","def _check_compat(sock_info):\n ...","def sanity_check(self):\n return True","def _check_types(self):\n if isinstance(self.unique_id, (int, str)): # should unique_id be a float?\n self.unique_id = str(self.unique_id)\n else:\n raise TypeError(f'unique_id incorrect type: {type(self.unique_id)}')\n try:\n self.ra = float(self.ra)\n except TypeError:\n print(f'ra incorrect type: {type(self.ra)}')\n try:\n self.dec = float(self.dec)\n except TypeError:\n print(f'dec incorrect type: {type(self.dec)}')\n try:\n self.z = float(self.z)\n except TypeError:\n print(f'z incorrect type: {type(self.z)}')\n if not isinstance(self.galcat, GCData):\n raise TypeError(f'galcat incorrect type: {type(self.galcat)}')\n if not -360. <= self.ra <= 360.:\n raise ValueError(f'ra={self.ra} not in valid bounds: [-360, 360]')\n if not -90. <= self.dec <= 90.:\n raise ValueError(f'dec={self.dec} not in valid bounds: [-90, 90]')\n if self.z < 0.:\n raise ValueError(f'z={self.z} must be greater than 0')","def visit(self, node: AstNode):\n raise CASTTypeError(f\"Unrecognized node type: {type(node)}\")","def cast_tech(self):\n # type: () -> string_types\n return self._cast_tech","def canAdapt(self, stru):\n return False","def __instancecheck__(self, instance):\n\n if isinstance(instance, ObjCInstance):\n return bool(instance.conformsToProtocol(self))\n else:\n return False","def _isinstance(cls, x):\n return isinstance(x, cls.PYTHON_TYPE_CHECK)","def cast(cls, value: 'dsl.Native') -> 'dsl.Native':\n try:\n return cls._cast(value)\n except (ValueError, TypeError) as err:\n raise _exception.CastError(f'Unable to cast {repr(value)} as {cls.__name__}') from err","def test_do_check_event_type(self):\n self.assertEqual(self.a.get_type(), None)\n self.assertEqual(self.b.get_type(), None)\n self.assertTrue(self.a.do_check_event_type(self.a))\n\n self.a = +self.a\n self.assertFalse(self.a.do_check_event_type(self.b))","def converted(self):\n if self.converters:\n return self.converters.converted(self)\n return False","def cast(current, new):\n typ = type(current)\n if typ == bool:\n try:\n return bool(int(new))\n except (ValueError, TypeError):\n pass\n try:\n new = new.lower()\n except AttributeError:\n pass\n if (new == 'on') or (new[0] in ('y', 't')):\n return True\n if (new == 'off') or (new[0] in ('n', 'f')):\n return False\n else:\n try:\n return typ(new)\n except (ValueError, TypeError):\n pass\n print(\"Problem setting parameter (now %s) to %s; incorrect type?\" % (current, new))\n return current","def bitcast(self, typ):","def __instancecheck__(self, instance):\n\n if isinstance(instance, ObjCInstance):\n return bool(instance.isKindOfClass(self))\n else:\n return False","def test_cast_works(self):\n parse.parse(setup_java_class(\"String x = (String) A.x() ;\"))","def _check_if_event_event_or_event_timex(self):\n if (type(self.source) is Timex and type(self.target) is Event) or (type(self.source) is Event and type(self.target) is Timex):\n self._is_event_timex = True\n elif type(self.source) is Event and type(self.target) is Event:\n self._is_event_event = True\n elif type(self.source) is Timex and type(self.target) is Timex:\n self._is_timex_timex = True","def test_cast_string_int(self) -> None:\n self.assertEqual(self.flag.cast_string('42'), 42)","def do_type(self, str_arg):\n try:\n self.adbc.type(validateString(str_arg))\n except Exception, e:\n printLog(self.threadName + 'TYPE FAILED: %s' % e.message)\n self.resultFlag = False\n finally:\n return self.resultFlag","def test_validCoerce(self):\n self.assertEquals(0, usage.portCoerce(\"0\"))\n self.assertEquals(3210, usage.portCoerce(\"3210\"))\n self.assertEquals(65535, usage.portCoerce(\"65535\"))","def should_coerce(self, parameter: inspect.Parameter, value: Any) -> bool:\n # No need to coerce defaults\n if value == parameter.default:\n return False\n\n annotation = checks.resolve_supertype(parameter.annotation)\n origin = self.get_origin(annotation)\n # If we can coerce the annotation,\n # and either the annotation has args,\n # or the value is not an instance of the origin\n return self.coerceable(annotation) and (\n getattr(annotation, \"__args__\", None) or not isinstance(value, origin)\n )","def cast(current, new):\n typ = type(current)\n if typ == bool:\n try:\n return bool(int(new))\n except (ValueError, TypeError):\n pass\n try:\n new = new.lower()\n except:\n pass\n if (new == 'on') or (new[0] in ('y', 't')):\n return True\n if (new == 'off') or (new[0] in ('n', 'f')):\n return False\n else:\n try:\n return typ(new)\n except:\n pass\n print(\"Problem setting parameter (now %s) to %s; incorrect type?\" % (current, new))\n return current","def check(self):\n raise NotImplementedError","def is_type_correct(*args):\n return _ida_hexrays.is_type_correct(*args)","def get_type_check(self, arg, option):\n pass","def has_acceptable_type(self, value):\n if not value:\n return False\n if super().has_acceptable_type(value):\n return True\n # Hmmm ok maybe we're running under IPython:\n try:\n import IPython\n return isinstance(value, IPython.kernel.zmq.iostream.OutStream)\n except ImportError:\n return False","def checkCompatibility(self):\n return _libsbml.CobraToFbcConverter_checkCompatibility(self)","def _check(self, event):\n\n if self._type is None:\n if isinstance(event, Event):\n self._type = Event\n elif isinstance(event, TimeRangeEvent):\n self._type = TimeRangeEvent\n elif isinstance(event, IndexedEvent):\n self._type = IndexedEvent\n else:\n if not isinstance(event, self._type):\n raise PipelineIOException('Homogeneous events expected')","def _check_type_compatibility(self, type_name1, type_name2,\n operation):\n if type_name1 != type_name2:\n raise TypeCompatibilityError(type_name1, type_name2, operation)","def test_cast_string_failure(self) -> None:\n self.flag.flag_type = None\n self.assertRaises(ValueError, self.flag.cast_string, '42')","def _check_other(self, other):\n # pylint: disable=protected-access\n if not isinstance(other, Spectrum):\n raise RuntimeError(\n \"Tried to binop Spectrum and %s\" % (type(other))\n )\n\n if (self._pot is None) != (other._pot is None):\n raise RuntimeError(\n \"Tried to binop Spectra with nonconformant POT\"\n )\n\n if (self._lt is None) != (other._lt is None):\n raise RuntimeError(\n \"Tried to binop Spectra with nonconformant Livetimes\"\n )","def can_auralise(self):\n\n if not self.sources:\n raise ValueError('No sources available')\n\n if not self.receivers:\n raise ValueError('No receivers available')\n\n if not self.atmosphere:\n raise ValueError('No atmosphere available.')\n\n if not self.geometry:\n raise ValueError('No geometry available.')\n\n return True","def _validability(self, ability):\n return isinstance(ability, AttributeAbility)","def cast_validator(caster):\n def validator(value):\n try:\n return caster(value)\n except ValueError:\n raise ValidationError()\n\n return validator","def _cast(cls, value: 'dsl.Native') -> 'dsl.Native':\n return cls.__type__(value)","def validate(self):\n self._check_type()","def deconstructable(self):\n return isinstance(self.get(), int)","def _check_type(self, new_value):\n raise NotImplementedError","def _check_mapper(self, mapper):\n if not hasattr(mapper, 'parse') or not callable(mapper.parse):\n raise ValueError('mapper must implement parse()')\n if not hasattr(mapper, 'format') or not callable(mapper.format):\n raise ValueError('mapper must implement format()')","def custom_cast(s):\n for cast_func in (int, float, str):\n try:\n return cast_func(s)\n except ValueError:\n pass\n raise BaseException('Could not cast as number/string!')","def _validability(self, ability):\n return (isinstance(ability, AttributeAbility) or\n isinstance(ability, WeaponAbility))","def is_interpretable(self):\n return bool(self.as_date() or self.as_time())","def check(self, runtime):\n return True","def _type_check_double(self, data):\n if type(data) not in self._VALID_TYPES:\n return False\n return True","def check(self, value: Any) -> None:\n if not isinstance(value, self.oktype):\n raise TypeError(value)","def supports(self, name):\r\n return hasattr(self, \"data_to_\" + name)","def _create_cast(cls, onnx_node, inputs, opset_version):\n to = onnx_node.getattr(\"to\")\n # singa only supports float32 and int32\n map_dict = {\n TensorProto.FLOAT: tensor.float32, # FLOAT to float32\n TensorProto.UINT8: None, # UINT8\n TensorProto.INT8: tensor.int32, # INT8 to int32\n TensorProto.UINT16: None, # UINT16\n TensorProto.INT16: tensor.int32, # INT16 to int32\n TensorProto.INT32: tensor.int32, # INT32 to int32\n TensorProto.INT64: tensor.int32, # INT64 to int32\n TensorProto.STRING: None, # stirng\n TensorProto.BOOL: None, # bool\n }\n to = map_dict[to]\n assert to != None, \"not support cast type: {}\".format(to)\n _, forward = cls._common_onnx_node_to_singa_op(onnx_node, inputs,\n opset_version)\n return _, forward(to)","def is_of_type(cmd):\r\n raise NotImplementedError()","def test_cast_bad_category(self):\n categories = list(range(10))\n dim = Categorical(\"yolo\", categories, shape=2)\n sample = np.array([\"asdfa\", \"1\"], dtype=object)\n with pytest.raises(ValueError) as exc:\n dim.cast(sample)\n assert \"Invalid category: asdfa\" in str(exc.value)","def hasVeryTrustedValue(self):","def cast(self, value):\n if value is None:\n return None\n return self.type(value)","def _valid_typable_object(ui_object, platform=Platform.ANDROID):\n if platform == Platform.ANDROID:\n return ui_object.obj_type in _TYPABLE_OBJECT_DESC.keys()\n else:\n assert False, 'Wrong Platform'","def checkCompatibility(self, *args):\n return _libsbml.SBase_checkCompatibility(self, *args)","def is_of_type(cls, value) -> bool:\n # UTF8 = 'utf-8'\n # UTF16 = 'utf-16'\n # UTF32 = 'utf-32'\n # ASCII = 'ascii'\n # BINARY = 'binary'\n # OCTAL = 'octal'\n # HEXADECIMAL = 'hexadecimal'\n # CP1252 = 'cp1252'\n # WINDOWS1252 = 'windows-1252'\n # UNICODEESCAPE = 'unicode-escape'\n\n v = None\n if cls == cls.UTF8 or cls == cls.UTF16 or cls == cls.UTF32 or cls == cls.UNICODEESCAPE:\n try:\n v = bytes(value)\n except:\n return False\n\n if cls == cls.ASCII:\n try:\n v = ascii(value)\n except:\n return False\n\n if cls == cls.BINARY:\n try:\n v = bin(value)\n except:\n return False\n\n if cls == cls.OCTAL:\n try:\n v = oct(value)\n except:\n return False\n\n if cls == cls.HEXADECIMAL:\n try:\n v = hex(value)\n except:\n return False\n\n if cls == cls.WINDOWS1252 or cls == cls.CP1252:\n try:\n v = str(value)\n except:\n return False\n return True","def check_dispel_magic(player):\n if \"Dispel Magic\" in player.spell_to_cast:\n return True\n return False","def cast(val):\n if val.lower() == str(True).lower():\n return True\n elif val.lower() == str(False).lower():\n return False\n\n try:\n return int(val)\n except ValueError:\n pass\n\n try:\n return float(val)\n except ValueError:\n pass\n\n return val","def sanity_check(self):\n pass","def _type_check(data):\n if data.__class__.__name__ != \"Matrix3\":\n return False\n return True","def type_cast(self, value, data_type):\n if isinstance(data_type, BaseArg):\n value = data_type(value)\n elif isinstance(value, data_type) is False:\n if self.auto_type_cast and isinstance(value, str) and data_type in (int, bool, float):\n if data_type is bool:\n value = value.lower()\n if value not in {\"true\", \"false\"}:\n raise Exception()\n value = True if value == \"true\" else False\n else:\n value = data_type(value)\n else:\n raise Exception()\n return value","def _isinstance(self, obj, raise_error=True):\n rv = isinstance(obj, self.__model__)\n if not rv and raise_error:\n raise ValueError('%s is not of type %s' % (obj, self.__model__))\n return rv","def check(self):\n raise NotImplementedError('Must be implemented by subclass.')","def _check_datatype_to_string(prediction):\n if isinstance(prediction, str):\n return True\n raise TypeError('Prediction is not in string type.')","def _valid_output_type(self, output_type):\n # pylint: disable=W0613, R0201\n return True","def tryCastToInt(number):\n try:\n return int(number)\n except:\n print(\"Error! Impossible to parse this variable\")\n return 0","def test_type_errors():\n\n\ttry:\n\t\ttransmissions = compute_transmissions(cal_directory, lines = 3.0)\n\texcept TypeError:\n\t\ttry:\n\t\t\ttransmissions = compute_transmissions(cal_directory, calibrator = 300.0)\n\t\texcept TypeError:\n\t\t\tassert True\n\t\telse:\n\t\t\tassert False\n\telse:\n\t\tassert False","def check_instance(self):\n self.assertIsInstance(self.amenity_1, amenity)\n self.assertIsInstance(self.amenity_2, amenity)","def cast(value):\n try:\n value = int(value)\n except ValueError:\n if value.lower().strip() in [\"true\", \"t\", \"1\", \"yes\"]:\n value = True\n elif value.lower().strip() in [\"false\", \"f\", \"no\", \"0\"]:\n value = False\n return value","def sanity_check(self):\n try:\n self._read()\n except tomlkit.exceptions.TOMLKitError:\n return False\n else:\n return True","def detect(cls):\n return False"],"string":"[\n \"def _can_cast_to(self, value, cast_type):\\n try:\\n _ = cast_type(value)\\n return True\\n except ValueError:\\n return False\",\n \"def is_casting(self):\\n # type: () -> bool\\n return self._is_casting\",\n \"def test_canConvert(string, cast, expected):\\n assert canConvert(string, cast) == expected\",\n \"def is_castxml(self):\\n return self._is_castxml\",\n \"def is_castxml1(self):\\n return self._is_castxml1\",\n \"def _assert_valid_value_and_cast(self, value):\\n raise NotImplementedError()\",\n \"def _can_cast(from_dtype, to_dtype):\\n if cudf.utils.utils.is_na_like(from_dtype):\\n return True\\n if isinstance(from_dtype, type):\\n from_dtype = cudf.dtype(from_dtype)\\n if isinstance(to_dtype, type):\\n to_dtype = cudf.dtype(to_dtype)\\n\\n # TODO : Add precision & scale checking for\\n # decimal types in future\\n\\n if isinstance(from_dtype, cudf.core.dtypes.DecimalDtype):\\n if isinstance(to_dtype, cudf.core.dtypes.DecimalDtype):\\n return True\\n elif isinstance(to_dtype, np.dtype):\\n if to_dtype.kind in {\\\"i\\\", \\\"f\\\", \\\"u\\\", \\\"U\\\", \\\"O\\\"}:\\n return True\\n else:\\n return False\\n elif isinstance(from_dtype, np.dtype):\\n if isinstance(to_dtype, np.dtype):\\n return np.can_cast(from_dtype, to_dtype)\\n elif isinstance(to_dtype, cudf.core.dtypes.DecimalDtype):\\n if from_dtype.kind in {\\\"i\\\", \\\"f\\\", \\\"u\\\", \\\"U\\\", \\\"O\\\"}:\\n return True\\n else:\\n return False\\n elif isinstance(to_dtype, cudf.core.types.CategoricalDtype):\\n return True\\n else:\\n return False\\n elif isinstance(from_dtype, cudf.core.dtypes.ListDtype):\\n # TODO: Add level based checks too once casting of\\n # list columns is supported\\n if isinstance(to_dtype, cudf.core.dtypes.ListDtype):\\n return np.can_cast(from_dtype.leaf_type, to_dtype.leaf_type)\\n else:\\n return False\\n elif isinstance(from_dtype, cudf.core.dtypes.CategoricalDtype):\\n if isinstance(to_dtype, cudf.core.dtypes.CategoricalDtype):\\n return True\\n elif isinstance(to_dtype, np.dtype):\\n return np.can_cast(from_dtype._categories.dtype, to_dtype)\\n else:\\n return False\\n else:\\n return np.can_cast(from_dtype, to_dtype)\",\n \"def is_casting(self, is_casting):\\n # type: (bool) -> None\\n\\n if is_casting is not None:\\n if not isinstance(is_casting, bool):\\n raise TypeError(\\\"Invalid type for `is_casting`, type has to be `bool`\\\")\\n\\n self._is_casting = is_casting\",\n \"def test_cast(self):\\n dim = Fidelity(\\\"epoch\\\", 1, 10)\\n with pytest.raises(NotImplementedError):\\n dim.cast()\",\n \"def is_convertible(source, target):\\n return __is_convertible_t(source, target).is_convertible()\",\n \"def test_cast(self, option, value, expected):\\n actual = option.cast(value)\\n assert actual == expected\",\n \"def backcast(self) -> bool:\\n return self.__backcast\",\n \"def check_type(self):\\n return True\",\n \"def test_cast_fail(self, option, value):\\n with pytest.raises(errors.BisonError):\\n option.cast(value)\",\n \"def type_check(self):\\n self.link_all_refs()\\n self.check_ast()\",\n \"def canAdapt(self, stru):\\n return isinstance(stru, crystal)\",\n \"def CheckType(self, *args, **kwargs):\\n pass\",\n \"def check_type_compat(input_a, input_b):\\n return return_family_type(input_a) is return_family_type(input_b)\",\n \"def checkType(self, value):\\n pass\",\n \"def test_casting_without_iterable(test_fixture, test_input, expected):\\n test_fixture.cast_prop = test_input\\n assert test_input == test_fixture.cast_prop == expected\\n assert type(test_fixture.cast_prop) == type(expected)\",\n \"def check_type(X, x_lat_dim, x_lon_dim, x_sample_dim, x_feature_dim):\\n\\tassert type(X) == xr.DataArray, 'XCast requires a dataset to be of type \\\"Xarray.DataArray\\\"'\",\n \"def test_as_type_invalid_cast(self, wrapped_unit: MyType) -> None:\\n # Arrange.\\n # Create a new fake UnitType.\\n other_type = mock.MagicMock()\\n # Give it a name for easier debugging.\\n other_type.__name__ = \\\"OtherType\\\"\\n\\n # A fake unit to convert.\\n to_convert = MyUnit(wrapped_unit, 10)\\n\\n # Act and assert.\\n # Conversion should fail since no cast is registered.\\n with pytest.raises(CastError):\\n wrapped_unit.as_type(to_convert, other_type)\",\n \"def can_convert(self, from_type, to_type):\\n #\\n # Test if the glpsol executable is available\\n #\\n if not pyomo.common.Executable(\\\"pico_convert\\\"):\\n return False\\n #\\n # Return True for specific from/to pairs\\n #\\n if from_type == ProblemFormat.nl and to_type == ProblemFormat.cpxlp:\\n return True\\n if from_type == ProblemFormat.nl and to_type == ProblemFormat.mps:\\n return True\\n if from_type == ProblemFormat.mps and to_type == ProblemFormat.cpxlp:\\n return True\\n if from_type == ProblemFormat.cpxlp and to_type == ProblemFormat.mps:\\n return True\\n return False\",\n \"def accepts(cls, value: Any) -> bool:\\n try:\\n cls.convert(value)\\n return True\\n except ValueError:\\n return False\",\n \"def CanConvert(*args, **kwargs):\\n return _gdi_.EncodingConverter_CanConvert(*args, **kwargs)\",\n \"def test_upcast(self):\\r\\n if config.cast_policy == 'custom':\\r\\n assert arange(iscalar()).dtype == iscalar().dtype\\r\\n assert arange(fscalar()).dtype == fscalar().dtype\\r\\n assert arange(dscalar()).dtype == dscalar().dtype\\r\\n\\r\\n # int32 + float32 -> float64\\r\\n assert arange(iscalar(), fscalar()).dtype == dscalar().dtype\\r\\n assert arange(iscalar(), dscalar()).dtype == dscalar().dtype\\r\\n assert arange(fscalar(), dscalar()).dtype == dscalar().dtype\\r\\n\\r\\n assert arange(iscalar(), fscalar(), dscalar()).dtype == \\\\\\r\\n dscalar().dtype\\r\\n elif config.cast_policy in ('numpy', 'numpy+floatX'):\\r\\n for dtype in get_numeric_types():\\r\\n # Test with a single argument.\\r\\n arange_dtype = arange(scalar(dtype=str(dtype))).dtype\\r\\n numpy_dtype = numpy.arange(numpy.array(1, dtype=dtype)).dtype\\r\\n if (dtype != 'float64' and\\r\\n numpy_dtype == 'float64' and\\r\\n config.cast_policy == 'numpy+floatX' and\\r\\n config.floatX == 'float32'):\\r\\n # We want a float32 arange.\\r\\n assert arange_dtype == 'float32'\\r\\n else:\\r\\n # Follow numpy.\\r\\n assert arange_dtype == numpy_dtype\\r\\n\\r\\n # Test with two arguments.\\r\\n for stop_dtype in get_numeric_types():\\r\\n arange_dtype = arange(\\r\\n start=scalar(dtype=str(dtype)),\\r\\n stop=scalar(dtype=str(stop_dtype))).dtype\\r\\n numpy_dtype = numpy.arange(\\r\\n start=numpy.array(0, dtype=dtype),\\r\\n stop=numpy.array(1, dtype=stop_dtype)).dtype\\r\\n if (dtype != 'float64' and\\r\\n stop_dtype != 'float64' and\\r\\n numpy_dtype == 'float64' and\\r\\n config.cast_policy == 'numpy+floatX' and\\r\\n config.floatX == 'float32'):\\r\\n # We want a float32 arange.\\r\\n assert arange_dtype == 'float32'\\r\\n else:\\r\\n # Follow numpy.\\r\\n assert arange_dtype == numpy_dtype\\r\\n\\r\\n # Test with three arguments.\\r\\n for step_dtype in get_numeric_types():\\r\\n arange_dtype = arange(\\r\\n start=scalar(dtype=str(dtype)),\\r\\n stop=scalar(dtype=str(stop_dtype)),\\r\\n step=scalar(dtype=str(step_dtype))).dtype\\r\\n numpy_dtype = numpy.arange(\\r\\n start=numpy.array(0, dtype=dtype),\\r\\n stop=numpy.array(1, dtype=stop_dtype),\\r\\n step=numpy.array(1, dtype=step_dtype)).dtype\\r\\n if (dtype != 'float64' and\\r\\n stop_dtype != 'float64' and\\r\\n step_dtype != 'float64' and\\r\\n numpy_dtype == 'float64' and\\r\\n config.cast_policy == 'numpy+floatX' and\\r\\n config.floatX == 'float32'):\\r\\n # We want a float32 arange.\\r\\n assert arange_dtype == 'float32'\\r\\n else:\\r\\n # Follow numpy.\\r\\n assert arange_dtype == numpy_dtype\\r\\n else:\\r\\n raise NotImplementedError(config.cast_policy)\",\n \"def applies(cls, obj):\\n return type(obj) in cls.types\",\n \"def __contains__(self, a):\\n try:\\n self.convert(a)\\n except CoercionFailed:\\n return False\\n\\n return True\",\n \"def check_force_cast(FROM, TO, operations, value):\\n import re\\n r = re.compile('(\\\\w+) \\\\%i\\\\d, \\\\$(-?\\\\d+)')\\n r2 = re.compile('(\\\\w+) \\\\%i\\\\d')\\n #\\n value = rffi.cast(FROM, value)\\n value = rffi.cast(lltype.Signed, value)\\n #\\n expected_value = rffi.cast(TO, value)\\n expected_value = rffi.cast(lltype.Signed, expected_value)\\n #\\n for op in operations:\\n match = r.match(op)\\n if match is None:\\n match = r2.match(op)\\n assert match, \\\"line %r does not match regexp\\\" % (op,)\\n opname = match.group(1)\\n if opname == 'int_and':\\n value &= int(match.group(2))\\n elif opname == 'int_signext':\\n numbytes = int(match.group(2))\\n value = int_signext(value, numbytes)\\n elif opname == 'int_is_true':\\n value = bool(value)\\n else:\\n assert 0, opname\\n #\\n assert rffi.cast(lltype.Signed, value) == expected_value\",\n \"def EncodingConverter_CanConvert(*args, **kwargs):\\n return _gdi_.EncodingConverter_CanConvert(*args, **kwargs)\",\n \"def test_evaluate_cast_expression(self):\\n value = self.evaluate_common(\\\"cast(2D,'Edm.Double')\\\")\\n self.assertTrue(\\n value.type_code == edm.SimpleType.Double, \\\"Expected Double\\\")\\n self.assertTrue(value.value == 2.0, \\\"Expected 2.0\\\")\\n value = self.evaluate_common(\\\"cast(2L,'Edm.Single')\\\")\\n self.assertTrue(\\n value.type_code == edm.SimpleType.Single, \\\"Expected Single\\\")\\n self.assertTrue(value.value == 2.0, \\\"Expected 2.0\\\")\\n value = self.evaluate_common(\\\"cast(2,'Edm.Int64')\\\")\\n self.assertTrue(\\n value.type_code == edm.SimpleType.Int64, \\\"Expected Int64\\\")\\n self.assertTrue(value.value == 2, \\\"Expected 2\\\")\\n try:\\n value = self.evaluate_common(\\\"cast(2.0D,'Edm.Single')\\\")\\n self.fail(\\\"Double cast to Single\\\")\\n except:\\n pass\\n value = self.evaluate_common(\\\"cast('x','Edm.String')\\\")\\n self.assertTrue(value.value == 'x', \\\"Expected 'x'\\\")\\n try:\\n value = self.evaluate_common(\\\"cast(X'DEAD','Edm.String')\\\")\\n self.fail(\\\"Binary cast to String\\\")\\n except:\\n pass\\n try:\\n value = self.evaluate_common(\\\"cast(1,'Edm.Boolean')\\\")\\n self.fail(\\\"1 cast to Boolean\\\")\\n except:\\n pass\\n value = self.evaluate_common(\\\"cast(null,'Edm.String')\\\")\\n self.assertTrue(\\n value.type_code == edm.SimpleType.String, \\\"Expected String\\\")\\n self.assertTrue(value.value is None, \\\"Expected None\\\")\\n value = self.evaluate_common(\\\"cast('Edm.Int16')\\\")\\n self.assertTrue(\\n value.type_code == edm.SimpleType.Int16, \\\"Expected Int16\\\")\\n self.assertTrue(value.value is None, \\\"Expected None\\\")\",\n \"def test_init_same_type(self, config: HandlerConfig) -> None:\\n # Arrange.\\n # Make it look like both units are the same type.\\n config.mock_from_unit.is_compatible.return_value = True\\n config.mock_to_unit.is_compatible.return_value = True\\n\\n # Act and assert.\\n # Creating the wrapper should fail when both units are the same\\n # type.\\n with pytest.raises(CastError):\\n CastHandler(config.mock_from_unit, config.mock_to_unit)\",\n \"def verify_type(self, obj):\\n return isinstance(obj, self.type_)\",\n \"def _check_compat(sock_info):\\n ...\",\n \"def sanity_check(self):\\n return True\",\n \"def _check_types(self):\\n if isinstance(self.unique_id, (int, str)): # should unique_id be a float?\\n self.unique_id = str(self.unique_id)\\n else:\\n raise TypeError(f'unique_id incorrect type: {type(self.unique_id)}')\\n try:\\n self.ra = float(self.ra)\\n except TypeError:\\n print(f'ra incorrect type: {type(self.ra)}')\\n try:\\n self.dec = float(self.dec)\\n except TypeError:\\n print(f'dec incorrect type: {type(self.dec)}')\\n try:\\n self.z = float(self.z)\\n except TypeError:\\n print(f'z incorrect type: {type(self.z)}')\\n if not isinstance(self.galcat, GCData):\\n raise TypeError(f'galcat incorrect type: {type(self.galcat)}')\\n if not -360. <= self.ra <= 360.:\\n raise ValueError(f'ra={self.ra} not in valid bounds: [-360, 360]')\\n if not -90. <= self.dec <= 90.:\\n raise ValueError(f'dec={self.dec} not in valid bounds: [-90, 90]')\\n if self.z < 0.:\\n raise ValueError(f'z={self.z} must be greater than 0')\",\n \"def visit(self, node: AstNode):\\n raise CASTTypeError(f\\\"Unrecognized node type: {type(node)}\\\")\",\n \"def cast_tech(self):\\n # type: () -> string_types\\n return self._cast_tech\",\n \"def canAdapt(self, stru):\\n return False\",\n \"def __instancecheck__(self, instance):\\n\\n if isinstance(instance, ObjCInstance):\\n return bool(instance.conformsToProtocol(self))\\n else:\\n return False\",\n \"def _isinstance(cls, x):\\n return isinstance(x, cls.PYTHON_TYPE_CHECK)\",\n \"def cast(cls, value: 'dsl.Native') -> 'dsl.Native':\\n try:\\n return cls._cast(value)\\n except (ValueError, TypeError) as err:\\n raise _exception.CastError(f'Unable to cast {repr(value)} as {cls.__name__}') from err\",\n \"def test_do_check_event_type(self):\\n self.assertEqual(self.a.get_type(), None)\\n self.assertEqual(self.b.get_type(), None)\\n self.assertTrue(self.a.do_check_event_type(self.a))\\n\\n self.a = +self.a\\n self.assertFalse(self.a.do_check_event_type(self.b))\",\n \"def converted(self):\\n if self.converters:\\n return self.converters.converted(self)\\n return False\",\n \"def cast(current, new):\\n typ = type(current)\\n if typ == bool:\\n try:\\n return bool(int(new))\\n except (ValueError, TypeError):\\n pass\\n try:\\n new = new.lower()\\n except AttributeError:\\n pass\\n if (new == 'on') or (new[0] in ('y', 't')):\\n return True\\n if (new == 'off') or (new[0] in ('n', 'f')):\\n return False\\n else:\\n try:\\n return typ(new)\\n except (ValueError, TypeError):\\n pass\\n print(\\\"Problem setting parameter (now %s) to %s; incorrect type?\\\" % (current, new))\\n return current\",\n \"def bitcast(self, typ):\",\n \"def __instancecheck__(self, instance):\\n\\n if isinstance(instance, ObjCInstance):\\n return bool(instance.isKindOfClass(self))\\n else:\\n return False\",\n \"def test_cast_works(self):\\n parse.parse(setup_java_class(\\\"String x = (String) A.x() ;\\\"))\",\n \"def _check_if_event_event_or_event_timex(self):\\n if (type(self.source) is Timex and type(self.target) is Event) or (type(self.source) is Event and type(self.target) is Timex):\\n self._is_event_timex = True\\n elif type(self.source) is Event and type(self.target) is Event:\\n self._is_event_event = True\\n elif type(self.source) is Timex and type(self.target) is Timex:\\n self._is_timex_timex = True\",\n \"def test_cast_string_int(self) -> None:\\n self.assertEqual(self.flag.cast_string('42'), 42)\",\n \"def do_type(self, str_arg):\\n try:\\n self.adbc.type(validateString(str_arg))\\n except Exception, e:\\n printLog(self.threadName + 'TYPE FAILED: %s' % e.message)\\n self.resultFlag = False\\n finally:\\n return self.resultFlag\",\n \"def test_validCoerce(self):\\n self.assertEquals(0, usage.portCoerce(\\\"0\\\"))\\n self.assertEquals(3210, usage.portCoerce(\\\"3210\\\"))\\n self.assertEquals(65535, usage.portCoerce(\\\"65535\\\"))\",\n \"def should_coerce(self, parameter: inspect.Parameter, value: Any) -> bool:\\n # No need to coerce defaults\\n if value == parameter.default:\\n return False\\n\\n annotation = checks.resolve_supertype(parameter.annotation)\\n origin = self.get_origin(annotation)\\n # If we can coerce the annotation,\\n # and either the annotation has args,\\n # or the value is not an instance of the origin\\n return self.coerceable(annotation) and (\\n getattr(annotation, \\\"__args__\\\", None) or not isinstance(value, origin)\\n )\",\n \"def cast(current, new):\\n typ = type(current)\\n if typ == bool:\\n try:\\n return bool(int(new))\\n except (ValueError, TypeError):\\n pass\\n try:\\n new = new.lower()\\n except:\\n pass\\n if (new == 'on') or (new[0] in ('y', 't')):\\n return True\\n if (new == 'off') or (new[0] in ('n', 'f')):\\n return False\\n else:\\n try:\\n return typ(new)\\n except:\\n pass\\n print(\\\"Problem setting parameter (now %s) to %s; incorrect type?\\\" % (current, new))\\n return current\",\n \"def check(self):\\n raise NotImplementedError\",\n \"def is_type_correct(*args):\\n return _ida_hexrays.is_type_correct(*args)\",\n \"def get_type_check(self, arg, option):\\n pass\",\n \"def has_acceptable_type(self, value):\\n if not value:\\n return False\\n if super().has_acceptable_type(value):\\n return True\\n # Hmmm ok maybe we're running under IPython:\\n try:\\n import IPython\\n return isinstance(value, IPython.kernel.zmq.iostream.OutStream)\\n except ImportError:\\n return False\",\n \"def checkCompatibility(self):\\n return _libsbml.CobraToFbcConverter_checkCompatibility(self)\",\n \"def _check(self, event):\\n\\n if self._type is None:\\n if isinstance(event, Event):\\n self._type = Event\\n elif isinstance(event, TimeRangeEvent):\\n self._type = TimeRangeEvent\\n elif isinstance(event, IndexedEvent):\\n self._type = IndexedEvent\\n else:\\n if not isinstance(event, self._type):\\n raise PipelineIOException('Homogeneous events expected')\",\n \"def _check_type_compatibility(self, type_name1, type_name2,\\n operation):\\n if type_name1 != type_name2:\\n raise TypeCompatibilityError(type_name1, type_name2, operation)\",\n \"def test_cast_string_failure(self) -> None:\\n self.flag.flag_type = None\\n self.assertRaises(ValueError, self.flag.cast_string, '42')\",\n \"def _check_other(self, other):\\n # pylint: disable=protected-access\\n if not isinstance(other, Spectrum):\\n raise RuntimeError(\\n \\\"Tried to binop Spectrum and %s\\\" % (type(other))\\n )\\n\\n if (self._pot is None) != (other._pot is None):\\n raise RuntimeError(\\n \\\"Tried to binop Spectra with nonconformant POT\\\"\\n )\\n\\n if (self._lt is None) != (other._lt is None):\\n raise RuntimeError(\\n \\\"Tried to binop Spectra with nonconformant Livetimes\\\"\\n )\",\n \"def can_auralise(self):\\n\\n if not self.sources:\\n raise ValueError('No sources available')\\n\\n if not self.receivers:\\n raise ValueError('No receivers available')\\n\\n if not self.atmosphere:\\n raise ValueError('No atmosphere available.')\\n\\n if not self.geometry:\\n raise ValueError('No geometry available.')\\n\\n return True\",\n \"def _validability(self, ability):\\n return isinstance(ability, AttributeAbility)\",\n \"def cast_validator(caster):\\n def validator(value):\\n try:\\n return caster(value)\\n except ValueError:\\n raise ValidationError()\\n\\n return validator\",\n \"def _cast(cls, value: 'dsl.Native') -> 'dsl.Native':\\n return cls.__type__(value)\",\n \"def validate(self):\\n self._check_type()\",\n \"def deconstructable(self):\\n return isinstance(self.get(), int)\",\n \"def _check_type(self, new_value):\\n raise NotImplementedError\",\n \"def _check_mapper(self, mapper):\\n if not hasattr(mapper, 'parse') or not callable(mapper.parse):\\n raise ValueError('mapper must implement parse()')\\n if not hasattr(mapper, 'format') or not callable(mapper.format):\\n raise ValueError('mapper must implement format()')\",\n \"def custom_cast(s):\\n for cast_func in (int, float, str):\\n try:\\n return cast_func(s)\\n except ValueError:\\n pass\\n raise BaseException('Could not cast as number/string!')\",\n \"def _validability(self, ability):\\n return (isinstance(ability, AttributeAbility) or\\n isinstance(ability, WeaponAbility))\",\n \"def is_interpretable(self):\\n return bool(self.as_date() or self.as_time())\",\n \"def check(self, runtime):\\n return True\",\n \"def _type_check_double(self, data):\\n if type(data) not in self._VALID_TYPES:\\n return False\\n return True\",\n \"def check(self, value: Any) -> None:\\n if not isinstance(value, self.oktype):\\n raise TypeError(value)\",\n \"def supports(self, name):\\r\\n return hasattr(self, \\\"data_to_\\\" + name)\",\n \"def _create_cast(cls, onnx_node, inputs, opset_version):\\n to = onnx_node.getattr(\\\"to\\\")\\n # singa only supports float32 and int32\\n map_dict = {\\n TensorProto.FLOAT: tensor.float32, # FLOAT to float32\\n TensorProto.UINT8: None, # UINT8\\n TensorProto.INT8: tensor.int32, # INT8 to int32\\n TensorProto.UINT16: None, # UINT16\\n TensorProto.INT16: tensor.int32, # INT16 to int32\\n TensorProto.INT32: tensor.int32, # INT32 to int32\\n TensorProto.INT64: tensor.int32, # INT64 to int32\\n TensorProto.STRING: None, # stirng\\n TensorProto.BOOL: None, # bool\\n }\\n to = map_dict[to]\\n assert to != None, \\\"not support cast type: {}\\\".format(to)\\n _, forward = cls._common_onnx_node_to_singa_op(onnx_node, inputs,\\n opset_version)\\n return _, forward(to)\",\n \"def is_of_type(cmd):\\r\\n raise NotImplementedError()\",\n \"def test_cast_bad_category(self):\\n categories = list(range(10))\\n dim = Categorical(\\\"yolo\\\", categories, shape=2)\\n sample = np.array([\\\"asdfa\\\", \\\"1\\\"], dtype=object)\\n with pytest.raises(ValueError) as exc:\\n dim.cast(sample)\\n assert \\\"Invalid category: asdfa\\\" in str(exc.value)\",\n \"def hasVeryTrustedValue(self):\",\n \"def cast(self, value):\\n if value is None:\\n return None\\n return self.type(value)\",\n \"def _valid_typable_object(ui_object, platform=Platform.ANDROID):\\n if platform == Platform.ANDROID:\\n return ui_object.obj_type in _TYPABLE_OBJECT_DESC.keys()\\n else:\\n assert False, 'Wrong Platform'\",\n \"def checkCompatibility(self, *args):\\n return _libsbml.SBase_checkCompatibility(self, *args)\",\n \"def is_of_type(cls, value) -> bool:\\n # UTF8 = 'utf-8'\\n # UTF16 = 'utf-16'\\n # UTF32 = 'utf-32'\\n # ASCII = 'ascii'\\n # BINARY = 'binary'\\n # OCTAL = 'octal'\\n # HEXADECIMAL = 'hexadecimal'\\n # CP1252 = 'cp1252'\\n # WINDOWS1252 = 'windows-1252'\\n # UNICODEESCAPE = 'unicode-escape'\\n\\n v = None\\n if cls == cls.UTF8 or cls == cls.UTF16 or cls == cls.UTF32 or cls == cls.UNICODEESCAPE:\\n try:\\n v = bytes(value)\\n except:\\n return False\\n\\n if cls == cls.ASCII:\\n try:\\n v = ascii(value)\\n except:\\n return False\\n\\n if cls == cls.BINARY:\\n try:\\n v = bin(value)\\n except:\\n return False\\n\\n if cls == cls.OCTAL:\\n try:\\n v = oct(value)\\n except:\\n return False\\n\\n if cls == cls.HEXADECIMAL:\\n try:\\n v = hex(value)\\n except:\\n return False\\n\\n if cls == cls.WINDOWS1252 or cls == cls.CP1252:\\n try:\\n v = str(value)\\n except:\\n return False\\n return True\",\n \"def check_dispel_magic(player):\\n if \\\"Dispel Magic\\\" in player.spell_to_cast:\\n return True\\n return False\",\n \"def cast(val):\\n if val.lower() == str(True).lower():\\n return True\\n elif val.lower() == str(False).lower():\\n return False\\n\\n try:\\n return int(val)\\n except ValueError:\\n pass\\n\\n try:\\n return float(val)\\n except ValueError:\\n pass\\n\\n return val\",\n \"def sanity_check(self):\\n pass\",\n \"def _type_check(data):\\n if data.__class__.__name__ != \\\"Matrix3\\\":\\n return False\\n return True\",\n \"def type_cast(self, value, data_type):\\n if isinstance(data_type, BaseArg):\\n value = data_type(value)\\n elif isinstance(value, data_type) is False:\\n if self.auto_type_cast and isinstance(value, str) and data_type in (int, bool, float):\\n if data_type is bool:\\n value = value.lower()\\n if value not in {\\\"true\\\", \\\"false\\\"}:\\n raise Exception()\\n value = True if value == \\\"true\\\" else False\\n else:\\n value = data_type(value)\\n else:\\n raise Exception()\\n return value\",\n \"def _isinstance(self, obj, raise_error=True):\\n rv = isinstance(obj, self.__model__)\\n if not rv and raise_error:\\n raise ValueError('%s is not of type %s' % (obj, self.__model__))\\n return rv\",\n \"def check(self):\\n raise NotImplementedError('Must be implemented by subclass.')\",\n \"def _check_datatype_to_string(prediction):\\n if isinstance(prediction, str):\\n return True\\n raise TypeError('Prediction is not in string type.')\",\n \"def _valid_output_type(self, output_type):\\n # pylint: disable=W0613, R0201\\n return True\",\n \"def tryCastToInt(number):\\n try:\\n return int(number)\\n except:\\n print(\\\"Error! Impossible to parse this variable\\\")\\n return 0\",\n \"def test_type_errors():\\n\\n\\ttry:\\n\\t\\ttransmissions = compute_transmissions(cal_directory, lines = 3.0)\\n\\texcept TypeError:\\n\\t\\ttry:\\n\\t\\t\\ttransmissions = compute_transmissions(cal_directory, calibrator = 300.0)\\n\\t\\texcept TypeError:\\n\\t\\t\\tassert True\\n\\t\\telse:\\n\\t\\t\\tassert False\\n\\telse:\\n\\t\\tassert False\",\n \"def check_instance(self):\\n self.assertIsInstance(self.amenity_1, amenity)\\n self.assertIsInstance(self.amenity_2, amenity)\",\n \"def cast(value):\\n try:\\n value = int(value)\\n except ValueError:\\n if value.lower().strip() in [\\\"true\\\", \\\"t\\\", \\\"1\\\", \\\"yes\\\"]:\\n value = True\\n elif value.lower().strip() in [\\\"false\\\", \\\"f\\\", \\\"no\\\", \\\"0\\\"]:\\n value = False\\n return value\",\n \"def sanity_check(self):\\n try:\\n self._read()\\n except tomlkit.exceptions.TOMLKitError:\\n return False\\n else:\\n return True\",\n \"def detect(cls):\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75801295","0.7562737","0.6442058","0.6417883","0.6306073","0.6138935","0.6129317","0.6065727","0.6046611","0.6044432","0.60181624","0.5920204","0.5906511","0.58403975","0.57817537","0.57176816","0.56767637","0.5561193","0.5528392","0.5521377","0.5496391","0.54688674","0.5466355","0.54585284","0.54421574","0.5433737","0.5411788","0.5370814","0.53635126","0.5362414","0.53470725","0.5343585","0.532648","0.5325261","0.531358","0.53008324","0.5299145","0.52652","0.5260204","0.5216542","0.5212242","0.52015615","0.5190835","0.51487994","0.5147306","0.51463664","0.5137787","0.51365286","0.51347834","0.51263183","0.5118228","0.5113617","0.5110467","0.5107094","0.50974196","0.50936586","0.5092022","0.50909877","0.5086638","0.50841","0.50820047","0.5077013","0.5065992","0.50488216","0.50428414","0.5040768","0.50339913","0.5024856","0.5019015","0.5016907","0.50072235","0.5006864","0.50028396","0.5002782","0.49961504","0.49942127","0.4993935","0.49933112","0.49927202","0.4991311","0.49894235","0.49885392","0.49858785","0.49757248","0.49755377","0.49751413","0.49622548","0.4951133","0.4950668","0.4949144","0.49332228","0.4912131","0.49001205","0.48964623","0.48908642","0.48851997","0.48841396","0.48836306","0.48815167","0.48793226","0.48761177"],"string":"[\n \"0.75801295\",\n \"0.7562737\",\n \"0.6442058\",\n \"0.6417883\",\n \"0.6306073\",\n \"0.6138935\",\n \"0.6129317\",\n \"0.6065727\",\n \"0.6046611\",\n \"0.6044432\",\n \"0.60181624\",\n \"0.5920204\",\n \"0.5906511\",\n \"0.58403975\",\n \"0.57817537\",\n \"0.57176816\",\n \"0.56767637\",\n \"0.5561193\",\n \"0.5528392\",\n \"0.5521377\",\n \"0.5496391\",\n \"0.54688674\",\n \"0.5466355\",\n \"0.54585284\",\n \"0.54421574\",\n \"0.5433737\",\n \"0.5411788\",\n \"0.5370814\",\n \"0.53635126\",\n \"0.5362414\",\n \"0.53470725\",\n \"0.5343585\",\n \"0.532648\",\n \"0.5325261\",\n \"0.531358\",\n \"0.53008324\",\n \"0.5299145\",\n \"0.52652\",\n \"0.5260204\",\n \"0.5216542\",\n \"0.5212242\",\n \"0.52015615\",\n \"0.5190835\",\n \"0.51487994\",\n \"0.5147306\",\n \"0.51463664\",\n \"0.5137787\",\n \"0.51365286\",\n \"0.51347834\",\n \"0.51263183\",\n \"0.5118228\",\n \"0.5113617\",\n \"0.5110467\",\n \"0.5107094\",\n \"0.50974196\",\n \"0.50936586\",\n \"0.5092022\",\n \"0.50909877\",\n \"0.5086638\",\n \"0.50841\",\n \"0.50820047\",\n \"0.5077013\",\n \"0.5065992\",\n \"0.50488216\",\n \"0.50428414\",\n \"0.5040768\",\n \"0.50339913\",\n \"0.5024856\",\n \"0.5019015\",\n \"0.5016907\",\n \"0.50072235\",\n \"0.5006864\",\n \"0.50028396\",\n \"0.5002782\",\n \"0.49961504\",\n \"0.49942127\",\n \"0.4993935\",\n \"0.49933112\",\n \"0.49927202\",\n \"0.4991311\",\n \"0.49894235\",\n \"0.49885392\",\n \"0.49858785\",\n \"0.49757248\",\n \"0.49755377\",\n \"0.49751413\",\n \"0.49622548\",\n \"0.4951133\",\n \"0.4950668\",\n \"0.4949144\",\n \"0.49332228\",\n \"0.4912131\",\n \"0.49001205\",\n \"0.48964623\",\n \"0.48908642\",\n \"0.48851997\",\n \"0.48841396\",\n \"0.48836306\",\n \"0.48815167\",\n \"0.48793226\",\n \"0.48761177\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":84991,"cells":{"query":{"kind":"string","value":"Implements the castling by moving the rook"},"document":{"kind":"string","value":"def castling_implement(self, turn, select, move):\n \n if select == self.coords.index(piece_class.KING_LOCATION[turn]):\n if self.board[self.coords.index(piece_class.KING_LOCATION[turn])].move_track == False:\n if move in [2, 58]:\n self.board[move+1] = self.board[move-2]\n self.board[move-2] = self.empty\n if move in [6, 62]:\n self.board[move-1] = self.board[move+1]\n self.board[move+1] = self.empty"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _walk(self):\n new_pos = self.rect.move((self.move, 0)) # move 9 pixel to the right per frame\n if self.rect.left < self.area.left or self.rect.right > self.area.right:\n self.move = -self.move # move to the opposite direction when the chimp position exceeds the screen\n new_pos = self.rect.move((self.move, 0))\n self.image = pygame.transform.flip(\n self.image, 1, 0\n ) # mirror the chimp to make it looks like turning around\n self.rect = new_pos","def move(self):\n pass","def movement(self):","def move(): #py:move\n RUR._move_()","def move(self, action):\n self.time += 1\n\n # If ship is destroyed ship can only contemplate sadness and despair\n if not action or not self.is_playable():\n return None\n\n self.actualise = False\n\n if self.leroy_time == 1:\n self.back_to_normal()\n if self.leroy_time > 0:\n self.leroy_time -= 1\n\n # there is a chance that the ia enter in leroy mode\n # the ia goes mad for some time, acting randomly\n # added to allow the ships to explore the possible actions and not stay passive\n if not self.player and self.leroy_time == 0 and self.agent.behavior == \"network\" and random() < LEROY_RATE:\n self.leroy_jenkins()\n\n # training reward depending on position\n # self.agent.reward = self.go_bottom_reward()\n\n if isinstance(action, ActionOneHot):\n if action.pointing:\n self.pointing = Point(randint(0, DEFAULT_WIDTH-1), randint(0, DEFAULT_HEIGHT-1))\n elif isinstance(action, Action):\n if action.pointing:\n self.pointing = action.pointing\n # print(\"action.pointing\", action.pointing)\n # print(\"turn \", self.direction)\n\n if action.thrust:\n self.thrust()\n if action.shoot:\n self.shoot()","def makeMove(self, move, player):","def move(self):\n # moving each of the obstacles\n time = rospy.get_rostime()\n self.vel_msg1.linear.x = math.sin(time.to_sec())\n self.pub1.publish(self.vel_msg1)\n self.vel_msg2.linear.x = math.cos(1.2 * time.to_sec())\n self.pub2.publish(self.vel_msg2)\n self.vel_msg3.linear.x = math.cos(2 * time.to_sec())\n self.pub3.publish(self.vel_msg3)\n self.vel_msg4.linear.x = math.sin(0.85 * time.to_sec())\n self.pub4.publish(self.vel_msg4)","def move(self, agent, action):\n\t\tpass","def _move_forward(self):\n\t\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\n\t\tif(self.y<=798):\n\t\t\tself.y = self.y+1\n\t\t\tif Board.board[self.x][self.y]=='0':\n\t\t\t\tMario.score += 1\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\n\t\t\telif Board.board[self.x][self.y]=='P':\n\t\t\t\tMario.lives+=1\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_1-up.wav\"])\n\n\t\t\telif Board.board[self.x][self.y]=='A':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\t\t\t\tMario.attack = 1\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_powerup.wav\"])\n\n\t\t\telif Board.board[self.x][self.y]=='@':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tMario.lives-=1\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_mariodie.wav\"])\n\t\t\t\tif Mario.lives<=0:\n\t\t\t\t\tcall([\"aplay\",\"-q\",\"smb_gameover.wav\"])\n\t\t\t\t\treturn \"exit\"\n\t\t\t\tos.system('clear')\n\t\t\t\tprint(\"\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\t\\t\\t\\t\\t\\tNumber of Mario left\",Mario.lives)\n\t\t\t\tMario.respawn(self.x,self.y)\n\t\t\t\ttime.sleep(2)\n\t\t\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\n\n\t\t\telif(Board.board[self.x][self.y]=='/'):\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x-1][self.y]='M'\n\n\t\t\telif Board.board[self.x][self.y]=='I':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_stage_clear.wav\"])\n\t\t\t\tBoard.bonus_round()\n\n\t\t\telif Board.board[self.x][self.y]=='K':\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tcall([\"aplay\",\"-q\",\"smb_stage_clear.wav\"])\n\t\t\t\tenemy.boss_round()\n\n\t\t\telif(Board.board[self.x][self.y] in obstacles):\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y-1]='M'\n\n\t\t\telif((Board.board[self.x+1][self.y-1]=='/' or Board.board[self.x+1][self.y-1]=='T') and Board.board[self.x+1][self.y]==' '):\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y+1]='M'\n\t\t\t\tMario.go_down(self)\n\t\t\telse:\n\t\t\t\tMario._pass(self.x,self.y-1)\n\t\t\t\tBoard.board[self.x][self.y]='M'\n\n\t\tif( self.y-1 >= ((Board.prev_j+Board.prev_k)/2) ):\n\t\t\tos.system('clear')\n\t\t\tBoard.prev_j += 1 \n\t\t\tBoard.prev_k += 1\n\t\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\n\t\telse:\n\t\t\tos.system('clear')\n\t\t\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)","def algo_mover(self):\n self.tick_count = 0 ## reset to start clock on delayed state change\n\n ### Move 1 ring ###\n de, aux, vers = self.instructions.pop(0)\n\n de = self.rods[de]\n aux = self.rods[aux]\n vers = self.rods[vers]\n\n ring = de.pop_ring()\n vers.add_ring(ring)\n\n ### Update the states after -- moved this to main update\n #self.state_updater()","def handleMove(self):\n pass","def _walk(self):\n \n newpos= self.rect.move((self.move, 0)) # x方向移動 .move, y方向不動。\n \n # 偵測碰撞左右牆壁，並處理(反彈)\n if not self.area.contains(newpos):\n if self.rect.left < self.area.left or \\\n self.rect.right > self.area.right:\n self.move = -self.move\n newpos = self.rect.move((self.move, 0))\n self.image = pygame.transform.flip(self.image, 1, 0)\n self.rect = newpos","def AeroMove(self, pos):\r\n\r\n pass","def move(self, direction):\n # replace with your code\n pass","def move(self, direction):\n # replace with your code\n pass","def player_movement(self):","def move(self, direction, cycles):\n\t\tpass","def _move(self, d, event):\n\n actor = None\n if event.source in self._crates:\n actor = self._crates[event.source]\n else:\n actor = self._clones[event.source][0]\n\n if d == Direction.NO_ACT or not event.success:\n actor.animation = actor.do_nothing_animation()\n return\n pos = actor.pos\n target = pos.dir_pos(d)\n actor.direction = d\n actor.animation = actor.walk_animation()\n self.repaint()","def move(self):\n # This block is used to move into the living room.\n if self.room_name == \"living\" or self.room_name == \"Living\":\n self.living_room()\n # This block is used to open the door.\n elif self.room_name == \"Door\" or self.room_name == \"door\":\n self.door()\n # This block is used to move into the kitchen.\n elif self.room_name == \"Kitchen\" or self.room_name == \"kitchen\":\n self.kitchen()\n # This block is used to move into the hallway.\n elif self.room_name == \"Hallway\" or self.room_name == \"hallway\":\n self.hallway()\n # This block is used to move into the bathroom.\n elif self.room_name == \"Bathroom\" or self.room_name == \"bathroom\":\n self.bathroom()\n # This block is used to move into the basement.\n elif self.room_name == \"Basement\" or self.room_name == \"basement\":\n self.basement()\n # This block is used to move into the bedroom.\n elif self.room_name == \"Bedroom\" or self.room_name == \"bedroom\":\n self.bedroom()\n # This block is used to move into the attic.\n elif self.room_name == \"Attic\" or \"attic\":\n self.attic()","def main():\n move()\n move()\n pick_beeper()\n move()\n turn_left()\n for i in range(2):\n move()\n put_beeper()\n turn_around()\n move_to_wall()\n turn_right()\n move_to_wall()\n turn_around()","def tick():\n move_balls(targets_speed)\n move_super_balls(targets_speed * 2)","def move(self):\n raise NotImplementedError","def move_forward():\n pass","def run(self):\n move_cmd = Twist()\n move_cmd.linear.x = 0\n move_cmd.angular.z = 0\n\n while not rospy.is_shutdown():\n # bump logic as previous psets\n if self.bump:\n self.bump = False\n # move backwards\n move_cmd.linear.x = LIN_SPEED * -1\n for i in range(5):\n self.cmd_vel.publish(move_cmd)\n self.rate.sleep()\n rospy.sleep(1)\n\n # turn randomly in a random direction\n move_cmd.linear.x = 0\n move_cmd.angular.z = ROT_SPEED * ((-1)**random.randint(1,2))\n\n if self.bump == 0:\n move_cmd.angular.z = ROT_SPEED * (-1)\n elif self.bump == 2:\n move_cmd.angular.z = ROT_SPEED\n\n for i in range(random.randint(5,15)):\n self.cmd_vel.publish(move_cmd)\n self.rate.sleep()\n rospy.sleep(1)\n\n move_cmd.angular.z = 0\n # if somethin in the screen is really close\n elif self.min_val < MIN_THRESHOLD:\n # make sure it's not the sock/leg warmer, and is actually an obstacle\n if self.obstacle_x <= self.x or self.obstacle_x >= self.x + self.w or abs(self.min_val - self.dist) > 0.1:\n move_cmd.linear.x = 0\n # turn away\n if self.obstacle_x > 320:\n move_cmd.angular.z = ROT_SPEED / 2\n else:\n move_cmd.angular.z = -ROT_SPEED / 2\n # self.min_val = 100\n for i in range(10):\n self.cmd_vel.publish(move_cmd)\n self.rate.sleep()\n self.last_move = rospy.Time.now()\n else:\n rospy.loginfo(\"Perimeter \" + str(self.perimeter_size))\n rospy.loginfo(\"Distance is \" + str(self.dist))\n\n # normalize angle error to rot speed\n ang_error_norm = -float(self.ang_error) / 100\n\n # set min and max rot speed\n if ang_error_norm < -ROT_SPEED:\n ang_error_norm = -ROT_SPEED\n elif ang_error_norm > ROT_SPEED:\n ang_error_norm = ROT_SPEED\n\n move_cmd.angular.z = ang_error_norm\n\n if RACE == False:\n # normalize dist error to lin speed\n self.dist_error = self.dist - 0.5\n dist_error_norm = float(self.dist_error) / 2\n\n if dist_error_norm < 0:\n # if NaN (self.dist gets set to -1)\n if dist_error_norm > -0.7:\n self.lost = 0\n # if too close\n else:\n self.lost += 1\n # if it's been more than 2 seconds\n if rospy.Time.now() > self.last_move + rospy.Duration(2):\n dist_error_norm = 0\n # if been lost for a while rotate and beep\n if self.lost > 20:\n move_cmd.angular.z = ROT_SPEED / 4\n self.beep.publish(4)\n else:\n # continue as previous\n dist_error_norm = self.last_speed\n else:\n # set max lin speed\n if dist_error_norm > LIN_SPEED:\n dist_error_norm = LIN_SPEED\n\n # reset lost stats\n self.lost = 0\n self.last_speed = dist_error_norm\n self.last_move = rospy.Time.now()\n\n move_cmd.linear.x = dist_error_norm\n else:\n move_cmd.linear.x = LIN_SPEED\n\n self.cmd_vel.publish(move_cmd)","def move(self, direction):\n pass","def step(self, action):\n # print(action)\n distances = self.agent.return_distances(self.agent.corners, self.agent.line_pos)\n\n left = distances[0]\n right = distances[1]\n self.agent.distances.append({\n 'left': left,\n 'right': right\n })\n reward = 0\n if action == 1:\n self.agent.angle -= 90\n if self.agent.angle < 0:\n self.agent.angle = 0\n self.agent.direction_history.append('left')\n self.reset_raycasts(self.agent.angle)\n self.render()\n if left > right:\n reward += 5\n else:\n reward -= 5\n\n elif action == 2:\n self.agent.angle += 90\n if self.agent.angle >= 360:\n self.agent.angle = 0\n\n self.reset_raycasts(self.agent.angle)\n self.render()\n self.agent.direction_history.append('right')\n if left < right:\n reward += 5\n else:\n reward -= 5\n\n elif action == 0:\n self.agent.direction_history.append('forward')\n if self.agent.angle >= 360: self.agent.angle == 0\n if self.agent.angle == 0 or self.agent.angle == 360:\n self.agent.agent_position['y'] -= 10\n self.reset_raycasts(self.agent.angle)\n elif self.agent.angle == 90: \n self.agent.agent_position['x'] += 10\n self.reset_raycasts(self.agent.angle)\n elif self.agent.angle == 180: \n self.agent.agent_position['y'] += 10\n self.reset_raycasts(self.agent.angle)\n elif self.agent.angle == 270:\n self.agent.agent_position['x'] -= 10\n self.reset_raycasts(self.agent.angle)\n \n if left + right >= 50:\n reward += 5\n\n self.render()\n\n elif action == 3:\n self.agent.direction_history.append('reverse')\n if self.agent.angle == 0:\n self.agent.agent_position['y'] += 10\n self.reset_raycasts(self.agent.angle)\n self.render()\n elif self.agent.angle == 90: \n self.agent.agent_position['x'] -= 10\n self.reset_raycasts(self.agent.angle)\n self.render()\n elif self.agent.angle == 180: \n self.agent.agent_position['y'] -= 10\n self.reset_raycasts(self.agent.angle)\n self.render()\n elif self.agent.angle == 270:\n self.agent.agent_position['x'] += 10\n self.reset_raycasts(self.agent.angle)\n self.render()\n \n if left + right <= 50:\n reward += 5\n\n \n else:\n reward -= 5\n\n if \"forward\" not in self.agent.direction_history[len(self.agent.direction_history)-6:len(self.agent.direction_history)-1]:\n reward -= 10\n\n \n info = {}\n if self.agent.check_collision():\n reward -= 10\n self.reset() \n self.agent.rewards.append({\n 'leftDistance': left,\n 'rightDistance': right,\n 'reward': reward,\n })\n self.render()\n print(f\"REWARD: {reward}\")\n # self.render()\n # print(self.agent.direction_history[-1])\n self.agent.rewards.append(reward)\n return np.array([left, right]), reward, False, info","def move():\n Robot.move()","def move_girl2(girl):\n global trainer_speed, girlcount2, girl_left2\n if girl_left2:\n for i in girl:\n i.x -= trainer_speed\n i.move_speed()\n girlcount2 += 0.5\n girl_frame = int(girlcount2) % 3\n camera.draw(girl[girl_frame])\n else:\n for i in girl:\n i.x += trainer_speed\n i.move_speed()\n girlcount2 += 0.5\n girl_frame = int(girlcount2) % 3\n camera.draw(girl[girl_frame + 3])\n for each in girl:\n if each.x < 20:\n girl_left2 = False\n if each.x > 780:\n girl_left2 = True","def main():\r\n\r\n movetwotimes()\r\n pick_beeper()\r\n move()\r\n turn_left()\r\n movetwotimes()\r\n put_beeper()\r\n turn_left()\r\n turn_left()\r\n movetwotimes()\r\n rotatethreetimes()\r\n movetwotimes()\r\n move()\r\n turn_left()\r\n turn_left()","def move(self):\n\n # get the location we WOULD go to\n newX = self.xcor() + self.dx\n newY = self.ycor() + self.dy\n while (abs (newX) > self.BOX_RANGE) or (abs(newY) > self.BOX_RANGE):\n # print(\"choosing new direction... \",end=\"\")\n self.chooseNewDirection()\n # print(self.dx, self.dy)\n newX = self.xcor() + self.dx\n newY = self.ycor() + self.dy\n\n # now move our monster\n super().move()","def sling_action():\n global mouse_distance\n global rope_length\n global angle\n global mouse_x_pos\n global mouse_y_pos\n\n #add code inside sling function\n # Fixing bird to the sling rope\n vec = vector((initial_x_sling, initial_y_sling), (mouse_x_pos, mouse_y_pos))\n unit_vec = unit_vector(vec)\n uv_1 = unit_vec[0]\n uv_2 = unit_vec[1]\n mouse_distance = distance(initial_x_sling, initial_y_sling, mouse_x_pos, mouse_y_pos) #point at which currrent bird id\n fix_pos = (uv_1*rope_length+initial_x_sling, uv_2*rope_length+initial_y_sling)\n highest_length = 102 #when stretched\n\n #to make bird stay within rope\n x_redbird = mouse_x_pos - 20\n y_redbird = mouse_y_pos - 20\n if mouse_distance > rope_length:\n pux, puy = fix_pos\n pux -= 20\n puy -= 20\n first_pos = pux, puy\n screen.blit(redbird, first_pos)\n second_pos = (uv_1*highest_length+initial_x_sling, uv_2*highest_length+initial_y_sling) #current position\n pygame.draw.line(screen, (255, 0, 0), (next_x_sling, next_y_sling), second_pos, 5) #catapult rope\n screen.blit(redbird, first_pos)\n pygame.draw.line(screen, (255, 0, 0), (initial_x_sling, initial_y_sling), second_pos, 5) #ANOTHER SIDE of catapult\n else:\n #when not fully stretched\n mouse_distance += 10\n third_pos = (uv_1*mouse_distance+initial_x_sling, uv_2*mouse_distance+initial_y_sling)\n pygame.draw.line(screen, (0, 0, 0), (next_x_sling, next_y_sling), third_pos, 5)\n screen.blit(redbird, (x_redbird, y_redbird))\n pygame.draw.line(screen, (0, 0, 0), (initial_x_sling, initial_y_sling), third_pos, 5)\n # Angle of impulse\n\n change_in_y = mouse_y_pos - initial_y_sling\n change_in_x = mouse_x_pos - initial_x_sling\n if change_in_x == 0:\n dx = 0.00000000000001\n angle = math.atan((float(change_in_y))/change_in_x)","def move(self):\r\n return 'rock'","def move(x,y):\r\n pass","def move_girl(girl):\n global trainer_speed, girlcount, girl_left\n if girl_left:\n for i in girl:\n i.x -= trainer_speed\n i.move_speed()\n girlcount += 0.5\n girl_frame = int(girlcount) % 3\n camera.draw(girl[girl_frame])\n else:\n for i in girl:\n i.x += trainer_speed\n i.move_speed()\n girlcount += 0.5\n girl_frame = int(girlcount) % 3\n camera.draw(girl[girl_frame + 3])\n for each in girl:\n if each.x < 20:\n girl_left = False\n if each.x > 780:\n girl_left = True","def example_move(self):\n self.right() # start rotating right\n time.sleep(1) # turn for a second\n self.stop() # stop\n self.servo(1000) # look right\n time.sleep(.25) # give your head time to move\n self.servo(2000) # look left","def example_move(self):\n self.right() # start rotating right\n time.sleep(1) # turn for a second\n self.stop() # stop\n self.servo(1000) # look right\n time.sleep(.25) # give your head time to move\n self.servo(2000) # look left","def turn():\r\n global turn_no\r\n turn_no += 1\r\n dlog('Starting Turn!')\r\n board_size = get_board_size()\r\n\r\n team = get_team()\r\n opp_team = Team.WHITE if team == Team.BLACK else team.BLACK\r\n dlog('Team: ' + str(team))\r\n\r\n robottype = get_type()\r\n dlog('Type: ' + str(robottype))\r\n\r\n if robottype == RobotType.PAWN:\r\n dlog('Human')\r\n\r\n r, c = get_location()\r\n dlog('My location is: ' + str(r) + ' ' + str(c))\r\n\r\n if team == Team.WHITE:\r\n forward = 1\r\n scan = 2\r\n else:\r\n forward = -1\r\n scan = -2\r\n \r\n # try capturing pieces\r\n if check_space_wrapper(r + forward, c + 1, board_size) == opp_team: # up and right\r\n capture(r + forward, c + 1)\r\n dlog('Captured at: (' + str(r + forward) + ', ' + str(c + 1) + ')') \r\n\r\n elif check_space_wrapper(r + forward, c - 1, board_size) == opp_team: # up and left\r\n capture(r + forward, c - 1)\r\n dlog('Captured at: (' + str(r + forward) + ', ' + str(c - 1) + ')')\r\n \r\n elif r + forward != -1 and r + forward != board_size and not check_space_wrapper(r + forward, c, board_size):\r\n try:\r\n if check_space(r, c-1) == get_team() or check_space(r-1, c-1) == get_team() or check_space(r, c+1) == get_team() or check_space(r-1, c+1) == get_team():\r\n move_forward()\r\n dlog('Moved forward!')\r\n except:\r\n pass\r\n\r\n \r\n else:\r\n board = get_board()\r\n dlog(str(board))\r\n if team == Team.WHITE:\r\n forward = 1\r\n index = 0\r\n\r\n else:\r\n forward = -1\r\n index = board_size - 1\r\n \r\n deep_accum = []\r\n c_indexes = []\r\n heuristic_accum = []\r\n heuristic = 0\r\n for c in range(board_size):\r\n close = []\r\n for r in range(board_size):\r\n dlog(str(check_space(r, c)))\r\n if check_space(r, c) == opp_team:\r\n if team == Team.WHITE:\r\n close.append(r)\r\n elif team == Team.BLACK:\r\n close.append(board_size-r-1)\r\n heuristic -= 1\r\n elif check_space(r, c) == team:\r\n heuristic += 1\r\n else:\r\n continue\r\n heuristic_accum.append([heuristic, c])\r\n if close != []:\r\n c_indexes.append(c)\r\n deep = sorted(close)\r\n deep_accum.append(deep[0])\r\n heuristic = 0\r\n close_index = sorted(list(zip(deep_accum, c_indexes)))\r\n for c in close_index:\r\n if c[0] == 0:\r\n continue \r\n col = c[1]\r\n weighted_val = heuristic_accum[col][0]-15\r\n heuristic_accum[col][0] = weighted_val\r\n break\r\n\r\n heuristic_accum = sorted(heuristic_accum)\r\n\r\n for heur in heuristic_accum:\r\n col = heur[1]\r\n if not check_space_wrapper(index, col, board_size) and not check_space_wrapper(index+forward, col+1, board_size) == opp_team and not check_space_wrapper(index+forward, col-1, board_size) == opp_team:\r\n spawn(index, col)\r\n dlog('Spawned unit at: (' + str(index) + ', ' + str(col) + ')')\r\n break\r\n\r\n bytecode = get_bytecode()\r\n dlog('Done! Bytecode left: ' + str(bytecode))","def move(self):\n movement = self.recognize_color()\n if movement is not None:\n if rand.random() <= self.proba_color:\n movement()\n else:\n r = rand.random()\n if r <= self.proba[0]:\n self.turn_left()\n elif r <= self.proba[0] + self.proba[1]:\n self.move_forward()\n else:\n self.turn_right()\n self.set_color()","def run(self):\n while self.direction != \"\":\n if self.direction == \"decrease\":\n if self.position > 200:\n self.position -= 15\n elif self.direction == \"increase\":\n if self.position < 800:\n self.position += 15\n if self.direction != \"neutral\":\n self.move_joint(self.position, 900)\n time.sleep(0.1)","def move(self):\n \n self.position = self.wander()","def turn(self):\n pass","def move_buildings(self):","def rop():\n return","def play_move(self,state):\n raise AIError(\"Must be implemented for child class!\")","def move_of_king_and_rook(self, from_row, from_col, to_row, to_col): \n #provjere da li su kraljevi ili topovi inicirali pomijeranje\n if(from_row == 7 and from_col == 0):\n self.wrl_moved = True\n elif(from_row == 7 and from_col == 7):\n self.wrr_moved = True\n elif(from_row == 7 and from_col == 4):\n self.wk_moved = True\n elif(from_row == 0 and from_col == 0):\n self.brl_moved = True\n elif(from_row == 0 and from_col == 7):\n self.brr_moved = True\n elif(from_row == 0 and from_col == 4):\n self.bk_moved = True\n \n #provjera da li je neko pojeo topove\n if(to_row == 7 and to_col == 0):\n self.wrl_moved = True\n elif(to_row == 7 and to_col == 7):\n self.wrr_moved = True\n elif(to_row == 0 and to_col == 0):\n self.brl_moved = True\n elif(to_row == 0 and to_col == 7):\n self.brr_moved = True","def move(self, board, move_dir):\n if move_dir == \"right\":\n # failsafe: do not move through other cars on board\n if board.positions[self.get_rows()[0]][self.get_cols()[1] + (self.size - 1)].isupper() or board.positions[self.get_rows()[0]][self.get_cols()[1] + (self.size - 1)] == 'r':\n print(\"No movement!\")\n return board\n \n # give board correct new positions (characters)\n else:\n board.positions[self.get_rows()[0]][self.get_cols()[1] + (self.size - 1)] = self.name[0]\n board.positions[self.get_rows()[0]][self.get_cols()[0]] = \"x\"\n\n # change car objects positions\n for i, col in enumerate(self.position):\n self.position[i] = str(self.get_rows()[0]) + \".\" + str(int(col[2]) + 1)\n return board\n elif move_dir == \"left\": \n if board.positions[self.get_rows()[0]][self.get_cols()[0] - 1].isupper() or board.positions[self.get_rows()[0]][self.get_cols()[0] - 1] == 'r':\n print(\"No movement!\")\n return board\n else: \n board.positions[self.get_rows()[0]][self.get_cols()[0] - 1] = self.name[0]\n board.positions[self.get_rows()[0]][self.get_cols()[1] + (self.size - 2)] = \"x\"\n\n for i, col in enumerate(self.position):\n self.position[i] = str(self.get_rows()[0]) + \".\" + str(int(col[2]) - 1)\n return board\n elif move_dir == \"up\":\n #print(board.positions[self.get_rows()[0] - 1][self.get_cols()[0]])\n if board.positions[self.get_rows()[0] - 1][self.get_cols()[0]].isupper() or board.positions[self.get_rows()[0] - 1][self.get_cols()[0]] == 'r':\n print(\"No movement!\")\n return board\n else:\n board.positions[self.get_rows()[0] - 1][self.get_cols()[0]] = self.name[0]\n board.positions[self.get_rows()[1] + (self.size - 2)][self.get_cols()[0]] = \"x\"\n\n for i, row in enumerate(self.position):\n self.position[i] = str(int(row[0]) - 1) + \".\" + str(self.get_cols()[0])\n\n #print(board)\n return board\n elif move_dir == \"down\": \n try: \n if board.positions[self.get_rows()[1] + (self.size - 1)][self.get_cols()[0]].isupper() or board.positions[self.get_rows()[1] + (self.size - 1)][self.get_cols()[0]] == 'r':\n print(\"No movement!\")\n return board\n except IndexError:\n return board\n else: \n board.positions[self.get_rows()[0]][self.get_cols()[0]] = \"x\" \n board.positions[self.get_rows()[1] + (self.size - 1)][self.get_cols()[0]] = self.name[0]\n\n for i, row in enumerate(self.position):\n self.position[i] = str(int(row[0]) + 1) + \".\" + str(self.get_cols()[0]) \n \n #print(self.position)\n #print(board)\n \n return board\n else:\n #print(\"NO MOVEMENT!\")\n return board","def __move(self):\n if self.goal is None:\n if self.tasks:\n self.goal = self.tasks.pop()\n self.goal_history.append(self.goal)\n self.logger.log(\n f\"Crewmate {self.agent_id} set as goal: {self.goal.name} in\" +\n f\" {self.game_map.room_names[self.goal.room_id]}\",\n Logger.LOG | Logger.PRINT_VISUAL)\n else:\n self.room = self.game_map.move_random(self)\n self.location_history.append(self.room)\n return\n\n if self.room is not self.goal.room_id:\n self.room = self.game_map.next_toward(self, self.goal.room_id)\n\n # Log the current room we are in: Either the room we moved to, or the room that happens to be the goal room\n self.location_history.append(self.room)","def _onmove(self, event):","def step(self, move):","def move(player, direction):\r\n \r\n curRoom = world.maps.World.mapGrid[player.room]\r\n door = curRoom.dirs[direction]\r\n roomid = world.maps.World.doors[door].getExitRoom(curRoom.id)\r\n newRoom = world.maps.World.mapGrid[roomid]\r\n brokesneak = False\r\n \r\n if player.getAttr(SNEAKING) and not utils.gameutils.stealthRoll(player):\r\n player.setAttr(SNEAKING, False)\r\n sendToRoomNotPlayer(player, \"{0}You notice {1} sneaking out to the {2}.\".format(LRED, player, DIRS[direction])) \r\n sendToPlayer(player, \"{0}You make a sound\".format(LRED))\r\n brokesneak = True\r\n elif not player.getAttr(SNEAKING): \r\n sendToRoomNotPlayer( player, \"{0} left to the {1}.\".format(player, DIRS[direction])) \r\n \r\n del curRoom.players[player.name]\r\n newRoom.players[player.name] = player\r\n player.room = newRoom.id\r\n\r\n if brokesneak:\r\n sendToRoomNotPlayer(player, \"{0}You notice {1} sneaking into the room from the {2}.\".format(LRED, player, DIRS[OPPOSITEDIRS[direction]])) \r\n elif not player.getAttr(SNEAKING):\r\n sendToRoomNotPlayer(player, \"{0} entered from the {1}.\".format(player, DIRS[OPPOSITEDIRS[direction]])) \r\n \r\n displayRoom(player, player.room)\r\n player.stats[MOVING] = False","def make_move(self):\n\n # If the agent is starting a game, make an \n # initial move\n if self.get_play_status() == False: \n self.initial_move()\n return\n\n # for speeds sake, allow the reflex agent to respond to manual\n # input. comment out for automatic running.\n x = int(input('hotwire x:'))\n y = int(input('hotwire y:'))\n return self.get_game_space().set_tile(x,y,self.get_affinity())\n\n # Check wheather the the agent side is going to \n # win by making one move, make the move\n # OR\n # Check if the oponent has a compromising move \n best_move = self.victory_check()\n if best_move is None: best_move = self.counter_opponent_win()\n if best_move is None: best_move = self.counter_opponent_adv()\n if best_move is None: best_move = self.best_last_option()\n if best_move != None: \n x = best_move[0]\n y = best_move[1]\n return self.get_game_space().set_tile(x,y,self.get_affinity())","def move(self, row, col, player):","def move(self): #py:UR.move\n RUR._UR.move_(self.body)","def play_one_move(self):\n self.print(\"top of move\")\n # 1) grab three cups\n c1 = self.take_cup_after(self.current_cup_idx())\n c2 = self.take_cup_after(self.current_cup_idx())\n c3 = self.take_cup_after(self.current_cup_idx())\n print(f\"pick up: {c1}, {c2}, {c3}\")\n self.print(\"after pickup\")\n\n # 2) find a destination cup\n destination_idx = self.find_next_destination(self.current_cup)\n print(\n f\"destination index is {destination_idx}, cup is {self.cups[destination_idx]}\"\n )\n\n # 3) insert cups back into the circle\n self.add_cups(destination_idx, [c1, c2, c3])\n self.print(\"after adding cups..\")\n\n # 4) select the next cup\n self.select_next_cup()\n\n self.print(\"post move\")","def castling(self, turn, ai):\n\n if self.board[self.coords.index(piece_class.KING_LOCATION[turn])].move_track == True:\n return None\n \n \n castling_queenside = [self.coords.index(piece_class.KING_LOCATION[turn]), self.coords.index(piece_class.KING_LOCATION[turn]) - 1,\n self.coords.index(piece_class.KING_LOCATION[turn]) - 2, self.coords.index(piece_class.KING_LOCATION[turn]) - 3,\n self.coords.index(piece_class.KING_LOCATION[turn]) - 4]\n castling_kingside = [self.coords.index(piece_class.KING_LOCATION[turn]), self.coords.index(piece_class.KING_LOCATION[turn]) + 1,\n self.coords.index(piece_class.KING_LOCATION[turn]) + 2, self.coords.index(piece_class.KING_LOCATION[turn]) + 3]\n \n if self.castling_valid(turn, castling_kingside):\n self.board[self.coords.index(piece_class.KING_LOCATION[turn])].possible_moves.append(self.coords[castling_kingside[2]])\n \n if self.castling_valid(turn, castling_queenside):\n self.board[self.coords.index(piece_class.KING_LOCATION[turn])].possible_moves.append(self.coords[castling_queenside[2]])","def step(self, action):\n # print(\"############################\")\n # print(\"action: {}\".format(action))\n\n self.movement_complete.data = False\n\n # 1) Read last joint positions by getting the observation before acting\n old_observation = self.get_obs()\n\n # 2) Get the new joint positions according to chosen action (actions here are the joint increments)\n if self._joint_increment is None:\n next_action_position = action\n else:\n next_action_position = self.get_action_to_position(action, old_observation[1:7])\n\n # 3) Move to position and wait for moveit to complete the execution\n self.publisher_to_moveit_object.pub_joints_to_moveit(next_action_position)\n # rospy.wait_for_message(\"/pickbot/movement_complete\", Bool)\n while not self.movement_complete.data:\n pass\n\n start_ros_time = rospy.Time.now()\n while True:\n # Check collision:\n # invalid_collision = self.get_collisions()\n # if invalid_collision:\n # print(\">>>>>>>>>> Collision: RESET <<<<<<<<<<<<<<<\")\n # observation = self.get_obs()\n # reward = UMath.compute_reward(observation, -200, True)\n # observation = self.get_obs()\n # print(\"Test Joint: {}\".format(np.around(observation[1:7], decimals=3)))\n # return U.get_state(observation), reward, True, {}\n\n elapsed_time = rospy.Time.now() - start_ros_time\n if np.isclose(next_action_position, self.joints_state.position, rtol=0.0, atol=0.01).all():\n break\n elif elapsed_time > rospy.Duration(2): # time out\n break\n # time.sleep(s\n\n \"\"\"\n #execute action as long as the current position is close to the target position and there is no invalid collision and time spend in the while loop is below 1.2 seconds to avoid beeing stuck touching the object and not beeing able to go to the desired position \n time1=time.time()\n while np.linalg.norm(np.asarray(self.joints_state.position)-np.asarray(next_action_position))>0.1 and self.get_collisions()==False and time.time()-time1<0.1: \n rospy.loginfo(\"Not yet reached target position and no collision\")\n \"\"\"\n # 4) Get new observation and update min_distance after performing the action\n new_observation = self.get_obs()\n if new_observation[0] < self.min_distace:\n self.min_distace = new_observation[0]\n # print(\"observ: {}\".format( np.around(new_observation[1:7], decimals=3)))\n\n # 5) Convert Observations into state\n state = U.get_state(new_observation)\n\n # 6) Check if its done, calculate done_reward\n done, done_reward, invalid_contact = self.is_done(new_observation)\n\n # 7) Calculate reward based on Observatin and done_reward and update the accumulated Episode Reward\n reward = UMath.compute_reward(new_observation, done_reward, invalid_contact)\n\n ### TEST ###\n if done:\n joint_pos = self.joints_state.position\n print(\"Joint in step (done): {}\".format(np.around(joint_pos, decimals=3)))\n ### END of TEST ###\n\n self.accumulated_episode_reward += reward\n\n self.episode_steps += 1\n\n return state, reward, done, {}","def move(self):\n if self.lanzar == True: # Permite el movimiento de la bala, solo ocurre cuando el jugador lo inique\n self.rect.move_ip(0,-self.speed_bullet)\n if self.rect.bottom < 100: # Permite eliminar el objeto cuando este alcanza una altura calculada con respecto a la dim de la pantalla\n self.kill()","def move(self):\r\n segments = len(self.all_turtles) - 1\r\n for i in range(len(self.all_turtles)):\r\n if segments == 0:\r\n self.all_turtles[segments].forward(MOVE_DISTANCE)\r\n else:\r\n new_x = self.all_turtles[segments - 1].xcor()\r\n new_y = self.all_turtles[segments - 1].ycor()\r\n self.all_turtles[segments].goto(new_x, new_y)\r\n segments -= 1","def __move__(self):\n v = self.velocity\n p = self.position\n p += v\n self.rect.x = round(p.x)\n self.rect.y = round(p.y)","def automove(self):\n if self.x < self.end_cinematic_x_pos:\n self.x += self.SHIP_SPEED\n if self.x > self.end_cinematic_x_pos:\n self.x -= self.SHIP_SPEED\n if self.y < self.end_cinematic_y_pos:\n self.y += self.SHIP_SPEED\n if self.y > self.end_cinematic_y_pos:\n self.y -= self.SHIP_SPEED","def __move_ball(self):\n while not self.__game_is_over():\n self.__ball.move(self.__dx, self.__dy)\n self.__handle_wall_collision()\n if self.__num_lives == 0:\n self.__game_over_picture()\n break\n elif self.__bricks_total == 0:\n self.__game_over_picture('You Win!!')\n break\n pause(FRAME_RATE)","def handle_make(self, move):\n# print \"handle make-\"+str(move.src)+\":\"+str(move.dst)\n if not move: return\n # remove en passant capture\n if move.is_en_passant():\n self.remove(move.dst + (8 if move.is_white() else -8))\n if move.is_capture():\n self.remove(move.dst)\n\n # move rook when castling\n if move.is_castling_short():\n if move.is_white():\n self.move((63,61))\n else:\n self.move((7,5))\n if move.is_castling_long():\n if move.is_white():\n self.move((56, 59))\n else:\n self.move((0, 3))\n # Actual move itself.\n self.move((move.src, move.dst))\n\n # Replace promoted piece.\n if move.is_promotion():\n self.remove(move.dst)\n p_piece = move.get_promotion_piece()\n if not move.is_white(): p_piece = p_piece.lower()\n# print \"promoting to \"+p_piece+\" at \"+move.dst\n self.put(move.dst, p_piece)\n self.update_vars_list()","def run(self):\n return self.move_bat()","def move_turtle(self):\n self.forward(self.move_speed)","def decide(self):\n self.maybe_shoot()\n next(self.move_cycle)\n\n pass","def turn():\n \n robottype = get_type()\n if robottype == RobotType.PAWN:\n pawn_turn()\n else:\n overlord_turn()\n bytecode = get_bytecode()","def turn():\n \n robottype = get_type()\n if robottype == RobotType.PAWN:\n pawn_turn()\n else:\n overlord_turn()\n bytecode = get_bytecode()","def move(self):\n \"\"\" Responsible for transformations \"\"\"\n pos, com, success = self.perception \n if self.destination is None:\n return array([0,0])\n\n if not self.awake:\n return array([0,0])\n\n\n if self.phase == 4 and self.proper_formation is not None:\n no_go = []\n for i in range(0,len(self.proper_formation)):\n if i != self.order and self.proper_formation[i][0] == self.proper_formation[self.order][0]:\n no_go.append(self.transform(self.proper_formation[i][1] - self.position))\n pos = merge_array_lists(pos, no_go)\n\n if self.phase == 2:\n point = self.destination.copy() - self.position\n elif self.phase > 2:\n point = self.transform(self.destination.copy() - self.position)\n else:\n point = self.destination.copy()\n\n if not array_equal(point, array([0,0])):\n reachable, path = findpathtoclosest(array([0,0]), point, pos)\n \n if len(path) == 0:\n move = array([0,0]) \n else:\n move = path[0]\n if not reachable and not array_equal(move,array([0,0])):\n if self.phase == 2:\n self.closest_i_could_get = path[-1] + self.position\n elif self.phase > 2:\n self.closest_i_could_get = self.transform2(path[-1]) + self.position\n else:\n self.closest_i_could_get = path[-1]\n elif not reachable:\n if self.phase > 1:\n self.closest_i_could_get = self.position\n else:\n self.closest_i_could_get = array([0,0])\n else:\n self.closest_i_could_get = None\n\n if reachable and self.phase == 4 and array_equal(move,array([0,0])):\n move = self.randomStep()\n self.closest_i_could_get = None\n\n else:\n move = array([0,0])\n self.closest_i_could_get = None\n\n return move","def move(self):\n \n # checks for bots nearby\n next_move = self.follow()\n \n # finds a random move if no bot\n if next_move is self.position:\n self.position = self.wander()\n else:\n self.position = next_move","def arm(self):\n self.set_destination(0, 0, 0, 0)\n\n for _ in range(100):\n self.local_pos_pub.publish(self.waypoint_g)\n rospy.sleep(0.01)\n\n rospy.loginfo(CBLUE2 + \"Arming Drone\" + CEND)\n\n arm_request = CommandBoolRequest(True)\n\n while not rospy.is_shutdown() and not self.current_state_g.armed:\n rospy.sleep(0.1)\n response = self.arming_client(arm_request)\n self.local_pos_pub.publish(self.waypoint_g)\n else:\n if response.success:\n rospy.loginfo(CGREEN2 + \"Arming successful\" + CEND)\n return 0\n else:\n rospy.logerr(CRED2 + \"Arming failed\" + CEND)\n return -1","def move(self, move):\n raise NotImplementedError()","def move(self, direction):\n # Store the values in the connection dictionary in a list\n self.room_num = self.current_room.connection[direction]\n\n # Check if there is a conditional movement and change the current room\n if len(self.room_num) == 1:\n self.current_room = self.rooms[int(self.room_num[0]) - 1]\n else:\n adventure.check(len(self.room_num))","def move(self):\n if self.ycor() > 280: self.y_dir = -1 # Set vertical movement to down if ball at top of screen\n if self.xcor() > 380: self.x_dir = -1 # Set horizontal movement to left if ball at right of screen\n if self.xcor() < -380: self.x_dir = 1 # Set horizontal movement to right if ball at left of screen\n new_x = self.xcor() + self.x_dir * 2 # Define 2 spaces forward in set horizontal dir of travel\n new_y = self.ycor() + self.y_dir * 2 # Define 2 spaces forward in set vertical dir of travel\n self.goto(new_x, new_y) # Move ball to newly defined position","def move2goal(self):\n vel_msg = Twist()\n\n # Linear velocity in the x-axis.\n vel_msg.linear.x = 0.4 # m/s\n vel_msg.linear.y = 0\n vel_msg.linear.z = 0\n\n # Angular velocity in the z-axis.\n vel_msg.angular.x = 0\n vel_msg.angular.y = 0\n vel_msg.angular.z = 1.5 # rad/s\n\n # Starting point reference\n goal_x = 1.0 \n goal_y = 1.0\n x_ref = 1.0\n y_ref = 1.0\n\n # Previous Reference\n x_prev_ref = 0.0\n y_prev_ref = 0.0\n theta_prev_ref = self.theta\n vrefA = 0.5\n wrefA = 0.0\n \n i = 0\n tPx, tPy, tPTheta = self.initiate_trajectory(\n x_ref, y_ref, vel_msg, \n x_prev_ref, y_prev_ref, \n theta_prev_ref, vrefA, wrefA\n )\n\n x_prev_ref = tPx[0]\n y_prev_ref = tPy[0]\n theta_prev_ref = tPTheta[0]\n\n print(f'X TRAJECTORY: {tPx}\\nY TRAJECTORY: {tPy}\\nTHETA TRAJ: {tPTheta}')\n print(f'ACTUAL THETA: {self.theta}')\n\n while not rospy.is_shutdown():\n \n if i >= 8:\n i = 0\n\n x_ref = goal_x\n y_ref = goal_y\n\n tPx, tPy, tPTheta = self.initiate_trajectory(\n x_ref, y_ref, vel_msg, \n x_prev_ref, y_prev_ref, \n theta_prev_ref, vrefA, wrefA\n )\n # inputRef = ControllerInput(\n # xref=x_ref,\n # yref=y_ref,\n # RstateX=self.x_position,\n # RstateY=self.y_position,\n # RstateTheta=self.theta,\n # RstateVelocity=vel_msg.linear.x,\n # RstateW=vel_msg.angular.z,\n # xrefA=x_prev_ref,\n # yrefA=y_prev_ref,\n # thetarefA=theta_prev_ref,\n # vrefA=vrefA,\n # wrefA=wrefA\n # )\n\n # rospy.loginfo(f'X: {self.x_position} \\tY: {self.y_position}\\t Theta: {self.theta} ')\n # nmpc = NMPC_Controller(inputRef)\n # tPx, tPy, tPTheta = nmpc.test_create_mini_path()\n\n # print(f'X TRAJECTORY: {tPx}\\nY TRAJECTORY: {tPy}\\nTHETA TRAJ: {tPTheta}')\n # print(f'ACTUAL THETA: {self.theta}')\n \n # new_v, new_w = nmpc.start_optmizer()\n # new_v = round(new_v, 4)\n # new_w = round(new_w, 4)\n\n # print(new_v, new_w)\n # rospy.loginfo(\n # f'X: {self.x_position}, Y: {self.y_position}, THETA: {self.theta}')\n \n # self.velocity_publisher.publish(vel_msg)\n # x_prev_ref = self.x_position\n # y_prev_ref = self.y_position\n # theta_prev_ref = self.theta\n # vrefA = vel_msg.linear.x\n # wrefA = vel_msg.angular.z\n \n\n # theta_prev_ref = self.theta\n # vel_msg.angular.z = 0.0\n\n\n '''Update the linear & angular velocity'''\n # vel_msg.linear.x = new_v\n # vel_msg.angular.z = new_w\n\n if i < 8:\n inputRef = ControllerInput(\n xref = tPx[i],\n yref = tPy[i],\n RstateX = self.x_position,\n RstateY = self.y_position,\n RstateTheta = self.theta,\n RstateVelocity = vel_msg.linear.x,\n RstateW = vel_msg.angular.z,\n xrefA = x_prev_ref,\n yrefA = y_prev_ref,\n thetarefA = theta_prev_ref,\n vrefA = vrefA,\n wrefA = wrefA\n )\n\n nmpc = NMPC_Controller(inputRef)\n new_v, new_w = nmpc.start_optmizer()\n new_v = round(new_v, 4)\n new_w = round(new_w, 4)\n\n print(f'(actual) X: {self.x_position}, Y: {self.x_position}, THETA: {self.theta}')\n print(f'(desired) X: {tPx[i]}, Y: {tPy[i]}')\n print(f'V: {vel_msg.linear.x}\\tW: {vel_msg.angular.z}')\n\n x_prev_ref = tPx[i-1]\n y_prev_ref = tPy[i-1]\n theta_prev_ref = tPTheta[i-1]\n vrefA = vel_msg.linear.x\n wrefA = vel_msg.angular.z\n\n vel_msg.linear.x = new_v\n vel_msg.angular.z = new_w\n # vel_msg.angular.z = 0.0\n\n print(f'index: {i}')\n\n distance = math.sqrt((self.x_position - tPx[i])**2 + (self.y_position - tPy[i])**2)\n if distance < 0.3:\n print(f'Distance: {distance}')\n i+=1\n\n\n self.velocity_publisher.publish(vel_msg)\n self.rate.sleep()\n\n rospy.spin()","def go(self, brd):\n return self.makeMove(brd)","def decide_next_move(self):\n pass","def act(self, action):\n\n self.get_frame(int(self.t))\n\n self.position = np.zeros((self.grid_size, self.grid_size))\n\n self.position[0:2,:]= -1\n self.position[:,0:2] = -1\n self.position[-2:, :] = -1\n self.position[:, -2:] = -1\n\n self.position[self.x, self.y] = 1\n if action == 0:\n if self.x == self.grid_size-3:\n self.x = self.x-1\n else:\n self.x = self.x + 1\n elif action == 1:\n if self.x == 2:\n self.x = self.x+1\n else:\n self.x = self.x-1\n elif action == 2:\n if self.y == self.grid_size - 3:\n self.y = self.y - 1\n else:\n self.y = self.y + 1\n elif action == 3:\n if self.y == 2:\n self.y = self.y + 1\n else:\n self.y = self.y - 1\n else:\n RuntimeError('Error: action not recognized')\n\n self.t = self.t + 1\n reward = self.board[self.x, self.y]\n \n self.hunter_move()\n removals = []\n for i in range(len(self.h_x)):\n if self.x == self.h_x[i] and self.y == self.h_y[i]:\n reward -= 100\n removals.append(i)\n \n for i in sorted(removals, reverse=True):\n del self.h_x[i]\n del self.h_y[i]\n \n self.board[self.x, self.y] = 0\n self.board_with_hunters[:,:] = 0\n \n for i in range(len(self.h_x)):\n self.board_with_hunters[self.h_x[i],self.h_y[i]] = -100\n \n self.trajectory[self.x,self.y] = 1\n game_over = self.t > self.max_time\n state = np.concatenate((self.board.reshape(self.grid_size, self.grid_size,1),\n self.position.reshape(self.grid_size, self.grid_size,1),\n self.trajectory.reshape(self.grid_size, self.grid_size,1),\n self.board_with_hunters.reshape(self.grid_size, self.grid_size,1)),axis=2)\n state = state[self.x-2:self.x+3,self.y-2:self.y+3,:]\n\n return state, reward, game_over","def _go(self, distance):\n ende = self._position + self._orient * distance\n self._goto(ende)","def _move(self):\n self.pos += self.direction # add direction vector\n self.direction += self.gravity # add gravity to direction\n self.direction = self.direction.elementwise() * self.drag # apply drag to direction","def move_aim(self):\n self.color = random.choice(COLORS)\n self.x += 3 * self.speed_x / FPS\n self.y += 3 * self.speed_y / FPS\n self.r -= 1\n self.draw_aim()\n if self.r <= 10:\n self.color = random.choice(COLORS)\n self.x = randint(100, 1000)\n self.y = randint(100, 800)\n self.r = randint(50, 100)\n self.speed_x = randint(-200, 200)\n self.speed_y = randint(-200, 200)\n if self.x >= 1100:\n self.speed_x = randint(-100, -10)\n if self.x <= 50:\n self.speed_x = randint(10, 100)\n if self.y >= 800:\n self.speed_y = randint(-100, -10)\n if self.y <= 50:\n self.speed_y = randint(10, 100)","def move_car(self):\n a = self.h / 50\n self.x += self.speed_x / FPS\n if self.x + 170 * a >= 1100:\n self.dir = -1\n self.speed_x = -self.speed_x\n if self.x - 170 * a <= 50:\n self.dir = 1\n self.speed_x = -self.speed_x","def mouse_move(self, obj, event):\n last_pos = self.iren.GetLastEventPosition()\n next_pos = self.iren.GetEventPosition()\n last_disp_coords = np.asarray([last_pos[0], last_pos[1], 0])\n next_disp_coords = np.asarray([next_pos[0], next_pos[1], 0])\n last_world_coords = self.display_to_world(last_disp_coords)\n next_world_coords = self.display_to_world(next_disp_coords)\n world_direction = (last_world_coords - next_world_coords)[0]\n\n if world_direction > 0:\n direction = 'forwards'\n elif world_direction < 0:\n direction = 'backwards'\n else:\n direction = 'none'\n\n if self.cone_dir == 'start':\n if direction == 'backwards':\n self.start_base_x += .5\n if self.start_base_x.is_integer():\n ind = str(int(self.start_base_x))\n isvalid = self.gaps.set_dragged_start(ind)\n if isvalid:\n self.ren_win.Render()\n else:\n self.start_base_x -= .5\n return\n\n elif direction == 'forwards':\n if self.start_base_x > 0:\n self.start_base_x -= .5\n if self.start_base_x.is_integer():\n ind = str(int(self.start_base_x))\n self.gaps.set_dragged_start(ind)\n self.ren_win.Render()\n\n if self.cone_dir == 'end':\n if direction == 'backwards':\n if self.end_base_x > 0:\n self.end_base_x -= .5\n if self.end_base_x.is_integer():\n ind = str(int(self.end_base_x))\n self.gaps.set_dragged_end(ind)\n self.ren_win.Render()\n\n elif direction == 'forwards':\n self.end_base_x += .5\n if self.end_base_x.is_integer():\n ind = str(int(self.end_base_x))\n isvalid = self.gaps.set_dragged_end(ind)\n if isvalid:\n self.ren_win.Render()\n else:\n self.end_base_x -= .5\n return","def move_rel(self):\n pass","def movement(self):\n self.rect.left -= self.speedx #to move the asteroid to the left","def move(self, direction):\n try:\n\n if self.in_thing:\n print(\"You have to get out of the \" + str(*self.in_thing[-1]) +\n \" first\")\n return self\n if direction == 'north':\n if self.finished_places == 12:\n self.finished_places += 1\n return North(self.items, self.finished_places)\n if direction == 'up':\n if self.finished_places == 4:\n self.finished_places += 1\n return Up(self.items, self.finished_places)\n if direction == 'east':\n if self.finished_places == 2:\n self.finished_places += 1\n return East(self.items, self.finished_places)\n except AttributeError:\n self.items = []\n return self.move(direction)\n print(' you didn\\'t listen to my very subtle hints, i know it was hard'\n ' your lost now. if you remember the commands i told you you can'\n ' go back to where you left off and continue, just type \"QUIT\"')\n return Place(self.items, self.finished_places)\n\n # implement\n # return new instance on class","def play(self):\n self._play(self._move_person)","def play_step(self, action):\n self.players[0].moving_left = False\n self.players[0].moving_right = False\n if action == MOVE_LEFT:\n self.players[0].moving_left = True\n for i in range(LOOP_AT_EACH_MOVE_UPDATE):\n self.update(is_a_star=True)\n if self.dead_player or not self.players[0].is_alive:\n break\n self.players[0].moving_left = False\n if self.dead_player or not self.players[0].is_alive:\n return\n elif action == MOVE_RIGHT:\n self.players[0].moving_right = True\n for i in range(LOOP_AT_EACH_MOVE_UPDATE):\n self.update(is_a_star=True)\n if self.dead_player or not self.players[0].is_alive:\n break\n self.players[0].moving_right = False\n if self.dead_player or not self.players[0].is_alive:\n return\n elif action == SHOOT:\n if self.dead_player or not self.players[0].is_alive:\n self.update(is_a_star=True)\n return\n if not self.players[0].weapon.is_active:\n self.players[0].shoot()\n for i in range(LOOP_AT_EACH_MOVE_UPDATE):\n self.update(is_a_star=True)\n if self.dead_player or not self.players[0].is_alive:\n break\n if self.dead_player or not self.players[0].is_alive:\n return","def computer_move(board,move,player):\r\n com_execution(board, move, player)","def knock_back(self):\n FORWARD_VEL = -2\n\n self.rect.x += self.x_vel\n\n if self.name == 'player':\n if self.rect.x >= (self.origin_pos[0] + 10):\n self.x_vel = FORWARD_VEL\n elif self.rect.x <= self.origin_pos[0]:\n self.rect.x = self.origin_pos[0]\n self.state = 'battle resting'\n self.x_vel = 0\n else:\n if self.rect.x <= (self.origin_pos[0] - 10):\n self.x_vel = 2\n elif self.rect.x >= self.origin_pos[0]:\n self.rect.x = self.origin_pos[0]\n self.state = 'battle resting'\n self.x_vel = 0","def make_move(self):\n self.owner = self.game.current_turn\n self.status = 'X' if self.owner == self.game.creator else 'O'\n ####\n #Random turn??\n ####\n self.save(update_fields=['status', 'owner'])\n\n # Add log entry for move\n self.game.add_log(f'cell made at ({self.row}, {self.col}) by {self.owner}')\n\n # Set the current turn for the other player if game is not over\n # Check if find winner\n if self.game.check_win(cell=self) or\\\n self.game.get_all_game_cells().filter(status='EMPTY').count() == 0:\n print(\"Winnnnnnnn\")\n self.game.mark_complete(winner=self.owner)\n\n # Switch player turn\n self.game.switch_turn()\n\n # Let the game know about the move and result\n self.send_game_update()","def move_player(direction):\n global ZERO_BASE_PLYR_POS\n if direction == \"north\":\n ZERO_BASE_PLYR_POS -= 10\n elif direction == \"south\":\n ZERO_BASE_PLYR_POS += 10\n elif direction == \"west\":\n ZERO_BASE_PLYR_POS -= 1\n elif direction == \"east\":\n ZERO_BASE_PLYR_POS += 1\n \n sleep(0.5) # all moves have a 0.5 second delay\n \n show_ground_feature()","def move_marble(self, coordinates, direction):\n pass","def move_asteroids(self):\n for i in range(len(self.asteroid_type)):\n self.game.move(self.asteroid_type[i], -self.asteroid_speed[i], 0)\n for i in range(len(self.asteroid_id_e)):\n self.game.move(self.asteroid_id_e[i], -self.asteroid_speed_e[i], 0)","def move_to_position2(self):","def moving(self):\n if self.sensors['proximity'][0].imminent_collision:\n self.motion.stopMove()\n self.posture.goToPosture('Stand', self.SPEED)\n self.behavior_ = self.BEHAVIORS.thinking\n\n elif not self.motion.moveIsActive():\n # Walker will always keep moving forward,\n # if no obstacles are found.\n move_to = (self.STRIDE, 0, 0)\n self.motion.post.moveTo(*move_to)\n logger.info('Walking: %s', move_to)","def moveable(self, board):\n # horizontal\n if self.direction == \"horizontal\":\n # the position to which the car wants to move is either 1 more or 1 less column wise\n right = self.get_cols()[1] + self.size - 1\n left = self.get_cols()[0] - 1\n # check if right or left is out of the boards margins \n if right > board.width_height:\n move_left = board.positions[self.get_rows()[0]][left]\n move_right = None\n elif left < 0:\n move_right = board.positions[self.get_rows()[0]][right]\n move_left = None\n else: \n move_right = board.positions[self.get_rows()[0]][right]\n move_left = board.positions[self.get_rows()[0]][left]\n\n # try to move left and right\n if move_right == \"x\" and move_left == \"x\":\n return \"leftright\"\n elif move_right == \"x\":\n return \"right\"\n elif move_left == \"x\":\n return \"left\"\n else: \n return \"none\"\n \n # vertical\n else:\n up = self.get_rows()[0] - 1\n #print(up)\n down = self.get_rows()[1] + self.size - 1\n # check if up or down is out of the boards margins \n if up < 0:\n # no room on the board for upward movement\n move_down = board.positions[down][self.get_cols()[0]]\n move_up = None\n elif down > board.width_height:\n # no room on the board for downward movement\n move_up = board.positions[up][self.get_cols()[0]]\n move_down = None\n else:\n # both up and down are possible positions on the board\n move_up = board.positions[up][self.get_cols()[0]]\n move_down = board.positions[down][self.get_cols()[0]]\n\n # try to move up and down\n if move_down == \"x\" and move_up == \"x\":\n return \"updown\"\n elif move_up == \"x\":\n return \"up\"\n elif move_down == \"x\":\n return \"down\"\n else: \n return \"none\"","def ProcessPlayerTileMove(self):\r\n # If the player steps on a door\r\n if 'doors' in self.data:\r\n for key in self.data['doors']:\r\n door = self.data['doors'][key]\r\n \r\n if self.game.player.pos.IsSame(door['pos']):\r\n Log('Entering door: %s: %s' % (key, door['map']))\r\n self.game.SelectMap(door['map'])\r\n \r\n if 'player_pos' in door:\r\n (player_x, player_y) = door['player_pos']\r\n else:\r\n (player_x, player_y) = self.game.map.data['player']['starting_pos']\r\n \r\n self.game.player.pos = rpg_base.Position(player_x, player_y)\r\n \r\n # Center the player on the screen\r\n tiles = self.game.map.GetTilesPerScreen()\r\n self.game.map.offset[0] = self.game.player.pos.x - tiles[0] / 2\r\n self.game.map.offset[1] = self.game.player.pos.y - tiles[1] / 2\r\n \r\n # Check if we have stepped next to an actor\r\n for name in self.actors:\r\n actor = self.actors[name]\r\n if self.game.player.pos.GetDistance(actor.pos) <= 1.0:\r\n self.game.dialogue = rpg_dialogue.Dialogue(self.game, actor)\r\n break","def moveBasedOnRetreatAction(self, time_passed):\n cpos = self.toScreenCoordinate()\n mpos = pygame.mouse.get_pos()\n toMouse = Vector2.from_points(cpos,mpos)\n toMouse.normalize()\n rheading = -toMouse\n \n heading = self.heading\n angle_between = heading.angle_between(rheading)\n if angle_between>=-30 and angle_between<=30:\n return\n \n distance = time_passed * self.speed\n movement = rheading * distance\n x = movement.get_x()\n y = movement.get_y()\n if not self.checkCollision(x, y) and self.checkValidCoord(x, y):\n self.move(x, y)","def move(self):\n for agent in self.agents:\n if not agent.fidelity:\n options = agent.get_move_options(agent.hex, self.kernel_size, None, extend=True)\n target = random36.choices(population=options,weights=[x.quality**2 for x in options])\n agent.move(target[0])","def move_car(self):\n import interface\n self.reset_position()\n print(\"move car during %f s...\" % self.portion_duration*self.nbr_portions)\n\n X, Y = [], []\n t_debut = time.time()\n while time.time() - t_debut < self.portion_duration*self.nbr_portions:\n current_time = time.time() - t_debut\n # On fait bouger les 4 roues.\n for numero_roue, speed in enumerate(self(current_time)):\n print(numero_roue)\n interface.move_wheel(numero_roue+1, speed)\n\n # Recuperation de la position reele\n (x, y), _ = interface.get_position()\n X.append(x)\n Y.append(y)\n\n interface.move_wheel(\"\", 0) # La voiture s'arette a la fin.\n print(\"\\tterminate\")\n return x, y"],"string":"[\n \"def _walk(self):\\n new_pos = self.rect.move((self.move, 0)) # move 9 pixel to the right per frame\\n if self.rect.left < self.area.left or self.rect.right > self.area.right:\\n self.move = -self.move # move to the opposite direction when the chimp position exceeds the screen\\n new_pos = self.rect.move((self.move, 0))\\n self.image = pygame.transform.flip(\\n self.image, 1, 0\\n ) # mirror the chimp to make it looks like turning around\\n self.rect = new_pos\",\n \"def move(self):\\n pass\",\n \"def movement(self):\",\n \"def move(): #py:move\\n RUR._move_()\",\n \"def move(self, action):\\n self.time += 1\\n\\n # If ship is destroyed ship can only contemplate sadness and despair\\n if not action or not self.is_playable():\\n return None\\n\\n self.actualise = False\\n\\n if self.leroy_time == 1:\\n self.back_to_normal()\\n if self.leroy_time > 0:\\n self.leroy_time -= 1\\n\\n # there is a chance that the ia enter in leroy mode\\n # the ia goes mad for some time, acting randomly\\n # added to allow the ships to explore the possible actions and not stay passive\\n if not self.player and self.leroy_time == 0 and self.agent.behavior == \\\"network\\\" and random() < LEROY_RATE:\\n self.leroy_jenkins()\\n\\n # training reward depending on position\\n # self.agent.reward = self.go_bottom_reward()\\n\\n if isinstance(action, ActionOneHot):\\n if action.pointing:\\n self.pointing = Point(randint(0, DEFAULT_WIDTH-1), randint(0, DEFAULT_HEIGHT-1))\\n elif isinstance(action, Action):\\n if action.pointing:\\n self.pointing = action.pointing\\n # print(\\\"action.pointing\\\", action.pointing)\\n # print(\\\"turn \\\", self.direction)\\n\\n if action.thrust:\\n self.thrust()\\n if action.shoot:\\n self.shoot()\",\n \"def makeMove(self, move, player):\",\n \"def move(self):\\n # moving each of the obstacles\\n time = rospy.get_rostime()\\n self.vel_msg1.linear.x = math.sin(time.to_sec())\\n self.pub1.publish(self.vel_msg1)\\n self.vel_msg2.linear.x = math.cos(1.2 * time.to_sec())\\n self.pub2.publish(self.vel_msg2)\\n self.vel_msg3.linear.x = math.cos(2 * time.to_sec())\\n self.pub3.publish(self.vel_msg3)\\n self.vel_msg4.linear.x = math.sin(0.85 * time.to_sec())\\n self.pub4.publish(self.vel_msg4)\",\n \"def move(self, agent, action):\\n\\t\\tpass\",\n \"def _move_forward(self):\\n\\t\\tself.x,self.y = Mario._get_coordinates(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\tif(self.y<=798):\\n\\t\\t\\tself.y = self.y+1\\n\\t\\t\\tif Board.board[self.x][self.y]=='0':\\n\\t\\t\\t\\tMario.score += 1\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='P':\\n\\t\\t\\t\\tMario.lives+=1\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_1-up.wav\\\"])\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='A':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\t\\t\\t\\tMario.attack = 1\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_powerup.wav\\\"])\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='@':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tMario.lives-=1\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_mariodie.wav\\\"])\\n\\t\\t\\t\\tif Mario.lives<=0:\\n\\t\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_gameover.wav\\\"])\\n\\t\\t\\t\\t\\treturn \\\"exit\\\"\\n\\t\\t\\t\\tos.system('clear')\\n\\t\\t\\t\\tprint(\\\"\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\n\\\\t\\\\t\\\\t\\\\t\\\\t\\\\tNumber of Mario left\\\",Mario.lives)\\n\\t\\t\\t\\tMario.respawn(self.x,self.y)\\n\\t\\t\\t\\ttime.sleep(2)\\n\\t\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\n\\t\\t\\telif(Board.board[self.x][self.y]=='/'):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x-1][self.y]='M'\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='I':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_stage_clear.wav\\\"])\\n\\t\\t\\t\\tBoard.bonus_round()\\n\\n\\t\\t\\telif Board.board[self.x][self.y]=='K':\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tcall([\\\"aplay\\\",\\\"-q\\\",\\\"smb_stage_clear.wav\\\"])\\n\\t\\t\\t\\tenemy.boss_round()\\n\\n\\t\\t\\telif(Board.board[self.x][self.y] in obstacles):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y-1]='M'\\n\\n\\t\\t\\telif((Board.board[self.x+1][self.y-1]=='/' or Board.board[self.x+1][self.y-1]=='T') and Board.board[self.x+1][self.y]==' '):\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y+1]='M'\\n\\t\\t\\t\\tMario.go_down(self)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tMario._pass(self.x,self.y-1)\\n\\t\\t\\t\\tBoard.board[self.x][self.y]='M'\\n\\n\\t\\tif( self.y-1 >= ((Board.prev_j+Board.prev_k)/2) ):\\n\\t\\t\\tos.system('clear')\\n\\t\\t\\tBoard.prev_j += 1 \\n\\t\\t\\tBoard.prev_k += 1\\n\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\\n\\t\\telse:\\n\\t\\t\\tos.system('clear')\\n\\t\\t\\tinit_board(Board.prev_i,Board.prev_j,Board.prev_k)\",\n \"def algo_mover(self):\\n self.tick_count = 0 ## reset to start clock on delayed state change\\n\\n ### Move 1 ring ###\\n de, aux, vers = self.instructions.pop(0)\\n\\n de = self.rods[de]\\n aux = self.rods[aux]\\n vers = self.rods[vers]\\n\\n ring = de.pop_ring()\\n vers.add_ring(ring)\\n\\n ### Update the states after -- moved this to main update\\n #self.state_updater()\",\n \"def handleMove(self):\\n pass\",\n \"def _walk(self):\\n \\n newpos= self.rect.move((self.move, 0)) # x方向移動 .move, y方向不動。\\n \\n # 偵測碰撞左右牆壁，並處理(反彈)\\n if not self.area.contains(newpos):\\n if self.rect.left < self.area.left or \\\\\\n self.rect.right > self.area.right:\\n self.move = -self.move\\n newpos = self.rect.move((self.move, 0))\\n self.image = pygame.transform.flip(self.image, 1, 0)\\n self.rect = newpos\",\n \"def AeroMove(self, pos):\\r\\n\\r\\n pass\",\n \"def move(self, direction):\\n # replace with your code\\n pass\",\n \"def move(self, direction):\\n # replace with your code\\n pass\",\n \"def player_movement(self):\",\n \"def move(self, direction, cycles):\\n\\t\\tpass\",\n \"def _move(self, d, event):\\n\\n actor = None\\n if event.source in self._crates:\\n actor = self._crates[event.source]\\n else:\\n actor = self._clones[event.source][0]\\n\\n if d == Direction.NO_ACT or not event.success:\\n actor.animation = actor.do_nothing_animation()\\n return\\n pos = actor.pos\\n target = pos.dir_pos(d)\\n actor.direction = d\\n actor.animation = actor.walk_animation()\\n self.repaint()\",\n \"def move(self):\\n # This block is used to move into the living room.\\n if self.room_name == \\\"living\\\" or self.room_name == \\\"Living\\\":\\n self.living_room()\\n # This block is used to open the door.\\n elif self.room_name == \\\"Door\\\" or self.room_name == \\\"door\\\":\\n self.door()\\n # This block is used to move into the kitchen.\\n elif self.room_name == \\\"Kitchen\\\" or self.room_name == \\\"kitchen\\\":\\n self.kitchen()\\n # This block is used to move into the hallway.\\n elif self.room_name == \\\"Hallway\\\" or self.room_name == \\\"hallway\\\":\\n self.hallway()\\n # This block is used to move into the bathroom.\\n elif self.room_name == \\\"Bathroom\\\" or self.room_name == \\\"bathroom\\\":\\n self.bathroom()\\n # This block is used to move into the basement.\\n elif self.room_name == \\\"Basement\\\" or self.room_name == \\\"basement\\\":\\n self.basement()\\n # This block is used to move into the bedroom.\\n elif self.room_name == \\\"Bedroom\\\" or self.room_name == \\\"bedroom\\\":\\n self.bedroom()\\n # This block is used to move into the attic.\\n elif self.room_name == \\\"Attic\\\" or \\\"attic\\\":\\n self.attic()\",\n \"def main():\\n move()\\n move()\\n pick_beeper()\\n move()\\n turn_left()\\n for i in range(2):\\n move()\\n put_beeper()\\n turn_around()\\n move_to_wall()\\n turn_right()\\n move_to_wall()\\n turn_around()\",\n \"def tick():\\n move_balls(targets_speed)\\n move_super_balls(targets_speed * 2)\",\n \"def move(self):\\n raise NotImplementedError\",\n \"def move_forward():\\n pass\",\n \"def run(self):\\n move_cmd = Twist()\\n move_cmd.linear.x = 0\\n move_cmd.angular.z = 0\\n\\n while not rospy.is_shutdown():\\n # bump logic as previous psets\\n if self.bump:\\n self.bump = False\\n # move backwards\\n move_cmd.linear.x = LIN_SPEED * -1\\n for i in range(5):\\n self.cmd_vel.publish(move_cmd)\\n self.rate.sleep()\\n rospy.sleep(1)\\n\\n # turn randomly in a random direction\\n move_cmd.linear.x = 0\\n move_cmd.angular.z = ROT_SPEED * ((-1)**random.randint(1,2))\\n\\n if self.bump == 0:\\n move_cmd.angular.z = ROT_SPEED * (-1)\\n elif self.bump == 2:\\n move_cmd.angular.z = ROT_SPEED\\n\\n for i in range(random.randint(5,15)):\\n self.cmd_vel.publish(move_cmd)\\n self.rate.sleep()\\n rospy.sleep(1)\\n\\n move_cmd.angular.z = 0\\n # if somethin in the screen is really close\\n elif self.min_val < MIN_THRESHOLD:\\n # make sure it's not the sock/leg warmer, and is actually an obstacle\\n if self.obstacle_x <= self.x or self.obstacle_x >= self.x + self.w or abs(self.min_val - self.dist) > 0.1:\\n move_cmd.linear.x = 0\\n # turn away\\n if self.obstacle_x > 320:\\n move_cmd.angular.z = ROT_SPEED / 2\\n else:\\n move_cmd.angular.z = -ROT_SPEED / 2\\n # self.min_val = 100\\n for i in range(10):\\n self.cmd_vel.publish(move_cmd)\\n self.rate.sleep()\\n self.last_move = rospy.Time.now()\\n else:\\n rospy.loginfo(\\\"Perimeter \\\" + str(self.perimeter_size))\\n rospy.loginfo(\\\"Distance is \\\" + str(self.dist))\\n\\n # normalize angle error to rot speed\\n ang_error_norm = -float(self.ang_error) / 100\\n\\n # set min and max rot speed\\n if ang_error_norm < -ROT_SPEED:\\n ang_error_norm = -ROT_SPEED\\n elif ang_error_norm > ROT_SPEED:\\n ang_error_norm = ROT_SPEED\\n\\n move_cmd.angular.z = ang_error_norm\\n\\n if RACE == False:\\n # normalize dist error to lin speed\\n self.dist_error = self.dist - 0.5\\n dist_error_norm = float(self.dist_error) / 2\\n\\n if dist_error_norm < 0:\\n # if NaN (self.dist gets set to -1)\\n if dist_error_norm > -0.7:\\n self.lost = 0\\n # if too close\\n else:\\n self.lost += 1\\n # if it's been more than 2 seconds\\n if rospy.Time.now() > self.last_move + rospy.Duration(2):\\n dist_error_norm = 0\\n # if been lost for a while rotate and beep\\n if self.lost > 20:\\n move_cmd.angular.z = ROT_SPEED / 4\\n self.beep.publish(4)\\n else:\\n # continue as previous\\n dist_error_norm = self.last_speed\\n else:\\n # set max lin speed\\n if dist_error_norm > LIN_SPEED:\\n dist_error_norm = LIN_SPEED\\n\\n # reset lost stats\\n self.lost = 0\\n self.last_speed = dist_error_norm\\n self.last_move = rospy.Time.now()\\n\\n move_cmd.linear.x = dist_error_norm\\n else:\\n move_cmd.linear.x = LIN_SPEED\\n\\n self.cmd_vel.publish(move_cmd)\",\n \"def move(self, direction):\\n pass\",\n \"def step(self, action):\\n # print(action)\\n distances = self.agent.return_distances(self.agent.corners, self.agent.line_pos)\\n\\n left = distances[0]\\n right = distances[1]\\n self.agent.distances.append({\\n 'left': left,\\n 'right': right\\n })\\n reward = 0\\n if action == 1:\\n self.agent.angle -= 90\\n if self.agent.angle < 0:\\n self.agent.angle = 0\\n self.agent.direction_history.append('left')\\n self.reset_raycasts(self.agent.angle)\\n self.render()\\n if left > right:\\n reward += 5\\n else:\\n reward -= 5\\n\\n elif action == 2:\\n self.agent.angle += 90\\n if self.agent.angle >= 360:\\n self.agent.angle = 0\\n\\n self.reset_raycasts(self.agent.angle)\\n self.render()\\n self.agent.direction_history.append('right')\\n if left < right:\\n reward += 5\\n else:\\n reward -= 5\\n\\n elif action == 0:\\n self.agent.direction_history.append('forward')\\n if self.agent.angle >= 360: self.agent.angle == 0\\n if self.agent.angle == 0 or self.agent.angle == 360:\\n self.agent.agent_position['y'] -= 10\\n self.reset_raycasts(self.agent.angle)\\n elif self.agent.angle == 90: \\n self.agent.agent_position['x'] += 10\\n self.reset_raycasts(self.agent.angle)\\n elif self.agent.angle == 180: \\n self.agent.agent_position['y'] += 10\\n self.reset_raycasts(self.agent.angle)\\n elif self.agent.angle == 270:\\n self.agent.agent_position['x'] -= 10\\n self.reset_raycasts(self.agent.angle)\\n \\n if left + right >= 50:\\n reward += 5\\n\\n self.render()\\n\\n elif action == 3:\\n self.agent.direction_history.append('reverse')\\n if self.agent.angle == 0:\\n self.agent.agent_position['y'] += 10\\n self.reset_raycasts(self.agent.angle)\\n self.render()\\n elif self.agent.angle == 90: \\n self.agent.agent_position['x'] -= 10\\n self.reset_raycasts(self.agent.angle)\\n self.render()\\n elif self.agent.angle == 180: \\n self.agent.agent_position['y'] -= 10\\n self.reset_raycasts(self.agent.angle)\\n self.render()\\n elif self.agent.angle == 270:\\n self.agent.agent_position['x'] += 10\\n self.reset_raycasts(self.agent.angle)\\n self.render()\\n \\n if left + right <= 50:\\n reward += 5\\n\\n \\n else:\\n reward -= 5\\n\\n if \\\"forward\\\" not in self.agent.direction_history[len(self.agent.direction_history)-6:len(self.agent.direction_history)-1]:\\n reward -= 10\\n\\n \\n info = {}\\n if self.agent.check_collision():\\n reward -= 10\\n self.reset() \\n self.agent.rewards.append({\\n 'leftDistance': left,\\n 'rightDistance': right,\\n 'reward': reward,\\n })\\n self.render()\\n print(f\\\"REWARD: {reward}\\\")\\n # self.render()\\n # print(self.agent.direction_history[-1])\\n self.agent.rewards.append(reward)\\n return np.array([left, right]), reward, False, info\",\n \"def move():\\n Robot.move()\",\n \"def move_girl2(girl):\\n global trainer_speed, girlcount2, girl_left2\\n if girl_left2:\\n for i in girl:\\n i.x -= trainer_speed\\n i.move_speed()\\n girlcount2 += 0.5\\n girl_frame = int(girlcount2) % 3\\n camera.draw(girl[girl_frame])\\n else:\\n for i in girl:\\n i.x += trainer_speed\\n i.move_speed()\\n girlcount2 += 0.5\\n girl_frame = int(girlcount2) % 3\\n camera.draw(girl[girl_frame + 3])\\n for each in girl:\\n if each.x < 20:\\n girl_left2 = False\\n if each.x > 780:\\n girl_left2 = True\",\n \"def main():\\r\\n\\r\\n movetwotimes()\\r\\n pick_beeper()\\r\\n move()\\r\\n turn_left()\\r\\n movetwotimes()\\r\\n put_beeper()\\r\\n turn_left()\\r\\n turn_left()\\r\\n movetwotimes()\\r\\n rotatethreetimes()\\r\\n movetwotimes()\\r\\n move()\\r\\n turn_left()\\r\\n turn_left()\",\n \"def move(self):\\n\\n # get the location we WOULD go to\\n newX = self.xcor() + self.dx\\n newY = self.ycor() + self.dy\\n while (abs (newX) > self.BOX_RANGE) or (abs(newY) > self.BOX_RANGE):\\n # print(\\\"choosing new direction... \\\",end=\\\"\\\")\\n self.chooseNewDirection()\\n # print(self.dx, self.dy)\\n newX = self.xcor() + self.dx\\n newY = self.ycor() + self.dy\\n\\n # now move our monster\\n super().move()\",\n \"def sling_action():\\n global mouse_distance\\n global rope_length\\n global angle\\n global mouse_x_pos\\n global mouse_y_pos\\n\\n #add code inside sling function\\n # Fixing bird to the sling rope\\n vec = vector((initial_x_sling, initial_y_sling), (mouse_x_pos, mouse_y_pos))\\n unit_vec = unit_vector(vec)\\n uv_1 = unit_vec[0]\\n uv_2 = unit_vec[1]\\n mouse_distance = distance(initial_x_sling, initial_y_sling, mouse_x_pos, mouse_y_pos) #point at which currrent bird id\\n fix_pos = (uv_1*rope_length+initial_x_sling, uv_2*rope_length+initial_y_sling)\\n highest_length = 102 #when stretched\\n\\n #to make bird stay within rope\\n x_redbird = mouse_x_pos - 20\\n y_redbird = mouse_y_pos - 20\\n if mouse_distance > rope_length:\\n pux, puy = fix_pos\\n pux -= 20\\n puy -= 20\\n first_pos = pux, puy\\n screen.blit(redbird, first_pos)\\n second_pos = (uv_1*highest_length+initial_x_sling, uv_2*highest_length+initial_y_sling) #current position\\n pygame.draw.line(screen, (255, 0, 0), (next_x_sling, next_y_sling), second_pos, 5) #catapult rope\\n screen.blit(redbird, first_pos)\\n pygame.draw.line(screen, (255, 0, 0), (initial_x_sling, initial_y_sling), second_pos, 5) #ANOTHER SIDE of catapult\\n else:\\n #when not fully stretched\\n mouse_distance += 10\\n third_pos = (uv_1*mouse_distance+initial_x_sling, uv_2*mouse_distance+initial_y_sling)\\n pygame.draw.line(screen, (0, 0, 0), (next_x_sling, next_y_sling), third_pos, 5)\\n screen.blit(redbird, (x_redbird, y_redbird))\\n pygame.draw.line(screen, (0, 0, 0), (initial_x_sling, initial_y_sling), third_pos, 5)\\n # Angle of impulse\\n\\n change_in_y = mouse_y_pos - initial_y_sling\\n change_in_x = mouse_x_pos - initial_x_sling\\n if change_in_x == 0:\\n dx = 0.00000000000001\\n angle = math.atan((float(change_in_y))/change_in_x)\",\n \"def move(self):\\r\\n return 'rock'\",\n \"def move(x,y):\\r\\n pass\",\n \"def move_girl(girl):\\n global trainer_speed, girlcount, girl_left\\n if girl_left:\\n for i in girl:\\n i.x -= trainer_speed\\n i.move_speed()\\n girlcount += 0.5\\n girl_frame = int(girlcount) % 3\\n camera.draw(girl[girl_frame])\\n else:\\n for i in girl:\\n i.x += trainer_speed\\n i.move_speed()\\n girlcount += 0.5\\n girl_frame = int(girlcount) % 3\\n camera.draw(girl[girl_frame + 3])\\n for each in girl:\\n if each.x < 20:\\n girl_left = False\\n if each.x > 780:\\n girl_left = True\",\n \"def example_move(self):\\n self.right() # start rotating right\\n time.sleep(1) # turn for a second\\n self.stop() # stop\\n self.servo(1000) # look right\\n time.sleep(.25) # give your head time to move\\n self.servo(2000) # look left\",\n \"def example_move(self):\\n self.right() # start rotating right\\n time.sleep(1) # turn for a second\\n self.stop() # stop\\n self.servo(1000) # look right\\n time.sleep(.25) # give your head time to move\\n self.servo(2000) # look left\",\n \"def turn():\\r\\n global turn_no\\r\\n turn_no += 1\\r\\n dlog('Starting Turn!')\\r\\n board_size = get_board_size()\\r\\n\\r\\n team = get_team()\\r\\n opp_team = Team.WHITE if team == Team.BLACK else team.BLACK\\r\\n dlog('Team: ' + str(team))\\r\\n\\r\\n robottype = get_type()\\r\\n dlog('Type: ' + str(robottype))\\r\\n\\r\\n if robottype == RobotType.PAWN:\\r\\n dlog('Human')\\r\\n\\r\\n r, c = get_location()\\r\\n dlog('My location is: ' + str(r) + ' ' + str(c))\\r\\n\\r\\n if team == Team.WHITE:\\r\\n forward = 1\\r\\n scan = 2\\r\\n else:\\r\\n forward = -1\\r\\n scan = -2\\r\\n \\r\\n # try capturing pieces\\r\\n if check_space_wrapper(r + forward, c + 1, board_size) == opp_team: # up and right\\r\\n capture(r + forward, c + 1)\\r\\n dlog('Captured at: (' + str(r + forward) + ', ' + str(c + 1) + ')') \\r\\n\\r\\n elif check_space_wrapper(r + forward, c - 1, board_size) == opp_team: # up and left\\r\\n capture(r + forward, c - 1)\\r\\n dlog('Captured at: (' + str(r + forward) + ', ' + str(c - 1) + ')')\\r\\n \\r\\n elif r + forward != -1 and r + forward != board_size and not check_space_wrapper(r + forward, c, board_size):\\r\\n try:\\r\\n if check_space(r, c-1) == get_team() or check_space(r-1, c-1) == get_team() or check_space(r, c+1) == get_team() or check_space(r-1, c+1) == get_team():\\r\\n move_forward()\\r\\n dlog('Moved forward!')\\r\\n except:\\r\\n pass\\r\\n\\r\\n \\r\\n else:\\r\\n board = get_board()\\r\\n dlog(str(board))\\r\\n if team == Team.WHITE:\\r\\n forward = 1\\r\\n index = 0\\r\\n\\r\\n else:\\r\\n forward = -1\\r\\n index = board_size - 1\\r\\n \\r\\n deep_accum = []\\r\\n c_indexes = []\\r\\n heuristic_accum = []\\r\\n heuristic = 0\\r\\n for c in range(board_size):\\r\\n close = []\\r\\n for r in range(board_size):\\r\\n dlog(str(check_space(r, c)))\\r\\n if check_space(r, c) == opp_team:\\r\\n if team == Team.WHITE:\\r\\n close.append(r)\\r\\n elif team == Team.BLACK:\\r\\n close.append(board_size-r-1)\\r\\n heuristic -= 1\\r\\n elif check_space(r, c) == team:\\r\\n heuristic += 1\\r\\n else:\\r\\n continue\\r\\n heuristic_accum.append([heuristic, c])\\r\\n if close != []:\\r\\n c_indexes.append(c)\\r\\n deep = sorted(close)\\r\\n deep_accum.append(deep[0])\\r\\n heuristic = 0\\r\\n close_index = sorted(list(zip(deep_accum, c_indexes)))\\r\\n for c in close_index:\\r\\n if c[0] == 0:\\r\\n continue \\r\\n col = c[1]\\r\\n weighted_val = heuristic_accum[col][0]-15\\r\\n heuristic_accum[col][0] = weighted_val\\r\\n break\\r\\n\\r\\n heuristic_accum = sorted(heuristic_accum)\\r\\n\\r\\n for heur in heuristic_accum:\\r\\n col = heur[1]\\r\\n if not check_space_wrapper(index, col, board_size) and not check_space_wrapper(index+forward, col+1, board_size) == opp_team and not check_space_wrapper(index+forward, col-1, board_size) == opp_team:\\r\\n spawn(index, col)\\r\\n dlog('Spawned unit at: (' + str(index) + ', ' + str(col) + ')')\\r\\n break\\r\\n\\r\\n bytecode = get_bytecode()\\r\\n dlog('Done! Bytecode left: ' + str(bytecode))\",\n \"def move(self):\\n movement = self.recognize_color()\\n if movement is not None:\\n if rand.random() <= self.proba_color:\\n movement()\\n else:\\n r = rand.random()\\n if r <= self.proba[0]:\\n self.turn_left()\\n elif r <= self.proba[0] + self.proba[1]:\\n self.move_forward()\\n else:\\n self.turn_right()\\n self.set_color()\",\n \"def run(self):\\n while self.direction != \\\"\\\":\\n if self.direction == \\\"decrease\\\":\\n if self.position > 200:\\n self.position -= 15\\n elif self.direction == \\\"increase\\\":\\n if self.position < 800:\\n self.position += 15\\n if self.direction != \\\"neutral\\\":\\n self.move_joint(self.position, 900)\\n time.sleep(0.1)\",\n \"def move(self):\\n \\n self.position = self.wander()\",\n \"def turn(self):\\n pass\",\n \"def move_buildings(self):\",\n \"def rop():\\n return\",\n \"def play_move(self,state):\\n raise AIError(\\\"Must be implemented for child class!\\\")\",\n \"def move_of_king_and_rook(self, from_row, from_col, to_row, to_col): \\n #provjere da li su kraljevi ili topovi inicirali pomijeranje\\n if(from_row == 7 and from_col == 0):\\n self.wrl_moved = True\\n elif(from_row == 7 and from_col == 7):\\n self.wrr_moved = True\\n elif(from_row == 7 and from_col == 4):\\n self.wk_moved = True\\n elif(from_row == 0 and from_col == 0):\\n self.brl_moved = True\\n elif(from_row == 0 and from_col == 7):\\n self.brr_moved = True\\n elif(from_row == 0 and from_col == 4):\\n self.bk_moved = True\\n \\n #provjera da li je neko pojeo topove\\n if(to_row == 7 and to_col == 0):\\n self.wrl_moved = True\\n elif(to_row == 7 and to_col == 7):\\n self.wrr_moved = True\\n elif(to_row == 0 and to_col == 0):\\n self.brl_moved = True\\n elif(to_row == 0 and to_col == 7):\\n self.brr_moved = True\",\n \"def move(self, board, move_dir):\\n if move_dir == \\\"right\\\":\\n # failsafe: do not move through other cars on board\\n if board.positions[self.get_rows()[0]][self.get_cols()[1] + (self.size - 1)].isupper() or board.positions[self.get_rows()[0]][self.get_cols()[1] + (self.size - 1)] == 'r':\\n print(\\\"No movement!\\\")\\n return board\\n \\n # give board correct new positions (characters)\\n else:\\n board.positions[self.get_rows()[0]][self.get_cols()[1] + (self.size - 1)] = self.name[0]\\n board.positions[self.get_rows()[0]][self.get_cols()[0]] = \\\"x\\\"\\n\\n # change car objects positions\\n for i, col in enumerate(self.position):\\n self.position[i] = str(self.get_rows()[0]) + \\\".\\\" + str(int(col[2]) + 1)\\n return board\\n elif move_dir == \\\"left\\\": \\n if board.positions[self.get_rows()[0]][self.get_cols()[0] - 1].isupper() or board.positions[self.get_rows()[0]][self.get_cols()[0] - 1] == 'r':\\n print(\\\"No movement!\\\")\\n return board\\n else: \\n board.positions[self.get_rows()[0]][self.get_cols()[0] - 1] = self.name[0]\\n board.positions[self.get_rows()[0]][self.get_cols()[1] + (self.size - 2)] = \\\"x\\\"\\n\\n for i, col in enumerate(self.position):\\n self.position[i] = str(self.get_rows()[0]) + \\\".\\\" + str(int(col[2]) - 1)\\n return board\\n elif move_dir == \\\"up\\\":\\n #print(board.positions[self.get_rows()[0] - 1][self.get_cols()[0]])\\n if board.positions[self.get_rows()[0] - 1][self.get_cols()[0]].isupper() or board.positions[self.get_rows()[0] - 1][self.get_cols()[0]] == 'r':\\n print(\\\"No movement!\\\")\\n return board\\n else:\\n board.positions[self.get_rows()[0] - 1][self.get_cols()[0]] = self.name[0]\\n board.positions[self.get_rows()[1] + (self.size - 2)][self.get_cols()[0]] = \\\"x\\\"\\n\\n for i, row in enumerate(self.position):\\n self.position[i] = str(int(row[0]) - 1) + \\\".\\\" + str(self.get_cols()[0])\\n\\n #print(board)\\n return board\\n elif move_dir == \\\"down\\\": \\n try: \\n if board.positions[self.get_rows()[1] + (self.size - 1)][self.get_cols()[0]].isupper() or board.positions[self.get_rows()[1] + (self.size - 1)][self.get_cols()[0]] == 'r':\\n print(\\\"No movement!\\\")\\n return board\\n except IndexError:\\n return board\\n else: \\n board.positions[self.get_rows()[0]][self.get_cols()[0]] = \\\"x\\\" \\n board.positions[self.get_rows()[1] + (self.size - 1)][self.get_cols()[0]] = self.name[0]\\n\\n for i, row in enumerate(self.position):\\n self.position[i] = str(int(row[0]) + 1) + \\\".\\\" + str(self.get_cols()[0]) \\n \\n #print(self.position)\\n #print(board)\\n \\n return board\\n else:\\n #print(\\\"NO MOVEMENT!\\\")\\n return board\",\n \"def __move(self):\\n if self.goal is None:\\n if self.tasks:\\n self.goal = self.tasks.pop()\\n self.goal_history.append(self.goal)\\n self.logger.log(\\n f\\\"Crewmate {self.agent_id} set as goal: {self.goal.name} in\\\" +\\n f\\\" {self.game_map.room_names[self.goal.room_id]}\\\",\\n Logger.LOG | Logger.PRINT_VISUAL)\\n else:\\n self.room = self.game_map.move_random(self)\\n self.location_history.append(self.room)\\n return\\n\\n if self.room is not self.goal.room_id:\\n self.room = self.game_map.next_toward(self, self.goal.room_id)\\n\\n # Log the current room we are in: Either the room we moved to, or the room that happens to be the goal room\\n self.location_history.append(self.room)\",\n \"def _onmove(self, event):\",\n \"def step(self, move):\",\n \"def move(player, direction):\\r\\n \\r\\n curRoom = world.maps.World.mapGrid[player.room]\\r\\n door = curRoom.dirs[direction]\\r\\n roomid = world.maps.World.doors[door].getExitRoom(curRoom.id)\\r\\n newRoom = world.maps.World.mapGrid[roomid]\\r\\n brokesneak = False\\r\\n \\r\\n if player.getAttr(SNEAKING) and not utils.gameutils.stealthRoll(player):\\r\\n player.setAttr(SNEAKING, False)\\r\\n sendToRoomNotPlayer(player, \\\"{0}You notice {1} sneaking out to the {2}.\\\".format(LRED, player, DIRS[direction])) \\r\\n sendToPlayer(player, \\\"{0}You make a sound\\\".format(LRED))\\r\\n brokesneak = True\\r\\n elif not player.getAttr(SNEAKING): \\r\\n sendToRoomNotPlayer( player, \\\"{0} left to the {1}.\\\".format(player, DIRS[direction])) \\r\\n \\r\\n del curRoom.players[player.name]\\r\\n newRoom.players[player.name] = player\\r\\n player.room = newRoom.id\\r\\n\\r\\n if brokesneak:\\r\\n sendToRoomNotPlayer(player, \\\"{0}You notice {1} sneaking into the room from the {2}.\\\".format(LRED, player, DIRS[OPPOSITEDIRS[direction]])) \\r\\n elif not player.getAttr(SNEAKING):\\r\\n sendToRoomNotPlayer(player, \\\"{0} entered from the {1}.\\\".format(player, DIRS[OPPOSITEDIRS[direction]])) \\r\\n \\r\\n displayRoom(player, player.room)\\r\\n player.stats[MOVING] = False\",\n \"def make_move(self):\\n\\n # If the agent is starting a game, make an \\n # initial move\\n if self.get_play_status() == False: \\n self.initial_move()\\n return\\n\\n # for speeds sake, allow the reflex agent to respond to manual\\n # input. comment out for automatic running.\\n x = int(input('hotwire x:'))\\n y = int(input('hotwire y:'))\\n return self.get_game_space().set_tile(x,y,self.get_affinity())\\n\\n # Check wheather the the agent side is going to \\n # win by making one move, make the move\\n # OR\\n # Check if the oponent has a compromising move \\n best_move = self.victory_check()\\n if best_move is None: best_move = self.counter_opponent_win()\\n if best_move is None: best_move = self.counter_opponent_adv()\\n if best_move is None: best_move = self.best_last_option()\\n if best_move != None: \\n x = best_move[0]\\n y = best_move[1]\\n return self.get_game_space().set_tile(x,y,self.get_affinity())\",\n \"def move(self, row, col, player):\",\n \"def move(self): #py:UR.move\\n RUR._UR.move_(self.body)\",\n \"def play_one_move(self):\\n self.print(\\\"top of move\\\")\\n # 1) grab three cups\\n c1 = self.take_cup_after(self.current_cup_idx())\\n c2 = self.take_cup_after(self.current_cup_idx())\\n c3 = self.take_cup_after(self.current_cup_idx())\\n print(f\\\"pick up: {c1}, {c2}, {c3}\\\")\\n self.print(\\\"after pickup\\\")\\n\\n # 2) find a destination cup\\n destination_idx = self.find_next_destination(self.current_cup)\\n print(\\n f\\\"destination index is {destination_idx}, cup is {self.cups[destination_idx]}\\\"\\n )\\n\\n # 3) insert cups back into the circle\\n self.add_cups(destination_idx, [c1, c2, c3])\\n self.print(\\\"after adding cups..\\\")\\n\\n # 4) select the next cup\\n self.select_next_cup()\\n\\n self.print(\\\"post move\\\")\",\n \"def castling(self, turn, ai):\\n\\n if self.board[self.coords.index(piece_class.KING_LOCATION[turn])].move_track == True:\\n return None\\n \\n \\n castling_queenside = [self.coords.index(piece_class.KING_LOCATION[turn]), self.coords.index(piece_class.KING_LOCATION[turn]) - 1,\\n self.coords.index(piece_class.KING_LOCATION[turn]) - 2, self.coords.index(piece_class.KING_LOCATION[turn]) - 3,\\n self.coords.index(piece_class.KING_LOCATION[turn]) - 4]\\n castling_kingside = [self.coords.index(piece_class.KING_LOCATION[turn]), self.coords.index(piece_class.KING_LOCATION[turn]) + 1,\\n self.coords.index(piece_class.KING_LOCATION[turn]) + 2, self.coords.index(piece_class.KING_LOCATION[turn]) + 3]\\n \\n if self.castling_valid(turn, castling_kingside):\\n self.board[self.coords.index(piece_class.KING_LOCATION[turn])].possible_moves.append(self.coords[castling_kingside[2]])\\n \\n if self.castling_valid(turn, castling_queenside):\\n self.board[self.coords.index(piece_class.KING_LOCATION[turn])].possible_moves.append(self.coords[castling_queenside[2]])\",\n \"def step(self, action):\\n # print(\\\"############################\\\")\\n # print(\\\"action: {}\\\".format(action))\\n\\n self.movement_complete.data = False\\n\\n # 1) Read last joint positions by getting the observation before acting\\n old_observation = self.get_obs()\\n\\n # 2) Get the new joint positions according to chosen action (actions here are the joint increments)\\n if self._joint_increment is None:\\n next_action_position = action\\n else:\\n next_action_position = self.get_action_to_position(action, old_observation[1:7])\\n\\n # 3) Move to position and wait for moveit to complete the execution\\n self.publisher_to_moveit_object.pub_joints_to_moveit(next_action_position)\\n # rospy.wait_for_message(\\\"/pickbot/movement_complete\\\", Bool)\\n while not self.movement_complete.data:\\n pass\\n\\n start_ros_time = rospy.Time.now()\\n while True:\\n # Check collision:\\n # invalid_collision = self.get_collisions()\\n # if invalid_collision:\\n # print(\\\">>>>>>>>>> Collision: RESET <<<<<<<<<<<<<<<\\\")\\n # observation = self.get_obs()\\n # reward = UMath.compute_reward(observation, -200, True)\\n # observation = self.get_obs()\\n # print(\\\"Test Joint: {}\\\".format(np.around(observation[1:7], decimals=3)))\\n # return U.get_state(observation), reward, True, {}\\n\\n elapsed_time = rospy.Time.now() - start_ros_time\\n if np.isclose(next_action_position, self.joints_state.position, rtol=0.0, atol=0.01).all():\\n break\\n elif elapsed_time > rospy.Duration(2): # time out\\n break\\n # time.sleep(s\\n\\n \\\"\\\"\\\"\\n #execute action as long as the current position is close to the target position and there is no invalid collision and time spend in the while loop is below 1.2 seconds to avoid beeing stuck touching the object and not beeing able to go to the desired position \\n time1=time.time()\\n while np.linalg.norm(np.asarray(self.joints_state.position)-np.asarray(next_action_position))>0.1 and self.get_collisions()==False and time.time()-time1<0.1: \\n rospy.loginfo(\\\"Not yet reached target position and no collision\\\")\\n \\\"\\\"\\\"\\n # 4) Get new observation and update min_distance after performing the action\\n new_observation = self.get_obs()\\n if new_observation[0] < self.min_distace:\\n self.min_distace = new_observation[0]\\n # print(\\\"observ: {}\\\".format( np.around(new_observation[1:7], decimals=3)))\\n\\n # 5) Convert Observations into state\\n state = U.get_state(new_observation)\\n\\n # 6) Check if its done, calculate done_reward\\n done, done_reward, invalid_contact = self.is_done(new_observation)\\n\\n # 7) Calculate reward based on Observatin and done_reward and update the accumulated Episode Reward\\n reward = UMath.compute_reward(new_observation, done_reward, invalid_contact)\\n\\n ### TEST ###\\n if done:\\n joint_pos = self.joints_state.position\\n print(\\\"Joint in step (done): {}\\\".format(np.around(joint_pos, decimals=3)))\\n ### END of TEST ###\\n\\n self.accumulated_episode_reward += reward\\n\\n self.episode_steps += 1\\n\\n return state, reward, done, {}\",\n \"def move(self):\\n if self.lanzar == True: # Permite el movimiento de la bala, solo ocurre cuando el jugador lo inique\\n self.rect.move_ip(0,-self.speed_bullet)\\n if self.rect.bottom < 100: # Permite eliminar el objeto cuando este alcanza una altura calculada con respecto a la dim de la pantalla\\n self.kill()\",\n \"def move(self):\\r\\n segments = len(self.all_turtles) - 1\\r\\n for i in range(len(self.all_turtles)):\\r\\n if segments == 0:\\r\\n self.all_turtles[segments].forward(MOVE_DISTANCE)\\r\\n else:\\r\\n new_x = self.all_turtles[segments - 1].xcor()\\r\\n new_y = self.all_turtles[segments - 1].ycor()\\r\\n self.all_turtles[segments].goto(new_x, new_y)\\r\\n segments -= 1\",\n \"def __move__(self):\\n v = self.velocity\\n p = self.position\\n p += v\\n self.rect.x = round(p.x)\\n self.rect.y = round(p.y)\",\n \"def automove(self):\\n if self.x < self.end_cinematic_x_pos:\\n self.x += self.SHIP_SPEED\\n if self.x > self.end_cinematic_x_pos:\\n self.x -= self.SHIP_SPEED\\n if self.y < self.end_cinematic_y_pos:\\n self.y += self.SHIP_SPEED\\n if self.y > self.end_cinematic_y_pos:\\n self.y -= self.SHIP_SPEED\",\n \"def __move_ball(self):\\n while not self.__game_is_over():\\n self.__ball.move(self.__dx, self.__dy)\\n self.__handle_wall_collision()\\n if self.__num_lives == 0:\\n self.__game_over_picture()\\n break\\n elif self.__bricks_total == 0:\\n self.__game_over_picture('You Win!!')\\n break\\n pause(FRAME_RATE)\",\n \"def handle_make(self, move):\\n# print \\\"handle make-\\\"+str(move.src)+\\\":\\\"+str(move.dst)\\n if not move: return\\n # remove en passant capture\\n if move.is_en_passant():\\n self.remove(move.dst + (8 if move.is_white() else -8))\\n if move.is_capture():\\n self.remove(move.dst)\\n\\n # move rook when castling\\n if move.is_castling_short():\\n if move.is_white():\\n self.move((63,61))\\n else:\\n self.move((7,5))\\n if move.is_castling_long():\\n if move.is_white():\\n self.move((56, 59))\\n else:\\n self.move((0, 3))\\n # Actual move itself.\\n self.move((move.src, move.dst))\\n\\n # Replace promoted piece.\\n if move.is_promotion():\\n self.remove(move.dst)\\n p_piece = move.get_promotion_piece()\\n if not move.is_white(): p_piece = p_piece.lower()\\n# print \\\"promoting to \\\"+p_piece+\\\" at \\\"+move.dst\\n self.put(move.dst, p_piece)\\n self.update_vars_list()\",\n \"def run(self):\\n return self.move_bat()\",\n \"def move_turtle(self):\\n self.forward(self.move_speed)\",\n \"def decide(self):\\n self.maybe_shoot()\\n next(self.move_cycle)\\n\\n pass\",\n \"def turn():\\n \\n robottype = get_type()\\n if robottype == RobotType.PAWN:\\n pawn_turn()\\n else:\\n overlord_turn()\\n bytecode = get_bytecode()\",\n \"def turn():\\n \\n robottype = get_type()\\n if robottype == RobotType.PAWN:\\n pawn_turn()\\n else:\\n overlord_turn()\\n bytecode = get_bytecode()\",\n \"def move(self):\\n \\\"\\\"\\\" Responsible for transformations \\\"\\\"\\\"\\n pos, com, success = self.perception \\n if self.destination is None:\\n return array([0,0])\\n\\n if not self.awake:\\n return array([0,0])\\n\\n\\n if self.phase == 4 and self.proper_formation is not None:\\n no_go = []\\n for i in range(0,len(self.proper_formation)):\\n if i != self.order and self.proper_formation[i][0] == self.proper_formation[self.order][0]:\\n no_go.append(self.transform(self.proper_formation[i][1] - self.position))\\n pos = merge_array_lists(pos, no_go)\\n\\n if self.phase == 2:\\n point = self.destination.copy() - self.position\\n elif self.phase > 2:\\n point = self.transform(self.destination.copy() - self.position)\\n else:\\n point = self.destination.copy()\\n\\n if not array_equal(point, array([0,0])):\\n reachable, path = findpathtoclosest(array([0,0]), point, pos)\\n \\n if len(path) == 0:\\n move = array([0,0]) \\n else:\\n move = path[0]\\n if not reachable and not array_equal(move,array([0,0])):\\n if self.phase == 2:\\n self.closest_i_could_get = path[-1] + self.position\\n elif self.phase > 2:\\n self.closest_i_could_get = self.transform2(path[-1]) + self.position\\n else:\\n self.closest_i_could_get = path[-1]\\n elif not reachable:\\n if self.phase > 1:\\n self.closest_i_could_get = self.position\\n else:\\n self.closest_i_could_get = array([0,0])\\n else:\\n self.closest_i_could_get = None\\n\\n if reachable and self.phase == 4 and array_equal(move,array([0,0])):\\n move = self.randomStep()\\n self.closest_i_could_get = None\\n\\n else:\\n move = array([0,0])\\n self.closest_i_could_get = None\\n\\n return move\",\n \"def move(self):\\n \\n # checks for bots nearby\\n next_move = self.follow()\\n \\n # finds a random move if no bot\\n if next_move is self.position:\\n self.position = self.wander()\\n else:\\n self.position = next_move\",\n \"def arm(self):\\n self.set_destination(0, 0, 0, 0)\\n\\n for _ in range(100):\\n self.local_pos_pub.publish(self.waypoint_g)\\n rospy.sleep(0.01)\\n\\n rospy.loginfo(CBLUE2 + \\\"Arming Drone\\\" + CEND)\\n\\n arm_request = CommandBoolRequest(True)\\n\\n while not rospy.is_shutdown() and not self.current_state_g.armed:\\n rospy.sleep(0.1)\\n response = self.arming_client(arm_request)\\n self.local_pos_pub.publish(self.waypoint_g)\\n else:\\n if response.success:\\n rospy.loginfo(CGREEN2 + \\\"Arming successful\\\" + CEND)\\n return 0\\n else:\\n rospy.logerr(CRED2 + \\\"Arming failed\\\" + CEND)\\n return -1\",\n \"def move(self, move):\\n raise NotImplementedError()\",\n \"def move(self, direction):\\n # Store the values in the connection dictionary in a list\\n self.room_num = self.current_room.connection[direction]\\n\\n # Check if there is a conditional movement and change the current room\\n if len(self.room_num) == 1:\\n self.current_room = self.rooms[int(self.room_num[0]) - 1]\\n else:\\n adventure.check(len(self.room_num))\",\n \"def move(self):\\n if self.ycor() > 280: self.y_dir = -1 # Set vertical movement to down if ball at top of screen\\n if self.xcor() > 380: self.x_dir = -1 # Set horizontal movement to left if ball at right of screen\\n if self.xcor() < -380: self.x_dir = 1 # Set horizontal movement to right if ball at left of screen\\n new_x = self.xcor() + self.x_dir * 2 # Define 2 spaces forward in set horizontal dir of travel\\n new_y = self.ycor() + self.y_dir * 2 # Define 2 spaces forward in set vertical dir of travel\\n self.goto(new_x, new_y) # Move ball to newly defined position\",\n \"def move2goal(self):\\n vel_msg = Twist()\\n\\n # Linear velocity in the x-axis.\\n vel_msg.linear.x = 0.4 # m/s\\n vel_msg.linear.y = 0\\n vel_msg.linear.z = 0\\n\\n # Angular velocity in the z-axis.\\n vel_msg.angular.x = 0\\n vel_msg.angular.y = 0\\n vel_msg.angular.z = 1.5 # rad/s\\n\\n # Starting point reference\\n goal_x = 1.0 \\n goal_y = 1.0\\n x_ref = 1.0\\n y_ref = 1.0\\n\\n # Previous Reference\\n x_prev_ref = 0.0\\n y_prev_ref = 0.0\\n theta_prev_ref = self.theta\\n vrefA = 0.5\\n wrefA = 0.0\\n \\n i = 0\\n tPx, tPy, tPTheta = self.initiate_trajectory(\\n x_ref, y_ref, vel_msg, \\n x_prev_ref, y_prev_ref, \\n theta_prev_ref, vrefA, wrefA\\n )\\n\\n x_prev_ref = tPx[0]\\n y_prev_ref = tPy[0]\\n theta_prev_ref = tPTheta[0]\\n\\n print(f'X TRAJECTORY: {tPx}\\\\nY TRAJECTORY: {tPy}\\\\nTHETA TRAJ: {tPTheta}')\\n print(f'ACTUAL THETA: {self.theta}')\\n\\n while not rospy.is_shutdown():\\n \\n if i >= 8:\\n i = 0\\n\\n x_ref = goal_x\\n y_ref = goal_y\\n\\n tPx, tPy, tPTheta = self.initiate_trajectory(\\n x_ref, y_ref, vel_msg, \\n x_prev_ref, y_prev_ref, \\n theta_prev_ref, vrefA, wrefA\\n )\\n # inputRef = ControllerInput(\\n # xref=x_ref,\\n # yref=y_ref,\\n # RstateX=self.x_position,\\n # RstateY=self.y_position,\\n # RstateTheta=self.theta,\\n # RstateVelocity=vel_msg.linear.x,\\n # RstateW=vel_msg.angular.z,\\n # xrefA=x_prev_ref,\\n # yrefA=y_prev_ref,\\n # thetarefA=theta_prev_ref,\\n # vrefA=vrefA,\\n # wrefA=wrefA\\n # )\\n\\n # rospy.loginfo(f'X: {self.x_position} \\\\tY: {self.y_position}\\\\t Theta: {self.theta} ')\\n # nmpc = NMPC_Controller(inputRef)\\n # tPx, tPy, tPTheta = nmpc.test_create_mini_path()\\n\\n # print(f'X TRAJECTORY: {tPx}\\\\nY TRAJECTORY: {tPy}\\\\nTHETA TRAJ: {tPTheta}')\\n # print(f'ACTUAL THETA: {self.theta}')\\n \\n # new_v, new_w = nmpc.start_optmizer()\\n # new_v = round(new_v, 4)\\n # new_w = round(new_w, 4)\\n\\n # print(new_v, new_w)\\n # rospy.loginfo(\\n # f'X: {self.x_position}, Y: {self.y_position}, THETA: {self.theta}')\\n \\n # self.velocity_publisher.publish(vel_msg)\\n # x_prev_ref = self.x_position\\n # y_prev_ref = self.y_position\\n # theta_prev_ref = self.theta\\n # vrefA = vel_msg.linear.x\\n # wrefA = vel_msg.angular.z\\n \\n\\n # theta_prev_ref = self.theta\\n # vel_msg.angular.z = 0.0\\n\\n\\n '''Update the linear & angular velocity'''\\n # vel_msg.linear.x = new_v\\n # vel_msg.angular.z = new_w\\n\\n if i < 8:\\n inputRef = ControllerInput(\\n xref = tPx[i],\\n yref = tPy[i],\\n RstateX = self.x_position,\\n RstateY = self.y_position,\\n RstateTheta = self.theta,\\n RstateVelocity = vel_msg.linear.x,\\n RstateW = vel_msg.angular.z,\\n xrefA = x_prev_ref,\\n yrefA = y_prev_ref,\\n thetarefA = theta_prev_ref,\\n vrefA = vrefA,\\n wrefA = wrefA\\n )\\n\\n nmpc = NMPC_Controller(inputRef)\\n new_v, new_w = nmpc.start_optmizer()\\n new_v = round(new_v, 4)\\n new_w = round(new_w, 4)\\n\\n print(f'(actual) X: {self.x_position}, Y: {self.x_position}, THETA: {self.theta}')\\n print(f'(desired) X: {tPx[i]}, Y: {tPy[i]}')\\n print(f'V: {vel_msg.linear.x}\\\\tW: {vel_msg.angular.z}')\\n\\n x_prev_ref = tPx[i-1]\\n y_prev_ref = tPy[i-1]\\n theta_prev_ref = tPTheta[i-1]\\n vrefA = vel_msg.linear.x\\n wrefA = vel_msg.angular.z\\n\\n vel_msg.linear.x = new_v\\n vel_msg.angular.z = new_w\\n # vel_msg.angular.z = 0.0\\n\\n print(f'index: {i}')\\n\\n distance = math.sqrt((self.x_position - tPx[i])**2 + (self.y_position - tPy[i])**2)\\n if distance < 0.3:\\n print(f'Distance: {distance}')\\n i+=1\\n\\n\\n self.velocity_publisher.publish(vel_msg)\\n self.rate.sleep()\\n\\n rospy.spin()\",\n \"def go(self, brd):\\n return self.makeMove(brd)\",\n \"def decide_next_move(self):\\n pass\",\n \"def act(self, action):\\n\\n self.get_frame(int(self.t))\\n\\n self.position = np.zeros((self.grid_size, self.grid_size))\\n\\n self.position[0:2,:]= -1\\n self.position[:,0:2] = -1\\n self.position[-2:, :] = -1\\n self.position[:, -2:] = -1\\n\\n self.position[self.x, self.y] = 1\\n if action == 0:\\n if self.x == self.grid_size-3:\\n self.x = self.x-1\\n else:\\n self.x = self.x + 1\\n elif action == 1:\\n if self.x == 2:\\n self.x = self.x+1\\n else:\\n self.x = self.x-1\\n elif action == 2:\\n if self.y == self.grid_size - 3:\\n self.y = self.y - 1\\n else:\\n self.y = self.y + 1\\n elif action == 3:\\n if self.y == 2:\\n self.y = self.y + 1\\n else:\\n self.y = self.y - 1\\n else:\\n RuntimeError('Error: action not recognized')\\n\\n self.t = self.t + 1\\n reward = self.board[self.x, self.y]\\n \\n self.hunter_move()\\n removals = []\\n for i in range(len(self.h_x)):\\n if self.x == self.h_x[i] and self.y == self.h_y[i]:\\n reward -= 100\\n removals.append(i)\\n \\n for i in sorted(removals, reverse=True):\\n del self.h_x[i]\\n del self.h_y[i]\\n \\n self.board[self.x, self.y] = 0\\n self.board_with_hunters[:,:] = 0\\n \\n for i in range(len(self.h_x)):\\n self.board_with_hunters[self.h_x[i],self.h_y[i]] = -100\\n \\n self.trajectory[self.x,self.y] = 1\\n game_over = self.t > self.max_time\\n state = np.concatenate((self.board.reshape(self.grid_size, self.grid_size,1),\\n self.position.reshape(self.grid_size, self.grid_size,1),\\n self.trajectory.reshape(self.grid_size, self.grid_size,1),\\n self.board_with_hunters.reshape(self.grid_size, self.grid_size,1)),axis=2)\\n state = state[self.x-2:self.x+3,self.y-2:self.y+3,:]\\n\\n return state, reward, game_over\",\n \"def _go(self, distance):\\n ende = self._position + self._orient * distance\\n self._goto(ende)\",\n \"def _move(self):\\n self.pos += self.direction # add direction vector\\n self.direction += self.gravity # add gravity to direction\\n self.direction = self.direction.elementwise() * self.drag # apply drag to direction\",\n \"def move_aim(self):\\n self.color = random.choice(COLORS)\\n self.x += 3 * self.speed_x / FPS\\n self.y += 3 * self.speed_y / FPS\\n self.r -= 1\\n self.draw_aim()\\n if self.r <= 10:\\n self.color = random.choice(COLORS)\\n self.x = randint(100, 1000)\\n self.y = randint(100, 800)\\n self.r = randint(50, 100)\\n self.speed_x = randint(-200, 200)\\n self.speed_y = randint(-200, 200)\\n if self.x >= 1100:\\n self.speed_x = randint(-100, -10)\\n if self.x <= 50:\\n self.speed_x = randint(10, 100)\\n if self.y >= 800:\\n self.speed_y = randint(-100, -10)\\n if self.y <= 50:\\n self.speed_y = randint(10, 100)\",\n \"def move_car(self):\\n a = self.h / 50\\n self.x += self.speed_x / FPS\\n if self.x + 170 * a >= 1100:\\n self.dir = -1\\n self.speed_x = -self.speed_x\\n if self.x - 170 * a <= 50:\\n self.dir = 1\\n self.speed_x = -self.speed_x\",\n \"def mouse_move(self, obj, event):\\n last_pos = self.iren.GetLastEventPosition()\\n next_pos = self.iren.GetEventPosition()\\n last_disp_coords = np.asarray([last_pos[0], last_pos[1], 0])\\n next_disp_coords = np.asarray([next_pos[0], next_pos[1], 0])\\n last_world_coords = self.display_to_world(last_disp_coords)\\n next_world_coords = self.display_to_world(next_disp_coords)\\n world_direction = (last_world_coords - next_world_coords)[0]\\n\\n if world_direction > 0:\\n direction = 'forwards'\\n elif world_direction < 0:\\n direction = 'backwards'\\n else:\\n direction = 'none'\\n\\n if self.cone_dir == 'start':\\n if direction == 'backwards':\\n self.start_base_x += .5\\n if self.start_base_x.is_integer():\\n ind = str(int(self.start_base_x))\\n isvalid = self.gaps.set_dragged_start(ind)\\n if isvalid:\\n self.ren_win.Render()\\n else:\\n self.start_base_x -= .5\\n return\\n\\n elif direction == 'forwards':\\n if self.start_base_x > 0:\\n self.start_base_x -= .5\\n if self.start_base_x.is_integer():\\n ind = str(int(self.start_base_x))\\n self.gaps.set_dragged_start(ind)\\n self.ren_win.Render()\\n\\n if self.cone_dir == 'end':\\n if direction == 'backwards':\\n if self.end_base_x > 0:\\n self.end_base_x -= .5\\n if self.end_base_x.is_integer():\\n ind = str(int(self.end_base_x))\\n self.gaps.set_dragged_end(ind)\\n self.ren_win.Render()\\n\\n elif direction == 'forwards':\\n self.end_base_x += .5\\n if self.end_base_x.is_integer():\\n ind = str(int(self.end_base_x))\\n isvalid = self.gaps.set_dragged_end(ind)\\n if isvalid:\\n self.ren_win.Render()\\n else:\\n self.end_base_x -= .5\\n return\",\n \"def move_rel(self):\\n pass\",\n \"def movement(self):\\n self.rect.left -= self.speedx #to move the asteroid to the left\",\n \"def move(self, direction):\\n try:\\n\\n if self.in_thing:\\n print(\\\"You have to get out of the \\\" + str(*self.in_thing[-1]) +\\n \\\" first\\\")\\n return self\\n if direction == 'north':\\n if self.finished_places == 12:\\n self.finished_places += 1\\n return North(self.items, self.finished_places)\\n if direction == 'up':\\n if self.finished_places == 4:\\n self.finished_places += 1\\n return Up(self.items, self.finished_places)\\n if direction == 'east':\\n if self.finished_places == 2:\\n self.finished_places += 1\\n return East(self.items, self.finished_places)\\n except AttributeError:\\n self.items = []\\n return self.move(direction)\\n print(' you didn\\\\'t listen to my very subtle hints, i know it was hard'\\n ' your lost now. if you remember the commands i told you you can'\\n ' go back to where you left off and continue, just type \\\"QUIT\\\"')\\n return Place(self.items, self.finished_places)\\n\\n # implement\\n # return new instance on class\",\n \"def play(self):\\n self._play(self._move_person)\",\n \"def play_step(self, action):\\n self.players[0].moving_left = False\\n self.players[0].moving_right = False\\n if action == MOVE_LEFT:\\n self.players[0].moving_left = True\\n for i in range(LOOP_AT_EACH_MOVE_UPDATE):\\n self.update(is_a_star=True)\\n if self.dead_player or not self.players[0].is_alive:\\n break\\n self.players[0].moving_left = False\\n if self.dead_player or not self.players[0].is_alive:\\n return\\n elif action == MOVE_RIGHT:\\n self.players[0].moving_right = True\\n for i in range(LOOP_AT_EACH_MOVE_UPDATE):\\n self.update(is_a_star=True)\\n if self.dead_player or not self.players[0].is_alive:\\n break\\n self.players[0].moving_right = False\\n if self.dead_player or not self.players[0].is_alive:\\n return\\n elif action == SHOOT:\\n if self.dead_player or not self.players[0].is_alive:\\n self.update(is_a_star=True)\\n return\\n if not self.players[0].weapon.is_active:\\n self.players[0].shoot()\\n for i in range(LOOP_AT_EACH_MOVE_UPDATE):\\n self.update(is_a_star=True)\\n if self.dead_player or not self.players[0].is_alive:\\n break\\n if self.dead_player or not self.players[0].is_alive:\\n return\",\n \"def computer_move(board,move,player):\\r\\n com_execution(board, move, player)\",\n \"def knock_back(self):\\n FORWARD_VEL = -2\\n\\n self.rect.x += self.x_vel\\n\\n if self.name == 'player':\\n if self.rect.x >= (self.origin_pos[0] + 10):\\n self.x_vel = FORWARD_VEL\\n elif self.rect.x <= self.origin_pos[0]:\\n self.rect.x = self.origin_pos[0]\\n self.state = 'battle resting'\\n self.x_vel = 0\\n else:\\n if self.rect.x <= (self.origin_pos[0] - 10):\\n self.x_vel = 2\\n elif self.rect.x >= self.origin_pos[0]:\\n self.rect.x = self.origin_pos[0]\\n self.state = 'battle resting'\\n self.x_vel = 0\",\n \"def make_move(self):\\n self.owner = self.game.current_turn\\n self.status = 'X' if self.owner == self.game.creator else 'O'\\n ####\\n #Random turn??\\n ####\\n self.save(update_fields=['status', 'owner'])\\n\\n # Add log entry for move\\n self.game.add_log(f'cell made at ({self.row}, {self.col}) by {self.owner}')\\n\\n # Set the current turn for the other player if game is not over\\n # Check if find winner\\n if self.game.check_win(cell=self) or\\\\\\n self.game.get_all_game_cells().filter(status='EMPTY').count() == 0:\\n print(\\\"Winnnnnnnn\\\")\\n self.game.mark_complete(winner=self.owner)\\n\\n # Switch player turn\\n self.game.switch_turn()\\n\\n # Let the game know about the move and result\\n self.send_game_update()\",\n \"def move_player(direction):\\n global ZERO_BASE_PLYR_POS\\n if direction == \\\"north\\\":\\n ZERO_BASE_PLYR_POS -= 10\\n elif direction == \\\"south\\\":\\n ZERO_BASE_PLYR_POS += 10\\n elif direction == \\\"west\\\":\\n ZERO_BASE_PLYR_POS -= 1\\n elif direction == \\\"east\\\":\\n ZERO_BASE_PLYR_POS += 1\\n \\n sleep(0.5) # all moves have a 0.5 second delay\\n \\n show_ground_feature()\",\n \"def move_marble(self, coordinates, direction):\\n pass\",\n \"def move_asteroids(self):\\n for i in range(len(self.asteroid_type)):\\n self.game.move(self.asteroid_type[i], -self.asteroid_speed[i], 0)\\n for i in range(len(self.asteroid_id_e)):\\n self.game.move(self.asteroid_id_e[i], -self.asteroid_speed_e[i], 0)\",\n \"def move_to_position2(self):\",\n \"def moving(self):\\n if self.sensors['proximity'][0].imminent_collision:\\n self.motion.stopMove()\\n self.posture.goToPosture('Stand', self.SPEED)\\n self.behavior_ = self.BEHAVIORS.thinking\\n\\n elif not self.motion.moveIsActive():\\n # Walker will always keep moving forward,\\n # if no obstacles are found.\\n move_to = (self.STRIDE, 0, 0)\\n self.motion.post.moveTo(*move_to)\\n logger.info('Walking: %s', move_to)\",\n \"def moveable(self, board):\\n # horizontal\\n if self.direction == \\\"horizontal\\\":\\n # the position to which the car wants to move is either 1 more or 1 less column wise\\n right = self.get_cols()[1] + self.size - 1\\n left = self.get_cols()[0] - 1\\n # check if right or left is out of the boards margins \\n if right > board.width_height:\\n move_left = board.positions[self.get_rows()[0]][left]\\n move_right = None\\n elif left < 0:\\n move_right = board.positions[self.get_rows()[0]][right]\\n move_left = None\\n else: \\n move_right = board.positions[self.get_rows()[0]][right]\\n move_left = board.positions[self.get_rows()[0]][left]\\n\\n # try to move left and right\\n if move_right == \\\"x\\\" and move_left == \\\"x\\\":\\n return \\\"leftright\\\"\\n elif move_right == \\\"x\\\":\\n return \\\"right\\\"\\n elif move_left == \\\"x\\\":\\n return \\\"left\\\"\\n else: \\n return \\\"none\\\"\\n \\n # vertical\\n else:\\n up = self.get_rows()[0] - 1\\n #print(up)\\n down = self.get_rows()[1] + self.size - 1\\n # check if up or down is out of the boards margins \\n if up < 0:\\n # no room on the board for upward movement\\n move_down = board.positions[down][self.get_cols()[0]]\\n move_up = None\\n elif down > board.width_height:\\n # no room on the board for downward movement\\n move_up = board.positions[up][self.get_cols()[0]]\\n move_down = None\\n else:\\n # both up and down are possible positions on the board\\n move_up = board.positions[up][self.get_cols()[0]]\\n move_down = board.positions[down][self.get_cols()[0]]\\n\\n # try to move up and down\\n if move_down == \\\"x\\\" and move_up == \\\"x\\\":\\n return \\\"updown\\\"\\n elif move_up == \\\"x\\\":\\n return \\\"up\\\"\\n elif move_down == \\\"x\\\":\\n return \\\"down\\\"\\n else: \\n return \\\"none\\\"\",\n \"def ProcessPlayerTileMove(self):\\r\\n # If the player steps on a door\\r\\n if 'doors' in self.data:\\r\\n for key in self.data['doors']:\\r\\n door = self.data['doors'][key]\\r\\n \\r\\n if self.game.player.pos.IsSame(door['pos']):\\r\\n Log('Entering door: %s: %s' % (key, door['map']))\\r\\n self.game.SelectMap(door['map'])\\r\\n \\r\\n if 'player_pos' in door:\\r\\n (player_x, player_y) = door['player_pos']\\r\\n else:\\r\\n (player_x, player_y) = self.game.map.data['player']['starting_pos']\\r\\n \\r\\n self.game.player.pos = rpg_base.Position(player_x, player_y)\\r\\n \\r\\n # Center the player on the screen\\r\\n tiles = self.game.map.GetTilesPerScreen()\\r\\n self.game.map.offset[0] = self.game.player.pos.x - tiles[0] / 2\\r\\n self.game.map.offset[1] = self.game.player.pos.y - tiles[1] / 2\\r\\n \\r\\n # Check if we have stepped next to an actor\\r\\n for name in self.actors:\\r\\n actor = self.actors[name]\\r\\n if self.game.player.pos.GetDistance(actor.pos) <= 1.0:\\r\\n self.game.dialogue = rpg_dialogue.Dialogue(self.game, actor)\\r\\n break\",\n \"def moveBasedOnRetreatAction(self, time_passed):\\n cpos = self.toScreenCoordinate()\\n mpos = pygame.mouse.get_pos()\\n toMouse = Vector2.from_points(cpos,mpos)\\n toMouse.normalize()\\n rheading = -toMouse\\n \\n heading = self.heading\\n angle_between = heading.angle_between(rheading)\\n if angle_between>=-30 and angle_between<=30:\\n return\\n \\n distance = time_passed * self.speed\\n movement = rheading * distance\\n x = movement.get_x()\\n y = movement.get_y()\\n if not self.checkCollision(x, y) and self.checkValidCoord(x, y):\\n self.move(x, y)\",\n \"def move(self):\\n for agent in self.agents:\\n if not agent.fidelity:\\n options = agent.get_move_options(agent.hex, self.kernel_size, None, extend=True)\\n target = random36.choices(population=options,weights=[x.quality**2 for x in options])\\n agent.move(target[0])\",\n \"def move_car(self):\\n import interface\\n self.reset_position()\\n print(\\\"move car during %f s...\\\" % self.portion_duration*self.nbr_portions)\\n\\n X, Y = [], []\\n t_debut = time.time()\\n while time.time() - t_debut < self.portion_duration*self.nbr_portions:\\n current_time = time.time() - t_debut\\n # On fait bouger les 4 roues.\\n for numero_roue, speed in enumerate(self(current_time)):\\n print(numero_roue)\\n interface.move_wheel(numero_roue+1, speed)\\n\\n # Recuperation de la position reele\\n (x, y), _ = interface.get_position()\\n X.append(x)\\n Y.append(y)\\n\\n interface.move_wheel(\\\"\\\", 0) # La voiture s'arette a la fin.\\n print(\\\"\\\\tterminate\\\")\\n return x, y\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7103401","0.7072305","0.6994009","0.69171774","0.68605596","0.6859176","0.6715968","0.66763794","0.6669459","0.6627489","0.6609323","0.6591225","0.6581653","0.65799546","0.65799546","0.6564521","0.65348953","0.6508202","0.64825106","0.6464618","0.6457356","0.64455193","0.6440003","0.64319277","0.6396406","0.6396015","0.6393564","0.6387987","0.63852215","0.63589424","0.6341879","0.6340497","0.63383555","0.6317116","0.6313125","0.6313125","0.6310552","0.6296015","0.6295576","0.62879556","0.6287693","0.6287056","0.6282281","0.6273938","0.6272346","0.62689126","0.62611943","0.62573695","0.6256801","0.62538815","0.62515783","0.62417936","0.6238533","0.62291414","0.6225168","0.6202384","0.6185096","0.61809826","0.6175588","0.61755174","0.617144","0.61692375","0.6159461","0.6153792","0.61525804","0.61508584","0.61508584","0.6145815","0.6140555","0.61291975","0.6129126","0.61166275","0.61132276","0.6112084","0.61119914","0.61074644","0.61054534","0.61009216","0.60999566","0.60962087","0.609443","0.60921246","0.608484","0.60826176","0.6069942","0.6061176","0.60567594","0.605354","0.6052478","0.60511476","0.6044556","0.6037775","0.60356516","0.60346895","0.60289156","0.60259116","0.6025705","0.6022907","0.60137236","0.6004601"],"string":"[\n \"0.7103401\",\n \"0.7072305\",\n \"0.6994009\",\n \"0.69171774\",\n \"0.68605596\",\n \"0.6859176\",\n \"0.6715968\",\n \"0.66763794\",\n \"0.6669459\",\n \"0.6627489\",\n \"0.6609323\",\n \"0.6591225\",\n \"0.6581653\",\n \"0.65799546\",\n \"0.65799546\",\n \"0.6564521\",\n \"0.65348953\",\n \"0.6508202\",\n \"0.64825106\",\n \"0.6464618\",\n \"0.6457356\",\n \"0.64455193\",\n \"0.6440003\",\n \"0.64319277\",\n \"0.6396406\",\n \"0.6396015\",\n \"0.6393564\",\n \"0.6387987\",\n \"0.63852215\",\n \"0.63589424\",\n \"0.6341879\",\n \"0.6340497\",\n \"0.63383555\",\n \"0.6317116\",\n \"0.6313125\",\n \"0.6313125\",\n \"0.6310552\",\n \"0.6296015\",\n \"0.6295576\",\n \"0.62879556\",\n \"0.6287693\",\n \"0.6287056\",\n \"0.6282281\",\n \"0.6273938\",\n \"0.6272346\",\n \"0.62689126\",\n \"0.62611943\",\n \"0.62573695\",\n \"0.6256801\",\n \"0.62538815\",\n \"0.62515783\",\n \"0.62417936\",\n \"0.6238533\",\n \"0.62291414\",\n \"0.6225168\",\n \"0.6202384\",\n \"0.6185096\",\n \"0.61809826\",\n \"0.6175588\",\n \"0.61755174\",\n \"0.617144\",\n \"0.61692375\",\n \"0.6159461\",\n \"0.6153792\",\n \"0.61525804\",\n \"0.61508584\",\n \"0.61508584\",\n \"0.6145815\",\n \"0.6140555\",\n \"0.61291975\",\n \"0.6129126\",\n \"0.61166275\",\n \"0.61132276\",\n \"0.6112084\",\n \"0.61119914\",\n \"0.61074644\",\n \"0.61054534\",\n \"0.61009216\",\n \"0.60999566\",\n \"0.60962087\",\n \"0.609443\",\n \"0.60921246\",\n \"0.608484\",\n \"0.60826176\",\n \"0.6069942\",\n \"0.6061176\",\n \"0.60567594\",\n \"0.605354\",\n \"0.6052478\",\n \"0.60511476\",\n \"0.6044556\",\n \"0.6037775\",\n \"0.60356516\",\n \"0.60346895\",\n \"0.60289156\",\n \"0.60259116\",\n \"0.6025705\",\n \"0.6022907\",\n \"0.60137236\",\n \"0.6004601\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6157545"},"document_rank":{"kind":"string","value":"63"}}},{"rowIdx":84992,"cells":{"query":{"kind":"string","value":"Resets all attributes after a game has been played by AIvAI"},"document":{"kind":"string","value":"def resets_attributes(self):\n \n self.path_dict = None\n self.poss_dict = None\n self.check_dict = None\n self.long_dict = None\n self.rep_counter = 0\n self.cap_counter = 0\n \n self.board = []\n self.coords = []\n self.chess_coords = []\n self.empty = \" \""},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def reset_game_fields(self):\n self.player = None\n self.event_status = 0 # set accordingly for player achievments\n self.event_status_list = [False, False, False, False, False, False, False, False]\n self.spaces = []\n self.characters = []\n self.exits = []\n self.items = []","def reset(self):\n self.ai.reset()","def reset_attributes():\n \n print(\"reset_attributes\")\n\n attributes = {\n \"scores\": {},\n \"state\": \"Start\",\n \"current_player\": 0,\n \"current_round\": 0,\n \"current_question\": \"\",\n \"sess_questions\":[],\n \"question_index\": 0,\n \"speech_output\": WELCOME_MESSAGE,\n \"reprompt_text\": HELP_MESSAGE}\n \n return attributes","def reset(self):\n print \"did a reset\"\n self.ai.reset()\n # print self.__repr__()","def reset(self):\n\n\t\tself.games_played = 0\n\t\tself.memory = {}\n\t\tself.movesThisGen = list()","def reset(self):\n\n\t\tself.games_played = 0\n\t\tself.memory = {}\n\t\tself.movesThisGen = list()","def reset_game(self):\n self.bHasWon = False\n self.game_over = False\n self.game_active = False\n self.player_points = 0\n self.enemy_points = 0\n self.remainingBalls = 0","def resetSkills(self):\r\n \"\"\" Reset the default attributes \"\"\"\r\n self.player['level'] = 1\r\n self.player['xp'] = 0\r\n self.player['credits'] = int(startCredits)\r\n self.player['popup'] = int(popupStatus)\r\n self.player['name'] = self.player.name\r\n self.player['lastconnected'] = int(time.time())\r\n\r\n \r\n \"\"\" Iterate through the skills list then set each skill to 0 \"\"\"\r\n for skill in skills:\r\n self.player[skill.name] = 0\r\n\r\n \"\"\" Slay the player \"\"\"\r\n es.server.queuecmd(\"damage %s %s\" % (self.userid, es.getplayerprop(self.userid, \"CBasePlayer.m_iHealth\")))\r\n \r\n \"\"\" Notify the user \"\"\"\r\n tell(self.userid, 'info deleted')","def reset(self, *args):\n self.state = GameStates.playing\n self.human = evilrps.Player('Human', self.get_player_choice)\n self.ai = evilrps.Player('AI', evilrps.create_ai())\n self.game = evilrps.Game(self.human, self.ai)","def reset_variables(self) -> None:\n self.attributs = {}\n self.data = []","def _reset_game(self):\n\t\tself.is_play = True\n\t\tself.is_gameover = False\n\t\tself.bolan.rect.y = self.bolan.default_y\n\t\tself.scoreboard.score = 0\n\t\tself.obstacles._reset_positions()","def resetPlayerDefaultAttributes(self):\r\n debug.write(\"[SourceRPG] Resetting player %s default attribute\" % self.name, 2)\r\n self.playerAttributes['baseHealth'] = 100\r\n self.playerAttributes['maxHealth'] = 100\r\n self.playerAttributes['maxSpeed'] = 1.0\r\n self.playerAttributes['maxGravity'] = 1.0\r\n self.playerAttributes['minStealth'] = 255\r\n self.playerAttributes['maxArmor'] = 100\r\n debug.write(\"[SourceRPG] Attributes reset\", 2)","def reset(self):\n self.score = None\n self.true = None\n self.meta = None","def reset(self):\n\n self._trained = False\n for attribute in self._trained_attributes:\n setattr(self, attribute, None)\n \n return None","def reset(self):\n self.damage_dealt = 0\n self.kills = 0\n self.got_killed = False\n self.fitness = 0","def reset(self):\r\n\t\tself.player_selected_actions = np.zeros((self.num_actions,), int)\r\n\t\tself.player_reward = np.zeros((self.num_timesteps,))\r\n\t\tself.player_optimum = np.zeros_like(self.player_reward, dtype=int)","def reset(self):\n\n game.reset()\n sm.get_screen('game_screen').reset()","def reset_game(self):\n self.ships_left = self.settings.ship_limit\n self.fleets_left = self.settings.fleet_waves\n self.target_miss = self.settings.target_max_miss\n self.reset_level()","def reset(self):\n # type: ()->None\n self._ifAttributes = {}\n self._ifAttributes['bridge-opts'] = {}\n self._ifAttributes['up'] = []\n self._ifAttributes['down'] = []\n self._ifAttributes['pre-up'] = []\n self._ifAttributes['pre-down'] = []\n self._ifAttributes['post-up'] = []\n self._ifAttributes['post-down'] = []","def reset_game():\n global x_pos, o_pos, frames, count\n\n count = 0\n x_pos = []\n o_pos = []\n result.set(\"Your Turn!\")\n for x in frames:\n for y in x.winfo_children():\n y.config(text=\" \", state='normal')","def reset_stats(self):\n print(\"Reseting stats\")\n self.player_lives = self.ai_stts.player_lives\n self.score = 0\n self.level = 1","def reset(self):\n self.disable()\n self.shoot_again = False\n self.balls_added_live = 0","def test_reset(self):\n\t\tfor AI in self.list_of_AIs:\n\t\t\tAI.reset()","def reset(self):\n self.current_exposure = None\n self.scores = {}","def reset(self):\n self.obstacles = []\n self._tick = 0","def gameReset(self):\n\n\t\tself.valuesEntered=0\n\t\tself.isActive=True\n\t\tself.currentPlayer=1\n\t\tfor i in range(self.gridSize**2):\n\t\t\tself.gridValues[i]='-'\n\n\n\t\treturn None","def reset(self):\n self.startSpawing()\n self.resetGirders()\n self.girderManager.startGirdersMoving()\n self.background1.startBackgroundMoving()\n self.background2.startBackgroundMoving()\n self.gameEnd = False\n self.player.setY(self.screenDim.y * 0.25)\n self.score = 0","def reset(self):\n self.creature.reset()\n self.current_world = copy.copy(self.init_world_rewards)\n self.draw_board()\n self.last_move = None","def clear_cached_attributes(self):\n setattr(self, '_atoms', None)\n setattr(self, '_bonds', None)\n setattr(self, '_rings', None)\n setattr(self, '_ring_systems', None)","def __reset(self):\n self.game_moves_history = []\n self.player_id = None","def reset(self):\n #print(self.agent.n_sim, self.score)\n # update the highscore if needed\n if self.isHuman and (self.score > self.highscore[0]):\n self.highscore[0] = self.score\n update_score(self.highscore, self.args.highscore_filename)\n elif (not self.isHuman) and (self.score > self.highscore[1]):\n self.highscore[1] = self.score\n update_score(self.highscore, self.args.highscore_filename)\n \n # reset the simulation\n self.bird = Bird(args)\n self.env = Environment(args, bird=self.bird)\n self.score = 0\n self.inGame = False\n self.hasJumped = False\n self.t = 0\n \n # update the state of the agent\n if not self.isHuman:\n state = self.env.get_state()\n self.agent.reset(state)\n \n # update the image \n if not self.muteDisplay:\n self.im.set_data(self.env.map)\n display_info(self.score, self.highscore, text_handle=self.text_score) \n plt.draw()","async def _reset_games(self, ctx):\n data = await self.get_data(ctx)\n await data.Games.clear()\n msg = (\"{0.name} ({0.id}) restored casino games to \"\n \"default settings.\").format(ctx.author)\n await ctx.send(msg)","def reset(self):\n # reset rewards\n self.reward = torch.zeros(self.batch_size, self.game_num_agents)\n\n self.has_been = torch.zeros(self.batch_size, self.nsteps, self.game_num_agents)\n\n self.terminal = torch.zeros(self.batch_size)\n\n self.step_counter = 1\n\n self.active","def Reset(self): \r\n self.player_draws.clear()\r\n self.player_points.clear()\r\n self.num_players=0\r\n self.cards.shuffle()","def reset(self) -> None:\n self.moves = OrderedDict()\n self.turn = random.choice(self.players)\n self.winner = None","def reset(self) -> None:\n self.best = self.mode_worse\n self.cooldown_counter = 0\n self.num_bad_epochs = 0","def reset(self):\n self.game_status = game_status.GameStatus(copy.deepcopy(self.initial_rosters),\n self.game_status.game_date)\n self._current_event_ndx = 0","def reset(self):\n self.last_round = False\n self.last_player = None\n self.scores = [0] * self.num_players\n self.current_player = 0\n self.turn = 0\n self.roll = None","def initialize_object_attributes(self, ai_game):\r\n self.ship_bullets = ai_game.ship_bullets\r\n self.alien_bullets = ai_game.alien_bullets\r\n self.aliens = ai_game.aliens\r\n self.explosions = ai_game.explosions\r\n self.stats = ai_game.stats\r\n self.sb = ai_game.sb\r\n self.ship = ai_game.ship","def reset(self):\n self.players = [Player(), Player(), Player(), Player()]\n self.hist = []\n self.round = 1\n self.current_player = 0\n self.first_winner_was = -1\n self.current_dice = -1\n self.observation_pending = False\n self.current_move_pieces = []\n self.current_enemys = []\n self.current_start_attempts = 0\n self.game_winners = []","def reset(self):\n self._stat = CardMeta()","def reset_hands(self):\r\n\r\n self.player_rock = False\r\n self.player_paper = False\r\n self.player_scissors = False\r\n self.opp_rock = False\r\n self.opp_paper = False\r\n self.opp_scissors = False","def round_reset(self):\n\t\tfor p in self.players: p.reset_state()\n\t\t#self.player = player(self.player.name, self.player.cards)\n\t\tself.small_blind.money -= self.blind // 2\n\t\tself.small_blind.stake += self.blind // 2\n\t\tself.big_blind.money -= self.blind\n\t\tself.big_blind.stake += self.blind\n\t\tself.last_bet_by = self.big_blind\n\t\tself.bank = self.blind\n\t\tself.bank_part = self.blind\n\t\tself.table_cards = []\n\t\tself.stage = stages.nocards","def reset_game_state(self):\n self.is_playing = False","def resetMission(myShip, myplanet):\n for var in vars(myShip):\n if var != 'name' and var != 'accel':\n myShip.setvar(str(var), None)\n for var in vars(myplanet):\n if var != 'name':\n myplanet.setvar(str(var), None)","def reset(self):\n self.test = 0\n self.hit = 0","def reset(self):\n self.test = 0\n self.hit = 0","def reset(self):\n self.test = 0\n self.hit = 0","def reset_all(self):\r\n for skill_string in self.__skills:\r\n self.reset(skill_string)\r\n self.check_skill_requirements(skill_string)","def reset_game(self):\n self._buttons_active = False\n self._laser_label.clear()\n self.add_items_labels()\n self.add_question_marks()\n QtCore.QTimer.singleShot(conf.second_display_duration,\n self.enter_phase_two)","def reset(self):\n\n # Ending variables\n self.time_idle = 0\n self.time_episode = 0\n self.done_time_idle = False\n self.done_falling = False\n self.done_time_episode = False\n\n # hero variables\n self.last_location = None\n self.last_velocity = 0\n\n # Sensor stack\n self.prev_image_0 = None\n self.prev_image_1 = None\n self.prev_image_2 = None\n\n self.last_heading_deviation = 0","def reset_cams(self):\n self.pseye.reset_cams()","def reset(self):\n self.dynamic_predictions = {}\n self.position = 0\n self.references = []","def clear_attributes(self):\n self.attrs = etad.AttributeContainer()","def reset(self) -> np.ndarray:\n # Initialize a new gamefield\n self.game.reset_game()\n self.turns_count = 0\n\n self.payoff_fields = constants.PAYOFF_LOCATIONS\n self.special_payoff_fields = random.sample(self.payoff_fields, 1)\n\n return self.make_alternative_states()","def _reset(self) -> None:\n\n self._reset_slots()\n self._paused = False\n self.latest_action = {}\n self.latest_message = [] #clear a list in python\n self.latest_bot_utterance = BotUttered.empty()\n self.followup_action = ACTION_LISTEN_NAME\n self.active_loop = {}","def reset(self):\r\n self._p = self._p_init\r\n self._r = self._r_init\r\n self._v = self._v_init\r\n self._w = self._w_init\r\n self._a = self._a_init\r\n self._alpha = self._alpha_init","def unload(self) -> None:\n for attr in self._attrs:\n setattr(self, attr, None)","def reset_fight():\n global FIGHT\n global BEARSTRENGTHVAL\n global MOVEMENT\n global ACTION\n global VALID_MOVE\n global FIGHTMOVES\n global GRIZZLY_BEAR\n\n FIGHT = False\n #ENEMY_LIST[ZERO_BASE_PLYR_POS] = len(ENEMY_LIST) # Last item is always None\n ENEMY_LIST[ZERO_BASE_PLYR_POS] = 4\n FIGHTMOVES = 0\n VALID_MOVE = True\n BEARSTRENGTHVAL = 100\n MOVEMENT = False\n ACTION = True\n GRIZZLY_BEAR = False\n process_context(\"None\")","def reset(self):\n \n if self._config.fix_seed:\n self._init_seed = (self._init_seed + 1) % 2**32 # set_seed requires int\n self.game.set_seed(self._init_seed)\n\n super(ShootEnv, self).reset()\n\n self._killcount = 0.0\n self._ammo = self.game.get_game_variable(GameVariable.AMMO2)\n self._health = self.game.get_game_variable(GameVariable.HEALTH)\n\n return self._get_observation()","def reset(self):\n Simulation.reset(self)","def reset(self):\n self.num_steps = 0\n self.world_state = self.action = None","def _reset(self):\n self.characterID = None\n self.myName = u''","def reset_stats(self):\n self.lives_left = self.game_settings.lives\n self.score = 0\n self.level = 1","def reset(self):\r\n # TODO: have reset flag such that it forces all the bottom changes\r\n self.pwm_freq = self._default[\"pwm_freq\"]\r\n self.gate_logic = self._default[\"gate_logic\"]\r\n self.max_pwm = self._default[\"max_pwm\"]\r\n self.lase_on_power_up = self._default[\"lase_on_power_up\"]\r\n\r\n self.mode = self._default[\"mode\"]\r\n self.lase = self._default[\"lase\"]\r\n self.percent = self._default[\"percent\"] # in percent\r","def _reset(self):\n\n # Checking one attribute is enough, because they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_\n del self.n_samples_seen_\n del self.mean_\n del self.var_","def reset(self):\r\n\t\t# Performs Player superclass reset.\r\n\t\tsuper().reset()\r\n\r\n\t\t# Resets the action preferences to the default value of 0 for all\r\n\t\t# timesteps and actions.\r\n\t\tself.H_gradient = np.zeros((self.num_timesteps, self.num_actions), dtype=float)","def _reset(self):\n\n # Checking one attribute is enough, becase they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_\n del self.mean_\n del self.var_","def _reset(self):\n\n # Checking one attribute is enough, becase they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_","def _reset(self):\n\n # Checking one attribute is enough, becase they are all set together\n # in partial_fit\n if hasattr(self, 'scale_'):\n del self.scale_","def reset_stats(self):\r\n self.ships_left = self.ai_settings.ship_limit\r\n self.score = 0\r\n self.level = 1","def reset(self):\n self.params.resetParams()","def reset(self):\n\n # person of interest\n self.person = None\n\n #coordinates of the person edwin's interacting with\n self.coordx = 0\n self.coordy = 0\n self.coordz = 0\n\n #edwin's own coordinates\n self.edwinx = 0\n self.edwiny = 0\n self.edwinz = 0\n\n #looping\n self.running = True\n\n #found or naw\n self.detected = False\n\n #tracking the user's information\n self.head = None\n self.torso = None\n self.Rhand = None\n self.Lhand = None\n\n # status\n self.status = None","def _hard_reset(self):\n self._reset_specific_envs(np.ones_like(self.episodes_done))\n self._update_other_info()","def reset(self):\n\n self.ids.score.text = '0'\n self.ids.end_button.text = 'submit'\n self.ids.end_button.disabled = True\n self.ids.end_button.opacity = 0\n self.time = None\n\n board = self.ids.board\n\n for item in board.children:\n if isinstance(item, Space) is True:\n space = game.spacelist[int(item.number)]\n item.text = ''\n item.atom = space.atom\n item.guess = False\n item.correct = False\n item.disabled = False\n\n elif isinstance(item, Marker) is True:\n item.text = ''\n item.disabled = False\n\n for i in range(1, 6):\n self.ids['tracker' + str(i)].color = scheme.white","def reset_turn_stats(self):\n\n # Set the attribute value to 0\n self._current_score = 0","def resetSettings(self):\n\n # it does this 4 times because for some reason it would not grab everything one time through. Investigate\n for i in range(4):\n\n networkNode = self.returnNetworkNode\n attrs = cmds.listAttr(networkNode, ud=True)\n\n for attr in attrs:\n attrType = str(cmds.getAttr(networkNode + \".\" + attr, type=True))\n\n if attrType == \"double\":\n cmds.setAttr(networkNode + \".\" + attr, lock=False)\n cmds.setAttr(networkNode + \".\" + attr, 0, lock=True)\n\n if attrType == \"bool\":\n cmds.setAttr(networkNode + \".\" + attr, lock=False)\n cmds.setAttr(networkNode + \".\" + attr, True, lock=True)\n\n if attrType == \"enum\":\n cmds.setAttr(networkNode + \".\" + attr, lock=False)\n cmds.setAttr(networkNode + \".\" + attr, 0, lock=True)\n\n # relaunch the UI\n self.updateSettingsUI()\n self.applyModuleChanges(self)","def clear_data(self):\n self.game_list.clear()\n self.game_scores.clear()","def __reset__(self):\n\n for i in self.__dict__.keys():\n self.__dict__[i] = None","def reset(self):\n self.reset_traits(('grow_hair', 'n_scaling_params', 'scale_x',\n 'scale_y', 'scale_z', 'rot_x', 'rot_y', 'rot_z',\n 'trans_x', 'trans_y', 'trans_z'))","def init_game_setting(self):\n ##################\n # YOUR CODE HERE #\n ##################\n self.state = np.zeros((1, 80, 80))\n self.clear_action()","def _reset(self) -> None:\n self.images = []\n self.activations = []\n self.labels = []\n self.preds = []\n self.n_found = 0","def reset_stats(self):\r\n self.pepes_left = self.ai_settings.pepe_limit\r\n self.score = 0\r\n self.level = 1","def reset(self, **kwargs):\n self.player = None\n self.player_list = list()\n\n self.events.post('mc_reset_phase_1')\n self.events.post('mc_reset_phase_2')\n self.events.post('mc_reset_phase_3')","def reset(self):\r\n\t\t# Performs Player superclass reset.\r\n\t\tsuper().reset()\r\n\r\n\t\t# Resets the Q value estimate to the default initial_Q for all actions.\r\n\t\tself.player_Q = np.ones_like(self.player_selected_actions, dtype=float) * self.initial_Q","def reset(self):\r\n\t\t# Performs Player superclass reset.\r\n\t\tsuper().reset()\r\n\r\n\t\t# Resets the Q value estimate to the default initial_Q for all actions.\r\n\t\tself.player_Q = np.ones_like(self.player_selected_actions, dtype=float) * self.initial_Q","def reset_components(self):\n self.menu_pipes = [PipeSet()]\n self.pipes = [PipeSet()]\n self.frame_number = 0 # Reset this one as it's also used by the actual game loop\n self.player_points = 0","def reset_obstacles(self):\n self.obstacles = np.array([])","def reset_state(self):\n for name in self._buffers:\n self._buffers[name] = self._defaults[name]","def reset(self, starting_player):\n self.__turn = starting_player.value\n self.__turns_played = 0\n self.__winner = MarkerType.NONE\n self.__loser = MarkerType.NONE\n\n for y in range(Settings.SIZE_Y):\n for x in range(Settings.SIZE_X):\n self.__grid[y][x] = MarkerType.NONE\n\n self.__state = GameState.PLAYING","def specific_reset(self) -> None:\n\n # first, set agent xy and adjust its height\n self.agent.specific_reset()\n agent_pos = np.zeros(3)\n agent_pos = np.concatenate((agent_pos[:2], [self.agent.init_xyz[2]]))\n self.agent.set_position(agent_pos)\n\n # second, reset obstacle positions\n if len(self.obstacles) > 0:\n obs_init_pos = env_utils.generate_obstacles_init_pos(\n num_obstacles=len(self.obstacles),\n agent_pos=self.agent.get_position(),\n goal_pos=np.array([]), # no goal in gather task\n world=self.world,\n min_allowed_distance=self.obstacle_obstacle_distance,\n agent_obstacle_distance=self.agent_obstacle_distance\n )\n for i, ob in enumerate(self.obstacles):\n ob.set_position(obs_init_pos[i])\n\n # finally, make all collected objects visible again\n [ob.update_visuals(make_visible=True) for ob in self.obstacles]","def interactive_reset(self):\n # Set the initial state\n\n self.dataset.reset()\n\n self.current_turn = 0\n self.current_function = None\n self.query = \"\"\n self.query_vector = np.ones(self.dataset.getVocabularySize())\n self.keywords={\"provided\":set(), \"rejected\":set()}\n self.functions_rejected=set()\n self.result_index = 0\n self.dont_know = False\n\n self.history={\n 'system_action': {\"action\": self.SYSTEM_OPENING_ACT},\n 'user_action': None\n }","def reset(self):\n self.objects = []\n hero = GameObject(self.__new_position(), 1, 1, 2, None, \"hero\")\n self.objects.append(hero)\n goal = GameObject(self.__new_position(), 1, 1, 1, 1, \"goal\")\n self.objects.append(goal)\n fire = GameObject(self.__new_position(), 1, 1, 0, -1, \"fire\")\n self.objects.append(fire)\n goal2 = GameObject(self.__new_position(), 1, 1, 1, 1, \"goal\")\n self.objects.append(goal2)\n fire2 = GameObject(self.__new_position(), 1, 1, 0, -1, \"fire\")\n self.objects.append(fire2)\n goal3 = GameObject(self.__new_position(), 1, 1, 1, 1, \"goal\")\n self.objects.append(goal3)\n goal4 = GameObject(self.__new_position(), 1, 1, 1, 1, \"goal\")\n self.objects.append(goal4)\n # print(self.objects)\n self.state = self.render_env()\n return self.state","def reset(self):\r\n # reset Wheel encoders\r\n self.start_time = time.time()\r\n [left_start, right_start] = self.Roomba.Query(43, 44)\r\n self.Motion.reset(left_start, right_start)\r\n # reset bumper\r\n self.bumper.reset()\r\n\r\n #reset grid world data\r\n self.action=[0.0,0.0]\r\n self.grid_state= [0,0,0]\r\n self.real_state = [0.0, 0.0, 0.0]\r\n self.trans_model = None\r\n pass","def reset_attributes(self):\n\n self.ell = None\n self.ell_jacobian = None\n self.ell_hessian = None\n\n self.ell_hyperparam = None\n self.ell_jacobian_hyperparam = None\n self.ell_hessian_hyperparam = None\n\n self.Y = None\n self.Cinv = None\n self.C = None\n self.Mz = None\n self.MMz = None\n self.sigma2 = None\n self.sigma02 = None\n self.Kninv = None\n self.KnpKninv = None\n\n self.Y_C_Mz_hyperparam = None\n self.sigma_hyperparam = None\n self.MMz_hyperparam = None\n self.Kninv_KnpKninv_hyperparam = None","def specific_reset(self) -> None:\n self.agent.specific_reset() # reset joints\n new_pos = self.agent.init_xyz\n new_pos[:2] = np.random.uniform(-0.01, 0.01, 2)\n self.agent.set_position(new_pos)\n self.old_potential = self.calculate_task_potential()","def reset(self):\n self.bbox = None\n self.true = None\n self.meta = None","def _soft_reset(self):\n self._reset_specific_envs(self.episodes_done)\n self._update_other_info()","def clear_attrs(self):\n self._attributes.clear()","def _reset(self):\n self._set(\"_n_init_features\", None)\n self._set(\"_n_output_features\", None)\n self._set(\"_n_intervals\", None)\n self._set(\"_mapper\", {})\n self._set(\"_cpp_preprocessor\", None)\n self._set(\"_fitted\", False)"],"string":"[\n \"def reset_game_fields(self):\\n self.player = None\\n self.event_status = 0 # set accordingly for player achievments\\n self.event_status_list = [False, False, False, False, False, False, False, False]\\n self.spaces = []\\n self.characters = []\\n self.exits = []\\n self.items = []\",\n \"def reset(self):\\n self.ai.reset()\",\n \"def reset_attributes():\\n \\n print(\\\"reset_attributes\\\")\\n\\n attributes = {\\n \\\"scores\\\": {},\\n \\\"state\\\": \\\"Start\\\",\\n \\\"current_player\\\": 0,\\n \\\"current_round\\\": 0,\\n \\\"current_question\\\": \\\"\\\",\\n \\\"sess_questions\\\":[],\\n \\\"question_index\\\": 0,\\n \\\"speech_output\\\": WELCOME_MESSAGE,\\n \\\"reprompt_text\\\": HELP_MESSAGE}\\n \\n return attributes\",\n \"def reset(self):\\n print \\\"did a reset\\\"\\n self.ai.reset()\\n # print self.__repr__()\",\n \"def reset(self):\\n\\n\\t\\tself.games_played = 0\\n\\t\\tself.memory = {}\\n\\t\\tself.movesThisGen = list()\",\n \"def reset(self):\\n\\n\\t\\tself.games_played = 0\\n\\t\\tself.memory = {}\\n\\t\\tself.movesThisGen = list()\",\n \"def reset_game(self):\\n self.bHasWon = False\\n self.game_over = False\\n self.game_active = False\\n self.player_points = 0\\n self.enemy_points = 0\\n self.remainingBalls = 0\",\n \"def resetSkills(self):\\r\\n \\\"\\\"\\\" Reset the default attributes \\\"\\\"\\\"\\r\\n self.player['level'] = 1\\r\\n self.player['xp'] = 0\\r\\n self.player['credits'] = int(startCredits)\\r\\n self.player['popup'] = int(popupStatus)\\r\\n self.player['name'] = self.player.name\\r\\n self.player['lastconnected'] = int(time.time())\\r\\n\\r\\n \\r\\n \\\"\\\"\\\" Iterate through the skills list then set each skill to 0 \\\"\\\"\\\"\\r\\n for skill in skills:\\r\\n self.player[skill.name] = 0\\r\\n\\r\\n \\\"\\\"\\\" Slay the player \\\"\\\"\\\"\\r\\n es.server.queuecmd(\\\"damage %s %s\\\" % (self.userid, es.getplayerprop(self.userid, \\\"CBasePlayer.m_iHealth\\\")))\\r\\n \\r\\n \\\"\\\"\\\" Notify the user \\\"\\\"\\\"\\r\\n tell(self.userid, 'info deleted')\",\n \"def reset(self, *args):\\n self.state = GameStates.playing\\n self.human = evilrps.Player('Human', self.get_player_choice)\\n self.ai = evilrps.Player('AI', evilrps.create_ai())\\n self.game = evilrps.Game(self.human, self.ai)\",\n \"def reset_variables(self) -> None:\\n self.attributs = {}\\n self.data = []\",\n \"def _reset_game(self):\\n\\t\\tself.is_play = True\\n\\t\\tself.is_gameover = False\\n\\t\\tself.bolan.rect.y = self.bolan.default_y\\n\\t\\tself.scoreboard.score = 0\\n\\t\\tself.obstacles._reset_positions()\",\n \"def resetPlayerDefaultAttributes(self):\\r\\n debug.write(\\\"[SourceRPG] Resetting player %s default attribute\\\" % self.name, 2)\\r\\n self.playerAttributes['baseHealth'] = 100\\r\\n self.playerAttributes['maxHealth'] = 100\\r\\n self.playerAttributes['maxSpeed'] = 1.0\\r\\n self.playerAttributes['maxGravity'] = 1.0\\r\\n self.playerAttributes['minStealth'] = 255\\r\\n self.playerAttributes['maxArmor'] = 100\\r\\n debug.write(\\\"[SourceRPG] Attributes reset\\\", 2)\",\n \"def reset(self):\\n self.score = None\\n self.true = None\\n self.meta = None\",\n \"def reset(self):\\n\\n self._trained = False\\n for attribute in self._trained_attributes:\\n setattr(self, attribute, None)\\n \\n return None\",\n \"def reset(self):\\n self.damage_dealt = 0\\n self.kills = 0\\n self.got_killed = False\\n self.fitness = 0\",\n \"def reset(self):\\r\\n\\t\\tself.player_selected_actions = np.zeros((self.num_actions,), int)\\r\\n\\t\\tself.player_reward = np.zeros((self.num_timesteps,))\\r\\n\\t\\tself.player_optimum = np.zeros_like(self.player_reward, dtype=int)\",\n \"def reset(self):\\n\\n game.reset()\\n sm.get_screen('game_screen').reset()\",\n \"def reset_game(self):\\n self.ships_left = self.settings.ship_limit\\n self.fleets_left = self.settings.fleet_waves\\n self.target_miss = self.settings.target_max_miss\\n self.reset_level()\",\n \"def reset(self):\\n # type: ()->None\\n self._ifAttributes = {}\\n self._ifAttributes['bridge-opts'] = {}\\n self._ifAttributes['up'] = []\\n self._ifAttributes['down'] = []\\n self._ifAttributes['pre-up'] = []\\n self._ifAttributes['pre-down'] = []\\n self._ifAttributes['post-up'] = []\\n self._ifAttributes['post-down'] = []\",\n \"def reset_game():\\n global x_pos, o_pos, frames, count\\n\\n count = 0\\n x_pos = []\\n o_pos = []\\n result.set(\\\"Your Turn!\\\")\\n for x in frames:\\n for y in x.winfo_children():\\n y.config(text=\\\" \\\", state='normal')\",\n \"def reset_stats(self):\\n print(\\\"Reseting stats\\\")\\n self.player_lives = self.ai_stts.player_lives\\n self.score = 0\\n self.level = 1\",\n \"def reset(self):\\n self.disable()\\n self.shoot_again = False\\n self.balls_added_live = 0\",\n \"def test_reset(self):\\n\\t\\tfor AI in self.list_of_AIs:\\n\\t\\t\\tAI.reset()\",\n \"def reset(self):\\n self.current_exposure = None\\n self.scores = {}\",\n \"def reset(self):\\n self.obstacles = []\\n self._tick = 0\",\n \"def gameReset(self):\\n\\n\\t\\tself.valuesEntered=0\\n\\t\\tself.isActive=True\\n\\t\\tself.currentPlayer=1\\n\\t\\tfor i in range(self.gridSize**2):\\n\\t\\t\\tself.gridValues[i]='-'\\n\\n\\n\\t\\treturn None\",\n \"def reset(self):\\n self.startSpawing()\\n self.resetGirders()\\n self.girderManager.startGirdersMoving()\\n self.background1.startBackgroundMoving()\\n self.background2.startBackgroundMoving()\\n self.gameEnd = False\\n self.player.setY(self.screenDim.y * 0.25)\\n self.score = 0\",\n \"def reset(self):\\n self.creature.reset()\\n self.current_world = copy.copy(self.init_world_rewards)\\n self.draw_board()\\n self.last_move = None\",\n \"def clear_cached_attributes(self):\\n setattr(self, '_atoms', None)\\n setattr(self, '_bonds', None)\\n setattr(self, '_rings', None)\\n setattr(self, '_ring_systems', None)\",\n \"def __reset(self):\\n self.game_moves_history = []\\n self.player_id = None\",\n \"def reset(self):\\n #print(self.agent.n_sim, self.score)\\n # update the highscore if needed\\n if self.isHuman and (self.score > self.highscore[0]):\\n self.highscore[0] = self.score\\n update_score(self.highscore, self.args.highscore_filename)\\n elif (not self.isHuman) and (self.score > self.highscore[1]):\\n self.highscore[1] = self.score\\n update_score(self.highscore, self.args.highscore_filename)\\n \\n # reset the simulation\\n self.bird = Bird(args)\\n self.env = Environment(args, bird=self.bird)\\n self.score = 0\\n self.inGame = False\\n self.hasJumped = False\\n self.t = 0\\n \\n # update the state of the agent\\n if not self.isHuman:\\n state = self.env.get_state()\\n self.agent.reset(state)\\n \\n # update the image \\n if not self.muteDisplay:\\n self.im.set_data(self.env.map)\\n display_info(self.score, self.highscore, text_handle=self.text_score) \\n plt.draw()\",\n \"async def _reset_games(self, ctx):\\n data = await self.get_data(ctx)\\n await data.Games.clear()\\n msg = (\\\"{0.name} ({0.id}) restored casino games to \\\"\\n \\\"default settings.\\\").format(ctx.author)\\n await ctx.send(msg)\",\n \"def reset(self):\\n # reset rewards\\n self.reward = torch.zeros(self.batch_size, self.game_num_agents)\\n\\n self.has_been = torch.zeros(self.batch_size, self.nsteps, self.game_num_agents)\\n\\n self.terminal = torch.zeros(self.batch_size)\\n\\n self.step_counter = 1\\n\\n self.active\",\n \"def Reset(self): \\r\\n self.player_draws.clear()\\r\\n self.player_points.clear()\\r\\n self.num_players=0\\r\\n self.cards.shuffle()\",\n \"def reset(self) -> None:\\n self.moves = OrderedDict()\\n self.turn = random.choice(self.players)\\n self.winner = None\",\n \"def reset(self) -> None:\\n self.best = self.mode_worse\\n self.cooldown_counter = 0\\n self.num_bad_epochs = 0\",\n \"def reset(self):\\n self.game_status = game_status.GameStatus(copy.deepcopy(self.initial_rosters),\\n self.game_status.game_date)\\n self._current_event_ndx = 0\",\n \"def reset(self):\\n self.last_round = False\\n self.last_player = None\\n self.scores = [0] * self.num_players\\n self.current_player = 0\\n self.turn = 0\\n self.roll = None\",\n \"def initialize_object_attributes(self, ai_game):\\r\\n self.ship_bullets = ai_game.ship_bullets\\r\\n self.alien_bullets = ai_game.alien_bullets\\r\\n self.aliens = ai_game.aliens\\r\\n self.explosions = ai_game.explosions\\r\\n self.stats = ai_game.stats\\r\\n self.sb = ai_game.sb\\r\\n self.ship = ai_game.ship\",\n \"def reset(self):\\n self.players = [Player(), Player(), Player(), Player()]\\n self.hist = []\\n self.round = 1\\n self.current_player = 0\\n self.first_winner_was = -1\\n self.current_dice = -1\\n self.observation_pending = False\\n self.current_move_pieces = []\\n self.current_enemys = []\\n self.current_start_attempts = 0\\n self.game_winners = []\",\n \"def reset(self):\\n self._stat = CardMeta()\",\n \"def reset_hands(self):\\r\\n\\r\\n self.player_rock = False\\r\\n self.player_paper = False\\r\\n self.player_scissors = False\\r\\n self.opp_rock = False\\r\\n self.opp_paper = False\\r\\n self.opp_scissors = False\",\n \"def round_reset(self):\\n\\t\\tfor p in self.players: p.reset_state()\\n\\t\\t#self.player = player(self.player.name, self.player.cards)\\n\\t\\tself.small_blind.money -= self.blind // 2\\n\\t\\tself.small_blind.stake += self.blind // 2\\n\\t\\tself.big_blind.money -= self.blind\\n\\t\\tself.big_blind.stake += self.blind\\n\\t\\tself.last_bet_by = self.big_blind\\n\\t\\tself.bank = self.blind\\n\\t\\tself.bank_part = self.blind\\n\\t\\tself.table_cards = []\\n\\t\\tself.stage = stages.nocards\",\n \"def reset_game_state(self):\\n self.is_playing = False\",\n \"def resetMission(myShip, myplanet):\\n for var in vars(myShip):\\n if var != 'name' and var != 'accel':\\n myShip.setvar(str(var), None)\\n for var in vars(myplanet):\\n if var != 'name':\\n myplanet.setvar(str(var), None)\",\n \"def reset(self):\\n self.test = 0\\n self.hit = 0\",\n \"def reset(self):\\n self.test = 0\\n self.hit = 0\",\n \"def reset(self):\\n self.test = 0\\n self.hit = 0\",\n \"def reset_all(self):\\r\\n for skill_string in self.__skills:\\r\\n self.reset(skill_string)\\r\\n self.check_skill_requirements(skill_string)\",\n \"def reset_game(self):\\n self._buttons_active = False\\n self._laser_label.clear()\\n self.add_items_labels()\\n self.add_question_marks()\\n QtCore.QTimer.singleShot(conf.second_display_duration,\\n self.enter_phase_two)\",\n \"def reset(self):\\n\\n # Ending variables\\n self.time_idle = 0\\n self.time_episode = 0\\n self.done_time_idle = False\\n self.done_falling = False\\n self.done_time_episode = False\\n\\n # hero variables\\n self.last_location = None\\n self.last_velocity = 0\\n\\n # Sensor stack\\n self.prev_image_0 = None\\n self.prev_image_1 = None\\n self.prev_image_2 = None\\n\\n self.last_heading_deviation = 0\",\n \"def reset_cams(self):\\n self.pseye.reset_cams()\",\n \"def reset(self):\\n self.dynamic_predictions = {}\\n self.position = 0\\n self.references = []\",\n \"def clear_attributes(self):\\n self.attrs = etad.AttributeContainer()\",\n \"def reset(self) -> np.ndarray:\\n # Initialize a new gamefield\\n self.game.reset_game()\\n self.turns_count = 0\\n\\n self.payoff_fields = constants.PAYOFF_LOCATIONS\\n self.special_payoff_fields = random.sample(self.payoff_fields, 1)\\n\\n return self.make_alternative_states()\",\n \"def _reset(self) -> None:\\n\\n self._reset_slots()\\n self._paused = False\\n self.latest_action = {}\\n self.latest_message = [] #clear a list in python\\n self.latest_bot_utterance = BotUttered.empty()\\n self.followup_action = ACTION_LISTEN_NAME\\n self.active_loop = {}\",\n \"def reset(self):\\r\\n self._p = self._p_init\\r\\n self._r = self._r_init\\r\\n self._v = self._v_init\\r\\n self._w = self._w_init\\r\\n self._a = self._a_init\\r\\n self._alpha = self._alpha_init\",\n \"def unload(self) -> None:\\n for attr in self._attrs:\\n setattr(self, attr, None)\",\n \"def reset_fight():\\n global FIGHT\\n global BEARSTRENGTHVAL\\n global MOVEMENT\\n global ACTION\\n global VALID_MOVE\\n global FIGHTMOVES\\n global GRIZZLY_BEAR\\n\\n FIGHT = False\\n #ENEMY_LIST[ZERO_BASE_PLYR_POS] = len(ENEMY_LIST) # Last item is always None\\n ENEMY_LIST[ZERO_BASE_PLYR_POS] = 4\\n FIGHTMOVES = 0\\n VALID_MOVE = True\\n BEARSTRENGTHVAL = 100\\n MOVEMENT = False\\n ACTION = True\\n GRIZZLY_BEAR = False\\n process_context(\\\"None\\\")\",\n \"def reset(self):\\n \\n if self._config.fix_seed:\\n self._init_seed = (self._init_seed + 1) % 2**32 # set_seed requires int\\n self.game.set_seed(self._init_seed)\\n\\n super(ShootEnv, self).reset()\\n\\n self._killcount = 0.0\\n self._ammo = self.game.get_game_variable(GameVariable.AMMO2)\\n self._health = self.game.get_game_variable(GameVariable.HEALTH)\\n\\n return self._get_observation()\",\n \"def reset(self):\\n Simulation.reset(self)\",\n \"def reset(self):\\n self.num_steps = 0\\n self.world_state = self.action = None\",\n \"def _reset(self):\\n self.characterID = None\\n self.myName = u''\",\n \"def reset_stats(self):\\n self.lives_left = self.game_settings.lives\\n self.score = 0\\n self.level = 1\",\n \"def reset(self):\\r\\n # TODO: have reset flag such that it forces all the bottom changes\\r\\n self.pwm_freq = self._default[\\\"pwm_freq\\\"]\\r\\n self.gate_logic = self._default[\\\"gate_logic\\\"]\\r\\n self.max_pwm = self._default[\\\"max_pwm\\\"]\\r\\n self.lase_on_power_up = self._default[\\\"lase_on_power_up\\\"]\\r\\n\\r\\n self.mode = self._default[\\\"mode\\\"]\\r\\n self.lase = self._default[\\\"lase\\\"]\\r\\n self.percent = self._default[\\\"percent\\\"] # in percent\\r\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, because they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\\n del self.n_samples_seen_\\n del self.mean_\\n del self.var_\",\n \"def reset(self):\\r\\n\\t\\t# Performs Player superclass reset.\\r\\n\\t\\tsuper().reset()\\r\\n\\r\\n\\t\\t# Resets the action preferences to the default value of 0 for all\\r\\n\\t\\t# timesteps and actions.\\r\\n\\t\\tself.H_gradient = np.zeros((self.num_timesteps, self.num_actions), dtype=float)\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, becase they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\\n del self.mean_\\n del self.var_\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, becase they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\",\n \"def _reset(self):\\n\\n # Checking one attribute is enough, becase they are all set together\\n # in partial_fit\\n if hasattr(self, 'scale_'):\\n del self.scale_\",\n \"def reset_stats(self):\\r\\n self.ships_left = self.ai_settings.ship_limit\\r\\n self.score = 0\\r\\n self.level = 1\",\n \"def reset(self):\\n self.params.resetParams()\",\n \"def reset(self):\\n\\n # person of interest\\n self.person = None\\n\\n #coordinates of the person edwin's interacting with\\n self.coordx = 0\\n self.coordy = 0\\n self.coordz = 0\\n\\n #edwin's own coordinates\\n self.edwinx = 0\\n self.edwiny = 0\\n self.edwinz = 0\\n\\n #looping\\n self.running = True\\n\\n #found or naw\\n self.detected = False\\n\\n #tracking the user's information\\n self.head = None\\n self.torso = None\\n self.Rhand = None\\n self.Lhand = None\\n\\n # status\\n self.status = None\",\n \"def _hard_reset(self):\\n self._reset_specific_envs(np.ones_like(self.episodes_done))\\n self._update_other_info()\",\n \"def reset(self):\\n\\n self.ids.score.text = '0'\\n self.ids.end_button.text = 'submit'\\n self.ids.end_button.disabled = True\\n self.ids.end_button.opacity = 0\\n self.time = None\\n\\n board = self.ids.board\\n\\n for item in board.children:\\n if isinstance(item, Space) is True:\\n space = game.spacelist[int(item.number)]\\n item.text = ''\\n item.atom = space.atom\\n item.guess = False\\n item.correct = False\\n item.disabled = False\\n\\n elif isinstance(item, Marker) is True:\\n item.text = ''\\n item.disabled = False\\n\\n for i in range(1, 6):\\n self.ids['tracker' + str(i)].color = scheme.white\",\n \"def reset_turn_stats(self):\\n\\n # Set the attribute value to 0\\n self._current_score = 0\",\n \"def resetSettings(self):\\n\\n # it does this 4 times because for some reason it would not grab everything one time through. Investigate\\n for i in range(4):\\n\\n networkNode = self.returnNetworkNode\\n attrs = cmds.listAttr(networkNode, ud=True)\\n\\n for attr in attrs:\\n attrType = str(cmds.getAttr(networkNode + \\\".\\\" + attr, type=True))\\n\\n if attrType == \\\"double\\\":\\n cmds.setAttr(networkNode + \\\".\\\" + attr, lock=False)\\n cmds.setAttr(networkNode + \\\".\\\" + attr, 0, lock=True)\\n\\n if attrType == \\\"bool\\\":\\n cmds.setAttr(networkNode + \\\".\\\" + attr, lock=False)\\n cmds.setAttr(networkNode + \\\".\\\" + attr, True, lock=True)\\n\\n if attrType == \\\"enum\\\":\\n cmds.setAttr(networkNode + \\\".\\\" + attr, lock=False)\\n cmds.setAttr(networkNode + \\\".\\\" + attr, 0, lock=True)\\n\\n # relaunch the UI\\n self.updateSettingsUI()\\n self.applyModuleChanges(self)\",\n \"def clear_data(self):\\n self.game_list.clear()\\n self.game_scores.clear()\",\n \"def __reset__(self):\\n\\n for i in self.__dict__.keys():\\n self.__dict__[i] = None\",\n \"def reset(self):\\n self.reset_traits(('grow_hair', 'n_scaling_params', 'scale_x',\\n 'scale_y', 'scale_z', 'rot_x', 'rot_y', 'rot_z',\\n 'trans_x', 'trans_y', 'trans_z'))\",\n \"def init_game_setting(self):\\n ##################\\n # YOUR CODE HERE #\\n ##################\\n self.state = np.zeros((1, 80, 80))\\n self.clear_action()\",\n \"def _reset(self) -> None:\\n self.images = []\\n self.activations = []\\n self.labels = []\\n self.preds = []\\n self.n_found = 0\",\n \"def reset_stats(self):\\r\\n self.pepes_left = self.ai_settings.pepe_limit\\r\\n self.score = 0\\r\\n self.level = 1\",\n \"def reset(self, **kwargs):\\n self.player = None\\n self.player_list = list()\\n\\n self.events.post('mc_reset_phase_1')\\n self.events.post('mc_reset_phase_2')\\n self.events.post('mc_reset_phase_3')\",\n \"def reset(self):\\r\\n\\t\\t# Performs Player superclass reset.\\r\\n\\t\\tsuper().reset()\\r\\n\\r\\n\\t\\t# Resets the Q value estimate to the default initial_Q for all actions.\\r\\n\\t\\tself.player_Q = np.ones_like(self.player_selected_actions, dtype=float) * self.initial_Q\",\n \"def reset(self):\\r\\n\\t\\t# Performs Player superclass reset.\\r\\n\\t\\tsuper().reset()\\r\\n\\r\\n\\t\\t# Resets the Q value estimate to the default initial_Q for all actions.\\r\\n\\t\\tself.player_Q = np.ones_like(self.player_selected_actions, dtype=float) * self.initial_Q\",\n \"def reset_components(self):\\n self.menu_pipes = [PipeSet()]\\n self.pipes = [PipeSet()]\\n self.frame_number = 0 # Reset this one as it's also used by the actual game loop\\n self.player_points = 0\",\n \"def reset_obstacles(self):\\n self.obstacles = np.array([])\",\n \"def reset_state(self):\\n for name in self._buffers:\\n self._buffers[name] = self._defaults[name]\",\n \"def reset(self, starting_player):\\n self.__turn = starting_player.value\\n self.__turns_played = 0\\n self.__winner = MarkerType.NONE\\n self.__loser = MarkerType.NONE\\n\\n for y in range(Settings.SIZE_Y):\\n for x in range(Settings.SIZE_X):\\n self.__grid[y][x] = MarkerType.NONE\\n\\n self.__state = GameState.PLAYING\",\n \"def specific_reset(self) -> None:\\n\\n # first, set agent xy and adjust its height\\n self.agent.specific_reset()\\n agent_pos = np.zeros(3)\\n agent_pos = np.concatenate((agent_pos[:2], [self.agent.init_xyz[2]]))\\n self.agent.set_position(agent_pos)\\n\\n # second, reset obstacle positions\\n if len(self.obstacles) > 0:\\n obs_init_pos = env_utils.generate_obstacles_init_pos(\\n num_obstacles=len(self.obstacles),\\n agent_pos=self.agent.get_position(),\\n goal_pos=np.array([]), # no goal in gather task\\n world=self.world,\\n min_allowed_distance=self.obstacle_obstacle_distance,\\n agent_obstacle_distance=self.agent_obstacle_distance\\n )\\n for i, ob in enumerate(self.obstacles):\\n ob.set_position(obs_init_pos[i])\\n\\n # finally, make all collected objects visible again\\n [ob.update_visuals(make_visible=True) for ob in self.obstacles]\",\n \"def interactive_reset(self):\\n # Set the initial state\\n\\n self.dataset.reset()\\n\\n self.current_turn = 0\\n self.current_function = None\\n self.query = \\\"\\\"\\n self.query_vector = np.ones(self.dataset.getVocabularySize())\\n self.keywords={\\\"provided\\\":set(), \\\"rejected\\\":set()}\\n self.functions_rejected=set()\\n self.result_index = 0\\n self.dont_know = False\\n\\n self.history={\\n 'system_action': {\\\"action\\\": self.SYSTEM_OPENING_ACT},\\n 'user_action': None\\n }\",\n \"def reset(self):\\n self.objects = []\\n hero = GameObject(self.__new_position(), 1, 1, 2, None, \\\"hero\\\")\\n self.objects.append(hero)\\n goal = GameObject(self.__new_position(), 1, 1, 1, 1, \\\"goal\\\")\\n self.objects.append(goal)\\n fire = GameObject(self.__new_position(), 1, 1, 0, -1, \\\"fire\\\")\\n self.objects.append(fire)\\n goal2 = GameObject(self.__new_position(), 1, 1, 1, 1, \\\"goal\\\")\\n self.objects.append(goal2)\\n fire2 = GameObject(self.__new_position(), 1, 1, 0, -1, \\\"fire\\\")\\n self.objects.append(fire2)\\n goal3 = GameObject(self.__new_position(), 1, 1, 1, 1, \\\"goal\\\")\\n self.objects.append(goal3)\\n goal4 = GameObject(self.__new_position(), 1, 1, 1, 1, \\\"goal\\\")\\n self.objects.append(goal4)\\n # print(self.objects)\\n self.state = self.render_env()\\n return self.state\",\n \"def reset(self):\\r\\n # reset Wheel encoders\\r\\n self.start_time = time.time()\\r\\n [left_start, right_start] = self.Roomba.Query(43, 44)\\r\\n self.Motion.reset(left_start, right_start)\\r\\n # reset bumper\\r\\n self.bumper.reset()\\r\\n\\r\\n #reset grid world data\\r\\n self.action=[0.0,0.0]\\r\\n self.grid_state= [0,0,0]\\r\\n self.real_state = [0.0, 0.0, 0.0]\\r\\n self.trans_model = None\\r\\n pass\",\n \"def reset_attributes(self):\\n\\n self.ell = None\\n self.ell_jacobian = None\\n self.ell_hessian = None\\n\\n self.ell_hyperparam = None\\n self.ell_jacobian_hyperparam = None\\n self.ell_hessian_hyperparam = None\\n\\n self.Y = None\\n self.Cinv = None\\n self.C = None\\n self.Mz = None\\n self.MMz = None\\n self.sigma2 = None\\n self.sigma02 = None\\n self.Kninv = None\\n self.KnpKninv = None\\n\\n self.Y_C_Mz_hyperparam = None\\n self.sigma_hyperparam = None\\n self.MMz_hyperparam = None\\n self.Kninv_KnpKninv_hyperparam = None\",\n \"def specific_reset(self) -> None:\\n self.agent.specific_reset() # reset joints\\n new_pos = self.agent.init_xyz\\n new_pos[:2] = np.random.uniform(-0.01, 0.01, 2)\\n self.agent.set_position(new_pos)\\n self.old_potential = self.calculate_task_potential()\",\n \"def reset(self):\\n self.bbox = None\\n self.true = None\\n self.meta = None\",\n \"def _soft_reset(self):\\n self._reset_specific_envs(self.episodes_done)\\n self._update_other_info()\",\n \"def clear_attrs(self):\\n self._attributes.clear()\",\n \"def _reset(self):\\n self._set(\\\"_n_init_features\\\", None)\\n self._set(\\\"_n_output_features\\\", None)\\n self._set(\\\"_n_intervals\\\", None)\\n self._set(\\\"_mapper\\\", {})\\n self._set(\\\"_cpp_preprocessor\\\", None)\\n self._set(\\\"_fitted\\\", False)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7516117","0.73008484","0.7209318","0.71248615","0.70516896","0.70516896","0.7050974","0.7039916","0.70002186","0.69693255","0.69182575","0.6885031","0.6839155","0.68244755","0.676734","0.67489743","0.67342496","0.67195565","0.6716343","0.66865003","0.6684202","0.66425955","0.66071755","0.65921044","0.65876895","0.65778375","0.6571659","0.6559154","0.6517914","0.6509977","0.65032303","0.64990616","0.64838123","0.6480669","0.64744663","0.64723825","0.64699715","0.6462367","0.64588374","0.64574206","0.6442206","0.6432499","0.64259285","0.6417372","0.641726","0.6405777","0.6405777","0.6405777","0.63905084","0.63827676","0.63797474","0.637819","0.6377198","0.63596666","0.6358096","0.6350805","0.63480926","0.63424987","0.6334947","0.6324629","0.6309428","0.629857","0.629758","0.6296102","0.62933517","0.6289079","0.627768","0.62743694","0.62520367","0.62520367","0.6243461","0.6237506","0.6234009","0.62308407","0.62274325","0.6224675","0.62178695","0.62168485","0.6207582","0.62038463","0.61843157","0.6174614","0.6168631","0.6159233","0.61534643","0.61534643","0.6151717","0.61492014","0.61459976","0.61433023","0.61378914","0.6130792","0.6127025","0.61264634","0.61211807","0.61124635","0.610494","0.61025804","0.6090057","0.6086659"],"string":"[\n \"0.7516117\",\n \"0.73008484\",\n \"0.7209318\",\n \"0.71248615\",\n \"0.70516896\",\n \"0.70516896\",\n \"0.7050974\",\n \"0.7039916\",\n \"0.70002186\",\n \"0.69693255\",\n \"0.69182575\",\n \"0.6885031\",\n \"0.6839155\",\n \"0.68244755\",\n \"0.676734\",\n \"0.67489743\",\n \"0.67342496\",\n \"0.67195565\",\n \"0.6716343\",\n \"0.66865003\",\n \"0.6684202\",\n \"0.66425955\",\n \"0.66071755\",\n \"0.65921044\",\n \"0.65876895\",\n \"0.65778375\",\n \"0.6571659\",\n \"0.6559154\",\n \"0.6517914\",\n \"0.6509977\",\n \"0.65032303\",\n \"0.64990616\",\n \"0.64838123\",\n \"0.6480669\",\n \"0.64744663\",\n \"0.64723825\",\n \"0.64699715\",\n \"0.6462367\",\n \"0.64588374\",\n \"0.64574206\",\n \"0.6442206\",\n \"0.6432499\",\n \"0.64259285\",\n \"0.6417372\",\n \"0.641726\",\n \"0.6405777\",\n \"0.6405777\",\n \"0.6405777\",\n \"0.63905084\",\n \"0.63827676\",\n \"0.63797474\",\n \"0.637819\",\n \"0.6377198\",\n \"0.63596666\",\n \"0.6358096\",\n \"0.6350805\",\n \"0.63480926\",\n \"0.63424987\",\n \"0.6334947\",\n \"0.6324629\",\n \"0.6309428\",\n \"0.629857\",\n \"0.629758\",\n \"0.6296102\",\n \"0.62933517\",\n \"0.6289079\",\n \"0.627768\",\n \"0.62743694\",\n \"0.62520367\",\n \"0.62520367\",\n \"0.6243461\",\n \"0.6237506\",\n \"0.6234009\",\n \"0.62308407\",\n \"0.62274325\",\n \"0.6224675\",\n \"0.62178695\",\n \"0.62168485\",\n \"0.6207582\",\n \"0.62038463\",\n \"0.61843157\",\n \"0.6174614\",\n \"0.6168631\",\n \"0.6159233\",\n \"0.61534643\",\n \"0.61534643\",\n \"0.6151717\",\n \"0.61492014\",\n \"0.61459976\",\n \"0.61433023\",\n \"0.61378914\",\n \"0.6130792\",\n \"0.6127025\",\n \"0.61264634\",\n \"0.61211807\",\n \"0.61124635\",\n \"0.610494\",\n \"0.61025804\",\n \"0.6090057\",\n \"0.6086659\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73064816"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":84993,"cells":{"query":{"kind":"string","value":"Calculates how long a game takes"},"document":{"kind":"string","value":"def time(self, start_time):\n \n TIME_LIST.append((time.time() - start_time))\n print(\"--- %s seconds ---\" % (time.time() - start_time))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def time(self) -> float:\n return self.state.game_loop / 22.4 # / (1/1.4) * (1/16)","def time(n_games, time_per_game):\n\n total_time = n_games * time_per_game / 60\n return total_time","def GAME_TIME_ADVANCE(dt):","def time_elapsed(session, player):\n #TODO (also needs to be added to bot logic)","def get_time_taken_sec(self) -> float:\n return self.time_stop - self.time_start","def runtime(self):\n return (self.time - self.start).total_seconds()","def remaining_ms():","def getTime():\n return float(time.perf_counter()*1000)","def getTime():\n return float(time.perf_counter()*1000)","def getTime():\n return float(time.perf_counter()*1000)","def getTime():\n return float(time.perf_counter()*1000)","def getTime():\n return float(time.perf_counter()*1000)","def getTime():\n return float(time.perf_counter()*1000)","def getTime():\n\n return float(time.perf_counter()*1000)","def runtime(self):\n return self.stop_time - self.start_time","def calculate_timeout(self):\n return self.total_estimated_words() / self.minimum_wpm * 60","def finish(self, secs=False):\n self.timeFinish = pygame.time.get_ticks()()\n elapsedTime = self.timeFinish - self.timeStart\n if secs:\n return elapsedTime / 1000\n else:\n return elapsedTime","def time(self):\n return pygame.time.get_ticks() - self.start_time","def elapseTime(self, gameState):\n\n \"*** YOUR CODE HERE ***\"\n\n allPossible = util.Counter()\n\n for oldPos in self.legalPositions:\n actions = gameState.getLegalActions(agentIndex)\n successorStates = [gameState.generateSuccessor(action) for action in actions]\n newPosDist = {}\n for state in successorStates:\n position = state.getAgentPosition(agentIndex)\n prob = 1.0/len(actions)\n newPosDist[position] = prob\n\n for newPos, prob in newPosDist.items():\n allPossible[newPos] += prob * self.beliefs[oldPos]\n\n allPossible.normalize()\n self.beliefs = allPossible","def time_left(self):\r\n return 10 - (int(time.time()) - self.start_time)","def timesLeft(self)->int:\n return self.maxTimes - self.timesUsed","def approximateTime(meal):\n RATE = 4.2535969274764765e-05 # seconds per character.\n time = len(meal)**1 * RATE\n return time","def part1() -> int:\n longest_sleeper = max(sleep_times, key=lambda g: len(sleep_times[g]))\n sleepiest_minute = max(\n sleep_times[longest_sleeper], key=sleep_times[longest_sleeper].count)\n\n return longest_sleeper * sleepiest_minute","def elapsed():\n global start_time\n return time.time() - start_time","def elapsed_time_in_minutes(number_of_layers: int, elapsed_bake_time: int) -> int:\n return preparation_time_in_minutes(number_of_layers) + elapsed_bake_time","def compute_go_duration(self, units='seconds'):\n go_duration = 0\n for trial in self.trials:\n max_time = 0\n for event in trial.events:\n if self.stop > max_time:\n max_time = self.stop\n\n go_duration += max_time\n\n self.go_duration = (go_duration, units)","def _run_time(func):\n start_time = datetime.datetime.now()\n func\n end_time = datetime.datetime.now()\n return end_time - start_time","def estimated_time(self):\n self._update()\n if not self.running_mode:\n return 0 if self._is_finished() else float(\"nan\")\n elif self.running_mode == \"local\":\n start = self.processes[0].create_time()\n elif self.running_mode == \"grid\":\n start = self.job[\"start_time\"]\n if start == 0:\n # Queued, but not started\n return float(\"nan\")\n else:\n logger.warning(\"Invalid running_mode attribute\")\n return float(\"nan\")\n current = self.current_step()\n if current <= 0: # If not dumped yet or error\n return float('nan')\n else:\n elapsed = time() - start\n return elapsed * (self.total_steps / current - 1)","def elapsed_time(self):\n # reset timer if game is not started\n if not self.started:\n self.timestamp = None\n return 0\n # sets the first timer\n if self.timestamp is None:\n self.timestamp = time.time()\n return 0\n # if there is a previous timer check elapsed time\n else:\n elapsed_time = time.time() - self.timestamp\n # if elapsed_time is larger than the maximum time, reset timer\n if elapsed_time >= self.max_time:\n self.timestamp = self.max_time\n return elapsed_time","def duration(self) -> float:\n return self.endTime()-self.startTime()","def failed_per_hour(self):\r\n return (3600.*(self.circ_failed+self.strm_failed))/self.current_uptime()","def elapsed_time():\r\n elapsed_time, duration = video_time()\r\n return elapsed_time","def duration(self):\r\n return (self.end_time or time.time()) - self.start_time","def duration():\r\n elapsed_time, duration = video_time()\r\n return duration","def time(self) -> int:\n pass","def elapsed_time_in_minutes(number_of_layers, elapsed_bake_time):\n\n preparation_time = preparation_time_in_minutes(number_of_layers)\n total_bake_time = preparation_time + elapsed_bake_time\n return total_bake_time","def time_passed(self):\n return (datetime.now(timezone.utc) - self._time_run).total_seconds()","def calculate_time(start_time):\r\n return round(time() - start_time, 2)","def time():\n master = MasterTimer.getMasterTimer()\n\n if master.end_time:\n return master.end_time - master.start_time\n else:\n return time.time() - master.start_time","def duration( self ):\n return (self.start and time.process_time()-self.start) or 0","def duration(self):\n\t\tif self.status():\n\t\t\t# Currently on, return time since session was started\n\t\t\treturn self.length()\n\t\telse:\n\t\t\t# Otherwise return time until last bit of work\n\t\t\t# Check that this isn't an empty session\n\t\t\tif not self.toggles: return timedelta()\n\t\t\treturn self.toggles[-1] - self.toggles[0]","def elapsed_time_in_minutes(number_of_layers, elapsed_bake_time):\n return elapsed_bake_time + (number_of_layers * 2)","def time(n):\n steps = 3 + math.ceil(n/5.0)*2\n return steps","def calculateWinRate():\n times = 10\n winRate = 0.0\n for i in range(times):\n game = Game('user', 6, 6)\n winRate += game.play(5, False, True, False, False)\n winRate = winRate/times\n print \"Winrate:\", winRate","def opponentscaredTime(self, gameState):\n opponents = self.getOpponents(gameState)\n for opponent in opponents:\n if gameState.getAgentState(opponent).scaredTimer > 1:\n return gameState.getAgentState(opponent).scaredTimer\n return None","def currentTimeSecs():\n return time.time()","def get_duration(self) -> int:\n return int( (self._frame_count / self._fps) * 1000 )","def run(self):\n game_time = 0\n while game_time < self.params.t:\n self.play_turn()\n game_time += 1\n\n reward = self.total_tx_reward\n successes = self.message_success_count\n self.reset() \n\n return reward / self.params.t, successes / self.params.t","def duration(self) -> int:\n return 0","def bake_time_remaining(elapsed_bake_time: int) -> int:\n return EXPECTED_BAKE_TIME - elapsed_bake_time","def cpu_time(self):","def speed(self) -> int:","def speed(self) -> int:","def duration(self):\n return int(\n (self.finish_checkpoint - self.start_checkpoint) * 1000000\n )","def time_remaining(self) -> float:\n\n return self.event.time - time.time()","def completeness_of_game(game):\n spaces = game.width * game.height\n played_spaces = len([x for x in game._board_state[:-3] if x == 1])\n return float(played_spaces / spaces)","def delta_time(self):\n delta_time = time.time() - self.time\n if delta_time >= 1.0 / self.target_fps:\n self.time = time.time()\n # end if\n return delta_time","def elapsed_time_in_minutes(no_of_layers, elapsed_bake_time):\n return preparation_time_in_minutes(no_of_layers) + elapsed_bake_time","def run_perft(self, depth):\n start = time()\n done = self.perft(self.game.board, self.game.current_turn, depth)\n end = time()\n print(\"Elapsed time for depth \" + str(depth) + \": \")\n print(str(end-start))\n return done","def duration(self):\r\n return self.t2 - self.t1","def time(self):\n return self._clock() - self._starttime","def mood_duration_millis():\n return max(200, int(state.normal(750, 500)))","def time_remaining(self, steps_done):\n if steps_done:\n seconds_per_step = (datetime.datetime.now() - self.__start_time).total_seconds() / steps_done\n seconds_remaining = (self.__total_steps - steps_done) * seconds_per_step\n return str(datetime.timedelta(seconds=round(seconds_remaining)))\n return 'not enough data to estimate'","def elapsed_time_in_minutes(number_of_layers, elapsed_bake_time):\n return preparation_time_in_minutes(number_of_layers) + elapsed_bake_time","def measure_time(n: int, max_delay: int) -> float:\n start_time = time.time()\n asyncio.run(wait_n(n, max_delay))\n return (time.time() - start_time) / n","def measure_time(n: int, max_delay: int) -> float:\n t0 = time.time()\n asyncio.run(wait_n(n, max_delay))\n t1 = time.time()\n total_time = t1 - t0\n return total_time / n","def test_countdown_performance():\n profiler = cProfile.Profile()\n profiler.enable()\n countdown(\"0 0 0 0 0 0 1000\")\n profiler.disable()\n stats = profiler.getstats()\n tot_time = stats[0].totaltime\n assert tot_time < 3, \"Wow, your computer is really slow. Or is it my code?\"","def getMaxTime(self,hashSpeed):\r\n\r\n possiblePasswords = 0\r\n length = self.length\r\n while length > 0:\r\n possiblePasswords += len(self.charset) ** length\r\n length -= 1\r\n seconds = possiblePasswords // hashSpeed\r\n\r\n self.log('Signal','Maximum seconds to run is %ds with a HashSpeed of %d and %d possibilities' % (seconds,hashSpeed,possiblePasswords),'getMaxTime')\r\n\r\n return seconds","def evaluate_time(self, pid, edge):\n return self.get_process_speed(pid) * edge.get_time()","def _get_time(self, state: State) -> int:\n benchmark_time = {\n 'resnet': state.timestamp.epoch.value,\n 'bert': state.timestamp.sample.value,\n }\n return benchmark_time[self.benchmark]","def duration(self):\r\n return self.stop - self.start","def time_ms():\n return int(1000 * time.time())","def performance_length(self):\n first_touch = self.touches[:1].index[0].to_pydatetime()\n last_touch = self.touches[-1:].index[0].to_pydatetime()\n return (last_touch - first_touch).total_seconds()","def duration(self):\n self._current_duration = time.perf_counter() - self._duration_start\n return round(self._current_duration, 4)","def get_elapsed_time(self):\n return self.app.get_elapsed_time() - self._pause_time","def __get_elapsed__(self):\n elapsed = (self.__end_time - self.__start_time)\n unit = \"seconds\"\n if elapsed >= 3600:\n unit = \"minutes\"\n hours = elapsed / 3600\n minutes = hours % 60\n hours = floor(hours)\n print(self.name, \"took\", str(hours), \"hours and\", \"{0:.2f}\".format(minutes), unit, \"to complete\")\n elif elapsed >= 60:\n minutes = floor(elapsed / 60)\n seconds = elapsed % 60\n print(self.name, \"took\", str(minutes), \"minutes and\", \"{0:.2f}\".format(seconds), unit, \"to complete\")\n else:\n print(self.name, \"took\", \"{0:.2f}\".format(elapsed), unit, \"to complete\")","def calculateWaitingTime(self, inputs):\n CollisionCounter.CollisionCounter.getInstance().waitingTimeCalculated(self.time)\n timeUntilDepature = self.getAtt('departure_time', inputs) - self.time\n remainingLoadingTime = self.calculateLoadingTime(inputs)\n # calculates first maximum possible waiting time\n sampleTime = int((timeUntilDepature - remainingLoadingTime) / self.participants)\n\n if sampleTime >= 1:\n # result is big enough for a standard treatment\n self.waitingTime = MyRandom.RandomNumber.getInstance().getRandomNumber(sampleTime + 1)\n elif sampleTime < 1:\n # reslut is too small, special treatment necessary\n upperLimit = (10 * (1 - (math.exp(sampleTime - 1)))) + 1\n self.waitingTime = MyRandom.RandomNumber.getInstance().getRandomNumber(max((min(upperLimit,\n timeUntilDepature)) + 1, 1))\n # decides whether charging is allowed during waiting time\n if not self.stayedConnected:\n self.stayConnected = True\n self.stayedConnected = True\n else:\n self.stayConnected = False\n self.stayedConnected = False","def calc_time_match(song: Song, result: Result) -> float:\n\n if result.duration > song.duration:\n return 100 - (result.duration - song.duration)\n\n return 100 - (song.duration - result.duration)","def millis() -> int:","def dpTime():\n start_time = time.time()\n subjects = loadSubjects(SUBJECT_FILENAME)\n maxWork = 50\n answer = dpAdvisor(subjects, maxWork)\n end_time = time.time()\n printSubjects(answer)\n print 'Time taken: ', end_time - start_time\n return None","def get_time_walking(self):\n return self.time_step_to_enqueue - self.time_entered","def get_game_length(self, duration):\n minutes = int(duration / 60)\n seconds = int(duration % 60)\n return \"{0}:{1}\".format(minutes, str(seconds).zfill(2))","def _STEPS2TIME(step):\n return step/1000.","def getRemainingRunTime(self) -> int:\n if not self.debug:\n self.myFieldFox.write(\"SYST:BATT:ARTT?\")\n ret = self.myFieldFox.read()\n else:\n ret = 60\n return ret","def compute_time(self):\n return self.compute_time_without_gc() + self.gc_time","def main():\n assert how_many_seconds(2) == 7200\n assert how_many_seconds(10) == 36000\n assert how_many_seconds(24) == 86400\n assert how_many_seconds(36) == 129600\n print('Passed.')","def duration(self):\n return float('{0:.2f}'.format(self.end_time - self.start_time))","def getTimeLeftSec(self):\n if self.sess is None: return 0\n since = self.sess.data.get('validSince')\n if not since: return 0\n\n sofar = time.time() - since\n if sofar < 0: return 0\n out = self.sess.data.get('validLifetime', 0) - sofar\n if out < 0: out = 0\n return out","def suspected_per_hour(self):\r\n return (3600.*(self.circ_suspected+self.strm_suspected\r\n +self.circ_failed+self.strm_failed))/self.current_uptime()","def duration(self):\n return self.end_time - self.start_time","def get_elapsed(self):\n delta = self._now() - self.start\n return delta.microseconds / 1000.0","def fight(self):\r\n\t\tif self.death():\r\n\t\t\treturn 0\r\n\t\tif self.ctime < 1:\r\n\t\t\tself.ctime += 0.05\r\n\t\telse:\r\n\t\t\tself.ctime = 0\r\n\t\t\tself.hit()","def get_bruteforce_time(combo, speed=1000000000):\n return (combo // speed) // (24 * 3600)","def fpct(self):\n # 1 is probably the best number in most cases because the game is often CPU-bound.\n # the following number could be chosen instead someday\n tps = self.real_speed * 1000 / VIRTUAL_TIME_INTERVAL\n # Avoid unrealistic ping values.\n ping = min(self.max_ping, self.ping)\n result = int(tps * ping * config.fpct_coef) + 1\n return min(config.fpct_max, result)","def duration(self):\n\n ended = time.time() if self.ended is None else self.ended\n return ended - self.started","def calculate_time_percentage_left(self):\n time_left = self.calculate_time_left()\n return time_left / self.attributes[AT.TIME_TO_EXPIRE]","def elapsed(self):\n return datetime.datetime.now() - self.start","def round_trip_time(self):\r\n return self.completion_time - self.launch_time","def secondsPassed(self)->int:\n return 0 if not self.used else int((datetime.utcnow() - self.firstAccessDate).total_seconds())","def utility_time_score(self, g, w):\n occ = parse_time(g.occurred)\n iss = parse_time(w.submitted)\n diff = occ - iss\n return diff.total_seconds() / 86400","def get_song_seconds_remaining(result):\n remaining_ms = get_song_length_milliseconds(result) - get_song_elapsed_milliseconds(result)\n return int(remaining_ms / 1000)"],"string":"[\n \"def time(self) -> float:\\n return self.state.game_loop / 22.4 # / (1/1.4) * (1/16)\",\n \"def time(n_games, time_per_game):\\n\\n total_time = n_games * time_per_game / 60\\n return total_time\",\n \"def GAME_TIME_ADVANCE(dt):\",\n \"def time_elapsed(session, player):\\n #TODO (also needs to be added to bot logic)\",\n \"def get_time_taken_sec(self) -> float:\\n return self.time_stop - self.time_start\",\n \"def runtime(self):\\n return (self.time - self.start).total_seconds()\",\n \"def remaining_ms():\",\n \"def getTime():\\n return float(time.perf_counter()*1000)\",\n \"def getTime():\\n return float(time.perf_counter()*1000)\",\n \"def getTime():\\n return float(time.perf_counter()*1000)\",\n \"def getTime():\\n return float(time.perf_counter()*1000)\",\n \"def getTime():\\n return float(time.perf_counter()*1000)\",\n \"def getTime():\\n return float(time.perf_counter()*1000)\",\n \"def getTime():\\n\\n return float(time.perf_counter()*1000)\",\n \"def runtime(self):\\n return self.stop_time - self.start_time\",\n \"def calculate_timeout(self):\\n return self.total_estimated_words() / self.minimum_wpm * 60\",\n \"def finish(self, secs=False):\\n self.timeFinish = pygame.time.get_ticks()()\\n elapsedTime = self.timeFinish - self.timeStart\\n if secs:\\n return elapsedTime / 1000\\n else:\\n return elapsedTime\",\n \"def time(self):\\n return pygame.time.get_ticks() - self.start_time\",\n \"def elapseTime(self, gameState):\\n\\n \\\"*** YOUR CODE HERE ***\\\"\\n\\n allPossible = util.Counter()\\n\\n for oldPos in self.legalPositions:\\n actions = gameState.getLegalActions(agentIndex)\\n successorStates = [gameState.generateSuccessor(action) for action in actions]\\n newPosDist = {}\\n for state in successorStates:\\n position = state.getAgentPosition(agentIndex)\\n prob = 1.0/len(actions)\\n newPosDist[position] = prob\\n\\n for newPos, prob in newPosDist.items():\\n allPossible[newPos] += prob * self.beliefs[oldPos]\\n\\n allPossible.normalize()\\n self.beliefs = allPossible\",\n \"def time_left(self):\\r\\n return 10 - (int(time.time()) - self.start_time)\",\n \"def timesLeft(self)->int:\\n return self.maxTimes - self.timesUsed\",\n \"def approximateTime(meal):\\n RATE = 4.2535969274764765e-05 # seconds per character.\\n time = len(meal)**1 * RATE\\n return time\",\n \"def part1() -> int:\\n longest_sleeper = max(sleep_times, key=lambda g: len(sleep_times[g]))\\n sleepiest_minute = max(\\n sleep_times[longest_sleeper], key=sleep_times[longest_sleeper].count)\\n\\n return longest_sleeper * sleepiest_minute\",\n \"def elapsed():\\n global start_time\\n return time.time() - start_time\",\n \"def elapsed_time_in_minutes(number_of_layers: int, elapsed_bake_time: int) -> int:\\n return preparation_time_in_minutes(number_of_layers) + elapsed_bake_time\",\n \"def compute_go_duration(self, units='seconds'):\\n go_duration = 0\\n for trial in self.trials:\\n max_time = 0\\n for event in trial.events:\\n if self.stop > max_time:\\n max_time = self.stop\\n\\n go_duration += max_time\\n\\n self.go_duration = (go_duration, units)\",\n \"def _run_time(func):\\n start_time = datetime.datetime.now()\\n func\\n end_time = datetime.datetime.now()\\n return end_time - start_time\",\n \"def estimated_time(self):\\n self._update()\\n if not self.running_mode:\\n return 0 if self._is_finished() else float(\\\"nan\\\")\\n elif self.running_mode == \\\"local\\\":\\n start = self.processes[0].create_time()\\n elif self.running_mode == \\\"grid\\\":\\n start = self.job[\\\"start_time\\\"]\\n if start == 0:\\n # Queued, but not started\\n return float(\\\"nan\\\")\\n else:\\n logger.warning(\\\"Invalid running_mode attribute\\\")\\n return float(\\\"nan\\\")\\n current = self.current_step()\\n if current <= 0: # If not dumped yet or error\\n return float('nan')\\n else:\\n elapsed = time() - start\\n return elapsed * (self.total_steps / current - 1)\",\n \"def elapsed_time(self):\\n # reset timer if game is not started\\n if not self.started:\\n self.timestamp = None\\n return 0\\n # sets the first timer\\n if self.timestamp is None:\\n self.timestamp = time.time()\\n return 0\\n # if there is a previous timer check elapsed time\\n else:\\n elapsed_time = time.time() - self.timestamp\\n # if elapsed_time is larger than the maximum time, reset timer\\n if elapsed_time >= self.max_time:\\n self.timestamp = self.max_time\\n return elapsed_time\",\n \"def duration(self) -> float:\\n return self.endTime()-self.startTime()\",\n \"def failed_per_hour(self):\\r\\n return (3600.*(self.circ_failed+self.strm_failed))/self.current_uptime()\",\n \"def elapsed_time():\\r\\n elapsed_time, duration = video_time()\\r\\n return elapsed_time\",\n \"def duration(self):\\r\\n return (self.end_time or time.time()) - self.start_time\",\n \"def duration():\\r\\n elapsed_time, duration = video_time()\\r\\n return duration\",\n \"def time(self) -> int:\\n pass\",\n \"def elapsed_time_in_minutes(number_of_layers, elapsed_bake_time):\\n\\n preparation_time = preparation_time_in_minutes(number_of_layers)\\n total_bake_time = preparation_time + elapsed_bake_time\\n return total_bake_time\",\n \"def time_passed(self):\\n return (datetime.now(timezone.utc) - self._time_run).total_seconds()\",\n \"def calculate_time(start_time):\\r\\n return round(time() - start_time, 2)\",\n \"def time():\\n master = MasterTimer.getMasterTimer()\\n\\n if master.end_time:\\n return master.end_time - master.start_time\\n else:\\n return time.time() - master.start_time\",\n \"def duration( self ):\\n return (self.start and time.process_time()-self.start) or 0\",\n \"def duration(self):\\n\\t\\tif self.status():\\n\\t\\t\\t# Currently on, return time since session was started\\n\\t\\t\\treturn self.length()\\n\\t\\telse:\\n\\t\\t\\t# Otherwise return time until last bit of work\\n\\t\\t\\t# Check that this isn't an empty session\\n\\t\\t\\tif not self.toggles: return timedelta()\\n\\t\\t\\treturn self.toggles[-1] - self.toggles[0]\",\n \"def elapsed_time_in_minutes(number_of_layers, elapsed_bake_time):\\n return elapsed_bake_time + (number_of_layers * 2)\",\n \"def time(n):\\n steps = 3 + math.ceil(n/5.0)*2\\n return steps\",\n \"def calculateWinRate():\\n times = 10\\n winRate = 0.0\\n for i in range(times):\\n game = Game('user', 6, 6)\\n winRate += game.play(5, False, True, False, False)\\n winRate = winRate/times\\n print \\\"Winrate:\\\", winRate\",\n \"def opponentscaredTime(self, gameState):\\n opponents = self.getOpponents(gameState)\\n for opponent in opponents:\\n if gameState.getAgentState(opponent).scaredTimer > 1:\\n return gameState.getAgentState(opponent).scaredTimer\\n return None\",\n \"def currentTimeSecs():\\n return time.time()\",\n \"def get_duration(self) -> int:\\n return int( (self._frame_count / self._fps) * 1000 )\",\n \"def run(self):\\n game_time = 0\\n while game_time < self.params.t:\\n self.play_turn()\\n game_time += 1\\n\\n reward = self.total_tx_reward\\n successes = self.message_success_count\\n self.reset() \\n\\n return reward / self.params.t, successes / self.params.t\",\n \"def duration(self) -> int:\\n return 0\",\n \"def bake_time_remaining(elapsed_bake_time: int) -> int:\\n return EXPECTED_BAKE_TIME - elapsed_bake_time\",\n \"def cpu_time(self):\",\n \"def speed(self) -> int:\",\n \"def speed(self) -> int:\",\n \"def duration(self):\\n return int(\\n (self.finish_checkpoint - self.start_checkpoint) * 1000000\\n )\",\n \"def time_remaining(self) -> float:\\n\\n return self.event.time - time.time()\",\n \"def completeness_of_game(game):\\n spaces = game.width * game.height\\n played_spaces = len([x for x in game._board_state[:-3] if x == 1])\\n return float(played_spaces / spaces)\",\n \"def delta_time(self):\\n delta_time = time.time() - self.time\\n if delta_time >= 1.0 / self.target_fps:\\n self.time = time.time()\\n # end if\\n return delta_time\",\n \"def elapsed_time_in_minutes(no_of_layers, elapsed_bake_time):\\n return preparation_time_in_minutes(no_of_layers) + elapsed_bake_time\",\n \"def run_perft(self, depth):\\n start = time()\\n done = self.perft(self.game.board, self.game.current_turn, depth)\\n end = time()\\n print(\\\"Elapsed time for depth \\\" + str(depth) + \\\": \\\")\\n print(str(end-start))\\n return done\",\n \"def duration(self):\\r\\n return self.t2 - self.t1\",\n \"def time(self):\\n return self._clock() - self._starttime\",\n \"def mood_duration_millis():\\n return max(200, int(state.normal(750, 500)))\",\n \"def time_remaining(self, steps_done):\\n if steps_done:\\n seconds_per_step = (datetime.datetime.now() - self.__start_time).total_seconds() / steps_done\\n seconds_remaining = (self.__total_steps - steps_done) * seconds_per_step\\n return str(datetime.timedelta(seconds=round(seconds_remaining)))\\n return 'not enough data to estimate'\",\n \"def elapsed_time_in_minutes(number_of_layers, elapsed_bake_time):\\n return preparation_time_in_minutes(number_of_layers) + elapsed_bake_time\",\n \"def measure_time(n: int, max_delay: int) -> float:\\n start_time = time.time()\\n asyncio.run(wait_n(n, max_delay))\\n return (time.time() - start_time) / n\",\n \"def measure_time(n: int, max_delay: int) -> float:\\n t0 = time.time()\\n asyncio.run(wait_n(n, max_delay))\\n t1 = time.time()\\n total_time = t1 - t0\\n return total_time / n\",\n \"def test_countdown_performance():\\n profiler = cProfile.Profile()\\n profiler.enable()\\n countdown(\\\"0 0 0 0 0 0 1000\\\")\\n profiler.disable()\\n stats = profiler.getstats()\\n tot_time = stats[0].totaltime\\n assert tot_time < 3, \\\"Wow, your computer is really slow. Or is it my code?\\\"\",\n \"def getMaxTime(self,hashSpeed):\\r\\n\\r\\n possiblePasswords = 0\\r\\n length = self.length\\r\\n while length > 0:\\r\\n possiblePasswords += len(self.charset) ** length\\r\\n length -= 1\\r\\n seconds = possiblePasswords // hashSpeed\\r\\n\\r\\n self.log('Signal','Maximum seconds to run is %ds with a HashSpeed of %d and %d possibilities' % (seconds,hashSpeed,possiblePasswords),'getMaxTime')\\r\\n\\r\\n return seconds\",\n \"def evaluate_time(self, pid, edge):\\n return self.get_process_speed(pid) * edge.get_time()\",\n \"def _get_time(self, state: State) -> int:\\n benchmark_time = {\\n 'resnet': state.timestamp.epoch.value,\\n 'bert': state.timestamp.sample.value,\\n }\\n return benchmark_time[self.benchmark]\",\n \"def duration(self):\\r\\n return self.stop - self.start\",\n \"def time_ms():\\n return int(1000 * time.time())\",\n \"def performance_length(self):\\n first_touch = self.touches[:1].index[0].to_pydatetime()\\n last_touch = self.touches[-1:].index[0].to_pydatetime()\\n return (last_touch - first_touch).total_seconds()\",\n \"def duration(self):\\n self._current_duration = time.perf_counter() - self._duration_start\\n return round(self._current_duration, 4)\",\n \"def get_elapsed_time(self):\\n return self.app.get_elapsed_time() - self._pause_time\",\n \"def __get_elapsed__(self):\\n elapsed = (self.__end_time - self.__start_time)\\n unit = \\\"seconds\\\"\\n if elapsed >= 3600:\\n unit = \\\"minutes\\\"\\n hours = elapsed / 3600\\n minutes = hours % 60\\n hours = floor(hours)\\n print(self.name, \\\"took\\\", str(hours), \\\"hours and\\\", \\\"{0:.2f}\\\".format(minutes), unit, \\\"to complete\\\")\\n elif elapsed >= 60:\\n minutes = floor(elapsed / 60)\\n seconds = elapsed % 60\\n print(self.name, \\\"took\\\", str(minutes), \\\"minutes and\\\", \\\"{0:.2f}\\\".format(seconds), unit, \\\"to complete\\\")\\n else:\\n print(self.name, \\\"took\\\", \\\"{0:.2f}\\\".format(elapsed), unit, \\\"to complete\\\")\",\n \"def calculateWaitingTime(self, inputs):\\n CollisionCounter.CollisionCounter.getInstance().waitingTimeCalculated(self.time)\\n timeUntilDepature = self.getAtt('departure_time', inputs) - self.time\\n remainingLoadingTime = self.calculateLoadingTime(inputs)\\n # calculates first maximum possible waiting time\\n sampleTime = int((timeUntilDepature - remainingLoadingTime) / self.participants)\\n\\n if sampleTime >= 1:\\n # result is big enough for a standard treatment\\n self.waitingTime = MyRandom.RandomNumber.getInstance().getRandomNumber(sampleTime + 1)\\n elif sampleTime < 1:\\n # reslut is too small, special treatment necessary\\n upperLimit = (10 * (1 - (math.exp(sampleTime - 1)))) + 1\\n self.waitingTime = MyRandom.RandomNumber.getInstance().getRandomNumber(max((min(upperLimit,\\n timeUntilDepature)) + 1, 1))\\n # decides whether charging is allowed during waiting time\\n if not self.stayedConnected:\\n self.stayConnected = True\\n self.stayedConnected = True\\n else:\\n self.stayConnected = False\\n self.stayedConnected = False\",\n \"def calc_time_match(song: Song, result: Result) -> float:\\n\\n if result.duration > song.duration:\\n return 100 - (result.duration - song.duration)\\n\\n return 100 - (song.duration - result.duration)\",\n \"def millis() -> int:\",\n \"def dpTime():\\n start_time = time.time()\\n subjects = loadSubjects(SUBJECT_FILENAME)\\n maxWork = 50\\n answer = dpAdvisor(subjects, maxWork)\\n end_time = time.time()\\n printSubjects(answer)\\n print 'Time taken: ', end_time - start_time\\n return None\",\n \"def get_time_walking(self):\\n return self.time_step_to_enqueue - self.time_entered\",\n \"def get_game_length(self, duration):\\n minutes = int(duration / 60)\\n seconds = int(duration % 60)\\n return \\\"{0}:{1}\\\".format(minutes, str(seconds).zfill(2))\",\n \"def _STEPS2TIME(step):\\n return step/1000.\",\n \"def getRemainingRunTime(self) -> int:\\n if not self.debug:\\n self.myFieldFox.write(\\\"SYST:BATT:ARTT?\\\")\\n ret = self.myFieldFox.read()\\n else:\\n ret = 60\\n return ret\",\n \"def compute_time(self):\\n return self.compute_time_without_gc() + self.gc_time\",\n \"def main():\\n assert how_many_seconds(2) == 7200\\n assert how_many_seconds(10) == 36000\\n assert how_many_seconds(24) == 86400\\n assert how_many_seconds(36) == 129600\\n print('Passed.')\",\n \"def duration(self):\\n return float('{0:.2f}'.format(self.end_time - self.start_time))\",\n \"def getTimeLeftSec(self):\\n if self.sess is None: return 0\\n since = self.sess.data.get('validSince')\\n if not since: return 0\\n\\n sofar = time.time() - since\\n if sofar < 0: return 0\\n out = self.sess.data.get('validLifetime', 0) - sofar\\n if out < 0: out = 0\\n return out\",\n \"def suspected_per_hour(self):\\r\\n return (3600.*(self.circ_suspected+self.strm_suspected\\r\\n +self.circ_failed+self.strm_failed))/self.current_uptime()\",\n \"def duration(self):\\n return self.end_time - self.start_time\",\n \"def get_elapsed(self):\\n delta = self._now() - self.start\\n return delta.microseconds / 1000.0\",\n \"def fight(self):\\r\\n\\t\\tif self.death():\\r\\n\\t\\t\\treturn 0\\r\\n\\t\\tif self.ctime < 1:\\r\\n\\t\\t\\tself.ctime += 0.05\\r\\n\\t\\telse:\\r\\n\\t\\t\\tself.ctime = 0\\r\\n\\t\\t\\tself.hit()\",\n \"def get_bruteforce_time(combo, speed=1000000000):\\n return (combo // speed) // (24 * 3600)\",\n \"def fpct(self):\\n # 1 is probably the best number in most cases because the game is often CPU-bound.\\n # the following number could be chosen instead someday\\n tps = self.real_speed * 1000 / VIRTUAL_TIME_INTERVAL\\n # Avoid unrealistic ping values.\\n ping = min(self.max_ping, self.ping)\\n result = int(tps * ping * config.fpct_coef) + 1\\n return min(config.fpct_max, result)\",\n \"def duration(self):\\n\\n ended = time.time() if self.ended is None else self.ended\\n return ended - self.started\",\n \"def calculate_time_percentage_left(self):\\n time_left = self.calculate_time_left()\\n return time_left / self.attributes[AT.TIME_TO_EXPIRE]\",\n \"def elapsed(self):\\n return datetime.datetime.now() - self.start\",\n \"def round_trip_time(self):\\r\\n return self.completion_time - self.launch_time\",\n \"def secondsPassed(self)->int:\\n return 0 if not self.used else int((datetime.utcnow() - self.firstAccessDate).total_seconds())\",\n \"def utility_time_score(self, g, w):\\n occ = parse_time(g.occurred)\\n iss = parse_time(w.submitted)\\n diff = occ - iss\\n return diff.total_seconds() / 86400\",\n \"def get_song_seconds_remaining(result):\\n remaining_ms = get_song_length_milliseconds(result) - get_song_elapsed_milliseconds(result)\\n return int(remaining_ms / 1000)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77314806","0.7411862","0.7296464","0.6975621","0.6766241","0.6730055","0.6683318","0.6671834","0.6671834","0.6671834","0.6671834","0.6671834","0.6671834","0.6656775","0.6643553","0.66259193","0.6610059","0.65386087","0.6536","0.6452645","0.64100575","0.6388198","0.6338689","0.6325187","0.6318473","0.630317","0.6293835","0.62856495","0.6252589","0.62401134","0.62400734","0.62389237","0.62283987","0.6205015","0.62038964","0.62024367","0.6198649","0.6190195","0.61848795","0.61827075","0.6176439","0.6176043","0.61735296","0.6171376","0.6171179","0.6167575","0.61656857","0.6160186","0.6157114","0.6154419","0.61483836","0.6121045","0.6121045","0.6118402","0.611833","0.6108039","0.6105753","0.61018336","0.6092226","0.60914046","0.608777","0.60791653","0.6071209","0.60625535","0.6048004","0.6045837","0.60435736","0.6020852","0.6016188","0.601584","0.6013628","0.60118604","0.60092443","0.6007066","0.59908086","0.5985665","0.598365","0.59800994","0.5979106","0.5963643","0.59586626","0.59572774","0.5945492","0.59402114","0.5935609","0.59287435","0.5923854","0.5919406","0.5919237","0.59100485","0.5907903","0.59052795","0.58997464","0.58991766","0.5898791","0.5889659","0.5887224","0.5878804","0.5878423","0.58766323","0.5872469"],"string":"[\n \"0.77314806\",\n \"0.7411862\",\n \"0.7296464\",\n \"0.6975621\",\n \"0.6766241\",\n \"0.6730055\",\n \"0.6683318\",\n \"0.6671834\",\n \"0.6671834\",\n \"0.6671834\",\n \"0.6671834\",\n \"0.6671834\",\n \"0.6671834\",\n \"0.6656775\",\n \"0.6643553\",\n \"0.66259193\",\n \"0.6610059\",\n \"0.65386087\",\n \"0.6536\",\n \"0.6452645\",\n \"0.64100575\",\n \"0.6388198\",\n \"0.6338689\",\n \"0.6325187\",\n \"0.6318473\",\n \"0.630317\",\n \"0.6293835\",\n \"0.62856495\",\n \"0.6252589\",\n \"0.62401134\",\n \"0.62400734\",\n \"0.62389237\",\n \"0.62283987\",\n \"0.6205015\",\n \"0.62038964\",\n \"0.62024367\",\n \"0.6198649\",\n \"0.6190195\",\n \"0.61848795\",\n \"0.61827075\",\n \"0.6176439\",\n \"0.6176043\",\n \"0.61735296\",\n \"0.6171376\",\n \"0.6171179\",\n \"0.6167575\",\n \"0.61656857\",\n \"0.6160186\",\n \"0.6157114\",\n \"0.6154419\",\n \"0.61483836\",\n \"0.6121045\",\n \"0.6121045\",\n \"0.6118402\",\n \"0.611833\",\n \"0.6108039\",\n \"0.6105753\",\n \"0.61018336\",\n \"0.6092226\",\n \"0.60914046\",\n \"0.608777\",\n \"0.60791653\",\n \"0.6071209\",\n \"0.60625535\",\n \"0.6048004\",\n \"0.6045837\",\n \"0.60435736\",\n \"0.6020852\",\n \"0.6016188\",\n \"0.601584\",\n \"0.6013628\",\n \"0.60118604\",\n \"0.60092443\",\n \"0.6007066\",\n \"0.59908086\",\n \"0.5985665\",\n \"0.598365\",\n \"0.59800994\",\n \"0.5979106\",\n \"0.5963643\",\n \"0.59586626\",\n \"0.59572774\",\n \"0.5945492\",\n \"0.59402114\",\n \"0.5935609\",\n \"0.59287435\",\n \"0.5923854\",\n \"0.5919406\",\n \"0.5919237\",\n \"0.59100485\",\n \"0.5907903\",\n \"0.59052795\",\n \"0.58997464\",\n \"0.58991766\",\n \"0.5898791\",\n \"0.5889659\",\n \"0.5887224\",\n \"0.5878804\",\n \"0.5878423\",\n \"0.58766323\",\n \"0.5872469\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":84994,"cells":{"query":{"kind":"string","value":"default formating as string"},"document":{"kind":"string","value":"def default(self, obj):\n if isinstance(obj, (dt.date, dt.datetime)):\n return obj.isoformat()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def format(self) -> str:","def formatted(self) -> str:\r\n ...","def format(self, *args, **kwargs) -> String:\n pass","def __format__(self, fmt):\n if not isinstance(fmt, str):\n raise TypeError(\"must be str, not %s\" % type(fmt).__name__)\n if len(fmt) != 0:\n return self.strftime(fmt)\n return str(self)","def asformat(self, format):","def __str__(self):\n return self.fmt.format(*self.args, **self.kwargs)","def __format__(self, format_specification=''):\n return super().__format__(format_specification=format_specification)","def format(self):\n ...","def format(self) -> str:\n return pulumi.get(self, \"format\")","def format(self) -> str:\n return self._format","def format(self) -> str:\n return self._format","def __str__(self):\n return self.format()","def get_format(self):\n pass","def format(obj): # pylint: disable=W0622\n# print '>>', obj\n if hasattr(obj, 'format'):\n return obj.format()\n return \"%s\" % obj","def format( self ) :\n\n return( self.__format )","def format(cls, value: Optional[T]) -> str:\n return str(value)","def __str__(self):\n if self.flaky:\n fmt = 'flaky | '\n else:\n fmt = ''\n fmt += '{2}: {0}'\n if self.variant:\n fmt += ' {1}'\n return fmt.format(*self)","def __format__(self, *args, **kwargs): # real signature unknown\r\n pass","def __makeFormatString(self):\n self.__formatString = \"\"\n for f in self.__columns:\n self.__formatString += \"%(\"+ f + \")-\" + str(self.__widths[f]) + \\\n \"s \"","def default_formatter(self, data):\n return data","def __str__(self):\n return super().formatter(\"{old}, {prefix}/{new}\")","def __repr__(self) -> str:\r\n\r\n saida = \"Format: \"\r\n x = self.getformat()\r\n for _ in range(len(x)):\r\n saida = f\"{saida}{x[_]}\"\r\n if _ < len(x)-1:\r\n saida += \", \"\r\n saida += \"\\n\"\r\n return saida","def format(value, arg):\n try:\n if value is not None:\n # return (str(arg)) % value\n return (str(value)) % arg\n else:\n return \"\"\n except (ValueError, TypeError):\n return \"\"","def _get_fmt_string(self):\n fmt = '>4s'\n for datatype in self.message_datatypes:\n if datatype in self.VALID_DATAYPES:\n if datatype == 'int':\n fmt += 'I'\n if datatype == 'float':\n fmt += 'f'\n if datatype == 'double':\n fmt += 'd'\n if datatype == 'char':\n fmt += 'c'\n if datatype == 'string':\n fmt += str(self.max_str_len)+'s'\n if datatype == 'bool':\n fmt += 'b'\n\n return fmt","def strftime(self, format):\n return \"\"","def strftime(self, format):\n return \"\"","def strftime(self, format):\n return \"\"","def format(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"format\")","def format(self, **kw):\n params = self.defaults.copy()\n params.update(kw)\n if self.filter:\n self.filter(self, params)\n msg = self.msg\n if self.key is not None:\n key = self.key.format(**params)\n msg = msg[key]\n return msg.format(**params)","def get_formatter(self, **kwargs):\n config = dict([\n (attr, getattr(self, attr))\n for attr in [\n \"include_sign\",\n \"group_with_commas\",\n \"num_decimal_places\",\n ]\n ])\n config.update(kwargs)\n return \"\".join([\n \"{\",\n config.get(\"field_name\", \"\"),\n \":\",\n \"+\" if config[\"include_sign\"] else \"\",\n \",\" if config[\"group_with_commas\"] else \"\",\n \".\", str(config[\"num_decimal_places\"]), \"f\",\n \"}\",\n ])","def sformatf(cls, msg, *args):\n #formats = {\"%t\": \"%d\", \"%0t\": \"%0d\"}\n #for s in formats:\n # msg = msg.replace(s, formats[s])\n #return sformatf(msg, *args)\n # TODO substitute old types %s/%d etc with {}\n #new_msg = cls.STR_RE.sub(r'{:\\1}', msg)\n #print(\"new_msg is \" + new_msg)\n for s in cls.formats:\n if s == \"%h\" or s == \"%0h\":\n msg = msg.replace(s, \"{:X}\")\n else:\n msg = msg.replace(s, \"{}\")\n return msg.format(*args)","def format(self, fmt: str, locale: str | None = None) -> str:\n return self._formatter.format(self, fmt, locale)","def format(self):\n return self._format","def format(cls):\n return super().format() + 'Hhiihh'","def __format__(self, formatstr):\n if formatstr.strip() == '': # Defualt behaviour mirrors self.__str__()\n formatstr = '+.3f'\n\n string = \\\n \"{:\" + formatstr +\"} \" + \\\n \"{:\" + formatstr +\"}i \" + \\\n \"{:\" + formatstr +\"}j \" + \\\n \"{:\" + formatstr +\"}k\"\n return string.format(self.q[0], self.q[1], self.q[2], self.q[3])","def default_detail_value_format(self, val):\n\n if val is None:\n msg = \"No Filter\"\n return \"<em>%s</em>\" % msg if self.report_format not in [XLS, CSV] else msg\n\n if isinstance(val, str):\n if val.lower() in relative_dates.ALL_DATE_RANGES:\n start, end = relative_dates(val).range()\n return \"%s (%s - %s)\" % (val, format_as_date(start), format_as_date(end))\n return val\n\n if isinstance(val, timezone.datetime):\n return format_as_date(val)\n\n try:\n if len(val) > 0 and isinstance(val[0], timezone.datetime):\n joiner = \" - \" if len(val) == 2 else \", \"\n return joiner.join(format_as_date(dt) for dt in val)\n except: # noqa: E722 # pragma: no cover\n pass\n\n try:\n return ', '.join(str(x) for x in val)\n except: # noqa: E722 # pragma: no cover\n pass\n\n return str(val)","def _get_nullformat(self, newformat):\n if self._type == int:\n length = len(str(newformat % 1))\n return '%'+str(length)+'s'\n elif self._type == float:\n length = len(str(newformat % 1.0))\n return '%'+str(length)+'s'\n else:\n return newformat","def __str__(self):\n # year, formatted unambiguously as YYYY, padded with zeros if not\n # four-digit\n yearString = \"0\" * (4 - len(str(self.get_year()))\n ) + str(self.get_year())\n # month, formatted unambiguously as MM, padded with zeros if not\n # two-digit\n monthString = \"0\" * (2 - len(str(self.get_month()))\n ) + str(self.get_month())\n # day, formatted unambiguously as DD, padded with zeros\n dayString = \"0\" * (2 - len(str(self.get_day()))) + str(self.get_day())\n # aggregate all the string representations\n return \"%s-%s-%s\" % (yearString, monthString, dayString)","def _format_default_value(self, default):\n return json.dumps(default)","def format(self):\n return self.getparam(\"FORMAT\")","def format(self):\n return self.getparam(\"FORMAT\")","def __format__(self, format_spec):\n if format_spec == \"polite\":\n return self.polite\n elif format_spec == \"casual\":\n return self.casual\n else:\n # Using string addition here to avoid triggering flake8-sfs\n # while still giving a meaningful self-contained example:\n raise ValueError(format_spec + \" not a format defined by Client object\")","def format_(self):\n return self.set_format or self.default_format or self.FALLBACK_FORMAT","def default(str):\n return str + ' [Default: %default]'","def __gen_fmt_str__(self, fmt):\n return '=' + (self.num_pts_recv * (fmt + ' '))","def format(self, message):","def __str__(self):\n if self.f_has_range():\n lenstr = \"len:%d\" % self.f_get_range_length()\n else:\n lenstr = \"\"\n\n if self.v_comment:\n commentstr = \"`%s`\" % self.v_comment\n else:\n commentstr = \"\"\n\n if commentstr or lenstr:\n if commentstr and lenstr:\n combined_str = \"%s, %s\" % (lenstr, commentstr)\n elif commentstr:\n combined_str = commentstr\n elif lenstr:\n combined_str = lenstr\n else:\n raise RuntimeError(\"You shall not pass!\")\n\n infostr = \" (%s)\" % combined_str\n\n else:\n infostr = \"\"\n\n return_string = \"%s %s%s\" % (self.f_get_class_name(), self.v_full_name, infostr)\n\n if not self.f_is_empty():\n return_string += \": \" + self.f_val_to_str()\n\n return return_string","def __str__(self):\n y, m, d = self._year, self._month, self._day\n h, mn, s, t = self._hour, self._minute, self._second, self._tz\n if s == int(s):\n # A whole number of seconds -- suppress milliseconds.\n return '%4.4d/%2.2d/%2.2d %2.2d:%2.2d:%2.2d %s' % (\n y, m, d, h, mn, s, t)\n else:\n # s is already rounded to the nearest microsecond, and\n # it's not a whole number of seconds. Be sure to print\n # 2 digits before the decimal point.\n return '%4.4d/%2.2d/%2.2d %2.2d:%2.2d:%06.6f %s' % (\n y, m, d, h, mn, s, t)","def TEXT(number, format_type):\n raise NotImplementedError()","def getFormatString(self):\n return ['S', 'P', 'w']","def get_format_opts(cls, format_=\"value\", fields=[]):\n return \" -f {0} {1}\".format(format_, \" \".join([\"-c \" + it for it in fields]))","def __format__(self, format_spec: str = \"\") -> str:\n if not format_spec:\n format_spec = \".4G\"\n array_string = np.array2string(\n self.data_in_display_units,\n formatter={\"float_kind\": lambda x: format(x, format_spec)},\n )\n return f\"{array_string} {self.display_unit}\"","def format_str() -> str:\r\n decimal_places = conf.instance[\"general\"][\"output\"][\"model_results_decimal_places\"]\r\n return f\"{{:.{decimal_places}f}}\"","def newFormatter(*args, **kw):\n originalResult = originalFormatter(*args, **kw)\n if all():\n originalResult += ' %r' % all()\n return originalResult","def to_format(self, format_string: str) -> str:\n return self.strftime(format_string)","def __format__(self, code):\n if not code:\n return self.name\n \n if code == 'c':\n return format(self.created_at, DATETIME_FORMAT_CODE)\n \n raise ValueError(\n f'Unknown format code {code!r} for {self.__class__.__name__}; {self!r}. '\n f'Available format codes: {\"\"!r}, {\"c\"!r}.'\n )","def format(self):\n groups = [g + \".\" for g in self.groups]\n params = [\";\" + p.format() for p in self.params]\n groups_name_params = \"\".join(groups) + self.name + \"\".join(params)\n return groups_name_params + \":\" + self.format_value() + CRLF","def assign_format(self):\n if self.is_output or self.is_req_output:\n if self.pname in self.tool_data[self.tool_name]['output_fmt']:\n return self.tool_data[self.tool_name]['output_fmt'][self.pname]\n elif self.pname in self.gen_out_fmt:\n return self.gen_out_fmt[self.pname]\n elif self.is_input:\n if self.pname in self.tool_data[self.tool_name]['input_fmt']:\n print(self.tool_data[self.tool_name])\n return self.tool_data[self.tool_name]['input_fmt'][self.pname]\n elif self.pname in self.gen_in_fmt:\n return self.gen_in_fmt[self.pname]\n else:\n # Not sure yet what this will be used for, but I think we need it.\n return ''","def format(self, *args, **kwargs):\n return self._format(args, kwargs)","def format_default(reg):\n\t\tif reg.size == \"accum\":\n\t\t\treturn str(float(reg.default)) + \"k\"\n\t\telse:\n\t\t\treturn str(int(reg.default)) + \"L\"","def __str__(self):\n return self.isoformat(sep=\" \")","def __format__(self, format_spec):\n # Reject anything that isn't an s\n if format_spec[-1] != 's':\n raise ValueError('{} format specifier not understood for this object',\n format_spec[:-1])\n # Output in this example will be (<a>,<b>,<c>)\n raw = \"(\" + \",\".join([str(self.a), str(self.b), str(self.c)]) + \")\"\n # Honor the format language by using the inbuilt string format\n # Since we know the original format_spec ends in an 's'\n # we can take advantage of the str.format method with a\n # string argument we constructed above\n return \"{r:{f}}\".format( r=raw, f=format_spec )","def set_formatter_string(config: dict):\n formatter_str = \"%(levelname)s %(name)s\"\n\n if config.get(\"formatter\"):\n return config[\"formatter\"]\n\n if config.get(\"extended\"):\n formatter_str += \".%(funcName)s():\"\n\n if config.get(\"timestamp\"):\n formatter_str = \"%(asctime)s \" + formatter_str\n\n formatter_str += \" %(message)s\"\n\n return formatter_str","def get_str(self, item: str, fmt: str = \"{}\") -> str:\n return fmt.format(self[item])","def __str__(self):\n return '[{0}, {1}]'.format(self.timeValuePairs, self.defaultValue)","def format(self, *args, **kwargs):\n raise NotImplementedError()","def format_value(self, value: float) -> str:\r\n ...","def format_value(self, value: float) -> str:\r\n ...","def __str__(self):\n #Format data from default fields\n template = \"{number:4}|{rep:4}|{time:5}{priority:+2}|\" \\\n \"{record_type:8}|{name:17}\"\n default_fields = template.format(**self)\n \n #Format data from custom fields\n custom_field_list = []\n for label in self.custom_labels:\n custom_field_list.append(\"|{:17}:{!s:<5}\".format(label,\n self[label]))\n custom_fields = \"\".join(custom_field_list)\n \n return default_fields + custom_fields","def __format__(self, format_spec: str) -> str:\n\n return format(self.balance, format_spec)","def __str__(self) -> str:\n return _get_default_str(self)","def strfdate(self, fmt):\n pattern = r'%({})'.format(reduce(lambda x, y: '{}|{}'.format(x, y), FORMAT_MAP.keys()))\n for f in re.findall(pattern, fmt):\n fmt = fmt.replace('%{}'.format(f), FORMAT_MAP[f](self))\n return fmt","def _process_str(self, fmt, *args, **kwargs):\n log_str = fmt\n if len(args) > 0 or len(kwargs) > 0:\n log_str = fmt.format(*args, **kwargs)\n\n return log_str","def __format__(self, formatstr):\n return self.df.__format__(formatstr)","def default_field_formatter(variable_name: str, field: Field) -> str:\n return \"{{ \" + f\"form.{variable_name}\" + \" }}\"","def isoformat(self):\n return \"\"","def default_format(data, color):\n if color:\n out = '\\n\\n\\nTitle: {0}\\nDate: {1}\\nLink: {2}\\n\\nImages links: {3}'.format(\n colored(data['title'], 'green'),\n data['pubDate'],\n colored(data['link'], 'blue'),\n colored(data['media'], 'blue'),)\n else:\n out = '\\n\\n\\nTitle: {0}\\nDate: {1}\\nLink: {2}\\n\\nImages links: {3}'.format(\n data['title'],\n data['pubDate'],\n data['link'],\n data['media'],)\n return out","def format(self):\n return self[\"format\"]","def format(self):\n return self[\"format\"]","def __repr__(self: GtinFormat) -> str:\n return f\"GtinFormat.{self.name}\"","def get_format(cls):\n return cls._format","def format(self) -> Optional[pulumi.Input['FlowLogFormatParametersArgs']]:\n return pulumi.get(self, \"format\")","def format(value):\n if isinstance(value, str):\n return '\"{}\"'.format(value)\n if isinstance(value, bool):\n return 'true' if value is True else 'false'\n elif isinstance(value, dict):\n assert False, 'Not implemented for dictionary type'\n elif hasattr(value, '__len__'): # should cover list and numpy array\n return '{{{}}}'.format(', '.join([str(v) for v in value]))\n else: # assume scalar value\n return value","def _format(val, valtype, floatfmt, intfmt, missingval=\"\", has_invisible=True): # noqa\n if val is None:\n return missingval\n\n if valtype is str:\n return f\"{val}\"\n elif valtype is int:\n return format(val, intfmt)\n elif valtype is bytes:\n try:\n return str(val, \"ascii\")\n except (TypeError, UnicodeDecodeError):\n return str(val)\n elif valtype is float:\n is_a_colored_number = has_invisible and isinstance(val, (str, bytes))\n if is_a_colored_number:\n raw_val = _strip_ansi(val)\n formatted_val = format(float(raw_val), floatfmt)\n return val.replace(raw_val, formatted_val)\n else:\n return format(float(val), floatfmt)\n else:\n return f\"{val}\"","def __str__(self: GtinFormat) -> str:\n return self.name.replace(\"_\", \"-\")","def __str__(self) -> str:\n str_ = self.method\n if self.basis is not None:\n str_ += f'/{self.basis}'\n if self.auxiliary_basis is not None:\n str_ += f', auxiliary_basis: {self.auxiliary_basis}'\n if self.dispersion is not None:\n str_ += f', dispersion: {self.dispersion}'\n if self.cabs is not None:\n str_ += f', cabs: {self.cabs}'\n if self.solvation_method is not None:\n str_ += f', solvation_method: {self.solvation_method}'\n if self.solvent is not None:\n str_ += f', solvent: {self.solvent}'\n if self.solvation_scheme_level is not None:\n str_ += f\", solvation_scheme_level: '{str(self.solvation_scheme_level)}'\"\n if self.software is not None:\n str_ += f', software: {self.software}'\n if self.software_version is not None:\n str_ += f', software_version: {self.software_version}'\n if self.args is not None and self.args and all([val for val in self.args.values()]):\n if any([key == 'keyword' for key in self.args.keys()]):\n str_ += ', keyword args:'\n for key, arg in self.args.items():\n if key == 'keyword':\n str_ += f' {arg}'\n if self.method_type is not None:\n str_ += f' ({self.method_type})'\n return str_","def __str__(self):\n if self.default_kind:\n kind_str = \"\"\n else:\n kind_str = \", kind={}\".format(self.kind)\n # End if\n return \"{}(len={}{})\".format(self.typestr, self.lenstr, kind_str)","def float_format(self):\n ...","def getFormattedText(self):\r\n h = \"00\"\r\n m = \"00\"\r\n s = \"00\"\r\n if(self.seconds < 10):\r\n s = \"0\" + str(self.seconds)\r\n else:\r\n s = str(self.seconds)\r\n\r\n if(self.minutes < 10):\r\n m = \"0\" + str(self.minutes)\r\n else:\r\n m = str(self.minutes)\r\n\r\n if(self.hours < 10):\r\n h = \"0\" + str(self.hours)\r\n else:\r\n h = str(self.hours)\r\n\r\n return h + \":\" + m + \":\" + s","def get_format(self):\n return self._format[0]","def pretty_str(self) -> str:\n ...","def w__format(self, string):\n s = ''\n for i in range(0, len(string) - 1, 2):\n s = s + \"%03s\" % string[i:i + 2]\n return s[1:]","def toString(self, format_):\n if format_ == 'fasta':\n return '>%s\\n%s\\n' % (self.id, self.sequence)\n elif format_ == 'fastq':\n if self.quality is None:\n raise ValueError('Read %r has no quality information' %\n self.id)\n else:\n return '@%s\\n%s\\n+%s\\n%s\\n' % (\n self.id, self.sequence, self.id, self.quality)\n else:\n raise ValueError(\"Format must be either 'fasta' or 'fastq'.\")","def getopt_format(self):\n self._print_enum_opt(\"format\", FORMATTERS)","def format(self):\r\n\r\n earth = \"???\" if self.maskearth else self.earth\r\n air = \"???\" if self.maskair else self.air\r\n fire = \"???\" if self.maskfire else self.fire\r\n water = \"???\" if self.maskwater else self.water\r\n\r\n if any((self.earth, self.fire, self.water)):\r\n statsline = f'Stats: {earth}/{air}/{fire}/{water}'\r\n elif self.air:\r\n statsline = f'Air: {air}'\r\n else:\r\n statsline = ''\r\n\r\n return (\r\n f'Character {self.name}, [{self.token}]. '\r\n f'Init: {self.init} {statsline} Owner: {self.user.name}'\r\n )","def _format_default(s):\n return u'\"%s%s\"' % (BASE_URL, s.get_absolute_url())","def _default_value_formatter(human_readable, field_limit, encoding='utf-8'):\n fields = {}\n if human_readable:\n fields = {\n eliot_ns(u'timestamp'): format.timestamp(\n include_microsecond=False),\n eliot_ns(u'duration'): format.duration(),\n }\n return compose(\n # We want tree-format to handle newlines.\n partial(format.escape_control_characters, overrides={0x0a: u'\\n'}),\n partial(format.truncate_value,\n field_limit) if field_limit else identity,\n format.some(\n format.fields(fields),\n format.text(),\n format.binary(encoding),\n format.anything(encoding)))","def get_format(self):\n return \"RAW\"","def date_format(date) -> str:\n if date is not None:\n str_ = date.strftime(DATE_FMT).upper()\n else:\n str_ = \" \"\n return str_","def format(self, valDict):\n return self._formatStr % valDict","def __str__(self):\r\n # Use the properties or you are not executing the formatting\r\n return f'{self.lname}, {self.fname} - {self.major}'"],"string":"[\n \"def format(self) -> str:\",\n \"def formatted(self) -> str:\\r\\n ...\",\n \"def format(self, *args, **kwargs) -> String:\\n pass\",\n \"def __format__(self, fmt):\\n if not isinstance(fmt, str):\\n raise TypeError(\\\"must be str, not %s\\\" % type(fmt).__name__)\\n if len(fmt) != 0:\\n return self.strftime(fmt)\\n return str(self)\",\n \"def asformat(self, format):\",\n \"def __str__(self):\\n return self.fmt.format(*self.args, **self.kwargs)\",\n \"def __format__(self, format_specification=''):\\n return super().__format__(format_specification=format_specification)\",\n \"def format(self):\\n ...\",\n \"def format(self) -> str:\\n return pulumi.get(self, \\\"format\\\")\",\n \"def format(self) -> str:\\n return self._format\",\n \"def format(self) -> str:\\n return self._format\",\n \"def __str__(self):\\n return self.format()\",\n \"def get_format(self):\\n pass\",\n \"def format(obj): # pylint: disable=W0622\\n# print '>>', obj\\n if hasattr(obj, 'format'):\\n return obj.format()\\n return \\\"%s\\\" % obj\",\n \"def format( self ) :\\n\\n return( self.__format )\",\n \"def format(cls, value: Optional[T]) -> str:\\n return str(value)\",\n \"def __str__(self):\\n if self.flaky:\\n fmt = 'flaky | '\\n else:\\n fmt = ''\\n fmt += '{2}: {0}'\\n if self.variant:\\n fmt += ' {1}'\\n return fmt.format(*self)\",\n \"def __format__(self, *args, **kwargs): # real signature unknown\\r\\n pass\",\n \"def __makeFormatString(self):\\n self.__formatString = \\\"\\\"\\n for f in self.__columns:\\n self.__formatString += \\\"%(\\\"+ f + \\\")-\\\" + str(self.__widths[f]) + \\\\\\n \\\"s \\\"\",\n \"def default_formatter(self, data):\\n return data\",\n \"def __str__(self):\\n return super().formatter(\\\"{old}, {prefix}/{new}\\\")\",\n \"def __repr__(self) -> str:\\r\\n\\r\\n saida = \\\"Format: \\\"\\r\\n x = self.getformat()\\r\\n for _ in range(len(x)):\\r\\n saida = f\\\"{saida}{x[_]}\\\"\\r\\n if _ < len(x)-1:\\r\\n saida += \\\", \\\"\\r\\n saida += \\\"\\\\n\\\"\\r\\n return saida\",\n \"def format(value, arg):\\n try:\\n if value is not None:\\n # return (str(arg)) % value\\n return (str(value)) % arg\\n else:\\n return \\\"\\\"\\n except (ValueError, TypeError):\\n return \\\"\\\"\",\n \"def _get_fmt_string(self):\\n fmt = '>4s'\\n for datatype in self.message_datatypes:\\n if datatype in self.VALID_DATAYPES:\\n if datatype == 'int':\\n fmt += 'I'\\n if datatype == 'float':\\n fmt += 'f'\\n if datatype == 'double':\\n fmt += 'd'\\n if datatype == 'char':\\n fmt += 'c'\\n if datatype == 'string':\\n fmt += str(self.max_str_len)+'s'\\n if datatype == 'bool':\\n fmt += 'b'\\n\\n return fmt\",\n \"def strftime(self, format):\\n return \\\"\\\"\",\n \"def strftime(self, format):\\n return \\\"\\\"\",\n \"def strftime(self, format):\\n return \\\"\\\"\",\n \"def format(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"format\\\")\",\n \"def format(self, **kw):\\n params = self.defaults.copy()\\n params.update(kw)\\n if self.filter:\\n self.filter(self, params)\\n msg = self.msg\\n if self.key is not None:\\n key = self.key.format(**params)\\n msg = msg[key]\\n return msg.format(**params)\",\n \"def get_formatter(self, **kwargs):\\n config = dict([\\n (attr, getattr(self, attr))\\n for attr in [\\n \\\"include_sign\\\",\\n \\\"group_with_commas\\\",\\n \\\"num_decimal_places\\\",\\n ]\\n ])\\n config.update(kwargs)\\n return \\\"\\\".join([\\n \\\"{\\\",\\n config.get(\\\"field_name\\\", \\\"\\\"),\\n \\\":\\\",\\n \\\"+\\\" if config[\\\"include_sign\\\"] else \\\"\\\",\\n \\\",\\\" if config[\\\"group_with_commas\\\"] else \\\"\\\",\\n \\\".\\\", str(config[\\\"num_decimal_places\\\"]), \\\"f\\\",\\n \\\"}\\\",\\n ])\",\n \"def sformatf(cls, msg, *args):\\n #formats = {\\\"%t\\\": \\\"%d\\\", \\\"%0t\\\": \\\"%0d\\\"}\\n #for s in formats:\\n # msg = msg.replace(s, formats[s])\\n #return sformatf(msg, *args)\\n # TODO substitute old types %s/%d etc with {}\\n #new_msg = cls.STR_RE.sub(r'{:\\\\1}', msg)\\n #print(\\\"new_msg is \\\" + new_msg)\\n for s in cls.formats:\\n if s == \\\"%h\\\" or s == \\\"%0h\\\":\\n msg = msg.replace(s, \\\"{:X}\\\")\\n else:\\n msg = msg.replace(s, \\\"{}\\\")\\n return msg.format(*args)\",\n \"def format(self, fmt: str, locale: str | None = None) -> str:\\n return self._formatter.format(self, fmt, locale)\",\n \"def format(self):\\n return self._format\",\n \"def format(cls):\\n return super().format() + 'Hhiihh'\",\n \"def __format__(self, formatstr):\\n if formatstr.strip() == '': # Defualt behaviour mirrors self.__str__()\\n formatstr = '+.3f'\\n\\n string = \\\\\\n \\\"{:\\\" + formatstr +\\\"} \\\" + \\\\\\n \\\"{:\\\" + formatstr +\\\"}i \\\" + \\\\\\n \\\"{:\\\" + formatstr +\\\"}j \\\" + \\\\\\n \\\"{:\\\" + formatstr +\\\"}k\\\"\\n return string.format(self.q[0], self.q[1], self.q[2], self.q[3])\",\n \"def default_detail_value_format(self, val):\\n\\n if val is None:\\n msg = \\\"No Filter\\\"\\n return \\\"<em>%s</em>\\\" % msg if self.report_format not in [XLS, CSV] else msg\\n\\n if isinstance(val, str):\\n if val.lower() in relative_dates.ALL_DATE_RANGES:\\n start, end = relative_dates(val).range()\\n return \\\"%s (%s - %s)\\\" % (val, format_as_date(start), format_as_date(end))\\n return val\\n\\n if isinstance(val, timezone.datetime):\\n return format_as_date(val)\\n\\n try:\\n if len(val) > 0 and isinstance(val[0], timezone.datetime):\\n joiner = \\\" - \\\" if len(val) == 2 else \\\", \\\"\\n return joiner.join(format_as_date(dt) for dt in val)\\n except: # noqa: E722 # pragma: no cover\\n pass\\n\\n try:\\n return ', '.join(str(x) for x in val)\\n except: # noqa: E722 # pragma: no cover\\n pass\\n\\n return str(val)\",\n \"def _get_nullformat(self, newformat):\\n if self._type == int:\\n length = len(str(newformat % 1))\\n return '%'+str(length)+'s'\\n elif self._type == float:\\n length = len(str(newformat % 1.0))\\n return '%'+str(length)+'s'\\n else:\\n return newformat\",\n \"def __str__(self):\\n # year, formatted unambiguously as YYYY, padded with zeros if not\\n # four-digit\\n yearString = \\\"0\\\" * (4 - len(str(self.get_year()))\\n ) + str(self.get_year())\\n # month, formatted unambiguously as MM, padded with zeros if not\\n # two-digit\\n monthString = \\\"0\\\" * (2 - len(str(self.get_month()))\\n ) + str(self.get_month())\\n # day, formatted unambiguously as DD, padded with zeros\\n dayString = \\\"0\\\" * (2 - len(str(self.get_day()))) + str(self.get_day())\\n # aggregate all the string representations\\n return \\\"%s-%s-%s\\\" % (yearString, monthString, dayString)\",\n \"def _format_default_value(self, default):\\n return json.dumps(default)\",\n \"def format(self):\\n return self.getparam(\\\"FORMAT\\\")\",\n \"def format(self):\\n return self.getparam(\\\"FORMAT\\\")\",\n \"def __format__(self, format_spec):\\n if format_spec == \\\"polite\\\":\\n return self.polite\\n elif format_spec == \\\"casual\\\":\\n return self.casual\\n else:\\n # Using string addition here to avoid triggering flake8-sfs\\n # while still giving a meaningful self-contained example:\\n raise ValueError(format_spec + \\\" not a format defined by Client object\\\")\",\n \"def format_(self):\\n return self.set_format or self.default_format or self.FALLBACK_FORMAT\",\n \"def default(str):\\n return str + ' [Default: %default]'\",\n \"def __gen_fmt_str__(self, fmt):\\n return '=' + (self.num_pts_recv * (fmt + ' '))\",\n \"def format(self, message):\",\n \"def __str__(self):\\n if self.f_has_range():\\n lenstr = \\\"len:%d\\\" % self.f_get_range_length()\\n else:\\n lenstr = \\\"\\\"\\n\\n if self.v_comment:\\n commentstr = \\\"`%s`\\\" % self.v_comment\\n else:\\n commentstr = \\\"\\\"\\n\\n if commentstr or lenstr:\\n if commentstr and lenstr:\\n combined_str = \\\"%s, %s\\\" % (lenstr, commentstr)\\n elif commentstr:\\n combined_str = commentstr\\n elif lenstr:\\n combined_str = lenstr\\n else:\\n raise RuntimeError(\\\"You shall not pass!\\\")\\n\\n infostr = \\\" (%s)\\\" % combined_str\\n\\n else:\\n infostr = \\\"\\\"\\n\\n return_string = \\\"%s %s%s\\\" % (self.f_get_class_name(), self.v_full_name, infostr)\\n\\n if not self.f_is_empty():\\n return_string += \\\": \\\" + self.f_val_to_str()\\n\\n return return_string\",\n \"def __str__(self):\\n y, m, d = self._year, self._month, self._day\\n h, mn, s, t = self._hour, self._minute, self._second, self._tz\\n if s == int(s):\\n # A whole number of seconds -- suppress milliseconds.\\n return '%4.4d/%2.2d/%2.2d %2.2d:%2.2d:%2.2d %s' % (\\n y, m, d, h, mn, s, t)\\n else:\\n # s is already rounded to the nearest microsecond, and\\n # it's not a whole number of seconds. Be sure to print\\n # 2 digits before the decimal point.\\n return '%4.4d/%2.2d/%2.2d %2.2d:%2.2d:%06.6f %s' % (\\n y, m, d, h, mn, s, t)\",\n \"def TEXT(number, format_type):\\n raise NotImplementedError()\",\n \"def getFormatString(self):\\n return ['S', 'P', 'w']\",\n \"def get_format_opts(cls, format_=\\\"value\\\", fields=[]):\\n return \\\" -f {0} {1}\\\".format(format_, \\\" \\\".join([\\\"-c \\\" + it for it in fields]))\",\n \"def __format__(self, format_spec: str = \\\"\\\") -> str:\\n if not format_spec:\\n format_spec = \\\".4G\\\"\\n array_string = np.array2string(\\n self.data_in_display_units,\\n formatter={\\\"float_kind\\\": lambda x: format(x, format_spec)},\\n )\\n return f\\\"{array_string} {self.display_unit}\\\"\",\n \"def format_str() -> str:\\r\\n decimal_places = conf.instance[\\\"general\\\"][\\\"output\\\"][\\\"model_results_decimal_places\\\"]\\r\\n return f\\\"{{:.{decimal_places}f}}\\\"\",\n \"def newFormatter(*args, **kw):\\n originalResult = originalFormatter(*args, **kw)\\n if all():\\n originalResult += ' %r' % all()\\n return originalResult\",\n \"def to_format(self, format_string: str) -> str:\\n return self.strftime(format_string)\",\n \"def __format__(self, code):\\n if not code:\\n return self.name\\n \\n if code == 'c':\\n return format(self.created_at, DATETIME_FORMAT_CODE)\\n \\n raise ValueError(\\n f'Unknown format code {code!r} for {self.__class__.__name__}; {self!r}. '\\n f'Available format codes: {\\\"\\\"!r}, {\\\"c\\\"!r}.'\\n )\",\n \"def format(self):\\n groups = [g + \\\".\\\" for g in self.groups]\\n params = [\\\";\\\" + p.format() for p in self.params]\\n groups_name_params = \\\"\\\".join(groups) + self.name + \\\"\\\".join(params)\\n return groups_name_params + \\\":\\\" + self.format_value() + CRLF\",\n \"def assign_format(self):\\n if self.is_output or self.is_req_output:\\n if self.pname in self.tool_data[self.tool_name]['output_fmt']:\\n return self.tool_data[self.tool_name]['output_fmt'][self.pname]\\n elif self.pname in self.gen_out_fmt:\\n return self.gen_out_fmt[self.pname]\\n elif self.is_input:\\n if self.pname in self.tool_data[self.tool_name]['input_fmt']:\\n print(self.tool_data[self.tool_name])\\n return self.tool_data[self.tool_name]['input_fmt'][self.pname]\\n elif self.pname in self.gen_in_fmt:\\n return self.gen_in_fmt[self.pname]\\n else:\\n # Not sure yet what this will be used for, but I think we need it.\\n return ''\",\n \"def format(self, *args, **kwargs):\\n return self._format(args, kwargs)\",\n \"def format_default(reg):\\n\\t\\tif reg.size == \\\"accum\\\":\\n\\t\\t\\treturn str(float(reg.default)) + \\\"k\\\"\\n\\t\\telse:\\n\\t\\t\\treturn str(int(reg.default)) + \\\"L\\\"\",\n \"def __str__(self):\\n return self.isoformat(sep=\\\" \\\")\",\n \"def __format__(self, format_spec):\\n # Reject anything that isn't an s\\n if format_spec[-1] != 's':\\n raise ValueError('{} format specifier not understood for this object',\\n format_spec[:-1])\\n # Output in this example will be (<a>,<b>,<c>)\\n raw = \\\"(\\\" + \\\",\\\".join([str(self.a), str(self.b), str(self.c)]) + \\\")\\\"\\n # Honor the format language by using the inbuilt string format\\n # Since we know the original format_spec ends in an 's'\\n # we can take advantage of the str.format method with a\\n # string argument we constructed above\\n return \\\"{r:{f}}\\\".format( r=raw, f=format_spec )\",\n \"def set_formatter_string(config: dict):\\n formatter_str = \\\"%(levelname)s %(name)s\\\"\\n\\n if config.get(\\\"formatter\\\"):\\n return config[\\\"formatter\\\"]\\n\\n if config.get(\\\"extended\\\"):\\n formatter_str += \\\".%(funcName)s():\\\"\\n\\n if config.get(\\\"timestamp\\\"):\\n formatter_str = \\\"%(asctime)s \\\" + formatter_str\\n\\n formatter_str += \\\" %(message)s\\\"\\n\\n return formatter_str\",\n \"def get_str(self, item: str, fmt: str = \\\"{}\\\") -> str:\\n return fmt.format(self[item])\",\n \"def __str__(self):\\n return '[{0}, {1}]'.format(self.timeValuePairs, self.defaultValue)\",\n \"def format(self, *args, **kwargs):\\n raise NotImplementedError()\",\n \"def format_value(self, value: float) -> str:\\r\\n ...\",\n \"def format_value(self, value: float) -> str:\\r\\n ...\",\n \"def __str__(self):\\n #Format data from default fields\\n template = \\\"{number:4}|{rep:4}|{time:5}{priority:+2}|\\\" \\\\\\n \\\"{record_type:8}|{name:17}\\\"\\n default_fields = template.format(**self)\\n \\n #Format data from custom fields\\n custom_field_list = []\\n for label in self.custom_labels:\\n custom_field_list.append(\\\"|{:17}:{!s:<5}\\\".format(label,\\n self[label]))\\n custom_fields = \\\"\\\".join(custom_field_list)\\n \\n return default_fields + custom_fields\",\n \"def __format__(self, format_spec: str) -> str:\\n\\n return format(self.balance, format_spec)\",\n \"def __str__(self) -> str:\\n return _get_default_str(self)\",\n \"def strfdate(self, fmt):\\n pattern = r'%({})'.format(reduce(lambda x, y: '{}|{}'.format(x, y), FORMAT_MAP.keys()))\\n for f in re.findall(pattern, fmt):\\n fmt = fmt.replace('%{}'.format(f), FORMAT_MAP[f](self))\\n return fmt\",\n \"def _process_str(self, fmt, *args, **kwargs):\\n log_str = fmt\\n if len(args) > 0 or len(kwargs) > 0:\\n log_str = fmt.format(*args, **kwargs)\\n\\n return log_str\",\n \"def __format__(self, formatstr):\\n return self.df.__format__(formatstr)\",\n \"def default_field_formatter(variable_name: str, field: Field) -> str:\\n return \\\"{{ \\\" + f\\\"form.{variable_name}\\\" + \\\" }}\\\"\",\n \"def isoformat(self):\\n return \\\"\\\"\",\n \"def default_format(data, color):\\n if color:\\n out = '\\\\n\\\\n\\\\nTitle: {0}\\\\nDate: {1}\\\\nLink: {2}\\\\n\\\\nImages links: {3}'.format(\\n colored(data['title'], 'green'),\\n data['pubDate'],\\n colored(data['link'], 'blue'),\\n colored(data['media'], 'blue'),)\\n else:\\n out = '\\\\n\\\\n\\\\nTitle: {0}\\\\nDate: {1}\\\\nLink: {2}\\\\n\\\\nImages links: {3}'.format(\\n data['title'],\\n data['pubDate'],\\n data['link'],\\n data['media'],)\\n return out\",\n \"def format(self):\\n return self[\\\"format\\\"]\",\n \"def format(self):\\n return self[\\\"format\\\"]\",\n \"def __repr__(self: GtinFormat) -> str:\\n return f\\\"GtinFormat.{self.name}\\\"\",\n \"def get_format(cls):\\n return cls._format\",\n \"def format(self) -> Optional[pulumi.Input['FlowLogFormatParametersArgs']]:\\n return pulumi.get(self, \\\"format\\\")\",\n \"def format(value):\\n if isinstance(value, str):\\n return '\\\"{}\\\"'.format(value)\\n if isinstance(value, bool):\\n return 'true' if value is True else 'false'\\n elif isinstance(value, dict):\\n assert False, 'Not implemented for dictionary type'\\n elif hasattr(value, '__len__'): # should cover list and numpy array\\n return '{{{}}}'.format(', '.join([str(v) for v in value]))\\n else: # assume scalar value\\n return value\",\n \"def _format(val, valtype, floatfmt, intfmt, missingval=\\\"\\\", has_invisible=True): # noqa\\n if val is None:\\n return missingval\\n\\n if valtype is str:\\n return f\\\"{val}\\\"\\n elif valtype is int:\\n return format(val, intfmt)\\n elif valtype is bytes:\\n try:\\n return str(val, \\\"ascii\\\")\\n except (TypeError, UnicodeDecodeError):\\n return str(val)\\n elif valtype is float:\\n is_a_colored_number = has_invisible and isinstance(val, (str, bytes))\\n if is_a_colored_number:\\n raw_val = _strip_ansi(val)\\n formatted_val = format(float(raw_val), floatfmt)\\n return val.replace(raw_val, formatted_val)\\n else:\\n return format(float(val), floatfmt)\\n else:\\n return f\\\"{val}\\\"\",\n \"def __str__(self: GtinFormat) -> str:\\n return self.name.replace(\\\"_\\\", \\\"-\\\")\",\n \"def __str__(self) -> str:\\n str_ = self.method\\n if self.basis is not None:\\n str_ += f'/{self.basis}'\\n if self.auxiliary_basis is not None:\\n str_ += f', auxiliary_basis: {self.auxiliary_basis}'\\n if self.dispersion is not None:\\n str_ += f', dispersion: {self.dispersion}'\\n if self.cabs is not None:\\n str_ += f', cabs: {self.cabs}'\\n if self.solvation_method is not None:\\n str_ += f', solvation_method: {self.solvation_method}'\\n if self.solvent is not None:\\n str_ += f', solvent: {self.solvent}'\\n if self.solvation_scheme_level is not None:\\n str_ += f\\\", solvation_scheme_level: '{str(self.solvation_scheme_level)}'\\\"\\n if self.software is not None:\\n str_ += f', software: {self.software}'\\n if self.software_version is not None:\\n str_ += f', software_version: {self.software_version}'\\n if self.args is not None and self.args and all([val for val in self.args.values()]):\\n if any([key == 'keyword' for key in self.args.keys()]):\\n str_ += ', keyword args:'\\n for key, arg in self.args.items():\\n if key == 'keyword':\\n str_ += f' {arg}'\\n if self.method_type is not None:\\n str_ += f' ({self.method_type})'\\n return str_\",\n \"def __str__(self):\\n if self.default_kind:\\n kind_str = \\\"\\\"\\n else:\\n kind_str = \\\", kind={}\\\".format(self.kind)\\n # End if\\n return \\\"{}(len={}{})\\\".format(self.typestr, self.lenstr, kind_str)\",\n \"def float_format(self):\\n ...\",\n \"def getFormattedText(self):\\r\\n h = \\\"00\\\"\\r\\n m = \\\"00\\\"\\r\\n s = \\\"00\\\"\\r\\n if(self.seconds < 10):\\r\\n s = \\\"0\\\" + str(self.seconds)\\r\\n else:\\r\\n s = str(self.seconds)\\r\\n\\r\\n if(self.minutes < 10):\\r\\n m = \\\"0\\\" + str(self.minutes)\\r\\n else:\\r\\n m = str(self.minutes)\\r\\n\\r\\n if(self.hours < 10):\\r\\n h = \\\"0\\\" + str(self.hours)\\r\\n else:\\r\\n h = str(self.hours)\\r\\n\\r\\n return h + \\\":\\\" + m + \\\":\\\" + s\",\n \"def get_format(self):\\n return self._format[0]\",\n \"def pretty_str(self) -> str:\\n ...\",\n \"def w__format(self, string):\\n s = ''\\n for i in range(0, len(string) - 1, 2):\\n s = s + \\\"%03s\\\" % string[i:i + 2]\\n return s[1:]\",\n \"def toString(self, format_):\\n if format_ == 'fasta':\\n return '>%s\\\\n%s\\\\n' % (self.id, self.sequence)\\n elif format_ == 'fastq':\\n if self.quality is None:\\n raise ValueError('Read %r has no quality information' %\\n self.id)\\n else:\\n return '@%s\\\\n%s\\\\n+%s\\\\n%s\\\\n' % (\\n self.id, self.sequence, self.id, self.quality)\\n else:\\n raise ValueError(\\\"Format must be either 'fasta' or 'fastq'.\\\")\",\n \"def getopt_format(self):\\n self._print_enum_opt(\\\"format\\\", FORMATTERS)\",\n \"def format(self):\\r\\n\\r\\n earth = \\\"???\\\" if self.maskearth else self.earth\\r\\n air = \\\"???\\\" if self.maskair else self.air\\r\\n fire = \\\"???\\\" if self.maskfire else self.fire\\r\\n water = \\\"???\\\" if self.maskwater else self.water\\r\\n\\r\\n if any((self.earth, self.fire, self.water)):\\r\\n statsline = f'Stats: {earth}/{air}/{fire}/{water}'\\r\\n elif self.air:\\r\\n statsline = f'Air: {air}'\\r\\n else:\\r\\n statsline = ''\\r\\n\\r\\n return (\\r\\n f'Character {self.name}, [{self.token}]. '\\r\\n f'Init: {self.init} {statsline} Owner: {self.user.name}'\\r\\n )\",\n \"def _format_default(s):\\n return u'\\\"%s%s\\\"' % (BASE_URL, s.get_absolute_url())\",\n \"def _default_value_formatter(human_readable, field_limit, encoding='utf-8'):\\n fields = {}\\n if human_readable:\\n fields = {\\n eliot_ns(u'timestamp'): format.timestamp(\\n include_microsecond=False),\\n eliot_ns(u'duration'): format.duration(),\\n }\\n return compose(\\n # We want tree-format to handle newlines.\\n partial(format.escape_control_characters, overrides={0x0a: u'\\\\n'}),\\n partial(format.truncate_value,\\n field_limit) if field_limit else identity,\\n format.some(\\n format.fields(fields),\\n format.text(),\\n format.binary(encoding),\\n format.anything(encoding)))\",\n \"def get_format(self):\\n return \\\"RAW\\\"\",\n \"def date_format(date) -> str:\\n if date is not None:\\n str_ = date.strftime(DATE_FMT).upper()\\n else:\\n str_ = \\\" \\\"\\n return str_\",\n \"def format(self, valDict):\\n return self._formatStr % valDict\",\n \"def __str__(self):\\r\\n # Use the properties or you are not executing the formatting\\r\\n return f'{self.lname}, {self.fname} - {self.major}'\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.837017","0.78593856","0.78285396","0.77283126","0.7577578","0.74514586","0.74457926","0.72434765","0.7181104","0.7149438","0.7149438","0.70565635","0.70547104","0.7050457","0.6980099","0.6979376","0.6970662","0.6964501","0.69569725","0.69133925","0.69112355","0.68897736","0.6888779","0.68849164","0.68664163","0.68664163","0.68664163","0.6837999","0.68247235","0.67944854","0.6791061","0.6787392","0.6771714","0.6754289","0.6746489","0.67216617","0.6695118","0.6694615","0.6679283","0.66499066","0.66499066","0.6649575","0.6644466","0.6608273","0.65872914","0.6581223","0.65738815","0.6571038","0.6542858","0.65414166","0.6533708","0.6518767","0.65181553","0.6516557","0.6516241","0.6511169","0.65110403","0.65086186","0.6506654","0.65004927","0.64945316","0.6467847","0.6466962","0.6458439","0.64584166","0.6447854","0.6444858","0.6444858","0.64383733","0.64311767","0.6420428","0.64170337","0.6406912","0.63974184","0.63972324","0.636847","0.6365089","0.63617444","0.63617444","0.6361163","0.6347681","0.63315225","0.632798","0.63095325","0.63089556","0.62973183","0.62731284","0.6272783","0.6262801","0.6256559","0.6255088","0.6252721","0.62381274","0.62295854","0.621827","0.6215391","0.62093264","0.6198414","0.6194657","0.618258","0.61733663"],"string":"[\n \"0.837017\",\n \"0.78593856\",\n \"0.78285396\",\n \"0.77283126\",\n \"0.7577578\",\n \"0.74514586\",\n \"0.74457926\",\n \"0.72434765\",\n \"0.7181104\",\n \"0.7149438\",\n \"0.7149438\",\n \"0.70565635\",\n \"0.70547104\",\n \"0.7050457\",\n \"0.6980099\",\n \"0.6979376\",\n \"0.6970662\",\n \"0.6964501\",\n \"0.69569725\",\n \"0.69133925\",\n \"0.69112355\",\n \"0.68897736\",\n \"0.6888779\",\n \"0.68849164\",\n \"0.68664163\",\n \"0.68664163\",\n \"0.68664163\",\n \"0.6837999\",\n \"0.68247235\",\n \"0.67944854\",\n \"0.6791061\",\n \"0.6787392\",\n \"0.6771714\",\n \"0.6754289\",\n \"0.6746489\",\n \"0.67216617\",\n \"0.6695118\",\n \"0.6694615\",\n \"0.6679283\",\n \"0.66499066\",\n \"0.66499066\",\n \"0.6649575\",\n \"0.6644466\",\n \"0.6608273\",\n \"0.65872914\",\n \"0.6581223\",\n \"0.65738815\",\n \"0.6571038\",\n \"0.6542858\",\n \"0.65414166\",\n \"0.6533708\",\n \"0.6518767\",\n \"0.65181553\",\n \"0.6516557\",\n \"0.6516241\",\n \"0.6511169\",\n \"0.65110403\",\n \"0.65086186\",\n \"0.6506654\",\n \"0.65004927\",\n \"0.64945316\",\n \"0.6467847\",\n \"0.6466962\",\n \"0.6458439\",\n \"0.64584166\",\n \"0.6447854\",\n \"0.6444858\",\n \"0.6444858\",\n \"0.64383733\",\n \"0.64311767\",\n \"0.6420428\",\n \"0.64170337\",\n \"0.6406912\",\n \"0.63974184\",\n \"0.63972324\",\n \"0.636847\",\n \"0.6365089\",\n \"0.63617444\",\n \"0.63617444\",\n \"0.6361163\",\n \"0.6347681\",\n \"0.63315225\",\n \"0.632798\",\n \"0.63095325\",\n \"0.63089556\",\n \"0.62973183\",\n \"0.62731284\",\n \"0.6272783\",\n \"0.6262801\",\n \"0.6256559\",\n \"0.6255088\",\n \"0.6252721\",\n \"0.62381274\",\n \"0.62295854\",\n \"0.621827\",\n \"0.6215391\",\n \"0.62093264\",\n \"0.6198414\",\n \"0.6194657\",\n \"0.618258\",\n \"0.61733663\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":84995,"cells":{"query":{"kind":"string","value":"Read scraped profiles parse them and write to json and yamls"},"document":{"kind":"string","value":"def main():\n # %%\n CFG.profiles_yamls_path.mkdir(parents=True, exist_ok=True)\n fpaths = list( _Config.raw_profiles_path.glob('*.html') )\n print( f'{len(fpaths)} htmls found' )\n # %%\n fpath = CFG.raw_profiles_path / 'luis-mario-urrea-murillo.html'\n # %%\n fpath = CFG.raw_profiles_path / 'cristian-david-montoya-saldarriaga-09638514a.html'\n # %%\n fpaths = [ CFG.raw_profiles_path / 'ricardo-alarcon-44079b105.html' ]\n # %%\n fpaths = [ Path('/home/teo/_data/talent/linkedin_raw_profiles/israellaguan.html')]\n # %%\n dics = {}\n # %%\n\n for i, fpath in enumerate(fpaths):\n if fpath in dics:\n continue\n\n with fpath.open('rt') as f_in:\n html = f_in.read()\n\n print( f'\\n***{i+1}/{len(fpaths)} {fpath.name}:')\n dic = extract_one( html, fpath )\n dic['linkedin_url'] = f\"https://www.linkedin.com/in/{fpath.name.split('.')[0]}\"\n dic['scraped_at'] = dt.datetime.fromtimestamp( fpath.stat().st_ctime )\n # pprint(dic['work_stats'])\n dics[fpath] = dic\n\n dics_arr = list(dics.values())\n # %%\n del dics\n # %%\n\n with (CFG.profiles_yamls_path / 'all_profiles.json').open('wt') as f_out:\n json.dump( dics_arr, f_out, cls=DateTimeEncoder, indent=4 )\n # %%\n with (CFG.profiles_yamls_path / 'all_profiles.yaml').open('wt') as f_out:\n yaml.safe_dump( dics_arr, f_out )\n # %%\n df = produce_summary_table( dics_arr )\n df.to_excel( CFG.raw_profiles_path.parent / 'mined_ruby_candidates_sample.xlsx',\n index=False)\n # %%"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def loadProfiles():\n with open(userProfilesDir, \"r\") as infile:\n profiles = json.loads(\"\\n\".join(infile.readlines()))\n infile.close()\n return profiles","def scrape_profile(inhandle, outfile, year, month):\n #Read file\n html = inhandle.read()\n soup = BeautifulSoup(html, 'html.parser')\n\n #Find main page content\n content = soup.find('div', {'class':'entry-content'})\n\n profile = {}\n\n #Fill in known info from URL\n profile['year_reported'] = year\n profile['month_reported'] = month\n\n #Extract and download images.\n profile['images'] = [save_image(img['src']) for img in content.findAll('img')]\n\n #Get visible text\n text = content.get_text().strip()\n\n #Parse information from text\n for key in extractors:\n match = extractors[key].search(text)\n if match:\n matchtext = match.group(1).strip()\n if key in ['justifications','messages']:\n vals = matchtext.split('\\n')\n else:\n vals = matchtext\n profile[key] = vals \n\n #Parse annotations\n content = soup.find('div', {'class':'entry-utility'})\n profile['tags'] = [node.get_text() for node in content.findAll('a', {'rel':'tag'})]\n profile['gender'] = 'female' if 'Female profiles' in profile['tags'] else 'male'\n\n #Save output\n json.dump(profile, open(outfile,'w'))","def _sloppy_parse_profiles (self, contents):\n profile_start = contents.find('profiles\":')\n profile_list_start = contents.find('profilesList')\n if int(profile_start) > -1 and int(profile_list_start) > -1:\n try:\n try:\n return json.loads('{\"a\":{\"' + contents[profile_start:profile_list_start-2].decode('string_escape') + '}}').get('a').get('profiles')\n except ValueError, e:\n return None\n except TypeError, e:\n return None\n return None","def _parse_profile_data (self, netflix_page_data):\n profiles = {};\n important_fields = [\n 'profileName',\n 'isActive',\n 'isAccountOwner',\n 'isKids'\n ]\n # values are accessible via dict (sloppy parsing successfull)\n if type(netflix_page_data) == dict:\n for profile_id in netflix_page_data.get('profiles'):\n if self._is_size_key(key=profile_id) == False and type(netflix_page_data['profiles'][profile_id]) == dict and netflix_page_data['profiles'][profile_id].get('avatar', False) != False:\n profile = {'id': profile_id}\n for important_field in important_fields:\n if important_field == 'profileName':\n profile.update({important_field: HTMLParser.HTMLParser().unescape(netflix_page_data['profiles'][profile_id]['summary'][important_field]).encode('utf8')})\n else:\n profile.update({important_field: netflix_page_data['profiles'][profile_id]['summary'][important_field]})\n avatar_base = netflix_page_data['nf'].get(netflix_page_data['profiles'][profile_id]['summary']['avatarName'], False);\n avatar = 'https://secure.netflix.com/ffe/profiles/avatars_v2/320x320/PICON_029.png' if avatar_base == False else avatar_base['images']['byWidth']['320']['value']\n profile.update({'avatar': avatar, 'isFirstUse': False})\n profiles.update({profile_id: profile})\n return profiles\n\n # values are stored in lists (returned from JS parser)\n # TODO: get rid of this christmas tree of doom\n for item in netflix_page_data:\n if 'hasViewedRatingWelcomeModal' in dict(item).keys():\n for profile_id in item:\n if self._is_size_key(key=profile_id) == False and type(item[profile_id]) == dict and item[profile_id].get('avatar', False) != False:\n profile = {'id': profile_id}\n for important_field in important_fields:\n profile.update({important_field: item[profile_id]['summary'][important_field]})\n avatar_base = item['nf'].get(item[profile_id]['summary']['avatarName'], False);\n avatar = 'https://secure.netflix.com/ffe/profiles/avatars_v2/320x320/PICON_029.png' if avatar_base == False else avatar_base['images']['byWidth']['320']['value']\n profile.update({'avatar': avatar})\n profiles.update({profile_id: profile})\n return profiles","def dumpProfiles(profiles):\n with open(userProfilesDir, \"w\") as outfile:\n outfile.writelines(json.dumps(profiles, indent=4))\n outfile.close()","def _deep_data(self, url):\n def _nested_persons(persons):\n _persons = list()\n for person_ in persons:\n person_ = [r.text.split(', ') for r in person_.find_all(class_='default-text')]\n person = {'name': person_[0][0].title()}\n if len(person_[0]) == 2:\n person['age'] = person_[0][1]\n\n if len(person_[1]) > 0:\n person['addressLocality'] = person_[1][0].title()\n if len(person_[1]) == 2:\n person['addressRegion'] = person_[1][1].upper()\n\n _persons.append(person)\n return _persons\n\n with self.driver(self.DRIVER_DIR) as driver:\n driver.get(url)\n driver.fullscreen_window()\n time.sleep(2)\n txt = driver.page_source\n\n soup = bs(txt, 'html.parser')\n\n profile_data = soup.find(type=\"application/ld+json\")\n if profile_data is None:\n self._raise_site_schema_change()\n profile_data = profile_data.string\n profile_data = json.loads(profile_data, strict=False)\n profile_data['@id'] = profile_data.pop('@id').split('/')[-1]\n\n try:\n about = profile_data.pop('about')\n for k, v in about.items():\n profile_data[k] = v\n except KeyError:\n pass\n\n name_ = profile_data.pop('name')\n profile_data['name'] = name_\n\n name_ = name_.split()\n profile_data['givenName'] = name_[0]\n profile_data['middleName'] = ' '.join(name_[1:-1])\n profile_data['familyName'] = name_[-1]\n\n if soup.find(class_='rep-vcard-score') is not None:\n profile_data['reputation_score'] = \"{min}-{max}\".format(\n min=soup.find(class_='rep-vcard-min').text,\n max=soup.find(class_='rep-vcard-max').text\n )\n\n address = list()\n address_ = soup.find_all(class_='card-address')\n for a in address_:\n street_address, locality_region_postal, *misc = [_.text for _ in a.find_all(class_='block-container')]\n address_locality, locality_region_postal = locality_region_postal.split(',')\n address_region, postal_code = locality_region_postal.split()\n address.append({\n 'streetAddress': street_address,\n 'addressLocality': address_locality,\n 'addressRegion': address_region,\n 'postalCode': postal_code,\n })\n\n profile_data['address'] = address\n\n personal_details = soup.find(class_='card-personal-details')\n if personal_details is not None:\n personal_details = personal_details.find_all(class_='item-container')\n personal_details = [detail.text.split(': ') for detail in personal_details]\n personal_details = [_ for _ in personal_details if len(_) == 2]\n personal_details = {detail.lower().replace(' ', '_'): value for\n detail, value in personal_details if value != 'Add Info'}\n\n birth_date = personal_details.pop('date_of_birth')\n if len(birth_date) > 0:\n profile_data['birthDate'] = birth_date\n\n for key_, value_ in personal_details.items():\n profile_data[key_] = value_\n\n # Education\n schools_ = soup.find(class_='card-education')\n if schools_ is not None:\n schools = list()\n schools_ = schools_.find_all(class_='card-content')\n for school in schools_:\n school = [detail.text.split(': ') for detail in school.find_all(class_='item-container')]\n school = {detail.lower().replace(' ', '_'): value for\n detail, value in school if value != 'Add Info'}\n\n if len(school) == 0:\n continue\n\n school['@type'] = 'EducationalOrganization'\n school['name'] = school.pop('school')\n school['streetAddress'], school['addressLocality'] = school.pop('city').split(', ')\n schools.append(school)\n\n # Work\n employers = soup.find(class_='card-job')\n if employers is not None:\n works_for = list()\n employers = employers.find_all(class_='card-content')\n for employer in employers:\n employer = [detail.text.split(': ') for detail in employer.find_all(class_='item-container')]\n employer = {detail.lower().replace(' ', '_'): value for\n detail, value in employer if value != 'Add Info'}\n\n if len(employer) == 0:\n continue\n\n employer['@type'] = 'Organization'\n try:\n employer['name'] = employer.pop('company')\n except KeyError:\n continue\n\n if len(employer.get('city', '')) > 0:\n employer['streetAddress'], employer['addressLocality'] = employer.pop('city').split(', ')\n\n works_for.append(employer)\n\n if len(works_for) > 0:\n profile_data['worksFor'] = works_for\n\n # Automobiles\n automobiles = soup.find(class_='card-auto')\n if automobiles is not None:\n owns = list()\n automobiles = automobiles.find_all(class_='card-content')\n for automobile in automobiles:\n automobile = [detail.text.split(': ') for detail in automobile.find_all(class_='item-container')]\n automobile = {detail.lower().replace(' ', '_'): value for\n detail, value in automobile if value != 'Add Info'}\n\n if len(automobile) == 0:\n continue\n\n automobile['@type'] = 'Product'\n automobile['model'] = ' '.join([\n automobile.pop('year'),\n automobile.pop('make'),\n automobile.pop('model')\n ])\n owns.append(automobile)\n\n if len(owns) > 0:\n profile_data['owns'] = owns\n\n profile_data['relatedTo'] = _nested_persons(soup.find_all(class_='relative-container'))\n profile_data['neighbors'] = _nested_persons(soup.find_all(class_='neighbor-container'))\n\n # Photos\n profile_data['pictures'] = list({photo['src'] for photo in soup.find_all(class_='profile-picture-holder')})\n return profile_data","def gather_all_profiles(year, month):\n page = 1\n urls = []\n\n print(\"{}-{} : Begin indexing.\".format(year, month))\n\n while (page > 0):\n urlstring = \"http://scamdigger.com/{}/{}/page/{}\".format(year,month,page) \n jitter = random.choice([0,1])\n try:\n urlhandle = urlopen(urlstring)\n urls += enumerate_profiles(urlhandle, page)\n # time.sleep(1+jitter)\n page += 1\n except:\n page = 0\n\n print(\"{}-{} : {} profiles\".format(year,month,len(urls)))\n\n for url in urls:\n uid = url[30:-1]\n outfile=PROFILES+os.sep+uid+'.json'\n jitter = random.choice([0,1])\n try:\n urlhandle = urlopen(url)\n scrape_profile(urlhandle, outfile, year, month)\n # time.sleep(1+jitter)\n except Exception as e:\n print(\"Exception when handling {}\".format(url))\n print(e)\n \n print(\"{}-{} : complete.\".format(year,month))","def parse(self):\n database = WriteToDatabase(\"config_profi_ru.conf\")\n database.create_base()\n # Start Webdriver with supressed logging\n options = webdriver.ChromeOptions() \n options.add_experimental_option(\"excludeSwitches\", [\"enable-logging\"])\n self.driver = webdriver.Chrome(options=options)\n # Get categories\n self.get_category_links()\n # Treat general categories\n for category in self.link_list:\n cat_name = category.split('/')[-2]\n # Check if category is already parsed\n if(os.path.exists(f\"profi_ru_json_data\\{cat_name}_data_file.json\")):\n self.logger.warning(f\"'{cat_name}' category is already parsed!\")\n continue\n self.logger.info(f\"Treating '{cat_name}' category\")\n counter = 0\n person_info = []\n try:\n category_profiles = self.get_profiles_by_category(f'{category}{self.profile_suffix}')\n self.logger.info(f\"Found {len(category_profiles)} profiles in '{cat_name}' category\")\n self.logger.info(f\"Start of parsing '{cat_name}' category...\")\n for person_link in category_profiles:\n person_info.append(self.get_person_info(person_link))\n counter+=1\n if(counter == len(category_profiles) - 1):\n self.logger.info(f\"End of parsing '{cat_name}' category...\")\n except:\n self.logger.critical(\"Problems with Internet connection or Web driver occured!\")\n self.logger.exception(f\"Only {counter} profiles of '{cat_name}' category were parsed\")\n self.cat_profiles_dict[cat_name] = person_info\n self.logger.info(f\"'{cat_name}' category was parsed successfully!\")\n self.write_json_file(cat_name, self.cat_profiles_dict[cat_name])\n self.logger.info(f'{cat_name}_data_file.json with parsed data was created successfully!')\n database.create_and_write_table(f'profi_ru_json_data\\{cat_name}_data_file.json')\n # Treat generic categories\n for cat_list in self.others_links:\n cat_name = cat_list[0]\n # Check if categroy is already parsed\n if(os.path.exists(f\"profi_ru_json_data\\{cat_name}_data_file.json\")):\n self.logger.warning(f\"'{cat_name}' category is already parsed!\")\n continue\n self.logger.info(f\"Treating '{cat_name}' category\")\n counter = 0\n person_info = []\n try:\n category_profiles = self.get_profiles_by_category(f'{category}{self.profile_suffix}')\n self.logger.info(f\"Found {len(category_profiles)} profiles in '{cat_name}' category\")\n for person_link in category_profiles:\n person_info.append(self.get_person_info(person_link))\n counter+=1\n if(counter == len(category_profiles) - 1):\n self.logger.info(f\"End of parsing {cat_name} category\")\n except:\n self.logger.critical(\"Problems with Internet connection or Web driver occured!\")\n self.logger.exception(f\"Only {counter} profiles of '{cat_name}' category were parsed\")\n self.cat_profiles_dict[cat_name] = person_info\n self.logger.info(f\"'{cat_name}' category was parsed successfully!\")\n self.write_json_file(cat_name, self.cat_profiles_dict[cat_name])\n self.logger.info(f'{cat_name}_data_file.json with parsed data was created successfully!')\n database.create_and_write_table(f'profi_ru_json_data\\{cat_name}_data_file.json')\n self.driver.quit()","def load_profiles_from_file(file_path):\r\n profiles = {}\r\n\r\n lastused = \"\"\r\n\r\n if File.Exists(file_path):\r\n try:\r\n with StreamReader(file_path) as xmlfile:\r\n xmldoc = XmlDocument()\r\n xmldoc.Load(xmlfile)\r\n\r\n if xmldoc.DocumentElement.Name == \"Profiles\":\r\n nodes = xmldoc.SelectNodes(\"Profiles/Profile\")\r\n #Individual exported profiles are saved with the document element as Profile\r\n elif xmldoc.DocumentElement.Name == \"Profile\":\r\n nodes = xmldoc.SelectNodes(\"Profile\")\r\n\r\n #Changed from 1.7 to 2.0 to use Profiles/Profile instead of Settings/Setting\r\n elif xmldoc.DocumentElement.Name == \"Settings\":\r\n nodes = xmldoc.SelectNodes(\"Settings/Setting\")\r\n elif xmldoc.DocumentElement.Name == \"Setting\":\r\n nodes = xmldoc.SelectNodes(\"Setting\")\r\n\r\n #No valid root elements\r\n else:\r\n MessageBox.Show(file_path + \" is not a valid Library Organizer profile file.\", \"Not a valid profile file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n return profiles, lastused\r\n\r\n if nodes.Count > 0:\r\n for node in nodes: \r\n profile = Profile()\r\n profile.Name = node.Attributes[\"Name\"].Value\r\n result = profile.load_from_xml(node)\r\n\r\n #Error loading the profile\r\n if result == False:\r\n MessageBox.Show(\"An error occured loading the profile \" + profile.Name + \". That profile has been skipped.\")\r\n\r\n else:\r\n profiles[profile.Name] = profile\r\n\r\n\r\n #Load the last used profile\r\n rootnode = xmldoc.DocumentElement\r\n if rootnode.HasAttribute(\"LastUsed\"):\r\n lastused = rootnode.Attributes[\"LastUsed\"].Value.split(\",\")\r\n\r\n except Exception, ex:\r\n MessageBox.Show(\"Something seems to have gone wrong loading the xml file.\\n\\nThe error was:\\n\" + str(ex), \"Error loading file\", MessageBoxButtons.OK, MessageBoxIcon.Error)\r\n\r\n return profiles, lastused","def get_profiles(count, credentials_type=constants.API_KEY_CREDENTIALS_TYPE, verbose=False):\n print('getting profiles through {}...'.format(credentials_type))\n\n # getting profiles information\n profile_ids = plus_api.get_unique_profile_ids(count, credentials_type=credentials_type, verbose=verbose)\n profile_urls = list(map(plus_api.profile_id_to_google_plus_url, profile_ids))\n # display_names, image_urls = get_profile_display_names_and_image_urls(profile_ids, credentials_type=credentials_type,\n # verbose=verbose)\n display_names, image_urls = get_profile_display_names_and_image_urls_with_getBatchGet(profile_ids,\n credentials_type=credentials_type,\n verbose=verbose)\n\n # saving as plain text\n txt_filename = 'results/people-api-profiles.txt'\n if verbose:\n print('saving profiles as plain text to {}...'.format(txt_filename))\n result_str = ''\n for profile in zip(profile_ids, profile_urls, display_names, image_urls):\n result_str += '\\n'.join(profile) + '\\n\\n'\n profiles_file = open(txt_filename, 'w', encoding='utf-8')\n profiles_file.write(result_str)\n profiles_file.close()\n\n # saving as json\n json_filename = 'results/people-api-profiles.json'\n if verbose:\n print('saving profiles as json to {}...'.format(json_filename))\n result_list = []\n json_fields = ('profileId', 'profileUrl', 'displayName', 'imageUrl')\n for profile in zip(profile_ids, profile_urls, display_names, image_urls):\n result_list.append(dict(zip(json_fields, profile)))\n json_str = json.dumps(result_list)\n profiles_file = open(json_filename, 'w', encoding='utf-8')\n profiles_file.write(json_str)\n profiles_file.close()","def get_profile(self, html_content: str) -> object:\n if not html_content:\n raise Exception(\"HTML content not found\")\n\n soup = BeautifulSoup(html_content, 'html.parser')\n\n self.profile = {\n \"employments\": [],\n \"others\": [],\n \"education\": []\n }\n # get sidebar\n sidebar = soup.select_one(self.tags.get(\"sidebar\"))\n items = sidebar.select(self.tags.get(\"sidebar.items\")) if sidebar else []\n\n # read each item on sidebar\n for item in items:\n\n label = item.select_one(self.tags.get(\"sidebar.items.header\"))\n section_title = label.text.strip()\n\n # Availability Item\n if section_title == 'Availability':\n status, avaibility = smart_split(\n get_value(item.select_one(self.tags.get(\"sidebar.items.availability\"))), '\\n')\n self.profile[\"status\"] = status\n self.profile[\"avaibility\"] = avaibility\n\n # Languages Item\n elif section_title == 'Languages':\n\n languages = [' '.join(get_value(li).split()) for li in\n item.select(self.tags.get(\"sidebar.items.languages.item\"))]\n self.profile[\"languages\"] = [dict(zip([\"name\", \"level\"], lng.split(\":\"))) for lng in languages]\n\n # Education Item\n elif section_title == 'Education':\n lis = item.select(self.tags.get(\"sidebar.items.education.item\"))\n\n for li in lis:\n school = \" \".join(\n get_value(li.select_one(self.tags.get(\"sidebar.items.education.school\"))).split())\n degree_area = get_value(li.select_one(self.tags.get(\"sidebar.items.education.degree_area\")))\n degree, area = smart_split(degree_area)\n attended_date = get_value(li.select_one(self.tags.get(\"sidebar.items.education.attended_date\")))\n\n self.profile[\"education\"].append({\n \"school\": school,\n \"degree\": degree,\n \"area\": area,\n \"attended_date\": attended_date\n })\n\n # Skill panel\n skills = soup.select(self.tags.get(\"skills\"))\n self.profile[\"skills\"] = set(get_value(s) for s in skills) # using set to remove duplications\n\n # Employment/Other Experience panels\n cards = soup.select(self.tags.get(\"profile.panels\"))\n for lis in cards:\n lis = lis.parent.select(\"li\")\n\n for li in lis:\n position, employer = smart_split(\n li.select_one(self.tags.get(\"employments.position_employer\")).get_text(strip=True), \"|\")\n period = ' '.join(li.select_one(self.tags.get(\"employments.period\")).get_text(strip=True).split())\n\n if \"Other\" != position.strip():\n self.profile[\"employments\"].append({\n \"employer\": employer,\n \"job_title\": position,\n \"hire_datetime\": period.split('-')[0].strip(),\n \"termination_datetime\": period.split('-')[1].strip()\n })\n else:\n self.profile[\"others\"].append({\n \"title\": employer,\n \"description\": period.split('-')[0]\n })\n\n title = soup.select_one(self.tags.get(\"title\"))\n hour = get_value(title.parent.parent.select_one(self.tags.get(\"hour_price\")))\n description = get_value(soup.select_one(self.tags.get(\"description\")))\n\n # define the last elements of profile\n self.profile[\"title\"] = get_value(title)\n self.profile[\"price\"] = hour\n self.profile[\"description\"] = description\n self.profile[\"full_name\"] = soup.select_one(self.tags.get(\"full_name\")).get_text(strip=True)\n self.profile[\"first_name\"] = self.profile[\"full_name\"].split()[0]\n self.profile[\"last_name\"] = self.profile[\"full_name\"].split()[-1]\n self.profile[\"address_city\"] = soup.select_one(self.tags.get(\"address_city\")).get_text(strip=True)\n self.profile[\"address_country\"] = soup.select_one(self.tags.get(\"address_country\")).get_text(strip=True)\n self.profile[\"picture_url\"] = soup.select_one(self.tags.get(\"picture\"))[\"src\"]\n\n return self.profile","def get_list(profiles_folder, logger):\n profile_list = []\n with scandir(profiles_folder) as it:\n for entry in it:\n if entry.is_file():\n filepath = profiles_folder + entry.name\n profile = json_from_file(filepath, logger)\n if profile is not None:\n try:\n profile_list.append({\"filepath\":filepath, \"name\":profile[\"name\"], \"description\":profile[\"description\"]})\n except AttributeError:\n logger.error(\"Missing attributes in \" + filepath)\n logger.error(str(profile))\n return profile_list","def _process_profiles(profiles):\n # keep_attributes = str.split(\"user_id public completion_percentage gender region last_login registration age\")\n # p2=profiles[keep_attributes]\n p2 = profiles\n p2['region'] = p2['region'].astype('category')\n p2['public'] = p2['public'].astype('category')\n p2['gender'] = p2['gender'].astype('category')\n p2['last_login'] = pd.to_datetime(p2['last_login'])\n p2['registration'] = pd.to_datetime(p2['registration'])\n p2.loc[p2.age == 0, 'age'] = np.nan\n\n return p2","def populate_profiles_from_directory(self):\n\n self.profiles = []\n\n # Go over all the files and create a profile object\n _profile_files = listdir(self.save_dir)\n\n for profile_filename in _profile_files:\n # Only check for .yaml files\n if path.splitext(profile_filename)[1] == '.yaml':\n # Get the data and create a new profile\n _file_data = self._load_data_from_file(path.join(self.save_dir, profile_filename))\n _profile = Profile(path.splitext(profile_filename)[0])\n _profile.player_data = _file_data\n self.profiles.append(_profile)","def _read_profiles(profile_directory):\n # Initialize key variables\n profiles = defaultdict(\n lambda: defaultdict(lambda: defaultdict()))\n\n # Read the yaml files in the profiles directory\n files = os.listdir(profile_directory)\n filenames = ['{}{}{}'.format(\n profile_directory, os.sep, nextfile) for nextfile in files]\n\n for _filename in sorted(filenames):\n # Get rid of excess os.sep separators\n pathitems = _filename.split(os.sep)\n filename = os.sep.join(pathitems)\n\n # Skip obvious\n if os.path.isfile(filename) is False:\n continue\n if filename.lower().endswith('.yaml') is False:\n continue\n\n with open(filename, 'r') as stream:\n try:\n _profiles = yaml.load(stream)['data']\n except yaml.YAMLError as exc:\n print(exc)\n\n # Create dictionary\n for item in _profiles:\n firstname = item['firstname']\n lastname = item['lastname']\n height = item['height']\n weight = item['weight']\n birthdate = item['birthdate']\n profiles[lastname][firstname][birthdate] = {\n 'height': height, 'weight': weight}\n\n return profiles","def scrap_profile(profile_name, number_of_posts):\r\n scraped_medias = []\r\n post_data = OrderedDict()\r\n # Populate OrderedDict with post_data fields\r\n medias = instagram.get_medias(profile_name, count=number_of_posts)\r\n for media in medias:\r\n media_id_ = media.identifier\r\n post_data['url'] = media.link\r\n\r\n post_data['text'] = media.caption\r\n\r\n try:\r\n post_data['media_url'] = media.image_high_resolution_url\r\n except Exception:\r\n post_data['media_url'] = media.video_url\r\n \r\n comments = instagram.get_media_comments_by_id(media_id_, 10000)\r\n post_data['comments'] = [comment.text for comment in comments['comments']]\r\n\r\n post_data['time'] = datetime.fromtimestamp(media.created_time).strftime(\"%d/%m/%Y\")\r\n\r\n post_data['likes'] = media.likes_count\r\n\r\n post_data['comments_count'] = len(post_data['comments'])\r\n \r\n post_data = dict(post_data)\r\n \r\n scraped_medias.append(post_data)\r\n \r\n return scraped_medias","def scrape_kprofiles(url):\n page = requests.get(url)\n tree = html.fromstring(page.content)\n results = tree.xpath('//article/div/div/div/p/a[@href]/text()')\n filtered_results = []\n discarded_results = 0\n for item in results:\n if item in [\"\\n\", \" \", \"\"]:\n discarded_results += 1\n continue\n else:\n if \"Profile\" in item:\n item = item.replace(\"Profile\", \"\")\n filtered_results.append(item.strip())\n return filtered_results","def _read_profile_file(cls, profile_file):\n qid2title: Dict[str, str] = {}\n qid2desc: Dict[str, str] = {}\n alias2qids: Dict[str, list] = {}\n type_systems: Dict[str, Dict[str, List[str]]] = {}\n qid2relations: Dict[str, Dict[str, List[str]]] = {}\n\n num_lines = sum(1 for _ in open(profile_file))\n with open(profile_file, \"r\") as in_f:\n for line in tqdm(in_f, total=num_lines, desc=\"Reading profile\"):\n line = ujson.loads(line)\n\n # Check keys and schema\n assert all(\n k in line.keys() for k in REQUIRED_KEYS\n ), f\"A key from {REQUIRED_KEYS} was not in {line}\"\n try:\n # Asserts the types are correct\n ent = EntityObj(\n entity_id=line[\"entity_id\"],\n mentions=line[\"mentions\"],\n title=line.get(\"title\", line[\"entity_id\"]),\n description=line.get(\"description\", \"\"),\n types=line.get(\"types\", {}),\n relations=line.get(\"relations\", []),\n )\n except ValidationError as e:\n print(e.json())\n raise e\n if ent.entity_id in qid2title:\n raise ValueError(f\"{ent.entity_id} is already in our dump\")\n qid2title[ent.entity_id] = ent.title\n qid2desc[ent.entity_id] = ent.description\n # For each [mention, score] value, create a value of mention -> [qid, score] in the alias2qid dict\n for men_pair in ent.mentions:\n # Lower case mentions for mention extraction\n new_men = get_lnrm(men_pair[0], strip=True, lower=True)\n if new_men not in alias2qids:\n alias2qids[new_men] = []\n alias2qids[new_men].append([ent.entity_id, men_pair[1]])\n # Add type systems of type_sys -> QID -> list of type names\n for type_sys in ent.types:\n if type_sys not in type_systems:\n type_systems[type_sys] = {}\n type_systems[type_sys][ent.entity_id] = ent.types[type_sys]\n # Add kg relations QID -> relation -> list of object QIDs\n for rel_pair in ent.relations:\n if \"relation\" not in rel_pair or \"object\" not in rel_pair:\n raise ValueError(\n \"For each value in relations, it must be a JSON with keys relation and object\"\n )\n if ent.entity_id not in qid2relations:\n qid2relations[ent.entity_id] = {}\n if rel_pair[\"relation\"] not in qid2relations[ent.entity_id]:\n qid2relations[ent.entity_id][rel_pair[\"relation\"]] = []\n qid2relations[ent.entity_id][rel_pair[\"relation\"]].append(\n rel_pair[\"object\"]\n )\n\n # Sort mentions based on score, highest first\n for al in list(alias2qids.keys()):\n alias2qids[al] = sorted(alias2qids[al], key=lambda x: x[1], reverse=True)\n # Add all qids to the type systems and KG connections with empty values\n # This isn't strictly required but can make the sets more clean as they'll have consistent keys\n for qid in qid2title:\n for type_sys in type_systems:\n if qid not in type_systems[type_sys]:\n type_systems[type_sys][qid] = []\n if qid not in qid2relations:\n qid2relations[qid] = {}\n return qid2title, qid2desc, alias2qids, type_systems, qid2relations","def list_profiles(self) -> dict:\n wsc = self.read_ws_configuration()\n out = OrderedDict()\n for name, json in wsc.profiles.items():\n out[name] = Profile(name, self.ws_data_folder / name, json)\n # Try to find current profile\n try:\n out[self.current_profile_name].is_current = True\n except Exception:\n pass\n return out","def load_profiles(profiles_file: TextIO) -> Tuple[Dict[str, List[str]],\n Dict[str, List[str]]]:\n individuals = [[]]\n sublist = 0\n content = profiles_file.readlines()\n for i in content:\n if i != '\\n':\n individuals[sublist].append(i)\n else:\n sublist += 1\n individuals.append([])\n return sort_profile(individuals)","def profile(username):\n link = \"https://myanimelist.net/animelist/{}\".format(username)\n mal_soup = get_soup(link)\n table = json.loads(mal_soup.find('table').get('data-items'))\n print(\"\\nRunning...\\n\")\n for dic in table:\n link = \"https://myanimelist.net{}\"\n res = mal_get_all_info(link.format(dic['anime_url']), dic['anime_title'])\n export_to_csv(res, 1)\n sleep(5)\n print(\"Finished.\")","def info_search(profiles: list, session: requests.Session):\n counter = 0\n results = []\n for profile in profiles:\n info = {}\n sleep(0.5)\n soup = BeautifulSoup(session.get(profile).text, \"lxml\")\n details = soup.find(\"div\", class_=\"page-content seaman-page-content\")\n info['name'] = details.h1.text\n try:\n for row in details.find_all(\"div\", class_=\"colmn3\"):\n row_content = row.text.split(\":\")\n if row_content[0] == \"Personal mobile number\":\n info['phone'] = row_content[1]\n if row_content[0] == \"E-Mail\":\n info['email'] = row_content[1]\n if 'phone' in info and duplicates_checker(results, info['phone']):\n results.append(info)\n vcard_handler(info)\n except AttributeError:\n continue\n if len(results) % 10 == 0:\n counter += 10\n logger.info(f'{counter} profiles added out of {len(profiles)}')\n logger.info(f'Finished adding {len(profiles)} profiles')\n return results","def get_profile():\n logger.debug(\"entering function get_profile\")\n response = read_user_profile()\n logger.debug(\"exiting function get_profile\")\n return jsonify(response)","def unite_imdb_profiles(verbose):\n if verbose:\n print(\"Uniting IMDB movie profiles to one csv file...\")\n if not os.path.exists(_IMDB_DIR_PATH):\n print(\"No IMDB profiles to unite!\")\n return\n profiles = []\n profile_files = os.listdir(_IMDB_DIR_PATH)\n if verbose:\n profile_files = tqdm(profile_files)\n for profile_file in profile_files:\n if verbose:\n profile_files.set_description('Reading {}'.format(profile_file))\n file_path = os.path.join(_IMDB_DIR_PATH, profile_file)\n _, ext = os.path.splitext(file_path)\n if ext == '.json':\n with open(file_path, 'r') as json_file:\n profiles.append(json.load(json_file))\n df = pd.DataFrame(profiles)\n df = _decompose_dict_column(df, 'avg_rating_per_demo', _DEMOGRAPHICS)\n df = _decompose_dict_column(df, 'votes_per_demo', _DEMOGRAPHICS)\n df = _decompose_dict_column(\n df, 'rating_freq', [str(i) for i in range(1, 11)])\n df = _dummy_list_column(df, 'genres')\n unison_fpath = os.path.join(\n _get_dataset_dir_path(), 'imdb_dataset.csv')\n df.to_csv(unison_fpath, index=False)","def write_tessprofiles(tessprofiles: Dict, online=False):\n logger.info(f'Writing tessprofiles')\n profile = TESSPROFILE_ONLINE_PATH if online else TESSPROFILE_PATH\n with open(profile, 'w') as f:\n json.dump(tessprofiles, f, indent=4)","def download_profiles_custom(self, profiles: Set[Profile],\n profile_pic: bool = True, posts: bool = True,\n tagged: bool = False,\n igtv: bool = False,\n highlights: bool = False,\n stories: bool = False,\n fast_update: bool = False,\n date_filter: Optional[Callable[[Post], bool]] = None,\n post_filter: Optional[Callable[[Post], bool]] = None,\n storyitem_filter: Optional[Callable[[Post], bool]] = None,\n raise_errors: bool = False):\n\n @contextmanager\n def _error_raiser(_str):\n yield\n\n # error_handler type is Callable[[Optional[str]], ContextManager[None]] (not supported with Python 3.5)\n error_handler = _error_raiser if raise_errors else self.context.error_catcher\n\n for profile in profiles:\n with error_handler(profile.username): # type: ignore\n profile_name = profile.username\n\n # Download profile picture\n if profile_pic:\n with self.context.error_catcher('Download profile picture of {}'.format(profile_name)):\n self.download_profilepic(profile)\n\n # Save metadata as JSON if desired.\n if self.save_metadata:\n json_filename = '{0}/{1}_{2}'.format(self.dirname_pattern.format(profile=profile_name,\n target=profile_name),\n profile_name, profile.userid)\n self.save_metadata_json(json_filename, profile)\n\n # Catch some errors\n if profile.is_private and (tagged or igtv or highlights or posts):\n if not self.context.is_logged_in:\n raise LoginRequiredException(\"--login=USERNAME required.\")\n if not profile.followed_by_viewer and self.context.username != profile.username:\n raise PrivateProfileNotFollowedException(\"Private but not followed.\")\n\n # Download tagged, if requested\n if tagged:\n with self.context.error_catcher('Download tagged of {}'.format(profile_name)):\n self.download_tagged(profile, fast_update=fast_update, post_filter=post_filter)\n\n # Download IGTV, if requested\n if igtv:\n with self.context.error_catcher('Download IGTV of {}'.format(profile_name)):\n self.download_igtv(profile, fast_update=fast_update, post_filter=post_filter)\n\n # Download highlights, if requested\n if highlights:\n with self.context.error_catcher('Download highlights of {}'.format(profile_name)):\n self.download_highlights(profile, fast_update=fast_update, storyitem_filter=storyitem_filter)\n\n # Iterate over pictures and download them\n if posts:\n self.context.log(\"Retrieving posts from profile {}.\".format(profile_name))\n self.posts_download_loop_custom(profile.get_posts(), profile_name, fast_update, date_filter, post_filter,\n total_count=profile.mediacount)\n\n if stories and profiles:\n with self.context.error_catcher(\"Download stories\"):\n self.context.log(\"Downloading stories\")\n self.download_stories(userids=list(profiles), fast_update=fast_update, filename_target=None,\n storyitem_filter=storyitem_filter)","def read_pardus_profiles(self):\n\n self.lan_config = ConfigParser.ConfigParser()\n self.lan_config.read(self.lan_config_path)\n connection_type = \"802-3-ethernet\"\n for section in self.lan_config.sections():\n lan_settings = {}\n for option in self.lan_config.options(section):\n if option == \"device\":\n #To strip device name from long device string\n lan_settings[option] = self.lan_config.get(section, option).split(\"_\")[-1]\n else:\n lan_settings[option] = self.lan_config.get(section, option)\n p = PardusNetworkProfile(section, connection_type, lan_settings)\n self.pardus_profiles.append(p)\n\n self.wlan_config = ConfigParser.ConfigParser()\n self.wlan_config.read(self.wlan_config_path)\n connection_type = \"802-11-wireless\"\n for section in self.wlan_config.sections():\n wlan_settings = {}\n for option in self.wlan_config.options(section):\n if option == \"device\":\n wlan_settings[option] = self.wlan_config.get(section, option).split(\"_\")[-1]\n else:\n wlan_settings[option] = self.wlan_config.get(section, option)\n p = PardusNetworkProfile(section, connection_type, wlan_settings)\n self.pardus_profiles.append(p)","def GetAllProfiles(self):\n profiles = []\n feed_uri = self._gd_client.GetFeedUri('profiles')\n while feed_uri:\n feed = self._gd_client.GetProfilesFeed(uri=feed_uri)\n profiles.extend(feed.entry)\n feed_uri = feed.FindNextLink()\n self._profiles = profiles","def get(self):\n self.response.headers['Content-Type'] = 'application/json'\n self.response.out.write(self._robot.GetProfileJson())","def parse_profile_list_page(page):\n page_tree = etree.HTML(page)\n profile_list = page_tree.xpath(\n '//table[@class=\"table_header\"]/tr[position() > 4 and position() < (last() - 3)]/td/node()[1]')\n if profile_list:\n profile_list[0] = \"main\"\n return [text.strip() for text in profile_list]\n else:\n raise ProfileException(\"Failed to get profile list\")","def profile():\n\n if not session.get('oauth_token'):\n return redirect(url_for('login'))\n tokenString = \"bearer {0}\".format(session['oauth_token']['access_token'])\n headers = {\"Authorization\": tokenString}\n profileInfo = {'access_token': session['oauth_token']['access_token']}\n\n # get user summary\n userinfourl = '{}/userinfo'.format(baseUAAurl)\n userinfo = json.loads(requests.get(\n userinfourl, headers=headers, verify=False).text)\n session['userinfo'] = userinfo\n profileInfo['userinfo'] = json.dumps(session['userinfo'])\n\n # Method 1 : get user roles by orgs and space\n usersummaryurl = '{0}/v2/users/{1}/summary'.format(\n baseAPIurl, userinfo['user_id'])\n usersummary = json.loads(requests.get(\n usersummaryurl, headers=headers, verify=False).text)\n\n if usersummary.get('entity'):\n spaceWiseUserRoles = getSpaceWiseUserRoles(usersummary['entity'])\n else:\n # Method 2 : get user roles by orgs and space\n spaceWiseUserRoles = {}\n spaceurl = baseAPIurl + '/v2/spaces'\n spaceresponse = requests.get(spaceurl, headers=headers, verify=False)\n space_json_data = json.loads(spaceresponse.text)\n for spaceresource in space_json_data['resources']:\n entity = spaceresource['entity']\n spaceGuid = spaceresource['metadata']['guid']\n\n # get all auditors\n auditorurl = baseAPIurl + entity['auditors_url']\n auditorresponse = json.loads(requests.get(\n auditorurl, headers=headers, verify=False).text)\n if isInThisRole(auditorresponse, userinfo['user_name']):\n spaceWiseUserRoles[spaceGuid] = {\n 'role': 'auditor',\n 'name': spaceresource['entity']['name']\n }\n\n # get all developers\n devurl = baseAPIurl + entity['developers_url']\n devresponse = json.loads(requests.get(\n devurl, headers=headers, verify=False).text)\n if isInThisRole(devresponse, userinfo['user_name']):\n spaceWiseUserRoles[spaceGuid] = {\n 'role': 'developer',\n 'name': spaceresource['entity']['name']\n }\n\n # get all managers\n managerurl = baseAPIurl + entity['managers_url']\n managerresponse = json.loads(requests.get(\n managerurl, headers=headers, verify=False).text)\n if isInThisRole(managerresponse, userinfo['user_name']):\n spaceWiseUserRoles[spaceGuid] = {\n 'role': 'manager',\n 'name': spaceresource['entity']['name']\n }\n\n profileInfo['spaceWiseUserRoles'] = json.dumps(spaceWiseUserRoles)\n session['spaceWiseUserRoles'] = spaceWiseUserRoles\n\n # get user apps from all spaces\n url = '{}/v2/apps'.format(baseAPIurl)\n response = requests.get(url, headers=headers, verify=False)\n appsData = json.loads(response.text)\n appsUrls = {}\n\n # user accessible app url\n for resource in appsData['resources']:\n routes_url = baseAPIurl + \\\n resource['entity']['routes_url']\n routes_url_response = json.loads(requests.get(\n routes_url, headers=headers, verify=False).text)\n for app in routes_url_response['resources']:\n hostname = app['entity']['host']\n appsUrls[hostname] = {\n 'url': 'http://{}.local.pcfdev.io'.format(hostname),\n 'space_guid': app['entity']['space_guid'],\n 'userRole': getSpaceRole(spaceWiseUserRoles, app['entity'][\n 'space_guid'], userinfo['user_name'])}\n profileInfo['apps'] = appsUrls\n\n organization_guid = getOrganizationId(session, appsData)\n profileInfo['org_id'] = organization_guid\n profileInfo['org_users'] = json.dumps(getOrganizationUsers(\n session, organization_guid))\n return render_template('profile.html', data=profileInfo)","def load_profiles(file_path):\r\n profiles, lastused = load_profiles_from_file(file_path)\r\n\r\n if len(profiles) == 0:\r\n #Just in case\r\n profiles[\"Default\"] = Profile()\r\n profiles[\"Default\"].Name = \"Default\"\r\n #Some default templates\r\n profiles[\"Default\"].FileTemplate = \"{<series>}{ Vol.<volume>}{ #<number2>}{ (of <count2>)}{ ({<month>, }<year>)}\"\r\n profiles[\"Default\"].FolderTemplate = \"{<publisher>}\\{<imprint>}\\{<series>}{ (<startyear>{ <format>})}\"\r\n \r\n if not lastused:\r\n lastused = [profiles.keys()[0]]\r\n \r\n return profiles, lastused","def fetch_my_profile(self, api_token: str) -> dict:\n query = \"\"\"\n query myProfile {\n myProfile {\n id\n firstName\n lastName\n }\n }\n \"\"\"\n path = jmespath.compile(\n \"\"\"\n data.myProfile.{\n id: id\n first_name: firstName\n last_name: lastName\n }\n \"\"\"\n )\n\n data = self.do_query(query, api_token=api_token)\n\n parsed_data = path.search(data)\n self.contains_keys(parsed_data, [\"id\", \"first_name\", \"last_name\"])\n return parsed_data","def get_student_profile_data():\n # pylint: disable=no-member\n try:\n regexp_string = self.regexp_from_users_included_email(self.users_included_email)\n re.compile(regexp_string)\n users = self.students_for_course(regexp_string)\n except:\n log.info(\"regexp is invalid: '%s'\", regexp_string)\n users = []\n\n for user in users:\n student_id = anonymous_id_for_user(user, self.course_id)\n profile = user.profile\n\n vip = self.get_vip(user)\n image_url = None\n if vip:\n image_url = \"https://my.imd.org/api/profile/{}/profile-picture-header\".format(vip)\n else:\n if self.is_course_staff:\n image_url = self.runtime.local_resource_url(self, 'public/images/profile-picture-header-no-vip.gif')\n else:\n image_url = self.runtime.local_resource_url(self, 'public/images/profile-picture-header.gif')\n\n cohort_name = None\n if (self.is_course_cohorted(self.course_id)):\n cohort_name = self.get_cohort(user, self.course_id).name\n\n yield {\n 'student_id': student_id,\n 'username': user.username,\n 'fullname': profile.name,\n 'vip': vip,\n 'image_url': image_url,\n 'email': user.email,\n 'cohort_name': cohort_name,\n }","def save_to_jsonl(self, profile_file):\n with open(profile_file, \"w\") as out_f:\n for qid in tqdm(self.get_all_qids(), disable=not self.verbose):\n mentions = self.get_mentions_with_scores(qid)\n title = self.get_title(qid)\n desc = self.get_desc(qid)\n ent_type_sys = {}\n for type_sys in self._type_systems:\n types = self.get_types(qid, type_sys)\n if len(types) > 0:\n ent_type_sys[type_sys] = types\n relations = []\n all_connections = self.get_all_connections(qid)\n for rel in all_connections:\n for qid2 in all_connections[rel]:\n relations.append({\"relation\": rel, \"object\": qid2})\n ent_obj = {\n \"entity_id\": qid,\n \"mentions\": mentions,\n \"title\": title,\n }\n # Add description if nonempty\n if len(desc) > 0:\n ent_obj[\"description\"] = desc\n if len(ent_type_sys) > 0:\n ent_obj[\"types\"] = ent_type_sys\n if len(relations) > 0:\n ent_obj[\"relations\"] = relations\n out_f.write(ujson.dumps(ent_obj) + \"\\n\")","def process_raw(self, raw: str) -> ListStreamProfilesT:\n data: ListStreamProfilesResponseT = orjson.loads(raw)\n stream_profiles = data.get(\"data\", {}).get(\"streamProfile\", [])\n return {\n stream_profile[\"name\"]: StreamProfile(\n id=stream_profile[\"name\"],\n name=stream_profile[\"name\"],\n description=stream_profile[\"description\"],\n parameters=stream_profile[\"parameters\"],\n )\n for stream_profile in stream_profiles\n }","def parse(self):\n details = self.details()\n\n return Profile(\n book_id=self.book_id,\n title=self.title(),\n user_id=self.user_id(),\n username=self.username(),\n summary=self.summary(),\n published=self.published(),\n updated=self.updated(),\n **details\n )","def parse_profiles(profiles):\n if type(profiles) == list:\n return [\n x\n for x in [ProfileSettingParser.parse(x) for x in profiles]\n if x is not None\n ]\n else:\n return []","def handle_current_profile(api_pipeline, current_profile):\n\n current_profile_saved_tweets = current_profile.tweet_set.all()\n profiles_tweets = api_pipeline.get_recent_tweets_for_author(current_profile.username, how_many=5)\n for profiles_tweet in profiles_tweets:\n if profiles_tweet not in current_profile_saved_tweets.filter(save_date=datetime.datetime.today().date()):\n profiles_tweet.save()\n current_profile.tweet_set.add(profiles_tweet)\n current_profile.save()\n profiles_tweets.sort(key=lambda tweet: (tweet.retweets, tweet.likes), reverse=True)\n profiles_tweets_chart = PlotPainter.plot_tweets(profiles_tweets) if profiles_tweets else None\n return profiles_tweets, profiles_tweets_chart","def scrape_profile(username: str, user_agents: typing.List[str]) -> typing.List[typing.Dict]:\n try:\n r = requests.get(f\"https://venmo.com/{username}\",\n headers={\"User-Agent\": random.choice(user_agents)})\n except requests.exceptions.ConnectionError:\n print(\"Error, unable to connect to host... check your network connection\")\n return 1\n\n soup = BeautifulSoup(r.text, \"lxml\")\n profile_data = []\n\n transactions = soup.find_all(\"div\", attrs={\"class\": \"single-payment content-wrap\"})\n\n for transaction in transactions:\n send, recv = transaction.find_all(\"a\")\n send, recv = send.text, recv.text\n message = transaction.find_all(\"div\", attrs={\"class\": \"paymentpage-text m_five_t\"})[0].text\n date = date_parser.parse(transaction.find_all(\"div\", attrs={\"class\": \"date\"})[0].text.split('-')[0], fuzzy=True)\n export_message = f\"{send} paid {recv} on {date.replace(microsecond=0).isoformat()} for {message}\"\n profile_data.append({\"sender\": send,\n \"recipient\": recv,\n \"date\": date.replace(microsecond=0).isoformat(),\n \"export_message\": export_message})\n\n return profile_data","def profiles_names(self):\n url = get_url('profiles')\n response = self._get(url)\n raise_on_error(response)\n return response.json()","async def all(self):\n log('retrieving profile cards..')\n start = time.monotonic()\n profiles = await asyncio.gather(*list(\n map(lambda profile: self.retrieve(str(profile['id'])), await self.list())\n ))\n elapsed = \"%0.2fs\" % (time.monotonic() - start,)\n log(\"retrieved {} profile cards in {}\".format(len(profiles), elapsed))\n return {\n \"hits\": len(profiles),\n \"updated\": time.time() * 1000,\n \"time\": elapsed,\n \"applicants\": list(filter(None, map(self.parse, profiles)))\n }","def test_list_profiles(self):\n response = self.client.get(self.url)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(len(response.data['results']), 1)","def get_profile(self,fields=('id','first-name','last-name','headline','summary')):\n\n if not self._access_token:\n raise FBError(\"Authentication needed!\")\n \n token = oauth.Token(self._access_token['oauth_token'], self._access_token['oauth_token_secret'])\n client = oauth.Client(self.consumer, token)\n profile_url = self.profile_url % \",\".join(fields)\n resp, content = client.request(profile_url,headers={\"x-li-format\":'json'})\n \n if resp['status'] != '200':\n print resp\n raise FBError(\"Invalid response %s.\" % resp['status'])\n \n try:\n return json.loads(content)\n except Exception, e:\n raise FBError(\"Invalid json %s.\" % unicode(e))","def refresh_session_data (self, account):\n # load the profiles page (to verify the user)\n response = self._session_get(component='profiles')\n # parse out the needed inline information\n only_script_tags = SoupStrainer('script')\n page_soup = BeautifulSoup(response.text, 'html.parser', parse_only=only_script_tags)\n page_data = self._parse_page_contents(page_soup=page_soup)\n account_hash = self._generate_account_hash(account=account)\n self._save_data(filename=self.data_path + '_' + account_hash)","def _username_to_profile(self, username: str) -> Dict[str, Any]:\n\n base_url = self.keys.pre_profile + username + self.keys.rank_token + self.keys.post_profile\n\n # Build the page source url for the given user's account\n con = urllib.request.urlopen(base_url)\n user_profile = con.read().decode('utf-8')\n\n # Convert the webpage to a profile JSON\n profile: dict = json.loads(str(user_profile))\n return profile","def profile(userid):\n # get bookmarked content\n bookmarks = db_bookmarks.find({\"user\": userid})\n bookmarks = [{\"search_term\": db_search_terms.find_one({\"_id\": b[\"search_id\"]}).get(\"value\"), \"source\": b[\"source\"], \"url\": b[\"url\"], \"date_saved\": b[\"date_saved\"].strftime(\"%H:%M %B %d, %Y\")} for b in bookmarks]\n # get previous searches\n searches = db_searches.find({\"user\": userid})\n searches = [{\"search_term\": db_search_terms.find_one({\"_id\": s[\"search_id\"]}).get(\"value\"), \"category\": s[\"category\"], \"datetime\": s[\"datetime\"].strftime(\"%H:%M %B %d, %Y\")} for s in searches]\n # get byod\n byod = db_byod.find({\"user\": userid})\n byod = [{\"content_type\": b[\"content_type\"], \"doc_name\": b[\"doc_name\"], \"text\": b[\"text\"], \"key_points\": b[\"key_points\"], \"datetime_uploaded\": b[\"datetime_uploaded\"].strftime(\"%H:%M %B %d, %Y\")} for b in byod]\n # get highlightds\n # highlights = db_highlights.find({\"user\": userid})\n # highlights = [{\"search_term\": db_search_terms.find_one({\"_id\": h[\"search_id\"]}).get(\"value\"), \"highlighted_word\": h[\"highlighted_word\"], \"results\": h[\"results\"], \"date_saved\": h[\"date_saved\"].strftime(\"%H:%M %B %d, %Y\")} for h in highlights]\n jsonob = jsonify(bookmarks=bookmarks,\n searches=searches,\n byod=byod)\n return jsonob","def profiles(self):\n if not self._profiles:\n self.GetAllProfiles()\n return self._profiles","def _sloppy_parse_inline_data (self, scripts):\n inline_data = {};\n for script in scripts:\n contents = str(script.contents[0])\n important_data = ['authURL', 'API_BASE_URL', 'API_ROOT', 'BUILD_IDENTIFIER', 'ICHNAEA_ROOT', 'gpsModel', 'guid', 'esn']\n res = {}\n for key in important_data:\n _res = self._sloppy_parse_user_and_api_data(key, contents)\n if _res != None:\n res.update({key: _res})\n if res != {}:\n inline_data.update(res)\n\n # parse profiles\n profiles = self._sloppy_parse_profiles(contents)\n avatars = self._sloppy_parse_avatars(contents)\n if profiles != None:\n inline_data.update({'profiles': profiles})\n if avatars != None:\n inline_data.update(avatars)\n return inline_data","def import_profiles(file_path):\r\n profiles, lastused = load_profiles_from_file(file_path)\r\n\r\n return profiles","def fetchJson(url):","def fetch_profile(access_token):\n\n headers = {\"Authorization\": \"Bearer \" + access_token}\n\n # Fetch athlete profile\n r = requests.get(API_URL + \"/athlete\", headers=headers)\n profile = r.json()\n if \"errors\" in profile:\n raise AuthError(profile[\"message\"])\n\n return {\n \"firstName\": profile[\"firstname\"],\n \"lastName\": profile[\"lastname\"],\n \"imgUrl\": profile[\"profile\"],\n \"profileUrl\": \"https://www.strava.com/athletes/{}\".format(profile[\"id\"]),\n }","def process_data (file):\n\n twitter_data = {}\n\n username = file.readline().strip()\n\n while username != '': # leave when all lines of the file have been read\n # initialize everything we need for entering data\n twitter_data [username] = {}\n twitter_data [username] ['bio'] = ''\n twitter_data [username] ['following'] = []\n\n twitter_data [username] ['name'] = file.readline().strip()\n twitter_data [username] ['location'] = file.readline().strip()\n twitter_data [username] ['web'] = file.readline().strip()\n\n bio = file.readline() # get the first line of the bio\n while bio.strip() != 'ENDBIO': # go until we get the line 'ENDBIO'\n twitter_data [username] ['bio'] += bio.strip() + '\\n'\n bio = file.readline()\n # we dont want the final '\\n' in the bio so we splice it\n twitter_data [username] ['bio'] = twitter_data [username] ['bio'] [:-1]\n\n following = file.readline() # get the first element for the list\n while following.strip() != 'END': # go until we get the line 'END'\n twitter_data [username] ['following'].append (following.strip())\n following = file.readline()\n\n username = file.readline().strip()\n return twitter_data","def get_parsed_data():\n\n echonest_data_files = [f for f in os.listdir('.') if re.match(\"^echonest_[\\w]+.txt$\", f)]\n\n # Setting up header with user id and attributes\n header = ['user_id']\n header.extend(ATTRIBUTES)\n\n # printing header to standard out\n print \",\".join(header) \n\n # Processing each file to obtain parsed data\n for data_file in echonest_data_files:\n user_id = data_file[9:-4] # strip file prefix/suffix to get username/id\n parse_echonest_data_file(data_file, user_id)","def test_list_profiles(\n api_client, enable_premium_requirement, profile_factory, user_factory\n):\n password = \"password\"\n user = user_factory(has_premium=True, password=password)\n api_client.log_in(user.primary_email.email, password)\n\n profile = profile_factory(km_user__user=user)\n\n url = f\"/know-me/users/{user.km_user.pk}/profiles/\"\n response = api_client.get(url)\n\n assert response.status_code == status.HTTP_200_OK\n assert response.json() == [\n {\n \"id\": profile.pk,\n \"url\": api_client.build_full_url(\n f\"/know-me/profile/profiles/{profile.pk}/\"\n ),\n \"created_at\": serialized_time(profile.created_at),\n \"updated_at\": serialized_time(profile.updated_at),\n \"is_private\": profile.is_private,\n \"name\": profile.name,\n \"permissions\": {\"read\": True, \"write\": True},\n \"topics_url\": api_client.build_full_url(\n f\"/know-me/profile/profiles/{profile.pk}/topics/\"\n ),\n }\n ]","def _parse_user_data (self, netflix_page_data):\n user_data = {};\n important_fields = [\n 'authURL',\n 'gpsModel',\n 'guid'\n ]\n\n # values are accessible via dict (sloppy parsing successfull)\n if type(netflix_page_data) == dict:\n for important_field in important_fields:\n user_data.update({important_field: netflix_page_data.get(important_field, '')})\n return user_data\n\n # values are stored in lists (returned from JS parser)\n for item in netflix_page_data:\n if 'memberContext' in dict(item).keys():\n for important_field in important_fields:\n user_data.update({important_field: item['memberContext']['data']['userInfo'][important_field]})\n\n return user_data","def scrape_story_metadata(self, story_id):\n url = '{0}/s/{1}'.format(self.base_url, story_id)\n result = requests.get(url)\n html = result.content\n #print html \n soup = BeautifulSoup(html, self.parser)\n\n # print soup\n try:\n pre_story_links = soup.find(id='pre_story_links').find_all('a')\n except AttributeError:\n pre_story_links = None\n if re.search(r\"var userid = (.*);\", str(soup)) is None:\n author_id = \"0\"\n else: \n author_id = int(re.search(r\"var userid = (.*);\", str(soup)).groups()[0]);\n #print re.search(r\"var title = (.*);\", str(soup))\n if re.search(r\"var title = (.*);\", str(soup)) is None:\n title = \"NO-TITLE\"\n else:\n title = re.search(r\"var title = (.*);\", str(soup)).groups()[0];\n title = unquote_plus(title)[1:-1]\n metadata_div = soup.find(id='profile_top')\n# times = metadata_div.find_all(attrs={'data-xutime':True})\n# metadata_text = metadata_div.find(class_='xgray xcontrast_txt').text\n# metadata_parts = metadata_text.split('-')\n# genres = self.get_genres(metadata_parts[2].strip())\n metadata = {\n 'id': story_id,\n# 'canon_type': pre_story_links[0].text,\n# 'canon': pre_story_links[1].text,\n 'author_id': author_id,\n 'title': title,\n# 'updated': int(times[0]['data-xutime']),\n# 'published': int(times[1]['data-xutime']),\n# 'lang': metadata_parts[1].strip(),\n# 'genres': genres\n }\n \"\"\"\n for parts in metadata_parts:\n parts = parts.strip()\n tag_and_val = parts.split(':')\n if len(tag_and_val) != 2:\n continue\n tag, val = tag_and_val\n tag = tag.strip().lower()\n if tag not in metadata:\n val = val.strip()\n try:\n val = int(val.replace(',', ''))\n metadata['num_'+tag] = val\n except:\n metadata[tag] = val\n if 'status' not in metadata:\n metadata['status'] = 'Incomplete'\n \"\"\"\n return metadata","def read_file(reviews):\n\n for review in reviews:\n yield json.loads(review)[\"text\"]","def parse(self, response, **kwargs):\n\n key_url = response._get_url().rsplit('/')[-2] # get the author nickname after the last slash\n name = response.xpath('//*[@id=\"woe\"]/div[2]/div/div[1]/div[2]/h3/text()').extract_first()\n job_title = response.xpath('//*[@id=\"woe\"]/div[2]/div/div[1]/div[2]/p/text()').extract_first()\n linkedin_url = response.xpath('//*[@id=\"woe\"]/div[2]/div/div[1]/div[1]/ul/li/a/@href').extract_first()\n date_title_part = response.xpath('//*[@id=\"woe\"]/div[2]/div/div[2]/div[position() > 1]')\n for row in date_title_part:\n row_extracted = row.extract()\n art_date = Selector(text=row_extracted).xpath('///span/text()').extract_first()\n date_formatted = conf.parse_dtts(art_date, '%B %d, %Y')\n article_title = Selector(text=row_extracted).xpath('///a/text()').extract_first()\n article_url = Selector(text=row_extracted).xpath('///a/@href').extract_first()\n\n if date_formatted >= conf.crawl_date[0].get('LastExecutionDate'):\n conf.write_data_append('authors.json', json.dumps({'keyUrl': key_url,\n 'name': name,\n 'jobTitle': job_title,\n 'linkedinUrl': linkedin_url,\n 'date': date_formatted,\n 'article_title': article_title,\n 'article_url': article_url}))","def get_all_profiles(self) -> List[Profile]:\n return [self.model.parse_obj(profile) for profile in self.read_records(SyncMode.full_refresh)]","def profiles():\n images = get_uploaded_images()\n records = db.session.query(UserProfile).all()\n return render_template('profiles.html', images=images, records =records)","def profiles(self, profiles):\n\n self._profiles = profiles","def _sloppy_parse_avatars (self, contents):\n avatars_start = contents.find('\"nf\":')\n avatars_list_start = contents.find('\"profiles\"')\n if int(avatars_start) > -1 and int(avatars_list_start) > -1:\n try:\n try:\n return json.loads('{' + contents[avatars_start:avatars_list_start-2].decode('string_escape') + '}')\n except ValueError, e:\n return None\n except TypeError, e:\n return None\n return None","def profiles_search(pages: list, session: requests.Session):\n result = []\n for page in pages:\n sleep(1)\n soup = BeautifulSoup(session.get(page).text, \"lxml\")\n table = soup.find(\"table\", class_=\"seaman-list-table va-top seaman-list-table-2\")\n for rows in table.find_all(\"tr\")[1:]:\n for column in rows.find(\"td\", 'seaman-name-td'):\n try:\n result.append(\"https://ukrcrewing.com.ua\" + column[\"href\"])\n except TypeError:\n continue\n logger.info(f'{len(result)} profiles detected')\n return result","def get_profiles(self):\n # print(self.uir) #checkpoint\n if os.path.isdir(self.uir+\"/profiles\"):\n profiles=os.listdir(self.uir+\"/profiles\")\n # print(profiles) #checkpoint\n for profile in profiles:\n wsadmin=self.uir+\"/profiles/\"+profile+\"/bin/wsadmin.bat\"\n if os.path.isfile(wsadmin): #check for wsadmin.bat.\n self.profiles.append(self.uir+\"/profiles/\"+profile)\n\n else: print(self.uir+' Instance does not have \"profile\" folder in '+self.uir)\n return","def prefetch_login(self):\n self.profiles = []\n email = self.credentials.get('email', '')\n password = self.credentials.get('password', '')\n if email != '' and password != '':\n if self.netflix_session.is_logged_in(account=self.credentials):\n refresh_session = self.netflix_session.refresh_session_data(\n account=self.credentials)\n if refresh_session:\n self.profiles = self.netflix_session.profiles\n else:\n if self.netflix_session.login(account=self.credentials):\n self.profiles = self.netflix_session.profiles\n self.kodi_helper.set_esn(self.netflix_session.esn)","def parse():\n with open('src/wator/Properties.json') as data_file:\n data = json.load(data_file)\n return data","def _load_profile(self, profile_name, cwd):\n\n profile_path = self.get_profile_path(profile_name, cwd)\n try:\n text = load_user_encoded(profile_path)\n except Exception as e:\n raise ConanException(f\"Cannot load profile:\\n{e}\")\n\n # All profiles will be now rendered with jinja2 as first pass\n base_path = os.path.dirname(profile_path)\n file_path = os.path.basename(profile_path)\n context = {\"platform\": platform,\n \"os\": os,\n \"profile_dir\": base_path,\n \"profile_name\": file_path,\n \"conan_version\": conan_version}\n rtemplate = Environment(loader=FileSystemLoader(base_path)).from_string(text)\n text = rtemplate.render(context)\n\n try:\n return self._recurse_load_profile(text, profile_path)\n except ConanException as exc:\n raise ConanException(\"Error reading '%s' profile: %s\" % (profile_name, exc))","def call(self, failed=[], deletion=False):\n\n if deletion == False:\n # NOTE we won't it to run only after the delete has completed\n return\n\n log.debug(\"Reading profiles from progran\")\n onos = ProgranHelpers.get_progran_onos_info(self.model_accessor)\n profile_url = \"http://%s:%s/onos/progran/profile/\" % (onos['url'], onos['port'])\n r = requests.get(profile_url, auth=HTTPBasicAuth(onos['username'], onos['password']))\n res = r.json()['ProfileArray']\n\n\n # remove default profiles\n res = [p for p in res if \"Default\" not in p['Name']]\n pnames = [p['Name'] for p in res]\n log.debug(\"Received Profiles: \", profiles=pnames)\n\n field_mapping = {\n 'Name': 'name',\n 'Start': 'start',\n 'End': 'end',\n }\n\n field_transformations = {\n 'Start': ProgranHelpers.date_to_time,\n 'End': ProgranHelpers.date_to_time\n }\n\n handover_mapping = {\n 'A5Hysteresis': 'HysteresisA5',\n 'A3Hysteresis': 'HysteresisA3'\n }\n\n updated_profiles = []\n\n for p in res:\n\n\n # checking for profiles\n try:\n si = ProgranServiceInstance.objects.get(name=p['Name'])\n log.debug(\"Profile %s already exists, updating it\" % p['Name'])\n\n except IndexError:\n si = ProgranServiceInstance()\n\n si.created_by = \"Progran\"\n\n log.debug(\"Profile %s is new, creating it\" % p['Name'])\n\n if not si.is_new:\n # update IMSI association\n xos_imsis_for_profile = [i.subscriber_service_instance.leaf_model for i in si.provided_links.all()]\n progran_imsis_for_profile = p['IMSIRuleArray']\n\n log.debug(\"List of imsis for profile %s in XOS\" % p[\"Name\"], imsis=xos_imsis_for_profile)\n log.debug(\"List of imsis for profile %s in ONOS\" % p[\"Name\"], imsis=progran_imsis_for_profile)\n\n for i in xos_imsis_for_profile:\n if not i.imsi_number in progran_imsis_for_profile:\n log.debug(\"Removing Imsi %s from profile %s\" % (i.imsi_number, p['Name']))\n\n imsi_link = ServiceInstanceLink.objects.get(subscriber_service_instance_id=i.id)\n\n # NOTE: this model has already been removed from the backend, no need to synchronize\n imsi_link.backend_need_delete = False\n imsi_link.no_sync = True\n imsi_link.save() # we need to save it to avoid a synchronization loop\n\n imsi_link.delete()\n else:\n # remove from imsi list coming from progran everything we already know about\n progran_imsis_for_profile.remove(i.imsi_number)\n\n for i in progran_imsis_for_profile:\n log.debug(\"Adding Imsi %s to profile %s\" % (i, p['Name']))\n imsi = MCordSubscriberInstance.objects.get(imsi_number=i)\n imsi_to_profile = ServiceInstanceLink(provider_service_instance=si,\n subscriber_service_instance=imsi)\n imsi_to_profile.save()\n\n # if the model has not been synchronized yet, skip it\n if not si.is_new and si.no_sync is False:\n log.debug(\"Skipping profile %s as not synchronized\" % p['Name'])\n # NOTE add it to the removed profiles to avoid deletion (this is ugly, I know)\n updated_profiles.append(si.name)\n continue\n\n # ugly fix\n if 'AdmControl' in p.keys():\n p['AdmControl'] = str(p['AdmControl'])\n\n si = ProgranHelpers.update_fields(si, p, field_mapping, field_transformations)\n\n # checking for handovers\n handover_dict = p['Handover']\n handover_dict = ProgranHelpers.convert_keys(handover_dict, handover_mapping)\n del p['Handover']\n\n if si.handover_id:\n handover = si.handover\n log.debug(\"handover already exists, updating it\", handover=handover_dict)\n else:\n handover = Handover()\n handover = ProgranHelpers.update_fields(handover, handover_dict)\n log.debug(\"handover is new, creating it\", handover=handover_dict)\n handover.created_by = \"Progran\"\n\n handover = ProgranHelpers.update_fields(handover, handover_dict)\n handover.save()\n\n # Assigning handover to profile\n si.handover = handover\n\n # si.backend_status = \"OK\"\n # si.backend_code = 1\n\n si.no_sync = True\n si.previously_sync = True\n\n if p[\"MMECfg\"]:\n si.mmeip = str(p[\"MMECfg\"][\"IPAddr\"])\n si.mmeport = str(p[\"MMECfg\"][\"Port\"])\n\n si.enacted = time.mktime(datetime.datetime.now().timetuple())\n\n si.save()\n\n updated_profiles.append(si.name)\n\n existing_profiles = [p.name for p in ProgranServiceInstance.objects.all() if not p.is_new]\n deleted_profiles = ProgranHelpers.list_diff(existing_profiles, updated_profiles)\n\n if len(deleted_profiles) > 0:\n for p in deleted_profiles:\n si = ProgranServiceInstance.objects.get(name=p)\n if si.created_by == 'XOS' and si.previously_sync == False:\n # don't delete if the profile has been created by XOS and it hasn't been sync'ed yet\n continue\n # TODO delete also the associated Handover\n log.debug(\"Profiles %s have been removed in progran, removing it from XOS\" % str(p))\n si.delete()","def get_strava_profile(self, create_csv = True, profile_csv_path =\n 'profile.csv'):\n # Get profile info. You need the URL, access_token and header information.\n access_token = self.strava_tokens['access_token']\n url = 'https://www.strava.com/api/v3/athlete'\n hdrs = {'Authorization': 'Bearer ' + access_token}\n\n # Make a request object.\n try :\n r = requests.get(url, headers=hdrs)\n except requests.exceptions.RequestException as e:\n print (\"Failed to make the API request when attempting to get profile. Received this exception:\", e)\n return None\n\n # Create a dataframe from the json content. This is just the profile info.\n # You have to orient by index because some of the fields may be empty\n # which causes a ValueError(\"arrays must all be same length\") otherwise.\n dataframe = pd.DataFrame.from_dict(r.json(), orient=\"index\")\n # Transpose it if you prefer to see it in the opposite orientation.\n #dataframe = dataframe.transpose()\n\n # Now convert the dataframe to a csv file if desired.\n if create_csv:\n dataframe.to_csv(profile_csv_path)\n\n return dataframe","def test_uu_test(self):\n\t\tbase = \"http://localhost:8000\"\n\t\t# Pro1 and Pro1a have no interests in common, Pro1 and Pro2 have two interests in common\n\t\tpro1 = requests.post(base+self.uri_creation, data=made_up_profiles(1), headers={'Content-Type': 'application/json'})\n\t\tpro1a = requests.post(base+self.uri_creation, data=made_up_profiles(0), headers={'Content-Type': 'application/json'})\n\t\tpro2= requests.post(base+self.uri_creation, data=made_up_profiles(2), headers={'Content-Type': 'application/json'})\n\n\t\tpro1_token = json.loads(pro1.content.decode('utf-8'))['profile']\n\t\tpro1a_token = json.loads(pro1a.content.decode('utf-8'))['profile']\n\t\tpro2_token = json.loads(pro2.content.decode('utf-8'))['profile']\n\n\t\tpro1_auth = json.loads(requests.get(base+self.uri_auth+pro1_token+'/').content.decode('utf-8'))['auth']\n\t\tpro1a_auth = json.loads(requests.get(base+self.uri_auth+pro1a_token+'/').content.decode('utf-8'))['auth']\n\t\tpro2_auth = json.loads(requests.get(base+self.uri_auth+pro2_token+'/').content.decode('utf-8'))['auth']\n\n\t\trequests.post(base+self.uri_add_selectable+pro1_token+'/', data=page_two_objects(pro1_auth, 5), headers={'Content-Type': 'application/json'})\n\t\trequests.post(base+self.uri_add_selectable+pro1a_token+'/', data=page_one_objects(pro1a_auth, 0), headers={'Content-Type': 'application/json'})\n\t\trequests.post(base+self.uri_add_selectable+pro2_token+'/', data=page_one_objects(pro2_auth, 1), headers={'Content-Type': 'application/json'})\n\t\tsuggestion_r = requests.post(base+self.uri_suggestion+pro1_token+'/', data=page_one(pro1_auth), headers={'Content-Type': 'application/json'})\n\t\tcontx_a = ast.literal_eval(json.loads(suggestion_r.content.decode('utf-8'))['base'])[0]\n\t\tcontx_b = ast.literal_eval(json.loads(suggestion_r.content.decode('utf-8'))['base'])[1]\n\t\tcontx_flag = json.loads(suggestion_r.content.decode('utf-8'))['state']\n\t\trequests.post(base+self.uri_add_selectable+pro1a_token+'/', data=page_two_objects(pro1a_auth, 3), headers={'Content-Type': 'application/json'})\n\t\tsuggestion2_r = requests.post(base+self.uri_suggestion+pro1_token+'/', data=page_one(pro1_auth), headers={'Content-Type': 'application/json'})\n\t\tcontx2_a = ast.literal_eval(json.loads(suggestion2_r.content.decode('utf-8'))['base'])[0]\n\t\tcontx2_b = ast.literal_eval(json.loads(suggestion2_r.content.decode('utf-8'))['base'])[1]\n\t\tself.assertTrue(contx2_a < contx_a)\n\t\tself.assertTrue(contx2_b > contx_b)\n\t\t# Element at index zero must become lower as it has a website in common with profile 1a\n\t\t# Element at index one must become higher as it has a no website in common with profile 2","def get_profiles(self):\n profiles = [['Profile name', 'GUID']]\n r = self.system_cursor.execute('{Call wtGetProfileList()}')\n for row in r.fetchall():\n profiles.append([row.PROFILE_NAME, row.PROFILE_GUID])\n return profiles","def merge_structural_profile(profile_path, merged_path):\n def list_to_str(lst):\n ''' Given a list, return the string of that list with tab separators\n '''\n return reduce( (lambda s, f: s + '\\t' + str(f)), lst, '')\n\n # external loop profile\n E_path = profile_path+'E_profile.txt'\n fEprofile = open(E_path)\n Eprofiles = fEprofile.readlines()\n\n # hairpin loop profiles\n H_path = profile_path+'H_profile.txt'\n fHprofile = open(H_path)\n Hprofiles = fHprofile.readlines()\n\n # internal loop profiles\n I_path = profile_path+'I_profile.txt'\n fIprofile = open(I_path)\n Iprofiles = fIprofile.readlines()\n\n # multi-loop profiles\n M_path = profile_path+ 'M_profile.txt'\n fMprofile = open(M_path)\n Mprofiles = fMprofile.readlines()\n\n num_seq = int(len(Eprofiles)/2)\n\n # parse into a single file\n fhout = open(merged_path, 'w')\n for i in range(num_seq):\n id = Eprofiles[i*2].split()[0]\n fhout.write(id+'\\n')\n H_prob = Hprofiles[i*2+1].split()\n I_prob = Iprofiles[i*2+1].split()\n M_prob = Mprofiles[i*2+1].split()\n E_prob = Eprofiles[i*2+1].split()\n P_prob = map( (lambda a, b, c, d: 1-float(a)-float(b)-float(c)-float(d)), H_prob, I_prob, M_prob, E_prob)\n fhout.write(list_to_str(P_prob[:len(P_prob)])+'\\n')\n fhout.write(list_to_str(H_prob[:len(P_prob)])+'\\n')\n fhout.write(list_to_str(I_prob[:len(P_prob)])+'\\n')\n fhout.write(list_to_str(M_prob[:len(P_prob)])+'\\n')\n fhout.write(list_to_str(E_prob[:len(P_prob)])+'\\n')\n fhout.close()\n\n return num_seq","def extract_data(file_name=default_file_name):\n fille = abs_path(file_name)\n try:\n jfile = open(fille)\n except FileNotFoundError:\n create_file(fille)\n jfile = open(fille)\n data = json.load(jfile)\n if(not 'Students' in data.keys()):\n create_file(fille)\n jfile = open(fille)\n data = json.load(jfile)\n return data","def generate_files(input_path, output_path):\n profiles = pd.read_csv(f'{input_path}/Profile/Profile.csv')\n\n # call all predictors\n predicted = {f: pr.all_predictors[f].predict(profiles=profiles, base_folder=input_path)\n for f in pr.features if f in pr.all_predictors}\n\n for i, (index, row) in enumerate(profiles.iterrows()):\n file = open(os.path.join(output_path, f'{row[\"userid\"]}.xml'), 'w')\n gender = pr.get_gender(predicted['gender'][row[1]]) if row[1] in predicted['gender'] else predicted['age_group_alternative'][row[1]]\n open_ = predicted['open'][row[1]] if row[1] in predicted['open'] else 'null'\n conscientious = predicted['conscientious'][row[1]] if row[1] in predicted['conscientious'] else 'null'\n extrovert = predicted['extrovert'][row[1]] if row[1] in predicted['extrovert'] else 'null'\n agreeable = predicted['agreeable'][row[1]] if row[1] in predicted['agreeable'] else 'null'\n neurotic = predicted['neurotic'][row[1]] if row[1] in predicted['neurotic'] else 'null'\n age = predicted['age_group'][row[1]] if row[1] in predicted['age_group'] else 'null'\n\n file.write(f'<user id=\"{row[1]}\" ')\n file.write(f'age_group=\"{age}\" ')\n file.write(f'gender=\"{gender}\" ')\n file.write(f'extrovert=\"{extrovert}\" ')\n file.write(f'neurotic=\"{neurotic}\" ')\n file.write(f'agreeable=\"{agreeable}\" ')\n file.write(f'open=\"{open_}\" ')\n file.write(f'conscientious=\"{conscientious}\" />')\n file.close()","async def test_retrieve_many(self):\n expected = [{\n '_id': 'id',\n 'name': 'name',\n 'version': 4,\n 'status': 'active'\n }]\n rsps = respx.get(f'{PROVISIONING_API_URL}/users/current/provisioning-profiles') \\\n .mock(return_value=Response(200, json=expected))\n profiles = await provisioning_client.get_provisioning_profiles(5, 'active')\n assert rsps.calls[0].request.url == \\\n f'{PROVISIONING_API_URL}/users/current/provisioning-profiles?version=5&status=active'\n assert rsps.calls[0].request.headers['auth-token'] == 'header.payload.sign'\n assert profiles == expected","def weather_profile(address, name, data='', update=''):\n filename = str(r\"C:\\Users\\thoma\\Desktop\\Python\\TH Projects\\Port\\tools\\weather\\weather_profiles/\" + str(address) + \"_weather_profile.txt\")\n filename_temp = str(r\"C:\\Users\\thoma\\Desktop\\Python\\TH Projects\\Port\\tools\\weather\\weather_profiles/\" + str(address) + \"_weather_profile_backup.txt\")\n\n #open as write - this takes care of the initial instance of the file\n # if we can read the profile read it, otherwise create an empty dict\n\n try:\n with open(filename) as f:\n contents = json.load(f)\n \n weather_profile_dict = contents\n \n #create backup file in case something goes wrong\n with open(filename_temp, 'w') as f:\n contents = weather_profile_dict\n json.dump(weather_profile_dict, f)\n\n except FileNotFoundError:\n weather_profile_dict = {}\n\n except ValueError:\n print(\"dictionary has a value error\")\n weather_profile_dict = {}\n \n # if there is an existing entry: \n if name in weather_profile_dict.keys():\n if update:\n del weather_profile_dict[name]\n weather_profile_dict[name] = data\n # store the amended profile \n with open(filename, 'w') as f:\n json.dump(weather_profile_dict,f)\n return weather_profile_dict[name]\n else:\n return weather_profile_dict[name]\n \n # if there isn't an existing entry\n else:\n if data:\n # create a new one if there is data\n weather_profile_dict[name] = data\n # store the amended profile \n with open(filename, 'w') as f:\n json.dump(weather_profile_dict,f)\n return weather_profile_dict[name]\n\n else:\n # return nothing if there isn't\n print(\"t/-There is no existing data for \" + str(name) + \" - add data...\")\n return None","def get_profile_metadata(config, timestamp):\n with open(config.profile) as profile:\n context = json.load(profile)\n data = {}\n expires_on = None\n expires_after = None\n version = str(context['data']['version'])\n\n if context['data']['removal_date'] is not None:\n expires_on = timezone.make_aware(datetime.datetime.strptime(\n context['data']['removal_date'], '%Y-%m-%dT%H:%M:%SZ')\n ).astimezone(pytz.UTC)\n if context['data']['removal_period'] is not None:\n expires_after = timestamp + timezone.timedelta(seconds=context['data']['removal_period'])\n if expires_on is not None and expires_after is not None:\n if expires_on < expires_after:\n expiration_date = expires_on\n else:\n expiration_date = expires_after\n elif expires_on is not None:\n expiration_date = expires_on\n else:\n expiration_date = expires_after\n\n if context['data']['auto_remove'] == 'default':\n expiration_date = None\n\n data['expires'] = expiration_date\n data['version'] = version\n return data","def profiles(self):\n with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\n return list(filter(lambda x: x is not None, executor.map(self.profile_details, self.profiles_names())))","def scrape_raw(self,lang='none'):\n pastes_json=self.scrape(lang);\n time.sleep(1)\n raw_pastes=dict()\n \n for paste in pastes_json:\n key=paste['key']\n if key not in self.scraped_pastes :\n raw_paste=self.scrape_paste(key)\n raw_pastes[key]=raw_paste\n self.scraped_pastes.append(key)\n time.sleep(1)\n \n return raw_pastes","def downloadvoterjson():\n \n # Get all State Names from CSV\n stnames = pd.read_csv(\"https://raw.githubusercontent.com/jasonong/List-of-US-States/master/states.csv\")\n statelist = [st for st in stnames.State]\n\n # Download a JSON for each State in State Names.\n for st in statelist:\n try:\n if len(st.split()) > 1:\n stsplit = st.split()\n st = '-'.join(stsplit).lower()\n \n with urllib.request.urlopen(f\"https://static01.nyt.com/elections-assets/2020/data/api/2020-11-03/race-page/{st.lower()}/president.json\") as url:\n data = json.loads(url.read().decode())\n with open(f\"{st}.json\", \"w\") as outfile:\n json.dump(data, outfile)\n \n except Exception as e:\n print(e)\n pass","def fetch_profile(self, api_token: str, id: str) -> dict:\n query = \"\"\"\n query Profile($id: ID!, $service_type: ServiceType!) {\n profile(id: $id, serviceType: $service_type) {\n id\n }\n }\n \"\"\"\n\n path = jmespath.compile(\n \"\"\"\n data.profile.{\n id: id\n }\n \"\"\"\n )\n\n data = self.do_query(\n query,\n api_token=api_token,\n variables={\n \"id\": id,\n \"service_type\": settings.HELSINKI_PROFILE_SERVICE_TYPE,\n },\n )\n\n parsed_data = path.search(data)\n self.contains_keys(parsed_data, [\"id\"])\n return parsed_data","def get_metadata(datas):\n\n required_profile_key, required_item_key = 'basics', 'category'\n utility_matrix = []\n\n item_details, profile_details = {}, {}\n\n for _ , data in datas.items():\n profile_name, item_name = None, None\n\n # extracted profile data for user, because two user may have the same nick name\n # so we will concatenate user nick name and its profile link to form the unique one.\n if type(data) is dict and required_profile_key in data.keys() :\n profile_name = \"%s|%s\" % (data[required_profile_key].get('name',''),\n data[required_profile_key].get('profile',''))\n\n profile_details[profile_name] = get_profile_detail(data)\n print ('extracted data of profile: %s ...' % data[required_profile_key].get('name',''))\n\n # for item\n if type(data) is dict and required_item_key in data.keys():\n if hasattr(data[required_item_key],'items'):\n for k,v in data[required_item_key].items():\n\n item_detail = get_item_detail(v)\n print ('extracted data for category %s ...' % k)\n\n #item_detail_to_str = json.dumps(item_detail) #\" ; \".join(list(set(item_detail)))\n if k in item_details:\n item_details[k]['work'].append(item_detail['work'])\n item_details[k]['skill'].append(item_detail['skill'])\n else:\n item_details[k] = {}\n item_details[k]['work'] = [item_detail['work']]\n item_details[k]['skill'] = [item_detail['skill']]\n\n utility_matrix.append({\n 'profile': profile_name,\n 'item': k,\n 'rating':v['point']\n })\n\n return utility_matrix, \\\n {k:{'work':json.dumps(v['work']),'skill':json.dumps(v['skill'])} for k,v in item_details.items()}, \\\n profile_details","def parse_json_export(json_file):\n\n json_file.seek(0)\n links = json.load(json_file)\n json_date = lambda s: datetime.strptime(s, '%Y-%m-%dT%H:%M:%SZ')\n\n for link in links:\n # example line\n # {\"href\":\"http:\\/\\/www.reddit.com\\/r\\/example\",\"description\":\"title here\",\"extended\":\"\",\"meta\":\"18a973f09c9cc0608c116967b64e0419\",\"hash\":\"910293f019c2f4bb1a749fb937ba58e3\",\"time\":\"2014-06-14T15:51:42Z\",\"shared\":\"no\",\"toread\":\"no\",\"tags\":\"reddit android\"}]\n if link:\n # Parse URL\n url = link.get('href') or link.get('url') or link.get('URL')\n if not url:\n raise Exception('JSON must contain URL in each entry [{\"url\": \"http://...\", ...}, ...]')\n\n # Parse the timestamp\n ts_str = str(datetime.now().timestamp())\n if link.get('timestamp'):\n # chrome/ff histories use a very precise timestamp\n ts_str = str(link['timestamp'] / 10000000) \n elif link.get('time'):\n ts_str = str(json_date(link['time'].split(',', 1)[0]).timestamp())\n elif link.get('created_at'):\n ts_str = str(json_date(link['created_at']).timestamp())\n elif link.get('created'):\n ts_str = str(json_date(link['created']).timestamp())\n elif link.get('date'):\n ts_str = str(json_date(link['date']).timestamp())\n elif link.get('bookmarked'):\n ts_str = str(json_date(link['bookmarked']).timestamp())\n elif link.get('saved'):\n ts_str = str(json_date(link['saved']).timestamp())\n \n # Parse the title\n title = None\n if link.get('title'):\n title = link['title'].strip() or None\n elif link.get('description'):\n title = link['description'].replace(' — Readability', '').strip() or None\n elif link.get('name'):\n title = link['name'].strip() or None\n\n yield {\n 'url': url,\n 'timestamp': ts_str,\n 'title': title,\n 'tags': link.get('tags') or '',\n 'sources': [json_file.name],\n }","def scrape(url, filename):\n courselist = []\n headers = ['title', 'description', 'course number', 'duration', 'difficulty', 'instructors', 'course url']\n with open(filename, 'wb') as outfile:\n wr = csv.writer(outfile)\n wr.writerow(headers)\n courses = json.load(urllib2.urlopen(url))\n for course in courses['courses']:\n c = Course()\n c.title = course['title']\n c.desc = course['summary']\n c.course_number = course['key']\n c.duration = str(course['expected_duration']) + ' ' + str(course['expected_duration_unit'])\n c.difficulty = course['level']\n c.url = 'https://www.udacity.com/course/' + course['slug']\n l = len(course['instructors'])\n for i in xrange(l):\n if(i == 0):\n c.instructors += course['instructors'][i]['name']\n else:\n c.instructors += ';' + course['instructors'][i]['name']\n with open(filename, 'ab') as outfile:\n wr = csv.writer(outfile)\n wr.writerow(c.getaslist())","def filterprofile(profile, settings):\n \n if settings.exclude is True and len(profile.description) == 0:\n print(\"EMPTY BIO\")\n return False\n\n if profile.description is None:\n return False\n\n if len(settings.include_keywords) > 1 and not any(kw in profile.description for kw in settings.include_keywords.splitlines()):\n print(\"NO KEYWORDS\")\n return False\n\n if profile.followers_count is None:\n return False\n \n if profile.followers_count < settings.followers:\n print(\"NUM FOLLOWERS\")\n return False\n\n if any(loc in profile.location for loc in settings.fromcountries.splitlines()):\n print(\"LOCATION\")\n return False\n\n if profile.statuses_count < settings.tweets:\n print(\"NUM TWEETS\")\n return False\n\n created = datetime.datetime.strptime(profile.created_at, \"%a %b %d %H:%M:%S %z %Y\")\n months = relativedelta(datetime.datetime.now(datetime.timezone.utc), created).years * 12\n if months == 0:\n if (profile.statuses_count / 12) > settings.tweetsperyear:\n print(\"TWEETS PER YEAR\")\n return False\n else:\n if (profile.statuses_count / months / 12) > settings.tweetsperyear:\n print(\"TWEETS PER YEAR\")\n return False\n\n if profile.status is not None:\n lasttweetdate = datetime.datetime.strptime(profile.status.created_at, \"%a %b %d %H:%M:%S %z %Y\").replace(tzinfo=None)\n \n lasttweetmonths = relativedelta(datetime.datetime.now(), lasttweetdate).years * 12\n if lasttweetmonths > settings.notweetsfor:\n print(\"LAST TWEET\")\n return False\n else:\n return False\n # else\n return True","def get_profile(ticker):\n PROFILE = \"{}/quote/{}/profile\".format(url, ticker)\n\n page = session.get(PROFILE)\n soup = BeautifulSoup(page.content, 'lxml')\n company_name = soup.find('h3', attrs={'class': \"Mb(10px)\"})\n company = soup.find('div', attrs={'class': \"Mb(35px)\"})\n click.echo(click.style(\"Profile\", fg='red', bold=True, underline=True))\n profile = []\n dic_key = []\n dic_val = []\n if company_name is not None:\n print company_name.get_text().encode('utf-8')\n company = soup.find('div', attrs={'class': \"Mb(25px)\"})\n info_2 = company.find('p', attrs={'class': \"D(ib) Va(t)\"})\n title = info_2.find_all('span')\n value = info_2.find_all('strong')\n for d in title:\n dic_key.append(d.get_text())\n for d in value:\n dic_val.append(d.get_text())\n comp_info = dict(zip(dic_key, dic_val))\n #df = pd.Series(comp_info)\n #print df\n data = soup.find(\"table\")\n data = data.find_all(\"tr\")\n for item in data:\n if not item('td') or item('td')[0].get_text() == '':\n continue\n name = item('td')[0].get_text().strip().encode('utf-8')\n title = item('td')[1].get_text().strip().encode('utf-8')\n pay = item('td')[2].get_text().strip().encode('utf-8')\n age = item('td')[4].get_text().strip().encode('utf-8')\n profile.append({\"Name\": name, \"Title\": title, \"Pay\": pay, \"Age\": age})\n for k, v in comp_info.iteritems():\n click.echo(\"{}: {}\".format(k, v))\n for item in profile:\n click.echo(\"Name: {}, Title: {}, Pay: {}, Age: {}\".format(item['Name'], item['Title'], item['Pay'], item['Age']))\n #print comp_info\n #print profile\n #pd.set_option(\"display.colheader_justify\",\"right\")\n #pd.set_option('display.width', 120)\n #df = pd.DataFrame(profile)\n #df.style.set_properties(**{'text-align': 'right'})\n #print(df)\n #print \"Name: {}, Title: {}, Pay: {}, Age: {}\".format(item('td')[0].get_text().encode('utf-8'), \n # item('td')[1].get_text().encode('utf-8'), item('td')[2].get_text().encode('utf-8'), item('td')[4].get_text().encode('utf-8'))\n click.pause(info='Press any key to continue ...', err=False)","def list_profiles(self):\n return self._get(\"posture\", box=BoxList)","def _parse_page_contents (self, page_soup):\n netflix_page_data = self.extract_inline_netflix_page_data(page_soup=page_soup)\n self.user_data = self._parse_user_data(netflix_page_data=netflix_page_data)\n self.esn = self._parse_esn_data(netflix_page_data=netflix_page_data)\n self.api_data = self._parse_api_base_data(netflix_page_data=netflix_page_data)\n self.profiles = self._parse_profile_data(netflix_page_data=netflix_page_data)\n self.log(msg='Found ESN \"' + self.esn + '\"')\n return netflix_page_data","def __init__(self):\r\n super(ProfileParser, self).__init__([self.ProfileEntryHandler()])","def list_profiles(request, pk=0):\n context = {'items': [], 'resource_type': 'Profile'}\n handle_expired_profiles()\n if pk == 0:\n context['h2'] = \"Configuration Profiles\"\n context['header_1'] = \"Type\"\n context['header_2'] = \"Last Modified\"\n profiles = ConfigurationProfile.objects.all().reverse()\n for profile in profiles:\n assignment_count = profile.pending_install.count()\n install_count = profile.installed.count()\n data = {'filename': str(profile), 'type': \"macOS\", 'meta': profile, 'assignment_count': assignment_count,\n 'install_count': install_count}\n context['items'].append(data)\n else:\n device = get_object_or_404(Laptop, pk=pk)\n context['h2'] = \"Profiles for {}\".format(device.name)\n context['header_1'] = \"Version\"\n context['header_2'] = \"Expires\"\n context['device_view'] = True\n context['device_id'] = pk\n profiles = ConfigurationProfile.objects.filter(pending_install__in=[device])\n profiles |= ConfigurationProfile.objects.filter(installed__in=[device])\n for profile in profiles:\n status = 'Not assigned'\n for entry in profile.installed.all():\n if entry == device:\n status = 'Installed'\n for entry in profile.pending_install.all():\n if entry == device:\n status = 'Assigned'\n record = InstallationRecord.objects.filter(profile=profile, device=device, active=True).first()\n expires_soon = False\n if record is not None and record.expires is not None:\n if timezone.now() < record.expires < timezone.now() + timezone.timedelta(days=30):\n expires_soon = True\n data = {'filename': str(profile), 'downloadable': False, 'install_record': record, 'meta': profile,\n 'status': status, 'expires_soon': expires_soon}\n context['items'].append(data)\n\n return render(request, 'mdm/resource_list.html', context)","def get_user_profiles(args): # GET\n limit = args.get(\"limit\", type=int) if args.get(\"limit\") else 0\n # NOTE checks if the string value of hasateam is equal to \"true\" because HTTP protocol only passes strings\n hasateam = args.get(\"hasateam\", \"\").lower() == \"true\"\n\n if hasateam:\n users = list(coll(\"users\").find({\"hasateam\": hasateam}).limit(limit))\n else:\n users = list(coll(\"users\").find({}).limit(limit))\n\n for user in users:\n user[\"user_id\"] = user.pop(\"_id\")\n return {\"user_profiles\": users}, 200","def weather_profile(address, name, data='', update=''):\n filename = str(r\"C:\\Users\\thoma\\Desktop\\Python\\TH Projects\\Port\\tools\\weather\\weather_profiles/\" + str(address) + \"_weather_profile_forecast.txt\")\n filename_temp = str(r\"C:\\Users\\thoma\\Desktop\\Python\\TH Projects\\Port\\tools\\weather\\weather_profiles/\" + str(address) + \"_weather_profile_forecast_backup.txt\")\n\n\n #open as write - this takes care of the initial instance of the file\n # if we can read the profile read it, otherwise create an empty dict\n\n try:\n with open(filename) as f:\n contents = json.load(f)\n \n weather_profile_dict = contents\n \n #create backup file in case something goes wrong\n with open(filename_temp, 'w') as f:\n contents = weather_profile_dict\n json.dump(weather_profile_dict, f)\n\n except FileNotFoundError:\n weather_profile_dict = {}\n\n except ValueError:\n print(\"dictionary has a value error\")\n weather_profile_dict = {}\n \n # if there is an existing entry: \n if name in weather_profile_dict.keys():\n if update:\n del weather_profile_dict[name]\n weather_profile_dict[name] = data\n # store the amended profile \n with open(filename, 'w') as f:\n json.dump(weather_profile_dict,f)\n return weather_profile_dict[name]\n else:\n return weather_profile_dict[name]\n \n # if there isn't an existing entry\n else:\n if data:\n # create a new one if there is data\n weather_profile_dict[name] = data\n # store the amended profile \n with open(filename, 'w') as f:\n json.dump(weather_profile_dict,f)\n return weather_profile_dict[name]\n\n else:\n # return nothing if there isn't\n print(\"t/-There is no existing data for \" + str(name) + \" - add data...\")\n return None","def feed_records(self):\n if not self.stats_file:\n return\n\n with open(self.stats_file) as fh:\n reader = reverse_file(fh)\n for line in reader:\n if line is None:\n return\n if not line:\n continue\n\n try:\n js = json.loads(line)\n except Exception as e:\n continue\n\n yield js","def list_profiles(self, params):\n return self.profiles","def retrieve_json():\n\n # Download the JSON files to a working directory.\n if not os.path.exists(JSON_FOLDER):\n os.makedirs(JSON_FOLDER)\n\n make_time_stamp_file()\n \n dump_all_json()\n \n # Report counts of what we got back.\n courses = load_courses_json()\n script_logging.log_status(\"Course count: %s\" % len(courses))\n \n for course in courses:\n course_id = course['id']\n\n students = load_students_json(course_id)\n script_logging.log_status(\"Student count for course %s: %s\" % (course_id, len(students)))\n \n users = load_users_json(course_id)\n script_logging.log_status(\"User count for course %s: %s\" % (course_id, len(users)))\n\n assignments = load_assignments_json(course_id)\n script_logging.log_status(\"Assignment count for course %s: %s\" % (course_id, len(assignments)))\n\n for assignment in assignments:\n assignment_id = assignment['id']\n submissions = load_submissions_json(assignment_id)\n script_logging.log_status(\"Submission count for assignment %s: %s\" % (assignment_id, len(submissions)))\n\n script_logging.log_status(\"Time stamp: %s\" % get_time_stamp())","def parseJsons(businessJson='pa_business.json', reviewJson='pa_review.json', userJson='pa_user.json'):\n\n # loops over all .json files in the argument\n parseJson(businessJson)\n print \"Success parsing \" + businessJson\n parseJson(reviewJson)\n print \"Success parsing \" + reviewJson\n parseJson(userJson)\n print \"Success parsing \" + userJson\n\n # we won't need the graph for the first part\n # UPDATE by James: initialized because needed to populate friendship_map\n (degree_centrality_map, closeness_centrality_map, betweenness_centrality_map) = initializeGraph()\n\n # business reviews are in the global business_reviews\n processReviews()\n\n pickle.dump(friendship_map, open( \"friendshipMap.p\", \"wb\" ) )\n pickle.dump(business_reviews, open( \"businessReviews.p\", \"wb\" ) )\n pickle.dump(degree_centrality_map, open( \"degreeCentrality.p\", \"wb\" ) )\n pickle.dump(closeness_centrality_map, open( \"closenessCentrality.p\", \"wb\" ) )\n pickle.dump(betweenness_centrality_map, open( \"betweennessCentrality.p\", \"wb\" ) )\n\n return (friendship_map, business_reviews, degree_centrality_map, closeness_centrality_map, betweenness_centrality_map, YGraph)","def transform_profiles(self):\n\n if len(self.pardus_profiles) > 0: # Make sure we have some profiles to migrate\n for profile in self.pardus_profiles:\n network_manager_profile = NetworkManagerProfile(profile.get_profile_name, profile)\n self.network_manager_profiles.append(network_manager_profile)","def list_network_profiles(arn=None, type=None, nextToken=None):\n pass","def __init__(self, user, username, password, cap=15):\n cap = 10; #50 images is the upper limit\n args = {'username': [user], 'media_types': ['none'], 'media_metadata': False, 'usernames': [user], 'verbose': 0, 'maximum': cap, 'login_user': username, 'login_only': False, 'destination': './', 'quiet': False, 'comments': True, 'filename': None, 'filter': None, 'search_location': False, 'tag': False, 'location': False, 'login_pass': password, 'retain_username': False, 'include_location': False, 'latest': False}\n\n self.json = None\n self.pic = None\n try : \n scraper = app.start(args)\n scraper.scrape()\n except:\n return\n\n\n scrap = json.dumps(scraper.posts, indent=4, sort_keys=True, ensure_ascii=False)\n self.json = json.loads(scrap)\n self.pic = scraper.profilepic\n\n if len(self.json) == 0:\n self.json = None\n return\n #rint self.json\n #print json.dumps(self.json, indent=4)"],"string":"[\n \"def loadProfiles():\\n with open(userProfilesDir, \\\"r\\\") as infile:\\n profiles = json.loads(\\\"\\\\n\\\".join(infile.readlines()))\\n infile.close()\\n return profiles\",\n \"def scrape_profile(inhandle, outfile, year, month):\\n #Read file\\n html = inhandle.read()\\n soup = BeautifulSoup(html, 'html.parser')\\n\\n #Find main page content\\n content = soup.find('div', {'class':'entry-content'})\\n\\n profile = {}\\n\\n #Fill in known info from URL\\n profile['year_reported'] = year\\n profile['month_reported'] = month\\n\\n #Extract and download images.\\n profile['images'] = [save_image(img['src']) for img in content.findAll('img')]\\n\\n #Get visible text\\n text = content.get_text().strip()\\n\\n #Parse information from text\\n for key in extractors:\\n match = extractors[key].search(text)\\n if match:\\n matchtext = match.group(1).strip()\\n if key in ['justifications','messages']:\\n vals = matchtext.split('\\\\n')\\n else:\\n vals = matchtext\\n profile[key] = vals \\n\\n #Parse annotations\\n content = soup.find('div', {'class':'entry-utility'})\\n profile['tags'] = [node.get_text() for node in content.findAll('a', {'rel':'tag'})]\\n profile['gender'] = 'female' if 'Female profiles' in profile['tags'] else 'male'\\n\\n #Save output\\n json.dump(profile, open(outfile,'w'))\",\n \"def _sloppy_parse_profiles (self, contents):\\n profile_start = contents.find('profiles\\\":')\\n profile_list_start = contents.find('profilesList')\\n if int(profile_start) > -1 and int(profile_list_start) > -1:\\n try:\\n try:\\n return json.loads('{\\\"a\\\":{\\\"' + contents[profile_start:profile_list_start-2].decode('string_escape') + '}}').get('a').get('profiles')\\n except ValueError, e:\\n return None\\n except TypeError, e:\\n return None\\n return None\",\n \"def _parse_profile_data (self, netflix_page_data):\\n profiles = {};\\n important_fields = [\\n 'profileName',\\n 'isActive',\\n 'isAccountOwner',\\n 'isKids'\\n ]\\n # values are accessible via dict (sloppy parsing successfull)\\n if type(netflix_page_data) == dict:\\n for profile_id in netflix_page_data.get('profiles'):\\n if self._is_size_key(key=profile_id) == False and type(netflix_page_data['profiles'][profile_id]) == dict and netflix_page_data['profiles'][profile_id].get('avatar', False) != False:\\n profile = {'id': profile_id}\\n for important_field in important_fields:\\n if important_field == 'profileName':\\n profile.update({important_field: HTMLParser.HTMLParser().unescape(netflix_page_data['profiles'][profile_id]['summary'][important_field]).encode('utf8')})\\n else:\\n profile.update({important_field: netflix_page_data['profiles'][profile_id]['summary'][important_field]})\\n avatar_base = netflix_page_data['nf'].get(netflix_page_data['profiles'][profile_id]['summary']['avatarName'], False);\\n avatar = 'https://secure.netflix.com/ffe/profiles/avatars_v2/320x320/PICON_029.png' if avatar_base == False else avatar_base['images']['byWidth']['320']['value']\\n profile.update({'avatar': avatar, 'isFirstUse': False})\\n profiles.update({profile_id: profile})\\n return profiles\\n\\n # values are stored in lists (returned from JS parser)\\n # TODO: get rid of this christmas tree of doom\\n for item in netflix_page_data:\\n if 'hasViewedRatingWelcomeModal' in dict(item).keys():\\n for profile_id in item:\\n if self._is_size_key(key=profile_id) == False and type(item[profile_id]) == dict and item[profile_id].get('avatar', False) != False:\\n profile = {'id': profile_id}\\n for important_field in important_fields:\\n profile.update({important_field: item[profile_id]['summary'][important_field]})\\n avatar_base = item['nf'].get(item[profile_id]['summary']['avatarName'], False);\\n avatar = 'https://secure.netflix.com/ffe/profiles/avatars_v2/320x320/PICON_029.png' if avatar_base == False else avatar_base['images']['byWidth']['320']['value']\\n profile.update({'avatar': avatar})\\n profiles.update({profile_id: profile})\\n return profiles\",\n \"def dumpProfiles(profiles):\\n with open(userProfilesDir, \\\"w\\\") as outfile:\\n outfile.writelines(json.dumps(profiles, indent=4))\\n outfile.close()\",\n \"def _deep_data(self, url):\\n def _nested_persons(persons):\\n _persons = list()\\n for person_ in persons:\\n person_ = [r.text.split(', ') for r in person_.find_all(class_='default-text')]\\n person = {'name': person_[0][0].title()}\\n if len(person_[0]) == 2:\\n person['age'] = person_[0][1]\\n\\n if len(person_[1]) > 0:\\n person['addressLocality'] = person_[1][0].title()\\n if len(person_[1]) == 2:\\n person['addressRegion'] = person_[1][1].upper()\\n\\n _persons.append(person)\\n return _persons\\n\\n with self.driver(self.DRIVER_DIR) as driver:\\n driver.get(url)\\n driver.fullscreen_window()\\n time.sleep(2)\\n txt = driver.page_source\\n\\n soup = bs(txt, 'html.parser')\\n\\n profile_data = soup.find(type=\\\"application/ld+json\\\")\\n if profile_data is None:\\n self._raise_site_schema_change()\\n profile_data = profile_data.string\\n profile_data = json.loads(profile_data, strict=False)\\n profile_data['@id'] = profile_data.pop('@id').split('/')[-1]\\n\\n try:\\n about = profile_data.pop('about')\\n for k, v in about.items():\\n profile_data[k] = v\\n except KeyError:\\n pass\\n\\n name_ = profile_data.pop('name')\\n profile_data['name'] = name_\\n\\n name_ = name_.split()\\n profile_data['givenName'] = name_[0]\\n profile_data['middleName'] = ' '.join(name_[1:-1])\\n profile_data['familyName'] = name_[-1]\\n\\n if soup.find(class_='rep-vcard-score') is not None:\\n profile_data['reputation_score'] = \\\"{min}-{max}\\\".format(\\n min=soup.find(class_='rep-vcard-min').text,\\n max=soup.find(class_='rep-vcard-max').text\\n )\\n\\n address = list()\\n address_ = soup.find_all(class_='card-address')\\n for a in address_:\\n street_address, locality_region_postal, *misc = [_.text for _ in a.find_all(class_='block-container')]\\n address_locality, locality_region_postal = locality_region_postal.split(',')\\n address_region, postal_code = locality_region_postal.split()\\n address.append({\\n 'streetAddress': street_address,\\n 'addressLocality': address_locality,\\n 'addressRegion': address_region,\\n 'postalCode': postal_code,\\n })\\n\\n profile_data['address'] = address\\n\\n personal_details = soup.find(class_='card-personal-details')\\n if personal_details is not None:\\n personal_details = personal_details.find_all(class_='item-container')\\n personal_details = [detail.text.split(': ') for detail in personal_details]\\n personal_details = [_ for _ in personal_details if len(_) == 2]\\n personal_details = {detail.lower().replace(' ', '_'): value for\\n detail, value in personal_details if value != 'Add Info'}\\n\\n birth_date = personal_details.pop('date_of_birth')\\n if len(birth_date) > 0:\\n profile_data['birthDate'] = birth_date\\n\\n for key_, value_ in personal_details.items():\\n profile_data[key_] = value_\\n\\n # Education\\n schools_ = soup.find(class_='card-education')\\n if schools_ is not None:\\n schools = list()\\n schools_ = schools_.find_all(class_='card-content')\\n for school in schools_:\\n school = [detail.text.split(': ') for detail in school.find_all(class_='item-container')]\\n school = {detail.lower().replace(' ', '_'): value for\\n detail, value in school if value != 'Add Info'}\\n\\n if len(school) == 0:\\n continue\\n\\n school['@type'] = 'EducationalOrganization'\\n school['name'] = school.pop('school')\\n school['streetAddress'], school['addressLocality'] = school.pop('city').split(', ')\\n schools.append(school)\\n\\n # Work\\n employers = soup.find(class_='card-job')\\n if employers is not None:\\n works_for = list()\\n employers = employers.find_all(class_='card-content')\\n for employer in employers:\\n employer = [detail.text.split(': ') for detail in employer.find_all(class_='item-container')]\\n employer = {detail.lower().replace(' ', '_'): value for\\n detail, value in employer if value != 'Add Info'}\\n\\n if len(employer) == 0:\\n continue\\n\\n employer['@type'] = 'Organization'\\n try:\\n employer['name'] = employer.pop('company')\\n except KeyError:\\n continue\\n\\n if len(employer.get('city', '')) > 0:\\n employer['streetAddress'], employer['addressLocality'] = employer.pop('city').split(', ')\\n\\n works_for.append(employer)\\n\\n if len(works_for) > 0:\\n profile_data['worksFor'] = works_for\\n\\n # Automobiles\\n automobiles = soup.find(class_='card-auto')\\n if automobiles is not None:\\n owns = list()\\n automobiles = automobiles.find_all(class_='card-content')\\n for automobile in automobiles:\\n automobile = [detail.text.split(': ') for detail in automobile.find_all(class_='item-container')]\\n automobile = {detail.lower().replace(' ', '_'): value for\\n detail, value in automobile if value != 'Add Info'}\\n\\n if len(automobile) == 0:\\n continue\\n\\n automobile['@type'] = 'Product'\\n automobile['model'] = ' '.join([\\n automobile.pop('year'),\\n automobile.pop('make'),\\n automobile.pop('model')\\n ])\\n owns.append(automobile)\\n\\n if len(owns) > 0:\\n profile_data['owns'] = owns\\n\\n profile_data['relatedTo'] = _nested_persons(soup.find_all(class_='relative-container'))\\n profile_data['neighbors'] = _nested_persons(soup.find_all(class_='neighbor-container'))\\n\\n # Photos\\n profile_data['pictures'] = list({photo['src'] for photo in soup.find_all(class_='profile-picture-holder')})\\n return profile_data\",\n \"def gather_all_profiles(year, month):\\n page = 1\\n urls = []\\n\\n print(\\\"{}-{} : Begin indexing.\\\".format(year, month))\\n\\n while (page > 0):\\n urlstring = \\\"http://scamdigger.com/{}/{}/page/{}\\\".format(year,month,page) \\n jitter = random.choice([0,1])\\n try:\\n urlhandle = urlopen(urlstring)\\n urls += enumerate_profiles(urlhandle, page)\\n # time.sleep(1+jitter)\\n page += 1\\n except:\\n page = 0\\n\\n print(\\\"{}-{} : {} profiles\\\".format(year,month,len(urls)))\\n\\n for url in urls:\\n uid = url[30:-1]\\n outfile=PROFILES+os.sep+uid+'.json'\\n jitter = random.choice([0,1])\\n try:\\n urlhandle = urlopen(url)\\n scrape_profile(urlhandle, outfile, year, month)\\n # time.sleep(1+jitter)\\n except Exception as e:\\n print(\\\"Exception when handling {}\\\".format(url))\\n print(e)\\n \\n print(\\\"{}-{} : complete.\\\".format(year,month))\",\n \"def parse(self):\\n database = WriteToDatabase(\\\"config_profi_ru.conf\\\")\\n database.create_base()\\n # Start Webdriver with supressed logging\\n options = webdriver.ChromeOptions() \\n options.add_experimental_option(\\\"excludeSwitches\\\", [\\\"enable-logging\\\"])\\n self.driver = webdriver.Chrome(options=options)\\n # Get categories\\n self.get_category_links()\\n # Treat general categories\\n for category in self.link_list:\\n cat_name = category.split('/')[-2]\\n # Check if category is already parsed\\n if(os.path.exists(f\\\"profi_ru_json_data\\\\{cat_name}_data_file.json\\\")):\\n self.logger.warning(f\\\"'{cat_name}' category is already parsed!\\\")\\n continue\\n self.logger.info(f\\\"Treating '{cat_name}' category\\\")\\n counter = 0\\n person_info = []\\n try:\\n category_profiles = self.get_profiles_by_category(f'{category}{self.profile_suffix}')\\n self.logger.info(f\\\"Found {len(category_profiles)} profiles in '{cat_name}' category\\\")\\n self.logger.info(f\\\"Start of parsing '{cat_name}' category...\\\")\\n for person_link in category_profiles:\\n person_info.append(self.get_person_info(person_link))\\n counter+=1\\n if(counter == len(category_profiles) - 1):\\n self.logger.info(f\\\"End of parsing '{cat_name}' category...\\\")\\n except:\\n self.logger.critical(\\\"Problems with Internet connection or Web driver occured!\\\")\\n self.logger.exception(f\\\"Only {counter} profiles of '{cat_name}' category were parsed\\\")\\n self.cat_profiles_dict[cat_name] = person_info\\n self.logger.info(f\\\"'{cat_name}' category was parsed successfully!\\\")\\n self.write_json_file(cat_name, self.cat_profiles_dict[cat_name])\\n self.logger.info(f'{cat_name}_data_file.json with parsed data was created successfully!')\\n database.create_and_write_table(f'profi_ru_json_data\\\\{cat_name}_data_file.json')\\n # Treat generic categories\\n for cat_list in self.others_links:\\n cat_name = cat_list[0]\\n # Check if categroy is already parsed\\n if(os.path.exists(f\\\"profi_ru_json_data\\\\{cat_name}_data_file.json\\\")):\\n self.logger.warning(f\\\"'{cat_name}' category is already parsed!\\\")\\n continue\\n self.logger.info(f\\\"Treating '{cat_name}' category\\\")\\n counter = 0\\n person_info = []\\n try:\\n category_profiles = self.get_profiles_by_category(f'{category}{self.profile_suffix}')\\n self.logger.info(f\\\"Found {len(category_profiles)} profiles in '{cat_name}' category\\\")\\n for person_link in category_profiles:\\n person_info.append(self.get_person_info(person_link))\\n counter+=1\\n if(counter == len(category_profiles) - 1):\\n self.logger.info(f\\\"End of parsing {cat_name} category\\\")\\n except:\\n self.logger.critical(\\\"Problems with Internet connection or Web driver occured!\\\")\\n self.logger.exception(f\\\"Only {counter} profiles of '{cat_name}' category were parsed\\\")\\n self.cat_profiles_dict[cat_name] = person_info\\n self.logger.info(f\\\"'{cat_name}' category was parsed successfully!\\\")\\n self.write_json_file(cat_name, self.cat_profiles_dict[cat_name])\\n self.logger.info(f'{cat_name}_data_file.json with parsed data was created successfully!')\\n database.create_and_write_table(f'profi_ru_json_data\\\\{cat_name}_data_file.json')\\n self.driver.quit()\",\n \"def load_profiles_from_file(file_path):\\r\\n profiles = {}\\r\\n\\r\\n lastused = \\\"\\\"\\r\\n\\r\\n if File.Exists(file_path):\\r\\n try:\\r\\n with StreamReader(file_path) as xmlfile:\\r\\n xmldoc = XmlDocument()\\r\\n xmldoc.Load(xmlfile)\\r\\n\\r\\n if xmldoc.DocumentElement.Name == \\\"Profiles\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Profiles/Profile\\\")\\r\\n #Individual exported profiles are saved with the document element as Profile\\r\\n elif xmldoc.DocumentElement.Name == \\\"Profile\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Profile\\\")\\r\\n\\r\\n #Changed from 1.7 to 2.0 to use Profiles/Profile instead of Settings/Setting\\r\\n elif xmldoc.DocumentElement.Name == \\\"Settings\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Settings/Setting\\\")\\r\\n elif xmldoc.DocumentElement.Name == \\\"Setting\\\":\\r\\n nodes = xmldoc.SelectNodes(\\\"Setting\\\")\\r\\n\\r\\n #No valid root elements\\r\\n else:\\r\\n MessageBox.Show(file_path + \\\" is not a valid Library Organizer profile file.\\\", \\\"Not a valid profile file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\\r\\n return profiles, lastused\\r\\n\\r\\n if nodes.Count > 0:\\r\\n for node in nodes: \\r\\n profile = Profile()\\r\\n profile.Name = node.Attributes[\\\"Name\\\"].Value\\r\\n result = profile.load_from_xml(node)\\r\\n\\r\\n #Error loading the profile\\r\\n if result == False:\\r\\n MessageBox.Show(\\\"An error occured loading the profile \\\" + profile.Name + \\\". That profile has been skipped.\\\")\\r\\n\\r\\n else:\\r\\n profiles[profile.Name] = profile\\r\\n\\r\\n\\r\\n #Load the last used profile\\r\\n rootnode = xmldoc.DocumentElement\\r\\n if rootnode.HasAttribute(\\\"LastUsed\\\"):\\r\\n lastused = rootnode.Attributes[\\\"LastUsed\\\"].Value.split(\\\",\\\")\\r\\n\\r\\n except Exception, ex:\\r\\n MessageBox.Show(\\\"Something seems to have gone wrong loading the xml file.\\\\n\\\\nThe error was:\\\\n\\\" + str(ex), \\\"Error loading file\\\", MessageBoxButtons.OK, MessageBoxIcon.Error)\\r\\n\\r\\n return profiles, lastused\",\n \"def get_profiles(count, credentials_type=constants.API_KEY_CREDENTIALS_TYPE, verbose=False):\\n print('getting profiles through {}...'.format(credentials_type))\\n\\n # getting profiles information\\n profile_ids = plus_api.get_unique_profile_ids(count, credentials_type=credentials_type, verbose=verbose)\\n profile_urls = list(map(plus_api.profile_id_to_google_plus_url, profile_ids))\\n # display_names, image_urls = get_profile_display_names_and_image_urls(profile_ids, credentials_type=credentials_type,\\n # verbose=verbose)\\n display_names, image_urls = get_profile_display_names_and_image_urls_with_getBatchGet(profile_ids,\\n credentials_type=credentials_type,\\n verbose=verbose)\\n\\n # saving as plain text\\n txt_filename = 'results/people-api-profiles.txt'\\n if verbose:\\n print('saving profiles as plain text to {}...'.format(txt_filename))\\n result_str = ''\\n for profile in zip(profile_ids, profile_urls, display_names, image_urls):\\n result_str += '\\\\n'.join(profile) + '\\\\n\\\\n'\\n profiles_file = open(txt_filename, 'w', encoding='utf-8')\\n profiles_file.write(result_str)\\n profiles_file.close()\\n\\n # saving as json\\n json_filename = 'results/people-api-profiles.json'\\n if verbose:\\n print('saving profiles as json to {}...'.format(json_filename))\\n result_list = []\\n json_fields = ('profileId', 'profileUrl', 'displayName', 'imageUrl')\\n for profile in zip(profile_ids, profile_urls, display_names, image_urls):\\n result_list.append(dict(zip(json_fields, profile)))\\n json_str = json.dumps(result_list)\\n profiles_file = open(json_filename, 'w', encoding='utf-8')\\n profiles_file.write(json_str)\\n profiles_file.close()\",\n \"def get_profile(self, html_content: str) -> object:\\n if not html_content:\\n raise Exception(\\\"HTML content not found\\\")\\n\\n soup = BeautifulSoup(html_content, 'html.parser')\\n\\n self.profile = {\\n \\\"employments\\\": [],\\n \\\"others\\\": [],\\n \\\"education\\\": []\\n }\\n # get sidebar\\n sidebar = soup.select_one(self.tags.get(\\\"sidebar\\\"))\\n items = sidebar.select(self.tags.get(\\\"sidebar.items\\\")) if sidebar else []\\n\\n # read each item on sidebar\\n for item in items:\\n\\n label = item.select_one(self.tags.get(\\\"sidebar.items.header\\\"))\\n section_title = label.text.strip()\\n\\n # Availability Item\\n if section_title == 'Availability':\\n status, avaibility = smart_split(\\n get_value(item.select_one(self.tags.get(\\\"sidebar.items.availability\\\"))), '\\\\n')\\n self.profile[\\\"status\\\"] = status\\n self.profile[\\\"avaibility\\\"] = avaibility\\n\\n # Languages Item\\n elif section_title == 'Languages':\\n\\n languages = [' '.join(get_value(li).split()) for li in\\n item.select(self.tags.get(\\\"sidebar.items.languages.item\\\"))]\\n self.profile[\\\"languages\\\"] = [dict(zip([\\\"name\\\", \\\"level\\\"], lng.split(\\\":\\\"))) for lng in languages]\\n\\n # Education Item\\n elif section_title == 'Education':\\n lis = item.select(self.tags.get(\\\"sidebar.items.education.item\\\"))\\n\\n for li in lis:\\n school = \\\" \\\".join(\\n get_value(li.select_one(self.tags.get(\\\"sidebar.items.education.school\\\"))).split())\\n degree_area = get_value(li.select_one(self.tags.get(\\\"sidebar.items.education.degree_area\\\")))\\n degree, area = smart_split(degree_area)\\n attended_date = get_value(li.select_one(self.tags.get(\\\"sidebar.items.education.attended_date\\\")))\\n\\n self.profile[\\\"education\\\"].append({\\n \\\"school\\\": school,\\n \\\"degree\\\": degree,\\n \\\"area\\\": area,\\n \\\"attended_date\\\": attended_date\\n })\\n\\n # Skill panel\\n skills = soup.select(self.tags.get(\\\"skills\\\"))\\n self.profile[\\\"skills\\\"] = set(get_value(s) for s in skills) # using set to remove duplications\\n\\n # Employment/Other Experience panels\\n cards = soup.select(self.tags.get(\\\"profile.panels\\\"))\\n for lis in cards:\\n lis = lis.parent.select(\\\"li\\\")\\n\\n for li in lis:\\n position, employer = smart_split(\\n li.select_one(self.tags.get(\\\"employments.position_employer\\\")).get_text(strip=True), \\\"|\\\")\\n period = ' '.join(li.select_one(self.tags.get(\\\"employments.period\\\")).get_text(strip=True).split())\\n\\n if \\\"Other\\\" != position.strip():\\n self.profile[\\\"employments\\\"].append({\\n \\\"employer\\\": employer,\\n \\\"job_title\\\": position,\\n \\\"hire_datetime\\\": period.split('-')[0].strip(),\\n \\\"termination_datetime\\\": period.split('-')[1].strip()\\n })\\n else:\\n self.profile[\\\"others\\\"].append({\\n \\\"title\\\": employer,\\n \\\"description\\\": period.split('-')[0]\\n })\\n\\n title = soup.select_one(self.tags.get(\\\"title\\\"))\\n hour = get_value(title.parent.parent.select_one(self.tags.get(\\\"hour_price\\\")))\\n description = get_value(soup.select_one(self.tags.get(\\\"description\\\")))\\n\\n # define the last elements of profile\\n self.profile[\\\"title\\\"] = get_value(title)\\n self.profile[\\\"price\\\"] = hour\\n self.profile[\\\"description\\\"] = description\\n self.profile[\\\"full_name\\\"] = soup.select_one(self.tags.get(\\\"full_name\\\")).get_text(strip=True)\\n self.profile[\\\"first_name\\\"] = self.profile[\\\"full_name\\\"].split()[0]\\n self.profile[\\\"last_name\\\"] = self.profile[\\\"full_name\\\"].split()[-1]\\n self.profile[\\\"address_city\\\"] = soup.select_one(self.tags.get(\\\"address_city\\\")).get_text(strip=True)\\n self.profile[\\\"address_country\\\"] = soup.select_one(self.tags.get(\\\"address_country\\\")).get_text(strip=True)\\n self.profile[\\\"picture_url\\\"] = soup.select_one(self.tags.get(\\\"picture\\\"))[\\\"src\\\"]\\n\\n return self.profile\",\n \"def get_list(profiles_folder, logger):\\n profile_list = []\\n with scandir(profiles_folder) as it:\\n for entry in it:\\n if entry.is_file():\\n filepath = profiles_folder + entry.name\\n profile = json_from_file(filepath, logger)\\n if profile is not None:\\n try:\\n profile_list.append({\\\"filepath\\\":filepath, \\\"name\\\":profile[\\\"name\\\"], \\\"description\\\":profile[\\\"description\\\"]})\\n except AttributeError:\\n logger.error(\\\"Missing attributes in \\\" + filepath)\\n logger.error(str(profile))\\n return profile_list\",\n \"def _process_profiles(profiles):\\n # keep_attributes = str.split(\\\"user_id public completion_percentage gender region last_login registration age\\\")\\n # p2=profiles[keep_attributes]\\n p2 = profiles\\n p2['region'] = p2['region'].astype('category')\\n p2['public'] = p2['public'].astype('category')\\n p2['gender'] = p2['gender'].astype('category')\\n p2['last_login'] = pd.to_datetime(p2['last_login'])\\n p2['registration'] = pd.to_datetime(p2['registration'])\\n p2.loc[p2.age == 0, 'age'] = np.nan\\n\\n return p2\",\n \"def populate_profiles_from_directory(self):\\n\\n self.profiles = []\\n\\n # Go over all the files and create a profile object\\n _profile_files = listdir(self.save_dir)\\n\\n for profile_filename in _profile_files:\\n # Only check for .yaml files\\n if path.splitext(profile_filename)[1] == '.yaml':\\n # Get the data and create a new profile\\n _file_data = self._load_data_from_file(path.join(self.save_dir, profile_filename))\\n _profile = Profile(path.splitext(profile_filename)[0])\\n _profile.player_data = _file_data\\n self.profiles.append(_profile)\",\n \"def _read_profiles(profile_directory):\\n # Initialize key variables\\n profiles = defaultdict(\\n lambda: defaultdict(lambda: defaultdict()))\\n\\n # Read the yaml files in the profiles directory\\n files = os.listdir(profile_directory)\\n filenames = ['{}{}{}'.format(\\n profile_directory, os.sep, nextfile) for nextfile in files]\\n\\n for _filename in sorted(filenames):\\n # Get rid of excess os.sep separators\\n pathitems = _filename.split(os.sep)\\n filename = os.sep.join(pathitems)\\n\\n # Skip obvious\\n if os.path.isfile(filename) is False:\\n continue\\n if filename.lower().endswith('.yaml') is False:\\n continue\\n\\n with open(filename, 'r') as stream:\\n try:\\n _profiles = yaml.load(stream)['data']\\n except yaml.YAMLError as exc:\\n print(exc)\\n\\n # Create dictionary\\n for item in _profiles:\\n firstname = item['firstname']\\n lastname = item['lastname']\\n height = item['height']\\n weight = item['weight']\\n birthdate = item['birthdate']\\n profiles[lastname][firstname][birthdate] = {\\n 'height': height, 'weight': weight}\\n\\n return profiles\",\n \"def scrap_profile(profile_name, number_of_posts):\\r\\n scraped_medias = []\\r\\n post_data = OrderedDict()\\r\\n # Populate OrderedDict with post_data fields\\r\\n medias = instagram.get_medias(profile_name, count=number_of_posts)\\r\\n for media in medias:\\r\\n media_id_ = media.identifier\\r\\n post_data['url'] = media.link\\r\\n\\r\\n post_data['text'] = media.caption\\r\\n\\r\\n try:\\r\\n post_data['media_url'] = media.image_high_resolution_url\\r\\n except Exception:\\r\\n post_data['media_url'] = media.video_url\\r\\n \\r\\n comments = instagram.get_media_comments_by_id(media_id_, 10000)\\r\\n post_data['comments'] = [comment.text for comment in comments['comments']]\\r\\n\\r\\n post_data['time'] = datetime.fromtimestamp(media.created_time).strftime(\\\"%d/%m/%Y\\\")\\r\\n\\r\\n post_data['likes'] = media.likes_count\\r\\n\\r\\n post_data['comments_count'] = len(post_data['comments'])\\r\\n \\r\\n post_data = dict(post_data)\\r\\n \\r\\n scraped_medias.append(post_data)\\r\\n \\r\\n return scraped_medias\",\n \"def scrape_kprofiles(url):\\n page = requests.get(url)\\n tree = html.fromstring(page.content)\\n results = tree.xpath('//article/div/div/div/p/a[@href]/text()')\\n filtered_results = []\\n discarded_results = 0\\n for item in results:\\n if item in [\\\"\\\\n\\\", \\\" \\\", \\\"\\\"]:\\n discarded_results += 1\\n continue\\n else:\\n if \\\"Profile\\\" in item:\\n item = item.replace(\\\"Profile\\\", \\\"\\\")\\n filtered_results.append(item.strip())\\n return filtered_results\",\n \"def _read_profile_file(cls, profile_file):\\n qid2title: Dict[str, str] = {}\\n qid2desc: Dict[str, str] = {}\\n alias2qids: Dict[str, list] = {}\\n type_systems: Dict[str, Dict[str, List[str]]] = {}\\n qid2relations: Dict[str, Dict[str, List[str]]] = {}\\n\\n num_lines = sum(1 for _ in open(profile_file))\\n with open(profile_file, \\\"r\\\") as in_f:\\n for line in tqdm(in_f, total=num_lines, desc=\\\"Reading profile\\\"):\\n line = ujson.loads(line)\\n\\n # Check keys and schema\\n assert all(\\n k in line.keys() for k in REQUIRED_KEYS\\n ), f\\\"A key from {REQUIRED_KEYS} was not in {line}\\\"\\n try:\\n # Asserts the types are correct\\n ent = EntityObj(\\n entity_id=line[\\\"entity_id\\\"],\\n mentions=line[\\\"mentions\\\"],\\n title=line.get(\\\"title\\\", line[\\\"entity_id\\\"]),\\n description=line.get(\\\"description\\\", \\\"\\\"),\\n types=line.get(\\\"types\\\", {}),\\n relations=line.get(\\\"relations\\\", []),\\n )\\n except ValidationError as e:\\n print(e.json())\\n raise e\\n if ent.entity_id in qid2title:\\n raise ValueError(f\\\"{ent.entity_id} is already in our dump\\\")\\n qid2title[ent.entity_id] = ent.title\\n qid2desc[ent.entity_id] = ent.description\\n # For each [mention, score] value, create a value of mention -> [qid, score] in the alias2qid dict\\n for men_pair in ent.mentions:\\n # Lower case mentions for mention extraction\\n new_men = get_lnrm(men_pair[0], strip=True, lower=True)\\n if new_men not in alias2qids:\\n alias2qids[new_men] = []\\n alias2qids[new_men].append([ent.entity_id, men_pair[1]])\\n # Add type systems of type_sys -> QID -> list of type names\\n for type_sys in ent.types:\\n if type_sys not in type_systems:\\n type_systems[type_sys] = {}\\n type_systems[type_sys][ent.entity_id] = ent.types[type_sys]\\n # Add kg relations QID -> relation -> list of object QIDs\\n for rel_pair in ent.relations:\\n if \\\"relation\\\" not in rel_pair or \\\"object\\\" not in rel_pair:\\n raise ValueError(\\n \\\"For each value in relations, it must be a JSON with keys relation and object\\\"\\n )\\n if ent.entity_id not in qid2relations:\\n qid2relations[ent.entity_id] = {}\\n if rel_pair[\\\"relation\\\"] not in qid2relations[ent.entity_id]:\\n qid2relations[ent.entity_id][rel_pair[\\\"relation\\\"]] = []\\n qid2relations[ent.entity_id][rel_pair[\\\"relation\\\"]].append(\\n rel_pair[\\\"object\\\"]\\n )\\n\\n # Sort mentions based on score, highest first\\n for al in list(alias2qids.keys()):\\n alias2qids[al] = sorted(alias2qids[al], key=lambda x: x[1], reverse=True)\\n # Add all qids to the type systems and KG connections with empty values\\n # This isn't strictly required but can make the sets more clean as they'll have consistent keys\\n for qid in qid2title:\\n for type_sys in type_systems:\\n if qid not in type_systems[type_sys]:\\n type_systems[type_sys][qid] = []\\n if qid not in qid2relations:\\n qid2relations[qid] = {}\\n return qid2title, qid2desc, alias2qids, type_systems, qid2relations\",\n \"def list_profiles(self) -> dict:\\n wsc = self.read_ws_configuration()\\n out = OrderedDict()\\n for name, json in wsc.profiles.items():\\n out[name] = Profile(name, self.ws_data_folder / name, json)\\n # Try to find current profile\\n try:\\n out[self.current_profile_name].is_current = True\\n except Exception:\\n pass\\n return out\",\n \"def load_profiles(profiles_file: TextIO) -> Tuple[Dict[str, List[str]],\\n Dict[str, List[str]]]:\\n individuals = [[]]\\n sublist = 0\\n content = profiles_file.readlines()\\n for i in content:\\n if i != '\\\\n':\\n individuals[sublist].append(i)\\n else:\\n sublist += 1\\n individuals.append([])\\n return sort_profile(individuals)\",\n \"def profile(username):\\n link = \\\"https://myanimelist.net/animelist/{}\\\".format(username)\\n mal_soup = get_soup(link)\\n table = json.loads(mal_soup.find('table').get('data-items'))\\n print(\\\"\\\\nRunning...\\\\n\\\")\\n for dic in table:\\n link = \\\"https://myanimelist.net{}\\\"\\n res = mal_get_all_info(link.format(dic['anime_url']), dic['anime_title'])\\n export_to_csv(res, 1)\\n sleep(5)\\n print(\\\"Finished.\\\")\",\n \"def info_search(profiles: list, session: requests.Session):\\n counter = 0\\n results = []\\n for profile in profiles:\\n info = {}\\n sleep(0.5)\\n soup = BeautifulSoup(session.get(profile).text, \\\"lxml\\\")\\n details = soup.find(\\\"div\\\", class_=\\\"page-content seaman-page-content\\\")\\n info['name'] = details.h1.text\\n try:\\n for row in details.find_all(\\\"div\\\", class_=\\\"colmn3\\\"):\\n row_content = row.text.split(\\\":\\\")\\n if row_content[0] == \\\"Personal mobile number\\\":\\n info['phone'] = row_content[1]\\n if row_content[0] == \\\"E-Mail\\\":\\n info['email'] = row_content[1]\\n if 'phone' in info and duplicates_checker(results, info['phone']):\\n results.append(info)\\n vcard_handler(info)\\n except AttributeError:\\n continue\\n if len(results) % 10 == 0:\\n counter += 10\\n logger.info(f'{counter} profiles added out of {len(profiles)}')\\n logger.info(f'Finished adding {len(profiles)} profiles')\\n return results\",\n \"def get_profile():\\n logger.debug(\\\"entering function get_profile\\\")\\n response = read_user_profile()\\n logger.debug(\\\"exiting function get_profile\\\")\\n return jsonify(response)\",\n \"def unite_imdb_profiles(verbose):\\n if verbose:\\n print(\\\"Uniting IMDB movie profiles to one csv file...\\\")\\n if not os.path.exists(_IMDB_DIR_PATH):\\n print(\\\"No IMDB profiles to unite!\\\")\\n return\\n profiles = []\\n profile_files = os.listdir(_IMDB_DIR_PATH)\\n if verbose:\\n profile_files = tqdm(profile_files)\\n for profile_file in profile_files:\\n if verbose:\\n profile_files.set_description('Reading {}'.format(profile_file))\\n file_path = os.path.join(_IMDB_DIR_PATH, profile_file)\\n _, ext = os.path.splitext(file_path)\\n if ext == '.json':\\n with open(file_path, 'r') as json_file:\\n profiles.append(json.load(json_file))\\n df = pd.DataFrame(profiles)\\n df = _decompose_dict_column(df, 'avg_rating_per_demo', _DEMOGRAPHICS)\\n df = _decompose_dict_column(df, 'votes_per_demo', _DEMOGRAPHICS)\\n df = _decompose_dict_column(\\n df, 'rating_freq', [str(i) for i in range(1, 11)])\\n df = _dummy_list_column(df, 'genres')\\n unison_fpath = os.path.join(\\n _get_dataset_dir_path(), 'imdb_dataset.csv')\\n df.to_csv(unison_fpath, index=False)\",\n \"def write_tessprofiles(tessprofiles: Dict, online=False):\\n logger.info(f'Writing tessprofiles')\\n profile = TESSPROFILE_ONLINE_PATH if online else TESSPROFILE_PATH\\n with open(profile, 'w') as f:\\n json.dump(tessprofiles, f, indent=4)\",\n \"def download_profiles_custom(self, profiles: Set[Profile],\\n profile_pic: bool = True, posts: bool = True,\\n tagged: bool = False,\\n igtv: bool = False,\\n highlights: bool = False,\\n stories: bool = False,\\n fast_update: bool = False,\\n date_filter: Optional[Callable[[Post], bool]] = None,\\n post_filter: Optional[Callable[[Post], bool]] = None,\\n storyitem_filter: Optional[Callable[[Post], bool]] = None,\\n raise_errors: bool = False):\\n\\n @contextmanager\\n def _error_raiser(_str):\\n yield\\n\\n # error_handler type is Callable[[Optional[str]], ContextManager[None]] (not supported with Python 3.5)\\n error_handler = _error_raiser if raise_errors else self.context.error_catcher\\n\\n for profile in profiles:\\n with error_handler(profile.username): # type: ignore\\n profile_name = profile.username\\n\\n # Download profile picture\\n if profile_pic:\\n with self.context.error_catcher('Download profile picture of {}'.format(profile_name)):\\n self.download_profilepic(profile)\\n\\n # Save metadata as JSON if desired.\\n if self.save_metadata:\\n json_filename = '{0}/{1}_{2}'.format(self.dirname_pattern.format(profile=profile_name,\\n target=profile_name),\\n profile_name, profile.userid)\\n self.save_metadata_json(json_filename, profile)\\n\\n # Catch some errors\\n if profile.is_private and (tagged or igtv or highlights or posts):\\n if not self.context.is_logged_in:\\n raise LoginRequiredException(\\\"--login=USERNAME required.\\\")\\n if not profile.followed_by_viewer and self.context.username != profile.username:\\n raise PrivateProfileNotFollowedException(\\\"Private but not followed.\\\")\\n\\n # Download tagged, if requested\\n if tagged:\\n with self.context.error_catcher('Download tagged of {}'.format(profile_name)):\\n self.download_tagged(profile, fast_update=fast_update, post_filter=post_filter)\\n\\n # Download IGTV, if requested\\n if igtv:\\n with self.context.error_catcher('Download IGTV of {}'.format(profile_name)):\\n self.download_igtv(profile, fast_update=fast_update, post_filter=post_filter)\\n\\n # Download highlights, if requested\\n if highlights:\\n with self.context.error_catcher('Download highlights of {}'.format(profile_name)):\\n self.download_highlights(profile, fast_update=fast_update, storyitem_filter=storyitem_filter)\\n\\n # Iterate over pictures and download them\\n if posts:\\n self.context.log(\\\"Retrieving posts from profile {}.\\\".format(profile_name))\\n self.posts_download_loop_custom(profile.get_posts(), profile_name, fast_update, date_filter, post_filter,\\n total_count=profile.mediacount)\\n\\n if stories and profiles:\\n with self.context.error_catcher(\\\"Download stories\\\"):\\n self.context.log(\\\"Downloading stories\\\")\\n self.download_stories(userids=list(profiles), fast_update=fast_update, filename_target=None,\\n storyitem_filter=storyitem_filter)\",\n \"def read_pardus_profiles(self):\\n\\n self.lan_config = ConfigParser.ConfigParser()\\n self.lan_config.read(self.lan_config_path)\\n connection_type = \\\"802-3-ethernet\\\"\\n for section in self.lan_config.sections():\\n lan_settings = {}\\n for option in self.lan_config.options(section):\\n if option == \\\"device\\\":\\n #To strip device name from long device string\\n lan_settings[option] = self.lan_config.get(section, option).split(\\\"_\\\")[-1]\\n else:\\n lan_settings[option] = self.lan_config.get(section, option)\\n p = PardusNetworkProfile(section, connection_type, lan_settings)\\n self.pardus_profiles.append(p)\\n\\n self.wlan_config = ConfigParser.ConfigParser()\\n self.wlan_config.read(self.wlan_config_path)\\n connection_type = \\\"802-11-wireless\\\"\\n for section in self.wlan_config.sections():\\n wlan_settings = {}\\n for option in self.wlan_config.options(section):\\n if option == \\\"device\\\":\\n wlan_settings[option] = self.wlan_config.get(section, option).split(\\\"_\\\")[-1]\\n else:\\n wlan_settings[option] = self.wlan_config.get(section, option)\\n p = PardusNetworkProfile(section, connection_type, wlan_settings)\\n self.pardus_profiles.append(p)\",\n \"def GetAllProfiles(self):\\n profiles = []\\n feed_uri = self._gd_client.GetFeedUri('profiles')\\n while feed_uri:\\n feed = self._gd_client.GetProfilesFeed(uri=feed_uri)\\n profiles.extend(feed.entry)\\n feed_uri = feed.FindNextLink()\\n self._profiles = profiles\",\n \"def get(self):\\n self.response.headers['Content-Type'] = 'application/json'\\n self.response.out.write(self._robot.GetProfileJson())\",\n \"def parse_profile_list_page(page):\\n page_tree = etree.HTML(page)\\n profile_list = page_tree.xpath(\\n '//table[@class=\\\"table_header\\\"]/tr[position() > 4 and position() < (last() - 3)]/td/node()[1]')\\n if profile_list:\\n profile_list[0] = \\\"main\\\"\\n return [text.strip() for text in profile_list]\\n else:\\n raise ProfileException(\\\"Failed to get profile list\\\")\",\n \"def profile():\\n\\n if not session.get('oauth_token'):\\n return redirect(url_for('login'))\\n tokenString = \\\"bearer {0}\\\".format(session['oauth_token']['access_token'])\\n headers = {\\\"Authorization\\\": tokenString}\\n profileInfo = {'access_token': session['oauth_token']['access_token']}\\n\\n # get user summary\\n userinfourl = '{}/userinfo'.format(baseUAAurl)\\n userinfo = json.loads(requests.get(\\n userinfourl, headers=headers, verify=False).text)\\n session['userinfo'] = userinfo\\n profileInfo['userinfo'] = json.dumps(session['userinfo'])\\n\\n # Method 1 : get user roles by orgs and space\\n usersummaryurl = '{0}/v2/users/{1}/summary'.format(\\n baseAPIurl, userinfo['user_id'])\\n usersummary = json.loads(requests.get(\\n usersummaryurl, headers=headers, verify=False).text)\\n\\n if usersummary.get('entity'):\\n spaceWiseUserRoles = getSpaceWiseUserRoles(usersummary['entity'])\\n else:\\n # Method 2 : get user roles by orgs and space\\n spaceWiseUserRoles = {}\\n spaceurl = baseAPIurl + '/v2/spaces'\\n spaceresponse = requests.get(spaceurl, headers=headers, verify=False)\\n space_json_data = json.loads(spaceresponse.text)\\n for spaceresource in space_json_data['resources']:\\n entity = spaceresource['entity']\\n spaceGuid = spaceresource['metadata']['guid']\\n\\n # get all auditors\\n auditorurl = baseAPIurl + entity['auditors_url']\\n auditorresponse = json.loads(requests.get(\\n auditorurl, headers=headers, verify=False).text)\\n if isInThisRole(auditorresponse, userinfo['user_name']):\\n spaceWiseUserRoles[spaceGuid] = {\\n 'role': 'auditor',\\n 'name': spaceresource['entity']['name']\\n }\\n\\n # get all developers\\n devurl = baseAPIurl + entity['developers_url']\\n devresponse = json.loads(requests.get(\\n devurl, headers=headers, verify=False).text)\\n if isInThisRole(devresponse, userinfo['user_name']):\\n spaceWiseUserRoles[spaceGuid] = {\\n 'role': 'developer',\\n 'name': spaceresource['entity']['name']\\n }\\n\\n # get all managers\\n managerurl = baseAPIurl + entity['managers_url']\\n managerresponse = json.loads(requests.get(\\n managerurl, headers=headers, verify=False).text)\\n if isInThisRole(managerresponse, userinfo['user_name']):\\n spaceWiseUserRoles[spaceGuid] = {\\n 'role': 'manager',\\n 'name': spaceresource['entity']['name']\\n }\\n\\n profileInfo['spaceWiseUserRoles'] = json.dumps(spaceWiseUserRoles)\\n session['spaceWiseUserRoles'] = spaceWiseUserRoles\\n\\n # get user apps from all spaces\\n url = '{}/v2/apps'.format(baseAPIurl)\\n response = requests.get(url, headers=headers, verify=False)\\n appsData = json.loads(response.text)\\n appsUrls = {}\\n\\n # user accessible app url\\n for resource in appsData['resources']:\\n routes_url = baseAPIurl + \\\\\\n resource['entity']['routes_url']\\n routes_url_response = json.loads(requests.get(\\n routes_url, headers=headers, verify=False).text)\\n for app in routes_url_response['resources']:\\n hostname = app['entity']['host']\\n appsUrls[hostname] = {\\n 'url': 'http://{}.local.pcfdev.io'.format(hostname),\\n 'space_guid': app['entity']['space_guid'],\\n 'userRole': getSpaceRole(spaceWiseUserRoles, app['entity'][\\n 'space_guid'], userinfo['user_name'])}\\n profileInfo['apps'] = appsUrls\\n\\n organization_guid = getOrganizationId(session, appsData)\\n profileInfo['org_id'] = organization_guid\\n profileInfo['org_users'] = json.dumps(getOrganizationUsers(\\n session, organization_guid))\\n return render_template('profile.html', data=profileInfo)\",\n \"def load_profiles(file_path):\\r\\n profiles, lastused = load_profiles_from_file(file_path)\\r\\n\\r\\n if len(profiles) == 0:\\r\\n #Just in case\\r\\n profiles[\\\"Default\\\"] = Profile()\\r\\n profiles[\\\"Default\\\"].Name = \\\"Default\\\"\\r\\n #Some default templates\\r\\n profiles[\\\"Default\\\"].FileTemplate = \\\"{<series>}{ Vol.<volume>}{ #<number2>}{ (of <count2>)}{ ({<month>, }<year>)}\\\"\\r\\n profiles[\\\"Default\\\"].FolderTemplate = \\\"{<publisher>}\\\\{<imprint>}\\\\{<series>}{ (<startyear>{ <format>})}\\\"\\r\\n \\r\\n if not lastused:\\r\\n lastused = [profiles.keys()[0]]\\r\\n \\r\\n return profiles, lastused\",\n \"def fetch_my_profile(self, api_token: str) -> dict:\\n query = \\\"\\\"\\\"\\n query myProfile {\\n myProfile {\\n id\\n firstName\\n lastName\\n }\\n }\\n \\\"\\\"\\\"\\n path = jmespath.compile(\\n \\\"\\\"\\\"\\n data.myProfile.{\\n id: id\\n first_name: firstName\\n last_name: lastName\\n }\\n \\\"\\\"\\\"\\n )\\n\\n data = self.do_query(query, api_token=api_token)\\n\\n parsed_data = path.search(data)\\n self.contains_keys(parsed_data, [\\\"id\\\", \\\"first_name\\\", \\\"last_name\\\"])\\n return parsed_data\",\n \"def get_student_profile_data():\\n # pylint: disable=no-member\\n try:\\n regexp_string = self.regexp_from_users_included_email(self.users_included_email)\\n re.compile(regexp_string)\\n users = self.students_for_course(regexp_string)\\n except:\\n log.info(\\\"regexp is invalid: '%s'\\\", regexp_string)\\n users = []\\n\\n for user in users:\\n student_id = anonymous_id_for_user(user, self.course_id)\\n profile = user.profile\\n\\n vip = self.get_vip(user)\\n image_url = None\\n if vip:\\n image_url = \\\"https://my.imd.org/api/profile/{}/profile-picture-header\\\".format(vip)\\n else:\\n if self.is_course_staff:\\n image_url = self.runtime.local_resource_url(self, 'public/images/profile-picture-header-no-vip.gif')\\n else:\\n image_url = self.runtime.local_resource_url(self, 'public/images/profile-picture-header.gif')\\n\\n cohort_name = None\\n if (self.is_course_cohorted(self.course_id)):\\n cohort_name = self.get_cohort(user, self.course_id).name\\n\\n yield {\\n 'student_id': student_id,\\n 'username': user.username,\\n 'fullname': profile.name,\\n 'vip': vip,\\n 'image_url': image_url,\\n 'email': user.email,\\n 'cohort_name': cohort_name,\\n }\",\n \"def save_to_jsonl(self, profile_file):\\n with open(profile_file, \\\"w\\\") as out_f:\\n for qid in tqdm(self.get_all_qids(), disable=not self.verbose):\\n mentions = self.get_mentions_with_scores(qid)\\n title = self.get_title(qid)\\n desc = self.get_desc(qid)\\n ent_type_sys = {}\\n for type_sys in self._type_systems:\\n types = self.get_types(qid, type_sys)\\n if len(types) > 0:\\n ent_type_sys[type_sys] = types\\n relations = []\\n all_connections = self.get_all_connections(qid)\\n for rel in all_connections:\\n for qid2 in all_connections[rel]:\\n relations.append({\\\"relation\\\": rel, \\\"object\\\": qid2})\\n ent_obj = {\\n \\\"entity_id\\\": qid,\\n \\\"mentions\\\": mentions,\\n \\\"title\\\": title,\\n }\\n # Add description if nonempty\\n if len(desc) > 0:\\n ent_obj[\\\"description\\\"] = desc\\n if len(ent_type_sys) > 0:\\n ent_obj[\\\"types\\\"] = ent_type_sys\\n if len(relations) > 0:\\n ent_obj[\\\"relations\\\"] = relations\\n out_f.write(ujson.dumps(ent_obj) + \\\"\\\\n\\\")\",\n \"def process_raw(self, raw: str) -> ListStreamProfilesT:\\n data: ListStreamProfilesResponseT = orjson.loads(raw)\\n stream_profiles = data.get(\\\"data\\\", {}).get(\\\"streamProfile\\\", [])\\n return {\\n stream_profile[\\\"name\\\"]: StreamProfile(\\n id=stream_profile[\\\"name\\\"],\\n name=stream_profile[\\\"name\\\"],\\n description=stream_profile[\\\"description\\\"],\\n parameters=stream_profile[\\\"parameters\\\"],\\n )\\n for stream_profile in stream_profiles\\n }\",\n \"def parse(self):\\n details = self.details()\\n\\n return Profile(\\n book_id=self.book_id,\\n title=self.title(),\\n user_id=self.user_id(),\\n username=self.username(),\\n summary=self.summary(),\\n published=self.published(),\\n updated=self.updated(),\\n **details\\n )\",\n \"def parse_profiles(profiles):\\n if type(profiles) == list:\\n return [\\n x\\n for x in [ProfileSettingParser.parse(x) for x in profiles]\\n if x is not None\\n ]\\n else:\\n return []\",\n \"def handle_current_profile(api_pipeline, current_profile):\\n\\n current_profile_saved_tweets = current_profile.tweet_set.all()\\n profiles_tweets = api_pipeline.get_recent_tweets_for_author(current_profile.username, how_many=5)\\n for profiles_tweet in profiles_tweets:\\n if profiles_tweet not in current_profile_saved_tweets.filter(save_date=datetime.datetime.today().date()):\\n profiles_tweet.save()\\n current_profile.tweet_set.add(profiles_tweet)\\n current_profile.save()\\n profiles_tweets.sort(key=lambda tweet: (tweet.retweets, tweet.likes), reverse=True)\\n profiles_tweets_chart = PlotPainter.plot_tweets(profiles_tweets) if profiles_tweets else None\\n return profiles_tweets, profiles_tweets_chart\",\n \"def scrape_profile(username: str, user_agents: typing.List[str]) -> typing.List[typing.Dict]:\\n try:\\n r = requests.get(f\\\"https://venmo.com/{username}\\\",\\n headers={\\\"User-Agent\\\": random.choice(user_agents)})\\n except requests.exceptions.ConnectionError:\\n print(\\\"Error, unable to connect to host... check your network connection\\\")\\n return 1\\n\\n soup = BeautifulSoup(r.text, \\\"lxml\\\")\\n profile_data = []\\n\\n transactions = soup.find_all(\\\"div\\\", attrs={\\\"class\\\": \\\"single-payment content-wrap\\\"})\\n\\n for transaction in transactions:\\n send, recv = transaction.find_all(\\\"a\\\")\\n send, recv = send.text, recv.text\\n message = transaction.find_all(\\\"div\\\", attrs={\\\"class\\\": \\\"paymentpage-text m_five_t\\\"})[0].text\\n date = date_parser.parse(transaction.find_all(\\\"div\\\", attrs={\\\"class\\\": \\\"date\\\"})[0].text.split('-')[0], fuzzy=True)\\n export_message = f\\\"{send} paid {recv} on {date.replace(microsecond=0).isoformat()} for {message}\\\"\\n profile_data.append({\\\"sender\\\": send,\\n \\\"recipient\\\": recv,\\n \\\"date\\\": date.replace(microsecond=0).isoformat(),\\n \\\"export_message\\\": export_message})\\n\\n return profile_data\",\n \"def profiles_names(self):\\n url = get_url('profiles')\\n response = self._get(url)\\n raise_on_error(response)\\n return response.json()\",\n \"async def all(self):\\n log('retrieving profile cards..')\\n start = time.monotonic()\\n profiles = await asyncio.gather(*list(\\n map(lambda profile: self.retrieve(str(profile['id'])), await self.list())\\n ))\\n elapsed = \\\"%0.2fs\\\" % (time.monotonic() - start,)\\n log(\\\"retrieved {} profile cards in {}\\\".format(len(profiles), elapsed))\\n return {\\n \\\"hits\\\": len(profiles),\\n \\\"updated\\\": time.time() * 1000,\\n \\\"time\\\": elapsed,\\n \\\"applicants\\\": list(filter(None, map(self.parse, profiles)))\\n }\",\n \"def test_list_profiles(self):\\n response = self.client.get(self.url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(len(response.data['results']), 1)\",\n \"def get_profile(self,fields=('id','first-name','last-name','headline','summary')):\\n\\n if not self._access_token:\\n raise FBError(\\\"Authentication needed!\\\")\\n \\n token = oauth.Token(self._access_token['oauth_token'], self._access_token['oauth_token_secret'])\\n client = oauth.Client(self.consumer, token)\\n profile_url = self.profile_url % \\\",\\\".join(fields)\\n resp, content = client.request(profile_url,headers={\\\"x-li-format\\\":'json'})\\n \\n if resp['status'] != '200':\\n print resp\\n raise FBError(\\\"Invalid response %s.\\\" % resp['status'])\\n \\n try:\\n return json.loads(content)\\n except Exception, e:\\n raise FBError(\\\"Invalid json %s.\\\" % unicode(e))\",\n \"def refresh_session_data (self, account):\\n # load the profiles page (to verify the user)\\n response = self._session_get(component='profiles')\\n # parse out the needed inline information\\n only_script_tags = SoupStrainer('script')\\n page_soup = BeautifulSoup(response.text, 'html.parser', parse_only=only_script_tags)\\n page_data = self._parse_page_contents(page_soup=page_soup)\\n account_hash = self._generate_account_hash(account=account)\\n self._save_data(filename=self.data_path + '_' + account_hash)\",\n \"def _username_to_profile(self, username: str) -> Dict[str, Any]:\\n\\n base_url = self.keys.pre_profile + username + self.keys.rank_token + self.keys.post_profile\\n\\n # Build the page source url for the given user's account\\n con = urllib.request.urlopen(base_url)\\n user_profile = con.read().decode('utf-8')\\n\\n # Convert the webpage to a profile JSON\\n profile: dict = json.loads(str(user_profile))\\n return profile\",\n \"def profile(userid):\\n # get bookmarked content\\n bookmarks = db_bookmarks.find({\\\"user\\\": userid})\\n bookmarks = [{\\\"search_term\\\": db_search_terms.find_one({\\\"_id\\\": b[\\\"search_id\\\"]}).get(\\\"value\\\"), \\\"source\\\": b[\\\"source\\\"], \\\"url\\\": b[\\\"url\\\"], \\\"date_saved\\\": b[\\\"date_saved\\\"].strftime(\\\"%H:%M %B %d, %Y\\\")} for b in bookmarks]\\n # get previous searches\\n searches = db_searches.find({\\\"user\\\": userid})\\n searches = [{\\\"search_term\\\": db_search_terms.find_one({\\\"_id\\\": s[\\\"search_id\\\"]}).get(\\\"value\\\"), \\\"category\\\": s[\\\"category\\\"], \\\"datetime\\\": s[\\\"datetime\\\"].strftime(\\\"%H:%M %B %d, %Y\\\")} for s in searches]\\n # get byod\\n byod = db_byod.find({\\\"user\\\": userid})\\n byod = [{\\\"content_type\\\": b[\\\"content_type\\\"], \\\"doc_name\\\": b[\\\"doc_name\\\"], \\\"text\\\": b[\\\"text\\\"], \\\"key_points\\\": b[\\\"key_points\\\"], \\\"datetime_uploaded\\\": b[\\\"datetime_uploaded\\\"].strftime(\\\"%H:%M %B %d, %Y\\\")} for b in byod]\\n # get highlightds\\n # highlights = db_highlights.find({\\\"user\\\": userid})\\n # highlights = [{\\\"search_term\\\": db_search_terms.find_one({\\\"_id\\\": h[\\\"search_id\\\"]}).get(\\\"value\\\"), \\\"highlighted_word\\\": h[\\\"highlighted_word\\\"], \\\"results\\\": h[\\\"results\\\"], \\\"date_saved\\\": h[\\\"date_saved\\\"].strftime(\\\"%H:%M %B %d, %Y\\\")} for h in highlights]\\n jsonob = jsonify(bookmarks=bookmarks,\\n searches=searches,\\n byod=byod)\\n return jsonob\",\n \"def profiles(self):\\n if not self._profiles:\\n self.GetAllProfiles()\\n return self._profiles\",\n \"def _sloppy_parse_inline_data (self, scripts):\\n inline_data = {};\\n for script in scripts:\\n contents = str(script.contents[0])\\n important_data = ['authURL', 'API_BASE_URL', 'API_ROOT', 'BUILD_IDENTIFIER', 'ICHNAEA_ROOT', 'gpsModel', 'guid', 'esn']\\n res = {}\\n for key in important_data:\\n _res = self._sloppy_parse_user_and_api_data(key, contents)\\n if _res != None:\\n res.update({key: _res})\\n if res != {}:\\n inline_data.update(res)\\n\\n # parse profiles\\n profiles = self._sloppy_parse_profiles(contents)\\n avatars = self._sloppy_parse_avatars(contents)\\n if profiles != None:\\n inline_data.update({'profiles': profiles})\\n if avatars != None:\\n inline_data.update(avatars)\\n return inline_data\",\n \"def import_profiles(file_path):\\r\\n profiles, lastused = load_profiles_from_file(file_path)\\r\\n\\r\\n return profiles\",\n \"def fetchJson(url):\",\n \"def fetch_profile(access_token):\\n\\n headers = {\\\"Authorization\\\": \\\"Bearer \\\" + access_token}\\n\\n # Fetch athlete profile\\n r = requests.get(API_URL + \\\"/athlete\\\", headers=headers)\\n profile = r.json()\\n if \\\"errors\\\" in profile:\\n raise AuthError(profile[\\\"message\\\"])\\n\\n return {\\n \\\"firstName\\\": profile[\\\"firstname\\\"],\\n \\\"lastName\\\": profile[\\\"lastname\\\"],\\n \\\"imgUrl\\\": profile[\\\"profile\\\"],\\n \\\"profileUrl\\\": \\\"https://www.strava.com/athletes/{}\\\".format(profile[\\\"id\\\"]),\\n }\",\n \"def process_data (file):\\n\\n twitter_data = {}\\n\\n username = file.readline().strip()\\n\\n while username != '': # leave when all lines of the file have been read\\n # initialize everything we need for entering data\\n twitter_data [username] = {}\\n twitter_data [username] ['bio'] = ''\\n twitter_data [username] ['following'] = []\\n\\n twitter_data [username] ['name'] = file.readline().strip()\\n twitter_data [username] ['location'] = file.readline().strip()\\n twitter_data [username] ['web'] = file.readline().strip()\\n\\n bio = file.readline() # get the first line of the bio\\n while bio.strip() != 'ENDBIO': # go until we get the line 'ENDBIO'\\n twitter_data [username] ['bio'] += bio.strip() + '\\\\n'\\n bio = file.readline()\\n # we dont want the final '\\\\n' in the bio so we splice it\\n twitter_data [username] ['bio'] = twitter_data [username] ['bio'] [:-1]\\n\\n following = file.readline() # get the first element for the list\\n while following.strip() != 'END': # go until we get the line 'END'\\n twitter_data [username] ['following'].append (following.strip())\\n following = file.readline()\\n\\n username = file.readline().strip()\\n return twitter_data\",\n \"def get_parsed_data():\\n\\n echonest_data_files = [f for f in os.listdir('.') if re.match(\\\"^echonest_[\\\\w]+.txt$\\\", f)]\\n\\n # Setting up header with user id and attributes\\n header = ['user_id']\\n header.extend(ATTRIBUTES)\\n\\n # printing header to standard out\\n print \\\",\\\".join(header) \\n\\n # Processing each file to obtain parsed data\\n for data_file in echonest_data_files:\\n user_id = data_file[9:-4] # strip file prefix/suffix to get username/id\\n parse_echonest_data_file(data_file, user_id)\",\n \"def test_list_profiles(\\n api_client, enable_premium_requirement, profile_factory, user_factory\\n):\\n password = \\\"password\\\"\\n user = user_factory(has_premium=True, password=password)\\n api_client.log_in(user.primary_email.email, password)\\n\\n profile = profile_factory(km_user__user=user)\\n\\n url = f\\\"/know-me/users/{user.km_user.pk}/profiles/\\\"\\n response = api_client.get(url)\\n\\n assert response.status_code == status.HTTP_200_OK\\n assert response.json() == [\\n {\\n \\\"id\\\": profile.pk,\\n \\\"url\\\": api_client.build_full_url(\\n f\\\"/know-me/profile/profiles/{profile.pk}/\\\"\\n ),\\n \\\"created_at\\\": serialized_time(profile.created_at),\\n \\\"updated_at\\\": serialized_time(profile.updated_at),\\n \\\"is_private\\\": profile.is_private,\\n \\\"name\\\": profile.name,\\n \\\"permissions\\\": {\\\"read\\\": True, \\\"write\\\": True},\\n \\\"topics_url\\\": api_client.build_full_url(\\n f\\\"/know-me/profile/profiles/{profile.pk}/topics/\\\"\\n ),\\n }\\n ]\",\n \"def _parse_user_data (self, netflix_page_data):\\n user_data = {};\\n important_fields = [\\n 'authURL',\\n 'gpsModel',\\n 'guid'\\n ]\\n\\n # values are accessible via dict (sloppy parsing successfull)\\n if type(netflix_page_data) == dict:\\n for important_field in important_fields:\\n user_data.update({important_field: netflix_page_data.get(important_field, '')})\\n return user_data\\n\\n # values are stored in lists (returned from JS parser)\\n for item in netflix_page_data:\\n if 'memberContext' in dict(item).keys():\\n for important_field in important_fields:\\n user_data.update({important_field: item['memberContext']['data']['userInfo'][important_field]})\\n\\n return user_data\",\n \"def scrape_story_metadata(self, story_id):\\n url = '{0}/s/{1}'.format(self.base_url, story_id)\\n result = requests.get(url)\\n html = result.content\\n #print html \\n soup = BeautifulSoup(html, self.parser)\\n\\n # print soup\\n try:\\n pre_story_links = soup.find(id='pre_story_links').find_all('a')\\n except AttributeError:\\n pre_story_links = None\\n if re.search(r\\\"var userid = (.*);\\\", str(soup)) is None:\\n author_id = \\\"0\\\"\\n else: \\n author_id = int(re.search(r\\\"var userid = (.*);\\\", str(soup)).groups()[0]);\\n #print re.search(r\\\"var title = (.*);\\\", str(soup))\\n if re.search(r\\\"var title = (.*);\\\", str(soup)) is None:\\n title = \\\"NO-TITLE\\\"\\n else:\\n title = re.search(r\\\"var title = (.*);\\\", str(soup)).groups()[0];\\n title = unquote_plus(title)[1:-1]\\n metadata_div = soup.find(id='profile_top')\\n# times = metadata_div.find_all(attrs={'data-xutime':True})\\n# metadata_text = metadata_div.find(class_='xgray xcontrast_txt').text\\n# metadata_parts = metadata_text.split('-')\\n# genres = self.get_genres(metadata_parts[2].strip())\\n metadata = {\\n 'id': story_id,\\n# 'canon_type': pre_story_links[0].text,\\n# 'canon': pre_story_links[1].text,\\n 'author_id': author_id,\\n 'title': title,\\n# 'updated': int(times[0]['data-xutime']),\\n# 'published': int(times[1]['data-xutime']),\\n# 'lang': metadata_parts[1].strip(),\\n# 'genres': genres\\n }\\n \\\"\\\"\\\"\\n for parts in metadata_parts:\\n parts = parts.strip()\\n tag_and_val = parts.split(':')\\n if len(tag_and_val) != 2:\\n continue\\n tag, val = tag_and_val\\n tag = tag.strip().lower()\\n if tag not in metadata:\\n val = val.strip()\\n try:\\n val = int(val.replace(',', ''))\\n metadata['num_'+tag] = val\\n except:\\n metadata[tag] = val\\n if 'status' not in metadata:\\n metadata['status'] = 'Incomplete'\\n \\\"\\\"\\\"\\n return metadata\",\n \"def read_file(reviews):\\n\\n for review in reviews:\\n yield json.loads(review)[\\\"text\\\"]\",\n \"def parse(self, response, **kwargs):\\n\\n key_url = response._get_url().rsplit('/')[-2] # get the author nickname after the last slash\\n name = response.xpath('//*[@id=\\\"woe\\\"]/div[2]/div/div[1]/div[2]/h3/text()').extract_first()\\n job_title = response.xpath('//*[@id=\\\"woe\\\"]/div[2]/div/div[1]/div[2]/p/text()').extract_first()\\n linkedin_url = response.xpath('//*[@id=\\\"woe\\\"]/div[2]/div/div[1]/div[1]/ul/li/a/@href').extract_first()\\n date_title_part = response.xpath('//*[@id=\\\"woe\\\"]/div[2]/div/div[2]/div[position() > 1]')\\n for row in date_title_part:\\n row_extracted = row.extract()\\n art_date = Selector(text=row_extracted).xpath('///span/text()').extract_first()\\n date_formatted = conf.parse_dtts(art_date, '%B %d, %Y')\\n article_title = Selector(text=row_extracted).xpath('///a/text()').extract_first()\\n article_url = Selector(text=row_extracted).xpath('///a/@href').extract_first()\\n\\n if date_formatted >= conf.crawl_date[0].get('LastExecutionDate'):\\n conf.write_data_append('authors.json', json.dumps({'keyUrl': key_url,\\n 'name': name,\\n 'jobTitle': job_title,\\n 'linkedinUrl': linkedin_url,\\n 'date': date_formatted,\\n 'article_title': article_title,\\n 'article_url': article_url}))\",\n \"def get_all_profiles(self) -> List[Profile]:\\n return [self.model.parse_obj(profile) for profile in self.read_records(SyncMode.full_refresh)]\",\n \"def profiles():\\n images = get_uploaded_images()\\n records = db.session.query(UserProfile).all()\\n return render_template('profiles.html', images=images, records =records)\",\n \"def profiles(self, profiles):\\n\\n self._profiles = profiles\",\n \"def _sloppy_parse_avatars (self, contents):\\n avatars_start = contents.find('\\\"nf\\\":')\\n avatars_list_start = contents.find('\\\"profiles\\\"')\\n if int(avatars_start) > -1 and int(avatars_list_start) > -1:\\n try:\\n try:\\n return json.loads('{' + contents[avatars_start:avatars_list_start-2].decode('string_escape') + '}')\\n except ValueError, e:\\n return None\\n except TypeError, e:\\n return None\\n return None\",\n \"def profiles_search(pages: list, session: requests.Session):\\n result = []\\n for page in pages:\\n sleep(1)\\n soup = BeautifulSoup(session.get(page).text, \\\"lxml\\\")\\n table = soup.find(\\\"table\\\", class_=\\\"seaman-list-table va-top seaman-list-table-2\\\")\\n for rows in table.find_all(\\\"tr\\\")[1:]:\\n for column in rows.find(\\\"td\\\", 'seaman-name-td'):\\n try:\\n result.append(\\\"https://ukrcrewing.com.ua\\\" + column[\\\"href\\\"])\\n except TypeError:\\n continue\\n logger.info(f'{len(result)} profiles detected')\\n return result\",\n \"def get_profiles(self):\\n # print(self.uir) #checkpoint\\n if os.path.isdir(self.uir+\\\"/profiles\\\"):\\n profiles=os.listdir(self.uir+\\\"/profiles\\\")\\n # print(profiles) #checkpoint\\n for profile in profiles:\\n wsadmin=self.uir+\\\"/profiles/\\\"+profile+\\\"/bin/wsadmin.bat\\\"\\n if os.path.isfile(wsadmin): #check for wsadmin.bat.\\n self.profiles.append(self.uir+\\\"/profiles/\\\"+profile)\\n\\n else: print(self.uir+' Instance does not have \\\"profile\\\" folder in '+self.uir)\\n return\",\n \"def prefetch_login(self):\\n self.profiles = []\\n email = self.credentials.get('email', '')\\n password = self.credentials.get('password', '')\\n if email != '' and password != '':\\n if self.netflix_session.is_logged_in(account=self.credentials):\\n refresh_session = self.netflix_session.refresh_session_data(\\n account=self.credentials)\\n if refresh_session:\\n self.profiles = self.netflix_session.profiles\\n else:\\n if self.netflix_session.login(account=self.credentials):\\n self.profiles = self.netflix_session.profiles\\n self.kodi_helper.set_esn(self.netflix_session.esn)\",\n \"def parse():\\n with open('src/wator/Properties.json') as data_file:\\n data = json.load(data_file)\\n return data\",\n \"def _load_profile(self, profile_name, cwd):\\n\\n profile_path = self.get_profile_path(profile_name, cwd)\\n try:\\n text = load_user_encoded(profile_path)\\n except Exception as e:\\n raise ConanException(f\\\"Cannot load profile:\\\\n{e}\\\")\\n\\n # All profiles will be now rendered with jinja2 as first pass\\n base_path = os.path.dirname(profile_path)\\n file_path = os.path.basename(profile_path)\\n context = {\\\"platform\\\": platform,\\n \\\"os\\\": os,\\n \\\"profile_dir\\\": base_path,\\n \\\"profile_name\\\": file_path,\\n \\\"conan_version\\\": conan_version}\\n rtemplate = Environment(loader=FileSystemLoader(base_path)).from_string(text)\\n text = rtemplate.render(context)\\n\\n try:\\n return self._recurse_load_profile(text, profile_path)\\n except ConanException as exc:\\n raise ConanException(\\\"Error reading '%s' profile: %s\\\" % (profile_name, exc))\",\n \"def call(self, failed=[], deletion=False):\\n\\n if deletion == False:\\n # NOTE we won't it to run only after the delete has completed\\n return\\n\\n log.debug(\\\"Reading profiles from progran\\\")\\n onos = ProgranHelpers.get_progran_onos_info(self.model_accessor)\\n profile_url = \\\"http://%s:%s/onos/progran/profile/\\\" % (onos['url'], onos['port'])\\n r = requests.get(profile_url, auth=HTTPBasicAuth(onos['username'], onos['password']))\\n res = r.json()['ProfileArray']\\n\\n\\n # remove default profiles\\n res = [p for p in res if \\\"Default\\\" not in p['Name']]\\n pnames = [p['Name'] for p in res]\\n log.debug(\\\"Received Profiles: \\\", profiles=pnames)\\n\\n field_mapping = {\\n 'Name': 'name',\\n 'Start': 'start',\\n 'End': 'end',\\n }\\n\\n field_transformations = {\\n 'Start': ProgranHelpers.date_to_time,\\n 'End': ProgranHelpers.date_to_time\\n }\\n\\n handover_mapping = {\\n 'A5Hysteresis': 'HysteresisA5',\\n 'A3Hysteresis': 'HysteresisA3'\\n }\\n\\n updated_profiles = []\\n\\n for p in res:\\n\\n\\n # checking for profiles\\n try:\\n si = ProgranServiceInstance.objects.get(name=p['Name'])\\n log.debug(\\\"Profile %s already exists, updating it\\\" % p['Name'])\\n\\n except IndexError:\\n si = ProgranServiceInstance()\\n\\n si.created_by = \\\"Progran\\\"\\n\\n log.debug(\\\"Profile %s is new, creating it\\\" % p['Name'])\\n\\n if not si.is_new:\\n # update IMSI association\\n xos_imsis_for_profile = [i.subscriber_service_instance.leaf_model for i in si.provided_links.all()]\\n progran_imsis_for_profile = p['IMSIRuleArray']\\n\\n log.debug(\\\"List of imsis for profile %s in XOS\\\" % p[\\\"Name\\\"], imsis=xos_imsis_for_profile)\\n log.debug(\\\"List of imsis for profile %s in ONOS\\\" % p[\\\"Name\\\"], imsis=progran_imsis_for_profile)\\n\\n for i in xos_imsis_for_profile:\\n if not i.imsi_number in progran_imsis_for_profile:\\n log.debug(\\\"Removing Imsi %s from profile %s\\\" % (i.imsi_number, p['Name']))\\n\\n imsi_link = ServiceInstanceLink.objects.get(subscriber_service_instance_id=i.id)\\n\\n # NOTE: this model has already been removed from the backend, no need to synchronize\\n imsi_link.backend_need_delete = False\\n imsi_link.no_sync = True\\n imsi_link.save() # we need to save it to avoid a synchronization loop\\n\\n imsi_link.delete()\\n else:\\n # remove from imsi list coming from progran everything we already know about\\n progran_imsis_for_profile.remove(i.imsi_number)\\n\\n for i in progran_imsis_for_profile:\\n log.debug(\\\"Adding Imsi %s to profile %s\\\" % (i, p['Name']))\\n imsi = MCordSubscriberInstance.objects.get(imsi_number=i)\\n imsi_to_profile = ServiceInstanceLink(provider_service_instance=si,\\n subscriber_service_instance=imsi)\\n imsi_to_profile.save()\\n\\n # if the model has not been synchronized yet, skip it\\n if not si.is_new and si.no_sync is False:\\n log.debug(\\\"Skipping profile %s as not synchronized\\\" % p['Name'])\\n # NOTE add it to the removed profiles to avoid deletion (this is ugly, I know)\\n updated_profiles.append(si.name)\\n continue\\n\\n # ugly fix\\n if 'AdmControl' in p.keys():\\n p['AdmControl'] = str(p['AdmControl'])\\n\\n si = ProgranHelpers.update_fields(si, p, field_mapping, field_transformations)\\n\\n # checking for handovers\\n handover_dict = p['Handover']\\n handover_dict = ProgranHelpers.convert_keys(handover_dict, handover_mapping)\\n del p['Handover']\\n\\n if si.handover_id:\\n handover = si.handover\\n log.debug(\\\"handover already exists, updating it\\\", handover=handover_dict)\\n else:\\n handover = Handover()\\n handover = ProgranHelpers.update_fields(handover, handover_dict)\\n log.debug(\\\"handover is new, creating it\\\", handover=handover_dict)\\n handover.created_by = \\\"Progran\\\"\\n\\n handover = ProgranHelpers.update_fields(handover, handover_dict)\\n handover.save()\\n\\n # Assigning handover to profile\\n si.handover = handover\\n\\n # si.backend_status = \\\"OK\\\"\\n # si.backend_code = 1\\n\\n si.no_sync = True\\n si.previously_sync = True\\n\\n if p[\\\"MMECfg\\\"]:\\n si.mmeip = str(p[\\\"MMECfg\\\"][\\\"IPAddr\\\"])\\n si.mmeport = str(p[\\\"MMECfg\\\"][\\\"Port\\\"])\\n\\n si.enacted = time.mktime(datetime.datetime.now().timetuple())\\n\\n si.save()\\n\\n updated_profiles.append(si.name)\\n\\n existing_profiles = [p.name for p in ProgranServiceInstance.objects.all() if not p.is_new]\\n deleted_profiles = ProgranHelpers.list_diff(existing_profiles, updated_profiles)\\n\\n if len(deleted_profiles) > 0:\\n for p in deleted_profiles:\\n si = ProgranServiceInstance.objects.get(name=p)\\n if si.created_by == 'XOS' and si.previously_sync == False:\\n # don't delete if the profile has been created by XOS and it hasn't been sync'ed yet\\n continue\\n # TODO delete also the associated Handover\\n log.debug(\\\"Profiles %s have been removed in progran, removing it from XOS\\\" % str(p))\\n si.delete()\",\n \"def get_strava_profile(self, create_csv = True, profile_csv_path =\\n 'profile.csv'):\\n # Get profile info. You need the URL, access_token and header information.\\n access_token = self.strava_tokens['access_token']\\n url = 'https://www.strava.com/api/v3/athlete'\\n hdrs = {'Authorization': 'Bearer ' + access_token}\\n\\n # Make a request object.\\n try :\\n r = requests.get(url, headers=hdrs)\\n except requests.exceptions.RequestException as e:\\n print (\\\"Failed to make the API request when attempting to get profile. Received this exception:\\\", e)\\n return None\\n\\n # Create a dataframe from the json content. This is just the profile info.\\n # You have to orient by index because some of the fields may be empty\\n # which causes a ValueError(\\\"arrays must all be same length\\\") otherwise.\\n dataframe = pd.DataFrame.from_dict(r.json(), orient=\\\"index\\\")\\n # Transpose it if you prefer to see it in the opposite orientation.\\n #dataframe = dataframe.transpose()\\n\\n # Now convert the dataframe to a csv file if desired.\\n if create_csv:\\n dataframe.to_csv(profile_csv_path)\\n\\n return dataframe\",\n \"def test_uu_test(self):\\n\\t\\tbase = \\\"http://localhost:8000\\\"\\n\\t\\t# Pro1 and Pro1a have no interests in common, Pro1 and Pro2 have two interests in common\\n\\t\\tpro1 = requests.post(base+self.uri_creation, data=made_up_profiles(1), headers={'Content-Type': 'application/json'})\\n\\t\\tpro1a = requests.post(base+self.uri_creation, data=made_up_profiles(0), headers={'Content-Type': 'application/json'})\\n\\t\\tpro2= requests.post(base+self.uri_creation, data=made_up_profiles(2), headers={'Content-Type': 'application/json'})\\n\\n\\t\\tpro1_token = json.loads(pro1.content.decode('utf-8'))['profile']\\n\\t\\tpro1a_token = json.loads(pro1a.content.decode('utf-8'))['profile']\\n\\t\\tpro2_token = json.loads(pro2.content.decode('utf-8'))['profile']\\n\\n\\t\\tpro1_auth = json.loads(requests.get(base+self.uri_auth+pro1_token+'/').content.decode('utf-8'))['auth']\\n\\t\\tpro1a_auth = json.loads(requests.get(base+self.uri_auth+pro1a_token+'/').content.decode('utf-8'))['auth']\\n\\t\\tpro2_auth = json.loads(requests.get(base+self.uri_auth+pro2_token+'/').content.decode('utf-8'))['auth']\\n\\n\\t\\trequests.post(base+self.uri_add_selectable+pro1_token+'/', data=page_two_objects(pro1_auth, 5), headers={'Content-Type': 'application/json'})\\n\\t\\trequests.post(base+self.uri_add_selectable+pro1a_token+'/', data=page_one_objects(pro1a_auth, 0), headers={'Content-Type': 'application/json'})\\n\\t\\trequests.post(base+self.uri_add_selectable+pro2_token+'/', data=page_one_objects(pro2_auth, 1), headers={'Content-Type': 'application/json'})\\n\\t\\tsuggestion_r = requests.post(base+self.uri_suggestion+pro1_token+'/', data=page_one(pro1_auth), headers={'Content-Type': 'application/json'})\\n\\t\\tcontx_a = ast.literal_eval(json.loads(suggestion_r.content.decode('utf-8'))['base'])[0]\\n\\t\\tcontx_b = ast.literal_eval(json.loads(suggestion_r.content.decode('utf-8'))['base'])[1]\\n\\t\\tcontx_flag = json.loads(suggestion_r.content.decode('utf-8'))['state']\\n\\t\\trequests.post(base+self.uri_add_selectable+pro1a_token+'/', data=page_two_objects(pro1a_auth, 3), headers={'Content-Type': 'application/json'})\\n\\t\\tsuggestion2_r = requests.post(base+self.uri_suggestion+pro1_token+'/', data=page_one(pro1_auth), headers={'Content-Type': 'application/json'})\\n\\t\\tcontx2_a = ast.literal_eval(json.loads(suggestion2_r.content.decode('utf-8'))['base'])[0]\\n\\t\\tcontx2_b = ast.literal_eval(json.loads(suggestion2_r.content.decode('utf-8'))['base'])[1]\\n\\t\\tself.assertTrue(contx2_a < contx_a)\\n\\t\\tself.assertTrue(contx2_b > contx_b)\\n\\t\\t# Element at index zero must become lower as it has a website in common with profile 1a\\n\\t\\t# Element at index one must become higher as it has a no website in common with profile 2\",\n \"def get_profiles(self):\\n profiles = [['Profile name', 'GUID']]\\n r = self.system_cursor.execute('{Call wtGetProfileList()}')\\n for row in r.fetchall():\\n profiles.append([row.PROFILE_NAME, row.PROFILE_GUID])\\n return profiles\",\n \"def merge_structural_profile(profile_path, merged_path):\\n def list_to_str(lst):\\n ''' Given a list, return the string of that list with tab separators\\n '''\\n return reduce( (lambda s, f: s + '\\\\t' + str(f)), lst, '')\\n\\n # external loop profile\\n E_path = profile_path+'E_profile.txt'\\n fEprofile = open(E_path)\\n Eprofiles = fEprofile.readlines()\\n\\n # hairpin loop profiles\\n H_path = profile_path+'H_profile.txt'\\n fHprofile = open(H_path)\\n Hprofiles = fHprofile.readlines()\\n\\n # internal loop profiles\\n I_path = profile_path+'I_profile.txt'\\n fIprofile = open(I_path)\\n Iprofiles = fIprofile.readlines()\\n\\n # multi-loop profiles\\n M_path = profile_path+ 'M_profile.txt'\\n fMprofile = open(M_path)\\n Mprofiles = fMprofile.readlines()\\n\\n num_seq = int(len(Eprofiles)/2)\\n\\n # parse into a single file\\n fhout = open(merged_path, 'w')\\n for i in range(num_seq):\\n id = Eprofiles[i*2].split()[0]\\n fhout.write(id+'\\\\n')\\n H_prob = Hprofiles[i*2+1].split()\\n I_prob = Iprofiles[i*2+1].split()\\n M_prob = Mprofiles[i*2+1].split()\\n E_prob = Eprofiles[i*2+1].split()\\n P_prob = map( (lambda a, b, c, d: 1-float(a)-float(b)-float(c)-float(d)), H_prob, I_prob, M_prob, E_prob)\\n fhout.write(list_to_str(P_prob[:len(P_prob)])+'\\\\n')\\n fhout.write(list_to_str(H_prob[:len(P_prob)])+'\\\\n')\\n fhout.write(list_to_str(I_prob[:len(P_prob)])+'\\\\n')\\n fhout.write(list_to_str(M_prob[:len(P_prob)])+'\\\\n')\\n fhout.write(list_to_str(E_prob[:len(P_prob)])+'\\\\n')\\n fhout.close()\\n\\n return num_seq\",\n \"def extract_data(file_name=default_file_name):\\n fille = abs_path(file_name)\\n try:\\n jfile = open(fille)\\n except FileNotFoundError:\\n create_file(fille)\\n jfile = open(fille)\\n data = json.load(jfile)\\n if(not 'Students' in data.keys()):\\n create_file(fille)\\n jfile = open(fille)\\n data = json.load(jfile)\\n return data\",\n \"def generate_files(input_path, output_path):\\n profiles = pd.read_csv(f'{input_path}/Profile/Profile.csv')\\n\\n # call all predictors\\n predicted = {f: pr.all_predictors[f].predict(profiles=profiles, base_folder=input_path)\\n for f in pr.features if f in pr.all_predictors}\\n\\n for i, (index, row) in enumerate(profiles.iterrows()):\\n file = open(os.path.join(output_path, f'{row[\\\"userid\\\"]}.xml'), 'w')\\n gender = pr.get_gender(predicted['gender'][row[1]]) if row[1] in predicted['gender'] else predicted['age_group_alternative'][row[1]]\\n open_ = predicted['open'][row[1]] if row[1] in predicted['open'] else 'null'\\n conscientious = predicted['conscientious'][row[1]] if row[1] in predicted['conscientious'] else 'null'\\n extrovert = predicted['extrovert'][row[1]] if row[1] in predicted['extrovert'] else 'null'\\n agreeable = predicted['agreeable'][row[1]] if row[1] in predicted['agreeable'] else 'null'\\n neurotic = predicted['neurotic'][row[1]] if row[1] in predicted['neurotic'] else 'null'\\n age = predicted['age_group'][row[1]] if row[1] in predicted['age_group'] else 'null'\\n\\n file.write(f'<user id=\\\"{row[1]}\\\" ')\\n file.write(f'age_group=\\\"{age}\\\" ')\\n file.write(f'gender=\\\"{gender}\\\" ')\\n file.write(f'extrovert=\\\"{extrovert}\\\" ')\\n file.write(f'neurotic=\\\"{neurotic}\\\" ')\\n file.write(f'agreeable=\\\"{agreeable}\\\" ')\\n file.write(f'open=\\\"{open_}\\\" ')\\n file.write(f'conscientious=\\\"{conscientious}\\\" />')\\n file.close()\",\n \"async def test_retrieve_many(self):\\n expected = [{\\n '_id': 'id',\\n 'name': 'name',\\n 'version': 4,\\n 'status': 'active'\\n }]\\n rsps = respx.get(f'{PROVISIONING_API_URL}/users/current/provisioning-profiles') \\\\\\n .mock(return_value=Response(200, json=expected))\\n profiles = await provisioning_client.get_provisioning_profiles(5, 'active')\\n assert rsps.calls[0].request.url == \\\\\\n f'{PROVISIONING_API_URL}/users/current/provisioning-profiles?version=5&status=active'\\n assert rsps.calls[0].request.headers['auth-token'] == 'header.payload.sign'\\n assert profiles == expected\",\n \"def weather_profile(address, name, data='', update=''):\\n filename = str(r\\\"C:\\\\Users\\\\thoma\\\\Desktop\\\\Python\\\\TH Projects\\\\Port\\\\tools\\\\weather\\\\weather_profiles/\\\" + str(address) + \\\"_weather_profile.txt\\\")\\n filename_temp = str(r\\\"C:\\\\Users\\\\thoma\\\\Desktop\\\\Python\\\\TH Projects\\\\Port\\\\tools\\\\weather\\\\weather_profiles/\\\" + str(address) + \\\"_weather_profile_backup.txt\\\")\\n\\n #open as write - this takes care of the initial instance of the file\\n # if we can read the profile read it, otherwise create an empty dict\\n\\n try:\\n with open(filename) as f:\\n contents = json.load(f)\\n \\n weather_profile_dict = contents\\n \\n #create backup file in case something goes wrong\\n with open(filename_temp, 'w') as f:\\n contents = weather_profile_dict\\n json.dump(weather_profile_dict, f)\\n\\n except FileNotFoundError:\\n weather_profile_dict = {}\\n\\n except ValueError:\\n print(\\\"dictionary has a value error\\\")\\n weather_profile_dict = {}\\n \\n # if there is an existing entry: \\n if name in weather_profile_dict.keys():\\n if update:\\n del weather_profile_dict[name]\\n weather_profile_dict[name] = data\\n # store the amended profile \\n with open(filename, 'w') as f:\\n json.dump(weather_profile_dict,f)\\n return weather_profile_dict[name]\\n else:\\n return weather_profile_dict[name]\\n \\n # if there isn't an existing entry\\n else:\\n if data:\\n # create a new one if there is data\\n weather_profile_dict[name] = data\\n # store the amended profile \\n with open(filename, 'w') as f:\\n json.dump(weather_profile_dict,f)\\n return weather_profile_dict[name]\\n\\n else:\\n # return nothing if there isn't\\n print(\\\"t/-There is no existing data for \\\" + str(name) + \\\" - add data...\\\")\\n return None\",\n \"def get_profile_metadata(config, timestamp):\\n with open(config.profile) as profile:\\n context = json.load(profile)\\n data = {}\\n expires_on = None\\n expires_after = None\\n version = str(context['data']['version'])\\n\\n if context['data']['removal_date'] is not None:\\n expires_on = timezone.make_aware(datetime.datetime.strptime(\\n context['data']['removal_date'], '%Y-%m-%dT%H:%M:%SZ')\\n ).astimezone(pytz.UTC)\\n if context['data']['removal_period'] is not None:\\n expires_after = timestamp + timezone.timedelta(seconds=context['data']['removal_period'])\\n if expires_on is not None and expires_after is not None:\\n if expires_on < expires_after:\\n expiration_date = expires_on\\n else:\\n expiration_date = expires_after\\n elif expires_on is not None:\\n expiration_date = expires_on\\n else:\\n expiration_date = expires_after\\n\\n if context['data']['auto_remove'] == 'default':\\n expiration_date = None\\n\\n data['expires'] = expiration_date\\n data['version'] = version\\n return data\",\n \"def profiles(self):\\n with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\\n return list(filter(lambda x: x is not None, executor.map(self.profile_details, self.profiles_names())))\",\n \"def scrape_raw(self,lang='none'):\\n pastes_json=self.scrape(lang);\\n time.sleep(1)\\n raw_pastes=dict()\\n \\n for paste in pastes_json:\\n key=paste['key']\\n if key not in self.scraped_pastes :\\n raw_paste=self.scrape_paste(key)\\n raw_pastes[key]=raw_paste\\n self.scraped_pastes.append(key)\\n time.sleep(1)\\n \\n return raw_pastes\",\n \"def downloadvoterjson():\\n \\n # Get all State Names from CSV\\n stnames = pd.read_csv(\\\"https://raw.githubusercontent.com/jasonong/List-of-US-States/master/states.csv\\\")\\n statelist = [st for st in stnames.State]\\n\\n # Download a JSON for each State in State Names.\\n for st in statelist:\\n try:\\n if len(st.split()) > 1:\\n stsplit = st.split()\\n st = '-'.join(stsplit).lower()\\n \\n with urllib.request.urlopen(f\\\"https://static01.nyt.com/elections-assets/2020/data/api/2020-11-03/race-page/{st.lower()}/president.json\\\") as url:\\n data = json.loads(url.read().decode())\\n with open(f\\\"{st}.json\\\", \\\"w\\\") as outfile:\\n json.dump(data, outfile)\\n \\n except Exception as e:\\n print(e)\\n pass\",\n \"def fetch_profile(self, api_token: str, id: str) -> dict:\\n query = \\\"\\\"\\\"\\n query Profile($id: ID!, $service_type: ServiceType!) {\\n profile(id: $id, serviceType: $service_type) {\\n id\\n }\\n }\\n \\\"\\\"\\\"\\n\\n path = jmespath.compile(\\n \\\"\\\"\\\"\\n data.profile.{\\n id: id\\n }\\n \\\"\\\"\\\"\\n )\\n\\n data = self.do_query(\\n query,\\n api_token=api_token,\\n variables={\\n \\\"id\\\": id,\\n \\\"service_type\\\": settings.HELSINKI_PROFILE_SERVICE_TYPE,\\n },\\n )\\n\\n parsed_data = path.search(data)\\n self.contains_keys(parsed_data, [\\\"id\\\"])\\n return parsed_data\",\n \"def get_metadata(datas):\\n\\n required_profile_key, required_item_key = 'basics', 'category'\\n utility_matrix = []\\n\\n item_details, profile_details = {}, {}\\n\\n for _ , data in datas.items():\\n profile_name, item_name = None, None\\n\\n # extracted profile data for user, because two user may have the same nick name\\n # so we will concatenate user nick name and its profile link to form the unique one.\\n if type(data) is dict and required_profile_key in data.keys() :\\n profile_name = \\\"%s|%s\\\" % (data[required_profile_key].get('name',''),\\n data[required_profile_key].get('profile',''))\\n\\n profile_details[profile_name] = get_profile_detail(data)\\n print ('extracted data of profile: %s ...' % data[required_profile_key].get('name',''))\\n\\n # for item\\n if type(data) is dict and required_item_key in data.keys():\\n if hasattr(data[required_item_key],'items'):\\n for k,v in data[required_item_key].items():\\n\\n item_detail = get_item_detail(v)\\n print ('extracted data for category %s ...' % k)\\n\\n #item_detail_to_str = json.dumps(item_detail) #\\\" ; \\\".join(list(set(item_detail)))\\n if k in item_details:\\n item_details[k]['work'].append(item_detail['work'])\\n item_details[k]['skill'].append(item_detail['skill'])\\n else:\\n item_details[k] = {}\\n item_details[k]['work'] = [item_detail['work']]\\n item_details[k]['skill'] = [item_detail['skill']]\\n\\n utility_matrix.append({\\n 'profile': profile_name,\\n 'item': k,\\n 'rating':v['point']\\n })\\n\\n return utility_matrix, \\\\\\n {k:{'work':json.dumps(v['work']),'skill':json.dumps(v['skill'])} for k,v in item_details.items()}, \\\\\\n profile_details\",\n \"def parse_json_export(json_file):\\n\\n json_file.seek(0)\\n links = json.load(json_file)\\n json_date = lambda s: datetime.strptime(s, '%Y-%m-%dT%H:%M:%SZ')\\n\\n for link in links:\\n # example line\\n # {\\\"href\\\":\\\"http:\\\\/\\\\/www.reddit.com\\\\/r\\\\/example\\\",\\\"description\\\":\\\"title here\\\",\\\"extended\\\":\\\"\\\",\\\"meta\\\":\\\"18a973f09c9cc0608c116967b64e0419\\\",\\\"hash\\\":\\\"910293f019c2f4bb1a749fb937ba58e3\\\",\\\"time\\\":\\\"2014-06-14T15:51:42Z\\\",\\\"shared\\\":\\\"no\\\",\\\"toread\\\":\\\"no\\\",\\\"tags\\\":\\\"reddit android\\\"}]\\n if link:\\n # Parse URL\\n url = link.get('href') or link.get('url') or link.get('URL')\\n if not url:\\n raise Exception('JSON must contain URL in each entry [{\\\"url\\\": \\\"http://...\\\", ...}, ...]')\\n\\n # Parse the timestamp\\n ts_str = str(datetime.now().timestamp())\\n if link.get('timestamp'):\\n # chrome/ff histories use a very precise timestamp\\n ts_str = str(link['timestamp'] / 10000000) \\n elif link.get('time'):\\n ts_str = str(json_date(link['time'].split(',', 1)[0]).timestamp())\\n elif link.get('created_at'):\\n ts_str = str(json_date(link['created_at']).timestamp())\\n elif link.get('created'):\\n ts_str = str(json_date(link['created']).timestamp())\\n elif link.get('date'):\\n ts_str = str(json_date(link['date']).timestamp())\\n elif link.get('bookmarked'):\\n ts_str = str(json_date(link['bookmarked']).timestamp())\\n elif link.get('saved'):\\n ts_str = str(json_date(link['saved']).timestamp())\\n \\n # Parse the title\\n title = None\\n if link.get('title'):\\n title = link['title'].strip() or None\\n elif link.get('description'):\\n title = link['description'].replace(' — Readability', '').strip() or None\\n elif link.get('name'):\\n title = link['name'].strip() or None\\n\\n yield {\\n 'url': url,\\n 'timestamp': ts_str,\\n 'title': title,\\n 'tags': link.get('tags') or '',\\n 'sources': [json_file.name],\\n }\",\n \"def scrape(url, filename):\\n courselist = []\\n headers = ['title', 'description', 'course number', 'duration', 'difficulty', 'instructors', 'course url']\\n with open(filename, 'wb') as outfile:\\n wr = csv.writer(outfile)\\n wr.writerow(headers)\\n courses = json.load(urllib2.urlopen(url))\\n for course in courses['courses']:\\n c = Course()\\n c.title = course['title']\\n c.desc = course['summary']\\n c.course_number = course['key']\\n c.duration = str(course['expected_duration']) + ' ' + str(course['expected_duration_unit'])\\n c.difficulty = course['level']\\n c.url = 'https://www.udacity.com/course/' + course['slug']\\n l = len(course['instructors'])\\n for i in xrange(l):\\n if(i == 0):\\n c.instructors += course['instructors'][i]['name']\\n else:\\n c.instructors += ';' + course['instructors'][i]['name']\\n with open(filename, 'ab') as outfile:\\n wr = csv.writer(outfile)\\n wr.writerow(c.getaslist())\",\n \"def filterprofile(profile, settings):\\n \\n if settings.exclude is True and len(profile.description) == 0:\\n print(\\\"EMPTY BIO\\\")\\n return False\\n\\n if profile.description is None:\\n return False\\n\\n if len(settings.include_keywords) > 1 and not any(kw in profile.description for kw in settings.include_keywords.splitlines()):\\n print(\\\"NO KEYWORDS\\\")\\n return False\\n\\n if profile.followers_count is None:\\n return False\\n \\n if profile.followers_count < settings.followers:\\n print(\\\"NUM FOLLOWERS\\\")\\n return False\\n\\n if any(loc in profile.location for loc in settings.fromcountries.splitlines()):\\n print(\\\"LOCATION\\\")\\n return False\\n\\n if profile.statuses_count < settings.tweets:\\n print(\\\"NUM TWEETS\\\")\\n return False\\n\\n created = datetime.datetime.strptime(profile.created_at, \\\"%a %b %d %H:%M:%S %z %Y\\\")\\n months = relativedelta(datetime.datetime.now(datetime.timezone.utc), created).years * 12\\n if months == 0:\\n if (profile.statuses_count / 12) > settings.tweetsperyear:\\n print(\\\"TWEETS PER YEAR\\\")\\n return False\\n else:\\n if (profile.statuses_count / months / 12) > settings.tweetsperyear:\\n print(\\\"TWEETS PER YEAR\\\")\\n return False\\n\\n if profile.status is not None:\\n lasttweetdate = datetime.datetime.strptime(profile.status.created_at, \\\"%a %b %d %H:%M:%S %z %Y\\\").replace(tzinfo=None)\\n \\n lasttweetmonths = relativedelta(datetime.datetime.now(), lasttweetdate).years * 12\\n if lasttweetmonths > settings.notweetsfor:\\n print(\\\"LAST TWEET\\\")\\n return False\\n else:\\n return False\\n # else\\n return True\",\n \"def get_profile(ticker):\\n PROFILE = \\\"{}/quote/{}/profile\\\".format(url, ticker)\\n\\n page = session.get(PROFILE)\\n soup = BeautifulSoup(page.content, 'lxml')\\n company_name = soup.find('h3', attrs={'class': \\\"Mb(10px)\\\"})\\n company = soup.find('div', attrs={'class': \\\"Mb(35px)\\\"})\\n click.echo(click.style(\\\"Profile\\\", fg='red', bold=True, underline=True))\\n profile = []\\n dic_key = []\\n dic_val = []\\n if company_name is not None:\\n print company_name.get_text().encode('utf-8')\\n company = soup.find('div', attrs={'class': \\\"Mb(25px)\\\"})\\n info_2 = company.find('p', attrs={'class': \\\"D(ib) Va(t)\\\"})\\n title = info_2.find_all('span')\\n value = info_2.find_all('strong')\\n for d in title:\\n dic_key.append(d.get_text())\\n for d in value:\\n dic_val.append(d.get_text())\\n comp_info = dict(zip(dic_key, dic_val))\\n #df = pd.Series(comp_info)\\n #print df\\n data = soup.find(\\\"table\\\")\\n data = data.find_all(\\\"tr\\\")\\n for item in data:\\n if not item('td') or item('td')[0].get_text() == '':\\n continue\\n name = item('td')[0].get_text().strip().encode('utf-8')\\n title = item('td')[1].get_text().strip().encode('utf-8')\\n pay = item('td')[2].get_text().strip().encode('utf-8')\\n age = item('td')[4].get_text().strip().encode('utf-8')\\n profile.append({\\\"Name\\\": name, \\\"Title\\\": title, \\\"Pay\\\": pay, \\\"Age\\\": age})\\n for k, v in comp_info.iteritems():\\n click.echo(\\\"{}: {}\\\".format(k, v))\\n for item in profile:\\n click.echo(\\\"Name: {}, Title: {}, Pay: {}, Age: {}\\\".format(item['Name'], item['Title'], item['Pay'], item['Age']))\\n #print comp_info\\n #print profile\\n #pd.set_option(\\\"display.colheader_justify\\\",\\\"right\\\")\\n #pd.set_option('display.width', 120)\\n #df = pd.DataFrame(profile)\\n #df.style.set_properties(**{'text-align': 'right'})\\n #print(df)\\n #print \\\"Name: {}, Title: {}, Pay: {}, Age: {}\\\".format(item('td')[0].get_text().encode('utf-8'), \\n # item('td')[1].get_text().encode('utf-8'), item('td')[2].get_text().encode('utf-8'), item('td')[4].get_text().encode('utf-8'))\\n click.pause(info='Press any key to continue ...', err=False)\",\n \"def list_profiles(self):\\n return self._get(\\\"posture\\\", box=BoxList)\",\n \"def _parse_page_contents (self, page_soup):\\n netflix_page_data = self.extract_inline_netflix_page_data(page_soup=page_soup)\\n self.user_data = self._parse_user_data(netflix_page_data=netflix_page_data)\\n self.esn = self._parse_esn_data(netflix_page_data=netflix_page_data)\\n self.api_data = self._parse_api_base_data(netflix_page_data=netflix_page_data)\\n self.profiles = self._parse_profile_data(netflix_page_data=netflix_page_data)\\n self.log(msg='Found ESN \\\"' + self.esn + '\\\"')\\n return netflix_page_data\",\n \"def __init__(self):\\r\\n super(ProfileParser, self).__init__([self.ProfileEntryHandler()])\",\n \"def list_profiles(request, pk=0):\\n context = {'items': [], 'resource_type': 'Profile'}\\n handle_expired_profiles()\\n if pk == 0:\\n context['h2'] = \\\"Configuration Profiles\\\"\\n context['header_1'] = \\\"Type\\\"\\n context['header_2'] = \\\"Last Modified\\\"\\n profiles = ConfigurationProfile.objects.all().reverse()\\n for profile in profiles:\\n assignment_count = profile.pending_install.count()\\n install_count = profile.installed.count()\\n data = {'filename': str(profile), 'type': \\\"macOS\\\", 'meta': profile, 'assignment_count': assignment_count,\\n 'install_count': install_count}\\n context['items'].append(data)\\n else:\\n device = get_object_or_404(Laptop, pk=pk)\\n context['h2'] = \\\"Profiles for {}\\\".format(device.name)\\n context['header_1'] = \\\"Version\\\"\\n context['header_2'] = \\\"Expires\\\"\\n context['device_view'] = True\\n context['device_id'] = pk\\n profiles = ConfigurationProfile.objects.filter(pending_install__in=[device])\\n profiles |= ConfigurationProfile.objects.filter(installed__in=[device])\\n for profile in profiles:\\n status = 'Not assigned'\\n for entry in profile.installed.all():\\n if entry == device:\\n status = 'Installed'\\n for entry in profile.pending_install.all():\\n if entry == device:\\n status = 'Assigned'\\n record = InstallationRecord.objects.filter(profile=profile, device=device, active=True).first()\\n expires_soon = False\\n if record is not None and record.expires is not None:\\n if timezone.now() < record.expires < timezone.now() + timezone.timedelta(days=30):\\n expires_soon = True\\n data = {'filename': str(profile), 'downloadable': False, 'install_record': record, 'meta': profile,\\n 'status': status, 'expires_soon': expires_soon}\\n context['items'].append(data)\\n\\n return render(request, 'mdm/resource_list.html', context)\",\n \"def get_user_profiles(args): # GET\\n limit = args.get(\\\"limit\\\", type=int) if args.get(\\\"limit\\\") else 0\\n # NOTE checks if the string value of hasateam is equal to \\\"true\\\" because HTTP protocol only passes strings\\n hasateam = args.get(\\\"hasateam\\\", \\\"\\\").lower() == \\\"true\\\"\\n\\n if hasateam:\\n users = list(coll(\\\"users\\\").find({\\\"hasateam\\\": hasateam}).limit(limit))\\n else:\\n users = list(coll(\\\"users\\\").find({}).limit(limit))\\n\\n for user in users:\\n user[\\\"user_id\\\"] = user.pop(\\\"_id\\\")\\n return {\\\"user_profiles\\\": users}, 200\",\n \"def weather_profile(address, name, data='', update=''):\\n filename = str(r\\\"C:\\\\Users\\\\thoma\\\\Desktop\\\\Python\\\\TH Projects\\\\Port\\\\tools\\\\weather\\\\weather_profiles/\\\" + str(address) + \\\"_weather_profile_forecast.txt\\\")\\n filename_temp = str(r\\\"C:\\\\Users\\\\thoma\\\\Desktop\\\\Python\\\\TH Projects\\\\Port\\\\tools\\\\weather\\\\weather_profiles/\\\" + str(address) + \\\"_weather_profile_forecast_backup.txt\\\")\\n\\n\\n #open as write - this takes care of the initial instance of the file\\n # if we can read the profile read it, otherwise create an empty dict\\n\\n try:\\n with open(filename) as f:\\n contents = json.load(f)\\n \\n weather_profile_dict = contents\\n \\n #create backup file in case something goes wrong\\n with open(filename_temp, 'w') as f:\\n contents = weather_profile_dict\\n json.dump(weather_profile_dict, f)\\n\\n except FileNotFoundError:\\n weather_profile_dict = {}\\n\\n except ValueError:\\n print(\\\"dictionary has a value error\\\")\\n weather_profile_dict = {}\\n \\n # if there is an existing entry: \\n if name in weather_profile_dict.keys():\\n if update:\\n del weather_profile_dict[name]\\n weather_profile_dict[name] = data\\n # store the amended profile \\n with open(filename, 'w') as f:\\n json.dump(weather_profile_dict,f)\\n return weather_profile_dict[name]\\n else:\\n return weather_profile_dict[name]\\n \\n # if there isn't an existing entry\\n else:\\n if data:\\n # create a new one if there is data\\n weather_profile_dict[name] = data\\n # store the amended profile \\n with open(filename, 'w') as f:\\n json.dump(weather_profile_dict,f)\\n return weather_profile_dict[name]\\n\\n else:\\n # return nothing if there isn't\\n print(\\\"t/-There is no existing data for \\\" + str(name) + \\\" - add data...\\\")\\n return None\",\n \"def feed_records(self):\\n if not self.stats_file:\\n return\\n\\n with open(self.stats_file) as fh:\\n reader = reverse_file(fh)\\n for line in reader:\\n if line is None:\\n return\\n if not line:\\n continue\\n\\n try:\\n js = json.loads(line)\\n except Exception as e:\\n continue\\n\\n yield js\",\n \"def list_profiles(self, params):\\n return self.profiles\",\n \"def retrieve_json():\\n\\n # Download the JSON files to a working directory.\\n if not os.path.exists(JSON_FOLDER):\\n os.makedirs(JSON_FOLDER)\\n\\n make_time_stamp_file()\\n \\n dump_all_json()\\n \\n # Report counts of what we got back.\\n courses = load_courses_json()\\n script_logging.log_status(\\\"Course count: %s\\\" % len(courses))\\n \\n for course in courses:\\n course_id = course['id']\\n\\n students = load_students_json(course_id)\\n script_logging.log_status(\\\"Student count for course %s: %s\\\" % (course_id, len(students)))\\n \\n users = load_users_json(course_id)\\n script_logging.log_status(\\\"User count for course %s: %s\\\" % (course_id, len(users)))\\n\\n assignments = load_assignments_json(course_id)\\n script_logging.log_status(\\\"Assignment count for course %s: %s\\\" % (course_id, len(assignments)))\\n\\n for assignment in assignments:\\n assignment_id = assignment['id']\\n submissions = load_submissions_json(assignment_id)\\n script_logging.log_status(\\\"Submission count for assignment %s: %s\\\" % (assignment_id, len(submissions)))\\n\\n script_logging.log_status(\\\"Time stamp: %s\\\" % get_time_stamp())\",\n \"def parseJsons(businessJson='pa_business.json', reviewJson='pa_review.json', userJson='pa_user.json'):\\n\\n # loops over all .json files in the argument\\n parseJson(businessJson)\\n print \\\"Success parsing \\\" + businessJson\\n parseJson(reviewJson)\\n print \\\"Success parsing \\\" + reviewJson\\n parseJson(userJson)\\n print \\\"Success parsing \\\" + userJson\\n\\n # we won't need the graph for the first part\\n # UPDATE by James: initialized because needed to populate friendship_map\\n (degree_centrality_map, closeness_centrality_map, betweenness_centrality_map) = initializeGraph()\\n\\n # business reviews are in the global business_reviews\\n processReviews()\\n\\n pickle.dump(friendship_map, open( \\\"friendshipMap.p\\\", \\\"wb\\\" ) )\\n pickle.dump(business_reviews, open( \\\"businessReviews.p\\\", \\\"wb\\\" ) )\\n pickle.dump(degree_centrality_map, open( \\\"degreeCentrality.p\\\", \\\"wb\\\" ) )\\n pickle.dump(closeness_centrality_map, open( \\\"closenessCentrality.p\\\", \\\"wb\\\" ) )\\n pickle.dump(betweenness_centrality_map, open( \\\"betweennessCentrality.p\\\", \\\"wb\\\" ) )\\n\\n return (friendship_map, business_reviews, degree_centrality_map, closeness_centrality_map, betweenness_centrality_map, YGraph)\",\n \"def transform_profiles(self):\\n\\n if len(self.pardus_profiles) > 0: # Make sure we have some profiles to migrate\\n for profile in self.pardus_profiles:\\n network_manager_profile = NetworkManagerProfile(profile.get_profile_name, profile)\\n self.network_manager_profiles.append(network_manager_profile)\",\n \"def list_network_profiles(arn=None, type=None, nextToken=None):\\n pass\",\n \"def __init__(self, user, username, password, cap=15):\\n cap = 10; #50 images is the upper limit\\n args = {'username': [user], 'media_types': ['none'], 'media_metadata': False, 'usernames': [user], 'verbose': 0, 'maximum': cap, 'login_user': username, 'login_only': False, 'destination': './', 'quiet': False, 'comments': True, 'filename': None, 'filter': None, 'search_location': False, 'tag': False, 'location': False, 'login_pass': password, 'retain_username': False, 'include_location': False, 'latest': False}\\n\\n self.json = None\\n self.pic = None\\n try : \\n scraper = app.start(args)\\n scraper.scrape()\\n except:\\n return\\n\\n\\n scrap = json.dumps(scraper.posts, indent=4, sort_keys=True, ensure_ascii=False)\\n self.json = json.loads(scrap)\\n self.pic = scraper.profilepic\\n\\n if len(self.json) == 0:\\n self.json = None\\n return\\n #rint self.json\\n #print json.dumps(self.json, indent=4)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6808882","0.67222965","0.66820586","0.6603268","0.6544514","0.6352096","0.625899","0.62085044","0.60912347","0.6039629","0.6026319","0.5911269","0.5884196","0.58651155","0.58599305","0.58528185","0.5841541","0.5834487","0.58279127","0.5772486","0.57361877","0.5694902","0.5616774","0.5595526","0.55759513","0.5564633","0.555445","0.5530859","0.5519071","0.5506901","0.54991794","0.54669297","0.5464554","0.5448907","0.5421897","0.5413284","0.5410661","0.53941035","0.5350173","0.5338228","0.5322905","0.53195816","0.52947307","0.5290411","0.5287302","0.5285124","0.5280122","0.52759564","0.5266535","0.5264536","0.5254785","0.5244929","0.52446616","0.5235587","0.5229091","0.52269065","0.5215436","0.52135897","0.5212049","0.52031887","0.51968473","0.5183916","0.5179987","0.5179308","0.5173317","0.51472527","0.51457196","0.51232135","0.5089942","0.50844955","0.50718033","0.5064405","0.50588316","0.5055535","0.5052784","0.5045159","0.5032931","0.5032029","0.5028268","0.5027043","0.5024408","0.50238055","0.5017216","0.5007847","0.50071716","0.50047296","0.500196","0.50017923","0.4997635","0.49958012","0.4980245","0.49798387","0.49735358","0.4972954","0.49728382","0.49596262","0.49592152","0.49562198","0.49427044","0.49423897"],"string":"[\n \"0.6808882\",\n \"0.67222965\",\n \"0.66820586\",\n \"0.6603268\",\n \"0.6544514\",\n \"0.6352096\",\n \"0.625899\",\n \"0.62085044\",\n \"0.60912347\",\n \"0.6039629\",\n \"0.6026319\",\n \"0.5911269\",\n \"0.5884196\",\n \"0.58651155\",\n \"0.58599305\",\n \"0.58528185\",\n \"0.5841541\",\n \"0.5834487\",\n \"0.58279127\",\n \"0.5772486\",\n \"0.57361877\",\n \"0.5694902\",\n \"0.5616774\",\n \"0.5595526\",\n \"0.55759513\",\n \"0.5564633\",\n \"0.555445\",\n \"0.5530859\",\n \"0.5519071\",\n \"0.5506901\",\n \"0.54991794\",\n \"0.54669297\",\n \"0.5464554\",\n \"0.5448907\",\n \"0.5421897\",\n \"0.5413284\",\n \"0.5410661\",\n \"0.53941035\",\n \"0.5350173\",\n \"0.5338228\",\n \"0.5322905\",\n \"0.53195816\",\n \"0.52947307\",\n \"0.5290411\",\n \"0.5287302\",\n \"0.5285124\",\n \"0.5280122\",\n \"0.52759564\",\n \"0.5266535\",\n \"0.5264536\",\n \"0.5254785\",\n \"0.5244929\",\n \"0.52446616\",\n \"0.5235587\",\n \"0.5229091\",\n \"0.52269065\",\n \"0.5215436\",\n \"0.52135897\",\n \"0.5212049\",\n \"0.52031887\",\n \"0.51968473\",\n \"0.5183916\",\n \"0.5179987\",\n \"0.5179308\",\n \"0.5173317\",\n \"0.51472527\",\n \"0.51457196\",\n \"0.51232135\",\n \"0.5089942\",\n \"0.50844955\",\n \"0.50718033\",\n \"0.5064405\",\n \"0.50588316\",\n \"0.5055535\",\n \"0.5052784\",\n \"0.5045159\",\n \"0.5032931\",\n \"0.5032029\",\n \"0.5028268\",\n \"0.5027043\",\n \"0.5024408\",\n \"0.50238055\",\n \"0.5017216\",\n \"0.5007847\",\n \"0.50071716\",\n \"0.50047296\",\n \"0.500196\",\n \"0.50017923\",\n \"0.4997635\",\n \"0.49958012\",\n \"0.4980245\",\n \"0.49798387\",\n \"0.49735358\",\n \"0.4972954\",\n \"0.49728382\",\n \"0.49596262\",\n \"0.49592152\",\n \"0.49562198\",\n \"0.49427044\",\n \"0.49423897\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6291534"},"document_rank":{"kind":"string","value":"6"}}},{"rowIdx":84996,"cells":{"query":{"kind":"string","value":"Extract data from one scraped html"},"document":{"kind":"string","value":"def extract_one( html: str, fpath: Path ):\n # %%\n doc = BeautifulSoup( html, features='html.parser')\n\n ret = { 'linkedin_handle': fpath.name.split('.')[0] }\n _parse_top_card( ret, doc )\n # %%\n ret['about'] = _extract_about( doc )\n # if len(ret['about']) < 100 and ret['about'].find('ver más') > 0:\n # print( f\"\\nVer más detected: \\nabout:{ret['about']} fpath={fpath}\" )\n\n ret['about_stats'] = {'about_eng_ratio': _common_english_ratio(ret['about'])}\n # %%\n ret['work_experience'] = _parse_experiences( doc )\n ret['work_stats'] = calc_work_stats( ret['work_experience'])\n # %%\n ret['skills'] = proc_skills_section( doc )\n ret['education'] = _parse_education( doc )\n ret['education_stats'] = _education_stats( ret['education'])\n ret['accomplishments'] = _extract_accomplishments(doc)\n ret['profile_text_stats'] = profile_text_stats( doc )\n # %%\n return ret\n # %%"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def extract_data_listing(html):\n id_finder = re.compile(r'PR[\\d]+~')\n return html.find_all('div', id=id_finder)","def page_data():\n return scrape()","def _extract_data(self,data,tag=None,cssid=None,cssclass=None,attrs=None,regexp=None,index=0):\n \n# cssclass = \"song\"\n# cssid = \"newsTable0\"\n# tag = \"div\"\n# import pdb\n# pdb.set_trace() \n \n if cssid: \n searchconstrain = SoupStrainer(tag, id=cssid)\n elif cssclass:\n searchconstrain = SoupStrainer(tag, attrs={\"class\":cssclass}) \n else:\n if isinstance(attrs, unicode):\n try:\n attrs = attrs.encode('utf-8')\n regexp = regexp.encode('utf-8')\n except:\n pass \n searchconstrain = SoupStrainer(tag, attrs={attrs:re.compile(regexp)})\n\n soup = BeautifulSoup(data,parseOnlyThese=searchconstrain)\n rslist = [ tp for tp in soup ]\n return rslist[index]","def _scrape(self):","def do_extract(self, xpath):\n s = Selector(self.driver.page_source)\n for i, result in enumerate(s.xpath(xpath).getall(), 1):\n print(i, result)","def _extract_html(self, url):\n self.response = requests.get(url, timeout=5)\n self.html = BeautifulSoup(self.response.content, \"lxml\") if self.response.ok else None\n # return self.html","def _parse(self):\n soup = BS(self._current_html, 'lxml')\n for item in soup.select('div.c'):\n temp = {}\n # main content\n ctt = item.select('span.ctt')\n if not ctt:\n continue\n weibo_body = item.select('div')\n if len(weibo_body) > 1:\n temp['content'] = weibo_body[0].text\n btn_group = weibo_body[1].text\n else:\n temp['content'] = weibo_body[0].select('span.ctt')[0].text\n btn_group = weibo_body[0].text\n temp['is_repost'] = True if REPO_TEST_PATTERN.match(\n temp['content']) else False\n try:\n temp['like_num'] = LIKE_NUM_PATTERN.findall(btn_group)[0]\n temp['cmt_num'] = COMMENT_NUM_PATTERN.findall(btn_group)[0]\n temp['repo_num'] = REPO_NUM_PATTERN.findall(btn_group)[0]\n except Exception:\n pass\n cmt = item.select('.cmt')\n # visibility\n if cmt:\n try:\n temp['visibility'] = VISIBILITY_PATTERN.findall(\n cmt[0].text)[0]\n except Exception:\n pass\n\n # img in main content\n img = item.select('div a img')\n img_src = img[0].attrs['src'] if img else None\n temp['img_src'] = img_src\n LOGGER.debug('img_src: {}'.format(img_src))\n # time & source device\n ct = item.select('span.ct')\n if ct:\n ct = ct[0]\n text = ct.text\n reg_result = TIME_PATTERN.findall(text)[0]\n\n temp['time'] = ar(\n '{}年{}'.format(self._current_year, reg_result[0]),\n DATE_FMTS[0]\n ).naive if reg_result[0] else ar(\n reg_result[1], DATE_FMTS[1]\n ).naive\n temp['source'] = SOURCE_DEVICE_PATTERN.findall(text)[0]\n self._post_item = Post(**temp)\n self._attachment_item = Attachment(\n uri=img_src, post=self._post_item)\n self._store()","def extract(soup, keyIndex):\n\n # getting the individual content\n content = soup.find('div', attrs={'data-key': keyIndex})\n\n # index\n pattern = r\"<span class=\\\"indexed-biz-name\\\">(\\d+)\"\n index = extractAttribute(content, pattern)\n #print index\n\n # link\n pattern = r\"biz-name\\\".*(/biz/[\\w?%?+-?]+)\\\"\"\n #pattern = r\"biz-name\\\".*(/biz/.*)\\\"\" \n link = extractAttribute(content, pattern)\n #print link\n\n # name\n #pattern = r\"biz-name.*>(.*)</a>\" \n pattern = r\"biz-name.*>(<.*>.*|\\w+\\s+<.*>.*)</a>\"\n name = extractAttribute(content, pattern) \n #print name\n\n # rating\n pattern = r\"star-img ([\\w+_?]+)\"\n rating = extractAttribute(content, pattern)\n #print rating\n\n # review\n pattern = r\"(\\d+) reviews\"\n review = extractAttribute(content, pattern)\n #print review\n\n # price\n pattern = r\"business-attribute price-range\\\">(.*)</span>\"\n price = extractAttribute(content, pattern)\n #print price\n\n # category\n pattern = r\"cflt=(\\w+)\"\n category = extractAttribute(content, pattern)\n #print category\n\n # neighborhood\n pattern = r\"neighborhood-str-list\\\">\\n\\s+(.*)\\s+</span>\"\n neighborhood = extractAttribute(content, pattern)\n #print neighborhood\n\n # address\n #pattern = r\"\\s+(.*)<br>\"\n pattern = r\"<address>\\n([\\s\\S]*)</address>\"\n address = extractAttribute(content, pattern)\n #print address\n\n # phone\n #pattern = r\"(\\([\\d]+\\) [\\d]+-[\\d]+)\"\n pattern = r\"biz-phone\\\">\\n\\s+(.*)\\n\\s+</span>\"\n phone = extractAttribute(content, pattern)\n #print phone\n\n # feedback\n pattern = r\"<p class=\\\"snippet\\\">(.*)</p>\"\n feedback = extractAttribute(content, pattern)\n #print feedback\n\n return [index, link, name, \\\n rating, review, price, category, \\\n neighborhood, address, phone, feedback]","def get_data_from_page(url):\n # Initialise a dictionary to store our information\n row = {'url': url}\n\n # get the html content of a website using the requests library and the get function\n # the '.content' returns the contents of the request, without it would return the HTTP status code\n page_contents = requests.get(url).content\n\n # parse the contents with beautiful soup\n soup = BeautifulSoup(page_contents, 'lxml')\n\n # Get the text relating to the campaign title which belong to that specific class.\n for element in soup.find_all(class_=\"a-campaign-title\"):\n row['title'] = element.text\n\n # Interrogating the source we found that the tags are URLs that always contain the 'discover' path\n # So we just need to find all the URLs that contain that string and return them\n tags = []\n for link in soup.find_all('a', href=True):\n if 'discover' in link['href']:\n tags.append(link.text)\n\n row['tags'] = tags\n\n # Progress meter is a single string in this class.\n for link in soup.findAll('h2', {'class': 'm-progress-meter-heading'}):\n goal = link.text\n\n # The format is strictly defined so we can do some string parsing to get the information we want\n row['current amount'] = goal.split()[0]\n row['total_amount'] = goal.split()[-2]\n\n for link in soup.findAll('div', {'class': 'p-campaign-description'}):\n row['description'] = link.text\n\n for link in soup.findAll('span', {'class': 'm-campaign-byline-created a-created-date'}):\n row['created'] = link.text\n\n # Some information exists only in the footer of the \"donations\" page, which is summarised in the side bar\n # We can't access it directly but the page URL is well formated so we can use some string manipulation to create\n # the URL and parse it.\n\n # the gofundme URLs are well formated along the lines of www.gofundme.com/f/name of fundraiser?qidSOMEHEXCODE\n # We can use the '?qid' as an anchor and replace it with '/donations?qid' to get the URL of the donations page\n donations_url = url.replace('?qid', '/donations?qid')\n\n soup = BeautifulSoup(requests.get(donations_url).content, 'lxml')\n text_soup = str(soup)\n\n # The information in this section is outside of the usual html format, but it is well structured\n # We can extract it using regular expressions that look for the information\n for dates in re.findall('launch_date\\\":\\\"[0-9-]+', text_soup):\n row['launch_date'] = dates.rsplit('\"', 1)[-1]\n\n for country in re.findall('country\\\":\\\"[A-Z]+', text_soup):\n row['country'] = country.rsplit('\"', 1)[-1]\n\n for donation_count in re.findall('donation_count\\\":[0-9]+', text_soup):\n row['donation_count'] = donation_count.rsplit(':', 1)[-1]\n\n for charity in re.findall('charity\\\":[a-z]+', text_soup):\n row['is_charity'] = charity.rsplit(':', 1)[-1]\n\n return row","def mine(self):\n collections = []\n # Getting HTML snapshot with selenium, storing a soup object in .data\n self.scrape()\n # Returns only the parts of the soup that surround each collection\n collection_elements = self.get_collection_elements()\n # Turns each soup element into a CollectionElement object\n collections = self.get_info_from_collections(collection_elements)\n # NOTE THE RETURN VALUE IS MERELY TO PASS TESTING< MUST BE CHANGED\n return self.data","def parse_page(html):\n\n soup = BeautifulSoup(html, \"html.parser\")\n review_soups = soup.find_all(\"script\", type=\"application/ld+json\")\n\n description_list = []\n for soup in review_soups:\n text = soup.string\n # decode the json into python dict\n js_dict = json.loads(text)\n\n if \"review\" in js_dict:\n review_list = js_dict[\"review\"]\n\n for i in range(len(review_list)):\n review_dict = review_list[i]\n description_list.append(review_dict[\"description\"])\n\n return description_list","def scrape_detail(self,url):\n soup=get_soup(url)\n self.zip=soup.find('p',class_='block-normal hide-for-small-only text-small hero-ranking-data-contact').stripped_strings.__next__()[-5::1]\n if self.zip in zips:\n #print('DUPLICATE!')\n zips.append(self.zip)\n info_tags=soup.find_all('span',class_='heading-small text-black text-tight block-flush display-block-for-large-up')\n self.type=info_tags[0].string.strip()\n self.year_founded=int(info_tags[1].string.strip())\n self.setting=info_tags[4].string.strip()\n self.endowment=info_tags[5].string.strip()","def extract_from_html(self, data, url):\n\n records = [ ]\n self.logger.debug(\"Found %d recipes in %s\" % (len(data.xpath(self.scope)), url))\n for rcp in data.xpath(self.scope):\n\n record = { }\n for prop in [ \"name\", \"recipeYield\", \"author\" ]:\n record[prop] = self.extract_text(prop, rcp)\n for prop in [ \"image\" ]:\n record[prop] = self.extract_attribute(prop, [ \"content\", \"src\" ], data)\n for prop in [ \"totalTime\", \"prepTime\", \"cookTime\", \"datePublished\" ]:\n record[prop] = self.extract_attribute(prop, [ \"content\" ], rcp)\n # I have no idea if cookingMethod should be text or a list because I've never seen it so\n # using the most general option for it\n for prop in [ \"recipeIngredient\", \"recipeInstructions\", \"cookingMethod\",\n \"recipeCategory\", \"recipeCuisine\" ]:\n record[prop] = self.extract_list(prop, rcp)\n\n # Older versions of the schema use \"ingredients\" rather than \"recipeIngredient\"\n if not record[\"recipeIngredient\"]:\n record[\"recipeIngredient\"] = self.extract_list(\"ingredients\", rcp)\n\n record = dict([ (k, v) for k, v in record.items() if k in self.store_fields ])\n record[\"url\"] = url\n record[\"collect_time\"] = datetime.utcnow()\n\n if self.validate(record):\n records.append(record)\n\n return records","def get_study_data(self, soup, url):\n pass","def scrape(self, page_src):\n soup = bs(page_src, 'html.parser')\n self._data = soup.text.split()","def extract_from_soup( target_url_soup ):\n\n datetime_now = datetime.datetime.now().strftime(\"%m.%d.%Y:%H.%M.%S\")\n\n # Declare a list for each variable extracted below.\n title_list = []\n href_list = []\n cost_list = []\n info_list = []\n loc_list = []\n datetime_pulled = []\n\n # Iteratively extract the data into a list that goes to a dictionary.\n for each in target_url_soup.find_all('p'):\n ## Filter this thing which gets caught in the craigslist data.\n if each.a.string == \"next 100 postings\":\n pass\n else:\n # Get the title, get none on an exception\n try: \n #print str(each.a.string)\n post_title = str(each.a.string)\n except:\n post_title = \"\"\n \n # Get the hyperlink, get none on an exception\n try:\n #print str(each.a.get('href'))\n post_href = str(each.a.get('href'))\n except:\n post_href = \"\"\n\n # Get the cost/info, get none on an exception\n post_cost = \"\"\n post_info = \"\"\n try:\n #print str(each.find(\"span\", \n # \"itemph\").string).strip().strip('-').strip()\n post_cost_info = str(each.find(\"span\", \n \"itemph\").string).strip().strip('-').strip()\n \n\n # Use a regular expression to parse this data further\n if re.match(\"\\$\", post_cost_info) is None:\n post_info = str(post_cost_info).strip()\n\n else:\n # If there is no /, assign as dollars.\n if re.search(\"/\", post_cost_info) is None:\n post_cost = str(post_cost_info).strip().strip('$')\n \n # chop up any entry with $COST / x br\n else:\n cost_info_list = [] # list to receive re.split()\n cost_info_list = re.split('/', post_cost_info, 1 )\n post_cost = str(cost_info_list[0]).strip().strip('$')\n post_info = str(cost_info_list[1]).strip()\n \n # Close the above try block for cost and info\n except:\n pass \n\n # Get the location, get none on an exception\n try: \n #print str(each.find(\"span\", \n # \"itempn\").string).strip().strip('()')\n post_loc = str(each.find(\"span\", \n \"itempn\").string).strip().strip('()')\n except:\n post_loc = \"\"\n \n ## Add all extracted items to their respective lists.\n ## We are still in the above loop here. All lists will get an entry.\n ## This keeps the lists in step in the case of bad entries, so they can\n ## still be zipped, but with blank spaces. Some data is better than\n ## no data.\n title_list.append( post_title )\n href_list.append( post_href )\n cost_list.append( post_cost )\n info_list.append( post_info )\n loc_list.append( post_loc )\n ## Append the datetime_now to each tuple, kept in step.\n datetime_pulled.append( datetime_now )\n\n # Zip the lists collected in the for loop into a tuple.\n # The tuple is the value of the dict/json.\n extracted_data_tuple = zip(title_list, \n href_list, \n cost_list,\n info_list,\n loc_list, \n datetime_pulled)\n \"\"\"\n This tuple is used for MD5 generation because it excludes the unique \n datetime attribute. This would salt the MD5 and we want the md5 to \n represent the data inside so we can detect duplicates.\n\n I have now also removed the href_list because of duplicate posts\n The likelihood of different people having the same title, cost, info, and\n location is still very low and won't affect data.\n \"\"\"\n extracted_data_tuple_nouniquetime = zip(title_list, \n cost_list, \n info_list, \n loc_list)\n \n md5_key_list = []\n # Generate a list of md5 keys from the data tuple, the md5s are the keys. \n for each in extracted_data_tuple_nouniquetime:\n eachmd5 = md5.new()\n eachmd5.update( str(each) )\n md5_key_list.append( str( eachmd5.hexdigest() ) )\n\n # Zip a tuple and convert into a dictionary for JSON extraction\n extracted_data_dict = dict( zip( md5_key_list, extracted_data_tuple ) )\n\n return ( extracted_data_dict )","def parse_announcement_data(self) -> 'Scraper':\n logger.info('Parsing extracted html partial')\n for tag in self.html_partial: # there are 63 tags\n if tag.name == 'h4':\n announcement_data = self.get_data_from_tag(tag)\n self.announcement_data_list.append(announcement_data)\n logger.info('Compiled announcement data list from html web page partial')\n return self","def scrape(self):\n pass","def process_level_1(content):\n\tsoup = BeautifulSoup(content, features=\"html.parser\")\n\n\t#r = soup.findAll('div',{\"class\":\"listing-item__content\"})\n\tr = soup.findAll('div',{\"class\":\"listing-item search-listing-result__item\"})\n\tres=list()\n\t# extract information from the source code if the page \n\tfor i in r:\n\t\tlisting_id = str(i)[str(i).index('data-wa-data=\"')+len(\"data-wa-data=\")+1:str(i).index(\"|source=listings_results\")+len(\"|source=listings_results\")]\n\t\ttype_area = i.find('div',{'class':'listing-characteristic margin-bottom'}).text\n\t\tprice = i.find(\"div\",{\"class\":\"listing-price margin-bottom\"}).text\n\t\tpalce = i.find(\"div\",{\"class\":\"text--muted text--small\"}).text\n\t\tres.append({\"id_\":listing_id,\n\t\t \"area\":type_area,\n\t\t \"price\":price,\n\t\t \"place\":palce})\n\treturn res","def get_doctor_info(url, html):\n \n # create a dictionary to save data\n doctor_info = {}\n \n # parsed html by using lxml\n # In the following processing\n # the one ending in org is the raw data in the HTML\n # the one ending in inf is the processed data by extracting from raw data\n select = etree.HTML(html)\n \n # part 1: get basic information about your doctor\n # attribute: Name, Title, Hospital, Department\n name_org = select.xpath('//div[@class=\"profile-text\"]//h1[@class=\"doctor-name\"]//text()')\n name_inf = name_org[0].strip()\n \n title_org = select.xpath('//div[@class=\"profile-text\"]//span[@class=\"positon\"]//text()')\n title_inf = [i.strip() for i in title_org if len(i.strip()) > 0]\n title_inf = ' '.join(title_inf)\n \n hospital_department_org = select.xpath('//div[@class=\"profile-text\"]//p[@class=\"doctor-faculty\"]//text()')\n hospital_department_inf = [i.strip() for i in hospital_department_org if len(i.strip()) > 0]\n hospital_inf = hospital_department_inf[0]\n department_inf = hospital_department_inf[1]\n \n doctor_info['姓名'] = name_inf\n doctor_info['职称'] = title_inf\n doctor_info['医院'] = hospital_inf\n doctor_info['科室'] = department_inf\n \n # part2: get header format data\n org = select.xpath('//div[@class=\"profile-sta\"]//text()')\n inf = [i.strip() for i in org if len(i.strip()) > 0 and i.strip() != '%']\n for i in range(len(inf)//2):\n doctor_info[inf[2*i]] = inf[2*i + 1]\n \n \n # part3: get sidebar format data\n org_1 = select.xpath('//div[@class=\"item-body\"]//div[@class=\"clearfix\"]//div[@class=\"per-sta-label\"]//text()')\n org_2 = select.xpath('//div[@class=\"item-body\"]//div[@class=\"clearfix\"]//div[@class=\"per-sta-data\"]//text()')\n for i in range(len(org_1)):\n doctor_info[org_1[i][:-1]] = org_2[i]\n \n # part4: get body format data\n honour_org = select.xpath('//div[@class=\"honour-header\"]//text()')\n honour_inf = ''.join([i.strip() for i in honour_org])\n \n honour_detail_org = select.xpath('//li[@class=\"honour-title\"]//text()')\n honour_detail_inf = [i.strip()[:4] for i in honour_detail_org if len(i.strip()) > 0]\n honour_detail_inf = ' '.join(honour_detail_inf)\n \n satisfaction_org = select.xpath('//div[@class=\"item-body\"]//div[@class=\"satisfaction clearfix\"]//i[@class=\"sta-num\"]//text()')\n satisfaction_inf = [i.strip() for i in satisfaction_org if len(i.strip()) > 0 and i.strip() != '%']\n \n resume_org = select.xpath('//div[@class=\"good-at-text\"]//text()')\n resume_inf = [i.strip() for i in resume_org]\n if len(resume_inf) <= 20:\n resume_inf = ''.join(resume_inf)\n resume_inf = ''.join(resume_inf[:20])\n \n star_org = select.xpath('//div[@class=\"experience-row clearfix\"]//span[@class=\"experience-label\"]//text()')\n star_inf = 1 if len(star_org) >= 1 else 0\n\n doctor_info['好大夫届数'] = honour_inf\n doctor_info['好大夫具体年份'] = honour_detail_inf\n doctor_info['简历'] = resume_inf \n doctor_info['诊后服务星'] = star_inf\n try:\n doctor_info['疗效满意度'] = satisfaction_inf[0]\n doctor_info['态度满意度'] = satisfaction_inf[1]\n except:\n pass\n \n # part5: personal url\n personal_url = url\n doctor_info['医生个人链接'] = personal_url\n \n return doctor_info","def get_info(url):\r\n soup = make_request(url)\r\n\r\n #get press release title\r\n title_text = soup.find(\"h2\", \"con-title\").text.strip()\r\n title = title_text.partition('\\n')[0]\r\n\r\n #get press release content and date\r\n div = soup.find_all(\"div\") #find div tags\r\n for ele in div:\r\n for div2 in ele(\"div\",\"text-right\"):\r\n if \"發佈日期\" in div2.text:\r\n text = ele.text\r\n date = re.findall(\"\\d\\d\\d\\d-\\d\\d-\\d\\d\", div2.text)[0]\r\n break #prevents reiterating upwards to all div parents\r\n return date, title, text","def __extract_info(self) -> Results:\n results: Results = []\n\n response = request(self.home_url)\n\n html = bs(response, \"lxml\")\n table = html.find(\"table\")\n for row in table.find_all(\"tr\")[1:]:\n col1, col2, col3 = row.find_all(\"td\")\n filename1, perc1 = col1.text.strip().split()\n filename2, perc2 = col2.text.strip().split()\n\n with ThreadPoolExecutor() as executor:\n future = executor.submit(self.__get_line_numbers, col1.a.get(\"href\"))\n lines = future.result()\n\n result_dict = Result(\n file1=filename1,\n file2=filename2,\n percentage_file1=perc_str_to_int(perc1),\n percentage_file2=perc_str_to_int(perc2),\n no_of_lines_matched=int(col3.text.strip()),\n lines_matched=lines,\n )\n results.append(result_dict)\n return results","def extract(self, data):","def getAdditionalDetails(self, soup):\n title_details = soup.find('div', id=\"titleDetails\")\n title_details = title_details.findAll('div', class_=\"txt-block\")\n return title_details","def extract(self, doc, raw_html):\n super(KenyaTodayCrawler, self).extract(doc, raw_html)\n\n soup = BeautifulSoup(raw_html)\n\n # gather title\n doc.title = soup.find(attrs={\"property\":\"og:title\"})['content']\n\n #gather publish date\n date = self.extract_plaintext(soup.select(\"main.content .entry-meta .entry-time\"))\n doc.published_at = self.parse_timestamp(date)\n\n nodes = soup.select(\".content .entry-content p\")\n self.log.info(nodes)\n if len(nodes) > 1:\n doc.summary = self.extract_plaintext(nodes[0:1])\n doc.text = \"\\n\\n\".join(p.text.strip() for p in nodes[2:])\n\n doc.author = Author.unknown()","def scrap_societe_site(page):\n\n req = requests.get(page)\n soup = BeautifulSoup(req.content, \"html.parser\")\n\n if soup.find(\"h1\", attrs={\"id\": \"identite_deno\"}):\n new_data.loc[i, \"enseigne_et1\"] = soup.find(\"h1\", attrs={\"id\":\"identite_deno\"}).get_text().strip()\n if soup.find(\"table\", attrs={\"id\":\"rensjur\"}):\n siret_det = soup.find(\"table\", attrs={\"id\":\"rensjur\"})\n siret_det = siret_det.get_text().split(\"\\n\")\n for j in range(len(siret_det)):\n if \"SIRET\" in siret_det[j]:\n new_data.loc[i, \"siret\"] = siret_det[j+1]\n break\n\n if soup.find(\"table\", attrs={\"id\":\"rensjurcomplete\"}):\n details = soup.find(\"table\", attrs={\"id\":\"rensjurcomplete\"})\n details_list = details.get_text().split(\"\\n\")\n for j in range(len(details_list)):\n if \"Adresse\" in details_list[j]:\n new_data.loc[i, \"adr_et_voie_lib\"] = details_list[j+1]\n if \"postal\" in details_list[j]:\n new_data.loc[i, \"adr_et_post\"] = details_list[j+1]\n if \"NAF\" in details_list[j] and is_nan(new_data.loc[i, \"naf\"]):\n new_data.loc[i, \"naf\"] = details_list[j+1]\n if \"Ville\" in details_list[j]:\n new_data.loc[i, \"adr_et_com_lib\"] = details_list[j+1]\n\n elif soup.find(\"table\", attrs={\"id\":\"etab\"}):\n details = soup.find(\"table\", attrs={\"id\":\"etab\"})\n details_list = details.get_text().split(\"\\n\")\n for j in range(len(details_list)):\n if \"Adresse\" in details_list[j]:\n new_data.loc[i, \"adr_et_voie_lib\"] = details_list[j+1]\n if \"postal\" in details_list[j]:\n new_data.loc[i, \"adr_et_post\"] = details_list[j+1]\n if \"NAF\" in details_list[j] and is_nan(new_data.loc[i, \"naf\"]):\n new_data.loc[i, \"naf\"] = details_list[j+1]\n if \"Ville\" in details_list[j]:\n new_data.loc[i, \"adr_et_com_lib\"] = details_list[j+1]","def scrape_html(self, html: str):\n\n soup = BeautifulSoup(html, features=\"lxml\")\n # Seprating tags using bs4\n data_tags_tags = soup.find_all(lambda tag: True if 'data-tags' in tag.attrs else False)\n self.complete_match = {tuple(sorted([v.strip() for v in tag.attrs['data-tags'].\n split(',')])): [tag, []] for tag in data_tags_tags}\n\n # Creating dict with all the complete tags\n for k, v in self.complete_match.items():\n hold_tags = set()\n for k2 in self.complete_match.keys():\n if k == k2:\n continue\n if set(k).issubset(k2):\n hold_tags.update(set(k2).difference(k))\n v[1] = sorted([{\"name\": tag} for tag in hold_tags], key=lambda tag: tag['name'])\n\n # Creating dict with all the partial and ambigious tags\n for k, v in self.complete_match.items():\n for L in range(0, len(k) + 1):\n for subset in itertools.permutations(k, L):\n if subset:\n if not self.partial_match.get(subset):\n self.partial_match[subset] = []\n self.partial_match[subset].append(v[0])\n\n # Inserting tags in the partial match dic for reference\n hold_partial_match = self.partial_match.copy()\n for k, v in self.partial_match.items():\n if len(v) > 1:\n values = set()\n for tags in v:\n values.update(tags.attrs['data-tags'].split(','))\n v2 = v.copy()\n v2.append(sorted([{\"name\": tag} for tag in set(values).difference(k)], key=lambda tag: tag['name']))\n hold_partial_match[k] = v2\n\n self.partial_match = hold_partial_match.copy()","def get_gremium_data(self, response):\n\n # e.g. <td class=\"smc_td smc_field_silink\"><a href=\"to0040.asp?__ksinr=11487\" title=\"Details anzeigen: Bezirksvertretung Bochum-Mitte 16.05.2019 \" class=\"smc_doc smc_datatype_si\">16.05.2019</a><!--SMCINFO:si.bi.1.4.1.1.16.1.3 --> 15:00-18:09</td>\n urls = response.xpath('//tr[contains(@class, \"smcrow1\") or contains(@class, \"smcrow2\") or contains(@class, \"smcrown\")]/*/a/@href').getall()\n dates = response.xpath('//tr[contains(@class, \"smcrow1\") or contains(@class, \"smcrow2\") or contains(@class, \"smcrown\")]/*/a/text()').getall()\n\n # e.g. <a href=\"getfile.asp?id=426409&type=do&\" title=\"Einladung \" target=\"_blank\">Einladung <span class=\"smcwrapsmall smcdosize\" title=\"Dateigröße\">266\\xa0KB </span></a>\n einladungen = response.xpath('//a[contains(text(), \"Einladung\")]/@href').getall()\n # e.g. <a href=\"getfile.asp?id=427859&type=do&\" title=\"Niederschrift öffentlich \" target=\"_blank\">Niederschrift öffentlich <span class=\"smcwrapsmall smcdosize\" title=\"Dateigröße\">570\\xa0KB </span></a>\n niederschriften = response.xpath('//a[contains(text(), \"Niederschrift\")]/@href').getall()\n\n # table layout in one table row; has either no, just one, or both Einladung and Niederschrift\n tables = response.xpath('//table[contains(@class, \"smcdocbox smcdocboxright\")]').getall()\n\n # not all einladungen have niederschriften vv. insert None accordingly\n for i in range(len(tables)):\n if \"Niederschrift\" not in tables[i]:\n niederschriften.insert(i, None)\n if \"Einladung\" not in tables[i]:\n einladungen.insert(i, None)\n\n return urls, dates, niederschriften, einladungen","def parse_html(html, search):\n \n def get_field_value(field, div):\n \"\"\"\n Finds class associated with each field and gets value\n \"\"\"\n if not field.get('html_class'):\n return\n \n tag = div.find(class_=field['html_class'])\n if not tag:\n return\n \n # Fix spans for title and listing date\n if field['name'] == 'title':\n for span in tag('span'):\n span.decompose()\n elif field['name'] == 'listing_date':\n for span in tag('span'):\n span.unwrap()\n \n # Get href for URL, or inner text for other fields\n if field['name'] == 'url':\n value = tag.get('href')\n else:\n value = tag.string\n \n # Clean fields by calling functions listed in JSON\n if field.get('cleaner'):\n value = clean_field(value, field['cleaner'])\n \n return value\n \n listings = []\n for div in html:\n listing = {'search': search.name}\n \n # Get field values from HTML\n for field in fields:\n listing[field['name']] = get_field_value(field, div)\n \n # Get calculated field values\n for field in [f for f in fields if f.get('calculated')]:\n listing[field['name']] = calculate_field_value(field['name'], listing, search)\n \n listings.append(listing)\n \n df = pandas.DataFrame(listings)\n return df","def scrap_mappy_site(page):\n\n req = requests.get(url)\n soup = BeautifulSoup(req.content, \"html.parser\")\n\n if soup.find(\"p\", attrs={\"class\": \"address\"}):\n ad = soup.find(\"p\", attrs={\"class\": \"address\"}).get_text()\n ad = ad.replace(\"\\n\",\"\").strip()\n\n new_data.loc[i, \"adr_et_voie_lib\"] = ad.split(\",\")[0]\n adr = ad.split(\",\")[1].strip()\n new_data.loc[i, \"adr_et_post\"] = adr[:5].strip()\n new_data.loc[i, \"adr_et_com_lib\"] = adr[5:].strip()\n\n else:\n ad_list_soup = soup.find_all(\"li\", attrs={\"class\":\"geoentity-fulllist\"})\n ad_list = []\n for el in ad_list_soup:\n ad_list.append(el.find(\"p\").get_text())\n ad_list = [el.lower() for el in ad_list]\n\n if len(ad_list) > 0:\n\n if not is_nan(miss_rp.loc[i, \"NOMVOI_X\"]):\n rp_numvoi_list = miss_rp.loc[i, \"NOMVOI_X\"].split(\" \")\n rp_numvoi_list = [el.lower() for el in rp_numvoi_list if\n el.lower() not in [\"un\",\"une\", \"de\", \"des\", \"du\",\n \"le\", \"la\", \"les\", \"rue\", \"boulevard\", \"route\", \"chemin\"]]\n\n ad = 0\n for el in ad_list:\n el2 = el.split(\",\")[0]\n for word in el2.split(\" \"):\n if word in rp_numvoi_list:\n ad = el\n if ad == 0:\n ad = ad_list[0]\n else:\n ad = ad_list[0]\n\n new_data.loc[i, \"adr_et_voie_lib\"] = ad.split(\",\")[0]\n adr = ad.split(\",\")[1].strip()\n new_data.loc[i, \"adr_et_post\"] = adr[:5].strip()\n new_data.loc[i, \"adr_et_com_lib\"] = adr[5:].strip()\n\n return \"+\"+str(new_data.loc[i, \"adr_et_voie_lib\"])+\"+\"+str(new_data.loc[i, \"adr_et_post\"])+\"+\"+str(new_data.loc[i, \"adr_et_com_lib\"])+\"+societe.com\"","def get_html_content():\n url = \"https://www.worldometers.info/coronavirus/\"\n req_data = requests.get(url).text\n soup = BeautifulSoup(req_data, 'html.parser')\n html_data = soup.select(\"#main_table_countries_today > tbody:nth-child(2) > tr[style='']\")\n return html_data","def getLineInformation(html, reg=['(?<=<\\/h2><li>).*?<\\/a>', '<.*a.*?>', '查公交上八六八四cn']):\n tmp = re.findall(reg[0], html)[0]\n Info = re.sub(reg[1], '', tmp)\n info = re.sub(reg[2], '', Info)\n return info","def _get_new_data(self, page_url, soup):\n data = {}\n data['url'] = page_url\n title = soup.find('dd', class_='lemmaWgt-lemmaTitle-title').find('h1')\n data['title'] = title.get_text()\n summary = soup.find('div', class_='lemma-summary')\n data['summary'] = summary.get_text()\n return data","def scrape_html(html):\n return YoutubeScrape(BeautifulSoup(html))","def __init__(self, html_soup):\n # Drilling down to the internal wrapper <div> tag\n self.data = html_soup.find('div', class_='sbkBrv_SingleResultDesc')","def scrape(self):\n #Get page\n soup, _ = getPage(self.url)\n\n #Check if page available\n if soup is None:\n #Not available, skip iteration\n self.na = True\n return\n\n #Get Price\n self.price = soup.find(class_=\"user-ad-price__price\").get_text()\n #Get list of attr names and values\n adAttrVals = soup.find_all(class_=\"vip-ad-attributes__value\")\n adAttrName = soup.find_all(class_=\"vip-ad-attributes__name\")\n #Search attrs for date listed\n for i in range(0,len(adAttrName)):\n if adAttrVals[i].contents[0] == \"Date Listed\":\n self.listDate = adAttrName[i].contents[0]\n break","def _parse_page_contents (self, page_soup):\n netflix_page_data = self.extract_inline_netflix_page_data(page_soup=page_soup)\n self.user_data = self._parse_user_data(netflix_page_data=netflix_page_data)\n self.esn = self._parse_esn_data(netflix_page_data=netflix_page_data)\n self.api_data = self._parse_api_base_data(netflix_page_data=netflix_page_data)\n self.profiles = self._parse_profile_data(netflix_page_data=netflix_page_data)\n self.log(msg='Found ESN \"' + self.esn + '\"')\n return netflix_page_data","def parse(html_doc):\n soup = BeautifulSoup(html_doc, 'html.parser')\n table = soup.find('div', class_=\"container-fluid cols_table show_visited\")\n# print table.prettify().encode('UTF-8')\n jobstats = []\n\n for row in table:\n jobstats.append({\n \"title\":row.find('div', class_=\"col-sm-7\").a.text,\n \"category\":row.find('div', class_=\"text-muted\").a.text,\n \"price\":row.find('div', class_=\"col-sm-2 amount title\").\n text.strip(),\n \"applications\":row.find(\n 'div', class_=\"col-sm-3 text-right text-nowrap hidden-xs\"\n ).text.strip()\n })\n return jobstats","def parse_soup(self, soup):\n # find all class_='gs_r gs_or gs_scl' => each result\n return soup.find_all('li', class_='ais-InfiniteHits-item')","def scrape_data():\n soup = get_page_source_code(\"https://www.tradingview.com/markets/stocks-usa/market-movers-active/\")\n stock_table = soup.select(\".tv-data-table__tbody tr\")\n \n stock_data = []\n\n for data in stock_table:\n stock_name = data.find(\"a\").get_text(strip=True)\n stock_price = data.select(\"td\")[1].get_text(strip=True)\n percentage_change = data.select(\"td\")[2].get_text(strip=True)\n rating = data.select(\"td\")[4].get_text(strip=True)\n stock_volume = data.select(\"td\")[5].get_text(strip=True)\n\n details = {\n\n \"name\": stock_name,\n \"price\": stock_price,\n \"change\": percentage_change,\n \"rating\": rating,\n \"volume\": stock_volume,\n \n }\n stock_data.append(details)\n return stock_data","def parse(self):\n result = {}\n if self.detail_statu:\n sel = Selector(text=self.driver.page_source)\n\n fact_table = sel.xpath(\n '//div[@class=\"facts-table\"]//text()').extract()\n result['facts'] = [list(i)\n for i in zip(fact_table[:: 2],\n fact_table[1:: 2])]\n\n tax_table = sel.xpath(\n '//div[@class=\"tax-values\"]//text()').extract()\n result['taxs'] = [list(i)\n for i in zip(tax_table[:: 2],\n tax_table[1:: 2])]\n\n listing_detail = sel.xpath(\n '//div[@class=\"amenities-container\"]//text()').extract()\n result['detail'] = listing_detail\n result['page_source'] = self.driver.page_source\n self.detail_statu = False\n else:\n self.log.warning(\n '---- Detail page url out of reach, use .search() first to get the detail page')\n return result","def scrape(self):\n\n self.url = self.headline.url\n\n # Should raise exception...\n if not self.parsing_template:\n return None, None, None, None, None\n\n try:\n response = self.download()\n self.source = response.text\n except:\n return None, None, None, None, None\n\n soup = BeautifulSoup(response.content, \"html.parser\")\n\n if soup:\n return self.parse(soup)\n else:\n return None, None, None, None, None","def extract_data(self, root, path, tag):\n data = []\n element = root.xpath(path)\n if element:\n url = self.PODEROPEDIA_BASE_URL + element[0].get('data-w2p_remote', None)\n if url:\n self.logger.debug('Querying {} from {}'.format(tag, url))\n try:\n response = self.session.get(url)\n response.raise_for_status()\n content = response.content\n html_tree = etree.HTML(content, parser=self.parser)\n if html_tree is None:\n return data\n rows = html_tree.xpath('.//*[starts-with(@id, \"collapse\")]/div/table/tr')\n for row in rows:\n target = target_name = target_path = relationship = None\n when = where = where_name = where_path = source = None\n row_id = row.get('id', '')\n cells = row.getchildren()\n idx = 0\n while idx < len(cells) - 1:\n try:\n cell_text = text_strip(cells[idx])\n except AttributeError:\n cell_text = ''\n sources = cells[idx].xpath('.//*[@class=\"fuente\"]')\n if len(sources) > 0:\n source = process_sources(cells[idx])\n elif cell_text == 'es' or cell_text == 'fue':\n when = cell_text\n idx = idx - 1\n target = cells[idx].find('a')\n if target is not None:\n target_path = target.get('href', None)\n target_name = text_strip(target)\n idx = idx + 2\n relationship = text_strip(cells[idx])\n elif cell_text == 'a' or cell_text == 'de':\n idx = idx - 1\n relationship = text_strip(cells[idx])\n idx = idx + 2\n target = cells[idx].find('a')\n if target is not None:\n target_path = target.get('href', None)\n target_name = text_strip(target)\n elif cell_text.startswith('desde'):\n when = cell_text\n elif 'es pasado' in cell_text:\n when = cell_text\n else:\n try:\n ignore = int(cell_text)\n when = cell_text\n except ValueError:\n potential_date = cell_text.split(' ')[0]\n try:\n ignore = datetime.strptime(potential_date, '%d-%m-%Y')\n when = cell_text\n except ValueError:\n try:\n ignore = datetime.strptime(potential_date, '%m-%Y')\n when = cell_text\n except ValueError:\n pass\n idx = idx + 1\n entry = {\n 'type': tag,\n 'target_path': target_path,\n 'relationship': relationship,\n 'when': when,\n 'where': where,\n 'source': source\n }\n data.append(entry)\n self.logger.debug('{}: {}'.format(tag, entry))\n except (requests.exceptions.HTTPError, etree.ParserError):\n self.logger.info('Something bad happened', exc_info=True)\n return data","def find_details_json(self, url):\n response = self.get_response(url)\n if response:\n html_soup = BeautifulSoup(response.text, 'html.parser')\n listings_json = html_soup.find('script', id='__NEXT_DATA__')\n if listings_json:\n listings_json = str(listings_json)\n listings_json = listings_json.replace(\"<script id=\\\"__NEXT_DATA__\\\" type=\\\"application/json\\\">\", \"\").replace(\"</script>\", \"\")\n listings = json.loads(listings_json)\n return listings\n else:\n skip_scraper(self.college, 'Trulia')","def _extract_channel_data(self, html_rsp):\n soup = BeautifulSoup(html_rsp, 'html.parser')\n script = self._filter_scripts(soup.find_all('script'))\n data_dict = self._parse_js(script)\n return data_dict","def scrape(self, page_src):\n soup = bs(page_src, 'html.parser')\n self._data = list(map(lambda a : a.get('href'), soup.find_all('a')))\n print(self._data)","def data_collector(self, n, url, ret):\n try:\n html = urllib2.urlopen(url).read()\n soup = BeautifulSoup(html)\n ret[n] = [soup.title.string, url, html[0:100]]\n except:\n ret[n] = [\"Error\", url, \"Error\"]","def retrieving_data():\n for x in range(1):\n page_number=random.randint(1,500)\n page_num=str(page_number)\n url = 'http://www.tastespotting.com/browse/'+page_num\n req = http.request('GET', url)\n data = BeautifulSoup(req.data,'html.parser')\n for each_div in data.find_all(\"div\", { \"class\": \"trendspotted-item\"}):\n for each_recipe in each_div.find_all('a', href=True):\n \"\"\"links starting with /clicks are the links of recipe to their original sites, so just retrieve those links\"\"\"\n if each_recipe['href'].startswith('/click'):\n retrieving_data.recipe_link=each_recipe['href'][16:-12]\n for each_img in each_recipe.find_all('img', alt=True):\n retrieving_data.recipe_image=each_img['src']\n for each_caption in each_div.find(\"p\", { \"class\": \"photo_caption\"}):\n retrieving_data.recipe_title=each_caption","def scrape(content):\n columns = ['name',\n 'description',\n 'location',\n 'link',\n 'price',\n 'operation',\n 'rooms',\n 'bathrooms',\n 'construction (m2)',\n 'terrain (m2)']\n\n data = pd.DataFrame(columns=columns)\n # Generate soup\n soup = BeautifulSoup(content, 'html.parser')\n # Get Characteristics\n for d in soup.find_all(class_=\"ListingCell-AllInfo\"):\n temp_dict = {}\n try:\n temp_dict['name'] = d.find(class_=\"ListingCell-KeyInfo-title\").text.strip()\n temp_dict['description'] = d.find(class_=\"ListingCell-shortDescription\").text.strip()\n temp_dict['location'] = ' '.join([j.strip() for j in d.find(class_=\"ListingCell-KeyInfo-address\").text.strip().split('\\n')])\n temp_dict['link'] = d.find(class_=\"ListingCell-KeyInfo-title\").find('a').get('href')\n temp_dict['price'] = d.find(class_=\"PriceSection-FirstPrice\").text.strip()\n temp_dict['operation'] = _operation\n for att in d.find(class_=\"KeyInformation\").find_all(class_=\"KeyInformation-attribute\"):\n att = att.text.lower()\n if 'recámara' in att:\n temp_dict['rooms'] = statistics.mean([int(s) for s in att.split() if s.isdigit()])\n elif 'baño' in att:\n temp_dict['bathrooms'] = statistics.mean([int(s) for s in att.split() if s.isdigit()])\n elif 'terreno' in att:\n temp_dict['terrain (m2)'] = statistics.mean([int(s) for s in att.split() if s.isdigit()])\n elif 'superficie' in att:\n temp_dict['construction (m2)'] = statistics.mean([int(s) for s in att.split() if s.isdigit()])\n except Exception as e:\n print(e)\n continue\n data = data.append(temp_dict, ignore_index=True)\n print('Found {} depts'.format(len(data['name'])))\n return data","def get_soup_general_data(soup):\n data_dict = {}\n\n name = soup.find(class_='product_title')\n if name:\n data_dict['name_of_game'] = name.h1.text\n\n pub = soup.find('li', class_='summary_detail publisher')\n if pub:\n data_dict['publisher'] = pub.a.text.strip()\n\n rel_date = soup.find('li', class_='summary_detail release_data')\n if rel_date:\n rel_date = rel_date.find('span', class_='data')\n if rel_date:\n data_dict['release_date'] = rel_date.text.strip()\n\n num_p = soup.find(\"li\", class_=\"summary_detail product_players\")\n if num_p:\n data_dict['num_players'] = num_p.find(class_=\"data\").text\n\n genres = soup.find(\"li\", class_='summary_detail product_genre')\n if genres:\n genres = genres.find_all('span', class_='data')\n data_dict['genres'] = [genre.text for genre in genres]\n\n age = soup.find(\"li\", class_=\"summary_detail product_rating\")\n if age:\n data_dict['age_rating'] = age.find('span', class_=\"data\").text\n\n return data_dict","def parse_html_data(html_data):\n\n contracts = []\n\n soup = BeautifulSoup(html_data, \"lxml\")\n\n for tr in soup.find_all(\"tr\", class_=\"grid-row\"):\n contract = get_new_contract(tr)\n if contract:\n contracts.append(contract)\n\n return contracts","def parseSearchHtml(self):\n pass","def parseSearchHtml(self):\n pass","def parsed_html():\n return utils.parse_html(\n \"\"\"\n <!doctype hmtl>\n <html>\n <head>\n <meta charset=\"utf-8\">\n <meta name=\"viewport\" content=\"width=device-width\">\n <title>Page title</title>\n <link rel=\"stylesheet\" href=\"/static/styles.css\" />\n </head>\n <body>\n <h1>Django Auto AMP</h1>\n <p>Generate automatic AMP from your Django templates</p>\n <img src=\"/static/img.jpg\" width=\"500\" height=\"300\" />\n <img src=\"/static/img.gif\" layout=\"nodisplay\" />\n <img src=\"/static/img.png\" />\n <script type=\"text/javascript\" src=\"/static/scripts.js\" />\n <script type=\"application/json\" src=\"/static/data.json\" />\n </body>\n </html>\n \"\"\"\n )","def get_data():\n info_html = urlopen(\"http://marci1368.getmarci.com\").read()\n div = Soup(info_html, \"html.parser\").find('div', {'id': 'letterbox1'})\n moreinfo = get_itunes_info(div[\"data-artist\"], div[\"data-title\"])\n if not moreinfo:\n return {\"artistName\": div[\"data-artist\"],\n \"trackName\": div[\"data-title\"],\n \"collectionName\": div[\"data-album\"]}\n return moreinfo","def extractSearchResults(self, html):\n results = list()\n soup = BeautifulSoup(html, 'html.parser')\n div = soup.find('div', id='main')\n if (type(div) == types.NoneType):\n div = soup.find('div', id='center_col')\n if (type(div) == types.NoneType):\n div = soup.find('body')\n if (type(div) != types.NoneType):\n lis = div.findAll('a')\n if(len(lis) > 0):\n for link in lis:\n if (type(link) == types.NoneType):\n continue\n \n url = link['href']\n if url.find(\".google\") > 6:\n continue\n \n url = self.extractUrl(url)\n if(cmp(url, '') == 0):\n continue\n title = link.renderContents()\n title = re.sub(r'<.+?>', '', title)\n result = SearchResult()\n result.setURL(url)\n print '### URL: ' + url\n result.setTitle(title)\n span = link.find('div')\n if (type(span) != types.NoneType):\n content = span.renderContents()\n content = re.sub(r'<.+?>', '', content)\n result.setContent(content)\n results.append(result)\n return results","def scrape_technical_data(self):\n\n page = requests.get(self.url)\n soup = BeautifulSoup(page.content, \"html.parser\")\n results = soup.find(\n \"section\", class_=\"titlereference-section-additional-details\"\n )\n results = results.find_all(\"tr\")\n\n data = []\n for res in results:\n data.append(res)\n\n runtime = data[1]\n runtime = re.sub(\"<.*?>\", \"\", str(runtime))\n runtime = (\n runtime.replace(\" \", \"\")\n .replace(\"Runtime\", \"\")\n .replace(\"\\n\", \"\")\n .replace(\"min\", \" min\")\n )\n\n country = \"\"\n languages = []\n color = \"\"\n social = {}\n if \"Country</td>\" in str(data[2]):\n country = data[2].find_all(\"a\")\n country = re.sub(\"<.*?>\", \"\", str(country))\n\n languages = data[3].find_all(\"a\")\n languages = re.sub(\"<.*?>\", \"\", str(languages))\n\n color = data[4].find_all(\"a\")\n color = re.sub(\"<.*?>\", \"\", str(color))\n elif \"Country</td>\" in str(data[3]):\n sites = data[2].find_all(\"a\")\n for site in sites:\n key = (\n re.sub(\"<.*?>\", \"\", str(site))\n .replace(\"\\n\", \"\")\n .replace(\" \", \"\")\n .replace(\" \", \"\")\n )\n social[key] = site[\"href\"]\n\n country = data[3].find_all(\"a\")\n country = re.sub(\"<.*?>\", \"\", str(country))\n\n languages = data[4].find_all(\"a\")\n languages = re.sub(\"<.*?>\", \"\", str(languages))\n\n color = data[5].find_all(\"a\")\n color = re.sub(\"<.*?>\", \"\", str(color))\n\n country = country.replace(\"[\", \"\").replace(\"]\", \"\")\n languages = languages.replace(\"[\", \"\").replace(\"]\", \"\")\n color = color.replace(\"[\", \"\").replace(\"]\", \"\")\n\n return (runtime, country, languages, color, social)","def _parse_events(self, html):\n print \"Parse events\"\n data = []\n soup = BeautifulSoup(html, \"html.parser\")\n events = soup.find_all(\"div\", {\"class\": \"program clearfix\"})\n \"\"\" Site's html is broken. We have to handle descriptions \n with a hack.\n \"\"\"\n descriptions = soup.find_all(\"div\", {\"class\": \"programpostingress\"})\n for index, event in enumerate(events):\n link_tag = event.find(\"a\")\n if link_tag:\n link = link_tag[\"href\"]\n else:\n link = None\n dates = self._parse_date(self._parse_text(event.find(\"span\", {\"class\": \"programpostdato\"})))\n row = {\n \"title\": self._parse_text(event.find(\"span\", {\"class\": \"programposttittel\"})),\n \"date_start\": dates[\"start\"],\n \"date_end\": dates[\"end\"],\n \"description\": self._parse_text(descriptions[index]),\n \"link\": link,\n \"country\": \"Norge\"\n }\n data.append(row)\n print \"Found %s events\" % len(data)\n return data","def parse_article_html(page_resp):\n article_url = page_resp.url\n \n article_page_soup = bs4.BeautifulSoup(page_resp.text, \"lxml\")\n \n title_html = article_page_soup.find_all(\"h1\")[0]\n title_text = title_html.contents[0]\n \n date = article_page_soup.find_all(\"small\", {'class': 'gray'})[0]\n date_text = date.contents[4].replace(\" \", \"\").split(\"\\n\")[3][:10]\n \n article_content = article_page_soup.find_all(\"div\", {'class': 'rich_media_content'})[0]\n article_text = article_content.get_text('\\n')\n is_original = check_if_original(article_content) or '[原创]' in title_text\n \n return {\n 'title': title_text,\n 'date': date_text,\n 'url': article_url,\n 'is_original': is_original,\n 'text': article_text\n \n}","def extract_movies(dom):\n\n # extract data per movie\n movies = dom.find_all('div', class_ = 'lister-item mode-advanced')\n\n # list to store scraped data\n movielist = []\n\n for movie in movies:\n\n # append extracted data to this dict\n moviedict = {}\n\n # scrape titles and add to dict\n moviedict['title'] = movie.h3.a.text\n\n # scrape ratings and add to dict\n moviedict['rating'] = float(movie.strong.text)\n\n # scrape year of release and add to dict\n year = movie.h3.find('span', class_ = 'lister-item-year text-muted unbold')\n moviedict['year'] = re.findall('\\d+', year.text.strip('()'))[0]\n\n # scrape actors and add to dict\n actors = movie.find_all(href=re.compile(\"adv_li_st\"))\n actorlist = []\n for actor in actors:\n actorlist.append(actor.text)\n actorstring = ', '.join(actorlist)\n moviedict['actors'] = actorstring\n\n # scrape runtime and add to dict\n moviedict['runtime'] = movie.p.find('span', class_ = 'runtime').text.split(' ')[0]\n movielist.append(moviedict)\n\n\n # ADD YOUR CODE HERE TO EXTRACT THE ABOVE INFORMATION ABOUT THE\n # HIGHEST RATED MOVIES\n # NOTE: FOR THIS EXERCISE YOU ARE ALLOWED (BUT NOT REQUIRED) TO IGNORE\n # UNICODE CHARACTERS AND SIMPLY LEAVE THEM OUT OF THE OUTPUT.\n\n return movielist # REPLACE THIS LINE AS WELL IF APPROPRIATE","def parse(content):\r\n soup = BeautifulSoup(content)\r\n submissions = soup.findAll('div')\r\n\r\n submission_data = []\r\n for s in submissions:\r\n t = s.getText()\r\n try:\r\n num, date, score = num_date_pattern.findall(t)[0]\r\n num = s.find('b').getText()\r\n data = json.loads(unescape(s.find('pre').getText()))\r\n d = {'submission_num': num,\r\n 'date': date,\r\n 'score': score,\r\n 'detail': data}\r\n submission_data.append(d)\r\n except Exception, e:\r\n logging.error(\"Error finding num_date_pattern: %s. Text:\\n%s\" % (e, t))\r\n return submission_data","def extract_data(self, id: str) -> dict:\r\n\r\n raw = self.session.get(f\"{self.host}/{id}\")\r\n soup = self.soup(raw)\r\n\r\n dlx = soup.find(class_=\"dlx\")\r\n result = {}\r\n\r\n for table in dlx.findAll(\"table\"):\r\n if not table.strong:\r\n continue\r\n\r\n if (h4 := table.findPrevious(\"h4\", style=\"text-align: center\")):\r\n eps = h4.text\r\n elif (prevTable := table.findPrevious(\"table\")):\r\n eps = prevTable.text\r\n\r\n d = {}\r\n res = None\r\n for tr in table.findAll(\"tr\"):\r\n if not tr.a:\r\n res = tr.text\r\n d[res] = {}\r\n continue\r\n for a in tr.findAll(\"a\"):\r\n d[res][a.text] = a[\"href\"]\r\n result[eps] = d\r\n return result","def extract(soup):\r\n table = soup.find('div', id='dnn_ctr11396_TimeTableView_PlaceHolder').find('table')\r\n rows = table.findChildren('tr', recursive=False)\r\n return [[col.findAll('div', {'class': 'TTLesson'}) for col in row.findChildren('td', recursive=False)[1:]]\r\n for row in rows[1:]]","def extract(self, response):\n # print response.url,\"extract response url\"\n sel = response.selector\n pages = []\n try:\n # print \"pages work\"\n pages = sel.xpath(\"//div[contains(@class,'fen_ye_nav')]//td/text()\").re(u\"共([\\d]{1,3})页\")\n # print pages\n except Exception, e:\n print e,\"error pages\"\n log.msg(e, level=log.ERROR)\n log.msg(response.url, level=log.ERROR)\n\n if len(pages) == 0:\n self.getUserName(response) #only one page\n else:\n for page in range(int(pages[0])+1)[1:]: #fro test\n url = response.url+\"_m0_p\"+str(page)\n yield Request(url, callback=self.getUserName,dont_filter=True)","def load_data(url: str):\n\n page = requests.get(url=url)\n soup = BeautifulSoup(page.content, 'html.parser')\n return soup","def gather_current(url=URL):\n page = requests.get(url)\n soup = BeautifulSoup(page.content, 'html.parser')\n return soup","def get_info(self):\r\n\r\n self.driver.get(WEBSITE)\r\n time.sleep(3)\r\n self.driver.find_element_by_xpath(\"\"\"//*[@id=\"modalContent\"]/div/button/i\"\"\").click()\r\n time.sleep(3)\r\n #gets prices and appends to list\r\n all_prices = self.driver.find_elements_by_class_name(\"firstPrice\")\r\n for price in all_prices:\r\n text = price.text\r\n new_p = text.replace(\".\", \"\")\r\n price_int = int(new_p.split(\" \")[1])\r\n self.price_list.append(price_int)\r\n #gets addresses\r\n all_addresses = self.driver.find_elements_by_class_name(\"postingCardLocationTitle\")\r\n for address in all_addresses:\r\n self.address_list.append(address.text)\r\n print(self.address_list)\r\n # gets info\r\n ad_info = self.driver.find_elements_by_css_selector(\"a.go-to-posting\")\r\n for info in ad_info:\r\n links = info.get_attribute('href') #gets href link inside the css\r\n self.all_links.append(links)\r\n self.all_info.append(info.text)\r\n\r\n # Just for tests\r\n print(self.price_list)\r\n print(self.all_info)\r\n print(self.all_links)","def extract_main_table_from_html(html):\n soup = bs(html, 'html.parser')\n table = soup.find('table')\n return(table)","def scrape_story_metadata(self, story_id):\n url = '{0}/s/{1}'.format(self.base_url, story_id)\n result = requests.get(url)\n html = result.content\n #print html \n soup = BeautifulSoup(html, self.parser)\n\n # print soup\n try:\n pre_story_links = soup.find(id='pre_story_links').find_all('a')\n except AttributeError:\n pre_story_links = None\n if re.search(r\"var userid = (.*);\", str(soup)) is None:\n author_id = \"0\"\n else: \n author_id = int(re.search(r\"var userid = (.*);\", str(soup)).groups()[0]);\n #print re.search(r\"var title = (.*);\", str(soup))\n if re.search(r\"var title = (.*);\", str(soup)) is None:\n title = \"NO-TITLE\"\n else:\n title = re.search(r\"var title = (.*);\", str(soup)).groups()[0];\n title = unquote_plus(title)[1:-1]\n metadata_div = soup.find(id='profile_top')\n# times = metadata_div.find_all(attrs={'data-xutime':True})\n# metadata_text = metadata_div.find(class_='xgray xcontrast_txt').text\n# metadata_parts = metadata_text.split('-')\n# genres = self.get_genres(metadata_parts[2].strip())\n metadata = {\n 'id': story_id,\n# 'canon_type': pre_story_links[0].text,\n# 'canon': pre_story_links[1].text,\n 'author_id': author_id,\n 'title': title,\n# 'updated': int(times[0]['data-xutime']),\n# 'published': int(times[1]['data-xutime']),\n# 'lang': metadata_parts[1].strip(),\n# 'genres': genres\n }\n \"\"\"\n for parts in metadata_parts:\n parts = parts.strip()\n tag_and_val = parts.split(':')\n if len(tag_and_val) != 2:\n continue\n tag, val = tag_and_val\n tag = tag.strip().lower()\n if tag not in metadata:\n val = val.strip()\n try:\n val = int(val.replace(',', ''))\n metadata['num_'+tag] = val\n except:\n metadata[tag] = val\n if 'status' not in metadata:\n metadata['status'] = 'Incomplete'\n \"\"\"\n return metadata","def extract_element_data(soup, params):\r\n \r\n # 1. Find the right tag\r\n if 'class' in params:\r\n elements_found = soup.find_all(params['tag'], params['class'])\r\n else:\r\n elements_found = soup.find_all(params['tag'])\r\n \r\n # 2. Extract text from these tags\r\n if 'get' in params:\r\n element_texts = [el.get(params['get']) for el in elements_found]\r\n else:\r\n element_texts = [el.get_text() for el in elements_found]\r\n \r\n # 3. Select a particular text or concatenate all of them\r\n tag_order = params.get('order', 0)\r\n if tag_order == -1:\r\n output = '**__**'.join(element_texts)\r\n else:\r\n output = element_texts[tag_order]\r\n \r\n return output","def decode_html(html):\n soup = BeautifulSoup(html, 'xml')\n return {\n 'personality': ProfileApi.get_personality(soup),\n 'ida_years': ProfileApi.get_ida_years(soup),\n 'years': ProfileApi.get_years(soup),\n 'choices': ProfileApi.get_choices(soup)\n }","def extract(self):\n\n table = self.soup.table\n table_rows = table.find_all('tr')\n\n for tr in table_rows:\n td = tr.find_all('td')\n data = [item.text for item in td]\n\n #Extracting each row and save it in a file as an object\n try:\n \n with open('data.txt', 'wb') as file:\n my_pickler = pickle.Pickler(file)\n my_pickler.dump(data)\n\n except Exception as exc:\n print('Error while saving data :', exc)\n\n #saving data in our dictionaries\n try: \n \n with open('data.txt', 'rb') as file:\n my_dipeck = pickle.Unpickler(file)\n my_data = my_dipeck.load()\n self.deaths_per_country[my_data[0]] = my_data[3]\n self.cases_per_country[my_data[0]] = my_data[1]\n\n except Exception as exc2:\n print('Error while loading data :', exc2)","def extract_data():\n books = WebScraper().get_top_100_data()\n time.sleep(2)\n BookDetailsWebScrapper().save_book_details(books)\n _save_extract_state(books)","def scrape(self):\n\n #Get page\n soup, _ = getPage(self.url)\n\n #Check page was found\n if soup is None:\n self.na = True\n return\n\n #Find price\n try:\n self.price = soup.find(class_=\"user-ad-price__price\").get_text()\n except:\n pass\n\n #Find attributes names/values\n adAttrVals = soup.find_all(class_=\"vip-ad-attributes__name\")\n adAttrName = soup.find_all(class_=\"vip-ad-attributes__value\")\n #Find description\n try:\n self.description = soup.find(class_=\"vip-ad-description__content--wrapped\").get_text()\n except:\n pass\n\n #Check all attributes for important information\n for i in range(0,len(adAttrName)):\n tempName = adAttrName[i].get_text()\n tempVal = adAttrVals[i].get_text()\n if \"Date Listed:\" in tempName:\n #Can be date or words (eg 16 minutes ago, yesterday)\n try:\n #Will work if date\n listDateLst = tempVal.lstrip().split('/')\n self.listDate = listDateLst[2]+'-'+listDateLst[1]+'-'+listDateLst[0]\n except:\n #Check not empty\n if tempVal is not None:\n if tempVal == \"Yesterday\":\n #Yesterday\n self.listDate = (datetime.today() - timedelta(days=1)).strftime('%Y-%m-%d')\n else:\n #Either hours or minutes\n self.listDate = datetime.today().strftime('%Y-%m-%d')\n \n elif \"Displacement (cc):\" in tempName:\n self.displacement = tempVal.lstrip()\n elif \"Make:\" in tempName:\n self.make = tempVal.lstrip()\n elif \"Model:\" in tempName:\n self.model = tempVal.lstrip()\n elif \"Year:\" in tempName:\n self.year = tempVal.lstrip()\n elif \"KMs:\" in tempName:\n self.kms = tempVal.lstrip()\n elif \"Registered:\" in tempName:\n if tempVal.lstrip() == \"Yes\":\n self.registered = \"Y\"\n elif tempVal.lstrip() == \"No\":\n self.registered = \"N\"\n elif \"Registration Expiry:\" in tempName:\n regExpLst = tempVal.lstrip().split('/')\n self.regExpiry = regExpLst[2]+'-'+regExpLst[1]+'-'+regExpLst[0]\n elif \"Colour:\" in tempName:\n self.colour = tempVal.lstrip()\n elif \"Learner Approved:\" in tempName:\n if tempVal.lstrip() == \"Yes\":\n self.learner = \"Y\"\n elif tempVal.lstrip() == \"No\":\n self.learner = \"N\"\n elif \"Listing Type:\" in tempName:\n self.listType = tempVal.lstrip()","def get_article1(url):\n # set agent\n agent = 'codeup ds germain' \n # query\n response = requests.get(url, headers={'User-Agent': agent}) \n # soup\n soup = BeautifulSoup(response.text) \n # get cat\n category = soup.find_all('li', {'class':'active-category selected'})[0].text \n # get raw cards\n cards = soup.select('.news-card') \n # create list of dicts for dataframe\n card_dict_list = [] \n # iterate each card\n for card in cards: \n # headline\n headline = card.find_all('span', {'itemprop':'headline'})[0].text \n # publish time\n publish_time = card.find_all('span', {'class':'time'})[0].text \n # content\n content = card.find_all('div', {'itemprop':'articleBody'})[0].text.strip() \n # create dict\n card_dict = {'headline':headline, 'publish_time':publish_time,\n 'category':category, 'content':content} \n # push dict to list\n card_dict_list.append(card_dict) \n # return dataframe\n return pd.DataFrame(card_dict_list)","def _grab_tags(self, url):\n a = self._api_request(url)\n return bs4.BeautifulSoup(a,features=\"html.parser\")","def parse(self, response: BeautifulSoup):\n raise NotImplementedError","def scrape_callback(url, html):\r\n fields = ('area', 'population', 'iso', 'country', 'capital',\r\n 'continent', 'tld', 'currency_code', 'currency_name',\r\n 'phone', 'postal_code_format', 'postal_code_regex',\r\n 'languages', 'neighbours')\r\n if re.search('/view/', url):\r\n tree = fromstring(html)\r\n all_rows = [\r\n tree.xpath('//tr[@id=\"places_%s__row\"]/td[@class=\"w2p_fw\"]' % field)[0].text_content()\r\n for field in fields]\r\n print(url, all_rows)","def parse_client_html(self):\n data = dict()\n data[\"date\"] = self.date.strftime(\"%m-%d-20%y\")\n name_tag = self.client.find_all(\"span\", {\"class\": \"client-info-name\"})\n data[\"name\"] = name_tag[0].text\n guests_tag = self.client.find_all(\"div\", {\"class\": \"col col-partysize number-fix\"})\n data[\"guests\"] = guests_tag[0].text\n min_tag = self.client.find_all(\"div\", {\"class\": \"col col-minimum\"})\n data[\"min\"] = min_tag[0].text\n table_tag = self.client.find_all(\"div\", {\"class\": \"col col-table-no ellipsis\"})\n data[\"table\"] = table_tag[0].text\n notes_tag = self.client.find_all(\"div\", {\"class\": \"is-mobile notes\"})\n if notes_tag:\n data[\"notes\"] = notes_tag[0].text\n else:\n data[\"notes\"] = \"\"\n data[\"booked\"] = self.client[\"sort_via\"]\n return data","def parse_page(url):\n\n page = requests.get(url)\n soup = BeautifulSoup(page.text, 'html.parser')\n\n listings = []\n\n # Loop throuhg all prices\n for offer in soup.findAll(\"div\", {\"class\": \"regular-ad\"}): # Scan regular-ad class to avoid featured ads realted to Kijiji Ads\n \n current_listing_dict = {}\n\n # Parse title\n title_list = offer.find_all(href=True)[0].text.split(\" \")\n title = [i for i in title_list if i]\n title = \" \".join(title).rstrip().strip(\"\\n\").strip(\" \")\n\n # Append title to dict\n current_listing_dict['title'] = title\n\n # Parse price\n price = \"\".join(offer.findAll(\"div\", {\"class\": \"price\"})[0].text.split(\" \")).rstrip().strip('\\n')\n\n if '$' in price:\n price = price.split('$')[-1].replace(',','')\n\n # Append price to dict\n current_listing_dict['price'] = price\n \n # Parse link\n link = offer.find_all(href=True)[0]['href']\n\n # Append link to dict\n current_listing_dict['link'] = link\n\n # Append to global listings list\n listings.append(current_listing_dict)\n\n return listings","def archive_parse_for_posts(page_html):\n # <div\\s+class=\"post.+data\\-post\\-id\\=['\"](\\d+)['\"].+?<span\\s+class=['\"]post_date['\"]>([^<]+)</span>\n post_info_regex = \"\"\"<div\\s+class=\"post.+?data\\-post\\-id\\=['\"](\\d+)['\"].+?<span\\s+class=['\"]post_date['\"]>([^<]+)</span>\"\"\"\n post_info = re.findall(post_info_regex, page_html, re.IGNORECASE|re.DOTALL)\n return post_info","def scrape_google(html_content):\n soup = BeautifulSoup(html_content)\n tag = soup.a\n company_name = tag.get_text()\n url = tag['href']\n #print(company_name,url)\n #return company_name, url\n return url","def scrape_layer(self):\n\t\t# azlyrics structure\n\t\ttry:\n\t\t\t#page = urlopen('http://www.azlyrics.com/lyrics/edsheeran/theateam.html')\n\t\t\tpage = urlopen('http://www.azlyrics.com/lyrics/edsheeran/theteam.html')\n\t\t\tsoup = BeautifulSoup(page.read(), \"lxml\")\n\t\t\tcontent_div = soup_az.find(\"div\", class_=\"col-xs-12 col-lg-8 text-center\")\n\t\t\tlyric_div = content_div.find(\"div\", class_=None)\n\t\t\t# change line break tag to dots\n\t\t\tprocessed = re.sub('<br\\s*?>', '.', lyric_div.text)\n\t\t\tlines = processed.split()\n\t\t\treturn lines\n\t\texcept URLError: # if no url or other connection errors, skip\n\t\t\tprint(\"no url\")\n\t\t\tpass\n\n\t\t# MetroLyrics structure\n\t\ttry:\n\t\t\t#page = urlopen('http://www.metrolyrics.com/a-team-lyrics-ed-sheeran.html')\n\t\t\tpage = urlopen('http://www.metrolyrics.com/aheeran.html')\n\t\t\tsoup = BeautifulSoup(page.read(), \"lxml\")\n\t\t\tlyrics = soup.find(\"div\", {\"id\": \"lyrics-body-text\"})\n\t\t\tprocessed = re.sub('\\n', '. ', lyrics.text)\n\t\t\tlines = processed.split()\n\t\t\treturn lines\n\t\texcept URLError: # if no url or other connection errors, skip\n\t\t\tprint(\"no url\")\n\t\t\tpass\n\n\t\t# Genius Lyrics structure\n\t\ttry:\n\t\t\t#page = urlopen('https://genius.com/Ed-sheeran-the-a-team-lyrics')\n\t\t\turl = 'https://genius.com/Ed-sheeran-the-a-team-lyrics'\n\t\t\tclient = webdriver.PhantomJS()\n\t\t\tclient.get(url)\n\t\t\tsoup = BeautifulSoup(client.page_source, 'lxml')\n\t\t\tlyrics_div = soup.find(\"div\", class_=\"lyrics\")\n\t\t\tlyrics_content = lyrics_div.find(\"p\")\n\t\t\t#print(lyrics_content)\n\t\t\tlyrics_a = lyrics_content.find_all(\"a\")\n\t\t\tlines = []\n\t\t\tfor obj in lyrics_a:\n\t\t\t\tfor c in obj.contents:\n\t\t\t\t\tif len(c) > 0: lines.append(c + '.')\t\t\t\n\t\t\treturn lines\n\t\t\t#lyrics_list.append(c)\n\t\t\t#print(lyrics_list)\n\t\texcept URLError:\n\t\t\tprint(\"no url\")\n\t\t\tpass\n\n\t\t# Song Lyrics structure IMPLEMENT THIS -------------------------------------------\n\t\ttry:\n\t\t\tpass\n\t\texcept URLError:\n\t\t\tprint(\"no url\")","def get_temp(html) -> None:\n\tif page_type_dict['general']:\n\t\tt_text = html.find('div', {'class': '_1HBR'}).text\n\t\tt_digit = ''.join([i for i in t_text if i.isdigit()])\n\t\tweather_dict['temperature'] = t_digit\n\telse:\n\t\tre_temp_class = re.compile('.*_2ezK.*') # regex template: str w/ '_2ezK'\n\t\ttemp_class = html.find('div', {'class': re_temp_class}) \n\t\t# we've got smth like: 'Ночью14°Утром19°Днём24°Вечером22°\n\t\tweather_lst = temp_class.text.split('°') # ['Ночью14','Утром19',...]\n\t\tint_weather_lst = [int(number.group()) for number in ( # for all the elems \n\t\t\tre.search(r'\\d+', word) for word in weather_lst) if number] # keep integers\n\t\t# result: [14, 19, 24, 22]\n\t\tweather_dict['temperature'] = int_weather_lst","def parse_top_movies(html: str) -> ResultSet:\n\n soup = BeautifulSoup(html, \"html.parser\")\n return soup.find_all(\"div\", class_=\"lister-item-content\")","def get_data_from_web():\n pass","def parse_html(city, html):\n return city.parse_html(html)","def _parse_doc(self, html_doc):\n\n c = itertools.count(start=1)\n\n # load adp page and parse out stats table\n doc = html.fromstring(html_doc)\n rows = doc.xpath(\n '//table[@id=\"adp_table\"]/tr[contains(@class, \"contentrow\")]')\n\n for row in rows:\n text = [v.strip() for v in row.xpath('.//text()')[1:]]\n text.insert(0, c.next())\n yield text","def scrape(self):\n try:\n self.result = urlfetch.fetch(self.url)\n except DownloadError:\n self.result = urlfetch.fetch(self.url) \n if ((self.result.status_code == 200) and\n (self.result.content_was_truncated == 0)):\n self.soup = BeautifulSoup(self.result.content)\n else:\n logging.critical(\"Bad Status Code: \", self.result.status_code, self.url)\n sys.exit(1)","def parse_source(html, encoding='utf-8'):\n return BeautifulSoup(html, from_encoding=encoding)","def parse_list_page_html(html):\n episode_list = []\n\n lines = html.split('\\n')\n for line in lines:\n if 'class=\"topictitle\"' in line and ' - ' in line and 'x' in line:\n datum = {}\n query = line.split('/viewtopic.php?f=177&t=')[1].split('&')[0]\n episode_season_str = line.split('class=\"topictitle\">')[1].split(' - ')[0]\n season_str = episode_season_str.split('x')[0]\n episode_str = episode_season_str.split('x')[1]\n datum['query'] = query\n datum['season'] = int(season_str)\n datum['episode'] = int(episode_str)\n episode_list.append(datum)\n\n return episode_list","def get_html_from_url(url):\n request = requests.get(url)\n data = request.text\n return data","def down_text(self, last_page):\n html_content = 3\n while html_content > 0:\n html_content -= 1\n try:\n\n html_content =urllib.urlopen(self.pageurl).read()\n break\n except Exception as e:\n print('Unable to download data [Time:%d][%s]' % (html_content, self.pageurl))\n insert_log('Unable to download data [Time:%d][%s]' % (html_content, self.pageurl))\n\n if isinstance(html_content, int):\n print('Unable to save data [%s]' % self.pageurl)\n insert_log('Unable to save data [%s]' % self.pageurl)\n return False\n\n print 'downling successfully from %s' % self.pageurl\n soup = BeautifulSoup(html_content)\n print soup\n #\n #对于帖子不存在了的情况进行判断\n titlep = soup.title.string\n if titlep == '403 Forbidden':\n print '出错了:%s' % self.pageurl\n insert_log('出错了:%s' % self.pageurl)\n return []\n\n alldata =[]\n audic = {}\n\n \"\"\"\n 由于第一页有主帖，因此第一页的解析会不同，要把主帖的所有信息保存下来，跟帖的信息也是如此\n \"\"\"\n #主帖的部分解析\n if((self.num == 1) and (self.rc == 0)):\n title = soup.find('div', id=\"content\").find(\"h1\").get_text().strip(\"\\r\\t\\n\")\n #section = soup.find('p', class_='crumbs').find_all('a')[1].get_text()\n auname = soup.find('div', id='content').find('span',class_=\"from\").find('a').get_text().strip(\"\\r\\t\\n\")\n auid = soup.find('div', id='content').find('span',class_=\"from\").find('a')['href'].split(\"/\")[-2]\n ctime = soup.find('div', id='content').find(\"h3\").find_all('span')[-1].get_text()\n\n audic['title'] = title\n audic['sec'] = self.section\n audic['uname'] = auname\n self.author = auname\n audic['uid'] = auid\n audic['ctime'] = ctime\n audic['ro'] = 0\n\n mydic = soup.find('div',id=\"content\").find_all(\"li\",class_=\"clearfix comment-item\")\n mi = 0\n #soup2 = BeautifulSoup((str)(htstr))\n #如果把self.rc放在外层if中用于判断是否没有记录，则对于有少量回复，而又有更新的情况，便不能跳过第一页的主帖\n #解析主帖部分内容\n if((mi == 0) and (self.num == 1)):\n mi += 1\n if(self.rc == 0):\n imgSrc = \"\"\n try:\n imgsrc = soup.find(\"div\",class_=\"topic-content\").find_all(\"div\",class_=\"topic-figure cc\")\n for img in imgsrc:\n try:\n for im in img.find_all(\"img\"):\n imgSrc += im[\"src\"] +\"\\n\"\n except:\n pass\n except:\n pass\n strtext = soup.find('div', id='link-report').find(\"div\",class_=\"topic-content\").get_text()\n\n audic['text'] = strtext.strip() + \"\\n\" + imgSrc\n\n alldata.append(audic)\n count = 0\n for htstr in mydic:\n soup2 = BeautifulSoup((str)(htstr))\n\n #解析跟帖内容\n redic = {}\n try:\n # irroder = (self.num-1)*100 +count\n #\n rtime = soup2.find('div', class_='bg-img-green').find('h4').find(\"span\").get_text()\n\n timeArray = time.strptime(rtime, \"%Y-%m-%d %H:%M:%S\")\n irorder = int(time.mktime(timeArray))\n redic['ro'] = irorder\n #update the reply count\n if (last_page and (irorder > self.rc)):\n self.rc = irorder\n imgSrc = \"\"\n try:\n imgsrc = soup2.find(\"div\",class_=\"topic-content\").find_all(\"div\",class_=\"topic-figure cc\")\n for img in imgsrc:\n try:\n for im in img.find_all(\"img\"):\n imgSrc += im[\"src\"] +\"\\n\"\n except:\n pass\n except:\n pass\n strtext = soup2.find('div', class_='reply-doc content').find(\"p\").get_text()\n redic['text'] = strtext.strip() +\"\\n\"+imgSrc\n\n\n\n uname = soup2.find('div', class_='bg-img-green').find('h4').find(\"a\").get_text()\n redic['un'] = uname\n\n if(uname != self.author):\n redic['au'] = False\n else:\n redic['au'] = True\n\n uid = soup2.find('div', class_='bg-img-green').find('h4').find(\"a\")[\"href\"].split(\"/\")[-2]\n\n redic['uid'] = uid\n\n\n redic['time'] = rtime\n\n alldata.append(redic)\n except AttributeError as e:\n alldata.append(redic)\n continue\n print 'All:%s' % self.pageurl\n return alldata","async def from_html(cls, document: str) -> Union[Dict[str, Any], List[Dict[str, Any]]]:\n doc = pq(document)\n segments = doc(cls.Meta.selector)\n if not segments:\n raise InformationError(\"Could not match selector '{selector}' to document.\".format(\n selector=cls.Meta.selector))\n total = []\n for segment in segments:\n values = {}\n for item_attr in cls._ATTRS:\n data = await getattr(cls, item_attr).load(segment)\n values[item_attr] = data\n total.append(values)\n if len(total) == 1:\n return total[0]\n return total","def get_certain_joke(html):\n soup = BeautifulSoup(html, 'lxml')\n\n # for i in len(soup.select('div.content')):\n # print(soup.select('div.content')[i].get_text())\n\n joke_content = soup.select('div.content')[0].get_text()\n\n return joke_content","def data():\r\n\r\n data = []\r\n m = soup.find('div', {'id': 'main-content'})\r\n\r\n for tag in m.children:\r\n\r\n \"\"\"Creates dict element with tag name and text for h-tags.\"\"\"\r\n if tag.name in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:\r\n data.append({'tag': tag.name,\r\n 'text': tag.text.strip(),\r\n 'ul': [],\r\n 'table': [],\r\n 'subheads': []})\r\n\r\n \"\"\"Adds unordered list data.\"\"\"\r\n if tag.name == 'ul':\r\n temp = _parse_list(tag)\r\n data[-1]['ul'].append(temp)\r\n\r\n \"\"\"Adds tabular data.\"\"\"\r\n try:\r\n lst = tag.get_attribute_list('class') # Finds table\r\n if 'table-wrap' in lst:\r\n tbl = []\r\n row = []\r\n\r\n for j in tag.find_all('tr'): # Adds table rows\r\n row = []\r\n\r\n for k in j.children:\r\n if k.name == \"th\" or k.name==\"td\":\r\n '''txt = _parse_list(l.ul)\r\n if txt == None:'''\r\n txt = k.get_text('\\n ').strip()\r\n row.append(txt) \r\n\r\n if row != []:\r\n tbl.append(row)\r\n\r\n if(tbl != []):\r\n data[-1]['table'].append(tbl)\r\n except:\r\n pass\r\n\r\n return data","def parse_detail(self, response):\n\n self.logger.log(self.log_lvl, 'scraping data @ {}'.format(response.url))\n\n item_list = list()\n image_urls = list()\n # extract image\n try:\n pattern = re.compile(r\"(.*imagearray:)(.*)(,.*displaymode.*)\", re.MULTILINE | re.DOTALL)\n javascript_containing_images = response.xpath('//script[contains(., \"var mygallery=\")]/text()').extract()[0]\n images = re.match(pattern, javascript_containing_images).group(2)\n image_array = json.loads(images)\n image_urls = [urlparse.urljoin(response.url, itm[1]) for itm in image_array]\n except Exception as e:\n print(\"{} - {}\".format(type(e), str(e)))\n\n tipe_mobil = response.css('#content font.vehicleinfo ~ font.warning::text').extract_first()\n model_mobil = response.css('#content font.vehicleinfo::text').extract_first()\n if tipe_mobil.lower() == model_mobil.lower():\n tipe_mobil = response.meta.get('type', None)\n main_group = response.meta.get('main_group', None)\n assembly_set = response.css('#content font.title b::text').extract_first()\n\n # sparepart items\n for row in response.css('div#content div.content table tr'):\n item = IsuzuSparepartItem()\n\n # source_url\n item['source_url'] = response.url\n\n # car model\n item['merk'] = self.name\n item['tipe_mobil'] = tipe_mobil\n item['model_mobil'] = model_mobil\n\n # images\n item['image_urls'] = image_urls\n\n # grouping/assembly\n item['main_group'] = main_group\n item['assembly_set'] = assembly_set\n\n item['key'] = row.css('td.intable:nth-child(1) .detailcontent::text').extract_first()\n item['part_number'] = row.css('td.intable:nth-child(2) .detailcontent::text').extract_first()\n item['itc'] = row.css('td.intable:nth-child(3) .detailcontent::text').extract_first()\n item['description'] = row.css('td.intable:nth-child(4) .detailcontent::text').extract_first()\n item['qty'] = row.css('td.intable:nth-child(5) .detailcontent::text').extract_first()\n item['app_date'] = row.css('td.intable:nth-child(6) .detailcontent::text').extract_first()\n item['lr'] = row.css('td.intable:nth-child(7) .detailcontent::text').extract_first()\n item['model'] = row.css('td.intable:nth-child(8) .detailcontent::text').extract_first()\n item['remarks'] = row.css('td.intable:nth-child(9) .detailcontent::text').extract_first()\n\n item_list.append(item)\n\n return item_list","def parse_details(pattern: str, data: BeautifulSoup) -> dict:\n answer = re.search(pattern, data)\n return answer.group(1) if answer else 'None'","def extract_news(parser):\n news_list = []\n\n titles = parser.find_all(\"tr\", class_=\"athing\")\n subtext = parser.find_all(\"td\", class_=\"subtext\")\n\n for i in range(len(titles)):\n x = titles[i].find_all(\"td\", class_=\"title\")[1]\n title = x.a.text\n url = x.a[\"href\"]\n c = subtext[i].find_all(\"a\")[4]\n if c.text == \"discuss\":\n comments = 0\n else:\n comments = c.text\n author = subtext[i].find(\"a\", class_=\"hnuser\").get_text()\n point = subtext[i].find(\"span\", class_=\"score\").text\n points = point.split(' ')[0]\n\n news_list.append({\"author\": author, \"comments\": comments, \"points\": points, \"title\": title, \"url\": url})\n\n return news_list","def btc_scraping():\n\n url_btc_web = requests.get('https://awebanalysis.com/es/coin-details/bitcoin/')\n soup = BeautifulSoup(url_btc_web.content, 'html.parser')\n\n # we need the class html\n result = soup.find('td', {'class': 'wbreak_word align-middle coin_price'})\n btc_price = result.text\n\n return btc_price"],"string":"[\n \"def extract_data_listing(html):\\n id_finder = re.compile(r'PR[\\\\d]+~')\\n return html.find_all('div', id=id_finder)\",\n \"def page_data():\\n return scrape()\",\n \"def _extract_data(self,data,tag=None,cssid=None,cssclass=None,attrs=None,regexp=None,index=0):\\n \\n# cssclass = \\\"song\\\"\\n# cssid = \\\"newsTable0\\\"\\n# tag = \\\"div\\\"\\n# import pdb\\n# pdb.set_trace() \\n \\n if cssid: \\n searchconstrain = SoupStrainer(tag, id=cssid)\\n elif cssclass:\\n searchconstrain = SoupStrainer(tag, attrs={\\\"class\\\":cssclass}) \\n else:\\n if isinstance(attrs, unicode):\\n try:\\n attrs = attrs.encode('utf-8')\\n regexp = regexp.encode('utf-8')\\n except:\\n pass \\n searchconstrain = SoupStrainer(tag, attrs={attrs:re.compile(regexp)})\\n\\n soup = BeautifulSoup(data,parseOnlyThese=searchconstrain)\\n rslist = [ tp for tp in soup ]\\n return rslist[index]\",\n \"def _scrape(self):\",\n \"def do_extract(self, xpath):\\n s = Selector(self.driver.page_source)\\n for i, result in enumerate(s.xpath(xpath).getall(), 1):\\n print(i, result)\",\n \"def _extract_html(self, url):\\n self.response = requests.get(url, timeout=5)\\n self.html = BeautifulSoup(self.response.content, \\\"lxml\\\") if self.response.ok else None\\n # return self.html\",\n \"def _parse(self):\\n soup = BS(self._current_html, 'lxml')\\n for item in soup.select('div.c'):\\n temp = {}\\n # main content\\n ctt = item.select('span.ctt')\\n if not ctt:\\n continue\\n weibo_body = item.select('div')\\n if len(weibo_body) > 1:\\n temp['content'] = weibo_body[0].text\\n btn_group = weibo_body[1].text\\n else:\\n temp['content'] = weibo_body[0].select('span.ctt')[0].text\\n btn_group = weibo_body[0].text\\n temp['is_repost'] = True if REPO_TEST_PATTERN.match(\\n temp['content']) else False\\n try:\\n temp['like_num'] = LIKE_NUM_PATTERN.findall(btn_group)[0]\\n temp['cmt_num'] = COMMENT_NUM_PATTERN.findall(btn_group)[0]\\n temp['repo_num'] = REPO_NUM_PATTERN.findall(btn_group)[0]\\n except Exception:\\n pass\\n cmt = item.select('.cmt')\\n # visibility\\n if cmt:\\n try:\\n temp['visibility'] = VISIBILITY_PATTERN.findall(\\n cmt[0].text)[0]\\n except Exception:\\n pass\\n\\n # img in main content\\n img = item.select('div a img')\\n img_src = img[0].attrs['src'] if img else None\\n temp['img_src'] = img_src\\n LOGGER.debug('img_src: {}'.format(img_src))\\n # time & source device\\n ct = item.select('span.ct')\\n if ct:\\n ct = ct[0]\\n text = ct.text\\n reg_result = TIME_PATTERN.findall(text)[0]\\n\\n temp['time'] = ar(\\n '{}年{}'.format(self._current_year, reg_result[0]),\\n DATE_FMTS[0]\\n ).naive if reg_result[0] else ar(\\n reg_result[1], DATE_FMTS[1]\\n ).naive\\n temp['source'] = SOURCE_DEVICE_PATTERN.findall(text)[0]\\n self._post_item = Post(**temp)\\n self._attachment_item = Attachment(\\n uri=img_src, post=self._post_item)\\n self._store()\",\n \"def extract(soup, keyIndex):\\n\\n # getting the individual content\\n content = soup.find('div', attrs={'data-key': keyIndex})\\n\\n # index\\n pattern = r\\\"<span class=\\\\\\\"indexed-biz-name\\\\\\\">(\\\\d+)\\\"\\n index = extractAttribute(content, pattern)\\n #print index\\n\\n # link\\n pattern = r\\\"biz-name\\\\\\\".*(/biz/[\\\\w?%?+-?]+)\\\\\\\"\\\"\\n #pattern = r\\\"biz-name\\\\\\\".*(/biz/.*)\\\\\\\"\\\" \\n link = extractAttribute(content, pattern)\\n #print link\\n\\n # name\\n #pattern = r\\\"biz-name.*>(.*)</a>\\\" \\n pattern = r\\\"biz-name.*>(<.*>.*|\\\\w+\\\\s+<.*>.*)</a>\\\"\\n name = extractAttribute(content, pattern) \\n #print name\\n\\n # rating\\n pattern = r\\\"star-img ([\\\\w+_?]+)\\\"\\n rating = extractAttribute(content, pattern)\\n #print rating\\n\\n # review\\n pattern = r\\\"(\\\\d+) reviews\\\"\\n review = extractAttribute(content, pattern)\\n #print review\\n\\n # price\\n pattern = r\\\"business-attribute price-range\\\\\\\">(.*)</span>\\\"\\n price = extractAttribute(content, pattern)\\n #print price\\n\\n # category\\n pattern = r\\\"cflt=(\\\\w+)\\\"\\n category = extractAttribute(content, pattern)\\n #print category\\n\\n # neighborhood\\n pattern = r\\\"neighborhood-str-list\\\\\\\">\\\\n\\\\s+(.*)\\\\s+</span>\\\"\\n neighborhood = extractAttribute(content, pattern)\\n #print neighborhood\\n\\n # address\\n #pattern = r\\\"\\\\s+(.*)<br>\\\"\\n pattern = r\\\"<address>\\\\n([\\\\s\\\\S]*)</address>\\\"\\n address = extractAttribute(content, pattern)\\n #print address\\n\\n # phone\\n #pattern = r\\\"(\\\\([\\\\d]+\\\\) [\\\\d]+-[\\\\d]+)\\\"\\n pattern = r\\\"biz-phone\\\\\\\">\\\\n\\\\s+(.*)\\\\n\\\\s+</span>\\\"\\n phone = extractAttribute(content, pattern)\\n #print phone\\n\\n # feedback\\n pattern = r\\\"<p class=\\\\\\\"snippet\\\\\\\">(.*)</p>\\\"\\n feedback = extractAttribute(content, pattern)\\n #print feedback\\n\\n return [index, link, name, \\\\\\n rating, review, price, category, \\\\\\n neighborhood, address, phone, feedback]\",\n \"def get_data_from_page(url):\\n # Initialise a dictionary to store our information\\n row = {'url': url}\\n\\n # get the html content of a website using the requests library and the get function\\n # the '.content' returns the contents of the request, without it would return the HTTP status code\\n page_contents = requests.get(url).content\\n\\n # parse the contents with beautiful soup\\n soup = BeautifulSoup(page_contents, 'lxml')\\n\\n # Get the text relating to the campaign title which belong to that specific class.\\n for element in soup.find_all(class_=\\\"a-campaign-title\\\"):\\n row['title'] = element.text\\n\\n # Interrogating the source we found that the tags are URLs that always contain the 'discover' path\\n # So we just need to find all the URLs that contain that string and return them\\n tags = []\\n for link in soup.find_all('a', href=True):\\n if 'discover' in link['href']:\\n tags.append(link.text)\\n\\n row['tags'] = tags\\n\\n # Progress meter is a single string in this class.\\n for link in soup.findAll('h2', {'class': 'm-progress-meter-heading'}):\\n goal = link.text\\n\\n # The format is strictly defined so we can do some string parsing to get the information we want\\n row['current amount'] = goal.split()[0]\\n row['total_amount'] = goal.split()[-2]\\n\\n for link in soup.findAll('div', {'class': 'p-campaign-description'}):\\n row['description'] = link.text\\n\\n for link in soup.findAll('span', {'class': 'm-campaign-byline-created a-created-date'}):\\n row['created'] = link.text\\n\\n # Some information exists only in the footer of the \\\"donations\\\" page, which is summarised in the side bar\\n # We can't access it directly but the page URL is well formated so we can use some string manipulation to create\\n # the URL and parse it.\\n\\n # the gofundme URLs are well formated along the lines of www.gofundme.com/f/name of fundraiser?qidSOMEHEXCODE\\n # We can use the '?qid' as an anchor and replace it with '/donations?qid' to get the URL of the donations page\\n donations_url = url.replace('?qid', '/donations?qid')\\n\\n soup = BeautifulSoup(requests.get(donations_url).content, 'lxml')\\n text_soup = str(soup)\\n\\n # The information in this section is outside of the usual html format, but it is well structured\\n # We can extract it using regular expressions that look for the information\\n for dates in re.findall('launch_date\\\\\\\":\\\\\\\"[0-9-]+', text_soup):\\n row['launch_date'] = dates.rsplit('\\\"', 1)[-1]\\n\\n for country in re.findall('country\\\\\\\":\\\\\\\"[A-Z]+', text_soup):\\n row['country'] = country.rsplit('\\\"', 1)[-1]\\n\\n for donation_count in re.findall('donation_count\\\\\\\":[0-9]+', text_soup):\\n row['donation_count'] = donation_count.rsplit(':', 1)[-1]\\n\\n for charity in re.findall('charity\\\\\\\":[a-z]+', text_soup):\\n row['is_charity'] = charity.rsplit(':', 1)[-1]\\n\\n return row\",\n \"def mine(self):\\n collections = []\\n # Getting HTML snapshot with selenium, storing a soup object in .data\\n self.scrape()\\n # Returns only the parts of the soup that surround each collection\\n collection_elements = self.get_collection_elements()\\n # Turns each soup element into a CollectionElement object\\n collections = self.get_info_from_collections(collection_elements)\\n # NOTE THE RETURN VALUE IS MERELY TO PASS TESTING< MUST BE CHANGED\\n return self.data\",\n \"def parse_page(html):\\n\\n soup = BeautifulSoup(html, \\\"html.parser\\\")\\n review_soups = soup.find_all(\\\"script\\\", type=\\\"application/ld+json\\\")\\n\\n description_list = []\\n for soup in review_soups:\\n text = soup.string\\n # decode the json into python dict\\n js_dict = json.loads(text)\\n\\n if \\\"review\\\" in js_dict:\\n review_list = js_dict[\\\"review\\\"]\\n\\n for i in range(len(review_list)):\\n review_dict = review_list[i]\\n description_list.append(review_dict[\\\"description\\\"])\\n\\n return description_list\",\n \"def scrape_detail(self,url):\\n soup=get_soup(url)\\n self.zip=soup.find('p',class_='block-normal hide-for-small-only text-small hero-ranking-data-contact').stripped_strings.__next__()[-5::1]\\n if self.zip in zips:\\n #print('DUPLICATE!')\\n zips.append(self.zip)\\n info_tags=soup.find_all('span',class_='heading-small text-black text-tight block-flush display-block-for-large-up')\\n self.type=info_tags[0].string.strip()\\n self.year_founded=int(info_tags[1].string.strip())\\n self.setting=info_tags[4].string.strip()\\n self.endowment=info_tags[5].string.strip()\",\n \"def extract_from_html(self, data, url):\\n\\n records = [ ]\\n self.logger.debug(\\\"Found %d recipes in %s\\\" % (len(data.xpath(self.scope)), url))\\n for rcp in data.xpath(self.scope):\\n\\n record = { }\\n for prop in [ \\\"name\\\", \\\"recipeYield\\\", \\\"author\\\" ]:\\n record[prop] = self.extract_text(prop, rcp)\\n for prop in [ \\\"image\\\" ]:\\n record[prop] = self.extract_attribute(prop, [ \\\"content\\\", \\\"src\\\" ], data)\\n for prop in [ \\\"totalTime\\\", \\\"prepTime\\\", \\\"cookTime\\\", \\\"datePublished\\\" ]:\\n record[prop] = self.extract_attribute(prop, [ \\\"content\\\" ], rcp)\\n # I have no idea if cookingMethod should be text or a list because I've never seen it so\\n # using the most general option for it\\n for prop in [ \\\"recipeIngredient\\\", \\\"recipeInstructions\\\", \\\"cookingMethod\\\",\\n \\\"recipeCategory\\\", \\\"recipeCuisine\\\" ]:\\n record[prop] = self.extract_list(prop, rcp)\\n\\n # Older versions of the schema use \\\"ingredients\\\" rather than \\\"recipeIngredient\\\"\\n if not record[\\\"recipeIngredient\\\"]:\\n record[\\\"recipeIngredient\\\"] = self.extract_list(\\\"ingredients\\\", rcp)\\n\\n record = dict([ (k, v) for k, v in record.items() if k in self.store_fields ])\\n record[\\\"url\\\"] = url\\n record[\\\"collect_time\\\"] = datetime.utcnow()\\n\\n if self.validate(record):\\n records.append(record)\\n\\n return records\",\n \"def get_study_data(self, soup, url):\\n pass\",\n \"def scrape(self, page_src):\\n soup = bs(page_src, 'html.parser')\\n self._data = soup.text.split()\",\n \"def extract_from_soup( target_url_soup ):\\n\\n datetime_now = datetime.datetime.now().strftime(\\\"%m.%d.%Y:%H.%M.%S\\\")\\n\\n # Declare a list for each variable extracted below.\\n title_list = []\\n href_list = []\\n cost_list = []\\n info_list = []\\n loc_list = []\\n datetime_pulled = []\\n\\n # Iteratively extract the data into a list that goes to a dictionary.\\n for each in target_url_soup.find_all('p'):\\n ## Filter this thing which gets caught in the craigslist data.\\n if each.a.string == \\\"next 100 postings\\\":\\n pass\\n else:\\n # Get the title, get none on an exception\\n try: \\n #print str(each.a.string)\\n post_title = str(each.a.string)\\n except:\\n post_title = \\\"\\\"\\n \\n # Get the hyperlink, get none on an exception\\n try:\\n #print str(each.a.get('href'))\\n post_href = str(each.a.get('href'))\\n except:\\n post_href = \\\"\\\"\\n\\n # Get the cost/info, get none on an exception\\n post_cost = \\\"\\\"\\n post_info = \\\"\\\"\\n try:\\n #print str(each.find(\\\"span\\\", \\n # \\\"itemph\\\").string).strip().strip('-').strip()\\n post_cost_info = str(each.find(\\\"span\\\", \\n \\\"itemph\\\").string).strip().strip('-').strip()\\n \\n\\n # Use a regular expression to parse this data further\\n if re.match(\\\"\\\\$\\\", post_cost_info) is None:\\n post_info = str(post_cost_info).strip()\\n\\n else:\\n # If there is no /, assign as dollars.\\n if re.search(\\\"/\\\", post_cost_info) is None:\\n post_cost = str(post_cost_info).strip().strip('$')\\n \\n # chop up any entry with $COST / x br\\n else:\\n cost_info_list = [] # list to receive re.split()\\n cost_info_list = re.split('/', post_cost_info, 1 )\\n post_cost = str(cost_info_list[0]).strip().strip('$')\\n post_info = str(cost_info_list[1]).strip()\\n \\n # Close the above try block for cost and info\\n except:\\n pass \\n\\n # Get the location, get none on an exception\\n try: \\n #print str(each.find(\\\"span\\\", \\n # \\\"itempn\\\").string).strip().strip('()')\\n post_loc = str(each.find(\\\"span\\\", \\n \\\"itempn\\\").string).strip().strip('()')\\n except:\\n post_loc = \\\"\\\"\\n \\n ## Add all extracted items to their respective lists.\\n ## We are still in the above loop here. All lists will get an entry.\\n ## This keeps the lists in step in the case of bad entries, so they can\\n ## still be zipped, but with blank spaces. Some data is better than\\n ## no data.\\n title_list.append( post_title )\\n href_list.append( post_href )\\n cost_list.append( post_cost )\\n info_list.append( post_info )\\n loc_list.append( post_loc )\\n ## Append the datetime_now to each tuple, kept in step.\\n datetime_pulled.append( datetime_now )\\n\\n # Zip the lists collected in the for loop into a tuple.\\n # The tuple is the value of the dict/json.\\n extracted_data_tuple = zip(title_list, \\n href_list, \\n cost_list,\\n info_list,\\n loc_list, \\n datetime_pulled)\\n \\\"\\\"\\\"\\n This tuple is used for MD5 generation because it excludes the unique \\n datetime attribute. This would salt the MD5 and we want the md5 to \\n represent the data inside so we can detect duplicates.\\n\\n I have now also removed the href_list because of duplicate posts\\n The likelihood of different people having the same title, cost, info, and\\n location is still very low and won't affect data.\\n \\\"\\\"\\\"\\n extracted_data_tuple_nouniquetime = zip(title_list, \\n cost_list, \\n info_list, \\n loc_list)\\n \\n md5_key_list = []\\n # Generate a list of md5 keys from the data tuple, the md5s are the keys. \\n for each in extracted_data_tuple_nouniquetime:\\n eachmd5 = md5.new()\\n eachmd5.update( str(each) )\\n md5_key_list.append( str( eachmd5.hexdigest() ) )\\n\\n # Zip a tuple and convert into a dictionary for JSON extraction\\n extracted_data_dict = dict( zip( md5_key_list, extracted_data_tuple ) )\\n\\n return ( extracted_data_dict )\",\n \"def parse_announcement_data(self) -> 'Scraper':\\n logger.info('Parsing extracted html partial')\\n for tag in self.html_partial: # there are 63 tags\\n if tag.name == 'h4':\\n announcement_data = self.get_data_from_tag(tag)\\n self.announcement_data_list.append(announcement_data)\\n logger.info('Compiled announcement data list from html web page partial')\\n return self\",\n \"def scrape(self):\\n pass\",\n \"def process_level_1(content):\\n\\tsoup = BeautifulSoup(content, features=\\\"html.parser\\\")\\n\\n\\t#r = soup.findAll('div',{\\\"class\\\":\\\"listing-item__content\\\"})\\n\\tr = soup.findAll('div',{\\\"class\\\":\\\"listing-item search-listing-result__item\\\"})\\n\\tres=list()\\n\\t# extract information from the source code if the page \\n\\tfor i in r:\\n\\t\\tlisting_id = str(i)[str(i).index('data-wa-data=\\\"')+len(\\\"data-wa-data=\\\")+1:str(i).index(\\\"|source=listings_results\\\")+len(\\\"|source=listings_results\\\")]\\n\\t\\ttype_area = i.find('div',{'class':'listing-characteristic margin-bottom'}).text\\n\\t\\tprice = i.find(\\\"div\\\",{\\\"class\\\":\\\"listing-price margin-bottom\\\"}).text\\n\\t\\tpalce = i.find(\\\"div\\\",{\\\"class\\\":\\\"text--muted text--small\\\"}).text\\n\\t\\tres.append({\\\"id_\\\":listing_id,\\n\\t\\t \\\"area\\\":type_area,\\n\\t\\t \\\"price\\\":price,\\n\\t\\t \\\"place\\\":palce})\\n\\treturn res\",\n \"def get_doctor_info(url, html):\\n \\n # create a dictionary to save data\\n doctor_info = {}\\n \\n # parsed html by using lxml\\n # In the following processing\\n # the one ending in org is the raw data in the HTML\\n # the one ending in inf is the processed data by extracting from raw data\\n select = etree.HTML(html)\\n \\n # part 1: get basic information about your doctor\\n # attribute: Name, Title, Hospital, Department\\n name_org = select.xpath('//div[@class=\\\"profile-text\\\"]//h1[@class=\\\"doctor-name\\\"]//text()')\\n name_inf = name_org[0].strip()\\n \\n title_org = select.xpath('//div[@class=\\\"profile-text\\\"]//span[@class=\\\"positon\\\"]//text()')\\n title_inf = [i.strip() for i in title_org if len(i.strip()) > 0]\\n title_inf = ' '.join(title_inf)\\n \\n hospital_department_org = select.xpath('//div[@class=\\\"profile-text\\\"]//p[@class=\\\"doctor-faculty\\\"]//text()')\\n hospital_department_inf = [i.strip() for i in hospital_department_org if len(i.strip()) > 0]\\n hospital_inf = hospital_department_inf[0]\\n department_inf = hospital_department_inf[1]\\n \\n doctor_info['姓名'] = name_inf\\n doctor_info['职称'] = title_inf\\n doctor_info['医院'] = hospital_inf\\n doctor_info['科室'] = department_inf\\n \\n # part2: get header format data\\n org = select.xpath('//div[@class=\\\"profile-sta\\\"]//text()')\\n inf = [i.strip() for i in org if len(i.strip()) > 0 and i.strip() != '%']\\n for i in range(len(inf)//2):\\n doctor_info[inf[2*i]] = inf[2*i + 1]\\n \\n \\n # part3: get sidebar format data\\n org_1 = select.xpath('//div[@class=\\\"item-body\\\"]//div[@class=\\\"clearfix\\\"]//div[@class=\\\"per-sta-label\\\"]//text()')\\n org_2 = select.xpath('//div[@class=\\\"item-body\\\"]//div[@class=\\\"clearfix\\\"]//div[@class=\\\"per-sta-data\\\"]//text()')\\n for i in range(len(org_1)):\\n doctor_info[org_1[i][:-1]] = org_2[i]\\n \\n # part4: get body format data\\n honour_org = select.xpath('//div[@class=\\\"honour-header\\\"]//text()')\\n honour_inf = ''.join([i.strip() for i in honour_org])\\n \\n honour_detail_org = select.xpath('//li[@class=\\\"honour-title\\\"]//text()')\\n honour_detail_inf = [i.strip()[:4] for i in honour_detail_org if len(i.strip()) > 0]\\n honour_detail_inf = ' '.join(honour_detail_inf)\\n \\n satisfaction_org = select.xpath('//div[@class=\\\"item-body\\\"]//div[@class=\\\"satisfaction clearfix\\\"]//i[@class=\\\"sta-num\\\"]//text()')\\n satisfaction_inf = [i.strip() for i in satisfaction_org if len(i.strip()) > 0 and i.strip() != '%']\\n \\n resume_org = select.xpath('//div[@class=\\\"good-at-text\\\"]//text()')\\n resume_inf = [i.strip() for i in resume_org]\\n if len(resume_inf) <= 20:\\n resume_inf = ''.join(resume_inf)\\n resume_inf = ''.join(resume_inf[:20])\\n \\n star_org = select.xpath('//div[@class=\\\"experience-row clearfix\\\"]//span[@class=\\\"experience-label\\\"]//text()')\\n star_inf = 1 if len(star_org) >= 1 else 0\\n\\n doctor_info['好大夫届数'] = honour_inf\\n doctor_info['好大夫具体年份'] = honour_detail_inf\\n doctor_info['简历'] = resume_inf \\n doctor_info['诊后服务星'] = star_inf\\n try:\\n doctor_info['疗效满意度'] = satisfaction_inf[0]\\n doctor_info['态度满意度'] = satisfaction_inf[1]\\n except:\\n pass\\n \\n # part5: personal url\\n personal_url = url\\n doctor_info['医生个人链接'] = personal_url\\n \\n return doctor_info\",\n \"def get_info(url):\\r\\n soup = make_request(url)\\r\\n\\r\\n #get press release title\\r\\n title_text = soup.find(\\\"h2\\\", \\\"con-title\\\").text.strip()\\r\\n title = title_text.partition('\\\\n')[0]\\r\\n\\r\\n #get press release content and date\\r\\n div = soup.find_all(\\\"div\\\") #find div tags\\r\\n for ele in div:\\r\\n for div2 in ele(\\\"div\\\",\\\"text-right\\\"):\\r\\n if \\\"發佈日期\\\" in div2.text:\\r\\n text = ele.text\\r\\n date = re.findall(\\\"\\\\d\\\\d\\\\d\\\\d-\\\\d\\\\d-\\\\d\\\\d\\\", div2.text)[0]\\r\\n break #prevents reiterating upwards to all div parents\\r\\n return date, title, text\",\n \"def __extract_info(self) -> Results:\\n results: Results = []\\n\\n response = request(self.home_url)\\n\\n html = bs(response, \\\"lxml\\\")\\n table = html.find(\\\"table\\\")\\n for row in table.find_all(\\\"tr\\\")[1:]:\\n col1, col2, col3 = row.find_all(\\\"td\\\")\\n filename1, perc1 = col1.text.strip().split()\\n filename2, perc2 = col2.text.strip().split()\\n\\n with ThreadPoolExecutor() as executor:\\n future = executor.submit(self.__get_line_numbers, col1.a.get(\\\"href\\\"))\\n lines = future.result()\\n\\n result_dict = Result(\\n file1=filename1,\\n file2=filename2,\\n percentage_file1=perc_str_to_int(perc1),\\n percentage_file2=perc_str_to_int(perc2),\\n no_of_lines_matched=int(col3.text.strip()),\\n lines_matched=lines,\\n )\\n results.append(result_dict)\\n return results\",\n \"def extract(self, data):\",\n \"def getAdditionalDetails(self, soup):\\n title_details = soup.find('div', id=\\\"titleDetails\\\")\\n title_details = title_details.findAll('div', class_=\\\"txt-block\\\")\\n return title_details\",\n \"def extract(self, doc, raw_html):\\n super(KenyaTodayCrawler, self).extract(doc, raw_html)\\n\\n soup = BeautifulSoup(raw_html)\\n\\n # gather title\\n doc.title = soup.find(attrs={\\\"property\\\":\\\"og:title\\\"})['content']\\n\\n #gather publish date\\n date = self.extract_plaintext(soup.select(\\\"main.content .entry-meta .entry-time\\\"))\\n doc.published_at = self.parse_timestamp(date)\\n\\n nodes = soup.select(\\\".content .entry-content p\\\")\\n self.log.info(nodes)\\n if len(nodes) > 1:\\n doc.summary = self.extract_plaintext(nodes[0:1])\\n doc.text = \\\"\\\\n\\\\n\\\".join(p.text.strip() for p in nodes[2:])\\n\\n doc.author = Author.unknown()\",\n \"def scrap_societe_site(page):\\n\\n req = requests.get(page)\\n soup = BeautifulSoup(req.content, \\\"html.parser\\\")\\n\\n if soup.find(\\\"h1\\\", attrs={\\\"id\\\": \\\"identite_deno\\\"}):\\n new_data.loc[i, \\\"enseigne_et1\\\"] = soup.find(\\\"h1\\\", attrs={\\\"id\\\":\\\"identite_deno\\\"}).get_text().strip()\\n if soup.find(\\\"table\\\", attrs={\\\"id\\\":\\\"rensjur\\\"}):\\n siret_det = soup.find(\\\"table\\\", attrs={\\\"id\\\":\\\"rensjur\\\"})\\n siret_det = siret_det.get_text().split(\\\"\\\\n\\\")\\n for j in range(len(siret_det)):\\n if \\\"SIRET\\\" in siret_det[j]:\\n new_data.loc[i, \\\"siret\\\"] = siret_det[j+1]\\n break\\n\\n if soup.find(\\\"table\\\", attrs={\\\"id\\\":\\\"rensjurcomplete\\\"}):\\n details = soup.find(\\\"table\\\", attrs={\\\"id\\\":\\\"rensjurcomplete\\\"})\\n details_list = details.get_text().split(\\\"\\\\n\\\")\\n for j in range(len(details_list)):\\n if \\\"Adresse\\\" in details_list[j]:\\n new_data.loc[i, \\\"adr_et_voie_lib\\\"] = details_list[j+1]\\n if \\\"postal\\\" in details_list[j]:\\n new_data.loc[i, \\\"adr_et_post\\\"] = details_list[j+1]\\n if \\\"NAF\\\" in details_list[j] and is_nan(new_data.loc[i, \\\"naf\\\"]):\\n new_data.loc[i, \\\"naf\\\"] = details_list[j+1]\\n if \\\"Ville\\\" in details_list[j]:\\n new_data.loc[i, \\\"adr_et_com_lib\\\"] = details_list[j+1]\\n\\n elif soup.find(\\\"table\\\", attrs={\\\"id\\\":\\\"etab\\\"}):\\n details = soup.find(\\\"table\\\", attrs={\\\"id\\\":\\\"etab\\\"})\\n details_list = details.get_text().split(\\\"\\\\n\\\")\\n for j in range(len(details_list)):\\n if \\\"Adresse\\\" in details_list[j]:\\n new_data.loc[i, \\\"adr_et_voie_lib\\\"] = details_list[j+1]\\n if \\\"postal\\\" in details_list[j]:\\n new_data.loc[i, \\\"adr_et_post\\\"] = details_list[j+1]\\n if \\\"NAF\\\" in details_list[j] and is_nan(new_data.loc[i, \\\"naf\\\"]):\\n new_data.loc[i, \\\"naf\\\"] = details_list[j+1]\\n if \\\"Ville\\\" in details_list[j]:\\n new_data.loc[i, \\\"adr_et_com_lib\\\"] = details_list[j+1]\",\n \"def scrape_html(self, html: str):\\n\\n soup = BeautifulSoup(html, features=\\\"lxml\\\")\\n # Seprating tags using bs4\\n data_tags_tags = soup.find_all(lambda tag: True if 'data-tags' in tag.attrs else False)\\n self.complete_match = {tuple(sorted([v.strip() for v in tag.attrs['data-tags'].\\n split(',')])): [tag, []] for tag in data_tags_tags}\\n\\n # Creating dict with all the complete tags\\n for k, v in self.complete_match.items():\\n hold_tags = set()\\n for k2 in self.complete_match.keys():\\n if k == k2:\\n continue\\n if set(k).issubset(k2):\\n hold_tags.update(set(k2).difference(k))\\n v[1] = sorted([{\\\"name\\\": tag} for tag in hold_tags], key=lambda tag: tag['name'])\\n\\n # Creating dict with all the partial and ambigious tags\\n for k, v in self.complete_match.items():\\n for L in range(0, len(k) + 1):\\n for subset in itertools.permutations(k, L):\\n if subset:\\n if not self.partial_match.get(subset):\\n self.partial_match[subset] = []\\n self.partial_match[subset].append(v[0])\\n\\n # Inserting tags in the partial match dic for reference\\n hold_partial_match = self.partial_match.copy()\\n for k, v in self.partial_match.items():\\n if len(v) > 1:\\n values = set()\\n for tags in v:\\n values.update(tags.attrs['data-tags'].split(','))\\n v2 = v.copy()\\n v2.append(sorted([{\\\"name\\\": tag} for tag in set(values).difference(k)], key=lambda tag: tag['name']))\\n hold_partial_match[k] = v2\\n\\n self.partial_match = hold_partial_match.copy()\",\n \"def get_gremium_data(self, response):\\n\\n # e.g. <td class=\\\"smc_td smc_field_silink\\\"><a href=\\\"to0040.asp?__ksinr=11487\\\" title=\\\"Details anzeigen: Bezirksvertretung Bochum-Mitte 16.05.2019 \\\" class=\\\"smc_doc smc_datatype_si\\\">16.05.2019</a><!--SMCINFO:si.bi.1.4.1.1.16.1.3 --> 15:00-18:09</td>\\n urls = response.xpath('//tr[contains(@class, \\\"smcrow1\\\") or contains(@class, \\\"smcrow2\\\") or contains(@class, \\\"smcrown\\\")]/*/a/@href').getall()\\n dates = response.xpath('//tr[contains(@class, \\\"smcrow1\\\") or contains(@class, \\\"smcrow2\\\") or contains(@class, \\\"smcrown\\\")]/*/a/text()').getall()\\n\\n # e.g. <a href=\\\"getfile.asp?id=426409&type=do&\\\" title=\\\"Einladung \\\" target=\\\"_blank\\\">Einladung <span class=\\\"smcwrapsmall smcdosize\\\" title=\\\"Dateigröße\\\">266\\\\xa0KB </span></a>\\n einladungen = response.xpath('//a[contains(text(), \\\"Einladung\\\")]/@href').getall()\\n # e.g. <a href=\\\"getfile.asp?id=427859&type=do&\\\" title=\\\"Niederschrift öffentlich \\\" target=\\\"_blank\\\">Niederschrift öffentlich <span class=\\\"smcwrapsmall smcdosize\\\" title=\\\"Dateigröße\\\">570\\\\xa0KB </span></a>\\n niederschriften = response.xpath('//a[contains(text(), \\\"Niederschrift\\\")]/@href').getall()\\n\\n # table layout in one table row; has either no, just one, or both Einladung and Niederschrift\\n tables = response.xpath('//table[contains(@class, \\\"smcdocbox smcdocboxright\\\")]').getall()\\n\\n # not all einladungen have niederschriften vv. insert None accordingly\\n for i in range(len(tables)):\\n if \\\"Niederschrift\\\" not in tables[i]:\\n niederschriften.insert(i, None)\\n if \\\"Einladung\\\" not in tables[i]:\\n einladungen.insert(i, None)\\n\\n return urls, dates, niederschriften, einladungen\",\n \"def parse_html(html, search):\\n \\n def get_field_value(field, div):\\n \\\"\\\"\\\"\\n Finds class associated with each field and gets value\\n \\\"\\\"\\\"\\n if not field.get('html_class'):\\n return\\n \\n tag = div.find(class_=field['html_class'])\\n if not tag:\\n return\\n \\n # Fix spans for title and listing date\\n if field['name'] == 'title':\\n for span in tag('span'):\\n span.decompose()\\n elif field['name'] == 'listing_date':\\n for span in tag('span'):\\n span.unwrap()\\n \\n # Get href for URL, or inner text for other fields\\n if field['name'] == 'url':\\n value = tag.get('href')\\n else:\\n value = tag.string\\n \\n # Clean fields by calling functions listed in JSON\\n if field.get('cleaner'):\\n value = clean_field(value, field['cleaner'])\\n \\n return value\\n \\n listings = []\\n for div in html:\\n listing = {'search': search.name}\\n \\n # Get field values from HTML\\n for field in fields:\\n listing[field['name']] = get_field_value(field, div)\\n \\n # Get calculated field values\\n for field in [f for f in fields if f.get('calculated')]:\\n listing[field['name']] = calculate_field_value(field['name'], listing, search)\\n \\n listings.append(listing)\\n \\n df = pandas.DataFrame(listings)\\n return df\",\n \"def scrap_mappy_site(page):\\n\\n req = requests.get(url)\\n soup = BeautifulSoup(req.content, \\\"html.parser\\\")\\n\\n if soup.find(\\\"p\\\", attrs={\\\"class\\\": \\\"address\\\"}):\\n ad = soup.find(\\\"p\\\", attrs={\\\"class\\\": \\\"address\\\"}).get_text()\\n ad = ad.replace(\\\"\\\\n\\\",\\\"\\\").strip()\\n\\n new_data.loc[i, \\\"adr_et_voie_lib\\\"] = ad.split(\\\",\\\")[0]\\n adr = ad.split(\\\",\\\")[1].strip()\\n new_data.loc[i, \\\"adr_et_post\\\"] = adr[:5].strip()\\n new_data.loc[i, \\\"adr_et_com_lib\\\"] = adr[5:].strip()\\n\\n else:\\n ad_list_soup = soup.find_all(\\\"li\\\", attrs={\\\"class\\\":\\\"geoentity-fulllist\\\"})\\n ad_list = []\\n for el in ad_list_soup:\\n ad_list.append(el.find(\\\"p\\\").get_text())\\n ad_list = [el.lower() for el in ad_list]\\n\\n if len(ad_list) > 0:\\n\\n if not is_nan(miss_rp.loc[i, \\\"NOMVOI_X\\\"]):\\n rp_numvoi_list = miss_rp.loc[i, \\\"NOMVOI_X\\\"].split(\\\" \\\")\\n rp_numvoi_list = [el.lower() for el in rp_numvoi_list if\\n el.lower() not in [\\\"un\\\",\\\"une\\\", \\\"de\\\", \\\"des\\\", \\\"du\\\",\\n \\\"le\\\", \\\"la\\\", \\\"les\\\", \\\"rue\\\", \\\"boulevard\\\", \\\"route\\\", \\\"chemin\\\"]]\\n\\n ad = 0\\n for el in ad_list:\\n el2 = el.split(\\\",\\\")[0]\\n for word in el2.split(\\\" \\\"):\\n if word in rp_numvoi_list:\\n ad = el\\n if ad == 0:\\n ad = ad_list[0]\\n else:\\n ad = ad_list[0]\\n\\n new_data.loc[i, \\\"adr_et_voie_lib\\\"] = ad.split(\\\",\\\")[0]\\n adr = ad.split(\\\",\\\")[1].strip()\\n new_data.loc[i, \\\"adr_et_post\\\"] = adr[:5].strip()\\n new_data.loc[i, \\\"adr_et_com_lib\\\"] = adr[5:].strip()\\n\\n return \\\"+\\\"+str(new_data.loc[i, \\\"adr_et_voie_lib\\\"])+\\\"+\\\"+str(new_data.loc[i, \\\"adr_et_post\\\"])+\\\"+\\\"+str(new_data.loc[i, \\\"adr_et_com_lib\\\"])+\\\"+societe.com\\\"\",\n \"def get_html_content():\\n url = \\\"https://www.worldometers.info/coronavirus/\\\"\\n req_data = requests.get(url).text\\n soup = BeautifulSoup(req_data, 'html.parser')\\n html_data = soup.select(\\\"#main_table_countries_today > tbody:nth-child(2) > tr[style='']\\\")\\n return html_data\",\n \"def getLineInformation(html, reg=['(?<=<\\\\/h2><li>).*?<\\\\/a>', '<.*a.*?>', '查公交上八六八四cn']):\\n tmp = re.findall(reg[0], html)[0]\\n Info = re.sub(reg[1], '', tmp)\\n info = re.sub(reg[2], '', Info)\\n return info\",\n \"def _get_new_data(self, page_url, soup):\\n data = {}\\n data['url'] = page_url\\n title = soup.find('dd', class_='lemmaWgt-lemmaTitle-title').find('h1')\\n data['title'] = title.get_text()\\n summary = soup.find('div', class_='lemma-summary')\\n data['summary'] = summary.get_text()\\n return data\",\n \"def scrape_html(html):\\n return YoutubeScrape(BeautifulSoup(html))\",\n \"def __init__(self, html_soup):\\n # Drilling down to the internal wrapper <div> tag\\n self.data = html_soup.find('div', class_='sbkBrv_SingleResultDesc')\",\n \"def scrape(self):\\n #Get page\\n soup, _ = getPage(self.url)\\n\\n #Check if page available\\n if soup is None:\\n #Not available, skip iteration\\n self.na = True\\n return\\n\\n #Get Price\\n self.price = soup.find(class_=\\\"user-ad-price__price\\\").get_text()\\n #Get list of attr names and values\\n adAttrVals = soup.find_all(class_=\\\"vip-ad-attributes__value\\\")\\n adAttrName = soup.find_all(class_=\\\"vip-ad-attributes__name\\\")\\n #Search attrs for date listed\\n for i in range(0,len(adAttrName)):\\n if adAttrVals[i].contents[0] == \\\"Date Listed\\\":\\n self.listDate = adAttrName[i].contents[0]\\n break\",\n \"def _parse_page_contents (self, page_soup):\\n netflix_page_data = self.extract_inline_netflix_page_data(page_soup=page_soup)\\n self.user_data = self._parse_user_data(netflix_page_data=netflix_page_data)\\n self.esn = self._parse_esn_data(netflix_page_data=netflix_page_data)\\n self.api_data = self._parse_api_base_data(netflix_page_data=netflix_page_data)\\n self.profiles = self._parse_profile_data(netflix_page_data=netflix_page_data)\\n self.log(msg='Found ESN \\\"' + self.esn + '\\\"')\\n return netflix_page_data\",\n \"def parse(html_doc):\\n soup = BeautifulSoup(html_doc, 'html.parser')\\n table = soup.find('div', class_=\\\"container-fluid cols_table show_visited\\\")\\n# print table.prettify().encode('UTF-8')\\n jobstats = []\\n\\n for row in table:\\n jobstats.append({\\n \\\"title\\\":row.find('div', class_=\\\"col-sm-7\\\").a.text,\\n \\\"category\\\":row.find('div', class_=\\\"text-muted\\\").a.text,\\n \\\"price\\\":row.find('div', class_=\\\"col-sm-2 amount title\\\").\\n text.strip(),\\n \\\"applications\\\":row.find(\\n 'div', class_=\\\"col-sm-3 text-right text-nowrap hidden-xs\\\"\\n ).text.strip()\\n })\\n return jobstats\",\n \"def parse_soup(self, soup):\\n # find all class_='gs_r gs_or gs_scl' => each result\\n return soup.find_all('li', class_='ais-InfiniteHits-item')\",\n \"def scrape_data():\\n soup = get_page_source_code(\\\"https://www.tradingview.com/markets/stocks-usa/market-movers-active/\\\")\\n stock_table = soup.select(\\\".tv-data-table__tbody tr\\\")\\n \\n stock_data = []\\n\\n for data in stock_table:\\n stock_name = data.find(\\\"a\\\").get_text(strip=True)\\n stock_price = data.select(\\\"td\\\")[1].get_text(strip=True)\\n percentage_change = data.select(\\\"td\\\")[2].get_text(strip=True)\\n rating = data.select(\\\"td\\\")[4].get_text(strip=True)\\n stock_volume = data.select(\\\"td\\\")[5].get_text(strip=True)\\n\\n details = {\\n\\n \\\"name\\\": stock_name,\\n \\\"price\\\": stock_price,\\n \\\"change\\\": percentage_change,\\n \\\"rating\\\": rating,\\n \\\"volume\\\": stock_volume,\\n \\n }\\n stock_data.append(details)\\n return stock_data\",\n \"def parse(self):\\n result = {}\\n if self.detail_statu:\\n sel = Selector(text=self.driver.page_source)\\n\\n fact_table = sel.xpath(\\n '//div[@class=\\\"facts-table\\\"]//text()').extract()\\n result['facts'] = [list(i)\\n for i in zip(fact_table[:: 2],\\n fact_table[1:: 2])]\\n\\n tax_table = sel.xpath(\\n '//div[@class=\\\"tax-values\\\"]//text()').extract()\\n result['taxs'] = [list(i)\\n for i in zip(tax_table[:: 2],\\n tax_table[1:: 2])]\\n\\n listing_detail = sel.xpath(\\n '//div[@class=\\\"amenities-container\\\"]//text()').extract()\\n result['detail'] = listing_detail\\n result['page_source'] = self.driver.page_source\\n self.detail_statu = False\\n else:\\n self.log.warning(\\n '---- Detail page url out of reach, use .search() first to get the detail page')\\n return result\",\n \"def scrape(self):\\n\\n self.url = self.headline.url\\n\\n # Should raise exception...\\n if not self.parsing_template:\\n return None, None, None, None, None\\n\\n try:\\n response = self.download()\\n self.source = response.text\\n except:\\n return None, None, None, None, None\\n\\n soup = BeautifulSoup(response.content, \\\"html.parser\\\")\\n\\n if soup:\\n return self.parse(soup)\\n else:\\n return None, None, None, None, None\",\n \"def extract_data(self, root, path, tag):\\n data = []\\n element = root.xpath(path)\\n if element:\\n url = self.PODEROPEDIA_BASE_URL + element[0].get('data-w2p_remote', None)\\n if url:\\n self.logger.debug('Querying {} from {}'.format(tag, url))\\n try:\\n response = self.session.get(url)\\n response.raise_for_status()\\n content = response.content\\n html_tree = etree.HTML(content, parser=self.parser)\\n if html_tree is None:\\n return data\\n rows = html_tree.xpath('.//*[starts-with(@id, \\\"collapse\\\")]/div/table/tr')\\n for row in rows:\\n target = target_name = target_path = relationship = None\\n when = where = where_name = where_path = source = None\\n row_id = row.get('id', '')\\n cells = row.getchildren()\\n idx = 0\\n while idx < len(cells) - 1:\\n try:\\n cell_text = text_strip(cells[idx])\\n except AttributeError:\\n cell_text = ''\\n sources = cells[idx].xpath('.//*[@class=\\\"fuente\\\"]')\\n if len(sources) > 0:\\n source = process_sources(cells[idx])\\n elif cell_text == 'es' or cell_text == 'fue':\\n when = cell_text\\n idx = idx - 1\\n target = cells[idx].find('a')\\n if target is not None:\\n target_path = target.get('href', None)\\n target_name = text_strip(target)\\n idx = idx + 2\\n relationship = text_strip(cells[idx])\\n elif cell_text == 'a' or cell_text == 'de':\\n idx = idx - 1\\n relationship = text_strip(cells[idx])\\n idx = idx + 2\\n target = cells[idx].find('a')\\n if target is not None:\\n target_path = target.get('href', None)\\n target_name = text_strip(target)\\n elif cell_text.startswith('desde'):\\n when = cell_text\\n elif 'es pasado' in cell_text:\\n when = cell_text\\n else:\\n try:\\n ignore = int(cell_text)\\n when = cell_text\\n except ValueError:\\n potential_date = cell_text.split(' ')[0]\\n try:\\n ignore = datetime.strptime(potential_date, '%d-%m-%Y')\\n when = cell_text\\n except ValueError:\\n try:\\n ignore = datetime.strptime(potential_date, '%m-%Y')\\n when = cell_text\\n except ValueError:\\n pass\\n idx = idx + 1\\n entry = {\\n 'type': tag,\\n 'target_path': target_path,\\n 'relationship': relationship,\\n 'when': when,\\n 'where': where,\\n 'source': source\\n }\\n data.append(entry)\\n self.logger.debug('{}: {}'.format(tag, entry))\\n except (requests.exceptions.HTTPError, etree.ParserError):\\n self.logger.info('Something bad happened', exc_info=True)\\n return data\",\n \"def find_details_json(self, url):\\n response = self.get_response(url)\\n if response:\\n html_soup = BeautifulSoup(response.text, 'html.parser')\\n listings_json = html_soup.find('script', id='__NEXT_DATA__')\\n if listings_json:\\n listings_json = str(listings_json)\\n listings_json = listings_json.replace(\\\"<script id=\\\\\\\"__NEXT_DATA__\\\\\\\" type=\\\\\\\"application/json\\\\\\\">\\\", \\\"\\\").replace(\\\"</script>\\\", \\\"\\\")\\n listings = json.loads(listings_json)\\n return listings\\n else:\\n skip_scraper(self.college, 'Trulia')\",\n \"def _extract_channel_data(self, html_rsp):\\n soup = BeautifulSoup(html_rsp, 'html.parser')\\n script = self._filter_scripts(soup.find_all('script'))\\n data_dict = self._parse_js(script)\\n return data_dict\",\n \"def scrape(self, page_src):\\n soup = bs(page_src, 'html.parser')\\n self._data = list(map(lambda a : a.get('href'), soup.find_all('a')))\\n print(self._data)\",\n \"def data_collector(self, n, url, ret):\\n try:\\n html = urllib2.urlopen(url).read()\\n soup = BeautifulSoup(html)\\n ret[n] = [soup.title.string, url, html[0:100]]\\n except:\\n ret[n] = [\\\"Error\\\", url, \\\"Error\\\"]\",\n \"def retrieving_data():\\n for x in range(1):\\n page_number=random.randint(1,500)\\n page_num=str(page_number)\\n url = 'http://www.tastespotting.com/browse/'+page_num\\n req = http.request('GET', url)\\n data = BeautifulSoup(req.data,'html.parser')\\n for each_div in data.find_all(\\\"div\\\", { \\\"class\\\": \\\"trendspotted-item\\\"}):\\n for each_recipe in each_div.find_all('a', href=True):\\n \\\"\\\"\\\"links starting with /clicks are the links of recipe to their original sites, so just retrieve those links\\\"\\\"\\\"\\n if each_recipe['href'].startswith('/click'):\\n retrieving_data.recipe_link=each_recipe['href'][16:-12]\\n for each_img in each_recipe.find_all('img', alt=True):\\n retrieving_data.recipe_image=each_img['src']\\n for each_caption in each_div.find(\\\"p\\\", { \\\"class\\\": \\\"photo_caption\\\"}):\\n retrieving_data.recipe_title=each_caption\",\n \"def scrape(content):\\n columns = ['name',\\n 'description',\\n 'location',\\n 'link',\\n 'price',\\n 'operation',\\n 'rooms',\\n 'bathrooms',\\n 'construction (m2)',\\n 'terrain (m2)']\\n\\n data = pd.DataFrame(columns=columns)\\n # Generate soup\\n soup = BeautifulSoup(content, 'html.parser')\\n # Get Characteristics\\n for d in soup.find_all(class_=\\\"ListingCell-AllInfo\\\"):\\n temp_dict = {}\\n try:\\n temp_dict['name'] = d.find(class_=\\\"ListingCell-KeyInfo-title\\\").text.strip()\\n temp_dict['description'] = d.find(class_=\\\"ListingCell-shortDescription\\\").text.strip()\\n temp_dict['location'] = ' '.join([j.strip() for j in d.find(class_=\\\"ListingCell-KeyInfo-address\\\").text.strip().split('\\\\n')])\\n temp_dict['link'] = d.find(class_=\\\"ListingCell-KeyInfo-title\\\").find('a').get('href')\\n temp_dict['price'] = d.find(class_=\\\"PriceSection-FirstPrice\\\").text.strip()\\n temp_dict['operation'] = _operation\\n for att in d.find(class_=\\\"KeyInformation\\\").find_all(class_=\\\"KeyInformation-attribute\\\"):\\n att = att.text.lower()\\n if 'recámara' in att:\\n temp_dict['rooms'] = statistics.mean([int(s) for s in att.split() if s.isdigit()])\\n elif 'baño' in att:\\n temp_dict['bathrooms'] = statistics.mean([int(s) for s in att.split() if s.isdigit()])\\n elif 'terreno' in att:\\n temp_dict['terrain (m2)'] = statistics.mean([int(s) for s in att.split() if s.isdigit()])\\n elif 'superficie' in att:\\n temp_dict['construction (m2)'] = statistics.mean([int(s) for s in att.split() if s.isdigit()])\\n except Exception as e:\\n print(e)\\n continue\\n data = data.append(temp_dict, ignore_index=True)\\n print('Found {} depts'.format(len(data['name'])))\\n return data\",\n \"def get_soup_general_data(soup):\\n data_dict = {}\\n\\n name = soup.find(class_='product_title')\\n if name:\\n data_dict['name_of_game'] = name.h1.text\\n\\n pub = soup.find('li', class_='summary_detail publisher')\\n if pub:\\n data_dict['publisher'] = pub.a.text.strip()\\n\\n rel_date = soup.find('li', class_='summary_detail release_data')\\n if rel_date:\\n rel_date = rel_date.find('span', class_='data')\\n if rel_date:\\n data_dict['release_date'] = rel_date.text.strip()\\n\\n num_p = soup.find(\\\"li\\\", class_=\\\"summary_detail product_players\\\")\\n if num_p:\\n data_dict['num_players'] = num_p.find(class_=\\\"data\\\").text\\n\\n genres = soup.find(\\\"li\\\", class_='summary_detail product_genre')\\n if genres:\\n genres = genres.find_all('span', class_='data')\\n data_dict['genres'] = [genre.text for genre in genres]\\n\\n age = soup.find(\\\"li\\\", class_=\\\"summary_detail product_rating\\\")\\n if age:\\n data_dict['age_rating'] = age.find('span', class_=\\\"data\\\").text\\n\\n return data_dict\",\n \"def parse_html_data(html_data):\\n\\n contracts = []\\n\\n soup = BeautifulSoup(html_data, \\\"lxml\\\")\\n\\n for tr in soup.find_all(\\\"tr\\\", class_=\\\"grid-row\\\"):\\n contract = get_new_contract(tr)\\n if contract:\\n contracts.append(contract)\\n\\n return contracts\",\n \"def parseSearchHtml(self):\\n pass\",\n \"def parseSearchHtml(self):\\n pass\",\n \"def parsed_html():\\n return utils.parse_html(\\n \\\"\\\"\\\"\\n <!doctype hmtl>\\n <html>\\n <head>\\n <meta charset=\\\"utf-8\\\">\\n <meta name=\\\"viewport\\\" content=\\\"width=device-width\\\">\\n <title>Page title</title>\\n <link rel=\\\"stylesheet\\\" href=\\\"/static/styles.css\\\" />\\n </head>\\n <body>\\n <h1>Django Auto AMP</h1>\\n <p>Generate automatic AMP from your Django templates</p>\\n <img src=\\\"/static/img.jpg\\\" width=\\\"500\\\" height=\\\"300\\\" />\\n <img src=\\\"/static/img.gif\\\" layout=\\\"nodisplay\\\" />\\n <img src=\\\"/static/img.png\\\" />\\n <script type=\\\"text/javascript\\\" src=\\\"/static/scripts.js\\\" />\\n <script type=\\\"application/json\\\" src=\\\"/static/data.json\\\" />\\n </body>\\n </html>\\n \\\"\\\"\\\"\\n )\",\n \"def get_data():\\n info_html = urlopen(\\\"http://marci1368.getmarci.com\\\").read()\\n div = Soup(info_html, \\\"html.parser\\\").find('div', {'id': 'letterbox1'})\\n moreinfo = get_itunes_info(div[\\\"data-artist\\\"], div[\\\"data-title\\\"])\\n if not moreinfo:\\n return {\\\"artistName\\\": div[\\\"data-artist\\\"],\\n \\\"trackName\\\": div[\\\"data-title\\\"],\\n \\\"collectionName\\\": div[\\\"data-album\\\"]}\\n return moreinfo\",\n \"def extractSearchResults(self, html):\\n results = list()\\n soup = BeautifulSoup(html, 'html.parser')\\n div = soup.find('div', id='main')\\n if (type(div) == types.NoneType):\\n div = soup.find('div', id='center_col')\\n if (type(div) == types.NoneType):\\n div = soup.find('body')\\n if (type(div) != types.NoneType):\\n lis = div.findAll('a')\\n if(len(lis) > 0):\\n for link in lis:\\n if (type(link) == types.NoneType):\\n continue\\n \\n url = link['href']\\n if url.find(\\\".google\\\") > 6:\\n continue\\n \\n url = self.extractUrl(url)\\n if(cmp(url, '') == 0):\\n continue\\n title = link.renderContents()\\n title = re.sub(r'<.+?>', '', title)\\n result = SearchResult()\\n result.setURL(url)\\n print '### URL: ' + url\\n result.setTitle(title)\\n span = link.find('div')\\n if (type(span) != types.NoneType):\\n content = span.renderContents()\\n content = re.sub(r'<.+?>', '', content)\\n result.setContent(content)\\n results.append(result)\\n return results\",\n \"def scrape_technical_data(self):\\n\\n page = requests.get(self.url)\\n soup = BeautifulSoup(page.content, \\\"html.parser\\\")\\n results = soup.find(\\n \\\"section\\\", class_=\\\"titlereference-section-additional-details\\\"\\n )\\n results = results.find_all(\\\"tr\\\")\\n\\n data = []\\n for res in results:\\n data.append(res)\\n\\n runtime = data[1]\\n runtime = re.sub(\\\"<.*?>\\\", \\\"\\\", str(runtime))\\n runtime = (\\n runtime.replace(\\\" \\\", \\\"\\\")\\n .replace(\\\"Runtime\\\", \\\"\\\")\\n .replace(\\\"\\\\n\\\", \\\"\\\")\\n .replace(\\\"min\\\", \\\" min\\\")\\n )\\n\\n country = \\\"\\\"\\n languages = []\\n color = \\\"\\\"\\n social = {}\\n if \\\"Country</td>\\\" in str(data[2]):\\n country = data[2].find_all(\\\"a\\\")\\n country = re.sub(\\\"<.*?>\\\", \\\"\\\", str(country))\\n\\n languages = data[3].find_all(\\\"a\\\")\\n languages = re.sub(\\\"<.*?>\\\", \\\"\\\", str(languages))\\n\\n color = data[4].find_all(\\\"a\\\")\\n color = re.sub(\\\"<.*?>\\\", \\\"\\\", str(color))\\n elif \\\"Country</td>\\\" in str(data[3]):\\n sites = data[2].find_all(\\\"a\\\")\\n for site in sites:\\n key = (\\n re.sub(\\\"<.*?>\\\", \\\"\\\", str(site))\\n .replace(\\\"\\\\n\\\", \\\"\\\")\\n .replace(\\\" \\\", \\\"\\\")\\n .replace(\\\" \\\", \\\"\\\")\\n )\\n social[key] = site[\\\"href\\\"]\\n\\n country = data[3].find_all(\\\"a\\\")\\n country = re.sub(\\\"<.*?>\\\", \\\"\\\", str(country))\\n\\n languages = data[4].find_all(\\\"a\\\")\\n languages = re.sub(\\\"<.*?>\\\", \\\"\\\", str(languages))\\n\\n color = data[5].find_all(\\\"a\\\")\\n color = re.sub(\\\"<.*?>\\\", \\\"\\\", str(color))\\n\\n country = country.replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\")\\n languages = languages.replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\")\\n color = color.replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\")\\n\\n return (runtime, country, languages, color, social)\",\n \"def _parse_events(self, html):\\n print \\\"Parse events\\\"\\n data = []\\n soup = BeautifulSoup(html, \\\"html.parser\\\")\\n events = soup.find_all(\\\"div\\\", {\\\"class\\\": \\\"program clearfix\\\"})\\n \\\"\\\"\\\" Site's html is broken. We have to handle descriptions \\n with a hack.\\n \\\"\\\"\\\"\\n descriptions = soup.find_all(\\\"div\\\", {\\\"class\\\": \\\"programpostingress\\\"})\\n for index, event in enumerate(events):\\n link_tag = event.find(\\\"a\\\")\\n if link_tag:\\n link = link_tag[\\\"href\\\"]\\n else:\\n link = None\\n dates = self._parse_date(self._parse_text(event.find(\\\"span\\\", {\\\"class\\\": \\\"programpostdato\\\"})))\\n row = {\\n \\\"title\\\": self._parse_text(event.find(\\\"span\\\", {\\\"class\\\": \\\"programposttittel\\\"})),\\n \\\"date_start\\\": dates[\\\"start\\\"],\\n \\\"date_end\\\": dates[\\\"end\\\"],\\n \\\"description\\\": self._parse_text(descriptions[index]),\\n \\\"link\\\": link,\\n \\\"country\\\": \\\"Norge\\\"\\n }\\n data.append(row)\\n print \\\"Found %s events\\\" % len(data)\\n return data\",\n \"def parse_article_html(page_resp):\\n article_url = page_resp.url\\n \\n article_page_soup = bs4.BeautifulSoup(page_resp.text, \\\"lxml\\\")\\n \\n title_html = article_page_soup.find_all(\\\"h1\\\")[0]\\n title_text = title_html.contents[0]\\n \\n date = article_page_soup.find_all(\\\"small\\\", {'class': 'gray'})[0]\\n date_text = date.contents[4].replace(\\\" \\\", \\\"\\\").split(\\\"\\\\n\\\")[3][:10]\\n \\n article_content = article_page_soup.find_all(\\\"div\\\", {'class': 'rich_media_content'})[0]\\n article_text = article_content.get_text('\\\\n')\\n is_original = check_if_original(article_content) or '[原创]' in title_text\\n \\n return {\\n 'title': title_text,\\n 'date': date_text,\\n 'url': article_url,\\n 'is_original': is_original,\\n 'text': article_text\\n \\n}\",\n \"def extract_movies(dom):\\n\\n # extract data per movie\\n movies = dom.find_all('div', class_ = 'lister-item mode-advanced')\\n\\n # list to store scraped data\\n movielist = []\\n\\n for movie in movies:\\n\\n # append extracted data to this dict\\n moviedict = {}\\n\\n # scrape titles and add to dict\\n moviedict['title'] = movie.h3.a.text\\n\\n # scrape ratings and add to dict\\n moviedict['rating'] = float(movie.strong.text)\\n\\n # scrape year of release and add to dict\\n year = movie.h3.find('span', class_ = 'lister-item-year text-muted unbold')\\n moviedict['year'] = re.findall('\\\\d+', year.text.strip('()'))[0]\\n\\n # scrape actors and add to dict\\n actors = movie.find_all(href=re.compile(\\\"adv_li_st\\\"))\\n actorlist = []\\n for actor in actors:\\n actorlist.append(actor.text)\\n actorstring = ', '.join(actorlist)\\n moviedict['actors'] = actorstring\\n\\n # scrape runtime and add to dict\\n moviedict['runtime'] = movie.p.find('span', class_ = 'runtime').text.split(' ')[0]\\n movielist.append(moviedict)\\n\\n\\n # ADD YOUR CODE HERE TO EXTRACT THE ABOVE INFORMATION ABOUT THE\\n # HIGHEST RATED MOVIES\\n # NOTE: FOR THIS EXERCISE YOU ARE ALLOWED (BUT NOT REQUIRED) TO IGNORE\\n # UNICODE CHARACTERS AND SIMPLY LEAVE THEM OUT OF THE OUTPUT.\\n\\n return movielist # REPLACE THIS LINE AS WELL IF APPROPRIATE\",\n \"def parse(content):\\r\\n soup = BeautifulSoup(content)\\r\\n submissions = soup.findAll('div')\\r\\n\\r\\n submission_data = []\\r\\n for s in submissions:\\r\\n t = s.getText()\\r\\n try:\\r\\n num, date, score = num_date_pattern.findall(t)[0]\\r\\n num = s.find('b').getText()\\r\\n data = json.loads(unescape(s.find('pre').getText()))\\r\\n d = {'submission_num': num,\\r\\n 'date': date,\\r\\n 'score': score,\\r\\n 'detail': data}\\r\\n submission_data.append(d)\\r\\n except Exception, e:\\r\\n logging.error(\\\"Error finding num_date_pattern: %s. Text:\\\\n%s\\\" % (e, t))\\r\\n return submission_data\",\n \"def extract_data(self, id: str) -> dict:\\r\\n\\r\\n raw = self.session.get(f\\\"{self.host}/{id}\\\")\\r\\n soup = self.soup(raw)\\r\\n\\r\\n dlx = soup.find(class_=\\\"dlx\\\")\\r\\n result = {}\\r\\n\\r\\n for table in dlx.findAll(\\\"table\\\"):\\r\\n if not table.strong:\\r\\n continue\\r\\n\\r\\n if (h4 := table.findPrevious(\\\"h4\\\", style=\\\"text-align: center\\\")):\\r\\n eps = h4.text\\r\\n elif (prevTable := table.findPrevious(\\\"table\\\")):\\r\\n eps = prevTable.text\\r\\n\\r\\n d = {}\\r\\n res = None\\r\\n for tr in table.findAll(\\\"tr\\\"):\\r\\n if not tr.a:\\r\\n res = tr.text\\r\\n d[res] = {}\\r\\n continue\\r\\n for a in tr.findAll(\\\"a\\\"):\\r\\n d[res][a.text] = a[\\\"href\\\"]\\r\\n result[eps] = d\\r\\n return result\",\n \"def extract(soup):\\r\\n table = soup.find('div', id='dnn_ctr11396_TimeTableView_PlaceHolder').find('table')\\r\\n rows = table.findChildren('tr', recursive=False)\\r\\n return [[col.findAll('div', {'class': 'TTLesson'}) for col in row.findChildren('td', recursive=False)[1:]]\\r\\n for row in rows[1:]]\",\n \"def extract(self, response):\\n # print response.url,\\\"extract response url\\\"\\n sel = response.selector\\n pages = []\\n try:\\n # print \\\"pages work\\\"\\n pages = sel.xpath(\\\"//div[contains(@class,'fen_ye_nav')]//td/text()\\\").re(u\\\"共([\\\\d]{1,3})页\\\")\\n # print pages\\n except Exception, e:\\n print e,\\\"error pages\\\"\\n log.msg(e, level=log.ERROR)\\n log.msg(response.url, level=log.ERROR)\\n\\n if len(pages) == 0:\\n self.getUserName(response) #only one page\\n else:\\n for page in range(int(pages[0])+1)[1:]: #fro test\\n url = response.url+\\\"_m0_p\\\"+str(page)\\n yield Request(url, callback=self.getUserName,dont_filter=True)\",\n \"def load_data(url: str):\\n\\n page = requests.get(url=url)\\n soup = BeautifulSoup(page.content, 'html.parser')\\n return soup\",\n \"def gather_current(url=URL):\\n page = requests.get(url)\\n soup = BeautifulSoup(page.content, 'html.parser')\\n return soup\",\n \"def get_info(self):\\r\\n\\r\\n self.driver.get(WEBSITE)\\r\\n time.sleep(3)\\r\\n self.driver.find_element_by_xpath(\\\"\\\"\\\"//*[@id=\\\"modalContent\\\"]/div/button/i\\\"\\\"\\\").click()\\r\\n time.sleep(3)\\r\\n #gets prices and appends to list\\r\\n all_prices = self.driver.find_elements_by_class_name(\\\"firstPrice\\\")\\r\\n for price in all_prices:\\r\\n text = price.text\\r\\n new_p = text.replace(\\\".\\\", \\\"\\\")\\r\\n price_int = int(new_p.split(\\\" \\\")[1])\\r\\n self.price_list.append(price_int)\\r\\n #gets addresses\\r\\n all_addresses = self.driver.find_elements_by_class_name(\\\"postingCardLocationTitle\\\")\\r\\n for address in all_addresses:\\r\\n self.address_list.append(address.text)\\r\\n print(self.address_list)\\r\\n # gets info\\r\\n ad_info = self.driver.find_elements_by_css_selector(\\\"a.go-to-posting\\\")\\r\\n for info in ad_info:\\r\\n links = info.get_attribute('href') #gets href link inside the css\\r\\n self.all_links.append(links)\\r\\n self.all_info.append(info.text)\\r\\n\\r\\n # Just for tests\\r\\n print(self.price_list)\\r\\n print(self.all_info)\\r\\n print(self.all_links)\",\n \"def extract_main_table_from_html(html):\\n soup = bs(html, 'html.parser')\\n table = soup.find('table')\\n return(table)\",\n \"def scrape_story_metadata(self, story_id):\\n url = '{0}/s/{1}'.format(self.base_url, story_id)\\n result = requests.get(url)\\n html = result.content\\n #print html \\n soup = BeautifulSoup(html, self.parser)\\n\\n # print soup\\n try:\\n pre_story_links = soup.find(id='pre_story_links').find_all('a')\\n except AttributeError:\\n pre_story_links = None\\n if re.search(r\\\"var userid = (.*);\\\", str(soup)) is None:\\n author_id = \\\"0\\\"\\n else: \\n author_id = int(re.search(r\\\"var userid = (.*);\\\", str(soup)).groups()[0]);\\n #print re.search(r\\\"var title = (.*);\\\", str(soup))\\n if re.search(r\\\"var title = (.*);\\\", str(soup)) is None:\\n title = \\\"NO-TITLE\\\"\\n else:\\n title = re.search(r\\\"var title = (.*);\\\", str(soup)).groups()[0];\\n title = unquote_plus(title)[1:-1]\\n metadata_div = soup.find(id='profile_top')\\n# times = metadata_div.find_all(attrs={'data-xutime':True})\\n# metadata_text = metadata_div.find(class_='xgray xcontrast_txt').text\\n# metadata_parts = metadata_text.split('-')\\n# genres = self.get_genres(metadata_parts[2].strip())\\n metadata = {\\n 'id': story_id,\\n# 'canon_type': pre_story_links[0].text,\\n# 'canon': pre_story_links[1].text,\\n 'author_id': author_id,\\n 'title': title,\\n# 'updated': int(times[0]['data-xutime']),\\n# 'published': int(times[1]['data-xutime']),\\n# 'lang': metadata_parts[1].strip(),\\n# 'genres': genres\\n }\\n \\\"\\\"\\\"\\n for parts in metadata_parts:\\n parts = parts.strip()\\n tag_and_val = parts.split(':')\\n if len(tag_and_val) != 2:\\n continue\\n tag, val = tag_and_val\\n tag = tag.strip().lower()\\n if tag not in metadata:\\n val = val.strip()\\n try:\\n val = int(val.replace(',', ''))\\n metadata['num_'+tag] = val\\n except:\\n metadata[tag] = val\\n if 'status' not in metadata:\\n metadata['status'] = 'Incomplete'\\n \\\"\\\"\\\"\\n return metadata\",\n \"def extract_element_data(soup, params):\\r\\n \\r\\n # 1. Find the right tag\\r\\n if 'class' in params:\\r\\n elements_found = soup.find_all(params['tag'], params['class'])\\r\\n else:\\r\\n elements_found = soup.find_all(params['tag'])\\r\\n \\r\\n # 2. Extract text from these tags\\r\\n if 'get' in params:\\r\\n element_texts = [el.get(params['get']) for el in elements_found]\\r\\n else:\\r\\n element_texts = [el.get_text() for el in elements_found]\\r\\n \\r\\n # 3. Select a particular text or concatenate all of them\\r\\n tag_order = params.get('order', 0)\\r\\n if tag_order == -1:\\r\\n output = '**__**'.join(element_texts)\\r\\n else:\\r\\n output = element_texts[tag_order]\\r\\n \\r\\n return output\",\n \"def decode_html(html):\\n soup = BeautifulSoup(html, 'xml')\\n return {\\n 'personality': ProfileApi.get_personality(soup),\\n 'ida_years': ProfileApi.get_ida_years(soup),\\n 'years': ProfileApi.get_years(soup),\\n 'choices': ProfileApi.get_choices(soup)\\n }\",\n \"def extract(self):\\n\\n table = self.soup.table\\n table_rows = table.find_all('tr')\\n\\n for tr in table_rows:\\n td = tr.find_all('td')\\n data = [item.text for item in td]\\n\\n #Extracting each row and save it in a file as an object\\n try:\\n \\n with open('data.txt', 'wb') as file:\\n my_pickler = pickle.Pickler(file)\\n my_pickler.dump(data)\\n\\n except Exception as exc:\\n print('Error while saving data :', exc)\\n\\n #saving data in our dictionaries\\n try: \\n \\n with open('data.txt', 'rb') as file:\\n my_dipeck = pickle.Unpickler(file)\\n my_data = my_dipeck.load()\\n self.deaths_per_country[my_data[0]] = my_data[3]\\n self.cases_per_country[my_data[0]] = my_data[1]\\n\\n except Exception as exc2:\\n print('Error while loading data :', exc2)\",\n \"def extract_data():\\n books = WebScraper().get_top_100_data()\\n time.sleep(2)\\n BookDetailsWebScrapper().save_book_details(books)\\n _save_extract_state(books)\",\n \"def scrape(self):\\n\\n #Get page\\n soup, _ = getPage(self.url)\\n\\n #Check page was found\\n if soup is None:\\n self.na = True\\n return\\n\\n #Find price\\n try:\\n self.price = soup.find(class_=\\\"user-ad-price__price\\\").get_text()\\n except:\\n pass\\n\\n #Find attributes names/values\\n adAttrVals = soup.find_all(class_=\\\"vip-ad-attributes__name\\\")\\n adAttrName = soup.find_all(class_=\\\"vip-ad-attributes__value\\\")\\n #Find description\\n try:\\n self.description = soup.find(class_=\\\"vip-ad-description__content--wrapped\\\").get_text()\\n except:\\n pass\\n\\n #Check all attributes for important information\\n for i in range(0,len(adAttrName)):\\n tempName = adAttrName[i].get_text()\\n tempVal = adAttrVals[i].get_text()\\n if \\\"Date Listed:\\\" in tempName:\\n #Can be date or words (eg 16 minutes ago, yesterday)\\n try:\\n #Will work if date\\n listDateLst = tempVal.lstrip().split('/')\\n self.listDate = listDateLst[2]+'-'+listDateLst[1]+'-'+listDateLst[0]\\n except:\\n #Check not empty\\n if tempVal is not None:\\n if tempVal == \\\"Yesterday\\\":\\n #Yesterday\\n self.listDate = (datetime.today() - timedelta(days=1)).strftime('%Y-%m-%d')\\n else:\\n #Either hours or minutes\\n self.listDate = datetime.today().strftime('%Y-%m-%d')\\n \\n elif \\\"Displacement (cc):\\\" in tempName:\\n self.displacement = tempVal.lstrip()\\n elif \\\"Make:\\\" in tempName:\\n self.make = tempVal.lstrip()\\n elif \\\"Model:\\\" in tempName:\\n self.model = tempVal.lstrip()\\n elif \\\"Year:\\\" in tempName:\\n self.year = tempVal.lstrip()\\n elif \\\"KMs:\\\" in tempName:\\n self.kms = tempVal.lstrip()\\n elif \\\"Registered:\\\" in tempName:\\n if tempVal.lstrip() == \\\"Yes\\\":\\n self.registered = \\\"Y\\\"\\n elif tempVal.lstrip() == \\\"No\\\":\\n self.registered = \\\"N\\\"\\n elif \\\"Registration Expiry:\\\" in tempName:\\n regExpLst = tempVal.lstrip().split('/')\\n self.regExpiry = regExpLst[2]+'-'+regExpLst[1]+'-'+regExpLst[0]\\n elif \\\"Colour:\\\" in tempName:\\n self.colour = tempVal.lstrip()\\n elif \\\"Learner Approved:\\\" in tempName:\\n if tempVal.lstrip() == \\\"Yes\\\":\\n self.learner = \\\"Y\\\"\\n elif tempVal.lstrip() == \\\"No\\\":\\n self.learner = \\\"N\\\"\\n elif \\\"Listing Type:\\\" in tempName:\\n self.listType = tempVal.lstrip()\",\n \"def get_article1(url):\\n # set agent\\n agent = 'codeup ds germain' \\n # query\\n response = requests.get(url, headers={'User-Agent': agent}) \\n # soup\\n soup = BeautifulSoup(response.text) \\n # get cat\\n category = soup.find_all('li', {'class':'active-category selected'})[0].text \\n # get raw cards\\n cards = soup.select('.news-card') \\n # create list of dicts for dataframe\\n card_dict_list = [] \\n # iterate each card\\n for card in cards: \\n # headline\\n headline = card.find_all('span', {'itemprop':'headline'})[0].text \\n # publish time\\n publish_time = card.find_all('span', {'class':'time'})[0].text \\n # content\\n content = card.find_all('div', {'itemprop':'articleBody'})[0].text.strip() \\n # create dict\\n card_dict = {'headline':headline, 'publish_time':publish_time,\\n 'category':category, 'content':content} \\n # push dict to list\\n card_dict_list.append(card_dict) \\n # return dataframe\\n return pd.DataFrame(card_dict_list)\",\n \"def _grab_tags(self, url):\\n a = self._api_request(url)\\n return bs4.BeautifulSoup(a,features=\\\"html.parser\\\")\",\n \"def parse(self, response: BeautifulSoup):\\n raise NotImplementedError\",\n \"def scrape_callback(url, html):\\r\\n fields = ('area', 'population', 'iso', 'country', 'capital',\\r\\n 'continent', 'tld', 'currency_code', 'currency_name',\\r\\n 'phone', 'postal_code_format', 'postal_code_regex',\\r\\n 'languages', 'neighbours')\\r\\n if re.search('/view/', url):\\r\\n tree = fromstring(html)\\r\\n all_rows = [\\r\\n tree.xpath('//tr[@id=\\\"places_%s__row\\\"]/td[@class=\\\"w2p_fw\\\"]' % field)[0].text_content()\\r\\n for field in fields]\\r\\n print(url, all_rows)\",\n \"def parse_client_html(self):\\n data = dict()\\n data[\\\"date\\\"] = self.date.strftime(\\\"%m-%d-20%y\\\")\\n name_tag = self.client.find_all(\\\"span\\\", {\\\"class\\\": \\\"client-info-name\\\"})\\n data[\\\"name\\\"] = name_tag[0].text\\n guests_tag = self.client.find_all(\\\"div\\\", {\\\"class\\\": \\\"col col-partysize number-fix\\\"})\\n data[\\\"guests\\\"] = guests_tag[0].text\\n min_tag = self.client.find_all(\\\"div\\\", {\\\"class\\\": \\\"col col-minimum\\\"})\\n data[\\\"min\\\"] = min_tag[0].text\\n table_tag = self.client.find_all(\\\"div\\\", {\\\"class\\\": \\\"col col-table-no ellipsis\\\"})\\n data[\\\"table\\\"] = table_tag[0].text\\n notes_tag = self.client.find_all(\\\"div\\\", {\\\"class\\\": \\\"is-mobile notes\\\"})\\n if notes_tag:\\n data[\\\"notes\\\"] = notes_tag[0].text\\n else:\\n data[\\\"notes\\\"] = \\\"\\\"\\n data[\\\"booked\\\"] = self.client[\\\"sort_via\\\"]\\n return data\",\n \"def parse_page(url):\\n\\n page = requests.get(url)\\n soup = BeautifulSoup(page.text, 'html.parser')\\n\\n listings = []\\n\\n # Loop throuhg all prices\\n for offer in soup.findAll(\\\"div\\\", {\\\"class\\\": \\\"regular-ad\\\"}): # Scan regular-ad class to avoid featured ads realted to Kijiji Ads\\n \\n current_listing_dict = {}\\n\\n # Parse title\\n title_list = offer.find_all(href=True)[0].text.split(\\\" \\\")\\n title = [i for i in title_list if i]\\n title = \\\" \\\".join(title).rstrip().strip(\\\"\\\\n\\\").strip(\\\" \\\")\\n\\n # Append title to dict\\n current_listing_dict['title'] = title\\n\\n # Parse price\\n price = \\\"\\\".join(offer.findAll(\\\"div\\\", {\\\"class\\\": \\\"price\\\"})[0].text.split(\\\" \\\")).rstrip().strip('\\\\n')\\n\\n if '$' in price:\\n price = price.split('$')[-1].replace(',','')\\n\\n # Append price to dict\\n current_listing_dict['price'] = price\\n \\n # Parse link\\n link = offer.find_all(href=True)[0]['href']\\n\\n # Append link to dict\\n current_listing_dict['link'] = link\\n\\n # Append to global listings list\\n listings.append(current_listing_dict)\\n\\n return listings\",\n \"def archive_parse_for_posts(page_html):\\n # <div\\\\s+class=\\\"post.+data\\\\-post\\\\-id\\\\=['\\\"](\\\\d+)['\\\"].+?<span\\\\s+class=['\\\"]post_date['\\\"]>([^<]+)</span>\\n post_info_regex = \\\"\\\"\\\"<div\\\\s+class=\\\"post.+?data\\\\-post\\\\-id\\\\=['\\\"](\\\\d+)['\\\"].+?<span\\\\s+class=['\\\"]post_date['\\\"]>([^<]+)</span>\\\"\\\"\\\"\\n post_info = re.findall(post_info_regex, page_html, re.IGNORECASE|re.DOTALL)\\n return post_info\",\n \"def scrape_google(html_content):\\n soup = BeautifulSoup(html_content)\\n tag = soup.a\\n company_name = tag.get_text()\\n url = tag['href']\\n #print(company_name,url)\\n #return company_name, url\\n return url\",\n \"def scrape_layer(self):\\n\\t\\t# azlyrics structure\\n\\t\\ttry:\\n\\t\\t\\t#page = urlopen('http://www.azlyrics.com/lyrics/edsheeran/theateam.html')\\n\\t\\t\\tpage = urlopen('http://www.azlyrics.com/lyrics/edsheeran/theteam.html')\\n\\t\\t\\tsoup = BeautifulSoup(page.read(), \\\"lxml\\\")\\n\\t\\t\\tcontent_div = soup_az.find(\\\"div\\\", class_=\\\"col-xs-12 col-lg-8 text-center\\\")\\n\\t\\t\\tlyric_div = content_div.find(\\\"div\\\", class_=None)\\n\\t\\t\\t# change line break tag to dots\\n\\t\\t\\tprocessed = re.sub('<br\\\\s*?>', '.', lyric_div.text)\\n\\t\\t\\tlines = processed.split()\\n\\t\\t\\treturn lines\\n\\t\\texcept URLError: # if no url or other connection errors, skip\\n\\t\\t\\tprint(\\\"no url\\\")\\n\\t\\t\\tpass\\n\\n\\t\\t# MetroLyrics structure\\n\\t\\ttry:\\n\\t\\t\\t#page = urlopen('http://www.metrolyrics.com/a-team-lyrics-ed-sheeran.html')\\n\\t\\t\\tpage = urlopen('http://www.metrolyrics.com/aheeran.html')\\n\\t\\t\\tsoup = BeautifulSoup(page.read(), \\\"lxml\\\")\\n\\t\\t\\tlyrics = soup.find(\\\"div\\\", {\\\"id\\\": \\\"lyrics-body-text\\\"})\\n\\t\\t\\tprocessed = re.sub('\\\\n', '. ', lyrics.text)\\n\\t\\t\\tlines = processed.split()\\n\\t\\t\\treturn lines\\n\\t\\texcept URLError: # if no url or other connection errors, skip\\n\\t\\t\\tprint(\\\"no url\\\")\\n\\t\\t\\tpass\\n\\n\\t\\t# Genius Lyrics structure\\n\\t\\ttry:\\n\\t\\t\\t#page = urlopen('https://genius.com/Ed-sheeran-the-a-team-lyrics')\\n\\t\\t\\turl = 'https://genius.com/Ed-sheeran-the-a-team-lyrics'\\n\\t\\t\\tclient = webdriver.PhantomJS()\\n\\t\\t\\tclient.get(url)\\n\\t\\t\\tsoup = BeautifulSoup(client.page_source, 'lxml')\\n\\t\\t\\tlyrics_div = soup.find(\\\"div\\\", class_=\\\"lyrics\\\")\\n\\t\\t\\tlyrics_content = lyrics_div.find(\\\"p\\\")\\n\\t\\t\\t#print(lyrics_content)\\n\\t\\t\\tlyrics_a = lyrics_content.find_all(\\\"a\\\")\\n\\t\\t\\tlines = []\\n\\t\\t\\tfor obj in lyrics_a:\\n\\t\\t\\t\\tfor c in obj.contents:\\n\\t\\t\\t\\t\\tif len(c) > 0: lines.append(c + '.')\\t\\t\\t\\n\\t\\t\\treturn lines\\n\\t\\t\\t#lyrics_list.append(c)\\n\\t\\t\\t#print(lyrics_list)\\n\\t\\texcept URLError:\\n\\t\\t\\tprint(\\\"no url\\\")\\n\\t\\t\\tpass\\n\\n\\t\\t# Song Lyrics structure IMPLEMENT THIS -------------------------------------------\\n\\t\\ttry:\\n\\t\\t\\tpass\\n\\t\\texcept URLError:\\n\\t\\t\\tprint(\\\"no url\\\")\",\n \"def get_temp(html) -> None:\\n\\tif page_type_dict['general']:\\n\\t\\tt_text = html.find('div', {'class': '_1HBR'}).text\\n\\t\\tt_digit = ''.join([i for i in t_text if i.isdigit()])\\n\\t\\tweather_dict['temperature'] = t_digit\\n\\telse:\\n\\t\\tre_temp_class = re.compile('.*_2ezK.*') # regex template: str w/ '_2ezK'\\n\\t\\ttemp_class = html.find('div', {'class': re_temp_class}) \\n\\t\\t# we've got smth like: 'Ночью14°Утром19°Днём24°Вечером22°\\n\\t\\tweather_lst = temp_class.text.split('°') # ['Ночью14','Утром19',...]\\n\\t\\tint_weather_lst = [int(number.group()) for number in ( # for all the elems \\n\\t\\t\\tre.search(r'\\\\d+', word) for word in weather_lst) if number] # keep integers\\n\\t\\t# result: [14, 19, 24, 22]\\n\\t\\tweather_dict['temperature'] = int_weather_lst\",\n \"def parse_top_movies(html: str) -> ResultSet:\\n\\n soup = BeautifulSoup(html, \\\"html.parser\\\")\\n return soup.find_all(\\\"div\\\", class_=\\\"lister-item-content\\\")\",\n \"def get_data_from_web():\\n pass\",\n \"def parse_html(city, html):\\n return city.parse_html(html)\",\n \"def _parse_doc(self, html_doc):\\n\\n c = itertools.count(start=1)\\n\\n # load adp page and parse out stats table\\n doc = html.fromstring(html_doc)\\n rows = doc.xpath(\\n '//table[@id=\\\"adp_table\\\"]/tr[contains(@class, \\\"contentrow\\\")]')\\n\\n for row in rows:\\n text = [v.strip() for v in row.xpath('.//text()')[1:]]\\n text.insert(0, c.next())\\n yield text\",\n \"def scrape(self):\\n try:\\n self.result = urlfetch.fetch(self.url)\\n except DownloadError:\\n self.result = urlfetch.fetch(self.url) \\n if ((self.result.status_code == 200) and\\n (self.result.content_was_truncated == 0)):\\n self.soup = BeautifulSoup(self.result.content)\\n else:\\n logging.critical(\\\"Bad Status Code: \\\", self.result.status_code, self.url)\\n sys.exit(1)\",\n \"def parse_source(html, encoding='utf-8'):\\n return BeautifulSoup(html, from_encoding=encoding)\",\n \"def parse_list_page_html(html):\\n episode_list = []\\n\\n lines = html.split('\\\\n')\\n for line in lines:\\n if 'class=\\\"topictitle\\\"' in line and ' - ' in line and 'x' in line:\\n datum = {}\\n query = line.split('/viewtopic.php?f=177&t=')[1].split('&')[0]\\n episode_season_str = line.split('class=\\\"topictitle\\\">')[1].split(' - ')[0]\\n season_str = episode_season_str.split('x')[0]\\n episode_str = episode_season_str.split('x')[1]\\n datum['query'] = query\\n datum['season'] = int(season_str)\\n datum['episode'] = int(episode_str)\\n episode_list.append(datum)\\n\\n return episode_list\",\n \"def get_html_from_url(url):\\n request = requests.get(url)\\n data = request.text\\n return data\",\n \"def down_text(self, last_page):\\n html_content = 3\\n while html_content > 0:\\n html_content -= 1\\n try:\\n\\n html_content =urllib.urlopen(self.pageurl).read()\\n break\\n except Exception as e:\\n print('Unable to download data [Time:%d][%s]' % (html_content, self.pageurl))\\n insert_log('Unable to download data [Time:%d][%s]' % (html_content, self.pageurl))\\n\\n if isinstance(html_content, int):\\n print('Unable to save data [%s]' % self.pageurl)\\n insert_log('Unable to save data [%s]' % self.pageurl)\\n return False\\n\\n print 'downling successfully from %s' % self.pageurl\\n soup = BeautifulSoup(html_content)\\n print soup\\n #\\n #对于帖子不存在了的情况进行判断\\n titlep = soup.title.string\\n if titlep == '403 Forbidden':\\n print '出错了:%s' % self.pageurl\\n insert_log('出错了:%s' % self.pageurl)\\n return []\\n\\n alldata =[]\\n audic = {}\\n\\n \\\"\\\"\\\"\\n 由于第一页有主帖，因此第一页的解析会不同，要把主帖的所有信息保存下来，跟帖的信息也是如此\\n \\\"\\\"\\\"\\n #主帖的部分解析\\n if((self.num == 1) and (self.rc == 0)):\\n title = soup.find('div', id=\\\"content\\\").find(\\\"h1\\\").get_text().strip(\\\"\\\\r\\\\t\\\\n\\\")\\n #section = soup.find('p', class_='crumbs').find_all('a')[1].get_text()\\n auname = soup.find('div', id='content').find('span',class_=\\\"from\\\").find('a').get_text().strip(\\\"\\\\r\\\\t\\\\n\\\")\\n auid = soup.find('div', id='content').find('span',class_=\\\"from\\\").find('a')['href'].split(\\\"/\\\")[-2]\\n ctime = soup.find('div', id='content').find(\\\"h3\\\").find_all('span')[-1].get_text()\\n\\n audic['title'] = title\\n audic['sec'] = self.section\\n audic['uname'] = auname\\n self.author = auname\\n audic['uid'] = auid\\n audic['ctime'] = ctime\\n audic['ro'] = 0\\n\\n mydic = soup.find('div',id=\\\"content\\\").find_all(\\\"li\\\",class_=\\\"clearfix comment-item\\\")\\n mi = 0\\n #soup2 = BeautifulSoup((str)(htstr))\\n #如果把self.rc放在外层if中用于判断是否没有记录，则对于有少量回复，而又有更新的情况，便不能跳过第一页的主帖\\n #解析主帖部分内容\\n if((mi == 0) and (self.num == 1)):\\n mi += 1\\n if(self.rc == 0):\\n imgSrc = \\\"\\\"\\n try:\\n imgsrc = soup.find(\\\"div\\\",class_=\\\"topic-content\\\").find_all(\\\"div\\\",class_=\\\"topic-figure cc\\\")\\n for img in imgsrc:\\n try:\\n for im in img.find_all(\\\"img\\\"):\\n imgSrc += im[\\\"src\\\"] +\\\"\\\\n\\\"\\n except:\\n pass\\n except:\\n pass\\n strtext = soup.find('div', id='link-report').find(\\\"div\\\",class_=\\\"topic-content\\\").get_text()\\n\\n audic['text'] = strtext.strip() + \\\"\\\\n\\\" + imgSrc\\n\\n alldata.append(audic)\\n count = 0\\n for htstr in mydic:\\n soup2 = BeautifulSoup((str)(htstr))\\n\\n #解析跟帖内容\\n redic = {}\\n try:\\n # irroder = (self.num-1)*100 +count\\n #\\n rtime = soup2.find('div', class_='bg-img-green').find('h4').find(\\\"span\\\").get_text()\\n\\n timeArray = time.strptime(rtime, \\\"%Y-%m-%d %H:%M:%S\\\")\\n irorder = int(time.mktime(timeArray))\\n redic['ro'] = irorder\\n #update the reply count\\n if (last_page and (irorder > self.rc)):\\n self.rc = irorder\\n imgSrc = \\\"\\\"\\n try:\\n imgsrc = soup2.find(\\\"div\\\",class_=\\\"topic-content\\\").find_all(\\\"div\\\",class_=\\\"topic-figure cc\\\")\\n for img in imgsrc:\\n try:\\n for im in img.find_all(\\\"img\\\"):\\n imgSrc += im[\\\"src\\\"] +\\\"\\\\n\\\"\\n except:\\n pass\\n except:\\n pass\\n strtext = soup2.find('div', class_='reply-doc content').find(\\\"p\\\").get_text()\\n redic['text'] = strtext.strip() +\\\"\\\\n\\\"+imgSrc\\n\\n\\n\\n uname = soup2.find('div', class_='bg-img-green').find('h4').find(\\\"a\\\").get_text()\\n redic['un'] = uname\\n\\n if(uname != self.author):\\n redic['au'] = False\\n else:\\n redic['au'] = True\\n\\n uid = soup2.find('div', class_='bg-img-green').find('h4').find(\\\"a\\\")[\\\"href\\\"].split(\\\"/\\\")[-2]\\n\\n redic['uid'] = uid\\n\\n\\n redic['time'] = rtime\\n\\n alldata.append(redic)\\n except AttributeError as e:\\n alldata.append(redic)\\n continue\\n print 'All:%s' % self.pageurl\\n return alldata\",\n \"async def from_html(cls, document: str) -> Union[Dict[str, Any], List[Dict[str, Any]]]:\\n doc = pq(document)\\n segments = doc(cls.Meta.selector)\\n if not segments:\\n raise InformationError(\\\"Could not match selector '{selector}' to document.\\\".format(\\n selector=cls.Meta.selector))\\n total = []\\n for segment in segments:\\n values = {}\\n for item_attr in cls._ATTRS:\\n data = await getattr(cls, item_attr).load(segment)\\n values[item_attr] = data\\n total.append(values)\\n if len(total) == 1:\\n return total[0]\\n return total\",\n \"def get_certain_joke(html):\\n soup = BeautifulSoup(html, 'lxml')\\n\\n # for i in len(soup.select('div.content')):\\n # print(soup.select('div.content')[i].get_text())\\n\\n joke_content = soup.select('div.content')[0].get_text()\\n\\n return joke_content\",\n \"def data():\\r\\n\\r\\n data = []\\r\\n m = soup.find('div', {'id': 'main-content'})\\r\\n\\r\\n for tag in m.children:\\r\\n\\r\\n \\\"\\\"\\\"Creates dict element with tag name and text for h-tags.\\\"\\\"\\\"\\r\\n if tag.name in ['h1', 'h2', 'h3', 'h4', 'h5', 'h6']:\\r\\n data.append({'tag': tag.name,\\r\\n 'text': tag.text.strip(),\\r\\n 'ul': [],\\r\\n 'table': [],\\r\\n 'subheads': []})\\r\\n\\r\\n \\\"\\\"\\\"Adds unordered list data.\\\"\\\"\\\"\\r\\n if tag.name == 'ul':\\r\\n temp = _parse_list(tag)\\r\\n data[-1]['ul'].append(temp)\\r\\n\\r\\n \\\"\\\"\\\"Adds tabular data.\\\"\\\"\\\"\\r\\n try:\\r\\n lst = tag.get_attribute_list('class') # Finds table\\r\\n if 'table-wrap' in lst:\\r\\n tbl = []\\r\\n row = []\\r\\n\\r\\n for j in tag.find_all('tr'): # Adds table rows\\r\\n row = []\\r\\n\\r\\n for k in j.children:\\r\\n if k.name == \\\"th\\\" or k.name==\\\"td\\\":\\r\\n '''txt = _parse_list(l.ul)\\r\\n if txt == None:'''\\r\\n txt = k.get_text('\\\\n ').strip()\\r\\n row.append(txt) \\r\\n\\r\\n if row != []:\\r\\n tbl.append(row)\\r\\n\\r\\n if(tbl != []):\\r\\n data[-1]['table'].append(tbl)\\r\\n except:\\r\\n pass\\r\\n\\r\\n return data\",\n \"def parse_detail(self, response):\\n\\n self.logger.log(self.log_lvl, 'scraping data @ {}'.format(response.url))\\n\\n item_list = list()\\n image_urls = list()\\n # extract image\\n try:\\n pattern = re.compile(r\\\"(.*imagearray:)(.*)(,.*displaymode.*)\\\", re.MULTILINE | re.DOTALL)\\n javascript_containing_images = response.xpath('//script[contains(., \\\"var mygallery=\\\")]/text()').extract()[0]\\n images = re.match(pattern, javascript_containing_images).group(2)\\n image_array = json.loads(images)\\n image_urls = [urlparse.urljoin(response.url, itm[1]) for itm in image_array]\\n except Exception as e:\\n print(\\\"{} - {}\\\".format(type(e), str(e)))\\n\\n tipe_mobil = response.css('#content font.vehicleinfo ~ font.warning::text').extract_first()\\n model_mobil = response.css('#content font.vehicleinfo::text').extract_first()\\n if tipe_mobil.lower() == model_mobil.lower():\\n tipe_mobil = response.meta.get('type', None)\\n main_group = response.meta.get('main_group', None)\\n assembly_set = response.css('#content font.title b::text').extract_first()\\n\\n # sparepart items\\n for row in response.css('div#content div.content table tr'):\\n item = IsuzuSparepartItem()\\n\\n # source_url\\n item['source_url'] = response.url\\n\\n # car model\\n item['merk'] = self.name\\n item['tipe_mobil'] = tipe_mobil\\n item['model_mobil'] = model_mobil\\n\\n # images\\n item['image_urls'] = image_urls\\n\\n # grouping/assembly\\n item['main_group'] = main_group\\n item['assembly_set'] = assembly_set\\n\\n item['key'] = row.css('td.intable:nth-child(1) .detailcontent::text').extract_first()\\n item['part_number'] = row.css('td.intable:nth-child(2) .detailcontent::text').extract_first()\\n item['itc'] = row.css('td.intable:nth-child(3) .detailcontent::text').extract_first()\\n item['description'] = row.css('td.intable:nth-child(4) .detailcontent::text').extract_first()\\n item['qty'] = row.css('td.intable:nth-child(5) .detailcontent::text').extract_first()\\n item['app_date'] = row.css('td.intable:nth-child(6) .detailcontent::text').extract_first()\\n item['lr'] = row.css('td.intable:nth-child(7) .detailcontent::text').extract_first()\\n item['model'] = row.css('td.intable:nth-child(8) .detailcontent::text').extract_first()\\n item['remarks'] = row.css('td.intable:nth-child(9) .detailcontent::text').extract_first()\\n\\n item_list.append(item)\\n\\n return item_list\",\n \"def parse_details(pattern: str, data: BeautifulSoup) -> dict:\\n answer = re.search(pattern, data)\\n return answer.group(1) if answer else 'None'\",\n \"def extract_news(parser):\\n news_list = []\\n\\n titles = parser.find_all(\\\"tr\\\", class_=\\\"athing\\\")\\n subtext = parser.find_all(\\\"td\\\", class_=\\\"subtext\\\")\\n\\n for i in range(len(titles)):\\n x = titles[i].find_all(\\\"td\\\", class_=\\\"title\\\")[1]\\n title = x.a.text\\n url = x.a[\\\"href\\\"]\\n c = subtext[i].find_all(\\\"a\\\")[4]\\n if c.text == \\\"discuss\\\":\\n comments = 0\\n else:\\n comments = c.text\\n author = subtext[i].find(\\\"a\\\", class_=\\\"hnuser\\\").get_text()\\n point = subtext[i].find(\\\"span\\\", class_=\\\"score\\\").text\\n points = point.split(' ')[0]\\n\\n news_list.append({\\\"author\\\": author, \\\"comments\\\": comments, \\\"points\\\": points, \\\"title\\\": title, \\\"url\\\": url})\\n\\n return news_list\",\n \"def btc_scraping():\\n\\n url_btc_web = requests.get('https://awebanalysis.com/es/coin-details/bitcoin/')\\n soup = BeautifulSoup(url_btc_web.content, 'html.parser')\\n\\n # we need the class html\\n result = soup.find('td', {'class': 'wbreak_word align-middle coin_price'})\\n btc_price = result.text\\n\\n return btc_price\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70408916","0.6867916","0.67792255","0.66598994","0.65740573","0.6546643","0.6389909","0.6372902","0.6365779","0.636015","0.63392663","0.6330602","0.63226384","0.63203055","0.6284733","0.6260619","0.6241101","0.6239079","0.6200324","0.61735535","0.617248","0.6166607","0.614181","0.61357385","0.6132099","0.6125842","0.6118627","0.60753185","0.60596746","0.6041369","0.6037712","0.6035602","0.6035286","0.6028283","0.60136116","0.59988785","0.599765","0.59834135","0.5983387","0.59731007","0.5963899","0.59574443","0.59432286","0.5940059","0.5913952","0.5905885","0.5904721","0.5894158","0.58847445","0.5881572","0.5867941","0.58571845","0.58571845","0.5857076","0.58472836","0.5844074","0.58405006","0.5833234","0.5826462","0.5825466","0.582358","0.5814387","0.58066666","0.5800695","0.57914126","0.57906413","0.57899487","0.57672286","0.576702","0.57534355","0.5743025","0.57408714","0.57374716","0.5735692","0.5735294","0.5727401","0.5721469","0.5720743","0.5702263","0.5698759","0.56959677","0.56957364","0.5693625","0.56822765","0.56772524","0.56714123","0.5669473","0.56633157","0.56631595","0.5662592","0.56616354","0.5659637","0.5656318","0.564781","0.5635454","0.56340796","0.5624382","0.5617804","0.5617227","0.5617104"],"string":"[\n \"0.70408916\",\n \"0.6867916\",\n \"0.67792255\",\n \"0.66598994\",\n \"0.65740573\",\n \"0.6546643\",\n \"0.6389909\",\n \"0.6372902\",\n \"0.6365779\",\n \"0.636015\",\n \"0.63392663\",\n \"0.6330602\",\n \"0.63226384\",\n \"0.63203055\",\n \"0.6284733\",\n \"0.6260619\",\n \"0.6241101\",\n \"0.6239079\",\n \"0.6200324\",\n \"0.61735535\",\n \"0.617248\",\n \"0.6166607\",\n \"0.614181\",\n \"0.61357385\",\n \"0.6132099\",\n \"0.6125842\",\n \"0.6118627\",\n \"0.60753185\",\n \"0.60596746\",\n \"0.6041369\",\n \"0.6037712\",\n \"0.6035602\",\n \"0.6035286\",\n \"0.6028283\",\n \"0.60136116\",\n \"0.59988785\",\n \"0.599765\",\n \"0.59834135\",\n \"0.5983387\",\n \"0.59731007\",\n \"0.5963899\",\n \"0.59574443\",\n \"0.59432286\",\n \"0.5940059\",\n \"0.5913952\",\n \"0.5905885\",\n \"0.5904721\",\n \"0.5894158\",\n \"0.58847445\",\n \"0.5881572\",\n \"0.5867941\",\n \"0.58571845\",\n \"0.58571845\",\n \"0.5857076\",\n \"0.58472836\",\n \"0.5844074\",\n \"0.58405006\",\n \"0.5833234\",\n \"0.5826462\",\n \"0.5825466\",\n \"0.582358\",\n \"0.5814387\",\n \"0.58066666\",\n \"0.5800695\",\n \"0.57914126\",\n \"0.57906413\",\n \"0.57899487\",\n \"0.57672286\",\n \"0.576702\",\n \"0.57534355\",\n \"0.5743025\",\n \"0.57408714\",\n \"0.57374716\",\n \"0.5735692\",\n \"0.5735294\",\n \"0.5727401\",\n \"0.5721469\",\n \"0.5720743\",\n \"0.5702263\",\n \"0.5698759\",\n \"0.56959677\",\n \"0.56957364\",\n \"0.5693625\",\n \"0.56822765\",\n \"0.56772524\",\n \"0.56714123\",\n \"0.5669473\",\n \"0.56633157\",\n \"0.56631595\",\n \"0.5662592\",\n \"0.56616354\",\n \"0.5659637\",\n \"0.5656318\",\n \"0.564781\",\n \"0.5635454\",\n \"0.56340796\",\n \"0.5624382\",\n \"0.5617804\",\n \"0.5617227\",\n \"0.5617104\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6600914"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":84997,"cells":{"query":{"kind":"string","value":"Calculate total_experience_yrs and other stats"},"document":{"kind":"string","value":"def calc_work_stats( work_xps: List[Dict[str, Any]] ):\n durations = [ rec['duration'] for rec in work_xps if 'duration' in rec ]\n total_years = sum( durations ) if durations else None\n avg_years = np.round( total_years / len(durations), 2) if durations else None\n poss_lt2_years = sum( 1 for dur in durations if dur < 2.0 )\n poss_lt1_2_years = sum(1 for dur in durations if dur < 1.2 )\n\n has_worked_abroad = any( rec for rec in work_xps\n if _is_location_abroad( rec.get('location_raw') ))\n\n return { \"total_experience_yrs\": total_years,\n 'avg_years': avg_years,\n 'n_work_positions': len(durations),\n 'poss_lt2_years': poss_lt2_years,\n 'poss_lt1.2_years': poss_lt1_2_years,\n 'has_worked_abroad': has_worked_abroad }\n # %%"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def years_job_experience(self) -> int:\n return sum([job.years_employed for job in self._history_jobs])","def get_experience(self):\n return sum([i.get_experience for i in self.__units])","async def yearlystats(self, ctx, *, iracing_id=None):\n await self.yearly_stats_db.call(ctx, iracing_id)","def getSalaryStat(self, year = 2014):\r\n year_str = str(year+1)\r\n \r\n df = pd.read_html('http://espn.go.com/nba/salaries/_/year/' + year_str)[0]\r\n df.columns = df.iloc[0]\r\n df = df[df.NAME != 'NAME']\r\n \r\n for page in range(2,12):\r\n page_df = pd.read_html('http://espn.go.com/nba/salaries/_/year/' + \\\r\n year_str + '/page/' + str(page))[0]\r\n page_df.columns = page_df.iloc[0]\r\n page_df = page_df[page_df.NAME != 'NAME']\r\n df = pd.concat([df, page_df])\r\n \r\n df['SALARY'] = df['SALARY'].replace('[\\$,]', '', regex=True).astype(int)\r\n cut_df = df[['NAME', 'SALARY']]\r\n cut_df = cut_df.reset_index(drop=True)\r\n cut_df.columns = [['PLAYER_NAME', 'SALARY']]\r\n \r\n salary_df = self.fixSalaryName(cut_df)\r\n \r\n return salary_df","def advancedStats():","def calculate_advantage(stage_0, stage_1):\n # Improvement in hp difference is good.\n hp_pct_0 = (float(stage_0.friendly_life)/MAX_FRIENDLY_LIFE) - (float(stage_0.enemy_life)/MAX_ENEMY_LIFE)\n hp_pct_1 = (float(stage_1.friendly_life)/MAX_FRIENDLY_LIFE) - (float(stage_1.enemy_life)/MAX_ENEMY_LIFE)\n return hp_pct_1 - hp_pct_0","def cumulative_stats_for_teams_each_year(self):\n self.cumulative_stats_for_team_each_year = (\n self.combine_both_winning_losing_games_stats\n .sort_values(['WTeamID','Season'])\n .groupby(['WTeamID'])\n .cumsum()\n .pipe(lambda x:x.assign(Season = self.combine_both_winning_losing_games_stats.Season.values))\n .pipe(lambda x:x.assign(TeamID = self.combine_both_winning_losing_games_stats.WTeamID.values))\n .drop(['LTeamID','win_rate'],1)\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\n .pipe(lambda x:x.assign(WFGP = x.WFGM/x.WFGA))\n .pipe(lambda x:x.assign(WFG3P = x.WFGM3/x.WFGA3))\n .pipe(lambda x:x.assign(WFTP = x.WFTM/x.WFTA))\n .pipe(lambda x:x.assign(LFGP = x.LFGM/x.LFGA))\n .pipe(lambda x:x.assign(LFG3P = x.LFGM3/x.LFGA3))\n .pipe(lambda x:x.assign(LFTP = x.LFTM/x.LFTA))\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\n # rebounds cumsum stats\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\n # assists, turnovers, steals, blocks and personal fouls\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\n # win or lose by how many points\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\n )","def yearlystats(self, table):\n # Drop unneeded header row\n tmp = table.iloc[1:,]\n # Create a header row removing spaces, /'s, and x's\n tmp.columns = [x.replace(\" \", \"\").replace(\"/\",\"\").replace(\".\",\"\") for x in tmp.iloc[0]]\n # Drop the row used to create header row\n tmp = tmp.drop(tmp.index[0])\n # Forward fill the year for analysis later \n tmp['Year'].fillna(method='ffill', inplace = True)\n # Create a new offense/defense variable \n tmp['OffenseDefense'] = tmp['Team']\n # Figure out which team we are working with \n curr_team = tmp.iloc[:1,1:3].values[0][0]\n # Create a new team variable\n tmp['Team'] = curr_team\n # In the offense defense variable, fill in the offense defense variable \n tmp['OffenseDefense'] = tmp['OffenseDefense'].apply(lambda x: 'Offense' if x == curr_team else x)\n return tmp","def life_insurance_to_recive_total(self):\n pass","def calculate_profit(self):","def year_emissions_intensity_rule(_m, y):\r\n\r\n return m.YEAR_EMISSIONS[y] / m.YEAR_DEMAND[y]","def annualized_perf(self):\n mean_return = round(self.data.log_returns.mean() * 252, 4)\n risk = round(self.data.log_returns.std() * np.sqrt(252), 4)\n print(\"Return: {} | Risk: {}\".format(mean_return, risk))","def do_damage(self) -> float:\n sum = 0\n for operator in self.__operators:\n if operator.is_alive:\n operator.experience += 1\n sum += operator.experience / 100\n return 0.1 + sum","def movie_stats(movies):\n def num_years():\n \"\"\"number of years covered in the dataset\"\"\"\n years = movies['Year']\n return ('num_years', years.nunique())\n\n def tot_movies():\n \"\"\"total number of movies over all years\"\"\"\n total = movies['Number of Movies'].sum()\n return ('tot_movies', total)\n\n def yr_fewest_movies():\n \"\"\"year with fewest movies made (earliest)\"\"\"\n copy = movies.copy()\n year = copy.sort_values(['Number of Movies', 'Year']).reset_index(drop = True).Year.loc[0]\n return ('yr_fewest_movies', year)\n\n def avg_gross():\n \"\"\"average amount of money grossed over all years\"\"\"\n avg = movies['Total Gross'].mean()\n if avg is np.nan:\n raise\n return ('avg_gross', avg)\n\n def highest_per_movie():\n \"\"\"The year with the highest gross per movie\"\"\"\n copy = movies.copy()\n copy['Gross Per Movie'] = copy['Total Gross'] / copy['Number of Movies'] #calculate gross per movie\n year = copy.sort_values(['Gross Per Movie'], ascending = False).reset_index(drop = True).Year.loc[0]\n return ('highest_per_movie', year)\n\n def second_lowest():\n \"\"\"name of the top movie during the second lowest (total) grossing year\"\"\"\n copy = movies.copy()\n name = copy.sort_values(['Total Gross']).reset_index(drop = True)['#1 Movie'].loc[1]\n return ('second_lowest', name)\n\n def avg_after_harry():\n \"\"\"avg number of movies made the year after an HP movie was #1 movie\"\"\"\n copy = movies.copy()\n copy = copy.sort_values(['Year']).reset_index(drop = True) #years early to present\n harry_years = copy[copy['#1 Movie'].str.contains('Harry')].Year #years where harry potter was #1\n next_years = harry_years + 1\n check = list(next_years.values)\n next_years_df = copy[copy['Year'].isin(check)]\n avg = next_years_df['Number of Movies'].mean()\n if avg is np.nan:\n raise\n return ('avg_after_harry', avg)\n\n functions = [num_years(), tot_movies(), yr_fewest_movies(), avg_gross(), highest_per_movie(), second_lowest(), avg_after_harry()]\n stats = {}\n for fxn in functions:\n try:\n info = fxn\n stats[info[0]] = info[1]\n except:\n continue\n series = pd.Series(stats)\n\n return series","def calc_perc_reducts():\n #Load RCP2.6 datq\n cubes = iris.load(data_dir+'DMSSO2NH3_18502100_RCP26_monthly.nc')\n #Get the surface and high level SO2 emissions\n surf_cube = cubes[3][:,0]\n high_cube = cubes[1][:,0]\n cubes = iris.cube.CubeList([surf_cube,high_cube])\n\n for i in range(0,len(cubes)):\n #Add the year and month to the cube and extract for 2009 onwards\n iris.coord_categorisation.add_year(cubes[i],'time',name='year')\n iris.coord_categorisation.add_month(cubes[i],'time',name='month')\n cubes[i] = cubes[i].extract(iris.Constraint(year = lambda y: y >=2009))\n\n #Make the year-on-year reduction rates\n yoy_rates = []\n for cube in cubes:\n #Calculate the global mean timeseries\n cube.coord('latitude').guess_bounds()\n cube.coord('longitude').guess_bounds()\n area_weights = iris.analysis.cartography.area_weights(cube)\n cube_mean = cube.collapsed(['latitude','longitude'],iris.analysis.MEAN,weights=area_weights)\n\n cube_rates = np.ones((cube_mean.shape))\n #Loop over the months and calculate the changes from the previous year\n #Calculate the year on year proportional changes in the global mean\n for i in range(12,cube_mean.shape[0]):\n cube_rates[i] = cube_mean[i].data / cube_mean[(i-12)].data\n\n yoy_rates.append(cube_rates)\n\n return yoy_rates","def compute_stats(self):\n if self.stats is not None:\n return\n self.stats = np.zeros(STEPS_MAX + 1)\n for m in self.missions:\n m.compute_stats()\n self.stats += 100 * m.stats\n self.stats /= len(self.missions)","def core_stats():\n data = get_tsv_dataset(os.path.join(DATA_DIR, TOTALS_FILE))\n if data is None:\n return make_response(jsonify({'error': 'Data could not be read'}), 500)\n # parse up so we can manipulate things.\n dataset = [int(x) for x in data]\n annual_sightings = sum(dataset)\n # for each 'month' (selection of x4\n monthly_sightings = []\n max_sightings = 0\n max_month = 0\n\n # grab each month's data into its own list for post processing.\n # also calculate some other numbers as we go.\n for i in range(0, len(dataset), 4):\n # select 4x data points.\n this_month = dataset[i:i + 4]\n total_sightings_this_month = sum(this_month)\n monthly_sightings.append(total_sightings_this_month)\n old_max = max_sightings\n max_sightings = max(max_sightings, total_sightings_this_month)\n if old_max < max_sightings:\n # it could be the 0th month.\n max_month = len(monthly_sightings)\n\n mean_monthly_sightings = mean(monthly_sightings)\n month_name = list(calendar.month_name)[max_month]\n return make_response(jsonify({'annual_sightings': annual_sightings,\n 'max_sightings': max_sightings,\n 'max_sighting_month': month_name,\n 'mean_monthly_sightings': mean_monthly_sightings}), 200)","def year_emissions_rule(_m, y):\r\n\r\n return sum(m.SCENARIO_EMISSIONS[y, s] for s in m.S)","def living_expenses(self):\n # Prepare arguments for call to `living_expenses_strategy`\n # NOTE: This is a pretty brittle way to determine the\n # retirement year. Issues #15 and #28 will require this\n # code to be changed in a future version.\n retirement_year = min(\n person.retirement_date.year for person in self.people)\n return self.living_expenses_strategy(\n year=self.this_year,\n people=self.people,\n retirement_year=retirement_year)","def evaluateY(self, time) -> float:\n ...","def year_cost_rule(_m, y):\r\n\r\n return sum(m.RHO[y, s] * m.SCEN[y, s] for s in m.S)","def setEmpireStats(self):\n totalEmpires = len(self.empires.keys())\n stats = {'Research':[], 'Fleet Size':[], 'Army Size':[], 'CR Production':[],\n 'AL Production':[],'EC Production':[],'IA Production':[]}\n \n # Calculate Research Stats\n d = {}\n for empireID, myEmpire in self.empires.iteritems():\n if empireID <> '1':\n num = 0\n for techID, myTech in myEmpire.techTree.iteritems():\n if myTech.complete == 1:\n num += 1\n d[empireID] = num\n stats['Research'] = anwp.func.funcs.sortDictByValue(d, True)\n \n # Calculate Fleet Stats\n d = {}\n for shipID, myShip in self.ships.iteritems():\n if myShip.empireID <> '1':\n (BV,CR,AL,EC,IA) = myShip.getMyValue()\n if myShip.empireID in d.keys():\n d[myShip.empireID] += BV\n else:\n d[myShip.empireID] = BV\n stats['Fleet Size'] = anwp.func.funcs.sortDictByValue(d, True)\n \n # Calculate Army Stats\n d = {}\n for regimentID, myRegiment in self.regiments.iteritems():\n if myRegiment.empireID <> '1':\n (BV,CR,AL,EC,IA) = myRegiment.getMyValue()\n if myRegiment.empireID in d.keys():\n d[myRegiment.empireID] += BV\n else:\n d[myRegiment.empireID] = BV\n stats['Army Size'] = anwp.func.funcs.sortDictByValue(d, True)\n\n # Calculate Production Stats\n for res in ['CR','AL','EC','IA']:\n d = {}\n for systemID, mySystem in self.systems.iteritems():\n if mySystem.myEmpireID <> '1':\n myValue = getattr(mySystem, 'prod%s' % res)\n if mySystem.myEmpireID in d.keys():\n d[mySystem.myEmpireID] += myValue\n else:\n d[mySystem.myEmpireID] = myValue\n myEmpire = self.empires[mySystem.myEmpireID]\n myEmpireValue = getattr(myEmpire, 'totalProd%s' % res)\n setattr(myEmpire, 'totalProd%s' % res, myEmpireValue+myValue)\n \n stats['%s Production' % res] = anwp.func.funcs.sortDictByValue(d, True)\n \n # calculate top captains\n d = {}\n for captainID, myCaptain in self.captains.iteritems():\n if myCaptain.myEmpire.id <> '1':\n myCaptain.resetData()\n d[myCaptain.id] = myCaptain.experience\n topCaptains = anwp.func.funcs.sortDictByValue(d, True)\n topCaptains = topCaptains[:2*len(self.empires.keys())]\n \n # Send out Stats to each Empire\n for empireID, myEmpire in self.empires.iteritems():\n if empireID <> '1':\n title = 'Round:%d Statistics' % self.currentRound\n body = ['%s ROUND %d STATS:' % (myEmpire.name, self.currentRound)]\n body.append('====================================================')\n for item in ['Research','Fleet Size', 'Army Size', 'CR Production',\n 'AL Production', 'EC Production', 'IA Production']:\n if empireID in stats[item]:\n body.append('You are %s in %s' % (anwp.func.funcs.getNiceNumber(stats[item].index(empireID)+1), item))\n \n # total production\n body.append('')\n body.append('TOTAL EMPIRE PRODUCTION OVER %d ROUNDS:' % self.currentRound)\n body.append('====================================================')\n for res in ['CR','AL','EC','IA']:\n body.append('Total %s Production: %d' % (res, getattr(myEmpire, 'totalProd%s' % res)))\n\n # legendary captains\n body.append('')\n body.append('TOP %d STARSHIP CAPTAINS in ROUND %d:' % ((2*len(self.empires.keys()), self.currentRound)))\n body.append('====================================================')\n for captainID in topCaptains:\n myCaptain = self.captains[captainID]\n myCaptain.promoteMe()\n body.append('%s ---> RANK:%s -- EXP:%d -- %s' % (string.upper(myCaptain.name), myCaptain.rank, myCaptain.experience, string.upper(myCaptain.myEmpire.name)))\n \n myEmpire.genMail({'fromEmpire':empireID, 'round':self.currentRound,\n 'messageType':'general', 'subject':title, 'body':body})","def test_yearly_report(self):\n self.register_user()\n result = self.login_user()\n access_token = json.loads(result.data.decode())['access_token']\n res = self.client().post('/expenses/', headers=dict(Authorization=\"Bearer \" + access_token),\n data=self.expense)\n self.assertEqual(res.status_code, 201)\n rv = self.client().post('/expenses/', headers=dict(Authorization=\"Bearer \" + access_token), data=\n {'name': 'soda', 'amount': 200, 'date_of_expense': '10-01-2021'})\n consolidated_total = 212.23\n res = self.client().get('/yearly_report?year=2021', headers=dict(Authorization=\"Bearer \" + access_token))\n self.assertEqual(res.status_code, 200)\n results = json.loads(res.data)\n self.assertEqual(results['consolidated_total'], consolidated_total)","def _get_age(self):\n for employee in self:\n if employee.sudo().birthday:\n employee.age = relativedelta(\n fields.Date.from_string(fields.Date.today()),\n fields.Date.from_string(employee.sudo().birthday)).years\n else:\n employee.age = 0","def _death_insurance(self, total_salary):\n return int(total_salary * self.death_insurance_rate)","def analyst_agent(currency_id, rates_past_future, days_predicted):\n currency = Currency.objects.get(id=currency_id)\n rate_yesterday = rates_past_future[:-days_predicted][-2]\n rate_today = rates_past_future[:-days_predicted][-1]\n rate_end = rates_past_future[-1]\n rate_growth_last_day = rate_today - rate_yesterday\n expected_rate_growth = rate_end - rate_today\n percentage_growth_last_day = 100 * float(rate_growth_last_day) / float(rate_yesterday)\n percentage_growth = 100 * float(expected_rate_growth) / float(rate_today)\n notification_agent(currency, expected_rate_growth, percentage_growth, days_predicted, rate_today, rate_end, percentage_growth_last_day)","def calc_annual_heating_savings (self):\n price = (self.diesel_prices + self.cd['heating fuel premium'])\n\n #~ self.base_heating_cost =\n\n #~ self.proposed_heating_cost =\n\n\n\n\n self.annual_heating_savings = self.reduction_diesel_used * price\n #~ print 'self.annual_heating_savings',self.annual_heating_savings","def monthly_fee(self):\n total_salary = self.base_salary\n if self.is_salary_allowances is True:\n fixed_allowances = self.summarize( self.fixed_allowances )\n non_fixed_allowances = self.summarize( self.non_fixed_allowances )\n total_salary = total_salary + non_fixed_allowances + fixed_allowances\n #end if\n\n company_old_age_insurance = 0\n individual_old_age_insurance = 0\n if self.old_age_insurance_status is True:\n company_old_age_insurance = \\\n self._company_old_age_insurance(total_salary)\n\n individual_old_age_insurance = \\\n self._individual_old_age_insurance(total_salary)\n #end if\n\n company_pension_insurance = 0\n individual_pension_insurance = 0\n if self.pension_insurance_status is True:\n company_pension_insurance = \\\n self._company_pension_insurance(total_salary)\n\n individual_pension_insurance = \\\n self._individual_pension_insurance(total_salary)\n #end if\n\n company_health_insurance = 0\n individual_health_insurance = 0\n if self.health_insurance_status is True:\n company_health_insurance = \\\n self._company_health_insurance(total_salary)\n\n individual_health_insurance = \\\n self._individual_health_insurance(total_salary)\n #end if\n\n death_insurance = 0\n if self.death_insurance_status is True:\n death_insurance = self._death_insurance(total_salary)\n #end if\n\n accident_insurance = 0\n if self.accident_insurance_status is True:\n accident_insurance = \\\n self._accident_insurance(total_salary, \\\n self.industry_risk_rate)\n #end if\n\n monthly = {\n \"old_age_insurance\" : {\n \"company\" : company_old_age_insurance,\n \"individual\" : individual_old_age_insurance,\n },\n \"pension_insurance\" : {\n \"company\" : company_pension_insurance,\n \"individual\" : individual_pension_insurance,\n },\n \"health_insurance\" : {\n \"company\" : company_health_insurance,\n \"individual\" : individual_health_insurance,\n },\n \"death_insurance\" : death_insurance,\n \"accident_insurance\" : accident_insurance\n }\n return monthly","def death_rate(self):\n raise NotImplementedError","def do_damage(self) -> float:\n res = 0.05 + self.experience / 100\n self.experience = self.experience + 1\n return res","def years_to_pay(self) -> float:\n return round(self.term / self.term_multiplier * self.n_periods / 12, 1)","def year_cumulative_price_difference_weighted_rule(_m, y):\r\n\r\n return sum(m.YEAR_ABSOLUTE_PRICE_DIFFERENCE_WEIGHTED[j] for j in m.Y if j <= y)","def per_hour(self):\n if self.is_salary():\n return 0.0\n return self.wage_cents / 100.0","def collect_payroll(team_dict):\n fourty_five_man = len(team_dict['roster'])\n fourty_man = sum(1 for x in team_dict['roster'] if x['contract'].type != 'AA')\n gross_payroll = collect_salaries(team_dict)\n payroll_adjustments = collect_adjustment_totals(team_dict)\n net_payroll = [0, 0, 0, 0, 0]\n payroll_remaining = [0, 0, 0, 0, 0]\n for i in range(5):\n net_payroll[i] = gross_payroll[i] + payroll_adjustments[i]\n payroll_remaining[i] = 135000000 - net_payroll[i]\n team_dict['payroll'] = {\n '40_man': fourty_man,\n '45_man': fourty_five_man,\n 'gross_payroll': gross_payroll,\n 'payroll_adjustments': payroll_adjustments,\n 'net_payroll': net_payroll,\n 'payroll_remaining': payroll_remaining,\n 'salary_cap': [135000000, 135000000, 135000000, 135000000, 135000000]\n }\n return team_dict","def _company_old_age_insurance(self, total_salary):\n return self.company_old_age_insurance_rate * total_salary","def determinePerformance(history):\n\n\tperformance = dict()\n\n\tn = 1\n\trecentDate = sorted(history.keys())[-1]\n\n\twhile True:\n\t\tlastYear = subtractYears(recentDate, n)\n\t\tif lastYear == None:\n\t\t\tbreak;\n\n\t\tentry = getEntryBefore(history, lastYear)\n\t\tif entry != None:\n\t\t\tppuChangePercent = ppuIncrease(history, lastYear, recentDate)\n\n\t\t\tif n > 1:\n\t\t\t\tyearEnd = subtractYears(recentDate, n-1)\n\t\t\t\toneYearPerformance = ppuIncrease(history, lastYear, yearEnd)\n\t\t\telse:\n\t\t\t\toneYearPerformance = ppuChangePercent\n\n\t\t\tarr = annualisedReturn(ppuChangePercent, n)\n\n\t\t\tperformance[n] = {\n\t\t\t\t'duration' : n,\n\t\t\t\t'indvidual' : oneYearPerformance,\n\t\t\t\t'cumulative' : ppuChangePercent,\n\t\t\t\t'effective' : arr\n\t\t\t}\n\t\t\tn = n + 1\n\t\t\tif n > 10:\n\t\t\t\tbreak\n\t\telse:\n\t\t\t# run out of whole years\n\t\t\tfirstDate = sorted(history.keys())[0]\n\t\t\tppuChangePercent = ppuIncrease(history, firstDate, recentDate)\n\t\t\tn = float((recentDate - firstDate).days) / 365\n\t\t\tarr = annualisedReturn(ppuChangePercent, n)\n\n\t\t\tperformance[n] = {\n\t\t\t\t'duration' : n,\n\t\t\t\t'indvidual' : None,\n\t\t\t\t'cumulative' : ppuChangePercent,\n\t\t\t\t'effective' : arr\n\t\t\t}\n\t\t\tbreak\n\n\treturn performance","def hit(self):\r\n self.life -= 1\r\n total_score = 1\r\n if self.life == 0:\r\n self.alive = False\r\n total_score += self.bonus\r\n return total_score","def _calculate(self):\n source = self.source\n res = {}\n l_cols = [[], [], [], []]\n r_lines = {}\n dateline=None\n ###delete the below code when fetch data from database(assume: data in database has been pretreatment)\n if source[t.ror].min() > -99.0:\n pass\n else:\n source[t.ror] = np.where(\n source[t.ror] > -99.0, source[t.ror], -99.0)\n ###\n for account in self.accounts:\n source_account = source[source[t.account] == account]\n source_account = source_account.reset_index(drop=True)\n dateline=source_account[t.effective_date]\n ror=source_account[t.ror]/100\n returns_cum = ROR.ror_cum_ann(source_account, self.annualized)\n # double_return_cum=round(double_return_cum,2)+1\n returns_cum = returns_cum + 1\n growth_amounts = returns_cum * self.starting_value\n returns_cum, growth_amounts = round(returns_cum - 1, 4), \\\n round(growth_amounts, 2)\n l_cols[0].append(growth_amounts.iloc[-1, 0])#account growth amount\n l_cols[1].append(growth_amounts.iloc[-1, 1])#bench growth amount\n l_cols[2].append(returns_cum.iloc[-1, 0])#account return\n l_cols[3].append(returns_cum.iloc[-1, 1])#bench return\n r_lines[account] = [list(returns_cum.iloc[:,0]), list(growth_amounts.iloc[:, 0]),#list(returns_cum.iloc[:, 0])\n list(growth_amounts.iloc[:, 1])]#account return, account growth amount, bench growth amount\n res['account_vs_benchmark'] = {'xAxis': self.accounts,\n 'series': l_cols}\n res['growth_of_unit'] = {'xAxis': list(dateline),\n 'series': r_lines}\n return res\n # ret_dict = self._ret(accounts, starting_value, source, annualized)\n # return ret_dict","def extras_total(self):\n total = self.wides + self.no_balls + self.byes + self.leg_byes\n return total","def base_experience(self) -> int:\n return self._base_experience","def get_score(self):\n for response in self.response_list:\n self.score += response.get_score","def calc_stat_values(self):","def get_total_expenses(self):\n return sum(self.expenses.values())","def user_stats(df):\n print('\\nCalculating User Stats...\\n')\n start_time = time.time()\n\n # Display counts of user types value_counts\n print('User Type Counts:')\n for key, value in dict(df['User Type'].value_counts()).items():\n print('{0:12}: {1:8}'.format(key, value))\n\n # Display counts of gender if that column is present\n if 'Gender' in df.columns:\n print('\\nGender Data:')\n gc = dict(df['Gender'].value_counts())\n\n print('\\tRides:')\n for key, value in gc.items():\n print('\\t{0:7}: {1:8}'.format(key, value))\n\n # get average duration by gender\n print('\\n\\tAverage Duration:')\n for gender in gc.keys():\n gender_df = df[df['Gender'] == gender]\n gender_duration_avg = get_human_readable_time(gender_df['Trip Duration'].mean())\n print('\\t{0:7}: {1:8}'.format(gender, gender_duration_avg))\n\n\n # Display earliest, most recent, and most common year of birth min/max/mode\n # if that column is present\n if 'Birth Year' in df.columns:\n current_year = dt.datetime.today().year\n df['Age'] = current_year - df['Birth Year']\n\n print('\\nAge Data:')\n print('\\tEarliest Birth Year: \\t{}'.format(int(df['Birth Year'].min())))\n print('\\tMost Recent Birth Year: {}'.format(int(df['Birth Year'].max())))\n\n most_common_year = ', '.join(map(str, map(int, list(df['Birth Year'].mode()))))\n print('\\tMost Common Birth Year: {}'.format(most_common_year))\n\n print('\\tOldest Rider: \\t\\t{}'.format(int(df['Age'].max())))\n print('\\tYoungest Rider: \\t{}'.format(int(df['Age'].min())))\n print('\\tAvergae Rider Age: \\t{}'.format(round(df['Age'].mean())))\n\n\n \n print('\\nThis took %s seconds.' % (time.time() - start_time))\n print('-'*40)","def marcels_players(goalie, date, df):\n # 0 = that year, 1 is year b4 ....\n marcel_weights = [.36, .29, .21, .14]\n reg_const = 2000\n reg_avg = 0 # Where to regress to\n\n # Use past 3 season to weight games played -> Just take weighted average\n gp_weights = [8, 4, 2, 0]\n\n season = int(helpers.get_season(date))\n\n weighted_goals_sum, weighted_fen_sum, weighted_xg_sum, weights_marcel_sum = 0, 0, 0, 0\n weighted_gp_sum, weights_gp_sum = 0, 0\n\n # Past 4 Seasons\n for i in range(0, 4):\n if season - i > 2006:\n # Subset from stats df\n df_goalie = df[(df['player'] == goalie) & (df['season'] == (season - i))]\n\n # Sanity Check\n if df_goalie.shape[0] > 1:\n print(\"Too many rows!!!!!!!\")\n exit()\n\n # If he played that year\n if not df_goalie.empty:\n weighted_goals_sum += df_goalie.iloc[0]['goals_a'] * marcel_weights[i]\n weighted_fen_sum += df_goalie.iloc[0]['fenwick_a'] * marcel_weights[i]\n weighted_xg_sum += df_goalie.iloc[0]['xg_a'] * marcel_weights[i]\n weighted_gp_sum += df_goalie.iloc[0]['games'] * gp_weights[i]\n\n # -> To divide by at end...normalize everything\n weights_marcel_sum += marcel_weights[i]\n weights_gp_sum += gp_weights[i]\n\n # Normalize weighted sums\n weighted_xg_sum = weighted_xg_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\n weighted_goals_sum = weighted_goals_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\n weighted_fen_sum = weighted_fen_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\n\n # Get Regressed fsv%\n if weighted_fen_sum != 0:\n weighted_adj_fsv = ((1 - weighted_goals_sum / weighted_fen_sum) - (1 - weighted_xg_sum / weighted_fen_sum)) * 100\n else:\n weighted_adj_fsv = 0\n reg_adj_fsv = weighted_adj_fsv - ((weighted_adj_fsv - reg_avg) * (reg_const / (reg_const + weighted_fen_sum)))\n\n # Get weighted gp\n weighted_gp_sum = weighted_gp_sum / weights_gp_sum if weights_gp_sum != 0 else 0\n\n return {'fsv': reg_adj_fsv, 'gp': weighted_gp_sum}","def year_sum_cumulative_price_difference_weighted_rule(_m, y):\r\n\r\n return sum(m.YEAR_CUMULATIVE_PRICE_DIFFERENCE_WEIGHTED[j] for j in m.Y if j <= y)","def payroll_calculation():\n\n name = search_employee()\n if name == None:\n return\n accrual_month = month('Accrual month: ')\n accrual_year = year('Accrual year: ')\n accrual = f'{accrual_month}-{accrual_year}'\n salary_value = month_salary()\n salary_base = salary(salary_value)\n overtime = value_input('Overtime: ')\n absences = value_input('Absences: ')\n late = value_input('Late: ')\n bonus = value_input('Bonus: ')\n\n hourly_wage = round(salary_value / 220, 2)\n overtime_value = round(float(hourly_wage * 1.5), 2)\n overtime_total = round(overtime_value * overtime, 2)\n daily_wage = round(salary_value / 30, 2)\n absences_value = round(daily_wage * absences, 2)\n late_value = round(daily_wage * late / 60, 2)\n inss_value = inss(salary_base, overtime_total)\n irrf_value = irrf(salary_base, overtime_total, inss_value, bonus)\n sleep(2)\n\n\n\n header('EARNINGS')\n print(f'Salary: {salary_base}')\n print(f'Bonus: {bonus}')\n print(f'Overtime: {overtime_total }')\n earnings_total = round(salary_base + overtime_total + bonus, 2)\n sleep(2)\n\n print(line())\n print(f'Earnings total: {earnings_total}')\n print(line())\n sleep(2)\n\n header('DISCOUNTS')\n\n transportation_vouchers = round(salary_base * 6 / 100, 2)\n health_care = round(salary_base * 2 / 100, 2)\n dental_care = round(salary_base * 0.5 / 100, 2)\n meal_ticket = round(salary_base * 1 / 100, 2)\n\n print(f'absences: {absences_value}')\n print(f'late: {late_value}')\n print(f'transportation_vouchers: {transportation_vouchers}')\n print(f'health_care: {health_care}')\n print(f'dental_care: {dental_care}')\n print(f'meal_ticket: {meal_ticket}')\n print(f'inss_value: {inss_value}')\n print(f'irrf_value: {irrf_value}')\n\n discounts_total = round(absences_value + late_value + transportation_vouchers + health_care +\n dental_care + meal_ticket + inss_value + irrf_value, 2)\n\n print(line())\n print(f'Discounts_total : {discounts_total }')\n print(line())\n liquid_salary = round(earnings_total - discounts_total, 2)\n print(f'Liquid_salary: {liquid_salary} ')\n print(line())\n\n conn = sqlite3.connect('data/people_management.db')\n cursor = conn.cursor()\n cursor.execute(f\"\"\"\n INSERT INTO salary (name, salary ,bonus, overtime, absences_value, late_value, \n t_vouchers, health_care, dental_care, meal_ticket, inss, irrf, \n earnings, discounts, liquid_salary, accrual)\n VALUES ('{name}', '{salary_base}' ,'{bonus}', '{overtime_total}', '{absences_value}', \n '{late_value}', '{transportation_vouchers}', '{health_care}', '{dental_care}', \n '{meal_ticket}', '{inss_value}', '{irrf_value}', '{earnings_total}', '{discounts_total}', \n '{liquid_salary}', '{accrual}')\n \"\"\")\n conn.commit()\n conn.close()","def getStockPerformance(stock, start_idx, end_idx, number_years, rounded=True):\r\n total_return = (stock['Adj Close'][end_idx]- stock['Adj Close'][start_idx])/stock['Adj Close'][start_idx] + 1\r\n total_return_percentage = total_return * 100\r\n num = ((total_return ** (1/number_years)) - 1) * 100\r\n annual_return = np.round(num, decimals=2) if rounded else num\r\n return (total_return_percentage, annual_return)","def processed_cum_overall(self):\n self.processed_cum_overall = (\n self.cumulative_stats_for_team_each_year\n [['Season','TeamID','win_rate','total_score','total_opponent_score','fgp','fg3p','ftp', 'total_rebounds','total_off_rebounds','total_def_rebounds',\n 'total_off_rebounds_percent','total_def_rebounds_percent','total_rebound_possession_percent','total_rebound_possessiongain_percent','total_blocks',\n 'total_assists','total_steals','total_turnover','total_personalfoul','total_block_opp_FGA_percent','total_assist_per_fgm','total_assist_turnover_ratio',\n 'expectation_per_game','avg_lose_score_by','avg_win_score_by']]\n )","def total_end_for(self, age, sex):\n initial = getattr(self, '%s_total_beginning_%s' % (age, sex), 0)\n admitted = getattr(self, '%s_admitted_%s' % (age, sex), 0)\n out = getattr(self, '%s_total_out_%s' % (age, sex), 0)\n return (initial + admitted) - out","def experiences(self):\n return self.client.call('GET',\n self.name + 'experiences')","def skills_to_salary(self):\n tree = html.fromstring(self.page)\n numerator = []\n denominator = []\n for salary in range(1, 6):\n salary_x_path = '//*[@id=\"SALARY_rbo\"]/ul/li[' + str(salary) + ']/a/text()'\n number_of_jobs_x_path = '//*[@id=\"SALARY_rbo\"]/ul/li[' + str(salary) + ']/text()'\n salary_str = tree.xpath(salary_x_path)\n num_of_jobs_str = tree.xpath(number_of_jobs_x_path)\n try:\n salary_float = float(salary_str[0].strip(\"$, +\").replace(',', ''))\n num_of_jobs_float = float(num_of_jobs_str[1].strip(\"( , )\"))\n except IndexError:\n salary_float = 0\n num_of_jobs_float = 1\n numerator.append(salary_float * num_of_jobs_float)\n denominator.append(num_of_jobs_float)\n weighted_average_salary = sum(numerator) / sum(denominator)\n total_number_of_jobs = sum(denominator)\n self.salary = weighted_average_salary\n self.number_of_jobs = total_number_of_jobs","def getTotalReward(self):\n return self.cumreward","def getTotalReward(self):\n return self.cumreward","def collect_salaries(team_dict):\n salaries = [0, 0, 0, 0, 0]\n for c in team_dict['roster']:\n for i in range(5):\n if (c['contract'].contract_season + i) <= c['contract'].length:\n salaries[i] += c['contract'].salary\n return salaries","async def score(self, ctx):\r\n with DB() as db:\r\n companies = db.query(Company).filter(Company.active == True).all()\r\n scores = []\r\n for company in companies:\r\n history = db.query(CompanyHistory).filter(CompanyHistory.company == company.id).order_by(CompanyHistory.date.desc()).first()\r\n scores.append([company.name, round(history.value, 2)])\r\n headers = ['Company', 'Net Worth']\r\n score_df = pd.DataFrame(scores, columns=headers)\r\n score_df = score_df.sort_values(['Net Worth'], ascending=False)\r\n aggregated = tabulate(score_df.values.tolist(), headers=headers)\r\n await ctx.send(f\"```{aggregated}```\")","def age_check(self):\r\n # check working status\r\n if 15 <= float(self.age) < 59:\r\n if self.work_status == 0:\r\n self.work_status = 1\r\n num_labor_list[self.hh_id] += 1\r\n labor_list.append(self.unique_id)\r\n if self.work_status == 1 and self.unique_id not in labor_list:\r\n labor_list.append(self.unique_id)\r\n else:\r\n self.work_status = 0\r\n\r\n # check education status; measured in years of education\r\n if 7 <= int(self.age) <= 19:\r\n if random.random() > 0.1:\r\n self.education += 1\r\n # most adults in the FNNR did not get a full 12-13 years of education\r\n elif 19 < float(self.age) < 23 and self.migration_status == 1:\r\n if random.random() < 0.5:\r\n self.education += 1 # went to college and got further education\r\n # this is rare; in the household list, a few received beyond 12 years of education\r\n\r\n # check age-based death rates\r\n if self.age > 65:\r\n self.death_rate = 0.001443 # 5-day death rate\r\n # The average death rate in China is 7.3 per 1,000 people/year, or 0.0073 (Google).\r\n # However, death rates should be higher for the elderly, or else the population structure will skew.\r\n # I set death rates for those over age 65 to be 10% per year--0.9 yearly survival rate.\r\n # The survival rate for each 5-day step is compounded 73 times, so x^73 = 0.85.\r\n # 0.998557 is the 5-day survival rate, and 1 - x is the 5-day death rate.\r\n else:\r\n self.death_rate = 0.00000425\r\n # I wanted people to have a 98% chance of reaching age 65 (death rate is lower if not elderly).\r\n # If a 'check' is every 5 days, 73 checks/year * 65 years = 4,745 checks.\r\n # x^4745 = 0.98; the 5-day survival rate is 0.99999575, and 1 - x is the 5-day death rate.\r\n\r\n # These rates are changeable later.\r","def get_age(self):\n return CURRENT_YEAR - self.year","def get_age(self):\n return CURRENT_YEAR - self.year","def year_average_price_rule(_m, y):\r\n\r\n # Total revenue\r\n return sum(m.SCENARIO_REVENUE[y, s] for s in m.S) / sum(m.SCENARIO_DEMAND[y, s] for s in m.S)","def calc_calories(gpx_track, wt = 175, activity='Run'):","def winning_games_stats(self):\n self.winning_games_up_to_2013 = (\n self.df\n .pipe(lambda x:x.assign(winning_num_counts = 1))\n .query(\"Season <= 2013\")\n .groupby(['Season','WTeamID'])\n .agg({\"WScore\":\"sum\",\"WFGM\":\"sum\",\"WFGA\":\"sum\",\"WFGM3\":\"sum\",\"WFGA3\":\"sum\",\"WFTM\":\"sum\",\"WFTA\":\"sum\",\"LScore\":\"sum\",\"winning_num_counts\":\"sum\",\n \"WOR\":\"sum\",\"WDR\":\"sum\",\"LFGM\":\"sum\",\"LFGA\":\"sum\",\n \"WAst\":\"sum\",\"WTO\":\"sum\",\"WStl\":\"sum\",\"WBlk\":\"sum\",\"WPF\":\"sum\"})\n .reset_index()\n .rename(columns={\"LScore\":\"losing_opponent_score\"})\n # rebounds\n .pipe(lambda x:x.assign(total_winning_rebounds = x.WOR + x.WDR))\n .pipe(lambda x:x.assign(winning_off_rebounds_percent = x.WOR/x.total_winning_rebounds))\n .pipe(lambda x:x.assign(winning_def_rebounds_percent = x.WDR/x.total_winning_rebounds))\n .pipe(lambda x:x.assign(team_missed_attempts = x.WFGA - x.WFGM))\n .pipe(lambda x:x.assign(opp_team_missed_attempts = x.LFGA - x.LFGM))\n .pipe(lambda x:x.assign(winning_rebound_possession_percent = x.WOR/x.team_missed_attempts))\n .pipe(lambda x:x.assign(winning_rebound_possessiongain_percent = x.WDR/x.opp_team_missed_attempts))\n # blocks, steals, assists and turnovers\n .pipe(lambda x:x.assign(winning_block_opp_FGA_percent = x.WBlk/x.LFGA))\n .pipe(lambda x:x.assign(winning_assist_per_fgm = x.WAst/x.WFGM))\n .pipe(lambda x:x.assign(winning_assist_turnover_ratio = x.WAst/x.WTO))\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\n .rename(columns={\"LFGA\":\"LFGA_opp\",\"LFGM\":\"LFGM_opp\"})\n )","def total_rewards(self) -> float:\n return self.__total_rewards","def annualized_gains(self, day='today'):\n assert day == 'today' or isinstance(day, date), 'Error! You have to pass a datetime.date istance to the day parameter.'\n if day == 'today':\n day = self.data.index[-1]\n if self.data.index[-1] >= day >= self.data.index[0]:\n day = self._first_good_date(day)\n initialValue = self.invested_amount(day)\n finalValue = self.value(day)\n numberOfDays = (day - self.data.index[0]).days\n return round(((finalValue / initialValue)**(365/numberOfDays) - 1) * 100, 2) \n else:\n return 0","def calc_statistics(self):\n\n self._ydata = np.zeros([4, len(self._depths)], dtype=float)\n\n exp_vals = np.zeros(self._ntrials, dtype=float)\n ideal_vals = np.zeros(self._ntrials, dtype=float)\n\n for depthidx, depth in enumerate(self._depths):\n\n exp_shots = 0\n\n for trialidx in range(self._ntrials):\n cname = 'qv_depth_%d_trial_%d' % (depth, trialidx)\n exp_vals[trialidx] = self._heavy_output_counts[cname]\n exp_shots += self._circ_shots[cname]\n ideal_vals[trialidx] = self._heavy_output_prob_ideal[cname]\n\n # Calculate mean and error for experimental data\n self._ydata[0][depthidx] = np.sum(exp_vals)/np.sum(exp_shots)\n self._ydata[1][depthidx] = (self._ydata[0][depthidx] *\n (1.0-self._ydata[0][depthidx])\n / self._ntrials)**0.5\n\n # Calculate mean and error for ideal data\n self._ydata[2][depthidx] = np.mean(ideal_vals)\n self._ydata[3][depthidx] = (self._ydata[2][depthidx] *\n (1.0-self._ydata[2][depthidx])\n / self._ntrials)**0.5","def test_result_years_ok(self):\n\n graphic = HeightCurve(\n gender=Constants.MALE,\n age=Constants.YEARS\n )\n\n # percentis_3\n self.assertEqual(graphic.result(83.24, 3), -1)\n self.assertEqual(graphic.result(83.25, 3), 0)\n self.assertEqual(graphic.result(83.26, 3), 0)\n self.assertEqual(graphic.result(118.42, 10), -1)\n self.assertEqual(graphic.result(118.43, 10), 0)\n self.assertEqual(graphic.result(118.44, 10), 0)\n\n # percentis_97\n self.assertEqual(graphic.result(100.50, 3), 0)\n self.assertEqual(graphic.result(100.51, 3), 0)\n self.assertEqual(graphic.result(100.52, 3), 1)\n self.assertEqual(graphic.result(143.42, 10), 0)\n self.assertEqual(graphic.result(143.43, 10), 0)\n self.assertEqual(graphic.result(143.44, 10), 1)","def patrimony_total(self):\n pass","def year_scheme_emissions_intensity_rule(_m, y):\r\n\r\n return m.YEAR_EMISSIONS[y] / m.YEAR_SCHEME_OUTPUT[y]","def annual_summary(self):\n \n #Initialize dict with info about all of year's storms\n hurdat_year = {'id':[],'operational_id':[],'name':[],'max_wspd':[],'min_mslp':[],'category':[],'ace':[]}\n \n #Search for corresponding entry in keys\n count_ss_pure = 0\n count_ss_partial = 0\n iterate_id = 1\n for key in self.dict.keys():\n\n #Retrieve info about storm\n temp_name = self.dict[key]['name']\n temp_vmax = np.array(self.dict[key]['vmax'])\n temp_mslp = np.array(self.dict[key]['mslp'])\n temp_type = np.array(self.dict[key]['type'])\n temp_time = np.array(self.dict[key]['date'])\n temp_ace = self.dict[key]['ace']\n\n #Get indices of all tropical/subtropical time steps\n idx = np.where((temp_type == 'SS') | (temp_type == 'SD') | (temp_type == 'TD') | (temp_type == 'TS') | (temp_type == 'HU'))\n\n #Get times during existence of trop/subtrop storms\n if len(idx[0]) == 0: continue\n trop_time = temp_time[idx]\n if 'season_start' not in hurdat_year.keys():\n hurdat_year['season_start'] = trop_time[0]\n hurdat_year['season_end'] = trop_time[-1]\n\n #Get max/min values and check for nan's\n np_wnd = np.array(temp_vmax[idx])\n np_slp = np.array(temp_mslp[idx])\n if len(np_wnd[~np.isnan(np_wnd)]) == 0:\n max_wnd = np.nan\n max_cat = -1\n else:\n max_wnd = int(np.nanmax(temp_vmax[idx]))\n max_cat = convert_category(np.nanmax(temp_vmax[idx]))\n if len(np_slp[~np.isnan(np_slp)]) == 0:\n min_slp = np.nan\n else:\n min_slp = int(np.nanmin(temp_mslp[idx]))\n\n #Append to dict\n hurdat_year['id'].append(key)\n hurdat_year['name'].append(temp_name)\n hurdat_year['max_wspd'].append(max_wnd)\n hurdat_year['min_mslp'].append(min_slp)\n hurdat_year['category'].append(max_cat)\n hurdat_year['ace'].append(temp_ace)\n hurdat_year['operational_id'].append(self.dict[key]['operational_id'])\n \n #Handle operational vs. non-operational storms\n\n #Check for purely subtropical storms\n if 'SS' in temp_type and True not in np.isin(temp_type,['TD','TS','HU']):\n count_ss_pure += 1\n\n #Check for partially subtropical storms\n if 'SS' in temp_type:\n count_ss_partial += 1\n\n #Add generic season info\n hurdat_year['season_storms'] = len(hurdat_year['name'])\n narray = np.array(hurdat_year['max_wspd'])\n narray = narray[~np.isnan(narray)]\n hurdat_year['season_named'] = len(narray[narray>=34])\n hurdat_year['season_hurricane'] = len(narray[narray>=65])\n hurdat_year['season_major'] = len(narray[narray>=100])\n hurdat_year['season_ace'] = np.sum(hurdat_year['ace'])\n hurdat_year['season_subtrop_pure'] = count_ss_pure\n hurdat_year['season_subtrop_partial'] = count_ss_partial\n \n #Return object\n return hurdat_year","def collect(self) -> int:\n self.get_logger().info('Generating experiences...')\n # Decay epsilon if requred\n self._wp.decay_epsilon(self.episode, 0.001)\n\n # Generate experiences\n trajectory_length, total_reward = self.step()\n self.publish(total_reward)\n self.flag.shift_to_compute_cycle()\n\n # Total reward section\n self._total_reward += total_reward\n if (self.episode + 1) % self._update == 0:\n self.get_logger().error(\n f'Expected Reward: {self._total_reward / self._update}')\n self._total_reward = 0\n\n return trajectory_length","def episode_total_score(self):\n return self.game.get_total_reward()","def career_talent(filtered_dict,WAR_batter_best, player_name='Nap Lajoie', year_proj = 2019):\n \n # Subsetting the dictionary for a particular player\n filtered_dict_new = filtered_dict[player_name]\n \n # Converting it to a dataframe\n df = pd.DataFrame(filtered_dict_new).sort_values(by=['yearID']).reset_index(drop = True)\n \n # Changing the player ID\n df['playerID'] = df['playerID'].astype(str) + '_proj'\n \n # Putting a counter for the number of iteration (we can use this counter later to determine the runtime of each loop)\n # j = 0\n \n # Creating an empty dataframe where we will be merging the rows\n emp_df = pd.DataFrame()\n \n # Iterating over all the years where the selected player has played baseball\n for idx, year in df['yearID'].iteritems():\n #j+=1\n \n # subsetting the Normalized WAR Talent Dataset for the year in which we want to find the projection\n batters_int = WAR_batter_best.loc[WAR_batter_best['yearID'] == year_proj]\n \n # creating a vector with all the WAR values in the projection year\n yy = batters_int.loc[:,'scale_WAR'].sort_values().reset_index(drop = True)\n \n # Finding the length of the vector created above\n n = len(yy)\n \n # Creating a zero vector with one more cell than the vector with all the scaled WAR values\n ytilde = [0] * (n+1)\n \n # Changing the first value in ytilde (Extrapolation logic developed by Shen and Daniel)\n ytilde[0] = yy[0] - 1/(yy[n-1] - yy[0])\n \n # Changing the last value in ytilde\n ytilde[n] = yy[n-1] + 1/(yy[n-1] - yy[n-4])\n \n # Iterating over all the cells of ytilde and updating their values\n # The updated ytilde is the new empirical distribution function of the WAR Talent\n for i in range(1,n):\n ytilde[i] = (yy[i]+yy[i-1])/2\n\n batters_int = pd.concat([batters_int,df.loc[df['yearID'] == year]]).reset_index(drop = True)\n batters_int['pops'][batters_int.shape[0]-1] = batters_int['pops'][0]\n batters_int = batters_int.sort_values(by = ['WAR_talent']).reset_index(drop = True)\n batters_int['foo'] = map_pareto_vals_vec(batters_int['WAR_talent'], batters_int['pops'])\n batters_int['foo'] = batters_int['foo'].reset_index(drop = True)\n batters_int['adj_WAR'] = order_qempirical(batters_int['foo'], ytilde)\n del batters_int['foo']\n batters_int = batters_int.loc[batters_int['playerID'] == df['playerID'].unique()[0]]\n emp_df = pd.concat([emp_df, batters_int])\n emp_df['target_year'] = year_proj\n \n return emp_df","def calculate_stats(team_id, current_matches, prev_matches, stats, targets):\n\n # Features\n current_formation = None\n opp_formation = None\n win = 0\n loss = 0\n recent_wins = 0\n recent_losses = 0\n prev_opp = []\n current_opp = 0\n total_goals = 0\n goals_for = 0\n goals_against = 0\n goal_diff = 0\n total_points = 0\n count = 1\n played = float(0)\n goal_efficiency = 0\n\n # Game Features\n game_features = {'possession': [], 'attacks': [], 'dangerous_attacks': [], 'yellow_cards': [],\n 'corner_kicks': [], 'shots_on_target': [], 'shots_total': [], 'ball_safe': [],\n 'goal_attempts': [], 'goal_attempts_allowed': [], 'saves': [], 'first_half_goals': [], 'sec_half_goals': [], 'goal_kicks': []}\n\n # Targets\n points = 0\n goals = 0\n opp_goals = 0\n\n goals_home = 0\n goals_away = 0\n opp_goals_at_home = 0 # Opponent goals when CURRENT team is AT Home\n opp_goals_at_away = 0 # Opponent goals when CURRENT team is Away\n\n total_games = len(prev_matches)\n # Pulling Data for PREVIOUS Matches\n for index, game in prev_matches.iterrows():\n\n # Home Wins and Road Losses are .8 while Road Wins and Home Losses are 1.2 / Draws remain the same\n if team_id == game['home_id']:\n\n if game['home_points'] == 3:\n win += .8\n\n elif game['home_points'] == 1:\n win += .5\n loss += .5\n\n else:\n loss += 1.2\n\n total_goals += game['home_score']\n goals_for += game['home_score']\n goals_against += game['away_score']\n goal_diff += game['home_score'] - game['away_score']\n total_points += game['home_points']\n\n goals_home += game['home_points']\n opp_goals_at_away += game['away_points']\n\n current_formation = game['home_formation']\n opp_formation = game['away_formation']\n\n prev_opp.append(game['away_id'])\n\n game_features['possession'].append(game['home_possession'])\n game_features['attacks'].append(game['home_attacks'])\n game_features['dangerous_attacks'].append(game['home_dangerous_attacks'])\n game_features['yellow_cards'].append(game['home_yellow_card'])\n game_features['corner_kicks'].append(game['home_corner_kicks'])\n game_features['shots_on_target'].append(game['home_shots_on_target'])\n game_features['shots_total'].append(game['home_shots_total'])\n game_features['ball_safe'].append(game['home_ball_safe'])\n game_features['goal_attempts'].append(game['home_goal_attempts'])\n game_features['goal_attempts_allowed'].append(game['away_goal_attempts'])\n game_features['saves'].append(game['home_saves'])\n game_features['first_half_goals'].append(game['home_first_half_score'])\n game_features['sec_half_goals'].append(game['home_second_half_score'])\n game_features['goal_kicks'].append(game['home_goal_kicks'])\n\n else:\n team_name = game[\"away_team\"]\n\n if game['away_points'] == 3:\n win += 1.2\n\n elif game['away_points'] == 1:\n win += .5\n loss += .5\n\n else:\n loss += .8\n\n total_goals += game['away_score']\n goals_for += game['away_score']\n goals_against += game['home_score']\n goal_diff += game['away_score'] - game['home_score']\n total_points += game['away_points']\n\n goals_away += game['away_points']\n opp_goals_at_home += game['home_points']\n\n current_formation = game['away_formation']\n opp_formation = game['home_formation']\n\n prev_opp.append(game['home_id'])\n\n game_features['possession'].append(game['away_possession'])\n game_features['attacks'].append(game['away_attacks'])\n game_features['dangerous_attacks'].append(game['away_dangerous_attacks'])\n game_features['yellow_cards'].append(game['away_yellow_card'])\n game_features['corner_kicks'].append(game['away_corner_kicks'])\n game_features['shots_on_target'].append(game['away_shots_on_target'])\n game_features['shots_total'].append(game['away_shots_total'])\n game_features['ball_safe'].append(game['away_ball_safe'])\n game_features['goal_attempts'].append(game['away_goal_attempts'])\n game_features['goal_attempts_allowed'].append(game['home_goal_attempts'])\n game_features['saves'].append(game['away_saves'])\n game_features['first_half_goals'].append(game['away_first_half_score'])\n game_features['sec_half_goals'].append(game['away_second_half_score'])\n game_features['goal_kicks'].append(game['away_goal_kicks'])\n\n played += 1\n count += 1\n\n # Pulling the data for the CURRENT MATCH\n for index, cur_game in current_matches.iterrows():\n\n match_id = cur_game[\"match_id\"]\n scheduled = cur_game[\"scheduled\"]\n if team_id == cur_game['home_id']:\n is_home = True\n team_name = cur_game[\"home_team\"]\n current_opp = cur_game['away_id']\n\n if targets:\n # Targets\n points = cur_game['home_points']\n goals = cur_game['home_score']\n opp_goals = cur_game['away_score']\n\n else:\n\n is_home = False\n team_name = cur_game[\"away_team\"]\n current_opp = cur_game['home_id']\n\n if targets:\n # Targets\n points = cur_game['away_points']\n goals = cur_game['away_score']\n opp_goals = cur_game['home_score']\n\n for k, v in game_features.items():\n game_features[k] = np.sum(np.array(v))\n \"\"\"if len(v) != 0:\n game_features[k] = np.nanmean(np.array(v))\n else:\n game_features[k] = np.nan\"\"\"\n\n\n if stats:\n print(\" ========================== \")\n print(\"Team Id : {} - Name : {}\".format(team_id, team_name))\n print(\"Prev Opponent Ids : {}\".format(prev_opp))\n print(\"FEATURES (Stats from * 3 Previous Matches)\")\n print(\"Total Goals : {}\".format(total_goals))\n print(\"Total Points : {}\".format(total_points))\n # print(\"Win Points : {}\".format(win))\n # print(\"Loss Points : {}\".format(loss))\n print(\"Played : {}\".format(played))\n print(\"Goal Diff : {}\".format(goal_diff))\n print(\"Margin : {}\".format(np.divide(goal_diff, played)))\n print(\"\")\n\n if targets:\n # Still can weight games more on most recent games\n print(\"\\nTARGETS (RESULTS OF CURRENT MATCH)\")\n print(\"Points : {}\".format(points))\n print(\"Goals : {}\".format(goals))\n print(\"Opp_Goals : {}\".format(opp_goals))\n\n return match_id, team_id, team_name, scheduled, int(is_home == True), total_points, \\\n goals_for, goals_against, goal_diff, goal_efficiency, played, win, loss, recent_wins, recent_losses, prev_opp, \\\n current_opp, points, goals, opp_goals, current_formation, opp_formation, game_features","def calculate_demo(self, request, parent_lookup_client, pk, format=None):\n\n retirement_plan = self.get_object()\n tickers = Ticker.objects.filter(~Q(state=Ticker.State.CLOSED.value))\n portfolio = []\n projection = []\n for idx, ticker in enumerate(tickers[:10]):\n percent = 0\n if idx <= 9:\n # 10% each for first 10 tickers\n percent = 10\n portfolio.append([ticker.id, percent])\n # grab 50 evenly spaced time points between dob and current time\n today = timezone.now().date()\n last_day = retirement_plan.client.date_of_birth + relativedelta(years=retirement_plan.selected_life_expectancy)\n day_interval = (last_day - today) / 49\n income_start = 20000\n assets_start = 100000\n for i in range(50):\n income = income_start + (i * 50)\n assets = assets_start + (i * 1000)\n dt = today + i * day_interval\n projection.append([d2ed(dt), assets, income])\n return Response({'portfolio': portfolio, 'projection': projection})","def calculateMarriedTax(husbandIncome, wifeIncome):\n pass","def compute_death_rate(dfs):\n \n dfcols = dfs[\"deaths\"].columns\n dfs[\"death_rate\"] = dfs[\"deaths\"].copy()\n\n for ccol in dfcols:\n dfs[\"death_rate\"][ccol] = dfs[\"deaths\"][ccol] / dfs[\"cases\"][ccol]\n \n return(dfs)","def age(self, agent):\n return (self.time - agent.born)/52.0","def global_stats(request):\n\n if request.user.is_authenticated:\n profile: IntervalsProfile = request.user.profile\n\n global_correct = 0\n global_answered = 0\n\n for r in profile.all_recent_results().values():\n global_correct += r.total_correct\n global_answered += r.total_completed\n\n print(global_correct, global_answered)\n\n return Response({\"global_correct\": global_correct,\n \"global_answered\": global_answered},\n status=status.HTTP_201_CREATED)\n return Response(status=status.HTTP_401_UNAUTHORIZED)","def user_stats(df):\n\n print('\\nCalculating User Stats...\\n')\n start_time = time.time()\n\n # TO DO: Display counts of user types\n\n user_type_count = df['User Type'].value_counts()\n print('The number of rentals per user type:')\n print(user_type_count.to_string())\n\n # TO DO: Display counts of gender\n \n if 'Gender' in df.columns:\n \n gender_type_count = df['Gender'].value_counts()\n print('\\nThe number of rental per gender:')\n print(gender_type_count.to_string())\n \n else:\n \n print('\\nNo data about gender available.\\n')\n \n # TO DO: Display earliest, most recent, and most common year of birth\n\n if 'Birth Year' in df.columns:\n \n youngest_user = df['Birth Year'].max()\n common_user = df['Birth Year'].mode()[0]\n oldest_user = df['Birth Year'].min()\n print('\\nThe youngest user was born', int(youngest_user))\n print('The most common user was born', int(common_user))\n print('The oldest user was born', int(oldest_user))\n \n else:\n \n print('\\nNo data about age available.\\n')\n \n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)","def _find_average_age():\r\n count, total = 0, 0\r\n for resource in resources:\r\n patient = resource[\"resource\"]\r\n if \"birthDate\" in patient:\r\n count += 1\r\n dob = patient[\"birthDate\"].split(\"-\")\r\n dob = datetime(int(dob[0]), int(dob[1]), int(dob[2]), 0, 0, 0, 0)\r\n if \"deceasedDateTime\" in patient:\r\n death_time = patient[\"deceasedDateTime\"].split(\"T\")[0].split(\r\n \"-\")\r\n death_time = datetime(int(death_time[0]), int(death_time[1]),\r\n int(death_time[2]), 0, 0, 0, 0)\r\n else:\r\n death_time = datetime.now()\r\n age = relativedelta(death_time, dob).years\r\n total += age\r\n if count == 0:\r\n return count, count\r\n return total / count, count","def total_price_target_difference_rule(_m):\r\n\r\n return sum(m.YEAR_PRICE_TARGET_DIFFERENCE[y] for y in m.Y)","def run_cumulative_wtrfclty_damage(self):\n\n\t\trt = [100, 250, 500, 1000, 2500, 5000, 10000]\n\t\t\n\t\tfor rt_val in rt:\n\t\t\tprint('\\twterfclty_dmg: cumulative rt_{}' .format(rt_val))\n\t\t\t# --- reading in damage results from above analysis\n\t\t\teq_damage_results_csv = os.path.join(self.wterfclty_output_path, \n\t\t\t\t\t\t\t\t\t\t\t\t 'wterfclty_eq_{}yr_dmg.csv' \n\t\t\t\t\t\t\t\t\t\t\t\t .format(rt_val))\n\t\t\ttsu_damage_results_csv = os.path.join(self.wterfclty_output_path, \n\t\t\t\t\t\t\t\t\t\t\t\t 'wterfclty_tsu_{}yr_dmg.csv' \n\t\t\t\t\t\t\t\t\t\t\t\t .format(rt_val))\n\t\t\teq_df = pd.read_csv(eq_damage_results_csv)\n\t\t\ttsu_df = pd.read_csv(tsu_damage_results_csv)\n\n\t\t\tcum_df = pd.DataFrame()\n\t\t\tcum_df['guid'] = eq_df['guid']\n\t\t\t\n\t\t\tcum_df['ds-complet'] = eq_df['ds-complet'] + tsu_df['ds-complet'] \\\n\t\t\t\t- eq_df['ds-complet']*tsu_df['ds-complet']\n\t\t\t\n\t\t\t# --- prob of exceeding each damage state\n\t\t\tcum_df['ls-complet'] = cum_df['ds-complet']\n\n\t\t\tcum_df['ls-extensi'] = eq_df['ls-extensi'] + tsu_df['ls-extensi'] \\\n\t\t\t\t- eq_df['ls-extensi']*tsu_df['ls-extensi']\n\n\t\t\tcum_df['ls-moderat'] = eq_df['ls-moderat'] + tsu_df['ls-moderat'] \\\n\t\t\t\t- eq_df['ls-moderat']*tsu_df['ls-moderat']\n\n\t\t\tcum_df['ls-slight'] = eq_df['ls-slight'] + tsu_df['ls-slight'] \\\n\t\t\t\t- eq_df['ls-slight']*tsu_df['ls-slight']\n\n\t\t\t# --- prob of being in each damage state\n\t\t\tcum_df['ds-extensi'] = cum_df['ls-extensi'] - cum_df['ds-complet']\n\t\t\tcum_df['ds-moderat'] = cum_df['ls-moderat'] - cum_df['ls-extensi']\n\t\t\tcum_df['ds-slight'] = cum_df['ls-slight'] - cum_df['ls-moderat']\n\t\t\tcum_df['ds-none'] = 1 - cum_df['ls-slight']\n\t\t\tcum_df['hazard'] = 'Earthquake+Tsunami'\n\n\t\t\tresult_name = os.path.join(self.wterfclty_output_path, \n\t\t\t\t\t\t\t\t\t 'wterfclty_cumulative_{}yr_dmg.csv' \n\t\t\t\t\t\t\t\t\t .format(rt_val))\n\t\t\tcum_df = cum_df[['guid', \n\t\t\t\t\t\t\t 'ls-slight',\n\t\t\t\t\t\t\t 'ls-moderat',\n\t\t\t\t\t\t\t 'ls-extensi',\n\t\t\t\t\t\t\t 'ls-complet',\n\t\t\t\t\t\t\t 'ds-none', \n\t\t\t\t\t\t\t 'ds-slight', \n\t\t\t\t\t\t\t 'ds-moderat', \n\t\t\t\t\t\t\t 'ds-extensi', \n\t\t\t\t\t\t\t 'ds-complet', \n\t\t\t\t\t\t\t 'hazard']]\n\t\t\tcum_df.to_csv(result_name, index=False)","def get_win_rate_regular_season_for_each_coach(self):\n self.games_won_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_win\",\"LastDayNum\":\"LastDayNum_win\",\"CoachName\":\"CoachName_win\",\"TeamID\":\"TeamID_win\"})\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\n .query(\"which_coach_for_win != 0\")\n .groupby(['Season','CoachName_win','WTeamID'])\n .agg({\"which_coach_for_win\":\"sum\"})\n .reset_index()\n )\n\n self.games_lose_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','LTeamID']]\n # merge for losing team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_lose\",\"LastDayNum\":\"LastDayNum_lose\",\"CoachName\":\"CoachName_lose\",\"TeamID\":\"TeamID_lose\"})\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\n .query(\"which_coach_for_lose != 0\")\n .groupby(['Season','CoachName_lose','LTeamID'])\n .agg({\"which_coach_for_lose\":\"sum\"})\n .reset_index()\n )\n\n # combine games won and lost df\n self.combine_regular_games_won_lose = (\n self.games_lose_for_coaches\n .merge(self.games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\n .pipe(lambda x:x.assign(win_rate_regular = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\n .drop(['CoachName_win','WTeamID'],1)\n .rename(columns={\"CoachName_lose\":\"CoachName\",\"LTeamID\":\"TeamID\",\"which_coach_for_lose\":\"games_lost\",\"which_coach_for_win\":\"games_won\"})\n )","def _compute_global_stats():\n global_stats = []\n \n wmt16_group = Group.objects.filter(name='WMT16')\n wmt16_users = _get_active_users_for_group(wmt16_group)\n \n # Check how many HITs have been completed. We now consider a HIT to be\n # completed once it has been annotated by one or more annotators.\n #\n # Before we required `hit.users.count() >= 3` for greater overlap.\n hits_completed = HIT.objects.filter(mturk_only=False, completed=True).count()\n \n # Check any remaining active HITs which are not yet marked complete.\n for hit in HIT.objects.filter(active=True, mturk_only=False, completed=False):\n if hit.users.count() >= 1:\n hits_completed = hits_completed + 1\n hit.completed = True\n hit.save()\n \n # Compute remaining HITs for all language pairs.\n hits_remaining = HIT.compute_remaining_hits()\n \n # Compute number of results contributed so far.\n ranking_results = RankingResult.objects.filter(\n item__hit__completed=True, item__hit__mturk_only=False)\n \n from math import factorial\n system_comparisons = 0\n for result in ranking_results:\n result.reload_dynamic_fields()\n # TODO: this implicitly counts A=B comparisons for multi systems.\n # Basically, inflating the number of pairwise comparisons... Fix!\n combinations = factorial(result.systems)/(factorial(result.systems-2) * 2) if result.systems > 2 else 0\n system_comparisons = system_comparisons + combinations\n \n # Aggregate information about participating groups.\n groups = set()\n for user in wmt16_users:\n for group in _identify_groups_for_user(user):\n groups.add(group)\n \n # Compute average/total duration over all results.\n durations = RankingResult.objects.all().values_list('duration', flat=True)\n total_time = sum([datetime_to_seconds(x) for x in durations])\n avg_time = total_time / float(hits_completed or 1)\n avg_user_time = total_time / float(3 * hits_completed or 1)\n \n global_stats.append(('Users', len(wmt16_users)))\n global_stats.append(('Groups', len(groups)))\n global_stats.append(('HITs completed', '{0:,}'.format(hits_completed)))\n global_stats.append(('HITs remaining', '{0:,}'.format(hits_remaining)))\n global_stats.append(('Ranking results', '{0:,}'.format(ranking_results.count())))\n global_stats.append(('System comparisons', '{0:,}'.format(system_comparisons)))\n global_stats.append(('Average duration (per HIT)', seconds_to_timedelta(avg_time)))\n global_stats.append(('Average duration (per task)', seconds_to_timedelta(avg_user_time)))\n global_stats.append(('Total duration', seconds_to_timedelta(total_time)))\n \n # Create new status data snapshot\n TimedKeyValueData.update_status_if_changed('users', str(len(wmt16_users)))\n TimedKeyValueData.update_status_if_changed('groups', str(len(groups)))\n TimedKeyValueData.update_status_if_changed('hits_completed', str(hits_completed))\n TimedKeyValueData.update_status_if_changed('hits_remaining', str(hits_remaining))\n TimedKeyValueData.update_status_if_changed('ranking_results', str(ranking_results.count()))\n TimedKeyValueData.update_status_if_changed('system_comparisons', str(system_comparisons))\n TimedKeyValueData.update_status_if_changed('duration_per_hit', str(seconds_to_timedelta(avg_time)))\n TimedKeyValueData.update_status_if_changed('duration_per_task', str(seconds_to_timedelta(avg_user_time)))\n TimedKeyValueData.update_status_if_changed('duration_total', str(seconds_to_timedelta(total_time)))\n \n return global_stats","def year_demand_rule(_m, y):\r\n\r\n return sum(m.SCENARIO_DEMAND[y, s] for s in m.S)","def calculate_employer_contribution(self):\r\n employer_contribution = 0\r\n for employer_match, tier_match in self.employer_match_tier_match_list:\r\n employer_contribution += (self.salary * employer_match * tier_match)\r\n return employer_contribution","def user_stats(df):\n\n print('\\nCalculating User Stats...\\n')\n start_time = time.time()\n\n count_user_type = df.groupby('User Type').count()\n print('The distribution of user types is: ',count_user_type)\n#used the suggested try except method, because i had difficutlties with an if clause an the variable scope of the 'city' variable - same with the 'birth year'\n try:\n count_gender = df.groupby('Gender').count()\n print('The distribution of genders is: ',count_gender)\n except:\n print('\\n No gender-data available in Washington.')\n\n try:\n earliest_yob = df['Birth Year'].min()\n print('\\n The earliest year of bearth is: ', earliest_yob)\n latest_yob = df['Birth Year'].max()\n print('\\n The most recent year of birth is: ', latest_yob)\n most_common_yob = df.loc[:,'Birth Year'].mode()\n print('The most common year of birth is: ', most_common_yob)\n except:\n print('\\n No birt-year-data available in Washington.')\n\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)","def _individual_old_age_insurance(self, total_salary):\n return self.individual_old_age_insurance_rate * total_salary","def daily_incidents(df2):\n\n if (df2[\"Holiday\"] == \"Thanksgiving Day\") | (df2[\"Holiday\"] == \"Christmas Day\"):\n d_inc = df2[\"Total\"] / 18\n elif df2[\"Holiday\"] == \"Non-holidays\":\n d_inc = df2[\"Total\"] / 6712\n else:\n d_inc = df2[\"Total\"] / 19\n\n return d_inc","def arithmetic_ret(self) -> float:\n return float(np.log(self.tsdf).diff().mean() * self.periods_in_a_year)","def estimate_runtime():\n cpu_count = multiprocessing.cpu_count()\n years = request.args.get('years', type=int)\n if not years:\n raise InvalidUsage(\n \"The 'years' param is required and must be an integer >= 1\")\n time_per_year = 4 # seconds\n diluted_cpu_effect = 1 + (1.0 * cpu_count / 10)\n raw_guess = time_per_year * math.ceil(1.0 * years / diluted_cpu_effect)\n result = int(max(raw_guess, 15))\n return jsonify({'seconds': result})","def calc_schedule_yearly(self):\n \n schedule = self.schedule\n\n # Yearly\n start_date = schedule['Date'].min()\n schedule['years_out'] = schedule['Date'].apply(lambda x: relativedelta(x, start_date).years)\n schedule['end_of_year_window'] = schedule['years_out'].diff(-1).fillna(-1).abs()\n \n schedule_yr = schedule\\\n .groupby([schedule['years_out']])\\\n .agg({'Payment': 'sum',\n 'Interest': 'sum',\n 'Additional_Payment': 'sum',\n 'Date': 'min',\n 'Date': 'max'\n }\n )\n\n schedule_yr.reset_index(drop=False, inplace=True)\n schedule_yr['Principal'] = schedule_yr['Payment'] - schedule_yr['Interest']\n \n yearly_end_balances = schedule.loc[schedule['end_of_year_window'] == 1, \n ['years_out', 'End Balance']]\n\n schedule_yr = schedule_yr.merge(yearly_end_balances, how='inner', on=['years_out'])\n\n return schedule_yr","def user_stats(df):\n\n print('\\nCalculating User Stats...\\n')\n start_time = time.time()\n\n # Display counts of user types\n user_types = df['User Type'].value_counts()\n print('The distribution of user types:\\n', user_types)\n\n # Display counts of gender\n if 'Gender' in df:\n gender = df['Gender'].value_counts()\n print('The distribution of genders:\\n', gender)\n else:\n print('There is no data on gender for this city/the selected time.')\n\n # Display earliest, most recent, and most common year of birth. Using integers for the years\n if 'Birth Year' in df:\n earliest_birth_year = df['Birth Year'].min()\n print('The earliest birth year: ', int(earliest_birth_year))\n most_recent_birth_year = df['Birth Year'].max()\n print('The most recent birth year: ', int(most_recent_birth_year))\n most_common_birth_year = df['Birth Year'].mode()[0]\n print('The most common birth year: ', int(most_common_birth_year))\n else:\n print(\"There is no data on birth years for this city/the selected time.\")\n\n\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)","def get_totals_across_years(df, tract_col, data_col):\n return df.groupby(tract_col)[data_col].agg([\"sum\", \"mean\"]).reset_index()","def user_stats(df):\n\n print('\\nCalculating User Stats...\\n')\n start_time = time.time()\n\n # Display counts of user types\n user_types = df['User Type'].value_counts()\n print('Count of user types:\\n', user_types)\n\n\n # Display counts of gender\n try:\n count_gender = df['Gender'].value_counts()\n print('Count of gender types:\\n', count_gender)\n except KeyError:\n print(\"No gender data available for this month.\")\n\n\n # Display earliest, most recent, and most common year of birth\n try:\n oldest_birth_year = df['Birth Year'].min()\n print(\"The oldest birth year is \", oldest_birth_year)\n except KeyError:\n print(\"No birth year data available for this month.\")\n\n try:\n youngest_birth_year = df['Birth Year'].max()\n print(\"The youngest birth year is \", youngest_birth_year)\n except KeyError:\n print(\"No birth year data available for this month.\")\n\n try:\n common_birth_year = df['Birth Year'].value_counts().idxmax()\n print(\"The most common birth year is \", common_birth_year)\n except KeyError:\n print(\"No birth year data available for this month.\")\n\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)","def annual_fee(self, working_months, year, with_bpjs=True):\n monthly_bpjs = []\n\n total_salary = self.base_salary\n if self.is_salary_allowances is True:\n fixed_allowances = self.summarize( self.fixed_allowances )\n non_fixed_allowances = self.summarize( self.non_fixed_allowances )\n total_salary = total_salary + non_fixed_allowances + fixed_allowances\n #end if\n\n # initialize variable for storing the annual bpjs\n annual_c_old_age_insurance = 0\n annual_i_old_age_insurance = 0\n annual_c_pension_insurance = 0\n annual_i_pension_insurance = 0\n annual_c_health_insurance = 0\n annual_i_health_insurance = 0\n annual_death_insurance = 0\n annual_accident_insurance = 0\n\n if with_bpjs is True:\n # only calculate bpjs if is enabled and automatically set everthing to zero when is false\n start_month = 1\n for month in range(start_month, working_months+1):\n\n company_old_age_insurance = 0\n individual_old_age_insurance = 0\n if self.old_age_insurance_status is True:\n company_old_age_insurance = \\\n self._company_old_age_insurance(total_salary)\n\n individual_old_age_insurance = \\\n self._individual_old_age_insurance(total_salary)\n #end if\n\n company_pension_insurance = 0\n individual_pension_insurance = 0\n if self.pension_insurance_status is True:\n company_pension_insurance = \\\n self._company_pension_insurance(total_salary, month, year)\n\n individual_pension_insurance = \\\n self._individual_pension_insurance(total_salary, month, year)\n #end if\n\n company_health_insurance = 0\n individual_health_insurance = 0\n if self.health_insurance_status is True:\n company_health_insurance = \\\n self._company_health_insurance(total_salary)\n\n individual_health_insurance = \\\n self._individual_health_insurance(total_salary)\n #end if\n\n death_insurance = 0\n if self.death_insurance_status is True:\n death_insurance = self._death_insurance(total_salary)\n #end if\n\n accident_insurance = 0\n if self.accident_insurance_status is True:\n accident_insurance = \\\n self._accident_insurance(total_salary, \\\n self.industry_risk_rate)\n #end if\n\n monthly = {\n \"old_age_insurance\" : {\n \"company\" : company_old_age_insurance,\n \"individual\" : individual_old_age_insurance,\n },\n \"pension_insurance\" : {\n \"company\" : company_pension_insurance,\n \"individual\" : individual_pension_insurance,\n },\n \"health_insurance\" : {\n \"company\" : company_health_insurance,\n \"individual\" : individual_health_insurance,\n },\n \"death_insurance\" : death_insurance,\n \"accident_insurance\" : accident_insurance\n }\n\n monthly_bpjs.append(monthly)\n\n annual_c_old_age_insurance = annual_c_old_age_insurance \\\n + company_old_age_insurance\n\n annual_i_old_age_insurance = annual_i_old_age_insurance \\\n + individual_old_age_insurance\n\n annual_c_pension_insurance = annual_c_pension_insurance \\\n + company_pension_insurance\n\n annual_i_pension_insurance = annual_i_pension_insurance \\\n + individual_pension_insurance\n\n annual_c_health_insurance = annual_c_health_insurance \\\n + company_health_insurance\n\n annual_i_health_insurance = annual_i_health_insurance \\\n + individual_health_insurance\n\n annual_death_insurance = annual_death_insurance\\\n + death_insurance\n\n annual_accident_insurance = annual_accident_insurance\\\n + accident_insurance\n #end for\n\n annual_bpjs = {\n \"old_age_insurance\" : {\n \"company\" : annual_c_old_age_insurance,\n \"individual\" : annual_i_old_age_insurance,\n },\n \"pension_insurance\" : {\n \"company\" : annual_c_pension_insurance,\n \"individual\" : annual_i_pension_insurance,\n },\n \"health_insurance\" : {\n \"company\" : annual_c_health_insurance,\n \"individual\" : annual_i_health_insurance,\n },\n \"death_insurance\" : annual_death_insurance,\n \"accident_insurance\" : annual_accident_insurance\n }\n return annual_bpjs","def wages(self):\n amount = (\n self.farmer.labor_cost()\n + self.reseller.labor_cost()\n + self.cofiring_plant.cofuel_om_wages()\n - self.coal_wages_lost\n )\n return display_as(amount, \"kUSD\")","def user_stats(df):\n\n print('\\nCalculating User Stats...\\n')\n start_time = time.time()\n\n # TO DO: Display counts of user types\n user_types = df['User Type'].value_counts()\n if city == 'washington':\n print(\"That info isn't available.\")\n break\n else:\n print(\"\\nUser types are: \", user_types)\n\n # TO DO: Display counts of gender\n gender = df['Gender'].value_counts()\n if city == 'washington':\n print(\"That info isn't available\")\n break\n else:\n print(\"\\nThe breakdown of gender is: \", gender)\n\n # TO DO: Display earliest, most recent, and most common year of birth\n oldest_birth=np.nanmin(df['Birth Year'])[0]\n print('\\nOldest birth year is', int(oldest_birth))\n\n youngest_birth=np.nanmax(df['Birth Year'])[0]\n print('\\nYoungest birth year is', int(youngest_birth))\n\n common_birth=df['Birth Year'].mode()[0]\n print('\\nMost common birth year is', int(common_birth))","def user_stats(df):\r\n\r\n print('\\nCalculating User Stats...\\n')\r\n start_time = time.time()\r\n\r\n # Display counts of user types \r\n count_user_types = df['User Type'].value_counts()\r\n print('The count of user types are:', count_user_types)\r\n\r\n # Display counts of gender - Chicago and New York ONLY were having Gender column\r\n try:\r\n gender_counts = df['Gender'].value_counts()\r\n print('The count of gender types are:', gender_counts)\r\n \r\n # Displays the earliest, the most recent, and most common year of birth\r\n # Chicago and New York ONLY were having Birth Year column\r\n \r\n earliest_year = df['Birth Year'].min()\r\n print('Earliest Year of Birth is:', earliest_year)\r\n most_recent_year = df['Birth Year'].max()\r\n print('Most Recent Year of Birth is:', most_recent_year)\r\n common_year = df['Birth Year'].mode()[0]\r\n print('Most Common Year of Birth is:', common_year)\r\n \r\n except KeyError:\r\n print('Sorry, Gender & Birth year data is not available for Washington')\r\n \r\n print(\"\\nThis process took %s seconds.\" % (time.time() - start_time))\r\n print('-'*40)","def calcAgeInYears(self, study_id):\n try:\n con = self.getMetadataDatabaseConnection()\n con.cursor().callproc('calc_age_in_years', [study_id])\n return True\n except Exception, e:\n print 'Exception caught: %s.\\nThe error is: %s' % (type(e), e)\n return False"],"string":"[\n \"def years_job_experience(self) -> int:\\n return sum([job.years_employed for job in self._history_jobs])\",\n \"def get_experience(self):\\n return sum([i.get_experience for i in self.__units])\",\n \"async def yearlystats(self, ctx, *, iracing_id=None):\\n await self.yearly_stats_db.call(ctx, iracing_id)\",\n \"def getSalaryStat(self, year = 2014):\\r\\n year_str = str(year+1)\\r\\n \\r\\n df = pd.read_html('http://espn.go.com/nba/salaries/_/year/' + year_str)[0]\\r\\n df.columns = df.iloc[0]\\r\\n df = df[df.NAME != 'NAME']\\r\\n \\r\\n for page in range(2,12):\\r\\n page_df = pd.read_html('http://espn.go.com/nba/salaries/_/year/' + \\\\\\r\\n year_str + '/page/' + str(page))[0]\\r\\n page_df.columns = page_df.iloc[0]\\r\\n page_df = page_df[page_df.NAME != 'NAME']\\r\\n df = pd.concat([df, page_df])\\r\\n \\r\\n df['SALARY'] = df['SALARY'].replace('[\\\\$,]', '', regex=True).astype(int)\\r\\n cut_df = df[['NAME', 'SALARY']]\\r\\n cut_df = cut_df.reset_index(drop=True)\\r\\n cut_df.columns = [['PLAYER_NAME', 'SALARY']]\\r\\n \\r\\n salary_df = self.fixSalaryName(cut_df)\\r\\n \\r\\n return salary_df\",\n \"def advancedStats():\",\n \"def calculate_advantage(stage_0, stage_1):\\n # Improvement in hp difference is good.\\n hp_pct_0 = (float(stage_0.friendly_life)/MAX_FRIENDLY_LIFE) - (float(stage_0.enemy_life)/MAX_ENEMY_LIFE)\\n hp_pct_1 = (float(stage_1.friendly_life)/MAX_FRIENDLY_LIFE) - (float(stage_1.enemy_life)/MAX_ENEMY_LIFE)\\n return hp_pct_1 - hp_pct_0\",\n \"def cumulative_stats_for_teams_each_year(self):\\n self.cumulative_stats_for_team_each_year = (\\n self.combine_both_winning_losing_games_stats\\n .sort_values(['WTeamID','Season'])\\n .groupby(['WTeamID'])\\n .cumsum()\\n .pipe(lambda x:x.assign(Season = self.combine_both_winning_losing_games_stats.Season.values))\\n .pipe(lambda x:x.assign(TeamID = self.combine_both_winning_losing_games_stats.WTeamID.values))\\n .drop(['LTeamID','win_rate'],1)\\n .pipe(lambda x:x.assign(win_rate = x.winning_num_counts/(x.winning_num_counts + x.losing_num_counts)))\\n .pipe(lambda x:x.assign(WFGP = x.WFGM/x.WFGA))\\n .pipe(lambda x:x.assign(WFG3P = x.WFGM3/x.WFGA3))\\n .pipe(lambda x:x.assign(WFTP = x.WFTM/x.WFTA))\\n .pipe(lambda x:x.assign(LFGP = x.LFGM/x.LFGA))\\n .pipe(lambda x:x.assign(LFG3P = x.LFGM3/x.LFGA3))\\n .pipe(lambda x:x.assign(LFTP = x.LFTM/x.LFTA))\\n .pipe(lambda x:x.assign(fgp = x.total_fgm/x.total_fga))\\n .pipe(lambda x:x.assign(fg3p = x.total_fg3m/x.total_fg3a))\\n .pipe(lambda x:x.assign(ftp = x.total_ftm/x.total_fta))\\n # rebounds cumsum stats\\n .pipe(lambda x:x.assign(total_def_rebounds_percent = x.total_def_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_off_rebounds_percent = x.total_off_rebounds/x.total_rebounds))\\n .pipe(lambda x:x.assign(total_rebound_possession_percent = x.total_off_rebounds/x.total_team_missed_attempts))\\n .pipe(lambda x:x.assign(total_rebound_possessiongain_percent = x.total_def_rebounds/x.total_opp_team_missed_attempts))\\n # assists, turnovers, steals, blocks and personal fouls\\n .pipe(lambda x:x.assign(total_block_opp_FGA_percent = x.total_blocks/x.total_opp_fga))\\n .pipe(lambda x:x.assign(total_assist_per_fgm = x.total_assists/x.total_fgm))\\n .pipe(lambda x:x.assign(total_assist_turnover_ratio = x.total_assists/x.total_turnover))\\n # win or lose by how many points\\n .pipe(lambda x:x.assign(lose_rate = 1-x.win_rate))\\n .pipe(lambda x:x.assign(win_score_by = x.WScore - x.losing_opponent_score))\\n .pipe(lambda x:x.assign(lose_score_by = x.LScore - x.winning_opponent_score))\\n .pipe(lambda x:x.assign(expectation_per_game = x.win_rate * x.win_score_by/x.winning_num_counts + x.lose_rate * x.lose_score_by/x.losing_num_counts))\\n .pipe(lambda x:x.assign(avg_win_score_by = x.win_score_by/x.winning_num_counts))\\n .pipe(lambda x:x.assign(avg_lose_score_by = x.lose_score_by/x.losing_num_counts))\\n )\",\n \"def yearlystats(self, table):\\n # Drop unneeded header row\\n tmp = table.iloc[1:,]\\n # Create a header row removing spaces, /'s, and x's\\n tmp.columns = [x.replace(\\\" \\\", \\\"\\\").replace(\\\"/\\\",\\\"\\\").replace(\\\".\\\",\\\"\\\") for x in tmp.iloc[0]]\\n # Drop the row used to create header row\\n tmp = tmp.drop(tmp.index[0])\\n # Forward fill the year for analysis later \\n tmp['Year'].fillna(method='ffill', inplace = True)\\n # Create a new offense/defense variable \\n tmp['OffenseDefense'] = tmp['Team']\\n # Figure out which team we are working with \\n curr_team = tmp.iloc[:1,1:3].values[0][0]\\n # Create a new team variable\\n tmp['Team'] = curr_team\\n # In the offense defense variable, fill in the offense defense variable \\n tmp['OffenseDefense'] = tmp['OffenseDefense'].apply(lambda x: 'Offense' if x == curr_team else x)\\n return tmp\",\n \"def life_insurance_to_recive_total(self):\\n pass\",\n \"def calculate_profit(self):\",\n \"def year_emissions_intensity_rule(_m, y):\\r\\n\\r\\n return m.YEAR_EMISSIONS[y] / m.YEAR_DEMAND[y]\",\n \"def annualized_perf(self):\\n mean_return = round(self.data.log_returns.mean() * 252, 4)\\n risk = round(self.data.log_returns.std() * np.sqrt(252), 4)\\n print(\\\"Return: {} | Risk: {}\\\".format(mean_return, risk))\",\n \"def do_damage(self) -> float:\\n sum = 0\\n for operator in self.__operators:\\n if operator.is_alive:\\n operator.experience += 1\\n sum += operator.experience / 100\\n return 0.1 + sum\",\n \"def movie_stats(movies):\\n def num_years():\\n \\\"\\\"\\\"number of years covered in the dataset\\\"\\\"\\\"\\n years = movies['Year']\\n return ('num_years', years.nunique())\\n\\n def tot_movies():\\n \\\"\\\"\\\"total number of movies over all years\\\"\\\"\\\"\\n total = movies['Number of Movies'].sum()\\n return ('tot_movies', total)\\n\\n def yr_fewest_movies():\\n \\\"\\\"\\\"year with fewest movies made (earliest)\\\"\\\"\\\"\\n copy = movies.copy()\\n year = copy.sort_values(['Number of Movies', 'Year']).reset_index(drop = True).Year.loc[0]\\n return ('yr_fewest_movies', year)\\n\\n def avg_gross():\\n \\\"\\\"\\\"average amount of money grossed over all years\\\"\\\"\\\"\\n avg = movies['Total Gross'].mean()\\n if avg is np.nan:\\n raise\\n return ('avg_gross', avg)\\n\\n def highest_per_movie():\\n \\\"\\\"\\\"The year with the highest gross per movie\\\"\\\"\\\"\\n copy = movies.copy()\\n copy['Gross Per Movie'] = copy['Total Gross'] / copy['Number of Movies'] #calculate gross per movie\\n year = copy.sort_values(['Gross Per Movie'], ascending = False).reset_index(drop = True).Year.loc[0]\\n return ('highest_per_movie', year)\\n\\n def second_lowest():\\n \\\"\\\"\\\"name of the top movie during the second lowest (total) grossing year\\\"\\\"\\\"\\n copy = movies.copy()\\n name = copy.sort_values(['Total Gross']).reset_index(drop = True)['#1 Movie'].loc[1]\\n return ('second_lowest', name)\\n\\n def avg_after_harry():\\n \\\"\\\"\\\"avg number of movies made the year after an HP movie was #1 movie\\\"\\\"\\\"\\n copy = movies.copy()\\n copy = copy.sort_values(['Year']).reset_index(drop = True) #years early to present\\n harry_years = copy[copy['#1 Movie'].str.contains('Harry')].Year #years where harry potter was #1\\n next_years = harry_years + 1\\n check = list(next_years.values)\\n next_years_df = copy[copy['Year'].isin(check)]\\n avg = next_years_df['Number of Movies'].mean()\\n if avg is np.nan:\\n raise\\n return ('avg_after_harry', avg)\\n\\n functions = [num_years(), tot_movies(), yr_fewest_movies(), avg_gross(), highest_per_movie(), second_lowest(), avg_after_harry()]\\n stats = {}\\n for fxn in functions:\\n try:\\n info = fxn\\n stats[info[0]] = info[1]\\n except:\\n continue\\n series = pd.Series(stats)\\n\\n return series\",\n \"def calc_perc_reducts():\\n #Load RCP2.6 datq\\n cubes = iris.load(data_dir+'DMSSO2NH3_18502100_RCP26_monthly.nc')\\n #Get the surface and high level SO2 emissions\\n surf_cube = cubes[3][:,0]\\n high_cube = cubes[1][:,0]\\n cubes = iris.cube.CubeList([surf_cube,high_cube])\\n\\n for i in range(0,len(cubes)):\\n #Add the year and month to the cube and extract for 2009 onwards\\n iris.coord_categorisation.add_year(cubes[i],'time',name='year')\\n iris.coord_categorisation.add_month(cubes[i],'time',name='month')\\n cubes[i] = cubes[i].extract(iris.Constraint(year = lambda y: y >=2009))\\n\\n #Make the year-on-year reduction rates\\n yoy_rates = []\\n for cube in cubes:\\n #Calculate the global mean timeseries\\n cube.coord('latitude').guess_bounds()\\n cube.coord('longitude').guess_bounds()\\n area_weights = iris.analysis.cartography.area_weights(cube)\\n cube_mean = cube.collapsed(['latitude','longitude'],iris.analysis.MEAN,weights=area_weights)\\n\\n cube_rates = np.ones((cube_mean.shape))\\n #Loop over the months and calculate the changes from the previous year\\n #Calculate the year on year proportional changes in the global mean\\n for i in range(12,cube_mean.shape[0]):\\n cube_rates[i] = cube_mean[i].data / cube_mean[(i-12)].data\\n\\n yoy_rates.append(cube_rates)\\n\\n return yoy_rates\",\n \"def compute_stats(self):\\n if self.stats is not None:\\n return\\n self.stats = np.zeros(STEPS_MAX + 1)\\n for m in self.missions:\\n m.compute_stats()\\n self.stats += 100 * m.stats\\n self.stats /= len(self.missions)\",\n \"def core_stats():\\n data = get_tsv_dataset(os.path.join(DATA_DIR, TOTALS_FILE))\\n if data is None:\\n return make_response(jsonify({'error': 'Data could not be read'}), 500)\\n # parse up so we can manipulate things.\\n dataset = [int(x) for x in data]\\n annual_sightings = sum(dataset)\\n # for each 'month' (selection of x4\\n monthly_sightings = []\\n max_sightings = 0\\n max_month = 0\\n\\n # grab each month's data into its own list for post processing.\\n # also calculate some other numbers as we go.\\n for i in range(0, len(dataset), 4):\\n # select 4x data points.\\n this_month = dataset[i:i + 4]\\n total_sightings_this_month = sum(this_month)\\n monthly_sightings.append(total_sightings_this_month)\\n old_max = max_sightings\\n max_sightings = max(max_sightings, total_sightings_this_month)\\n if old_max < max_sightings:\\n # it could be the 0th month.\\n max_month = len(monthly_sightings)\\n\\n mean_monthly_sightings = mean(monthly_sightings)\\n month_name = list(calendar.month_name)[max_month]\\n return make_response(jsonify({'annual_sightings': annual_sightings,\\n 'max_sightings': max_sightings,\\n 'max_sighting_month': month_name,\\n 'mean_monthly_sightings': mean_monthly_sightings}), 200)\",\n \"def year_emissions_rule(_m, y):\\r\\n\\r\\n return sum(m.SCENARIO_EMISSIONS[y, s] for s in m.S)\",\n \"def living_expenses(self):\\n # Prepare arguments for call to `living_expenses_strategy`\\n # NOTE: This is a pretty brittle way to determine the\\n # retirement year. Issues #15 and #28 will require this\\n # code to be changed in a future version.\\n retirement_year = min(\\n person.retirement_date.year for person in self.people)\\n return self.living_expenses_strategy(\\n year=self.this_year,\\n people=self.people,\\n retirement_year=retirement_year)\",\n \"def evaluateY(self, time) -> float:\\n ...\",\n \"def year_cost_rule(_m, y):\\r\\n\\r\\n return sum(m.RHO[y, s] * m.SCEN[y, s] for s in m.S)\",\n \"def setEmpireStats(self):\\n totalEmpires = len(self.empires.keys())\\n stats = {'Research':[], 'Fleet Size':[], 'Army Size':[], 'CR Production':[],\\n 'AL Production':[],'EC Production':[],'IA Production':[]}\\n \\n # Calculate Research Stats\\n d = {}\\n for empireID, myEmpire in self.empires.iteritems():\\n if empireID <> '1':\\n num = 0\\n for techID, myTech in myEmpire.techTree.iteritems():\\n if myTech.complete == 1:\\n num += 1\\n d[empireID] = num\\n stats['Research'] = anwp.func.funcs.sortDictByValue(d, True)\\n \\n # Calculate Fleet Stats\\n d = {}\\n for shipID, myShip in self.ships.iteritems():\\n if myShip.empireID <> '1':\\n (BV,CR,AL,EC,IA) = myShip.getMyValue()\\n if myShip.empireID in d.keys():\\n d[myShip.empireID] += BV\\n else:\\n d[myShip.empireID] = BV\\n stats['Fleet Size'] = anwp.func.funcs.sortDictByValue(d, True)\\n \\n # Calculate Army Stats\\n d = {}\\n for regimentID, myRegiment in self.regiments.iteritems():\\n if myRegiment.empireID <> '1':\\n (BV,CR,AL,EC,IA) = myRegiment.getMyValue()\\n if myRegiment.empireID in d.keys():\\n d[myRegiment.empireID] += BV\\n else:\\n d[myRegiment.empireID] = BV\\n stats['Army Size'] = anwp.func.funcs.sortDictByValue(d, True)\\n\\n # Calculate Production Stats\\n for res in ['CR','AL','EC','IA']:\\n d = {}\\n for systemID, mySystem in self.systems.iteritems():\\n if mySystem.myEmpireID <> '1':\\n myValue = getattr(mySystem, 'prod%s' % res)\\n if mySystem.myEmpireID in d.keys():\\n d[mySystem.myEmpireID] += myValue\\n else:\\n d[mySystem.myEmpireID] = myValue\\n myEmpire = self.empires[mySystem.myEmpireID]\\n myEmpireValue = getattr(myEmpire, 'totalProd%s' % res)\\n setattr(myEmpire, 'totalProd%s' % res, myEmpireValue+myValue)\\n \\n stats['%s Production' % res] = anwp.func.funcs.sortDictByValue(d, True)\\n \\n # calculate top captains\\n d = {}\\n for captainID, myCaptain in self.captains.iteritems():\\n if myCaptain.myEmpire.id <> '1':\\n myCaptain.resetData()\\n d[myCaptain.id] = myCaptain.experience\\n topCaptains = anwp.func.funcs.sortDictByValue(d, True)\\n topCaptains = topCaptains[:2*len(self.empires.keys())]\\n \\n # Send out Stats to each Empire\\n for empireID, myEmpire in self.empires.iteritems():\\n if empireID <> '1':\\n title = 'Round:%d Statistics' % self.currentRound\\n body = ['%s ROUND %d STATS:' % (myEmpire.name, self.currentRound)]\\n body.append('====================================================')\\n for item in ['Research','Fleet Size', 'Army Size', 'CR Production',\\n 'AL Production', 'EC Production', 'IA Production']:\\n if empireID in stats[item]:\\n body.append('You are %s in %s' % (anwp.func.funcs.getNiceNumber(stats[item].index(empireID)+1), item))\\n \\n # total production\\n body.append('')\\n body.append('TOTAL EMPIRE PRODUCTION OVER %d ROUNDS:' % self.currentRound)\\n body.append('====================================================')\\n for res in ['CR','AL','EC','IA']:\\n body.append('Total %s Production: %d' % (res, getattr(myEmpire, 'totalProd%s' % res)))\\n\\n # legendary captains\\n body.append('')\\n body.append('TOP %d STARSHIP CAPTAINS in ROUND %d:' % ((2*len(self.empires.keys()), self.currentRound)))\\n body.append('====================================================')\\n for captainID in topCaptains:\\n myCaptain = self.captains[captainID]\\n myCaptain.promoteMe()\\n body.append('%s ---> RANK:%s -- EXP:%d -- %s' % (string.upper(myCaptain.name), myCaptain.rank, myCaptain.experience, string.upper(myCaptain.myEmpire.name)))\\n \\n myEmpire.genMail({'fromEmpire':empireID, 'round':self.currentRound,\\n 'messageType':'general', 'subject':title, 'body':body})\",\n \"def test_yearly_report(self):\\n self.register_user()\\n result = self.login_user()\\n access_token = json.loads(result.data.decode())['access_token']\\n res = self.client().post('/expenses/', headers=dict(Authorization=\\\"Bearer \\\" + access_token),\\n data=self.expense)\\n self.assertEqual(res.status_code, 201)\\n rv = self.client().post('/expenses/', headers=dict(Authorization=\\\"Bearer \\\" + access_token), data=\\n {'name': 'soda', 'amount': 200, 'date_of_expense': '10-01-2021'})\\n consolidated_total = 212.23\\n res = self.client().get('/yearly_report?year=2021', headers=dict(Authorization=\\\"Bearer \\\" + access_token))\\n self.assertEqual(res.status_code, 200)\\n results = json.loads(res.data)\\n self.assertEqual(results['consolidated_total'], consolidated_total)\",\n \"def _get_age(self):\\n for employee in self:\\n if employee.sudo().birthday:\\n employee.age = relativedelta(\\n fields.Date.from_string(fields.Date.today()),\\n fields.Date.from_string(employee.sudo().birthday)).years\\n else:\\n employee.age = 0\",\n \"def _death_insurance(self, total_salary):\\n return int(total_salary * self.death_insurance_rate)\",\n \"def analyst_agent(currency_id, rates_past_future, days_predicted):\\n currency = Currency.objects.get(id=currency_id)\\n rate_yesterday = rates_past_future[:-days_predicted][-2]\\n rate_today = rates_past_future[:-days_predicted][-1]\\n rate_end = rates_past_future[-1]\\n rate_growth_last_day = rate_today - rate_yesterday\\n expected_rate_growth = rate_end - rate_today\\n percentage_growth_last_day = 100 * float(rate_growth_last_day) / float(rate_yesterday)\\n percentage_growth = 100 * float(expected_rate_growth) / float(rate_today)\\n notification_agent(currency, expected_rate_growth, percentage_growth, days_predicted, rate_today, rate_end, percentage_growth_last_day)\",\n \"def calc_annual_heating_savings (self):\\n price = (self.diesel_prices + self.cd['heating fuel premium'])\\n\\n #~ self.base_heating_cost =\\n\\n #~ self.proposed_heating_cost =\\n\\n\\n\\n\\n self.annual_heating_savings = self.reduction_diesel_used * price\\n #~ print 'self.annual_heating_savings',self.annual_heating_savings\",\n \"def monthly_fee(self):\\n total_salary = self.base_salary\\n if self.is_salary_allowances is True:\\n fixed_allowances = self.summarize( self.fixed_allowances )\\n non_fixed_allowances = self.summarize( self.non_fixed_allowances )\\n total_salary = total_salary + non_fixed_allowances + fixed_allowances\\n #end if\\n\\n company_old_age_insurance = 0\\n individual_old_age_insurance = 0\\n if self.old_age_insurance_status is True:\\n company_old_age_insurance = \\\\\\n self._company_old_age_insurance(total_salary)\\n\\n individual_old_age_insurance = \\\\\\n self._individual_old_age_insurance(total_salary)\\n #end if\\n\\n company_pension_insurance = 0\\n individual_pension_insurance = 0\\n if self.pension_insurance_status is True:\\n company_pension_insurance = \\\\\\n self._company_pension_insurance(total_salary)\\n\\n individual_pension_insurance = \\\\\\n self._individual_pension_insurance(total_salary)\\n #end if\\n\\n company_health_insurance = 0\\n individual_health_insurance = 0\\n if self.health_insurance_status is True:\\n company_health_insurance = \\\\\\n self._company_health_insurance(total_salary)\\n\\n individual_health_insurance = \\\\\\n self._individual_health_insurance(total_salary)\\n #end if\\n\\n death_insurance = 0\\n if self.death_insurance_status is True:\\n death_insurance = self._death_insurance(total_salary)\\n #end if\\n\\n accident_insurance = 0\\n if self.accident_insurance_status is True:\\n accident_insurance = \\\\\\n self._accident_insurance(total_salary, \\\\\\n self.industry_risk_rate)\\n #end if\\n\\n monthly = {\\n \\\"old_age_insurance\\\" : {\\n \\\"company\\\" : company_old_age_insurance,\\n \\\"individual\\\" : individual_old_age_insurance,\\n },\\n \\\"pension_insurance\\\" : {\\n \\\"company\\\" : company_pension_insurance,\\n \\\"individual\\\" : individual_pension_insurance,\\n },\\n \\\"health_insurance\\\" : {\\n \\\"company\\\" : company_health_insurance,\\n \\\"individual\\\" : individual_health_insurance,\\n },\\n \\\"death_insurance\\\" : death_insurance,\\n \\\"accident_insurance\\\" : accident_insurance\\n }\\n return monthly\",\n \"def death_rate(self):\\n raise NotImplementedError\",\n \"def do_damage(self) -> float:\\n res = 0.05 + self.experience / 100\\n self.experience = self.experience + 1\\n return res\",\n \"def years_to_pay(self) -> float:\\n return round(self.term / self.term_multiplier * self.n_periods / 12, 1)\",\n \"def year_cumulative_price_difference_weighted_rule(_m, y):\\r\\n\\r\\n return sum(m.YEAR_ABSOLUTE_PRICE_DIFFERENCE_WEIGHTED[j] for j in m.Y if j <= y)\",\n \"def per_hour(self):\\n if self.is_salary():\\n return 0.0\\n return self.wage_cents / 100.0\",\n \"def collect_payroll(team_dict):\\n fourty_five_man = len(team_dict['roster'])\\n fourty_man = sum(1 for x in team_dict['roster'] if x['contract'].type != 'AA')\\n gross_payroll = collect_salaries(team_dict)\\n payroll_adjustments = collect_adjustment_totals(team_dict)\\n net_payroll = [0, 0, 0, 0, 0]\\n payroll_remaining = [0, 0, 0, 0, 0]\\n for i in range(5):\\n net_payroll[i] = gross_payroll[i] + payroll_adjustments[i]\\n payroll_remaining[i] = 135000000 - net_payroll[i]\\n team_dict['payroll'] = {\\n '40_man': fourty_man,\\n '45_man': fourty_five_man,\\n 'gross_payroll': gross_payroll,\\n 'payroll_adjustments': payroll_adjustments,\\n 'net_payroll': net_payroll,\\n 'payroll_remaining': payroll_remaining,\\n 'salary_cap': [135000000, 135000000, 135000000, 135000000, 135000000]\\n }\\n return team_dict\",\n \"def _company_old_age_insurance(self, total_salary):\\n return self.company_old_age_insurance_rate * total_salary\",\n \"def determinePerformance(history):\\n\\n\\tperformance = dict()\\n\\n\\tn = 1\\n\\trecentDate = sorted(history.keys())[-1]\\n\\n\\twhile True:\\n\\t\\tlastYear = subtractYears(recentDate, n)\\n\\t\\tif lastYear == None:\\n\\t\\t\\tbreak;\\n\\n\\t\\tentry = getEntryBefore(history, lastYear)\\n\\t\\tif entry != None:\\n\\t\\t\\tppuChangePercent = ppuIncrease(history, lastYear, recentDate)\\n\\n\\t\\t\\tif n > 1:\\n\\t\\t\\t\\tyearEnd = subtractYears(recentDate, n-1)\\n\\t\\t\\t\\toneYearPerformance = ppuIncrease(history, lastYear, yearEnd)\\n\\t\\t\\telse:\\n\\t\\t\\t\\toneYearPerformance = ppuChangePercent\\n\\n\\t\\t\\tarr = annualisedReturn(ppuChangePercent, n)\\n\\n\\t\\t\\tperformance[n] = {\\n\\t\\t\\t\\t'duration' : n,\\n\\t\\t\\t\\t'indvidual' : oneYearPerformance,\\n\\t\\t\\t\\t'cumulative' : ppuChangePercent,\\n\\t\\t\\t\\t'effective' : arr\\n\\t\\t\\t}\\n\\t\\t\\tn = n + 1\\n\\t\\t\\tif n > 10:\\n\\t\\t\\t\\tbreak\\n\\t\\telse:\\n\\t\\t\\t# run out of whole years\\n\\t\\t\\tfirstDate = sorted(history.keys())[0]\\n\\t\\t\\tppuChangePercent = ppuIncrease(history, firstDate, recentDate)\\n\\t\\t\\tn = float((recentDate - firstDate).days) / 365\\n\\t\\t\\tarr = annualisedReturn(ppuChangePercent, n)\\n\\n\\t\\t\\tperformance[n] = {\\n\\t\\t\\t\\t'duration' : n,\\n\\t\\t\\t\\t'indvidual' : None,\\n\\t\\t\\t\\t'cumulative' : ppuChangePercent,\\n\\t\\t\\t\\t'effective' : arr\\n\\t\\t\\t}\\n\\t\\t\\tbreak\\n\\n\\treturn performance\",\n \"def hit(self):\\r\\n self.life -= 1\\r\\n total_score = 1\\r\\n if self.life == 0:\\r\\n self.alive = False\\r\\n total_score += self.bonus\\r\\n return total_score\",\n \"def _calculate(self):\\n source = self.source\\n res = {}\\n l_cols = [[], [], [], []]\\n r_lines = {}\\n dateline=None\\n ###delete the below code when fetch data from database(assume: data in database has been pretreatment)\\n if source[t.ror].min() > -99.0:\\n pass\\n else:\\n source[t.ror] = np.where(\\n source[t.ror] > -99.0, source[t.ror], -99.0)\\n ###\\n for account in self.accounts:\\n source_account = source[source[t.account] == account]\\n source_account = source_account.reset_index(drop=True)\\n dateline=source_account[t.effective_date]\\n ror=source_account[t.ror]/100\\n returns_cum = ROR.ror_cum_ann(source_account, self.annualized)\\n # double_return_cum=round(double_return_cum,2)+1\\n returns_cum = returns_cum + 1\\n growth_amounts = returns_cum * self.starting_value\\n returns_cum, growth_amounts = round(returns_cum - 1, 4), \\\\\\n round(growth_amounts, 2)\\n l_cols[0].append(growth_amounts.iloc[-1, 0])#account growth amount\\n l_cols[1].append(growth_amounts.iloc[-1, 1])#bench growth amount\\n l_cols[2].append(returns_cum.iloc[-1, 0])#account return\\n l_cols[3].append(returns_cum.iloc[-1, 1])#bench return\\n r_lines[account] = [list(returns_cum.iloc[:,0]), list(growth_amounts.iloc[:, 0]),#list(returns_cum.iloc[:, 0])\\n list(growth_amounts.iloc[:, 1])]#account return, account growth amount, bench growth amount\\n res['account_vs_benchmark'] = {'xAxis': self.accounts,\\n 'series': l_cols}\\n res['growth_of_unit'] = {'xAxis': list(dateline),\\n 'series': r_lines}\\n return res\\n # ret_dict = self._ret(accounts, starting_value, source, annualized)\\n # return ret_dict\",\n \"def extras_total(self):\\n total = self.wides + self.no_balls + self.byes + self.leg_byes\\n return total\",\n \"def base_experience(self) -> int:\\n return self._base_experience\",\n \"def get_score(self):\\n for response in self.response_list:\\n self.score += response.get_score\",\n \"def calc_stat_values(self):\",\n \"def get_total_expenses(self):\\n return sum(self.expenses.values())\",\n \"def user_stats(df):\\n print('\\\\nCalculating User Stats...\\\\n')\\n start_time = time.time()\\n\\n # Display counts of user types value_counts\\n print('User Type Counts:')\\n for key, value in dict(df['User Type'].value_counts()).items():\\n print('{0:12}: {1:8}'.format(key, value))\\n\\n # Display counts of gender if that column is present\\n if 'Gender' in df.columns:\\n print('\\\\nGender Data:')\\n gc = dict(df['Gender'].value_counts())\\n\\n print('\\\\tRides:')\\n for key, value in gc.items():\\n print('\\\\t{0:7}: {1:8}'.format(key, value))\\n\\n # get average duration by gender\\n print('\\\\n\\\\tAverage Duration:')\\n for gender in gc.keys():\\n gender_df = df[df['Gender'] == gender]\\n gender_duration_avg = get_human_readable_time(gender_df['Trip Duration'].mean())\\n print('\\\\t{0:7}: {1:8}'.format(gender, gender_duration_avg))\\n\\n\\n # Display earliest, most recent, and most common year of birth min/max/mode\\n # if that column is present\\n if 'Birth Year' in df.columns:\\n current_year = dt.datetime.today().year\\n df['Age'] = current_year - df['Birth Year']\\n\\n print('\\\\nAge Data:')\\n print('\\\\tEarliest Birth Year: \\\\t{}'.format(int(df['Birth Year'].min())))\\n print('\\\\tMost Recent Birth Year: {}'.format(int(df['Birth Year'].max())))\\n\\n most_common_year = ', '.join(map(str, map(int, list(df['Birth Year'].mode()))))\\n print('\\\\tMost Common Birth Year: {}'.format(most_common_year))\\n\\n print('\\\\tOldest Rider: \\\\t\\\\t{}'.format(int(df['Age'].max())))\\n print('\\\\tYoungest Rider: \\\\t{}'.format(int(df['Age'].min())))\\n print('\\\\tAvergae Rider Age: \\\\t{}'.format(round(df['Age'].mean())))\\n\\n\\n \\n print('\\\\nThis took %s seconds.' % (time.time() - start_time))\\n print('-'*40)\",\n \"def marcels_players(goalie, date, df):\\n # 0 = that year, 1 is year b4 ....\\n marcel_weights = [.36, .29, .21, .14]\\n reg_const = 2000\\n reg_avg = 0 # Where to regress to\\n\\n # Use past 3 season to weight games played -> Just take weighted average\\n gp_weights = [8, 4, 2, 0]\\n\\n season = int(helpers.get_season(date))\\n\\n weighted_goals_sum, weighted_fen_sum, weighted_xg_sum, weights_marcel_sum = 0, 0, 0, 0\\n weighted_gp_sum, weights_gp_sum = 0, 0\\n\\n # Past 4 Seasons\\n for i in range(0, 4):\\n if season - i > 2006:\\n # Subset from stats df\\n df_goalie = df[(df['player'] == goalie) & (df['season'] == (season - i))]\\n\\n # Sanity Check\\n if df_goalie.shape[0] > 1:\\n print(\\\"Too many rows!!!!!!!\\\")\\n exit()\\n\\n # If he played that year\\n if not df_goalie.empty:\\n weighted_goals_sum += df_goalie.iloc[0]['goals_a'] * marcel_weights[i]\\n weighted_fen_sum += df_goalie.iloc[0]['fenwick_a'] * marcel_weights[i]\\n weighted_xg_sum += df_goalie.iloc[0]['xg_a'] * marcel_weights[i]\\n weighted_gp_sum += df_goalie.iloc[0]['games'] * gp_weights[i]\\n\\n # -> To divide by at end...normalize everything\\n weights_marcel_sum += marcel_weights[i]\\n weights_gp_sum += gp_weights[i]\\n\\n # Normalize weighted sums\\n weighted_xg_sum = weighted_xg_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\\n weighted_goals_sum = weighted_goals_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\\n weighted_fen_sum = weighted_fen_sum / weights_marcel_sum if weights_marcel_sum != 0 else 0\\n\\n # Get Regressed fsv%\\n if weighted_fen_sum != 0:\\n weighted_adj_fsv = ((1 - weighted_goals_sum / weighted_fen_sum) - (1 - weighted_xg_sum / weighted_fen_sum)) * 100\\n else:\\n weighted_adj_fsv = 0\\n reg_adj_fsv = weighted_adj_fsv - ((weighted_adj_fsv - reg_avg) * (reg_const / (reg_const + weighted_fen_sum)))\\n\\n # Get weighted gp\\n weighted_gp_sum = weighted_gp_sum / weights_gp_sum if weights_gp_sum != 0 else 0\\n\\n return {'fsv': reg_adj_fsv, 'gp': weighted_gp_sum}\",\n \"def year_sum_cumulative_price_difference_weighted_rule(_m, y):\\r\\n\\r\\n return sum(m.YEAR_CUMULATIVE_PRICE_DIFFERENCE_WEIGHTED[j] for j in m.Y if j <= y)\",\n \"def payroll_calculation():\\n\\n name = search_employee()\\n if name == None:\\n return\\n accrual_month = month('Accrual month: ')\\n accrual_year = year('Accrual year: ')\\n accrual = f'{accrual_month}-{accrual_year}'\\n salary_value = month_salary()\\n salary_base = salary(salary_value)\\n overtime = value_input('Overtime: ')\\n absences = value_input('Absences: ')\\n late = value_input('Late: ')\\n bonus = value_input('Bonus: ')\\n\\n hourly_wage = round(salary_value / 220, 2)\\n overtime_value = round(float(hourly_wage * 1.5), 2)\\n overtime_total = round(overtime_value * overtime, 2)\\n daily_wage = round(salary_value / 30, 2)\\n absences_value = round(daily_wage * absences, 2)\\n late_value = round(daily_wage * late / 60, 2)\\n inss_value = inss(salary_base, overtime_total)\\n irrf_value = irrf(salary_base, overtime_total, inss_value, bonus)\\n sleep(2)\\n\\n\\n\\n header('EARNINGS')\\n print(f'Salary: {salary_base}')\\n print(f'Bonus: {bonus}')\\n print(f'Overtime: {overtime_total }')\\n earnings_total = round(salary_base + overtime_total + bonus, 2)\\n sleep(2)\\n\\n print(line())\\n print(f'Earnings total: {earnings_total}')\\n print(line())\\n sleep(2)\\n\\n header('DISCOUNTS')\\n\\n transportation_vouchers = round(salary_base * 6 / 100, 2)\\n health_care = round(salary_base * 2 / 100, 2)\\n dental_care = round(salary_base * 0.5 / 100, 2)\\n meal_ticket = round(salary_base * 1 / 100, 2)\\n\\n print(f'absences: {absences_value}')\\n print(f'late: {late_value}')\\n print(f'transportation_vouchers: {transportation_vouchers}')\\n print(f'health_care: {health_care}')\\n print(f'dental_care: {dental_care}')\\n print(f'meal_ticket: {meal_ticket}')\\n print(f'inss_value: {inss_value}')\\n print(f'irrf_value: {irrf_value}')\\n\\n discounts_total = round(absences_value + late_value + transportation_vouchers + health_care +\\n dental_care + meal_ticket + inss_value + irrf_value, 2)\\n\\n print(line())\\n print(f'Discounts_total : {discounts_total }')\\n print(line())\\n liquid_salary = round(earnings_total - discounts_total, 2)\\n print(f'Liquid_salary: {liquid_salary} ')\\n print(line())\\n\\n conn = sqlite3.connect('data/people_management.db')\\n cursor = conn.cursor()\\n cursor.execute(f\\\"\\\"\\\"\\n INSERT INTO salary (name, salary ,bonus, overtime, absences_value, late_value, \\n t_vouchers, health_care, dental_care, meal_ticket, inss, irrf, \\n earnings, discounts, liquid_salary, accrual)\\n VALUES ('{name}', '{salary_base}' ,'{bonus}', '{overtime_total}', '{absences_value}', \\n '{late_value}', '{transportation_vouchers}', '{health_care}', '{dental_care}', \\n '{meal_ticket}', '{inss_value}', '{irrf_value}', '{earnings_total}', '{discounts_total}', \\n '{liquid_salary}', '{accrual}')\\n \\\"\\\"\\\")\\n conn.commit()\\n conn.close()\",\n \"def getStockPerformance(stock, start_idx, end_idx, number_years, rounded=True):\\r\\n total_return = (stock['Adj Close'][end_idx]- stock['Adj Close'][start_idx])/stock['Adj Close'][start_idx] + 1\\r\\n total_return_percentage = total_return * 100\\r\\n num = ((total_return ** (1/number_years)) - 1) * 100\\r\\n annual_return = np.round(num, decimals=2) if rounded else num\\r\\n return (total_return_percentage, annual_return)\",\n \"def processed_cum_overall(self):\\n self.processed_cum_overall = (\\n self.cumulative_stats_for_team_each_year\\n [['Season','TeamID','win_rate','total_score','total_opponent_score','fgp','fg3p','ftp', 'total_rebounds','total_off_rebounds','total_def_rebounds',\\n 'total_off_rebounds_percent','total_def_rebounds_percent','total_rebound_possession_percent','total_rebound_possessiongain_percent','total_blocks',\\n 'total_assists','total_steals','total_turnover','total_personalfoul','total_block_opp_FGA_percent','total_assist_per_fgm','total_assist_turnover_ratio',\\n 'expectation_per_game','avg_lose_score_by','avg_win_score_by']]\\n )\",\n \"def total_end_for(self, age, sex):\\n initial = getattr(self, '%s_total_beginning_%s' % (age, sex), 0)\\n admitted = getattr(self, '%s_admitted_%s' % (age, sex), 0)\\n out = getattr(self, '%s_total_out_%s' % (age, sex), 0)\\n return (initial + admitted) - out\",\n \"def experiences(self):\\n return self.client.call('GET',\\n self.name + 'experiences')\",\n \"def skills_to_salary(self):\\n tree = html.fromstring(self.page)\\n numerator = []\\n denominator = []\\n for salary in range(1, 6):\\n salary_x_path = '//*[@id=\\\"SALARY_rbo\\\"]/ul/li[' + str(salary) + ']/a/text()'\\n number_of_jobs_x_path = '//*[@id=\\\"SALARY_rbo\\\"]/ul/li[' + str(salary) + ']/text()'\\n salary_str = tree.xpath(salary_x_path)\\n num_of_jobs_str = tree.xpath(number_of_jobs_x_path)\\n try:\\n salary_float = float(salary_str[0].strip(\\\"$, +\\\").replace(',', ''))\\n num_of_jobs_float = float(num_of_jobs_str[1].strip(\\\"( , )\\\"))\\n except IndexError:\\n salary_float = 0\\n num_of_jobs_float = 1\\n numerator.append(salary_float * num_of_jobs_float)\\n denominator.append(num_of_jobs_float)\\n weighted_average_salary = sum(numerator) / sum(denominator)\\n total_number_of_jobs = sum(denominator)\\n self.salary = weighted_average_salary\\n self.number_of_jobs = total_number_of_jobs\",\n \"def getTotalReward(self):\\n return self.cumreward\",\n \"def getTotalReward(self):\\n return self.cumreward\",\n \"def collect_salaries(team_dict):\\n salaries = [0, 0, 0, 0, 0]\\n for c in team_dict['roster']:\\n for i in range(5):\\n if (c['contract'].contract_season + i) <= c['contract'].length:\\n salaries[i] += c['contract'].salary\\n return salaries\",\n \"async def score(self, ctx):\\r\\n with DB() as db:\\r\\n companies = db.query(Company).filter(Company.active == True).all()\\r\\n scores = []\\r\\n for company in companies:\\r\\n history = db.query(CompanyHistory).filter(CompanyHistory.company == company.id).order_by(CompanyHistory.date.desc()).first()\\r\\n scores.append([company.name, round(history.value, 2)])\\r\\n headers = ['Company', 'Net Worth']\\r\\n score_df = pd.DataFrame(scores, columns=headers)\\r\\n score_df = score_df.sort_values(['Net Worth'], ascending=False)\\r\\n aggregated = tabulate(score_df.values.tolist(), headers=headers)\\r\\n await ctx.send(f\\\"```{aggregated}```\\\")\",\n \"def age_check(self):\\r\\n # check working status\\r\\n if 15 <= float(self.age) < 59:\\r\\n if self.work_status == 0:\\r\\n self.work_status = 1\\r\\n num_labor_list[self.hh_id] += 1\\r\\n labor_list.append(self.unique_id)\\r\\n if self.work_status == 1 and self.unique_id not in labor_list:\\r\\n labor_list.append(self.unique_id)\\r\\n else:\\r\\n self.work_status = 0\\r\\n\\r\\n # check education status; measured in years of education\\r\\n if 7 <= int(self.age) <= 19:\\r\\n if random.random() > 0.1:\\r\\n self.education += 1\\r\\n # most adults in the FNNR did not get a full 12-13 years of education\\r\\n elif 19 < float(self.age) < 23 and self.migration_status == 1:\\r\\n if random.random() < 0.5:\\r\\n self.education += 1 # went to college and got further education\\r\\n # this is rare; in the household list, a few received beyond 12 years of education\\r\\n\\r\\n # check age-based death rates\\r\\n if self.age > 65:\\r\\n self.death_rate = 0.001443 # 5-day death rate\\r\\n # The average death rate in China is 7.3 per 1,000 people/year, or 0.0073 (Google).\\r\\n # However, death rates should be higher for the elderly, or else the population structure will skew.\\r\\n # I set death rates for those over age 65 to be 10% per year--0.9 yearly survival rate.\\r\\n # The survival rate for each 5-day step is compounded 73 times, so x^73 = 0.85.\\r\\n # 0.998557 is the 5-day survival rate, and 1 - x is the 5-day death rate.\\r\\n else:\\r\\n self.death_rate = 0.00000425\\r\\n # I wanted people to have a 98% chance of reaching age 65 (death rate is lower if not elderly).\\r\\n # If a 'check' is every 5 days, 73 checks/year * 65 years = 4,745 checks.\\r\\n # x^4745 = 0.98; the 5-day survival rate is 0.99999575, and 1 - x is the 5-day death rate.\\r\\n\\r\\n # These rates are changeable later.\\r\",\n \"def get_age(self):\\n return CURRENT_YEAR - self.year\",\n \"def get_age(self):\\n return CURRENT_YEAR - self.year\",\n \"def year_average_price_rule(_m, y):\\r\\n\\r\\n # Total revenue\\r\\n return sum(m.SCENARIO_REVENUE[y, s] for s in m.S) / sum(m.SCENARIO_DEMAND[y, s] for s in m.S)\",\n \"def calc_calories(gpx_track, wt = 175, activity='Run'):\",\n \"def winning_games_stats(self):\\n self.winning_games_up_to_2013 = (\\n self.df\\n .pipe(lambda x:x.assign(winning_num_counts = 1))\\n .query(\\\"Season <= 2013\\\")\\n .groupby(['Season','WTeamID'])\\n .agg({\\\"WScore\\\":\\\"sum\\\",\\\"WFGM\\\":\\\"sum\\\",\\\"WFGA\\\":\\\"sum\\\",\\\"WFGM3\\\":\\\"sum\\\",\\\"WFGA3\\\":\\\"sum\\\",\\\"WFTM\\\":\\\"sum\\\",\\\"WFTA\\\":\\\"sum\\\",\\\"LScore\\\":\\\"sum\\\",\\\"winning_num_counts\\\":\\\"sum\\\",\\n \\\"WOR\\\":\\\"sum\\\",\\\"WDR\\\":\\\"sum\\\",\\\"LFGM\\\":\\\"sum\\\",\\\"LFGA\\\":\\\"sum\\\",\\n \\\"WAst\\\":\\\"sum\\\",\\\"WTO\\\":\\\"sum\\\",\\\"WStl\\\":\\\"sum\\\",\\\"WBlk\\\":\\\"sum\\\",\\\"WPF\\\":\\\"sum\\\"})\\n .reset_index()\\n .rename(columns={\\\"LScore\\\":\\\"losing_opponent_score\\\"})\\n # rebounds\\n .pipe(lambda x:x.assign(total_winning_rebounds = x.WOR + x.WDR))\\n .pipe(lambda x:x.assign(winning_off_rebounds_percent = x.WOR/x.total_winning_rebounds))\\n .pipe(lambda x:x.assign(winning_def_rebounds_percent = x.WDR/x.total_winning_rebounds))\\n .pipe(lambda x:x.assign(team_missed_attempts = x.WFGA - x.WFGM))\\n .pipe(lambda x:x.assign(opp_team_missed_attempts = x.LFGA - x.LFGM))\\n .pipe(lambda x:x.assign(winning_rebound_possession_percent = x.WOR/x.team_missed_attempts))\\n .pipe(lambda x:x.assign(winning_rebound_possessiongain_percent = x.WDR/x.opp_team_missed_attempts))\\n # blocks, steals, assists and turnovers\\n .pipe(lambda x:x.assign(winning_block_opp_FGA_percent = x.WBlk/x.LFGA))\\n .pipe(lambda x:x.assign(winning_assist_per_fgm = x.WAst/x.WFGM))\\n .pipe(lambda x:x.assign(winning_assist_turnover_ratio = x.WAst/x.WTO))\\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\\n .rename(columns={\\\"LFGA\\\":\\\"LFGA_opp\\\",\\\"LFGM\\\":\\\"LFGM_opp\\\"})\\n )\",\n \"def total_rewards(self) -> float:\\n return self.__total_rewards\",\n \"def annualized_gains(self, day='today'):\\n assert day == 'today' or isinstance(day, date), 'Error! You have to pass a datetime.date istance to the day parameter.'\\n if day == 'today':\\n day = self.data.index[-1]\\n if self.data.index[-1] >= day >= self.data.index[0]:\\n day = self._first_good_date(day)\\n initialValue = self.invested_amount(day)\\n finalValue = self.value(day)\\n numberOfDays = (day - self.data.index[0]).days\\n return round(((finalValue / initialValue)**(365/numberOfDays) - 1) * 100, 2) \\n else:\\n return 0\",\n \"def calc_statistics(self):\\n\\n self._ydata = np.zeros([4, len(self._depths)], dtype=float)\\n\\n exp_vals = np.zeros(self._ntrials, dtype=float)\\n ideal_vals = np.zeros(self._ntrials, dtype=float)\\n\\n for depthidx, depth in enumerate(self._depths):\\n\\n exp_shots = 0\\n\\n for trialidx in range(self._ntrials):\\n cname = 'qv_depth_%d_trial_%d' % (depth, trialidx)\\n exp_vals[trialidx] = self._heavy_output_counts[cname]\\n exp_shots += self._circ_shots[cname]\\n ideal_vals[trialidx] = self._heavy_output_prob_ideal[cname]\\n\\n # Calculate mean and error for experimental data\\n self._ydata[0][depthidx] = np.sum(exp_vals)/np.sum(exp_shots)\\n self._ydata[1][depthidx] = (self._ydata[0][depthidx] *\\n (1.0-self._ydata[0][depthidx])\\n / self._ntrials)**0.5\\n\\n # Calculate mean and error for ideal data\\n self._ydata[2][depthidx] = np.mean(ideal_vals)\\n self._ydata[3][depthidx] = (self._ydata[2][depthidx] *\\n (1.0-self._ydata[2][depthidx])\\n / self._ntrials)**0.5\",\n \"def test_result_years_ok(self):\\n\\n graphic = HeightCurve(\\n gender=Constants.MALE,\\n age=Constants.YEARS\\n )\\n\\n # percentis_3\\n self.assertEqual(graphic.result(83.24, 3), -1)\\n self.assertEqual(graphic.result(83.25, 3), 0)\\n self.assertEqual(graphic.result(83.26, 3), 0)\\n self.assertEqual(graphic.result(118.42, 10), -1)\\n self.assertEqual(graphic.result(118.43, 10), 0)\\n self.assertEqual(graphic.result(118.44, 10), 0)\\n\\n # percentis_97\\n self.assertEqual(graphic.result(100.50, 3), 0)\\n self.assertEqual(graphic.result(100.51, 3), 0)\\n self.assertEqual(graphic.result(100.52, 3), 1)\\n self.assertEqual(graphic.result(143.42, 10), 0)\\n self.assertEqual(graphic.result(143.43, 10), 0)\\n self.assertEqual(graphic.result(143.44, 10), 1)\",\n \"def patrimony_total(self):\\n pass\",\n \"def year_scheme_emissions_intensity_rule(_m, y):\\r\\n\\r\\n return m.YEAR_EMISSIONS[y] / m.YEAR_SCHEME_OUTPUT[y]\",\n \"def annual_summary(self):\\n \\n #Initialize dict with info about all of year's storms\\n hurdat_year = {'id':[],'operational_id':[],'name':[],'max_wspd':[],'min_mslp':[],'category':[],'ace':[]}\\n \\n #Search for corresponding entry in keys\\n count_ss_pure = 0\\n count_ss_partial = 0\\n iterate_id = 1\\n for key in self.dict.keys():\\n\\n #Retrieve info about storm\\n temp_name = self.dict[key]['name']\\n temp_vmax = np.array(self.dict[key]['vmax'])\\n temp_mslp = np.array(self.dict[key]['mslp'])\\n temp_type = np.array(self.dict[key]['type'])\\n temp_time = np.array(self.dict[key]['date'])\\n temp_ace = self.dict[key]['ace']\\n\\n #Get indices of all tropical/subtropical time steps\\n idx = np.where((temp_type == 'SS') | (temp_type == 'SD') | (temp_type == 'TD') | (temp_type == 'TS') | (temp_type == 'HU'))\\n\\n #Get times during existence of trop/subtrop storms\\n if len(idx[0]) == 0: continue\\n trop_time = temp_time[idx]\\n if 'season_start' not in hurdat_year.keys():\\n hurdat_year['season_start'] = trop_time[0]\\n hurdat_year['season_end'] = trop_time[-1]\\n\\n #Get max/min values and check for nan's\\n np_wnd = np.array(temp_vmax[idx])\\n np_slp = np.array(temp_mslp[idx])\\n if len(np_wnd[~np.isnan(np_wnd)]) == 0:\\n max_wnd = np.nan\\n max_cat = -1\\n else:\\n max_wnd = int(np.nanmax(temp_vmax[idx]))\\n max_cat = convert_category(np.nanmax(temp_vmax[idx]))\\n if len(np_slp[~np.isnan(np_slp)]) == 0:\\n min_slp = np.nan\\n else:\\n min_slp = int(np.nanmin(temp_mslp[idx]))\\n\\n #Append to dict\\n hurdat_year['id'].append(key)\\n hurdat_year['name'].append(temp_name)\\n hurdat_year['max_wspd'].append(max_wnd)\\n hurdat_year['min_mslp'].append(min_slp)\\n hurdat_year['category'].append(max_cat)\\n hurdat_year['ace'].append(temp_ace)\\n hurdat_year['operational_id'].append(self.dict[key]['operational_id'])\\n \\n #Handle operational vs. non-operational storms\\n\\n #Check for purely subtropical storms\\n if 'SS' in temp_type and True not in np.isin(temp_type,['TD','TS','HU']):\\n count_ss_pure += 1\\n\\n #Check for partially subtropical storms\\n if 'SS' in temp_type:\\n count_ss_partial += 1\\n\\n #Add generic season info\\n hurdat_year['season_storms'] = len(hurdat_year['name'])\\n narray = np.array(hurdat_year['max_wspd'])\\n narray = narray[~np.isnan(narray)]\\n hurdat_year['season_named'] = len(narray[narray>=34])\\n hurdat_year['season_hurricane'] = len(narray[narray>=65])\\n hurdat_year['season_major'] = len(narray[narray>=100])\\n hurdat_year['season_ace'] = np.sum(hurdat_year['ace'])\\n hurdat_year['season_subtrop_pure'] = count_ss_pure\\n hurdat_year['season_subtrop_partial'] = count_ss_partial\\n \\n #Return object\\n return hurdat_year\",\n \"def collect(self) -> int:\\n self.get_logger().info('Generating experiences...')\\n # Decay epsilon if requred\\n self._wp.decay_epsilon(self.episode, 0.001)\\n\\n # Generate experiences\\n trajectory_length, total_reward = self.step()\\n self.publish(total_reward)\\n self.flag.shift_to_compute_cycle()\\n\\n # Total reward section\\n self._total_reward += total_reward\\n if (self.episode + 1) % self._update == 0:\\n self.get_logger().error(\\n f'Expected Reward: {self._total_reward / self._update}')\\n self._total_reward = 0\\n\\n return trajectory_length\",\n \"def episode_total_score(self):\\n return self.game.get_total_reward()\",\n \"def career_talent(filtered_dict,WAR_batter_best, player_name='Nap Lajoie', year_proj = 2019):\\n \\n # Subsetting the dictionary for a particular player\\n filtered_dict_new = filtered_dict[player_name]\\n \\n # Converting it to a dataframe\\n df = pd.DataFrame(filtered_dict_new).sort_values(by=['yearID']).reset_index(drop = True)\\n \\n # Changing the player ID\\n df['playerID'] = df['playerID'].astype(str) + '_proj'\\n \\n # Putting a counter for the number of iteration (we can use this counter later to determine the runtime of each loop)\\n # j = 0\\n \\n # Creating an empty dataframe where we will be merging the rows\\n emp_df = pd.DataFrame()\\n \\n # Iterating over all the years where the selected player has played baseball\\n for idx, year in df['yearID'].iteritems():\\n #j+=1\\n \\n # subsetting the Normalized WAR Talent Dataset for the year in which we want to find the projection\\n batters_int = WAR_batter_best.loc[WAR_batter_best['yearID'] == year_proj]\\n \\n # creating a vector with all the WAR values in the projection year\\n yy = batters_int.loc[:,'scale_WAR'].sort_values().reset_index(drop = True)\\n \\n # Finding the length of the vector created above\\n n = len(yy)\\n \\n # Creating a zero vector with one more cell than the vector with all the scaled WAR values\\n ytilde = [0] * (n+1)\\n \\n # Changing the first value in ytilde (Extrapolation logic developed by Shen and Daniel)\\n ytilde[0] = yy[0] - 1/(yy[n-1] - yy[0])\\n \\n # Changing the last value in ytilde\\n ytilde[n] = yy[n-1] + 1/(yy[n-1] - yy[n-4])\\n \\n # Iterating over all the cells of ytilde and updating their values\\n # The updated ytilde is the new empirical distribution function of the WAR Talent\\n for i in range(1,n):\\n ytilde[i] = (yy[i]+yy[i-1])/2\\n\\n batters_int = pd.concat([batters_int,df.loc[df['yearID'] == year]]).reset_index(drop = True)\\n batters_int['pops'][batters_int.shape[0]-1] = batters_int['pops'][0]\\n batters_int = batters_int.sort_values(by = ['WAR_talent']).reset_index(drop = True)\\n batters_int['foo'] = map_pareto_vals_vec(batters_int['WAR_talent'], batters_int['pops'])\\n batters_int['foo'] = batters_int['foo'].reset_index(drop = True)\\n batters_int['adj_WAR'] = order_qempirical(batters_int['foo'], ytilde)\\n del batters_int['foo']\\n batters_int = batters_int.loc[batters_int['playerID'] == df['playerID'].unique()[0]]\\n emp_df = pd.concat([emp_df, batters_int])\\n emp_df['target_year'] = year_proj\\n \\n return emp_df\",\n \"def calculate_stats(team_id, current_matches, prev_matches, stats, targets):\\n\\n # Features\\n current_formation = None\\n opp_formation = None\\n win = 0\\n loss = 0\\n recent_wins = 0\\n recent_losses = 0\\n prev_opp = []\\n current_opp = 0\\n total_goals = 0\\n goals_for = 0\\n goals_against = 0\\n goal_diff = 0\\n total_points = 0\\n count = 1\\n played = float(0)\\n goal_efficiency = 0\\n\\n # Game Features\\n game_features = {'possession': [], 'attacks': [], 'dangerous_attacks': [], 'yellow_cards': [],\\n 'corner_kicks': [], 'shots_on_target': [], 'shots_total': [], 'ball_safe': [],\\n 'goal_attempts': [], 'goal_attempts_allowed': [], 'saves': [], 'first_half_goals': [], 'sec_half_goals': [], 'goal_kicks': []}\\n\\n # Targets\\n points = 0\\n goals = 0\\n opp_goals = 0\\n\\n goals_home = 0\\n goals_away = 0\\n opp_goals_at_home = 0 # Opponent goals when CURRENT team is AT Home\\n opp_goals_at_away = 0 # Opponent goals when CURRENT team is Away\\n\\n total_games = len(prev_matches)\\n # Pulling Data for PREVIOUS Matches\\n for index, game in prev_matches.iterrows():\\n\\n # Home Wins and Road Losses are .8 while Road Wins and Home Losses are 1.2 / Draws remain the same\\n if team_id == game['home_id']:\\n\\n if game['home_points'] == 3:\\n win += .8\\n\\n elif game['home_points'] == 1:\\n win += .5\\n loss += .5\\n\\n else:\\n loss += 1.2\\n\\n total_goals += game['home_score']\\n goals_for += game['home_score']\\n goals_against += game['away_score']\\n goal_diff += game['home_score'] - game['away_score']\\n total_points += game['home_points']\\n\\n goals_home += game['home_points']\\n opp_goals_at_away += game['away_points']\\n\\n current_formation = game['home_formation']\\n opp_formation = game['away_formation']\\n\\n prev_opp.append(game['away_id'])\\n\\n game_features['possession'].append(game['home_possession'])\\n game_features['attacks'].append(game['home_attacks'])\\n game_features['dangerous_attacks'].append(game['home_dangerous_attacks'])\\n game_features['yellow_cards'].append(game['home_yellow_card'])\\n game_features['corner_kicks'].append(game['home_corner_kicks'])\\n game_features['shots_on_target'].append(game['home_shots_on_target'])\\n game_features['shots_total'].append(game['home_shots_total'])\\n game_features['ball_safe'].append(game['home_ball_safe'])\\n game_features['goal_attempts'].append(game['home_goal_attempts'])\\n game_features['goal_attempts_allowed'].append(game['away_goal_attempts'])\\n game_features['saves'].append(game['home_saves'])\\n game_features['first_half_goals'].append(game['home_first_half_score'])\\n game_features['sec_half_goals'].append(game['home_second_half_score'])\\n game_features['goal_kicks'].append(game['home_goal_kicks'])\\n\\n else:\\n team_name = game[\\\"away_team\\\"]\\n\\n if game['away_points'] == 3:\\n win += 1.2\\n\\n elif game['away_points'] == 1:\\n win += .5\\n loss += .5\\n\\n else:\\n loss += .8\\n\\n total_goals += game['away_score']\\n goals_for += game['away_score']\\n goals_against += game['home_score']\\n goal_diff += game['away_score'] - game['home_score']\\n total_points += game['away_points']\\n\\n goals_away += game['away_points']\\n opp_goals_at_home += game['home_points']\\n\\n current_formation = game['away_formation']\\n opp_formation = game['home_formation']\\n\\n prev_opp.append(game['home_id'])\\n\\n game_features['possession'].append(game['away_possession'])\\n game_features['attacks'].append(game['away_attacks'])\\n game_features['dangerous_attacks'].append(game['away_dangerous_attacks'])\\n game_features['yellow_cards'].append(game['away_yellow_card'])\\n game_features['corner_kicks'].append(game['away_corner_kicks'])\\n game_features['shots_on_target'].append(game['away_shots_on_target'])\\n game_features['shots_total'].append(game['away_shots_total'])\\n game_features['ball_safe'].append(game['away_ball_safe'])\\n game_features['goal_attempts'].append(game['away_goal_attempts'])\\n game_features['goal_attempts_allowed'].append(game['home_goal_attempts'])\\n game_features['saves'].append(game['away_saves'])\\n game_features['first_half_goals'].append(game['away_first_half_score'])\\n game_features['sec_half_goals'].append(game['away_second_half_score'])\\n game_features['goal_kicks'].append(game['away_goal_kicks'])\\n\\n played += 1\\n count += 1\\n\\n # Pulling the data for the CURRENT MATCH\\n for index, cur_game in current_matches.iterrows():\\n\\n match_id = cur_game[\\\"match_id\\\"]\\n scheduled = cur_game[\\\"scheduled\\\"]\\n if team_id == cur_game['home_id']:\\n is_home = True\\n team_name = cur_game[\\\"home_team\\\"]\\n current_opp = cur_game['away_id']\\n\\n if targets:\\n # Targets\\n points = cur_game['home_points']\\n goals = cur_game['home_score']\\n opp_goals = cur_game['away_score']\\n\\n else:\\n\\n is_home = False\\n team_name = cur_game[\\\"away_team\\\"]\\n current_opp = cur_game['home_id']\\n\\n if targets:\\n # Targets\\n points = cur_game['away_points']\\n goals = cur_game['away_score']\\n opp_goals = cur_game['home_score']\\n\\n for k, v in game_features.items():\\n game_features[k] = np.sum(np.array(v))\\n \\\"\\\"\\\"if len(v) != 0:\\n game_features[k] = np.nanmean(np.array(v))\\n else:\\n game_features[k] = np.nan\\\"\\\"\\\"\\n\\n\\n if stats:\\n print(\\\" ========================== \\\")\\n print(\\\"Team Id : {} - Name : {}\\\".format(team_id, team_name))\\n print(\\\"Prev Opponent Ids : {}\\\".format(prev_opp))\\n print(\\\"FEATURES (Stats from * 3 Previous Matches)\\\")\\n print(\\\"Total Goals : {}\\\".format(total_goals))\\n print(\\\"Total Points : {}\\\".format(total_points))\\n # print(\\\"Win Points : {}\\\".format(win))\\n # print(\\\"Loss Points : {}\\\".format(loss))\\n print(\\\"Played : {}\\\".format(played))\\n print(\\\"Goal Diff : {}\\\".format(goal_diff))\\n print(\\\"Margin : {}\\\".format(np.divide(goal_diff, played)))\\n print(\\\"\\\")\\n\\n if targets:\\n # Still can weight games more on most recent games\\n print(\\\"\\\\nTARGETS (RESULTS OF CURRENT MATCH)\\\")\\n print(\\\"Points : {}\\\".format(points))\\n print(\\\"Goals : {}\\\".format(goals))\\n print(\\\"Opp_Goals : {}\\\".format(opp_goals))\\n\\n return match_id, team_id, team_name, scheduled, int(is_home == True), total_points, \\\\\\n goals_for, goals_against, goal_diff, goal_efficiency, played, win, loss, recent_wins, recent_losses, prev_opp, \\\\\\n current_opp, points, goals, opp_goals, current_formation, opp_formation, game_features\",\n \"def calculate_demo(self, request, parent_lookup_client, pk, format=None):\\n\\n retirement_plan = self.get_object()\\n tickers = Ticker.objects.filter(~Q(state=Ticker.State.CLOSED.value))\\n portfolio = []\\n projection = []\\n for idx, ticker in enumerate(tickers[:10]):\\n percent = 0\\n if idx <= 9:\\n # 10% each for first 10 tickers\\n percent = 10\\n portfolio.append([ticker.id, percent])\\n # grab 50 evenly spaced time points between dob and current time\\n today = timezone.now().date()\\n last_day = retirement_plan.client.date_of_birth + relativedelta(years=retirement_plan.selected_life_expectancy)\\n day_interval = (last_day - today) / 49\\n income_start = 20000\\n assets_start = 100000\\n for i in range(50):\\n income = income_start + (i * 50)\\n assets = assets_start + (i * 1000)\\n dt = today + i * day_interval\\n projection.append([d2ed(dt), assets, income])\\n return Response({'portfolio': portfolio, 'projection': projection})\",\n \"def calculateMarriedTax(husbandIncome, wifeIncome):\\n pass\",\n \"def compute_death_rate(dfs):\\n \\n dfcols = dfs[\\\"deaths\\\"].columns\\n dfs[\\\"death_rate\\\"] = dfs[\\\"deaths\\\"].copy()\\n\\n for ccol in dfcols:\\n dfs[\\\"death_rate\\\"][ccol] = dfs[\\\"deaths\\\"][ccol] / dfs[\\\"cases\\\"][ccol]\\n \\n return(dfs)\",\n \"def age(self, agent):\\n return (self.time - agent.born)/52.0\",\n \"def global_stats(request):\\n\\n if request.user.is_authenticated:\\n profile: IntervalsProfile = request.user.profile\\n\\n global_correct = 0\\n global_answered = 0\\n\\n for r in profile.all_recent_results().values():\\n global_correct += r.total_correct\\n global_answered += r.total_completed\\n\\n print(global_correct, global_answered)\\n\\n return Response({\\\"global_correct\\\": global_correct,\\n \\\"global_answered\\\": global_answered},\\n status=status.HTTP_201_CREATED)\\n return Response(status=status.HTTP_401_UNAUTHORIZED)\",\n \"def user_stats(df):\\n\\n print('\\\\nCalculating User Stats...\\\\n')\\n start_time = time.time()\\n\\n # TO DO: Display counts of user types\\n\\n user_type_count = df['User Type'].value_counts()\\n print('The number of rentals per user type:')\\n print(user_type_count.to_string())\\n\\n # TO DO: Display counts of gender\\n \\n if 'Gender' in df.columns:\\n \\n gender_type_count = df['Gender'].value_counts()\\n print('\\\\nThe number of rental per gender:')\\n print(gender_type_count.to_string())\\n \\n else:\\n \\n print('\\\\nNo data about gender available.\\\\n')\\n \\n # TO DO: Display earliest, most recent, and most common year of birth\\n\\n if 'Birth Year' in df.columns:\\n \\n youngest_user = df['Birth Year'].max()\\n common_user = df['Birth Year'].mode()[0]\\n oldest_user = df['Birth Year'].min()\\n print('\\\\nThe youngest user was born', int(youngest_user))\\n print('The most common user was born', int(common_user))\\n print('The oldest user was born', int(oldest_user))\\n \\n else:\\n \\n print('\\\\nNo data about age available.\\\\n')\\n \\n print(\\\"\\\\nThis took %s seconds.\\\" % (time.time() - start_time))\\n print('-'*40)\",\n \"def _find_average_age():\\r\\n count, total = 0, 0\\r\\n for resource in resources:\\r\\n patient = resource[\\\"resource\\\"]\\r\\n if \\\"birthDate\\\" in patient:\\r\\n count += 1\\r\\n dob = patient[\\\"birthDate\\\"].split(\\\"-\\\")\\r\\n dob = datetime(int(dob[0]), int(dob[1]), int(dob[2]), 0, 0, 0, 0)\\r\\n if \\\"deceasedDateTime\\\" in patient:\\r\\n death_time = patient[\\\"deceasedDateTime\\\"].split(\\\"T\\\")[0].split(\\r\\n \\\"-\\\")\\r\\n death_time = datetime(int(death_time[0]), int(death_time[1]),\\r\\n int(death_time[2]), 0, 0, 0, 0)\\r\\n else:\\r\\n death_time = datetime.now()\\r\\n age = relativedelta(death_time, dob).years\\r\\n total += age\\r\\n if count == 0:\\r\\n return count, count\\r\\n return total / count, count\",\n \"def total_price_target_difference_rule(_m):\\r\\n\\r\\n return sum(m.YEAR_PRICE_TARGET_DIFFERENCE[y] for y in m.Y)\",\n \"def run_cumulative_wtrfclty_damage(self):\\n\\n\\t\\trt = [100, 250, 500, 1000, 2500, 5000, 10000]\\n\\t\\t\\n\\t\\tfor rt_val in rt:\\n\\t\\t\\tprint('\\\\twterfclty_dmg: cumulative rt_{}' .format(rt_val))\\n\\t\\t\\t# --- reading in damage results from above analysis\\n\\t\\t\\teq_damage_results_csv = os.path.join(self.wterfclty_output_path, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t 'wterfclty_eq_{}yr_dmg.csv' \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t .format(rt_val))\\n\\t\\t\\ttsu_damage_results_csv = os.path.join(self.wterfclty_output_path, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t 'wterfclty_tsu_{}yr_dmg.csv' \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t .format(rt_val))\\n\\t\\t\\teq_df = pd.read_csv(eq_damage_results_csv)\\n\\t\\t\\ttsu_df = pd.read_csv(tsu_damage_results_csv)\\n\\n\\t\\t\\tcum_df = pd.DataFrame()\\n\\t\\t\\tcum_df['guid'] = eq_df['guid']\\n\\t\\t\\t\\n\\t\\t\\tcum_df['ds-complet'] = eq_df['ds-complet'] + tsu_df['ds-complet'] \\\\\\n\\t\\t\\t\\t- eq_df['ds-complet']*tsu_df['ds-complet']\\n\\t\\t\\t\\n\\t\\t\\t# --- prob of exceeding each damage state\\n\\t\\t\\tcum_df['ls-complet'] = cum_df['ds-complet']\\n\\n\\t\\t\\tcum_df['ls-extensi'] = eq_df['ls-extensi'] + tsu_df['ls-extensi'] \\\\\\n\\t\\t\\t\\t- eq_df['ls-extensi']*tsu_df['ls-extensi']\\n\\n\\t\\t\\tcum_df['ls-moderat'] = eq_df['ls-moderat'] + tsu_df['ls-moderat'] \\\\\\n\\t\\t\\t\\t- eq_df['ls-moderat']*tsu_df['ls-moderat']\\n\\n\\t\\t\\tcum_df['ls-slight'] = eq_df['ls-slight'] + tsu_df['ls-slight'] \\\\\\n\\t\\t\\t\\t- eq_df['ls-slight']*tsu_df['ls-slight']\\n\\n\\t\\t\\t# --- prob of being in each damage state\\n\\t\\t\\tcum_df['ds-extensi'] = cum_df['ls-extensi'] - cum_df['ds-complet']\\n\\t\\t\\tcum_df['ds-moderat'] = cum_df['ls-moderat'] - cum_df['ls-extensi']\\n\\t\\t\\tcum_df['ds-slight'] = cum_df['ls-slight'] - cum_df['ls-moderat']\\n\\t\\t\\tcum_df['ds-none'] = 1 - cum_df['ls-slight']\\n\\t\\t\\tcum_df['hazard'] = 'Earthquake+Tsunami'\\n\\n\\t\\t\\tresult_name = os.path.join(self.wterfclty_output_path, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t 'wterfclty_cumulative_{}yr_dmg.csv' \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t .format(rt_val))\\n\\t\\t\\tcum_df = cum_df[['guid', \\n\\t\\t\\t\\t\\t\\t\\t 'ls-slight',\\n\\t\\t\\t\\t\\t\\t\\t 'ls-moderat',\\n\\t\\t\\t\\t\\t\\t\\t 'ls-extensi',\\n\\t\\t\\t\\t\\t\\t\\t 'ls-complet',\\n\\t\\t\\t\\t\\t\\t\\t 'ds-none', \\n\\t\\t\\t\\t\\t\\t\\t 'ds-slight', \\n\\t\\t\\t\\t\\t\\t\\t 'ds-moderat', \\n\\t\\t\\t\\t\\t\\t\\t 'ds-extensi', \\n\\t\\t\\t\\t\\t\\t\\t 'ds-complet', \\n\\t\\t\\t\\t\\t\\t\\t 'hazard']]\\n\\t\\t\\tcum_df.to_csv(result_name, index=False)\",\n \"def get_win_rate_regular_season_for_each_coach(self):\\n self.games_won_for_coaches = (\\n self.raw_data_regularseason\\n [['Season','DayNum','WTeamID']]\\n # merge for winning team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_win\\\",\\\"LastDayNum\\\":\\\"LastDayNum_win\\\",\\\"CoachName\\\":\\\"CoachName_win\\\",\\\"TeamID\\\":\\\"TeamID_win\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_win != 0\\\")\\n .groupby(['Season','CoachName_win','WTeamID'])\\n .agg({\\\"which_coach_for_win\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n self.games_lose_for_coaches = (\\n self.raw_data_regularseason\\n [['Season','DayNum','LTeamID']]\\n # merge for losing team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_lose\\\",\\\"LastDayNum\\\":\\\"LastDayNum_lose\\\",\\\"CoachName\\\":\\\"CoachName_lose\\\",\\\"TeamID\\\":\\\"TeamID_lose\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_lose != 0\\\")\\n .groupby(['Season','CoachName_lose','LTeamID'])\\n .agg({\\\"which_coach_for_lose\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # combine games won and lost df\\n self.combine_regular_games_won_lose = (\\n self.games_lose_for_coaches\\n .merge(self.games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\\n .pipe(lambda x:x.assign(win_rate_regular = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\\n .drop(['CoachName_win','WTeamID'],1)\\n .rename(columns={\\\"CoachName_lose\\\":\\\"CoachName\\\",\\\"LTeamID\\\":\\\"TeamID\\\",\\\"which_coach_for_lose\\\":\\\"games_lost\\\",\\\"which_coach_for_win\\\":\\\"games_won\\\"})\\n )\",\n \"def _compute_global_stats():\\n global_stats = []\\n \\n wmt16_group = Group.objects.filter(name='WMT16')\\n wmt16_users = _get_active_users_for_group(wmt16_group)\\n \\n # Check how many HITs have been completed. We now consider a HIT to be\\n # completed once it has been annotated by one or more annotators.\\n #\\n # Before we required `hit.users.count() >= 3` for greater overlap.\\n hits_completed = HIT.objects.filter(mturk_only=False, completed=True).count()\\n \\n # Check any remaining active HITs which are not yet marked complete.\\n for hit in HIT.objects.filter(active=True, mturk_only=False, completed=False):\\n if hit.users.count() >= 1:\\n hits_completed = hits_completed + 1\\n hit.completed = True\\n hit.save()\\n \\n # Compute remaining HITs for all language pairs.\\n hits_remaining = HIT.compute_remaining_hits()\\n \\n # Compute number of results contributed so far.\\n ranking_results = RankingResult.objects.filter(\\n item__hit__completed=True, item__hit__mturk_only=False)\\n \\n from math import factorial\\n system_comparisons = 0\\n for result in ranking_results:\\n result.reload_dynamic_fields()\\n # TODO: this implicitly counts A=B comparisons for multi systems.\\n # Basically, inflating the number of pairwise comparisons... Fix!\\n combinations = factorial(result.systems)/(factorial(result.systems-2) * 2) if result.systems > 2 else 0\\n system_comparisons = system_comparisons + combinations\\n \\n # Aggregate information about participating groups.\\n groups = set()\\n for user in wmt16_users:\\n for group in _identify_groups_for_user(user):\\n groups.add(group)\\n \\n # Compute average/total duration over all results.\\n durations = RankingResult.objects.all().values_list('duration', flat=True)\\n total_time = sum([datetime_to_seconds(x) for x in durations])\\n avg_time = total_time / float(hits_completed or 1)\\n avg_user_time = total_time / float(3 * hits_completed or 1)\\n \\n global_stats.append(('Users', len(wmt16_users)))\\n global_stats.append(('Groups', len(groups)))\\n global_stats.append(('HITs completed', '{0:,}'.format(hits_completed)))\\n global_stats.append(('HITs remaining', '{0:,}'.format(hits_remaining)))\\n global_stats.append(('Ranking results', '{0:,}'.format(ranking_results.count())))\\n global_stats.append(('System comparisons', '{0:,}'.format(system_comparisons)))\\n global_stats.append(('Average duration (per HIT)', seconds_to_timedelta(avg_time)))\\n global_stats.append(('Average duration (per task)', seconds_to_timedelta(avg_user_time)))\\n global_stats.append(('Total duration', seconds_to_timedelta(total_time)))\\n \\n # Create new status data snapshot\\n TimedKeyValueData.update_status_if_changed('users', str(len(wmt16_users)))\\n TimedKeyValueData.update_status_if_changed('groups', str(len(groups)))\\n TimedKeyValueData.update_status_if_changed('hits_completed', str(hits_completed))\\n TimedKeyValueData.update_status_if_changed('hits_remaining', str(hits_remaining))\\n TimedKeyValueData.update_status_if_changed('ranking_results', str(ranking_results.count()))\\n TimedKeyValueData.update_status_if_changed('system_comparisons', str(system_comparisons))\\n TimedKeyValueData.update_status_if_changed('duration_per_hit', str(seconds_to_timedelta(avg_time)))\\n TimedKeyValueData.update_status_if_changed('duration_per_task', str(seconds_to_timedelta(avg_user_time)))\\n TimedKeyValueData.update_status_if_changed('duration_total', str(seconds_to_timedelta(total_time)))\\n \\n return global_stats\",\n \"def year_demand_rule(_m, y):\\r\\n\\r\\n return sum(m.SCENARIO_DEMAND[y, s] for s in m.S)\",\n \"def calculate_employer_contribution(self):\\r\\n employer_contribution = 0\\r\\n for employer_match, tier_match in self.employer_match_tier_match_list:\\r\\n employer_contribution += (self.salary * employer_match * tier_match)\\r\\n return employer_contribution\",\n \"def user_stats(df):\\n\\n print('\\\\nCalculating User Stats...\\\\n')\\n start_time = time.time()\\n\\n count_user_type = df.groupby('User Type').count()\\n print('The distribution of user types is: ',count_user_type)\\n#used the suggested try except method, because i had difficutlties with an if clause an the variable scope of the 'city' variable - same with the 'birth year'\\n try:\\n count_gender = df.groupby('Gender').count()\\n print('The distribution of genders is: ',count_gender)\\n except:\\n print('\\\\n No gender-data available in Washington.')\\n\\n try:\\n earliest_yob = df['Birth Year'].min()\\n print('\\\\n The earliest year of bearth is: ', earliest_yob)\\n latest_yob = df['Birth Year'].max()\\n print('\\\\n The most recent year of birth is: ', latest_yob)\\n most_common_yob = df.loc[:,'Birth Year'].mode()\\n print('The most common year of birth is: ', most_common_yob)\\n except:\\n print('\\\\n No birt-year-data available in Washington.')\\n\\n print(\\\"\\\\nThis took %s seconds.\\\" % (time.time() - start_time))\\n print('-'*40)\",\n \"def _individual_old_age_insurance(self, total_salary):\\n return self.individual_old_age_insurance_rate * total_salary\",\n \"def daily_incidents(df2):\\n\\n if (df2[\\\"Holiday\\\"] == \\\"Thanksgiving Day\\\") | (df2[\\\"Holiday\\\"] == \\\"Christmas Day\\\"):\\n d_inc = df2[\\\"Total\\\"] / 18\\n elif df2[\\\"Holiday\\\"] == \\\"Non-holidays\\\":\\n d_inc = df2[\\\"Total\\\"] / 6712\\n else:\\n d_inc = df2[\\\"Total\\\"] / 19\\n\\n return d_inc\",\n \"def arithmetic_ret(self) -> float:\\n return float(np.log(self.tsdf).diff().mean() * self.periods_in_a_year)\",\n \"def estimate_runtime():\\n cpu_count = multiprocessing.cpu_count()\\n years = request.args.get('years', type=int)\\n if not years:\\n raise InvalidUsage(\\n \\\"The 'years' param is required and must be an integer >= 1\\\")\\n time_per_year = 4 # seconds\\n diluted_cpu_effect = 1 + (1.0 * cpu_count / 10)\\n raw_guess = time_per_year * math.ceil(1.0 * years / diluted_cpu_effect)\\n result = int(max(raw_guess, 15))\\n return jsonify({'seconds': result})\",\n \"def calc_schedule_yearly(self):\\n \\n schedule = self.schedule\\n\\n # Yearly\\n start_date = schedule['Date'].min()\\n schedule['years_out'] = schedule['Date'].apply(lambda x: relativedelta(x, start_date).years)\\n schedule['end_of_year_window'] = schedule['years_out'].diff(-1).fillna(-1).abs()\\n \\n schedule_yr = schedule\\\\\\n .groupby([schedule['years_out']])\\\\\\n .agg({'Payment': 'sum',\\n 'Interest': 'sum',\\n 'Additional_Payment': 'sum',\\n 'Date': 'min',\\n 'Date': 'max'\\n }\\n )\\n\\n schedule_yr.reset_index(drop=False, inplace=True)\\n schedule_yr['Principal'] = schedule_yr['Payment'] - schedule_yr['Interest']\\n \\n yearly_end_balances = schedule.loc[schedule['end_of_year_window'] == 1, \\n ['years_out', 'End Balance']]\\n\\n schedule_yr = schedule_yr.merge(yearly_end_balances, how='inner', on=['years_out'])\\n\\n return schedule_yr\",\n \"def user_stats(df):\\n\\n print('\\\\nCalculating User Stats...\\\\n')\\n start_time = time.time()\\n\\n # Display counts of user types\\n user_types = df['User Type'].value_counts()\\n print('The distribution of user types:\\\\n', user_types)\\n\\n # Display counts of gender\\n if 'Gender' in df:\\n gender = df['Gender'].value_counts()\\n print('The distribution of genders:\\\\n', gender)\\n else:\\n print('There is no data on gender for this city/the selected time.')\\n\\n # Display earliest, most recent, and most common year of birth. Using integers for the years\\n if 'Birth Year' in df:\\n earliest_birth_year = df['Birth Year'].min()\\n print('The earliest birth year: ', int(earliest_birth_year))\\n most_recent_birth_year = df['Birth Year'].max()\\n print('The most recent birth year: ', int(most_recent_birth_year))\\n most_common_birth_year = df['Birth Year'].mode()[0]\\n print('The most common birth year: ', int(most_common_birth_year))\\n else:\\n print(\\\"There is no data on birth years for this city/the selected time.\\\")\\n\\n\\n print(\\\"\\\\nThis took %s seconds.\\\" % (time.time() - start_time))\\n print('-'*40)\",\n \"def get_totals_across_years(df, tract_col, data_col):\\n return df.groupby(tract_col)[data_col].agg([\\\"sum\\\", \\\"mean\\\"]).reset_index()\",\n \"def user_stats(df):\\n\\n print('\\\\nCalculating User Stats...\\\\n')\\n start_time = time.time()\\n\\n # Display counts of user types\\n user_types = df['User Type'].value_counts()\\n print('Count of user types:\\\\n', user_types)\\n\\n\\n # Display counts of gender\\n try:\\n count_gender = df['Gender'].value_counts()\\n print('Count of gender types:\\\\n', count_gender)\\n except KeyError:\\n print(\\\"No gender data available for this month.\\\")\\n\\n\\n # Display earliest, most recent, and most common year of birth\\n try:\\n oldest_birth_year = df['Birth Year'].min()\\n print(\\\"The oldest birth year is \\\", oldest_birth_year)\\n except KeyError:\\n print(\\\"No birth year data available for this month.\\\")\\n\\n try:\\n youngest_birth_year = df['Birth Year'].max()\\n print(\\\"The youngest birth year is \\\", youngest_birth_year)\\n except KeyError:\\n print(\\\"No birth year data available for this month.\\\")\\n\\n try:\\n common_birth_year = df['Birth Year'].value_counts().idxmax()\\n print(\\\"The most common birth year is \\\", common_birth_year)\\n except KeyError:\\n print(\\\"No birth year data available for this month.\\\")\\n\\n print(\\\"\\\\nThis took %s seconds.\\\" % (time.time() - start_time))\\n print('-'*40)\",\n \"def annual_fee(self, working_months, year, with_bpjs=True):\\n monthly_bpjs = []\\n\\n total_salary = self.base_salary\\n if self.is_salary_allowances is True:\\n fixed_allowances = self.summarize( self.fixed_allowances )\\n non_fixed_allowances = self.summarize( self.non_fixed_allowances )\\n total_salary = total_salary + non_fixed_allowances + fixed_allowances\\n #end if\\n\\n # initialize variable for storing the annual bpjs\\n annual_c_old_age_insurance = 0\\n annual_i_old_age_insurance = 0\\n annual_c_pension_insurance = 0\\n annual_i_pension_insurance = 0\\n annual_c_health_insurance = 0\\n annual_i_health_insurance = 0\\n annual_death_insurance = 0\\n annual_accident_insurance = 0\\n\\n if with_bpjs is True:\\n # only calculate bpjs if is enabled and automatically set everthing to zero when is false\\n start_month = 1\\n for month in range(start_month, working_months+1):\\n\\n company_old_age_insurance = 0\\n individual_old_age_insurance = 0\\n if self.old_age_insurance_status is True:\\n company_old_age_insurance = \\\\\\n self._company_old_age_insurance(total_salary)\\n\\n individual_old_age_insurance = \\\\\\n self._individual_old_age_insurance(total_salary)\\n #end if\\n\\n company_pension_insurance = 0\\n individual_pension_insurance = 0\\n if self.pension_insurance_status is True:\\n company_pension_insurance = \\\\\\n self._company_pension_insurance(total_salary, month, year)\\n\\n individual_pension_insurance = \\\\\\n self._individual_pension_insurance(total_salary, month, year)\\n #end if\\n\\n company_health_insurance = 0\\n individual_health_insurance = 0\\n if self.health_insurance_status is True:\\n company_health_insurance = \\\\\\n self._company_health_insurance(total_salary)\\n\\n individual_health_insurance = \\\\\\n self._individual_health_insurance(total_salary)\\n #end if\\n\\n death_insurance = 0\\n if self.death_insurance_status is True:\\n death_insurance = self._death_insurance(total_salary)\\n #end if\\n\\n accident_insurance = 0\\n if self.accident_insurance_status is True:\\n accident_insurance = \\\\\\n self._accident_insurance(total_salary, \\\\\\n self.industry_risk_rate)\\n #end if\\n\\n monthly = {\\n \\\"old_age_insurance\\\" : {\\n \\\"company\\\" : company_old_age_insurance,\\n \\\"individual\\\" : individual_old_age_insurance,\\n },\\n \\\"pension_insurance\\\" : {\\n \\\"company\\\" : company_pension_insurance,\\n \\\"individual\\\" : individual_pension_insurance,\\n },\\n \\\"health_insurance\\\" : {\\n \\\"company\\\" : company_health_insurance,\\n \\\"individual\\\" : individual_health_insurance,\\n },\\n \\\"death_insurance\\\" : death_insurance,\\n \\\"accident_insurance\\\" : accident_insurance\\n }\\n\\n monthly_bpjs.append(monthly)\\n\\n annual_c_old_age_insurance = annual_c_old_age_insurance \\\\\\n + company_old_age_insurance\\n\\n annual_i_old_age_insurance = annual_i_old_age_insurance \\\\\\n + individual_old_age_insurance\\n\\n annual_c_pension_insurance = annual_c_pension_insurance \\\\\\n + company_pension_insurance\\n\\n annual_i_pension_insurance = annual_i_pension_insurance \\\\\\n + individual_pension_insurance\\n\\n annual_c_health_insurance = annual_c_health_insurance \\\\\\n + company_health_insurance\\n\\n annual_i_health_insurance = annual_i_health_insurance \\\\\\n + individual_health_insurance\\n\\n annual_death_insurance = annual_death_insurance\\\\\\n + death_insurance\\n\\n annual_accident_insurance = annual_accident_insurance\\\\\\n + accident_insurance\\n #end for\\n\\n annual_bpjs = {\\n \\\"old_age_insurance\\\" : {\\n \\\"company\\\" : annual_c_old_age_insurance,\\n \\\"individual\\\" : annual_i_old_age_insurance,\\n },\\n \\\"pension_insurance\\\" : {\\n \\\"company\\\" : annual_c_pension_insurance,\\n \\\"individual\\\" : annual_i_pension_insurance,\\n },\\n \\\"health_insurance\\\" : {\\n \\\"company\\\" : annual_c_health_insurance,\\n \\\"individual\\\" : annual_i_health_insurance,\\n },\\n \\\"death_insurance\\\" : annual_death_insurance,\\n \\\"accident_insurance\\\" : annual_accident_insurance\\n }\\n return annual_bpjs\",\n \"def wages(self):\\n amount = (\\n self.farmer.labor_cost()\\n + self.reseller.labor_cost()\\n + self.cofiring_plant.cofuel_om_wages()\\n - self.coal_wages_lost\\n )\\n return display_as(amount, \\\"kUSD\\\")\",\n \"def user_stats(df):\\n\\n print('\\\\nCalculating User Stats...\\\\n')\\n start_time = time.time()\\n\\n # TO DO: Display counts of user types\\n user_types = df['User Type'].value_counts()\\n if city == 'washington':\\n print(\\\"That info isn't available.\\\")\\n break\\n else:\\n print(\\\"\\\\nUser types are: \\\", user_types)\\n\\n # TO DO: Display counts of gender\\n gender = df['Gender'].value_counts()\\n if city == 'washington':\\n print(\\\"That info isn't available\\\")\\n break\\n else:\\n print(\\\"\\\\nThe breakdown of gender is: \\\", gender)\\n\\n # TO DO: Display earliest, most recent, and most common year of birth\\n oldest_birth=np.nanmin(df['Birth Year'])[0]\\n print('\\\\nOldest birth year is', int(oldest_birth))\\n\\n youngest_birth=np.nanmax(df['Birth Year'])[0]\\n print('\\\\nYoungest birth year is', int(youngest_birth))\\n\\n common_birth=df['Birth Year'].mode()[0]\\n print('\\\\nMost common birth year is', int(common_birth))\",\n \"def user_stats(df):\\r\\n\\r\\n print('\\\\nCalculating User Stats...\\\\n')\\r\\n start_time = time.time()\\r\\n\\r\\n # Display counts of user types \\r\\n count_user_types = df['User Type'].value_counts()\\r\\n print('The count of user types are:', count_user_types)\\r\\n\\r\\n # Display counts of gender - Chicago and New York ONLY were having Gender column\\r\\n try:\\r\\n gender_counts = df['Gender'].value_counts()\\r\\n print('The count of gender types are:', gender_counts)\\r\\n \\r\\n # Displays the earliest, the most recent, and most common year of birth\\r\\n # Chicago and New York ONLY were having Birth Year column\\r\\n \\r\\n earliest_year = df['Birth Year'].min()\\r\\n print('Earliest Year of Birth is:', earliest_year)\\r\\n most_recent_year = df['Birth Year'].max()\\r\\n print('Most Recent Year of Birth is:', most_recent_year)\\r\\n common_year = df['Birth Year'].mode()[0]\\r\\n print('Most Common Year of Birth is:', common_year)\\r\\n \\r\\n except KeyError:\\r\\n print('Sorry, Gender & Birth year data is not available for Washington')\\r\\n \\r\\n print(\\\"\\\\nThis process took %s seconds.\\\" % (time.time() - start_time))\\r\\n print('-'*40)\",\n \"def calcAgeInYears(self, study_id):\\n try:\\n con = self.getMetadataDatabaseConnection()\\n con.cursor().callproc('calc_age_in_years', [study_id])\\n return True\\n except Exception, e:\\n print 'Exception caught: %s.\\\\nThe error is: %s' % (type(e), e)\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6512815","0.63359934","0.5996619","0.5913304","0.57828826","0.5751886","0.5728529","0.5614757","0.5604025","0.5573763","0.55307776","0.55264235","0.5497981","0.5462642","0.5450629","0.54372895","0.54367644","0.5427174","0.5389119","0.5351539","0.5340693","0.5322953","0.5313853","0.5313573","0.5311389","0.5282752","0.5275318","0.52718383","0.52675265","0.52390003","0.5238171","0.5233396","0.522256","0.521958","0.5216372","0.5207312","0.52059805","0.5193368","0.51928186","0.51918364","0.51885605","0.51838654","0.5175282","0.5173796","0.51736265","0.5155361","0.51527655","0.51480657","0.51399434","0.5124905","0.5063835","0.5061306","0.50589544","0.50589544","0.5052045","0.50498456","0.5047875","0.50342864","0.50342864","0.5030178","0.50282645","0.50257707","0.50240654","0.5021973","0.50210494","0.50063694","0.5005112","0.5004569","0.5001674","0.49995264","0.4999351","0.49809757","0.49805483","0.49794444","0.497723","0.49767768","0.49731088","0.49724954","0.49703178","0.49698487","0.49684912","0.49676022","0.49574247","0.49464515","0.49461916","0.49423733","0.49396843","0.493796","0.4936475","0.49326947","0.4921477","0.49155825","0.49140868","0.49137494","0.4912833","0.49123695","0.49078345","0.49077514","0.49055073","0.49041295"],"string":"[\n \"0.6512815\",\n \"0.63359934\",\n \"0.5996619\",\n \"0.5913304\",\n \"0.57828826\",\n \"0.5751886\",\n \"0.5728529\",\n \"0.5614757\",\n \"0.5604025\",\n \"0.5573763\",\n \"0.55307776\",\n \"0.55264235\",\n \"0.5497981\",\n \"0.5462642\",\n \"0.5450629\",\n \"0.54372895\",\n \"0.54367644\",\n \"0.5427174\",\n \"0.5389119\",\n \"0.5351539\",\n \"0.5340693\",\n \"0.5322953\",\n \"0.5313853\",\n \"0.5313573\",\n \"0.5311389\",\n \"0.5282752\",\n \"0.5275318\",\n \"0.52718383\",\n \"0.52675265\",\n \"0.52390003\",\n \"0.5238171\",\n \"0.5233396\",\n \"0.522256\",\n \"0.521958\",\n \"0.5216372\",\n \"0.5207312\",\n \"0.52059805\",\n \"0.5193368\",\n \"0.51928186\",\n \"0.51918364\",\n \"0.51885605\",\n \"0.51838654\",\n \"0.5175282\",\n \"0.5173796\",\n \"0.51736265\",\n \"0.5155361\",\n \"0.51527655\",\n \"0.51480657\",\n \"0.51399434\",\n \"0.5124905\",\n \"0.5063835\",\n \"0.5061306\",\n \"0.50589544\",\n \"0.50589544\",\n \"0.5052045\",\n \"0.50498456\",\n \"0.5047875\",\n \"0.50342864\",\n \"0.50342864\",\n \"0.5030178\",\n \"0.50282645\",\n \"0.50257707\",\n \"0.50240654\",\n \"0.5021973\",\n \"0.50210494\",\n \"0.50063694\",\n \"0.5005112\",\n \"0.5004569\",\n \"0.5001674\",\n \"0.49995264\",\n \"0.4999351\",\n \"0.49809757\",\n \"0.49805483\",\n \"0.49794444\",\n \"0.497723\",\n \"0.49767768\",\n \"0.49731088\",\n \"0.49724954\",\n \"0.49703178\",\n \"0.49698487\",\n \"0.49684912\",\n \"0.49676022\",\n \"0.49574247\",\n \"0.49464515\",\n \"0.49461916\",\n \"0.49423733\",\n \"0.49396843\",\n \"0.493796\",\n \"0.4936475\",\n \"0.49326947\",\n \"0.4921477\",\n \"0.49155825\",\n \"0.49140868\",\n \"0.49137494\",\n \"0.4912833\",\n \"0.49123695\",\n \"0.49078345\",\n \"0.49077514\",\n \"0.49055073\",\n \"0.49041295\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6127486"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":84998,"cells":{"query":{"kind":"string","value":"some metrics on the whole profile text"},"document":{"kind":"string","value":"def profile_text_stats( doc: BeautifulSoup ):\n text = doc.find('main', {'class': 'core-rail'}).text.strip()\n words = text.split()\n eng_ratio = sum(1 for word in words if word in COMMON_ENGLISH) * 10/ (len(words) + 0.001)\n return { 'length': len( text ),\n 'eng_ratio': np.round( eng_ratio, 2)}\n # %%"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _profile(self, text):\n prof = zeros(len(self.alph)**self.N)\n ngs = ngrams(text, self.N)\n for tup in ngs:\n loc = 0\n for i in range(len(tup)):\n loc += (len(self.alph)**i) * self.alph.index(tup[i])\n prof[loc] += 1\n return prof","def analize(self, text):\n\n try:\n profile = self.personality_insights.profile(text,\n raw_scores=True,\n consumption_preferences=True)\n except Exception as e:\n print(\"Error during API call\", e)\n profile = \"\"\n\n return profile","def advancedStats():","def stats_text(test):\n\n stats_text_en(test) \n \n stats_text_cn(test)","def getTextStatsFeat(text, stemmRequired = True,\r\n excludeStopwordsRequired = True):\r\n #length = len(text)\r\n sentenceCount = len(re.findall(\"[.?!]\", text))\r\n exclamationMarkCount = len(re.findall(\"[!]\", text))\r\n questionMarkCount = len(re.findall(\"[?]\", text))\r\n digitsCount = len(re.findall(\"[0-9]+\", text))\r\n text = text.replace(\",\", \" \").replace(\".\", \" \")\r\n cleanText = re.sub('[^a-zа-я0-9]', ' ', text.lower())\r\n wordCount = 0.0\r\n charCount = 0.0\r\n rusCharCount = 0.0\r\n engCharCount = 0.0\r\n if excludeStopwordsRequired:\r\n for w in cleanText.split():\r\n if len(w)>1 and w not in stopwords:\r\n if not (not stemmRequired or re.search(\"[0-9a-z]\", w)):\r\n w = stemmer.stem(w)\r\n wordCount += 1\r\n c, rus, eng = getWordCharCount(w)\r\n charCount += c\r\n rusCharCount += rus\r\n engCharCount += eng\r\n else:\r\n for w in cleanText.split():\r\n if len(w)>1:\r\n if not (not stemmRequired or re.search(\"[0-9a-z]\", w)):\r\n w = stemmer.stem(w)\r\n wordCount += 1\r\n c, rus, eng = getWordCharCount(w)\r\n charCount += c\r\n rusCharCount += rus\r\n engCharCount += eng\r\n # per sentence\r\n wordPerSentence = tryDivide(wordCount, sentenceCount)\r\n charPerSentence = tryDivide(charCount, sentenceCount)\r\n rusCharPerSentence = tryDivide(rusCharCount, sentenceCount)\r\n engCharPerSentence = tryDivide(engCharCount, sentenceCount)\r\n # per word\r\n charPerWord = tryDivide(charCount, wordCount)\r\n rusCharPerWord = tryDivide(rusCharCount, wordCount)\r\n engCharPerWord = tryDivide(engCharCount, wordCount)\r\n # ratio\r\n rusCharRatio = tryDivide(rusCharCount, charCount)\r\n engCharRatio = tryDivide(engCharCount, charCount)\r\n rusCharVsEngChar = tryDivide(rusCharCount, engCharCount)\r\n engCharVsRusChar = tryDivide(engCharCount, rusCharCount)\r\n \r\n stats = [\r\n sentenceCount,\r\n wordCount,\r\n charCount,\r\n rusCharCount,\r\n engCharCount,\r\n digitsCount,\r\n exclamationMarkCount,\r\n questionMarkCount,\r\n wordPerSentence,\r\n charPerSentence,\r\n rusCharPerSentence,\r\n engCharPerSentence,\r\n charPerWord,\r\n rusCharPerWord,\r\n engCharPerWord,\r\n rusCharRatio,\r\n engCharRatio,\r\n rusCharVsEngChar,\r\n engCharVsRusChar,\r\n ]\r\n statsFeat = \"\"\r\n for i,f in enumerate(stats):\r\n if f != 0:\r\n statsFeat += \"%s:%s \" % (i+1, f)\r\n statsFeat = statsFeat[:-1] \r\n return statsFeat","def get_summarization(text):\n DOCUMENT = re.sub(r'\\n|\\r', ' ', text)\n DOCUMENT = re.sub(r' +', ' ', DOCUMENT)\n DOCUMENT = DOCUMENT.strip()\n print(\"Summary 1\")\n print(summarize(DOCUMENT, word_count=75, split=False))\n print(\"Summary 2\")\n print(summarize(DOCUMENT, ratio=0.2, split=False))","def readability_measurements(passage: str):\n results = readability.getmeasures(passage, lang='en')\n \n chars_per_word = results['sentence info']['characters_per_word']\n syll_per_word = results['sentence info']['syll_per_word']\n words_per_sent = results['sentence info']['words_per_sentence']\n \n kincaid = results['readability grades']['Kincaid']\n ari = results['readability grades']['ARI']\n coleman_liau = results['readability grades']['Coleman-Liau']\n flesch = results['readability grades']['FleschReadingEase']\n gunning_fog = results['readability grades']['GunningFogIndex']\n lix = results['readability grades']['LIX']\n smog = results['readability grades']['SMOGIndex']\n rix = results['readability grades']['RIX']\n dale_chall = results['readability grades']['DaleChallIndex']\n \n tobeverb = results['word usage']['tobeverb']\n auxverb = results['word usage']['auxverb']\n conjunction = results['word usage']['conjunction']\n pronoun = results['word usage']['pronoun']\n preposition = results['word usage']['preposition']\n nominalization = results['word usage']['nominalization']\n \n pronoun_b = results['sentence beginnings']['pronoun']\n interrogative = results['sentence beginnings']['interrogative']\n article = results['sentence beginnings']['article']\n subordination = results['sentence beginnings']['subordination']\n conjunction_b = results['sentence beginnings']['conjunction']\n preposition_b = results['sentence beginnings']['preposition']\n \n return [chars_per_word, syll_per_word, words_per_sent,\n kincaid, ari, coleman_liau, flesch, gunning_fog, lix, smog, rix, dale_chall,\n tobeverb, auxverb, conjunction, pronoun, preposition, nominalization,\n pronoun_b, interrogative, article, subordination, conjunction_b, preposition_b]","def metrics_text(self, x, extra=None):\n metrics = self.compute_metrics(x, extra=extra)\n if metrics is not None:\n s = \", \".join([\"%s=%0.5f\"%(k, v) for k, v in metrics.items()])\n return s\n return \"\"","def stats(self):","def script_stats_analysis(self, data):\n anomal_chars = [r'^', r'{', r'}', r'\"', r',', r'<', r'>', ';']\n anomal_char_stats = {}\n char_stats = {\"upper\": 0, \"lower\": 0, \"numbers\": 0, \"symbols\": 0, \"spaces\": 0}\n anomalies = []\n c = Counter(data)\n anomaly_score = 0\n\n # Check the characters\n for char in c.most_common():\n if char[0] in anomal_chars:\n anomal_char_stats[char[0]] = char[1]\n if char[0].isupper():\n char_stats[\"upper\"] += char[1]\n elif char[0].islower():\n char_stats[\"lower\"] += char[1]\n elif char[0].isdigit():\n char_stats[\"numbers\"] += char[1]\n elif char[0].isspace():\n char_stats[\"spaces\"] += char[1]\n else:\n char_stats[\"symbols\"] += char[1]\n # Totals\n char_stats[\"total\"] = len(data)\n char_stats[\"alpha\"] = char_stats[\"upper\"] + char_stats[\"lower\"]\n\n # Detect Anomalies\n if char_stats[\"alpha\"] > 40 and char_stats[\"upper\"] > (char_stats[\"lower\"] * 0.9):\n anomalies.append(\"upper to lower ratio\")\n anomaly_score += 20\n if char_stats[\"symbols\"] > char_stats[\"alpha\"]:\n anomalies.append(\"more symbols than alphanum chars\")\n anomaly_score += 40\n for ac, count in anomal_char_stats.iteritems():\n if (count/char_stats[\"alpha\"]) > 0.05:\n anomalies.append(\"symbol count of '%s' very high\" % ac)\n anomaly_score += 40\n\n # Generate message\n message = \"Anomaly detected ANOMALIES: '{0}'\".format(\"', '\".join(anomalies))\n if anomaly_score > 40:\n return message, anomaly_score\n\n return \"\", 0","def show_profile(self, aggregate=0):\n s = ''\n if aggregate == 0:\n s += '%50s\\t %7s \\t %4s \\t %4s \\t %s \\n' % (\"call\", \"time\", \"#sol\", \"#call\", \"location\")\n s += '-' * 100 + '\\n'\n for tm, key in sorted((t, k) for k, t in self.timestats.items()):\n term, location = key\n nb = self.resultstats[key]\n cl = self.callstats[key]\n location = location_string(location)\n s += '%50s\\t %.5f \\t %d \\t %d \\t [%s]\\n' % (term, tm, nb, cl, location)\n else:\n timestats_agg = defaultdict(float)\n resultstats_agg = defaultdict(int)\n callstats_agg = defaultdict(int)\n\n for k, t in self.timestats.items():\n if aggregate == 1:\n key = k[0]\n else:\n key = k[0].signature\n timestats_agg[key] += t\n resultstats_agg[key] += self.resultstats[k]\n callstats_agg[key] += self.callstats[k]\n\n s += '%50s\\t %7s \\t %4s \\t %4s\\n' % (\"call\", \"time\", \"#sol\", \"#call\")\n s += '-' * 100 + '\\n'\n for tm, key in sorted((t, k) for k, t in timestats_agg.items()):\n nb = resultstats_agg[key]\n cl = callstats_agg[key]\n s += '%50s\\t %.5f \\t %d \\t %d\\n' % (key, tm, nb, cl)\n\n return s","def measureAll(authors_texts,sectorialized_agents):\n authors_texts=P.text.aux.textFromAuthors(authors_texts,self.topm_dict[\"sectorialized_agents\"])\n authors_measures={}\n # análise de cada mensagem e de cada autor\n for author in authors_texts:\n authors_measures[author]={}\n texts=authors_texts[author]\n authors_measures[author][\"raw_strings\"]=P.text.raw.analyseAll(texts)\n authors_measures[author][\"pos\"]= P.text.pos.analyseAll(authors_analysis[author][\"raw_analysis\"])\n authors_measures[author][ \"wordnet\" ]=P.text.wordnet.analyseAll(authors_analysis[author][\"pos_analysis\"])\n authors_measures[author][\"tfIdf\"]=P.text.tfIdf.analyseAll(texts) # tfIdf de cada texto e do autor, numeric: mean e std das distancias\n # análise de cada setor e da estrutura toda\n# sectors_texts=P.text.aux.textFromSectors(authors_text,sectorialized_agents)\n sectors_measures={}\n for sector in sectorialized_agents:\n sectors_measures[sector][\"raw_strings\"]=P.text.raw.sectorsAnalyseAll(authors_analysis,sectorialized_agents[sector])\n sectors_measures[sector][\"pos\"]= P.text.pos.sectorsAnalyseAll(authors_analysis,sectorialized_agents[sector])\n sectors_measures[sector][\"wordnet\"]= P.text.wordnet.sectorsAnalyseAll(authors_analysis,sectorialized_agents[sector])\n # tfIdf de cada texto e de cada autor, numeric: mean e std das distancias por texto e por autor, e media e etd dos autores\n sectors_measures[sector][\"tfIdf\"]= P.text.tfIdf.sectorsAnalyseAll(authors_analysis,sectorialized_agents[sector])\n\n# texts=[sectors_texts[i] for i in (\"peripherals\",\"intermediaries\",\"hubs\")]\n# sectors_analysis[\"raw_strings\"]=P.text.raw.analyseAll(texts)\n# sectors_analysis[\"pos\"]= P.text.pos.analyseAll(sectors_analysis[\"raw_analysis\"])\n# sectors_analysis[ \"wordnet\" ]=P.text.wordnet.analyseAll(sectors_analysis[\"pos_analysis\"])\n# sectors_analysis[\"tfIdf\"]=P.text.tfIdf.tfIdf(texts)\n\n overall_measures[\"raw_strings\"]=P.text.raw.systemAnalysis(sectors_analysis) # medias de toda a rede por mensagem, por autor e por setor\n overall_measures[\"pos\"]=P.text.raw.systemAnalysis(sectors_analysis) # medias de toda a rede por mensagem, por autor e por setor\n overall_measures[\"wordnet\"]=P.text.raw.systemAnalysis(sectors_analysis) # medias de toda a rede por mensagem, por autor e por setor\n # tfIdf measurespor texto, autor e setor, numeric: media e desvio das distancias por cada grupo, media e desvio dos setores e dos autores\n overall_measures[\"tfIdf\"]=P.text.tfIdf.systemAnalysis(sectors_analysis) # medias de toda a rede por mensagem, por autor e por setor\n\n del authors_texts,sectorialized_agents,author, sector\n return locals()","def __profanity_scan(self, title, text):\n profane_list = set(PROFANITY)\n text_words = nltk.word_tokenize(text)\n text_count = 0\n title_count = 0\n for word in text_words:\n if word.lower() in profane_list:\n text_count += 1\n for word in title.split():\n if word.lower() in profane_list:\n title_count += 1\n return title_count, text_count","def text_report(self):\n\n word_count = self.word_count()\n\n print(\"\\nThere are {} words in the text.\".format(word_count))\n mean, median, mode = self.average_word_length()\n\n print(\"\\nMean, median and mode word length is {}, {}, {}.\".format(mean, median, mode))\n\n if word_count < 10:\n print(\"\\nLongest words:\")\n else:\n print(\"\\n10 longest words:\")\n for s in self.longest_words():\n print(s)\n\n print(\"\\nMost common words:\")\n for s in self.common_words():\n print(\"{} x {}\".format(s[1], s[0]))\n\n longest_grams = []\n\n # find n_longest n-grams\n n_longest = 10\n # strongly doubt that there will be n-grams longer than 50\n for i in range(min(50, word_count), 1, -1):\n if len(longest_grams) >= n_longest:\n break\n grams = self.find_ngrams(i)\n grams_list = sorted(grams, key=grams.get, reverse=True)\n\n for g in grams_list:\n if grams[g] > 4:\n # do not want to include n-grams which are substrings of longer n-grams\n substring = False\n for s in longest_grams:\n if g in s[1]:\n substring = True\n break\n if not substring:\n longest_grams.append([grams[g], g])\n\n print(\"\\nLongest n-grams:\")\n for g in longest_grams:\n print(\"{} x {}\".format(g[0], g[1]))\n print('\\n')","def summary_line_and_description():","def report(self):\n m = {}\n num_tok = self.metrics['num_tokens']\n if num_tok > 0:\n if self.metrics['correct_tokens'] > 0:\n m['token_acc'] = self.metrics['correct_tokens'] / num_tok\n m['loss'] = self.metrics['loss'] / num_tok\n if self.metrics['pred_count'] > 0:\n m['pred'] = self.metrics['correct_pred'] / self.metrics['pred_count']\n try:\n m['ppl'] = math.exp(m['loss'])\n except OverflowError:\n m['ppl'] = float('inf')\n if self.metrics['total_skipped_batches'] > 0:\n m['total_skipped_batches'] = self.metrics['total_skipped_batches']\n for k, v in m.items():\n # clean up: rounds to sigfigs and converts tensors to floats\n m[k] = round_sigfigs(v, 4)\n return m","def stats_preprocessing(self):\n output = {'before_tot':[],\n 'before_unique':[],\n 'after_tot':[],\n 'after_unique':[]}\n for i in range(len(self.table)):\n description_raw = self.table.description.iloc[i].split(' ')\n clean_txt = self.table.clean_text.iloc[i].split(' ')\n\n output['before_tot'].append(len(description_raw))\n output['before_unique'].append(len(set(description_raw)))\n output['after_tot'].append(len(clean_txt))\n output['after_unique'].append(len(set(clean_txt)))\n \n print(\"\"\"Before preprocessing a description had on average {0} words with standard deviation {1}. \\n\nMoreover, the average of unique words was {2} and the standard deviation {3}.\"\"\"\\\n .format(round(mean(output['before_tot']), 2), round(stdev(output['before_tot']), 2), \n round(mean(output['before_unique']), 2), round(stdev(output['before_unique'])), 2))\n \n print(\"\"\"\\nAfter preprocessing a description has on average {0} words with standard deviation {1}. \\n \nThe average of unique words is now {2} and the standard deviation {3}.\"\"\"\\\n .format(round(mean(output['after_tot']), 2), round(stdev(output['after_tot']), 2), \n round(mean(output['after_unique']),2), round(stdev(output['after_unique']), 2)))\n\n return output","def evaluate_text(bio_text):\n\n\ts_df = bert_evaluator.evaluate_text(bio_text)\n\trealness_vals = s_df['prob_real'].values \n\n\t# return tuple (float mininal_chunk_val, float avg_chunk_val, \n\t# float[] all_vals)\n\treturn (np.min(realness_vals), np.mean(realness_vals), vals)","def summaryText(self):\n\n print('\\nReport Summary:\\n')\n for author in self.lowQuality.keys():\n if len(self.lowQuality[author]) > 0:\n print('Author: ' + author)\n print('---------------------')\n # do some sorting for readability\n files = []\n file2rating = {}\n for fileRating in self.lowQuality[author]:\n files.append(fileRating[1])\n file2rating[fileRating[1]] = fileRating[0]\n files.sort()\n for fileRating in files:\n print(file2rating[fileRating] + ' :: ' + fileRating)\n print('\\n\\n')","def display_metrics3(self):\n messagebox.showinfo(\"Processed Image Metrics\", self.pro_metrics)","def acc(ev):\n profData = getProfilingData(ev)\n if profData is not None:\n return profData.Tacc()\n return \"\"","def measure(self, recommender):","def summary_stats(tile_summary):\n return \"Original Dimensions: %dx%d\\n\" % (tile_summary.orig_w, tile_summary.orig_h) + \\\n \"Original Tile Size: %dx%d\\n\" % (tile_summary.orig_tile_w, tile_summary.orig_tile_h) + \\\n \"Scale Factor: 1/%dx\\n\" % tile_summary.scale_factor + \\\n \"Scaled Dimensions: %dx%d\\n\" % (tile_summary.scaled_w, tile_summary.scaled_h) + \\\n \"Scaled Tile Size: %dx%d\\n\" % (tile_summary.scaled_tile_w, tile_summary.scaled_tile_w) + \\\n \"Total Mask: %3.2f%%, Total Tissue: %3.2f%%\\n\" % (\n tile_summary.mask_percentage(), tile_summary.tissue_percentage) + \\\n \"Tiles: %dx%d = %d\\n\" % (tile_summary.num_col_tiles, tile_summary.num_row_tiles, tile_summary.count) + \\\n \" %5d (%5.2f%%) tiles >=%d%% tissue\\n\" % (\n tile_summary.high, tile_summary.high / tile_summary.count * 100, TISSUE_HIGH_THRESH) + \\\n \" %5d (%5.2f%%) tiles >=%d%% and <%d%% tissue\\n\" % (\n tile_summary.medium, tile_summary.medium / tile_summary.count * 100, TISSUE_LOW_THRESH,\n TISSUE_HIGH_THRESH) + \\\n \" %5d (%5.2f%%) tiles >0%% and <%d%% tissue\\n\" % (\n tile_summary.low, tile_summary.low / tile_summary.count * 100, TISSUE_LOW_THRESH) + \\\n \" %5d (%5.2f%%) tiles =0%% tissue\" % (tile_summary.none, tile_summary.none / tile_summary.count * 100)","def measure(self):\n pass","def summary_string(self) -> str:","def display_metrics2(self):\n messagebox.showinfo(\"Original Image Metrics\", self.raw_metrics)","def _createTextProfile(self, indict):\r\n\r\n keys = map(lambda x: x[0], indict)\r\n vals = map(lambda x: x[1], indict)\r\n\r\n outstrs = [\"\\n\"]\r\n propDict = {}\r\n total = sum(vals)\r\n maxLenKey = max([len(a) for a in keys])\r\n maxLenVal = max([len(repr(a)) for a in vals]) \r\n\r\n for k, v in indict:\r\n outstr = \" \"\r\n outstr += k.ljust(maxLenKey + 1)\r\n outstr += (\"%.2f\" % v).ljust(maxLenVal + 1)\r\n outstr += \"-\" * int(self.numBars * (v / total))\r\n outstrs.append(outstr)\r\n\r\n return \"\\n\".join(outstrs)","def compute_statistics(self):","def profile(self):\n return NumericStatsMixin.profile(self)","def analyze(self, text):\n #analize every word in the text a value -1, 1 or 0 and calculate total score\n #tokens allow us to split words in single tokens we can initialize tokens like this:\n\n tokenizer = nltk.tokenize.TweetTokenizer()\n tokens = tokenizer.tokenize(text.lower())\n\n score = 0\n\n if tokens[0] in self.negatives:\n score =- 1\n elif tokens[0] in self.positives:\n score =+ 1\n else:\n score = 0\n\n #print('', text)\n\n return score","def summary(self):\n return self.pfm","def analyze(self, text):\n #Check each word in text\n tokenizer = nltk.tokenize.TweetTokenizer()\n tokens = tokenizer.tokenize(text)\n total_score = 0\n #Sum the total score\n for token in tokens:\n token = token.lower()\n if token in self.positives:\n total_score = total_score + 1\n elif token in self.negatives:\n total_score = total_score - 1\n else:\n total_score = total_score + 0\n \n return total_score","def stats(self):\n stats = {\n 'lines' : '', # This will count the lines under each split\n 'status_code': self.status_code,\n 'content_type': self.mime,\n 'hop': self.hop_path[-1:],\n 'sum:content_length': self.content_length,\n 'host': self.host(),\n 'source': self.source\n }\n # Add in annotations:\n for annot in self.annotations:\n # Set a prefix based on what it is:\n prefix = ''\n if self.re_tries.match(annot):\n prefix = 'tries:'\n elif self.re_ip.match(annot):\n prefix = \"ip:\"\n # Only emit lines with annotations:\n if annot != \"-\":\n stats[\"%s%s\" % (prefix, annot)] = \"\"\n return stats","def compute_metrics(self):\n pass","def analyze(self, text):\n\n # TODO\n # tokens = tokenizer.tokenize(tweet)\n tokenizer = nltk.tokenize.TweetTokenizer()\n tokens = tokenizer.tokenize(text)\n score = 0\n\n for word in tokens:\n # iterate over tokens#str.lower\n\n if word.lower() in self.positives:\n score = score+1\n\n elif word.lower() in self.negatives:\n score = score-1\n\n else:\n continue\n return score","def evaluate(self, text):\n text = ' '.join(['<'] * (self.n - 1) + [text.replace(' . ', ' .%s ' % (' <' * (self.n - 1)))])\n tokens = self.split(text)\n sum = 0\n for i in range(len(tokens) - self.n + 1):\n n_gram = self.join(tokens[i: i + self.n])\n prob = self.get_probability(n_gram, True)\n sum += math.log(prob, self.log_base)\n return sum","def summary(self):\n if not self: return u''\n s = u'Character\\n=====\\nName: %s\\n' % \\\n self.get('name', u'')\n bio = self.get('biography')\n if bio:\n s += u'Biography: %s\\n' % bio[0]\n filmo = self.get('filmography')\n if filmo:\n a_list = [x.get('long imdb canonical title', u'')\n for x in filmo[:5]]\n s += u'Last movies with this character: %s.\\n' % u'; '.join(a_list)\n return s","def num_words():\n # Load the GT.\n df = pd.read_csv(config.META_FQN, sep=\"\\t\")\n stats = {\n \"T\": {\"words\": [], \"duration\": []},\n \"P\": {\"words\": [], \"duration\": []},\n \"sess\": {\"words\": [], \"duration\": []},\n }\n\n for _, row in df.iterrows():\n if row[\"asr_test\"]:\n stats[\"P\"][\"words\"].append(float(row[\"gt_patient_num_words\"]))\n stats[\"T\"][\"words\"].append(float(row[\"gt_therapist_num_words\"]))\n stats[\"P\"][\"duration\"].append(float(row[\"gt_patient_time_spoken\"]))\n stats[\"T\"][\"duration\"].append(\n float(row[\"gt_therapist_time_spoken\"])\n )\n stats[\"sess\"][\"duration\"].append(float(row[\"sess_dur\"]))\n n_words = (\n row[\"gt_therapist_num_words\"] + row[\"gt_patient_num_words\"]\n )\n stats[\"sess\"][\"words\"].append(n_words)\n\n for speaker in stats:\n for metric in stats[speaker]:\n print(f\"------ {speaker} | {metric} ------\")\n print_stats(stats[speaker][metric])","def print_metrics(result):\n logging.log(LOG_LEVEL_OUTPUT_INFO,\n '------------------------------------------------')\n logging.log(LOG_LEVEL_OUTPUT_INFO, ' KEY METRICS: ')\n logging.log(LOG_LEVEL_OUTPUT_INFO,\n '------------------------------------------------')\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* pages_count: %d',\n get_counter_metric(result, 'pages_count'))\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_count: %d',\n get_counter_metric(result, 'revisions_count'))\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* very_long_page_histories_count: %d',\n get_counter_metric(result, 'very_long_page_histories_count'))\n revisions_per_page_distr = get_distributions_metric(\n result, 'revisions_per_page_distr')\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.mean: %d',\n revisions_per_page_distr.mean)\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.sum: %d',\n revisions_per_page_distr.sum)\n cumulative_page_rev_size_distr = get_distributions_metric(\n result, 'cumulative_page_rev_size_distr')\n logging.log(LOG_LEVEL_OUTPUT_INFO,\n '* cumulative_page_rev_size_distr.mean: %d',\n cumulative_page_rev_size_distr.mean)\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* cumulative_page_rev_size_distr.sum: %d',\n cumulative_page_rev_size_distr.sum)","def printStats(m2, m3, actual, top):\n print(\"\\nThe actual categories for this page are: %s\" % \", \".join(sorted(actual)))\n print(\"\\nThe suggested categories for this page are: %s\" % \", \".join(sorted([v for v, count in top])))\n print(\"\\nBOOLEAN MEASURE = %s\" %(m2 != 0))\n print(\"FRACTIONAL MEASURE = %0.2f\" %(m2))\n print(\"HIERARCHICAL MEASURE = %0.2f\\n\" %(m3))\n print(\"*\" * 150)","def extract_core_stats(self):\n titleLabel = QLabel(\"Core Statistics\")\n titleLabel.setStyleSheet('font: 20pt \"Imprint MT Shadow\"; color: #ffffff;')\n self.filterVbox.addWidget(titleLabel, alignment=Qt.AlignCenter)\n potentialLabels = [\"Primary\", \"Secondary\", \"Class\", \"Subclass\",\n \"Background\", \"Race\", \"Subrace\", \"Minimum AC\"]\n\n for label in potentialLabels:\n if label in [\"Primary\", \"Secondary\"]:\n textAddition = \" Archetype\"\n else:\n textAddition = \"\"\n try:\n nextLabel = QLabel(f\"{label + textAddition}: {self.filters[label]}\")\n nextLabel.setStyleSheet('font: 14pt \"Times New Roman\"; color: rgb(188, 189, 177);')\n self.filterVbox.addWidget(nextLabel, alignment=Qt.AlignCenter)\n except KeyError:\n pass","def test_execution_profiling(self):\n self._test_reports_helper({\"--profile-execution\": \"\"}, [\"report.txt\"])","def compute_and_print_eval_metrics(self):\n s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')\n precision, recall, f1, mean_precision, mean_recall, map50, map = 0., 0., 0., 0., 0., 0., 0.\n ap = []\n eval_stats = [np.concatenate(x, 0) for x in zip(*self.eval_stats)]\n if len(eval_stats) and eval_stats[0].any():\n precision, recall, ap, f1, ap_class = ap_per_class(*eval_stats)\n precision, recall, ap50, ap = precision[:, 0], recall[:, 0], ap[:, 0], ap.mean(1)\n mean_precision, mean_recall, map50, map = precision.mean(), recall.mean(), ap50.mean(), ap.mean()\n nt = np.bincount(eval_stats[3].astype(np.int64), minlength=len(self.class_names)) # number of targets per class\n else:\n nt = np.zeros(1)\n\n pf = '%20s' + '%12.5g' * 6 # print format\n print(\"\\n EVALUTAION \\n\")\n print(s)\n print(pf % ('all', self.seen, nt.sum(), mean_precision, mean_recall, map50, map))\n if self.cfg.eval.verbose:\n for indx, cls in enumerate(ap_class):\n print(pf % (self.class_names[cls], self.seen, nt[cls], precision[indx], recall[indx], ap50[indx], ap[indx]))","def analyze_text (self, testing_string): \n self.length = len(self.testing_string)\n self.total_words = (self.testing_string).split()\n self.total_unique_words = set(self.total_words)\n\n self.total_characters = (int)(0)\n for ch in self.testing_string :\n if(ch.isspace() != True):\n self.total_characters = self.total_characters + 1 \n\n self.total_unique_characters = set(self.testing_string)\n \n Linguist.about_given_string[\"Length\"] = self.length\n Linguist.about_given_string[\"Total_words\"] = len(self.total_words)\n Linguist.about_given_string[\"Total_unique_words\"] = len(self.total_unique_words)\n Linguist.about_given_string[\"Total_characters\"] = self.total_characters\n Linguist.about_given_string[\"Total_unique_characters\"] = len(self.total_unique_characters)","def get_stats(text):\n stats_wP = ['HP', 'Def', 'Atk']\n regex = r\"(Speed|Health|Defense|Attack|Critical Hit Damage|Critical Hit Chance|Effectiveness|Effect Resistance) \\d.*\"\n\n line_num = 0\n sub_num = 1\n text = text.strip()\n item_stats = {}\n for key, value in change_lst.items():\n text = text.replace(key, str(value))\n\n for line in text.split(\"\\n\"):\n line_num += 1\n if re.search(r\"set\", line, re.IGNORECASE) is not None: # \"Set\" in line:\n set_found = re.search(r\"([a-zA-Z]+) Set\", line, re.IGNORECASE).group(1).capitalize()\n if set_found in sets_name:\n item_stats[\"set\"] = set_found\n else:\n item_stats[\"set\"] = difflib.get_close_matches(set_found, sets_name, n=1, cutoff=0.1)[0]\n\n if re.search(regex, line):\n stat, value = line.rsplit(\" \", 1)\n stat = rename_stat(stat)\n # if \" \" in stat:\n # stat = stat.rsplit(\" \", 1)[1]\n\n if \",\" in value:\n value = re.sub(\",\", \"\", value)\n\n if \"%\" in value:\n value = int(value[:-1])\n if stat in stats_wP:\n stat += \"P\"\n\n if line_num == 1:\n # Record Main Stat\n item_stats[\"mainStat\"] = [stat, int(value)]\n # item_stats[str(\"Main \" + stat)] = value\n else:\n # Record substats\n item_stats[\"subStat\" + str(sub_num)] = [stat, int(value)]\n sub_num += 1\n\n # if no subStats, raise error\n if sub_num <= 2:\n raise\n\n return item_stats","def analyze_text(text):\n # Your code here\n total=0\n total_es = 0\n\n for c in text:\n if c.isalpha():\n total += 1\n\n total_es = text.lower().count('e')\n percentage = float(total_es/total)\n\n print(total, total_es, percentage * 100)","def stats(self):\n pass","def _run_profile(self, profile: Tuple[str, List[int], int, float]) -> List[int]:\n # pylint: disable=unused-variable\n name, command, signals, delay = profile\n # pylint: enable=unused-variable\n\n # print(\"\\trunning profile: %s, command %s, %d, delay %0.02f\" %\n # (name, [\"0x%02x\" % i for i in command], signals, delay))\n return self._i2c_read_words_from_cmd(command, delay, signals)","def raw_measure(self) -> List[int]:\n # name, command, signals, delay\n return self._run_profile((\"raw_measure\", [0x20, 0x50], 2, 0.025))","def get_str_metadata(self):\n return \"\\n\".join([\"Guessed by {}\".format(self.guessed_by), \"{} metaphors used\".format(self.metaphors_used)])","def _get_accuracy(text):\n sta_obj = [m.start() for m in re.finditer('%',text)]\n return([float(text[x-3:x:1]) for x in sta_obj])","def analyze(self, text):\n score =0\n token = TweetTokenizer()\n tokens = token.tokenize(text)\n for token in tokens:\n if token.lower() in self.pos_list:\n score+=1\n elif token.lower() in self.neg_list:\n score-=1\n\n return score","def measure(colloc_tag, colloc_count, n1grams, unigrams, n):\r\n collocation_words, collocation_tags = colloc_tag.split('/')\r\n collocation_words = collocation_words.split(' ')\r\n collocation_tags = collocation_tags.split(' ')\r\n pattern_words = ' '.join(collocation_words[:-1])\r\n pattern_tags = ' '.join(collocation_tags[:-1])\r\n\r\n pattern = pattern_words + '/' + pattern_tags\r\n last_word = collocation_words[-1] + '/' + collocation_tags[-1]\r\n c_pattern = int(n1grams[pattern])\r\n c_lw = int(unigrams[last_word])\r\n colloc_count = int(colloc_count)\r\n\r\n tsc = t_score(colloc_count, c_pattern, c_lw, n)\r\n pmisc = pmi(colloc_count, c_pattern, c_lw, n)\r\n logdsc = logDice(colloc_count, c_pattern, c_lw)\r\n return logdsc, pmisc, tsc","def get_profile_stats():\n return p_stats","def _printSummary(self):\n\t\t### COP OUT\n\t\tif self.params['background'] is True:\n\t\t\tself.stats['count'] += 1\n\t\t\treturn\n\n\t\t### THIS NEEDS TO BECOME MUCH MORE GENERAL, e.g. Peaks\n\t\ttdiff = time.time()-self.stats['startseries']\n\t\tif not self.params['continue'] or tdiff > 0.1:\n\t\t\tcount = self.stats['count']\n\t\t\t#if(count != self.stats['lastcount']):\n\t\t\tsys.stderr.write(\"\\n\\tSUMMARY: \"+self.functionname+\"\\n\")\n\t\t\tself._printLine()\n\t\t\tsys.stderr.write(\"\\tTIME: \\t\"+apDisplay.timeString(tdiff)+\"\\n\")\n\t\t\tself.stats['timesum'] = self.stats['timesum'] + tdiff\n\t\t\tself.stats['timesumsq'] = self.stats['timesumsq'] + (tdiff**2)\n\t\t\ttimesum = self.stats['timesum']\n\t\t\ttimesumsq = self.stats['timesumsq']\n\t\t\tif(count > 1):\n\t\t\t\ttimeavg = float(timesum)/float(count)\n\t\t\t\ttimestdev = math.sqrt(float(count*timesumsq - timesum**2) / float(count*(count-1)))\n\t\t\t\ttimeremain = (float(timeavg)+float(timestdev))*self.stats['seriesleft']\n\t\t\t\tsys.stderr.write(\"\\tAVG TIME: \\t\"+apDisplay.timeString(timeavg,timestdev)+\"\\n\")\n\t\t\t\t#print \"\\t(- TOTAL:\",apDisplay.timeString(timesum),\" -)\"\n\t\t\t\tif(self.stats['seriesleft'] > 0):\n\t\t\t\t\tsys.stderr.write(\"\\t(- REMAINING TIME: \"+apDisplay.timeString(timeremain)+\" for \"\n\t\t\t\t\t\t+str(self.stats['seriesleft'])+\" series -)\\n\")\n\t\t\t#print \"\\tMEM: \",(mem.active()-startmem)/1024,\"M (\",(mem.active()-startmem)/(1024*count),\"M)\"\n\t\t\tself.stats['count'] += 1\n\t\t\tself._printLine()","def analyze(self, text):\n\n tknzr = nltk.tokenize.TweetTokenizer()\n words = tknzr.tokenize(text)\n \n score = 0\n \n for word in words:\n if word.lower() in self.positives:\n score += 1\n elif word.lower() in self.negatives:\n score -= 1\n else:\n continue\n \n return score","def get_text_summarization_gensim(text, summary_ratio=0.4):\n sentences = extract_sentences(text)\n text = ' '.join(sentences)\n summary = summarize(text, split=True, ratio=summary_ratio)\n return summary","def analyze(self, text):\n tokenizer = nltk.tokenize.TweetTokenizer()\n tokens = tokenizer.tokenize(text)\n score = 0\n\n for token in tokens:\n if token in self.positives_list:\n score += 1\n elif token in self.negatives_list:\n score -= 1\n\n return score","def user_stats(df):\r\n print('\\nCalculating User Stats...\\n')\r\n start_time = time.time()\r\n # TO DO: Display counts of user types\r\n df = ['user type'].value_counts()\r\n print('count of user typs:\\n')\r\n # TO DO: Display counts of gender\r\n df = ['grnder'].value_counts()\r\n if 'Gender' in df:\r\n print('count of gender:\\n')\r\n # TO DO: Display earliest, most recent, and most common year of birth\r\n year = df['birth year'].value_counts()\r\n if 'birth year' in df:\r\n print('earliset birth year is:{year.min()}\\nmost recent is: {year.max()}\\nand most common birth year is: (year.mode()[0]')\r\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\r\n print('-'*40)","def analyze(self, word_count_thresh):","def _scan_profilewidth(self,handle,consumer):\n read_and_call(handle, consumer.profilewidth, contains=\"Nseqs1\")","def get_word_stats(segments, feats_dict):\r\n word_count_list = []\r\n word_lengths = []\r\n long_count = 0\r\n for segment in segments:\r\n word_count_list.append(len(segment))\r\n for word in segment:\r\n word_lengths.append(len(word))\r\n if len(word) > 6:\r\n long_count += 1\r\n # Compute segment level statistics\r\n feats_dict['wc_mean'] = np.mean(word_count_list) if word_count_list else float('nan')\r\n feats_dict['wc_median'] = np.median(word_count_list) if word_count_list else float('nan')\r\n feats_dict['wc_stdev'] = np.std(word_count_list) if word_count_list else float('nan')\r\n feats_dict['wc_min'] = min(word_count_list) if word_count_list else float('nan')\r\n feats_dict['wc_max'] = max(word_count_list) if word_count_list else float('nan')\r\n feats_dict['total_count'] = sum(word_count_list) if word_count_list else float('nan')\r\n\r\n # Compute fraction of words across whole call that are long (i.e. 6+ words)\r\n feats_dict['lw_count'] = (long_count / feats_dict['total_count']) if feats_dict['total_count'] else float('nan')\r\n # Compute mean length of any word used\r\n feats_dict['word_len'] = np.mean(word_lengths) if word_lengths else float('nan')","def karma(self, msg):\n if len(msg.msg.split(' ')) > 1:\n word = msg.msg.split(' ')[1].lower()\n if self._word_exists(word):\n pos = self.vault[word][0]\n neg = self.vault[word][1]\n sum = self.vault[word][2]\n if sum > 99:\n comment = _('%s is far too good! Practice Evil, my friend.'\n % word)\n elif 100 > sum > 0:\n comment = _('What a nice %s, so many good deeds...') % word\n elif sum == 0:\n comment = _('The ultimate way to Nirvana.')\n elif -50 < sum < 0:\n comment = _('Such a bad boy.')\n elif -49 < sum:\n comment = _('%s seems to be an evil genius in training.' %\n word)\n return _('Karma statistics for %s: +%i -%i => %i -- %s' % \n (word, pos, neg, sum, comment))\n else:\n return _('%s has no karma recordings yet.' % word)\n else:\n highest = ['None', 0]\n lowest = ['None', 0]\n pos = 0\n neg = 0\n sum = 0\n for k, v in self.vault.items():\n log('i', '%s#%s' % (str(k), str(v)))\n if v[2] > highest[1]:\n highest = [k, v[2]]\n if v[2] < lowest[1]:\n lowest = [k, v[2]]\n pos += v[0]\n neg += v[1]\n sum += v[2]\n return _('I keep track of %i people\\'s karma. Overall I noticed \\\n%i positive and %i negative karma points. %s is the most well behaved I \\\nknow. %s seems to have a Bachelor\\'s degree in Darkness. The common karma of \\\nall people I know is %i.' % (len(self.vault), pos, neg, highest[0], lowest[0],\n sum))","def analyze(data):\n ## Do welch periodogram here\n pass","def calc_profile(self, phases):\n raise NotImplementedError()","def analyse_text(custom_text, classifier, Resource, threshold, language='en'):\n return [(bytes(custom_text, 'utf-8'),\n _minimal_analysis(bytes(custom_text, 'utf-8'), classifier, Resource, threshold, language))]","def print_stats(self):\n if self.n_iter % 5 != 0:\n return\n\n s_iter = \"%7i - \" % self.n_iter\n s_stat = ' || '.join([\n '{}: {:7.4f}'.format(k, np.mean(v)) for k, v in self.stats.items()\n if type(v) is list and len(v) > 0\n ])\n for k in self.stats.keys():\n if type(self.stats[k]) is list:\n del self.stats[k][:]\n\n # transformer learning rate\n # learning rates\n s_lr = \" - \"\n for k, v in self.optimizers.items():\n s_lr = s_lr + (\" - %s LR: \" % k) + \" / \".join(\n \"{:.4e}\".format(group['lr']) for group in v.param_groups)\n\n # processing speed\n new_time = time.time()\n diff = new_time - self.last_time\n s_speed = \"{:7.2f} sent/s - {:8.2f} words/s - \".format(\n self.stats['processed_s'] * 1.0 / diff,\n self.stats['processed_w'] * 1.0 / diff\n )\n self.stats['processed_s'] = 0\n self.stats['processed_w'] = 0\n self.last_time = new_time\n\n # log speed + stats + learning rate\n logger.info(s_iter + s_speed + s_stat + s_lr)","def stat(data, data_word, data_sent):\n #basic counts\n sent = len(data_sent)\n syll = textstat.syllable_count(data)\n word = len(data_word)\n\n #average calcs\n avg_syll = syll / word\n avg_word = word / sent\n read_time = word/265\n\n #advance stat\n flesch_kincaid_grade = fkg(int(word), int(sent), int(syll))\n verbose = len([word for word in data_word if textstat.syllable_count(word) > 3])\n\n wordy = 0\n for item in data_sent:\n token = word_tokenize(item)\n if len(token) > 40:\n wordy += 1\n #writing to list\n stats = [syll,word,sent,avg_syll,avg_word,read_time,flesch_kincaid_grade, verbose, wordy]\n\n return stats","def print_performance_info(self):\n pass","def evaluate(self):\n scores = []\n scores.append(self.word_analogy())\n print(\"Word Analogy (acc): \", scores[0])\n scores.append(self.word_similarity())\n print(\"Word Similarity (MSE): \", scores[1])\n scores.append(self.concept_categorization())\n print(\"Concept Categorization (purity): \", scores[2])\n scores.append(self.sentiment_analysis())\n print(\"Sentiment Analysis (acc): \", scores[3])\n return scores","def _stats(self):\n return (\"size = \" + str(self.size())\n + \"; height = \" + str(self.height()))","def profile(x):\n return x","def get_summary(self):\n \n text = \"word: {}, total_score: {} \\n\".format(self.clue, self.total_score)\n for card, score in self.sorted_card_score_pairs:\n card_text = \"\\t card.name:{} (team:{}), similarity: {} \\n\".format(card.name, card.color, score)\n text += card_text\n return text","def text_summary(samples,labels,axobj): \n \n #axobj.set_axis_on()\n axobj.text(-0.8,0.9,'Chain parameters:',fontsize='xx-large',transform=axobj.transAxes)\n tloc = 0.\n for par in range(0,samples.shape[1]):\n x = deepcopy(samples[:,par])\n xstd = np.ediff1d(np.percentile(x,[15.87,84.13]))[0]/2.\n xmed = np.median(x)\n if 'lambda' in labels[par]: xmed,xstd = 1e6*xmed,1e6*xstd\n if 'L$_\\\\odot$' in labels[par]: xmed,xstd = xmed/1e12, xstd/1e12\n axobj.text(-0.8,0.7-tloc,'{0:10s} = {1:.2f} $\\\\pm$ {2:.2f}'.\n format(labels[par],xmed,xstd),fontsize='xx-large',transform=axobj.transAxes)\n tloc += 0.18\n \n return axobj","def score_seq(self, seq, verbose=False):\n score = 0.0\n count = 0\n # Start at third word, since we need a full context.\n for i in range(2, len(seq)):\n if (seq[i] == \"<s>\" or seq[i] == \"</s>\"):\n continue # Don't count special tokens in score.\n s = np.log2(self.next_word_proba(seq[i], seq[i-2:i]))\n score += s\n count += 1\n # DEBUG\n if verbose:\n print(\"log P({:s} | {:s}) = {.03f}\".format(seq[i], \" \".join(seq[i-2:i]), s))\n return score, count","def test_summarize(self):\n\n input_text = (\n \"\"\"Lior Degani, the Co-Founder and head of Marketing of Swayy, pinged me last week when I was in California to tell me about his startup and give me beta access. I heard his pitch and was skeptical. I was also tired, cranky and missing my kids – so my frame of mind wasn’t the most positive.\n\n I went into Swayy to check it out, and when it asked for access to my Twitter and permission to tweet from my account, all I could think was, “If this thing spams my Twitter account I am going to bitch-slap him all over the Internet.” Fortunately that thought stayed in my head, and not out of my mouth.\n\n One week later, I’m totally addicted to Swayy and glad I said nothing about the spam (it doesn’t send out spam tweets but I liked the line too much to not use it for this article). I pinged Lior on Facebook with a request for a beta access code for TNW readers. I also asked how soon can I write about it. It’s that good. Seriously. I use every content curation service online. It really is That Good.\n\n What is Swayy? It’s like Percolate and LinkedIn recommended articles, mixed with trending keywords for the topics you find interesting, combined with an analytics dashboard that shows the trends of what you do and how people react to it. I like it for the simplicity and accuracy of the content curation. Everything I’m actually interested in reading is in one place – I don’t have to skip from another major tech blog over to Harvard Business Review then hop over to another major tech or business blog. It’s all in there. And it has saved me So Much Time\n\n\n\n After I decided that I trusted the service, I added my Facebook and LinkedIn accounts. The content just got That Much Better. I can share from the service itself, but I generally prefer reading the actual post first – so I end up sharing it from the main link, using Swayy more as a service for discovery.\n\n I’m also finding myself checking out trending keywords more often (more often than never, which is how often I do it on Twitter.com).\n\n\n\n The analytics side isn’t as interesting for me right now, but that could be due to the fact that I’ve barely been online since I came back from the US last weekend. The graphs also haven’t given me any particularly special insights as I can’t see which post got the actual feedback on the graph side (however there are numbers on the Timeline side.) This is a Beta though, and new features are being added and improved daily. I’m sure this is on the list. As they say, if you aren’t launching with something you’re embarrassed by, you’ve waited too long to launch.\n\n It was the suggested content that impressed me the most. The articles really are spot on – which is why I pinged Lior again to ask a few questions:\n\n How do you choose the articles listed on the site? Is there an algorithm involved? And is there any IP?\n\n Yes, we’re in the process of filing a patent for it. But basically the system works with a Natural Language Processing Engine. Actually, there are several parts for the content matching, but besides analyzing what topics the articles are talking about, we have machine learning algorithms that match you to the relevant suggested stuff. For example, if you shared an article about Zuck that got a good reaction from your followers, we might offer you another one about Kevin Systrom (just a simple example).\n\n Who came up with the idea for Swayy, and why? And what’s your business model?\"\"\"\n )\n\n input_length = 5\n\n result = self.summarizer.summarize(input_text, input_length)\n\n self.assertIsInstance(result, tuple)\n self.assertEqual(input_length, len(result))","def parse(conlleval_stdout):\n for line in conlleval_stdout.split('\\n'):\n if 'accuracy' in line:\n p = float(re.findall(r'precision:\\s*(\\d+\\.\\d+)%', line)[0])\n r = float(re.findall(r'recall:\\s*(\\d+\\.\\d+)%', line)[0])\n f = float(re.findall(r'FB1:\\s*(\\d+\\.\\d+)', line)[0])\n return {'p':p, 'r':r, 'f1':f}","def human_readable_info(self) -> str:\n next_session = unix_str(self._stat.next_session)\n last_session = unix_str(self._stat.last_session)\n return \"\"\"\n Next Session: {}\n Last Session: {}\n Repetitions: {}\n Health: {}\n ------------------------\n Past Quality (last 20):\n ------------------------\n {}\n \"\"\".format(\n next_session,\n last_session,\n self._stat.actual_repetitions,\n self._health(),\n self._past_quality_graph(),\n )","def getprofile(): # real signature unknown; restored from __doc__\n pass","def displayStatistics(self):\n return \"\"","def analyse(self):\n pass","def output_stats(self):\n elapsed = self.timer.elapsed.total_seconds()\n count = self.copied + self.errored\n total = self.total\n # Time per key in milliseconds\n avg = round(elapsed / count * 1000, 3)\n # Time remaining in seconds\n remaining = 1.0 * elapsed / count * (total - count)\n # Time remaining in minutes\n remaining = round(remaining / 60.0, 1)\n # Time taken in minutes\n elapsed = round(elapsed / 60.0, 1)\n\n self.log.info(f\"{self.prefix}: {avg}ms avg, {elapsed}min passed, \"\n f\"{remaining}min remaining. ({count:,}/{total:,})\")","def print_metrics(self):\n # num times regular barcodes appear in a simulated doublet nearest neighbors, grouped by value\n # TODO: this list is 2 dimensional... need to extract dimensione with counts for the counter\n frequencies = [i[1] for i in self.num_times_knn]\n counter = collections.Counter(frequencies)\n print(\"##\\nNumber time barcoded in sim doub KNN: {}\".format(counter))\n\n # artificial fraction\n print(\"##\\nArtificial fraction: {}\".format(self.artificial_fraction))\n\n # num doublets\n print(\"##\\nNumber of doublets called: {}\".format(self.num_doublets))","def basic_statistics(self,measure):\n for line in self.data('Basic Statistics'):\n key,value = line.split('\\t')\n if key == measure:\n return value\n raise KeyError(\"No key '%s'\" % key)","def analyze(self, text):\n\n # start from 0 for each Analyser variable\n self.positives = 0\n self.negatives = 0\n\n # precise self text value\n self.text = text\n\n # declare a tokenased word\n tokenizer = nltk.tokenize.TweetTokenizer()\n tokens = tokenizer.tokenize(text)\n\n # indicate the length of list tokens\n size = len(tokens)\n\n # all the word stuff to ckeck\n for word in tokens:\n\n # chaque mots est converti en mot sans majuscule\n word = str.lower(word)\n\n linespos = [line.rstrip('\\n') for line in open('positive-words.txt')]\n linesneg = [line.rstrip('\\n') for line in open('negative-words.txt')]\n\n # check for positive or negative or neutral words\n if word in linespos:\n self.positives += 1\n elif word in linesneg:\n self.negatives += 1\n else:\n continue\n\n # score calculculated and reurned\n score = self.positives - self.negatives\n\n return score","def get_text(self):\n text_complet = \"\"\n rez_dict = self.__results\n for i in range(0, len(rez_dict[\"text\"])):\n text = rez_dict[\"text\"][i]\n conf = int(rez_dict[\"conf\"][i])\n if conf > self.__min_confidence:\n text_complet += text + \" \"\n return text_complet","def dataStats(self):\n print (\"Performing statistical analysis of the data\")\n # stuff to do","def disp_score():","def profile(self, data):\n\n # need to store the output of each morphism in the analysis, which forms the training data later on\n self.profile = {}\n cur_output = data\n for morph in self.analysis.morphisms:\n cur_name = morph.name\n cur_output = morph.apply(cur_output)\n self.profile[cur_name] = cur_output","def get_summary_line(text):\n try:\n i = text.rfind('\\n')\n j = text.rfind('\\n', 0, i-1)\n #print(\"i, j:\", i, j)\n #print(\"j repr:\", repr(text[j+1:i]))\n k = text.rfind('\\n', 0, j-1)\n #print(\"k repr:\", repr(text[k+1:j]))\n s = text[k+1:j].replace('=', \"\")\n s = s.strip()\n #print(\"s repr:\", repr(s))\n except Exception:\n s = \"???\"\n return s","def getTextUnits_old(filename):\n doc = Document(filename)\n units = list()\n \n unit_tracker = defaultdict(int)\n \n non_units = [\"name:\", \"date:\", \"date\", \"series\", \"transcriber\", \"thesis:\", \"currently:\", \"note\", \"comment\", \"grandparents:\", \"transcript:\", \"note:\"]\n\n ongoing_answer = \"\"\n\n \n\n # iterate over all paragraphs to get text units\n for para in doc.paragraphs:\n paragraph = para.text\n # ensure it is not an empty line\n if len(paragraph.strip())>0:\n # get first word\n formatted_para = paragraph.lstrip()\n unit_type = formatted_para.partition(' ')[0]\n # in case it is in the format of e.g 'George Salton:'\n \n # e.g AJ:, B.\n m = re.compile('[A-Z][A-Z]?[.|:|-]')\n type1 = m.match(unit_type)\n\n # e.g [WJ]\n n = re.compile('\\[[A-Z][A-Z]\\]')\n typer2= n.match(unit_type)\n\n # timestamp e.g 15:01:27\n o = re.compile('[0-9]?[0-9]:[0-9][0-9]:[0-9][0-9]')\n type3 = o.match(unit_type) \n \n \n \n\n\n # else parse them according to formatting guidelines\n if (\"Question:\" in unit_type or\n type1 or\n \"Answer:\" in unit_type or \n typer2 or\n type3):\n\n # check if there was an ongoing paragraph\n #units.append({'unit': paragraph})\n \n if ongoing_answer: \n units.append({'unit': ongoing_answer})\n\n # reset it\n ongoing_answer = \"\"\n ongoing_answer += paragraph\n \n # update tracker\n unit_tracker[unit_type] += 1\n\n elif (unit_type.endswith(':') and\n unit_type.lower() not in non_units and\n unit_type[:-1].isalpha()):\n \n \n units.append({'unit': paragraph})\n # update tracker\n unit_tracker[unit_type] += 1\n \n \n # backup method,in case it is in the format of e.g 'George Salton:'\n elif len(paragraph.split()) > 3:\n backup_type = paragraph.split()[1]\n backup_two = paragraph.split()[2]\n\n if ((':' in backup_type or backup_type.lower() not in non_units) or\n (':' in backup_two or backup_two.lower() not in non_units)): \n \n if ((paragraph.strip()[0].islower() and len(paragraph.strip()) > 5) or (paragraph.strip()[-1] in ['.','!','?'])) and len(units) >0:\n units[-1]['unit']=units[-1]['unit']+ ' '+paragraph\n # update tracker\n unit_tracker[unit_type] += 1\n else:\n units.append({'unit':paragraph})\n unit_tracker[unit_type] += 1\n # if it is none of these cases, maybe there is an ongoing answer\n \n elif (ongoing_answer and ongoing_answer != paragraph):\n \n if not any(non_unit in paragraph.lower() for non_unit in non_units):\n ongoing_answer += paragraph\n else:\n units.append({'unit':paragraph})\n\n if len(unit_tracker) < 2:\n return []\n \n return units","def __str__(self):\n\t\tprint \"generating graph stats...\\n\"\n\t\tstart_time = time.time()\n\t\tbf = self.branching_factor()\n\t\treq_time = float(time.time() - start_time)\n\t\tb = str(bf[0])\n\t\tn = str(bf[1])\n\t\terr = str(100.0 * float(self.default_num_samples-bf[1])/self.default_num_samples)\n\t\tsize = str(self.num_articles())\n\t\tpg_time = str(req_time/bf[1])\n\t\tt = str(time.time() - start_time)\n\t\treturn \"_______Wikipedia Graph Stats_______\\n\" + \\\n\t\t\t\t\"# of nodes:\\t\\t\"+size+\"\\n\" + \\\n\t\t\t\t\"Avg. branching factor\\t\"+b+\"\\n\" + \\\n\t\t\t\t\"\\t\\t\\t(n=\"+n+\")\\n\" + \\\n\t\t\t\t\"Page Req. Fail Rate:\\t\"+err+\"%\\n\" + \\\n\t\t\t\t\"Avg. Page Req. Time:\\t\"+pg_time+\" sec\\n\" + \\\n\t\t\t\t\"<stats generated in \"+t+ \" sec>\"","def printsummary(text):\r\n\r\n print('Summary:')\r\n print('--------')\r\n print(text)","def analyze_data():\n attack_free_1 = load_messages(\"data/csv/Attack_free_dataset.csv\", verbose=True)\n\n impersonation_1 = load_messages(\"data/csv/170907_impersonation.csv\", verbose=True)\n impersonation_2 = load_messages(\"data/csv/170907_impersonation_2.csv\", verbose=True)\n impersonation_3 = load_messages(\"data/csv/Impersonation_attack_dataset.csv\", verbose=True)\n\n information = {\n \"Mean time between normal messages\":\n get_mean_time_between_normal_messages(attack_free_1),\n \"Mean time between split messages\":\n get_mean_time_between_split_messages(attack_free_1),\n \"Sum of removed intervals in '170907_impersonation.csv'\":\n get_sum_of_removed_intervals(impersonation_1, 250),\n \"Sum of removed intervals in '170907_impersonation_2.csv'\":\n get_sum_of_removed_intervals(impersonation_2, 250),\n \"Sum of removed intervals in 'Impersonation_attack_dataset.csv'\":\n get_sum_of_removed_intervals(impersonation_3, 250),\n \"Index of split in '170907_impersonation.csv'\":\n get_index_before_time(impersonation_1, 250 - 23.434627056121826),\n \"Index of split in '170907_impersonation_2.csv'\":\n get_index_before_time(impersonation_2, 250 - 20.980855226516724),\n \"Index of split in 'Impersonation_attack_dataset.csv'\":\n get_index_before_time(impersonation_3, 250 - 2.1056361198425293)\n }\n\n return information","def handle(text, mic, profile, wxbot=None):\n logger = logging.getLogger(__name__)\n # get config\n if SLUG not in profile or \\\n 'age' not in profile[SLUG]:\n mic.say('性别检测插件配置有误，插件使用失败', cache=True)\n return\n age = profile[SLUG]['age']\n try:\n gen = guess()\n age = guess(model_dir='/home/.dingdang/myplugins/plugincode/22801',class_type='age')#使用绝对路径路径\n logger.debug(\"genda report: \", gen)\n if gen=='M':\n mic.say('帅哥你好！', cache=True)\n print('prediction:',age)\n else:\n mic.say('美女你好！', cache=True)\n print('prediction:',age)\n except Exception, e:\n logger.error(e)","def evaluate(self,text):\n tokens=tokenize(text)\n if len(tokens)<self.n:\n return self.evaluate_n_minus1(tuple(tokens))\n \n ngrams_list = list(ngrams(tokens,self.n))\n value=0\n\n for i, ng in enumerate(ngrams_list):\n gram2,gram1=tuple(ng[:-1]),ng[-1]\n# if not i:\n# value = self.evaluate_n_minus1(gram2)\n denominator = sum(self.lm_dict[gram2].values())\n numerator = self.lm_dict[gram2][gram1] \n if numerator == 0 or denominator == 0:\n prob = self.smooth(numerator , denominator)\n else:\n prob = numerator / denominator\n value += math.log(prob)\n return value","def metrics(self):\n return self.verificationRun.metrics()","def test_counts(self):\n lines, words, chars = analyze_text(self.filename)\n self.assertEqual(lines, 4)\n self.assertEqual(words, 8)\n self.assertEqual(chars, 36)","def basic_statistics_of_email(data):\n word_counts = []\n character_count = 0\n\n for ind, row in data.iterrows():\n tokenizer = RegexpTokenizer(r'\\w+')\n real_words = tokenizer.tokenize(row[\"RawText\"].lower())\n\n character_count += sum(map(len, real_words))\n word_counts.append(len(real_words))\n\n return character_count, pd.Series(word_counts)","def _evaluation_summary(self, metric: str) -> str:\n fold_score_lists = self.fold_evaluations[metric].values()\n\n # Get the mean and std over all repetitions\n rep_means = [np.mean(list(x.values())) for x in fold_score_lists]\n rep_stds = [np.std(list(x.values())) for x in fold_score_lists]\n\n return \"{:.4f} +- {:.4f}\".format(np.mean(rep_means), np.mean(rep_stds))"],"string":"[\n \"def _profile(self, text):\\n prof = zeros(len(self.alph)**self.N)\\n ngs = ngrams(text, self.N)\\n for tup in ngs:\\n loc = 0\\n for i in range(len(tup)):\\n loc += (len(self.alph)**i) * self.alph.index(tup[i])\\n prof[loc] += 1\\n return prof\",\n \"def analize(self, text):\\n\\n try:\\n profile = self.personality_insights.profile(text,\\n raw_scores=True,\\n consumption_preferences=True)\\n except Exception as e:\\n print(\\\"Error during API call\\\", e)\\n profile = \\\"\\\"\\n\\n return profile\",\n \"def advancedStats():\",\n \"def stats_text(test):\\n\\n stats_text_en(test) \\n \\n stats_text_cn(test)\",\n \"def getTextStatsFeat(text, stemmRequired = True,\\r\\n excludeStopwordsRequired = True):\\r\\n #length = len(text)\\r\\n sentenceCount = len(re.findall(\\\"[.?!]\\\", text))\\r\\n exclamationMarkCount = len(re.findall(\\\"[!]\\\", text))\\r\\n questionMarkCount = len(re.findall(\\\"[?]\\\", text))\\r\\n digitsCount = len(re.findall(\\\"[0-9]+\\\", text))\\r\\n text = text.replace(\\\",\\\", \\\" \\\").replace(\\\".\\\", \\\" \\\")\\r\\n cleanText = re.sub('[^a-zа-я0-9]', ' ', text.lower())\\r\\n wordCount = 0.0\\r\\n charCount = 0.0\\r\\n rusCharCount = 0.0\\r\\n engCharCount = 0.0\\r\\n if excludeStopwordsRequired:\\r\\n for w in cleanText.split():\\r\\n if len(w)>1 and w not in stopwords:\\r\\n if not (not stemmRequired or re.search(\\\"[0-9a-z]\\\", w)):\\r\\n w = stemmer.stem(w)\\r\\n wordCount += 1\\r\\n c, rus, eng = getWordCharCount(w)\\r\\n charCount += c\\r\\n rusCharCount += rus\\r\\n engCharCount += eng\\r\\n else:\\r\\n for w in cleanText.split():\\r\\n if len(w)>1:\\r\\n if not (not stemmRequired or re.search(\\\"[0-9a-z]\\\", w)):\\r\\n w = stemmer.stem(w)\\r\\n wordCount += 1\\r\\n c, rus, eng = getWordCharCount(w)\\r\\n charCount += c\\r\\n rusCharCount += rus\\r\\n engCharCount += eng\\r\\n # per sentence\\r\\n wordPerSentence = tryDivide(wordCount, sentenceCount)\\r\\n charPerSentence = tryDivide(charCount, sentenceCount)\\r\\n rusCharPerSentence = tryDivide(rusCharCount, sentenceCount)\\r\\n engCharPerSentence = tryDivide(engCharCount, sentenceCount)\\r\\n # per word\\r\\n charPerWord = tryDivide(charCount, wordCount)\\r\\n rusCharPerWord = tryDivide(rusCharCount, wordCount)\\r\\n engCharPerWord = tryDivide(engCharCount, wordCount)\\r\\n # ratio\\r\\n rusCharRatio = tryDivide(rusCharCount, charCount)\\r\\n engCharRatio = tryDivide(engCharCount, charCount)\\r\\n rusCharVsEngChar = tryDivide(rusCharCount, engCharCount)\\r\\n engCharVsRusChar = tryDivide(engCharCount, rusCharCount)\\r\\n \\r\\n stats = [\\r\\n sentenceCount,\\r\\n wordCount,\\r\\n charCount,\\r\\n rusCharCount,\\r\\n engCharCount,\\r\\n digitsCount,\\r\\n exclamationMarkCount,\\r\\n questionMarkCount,\\r\\n wordPerSentence,\\r\\n charPerSentence,\\r\\n rusCharPerSentence,\\r\\n engCharPerSentence,\\r\\n charPerWord,\\r\\n rusCharPerWord,\\r\\n engCharPerWord,\\r\\n rusCharRatio,\\r\\n engCharRatio,\\r\\n rusCharVsEngChar,\\r\\n engCharVsRusChar,\\r\\n ]\\r\\n statsFeat = \\\"\\\"\\r\\n for i,f in enumerate(stats):\\r\\n if f != 0:\\r\\n statsFeat += \\\"%s:%s \\\" % (i+1, f)\\r\\n statsFeat = statsFeat[:-1] \\r\\n return statsFeat\",\n \"def get_summarization(text):\\n DOCUMENT = re.sub(r'\\\\n|\\\\r', ' ', text)\\n DOCUMENT = re.sub(r' +', ' ', DOCUMENT)\\n DOCUMENT = DOCUMENT.strip()\\n print(\\\"Summary 1\\\")\\n print(summarize(DOCUMENT, word_count=75, split=False))\\n print(\\\"Summary 2\\\")\\n print(summarize(DOCUMENT, ratio=0.2, split=False))\",\n \"def readability_measurements(passage: str):\\n results = readability.getmeasures(passage, lang='en')\\n \\n chars_per_word = results['sentence info']['characters_per_word']\\n syll_per_word = results['sentence info']['syll_per_word']\\n words_per_sent = results['sentence info']['words_per_sentence']\\n \\n kincaid = results['readability grades']['Kincaid']\\n ari = results['readability grades']['ARI']\\n coleman_liau = results['readability grades']['Coleman-Liau']\\n flesch = results['readability grades']['FleschReadingEase']\\n gunning_fog = results['readability grades']['GunningFogIndex']\\n lix = results['readability grades']['LIX']\\n smog = results['readability grades']['SMOGIndex']\\n rix = results['readability grades']['RIX']\\n dale_chall = results['readability grades']['DaleChallIndex']\\n \\n tobeverb = results['word usage']['tobeverb']\\n auxverb = results['word usage']['auxverb']\\n conjunction = results['word usage']['conjunction']\\n pronoun = results['word usage']['pronoun']\\n preposition = results['word usage']['preposition']\\n nominalization = results['word usage']['nominalization']\\n \\n pronoun_b = results['sentence beginnings']['pronoun']\\n interrogative = results['sentence beginnings']['interrogative']\\n article = results['sentence beginnings']['article']\\n subordination = results['sentence beginnings']['subordination']\\n conjunction_b = results['sentence beginnings']['conjunction']\\n preposition_b = results['sentence beginnings']['preposition']\\n \\n return [chars_per_word, syll_per_word, words_per_sent,\\n kincaid, ari, coleman_liau, flesch, gunning_fog, lix, smog, rix, dale_chall,\\n tobeverb, auxverb, conjunction, pronoun, preposition, nominalization,\\n pronoun_b, interrogative, article, subordination, conjunction_b, preposition_b]\",\n \"def metrics_text(self, x, extra=None):\\n metrics = self.compute_metrics(x, extra=extra)\\n if metrics is not None:\\n s = \\\", \\\".join([\\\"%s=%0.5f\\\"%(k, v) for k, v in metrics.items()])\\n return s\\n return \\\"\\\"\",\n \"def stats(self):\",\n \"def script_stats_analysis(self, data):\\n anomal_chars = [r'^', r'{', r'}', r'\\\"', r',', r'<', r'>', ';']\\n anomal_char_stats = {}\\n char_stats = {\\\"upper\\\": 0, \\\"lower\\\": 0, \\\"numbers\\\": 0, \\\"symbols\\\": 0, \\\"spaces\\\": 0}\\n anomalies = []\\n c = Counter(data)\\n anomaly_score = 0\\n\\n # Check the characters\\n for char in c.most_common():\\n if char[0] in anomal_chars:\\n anomal_char_stats[char[0]] = char[1]\\n if char[0].isupper():\\n char_stats[\\\"upper\\\"] += char[1]\\n elif char[0].islower():\\n char_stats[\\\"lower\\\"] += char[1]\\n elif char[0].isdigit():\\n char_stats[\\\"numbers\\\"] += char[1]\\n elif char[0].isspace():\\n char_stats[\\\"spaces\\\"] += char[1]\\n else:\\n char_stats[\\\"symbols\\\"] += char[1]\\n # Totals\\n char_stats[\\\"total\\\"] = len(data)\\n char_stats[\\\"alpha\\\"] = char_stats[\\\"upper\\\"] + char_stats[\\\"lower\\\"]\\n\\n # Detect Anomalies\\n if char_stats[\\\"alpha\\\"] > 40 and char_stats[\\\"upper\\\"] > (char_stats[\\\"lower\\\"] * 0.9):\\n anomalies.append(\\\"upper to lower ratio\\\")\\n anomaly_score += 20\\n if char_stats[\\\"symbols\\\"] > char_stats[\\\"alpha\\\"]:\\n anomalies.append(\\\"more symbols than alphanum chars\\\")\\n anomaly_score += 40\\n for ac, count in anomal_char_stats.iteritems():\\n if (count/char_stats[\\\"alpha\\\"]) > 0.05:\\n anomalies.append(\\\"symbol count of '%s' very high\\\" % ac)\\n anomaly_score += 40\\n\\n # Generate message\\n message = \\\"Anomaly detected ANOMALIES: '{0}'\\\".format(\\\"', '\\\".join(anomalies))\\n if anomaly_score > 40:\\n return message, anomaly_score\\n\\n return \\\"\\\", 0\",\n \"def show_profile(self, aggregate=0):\\n s = ''\\n if aggregate == 0:\\n s += '%50s\\\\t %7s \\\\t %4s \\\\t %4s \\\\t %s \\\\n' % (\\\"call\\\", \\\"time\\\", \\\"#sol\\\", \\\"#call\\\", \\\"location\\\")\\n s += '-' * 100 + '\\\\n'\\n for tm, key in sorted((t, k) for k, t in self.timestats.items()):\\n term, location = key\\n nb = self.resultstats[key]\\n cl = self.callstats[key]\\n location = location_string(location)\\n s += '%50s\\\\t %.5f \\\\t %d \\\\t %d \\\\t [%s]\\\\n' % (term, tm, nb, cl, location)\\n else:\\n timestats_agg = defaultdict(float)\\n resultstats_agg = defaultdict(int)\\n callstats_agg = defaultdict(int)\\n\\n for k, t in self.timestats.items():\\n if aggregate == 1:\\n key = k[0]\\n else:\\n key = k[0].signature\\n timestats_agg[key] += t\\n resultstats_agg[key] += self.resultstats[k]\\n callstats_agg[key] += self.callstats[k]\\n\\n s += '%50s\\\\t %7s \\\\t %4s \\\\t %4s\\\\n' % (\\\"call\\\", \\\"time\\\", \\\"#sol\\\", \\\"#call\\\")\\n s += '-' * 100 + '\\\\n'\\n for tm, key in sorted((t, k) for k, t in timestats_agg.items()):\\n nb = resultstats_agg[key]\\n cl = callstats_agg[key]\\n s += '%50s\\\\t %.5f \\\\t %d \\\\t %d\\\\n' % (key, tm, nb, cl)\\n\\n return s\",\n \"def measureAll(authors_texts,sectorialized_agents):\\n authors_texts=P.text.aux.textFromAuthors(authors_texts,self.topm_dict[\\\"sectorialized_agents\\\"])\\n authors_measures={}\\n # análise de cada mensagem e de cada autor\\n for author in authors_texts:\\n authors_measures[author]={}\\n texts=authors_texts[author]\\n authors_measures[author][\\\"raw_strings\\\"]=P.text.raw.analyseAll(texts)\\n authors_measures[author][\\\"pos\\\"]= P.text.pos.analyseAll(authors_analysis[author][\\\"raw_analysis\\\"])\\n authors_measures[author][ \\\"wordnet\\\" ]=P.text.wordnet.analyseAll(authors_analysis[author][\\\"pos_analysis\\\"])\\n authors_measures[author][\\\"tfIdf\\\"]=P.text.tfIdf.analyseAll(texts) # tfIdf de cada texto e do autor, numeric: mean e std das distancias\\n # análise de cada setor e da estrutura toda\\n# sectors_texts=P.text.aux.textFromSectors(authors_text,sectorialized_agents)\\n sectors_measures={}\\n for sector in sectorialized_agents:\\n sectors_measures[sector][\\\"raw_strings\\\"]=P.text.raw.sectorsAnalyseAll(authors_analysis,sectorialized_agents[sector])\\n sectors_measures[sector][\\\"pos\\\"]= P.text.pos.sectorsAnalyseAll(authors_analysis,sectorialized_agents[sector])\\n sectors_measures[sector][\\\"wordnet\\\"]= P.text.wordnet.sectorsAnalyseAll(authors_analysis,sectorialized_agents[sector])\\n # tfIdf de cada texto e de cada autor, numeric: mean e std das distancias por texto e por autor, e media e etd dos autores\\n sectors_measures[sector][\\\"tfIdf\\\"]= P.text.tfIdf.sectorsAnalyseAll(authors_analysis,sectorialized_agents[sector])\\n\\n# texts=[sectors_texts[i] for i in (\\\"peripherals\\\",\\\"intermediaries\\\",\\\"hubs\\\")]\\n# sectors_analysis[\\\"raw_strings\\\"]=P.text.raw.analyseAll(texts)\\n# sectors_analysis[\\\"pos\\\"]= P.text.pos.analyseAll(sectors_analysis[\\\"raw_analysis\\\"])\\n# sectors_analysis[ \\\"wordnet\\\" ]=P.text.wordnet.analyseAll(sectors_analysis[\\\"pos_analysis\\\"])\\n# sectors_analysis[\\\"tfIdf\\\"]=P.text.tfIdf.tfIdf(texts)\\n\\n overall_measures[\\\"raw_strings\\\"]=P.text.raw.systemAnalysis(sectors_analysis) # medias de toda a rede por mensagem, por autor e por setor\\n overall_measures[\\\"pos\\\"]=P.text.raw.systemAnalysis(sectors_analysis) # medias de toda a rede por mensagem, por autor e por setor\\n overall_measures[\\\"wordnet\\\"]=P.text.raw.systemAnalysis(sectors_analysis) # medias de toda a rede por mensagem, por autor e por setor\\n # tfIdf measurespor texto, autor e setor, numeric: media e desvio das distancias por cada grupo, media e desvio dos setores e dos autores\\n overall_measures[\\\"tfIdf\\\"]=P.text.tfIdf.systemAnalysis(sectors_analysis) # medias de toda a rede por mensagem, por autor e por setor\\n\\n del authors_texts,sectorialized_agents,author, sector\\n return locals()\",\n \"def __profanity_scan(self, title, text):\\n profane_list = set(PROFANITY)\\n text_words = nltk.word_tokenize(text)\\n text_count = 0\\n title_count = 0\\n for word in text_words:\\n if word.lower() in profane_list:\\n text_count += 1\\n for word in title.split():\\n if word.lower() in profane_list:\\n title_count += 1\\n return title_count, text_count\",\n \"def text_report(self):\\n\\n word_count = self.word_count()\\n\\n print(\\\"\\\\nThere are {} words in the text.\\\".format(word_count))\\n mean, median, mode = self.average_word_length()\\n\\n print(\\\"\\\\nMean, median and mode word length is {}, {}, {}.\\\".format(mean, median, mode))\\n\\n if word_count < 10:\\n print(\\\"\\\\nLongest words:\\\")\\n else:\\n print(\\\"\\\\n10 longest words:\\\")\\n for s in self.longest_words():\\n print(s)\\n\\n print(\\\"\\\\nMost common words:\\\")\\n for s in self.common_words():\\n print(\\\"{} x {}\\\".format(s[1], s[0]))\\n\\n longest_grams = []\\n\\n # find n_longest n-grams\\n n_longest = 10\\n # strongly doubt that there will be n-grams longer than 50\\n for i in range(min(50, word_count), 1, -1):\\n if len(longest_grams) >= n_longest:\\n break\\n grams = self.find_ngrams(i)\\n grams_list = sorted(grams, key=grams.get, reverse=True)\\n\\n for g in grams_list:\\n if grams[g] > 4:\\n # do not want to include n-grams which are substrings of longer n-grams\\n substring = False\\n for s in longest_grams:\\n if g in s[1]:\\n substring = True\\n break\\n if not substring:\\n longest_grams.append([grams[g], g])\\n\\n print(\\\"\\\\nLongest n-grams:\\\")\\n for g in longest_grams:\\n print(\\\"{} x {}\\\".format(g[0], g[1]))\\n print('\\\\n')\",\n \"def summary_line_and_description():\",\n \"def report(self):\\n m = {}\\n num_tok = self.metrics['num_tokens']\\n if num_tok > 0:\\n if self.metrics['correct_tokens'] > 0:\\n m['token_acc'] = self.metrics['correct_tokens'] / num_tok\\n m['loss'] = self.metrics['loss'] / num_tok\\n if self.metrics['pred_count'] > 0:\\n m['pred'] = self.metrics['correct_pred'] / self.metrics['pred_count']\\n try:\\n m['ppl'] = math.exp(m['loss'])\\n except OverflowError:\\n m['ppl'] = float('inf')\\n if self.metrics['total_skipped_batches'] > 0:\\n m['total_skipped_batches'] = self.metrics['total_skipped_batches']\\n for k, v in m.items():\\n # clean up: rounds to sigfigs and converts tensors to floats\\n m[k] = round_sigfigs(v, 4)\\n return m\",\n \"def stats_preprocessing(self):\\n output = {'before_tot':[],\\n 'before_unique':[],\\n 'after_tot':[],\\n 'after_unique':[]}\\n for i in range(len(self.table)):\\n description_raw = self.table.description.iloc[i].split(' ')\\n clean_txt = self.table.clean_text.iloc[i].split(' ')\\n\\n output['before_tot'].append(len(description_raw))\\n output['before_unique'].append(len(set(description_raw)))\\n output['after_tot'].append(len(clean_txt))\\n output['after_unique'].append(len(set(clean_txt)))\\n \\n print(\\\"\\\"\\\"Before preprocessing a description had on average {0} words with standard deviation {1}. \\\\n\\nMoreover, the average of unique words was {2} and the standard deviation {3}.\\\"\\\"\\\"\\\\\\n .format(round(mean(output['before_tot']), 2), round(stdev(output['before_tot']), 2), \\n round(mean(output['before_unique']), 2), round(stdev(output['before_unique'])), 2))\\n \\n print(\\\"\\\"\\\"\\\\nAfter preprocessing a description has on average {0} words with standard deviation {1}. \\\\n \\nThe average of unique words is now {2} and the standard deviation {3}.\\\"\\\"\\\"\\\\\\n .format(round(mean(output['after_tot']), 2), round(stdev(output['after_tot']), 2), \\n round(mean(output['after_unique']),2), round(stdev(output['after_unique']), 2)))\\n\\n return output\",\n \"def evaluate_text(bio_text):\\n\\n\\ts_df = bert_evaluator.evaluate_text(bio_text)\\n\\trealness_vals = s_df['prob_real'].values \\n\\n\\t# return tuple (float mininal_chunk_val, float avg_chunk_val, \\n\\t# float[] all_vals)\\n\\treturn (np.min(realness_vals), np.mean(realness_vals), vals)\",\n \"def summaryText(self):\\n\\n print('\\\\nReport Summary:\\\\n')\\n for author in self.lowQuality.keys():\\n if len(self.lowQuality[author]) > 0:\\n print('Author: ' + author)\\n print('---------------------')\\n # do some sorting for readability\\n files = []\\n file2rating = {}\\n for fileRating in self.lowQuality[author]:\\n files.append(fileRating[1])\\n file2rating[fileRating[1]] = fileRating[0]\\n files.sort()\\n for fileRating in files:\\n print(file2rating[fileRating] + ' :: ' + fileRating)\\n print('\\\\n\\\\n')\",\n \"def display_metrics3(self):\\n messagebox.showinfo(\\\"Processed Image Metrics\\\", self.pro_metrics)\",\n \"def acc(ev):\\n profData = getProfilingData(ev)\\n if profData is not None:\\n return profData.Tacc()\\n return \\\"\\\"\",\n \"def measure(self, recommender):\",\n \"def summary_stats(tile_summary):\\n return \\\"Original Dimensions: %dx%d\\\\n\\\" % (tile_summary.orig_w, tile_summary.orig_h) + \\\\\\n \\\"Original Tile Size: %dx%d\\\\n\\\" % (tile_summary.orig_tile_w, tile_summary.orig_tile_h) + \\\\\\n \\\"Scale Factor: 1/%dx\\\\n\\\" % tile_summary.scale_factor + \\\\\\n \\\"Scaled Dimensions: %dx%d\\\\n\\\" % (tile_summary.scaled_w, tile_summary.scaled_h) + \\\\\\n \\\"Scaled Tile Size: %dx%d\\\\n\\\" % (tile_summary.scaled_tile_w, tile_summary.scaled_tile_w) + \\\\\\n \\\"Total Mask: %3.2f%%, Total Tissue: %3.2f%%\\\\n\\\" % (\\n tile_summary.mask_percentage(), tile_summary.tissue_percentage) + \\\\\\n \\\"Tiles: %dx%d = %d\\\\n\\\" % (tile_summary.num_col_tiles, tile_summary.num_row_tiles, tile_summary.count) + \\\\\\n \\\" %5d (%5.2f%%) tiles >=%d%% tissue\\\\n\\\" % (\\n tile_summary.high, tile_summary.high / tile_summary.count * 100, TISSUE_HIGH_THRESH) + \\\\\\n \\\" %5d (%5.2f%%) tiles >=%d%% and <%d%% tissue\\\\n\\\" % (\\n tile_summary.medium, tile_summary.medium / tile_summary.count * 100, TISSUE_LOW_THRESH,\\n TISSUE_HIGH_THRESH) + \\\\\\n \\\" %5d (%5.2f%%) tiles >0%% and <%d%% tissue\\\\n\\\" % (\\n tile_summary.low, tile_summary.low / tile_summary.count * 100, TISSUE_LOW_THRESH) + \\\\\\n \\\" %5d (%5.2f%%) tiles =0%% tissue\\\" % (tile_summary.none, tile_summary.none / tile_summary.count * 100)\",\n \"def measure(self):\\n pass\",\n \"def summary_string(self) -> str:\",\n \"def display_metrics2(self):\\n messagebox.showinfo(\\\"Original Image Metrics\\\", self.raw_metrics)\",\n \"def _createTextProfile(self, indict):\\r\\n\\r\\n keys = map(lambda x: x[0], indict)\\r\\n vals = map(lambda x: x[1], indict)\\r\\n\\r\\n outstrs = [\\\"\\\\n\\\"]\\r\\n propDict = {}\\r\\n total = sum(vals)\\r\\n maxLenKey = max([len(a) for a in keys])\\r\\n maxLenVal = max([len(repr(a)) for a in vals]) \\r\\n\\r\\n for k, v in indict:\\r\\n outstr = \\\" \\\"\\r\\n outstr += k.ljust(maxLenKey + 1)\\r\\n outstr += (\\\"%.2f\\\" % v).ljust(maxLenVal + 1)\\r\\n outstr += \\\"-\\\" * int(self.numBars * (v / total))\\r\\n outstrs.append(outstr)\\r\\n\\r\\n return \\\"\\\\n\\\".join(outstrs)\",\n \"def compute_statistics(self):\",\n \"def profile(self):\\n return NumericStatsMixin.profile(self)\",\n \"def analyze(self, text):\\n #analize every word in the text a value -1, 1 or 0 and calculate total score\\n #tokens allow us to split words in single tokens we can initialize tokens like this:\\n\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n tokens = tokenizer.tokenize(text.lower())\\n\\n score = 0\\n\\n if tokens[0] in self.negatives:\\n score =- 1\\n elif tokens[0] in self.positives:\\n score =+ 1\\n else:\\n score = 0\\n\\n #print('', text)\\n\\n return score\",\n \"def summary(self):\\n return self.pfm\",\n \"def analyze(self, text):\\n #Check each word in text\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n tokens = tokenizer.tokenize(text)\\n total_score = 0\\n #Sum the total score\\n for token in tokens:\\n token = token.lower()\\n if token in self.positives:\\n total_score = total_score + 1\\n elif token in self.negatives:\\n total_score = total_score - 1\\n else:\\n total_score = total_score + 0\\n \\n return total_score\",\n \"def stats(self):\\n stats = {\\n 'lines' : '', # This will count the lines under each split\\n 'status_code': self.status_code,\\n 'content_type': self.mime,\\n 'hop': self.hop_path[-1:],\\n 'sum:content_length': self.content_length,\\n 'host': self.host(),\\n 'source': self.source\\n }\\n # Add in annotations:\\n for annot in self.annotations:\\n # Set a prefix based on what it is:\\n prefix = ''\\n if self.re_tries.match(annot):\\n prefix = 'tries:'\\n elif self.re_ip.match(annot):\\n prefix = \\\"ip:\\\"\\n # Only emit lines with annotations:\\n if annot != \\\"-\\\":\\n stats[\\\"%s%s\\\" % (prefix, annot)] = \\\"\\\"\\n return stats\",\n \"def compute_metrics(self):\\n pass\",\n \"def analyze(self, text):\\n\\n # TODO\\n # tokens = tokenizer.tokenize(tweet)\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n tokens = tokenizer.tokenize(text)\\n score = 0\\n\\n for word in tokens:\\n # iterate over tokens#str.lower\\n\\n if word.lower() in self.positives:\\n score = score+1\\n\\n elif word.lower() in self.negatives:\\n score = score-1\\n\\n else:\\n continue\\n return score\",\n \"def evaluate(self, text):\\n text = ' '.join(['<'] * (self.n - 1) + [text.replace(' . ', ' .%s ' % (' <' * (self.n - 1)))])\\n tokens = self.split(text)\\n sum = 0\\n for i in range(len(tokens) - self.n + 1):\\n n_gram = self.join(tokens[i: i + self.n])\\n prob = self.get_probability(n_gram, True)\\n sum += math.log(prob, self.log_base)\\n return sum\",\n \"def summary(self):\\n if not self: return u''\\n s = u'Character\\\\n=====\\\\nName: %s\\\\n' % \\\\\\n self.get('name', u'')\\n bio = self.get('biography')\\n if bio:\\n s += u'Biography: %s\\\\n' % bio[0]\\n filmo = self.get('filmography')\\n if filmo:\\n a_list = [x.get('long imdb canonical title', u'')\\n for x in filmo[:5]]\\n s += u'Last movies with this character: %s.\\\\n' % u'; '.join(a_list)\\n return s\",\n \"def num_words():\\n # Load the GT.\\n df = pd.read_csv(config.META_FQN, sep=\\\"\\\\t\\\")\\n stats = {\\n \\\"T\\\": {\\\"words\\\": [], \\\"duration\\\": []},\\n \\\"P\\\": {\\\"words\\\": [], \\\"duration\\\": []},\\n \\\"sess\\\": {\\\"words\\\": [], \\\"duration\\\": []},\\n }\\n\\n for _, row in df.iterrows():\\n if row[\\\"asr_test\\\"]:\\n stats[\\\"P\\\"][\\\"words\\\"].append(float(row[\\\"gt_patient_num_words\\\"]))\\n stats[\\\"T\\\"][\\\"words\\\"].append(float(row[\\\"gt_therapist_num_words\\\"]))\\n stats[\\\"P\\\"][\\\"duration\\\"].append(float(row[\\\"gt_patient_time_spoken\\\"]))\\n stats[\\\"T\\\"][\\\"duration\\\"].append(\\n float(row[\\\"gt_therapist_time_spoken\\\"])\\n )\\n stats[\\\"sess\\\"][\\\"duration\\\"].append(float(row[\\\"sess_dur\\\"]))\\n n_words = (\\n row[\\\"gt_therapist_num_words\\\"] + row[\\\"gt_patient_num_words\\\"]\\n )\\n stats[\\\"sess\\\"][\\\"words\\\"].append(n_words)\\n\\n for speaker in stats:\\n for metric in stats[speaker]:\\n print(f\\\"------ {speaker} | {metric} ------\\\")\\n print_stats(stats[speaker][metric])\",\n \"def print_metrics(result):\\n logging.log(LOG_LEVEL_OUTPUT_INFO,\\n '------------------------------------------------')\\n logging.log(LOG_LEVEL_OUTPUT_INFO, ' KEY METRICS: ')\\n logging.log(LOG_LEVEL_OUTPUT_INFO,\\n '------------------------------------------------')\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* pages_count: %d',\\n get_counter_metric(result, 'pages_count'))\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_count: %d',\\n get_counter_metric(result, 'revisions_count'))\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* very_long_page_histories_count: %d',\\n get_counter_metric(result, 'very_long_page_histories_count'))\\n revisions_per_page_distr = get_distributions_metric(\\n result, 'revisions_per_page_distr')\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.mean: %d',\\n revisions_per_page_distr.mean)\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* revisions_per_page_distr.sum: %d',\\n revisions_per_page_distr.sum)\\n cumulative_page_rev_size_distr = get_distributions_metric(\\n result, 'cumulative_page_rev_size_distr')\\n logging.log(LOG_LEVEL_OUTPUT_INFO,\\n '* cumulative_page_rev_size_distr.mean: %d',\\n cumulative_page_rev_size_distr.mean)\\n logging.log(LOG_LEVEL_OUTPUT_INFO, '* cumulative_page_rev_size_distr.sum: %d',\\n cumulative_page_rev_size_distr.sum)\",\n \"def printStats(m2, m3, actual, top):\\n print(\\\"\\\\nThe actual categories for this page are: %s\\\" % \\\", \\\".join(sorted(actual)))\\n print(\\\"\\\\nThe suggested categories for this page are: %s\\\" % \\\", \\\".join(sorted([v for v, count in top])))\\n print(\\\"\\\\nBOOLEAN MEASURE = %s\\\" %(m2 != 0))\\n print(\\\"FRACTIONAL MEASURE = %0.2f\\\" %(m2))\\n print(\\\"HIERARCHICAL MEASURE = %0.2f\\\\n\\\" %(m3))\\n print(\\\"*\\\" * 150)\",\n \"def extract_core_stats(self):\\n titleLabel = QLabel(\\\"Core Statistics\\\")\\n titleLabel.setStyleSheet('font: 20pt \\\"Imprint MT Shadow\\\"; color: #ffffff;')\\n self.filterVbox.addWidget(titleLabel, alignment=Qt.AlignCenter)\\n potentialLabels = [\\\"Primary\\\", \\\"Secondary\\\", \\\"Class\\\", \\\"Subclass\\\",\\n \\\"Background\\\", \\\"Race\\\", \\\"Subrace\\\", \\\"Minimum AC\\\"]\\n\\n for label in potentialLabels:\\n if label in [\\\"Primary\\\", \\\"Secondary\\\"]:\\n textAddition = \\\" Archetype\\\"\\n else:\\n textAddition = \\\"\\\"\\n try:\\n nextLabel = QLabel(f\\\"{label + textAddition}: {self.filters[label]}\\\")\\n nextLabel.setStyleSheet('font: 14pt \\\"Times New Roman\\\"; color: rgb(188, 189, 177);')\\n self.filterVbox.addWidget(nextLabel, alignment=Qt.AlignCenter)\\n except KeyError:\\n pass\",\n \"def test_execution_profiling(self):\\n self._test_reports_helper({\\\"--profile-execution\\\": \\\"\\\"}, [\\\"report.txt\\\"])\",\n \"def compute_and_print_eval_metrics(self):\\n s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')\\n precision, recall, f1, mean_precision, mean_recall, map50, map = 0., 0., 0., 0., 0., 0., 0.\\n ap = []\\n eval_stats = [np.concatenate(x, 0) for x in zip(*self.eval_stats)]\\n if len(eval_stats) and eval_stats[0].any():\\n precision, recall, ap, f1, ap_class = ap_per_class(*eval_stats)\\n precision, recall, ap50, ap = precision[:, 0], recall[:, 0], ap[:, 0], ap.mean(1)\\n mean_precision, mean_recall, map50, map = precision.mean(), recall.mean(), ap50.mean(), ap.mean()\\n nt = np.bincount(eval_stats[3].astype(np.int64), minlength=len(self.class_names)) # number of targets per class\\n else:\\n nt = np.zeros(1)\\n\\n pf = '%20s' + '%12.5g' * 6 # print format\\n print(\\\"\\\\n EVALUTAION \\\\n\\\")\\n print(s)\\n print(pf % ('all', self.seen, nt.sum(), mean_precision, mean_recall, map50, map))\\n if self.cfg.eval.verbose:\\n for indx, cls in enumerate(ap_class):\\n print(pf % (self.class_names[cls], self.seen, nt[cls], precision[indx], recall[indx], ap50[indx], ap[indx]))\",\n \"def analyze_text (self, testing_string): \\n self.length = len(self.testing_string)\\n self.total_words = (self.testing_string).split()\\n self.total_unique_words = set(self.total_words)\\n\\n self.total_characters = (int)(0)\\n for ch in self.testing_string :\\n if(ch.isspace() != True):\\n self.total_characters = self.total_characters + 1 \\n\\n self.total_unique_characters = set(self.testing_string)\\n \\n Linguist.about_given_string[\\\"Length\\\"] = self.length\\n Linguist.about_given_string[\\\"Total_words\\\"] = len(self.total_words)\\n Linguist.about_given_string[\\\"Total_unique_words\\\"] = len(self.total_unique_words)\\n Linguist.about_given_string[\\\"Total_characters\\\"] = self.total_characters\\n Linguist.about_given_string[\\\"Total_unique_characters\\\"] = len(self.total_unique_characters)\",\n \"def get_stats(text):\\n stats_wP = ['HP', 'Def', 'Atk']\\n regex = r\\\"(Speed|Health|Defense|Attack|Critical Hit Damage|Critical Hit Chance|Effectiveness|Effect Resistance) \\\\d.*\\\"\\n\\n line_num = 0\\n sub_num = 1\\n text = text.strip()\\n item_stats = {}\\n for key, value in change_lst.items():\\n text = text.replace(key, str(value))\\n\\n for line in text.split(\\\"\\\\n\\\"):\\n line_num += 1\\n if re.search(r\\\"set\\\", line, re.IGNORECASE) is not None: # \\\"Set\\\" in line:\\n set_found = re.search(r\\\"([a-zA-Z]+) Set\\\", line, re.IGNORECASE).group(1).capitalize()\\n if set_found in sets_name:\\n item_stats[\\\"set\\\"] = set_found\\n else:\\n item_stats[\\\"set\\\"] = difflib.get_close_matches(set_found, sets_name, n=1, cutoff=0.1)[0]\\n\\n if re.search(regex, line):\\n stat, value = line.rsplit(\\\" \\\", 1)\\n stat = rename_stat(stat)\\n # if \\\" \\\" in stat:\\n # stat = stat.rsplit(\\\" \\\", 1)[1]\\n\\n if \\\",\\\" in value:\\n value = re.sub(\\\",\\\", \\\"\\\", value)\\n\\n if \\\"%\\\" in value:\\n value = int(value[:-1])\\n if stat in stats_wP:\\n stat += \\\"P\\\"\\n\\n if line_num == 1:\\n # Record Main Stat\\n item_stats[\\\"mainStat\\\"] = [stat, int(value)]\\n # item_stats[str(\\\"Main \\\" + stat)] = value\\n else:\\n # Record substats\\n item_stats[\\\"subStat\\\" + str(sub_num)] = [stat, int(value)]\\n sub_num += 1\\n\\n # if no subStats, raise error\\n if sub_num <= 2:\\n raise\\n\\n return item_stats\",\n \"def analyze_text(text):\\n # Your code here\\n total=0\\n total_es = 0\\n\\n for c in text:\\n if c.isalpha():\\n total += 1\\n\\n total_es = text.lower().count('e')\\n percentage = float(total_es/total)\\n\\n print(total, total_es, percentage * 100)\",\n \"def stats(self):\\n pass\",\n \"def _run_profile(self, profile: Tuple[str, List[int], int, float]) -> List[int]:\\n # pylint: disable=unused-variable\\n name, command, signals, delay = profile\\n # pylint: enable=unused-variable\\n\\n # print(\\\"\\\\trunning profile: %s, command %s, %d, delay %0.02f\\\" %\\n # (name, [\\\"0x%02x\\\" % i for i in command], signals, delay))\\n return self._i2c_read_words_from_cmd(command, delay, signals)\",\n \"def raw_measure(self) -> List[int]:\\n # name, command, signals, delay\\n return self._run_profile((\\\"raw_measure\\\", [0x20, 0x50], 2, 0.025))\",\n \"def get_str_metadata(self):\\n return \\\"\\\\n\\\".join([\\\"Guessed by {}\\\".format(self.guessed_by), \\\"{} metaphors used\\\".format(self.metaphors_used)])\",\n \"def _get_accuracy(text):\\n sta_obj = [m.start() for m in re.finditer('%',text)]\\n return([float(text[x-3:x:1]) for x in sta_obj])\",\n \"def analyze(self, text):\\n score =0\\n token = TweetTokenizer()\\n tokens = token.tokenize(text)\\n for token in tokens:\\n if token.lower() in self.pos_list:\\n score+=1\\n elif token.lower() in self.neg_list:\\n score-=1\\n\\n return score\",\n \"def measure(colloc_tag, colloc_count, n1grams, unigrams, n):\\r\\n collocation_words, collocation_tags = colloc_tag.split('/')\\r\\n collocation_words = collocation_words.split(' ')\\r\\n collocation_tags = collocation_tags.split(' ')\\r\\n pattern_words = ' '.join(collocation_words[:-1])\\r\\n pattern_tags = ' '.join(collocation_tags[:-1])\\r\\n\\r\\n pattern = pattern_words + '/' + pattern_tags\\r\\n last_word = collocation_words[-1] + '/' + collocation_tags[-1]\\r\\n c_pattern = int(n1grams[pattern])\\r\\n c_lw = int(unigrams[last_word])\\r\\n colloc_count = int(colloc_count)\\r\\n\\r\\n tsc = t_score(colloc_count, c_pattern, c_lw, n)\\r\\n pmisc = pmi(colloc_count, c_pattern, c_lw, n)\\r\\n logdsc = logDice(colloc_count, c_pattern, c_lw)\\r\\n return logdsc, pmisc, tsc\",\n \"def get_profile_stats():\\n return p_stats\",\n \"def _printSummary(self):\\n\\t\\t### COP OUT\\n\\t\\tif self.params['background'] is True:\\n\\t\\t\\tself.stats['count'] += 1\\n\\t\\t\\treturn\\n\\n\\t\\t### THIS NEEDS TO BECOME MUCH MORE GENERAL, e.g. Peaks\\n\\t\\ttdiff = time.time()-self.stats['startseries']\\n\\t\\tif not self.params['continue'] or tdiff > 0.1:\\n\\t\\t\\tcount = self.stats['count']\\n\\t\\t\\t#if(count != self.stats['lastcount']):\\n\\t\\t\\tsys.stderr.write(\\\"\\\\n\\\\tSUMMARY: \\\"+self.functionname+\\\"\\\\n\\\")\\n\\t\\t\\tself._printLine()\\n\\t\\t\\tsys.stderr.write(\\\"\\\\tTIME: \\\\t\\\"+apDisplay.timeString(tdiff)+\\\"\\\\n\\\")\\n\\t\\t\\tself.stats['timesum'] = self.stats['timesum'] + tdiff\\n\\t\\t\\tself.stats['timesumsq'] = self.stats['timesumsq'] + (tdiff**2)\\n\\t\\t\\ttimesum = self.stats['timesum']\\n\\t\\t\\ttimesumsq = self.stats['timesumsq']\\n\\t\\t\\tif(count > 1):\\n\\t\\t\\t\\ttimeavg = float(timesum)/float(count)\\n\\t\\t\\t\\ttimestdev = math.sqrt(float(count*timesumsq - timesum**2) / float(count*(count-1)))\\n\\t\\t\\t\\ttimeremain = (float(timeavg)+float(timestdev))*self.stats['seriesleft']\\n\\t\\t\\t\\tsys.stderr.write(\\\"\\\\tAVG TIME: \\\\t\\\"+apDisplay.timeString(timeavg,timestdev)+\\\"\\\\n\\\")\\n\\t\\t\\t\\t#print \\\"\\\\t(- TOTAL:\\\",apDisplay.timeString(timesum),\\\" -)\\\"\\n\\t\\t\\t\\tif(self.stats['seriesleft'] > 0):\\n\\t\\t\\t\\t\\tsys.stderr.write(\\\"\\\\t(- REMAINING TIME: \\\"+apDisplay.timeString(timeremain)+\\\" for \\\"\\n\\t\\t\\t\\t\\t\\t+str(self.stats['seriesleft'])+\\\" series -)\\\\n\\\")\\n\\t\\t\\t#print \\\"\\\\tMEM: \\\",(mem.active()-startmem)/1024,\\\"M (\\\",(mem.active()-startmem)/(1024*count),\\\"M)\\\"\\n\\t\\t\\tself.stats['count'] += 1\\n\\t\\t\\tself._printLine()\",\n \"def analyze(self, text):\\n\\n tknzr = nltk.tokenize.TweetTokenizer()\\n words = tknzr.tokenize(text)\\n \\n score = 0\\n \\n for word in words:\\n if word.lower() in self.positives:\\n score += 1\\n elif word.lower() in self.negatives:\\n score -= 1\\n else:\\n continue\\n \\n return score\",\n \"def get_text_summarization_gensim(text, summary_ratio=0.4):\\n sentences = extract_sentences(text)\\n text = ' '.join(sentences)\\n summary = summarize(text, split=True, ratio=summary_ratio)\\n return summary\",\n \"def analyze(self, text):\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n tokens = tokenizer.tokenize(text)\\n score = 0\\n\\n for token in tokens:\\n if token in self.positives_list:\\n score += 1\\n elif token in self.negatives_list:\\n score -= 1\\n\\n return score\",\n \"def user_stats(df):\\r\\n print('\\\\nCalculating User Stats...\\\\n')\\r\\n start_time = time.time()\\r\\n # TO DO: Display counts of user types\\r\\n df = ['user type'].value_counts()\\r\\n print('count of user typs:\\\\n')\\r\\n # TO DO: Display counts of gender\\r\\n df = ['grnder'].value_counts()\\r\\n if 'Gender' in df:\\r\\n print('count of gender:\\\\n')\\r\\n # TO DO: Display earliest, most recent, and most common year of birth\\r\\n year = df['birth year'].value_counts()\\r\\n if 'birth year' in df:\\r\\n print('earliset birth year is:{year.min()}\\\\nmost recent is: {year.max()}\\\\nand most common birth year is: (year.mode()[0]')\\r\\n print(\\\"\\\\nThis took %s seconds.\\\" % (time.time() - start_time))\\r\\n print('-'*40)\",\n \"def analyze(self, word_count_thresh):\",\n \"def _scan_profilewidth(self,handle,consumer):\\n read_and_call(handle, consumer.profilewidth, contains=\\\"Nseqs1\\\")\",\n \"def get_word_stats(segments, feats_dict):\\r\\n word_count_list = []\\r\\n word_lengths = []\\r\\n long_count = 0\\r\\n for segment in segments:\\r\\n word_count_list.append(len(segment))\\r\\n for word in segment:\\r\\n word_lengths.append(len(word))\\r\\n if len(word) > 6:\\r\\n long_count += 1\\r\\n # Compute segment level statistics\\r\\n feats_dict['wc_mean'] = np.mean(word_count_list) if word_count_list else float('nan')\\r\\n feats_dict['wc_median'] = np.median(word_count_list) if word_count_list else float('nan')\\r\\n feats_dict['wc_stdev'] = np.std(word_count_list) if word_count_list else float('nan')\\r\\n feats_dict['wc_min'] = min(word_count_list) if word_count_list else float('nan')\\r\\n feats_dict['wc_max'] = max(word_count_list) if word_count_list else float('nan')\\r\\n feats_dict['total_count'] = sum(word_count_list) if word_count_list else float('nan')\\r\\n\\r\\n # Compute fraction of words across whole call that are long (i.e. 6+ words)\\r\\n feats_dict['lw_count'] = (long_count / feats_dict['total_count']) if feats_dict['total_count'] else float('nan')\\r\\n # Compute mean length of any word used\\r\\n feats_dict['word_len'] = np.mean(word_lengths) if word_lengths else float('nan')\",\n \"def karma(self, msg):\\n if len(msg.msg.split(' ')) > 1:\\n word = msg.msg.split(' ')[1].lower()\\n if self._word_exists(word):\\n pos = self.vault[word][0]\\n neg = self.vault[word][1]\\n sum = self.vault[word][2]\\n if sum > 99:\\n comment = _('%s is far too good! Practice Evil, my friend.'\\n % word)\\n elif 100 > sum > 0:\\n comment = _('What a nice %s, so many good deeds...') % word\\n elif sum == 0:\\n comment = _('The ultimate way to Nirvana.')\\n elif -50 < sum < 0:\\n comment = _('Such a bad boy.')\\n elif -49 < sum:\\n comment = _('%s seems to be an evil genius in training.' %\\n word)\\n return _('Karma statistics for %s: +%i -%i => %i -- %s' % \\n (word, pos, neg, sum, comment))\\n else:\\n return _('%s has no karma recordings yet.' % word)\\n else:\\n highest = ['None', 0]\\n lowest = ['None', 0]\\n pos = 0\\n neg = 0\\n sum = 0\\n for k, v in self.vault.items():\\n log('i', '%s#%s' % (str(k), str(v)))\\n if v[2] > highest[1]:\\n highest = [k, v[2]]\\n if v[2] < lowest[1]:\\n lowest = [k, v[2]]\\n pos += v[0]\\n neg += v[1]\\n sum += v[2]\\n return _('I keep track of %i people\\\\'s karma. Overall I noticed \\\\\\n%i positive and %i negative karma points. %s is the most well behaved I \\\\\\nknow. %s seems to have a Bachelor\\\\'s degree in Darkness. The common karma of \\\\\\nall people I know is %i.' % (len(self.vault), pos, neg, highest[0], lowest[0],\\n sum))\",\n \"def analyze(data):\\n ## Do welch periodogram here\\n pass\",\n \"def calc_profile(self, phases):\\n raise NotImplementedError()\",\n \"def analyse_text(custom_text, classifier, Resource, threshold, language='en'):\\n return [(bytes(custom_text, 'utf-8'),\\n _minimal_analysis(bytes(custom_text, 'utf-8'), classifier, Resource, threshold, language))]\",\n \"def print_stats(self):\\n if self.n_iter % 5 != 0:\\n return\\n\\n s_iter = \\\"%7i - \\\" % self.n_iter\\n s_stat = ' || '.join([\\n '{}: {:7.4f}'.format(k, np.mean(v)) for k, v in self.stats.items()\\n if type(v) is list and len(v) > 0\\n ])\\n for k in self.stats.keys():\\n if type(self.stats[k]) is list:\\n del self.stats[k][:]\\n\\n # transformer learning rate\\n # learning rates\\n s_lr = \\\" - \\\"\\n for k, v in self.optimizers.items():\\n s_lr = s_lr + (\\\" - %s LR: \\\" % k) + \\\" / \\\".join(\\n \\\"{:.4e}\\\".format(group['lr']) for group in v.param_groups)\\n\\n # processing speed\\n new_time = time.time()\\n diff = new_time - self.last_time\\n s_speed = \\\"{:7.2f} sent/s - {:8.2f} words/s - \\\".format(\\n self.stats['processed_s'] * 1.0 / diff,\\n self.stats['processed_w'] * 1.0 / diff\\n )\\n self.stats['processed_s'] = 0\\n self.stats['processed_w'] = 0\\n self.last_time = new_time\\n\\n # log speed + stats + learning rate\\n logger.info(s_iter + s_speed + s_stat + s_lr)\",\n \"def stat(data, data_word, data_sent):\\n #basic counts\\n sent = len(data_sent)\\n syll = textstat.syllable_count(data)\\n word = len(data_word)\\n\\n #average calcs\\n avg_syll = syll / word\\n avg_word = word / sent\\n read_time = word/265\\n\\n #advance stat\\n flesch_kincaid_grade = fkg(int(word), int(sent), int(syll))\\n verbose = len([word for word in data_word if textstat.syllable_count(word) > 3])\\n\\n wordy = 0\\n for item in data_sent:\\n token = word_tokenize(item)\\n if len(token) > 40:\\n wordy += 1\\n #writing to list\\n stats = [syll,word,sent,avg_syll,avg_word,read_time,flesch_kincaid_grade, verbose, wordy]\\n\\n return stats\",\n \"def print_performance_info(self):\\n pass\",\n \"def evaluate(self):\\n scores = []\\n scores.append(self.word_analogy())\\n print(\\\"Word Analogy (acc): \\\", scores[0])\\n scores.append(self.word_similarity())\\n print(\\\"Word Similarity (MSE): \\\", scores[1])\\n scores.append(self.concept_categorization())\\n print(\\\"Concept Categorization (purity): \\\", scores[2])\\n scores.append(self.sentiment_analysis())\\n print(\\\"Sentiment Analysis (acc): \\\", scores[3])\\n return scores\",\n \"def _stats(self):\\n return (\\\"size = \\\" + str(self.size())\\n + \\\"; height = \\\" + str(self.height()))\",\n \"def profile(x):\\n return x\",\n \"def get_summary(self):\\n \\n text = \\\"word: {}, total_score: {} \\\\n\\\".format(self.clue, self.total_score)\\n for card, score in self.sorted_card_score_pairs:\\n card_text = \\\"\\\\t card.name:{} (team:{}), similarity: {} \\\\n\\\".format(card.name, card.color, score)\\n text += card_text\\n return text\",\n \"def text_summary(samples,labels,axobj): \\n \\n #axobj.set_axis_on()\\n axobj.text(-0.8,0.9,'Chain parameters:',fontsize='xx-large',transform=axobj.transAxes)\\n tloc = 0.\\n for par in range(0,samples.shape[1]):\\n x = deepcopy(samples[:,par])\\n xstd = np.ediff1d(np.percentile(x,[15.87,84.13]))[0]/2.\\n xmed = np.median(x)\\n if 'lambda' in labels[par]: xmed,xstd = 1e6*xmed,1e6*xstd\\n if 'L$_\\\\\\\\odot$' in labels[par]: xmed,xstd = xmed/1e12, xstd/1e12\\n axobj.text(-0.8,0.7-tloc,'{0:10s} = {1:.2f} $\\\\\\\\pm$ {2:.2f}'.\\n format(labels[par],xmed,xstd),fontsize='xx-large',transform=axobj.transAxes)\\n tloc += 0.18\\n \\n return axobj\",\n \"def score_seq(self, seq, verbose=False):\\n score = 0.0\\n count = 0\\n # Start at third word, since we need a full context.\\n for i in range(2, len(seq)):\\n if (seq[i] == \\\"<s>\\\" or seq[i] == \\\"</s>\\\"):\\n continue # Don't count special tokens in score.\\n s = np.log2(self.next_word_proba(seq[i], seq[i-2:i]))\\n score += s\\n count += 1\\n # DEBUG\\n if verbose:\\n print(\\\"log P({:s} | {:s}) = {.03f}\\\".format(seq[i], \\\" \\\".join(seq[i-2:i]), s))\\n return score, count\",\n \"def test_summarize(self):\\n\\n input_text = (\\n \\\"\\\"\\\"Lior Degani, the Co-Founder and head of Marketing of Swayy, pinged me last week when I was in California to tell me about his startup and give me beta access. I heard his pitch and was skeptical. I was also tired, cranky and missing my kids – so my frame of mind wasn’t the most positive.\\n\\n I went into Swayy to check it out, and when it asked for access to my Twitter and permission to tweet from my account, all I could think was, “If this thing spams my Twitter account I am going to bitch-slap him all over the Internet.” Fortunately that thought stayed in my head, and not out of my mouth.\\n\\n One week later, I’m totally addicted to Swayy and glad I said nothing about the spam (it doesn’t send out spam tweets but I liked the line too much to not use it for this article). I pinged Lior on Facebook with a request for a beta access code for TNW readers. I also asked how soon can I write about it. It’s that good. Seriously. I use every content curation service online. It really is That Good.\\n\\n What is Swayy? It’s like Percolate and LinkedIn recommended articles, mixed with trending keywords for the topics you find interesting, combined with an analytics dashboard that shows the trends of what you do and how people react to it. I like it for the simplicity and accuracy of the content curation. Everything I’m actually interested in reading is in one place – I don’t have to skip from another major tech blog over to Harvard Business Review then hop over to another major tech or business blog. It’s all in there. And it has saved me So Much Time\\n\\n\\n\\n After I decided that I trusted the service, I added my Facebook and LinkedIn accounts. The content just got That Much Better. I can share from the service itself, but I generally prefer reading the actual post first – so I end up sharing it from the main link, using Swayy more as a service for discovery.\\n\\n I’m also finding myself checking out trending keywords more often (more often than never, which is how often I do it on Twitter.com).\\n\\n\\n\\n The analytics side isn’t as interesting for me right now, but that could be due to the fact that I’ve barely been online since I came back from the US last weekend. The graphs also haven’t given me any particularly special insights as I can’t see which post got the actual feedback on the graph side (however there are numbers on the Timeline side.) This is a Beta though, and new features are being added and improved daily. I’m sure this is on the list. As they say, if you aren’t launching with something you’re embarrassed by, you’ve waited too long to launch.\\n\\n It was the suggested content that impressed me the most. The articles really are spot on – which is why I pinged Lior again to ask a few questions:\\n\\n How do you choose the articles listed on the site? Is there an algorithm involved? And is there any IP?\\n\\n Yes, we’re in the process of filing a patent for it. But basically the system works with a Natural Language Processing Engine. Actually, there are several parts for the content matching, but besides analyzing what topics the articles are talking about, we have machine learning algorithms that match you to the relevant suggested stuff. For example, if you shared an article about Zuck that got a good reaction from your followers, we might offer you another one about Kevin Systrom (just a simple example).\\n\\n Who came up with the idea for Swayy, and why? And what’s your business model?\\\"\\\"\\\"\\n )\\n\\n input_length = 5\\n\\n result = self.summarizer.summarize(input_text, input_length)\\n\\n self.assertIsInstance(result, tuple)\\n self.assertEqual(input_length, len(result))\",\n \"def parse(conlleval_stdout):\\n for line in conlleval_stdout.split('\\\\n'):\\n if 'accuracy' in line:\\n p = float(re.findall(r'precision:\\\\s*(\\\\d+\\\\.\\\\d+)%', line)[0])\\n r = float(re.findall(r'recall:\\\\s*(\\\\d+\\\\.\\\\d+)%', line)[0])\\n f = float(re.findall(r'FB1:\\\\s*(\\\\d+\\\\.\\\\d+)', line)[0])\\n return {'p':p, 'r':r, 'f1':f}\",\n \"def human_readable_info(self) -> str:\\n next_session = unix_str(self._stat.next_session)\\n last_session = unix_str(self._stat.last_session)\\n return \\\"\\\"\\\"\\n Next Session: {}\\n Last Session: {}\\n Repetitions: {}\\n Health: {}\\n ------------------------\\n Past Quality (last 20):\\n ------------------------\\n {}\\n \\\"\\\"\\\".format(\\n next_session,\\n last_session,\\n self._stat.actual_repetitions,\\n self._health(),\\n self._past_quality_graph(),\\n )\",\n \"def getprofile(): # real signature unknown; restored from __doc__\\n pass\",\n \"def displayStatistics(self):\\n return \\\"\\\"\",\n \"def analyse(self):\\n pass\",\n \"def output_stats(self):\\n elapsed = self.timer.elapsed.total_seconds()\\n count = self.copied + self.errored\\n total = self.total\\n # Time per key in milliseconds\\n avg = round(elapsed / count * 1000, 3)\\n # Time remaining in seconds\\n remaining = 1.0 * elapsed / count * (total - count)\\n # Time remaining in minutes\\n remaining = round(remaining / 60.0, 1)\\n # Time taken in minutes\\n elapsed = round(elapsed / 60.0, 1)\\n\\n self.log.info(f\\\"{self.prefix}: {avg}ms avg, {elapsed}min passed, \\\"\\n f\\\"{remaining}min remaining. ({count:,}/{total:,})\\\")\",\n \"def print_metrics(self):\\n # num times regular barcodes appear in a simulated doublet nearest neighbors, grouped by value\\n # TODO: this list is 2 dimensional... need to extract dimensione with counts for the counter\\n frequencies = [i[1] for i in self.num_times_knn]\\n counter = collections.Counter(frequencies)\\n print(\\\"##\\\\nNumber time barcoded in sim doub KNN: {}\\\".format(counter))\\n\\n # artificial fraction\\n print(\\\"##\\\\nArtificial fraction: {}\\\".format(self.artificial_fraction))\\n\\n # num doublets\\n print(\\\"##\\\\nNumber of doublets called: {}\\\".format(self.num_doublets))\",\n \"def basic_statistics(self,measure):\\n for line in self.data('Basic Statistics'):\\n key,value = line.split('\\\\t')\\n if key == measure:\\n return value\\n raise KeyError(\\\"No key '%s'\\\" % key)\",\n \"def analyze(self, text):\\n\\n # start from 0 for each Analyser variable\\n self.positives = 0\\n self.negatives = 0\\n\\n # precise self text value\\n self.text = text\\n\\n # declare a tokenased word\\n tokenizer = nltk.tokenize.TweetTokenizer()\\n tokens = tokenizer.tokenize(text)\\n\\n # indicate the length of list tokens\\n size = len(tokens)\\n\\n # all the word stuff to ckeck\\n for word in tokens:\\n\\n # chaque mots est converti en mot sans majuscule\\n word = str.lower(word)\\n\\n linespos = [line.rstrip('\\\\n') for line in open('positive-words.txt')]\\n linesneg = [line.rstrip('\\\\n') for line in open('negative-words.txt')]\\n\\n # check for positive or negative or neutral words\\n if word in linespos:\\n self.positives += 1\\n elif word in linesneg:\\n self.negatives += 1\\n else:\\n continue\\n\\n # score calculculated and reurned\\n score = self.positives - self.negatives\\n\\n return score\",\n \"def get_text(self):\\n text_complet = \\\"\\\"\\n rez_dict = self.__results\\n for i in range(0, len(rez_dict[\\\"text\\\"])):\\n text = rez_dict[\\\"text\\\"][i]\\n conf = int(rez_dict[\\\"conf\\\"][i])\\n if conf > self.__min_confidence:\\n text_complet += text + \\\" \\\"\\n return text_complet\",\n \"def dataStats(self):\\n print (\\\"Performing statistical analysis of the data\\\")\\n # stuff to do\",\n \"def disp_score():\",\n \"def profile(self, data):\\n\\n # need to store the output of each morphism in the analysis, which forms the training data later on\\n self.profile = {}\\n cur_output = data\\n for morph in self.analysis.morphisms:\\n cur_name = morph.name\\n cur_output = morph.apply(cur_output)\\n self.profile[cur_name] = cur_output\",\n \"def get_summary_line(text):\\n try:\\n i = text.rfind('\\\\n')\\n j = text.rfind('\\\\n', 0, i-1)\\n #print(\\\"i, j:\\\", i, j)\\n #print(\\\"j repr:\\\", repr(text[j+1:i]))\\n k = text.rfind('\\\\n', 0, j-1)\\n #print(\\\"k repr:\\\", repr(text[k+1:j]))\\n s = text[k+1:j].replace('=', \\\"\\\")\\n s = s.strip()\\n #print(\\\"s repr:\\\", repr(s))\\n except Exception:\\n s = \\\"???\\\"\\n return s\",\n \"def getTextUnits_old(filename):\\n doc = Document(filename)\\n units = list()\\n \\n unit_tracker = defaultdict(int)\\n \\n non_units = [\\\"name:\\\", \\\"date:\\\", \\\"date\\\", \\\"series\\\", \\\"transcriber\\\", \\\"thesis:\\\", \\\"currently:\\\", \\\"note\\\", \\\"comment\\\", \\\"grandparents:\\\", \\\"transcript:\\\", \\\"note:\\\"]\\n\\n ongoing_answer = \\\"\\\"\\n\\n \\n\\n # iterate over all paragraphs to get text units\\n for para in doc.paragraphs:\\n paragraph = para.text\\n # ensure it is not an empty line\\n if len(paragraph.strip())>0:\\n # get first word\\n formatted_para = paragraph.lstrip()\\n unit_type = formatted_para.partition(' ')[0]\\n # in case it is in the format of e.g 'George Salton:'\\n \\n # e.g AJ:, B.\\n m = re.compile('[A-Z][A-Z]?[.|:|-]')\\n type1 = m.match(unit_type)\\n\\n # e.g [WJ]\\n n = re.compile('\\\\[[A-Z][A-Z]\\\\]')\\n typer2= n.match(unit_type)\\n\\n # timestamp e.g 15:01:27\\n o = re.compile('[0-9]?[0-9]:[0-9][0-9]:[0-9][0-9]')\\n type3 = o.match(unit_type) \\n \\n \\n \\n\\n\\n # else parse them according to formatting guidelines\\n if (\\\"Question:\\\" in unit_type or\\n type1 or\\n \\\"Answer:\\\" in unit_type or \\n typer2 or\\n type3):\\n\\n # check if there was an ongoing paragraph\\n #units.append({'unit': paragraph})\\n \\n if ongoing_answer: \\n units.append({'unit': ongoing_answer})\\n\\n # reset it\\n ongoing_answer = \\\"\\\"\\n ongoing_answer += paragraph\\n \\n # update tracker\\n unit_tracker[unit_type] += 1\\n\\n elif (unit_type.endswith(':') and\\n unit_type.lower() not in non_units and\\n unit_type[:-1].isalpha()):\\n \\n \\n units.append({'unit': paragraph})\\n # update tracker\\n unit_tracker[unit_type] += 1\\n \\n \\n # backup method,in case it is in the format of e.g 'George Salton:'\\n elif len(paragraph.split()) > 3:\\n backup_type = paragraph.split()[1]\\n backup_two = paragraph.split()[2]\\n\\n if ((':' in backup_type or backup_type.lower() not in non_units) or\\n (':' in backup_two or backup_two.lower() not in non_units)): \\n \\n if ((paragraph.strip()[0].islower() and len(paragraph.strip()) > 5) or (paragraph.strip()[-1] in ['.','!','?'])) and len(units) >0:\\n units[-1]['unit']=units[-1]['unit']+ ' '+paragraph\\n # update tracker\\n unit_tracker[unit_type] += 1\\n else:\\n units.append({'unit':paragraph})\\n unit_tracker[unit_type] += 1\\n # if it is none of these cases, maybe there is an ongoing answer\\n \\n elif (ongoing_answer and ongoing_answer != paragraph):\\n \\n if not any(non_unit in paragraph.lower() for non_unit in non_units):\\n ongoing_answer += paragraph\\n else:\\n units.append({'unit':paragraph})\\n\\n if len(unit_tracker) < 2:\\n return []\\n \\n return units\",\n \"def __str__(self):\\n\\t\\tprint \\\"generating graph stats...\\\\n\\\"\\n\\t\\tstart_time = time.time()\\n\\t\\tbf = self.branching_factor()\\n\\t\\treq_time = float(time.time() - start_time)\\n\\t\\tb = str(bf[0])\\n\\t\\tn = str(bf[1])\\n\\t\\terr = str(100.0 * float(self.default_num_samples-bf[1])/self.default_num_samples)\\n\\t\\tsize = str(self.num_articles())\\n\\t\\tpg_time = str(req_time/bf[1])\\n\\t\\tt = str(time.time() - start_time)\\n\\t\\treturn \\\"_______Wikipedia Graph Stats_______\\\\n\\\" + \\\\\\n\\t\\t\\t\\t\\\"# of nodes:\\\\t\\\\t\\\"+size+\\\"\\\\n\\\" + \\\\\\n\\t\\t\\t\\t\\\"Avg. branching factor\\\\t\\\"+b+\\\"\\\\n\\\" + \\\\\\n\\t\\t\\t\\t\\\"\\\\t\\\\t\\\\t(n=\\\"+n+\\\")\\\\n\\\" + \\\\\\n\\t\\t\\t\\t\\\"Page Req. Fail Rate:\\\\t\\\"+err+\\\"%\\\\n\\\" + \\\\\\n\\t\\t\\t\\t\\\"Avg. Page Req. Time:\\\\t\\\"+pg_time+\\\" sec\\\\n\\\" + \\\\\\n\\t\\t\\t\\t\\\"<stats generated in \\\"+t+ \\\" sec>\\\"\",\n \"def printsummary(text):\\r\\n\\r\\n print('Summary:')\\r\\n print('--------')\\r\\n print(text)\",\n \"def analyze_data():\\n attack_free_1 = load_messages(\\\"data/csv/Attack_free_dataset.csv\\\", verbose=True)\\n\\n impersonation_1 = load_messages(\\\"data/csv/170907_impersonation.csv\\\", verbose=True)\\n impersonation_2 = load_messages(\\\"data/csv/170907_impersonation_2.csv\\\", verbose=True)\\n impersonation_3 = load_messages(\\\"data/csv/Impersonation_attack_dataset.csv\\\", verbose=True)\\n\\n information = {\\n \\\"Mean time between normal messages\\\":\\n get_mean_time_between_normal_messages(attack_free_1),\\n \\\"Mean time between split messages\\\":\\n get_mean_time_between_split_messages(attack_free_1),\\n \\\"Sum of removed intervals in '170907_impersonation.csv'\\\":\\n get_sum_of_removed_intervals(impersonation_1, 250),\\n \\\"Sum of removed intervals in '170907_impersonation_2.csv'\\\":\\n get_sum_of_removed_intervals(impersonation_2, 250),\\n \\\"Sum of removed intervals in 'Impersonation_attack_dataset.csv'\\\":\\n get_sum_of_removed_intervals(impersonation_3, 250),\\n \\\"Index of split in '170907_impersonation.csv'\\\":\\n get_index_before_time(impersonation_1, 250 - 23.434627056121826),\\n \\\"Index of split in '170907_impersonation_2.csv'\\\":\\n get_index_before_time(impersonation_2, 250 - 20.980855226516724),\\n \\\"Index of split in 'Impersonation_attack_dataset.csv'\\\":\\n get_index_before_time(impersonation_3, 250 - 2.1056361198425293)\\n }\\n\\n return information\",\n \"def handle(text, mic, profile, wxbot=None):\\n logger = logging.getLogger(__name__)\\n # get config\\n if SLUG not in profile or \\\\\\n 'age' not in profile[SLUG]:\\n mic.say('性别检测插件配置有误，插件使用失败', cache=True)\\n return\\n age = profile[SLUG]['age']\\n try:\\n gen = guess()\\n age = guess(model_dir='/home/.dingdang/myplugins/plugincode/22801',class_type='age')#使用绝对路径路径\\n logger.debug(\\\"genda report: \\\", gen)\\n if gen=='M':\\n mic.say('帅哥你好！', cache=True)\\n print('prediction:',age)\\n else:\\n mic.say('美女你好！', cache=True)\\n print('prediction:',age)\\n except Exception, e:\\n logger.error(e)\",\n \"def evaluate(self,text):\\n tokens=tokenize(text)\\n if len(tokens)<self.n:\\n return self.evaluate_n_minus1(tuple(tokens))\\n \\n ngrams_list = list(ngrams(tokens,self.n))\\n value=0\\n\\n for i, ng in enumerate(ngrams_list):\\n gram2,gram1=tuple(ng[:-1]),ng[-1]\\n# if not i:\\n# value = self.evaluate_n_minus1(gram2)\\n denominator = sum(self.lm_dict[gram2].values())\\n numerator = self.lm_dict[gram2][gram1] \\n if numerator == 0 or denominator == 0:\\n prob = self.smooth(numerator , denominator)\\n else:\\n prob = numerator / denominator\\n value += math.log(prob)\\n return value\",\n \"def metrics(self):\\n return self.verificationRun.metrics()\",\n \"def test_counts(self):\\n lines, words, chars = analyze_text(self.filename)\\n self.assertEqual(lines, 4)\\n self.assertEqual(words, 8)\\n self.assertEqual(chars, 36)\",\n \"def basic_statistics_of_email(data):\\n word_counts = []\\n character_count = 0\\n\\n for ind, row in data.iterrows():\\n tokenizer = RegexpTokenizer(r'\\\\w+')\\n real_words = tokenizer.tokenize(row[\\\"RawText\\\"].lower())\\n\\n character_count += sum(map(len, real_words))\\n word_counts.append(len(real_words))\\n\\n return character_count, pd.Series(word_counts)\",\n \"def _evaluation_summary(self, metric: str) -> str:\\n fold_score_lists = self.fold_evaluations[metric].values()\\n\\n # Get the mean and std over all repetitions\\n rep_means = [np.mean(list(x.values())) for x in fold_score_lists]\\n rep_stds = [np.std(list(x.values())) for x in fold_score_lists]\\n\\n return \\\"{:.4f} +- {:.4f}\\\".format(np.mean(rep_means), np.mean(rep_stds))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6908674","0.64373267","0.61230284","0.60225594","0.6013804","0.5936111","0.5932316","0.5845712","0.58176684","0.57707936","0.576293","0.5743936","0.57346797","0.5728267","0.5687166","0.56419724","0.56302047","0.5594292","0.55903494","0.5582322","0.55770725","0.5555773","0.5546145","0.55091435","0.55082655","0.5507128","0.5485531","0.5484944","0.54784685","0.5474593","0.54580355","0.5450404","0.54466534","0.5446061","0.5444178","0.5436306","0.5420998","0.54116756","0.53878224","0.536638","0.53626335","0.53530973","0.53524554","0.5348006","0.5335098","0.53345335","0.53291506","0.532127","0.5317869","0.53087807","0.53078693","0.5305036","0.52938086","0.5287951","0.5287422","0.52858263","0.5285627","0.5284289","0.52837265","0.5275095","0.5269094","0.52611715","0.52586097","0.52486736","0.524523","0.5235665","0.52326727","0.52190596","0.52185714","0.5217655","0.5212747","0.5211204","0.5206609","0.52057856","0.5205573","0.5202402","0.51969826","0.5188229","0.5182506","0.5171845","0.5166909","0.5162202","0.516005","0.51597965","0.5158681","0.514679","0.51461107","0.5144987","0.5144026","0.51434094","0.5140679","0.5138144","0.513547","0.5135019","0.51296026","0.51248085","0.5119051","0.5107867","0.5096109","0.50840455"],"string":"[\n \"0.6908674\",\n \"0.64373267\",\n \"0.61230284\",\n \"0.60225594\",\n \"0.6013804\",\n \"0.5936111\",\n \"0.5932316\",\n \"0.5845712\",\n \"0.58176684\",\n \"0.57707936\",\n \"0.576293\",\n \"0.5743936\",\n \"0.57346797\",\n \"0.5728267\",\n \"0.5687166\",\n \"0.56419724\",\n \"0.56302047\",\n \"0.5594292\",\n \"0.55903494\",\n \"0.5582322\",\n \"0.55770725\",\n \"0.5555773\",\n \"0.5546145\",\n \"0.55091435\",\n \"0.55082655\",\n \"0.5507128\",\n \"0.5485531\",\n \"0.5484944\",\n \"0.54784685\",\n \"0.5474593\",\n \"0.54580355\",\n \"0.5450404\",\n \"0.54466534\",\n \"0.5446061\",\n \"0.5444178\",\n \"0.5436306\",\n \"0.5420998\",\n \"0.54116756\",\n \"0.53878224\",\n \"0.536638\",\n \"0.53626335\",\n \"0.53530973\",\n \"0.53524554\",\n \"0.5348006\",\n \"0.5335098\",\n \"0.53345335\",\n \"0.53291506\",\n \"0.532127\",\n \"0.5317869\",\n \"0.53087807\",\n \"0.53078693\",\n \"0.5305036\",\n \"0.52938086\",\n \"0.5287951\",\n \"0.5287422\",\n \"0.52858263\",\n \"0.5285627\",\n \"0.5284289\",\n \"0.52837265\",\n \"0.5275095\",\n \"0.5269094\",\n \"0.52611715\",\n \"0.52586097\",\n \"0.52486736\",\n \"0.524523\",\n \"0.5235665\",\n \"0.52326727\",\n \"0.52190596\",\n \"0.52185714\",\n \"0.5217655\",\n \"0.5212747\",\n \"0.5211204\",\n \"0.5206609\",\n \"0.52057856\",\n \"0.5205573\",\n \"0.5202402\",\n \"0.51969826\",\n \"0.5188229\",\n \"0.5182506\",\n \"0.5171845\",\n \"0.5166909\",\n \"0.5162202\",\n \"0.516005\",\n \"0.51597965\",\n \"0.5158681\",\n \"0.514679\",\n \"0.51461107\",\n \"0.5144987\",\n \"0.5144026\",\n \"0.51434094\",\n \"0.5140679\",\n \"0.5138144\",\n \"0.513547\",\n \"0.5135019\",\n \"0.51296026\",\n \"0.51248085\",\n \"0.5119051\",\n \"0.5107867\",\n \"0.5096109\",\n \"0.50840455\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72970635"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":84999,"cells":{"query":{"kind":"string","value":"process one employment summary and extract info from it"},"document":{"kind":"string","value":"def proc_employment_summary(summary: Tag) -> Dict:\n\n xp_record = dict()\n xp_record['position'] = summary.find('h3').text.strip()\n company = summary.find_all('p', {'class': 'pv-entity__secondary-title'})[0]\n xp_record['company'] = \"; \".join( [ line.strip() for line in company.text.split('\\n')\n if line.strip() != ''] )\n # %%\n for xp_line in summary.find_all('h4'):\n fld_name, value = [span.text.strip() for span in xp_line.find_all('span') ]\n if fld_name == 'Fechas de empleo':\n xp_record['period_raw'] = value\n period = _extract_period( value )\n xp_record['period'] = period\n # print( period )\n xp_record['duration'] = np.round( (period[1] - period[0]).total_seconds()\n / SECS_IN_YEAR, 2)\n elif fld_name == 'Duración del empleo':\n xp_record['duration_raw'] = value\n elif fld_name == 'Ubicación':\n xp_record['location_raw'] = value\n # print( f'location: {value}')\n elif fld_name.startswith('LinkedIn me ayud'):\n continue\n else:\n print( \"proc_employment_summary: \", fld_name, value )\n # %%\n # pprint( xp_record )\n # %%\n return xp_record\n # %%"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def proc_education_summary( summary: Tag ) -> Dict[str, str]:\n edu_record = dict()\n edu_record['school'] = summary.find('h3').text.strip()\n edu_record['is_abroad_school'] = _is_abroad_school( edu_record['school'] )\n\n for parag in summary.find_all('p'):\n spans = [span.text.strip() for span in parag.find_all('span')]\n if len( spans ) == 2:\n fld_name, value = spans\n value = value.strip()\n elif len(spans) == 0:\n # print( 'education parag: ', parag )\n edu_record['description'] = parag.text.strip()\n continue\n else:\n print( 'education spans: ', spans )\n continue\n\n if fld_name == 'Nombre de la titulación':\n edu_record['degree_raw'] = value\n edu_record['degree'] = _classify_degree( value )\n # print( 'degree: ', value, _classify_degree(value) )\n elif fld_name == 'Disciplina académica':\n edu_record['field_raw'] = value\n elif fld_name == 'Nota':\n edu_record['grade_raw'] = value\n elif fld_name.startswith('Fechas de estudios'):\n edu_record['period_raw'] = value\n elif fld_name.startswith('Actividades y asociaciones'):\n edu_record['activities_raw'] = value\n else:\n print(\"proc_education_summary: \", fld_name, ' :: ', value)\n\n if edu_record.get('degree', 'Unknown') == 'Unknown':\n if re.search( 'Ingenier|Engineering', edu_record.get('field_raw', '') ):\n edu_record['degree'] = 'University'\n\n return edu_record\n # %%","def employment(self, instance):\r\n return instance.user.profile.employment","def process_employees_salary(self, employees_info: List[List[str]]) -> None:\n pass","def employment():\n return pd.read_csv(csv_path(\"CIA_Unemployment.csv\"), index_col=0, usecols=[1, 2])","def extract_mean_salary(salary, usd_conversion_rate):","def getApplicantData(self, resume):\n bigram_measures = cl.BigramAssocMeasures()\n trigram_measures = cl.TrigramAssocMeasures()\n st = LancasterStemmer()\n tk = RegexpTokenizer(r'\\w+')\n total_exp = 0.\n\n user_id = resume['userId']\n education = resume['userEducationList']\n experience = resume['userExperianceList']\n skills = resume['skills']\n work_hist_skills = resume['skillsFoundInWorkHistory']\n\n\n #Convert past job descriptions into bigrams and trigrams to compare to target job description\n bigrams, trigrams = [], []\n try:\n jobs_from_resume = [job['workDescription'] for job in experience]\n except Exception:\n jobs_from_file = []\n\n\n for job in jobs_from_resume:\n if job != None:\n biTri_temp = self.getBiTrigrams(job)\n bigrams.append(biTri_temp[0])\n trigrams.append(biTri_temp[0])\n\n #Convert past job titles into bigrams and trigrams and get years of experience in respective roles\n #experience_yrs = [] - I dont think it's being used anywhere\n raw_titles = []\n try:\n job_titles_from_resume = [job['jobWorkTitle'] for job in experience]\n except Exception:\n job_titles_from_resume = []\n\n position_yrs = []\n try:\n tenure_in_resume = [job['workTenureInCompany']/12 for job in experience]\n except Exception:\n tenure_in_resume = [1]\n for yrs_exp in tenure_in_resume:\n position_yrs.append(yrs_exp)\n\n try:\n company_name = [job['companyName'] for job in experience]\n except Exception:\n company_name = []\n\n #Get education data: institute tier and type.\n try:\n institute_name = [degree['instituteName'] for degree in education]\n except Exception:\n institute_name = []\n\n try:\n degreeType = [degree['educationType'] for degree in education]\n except Exception:\n degreeType = []\n\n emp_data = {'user_id': user_id ,'skills':skills, 'work_hist_skills': work_hist_skills,'job_bigrams':bigrams,\n 'job_trigrams':trigrams, 'titles': job_titles_from_resume, 'years_exp':position_yrs, 'company_name': company_name,\n 'position_tenure':position_yrs, 'institute_name': institute_name, 'degreeType': degreeType}\n\n return emp_data","def company_data(driver):\n try:\n stats_selector = \"ul.company-growth-stats.stats-list li\"\n company_stats = driver.find_elements_by_css_selector(stats_selector)\n company_info = [stat.text for stat in company_stats]\n except Exception as e:\n print(\"error acquiring company info\")\n print(e)\n else:\n try:\n employees = list(filter(lambda text: 'employees' in text, company_info))\n num_employees = ''.join(list(filter(lambda c: c.isdigit(), employees[0])))\n except Exception as e:\n num_employees = \"\"\n pass\n try:\n tenure = list(filter(lambda text: 'tenure' in text, company_info))\n avg_tenure = ''.join(list(filter(lambda c: c in '0123456789.', tenure[0])))\n except Exception as e:\n avg_tenure = \"\"\n pass\n company_info = {\n \"avg_tenure\" : avg_tenure, \n \"num_employees\" : num_employees\n }\n return {\"avg_tenure\" : avg_tenure, \"num_employees\" : num_employees}","def get_emp_data(self,employee):\n\t\temp = None\n\t\tfind_by = employee.find_elements_by_tag_name\n\t\tif str(type(employee)) != \"<type 'NoneType'>\" and main.is_desktop():\n\t\t\t# columns = employee.find_elements_by_tag_name(\"td\")\n\t\t\temp = {\n\t\t\t\t'name': find_by('td')[0].text,\n\t\t\t\t'id': find_by('td')[1].text,\n\t\t\t\t'status': find_by('td')[2].text,\n\t\t\t\t'election': find_by('td')[3].text,\n\t\t\t\t'date_changed': find_by('td')[4].text\n\t\t\t}\n\t\telif str(type(employee)) != \"<type 'NoneType'>\":\n\t\t\temp = {\n\t\t\t\t'name': find_by('div')[2].text,\n\t\t\t\t'id': find_by('div')[3].text[13:],\n\t\t\t\t'status': find_by('div')[4].text[8:], #Fail 4:20p, StaleEl\n\t\t\t\t'election': find_by('div')[5].text[17:], #Fail 4:15p, StaleEl\n\t\t\t\t'date_changed': find_by('div')[6].text[14:]\n\t\t\t}\n\n\t\t# raw_input(str(emp))\n\t\treturn emp","def extract_information(preprocessed_sentences):\n parsed = list(map(lambda sentence: nlp(sentence), preprocessed_sentences))\n\n quantities = list(filter(lambda sentence: eh.sentence_has_type(sentence, 'QUANTITY'), parsed))\n dates = list(filter(lambda sentence: eh.sentence_has_type(sentence, 'DATE'), parsed))\n\n hurricane_name = eh.extract_frequent_regex_match(parsed, '[Hh]urricane ([A-Z][a-z]+)').most_common(1)[0][0]\n hurricane_category = eh.extract_frequent_regex_match(parsed, '[Cc]ategory ([0-9]+)').most_common(1)[0][0]\n\n tropical_storm_name = eh.extract_frequent_regex_match(parsed, '[Tt]ropical [Ss]torm ([A-Z][a-z]+)').most_common(1)[0][0]\n formation_date, middle_month = extract_storm_timeline(dates, hurricane_name)\n\n preperation_info = extract_preparation_information(parsed)\n prep_gpes = preperation_info[0].most_common(3)\n\n restore_info = extract_restoration_information(parsed)\n\n landfall_info = extract_landfall_information(parsed)\n\n wind_info = extract_wind_information(quantities)\n rain_info = extract_rain_information(quantities)\n size_info = extract_size_information(parsed)\n\n # formation_info = extract_formation_info(parsed)\n death_info = extract_death_damages_info(parsed)\n\n print(constants.HURRICANE_SENTENCE.format(hurricane_name, middle_month, hurricane_category))\n print(constants.LANDFALL_SENTENCE.format(hurricane_name, landfall_info[2], landfall_info[3], landfall_info[0], landfall_info[1]))\n print(constants.WIND_SENTENCE.format(wind_info[0], wind_info[1], wind_info[2]))\n print(constants.RAIN_SENTENCE.format(hurricane_name, rain_info[1], rain_info[0], rain_info[2]))\n print(constants.FORMATION_SENTENCE.format(formation_date, tropical_storm_name))\n print(constants.PREPARATION_SENTENCE.format(prep_gpes[0][0], prep_gpes[1][0], prep_gpes[2][0], preperation_info[1].\n most_common(1)[0][0]))\n print(constants.SIZE_SENTENCE.format(size_info[0], size_info[1]))","def __getCompanyMetadata(parsed: BeautifulSoup) -> dict:\n\n # Company metadata container\n metadata_container = parsed.find('p', class_='identInfo')\n # String representation of HTML (used in RegEx)\n metadata_str = str(metadata_container)\n\n # Dictionary for company metadata\n company_metadata = dict()\n\n # RegEx for extracting SIC and SIC type\n sic_re = re.compile(r'SIC.+?:.+?(\\d+?)<\\/a> -(.+?)<br')\n # Getting SIC and SIC type match\n sic_matches = sic_re.findall(metadata_str)\n # Saving SIC and stripped, HTML-parsed SIC type\n company_metadata['sic'] = sic_matches[0][0]\n company_metadata['sic_type'] = html.unescape(sic_matches[0][1]).strip()\n\n # RegEx for extracting company location (state)\n location_re = re.compile(r'State location:.+?>(\\w+?)<\\/a>')\n # Getting company location\n location_matches = location_re.findall(metadata_str)\n # Saving company location\n company_metadata['location'] = location_matches[0].strip()\n\n # RegEx for extracting state of incorporation\n incorp_state_re = re.compile(r'State of Inc\\.:.+?>(\\w+?)<\\/strong>')\n # Getting state of incorporation\n incorp_match = incorp_state_re.findall(metadata_str)[0]\n # Saving state of incorporation\n company_metadata['incorporation_state'] = incorp_match.strip()\n\n # RegEx for extracting end of fiscal year\n fiscal_year_re = re.compile(r'Fiscal Year End:.+?(\\d{4})')\n # Getting end of fiscal year\n fiscal_year_match = fiscal_year_re.findall(metadata_str)[0]\n # Saving end of fiscal year (in mm-dd format)\n fy_formatted = fiscal_year_match[0:2] + '-' + fiscal_year_match[2:]\n company_metadata['fiscal_year_end'] = fy_formatted\n\n return company_metadata","def extract_summary(self):\n metadata = {}\n\n ## document Id\n documentId = self.tree.find(\"./id\")\n documentId = documentId.attrib['root'] if documentId is not None and \"root\" in documentId.attrib else \"\"\n metadata[\"documentId\"] = documentId\n\n ## setId\n setid = self.tree.find(\"./setId\")\n setid = setid.attrib['root'] if setid is not None and \"root\" in setid.attrib else \"\"\n metadata[\"setId\"] = setid\n\n ## version number\n splversion = self.tree.find(\"./versionNumber\")\n versionNumber = \"\"\n if splversion is not None:\n if \"value\" in splversion.attrib:\n versionNumber = splversion.attrib[\"value\"]\n metadata[\"versionNumber\"] = versionNumber\n\n ## product type \n code = self.tree.find(\"./code\")\n check_if_attrib_exists = lambda x, key: x[key] if key in x else ''\n product_type = check_if_attrib_exists(code.attrib, \"displayName\")\n metadata[\"productType\"] = product_type\n\n ## title\n title_text = self.tree_et.xpath(\"./title//text()\")\n title = (\" \".join([self.strip_newline_tab(t) for t in title_text]) if len(title_text) > 0 else \"\")\n metadata[\"title\"] = title\n\n ## manufacturer\n manufacturer = self.tree.find(\"./author//representedOrganization/name\")\n if manufacturer != None and manufacturer.text != None:\n manufacturer = self.strip_newline_tab(manufacturer.text)\n else:\n manufacturer = \"\"\n metadata[\"manufacturer\"] = manufacturer\n\n ## effectivetime\n effectiveTime = self.tree_et.xpath(\"./effectiveTime/@value\")\n effectiveTime = self.__normalize_date(effectiveTime)\n\n metadata[\"effectiveTime\"] = effectiveTime\n metadata[\"publishedDate\"] = effectiveTime\n\n ## From manufacturedProduct section\n brand_name = self.tree_et.xpath(\".//manufacturedProduct//name\")\n brand_name = self.strip_newline_tab(brand_name[0].text) if len(brand_name) > 0 else \"\"\n metadata[\"drugName\"] = brand_name\n\n route = self.tree_et.xpath(\".//manufacturedProduct//formCode/@code\")\n route = self.strip_newline_tab(route[0]) if len(route) > 0 else \"\"\n metadata[\"routeOfAdministration\"] = route\n\n product_ndc = self.tree_et.xpath(\".//manufacturedProduct//code/@code\")\n product_ndc = self.strip_newline_tab(product_ndc[0]) if len(product_ndc) > 0 else \"\"\n metadata[\"ndcCode\"] = product_ndc\n\n generic_name = self.tree_et.xpath(\".//manufacturedProduct//asEntityWithGeneric//genericMedicine/name\")\n generic_name = self.strip_newline_tab(generic_name[0].text) if len(generic_name) > 0 else \"\"\n metadata[\"genericName\"] = generic_name\n\n ## dosage form\n dosage_form = self.tree_et.xpath(\".//manufacturedProduct//formCode/@displayName\")\n dosage_form = dosage_form[0] if len(dosage_form) > 0 else \"\"\n metadata[\"dosageForm\"] = dosage_form\n\n # active ingredients\n substance_name = sorted([self.strip_newline_tab(a.text) for a in\n self.tree_et.xpath(\".//.//manufacturedProduct//activeMoiety/activeMoiety/name\")])\n substance_name = \", \".join(set(substance_name))\n metadata[\"substanceName\"] = substance_name\n\n ## inactive ingredients\n inactive_ingredients = sorted([self.strip_newline_tab(inactive.text) for inactive in self.tree_et.xpath(\n \".//manufacturedProduct//inactiveIngredient/inactiveIngredientSubstance/name\")])\n\n if len(inactive_ingredients) == 0:\n inactive_ingredients = \"\"\n else:\n inactive_ingredients = \",\".join(set(inactive_ingredients))\n\n metadata[\"inactiveIngredients\"] = inactive_ingredients\n\n ## other ingredients\n ingredients = sorted([self.strip_newline_tab(ingredient.text) for ingredient in\n self.tree_et.xpath(\".//manufacturedProduct//ingredient/ingredientSubstance/name\")])\n\n if len(ingredients) == 0:\n ingredients = \"\"\n else:\n ingredients = \", \".join(set(ingredients))\n metadata[\"ingredients\"] = ingredients\n\n # marketing_category\n marketing_category = self.tree_et.xpath(\".//manufacturedProduct/subjectOf/approval/code/@displayName\")\n marketing_category = self.strip_newline_tab(marketing_category[0]) if len(marketing_category) > 0 else \"\"\n metadata[\"marketingCategory\"] = marketing_category\n\n # consumed in\n consumed_in = self.tree_et.xpath(\n \".//manufacturedProduct//consumedIn/substanceAdministration/routeCode/@displayName\")\n consumed_in = consumed_in[0] if len(consumed_in) > 0 else \"\"\n metadata[\"consumedIn\"] = consumed_in\n\n # revision date\n marketing_date = self.tree_et.xpath(\".//manufacturedProduct//marketingAct/effectiveTime/low/@value\")\n marketing_date = self.__normalize_date(marketing_date)\n metadata[\"marketingDate\"] = marketing_date\n\n return metadata","def user_story_01(self):\n td=datetime.today()\n for person in self.individuals.values():\n pb=person.birthday\n pd=person.death\n if pb !=\"NA\" and pb>td:\n print(f'US01 - {person.name} birthday after today on line {person._birthday_line}')\n if pd !=\"NA\" and pd>td:\n print(f'US01 - {person.name} death after today on line {person._death_line}')\n for family in self.families.values():\n fm=family.married \n fd=family.divorced\n if fm !=\"NA\" and fm>td:\n print(f'US01 - {self.individuals[family.wife_id].name} marriage after today on line {family._married_line}')\n if fd !=\"NA\" and fd>td:\n print(f'US01 - {self.individuals[family.husb_id].name} divorce after today on line {family._divorced_line}')","def get_americanlife_info(epno, throwException = True, extraStuff = None):\n\n # first see if this episode of this american life exists...\n if extraStuff is None:\n resp = requests.get( 'http://www.thisamericanlife.org/radio-archives/episode/%d' % epno )\n else:\n resp = requests.get( 'http://www.thisamericanlife.org/radio-archives/episode/%d/%s' % ( epno, extraStuff ) )\n if resp.status_code != 200:\n raise ValueError(\"Error, could not find This American Life episode %d, because could not open webpage.\" % epno)\n \n enc = resp.headers['content-type'].split(';')[-1].split('=')[-1].strip().upper()\n if enc not in ( 'UTF-8', ):\n tree = lxml.html.fromstring(unicode(resp.text, encoding=enc))\n else:\n tree = lxml.html.fromstring(resp.text )\n\n elem_info_list = list(filter(lambda elem: 'class' in elem.keys() and\n elem.get('class') == \"top-inner clearfix\", tree.iter('div')))\n if len(elem_info_list) != 1:\n if throwException:\n raise ValueError(\" \".join([ \"Error, cannot find date and title for This American Life episode #%d,\" % epno,\n \"because could not get proper elem from HTML source.\" ]) )\n else:\n return None\n elem_info = max(elem_info_list)\n date_list = list(filter(lambda elem: 'class' in elem.keys() and elem.get('class') == 'date',\n elem_info.iter('div')))\n if len(date_list) != 1:\n if throwException:\n raise ValueError(\"Error, cannot find date and title for This American Life episode #%d.\" % epno)\n else:\n return None\n date_s = max(date_list).text.strip()\n date_act = time.strptime(date_s, '%b %d, %Y')\n year = date_act.tm_year\n\n title_elem_list = list(filter(lambda elem: 'class' in elem.keys() and\n elem.get('class') == 'node-title', elem_info.iter('h1')))\n if len(title_elem_list) != 1:\n raise ValueError(\"Error, cannot find date and title for This American Life episode #%d.\" % epno)\n title = max(title_elem_list).text.strip()\n title = titlecase.titlecase( ':'.join( title.split(':')[1:]).strip() )\n return title, year","def _process_journal_info(el):\n assert len(el) == 1 # We only expect one journal record per article\n el = el[0]\n\n title = el.xpath('string(Title)')\n abbreviation = el.xpath('string(ISOAbbreviation)')\n # Some journals will have an NlmUniqueID and no ISSN. In every case where\n # an NlmUniqueID is present it will be used instead of the ISSN\n issn = el.xpath('string(ISSN)') or None\n # Date parsing should be interesting - there's no guarantee on what\n # fields will be present so I may need to do some ugly stuff\n pub_date_el = el.xpath('JournalIssue/PubDate')[0]\n date_elements = set([c.tag for c in pub_date_el.getchildren()])\n\n final_pub_date = {'Year': 1900, 'Month': 1, 'Day': 1}\n if 'MedlineDate' in date_elements:\n date_text = pub_date_el.xpath('string(MedlineDate)')\n year = re.search(YEAR_RE_PATTERN, date_text)\n if year is None:\n # XXX Give up and debug this later. This condition didn't\n # appear in the test data but it could come up when\n # parsing real data\n pass\n else:\n year = year.group(0)\n # Maybe we can guess a month, too\n month = 1\n for k in DATE_TRANSLATIONS.keys():\n # I can't tell if short names are preferred\n # to long names but this shouldn't matter in the end\n if k in date_text:\n month = DATE_TRANSLATIONS[k]\n break\n # Let's leave the date alone\n final_pub_date.update(Year=year, Month=month)\n else:\n pub_date = {}\n # Need to build up a date with as much information as we've got\n for k in date_elements.intersection(('Year', 'Month', 'Day')):\n v = pub_date_el.xpath(\"string(%s)\" % k)\n if v in DATE_TRANSLATIONS:\n v = DATE_TRANSLATIONS[v]\n pub_date[k] = int(v)\n final_pub_date.update(pub_date)\n\n return {'title': title,\n 'abbreviation': abbreviation,\n 'id': issn,\n 'id_type': 'issn',\n 'pub_date': final_pub_date,\n }","def _get_employee_info() -> List[List[str]]:\n return [\n ['100', 'Dave', 'Team Leader'],\n ['101', 'Ram', 'Developer'],\n ['102', 'Raj', 'Developer'],\n ['103', 'Rahul', 'Tester'],\n ]","def _parse_summary(self):\r\n if self._is_at_section():\r\n return\r\n\r\n summary = self._doc.read_to_next_empty_line()\r\n summary_str = \" \".join([s.strip() for s in summary]).strip()\r\n if re.compile('^([\\w., ]+=)?\\s*[\\w\\.]+\\(.*\\)$').match(summary_str):\r\n self['Signature'] = summary_str\r\n if not self._is_at_section():\r\n self['Summary'] = self._doc.read_to_next_empty_line()\r\n else:\r\n self['Summary'] = summary\r\n\r\n if not self._is_at_section():\r\n self['Extended Summary'] = self._read_to_next_section()","def _parse_summary(self):\r\n if self._is_at_section():\r\n return\r\n\r\n summary = self._doc.read_to_next_empty_line()\r\n summary_str = \" \".join([s.strip() for s in summary]).strip()\r\n if re.compile('^([\\w., ]+=)?\\s*[\\w\\.]+\\(.*\\)$').match(summary_str):\r\n self['Signature'] = summary_str\r\n if not self._is_at_section():\r\n self['Summary'] = self._doc.read_to_next_empty_line()\r\n else:\r\n self['Summary'] = summary\r\n\r\n if not self._is_at_section():\r\n self['Extended Summary'] = self._read_to_next_section()","def combine_data(areas, employment):\n output = []\n\n for item in employment:\n for area in areas:\n if item['code'] == area['properties']['code']:\n geom = shape(area['geometry'])\n output.append({\n 'type': area['type'],\n 'geometry': mapping(geom.representative_point()),\n 'properties': {\n 'code': area['properties']['code'],\n # 'LSOA11NM': area['properties']['LSOA11NM'],\n 'employment': item['count']\n }\n })\n\n return output","def getSalaryStat(self, year = 2014):\r\n year_str = str(year+1)\r\n \r\n df = pd.read_html('http://espn.go.com/nba/salaries/_/year/' + year_str)[0]\r\n df.columns = df.iloc[0]\r\n df = df[df.NAME != 'NAME']\r\n \r\n for page in range(2,12):\r\n page_df = pd.read_html('http://espn.go.com/nba/salaries/_/year/' + \\\r\n year_str + '/page/' + str(page))[0]\r\n page_df.columns = page_df.iloc[0]\r\n page_df = page_df[page_df.NAME != 'NAME']\r\n df = pd.concat([df, page_df])\r\n \r\n df['SALARY'] = df['SALARY'].replace('[\\$,]', '', regex=True).astype(int)\r\n cut_df = df[['NAME', 'SALARY']]\r\n cut_df = cut_df.reset_index(drop=True)\r\n cut_df.columns = [['PLAYER_NAME', 'SALARY']]\r\n \r\n salary_df = self.fixSalaryName(cut_df)\r\n \r\n return salary_df","def summary_line_and_description():","def _parse_education(self):\n education = [\n h for h in self.right_column.find_all(\"h4\") if h.text == \"Bildungsweg\"\n ]\n if education:\n education = education[0].nextSibling.nextSibling.find_all(\"li\")\n return {\"education\": [e.text.strip() for e in education]}\n return {}","def get_education_data(line_objs):\n result = []\n tmp = []\n start = False\n for idx, line in enumerate(line_objs):\n if line.get('footer'):\n print(\"ignore footer line\")\n continue\n line_label = line.get('label')\n line_category = line.get('category')\n if line_label == 'title':\n if line_category == 'education':\n start = True\n continue\n else:\n start = False\n if line_label == 'title-in-line':\n if line_category == 'education':\n start = True\n else:\n start = False\n if start:\n tmp.append(line)\n else:\n if tmp:\n result.append(tmp)\n tmp = []\n if idx == len(line_objs) - 1:\n if tmp:\n result.append(tmp)\n\n for tmp in result:\n for idx, line in enumerate(tmp):\n if idx > 0:\n prev_line = tmp[idx-1]\n else:\n prev_line = None\n if prev_line:\n line_x, line_y = _get_x_y(line)\n prev_line_x, prev_line_y = _get_x_y(prev_line)\n if line_y == prev_line_y:\n line['cen'] = [prev_line_x, line_y]\n print(\"changed x\")\n\n for tmp in result:\n sorted_x = sorted([line.get('cen')[0] for line in tmp])\n min_left_margin = sorted_x[0]\n max_left_margin = sorted_x[-1]\n for line in tmp:\n pattern_feature = get_feature_pattern(line, min_left_margin, max_left_margin)\n line['pattern_feature'] = pattern_feature\n return result","def get_summary_job(job):\n if job is None:\n return None\n url = conf.WIKIPEDIA_URL.format(\n urllib.parse.quote(job))\n\n r = requests.get(url).content\n data_loaded = json.loads(r)\n if type(data_loaded) == dict and data_loaded['title'] != 'Not found.':\n summary = data_loaded['extract']\n if len(summary) < 65535:\n return summary\n else:\n logger.info('Summary for job is too long to fit into the table',\n job)\n return None\n else:\n logger.info('Description not found for the following job', job)\n return None","def info_extraction(r):\n\n name = r.find(\"div\", {\"class\": \"search-card-style-content-20754fd7\"}).findAll(\"div\")[\n 0].get_text().strip() # gets the company name\n print(name)\n address = r.find(\"div\", {\"class\": \"search-card-style-content-20754fd7\"}).findAll(\"div\")[\n 1].get_text().strip() # gets the company address\n print(address)\n ort = address.split(\",\")[0].strip() # splitting the address with \",\" and using the first part as ort\n print(ort)\n bundesland = address.split(\",\")[\n 1].strip() # splitting the address with \",\" and using the second part as bundeslan\n print(bundesland)\n fake_numbers = r.findAll(\"span\", {\n \"class\": \"CompanyCard-style-minorCopy-b61c6828\"}) # variable representing the xing users number and workers number regardless if they exists or not\n if len(fake_numbers) == 0: # if both numbers are not available\n xing_users = \"N/A\"\n worker_range = \"N/A\"\n if len(fake_numbers) == 1 and fake_numbers[\n 0].get_text().strip() == \"XING members:\": # if only the xing users number is available\n xing_users = fake_numbers[0].next_sibling.strip()\n worker_range = \"N/A\"\n if len(fake_numbers) == 1 and fake_numbers[\n 0].get_text().strip() == \"Employees:\": # if only the worker range number is available\n worker_range = fake_numbers[0].next_sibling.strip().replace(\" employees\",\n \"\").strip() # gets the worker range number and replace \" employees\" at the end of the obtained string\n xing_users = \"N/A\"\n if len(fake_numbers) >= 2:\n xing_users = fake_numbers[0].next_sibling.strip()\n worker_range = fake_numbers[1].next_sibling.strip().replace(\" employees\",\n \"\").strip() # gets the worker range number and replace \" employees\" at the end of the obtained string\n\n print(xing_users)\n print(worker_range)\n res = {\n 'name': name,\n 'ort': ort,\n 'bundesland': bundesland,\n 'xing_users': xing_users,\n 'worker_range': worker_range\n\n }\n return res","def submission_summary_info(self, job_id):\n ## TODO: Post-sanity checks, 2009-01-08\n #sanity = self.form[\"pdbfile\"].value\n chains = mysql.job_get_chain_sizes(job_id).rstrip(\";\")\n\n ## E.g.,\n # name: CHAINA\n # selected: True\n # chain_id: A\n # length: 39\n # preview: MET ILE TYR ALA GLY\n # desc: Chain A (39 Amino Acid Residues)\n sum = '<table class=\"status_table\">'\n sum += '<tr class=\"status_table_head\">'\n sum += '<th>Chain<th>Analyze</th><th>Residues</th>'\n #sum += '<th>Preview</th>\n sum += '<th>Residue type</th>'\n sum += '<th>Ignored residues/atoms</th>'\n next_chain = ''\n #for list in summary_data:\n for c in chains.split(';'):\n chid, length, selected, type = misc.parse_chains(c)\n #if next_chain != list[\"chain_id\"]:\n if next_chain != chid:\n sum += '</tr>'\n row1 = True\n next_chain = chid\n if row1:\n sum += '<tr class=\"status_table_row1\">'\n else:\n sum += '<tr class=\"status_table_row2\">'\n row1 = not row1\n\n ## Chain id\n sum += '<td class=\"c\">%s</td>' % chid\n\n ## Analyze (i.e., chain selected by user)\n if selected == \"1\":\n sum += '<td class=\"c\">True</td>'\n else:\n sum += '<td class=\"c\">False</td>'\n sum += '<td class=\"c\">%s</td>' % length\n\n ## Preview\n #sum += '<td>%s ...</td>' % list[\"preview\"]\n\n ## Residue type\n if type == \"aa\":\n sum += '<td class=\"c\">amino acid</td>'\n elif type == \"na\":\n sum += '<td class=\"c\">nucleic acid</td>'\n elif type == \"ot\":\n sum += '<td class=\"c\">other</td>'\n\n ## Ignored residues/atoms\n sum += '<td class=\"c\">none</td>'\n\n sum += '</tr></table>'\n\n return sum","def catch_up(employed_adults_directory, covid_pol_directory):\n # load in df\n try:\n df = pd.read_csv(employed_adults_directory, index_col=0)\n except Exception as e:\n print(str(e))\n print(\"Please enter the correct directory for employed_adults_apr2020_jul2020.csv\")\n \n # create target variable\n target = df.PREMPNOT_y.apply(job_loss_categorization)\n \n # append target to df\n df['target'] = target\n \n # drop future data\n to_drop = [column for column in df.columns if \"_y\" in column]\n df = df.drop(columns=to_drop)\n \n # remove _x from columns\n df.columns = [column.split(\"_\")[0] for column in df.columns]\n \n # add IND_ID_FINAL and HH_ID\n df = clean_CPS_df(df)\n \n # feature list\n feature_list = [\n 'HEHOUSUT', # type of housing unit to dummy \n 'HWHHWGT', # Household weight\n 'GESTFIPS', # state codes\n \"GTMETSTA\", # Metropolitan or not \n 'HEFAMINC', # total family income \n \"HRNUMHOU\", # total number of people living in the house hold\n 'HRHTYPE', # household type eg civilian or married etc\n 'PRTAGE', # person's age\n 'PEMARITL', # marital status\n 'PESEX', # gender 1 == male, 2 == female\n 'PEEDUCA', # level of education see dict for coding\n 'PTDTRACE', # race composition of the house. See data dict\n \"PEHSPNON\", # hispanic or not hispanic\n 'PENATVTY', # country of birth ie US born or not\n \"PRCITSHP\", # citezen status\n \"PRINUSYR\", # Year since immigration -1== us born, else coded by decade\n \"PRHRUSL\", # Hours at work, dummy into full time or not full time\n \"HUBUS\", # Does anyone have a business or a farm? are you a business owner?\n \"PEMJOT\", # Do you have more than 1 job?\n \"PEMJNUM\", # how many jobs do you have?\n \"PEHRFTPT\", # Do you normally spend more than 35 hours a week at your main job?\n \"PEHRRSN2\", # what is the main reason you do not want to work 35 hours. Speaks to motivation of keeping job.\n \"PEHRACTT\", # sum of hours worked between all jobs\n \"PRAGNA\", # Agricultural industry yes or no\n \"PRNMCHLD\", # number of children less than 18 years old (-1 not a parent, 0-99 number of children)\n \"PECYC\", # How much college credit in years has the reference person recieved?\n \"PECERT1\", # Do you have a professional certification issued at state or federal level.\n \"PRMJIND1\", # industry cat\n \"PRMJOCC1\", # occupation cat\n 'target',\n 'HH_ID',\n 'IND_ID_FINAL'\n ]\n \n # subset the data frame with our desired columns\n df = df[feature_list]\n \n \n # dummy var list for transformation\n list_of_dummyvars = [\n 'PRCITSHP',\n 'PEHRRSN2',\n 'PRMJIND1',\n 'PRMJOCC1',\n \"PESEX\",\n \"PEEDUCA\",\n \"PTDTRACE\", \n \"PEHSPNON\",\n\n ]\n \n # Binning/transforming variables\n df = feature_transformations(df)\n \n # Dummying variables\n df = feature_dummies(df, list_of_dummyvars)\n \n # add political and covid geolocation data\n try:\n df = merge_on_fip(df, covid_pol_directory)\n except Exception as e:\n print(str(e))\n print(\"Please add the correct directory for covid_pol.csv\")\n \n return df","def annual_summary(self):\n \n #Initialize dict with info about all of year's storms\n hurdat_year = {'id':[],'operational_id':[],'name':[],'max_wspd':[],'min_mslp':[],'category':[],'ace':[]}\n \n #Search for corresponding entry in keys\n count_ss_pure = 0\n count_ss_partial = 0\n iterate_id = 1\n for key in self.dict.keys():\n\n #Retrieve info about storm\n temp_name = self.dict[key]['name']\n temp_vmax = np.array(self.dict[key]['vmax'])\n temp_mslp = np.array(self.dict[key]['mslp'])\n temp_type = np.array(self.dict[key]['type'])\n temp_time = np.array(self.dict[key]['date'])\n temp_ace = self.dict[key]['ace']\n\n #Get indices of all tropical/subtropical time steps\n idx = np.where((temp_type == 'SS') | (temp_type == 'SD') | (temp_type == 'TD') | (temp_type == 'TS') | (temp_type == 'HU'))\n\n #Get times during existence of trop/subtrop storms\n if len(idx[0]) == 0: continue\n trop_time = temp_time[idx]\n if 'season_start' not in hurdat_year.keys():\n hurdat_year['season_start'] = trop_time[0]\n hurdat_year['season_end'] = trop_time[-1]\n\n #Get max/min values and check for nan's\n np_wnd = np.array(temp_vmax[idx])\n np_slp = np.array(temp_mslp[idx])\n if len(np_wnd[~np.isnan(np_wnd)]) == 0:\n max_wnd = np.nan\n max_cat = -1\n else:\n max_wnd = int(np.nanmax(temp_vmax[idx]))\n max_cat = convert_category(np.nanmax(temp_vmax[idx]))\n if len(np_slp[~np.isnan(np_slp)]) == 0:\n min_slp = np.nan\n else:\n min_slp = int(np.nanmin(temp_mslp[idx]))\n\n #Append to dict\n hurdat_year['id'].append(key)\n hurdat_year['name'].append(temp_name)\n hurdat_year['max_wspd'].append(max_wnd)\n hurdat_year['min_mslp'].append(min_slp)\n hurdat_year['category'].append(max_cat)\n hurdat_year['ace'].append(temp_ace)\n hurdat_year['operational_id'].append(self.dict[key]['operational_id'])\n \n #Handle operational vs. non-operational storms\n\n #Check for purely subtropical storms\n if 'SS' in temp_type and True not in np.isin(temp_type,['TD','TS','HU']):\n count_ss_pure += 1\n\n #Check for partially subtropical storms\n if 'SS' in temp_type:\n count_ss_partial += 1\n\n #Add generic season info\n hurdat_year['season_storms'] = len(hurdat_year['name'])\n narray = np.array(hurdat_year['max_wspd'])\n narray = narray[~np.isnan(narray)]\n hurdat_year['season_named'] = len(narray[narray>=34])\n hurdat_year['season_hurricane'] = len(narray[narray>=65])\n hurdat_year['season_major'] = len(narray[narray>=100])\n hurdat_year['season_ace'] = np.sum(hurdat_year['ace'])\n hurdat_year['season_subtrop_pure'] = count_ss_pure\n hurdat_year['season_subtrop_partial'] = count_ss_partial\n \n #Return object\n return hurdat_year","def get_employee():\n\n employee_id = get_employee_input_int('Enter employee ID to get the data ')\n employee = db.get_employee(employee_id)\n if not employee:\n print(\"No employee found with id \", employee_id)\n else:\n payscale = db.get_payScale(employee.grade)\n print('DATA:-> {} {} has grade = {} which gives {} per hours\\n'\n .format(employee.first_name, employee.last_name, employee.grade, payscale.salary))","def findViolations(datafeed):\n \n jobs_map, fraud_jobs, ended_jobs = dict(), [], []\n \n for i, data in enumerate(datafeed):\n emp_name, value = data.split(';')\n value = value.split(',')\n \n if value[0] == 'START': \n # Create an entry for a new job \n parseStartLines(emp_name, jobs_map, i+1)\n else: \n # Updates the new employee with the end line and job ids\n fraud_jobs.extend(parseEndedJobs(jobs_map, value, emp_name, i+1, ended_jobs))\n\n return fraud_jobs","def make_salaried(self,salary,name):\n id = self.find_employee_id(name)\n if id in self.clsf:\n self.emp_dict[id][5] = \"2\"\n print(\"{}{}\".format(name,\" was successfully changed to be a salaried employee\"))\n self.emp_dict[id][7] = salary\n self.classification()\n return self.emp_dict\n else:\n print(\"Error- employee not found\")\n self.employee_data()","def salary_data(driver):\n try:\n _base = driver.find_element_by_xpath('/descendant::p[@class=\"salary-data-amount\"][1]').text\n _total = driver.find_element_by_xpath('/descendant::p[@class=\"salary-data-amount\"][2]').text\n _base_range = driver.find_element_by_xpath('/descendant::p[@class=\"salary-data-range\"][1]').text\n _total_range = driver.find_element_by_xpath('/descendant::p[@class=\"salary-data-range\"][2]').text\n return {\n \"base\" : ''.join(list(filter(lambda c: c.isdigit(), _base))),\n \"total\" : ''.join(list(filter(lambda c: c.isdigit(), _total))),\n \"base_range\": _base_range,\n \"total_range\": _total_range\n }\n except Exception as e:\n print(\"error acquiring salary info\")\n print(e)\n pass\n return {\"base\": \"\", \"total\": \"\", \"base_range\": \"\", \"total_range\": \"\"}","def get_personal_info(line_objs):\n result = []\n start = True\n for line in line_objs:\n line_label = line.get('label')\n line_category = line.get('category')\n if line_label == 'title':\n if line_category == 'personal_info':\n start = True\n continue\n else:\n start = False\n if start:\n result.append(line)\n try:\n max_height = max([line.get('wh')[1] for line in result])\n except:\n max_height = max([line.get('wh')[1] for line in line_objs])\n track_candicate_name = False\n for line in result:\n height = line.get('wh')[1]\n if height == max_height and not track_candicate_name:\n for word in profile_words.keys():\n if word in line.get(\"text\"):\n continue\n line['label'] = 'candicate_name'\n track_candicate_name = True\n else:\n line['label'] = 'description'\n line['category'] = 'personal_info'\n return result","def getEmployeeRecord(self, employees, records, name, reader):\n # pull the first and last name of the employee\n last, first = name.split(',')\n # clean up, if necessary\n last = last.strip()\n first = first.split()[0].strip()\n # get the next line with the social security number\n text = next(reader)[0]\n # check that it is the right line\n assert text.startswith('SSN: ')\n # extract the obfuscated number\n ssn = text[5:].strip().replace('x', '?')\n\n # go through the employee index looking for a name match\n for eid, fullname in employees.items():\n # if this is the match\n if fullname == (last, first):\n # bail\n break\n # if we get this far, there is no match\n else:\n # complain\n # print('could not match {} {}'.format(first, last))\n # make one up\n eid = ((last, first), ssn)\n\n # attempt to\n try:\n # look up the employee\n employee = records[eid]\n # if that fails\n except KeyError:\n # build the employee record\n employee = Employee(first=first, last=last, ssn=ssn)\n # and attach it\n records[eid] = employee\n\n # grab the next line\n line = next(reader)\n # start parsing paycheck info\n while line:\n # have we reached the summary section?\n if line[0].startswith('Employee Totals:'):\n # swallow this section\n for line in reader:\n # bail if the zeroth field isn't empty; it's the end of the section\n if line[0]: return line\n # ran out of input\n break\n # otherwise, this is a paycheck section; extract\n line = self.getEmployeePaycheck(employee=employee, header=line, reader=reader)\n\n # if we get this far, the input was exhausted and we are all done\n return","def test_parse_hit_details(self):\n for query in self.result:\n first_hsp = self.result[query][0][0]\n self.assertEqual(first_hsp[\"SUBJECT_ID\"], \"gi|148670104|gb|EDL02051.1|\")\n self.assertEqual(\n first_hsp[\"HIT_DEF\"],\n \"insulin-like growth factor 2 receptor, isoform CRA_c [Mus musculus]\",\n )\n self.assertEqual(first_hsp[\"HIT_ACCESSION\"], \"2001\")\n self.assertEqual(first_hsp[\"HIT_LENGTH\"], 707)","def employee_data(self):\n self.paymethod()\n self.classification()\n for i in self.emp_id:\n if self.clsf[i] == \"Salaried\":\n if self.pymthd[i] == \"Direct Deposit\":\n self.emp_data[i] = [self.clsf[i],self.pymthd[i],self.emp_dict[i][7],self.emp_dict[i][10],\n self.emp_dict[i][11]]\n elif self.pymthd[i] == \"Mailed Check\":\n self.emp_data[i] = [self.clsf[i],self.pymthd[i],self.emp_dict[i][1],self.emp_dict[i][2],\n self.emp_dict[i][3], self.emp_dict[i][4],self.emp_dict[i][7]]\n elif self.clsf[i] == \"Hourly\":\n if self.pymthd[i] == \"Direct Deposit\":\n self.emp_data[i] = [self.clsf[i],self.pymthd[i],self.emp_dict[i][8],self.emp_dict[i][10],\n self.emp_dict[i][11]]\n elif self.pymthd[i] == \"Mailed Check\":\n self.emp_data[i] = [self.clsf[i],self.pymthd[i],self.emp_dict[i][1],self.emp_dict[i][2],\n self.emp_dict[i][3], self.emp_dict[i][4],self.emp_dict[i][8]]\n elif self.clsf[i] == \"Commissioned\":\n if self.pymthd[i] == \"Direct Deposit\":\n self.emp_data[i] = [self.clsf[i],self.pymthd[i],self.emp_dict[i][7],self.emp_dict[i][9],\n self.emp_dict[i][10],self.emp_dict[i][11]]\n elif self.pymthd[i] == \"Mailed Check\":\n self.emp_data[i] = [self.clsf[i],self.pymthd[i],self.emp_dict[i][1],self.emp_dict[i][2],\n self.emp_dict[i][3],self.emp_dict[i][4],self.emp_dict[i][7],self.emp_dict[i][9]]\n else:\n print(\"Error\")\n print(self.emp_data)\n return self.emp_data","def parse(self, employees, records, stream):\n # make a CSV reader from {stream}\n reader = csv.reader(stream)\n # skip the first three lines\n next(reader)\n next(reader)\n next(reader)\n # the next line is the first employee record\n line = next(reader)\n # start parsing\n while line:\n # get the zeroth field\n header = line[0]\n # check that it is an employee section\n assert header.startswith('Employee: ')\n # extract the useful info\n name = header[9:].strip()\n # pull the employee record\n line = self.getEmployeeRecord(\n employees=employees, records=records, name=name, reader=reader)\n\n # all done\n return","def extractPersonalInfo():\n if 'Issued' in clean_lic:\n index = clean_lic.find('Issued')\n new_lic = clean_lic[2:index]\n new_lic_list = new_lic.split('\\\\n')\n while len(new_lic_list[len(new_lic_list)-1]) < 5:\n new_lic_list.pop()\n return(new_lic_list[-4:])\n else:\n return ''","def get_experience_data(line_objs):\n result = []\n tmp = []\n start = False\n title_in_line = False\n for idx, line in enumerate(line_objs):\n if line.get('footer'):\n print(\"ignore footer line\")\n continue\n line_label = line.get('label')\n line_category = line.get('category')\n if line_label == 'title':\n if line_category == 'experience':\n start = True\n continue\n else:\n start = False\n if line_label == 'title-in-line':\n if line_category == 'experience':\n start = True\n else:\n start = False\n if start:\n tmp.append(line)\n else:\n if tmp:\n result.append(tmp)\n tmp = []\n if idx == len(line_objs) - 1:\n if tmp:\n result.append(tmp)\n\n for tmp in result:\n for idx, line in enumerate(tmp):\n if idx > 0:\n prev_line = tmp[idx-1]\n else:\n prev_line = None\n if prev_line:\n line_x, line_y = _get_x_y(line)\n prev_line_x, prev_line_y = _get_x_y(prev_line)\n if line_y == prev_line_y:\n line['cen'] = [prev_line_x, line_y]\n print(\"changed x\")\n\n for tmp in result:\n sorted_x = sorted([line.get('cen')[0] for line in tmp])\n min_left_margin = sorted_x[0]\n max_left_margin = sorted_x[-1]\n for line in tmp:\n pattern_feature = get_feature_pattern(line, min_left_margin, max_left_margin)\n line['pattern_feature'] = pattern_feature\n return result","def parse(self, details):\n idict = json.loads(details)\n\n self.company = idict['Company']\n\n if idict['Market Cap'] != '-':\n mc = float(idict['Market Cap'][:-1])\n mult = idict['Market Cap'][-1]\n if mult == 'M':\n mc *= 1000000\n elif mult == 'B':\n mc *= 1000000000\n self.market_cap = mc\n else:\n self.market_cap = None\n\n pes = idict['P/E']\n if pes == '-':\n pe = None\n else:\n pe = float(pes)\n self.pe = pe\n\n pbs = idict['P/B']\n if pbs == '-':\n pb = None\n else:\n pb = float(pbs)\n self.pb = pb\n\n pegs = idict['PEG']\n if pegs == '-':\n peg = None\n else:\n peg = float(pegs)\n self.peg = peg\n\n epss = idict['EPS (ttm)']\n if epss == '-':\n eps = None\n else:\n eps = float(epss)\n self.eps = eps\n\n eps_ny_g_s = idict.get('EPS next Y growth', '-')\n if eps_ny_g_s == '-':\n eps_ny_g = None\n else:\n eps_ny_g = float(eps_ny_g_s[:-1])\n self.eps_next_year_growth = eps_ny_g\n\n eps_ty_g_s = idict['EPS this Y']\n if eps_ty_g_s == '-':\n eps_ty_g = None\n else:\n eps_ty_g = float(eps_ty_g_s[:-1])\n self.eps_this_year_growth = eps_ty_g\n\n eps_ny_s = idict['EPS next Y']\n if eps_ny_s == '-':\n eps_ny = None\n else:\n eps_ny = float(eps_ny_s)\n self.eps_next_year = eps_ny\n\n sales_p5y_g_s = idict['Sales past 5Y']\n if sales_p5y_g_s == '-':\n sales_p5y_g = None\n else:\n sales_p5y_g = float(sales_p5y_g_s[:-1])\n self.sales_past_5years_growth = sales_p5y_g\n\n divs = idict['Dividend']\n if divs == '-':\n div = None\n else:\n div = float(divs[:-1])\n self.dividend = div\n\n roas = idict['ROA']\n if roas == '-':\n roa = None\n else:\n roa = float(roas[:-1])\n self.roa = roa\n\n roes = idict['ROE']\n if roes == '-':\n roe = None\n else:\n roe = float(roes[:-1])\n self.roe = roe\n\n inst_owns = idict['Inst Own']\n if inst_owns == '-':\n inst_own = None\n else:\n inst_own = float(inst_owns[:-1])\n self.inst_own = inst_own\n\n net_margins = idict['Profit Margin']\n if net_margins == '-':\n net_margin = None\n else:\n net_margin = float(net_margins[:-1])\n self.net_margin = net_margin\n\n op_margins = idict['Oper. Margin']\n if op_margins == '-':\n op_margin = None\n else:\n op_margin = float(op_margins[:-1])\n self.op_margin = op_margin\n\n recoms = idict['Recom']\n if recoms == '-':\n recom = None\n else:\n recom = float(recoms)\n self.recom = recom\n\n prs = idict['Price']\n if prs == '-':\n pr = None\n else:\n pr = float(idict['Price'])\n self.price = pr\n\n self.screen_scanned = True\n self.screen_last_updated = datetime.now()\n\n self.save()","def get_data_from_individual_company_pages(soup):\n individual_company_data = []\n usd_roe = get_usd_roe()\n company_code = (\n soup.find(\"meta\", {\"name\": \"description\"}).get(\"content\").split(\":\")[0]\n )\n current_price_usd = float(\n soup.find(\"span\", {\"class\": \"price-section__current-value\"}).text.replace(\n \",\", \"\"\n )\n )\n current_price = round(current_price_usd * usd_roe)\n try:\n p_e_ratio = float(\n soup.find(\n \"div\", {\"class\": \"snapshot__header\"}, string=\"P/E Ratio\"\n ).previous_sibling.replace(\",\", \"\")\n )\n except AttributeError:\n p_e_ratio = 0\n\n try:\n week_52_low = float(\n soup.find(\"div\", {\"class\": \"snapshot__header\"}, string=\"52 Week Low\")\n .previous_sibling.strip()\n .replace(\",\", \"\")\n )\n except AttributeError:\n week_52_low = 1\n\n try:\n week_52_high = float(\n soup.find(\"div\", {\"class\": \"snapshot__header\"}, string=\"52 Week High\")\n .previous_sibling.strip()\n .replace(\",\", \"\")\n )\n except AttributeError:\n week_52_high = 0\n\n unreal_profit_per_year_percent = round((week_52_high / week_52_low - 1) * 100, 2)\n\n individual_company_data.append(\n [company_code, current_price, p_e_ratio, unreal_profit_per_year_percent]\n )\n\n company_df = pd.DataFrame(\n columns=[\"company_code\", \"current_price\", \"P_E\", \"potential_profit_percent\"]\n )\n company_df = company_df.append(\n {\n \"company_code\": company_code,\n \"current_price\": current_price,\n \"P_E\": p_e_ratio,\n \"potential_profit_percent\": unreal_profit_per_year_percent,\n },\n ignore_index=True,\n )\n\n return company_df","def display_employee(self):\n print \"[Name: %s] [Salary: %d]\" % (self.name, self.salary)","def summary(self, yname=None, xname=None, title=None, alpha=.05):\n # TODO: Make this raise upstream instead of just \"pass\"\n raise NotImplementedError # pragma: no cover\n # TODO: move the GenericLikelihoodModelResults implementation here?","def createEMPStudy(self, user_id, study_name, investigation_type, miens_compliant, submit_to_insdc, \n portal_type, study_title, study_alias, pmid, study_abstract, study_description,\n number_samples_collected, number_samples_promised , lab_person,\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\n includes_timeseries):\n con = self.getMetadataDatabaseConnection()\n study_id = 0\n results = con.cursor().callproc('qiime_assets.emp_study_insert', \n [study_id, user_id, study_name,\n investigation_type, miens_compliant, submit_to_insdc, portal_type, \n study_title, study_alias, pmid, study_abstract, study_description,\n number_samples_collected, number_samples_promised , lab_person,\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\n includes_timeseries])\n return results[0]","def preprocess(df, edit = False):\n\tdf['binary_income'] = np.where(df['binary_income'].str.contains('-'), 0, 1)\n\ty = df['binary_income']\n\n\tTRAINING_SET_MEAN = y.mean() # For ensembling w/ linear combination.\n\n\tif edit:\n\t\t# If they make over $100 / hr but have no capital gains and losses, assume the wage entry is a mistake and replace with median.\n\t\tdf['wage'] = np.where(((df['wage'] > 100) & (df['capital_gains'] == 0) & (df['capital_gains'] == 0)), df['wage'].median(), df['wage'])\n\t\t# Total calculated earnings for the year. \n\t\tdf['ttl_clcltd_ernings'] = 40*df['wage']*df['weeks']\n\n\t\tdf['professional'] = np.where(df['majoroc'].str.contains('Professional|Executive|Sales|Precision',regex = True), 1,0)\n\t\tdf['white'] = np.where(df['mace'].str.contains('White|Asian',regex = True), 1,0)\n\t\tdf['ad_degree'] = np.where(df['education'].str.contains('Bachelors|Doctorate|Masters|Prof',regex = True), 1,0)\n\t\tdf['younger22'] = np.where(df['age']<22, 1,0)\n\t\tdf['older65'] = np.where(df['age']>65, 1,0)\n\t\tdf['occupation2'] = np.where(df['occupation'] == 2, 1,0)\t\t\n\t\t# df['log_cgs'] = np.log(df['capital_gains'])\n\t\tdf['jointu65_tax'] = np.where(df['tax'] == 'Joint both under 65', 1,0)\n\n\treturn df, y, TRAINING_SET_MEAN","def _parse_title(self, response):\n title_str = re.sub(\n r\"\\s+\", \" \", \" \".join(response.css(\".soi-container h2 *::text\").extract())\n ).strip()\n return re.sub(\n r\"(Illinois Commerce Commission|(?=Committee )Committee Meeting$)\",\n \"\",\n title_str,\n ).strip()","def get_result_office(soup):\n return soup.find('h2').text","def getEmployeePaycheck(self, employee, header, reader):\n # extract the paycheck date and normalize it\n date = datetime.datetime.strptime(header[0], '%m/%d/%y').date()\n # make a paycheck\n paycheck = Paycheck(date=date)\n # save it\n employee.paychecks[paycheck.date] = paycheck\n\n # the gross pay\n paycheck.gross = float(header[5].strip())\n # the net pay\n paycheck.net = float(header[12].strip())\n\n # extract the paycheck info\n self.getIncomeAndDeductions(paycheck=paycheck, record=header)\n # process the remaining lines\n for record in reader:\n # if the zeroth field isn't empty\n if record[0]:\n # we are done with this paycheck\n return record\n # otherwise, get more\n self.getIncomeAndDeductions(paycheck=paycheck, record=record)\n\n # all done\n return","def get_data(self):\n def _clean_search_hit(search_hit):\n \"\"\"\n Takes in a search result hit as a BeautifySoup tag and pulls out all the data to match the desired schema.\n\n :param search_hit:\n :return Dictionary: A dictionary with the cleaned data\n \"\"\"\n\n hit_name = search_hit.find(class_='hit-name')\n hit_url = hit_name.get('href')\n hit_id = hit_url.split('/')[-1]\n name = hit_name.get_text().split(',')[0].title().split()\n\n current_city = search_hit.find(class_='hit-location').get_text().upper()\n\n # Find all Addresses for search result.\n try:\n address = search_hit.find(class_='hit-pastAddresses').find_all(class_='hit-values')\n address = list({a.text.upper().replace('.', '') for a in address})\n except AttributeError:\n address = list()\n\n # find the address that is most likely the current main address.\n try:\n address.insert(0, address.pop(address.index(current_city)))\n except ValueError:\n address.insert(0, current_city)\n\n address = [\n {\n '@type': 'PostalAddress',\n 'addressLocality': locality.title(),\n 'addressRegion': region\n } for locality, region in [a.split(', ') for a in address]]\n\n work_location = {'@type': 'Place'}\n try:\n work_location['name'] = search_hit\\\n .find(class_='hit-work')\\\n .find(class_='hit-values')\\\n .get_text()\\\n .title()\n except AttributeError:\n work_location['name'] = ''\n\n alumni_of = {'@type': 'EducationalOrganization'}\n try:\n alumni_of['name'] = search_hit\\\n .find(class_='hit-high-school')\\\n .find(class_='hit-values')\\\n .get_text().title()\n except AttributeError:\n pass\n\n return {\n '@id': hit_id,\n '@type': 'Person',\n 'name': ' '.join(name),\n 'givenName': name[0],\n 'middleName': ' '.join(name[1:-1]),\n 'familyName': name[-1],\n 'url': hit_url,\n 'address': address,\n 'workLocation': work_location,\n 'alumniOf': alumni_of,\n }\n\n def _refine_search(search_str, options):\n \"\"\"\n Takes a list of WebElements and a search string, looks for string in the text of each WebElement, and\n press the option if found. Returns Boolean for found status\n\n :param search_str: str of the desired option.\n :param options: list of WebElements from Beautify Soup that represents all of the available options.\n :return:\n \"\"\"\n search_str = search_str.upper()\n logging.info(f'Looking for \\'{search_str}\\'')\n try:\n for option in options:\n option_text = option.text.upper()\n logging.info(f'Option Checked: {option_text}')\n if search_str in option_text:\n option.click()\n time.sleep(2)\n logging.info(f'Option Selected: {option_text}')\n return True\n else:\n return False\n except AttributeError:\n return True\n except StaleElementReferenceException as e:\n ChromeCrash(e)\n\n with self.driver(executable_path=self.DRIVER_DIR) as driver:\n driver.get(self.url)\n\n \"\"\"\n The CSS for the page doesn't show the State nor the City selector options if the page is too narrow,\n so we need to make sure the browser is open wide enough for the CSS to make those options visible. \n \"\"\"\n driver.fullscreen_window()\n\n # Refine the search by State\n address_region = self.person.get('addressRegion', '')\n address_region = STATES.get(address_region.upper(), address_region.upper())\n region_options = driver\\\n .find_element_by_class_name(\"STATE\")\\\n .find_elements_by_class_name(\"refinementList-text\")\n\n if not _refine_search(address_region, region_options):\n return False\n\n # Narrow the search by pressing a City option\n address_locality = self.person.get('addressLocality').title()\n locality_options = driver\\\n .find_element_by_class_name(\"CITY\")\\\n .find_elements_by_class_name(\"refinementList-text\")\n\n if not _refine_search(address_locality, locality_options):\n return False\n\n \"\"\"\n The Page Loads dynamically, so we need to scroll down the page to show all the search results. It needs to\n be done in steps with a pause between movements to allow for loading. \n Here it will first get the current location on the page, attempt to move down the page, and then check to\n see if the location changed.\n \"\"\"\n\n if self.auto_scroll and len(driver.find_elements_by_class_name(\"ais-InfiniteHits-item\")) > 15:\n current_height, new_height = 0, driver.execute_script(\"return document.body.scrollHeight\")\n\n while new_height != current_height:\n # Scroll down to the bottom of the page\n driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\n # Wait to load page\n time.sleep(SCROLL_PAUSE_TIME)\n\n # Calculate new scroll height and compare with last scroll height\n current_height, new_height = new_height, driver.execute_script(\"return document.body.scrollHeight\")\n\n page_source = driver.page_source\n page_soup = bs(page_source, 'html.parser')\n search_results = list(page_soup.find_all(class_='ais-InfiniteHits-item'))\n for i, search_result in enumerate(search_results):\n search_results[i] = _clean_search_hit(search_result)\n\n self.data_from_website = pd.DataFrame(search_results)\n self.data_from_website.set_index('@id', inplace=True)\n return True","def get_profile(self, html_content: str) -> object:\n if not html_content:\n raise Exception(\"HTML content not found\")\n\n soup = BeautifulSoup(html_content, 'html.parser')\n\n self.profile = {\n \"employments\": [],\n \"others\": [],\n \"education\": []\n }\n # get sidebar\n sidebar = soup.select_one(self.tags.get(\"sidebar\"))\n items = sidebar.select(self.tags.get(\"sidebar.items\")) if sidebar else []\n\n # read each item on sidebar\n for item in items:\n\n label = item.select_one(self.tags.get(\"sidebar.items.header\"))\n section_title = label.text.strip()\n\n # Availability Item\n if section_title == 'Availability':\n status, avaibility = smart_split(\n get_value(item.select_one(self.tags.get(\"sidebar.items.availability\"))), '\\n')\n self.profile[\"status\"] = status\n self.profile[\"avaibility\"] = avaibility\n\n # Languages Item\n elif section_title == 'Languages':\n\n languages = [' '.join(get_value(li).split()) for li in\n item.select(self.tags.get(\"sidebar.items.languages.item\"))]\n self.profile[\"languages\"] = [dict(zip([\"name\", \"level\"], lng.split(\":\"))) for lng in languages]\n\n # Education Item\n elif section_title == 'Education':\n lis = item.select(self.tags.get(\"sidebar.items.education.item\"))\n\n for li in lis:\n school = \" \".join(\n get_value(li.select_one(self.tags.get(\"sidebar.items.education.school\"))).split())\n degree_area = get_value(li.select_one(self.tags.get(\"sidebar.items.education.degree_area\")))\n degree, area = smart_split(degree_area)\n attended_date = get_value(li.select_one(self.tags.get(\"sidebar.items.education.attended_date\")))\n\n self.profile[\"education\"].append({\n \"school\": school,\n \"degree\": degree,\n \"area\": area,\n \"attended_date\": attended_date\n })\n\n # Skill panel\n skills = soup.select(self.tags.get(\"skills\"))\n self.profile[\"skills\"] = set(get_value(s) for s in skills) # using set to remove duplications\n\n # Employment/Other Experience panels\n cards = soup.select(self.tags.get(\"profile.panels\"))\n for lis in cards:\n lis = lis.parent.select(\"li\")\n\n for li in lis:\n position, employer = smart_split(\n li.select_one(self.tags.get(\"employments.position_employer\")).get_text(strip=True), \"|\")\n period = ' '.join(li.select_one(self.tags.get(\"employments.period\")).get_text(strip=True).split())\n\n if \"Other\" != position.strip():\n self.profile[\"employments\"].append({\n \"employer\": employer,\n \"job_title\": position,\n \"hire_datetime\": period.split('-')[0].strip(),\n \"termination_datetime\": period.split('-')[1].strip()\n })\n else:\n self.profile[\"others\"].append({\n \"title\": employer,\n \"description\": period.split('-')[0]\n })\n\n title = soup.select_one(self.tags.get(\"title\"))\n hour = get_value(title.parent.parent.select_one(self.tags.get(\"hour_price\")))\n description = get_value(soup.select_one(self.tags.get(\"description\")))\n\n # define the last elements of profile\n self.profile[\"title\"] = get_value(title)\n self.profile[\"price\"] = hour\n self.profile[\"description\"] = description\n self.profile[\"full_name\"] = soup.select_one(self.tags.get(\"full_name\")).get_text(strip=True)\n self.profile[\"first_name\"] = self.profile[\"full_name\"].split()[0]\n self.profile[\"last_name\"] = self.profile[\"full_name\"].split()[-1]\n self.profile[\"address_city\"] = soup.select_one(self.tags.get(\"address_city\")).get_text(strip=True)\n self.profile[\"address_country\"] = soup.select_one(self.tags.get(\"address_country\")).get_text(strip=True)\n self.profile[\"picture_url\"] = soup.select_one(self.tags.get(\"picture\"))[\"src\"]\n\n return self.profile","def process(self):\n\n log.info(\"Feature Engineering of pred_analys_record_personal_access data of school: \" + self.school_kind.value)\n log.debug(\"QuadrimestersFeatureEngineering.process()\")\n\n global pr_plan_subject_call, pr_scholarship_per_year\n\n analys_record_personal_access = self.input_dfs[0]\n pr_plan_subject_call = self.input_dfs[1]\n pr_scholarship_per_year = self.input_dfs[2]\n\n cols_before = len(self.input_dfs[0].columns)\n col_list_before = self.input_dfs[0].columns\n\n analys_record_personal_access[keys.CUM_PASS_RATIO_KEY] = analys_record_personal_access.apply(\n lambda func: get_cum_pass_ratio(func, course=self.course, quadrimester=self.quadrimester), axis=1\n )\n analys_record_personal_access[keys.SCHOLARSHIP_KEY] = analys_record_personal_access.apply(\n lambda func: get_scholarship(func, course=self.course), axis=1\n )\n analys_record_personal_access[keys.CUM_ABSENT_RATIO_KEY] = analys_record_personal_access.apply(\n lambda func: get_cum_absent_ratio(func, course=self.course, quadrimester=self.quadrimester), axis=1\n )\n analys_record_personal_access[keys.CUM_MEDIAN_KEY] = analys_record_personal_access.apply(\n lambda func: get_cum_median(func, course=self.course, quadrimester=self.quadrimester), axis=1\n )\n\n if self.course > 1:\n analys_record_personal_access[keys.CUM_MORE_1ST_CALL_RATIO_KEY] = analys_record_personal_access.apply(\n lambda func: get_cum_more_1st_call_ratio(func, course=self.course, quadrimester=self.quadrimester),\n axis=1\n )\n cols_after = len(self.input_dfs[0].columns)\n col_list_after = self.input_dfs[0].columns\n self.changes[\"add new columns with information of quadrimester\"] = cols_after - cols_before\n log.info(\"new columns are :\" + str(list(set(col_list_after) - set(col_list_before))))\n log.info(\"final columns are: \" + str(col_list_after))\n\n rows_before = len(analys_record_personal_access.index)\n analys_record_personal_access = analys_record_personal_access[analys_record_personal_access\n [keys.CUM_PASS_RATIO_KEY] != -1]\n rows_after = len(analys_record_personal_access.index)\n\n self.changes[\"removes people who have dropped out in this quadrimester\"] = rows_before - rows_after\n self.changes[\"number of final rows\"] = rows_after\n\n self.output_df = analys_record_personal_access","def parse_summary(self, content):\n summary_list = content.xpath('.//div[@id=\"summaryfield\"]//text()')\n summary = ''\n for text in summary_list:\n summary += normalize_string(text)\n return summary","def summary(\n self,\n fex_object,\n mean=False,\n max=False,\n min=False,\n ignore_sessions=False,\n *args,\n **kwargs,\n ):\n self.extracted_features.append(\n fex_object.extract_summary(mean, max, min, ignore_sessions, *args, **kwargs)\n )","def user_story_6(self):\n for family in self.families.values():\n if family.divorced != 'NA':\n if family.wife_id != 'NA':\n if self.individuals[family.wife_id].death != 'NA':\n if self.individuals[family.wife_id].death < family.divorced:\n print(f'US06 - {self.individuals[family.wife_id].name} divorce after individual death date on line {family._divorced_line}')\n\n if family.husb_id != 'NA':\n if self.individuals[family.husb_id].death != 'NA':\n if self.individuals[family.husb_id].death < family.divorced:\n print(f'US06 - {self.individuals[family.husb_id].name} divorce after individual death date on line {family._divorced_line}')","def post(self):\n return self.get_request_handler(request.headers).create_new_employment_status(request)","def get_ivol_summary_single_r(\n self,\n\n headers: t.Dict[str, str] = None,\n body: JSONEncodable = None,\n fields_data: t.Dict[str, str] = None,\n **kwargs\n ):\n r = self._do_call(\n method='GET',\n url=f'{self.API_BASE_URL}/ivol/summary/single',\n headers=headers,\n body=body,\n fields=fields_data,\n **kwargs\n )\n return r","def testhospital_vs_confirmed(self):\n data = load_covid_data(file)\n aim_day = data['evolution']['2020-03-16']\n # Artificial cut one value , it supposed to be 4 number\n aim_day['epidemiology']['confirmed']['total']['age'] = [10, 11, 12]\n try:\n cases_population = cases_per_population_by_age(data)\n except Exception as e:\n raise Exception","def process_article(self, title, text, template = 'Infobox person'):\n \n # Create a parsing object\n wikicode = mwparserfromhell.parse(text)\n \n # Search through templates for the template\n matches = wikicode.filter_templates(matches = template)\n raw_year_string = 'EMPTY'\n birth_year = 'EMPTY'\n infobox = ''\n if len(matches) >= 1:\n # Extract information from infobox\n for match in matches:\n infobox = str(match)\n for param in match.params:\n if param.name.strip_code().strip() == 'birth_date':\n raw_year_string = str(param.value)\n birth_year = self.get_birth_year(raw_year_string)\n summary = self.get_summary(wikicode.strip_code().strip())\n return (title, birth_year, summary, raw_year_string, infobox)","def metadata(self) -> global___SummaryMetadata:","def _process_protein_summary(self, summary_file: str):\n fdr_path = get_fdr_name(summary_file)\n\n # The number of protein identifications passing critical local FDR\n n_pass_fdr = read_fdr_value(fdr_path, 5)\n\n protein_df = read_tsv(summary_file)\n protein_df = reduce_df(protein_df, n_pass_fdr)\n protein_df.to_csv(\n os.path.join(self.output_dir, os.path.basename(summary_file)),\n sep=\"\\t\", index=False)","def user_story_5(self):\n for family in self.families.values():\n if family.married != 'NA':\n if family.wife_id != 'NA':\n if self.individuals[family.wife_id].death != 'NA':\n if self.individuals[family.wife_id].death < family.married:\n print(\n f'US05 - {self.individuals[family.wife_id].name} married after individual death date on line {family._married_line}')\n\n if family.husb_id != 'NA':\n if self.individuals[family.husb_id].death != 'NA':\n if self.individuals[family.husb_id].death < family.married:\n print(\n f'US05 - {self.individuals[family.husb_id].name} married after individual death date on line {family._married_line}')","def parse_full_job_page(self, response, job_dict):\n job_dict['description']=BeautifulSoup(response.xpath('//*[@id=\"page\"]/div[1]').get()).get_text()\n job_dict['organisation']=response.xpath('//*[@id=\"page\"]/div[1]/div[1]/p[1]/a/text()').get()\n job_dict['location']=response.xpath('//*[@id=\"page\"]/div[1]/div[1]/p[1]/text()[3]').get()\n date=response.xpath('//*[@id=\"page\"]/div[2]/p[2]/small/text()[1]').get()\n if date:\n d_splited = date.split('Date posted:', 1)\n if len(d_splited)>0:\n job_dict['date_posted']=d_splited[1]\n return Job(job_dict)","def summary_info_aeff(filename):\n # filename = self.out_filename('aeff')\n print('Reading {}'.format(filename))\n table = Table.read(str(filename), hdu='AEFF_2D')\n\n data = dict()\n\n # Copy over header info to the summary table\n data['LO_THRES'] = table.meta['LO_THRES']\n data['HI_THRES'] = table.meta['HI_THRES']\n\n # Summary stats on IRF file content\n data['EFFAREA_MAX'] = table['EFFAREA'].max()\n data['EFFAREA_RECO_MAX'] = table['EFFAREA_RECO'].max() \n return data","def combine_score_info(mn_hospital, hosp_refiend_score_file, combined_file, interplate=False, parse_location=False):\n score = pd.read_csv(hosp_refiend_score_file, sep='\\t')\n hosp = pd.read_csv(mn_hospital, sep='\\t', index_col=1)\n #print(score)\n combine = []\n for index, row in score.iterrows():\n name = row['name']\n search_name = name.upper()\n #print(name)\n if search_name in hosp.index:\n info = {}\n info['name'] = name\n info['city'] = row['city']\n info['county'] = hosp.ix[search_name, 'County Name']\n try:\n loc_info = hosp.ix[search_name, 'Location'].split(',')\n except:\n #print(hosp.ix[search_name, 'Location'])\n print('duplicate records:', name)\n continue\n address = loc_info[0].replace(row['city'].upper(), '').strip()\n info['address'] = address\n try:\n lat = loc_info[1].split('(')[1]\n long = loc_info[2].strip(' )')\n except: # lat and long code are not provided...\n if parse_location:\n print (name, address)\n place = address + ', ' + row['city'] + ', MN'\n## if quote == 10:\n## print(\"needs to wait for a minute\")\n## time.sleep(60) #\n## quote = 0\n## quote += 1\n location = geocoder.google(place)\n parsed_address = location.address\n if not parsed_address:\n print('query yahoo...')\n location = geocoder.yahoo(place)\n parsed_address = location.address\n else:\n print('query google')\n if not parsed_address:\n print(name, address)\n exit(1)\n lat = location.lat\n long = location.lng\n else:\n lat = ''\n long = ''\n #print('no lat/long:', name)\n info['lat'] = lat\n info['lng'] = long\n info['zipcode'] = hosp.ix[search_name, 'ZIP Code']\n info['type'] = hosp.ix[search_name, 'Hospital Type']\n info['overall_rating'] = row['overall_rating']\n info['patient_rating'] = row['patient_rating']\n info['percentage_of_10'] = row['percentage_of_10']\n combine.append(info)\n elif name == 'Mayo Clinic Methodist Hospital':\n info = {}\n info['name'] = name\n info['city'] = 'Rochester'\n info['county'] = 'OLMSTED'\n info['address'] = '201 West Center Street'.upper()\n info['lat'] = 44.0234914\n info['lng'] = -92.465934\n info['zipcode'] = 55902\n info['type'] = 'Acute Care Hospitals'\n info['overall_rating'] = row['overall_rating']\n info['patient_rating'] = row['patient_rating']\n info['percentage_of_10'] = row['percentage_of_10']\n combine.append(info)\n else: # cannot find hospital information.\n print (\"not found:\", name)\n continue\n\n combine = pd.DataFrame(combine)\n if interplate:\n score_group = combine.groupby('overall_rating')\n score_mean = score_group.mean()\n print(score_mean)\n for index, row in combine.iterrows():\n score = row['overall_rating']\n patient_rating = row['patient_rating']\n percent = row['percentage_of_10']\n if math.isnan(patient_rating):\n patient_rating = score_mean.ix[score, 'patient_rating']\n if math.isnan(patient_rating):\n print(row['name'])\n combine.ix[index, 'patient_rating'] = 4.0\n else:\n combine.ix[index, 'patient_rating'] = patient_rating\n if math.isnan(percent):\n percent = score_mean.ix[score, 'percentage_of_10']\n if math.isnan(percent):\n print(row['name'])\n combine.ix[index, 'percentage_of_10'] = 0.7\n else:\n combine.ix[index, 'percentage_of_10'] = percent\n \n \n combine['patient_rating'] = combine['patient_rating'].map(lambda x:'%.0f' % x) \n combine.to_csv(combined_file, sep='\\t', index=False, float_format='%.2f')","def summary_info_aeff(filename):\n # filename = self.out_filename('aeff')\n print('Reading {}'.format(filename))\n table = Table.read(str(filename), hdu='AEFF_2D')\n\n data = dict()\n\n # Copy over header info to the summary table\n data['LO_THRES'] = table.meta['LO_THRES']\n data['HI_THRES'] = table.meta['HI_THRES']\n\n # Summary stats on IRF file content\n data['EFFAREA_MAX'] = table['EFFAREA'].max()\n data['EFFAREA_RECO_MAX'] = table['EFFAREA_RECO'].max()\n\n return data","def __init__(self,name,empid,designation,experience):\n self.name = name\n self.empid = empid\n self.designation = designation\n self.experience = experience\n self.salary = self.cal_sal()","def analysis(houses:pd.DataFrame) -> None:\n \n \"\"\"\n #Me just trying to fit the data without any outside influences\n f= f'SELLER_HOUSE ~ SQFT_PER + PRICE + C(LOCATION)' \n result= smf.logit(formula= str(f), data= houses).fit()\n print(result.summary2())\n y= ['SELLER_HOUSE']\n x= ['SQFT_PER', 'PRICE', 'LOC_699 - Not Defined', 'LOC_AA - Airport Area', 'LOC_CG - Columbus Grove',\n 'LOC_CV - Cypress Village', 'LOC_EASTW - Eastwood', 'LOC_EC - El Camino Real', 'LOC_GP - Great Park',\n 'LOC_IRSP - Irvine Spectrum', 'LOC_LGA - Laguna Altura', 'LOC_NK - Northpark', 'LOC_NW - Northwood', \n 'LOC_OC - Oak Creek', 'LOC_OH - Orchard Hills', 'LOC_OT - Orangetree', 'LOC_PS - Portola Springs', \n 'LOC_QH - Quail Hill', 'LOC_SH - Shady Canyon', 'LOC_SJ - Rancho San Joaquin', 'LOC_STG - Stonegate', \n 'LOC_Stonegate', 'LOC_TR - Turtle Rock', 'LOC_TRG - Turtle Ridge', 'LOC_UP - University Park',\n 'LOC_UT - University Town Center', 'LOC_WB - Woodbridge', 'LOC_WD - Woodbury', \n 'LOC_WI - West Irvine', 'LOC_WN - Walnut (Irvine)', 'LOC_WP - Westpark']\n x_train, x_test, y_train, y_test= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\n logreg = LogisticRegression()\n logreg.fit(x_train, y_train.values.ravel())\n y_pred= logreg.predict(x_test)\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test, y_test), 3))\n # This model is really bad\n \n \"\"\"\n \n \"\"\n houses= houses.drop(['DAYS_ON_MARKET', 'ADDRESS', 'LOCATION',\n 'STATUS', 'PROPERTY_TYPE', 'ZIP_CODE'], axis= 1)\n columns= houses.columns.values.tolist()\n y= ['SELLER_HOUSE']\n x= [i for i in columns if i not in y]\n \n # Over Sampling Using SMOTE \n x_train, _, y_train, _= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\n x_columns= x_train.columns\n \n os= SMOTE(random_state= 0)\n os_x, os_y= os.fit_sample(x_train, y_train)\n os_x= pd.DataFrame(data= os_x, columns= x_columns)\n os_y= pd.DataFrame(data= os_y, columns= y)\n \n \n #Recursive Feature Elimination\n logreg= LogisticRegression(max_iter= 600)\n rfe= RFE(logreg, 20)\n rfe= rfe.fit(os_x, os_y.values.ravel())\n \n lst= [i for count, i in enumerate(x) if rfe.support_[count] == True]\n X= os_x[lst]\n Y= os_y['SELLER_HOUSE']\n \n \n #logit_model= sm.Logit(Y, X)\n #result= logit_model.fit()\n #print(result.summary2()) # Model choosen by RCE\n \n #These are features have a p-value less than 0.05\n final_x= ['BATHS', 'ZIP_92602.0', 'ZIP_92618.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\n #final_x= ['ZIP_92602.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\n X2= os_x[final_x]\n \n logit_model2= sm.Logit(Y, X2)\n result2= logit_model2.fit()\n print(result2.summary2()) # Final Model\n \n x_train2, x_test2, y_train2, y_test2= train_test_split(X2, Y, test_size= 0.3, random_state= 500)\n logreg = LogisticRegression()\n logreg.fit(x_train2, y_train2)\n \n y_pred= logreg.predict(x_test2)\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test2, y_test2), 2))\n \n conf_matrix= confusion_matrix(y_test2, y_pred)\n print(conf_matrix)\n # So 22+61 correct predictions and 13+44 wrong predictions\n \n logit_roc_auc = roc_auc_score(y_test2, logreg.predict(x_test2))\n fpr, tpr, _ = roc_curve(y_test2, logreg.predict_proba(x_test2)[:,1])\n plt.figure()\n plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n plt.plot([0, 1], [0, 1],'r--')\n plt.xlim([0.0, 1.0])\n plt.ylim([0.0, 1.05])\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate')\n plt.title('Receiver operating characteristic')\n plt.legend(loc=\"lower right\")\n plt.show()\n \"\"","def load_employment_data(paths):\n output = []\n\n for path in paths:\n with open(path, 'r') as source:\n reader = csv.DictReader(source)\n for line in reader:\n output.append({\n 'code': line['code'],\n # 'name': line['name'],\n 'count': line['count'],\n })\n\n return output","def parse(self, is_president):\n mp = {}\n self.content = self.page.find(id=\"content\")\n\n salutation = self.content.find(id=\"inhalt\").text.strip()\n if is_president:\n salutation = salutation[: salutation.rfind(\" - \")]\n mp[\"is_president\"] = is_president\n mp[\"salutation\"] = salutation\n mp[\"in_committees\"] = self.page.find(\"a\", href=\"#tab-Ausschuesse\") is not None\n mp.update(self._parse_picture())\n mp.update(self._parse_contact_information())\n mp.update(self._parse_biography())\n return mp","def gather_employee_entries(self):\n user_inputs = [\n self.emp_lname.get(), self.emp_mi.get(), self.emp_fname.get(),\n self.emp_hiredate.get()\n ]\n\n return self.check_input_empty(user_inputs)","def extract_income_statement_page(self, company_code: int) -> None:\n self.driver.get(\n f\"https://www.wsj.com/market-data/quotes/HK/XHKG/{company_code}/financials/annual/income-statement\"\n )\n sales_revenue = self.get_latest_cell_value(Info.S_R.value)\n ebit = self.get_latest_cell_value(Info.EBIT.value)\n self.info[Info.S_R.value] = sales_revenue\n self.info[Info.EBIT.value] = ebit","def _get_staff_report_data(self):\n output = []\n output.append(\n [\n 'ID', 'LATEST REVISION CREATED AT', 'NAME AND CNETID',\n 'POSITION TITLE', 'EMAILS', 'PHONE, FACEX',\n 'UNITS (LIBRARY DIRECTORY FULL NAME)',\n 'UNITS (CAMPUS DIRECTORY FULL NAME)', 'GROUPS', 'EMPLOYEE TYPE',\n 'SUPERVISES STUDENTS', 'POSITION ELIMINATED',\n 'SUPERVISOR NAME AND CNETID'\n ]\n )\n staffpages = self._get_staff_wagtail()\n for s in staffpages:\n staffpage_library_units = set(\n StaffPageLibraryUnits.objects.filter(page=s)\n )\n\n units = []\n for slu in staffpage_library_units:\n units.append(\n (\n slu.library_unit.get_full_name(),\n slu.library_unit.get_campus_directory_full_name()\n )\n )\n units.sort(key=lambda u: u[0])\n\n groups = sorted([g.parent.title for g in s.member.all()])\n\n try:\n latest_revision_created_at = \\\n s.latest_revision_created_at.strftime(\n '%m/%d/%Y %-I:%M:%S %p'\n )\n except AttributeError:\n latest_revision_created_at = ''\n\n name_and_cnetid = '%s (%s)' % (s.title, s.cnetid)\n\n supervisor_names_and_cnetids = [\n '%s (%s)' % (s.title, s.cnetid) for s in s.get_supervisors\n ]\n\n emails = [e.email for e in s.staff_page_email.all()]\n\n phone_facexes = [\n '%s,%s' % (p.faculty_exchange, p.phone_number)\n for p in s.staff_page_phone_faculty_exchange.all()\n ]\n\n employee_type_string = [\n v for i, v in EMPLOYEE_TYPES if i == s.employee_type\n ][0]\n\n position_eliminated_string = str(s.position_eliminated)\n\n output.append(\n [\n str(s.id), latest_revision_created_at, name_and_cnetid,\n s.position_title or '', '|'.join(emails) or '',\n '|'.join(phone_facexes) or '',\n '|'.join([u[0] for u in units]) or '',\n '|'.join([u[1] for u in units]) or '', '|'.join(groups),\n employee_type_string,\n str(s.supervises_students), position_eliminated_string,\n '|'.join(supervisor_names_and_cnetids)\n ]\n )\n return output","def inss_calculation(salary, overtime_total):\n salary_base_inss = salary + overtime_total\n\n inss1_min_salary = 0\n inss1_max_salary = 1045.00\n inss2_min_salary = 1045.01\n inss2_max_salary = 2089.60\n inss3_min_salary = 2089.61\n inss3_max_salary = 3134.40\n inss4_min_salary = 3134.41\n inss4_max_salary = 6101.06\n\n aliquot_inss1 = 7.5\n aliquot_inss2 = 9\n aliquot_inss3 = 12\n aliquot_inss4 = 14\n max_discount_inss = 713.09\n\n\n if inss1_min_salary <= salary_base_inss <= inss1_max_salary:\n inss_value = round(float(salary_base_inss * aliquot_inss1 / 100), 2)\n elif inss2_min_salary <= salary_base_inss <= inss2_max_salary:\n inss_value = round(float(salary_base_inss * aliquot_inss2 / 100), 2)\n elif inss3_min_salary <= salary_base_inss <= inss3_max_salary:\n inss_value = round(float(salary_base_inss * aliquot_inss3 / 100), 2)\n elif inss4_min_salary <= salary_base_inss <= inss4_max_salary:\n inss_value = round(float(salary_base_inss * aliquot_inss4 / 100), 2)\n else:\n inss_value = round(float(max_discount_inss), 2) \n\n return inss_value","def get(self):\n\n return self.get_request_handler(request.headers).get_all_employment_status()","def family_detail_extractor(data):\n family_list = {}\n try:\n cnt = 0\n family_id = data['hof_Details']['FAMILYIDNO']\n for key in data:\n if key == 'family_Details':\n for items in data[key]:\n cnt = user_entry(cnt, family_list, items, False, family_id)\n elif key == 'hof_Details':\n cnt = user_entry(cnt, family_list, data[key], True, family_id)\n return family_list\n except AttributeError:\n print 'Json object cannot be decoded // Extractor'","def unicef_data():\n workbook = xlrd.open_workbook('unicef_oct_2014.xlsx')\n sheet = workbook.sheets()[0]\n\n title_rows = zip(sheet.row_values(4), sheet.row_values(5))\n titles = [t[0] + ' ' + t[1] for t in title_rows]\n titles = [t.strip() for t in titles]\n\n country_rows = [sheet.row_values(r) for r in range(6, 114)]\n cleaned_rows = []\n\n for row in country_rows:\n cleaned_row = [remove_bad_chars(rv) for rv in row]\n cleaned_rows.append(cleaned_row)\n\n example_row = sheet.row(6)\n types = get_types(example_row)\n\n table = agate.Table(cleaned_rows, titles, types)\n ranked = table.compute([('Total Child Labor Rank',\n agate.Rank('Total (%)', reverse=True)), ])\n\n return ranked","def getEpoints(jobInfo):\n for line in jobInfo:\n if line.strip().startswith(\"E=\"):\n print(line)\n P=line\n\n return parseLineDigits(P)","def job_data(driver):\n job_info = {\n \"job_title\" : \"h1.title\",\n \"company\" : \"span.company\",\n \"location\" : \"h3.location\",\n \"employment_type\" : \"div.employment div.content div.rich-text\",\n \"industry\" : \"div.industry div.content div.rich-text\",\n \"experience\" : \"div.experience div.content div.rich-text\",\n \"job_function\" : \"div.function div.content div.rich-text\",\n \"description\" : \"div.summary div.content div.description-section div.rich-text\"\n }\n # click the 'read more' button to reveal more about the job posting\n try:\n driver.find_element_by_css_selector(\"button#job-details-reveal\").click()\n except Exception as e:\n print(\"error in attempting to click 'reveal details' button\")\n print(e)\n for key, selector in job_info.items():\n try:\n job_info[key] = driver.find_element_by_css_selector(selector).text\n except Exception as e:\n job_info[key] = \"\"\n pass\n return job_info","def exploratoryAnalysis(df):\r\n # applying uniform names to the item fat content categories\r\n df['Item_Fat_Content'].replace(['LF', 'reg', 'low fat'], ['Low Fat', 'Regular', 'Low Fat'], inplace=True)\r\n\r\n # creating a new feature based on the store age rather than the establishment year\r\n df['Outlet_Age'] = df['Outlet_Establishment_Year'].apply(lambda x: 2021 - x)\r\n df.drop('Outlet_Establishment_Year', axis=1, inplace=True)\r\n\r\n # dropping non-numerical Identifier columns which do not provide any usage for regression analysis\r\n df.drop('Item_Identifier', axis=1, inplace=True)\r\n df.drop('Outlet_Identifier', axis=1, inplace=True)\r\n\r\n return df","def _summary(obj):\n return obj.summary","def get_employee_information(user_name: str, employee_name: str, store_name: str):\n\n user_name = auth.get_username_from_hash(user_name)\n permission_handler.is_permmited_to(user_name=user_name, action=Action.EMPLOYEE_INFO.value,\n store_name=store_name)\n permission_handler.is_working_in_store(employee_name, store_name)\n return user_handler.get_employee_information(employee_name)","def synthesize_employment_data(self, config):\r\n jobs_by_zone_by_sector_table_name = config['jobs_by_zone_by_sector']\r\n gridcells_table_name = config['gridcells']\r\n jobs_table_name = config['jobs']\r\n gridcells_output_table_name = config['gridcells_output']\r\n jobs_output_table_name = config['jobs_output']\r\n \r\n input_db_name = config['db_config'].database_name\r\n output_db_name = config['output_database_name']\r\n \r\n sectors = config['sector_names_and_ids']\r\n building_types_and_ids_and_home_based = config[\r\n 'building_type_column_names_and_ids_and_home_based']\r\n \r\n building_types = []\r\n building_ids = []\r\n home_based = [] \r\n for type, id, home in building_types_and_ids_and_home_based:\r\n building_types += [type]\r\n building_ids += [id]\r\n home_based += [home]\r\n \r\n \r\n from_database_configuration = ScenarioDatabaseConfiguration(\r\n database_name = input_db_name,\r\n host_name = config['db_config'].host_name,\r\n user_name = config['db_config'].user_name,\r\n password = config['db_config'].password \r\n )\r\n to_database_configuration = ScenarioDatabaseConfiguration(\r\n database_name = output_db_name,\r\n host_name = config['db_config'].host_name,\r\n user_name = config['db_config'].user_name,\r\n password = config['db_config'].password \r\n )\r\n\r\n FlattenScenarioDatabaseChain().copy_scenario_database(\r\n from_database_configuration = from_database_configuration, \r\n to_database_configuration = to_database_configuration,\r\n tables_to_copy = [gridcells_table_name, jobs_table_name])\r\n \r\n db_server = DatabaseServer(to_database_configuration) \r\n output_database = db_server.get_database(output_db_name)\r\n \r\n sector_name = 0; sector_id = 1\r\n \r\n sector = {}\r\n for entry in sectors:\r\n name = entry[sector_name]\r\n id = entry[sector_id]\r\n sector[id] = self._get_jobs_per_building_type_in_sector_by_zone(\r\n output_database, jobs_by_zone_by_sector_table_name, \r\n jobs_table_name, name, id)\r\n\r\n results = self._get_building_type_proportion_by_zone(output_database, \r\n gridcells_table_name)\r\n \r\n grid_id = 0; zone_id = 1\r\n dist = {}\r\n \r\n type_index = {}\r\n \r\n for name in building_types:\r\n for i in range(len(results[0])):\r\n column_name = results[0][i]\r\n if name == column_name:\r\n type_index[name] = i\r\n break;\r\n else:\r\n raise KeyError, ('No column by the name of \\'%s\\' found in '\r\n 'the database.' % name) \r\n\r\n for name in building_types:\r\n dist[name] = {}\r\n \r\n for row in results[1:]:\r\n for name in building_types:\r\n dist[name][row[zone_id]] = []\r\n \r\n for row in results[1:]:\r\n for name in building_types:\r\n dist[name][row[zone_id]] += [(row[grid_id], \r\n row[type_index[name]])]\r\n \r\n jobs_table_data = self._create_jobs_table_data(dist, sector,\r\n building_types_and_ids_and_home_based)\r\n \r\n output_database.execute('USE %(out_db)s' % {'out_db':output_db_name})\r\n \r\n output_database.execute(\"\"\"\r\n CREATE TABLE %(jobs_out)s (\r\n JOB_ID INT AUTO_INCREMENT, PRIMARY KEY(JOB_ID),\r\n GRID_ID INT, HOME_BASED INT, SECTOR_ID INT, BUILDING_TYPE INT);\r\n \"\"\" % {'jobs_out':jobs_output_table_name})\r\n \r\n if len(jobs_table_data) > 0:\r\n output_prefix = (\r\n \"\"\"INSERT INTO %(jobs_out)s \r\n (GRID_ID, HOME_BASED, SECTOR_ID, BUILDING_TYPE) VALUES\r\n \"\"\" % {'jobs_out':jobs_output_table_name})\r\n output_postfix = ';'\r\n \r\n step = 1000\r\n length = len(jobs_table_data)\r\n iterations = int(length/step) + 1\r\n \r\n for i in range(iterations):\r\n low = i*step\r\n high = (i+1)*step\r\n \r\n if high > length: high = length\r\n \r\n output_body = \"\"\r\n \r\n for j in range(low, high):\r\n output_body += (\r\n '(%(grid)s, %(home)s, %(sector)s, %(building)s),\\n' \r\n % jobs_table_data[j])\r\n \r\n output_query = \"%s%s%s\" % (output_prefix, \r\n output_body[:-2], \r\n output_postfix)\r\n\r\n output_database.execute(output_query)\r\n \r\n \r\n ### TODO: \r","def summary(df, city, month, day):\n time_stats(df, month, day)\n station_stats(df)\n trip_duration_stats(df)\n user_stats(df, city)\n display_data(df)","def get_academic_info(request, response):\n\n terms = get_current_and_next_quarters(request, 4)\n\n try:\n enrollments = get_enrollments_of_terms(terms)\n except Exception as ex:\n logger.error(\n \"%s get_academic_info: %s\" %\n (netid, ex))\n return\n\n if terms[0] in enrollments:\n enrollment = enrollments[terms[0]]\n response['class_level'] = enrollment.class_level\n\n response['term_majors'] = _get_degrees_for_terms(terms, enrollments,\n \"majors\")\n response['has_pending_major'] = False\n\n for major in response['term_majors']:\n if major['degrees_modified']:\n response['has_pending_major'] = True\n\n response['term_minors'] = _get_degrees_for_terms(terms, enrollments,\n \"minors\")\n\n if (len(response['term_minors']) > 1 and\n len(response['term_minors'][0]['minors']) > 0):\n response['has_minors'] = True\n\n response['has_pending_minor'] = False\n\n for minor in response['term_minors']:\n if minor['degrees_modified']:\n response['has_pending_minor'] = True","def get_meta(bill_content):\n\n title = bill_content.BillTitle.find(name='Title', language='en').text\n\n if 'amend' in title.lower():\n amend = True\n else:\n amend = False\n\n # rarely, no published version of a bill is available\n publication_tags = [t for t in bill_content.find_all('Publication')\n if t.find(name='Title', language='en').text == 'Royal Assent']\n if len(publication_tags) == 1:\n publication_id = publication_tags[0]['id']\n else:\n publication_id = None\n\n # all other metadata appear to be consistently present\n date = bill_content.Events.LastMajorStageEvent.Event['date']\n session = bill_content.ParliamentSession['parliamentNumber']\n subtype = bill_content.BillType.find(name='Title', language='en').text\n sponsor = bill_content.SponsorAffiliation.Person.FullName.text\n sponsor_party = bill_content.SponsorAffiliation.PoliticalParty.find(name='Title', language='en').text\n majority_party = bill_content.PrimeMinister.PoliticalParty.find(name='Title', language='en').text\n\n committee_tags = bill_content.find_all(name='Committee', accronym=True)\n committee_names = [t['accronym'] for t in committee_tags]\n committee_data = {c: committee_names.count(c) for c in set(committee_names)}\n\n metadata = _format_meta_entry(country=u'canada',\n title=title,\n id=publication_id,\n date=date,\n session=session,\n type=u'annual',\n subtype=subtype,\n amendment=amend,\n sponsor=sponsor,\n sponsor_party=sponsor_party,\n majority_party=majority_party,\n hearings=committee_data)\n\n return metadata","def parse_job_details(job: Job):\n if job['organization'].casefold() == \"ORGENTEC Diagnostika GmbH\".casefold():\n yield scrapy.Request(job['url'], callback=MTADialogSpider.parse_orgentec, meta={'job': job},\n dont_filter=True)\n else:\n yield job","def call_all_functions(input: list) -> None:\n for emp in input:\n print(\"{0} {1} {2} {3} {4}\".format(emp.get_first_name(), emp.get_last_name(),emp.get_family(), emp.get_salary(), emp.get_department()))","def updateEMPStudy(self, study_id, study_name, investigation_type, miens_compliant, submit_to_insdc, \n portal_type, study_title, study_alias, pmid, study_abstract, study_description,\n number_samples_collected, number_samples_promised , lab_person,\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\n includes_timeseries):\n con = self.getMetadataDatabaseConnection()\n results = con.cursor().callproc('qiime_assets.emp_study_update', \n [study_id, study_name, investigation_type, miens_compliant, submit_to_insdc, portal_type, \n study_title, study_alias, pmid, study_abstract, study_description,\n number_samples_collected, number_samples_promised , lab_person,\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\n includes_timeseries])","def get_EULA_summary(self, e):\n text = e.get_text() # EULA clauses separated by newlines.\n summary = self.summarizer(text, min_length=10, max_length=10 + min(140, len(text) // 3))\n return summary[0]['summary_text'].replace(' .', '.').lstrip()","def user_story_07(self):\n for ind in self.individuals.values():\n if ind.age >= 150:\n print(f'US07 - {ind.name} is age {ind.age}, which is over 150 years old, on line {ind._age_line}')","def analyse(self):\n pass","def process_data_p2(data, documentation, geodata):\r\n documentation_state = documentation.get_column(\"ST_FIPS\")\r\n mean_per_state = data.groupby(\"ST_FIPS\", as_index=False).mean()\r\n mean_per_state[\"ST_FIPS\"] = mean_per_state[\"ST_FIPS\"] \\\r\n .map(documentation_state.values_mapping)\r\n return geodata.merge(mean_per_state, how=\"left\",\r\n left_on=\"name\", right_on=\"ST_FIPS\")","def main():\n\n #get the csv file into a data-frame\n universities_df = pd.read_csv('universities_data.csv', encoding = 'utf-8-sig')\n universities_names_list = universities_df['name'].tolist()\n\n #get list of university objects\n url = 'http://universities.hipolabs.com/search?country=Israel'\n api_universities = Get_universities(url)\n list_of_universities = api_universities.get_universities_info()\n\n #to see if we got new entities or not for exporting to csv later..\n is_new_entities = False\n\n for university in list_of_universities:\n if university.name not in universities_names_list:\n is_new_entities = True\n universities_df= universities_df.append(pd.DataFrame({\n 'alpha_two_code': [university.alpha_two_code], \n 'country': [university.country],\n 'web_pages': [str(university.web_pages)],\n 'domains': [str(university.domains)],\n 'name': [university.name],\n 'state_province':[str(university.state_province)]}) , ignore_index = True)\n\n #export back to csv if true\n if is_new_entities: \n print('we got new entities!') \n universities_df.to_csv('universities_data.csv', encoding = 'utf-8-sig', index = False)\n else:print('no new universities for now!')","def _clean_search_hit(search_hit):\n\n hit_name = search_hit.find(class_='hit-name')\n hit_url = hit_name.get('href')\n hit_id = hit_url.split('/')[-1]\n name = hit_name.get_text().split(',')[0].title().split()\n\n current_city = search_hit.find(class_='hit-location').get_text().upper()\n\n # Find all Addresses for search result.\n try:\n address = search_hit.find(class_='hit-pastAddresses').find_all(class_='hit-values')\n address = list({a.text.upper().replace('.', '') for a in address})\n except AttributeError:\n address = list()\n\n # find the address that is most likely the current main address.\n try:\n address.insert(0, address.pop(address.index(current_city)))\n except ValueError:\n address.insert(0, current_city)\n\n address = [\n {\n '@type': 'PostalAddress',\n 'addressLocality': locality.title(),\n 'addressRegion': region\n } for locality, region in [a.split(', ') for a in address]]\n\n work_location = {'@type': 'Place'}\n try:\n work_location['name'] = search_hit\\\n .find(class_='hit-work')\\\n .find(class_='hit-values')\\\n .get_text()\\\n .title()\n except AttributeError:\n work_location['name'] = ''\n\n alumni_of = {'@type': 'EducationalOrganization'}\n try:\n alumni_of['name'] = search_hit\\\n .find(class_='hit-high-school')\\\n .find(class_='hit-values')\\\n .get_text().title()\n except AttributeError:\n pass\n\n return {\n '@id': hit_id,\n '@type': 'Person',\n 'name': ' '.join(name),\n 'givenName': name[0],\n 'middleName': ' '.join(name[1:-1]),\n 'familyName': name[-1],\n 'url': hit_url,\n 'address': address,\n 'workLocation': work_location,\n 'alumniOf': alumni_of,\n }","def University_calculation(jobid):\r\n min_cgpa=90\r\n \"\"\"~~~~~~~~~\"\"\"\r\n dbconnect= connect_to_db()\r\n Candidate_qualifications=pd.read_sql(\"select candidate_id,university_name,institute_name,aggregate from candidate_qualification where candidate_id in(select candidate_id from master_id where job_id=\"+str(jobid)+\")\",con=dbconnect)\r\n College_data=pd.read_sql(\"select * from college_tiers\",con=dbconnect)\r\n Candidate_qualifications[\"aggregate\"]=Candidate_qualifications[\"aggregate\"].apply(lambda x: 0 if x<0 or x>100 else x)\r\n Candidate_qualifications[\"aggregate\"]=Candidate_qualifications[\"aggregate\"].apply(lambda x: x*10 if 5<x<10 else x)\r\n Candidate_qualifications[\"aggregate\"]=Candidate_qualifications[\"aggregate\"].apply(lambda x: 25*x if 0<x<4 else x)\r\n\r\n def Aggregate():\r\n Unique_candids=Candidate_qualifications[[\"candidate_id\",\"aggregate\"]].groupby(\"candidate_id\").mean()\r\n Unique_candids[\"aggregate\"]=Unique_candids[\"aggregate\"].apply(lambda x:x-min_cgpa)\r\n minval=min(Unique_candids[\"aggregate\"])\r\n maxval=max(Unique_candids[\"aggregate\"])\r\n Unique_candids[\"aggregate\"]=Unique_candids[\"aggregate\"].apply(lambda x:(x-minval)*100/(maxval-minval))\r\n Unique_candids=Unique_candids.reset_index()\r\n return Unique_candids\r\n \r\n def University_name():\r\n stop_words=[\"of\",\"on\",\"for\",\"the\",\"&\",\"and\"]\r\n unique_candids=list(np.unique(Candidate_qualifications[\"candidate_id\"]))\r\n candidate_univdict={}\r\n for i in unique_candids:\r\n candidate_univdict[i]=Candidate_qualifications[[\"university_name\",\"institute_name\"]][Candidate_qualifications[\"candidate_id\"]==i].values.tolist()\r\n candidate_univdict={k:list(map(lambda x:list(filter(lambda y:str(y).strip() not in[\"\",\"None\"],x)),v)) for k,v in candidate_univdict.items()}\r\n candidate_univdict={k: np.unique(list(itertools.chain.from_iterable(v))).tolist() for k,v in candidate_univdict.items()}\r\n for i in candidate_univdict.keys():\r\n for j in candidate_univdict[i]:\r\n if j in list(map(lambda x: str(x).lower(),College_data[\"College\"].tolist())):\r\n candidate_univdict[i][candidate_univdict[i].index(j)]=list(College_data[\"Tier\"][College_data[\"College\"]==j])[0]\r\n continue;\r\n if j in list(map(lambda x: str(x).lower(),College_data[\"College\"].tolist())):\r\n candidate_univdict[i][candidate_univdict[i].index(j)]=list(College_data[\"Tier\"][College_data[\"Ancronym\"]==j])[0]\r\n continue;\r\n else:\r\n Processed_collegedata=pd.DataFrame(College_data[\"College\"].apply(lambda x: [i for i in re.sub(\"[\\.-]\",\" \",x.lower()).split() if i not in stop_words]))\r\n Processed_collegedata[\"Ancronym\"]=College_data[\"Ancronym\"].apply(lambda x: [i for i in re.sub(\"[\\.-]\",\" \",x.lower()).split() if i not in stop_words])\r\n val=[w for w in re.sub(\"[\\.-]\",\" \",j.lower()).split() if w not in stop_words]\r\n Processed_collegedata[\"College\"]=Processed_collegedata[\"College\"].apply(lambda x:(len(set(val).intersection(set(x))))/len(set(val).union(set(x))))\r\n Processed_collegedata[\"Ancronym\"]=Processed_collegedata[\"Ancronym\"].apply(lambda x:(len(set(val).intersection(set(x))))/len(set(val).union(set(x))))\r\n maxval=Processed_collegedata.max().idxmax()\r\n if Processed_collegedata[maxval].idxmax()>0.5:\r\n candidate_univdict[i][candidate_univdict[i].index(j)]=College_data.ix[Processed_collegedata[maxval].idxmax(),\"Tier\"]\r\n else:\r\n candidate_univdict[i][candidate_univdict[i].index(j)]=3\r\n \r\n candidate_univdict={k:100/min(v) for k,v in candidate_univdict.items() if len(v)>0}\r\n College_score=pd.DataFrame.from_dict(candidate_univdict,orient=\"index\")\r\n College_score=College_score.reset_index()\r\n College_score.columns=[\"candidate_id\",\"Tier_score\"]\r\n return College_score\r\n result=pd.merge(Aggregate(),University_name(),how=\"outer\",on=\"candidate_id\")\r\n result=pd.merge(pd.DataFrame(np.unique(Candidate_qualifications[\"candidate_id\"]),columns=[\"candidate_id\"]),result,how=\"left\",on=\"candidate_id\")\r\n result=result.fillna(0)\r\n return result","def dataDescription(train):\n print(train.describe())\n # No NaNs\n print(train.head())\n # 5 columns: store number, department number, date, weekly sales, isHoliday\n\n # Lets view the weekly sales, grouped by date\n fig, ax = plt.subplots()\n salesByDate = train.groupby('Date').mean().Weekly_Sales\n salesByDate.plot()\n plt.show()\n fig, ax = plt.subplots()\n autocorrelation_plot(salesByDate)\n plt.show()\n # Lets plot all store's sales by date on the same graph\n salesByStore = train.groupby(by=['Store', 'Date']).mean().Weekly_Sales\n salesByStore.unstack(level=0).plot()\n # Lets plot all department's sales by date on the same graph\n salesByStore = train.groupby(by=['Dept', 'Date']).mean().Weekly_Sales\n salesByStore.unstack(level=0).plot()\n # It looks like all stores have peaks at the same times, however just a\n # single department. Could this be the holiday department?\n fig, ax = plt.subplots()\n holidayDates = train.groupby('Date').mean().IsHoliday\n holidayDates.plot()\n plt.show()\n # Very few holiday days...\n # What are the average sales on holidays vs not holidays\n holidaySales = train[train['IsHoliday'] == True].Weekly_Sales.mean()\n nonHolidaySales = train[train['IsHoliday'] == False].Weekly_Sales.mean()\n print(\"The mean weekly sales on holidays is {:.2f} \".format(holidaySales) +\n \"and then {:.2f} for non-holidays.\".format(nonHolidaySales))\n\n # Lets looks at how complete the data is. Create a series with all possible\n # dates in. Compare with each store and department.\n trainDates = pd.to_datetime(train.Date)\n trainDates = pd.DatetimeIndex(trainDates.unique())\n# First confirm there are no missing dates in the whole range.\n# trainDatesTest = pd.date_range(trainDates.min(),\n# trainDates.max(), freq='7D')\n# (trainDates == trainDatesTest).all()\n stores = np.unique(train['Store'])\n depts = np.unique(train['Dept'])\n missingDates = defaultdict(int)\n for store in stores:\n for dept in depts:\n trainThis = train[train['Store'] == store]\n trainThis = trainThis[trainThis['Dept'] == dept]\n missing = len(trainDates) - len(pd.DatetimeIndex(trainThis.Date))\n missingDates[missing] += 1\n\n # The majority miss nothing. 314 stores don't have certain departments\n # - as can be expected!\n fig, ax = plt.subplots()\n ax.bar(list(missingDates.keys()), missingDates.values())","def setEmpireStats(self):\n totalEmpires = len(self.empires.keys())\n stats = {'Research':[], 'Fleet Size':[], 'Army Size':[], 'CR Production':[],\n 'AL Production':[],'EC Production':[],'IA Production':[]}\n \n # Calculate Research Stats\n d = {}\n for empireID, myEmpire in self.empires.iteritems():\n if empireID <> '1':\n num = 0\n for techID, myTech in myEmpire.techTree.iteritems():\n if myTech.complete == 1:\n num += 1\n d[empireID] = num\n stats['Research'] = anwp.func.funcs.sortDictByValue(d, True)\n \n # Calculate Fleet Stats\n d = {}\n for shipID, myShip in self.ships.iteritems():\n if myShip.empireID <> '1':\n (BV,CR,AL,EC,IA) = myShip.getMyValue()\n if myShip.empireID in d.keys():\n d[myShip.empireID] += BV\n else:\n d[myShip.empireID] = BV\n stats['Fleet Size'] = anwp.func.funcs.sortDictByValue(d, True)\n \n # Calculate Army Stats\n d = {}\n for regimentID, myRegiment in self.regiments.iteritems():\n if myRegiment.empireID <> '1':\n (BV,CR,AL,EC,IA) = myRegiment.getMyValue()\n if myRegiment.empireID in d.keys():\n d[myRegiment.empireID] += BV\n else:\n d[myRegiment.empireID] = BV\n stats['Army Size'] = anwp.func.funcs.sortDictByValue(d, True)\n\n # Calculate Production Stats\n for res in ['CR','AL','EC','IA']:\n d = {}\n for systemID, mySystem in self.systems.iteritems():\n if mySystem.myEmpireID <> '1':\n myValue = getattr(mySystem, 'prod%s' % res)\n if mySystem.myEmpireID in d.keys():\n d[mySystem.myEmpireID] += myValue\n else:\n d[mySystem.myEmpireID] = myValue\n myEmpire = self.empires[mySystem.myEmpireID]\n myEmpireValue = getattr(myEmpire, 'totalProd%s' % res)\n setattr(myEmpire, 'totalProd%s' % res, myEmpireValue+myValue)\n \n stats['%s Production' % res] = anwp.func.funcs.sortDictByValue(d, True)\n \n # calculate top captains\n d = {}\n for captainID, myCaptain in self.captains.iteritems():\n if myCaptain.myEmpire.id <> '1':\n myCaptain.resetData()\n d[myCaptain.id] = myCaptain.experience\n topCaptains = anwp.func.funcs.sortDictByValue(d, True)\n topCaptains = topCaptains[:2*len(self.empires.keys())]\n \n # Send out Stats to each Empire\n for empireID, myEmpire in self.empires.iteritems():\n if empireID <> '1':\n title = 'Round:%d Statistics' % self.currentRound\n body = ['%s ROUND %d STATS:' % (myEmpire.name, self.currentRound)]\n body.append('====================================================')\n for item in ['Research','Fleet Size', 'Army Size', 'CR Production',\n 'AL Production', 'EC Production', 'IA Production']:\n if empireID in stats[item]:\n body.append('You are %s in %s' % (anwp.func.funcs.getNiceNumber(stats[item].index(empireID)+1), item))\n \n # total production\n body.append('')\n body.append('TOTAL EMPIRE PRODUCTION OVER %d ROUNDS:' % self.currentRound)\n body.append('====================================================')\n for res in ['CR','AL','EC','IA']:\n body.append('Total %s Production: %d' % (res, getattr(myEmpire, 'totalProd%s' % res)))\n\n # legendary captains\n body.append('')\n body.append('TOP %d STARSHIP CAPTAINS in ROUND %d:' % ((2*len(self.empires.keys()), self.currentRound)))\n body.append('====================================================')\n for captainID in topCaptains:\n myCaptain = self.captains[captainID]\n myCaptain.promoteMe()\n body.append('%s ---> RANK:%s -- EXP:%d -- %s' % (string.upper(myCaptain.name), myCaptain.rank, myCaptain.experience, string.upper(myCaptain.myEmpire.name)))\n \n myEmpire.genMail({'fromEmpire':empireID, 'round':self.currentRound,\n 'messageType':'general', 'subject':title, 'body':body})","def scrape_profile(inhandle, outfile, year, month):\n #Read file\n html = inhandle.read()\n soup = BeautifulSoup(html, 'html.parser')\n\n #Find main page content\n content = soup.find('div', {'class':'entry-content'})\n\n profile = {}\n\n #Fill in known info from URL\n profile['year_reported'] = year\n profile['month_reported'] = month\n\n #Extract and download images.\n profile['images'] = [save_image(img['src']) for img in content.findAll('img')]\n\n #Get visible text\n text = content.get_text().strip()\n\n #Parse information from text\n for key in extractors:\n match = extractors[key].search(text)\n if match:\n matchtext = match.group(1).strip()\n if key in ['justifications','messages']:\n vals = matchtext.split('\\n')\n else:\n vals = matchtext\n profile[key] = vals \n\n #Parse annotations\n content = soup.find('div', {'class':'entry-utility'})\n profile['tags'] = [node.get_text() for node in content.findAll('a', {'rel':'tag'})]\n profile['gender'] = 'female' if 'Female profiles' in profile['tags'] else 'male'\n\n #Save output\n json.dump(profile, open(outfile,'w'))","def parse_orgentec(response: scrapy.http.Response):\n job = response.meta['job']\n\n yield job","def summary(self):\n raise NotImplementedError","def _run_household_year(queue, residents, year, work_seed, **elp_kwargs):\n try:\n np.random.seed(work_seed)\n data_ex_main = DataExchangeCsv()\n elp = ElectricalLoadProfile(data_ex_main, residents, **elp_kwargs)\n result = elp.get_rescheduled_profiles(year)\n queue.put((None, result))\n return\n except Exception as e:\n queue.put((e, traceback.format_exc()))\n # traceback.print_exc(file=sys.stdout)\n return"],"string":"[\n \"def proc_education_summary( summary: Tag ) -> Dict[str, str]:\\n edu_record = dict()\\n edu_record['school'] = summary.find('h3').text.strip()\\n edu_record['is_abroad_school'] = _is_abroad_school( edu_record['school'] )\\n\\n for parag in summary.find_all('p'):\\n spans = [span.text.strip() for span in parag.find_all('span')]\\n if len( spans ) == 2:\\n fld_name, value = spans\\n value = value.strip()\\n elif len(spans) == 0:\\n # print( 'education parag: ', parag )\\n edu_record['description'] = parag.text.strip()\\n continue\\n else:\\n print( 'education spans: ', spans )\\n continue\\n\\n if fld_name == 'Nombre de la titulación':\\n edu_record['degree_raw'] = value\\n edu_record['degree'] = _classify_degree( value )\\n # print( 'degree: ', value, _classify_degree(value) )\\n elif fld_name == 'Disciplina académica':\\n edu_record['field_raw'] = value\\n elif fld_name == 'Nota':\\n edu_record['grade_raw'] = value\\n elif fld_name.startswith('Fechas de estudios'):\\n edu_record['period_raw'] = value\\n elif fld_name.startswith('Actividades y asociaciones'):\\n edu_record['activities_raw'] = value\\n else:\\n print(\\\"proc_education_summary: \\\", fld_name, ' :: ', value)\\n\\n if edu_record.get('degree', 'Unknown') == 'Unknown':\\n if re.search( 'Ingenier|Engineering', edu_record.get('field_raw', '') ):\\n edu_record['degree'] = 'University'\\n\\n return edu_record\\n # %%\",\n \"def employment(self, instance):\\r\\n return instance.user.profile.employment\",\n \"def process_employees_salary(self, employees_info: List[List[str]]) -> None:\\n pass\",\n \"def employment():\\n return pd.read_csv(csv_path(\\\"CIA_Unemployment.csv\\\"), index_col=0, usecols=[1, 2])\",\n \"def extract_mean_salary(salary, usd_conversion_rate):\",\n \"def getApplicantData(self, resume):\\n bigram_measures = cl.BigramAssocMeasures()\\n trigram_measures = cl.TrigramAssocMeasures()\\n st = LancasterStemmer()\\n tk = RegexpTokenizer(r'\\\\w+')\\n total_exp = 0.\\n\\n user_id = resume['userId']\\n education = resume['userEducationList']\\n experience = resume['userExperianceList']\\n skills = resume['skills']\\n work_hist_skills = resume['skillsFoundInWorkHistory']\\n\\n\\n #Convert past job descriptions into bigrams and trigrams to compare to target job description\\n bigrams, trigrams = [], []\\n try:\\n jobs_from_resume = [job['workDescription'] for job in experience]\\n except Exception:\\n jobs_from_file = []\\n\\n\\n for job in jobs_from_resume:\\n if job != None:\\n biTri_temp = self.getBiTrigrams(job)\\n bigrams.append(biTri_temp[0])\\n trigrams.append(biTri_temp[0])\\n\\n #Convert past job titles into bigrams and trigrams and get years of experience in respective roles\\n #experience_yrs = [] - I dont think it's being used anywhere\\n raw_titles = []\\n try:\\n job_titles_from_resume = [job['jobWorkTitle'] for job in experience]\\n except Exception:\\n job_titles_from_resume = []\\n\\n position_yrs = []\\n try:\\n tenure_in_resume = [job['workTenureInCompany']/12 for job in experience]\\n except Exception:\\n tenure_in_resume = [1]\\n for yrs_exp in tenure_in_resume:\\n position_yrs.append(yrs_exp)\\n\\n try:\\n company_name = [job['companyName'] for job in experience]\\n except Exception:\\n company_name = []\\n\\n #Get education data: institute tier and type.\\n try:\\n institute_name = [degree['instituteName'] for degree in education]\\n except Exception:\\n institute_name = []\\n\\n try:\\n degreeType = [degree['educationType'] for degree in education]\\n except Exception:\\n degreeType = []\\n\\n emp_data = {'user_id': user_id ,'skills':skills, 'work_hist_skills': work_hist_skills,'job_bigrams':bigrams,\\n 'job_trigrams':trigrams, 'titles': job_titles_from_resume, 'years_exp':position_yrs, 'company_name': company_name,\\n 'position_tenure':position_yrs, 'institute_name': institute_name, 'degreeType': degreeType}\\n\\n return emp_data\",\n \"def company_data(driver):\\n try:\\n stats_selector = \\\"ul.company-growth-stats.stats-list li\\\"\\n company_stats = driver.find_elements_by_css_selector(stats_selector)\\n company_info = [stat.text for stat in company_stats]\\n except Exception as e:\\n print(\\\"error acquiring company info\\\")\\n print(e)\\n else:\\n try:\\n employees = list(filter(lambda text: 'employees' in text, company_info))\\n num_employees = ''.join(list(filter(lambda c: c.isdigit(), employees[0])))\\n except Exception as e:\\n num_employees = \\\"\\\"\\n pass\\n try:\\n tenure = list(filter(lambda text: 'tenure' in text, company_info))\\n avg_tenure = ''.join(list(filter(lambda c: c in '0123456789.', tenure[0])))\\n except Exception as e:\\n avg_tenure = \\\"\\\"\\n pass\\n company_info = {\\n \\\"avg_tenure\\\" : avg_tenure, \\n \\\"num_employees\\\" : num_employees\\n }\\n return {\\\"avg_tenure\\\" : avg_tenure, \\\"num_employees\\\" : num_employees}\",\n \"def get_emp_data(self,employee):\\n\\t\\temp = None\\n\\t\\tfind_by = employee.find_elements_by_tag_name\\n\\t\\tif str(type(employee)) != \\\"<type 'NoneType'>\\\" and main.is_desktop():\\n\\t\\t\\t# columns = employee.find_elements_by_tag_name(\\\"td\\\")\\n\\t\\t\\temp = {\\n\\t\\t\\t\\t'name': find_by('td')[0].text,\\n\\t\\t\\t\\t'id': find_by('td')[1].text,\\n\\t\\t\\t\\t'status': find_by('td')[2].text,\\n\\t\\t\\t\\t'election': find_by('td')[3].text,\\n\\t\\t\\t\\t'date_changed': find_by('td')[4].text\\n\\t\\t\\t}\\n\\t\\telif str(type(employee)) != \\\"<type 'NoneType'>\\\":\\n\\t\\t\\temp = {\\n\\t\\t\\t\\t'name': find_by('div')[2].text,\\n\\t\\t\\t\\t'id': find_by('div')[3].text[13:],\\n\\t\\t\\t\\t'status': find_by('div')[4].text[8:], #Fail 4:20p, StaleEl\\n\\t\\t\\t\\t'election': find_by('div')[5].text[17:], #Fail 4:15p, StaleEl\\n\\t\\t\\t\\t'date_changed': find_by('div')[6].text[14:]\\n\\t\\t\\t}\\n\\n\\t\\t# raw_input(str(emp))\\n\\t\\treturn emp\",\n \"def extract_information(preprocessed_sentences):\\n parsed = list(map(lambda sentence: nlp(sentence), preprocessed_sentences))\\n\\n quantities = list(filter(lambda sentence: eh.sentence_has_type(sentence, 'QUANTITY'), parsed))\\n dates = list(filter(lambda sentence: eh.sentence_has_type(sentence, 'DATE'), parsed))\\n\\n hurricane_name = eh.extract_frequent_regex_match(parsed, '[Hh]urricane ([A-Z][a-z]+)').most_common(1)[0][0]\\n hurricane_category = eh.extract_frequent_regex_match(parsed, '[Cc]ategory ([0-9]+)').most_common(1)[0][0]\\n\\n tropical_storm_name = eh.extract_frequent_regex_match(parsed, '[Tt]ropical [Ss]torm ([A-Z][a-z]+)').most_common(1)[0][0]\\n formation_date, middle_month = extract_storm_timeline(dates, hurricane_name)\\n\\n preperation_info = extract_preparation_information(parsed)\\n prep_gpes = preperation_info[0].most_common(3)\\n\\n restore_info = extract_restoration_information(parsed)\\n\\n landfall_info = extract_landfall_information(parsed)\\n\\n wind_info = extract_wind_information(quantities)\\n rain_info = extract_rain_information(quantities)\\n size_info = extract_size_information(parsed)\\n\\n # formation_info = extract_formation_info(parsed)\\n death_info = extract_death_damages_info(parsed)\\n\\n print(constants.HURRICANE_SENTENCE.format(hurricane_name, middle_month, hurricane_category))\\n print(constants.LANDFALL_SENTENCE.format(hurricane_name, landfall_info[2], landfall_info[3], landfall_info[0], landfall_info[1]))\\n print(constants.WIND_SENTENCE.format(wind_info[0], wind_info[1], wind_info[2]))\\n print(constants.RAIN_SENTENCE.format(hurricane_name, rain_info[1], rain_info[0], rain_info[2]))\\n print(constants.FORMATION_SENTENCE.format(formation_date, tropical_storm_name))\\n print(constants.PREPARATION_SENTENCE.format(prep_gpes[0][0], prep_gpes[1][0], prep_gpes[2][0], preperation_info[1].\\n most_common(1)[0][0]))\\n print(constants.SIZE_SENTENCE.format(size_info[0], size_info[1]))\",\n \"def __getCompanyMetadata(parsed: BeautifulSoup) -> dict:\\n\\n # Company metadata container\\n metadata_container = parsed.find('p', class_='identInfo')\\n # String representation of HTML (used in RegEx)\\n metadata_str = str(metadata_container)\\n\\n # Dictionary for company metadata\\n company_metadata = dict()\\n\\n # RegEx for extracting SIC and SIC type\\n sic_re = re.compile(r'SIC.+?:.+?(\\\\d+?)<\\\\/a> -(.+?)<br')\\n # Getting SIC and SIC type match\\n sic_matches = sic_re.findall(metadata_str)\\n # Saving SIC and stripped, HTML-parsed SIC type\\n company_metadata['sic'] = sic_matches[0][0]\\n company_metadata['sic_type'] = html.unescape(sic_matches[0][1]).strip()\\n\\n # RegEx for extracting company location (state)\\n location_re = re.compile(r'State location:.+?>(\\\\w+?)<\\\\/a>')\\n # Getting company location\\n location_matches = location_re.findall(metadata_str)\\n # Saving company location\\n company_metadata['location'] = location_matches[0].strip()\\n\\n # RegEx for extracting state of incorporation\\n incorp_state_re = re.compile(r'State of Inc\\\\.:.+?>(\\\\w+?)<\\\\/strong>')\\n # Getting state of incorporation\\n incorp_match = incorp_state_re.findall(metadata_str)[0]\\n # Saving state of incorporation\\n company_metadata['incorporation_state'] = incorp_match.strip()\\n\\n # RegEx for extracting end of fiscal year\\n fiscal_year_re = re.compile(r'Fiscal Year End:.+?(\\\\d{4})')\\n # Getting end of fiscal year\\n fiscal_year_match = fiscal_year_re.findall(metadata_str)[0]\\n # Saving end of fiscal year (in mm-dd format)\\n fy_formatted = fiscal_year_match[0:2] + '-' + fiscal_year_match[2:]\\n company_metadata['fiscal_year_end'] = fy_formatted\\n\\n return company_metadata\",\n \"def extract_summary(self):\\n metadata = {}\\n\\n ## document Id\\n documentId = self.tree.find(\\\"./id\\\")\\n documentId = documentId.attrib['root'] if documentId is not None and \\\"root\\\" in documentId.attrib else \\\"\\\"\\n metadata[\\\"documentId\\\"] = documentId\\n\\n ## setId\\n setid = self.tree.find(\\\"./setId\\\")\\n setid = setid.attrib['root'] if setid is not None and \\\"root\\\" in setid.attrib else \\\"\\\"\\n metadata[\\\"setId\\\"] = setid\\n\\n ## version number\\n splversion = self.tree.find(\\\"./versionNumber\\\")\\n versionNumber = \\\"\\\"\\n if splversion is not None:\\n if \\\"value\\\" in splversion.attrib:\\n versionNumber = splversion.attrib[\\\"value\\\"]\\n metadata[\\\"versionNumber\\\"] = versionNumber\\n\\n ## product type \\n code = self.tree.find(\\\"./code\\\")\\n check_if_attrib_exists = lambda x, key: x[key] if key in x else ''\\n product_type = check_if_attrib_exists(code.attrib, \\\"displayName\\\")\\n metadata[\\\"productType\\\"] = product_type\\n\\n ## title\\n title_text = self.tree_et.xpath(\\\"./title//text()\\\")\\n title = (\\\" \\\".join([self.strip_newline_tab(t) for t in title_text]) if len(title_text) > 0 else \\\"\\\")\\n metadata[\\\"title\\\"] = title\\n\\n ## manufacturer\\n manufacturer = self.tree.find(\\\"./author//representedOrganization/name\\\")\\n if manufacturer != None and manufacturer.text != None:\\n manufacturer = self.strip_newline_tab(manufacturer.text)\\n else:\\n manufacturer = \\\"\\\"\\n metadata[\\\"manufacturer\\\"] = manufacturer\\n\\n ## effectivetime\\n effectiveTime = self.tree_et.xpath(\\\"./effectiveTime/@value\\\")\\n effectiveTime = self.__normalize_date(effectiveTime)\\n\\n metadata[\\\"effectiveTime\\\"] = effectiveTime\\n metadata[\\\"publishedDate\\\"] = effectiveTime\\n\\n ## From manufacturedProduct section\\n brand_name = self.tree_et.xpath(\\\".//manufacturedProduct//name\\\")\\n brand_name = self.strip_newline_tab(brand_name[0].text) if len(brand_name) > 0 else \\\"\\\"\\n metadata[\\\"drugName\\\"] = brand_name\\n\\n route = self.tree_et.xpath(\\\".//manufacturedProduct//formCode/@code\\\")\\n route = self.strip_newline_tab(route[0]) if len(route) > 0 else \\\"\\\"\\n metadata[\\\"routeOfAdministration\\\"] = route\\n\\n product_ndc = self.tree_et.xpath(\\\".//manufacturedProduct//code/@code\\\")\\n product_ndc = self.strip_newline_tab(product_ndc[0]) if len(product_ndc) > 0 else \\\"\\\"\\n metadata[\\\"ndcCode\\\"] = product_ndc\\n\\n generic_name = self.tree_et.xpath(\\\".//manufacturedProduct//asEntityWithGeneric//genericMedicine/name\\\")\\n generic_name = self.strip_newline_tab(generic_name[0].text) if len(generic_name) > 0 else \\\"\\\"\\n metadata[\\\"genericName\\\"] = generic_name\\n\\n ## dosage form\\n dosage_form = self.tree_et.xpath(\\\".//manufacturedProduct//formCode/@displayName\\\")\\n dosage_form = dosage_form[0] if len(dosage_form) > 0 else \\\"\\\"\\n metadata[\\\"dosageForm\\\"] = dosage_form\\n\\n # active ingredients\\n substance_name = sorted([self.strip_newline_tab(a.text) for a in\\n self.tree_et.xpath(\\\".//.//manufacturedProduct//activeMoiety/activeMoiety/name\\\")])\\n substance_name = \\\", \\\".join(set(substance_name))\\n metadata[\\\"substanceName\\\"] = substance_name\\n\\n ## inactive ingredients\\n inactive_ingredients = sorted([self.strip_newline_tab(inactive.text) for inactive in self.tree_et.xpath(\\n \\\".//manufacturedProduct//inactiveIngredient/inactiveIngredientSubstance/name\\\")])\\n\\n if len(inactive_ingredients) == 0:\\n inactive_ingredients = \\\"\\\"\\n else:\\n inactive_ingredients = \\\",\\\".join(set(inactive_ingredients))\\n\\n metadata[\\\"inactiveIngredients\\\"] = inactive_ingredients\\n\\n ## other ingredients\\n ingredients = sorted([self.strip_newline_tab(ingredient.text) for ingredient in\\n self.tree_et.xpath(\\\".//manufacturedProduct//ingredient/ingredientSubstance/name\\\")])\\n\\n if len(ingredients) == 0:\\n ingredients = \\\"\\\"\\n else:\\n ingredients = \\\", \\\".join(set(ingredients))\\n metadata[\\\"ingredients\\\"] = ingredients\\n\\n # marketing_category\\n marketing_category = self.tree_et.xpath(\\\".//manufacturedProduct/subjectOf/approval/code/@displayName\\\")\\n marketing_category = self.strip_newline_tab(marketing_category[0]) if len(marketing_category) > 0 else \\\"\\\"\\n metadata[\\\"marketingCategory\\\"] = marketing_category\\n\\n # consumed in\\n consumed_in = self.tree_et.xpath(\\n \\\".//manufacturedProduct//consumedIn/substanceAdministration/routeCode/@displayName\\\")\\n consumed_in = consumed_in[0] if len(consumed_in) > 0 else \\\"\\\"\\n metadata[\\\"consumedIn\\\"] = consumed_in\\n\\n # revision date\\n marketing_date = self.tree_et.xpath(\\\".//manufacturedProduct//marketingAct/effectiveTime/low/@value\\\")\\n marketing_date = self.__normalize_date(marketing_date)\\n metadata[\\\"marketingDate\\\"] = marketing_date\\n\\n return metadata\",\n \"def user_story_01(self):\\n td=datetime.today()\\n for person in self.individuals.values():\\n pb=person.birthday\\n pd=person.death\\n if pb !=\\\"NA\\\" and pb>td:\\n print(f'US01 - {person.name} birthday after today on line {person._birthday_line}')\\n if pd !=\\\"NA\\\" and pd>td:\\n print(f'US01 - {person.name} death after today on line {person._death_line}')\\n for family in self.families.values():\\n fm=family.married \\n fd=family.divorced\\n if fm !=\\\"NA\\\" and fm>td:\\n print(f'US01 - {self.individuals[family.wife_id].name} marriage after today on line {family._married_line}')\\n if fd !=\\\"NA\\\" and fd>td:\\n print(f'US01 - {self.individuals[family.husb_id].name} divorce after today on line {family._divorced_line}')\",\n \"def get_americanlife_info(epno, throwException = True, extraStuff = None):\\n\\n # first see if this episode of this american life exists...\\n if extraStuff is None:\\n resp = requests.get( 'http://www.thisamericanlife.org/radio-archives/episode/%d' % epno )\\n else:\\n resp = requests.get( 'http://www.thisamericanlife.org/radio-archives/episode/%d/%s' % ( epno, extraStuff ) )\\n if resp.status_code != 200:\\n raise ValueError(\\\"Error, could not find This American Life episode %d, because could not open webpage.\\\" % epno)\\n \\n enc = resp.headers['content-type'].split(';')[-1].split('=')[-1].strip().upper()\\n if enc not in ( 'UTF-8', ):\\n tree = lxml.html.fromstring(unicode(resp.text, encoding=enc))\\n else:\\n tree = lxml.html.fromstring(resp.text )\\n\\n elem_info_list = list(filter(lambda elem: 'class' in elem.keys() and\\n elem.get('class') == \\\"top-inner clearfix\\\", tree.iter('div')))\\n if len(elem_info_list) != 1:\\n if throwException:\\n raise ValueError(\\\" \\\".join([ \\\"Error, cannot find date and title for This American Life episode #%d,\\\" % epno,\\n \\\"because could not get proper elem from HTML source.\\\" ]) )\\n else:\\n return None\\n elem_info = max(elem_info_list)\\n date_list = list(filter(lambda elem: 'class' in elem.keys() and elem.get('class') == 'date',\\n elem_info.iter('div')))\\n if len(date_list) != 1:\\n if throwException:\\n raise ValueError(\\\"Error, cannot find date and title for This American Life episode #%d.\\\" % epno)\\n else:\\n return None\\n date_s = max(date_list).text.strip()\\n date_act = time.strptime(date_s, '%b %d, %Y')\\n year = date_act.tm_year\\n\\n title_elem_list = list(filter(lambda elem: 'class' in elem.keys() and\\n elem.get('class') == 'node-title', elem_info.iter('h1')))\\n if len(title_elem_list) != 1:\\n raise ValueError(\\\"Error, cannot find date and title for This American Life episode #%d.\\\" % epno)\\n title = max(title_elem_list).text.strip()\\n title = titlecase.titlecase( ':'.join( title.split(':')[1:]).strip() )\\n return title, year\",\n \"def _process_journal_info(el):\\n assert len(el) == 1 # We only expect one journal record per article\\n el = el[0]\\n\\n title = el.xpath('string(Title)')\\n abbreviation = el.xpath('string(ISOAbbreviation)')\\n # Some journals will have an NlmUniqueID and no ISSN. In every case where\\n # an NlmUniqueID is present it will be used instead of the ISSN\\n issn = el.xpath('string(ISSN)') or None\\n # Date parsing should be interesting - there's no guarantee on what\\n # fields will be present so I may need to do some ugly stuff\\n pub_date_el = el.xpath('JournalIssue/PubDate')[0]\\n date_elements = set([c.tag for c in pub_date_el.getchildren()])\\n\\n final_pub_date = {'Year': 1900, 'Month': 1, 'Day': 1}\\n if 'MedlineDate' in date_elements:\\n date_text = pub_date_el.xpath('string(MedlineDate)')\\n year = re.search(YEAR_RE_PATTERN, date_text)\\n if year is None:\\n # XXX Give up and debug this later. This condition didn't\\n # appear in the test data but it could come up when\\n # parsing real data\\n pass\\n else:\\n year = year.group(0)\\n # Maybe we can guess a month, too\\n month = 1\\n for k in DATE_TRANSLATIONS.keys():\\n # I can't tell if short names are preferred\\n # to long names but this shouldn't matter in the end\\n if k in date_text:\\n month = DATE_TRANSLATIONS[k]\\n break\\n # Let's leave the date alone\\n final_pub_date.update(Year=year, Month=month)\\n else:\\n pub_date = {}\\n # Need to build up a date with as much information as we've got\\n for k in date_elements.intersection(('Year', 'Month', 'Day')):\\n v = pub_date_el.xpath(\\\"string(%s)\\\" % k)\\n if v in DATE_TRANSLATIONS:\\n v = DATE_TRANSLATIONS[v]\\n pub_date[k] = int(v)\\n final_pub_date.update(pub_date)\\n\\n return {'title': title,\\n 'abbreviation': abbreviation,\\n 'id': issn,\\n 'id_type': 'issn',\\n 'pub_date': final_pub_date,\\n }\",\n \"def _get_employee_info() -> List[List[str]]:\\n return [\\n ['100', 'Dave', 'Team Leader'],\\n ['101', 'Ram', 'Developer'],\\n ['102', 'Raj', 'Developer'],\\n ['103', 'Rahul', 'Tester'],\\n ]\",\n \"def _parse_summary(self):\\r\\n if self._is_at_section():\\r\\n return\\r\\n\\r\\n summary = self._doc.read_to_next_empty_line()\\r\\n summary_str = \\\" \\\".join([s.strip() for s in summary]).strip()\\r\\n if re.compile('^([\\\\w., ]+=)?\\\\s*[\\\\w\\\\.]+\\\\(.*\\\\)$').match(summary_str):\\r\\n self['Signature'] = summary_str\\r\\n if not self._is_at_section():\\r\\n self['Summary'] = self._doc.read_to_next_empty_line()\\r\\n else:\\r\\n self['Summary'] = summary\\r\\n\\r\\n if not self._is_at_section():\\r\\n self['Extended Summary'] = self._read_to_next_section()\",\n \"def _parse_summary(self):\\r\\n if self._is_at_section():\\r\\n return\\r\\n\\r\\n summary = self._doc.read_to_next_empty_line()\\r\\n summary_str = \\\" \\\".join([s.strip() for s in summary]).strip()\\r\\n if re.compile('^([\\\\w., ]+=)?\\\\s*[\\\\w\\\\.]+\\\\(.*\\\\)$').match(summary_str):\\r\\n self['Signature'] = summary_str\\r\\n if not self._is_at_section():\\r\\n self['Summary'] = self._doc.read_to_next_empty_line()\\r\\n else:\\r\\n self['Summary'] = summary\\r\\n\\r\\n if not self._is_at_section():\\r\\n self['Extended Summary'] = self._read_to_next_section()\",\n \"def combine_data(areas, employment):\\n output = []\\n\\n for item in employment:\\n for area in areas:\\n if item['code'] == area['properties']['code']:\\n geom = shape(area['geometry'])\\n output.append({\\n 'type': area['type'],\\n 'geometry': mapping(geom.representative_point()),\\n 'properties': {\\n 'code': area['properties']['code'],\\n # 'LSOA11NM': area['properties']['LSOA11NM'],\\n 'employment': item['count']\\n }\\n })\\n\\n return output\",\n \"def getSalaryStat(self, year = 2014):\\r\\n year_str = str(year+1)\\r\\n \\r\\n df = pd.read_html('http://espn.go.com/nba/salaries/_/year/' + year_str)[0]\\r\\n df.columns = df.iloc[0]\\r\\n df = df[df.NAME != 'NAME']\\r\\n \\r\\n for page in range(2,12):\\r\\n page_df = pd.read_html('http://espn.go.com/nba/salaries/_/year/' + \\\\\\r\\n year_str + '/page/' + str(page))[0]\\r\\n page_df.columns = page_df.iloc[0]\\r\\n page_df = page_df[page_df.NAME != 'NAME']\\r\\n df = pd.concat([df, page_df])\\r\\n \\r\\n df['SALARY'] = df['SALARY'].replace('[\\\\$,]', '', regex=True).astype(int)\\r\\n cut_df = df[['NAME', 'SALARY']]\\r\\n cut_df = cut_df.reset_index(drop=True)\\r\\n cut_df.columns = [['PLAYER_NAME', 'SALARY']]\\r\\n \\r\\n salary_df = self.fixSalaryName(cut_df)\\r\\n \\r\\n return salary_df\",\n \"def summary_line_and_description():\",\n \"def _parse_education(self):\\n education = [\\n h for h in self.right_column.find_all(\\\"h4\\\") if h.text == \\\"Bildungsweg\\\"\\n ]\\n if education:\\n education = education[0].nextSibling.nextSibling.find_all(\\\"li\\\")\\n return {\\\"education\\\": [e.text.strip() for e in education]}\\n return {}\",\n \"def get_education_data(line_objs):\\n result = []\\n tmp = []\\n start = False\\n for idx, line in enumerate(line_objs):\\n if line.get('footer'):\\n print(\\\"ignore footer line\\\")\\n continue\\n line_label = line.get('label')\\n line_category = line.get('category')\\n if line_label == 'title':\\n if line_category == 'education':\\n start = True\\n continue\\n else:\\n start = False\\n if line_label == 'title-in-line':\\n if line_category == 'education':\\n start = True\\n else:\\n start = False\\n if start:\\n tmp.append(line)\\n else:\\n if tmp:\\n result.append(tmp)\\n tmp = []\\n if idx == len(line_objs) - 1:\\n if tmp:\\n result.append(tmp)\\n\\n for tmp in result:\\n for idx, line in enumerate(tmp):\\n if idx > 0:\\n prev_line = tmp[idx-1]\\n else:\\n prev_line = None\\n if prev_line:\\n line_x, line_y = _get_x_y(line)\\n prev_line_x, prev_line_y = _get_x_y(prev_line)\\n if line_y == prev_line_y:\\n line['cen'] = [prev_line_x, line_y]\\n print(\\\"changed x\\\")\\n\\n for tmp in result:\\n sorted_x = sorted([line.get('cen')[0] for line in tmp])\\n min_left_margin = sorted_x[0]\\n max_left_margin = sorted_x[-1]\\n for line in tmp:\\n pattern_feature = get_feature_pattern(line, min_left_margin, max_left_margin)\\n line['pattern_feature'] = pattern_feature\\n return result\",\n \"def get_summary_job(job):\\n if job is None:\\n return None\\n url = conf.WIKIPEDIA_URL.format(\\n urllib.parse.quote(job))\\n\\n r = requests.get(url).content\\n data_loaded = json.loads(r)\\n if type(data_loaded) == dict and data_loaded['title'] != 'Not found.':\\n summary = data_loaded['extract']\\n if len(summary) < 65535:\\n return summary\\n else:\\n logger.info('Summary for job is too long to fit into the table',\\n job)\\n return None\\n else:\\n logger.info('Description not found for the following job', job)\\n return None\",\n \"def info_extraction(r):\\n\\n name = r.find(\\\"div\\\", {\\\"class\\\": \\\"search-card-style-content-20754fd7\\\"}).findAll(\\\"div\\\")[\\n 0].get_text().strip() # gets the company name\\n print(name)\\n address = r.find(\\\"div\\\", {\\\"class\\\": \\\"search-card-style-content-20754fd7\\\"}).findAll(\\\"div\\\")[\\n 1].get_text().strip() # gets the company address\\n print(address)\\n ort = address.split(\\\",\\\")[0].strip() # splitting the address with \\\",\\\" and using the first part as ort\\n print(ort)\\n bundesland = address.split(\\\",\\\")[\\n 1].strip() # splitting the address with \\\",\\\" and using the second part as bundeslan\\n print(bundesland)\\n fake_numbers = r.findAll(\\\"span\\\", {\\n \\\"class\\\": \\\"CompanyCard-style-minorCopy-b61c6828\\\"}) # variable representing the xing users number and workers number regardless if they exists or not\\n if len(fake_numbers) == 0: # if both numbers are not available\\n xing_users = \\\"N/A\\\"\\n worker_range = \\\"N/A\\\"\\n if len(fake_numbers) == 1 and fake_numbers[\\n 0].get_text().strip() == \\\"XING members:\\\": # if only the xing users number is available\\n xing_users = fake_numbers[0].next_sibling.strip()\\n worker_range = \\\"N/A\\\"\\n if len(fake_numbers) == 1 and fake_numbers[\\n 0].get_text().strip() == \\\"Employees:\\\": # if only the worker range number is available\\n worker_range = fake_numbers[0].next_sibling.strip().replace(\\\" employees\\\",\\n \\\"\\\").strip() # gets the worker range number and replace \\\" employees\\\" at the end of the obtained string\\n xing_users = \\\"N/A\\\"\\n if len(fake_numbers) >= 2:\\n xing_users = fake_numbers[0].next_sibling.strip()\\n worker_range = fake_numbers[1].next_sibling.strip().replace(\\\" employees\\\",\\n \\\"\\\").strip() # gets the worker range number and replace \\\" employees\\\" at the end of the obtained string\\n\\n print(xing_users)\\n print(worker_range)\\n res = {\\n 'name': name,\\n 'ort': ort,\\n 'bundesland': bundesland,\\n 'xing_users': xing_users,\\n 'worker_range': worker_range\\n\\n }\\n return res\",\n \"def submission_summary_info(self, job_id):\\n ## TODO: Post-sanity checks, 2009-01-08\\n #sanity = self.form[\\\"pdbfile\\\"].value\\n chains = mysql.job_get_chain_sizes(job_id).rstrip(\\\";\\\")\\n\\n ## E.g.,\\n # name: CHAINA\\n # selected: True\\n # chain_id: A\\n # length: 39\\n # preview: MET ILE TYR ALA GLY\\n # desc: Chain A (39 Amino Acid Residues)\\n sum = '<table class=\\\"status_table\\\">'\\n sum += '<tr class=\\\"status_table_head\\\">'\\n sum += '<th>Chain<th>Analyze</th><th>Residues</th>'\\n #sum += '<th>Preview</th>\\n sum += '<th>Residue type</th>'\\n sum += '<th>Ignored residues/atoms</th>'\\n next_chain = ''\\n #for list in summary_data:\\n for c in chains.split(';'):\\n chid, length, selected, type = misc.parse_chains(c)\\n #if next_chain != list[\\\"chain_id\\\"]:\\n if next_chain != chid:\\n sum += '</tr>'\\n row1 = True\\n next_chain = chid\\n if row1:\\n sum += '<tr class=\\\"status_table_row1\\\">'\\n else:\\n sum += '<tr class=\\\"status_table_row2\\\">'\\n row1 = not row1\\n\\n ## Chain id\\n sum += '<td class=\\\"c\\\">%s</td>' % chid\\n\\n ## Analyze (i.e., chain selected by user)\\n if selected == \\\"1\\\":\\n sum += '<td class=\\\"c\\\">True</td>'\\n else:\\n sum += '<td class=\\\"c\\\">False</td>'\\n sum += '<td class=\\\"c\\\">%s</td>' % length\\n\\n ## Preview\\n #sum += '<td>%s ...</td>' % list[\\\"preview\\\"]\\n\\n ## Residue type\\n if type == \\\"aa\\\":\\n sum += '<td class=\\\"c\\\">amino acid</td>'\\n elif type == \\\"na\\\":\\n sum += '<td class=\\\"c\\\">nucleic acid</td>'\\n elif type == \\\"ot\\\":\\n sum += '<td class=\\\"c\\\">other</td>'\\n\\n ## Ignored residues/atoms\\n sum += '<td class=\\\"c\\\">none</td>'\\n\\n sum += '</tr></table>'\\n\\n return sum\",\n \"def catch_up(employed_adults_directory, covid_pol_directory):\\n # load in df\\n try:\\n df = pd.read_csv(employed_adults_directory, index_col=0)\\n except Exception as e:\\n print(str(e))\\n print(\\\"Please enter the correct directory for employed_adults_apr2020_jul2020.csv\\\")\\n \\n # create target variable\\n target = df.PREMPNOT_y.apply(job_loss_categorization)\\n \\n # append target to df\\n df['target'] = target\\n \\n # drop future data\\n to_drop = [column for column in df.columns if \\\"_y\\\" in column]\\n df = df.drop(columns=to_drop)\\n \\n # remove _x from columns\\n df.columns = [column.split(\\\"_\\\")[0] for column in df.columns]\\n \\n # add IND_ID_FINAL and HH_ID\\n df = clean_CPS_df(df)\\n \\n # feature list\\n feature_list = [\\n 'HEHOUSUT', # type of housing unit to dummy \\n 'HWHHWGT', # Household weight\\n 'GESTFIPS', # state codes\\n \\\"GTMETSTA\\\", # Metropolitan or not \\n 'HEFAMINC', # total family income \\n \\\"HRNUMHOU\\\", # total number of people living in the house hold\\n 'HRHTYPE', # household type eg civilian or married etc\\n 'PRTAGE', # person's age\\n 'PEMARITL', # marital status\\n 'PESEX', # gender 1 == male, 2 == female\\n 'PEEDUCA', # level of education see dict for coding\\n 'PTDTRACE', # race composition of the house. See data dict\\n \\\"PEHSPNON\\\", # hispanic or not hispanic\\n 'PENATVTY', # country of birth ie US born or not\\n \\\"PRCITSHP\\\", # citezen status\\n \\\"PRINUSYR\\\", # Year since immigration -1== us born, else coded by decade\\n \\\"PRHRUSL\\\", # Hours at work, dummy into full time or not full time\\n \\\"HUBUS\\\", # Does anyone have a business or a farm? are you a business owner?\\n \\\"PEMJOT\\\", # Do you have more than 1 job?\\n \\\"PEMJNUM\\\", # how many jobs do you have?\\n \\\"PEHRFTPT\\\", # Do you normally spend more than 35 hours a week at your main job?\\n \\\"PEHRRSN2\\\", # what is the main reason you do not want to work 35 hours. Speaks to motivation of keeping job.\\n \\\"PEHRACTT\\\", # sum of hours worked between all jobs\\n \\\"PRAGNA\\\", # Agricultural industry yes or no\\n \\\"PRNMCHLD\\\", # number of children less than 18 years old (-1 not a parent, 0-99 number of children)\\n \\\"PECYC\\\", # How much college credit in years has the reference person recieved?\\n \\\"PECERT1\\\", # Do you have a professional certification issued at state or federal level.\\n \\\"PRMJIND1\\\", # industry cat\\n \\\"PRMJOCC1\\\", # occupation cat\\n 'target',\\n 'HH_ID',\\n 'IND_ID_FINAL'\\n ]\\n \\n # subset the data frame with our desired columns\\n df = df[feature_list]\\n \\n \\n # dummy var list for transformation\\n list_of_dummyvars = [\\n 'PRCITSHP',\\n 'PEHRRSN2',\\n 'PRMJIND1',\\n 'PRMJOCC1',\\n \\\"PESEX\\\",\\n \\\"PEEDUCA\\\",\\n \\\"PTDTRACE\\\", \\n \\\"PEHSPNON\\\",\\n\\n ]\\n \\n # Binning/transforming variables\\n df = feature_transformations(df)\\n \\n # Dummying variables\\n df = feature_dummies(df, list_of_dummyvars)\\n \\n # add political and covid geolocation data\\n try:\\n df = merge_on_fip(df, covid_pol_directory)\\n except Exception as e:\\n print(str(e))\\n print(\\\"Please add the correct directory for covid_pol.csv\\\")\\n \\n return df\",\n \"def annual_summary(self):\\n \\n #Initialize dict with info about all of year's storms\\n hurdat_year = {'id':[],'operational_id':[],'name':[],'max_wspd':[],'min_mslp':[],'category':[],'ace':[]}\\n \\n #Search for corresponding entry in keys\\n count_ss_pure = 0\\n count_ss_partial = 0\\n iterate_id = 1\\n for key in self.dict.keys():\\n\\n #Retrieve info about storm\\n temp_name = self.dict[key]['name']\\n temp_vmax = np.array(self.dict[key]['vmax'])\\n temp_mslp = np.array(self.dict[key]['mslp'])\\n temp_type = np.array(self.dict[key]['type'])\\n temp_time = np.array(self.dict[key]['date'])\\n temp_ace = self.dict[key]['ace']\\n\\n #Get indices of all tropical/subtropical time steps\\n idx = np.where((temp_type == 'SS') | (temp_type == 'SD') | (temp_type == 'TD') | (temp_type == 'TS') | (temp_type == 'HU'))\\n\\n #Get times during existence of trop/subtrop storms\\n if len(idx[0]) == 0: continue\\n trop_time = temp_time[idx]\\n if 'season_start' not in hurdat_year.keys():\\n hurdat_year['season_start'] = trop_time[0]\\n hurdat_year['season_end'] = trop_time[-1]\\n\\n #Get max/min values and check for nan's\\n np_wnd = np.array(temp_vmax[idx])\\n np_slp = np.array(temp_mslp[idx])\\n if len(np_wnd[~np.isnan(np_wnd)]) == 0:\\n max_wnd = np.nan\\n max_cat = -1\\n else:\\n max_wnd = int(np.nanmax(temp_vmax[idx]))\\n max_cat = convert_category(np.nanmax(temp_vmax[idx]))\\n if len(np_slp[~np.isnan(np_slp)]) == 0:\\n min_slp = np.nan\\n else:\\n min_slp = int(np.nanmin(temp_mslp[idx]))\\n\\n #Append to dict\\n hurdat_year['id'].append(key)\\n hurdat_year['name'].append(temp_name)\\n hurdat_year['max_wspd'].append(max_wnd)\\n hurdat_year['min_mslp'].append(min_slp)\\n hurdat_year['category'].append(max_cat)\\n hurdat_year['ace'].append(temp_ace)\\n hurdat_year['operational_id'].append(self.dict[key]['operational_id'])\\n \\n #Handle operational vs. non-operational storms\\n\\n #Check for purely subtropical storms\\n if 'SS' in temp_type and True not in np.isin(temp_type,['TD','TS','HU']):\\n count_ss_pure += 1\\n\\n #Check for partially subtropical storms\\n if 'SS' in temp_type:\\n count_ss_partial += 1\\n\\n #Add generic season info\\n hurdat_year['season_storms'] = len(hurdat_year['name'])\\n narray = np.array(hurdat_year['max_wspd'])\\n narray = narray[~np.isnan(narray)]\\n hurdat_year['season_named'] = len(narray[narray>=34])\\n hurdat_year['season_hurricane'] = len(narray[narray>=65])\\n hurdat_year['season_major'] = len(narray[narray>=100])\\n hurdat_year['season_ace'] = np.sum(hurdat_year['ace'])\\n hurdat_year['season_subtrop_pure'] = count_ss_pure\\n hurdat_year['season_subtrop_partial'] = count_ss_partial\\n \\n #Return object\\n return hurdat_year\",\n \"def get_employee():\\n\\n employee_id = get_employee_input_int('Enter employee ID to get the data ')\\n employee = db.get_employee(employee_id)\\n if not employee:\\n print(\\\"No employee found with id \\\", employee_id)\\n else:\\n payscale = db.get_payScale(employee.grade)\\n print('DATA:-> {} {} has grade = {} which gives {} per hours\\\\n'\\n .format(employee.first_name, employee.last_name, employee.grade, payscale.salary))\",\n \"def findViolations(datafeed):\\n \\n jobs_map, fraud_jobs, ended_jobs = dict(), [], []\\n \\n for i, data in enumerate(datafeed):\\n emp_name, value = data.split(';')\\n value = value.split(',')\\n \\n if value[0] == 'START': \\n # Create an entry for a new job \\n parseStartLines(emp_name, jobs_map, i+1)\\n else: \\n # Updates the new employee with the end line and job ids\\n fraud_jobs.extend(parseEndedJobs(jobs_map, value, emp_name, i+1, ended_jobs))\\n\\n return fraud_jobs\",\n \"def make_salaried(self,salary,name):\\n id = self.find_employee_id(name)\\n if id in self.clsf:\\n self.emp_dict[id][5] = \\\"2\\\"\\n print(\\\"{}{}\\\".format(name,\\\" was successfully changed to be a salaried employee\\\"))\\n self.emp_dict[id][7] = salary\\n self.classification()\\n return self.emp_dict\\n else:\\n print(\\\"Error- employee not found\\\")\\n self.employee_data()\",\n \"def salary_data(driver):\\n try:\\n _base = driver.find_element_by_xpath('/descendant::p[@class=\\\"salary-data-amount\\\"][1]').text\\n _total = driver.find_element_by_xpath('/descendant::p[@class=\\\"salary-data-amount\\\"][2]').text\\n _base_range = driver.find_element_by_xpath('/descendant::p[@class=\\\"salary-data-range\\\"][1]').text\\n _total_range = driver.find_element_by_xpath('/descendant::p[@class=\\\"salary-data-range\\\"][2]').text\\n return {\\n \\\"base\\\" : ''.join(list(filter(lambda c: c.isdigit(), _base))),\\n \\\"total\\\" : ''.join(list(filter(lambda c: c.isdigit(), _total))),\\n \\\"base_range\\\": _base_range,\\n \\\"total_range\\\": _total_range\\n }\\n except Exception as e:\\n print(\\\"error acquiring salary info\\\")\\n print(e)\\n pass\\n return {\\\"base\\\": \\\"\\\", \\\"total\\\": \\\"\\\", \\\"base_range\\\": \\\"\\\", \\\"total_range\\\": \\\"\\\"}\",\n \"def get_personal_info(line_objs):\\n result = []\\n start = True\\n for line in line_objs:\\n line_label = line.get('label')\\n line_category = line.get('category')\\n if line_label == 'title':\\n if line_category == 'personal_info':\\n start = True\\n continue\\n else:\\n start = False\\n if start:\\n result.append(line)\\n try:\\n max_height = max([line.get('wh')[1] for line in result])\\n except:\\n max_height = max([line.get('wh')[1] for line in line_objs])\\n track_candicate_name = False\\n for line in result:\\n height = line.get('wh')[1]\\n if height == max_height and not track_candicate_name:\\n for word in profile_words.keys():\\n if word in line.get(\\\"text\\\"):\\n continue\\n line['label'] = 'candicate_name'\\n track_candicate_name = True\\n else:\\n line['label'] = 'description'\\n line['category'] = 'personal_info'\\n return result\",\n \"def getEmployeeRecord(self, employees, records, name, reader):\\n # pull the first and last name of the employee\\n last, first = name.split(',')\\n # clean up, if necessary\\n last = last.strip()\\n first = first.split()[0].strip()\\n # get the next line with the social security number\\n text = next(reader)[0]\\n # check that it is the right line\\n assert text.startswith('SSN: ')\\n # extract the obfuscated number\\n ssn = text[5:].strip().replace('x', '?')\\n\\n # go through the employee index looking for a name match\\n for eid, fullname in employees.items():\\n # if this is the match\\n if fullname == (last, first):\\n # bail\\n break\\n # if we get this far, there is no match\\n else:\\n # complain\\n # print('could not match {} {}'.format(first, last))\\n # make one up\\n eid = ((last, first), ssn)\\n\\n # attempt to\\n try:\\n # look up the employee\\n employee = records[eid]\\n # if that fails\\n except KeyError:\\n # build the employee record\\n employee = Employee(first=first, last=last, ssn=ssn)\\n # and attach it\\n records[eid] = employee\\n\\n # grab the next line\\n line = next(reader)\\n # start parsing paycheck info\\n while line:\\n # have we reached the summary section?\\n if line[0].startswith('Employee Totals:'):\\n # swallow this section\\n for line in reader:\\n # bail if the zeroth field isn't empty; it's the end of the section\\n if line[0]: return line\\n # ran out of input\\n break\\n # otherwise, this is a paycheck section; extract\\n line = self.getEmployeePaycheck(employee=employee, header=line, reader=reader)\\n\\n # if we get this far, the input was exhausted and we are all done\\n return\",\n \"def test_parse_hit_details(self):\\n for query in self.result:\\n first_hsp = self.result[query][0][0]\\n self.assertEqual(first_hsp[\\\"SUBJECT_ID\\\"], \\\"gi|148670104|gb|EDL02051.1|\\\")\\n self.assertEqual(\\n first_hsp[\\\"HIT_DEF\\\"],\\n \\\"insulin-like growth factor 2 receptor, isoform CRA_c [Mus musculus]\\\",\\n )\\n self.assertEqual(first_hsp[\\\"HIT_ACCESSION\\\"], \\\"2001\\\")\\n self.assertEqual(first_hsp[\\\"HIT_LENGTH\\\"], 707)\",\n \"def employee_data(self):\\n self.paymethod()\\n self.classification()\\n for i in self.emp_id:\\n if self.clsf[i] == \\\"Salaried\\\":\\n if self.pymthd[i] == \\\"Direct Deposit\\\":\\n self.emp_data[i] = [self.clsf[i],self.pymthd[i],self.emp_dict[i][7],self.emp_dict[i][10],\\n self.emp_dict[i][11]]\\n elif self.pymthd[i] == \\\"Mailed Check\\\":\\n self.emp_data[i] = [self.clsf[i],self.pymthd[i],self.emp_dict[i][1],self.emp_dict[i][2],\\n self.emp_dict[i][3], self.emp_dict[i][4],self.emp_dict[i][7]]\\n elif self.clsf[i] == \\\"Hourly\\\":\\n if self.pymthd[i] == \\\"Direct Deposit\\\":\\n self.emp_data[i] = [self.clsf[i],self.pymthd[i],self.emp_dict[i][8],self.emp_dict[i][10],\\n self.emp_dict[i][11]]\\n elif self.pymthd[i] == \\\"Mailed Check\\\":\\n self.emp_data[i] = [self.clsf[i],self.pymthd[i],self.emp_dict[i][1],self.emp_dict[i][2],\\n self.emp_dict[i][3], self.emp_dict[i][4],self.emp_dict[i][8]]\\n elif self.clsf[i] == \\\"Commissioned\\\":\\n if self.pymthd[i] == \\\"Direct Deposit\\\":\\n self.emp_data[i] = [self.clsf[i],self.pymthd[i],self.emp_dict[i][7],self.emp_dict[i][9],\\n self.emp_dict[i][10],self.emp_dict[i][11]]\\n elif self.pymthd[i] == \\\"Mailed Check\\\":\\n self.emp_data[i] = [self.clsf[i],self.pymthd[i],self.emp_dict[i][1],self.emp_dict[i][2],\\n self.emp_dict[i][3],self.emp_dict[i][4],self.emp_dict[i][7],self.emp_dict[i][9]]\\n else:\\n print(\\\"Error\\\")\\n print(self.emp_data)\\n return self.emp_data\",\n \"def parse(self, employees, records, stream):\\n # make a CSV reader from {stream}\\n reader = csv.reader(stream)\\n # skip the first three lines\\n next(reader)\\n next(reader)\\n next(reader)\\n # the next line is the first employee record\\n line = next(reader)\\n # start parsing\\n while line:\\n # get the zeroth field\\n header = line[0]\\n # check that it is an employee section\\n assert header.startswith('Employee: ')\\n # extract the useful info\\n name = header[9:].strip()\\n # pull the employee record\\n line = self.getEmployeeRecord(\\n employees=employees, records=records, name=name, reader=reader)\\n\\n # all done\\n return\",\n \"def extractPersonalInfo():\\n if 'Issued' in clean_lic:\\n index = clean_lic.find('Issued')\\n new_lic = clean_lic[2:index]\\n new_lic_list = new_lic.split('\\\\\\\\n')\\n while len(new_lic_list[len(new_lic_list)-1]) < 5:\\n new_lic_list.pop()\\n return(new_lic_list[-4:])\\n else:\\n return ''\",\n \"def get_experience_data(line_objs):\\n result = []\\n tmp = []\\n start = False\\n title_in_line = False\\n for idx, line in enumerate(line_objs):\\n if line.get('footer'):\\n print(\\\"ignore footer line\\\")\\n continue\\n line_label = line.get('label')\\n line_category = line.get('category')\\n if line_label == 'title':\\n if line_category == 'experience':\\n start = True\\n continue\\n else:\\n start = False\\n if line_label == 'title-in-line':\\n if line_category == 'experience':\\n start = True\\n else:\\n start = False\\n if start:\\n tmp.append(line)\\n else:\\n if tmp:\\n result.append(tmp)\\n tmp = []\\n if idx == len(line_objs) - 1:\\n if tmp:\\n result.append(tmp)\\n\\n for tmp in result:\\n for idx, line in enumerate(tmp):\\n if idx > 0:\\n prev_line = tmp[idx-1]\\n else:\\n prev_line = None\\n if prev_line:\\n line_x, line_y = _get_x_y(line)\\n prev_line_x, prev_line_y = _get_x_y(prev_line)\\n if line_y == prev_line_y:\\n line['cen'] = [prev_line_x, line_y]\\n print(\\\"changed x\\\")\\n\\n for tmp in result:\\n sorted_x = sorted([line.get('cen')[0] for line in tmp])\\n min_left_margin = sorted_x[0]\\n max_left_margin = sorted_x[-1]\\n for line in tmp:\\n pattern_feature = get_feature_pattern(line, min_left_margin, max_left_margin)\\n line['pattern_feature'] = pattern_feature\\n return result\",\n \"def parse(self, details):\\n idict = json.loads(details)\\n\\n self.company = idict['Company']\\n\\n if idict['Market Cap'] != '-':\\n mc = float(idict['Market Cap'][:-1])\\n mult = idict['Market Cap'][-1]\\n if mult == 'M':\\n mc *= 1000000\\n elif mult == 'B':\\n mc *= 1000000000\\n self.market_cap = mc\\n else:\\n self.market_cap = None\\n\\n pes = idict['P/E']\\n if pes == '-':\\n pe = None\\n else:\\n pe = float(pes)\\n self.pe = pe\\n\\n pbs = idict['P/B']\\n if pbs == '-':\\n pb = None\\n else:\\n pb = float(pbs)\\n self.pb = pb\\n\\n pegs = idict['PEG']\\n if pegs == '-':\\n peg = None\\n else:\\n peg = float(pegs)\\n self.peg = peg\\n\\n epss = idict['EPS (ttm)']\\n if epss == '-':\\n eps = None\\n else:\\n eps = float(epss)\\n self.eps = eps\\n\\n eps_ny_g_s = idict.get('EPS next Y growth', '-')\\n if eps_ny_g_s == '-':\\n eps_ny_g = None\\n else:\\n eps_ny_g = float(eps_ny_g_s[:-1])\\n self.eps_next_year_growth = eps_ny_g\\n\\n eps_ty_g_s = idict['EPS this Y']\\n if eps_ty_g_s == '-':\\n eps_ty_g = None\\n else:\\n eps_ty_g = float(eps_ty_g_s[:-1])\\n self.eps_this_year_growth = eps_ty_g\\n\\n eps_ny_s = idict['EPS next Y']\\n if eps_ny_s == '-':\\n eps_ny = None\\n else:\\n eps_ny = float(eps_ny_s)\\n self.eps_next_year = eps_ny\\n\\n sales_p5y_g_s = idict['Sales past 5Y']\\n if sales_p5y_g_s == '-':\\n sales_p5y_g = None\\n else:\\n sales_p5y_g = float(sales_p5y_g_s[:-1])\\n self.sales_past_5years_growth = sales_p5y_g\\n\\n divs = idict['Dividend']\\n if divs == '-':\\n div = None\\n else:\\n div = float(divs[:-1])\\n self.dividend = div\\n\\n roas = idict['ROA']\\n if roas == '-':\\n roa = None\\n else:\\n roa = float(roas[:-1])\\n self.roa = roa\\n\\n roes = idict['ROE']\\n if roes == '-':\\n roe = None\\n else:\\n roe = float(roes[:-1])\\n self.roe = roe\\n\\n inst_owns = idict['Inst Own']\\n if inst_owns == '-':\\n inst_own = None\\n else:\\n inst_own = float(inst_owns[:-1])\\n self.inst_own = inst_own\\n\\n net_margins = idict['Profit Margin']\\n if net_margins == '-':\\n net_margin = None\\n else:\\n net_margin = float(net_margins[:-1])\\n self.net_margin = net_margin\\n\\n op_margins = idict['Oper. Margin']\\n if op_margins == '-':\\n op_margin = None\\n else:\\n op_margin = float(op_margins[:-1])\\n self.op_margin = op_margin\\n\\n recoms = idict['Recom']\\n if recoms == '-':\\n recom = None\\n else:\\n recom = float(recoms)\\n self.recom = recom\\n\\n prs = idict['Price']\\n if prs == '-':\\n pr = None\\n else:\\n pr = float(idict['Price'])\\n self.price = pr\\n\\n self.screen_scanned = True\\n self.screen_last_updated = datetime.now()\\n\\n self.save()\",\n \"def get_data_from_individual_company_pages(soup):\\n individual_company_data = []\\n usd_roe = get_usd_roe()\\n company_code = (\\n soup.find(\\\"meta\\\", {\\\"name\\\": \\\"description\\\"}).get(\\\"content\\\").split(\\\":\\\")[0]\\n )\\n current_price_usd = float(\\n soup.find(\\\"span\\\", {\\\"class\\\": \\\"price-section__current-value\\\"}).text.replace(\\n \\\",\\\", \\\"\\\"\\n )\\n )\\n current_price = round(current_price_usd * usd_roe)\\n try:\\n p_e_ratio = float(\\n soup.find(\\n \\\"div\\\", {\\\"class\\\": \\\"snapshot__header\\\"}, string=\\\"P/E Ratio\\\"\\n ).previous_sibling.replace(\\\",\\\", \\\"\\\")\\n )\\n except AttributeError:\\n p_e_ratio = 0\\n\\n try:\\n week_52_low = float(\\n soup.find(\\\"div\\\", {\\\"class\\\": \\\"snapshot__header\\\"}, string=\\\"52 Week Low\\\")\\n .previous_sibling.strip()\\n .replace(\\\",\\\", \\\"\\\")\\n )\\n except AttributeError:\\n week_52_low = 1\\n\\n try:\\n week_52_high = float(\\n soup.find(\\\"div\\\", {\\\"class\\\": \\\"snapshot__header\\\"}, string=\\\"52 Week High\\\")\\n .previous_sibling.strip()\\n .replace(\\\",\\\", \\\"\\\")\\n )\\n except AttributeError:\\n week_52_high = 0\\n\\n unreal_profit_per_year_percent = round((week_52_high / week_52_low - 1) * 100, 2)\\n\\n individual_company_data.append(\\n [company_code, current_price, p_e_ratio, unreal_profit_per_year_percent]\\n )\\n\\n company_df = pd.DataFrame(\\n columns=[\\\"company_code\\\", \\\"current_price\\\", \\\"P_E\\\", \\\"potential_profit_percent\\\"]\\n )\\n company_df = company_df.append(\\n {\\n \\\"company_code\\\": company_code,\\n \\\"current_price\\\": current_price,\\n \\\"P_E\\\": p_e_ratio,\\n \\\"potential_profit_percent\\\": unreal_profit_per_year_percent,\\n },\\n ignore_index=True,\\n )\\n\\n return company_df\",\n \"def display_employee(self):\\n print \\\"[Name: %s] [Salary: %d]\\\" % (self.name, self.salary)\",\n \"def summary(self, yname=None, xname=None, title=None, alpha=.05):\\n # TODO: Make this raise upstream instead of just \\\"pass\\\"\\n raise NotImplementedError # pragma: no cover\\n # TODO: move the GenericLikelihoodModelResults implementation here?\",\n \"def createEMPStudy(self, user_id, study_name, investigation_type, miens_compliant, submit_to_insdc, \\n portal_type, study_title, study_alias, pmid, study_abstract, study_description,\\n number_samples_collected, number_samples_promised , lab_person,\\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \\n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\\n includes_timeseries):\\n con = self.getMetadataDatabaseConnection()\\n study_id = 0\\n results = con.cursor().callproc('qiime_assets.emp_study_insert', \\n [study_id, user_id, study_name,\\n investigation_type, miens_compliant, submit_to_insdc, portal_type, \\n study_title, study_alias, pmid, study_abstract, study_description,\\n number_samples_collected, number_samples_promised , lab_person,\\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \\n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\\n includes_timeseries])\\n return results[0]\",\n \"def preprocess(df, edit = False):\\n\\tdf['binary_income'] = np.where(df['binary_income'].str.contains('-'), 0, 1)\\n\\ty = df['binary_income']\\n\\n\\tTRAINING_SET_MEAN = y.mean() # For ensembling w/ linear combination.\\n\\n\\tif edit:\\n\\t\\t# If they make over $100 / hr but have no capital gains and losses, assume the wage entry is a mistake and replace with median.\\n\\t\\tdf['wage'] = np.where(((df['wage'] > 100) & (df['capital_gains'] == 0) & (df['capital_gains'] == 0)), df['wage'].median(), df['wage'])\\n\\t\\t# Total calculated earnings for the year. \\n\\t\\tdf['ttl_clcltd_ernings'] = 40*df['wage']*df['weeks']\\n\\n\\t\\tdf['professional'] = np.where(df['majoroc'].str.contains('Professional|Executive|Sales|Precision',regex = True), 1,0)\\n\\t\\tdf['white'] = np.where(df['mace'].str.contains('White|Asian',regex = True), 1,0)\\n\\t\\tdf['ad_degree'] = np.where(df['education'].str.contains('Bachelors|Doctorate|Masters|Prof',regex = True), 1,0)\\n\\t\\tdf['younger22'] = np.where(df['age']<22, 1,0)\\n\\t\\tdf['older65'] = np.where(df['age']>65, 1,0)\\n\\t\\tdf['occupation2'] = np.where(df['occupation'] == 2, 1,0)\\t\\t\\n\\t\\t# df['log_cgs'] = np.log(df['capital_gains'])\\n\\t\\tdf['jointu65_tax'] = np.where(df['tax'] == 'Joint both under 65', 1,0)\\n\\n\\treturn df, y, TRAINING_SET_MEAN\",\n \"def _parse_title(self, response):\\n title_str = re.sub(\\n r\\\"\\\\s+\\\", \\\" \\\", \\\" \\\".join(response.css(\\\".soi-container h2 *::text\\\").extract())\\n ).strip()\\n return re.sub(\\n r\\\"(Illinois Commerce Commission|(?=Committee )Committee Meeting$)\\\",\\n \\\"\\\",\\n title_str,\\n ).strip()\",\n \"def get_result_office(soup):\\n return soup.find('h2').text\",\n \"def getEmployeePaycheck(self, employee, header, reader):\\n # extract the paycheck date and normalize it\\n date = datetime.datetime.strptime(header[0], '%m/%d/%y').date()\\n # make a paycheck\\n paycheck = Paycheck(date=date)\\n # save it\\n employee.paychecks[paycheck.date] = paycheck\\n\\n # the gross pay\\n paycheck.gross = float(header[5].strip())\\n # the net pay\\n paycheck.net = float(header[12].strip())\\n\\n # extract the paycheck info\\n self.getIncomeAndDeductions(paycheck=paycheck, record=header)\\n # process the remaining lines\\n for record in reader:\\n # if the zeroth field isn't empty\\n if record[0]:\\n # we are done with this paycheck\\n return record\\n # otherwise, get more\\n self.getIncomeAndDeductions(paycheck=paycheck, record=record)\\n\\n # all done\\n return\",\n \"def get_data(self):\\n def _clean_search_hit(search_hit):\\n \\\"\\\"\\\"\\n Takes in a search result hit as a BeautifySoup tag and pulls out all the data to match the desired schema.\\n\\n :param search_hit:\\n :return Dictionary: A dictionary with the cleaned data\\n \\\"\\\"\\\"\\n\\n hit_name = search_hit.find(class_='hit-name')\\n hit_url = hit_name.get('href')\\n hit_id = hit_url.split('/')[-1]\\n name = hit_name.get_text().split(',')[0].title().split()\\n\\n current_city = search_hit.find(class_='hit-location').get_text().upper()\\n\\n # Find all Addresses for search result.\\n try:\\n address = search_hit.find(class_='hit-pastAddresses').find_all(class_='hit-values')\\n address = list({a.text.upper().replace('.', '') for a in address})\\n except AttributeError:\\n address = list()\\n\\n # find the address that is most likely the current main address.\\n try:\\n address.insert(0, address.pop(address.index(current_city)))\\n except ValueError:\\n address.insert(0, current_city)\\n\\n address = [\\n {\\n '@type': 'PostalAddress',\\n 'addressLocality': locality.title(),\\n 'addressRegion': region\\n } for locality, region in [a.split(', ') for a in address]]\\n\\n work_location = {'@type': 'Place'}\\n try:\\n work_location['name'] = search_hit\\\\\\n .find(class_='hit-work')\\\\\\n .find(class_='hit-values')\\\\\\n .get_text()\\\\\\n .title()\\n except AttributeError:\\n work_location['name'] = ''\\n\\n alumni_of = {'@type': 'EducationalOrganization'}\\n try:\\n alumni_of['name'] = search_hit\\\\\\n .find(class_='hit-high-school')\\\\\\n .find(class_='hit-values')\\\\\\n .get_text().title()\\n except AttributeError:\\n pass\\n\\n return {\\n '@id': hit_id,\\n '@type': 'Person',\\n 'name': ' '.join(name),\\n 'givenName': name[0],\\n 'middleName': ' '.join(name[1:-1]),\\n 'familyName': name[-1],\\n 'url': hit_url,\\n 'address': address,\\n 'workLocation': work_location,\\n 'alumniOf': alumni_of,\\n }\\n\\n def _refine_search(search_str, options):\\n \\\"\\\"\\\"\\n Takes a list of WebElements and a search string, looks for string in the text of each WebElement, and\\n press the option if found. Returns Boolean for found status\\n\\n :param search_str: str of the desired option.\\n :param options: list of WebElements from Beautify Soup that represents all of the available options.\\n :return:\\n \\\"\\\"\\\"\\n search_str = search_str.upper()\\n logging.info(f'Looking for \\\\'{search_str}\\\\'')\\n try:\\n for option in options:\\n option_text = option.text.upper()\\n logging.info(f'Option Checked: {option_text}')\\n if search_str in option_text:\\n option.click()\\n time.sleep(2)\\n logging.info(f'Option Selected: {option_text}')\\n return True\\n else:\\n return False\\n except AttributeError:\\n return True\\n except StaleElementReferenceException as e:\\n ChromeCrash(e)\\n\\n with self.driver(executable_path=self.DRIVER_DIR) as driver:\\n driver.get(self.url)\\n\\n \\\"\\\"\\\"\\n The CSS for the page doesn't show the State nor the City selector options if the page is too narrow,\\n so we need to make sure the browser is open wide enough for the CSS to make those options visible. \\n \\\"\\\"\\\"\\n driver.fullscreen_window()\\n\\n # Refine the search by State\\n address_region = self.person.get('addressRegion', '')\\n address_region = STATES.get(address_region.upper(), address_region.upper())\\n region_options = driver\\\\\\n .find_element_by_class_name(\\\"STATE\\\")\\\\\\n .find_elements_by_class_name(\\\"refinementList-text\\\")\\n\\n if not _refine_search(address_region, region_options):\\n return False\\n\\n # Narrow the search by pressing a City option\\n address_locality = self.person.get('addressLocality').title()\\n locality_options = driver\\\\\\n .find_element_by_class_name(\\\"CITY\\\")\\\\\\n .find_elements_by_class_name(\\\"refinementList-text\\\")\\n\\n if not _refine_search(address_locality, locality_options):\\n return False\\n\\n \\\"\\\"\\\"\\n The Page Loads dynamically, so we need to scroll down the page to show all the search results. It needs to\\n be done in steps with a pause between movements to allow for loading. \\n Here it will first get the current location on the page, attempt to move down the page, and then check to\\n see if the location changed.\\n \\\"\\\"\\\"\\n\\n if self.auto_scroll and len(driver.find_elements_by_class_name(\\\"ais-InfiniteHits-item\\\")) > 15:\\n current_height, new_height = 0, driver.execute_script(\\\"return document.body.scrollHeight\\\")\\n\\n while new_height != current_height:\\n # Scroll down to the bottom of the page\\n driver.execute_script(\\\"window.scrollTo(0, document.body.scrollHeight);\\\")\\n\\n # Wait to load page\\n time.sleep(SCROLL_PAUSE_TIME)\\n\\n # Calculate new scroll height and compare with last scroll height\\n current_height, new_height = new_height, driver.execute_script(\\\"return document.body.scrollHeight\\\")\\n\\n page_source = driver.page_source\\n page_soup = bs(page_source, 'html.parser')\\n search_results = list(page_soup.find_all(class_='ais-InfiniteHits-item'))\\n for i, search_result in enumerate(search_results):\\n search_results[i] = _clean_search_hit(search_result)\\n\\n self.data_from_website = pd.DataFrame(search_results)\\n self.data_from_website.set_index('@id', inplace=True)\\n return True\",\n \"def get_profile(self, html_content: str) -> object:\\n if not html_content:\\n raise Exception(\\\"HTML content not found\\\")\\n\\n soup = BeautifulSoup(html_content, 'html.parser')\\n\\n self.profile = {\\n \\\"employments\\\": [],\\n \\\"others\\\": [],\\n \\\"education\\\": []\\n }\\n # get sidebar\\n sidebar = soup.select_one(self.tags.get(\\\"sidebar\\\"))\\n items = sidebar.select(self.tags.get(\\\"sidebar.items\\\")) if sidebar else []\\n\\n # read each item on sidebar\\n for item in items:\\n\\n label = item.select_one(self.tags.get(\\\"sidebar.items.header\\\"))\\n section_title = label.text.strip()\\n\\n # Availability Item\\n if section_title == 'Availability':\\n status, avaibility = smart_split(\\n get_value(item.select_one(self.tags.get(\\\"sidebar.items.availability\\\"))), '\\\\n')\\n self.profile[\\\"status\\\"] = status\\n self.profile[\\\"avaibility\\\"] = avaibility\\n\\n # Languages Item\\n elif section_title == 'Languages':\\n\\n languages = [' '.join(get_value(li).split()) for li in\\n item.select(self.tags.get(\\\"sidebar.items.languages.item\\\"))]\\n self.profile[\\\"languages\\\"] = [dict(zip([\\\"name\\\", \\\"level\\\"], lng.split(\\\":\\\"))) for lng in languages]\\n\\n # Education Item\\n elif section_title == 'Education':\\n lis = item.select(self.tags.get(\\\"sidebar.items.education.item\\\"))\\n\\n for li in lis:\\n school = \\\" \\\".join(\\n get_value(li.select_one(self.tags.get(\\\"sidebar.items.education.school\\\"))).split())\\n degree_area = get_value(li.select_one(self.tags.get(\\\"sidebar.items.education.degree_area\\\")))\\n degree, area = smart_split(degree_area)\\n attended_date = get_value(li.select_one(self.tags.get(\\\"sidebar.items.education.attended_date\\\")))\\n\\n self.profile[\\\"education\\\"].append({\\n \\\"school\\\": school,\\n \\\"degree\\\": degree,\\n \\\"area\\\": area,\\n \\\"attended_date\\\": attended_date\\n })\\n\\n # Skill panel\\n skills = soup.select(self.tags.get(\\\"skills\\\"))\\n self.profile[\\\"skills\\\"] = set(get_value(s) for s in skills) # using set to remove duplications\\n\\n # Employment/Other Experience panels\\n cards = soup.select(self.tags.get(\\\"profile.panels\\\"))\\n for lis in cards:\\n lis = lis.parent.select(\\\"li\\\")\\n\\n for li in lis:\\n position, employer = smart_split(\\n li.select_one(self.tags.get(\\\"employments.position_employer\\\")).get_text(strip=True), \\\"|\\\")\\n period = ' '.join(li.select_one(self.tags.get(\\\"employments.period\\\")).get_text(strip=True).split())\\n\\n if \\\"Other\\\" != position.strip():\\n self.profile[\\\"employments\\\"].append({\\n \\\"employer\\\": employer,\\n \\\"job_title\\\": position,\\n \\\"hire_datetime\\\": period.split('-')[0].strip(),\\n \\\"termination_datetime\\\": period.split('-')[1].strip()\\n })\\n else:\\n self.profile[\\\"others\\\"].append({\\n \\\"title\\\": employer,\\n \\\"description\\\": period.split('-')[0]\\n })\\n\\n title = soup.select_one(self.tags.get(\\\"title\\\"))\\n hour = get_value(title.parent.parent.select_one(self.tags.get(\\\"hour_price\\\")))\\n description = get_value(soup.select_one(self.tags.get(\\\"description\\\")))\\n\\n # define the last elements of profile\\n self.profile[\\\"title\\\"] = get_value(title)\\n self.profile[\\\"price\\\"] = hour\\n self.profile[\\\"description\\\"] = description\\n self.profile[\\\"full_name\\\"] = soup.select_one(self.tags.get(\\\"full_name\\\")).get_text(strip=True)\\n self.profile[\\\"first_name\\\"] = self.profile[\\\"full_name\\\"].split()[0]\\n self.profile[\\\"last_name\\\"] = self.profile[\\\"full_name\\\"].split()[-1]\\n self.profile[\\\"address_city\\\"] = soup.select_one(self.tags.get(\\\"address_city\\\")).get_text(strip=True)\\n self.profile[\\\"address_country\\\"] = soup.select_one(self.tags.get(\\\"address_country\\\")).get_text(strip=True)\\n self.profile[\\\"picture_url\\\"] = soup.select_one(self.tags.get(\\\"picture\\\"))[\\\"src\\\"]\\n\\n return self.profile\",\n \"def process(self):\\n\\n log.info(\\\"Feature Engineering of pred_analys_record_personal_access data of school: \\\" + self.school_kind.value)\\n log.debug(\\\"QuadrimestersFeatureEngineering.process()\\\")\\n\\n global pr_plan_subject_call, pr_scholarship_per_year\\n\\n analys_record_personal_access = self.input_dfs[0]\\n pr_plan_subject_call = self.input_dfs[1]\\n pr_scholarship_per_year = self.input_dfs[2]\\n\\n cols_before = len(self.input_dfs[0].columns)\\n col_list_before = self.input_dfs[0].columns\\n\\n analys_record_personal_access[keys.CUM_PASS_RATIO_KEY] = analys_record_personal_access.apply(\\n lambda func: get_cum_pass_ratio(func, course=self.course, quadrimester=self.quadrimester), axis=1\\n )\\n analys_record_personal_access[keys.SCHOLARSHIP_KEY] = analys_record_personal_access.apply(\\n lambda func: get_scholarship(func, course=self.course), axis=1\\n )\\n analys_record_personal_access[keys.CUM_ABSENT_RATIO_KEY] = analys_record_personal_access.apply(\\n lambda func: get_cum_absent_ratio(func, course=self.course, quadrimester=self.quadrimester), axis=1\\n )\\n analys_record_personal_access[keys.CUM_MEDIAN_KEY] = analys_record_personal_access.apply(\\n lambda func: get_cum_median(func, course=self.course, quadrimester=self.quadrimester), axis=1\\n )\\n\\n if self.course > 1:\\n analys_record_personal_access[keys.CUM_MORE_1ST_CALL_RATIO_KEY] = analys_record_personal_access.apply(\\n lambda func: get_cum_more_1st_call_ratio(func, course=self.course, quadrimester=self.quadrimester),\\n axis=1\\n )\\n cols_after = len(self.input_dfs[0].columns)\\n col_list_after = self.input_dfs[0].columns\\n self.changes[\\\"add new columns with information of quadrimester\\\"] = cols_after - cols_before\\n log.info(\\\"new columns are :\\\" + str(list(set(col_list_after) - set(col_list_before))))\\n log.info(\\\"final columns are: \\\" + str(col_list_after))\\n\\n rows_before = len(analys_record_personal_access.index)\\n analys_record_personal_access = analys_record_personal_access[analys_record_personal_access\\n [keys.CUM_PASS_RATIO_KEY] != -1]\\n rows_after = len(analys_record_personal_access.index)\\n\\n self.changes[\\\"removes people who have dropped out in this quadrimester\\\"] = rows_before - rows_after\\n self.changes[\\\"number of final rows\\\"] = rows_after\\n\\n self.output_df = analys_record_personal_access\",\n \"def parse_summary(self, content):\\n summary_list = content.xpath('.//div[@id=\\\"summaryfield\\\"]//text()')\\n summary = ''\\n for text in summary_list:\\n summary += normalize_string(text)\\n return summary\",\n \"def summary(\\n self,\\n fex_object,\\n mean=False,\\n max=False,\\n min=False,\\n ignore_sessions=False,\\n *args,\\n **kwargs,\\n ):\\n self.extracted_features.append(\\n fex_object.extract_summary(mean, max, min, ignore_sessions, *args, **kwargs)\\n )\",\n \"def user_story_6(self):\\n for family in self.families.values():\\n if family.divorced != 'NA':\\n if family.wife_id != 'NA':\\n if self.individuals[family.wife_id].death != 'NA':\\n if self.individuals[family.wife_id].death < family.divorced:\\n print(f'US06 - {self.individuals[family.wife_id].name} divorce after individual death date on line {family._divorced_line}')\\n\\n if family.husb_id != 'NA':\\n if self.individuals[family.husb_id].death != 'NA':\\n if self.individuals[family.husb_id].death < family.divorced:\\n print(f'US06 - {self.individuals[family.husb_id].name} divorce after individual death date on line {family._divorced_line}')\",\n \"def post(self):\\n return self.get_request_handler(request.headers).create_new_employment_status(request)\",\n \"def get_ivol_summary_single_r(\\n self,\\n\\n headers: t.Dict[str, str] = None,\\n body: JSONEncodable = None,\\n fields_data: t.Dict[str, str] = None,\\n **kwargs\\n ):\\n r = self._do_call(\\n method='GET',\\n url=f'{self.API_BASE_URL}/ivol/summary/single',\\n headers=headers,\\n body=body,\\n fields=fields_data,\\n **kwargs\\n )\\n return r\",\n \"def testhospital_vs_confirmed(self):\\n data = load_covid_data(file)\\n aim_day = data['evolution']['2020-03-16']\\n # Artificial cut one value , it supposed to be 4 number\\n aim_day['epidemiology']['confirmed']['total']['age'] = [10, 11, 12]\\n try:\\n cases_population = cases_per_population_by_age(data)\\n except Exception as e:\\n raise Exception\",\n \"def process_article(self, title, text, template = 'Infobox person'):\\n \\n # Create a parsing object\\n wikicode = mwparserfromhell.parse(text)\\n \\n # Search through templates for the template\\n matches = wikicode.filter_templates(matches = template)\\n raw_year_string = 'EMPTY'\\n birth_year = 'EMPTY'\\n infobox = ''\\n if len(matches) >= 1:\\n # Extract information from infobox\\n for match in matches:\\n infobox = str(match)\\n for param in match.params:\\n if param.name.strip_code().strip() == 'birth_date':\\n raw_year_string = str(param.value)\\n birth_year = self.get_birth_year(raw_year_string)\\n summary = self.get_summary(wikicode.strip_code().strip())\\n return (title, birth_year, summary, raw_year_string, infobox)\",\n \"def metadata(self) -> global___SummaryMetadata:\",\n \"def _process_protein_summary(self, summary_file: str):\\n fdr_path = get_fdr_name(summary_file)\\n\\n # The number of protein identifications passing critical local FDR\\n n_pass_fdr = read_fdr_value(fdr_path, 5)\\n\\n protein_df = read_tsv(summary_file)\\n protein_df = reduce_df(protein_df, n_pass_fdr)\\n protein_df.to_csv(\\n os.path.join(self.output_dir, os.path.basename(summary_file)),\\n sep=\\\"\\\\t\\\", index=False)\",\n \"def user_story_5(self):\\n for family in self.families.values():\\n if family.married != 'NA':\\n if family.wife_id != 'NA':\\n if self.individuals[family.wife_id].death != 'NA':\\n if self.individuals[family.wife_id].death < family.married:\\n print(\\n f'US05 - {self.individuals[family.wife_id].name} married after individual death date on line {family._married_line}')\\n\\n if family.husb_id != 'NA':\\n if self.individuals[family.husb_id].death != 'NA':\\n if self.individuals[family.husb_id].death < family.married:\\n print(\\n f'US05 - {self.individuals[family.husb_id].name} married after individual death date on line {family._married_line}')\",\n \"def parse_full_job_page(self, response, job_dict):\\n job_dict['description']=BeautifulSoup(response.xpath('//*[@id=\\\"page\\\"]/div[1]').get()).get_text()\\n job_dict['organisation']=response.xpath('//*[@id=\\\"page\\\"]/div[1]/div[1]/p[1]/a/text()').get()\\n job_dict['location']=response.xpath('//*[@id=\\\"page\\\"]/div[1]/div[1]/p[1]/text()[3]').get()\\n date=response.xpath('//*[@id=\\\"page\\\"]/div[2]/p[2]/small/text()[1]').get()\\n if date:\\n d_splited = date.split('Date posted:', 1)\\n if len(d_splited)>0:\\n job_dict['date_posted']=d_splited[1]\\n return Job(job_dict)\",\n \"def summary_info_aeff(filename):\\n # filename = self.out_filename('aeff')\\n print('Reading {}'.format(filename))\\n table = Table.read(str(filename), hdu='AEFF_2D')\\n\\n data = dict()\\n\\n # Copy over header info to the summary table\\n data['LO_THRES'] = table.meta['LO_THRES']\\n data['HI_THRES'] = table.meta['HI_THRES']\\n\\n # Summary stats on IRF file content\\n data['EFFAREA_MAX'] = table['EFFAREA'].max()\\n data['EFFAREA_RECO_MAX'] = table['EFFAREA_RECO'].max() \\n return data\",\n \"def combine_score_info(mn_hospital, hosp_refiend_score_file, combined_file, interplate=False, parse_location=False):\\n score = pd.read_csv(hosp_refiend_score_file, sep='\\\\t')\\n hosp = pd.read_csv(mn_hospital, sep='\\\\t', index_col=1)\\n #print(score)\\n combine = []\\n for index, row in score.iterrows():\\n name = row['name']\\n search_name = name.upper()\\n #print(name)\\n if search_name in hosp.index:\\n info = {}\\n info['name'] = name\\n info['city'] = row['city']\\n info['county'] = hosp.ix[search_name, 'County Name']\\n try:\\n loc_info = hosp.ix[search_name, 'Location'].split(',')\\n except:\\n #print(hosp.ix[search_name, 'Location'])\\n print('duplicate records:', name)\\n continue\\n address = loc_info[0].replace(row['city'].upper(), '').strip()\\n info['address'] = address\\n try:\\n lat = loc_info[1].split('(')[1]\\n long = loc_info[2].strip(' )')\\n except: # lat and long code are not provided...\\n if parse_location:\\n print (name, address)\\n place = address + ', ' + row['city'] + ', MN'\\n## if quote == 10:\\n## print(\\\"needs to wait for a minute\\\")\\n## time.sleep(60) #\\n## quote = 0\\n## quote += 1\\n location = geocoder.google(place)\\n parsed_address = location.address\\n if not parsed_address:\\n print('query yahoo...')\\n location = geocoder.yahoo(place)\\n parsed_address = location.address\\n else:\\n print('query google')\\n if not parsed_address:\\n print(name, address)\\n exit(1)\\n lat = location.lat\\n long = location.lng\\n else:\\n lat = ''\\n long = ''\\n #print('no lat/long:', name)\\n info['lat'] = lat\\n info['lng'] = long\\n info['zipcode'] = hosp.ix[search_name, 'ZIP Code']\\n info['type'] = hosp.ix[search_name, 'Hospital Type']\\n info['overall_rating'] = row['overall_rating']\\n info['patient_rating'] = row['patient_rating']\\n info['percentage_of_10'] = row['percentage_of_10']\\n combine.append(info)\\n elif name == 'Mayo Clinic Methodist Hospital':\\n info = {}\\n info['name'] = name\\n info['city'] = 'Rochester'\\n info['county'] = 'OLMSTED'\\n info['address'] = '201 West Center Street'.upper()\\n info['lat'] = 44.0234914\\n info['lng'] = -92.465934\\n info['zipcode'] = 55902\\n info['type'] = 'Acute Care Hospitals'\\n info['overall_rating'] = row['overall_rating']\\n info['patient_rating'] = row['patient_rating']\\n info['percentage_of_10'] = row['percentage_of_10']\\n combine.append(info)\\n else: # cannot find hospital information.\\n print (\\\"not found:\\\", name)\\n continue\\n\\n combine = pd.DataFrame(combine)\\n if interplate:\\n score_group = combine.groupby('overall_rating')\\n score_mean = score_group.mean()\\n print(score_mean)\\n for index, row in combine.iterrows():\\n score = row['overall_rating']\\n patient_rating = row['patient_rating']\\n percent = row['percentage_of_10']\\n if math.isnan(patient_rating):\\n patient_rating = score_mean.ix[score, 'patient_rating']\\n if math.isnan(patient_rating):\\n print(row['name'])\\n combine.ix[index, 'patient_rating'] = 4.0\\n else:\\n combine.ix[index, 'patient_rating'] = patient_rating\\n if math.isnan(percent):\\n percent = score_mean.ix[score, 'percentage_of_10']\\n if math.isnan(percent):\\n print(row['name'])\\n combine.ix[index, 'percentage_of_10'] = 0.7\\n else:\\n combine.ix[index, 'percentage_of_10'] = percent\\n \\n \\n combine['patient_rating'] = combine['patient_rating'].map(lambda x:'%.0f' % x) \\n combine.to_csv(combined_file, sep='\\\\t', index=False, float_format='%.2f')\",\n \"def summary_info_aeff(filename):\\n # filename = self.out_filename('aeff')\\n print('Reading {}'.format(filename))\\n table = Table.read(str(filename), hdu='AEFF_2D')\\n\\n data = dict()\\n\\n # Copy over header info to the summary table\\n data['LO_THRES'] = table.meta['LO_THRES']\\n data['HI_THRES'] = table.meta['HI_THRES']\\n\\n # Summary stats on IRF file content\\n data['EFFAREA_MAX'] = table['EFFAREA'].max()\\n data['EFFAREA_RECO_MAX'] = table['EFFAREA_RECO'].max()\\n\\n return data\",\n \"def __init__(self,name,empid,designation,experience):\\n self.name = name\\n self.empid = empid\\n self.designation = designation\\n self.experience = experience\\n self.salary = self.cal_sal()\",\n \"def analysis(houses:pd.DataFrame) -> None:\\n \\n \\\"\\\"\\\"\\n #Me just trying to fit the data without any outside influences\\n f= f'SELLER_HOUSE ~ SQFT_PER + PRICE + C(LOCATION)' \\n result= smf.logit(formula= str(f), data= houses).fit()\\n print(result.summary2())\\n y= ['SELLER_HOUSE']\\n x= ['SQFT_PER', 'PRICE', 'LOC_699 - Not Defined', 'LOC_AA - Airport Area', 'LOC_CG - Columbus Grove',\\n 'LOC_CV - Cypress Village', 'LOC_EASTW - Eastwood', 'LOC_EC - El Camino Real', 'LOC_GP - Great Park',\\n 'LOC_IRSP - Irvine Spectrum', 'LOC_LGA - Laguna Altura', 'LOC_NK - Northpark', 'LOC_NW - Northwood', \\n 'LOC_OC - Oak Creek', 'LOC_OH - Orchard Hills', 'LOC_OT - Orangetree', 'LOC_PS - Portola Springs', \\n 'LOC_QH - Quail Hill', 'LOC_SH - Shady Canyon', 'LOC_SJ - Rancho San Joaquin', 'LOC_STG - Stonegate', \\n 'LOC_Stonegate', 'LOC_TR - Turtle Rock', 'LOC_TRG - Turtle Ridge', 'LOC_UP - University Park',\\n 'LOC_UT - University Town Center', 'LOC_WB - Woodbridge', 'LOC_WD - Woodbury', \\n 'LOC_WI - West Irvine', 'LOC_WN - Walnut (Irvine)', 'LOC_WP - Westpark']\\n x_train, x_test, y_train, y_test= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\\n logreg = LogisticRegression()\\n logreg.fit(x_train, y_train.values.ravel())\\n y_pred= logreg.predict(x_test)\\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test, y_test), 3))\\n # This model is really bad\\n \\n \\\"\\\"\\\"\\n \\n \\\"\\\"\\n houses= houses.drop(['DAYS_ON_MARKET', 'ADDRESS', 'LOCATION',\\n 'STATUS', 'PROPERTY_TYPE', 'ZIP_CODE'], axis= 1)\\n columns= houses.columns.values.tolist()\\n y= ['SELLER_HOUSE']\\n x= [i for i in columns if i not in y]\\n \\n # Over Sampling Using SMOTE \\n x_train, _, y_train, _= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\\n x_columns= x_train.columns\\n \\n os= SMOTE(random_state= 0)\\n os_x, os_y= os.fit_sample(x_train, y_train)\\n os_x= pd.DataFrame(data= os_x, columns= x_columns)\\n os_y= pd.DataFrame(data= os_y, columns= y)\\n \\n \\n #Recursive Feature Elimination\\n logreg= LogisticRegression(max_iter= 600)\\n rfe= RFE(logreg, 20)\\n rfe= rfe.fit(os_x, os_y.values.ravel())\\n \\n lst= [i for count, i in enumerate(x) if rfe.support_[count] == True]\\n X= os_x[lst]\\n Y= os_y['SELLER_HOUSE']\\n \\n \\n #logit_model= sm.Logit(Y, X)\\n #result= logit_model.fit()\\n #print(result.summary2()) # Model choosen by RCE\\n \\n #These are features have a p-value less than 0.05\\n final_x= ['BATHS', 'ZIP_92602.0', 'ZIP_92618.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\\n #final_x= ['ZIP_92602.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\\n X2= os_x[final_x]\\n \\n logit_model2= sm.Logit(Y, X2)\\n result2= logit_model2.fit()\\n print(result2.summary2()) # Final Model\\n \\n x_train2, x_test2, y_train2, y_test2= train_test_split(X2, Y, test_size= 0.3, random_state= 500)\\n logreg = LogisticRegression()\\n logreg.fit(x_train2, y_train2)\\n \\n y_pred= logreg.predict(x_test2)\\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test2, y_test2), 2))\\n \\n conf_matrix= confusion_matrix(y_test2, y_pred)\\n print(conf_matrix)\\n # So 22+61 correct predictions and 13+44 wrong predictions\\n \\n logit_roc_auc = roc_auc_score(y_test2, logreg.predict(x_test2))\\n fpr, tpr, _ = roc_curve(y_test2, logreg.predict_proba(x_test2)[:,1])\\n plt.figure()\\n plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\\n plt.plot([0, 1], [0, 1],'r--')\\n plt.xlim([0.0, 1.0])\\n plt.ylim([0.0, 1.05])\\n plt.xlabel('False Positive Rate')\\n plt.ylabel('True Positive Rate')\\n plt.title('Receiver operating characteristic')\\n plt.legend(loc=\\\"lower right\\\")\\n plt.show()\\n \\\"\\\"\",\n \"def load_employment_data(paths):\\n output = []\\n\\n for path in paths:\\n with open(path, 'r') as source:\\n reader = csv.DictReader(source)\\n for line in reader:\\n output.append({\\n 'code': line['code'],\\n # 'name': line['name'],\\n 'count': line['count'],\\n })\\n\\n return output\",\n \"def parse(self, is_president):\\n mp = {}\\n self.content = self.page.find(id=\\\"content\\\")\\n\\n salutation = self.content.find(id=\\\"inhalt\\\").text.strip()\\n if is_president:\\n salutation = salutation[: salutation.rfind(\\\" - \\\")]\\n mp[\\\"is_president\\\"] = is_president\\n mp[\\\"salutation\\\"] = salutation\\n mp[\\\"in_committees\\\"] = self.page.find(\\\"a\\\", href=\\\"#tab-Ausschuesse\\\") is not None\\n mp.update(self._parse_picture())\\n mp.update(self._parse_contact_information())\\n mp.update(self._parse_biography())\\n return mp\",\n \"def gather_employee_entries(self):\\n user_inputs = [\\n self.emp_lname.get(), self.emp_mi.get(), self.emp_fname.get(),\\n self.emp_hiredate.get()\\n ]\\n\\n return self.check_input_empty(user_inputs)\",\n \"def extract_income_statement_page(self, company_code: int) -> None:\\n self.driver.get(\\n f\\\"https://www.wsj.com/market-data/quotes/HK/XHKG/{company_code}/financials/annual/income-statement\\\"\\n )\\n sales_revenue = self.get_latest_cell_value(Info.S_R.value)\\n ebit = self.get_latest_cell_value(Info.EBIT.value)\\n self.info[Info.S_R.value] = sales_revenue\\n self.info[Info.EBIT.value] = ebit\",\n \"def _get_staff_report_data(self):\\n output = []\\n output.append(\\n [\\n 'ID', 'LATEST REVISION CREATED AT', 'NAME AND CNETID',\\n 'POSITION TITLE', 'EMAILS', 'PHONE, FACEX',\\n 'UNITS (LIBRARY DIRECTORY FULL NAME)',\\n 'UNITS (CAMPUS DIRECTORY FULL NAME)', 'GROUPS', 'EMPLOYEE TYPE',\\n 'SUPERVISES STUDENTS', 'POSITION ELIMINATED',\\n 'SUPERVISOR NAME AND CNETID'\\n ]\\n )\\n staffpages = self._get_staff_wagtail()\\n for s in staffpages:\\n staffpage_library_units = set(\\n StaffPageLibraryUnits.objects.filter(page=s)\\n )\\n\\n units = []\\n for slu in staffpage_library_units:\\n units.append(\\n (\\n slu.library_unit.get_full_name(),\\n slu.library_unit.get_campus_directory_full_name()\\n )\\n )\\n units.sort(key=lambda u: u[0])\\n\\n groups = sorted([g.parent.title for g in s.member.all()])\\n\\n try:\\n latest_revision_created_at = \\\\\\n s.latest_revision_created_at.strftime(\\n '%m/%d/%Y %-I:%M:%S %p'\\n )\\n except AttributeError:\\n latest_revision_created_at = ''\\n\\n name_and_cnetid = '%s (%s)' % (s.title, s.cnetid)\\n\\n supervisor_names_and_cnetids = [\\n '%s (%s)' % (s.title, s.cnetid) for s in s.get_supervisors\\n ]\\n\\n emails = [e.email for e in s.staff_page_email.all()]\\n\\n phone_facexes = [\\n '%s,%s' % (p.faculty_exchange, p.phone_number)\\n for p in s.staff_page_phone_faculty_exchange.all()\\n ]\\n\\n employee_type_string = [\\n v for i, v in EMPLOYEE_TYPES if i == s.employee_type\\n ][0]\\n\\n position_eliminated_string = str(s.position_eliminated)\\n\\n output.append(\\n [\\n str(s.id), latest_revision_created_at, name_and_cnetid,\\n s.position_title or '', '|'.join(emails) or '',\\n '|'.join(phone_facexes) or '',\\n '|'.join([u[0] for u in units]) or '',\\n '|'.join([u[1] for u in units]) or '', '|'.join(groups),\\n employee_type_string,\\n str(s.supervises_students), position_eliminated_string,\\n '|'.join(supervisor_names_and_cnetids)\\n ]\\n )\\n return output\",\n \"def inss_calculation(salary, overtime_total):\\n salary_base_inss = salary + overtime_total\\n\\n inss1_min_salary = 0\\n inss1_max_salary = 1045.00\\n inss2_min_salary = 1045.01\\n inss2_max_salary = 2089.60\\n inss3_min_salary = 2089.61\\n inss3_max_salary = 3134.40\\n inss4_min_salary = 3134.41\\n inss4_max_salary = 6101.06\\n\\n aliquot_inss1 = 7.5\\n aliquot_inss2 = 9\\n aliquot_inss3 = 12\\n aliquot_inss4 = 14\\n max_discount_inss = 713.09\\n\\n\\n if inss1_min_salary <= salary_base_inss <= inss1_max_salary:\\n inss_value = round(float(salary_base_inss * aliquot_inss1 / 100), 2)\\n elif inss2_min_salary <= salary_base_inss <= inss2_max_salary:\\n inss_value = round(float(salary_base_inss * aliquot_inss2 / 100), 2)\\n elif inss3_min_salary <= salary_base_inss <= inss3_max_salary:\\n inss_value = round(float(salary_base_inss * aliquot_inss3 / 100), 2)\\n elif inss4_min_salary <= salary_base_inss <= inss4_max_salary:\\n inss_value = round(float(salary_base_inss * aliquot_inss4 / 100), 2)\\n else:\\n inss_value = round(float(max_discount_inss), 2) \\n\\n return inss_value\",\n \"def get(self):\\n\\n return self.get_request_handler(request.headers).get_all_employment_status()\",\n \"def family_detail_extractor(data):\\n family_list = {}\\n try:\\n cnt = 0\\n family_id = data['hof_Details']['FAMILYIDNO']\\n for key in data:\\n if key == 'family_Details':\\n for items in data[key]:\\n cnt = user_entry(cnt, family_list, items, False, family_id)\\n elif key == 'hof_Details':\\n cnt = user_entry(cnt, family_list, data[key], True, family_id)\\n return family_list\\n except AttributeError:\\n print 'Json object cannot be decoded // Extractor'\",\n \"def unicef_data():\\n workbook = xlrd.open_workbook('unicef_oct_2014.xlsx')\\n sheet = workbook.sheets()[0]\\n\\n title_rows = zip(sheet.row_values(4), sheet.row_values(5))\\n titles = [t[0] + ' ' + t[1] for t in title_rows]\\n titles = [t.strip() for t in titles]\\n\\n country_rows = [sheet.row_values(r) for r in range(6, 114)]\\n cleaned_rows = []\\n\\n for row in country_rows:\\n cleaned_row = [remove_bad_chars(rv) for rv in row]\\n cleaned_rows.append(cleaned_row)\\n\\n example_row = sheet.row(6)\\n types = get_types(example_row)\\n\\n table = agate.Table(cleaned_rows, titles, types)\\n ranked = table.compute([('Total Child Labor Rank',\\n agate.Rank('Total (%)', reverse=True)), ])\\n\\n return ranked\",\n \"def getEpoints(jobInfo):\\n for line in jobInfo:\\n if line.strip().startswith(\\\"E=\\\"):\\n print(line)\\n P=line\\n\\n return parseLineDigits(P)\",\n \"def job_data(driver):\\n job_info = {\\n \\\"job_title\\\" : \\\"h1.title\\\",\\n \\\"company\\\" : \\\"span.company\\\",\\n \\\"location\\\" : \\\"h3.location\\\",\\n \\\"employment_type\\\" : \\\"div.employment div.content div.rich-text\\\",\\n \\\"industry\\\" : \\\"div.industry div.content div.rich-text\\\",\\n \\\"experience\\\" : \\\"div.experience div.content div.rich-text\\\",\\n \\\"job_function\\\" : \\\"div.function div.content div.rich-text\\\",\\n \\\"description\\\" : \\\"div.summary div.content div.description-section div.rich-text\\\"\\n }\\n # click the 'read more' button to reveal more about the job posting\\n try:\\n driver.find_element_by_css_selector(\\\"button#job-details-reveal\\\").click()\\n except Exception as e:\\n print(\\\"error in attempting to click 'reveal details' button\\\")\\n print(e)\\n for key, selector in job_info.items():\\n try:\\n job_info[key] = driver.find_element_by_css_selector(selector).text\\n except Exception as e:\\n job_info[key] = \\\"\\\"\\n pass\\n return job_info\",\n \"def exploratoryAnalysis(df):\\r\\n # applying uniform names to the item fat content categories\\r\\n df['Item_Fat_Content'].replace(['LF', 'reg', 'low fat'], ['Low Fat', 'Regular', 'Low Fat'], inplace=True)\\r\\n\\r\\n # creating a new feature based on the store age rather than the establishment year\\r\\n df['Outlet_Age'] = df['Outlet_Establishment_Year'].apply(lambda x: 2021 - x)\\r\\n df.drop('Outlet_Establishment_Year', axis=1, inplace=True)\\r\\n\\r\\n # dropping non-numerical Identifier columns which do not provide any usage for regression analysis\\r\\n df.drop('Item_Identifier', axis=1, inplace=True)\\r\\n df.drop('Outlet_Identifier', axis=1, inplace=True)\\r\\n\\r\\n return df\",\n \"def _summary(obj):\\n return obj.summary\",\n \"def get_employee_information(user_name: str, employee_name: str, store_name: str):\\n\\n user_name = auth.get_username_from_hash(user_name)\\n permission_handler.is_permmited_to(user_name=user_name, action=Action.EMPLOYEE_INFO.value,\\n store_name=store_name)\\n permission_handler.is_working_in_store(employee_name, store_name)\\n return user_handler.get_employee_information(employee_name)\",\n \"def synthesize_employment_data(self, config):\\r\\n jobs_by_zone_by_sector_table_name = config['jobs_by_zone_by_sector']\\r\\n gridcells_table_name = config['gridcells']\\r\\n jobs_table_name = config['jobs']\\r\\n gridcells_output_table_name = config['gridcells_output']\\r\\n jobs_output_table_name = config['jobs_output']\\r\\n \\r\\n input_db_name = config['db_config'].database_name\\r\\n output_db_name = config['output_database_name']\\r\\n \\r\\n sectors = config['sector_names_and_ids']\\r\\n building_types_and_ids_and_home_based = config[\\r\\n 'building_type_column_names_and_ids_and_home_based']\\r\\n \\r\\n building_types = []\\r\\n building_ids = []\\r\\n home_based = [] \\r\\n for type, id, home in building_types_and_ids_and_home_based:\\r\\n building_types += [type]\\r\\n building_ids += [id]\\r\\n home_based += [home]\\r\\n \\r\\n \\r\\n from_database_configuration = ScenarioDatabaseConfiguration(\\r\\n database_name = input_db_name,\\r\\n host_name = config['db_config'].host_name,\\r\\n user_name = config['db_config'].user_name,\\r\\n password = config['db_config'].password \\r\\n )\\r\\n to_database_configuration = ScenarioDatabaseConfiguration(\\r\\n database_name = output_db_name,\\r\\n host_name = config['db_config'].host_name,\\r\\n user_name = config['db_config'].user_name,\\r\\n password = config['db_config'].password \\r\\n )\\r\\n\\r\\n FlattenScenarioDatabaseChain().copy_scenario_database(\\r\\n from_database_configuration = from_database_configuration, \\r\\n to_database_configuration = to_database_configuration,\\r\\n tables_to_copy = [gridcells_table_name, jobs_table_name])\\r\\n \\r\\n db_server = DatabaseServer(to_database_configuration) \\r\\n output_database = db_server.get_database(output_db_name)\\r\\n \\r\\n sector_name = 0; sector_id = 1\\r\\n \\r\\n sector = {}\\r\\n for entry in sectors:\\r\\n name = entry[sector_name]\\r\\n id = entry[sector_id]\\r\\n sector[id] = self._get_jobs_per_building_type_in_sector_by_zone(\\r\\n output_database, jobs_by_zone_by_sector_table_name, \\r\\n jobs_table_name, name, id)\\r\\n\\r\\n results = self._get_building_type_proportion_by_zone(output_database, \\r\\n gridcells_table_name)\\r\\n \\r\\n grid_id = 0; zone_id = 1\\r\\n dist = {}\\r\\n \\r\\n type_index = {}\\r\\n \\r\\n for name in building_types:\\r\\n for i in range(len(results[0])):\\r\\n column_name = results[0][i]\\r\\n if name == column_name:\\r\\n type_index[name] = i\\r\\n break;\\r\\n else:\\r\\n raise KeyError, ('No column by the name of \\\\'%s\\\\' found in '\\r\\n 'the database.' % name) \\r\\n\\r\\n for name in building_types:\\r\\n dist[name] = {}\\r\\n \\r\\n for row in results[1:]:\\r\\n for name in building_types:\\r\\n dist[name][row[zone_id]] = []\\r\\n \\r\\n for row in results[1:]:\\r\\n for name in building_types:\\r\\n dist[name][row[zone_id]] += [(row[grid_id], \\r\\n row[type_index[name]])]\\r\\n \\r\\n jobs_table_data = self._create_jobs_table_data(dist, sector,\\r\\n building_types_and_ids_and_home_based)\\r\\n \\r\\n output_database.execute('USE %(out_db)s' % {'out_db':output_db_name})\\r\\n \\r\\n output_database.execute(\\\"\\\"\\\"\\r\\n CREATE TABLE %(jobs_out)s (\\r\\n JOB_ID INT AUTO_INCREMENT, PRIMARY KEY(JOB_ID),\\r\\n GRID_ID INT, HOME_BASED INT, SECTOR_ID INT, BUILDING_TYPE INT);\\r\\n \\\"\\\"\\\" % {'jobs_out':jobs_output_table_name})\\r\\n \\r\\n if len(jobs_table_data) > 0:\\r\\n output_prefix = (\\r\\n \\\"\\\"\\\"INSERT INTO %(jobs_out)s \\r\\n (GRID_ID, HOME_BASED, SECTOR_ID, BUILDING_TYPE) VALUES\\r\\n \\\"\\\"\\\" % {'jobs_out':jobs_output_table_name})\\r\\n output_postfix = ';'\\r\\n \\r\\n step = 1000\\r\\n length = len(jobs_table_data)\\r\\n iterations = int(length/step) + 1\\r\\n \\r\\n for i in range(iterations):\\r\\n low = i*step\\r\\n high = (i+1)*step\\r\\n \\r\\n if high > length: high = length\\r\\n \\r\\n output_body = \\\"\\\"\\r\\n \\r\\n for j in range(low, high):\\r\\n output_body += (\\r\\n '(%(grid)s, %(home)s, %(sector)s, %(building)s),\\\\n' \\r\\n % jobs_table_data[j])\\r\\n \\r\\n output_query = \\\"%s%s%s\\\" % (output_prefix, \\r\\n output_body[:-2], \\r\\n output_postfix)\\r\\n\\r\\n output_database.execute(output_query)\\r\\n \\r\\n \\r\\n ### TODO: \\r\",\n \"def summary(df, city, month, day):\\n time_stats(df, month, day)\\n station_stats(df)\\n trip_duration_stats(df)\\n user_stats(df, city)\\n display_data(df)\",\n \"def get_academic_info(request, response):\\n\\n terms = get_current_and_next_quarters(request, 4)\\n\\n try:\\n enrollments = get_enrollments_of_terms(terms)\\n except Exception as ex:\\n logger.error(\\n \\\"%s get_academic_info: %s\\\" %\\n (netid, ex))\\n return\\n\\n if terms[0] in enrollments:\\n enrollment = enrollments[terms[0]]\\n response['class_level'] = enrollment.class_level\\n\\n response['term_majors'] = _get_degrees_for_terms(terms, enrollments,\\n \\\"majors\\\")\\n response['has_pending_major'] = False\\n\\n for major in response['term_majors']:\\n if major['degrees_modified']:\\n response['has_pending_major'] = True\\n\\n response['term_minors'] = _get_degrees_for_terms(terms, enrollments,\\n \\\"minors\\\")\\n\\n if (len(response['term_minors']) > 1 and\\n len(response['term_minors'][0]['minors']) > 0):\\n response['has_minors'] = True\\n\\n response['has_pending_minor'] = False\\n\\n for minor in response['term_minors']:\\n if minor['degrees_modified']:\\n response['has_pending_minor'] = True\",\n \"def get_meta(bill_content):\\n\\n title = bill_content.BillTitle.find(name='Title', language='en').text\\n\\n if 'amend' in title.lower():\\n amend = True\\n else:\\n amend = False\\n\\n # rarely, no published version of a bill is available\\n publication_tags = [t for t in bill_content.find_all('Publication')\\n if t.find(name='Title', language='en').text == 'Royal Assent']\\n if len(publication_tags) == 1:\\n publication_id = publication_tags[0]['id']\\n else:\\n publication_id = None\\n\\n # all other metadata appear to be consistently present\\n date = bill_content.Events.LastMajorStageEvent.Event['date']\\n session = bill_content.ParliamentSession['parliamentNumber']\\n subtype = bill_content.BillType.find(name='Title', language='en').text\\n sponsor = bill_content.SponsorAffiliation.Person.FullName.text\\n sponsor_party = bill_content.SponsorAffiliation.PoliticalParty.find(name='Title', language='en').text\\n majority_party = bill_content.PrimeMinister.PoliticalParty.find(name='Title', language='en').text\\n\\n committee_tags = bill_content.find_all(name='Committee', accronym=True)\\n committee_names = [t['accronym'] for t in committee_tags]\\n committee_data = {c: committee_names.count(c) for c in set(committee_names)}\\n\\n metadata = _format_meta_entry(country=u'canada',\\n title=title,\\n id=publication_id,\\n date=date,\\n session=session,\\n type=u'annual',\\n subtype=subtype,\\n amendment=amend,\\n sponsor=sponsor,\\n sponsor_party=sponsor_party,\\n majority_party=majority_party,\\n hearings=committee_data)\\n\\n return metadata\",\n \"def parse_job_details(job: Job):\\n if job['organization'].casefold() == \\\"ORGENTEC Diagnostika GmbH\\\".casefold():\\n yield scrapy.Request(job['url'], callback=MTADialogSpider.parse_orgentec, meta={'job': job},\\n dont_filter=True)\\n else:\\n yield job\",\n \"def call_all_functions(input: list) -> None:\\n for emp in input:\\n print(\\\"{0} {1} {2} {3} {4}\\\".format(emp.get_first_name(), emp.get_last_name(),emp.get_family(), emp.get_salary(), emp.get_department()))\",\n \"def updateEMPStudy(self, study_id, study_name, investigation_type, miens_compliant, submit_to_insdc, \\n portal_type, study_title, study_alias, pmid, study_abstract, study_description,\\n number_samples_collected, number_samples_promised , lab_person,\\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \\n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\\n includes_timeseries):\\n con = self.getMetadataDatabaseConnection()\\n results = con.cursor().callproc('qiime_assets.emp_study_update', \\n [study_id, study_name, investigation_type, miens_compliant, submit_to_insdc, portal_type, \\n study_title, study_alias, pmid, study_abstract, study_description,\\n number_samples_collected, number_samples_promised , lab_person,\\n lab_person_contact, emp_person, first_contact, most_recent_contact, sample_type, \\n has_physical_specimen, has_extracted_data, timeseries, spatial_series,\\n principal_investigator, principal_investigator_contact, default_emp_status, funding,\\n includes_timeseries])\",\n \"def get_EULA_summary(self, e):\\n text = e.get_text() # EULA clauses separated by newlines.\\n summary = self.summarizer(text, min_length=10, max_length=10 + min(140, len(text) // 3))\\n return summary[0]['summary_text'].replace(' .', '.').lstrip()\",\n \"def user_story_07(self):\\n for ind in self.individuals.values():\\n if ind.age >= 150:\\n print(f'US07 - {ind.name} is age {ind.age}, which is over 150 years old, on line {ind._age_line}')\",\n \"def analyse(self):\\n pass\",\n \"def process_data_p2(data, documentation, geodata):\\r\\n documentation_state = documentation.get_column(\\\"ST_FIPS\\\")\\r\\n mean_per_state = data.groupby(\\\"ST_FIPS\\\", as_index=False).mean()\\r\\n mean_per_state[\\\"ST_FIPS\\\"] = mean_per_state[\\\"ST_FIPS\\\"] \\\\\\r\\n .map(documentation_state.values_mapping)\\r\\n return geodata.merge(mean_per_state, how=\\\"left\\\",\\r\\n left_on=\\\"name\\\", right_on=\\\"ST_FIPS\\\")\",\n \"def main():\\n\\n #get the csv file into a data-frame\\n universities_df = pd.read_csv('universities_data.csv', encoding = 'utf-8-sig')\\n universities_names_list = universities_df['name'].tolist()\\n\\n #get list of university objects\\n url = 'http://universities.hipolabs.com/search?country=Israel'\\n api_universities = Get_universities(url)\\n list_of_universities = api_universities.get_universities_info()\\n\\n #to see if we got new entities or not for exporting to csv later..\\n is_new_entities = False\\n\\n for university in list_of_universities:\\n if university.name not in universities_names_list:\\n is_new_entities = True\\n universities_df= universities_df.append(pd.DataFrame({\\n 'alpha_two_code': [university.alpha_two_code], \\n 'country': [university.country],\\n 'web_pages': [str(university.web_pages)],\\n 'domains': [str(university.domains)],\\n 'name': [university.name],\\n 'state_province':[str(university.state_province)]}) , ignore_index = True)\\n\\n #export back to csv if true\\n if is_new_entities: \\n print('we got new entities!') \\n universities_df.to_csv('universities_data.csv', encoding = 'utf-8-sig', index = False)\\n else:print('no new universities for now!')\",\n \"def _clean_search_hit(search_hit):\\n\\n hit_name = search_hit.find(class_='hit-name')\\n hit_url = hit_name.get('href')\\n hit_id = hit_url.split('/')[-1]\\n name = hit_name.get_text().split(',')[0].title().split()\\n\\n current_city = search_hit.find(class_='hit-location').get_text().upper()\\n\\n # Find all Addresses for search result.\\n try:\\n address = search_hit.find(class_='hit-pastAddresses').find_all(class_='hit-values')\\n address = list({a.text.upper().replace('.', '') for a in address})\\n except AttributeError:\\n address = list()\\n\\n # find the address that is most likely the current main address.\\n try:\\n address.insert(0, address.pop(address.index(current_city)))\\n except ValueError:\\n address.insert(0, current_city)\\n\\n address = [\\n {\\n '@type': 'PostalAddress',\\n 'addressLocality': locality.title(),\\n 'addressRegion': region\\n } for locality, region in [a.split(', ') for a in address]]\\n\\n work_location = {'@type': 'Place'}\\n try:\\n work_location['name'] = search_hit\\\\\\n .find(class_='hit-work')\\\\\\n .find(class_='hit-values')\\\\\\n .get_text()\\\\\\n .title()\\n except AttributeError:\\n work_location['name'] = ''\\n\\n alumni_of = {'@type': 'EducationalOrganization'}\\n try:\\n alumni_of['name'] = search_hit\\\\\\n .find(class_='hit-high-school')\\\\\\n .find(class_='hit-values')\\\\\\n .get_text().title()\\n except AttributeError:\\n pass\\n\\n return {\\n '@id': hit_id,\\n '@type': 'Person',\\n 'name': ' '.join(name),\\n 'givenName': name[0],\\n 'middleName': ' '.join(name[1:-1]),\\n 'familyName': name[-1],\\n 'url': hit_url,\\n 'address': address,\\n 'workLocation': work_location,\\n 'alumniOf': alumni_of,\\n }\",\n \"def University_calculation(jobid):\\r\\n min_cgpa=90\\r\\n \\\"\\\"\\\"~~~~~~~~~\\\"\\\"\\\"\\r\\n dbconnect= connect_to_db()\\r\\n Candidate_qualifications=pd.read_sql(\\\"select candidate_id,university_name,institute_name,aggregate from candidate_qualification where candidate_id in(select candidate_id from master_id where job_id=\\\"+str(jobid)+\\\")\\\",con=dbconnect)\\r\\n College_data=pd.read_sql(\\\"select * from college_tiers\\\",con=dbconnect)\\r\\n Candidate_qualifications[\\\"aggregate\\\"]=Candidate_qualifications[\\\"aggregate\\\"].apply(lambda x: 0 if x<0 or x>100 else x)\\r\\n Candidate_qualifications[\\\"aggregate\\\"]=Candidate_qualifications[\\\"aggregate\\\"].apply(lambda x: x*10 if 5<x<10 else x)\\r\\n Candidate_qualifications[\\\"aggregate\\\"]=Candidate_qualifications[\\\"aggregate\\\"].apply(lambda x: 25*x if 0<x<4 else x)\\r\\n\\r\\n def Aggregate():\\r\\n Unique_candids=Candidate_qualifications[[\\\"candidate_id\\\",\\\"aggregate\\\"]].groupby(\\\"candidate_id\\\").mean()\\r\\n Unique_candids[\\\"aggregate\\\"]=Unique_candids[\\\"aggregate\\\"].apply(lambda x:x-min_cgpa)\\r\\n minval=min(Unique_candids[\\\"aggregate\\\"])\\r\\n maxval=max(Unique_candids[\\\"aggregate\\\"])\\r\\n Unique_candids[\\\"aggregate\\\"]=Unique_candids[\\\"aggregate\\\"].apply(lambda x:(x-minval)*100/(maxval-minval))\\r\\n Unique_candids=Unique_candids.reset_index()\\r\\n return Unique_candids\\r\\n \\r\\n def University_name():\\r\\n stop_words=[\\\"of\\\",\\\"on\\\",\\\"for\\\",\\\"the\\\",\\\"&\\\",\\\"and\\\"]\\r\\n unique_candids=list(np.unique(Candidate_qualifications[\\\"candidate_id\\\"]))\\r\\n candidate_univdict={}\\r\\n for i in unique_candids:\\r\\n candidate_univdict[i]=Candidate_qualifications[[\\\"university_name\\\",\\\"institute_name\\\"]][Candidate_qualifications[\\\"candidate_id\\\"]==i].values.tolist()\\r\\n candidate_univdict={k:list(map(lambda x:list(filter(lambda y:str(y).strip() not in[\\\"\\\",\\\"None\\\"],x)),v)) for k,v in candidate_univdict.items()}\\r\\n candidate_univdict={k: np.unique(list(itertools.chain.from_iterable(v))).tolist() for k,v in candidate_univdict.items()}\\r\\n for i in candidate_univdict.keys():\\r\\n for j in candidate_univdict[i]:\\r\\n if j in list(map(lambda x: str(x).lower(),College_data[\\\"College\\\"].tolist())):\\r\\n candidate_univdict[i][candidate_univdict[i].index(j)]=list(College_data[\\\"Tier\\\"][College_data[\\\"College\\\"]==j])[0]\\r\\n continue;\\r\\n if j in list(map(lambda x: str(x).lower(),College_data[\\\"College\\\"].tolist())):\\r\\n candidate_univdict[i][candidate_univdict[i].index(j)]=list(College_data[\\\"Tier\\\"][College_data[\\\"Ancronym\\\"]==j])[0]\\r\\n continue;\\r\\n else:\\r\\n Processed_collegedata=pd.DataFrame(College_data[\\\"College\\\"].apply(lambda x: [i for i in re.sub(\\\"[\\\\.-]\\\",\\\" \\\",x.lower()).split() if i not in stop_words]))\\r\\n Processed_collegedata[\\\"Ancronym\\\"]=College_data[\\\"Ancronym\\\"].apply(lambda x: [i for i in re.sub(\\\"[\\\\.-]\\\",\\\" \\\",x.lower()).split() if i not in stop_words])\\r\\n val=[w for w in re.sub(\\\"[\\\\.-]\\\",\\\" \\\",j.lower()).split() if w not in stop_words]\\r\\n Processed_collegedata[\\\"College\\\"]=Processed_collegedata[\\\"College\\\"].apply(lambda x:(len(set(val).intersection(set(x))))/len(set(val).union(set(x))))\\r\\n Processed_collegedata[\\\"Ancronym\\\"]=Processed_collegedata[\\\"Ancronym\\\"].apply(lambda x:(len(set(val).intersection(set(x))))/len(set(val).union(set(x))))\\r\\n maxval=Processed_collegedata.max().idxmax()\\r\\n if Processed_collegedata[maxval].idxmax()>0.5:\\r\\n candidate_univdict[i][candidate_univdict[i].index(j)]=College_data.ix[Processed_collegedata[maxval].idxmax(),\\\"Tier\\\"]\\r\\n else:\\r\\n candidate_univdict[i][candidate_univdict[i].index(j)]=3\\r\\n \\r\\n candidate_univdict={k:100/min(v) for k,v in candidate_univdict.items() if len(v)>0}\\r\\n College_score=pd.DataFrame.from_dict(candidate_univdict,orient=\\\"index\\\")\\r\\n College_score=College_score.reset_index()\\r\\n College_score.columns=[\\\"candidate_id\\\",\\\"Tier_score\\\"]\\r\\n return College_score\\r\\n result=pd.merge(Aggregate(),University_name(),how=\\\"outer\\\",on=\\\"candidate_id\\\")\\r\\n result=pd.merge(pd.DataFrame(np.unique(Candidate_qualifications[\\\"candidate_id\\\"]),columns=[\\\"candidate_id\\\"]),result,how=\\\"left\\\",on=\\\"candidate_id\\\")\\r\\n result=result.fillna(0)\\r\\n return result\",\n \"def dataDescription(train):\\n print(train.describe())\\n # No NaNs\\n print(train.head())\\n # 5 columns: store number, department number, date, weekly sales, isHoliday\\n\\n # Lets view the weekly sales, grouped by date\\n fig, ax = plt.subplots()\\n salesByDate = train.groupby('Date').mean().Weekly_Sales\\n salesByDate.plot()\\n plt.show()\\n fig, ax = plt.subplots()\\n autocorrelation_plot(salesByDate)\\n plt.show()\\n # Lets plot all store's sales by date on the same graph\\n salesByStore = train.groupby(by=['Store', 'Date']).mean().Weekly_Sales\\n salesByStore.unstack(level=0).plot()\\n # Lets plot all department's sales by date on the same graph\\n salesByStore = train.groupby(by=['Dept', 'Date']).mean().Weekly_Sales\\n salesByStore.unstack(level=0).plot()\\n # It looks like all stores have peaks at the same times, however just a\\n # single department. Could this be the holiday department?\\n fig, ax = plt.subplots()\\n holidayDates = train.groupby('Date').mean().IsHoliday\\n holidayDates.plot()\\n plt.show()\\n # Very few holiday days...\\n # What are the average sales on holidays vs not holidays\\n holidaySales = train[train['IsHoliday'] == True].Weekly_Sales.mean()\\n nonHolidaySales = train[train['IsHoliday'] == False].Weekly_Sales.mean()\\n print(\\\"The mean weekly sales on holidays is {:.2f} \\\".format(holidaySales) +\\n \\\"and then {:.2f} for non-holidays.\\\".format(nonHolidaySales))\\n\\n # Lets looks at how complete the data is. Create a series with all possible\\n # dates in. Compare with each store and department.\\n trainDates = pd.to_datetime(train.Date)\\n trainDates = pd.DatetimeIndex(trainDates.unique())\\n# First confirm there are no missing dates in the whole range.\\n# trainDatesTest = pd.date_range(trainDates.min(),\\n# trainDates.max(), freq='7D')\\n# (trainDates == trainDatesTest).all()\\n stores = np.unique(train['Store'])\\n depts = np.unique(train['Dept'])\\n missingDates = defaultdict(int)\\n for store in stores:\\n for dept in depts:\\n trainThis = train[train['Store'] == store]\\n trainThis = trainThis[trainThis['Dept'] == dept]\\n missing = len(trainDates) - len(pd.DatetimeIndex(trainThis.Date))\\n missingDates[missing] += 1\\n\\n # The majority miss nothing. 314 stores don't have certain departments\\n # - as can be expected!\\n fig, ax = plt.subplots()\\n ax.bar(list(missingDates.keys()), missingDates.values())\",\n \"def setEmpireStats(self):\\n totalEmpires = len(self.empires.keys())\\n stats = {'Research':[], 'Fleet Size':[], 'Army Size':[], 'CR Production':[],\\n 'AL Production':[],'EC Production':[],'IA Production':[]}\\n \\n # Calculate Research Stats\\n d = {}\\n for empireID, myEmpire in self.empires.iteritems():\\n if empireID <> '1':\\n num = 0\\n for techID, myTech in myEmpire.techTree.iteritems():\\n if myTech.complete == 1:\\n num += 1\\n d[empireID] = num\\n stats['Research'] = anwp.func.funcs.sortDictByValue(d, True)\\n \\n # Calculate Fleet Stats\\n d = {}\\n for shipID, myShip in self.ships.iteritems():\\n if myShip.empireID <> '1':\\n (BV,CR,AL,EC,IA) = myShip.getMyValue()\\n if myShip.empireID in d.keys():\\n d[myShip.empireID] += BV\\n else:\\n d[myShip.empireID] = BV\\n stats['Fleet Size'] = anwp.func.funcs.sortDictByValue(d, True)\\n \\n # Calculate Army Stats\\n d = {}\\n for regimentID, myRegiment in self.regiments.iteritems():\\n if myRegiment.empireID <> '1':\\n (BV,CR,AL,EC,IA) = myRegiment.getMyValue()\\n if myRegiment.empireID in d.keys():\\n d[myRegiment.empireID] += BV\\n else:\\n d[myRegiment.empireID] = BV\\n stats['Army Size'] = anwp.func.funcs.sortDictByValue(d, True)\\n\\n # Calculate Production Stats\\n for res in ['CR','AL','EC','IA']:\\n d = {}\\n for systemID, mySystem in self.systems.iteritems():\\n if mySystem.myEmpireID <> '1':\\n myValue = getattr(mySystem, 'prod%s' % res)\\n if mySystem.myEmpireID in d.keys():\\n d[mySystem.myEmpireID] += myValue\\n else:\\n d[mySystem.myEmpireID] = myValue\\n myEmpire = self.empires[mySystem.myEmpireID]\\n myEmpireValue = getattr(myEmpire, 'totalProd%s' % res)\\n setattr(myEmpire, 'totalProd%s' % res, myEmpireValue+myValue)\\n \\n stats['%s Production' % res] = anwp.func.funcs.sortDictByValue(d, True)\\n \\n # calculate top captains\\n d = {}\\n for captainID, myCaptain in self.captains.iteritems():\\n if myCaptain.myEmpire.id <> '1':\\n myCaptain.resetData()\\n d[myCaptain.id] = myCaptain.experience\\n topCaptains = anwp.func.funcs.sortDictByValue(d, True)\\n topCaptains = topCaptains[:2*len(self.empires.keys())]\\n \\n # Send out Stats to each Empire\\n for empireID, myEmpire in self.empires.iteritems():\\n if empireID <> '1':\\n title = 'Round:%d Statistics' % self.currentRound\\n body = ['%s ROUND %d STATS:' % (myEmpire.name, self.currentRound)]\\n body.append('====================================================')\\n for item in ['Research','Fleet Size', 'Army Size', 'CR Production',\\n 'AL Production', 'EC Production', 'IA Production']:\\n if empireID in stats[item]:\\n body.append('You are %s in %s' % (anwp.func.funcs.getNiceNumber(stats[item].index(empireID)+1), item))\\n \\n # total production\\n body.append('')\\n body.append('TOTAL EMPIRE PRODUCTION OVER %d ROUNDS:' % self.currentRound)\\n body.append('====================================================')\\n for res in ['CR','AL','EC','IA']:\\n body.append('Total %s Production: %d' % (res, getattr(myEmpire, 'totalProd%s' % res)))\\n\\n # legendary captains\\n body.append('')\\n body.append('TOP %d STARSHIP CAPTAINS in ROUND %d:' % ((2*len(self.empires.keys()), self.currentRound)))\\n body.append('====================================================')\\n for captainID in topCaptains:\\n myCaptain = self.captains[captainID]\\n myCaptain.promoteMe()\\n body.append('%s ---> RANK:%s -- EXP:%d -- %s' % (string.upper(myCaptain.name), myCaptain.rank, myCaptain.experience, string.upper(myCaptain.myEmpire.name)))\\n \\n myEmpire.genMail({'fromEmpire':empireID, 'round':self.currentRound,\\n 'messageType':'general', 'subject':title, 'body':body})\",\n \"def scrape_profile(inhandle, outfile, year, month):\\n #Read file\\n html = inhandle.read()\\n soup = BeautifulSoup(html, 'html.parser')\\n\\n #Find main page content\\n content = soup.find('div', {'class':'entry-content'})\\n\\n profile = {}\\n\\n #Fill in known info from URL\\n profile['year_reported'] = year\\n profile['month_reported'] = month\\n\\n #Extract and download images.\\n profile['images'] = [save_image(img['src']) for img in content.findAll('img')]\\n\\n #Get visible text\\n text = content.get_text().strip()\\n\\n #Parse information from text\\n for key in extractors:\\n match = extractors[key].search(text)\\n if match:\\n matchtext = match.group(1).strip()\\n if key in ['justifications','messages']:\\n vals = matchtext.split('\\\\n')\\n else:\\n vals = matchtext\\n profile[key] = vals \\n\\n #Parse annotations\\n content = soup.find('div', {'class':'entry-utility'})\\n profile['tags'] = [node.get_text() for node in content.findAll('a', {'rel':'tag'})]\\n profile['gender'] = 'female' if 'Female profiles' in profile['tags'] else 'male'\\n\\n #Save output\\n json.dump(profile, open(outfile,'w'))\",\n \"def parse_orgentec(response: scrapy.http.Response):\\n job = response.meta['job']\\n\\n yield job\",\n \"def summary(self):\\n raise NotImplementedError\",\n \"def _run_household_year(queue, residents, year, work_seed, **elp_kwargs):\\n try:\\n np.random.seed(work_seed)\\n data_ex_main = DataExchangeCsv()\\n elp = ElectricalLoadProfile(data_ex_main, residents, **elp_kwargs)\\n result = elp.get_rescheduled_profiles(year)\\n queue.put((None, result))\\n return\\n except Exception as e:\\n queue.put((e, traceback.format_exc()))\\n # traceback.print_exc(file=sys.stdout)\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.647423","0.61216635","0.59458005","0.56278527","0.5343209","0.53211635","0.52993035","0.52750313","0.5241437","0.5197236","0.51397026","0.5090858","0.5073519","0.50543827","0.5022594","0.5021494","0.5021494","0.5011691","0.4984092","0.49774346","0.4972862","0.49456128","0.49362305","0.49225333","0.49153215","0.49137247","0.48956123","0.48796397","0.48626456","0.4855084","0.48324683","0.48280868","0.47752514","0.47605035","0.4759379","0.47525033","0.4742001","0.47227043","0.47204256","0.47155294","0.46975967","0.46877012","0.46863732","0.4683702","0.4683394","0.4677627","0.4676879","0.46724954","0.46700355","0.46403223","0.46355063","0.46213773","0.46192428","0.4607446","0.4605583","0.46025944","0.45993456","0.45910177","0.45854962","0.45763198","0.45723912","0.45703945","0.4569905","0.45504096","0.45403183","0.453957","0.45393255","0.45339727","0.45297208","0.4526753","0.4520547","0.45105043","0.45067027","0.4504988","0.44988462","0.4497818","0.4495825","0.44885695","0.44877487","0.44873366","0.4486571","0.448235","0.44812214","0.4477156","0.44764185","0.44745478","0.44699028","0.44637296","0.44614655","0.44611326","0.4457561","0.44574794","0.44539732","0.4452169","0.44454774","0.44302702","0.44300374","0.4427835","0.44251516","0.4423633"],"string":"[\n \"0.647423\",\n \"0.61216635\",\n \"0.59458005\",\n \"0.56278527\",\n \"0.5343209\",\n \"0.53211635\",\n \"0.52993035\",\n \"0.52750313\",\n \"0.5241437\",\n \"0.5197236\",\n \"0.51397026\",\n \"0.5090858\",\n \"0.5073519\",\n \"0.50543827\",\n \"0.5022594\",\n \"0.5021494\",\n \"0.5021494\",\n \"0.5011691\",\n \"0.4984092\",\n \"0.49774346\",\n \"0.4972862\",\n \"0.49456128\",\n \"0.49362305\",\n \"0.49225333\",\n \"0.49153215\",\n \"0.49137247\",\n \"0.48956123\",\n \"0.48796397\",\n \"0.48626456\",\n \"0.4855084\",\n \"0.48324683\",\n \"0.48280868\",\n \"0.47752514\",\n \"0.47605035\",\n \"0.4759379\",\n \"0.47525033\",\n \"0.4742001\",\n \"0.47227043\",\n \"0.47204256\",\n \"0.47155294\",\n \"0.46975967\",\n \"0.46877012\",\n \"0.46863732\",\n \"0.4683702\",\n \"0.4683394\",\n \"0.4677627\",\n \"0.4676879\",\n \"0.46724954\",\n \"0.46700355\",\n \"0.46403223\",\n \"0.46355063\",\n \"0.46213773\",\n \"0.46192428\",\n \"0.4607446\",\n \"0.4605583\",\n \"0.46025944\",\n \"0.45993456\",\n \"0.45910177\",\n \"0.45854962\",\n \"0.45763198\",\n \"0.45723912\",\n \"0.45703945\",\n \"0.4569905\",\n \"0.45504096\",\n \"0.45403183\",\n \"0.453957\",\n \"0.45393255\",\n \"0.45339727\",\n \"0.45297208\",\n \"0.4526753\",\n \"0.4520547\",\n \"0.45105043\",\n \"0.45067027\",\n \"0.4504988\",\n \"0.44988462\",\n \"0.4497818\",\n \"0.4495825\",\n \"0.44885695\",\n \"0.44877487\",\n \"0.44873366\",\n \"0.4486571\",\n \"0.448235\",\n \"0.44812214\",\n \"0.4477156\",\n \"0.44764185\",\n \"0.44745478\",\n \"0.44699028\",\n \"0.44637296\",\n \"0.44614655\",\n \"0.44611326\",\n \"0.4457561\",\n \"0.44574794\",\n \"0.44539732\",\n \"0.4452169\",\n \"0.44454774\",\n \"0.44302702\",\n \"0.44300374\",\n \"0.4427835\",\n \"0.44251516\",\n \"0.4423633\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81081915"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":849,"numItemsPerPage":100,"numTotalItems":95772,"offset":84900,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjAwMzgxNSwianRpIjoiMGRjMmJjNjQtOGYyZi00MzUxLWI0OTctZDE3ZGVkMzc3ZDI2Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYwMDc0MTUsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.sXlb1mDvotWSHC9mqvK9ADGmRgQazLkAerFWHiDgZh3bdTsko-5tvTt034wUfYa6KfkO5MEgzAZ6BtzgmF_yCQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Splits data into train, dev, and test sets and saves these into separate directories. | def split_train_test_dev(self):
for dir_name in (self.config.train_dir, self.config.dev_dir,
self.config.test_dir):
create_dir(dir_name)
self.split_helper(self.config.parsed_train_file_pos, 'pos')
self.split_helper(self.config.parsed_train_file_neg, 'neg') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MakeDataSetFiles(dirname):\n\n\n if not os.path.exists(dirname):\n os.mkdir(dirname)\n if not os.path.exists(os.path.join(dirname, 'train')):\n os.mkdir(os.path.join(dirname, 'train'))\n if not os.path.exists(os.path.join(dirname, 'test')):\n os.mkdir(os.path.join(dirname, 'test'))\n data_train = ... | [
"0.7316146",
"0.71866006",
"0.7114873",
"0.7022753",
"0.68888664",
"0.6882073",
"0.6838386",
"0.6802326",
"0.67861754",
"0.6763696",
"0.6753951",
"0.6730403",
"0.6691835",
"0.6685175",
"0.66725105",
"0.6671246",
"0.6665487",
"0.66565126",
"0.6647751",
"0.660803",
"0.66024566"... | 0.80809546 | 0 |
Yields sequence, sequence length, count of unique tokens from input file. Reads file from beginning after reaching the end. | def train_sample_generator(self, fi):
while True:
line = fi.readline()
if not line:
fi.seek(0)
continue
sequence = np.array(line.split(" "), dtype=np.intp)
yield sequence, sequence.shape[0], np.unique(sequence).shape[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_counts(counts_file):\n try:\n fi = open(counts_file, 'r')\n except IOError:\n sys.stderr.write('ERROR: Cannot open %s.\\n' % counts_file)\n sys.exit(1)\n\n for line in fi:\n fields = line.strip().split(' ')\n yield fields # yields a list of fields",
"def solve... | [
"0.63293844",
"0.6184904",
"0.5920593",
"0.5848726",
"0.58256215",
"0.58110034",
"0.576691",
"0.5766602",
"0.57536906",
"0.57464147",
"0.5743451",
"0.5698512",
"0.56470245",
"0.5639647",
"0.5633243",
"0.5612199",
"0.56089216",
"0.5563795",
"0.55498075",
"0.5525601",
"0.551029... | 0.5979231 | 2 |
Yields sequence, sequence length, and label from input file. | def dev_sample_generator(self, fi):
for line in fi:
line_list = line.split(" ")
label = int(line_list[-1])
sequence = np.array(line_list[:-1], dtype=np.intp)
yield sequence, sequence.shape[0], np.unique(sequence).shape[0], label | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fasta(path):\n label = None\n sequence = None\n with open(path, 'r') as data:\n for line in data:\n line = line.strip()\n if line.startswith('>'):\n if label and sequence:\n yield (label, sequence)\n label = line[1:]\n ... | [
"0.68528914",
"0.6591686",
"0.6564022",
"0.64176744",
"0.6332086",
"0.62952423",
"0.62774473",
"0.620664",
"0.6127451",
"0.6097349",
"0.60575753",
"0.5990235",
"0.5986205",
"0.591602",
"0.5905646",
"0.58811617",
"0.58803195",
"0.5848473",
"0.58155936",
"0.580522",
"0.5799044"... | 0.643483 | 3 |
Yields sequence, sequence length from input file. | def predict_sample_generator(self, fi):
for line in fi:
sequence = np.array(line.split(" "), dtype=np.intp)
yield sequence, sequence.shape[0], np.unique(sequence).shape[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fasta_read_generator(file_handler):\r\n seq = []\r\n name = ''\r\n for line in file_handler:\r\n if line[0] == '>':\r\n sequence = ''.join(seq)\r\n if name: # only yield when we already have all data for the first sequence\r\n yield name, sequence\r\n ... | [
"0.67658085",
"0.6689718",
"0.66688454",
"0.6580756",
"0.65676934",
"0.65368074",
"0.6521221",
"0.65069735",
"0.646596",
"0.645717",
"0.6447958",
"0.6339892",
"0.6260382",
"0.61856395",
"0.6143909",
"0.61225396",
"0.6086098",
"0.6078724",
"0.60098237",
"0.5990917",
"0.5990292... | 0.62304157 | 13 |
Generates train batches by randomly selecting labels from both classes to avoid imbalance. Yields | def train_batch_generator(self):
seq_lengths = np.zeros((self.batch_size), dtype=np.intp)
unique_count = np.zeros((self.batch_size), dtype=np.intp)
fis = (self.config.train_dir + "pos.txt",
self.config.train_dir + "neg.txt")
fi_pos, fi_neg = map(open, fis)
sample_g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sample_train_batch(self):\r\n batch = []\r\n labels =[]\r\n num_groups = self.batch_size // self.batch_k\r\n sampleed_classes = np.random.choice(self.train_class_ids,num_groups,replace=False)\r\n for class_id in sampleed_classes:\r\n img_fname = np.random.choice(se... | [
"0.71604943",
"0.7110667",
"0.68857807",
"0.68060225",
"0.6768268",
"0.6751107",
"0.67276067",
"0.6708528",
"0.658012",
"0.65510166",
"0.65371096",
"0.6465802",
"0.6404252",
"0.63875073",
"0.6372499",
"0.63602597",
"0.63326573",
"0.6307031",
"0.6297274",
"0.6293418",
"0.62921... | 0.0 | -1 |
Generates test batches from all.txt in train/ test/ or dev/ directories where samples are shuffled and labeled. Yields | def test_batch_generator(self, dir_name):
input = np.zeros((self.batch_size, self.max_seq_len,
self.embedding_size))
seq_lengths = np.zeros((self.batch_size), dtype=np.intp)
unique_counts = np.zeros((self.batch_size), dtype=np.intp)
labels = np.zeros((self.batch... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_test_batches(data_dir='/home/yunhan/batchified'):\n # train 3 valid 1\n # Use batch 1 - 53 as train (60%), 54 - 71 as validation (20%), 72 - 89 as test (20%)\n n = 18\n idx = np.random.permutation(n)\n idx = idx + 72\n for i in range(n):\n X = np.load(\"%s/X%d.npy\" % (data_dir, id... | [
"0.7377898",
"0.7097675",
"0.70060813",
"0.66782206",
"0.6657778",
"0.65936774",
"0.6588651",
"0.65786034",
"0.65503895",
"0.6515142",
"0.65122056",
"0.6450401",
"0.64411056",
"0.64382833",
"0.6385059",
"0.6330969",
"0.6320016",
"0.6313816",
"0.6294479",
"0.62892824",
"0.6274... | 0.7086116 | 2 |
Generates test batches from all.txt in test/ or dev/ directories where samples are shuffled and labeled. Yields | def predict_batch_generator(self):
input = np.zeros((self.batch_size, self.max_seq_len,
self.embedding_size))
seq_lengths = np.zeros((self.batch_size), dtype=np.intp)
unique_counts = np.zeros((self.batch_size), dtype=np.intp)
i = 0
fi = open(self.config... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_test_batches(data_dir='/home/yunhan/batchified'):\n # train 3 valid 1\n # Use batch 1 - 53 as train (60%), 54 - 71 as validation (20%), 72 - 89 as test (20%)\n n = 18\n idx = np.random.permutation(n)\n idx = idx + 72\n for i in range(n):\n X = np.load(\"%s/X%d.npy\" % (data_dir, id... | [
"0.7177904",
"0.71596247",
"0.7101582",
"0.7053556",
"0.6844728",
"0.67826915",
"0.66571736",
"0.6609596",
"0.6556099",
"0.65444964",
"0.6511416",
"0.64694655",
"0.6429852",
"0.6417383",
"0.63924825",
"0.6333554",
"0.6333277",
"0.6291732",
"0.62873936",
"0.62851316",
"0.62671... | 0.0 | -1 |
Generates placeholder variables to represent the input tensors. | def add_placeholders(self):
self.input_placeholder = tf.placeholder(
tf.float32,
(None, self.max_seq_len, self.embedding_size),
"input"
)
self.batch_seq_length_placeholder = tf.placeholder(tf.int32, (None, ),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_placeholders(self):\n # \"None\" means the batches may have a variable batch size and length.\n self.x = tf.placeholder(tf.int64, shape=[None, None])",
"def _create_placeholders(self):\n\n\t\twith tf.name_scope(\"input_data\"):\n\t\t\tself.input_words=tf.placeholder(shape=(None,self.look_back),... | [
"0.752378",
"0.75216216",
"0.7436447",
"0.73585546",
"0.7357306",
"0.71614146",
"0.7106802",
"0.70997226",
"0.7085868",
"0.7065236",
"0.70339906",
"0.70315886",
"0.7022586",
"0.69984174",
"0.6973781",
"0.69555634",
"0.6947508",
"0.6937862",
"0.6934914",
"0.6903116",
"0.688508... | 0.6893304 | 20 |
Creates the feed_dict for training the given step. If label_batch is None, then no labels are added to feed_dict | def create_feed_dict(self, inputs_batch, batch_seq_length,
batch_unique_count, labels_batch=None, dropout=1,
lr=None):
feed_dict = dict()
feed_dict[self.input_placeholder] = inputs_batch
feed_dict[self.batch_seq_length_placeholder] = batch_seq_le... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_feed_dict(self, inputs_batch, labels_batch=None):\n ### YOUR CODE HERE\n feed_dict=dict()\n feed_dict[self.input_placeholder]=inputs_batch\n if labels_batch is not None:\n feed_dict[self.labels_placeholder]=labels_batch\n ### END YOUR CODE\n return fe... | [
"0.74259585",
"0.714005",
"0.70168495",
"0.69314265",
"0.68711233",
"0.6858954",
"0.6803167",
"0.67105776",
"0.65097845",
"0.62538755",
"0.6213174",
"0.60656565",
"0.60462654",
"0.60310936",
"0.60170025",
"0.59969974",
"0.5960441",
"0.5938122",
"0.5918129",
"0.590943",
"0.590... | 0.6911674 | 4 |
Adds the core transformation for this model which transforms a batch of input data into a batch of predictions. | def add_prediction_op(self):
pred = tf.get_variable(
name='pred',
shape=(self.batch_size, self.config.n_classes),
initializer=tf.zeros_initializer()
)
return pred | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _transform(self, dataset):\n raise NotImplementedError()",
"def preprocess_transform(self, X: Tensor) -> Tensor:\n for tf in self.values():\n X = tf.preprocess_transform(X)\n return X",
"def transform_batch(self, inputs_batch, target_ids_batch, targets_batch):\n # ext... | [
"0.64923996",
"0.63494116",
"0.6219738",
"0.6142022",
"0.61072344",
"0.60516727",
"0.60433406",
"0.60370857",
"0.6031921",
"0.60278577",
"0.60278577",
"0.60278577",
"0.60278577",
"0.60278577",
"0.60278577",
"0.60278577",
"0.59975016",
"0.59867114",
"0.59849584",
"0.5957172",
... | 0.0 | -1 |
Adds ops for the cross entropy loss to the computational graph. The loss is averaged over all examples in the current minibatch. | def add_loss_op(self, pred):
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=self.labels_placeholder,
logits=pred,
name="loss"
)
loss = tf.reduce_mean(loss)
return loss | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_loss_op(self, pred):\n ### YOUR CODE HERE\n loss = cross_entropy_loss(self.labels_placeholder,pred)\n ### END YOUR CODE\n return loss",
"def loss_op(logits, labels):\n cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, labels, name='xentropy_eval')\n... | [
"0.7201984",
"0.6916799",
"0.6771412",
"0.67244655",
"0.66884035",
"0.66647446",
"0.6586543",
"0.6573803",
"0.6466889",
"0.6453262",
"0.64367396",
"0.6422562",
"0.64079267",
"0.6380531",
"0.63719505",
"0.6367178",
"0.63509816",
"0.63509816",
"0.63411987",
"0.6335173",
"0.6322... | 0.70479894 | 1 |
Creates an optimizer and applies the gradients to all trainable variables. | def add_training_op(self, loss, global_step):
# Calculate and clip gradients
params = tf.trainable_variables()
gradients = tf.gradients(loss, params)
clipped_gradients, _ = tf.clip_by_global_norm(
gradients,
self.config.max_gradient_norm
)
# Opti... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _optimize(self):\n # Retrieve all trainable variables\n train_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\n\n # Compute the gradient (return a pair of variable and their respective gradient)\n grads = self.optimizer.compute_gradients(loss=self.loss, var_list=trai... | [
"0.7602666",
"0.747355",
"0.73952514",
"0.7230327",
"0.7107085",
"0.7087083",
"0.7053867",
"0.69550973",
"0.68434846",
"0.68424076",
"0.68309206",
"0.6783665",
"0.6721693",
"0.67049515",
"0.66924775",
"0.6641975",
"0.6584569",
"0.6563159",
"0.65391326",
"0.6534081",
"0.652209... | 0.6325796 | 36 |
Creates f1 evaluator of classifier | def add_eval_op(self, pred):
f1_score, metric_update_op = tf.contrib.metrics.f1_score(
self.labels_placeholder,
tf.slice(tf.nn.softmax(self.pred), [0, 1], [-1, 1]),
name='f1_score'
)
return f1_score, metric_update_op | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate(classifier, normalizer, transformer, x, y):\n if normalizer is not None:\n x = normalizer.transform(x)\n\n if transformer is not None:\n x = transformer.transform(x)\n\n preds = classifier.predict(x)\n acc = np.sum(y == preds) / len(y)\n f1 = f1_score(y, preds, average='ma... | [
"0.67267",
"0.64495933",
"0.6437079",
"0.6279391",
"0.6247813",
"0.6081725",
"0.60645205",
"0.6030185",
"0.6019579",
"0.59954196",
"0.59708714",
"0.5969949",
"0.5936133",
"0.5895928",
"0.58899134",
"0.5847119",
"0.582209",
"0.580883",
"0.58036584",
"0.5793276",
"0.5783115",
... | 0.57672435 | 21 |
Evaluates model on dev dataset and returns f1_score and predicted labels | def evaluate(self, sess, data_gen):
pred_labels = np.array([], dtype=np.intp)
labels = np.array([], dtype=np.intp)
for inputs, seq_length, batch_labels in data_gen:
feed_dict = self.create_feed_dict(inputs, seq_length, batch_labels,
self.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate(model, eval_data, num_labels): \n # Turn on the evaluation state to ignore dropouts\n model.eval()\n results = [predict(model, x) for x, y in eval_data]\n f1_score, accuracy = get_metrics(np.array([y for x, y in eval_data]), results, num_labels)\n return f1_score, accuracy",
"def e... | [
"0.7339661",
"0.72443473",
"0.72425336",
"0.72048825",
"0.7053383",
"0.6888309",
"0.68621725",
"0.68598145",
"0.6721542",
"0.6717622",
"0.6713856",
"0.6701493",
"0.67008084",
"0.6699391",
"0.6676458",
"0.66709936",
"0.66066396",
"0.6598177",
"0.6550447",
"0.65374744",
"0.6524... | 0.72352886 | 3 |
Predicts labels on unlabeled test set. | def predict(self, sess, data_gen):
pred_labels = np.array([], dtype=np.intp)
for inputs, seq_length in data_gen:
feed_dict = self.create_feed_dict(inputs, seq_length,
dropout=self.config.dropout)
pred = sess.run(self.pred, feed_dict)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def predict_labels(model, x_test):\n \n pred = model.predict(x_test)\n #pred_labels = model.predict_classes(x_test) # depricated\n pred_labels = np.argmax(model.predict(x_test), axis=-1)\n \n return pred, pred_labels",
"def predict(self, test_set, test_labels):\n\n with tf.Session() a... | [
"0.7749779",
"0.75957185",
"0.75269455",
"0.7390682",
"0.7354112",
"0.7269686",
"0.71631753",
"0.7143413",
"0.70833015",
"0.70598036",
"0.6953809",
"0.6916104",
"0.6856251",
"0.6856251",
"0.68497777",
"0.6846053",
"0.68418294",
"0.6825785",
"0.68203235",
"0.6814053",
"0.67928... | 0.0 | -1 |
Saves best model during train. | def save_best(self, sess, score):
if score > self.best_score:
self.best_score = score
path_prefix = self.saver.save(sess, self.config.save_path,
self.global_step)
self.best_model_path = path_prefix
return path_prefix
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_model(self, is_best, state, epoch):\n path = os.path.join(self.logpath_models, 'model-%d.pth.tar' % epoch)\n torch.save(state, path)\n if is_best:\n shutil.copyfile(path, path + 'model_best.pth.tar')",
"def save_model(self, epoch):\n\n # Reload weights form the che... | [
"0.77143276",
"0.76434976",
"0.76278317",
"0.76071495",
"0.7401447",
"0.73491347",
"0.7321327",
"0.73050785",
"0.725297",
"0.7215255",
"0.72109216",
"0.72031134",
"0.71639895",
"0.7157207",
"0.7136594",
"0.71315217",
"0.7115645",
"0.7108602",
"0.71053714",
"0.71004444",
"0.70... | 0.6884816 | 49 |
Generates placeholder variables to represent the input tensors. | def add_placeholders(self):
self.input_placeholder = tf.placeholder(
tf.float32,
(None, self.max_seq_len, self.embedding_size),
"input"
)
self.batch_seq_length_placeholder = tf.placeholder(tf.int32, (None, ),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_placeholders(self):\n # \"None\" means the batches may have a variable batch size and length.\n self.x = tf.placeholder(tf.int64, shape=[None, None])",
"def _create_placeholders(self):\n\n\t\twith tf.name_scope(\"input_data\"):\n\t\t\tself.input_words=tf.placeholder(shape=(None,self.look_back),... | [
"0.7523517",
"0.7521597",
"0.74357694",
"0.73583406",
"0.73567647",
"0.71616036",
"0.7107284",
"0.7100283",
"0.70864344",
"0.70652926",
"0.7033381",
"0.70321536",
"0.7022589",
"0.6998019",
"0.697382",
"0.69554234",
"0.6947222",
"0.6937978",
"0.69340724",
"0.68936676",
"0.6885... | 0.6903461 | 19 |
Creates an optimizer and applies the gradients to all trainable variables. | def add_training_op(self, loss, global_step):
# Calculate and clip gradients
params = tf.trainable_variables()
gradients = tf.gradients(loss, params)
clipped_gradients, _ = tf.clip_by_global_norm(
gradients,
self.config.max_gradient_norm
)
# Opti... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _optimize(self):\n # Retrieve all trainable variables\n train_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\n\n # Compute the gradient (return a pair of variable and their respective gradient)\n grads = self.optimizer.compute_gradients(loss=self.loss, var_list=trai... | [
"0.76019984",
"0.74737346",
"0.73964405",
"0.7229506",
"0.710572",
"0.7086818",
"0.70538044",
"0.69551605",
"0.68436944",
"0.6842918",
"0.6831712",
"0.6783377",
"0.67218363",
"0.67048717",
"0.66930056",
"0.6641727",
"0.65861505",
"0.6564599",
"0.6539085",
"0.65337276",
"0.652... | 0.63445395 | 32 |
Adds the core transformation for this model which transforms a batch of input data into a batch of predictions. | def add_prediction_op(self):
#x_dropout = tf.keras.layers.SpatialDropout1D(0.4).apply(self.input_placeholder)
layer_1_size = 150
layer_2_size = 25
num_aux_feats = 4
rnn_cell_layer_1_fwd = tf.nn.rnn_cell.GRUCell(
layer_1_size,
activation='relu',
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _transform(self, dataset):\n raise NotImplementedError()",
"def preprocess_transform(self, X: Tensor) -> Tensor:\n for tf in self.values():\n X = tf.preprocess_transform(X)\n return X",
"def transform_batch(self, inputs_batch, target_ids_batch, targets_batch):\n # ext... | [
"0.64929265",
"0.63505316",
"0.621995",
"0.61421704",
"0.61059344",
"0.6052137",
"0.6044346",
"0.60373497",
"0.60322785",
"0.6027754",
"0.6027754",
"0.6027754",
"0.6027754",
"0.6027754",
"0.6027754",
"0.6027754",
"0.5998109",
"0.5987142",
"0.5984651",
"0.59563875",
"0.595031"... | 0.0 | -1 |
Returns database cursor to interact with PostgreSQL database. | def yield_db_cursor(connect_params=DB_PARAMS, cursor_type=DictCursor):
with psycopg2.connect(**connect_params) as con:
with con.cursor(cursor_factory=cursor_type) as cur:
yield cur | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cursor():\n dbh = handle()\n return dbh.cursor()",
"def managed_cursor(self, cursor_factory=None):\n\n self.conn_url = (f'postgresql://{self.user}:{self.password}@{self.host}:{self.port}/{self.db}')\n self.conn = psycopg2.connect(self.conn_url)\n self.conn.autocommit = True\n ... | [
"0.77635354",
"0.75554824",
"0.74691576",
"0.73980325",
"0.7387887",
"0.73217696",
"0.7303565",
"0.72945076",
"0.7289811",
"0.72576785",
"0.7194207",
"0.7159727",
"0.71519923",
"0.71519923",
"0.7132154",
"0.70944715",
"0.70845306",
"0.7071748",
"0.70472896",
"0.6998076",
"0.6... | 0.71329457 | 14 |
convert MySQL timestamp to datetime | def convert_timestamp(ts):
format = '%Y-%m-%d %H:%M:%S'
return datetime.strptime(ts, format) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mysql_timestamp_converter(timestamp):\n if timestamp[4] == '-':\n return datetime_or_None(timestamp)\n timestamp += \"0\"*(14-len(timestamp)) # padding\n year, month, day, hour, minute, second = \\\n int(timestamp[:4]), int(timestamp[4:6]), int(timestamp[6:8]), \\\n int(timestamp[... | [
"0.77829003",
"0.7010029",
"0.6845619",
"0.67591673",
"0.6717474",
"0.67072576",
"0.6706572",
"0.6541347",
"0.640038",
"0.63838106",
"0.63502055",
"0.6260463",
"0.6218308",
"0.6216306",
"0.6183532",
"0.6134314",
"0.61161196",
"0.61146975",
"0.61005396",
"0.6095403",
"0.608007... | 0.6650869 | 7 |
If active is true, only return active potholes. If active is false, return all potholes. If date is set, then return potholes with ledger information up to the specified date | def get_geojson_potholes(active=True, date=None):
potholes = VwPothole.objects.all() if date is None \
else VwPothole.objects.raw(vw_pothole_by_date, {'datetime': '{} 23:59:59'.format(date)})
pothole_features = [Feature(
geometry=Point((float(pothole.lon), float(pothole.lat)), precision=8),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def active (self, after = None, before = None):\n\n active = ActivityList()\n active.list = [actor for actor in self.list\n if (after == None or\n actor[\"period\"].end >= after) and\n (before == None or\n ... | [
"0.50194657",
"0.49823934",
"0.4923843",
"0.48555967",
"0.48555368",
"0.47429028",
"0.47279143",
"0.46891028",
"0.4629333",
"0.46156973",
"0.4614425",
"0.45901078",
"0.45899412",
"0.45639297",
"0.45589805",
"0.45522225",
"0.45316374",
"0.4529043",
"0.4516528",
"0.45120677",
"... | 0.6141142 | 0 |
Can construct a package from a S3 Key | def test_list(self):
key = Key(self.bucket)
name, version, filename = 'mypkg', '1.2', 'pkg.tar.gz'
key.key = name + '/' + filename
key.set_metadata('name', name)
key.set_metadata('version', version)
key.set_contents_from_string('foobar')
package = list(self.storag... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_key():\n conn = boto.connect_s3()\n bucket = conn.create_bucket(settings.MESSY_BUCKET)\n key = Key(bucket)\n key.key = settings.MESSY_KEY\n return key",
"def make_s3(sitename):\n return s3.S3(sitename)",
"def from_s3(cls, bucket_name, mos_file_key):\n xml = s3.get_file_con... | [
"0.6773442",
"0.6403095",
"0.6348497",
"0.61896443",
"0.6135538",
"0.6134094",
"0.6044005",
"0.6023191",
"0.5896879",
"0.5869504",
"0.58617014",
"0.58217484",
"0.57936394",
"0.57654315",
"0.5761166",
"0.5751859",
"0.5741993",
"0.57388633",
"0.573869",
"0.57145077",
"0.5685236... | 0.54021513 | 67 |
Test that list works on old keys with no metadata | def test_list_no_metadata(self):
key = Key(self.bucket)
name, version = 'mypkg', '1.2'
filename = '%s-%s.tar.gz' % (name, version)
key.key = name + '/' + filename
key.set_contents_from_string('foobar')
package = list(self.storage.list(Package))[0]
self.assertEqual... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_ls(self):\n fake_key = namedtuple('Key', ['name', 'last_modified', 'size'])\n num_items = 10\n items = []\n for i in range(num_items):\n items.append({'name': 'item_%d' % i, 'last_modified': 'fake_date', 'size': 100})\n\n backend = self.test_init_valid()\n\n ... | [
"0.7025229",
"0.63540006",
"0.63520336",
"0.62515557",
"0.6204083",
"0.61587137",
"0.61037785",
"0.60956293",
"0.60297567",
"0.6005905",
"0.600304",
"0.59855175",
"0.5985121",
"0.59804595",
"0.5962854",
"0.59540826",
"0.59345037",
"0.58855706",
"0.5871708",
"0.5871708",
"0.58... | 0.66902786 | 1 |
Mock s3 and test package url generation | def test_get_url(self):
package = make_package()
response = self.storage.download_response(package)
parts = urlparse(response.location)
self.assertEqual(parts.scheme, 'https')
self.assertEqual(parts.netloc, 'mybucket.s3.amazonaws.com')
self.assertEqual(parts.path, '/' + ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mock_s3_fixture():\n with mock_s3():\n yield",
"def mock_s3_client():\n with mock_s3():\n yield",
"def mock_s3_bucket():\n with moto.mock_s3():\n bucket_name = \"mock-bucket\"\n my_config = Config(region_name=\"us-east-1\")\n s3_client = boto3.client(\"s3\", conf... | [
"0.7160128",
"0.7098021",
"0.70244277",
"0.70041245",
"0.68894506",
"0.68372697",
"0.6746593",
"0.6678674",
"0.6602817",
"0.6598",
"0.65938234",
"0.6588515",
"0.6581509",
"0.65780693",
"0.65651834",
"0.6470033",
"0.6459814",
"0.64573693",
"0.64334434",
"0.64143026",
"0.641260... | 0.70476264 | 2 |
delete() should remove package from storage | def test_delete(self):
package = make_package()
self.storage.upload(package, StringIO())
self.storage.delete(package)
keys = list(self.bucket.list())
self.assertEqual(len(keys), 0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete(self):\n self.package = None",
"def test_delete(self):\n package = make_package()\n path = self.storage.get_path(package)\n os.makedirs(os.path.dirname(path))\n with open(path, 'w') as ofile:\n ofile.write('foobar')\n self.storage.delete(package)\n ... | [
"0.79596126",
"0.79171205",
"0.74405926",
"0.74049443",
"0.73858505",
"0.735797",
"0.72527796",
"0.7154662",
"0.703437",
"0.6974038",
"0.6930558",
"0.68803126",
"0.6863968",
"0.6856768",
"0.6781866",
"0.67654777",
"0.67367566",
"0.6721418",
"0.6714018",
"0.66926026",
"0.66661... | 0.75034857 | 2 |
Uploading package sets metadata and sends to S3 | def test_upload(self):
package = make_package()
datastr = 'foobar'
data = StringIO(datastr)
self.storage.upload(package, data)
key = list(self.bucket.list())[0]
self.assertEqual(key.get_contents_as_string(), datastr)
self.assertEqual(key.get_metadata('name'), pack... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upload_package(self, filename=None):\n logger.info(\"Uploading the package to S3\")\n s3f = S3FunctionUploader(self.function_config['Code']['S3Bucket'])\n self.s3_filename = path.join(\n self.function_config['Code']['S3KeyPath'],\n path.basename(filename or self.local... | [
"0.7508901",
"0.71368754",
"0.69945604",
"0.6988492",
"0.69506544",
"0.66899306",
"0.6670776",
"0.65964633",
"0.6553598",
"0.65402174",
"0.65022314",
"0.64736867",
"0.6442402",
"0.63931197",
"0.6360422",
"0.63000345",
"0.6274222",
"0.62663007",
"0.626292",
"0.6248924",
"0.623... | 0.70516366 | 2 |
If prepend_hash = True, attach a hash to the file path | def test_upload_prepend_hash(self):
self.storage.prepend_hash = True
package = make_package()
data = StringIO()
self.storage.upload(package, data)
key = list(self.bucket.list())[0]
pattern = r'^[0-9a-f]{4}/%s/%s$' % (re.escape(package.name),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_hash(path):\n if re.search(r\"^/.+\", path):\n path = path[1:]\n\n # If a story, fix the path.\n is_story = False\n original_path = path\n if re.search(r\"^\\d{4}\\-\\d{2}\\-\\d{2}\", path):\n path = \"static/stories/%s.json\" % path\n is_story = True\n\n blocksize = ... | [
"0.6296029",
"0.61588377",
"0.6156242",
"0.59895957",
"0.5908115",
"0.5859219",
"0.5853377",
"0.57811344",
"0.5710734",
"0.56959283",
"0.5666711",
"0.566154",
"0.5638332",
"0.5616522",
"0.5597391",
"0.55963844",
"0.55830014",
"0.5578103",
"0.55753094",
"0.5553651",
"0.5540363... | 0.6148258 | 3 |
If S3 bucket doesn't exist, create it | def test_create_bucket(self, boto_mock):
conn = boto_mock.s3.connect_to_region()
boto_mock.exception.S3ResponseError = boto.exception.S3ResponseError
def raise_not_found(*_, **__):
""" Raise a 'bucket not found' exception """
e = boto.exception.S3ResponseError(400, 'miss... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def s3_create_bucket(self):\n self.conn.create_bucket(DEFAULT_BUCKET_NAME)",
"def _create_s3_bucket_if_not_exist(self, prefix):\n account = self.boto_session.client(\"sts\").get_caller_identity()[\"Account\"]\n region = self.boto_session.region_name\n s3_bucket_name = \"{}-{}-{}\".format(... | [
"0.8412326",
"0.8206333",
"0.81882066",
"0.7882016",
"0.77453667",
"0.768874",
"0.7617038",
"0.7584526",
"0.7462588",
"0.74349284",
"0.73654705",
"0.73503596",
"0.7315933",
"0.72065103",
"0.71941084",
"0.71765643",
"0.7155236",
"0.7097696",
"0.70871484",
"0.7078128",
"0.70769... | 0.73255473 | 12 |
Raise a 'bucket not found' exception | def raise_not_found(*_, **__):
e = boto.exception.S3ResponseError(400, 'missing')
e.error_code = 'NoSuchBucket'
raise e | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_empty(empty_bucket): # pylint: disable=redefined-outer-name\n with pytest.raises(KeyError):\n empty_bucket.get(\"key 1\")",
"def test_get_bucket(self):\n pass",
"def test_api_get_bucketlist_by_id_not_exist(self):\n res = self.client().get(f\"/bucketlist/99\")\n self... | [
"0.7205869",
"0.71065015",
"0.7096352",
"0.7038852",
"0.6997341",
"0.6958753",
"0.6955459",
"0.6913117",
"0.6909686",
"0.6773827",
"0.6682321",
"0.6665191",
"0.6648655",
"0.6552224",
"0.6528791",
"0.64729685",
"0.6464192",
"0.6383199",
"0.63815653",
"0.63114816",
"0.62645215"... | 0.8030355 | 0 |
Mock s3 and test package url generation | def test_get_url(self):
package = make_package(version="1.1+g12345")
response = self.storage.download_response(package)
parts = urlparse(response.location)
self.assertEqual(parts.scheme, 'https')
self.assertEqual(parts.netloc, 'abcdef.cloudfront.net')
self.assertEqual(pa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mock_s3_fixture():\n with mock_s3():\n yield",
"def mock_s3_client():\n with mock_s3():\n yield",
"def test_get_url(self):\n package = make_package()\n response = self.storage.download_response(package)\n\n parts = urlparse(response.location)\n self.assertEqu... | [
"0.7160128",
"0.7098021",
"0.70476264",
"0.70244277",
"0.70041245",
"0.68894506",
"0.68372697",
"0.6746593",
"0.6678674",
"0.6602817",
"0.6598",
"0.65938234",
"0.6588515",
"0.6581509",
"0.65780693",
"0.65651834",
"0.6470033",
"0.6459814",
"0.64573693",
"0.64334434",
"0.641430... | 0.616857 | 35 |
Uploading package saves file | def test_upload(self):
package = make_package()
datastr = 'foobar'
data = StringIO(datastr)
self.storage.upload(package, data)
filename = self.storage.get_path(package)
self.assertTrue(os.path.exists(filename))
with open(filename, 'r') as ifile:
self.a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upload_package(self, __contents):\n raise NotImplementedError",
"def upload_file(self, file_path, file_name, output_path):",
"def upload(self, filename, file_path):\n return",
"def store(self, filename):",
"def upload_package(self, filename=None):\n logger.info(\"Uploading the pack... | [
"0.7098982",
"0.70311284",
"0.673023",
"0.64792013",
"0.6445228",
"0.63774496",
"0.6279477",
"0.6275277",
"0.62648404",
"0.62244755",
"0.6175778",
"0.61720103",
"0.6160675",
"0.6138877",
"0.61355174",
"0.6126883",
"0.60978365",
"0.6096455",
"0.6072449",
"0.6071755",
"0.607072... | 0.6412353 | 5 |
Can iterate over uploaded packages | def test_list(self):
package = make_package()
path = self.storage.get_path(package)
os.makedirs(os.path.dirname(path))
with open(path, 'w') as ofile:
ofile.write('foobar')
pkg = list(self.storage.list(Package))[0]
self.assertEquals(pkg.name, package.name)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def iter_upload(\n self,\n dandiset: RemoteDandiset,\n metadata: dict[str, Any],\n jobs: Optional[int] = None,\n replacing: Optional[RemoteAsset] = None,\n ) -> Iterator[dict]:\n ...",
"def uploadPackages(self, directory):\n files_to_upload_dict = {}\n f... | [
"0.661685",
"0.6494102",
"0.60327435",
"0.6024599",
"0.5999073",
"0.5940509",
"0.5845385",
"0.5839594",
"0.5809949",
"0.5790439",
"0.5780649",
"0.5731289",
"0.57043016",
"0.56614417",
"0.5652788",
"0.5590161",
"0.55652773",
"0.55527717",
"0.55380183",
"0.55373126",
"0.5513332... | 0.5469933 | 23 |
delete() should remove package from storage | def test_delete(self):
package = make_package()
path = self.storage.get_path(package)
os.makedirs(os.path.dirname(path))
with open(path, 'w') as ofile:
ofile.write('foobar')
self.storage.delete(package)
self.assertFalse(os.path.exists(path)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete(self):\n self.package = None",
"def test_delete(self):\n package = make_package()\n self.storage.upload(package, StringIO())\n self.storage.delete(package)\n keys = list(self.bucket.list())\n self.assertEqual(len(keys), 0)",
"def test_delete_package(self):\n... | [
"0.79596126",
"0.75034857",
"0.74405926",
"0.74049443",
"0.73858505",
"0.735797",
"0.72527796",
"0.7154662",
"0.703437",
"0.6974038",
"0.6930558",
"0.68803126",
"0.6863968",
"0.6856768",
"0.6781866",
"0.67654777",
"0.67367566",
"0.6721418",
"0.6714018",
"0.66926026",
"0.66661... | 0.79171205 | 1 |
configure() will create the package dir if it doesn't exist | def test_create_package_dir(self):
tempdir = tempfile.mkdtemp()
os.rmdir(tempdir)
settings = {
'storage.dir': tempdir,
}
FileStorage.configure(settings)
try:
self.assertTrue(os.path.exists(tempdir))
finally:
os.rmdir(tempdir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_package_dir(self):\n\n recursive = True if self.format == 'src' else False\n return self.create_dir(self.packageDir, recursive=recursive)",
"def mkconfig():\n basedir = os.path.join(os.path.expanduser('~'), '.strikepackage')\n\n # Try to populate dirs\n defaultdirs = [os.path.j... | [
"0.67424464",
"0.6623338",
"0.66134506",
"0.6508852",
"0.6499385",
"0.63470924",
"0.6320616",
"0.6299468",
"0.62711537",
"0.62512326",
"0.62406296",
"0.6187436",
"0.6186941",
"0.61614126",
"0.61340195",
"0.6113963",
"0.6108494",
"0.60833925",
"0.60372895",
"0.6028414",
"0.600... | 0.6465807 | 5 |
Adds a directory to sys.path and processes its pth files. | def addsitedir(sitedir,known_paths=None):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_path():\n\timport sys\n\tsys.path.append(directory_root())",
"def add_directory(self, directory, prepend=False):\r\n if prepend:\r\n self.directories.insert(0, os.path.normpath(directory))\r\n else:\r\n self.directories.append(os.path.normpath(directory))",
"def m... | [
"0.6933244",
"0.6737644",
"0.67238295",
"0.6666068",
"0.652336",
"0.652336",
"0.6474273",
"0.645103",
"0.6360454",
"0.635467",
"0.6256308",
"0.6189984",
"0.61317366",
"0.61105937",
"0.60868436",
"0.60415703",
"0.6032115",
"0.6032115",
"0.5943574",
"0.59285384",
"0.5898018",
... | 0.5466277 | 38 |
Returns a list containing all global sitepackages directories (and possibly sitepython). | def getsitepackages():
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_sitepackage_dirs():\n if 'getsitepackages' in dir(site):\n return site.getsitepackages()\n else:\n # workaround for https://github.com/pypa/virtualenv/issues/355\n return sys.path",
"def get_site_packages():\n # Another hack...\n # Relies on the fact that os.py is in the ... | [
"0.8410711",
"0.8401981",
"0.7826747",
"0.7820178",
"0.7389793",
"0.6985517",
"0.68044204",
"0.677929",
"0.677861",
"0.66732484",
"0.6565434",
"0.6558092",
"0.6371193",
"0.6354965",
"0.63515717",
"0.63431376",
"0.63176894",
"0.63109446",
"0.6305077",
"0.6305077",
"0.6305077",... | 0.77451956 | 4 |
Returns the "user base" directory path. The "user base" directory can be used to store data. If the global variable ``USER_BASE`` is not initialized yet, this function will also set it. | def getuserbase():
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_user_home(self):\n return os.environ['HOME']",
"def get_user_home(self):\n return os.environ['HOME']",
"def user_home_path(self):\n return path.join(env.user_home, self._user_home_path)",
"def getUserDir() -> str:\n\n if os.name == \"nt\": # Windows system, try to return docu... | [
"0.72320074",
"0.72320074",
"0.7169158",
"0.71514034",
"0.7037741",
"0.6934056",
"0.691502",
"0.6901557",
"0.6891382",
"0.6862563",
"0.68151444",
"0.6769605",
"0.6735248",
"0.66909224",
"0.6690817",
"0.65635455",
"0.65374714",
"0.6530841",
"0.65104234",
"0.6503841",
"0.649601... | 0.79859984 | 0 |
Returns the userspecific sitepackages directory path. If the global variable ``USER_SITE`` is not initialized yet, this function will also set it. | def getusersitepackages():
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def path(self):\n installed_packages_folder_path = site.getsitepackages()[0]\n return f'{installed_packages_folder_path}/{SITE_PACKAGES_FOLDER_NAME}'",
"def user_site_packages() -> str:\n if os.name == 'nt':\n return os.path.join(user_plugin_dir(), 'Lib', 'site-packages')\n\n python_di... | [
"0.727083",
"0.70846105",
"0.6716054",
"0.6541481",
"0.6492487",
"0.6406417",
"0.6365474",
"0.6347431",
"0.6347431",
"0.62456554",
"0.62294674",
"0.6225628",
"0.6074075",
"0.602471",
"0.6020545",
"0.5998052",
"0.59796757",
"0.59656936",
"0.59631383",
"0.5927635",
"0.5906016",... | 0.5686725 | 41 |
Read API key from /data/credential.txt | def read_key():
path = os.path.join(os.path.dirname(__file__), 'data')
f = open(os.path.join(path, 'credential.txt'), 'r')
key = f.read()
f.close()
return key | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_api_key ():\n PROJECT_PATH = os.path.abspath(os.path.dirname(__name__))\n key_file = open(PROJECT_PATH + \"/key_api.txt\", \"r\")\n return (key_file.read()).rstrip('\\n')",
"def read_api_key():\n script_path = os.path.dirname(os.path.realpath(__file__)) \n config = open(script_path + '/... | [
"0.77033496",
"0.76855683",
"0.75721073",
"0.75271016",
"0.7320017",
"0.72772956",
"0.72321165",
"0.7134715",
"0.71150076",
"0.7113407",
"0.69248843",
"0.68938637",
"0.6853688",
"0.67965347",
"0.675196",
"0.67040294",
"0.6643031",
"0.6438197",
"0.6428389",
"0.6417984",
"0.634... | 0.8547963 | 0 |
get champion name by its id | def get_champion_name(champion_id, api_key=read_key(), region='na'):
response = urllib2.urlopen('https://global.api.pvp.net/api/lol/static-data/'+region+'/v1.2/champion/' +
str(champion_id)+'?api_key=' + api_key)
champion = json.load(response)
return champion['name'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_name(self, id):\n\t\treturn self.name_by_index[id]",
"def champion_key_from_id(champion_id):\n return champions[\"data\"][str(champion_id)][\"key\"]",
"def getPlayerCardName(self, playerid):\n with open('./data/players_database.csv', 'r', encoding=\"utf8\") as read_obj:\n csv_reade... | [
"0.70235884",
"0.701553",
"0.66856205",
"0.6443642",
"0.6425609",
"0.6255943",
"0.621068",
"0.6080701",
"0.6069413",
"0.60660994",
"0.60368574",
"0.60175383",
"0.5974954",
"0.5973603",
"0.59718186",
"0.5946285",
"0.59321064",
"0.5921488",
"0.5900459",
"0.5856542",
"0.5848437"... | 0.81229717 | 0 |
Create new container, optionally preset with given items. | def __init__(self, items=None):
if items is None:
self.items = []
else:
self.items = items | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def createContainer(tag, data={}): #@NoSelf",
"def _create_stack_item(container='gl-stack', children=None, viewers=None):\n children = [] if children is None else children\n viewers = [] if viewers is None else viewers\n\n return {\n 'id': str(uuid.uuid4()),\n 'contain... | [
"0.6197168",
"0.5949519",
"0.5759078",
"0.5747322",
"0.5676962",
"0.56560975",
"0.5639722",
"0.56048506",
"0.55990714",
"0.5537253",
"0.5523977",
"0.5512537",
"0.551178",
"0.55078644",
"0.54995126",
"0.54995126",
"0.54995126",
"0.54995126",
"0.54995126",
"0.54977703",
"0.5477... | 0.51043123 | 42 |
Add a new item to the container data. | def append(self, item):
self.items.append(item) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def push(self, Item):\n self.data_container.insert(0, Item)",
"def add_item(self, item):\n self.items.append(item)",
"def add(self, item):\n self.contents.append(item)",
"def push(self, item):\n self._data.append(item)",
"def push(self, item: Any) -> None:\n self._data.ap... | [
"0.8293996",
"0.79781246",
"0.79551023",
"0.7854814",
"0.7808611",
"0.7806071",
"0.77346116",
"0.7652134",
"0.7637927",
"0.756785",
"0.7530717",
"0.7530717",
"0.7530717",
"0.7530717",
"0.7530717",
"0.75287896",
"0.7527001",
"0.7518286",
"0.7480627",
"0.74373317",
"0.74295294"... | 0.7446481 | 19 |
Allow support of the [] operator. | def __getitem__(self, idx):
return self.items[idx] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __getitem__ (self, index):\n pass",
"def __getitem__(self):\n pass",
"def __getitem__(self, index):\n raise NotImplementedError",
"def __getitem__(self, index):\n pass",
"def __getitem__(self, index):\n pass",
"def __getitem__(self, idx):\n pass",
"def __getitem__(... | [
"0.7602952",
"0.7474964",
"0.74716824",
"0.7461197",
"0.7461197",
"0.74117994",
"0.74117994",
"0.72971886",
"0.72691345",
"0.72691345",
"0.72138184",
"0.7195102",
"0.7188712",
"0.7188712",
"0.7169328",
"0.71553564",
"0.7142823",
"0.71186095",
"0.7032734",
"0.7000797",
"0.6989... | 0.66228575 | 75 |
Number of items in this container. | def __len__(self):
return len(self.items) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def items_num(self):\n return len(self.items)",
"def items_count(self):\n return len(self.items)",
"def items_num(self):\n\t\treturn len(self.items)",
"def items_num(self):\n\t\treturn len(self.items)",
"def size(self) -> int:\n return self.num_items",
"def get_num_items(self):\r\n ... | [
"0.8741414",
"0.870422",
"0.8616444",
"0.8616444",
"0.8564642",
"0.8551799",
"0.84417975",
"0.8433523",
"0.83744204",
"0.8125933",
"0.81075835",
"0.8097785",
"0.8085924",
"0.8085924",
"0.8085924",
"0.8085924",
"0.8085924",
"0.80436784",
"0.8008414",
"0.8006885",
"0.79698616",... | 0.7501497 | 49 |
Create a new Token representation. | def __init__(self, kind, value=None, neg_kind=False, neg_value=False):
self._kind = kind
self._value = value
self.neg_kind = neg_kind
self.neg_value = neg_value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_token(self, token_id, data):\n raise exception.NotImplemented() # pragma: no cover",
"def token(self, id):\r\n return Token(self, id)",
"async def create_token(self, *args, **kwargs) -> OAuth2Token:\n token = await super().create_token(*args, **kwargs)\n # NOTE: Save dat... | [
"0.6957312",
"0.69491285",
"0.68020576",
"0.67987514",
"0.67915785",
"0.67016095",
"0.6622861",
"0.6608519",
"0.64910173",
"0.64553404",
"0.6414626",
"0.63857186",
"0.6378225",
"0.63423055",
"0.6304656",
"0.6271824",
"0.62174344",
"0.6169603",
"0.6169603",
"0.6169603",
"0.616... | 0.0 | -1 |
Get the kind of this token. | def kind(self):
return self._kind | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def kind(self):\r\n return TokenKind.from_value(conf.lib.clang_getTokenKind(self))",
"def token_type(self) -> str:\n return self._token_type",
"def token_type(self) -> str:\n return self._token_type",
"def token_type(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self,... | [
"0.7913713",
"0.7874426",
"0.7874426",
"0.77519107",
"0.7514257",
"0.7514257",
"0.7482885",
"0.7372285",
"0.7361082",
"0.7339658",
"0.73053753",
"0.73053753",
"0.73053753",
"0.73053753",
"0.73053753",
"0.73053753",
"0.73053753",
"0.73053753",
"0.73053753",
"0.73053753",
"0.73... | 0.7303887 | 36 |
Get the value of this token. | def value(self):
return self._value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getValue(self):\n \n return self._value",
"def get(self):\n return self._value",
"def get_value(self):\n return self._value",
"def get_value(self):\n return self._value",
"def get_value(self):\n return self._value",
"def get_value(self):\n return s... | [
"0.7911703",
"0.78716505",
"0.7860829",
"0.7860829",
"0.7860829",
"0.7810166",
"0.7810166",
"0.7808797",
"0.7806703",
"0.7791883",
"0.7758687",
"0.7758687",
"0.7758687",
"0.775261",
"0.7745407",
"0.7717471",
"0.77137625",
"0.7697193",
"0.76841635",
"0.76841635",
"0.76841635",... | 0.75800484 | 48 |
Compare two tokens (used for matching). | def __eq__(self, other):
if not isinstance(other.kind, list):
k0 = [other.kind]
else:
k0 = other.kind
if not isinstance(other.value, list):
v0 = [other.value]
else:
v0 = other.value
if not isinstance(self.kind, list):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _compare_unordered(self, other):\n self_token_set = set(self.tokens)\n other_token_set = set(other.tokens)\n\n if len(self_token_set.intersection(other_token_set)) == 0:\n return MatchResult(\"NO\", 0.0)\n\n if self.address_type != other.address_type:\n return ... | [
"0.7164239",
"0.70105475",
"0.69408226",
"0.6940618",
"0.6847735",
"0.67941594",
"0.659077",
"0.6555441",
"0.6490086",
"0.6489438",
"0.64550525",
"0.63669354",
"0.63249445",
"0.6286866",
"0.62383115",
"0.6128456",
"0.60696906",
"0.60504436",
"0.6007138",
"0.59917057",
"0.5962... | 0.0 | -1 |
Represent a consumer as a string. | def __repr__(self):
param = ""
action = None
if isinstance(self.items, list):
for i in self.items:
if len(param) > 0:
param += ", "
param += i.__repr__()
if self.action is not None:
action = self.action.__name... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self) -> str:\n return str(self.getvalue())",
"def __str__(self) -> str:\n return str(self.getvalue())",
"def __str__(self) -> str:\n return str(self.getvalue())",
"def __str__(self):\n return str(self._inner)",
"async def consumer(message):\n # TODO\n print(me... | [
"0.5992803",
"0.5992803",
"0.5992803",
"0.5777372",
"0.562348",
"0.56132424",
"0.5595922",
"0.555455",
"0.5551444",
"0.55506545",
"0.55497307",
"0.5548537",
"0.55457306",
"0.55349976",
"0.553099",
"0.553099",
"0.553099",
"0.553099",
"0.5517856",
"0.55134",
"0.551042",
"0.55... | 0.0 | -1 |
Try to match expected tokens against input, and if they match , consume them from the input. | def match(self, inp):
return 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def match(self, input_reader):\n retval = []\n # skip the whitespace here to prevent errors at the end of the string\n if input_reader.getIgnoreState():\n input_reader.skipWhite()\n retval.append(self.__rule.match(input_reader))\n try:\n while True:\n ... | [
"0.6492945",
"0.64347273",
"0.64072734",
"0.6325343",
"0.6323796",
"0.6185672",
"0.61034584",
"0.5995298",
"0.5961961",
"0.5961961",
"0.5961961",
"0.5897409",
"0.5832142",
"0.58187217",
"0.58052206",
"0.58036894",
"0.5779724",
"0.5731637",
"0.57204294",
"0.57186896",
"0.57164... | 0.5457896 | 39 |
Try to match expected tokens against input, and if they match , consume them from the input. This consumer only consumes when all subconsumers consumed (AND). | def match(self, inp):
and_complete = len(self.items)
matches = 0
work = inp
for t in self.items:
if isinstance(t, Consumer) and len(work):
r = t.match(work)
if r:
and_complete -= 1
matches += r
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def consume():\n depth = tokenizer.depth()\n for token in source:\n yield token\n if tokenizer.depth() < depth:\n return",
"def consume():\n depth = tokenizer.depth()\n for token in source:\n yield token\n if tokenizer.depth()... | [
"0.64358234",
"0.64358234",
"0.64358234",
"0.61860573",
"0.60832894",
"0.6072646",
"0.59237236",
"0.5916269",
"0.58925706",
"0.5841462",
"0.5817155",
"0.5734779",
"0.57143986",
"0.5662163",
"0.56231654",
"0.5539906",
"0.54996157",
"0.5483906",
"0.5459921",
"0.5455314",
"0.543... | 0.61943865 | 3 |
Try to match expected tokens against input, and if they match , consume them from the input. This consumer only consumes when one of the subconsumers consumed (OR). | def match(self, inp):
matches = 0
work = inp
for i in self.items:
if isinstance(i, Consumer) and len(work):
matches += i.match(work)
if matches:
return matches
elif len(work):
if work[0] == i:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def consume():\n depth = tokenizer.depth()\n for token in source:\n yield token\n if tokenizer.depth() < depth:\n return",
"def consume():\n depth = tokenizer.depth()\n for token in source:\n yield token\n if tokenizer.depth()... | [
"0.6473479",
"0.6473479",
"0.6473479",
"0.63934284",
"0.6198775",
"0.6061931",
"0.5906932",
"0.58926016",
"0.5851433",
"0.58479047",
"0.5828107",
"0.5804186",
"0.5762405",
"0.5644706",
"0.56113833",
"0.55958414",
"0.5543219",
"0.5503156",
"0.5490929",
"0.5426931",
"0.5384671"... | 0.60854316 | 5 |
This consumer executes the containing root consumer as long as that consumer consumed. | def match(self, inp):
matches = 0
while True:
m = self.items[0].match(inp)
inp = inp[m:]
if m > 0:
matches += m
else:
break
if matches and self.action is not None:
self.action(inp[:matches])
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _consumer(self) -> None:\n while (data := self._q.get()) is not None:\n write_data(data, self.writer)\n self._q.task_done()\n else:\n logging.info(\"None received. Queue consumed.\")\n self._q.task_done()\n return",
"def consume(self, handl... | [
"0.6843543",
"0.6778191",
"0.6764056",
"0.6676201",
"0.6596627",
"0.6564594",
"0.6454761",
"0.6452899",
"0.636782",
"0.63429266",
"0.6336376",
"0.63289666",
"0.6315336",
"0.6311098",
"0.6297736",
"0.62942386",
"0.62673885",
"0.6200036",
"0.61780053",
"0.61507535",
"0.6141587"... | 0.0 | -1 |
Create a rule with a given root consumer. | def __init__(self, root_cons):
assert isinstance(root_cons, Consumer)
self.root_cons = root_cons | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_acl_rule(self, context, sgr):\n self.security_group_driver.create_acl_rule(context, sgr)",
"def create_resolver_rule(CreatorRequestId=None, Name=None, RuleType=None, DomainName=None, TargetIps=None, ResolverEndpointId=None, Tags=None):\n pass",
"def create_snat_rule(self, **attrs):\n ... | [
"0.5656426",
"0.55104995",
"0.5498274",
"0.54220843",
"0.5255137",
"0.51557946",
"0.5137536",
"0.5112721",
"0.5094363",
"0.5049951",
"0.5020241",
"0.50156814",
"0.50156814",
"0.501336",
"0.50028026",
"0.4972401",
"0.49509874",
"0.49509874",
"0.49509874",
"0.49509874",
"0.4950... | 0.53467864 | 4 |
Try to match this rule to the input tokens. | def match(self, inp):
matched = self.root_cons.match(inp)
return matched | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def match(self, input_reader):\n try:\n rule_match = self.__rule.match(input_reader)\n logging.debug(\"Matched %s\" % self)\n return self.returnToken(self.callAction(rule_match))\n except ParseException as e:\n if e.final:\n raise\n ... | [
"0.65860355",
"0.6569237",
"0.648908",
"0.6396153",
"0.6340394",
"0.61685085",
"0.61111885",
"0.6084709",
"0.60695326",
"0.6060048",
"0.6018276",
"0.59840447",
"0.5964592",
"0.5909785",
"0.59013426",
"0.5880305",
"0.5824402",
"0.57897544",
"0.57223314",
"0.5671744",
"0.559937... | 0.0 | -1 |
Represent a rule as a string. | def __repr__(self):
return "%s(%s)" % (self.__class__.__name__, self.root_cons.__repr__()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return \"{ %s }\" % str(self.__rule)",
"def __str__(self):\n return \"[ %s ]\" % str(self.__rule)",
"def __str__(self):\n return \"{ %s }1\" % str(self.__rule)",
"def rule_to_str(self, t):\r\n\r\n if(t[0] == TERMINAL):\r\n return self.terminal_to_st... | [
"0.82408535",
"0.8018108",
"0.79563034",
"0.7597699",
"0.7547775",
"0.72459847",
"0.7131237",
"0.70973545",
"0.703556",
"0.70346487",
"0.6995514",
"0.68326235",
"0.6769843",
"0.67501",
"0.67490554",
"0.6587217",
"0.6539035",
"0.6535876",
"0.6502795",
"0.64148235",
"0.6307539"... | 0.0 | -1 |
Create a parser from a given lexicon. | def __init__(self, lexicon, flags=0):
import sre_parse
import sre_compile
from sre_constants import BRANCH, SUBPATTERN
self.lexicon = lexicon
# combine phrases into a compound pattern
p = []
s = sre_parse.Pattern()
s.flags = flags
for phrase, ac... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_parser():\n pass",
"def make_minilang_parser():\n gramm = Grammar.from_string(GRAMMAR)\n return parser_from_grammar(gramm, 'program')",
"def make_parser(data):\n # type: (str) -> RelayParser\n input_stream = InputStream(data)\n lexer = RelayLexer(input_stream)\n token_stream = C... | [
"0.6069957",
"0.6066554",
"0.60480183",
"0.5750524",
"0.5577068",
"0.55530554",
"0.55429524",
"0.5505798",
"0.5487193",
"0.5439394",
"0.5416125",
"0.541545",
"0.5408069",
"0.5406951",
"0.5396058",
"0.537577",
"0.5354577",
"0.52744836",
"0.5261595",
"0.5259573",
"0.52374434",
... | 0.621701 | 0 |
Scan the input string, return a list of tokens. | def scan(self, string):
result = []
append = result.append
match = self.scanner.scanner(string).match
i = 0
while 1:
m = match()
if not m:
break
j = m.end()
if i == j:
break
action = sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tokenize(self, input_string: str) -> List[str]:",
"def tokenise_str(input_str):\n t = Tokeniser(input_str)\n tokens = []\n while True:\n token = t.next()\n if token is None:\n break\n tokens.append(token)\n return tokens",
"def _get_tokens(s: str) ->List[str]:\n ... | [
"0.77675945",
"0.7501283",
"0.73545146",
"0.7273766",
"0.7175815",
"0.7150616",
"0.71369165",
"0.70631945",
"0.70483094",
"0.6998969",
"0.69848484",
"0.69318676",
"0.69164383",
"0.69087684",
"0.68783075",
"0.68697166",
"0.6829753",
"0.68255985",
"0.68176705",
"0.67455864",
"0... | 0.6573386 | 34 |
Create a tokenizer from a list of patterns. | def __init__(self, patterns=None):
Container.__init__(self, patterns) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_tokenizer(self):\n def tokenizer(doc):\n token_pattern = re.compile(self.token_pattern)\n return token_pattern.findall(doc)\n \n return tokenizer",
"def tokenize(self, texts: List[str]) -> List[Token]:\n raise NotImplementedError",
"def tokenizer(... | [
"0.658101",
"0.63527864",
"0.61232406",
"0.6110007",
"0.58901197",
"0.586808",
"0.5829946",
"0.5823161",
"0.57966834",
"0.56878775",
"0.5638406",
"0.5624384",
"0.56119484",
"0.558832",
"0.55631876",
"0.5546463",
"0.5538981",
"0.5531948",
"0.551695",
"0.5488704",
"0.5483571",
... | 0.0 | -1 |
Tokenize the input string. | def tokenize(self, inp):
scanner = Scanner(self.items)
return scanner.scan(inp)[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tokenize(self, input_string: str) -> List[str]:",
"def tokenize(str):\n return str.split()",
"def _tokenize(self, string_):\n return \" \".join(word_tokenize(string_))",
"def _tokenize(self, string):\n self._tokens = []\n\n # Split and strip the input string by newlines\n f... | [
"0.78138953",
"0.7647113",
"0.7621142",
"0.7614912",
"0.7563403",
"0.7274134",
"0.7199052",
"0.7167596",
"0.71064675",
"0.7049297",
"0.70231694",
"0.6993997",
"0.68852586",
"0.6804387",
"0.67763877",
"0.6765216",
"0.67639923",
"0.6758892",
"0.674922",
"0.67352617",
"0.6723727... | 0.0 | -1 |
String representation of this Tokenizer. | def __repr__(self):
return "%s()" % self.__class__.__name__ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return 'Tokenizer({type}, {value})'.format(\n type=self.type,\n value=repr(self.value)\n )",
"def __repr__ (self) -> String:\n\n st = (\"_Token(%r, '%s', %s, %r)\"\n % (self.start, self.text, self.kind, self.value))\n return st",
"def toString(sel... | [
"0.80519426",
"0.7545306",
"0.75383246",
"0.73899835",
"0.7332152",
"0.7317708",
"0.7317708",
"0.7317708",
"0.7317708",
"0.7237351",
"0.7175742",
"0.71049696",
"0.7057985",
"0.68212074",
"0.6681456",
"0.6658947",
"0.6633161",
"0.6614109",
"0.645334",
"0.63983494",
"0.63702554... | 0.0 | -1 |
Parse the input by first tokenizing it, and than applying the grammar. | def parse(self, inp):
tokens = self.tokenizer.tokenize(inp)
tokens_left = len(tokens)
# print(tokens)
while tokens_left:
for rule in self.grammar:
tokens = tokens[rule.match(tokens):]
if len(tokens) < tokens_left:
tokens_left =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_input(self, input):\r\n return self._parse(antlr3.ANTLRInputStream(input))",
"def parse(self, input):\n pass",
"def _parse(self):\n try:\n # parse token stream into abstract syntax tree (AST)\n self._ast = self._rule_container()\n\n except ParseError:\n ... | [
"0.7399071",
"0.7176999",
"0.68727535",
"0.6820916",
"0.6671495",
"0.6598285",
"0.6544924",
"0.64858556",
"0.6451945",
"0.6421137",
"0.6334191",
"0.62509596",
"0.6192237",
"0.61716855",
"0.60511875",
"0.6048852",
"0.603809",
"0.5991771",
"0.596822",
"0.5967969",
"0.59347737",... | 0.7061553 | 2 |
String representation of this Parser. | def __repr__(self):
if self.tokenizer is not None:
tok = self.tokenizer.__repr__()
else:
tok = None
if self.grammar is not None:
gr = self.grammar.__repr__()
else:
gr = None
return "%s(%s, %s)" % (self.__class__.__name__, tok, gr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return self.str_parse_tree(0)",
"def __str__(self) :\n if not self.parsed :\n return \"\"\n mybuffer = []\n mybuffer.append(\"IPP version : %s.%s\" % self.version)\n mybuffer.append(\"IPP operation Id : 0x%04x\" % self.operation_id)\n mybu... | [
"0.7421955",
"0.7210431",
"0.7145681",
"0.71311057",
"0.7080356",
"0.7047601",
"0.7026195",
"0.70037293",
"0.6999853",
"0.6991449",
"0.6958756",
"0.6935437",
"0.6929929",
"0.6928658",
"0.6918804",
"0.6912121",
"0.6911741",
"0.6909374",
"0.68920237",
"0.6884707",
"0.6884707",
... | 0.69677925 | 10 |
Test dummpy molecule for 90 degree rotations on global zaxis | def test_diatomic_dummy_molecule_rotation_around_global_axis():
mol = Molecule()
mol.atoms = ['C'] * 2
mol.coordinates = np.array([[1, 0, 0], [0, 1, 0]])
mol.rotate(([0, 0, 0], [0, 0, 1]), np.pi / 2, center=False)
assert np.allclose(mol.coordinates, [[0, 1, 0], [-1, 0, 0]])
mol.rotate(([0, 0, 0]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_diatomic_dummy_molecule_rotation_around_molecule_axis():\n mol = Molecule()\n mol.atoms = ['C'] * 2\n mol.coordinates = np.array([[1, 0, 0], [0, 1, 0]])\n mol.rotate(([0, 0, 0], [0, 0, 1]), np.pi / 2, center=True)\n assert np.allclose(mol.coordinates, [[1, 1, 0], [0, 0, 0]])\n mol.rotate... | [
"0.68779147",
"0.6699214",
"0.6467635",
"0.6058268",
"0.59421706",
"0.5833135",
"0.5812672",
"0.5786473",
"0.5781003",
"0.57146466",
"0.5709731",
"0.5701399",
"0.5697652",
"0.5675569",
"0.56018823",
"0.5592052",
"0.5585152",
"0.55424553",
"0.55419815",
"0.55203927",
"0.549647... | 0.72263396 | 0 |
Test dummpy molecule for 90 degree rotations on zaxis of the molecule | def test_diatomic_dummy_molecule_rotation_around_molecule_axis():
mol = Molecule()
mol.atoms = ['C'] * 2
mol.coordinates = np.array([[1, 0, 0], [0, 1, 0]])
mol.rotate(([0, 0, 0], [0, 0, 1]), np.pi / 2, center=True)
assert np.allclose(mol.coordinates, [[1, 1, 0], [0, 0, 0]])
mol.rotate(([0, 0, 0]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_z_rot(self):\n\n # Create a Matrix representing 90 deg z rot.\n mat = Matrix44.from_rot_z(90)\n # Use from_matrix44()\n quat = Quat.from_matrix44(mat)\n\n # Ensure the quat matches a 90 degree x rotation.\n expected = Quat.from_axis_angle_deg(Vec3(0, 0, 1), 90)\n ... | [
"0.70797455",
"0.68893856",
"0.6713505",
"0.6137352",
"0.60241646",
"0.6014459",
"0.59592366",
"0.5933176",
"0.5881573",
"0.58782434",
"0.58767945",
"0.5848994",
"0.584663",
"0.583147",
"0.58155316",
"0.5810699",
"0.58072495",
"0.57150024",
"0.568535",
"0.56782866",
"0.567333... | 0.70375586 | 1 |
Call recursive glob iff is in the pattern. | def recursive_glob(path):
if "*" not in path:
# Glob isn't needed.
return [path]
elif "**" not in path:
# Recursive glob isn't needed.
return path_utils.glob(path)
else:
return path_utils.glob(path, recursive=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recursive_glob(stem, file_pattern):\n\n if sys.version_info >= (3, 5):\n return glob(stem + \"/**/\" + file_pattern, recursive=True)\n else:\n # gh-316: this will avoid invalid unicode comparisons in Python 2.x\n if stem == str(\"*\"):\n stem = \".\"\n matches = []\... | [
"0.751194",
"0.6974341",
"0.68034536",
"0.6758196",
"0.67542297",
"0.65902424",
"0.6563652",
"0.6520523",
"0.64885634",
"0.6402769",
"0.6326716",
"0.6301645",
"0.6283898",
"0.6252692",
"0.6246691",
"0.62367874",
"0.6196066",
"0.61890477",
"0.6177601",
"0.6141388",
"0.6139274"... | 0.7050668 | 1 |
Create a nested directory, but don't fail if any of it already exists. | def makedirs(path):
try:
os.makedirs(path)
except OSError as e:
if e.errno != errno.EEXIST:
raise | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_dir_if_necessary(path):\n try:\n os.makedirs(path)\n except OSError as exception:\n if exception.errno != errno.EEXIST:\n raise",
"def create_dir():\n if check_dir_exist():\n return False\n else:\n os.makedirs(path_structure)\n return True",
... | [
"0.74229234",
"0.72851455",
"0.7262007",
"0.7246796",
"0.7240233",
"0.7218255",
"0.718408",
"0.7164283",
"0.7142678",
"0.7140523",
"0.7129466",
"0.7109018",
"0.70924854",
"0.70889115",
"0.7085182",
"0.70810604",
"0.70569605",
"0.704916",
"0.7046928",
"0.70466846",
"0.7039653"... | 0.0 | -1 |
Context manager. Change the directory, and restore it afterwards. | def cd(path):
if not path:
yield
return
curdir = path_utils.getcwd()
os.chdir(path)
try:
yield
finally:
os.chdir(curdir) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __enter__(self):\n self.savedPath = os.getcwd()\n os.chdir(self.newPath)",
"def restore_cwd():\n cwd = os.getcwd()\n try:\n yield\n finally:\n os.chdir(cwd)",
"def _restore_orig_directory(self):\n if not self._is_temp_dir:\n return\n self._base_... | [
"0.78329426",
"0.72657293",
"0.71698695",
"0.7141132",
"0.7084793",
"0.7076439",
"0.7041844",
"0.68930256",
"0.68384814",
"0.68027496",
"0.6796666",
"0.67186296",
"0.6703635",
"0.6698865",
"0.66618675",
"0.6634945",
"0.6622206",
"0.66195375",
"0.6611089",
"0.65799147",
"0.657... | 0.60399324 | 58 |
Checks if a pyi file is path/to/dir/__init__.pyi. | def is_pyi_directory_init(filename):
if filename is None:
return False
return path_utils.splitext(path_utils.basename(filename))[0] == "__init__" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def IsPackage(path):\n init_base_path = os.path.join(path, '__init__.py')\n return (os.path.isfile(init_base_path) or\n os.path.isfile(init_base_path + 'c') or\n os.path.isfile(init_base_path + 'o'))",
"def is_pkg(cls, path):\n return exists(join(path, '__init__.py'))",
"def ... | [
"0.7422277",
"0.7233876",
"0.7077706",
"0.70653397",
"0.6987086",
"0.6987086",
"0.6595339",
"0.6401514",
"0.6298225",
"0.62583286",
"0.6258052",
"0.6250494",
"0.61958045",
"0.60404485",
"0.6009115",
"0.60048294",
"0.58957773",
"0.5888118",
"0.58545226",
"0.58261836",
"0.58251... | 0.79479814 | 0 |
Checks if the filename is a pickle file. | def is_pickle(filename):
return path_utils.splitext(filename)[1].startswith(PICKLE_EXT) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pickleExists(name):\n fileNames = [f for f in os.listdir(PICKLE_DIR) if name in f]\n return not len(fileNames) == 0",
"def verify_filename(filename):\n\n if is_fileobj(filename):\n raise ValueError(\"%r not a filename\" % filename)",
"def is_mp3_file(filename):\n ext = str(os.path.splite... | [
"0.643423",
"0.6430957",
"0.6344804",
"0.6319534",
"0.62992567",
"0.6294618",
"0.6250568",
"0.62361664",
"0.6185609",
"0.61671084",
"0.61487937",
"0.61480135",
"0.61176413",
"0.6105346",
"0.6073736",
"0.6073736",
"0.6023189",
"0.60059375",
"0.6004802",
"0.5994341",
"0.5991949... | 0.8553102 | 0 |
Fully expand a path, optionally with an explicit cwd. | def expand_path(path, cwd=None):
expand = lambda path: path_utils.realpath(path_utils.expanduser(path))
with cd(cwd):
return expand(path) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def expandpath(path):\n return os.path.abspath(os.path.expanduser(path))",
"def expand_path(path):\n return expanduser(expandvars(path))",
"def ExpandPath(path):\n return os.path.realpath(os.path.expanduser(path))",
"def expand_path(path):\n\n return os.path.abspath(os.path.expanduser(os.path.expan... | [
"0.74701923",
"0.73939276",
"0.73707473",
"0.7219343",
"0.7219343",
"0.71664995",
"0.7078088",
"0.6832545",
"0.6721617",
"0.6641923",
"0.6560282",
"0.655823",
"0.65138286",
"0.65120006",
"0.6336295",
"0.62854826",
"0.62736595",
"0.62604237",
"0.61930496",
"0.6163498",
"0.6128... | 0.85340816 | 0 |
Fully expand a list of paths, optionally with an explicit cwd. | def expand_paths(paths, cwd=None):
return [expand_path(x, cwd) for x in paths] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def expand_globpaths(globpaths, cwd=None):\n with cd(cwd):\n paths = sum((recursive_glob(p) for p in globpaths), [])\n return expand_paths(paths, cwd)",
"def expand_paths(__file__, paths_with_globs):\n if isinstance(paths_with_globs, str):\n return expand_path(__file__, paths_with_globs)\n else... | [
"0.7456049",
"0.70066786",
"0.6774307",
"0.66487193",
"0.65742046",
"0.624558",
"0.61956173",
"0.59257555",
"0.56479543",
"0.5538372",
"0.55290365",
"0.54953575",
"0.54544365",
"0.5436541",
"0.5423522",
"0.5376047",
"0.5348702",
"0.5323587",
"0.53146243",
"0.5308345",
"0.5304... | 0.8460689 | 0 |
Expand a list of glob expressions into a list of full paths. | def expand_globpaths(globpaths, cwd=None):
with cd(cwd):
paths = sum((recursive_glob(p) for p in globpaths), [])
return expand_paths(paths, cwd) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def expand(self, path_list):\n path_list2 = []\n for path in path_list:\n if glob.has_magic(path):\n iterator = glob.iglob(path)\n path_list2.extend(iterator)\n else:\n path_list2.append(path)\n return path_list2",
"def expan... | [
"0.8407495",
"0.80332714",
"0.7528493",
"0.7383241",
"0.73741955",
"0.6872758",
"0.6813668",
"0.6686918",
"0.6616619",
"0.65312225",
"0.64324456",
"0.6402898",
"0.6316146",
"0.63040036",
"0.6291287",
"0.62841296",
"0.62715226",
"0.6246781",
"0.6219632",
"0.6194844",
"0.618852... | 0.7691867 | 2 |
Expand a spaceseparated string of filenames passed in as sources. This is a helper function for handling command line arguments that specify a list of source files and directories. Any directories in filenames will be scanned recursively for .py files. Any files that do not end with ".py" will be dropped. | def expand_source_files(filenames, cwd=None):
out = []
for f in expand_globpaths(filenames.split(), cwd):
if path_utils.isdir(f):
# If we have a directory, collect all the .py files within it....
out += recursive_glob(path_utils.join(f, "**", "*.py"))
elif f.endswith(".py"):
out.append(f)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def preprocess(\n self,\n source: str,\n name: t.Optional[str] = None,\n filename: t.Optional[str] = None,\n ) -> str:\n return reduce(\n lambda s, e: e.preprocess(s, name, filename),\n self.iter_extensions(),\n str(source),\n )",
"def... | [
"0.57943326",
"0.56623256",
"0.56462324",
"0.56194705",
"0.56137234",
"0.55241853",
"0.54940057",
"0.5488685",
"0.54875094",
"0.5447087",
"0.5410495",
"0.5335001",
"0.5287344",
"0.5250983",
"0.5189532",
"0.5180171",
"0.5116171",
"0.5083184",
"0.5078157",
"0.506521",
"0.505824... | 0.689316 | 0 |
Shows possible moves interpreted as chess coords | def chess_coord_moves(self, select):
hold = []
for i in self.board[select].possible_moves:
hold.append(self.chess_coords[self.coords.index(i)])
self.print_message(("my possible moves are:",hold)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def render_possible_moves(self):\n for move in self.env.possible_moves(self.env.turn):\n self.render_marker(move, Colors.BLUE)",
"def chess_map(self):\n print('<>\\t', end='')\n for i in range(1, self.mapX + 1): # 打印列坐标\n print(i, end='\\t')\n print()\n f... | [
"0.75572604",
"0.73018354",
"0.6853283",
"0.6808992",
"0.6737718",
"0.66602975",
"0.6623016",
"0.66016227",
"0.6577454",
"0.65595424",
"0.6546937",
"0.65451103",
"0.64989746",
"0.6481591",
"0.6444175",
"0.64146894",
"0.6391575",
"0.63840497",
"0.6382809",
"0.6382281",
"0.6379... | 0.7084934 | 2 |
Prints messages. Makes it easier to turn off when running AI | def print_message(self, message):
print(message) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hide_messages():\n\n print(\"Keep uncertainty data?\")\n print(\"NewDatabase(..., keep_uncertainty_data=True)\")\n print(\"\")\n print(\"Hide these messages?\")\n print(\"NewDatabase(..., quiet=True)\")",
"def print_messages(self):\n if self.messages:\n self.messages.append(\"\")\n ... | [
"0.6853183",
"0.67243636",
"0.66580886",
"0.6560094",
"0.65378034",
"0.63819957",
"0.63348514",
"0.62748116",
"0.62620103",
"0.62291723",
"0.62229854",
"0.62218237",
"0.62142426",
"0.62052816",
"0.6195678",
"0.619227",
"0.6191761",
"0.6156673",
"0.6156401",
"0.61522394",
"0.6... | 0.5950852 | 35 |
Fixes the problem where the mover method infinetly recurses due to the computer trying to capture a piece while in check. self.recursive_move is a list of blacklisted moves | def recurse_fix(self, recurse):
#Removes the recursive moves from <piece>.possible_moves
if recurse == True:
# print(self.recursive_move)
for i in self.recursive_move:
select = self.recursive_move[self.recursive_move.index(i)][0]
move = sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_valid_moves(self):\r\n # castling and en-passant rights are stored, because move affects these values\r\n temp_enpassant_possible = self.enpas_pos\r\n temp_castle = CastleRights(self.cr_castle_r.wks, self.cr_castle_r.bks,\r\n self.cr_castle_r.wqs, self... | [
"0.6435797",
"0.6278216",
"0.6252017",
"0.61592704",
"0.61344117",
"0.61085063",
"0.6066249",
"0.60613513",
"0.60312474",
"0.60108995",
"0.6007377",
"0.6001272",
"0.59988976",
"0.5975665",
"0.597399",
"0.5933734",
"0.592724",
"0.5924062",
"0.59091336",
"0.5909046",
"0.5904532... | 0.75138515 | 0 |
Display game board on screen | def display_board(self, board):
print("\n\t - A - B - C - D - E - F - G - H - \n")
print("\t8 ", board[56], "|", board[57], "|", board[58], "|", board[59], "|", board[60], "|", board[61], "|", board[62], "|", board[63])
print("\t ", "---------------------------------------")
pr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_board(self):\n self._game_board.display()",
"def show_board(self, game):\n\n self.screen.clear()\n self.show_banner()\n self.show_score(game)\n self.show_towers(game.board)\n self.screen.refresh()",
"def display_board(self):\n print(self.game_board)",
... | [
"0.8678167",
"0.83532125",
"0.8349347",
"0.83242863",
"0.8264461",
"0.824662",
"0.79697764",
"0.7959786",
"0.7956839",
"0.7923558",
"0.7922038",
"0.7910964",
"0.79016423",
"0.78563464",
"0.7760773",
"0.7742415",
"0.7722158",
"0.7712943",
"0.7707028",
"0.764252",
"0.7638022",
... | 0.77047324 | 19 |
Populates the board with piece objects | def populate(self):
counter = 0
placers = [piece_class.Rook, piece_class.Knight, piece_class.Bishop,
piece_class.Queen, piece_class.King, piece_class.Bishop,
piece_class.Knight, piece_class.Rook, piece_class.Pawn,
piece_class.Pawn, piece_class.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def populate_board(self):\n for key, value in self.game.white_pieces.items():\n x_pos = self.width * value.x_pos\n y_pos = self.width * value.y_pos\n img = self.load_image(\"images/\" + value.image, value.starting_position)\n self.place_image_on_canvas(x_pos, y_po... | [
"0.76593214",
"0.75936663",
"0.7367415",
"0.72926337",
"0.7115361",
"0.7082727",
"0.70713323",
"0.7070828",
"0.7015153",
"0.69468915",
"0.6837631",
"0.68237364",
"0.68121535",
"0.6800702",
"0.66739005",
"0.6650878",
"0.6610713",
"0.659756",
"0.6571999",
"0.6550281",
"0.651255... | 0.8106084 | 0 |
Tracks available moves for every piece | def loads_pathways(self, turn):
black_coords, white_coords = self.parser()
counter = 0
path_dict, poss_dict, check_dict, long_dict = {BLACK : [], WHITE : []}, {BLACK : [], WHITE : []}, {BLACK : [], WHITE : []}, {BLACK : [], WHITE : []}
for i in self.board:
if i != se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_moves(self):",
"def getPossibleMoves(self): # called to get possible positions this piece can go\r\n \r\n moves = {}\r\n\r\n ids = []\r\n\r\n for piece in self.board.pieces.values():\r\n if piece.name == \"empty\":\r\n piece.glow = False\r\n ... | [
"0.7183788",
"0.71057975",
"0.68767446",
"0.6852259",
"0.6705842",
"0.6636046",
"0.6629116",
"0.6602298",
"0.651185",
"0.6503754",
"0.64848596",
"0.64755505",
"0.64447147",
"0.63818496",
"0.6336451",
"0.63071597",
"0.62893933",
"0.62332284",
"0.6224337",
"0.62011695",
"0.6181... | 0.5866029 | 71 |
Turns the board index' which hold pieces into coordinates | def parser(self):
hold = [i for i, val in enumerate(self.board) if val != self.empty and val.colour == BLACK]
hold2 = [i for i, val in enumerate(self.board) if val != self.empty and val.colour == WHITE]
#This is why dictionaries are better
black_coords = []
white_coords ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_board_coordinates(self):\n\n temp_board = copy.deepcopy(self.get_board())\n board_columns = self.get_board_columns()\n board_rows = self.get_board_rows()\n\n for board_row in board_rows:\n\n for board_column in board_columns:\n \n index_colum... | [
"0.71379375",
"0.70625293",
"0.6820346",
"0.67808604",
"0.6744463",
"0.6730689",
"0.66997415",
"0.6699165",
"0.66525185",
"0.662486",
"0.65777856",
"0.65033275",
"0.64912",
"0.6417787",
"0.6387271",
"0.638245",
"0.63251114",
"0.6315006",
"0.625296",
"0.6216417",
"0.6207858",
... | 0.0 | -1 |
Checks to see if a player is in check | def checks_check(self, turn):
opposite_colour = next_turn(turn)
if piece_class.KING_LOCATION[opposite_colour] in self.path_dict[turn]:
self.print_message("CHECK!")
# self.checkmate(turn)
self.mate_double(turn)
self.mate_pinned(turn)
self.mate_n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_in_check(self, player):\n\n current_player = player.upper()\n\n # Gathers current player's General and sets opponent player.\n if current_player == 'BLUE':\n\n current_player_general_position = self.get_general_position_blue()\n opponent_player = 'RED'\n\n e... | [
"0.7478128",
"0.7422957",
"0.7306489",
"0.72766924",
"0.72634876",
"0.7202797",
"0.71440995",
"0.6985347",
"0.6975687",
"0.6958636",
"0.6958488",
"0.6942686",
"0.6923067",
"0.68779784",
"0.6877642",
"0.68572235",
"0.685284",
"0.6837244",
"0.6819755",
"0.6790949",
"0.6739893",... | 0.0 | -1 |
Tests if a double check causes a checkmate | def mate_double(self, turn):
opposite_colour = next_turn(turn)
opp_king_index = (piece_class.KING_LOCATION[opposite_colour][0] + piece_class.KING_LOCATION[opposite_colour][1] * 8)
opp_poss_moves = {tuple(i) for i in self.poss_dict[opposite_colour]}
check_path = {tuple(i) for i in self.c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check():",
"def test_recheck_fails(self):\n raise NotImplementedError",
"def check(self) -> None:",
"def test_version_check_outdated(self):\n output = self.run_command(\"selfupdate --check bennr01:selfupdate_test_outdated\", exitcode=0)\n self.assertIn(\"Target: bennr01:selfupdate_te... | [
"0.64797485",
"0.6376622",
"0.6094995",
"0.60793626",
"0.6064576",
"0.6052561",
"0.5955447",
"0.5947425",
"0.5947425",
"0.5947425",
"0.5947425",
"0.59290624",
"0.5913185",
"0.58940095",
"0.58906275",
"0.5886973",
"0.5885773",
"0.58225274",
"0.5817582",
"0.58138984",
"0.580961... | 0.59306425 | 11 |
Checks piece(s) are pinned to the king, and can't prevent checkmate | def mate_pinned(self, turn):
pinned_list = []
opposite_colour = next_turn(turn)
opp_king_index = (piece_class.KING_LOCATION[opposite_colour][0] + piece_class.KING_LOCATION[opposite_colour][1] * 8)
opp_poss_moves = {tuple(i) for i in self.poss_dict[opposite_colour]}
check... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_move_knight_legally_blocked(self):\n for piece in [('N', True), ('N', False)]:\n self.c.board = \\\n [[('K', piece[1]) for i in range(8)] for i in range(8)]\n self.c.turn = piece[1]\n self.c.board[4][4] = piece\n for dest in ['d6', 'f6', 'c... | [
"0.68501544",
"0.66945",
"0.6639427",
"0.64998174",
"0.64502615",
"0.644943",
"0.64347035",
"0.64299804",
"0.6413221",
"0.6408485",
"0.6388782",
"0.63560796",
"0.6353194",
"0.63430786",
"0.6337254",
"0.63203883",
"0.6306646",
"0.6304615",
"0.62882274",
"0.6250088",
"0.6243981... | 0.7173512 | 0 |
Tests for the most usual checkmate | def mate_normal(self, turn):
opposite_colour = next_turn(turn)
opp_king_index = (piece_class.KING_LOCATION[opposite_colour][0] + piece_class.KING_LOCATION[opposite_colour][1] * 8)
opp_poss_moves = {tuple(i) for i in self.poss_dict[opposite_colour]}
check_path = {tuple(i) for i i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check():",
"def test_check(self):\n\n self.assertTrue(Naive().check(self.file_gitignore))\n self.assertTrue(Naive().check(self.file_tests))\n self.assertTrue(Naive().check(self.file_bin))\n self.assertTrue(Naive().check(self.file_py))\n self.assertTrue(Naive().check(self.fi... | [
"0.76886064",
"0.7633155",
"0.7092025",
"0.6998297",
"0.691067",
"0.68761855",
"0.6818562",
"0.6818562",
"0.6818562",
"0.6818562",
"0.6790198",
"0.678656",
"0.67435277",
"0.6642163",
"0.6612691",
"0.6592765",
"0.6569492",
"0.6565059",
"0.65569156",
"0.6540621",
"0.6509107",
... | 0.0 | -1 |
Checks if there's a draw by repitition | def draw_by_rep(self, turn, select, move):
REPETITION_CURR[turn] = [self.coords[select], self.coords[move]]
if REPETITION_PREV[turn] != []:
if REPETITION_CURR[turn][0] == REPETITION_PREV[turn][1] and REPETITION_CURR[turn][1] == REPETITION_PREV[turn][0]:
self.rep_counter += 1... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isdrawn(self):\n return hasattr(self, 'drawn')",
"def checkDraw(self) -> D:\n if self.board.positions.count(\" \") == 0:\n print(\"DRAW!\")\n return True",
"def draw(self):\n\n for row in self._board:\n for slot in row:\n if slot == 0:\n ... | [
"0.72048205",
"0.68884087",
"0.67064244",
"0.656032",
"0.65569574",
"0.6467784",
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"0.6077393",
"0.6041107",
"0.60317785",
"0.5978021",
"0.59643847",
"0.594096... | 0.57615733 | 30 |
Checks if there's a draw due to insufficient material | def draw_by_insufficient(self):
if self.cap_counter > 100:
self.draw_loop("draw due to none in 50")
if self.board.count(self.empty) == 62:
self.draw_loop("draw due to insufficient")
if self.board.count(self.empty) == 61:
for i i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def insufficient_material(_user_id):\n _board = boards[_user_id]\n return _board.is_insufficient_material()",
"def insufficient_material(self):\n piece_set = set()\n for row in range(8):\n for col in range(8):\n piece = self.board.squares[row][col]\n i... | [
"0.6823158",
"0.65014744",
"0.6312815",
"0.62909424",
"0.62909424",
"0.5994646",
"0.5992354",
"0.5942213",
"0.59327585",
"0.58796394",
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"0.56119996",
"0.5551076",
"0.5534958",
"0.5529477",
"0.5487489",
"0.5461284",
"0.546109... | 0.63086367 | 3 |
Adjusts the king moves to take into account opponent paths | def king_adjust(self, turn):
opposite_turn = next_turn(turn)
original_location_index = (piece_class.KING_LOCATION[turn][0] + piece_class.KING_LOCATION[turn][1] * 8)
# if self.board[original_location_index] == self.empty:
# print("yo")
self.board[original_loc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_king_moves(self, state):\n #king_moves = []\n possible_moves = []\n if self.color == cc.WHITE_ACTIVE:\n enemy_color = cc.BLACK_ACTIVE\n enemy_pieces = cc.BLACK_PIECES\n elif self.color == cc.BLACK_ACTIVE:\n enemy_color = cc.WHITE_ACTIVE\n ... | [
"0.65922093",
"0.6517307",
"0.6433317",
"0.642922",
"0.63771147",
"0.63771147",
"0.63771147",
"0.63081336",
"0.6298383",
"0.6261358",
"0.6192195",
"0.6176605",
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"0.6143453",
"0.61155224",
"0.6101044",
"0.6099099",
"0.6098332",
"0.6097228",
"0.60883033... | 0.85099286 | 0 |
Appends the possible move of the king if castling is possible | def castling(self, turn, ai):
if self.board[self.coords.index(piece_class.KING_LOCATION[turn])].move_track == True:
return None
castling_queenside = [self.coords.index(piece_class.KING_LOCATION[turn]), self.coords.index(piece_class.KING_LOCATION[turn]) - 1,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def king_adjust(self, turn):\n\n opposite_turn = next_turn(turn)\n\n original_location_index = (piece_class.KING_LOCATION[turn][0] + piece_class.KING_LOCATION[turn][1] * 8)\n \n# if self.board[original_location_index] == self.empty:\n# print(\"yo\")\n \n self.bo... | [
"0.7224072",
"0.6627195",
"0.65229857",
"0.6470024",
"0.6182009",
"0.6177418",
"0.61430883",
"0.6139011",
"0.61076343",
"0.6091473",
"0.6086108",
"0.60822123",
"0.6072754",
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"0.6068784",
"0.6068784",
"0.60567504",
"0.60416263",
"0.6029341",
"0.6026139",
"0.6018981... | 0.72807145 | 0 |
Checks if castling is possible | def castling_valid(self, turn, direction):
opposite_colour = next_turn(turn)
if self.board[direction[0]] and self.board[direction[-1]] != self.empty:
if ((self.board[direction[0]].graphic) == piece_class.PIECEDICT[turn][piece_class.King] and
(self.board[direct... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _can_cast_to(self, value, cast_type):\n try:\n _ = cast_type(value)\n return True\n except ValueError:\n return False",
"def is_casting(self):\n # type: () -> bool\n return self._is_casting",
"def test_canConvert(string, cast, expected):\n assert canConvert(string, cast)... | [
"0.75801295",
"0.7562737",
"0.6442058",
"0.6417883",
"0.6306073",
"0.6138935",
"0.6129317",
"0.6065727",
"0.6046611",
"0.6044432",
"0.60181624",
"0.5920204",
"0.5906511",
"0.58403975",
"0.57817537",
"0.57176816",
"0.56767637",
"0.5561193",
"0.5528392",
"0.5521377",
"0.5496391... | 0.0 | -1 |
Implements the castling by moving the rook | def castling_implement(self, turn, select, move):
if select == self.coords.index(piece_class.KING_LOCATION[turn]):
if self.board[self.coords.index(piece_class.KING_LOCATION[turn])].move_track == False:
if move in [2, 58]:
self.board[move+1] = self.board[m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _walk(self):\n new_pos = self.rect.move((self.move, 0)) # move 9 pixel to the right per frame\n if self.rect.left < self.area.left or self.rect.right > self.area.right:\n self.move = -self.move # move to the opposite direction when the chimp position exceeds the screen\n n... | [
"0.7103401",
"0.7072305",
"0.6994009",
"0.69171774",
"0.68605596",
"0.6859176",
"0.6715968",
"0.66763794",
"0.6669459",
"0.6627489",
"0.6609323",
"0.6591225",
"0.6581653",
"0.65799546",
"0.65799546",
"0.6564521",
"0.65348953",
"0.6508202",
"0.64825106",
"0.6464618",
"0.645735... | 0.6157545 | 63 |
Resets all attributes after a game has been played by AIvAI | def resets_attributes(self):
self.path_dict = None
self.poss_dict = None
self.check_dict = None
self.long_dict = None
self.rep_counter = 0
self.cap_counter = 0
self.board = []
self.coords = []
self.chess_coords = []
self.e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset_game_fields(self):\n self.player = None\n self.event_status = 0 # set accordingly for player achievments\n self.event_status_list = [False, False, False, False, False, False, False, False]\n self.spaces = []\n self.characters = []\n self.exits = []\n self.... | [
"0.7516117",
"0.73008484",
"0.7209318",
"0.71248615",
"0.70516896",
"0.70516896",
"0.7050974",
"0.7039916",
"0.70002186",
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"0.67195565",
"0.6716343",
"0.66865003",
"0.66... | 0.73064816 | 1 |
Calculates how long a game takes | def time(self, start_time):
TIME_LIST.append((time.time() - start_time))
print("--- %s seconds ---" % (time.time() - start_time)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def time(self) -> float:\n return self.state.game_loop / 22.4 # / (1/1.4) * (1/16)",
"def time(n_games, time_per_game):\n\n total_time = n_games * time_per_game / 60\n return total_time",
"def GAME_TIME_ADVANCE(dt):",
"def time_elapsed(session, player):\n #TODO (also needs to be added to bot... | [
"0.77314806",
"0.7411862",
"0.7296464",
"0.6975621",
"0.6766241",
"0.6730055",
"0.6683318",
"0.6671834",
"0.6671834",
"0.6671834",
"0.6671834",
"0.6671834",
"0.6671834",
"0.6656775",
"0.6643553",
"0.66259193",
"0.6610059",
"0.65386087",
"0.6536",
"0.6452645",
"0.64100575",
... | 0.0 | -1 |
default formating as string | def default(self, obj):
if isinstance(obj, (dt.date, dt.datetime)):
return obj.isoformat() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format(self) -> str:",
"def formatted(self) -> str:\r\n ...",
"def format(self, *args, **kwargs) -> String:\n pass",
"def __format__(self, fmt):\n if not isinstance(fmt, str):\n raise TypeError(\"must be str, not %s\" % type(fmt).__name__)\n if len(fmt) != 0:\n ... | [
"0.837017",
"0.78593856",
"0.78285396",
"0.77283126",
"0.7577578",
"0.74514586",
"0.74457926",
"0.72434765",
"0.7181104",
"0.7149438",
"0.7149438",
"0.70565635",
"0.70547104",
"0.7050457",
"0.6980099",
"0.6979376",
"0.6970662",
"0.6964501",
"0.69569725",
"0.69133925",
"0.6911... | 0.0 | -1 |
Read scraped profiles parse them and write to json and yamls | def main():
# %%
CFG.profiles_yamls_path.mkdir(parents=True, exist_ok=True)
fpaths = list( _Config.raw_profiles_path.glob('*.html') )
print( f'{len(fpaths)} htmls found' )
# %%
fpath = CFG.raw_profiles_path / 'luis-mario-urrea-murillo.html'
# %%
fpath = CFG.raw_profiles_path / 'cristian-... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadProfiles():\n with open(userProfilesDir, \"r\") as infile:\n profiles = json.loads(\"\\n\".join(infile.readlines()))\n infile.close()\n return profiles",
"def scrape_profile(inhandle, outfile, year, month):\n #Read file\n html = inhandle.read()\n soup = BeautifulSoup(html, 'htm... | [
"0.6808882",
"0.67222965",
"0.66820586",
"0.6603268",
"0.6544514",
"0.6352096",
"0.625899",
"0.62085044",
"0.60912347",
"0.6039629",
"0.6026319",
"0.5911269",
"0.5884196",
"0.58651155",
"0.58599305",
"0.58528185",
"0.5841541",
"0.5834487",
"0.58279127",
"0.5772486",
"0.573618... | 0.6291534 | 6 |
Extract data from one scraped html | def extract_one( html: str, fpath: Path ):
# %%
doc = BeautifulSoup( html, features='html.parser')
ret = { 'linkedin_handle': fpath.name.split('.')[0] }
_parse_top_card( ret, doc )
# %%
ret['about'] = _extract_about( doc )
# if len(ret['about']) < 100 and ret['about'].find('ver más') > 0:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_data_listing(html):\n id_finder = re.compile(r'PR[\\d]+~')\n return html.find_all('div', id=id_finder)",
"def page_data():\n return scrape()",
"def _extract_data(self,data,tag=None,cssid=None,cssclass=None,attrs=None,regexp=None,index=0):\n \n# cssclass = \"song\"\n# c... | [
"0.70408916",
"0.6867916",
"0.67792255",
"0.66598994",
"0.65740573",
"0.6546643",
"0.6389909",
"0.6372902",
"0.6365779",
"0.636015",
"0.63392663",
"0.6330602",
"0.63226384",
"0.63203055",
"0.6284733",
"0.6260619",
"0.6241101",
"0.6239079",
"0.6200324",
"0.61735535",
"0.617248... | 0.6600914 | 4 |
Calculate total_experience_yrs and other stats | def calc_work_stats( work_xps: List[Dict[str, Any]] ):
durations = [ rec['duration'] for rec in work_xps if 'duration' in rec ]
total_years = sum( durations ) if durations else None
avg_years = np.round( total_years / len(durations), 2) if durations else None
poss_lt2_years = sum( 1 for dur in durations... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def years_job_experience(self) -> int:\n return sum([job.years_employed for job in self._history_jobs])",
"def get_experience(self):\n return sum([i.get_experience for i in self.__units])",
"async def yearlystats(self, ctx, *, iracing_id=None):\n await self.yearly_stats_db.call(ctx, iracin... | [
"0.6512815",
"0.63359934",
"0.5996619",
"0.5913304",
"0.57828826",
"0.5751886",
"0.5728529",
"0.5614757",
"0.5604025",
"0.5573763",
"0.55307776",
"0.55264235",
"0.5497981",
"0.5462642",
"0.5450629",
"0.54372895",
"0.54367644",
"0.5427174",
"0.5389119",
"0.5351539",
"0.5340693... | 0.6127486 | 2 |
some metrics on the whole profile text | def profile_text_stats( doc: BeautifulSoup ):
text = doc.find('main', {'class': 'core-rail'}).text.strip()
words = text.split()
eng_ratio = sum(1 for word in words if word in COMMON_ENGLISH) * 10/ (len(words) + 0.001)
return { 'length': len( text ),
'eng_ratio': np.round( eng_ratio, 2)}
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _profile(self, text):\n prof = zeros(len(self.alph)**self.N)\n ngs = ngrams(text, self.N)\n for tup in ngs:\n loc = 0\n for i in range(len(tup)):\n loc += (len(self.alph)**i) * self.alph.index(tup[i])\n prof[loc] += 1\n return prof",
... | [
"0.6908674",
"0.64373267",
"0.61230284",
"0.60225594",
"0.6013804",
"0.5936111",
"0.5932316",
"0.5845712",
"0.58176684",
"0.57707936",
"0.576293",
"0.5743936",
"0.57346797",
"0.5728267",
"0.5687166",
"0.56419724",
"0.56302047",
"0.5594292",
"0.55903494",
"0.5582322",
"0.55770... | 0.72970635 | 0 |
process one employment summary and extract info from it | def proc_employment_summary(summary: Tag) -> Dict:
xp_record = dict()
xp_record['position'] = summary.find('h3').text.strip()
company = summary.find_all('p', {'class': 'pv-entity__secondary-title'})[0]
xp_record['company'] = "; ".join( [ line.strip() for line in company.text.split('\n')
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def proc_education_summary( summary: Tag ) -> Dict[str, str]:\n edu_record = dict()\n edu_record['school'] = summary.find('h3').text.strip()\n edu_record['is_abroad_school'] = _is_abroad_school( edu_record['school'] )\n\n for parag in summary.find_all('p'):\n spans = [span.text.strip() for span ... | [
"0.647423",
"0.61216635",
"0.59458005",
"0.56278527",
"0.5343209",
"0.53211635",
"0.52993035",
"0.52750313",
"0.5241437",
"0.5197236",
"0.51397026",
"0.5090858",
"0.5073519",
"0.50543827",
"0.5022594",
"0.5021494",
"0.5021494",
"0.5011691",
"0.4984092",
"0.49774346",
"0.49728... | 0.81081915 | 0 |
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