Datasets:

jon-tow

/

starcoderdata-python-edu

like 8

Chapter__7/unittest/test_cap.py

nil1729/python__noob

12799951

import unittest import cap class TestCap(unittest.TestCase): def test_single_word(self): text = 'python' result = cap.cap_text(text) self.assertEquals(result, 'Python') def test_multiple_word(self): text = 'python django' result = cap.cap_text(text) self.assertEquals(result, 'Python Django') if __name__ == '__main__': unittest.main()

top_secret/_vault.py

trym-inc/top-secret

12799952

from typing import List, Dict, Callable from .cast_handlers import bool_cast_handler from .exceptions import CastHandlerMissingError from .exceptions import SecretMissingError from .exceptions import SecretSourceMissing from .secret_sources import BaseSecretSource from .secret_sources import EnvironmentVariableSecretSource class NoDefault: pass DEFAULT_SECRET_SOURCES = [ EnvironmentVariableSecretSource() ] DEFAULT_CAST_HANDLERS = { bool: bool_cast_handler, } class Vault: _cache = {} cast_handlers: 'Dict[Callable]' = {} secret_sources: 'List[BaseSecretSource]' = [] preprocessors: 'List[Callable[str, str]]' = [] def __init__(self, secret_sources=None, cast_handlers=None, preprocessors=None): if cast_handlers is None: cast_handlers = {} if secret_sources is None: secret_sources = [] if preprocessors is None: preprocessors = [] self.default_secret_sources = secret_sources self.default_cast_handlers = cast_handlers self.default_preprocessors = preprocessors self.reset() def add_secret_source(self, source: 'BaseSecretSource'): if source in self.secret_sources: return self.secret_sources.append(source) def clear_secret_sources(self): self.secret_sources = [] def reset_secret_sources(self): self.secret_sources = list(self.default_secret_sources) def add_cast_handler(self, handler_key, handler): self.cast_handlers[handler_key] = handler def clear_cast_handlers(self): self.cast_handlers = {} def reset_cast_handlers(self): self.cast_handlers = {**self.default_cast_handlers} def add_preprocessor(self, fn): self.preprocessors.append(fn) def clear_preprocessors(self): self.preprocessors = [] def reset_preprocessors(self): self.preprocessors = list(self.default_preprocessors) def clear_cache(self): self._cache = {} def reset(self): self.reset_secret_sources() self.reset_cast_handlers() self.reset_preprocessors() self.clear_cache() def get( self, name, default=NoDefault, *, source=None, preprocessors=None, cast_to=None, no_cache=False, cache_result=True ): if no_cache is False and name in self._cache: return self._cache[name] value = self._get_from_source(name, default, source) value = self._preprocess(value, preprocessors) value = self._cast_to(value, cast_to, default) if cache_result: self._cache[name] = value return value def _get_from_source(self, name, default, source): if source is not None: return source.get(name) if not self.secret_sources: raise SecretSourceMissing for source in self.secret_sources: try: value = source.get(name) break except SecretMissingError: pass else: if default is NoDefault: raise SecretMissingError(name) else: value = default return value def _preprocess(self, value, preprocessors): if preprocessors is None: preprocessors = self.preprocessors else: preprocessors = preprocessors for preprocessor in preprocessors: value = preprocessor(value) return value def _cast_to(self, value, cast_to, default): if value is default: return value if cast_to is not None: handler = self.cast_handlers.get(cast_to, cast_to) if not callable(handler): raise CastHandlerMissingError( f'Cast handler: {handler!r}, is not registered.' ) value = handler(value) return value vault = Vault(DEFAULT_SECRET_SOURCES, DEFAULT_CAST_HANDLERS)

pycode/demo_km.py

Skielex/InSegt

12799953

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Demo showing how km_dict and insegtannotator may be used together for interactive segmentation. @author: vand and abda """ import sys import insegtannotator import skimage.io import skimage.data import km_dict import numpy as np #%% EXAMPLE 1: glass fibres ## loading image print('Loading image') filename = '../data/glass.png' image = skimage.io.imread(filename) #%% EXAMPLE 2: nerve fibres ## loading image print('Loading image') filename = '../data/nerve_im_scale.png' image = skimage.io.imread(filename) #%% COMMON PART patch_size = 11 branching_factor = 5 number_layers = 5 number_training_patches = 35000 normalization = False image_float = image.astype(np.float)/255 # Build tree T = km_dict.build_km_tree(image_float, patch_size, branching_factor, number_training_patches, number_layers, normalization) # Search km-tree and get assignment A, number_nodes = km_dict.search_km_tree(image_float, T, branching_factor, normalization) # number of repetitions for updating the segmentation number_repetitions = 2 def processing_function(labels): r,c = labels.shape l = np.max(labels)+1 label_image = np.zeros((r,c,l)) for k in range(number_repetitions): for i in range(1,l): label_image[:,:,i] = (labels == i).astype(float) D = km_dict.improb_to_dictprob(A, label_image, number_nodes, patch_size) # Dictionary P = km_dict.dictprob_to_improb(A, D, patch_size) # Probability map labels = np.argmax(P,axis=2) # Segmentation return labels print('Showtime') # showtime app = insegtannotator.PyQt5.QtWidgets.QApplication([]) ex = insegtannotator.InSegtAnnotator(image, processing_function) app.exec() sys.exit()

src/eltetrado/analysis.py

tzok/el_tetrado

12799954

<gh_stars>0 import itertools import logging import math import os import string import subprocess import tempfile from collections import defaultdict, Counter from dataclasses import dataclass, field from typing import Dict, Iterable, List, Tuple, Optional, Set import numpy from eltetrado.model import Atom3D, Structure3D, Structure2D, BasePair3D, Residue3D, GlycosidicBond, ONZ, \ GbaTetradClassification, Ion, Direction, LoopType, ONZM, GbaQuadruplexClassification, LoopClassification logging.basicConfig(level=os.environ.get("LOGLEVEL", "INFO")) @dataclass(order=True) class Tetrad: @staticmethod def is_valid(nt1: Residue3D, nt2: Residue3D, nt3: Residue3D, nt4: Residue3D, pair_dictionary: Dict[Tuple[Residue3D, Residue3D], BasePair3D]) -> bool: lw1 = pair_dictionary[(nt1, nt2)].lw lw2 = pair_dictionary[(nt2, nt3)].lw lw3 = pair_dictionary[(nt3, nt4)].lw lw4 = pair_dictionary[(nt4, nt1)].lw for lw_i, lw_j in ((lw1, lw4), (lw2, lw1), (lw3, lw2), (lw4, lw3)): if lw_i.name[1] == lw_j.name[2]: return False return True nt1: Residue3D nt2: Residue3D nt3: Residue3D nt4: Residue3D pair_12: BasePair3D pair_23: BasePair3D pair_34: BasePair3D pair_41: BasePair3D onz: ONZ = field(init=False) gba_class: Optional[GbaTetradClassification] = field(init=False) planarity_deviation: float = field(init=False) ions_channel: List[Atom3D] = field(default_factory=list) ions_outside: Dict[Residue3D, List[Atom3D]] = field(default_factory=dict) def __post_init__(self): self.reorder_to_match_5p_3p() self.planarity_deviation = self.__calculate_planarity_deviation() def reorder_to_match_5p_3p(self): # transform into (0, 1, 2, 3) ni, nj, nk, nl = map(lambda nt: nt.index, self.nucleotides) indices = sorted((ni, nj, nk, nl)) ni, nj, nk, nl = (indices.index(x) for x in (ni, nj, nk, nl)) nmin = min(ni, nj, nk, nl) if nmin == ni: pass elif nmin == nj: self.nt1, self.nt2, self.nt3, self.nt4 = self.nt2, self.nt3, self.nt4, self.nt1 self.pair_12, self.pair_23, self.pair_34, self.pair_41 = self.pair_23, self.pair_34, self.pair_41, self.pair_12 elif nmin == nk: self.nt1, self.nt2, self.nt3, self.nt4 = self.nt3, self.nt4, self.nt1, self.nt2 self.pair_12, self.pair_23, self.pair_34, self.pair_41 = self.pair_34, self.pair_41, self.pair_12, self.pair_23 else: self.nt1, self.nt2, self.nt3, self.nt4 = self.nt4, self.nt1, self.nt2, self.nt3 self.pair_12, self.pair_23, self.pair_34, self.pair_41 = self.pair_41, self.pair_12, self.pair_23, self.pair_34 # flip order if necessary if self.pair_12.score() > self.pair_41.reverse().score(): self.nt1, self.nt2, self.nt3, self.nt4 = self.nt1, self.nt4, self.nt3, self.nt2 self.pair_12, self.pair_23, self.pair_34, self.pair_41 = self.pair_41.reverse(), self.pair_34.reverse(), self.pair_23.reverse(), self.pair_12.reverse() # ONZ and da Silva's classification are valid in 5'-3' order self.onz = self.__classify_onz() self.gba_class = self.__classify_by_gba() def reorder_to_match_other_tetrad(self, order: Tuple[Residue3D, Residue3D, Residue3D, Residue3D]): if order == (self.nt1, self.nt2, self.nt3, self.nt4): pass elif order == (self.nt2, self.nt3, self.nt4, self.nt1): self.pair_12, self.pair_23, self.pair_34, self.pair_41 = self.pair_23, self.pair_34, self.pair_41, self.pair_12 elif order == (self.nt3, self.nt4, self.nt1, self.nt2): self.pair_12, self.pair_23, self.pair_34, self.pair_41 = self.pair_34, self.pair_41, self.pair_12, self.pair_23 elif order == (self.nt4, self.nt1, self.nt2, self.nt3): self.pair_12, self.pair_23, self.pair_34, self.pair_41 = self.pair_41, self.pair_12, self.pair_23, self.pair_34 elif order == (self.nt4, self.nt3, self.nt2, self.nt1): self.pair_12, self.pair_23, self.pair_34, self.pair_41 = self.pair_34.reverse(), self.pair_23.reverse(), self.pair_12.reverse(), self.pair_41.reverse() elif order == (self.nt3, self.nt2, self.nt1, self.nt4): self.pair_12, self.pair_23, self.pair_34, self.pair_41 = self.pair_23.reverse(), self.pair_12.reverse(), self.pair_41.reverse(), self.pair_34.reverse() elif order == (self.nt2, self.nt1, self.nt4, self.nt3): self.pair_12, self.pair_23, self.pair_34, self.pair_41 = self.pair_12.reverse(), self.pair_41.reverse(), self.pair_34.reverse(), self.pair_23.reverse() elif order == (self.nt1, self.nt4, self.nt3, self.nt2): self.pair_12, self.pair_23, self.pair_34, self.pair_41 = self.pair_41.reverse(), self.pair_34.reverse(), self.pair_23.reverse(), self.pair_12.reverse() else: raise RuntimeError(f'Cannot apply order: {order}') self.nt1, self.nt2, self.nt3, self.nt4 = order def __classify_onz(self) -> ONZ: # transform into (0, 1, 2, 3) ni, nj, nk, nl = (nt.index for nt in self.nucleotides) indices = sorted((ni, nj, nk, nl)) ni, nj, nk, nl = (indices.index(x) for x in (ni, nj, nk, nl)) while ni != 0: ni, nj, nk, nl = nl, ni, nj, nk order = (nj, nk, nl) if order == (1, 2, 3): return ONZ.O_PLUS elif order == (3, 2, 1): return ONZ.O_MINUS elif order == (1, 3, 2): return ONZ.N_PLUS elif order == (2, 3, 1): return ONZ.N_MINUS elif order == (2, 1, 3): return ONZ.Z_PLUS elif order == (3, 1, 2): return ONZ.Z_MINUS raise RuntimeError(f'Impossible combination: {ni} {nj} {nk} {nl}') def __classify_by_gba(self) -> Optional[GbaTetradClassification]: """ See: <NAME>. (2007). Geometric Formalism for DNA Quadruplex Folding. Chemistry - A European Journal, 13(35), 9738–9745. https://doi.org/10.1002/chem.200701255 :return: Classification according to Webba da Silva or n/a """ # without all nucleotides having a valid syn/anti, this classification is impossible if not all([nt.chi_class in (GlycosidicBond.syn, GlycosidicBond.anti) for nt in self.nucleotides]): return None # this will create a 4-letter string made of 's' for syn or 'a' for anti fingerprint = ''.join([nt.chi_class.value[0] for nt in self.nucleotides]) # this dict has all classes mapped to fingerprints gba_classes = { 'aass': GbaTetradClassification.Ia, 'ssaa': GbaTetradClassification.Ib, 'asas': GbaTetradClassification.IIa, 'sasa': GbaTetradClassification.IIb, 'asaa': GbaTetradClassification.IIIa, 'sass': GbaTetradClassification.IIIb, 'aaas': GbaTetradClassification.IVa, 'sssa': GbaTetradClassification.IVb, 'aasa': GbaTetradClassification.Va, 'ssas': GbaTetradClassification.Vb, 'assa': GbaTetradClassification.VIa, 'saas': GbaTetradClassification.VIb, 'asss': GbaTetradClassification.VIIa, 'saaa': GbaTetradClassification.VIIb, 'aaaa': GbaTetradClassification.VIIIa, 'ssss': GbaTetradClassification.VIIIb } if fingerprint not in gba_classes: logging.error(f'Impossible combination of syn/anti: {[nt.chi_class for nt in self.nucleotides]}') return None return gba_classes[fingerprint] def __calculate_planarity_deviation(self) -> float: outer = [nt.outermost_atom for nt in self.nucleotides] inner = [nt.innermost_atom for nt in self.nucleotides] return numpy.linalg.norm(center_of_mass(outer) - center_of_mass(inner)) @property def nucleotides(self) -> Tuple[Residue3D, Residue3D, Residue3D, Residue3D]: return self.nt1, self.nt2, self.nt3, self.nt4 def __hash__(self): return hash(frozenset([self.nt1, self.nt2, self.nt3, self.nt4])) def __str__(self): return f' ' \ f'{self.nt1.full_name} {self.nt2.full_name} {self.nt3.full_name} {self.nt4.full_name} ' \ f'{self.pair_12.lw.value} {self.pair_23.lw.value} {self.pair_34.lw.value} {self.pair_41.lw.value} ' \ f'{self.onz.value} {self.gba_class.value} ' \ f'planarity={round(self.planarity_deviation, 2)} ' \ f'{self.__ions_channel_str()} ' \ f'{self.__ions_outside_str()}\n' def chains(self) -> Set[str]: return set([nt.chain for nt in self.nucleotides]) def is_disjoint(self, other) -> bool: return frozenset(self.nucleotides).isdisjoint(frozenset(other.nucleotides)) def center(self) -> numpy.ndarray: return center_of_mass(self.outer_and_inner_atoms()) def outer_and_inner_atoms(self) -> List[Atom3D]: return list(map(lambda residue: residue.outermost_atom, self.nucleotides)) + \ list(map(lambda residue: residue.innermost_atom, self.nucleotides)) def __ions_channel_str(self) -> str: if self.ions_channel: return 'ions_channel=' + ','.join([atom.atomName for atom in self.ions_channel]) return '' def __ions_outside_str(self) -> str: if self.ions_outside: result = [] for residue, ions in self.ions_outside.items(): result.append(f'{residue.full_name}: [{",".join([ion.atomName for ion in ions])}]') return 'ions_outside=' + ' '.join(result) return '' @dataclass class TetradPair: tetrad1: Tetrad tetrad2: Tetrad stacked: Dict[Residue3D, Residue3D] tetrad2_nts_best_order: Tuple[Residue3D, Residue3D, Residue3D, Residue3D] = field(init=False) direction: Direction = field(init=False) rise: float = field(init=False) twist: float = field(init=False) def __post_init__(self): self.tetrad2_nts_best_order = ( self.stacked[self.tetrad1.nt1], self.stacked[self.tetrad1.nt2], self.stacked[self.tetrad1.nt3], self.stacked[self.tetrad1.nt4] ) self.direction = self.__determine_direction() self.rise = self.__calculate_rise() self.twist = self.__calculate_twist() def __determine_direction(self) -> Direction: indices1 = list(map(lambda nt: nt.index, self.tetrad1.nucleotides)) indices2 = list(map(lambda nt: nt.index, self.tetrad2_nts_best_order)) # count directions 5' -> 3' as +1 or -1 counter = Counter(1 if j - i > 0 else -1 for i, j in zip(indices1, indices2)) direction, count = counter.most_common()[0] if count == 4: # all in the same direction return Direction.parallel elif count == 2: # two in +, one in - direction return Direction.antiparallel return Direction.hybrid def __calculate_rise(self) -> float: t1 = self.tetrad1.outer_and_inner_atoms() t2 = self.tetrad2.outer_and_inner_atoms() return numpy.linalg.norm(center_of_mass(t1) - center_of_mass(t2)) def __calculate_twist(self) -> float: nt1_1, nt1_2, _, _ = self.tetrad1.nucleotides nt2_1, nt2_2, _, _ = self.tetrad2_nts_best_order v1 = nt1_1.find_atom("C1'").coordinates() - nt1_2.find_atom("C1'").coordinates() v1 = v1 / numpy.linalg.norm(v1) v2 = nt2_1.find_atom("C1'").coordinates() - nt2_2.find_atom("C1'").coordinates() v2 = v2 / numpy.linalg.norm(v2) return math.degrees(numpy.arccos(numpy.clip(numpy.dot(v1, v2), -1.0, 1.0))) def __str__(self): return f' direction={self.direction.value} rise={round(self.rise, 2)} twist={round(self.twist, 2)}\n' @dataclass class Tract: nucleotides: List[Residue3D] def __str__(self): return f' {", ".join(map(lambda nt: nt.full_name, self.nucleotides))}' @dataclass class Loop: nucleotides: List[Residue3D] loop_type: Optional[LoopType] def __str__(self): return f' {self.loop_type.value if self.loop_type else "n/a"} ' \ f'{", ".join(map(lambda nt: nt.full_name, self.nucleotides))}' @dataclass class Quadruplex: tetrads: List[Tetrad] tetrad_pairs: List[TetradPair] structure3d: Structure3D onzm: Optional[ONZM] = field(init=False) gba_classes: List[GbaQuadruplexClassification] = field(init=False) tracts: List[Tract] = field(init=False) loops: List[Loop] = field(init=False) loop_class: Optional[LoopClassification] = field(init=False) def __post_init__(self): self.onzm = self.__classify_onzm() self.gba_classes = self.__classify_by_gba() self.tracts = self.__find_tracts() self.loops = self.__find_loops() self.loop_class = self.__classify_by_loops() def __classify_onzm(self) -> Optional[ONZM]: if len(self.tetrads) == 1: return None if any([t.onz is None for t in self.tetrads]): return None counter = Counter([t.onz.value[0] for t in self.tetrads]) onz, support = counter.most_common()[0] if support != len(self.tetrads): onz = 'M' counter = Counter([tp.direction.value[0] for tp in self.tetrad_pairs]) direction, support = counter.most_common()[0] if support != len(self.tetrad_pairs): direction = 'h' counter = Counter([t.onz.value[1] for t in self.tetrads]) plus_minus, support = counter.most_common()[0] if support != len(self.tetrads): plus_minus = '*' return ONZM.from_value(f'{onz}{direction}{plus_minus}') def __classify_by_gba(self) -> List[GbaQuadruplexClassification]: gbas = set() for t in self.tetrads: gba = t.gba_class if gba is not None: gbas.add(gba.value[:-1]) # discard 'a' or 'b' subvariant roman_numerals = {'I': 1, 'II': 2, 'III': 3, 'IV': 4, 'V': 5, 'VI': 6, 'VII': 7, 'VIII': 8} gbas = sorted(gbas, key=lambda gba: roman_numerals.get(gba, 100)) return list(map(lambda x: GbaQuadruplexClassification[x], gbas)) def __find_tracts(self) -> List[Tract]: tracts = [[self.tetrads[0].nt1], [self.tetrads[0].nt2], [self.tetrads[0].nt3], [self.tetrads[0].nt4]] if len(self.tetrad_pairs) > 0: for tetrad_pair in self.tetrad_pairs: nt_dict = { tetrad_pair.tetrad1.nt1: tetrad_pair.tetrad2_nts_best_order[0], tetrad_pair.tetrad1.nt2: tetrad_pair.tetrad2_nts_best_order[1], tetrad_pair.tetrad1.nt3: tetrad_pair.tetrad2_nts_best_order[2], tetrad_pair.tetrad1.nt4: tetrad_pair.tetrad2_nts_best_order[3], } for i in range(4): tracts[i].append(nt_dict[tracts[i][-1]]) return [Tract(nts) for nts in tracts] def __find_loops(self) -> List[Loop]: if len(self.tetrads) == 1: return [] loops = [] tetrad_nucleotides = sorted([nt for tetrad in self.tetrads for nt in tetrad.nucleotides], key=lambda nt: nt.index) for i in range(1, len(tetrad_nucleotides)): nprev = tetrad_nucleotides[i - 1] ncur = tetrad_nucleotides[i] if ncur.index - nprev.index > 1 and ncur.chain == nprev.chain: for tract in self.tracts: if nprev in tract.nucleotides and ncur in tract.nucleotides: break else: nts = list(filter(lambda nt: nprev.index < nt.index < ncur.index, self.structure3d.residues)) loop_type = self.__detect_loop_type(nprev, ncur) loops.append(Loop(nts, loop_type)) return loops def __detect_loop_type(self, nt_first: Residue3D, nt_last: Residue3D) -> Optional[LoopType]: tetrad_with_first = self.__find_tetrad_with_nt(nt_first) tetrad_with_last = self.__find_tetrad_with_nt(nt_last) if tetrad_with_first is None or tetrad_with_last is None: logging.warning(f'Failed to classify the loop between {nt_first} and {nt_last}') return None if tetrad_with_first == tetrad_with_last: # diagonal or laterals happen when first and last nt of a loop is in the same tetrad sign = self.__detect_loop_sign(nt_first, nt_last, tetrad_with_first) if sign is not None: return LoopType.from_value(f'lateral{sign}') return LoopType.diagonal tract_with_last = self.__find_tract_with_nt(nt_last) if tract_with_last is not None: # search along the tract to check what pairs with nt_first for nt in tract_with_last.nucleotides: if nt in tetrad_with_first.nucleotides: sign = self.__detect_loop_sign(nt_first, nt, tetrad_with_first) if sign is not None: return LoopType.from_value(f'propeller{sign}') logging.warning(f'Failed to classify the loop between {nt_first} and {nt_last}') return None def __find_tetrad_with_nt(self, nt: Residue3D) -> Optional[Tetrad]: for tetrad in self.tetrads: if nt in tetrad.nucleotides: return tetrad return None def __find_tract_with_nt(self, nt: Residue3D) -> Optional[Tract]: for tract in self.tracts: if nt in tract.nucleotides: return tract return None def __detect_loop_sign(self, first: Residue3D, last: Residue3D, tetrad: Tetrad) -> Optional[str]: for pair in [tetrad.pair_12, tetrad.pair_23, tetrad.pair_34, tetrad.pair_41]: # main check if pair.nt1 == first and pair.nt2 == last: if pair.score() < pair.reverse().score(): return '-' return '+' # reverse check if pair.nt1 == last and pair.nt2 == first: if pair.score() < pair.reverse().score(): return '+' return '-' return None def __classify_by_loops(self) -> Optional[LoopClassification]: if len(self.loops) != 3 or any([loop.loop_type is None for loop in self.loops]): return None loop_classes = { 'ppp': '1', 'ppl': '2', 'plp': '3', 'lpp': '4', 'pdp': '5', 'lll': '6', 'llp': '7', 'lpl': '8', 'pll': '9', 'pdl': '10', 'ldl': '11', 'dpd': '12', 'ldp': '13' } fingerprint = ''.join([loop.loop_type.value[0] for loop in self.loops]) if fingerprint not in loop_classes: logging.error(f'Unknown loop classification: {fingerprint}') return None subtype = 'a' if self.loops[0 if fingerprint != 'dpd' else 1].loop_type.value[-1] == '-' else 'b' return LoopClassification.from_value(f'{loop_classes[fingerprint]}{subtype}') def __str__(self): builder = '' if len(self.tetrads) == 1: builder += ' single tetrad\n' builder += str(self.tetrads[0]) else: builder += f' {self.onzm.value if self.onzm is not None else "R"}' builder += f' {",".join(map(lambda gba: gba.value, self.gba_classes))}' if self.loop_class: builder += f' {self.loop_class.value} {self.loop_class.loop_progression()}' else: builder += f' n/a' builder += f' quadruplex with {len(self.tetrads)} tetrads\n' builder += str(self.tetrad_pairs[0].tetrad1) for tetrad_pair in self.tetrad_pairs: builder += str(tetrad_pair) builder += str(tetrad_pair.tetrad2) if self.tracts: builder += '\n Tracts:\n' for tract in self.tracts: builder += f'{tract}\n' if self.loops: builder += '\n Loops:\n' for loop in self.loops: builder += f'{loop}\n' builder += '\n' return builder @dataclass class Helix: tetrads: List[Tetrad] tetrad_pairs: List[TetradPair] structure3d: Structure3D quadruplexes: List[Quadruplex] = field(init=False) def __post_init__(self): self.quadruplexes = self.__find_quadruplexes() def __find_quadruplexes(self): if len(self.tetrad_pairs) == 0: return [Quadruplex(self.tetrads, [], self.structure3d)] quadruplexes = list() tetrads = list() for tetrad in [self.tetrad_pairs[0].tetrad1] + [tetrad_pair.tetrad2 for tetrad_pair in self.tetrad_pairs]: if tetrads: if tetrad.chains().isdisjoint(tetrads[-1].chains()): quadruplexes.append(Quadruplex(tetrads, self.__filter_tetrad_pairs(tetrads), self.structure3d)) tetrads = list() tetrads.append(tetrad) quadruplexes.append(Quadruplex(tetrads, self.__filter_tetrad_pairs(tetrads), self.structure3d)) return quadruplexes def __filter_tetrad_pairs(self, tetrads: List[Tetrad]) -> List[TetradPair]: chains = set() for tetrad in tetrads: chains.update(tetrad.chains()) def check_tetrad(t: Tetrad) -> bool: return not t.chains().isdisjoint(chains) def check_pair(tp: TetradPair) -> bool: return check_tetrad(tp.tetrad1) and check_tetrad(tp.tetrad2) return list(filter(check_pair, self.tetrad_pairs)) def __str__(self): builder = '' if len(self.tetrads) > 1: builder += f'n4-helix with {len(self.tetrads)} tetrads\n' for quadruplex in self.quadruplexes: builder += str(quadruplex) elif len(self.tetrads) == 1: builder += 'single tetrad without stacking\n' builder += str(self.tetrads[0]) return builder @dataclass class Analysis: structure2d: Structure2D structure3d: Structure3D strict: bool no_reorder: bool stacking_mismatch: int base_pairs: List[BasePair3D] = field(init=False) base_pair_graph: Dict[Residue3D, List[Residue3D]] = field(init=False) base_pair_dict: Dict[Tuple[Residue3D, Residue3D], BasePair3D] = field(init=False) stacking_graph: Dict[Residue3D, List[Residue3D]] = field(init=False) tetrads: List[Tetrad] = field(init=False) tetrad_scores: Dict[Tetrad, Dict[Tetrad, Tuple[int, Tuple, Tuple]]] = field(init=False) tetrad_pairs: List[TetradPair] = field(init=False) helices: List[Helix] = field(init=False) ions: List[Atom3D] = field(init=False) sequence: str = field(init=False) line1: str = field(init=False) line2: str = field(init=False) shifts: Dict[Residue3D, int] = field(init=False) def __post_init__(self): self.base_pairs = self.structure3d.base_pairs(self.structure2d) self.base_pair_graph = self.structure3d.base_pair_graph(self.structure2d, self.strict) self.base_pair_dict = self.structure3d.base_pair_dict(self.structure2d, self.strict) self.stacking_graph = self.structure3d.stacking_graph(self.structure2d) self.tetrads = self.__find_tetrads(self.no_reorder) self.tetrad_scores = self.__calculate_tetrad_scores() self.tetrad_pairs = self.__find_tetrad_pairs(self.stacking_mismatch) self.helices = self.__find_helices() if not self.no_reorder: self.__find_best_chain_order() self.sequence, self.line1, self.line2, self.shifts = self.__generate_twoline_dotbracket() self.ions = self.__find_ions() self.__assign_ions_to_tetrads() def __find_tetrads(self, no_reorder=False) -> List[Tetrad]: # search for a tetrad: i -> j -> k -> l # ^--------------^ tetrads = [] for i in self.base_pair_graph: for j in filter(lambda x: x != i, self.base_pair_graph[i]): for k in filter(lambda x: x not in (i, j), self.base_pair_graph[j]): for l in filter(lambda x: x not in (i, j, k) and i in self.base_pair_graph[x], self.base_pair_graph[k]): if Tetrad.is_valid(i, j, k, l, self.base_pair_dict): pair_12 = self.base_pair_dict[(i, j)] pair_23 = self.base_pair_dict[(j, k)] pair_34 = self.base_pair_dict[(k, l)] pair_41 = self.base_pair_dict[(l, i)] tetrads.append(Tetrad(i, j, k, l, pair_12, pair_23, pair_34, pair_41)) # build graph of tetrads while tetrads: graph = defaultdict(list) for (ti, tj) in itertools.combinations(tetrads, 2): if not ti.is_disjoint(tj): graph[ti].append(tj) graph[tj].append(ti) # remove tetrad which conflicts the most with others # in case of a tie, remove one which has the worst planarity deviation candidates = sorted(tetrads, key=lambda t: (len(graph[t]), t.planarity_deviation), reverse=True) if len(graph[candidates[0]]) > 0: tetrads.remove(candidates[0]) else: break return sorted(tetrads, key=lambda t: min(map(lambda nt: nt.index, t.nucleotides))) def __calculate_tetrad_scores(self) \ -> Dict[Tetrad, Dict[Tetrad, Tuple[int, Tuple, Tuple]]]: def is_next_by_stacking(nt1: Residue3D, nt2: Residue3D) -> bool: return nt2 in self.stacking_graph.get(nt1, []) def is_next_sequentially(nt1: Residue3D, nt2: Residue3D) -> bool: return nt1.chain == nt2.chain and abs(nt1.index - nt2.index) == 1 tetrad_scores = defaultdict(dict) for ti, tj in itertools.combinations(self.tetrads, 2): nts1 = ti.nucleotides best_score = 0 best_score_sequential = 0 best_score_stacking = 0 best_order = tj.nucleotides n1, n2, n3, n4 = tj.nucleotides viable_permutations = [(n1, n2, n3, n4), (n2, n3, n4, n1), (n3, n4, n1, n2), (n4, n1, n2, n3), (n1, n4, n3, n2), (n4, n3, n2, n1), (n3, n2, n1, n4), (n2, n1, n4, n3)] for nts2 in viable_permutations: score_stacking = [1 if is_next_by_stacking(nts1[i], nts2[i]) else 0 for i in range(4)] score_sequential = [1 if is_next_sequentially(nts1[i], nts2[i]) else 0 for i in range(4)] score = sum([max(score_stacking[i], score_sequential[i]) for i in range(4)]) score_sequential = sum(score_sequential) score_stacking = sum(score_stacking) if (score, score_sequential, score_stacking) > (best_score, best_score_sequential, best_score_stacking): best_score, best_score_sequential, best_score_stacking = score, score_sequential, score_stacking best_order = nts2 if best_score == 4: break tetrad_scores[ti][tj] = (best_score, nts1, best_order) tetrad_scores[tj][ti] = (best_score, best_order, nts1) return tetrad_scores def __find_tetrad_pairs(self, stacking_mismatch: int) -> List[TetradPair]: tetrads = list(self.tetrads) best_score = 0 best_order = tetrads for ti in tetrads: score = 0 order = [ti] candidates = set(self.tetrads) - {ti} while candidates: tj = max([tj for tj in candidates], key=lambda tk: self.tetrad_scores[ti][tk][0]) score += self.tetrad_scores[ti][tj][0] order.append(tj) candidates.remove(tj) ti = tj if score > best_score: best_score = score best_order = order if best_score == (len(self.tetrads) - 1) * 4: break tetrad_pairs = [] for i in range(1, len(best_order)): ti, tj = best_order[i - 1], best_order[i] score = self.tetrad_scores[ti][tj][0] if score >= (4 - stacking_mismatch): nts1, nts2 = self.tetrad_scores[ti][tj][1:] stacked = {nts1[i]: nts2[i] for i in range(4)} stacked.update({v: k for k, v in stacked.items()}) tetrad_pairs.append(TetradPair(ti, tj, stacked)) order = (stacked[ti.nt1], stacked[ti.nt2], stacked[ti.nt3], stacked[ti.nt4]) tj.reorder_to_match_other_tetrad(order) return tetrad_pairs def __find_helices(self): helices = [] helix_tetrads = [] helix_tetrad_pairs = [] for tp in self.tetrad_pairs: ti, tj = tp.tetrad1, tp.tetrad2 if not helix_tetrads: helix_tetrads.append(ti) score = self.tetrad_scores[helix_tetrads[-1]][tj][0] if score >= (4 - self.stacking_mismatch): helix_tetrads.append(tj) helix_tetrad_pairs.append(tp) else: helices.append(Helix(helix_tetrads, helix_tetrad_pairs, self.structure3d)) helix_tetrads = [ti, tj] helix_tetrad_pairs = [tp] if helix_tetrads: helices.append(Helix(helix_tetrads, helix_tetrad_pairs, self.structure3d)) for tetrad in self.tetrads: if not any([tetrad in helix.tetrads for helix in helices]): helices.append(Helix([tetrad], [], self.structure3d)) return helices def __find_best_chain_order(self): chain_groups = self.__group_related_chains() final_order = [] for chains in chain_groups: best_permutation, best_score = chains, (1e10, 1e10) if len(chains) > 1: for permutation in itertools.permutations(chains): self.__reorder_chains(permutation) classifications = [t.onz for h in self.helices for t in h.tetrads] logging.debug( f'Checking reorder: {" ".join(permutation)} {" ".join(map(lambda c: c.value, classifications))}') onz_score = sum(c.score() for c in classifications) chain_order_score = self.__chain_order_score(permutation) score = (onz_score, chain_order_score) if score < best_score: best_score = score best_permutation = permutation elif score == best_score: # in case of a tie, pick permutation earlier in lexicographical sense if permutation < best_permutation: best_permutation = permutation final_order.extend(best_permutation) if len(final_order) > 1: self.__reorder_chains(final_order) classifications = [t.onz for h in self.helices for t in h.tetrads] logging.debug(f'Selected chain order: {" ".join(final_order)} ' f'{" ".join(map(lambda onz: onz.value, classifications))}') self.tetrads = self.__find_tetrads(True) self.tetrad_scores = self.__calculate_tetrad_scores() self.tetrad_pairs = self.__find_tetrad_pairs(self.stacking_mismatch) self.helices = self.__find_helices() def __group_related_chains(self) -> List[List[str]]: candidates = set() for h in self.helices: for t in h.tetrads: candidates.add(frozenset([t.nt1.chain, t.nt2.chain, t.nt3.chain, t.nt4.chain])) candidates = [set(c) for c in candidates] changed = True while changed: changed = False for i, j in itertools.combinations(range(len(candidates)), 2): qi, qj = candidates[i], candidates[j] if not qi.isdisjoint(qj): qi.update(qj) del candidates[j] changed = True break candidates = sorted(candidates, key=lambda x: len(x), reverse=True) groups = [] for candidate in candidates: if any([group.issuperset(candidate) for group in groups]): continue groups.append(candidate) return sorted([sorted(group) for group in groups], key=lambda x: x[0]) def __reorder_chains(self, chain_order: Iterable[str]): i = 1 for chain in chain_order: for nt in self.structure3d.residues: if nt.chain == chain: nt.index = i i += 1 for nt in self.structure3d.residues: if nt.chain not in chain_order: nt.index = i i += 1 if len(self.tetrad_pairs) > 0: self.tetrad_pairs[0].tetrad1.reorder_to_match_5p_3p() for tp in self.tetrad_pairs: order = (tp.stacked[tp.tetrad1.nt1], tp.stacked[tp.tetrad1.nt2], tp.stacked[tp.tetrad1.nt3], tp.stacked[tp.tetrad1.nt4]) tp.tetrad2.reorder_to_match_5p_3p() # this is required to recalculate ONZ tp.tetrad2.reorder_to_match_other_tetrad(order) def __chain_order_score(self, chain_order: Tuple[str, ...]) -> int: chain_pairs = [] for h in self.helices: for t in h.tetrads: for p in [t.pair_12, t.pair_23, t.pair_34, t.pair_41]: c1 = p.nt1.chain c2 = p.nt2.chain if c1 != c2 and c1 in chain_order and c2 in chain_order: chain_pairs.append([c1, c2]) sum_sq = 0 for c1, c2 in chain_pairs: sum_sq += (chain_order.index(c1) - chain_order.index(c2)) ** 2 return sum_sq def __find_ions(self) -> List[Atom3D]: metal_atom_names = set([ion.value.upper() for ion in Ion]) ions = [] used = set() for residue in self.structure3d.residues: for atom in residue.atoms: if atom.atomName.upper() in metal_atom_names: coordinates = tuple(atom.coordinates()) if coordinates not in used: ions.append(atom) used.add(coordinates) return ions def __assign_ions_to_tetrads(self) \ -> Tuple[Dict[Tetrad, List[Atom3D]], Dict[Tuple[Tetrad, Residue3D], List[Atom3D]]]: if len(self.tetrads) == 0: return {}, {} ions_channel = defaultdict(list) ions_outside = defaultdict(list) for ion in self.ions: min_distance = math.inf min_tetrad = self.tetrads[0] for tetrad in self.tetrads: distance = numpy.linalg.norm(ion.coordinates() - tetrad.center()) if distance < min_distance: min_distance = distance min_tetrad = tetrad # TODO: verify threshold of 6A between an ion and tetrad channel if min_distance < 6.0: ions_channel[min_tetrad].append(ion) continue min_distance = math.inf min_tetrad = self.tetrads[0] min_nt = min_tetrad.nt1 for tetrad in self.tetrads: for nt in tetrad.nucleotides: for atom in nt.atoms: distance = numpy.linalg.norm(ion.coordinates() - atom.coordinates()) if distance < min_distance: min_distance = distance min_tetrad = tetrad min_nt = nt # TODO: verify threshold of 3A between an ion and an atom if min_distance < 3.0: ions_outside[(min_tetrad, min_nt)].append(ion) continue logging.debug(f'Skipping an ion, because it is too far from any tetrad (distance={min_distance})') for tetrad, ions in ions_channel.items(): tetrad.ions_channel = ions for pair, ions in ions_outside.items(): tetrad, residue = pair tetrad.ions_outside[residue] = ions def __generate_twoline_dotbracket(self) -> Tuple[str, str, str, Dict[Residue3D, int]]: layer1, layer2 = [], [] for tetrad in self.tetrads: layer1.extend([tetrad.pair_12, tetrad.pair_34]) layer2.extend([tetrad.pair_23, tetrad.pair_41]) sequence, line1, shifts = self.__elimination_conflicts(layer1) _, line2, _ = self.__elimination_conflicts(layer2) return sequence, line1, line2, shifts def __elimination_conflicts(self, pairs: List[BasePair3D]) -> Tuple[str, str, Dict[Residue3D, int]]: orders = dict() order = 0 queue = list(pairs) removed = [] while queue: conflicts = defaultdict(list) for pi, pj in itertools.combinations(queue, 2): if pi.conflicts_with(pj): conflicts[pi].append(pj) conflicts[pj].append(pi) if conflicts: pair, _ = max(conflicts.items(), key=lambda x: (len(x[1]), x[0].nt1)) removed.append(pair) queue.remove(pair) else: orders.update({pair: order for pair in queue}) queue, removed = removed, [] order += 1 opening = '([{<' + string.ascii_uppercase closing = ')]}>' + string.ascii_lowercase dotbracket = dict() for pair, order in orders.items(): nt1, nt2 = sorted([pair.nt1, pair.nt2]) dotbracket[nt1] = opening[order] dotbracket[nt2] = closing[order] sequence = '' structure = '' shifts = dict() shift_value = 0 chain = None for nt in sorted(filter(lambda nt: nt.is_nucleotide, self.structure3d.residues), key=lambda nt: nt.index): if chain and chain != nt.chain: sequence += '-' structure += '-' shift_value += 1 sequence += nt.one_letter_name structure += dotbracket.get(nt, '.') shifts[nt] = shift_value chain = nt.chain return sequence, structure, shifts def __str__(self): builder = f'Chain order: {" ".join(self.__chain_order())}\n' for helix in self.helices: builder += str(helix) builder += f'{self.sequence}\n{self.line1}\n{self.line2}' return builder def __chain_order(self) -> List[str]: only_nucleic_acids = filter(lambda nt: nt.is_nucleotide, self.structure3d.residues) return list({nt.chain: 0 for nt in sorted(only_nucleic_acids, key=lambda nt: nt.index)}.keys()) def canonical(self) -> List[BasePair3D]: return [base_pair for base_pair in self.base_pairs if base_pair.is_canonical()] @dataclass class Visualizer: analysis: Analysis tetrads: List[Tetrad] complete2d: bool onz_dict: Dict[BasePair3D, ONZ] = field(init=False) def __post_init__(self): self.onz_dict = {pair: tetrad.onz for tetrad in self.tetrads for pair in [tetrad.pair_12, tetrad.pair_23, tetrad.pair_34, tetrad.pair_41]} def visualize(self, prefix: str, suffix: str): fasta = tempfile.NamedTemporaryFile('w+', suffix='.fasta') fasta.write(f'>{prefix}-{suffix}\n') fasta.write(self.analysis.sequence) fasta.flush() layer1, layer2 = [], [] for tetrad in self.tetrads: layer1.extend([tetrad.pair_12, tetrad.pair_34]) layer2.extend([tetrad.pair_23, tetrad.pair_41]) helix1 = self.__to_helix(layer1, self.analysis.canonical() if self.complete2d else []) helix2 = self.__to_helix(layer2) currdir = os.path.dirname(os.path.realpath(__file__)) output_pdf = f'{prefix}-{suffix}.pdf' run = subprocess.run([os.path.join(currdir, 'quadraw.R'), fasta.name, helix1.name, helix2.name, output_pdf], stdout=subprocess.PIPE, stderr=subprocess.PIPE) if run.returncode == 0: print('\nPlot:', output_pdf) else: logging.error(f'Failed to prepare visualization, reason:\n {run.stderr.decode()}') def __to_helix(self, layer: List[BasePair3D], canonical: Optional[List[BasePair3D]] = None) -> tempfile.NamedTemporaryFile(): onz_value = {ONZ.O_PLUS: 1, ONZ.O_MINUS: 2, ONZ.N_PLUS: 3, ONZ.N_MINUS: 4, ONZ.Z_PLUS: 5, ONZ.Z_MINUS: 6} nucleotides = self.analysis.structure3d.residues shifts = self.analysis.shifts helix = tempfile.NamedTemporaryFile('w+', suffix='.helix') helix.write(f'#{len(self.analysis.sequence) + 1}\n') helix.write('i\tj\tlength\tvalue\n') for pair in layer: x, y = pair.nt1, pair.nt2 x, y = nucleotides.index(x) + 1 + shifts[x], nucleotides.index(y) + 1 + shifts[y] onz = self.onz_dict[pair] helix.write(f'{x}\t{y}\t1\t{onz_value.get(onz, 7)}\n') if canonical: for pair in canonical: x, y = pair.nt1, pair.nt2 x, y = nucleotides.index(x) + 1 + shifts[x], nucleotides.index(y) + 1 + shifts[y] helix.write(f'{x}\t{y}\t1\t8\n') helix.flush() return helix class AnalysisSimple: def __init__(self, structure2d: Structure2D, structure3d: Structure3D): self.pairs: List[BasePair3D] = structure3d.base_pairs(structure2d) self.graph: Dict[Residue3D, List[Residue3D]] = structure3d.base_pair_graph(structure2d) self.pair_dict: Dict[Tuple[Residue3D, Residue3D], BasePair3D] = structure3d.base_pair_dict(structure2d) def has_tetrads(self): tetrads = set() for i in self.graph: for j in filter(lambda x: x != i, self.graph[i]): for k in filter(lambda x: x not in (i, j), self.graph[j]): for l in filter(lambda x: x not in (i, j, k) and x in self.graph[i], self.graph[k]): if Tetrad.is_valid(i, j, k, l, self.pair_dict): tetrads.add(frozenset([i, j, k, l])) if len(tetrads) > 1: return True return False def center_of_mass(atoms): coords = [atom.coordinates() for atom in atoms] xs = (coord[0] for coord in coords) ys = (coord[1] for coord in coords) zs = (coord[2] for coord in coords) return numpy.array((sum(xs) / len(coords), sum(ys) / len(coords), sum(zs) / len(coords))) def eltetrado(structure2d: Structure2D, structure3d: Structure3D, strict: bool, no_reorder: bool, stacking_mismatch: int) -> Analysis: return Analysis(structure2d, structure3d, strict, no_reorder, stacking_mismatch) def has_tetrad(structure2d: Structure2D, structure3d: Structure3D) -> bool: structure = AnalysisSimple(structure2d, structure3d) return structure.has_tetrads()

cubes/__init__.py

lmjohns3/cube-experiment

12799955

from .database import Experiment, Movement, Trial from . import plots from . import utils

tests/test_ndvisummarytimeseries.py

tmilliman/python-vegindex

12799956

<reponame>tmilliman/python-vegindex # -*- coding: utf-8 -*- """ test_ndvisummarytimeseries -------------------------- Tests for `vegindex.ndvi_summary_timeseries` module. """ import os import numpy as np from PIL import Image from pkg_resources import Requirement from pkg_resources import resource_filename from vegindex import config from vegindex import ndvi_summary_timeseries from vegindex import vegindex as vi SAMPLE_DATA_DIR = os.path.join(os.path.dirname(__file__), "sample_data") config.archive_dir = SAMPLE_DATA_DIR def test_reading_ndvits_summary_file(): """ test reading in existing ndvits summary timeseries """ sitename = "dukehw" roiname = "DB_1000" ndvi_file = "{}_{}_ndvi_3day.csv".format(sitename, roiname) # set up path to roistats file ndvi_path = os.path.join(SAMPLE_DATA_DIR, sitename, "ROI", ndvi_file) ndvits = ndvi_summary_timeseries.NDVISummaryTimeSeries( site=sitename, ROIListID=roiname ) ndvits.readCSV(ndvi_path) first_row = ndvits.rows[0] last_row = ndvits.rows[-1] # test that we're getting header metadata correctly np.testing.assert_equal(ndvits.site, "dukehw") np.testing.assert_equal(ndvits.nday, 3) np.testing.assert_equal(ndvits.roitype, "DB") np.testing.assert_equal(ndvits.sequence_number, "1000") # spot check a couple of rows np.testing.assert_equal( last_row["midday_rgb_filename"], "dukehw_2020_07_15_115405.jpg" ) np.testing.assert_equal( last_row["midday_ir_filename"], "dukehw_IR_2020_07_15_115405.jpg" ) np.testing.assert_equal(first_row["ndvi_mean"], 0.22027) np.testing.assert_equal(first_row["ndvi_std"], 0.16966) np.testing.assert_equal(first_row["max_solar_elev"], 75.9963) np.testing.assert_equal(len(ndvits.rows), 870) def test_get_ndvi_summary_file(): """ test reading in existing ndvits summary timeseries using the helper function. """ sitename = "dukehw" roiname = "DB_1000" ndvits = vi.get_ndvi_summary(sitename, roiname, nday=3) first_row = ndvits.rows[0] last_row = ndvits.rows[-1] # test that we're getting header metadata correctly np.testing.assert_equal(ndvits.site, "dukehw") np.testing.assert_equal(ndvits.nday, 3) np.testing.assert_equal(ndvits.roitype, "DB") np.testing.assert_equal(ndvits.sequence_number, "1000") # spot check a couple of rows np.testing.assert_equal( last_row["midday_rgb_filename"], "dukehw_2020_07_15_115405.jpg" ) np.testing.assert_equal( last_row["midday_ir_filename"], "dukehw_IR_2020_07_15_115405.jpg" ) np.testing.assert_equal(first_row["ndvi_mean"], 0.22027) np.testing.assert_equal(first_row["ndvi_std"], 0.16966) np.testing.assert_equal(first_row["max_solar_elev"], 75.9963) np.testing.assert_equal(len(ndvits.rows), 870)

GreenPonik_Atlas_Scientific_OEM_i2c/CommonsI2c.py

GreenPonik/GreenPonik_Atlas_Scientific_OEM_i2c

12799957

#! /usr/bin/python3 """ Description ----------- Class to communicate with Atlas Scientific OEM sensors in I2C mode. Atlas Scientific i2c by GreenPonik Source code is based on Atlas Scientific documentations: https://www.atlas-scientific.com/files/EC_oem_datasheet.pdf https://atlas-scientific.com/files/oem_pH_datasheet.pdf """ import time from GreenPonik_Atlas_Scientific_OEM_i2c.AtlasOEMI2c import _AtlasOEMI2c class _CommonsI2c(_AtlasOEMI2c): """ commons methods for EC and PH OEM circuits """ def _convert_raw_hex_to_float(self, byte_array): """ convert bytearray response to float result return float converted value """ hexstr = byte_array.hex() float_from_hexa = float.fromhex(byte_array.hex()) converted = float_from_hexa if self.debug: print("Byte Array to decode: ", byte_array) print("Byte Array decoded to hexa string: %s" % hexstr) print("float from hexa: %.3f" % float_from_hexa) return converted def _check_calibration_confirm(self, confirm): """ check the response of calibration confirm register """ if self.debug: if hex(0x00) == hex(confirm): print("Calibration applied") else: raise Exception("Cannot confirm the operation was correctly executed") # ----- Getters ----- ######## def get_device_info(self): """ Get device information @return string module type, firmware version """ if "EC" == self.moduletype or "PH" == self.moduletype: info = self.read( self.OEM_EC_REGISTERS["device_type"], self.TWO_BYTE_READ, ) return "SUCCESS: %s, module type: %s and firmware is: %s" % ( self.moduletype, info[0], info[1], ) def get_type(self): """ Read sensor type @return int the sensor type (1=EC, 4=PH) """ if "EC" == self.moduletype or "PH" == self.moduletype: device_type = self.read( self.OEM_EC_REGISTERS["device_type"], self.ONE_BYTE_READ, ) if self.debug: print("Device type is: %s" % device_type) return device_type def get_firmware(self): """ Read sensor firmware @return int the firmware revision """ if "EC" == self.moduletype or "PH" == self.moduletype: firmware = self.read( self.OEM_EC_REGISTERS["device_firmware"], self.ONE_BYTE_READ, ) if self.debug: print("Firmware type is: %s" % firmware) return firmware def get_new_read_available(self): """ New Read is available @return int 1 if new read available, 0 if not """ is_new_read = self.read( self.OEM_EC_REGISTERS["device_new_reading"], self.ONE_BYTE_READ, ) return is_new_read def get_read(self): """ Read sensor value @return float the sensor value """ # self.set_wakeup_sleep_mode(0x01) # wake device before read time.sleep(self._long_timeout) if "EC" == self.moduletype: rawhex = self.read( self.OEM_EC_REGISTERS["device_ec_msb"], self.FOUR_BYTE_READ, ) value = self._convert_raw_hex_to_float(rawhex) / 100 elif "PH" == self.moduletype: rawhex = self.read( self.OEM_PH_REGISTERS["device_ph_msb"], self.FOUR_BYTE_READ, ) value = self._convert_raw_hex_to_float(rawhex) / 1000 if self.debug: print( "%s: %s%s" % ( self.moduletype, value, "µs" if "EC" == self.moduletype else "", ) ) # self.set_wakeup_sleep_mode(0x00) # sleep device after read return value def get_temperature(self): """ Get current compensation temperature @return float temperature value """ if "EC" == self.moduletype: rawhex = self.read( self.OEM_EC_REGISTERS["device_temperature_comp_msb"], self.FOUR_BYTE_READ, ) elif "PH" == self.moduletype: rawhex = self.read( self.OEM_PH_REGISTERS["device_temperature_comp_msb"], self.FOUR_BYTE_READ, ) value = self._convert_raw_hex_to_float(rawhex) / 100 if self.debug: print("%s compensation Temperature: %s°c" % (self.moduletype, value)) return value def get_calibration(self): """ Get current calibrations data :return: string with current points calibrated :rtype: """ if "EC" == self.moduletype: register = self.OEM_EC_REGISTERS["device_calibration_confirm"] """ bits - "dry": 0, - "single": 1, - "low": 2, - "high": 3, """ binary_calib_status = self.EC_BINARY_CALIB_STATUS elif "PH" == self.moduletype: register = self.OEM_PH_REGISTERS["device_calibration_confirm"] """ bits - "low": 1, - "mid": 2, - "high": 3, """ binary_calib_status = self.PH_BINARY_CALIB_STATUS r = self.read(register) if self.debug: print("Binary result from OEM", r) print("Who is calibrated? >", binary_calib_status[r]) return binary_calib_status[r] def get_led(self) -> int: """ Get led state register is the same for EC and PH OEM circuit :return: int 0x00 = OFF or 0x01 = ON :rtype: int """ register = self.OEM_EC_REGISTERS["device_led"] led_status = self.read(register) if self.debug: print("Led status is currently: %s" % hex(led_status)) return led_status def get_wakeup_sleep_mode(self) -> int: """ get Active or Hibernate device mode register is the same for EC and PH OEM circuit :return: int 0x01 = WakeUp or 0x00 = Hibernate :rtype: int """ register = self.OEM_EC_REGISTERS["device_sleep"] mode = self.read(register) if self.debug: print( "Device is currently in mode: %s" % ("wakeup" if hex(0x01) == hex(mode) else "sleep") ) return mode # ----- Setters ----- ######## def set_temperature(self, t=25.0): """Set the compensation temperature :param t: float temperature value """ self.set_wakeup_sleep_mode(0x01) # wake device before set temperature time.sleep(self._long_timeout) if "EC" == self.moduletype: register = self.OEM_EC_REGISTERS["device_temperature_comp_msb"] elif "PH" == self.moduletype: register = self.OEM_PH_REGISTERS["device_temperature_comp_msb"] byte_array = int(round(t * 100)).to_bytes(4, "big") if self.debug: print("Temperature to set: %.2f" % t) print( "%s sent converted temp to bytes: " % (self.moduletype), byte_array, ) time.sleep(self.short_timeout) self.write(register, byte_array) self.set_wakeup_sleep_mode(0x00) # sleep device after set temperature def _set_calibration_registers(self, value): """calibration registers do not use alone because calibration is apply by using set_calibration_apply /!in float micro siemens µS for EC/! /! in float for pH/! """ if "EC" == self.moduletype: register = self.OEM_EC_REGISTERS["device_calibration_msb"] # ec calibration wait for µSiemens byte_array = int(round(value * 100)).to_bytes(4, "big") elif "PH" == self.moduletype: register = self.OEM_PH_REGISTERS["device_calibration_msb"] byte_array = int(round(value * 1000)).to_bytes(4, "big") self.write(register, byte_array) if self.debug: print("Value to send: %.2f" % value) print( "%s sent converted value to bytes: " % (self.moduletype), byte_array, ) def set_calibration_apply(self, value, point=""): """apply the calibration :param value: float solution calibration value converted in float. EC waiting for µS e.g. 1.413 = > 1413.0 :param point: string "dry", "single", "low", "mid", "high" only """ if point not in ("dry", "single", "low", "mid", "high"): raise Exception( 'missing string point argument, \ can only be "dry", "single", "low", "mid", "high"' ) if "EC" == self.moduletype: points = {"dry": 0x02, "single": 0x03, "low": 0x04, "high": 0x05} register = self.OEM_EC_REGISTERS["device_calibration_request"] elif "PH" == self.moduletype: points = {"low": 0x02, "mid": 0x03, "high": 0x04} register = self.OEM_PH_REGISTERS["device_calibration_request"] self._set_calibration_registers(value) time.sleep(self.long_timeout) self.write(register, points[point]) # apply point calibration data time.sleep(self.short_timeout) # wait before read register to get confirmation conf = self.read(register) self._check_calibration_confirm(conf) return conf def set_calibration_clear(self): """clear calibration data """ if "EC" == self.moduletype: register = self.OEM_EC_REGISTERS["device_calibration_request"] elif "PH" == self.moduletype: register = self.OEM_PH_REGISTERS["device_calibration_request"] self.write(register, 0x01) # send 0x01 to clear calibration data time.sleep(self.short_timeout) # wait before read register to get confirmation conf = self.read(register) self._check_calibration_confirm(conf) return conf def set_i2c_addr(self, addr): """Change the device i2c address :param addr: int = new i2c add """ if addr not in self.ADDR_OEM_HEXA and addr not in self.ADDR_OEM_DECIMAL: raise Exception( "only decimal address expected, convert hexa by using \ AtlasI2c.ADDR_OEM_DECIMAL or AtlasI2c.ADDR_EZO_DECIMAL" ) else: """ write workflow to change physical i2c address """ self.address(addr) raise NotImplementedError("write workflow to change physical i2c address") def set_led(self, state=0x01): """Change Led state :param state: byte state => 0x01 = ON or 0x00 = OFF """ register = self.OEM_EC_REGISTERS["device_led"] self.write(register, state) if self.debug: print( "Led status change to: %s" % ("On" if hex(0x01) == hex(state) else "OFF") ) def set_wakeup_sleep_mode(self, action=0x01): """change device mode to Active or Hibernate register is the same for EC and PH OEM circuit :param byte: action => 0x01 = WakeUp or 0x00 = Hibernate """ register = self.OEM_EC_REGISTERS["device_sleep"] self.write(register, action) if self.debug: print( "Device is now: %s" % ("wakeup" if hex(0x01) == hex(action) else "sleep") ) def set_ack_new_read_available(self): """Ack new Read available """ register = self.OEM_EC_REGISTERS["device_new_reading"] ack = 0x00 self.write(register, ack) if self.debug: print("ack new reading available register %s to %s" % (register, ack))

tools/loadKar.py

hidura/sugelico

12799958

from datetime import datetime import os class core: def __init__(self, environ = None, location = None): self.response = None if environ == None and location == None: #If the environ and the location are None, no make anything. self.response = """<h1>Petition doesn't have <u>Environ</u> or <u>Location</u></h1>""" elif environ != None: self.environ = environ from tools.Utilities import buildRq buildReq = buildRq() from tools.main import main request = buildReq.extrctEnv(environ, environ['DOCUMENT_ROOT']) # try: # self.response = main(request, environ).getResult() # except Exception as ex: # error = {"error": str(ex.args[0])} # self.response = {"status": 200, "value": error, "type": "application/json"} self.response = main(request, environ).getResult() def result(self): if self.response != None: return self.response else: return ["plain", "Problem with the communication with the core..."] def logs(self, logData): logdir = os.listdir(self.environ['DOCUMENT_ROOT']+'logs/') a = datetime.now() logtime = '' for piece in a.timetuple()[:3]: logtime += str(piece)+'-' logtime = logtime[:-1]+" " for piece in a.timetuple()[3:]: logtime += str(piece)+':' logtime = logtime[:-3] if len(logdir) < 1: log = open(self.environ['DOCUMENT_ROOT']+'logs/error.log', 'w') for piece in str(logData).split('\n'): log.write('['+logtime+']'+str(piece)+'\n') log.close() else: log = open(self.environ['DOCUMENT_ROOT']+'logs/error.log', 'r') if len(log.readlines()) > 500: self.cleanLogs(self.environ['DOCUMENT_ROOT']+'logs') log = open(self.environ['DOCUMENT_ROOT']+'logs/error.log', 'w') else: log = open(self.environ['DOCUMENT_ROOT']+'logs/error.log', 'a') for piece in str(logData).split('\n'): log.write('['+logtime+']'+str(piece)+'\n') log.close() return str(logData) def cleanLogs(self, location): logfiles = os.listdir(location) if len(logfiles) == 9: os.remove(logfiles[-1]) logfiles = logfiles[:-1] cont = 1 for log in logfiles: os.rename(self.environ['DOCUMENT_ROOT']+'logs/'+log, self.environ['DOCUMENT_ROOT']+'logs/error_'+str(cont)+'.log') else: cont = 1 for log in logfiles: os.rename(self.environ['DOCUMENT_ROOT']+'logs/'+log, self.environ['DOCUMENT_ROOT']+'logs/error_'+str(cont)+'.log')

IMU/VTK-6.2.0/ThirdParty/Twisted/twisted/scripts/__init__.py

timkrentz/SunTracker

12799959

<filename>IMU/VTK-6.2.0/ThirdParty/Twisted/twisted/scripts/__init__.py # Copyright (c) Twisted Matrix Laboratories. # See LICENSE for details. """ Subpackage containing the modules that implement the command line tools. Note that these are imported by top-level scripts which are intended to be invoked directly from a shell. """ from twisted.python.versions import Version from twisted.python.deprecate import deprecatedModuleAttribute deprecatedModuleAttribute( Version("Twisted", 11, 1, 0), "Seek unzipping software outside of Twisted.", __name__, "tkunzip") deprecatedModuleAttribute( Version("Twisted", 12, 1, 0), "tapconvert has been deprecated.", __name__, "tapconvert") del Version, deprecatedModuleAttribute

API/controller/routes.py

GeoscienceAustralia/FSDF-Roads

12799960

from flask import Blueprint, request, Response, render_template from model.roads import Roads from pyldapi import ContainerRenderer import conf import ast import folium print(__name__) routes = Blueprint('controller', __name__) DEFAULT_ITEMS_PER_PAGE=50 @routes.route('/', strict_slashes=True) def home(): return render_template('home.html') @routes.route('/rds/') def roads(): # Search specific items using keywords search_string = request.values.get('search') try: # get the register length from the online DB sql = 'SELECT COUNT(*) FROM "transportroads"' if search_string: sql += '''WHERE UPPER(cast("id" as text)) LIKE '%{search_string}%' OR UPPER("name") LIKE '%{search_string}%'; '''.format(search_string=search_string.strip().upper()) no_of_items = conf.db_select(sql)[0][0] page = int(request.values.get('page')) if request.values.get('page') is not None else 1 per_page = int(request.values.get('per_page')) \ if request.values.get('per_page') is not None else DEFAULT_ITEMS_PER_PAGE offset = (page - 1) * per_page # get the id and name for each record in the database sql = '''SELECT "id", "name" FROM "transportroads"''' if search_string: sql += '''WHERE UPPER(cast("id" as text)) LIKE '%{search_string}%' OR UPPER("name") LIKE '%{search_string}%' '''.format(search_string=search_string.strip().upper()) sql += '''ORDER BY "name" OFFSET {} LIMIT {}'''.format(offset, per_page) items = [] for item in conf.db_select(sql): items.append( (item[0], item[1]) ) except Exception as e: print(e) return Response('The Roads database is offline', mimetype='text/plain', status=500) return ContainerRenderer(request=request, instance_uri=request.url, label='Roads Register', comment='A register of Roads', parent_container_uri='http://linked.data.gov.au/def/placenames/PlaceName', parent_container_label='QLD_Roads', members=items, members_total_count=no_of_items, profiles=None, default_profile_token=None, super_register=None, page_size_max=1000, register_template=None, per_page=per_page, search_query=search_string, search_enabled=True ).render() @routes.route('/map') def show_map(): ''' Function to render a map around the specified line ''' name = request.values.get('name') coords_list = ast.literal_eval(request.values.get('coords'))[0] # swap x & y for mapping points = [] for coords in coords_list: points.append(tuple([coords[1], coords[0]])) ave_lat = sum(p[0] for p in points) / len(points) ave_lon = sum(p[1] for p in points) / len(points) # create a new map object folium_map = folium.Map(location=[ave_lat, ave_lon], zoom_start=15) tooltip = 'Click for more information' folium.PolyLine(points, color="red", weight=2.5, opacity=1, popup = name, tooltip=tooltip).add_to(folium_map) return folium_map.get_root().render() @routes.route('/rds/<string:roads_id>') def road(roads_id): roads = Roads(request, request.base_url) return roads.render()

backend/chat/routing.py

CSCI34284/group4_project

12799961

""" Prepared by Backend/Server Team - Sheldon, Martin, Brian, Sarah, Veronica. """ from django.urls import re_path from .consumers import ChatConsumer # Assign pattern to activate selected websocket websocket_urlpatterns = [ re_path(r'^ws/chat/(?P<room_name>[^/]+)/$', ChatConsumer), ]

ebs_snatcher/main.py

Cobliteam/ebs_snatcher

12799962

<reponame>Cobliteam/ebs_snatcher from __future__ import unicode_literals from builtins import str, bytes import argparse import json import logging import random from . import ebs logger = logging.getLogger('ebs-snatcher.main') def get_args(): # pragma: no cover argp = argparse.ArgumentParser( 'ebs-snatcher', description='Automatically provision AWS EBS volumes from snapshots') argp.add_argument( '--instance-id', metavar='ID', required=True, help='Instance ID to attach volumes to') argp.add_argument( '--volume-id-tag', metavar='KEY=VALUE', type=key_tag_pair, required=True, action='append', help='Tag used to identify desired volumes. Will be used to search ' 'currently attached volumes to determine if a new one is needed ' 'and applied to new volumes. Can be provided multiple times, in ' 'which case tags will be combined as an AND condition.') argp.add_argument( '--volume-size', metavar='GB', type=positive_int, required=True, help='Size to assign to newly created volumes, in GBs.') argp.add_argument( '--snapshot-search-tag', metavar='KEY=VALUE', type=key_tag_pair, required=True, action='append', help='Tag used to identify snapshots to create new volumes from.' 'Can be provided multiple times, in which case tags will be ' 'combined as an AND condition.') argp.add_argument( '--attach-device', metavar='PATH|auto', required=True, help='Name of device to use when attaching a volume, such as ' '"/dev/sdb". Can be set to "auto" to use a safe default. ' 'Device names found to be already in use will be skipped, and the ' 'next name in alphabetical order will be tried until attachment ' 'succeeds') argp.add_argument( '--volume-extra-tag', metavar='KEY=VALUE', type=key_tag_pair, action='append', help='Extra tags to be applied to newly create volumes, but which are ' 'not used for identification') argp.add_argument( '--encrypt-kms-key-id', metavar='KEY-ID', default=None, help='Enable encryption and use the given KMS key ID for newly created ' 'volumes') argp.add_argument( '--volume-type', metavar='TYPE', choices=ebs.VOLUME_TYPES, default='gp2', help='Volume type to use for newly created volumes') argp.add_argument( '--volume-iops', metavar='COUNT', type=positive_int, default=None, help='Number of provisioned I/O operations to assign to newly created ' 'volumes. Make sure to choose an appropriate volume type to ' 'match.') argp.add_argument( '--move-to-current-az', action='store_true', default=False, help="If there is a volume available in a different AZ than the " "current one, instead of skipping it and looking for snapshots " "by tag, try to move it to the current AZ, by cloning it and " "deleting the original.") return argp.parse_args() def positive_int(s): n = int(s) if n <= 0: raise ValueError('Value must be positive: {}'.format(n)) return n def key_tag_pair(s): if isinstance(s, bytes): s = str(s, 'utf-8') elif not isinstance(s, str): raise TypeError('Input must be a string') try: key, value = s.split('=', 1) except ValueError: raise ValueError('Missing tag value: {}'.format(s)) return key, value class ResourceState(object): def __init__(self, args, instance_info): self.args = args self.instance_info = instance_info self.state = None self.volume_id = None self.old_volume_id = None self.snapshot_id = None self.attached_device = None def survey(self): logger.debug('Looking up currently attached volumes') attached_volumes = \ ebs.find_attached_volumes(self.args.volume_id_tag, self.instance_info) if attached_volumes: volume_id = attached_volumes[0]['VolumeId'] attached_device = attached_volumes[0]['Attachments'][0]['Device'] logger.info( 'Found volume already attached to instance: %s', volume_id) self.state = 'present' self.volume_id = volume_id self.attached_device = attached_device return logger.debug('Looking up existing available volumes in AZ') volumes = \ ebs.find_available_volumes(self.args.volume_id_tag, self.instance_info, current_az=True) if volumes: logger.info( 'Found available volumes with given specifications in current ' 'AZ: %s', ', '.join(map(lambda v: v['VolumeId'], volumes))) self.state = 'attached' self.volume_id = random.choice(volumes)['VolumeId'] return if self.args.move_to_current_az: logger.info('Did not find any available volumes in current AZ. ' 'Searching for available volumes to move in other AZ') other_az_volumes = \ ebs.find_available_volumes(self.args.volume_id_tag, self.instance_info, current_az=False) for old_volume in other_az_volumes: old_volume_id = old_volume['VolumeId'] old_az = old_volume['AvailabilityZone'] new_az = self.instance_info['Placement']['AvailabilityZone'] filters = [{'Name': 'volume-id', 'Values': [old_volume_id]}] snapshot = ebs.find_existing_snapshot(filters=filters) if snapshot: snapshot_id = snapshot['SnapshotId'] logger.info( 'Found volume %s in AZ %s, will attempt to move ' 'it to current AZ %s. Using snapshot %s.', old_volume_id, old_az, new_az, snapshot_id) self.state = 'created' self.snapshot_id = snapshot_id self.old_volume_id = old_volume_id break else: logger.info('Did not find any available volumes in other AZ ' 'move. Creating new volume from scratch.') self.state = 'created' else: logger.info('Did not find any available volumes. Searching for a ' 'suitable snapshot instead') snapshot = ebs.find_existing_snapshot( search_tags=self.args.snapshot_search_tag) self.state = 'created' self.snapshot_id = snapshot and snapshot['SnapshotId'] def converge(self): if not self.volume_id: availability_zone = \ self.instance_info['Placement']['AvailabilityZone'] logger.info('About to create volume in AZ %s', availability_zone) if not self.snapshot_id: logger.info('Creating volume from scratch') else: logger.info('Creating volume from snapshot %s', self.snapshot_id) new_volume = ebs.create_volume( id_tags=self.args.volume_id_tag, extra_tags=self.args.volume_extra_tag, availability_zone=availability_zone, volume_type=self.args.volume_type, size=self.args.volume_size, iops=self.args.volume_iops, kms_key_id=self.args.encrypt_kms_key_id, src_snapshot_id=self.snapshot_id) self.volume_id = new_volume['VolumeId'] if not self.attached_device: self.attached_device = ebs.attach_volume( volume_id=self.volume_id, instance_info=self.instance_info, device_name=self.args.attach_device) self.attached_device = \ ebs.find_system_block_device(self.volume_id, self.attached_device) if self.old_volume_id: ebs.delete_volume(volume_id=self.old_volume_id) def to_json(self): return {'volume_id': self.volume_id, 'attached_device': self.attached_device, 'result': self.state, 'src_snapshot_id': self.snapshot_id} def main(): logging.basicConfig(level=logging.DEBUG) args = get_args() instance_info = ebs.get_instance_info(args.instance_id) resource_state = ResourceState(args, instance_info) resource_state.survey() resource_state.converge() print(json.dumps(resource_state.to_json())) return 0 if __name__ == '__main__': main() # pragma: no cover

manager/integration/tests/test_ha.py

JacieChao/longhorn-tests

12799963

import pytest import common import time from common import client, volume_name # NOQA from common import SIZE, DEV_PATH from common import check_volume_data, get_self_host_id, get_volume_endpoint from common import write_volume_random_data from common import RETRY_COUNTS, RETRY_ITERVAL @pytest.mark.coretest # NOQA def test_ha_simple_recovery(client, volume_name): # NOQA ha_simple_recovery_test(client, volume_name, SIZE) def ha_simple_recovery_test(client, volume_name, size, base_image=""): # NOQA volume = client.create_volume(name=volume_name, size=size, numberOfReplicas=2, baseImage=base_image) volume = common.wait_for_volume_detached(client, volume_name) assert volume["name"] == volume_name assert volume["size"] == size assert volume["numberOfReplicas"] == 2 assert volume["state"] == "detached" assert volume["created"] != "" assert volume["baseImage"] == base_image host_id = get_self_host_id() volume = volume.attach(hostId=host_id) volume = common.wait_for_volume_healthy(client, volume_name) volume = client.by_id_volume(volume_name) assert get_volume_endpoint(volume) == DEV_PATH + volume_name assert len(volume["replicas"]) == 2 replica0 = volume["replicas"][0] assert replica0["name"] != "" replica1 = volume["replicas"][1] assert replica1["name"] != "" data = write_volume_random_data(volume) volume = volume.replicaRemove(name=replica0["name"]) # wait until we saw a replica starts rebuilding new_replica_found = False for i in range(RETRY_COUNTS): v = client.by_id_volume(volume_name) for r in v["replicas"]: if r["name"] != replica0["name"] and \ r["name"] != replica1["name"]: new_replica_found = True break if new_replica_found: break time.sleep(RETRY_ITERVAL) assert new_replica_found volume = common.wait_for_volume_healthy(client, volume_name) volume = client.by_id_volume(volume_name) assert volume["state"] == common.VOLUME_STATE_ATTACHED assert volume["robustness"] == common.VOLUME_ROBUSTNESS_HEALTHY assert len(volume["replicas"]) >= 2 found = False for replica in volume["replicas"]: if replica["name"] == replica1["name"]: found = True break assert found check_volume_data(volume, data) volume = volume.detach() volume = common.wait_for_volume_detached(client, volume_name) client.delete(volume) common.wait_for_volume_delete(client, volume_name) volumes = client.list_volume() assert len(volumes) == 0 @pytest.mark.coretest # NOQA def test_ha_salvage(client, volume_name): # NOQA ha_salvage_test(client, volume_name) def ha_salvage_test(client, volume_name, base_image=""): # NOQA volume = client.create_volume(name=volume_name, size=SIZE, numberOfReplicas=2, baseImage=base_image) volume = common.wait_for_volume_detached(client, volume_name) assert volume["name"] == volume_name assert volume["size"] == SIZE assert volume["numberOfReplicas"] == 2 assert volume["state"] == "detached" assert volume["created"] != "" assert volume["baseImage"] == base_image host_id = get_self_host_id() volume = volume.attach(hostId=host_id) volume = common.wait_for_volume_healthy(client, volume_name) assert len(volume["replicas"]) == 2 replica0_name = volume["replicas"][0]["name"] replica1_name = volume["replicas"][1]["name"] data = write_volume_random_data(volume) common.k8s_delete_replica_pods_for_volume(volume_name) volume = common.wait_for_volume_faulted(client, volume_name) assert len(volume["replicas"]) == 2 assert volume["replicas"][0]["failedAt"] != "" assert volume["replicas"][1]["failedAt"] != "" volume.salvage(names=[replica0_name, replica1_name]) volume = common.wait_for_volume_detached(client, volume_name) assert len(volume["replicas"]) == 2 assert volume["replicas"][0]["failedAt"] == "" assert volume["replicas"][1]["failedAt"] == "" volume = volume.attach(hostId=host_id) volume = common.wait_for_volume_healthy(client, volume_name) check_volume_data(volume, data) volume = volume.detach() volume = common.wait_for_volume_detached(client, volume_name) client.delete(volume) common.wait_for_volume_delete(client, volume_name) volumes = client.list_volume() assert len(volumes) == 0

neural_cdes/F.py

jb-c/dissertation

12799964

<reponame>jb-c/dissertation<gh_stars>0 import torch class F(torch.nn.Module): ''' Defines the neural network denoted f_{\theta} in our neural CDE model ''' def __init__(self, input_channels, hidden_channels, width = 128): ''' :param input_channels: the number of input channels in the data X. :param hidden_channels: the number of channels for z_t. (We use h = 32) ''' #torch.manual_seed(3) super(F, self).__init__() self.input_channels = input_channels self.hidden_channels = hidden_channels self.linear1 = torch.nn.Linear(hidden_channels, width) self.linear2 = torch.nn.Linear(width, width) self.linear3 = torch.nn.Linear(width, input_channels * hidden_channels) def forward(self, t, z): ''' :param t: t is normally embedded in the data :param z: input to the network & has shape (batch, hidden_channels) :return: F(z) ''' z = self.linear1(z) z = z.tanh() z = self.linear2(z) z = z.tanh() z = self.linear3(z) # A final tanh non-linearity. z = z.tanh() # Ignoring the batch dimension, the shape of the output tensor must be a matrix, # because we need it to represent a linear map from R^input_channels to R^hidden_channels. z = z.view(z.size(0), self.hidden_channels, self.input_channels) return z

UD/test.py

Anon-LeoH/UncleDaLearn

12799965

from LinearRegression.HugeScaleLR import hugeScaleLR as hlr from LinearRegression.GradientDescent import gradientDescent as glr from LinearRegression.regularization import regularization as rg import numpy as np from copy import deepcopy as dp import random import math from Function import * from Distribution import NormalDistribution as nd import matplotlib.pyplot as plt from NeuralNetwork import BPnetwork as bpn fig = plt.figure() fig.suptitle(u'Informations gragh paramed by level', fontsize=14, fontweight='bold') ax = fig.add_subplot(111) set11 = nd(3, 2.5) set12 = nd(3, 2.5) set13 = nd(3, 2.5) set21 = nd(-6, 1.0) set22 = nd(-6, 1.0) set23 = nd(-6, 1.0) class testFunc(functionObject): def __init__(self): pass def cal(self, x): return ( 0.7 * x - 7 * (x ** 2) + 0.1 * (x ** 3) + 7 ) tf = testFunc() x = [] y = [] y2 = [] for i in xrange(1000): x.append([set11.val(), set12.val(), set13.val()]) y.append([1, 0]) for i in xrange(1000): x.append([set21.val(), set22.val(), set23.val()]) y.append([0, 1]) y2 = [[item] for item in y2] jg = bpn([3, 4, 4, 2]) jg.train(x, y, 0.03, 0.0005) #for i in xrange(10): # tmp = random.uniform(0.1, 25.0) # ty1 = tf.cal(tmp) # tmp = [tmp * 1, tmp ** 2, tmp ** 3] # tmp1 = dp(tmp) # ty2 = jg1.cal(tmp)[0] # ty3 = jg2.cal(tmp1) # ty2 = ty2 * param[1] + param[0] # rlt = (ty1, ty2, ty3) # print rlt #x = np.arange(-20.0, 20.0, 0.01) #y1 = [tf.cal(item) for item in x] #y2 = [jg1.cal([item, item ** 2, item ** 3])[0] * param[1] + param[0] for item in x] #y3 = [jg2.cal([item, item ** 2, item ** 3]) for item in x] #ax.plot(x, y1, 'r', x, y2, 'b', x, y3, 'y') #fig.savefig('level.png', dpi=100) #fig.show() tr = 0 fl = 0 for i in xrange(100): p = random.random() if p >= 0.5: tmpx = [set11.val(), set12.val(), set13.val()] tmpy = 1 else: tmpx = [set21.val(), set22.val(), set23.val()] tmpy = 2 rlt = jg.cal(tmpx) if rlt[0] > rlt[1]: rlt = 1 else: rlt = 2 if rlt == tmpy: tr += 1 else: fl += 1 print "result: " + str(float(tr) / (tr + fl))

Ex81.py

ErickTeixeira777/Python-3

12799966

<filename>Ex81.py '''Faça um programa que vai ler vários números e colocar em uma lista, depois disso,mostre: A) Quantos números foram digitados. B) A Lista de valores, ordenada de forma decrescente. C) Se o valor 5 foi digitado e está ou não na lista''' valores = [] while True: valores.append(int(input('Digite um valor: '))) resp = str(input('Gostaria de continuar? [S/N] ')) if resp in 'Nn': break print('='*30) print(f'Você digitou {len(valores)} elementos. ') valores.sort(reverse=True) print(f'Os valores em ordem decrescente são {valores}') if 5 in valores: print('O valor 5 faz parte da lista! ') else: print('O valor 5 não foi encontrado na lista! ')

CreateAccount.py

manijamali2003/Nava

12799967

<reponame>manijamali2003/Nava #!/usr/bin/env python3 import os import socket, random,hashlib from Nava import * HOST = '127.0.0.1' # Standard loopback interface address (localhost) PORT = 65433 # Port to listen on (non-privileged ports are > 1023) with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind((HOST, PORT)) s.listen() conn, addr = s.accept() with conn: print('Connected by', addr) while True: data = conn.recv(1024).decode('utf-8') if data: try: split = data.split(',') username = split[0] # manijamali2003 if not os.path.isfile (f'Etc/Users Account/{username}'): fullname = split[1] # <NAME> gender = split[2] # 0: Male, 1: Female birthday = split[3] # yyyy/mm/dd countryc = split[4] # IR city = split[5] # Mashhad zipcode = split[6] # 11111111 hashcode = split[7] # hash of password sha3_513 f = open(f'Etc/Users Account/{username}','wb') f.write(f'{fullname},{gender},{birthday},{countryc},{city},{zipcode},{hashcode}'.encode()) f.close() key = KeyCreator() f = open(f'Etc/Users Account/Public Keys/{username}.pem','wb') f.write(key.public) # you should create it f.close() conn.sendall(key.private) conn.sendall(key.public) else: conn.sendall(b'e: account exists') except: conn.sendall(b'e: some errors')

diventi/landing/migrations/0009_auto_20180220_0745.py

flavoi/diven

12799968

# -*- coding: utf-8 -*- # Generated by Django 1.11.5 on 2018-02-20 06:45 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('landing', '0008_remove_featurescover_active'), ] operations = [ migrations.CreateModel( name='PresentationCover', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('image', models.URLField(verbose_name='image')), ('label', models.CharField(blank=True, max_length=50, verbose_name='label')), ('label_it', models.CharField(blank=True, max_length=50, null=True, verbose_name='label')), ('label_en', models.CharField(blank=True, max_length=50, null=True, verbose_name='label')), ('section', models.CharField(choices=[('DES', 'description'), ('FEA', 'features')], default='DES', max_length=3)), ('default', models.BooleanField(default=False)), ], options={ 'verbose_name': 'Presentation Cover', 'verbose_name_plural': 'Presentation Covers', }, ), migrations.RemoveField( model_name='presentation', name='features_cover', ), migrations.DeleteModel( name='FeaturesCover', ), migrations.AddField( model_name='presentation', name='presentation_covers', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='landing.PresentationCover', verbose_name='presentation cover'), ), ]

FAUSTPy/__main__.py

mathandy/faust_python

12799969

import argparse import numpy as np import matplotlib.pyplot as plt from FAUSTPy import * ####################################################### # set up command line arguments ####################################################### parser = argparse.ArgumentParser() parser.add_argument('-f', '--faustfloat', dest="faustfloat", default="float", help="The value of FAUSTFLOAT.") parser.add_argument('-p', '--path', dest="faust_path", default="", help="The path to the FAUST compiler.") parser.add_argument('-c', '--cflags', dest="cflags", default=[], type=str.split, help="Extra compiler flags") parser.add_argument('-s', '--fs', dest="fs", default=48000, type=int, help="The sampling frequency") args = parser.parse_args() ####################################################### # initialise the FAUST object and get the default parameters ####################################################### wrapper.FAUST_PATH = args.faust_path dattorro = FAUST("dattorro_notch_cut_regalia.dsp", args.fs, args.faustfloat, extra_compile_args=args.cflags) def_Q = dattorro.dsp.ui.p_Q def_Gain = dattorro.dsp.ui.p_Gain def_Freq = dattorro.dsp.ui.p_Center_Freq ####################################################### # plot the frequency response with the default settings ####################################################### audio = np.zeros((dattorro.dsp.num_in, args.fs), dtype=dattorro.dsp.dtype) audio[:, 0] = 1 out = dattorro.compute(audio) print(audio) print(out) spec = np.fft.fft(out)[:, :args.fs/2] fig = plt.figure() p = fig.add_subplot( 1, 1, 1, title="Frequency response with the default settings\n" "(Q={}, F={:.2f} Hz, G={:.0f} dB FS)".format( def_Q.zone, def_Freq.zone, 20*np.log10(def_Gain.zone+1e-8) ), xlabel="Frequency in Hz (log)", ylabel="Magnitude in dB FS", xscale="log" ) p.plot(20*np.log10(np.absolute(spec.T)+1e-8)) p.legend(("Left channel", "Right channel"), loc="best") ####################################################### # plot the frequency response with varying Q ####################################################### Q = np.linspace(def_Q.min, def_Q.max, 10) dattorro.dsp.ui.p_Center_Freq = 1e2 dattorro.dsp.ui.p_Gain = 10**(-0.5) # -10 dB cur_G = dattorro.dsp.ui.p_Gain.zone cur_F = dattorro.dsp.ui.p_Center_Freq.zone fig = plt.figure() p = fig.add_subplot( 1, 1, 1, title="Frequency response " "(G={:.0f} dB FS, F={} Hz)".format(20*np.log10(cur_G+1e-8), cur_F), xlabel="Frequency in Hz (log)", ylabel="Magnitude in dB FS", xscale="log" ) for q in Q: dattorro.dsp.ui.p_Q = q out = dattorro.compute(audio) spec = np.fft.fft(out)[0, :args.fs/2] p.plot(20*np.log10(np.absolute(spec.T)+1e-8), label="Q={}".format(q)) p.legend(loc="best") ####################################################### # plot the frequency response with varying gain ####################################################### # start at -60 dB because the minimum is at an extremely low -160 dB G = np.logspace(-3, np.log10(def_Gain.max), 10) dattorro.dsp.ui.p_Q = 2 cur_Q = dattorro.dsp.ui.p_Q.zone cur_F = dattorro.dsp.ui.p_Center_Freq.zone fig = plt.figure() p = fig.add_subplot( 1, 1, 1, title="Frequency response (Q={}, F={} Hz)".format(cur_Q, cur_F), xlabel="Frequency in Hz (log)", ylabel="Magnitude in dB FS", xscale="log" ) for g in G: dattorro.dsp.ui.p_Gain = g out = dattorro.compute(audio) spec = np.fft.fft(out)[0, :args.fs/2] p.plot(20*np.log10(np.absolute(spec.T)+1e-8), label="G={:.3g} dB FS".format(20*np.log10(g+1e-8))) p.legend(loc="best") ########################################################### # plot the frequency response with varying center frequency ########################################################### F = np.logspace(np.log10(def_Freq.min), np.log10(def_Freq.max), 10) dattorro.dsp.ui.p_Q = def_Q.default dattorro.dsp.ui.p_Gain = 10**(-0.5) # -10 dB cur_Q = dattorro.dsp.ui.p_Q.zone cur_G = dattorro.dsp.ui.p_Gain.zone fig = plt.figure() p = fig.add_subplot( 1, 1, 1, title="Frequency response " "(Q={}, G={:.0f} dB FS)".format(cur_Q, 20*np.log10(cur_G+1e-8)), xlabel="Frequency in Hz (log)", ylabel="Magnitude in dB FS", xscale="log" ) for f in F: dattorro.dsp.ui.p_Center_Freq = f out = dattorro.compute(audio) spec = np.fft.fft(out)[0, :args.fs/2] p.plot(20*np.log10(np.absolute(spec.T)+1e-8), label="F={:.2f} Hz".format(f)) p.legend(loc="best") ################ # show the plots ################ plt.show() print("everything passes!")

src/schemathesis/runner/impl/__init__.py

gluhar2006/schemathesis

12799970

<gh_stars>100-1000 from .core import BaseRunner from .solo import SingleThreadASGIRunner, SingleThreadRunner, SingleThreadWSGIRunner from .threadpool import ThreadPoolASGIRunner, ThreadPoolRunner, ThreadPoolWSGIRunner

setup.py

sqxccdy/tcp-tunnel

12799971

<gh_stars>1-10 """ This is a setup.py script generated by py2applet Usage: python setup.py py2app """ try: from setuptools import setup except ImportError: from distutils.core import setup from setuptools import setup, find_packages import tcp_tunnel setup(name="tcp-tunnel", version=tcp_tunnel.VERSION, packages=find_packages(include=['tcp_tunnel', 'tcp_tunnel.*',]), author='<NAME>', author_email='<EMAIL>', long_description=open("README.md", "r").read(), long_description_content_type="text/markdown", include_package_data=True, exclude_package_date={'': ['.gitignore']}, keywords='tcp, tunnel, tcp-tunnel, tcptunnel', license='MIT License', url='https://github.com/sqxccdy/tcp-tunnel.git', entry_points={ 'console_scripts': [ 'aiomq_server=aiomq.server:run' ] }, install_requires=[], classifiers=[ 'Development Status :: 1 - Planning', 'Intended Audience :: Developers', 'License :: OSI Approved :: GNU Library or Lesser General Public License (LGPL)', 'Operating System :: OS Independent', 'Natural Language :: Chinese (Simplified)', 'Programming Language :: Python', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.6', 'Topic :: Software Development :: Libraries', 'Topic :: Utilities', ], )

pertemuan_8/7_HTTP_API_With_Sensor/app/forms/_action.py

Muhammad-Yunus/Flask-Web-Development

12799972

<filename>pertemuan_8/7_HTTP_API_With_Sensor/app/forms/_action.py from . import FlaskForm from . import SubmitField class ActionTable(FlaskForm): activate = SubmitField('Activate') deactivate = SubmitField('Deactivate') delete = SubmitField('Delete')

examples/html_test/static/generate.py

bitterfly/kuho

12799973

#!/usr/bin/python3 import os import sys import http.server import socketserver import socket import shutil from base64 import b64encode from urllib.parse import quote from os.path import basename, splitext, join, isfile from collections import defaultdict from subprocess import run from distutils.dir_util import copy_tree from distutils.file_util import copy_file build_dir = 'build' source_dir = 'source' dest_dir = 'built_static' css_dir = join(build_dir, 'css') images_dir = join(build_dir, 'images') class TemporaryTCPServer(socketserver.TCPServer): def server_bind(self): self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) self.socket.bind(self.server_address) def serve(port): os.chdir(dest_dir) handler = http.server.SimpleHTTPRequestHandler httpd = TemporaryTCPServer(("", port), handler) print("[serve] serving on port " + str(port)) httpd.serve_forever() def clean(): shutil.rmtree(build_dir) shutil.rmtree(dest_dir) def build(): copy_tree(source_dir, build_dir, update=1) make_fallback_images(images_dir) print('[create] _images.scss ', end='') save_images_css(images_dir, join(css_dir, '_images.scss')) print('[ok]') run_sass(css_dir, join(dest_dir, 'css')) print('[update] asis ', end='') copy_tree(join(source_dir, 'asis'), join(dest_dir, 'asis'), update=1) print('[ok]') def run_sass(css_source_dir, css_dest_dir): os.makedirs(css_dest_dir, exist_ok=True) for (dirpath, dirnames, filenames) in os.walk(css_source_dir): for f in filenames: name, ext = splitext(f) if ext == '.scss' and name[0] != '_': print("[sass] " + f + ' ', end='') run([ 'sass', join(css_source_dir, f), join(css_dest_dir, name + '.css') ], check = True) print("[ok]") elif ext == '.css': print("[copy] " + f + ' ', end='') copy_file(join(css_source_dir, f), join(css_dest_dir, f), update=1) print("[ok]") break def make_fallback_images(images_dir): images = find_built_images(images_dir) for image, files in images.items(): f = files[0] pngimage = image + '.png' if pngimage not in files: print("[create] " + pngimage + ' ', end='') run([ 'convert', '-background', 'none', join(images_dir, f), join(images_dir, pngimage) ], check = True) print("[ok]") def images_in_dir(dir): vectors = [] rasters = [] dumb_rasters = [] lossy = [] for (dirpath, dirnames, filenames) in os.walk(dir): for f in filenames: name, ext = splitext(basename(f)) if ext in ['.svg']: vectors += [f] if ext in ['.png']: rasters += [f] if ext in ['.gif']: dumb_rasters += [f] if ext in ['.jpg', '.jpeg']: lossy += [f] break return vectors + rasters + dumb_rasters + lossy def find_built_images(images_dir): images = defaultdict(list) for image in images_in_dir(images_dir): name, _ = splitext(basename(image)) images[name] += [image] return dict(images) def images_to_css(images_dir): images = find_built_images(images_dir) csseses = [] for name, files in images.items(): css = '.image-' + name + " {\n" files_and_extensions = [(f, splitext(f)[1][1:]) for f in files] for image, ext in [(f, ext) for f, ext in files_and_extensions if ext != 'svg']: data = raster_data(join(images_dir, image), ext) css += 'background-image: url(' + data + ");\n" for svg, ext in [(f, ext) for f, ext in files_and_extensions if ext == 'svg']: data = xml_data(join(images_dir, svg), ext) css += 'background-image: url(' + data + "), linear-gradient(transparent, transparent);\n" css += "}\n" csseses += [css] return "\n".join(csseses) def save_images_css(images_dir, css_file): with open(css_file, 'w') as f: f.write(images_to_css(images_dir)) def raster_data(image_filename, ext): with open(image_filename, 'rb') as f: data = b64encode(f.read()).decode('utf-8') return 'data:image/' + ext + ';base64,' + data def xml_data(image_filename, ext): with open(image_filename, 'r') as f: data = quote(f.read()) return 'data:image/' + ext + '+xml;charset=US-ASCII,' + data def image_data(image_filename): _, ext = splitext(image_filename) if ext == '.svg': return xml_data(image_filename, ext) else: return raster_data(image_filename, ext) if __name__ == '__main__': try: arg = sys.argv[1] except IndexError: arg = None if arg == 'build': build() elif arg == 'clean': clean() elif arg == 'serve': try: port = int(sys.argv[2]) except IndexError: port = 8000 build() serve(port) else: print('please use "build", "clean" or "serve" as a first argument.')

py_utils/readFile.py

mrrgeresez/my-python-approach

12799974

# -*- coding: utf-8 -*- """ Doc: show how to use it $ python readFile.py data.txt Show content of data.txt """ import sys if __name__ == "__main__": with open(sys.argv[1],'r',encoding = 'utf8') as f: # indicates that the second argument in terminal is to be used for line in f: print(line[:-1])

apps/operations/urls.py

bopopescu/diandian_online

12799975

# _*_ coding: utf-8 _*_ __author__ = 'nick' __date__ = '2019/2/25 18:14' from django.urls import path from . import views app_name = 'operations' urlpatterns = [ # 用户个人中心 path('user_center_information/', views.UserCenterInformation.as_view(), name='user_center_information'), # 用户学习的课程 path('user_center_my_courses/', views.UserStudyCourse.as_view(), name='user_center_my_courses'), # 用户消息 path('user_center_messages/', views.UserMessageView.as_view(), name='user_center_messages'), # 用户收藏的教师 path('user_center_fav_teachers/', views.UserCollectedTeacher.as_view(), name='user_center_fav_teachers'), # 用户收藏的教师 path('user_center_fav_courses/', views.UserCollectedCourse.as_view(), name='user_center_fav_courses'), # 用户收藏的教师 path('user_center_fav_organizations/', views.UserCollectedOrganization.as_view(), name='user_center_fav_organizations'), # 修改用户头像 path('reset_head_portrait/', views.ResetUserHeaderPortraitView.as_view(), name='reset_head_portrait'), # 修改用户密码 path('reset_user_password/', views.ResetUserPasswordView.as_view(), name='reset_user_password'), # 修改用户信息 path('reset_user_information/', views.ResetUserInformationView.as_view(), name='reset_user_information'), # 修改邮箱是发送的验证码 path('send_email_verify_record/', views.SendEmailView.as_view(), name='send_email_verify_record'), # 修改邮箱 path('reset_user_email/', views.ResetUserEmailView.as_view(), name='reset_user_email'), # 读取用户消息 path('read_message/', views.ReadMessageView.as_view(), name='read_message'), ]

utils/ImageProcesser.py

LLRukia/kkrbot

12799976

import os import re import uuid import globals from PIL import Image, ImageDraw, ImageFont from utils.Asset import ImageAsset SPACING = 5 back_regex = re.compile(r'back_([0-9]*)\.jpg') BACK_PIC_UNIT_WIDTH, BACK_PIC_UNIT_HEIGHT = 140, 130 BACK_PIC_NUM_EACH_LINE = 5 def bg_image_gen(back_number, s): def half_en_len(s): return (len(s) + (len(s.encode(encoding='utf-8')) - len(s)) // 2) // 2 back_number = f'back_{back_number}' img_path = os.path.join(globals.staticpath, f'bg/{back_number}.jpg') im_src = Image.open(img_path) if back_number in [f'back_{n}' for n in [38, 46, 47, 51, 52, 53]]: real_width = max(3, im_src.width // max(6, half_en_len(s)) * 4 // 5) font = ImageFont.truetype(os.path.join(globals.staticpath, 'simhei.ttf'), real_width) real_height = real_width + SPACING im = Image.new('RGB', (im_src.width, im_src.height), (255, 255, 255)) im.paste(im_src) text_width = im_src.width draw = ImageDraw.Draw(im) sz = draw.textsize(s, font=font) x = (text_width - sz[0]) / 2 y = im_src.height - real_height draw.text((x, y), s, fill=(245, 255, 250), font=font) elif back_number in [f'back_{n}' for n in [33]]: real_width = max(3, im_src.width // max(6, half_en_len(s)) * 4 // 5) font = ImageFont.truetype(os.path.join(globals.staticpath, 'simhei.ttf'), real_width) real_height = real_width + SPACING im = Image.new('RGB', (im_src.width, im_src.height), (255, 255, 255)) im.paste(im_src) text_width = im_src.width draw = ImageDraw.Draw(im) sz = draw.textsize(s, font=font) x = (text_width - sz[0]) / 2 y = im_src.height - 2 * real_height draw.text((x, y), s, fill=(245, 255, 250), font=font) elif back_number in [f'back_{n}' for n in [50]]: real_width = max(3, im_src.width // max(6, half_en_len(s)) * 4 // 5) font = ImageFont.truetype(os.path.join(globals.staticpath, 'simhei.ttf'), real_width) real_height = real_width + SPACING im = Image.new('RGB', (im_src.width, im_src.height), (255, 255, 255)) im.paste(im_src) text_width = im_src.width draw = ImageDraw.Draw(im) sz = draw.textsize(s, font=font) x = (text_width - sz[0]) / 2 y = 5 draw.text((x, y), s, fill=(23, 0, 0), font=font) else: real_width = max(3, im_src.width // max(6, half_en_len(s))) font = ImageFont.truetype(os.path.join(globals.staticpath, 'simhei.ttf'), real_width) real_height = real_width + SPACING im = Image.new('RGB', (im_src.width, im_src.height + real_height), (255, 255, 255)) im.paste(im_src) text_width = im_src.width draw = ImageDraw.Draw(im) sz = draw.textsize(s, font=font) x = (text_width - sz[0]) / 2 y = im_src.height draw.text((x, y), s, fill=(23, 0, 0), font=font) return im def get_back_pics(): raw = ImageAsset.get('back_catalogue') if raw: return raw back_pic_set = set() for _, _, files in os.walk(os.path.join(globals.staticpath, 'bg')): for f in files: if f.startswith('back_') and f.endswith('.jpg'): num = int(back_regex.findall(f)[0]) back_pic_set.add(num) cur_back_pic_nums = len(back_pic_set) if cur_back_pic_nums == 0: return im = Image.new('RGB', (BACK_PIC_NUM_EACH_LINE * BACK_PIC_UNIT_WIDTH, BACK_PIC_UNIT_HEIGHT * (((cur_back_pic_nums - 1) // BACK_PIC_NUM_EACH_LINE) + 1)), (255, 255, 255)) for i, num in enumerate(back_pic_set): im_o = bg_image_gen(num, f'底图 {num}') im_o = im_o.resize((BACK_PIC_UNIT_WIDTH, BACK_PIC_UNIT_HEIGHT)) box = (i % BACK_PIC_NUM_EACH_LINE * BACK_PIC_UNIT_WIDTH, i // BACK_PIC_NUM_EACH_LINE * BACK_PIC_UNIT_HEIGHT) im.paste(im_o, box) return ImageAsset.image_raw(im, 'back_catalogue') def merge_image(rsn, rarity, attribute, band_id, thumbnail=True, trained=False, return_fn=False): if thumbnail: try: if return_fn: fn = os.path.join(globals.datapath, 'image', f'auto_reply/cards/thumb/m_{rsn}_{"normal" if not trained else "after_training"}.png') if os.access(fn, os.R_OK): return fn attribute_icon = Image.open(os.path.join(globals.asset_resource_path, f'{attribute}.png')) band_icon = Image.open(os.path.join(globals.asset_resource_path, f'band_{band_id}.png')) if not trained: back_image = Image.open(f'{os.path.join(globals.asset_card_thumb_path, f"{rsn}_normal.png")}') star = Image.open(os.path.join(globals.asset_resource_path, 'star.png')).resize((32, 32), Image.ANTIALIAS) else: back_image = Image.open(f'{os.path.join(globals.asset_card_thumb_path, f"{rsn}_after_training.png")}') star = Image.open(os.path.join(globals.asset_resource_path, 'star_trained.png')).resize((32, 32), Image.ANTIALIAS) if rarity == 1: frame = Image.open(os.path.join(globals.asset_resource_path, f'card-1-{attribute}.png')) else: frame = Image.open(os.path.join(globals.asset_resource_path, f'card-{rarity}.png')) back_image.paste(frame, (0, 0), mask=frame) back_image.paste(band_icon, (0, 0), mask=band_icon) back_image.paste(attribute_icon, (180 - 50, 0), mask=attribute_icon) for i in range(rarity): back_image.paste(star, (2, 170 - 27 * (i + 1)), mask=star) if return_fn: fn = os.path.join(globals.datapath, 'image', f'auto_reply/cards/thumb/m_{rsn}_{"normal" if not trained else "after_training"}.png') back_image.save(fn) return fn return back_image except: import sys sys.excepthook(*sys.exc_info()) return None else: fn = os.path.join(globals.datapath, 'image', f'auto_reply/cards/m_{rsn}_{"normal" if not trained else "after_training"}.png') if os.access(fn, os.R_OK): return fn try: OUT_WIDTH, OUT_HEIGHT = 1364, 1020 INNER_WIDTH, INNER_HEIGHT = 1334, 1002 STAR_SIZE, ICON_SIZE = 100, 150 TOP_OFFSET, RIGHT_OFFSET, BOTTOM_OFFSET, LEFT_OFFSET = 22, 165, 20, 10 STAT_STEP = 95 back_image = Image.new('RGB', (OUT_WIDTH, OUT_HEIGHT)) attribute_icon = Image.open(os.path.join(globals.asset_resource_path, f'{attribute}.png')).resize((ICON_SIZE, ICON_SIZE), Image.ANTIALIAS) band_icon = Image.open(os.path.join(globals.asset_resource_path, f'band_{band_id}.png')).resize((ICON_SIZE, ICON_SIZE), Image.ANTIALIAS) if not trained: card = Image.open(f'{os.path.join(globals.asset_card_path, f"{rsn}_card_normal.png")}') star = Image.open(os.path.join(globals.asset_resource_path, 'star.png')).resize((STAR_SIZE, STAR_SIZE), Image.ANTIALIAS) else: card = Image.open(f'{os.path.join(globals.asset_card_path, f"{rsn}_card_after_training.png")}') star = Image.open(os.path.join(globals.asset_resource_path, 'star_trained.png')).resize((STAR_SIZE, STAR_SIZE), Image.ANTIALIAS) if rarity == 1: frame = Image.open(os.path.join(globals.asset_resource_path, f'frame-1-{attribute}.png')).resize((OUT_WIDTH, OUT_HEIGHT), Image.ANTIALIAS) else: frame = Image.open(os.path.join(globals.asset_resource_path, f'frame-{rarity}.png')).resize((OUT_WIDTH, OUT_HEIGHT), Image.ANTIALIAS) back_image.paste(card, ((OUT_WIDTH - INNER_WIDTH) // 2, (OUT_HEIGHT - INNER_HEIGHT) // 2), mask=card) back_image.paste(frame, (0, 0), mask=frame) back_image.paste(band_icon, (LEFT_OFFSET, TOP_OFFSET), mask=band_icon) back_image.paste(attribute_icon, (OUT_WIDTH - RIGHT_OFFSET, TOP_OFFSET), mask=attribute_icon) for i in range(rarity): back_image.paste(star, (LEFT_OFFSET, OUT_HEIGHT - BOTTOM_OFFSET - STAT_STEP * (i + 1)), mask=star) back_image.save(fn) return fn except: return '' def white_padding(width, height): return Image.new('RGB', (width, height), (255, 255, 255)) def thumbnail(**options): # images: a list of Image objects, or a list of lists(tuples) of Image objects # labels: a list of strings shown at the bottom # image_style: if not assigned, take the params of the first image; if both assigned, will be forced to resize # width: width of each image, if not assigned, will be min(scaled value by height, 180) # height: height of each image, if not assigned, will be min(scaled value by width, 180) # label_style: # font_size: font_size of each label # col_num (images are arranged row by row) # col_space: (space between two columns) # row_space (space between two rows, if labels exist, it means the space between the label of row1 and the image of row2) images = options['images'] first_image = images[0] if not isinstance(first_image, Image.Image): if isinstance(first_image, (list, tuple)): first_image = first_image[0] if not isinstance(first_image, Image.Image): raise Exception('images must be a list of Image objects, or a list of lists(tuples) of Image objects') else: raise Exception('images must be a list of Image objects, or a list of lists(tuples) of Image objects') else: images = [[im] for im in images] if not options.get('image_style'): box_width, box_height = first_image.size else: if options['image_style'].get('width') and options['image_style'].get('height'): box_width, box_height = options['image_style']['width'], options['image_style']['height'] images = [[im.resize((box_width, box_height)) for im in im_list] for im_list in images] elif options['image_style'].get('width') and not options['image_style'].get('height'): images = [[im.resize((options['image_style']['width'], options['image_style']['width'] * im.size[1] // im.size[0])) for im in im_list] for im_list in images] box_width, box_height = options['image_style']['width'], max([im.size[1] for im_list in images for im in im_list]) elif not options['image_style'].get('width') and options['image_style'].get('height'): images = [[im.resize((options['image_style']['height'] * im.size[0] // im.size[1], options['image_style']['height'])) for im in im_list] for im_list in images] box_width, box_height = max([im.size[0] for im_list in images for im in im_list]), options['image_style']['height'] col_num = options.get('col_num', 4) row_num = (len(images) - 1) // col_num + 1 col_space = options.get('col_space', 0) row_space = options.get('row_space', 0) if options.get('labels'): font = ImageFont.truetype(os.path.join(globals.staticpath, 'simhei.ttf'), options.get('label_style', {}).get('font_size', 20)) all_chars = set() max_label_width = 0 for label in options['labels']: max_label_width = max(max_label_width, ImageDraw.Draw(Image.new('RGB', (0, 0))).textsize(label, font=font)[0]) all_chars |= set(label) label_height = ImageDraw.Draw(Image.new('RGB', (0, 0))).textsize(''.join(all_chars), font=font)[1] box_width = max(box_width * len(images[0]), max_label_width) // len(images[0]) back_image = Image.new('RGB', ( col_num * len(images[0]) * box_width + (col_num - 1) * col_space, (box_height + label_height) * row_num + row_num * row_space, ), (255, 255, 255)) draw = ImageDraw.Draw(back_image) labels = options['labels'] for r in range(row_num): for c in range(col_num): if r * col_num + c >= len(images): break image_group = images[r * col_num + c] for i, im in enumerate(image_group): back_image.paste(im, ( (len(image_group) * c + i) * box_width + (box_width - im.size[0]) // 2 + col_space * c, r * (box_height + label_height + row_space) )) sz = draw.textsize(labels[r * col_num + c], font=font) draw.text(( len(image_group) * c * box_width + (len(image_group) * box_width - sz[0]) // 2 + c * col_space, r * (box_height + label_height + row_space) + box_height ), labels[r * col_num + c], fill=(0, 0, 0), font=font) else: back_image = Image.new('RGB', ( col_num * len(images[0]) * box_width + (col_num - 1) * col_space, box_height * row_num + (row_num - 1) * row_space ), (255, 255, 255)) draw = ImageDraw.Draw(back_image) for r in range(row_num): for c in range(col_num): if r * col_num + c >= len(images): break image_group = images[r * col_num + c] for i, im in enumerate(image_group): back_image.paste(im, ( (len(image_group) * c + i) * box_width + (box_width - im.size[0]) // 2 + c * col_space * int(i == len(image_group) - 1), r * (box_height + row_space) )) return ImageAsset.image_raw(back_image) def open_nontransparent(filename): try: image = Image.open(filename).convert('RGBA') new_image = Image.new('RGBA', image.size, (255, 255, 255, 255)) new_image.paste(image, (0, 0), image) return new_image except: pass def manual(): raw = ImageAsset.get('manual') if raw: return raw row_space = 20 col_space = 50 font = ImageFont.truetype(os.path.join(globals.staticpath, 'simhei.ttf'), 20) lines = [ 'ycm/有车吗: 查询车牌(来源: https://bandoristation.com/)', '底图目录: 查询底图目录(是的，不仅功能一样，连图都盗过来了，虽然还没更新。底图31，Tsugu！.jpg)', '底图+数字: 切换底图', 'xx.jpg: 图片合成', '', '以下查询功能数据来源Bestdori', '查卡 [稀有度] [颜色] [人物] [乐团] [技能类型]: 按条件筛选符合要求的卡片，同类条件取并集，不同类条件取交集。例如: 查卡 4x pure ksm 分', '查卡+数字: 按id查询单卡信息', '无框+数字: 按id查询单卡无框卡面', '活动列表 [活动类型]: 按条件筛选符合要求的活动，活动类型包括“一般活动”，“竞演LIVE”或“对邦”，“挑战LIVE”或“CP”，“LIVE试炼”，“任务LIVE”', '活动+数字 [服务器]: 按id查询单活动信息，默认国服，可选“日服”，“国际服”，“台服”，“国服”，“韩服”', '卡池列表 [卡池类型]: 按条件筛选符合要求的卡池，卡池类型包括“常驻”或“无期限”，“限时”或“限定”或“期间限定”，“特殊”（该条件慎加，因为没啥特别的卡池），“必4”', '卡池+数字 [服务器]: 按id查询单卡池信息，默认国服，可选“日服”，“国际服”，“台服”，“国服”，“韩服”', '', '以下查询功能数据来源bilibili开放的豹跳接口，慎用', '查抽卡名字 名字: 查用户名称包含该名字的玩家出的4星', ] line_height = ImageDraw.Draw(Image.new('RGB', (0, 0))).textsize('底图目录', font=font)[1] image = Image.new('RGB', (ImageDraw.Draw(Image.new('RGB', (0, 0))).textsize(max(lines, key=lambda line: len(line)), font=font)[0] + 2 * col_space, (line_height + row_space) * len(lines)), (255, 255, 255)) draw = ImageDraw.Draw(image) line_pos = row_space for i, line in enumerate(lines): sz = draw.textsize(line, font=font) draw.text((col_space, line_pos), line, fill=(0, 0, 0), font=font) line_pos += sz[1] + row_space return ImageAsset.image_raw(image, 'manual') def compress(infile, mb=None, step=10, quality=80, isabs=False): if not isabs: absinfile = os.path.join(globals.datapath, 'image', infile) else: absinfile = infile outfile = infile[infile.rfind('/') + 1:infile.rfind('.')] + '-c.jpg' absoutfile = os.path.join(globals.datapath, 'image', outfile) if os.path.exists(absoutfile): return outfile if mb is None: im = Image.open(absinfile) im = im.convert('RGB') im.save(absoutfile, quality=quality) return absoutfile o_size = os.path.getsize(absinfile) / 1024 if o_size <= mb: return infile while o_size > mb: im = Image.open(absinfile) im = im.convert('RGB') im.save(absoutfile, quality=quality) if quality - step < 0: break quality -= step o_size = os.path.getsize(absoutfile) / 1024 return absoutfile

Maths/7. Reverse Integer.py

thewires2/Leetcode

12799977

class Solution: def reverse(self, x: int) -> int: f=False if x<0: f=True y=str(abs(x)) y=y[::-1] x=int(y) if -2147483648<=x and x<=2147483647: if f==False: return x return -x return 0

rockpaperscissors.py

tarellmorris/RockPaperScissors

12799978

<filename>rockpaperscissors.py from random import randint options = {1: 'ROCK', 2: 'PAPER', 3: 'SCISSORS'} def cpu_move(): cpu_rand = randint(1, 3) return cpu_rand def player_move(): player = input("Choose a move: ('ROCK', 'PAPER', or 'SCISSORS'): ") # if player.upper() != 'ROCK' or player.upper() != 'PAPER' or player.upper() != 'SCISSORS': # print("Syntax error. Please re-enter your move; either 'ROCK', 'PAPER', or 'SCISSORS'...") # player_move() # else: return player.upper() def results(cpu, player): while player == 'ROCK': if cpu == 2: return 'You lose!' elif cpu == 3: return 'You win!' else: return 'Draw!' while player == 'PAPER': if cpu == 3: return 'You lose!' elif cpu == 1: return 'You win!' else: return 'Draw!' while player == 'SCISSORS': if cpu == 1: return 'You lose!' elif cpu == 2: return 'You win!' else: return 'Draw!' def main(): print("This game will be played best two out of three.") match = 1 cpu_tally = 0 player_tally = 0 while cpu_tally != 2 and player_tally != 2: print("Round {}...".format(match)) player = player_move() cpu = cpu_move() print("CPU chose {}!".format(options[cpu])) score = results(cpu, player) if score == 'You win!': print('You win!') match += 1 player_tally += 1 elif score == 'You lose!': print('You lose!') match += 1 cpu_tally += 1 else: print('Draw!') match += 1 if cpu_tally == 2: print("Oh no! You lose!") if player_tally == 2: print("Congratulations! You win!") def play_again(): confirm = input("Would you like to play again?: ('YES' or 'NO') ") if confirm.upper() == 'YES': main() else: SystemExit() print("Are you ready to play Rock Paper Scissors?") confirm = input("Enter 'YES' to continue or 'NO' to exit program: ") if confirm.upper() == 'YES': main() else: SystemExit() play_again()

src/control_node/control_node/errors.py

tessia-project/tessia-mesh

12799979

# Copyright 2021 IBM Corp. # # 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. """ Custom service layer errors that should be differentiated """ # # IMPORTS # # # CONSTANTS AND DEFINITIONS # # # CODE # class StartInstanceError(RuntimeError): """Instance could not be started""" # StartInstanceError class ComponentProbeError(RuntimeError): """Component returned an erroneous response""" # ComponentProbeError class ConfigurationError(ValueError): """Invalid configuration values""" # ConfigurationError class ValidationError(ValueError): """JSON Schema validation error""" def __init__(self, error_iterator) -> None: errors = [f'{"/".join(map(str, item.path))}: {item.message}' for item in error_iterator] super().__init__(f'Task validation failed: {", ".join(errors)}') # __init__() # ValidationError

code/route_betweenness.py

tlarock/shipping

12799980

<gh_stars>0 import numpy as np import networkx as nx def route_node_betweenness_from_paths(G, filtered_paths): ''' Computes route betweenness for nodes starting from set of paths filtered_paths. Uses G only to get the number of nodes; could be done by iterating over pairs of nodes in filtered_paths or given as input parameter. Uses dense numpy arrays for computations. ''' ## Zero array of dimensions len(G.nodes()) by len(filtered_paths) node_to_idx = {node:idx for idx, node in enumerate(G.nodes())} pair_to_idx = {pair:idx for idx, pair in enumerate(filtered_paths.keys())} numerator = np.zeros((len(G.nodes()), len(filtered_paths))) denominator = [] for pair in filtered_paths: denominator.append(len(filtered_paths[pair])) for path in filtered_paths[pair]: for node in path: numerator[node_to_idx[node], pair_to_idx[pair]] += 1 denominator = np.array(denominator) normalized_counts = numerator / denominator total_betweenness = normalized_counts.sum(axis=1) route_betweenness = {node:total_betweenness[idx] for node, idx in node_to_idx.items()} return route_betweenness def route_node_betweenness_from_file(filename): ''' Computes route betweenness for nodes by reading file filename. Uses dictionaries for computations. ''' pair_counter = 0 total_pairs = 0 first = True node_to_pair_dict = dict() prev_pair = (-1, -1) filtered_paths = dict() with open(filename, 'r') as fin: for line in fin: path, *_ = line.strip().split('|') path = path.strip().split(',') pair = (path[0], path[-1]) filtered_paths.setdefault(pair, list()) filtered_paths[pair].append(path) if pair != prev_pair and not first: nodes_to_norm = set() for path in filtered_paths[prev_pair]: for node in path: node_to_pair_dict.setdefault(node, dict()) node_to_pair_dict[node].setdefault(prev_pair, 0) node_to_pair_dict[node][prev_pair] += 1 nodes_to_norm.add(node) ## Normalize for node in nodes_to_norm: node_to_pair_dict[node][prev_pair] /= len(filtered_paths[prev_pair]) pair_counter += 1 total_pairs += 1 if pair_counter == 150_000: print(f"{total_pairs} processed.", flush=True) pair_counter = 0 prev_pair = pair if first: first = False ## Handle the last pair for path in filtered_paths[prev_pair]: nodes_to_norm = set() for node in path: node_to_pair_dict.setdefault(node, dict()) node_to_pair_dict[node].setdefault(prev_pair, 0) node_to_pair_dict[node][prev_pair] += 1 nodes_to_norm.add(node) ## Normalize for node in nodes_to_norm: node_to_pair_dict[node][prev_pair] /= len(filtered_paths[prev_pair]) ## Compute betweenness by summing over all pairs for each node route_betweenness = {node:sum(node_to_pair_dict[node].values()) for node in node_to_pair_dict} return route_betweenness def route_edge_betweenness_from_paths(G, filtered_paths): ''' Computes route betweenness for edges starting from set of paths filtered_paths. Uses G only to get the number of nodes; could be done by iterating over pairs of nodes in filtered_paths or given as input parameter. Uses dense numpy arrays for computations. ''' ## Zero array of dimensions len(G.edges()) by len(filtered_paths) edge_to_idx = {edge:idx for idx, edge in enumerate(G.edges())} pair_to_idx = {pair:idx for idx, pair in enumerate(filtered_paths.keys())} numerator = np.zeros((len(G.edges()), len(filtered_paths))) denominator = [] for pair in filtered_paths: denominator.append(len(filtered_paths[pair])) for path in filtered_paths[pair]: for i in range(1, len(path)): numerator[edge_to_idx[(path[i-1], path[i])], pair_to_idx[pair]] += 1 denominator = np.array(denominator) normalized_counts = numerator / denominator total_betweenness = normalized_counts.sum(axis=1) route_betweenness = {edge:total_betweenness[idx] for edge, idx in edge_to_idx.items()} return route_betweenness def route_edge_betweenness_from_file(filename): ''' Computes route betweenness for edges by reading file filename. Uses dictionaries for computations. ''' pair_counter = 0 total_pairs = 0 first = True edge_to_pair_dict = dict() prev_pair = (-1, -1) filtered_paths = dict() with open(filename, 'r') as fin: for line in fin: path, *_ = line.strip().split('|') path = path.strip().split(',') pair = (path[0], path[-1]) filtered_paths.setdefault(pair, list()) filtered_paths[pair].append(path) if pair != prev_pair and not first: edges_to_norm = set() for path in filtered_paths[prev_pair]: for i in range(1, len(path)): edge = path[i-1], path[i] edge_to_pair_dict.setdefault(edge, dict()) edge_to_pair_dict[edge].setdefault(prev_pair, 0) edge_to_pair_dict[edge][prev_pair] += 1 edges_to_norm.add(edge) ## Normalize for edge in edges_to_norm: edge_to_pair_dict[edge][prev_pair] /= len(filtered_paths[prev_pair]) pair_counter += 1 total_pairs += 1 if pair_counter == 150_000: print(f"{total_pairs} processed.", flush=True) pair_counter = 0 prev_pair = pair if first: first = False ## Handle the last pair for path in filtered_paths[prev_pair]: edges_to_norm = set() for i in range(1, len(path)): edge = path[i-1], path[i] edge_to_pair_dict.setdefault(edge, dict()) edge_to_pair_dict[edge].setdefault(prev_pair, 0) edge_to_pair_dict[edge][prev_pair] += 1 edges_to_norm.add(edge) ## Normalize for edge in edges_to_norm: edge_to_pair_dict[edge][prev_pair] /= len(filtered_paths[prev_pair]) ## Compute betweenness by summing over all pairs for each edge route_betweenness = {edge:sum(edge_to_pair_dict[edge].values()) for edge in edge_to_pair_dict} return route_betweenness def route_path_betweenness_from_file(filename, k): ''' Computes route betweenness for paths by reading file filename. Uses dictionaries for computations. ''' pair_counter = 0 total_pairs = 0 first = True path_to_pair_dict = dict() prev_pair = (-1, -1) filtered_paths = dict() with open(filename, 'r') as fin: for line in fin: path, *_ = line.strip().split('|') path = path.strip().split(',') pair = (path[0], path[-1]) filtered_paths.setdefault(pair, list()) filtered_paths[pair].append(path) if pair != prev_pair and not first: paths_to_norm = set() for path in filtered_paths[prev_pair]: for i in range(0, len(path)-k): kpath = tuple(path[i:i+k+1]) path_to_pair_dict.setdefault(kpath, dict()) path_to_pair_dict[kpath].setdefault(prev_pair, 0) path_to_pair_dict[kpath][prev_pair] += 1 paths_to_norm.add(kpath) ## Normalize for path in paths_to_norm: path_to_pair_dict[path][prev_pair] /= len(filtered_paths[prev_pair]) pair_counter += 1 total_pairs += 1 if pair_counter == 150_000: print(f"{total_pairs} processed.", flush=True) pair_counter = 0 prev_pair = pair if first: first = False ## Handle the last pair for path in filtered_paths[prev_pair]: paths_to_norm = set() for i in range(0, len(path)-k): kpath = tuple(path[i:i+k+1]) path_to_pair_dict.setdefault(kpath, dict()) path_to_pair_dict[kpath].setdefault(prev_pair, 0) path_to_pair_dict[kpath][prev_pair] += 1 paths_to_norm.add(kpath) ## Normalize for path in paths_to_norm: path_to_pair_dict[path][prev_pair] /= len(filtered_paths[prev_pair]) ## Compute betweenness by summing over all pairs for each path route_betweenness = {path:sum(path_to_pair_dict[path].values()) for path in path_to_pair_dict} return route_betweenness

language/python/pocket-primer/CH5 - Files Input and Output/planets.py

pendraic/learn

12799981

<filename>language/python/pocket-primer/CH5 - Files Input and Output/planets.py # This program prints the names of planets having fewer than ten (10) moons # Open the file try: infile = open ("planets.txt", "r") # Read (skip over) the header line s = infile.readline() # For each planet for i in range (0, 8): # Read a line as string s s = infile.readline() # Break s into components based on commas giving list P p = s.split (",") # If p[10] < 10 print the planet name, which is p[0] if int(p[10])<10: print (p[0], "has fewer than 10 moons.") except FileNotFoundError: print ("There is no file named 'planets.txt'. Please try again")

runtests.py

tushargoel97/WebAdmin

12799982

<gh_stars>0 from fabric import Connection class OperationsUtil(): def __init__(self): self.rootUser="ubuntu20" self.rootPass="<PASSWORD>!" self.conn=Connection(host='[email protected]',connect_kwargs={"password": self.rootPass}) def createUser(self, username, password, dirname): if not dirname: dirname = username command = "useradd -p "+password+" -m -d /home/"+dirname+"/ -g users -s /bin/bash "+username try: val = self.conn.sudo(command,password=self.rootPass,hide=True).stdout.strip() return ('User Created: '+username,0) except Exception as e: return ('Cannot create user',1) def viewUser(self): command = "awk -F: '{ print $1}' /etc/passwd" return self.conn.run(command,hide=True).stdout.strip() def deluser(self,username): command = "userdel -f "+username try: val = self.conn.sudo(command,password=self.<PASSWORD>,hide=True).stdout.strip() return ('User Deleted: '+username,0) except Exception as e: return ('Cannot delete user',1) def updatePriv(self,username): command = "usermod -aG sudo "+username try: val = self.conn.sudo(command,password=self.rootPass).stdout.strip() return ('User Privilege Granted',0) except Exception as e: return ('Cannot Grant user Privileges',1) op = OperationsUtil() # print(op.createUser("test", "test")[0]) # print(op.viewUser()) # print(op.updatePriv("test")[0]) print(op.deluser("test1")[0])

src/tfchain/types/PrimitiveTypes.py

GlenDC/threefold-wallet-electron

12799983

<reponame>GlenDC/threefold-wallet-electron import tfchain.errors as tferrors import tfchain.polyfill.encoding.base64 as jsbase64 import tfchain.polyfill.encoding.hex as jshex import tfchain.polyfill.encoding.str as jsstr import tfchain.polyfill.encoding.decimal as jsdec import tfchain.polyfill.array as jsarray from tfchain.types.BaseDataType import BaseDataTypeClass class BinaryData(BaseDataTypeClass): """ BinaryData is the data type used for any binary data used in tfchain. """ def __init__(self, value=None, fixed_size=None, strencoding=None): # define string encoding if strencoding != None and not isinstance(strencoding, str): raise TypeError( "strencoding should be None or a str, not be of type {}".format(strencoding)) if strencoding == None or jsstr.String(strencoding).lower().strip().__eq__('hex'): self._from_str = lambda s: jshex.bytes_from_hex(s) self._to_str = lambda value: jshex.bytes_to_hex(value) elif jsstr.String(strencoding).lower().strip().__eq__('base64'): self._from_str = lambda s: jsbase64.bytes_from_b64(s) self._to_str = lambda value: jsbase64.bytes_to_b64(value) elif jsstr.String(strencoding).lower().strip().__eq__('hexprefix'): self._from_str = lambda s: jshex.bytes_from_hex( s[2:] if (s.startswith("0x") or s.startswith("0X")) else s) self._to_str = lambda value: '0x' + jshex.bytes_to_hex(value) else: raise TypeError( "{} is not a valid string encoding".format(strencoding)) self._strencoding = strencoding # define fixed size if fixed_size != None: if not isinstance(fixed_size, int): raise TypeError( "fixed size should be None or int, not be of type {}".format(type(fixed_size))) if fixed_size < 0: raise TypeError( "fixed size should be at least 0, {} is not allowed".format(fixed_size)) if fixed_size != 0: self._fixed_size = fixed_size else: self._fixed_size = None # for now use no fixed size # define the value (finally) self._value = None self.value = value if fixed_size == 0: # define the fixed size now, if the fixed_size was 0 # based on the binary length of the value self._fixed_size = len(self.value) @classmethod def from_json(cls, obj, fixed_size=None, strencoding=None): if obj != None and not isinstance(obj, str): raise TypeError( "binary data is expected to be an encoded string when part of a JSON object") if obj == '': obj = None return cls(value=obj, fixed_size=fixed_size, strencoding=strencoding) @property def value(self): return self._value @value.setter def value(self, value): # normalize the value if isinstance(value, BinaryData): value = value.value elif value == None: if self._fixed_size != None: value = bytes(jsarray.new_array(self._fixed_size)) else: value = bytes(jsarray.new_array(0)) elif isinstance(value, str): value = self._from_str(value) elif isinstance(value, bytearray): value = bytes(value) elif not isinstance(value, bytes) and not jsarray.is_uint8_array(value): raise TypeError( "binary data can only be set to a BinaryData, str, bytes or bytearray, not {}".format(type(value))) # if fixed size, check this now lvalue = len(value) if self._fixed_size != None and lvalue != 0 and lvalue != self._fixed_size: raise ValueError( "binary data was expected to be of fixed size {}, length {} is not allowed".format( self._fixed_size, len(value))) # all good, assign the bytearray value self._value = value def __len__(self): return len(self.value) def __str__(self): return self._to_str(self._value) def str(self): return self.__str__() def __repr__(self): return self.__str__() def json(self): return self.__str__() def __eq__(self, other): other = self._op_other_as_binary_data(other) return self.value == other.value def __ne__(self, other): other = self._op_other_as_binary_data(other) return self.value != other.value def _op_other_as_binary_data(self, other): if isinstance(other, (str, bytes, bytearray)): other = BinaryData( value=other, fixed_size=self._fixed_size, strencoding=self._strencoding) elif not isinstance(other, BinaryData): raise TypeError( "Binary data of type {} is not supported".format(type(other))) if self._fixed_size != other._fixed_size: raise TypeError( "Cannot compare binary data with different fixed size: self({}) != other({})".format( self._fixed_size, other._fixed_size)) if self._strencoding != other._strencoding: raise TypeError( "Cannot compare binary data with different strencoding: self({}) != other({})".format( self._strencoding, other._strencoding)) return other def __hash__(self): return hash(self.__str__()) def sia_binary_encode(self, encoder): """ Encode this binary data according to the Sia Binary Encoding format. Either encoded as a slice or an array, depending on whether or not it is fixed sized. """ if self._fixed_size == None: encoder.add_slice(self._value) else: encoder.add_array(self._value) def rivine_binary_encode(self, encoder): """ Encode this binary data according to the Rivine Binary Encoding format. Either encoded as a slice or an array, depending on whether or not it is fixed sized. """ if self._fixed_size == None: encoder.add_slice(self._value) else: encoder.add_array(self._value) class Hash(BinaryData): SIZE = 32 """ TFChain Hash Object, a special type of BinaryData """ def __init__(self, value=None): super().__init__(value, fixed_size=Hash.SIZE, strencoding='hex') @classmethod def from_json(cls, obj): if obj != None and not isinstance(obj, str): raise TypeError( "hash is expected to be an encoded string when part of a JSON object, not {}".format(type(obj))) if obj == '': obj = None return cls(value=obj) def __str__(self): s = super().__str__() if jsstr.isempty(s): return jsstr.repeat('0', Hash.SIZE*2) return s class Currency(BaseDataTypeClass): """ TFChain Currency Object. """ def __init__(self, value=None): self._value = None self.value = value @classmethod def sum(cls, *values): s = cls() for value in values: s.__iadd__(value) return s @classmethod def from_str(cls, obj, lowest_unit=False): if obj != None and not isinstance(obj, str): raise TypeError( "currency is expected to be a string , not type {}".format(type(obj))) if obj == '': obj = None c = cls() c.value = jsdec.Decimal(obj) if lowest_unit: c.value.__imul__(jsdec.Decimal('0.000000001')) return c @classmethod def from_json(_, obj): return Currency.from_str(obj, lowest_unit=True) @property def value(self): return self._value def plus(self, other): return self.__add__(other) def minus(self, other): return self.__sub__(other) def times(self, other): return self.__mul__(other) def divided_by(self, other): return self.__truediv__(other) def equal_to(self, other): return self.__eq__(other) def not_equal_to(self, other): return self.__ne__(other) def less_than(self, other): return self.__lt__(other) def greater_than(self, other): return self.__gt__(other) def less_than_or_equal_to(self, other): return self.__le__(other) def greater_than_or_equal_to(self, other): return self.__ge__(other) def negate(self): return Currency(self.value.negate()) @value.setter def value(self, value): if value == None: self._value = jsdec.Decimal() return if isinstance(value, Currency): self._value = value.value return if isinstance(value, (int, str, jsdec.Decimal)): inner_value = value if isinstance(inner_value, str): inner_value = jsstr.String(inner_value).upper().strip().value if len(inner_value) >= 4 and inner_value[-3:] == 'TFT': inner_value = jsstr.rstrip(inner_value[:-3]) d = jsdec.Decimal(inner_value) _, _, exp = d.as_tuple() # sign is first return value if exp < -9: raise tferrors.CurrencyPrecisionOverflow(d.__str__()) # if sign != 0: # allow negative values for intermediate computations # raise tferrors.CurrencyNegativeValue(d.__str__()) self._value = d return raise TypeError( "cannot set value of type {} as Currency (invalid type): {}".format(type(value), value)) # operator overloading to allow currencies to be summed def __add__(self, other): if not isinstance(other, Currency): return self.__add__(Currency(other)) return Currency(self.value.__add__(other.value)) def __radd__(self, other): return self.__add__(other) def __iadd__(self, other): if not isinstance(other, Currency): return self.__iadd__(Currency(other)) self._value.__iadd__(other.value) return self # operator overloading to allow currencies to be multiplied def __mul__(self, other): if not isinstance(other, Currency): return self.__mul__(Currency(other)) return Currency(self.value.__mul__(other.value).to_nearest(9)) def __rmul__(self, other): return self.__mul__(other) def __imul__(self, other): if not isinstance(other, Currency): return self.__imul__(Currency(other)) self._value.__imul__(other.value) return self # operator overloading to allow currencies to be divided def __truediv__(self, other): if not isinstance(other, Currency): return self.__truediv__(Currency(other)) return Currency(self.value.__truediv__(other.value).to_nearest(9)) # operator overloading to allow currencies to be subtracted def __sub__(self, other): if not isinstance(other, Currency): return self.__sub__(Currency(other)) return Currency(self.value.__sub__(other.value)) def __rsub__(self, other): return self.__sub__(other) def __isub__(self, other): if not isinstance(other, Currency): return self.__isub__(Currency(other)) self._value.__isub__(other.value) return self # operator overloading to allow currencies to be compared def __lt__(self, other): if not isinstance(other, Currency): return self.__lt__(Currency(other)) return self.value.__lt__(other.value) def __le__(self, other): if not isinstance(other, Currency): return self.__le__(Currency(other)) return self.value.__le__(other.value) def __eq__(self, other): if not isinstance(other, Currency): return self.__eq__(Currency(other)) return self.value.__eq__(other.value) def __ne__(self, other): if not isinstance(other, Currency): return self.__ne__(Currency(other)) return self.value.__ne__(other.value) def __gt__(self, other): if not isinstance(other, Currency): return self.__gt__(Currency(other)) return self.value.__gt__(other.value) def __ge__(self, other): if not isinstance(other, Currency): return self.__ge__(Currency(other)) return self.value.__ge__(other.value) @staticmethod def _op_other_as_currency(other): if isinstance(other, (int, str)): other = Currency(value=other) elif isinstance(other, float): other = Currency(value=jsdec.Decimal(str(other))) elif not isinstance(other, Currency): raise TypeError( "currency of type {} is not supported".format(type(other))) return other # allow our currency to be turned into an int def __int__(self): return jsstr.to_int(self.str(lowest_unit=True)) def bytes(self): return self.value.bytes(prec=9) def __str__(self): return self.str() def str(self, with_unit=False, lowest_unit=False, precision=9): """ Turn this Currency value into a str TFT unit-based value, optionally with the currency notation. @param with_unit: include the TFT currency suffix unit with the str """ s = self.value.str(precision) if lowest_unit: s = jsstr.lstrip(jsstr.replace(s, ".", ""), "0") elif jsstr.contains(s, "."): s = jsstr.rstrip(jsstr.rstrip(s, "0 "), '.') if jsstr.isempty(s): s = "0" if with_unit: s += " TFT" return s def __repr__(self): return self.str(with_unit=True) def json(self): return self.str(lowest_unit=True) def sia_binary_encode(self, encoder): """ Encode this currency according to the Sia Binary Encoding format. """ b = self.bytes() encoder.add_int(len(b)) encoder.add_array(b) def rivine_binary_encode(self, encoder): """ Encode this currency according to the Rivine Binary Encoding format. """ b = self.bytes() encoder.add_slice(b) class Blockstake(BaseDataTypeClass): """ TFChain Blockstake Object. """ def __init__(self, value=None): self._value = Currency(value) @classmethod def from_json(cls, obj): if obj != None and not isinstance(obj, str): raise TypeError( "block stake is expected to be a string when part of a JSON object, not type {}".format(type(obj))) if obj == '': obj = None return cls(value=obj) @property def value(self): return self._value @value.setter def value(self, value): value._value = Currency(value=value) # allow our block stake to be turned into an int def __int__(self): return jsstr.to_int(self.value.str(lowest_unit=False)) def str(self): return jsstr.from_int(self.__int__()) def __str__(self): return self.str() def __repr__(self): return self.__str__() def json(self): return self.__str__() def bytes(self): return self.value.bytes() def sia_binary_encode(self, encoder): """ Encode this block stake (==Currency) according to the Sia Binary Encoding format. """ b = self.bytes() encoder.add_int(len(b)) encoder.add_array(b) def rivine_binary_encode(self, encoder): """ Encode this block stake (==Currency) according to the Rivine Binary Encoding format. """ b = self.bytes() encoder.add_slice(b)

dimensionality_reduction/LDA.py

jonathangouvea/PatternRecognition

12799984

import numpy as np from numpy import linalg as LA class LDA(): def __init__(self, dim = 2): self.dim = dim self.matrixTransf = None def fit_transform(self, X, labels): positive = [] negative = [] for i in range(len(labels)): if labels[i] == 1: positive.append(X[i]) else: negative.append(X[i]) positive = np.array(positive) negative = np.array(negative) media_pos = np.mean(positive, axis = 0) media_neg = np.mean(negative, axis = 0) cov_pos = np.cov(positive.T) cov_neg = np.cov(negative.T) SW = cov_pos + cov_neg sub = (media_pos - media_neg) print(SW.shape) print(sub.shape) wLDA = np.matmul(LA.pinv(SW), sub) self.matrixTransf = np.array(wLDA) print("Matriz de transformação") print(self.matrixTransf) res = np.matmul(X, self.matrixTransf.T) return res

wowgic/wowgic_flask/lib/tweepy/__init__.py

chelladurai89/wowgicbackend2.0

12799985

# Tweepy # Copyright 2009-2010 <NAME> # See LICENSE for details. """ Tweepy Twitter API library """ __version__ = '3.5.0' __author__ = '<NAME>' __license__ = 'MIT' from tweepy.models import Status, User, DirectMessage, Friendship, SavedSearch, SearchResults, ModelFactory, Category from tweepy.error import TweepError, RateLimitError from tweepy.api import API from tweepy.cache import Cache, MemoryCache, FileCache from tweepy.auth import OAuthHandler, AppAuthHandler from tweepy.limit import RateLimitHandler from tweepy.streaming import Stream, StreamListener from tweepy.cursor import Cursor # Global, unauthenticated instance of API api = API() def debug(enable=True, level=1): from six.moves.http_client import HTTPConnection HTTPConnection.debuglevel = level def chunks(l, n): for i in xrange(0, len(l), n): yield l[i:i+n] class Twitter(object): """ Twitter API wrapper based on Tweepy using the RateLimitHandler with multiple access tokens (see https://github.com/svven/tweepy). It also handles API method cursors and splits input param lists in chunks if neccessary. """ def __init__(self, consumer_key, consumer_secret, access_tokens=None): """ Initialize params for RateLimitHandler to pass to Tweepy API. Param `access_tokens` must be a dictionary but it can be loaded later just before the first API method call, and has to be like {user_id: (access_token_key, access_token_secret)}. """ self.consumer_key = consumer_key self.consumer_secret = consumer_secret self.access_tokens = access_tokens _api = None def _get_api(self): "Initialize Tweepy API object with RateLimitHandler auth." auth = RateLimitHandler(self.consumer_key, self.consumer_secret) for key, secret in self.access_tokens.values(): auth.add_access_token(key, secret) # print 'Token pool size: %d' % len(auth.tokens) return API(auth) # retry_count=2, retry_delay=3, # wait_on_rate_limit=True, wait_on_rate_limit_notify=True @property def api(self): "Lazy loaded Tweepy API object." if not self._api: self._api = self._get_api() return self._api

Section 2 - Data (variables, assignments and expressions)/Breakouts/Breakout 2.1 - Turtle Graphics/Q5 - random walk solution.py

gitjot/python-for-lccs

12799986

<gh_stars>1-10 # Event: LCCS Python Fundamental Skills Workshop # Date: May 2018 # Author: <NAME>, PDST # eMail: <EMAIL> # Purpose: Solution to Q5 page 53 from turtle import * from random import * angle = randint(0, 360) lineLen = randint(50, 100) left(angle) forward(lineLen) angle = randint(0, 360) lineLen = randint(50, 100) left(angle) forward(lineLen) angle = randint(0, 360) lineLen = randint(50, 100) left(angle) forward(lineLen) angle = randint(0, 360) lineLen = randint(50, 100) left(angle) forward(lineLen) angle = randint(0, 360) lineLen = randint(50, 100) left(angle) forward(lineLen)

QianProj.py

Bachery/sqlova

12799987

from datetime import timedelta import numpy as np import pandas as pd import argparse import torch import json import os from add_csv import csv_to_sqlite, csv_to_json from sqlnet.dbengine import DBEngine from sqlova.utils.utils_wikisql import * from train import construct_hyper_param, get_models #### prediction #################### def get_args(): parser = argparse.ArgumentParser() # parser.add_argument("--model_file", required=True, help='model file to use (e.g. model_best.pt)') # parser.add_argument("--bert_model_file", required=True, help='bert model file to use (e.g. model_bert_best.pt)') # parser.add_argument("--bert_path", required=True, help='path to bert files (bert_config*.json etc)') # parser.add_argument("--data_path", required=True, help='path to *.jsonl and *.db files') # parser.add_argument("--split", required=True, help='prefix of jsonl and db files (e.g. dev)') # parser.add_argument("--result_path", required=True, help='directory in which to place results') args = construct_hyper_param(parser) return args def load_models(args): BERT_PT_PATH = './data_and_model' path_model_bert = './model_bert_best.pt' path_model = './model_best.pt' model, model_bert, tokenizer, bert_config = get_models(args, BERT_PT_PATH, True, path_model_bert, path_model) return model, model_bert, tokenizer, bert_config def my_get_fields(t, data_tables): ### t: list of dict ### data_tables: dict nlu, nlu_t, tb, hds = [], [], [], [] for t1 in t: nlu.append( t1['question']) nlu_t.append( t1['question_tok']) tbid = t1['table_id'] tb.append(data_tables[tbid]) hds.append(data_tables[tbid]['header']) return nlu, nlu_t, tb, hds def my_predict( data_loader, data_table, model, model_bert, bert_config, tokenizer, max_seq_length, num_target_layers, path_db, dset_name, EG=False, beam_size=4): model.eval() model_bert.eval() # engine = DBEngine(os.path.join(path_db, f"{dset_name}.db")) engine = DBEngine(path_db) results = [] for _, t in enumerate(data_loader): nlu, nlu_t, tb, hds = my_get_fields(t, data_table) wemb_n, wemb_h, l_n, l_hpu, l_hs, \ nlu_tt, t_to_tt_idx, tt_to_t_idx \ = get_wemb_bert(bert_config, model_bert, tokenizer, nlu_t, hds, max_seq_length, num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers) if not EG: # No Execution guided decoding s_sc, s_sa, s_wn, s_wc, s_wo, s_wv = model(wemb_n, l_n, wemb_h, l_hpu, l_hs) pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi = pred_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, ) pr_wv_str, pr_wv_str_wp = convert_pr_wvi_to_string(pr_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu) pr_sql_i = generate_sql_i(pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, nlu) else: # Execution guided decoding prob_sca, prob_w, prob_wn_w, \ pr_sc, pr_sa, pr_wn, pr_sql_i \ = model.beam_forward(wemb_n, l_n, wemb_h, l_hpu, l_hs, engine, tb, nlu_t, nlu_tt, tt_to_t_idx, nlu, beam_size=beam_size) # sort and generate pr_wc, pr_wo, pr_wv, pr_sql_i = sort_and_generate_pr_w(pr_sql_i) # Following variables are just for consistency with no-EG case. pr_wvi = None # not used pr_wv_str=None pr_wv_str_wp=None pr_sql_q = generate_sql_q(pr_sql_i, tb) for b, (pr_sql_i1, pr_sql_q1) in enumerate(zip(pr_sql_i, pr_sql_q)): results1 = {} results1["query"] = pr_sql_i1 results1["table_id"] = tb[b]["id"] results1["nlu"] = nlu[b] results1["sql"] = pr_sql_q1 results.append(results1) return results #### deal with data ################### ## 不需要了 def read_csv_to_table(csv_path): # file_name as table_id table_id = csv_path.split('/')[-1][:-4] df = pd.read_csv(csv_path) headers = df.columns.tolist() rows = [] for _, row in df.iterrows(): rows.append(row.tolist()) print(rows) ## TODO: add_csv def create_table_and_db(): pass def read_scripts(txt_path): nlu = [] with open(txt_path, 'r') as f: line = f.readline() while line: if line.endswith('\n'): nlu.append(line[:-1]) else: nlu.append(line) line = f.readline() return nlu ## TODO: with tools in annotate_ws.py def split_scripts(nlu): nlu_t = [] for nlu1 in nlu: nlu_t.append(nlu1.split(' ')) return nlu_t def get_tables(tb_path): table = {} with open(tb_path) as f: for _, line in enumerate(f): t1 = json.loads(line.strip()) table[t1['id']] = t1 return table def prepare_data(): sc_paths = [ './Qian_data/company_script.txt', './Qian_data/product_script.txt',] sc_tableids = [ 'company_table', 'product_table',] nlu = [] nlu_t = [] tbid = [] for i in range(len(sc_paths)): nlu_i = read_scripts(sc_paths[i]) nlu_t_i = split_scripts(nlu_i) nlu.extend(nlu_i) nlu_t.extend(nlu_t_i) tbid.extend([sc_tableids[i]] * len(nlu_i)) data = [] for i in range(len(nlu)): data.append({ 'question': nlu[i], 'question_tok': nlu_t[i], 'table_id': tbid[i], }) return data if __name__ == '__main__': dset_name = 'qian' save_path = './Qian_data/' ### model args = get_args() model, model_bert, tokenizer, bert_config = load_models(args) ### data db_path = './Qian_data/qian.db' tb_path = './Qian_data/qian.tables.jsonl' data_table = get_tables(tb_path) data = prepare_data() data_loader = torch.utils.data.DataLoader( batch_size=args.bS, dataset=data, shuffle=False, num_workers=1, collate_fn=lambda x: x # now dictionary values are not merged! ) ### predict with torch.no_grad(): results = my_predict(data_loader, data_table, model, model_bert, bert_config, tokenizer, max_seq_length=args.max_seq_length, num_target_layers=args.num_target_layers, path_db=db_path, dset_name=dset_name, EG=False, #args.EG, ) # save results save_for_evaluation(save_path, results, dset_name)

scalyr_agent/third_party_tls/tlslite/defragmenter.py

zak905/scalyr-agent-2

12799988

<gh_stars>0 # Copyright (c) 2015, <NAME> # # See the LICENSE file for legal information regarding use of this file. """ Helper package for handling fragmentation of messages """ from __future__ import generators from .utils.codec import Parser class Defragmenter(object): """ Class for demultiplexing TLS messages. Since the messages can be interleaved and fragmented between each other we need to cache not complete ones and return in order of urgency. Supports messages with given size (like Alerts) or with a length header in specific place (like Handshake messages). :ivar priorities: order in which messages from given types should be returned. :ivar buffers: data buffers for message types :ivar decoders: functions which check buffers if a message of given type is complete """ def __init__(self): """Set up empty defregmenter""" self.priorities = [] self.buffers = {} self.decoders = {} def addStaticSize(self, msgType, size): """Add a message type which all messages are of same length""" if msgType in self.priorities: raise ValueError("Message type already defined") if size < 1: raise ValueError("Message size must be positive integer") self.priorities += [msgType] self.buffers[msgType] = bytearray(0) def sizeHandler(data): """ Size of message in parameter If complete message is present in parameter returns its size, None otherwise. """ if len(data) < size: return None else: return size self.decoders[msgType] = sizeHandler def addDynamicSize(self, msgType, sizeOffset, sizeOfSize): """Add a message type which has a dynamic size set in a header""" if msgType in self.priorities: raise ValueError("Message type already defined") if sizeOfSize < 1: raise ValueError("Size of size must be positive integer") if sizeOffset < 0: raise ValueError("Offset can't be negative") self.priorities += [msgType] self.buffers[msgType] = bytearray(0) def sizeHandler(data): """ Size of message in parameter If complete message is present in parameter returns its size, None otherwise. """ if len(data) < sizeOffset+sizeOfSize: return None else: parser = Parser(data) # skip the header parser.getFixBytes(sizeOffset) payloadLength = parser.get(sizeOfSize) if parser.getRemainingLength() < payloadLength: # not enough bytes in buffer return None return sizeOffset + sizeOfSize + payloadLength self.decoders[msgType] = sizeHandler def addData(self, msgType, data): """Adds data to buffers""" if msgType not in self.priorities: raise ValueError("Message type not defined") self.buffers[msgType] += data def getMessage(self): """Extract the highest priority complete message from buffer""" for msgType in self.priorities: length = self.decoders[msgType](self.buffers[msgType]) if length is None: continue # extract message data = self.buffers[msgType][:length] # remove it from buffer self.buffers[msgType] = self.buffers[msgType][length:] return (msgType, data) return None def clearBuffers(self): """Remove all data from buffers""" for key in self.buffers.keys(): self.buffers[key] = bytearray(0)

tfx/orchestration/portable/execution_watcher.py

avelez93/tfx

12799989

<filename>tfx/orchestration/portable/execution_watcher.py # Copyright 2021 Google LLC. All Rights Reserved. # # 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. """This module provides a gRPC service for updating remote job info to MLMD.""" from concurrent import futures from typing import Optional from absl import logging import grpc from tfx.orchestration import metadata from tfx.proto.orchestration import execution_watcher_pb2 from tfx.proto.orchestration import execution_watcher_pb2_grpc from ml_metadata.proto import metadata_store_pb2 def generate_service_stub( address: str, creds: Optional[grpc.ChannelCredentials] = None, ) -> execution_watcher_pb2_grpc.ExecutionWatcherServiceStub: """Generates a gRPC service stub for a given server address.""" channel = grpc.secure_channel( address, creds) if creds else grpc.insecure_channel(address) return execution_watcher_pb2_grpc.ExecutionWatcherServiceStub(channel) class ExecutionWatcher( execution_watcher_pb2_grpc.ExecutionWatcherServiceServicer): """A gRPC service server for updating remote job info to MLMD. Attributes: local_address: Local network address to the server. address: Remote network address to the server, same as local_address if not configured. """ def __init__(self, port: int, mlmd_connection: metadata.Metadata, execution: metadata_store_pb2.Execution, address: Optional[str] = None, creds: Optional[grpc.ServerCredentials] = None): """Initializes the gRPC server. Args: port: Which port the service will be using. mlmd_connection: ML metadata connection. execution: The MLMD Execution to keep track of. address: Remote address used to contact the server. Should be formatted as an ipv4 or ipv6 address in the format `address:port`. If left as None, server will use local address. creds: gRPC server credentials. If left as None, server will use an insecure port. """ super().__init__() self._port = port self._address = address self._creds = creds self._mlmd_connection = mlmd_connection self._server = self._create_server() if not execution.HasField('id'): raise ValueError( 'execution id must be set to be tracked by ExecutionWatcher.') self._execution = execution def UpdateExecutionInfo( self, request: execution_watcher_pb2.UpdateExecutionInfoRequest, context: grpc.ServicerContext ) -> execution_watcher_pb2.UpdateExecutionInfoResponse: """Updates the `custom_properties` field of Execution object in MLMD.""" logging.info('Received request to update execution info: updates %s, ' 'execution_id %s', request.updates, request.execution_id) if request.execution_id != self._execution.id: context.set_code(grpc.StatusCode.NOT_FOUND) context.set_details( 'Execution with given execution_id not tracked by server: ' f'{request.execution_id}') return execution_watcher_pb2.UpdateExecutionInfoResponse() for key, value in request.updates.items(): self._execution.custom_properties[key].CopyFrom( value) # Only the execution is needed with self._mlmd_connection as m: m.store.put_executions((self._execution,)) return execution_watcher_pb2.UpdateExecutionInfoResponse() def _create_server(self): """Creates a gRPC server and add `self` on to it.""" result = grpc.server(futures.ThreadPoolExecutor()) execution_watcher_pb2_grpc.add_ExecutionWatcherServiceServicer_to_server( self, result) if self._creds is None: result.add_insecure_port(self.local_address) else: result.add_secure_port(self.local_address, self._creds) return result @property def local_address(self) -> str: # Local network address to the server. return f'localhost:{self._port}' @property def address(self) -> str: return self._address or self.local_address def start(self): """Starts the server.""" self._server.start() def stop(self): """Stops the server.""" self._server.stop(grace=None)

main.py

xiangsheng1325/CPGAN

12799990

<filename>main.py import warnings import pprint, os, random, torch from CPGAN.data_utils import * import numpy as np from CPGAN.options import * from CPGAN.train import * warnings.filterwarnings("ignore") def get_options(): opt = Options() opt = opt.initialize() return opt def seed_torch(seed=1029): random.seed(seed) os.environ['PYTHONHASHSEED'] = str(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) # if you are using multi-GPU. # torch.backends.cudnn.benchmark = False # torch.backends.cudnn.deterministic = True if __name__ == '__main__': opt = get_options() ##{ 临时改超参数 opt.gpu = '1' opt.batch_size = 1 opt.max_epochs = 160 opt.method = 'cpgan' opt.graph_type = "yeast" opt.data_dir = "./data" ## 正式训练时收起 } os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu seed = int(time.time()*opt.random_seed) % (2**32) seed_torch(seed=seed) print('=========== OPTIONS ===========') pprint.pprint(vars(opt)) print(' ======== END OPTIONS ========\n\n') graphs = train_adj_mats, test_adj_mats, train_attr_vecs, test_attr_vecs = load_data( data_filepath=opt.data_dir, ) modeldict, logdict = train_cpgan(graphs, train_adj_mats, train_attr_vecs, opt)

src/drone_control/mavros/sender.py

Adrien4193/drone_control

12799991

<reponame>Adrien4193/drone_control<gh_stars>0 import rospy from tf.transformations import quaternion_from_euler from geometry_msgs.msg import Point, PoseStamped, Quaternion from mavros_msgs.msg import Thrust class Sender(object): def __init__(self): self._pubs = {} self._pubs['position'] = rospy.Publisher( '/mavros/setpoint_position/local', PoseStamped, queue_size=10 ) self._pubs['attitude'] = rospy.Publisher( '/mavros/setpoint_attitude/attitude', PoseStamped, queue_size=10 ) self._pubs['velocity'] = rospy.Publisher( '/mavros/setpoint_attitude/velocity', PoseStamped, queue_size=10 ) self._pubs['thrust'] = rospy.Publisher( '/mavros/setpoint_attitude/thrust', Thrust, queue_size=10 ) self._pubs['mocap'] = rospy.Publisher( '/mavros/mocap/pose', PoseStamped, queue_size=10 ) def __del__(self): for pub in self._pubs.values(): pass#pub.unregister() def send_attitude(self, attitude): self._pubs['attitude'].publish(attitude.get_message()) self._pubs['thrust'].publish(Thrust(thrust=attitude.thrust)) def send_velocity(self, attitude): self._pubs['velocity'].publish(attitude.get_message()) self._pubs['thrust'].publish(Thrust(thrust=attitude.thrust)) def send_position(self, pose): self._pubs['position'].publish(pose.get_message()) def send_mocap(self, pose): self._pubs['mocap'].publish(pose.get_message())

introducing-python-answers/chapter10.py

DailyYu/python-study

12799992

<reponame>DailyYu/python-study # Q1 from datetime import date now = date.today() now_string = now.isoformat() with open('today.txt', 'w') as file: print(now, file=file) # Q2 today_string = None with open('today.txt') as file: today_string = file.read() print(today_string) # Q3 from datetime import datetime format = '%Y-%m-%d\n' print(datetime.strptime(today_string, format)) # Q4 import os print(os.listdir('.')) # Q5 print(os.listdir('..')) # Q6 import multiprocessing def print_current_time(seconds): from time import sleep sleep(seconds) print(f'Wait for {seconds} seconds, Current time is {datetime.today().time()}') import random # 由于Windows下multiprocess会执行整个代码块，所以会引起循环创建进程的问题 # 这需要下面的代码来避免 if __name__ == '__main__': for n in range(3): seconds = random.random() process = multiprocessing.Process(target=print_current_time, args=(seconds,)) process.start() # Q7 my_birthday = date(1993, 8, 13) print(my_birthday) # Q8 # 星期从零开始计数 print(my_birthday.weekday()) # 星期从一开始计数 print(my_birthday.isoweekday()) # Q9 from datetime import timedelta ten_thousand_day_after_my_birthday = my_birthday + timedelta(days=10000) print(ten_thousand_day_after_my_birthday)

things.py

racinmat/depth-voxelmap-estimation

12799993

import tensorflow as tf import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from PIL import Image import Network import dataset from Network import BATCH_SIZE from dataset import DataSet def output_predict(depths, images, depths_discretized, depths_reconstructed, output_dir): print("output predict into %s" % output_dir) if not tf.gfile.Exists(output_dir): tf.gfile.MakeDirs(output_dir) for i, _ in enumerate(images): image, depth, depth_discretized, depth_reconstructed = images[i], depths[i], depths_discretized[i], \ depths_reconstructed[i] pilimg = Image.fromarray(np.uint8(image)) image_name = "%s/%03d_org.png" % (output_dir, i) pilimg.save(image_name) depth = depth.transpose(2, 0, 1) if np.max(depth) != 0: ra_depth = (depth / np.max(depth)) * 255.0 else: ra_depth = depth * 255.0 depth_pil = Image.fromarray(np.uint8(ra_depth[0]), mode="L") depth_name = "%s/%03d.png" % (output_dir, i) depth_pil.save(depth_name) for j in range(dataset.DEPTH_DIM): ra_depth = depth_discretized[:, :, j] * 255.0 depth_discr_pil = Image.fromarray(np.uint8(ra_depth), mode="L") depth_discr_name = "%s/%03d_%03d_discr.png" % (output_dir, i, j) depth_discr_pil.save(depth_discr_name) # for j in range(DEPTH_DIM): # ra_depth = mask[:, :, j] # depth_discr_pil = Image.fromarray(np.uint8(ra_depth), mode="L") # depth_discr_name = "%s/%03d_%03d_discr_m.png" % (output_dir, i, j) # depth_discr_pil.save(depth_discr_name) # # for j in range(DEPTH_DIM): # ra_depth = mask_lower[:, :, j] # depth_discr_pil = Image.fromarray(np.uint8(ra_depth), mode="L") # depth_discr_name = "%s/%03d_%03d_discr_ml.png" % (output_dir, i, j) # depth_discr_pil.save(depth_discr_name) depth = depth_reconstructed[:, :, 0] if np.max(depth) != 0: ra_depth = (depth / np.max(depth)) * 255.0 else: ra_depth = depth * 255.0 depth_pil = Image.fromarray(np.uint8(ra_depth), mode="L") depth_name = "%s/%03d_reconstructed.png" % (output_dir, i) depth_pil.save(depth_name) def playground_loss_function(labels, logits): # in rank 2, [elements, classes] # tf.nn.weighted_cross_entropy_with_logits(labels, logits, weights) losses = tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=logits) return losses def prob_to_logit(probs): return np.log(probs / (1 - probs)) def softmax(x): """Same behaviour as tf.nn.softmax in tensorflow""" e_x = np.exp(x) sum_per_row = np.tile(e_x.sum(axis=1), (x.shape[1], 1)).T print('e_x', '\n', e_x) print('sum_per_row', '\n', sum_per_row) return e_x / sum_per_row def softmax_cross_entropy_loss(labels, logits): """Same behaviour as tf.nn.softmax_cross_entropy_with_logits in tensorflow""" loss_per_row = - np.sum(labels * np.log(softmax(logits)), axis=1) return loss_per_row def labels_to_info_gain(labels, logits, alpha=0.2): last_axis = len(logits.shape) - 1 label_idx = np.tile(np.argmax(labels, axis=last_axis), (labels.shape[last_axis], 1)).T prob_bin_idx = np.tile(range(logits.shape[last_axis]), (labels.shape[0], 1)) # print('label_idx', '\n', label_idx) # print('probs_idx', '\n', prob_bin_idx) info_gain = np.exp(-alpha * (label_idx - prob_bin_idx)**2) print('info gain', '\n', info_gain) return info_gain def tf_labels_to_info_gain(labels, logits, alpha=0.2): last_axis = len(logits.shape) - 1 label_idx = tf.expand_dims(tf.argmax(labels, axis=last_axis), 0) label_idx = tf.cast(label_idx, dtype=tf.int32) label_idx = tf.tile(label_idx, [labels.shape[last_axis], 1]) label_idx = tf.transpose(label_idx) prob_bin_idx = tf.expand_dims(tf.range(logits.shape[last_axis], dtype=tf.int32), last_axis) prob_bin_idx = tf.transpose(prob_bin_idx) prob_bin_idx = tf.tile(prob_bin_idx, [labels.shape[0], 1]) difference = (label_idx - prob_bin_idx)**2 difference = tf.cast(difference, dtype=tf.float32) info_gain = tf.exp(-alpha * difference) return info_gain def informed_cross_entropy_loss(labels, logits): """Same behaviour as tf.nn.weighted_cross_entropy_with_logits in tensorflow""" probs = softmax(logits) print('probs', '\n', probs) logged_probs = np.log(probs) print('logged probs', '\n', logged_probs) loss_per_row = - np.sum(labels_to_info_gain(labels, logits) * logged_probs, axis=1) return loss_per_row def playing_with_losses(): labels = np.array([ [0, 1, 0, 0, 0], [0, 1, 0, 0, 0], [0, 1, 0, 0, 0], [0, 1, 0, 0, 0], [0, 1, 0, 0, 0], [0, 1, 0, 0, 0], # [0, 1, 0, 0, 0], # [0, 0, 1, 0, 0], # [0, 0, 0, 1, 0], # [0, 0, 0, 0, 1], # [1, 0, 0, 0, 0], ]) logits = np.array([ [0, 20, 0, 0, 0], [0, 10, 0, 0, 0], [0, 2, 0, 0, 0], [1, 1, 1, 0, 0], [0, 1, 0, 0, 1], [0, 1, 0, 0, 0], # [3, 1, 1, 1, 1], # [0, 10, 0, 0, 0], # [1, 5, 1, 1, 1], # [0, 0, 1, 0, 0], # [1, 1, 4, 1, 1], # [1, 1, 1, 4, 1], # [1, 1, 1, 1, 4], # [4, 1, 1, 1, 1], ]) probs = softmax(logits) loss = softmax_cross_entropy_loss(labels=labels, logits=logits) new_loss = informed_cross_entropy_loss(labels=labels, logits=logits) with tf.Graph().as_default(): with tf.Session() as sess: logits_tf = tf.constant(logits, dtype=tf.float32) labels_tf = tf.constant(labels, dtype=tf.float32) probs_tf = sess.run(tf.nn.softmax(logits_tf)) loss_tf = sess.run(tf.nn.softmax_cross_entropy_with_logits(labels=labels_tf, logits=logits_tf)) new_loss_tf = sess.run(tf.nn.softmax_cross_entropy_with_logits(labels=tf_labels_to_info_gain(labels, logits_tf), logits=logits_tf)) # print('labels', '\n', labels) # print('logits', '\n', logits) # print('probs', '\n', probs) # print('probs diff', '\n', probs - probs_tf) print('loss', '\n', loss) print('loss_tf', '\n', loss_tf) print('loss diff', '\n', loss - loss_tf) print('new_loss', '\n', new_loss) print('new_loss_tf', '\n', new_loss_tf) print('new loss diff', '\n', new_loss - new_loss_tf) # f, axarr = plt.subplots(2, 3) # axarr[0, 0].set_title('sample 1') # axarr[0, 0].plot(probs[0, :]) # axarr[0, 1].set_title('sample 2') # axarr[0, 1].plot(probs[1, :]) # axarr[1, 0].set_title('sample 3') # axarr[1, 0].plot(probs[2, :]) # axarr[1, 1].set_title('sample 4') # axarr[1, 1].plot(probs[3, :]) plt.plot(probs[0, :], color='r') plt.plot(probs[1, :], color='g') plt.plot(probs[2, :], color='b') plt.plot(probs[3, :], color='y') plt.show() def input_parser(filename): assert tf.get_default_session() is sess tf.logging.warning(('filename', filename)) channel_data = tf.data.TextLineDataset(filename).map(lambda line: tf.decode_csv(line, [["path"], ["annotation"]])) return channel_data def filenames_to_data(rgb_filename, voxelmap_filename): tf.logging.warning(('rgb_filename', rgb_filename)) rgb_image = dataset.DataSet.filename_to_input_image(rgb_filename) voxelmap = tf.py_func(dataset.DataSet.filename_to_target_voxelmap, [voxelmap_filename], tf.int32) voxelmap.set_shape([dataset.TARGET_WIDTH, dataset.TARGET_HEIGHT, dataset.DEPTH_DIM]) # voxelmap = dataset.DataSet.filename_to_target_voxelmap(voxelmap_filename) depth_reconstructed = dataset.DataSet.tf_voxelmap_to_depth(voxelmap) return rgb_image, voxelmap, depth_reconstructed def tf_new_data_api_experiments(): # global sess batch_size = 4 with sess.as_default(): tf.logging.set_verbosity(tf.logging.INFO) # dataset = tf.data.TFRecordDataset(['train-voxel-gta.csv', 'test-voxel-gta.csv']) train_imgs = tf.constant(['train-voxel-gta.csv']) filename_list = tf.data.Dataset.from_tensor_slices(train_imgs) filename_pairs = filename_list.flat_map(input_parser) data_pairs = filename_pairs.map(filenames_to_data) data_pairs = data_pairs.batch(batch_size) # # # input # image = dataset.DataSet.filename_to_input_image(filename) # # target # voxelmap = dataset.DataSet.filename_to_target_voxelmap(voxelmap_filename) # depth_reconstructed = dataset.DataSet.tf_voxelmap_to_depth(voxelmap) iterator = data_pairs.make_one_shot_iterator() batch_images, batch_voxels, batch_depths = iterator.get_next() for i in range(1): images_values, voxels_values, depths_values = sess.run([batch_images, batch_voxels, batch_depths]) for j in range(batch_size): plt.figure(figsize=(10, 6)) plt.axis('off') plt.imshow(images_values[j, :, :, :].astype(dtype=np.uint8)) plt.savefig('inspections/out-{}-rgb.png'.format(j), bbox_inches='tight') plt.figure(figsize=(10, 6)) plt.axis('off') plt.imshow(depths_values[j, :, :].T, cmap='gray') plt.savefig('inspections/out-{}-depth.png'.format(j), bbox_inches='tight') # pure numpy calculation of depth image from voxelmap occupied_ndc_grid = voxels_values[j, :, :, :] occupied_ndc_grid = np.flip(occupied_ndc_grid, axis=2) depth_size = occupied_ndc_grid.shape[2] new_depth = np.argmax(occupied_ndc_grid, axis=2) new_depth = new_depth.T new_depth *= int(255/depth_size) plt.figure(figsize=(10, 7)) plt.axis('off') plt.imshow(new_depth, cmap='gray') plt.savefig('inspections/out-{}-depth-np.png'.format(j), bbox_inches='tight') def load_numpy_bin(): # name = 'inspections/2018-03-07--17-57-32--527.bin' name = 'inspections/2018-03-07--17-57-32--527.npy' # numpy_voxelmap = np.fromfile(name, sep=';') numpy_voxelmap = np.load(name) print(numpy_voxelmap.shape) # numpy_voxelmap = numpy_voxelmap.reshape([240, 160, 100]) numpy_voxelmap = np.flip(numpy_voxelmap, axis=2) # now I have just boolean for each value # so I create mask to assign higher value to booleans in higher index depth_size = numpy_voxelmap.shape[2] new_depth = np.argmax(numpy_voxelmap, axis=2) new_depth = new_depth.T new_depth *= int(255 / depth_size) plt.figure(figsize=(10, 6)) plt.axis('off') plt.imshow(new_depth, cmap='gray') plt.savefig('inspections/2018-03-07--17-57-32--527.png', bbox_inches='tight') sess = tf.Session(config=tf.ConfigProto(device_count={'GPU': 0})) if __name__ == '__main__': # playing_with_losses() # tf_dataset_experiments() # load_numpy_bin() tf_new_data_api_experiments() # arr = np.array([ # [1, 1, 1, 2], # [2, 2, 2, 4], # [4, 4, 4, 8], # ]) # with tf.Graph().as_default(): # with tf.Session() as sess: # logits_tf = tf.constant(arr, dtype=tf.float32) # tf_mean = sess.run(tf.reduce_mean(logits_tf)) # print('tf_mean\n', tf_mean) # # print('mean\n', np.mean(arr)) # print('sum_per_row\n', np.sum(arr, axis=1)) # print('mean_of_sum\n', np.mean(np.sum(arr, axis=1), axis=0)) # ds = DataSet(8) # ds.load_params('train.csv') # # d = list(range(1, 100)) # d_min = np.min(d) # d_max = 20 # num_bins = 10 # q_calc = (np.log(np.max(d)) - np.log(d_min)) / (num_bins - 1) # # q = 0.5 # width of quantization bin # l = np.round((np.log(d) - np.log(d_min)) / q_calc) # # print(d) # print(l) # # print('q_calc', q_calc) # # f, axarr = plt.subplots(2, 2) # axarr[0, 0].plot(d) # axarr[0, 1].plot(np.log(d)) # axarr[1, 0].plot(np.log(d) - np.log(d_min)) # axarr[1, 1].plot((np.log(d) - np.log(d_min)) / q_calc) # plt.show() # with tf.Graph().as_default(): # with tf.Session() as sess: # x = tf.constant(d) # # # for i in range(500): # # if i % 500 == 0: # # print('hi', i) # # IMAGE_HEIGHT = 240 # IMAGE_WIDTH = 320 # TARGET_HEIGHT = 120 # TARGET_WIDTH = 160 # DEPTH_DIM = 10 # # filename_queue = tf.train.string_input_producer(['train.csv'], shuffle=True) # reader = tf.TextLineReader() # _, serialized_example = reader.read(filename_queue) # filename, depth_filename = tf.decode_csv(serialized_example, [["path"], ["annotation"]]) # # input # jpg = tf.read_file(filename) # image = tf.image.decode_jpeg(jpg, channels=3) # image = tf.cast(image, tf.float32) # # target # depth_png = tf.read_file(depth_filename) # depth = tf.image.decode_png(depth_png, channels=1) # depth = tf.cast(depth, tf.float32) # depth = depth / 255.0 # # depth = tf.cast(depth, tf.int64) # # resize # image = tf.image.resize_images(image, (IMAGE_HEIGHT, IMAGE_WIDTH)) # depth = tf.image.resize_images(depth, (TARGET_HEIGHT, TARGET_WIDTH)) # # depth_discretized = dataset.DataSet.discretize_depth(depth) # # invalid_depth = tf.sign(depth) # # batch_size = 8 # # generate batch # images, depths, depths_discretized, invalid_depths = tf.train.batch( # [image, depth, depth_discretized, invalid_depth], # batch_size=batch_size, # num_threads=4, # capacity=40) # # depth_reconstructed, weights, mask, mask_multiplied, mask_multiplied_sum = Network.Network.bins_to_depth(depths_discretized) # # print('weights: ', weights) # # coord = tf.train.Coordinator() # threads = tf.train.start_queue_runners(sess=sess, coord=coord) # # images_val, depths_val, depths_discretized_val, invalid_depths_val, depth_reconstructed_val, mask_val, mask_multiplied_val, mask_multiplied_sum_val = sess.run( # [images, depths, depths_discretized, invalid_depths, depth_reconstructed, mask, mask_multiplied, mask_multiplied_sum]) # sess.run(images) # # output_predict(depths_val, images_val, depths_discretized_val, # depth_reconstructed_val, 'kunda') # # depth_reconstructed_val = depth_reconstructed_val[:, :, :, 0] # coord.request_stop() # coord.join(threads) # # layer = 2 # f, axarr = plt.subplots(2, 3) # axarr[0, 0].set_title('masks_val') # axarr[0, 0].imshow(mask_val[0, :, :, layer]) # axarr[0, 1].set_title('mask_multiplied_val') # axarr[0, 1].imshow(mask_multiplied_val[0, :, :, layer]) # axarr[1, 0].set_title('depths_val') # axarr[1, 0].imshow(depths_val[0, :, :, 0]) # axarr[1, 1].set_title('depths_discretized_val') # axarr[1, 1].imshow(depths_discretized_val[0, :, :, layer]) # axarr[0, 2].set_title('mask_multiplied_sum_val') # axarr[0, 2].imshow(mask_multiplied_sum_val[0, :, :]) # axarr[1, 2].set_title('depth_reconstructed_val') # axarr[1, 2].imshow(depth_reconstructed_val[0, :, :]) # plt.show() # network = Network.Network() # network.prepare() # total_vars = np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()]) # print('trainable vars: ', total_vars) # for output bins = 200: 73 696 786 # for output bins = 100: 65 312 586

src/python/twitter/pants/tasks/cache_manager.py

wfarner/commons

12799994

# ================================================================================================== # Copyright 2012 Twitter, Inc. # -------------------------------------------------------------------------------------------------- # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this work except in compliance with the License. # You may obtain a copy of the License in the LICENSE file, or 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. # ================================================================================================== try: import cPickle as pickle except ImportError: import pickle from twitter.pants import has_sources from twitter.pants.base.build_invalidator import ( BuildInvalidator, CacheKeyGenerator, NO_SOURCES, TARGET_SOURCES) from twitter.pants.base.target import Target from twitter.pants.targets import TargetWithSources from twitter.pants.targets.external_dependency import ExternalDependency from twitter.pants.targets.internal import InternalTarget class VersionedTargetSet(object): """Represents a list of targets, a corresponding CacheKey, and a flag determining whether the list of targets is currently valid. When invalidating a single target, this can be used to represent that target as a singleton. When checking the artifact cache, this can also be used to represent a list of targets that are built together into a single artifact. """ @classmethod def from_versioned_targets(cls, versioned_targets): first_target = versioned_targets[0] cache_manager = first_target._cache_manager # Quick sanity check; all the versioned targets should have the same cache manager. # TODO(ryan): the way VersionedTargets store their own links to a single CacheManager instance # feels hacky; see if there's a cleaner way for callers to handle awareness of the CacheManager. for versioned_target in versioned_targets: if versioned_target._cache_manager != cache_manager: raise ValueError("Attempting to combine versioned targets %s and %s with different" " CacheManager instances: %s and %s" % (first_target, versioned_target, cache_manager, versioned_target._cache_manager)) return cls(cache_manager, versioned_targets) def __init__(self, cache_manager, versioned_targets): self._cache_manager = cache_manager self.versioned_targets = versioned_targets self.targets = [vt.target for vt in versioned_targets] # The following line is a no-op if cache_key was set in the VersionedTarget __init__ method. self.cache_key = CacheKeyGenerator.combine_cache_keys([vt.cache_key for vt in versioned_targets]) self.num_sources = self.cache_key.num_sources self.valid = not cache_manager.needs_update(self.cache_key) def update(self): self._cache_manager.update(self) def force_invalidate(self): self._cache_manager.force_invalidate(self) def __repr__(self): return "VTS(%s. %d)" % (','.join(target.id for target in self.targets), 1 if self.valid else 0) class VersionedTarget(VersionedTargetSet): """This class represents a singleton VersionedTargetSet, and has links to VersionedTargets that the wrapped target depends on (after having resolved through any "alias" targets. """ def __init__(self, cache_manager, target, cache_key): if not isinstance(target, TargetWithSources): raise ValueError("The target %s must support sources and does not." % target.id) self.target = target self.cache_key = cache_key # Must come after the assignments above, as they are used in the parent's __init__. VersionedTargetSet.__init__(self, cache_manager, [self]) self.id = target.id self.dependencies = set() # The result of calling check() on a CacheManager. # Each member is a list of VersionedTargetSet objects in topological order. # Tasks may need to perform no, some or all operations on either of these, depending on how they # are implemented. class InvalidationCheck(object): @classmethod def _partition_versioned_targets(cls, versioned_targets, partition_size_hint): """Groups versioned targets so that each group has roughly the same number of sources. versioned_targets is a list of VersionedTarget objects [vt1, vt2, vt3, vt4, vt5, vt6, ...]. Returns a list of VersionedTargetSet objects, e.g., [VT1, VT2, VT3, ...] representing the same underlying targets. E.g., VT1 is the combination of [vt1, vt2, vt3], VT2 is the combination of [vt4, vt5] and VT3 is [vt6]. The new versioned targets are chosen to have roughly partition_size_hint sources. This is useful as a compromise between flat mode, where we build all targets in a single compiler invocation, and non-flat mode, where we invoke a compiler for each target, which may lead to lots of compiler startup overhead. A task can choose instead to build one group at a time. """ res = [] # Hack around the python outer scope problem. class VtGroup(object): def __init__(self): self.vts = [] self.total_sources = 0 current_group = VtGroup() def add_to_current_group(vt): current_group.vts.append(vt) current_group.total_sources += vt.num_sources def close_current_group(): if len(current_group.vts) > 0: new_vt = VersionedTargetSet.from_versioned_targets(current_group.vts) res.append(new_vt) current_group.vts = [] current_group.total_sources = 0 for vt in versioned_targets: add_to_current_group(vt) if current_group.total_sources > 1.5 * partition_size_hint and len(current_group.vts) > 1: # Too big. Close the current group without this vt and add it to the next one. current_group.vts.pop() close_current_group() add_to_current_group(vt) elif current_group.total_sources > partition_size_hint: close_current_group() close_current_group() # Close the last group, if any. return res def __init__(self, all_vts, invalid_vts, partition_size_hint=None): # All the targets, valid and invalid. self.all_vts = all_vts # All the targets, partitioned if so requested. self.all_vts_partitioned = self._partition_versioned_targets( all_vts, partition_size_hint) if partition_size_hint else all_vts # Just the invalid targets. self.invalid_vts = invalid_vts # Just the invalid targets, partitioned if so requested. self.invalid_vts_partitioned = self._partition_versioned_targets( invalid_vts, partition_size_hint) if partition_size_hint else invalid_vts class CacheManager(object): """Manages cache checks, updates and invalidation keeping track of basic change and invalidation statistics. Note that this is distinct from the ArtifactCache concept, and should probably be renamed. """ def __init__(self, cache_key_generator, build_invalidator_dir, invalidate_dependents, extra_data, only_externaldeps): self._cache_key_generator = cache_key_generator self._invalidate_dependents = invalidate_dependents self._extra_data = pickle.dumps(extra_data) # extra_data may be None. self._sources = NO_SOURCES if only_externaldeps else TARGET_SOURCES self._invalidator = BuildInvalidator(build_invalidator_dir) def update(self, vts): """Mark a changed or invalidated VersionedTargetSet as successfully processed.""" for vt in vts.versioned_targets: self._invalidator.update(vt.cache_key) vt.valid = True self._invalidator.update(vts.cache_key) vts.valid = True def force_invalidate(self, vts): """Force invalidation of a VersionedTargetSet.""" for vt in vts.versioned_targets: self._invalidator.force_invalidate(vt.cache_key) vt.valid = False self._invalidator.force_invalidate(vts.cache_key) vts.valid = False def check(self, targets, partition_size_hint=None): """Checks whether each of the targets has changed and invalidates it if so. Returns a list of VersionedTargetSet objects (either valid or invalid). The returned sets 'cover' the input targets, possibly partitioning them, and are in topological order. The caller can inspect these in order and, e.g., rebuild the invalid ones. """ all_vts = self._sort_and_validate_targets(targets) invalid_vts = filter(lambda vt: not vt.valid, all_vts) return InvalidationCheck(all_vts, invalid_vts, partition_size_hint) def _sort_and_validate_targets(self, targets): """Validate each target. Returns a topologically ordered set of VersionedTargets, each representing one input target. """ # We must check the targets in this order, to ensure correctness if invalidate_dependents=True, # since we use earlier cache keys to compute later cache keys in this case. ordered_targets = self._order_target_list(targets) # This will be a list of VersionedTargets that correspond to @targets. versioned_targets = [] # This will be a mapping from each target to its corresponding VersionedTarget. versioned_targets_by_target = {} # Map from id to current fingerprint of the target with that id. We update this as we iterate, # in topological order, so when handling a target, this will already contain all its deps (in # this round). id_to_hash = {} for target in ordered_targets: dependency_keys = set() if self._invalidate_dependents and hasattr(target, 'dependencies'): # Note that we only need to do this for the immediate deps, because those will already # reflect changes in their own deps. for dep in target.dependencies: # We rely on the fact that any deps have already been processed, either in an earlier # round or because they came first in ordered_targets. if isinstance(dep, ExternalDependency): dependency_keys.add(dep.cache_key()) elif isinstance(dep, Target): fprint = id_to_hash.get(dep.id, None) if fprint is None: # It may have been processed in a prior round, and therefore the fprint should # have been written out by the invalidator. fprint = self._invalidator.existing_hash(dep.id) # Note that fprint may be None here, indicating that the dependency will not be # processed until a later phase. For example, if a codegen target depends on a # library target (because the generated code needs that library). if fprint is not None: dependency_keys.add(fprint) else: raise ValueError('Cannot calculate a cache_key for a dependency: %s' % dep) cache_key = self._key_for(target, dependency_keys) id_to_hash[target.id] = cache_key.hash # Create a VersionedTarget corresponding to @target. versioned_target = VersionedTarget(self, target, cache_key) # Add the new VersionedTarget to the list of computed VersionedTargets. versioned_targets.append(versioned_target) # Add to the mapping from Targets to VersionedTargets, for use in hooking up VersionedTarget # dependencies below. versioned_targets_by_target[target] = versioned_target # Having created all applicable VersionedTargets, now we build the VersionedTarget dependency # graph, looking through targets that don't correspond to VersionedTargets themselves. versioned_target_deps_by_target = {} def get_versioned_target_deps_for_target(target): # For every dependency of @target, we will store its corresponding VersionedTarget here. For # dependencies that don't correspond to a VersionedTarget (e.g. pass-through dependency # wrappers), we will resolve their actual dependencies and find VersionedTargets for them. versioned_target_deps = set([]) if hasattr(target, 'dependencies'): for dep in target.dependencies: for dependency in dep.resolve(): if dependency in versioned_targets_by_target: # If there exists a VersionedTarget corresponding to this Target, store it and # continue. versioned_target_deps.add(versioned_targets_by_target[dependency]) elif dependency in versioned_target_deps_by_target: # Otherwise, see if we've already resolved this dependency to the VersionedTargets it # depends on, and use those. versioned_target_deps.update(versioned_target_deps_by_target[dependency]) else: # Otherwise, compute the VersionedTargets that correspond to this dependency's # dependencies, cache and use the computed result. versioned_target_deps_by_target[dependency] = get_versioned_target_deps_for_target( dependency) versioned_target_deps.update(versioned_target_deps_by_target[dependency]) # Return the VersionedTarget dependencies that this target's VersionedTarget should depend on. return versioned_target_deps # Initialize all VersionedTargets to point to the VersionedTargets they depend on. for versioned_target in versioned_targets: versioned_target.dependencies = get_versioned_target_deps_for_target(versioned_target.target) return versioned_targets def needs_update(self, cache_key): return self._invalidator.needs_update(cache_key) def _order_target_list(self, targets): """Orders the targets topologically, from least to most dependent.""" targets = set(filter(has_sources, targets)) return filter(targets.__contains__, reversed(InternalTarget.sort_targets(targets))) def _key_for(self, target, dependency_keys): def fingerprint_extra(sha): sha.update(self._extra_data) for key in sorted(dependency_keys): # Sort to ensure hashing in a consistent order. sha.update(key) return self._cache_key_generator.key_for_target( target, sources=self._sources, fingerprint_extra=fingerprint_extra )

configman/tests/test_val_for_json.py

peterbe/configman

12799995

# ***** BEGIN LICENSE BLOCK ***** # Version: MPL 1.1/GPL 2.0/LGPL 2.1 # # The contents of this file are subject to the Mozilla Public License Version # 1.1 (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.mozilla.org/MPL/ # # Software distributed under the License is distributed on an "AS IS" basis, # WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License # for the specific language governing rights and limitations under the # License. # # The Original Code is configman # # The Initial Developer of the Original Code is # Mozilla Foundation # Portions created by the Initial Developer are Copyright (C) 2011 # the Initial Developer. All Rights Reserved. # # Contributor(s): # <NAME>, <EMAIL> # <NAME>, <EMAIL> # # Alternatively, the contents of this file may be used under the terms of # either the GNU General Public License Version 2 or later (the "GPL"), or # the GNU Lesser General Public License Version 2.1 or later (the "LGPL"), # in which case the provisions of the GPL or the LGPL are applicable instead # of those above. If you wish to allow use of your version of this file only # under the terms of either the GPL or the LGPL, and not to allow others to # use your version of this file under the terms of the MPL, indicate your # decision by deleting the provisions above and replace them with the notice # and other provisions required by the GPL or the LGPL. If you do not delete # the provisions above, a recipient may use your version of this file under # the terms of any one of the MPL, the GPL or the LGPL. # # ***** END LICENSE BLOCK ***** import unittest import os import json import tempfile from cStringIO import StringIO import configman.config_manager as config_manager import configman.datetime_util as dtu from configman.value_sources.for_json import ValueSource #from ..value_sources.for_json import ValueSource def bbb_minus_one(config, local_config, args): return config.bbb - 1 class TestCase(unittest.TestCase): def test_for_json_basics(self): tmp_filename = os.path.join(tempfile.gettempdir(), 'test.json') j = {'fred': 'wilma', 'number': 23, } with open(tmp_filename, 'w') as f: json.dump(j, f) try: jvs = ValueSource(tmp_filename) vals = jvs.get_values(None, True) self.assertEqual(vals['fred'], 'wilma') self.assertEqual(vals['number'], 23) finally: if os.path.isfile(tmp_filename): os.remove(tmp_filename) def test_write_json(self): n = config_manager.Namespace(doc='top') n.add_option('aaa', '2011-05-04T15:10:00', 'the a', short_form='a', from_string_converter=dtu.datetime_from_ISO_string ) def value_iter(): yield 'aaa', 'aaa', n.aaa s = StringIO() ValueSource.write(value_iter, output_stream=s) received = s.getvalue() s.close() jrec = json.loads(received) expect_to_find = { "short_form": "a", "default": "2011-05-04T15:10:00", "doc": "the a", "value": "2011-05-04T15:10:00", "from_string_converter": "configman.datetime_util.datetime_from_ISO_string", "name": "aaa" } for key, value in expect_to_find.items(): self.assertEqual(jrec['aaa'][key], value) def test_json_round_trip(self): n = config_manager.Namespace(doc='top') n.add_option('aaa', '2011-05-04T15:10:00', 'the a', short_form='a', from_string_converter=dtu.datetime_from_ISO_string ) expected_date = dtu.datetime_from_ISO_string('2011-05-04T15:10:00') n.add_option('bbb', '37', 'the a', short_form='a', from_string_converter=int ) n.add_option('write', 'json') n.add_aggregation('bbb_minus_one', bbb_minus_one) #t = tempfile.NamedTemporaryFile('w', suffix='.json', delete=False) name = '/tmp/test.json' import functools opener = functools.partial(open, name, 'w') c1 = config_manager.ConfigurationManager([n], [], use_admin_controls=True, use_auto_help=False, app_name='/tmp/test', app_version='0', app_description='', argv_source=[]) c1.write_conf('json', opener) d1 = {'bbb': 88} d2 = {'bbb': '-99'} try: with open(name) as jfp: j = json.load(jfp) c2 = config_manager.ConfigurationManager((j,), (d1, d2), use_admin_controls=True, use_auto_help=False, argv_source=[]) config = c2.get_config() self.assertEqual(config.aaa, expected_date) self.assertEqual(config.bbb, -99) self.assertEqual(config.bbb_minus_one, -100) finally: os.unlink(name)

Python/Programming Basics/Simple Conditional Statements/09. Password Guess.py

teodoramilcheva/softuni-software-engineering

12799996

<gh_stars>0 a = input() b = '<PASSWORD>' if a == b: print('Welcome') else: print('Wrong password!')

scripts/addons/uvpackmaster2/operator.py

Tilapiatsu/blender-custom_conf

12799997

<gh_stars>1-10 # ##### BEGIN GPL LICENSE BLOCK ##### # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # as published by the Free Software Foundation; either version 2 # of the License, or (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software Foundation, # Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. # # ##### END GPL LICENSE BLOCK ##### import subprocess import queue import threading import signal import webbrowser from .utils import * from .pack_context import * from .connection import * from .prefs import * from .os_iface import * from .island_params import * from .labels import UvpLabels from .register import check_uvp, unregister_uvp import bmesh import bpy import mathutils import tempfile class InvalidIslandsError(Exception): pass class NoUvFaceError(Exception): pass class UVP2_OT_PackOperatorGeneric(bpy.types.Operator): bl_options = {'UNDO'} MODAL_INTERVAL_S = 0.1 interactive = False @classmethod def poll(cls, context): prefs = get_prefs() return prefs.uvp_initialized and context.active_object is not None and context.active_object.mode == 'EDIT' def check_uvp_retcode(self, retcode): self.prefs.uvp_retcode = retcode if retcode in {UvPackerErrorCode.SUCCESS, UvPackerErrorCode.INVALID_ISLANDS, UvPackerErrorCode.NO_SPACE, UvPackerErrorCode.PRE_VALIDATION_FAILED}: return if retcode == UvPackerErrorCode.CANCELLED: raise OpCancelledException() if retcode == UvPackerErrorCode.NO_VALID_STATIC_ISLAND: raise RuntimeError("'Pack To Others' option enabled, but no unselected island found in the packing box") if retcode == UvPackerErrorCode.MAX_GROUP_COUNT_EXCEEDED: raise RuntimeError("Maximal group count exceeded") if retcode == UvPackerErrorCode.DEVICE_NOT_SUPPORTED: raise RuntimeError("Selected device is not supported") if retcode == UvPackerErrorCode.DEVICE_DOESNT_SUPPORT_GROUPS_TOGETHER: raise RuntimeError("Selected device doesn't support packing groups together") raise RuntimeError('Pack process returned an error') def raiseUnexpectedOutputError(self): raise RuntimeError('Unexpected output from the pack process') def set_status(self, status_type, status): self.op_status_type = status_type self.op_status = status def add_warning(self, warn_msg): self.op_warnings.append(warn_msg) def report_status(self): if self.op_status is not None: self.prefs['op_status'] = self.op_status op_status_type = self.op_status_type if self.op_status_type is not None else 'INFO' op_status = self.op_status if len(self.op_warnings) > 0: if op_status_type == 'INFO': op_status_type = 'WARNING' op_status += '. (WARNINGS were reported - check the UVP tab for details)' self.report({op_status_type}, op_status) self.prefs['op_warnings'] = self.op_warnings # self.prefs.stats_op_warnings.add(warning_msg) def exit_common(self): if self.interactive: wm = self.p_context.context.window_manager wm.event_timer_remove(self._timer) self.p_context.update_meshes() self.report_status() if in_debug_mode(): print('UVP operation time: ' + str(time.time() - self.start_time)) def read_islands(self, islands_msg): islands = [] island_cnt = force_read_int(islands_msg) selected_cnt = force_read_int(islands_msg) for i in range(island_cnt): islands.append(read_int_array(islands_msg)) self.p_context.set_islands(selected_cnt, islands) def process_invalid_islands(self): if self.uvp_proc.returncode != UvPackerErrorCode.INVALID_ISLANDS: return if self.invalid_islands_msg is None: self.raiseUnexpectedOutputError() code = force_read_int(self.invalid_islands_msg) subcode = force_read_int(self.invalid_islands_msg) invalid_islands = read_int_array(self.invalid_islands_msg) if len(invalid_islands) == 0: self.raiseUnexpectedOutputError() self.p_context.handle_invalid_islands(invalid_islands) if code == UvInvalidIslandCode.TOPOLOGY: error_msg = "Invalid topology encountered in the selected islands. Check the Help panel to learn more" elif code == UvInvalidIslandCode.INT_PARAM: param_array = IslandParamInfo.get_param_info_array() error_msg = "Faces with inconsistent {} values found in the selected islands".format(param_array[subcode].NAME) else: self.raiseUnexpectedOutputError() raise InvalidIslandsError(error_msg) def require_selection(self): return True def finish_after_op_done(self): return True def handle_op_done(self): self.op_done = True send_finish_confirmation(self.uvp_proc) try: self.uvp_proc.wait(5) except: raise RuntimeError('The UVP process wait timeout reached') self.connection_thread.join() self.check_uvp_retcode(self.uvp_proc.returncode) if not self.p_context.islands_received(): self.raiseUnexpectedOutputError() self.process_invalid_islands() self.process_result() if self.finish_after_op_done(): raise OpFinishedException() def finish(self, context): self.exit_common() return {'FINISHED', 'PASS_THROUGH'} def cancel(self, context): self.uvp_proc.terminate() # self.progress_thread.terminate() self.exit_common() return {'FINISHED'} def get_progress_msg_spec(self): return False def get_progress_msg(self): if self.hang_detected: return 'Packer process not responding for a longer time (press ESC to abort)' if self.curr_phase is None: return False progress_msg_spec = self.get_progress_msg_spec() if progress_msg_spec: return progress_msg_spec if self.curr_phase == UvPackingPhaseCode.INITIALIZATION: return 'Initialization (press ESC to cancel)' if self.curr_phase == UvPackingPhaseCode.TOPOLOGY_ANALYSIS: return "Topology analysis: {:3}% (press ESC to cancel)".format(self.progress_array[0]) if self.curr_phase == UvPackingPhaseCode.OVERLAP_CHECK: return 'Overlap check in progress (press ESC to cancel)' if self.curr_phase == UvPackingPhaseCode.AREA_MEASUREMENT: return 'Area measurement in progress (press ESC to cancel)' if self.curr_phase == UvPackingPhaseCode.SIMILAR_SELECTION: return 'Searching for similar islands (press ESC to cancel)' if self.curr_phase == UvPackingPhaseCode.SIMILAR_ALIGNING: return 'Similar islands aligning (press ESC to cancel)' if self.curr_phase == UvPackingPhaseCode.RENDER_PRESENTATION: return 'Close the demo window to finish' if self.curr_phase == UvPackingPhaseCode.TOPOLOGY_VALIDATION: return "Topology validation: {:3}% (press ESC to cancel)".format(self.progress_array[0]) if self.curr_phase == UvPackingPhaseCode.VALIDATION: return "Per-face overlap check: {:3}% (press ESC to cancel)".format(self.progress_array[0]) raise RuntimeError('Unexpected packing phase encountered') def handle_uvp_msg_spec(self, msg_code, msg): return False def handle_event_spec(self, event): return False def handle_progress_msg(self): if self.op_done: return msg_refresh_interval = 2.0 new_progress_msg = self.get_progress_msg() if not new_progress_msg: return now = time.time() if now - self.progress_last_update_time > msg_refresh_interval or new_progress_msg != self.progress_msg: self.progress_last_update_time = now self.progress_msg = new_progress_msg self.report({'INFO'}, self.progress_msg) def handle_uvp_msg(self, msg): msg_code = force_read_int(msg) if self.handle_uvp_msg_spec(msg_code, msg): return if msg_code == UvPackMessageCode.PROGRESS_REPORT: self.curr_phase = force_read_int(msg) progress_size = force_read_int(msg) if progress_size > len(self.progress_array): self.progress_array = [0] * (progress_size) for i in range(progress_size): self.progress_array[i] = force_read_int(msg) self.progress_sec_left = force_read_int(msg) self.progress_iter_done = force_read_int(msg) # Inform the upper layer wheter it should finish if self.curr_phase == UvPackingPhaseCode.DONE: self.handle_op_done() elif msg_code == UvPackMessageCode.INVALID_ISLANDS: if self.invalid_islands_msg is not None: self.raiseUnexpectedOutputError() self.invalid_islands_msg = msg elif msg_code == UvPackMessageCode.ISLAND_FLAGS: if self.island_flags_msg is not None: self.raiseUnexpectedOutputError() self.island_flags_msg = msg elif msg_code == UvPackMessageCode.PACK_SOLUTION: if self.pack_solution_msg is not None: self.raiseUnexpectedOutputError() self.pack_solution_msg = msg elif msg_code == UvPackMessageCode.AREA: if self.area_msg is not None: self.raiseUnexpectedOutputError() self.area_msg = msg elif msg_code == UvPackMessageCode.INVALID_FACES: if self.invalid_faces_msg is not None: self.raiseUnexpectedOutputError() self.invalid_faces_msg = msg elif msg_code == UvPackMessageCode.SIMILAR_ISLANDS: if self.similar_islands_msg is not None: self.raiseUnexpectedOutputError() self.similar_islands_msg = msg elif msg_code == UvPackMessageCode.ISLANDS: self.read_islands(msg) elif msg_code == UvPackMessageCode.ISLANDS_METADATA: if self.islands_metadata_msg is not None: self.raiseUnexpectedOutputError() self.islands_metadata_msg = msg else: self.raiseUnexpectedOutputError() def handle_communication(self): if self.op_done: return msg_received = 0 while True: try: item = self.progress_queue.get_nowait() except queue.Empty as ex: break if isinstance(item, str): raise RuntimeError(item) elif isinstance(item, io.BytesIO): self.handle_uvp_msg(item) else: raise RuntimeError('Unexpected output from the connection thread') msg_received += 1 curr_time = time.time() if msg_received > 0: self.last_msg_time = curr_time self.hang_detected = False else: if self.curr_phase != UvPackingPhaseCode.RENDER_PRESENTATION and curr_time - self.last_msg_time > self.hang_timeout: self.hang_detected = True def handle_event(self, event): # Kill the UVP process unconditionally if a hang was detected if self.hang_detected and event.type == 'ESC': raise OpAbortedException() if self.handle_event_spec(event): return # Generic event processing code if event.type == 'ESC': raise OpCancelledException() elif event.type == 'TIMER': self.handle_communication() def modal(self, context, event): cancel = False finish = False try: try: self.handle_event(event) # Check whether the uvp process is alive if not self.op_done and self.uvp_proc.poll() is not None: # It should not be required to but check once again to be on the safe side self.handle_communication() if not self.op_done: # Special value indicating a crash self.prefs.uvp_retcode = -1 raise RuntimeError('Packer process died unexpectedly') self.handle_progress_msg() except OpFinishedException: finish = True except: raise if finish: return self.finish(context) except OpAbortedException: self.set_status('INFO', 'Packer process killed') cancel = True except OpCancelledException: self.set_status('INFO', 'Operation cancelled by the user') cancel = True except InvalidIslandsError as err: self.set_status('ERROR', str(err)) cancel = True except RuntimeError as ex: if in_debug_mode(): print_backtrace(ex) self.set_status('ERROR', str(ex)) cancel = True except Exception as ex: if in_debug_mode(): print_backtrace(ex) self.set_status('ERROR', 'Unexpected error') cancel = True if cancel: return self.cancel(context) return {'RUNNING_MODAL'} if not self.op_done else {'PASS_THROUGH'} def pre_op_initialize(self): pass def execute(self, context): cancel = False self.op_done = False self.uvp_proc = None self.prefs = get_prefs() self.scene_props = context.scene.uvp2_props self.p_context = None self.pack_ratio = 1.0 self.target_box = None self.op_status_type = None self.op_status = None self.op_warnings = [] try: if not check_uvp(): unregister_uvp() redraw_ui(context) raise RuntimeError("UVP engine broken") reset_stats(self.prefs) self.p_context = PackContext(context) self.pre_op_initialize() send_unselected = self.send_unselected_islands() send_rot_step = self.send_rot_step() send_groups = self.grouping_enabled() and (to_uvp_group_method(self.get_group_method()) == UvGroupingMethodUvp.EXTERNAL) send_lock_groups = self.lock_groups_enabled() send_verts_3d = self.send_verts_3d() selected_cnt, unselected_cnt = self.p_context.serialize_uv_maps(send_unselected, send_groups, send_rot_step, send_lock_groups, send_verts_3d, self.get_group_method() if send_groups else None) if self.require_selection(): if selected_cnt == 0: raise NoUvFaceError('No UV face selected') else: if selected_cnt + unselected_cnt == 0: raise NoUvFaceError('No UV face visible') self.validate_pack_params() if self.prefs.write_to_file: out_filepath = os.path.join(tempfile.gettempdir(), 'uv_islands.data') out_file = open(out_filepath, 'wb') out_file.write(self.p_context.serialized_maps) out_file.close() uvp_args_final = [get_uvp_execpath(), '-E', '-e', str(UvTopoAnalysisLevel.FORCE_EXTENDED), '-t', str(self.prefs.thread_count)] + self.get_uvp_args() if send_unselected: uvp_args_final.append('-s') if self.grouping_enabled(): uvp_args_final += ['-a', str(to_uvp_group_method(self.get_group_method()))] if self.send_rot_step(): uvp_args_final += ['-R'] if self.lock_groups_enabled(): uvp_args_final += ['-Q'] if in_debug_mode(): if self.prefs.seed > 0: uvp_args_final += ['-S', str(self.prefs.seed)] if self.prefs.wait_for_debugger: uvp_args_final.append('-G') uvp_args_final += ['-T', str(self.prefs.test_param)] print('Pakcer args: ' + ' '.join(x for x in uvp_args_final)) creation_flags = os_uvp_creation_flags() popen_args = dict() if creation_flags is not None: popen_args['creationflags'] = creation_flags self.uvp_proc = subprocess.Popen(uvp_args_final, stdin=subprocess.PIPE, stdout=subprocess.PIPE, **popen_args) out_stream = self.uvp_proc.stdin out_stream.write(self.p_context.serialized_maps) out_stream.flush() self.start_time = time.time() self.last_msg_time = self.start_time self.hang_detected = False self.hang_timeout = 10.0 # Start progress monitor thread self.progress_queue = queue.Queue() self.connection_thread = threading.Thread(target=connection_thread_func, args=(self.uvp_proc.stdout, self.progress_queue)) self.connection_thread.daemon = True self.connection_thread.start() self.progress_array = [0] self.progress_msg = '' self.progress_sec_left = -1 self.progress_iter_done = -1 self.progress_last_update_time = 0.0 self.curr_phase = UvPackingPhaseCode.INITIALIZATION self.invalid_islands_msg = None self.island_flags_msg = None self.pack_solution_msg = None self.area_msg = None self.invalid_faces_msg = None self.similar_islands_msg = None self.islands_metadata_msg = None except NoUvFaceError as ex: self.set_status('WARNING', str(ex)) cancel = True except RuntimeError as ex: if in_debug_mode(): print_backtrace(ex) self.set_status('ERROR', str(ex)) cancel = True except Exception as ex: if in_debug_mode(): print_backtrace(ex) self.set_status('ERROR', 'Unexpected error') cancel = True if self.p_context is not None: self.p_context.update_meshes() if cancel: if self.uvp_proc is not None: self.uvp_proc.terminate() self.report_status() return {'FINISHED'} if self.interactive: wm = context.window_manager self._timer = wm.event_timer_add(self.MODAL_INTERVAL_S, window=context.window) wm.modal_handler_add(self) return {'RUNNING_MODAL'} class FakeTimerEvent: def __init__(self): self.type = 'TIMER' self.value = 'NOTHING' self.ctrl = False while True: event = FakeTimerEvent() ret = self.modal(context, event) if ret.intersection({'FINISHED', 'CANCELLED'}): return ret time.sleep(self.MODAL_INTERVAL_S) def invoke(self, context, event): self.interactive = True self.prefs = get_prefs() self.scene_props = context.scene.uvp2_props self.confirmation_msg = self.get_confirmation_msg() wm = context.window_manager if self.confirmation_msg != '': pix_per_char = 5 dialog_width = pix_per_char * len(self.confirmation_msg) + 50 return wm.invoke_props_dialog(self, width=dialog_width) return self.execute(context) def draw(self, context): layout = self.layout col = layout.column() col.label(text=self.confirmation_msg) def get_confirmation_msg(self): return '' def send_unselected_islands(self): return False def grouping_enabled(self): return False def get_group_method(self): raise RuntimeError('Unexpected grouping requested') def send_rot_step(self): return False def lock_groups_enabled(self): return False def send_verts_3d(self): return False def read_area(self, area_msg): return round(force_read_float(area_msg) / self.pack_ratio, 3) class UVP2_OT_PackOperator(UVP2_OT_PackOperatorGeneric): bl_idname = 'uvpackmaster2.uv_pack' bl_label = 'Pack' bl_description = 'Pack selected UV islands' def __init__(self): self.cancel_sig_sent = False self.area = None def get_confirmation_msg(self): if platform.system() == 'Darwin': active_dev = self.prefs.dev_array[self.prefs.sel_dev_idx] if self.prefs.sel_dev_idx < len(self.prefs.dev_array) else None if active_dev is not None and active_dev.id.startswith('cuda'): return UvpLabels.CUDA_MACOS_CONFIRM_MSG if self.prefs.pack_groups_together(self.scene_props) and not self.prefs.heuristic_enabled(self.scene_props): return UvpLabels.GROUPS_TOGETHER_CONFIRM_MSG return '' def send_unselected_islands(self): return self.prefs.pack_to_others_enabled(self.scene_props) def grouping_enabled(self): return self.prefs.grouping_enabled(self.scene_props) def get_group_method(self): return self.scene_props.group_method def send_rot_step(self): return self.prefs.FEATURE_island_rotation_step and self.scene_props.rot_enable and self.scene_props.island_rot_step_enable def lock_groups_enabled(self): return self.prefs.FEATURE_lock_overlapping and self.scene_props.lock_groups_enable def send_verts_3d(self): return self.scene_props.normalize_islands def get_progress_msg_spec(self): if self.curr_phase in { UvPackingPhaseCode.PACKING, UvPackingPhaseCode.PIXEL_MARGIN_ADJUSTMENT }: if self.curr_phase == UvPackingPhaseCode.PIXEL_MARGIN_ADJUSTMENT: header_str = 'Pixel margin adjustment. ' elif self.prefs.heuristic_enabled(self.scene_props): header_str = 'Current area: {}. '.format(self.area if self.area is not None else 'none') else: header_str = '' if self.progress_iter_done >= 0: iter_str = 'Iter. done: {}. '.format(self.progress_iter_done) else: iter_str = '' if self.progress_sec_left >= 0: time_left_str = "Time left: {} sec. ".format(self.progress_sec_left) else: time_left_str = '' percent_progress_str = '' for prog in self.progress_array: percent_progress_str += str(prog).rjust(3, ' ') + '%, ' percent_progress_str = percent_progress_str[:-2] progress_str = 'Pack progress: {} '.format(percent_progress_str) if self.area is not None: end_str = '(press ESC to apply result) ' else: end_str = '(press ESC to cancel) ' return header_str + iter_str + time_left_str + progress_str + end_str return False def handle_uvp_msg_spec(self, msg_code, msg): if msg_code == UvPackMessageCode.AREA: self.area = self.read_area(msg) return True elif msg_code == UvPackMessageCode.PACK_SOLUTION: pack_solution = read_pack_solution(msg) self.p_context.apply_pack_solution(self.pack_ratio, pack_solution) return True elif msg_code == UvPackMessageCode.BENCHMARK: stats = self.prefs.stats_array.add() dev_name_len = force_read_int(msg) stats.dev_name = msg.read(dev_name_len).decode('ascii') stats.iter_count = force_read_int(msg) stats.total_time = force_read_int(msg) stats.avg_time = force_read_int(msg) return True return False def handle_event_spec(self, event): if event.type == 'ESC': if not self.cancel_sig_sent: self.uvp_proc.send_signal(os_cancel_sig()) self.cancel_sig_sent = True return True return False def process_result(self): overlap_detected = False outside_detected = False if self.invalid_faces_msg is not None: invalid_face_count = force_read_int(self.invalid_faces_msg) invalid_faces = read_int_array(self.invalid_faces_msg) if not self.prefs.FEATURE_demo: if len(invalid_faces) != invalid_face_count: self.raiseUnexpectedOutputError() if invalid_face_count > 0: # Switch to the face selection mode if self.p_context.context.tool_settings.use_uv_select_sync: self.p_context.context.tool_settings.mesh_select_mode = (False, False, True) else: self.p_context.context.tool_settings.uv_select_mode = 'FACE' self.p_context.select_all_faces(False) self.p_context.select_faces(list(invalid_faces), True) if invalid_face_count > 0: self.set_status('WARNING', 'Pre-validation failed. Number of invalid faces found: ' + str(invalid_face_count) + '. Packing aborted') return if not self.prefs.FEATURE_demo: if self.island_flags_msg is None: self.raiseUnexpectedOutputError() island_flags = read_int_array(self.island_flags_msg) overlap_detected, outside_detected = self.p_context.handle_island_flags(island_flags) if self.area is not None: self.prefs.stats_area = self.area if self.uvp_proc.returncode == UvPackerErrorCode.NO_SPACE: op_status = 'Packing stopped - no space to pack all islands' self.add_warning("Overlap check was performed only on the islands which were packed") else: op_status = 'Packing done' if self.area is not None: op_status += ', packed islands area: ' + str(self.area) self.set_status('INFO', op_status) if overlap_detected: self.add_warning("Overlapping islands were detected after packing (check the selected islands). Consider increasing the 'Precision' parameter. Sometimes increasing the 'Adjustment Time' may solve the problem (if used in the operation).") if outside_detected: self.add_warning("Some islands are outside their packing box after packing (check the selected islands). This usually happens when 'Pixel Padding' is set to a small value and the 'Adjustment Time' is not long enough.") def validate_pack_params(self): active_dev = self.prefs.dev_array[self.prefs.sel_dev_idx] if self.prefs.sel_dev_idx < len(self.prefs.dev_array) else None if active_dev is None: raise RuntimeError('Could not find a packing device') if not active_dev.supported: raise RuntimeError('Selected packing device is not supported in this engine edition') # Validate pack mode pack_mode = UvPackingMode.get_mode(self.scene_props.pack_mode) if pack_mode.req_feature != '' and not getattr(self.prefs, 'FEATURE_' + pack_mode.req_feature): raise RuntimeError('Selected packing mode is not supported in this engine edition') if self.grouping_enabled(): if self.get_group_method() == UvGroupingMethod.SIMILARITY.code: if self.prefs.pack_to_others_enabled(self.scene_props): raise RuntimeError("'Pack To Others' is not supported with grouping by similarity") if not self.scene_props.rot_enable: raise RuntimeError("Island rotations must be enabled in order to group by similarity") if self.scene_props.prerot_disable: raise RuntimeError("'Pre-Rotation Disable' option must be off in order to group by similarity") if self.prefs.FEATURE_target_box and self.prefs.target_box_enable: validate_target_box(self.scene_props) def get_target_box_string(self, target_box): prec = 4 return "{}:{}:{}:{}".format( round(target_box[0].x, prec), round(target_box[0].y, prec), round(target_box[1].x, prec), round(target_box[1].y, prec)) def get_uvp_args(self): uvp_args = ['-o', str(UvPackerOpcode.PACK), '-i', str(self.scene_props.precision), '-m', str(self.scene_props.margin)] uvp_args += ['-d', self.prefs.dev_array[self.prefs.sel_dev_idx].id] if self.prefs.pixel_margin_enabled(self.scene_props): uvp_args += ['-M', str(self.scene_props.pixel_margin)] uvp_args += ['-y', str(self.prefs.pixel_margin_tex_size(self.scene_props, self.p_context.context))] if self.prefs.pixel_padding_enabled(self.scene_props): uvp_args += ['-N', str(self.scene_props.pixel_padding)] uvp_args += ['-W', self.scene_props.pixel_margin_method] uvp_args += ['-Y', str(self.scene_props.pixel_margin_adjust_time)] if self.prefs.fixed_scale_enabled(self.scene_props): uvp_args += ['-O'] uvp_args += ['-F', self.scene_props.fixed_scale_strategy] if self.prefs.FEATURE_island_rotation: if self.scene_props.rot_enable: rot_step_value = self.scene_props.rot_step if self.scene_props.prerot_disable: uvp_args += ['-w'] else: rot_step_value = -1 uvp_args += ['-r', str(rot_step_value)] if self.prefs.heuristic_enabled(self.scene_props): uvp_args += ['-h', str(self.scene_props.heuristic_search_time), '-j', str(self.scene_props.heuristic_max_wait_time)] if self.prefs.FEATURE_advanced_heuristic and self.scene_props.advanced_heuristic: uvp_args.append('-H') uvp_args += ['-g', self.scene_props.pack_mode] tile_count, tiles_in_row = self.prefs.tile_grid_config(self.scene_props, self.p_context.context) if self.prefs.pack_to_tiles(self.scene_props): uvp_args += ['-V', str(tile_count)] if self.prefs.tiles_enabled(self.scene_props): uvp_args += ['-C', str(tiles_in_row)] if self.grouping_enabled(): if to_uvp_group_method(self.get_group_method()) == UvGroupingMethodUvp.SIMILARITY: uvp_args += ['-I', str(self.scene_props.similarity_threshold)] if self.prefs.pack_groups_together(self.scene_props): uvp_args += ['-U', str(self.scene_props.group_compactness)] if self.prefs.multi_device_enabled(self.scene_props): uvp_args.append('-u') if self.prefs.lock_overlap_enabled(self.scene_props): uvp_args += ['-l', self.scene_props.lock_overlapping_mode] if self.prefs.pack_to_others_enabled(self.scene_props): uvp_args += ['-x'] if self.prefs.FEATURE_validation and self.scene_props.pre_validate: uvp_args.append('-v') if self.prefs.normalize_islands_enabled(self.scene_props): uvp_args.append('-L') if self.prefs.FEATURE_target_box and self.prefs.target_box_enable: self.target_box = self.prefs.target_box(self.scene_props) if self.prefs.pack_ratio_enabled(self.scene_props): self.pack_ratio = get_active_image_ratio(self.p_context.context) if self.pack_ratio != 1.0: uvp_args += ['-q', str(self.pack_ratio)] if self.target_box is not None: self.target_box[0].x *= self.pack_ratio self.target_box[1].x *= self.pack_ratio else: self.target_box = (Vector((0.0, 0.0)), Vector((self.pack_ratio, 1.0))) if self.target_box is not None: uvp_args += ['-B', self.get_target_box_string(self.target_box)] uvp_args.append('-b') return uvp_args class UVP2_OT_OverlapCheckOperator(UVP2_OT_PackOperatorGeneric): bl_idname = 'uvpackmaster2.uv_overlap_check' bl_label = 'Overlap Check' bl_description = 'Check wheter selected UV islands overlap each other' def process_result(self): if self.island_flags_msg is None: self.raiseUnexpectedOutputError() island_flags = read_int_array(self.island_flags_msg) overlap_detected, outside_detected = self.p_context.handle_island_flags(island_flags) if overlap_detected: self.set_status('WARNING', 'Overlapping islands detected') else: self.set_status('INFO', 'No overlapping islands detected') def validate_pack_params(self): pass def get_uvp_args(self): uvp_args = ['-o', str(UvPackerOpcode.OVERLAP_CHECK)] return uvp_args class UVP2_OT_MeasureAreaOperator(UVP2_OT_PackOperatorGeneric): bl_idname = 'uvpackmaster2.uv_measure_area' bl_label = 'Measure Area' bl_description = 'Measure area of selected UV islands' def process_result(self): if self.area_msg is None: self.raiseUnexpectedOutputError() area = self.read_area(self.area_msg) self.prefs.stats_area = area self.set_status('INFO', 'Islands area: ' + str(area)) def validate_pack_params(self): pass def get_uvp_args(self): uvp_args = ['-o', str(UvPackerOpcode.MEASURE_AREA)] return uvp_args class UVP2_OT_ValidateOperator(UVP2_OT_PackOperatorGeneric): bl_idname = 'uvpackmaster2.uv_validate' bl_label = 'Validate UVs' bl_description = 'Validate selected UV faces. The validation procedure looks for invalid UV faces i.e. faces with area close to 0, self-intersecting faces, faces overlapping each other' def get_confirmation_msg(self): if self.prefs.FEATURE_demo: return 'WARNING: in the demo mode only the number of invalid faces found is reported, invalid faces will not be selected. Click OK to continue' return '' def process_result(self): if self.invalid_faces_msg is None: self.raiseUnexpectedOutputError() invalid_face_count = force_read_int(self.invalid_faces_msg) invalid_faces = read_int_array(self.invalid_faces_msg) if not self.prefs.FEATURE_demo: if len(invalid_faces) != invalid_face_count: self.raiseUnexpectedOutputError() if invalid_face_count > 0: # Switch to the face selection mode if self.p_context.context.tool_settings.use_uv_select_sync: self.p_context.context.tool_settings.mesh_select_mode = (False, False, True) else: self.p_context.context.tool_settings.uv_select_mode = 'FACE' self.p_context.select_all_faces(False) self.p_context.select_faces(list(invalid_faces), True) else: if len(invalid_faces) > 0: self.raiseUnexpectedOutputError() if invalid_face_count > 0: self.set_status('WARNING', 'Number of invalid faces found: ' + str(invalid_face_count)) else: self.set_status('INFO', 'No invalid faces found') def validate_pack_params(self): pass def get_uvp_args(self): uvp_args = ['-o', str(UvPackerOpcode.VALIDATE_UVS)] return uvp_args class UVP2_OT_ProcessSimilar(UVP2_OT_PackOperatorGeneric): def validate_pack_params(self): pass def get_uvp_args(self): uvp_args = ['-o', str(self.get_uvp_opcode()), '-I', str(self.scene_props.similarity_threshold)] uvp_args += ['-i', str(self.scene_props.precision)] uvp_args += ['-r', str(90)] if self.prefs.pack_ratio_enabled(self.scene_props): self.pack_ratio = get_active_image_ratio(self.p_context.context) uvp_args += ['-q', str(self.pack_ratio)] return uvp_args class UVP2_OT_SelectSimilar(UVP2_OT_ProcessSimilar): bl_idname = 'uvpackmaster2.uv_select_similar' bl_label = 'Select Similar' bl_description = "Selects all islands which have similar shape to islands which are already selected. For more info regarding similarity detection click the help button" def get_confirmation_msg(self): if self.prefs.FEATURE_demo: return 'WARNING: in the demo mode only the number of similar islands found is reported, islands will not be selected. Click OK to continue' return '' def send_unselected_islands(self): return True def get_uvp_opcode(self): return UvPackerOpcode.SELECT_SIMILAR def process_result(self): if self.similar_islands_msg is None: self.raiseUnexpectedOutputError() similar_island_count = force_read_int(self.similar_islands_msg) similar_islands = read_int_array(self.similar_islands_msg) if not self.prefs.FEATURE_demo: if len(similar_islands) != similar_island_count: self.raiseUnexpectedOutputError() for island_idx in similar_islands: self.p_context.select_island_faces(island_idx, self.p_context.uv_island_faces_list[island_idx], True) else: if len(similar_islands) > 0: self.raiseUnexpectedOutputError() self.set_status('INFO', 'Similar islands found: ' + str(similar_island_count)) class UVP2_OT_AlignSimilar(UVP2_OT_ProcessSimilar): bl_idname = 'uvpackmaster2.uv_align_similar' bl_label = 'Align Similar' bl_description = "Align selected islands, so islands which are similar are placed on top of each other. For more info regarding similarity detection click the help button" def get_uvp_opcode(self): return UvPackerOpcode.ALIGN_SIMILAR def process_result(self): if self.prefs.FEATURE_demo: return if self.pack_solution_msg is None: self.raiseUnexpectedOutputError() pack_solution = read_pack_solution(self.pack_solution_msg) self.p_context.apply_pack_solution(self.pack_ratio, pack_solution) self.set_status('INFO', 'Islands aligned') class UVP2_OT_ScaleIslands(bpy.types.Operator): bl_options = {'UNDO'} @classmethod def poll(cls, context): return context.active_object is not None and context.active_object.mode == 'EDIT' def execute(self, context): try: self.p_context = PackContext(context) ratio = get_active_image_ratio(self.p_context.context) self.p_context.scale_selected_faces(self.get_scale_factors()) except RuntimeError as ex: if in_debug_mode(): print_backtrace(ex) self.report({'ERROR'}, str(ex)) except Exception as ex: if in_debug_mode(): print_backtrace(ex) self.report({'ERROR'}, 'Unexpected error') self.p_context.update_meshes() return {'FINISHED'} def get_scale_factors(self): return (1.0, 1.0) class UVP2_OT_AdjustIslandsToTexture(UVP2_OT_ScaleIslands): bl_idname = 'uvpackmaster2.uv_adjust_islands_to_texture' bl_label = 'Adjust Islands To Texture' bl_description = "Adjust scale of selected islands so they are suitable for packing into the active texture. CAUTION: this operator should be used only when packing to a non-square texture. For for info regarding non-square packing click the help icon" def get_scale_factors(self): ratio = get_active_image_ratio(self.p_context.context) return (1.0 / ratio, 1.0) class UVP2_OT_UndoIslandsAdjustemntToTexture(UVP2_OT_ScaleIslands): bl_idname = 'uvpackmaster2.uv_undo_islands_adjustment_to_texture' bl_label = 'Undo Islands Adjustment' bl_description = "Undo adjustment performed by the 'Adjust Islands To Texture' operator so islands are again suitable for packing into a square texture. For for info regarding non-square packing read the documentation" def get_scale_factors(self): ratio = get_active_image_ratio(self.p_context.context) return (ratio, 1.0) class UVP2_OT_Help(bpy.types.Operator): bl_label = 'Help' def execute(self, context): webbrowser.open(UvpLabels.HELP_BASEURL + self.URL_SUFFIX) return {'FINISHED'} class UVP2_OT_UvpSetupHelp(UVP2_OT_Help): bl_label = 'UVP Setup Help' bl_idname = 'uvpackmaster2.uv_uvp_setup_help' bl_description = "Show help for UVP setup" URL_SUFFIX = "uvp-setup" class UVP2_OT_HeuristicSearchHelp(UVP2_OT_Help): bl_label = 'Non-Square Packing Help' bl_idname = 'uvpackmaster2.uv_heuristic_search_help' bl_description = "Show help for heuristic search" URL_SUFFIX = "heuristic-search" class UVP2_OT_NonSquarePackingHelp(UVP2_OT_Help): bl_label = 'Non-Square Packing Help' bl_idname = 'uvpackmaster2.uv_nonsquare_packing_help' bl_description = "Show help for non-square packing" URL_SUFFIX = "non-square-packing" class UVP2_OT_SimilarityDetectionHelp(UVP2_OT_Help): bl_label = 'Similarity Detection Help' bl_idname = 'uvpackmaster2.uv_similarity_detection_help' bl_description = "Show help for similarity detection" URL_SUFFIX = "similarity-detection" class UVP2_OT_InvalidTopologyHelp(UVP2_OT_Help): bl_label = 'Invalid Topology Help' bl_idname = 'uvpackmaster2.uv_invalid_topology_help' bl_description = "Show help for handling invalid topology errors" URL_SUFFIX = "invalid-topology-issues" class UVP2_OT_PixelMarginHelp(UVP2_OT_Help): bl_label = 'Pixel Margin Help' bl_idname = 'uvpackmaster2.uv_pixel_margin_help' bl_description = "Show help for setting margin in pixels" URL_SUFFIX = "pixel-margin" class UVP2_OT_IslandRotStepHelp(UVP2_OT_Help): bl_label = 'Island Rotation Step Help' bl_idname = 'uvpackmaster2.uv_island_rot_step_help' bl_description = "Show help for setting rotation step on per-island level" URL_SUFFIX = "island-rotation-step" class UVP2_OT_UdimSupportHelp(UVP2_OT_Help): bl_label = 'UDIM Support Help' bl_idname = 'uvpackmaster2.uv_udim_support_help' bl_description = "Show help for UDIM support" URL_SUFFIX = "udim-support" class UVP2_OT_ManualGroupingHelp(UVP2_OT_Help): bl_label = 'Manual Grouping Help' bl_idname = 'uvpackmaster2.uv_manual_grouping_help' bl_description = "Show help for manual grouping" URL_SUFFIX = "udim-support#manual-grouping"

src/data_gen/train_tokenizer.py

spacemanidol/Image2smiles

12799998

<filename>src/data_gen/train_tokenizer.py import os import argparse from tokenizers.decoders import ByteLevel as ByteLevelDecoder from tokenizers.models import BPE from tokenizers.normalizers import Lowercase, NFKC, Sequence from tokenizers.pre_tokenizers import ByteLevel from tokenizers.trainers import BpeTrainer from tokenizers import Tokenizer, models, pre_tokenizers, decoders, trainers, processors def get_smi_files(directory): files = [] for filename in os.listdir(directory): if filename.endswith(".smi"): files.append(os.path.join(directory, filename)) else: continue return files def main(args): if args.do_train: # Initialize a tokenizer files = get_smi_files(args.training_files) print("Training BPE tokenizer using the following files:{}".format(files)) tokenizer = Tokenizer(models.BPE(unk_token="<unk>")) tokenizer.enable_padding(pad_id=args.vocab_size+2, pad_token="<pad>", length=args.pad_len) tokenizer.enable_truncation(max_length=args.pad_len,strategy='only_first') tokenizer.normalizer = Sequence([NFKC()]) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False) tokenizer.decoder = decoders.ByteLevel() tokenizer.post_processor = processors.ByteLevel(trim_offsets=True) # Train the tokenizer trainer = trainers.BpeTrainer(show_progress=True, vocab_size=args.vocab_size, min_frequency=args.min_frequency) tokenizer.train(files, trainer=trainer) tokenizer.add_tokens(["<start>", "<end>" ]) tokenizer.save(os.path.join('tokenizers',args.tokenizer_name), pretty=True) print("Trained vocab size: {}".format(tokenizer.get_vocab_size())) if args.do_test: # Test the tokenizer tokenizer = Tokenizer.from_file(os.path.join('tokenizers',args.tokenizer_name)) print("Testing with SMILES String: {}".format(args.test_string)) encoding = tokenizer.encode(args.test_string) print("Encoded string: {}".format(encoding.tokens)) print(encoding.ids) decoded = tokenizer.decode(encoding.ids) print("Decoded string: {}".format(decoded)) if __name__ == '__main__': parser = argparse.ArgumentParser(description='Generate a smiles tokenizer given candidate files and target configs.') parser.add_argument('--training_files', type=str, default='data/', help='source of input smiles data') parser.add_argument('--pad_len', type=int, default=150, help='how much to pad tokenized input') parser.add_argument('--do_train', action='store_true', help='Train a tokenizer' ) parser.add_argument('--do_test', action='store_true', help='Test the tokenizer found in tokenizer dir file') parser.add_argument('--test_string', type=str, default='CC(C)CCNc1cnnc(NCCc2ccc(S(N)(=O)=O)cc2)n1', help='a SMILES string to test tokenizer with') parser.add_argument('--tokenizer_name', type=str, default='tokenizer_vocab_2000.json') parser.add_argument('--vocab_size', type=int, default=2000, help='Size of the vocab to rain') parser.add_argument('--min_frequency', type=int, default=2, help='min fequency of word in corpus') args = parser.parse_args() main(args)

hub/tests/test_models_custom_link.py

yevgenykuz/dev-team-hub

12799999

<reponame>yevgenykuz/dev-team-hub from django.test import TestCase from ..models import CustomLink class CustomLinkModelTests(TestCase): def setUp(self): self.custom_link = CustomLink.objects.create(name='Google', url='https://www.google.com', order_id=1) def test_new_object(self): self.assertEquals(self.custom_link.name, 'Google') self.assertEquals(self.custom_link.url, 'https://www.google.com') self.assertEquals(self.custom_link.order_id, 1) def test_field_name(self): field_label = self.custom_link._meta.get_field('name').verbose_name max_length = self.custom_link._meta.get_field('name').max_length unique = self.custom_link._meta.get_field('name').unique self.assertEquals(field_label, 'name') self.assertEquals(max_length, 50) self.assertEquals(unique, True) def test_field_url(self): field_label = self.custom_link._meta.get_field('url').verbose_name max_length = self.custom_link._meta.get_field('url').max_length self.assertEquals(field_label, 'url') self.assertEquals(max_length, 255) def test_field_order_id(self): field_label = self.custom_link._meta.get_field('order_id').verbose_name max_length = self.custom_link._meta.get_field('order_id').default self.assertEquals(field_label, 'order id') self.assertEquals(max_length, 0) def test_object_presentation(self): expected_presentation = f"[{self.custom_link.name}]: {self.custom_link.url}" self.assertEquals(expected_presentation, str(self.custom_link)) def test_object_ordering(self): self.assertEquals(self.custom_link._meta.ordering, ['order_id'])