nilq/baby-python-and-lua · Datasets at Hugging Face

content stringlengths 0 1.05M	origin stringclasses 2 values	type stringclasses 2 values
from typing import Any, Dict, List, Optional from pydantic import BaseModel, Field class RecordSearch(BaseModel): """ Dao search Attributes: ----------- query: The elasticsearch search query portion aggregations: The elasticsearch search aggregations """ query: Optional[Dict[str, Any]] aggregations: Optional[Dict[str, Any]] class RecordSearchResults(BaseModel): """ Dao search results Attributes: ----------- total: int The total of query results records: List[T] List of records retrieved for the pagination configuration aggregations: Optional[Dict[str, Dict[str, Any]]] The query aggregations grouped by task. Optional words: Optional[Dict[str, int]] The words cloud aggregations metadata: Optional[Dict[str, int]] Metadata fields aggregations """ total: int records: List[Dict[str, Any]] aggregations: Optional[Dict[str, Dict[str, Any]]] = Field(default_factory=dict) words: Optional[Dict[str, int]] = None metadata: Optional[Dict[str, int]] = None	nilq/baby-python	python
from typing import Tuple, Callable from .template import Processor from .concat import BatchConcat, BatchPool from .denoise import Dada2SingleEnd, Dada2PairedEnd from .importing import ImportSingleEndFastq, ImportPairedEndFastq from .trimming import BatchTrimGalorePairedEnd, BatchTrimGaloreSingleEnd class GenerateASVPairedEnd(Processor): fq_dir: str fq1_suffix: str fq2_suffix: str clip_r1_5_prime: int clip_r2_5_prime: int trimmed_fq_dir: str feature_sequence_qza: str feature_table_qza: str def main( self, fq_dir: str, fq1_suffix: str, fq2_suffix: str, clip_r1_5_prime: int, clip_r2_5_prime: int) -> Tuple[str, str]: return self.feature_table_qza, self.feature_sequence_qza def trimming(self): self.trimmed_fq_dir = BatchTrimGalorePairedEnd(self.settings).main( fq_dir=self.fq_dir, fq1_suffix=self.fq1_suffix, fq2_suffix=self.fq2_suffix, clip_r1_5_prime=self.clip_r1_5_prime, clip_r2_5_prime=self.clip_r2_5_prime) class GenerateASVConcatPairedEnd(GenerateASVPairedEnd): concat_fq_dir: str fq_suffix: str single_end_seq_qza: str def main( self, fq_dir: str, fq1_suffix: str, fq2_suffix: str, clip_r1_5_prime: int, clip_r2_5_prime: int) -> Tuple[str, str]: self.fq_dir = fq_dir self.fq1_suffix = fq1_suffix self.fq2_suffix = fq2_suffix self.clip_r1_5_prime = clip_r1_5_prime self.clip_r2_5_prime = clip_r2_5_prime self.trimming() self.concat() self.importing() self.denoise() return self.feature_table_qza, self.feature_sequence_qza def concat(self): self.concat_fq_dir, self.fq_suffix = BatchConcat(self.settings).main( fq_dir=self.trimmed_fq_dir, fq1_suffix=self.fq1_suffix, fq2_suffix=self.fq2_suffix) def importing(self): self.single_end_seq_qza = ImportSingleEndFastq(self.settings).main( fq_dir=self.concat_fq_dir, fq_suffix=self.fq_suffix) def denoise(self): self.feature_table_qza, self.feature_sequence_qza = Dada2SingleEnd(self.settings).main( demultiplexed_seq_qza=self.single_end_seq_qza) class GenerateASVMergePairedEnd(GenerateASVPairedEnd): paired_end_seq_qza: str def main( self, fq_dir: str, fq1_suffix: str, fq2_suffix: str, clip_r1_5_prime: int, clip_r2_5_prime: int) -> Tuple[str, str]: self.fq_dir = fq_dir self.fq1_suffix = fq1_suffix self.fq2_suffix = fq2_suffix self.clip_r1_5_prime = clip_r1_5_prime self.clip_r2_5_prime = clip_r2_5_prime self.trimming() self.importing() self.denoise() return self.feature_table_qza, self.feature_sequence_qza def importing(self): self.paired_end_seq_qza = ImportPairedEndFastq(self.settings).main( fq_dir=self.trimmed_fq_dir, fq1_suffix=self.fq1_suffix, fq2_suffix=self.fq2_suffix) def denoise(self): self.feature_table_qza, self.feature_sequence_qza = Dada2PairedEnd(self.settings).main( demultiplexed_seq_qza=self.paired_end_seq_qza) class GenerateASVPoolPairedEnd(GenerateASVPairedEnd): pooled_fq_dir: str fq_suffix: str single_end_seq_qza: str def main( self, fq_dir: str, fq1_suffix: str, fq2_suffix: str, clip_r1_5_prime: int, clip_r2_5_prime: int) -> Tuple[str, str]: self.fq_dir = fq_dir self.fq1_suffix = fq1_suffix self.fq2_suffix = fq2_suffix self.clip_r1_5_prime = clip_r1_5_prime self.clip_r2_5_prime = clip_r2_5_prime self.trimming() self.pool() self.importing() self.denoise() return self.feature_table_qza, self.feature_sequence_qza def pool(self): self.pooled_fq_dir, self.fq_suffix = BatchPool(self.settings).main( fq_dir=self.trimmed_fq_dir, fq1_suffix=self.fq1_suffix, fq2_suffix=self.fq2_suffix) def importing(self): self.single_end_seq_qza = ImportSingleEndFastq(self.settings).main( fq_dir=self.pooled_fq_dir, fq_suffix=self.fq_suffix) def denoise(self): self.feature_table_qza, self.feature_sequence_qza = Dada2SingleEnd(self.settings).main( demultiplexed_seq_qza=self.single_end_seq_qza) class FactoryGenerateASVPairedEnd(Processor): MODE_TO_CLASS = { 'concat': GenerateASVConcatPairedEnd, 'merge': GenerateASVMergePairedEnd, 'pool': GenerateASVPoolPairedEnd } def main(self, paired_end_mode: str) -> Callable: assert paired_end_mode in self.MODE_TO_CLASS.keys(), \ f'"{paired_end_mode}" is not a valid mode for GenerateASV' _Class = self.MODE_TO_CLASS[paired_end_mode] return _Class(self.settings).main class GenerateASVSingleEnd(Processor): fq_dir: str fq_suffix: str clip_5_prime: int trimmed_fq_dir: str single_end_seq_qza: str feature_sequence_qza: str feature_table_qza: str def main( self, fq_dir: str, fq_suffix: str, clip_5_prime: int) -> Tuple[str, str]: self.fq_dir = fq_dir self.fq_suffix = fq_suffix self.clip_5_prime = clip_5_prime self.trimming() self.importing() self.denoise() return self.feature_table_qza, self.feature_sequence_qza def trimming(self): self.trimmed_fq_dir = BatchTrimGaloreSingleEnd(self.settings).main( fq_dir=self.fq_dir, fq_suffix=self.fq_suffix, clip_5_prime=self.clip_5_prime) def importing(self): self.single_end_seq_qza = ImportSingleEndFastq(self.settings).main( fq_dir=self.trimmed_fq_dir, fq_suffix=self.fq_suffix) def denoise(self): self.feature_table_qza, self.feature_sequence_qza = Dada2SingleEnd(self.settings).main( demultiplexed_seq_qza=self.single_end_seq_qza)	nilq/baby-python	python
import personalnames.titles as titles import bisect # noinspection PyTypeChecker def gen_initials(lastname, firstname, formats, title=None, post_nominal=None, no_ws=False): """ Generate the name formats with initials. :param lastname: person's lastname :param firstname: person's firstname :param title: person's title :param post_nominal: suffix, e.g. 'junior', or 'esq.' :param formats: list of formats ['firstnamelastname', 'lastnamefirstname'] :param no_ws: add a form with no whitespace. :return: de-duplicated list of names with initials. """ # Normalise whitespace to single space lastname = normalise_whitespace(lastname) parts = normalise_whitespace(firstname).split() forms = [] for x in range(1, len(parts) + 1): initials = [part[0:1] + "." for part in parts[0:x]] initials += parts[x:] if "firstnamelastname" in formats: forms.append(" ".join([" ".join(initials), lastname])) if title: forms.append(" ".join([title, " ".join(initials), lastname])) if "lastnamefirstname" in formats: forms.append(", ".join([lastname, " ".join(initials)])) if title: forms.append(", ".join([lastname, title + " " + " ".join(initials)])) for x in range(1, len(parts) + 1): initials = [part[0:1] + "." for part in parts[1:x]] initials += parts[x:] if "firstnamelastname" in formats: forms.append(" ".join([parts[0], " ".join(initials), lastname])) if title: forms.append(" ".join([title, parts[0], " ".join(initials), lastname])) if "lastnamefirstname" in formats: forms.append(lastname + ", " + " ".join([parts[0], " ".join(initials)])) if title: forms.append( lastname + ", " + " ".join([title, parts[0], " ".join(initials)]) ) if post_nominal: forms.extend([x + ", " + post_nominal for x in forms[:]]) if no_ws: forms.extend([removewhitespace(x) for x in forms[:]]) return list(set(forms)) def parse_titles(parts): title_parts = [] suffix_parts = [] nominal_parts = [] for part in parts: if part.lower() in titles.prefixes: title_parts.append(part) elif part.lower() in titles.suffixes: suffix_parts.append(part) else: nominal_parts.append(part) return title_parts, nominal_parts, suffix_parts def name_split(name, split_char=","): """ Split a name into a list of name parts (not categorised, just an ordered list). Retain commas for later use in splitting the list into surname and forename parts. :param name: string for personal name :param split_char: character to split on (default to comma) :return: list of strings, including commas. """ name_list = [] split_split = name.split(split_char) for split_item in split_split[:-1]: [name_list.append(normalise_whitespace(x)) for x in split_item.split()] name_list.append(split_char) [name_list.append(normalise_whitespace(x)) for x in split_split[-1].split()] return name_list def name_parts(name, split_c=","): """ TO DO: handle case with multiple commas (if this is a genuine case) :param name: :param split_c: :return: """ n_parts = name_split(name, split_char=split_c) title, personal_name, suffix = parse_titles(n_parts) if personal_name[-1] == split_c: # case where multiple commas in name, or only comma is a post-nominal, e.g. Esq. n = personal_name[:-1] else: n = personal_name if split_c in n: lastname = whitespace_list( n[: bisect.bisect(n, split_c) - 1] ) firstname = whitespace_list( n[bisect.bisect(n, split_c):] ) else: firstname = whitespace_list(n[:-1]) lastname = whitespace_list([n[-1]]) title = whitespace_list(title) suffix = whitespace_list(suffix) return title, firstname, lastname, suffix def name_initials(name, name_formats=None, non_ws=False): """ Generate a set of initials from a name provided as a string. :param name: string, e.g. Dr. Martin Luther King :param name_formats: list of formats for the name. :param non_ws: no whitespace form :return: list of formats including initials """ if name_formats is None: name_formats = ["firstnamelastname", "lastnamefirstname"] honorific, forename, surname, suffix = name_parts(name) initials = gen_initials( lastname=surname, firstname=forename, title=honorific, post_nominal=suffix, formats=name_formats, no_ws=non_ws ) return [normalise_whitespace(x) for x in initials] def whitespace_list(text_list): return normalise_whitespace(" ".join(text_list)) def normalise_whitespace(text): """ Normalise the whitespace in the string :param text: string :return: string with whitespace normalised to single space """ return " ".join(text.strip().split()) def removewhitespace(text): """ Remove the whitespace in the string :param text: string :return: string with no whitespace """ return "".join(text.strip().split())	nilq/baby-python	python
from typing import List, Union def is_valid(sides: List[Union[float,int]]) -> bool: [x, y, z] = sides return x > 0 and y > 0 and z > 0 and x + y > z def equilateral(sides: List[Union[float,int]]) -> bool: sides.sort() return is_valid(sides) and sides.count(sides[0]) == 3 def isosceles(sides: List[Union[float,int]]) -> bool: sides.sort() return is_valid(sides) and sides[0] == sides[1] or sides[1] == sides[2] def scalene(sides: List[Union[float,int]]) -> bool: sides.sort() return is_valid(sides) and sides[0] != sides[1] and sides[1] != sides[2]	nilq/baby-python	python
import numpy as np import scipy.sparse as sparse import scipy.sparse.linalg as linalg from .mesh import UniformMesh from .laminate_model import LaminateModel from .laminate_dof import LaminateDOF class LaminateFEM(object): def __init__(self, material, cantilever): self.cantilever = cantilever self.mesh = UniformMesh(cantilever.topology) self.dof = LaminateDOF(self.mesh) self.model = LaminateModel(material, cantilever.a, cantilever.b) self.a = cantilever.a self.b = cantilever.b self.assemble() def get_mass_matrix(self, free=False): muu = self.muu.tocsr() if free is False: return muu return muu[self.dof.free_dofs, :][:, self.dof.free_dofs] def get_stiffness_matrix(self, free=False): kuu = self.kuu.tocsr() if free is False: return kuu return kuu[self.dof.free_dofs, :][:, self.dof.free_dofs] def get_piezoelectric_matrix(self, free=False): kuv = self.kuv.tocsr() if free is False: return kuv return kuv[self.dof.free_dofs, :] def get_capacitance_matrix(self): return self.kvv def modal_analysis(self, n_modes): """The return value (w) are the eigenvalues and the return value (v) are the eigenvectors. """ m = self.muu.tocsc()[self.dof.free_dofs, :][:, self.dof.free_dofs] k = self.kuu.tocsc()[self.dof.free_dofs, :][:, self.dof.free_dofs] w, v = linalg.eigsh(k, k=n_modes, M=m, sigma=0, which='LM') vall = np.zeros((self.dof.n_mdof, n_modes)) vall[self.dof.free_dofs, :] = v return w, v, vall def assemble(self): """The mass, stiffness, piezoelectric, and capacitance matricies are assembled in this function. """ muue = self.model.get_mass_element() kuue = self.model.get_stiffness_element() kuve = self.model.get_piezoelectric_element() kvve = self.model.get_capacitance_element() nm, ne = kuve.shape k_num = nm * nm * self.mesh.n_elem p_num = nm * ne * self.mesh.n_elem c_num = ne * ne * self.mesh.n_elem k_index = list(np.ndindex(nm, nm)) p_index = list(np.ndindex(nm, ne)) c_index = list(np.ndindex(ne, ne)) k_row = np.zeros(k_num) k_col = np.zeros(k_num) k_val = np.zeros(k_num) m_val = np.zeros(k_num) p_row = np.zeros(p_num) p_col = np.zeros(p_num) p_val = np.zeros(p_num) c_row = np.zeros(c_num) c_col = np.zeros(c_num) c_val = np.zeros(c_num) k_ntriplet = 0 p_ntriplet = 0 c_ntriplet = 0 for ni, e in enumerate(self.dof.dof_elements): for ii, jj in k_index: k_row[k_ntriplet] = e.mechanical_dof[ii] k_col[k_ntriplet] = e.mechanical_dof[jj] k_val[k_ntriplet] = kuue[ii, jj] m_val[k_ntriplet] = muue[ii, jj] k_ntriplet += 1 for ii, jj in p_index: p_row[p_ntriplet] = e.mechanical_dof[ii] p_col[p_ntriplet] = e.electrical_dof[jj] p_val[p_ntriplet] = kuve[ii, jj] p_ntriplet += 1 for ii, jj in c_index: c_row[c_ntriplet] = e.electrical_dof[ii] c_col[c_ntriplet] = e.electrical_dof[jj] c_val[c_ntriplet] = kvve[ii, jj] c_ntriplet += 1 muu_shape = (self.dof.n_mdof, self.dof.n_mdof) kuu_shape = (self.dof.n_mdof, self.dof.n_mdof) kuv_shape = (self.dof.n_mdof, self.dof.n_edof) kvv_shape = (self.dof.n_edof, self.dof.n_edof) self.muu = sparse.coo_matrix((m_val, (k_row, k_col)), shape=muu_shape) self.kuu = sparse.coo_matrix((k_val, (k_row, k_col)), shape=kuu_shape) self.kuv = sparse.coo_matrix((p_val, (p_row, p_col)), shape=kuv_shape) self.kvv = sparse.coo_matrix((c_val, (c_row, c_col)), shape=kvv_shape)	nilq/baby-python	python
"""Falcon benchmarks""" from bench import main # NOQA	nilq/baby-python	python
import os from pathlib import Path import pyspark.sql.types as st from pyspark.sql.types import Row from pyspark.ml.regression import GBTRegressor from pyspark.sql import DataFrame, SparkSession spark = SparkSession.builder \ .appName("karl02") \ .getOrCreate() datadir: str = os.getenv("DATADIR") if datadir is None: raise ValueError("Environment variable DATADIR must be defined") print(f"datadir = '{datadir}'") schema = st.StructType([ st.StructField('year', st.IntegerType(), True), st.StructField('month', st.IntegerType(), True), st.StructField('dn', st.IntegerType(), True), st.StructField('wday', st.IntegerType(), True), st.StructField('snap', st.IntegerType(), True), st.StructField('dept_id', st.StringType(), True), st.StructField('item_id', st.StringType(), True), st.StructField('store_id', st.StringType(), True), st.StructField('sales', st.DoubleType(), True), st.StructField('flag_ram', st.IntegerType(), True), st.StructField('Sales_Pred', st.DoubleType(), True) ]) p = str(Path(datadir, "Sales5_Ab2011_InklPred.csv")) print(f"Reading: '{p}'") train: DataFrame = spark.read.csv(p, header='true', schema=schema) rows = train.rdd.take(5) for r in rows: dn = r["sales"] d = r.asDict() v = list(d.values()) print(v) print(type(v)) print("------------------------- R E A D Y --------------------------------") def train(df: DataFrame): def astraining(row: Row) -> Row: df = row.asDict() del df['Sales_Pred'] del df['sales'] sales = row.asDict()['sales'] return Row(label=sales, features=list(df.values())) t3 = train.rdd \ .filter(lambda r: r["sales"] is not None) \ .map(astraining) gbt = GBTRegressor(maxIter=10) df = spark.createDataFrame(t3) df.show() gbt.fit(df) print("----------- after fit ------------")	nilq/baby-python	python
############################################################################### # Imports ############################################################################### from layer import Layer import numpy as np class HiddenLayer(Layer): def setDownstreamSum(self, w, delta): """Sum the product of w and delta for the next layer Needed for calculating delta for this layer Parameters ---------- w : np.ndarray Matrix of weight values for the next layer delta : np.ndarray Matrix of delta values for the next layer """ self.downstream_sum = np.matmul(w[:,:-1].transpose(), delta) def setDelta(self): """Calculate delta for the hidden layer """ # Derivative of sigmoid using last forward pass output_der = self.y * (1 - self.y) self.delta = output_der * self.downstream_sum if __name__ == '__main__': print('Warning: Tests for this file are deprecated')	nilq/baby-python	python
# -- coding: utf-8 -- ''' @Time : 2021/8/30 @Author : Yanyuxiang @Email : yanyuxiangtoday@163.com @FileName: send_message.py @Software: PyCharm ''' import itchat def main(): itchat.auto_login() friends = itchat.get_friends(update=True) # itchat.send('这是来自python程序的一条消息', toUserName='filehelper') return if __name__ == '__main__': main()	nilq/baby-python	python
from . import scheduler from app.utils.refresh_mat_views import refresh_all_mat_views from app.utils.constants import COUNTRIES # 5/9 = 5am, 2pm, and 11pm # https://cron.help/#0_5/9___* @scheduler.task("cron", minute="0", hour="5") def run_task_ALL(): with scheduler.app.app_context(): from app.service.routes import call_loader for country in COUNTRIES: call_loader( country=country, search=dict(ad_reached_countries=[country], ad_active_status="ALL"), ) @scheduler.task("cron", minute="35", hour="*") def refresh_views(): with scheduler.app.app_context(): refresh_all_mat_views(False)	nilq/baby-python	python
import torch from torch.utils.data import DataLoader from torch import optim from torch.optim.lr_scheduler import StepLR from snf.layers.flowsequential import FlowSequential from snf.layers.selfnorm import SelfNormConv, SelfNormFC from snf.train.losses import NegativeGaussianLoss from snf.train.experiment import Experiment def create_model(data_size, layer='conv'): layers = [] c_in = data_size[0] h = data_size[1] w = data_size[2] if layer == 'fc': size = c_in * h * w layers.append(SelfNormFC(size, size, bias=True, sym_recon_grad=False, only_R_recon=False)) model = FlowSequential(NegativeGaussianLoss(size=(size,)), layers) elif layer == 'conv': layers.append(SelfNormConv(c_in, c_in, (3,3), bias=True, stride=1, padding=1, sym_recon_grad=False, only_R_recon=False)) model = FlowSequential(NegativeGaussianLoss(size=data_size), layers) return model def load_data(batch_size=100, im_size=(1,28,28), n_train=60_000, n_val=10_0000, n_test=10_000): trainx = torch.randn(n_train, im_size) testx = torch.randn(n_test, im_size) trainy = torch.zeros(n_train) testy = torch.zeros(n_test) trainvalset = torch.utils.data.TensorDataset(trainx, trainy) testset = torch.utils.data.TensorDataset(testx, testy) trainset = torch.utils.data.Subset(trainvalset, range(0, n_train - n_val)) valset = torch.utils.data.Subset(trainvalset, range(n_train - n_val, n_train)) train_loader = DataLoader(trainset, batch_size=batch_size) val_loader = DataLoader(valset, batch_size=batch_size) test_loader = DataLoader(testset, batch_size=batch_size) return train_loader, val_loader, test_loader def run_timing_experiment(name, snf_name, config, sz, m, results): train_loader, val_loader, test_loader = load_data(batch_size=config['batch_size'], im_size=sz, n_train=50_000, n_val=100, n_test=100) model = create_model(data_size=sz, layer=m).to('cuda') optimizer = optim.Adam(model.parameters(), lr=config['lr'], betas=(0.9, 0.999)) scheduler = StepLR(optimizer, step_size=1, gamma=1.0) experiment = Experiment(model, train_loader, val_loader, test_loader, optimizer, scheduler, *config) experiment.run() mean_time = experiment.summary['Batch Time Mean'] std_time = experiment.summary['Batch Time Std'] print(f"{name}: {mean_time} +/- {std_time}") results[f'{m} {snf_name}']['n_params'].append(sz[0] sz[1] * sz[2]) results[f'{m} {snf_name}']['mean'].append(mean_time) results[f'{m} {snf_name}']['std'].append(std_time) return results def main(): image_sizes = [(1, x*32, 1) for x in range(1, 130, 3)] model_type = ['fc', 'conv'] self_normalized = [True, False] name = 'Timing Experiment ' results = {} for m in model_type: for snf in self_normalized: if snf: snf_name = 'SNF' else: snf_name = 'Reg' results[f'{m} {snf_name}'] = { 'n_params': [], 'mean': [], 'std': [] } for sz in image_sizes: name = f'Timing Experiment {m} {snf_name} {sz}' config = { 'name': name, 'eval_epochs': 1, 'sample_epochs': 1000, 'log_interval': 10000, 'lr': 1e-4, 'batch_size': 128, 'modified_grad': snf, 'add_recon_grad': snf, 'sym_recon_grad': False, 'only_R_recon': False, 'actnorm': False, 'split_prior': False, 'activation': 'None', 'log_timing': True, 'epochs': 10 } results = run_timing_experiment(name, snf_name, config, sz, m, results) print(results[f'{m} {snf_name}']) print(results) print(results) print(results)	nilq/baby-python	python
from VcfNormalize import VcfNormalize import argparse import os #get command line arguments parser = argparse.ArgumentParser(description='Script to run GATK VariantsToAllelicPrimitives in order to decompose MNPs into more basic/primitive alleles') parser.add_argument('--gatk_folder', type=str, required=True, help='Folder containing GATK jar file' ) parser.add_argument('--bgzip_folder', type=str, required=True, help='Folder containing bgzip' ) parser.add_argument('--vcf', type=str, required=True, help='Path to the VCF file that will be analysed' ) parser.add_argument('--outprefix', type=str, required=True, help='Prefix for output file' ) parser.add_argument('--reference', type=str, required=True, help='Path to the reference Fasta file' ) parser.add_argument('--compress', type=str, required=False, help='Compress the output file' ) args = parser.parse_args() if __name__ == '__main__': vcfallprim = VcfNormalize(vcf=args.vcf,gatk_folder=args.gatk_folder,bgzip_folder=args.bgzip_folder) vcfallprim.run_gatk_VariantsToAllelicPrimitives(outprefix=args.outprefix,reference=args.reference,compress=args.compress)	nilq/baby-python	python
# This file was automatically created by FeynRules 2.3.36 # Mathematica version: 11.3.0 for Linux x86 (64-bit) (March 7, 2018) # Date: Wed 24 Feb 2021 15:52:48 from object_library import all_couplings, Coupling from function_library import complexconjugate, re, im, csc, sec, acsc, asec, cot GC_1 = Coupling(name = 'GC_1', value = '-(eecomplex(0,1))/3.', order = {'QED':1}) GC_2 = Coupling(name = 'GC_2', value = '(2eecomplex(0,1))/3.', order = {'QED':1}) GC_3 = Coupling(name = 'GC_3', value = '-(eecomplex(0,1))', order = {'QED':1}) GC_4 = Coupling(name = 'GC_4', value = 'eecomplex(0,1)', order = {'QED':1}) GC_5 = Coupling(name = 'GC_5', value = 'ee2complex(0,1)', order = {'QED':2}) GC_6 = Coupling(name = 'GC_6', value = '2ee2complex(0,1)', order = {'QED':2}) GC_7 = Coupling(name = 'GC_7', value = '-ee*2/(2.cw)', order = {'QED':2}) GC_8 = Coupling(name = 'GC_8', value = 'ee*2/(2.cw)', order = {'QED':2}) GC_9 = Coupling(name = 'GC_9', value = '-(cabee2complex(0,1))/(2.cw)', order = {'QED':2}) GC_10 = Coupling(name = 'GC_10', value = '-(cphiee*2)/(2.cw)', order = {'QED':2}) GC_11 = Coupling(name = 'GC_11', value = '(cphiee2)/(2.cw)', order = {'QED':2}) GC_12 = Coupling(name = 'GC_12', value = '-(cabcphiee*2complex(0,1))/(2.cw)', order = {'QED':2}) GC_13 = Coupling(name = 'GC_13', value = 'cphifl1x2complex(0,1) - cphifl2x1complex(0,1)', order = {'ZEE':1}) GC_14 = Coupling(name = 'GC_14', value = '-(cphifl1x2complex(0,1)) + cphifl2x1complex(0,1)', order = {'ZEE':1}) GC_15 = Coupling(name = 'GC_15', value = 'cphifl1x3complex(0,1) - cphifl3x1complex(0,1)', order = {'ZEE':1}) GC_16 = Coupling(name = 'GC_16', value = '-(cphifl1x3complex(0,1)) + cphifl3x1complex(0,1)', order = {'ZEE':1}) GC_17 = Coupling(name = 'GC_17', value = 'cphifl2x3complex(0,1) - cphifl3x2complex(0,1)', order = {'ZEE':1}) GC_18 = Coupling(name = 'GC_18', value = '-(cphifl2x3complex(0,1)) + cphifl3x2complex(0,1)', order = {'ZEE':1}) GC_19 = Coupling(name = 'GC_19', value = '-G', order = {'QCD':1}) GC_20 = Coupling(name = 'GC_20', value = 'complex(0,1)G', order = {'QCD':1}) GC_21 = Coupling(name = 'GC_21', value = 'complex(0,1)G2', order = {'QCD':2}) GC_22 = Coupling(name = 'GC_22', value = '-(complex(0,1)I1a11)', order = {'QED':1}) GC_23 = Coupling(name = 'GC_23', value = '-(complex(0,1)I1a12)', order = {'QED':1}) GC_24 = Coupling(name = 'GC_24', value = '-(complex(0,1)I1a13)', order = {'QED':1}) GC_25 = Coupling(name = 'GC_25', value = '-(complex(0,1)I1a21)', order = {'QED':1}) GC_26 = Coupling(name = 'GC_26', value = '-(complex(0,1)I1a22)', order = {'QED':1}) GC_27 = Coupling(name = 'GC_27', value = '-(complex(0,1)I1a23)', order = {'QED':1}) GC_28 = Coupling(name = 'GC_28', value = '-(complex(0,1)I1a31)', order = {'QED':1}) GC_29 = Coupling(name = 'GC_29', value = '-(complex(0,1)I1a32)', order = {'QED':1}) GC_30 = Coupling(name = 'GC_30', value = '-(complex(0,1)I1a33)', order = {'QED':1}) GC_31 = Coupling(name = 'GC_31', value = 'complex(0,1)I2a11', order = {'QED':1}) GC_32 = Coupling(name = 'GC_32', value = 'complex(0,1)I2a12', order = {'QED':1}) GC_33 = Coupling(name = 'GC_33', value = 'complex(0,1)I2a13', order = {'QED':1}) GC_34 = Coupling(name = 'GC_34', value = 'complex(0,1)I2a21', order = {'QED':1}) GC_35 = Coupling(name = 'GC_35', value = 'complex(0,1)I2a22', order = {'QED':1}) GC_36 = Coupling(name = 'GC_36', value = 'complex(0,1)I2a23', order = {'QED':1}) GC_37 = Coupling(name = 'GC_37', value = 'complex(0,1)I2a31', order = {'QED':1}) GC_38 = Coupling(name = 'GC_38', value = 'complex(0,1)I2a32', order = {'QED':1}) GC_39 = Coupling(name = 'GC_39', value = 'complex(0,1)I2a33', order = {'QED':1}) GC_40 = Coupling(name = 'GC_40', value = 'complex(0,1)I3a11', order = {'QED':1}) GC_41 = Coupling(name = 'GC_41', value = 'complex(0,1)I3a12', order = {'QED':1}) GC_42 = Coupling(name = 'GC_42', value = 'complex(0,1)I3a13', order = {'QED':1}) GC_43 = Coupling(name = 'GC_43', value = 'complex(0,1)I3a21', order = {'QED':1}) GC_44 = Coupling(name = 'GC_44', value = 'complex(0,1)I3a22', order = {'QED':1}) GC_45 = Coupling(name = 'GC_45', value = 'complex(0,1)I3a23', order = {'QED':1}) GC_46 = Coupling(name = 'GC_46', value = 'complex(0,1)I3a31', order = {'QED':1}) GC_47 = Coupling(name = 'GC_47', value = 'complex(0,1)I3a32', order = {'QED':1}) GC_48 = Coupling(name = 'GC_48', value = 'complex(0,1)I3a33', order = {'QED':1}) GC_49 = Coupling(name = 'GC_49', value = '-(complex(0,1)I4a11)', order = {'QED':1}) GC_50 = Coupling(name = 'GC_50', value = '-(complex(0,1)I4a12)', order = {'QED':1}) GC_51 = Coupling(name = 'GC_51', value = '-(complex(0,1)I4a13)', order = {'QED':1}) GC_52 = Coupling(name = 'GC_52', value = '-(complex(0,1)I4a21)', order = {'QED':1}) GC_53 = Coupling(name = 'GC_53', value = '-(complex(0,1)I4a22)', order = {'QED':1}) GC_54 = Coupling(name = 'GC_54', value = '-(complex(0,1)I4a23)', order = {'QED':1}) GC_55 = Coupling(name = 'GC_55', value = '-(complex(0,1)I4a31)', order = {'QED':1}) GC_56 = Coupling(name = 'GC_56', value = '-(complex(0,1)I4a32)', order = {'QED':1}) GC_57 = Coupling(name = 'GC_57', value = '-(complex(0,1)I4a33)', order = {'QED':1}) GC_58 = Coupling(name = 'GC_58', value = '-(complex(0,1)lam1)', order = {'QED':2}) GC_59 = Coupling(name = 'GC_59', value = '-2complex(0,1)lam1', order = {'QED':2}) GC_60 = Coupling(name = 'GC_60', value = '-3complex(0,1)lam1', order = {'QED':2}) GC_61 = Coupling(name = 'GC_61', value = '-3complex(0,1)lam2', order = {'QED':2}) GC_62 = Coupling(name = 'GC_62', value = '-(complex(0,1)lam3)', order = {'QED':2}) GC_63 = Coupling(name = 'GC_63', value = '-(complex(0,1)lam3) - complex(0,1)lam4 - complex(0,1)lam5', order = {'QED':2}) GC_64 = Coupling(name = 'GC_64', value = '-2cphi2complex(0,1)lam5', order = {'QED':2}) GC_65 = Coupling(name = 'GC_65', value = '-(cphicomplex(0,1)lam4)/2. - (cphicomplex(0,1)lam5)/2.', order = {'QED':2}) GC_66 = Coupling(name = 'GC_66', value = '(cabcphilam4)/2. - (cabcphilam5)/2.', order = {'QED':2}) GC_67 = Coupling(name = 'GC_67', value = '-(cabcphilam4)/2. + (cabcphilam5)/2.', order = {'QED':2}) GC_68 = Coupling(name = 'GC_68', value = '-(complex(0,1)lam6)', order = {'QED':2}) GC_69 = Coupling(name = 'GC_69', value = '-3complex(0,1)lam6', order = {'QED':2}) GC_70 = Coupling(name = 'GC_70', value = '-(cphicomplex(0,1)lam6)', order = {'QED':2}) GC_71 = Coupling(name = 'GC_71', value = '-2cphicomplex(0,1)lam6', order = {'QED':2}) GC_72 = Coupling(name = 'GC_72', value = '-3complex(0,1)lam7', order = {'QED':2}) GC_73 = Coupling(name = 'GC_73', value = '-(cphicomplex(0,1)lam7)', order = {'QED':2}) GC_74 = Coupling(name = 'GC_74', value = '(ee2complex(0,1)sab)/(2.cw)', order = {'QED':2}) GC_75 = Coupling(name = 'GC_75', value = '-(cphiee2complex(0,1)sab)/(2.cw)', order = {'QED':2}) GC_76 = Coupling(name = 'GC_76', value = '(cphilam4sab)/2. - (cphilam5sab)/2.', order = {'QED':2}) GC_77 = Coupling(name = 'GC_77', value = '-(cphilam4sab)/2. + (cphilam5sab)/2.', order = {'QED':2}) GC_78 = Coupling(name = 'GC_78', value = '-(cab*2complex(0,1)lam3) + 2cabcomplex(0,1)lam6sab - complex(0,1)lam1sab2', order = {'QED':2}) GC_79 = Coupling(name = 'GC_79', value = '-(cab2complex(0,1)lam3) - cab2complex(0,1)lam4 + cab2complex(0,1)lam5 + 2cabcomplex(0,1)lam6sab - complex(0,1)lam1sab2', order = {'QED':2}) GC_80 = Coupling(name = 'GC_80', value = '-(cab2complex(0,1)lam3) - cab2complex(0,1)lam4 + cab2complex(0,1)lam5 - 2cabcomplex(0,1)lam7sab - complex(0,1)lam2sab2', order = {'QED':2}) GC_81 = Coupling(name = 'GC_81', value = '-(cab2complex(0,1)lam1) - 2cabcomplex(0,1)lam6sab - complex(0,1)lam3sab2', order = {'QED':2}) GC_82 = Coupling(name = 'GC_82', value = '-(cab2complex(0,1)lam5) + cabcomplex(0,1)lam6sab - cabcomplex(0,1)lam7sab + complex(0,1)lam5sab2', order = {'QED':2}) GC_83 = Coupling(name = 'GC_83', value = '-(cab2complex(0,1)lam1) - 2cabcomplex(0,1)lam6sab - complex(0,1)lam3sab2 - complex(0,1)lam4sab2 + complex(0,1)lam5sab2', order = {'QED':2}) GC_84 = Coupling(name = 'GC_84', value = '-(cab2complex(0,1)lam2) + 2cabcomplex(0,1)lam7sab - complex(0,1)lam3sab2 - complex(0,1)lam4sab2 + complex(0,1)lam5sab2', order = {'QED':2}) GC_85 = Coupling(name = 'GC_85', value = '-(cab2cphicomplex(0,1)lam4)/2. - (cab*2cphicomplex(0,1)lam5)/2. + cabcphicomplex(0,1)lam6sab - cabcphicomplex(0,1)lam7sab + (cphicomplex(0,1)lam4sab2)/2. + (cphicomplex(0,1)lam5sab2)/2.', order = {'QED':2}) GC_86 = Coupling(name = 'GC_86', value = '-(cab2complex(0,1)lam7) + 2cabcomplex(0,1)lam5sab - complex(0,1)lam6sab2', order = {'QED':2}) GC_87 = Coupling(name = 'GC_87', value = '-(cab2complex(0,1)lam6) + cabcomplex(0,1)lam1sab - cabcomplex(0,1)lam3sab + complex(0,1)lam6sab2', order = {'QED':2}) GC_88 = Coupling(name = 'GC_88', value = '-(cab2complex(0,1)lam6) + cabcomplex(0,1)lam1sab - cabcomplex(0,1)lam3sab - cabcomplex(0,1)lam4sab + cabcomplex(0,1)lam5sab + complex(0,1)lam6sab2', order = {'QED':2}) GC_89 = Coupling(name = 'GC_89', value = '-(cab2cphicomplex(0,1)lam7) + cabcphicomplex(0,1)lam4sab + cabcphicomplex(0,1)lam5sab - cphicomplex(0,1)lam6sab2', order = {'QED':2}) GC_90 = Coupling(name = 'GC_90', value = '-(cab2complex(0,1)lam6) - 2cabcomplex(0,1)lam5sab - complex(0,1)lam7sab2', order = {'QED':2}) GC_91 = Coupling(name = 'GC_91', value = '-(cab2complex(0,1)lam7) - cabcomplex(0,1)lam2sab + cabcomplex(0,1)lam3sab + cabcomplex(0,1)lam4sab - cabcomplex(0,1)lam5sab + complex(0,1)lam7sab2', order = {'QED':2}) GC_92 = Coupling(name = 'GC_92', value = '-(cab2cphicomplex(0,1)lam6) - cabcphicomplex(0,1)lam4sab - cabcphicomplex(0,1)lam5sab - cphicomplex(0,1)lam7sab*2', order = {'QED':2}) GC_93 = Coupling(name = 'GC_93', value = '-3cab*4complex(0,1)lam2 + 12cab*3complex(0,1)lam7sab - 6cab2complex(0,1)lam3sab*2 - 6cab*2complex(0,1)lam4sab*2 - 6cab*2complex(0,1)lam5sab*2 + 12cabcomplex(0,1)lam6sab3 - 3complex(0,1)lam1sab*4', order = {'QED':2}) GC_94 = Coupling(name = 'GC_94', value = '-3cab*4complex(0,1)lam1 - 12cab*3complex(0,1)lam6sab - 6cab2complex(0,1)lam3sab*2 - 6cab*2complex(0,1)lam4sab*2 - 6cab*2complex(0,1)lam5sab*2 - 12cabcomplex(0,1)lam7sab3 - 3complex(0,1)lam2sab4', order = {'QED':2}) GC_95 = Coupling(name = 'GC_95', value = '-(cab4complex(0,1)lam3) - cab*4complex(0,1)lam4 - cab4complex(0,1)lam5 + 6cab*3complex(0,1)lam6sab - 6cab3complex(0,1)lam7sab - 3cab2complex(0,1)lam1sab*2 - 3cab*2complex(0,1)lam2sab*2 + 4cab*2complex(0,1)lam3sab*2 + 4cab*2complex(0,1)lam4sab*2 + 4cab*2complex(0,1)lam5sab*2 - 6cabcomplex(0,1)lam6sab3 + 6cabcomplex(0,1)lam7sab3 - complex(0,1)lam3sab4 - complex(0,1)lam4sab4 - complex(0,1)lam5sab4', order = {'QED':2}) GC_96 = Coupling(name = 'GC_96', value = '-3cab*4complex(0,1)lam7 - 3cab*3complex(0,1)lam2sab + 3cab3complex(0,1)lam3sab + 3cab3complex(0,1)lam4sab + 3cab3complex(0,1)lam5sab - 9cab2complex(0,1)lam6sab*2 + 9cab*2complex(0,1)lam7sab*2 + 3cabcomplex(0,1)lam1sab3 - 3cabcomplex(0,1)lam3sab3 - 3cabcomplex(0,1)lam4sab3 - 3cabcomplex(0,1)lam5sab3 + 3complex(0,1)lam6sab*4', order = {'QED':2}) GC_97 = Coupling(name = 'GC_97', value = '-3cab*4complex(0,1)lam6 + 3cab*3complex(0,1)lam1sab - 3cab3complex(0,1)lam3sab - 3cab3complex(0,1)lam4sab - 3cab3complex(0,1)lam5sab + 9cab2complex(0,1)lam6sab*2 - 9cab*2complex(0,1)lam7sab*2 - 3cabcomplex(0,1)lam2sab3 + 3cabcomplex(0,1)lam3sab3 + 3cabcomplex(0,1)lam4sab3 + 3cabcomplex(0,1)lam5sab3 + 3complex(0,1)lam7sab4', order = {'QED':2}) GC_98 = Coupling(name = 'GC_98', value = '-(ee2sphi)/(2.cw)', order = {'QED':2}) GC_99 = Coupling(name = 'GC_99', value = '(ee*2sphi)/(2.cw)', order = {'QED':2}) GC_100 = Coupling(name = 'GC_100', value = '-(cabee*2complex(0,1)sphi)/(2.cw)', order = {'QED':2}) GC_101 = Coupling(name = 'GC_101', value = '-2cphicomplex(0,1)lam5sphi', order = {'QED':2}) GC_102 = Coupling(name = 'GC_102', value = '-(complex(0,1)lam6sphi)', order = {'QED':2}) GC_103 = Coupling(name = 'GC_103', value = '-2complex(0,1)lam6sphi', order = {'QED':2}) GC_104 = Coupling(name = 'GC_104', value = '-(complex(0,1)lam7sphi)', order = {'QED':2}) GC_105 = Coupling(name = 'GC_105', value = '-(ee2complex(0,1)sabsphi)/(2.cw)', order = {'QED':2}) GC_106 = Coupling(name = 'GC_106', value = '-2complex(0,1)lam5sphi*2', order = {'QED':2}) GC_107 = Coupling(name = 'GC_107', value = 'fl1x2complex(0,1)sphi - fl2x1complex(0,1)sphi', order = {'ZEE':1}) GC_108 = Coupling(name = 'GC_108', value = '-(fl1x2complex(0,1)sphi) + fl2x1complex(0,1)sphi', order = {'ZEE':1}) GC_109 = Coupling(name = 'GC_109', value = 'fl1x3complex(0,1)sphi - fl3x1complex(0,1)sphi', order = {'ZEE':1}) GC_110 = Coupling(name = 'GC_110', value = '-(fl1x3complex(0,1)sphi) + fl3x1complex(0,1)sphi', order = {'ZEE':1}) GC_111 = Coupling(name = 'GC_111', value = 'fl2x3complex(0,1)sphi - fl3x2complex(0,1)sphi', order = {'ZEE':1}) GC_112 = Coupling(name = 'GC_112', value = '-(fl2x3complex(0,1)sphi) + fl3x2complex(0,1)sphi', order = {'ZEE':1}) GC_113 = Coupling(name = 'GC_113', value = '-(complex(0,1)lam4sphi)/2. - (complex(0,1)lam5sphi)/2.', order = {'QED':2}) GC_114 = Coupling(name = 'GC_114', value = '(cablam4sphi)/2. - (cablam5sphi)/2.', order = {'QED':2}) GC_115 = Coupling(name = 'GC_115', value = '-(cablam4sphi)/2. + (cablam5sphi)/2.', order = {'QED':2}) GC_116 = Coupling(name = 'GC_116', value = 'cphicomplex(0,1)lam10sphi - cphicomplex(0,1)lam7sphi', order = {'QED':2}) GC_117 = Coupling(name = 'GC_117', value = '2cphi*2complex(0,1)lam10sphi - 2cphi2complex(0,1)lam7sphi', order = {'QED':2}) GC_118 = Coupling(name = 'GC_118', value = '-(cphicomplex(0,1)lam3sphi) + cphicomplex(0,1)lam8sphi', order = {'QED':2}) GC_119 = Coupling(name = 'GC_119', value = '-(cphicomplex(0,1)lam3sphi) - cphicomplex(0,1)lam4sphi + cphicomplex(0,1)lam8sphi', order = {'QED':2}) GC_120 = Coupling(name = 'GC_120', value = '-(cphicomplex(0,1)lam2sphi) + cphicomplex(0,1)lam9sphi', order = {'QED':2}) GC_121 = Coupling(name = 'GC_121', value = '(lam4sabsphi)/2. - (lam5sabsphi)/2.', order = {'QED':2}) GC_122 = Coupling(name = 'GC_122', value = '-(lam4sabsphi)/2. + (lam5sabsphi)/2.', order = {'QED':2}) GC_123 = Coupling(name = 'GC_123', value = '-(cab2complex(0,1)lam4sphi)/2. - (cab*2complex(0,1)lam5sphi)/2. + cabcomplex(0,1)lam6sabsphi - cabcomplex(0,1)lam7sabsphi + (complex(0,1)lam4sab*2sphi)/2. + (complex(0,1)lam5sab*2sphi)/2.', order = {'QED':2}) GC_124 = Coupling(name = 'GC_124', value = '-(cab*2complex(0,1)lam7sphi) + cabcomplex(0,1)lam4sabsphi + cabcomplex(0,1)lam5sabsphi - complex(0,1)lam6sab*2sphi', order = {'QED':2}) GC_125 = Coupling(name = 'GC_125', value = '-(cab*2complex(0,1)lam6sphi) - cabcomplex(0,1)lam4sabsphi - cabcomplex(0,1)lam5sabsphi - complex(0,1)lam7sab*2sphi', order = {'QED':2}) GC_126 = Coupling(name = 'GC_126', value = 'cab*2cphicomplex(0,1)lam10sphi - cab2cphicomplex(0,1)lam7sphi - cabcphicomplex(0,1)lam2sabsphi + cabcphicomplex(0,1)lam3sabsphi - cabcphicomplex(0,1)lam8sabsphi + cabcphicomplex(0,1)lam9sabsphi - cphicomplex(0,1)lam10sab*2sphi + cphicomplex(0,1)lam7sab2sphi', order = {'QED':2}) GC_127 = Coupling(name = 'GC_127', value = '-(cab*2cphicomplex(0,1)lam2sphi) + cab2cphicomplex(0,1)lam9sphi - 2cabcphicomplex(0,1)lam10sabsphi + 2cabcphicomplex(0,1)lam7sabsphi - cphicomplex(0,1)lam3sab*2sphi + cphicomplex(0,1)lam8sab2sphi', order = {'QED':2}) GC_128 = Coupling(name = 'GC_128', value = '-(cab*2cphicomplex(0,1)lam3sphi) + cab2cphicomplex(0,1)lam8sphi + 2cabcphicomplex(0,1)lam10sabsphi - 2cabcphicomplex(0,1)lam7sabsphi - cphicomplex(0,1)lam2sab*2sphi + cphicomplex(0,1)lam9sab2sphi', order = {'QED':2}) GC_129 = Coupling(name = 'GC_129', value = '-(cphi*2eecomplex(0,1)) - eecomplex(0,1)sphi2', order = {'QED':1}) GC_130 = Coupling(name = 'GC_130', value = '2cphi*2ee*2complex(0,1) + 2ee2complex(0,1)sphi2', order = {'QED':2}) GC_131 = Coupling(name = 'GC_131', value = '-(cphi2complex(0,1)lam7) - complex(0,1)lam10sphi2', order = {'QED':2}) GC_132 = Coupling(name = 'GC_132', value = '-2cphi*3complex(0,1)lam7 - 2cphicomplex(0,1)lam10sphi2', order = {'QED':2}) GC_133 = Coupling(name = 'GC_133', value = '-(cphi2complex(0,1)lam9) - complex(0,1)lam2sphi2', order = {'QED':2}) GC_134 = Coupling(name = 'GC_134', value = '-(cphi2complex(0,1)lam8) - complex(0,1)lam3sphi2', order = {'QED':2}) GC_135 = Coupling(name = 'GC_135', value = '-(cphi2complex(0,1)lam8) - complex(0,1)lam3sphi2 - complex(0,1)lam4sphi2', order = {'QED':2}) GC_136 = Coupling(name = 'GC_136', value = '-(cphi2complex(0,1)lam10) - complex(0,1)lam7sphi2', order = {'QED':2}) GC_137 = Coupling(name = 'GC_137', value = '-(cphi3complex(0,1)lam10) + cphicomplex(0,1)lam10sphi*2 - 2cphicomplex(0,1)lam7sphi2', order = {'QED':2}) GC_138 = Coupling(name = 'GC_138', value = '2cphicomplex(0,1)lam10sphi2 - 2cphicomplex(0,1)lam7sphi2', order = {'QED':2}) GC_139 = Coupling(name = 'GC_139', value = '-(cphi2complex(0,1)lam3) - complex(0,1)lam8sphi2', order = {'QED':2}) GC_140 = Coupling(name = 'GC_140', value = '-(cphi2complex(0,1)lam3) - cphi2complex(0,1)lam4 - complex(0,1)lam8sphi2', order = {'QED':2}) GC_141 = Coupling(name = 'GC_141', value = '-(cphi2complex(0,1)lam2) - complex(0,1)lam9sphi2', order = {'QED':2}) GC_142 = Coupling(name = 'GC_142', value = '-2cphi*2complex(0,1)lam2sphi*2 + 4cphi*2complex(0,1)lam9sphi*2 - 4cphi*2complex(0,1)lametasphi2', order = {'QED':2}) GC_143 = Coupling(name = 'GC_143', value = '-((cphi2muzee)/cmath.sqrt(2)) - (muzeesphi2)/cmath.sqrt(2)', order = {'QED':1}) GC_144 = Coupling(name = 'GC_144', value = '(cphi2muzee)/cmath.sqrt(2) + (muzeesphi2)/cmath.sqrt(2)', order = {'QED':1}) GC_145 = Coupling(name = 'GC_145', value = '-(cab2cphi2complex(0,1)lam7) - cabcphi*2complex(0,1)lam2sab + cabcphi2complex(0,1)lam3sab + cphi*2complex(0,1)lam7sab2 - cab2complex(0,1)lam10sphi2 + cabcomplex(0,1)lam8sabsphi2 - cabcomplex(0,1)lam9sabsphi2 + complex(0,1)lam10sab2sphi2', order = {'QED':2}) GC_146 = Coupling(name = 'GC_146', value = '-(cab2cphi2complex(0,1)lam8) - 2cabcphi2complex(0,1)lam10sab - cphi*2complex(0,1)lam9sab2 - cab2complex(0,1)lam3sphi2 - 2cabcomplex(0,1)lam7sabsphi*2 - complex(0,1)lam2sab2sphi2', order = {'QED':2}) GC_147 = Coupling(name = 'GC_147', value = '-(cab2cphi2complex(0,1)lam9) + 2cabcphi2complex(0,1)lam10sab - cphi*2complex(0,1)lam8sab2 - cab2complex(0,1)lam2sphi2 + 2cabcomplex(0,1)lam7sabsphi*2 - complex(0,1)lam3sab2sphi2', order = {'QED':2}) GC_148 = Coupling(name = 'GC_148', value = '-(cab2cphi2complex(0,1)lam10) + cabcphi*2complex(0,1)lam8sab - cabcphi2complex(0,1)lam9sab + cphi*2complex(0,1)lam10sab2 - cab2complex(0,1)lam7sphi2 - cabcomplex(0,1)lam2sabsphi2 + cabcomplex(0,1)lam3sabsphi2 + complex(0,1)lam7sab2sphi2', order = {'QED':2}) GC_149 = Coupling(name = 'GC_149', value = '-(cab2cphi2complex(0,1)lam2) + 2cabcphi2complex(0,1)lam7sab - cphi*2complex(0,1)lam3sab2 - cab2complex(0,1)lam9sphi2 + 2cabcomplex(0,1)lam10sabsphi*2 - complex(0,1)lam8sab2sphi2', order = {'QED':2}) GC_150 = Coupling(name = 'GC_150', value = '-(cab2cphi2complex(0,1)lam3) - 2cabcphi2complex(0,1)lam7sab - cphi*2complex(0,1)lam2sab2 - cab2complex(0,1)lam8sphi2 - 2cabcomplex(0,1)lam10sabsphi*2 - complex(0,1)lam9sab2sphi2', order = {'QED':2}) GC_151 = Coupling(name = 'GC_151', value = 'cphi2complex(0,1)lam10sphi - 2cphi*2complex(0,1)lam7sphi - complex(0,1)lam10sphi*3', order = {'QED':2}) GC_152 = Coupling(name = 'GC_152', value = '-2cphi*2complex(0,1)lam10sphi - 2complex(0,1)lam7sphi3', order = {'QED':2}) GC_153 = Coupling(name = 'GC_153', value = '-2cphi*3complex(0,1)lam9sphi + 4cphi3complex(0,1)lametasphi - 2cphicomplex(0,1)lam2sphi*3 + 2cphicomplex(0,1)lam9sphi3', order = {'QED':2}) GC_154 = Coupling(name = 'GC_154', value = '-2cphi*3complex(0,1)lam2sphi + 2cphi3complex(0,1)lam9sphi - 2cphicomplex(0,1)lam9sphi*3 + 4cphicomplex(0,1)lametasphi3', order = {'QED':2}) GC_155 = Coupling(name = 'GC_155', value = '-4cphi*4complex(0,1)lameta - 4cphi*2complex(0,1)lam9sphi*2 - 2complex(0,1)lam2sphi4', order = {'QED':2}) GC_156 = Coupling(name = 'GC_156', value = '-(cphi4complex(0,1)lam9) - 2cphi2complex(0,1)lam2sphi*2 + 2cphi*2complex(0,1)lam9sphi*2 - 4cphi*2complex(0,1)lametasphi*2 - complex(0,1)lam9sphi4', order = {'QED':2}) GC_157 = Coupling(name = 'GC_157', value = '-2cphi*4complex(0,1)lam2 - 4cphi*2complex(0,1)lam9sphi*2 - 4complex(0,1)lametasphi4', order = {'QED':2}) GC_158 = Coupling(name = 'GC_158', value = '(cab2ee2complex(0,1))/(2.sw2) + (ee2complex(0,1)sab2)/(2.sw2)', order = {'QED':2}) GC_159 = Coupling(name = 'GC_159', value = '(ee2complex(0,1))/(2.sw2)', order = {'QED':2}) GC_160 = Coupling(name = 'GC_160', value = '-((ee2complex(0,1))/sw2)', order = {'QED':2}) GC_161 = Coupling(name = 'GC_161', value = '(cphi2ee*2complex(0,1))/(2.sw2)', order = {'QED':2}) GC_162 = Coupling(name = 'GC_162', value = '(cw2ee*2complex(0,1))/sw*2', order = {'QED':2}) GC_163 = Coupling(name = 'GC_163', value = '(cphiee*2complex(0,1)sphi)/(2.sw2)', order = {'QED':2}) GC_164 = Coupling(name = 'GC_164', value = '(ee2complex(0,1)sphi*2)/(2.sw*2)', order = {'QED':2}) GC_165 = Coupling(name = 'GC_165', value = 'ee/(2.sw)', order = {'QED':1}) GC_166 = Coupling(name = 'GC_166', value = '(eecomplex(0,1))/(swcmath.sqrt(2))', order = {'QED':1}) GC_167 = Coupling(name = 'GC_167', value = '-(cabeecomplex(0,1))/(2.sw)', order = {'QED':1}) GC_168 = Coupling(name = 'GC_168', value = '(cabeecomplex(0,1))/(2.sw)', order = {'QED':1}) GC_169 = Coupling(name = 'GC_169', value = '(CKM1x1eecomplex(0,1))/(swcmath.sqrt(2))', order = {'QED':1}) GC_170 = Coupling(name = 'GC_170', value = '(CKM1x2eecomplex(0,1))/(swcmath.sqrt(2))', order = {'QED':1}) GC_171 = Coupling(name = 'GC_171', value = '(CKM1x3eecomplex(0,1))/(swcmath.sqrt(2))', order = {'QED':1}) GC_172 = Coupling(name = 'GC_172', value = '(CKM2x1eecomplex(0,1))/(swcmath.sqrt(2))', order = {'QED':1}) GC_173 = Coupling(name = 'GC_173', value = '(CKM2x2eecomplex(0,1))/(swcmath.sqrt(2))', order = {'QED':1}) GC_174 = Coupling(name = 'GC_174', value = '(CKM2x3eecomplex(0,1))/(swcmath.sqrt(2))', order = {'QED':1}) GC_175 = Coupling(name = 'GC_175', value = '(CKM3x1eecomplex(0,1))/(swcmath.sqrt(2))', order = {'QED':1}) GC_176 = Coupling(name = 'GC_176', value = '(CKM3x2eecomplex(0,1))/(swcmath.sqrt(2))', order = {'QED':1}) GC_177 = Coupling(name = 'GC_177', value = '(CKM3x3eecomplex(0,1))/(swcmath.sqrt(2))', order = {'QED':1}) GC_178 = Coupling(name = 'GC_178', value = '(cphiee)/(2.sw)', order = {'QED':1}) GC_179 = Coupling(name = 'GC_179', value = '-(cabcphieecomplex(0,1))/(2.sw)', order = {'QED':1}) GC_180 = Coupling(name = 'GC_180', value = '(cabcphieecomplex(0,1))/(2.sw)', order = {'QED':1}) GC_181 = Coupling(name = 'GC_181', value = '-((cweecomplex(0,1))/sw)', order = {'QED':1}) GC_182 = Coupling(name = 'GC_182', value = '(cweecomplex(0,1))/sw', order = {'QED':1}) GC_183 = Coupling(name = 'GC_183', value = '-ee2/(2.sw)', order = {'QED':2}) GC_184 = Coupling(name = 'GC_184', value = 'ee*2/(2.sw)', order = {'QED':2}) GC_185 = Coupling(name = 'GC_185', value = '(cabee2complex(0,1))/(2.sw)', order = {'QED':2}) GC_186 = Coupling(name = 'GC_186', value = '-(cphiee*2)/(2.sw)', order = {'QED':2}) GC_187 = Coupling(name = 'GC_187', value = '(cphiee2)/(2.sw)', order = {'QED':2}) GC_188 = Coupling(name = 'GC_188', value = '(cabcphiee*2complex(0,1))/(2.sw)', order = {'QED':2}) GC_189 = Coupling(name = 'GC_189', value = '(-2cwee2complex(0,1))/sw', order = {'QED':2}) GC_190 = Coupling(name = 'GC_190', value = '-(eecomplex(0,1)sab)/(2.sw)', order = {'QED':1}) GC_191 = Coupling(name = 'GC_191', value = '(eecomplex(0,1)sab)/(2.sw)', order = {'QED':1}) GC_192 = Coupling(name = 'GC_192', value = '-(cphieecomplex(0,1)sab)/(2.sw)', order = {'QED':1}) GC_193 = Coupling(name = 'GC_193', value = '(cphieecomplex(0,1)sab)/(2.sw)', order = {'QED':1}) GC_194 = Coupling(name = 'GC_194', value = '-(ee*2complex(0,1)sab)/(2.sw)', order = {'QED':2}) GC_195 = Coupling(name = 'GC_195', value = '(cphiee2complex(0,1)sab)/(2.sw)', order = {'QED':2}) GC_196 = Coupling(name = 'GC_196', value = '(eesphi)/(2.sw)', order = {'QED':1}) GC_197 = Coupling(name = 'GC_197', value = '-(cabeecomplex(0,1)sphi)/(2.sw)', order = {'QED':1}) GC_198 = Coupling(name = 'GC_198', value = '(cabeecomplex(0,1)sphi)/(2.sw)', order = {'QED':1}) GC_199 = Coupling(name = 'GC_199', value = '-(ee*2sphi)/(2.sw)', order = {'QED':2}) GC_200 = Coupling(name = 'GC_200', value = '(ee2sphi)/(2.sw)', order = {'QED':2}) GC_201 = Coupling(name = 'GC_201', value = '(cabee*2complex(0,1)sphi)/(2.sw)', order = {'QED':2}) GC_202 = Coupling(name = 'GC_202', value = '-(eecomplex(0,1)sabsphi)/(2.sw)', order = {'QED':1}) GC_203 = Coupling(name = 'GC_203', value = '(eecomplex(0,1)sabsphi)/(2.sw)', order = {'QED':1}) GC_204 = Coupling(name = 'GC_204', value = '(ee*2complex(0,1)sabsphi)/(2.sw)', order = {'QED':2}) GC_205 = Coupling(name = 'GC_205', value = '(eecomplex(0,1)sw)/(3.cw)', order = {'QED':1}) GC_206 = Coupling(name = 'GC_206', value = '(-2eecomplex(0,1)sw)/(3.cw)', order = {'QED':1}) GC_207 = Coupling(name = 'GC_207', value = '(eecomplex(0,1)sw)/cw', order = {'QED':1}) GC_208 = Coupling(name = 'GC_208', value = '-(cweecomplex(0,1))/(2.sw) - (eecomplex(0,1)sw)/(6.cw)', order = {'QED':1}) GC_209 = Coupling(name = 'GC_209', value = '(cweecomplex(0,1))/(2.sw) - (eecomplex(0,1)sw)/(6.cw)', order = {'QED':1}) GC_210 = Coupling(name = 'GC_210', value = '-(cweecomplex(0,1))/(2.sw) + (eecomplex(0,1)sw)/(2.cw)', order = {'QED':1}) GC_211 = Coupling(name = 'GC_211', value = '(cweecomplex(0,1))/(2.sw) + (eecomplex(0,1)sw)/(2.cw)', order = {'QED':1}) GC_212 = Coupling(name = 'GC_212', value = '-(cabcwee)/(2.sw) - (cabeesw)/(2.cw)', order = {'QED':1}) GC_213 = Coupling(name = 'GC_213', value = '(cwee2complex(0,1))/sw - (ee*2complex(0,1)sw)/cw', order = {'QED':2}) GC_214 = Coupling(name = 'GC_214', value = '-(cweesab)/(2.sw) - (eesabsw)/(2.cw)', order = {'QED':1}) GC_215 = Coupling(name = 'GC_215', value = '(cweesab)/(2.sw) + (eesabsw)/(2.cw)', order = {'QED':1}) GC_216 = Coupling(name = 'GC_216', value = '-(cphicweecomplex(0,1)sphi)/(2.sw) - (cphieecomplex(0,1)sphisw)/(2.cw)', order = {'QED':1}) GC_217 = Coupling(name = 'GC_217', value = '(cphicweecomplex(0,1)sphi)/(2.sw) + (cphieecomplex(0,1)sphisw)/(2.cw)', order = {'QED':1}) GC_218 = Coupling(name = 'GC_218', value = '(cphicwee2complex(0,1)sphi)/sw + (cphiee*2complex(0,1)sphisw)/cw', order = {'QED':2}) GC_219 = Coupling(name = 'GC_219', value = '-(cweecomplex(0,1)sphi2)/(2.sw) + (cphi*2eecomplex(0,1)sw)/cw + (eecomplex(0,1)sphi*2sw)/(2.cw)', order = {'QED':1}) GC_220 = Coupling(name = 'GC_220', value = '-(cphi2cweecomplex(0,1))/(2.sw) + (cphi2eecomplex(0,1)sw)/(2.cw) + (eecomplex(0,1)sphi2sw)/cw', order = {'QED':1}) GC_221 = Coupling(name = 'GC_221', value = '(cwee2complex(0,1)sphi2)/sw - (2cphi*2ee*2complex(0,1)sw)/cw - (ee2complex(0,1)sphi2sw)/cw', order = {'QED':2}) GC_222 = Coupling(name = 'GC_222', value = '(cphi*2cwee2complex(0,1))/sw - (cphi*2ee*2complex(0,1)sw)/cw - (2ee*2complex(0,1)sphi2sw)/cw', order = {'QED':2}) GC_223 = Coupling(name = 'GC_223', value = '-(ee*2complex(0,1)) + (cw*2ee*2complex(0,1))/(2.sw2) + (ee2complex(0,1)sw2)/(2.cw2)', order = {'QED':2}) GC_224 = Coupling(name = 'GC_224', value = 'ee2complex(0,1) + (cw2ee*2complex(0,1))/(2.sw2) + (ee2complex(0,1)sw2)/(2.cw2)', order = {'QED':2}) GC_225 = Coupling(name = 'GC_225', value = 'cab2ee2complex(0,1) + ee*2complex(0,1)sab2 + (cab2cw*2ee*2complex(0,1))/(2.sw2) + (cw2ee*2complex(0,1)sab2)/(2.sw2) + (cab2ee2complex(0,1)sw2)/(2.cw2) + (ee2complex(0,1)sab*2sw*2)/(2.cw*2)', order = {'QED':2}) GC_226 = Coupling(name = 'GC_226', value = '-(cphiee*2complex(0,1)sphi) + (cphicw*2ee*2complex(0,1)sphi)/(2.sw*2) - (3cphiee2complex(0,1)sphisw*2)/(2.cw2)', order = {'QED':2}) GC_227 = Coupling(name = 'GC_227', value = '-(ee2complex(0,1)sphi2) + (cw2ee2complex(0,1)sphi2)/(2.sw*2) + (2cphi*2ee*2complex(0,1)sw2)/cw2 + (ee2complex(0,1)sphi2sw*2)/(2.cw2)', order = {'QED':2}) GC_228 = Coupling(name = 'GC_228', value = '-(cphi2ee2complex(0,1)) + (cphi*2cw*2ee*2complex(0,1))/(2.sw2) + (cphi2ee*2complex(0,1)sw2)/(2.cw*2) + (2ee*2complex(0,1)sphi2sw2)/cw2', order = {'QED':2}) GC_229 = Coupling(name = 'GC_229', value = '-(ee*2complex(0,1)vev)/(2.cw)', order = {'QED':1}) GC_230 = Coupling(name = 'GC_230', value = '(cabee2complex(0,1)vev)/(2.sw2)', order = {'QED':1}) GC_231 = Coupling(name = 'GC_231', value = '-(ee2complex(0,1)sabvev)/(2.sw2)', order = {'QED':1}) GC_232 = Coupling(name = 'GC_232', value = '-(ee2vev)/(2.sw)', order = {'QED':1}) GC_233 = Coupling(name = 'GC_233', value = '(ee*2complex(0,1)vev)/(2.sw)', order = {'QED':1}) GC_234 = Coupling(name = 'GC_234', value = '(ee*2vev)/(2.sw)', order = {'QED':1}) GC_235 = Coupling(name = 'GC_235', value = '(cphilam4vev)/2. - (cphilam5vev)/2. - (muzeesphi)/cmath.sqrt(2)', order = {'QED':1}) GC_236 = Coupling(name = 'GC_236', value = '-(cphilam4vev)/2. + (cphilam5vev)/2. + (muzeesphi)/cmath.sqrt(2)', order = {'QED':1}) GC_237 = Coupling(name = 'GC_237', value = '-(cabcomplex(0,1)lam6vev) + complex(0,1)lam1sabvev', order = {'QED':1}) GC_238 = Coupling(name = 'GC_238', value = '-(cabcomplex(0,1)lam6vev) - complex(0,1)lam5sabvev', order = {'QED':1}) GC_239 = Coupling(name = 'GC_239', value = '-(cabcomplex(0,1)lam7vev) + complex(0,1)lam3sabvev + complex(0,1)lam4sabvev - complex(0,1)lam5sabvev', order = {'QED':1}) GC_240 = Coupling(name = 'GC_240', value = '-(cabcphicomplex(0,1)lam6vev) - (cphicomplex(0,1)lam4sabvev)/2. - (cphicomplex(0,1)lam5sabvev)/2. + (complex(0,1)muzeesabsphi)/cmath.sqrt(2)', order = {'QED':1}) GC_241 = Coupling(name = 'GC_241', value = '-(cabcomplex(0,1)lam1vev) - complex(0,1)lam6sabvev', order = {'QED':1}) GC_242 = Coupling(name = 'GC_242', value = '-(cabcomplex(0,1)lam5vev) + complex(0,1)lam6sabvev', order = {'QED':1}) GC_243 = Coupling(name = 'GC_243', value = '-(cabcphicomplex(0,1)lam4vev)/2. - (cabcphicomplex(0,1)lam5vev)/2. + cphicomplex(0,1)lam6sabvev + (cabcomplex(0,1)muzeesphi)/cmath.sqrt(2)', order = {'QED':1}) GC_244 = Coupling(name = 'GC_244', value = '-(cabcomplex(0,1)lam3vev) - cabcomplex(0,1)lam4vev + cabcomplex(0,1)lam5vev - complex(0,1)lam7sabvev', order = {'QED':1}) GC_245 = Coupling(name = 'GC_245', value = '-3cab*3complex(0,1)lam7vev + 3cab2complex(0,1)lam3sabvev + 3cab*2complex(0,1)lam4sabvev + 3cab*2complex(0,1)lam5sabvev - 9cabcomplex(0,1)lam6sab2vev + 3complex(0,1)lam1sab3vev', order = {'QED':1}) GC_246 = Coupling(name = 'GC_246', value = '-3cab3complex(0,1)lam6vev + 3cab2complex(0,1)lam1sabvev - 2cab*2complex(0,1)lam3sabvev - 2cab*2complex(0,1)lam4sabvev - 2cab*2complex(0,1)lam5sabvev + 6cabcomplex(0,1)lam6sab2vev - 3cabcomplex(0,1)lam7sab*2vev + complex(0,1)lam3sab*3vev + complex(0,1)lam4sab*3vev + complex(0,1)lam5sab*3vev', order = {'QED':1}) GC_247 = Coupling(name = 'GC_247', value = '-(cab*3complex(0,1)lam3vev) - cab*3complex(0,1)lam4vev - cab*3complex(0,1)lam5vev + 6cab2complex(0,1)lam6sabvev - 3cab*2complex(0,1)lam7sabvev - 3cabcomplex(0,1)lam1sab2vev + 2cabcomplex(0,1)lam3sab*2vev + 2cabcomplex(0,1)lam4sab*2vev + 2cabcomplex(0,1)lam5sab*2vev - 3complex(0,1)lam6sab3vev', order = {'QED':1}) GC_248 = Coupling(name = 'GC_248', value = '-3cab3complex(0,1)lam1vev - 9cab2complex(0,1)lam6sabvev - 3cabcomplex(0,1)lam3sab2vev - 3cabcomplex(0,1)lam4sab*2vev - 3cabcomplex(0,1)lam5sab*2vev - 3complex(0,1)lam7sab3vev', order = {'QED':1}) GC_249 = Coupling(name = 'GC_249', value = '(lam4sphivev)/2. - (lam5sphivev)/2. + (cphimuzee)/cmath.sqrt(2)', order = {'QED':1}) GC_250 = Coupling(name = 'GC_250', value = '-(lam4sphivev)/2. + (lam5sphivev)/2. - (cphimuzee)/cmath.sqrt(2)', order = {'QED':1}) GC_251 = Coupling(name = 'GC_251', value = '-(cabcomplex(0,1)lam6sphivev) - (complex(0,1)lam4sabsphivev)/2. - (complex(0,1)lam5sabsphivev)/2. - (cphicomplex(0,1)muzeesab)/cmath.sqrt(2)', order = {'QED':1}) GC_252 = Coupling(name = 'GC_252', value = '-(cabcomplex(0,1)lam4sphivev)/2. - (cabcomplex(0,1)lam5sphivev)/2. + complex(0,1)lam6sabsphivev - (cabcphicomplex(0,1)muzee)/cmath.sqrt(2)', order = {'QED':1}) GC_253 = Coupling(name = 'GC_253', value = '-(cabcphicomplex(0,1)lam3sphivev) + cabcphicomplex(0,1)lam8sphivev + cphicomplex(0,1)lam10sabsphivev - cphicomplex(0,1)lam7sabsphivev + (cabcphi2complex(0,1)muzee)/cmath.sqrt(2) - (cabcomplex(0,1)muzeesphi*2)/cmath.sqrt(2)', order = {'QED':1}) GC_254 = Coupling(name = 'GC_254', value = 'cabcphicomplex(0,1)lam10sphivev - cabcphicomplex(0,1)lam7sphivev + cphicomplex(0,1)lam3sabsphivev - cphicomplex(0,1)lam8sabsphivev - (cphi2complex(0,1)muzeesab)/cmath.sqrt(2) + (complex(0,1)muzeesabsphi2)/cmath.sqrt(2)', order = {'QED':1}) GC_255 = Coupling(name = 'GC_255', value = '-(cabcphi*2complex(0,1)lam3vev) - cphi*2complex(0,1)lam7sabvev - cabcomplex(0,1)lam8sphi*2vev - complex(0,1)lam10sabsphi2vev - cabcphicomplex(0,1)muzeesphicmath.sqrt(2)', order = {'QED':1}) GC_256 = Coupling(name = 'GC_256', value = '-(cabcphi*2complex(0,1)lam10vev) + cphi*2complex(0,1)lam8sabvev - cabcomplex(0,1)lam7sphi*2vev + complex(0,1)lam3sabsphi2vev - cphicomplex(0,1)muzeesabsphicmath.sqrt(2)', order = {'QED':1}) GC_257 = Coupling(name = 'GC_257', value = '-(cabcphi*2complex(0,1)lam8vev) - cphi*2complex(0,1)lam10sabvev - cabcomplex(0,1)lam3sphi*2vev - complex(0,1)lam7sabsphi2vev + cabcphicomplex(0,1)muzeesphicmath.sqrt(2)', order = {'QED':1}) GC_258 = Coupling(name = 'GC_258', value = '-(cabcphi*2complex(0,1)lam7vev) + cphi*2complex(0,1)lam3sabvev - cabcomplex(0,1)lam10sphi*2vev + complex(0,1)lam8sabsphi2vev + cphicomplex(0,1)muzeesabsphicmath.sqrt(2)', order = {'QED':1}) GC_259 = Coupling(name = 'GC_259', value = '-(ee2vev)/(4.cw) - (cwee*2vev)/(4.sw2)', order = {'QED':1}) GC_260 = Coupling(name = 'GC_260', value = '(ee2vev)/(4.cw) - (cwee*2vev)/(4.sw2)', order = {'QED':1}) GC_261 = Coupling(name = 'GC_261', value = '-(ee2vev)/(4.cw) + (cwee*2vev)/(4.sw2)', order = {'QED':1}) GC_262 = Coupling(name = 'GC_262', value = '(ee2vev)/(4.cw) + (cwee*2vev)/(4.sw2)', order = {'QED':1}) GC_263 = Coupling(name = 'GC_263', value = 'cabee*2complex(0,1)vev + (cabcw*2ee*2complex(0,1)vev)/(2.sw*2) + (cabee*2complex(0,1)sw2vev)/(2.cw2)', order = {'QED':1}) GC_264 = Coupling(name = 'GC_264', value = '-(ee2complex(0,1)sabvev) - (cw*2ee*2complex(0,1)sabvev)/(2.sw2) - (ee2complex(0,1)sabsw*2vev)/(2.cw2)', order = {'QED':1}) GC_265 = Coupling(name = 'GC_265', value = '-(yb/cmath.sqrt(2))', order = {'QED':1}) GC_266 = Coupling(name = 'GC_266', value = '-((cabcomplex(0,1)yb)/cmath.sqrt(2))', order = {'QED':1}) GC_267 = Coupling(name = 'GC_267', value = '(complex(0,1)sabyb)/cmath.sqrt(2)', order = {'QED':1}) GC_268 = Coupling(name = 'GC_268', value = '-(yc/cmath.sqrt(2))', order = {'QED':1}) GC_269 = Coupling(name = 'GC_269', value = 'yc/cmath.sqrt(2)', order = {'QED':1}) GC_270 = Coupling(name = 'GC_270', value = '-((cabcomplex(0,1)yc)/cmath.sqrt(2))', order = {'QED':1}) GC_271 = Coupling(name = 'GC_271', value = '(complex(0,1)sabyc)/cmath.sqrt(2)', order = {'QED':1}) GC_272 = Coupling(name = 'GC_272', value = '-(ydo/cmath.sqrt(2))', order = {'QED':1}) GC_273 = Coupling(name = 'GC_273', value = '-((cabcomplex(0,1)ydo)/cmath.sqrt(2))', order = {'QED':1}) GC_274 = Coupling(name = 'GC_274', value = '(complex(0,1)sabydo)/cmath.sqrt(2)', order = {'QED':1}) GC_275 = Coupling(name = 'GC_275', value = '-(complex(0,1)ye)', order = {'QED':1}) GC_276 = Coupling(name = 'GC_276', value = '-(ye/cmath.sqrt(2))', order = {'QED':1}) GC_277 = Coupling(name = 'GC_277', value = 'ye/cmath.sqrt(2)', order = {'QED':1}) GC_278 = Coupling(name = 'GC_278', value = '-((cabcomplex(0,1)ye)/cmath.sqrt(2))', order = {'QED':1}) GC_279 = Coupling(name = 'GC_279', value = '(complex(0,1)sabye)/cmath.sqrt(2)', order = {'QED':1}) GC_280 = Coupling(name = 'GC_280', value = '-(complex(0,1)ym)', order = {'QED':1}) GC_281 = Coupling(name = 'GC_281', value = '-(ym/cmath.sqrt(2))', order = {'QED':1}) GC_282 = Coupling(name = 'GC_282', value = 'ym/cmath.sqrt(2)', order = {'QED':1}) GC_283 = Coupling(name = 'GC_283', value = '-((cabcomplex(0,1)ym)/cmath.sqrt(2))', order = {'QED':1}) GC_284 = Coupling(name = 'GC_284', value = '(complex(0,1)sabym)/cmath.sqrt(2)', order = {'QED':1}) GC_285 = Coupling(name = 'GC_285', value = '-(ys/cmath.sqrt(2))', order = {'QED':1}) GC_286 = Coupling(name = 'GC_286', value = '-((cabcomplex(0,1)ys)/cmath.sqrt(2))', order = {'QED':1}) GC_287 = Coupling(name = 'GC_287', value = '(complex(0,1)sabys)/cmath.sqrt(2)', order = {'QED':1}) GC_288 = Coupling(name = 'GC_288', value = '-(yt/cmath.sqrt(2))', order = {'QED':1}) GC_289 = Coupling(name = 'GC_289', value = 'yt/cmath.sqrt(2)', order = {'QED':1}) GC_290 = Coupling(name = 'GC_290', value = '-((cabcomplex(0,1)yt)/cmath.sqrt(2))', order = {'QED':1}) GC_291 = Coupling(name = 'GC_291', value = '(complex(0,1)sabyt)/cmath.sqrt(2)', order = {'QED':1}) GC_292 = Coupling(name = 'GC_292', value = '-(complex(0,1)ytau)', order = {'QED':1}) GC_293 = Coupling(name = 'GC_293', value = '-(ytau/cmath.sqrt(2))', order = {'QED':1}) GC_294 = Coupling(name = 'GC_294', value = 'ytau/cmath.sqrt(2)', order = {'QED':1}) GC_295 = Coupling(name = 'GC_295', value = '-((cabcomplex(0,1)ytau)/cmath.sqrt(2))', order = {'QED':1}) GC_296 = Coupling(name = 'GC_296', value = '(complex(0,1)sabytau)/cmath.sqrt(2)', order = {'QED':1}) GC_297 = Coupling(name = 'GC_297', value = '-(yup/cmath.sqrt(2))', order = {'QED':1}) GC_298 = Coupling(name = 'GC_298', value = 'yup/cmath.sqrt(2)', order = {'QED':1}) GC_299 = Coupling(name = 'GC_299', value = '-((cabcomplex(0,1)yup)/cmath.sqrt(2))', order = {'QED':1}) GC_300 = Coupling(name = 'GC_300', value = '(complex(0,1)sabyup)/cmath.sqrt(2)', order = {'QED':1}) GC_301 = Coupling(name = 'GC_301', value = '-(yzee/cmath.sqrt(2))', order = {'ZEE':1}) GC_302 = Coupling(name = 'GC_302', value = 'yzee/cmath.sqrt(2)', order = {'ZEE':1}) GC_303 = Coupling(name = 'GC_303', value = '-((cabcomplex(0,1)yzee)/cmath.sqrt(2))', order = {'ZEE':1}) GC_304 = Coupling(name = 'GC_304', value = '-(cphicomplex(0,1)yzee)', order = {'ZEE':1}) GC_305 = Coupling(name = 'GC_305', value = '-((complex(0,1)sabyzee)/cmath.sqrt(2))', order = {'ZEE':1}) GC_306 = Coupling(name = 'GC_306', value = '-(complex(0,1)sphiyzee)', order = {'ZEE':1}) GC_307 = Coupling(name = 'GC_307', value = '-(yzem/cmath.sqrt(2))', order = {'ZEE':1}) GC_308 = Coupling(name = 'GC_308', value = 'yzem/cmath.sqrt(2)', order = {'ZEE':1}) GC_309 = Coupling(name = 'GC_309', value = '-((cabcomplex(0,1)yzem)/cmath.sqrt(2))', order = {'ZEE':1}) GC_310 = Coupling(name = 'GC_310', value = '-(cphicomplex(0,1)yzem)', order = {'ZEE':1}) GC_311 = Coupling(name = 'GC_311', value = '-((complex(0,1)sabyzem)/cmath.sqrt(2))', order = {'ZEE':1}) GC_312 = Coupling(name = 'GC_312', value = '-(complex(0,1)sphiyzem)', order = {'ZEE':1}) GC_313 = Coupling(name = 'GC_313', value = '-(yzet/cmath.sqrt(2))', order = {'ZEE':1}) GC_314 = Coupling(name = 'GC_314', value = 'yzet/cmath.sqrt(2)', order = {'ZEE':1}) GC_315 = Coupling(name = 'GC_315', value = '-((cabcomplex(0,1)yzet)/cmath.sqrt(2))', order = {'ZEE':1}) GC_316 = Coupling(name = 'GC_316', value = '-(cphicomplex(0,1)yzet)', order = {'ZEE':1}) GC_317 = Coupling(name = 'GC_317', value = '-((complex(0,1)sabyzet)/cmath.sqrt(2))', order = {'ZEE':1}) GC_318 = Coupling(name = 'GC_318', value = '-(complex(0,1)sphiyzet)', order = {'ZEE':1}) GC_319 = Coupling(name = 'GC_319', value = '-(yzme/cmath.sqrt(2))', order = {'ZEE':1}) GC_320 = Coupling(name = 'GC_320', value = 'yzme/cmath.sqrt(2)', order = {'ZEE':1}) GC_321 = Coupling(name = 'GC_321', value = '-((cabcomplex(0,1)yzme)/cmath.sqrt(2))', order = {'ZEE':1}) GC_322 = Coupling(name = 'GC_322', value = '-(cphicomplex(0,1)yzme)', order = {'ZEE':1}) GC_323 = Coupling(name = 'GC_323', value = '-((complex(0,1)sabyzme)/cmath.sqrt(2))', order = {'ZEE':1}) GC_324 = Coupling(name = 'GC_324', value = '-(complex(0,1)sphiyzme)', order = {'ZEE':1}) GC_325 = Coupling(name = 'GC_325', value = '-(yzmm/cmath.sqrt(2))', order = {'ZEE':1}) GC_326 = Coupling(name = 'GC_326', value = 'yzmm/cmath.sqrt(2)', order = {'ZEE':1}) GC_327 = Coupling(name = 'GC_327', value = '-((cabcomplex(0,1)yzmm)/cmath.sqrt(2))', order = {'ZEE':1}) GC_328 = Coupling(name = 'GC_328', value = '-(cphicomplex(0,1)yzmm)', order = {'ZEE':1}) GC_329 = Coupling(name = 'GC_329', value = '-((complex(0,1)sabyzmm)/cmath.sqrt(2))', order = {'ZEE':1}) GC_330 = Coupling(name = 'GC_330', value = '-(complex(0,1)sphiyzmm)', order = {'ZEE':1}) GC_331 = Coupling(name = 'GC_331', value = '-(yzmt/cmath.sqrt(2))', order = {'ZEE':1}) GC_332 = Coupling(name = 'GC_332', value = 'yzmt/cmath.sqrt(2)', order = {'ZEE':1}) GC_333 = Coupling(name = 'GC_333', value = '-((cabcomplex(0,1)yzmt)/cmath.sqrt(2))', order = {'ZEE':1}) GC_334 = Coupling(name = 'GC_334', value = '-(cphicomplex(0,1)yzmt)', order = {'ZEE':1}) GC_335 = Coupling(name = 'GC_335', value = '-((complex(0,1)sabyzmt)/cmath.sqrt(2))', order = {'ZEE':1}) GC_336 = Coupling(name = 'GC_336', value = '-(complex(0,1)sphiyzmt)', order = {'ZEE':1}) GC_337 = Coupling(name = 'GC_337', value = '-(yzte/cmath.sqrt(2))', order = {'ZEE':1}) GC_338 = Coupling(name = 'GC_338', value = 'yzte/cmath.sqrt(2)', order = {'ZEE':1}) GC_339 = Coupling(name = 'GC_339', value = '-((cabcomplex(0,1)yzte)/cmath.sqrt(2))', order = {'ZEE':1}) GC_340 = Coupling(name = 'GC_340', value = '-(cphicomplex(0,1)yzte)', order = {'ZEE':1}) GC_341 = Coupling(name = 'GC_341', value = '-((complex(0,1)sabyzte)/cmath.sqrt(2))', order = {'ZEE':1}) GC_342 = Coupling(name = 'GC_342', value = '-(complex(0,1)sphiyzte)', order = {'ZEE':1}) GC_343 = Coupling(name = 'GC_343', value = '-(yztm/cmath.sqrt(2))', order = {'ZEE':1}) GC_344 = Coupling(name = 'GC_344', value = 'yztm/cmath.sqrt(2)', order = {'ZEE':1}) GC_345 = Coupling(name = 'GC_345', value = '-((cabcomplex(0,1)yztm)/cmath.sqrt(2))', order = {'ZEE':1}) GC_346 = Coupling(name = 'GC_346', value = '-(cphicomplex(0,1)yztm)', order = {'ZEE':1}) GC_347 = Coupling(name = 'GC_347', value = '-((complex(0,1)sabyztm)/cmath.sqrt(2))', order = {'ZEE':1}) GC_348 = Coupling(name = 'GC_348', value = '-(complex(0,1)sphiyztm)', order = {'ZEE':1}) GC_349 = Coupling(name = 'GC_349', value = '-(yztt/cmath.sqrt(2))', order = {'ZEE':1}) GC_350 = Coupling(name = 'GC_350', value = 'yztt/cmath.sqrt(2)', order = {'ZEE':1}) GC_351 = Coupling(name = 'GC_351', value = '-((cabcomplex(0,1)yztt)/cmath.sqrt(2))', order = {'ZEE':1}) GC_352 = Coupling(name = 'GC_352', value = '-(cphicomplex(0,1)yztt)', order = {'ZEE':1}) GC_353 = Coupling(name = 'GC_353', value = '-((complex(0,1)sabyztt)/cmath.sqrt(2))', order = {'ZEE':1}) GC_354 = Coupling(name = 'GC_354', value = '-(complex(0,1)sphiyztt)', order = {'ZEE':1}) GC_355 = Coupling(name = 'GC_355', value = '(eecomplex(0,1)complexconjugate(CKM1x1))/(swcmath.sqrt(2))', order = {'QED':1}) GC_356 = Coupling(name = 'GC_356', value = '(eecomplex(0,1)complexconjugate(CKM1x2))/(swcmath.sqrt(2))', order = {'QED':1}) GC_357 = Coupling(name = 'GC_357', value = '(eecomplex(0,1)complexconjugate(CKM1x3))/(swcmath.sqrt(2))', order = {'QED':1}) GC_358 = Coupling(name = 'GC_358', value = '(eecomplex(0,1)complexconjugate(CKM2x1))/(swcmath.sqrt(2))', order = {'QED':1}) GC_359 = Coupling(name = 'GC_359', value = '(eecomplex(0,1)complexconjugate(CKM2x2))/(swcmath.sqrt(2))', order = {'QED':1}) GC_360 = Coupling(name = 'GC_360', value = '(eecomplex(0,1)complexconjugate(CKM2x3))/(swcmath.sqrt(2))', order = {'QED':1}) GC_361 = Coupling(name = 'GC_361', value = '(eecomplex(0,1)complexconjugate(CKM3x1))/(swcmath.sqrt(2))', order = {'QED':1}) GC_362 = Coupling(name = 'GC_362', value = '(eecomplex(0,1)complexconjugate(CKM3x2))/(swcmath.sqrt(2))', order = {'QED':1}) GC_363 = Coupling(name = 'GC_363', value = '(eecomplex(0,1)complexconjugate(CKM3x3))/(sw*cmath.sqrt(2))', order = {'QED':1})	nilq/baby-python	python
import AnimatedProp from direct.actor import Actor from direct.interval.IntervalGlobal import * class HQPeriscopeAnimatedProp(AnimatedProp.AnimatedProp): def __init__(self, node): AnimatedProp.AnimatedProp.__init__(self, node) parent = node.getParent() self.periscope = Actor.Actor(node, copy=0) self.periscope.reparentTo(parent) self.periscope.loadAnims({'anim': 'phase_3.5/models/props/HQ_periscope-chan'}) self.periscope.pose('anim', 0) self.node = self.periscope self.track = Sequence(Wait(2.0), self.periscope.actorInterval('anim', startFrame=0, endFrame=40), Wait(0.7), self.periscope.actorInterval('anim', startFrame=40, endFrame=90), Wait(0.7), self.periscope.actorInterval('anim', startFrame=91, endFrame=121), Wait(0.7), self.periscope.actorInterval('anim', startFrame=121, endFrame=91), Wait(0.7), self.periscope.actorInterval('anim', startFrame=90, endFrame=40), Wait(0.7), self.periscope.actorInterval('anim', startFrame=40, endFrame=90), Wait(0.7), self.periscope.actorInterval('anim', startFrame=91, endFrame=121), Wait(0.5), self.periscope.actorInterval('anim', startFrame=121, endFrame=148), Wait(3.0), name=self.uniqueName('HQPeriscope')) def delete(self): AnimatedProp.AnimatedProp.delete(self) self.node.cleanup() del self.node del self.periscope del self.track def enter(self): AnimatedProp.AnimatedProp.enter(self) self.track.loop() def exit(self): AnimatedProp.AnimatedProp.exit(self) self.track.finish()	nilq/baby-python	python
from threading import Lock, Thread from time import sleep class Ingresso: def __init__(self, estoque): self.estoque = estoque self.lock = Lock() def comprar(self, quantidade): self.lock.acquire() if self.estoque < quantidade: print("-Não temos ingresso suficientes.") self.lock.release() return sleep(1) self.estoque -= quantidade print( f"-Você comprou {quantidade} de ingresso(s), restando {self.estoque} no estoque." ) self.lock.release() if __name__ == "__main__": ingresso = Ingresso(10) for i in range(1, 20): t = Thread(target=ingresso.comprar, args=(i,)) t.start()	nilq/baby-python	python
from functools import partial, wraps from slm_lab import ROOT_DIR from slm_lab.lib import logger, util import os import pydash as ps import torch import torch.nn as nn logger = logger.get_logger(__name__) class NoOpLRScheduler: '''Symbolic LRScheduler class for API consistency''' def __init__(self, optim): self.optim = optim def step(self, epoch=None): pass def get_lr(self): return self.optim.defaults['lr'] def build_fc_model(dims, activation=None): '''Build a full-connected model by interleaving nn.Linear and activation_fn''' assert len(dims) >= 2, 'dims need to at least contain input, output' # shift dims and make pairs of (in, out) dims per layer dim_pairs = list(zip(dims[:-1], dims[1:])) layers = [] for in_d, out_d in dim_pairs: layers.append(nn.Linear(in_d, out_d)) if activation is not None: layers.append(get_activation_fn(activation)) model = nn.Sequential(layers) return model def get_nn_name(uncased_name): '''Helper to get the proper name in PyTorch nn given a case-insensitive name''' for nn_name in nn.__dict__: if uncased_name.lower() == nn_name.lower(): return nn_name raise ValueError(f'Name {uncased_name} not found in {nn.__dict__}') def get_activation_fn(activation): '''Helper to generate activation function layers for net''' activation = activation or 'relu' ActivationClass = getattr(nn, get_nn_name(activation)) return ActivationClass() def get_loss_fn(cls, loss_spec): '''Helper to parse loss param and construct loss_fn for net''' LossClass = getattr(nn, get_nn_name(loss_spec['name'])) loss_spec = ps.omit(loss_spec, 'name') loss_fn = LossClass(loss_spec) return loss_fn def get_lr_scheduler(cls, lr_scheduler_spec): '''Helper to parse lr_scheduler param and construct Pytorch optim.lr_scheduler''' if ps.is_empty(lr_scheduler_spec): lr_scheduler = NoOpLRScheduler(cls.optim) elif lr_scheduler_spec['name'] == 'LinearToZero': LRSchedulerClass = getattr(torch.optim.lr_scheduler, 'LambdaLR') total_t = float(lr_scheduler_spec['total_t']) lr_scheduler = LRSchedulerClass(cls.optim, lr_lambda=lambda x: 1 - x / total_t) else: LRSchedulerClass = getattr(torch.optim.lr_scheduler, lr_scheduler_spec['name']) lr_scheduler_spec = ps.omit(lr_scheduler_spec, 'name') lr_scheduler = LRSchedulerClass(cls.optim, lr_scheduler_spec) return lr_scheduler def get_optim(cls, optim_spec): '''Helper to parse optim param and construct optim for net''' OptimClass = getattr(torch.optim, optim_spec['name']) optim_spec = ps.omit(optim_spec, 'name') optim = OptimClass(cls.parameters(), optim_spec) return optim def get_policy_out_dim(body): '''Helper method to construct the policy network out_dim for a body according to is_discrete, action_type''' action_dim = body.action_dim if body.is_discrete: if body.action_type == 'multi_discrete': assert ps.is_list(action_dim), action_dim policy_out_dim = action_dim else: assert ps.is_integer(action_dim), action_dim policy_out_dim = action_dim else: if body.action_type == 'multi_continuous': assert ps.is_list(action_dim), action_dim raise NotImplementedError('multi_continuous not supported yet') else: assert ps.is_integer(action_dim), action_dim if action_dim == 1: policy_out_dim = 2 # singleton stay as int else: # TODO change this to one slicable layer for efficiency policy_out_dim = action_dim [2] return policy_out_dim def get_out_dim(body, add_critic=False): '''Construct the NetClass out_dim for a body according to is_discrete, action_type, and whether to add a critic unit''' policy_out_dim = get_policy_out_dim(body) if add_critic: if ps.is_list(policy_out_dim): out_dim = policy_out_dim + [1] else: out_dim = [policy_out_dim, 1] else: out_dim = policy_out_dim return out_dim def init_layers(net, init_fn): if init_fn is None: return nonlinearity = get_nn_name(net.hid_layers_activation).lower() if nonlinearity == 'leakyrelu': nonlinearity = 'leaky_relu' if init_fn == 'xavier_uniform_': try: gain = nn.init.calculate_gain(nonlinearity) except ValueError: gain = 1 init_fn = partial(nn.init.xavier_uniform_, gain=gain) elif 'kaiming' in init_fn: assert nonlinearity in ['relu', 'leaky_relu'], f'Kaiming initialization not supported for {nonlinearity}' init_fn = nn.init.__dict__[init_fn] init_fn = partial(init_fn, nonlinearity=nonlinearity) else: init_fn = nn.init.__dict__[init_fn] net.apply(partial(init_parameters, init_fn=init_fn)) def init_parameters(module, init_fn): ''' Initializes module's weights using init_fn, which is the name of function from from nn.init Initializes module's biases to either 0.01 or 0.0, depending on module The only exception is BatchNorm layers, for which we use uniform initialization ''' bias_init = 0.0 classname = util.get_class_name(module) if 'BatchNorm' in classname: init_fn(module.weight) nn.init.constant_(module.bias, bias_init) elif 'GRU' in classname: for name, param in module.named_parameters(): if 'weight' in name: init_fn(param) elif 'bias' in name: nn.init.constant_(param, 0.0) elif 'Linear' in classname or ('Conv' in classname and 'Net' not in classname): init_fn(module.weight) nn.init.constant_(module.bias, bias_init) # params methods def save(net, model_path): '''Save model weights to path''' torch.save(net.state_dict(), util.smart_path(model_path)) logger.info(f'Saved model to {model_path}') def save_algorithm(algorithm, ckpt=None): '''Save all the nets for an algorithm''' agent = algorithm.agent net_names = algorithm.net_names prepath = util.get_prepath(agent.spec, agent.info_space, unit='session') if ckpt is not None: prepath = f'{prepath}_ckpt-{ckpt}' logger.info(f'Saving algorithm {util.get_class_name(algorithm)} nets {net_names}') for net_name in net_names: net = getattr(algorithm, net_name) model_path = f'{prepath}_{net_name}_model.pth' save(net, model_path) optim_path = f'{prepath}_{net_name}_optim.pth' save(net.optim, optim_path) def load(net, model_path): '''Save model weights from a path into a net module''' device = None if torch.cuda.is_available() else 'cpu' net.load_state_dict(torch.load(util.smart_path(model_path), map_location=device)) logger.info(f'Loaded model from {model_path}') def load_algorithm(algorithm): '''Save all the nets for an algorithm''' agent = algorithm.agent net_names = algorithm.net_names if util.in_eval_lab_modes(): # load specific model in eval mode prepath = agent.info_space.eval_model_prepath else: prepath = util.get_prepath(agent.spec, agent.info_space, unit='session') logger.info(f'Loading algorithm {util.get_class_name(algorithm)} nets {net_names}') for net_name in net_names: net = getattr(algorithm, net_name) model_path = f'{prepath}_{net_name}_model.pth' load(net, model_path) optim_path = f'{prepath}_{net_name}_optim.pth' load(net.optim, optim_path) def copy(src_net, tar_net): '''Copy model weights from src to target''' tar_net.load_state_dict(src_net.state_dict()) def polyak_update(src_net, tar_net, old_ratio=0.5): ''' Polyak weight update to update a target tar_net, retain old weights by its ratio, i.e. target <- old_ratio * source + (1 - old_ratio) * target ''' for src_param, tar_param in zip(src_net.parameters(), tar_net.parameters()): tar_param.data.copy_(old_ratio * src_param.data + (1.0 - old_ratio) * tar_param.data) def to_check_training_step(): '''Condition for running assert_trained''' return os.environ.get('PY_ENV') == 'test' or util.get_lab_mode() == 'dev' def dev_check_training_step(fn): ''' Decorator to check if net.training_step actually updates the network weights properly Triggers only if to_check_training_step is True (dev/test mode) @example @net_util.dev_check_training_step def training_step(self, ...): ... ''' @wraps(fn) def check_fn(args, kwargs): if not to_check_training_step(): return fn(args, *kwargs) net = args[0] # first arg self # get pre-update parameters to compare pre_params = [param.clone() for param in net.parameters()] # run training_step, get loss loss = fn(args, **kwargs) # get post-update parameters to compare post_params = [param.clone() for param in net.parameters()] if loss == 0.0: # if loss is 0, there should be no updates # TODO if without momentum, parameters should not change too for p_name, param in net.named_parameters(): assert param.grad.norm() == 0 else: # check parameter updates try: assert not all(torch.equal(w1, w2) for w1, w2 in zip(pre_params, post_params)), f'Model parameter is not updated in training_step(), check if your tensor is detached from graph. Loss: {loss:g}' logger.info(f'Model parameter is updated in training_step(). Loss: {loss: g}') except Exception as e: logger.error(e) if os.environ.get('PY_ENV') == 'test': # raise error if in unit test raise(e) # check grad norms min_norm, max_norm = 0.0, 1e5 for p_name, param in net.named_parameters(): try: grad_norm = param.grad.norm() assert min_norm < grad_norm < max_norm, f'Gradient norm for {p_name} is {grad_norm:g}, fails the extreme value check {min_norm} < grad_norm < {max_norm}. Loss: {loss:g}. Check your network and loss computation.' logger.info(f'Gradient norm for {p_name} is {grad_norm:g}; passes value check.') except Exception as e: logger.warn(e) logger.debug('Passed network parameter update check.') # store grad norms for debugging net.store_grad_norms() return loss return check_fn def get_grad_norms(algorithm): '''Gather all the net's grad norms of an algorithm for debugging''' grad_norms = [] for net_name in algorithm.net_names: net = getattr(algorithm, net_name) if net.grad_norms is not None: grad_norms.extend(net.grad_norms) return grad_norms	nilq/baby-python	python
""" Minimize the Himmelblau function. http://en.wikipedia.org/wiki/Himmelblau%27s_function """ import numpy import minhelper def himmelblau(X): """ This R^2 -> R^1 function should be compatible with algopy. http://en.wikipedia.org/wiki/Himmelblau%27s_function This function has four local minima where the value of the function is 0. """ x = X[0] y = X[1] a = xx + y - 11 b = x + yy - 7 return aa + bb def main(): target = [3, 2] easy_init = [3.1, 2.1] hard_init = [-0.27, -0.9] minhelper.show_minimization_results( himmelblau, target, easy_init, hard_init) if __name__ == '__main__': main()	nilq/baby-python	python
import os import tempfile from unittest import TestCase from pubmed_bpe_tokeniser import PubmedBPETokenisor class TestPubmedBPETokenisor(TestCase): def test_train(self): # Arrange data_file = os.path.join(os.path.dirname(__file__), "data", "sample_pubmed.json") sut = PubmedBPETokenisor(vocab_size=300) tempdir = tempfile.mkdtemp() output_file_json = os.path.join(tempdir, "vocab.json") # Act sut.train([data_file], output_file_json) # Assert self.assertTrue(os.path.getsize(output_file_json) > 100, "Expected the vocab file size {} to be greater than 100")	nilq/baby-python	python
# # -- coding: utf-8 -- # # Copyright (c) 2020 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import os from common.base_model_init import BaseModelInitializer, set_env_var class ModelInitializer(BaseModelInitializer): # SSD-MobileNet BFloat16 inference model initialization args = None custom_args = [] def __init__(self, args, custom_args=[], platform_util=None): super(ModelInitializer, self).__init__(args, custom_args, platform_util) # Set the num_inter_threads and num_intra_threads # if user did not provide then default value based on platform will be set self.set_num_inter_intra_threads(self.args.num_inter_threads, self.args.num_intra_threads) # Set KMP env vars, if they haven't already been set config_file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "config.json") self.set_kmp_vars(config_file_path) benchmark_script = os.path.join(self.args.intelai_models, self.args.mode, "infer_detections.py") self.command_prefix = self.get_command_prefix(self.args.socket_id) \ + "{} {}".format(self.python_exe, benchmark_script) set_env_var("OMP_NUM_THREADS", self.args.num_intra_threads) self.command_prefix += " -g {0}".format(self.args.input_graph) self.command_prefix += " -i 1000" self.command_prefix += " -w 200" self.command_prefix += " -a {0}".format(self.args.num_intra_threads) self.command_prefix += " -e {0}".format(self.args.num_inter_threads) self.command_prefix += " -p {0}".format(self.args.precision) if self.args.data_location: self.command_prefix += " -d {0}".format(self.args.data_location) if self.args.accuracy_only: self.command_prefix += " -r" assert self.args.data_location, "accuracy must provide the data." else: # Did not support multi-batch accuracy check. self.command_prefix += " -b {0}".format(self.args.batch_size) def run(self): # Run script from the tensorflow models research directory self.run_command(self.command_prefix)	nilq/baby-python	python
import os from ament_index_python.packages import get_package_share_directory from launch import LaunchDescription from launch.actions import IncludeLaunchDescription from launch.launch_description_sources import PythonLaunchDescriptionSource def generate_launch_description(): # # launch 海龟节点<正常版> # turtlesim_world_1 = IncludeLaunchDescription( # PythonLaunchDescriptionSource([os.path.join( # get_package_share_directory('launch_tutorial'), 'launch'), # '/turtlesim_world_1.launch.py']) # ) # # launch 海龟节点 <YAML> # turtlesim_world_2 = IncludeLaunchDescription( # PythonLaunchDescriptionSource([os.path.join( # get_package_share_directory('launch_tutorial'), 'launch'), # '/turtlesim_world_2.launch.py']) # ) # launch 海龟节点 <YAML> 使用wildcards 通配符 /**: turtlesim_world_3 = IncludeLaunchDescription( PythonLaunchDescriptionSource([os.path.join( get_package_share_directory('launch_tutorial'), 'launch'), '/turtlesim_world_3.launch.py']) ) # broadcaster_listener_nodes = IncludeLaunchDescription( # PythonLaunchDescriptionSource([os.path.join( # get_package_share_directory('launch_tutorial'), 'launch'), # '/broadcaster_listener.launch.py']), # launch_arguments={'target_frame': 'carrot1'}.items(), # ) # mimic_node = IncludeLaunchDescription( # PythonLaunchDescriptionSource([os.path.join( # get_package_share_directory('launch_tutorial'), 'launch'), # '/mimic.launch.py']) # ) # fixed_frame_node = IncludeLaunchDescription( # PythonLaunchDescriptionSource([os.path.join( # get_package_share_directory('launch_tutorial'), 'launch'), # '/fixed_broadcaster.launch.py']) # ) # rviz_node = IncludeLaunchDescription( # PythonLaunchDescriptionSource([os.path.join( # get_package_share_directory('launch_tutorial'), 'launch'), # '/turtlesim_rviz.launch.py']) # ) return LaunchDescription([ # turtlesim_world_1, # turtlesim_world_2, turtlesim_world_3, # broadcaster_listener_nodes, # mimic_node, # fixed_frame_node, # rviz_node ])	nilq/baby-python	python
from setuptools import setup,find_packages import lixtools setup( name='lixtools', description="""software tools for data collection/processing at LiX""", version=lixtools.__version__, author='Lin Yang', author_email='lyang@bnl.gov', license="BSD-3-Clause", url="https://github.com/NSLS-II-LIX/lixtools", packages=find_packages(), package_data={'': ['plate_label_template.html', 'template_report.ipynb']}, include_package_data=True, install_requires=['py4xs', 'numpy', 'pandas', 'python-barcode', 'matplotlib', 'pillow', 'openpyxl>=3', 'qrcode'], python_requires='>=3.6', classifiers=[ "Intended Audience :: Science/Research", "Topic :: Scientific/Engineering :: Information Analysis", "Development Status :: 3 - Alpha", "Programming Language :: Python :: 3.6", ], keywords='x-ray scattering', )	nilq/baby-python	python
''' * 'show system status' ''' import re from genie.metaparser import MetaParser from genie.metaparser.util.schemaengine import Optional # =========================================== # Schema for 'show system status' # =========================================== class ShowSystemStatusSchema(MetaParser): """ Schema for "show system status" """ schema = { 'boot_loader_version': str, 'build': str, 'chassis_serial_number': str, 'commit_pending': str, 'configuration_template': str, Optional('engineering_signed'): bool, Optional('controller_compatibility'): str, Optional('cpu_allocation'): { Optional('total'): int, Optional('control'): int, Optional('data'): int }, 'cpu_reported_reboot': str, 'cpu_states': { 'idle': float, 'system': float, 'user': float }, 'current_time': str, 'disk_usage': { 'avail_mega': int, 'filesystem': str, 'mounted_on': str, 'size_mega': int, 'use_pc': int, 'used_mega': int }, Optional('vmanage_storage_usage'): { Optional('filesystem'): str, Optional('size_mega'): int, Optional('used_mega'): int, Optional('avail_mega'): int, Optional('use_pc'): int, Optional('mounted_on'): str }, 'last_reboot': str, Optional('load_average'): { Optional('minute_1'): float, Optional('minute_15'): float, Optional('minute_5'): float }, 'memory_usage': { 'buffers_kilo': int, 'cache_kilo': int, 'free_kilo': int, 'total_kilo': int, 'used_kilo': int }, Optional('hypervisor_type'):str, Optional('cloud_hosted_instance'):str, 'model_name': str, 'personality': str, 'processes': int, 'services': str, 'system_fips_state': str, 'system_logging_disk': str, 'system_logging_host': str, 'system_state': str, 'system_uptime': str, Optional('device_role'): str, Optional('testbed_mode'): str, 'version': str, 'vmanaged': str } # =========================================== # Parser for 'show system status' # =========================================== class ShowSystemStatus(ShowSystemStatusSchema): """ Parser for "show system status" """ cli_command = "show system status" def cli(self, output=None): if output is None: output = self.device.execute(self.cli_command) parsed_dict = {} # System logging to host is disabled # System logging to disk is enabled p1 = re.compile(r'^System\s+logging\s+to\s+(?P<type>\w+)\s+is\s+(?P<value>enabled\|disabled)$') # CPU allocation: 4 total, 1 control, 3 data # CPU allocation: 16 total p2 = re.compile(r'^CPU\s+allocation:\s+(?P<total>\d+)\s+total(,\s+(?P<control>\d+)\s+control)?(,\s+(?P<data>\d+)\s+data)?$') # CPU states: 1.25% user, 5.26% system, 93.48% idle p3 = re.compile(r'^CPU\s+states:\s+(?P<user>[\d\.]+)\%\s+user,\s+(?P<system>[\d\.]+)\%\s+system,\s+(?P<idle>[\d\.]+)\%\s+idle$') # Load average: 1 minute: 3.20, 5 minutes: 3.13, 15 minutes: 3.10 p4 = re.compile(r'^Load\s+average:\s+1\s+minute:\s+(?P<minute_1>[\d\.]+),\s+5\s+minutes:\s+(?P<minute_5>[\d\.]+),\s+15\s+minutes:\s+(?P<minute_15>[\d\.]+)$') # Engineering Signed True p5 = re.compile(r'^Engineering +Signed +(?P<value>True\|False)$') # Memory usage: 1907024K total, 1462908K used, 444116K free p6 = re.compile(r'^Memory\s+usage:\s+(?P<total_kilo>\d+)K\s+total,\s+(?P<used_kilo>\d+)K\s+used,\s+(?P<free_kilo>\d+)K\s+free$') # 0K buffers, 0K cache p7 = re.compile(r'^(?P<buffers_kilo>\d+)K\s+buffers,\s+(?P<cache_kilo>\d+)K\s+cache$') # Disk usage: Filesystem Size Used Avail Use % Mounted on # vManage storage usage: Filesystem Size Used Avail Use% Mounted on p8 = re.compile(r'^(?P<usage_dict>.+usage):\s+Filesystem\s+Size\s+Used\s+Avail\s+Use\s%\s+Mounted\s+on$') # /dev/root 7615M 447M 6741M 6% / # /dev/disk/by-label/fs-bootflash 11039M 1240M 9219M 12% /bootflash # /dev/bootflash1 28748M 2031M 25257M 7% /bootflash p9 = re.compile(r'^(?P<filesystem>.+\S)\s+(?P<size_mega>\d+)M\s+(?P<used_mega>\d+)M\s+(?P<avail_mega>\d+)M\s+(?P<use_pc>\d+)\%\s+(?P<mounted_on>.+)$') # Controller Compatibility: 20.3 # Version: 99.99.999-4567 # Build: 4567 # System state: GREEN. All daemons up # System FIPS state: Enabled # Testbed mode: Enabled # Hypervisor Type: None # Cloud Hosted Instance: false # Last reboot: Initiated by user - activate 99.99.999-4567. # CPU-reported reboot: Not Applicable # Boot loader version: Not applicable # System uptime: 0 days 21 hrs 35 min 28 sec # Current time: Thu Aug 06 02:49:25 PDT 2020 # Processes: 250 total # Personality: vedge # Model name: vedge-cloud # Services: None # vManaged: true # Commit pending: false # Configuration template: CLItemplate_srp_vedge # Chassis serial number: None p10 = re.compile(r'^(?P<key>.):\s+(?P<value>.*)$') for line in output.splitlines(): line = line.strip() # System logging to host is disabled # System logging to disk is enabled m1 = p1.match(line) if m1: group = m1.groupdict() parsed_dict['system_logging_' + group['type']] = group['value'] continue # CPU allocation: 4 total, 1 control, 3 data # CPU allocation: 16 total m2 = p2.match(line) if m2: group = m2.groupdict() group = {key: int(group[key]) for key in group if group[key]} parsed_dict.update({'cpu_allocation': group}) continue # CPU states: 1.25% user, 5.26% system, 93.48% idle m3 = p3.match(line) if m3: group = m3.groupdict() for keys in group: group[keys] = float(group[keys]) parsed_dict.update({'cpu_states': group}) continue # Load average: 1 minute: 3.20, 5 minutes: 3.13, 15 minutes: 3.10 m4 = p4.match(line) if m4: group = m4.groupdict() for keys in group: group[keys] = float(group[keys]) parsed_dict.update({'load_average': group}) continue # Engineering Signed True m5 = p5.match(line) if m5: group = m5.groupdict() group = bool(group['value']) parsed_dict.update({'engineering_signed': group}) continue # Memory usage: 1907024K total, 1462908K used, 444116K free m6 = p6.match(line) if m6: group = m6.groupdict() parsed_dict.update({'memory_usage': { key:int(group[key]) for key in group }}) continue # 0K buffers, 0K cache m7 = p7.match(line) if m7: group = m7.groupdict() parsed_dict['memory_usage'].update({ key:int(group[key]) for key in group }) continue # Disk usage: Filesystem Size Used Avail Use % Mounted on # vManage storage usage: Filesystem Size Used Avail Use% Mounted on m8 = p8.match(line) if m8: group = m8.groupdict() usage_dict_name = group['usage_dict'].replace(' ', '_').lower() usage_dict = parsed_dict.setdefault(usage_dict_name, {}) continue # /dev/sda 503966M 6162M 472203M 1% /opt/data # /dev/bootflash1 28748M 2031M 25257M 7% /bootflash m9 = p9.match(line) if m9: group = m9.groupdict() usage_dict.update({'filesystem': group.pop('filesystem')}) usage_dict.update({'mounted_on': group.pop('mounted_on')}) usage_dict.update({ key: int(group[key]) for key in group }) continue # Controller Compatibility: 20.3 # Version: 99.99.999-4567 # Build: 4567 # System state: GREEN. All daemons up # System FIPS state: Enabled # Testbed mode: Enabled # Hypervisor Type: None # Cloud Hosted Instance: false # Last reboot: Initiated by user - activate 99.99.999-4567. # CPU-reported reboot: Not Applicable # Boot loader version: Not applicable # System uptime: 0 days 21 hrs 35 min 28 sec # Current time: Thu Aug 06 02:49:25 PDT 2020 # Processes: 250 total # Personality: vedge # Model name: vedge-cloud # Services: None # vManaged: true # Commit pending: false # Configuration template: CLItemplate_srp_vedge # Chassis serial number: None m10 = p10.match(line) if m10: group = m10.groupdict() key = group['key'].replace('-', '_').replace(' ','_').replace(':','').lower() if key == 'processes': group['value'] = int(group['value'].replace('total','')) parsed_dict.update({key: (group['value'])}) continue return parsed_dict	nilq/baby-python	python
from django.conf.urls import patterns, include, url from tastypie.api import Api from tastypie_evostream.api import StreamResource tastypie_evostream_api = Api() tastypie_evostream_api.register(StreamResource()) urlpatterns = patterns( '', url(r'', include(tastypie_evostream_api.urls)), )	nilq/baby-python	python
# -- coding: UTF-8 -- from django.db import models from django.contrib.contenttypes import fields from django.contrib.contenttypes.models import ContentType # from south.modelsinspector import add_introspection_rules # from tagging.models import Tag # from tagging_autocomplete.models import TagAutocompleteField from taggit_autosuggest.managers import TaggableManager from django.contrib.auth.models import User #from contrapartes.models import Usuarios # from thumbs import ImageWithThumbsField from sorl.thumbnail import ImageField from utils import * import datetime # from south.modelsinspector import add_introspection_rules from ckeditor_uploader.fields import RichTextUploadingField # add_introspection_rules ([], ["^ckeditor\.fields\.RichTextField"]) # add_introspection_rules ([], ["^tagging_autocomplete\.models\.TagAutocompleteField"]) # Create your models here. class Imagen(models.Model): ''' Modelo generico para subir imagenes en todos los demas app :)''' content_type = models.ForeignKey(ContentType,on_delete=models.DO_NOTHING) object_id = models.IntegerField(db_index=True) content_object = fields.GenericForeignKey('content_type', 'object_id') nombre_img = models.CharField("Nombre",max_length=200, null=True, blank=True) foto = ImageField("Foto",upload_to=get_file_path,null=True, blank=True) tags_img = TaggableManager("Tags",help_text='Separar elementos con "," ', blank=True) fileDir = 'fotos/' class Meta: verbose_name_plural = "Imágenes" def __str__(self): return self.nombre_img class Documentos(models.Model): ''' Modelo generico para subir los documentos en distintos app''' content_type = models.ForeignKey(ContentType,on_delete=models.DO_NOTHING) object_id = models.IntegerField(db_index=True) content_object = fields.GenericForeignKey('content_type', 'object_id') nombre_doc = models.CharField("Nombre",max_length=200, null=True, blank=True) adjunto = models.FileField("Adjunto",upload_to=get_file_path, null=True, blank=True) tags_doc = TaggableManager("Tags",help_text='Separar elementos con "," ', blank=True) fileDir = 'documentos/' class Meta: verbose_name_plural = "Documentos" def __str__(self): return self.nombre_doc class Videos(models.Model): ''' Modelo generico para subir videos en todos los app''' content_type = models.ForeignKey(ContentType,on_delete=models.DO_NOTHING) object_id = models.IntegerField(db_index=True) content_object = fields.GenericForeignKey('content_type', 'object_id') nombre_video = models.CharField(max_length=200, null=True, blank=True) url = models.URLField(null=True, blank=True) tags_vid = TaggableManager(help_text='Separar elementos con "," ', blank=True) class Meta: verbose_name_plural = "Videos" def __str__(self): return self.nombre_video class Audios(models.Model): '''' Modelo generico para subir audios en todos los demas app ''' content_type = models.ForeignKey(ContentType,on_delete=models.DO_NOTHING) object_id = models.IntegerField(db_index=True) content_object = fields.GenericForeignKey('content_type', 'object_id') nombre_audio = models.CharField(max_length=200, null=True, blank=True) audio = models.FileField(upload_to=get_file_path, null=True, blank=True) tags_aud = TaggableManager(help_text='Separar elementos con "," ', blank=True) fileDir = 'audios/' class Meta: verbose_name_plural = "Audios" def __str__(self): return self.nombre_audio class Foros(models.Model): nombre = models.CharField(max_length=200) creacion = models.DateField(auto_now_add=True) apertura = models.DateField('Apertura y recepción de aportes') cierre = models.DateField('Cierre de aportes') fecha_skype = models.DateField('Propuesta de reunión skype') memoria = models.DateField('Propuesta entrega de memoria') contenido = RichTextUploadingField() contraparte = models.ForeignKey(User,on_delete=models.DO_NOTHING) #documentos = fields.GenericRelation(Documentos) #fotos = fields.GenericRelation(Imagen) #video = fields.GenericRelation(Videos) #audio = fields.GenericRelation(Audios) correo_enviado = models.BooleanField(editable=False) class Meta: verbose_name_plural = "Foros" ordering = ['-creacion'] def __str__(self): return self.nombre def __documento__(self): lista = [] for obj in self.documentos.all(): lista.append(obj) return lista def __fotos__(self): lista = [] for obj in self.fotos.all(): lista.append(obj) return lista def __video__(self): lista = [] for obj in self.video.all(): lista.append(obj) return lista def __audio__(self): lista = [] for obj in self.audio.all(): lista.append(obj) return lista def get_absolute_url(self): return "/foros/ver/%d" % (self.id) class Aportes(models.Model): foro = models.ForeignKey(Foros,on_delete=models.CASCADE) fecha = models.DateField(auto_now_add=True) contenido = RichTextUploadingField() user = models.ForeignKey(User,on_delete=models.DO_NOTHING) adjuntos = fields.GenericRelation(Documentos) fotos = fields.GenericRelation(Imagen) video = fields.GenericRelation(Videos) audio = fields.GenericRelation(Audios) class Meta: verbose_name_plural = "Aportes" def __str__(self): return self.foro.nombre def __documento__(self): lista = [] for obj in self.adjuntos.all(): lista.append(obj) return lista def __fotos__(self): lista = [] for obj in self.fotos.all(): lista.append(obj) return lista def __video__(self): lista = [] for obj in self.video.all(): lista.append(obj) return lista def __audio__(self): lista = [] for obj in self.audio.all(): lista.append(obj) return lista class Comentarios(models.Model): fecha = models.DateField(auto_now_add=True) usuario = models.ForeignKey(User,on_delete=models.DO_NOTHING) comentario = RichTextUploadingField() aporte = models.ForeignKey(Aportes,on_delete=models.CASCADE) class Meta: verbose_name_plural = "Comentarios" def __str__(self): return self.usuario.username	nilq/baby-python	python
# coding: utf-8 ''' ------------------------------------------------------------------------------ Copyright 2015 - 2017 Esri 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. ------------------------------------------------------------------------------ UnitTestUtiliites.py -------------------------------------------------- requirments: * ArcGIS Desktop 10.X+ or ArcGIS Pro 1.X+ * Python 2.7 or Python 3.4 author: ArcGIS Solutions company: Esri ================================================== description: Basic methods used in unit tests ================================================== history: 10/06/2015 - JH - original coding 10/23/2015 - MF - mods for tests ================================================== ''' import arcpy import os import sys import traceback import platform import logging import Configuration import datetime def getLoggerName(): ''' get unique log file name ''' if Configuration.DEBUG == True: print("UnitTestUtilities - getLoggerName") seq = 0 name = nameFromDate(seq) #add +=1 to seq until name doesn't exist as a path while os.path.exists(os.path.join(Configuration.logPath, name)): seq += 1 name = nameFromDate(seq) #logFilePath = os.path.join(Configuration.logPath, name) return name def getCurrentDateTimeForLogFile(): ''' Get current date/time string as: YYYY-MM-DD_HH-MM-SS ''' return datetime.datetime.now().strftime("%Y-%B-%d_%H-%M-%S") def getCurrentDateTime(): ''' Get current date/time string as: DD/MM/YYYY HH:MM:SS ''' return datetime.datetime.now().strftime("%d/%B/%Y %H:%M:%S") def nameFromDate(seq): ''' Make log file name''' return 'SGT_' + str(getCurrentDateTimeForLogFile()) + '_seq' + str(seq) + '.log' def makeFileFromPath(filePath): ''' make a file object from a path to that file if it doesn't already exist ''' if not checkExists(filePath): try: fd = open(filePath, 'a') fd.close() except: print("Can't make file for some reason.") return filePath def makeFolderFromPath(folderPath): ''' make a folder(s) from a path if it doesn't already exist ''' if not checkExists(folderPath): try: os.makedirs(folderPath) except: print("Can't make the folder for some reason.") return folderPath def initializeLogger(name, logLevel = logging.DEBUG): ''' get and return named logger ''' if Configuration.DEBUG == True: print("UnitTestUtilities - initializeLogger") # Check if the path to the log files exists, and create if not if not os.path.exists(Configuration.logPath): dummy = makeFolderFromPath(Configuration.logPath) # get a unique log file name if we don't have a name already if name == None or name == "": name = getLoggerName() logFile = os.path.join(Configuration.logPath, name) Configuration.LoggerFile = logFile # if the log file does NOT exist, create it if not os.path.exists(logFile): logFile = makeFileFromPath(logFile) logger = logging.getLogger(name) logger.setLevel(logLevel) logFormatter = logging.Formatter('%(levelname)s: %(asctime)s %(message)s') fileHandler = logging.FileHandler(logFile) fileHandler.setFormatter(logFormatter) logger.addHandler(fileHandler) consoleHandler = logging.StreamHandler(sys.stdout) consoleHandler.setFormatter(logFormatter) logger.addHandler(consoleHandler) return logger def setUpLogFileHeader(): ''' Add a header to log file when initialized ''' Configuration.Logger.debug("UnitTestUtilities - setUpLogFileHeader") Configuration.Logger.info("------------ Begin Tests ------------------") Configuration.Logger.info("Platform: {0}".format(platform.platform())) Configuration.Logger.info("Python Version {0}".format(sys.version)) agsInstallInfo = arcpy.GetInstallInfo() Configuration.Logger.info("Product: {0}, Version: {1}, Installed on: {2}, Build: {3}.".format(agsInstallInfo['ProductName'], \ agsInstallInfo['Version'], agsInstallInfo['InstallDate'], agsInstallInfo['BuildNumber'])) Configuration.Logger.info("-------------------------------------------") def checkArcPy(): ''' sanity check that ArcPy is working ''' if Configuration.DEBUG == True: print("UnitTestUtilities - checkArcPy") arcpy.AddMessage("ArcPy works") def checkExists(p): ''' Python check for existence ''' if Configuration.DEBUG == True: print("UnitTestUtilities - checkExists") return os.path.exists(p) def createScratch(scratchPath): ''' create scratch geodatabase ''' if Configuration.DEBUG == True: print("UnitTestUtilities - createScratch") scratchName = 'scratch.gdb' scratchGDB = os.path.join(scratchPath, scratchName) if checkExists(scratchGDB): print("Scratch already exists") return scratchGDB try: if Configuration.DEBUG == True: print("Creating scratch geodatabase...") arcpy.CreateFileGDB_management(scratchPath, scratchName) if Configuration.DEBUG == True: print("Created scratch gdb.") except: print("Problem creating scratch.gdb") return scratchGDB def deleteScratch(scratchPath): ''' delete scratch geodatabase ''' if Configuration.DEBUG == True: print("UnitTestUtilities - deleteScratch") try: arcpy.Delete_management(scratchPath) if Configuration.DEBUG == True: print("Deleted scratch gdb.") except: print("scratch.gdb delete failed") return def checkFilePaths(paths): ''' check file/folder paths exist ''' if Configuration.DEBUG == True: print("UnitTestUtilities - checkFilePaths") for path2check in paths: if os.path.exists(path2check): if Configuration.DEBUG == True: print("Valid Path: " + path2check) else: raise Exception('Bad Path: ' + str(path2check)) def checkGeoObjects(objects): ''' check geospatial stuff exists ''' if Configuration.DEBUG == True: print("UnitTestUtilities - checkGeoObjects") for object2Check in objects: #TODO: Shouldn't we be using arcpy.Exists()? desc = arcpy.Describe(object2Check) if desc == None: print("--> Invalid Object: " + str(object2Check)) arcpy.AddError("Bad Input") raise Exception('Bad Input') else: if Configuration.DEBUG == True: print("Valid Object: " + desc.Name) def handleArcPyError(): ''' Basic GP error handling, errors printed to console and logger ''' if Configuration.DEBUG == True: print("UnitTestUtilities - handleArcPyError") # Get the arcpy error messages msgs = arcpy.GetMessages() arcpy.AddError(msgs) print(msgs) Configuration.Logger.error(msgs) raise Exception('ArcPy Error') def handleGeneralError(exception = None): ''' Basic error handler, errors printed to console and logger ''' if Configuration.DEBUG == True: print("UnitTestUtilities - handleGeneralError") if isinstance(exception, Exception): print(str(exception)) Configuration.Logger.error(str(exception)) # Get the traceback object tb = sys.exc_info()[2] tbinfo = traceback.format_tb(tb)[0] # Concatenate information together concerning the error into a message string pymsg = "PYTHON ERRORS:\nTraceback info:\n" + tbinfo + "\nError Info:\n" + str(sys.exc_info()[1]) msgs = "ArcPy ERRORS:\n" + arcpy.GetMessages() + "\n" # Print Python error messages for use in Python / Python Window print(pymsg + "\n") Configuration.Logger.error(pymsg) print(msgs) Configuration.Logger.error(msgs) if isinstance(exception, Exception): raise exception else: raise Exception('General Error') def geoObjectsExist(objects): ''' Return true if all of the input list of geo-objects exist, false otherwise ''' allExist = True for obj in objects: if not arcpy.Exists(obj): allExist = False return allExist def folderPathsExist(paths): ''' Return true if all input paths exist, false otherwise ''' allExist = True for p in paths: if not os.path.exists(p): allExist = False return allExist def deleteIfExists(dataset): ''' Delete the input dataset if it exists ''' if (arcpy.Exists(dataset)): arcpy.Delete_management(dataset) arcpy.AddMessage("deleted dataset: " + dataset)	nilq/baby-python	python
from flask_restful import Resource, reqparse, request from flask_restful import fields, marshal_with, marshal from sqlalchemy.exc import IntegrityError from sqlalchemy import or_, and_, text from flask_jwt_extended import jwt_required from models.keyword import Keyword from app import db from utils.util import max_res from helpers.keywords_resource_helper import * class KeywordsResource(Resource): @jwt_required def get(self, keyword_id=None): if keyword_id: keyword = Keyword.find_by_id(keyword_id) return max_res(marshal(keyword, keyword_fields)) else: conditions = [] args = keyword_query_parser.parse_args() page = args['page'] per_page = args['pagesize'] if args['orderby'] not in sortable_fields: return max_res('', code=500, errmsg='排序非法字段') sort = args['orderby'] if args['desc']>0: sort = args['orderby'] + ' desc' conditions = make_conditions(conditions,args) # 在这里添加更多的条件查询例如 # if args['name'] is not None: # conditions.append(Keyword.name.like('%'+args['name']+'%')) if conditions is []: pagination = Keyword.query.order_by(text(sort)).paginate(page, per_page, error_out=False) else: pagination = Keyword.query.filter(conditions).order_by(text(sort)).paginate(page, per_page, error_out=False) paginate = { 'total':pagination.total, 'pageSize': pagination.per_page, 'current': pagination.page } print(pagination.items) return max_res(marshal({ 'pagination': paginate, 'list': [marshal(u, keyword_fields) for u in pagination.items] }, keyword_list_fields)) @jwt_required def post(self): args = keyword_post_parser.parse_args() keyword = Keyword(*args) try: keyword.add() except IntegrityError: return max_res('', code=401, errmsg='名称重复') return max_res(marshal(keyword, keyword_fields)) def put(self, keyword_id=None): keyword = Keyword.find_by_id(keyword_id) args = keyword_update_parser.parse_args() keyword = update_all_fields(args, keyword) #可以在这里继续添加需要更新的字段如 # if args['name']: # o.name = args['name'] # db.session.commit() try: keyword.update() except Exception as e: return max_res('',500, 'Failed to modify.') return max_res(marshal(keyword, keyword_fields)) def delete(self, keyword_id=None): keyword = Keyword.find_by_id(keyword_id) try: keyword.delete() except Exception as e: return max_res('',500, 'The record has already deleted.') return max_res('The keyword has been deleted.')	nilq/baby-python	python
# -- coding: utf-8 -- import unittest from iemlav.lib.log_monitor.server_log.parser.apache import ApacheParser from iemlav.lib.log_monitor.server_log.server_logger import ServerLogger try: # if python 3.x.x from unittest.mock import patch except ImportError: # python 2.x.x from mock import patch class TestApacheParser(unittest.TestCase): """ Test class for SecureTea Server Log Apache Log Parser. """ def setUp(self): """ Setup class for TestApacheParser. """ # Initialize Apache object self.apache_obj = ApacheParser(window=30, path="random_path") # Mock log data self.data = ['83.149.9.216 - - [14/Jun/2019:10:30:00 +0000] ' \ '"GET /presentations/logstash-monitorama-2013/images/kibana-dashboard3.png HTTP/1.1" ' \ '400 171717 "http://semicomplete.com/presentations/logstash-monitorama-2013/" ' \ '"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like Gecko) '\ 'Chrome/32.0.1700.77 Safari/537.36'] # Mock parsed log data self.parsed_dict = {'83.149.9.216': { 'ep_time': [1560508200], 'get': ['/presentations/logstash-monitorama-2013/images/kibana-dashboard3.png HTTP/1.1'], 'status_code': [400], 'ua': ['Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) ' \ 'AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.77 Safari/537.36'], 'count': 1, 'unique_get': ['/presentations/logstash-monitorama-2013/images/kibana-dashboard3.png HTTP/1.1'] } } @patch.object(ApacheParser, "check_within_window") @patch.object(ServerLogger, "log") @patch("iemlav.lib.log_monitor.server_log.parser.apache.utils") def test_parse(self, mck_utils, mock_log, mck_window): """ Test parse. """ mck_utils.open_file.return_value = self.data mck_window.return_value = True mck_utils.get_epoch_time.return_value = 1560508200 # Check if the parsing is correct self.assertEqual(self.apache_obj.parse(), self.parsed_dict) @patch("iemlav.lib.log_monitor.server_log.parser.apache.time") def test_check_within_window(self, mock_time): """ Test check_within_window. """ # Case 1: When time difference is less than window mock_time.time.return_value = 1560508200 res = self.apache_obj.check_within_window(1560508200) self.assertTrue(res) # Case 2: When time difference is greater than window res = self.apache_obj.check_within_window(1557916100) self.assertFalse(res) def test_update_dict(self): """ Test update_dict. """ self.apache_obj.update_dict( ip="1.1.1.1", ep_time=1500, get="/random/get/req", status_code=200, user_agent="random-user-agent" ) temp_dict = {'ep_time': [1500], 'get': ['/random/get/req'], 'status_code': [200], 'ua': ['random-user-agent'], 'count': 1, 'unique_get': ['/random/get/req']} # Check if the key exists self.assertTrue(self.apache_obj.apache_dict.get("1.1.1.1")) # Check if the updated dict is correct self.assertEqual(self.apache_obj.apache_dict["1.1.1.1"], temp_dict)	nilq/baby-python	python
# Copyright (c) 2019 fortiss GmbH # # This software is released under the MIT License. # https://opensource.org/licenses/MIT from bark.world.agent import * from bark.models.behavior import * from bark.world import * from bark.world.map import * from modules.runtime.commons.parameters import ParameterServer from modules.runtime.commons.xodr_parser import XodrParser import copy class Scenario: def __init__(self, agent_list=None, eval_agent_ids=None, map_file_name=None, json_params=None, map_interface=None): self._agent_list = agent_list or [] self._eval_agent_ids = eval_agent_ids or [] self._map_file_name = map_file_name self._json_params = json_params self._map_interface = map_interface def get_world_state(self): """get initial world state of scenario to start simulation from here Returns: [bark.world.World] """ return self._build_world_state() def copy(self): return Scenario(agent_list=copy.deepcopy(self._agent_list), eval_agent_ids=self._eval_agent_ids.copy(), map_file_name=self._map_file_name, json_params=self._json_params.copy(), map_interface=self._map_interface) def _build_world_state(self): param_server = ParameterServer(json=self._json_params) world = World(param_server) if self._map_interface is None: world = self.setup_map(world, self._map_file_name) else: world.set_map(self._map_interface) for agent in self._agent_list: world.add_agent(agent) return world def __getstate__(self): odict = self.__dict__.copy() print(odict['_map_interface']) del odict['_map_interface'] print(odict) return odict def __setstate__(self, sdict): sdict['_map_interface'] = None self.__dict__.update(sdict) def setup_map(self, world, _map_file_name): if not _map_file_name: return world xodr_parser = XodrParser(_map_file_name ) map_interface = MapInterface() map_interface.set_open_drive_map(xodr_parser.map) self._map_interface = map_interface world.set_map(map_interface) return world	nilq/baby-python	python
import os import pytest from h2_conf import HttpdConf def setup_data(env): s100 = "012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678\n" with open(os.path.join(env.gen_dir, "data-1k"), 'w') as f: for i in range(10): f.write(s100) # The trailer tests depend on "nghttp" as no other client seems to be able to send those # rare things. class TestStore: @pytest.fixture(autouse=True, scope='class') def _class_scope(self, env): setup_data(env) HttpdConf(env).add_vhost_cgi(h2proxy_self=True).install() assert env.apache_restart() == 0 # check if the server survives a trailer or two def test_202_01(self, env): url = env.mkurl("https", "cgi", "/echo.py") fpath = os.path.join(env.gen_dir, "data-1k") r = env.nghttp().upload(url, fpath, options=["--trailer", "test: 1"]) assert 300 > r.response["status"] assert 1000 == len(r.response["body"]) r = env.nghttp().upload(url, fpath, options=["--trailer", "test: 1b", "--trailer", "XXX: test"]) assert 300 > r.response["status"] assert 1000 == len(r.response["body"]) # check if the server survives a trailer without content-length def test_202_02(self, env): url = env.mkurl("https", "cgi", "/echo.py") fpath = os.path.join(env.gen_dir, "data-1k") r = env.nghttp().upload(url, fpath, options=["--trailer", "test: 2", "--no-content-length"]) assert 300 > r.response["status"] assert 1000 == len(r.response["body"]) # check if echoing request headers in response from GET works def test_202_03(self, env): url = env.mkurl("https", "cgi", "/echohd.py?name=X") r = env.nghttp().get(url, options=["--header", "X: 3"]) assert 300 > r.response["status"] assert b"X: 3\n" == r.response["body"] # check if echoing request headers in response from POST works def test_202_03b(self, env): url = env.mkurl("https", "cgi", "/echohd.py?name=X") r = env.nghttp().post_name(url, "Y", options=["--header", "X: 3b"]) assert 300 > r.response["status"] assert b"X: 3b\n" == r.response["body"] # check if echoing request headers in response from POST works, but trailers are not seen # This is the way CGI invocation works. def test_202_04(self, env): url = env.mkurl("https", "cgi", "/echohd.py?name=X") r = env.nghttp().post_name(url, "Y", options=["--header", "X: 4a", "--trailer", "X: 4b"]) assert 300 > r.response["status"] assert b"X: 4a\n" == r.response["body"]	nilq/baby-python	python
# -- coding: utf-8 -- from mimetypes import MimeTypes from hashlib import md5 def list_of(_list, _class): """ Chequea que la lista _list contenga elementos del mismo tipo, desciptos en _class. Args: - _list: - list(). - Lista de elementos sobre la que se desea trabajar. - El argumento solo acepta objetos de class list. - _class: - Clase esperada en los elemntos de la lista. - admite que se chequee cualquier tipo, incuso, NoneType. Returns: - bool(). - True: La lista posee todos sus elementos de la clase _class - False: Al menos uno de los elementos no es de clase _class """ if not isinstance(_list, list): raise TypeError('check_list_type() solo acepta type(_list)==list') return not False in [isinstance(element, _class) for element in _list] def get_mimetype(_filename=None): """ Retorna Mime Type de un archivo (_filename). Args: ==== - _filename: Str(). path al archivo que se desea chequear. Require: ======= - Python Builtin lib: mimetypes. Returns: ======= - Str(). MimeType. Exito. - None: Fallo. """ try: mime = MimeTypes() return mime.guess_type(_filename)[0] except TypeError: pass except IOError: pass def build_hash(_filename): """ Crear hash de recurso. Args: ==== - _filename: Str(). Archivo sobre el cual se desea calcular HASH. Return: ====== - Exito: Str() MD5-Hash. - Fallo: None. """ hash_md5 = md5() try: with open(_filename, "rb") as f: for chunk in iter(lambda: f.read(4096), b""): hash_md5.update(chunk) return hash_md5.hexdigest() except IOError: pass	nilq/baby-python	python
import unittest import random from hypothesis import given, settings, assume, Verbosity, strategies as st from src.poker.app.card import Deck, Card, Suit, Value from src.poker.app.hand import Hand, Play, Result, calculate_play_hand DeckStrategy = st.builds(Deck) NaiveHandStrategy = st.builds(Hand, st.sets( st.builds(Card, st.sampled_from(Suit), st.sampled_from(Value)) , max_size = 5 , min_size = 5)) @st.composite def three_of_a_kind_in_hand(draw) -> Hand: d = draw(DeckStrategy) r = draw(st.randoms()) #1 sample = r.choice(list(d.deck)) cards = set([sample]) #2 sample = Card(sample.suit.next(), sample.val.next()) cards.add(sample) #3 sample = Card(sample.suit.next(), sample.val.next()) cards.add(sample) #4 sample = Card(sample.suit.next(), sample.val) cards.add(sample) #5 sample = Card(sample.suit.next(), sample.val) cards.add(sample) return Hand(cards) @st.composite def full_house_in_hand(draw) -> Hand: d = draw(DeckStrategy) r = draw(st.randoms()) #1 sample = r.choice(list(d.deck)) cards = set([sample]) #2 sample = Card(sample.suit.next(), sample.val) cards.add(sample) #3 sample = Card(sample.suit.next(), sample.val.next()) cards.add(sample) #4 sample = Card(sample.suit.next(), sample.val) cards.add(sample) #5 sample = Card(sample.suit.next(), sample.val) cards.add(sample) return Hand(cards) @st.composite def straight_in_hand(draw) -> Hand: blacklist = {Value.JACK, Value.QUEEN, Value.KING} d = draw(DeckStrategy) r = draw(st.randoms()) sample = r.choice(list(d.deck)) assume(not sample.val in blacklist) # while v in blacklist: # v = random.choice(list(Value)) cards = set([sample]) for _ in range(4): sample = Card(sample.suit.next(), sample.val.next()) cards.add(sample) return Hand(cards) class PokerTest(unittest.TestCase): @given(d=DeckStrategy, n_gets=st.integers(min_value=0, max_value=55), m_sets=st.integers(min_value=0, max_value=55)) #@settings(verbosity=Verbosity.verbose) def test_deck_gets_and_sets(self, d: Deck, n_gets, m_sets) -> None: """ Tests if the deck class takes and returns properly cards """ withdraws = list() for _ in range(n_gets+1): card = d.get_random_card() if card: withdraws.append(card) for _ in range(m_sets+1): if withdraws: card = random.choice(withdraws) withdraws.remove(card) d.set_card(card) self.assertEqual(len(withdraws) + len(d.deck), Deck.TOTAL_CARDS) @given(hand=NaiveHandStrategy) @settings(max_examples=150) def test_hand_plays_value(self, hand: Hand) -> None: calculate_play_hand(hand) assert hand.value > 0 and len(hand.cards) == 5 @given(hand=three_of_a_kind_in_hand()) def test_three_of_a_kind(self, hand: Hand) -> None: calculate_play_hand(hand) self.assertEqual(hand.play, Play.THREE_OF_A_KIND) @given(hand=full_house_in_hand()) def test_full_house(self, hand: Hand) -> None: calculate_play_hand(hand) self.assertEqual(hand.play, Play.FULL_HOUSE) @given(hand=straight_in_hand()) def test_straight(self, hand: Hand) -> None: calculate_play_hand(hand) self.assertEqual(hand.play, Play.STRAIGHT) @given(hand1=st.one_of(full_house_in_hand(), straight_in_hand()), hand2=st.one_of(three_of_a_kind_in_hand())) #@settings(verbosity=Verbosity.verbose) def test_two_hands(self, hand1: Hand, hand2: Hand) -> None: calculate_play_hand(hand1) calculate_play_hand(hand2) self.assertEqual(Result.WIN, hand1.compare(hand2)) if __name__ == "__main__": unittest.main()	nilq/baby-python	python
import numpy as np # Reshaping arrays: # Reshaping means changing the shape of an array. # The shape of an array is the number of elements in each dimension. # By reshaping we can add or remove dimensions or change number of elements in each dimension. # **Note: The product of the number of elements inside the Reshape must be equal to the number of elements of the array arr = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5]) print(arr.reshape(4, 4, 2)) print(arr.reshape(2, 2, 2, 4)) copy_or_view = arr.reshape(4, 8) print(copy_or_view.base) print(arr.reshape(2, 4, -1)) arr = np.array([ [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 5]] ]) print(arr.reshape(-1))	nilq/baby-python	python
import re j_format = { "j": "000010", } i_format = { 'beq': "000100", 'bne': None, 'addi': "001000", 'addiu': None, 'andi': None, 'ori': None, 'slti': None, 'sltiu': None, 'lui': None, 'lw': "100011", 'sw': "101011", } r_format = { 'add': "100000", 'addu': None, 'sub': "100010", 'subu': None, 'and': "100100", 'or': "100101", 'xor': "100110", 'slt': "101010", 'sltu': None, 'sll': "101001", 'srl': None, 'jr': None } reg_name = { '$zero': 1, '$at': 1, '$v': 2, '$a': 4, '$t': 8, '$s': 8, '$tt': 2, '$k': 2, '$gp': 1, '$sp': 1, '$fp': 1, '$ra': 1 } def reg_init(): registers = {} n = 0 for key, value in reg_name.items(): for i in range(value): b = bin(n)[2:].zfill(5) if key == "$zero": registers[f"{key}"] = b elif key == "$tt": registers[f"$t{i + 8}"] = b else: registers[f"{key}{i}"] = b n += 1 return registers def check_dependency(ins, regs): if ins[1] in regs: return True return False def twos_comp(binary): binary = binary[::-1] new_b = "" flag = False for i in range(len(binary)): if flag: new_b += '0' if binary[i] == '1' else '1' else: if binary[i] == '1': flag = True new_b += binary[i] return new_b[::-1] def compile_asm(lines, registers): # for beq, if R -> NOP, if LW -> NOP2 instructions = [] labels = {} add = 0 for line in lines: if line[-1] == ':': labels[line[:-1]] = add continue ins = re.findall("^[a-z]+", line) if ins[0] != 'j': regs = re.findall(" [a-zA-Z0-9]+\|\$[a-z]+[0-9]\|[0-9]+\|\$zero\|-[0-9]+", line) else: regs = [line.split(" ")[1]] if ins[0] == 'beq' and check_dependency(instructions[-1], regs): if instructions[-1][0] == 'lw': instructions.append(['nop']) instructions.append(['nop']) add += 2 elif r_format.get(instructions[-1][0]): instructions.append(['nop']) add += 1 elif instructions[-1][0] in list(i_format.keys())[2:9] and i_format.get(instructions[-1][0]): instructions.append(['nop']) add += 1 add += 1 instructions.append(ins + regs) binary = [] for add, ins in enumerate(instructions): b = [] if ins[0] == 'nop': b.append('0' 32) elif ins[0] in i_format: b.append(i_format[ins[0]]) im, reg = (ins[2], ins[3]) if ins[0] in ['lw', 'sw'] else (ins[3], ins[2]) im = im.strip() b.append(registers[reg]) b.append(registers[ins[1]]) if im.isnumeric() or (im[0] == '-' and im[1:].isnumeric()): immediate = int(im) while ins[0] == "beq" and instructions[immediate][0] == "nop": immediate += 1 if immediate < 0: b.append(twos_comp(bin(immediate)[2:].zfill(16))) else: b.append(bin(immediate)[2:].zfill(16)) else: r_ad = labels[im.strip()] - add - 1 while instructions[add + 1 + r_ad][0] == "nop": r_ad += 1 if r_ad < 0: r_ad_bin = twos_comp(bin(r_ad)[2:].zfill(16)) else: r_ad_bin = bin(r_ad)[2:].zfill(16) b.append(r_ad_bin) elif ins[0] in r_format: b.append("000000") # OPCODE if ins[0] == "sll" or ins[0] == "srl": b.append(registers[ins[2]]) # RT b.append("00000") # RS b.append(registers[ins[1]]) # RD shamt = bin(int(ins[3]))[2:].zfill(5) b.append(shamt) # SHAMT else: b.append(registers[ins[2]]) # RS b.append(registers[ins[3]]) # RT b.append(registers[ins[1]]) # RD b.append("00000") # SHAMT b.append(r_format[ins[0]]) # FUNCT elif ins[0] in j_format: b.append(j_format[ins[0]]) if ins[1].isnumeric(): ad = int(ins[1]) while instructions[ad][0] == "nop": ad += 1 b.append(bin(ad)[2:].zfill(26)) else: ad = labels[ins[1]] while instructions[ad][0] == "nop": ad += 1 b.append(bin(ad)[2:].zfill(26)) binary.append("".join(b)) return binary def compiler(file_name): registers = reg_init() lines = open(file_name).read().split('\n') return compile_asm(lines, registers) # compiler("p.art")	nilq/baby-python	python
import random from collections import defaultdict import numpy as np from maddpg.common.utils_common import zip_map class ReplayBuffer(object): def __init__(self, size): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. """ self._storage = [] self._maxsize = int(size) self._next_idx = 0 def __len__(self): return len(self._storage) def clear(self): self._storage = [] self._next_idx = 0 def add(self, obs_t, action, reward, obs_tp1, done): data = (obs_t, action, reward, obs_tp1, done) if self._next_idx >= len(self._storage): self._storage.append(data) else: self._storage[self._next_idx] = data self._next_idx = (self._next_idx + 1) % self._maxsize def _encode_sample(self, idxes): obses_t = defaultdict(list) actions = defaultdict(list) rewards = defaultdict(list) obses_tp1 = defaultdict(list) dones = defaultdict(list) for i in idxes: data = self._storage[i] obs_t, action, reward, obs_tp1, done = data for key, (obs_t, action, reward, obs_tp1, done) in zip_map(*data): obses_t[key].append(obs_t) actions[key].append(action) rewards[key].append(reward) obses_tp1[key].append(obs_tp1) dones[key].append(done) return obses_t, actions, rewards, obses_tp1, dones def make_index(self, batch_size): return [random.randint(0, len(self._storage) - 1) for _ in range(batch_size)] def make_latest_index(self, batch_size): idx = [(self._next_idx - 1 - i) % self._maxsize for i in range(batch_size)] np.random.shuffle(idx) return idx def sample_index(self, idxes): return self._encode_sample(idxes) def sample(self, batch_size): """Sample a batch of experiences. Parameters ---------- batch_size: int How many transitions to sample. Returns ------- obs_batch: np.array batch of observations act_batch: np.array batch of actions executed given obs_batch rew_batch: np.array rewards received as results of executing act_batch next_obs_batch: np.array next set of observations seen after executing act_batch done_mask: np.array done_mask[i] = 1 if executing act_batch[i] resulted in the end of an episode and 0 otherwise. """ if batch_size > 0: idxes = self.make_index(batch_size) else: idxes = range(0, len(self._storage)) return self._encode_sample(idxes) def collect(self): return self.sample(-1)	nilq/baby-python	python
from arbre_binaire_jeu import * #------------------------------------------------------------------------------- # DM MISSION # # Objectif : Construire un jeu à partir d'un texte préconstruit # # Contrainte : utiliser un arbre binaire #------------------------------------------------------------------------------- # Phrases préconstruites phrases = [None] * 18 phrases[0] = """21/10/2024 - 03h30, New York, un quartier mal famé et mal éclairé. Vous êtes au pied de l'immeuble auquel vous a mené votre enquête. 25 étages à vue de nez. Même au coeur de la nuit, on voit qu'il aurait besoin au minimum d'un sacré coup de peinture ; on le donnerait pour abandonné si on ne percevait pas ça et là de faibles rayons de lumière. Tout est calme alentour, un silence à couper au couteau. D'après mon informateur, le fils du Président, retenu prisonnier par le Gang des Ignares, est situé tout en haut au dernier étage. Il est probablement sous surveillance. Pour le libérer, il va falloir être discret...""" phrases[1] = "Damnation, je ne peux plus avancer! Il va falloir tenter la voie des airs, ça va être compliqué ..." phrases[2] = "La porte d'entrée n'offre pas de résistance, je traverse le hall vers les accès aux étages." phrases[3] = "Quelle malchance, l'ascenseur n' a plus d 'électricité. je vois un boîtier avec des fils qui dépassent." phrases[4] = "L'escalier est fermé par une énorme grille. Il y a un boîtier avec un code à taper." phrases[5] = "Ca y est, l'ascenseur fonctionne. Voilà, je suis dedans et je monte." phrases[6] = "Ca y est, la grille s'ouvre. Prenons l' escalier. A moi les 25 étages...pfff !" phrases[7] = """Ascension terminée, me voici à pied d'oeuvre! Je découvre un couloir. Il y a une porte latérale ouverte, ce doit être celle du gardien. La porte fermée au fond doit être celle du prisonnier""" phrases[8] = "Sacrebleu ! J 'ai foutu en l'air cette boîte rouillée qui s'est appelée un jour ascenseur. Voyons l'escalier..." phrases[9] = "Enfer, le code ne marche pas, la grille est bloquée l'escalier est inaccessible. Voyons l'ascenseur..." phrases[10] = "C'est la catastrophe, je ne peux pas monter, j' abandonne la mission en attendant de trouver un autre moyen" phrases[11] = "Malédiction, le couloir est allumé, je vais me faire repérer, à moins qu' il dorme" phrases[12] = "Le couloir est dans l'obscurité, pas de lumière, je vais me glisser dans l'ombre, il ne me verra pas" phrases[13] = "Pas de bruit sauf une légère respiration, le surveillant doit dormir, je tente ma chance" phrases[14] = "Des bruits de table et de chaise, le surveillant est apparemment bien éveillé." phrases[15] = """Ouf, j'ai pu passer le couloir sans encombre. J'ouvre la porte au fond. Le prisonnier tourne la tête lentement vers moi et me lance un regard ébahi. Je prend la pose et je lui lance un « Salut fiston, ton sauveur est arrivé! »""" phrases[16] = """Un jet de lumière, le gardien braque sur moi un gros flingue, un sourire mauvais éclaire son visage ; manifestement il m'attendait c'est un piège !""" phrases[17] = "C'est trop risqué pour l' instant, je reviendrai dans quelques heures." questions = [None] * 6 questions[0] = "La porte de l'immeuble est-elle ouverte (taper 1) ou verrouillée (taper 0) ? " questions[1] = "Choisissez vous de prendre l'ascenseur (taper 1) ou l'escalier (taper 0)? " questions[2] = "Branchez vous le fil vert avec noir (taper 1) ou le rouge avec le noir (taper 0)? " questions[3] = "Choisissez vous le code 1111 (taper 1) ou le code 9999 (taper 0)? " questions[4] = "Le couloir en face est-il éclairé (taper 1) ou dans le noir (taper 0)? " questions[5] = "Entendez vous quelqu'un qui s'agite dans la pièce de surveillance (taper 1) ou est elle silencieuse (taper 0) ? " # déroulement de l'histoire (aide) ''' porte ouverte ou fermée ? porte ouverte, choix ascenseur ou escalier porte fermée, fin de l' histoire ascenseur, fil vert ou rouge ascenseur marche, arrivée en haut, question sur lumière couloir ascenseur en panne, test escalier couloir allumé, bruit ? trop risqué sauvé couloir éteint, bruit ? sauvé piège escalier, 1111 ou 9999 ? escalier marche, arrivée en haut, question sur lumière couloir ... escalier foutu, test ascenseur escalier bloqué, mission reportée ''' arbre_jeu = Noeud((phrases[0] + questions[0]), Noeud(phrases[1], None, None ), Noeud((phrases[2] + questions[1]), Noeud((phrases[3] + questions[2]), Noeud((phrases[8] + phrases[4] + questions[3]), "Fin Bis", None ), Noeud((phrases[6] + phrases[7] + questions[4]), Noeud((phrases[11] + questions[5]), Noeud((phrases[13] + phrases[16]), # Fin None, None ) ), Noeud((phrases[12] + phrases[15])) ) ), Noeud((phrases[4] + phrases[3]), Noeud, Noeud((phrases[6] + phrases[7] + questions[4]) Noeud((phrases[11] + questions[5]), Noeud((phrases[13] + phrases[16]), # Fin None, None ) ), Noeud((phrases[12] + phrases[15])) ) ), ) ) affichage(arbre_jeu) print("....!! FIN !!...") #------------------------------------------------------------------------------- # QUESTIONS # # 1. a) Sur papier, construire l'arbre en notant comme étiquette de noeud les phrases et la question éventuelle associées # b) Déterminer la taille et la hauteur de l'arbre. Combien comporte-t-il de feuilles ? # 2. A l'aide du module arbre_binaire importé, constuire l'arbre précédent en python. # 3. Ecrire une fonction qui parcours l'arbre comme dans en affichant le texte dans la console et les questions # sous forme d'input pour lequel le joueur répond 0 ou 1. # 4. Tester le jeu.	nilq/baby-python	python
# coding: utf-8 from olo import funcs from olo.funcs import COUNT, SUM, AVG, MAX, DISTINCT from .base import TestCase, Foo, Bar, Dummy from .fixture import is_pg from .utils import ( patched_execute, no_pk ) attrs = dict( name='foo', tags=['a', 'b', 'c'], password='password', payload={ 'abc': ['1', 2, 3], 'def': [4, '5', 6] } ) class TestCachedQuery(TestCase): def test_fallback(self): bar = Bar.create(name='a', xixi='a', age=1) with patched_execute as execute: bar = Bar.cq.filter(age=MAX(Bar.cq('age'))).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq.filter(age=MAX(Bar.cq('age'))).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq.filter(Bar.age > 0).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq.filter(Bar.age > 0).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq('age').filter(Bar.age > 0).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq('age').filter(Bar.age > 0).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) def test_first(self): with patched_execute as execute: bar = Bar.cq.filter(xixi='a', age=1).first() self.assertIsNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq.filter(xixi='a', age=1).first() self.assertIsNone(bar) self.assertFalse(execute.called) bar = Bar.create(name='a', xixi='a', age=1) with patched_execute as execute: bar = Bar.cq.filter(xixi='a', age=1).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq.filter(xixi='a', age=1).first() self.assertIsNotNone(bar) self.assertFalse(execute.called) def test_all(self): with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).all() self.assertEqual(bars, []) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).all() self.assertEqual(bars, []) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(10).all() self.assertEqual(bars, []) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(bars, []) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.limit(10).all() self.assertEqual(bars, []) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.limit(11).all() self.assertEqual(bars, []) self.assertFalse(execute.called) bar = Bar.create(name='a', xixi='a', age=1) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) self.assertEqual(execute.call_count, 2) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.limit(10).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) bar.update(name='a+') with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) self.assertEqual(execute.call_count, 2) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) bar.update(name='a') with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) self.assertEqual(execute.call_count, 2) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) bar.update(word='1') with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) self.assertEqual(execute.call_count, 1) self.assertEqual(bars[0].word, bar.word) bar.update(word='2') Bar.cache.get(bar.name) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) self.assertEqual(bars[0].word, bar.word) bar.update(xixi='b') with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 0) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 0) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) bar.update(word='a') bar = Bar.create(name='b', xixi='b', age=1, word='b') bar = Bar.create(name='c', xixi='b', age=1, word='c') bar = Bar.create(name='d', xixi='b', age=1, word='d') with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).limit(11).all() self.assertEqual(len(bars), 4) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).limit(11).all() self.assertEqual(len(bars), 4) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).limit(2).all() self.assertEqual(len(bars), 2) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cache.gets_by(xixi='b', age=1, start=3, limit=2) self.assertEqual(len(bars), 1) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( '-name' ).limit(3).all() self.assertEqual(len(bars), 3) self.assertEqual(['d', 'c', 'b'], list(map(lambda x: x.name, bars))) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( '-name' ).limit(3).all() self.assertEqual(len(bars), 3) self.assertEqual(['d', 'c', 'b'], list(map(lambda x: x.name, bars))) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( 'name' ).limit(3).all() self.assertEqual(len(bars), 3) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( 'name' ).limit(3).all() self.assertEqual(len(bars), 3) self.assertEqual(['a', 'b', 'c'], list(map(lambda x: x.name, bars))) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( '-age', 'word' ).offset(3).limit(2).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( '-age', 'word' ).offset(3).limit(2).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) _bar = bars[0] _bar.update(xixi='c') with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( '-age', 'word' ).offset(2).limit(2).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) _bar.update(xixi='b') with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( 'word', 'age' ).offset(3).limit(2).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( 'word', 'age' ).offset(3).limit(2).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) Bar.create(name='e', xixi='b', age=1, word='e') Bar.create(name='f', xixi='b', age=1, word='f') with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( 'word', 'age' ).offset(3).limit(2).all() self.assertEqual(len(bars), 2) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( 'word', 'age' ).offset(3).limit(2).all() self.assertEqual(len(bars), 2) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(name='e').all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) Foo.create(name='1', age=1) Foo.create(name='2', age=1) Foo.create(name='3', age=2) with no_pk(Foo): Foo.cq.filter(age=1).limit(3).all() foos = Foo.cq.filter(age=3).limit(3).all() self.assertEqual(foos, []) def test_count_by(self): with patched_execute as execute: c = Bar.cq.filter(xixi='a', age=1).count() self.assertEqual(c, 0) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='a', age=1).count() self.assertEqual(c, 0) self.assertFalse(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 0) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 0) self.assertFalse(execute.called) with patched_execute as execute: c = Bar.cq.filter().count() self.assertEqual(c, 0) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter().count() self.assertEqual(c, 0) self.assertFalse(execute.called) with patched_execute as execute: c = Bar.cq.filter(name='a').count() self.assertEqual(c, 0) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(name='a').count() self.assertEqual(c, 0) self.assertFalse(execute.called) with patched_execute as execute: c = Bar.cq.filter(word='a').count() self.assertEqual(c, 0) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(word='a').count() self.assertEqual(c, 0) self.assertTrue(execute.called) Bar.create(name='a', xixi='b', age=1) with patched_execute as execute: c = Bar.cq.filter(xixi='a', age=1).count() self.assertEqual(c, 0) self.assertFalse(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 1) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter().count() self.assertEqual(c, 1) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(name='a').count() self.assertEqual(c, 1) self.assertTrue(execute.called) Bar.create(name='b', xixi='a', age=1) with patched_execute as execute: c = Bar.cq.filter(xixi='a', age=1).count() self.assertEqual(c, 1) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 1) self.assertFalse(execute.called) bar = Bar.create(name='c', xixi='b', age=1) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 2) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 2) self.assertFalse(execute.called) bar.update(xixi='c') with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 1) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 1) self.assertFalse(execute.called) def test_order_by(self): Dummy.create(name='foo0', age=3) Dummy.create(name='foo2', age=6) Dummy.create(name='foo2', age=7) Dummy.create(name='foo3', age=4) Dummy.create(name='foo4', age=2) rv = Dummy.cq('age').order_by('age').all() self.assertEqual(rv, [2, 3, 4, 6, 7]) rv = Dummy.cq('age').order_by(Dummy.age).all() self.assertEqual(rv, [2, 3, 4, 6, 7]) rv = Dummy.cq('age').order_by(Dummy.age.desc()).all() self.assertEqual(rv, [7, 6, 4, 3, 2]) age = Dummy.age.alias('a') rv = Dummy.cq(age).order_by(age).all() self.assertEqual(rv, [2, 3, 4, 6, 7]) rv = Dummy.cq(age).order_by(age.desc()).all() self.assertEqual(rv, [7, 6, 4, 3, 2]) rv = Dummy.cq(age).order_by(Dummy.id.asc(), Dummy.age.desc()).all() self.assertEqual(rv, [3, 6, 7, 4, 2]) rv = Dummy.cq(age).order_by(Dummy.age.in_([2, 4]).desc(), Dummy.id.desc()).all() # noqa self.assertEqual(rv, [2, 4, 7, 6, 3]) rv = Dummy.cq(age).order_by(Dummy.age.in_([2, 4]).desc()).order_by(Dummy.id.desc()).all() # noqa self.assertEqual(rv, [2, 4, 7, 6, 3]) def test_group_by(self): Dummy.create(name='foo0', age=1) Dummy.create(name='foo2', age=2) Dummy.create(name='foo2', age=2) Dummy.create(name='foo3', age=3) Dummy.create(name='foo4', age=3) rv = Dummy.cq('age', funcs.COUNT(1)).group_by('age').order_by('age').all() self.assertEqual(rv, [(1, 1), (2, 2), (3, 2)]) rv = Dummy.cq('name', 'age').group_by('name', 'age').order_by('age').all() self.assertEqual(rv, [('foo0', 1), ('foo2', 2), ('foo3', 3), ('foo4', 3)]) rv = Dummy.cq('name', 'age').group_by('name').group_by('age').order_by('age').all() self.assertEqual(rv, [('foo0', 1), ('foo2', 2), ('foo3', 3), ('foo4', 3)]) def test_having(self): # FIXME(PG) if is_pg: return Dummy.create(name='foo0', age=1) Dummy.create(name='foo2', age=2) Dummy.create(name='foo2', age=2) Dummy.create(name='foo3', age=3) Dummy.create(name='foo4', age=3) Dummy.create(name='foo5', age=3) c = COUNT(1).alias('c') rv = Dummy.cq('age', c).group_by( 'age' ).having(c > 2).all() self.assertEqual(rv, [(3, 3)]) def test_join(self): Dummy.create(name='dummy0', age=3) Dummy.create(name='dummy1', age=6) Dummy.create(name='dummy2', age=9) Foo.create(name='foo0', age=1) Foo.create(name='foo1', age=2) Foo.create(name='foo2', age=3) Foo.create(name='foo3', age=3) Foo.create(name='foo4', age=6) Foo.create(name='foo5', age=6) Foo.create(name='foo6', age=6) q = Foo.cq.join(Dummy).on(Foo.age == Dummy.age) res = q.all() self.assertEqual(len(res), 5) self.assertEqual({x.name for x in res}, { 'foo2', 'foo3', 'foo4', 'foo5', 'foo6' }) q = Dummy.cq.join(Foo).on(Foo.age == Dummy.age) res = q.all() self.assertEqual(len(res), 5) self.assertEqual({x.name for x in res}, { 'dummy0', 'dummy0', 'dummy1', 'dummy1', 'dummy1' }) q = Dummy.cq.join(Foo).on(Foo.age == Dummy.age, Dummy.age == 6) res = q.all() self.assertEqual(len(res), 3) self.assertEqual({x.name for x in res}, { 'dummy1', 'dummy1', 'dummy1' }) q = Dummy.cq(DISTINCT(Dummy.id)).join(Foo).on( Foo.age == Dummy.age ).order_by( Foo.id.desc(), Dummy.age.desc() ) res = q.all() self.assertEqual(res, [2, 1]) q = Dummy.cq(DISTINCT(Dummy.id)).left_join(Foo).on( Foo.age == Dummy.age ).order_by( Foo.id.desc(), Dummy.age.desc() ) res = q.all() if is_pg: self.assertEqual(res, [3, 2, 1]) else: self.assertEqual(res, [2, 1, 3]) q = Dummy.cq(DISTINCT(Dummy.id)).right_join(Foo).on( Foo.age == Dummy.age ).order_by( Foo.id.desc(), Dummy.age.desc() ) res = q.all() self.assertEqual(res, [2, 1, None]) def test_sum(self): Dummy.create(name='foo0', age=1) Dummy.create(name='foo2', age=2) Dummy.create(name='foo3', age=3) rv = Dummy.cq(SUM(Dummy.age)).first() self.assertEqual(rv, 6) def test_avg(self): Dummy.create(name='foo0', age=1) Dummy.create(name='foo2', age=2) Dummy.create(name='foo3', age=3) rv = Dummy.cq(AVG(Dummy.age)).first() self.assertEqual(rv, 2)	nilq/baby-python	python
from setuptools import setup setup( name='german_transliterate', version='0.1.3', author='repodiac', author_email='spamornot@gmx.net', packages=['german_transliterate'], url='http://github.com/repodiac/german_transliterate', license='CC-BY-4.0 License', description='german_transliterate can clean and transliterate (i.e. normalize) German text including abbreviations, numbers, timestamps etc.', long_description=open('README.md', encoding="UTF-8").read(), install_requires=[ "num2words", ], )	nilq/baby-python	python
# This file is part of Sequana software # # Copyright (c) 2016-2021 - Sequana Development Team # # # Distributed under the terms of the 3-clause BSD license. # The full license is in the LICENSE file, distributed with this software. # # website: https://github.com/sequana/sequana # documentation: http://sequana.readthedocs.io # ############################################################################## import os import sys import shutil from easydev import execute, TempFile, md5 from sequana.lazy import pandas as pd from sequana.lazy import pylab from sequana.lazy import numpy as np from sequana.misc import wget from sequana import sequana_config_path from colormap import Colormap import colorlog logger = colorlog.getLogger(__name__) __all__ = [ "KrakenResults", "KrakenPipeline", "KrakenAnalysis", "KrakenDownload", "KrakenSequential", "KrakenDB", ] class KrakenDB: """Class to handle a kraken DB""" def __init__(self, filename): if isinstance(filename, KrakenDB): filename = filename.path if os.path.exists(filename) is False: possible_path = sequana_config_path + "/kraken2_dbs/" + filename if os.path.exists(possible_path) is True: self.path = possible_path else: msg = f"{filename} not found locally or in {sequana_config_path}." raise IOError(msg) else: self.path = os.path.abspath(filename) self.name = os.path.basename(self.path) def _get_database_version(self): if os.path.exists(self.path + os.sep + "hash.k2d"): return "kraken2" else: # pragma: no cover logger.error( "Sequana supports kraken2 only. Looks like an invalid kraken database directory" ) version = property(_get_database_version) def __repr__(self): return self.name class KrakenResults(object): """Translate Kraken results into a Krona-compatible file If you run a kraken analysis with :class:`KrakenAnalysis`, you will end up with a file e.g. named kraken.out (by default). You could use kraken-translate but then you need extra parsing to convert into a Krona-compatible file. Here, we take the output from kraken and directly transform it to a krona-compatible file. kraken2 uses the --use-names that needs extra parsing. :: k = KrakenResults("kraken.out") k.kraken_to_krona() Then format expected looks like:: C HISEQ:426:C5T65ACXX:5:2301:18719:16377 1 203 1:71 A:31 1:71 C HISEQ:426:C5T65ACXX:5:2301:21238:16397 1 202 1:71 A:31 1:71 Where each row corresponds to one read. :: "562:13 561:4 A:31 0:1 562:3" would indicate that: the first 13 k-mers mapped to taxonomy ID #562 the next 4 k-mers mapped to taxonomy ID #561 the next 31 k-mers contained an ambiguous nucleotide the next k-mer was not in the database the last 3 k-mers mapped to taxonomy ID #562 For kraken2, format is slighlty different since it depends on paired or not. If paired, :: C read1 2697049 151\|151 2697049:117 \|:\| 0:1 2697049:116 See kraken documentation for details. .. note:: a taxon of ID 1 (root) means that the read is classified but in differen domain. https://github.com/DerrickWood/kraken/issues/100 .. note:: This takes care of fetching taxons and the corresponding lineages from online web services. """ def __init__(self, filename="kraken.out", verbose=True): """.. rubric:: constructor :param filename: the input from KrakenAnalysis class """ self.filename = filename on_rtd = os.environ.get("READTHEDOCS", None) == "True" if on_rtd is False: from sequana.taxonomy import Taxonomy self.tax = Taxonomy(verbose=verbose) self.tax.download_taxonomic_file() # make sure it is available locally else: # pragma: no cover class Taxonomy(object): # pragma: no cover from sequana import sequana_data # must be local df = pd.read_csv(sequana_data("test_taxon_rtd.csv"), index_col=0) def get_lineage_and_rank(self, x): # Note that we add the name as well here ranks = [ "kingdom", "phylum", "class", "order", "family", "genus", "species", "name", ] return [(self.df.loc[x][rank], rank) for rank in ranks] self.tax = Taxonomy() if filename: # This initialise the data self._parse_data() self._data_created = False def get_taxonomy_db(self, ids): """Retrieve taxons given a list of taxons :param list ids: list of taxons as strings or integers. Could also be a single string or a single integer :return: a dataframe .. note:: the first call first loads all taxons in memory and takes a few seconds but subsequent calls are much faster """ # filter the lineage to keep only information from one of the main rank # that is superkingdom, kingdom, phylum, class, order, family, genus and # species ranks = ("kingdom", "phylum", "class", "order", "family", "genus", "species") if isinstance(ids, int): ids = [ids] if len(ids) == 0: return pd.DataFrame() if isinstance(ids, list) is False: ids = [ids] lineage = [self.tax.get_lineage_and_rank(x) for x in ids] # Now, we filter each lineage to keep only relevant ranks # There are a few caveats though as explained hereafter # we merge the kingdom and superkingdom and subkingdom results = [] for i, this in enumerate(lineage): default = dict.fromkeys(ranks, " ") for entry in this: if entry[1] in ranks: default[entry[1]] = entry[0] # if there is a superkingdom, overwrite the kingdom for entry in this: if entry[1] == "superkingdom": default["kingdom"] = entry[0] if default["kingdom"] == " ": for entry in this: if entry[1] == "subkingdom": default["kingdom"] = entry[0] # in theory, we have now populated all ranks; # Yet, there are several special cases (need examples): # 1. all ranks are filled: perfect # 2. some ranks are empty: we fill them with a space. # 3. all ranks are empty: # a. this is the root # b. this may be expected. e.g for an artifical sequence # c. all ranks below species are empty --> this is probably # what we will get e.g. for plasmids # case 3.b if set([x[1] for x in this]) == {"no rank", "species"}: # we can ignore the root and keep the others # if we end up with more than 6 entries, this is annoying # let us put a warning for now. count = 0 for x in this: if x[1] == "no rank" and x[0] != "root": default[ranks[count]] = x[0] count += 1 if count > 6: logger.warning("too many no_rank in taxon{}".format(ids[i])) break # for the name, we take the last entry, which is suppose to be the # scientific name found, so the scientific name of the taxon itself. # Note that this is not alwyas the species rank name # For instance for the taxon 2509511, the ID correspond to # a subgenus of Sarbecovirus and has no species entry. last_name, last_rank = this[-1] if last_rank not in ["species", "no rank"]: default["name"] = f"{last_rank}:{last_name}" else: default["name"] = "" results.append(default) df = pd.DataFrame.from_records(results) df.index = ids df = df[list(ranks) + ["name"]] df.index = df.index.astype(int) return df def _parse_data(self): taxonomy = {} logger.info("Reading kraken data from {}".format(self.filename)) columns = ["status", "taxon", "length"] # we select only col 0,2,3 to save memory, which is required on very # large files try: # each call to concat in the for loop below # will take time and increase with chunk position. # for 15M reads, this has a big cost. So chunksize set to 1M # is better than 1000 and still reasonable in memory reader = pd.read_csv( self.filename, sep="\t", header=None, usecols=[0, 2, 3], chunksize=1000000, ) except pd.errors.EmptyDataError: # pragma: no cover logger.warning("Empty files. 100%% unclassified ?") self.unclassified = "?" # size of the input data set self.classified = 0 self._df = pd.DataFrame([], columns=columns) self._taxons = self._df.taxon return except pd.errors.ParserError: # raise NotImplementedError # this section is for the case # #only_classified_output when there is no found classified read raise NotImplementedError for chunk in reader: try: self._df self._df = pd.concat([self._df, chunk]) except AttributeError: self._df = chunk self._df.columns = columns count = sum(self._df.taxon == 1) percentage = count / len(self._df) * 100 if percentage >= 1: logger.warning( "Found {} taxons of classified reads with root ID (1) ({} %)".format( count, round(percentage, 2) ) ) # This gives the list of taxons as index and their amount # above, we select only columns 0, 2, 3 the column are still labelled # 0, 2, 3 in the df self._taxons = self._df.groupby("taxon").size() try: self._taxons.drop(0, inplace=True) except: pass # 0 may not be there self._taxons.sort_values(ascending=False, inplace=True) category = self.df.groupby("status").size() if "C" in category.index: self.classified = category["C"] else: self.classified = 0 if "U" in category.index: self.unclassified = category["U"] else: self.unclassified = 0 logger.debug(self.taxons.iloc[0:10]) def _get_taxons(self): try: return self._taxons except: self._parse_data() return self._taxons taxons = property(_get_taxons) def _get_df(self): try: return self._df except: self._parse_data() return self._df df = property(_get_df) def _get_df_with_taxon(self, dbname): df = self.get_taxonomy_db([int(x) for x in self.taxons.index]) df["count"] = self.taxons.values df.reset_index(inplace=True) newrow = len(df) df.loc[newrow] = "Unclassified" df.loc[newrow, "count"] = self.unclassified df.loc[newrow, "index"] = -1 df.rename(columns={"index": "taxon"}, inplace=True) df["percentage"] = df["count"] / df["count"].sum() * 100 starter = ["taxon", "count", "percentage"] df = df[starter + [x for x in df.columns if x not in starter]] df.sort_values(by="percentage", inplace=True, ascending=False) return df def kraken_to_csv(self, filename, dbname): df = self._get_df_with_taxon(dbname) df.to_csv(filename, index=False) return df def kraken_to_json(self, filename, dbname): df = self._get_df_with_taxon(dbname) try: df.to_json(filename, indent=4, orient="records") except: df.to_json(filename, orient="records") return df def kraken_to_krona(self, output_filename=None, nofile=False): """ :return: status: True is everything went fine otherwise False """ if output_filename is None: output_filename = self.filename + ".summary" taxon_to_find = list(self.taxons.index) if len(taxon_to_find) == 0: logger.warning( "No reads were identified. You will need a more complete database" ) self.output_filename = output_filename with open(output_filename, "w") as fout: fout.write("%s\t%s" % (self.unclassified, "Unclassified")) return False if len(taxon_to_find) == 0: return False df = self.get_taxonomy_db(taxon_to_find) self.lineage = [";".join(this) for this in df[df.columns[0:-1]].values] self.scnames = list(df["name"].values) # do we need a cast ? # Now save the file self.output_filename = output_filename with open(output_filename, "w") as fout: for i, this in enumerate(self.lineage): taxon = taxon_to_find[i] count = self.taxons.loc[taxon] line = str(count) + "\t" + "\t".join(this.split(";")) line += " " + self.scnames[i] fout.write(line + "\n") try: fout.write("%s\t%s" % (self.unclassified, "Unclassified")) except: pass # unclassified may not exists if all classified self._data_created = True return True def plot2(self, kind="pie", fontsize=12): """This is the simplified static krona-like plot included in HTML reports""" import matplotlib.pyplot as plt taxons = self.taxons.copy() if len(self.taxons.index) == 0: return None df = self.get_taxonomy_db(list(self.taxons.index)) self.dd = df if self.unclassified > 0: df.loc[-1] = ["Unclassified"] * 8 taxons[-1] = self.unclassified df["ratio"] = taxons / taxons.sum() * 100 data_class = df.groupby(["kingdom", "class"]).sum() data_species = df.groupby(["kingdom", "species"]).sum() X = [] Y = [] Z = [] labels = [] zlabels, ztaxons = [], [] kingdom_colors = [] inner_colors = [] inner_labels = [] species_colors = [] taxons = df["species"].reset_index().set_index("species") for kingdom in data_class.index.levels[0]: # kingdom info X.append(data_class.loc[kingdom].ratio.sum()) # class info y = list(data_class.loc[kingdom].ratio.values) temp = data_class.loc[kingdom] y1 = temp.query("ratio>=0.5") y2 = temp.query("ratio<0.5") y = list(y1.ratio.values) + list(y2.ratio.values) inner_labels += list(y1.ratio.index) + [""] * len(y2.ratio) Y.extend(y) # species info temp = data_species.loc[kingdom] z1 = temp.query("ratio>=0.5") z2 = temp.query("ratio<0.5") z = list(z1.ratio.values) + list(z2.ratio.values) zlabels += list(z1.ratio.index) + [""] * len(z2.ratio) Z.extend(z) if kingdom.strip(): labels.append(kingdom) else: labels.append("undefined/unknown taxon") if kingdom == "Eukaryota": this_cmap = plt.cm.Purples elif kingdom == "Unclassified": this_cmap = plt.cm.Greys elif kingdom == "Bacteria": this_cmap = plt.cm.Reds elif kingdom == "Viruses": this_cmap = plt.cm.Greens elif kingdom == "Archaea": this_cmap = Colormap().cmap_linear("yellow", "yellow", "orange") else: this_cmap = Colormap().cmap_linear( "light gray", "gray(w3c)", "dark gray" ) kingdom_colors.append(this_cmap(0.8)) inner_colors.extend(this_cmap(np.linspace(0.6, 0.2, len(y)))) species_colors.extend(this_cmap(np.linspace(0.6, 0.2, len(z)))) fig, ax = pylab.subplots(figsize=(9.5, 7)) size = 0.2 pct_distance = 0 w1, l1 = ax.pie( X, radius=1 - 2 * size, colors=kingdom_colors, wedgeprops=dict(width=size, edgecolor="w"), labels=labels, labeldistance=0.4, ) w2, l2 = ax.pie( Y, radius=1 - size, colors=inner_colors, labels=[x.replace("Unclassified", "") for x in inner_labels], wedgeprops=dict(width=size, edgecolor="w"), labeldistance=0.65, ) # labels can be long. Let us cut them zlabels2 = [] for this in zlabels: if len(this) > 30: zlabels2.append(this[0:30] + "...") else: zlabels2.append(this) w3, l3 = ax.pie( Z, radius=1, colors=species_colors, labels=[x.replace("Unclassified", "") for x in zlabels2], wedgeprops=dict(width=size, edgecolor="w"), labeldistance=0.9, ) ax.set(aspect="equal") pylab.subplots_adjust(right=1, left=0, bottom=0, top=1) pylab.legend(labels, title="kingdom", loc="upper right", fontsize=fontsize) import webbrowser mapper = {k: v for k, v in zip(zlabels, Z)} def on_pick(event): wedge = event.artist label = wedge.get_label() if mapper[label] > 1: taxon = taxons.loc[label, "index"] webbrowser.open( "https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id={}".format( taxon ) ) else: wedge.set_color("white") for wedge in w3: wedge.set_picker(True) fig.canvas.mpl_connect("pick_event", on_pick) # this is used to check that everything was okay in the rules return df def plot( self, kind="pie", cmap="tab20c", threshold=1, radius=0.9, textcolor="red", delete_krona_file=False, kargs, ): """A simple non-interactive plot of taxons :return: None if no taxon were found and a dataframe otherwise A Krona Javascript output is also available in :meth:`kraken_to_krona` .. plot:: :include-source: from sequana import KrakenResults, sequana_data test_file = sequana_data("kraken.out", "doc") k = KrakenResults(test_file) df = k.plot(kind='pie') .. seealso:: to generate the data see :class:`KrakenPipeline` or the standalone application sequana_taxonomy*. .. todo:: For a future release, we could use this kind of plot https://stackoverflow.com/questions/57720935/how-to-use-correct-cmap-colors-in-nested-pie-chart-in-matplotlib """ if len(self._df) == 0: return if self._data_created == False: status = self.kraken_to_krona() if kind not in ["barh", "pie"]: logger.error("kind parameter: Only barh and pie are supported") return # This may have already been called but maybe not. This is not time # consuming, so we call it again here if len(self.taxons.index) == 0: return None df = self.get_taxonomy_db(list(self.taxons.index)) if self.unclassified > 0: df.loc[-1] = ["Unclassified"] 8 data = self.taxons.copy() # we add the unclassified only if needed if self.unclassified > 0: data.loc[-1] = self.unclassified data = data / data.sum() * 100 assert threshold > 0 and threshold < 100 # everything below the threshold (1) is gather together and summarised # into 'others' others = data[data < threshold].sum() data = data[data >= threshold] names = df.loc[data.index]["name"] data.index = names.values if others > 0: data.loc["others"] = others try: data.sort_values(inplace=True) except: data.sort(inplace=True) pylab.figure(figsize=(10, 8)) pylab.clf() self.dd = data if kind == "pie": ax = data.plot( kind=kind, cmap=cmap, autopct="%1.1f%%", radius=radius, kargs ) pylab.ylabel(" ") for text in ax.texts: # large, x-small, small, None, x-large, medium, xx-small, # smaller, xx-large, larger text.set_size("small") text.set_color(textcolor) for wedge in ax.patches: wedge.set_linewidth(1) wedge.set_edgecolor("k") self.ax = ax elif kind == "barh": ax = data.plot(kind=kind, kargs) pylab.xlabel(" percentage ") if delete_krona_file: os.remove(self.filename + ".summary") return data def to_js(self, output="krona.html"): if self._data_created == False: status = self.kraken_to_krona() execute("ktImportText %s -o %s" % (self.output_filename, output)) def boxplot_classified_vs_read_length(self): """Show distribution of the read length grouped by classified or not""" # if paired and kraken2, there are \| in length to separate both reads. # to simplify, if this is the case, we will just take the first read # length for now. df = self.df.copy() try: # kraken2 df.length = df.length.apply(lambda x: int(x.split("\|")[0])) except: pass df[["status", "length"]].groupby("status").boxplot() return df def histo_classified_vs_read_length(self): """Show distribution of the read length grouped by classified or not""" # if paired and kraken2, there are \| in length to separate both reads. # to simplify, if this is the case, we will just take the first read # length for now. df = self.df.copy() if "\|" in str(df.length.values[0]): df.length = df.length.apply(lambda x: int(x.split("\|")[0])) df = df[["status", "length"]] M = df["length"].max() df.hist(by="status", sharey=True, bins=pylab.linspace(0, M, int(M / 5))) axes = pylab.gcf().get_axes() axes[0].set_xlabel("read length") axes[1].set_xlabel("read length") axes[1].grid(True) axes[0].grid(True) return df class KrakenPipeline(object): """Used by the standalone application sequana_taxonomy This runs Kraken on a set of FastQ files, transform the results in a format compatible for Krona, and creates a Krona HTML report. :: from sequana import KrakenPipeline kt = KrakenPipeline(["R1.fastq.gz", "R2.fastq.gz"], database="krakendb") kt.run() kt.show() .. warning:: We do not provide Kraken database within sequana. You may either download a database from https://ccb.jhu.edu/software/kraken/ or use this class to download a toy example that will be stored in e.g .config/sequana under Unix platforms. See :class:`KrakenDownload`. .. seealso:: We provide a standalone application of this class, which is called sequana_taxonomy and can be used within a command shell. """ def __init__( self, fastq, database, threads=4, output_directory="kraken", dbname=None, confidence=0, ): """.. rubric:: Constructor :param fastq: either a fastq filename or a list of 2 fastq filenames :param database: the path to a valid Kraken database :param threads: number of threads to be used by Kraken :param output_directory: output filename of the Krona HTML page :param dbname: Description: internally, once Kraken has performed an analysis, reads are associated to a taxon (or not). We then find the correponding lineage and scientific names to be stored within a Krona formatted file. KtImportTex is then used to create the Krona page. """ # Set and create output directory self.output_directory = output_directory try: os.makedirs(output_directory) except FileExistsError: pass self.database = database self.ka = KrakenAnalysis(fastq, database, threads, confidence=confidence) if dbname is None: self.dbname = os.path.basename(database) else: self.dbname = dbname def run( self, output_filename_classified=None, output_filename_unclassified=None, only_classified_output=False, ): """Run the analysis using Kraken and create the Krona output .. todo:: reuse the KrakenResults code to simplify this method. """ # Run Kraken (KrakenAnalysis) kraken_results = self.output_directory + os.sep + "kraken.out" self.ka.run( output_filename=kraken_results, output_filename_unclassified=output_filename_unclassified, output_filename_classified=output_filename_classified, only_classified_output=only_classified_output, ) # Translate kraken output to a format understood by Krona and save png # image self.kr = KrakenResults(kraken_results, verbose=False) # we save the pie chart try: self.kr.plot2(kind="pie") except Exception as err: logger.warning(err) self.kr.plot(kind="pie") pylab.savefig(self.output_directory + os.sep + "kraken.png") # we save information about the unclassified reads (length) try: self.kr.boxplot_classified_vs_read_length() pylab.savefig(self.output_directory + os.sep + "boxplot_read_length.png") except Exception as err: logger.warning("boxplot read length could not be computed") try: self.kr.histo_classified_vs_read_length() pylab.savefig(self.output_directory + os.sep + "hist_read_length.png") except Exception as err: logger.warning("hist read length could not be computed") prefix = self.output_directory + os.sep self.kr.kraken_to_json(prefix + "kraken.json", self.dbname) self.kr.kraken_to_csv(prefix + "kraken.csv", self.dbname) # Transform to Krona HTML from snakemake import shell kraken_html = self.output_directory + os.sep + "kraken.html" status = self.kr.kraken_to_krona(output_filename=prefix + "kraken.out.summary") if status is True: shell( "ktImportText %s -o %s" % (prefix + "kraken.out.summary", kraken_html) ) else: shell("touch {}".format(kraken_html)) # finally a summary database = KrakenDB(self.database) summary = {"database": [database.name]} summary[database.name] = {"C": int(self.kr.classified)} summary["U"] = int(self.kr.unclassified) summary["total"] = int(self.kr.unclassified + self.kr.classified) # redundant but useful and compatible with sequential approach summary["unclassified"] = int(self.kr.unclassified) summary["classified"] = int(self.kr.classified) return summary def show(self): """Opens the filename defined in the constructor""" from easydev import onweb onweb(self.output) class KrakenAnalysis(object): """Run kraken on a set of FastQ files In order to run a Kraken analysis, we firtst need a local database. We provide a Toy example. The ToyDB is downloadable as follows ( you will need to run the following code only once):: from sequana import KrakenDownload kd = KrakenDownload() kd.download_kraken_toydb() .. seealso:: :class:`KrakenDownload` for more databases The path to the database is required to run the analysis. It has been stored in the directory ./config/sequana/kraken_toydb under Linux platforms The following code should be platform independent:: import os from sequana import sequana_config_path database = sequana_config_path + os.sep + "kraken_toydb") Finally, we can run the analysis on the toy data set:: from sequana import sequana_data data = sequana_data("Hm2_GTGAAA_L005_R1_001.fastq.gz", "data") ka = KrakenAnalysis(data, database=database) ka.run() This creates a file named kraken.out. It can be interpreted with :class:`KrakenResults` """ def __init__(self, fastq, database, threads=4, confidence=0): """.. rubric:: Constructor :param fastq: either a fastq filename or a list of 2 fastq filenames :param database: the path to a valid Kraken database :param threads: number of threads to be used by Kraken :param confidence: parameter used by kraken2 :param return: """ self.database = KrakenDB(database) self.threads = threads self.confidence = confidence # Fastq input if isinstance(fastq, str): self.paired = False self.fastq = [fastq] elif isinstance(fastq, list): if len(fastq) == 2: self.paired = True elif len(fastq) == 1: self.paired = False else: raise IOError(("You must provide 1 or 2 files")) self.fastq = fastq else: raise ValueError("Expected a fastq filename or list of 2 fastq filenames") def run( self, output_filename=None, output_filename_classified=None, output_filename_unclassified=None, only_classified_output=False, ): """Performs the kraken analysis :param str output_filename: if not provided, a temporary file is used and stored in :attr:`kraken_output`. :param str output_filename_classified: not compressed :param str output_filename_unclassified: not compressed """ if self.database.version != "kraken2": logger.error(f"input database is not valid kraken2 database") sys.exit(1) if output_filename is None: self.kraken_output = TempFile().name else: self.kraken_output = output_filename dirname = os.path.dirname(output_filename) if os.path.exists(dirname) is False: os.makedirs(dirname) # make sure the required output directories exist: # and that the output filenames ends in .fastq if output_filename_classified: assert output_filename_classified.endswith(".fastq") dirname = os.path.dirname(output_filename_classified) if os.path.exists(dirname) is False: os.makedirs(dirname) if output_filename_unclassified: assert output_filename_unclassified.endswith(".fastq") dirname = os.path.dirname(output_filename_unclassified) if os.path.exists(dirname) is False: os.makedirs(dirname) params = { "database": self.database.path, "thread": self.threads, "file1": self.fastq[0], "kraken_output": self.kraken_output, "output_filename_unclassified": output_filename_unclassified, "output_filename_classified": output_filename_classified, } if self.paired: params["file2"] = self.fastq[1] command = f"kraken2 --confidence {self.confidence}" command += f" {params['file1']}" if self.paired: command += f" {params['file2']} --paired" command += f" --db {params['database']} " command += f" --threads {params['thread']} " command += f" --output {params['kraken_output']} " # If N is number of reads unclassified 3 cases depending on out-fmt # choice # case1 --paired and out-fmt legacy saved fasta R1 and R2 together on N lines # case2 --paired and out-fmt interleaved saved fasta R1 and R2 alternatively on 2N lines # case3 --paired and out-fmt paired saved R1 on N lines. Where is R2 ???? # Note, that there is always one single file. So, the only way for # kraken to know that this new files (used as input) is paired, is to # use --paired. # In any case, this new file looks like an R1-only file. Indeed, if # interleaved, all data inside the file, if legacy, The R1 and R2 are # separated by N but a unique sequence. If --out-fmt is paired, this is # annoying. Indeed, half of the data is lost. # So, if now input is # case1, we cannot provide --paired # case2 we cannot either, so how are R1 and R2 taken care of ? # besides, if provided, the interleaved input is seen as single ended. # Indeed, if provided, --out-fmt cannot be interleaved since krakne1 # complains that input is not paired. # case3, only R1 so we cannot use --paired # if kraken2, there is no --out-fmt option, so output is always a fastq # with either R1 only or two output files. # If we omit the --paired options, the 2 input R1 and R2 are considered # as 2 different unrelated samples # if we use --paired we now must have # in the file name, and then # the two files are created if self.database.version == "kraken2": if output_filename_unclassified: command += " --unclassified-out %(output_filename_unclassified)s " if output_filename_classified: command += " --classified-out %(output_filename_classified)s " command = command % params logger.debug(command) from snakemake import shell shell(command) if only_classified_output: # kraken2 has no classified_output option. we mimic it here below # just to get a temporary filename fout = TempFile() outname = fout.name newfile = open(outname, "w") with open(output_filename, "r") as fin: for line in fin.readlines(): if line.startswith("C"): newfile.write(line) newfile.close() shutil.move(outname, output_filename) # a simple utility function try: from itertools import izip_longest except: from itertools import zip_longest as izip_longest def grouper(iterable): args = [iter(iterable)] * 8 return izip_longest(args) class KrakenSequential(object): """Kraken Sequential Analysis This runs Kraken on a FastQ file with multiple k-mer databases in a sequencial way way. Unclassified sequences with the first database are input for the second, and so on. The input may be a single FastQ file or paired, gzipped or not. FastA are also accepted. """ def __init__( self, filename_fastq, fof_databases, threads=1, output_directory="./kraken_sequential/", keep_temp_files=False, output_filename_unclassified=None, output_filename_classified=None, force=False, confidence=0, ): """.. rubric:: constructor* :param filename_fastq: FastQ file to analyse :param fof_databases: file that contains a list of databases paths (one per line). The order is important. Note that you may also provide a list of datab ase paths. :param threads: number of threads to be used by Kraken :param output_directory: name of the output directory :param keep_temp_files: bool, if True, will keep intermediate files from each Kraken analysis, and save html report at each step :param bool force: if the output directory already exists, the instanciation fails so that the existing data is not overrwritten. If you wish to overwrite the existing directory, set this parameter to iTrue. """ self.filename_fastq = filename_fastq self.confidence = confidence # input databases may be stored in a file if isinstance(fof_databases, str) and os.path.exists(fof_databases): with open(fof_databases, "r") as fof: self.databases = [ absolute_path.split("\n")[0] for absolute_path in fof.readlines() ] # or simply provided as a list elif isinstance(fof_databases, list): self.databases = fof_databases[:] else: raise TypeError( "input databases must be a list of valid kraken2 " "databases or a file (see documebntation)" ) self.databases = [KrakenDB(x) for x in self.databases] for d in self.databases: if d.version != "kraken2": logger.error(f"input database {d} is not valid kraken2 ") sys.exit(1) self.threads = threads self.output_directory = output_directory self.keep_temp_files = keep_temp_files # check if the output directory already exist try: os.mkdir(output_directory) except OSError: if os.path.isdir(output_directory) and force is False: logger.error("Output directory %s already exists" % output_directory) raise Exception elif force is True: logger.warning( "Output directory %s already exists. You may " "overwrite existing results" % output_directory ) # list of input fastq files if isinstance(filename_fastq, list) and len(filename_fastq) in [1, 2]: self.inputs = filename_fastq[:] elif isinstance(filename_fastq, str): self.inputs = [filename_fastq] else: msg = "input file must be a string or list of 2 filenames" msg += "\nYou provided {}".format(filename_fastq) raise TypeError(msg) if len(self.inputs) == 1: self.paired = False elif len(self.inputs) == 2: self.paired = True self.unclassified_output = output_filename_unclassified self.classified_output = output_filename_classified def _run_one_analysis(self, iteration): """Run one analysis""" db = self.databases[iteration] logger.info("Analysing data using database {}".format(db)) # a convenient alias _pathto = lambda x: self.output_directory + x # the output is saved in this file if self.paired: # if paired, kraken2 expect a # and then will create 2 files (1 and 2 # ) # Note that kraken adds a _ before the # (1,2) so no need to add one output_filename_unclassified = _pathto("unclassified_%d#.fastq" % iteration) file_fastq_unclass = [ _pathto("unclassified_%d_1.fastq" % iteration), _pathto("unclassified_%d_2.fastq" % iteration), ] else: output_filename_unclassified = _pathto("unclassified_%d.fastq" % iteration) file_fastq_unclass = _pathto("unclassified_%d.fastq" % iteration) if iteration == 0: inputs = self.inputs else: inputs = self._list_kraken_input[iteration - 1] # if this is the last iteration (even if iteration is zero), save # classified and unclassified in the final kraken results. if iteration == len(self.databases) - 1: only_classified_output = False else: only_classified_output = True file_kraken_out = self.output_directory + "/kraken_{}.out".format(iteration) # The analysis itself analysis = KrakenAnalysis(inputs, db, self.threads, confidence=self.confidence) analysis.run( output_filename=file_kraken_out, output_filename_unclassified=output_filename_unclassified, only_classified_output=only_classified_output, ) # save input/output files. self._list_kraken_input.append(file_fastq_unclass) self._list_kraken_output.append(file_kraken_out) def run(self, dbname="multiple", output_prefix="kraken_final"): """Run the sequential analysis :param dbname: :param output_prefix: :return: dictionary summarizing the databases names and classified/unclassied This method does not return anything creates a set of files: - kraken_final.out - krona_final.html - kraken.png (pie plot of the classified/unclassified reads) .. note:: the databases are run in the order provided in the constructor. """ # list of all output to merge at the end self._list_kraken_output = [] self._list_kraken_input = [] # Iteration over the databases for iteration in range(len(self.databases)): # The analysis itself status = self._run_one_analysis(iteration) last_unclassified = self._list_kraken_input[-1] # If everything was classified, we can stop here if isinstance(last_unclassified, str): stat = os.stat(last_unclassified) if stat.st_size == 0: break elif isinstance(last_unclassified, list): stat = os.stat(last_unclassified[0]) if stat.st_size == 0: break # concatenate all kraken output files file_output_final = self.output_directory + os.sep + "%s.out" % output_prefix with open(file_output_final, "w") as outfile: for fname in self._list_kraken_output: with open(fname) as infile: for line in infile: outfile.write(line) logger.info("Analysing final results") result = KrakenResults(file_output_final, verbose=False) try: result.histo_classified_vs_read_length() pylab.savefig(self.output_directory + os.sep + "hist_read_length.png") except Exception as err: logger.warning("hist read length could not be computed") try: result.boxplot_classified_vs_read_length() pylab.savefig(self.output_directory + os.sep + "boxplot_read_length.png") except Exception as err: logger.warning("hist read length could not be computed") # TODO: this looks similar to the code in KrakenPipeline. could be factorised result.to_js("%s%s%s.html" % (self.output_directory, os.sep, output_prefix)) try: result.plot2(kind="pie") except Exception as err: logger.warning(err) result.plot(kind="pie") pylab.savefig(self.output_directory + os.sep + "kraken.png") prefix = self.output_directory + os.sep result.kraken_to_json(prefix + "kraken.json", dbname) result.kraken_to_csv(prefix + "kraken.csv", dbname) # remove kraken intermediate files (including unclassified files) if self.unclassified_output: # Just cp the last unclassified file try: # single-end data (one file) shutil.copy2(self._list_kraken_input[-1], self.unclassified_output) except: for i, x in enumerate(self._list_kraken_input[-1]): shutil.copy2(x, self.unclassified_output.replace("#", str(i + 1))) if self.classified_output: # Just cp the last classified file shutil.copy2(self._list_kraken_input[-1], self.classified_output) summary = {"databases": [x.name for x in self.databases]} total = 0 classified = 0 for f_temp, db in zip(self._list_kraken_output, self.databases): # In theory, the first N-1 DB returns only classified (C) read # and the last one contains both try: df = pd.read_csv(f_temp, sep="\t", header=None, usecols=[0]) C = sum(df[0] == "C") U = sum(df[0] == "U") except pd.errors.EmptyDataError: # if no read classified, C = 0 U = 0 total += U total += C classified += C summary[db.name] = {"C": C} if U != 0: # the last one summary["unclassified"] = U summary["total"] = total summary["classified"] = classified if not self.keep_temp_files: # kraken_0.out for f_temp in self._list_kraken_output: os.remove(f_temp) # unclassified for f_temp in self._list_kraken_input: if isinstance(f_temp, str): os.remove(f_temp) elif isinstance(f_temp, list): for this in f_temp: os.remove(this) return summary class KrakenDownload(object): """Utility to download Kraken DB and place them in a local directory :: from sequana import KrakenDownload kd = KrakenDownload() kd.download('toydb') """ def __init__(self, output_dir=None): if output_dir is None: self.output_dir = f"{sequana_config_path}{os.sep}kraken2_dbs" else: self.output_dir = output_dir def download(self, name, verbose=True): if name == "toydb": self._download_kraken2_toydb(verbose=verbose) else: raise ValueError("name must be 'toydb' for now") def _download_kraken2_toydb(self, verbose=True): """Download the kraken DB toy example from sequana_data into .config/sequana directory Checks the md5 checksums. About 32Mb of data """ base = f"{self.output_dir}{os.sep}toydb" try: os.makedirs(base) except FileExistsError: pass baseurl = "https://github.com/sequana/data/raw/master/" # download only if required logger.info("Downloading the database into %s" % base) md5sums = [ "31f4b20f9e5c6beb9e1444805264a6e5", "733f7587f9c0c7339666d5906ec6fcd3", "7bb56a0f035b27839fb5c18590b79263", ] filenames = ["hash.k2d", "opts.k2d", "taxo.k2d"] for filename, md5sum in zip(filenames, md5sums): url = baseurl + f"kraken2_toydb/{filename}" filename = base + os.sep + filename if os.path.exists(filename) and md5(filename) == md5sum: logger.warning(f"{filename} already present with good md5sum") else: logger.info(f"Downloading {url}") wget(url, filename)	nilq/baby-python	python
import planckStyle as s g = s.getSubplotPlotter() g.settings.legend_fontsize -= 3.5 g.settings.lineM = ['-g', '-r', '-b', '-k', '--r', '--b'] pol = ['TT', 'TE', 'EE', 'TTTEEE'] dataroots = [ getattr(s, 'defdata_' + p) for p in pol] dataroots += [dataroots[1].replace('lowEB', 'lowTEB'), dataroots[2].replace('lowEB', 'lowTEB')] for par, marker in zip(['', 'nnu', 'mnu', 'Alens', 'r', 'yhe', 'nrun'], [None, 3.046, 0.06, 1, None, 0.2449, 0]): g.newPlot() base = 'base_' if par: base += par + '_' roots = [base + dat for dat in dataroots] labels = [s.datalabel[r] for r in dataroots] g.settings.legend_frac_subplot_margin = 0.15 plotpars = [ 'zrei', 'H0', 'omegabh2', 'thetastar', 'A', 'tau', 'omegam', 'omegach2', 'ns', 'sigma8'] if par: plotpars[0] = par g.plots_1d(roots, plotpars, nx=5, legend_ncol=len(roots), legend_labels=labels, share_y=True, markers=[marker]) g.export(tag=par)	nilq/baby-python	python
#!/usr/bin/env python # -- coding: utf-8 -- """Python KISS Module Test Constants.""" __author__ = 'Greg Albrecht W2GMD <oss@undef.net>' # NOQA pylint: disable=R0801 __copyright__ = 'Copyright 2017 Greg Albrecht and Contributors' # NOQA pylint: disable=R0801 __license__ = 'Apache License, Version 2.0' # NOQA pylint: disable=R0801 PANGRAM = 'the quick brown fox jumps over the lazy dog' ALPHABET = PANGRAM.replace(' ', '') NUMBERS = ''.join([str(x) for x in range(0, 10)]) POSITIVE_NUMBERS = NUMBERS[1:] ALPHANUM = ''.join([ALPHABET, NUMBERS]) TEST_FRAMES = 'tests/test_frames.log' TEST_FRAME = ( '82a0a4b0646860ae648e9a88406cae92888a62406303f021333734352e3735' '4e4931323232382e303557235732474d442d3620496e6e65722053756e73657' '42c2053462069476174652f4469676970656174657220687474703a2f2f7732' '676d642e6f7267')	nilq/baby-python	python
from toolz import get from functools import partial pairs = [(1, 2) for i in range(100000)] def test_get(): first = partial(get, 0) for p in pairs: first(p)	nilq/baby-python	python
import torch import torch.nn as nn import torch.optim as optim import pytorch_lightning as pl from network.dla import MOC_DLA from network.resnet import MOC_ResNet from trainer.losses import MOCLoss from MOC_utils.model import load_coco_pretrained_model backbone = { 'dla': MOC_DLA, 'resnet': MOC_ResNet } def fill_fc_weights(layers): for m in layers.modules(): if isinstance(m, nn.Conv2d): if m.bias is not None: nn.init.constant_(m.bias, 0) class MOC_Branch(nn.Module): def __init__(self, input_channel, arch, head_conv, branch_info, K): super(MOC_Branch, self).__init__() assert head_conv > 0 wh_head_conv = 64 if arch == 'resnet' else head_conv self.hm = nn.Sequential( nn.Conv2d(K * input_channel, head_conv, kernel_size=3, padding=1, bias=True), nn.ReLU(inplace=True), nn.Conv2d(head_conv, branch_info['hm'], kernel_size=1, stride=1, padding=0, bias=True)) self.hm[-1].bias.data.fill_(-2.19) self.mov = nn.Sequential( nn.Conv2d(K * input_channel, head_conv, kernel_size=3, padding=1, bias=True), nn.ReLU(inplace=True), nn.Conv2d(head_conv, branch_info['mov'], kernel_size=1, stride=1, padding=0, bias=True)) fill_fc_weights(self.mov) self.wh = nn.Sequential( nn.Conv2d(input_channel, wh_head_conv, kernel_size=3, padding=1, bias=True), nn.ReLU(inplace=True), nn.Conv2d(wh_head_conv, branch_info['wh'] // K, kernel_size=1, stride=1, padding=0, bias=True)) fill_fc_weights(self.wh) def forward(self, input_chunk): output = {} output_wh = [] for feature in input_chunk: output_wh.append(self.wh(feature)) input_chunk = torch.cat(input_chunk, dim=1) output_wh = torch.cat(output_wh, dim=1) output['hm'] = self.hm(input_chunk) output['mov'] = self.mov(input_chunk) output['wh'] = output_wh return output class MOC_Net(pl.LightningModule): def __init__(self, arch, num_classes, head_conv=256, K=7, *kwargs): super().__init__() self.save_hyperparameters() num_layers = int(arch[arch.find('_') + 1:]) if '_' in arch else 0 arch = arch[:arch.find('_')] if '_' in arch else arch branch_info = {'hm': num_classes, 'mov': 2 K, 'wh': 2 * K} self.K = K self.backbone = backbone[arch](num_layers) self.branch = MOC_Branch(self.backbone.output_channel, arch, head_conv, branch_info, K) # Define the loss function self.loss = MOCLoss() def forward(self, x): chunk = [self.backbone(x[i]) for i in range(self.K)] return [self.branch(chunk)] def configure_optimizers(self): if self.hparams.optimizer == 'sgd': return optim.SGD(self.parameters(), self.hparams.lr, momentum = 0.9) elif self.hparams.optimizer == 'adam': return optim.Adam(self.parameters(), self.hparams.lr) elif self.hparams.optimizer == 'adamax': return optim.Adamax(self.parameters(), self.hparams.lr) def run_epoch(self, phase, batch, batch_idx): assert len(batch['input']) == self.K output = self(batch['input'])[0] loss, loss_stats = self.loss(output, batch) self.log(f'{phase}_loss', loss, prog_bar=True, logger=True) self.log(f'{phase}_loss_hm', loss_stats['loss_hm'], prog_bar=True, logger=True) self.log(f'{phase}_loss_mov', loss_stats['loss_mov'], prog_bar=True, logger=True) self.log(f'{phase}_loss_wh', loss_stats['loss_wh'], prog_bar=True, logger=True) return loss.mean() def training_step(self, batch, batch_idx): return self.run_epoch("train", batch, batch_idx) def validation_step(self, batch, batch_idx): self.run_epoch("val", batch, batch_idx) def test_step(self, batch, batch_idx): self.run_epoch("test", batch, batch_idx) if __name__ == '__main__': num_classes = 24 K = 7 arch = 'resnet_18' head_conv = 256 model = MOC_Net(arch, num_classes, head_conv, K, lr=0.001, optimizer='adam') model = load_coco_pretrained_model(model, arch, print_log=False) input_shape = (1, 3, 288, 288) x = [torch.randn(input_shape)] * K # y = model.backbone(x) #1, 64, 72, 72 y = model(x) # print(len(y)) print(y[0].keys()) hm = y[0]['hm'] mov = y[0]['mov'] wh = y[0]['wh'] print(hm.shape) print(mov.shape) print(wh.shape) print(model.hparams) model.configure_optimizers()	nilq/baby-python	python
import os from django.http import HttpResponse from django.template import Context, RequestContext, loader def ajax_aware_render(request, template_list, context=None, kwargs): """ Render a template, using a different one automatically for AJAX requests. :param template_list: Either a template name or a list of template names. :param context: Optional extra context to pass to the template. For AJAX requests, the template list is altered to look for alternate templates first and the ``is_ajax`` context variable is set to ``True``. For example, if ``template_list`` was set to ``['custom/login.html', 'login.html']``, then an AJAX request will change this to:: ['custom/login.ajax.html', 'login.ajax.html', 'custom/login.html', 'login.html'] """ if not isinstance(context, Context): context = RequestContext(request, context) if isinstance(template_list, basestring): template_list = [template_list] if request.is_ajax(): ajax_template_list = [] for name in template_list: ajax_template_list.append('%s.ajax%s' % os.path.splitext(name)) template_list = ajax_template_list + list(template_list) context['is_ajax'] = True context['current_url'] = request.get_full_path() template = loader.select_template(template_list) return HttpResponse(template.render(context), kwargs)	nilq/baby-python	python
import logging import collections import time import six from six.moves import http_client from flask import url_for, g, jsonify from flask.views import MethodView import marshmallow as ma from flask_restx import reqparse from flask_smorest import Blueprint, abort from drift.core.extensions.urlregistry import Endpoints from driftbase.models.db import CorePlayer, Counter, CounterEntry from driftbase.utils import get_all_counters, get_counter from driftbase.players import get_playergroup_ids log = logging.getLogger(__name__) bp = Blueprint("counters", __name__, url_prefix="/counters", description="Counters") endpoints = Endpoints() NUM_RESULTS = 100 def drift_init_extension(app, api, **kwargs): api.register_blueprint(bp) endpoints.init_app(app) @bp.route('/', endpoint='list') class CountersApi(MethodView): get_args = reqparse.RequestParser() def get(self): """ Get a list of all 'leaderboards' """ all_counters = g.db.query(Counter).order_by(Counter.name).distinct() ret = [] for s in all_counters: ret.append({ "name": s.name, "label": s.label, "counter_id": s.counter_id, "url": url_for("counters.entry", counter_id=s.counter_id, _external=True) }) return jsonify(ret), http_client.OK, {'Cache-Control': "max_age=60"} @bp.route('/<int:counter_id>', endpoint='entry') class CounterApi(MethodView): get_args = reqparse.RequestParser() get_args.add_argument("num", type=int, default=NUM_RESULTS) get_args.add_argument("include", type=int, action='append') # TODO: Sunset this in favour of player_group get_args.add_argument("player_id", type=int, action='append') get_args.add_argument("player_group", type=str) get_args.add_argument("reverse", type=bool) #@namespace.expect(get_args) def get(self, counter_id): start_time = time.time() args = self.get_args.parse_args() num = args.get("num") or NUM_RESULTS counter = get_counter(counter_id) if not counter: abort(404) filter_player_ids = [] reverse = not not args.reverse if args.player_id: filter_player_ids = args.player_id query = g.db.query(CounterEntry, CorePlayer) query = query.filter(CounterEntry.counter_id == counter_id, CounterEntry.period == "total", CounterEntry.player_id == CorePlayer.player_id, CorePlayer.status == "active", CorePlayer.player_name != u"",) if filter_player_ids: query = query.filter(CounterEntry.player_id.in_(filter_player_ids)) if args.player_group: filter_player_ids = get_playergroup_ids(args.player_group) query = query.filter(CounterEntry.player_id.in_(filter_player_ids)) if reverse: query = query.order_by(CounterEntry.value) else: query = query.order_by(-CounterEntry.value) query = query.limit(num) rows = query.all() counter_totals = collections.defaultdict(list) counter_names = {} if args.include: all_counters = get_all_counters() # inline other counters for the players player_ids = [r[0].player_id for r in rows] counter_rows = g.db.query(CounterEntry.player_id, CounterEntry.counter_id, CounterEntry.value) \ .filter(CounterEntry.period == "total", CounterEntry.player_id.in_(player_ids), CounterEntry.counter_id.in_(args.include)) \ .all() for r in counter_rows: this_player_id = r[0] this_counter_id = r[1] this_value = r[2] # find the name of this counter. We cache this locally for performance try: counter_name = counter_names[this_counter_id] except KeyError: c = all_counters.get(six.text_type(this_counter_id), {}) name = c.get("name", this_counter_id) counter_names[this_counter_id] = name counter_name = name entry = { "name": counter_name, "counter_id": this_counter_id, "counter_url": url_for("player_counters.entry", player_id=this_player_id, counter_id=this_counter_id, _external=True), "total": this_value } counter_totals[r.player_id].append(entry) ret = [] for i, row in enumerate(rows): player_id = row[0].player_id entry = { "name": counter["name"], "counter_id": counter_id, "player_id": player_id, "player_name": row[1].player_name, "player_url": url_for("players.entry", player_id=player_id, _external=True), "counter_url": url_for("player_counters.entry", player_id=player_id, counter_id=row[0].counter_id, _external=True), "total": row[0].value, "position": i + 1, "include": counter_totals.get(player_id, {}) } ret.append(entry) log.info("Returning counters in %.2fsec", time.time() - start_time) return jsonify(ret), http_client.OK, {'Cache-Control': "max_age=60"} @endpoints.register def endpoint_info(current_user): ret = {} ret["counters"] = url_for("counters.list", _external=True) return ret	nilq/baby-python	python
#!/usr/bin/env python """ Recursively find and replace text in files under a specific folder with preview of changed data in dry-run mode ============ Example Usage --------------- See what is going to change (dry run): > flip all dates from 2017-12-31 to 31-12-2017 find_replace.py --dir project/myfolder --search-regex "\d{4}-\d{2}-\d{2}" --replace-regex "\3-\2-\1" --dry-run Do actual replacement: find_replace.py --dir project/myfolder --search-regex "\d{4}-\d{2}-\d{2}" --replace-regex "\3-\2-\1" Do actual replacement and create backup files: find_replace.py --dir project/myfolder --search-regex "\d{4}-\d{2}-\d{2}" --replace-regex "\3-\2-\1" --create-backup Same action as previous command with short-hand syntax: find_replace.py -d project/myfolder -s "\d{4}-\d{2}-\d{2}" -r "\3-\2-\1" -b Output of `find_replace.py -h`: usage: find-replace-in-files-regex.py [-h] [--dir DIR] --search-regex SEARCH_REGEX --replace-regex REPLACE_REGEX [--glob GLOB] [--dry-run] [--create-backup] [--verbose] [--print-parent-folder] USAGE: find-replace-in-files-regex.py -d [my_folder] -s <search_regex> -r <replace_regex> -g [glob_pattern] """ from __future__ import print_function import os import fnmatch import sys import shutil import re import argparse class Colors: Default = "\033[39m" Black = "\033[30m" Red = "\033[31m" Green = "\033[32m" Yellow = "\033[33m" Blue = "\033[34m" Magenta = "\033[35m" Cyan = "\033[36m" LightGray = "\033[37m" DarkGray = "\033[90m" LightRed = "\033[91m" LightGreen = "\033[92m" LightYellow = "\033[93m" LightBlue = "\033[94m" LightMagenta = "\033[95m" LightCyan = "\033[96m" White = "\033[97m" NoColor = "\033[0m" def find_replace(cfg): search_pattern = re.compile(cfg.search_regex) if cfg.dry_run: print('THIS IS A DRY RUN -- NO FILES WILL BE CHANGED!') for path, dirs, files in os.walk(os.path.abspath(cfg.dir)): for filename in fnmatch.filter(files, cfg.glob): if cfg.print_parent_folder: pardir = os.path.normpath(os.path.join(path, '..')) pardir = os.path.split(pardir)[-1] print('[%s]' % pardir) full_path = os.path.join(path, filename) # backup original file if cfg.create_backup: backup_path = full_path + '.bak' while os.path.exists(backup_path): backup_path += '.bak' print('DBG: creating backup', backup_path) shutil.copyfile(full_path, backup_path) if os.path.islink(full_path): print("{}File {} is a symlink. Skipping{}".format(Colors.Red, full_path, Colors.NoColor)) continue with open(full_path) as f: old_text = f.read() all_matches = search_pattern.findall(old_text) if all_matches: print('{}Found {} match(es) in file {}{}'.format(Colors.LightMagenta, len(all_matches), filename, Colors.NoColor)) new_text = search_pattern.sub(cfg.replace_regex, old_text) if not cfg.dry_run: with open(full_path, "w") as f: print('DBG: replacing in file', full_path) f.write(new_text) # else: # for idx, matches in enumerate(all_matches): # print("Match #{}: {}".format(idx, matches)) if cfg.verbose or cfg.dry_run: colorized_old = search_pattern.sub(Colors.LightBlue + r"\g<0>" + Colors.NoColor, old_text) colorized_old = '\n'.join(['\t' + line.strip() for line in colorized_old.split('\n') if Colors.LightBlue in line]) colorized = search_pattern.sub(Colors.Green + cfg.replace_regex + Colors.NoColor, old_text) colorized = '\n'.join(['\t' + line.strip() for line in colorized.split('\n') if Colors.Green in line]) print("{}BEFORE:{}\n{}".format(Colors.White, Colors.NoColor, colorized_old)) print("{}AFTER :{}\n{}".format(Colors.Yellow, Colors.NoColor, colorized)) elif cfg.list_non_matching: print('File {} does not contain search regex "{}"'.format(filename, cfg.search_regex)) if __name__ == '__main__': parser = argparse.ArgumentParser(description='''DESCRIPTION: Find and replace recursively from the given folder using regular expressions''', formatter_class=argparse.RawDescriptionHelpFormatter, epilog='''USAGE: {0} -d [my_folder] -s <search_regex> -r <replace_regex> -g [glob_pattern] '''.format(os.path.basename(sys.argv[0]))) parser.add_argument('--dir', '-d', help='folder to search in; by default current folder', default='.') parser.add_argument('--search-regex', '-s', help='search regex', required=True) parser.add_argument('--replace-regex', '-r', help='replacement regex', required=True) parser.add_argument('--glob', '-g', help='glob pattern, i.e. .html', default=".*") parser.add_argument('--dry-run', '-dr', action='store_true', help="don't replace anything just show what is going to be done", default=False) parser.add_argument('--create-backup', '-b', action='store_true', help='Create backup files', default=False) parser.add_argument('--verbose', '-v', action='store_true', help="Show files which don't match the search regex", default=False) parser.add_argument('--print-parent-folder', '-p', action='store_true', help="Show the parent info for debug", default=False) parser.add_argument('--list-non-matching', '-n', action='store_true', help="Supress colors", default=False) config = parser.parse_args(sys.argv[1:]) find_replace(config)	nilq/baby-python	python
# ___________________________________________________________________________ # # EGRET: Electrical Grid Research and Engineering Tools # Copyright 2019 National Technology & Engineering Solutions of Sandia, LLC # (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. # Government retains certain rights in this software. # This software is distributed under the Revised BSD License. # ___________________________________________________________________________ """ This is the logging configuration for EGRET. The documentation below is primarily for EGRET developers. Examples ======== To use the logger in your code, add the following after your import .. code-block:: python import logging logger = logging.getLogger('egret.path.to.module') Then, you can use the standard logging functions .. code-block:: python logger.debug('message') logger.info('message') logger.warning('message') logger.error('message') logger.critical('message') Note that by default, any message that has a logging level of warning or higher (warning, error, critical) will be logged. To log an exception and capture the stack trace .. code-block:: python try: c = a / b except Exception as e: logging.error("Exception occurred", exc_info=True) """ import sys import logging log_format = '%(message)s' # configure the root logger for egret logger = logging.getLogger('egret') logger.setLevel(logging.INFO) console_handler = logging.StreamHandler(sys.stdout) fmtr = logging.Formatter(log_format) console_handler.setFormatter(fmtr) logger.addHandler(console_handler)	nilq/baby-python	python
import os import cv2 import numpy as np import random classnames = ["no weather degradation", "fog", "rain", "snow"] modes = ["train", "val", "test"] for classname in classnames: input_path = "./jhucrowd+weather dataset/{}".format(classname) images = os.listdir(input_path) random.shuffle(images) N = len(images) tot_train = int(N * 0.7) tot_val = int(N * 0.1) tot_test = int(N * 0.2) r = N - (tot_train + tot_val + tot_test) tot_train = tot_train + r start_index_train = 0 start_index_val = tot_train start_index_test = tot_train + tot_val for i_img, img_name in enumerate(images): if i_img < start_index_val: mode = modes[0] elif i_img < start_index_test and i_img >= start_index_val: mode = modes[1] else: mode = modes[2] output_path = "./preprocessed_data/{}/{}".format(mode, classname) print(os.path.join(output_path, img_name)) image = cv2.imread(os.path.join(input_path, img_name)) cv2.imwrite(os.path.join(output_path, img_name), image)	nilq/baby-python	python
#!/usr/bin/env python import sys import time import random mn,mx,count = map(int,sys.argv[1:4]) seed = sys.argv[4] if len(sys.argv) > 4 else time.time() random.seed(seed) print 'x,y' for i in xrange(count): print ','.join(map(str,[random.randint(mn,mx),random.randint(mn,mx)]))	nilq/baby-python	python
# -- coding: utf-8 -- from __future__ import unicode_literals from django.utils.encoding import python_2_unicode_compatible from django.db import models # Create your models here. @python_2_unicode_compatible class SummaryNote(models.Model): title = models.CharField(max_length=60) content = models.TextField() def __str__ (self): return self.title def __repr__ (self): return '<SummaryNote %s>' % self.title	nilq/baby-python	python
# Generated by Django 3.0.8 on 2020-07-29 13:57 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('wagtailimages', '0022_uploadedimage'), ('events', '0003_auto_20200725_2158'), ] operations = [ migrations.AddField( model_name='eventtype', name='list_image', field=models.ForeignKey(blank=True, help_text='This image will be displayed above the event on the front page', null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image'), ), ]	nilq/baby-python	python
"""A CLI utility that aggregates configuration sources into a JSON object.""" import json import logging import os import typing import cleo import structlog import toml import pitstop import pitstop.backends.base import pitstop.strategies import pitstop.strategies.base import pitstop.types __all__ = ('app', 'main') app = cleo.Application("pitstop", pitstop.__version__, complete=True) def load_strategy( path: str, strategy_name: typing.Optional[str] = None ) -> pitstop.strategies.base.BaseStrategy: """Load a configuration strategy from a pitstop configuration file.""" filename = os.path.basename(path) with open(path, 'r') as f: config = toml.loads(f.read()) if filename == 'pyproject.toml': config = config['tool']['pitstop'] return pitstop.strategies.strategy_factory(config, strategy_name) def main() -> None: """``pitstop`` entrypoint.""" shared_processors = [ structlog.stdlib.add_logger_name, structlog.stdlib.add_log_level, structlog.stdlib.PositionalArgumentsFormatter(), structlog.processors.TimeStamper(fmt='%Y-%m-%d %H:%M:%S'), structlog.processors.StackInfoRenderer(), structlog.processors.format_exc_info, structlog.processors.UnicodeDecoder(), ] structlog.configure( processors=shared_processors + [structlog.stdlib.ProcessorFormatter.wrap_for_formatter], context_class=dict, logger_factory=structlog.stdlib.LoggerFactory(), wrapper_class=structlog.stdlib.BoundLogger, cache_logger_on_first_use=True, ) formatter = structlog.stdlib.ProcessorFormatter( processor=structlog.dev.ConsoleRenderer(), foreign_pre_chain=shared_processors, ) handler = logging.StreamHandler() handler.setFormatter(formatter) root_logger = logging.getLogger() root_logger.addHandler(handler) app.add(ResolveCommand()) app.run() class BaseCommand(cleo.Command): """Base :class:`cleo.Command`.""" def handle(self) -> None: """Perform shared CLI application setup. All CLI commands should subclass :class:`BaseCommand` and call :func:`super` when overriding this method. """ verbosity = self.output.get_verbosity() if verbosity == cleo.Output.VERBOSITY_QUIET: level = logging.FATAL elif verbosity == cleo.Output.VERBOSITY_NORMAL: level = logging.WARN elif verbosity <= cleo.Output.VERBOSITY_VERBOSE: level = logging.INFO elif verbosity <= cleo.Output.VERBOSITY_DEBUG: level = logging.DEBUG root_logger = logging.getLogger() root_logger.setLevel(level) class ResolveCommand(BaseCommand): """ Resolve all backend sources and output resolved configuration. resolve {config? : pitstop configuration file} {--s\|strategy=v1 : pitstop strategy version} {--c\|compact : enable compact output} """ def handle(self) -> None: # noqa: D102 super().handle() config = self.argument('config') strategy = self.option('strategy') if config is None: config = 'pyproject.toml' strategy = load_strategy(config, strategy_name=strategy) config = strategy.resolve() self.line( json.dumps(config, indent=None if self.option('compact') else 4) ) if __name__ == '__main__': main()	nilq/baby-python	python
from django.shortcuts import render, get_object_or_404 from django.http import HttpResponseRedirect, JsonResponse from django.contrib.auth.decorators import login_required from django.core.urlresolvers import reverse from django.template import loader, Context from pure_pagination import Paginator, EmptyPage, PageNotAnInteger from .decorators import valid_character_selected from .forms import FilterJournalForm from apps.apies.forms import ApiForm from apps.characters.models import CharacterApi, CharacterJournal import utils @login_required def characters(request): api_form = ApiForm(request.POST or None, user=request.user) if request.POST and api_form.is_valid(): api_form.save(request.user) api_form = ApiForm(user=request.user) characters = CharacterApi.objects.filter(api__user=request.user) if request.user.groups.filter( name="moderator" ).exists() or request.user.is_superuser: members = CharacterApi.objects.exclude(api__user=request.user) return render( request, "characters/characters.html", { "api_form": api_form, "characters": characters, "members": members } ) return render( request, "characters/characters.html", { "api_form": api_form, "characters": characters } ) @login_required def select_character(request, pk): if request.user.groups.filter( name="moderator" ).exists() or request.user.is_superuser: character = get_object_or_404(CharacterApi, pk=pk) request.session['moderator'] = True else: character = get_object_or_404( CharacterApi, pk=pk, api__user=request.user ) request.session['moderator'] = False request.session['charpk'] = character.pk request.session['access'] = character.api.access() return HttpResponseRedirect(reverse("character_sheet")) @login_required @valid_character_selected def character_sheet(request): character = get_object_or_404(CharacterApi, pk=request.session['charpk']) cache_key = character.sheet_cache_key() result = utils.connection.get_cache(cache_key) if not result: #or time to live is to long character.sheet_set_cache_job() #sheet, employment = character.character_sheet() #account = character.api.account_status() #in_training = character.skill_in_training() # "employment": employment, # "in_training": in_training, # "sheet": sheet, # "account": account, #"character": character, return render( request, "characters/sheet.html", ) @login_required @valid_character_selected def character_sheet_data(request): character = get_object_or_404(CharacterApi, pk=request.session['charpk']) cache_key = character.sheet_cache_key() result = utils.connection.get_cache(cache_key) if result: #render template sheet, employment = character.character_sheet() paid_until = character.api.account_status() in_training = None #character.skill_in_training() context = Context( { "employment": employment, "in_training": in_training, "sheet": sheet, "paid_until": paid_until, "character": character, } ) template = loader.get_template('characters/sheet_content.html') content = template.render(context) refresh_timer = 60 * 10 * 1000 else: content = """<i class="fa fa-spinner fa-spin text-center"></i>""" refrsh_timer = 0.3 return JsonResponse( { "content": content, "refresh_timer": refresh_timer, } ) @login_required @valid_character_selected def character_skills(request): character = get_object_or_404(CharacterApi, pk=request.session['charpk']) if not character.api.access_to("CharacterSheet"): return HttpResponseRedirect(reverse("characters")) skills = character.trained_skills() queue = character.skill_queue() return render( request, "characters/character_skills.html", { "character": character, "skills": skills, "queue": queue, } ) @login_required @valid_character_selected def character_journal(request): character = get_object_or_404(CharacterApi, pk=request.session['charpk']) if not character.api.access_to("WalletJournal"): return HttpResponseRedirect(reverse("characters")) all_transactions = character.wallet_journal() filter_form = FilterJournalForm( request.POST or None, characterapi=character ) paginator = Paginator( all_transactions, 50, request=request ) page = request.GET.get('page', 1) try: transactions = paginator.page(page) except PageNotAnInteger: transactions = paginator.page(1) except EmptyPage: transactions = paginator.page(paginator.num_pages) chart_list = CharacterJournal.monthly_balance(character) return render( request, "characters/wallet_journal.html", { "character": character, "transactions": transactions, "chart_list": chart_list, "filter_form": filter_form, } )	nilq/baby-python	python
import os import unittest from bs4 import BeautifulSoup from parser import Parser class ParserTestCase(unittest.TestCase): def setUp(self): pass def test_item_info_images(self): base_url = "https://www.akusherstvo.ru" page_url = "/catalog/50666-avtokreslo-rant-star/" page_mock_url = base_url + page_url dump_folder = "test" parser = Parser(base_url, dump_folder) page = self.get_page_mock(parser, page_mock_url) page_url = "/catalog/36172-carmela/" item_info = parser.get_item_info(page, page_url) more_photos = item_info["more_photos"] color_photos = item_info["color_photos"] self.assertEqual(len(more_photos), 4) self.assertEqual(len(color_photos), 4) self.assertEqual(any([ "_b." in photo_url for photo_url in color_photos]), False, "all paths should be without and postfix") self.assertEqual(any([ "_s." in photo_url for photo_url in more_photos]), False, "all paths should be without and postfix") def get_page_mock(self, parser, url): normalized_url = url.replace("/", "_") full_path = "./test_data/mock_{}.html".format(normalized_url) if os.path.exists(full_path): with open(full_path, "r") as f: raw_text = f.read() page = BeautifulSoup(raw_text, features="html5lib") else: page = parser.get_bs(url, codec="cp1251") os.makedirs("./test_data", exist_ok=True) with open(full_path, "w") as f: f.write(str(page)) return page if __name__ == '__main__': unittest.main()	nilq/baby-python	python
from modules import engine from modules import out @engine.prepare_and_clean def execute(key = None): out.log('These are all configuration settings.') config_vars = engine.get_config(key) if key is None: for k in config_vars: out.log(k + ' = ' + str(config_vars[k])) else: out.log(key + ' = ' + config_vars) def help(): out.log("This command will print all the variables, that are set in the engines environment that look like config variables.", 'help')	nilq/baby-python	python
# 2D dataset loaders import data.data_hcp as data_hcp import data.data_abide as data_abide import data.data_nci as data_nci import data.data_promise as data_promise import data.data_pirad_erc as data_pirad_erc import data.data_mnms as data_mnms import data.data_wmh as data_wmh import data.data_scgm as data_scgm # other imports import logging import config.system_paths as sys_config import numpy as np # ================================================================== # TRAINING DATA LOADER # ================================================================== def load_test_data(dataset, image_size, target_resolution, cv_fold_num = 1): # ================================================================ # NCI # ================================================================ if dataset in ['RUNMC', 'BMC']: logging.info('Reading NCI - ' + dataset + ' images...') logging.info('Data root directory: ' + sys_config.orig_data_root_nci) data_pros = data_nci.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_nci, preprocessing_folder = sys_config.preproc_folder_nci, size = image_size, target_resolution = target_resolution, force_overwrite = False, sub_dataset = dataset, cv_fold_num = cv_fold_num) imtr = data_pros['images_train'] gttr = data_pros['labels_train'] orig_data_res_x = data_pros['px_train'][:] orig_data_res_y = data_pros['py_train'][:] orig_data_res_z = data_pros['pz_train'][:] orig_data_siz_x = data_pros['nx_train'][:] orig_data_siz_y = data_pros['ny_train'][:] orig_data_siz_z = data_pros['nz_train'][:] num_train_subjects = orig_data_siz_z.shape[0] imvl = data_pros['images_validation'] gtvl = data_pros['labels_validation'] orig_data_siz_z_val = data_pros['nz_validation'][:] num_val_subjects = orig_data_siz_z_val.shape[0] elif dataset in ['UCL', 'BIDMC', 'HK']: logging.info('Reading' + dataset + ' images...') logging.info('Data root directory: ' + sys_config.orig_data_root_promise) data_pros = data_promise.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_promise, preprocessing_folder = sys_config.preproc_folder_promise, size = image_size, target_resolution = target_resolution, force_overwrite = False, sub_dataset = dataset, cv_fold_num = cv_fold_num) imtr = data_pros['images_train'] gttr = data_pros['labels_train'] orig_data_res_x = data_pros['px_train'][:] orig_data_res_y = data_pros['py_train'][:] orig_data_res_z = data_pros['pz_train'][:] orig_data_siz_x = data_pros['nx_train'][:] orig_data_siz_y = data_pros['ny_train'][:] orig_data_siz_z = data_pros['nz_train'][:] num_train_subjects = orig_data_siz_z.shape[0] imvl = data_pros['images_validation'] gtvl = data_pros['labels_validation'] orig_data_siz_z_val = data_pros['nz_validation'][:] num_val_subjects = orig_data_siz_z_val.shape[0] elif dataset in ['USZ']: logging.info('Reading PIRAD_ERC images...') logging.info('Data root directory: ' + sys_config.orig_data_root_pirad_erc) data_pros_train = data_pirad_erc.load_data(input_folder = sys_config.orig_data_root_pirad_erc, preproc_folder = sys_config.preproc_folder_pirad_erc, idx_start = 40, idx_end = 68, size = image_size, target_resolution = target_resolution, labeller = 'ek', force_overwrite = False) imtr = data_pros_train['images'] gttr = data_pros_train['labels'] orig_data_res_x = data_pros_train['px'][:] orig_data_res_y = data_pros_train['py'][:] orig_data_res_z = data_pros_train['pz'][:] orig_data_siz_x = data_pros_train['nx'][:] orig_data_siz_y = data_pros_train['ny'][:] orig_data_siz_z = data_pros_train['nz'][:] num_train_subjects = orig_data_siz_z.shape[0] data_pros_val = data_pirad_erc.load_data(input_folder = sys_config.orig_data_root_pirad_erc, preproc_folder = sys_config.preproc_folder_pirad_erc, idx_start = 20, idx_end = 40, size = image_size, target_resolution = target_resolution, labeller = 'ek', force_overwrite = False) imvl = data_pros_val['images'] gtvl = data_pros_val['labels'] orig_data_siz_z_val = data_pros_val['nz'][:] num_val_subjects = orig_data_siz_z_val.shape[0] # ================================================================ # CARDIAC (MNMS) # ================================================================ elif dataset in ['HVHD', 'CSF', 'UHE']: logging.info('Reading MNMS - ' + dataset + ' images...') logging.info('Data root directory: ' + sys_config.orig_data_root_mnms) data_cardiac = data_mnms.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_mnms, preprocessing_folder = sys_config.preproc_folder_mnms, size = image_size, target_resolution = target_resolution, force_overwrite = False, sub_dataset = dataset) imtr = data_cardiac['images_train'] gttr = data_cardiac['labels_train'] orig_data_res_x = data_cardiac['px_train'][:] orig_data_res_y = data_cardiac['py_train'][:] orig_data_res_z = data_cardiac['pz_train'][:] orig_data_siz_x = data_cardiac['nx_train'][:] orig_data_siz_y = data_cardiac['ny_train'][:] orig_data_siz_z = data_cardiac['nz_train'][:] num_train_subjects = orig_data_siz_z.shape[0] imvl = data_cardiac['images_validation'] gtvl = data_cardiac['labels_validation'] orig_data_siz_z_val = data_cardiac['nz_validation'][:] num_val_subjects = orig_data_siz_z_val.shape[0] # ================================================================ # Brain lesions (WMH) # ================================================================ elif dataset in ['UMC', 'NUHS']: data_brain_lesions = data_wmh.load_and_maybe_process_data(sys_config.orig_data_root_wmh, sys_config.preproc_folder_wmh, image_size, target_resolution, force_overwrite=False, sub_dataset = dataset, cv_fold_number = cv_fold_num, protocol = 'FLAIR') imtr = data_brain_lesions['images_train'] gttr = data_brain_lesions['labels_train'] orig_data_res_x = data_brain_lesions['px_train'][:] orig_data_res_y = data_brain_lesions['py_train'][:] orig_data_res_z = data_brain_lesions['pz_train'][:] orig_data_siz_x = data_brain_lesions['nx_train'][:] orig_data_siz_y = data_brain_lesions['ny_train'][:] orig_data_siz_z = data_brain_lesions['nz_train'][:] num_train_subjects = orig_data_siz_z.shape[0] imvl = data_brain_lesions['images_validation'] gtvl = data_brain_lesions['labels_validation'] orig_data_siz_z_val = data_brain_lesions['nz_validation'][:] num_val_subjects = orig_data_siz_z_val.shape[0] elif dataset in ['site1', 'site2', 'site3', 'site4']: data_gm = data_scgm.load_and_maybe_process_data(sys_config.orig_data_root_scgm, sys_config.preproc_folder_scgm, image_size, target_resolution, force_overwrite=False, sub_dataset = dataset, cv_fold_number = cv_fold_num) imtr = data_gm['images_train'] gttr = data_gm['labels_train'] orig_data_res_x = data_gm['px_train'][:] orig_data_res_y = data_gm['py_train'][:] orig_data_res_z = data_gm['pz_train'][:] orig_data_siz_x = data_gm['nx_train'][:] orig_data_siz_y = data_gm['ny_train'][:] orig_data_siz_z = data_gm['nz_train'][:] num_train_subjects = orig_data_siz_z.shape[0] imvl = data_gm['images_validation'] gtvl = data_gm['labels_validation'] orig_data_siz_z_val = data_gm['nz_validation'][:] num_val_subjects = orig_data_siz_z_val.shape[0] # ================================================================ # HCP T1 / T2 # ================================================================ elif dataset in ['HCPT1', 'HCPT2']: logging.info('Reading ' + str(dataset) + ' images...') logging.info('Data root directory: ' + sys_config.orig_data_root_hcp) data_brain_train = data_hcp.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_hcp, preprocessing_folder = sys_config.preproc_folder_hcp, idx_start = 0, idx_end = 20, protocol = dataset[-2:], size = image_size, depth = 256, target_resolution = target_resolution) imtr = data_brain_train['images'] gttr = data_brain_train['labels'] orig_data_res_x = data_brain_train['px'][:] orig_data_res_y = data_brain_train['py'][:] orig_data_res_z = data_brain_train['pz'][:] orig_data_siz_x = data_brain_train['nx'][:] orig_data_siz_y = data_brain_train['ny'][:] orig_data_siz_z = data_brain_train['nz'][:] num_train_subjects = orig_data_siz_z.shape[0] data_brain_val = data_hcp.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_hcp, preprocessing_folder = sys_config.preproc_folder_hcp, idx_start = 20, idx_end = 25, protocol = dataset[-2:], size = image_size, depth = 256, target_resolution = target_resolution) imvl = data_brain_val['images'] gtvl = data_brain_val['labels'] orig_data_siz_z_val = data_brain_val['nz'][:] num_val_subjects = orig_data_siz_z_val.shape[0] elif dataset in ['CALTECH']: logging.info('Reading CALTECH images...') logging.info('Data root directory: ' + sys_config.orig_data_root_abide + 'CALTECH/') data_brain_train = data_abide.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_abide, preprocessing_folder = sys_config.preproc_folder_abide, site_name = 'CALTECH', idx_start = 0, idx_end = 10, protocol = 'T1', size = image_size, depth = 256, target_resolution = target_resolution) imtr = data_brain_train['images'] gttr = data_brain_train['labels'] orig_data_res_x = data_brain_train['px'][:] orig_data_res_y = data_brain_train['py'][:] orig_data_res_z = data_brain_train['pz'][:] orig_data_siz_x = data_brain_train['nx'][:] orig_data_siz_y = data_brain_train['ny'][:] orig_data_siz_z = data_brain_train['nz'][:] num_train_subjects = orig_data_siz_z.shape[0] data_brain_val = data_abide.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_abide, preprocessing_folder = sys_config.preproc_folder_abide, site_name = 'CALTECH', idx_start = 10, idx_end = 15, protocol = 'T1', size = image_size, depth = 256, target_resolution = target_resolution) imvl = data_brain_val['images'] gtvl = data_brain_val['labels'] orig_data_siz_z_val = data_brain_val['nz'][:] num_val_subjects = orig_data_siz_z_val.shape[0] return (imtr, # 0 gttr, # 1 orig_data_res_x, # 2 orig_data_res_y, # 3 orig_data_res_z, # 4 orig_data_siz_x, # 5 orig_data_siz_y, # 6 orig_data_siz_z, # 7 num_train_subjects, # 8 imvl, # 9 gtvl, # 10 orig_data_siz_z_val, # 11 num_val_subjects) # 12 # ================================================================== # TEST DATA LOADER # ================================================================== def load_testing_data(test_dataset, cv_fold_num, image_size, target_resolution, image_depth): # ================================================================ # PROMISE # ================================================================ if test_dataset in ['UCL', 'BIDMC', 'HK']: data_pros = data_promise.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_promise, preprocessing_folder = sys_config.preproc_folder_promise, size = image_size, target_resolution = target_resolution, force_overwrite = False, sub_dataset = test_dataset, cv_fold_num = cv_fold_num) imts = data_pros['images_test'] gtts = data_pros['labels_test'] orig_data_res_x = data_pros['px_test'][:] orig_data_res_y = data_pros['py_test'][:] orig_data_res_z = data_pros['pz_test'][:] orig_data_siz_x = data_pros['nx_test'][:] orig_data_siz_y = data_pros['ny_test'][:] orig_data_siz_z = data_pros['nz_test'][:] name_test_subjects = data_pros['patnames_test'] num_test_subjects = orig_data_siz_z.shape[0] ids = np.arange(num_test_subjects) # ================================================================ # USZ # ================================================================ elif test_dataset == 'USZ': image_depth = 32 z_resolution = 2.5 idx_start = 0 idx_end = 20 data_pros = data_pirad_erc.load_data(input_folder = sys_config.orig_data_root_pirad_erc, preproc_folder = sys_config.preproc_folder_pirad_erc, idx_start = idx_start, idx_end = idx_end, size = image_size, target_resolution = target_resolution, labeller = 'ek') imts = data_pros['images'] gtts = data_pros['labels'] orig_data_res_x = data_pros['px'][:] orig_data_res_y = data_pros['py'][:] orig_data_res_z = data_pros['pz'][:] orig_data_siz_x = data_pros['nx'][:] orig_data_siz_y = data_pros['ny'][:] orig_data_siz_z = data_pros['nz'][:] name_test_subjects = data_pros['patnames'] num_test_subjects = 10 # orig_data_siz_z.shape[0] ids = np.arange(idx_start, idx_end) # ================================================================ # NCI # ================================================================ elif test_dataset in ['BMC', 'RUNMC']: logging.info('Reading ' + test_dataset + ' images...') logging.info('Data root directory: ' + sys_config.orig_data_root_nci) data_pros = data_nci.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_nci, preprocessing_folder = sys_config.preproc_folder_nci, size = image_size, target_resolution = target_resolution, force_overwrite = False, sub_dataset = test_dataset, cv_fold_num = cv_fold_num) imts = data_pros['images_test'] gtts = data_pros['labels_test'] orig_data_res_x = data_pros['px_test'][:] orig_data_res_y = data_pros['py_test'][:] orig_data_res_z = data_pros['pz_test'][:] orig_data_siz_x = data_pros['nx_test'][:] orig_data_siz_y = data_pros['ny_test'][:] orig_data_siz_z = data_pros['nz_test'][:] name_test_subjects = data_pros['patnames_test'] num_test_subjects = orig_data_siz_z.shape[0] ids = np.arange(num_test_subjects) # ================================================================ # CARDIAC (MNMS) # ================================================================ elif test_dataset == 'HVHD' or test_dataset == 'CSF' or test_dataset == 'UHE': logging.info('Reading MNMS - ' + test_dataset + ' images...') logging.info('Data root directory: ' + sys_config.orig_data_root_mnms) data_cardiac = data_mnms.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_mnms, preprocessing_folder = sys_config.preproc_folder_mnms, size = image_size, target_resolution = target_resolution, force_overwrite = False, sub_dataset = test_dataset) imts = data_cardiac['images_test'] gtts = data_cardiac['labels_test'] orig_data_res_x = data_cardiac['px_test'][:] orig_data_res_y = data_cardiac['py_test'][:] orig_data_res_z = data_cardiac['pz_test'][:] orig_data_siz_x = data_cardiac['nx_test'][:] orig_data_siz_y = data_cardiac['ny_test'][:] orig_data_siz_z = data_cardiac['nz_test'][:] name_test_subjects = data_cardiac['patnames_test'] num_test_subjects = orig_data_siz_z.shape[0] ids = np.arange(num_test_subjects) # ================================================================ # Brain lesions (WMH) # ================================================================ elif test_dataset == 'UMC' or test_dataset == 'NUHS': data_brain_lesions = data_wmh.load_and_maybe_process_data(sys_config.orig_data_root_wmh, sys_config.preproc_folder_wmh, image_size, target_resolution, force_overwrite=False, sub_dataset = test_dataset, cv_fold_number = cv_fold_num, protocol = 'FLAIR') imts = data_brain_lesions['images_test'] gtts = data_brain_lesions['labels_test'] orig_data_res_x = data_brain_lesions['px_test'][:] orig_data_res_y = data_brain_lesions['py_test'][:] orig_data_res_z = data_brain_lesions['pz_test'][:] orig_data_siz_x = data_brain_lesions['nx_test'][:] orig_data_siz_y = data_brain_lesions['ny_test'][:] orig_data_siz_z = data_brain_lesions['nz_test'][:] name_test_subjects = data_brain_lesions['patnames_test'] num_test_subjects = orig_data_siz_z.shape[0] ids = np.arange(num_test_subjects) # ================================================================ # SPINE # ================================================================ elif test_dataset == 'site1' or test_dataset == 'site2' or test_dataset == 'site3' or test_dataset == 'site4': data_spine = data_scgm.load_and_maybe_process_data(sys_config.orig_data_root_scgm, sys_config.preproc_folder_scgm, image_size, target_resolution, force_overwrite=False, sub_dataset = test_dataset, cv_fold_number = cv_fold_num) imts = data_spine['images_test'] gtts = data_spine['labels_test'] orig_data_res_x = data_spine['px_test'][:] orig_data_res_y = data_spine['py_test'][:] orig_data_res_z = data_spine['pz_test'][:] orig_data_siz_x = data_spine['nx_test'][:] orig_data_siz_y = data_spine['ny_test'][:] orig_data_siz_z = data_spine['nz_test'][:] name_test_subjects = data_spine['patnames_test'] num_test_subjects = orig_data_siz_z.shape[0] ids = np.arange(num_test_subjects) # ================================================================ # HCP T1 # ================================================================ elif test_dataset == 'HCPT1': logging.info('Reading HCPT1 images...') logging.info('Data root directory: ' + sys_config.orig_data_root_hcp) idx_start = 50 idx_end = 70 data_brain = data_hcp.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_hcp, preprocessing_folder = sys_config.preproc_folder_hcp, idx_start = idx_start, idx_end = idx_end, protocol = 'T1', size = image_size, depth = image_depth, target_resolution = target_resolution) imts = data_brain['images'] gtts = data_brain['labels'] orig_data_res_x = data_brain['px'][:] orig_data_res_y = data_brain['py'][:] orig_data_res_z = data_brain['pz'][:] orig_data_siz_x = data_brain['nx'][:] orig_data_siz_y = data_brain['ny'][:] orig_data_siz_z = data_brain['nz'][:] name_test_subjects = data_brain['patnames'] num_test_subjects = 10 # imts.shape[0] // image_depth ids = np.arange(idx_start, idx_end) # ================================================================ # HCP T2 # ================================================================ elif test_dataset == 'HCPT2': logging.info('Reading HCPT2 images...') logging.info('Data root directory: ' + sys_config.orig_data_root_hcp) idx_start = 50 idx_end = 70 data_brain = data_hcp.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_hcp, preprocessing_folder = sys_config.preproc_folder_hcp, idx_start = idx_start, idx_end = idx_end, protocol = 'T2', size = image_size, depth = image_depth, target_resolution = target_resolution) imts = data_brain['images'] gtts = data_brain['labels'] orig_data_res_x = data_brain['px'][:] orig_data_res_y = data_brain['py'][:] orig_data_res_z = data_brain['pz'][:] orig_data_siz_x = data_brain['nx'][:] orig_data_siz_y = data_brain['ny'][:] orig_data_siz_z = data_brain['nz'][:] name_test_subjects = data_brain['patnames'] num_test_subjects = 10 # imts.shape[0] // image_depth ids = np.arange(idx_start, idx_end) # ================================================================ # ABIDE CALTECH T1 # ================================================================ elif test_dataset == 'CALTECH': logging.info('Reading CALTECH images...') logging.info('Data root directory: ' + sys_config.orig_data_root_abide + 'CALTECH/') idx_start = 16 idx_end = 36 data_brain = data_abide.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_abide, preprocessing_folder = sys_config.preproc_folder_abide, site_name = 'CALTECH', idx_start = idx_start, idx_end = idx_end, protocol = 'T1', size = image_size, depth = image_depth, target_resolution = target_resolution) imts = data_brain['images'] gtts = data_brain['labels'] orig_data_res_x = data_brain['px'][:] orig_data_res_y = data_brain['py'][:] orig_data_res_z = data_brain['pz'][:] orig_data_siz_x = data_brain['nx'][:] orig_data_siz_y = data_brain['ny'][:] orig_data_siz_z = data_brain['nz'][:] name_test_subjects = data_brain['patnames'] num_test_subjects = 10 # imts.shape[0] // image_depth ids = np.arange(idx_start, idx_end) # ================================================================ # ABIDE STANFORD T1 # ================================================================ elif test_dataset == 'STANFORD': logging.info('Reading STANFORD images...') logging.info('Data root directory: ' + sys_config.orig_data_root_abide + 'STANFORD/') idx_start = 16 idx_end = 36 data_brain = data_abide.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_abide, preprocessing_folder = sys_config.preproc_folder_abide, site_name = 'STANFORD', idx_start = idx_start, idx_end = idx_end, protocol = 'T1', size = image_size, depth = image_depth, target_resolution = target_resolution) imts = data_brain['images'] gtts = data_brain['labels'] orig_data_res_x = data_brain['px'][:] orig_data_res_y = data_brain['py'][:] orig_data_res_z = data_brain['pz'][:] orig_data_siz_x = data_brain['nx'][:] orig_data_siz_y = data_brain['ny'][:] orig_data_siz_z = data_brain['nz'][:] name_test_subjects = data_brain['patnames'] num_test_subjects = 10 # imts.shape[0] // image_depth ids = np.arange(idx_start, idx_end) return (imts, # 0 gtts, # 1 orig_data_res_x, # 2 orig_data_res_y, # 3 orig_data_res_z, # 4 orig_data_siz_x, # 5 orig_data_siz_y, # 6 orig_data_siz_z, # 7 name_test_subjects, # 8 num_test_subjects, # 9 ids) # 10 # ================================================================ # ================================================================ def load_testing_data_wo_preproc(test_dataset_name, ids, sub_num, subject_name, image_depth): if test_dataset_name == 'HCPT1': # image will be normalized to [0,1] image_orig, labels_orig = data_hcp.load_without_size_preprocessing(input_folder = sys_config.orig_data_root_hcp, idx = ids[sub_num], protocol = 'T1', preprocessing_folder = sys_config.preproc_folder_hcp, depth = image_depth) elif test_dataset_name == 'HCPT2': # image will be normalized to [0,1] image_orig, labels_orig = data_hcp.load_without_size_preprocessing(input_folder = sys_config.orig_data_root_hcp, idx = ids[sub_num], protocol = 'T2', preprocessing_folder = sys_config.preproc_folder_hcp, depth = image_depth) elif test_dataset_name == 'CALTECH': # image will be normalized to [0,1] image_orig, labels_orig = data_abide.load_without_size_preprocessing(input_folder = sys_config.orig_data_root_abide, site_name = 'CALTECH', idx = ids[sub_num], depth = image_depth) elif test_dataset_name == 'STANFORD': # image will be normalized to [0,1] image_orig, labels_orig = data_abide.load_without_size_preprocessing(input_folder = sys_config.orig_data_root_abide, site_name = 'STANFORD', idx = ids[sub_num], depth = image_depth) elif test_dataset_name in ['BMC', 'RUNMC']: # image will be normalized to [0,1] image_orig, labels_orig = data_nci.load_without_size_preprocessing(sys_config.orig_data_root_nci, sys_config.preproc_folder_nci, test_dataset_name, cv_fold_num=1, train_test='test', idx=ids[sub_num]) elif test_dataset_name == 'USZ': # image will be normalized to [0,1] image_orig, labels_orig = data_pirad_erc.load_without_size_preprocessing(sys_config.orig_data_root_pirad_erc, subject_name, labeller='ek') elif test_dataset_name in ['UCL', 'BIDMC', 'HK']: # image will be normalized to [0,1] image_orig, labels_orig = data_promise.load_without_size_preprocessing(sys_config.preproc_folder_promise, subject_name[4:6]) elif test_dataset_name in ['CSF', 'UHE', 'HVHD']: # image will be normalized to [0,1] image_orig, labels_orig = data_mnms.load_without_size_preprocessing(sys_config.preproc_folder_mnms, subject_name) elif test_dataset_name in ['VU', 'UMC', 'NUHS']: # image will be normalized to [0,1] image_orig, labels_orig = data_wmh.load_without_size_preprocessing(sys_config.orig_data_root_wmh, test_dataset_name, subject_name, 'FLAIR') elif test_dataset_name in ['site1', 'site2', 'site3', 'site4']: # image will be normalized to [0,1] image_orig, labels_orig = data_scgm.load_without_size_preprocessing(sys_config.orig_data_root_scgm, sys_config.preproc_folder_scgm, test_dataset_name, subject_name) return image_orig, labels_orig def load_and_maybe_process_data(input_folder, preprocessing_folder, size, target_resolution, force_overwrite=False, sub_dataset = 'RUNMC', # RUNMC / BMC cv_fold_num = 1): #size_str = '_'.join([str(i) for i in size]) #res_str = '_'.join([str(i) for i in target_resolution]) data_file_name = 'data_2d_size_%s_res_%s_cv_fold_%d_%s.hdf5' % (size, target_resolution, cv_fold_num, sub_dataset) data_file_path = os.path.join(preprocessing_folder, data_file_name) utils.makefolder(preprocessing_folder) if not os.path.exists(data_file_path) or force_overwrite: logging.info('This configuration of mode, size and target resolution has not yet been preprocessed') logging.info('Preprocessing now!') prepare_data(input_folder, preprocessing_folder, data_file_path, size, target_resolution, sub_dataset, cv_fold_num) else: logging.info('Already preprocessed this configuration. Loading now!') return h5py.File(data_file_path, 'r') # =============================================================== # function to read a single subjects image and labels without any pre-processing # =============================================================== def load_without_size_preprocessing(input_folder, preprocessing_folder, sub_dataset, cv_fold_num, train_test, idx): # ======================= # ======================= if sub_dataset == 'RUNMC': image_folder = input_folder + 'Images/Prostate-3T/' folder_base = 'Prostate3T' elif sub_dataset == 'BMC': image_folder = input_folder + 'Images/PROSTATE-DIAGNOSIS/' folder_base = 'ProstateDx' # ======================= # ======================= folder_list = get_patient_folders(image_folder, folder_base, sub_dataset, cv_fold_num) folder = folder_list[train_test][idx] patname = folder_base + '-' + str(folder.split('-')[-2]) + '-' + str(folder.split('-')[-1]) nifti_img_path = preprocessing_folder + 'Individual_NIFTI/' + patname # ============ # read the image and normalize the image to be between 0 and 1 # ============ image = utils.load_nii(img_path = nifti_img_path + '_img_n4.nii.gz')[0] image = utils.normalise_image(image, norm_type='div_by_max') # ================== # read the label file # ================== label = utils.load_nii(img_path = nifti_img_path + '_lbl.nii.gz')[0] return image, label	nilq/baby-python	python
# -- coding: utf-8 -- from sqlalchemy.ext.hybrid import hybrid_property from . import db, bcrypt from datetime import datetime class User(db.Model): __tablename__ = 'users' id = db.Column(db.Integer, primary_key=True, autoincrement=True) username = db.Column(db.String(32), index=True, unique=True) email = db.Column(db.String(64), unique=True) _password = db.Column(db.String(64)) reg_time = db.Column(db.DateTime, default=datetime.utcnow) last_login = db.Column(db.DateTime) @property def is_authenticated(self): return True @property def is_active(self): return True @property def is_anonymous(self): return False def get_id(self): try: return unicode(self.id) except NameError: return str(self.id) @hybrid_property def password(self): return self._password @password.setter def _set_password(self, plaintext): self._password = bcrypt.generate_password_hash(plaintext) def is_correct_password(self, plaintext): if bcrypt.check_password_hash(self._password, plaintext): return True return False def __repr__(self): return '<User %r>' % self.username class Entry(db.Model): __tablename__ = 'entries' id = db.Column(db.Integer, primary_key=True, autoincrement=True) good = db.Column(db.String(64)) bad = db.Column(db.String(64)) def __repr__(self): return '<Entry %r & %r>' % (self.good, self.bad) class Hobby(db.Model): __tablename__ = 'hobbies' id = db.Column(db.Integer, primary_key=True, autoincrement=True) hobby = db.Column(db.String(12)) hb_entries = db.relationship('HBEntry', backref='hobby', lazy='dynamic') class HBEntry(db.Model): __tablename__ = 'hbentries' id = db.Column(db.Integer, primary_key=True, autoincrement=True) hb_id = db.Column(db.Integer, db.ForeignKey('hobbies.id')) good = db.Column(db.String(64)) bad = db.Column(db.String(64)) def __repr__(self): return '<Entry %r & %r>' % (self.good, self.bad)	nilq/baby-python	python
""" Stingy OLX ad message forwarder: check for new message(s) and send them to your email @author yohanes.gultom@gmail.com """ from stingy_olx import StingyOLX import re import argparse import smtplib email_tpl = '''From: {0}\r\nTo: {1}\r\nSubject: {2}\r\nMIME-Version: 1.0\r\nContent-Type: text/html\r\n\r\n {3} ''' message_group_tpl = ''' <strong><a href="{}">{}</a></strong> {} ''' message_tpl = ''' <div style="padding-bottom:5px"> <em>{} ({})</em> <div>{}</div> </div> ''' def send_email(smtp_config, to, body): server_ssl = smtplib.SMTP_SSL(smtp_config['server'], smtp_config['port']) server_ssl.ehlo() server_ssl.login(smtp_config['username'], smtp_config['password']) email = email_tpl.format( smtp_config['from'], to, smtp_config['subject'], body, ) server_ssl.sendmail(smtp_config['from'], to, email) server_ssl.close() print('Email sent') def build_email(ads): """ Build HTML email format based on template and ad messages """ email = [] for ad in ads: html_messages = [] for msg in ad['messages']: html_messages.append(message_tpl.format(msg['sender'], msg['time'], msg['body'])) email.append(message_group_tpl.format(ad['url'], ad['title'], '\n'.join(html_messages))) return '\n'.join(email) def main(): parser = argparse.ArgumentParser() parser.add_argument("olx_username", help="OLX username") parser.add_argument("olx_password", help="OLX password") parser.add_argument("smtp_username", help="SMTP username") parser.add_argument("smtp_password", help="SMTP password") parser.add_argument("email_to", help="Email recipient") parser.add_argument("-s", "--smtp_server", help="SMTP server", default="smtp.gmail.com") parser.add_argument("-p", "--smtp_port", help="SMTP port", type=int, default=465) args = parser.parse_args() smtp_config = { 'username': args.smtp_username, 'password': args.smtp_password, 'server': args.smtp_server, 'port': args.smtp_port, 'from': 'Yohanes Gultom', 'subject': 'Pesan baru di olx.co.id' } olx = StingyOLX() olx.login(args.olx_username, args.olx_password) ads = olx.check_unread_message() if ads: email = build_email(ads) send_email(smtp_config, args.email_to, email) olx.logout() if __name__ == '__main__': main()	nilq/baby-python	python
# encoding: utf-8 # module pandas._libs.reduction # from C:\Python27\lib\site-packages\pandas\_libs\reduction.pyd # by generator 1.147 # no doc # imports import __builtin__ as __builtins__ # <module '__builtin__' (built-in)> import numpy as np # C:\Python27\lib\site-packages\numpy\__init__.pyc from pandas._libs.lib import maybe_convert_objects import distutils.version as __distutils_version # functions def apply_frame_axis0(args, kwargs): # real signature unknown pass def reduce(args, *kwargs): # real signature unknown """ Parameters ----------- arr : NDFrame object f : function axis : integer axis dummy : type of reduced output (series) labels : Index or None """ pass def __pyx_unpickle_Reducer(args, *kwargs): # real signature unknown pass def __pyx_unpickle_SeriesBinGrouper(args, *kwargs): # real signature unknown pass def __pyx_unpickle_SeriesGrouper(args, *kwargs): # real signature unknown pass def __pyx_unpickle_Slider(args, *kwargs): # real signature unknown pass # classes class BlockSlider(object): """ Only capable of sliding on axis=0 """ def move(self, args, *kwargs): # real signature unknown pass def __init__(self, args, *kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(S, more): # real signature unknown; restored from __doc__ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """ pass def __reduce__(self, args, kwargs): # real signature unknown pass def __setstate__(self, args, *kwargs): # real signature unknown pass blocks = property(lambda self: object(), lambda self, v: None, lambda self: None) # default dummy = property(lambda self: object(), lambda self, v: None, lambda self: None) # default frame = property(lambda self: object(), lambda self, v: None, lambda self: None) # default idx_slider = property(lambda self: object(), lambda self, v: None, lambda self: None) # default index = property(lambda self: object(), lambda self, v: None, lambda self: None) # default nblocks = property(lambda self: object(), lambda self, v: None, lambda self: None) # default __pyx_vtable__ = None # (!) real value is '<capsule object NULL at 0x0000000006A57CC0>' class InvalidApply(Exception): # no doc def __init__(self, args, *kwargs): # real signature unknown pass __weakref__ = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """list of weak references to the object (if defined)""" __qualname__ = 'InvalidApply' class LooseVersion(__distutils_version.Version): """ Version numbering for anarchists and software realists. Implements the standard interface for version number classes as described above. A version number consists of a series of numbers, separated by either periods or strings of letters. When comparing version numbers, the numeric components will be compared numerically, and the alphabetic components lexically. The following are all valid version numbers, in no particular order: 1.5.1 1.5.2b2 161 3.10a 8.02 3.4j 1996.07.12 3.2.pl0 3.1.1.6 2g6 11g 0.960923 2.2beta29 1.13++ 5.5.kw 2.0b1pl0 In fact, there is no such thing as an invalid version number under this scheme; the rules for comparison are simple and predictable, but may not always give the results you want (for some definition of "want"). """ def parse(self, args, *kwargs): # real signature unknown pass def __cmp__(self, args, *kwargs): # real signature unknown pass def __init__(self, args, *kwargs): # real signature unknown pass def __repr__(self, args, *kwargs): # real signature unknown pass def __str__(self, args, *kwargs): # real signature unknown pass component_re = None # (!) real value is '<_sre.SRE_Pattern object at 0x0000000003C98470>' class Reducer(object): """ Performs generic reduction operation on a C or Fortran-contiguous ndarray while avoiding ndarray construction overhead """ def get_result(self, args, *kwargs): # real signature unknown pass def _check_dummy(self, args, *kwargs): # real signature unknown pass def __init__(self, args, *kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(S, more): # real signature unknown; restored from __doc__ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """ pass def __reduce__(self, args, kwargs): # real signature unknown pass def __setstate__(self, args, *kwargs): # real signature unknown pass class SeriesBinGrouper(object): """ Performs grouping operation according to bin edges, rather than labels """ def get_result(self, args, *kwargs): # real signature unknown pass def _check_dummy(self, args, *kwargs): # real signature unknown pass def __init__(self, args, *kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(S, more): # real signature unknown; restored from __doc__ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """ pass def __reduce__(self, args, kwargs): # real signature unknown pass def __setstate__(self, args, *kwargs): # real signature unknown pass arr = property(lambda self: object(), lambda self, v: None, lambda self: None) # default bins = property(lambda self: object(), lambda self, v: None, lambda self: None) # default dummy_arr = property(lambda self: object(), lambda self, v: None, lambda self: None) # default dummy_index = property(lambda self: object(), lambda self, v: None, lambda self: None) # default f = property(lambda self: object(), lambda self, v: None, lambda self: None) # default index = property(lambda self: object(), lambda self, v: None, lambda self: None) # default ityp = property(lambda self: object(), lambda self, v: None, lambda self: None) # default name = property(lambda self: object(), lambda self, v: None, lambda self: None) # default typ = property(lambda self: object(), lambda self, v: None, lambda self: None) # default values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default class SeriesGrouper(object): """ Performs generic grouping operation while avoiding ndarray construction overhead """ def get_result(self, args, *kwargs): # real signature unknown pass def _check_dummy(self, args, *kwargs): # real signature unknown pass def __init__(self, args, *kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(S, more): # real signature unknown; restored from __doc__ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """ pass def __reduce__(self, args, kwargs): # real signature unknown pass def __setstate__(self, args, *kwargs): # real signature unknown pass arr = property(lambda self: object(), lambda self, v: None, lambda self: None) # default dummy_arr = property(lambda self: object(), lambda self, v: None, lambda self: None) # default dummy_index = property(lambda self: object(), lambda self, v: None, lambda self: None) # default f = property(lambda self: object(), lambda self, v: None, lambda self: None) # default index = property(lambda self: object(), lambda self, v: None, lambda self: None) # default ityp = property(lambda self: object(), lambda self, v: None, lambda self: None) # default labels = property(lambda self: object(), lambda self, v: None, lambda self: None) # default name = property(lambda self: object(), lambda self, v: None, lambda self: None) # default typ = property(lambda self: object(), lambda self, v: None, lambda self: None) # default values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default class Slider(object): """ Only handles contiguous data for now """ def advance(self, args, *kwargs): # real signature unknown pass def reset(self, args, *kwargs): # real signature unknown pass def set_length(self, args, *kwargs): # real signature unknown pass def __init__(self, args, *kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(S, more): # real signature unknown; restored from __doc__ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """ pass def __reduce__(self, args, kwargs): # real signature unknown pass def __setstate__(self, args, **kwargs): # real signature unknown pass __pyx_vtable__ = None # (!) real value is '<capsule object NULL at 0x0000000006A57C60>' # variables with complex values __test__ = {}	nilq/baby-python	python
""" Base threading server class """ from threading import Thread class ThreadServer: def __init__(self): self.server_thread = None self.running = False def start(self, args, kwargs): if self.running: return self.running = True self.server_thread = Thread(target=self.run, args=args, kwargs=kwargs) self.server_thread.start() def stop(self): self.running = False def run(self): """ Server main function """ pass class StaticServer: def start(self, args, **kwargs): pass def stop(self): pass	nilq/baby-python	python
from dynaconf import FlaskDynaconf flask_dynaconf = FlaskDynaconf() def init_app(app, config): flask_dynaconf.init_app(app, config) app.config.load_extensions()	nilq/baby-python	python
# Copyright 2019 The TensorFlow Authors. 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. # ============================================================================== """Model utilities.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import json import os import sys from absl import logging import numpy as np import tensorflow.compat.v1 as tf class BatchNormalization(tf.layers.BatchNormalization): """Fixed default name of BatchNormalization to match TpuBatchNormalization.""" def __init__(self, name='tpu_batch_normalization', kwargs): super(BatchNormalization, self).__init__(name=name, kwargs) def drop_connect(inputs, is_training, survival_prob): """Drop the entire conv with given survival probability.""" # "Deep Networks with Stochastic Depth", https://arxiv.org/pdf/1603.09382.pdf if not is_training: return inputs # Compute tensor. batch_size = tf.shape(inputs)[0] random_tensor = survival_prob random_tensor += tf.random_uniform([batch_size, 1, 1, 1], dtype=inputs.dtype) binary_tensor = tf.floor(random_tensor) # Unlike conventional way that multiply survival_prob at test time, here we # divide survival_prob at training time, such that no addition compute is # needed at test time. output = tf.div(inputs, survival_prob) * binary_tensor return output def get_ema_vars(): """Get all exponential moving average (ema) variables.""" ema_vars = tf.trainable_variables() + tf.get_collection('moving_vars') for v in tf.global_variables(): # We maintain mva for batch norm moving mean and variance as well. if 'moving_mean' in v.name or 'moving_variance' in v.name: ema_vars.append(v) return list(set(ema_vars)) class DepthwiseConv2D(tf.keras.layers.DepthwiseConv2D, tf.layers.Layer): """Wrap keras DepthwiseConv2D to tf.layers.""" pass class Conv2D(tf.layers.Conv2D): """Wrapper for Conv2D with specialization for fast inference.""" def _bias_activation(self, outputs): if self.use_bias: outputs = tf.nn.bias_add(outputs, self.bias, data_format='NCHW') if self.activation is not None: return self.activation(outputs) return outputs def _can_run_fast_1x1(self, inputs): batch_size = inputs.shape.as_list()[0] return (self.data_format == 'channels_first' and batch_size == 1 and self.kernel_size == (1, 1)) def _call_fast_1x1(self, inputs): # Compute the 1x1 convolution as a matmul. inputs_shape = tf.shape(inputs) flat_inputs = tf.reshape(inputs, [inputs_shape[1], -1]) flat_outputs = tf.matmul( tf.squeeze(self.kernel), flat_inputs, transpose_a=True) outputs_shape = tf.concat([[1, self.filters], inputs_shape[2:]], axis=0) outputs = tf.reshape(flat_outputs, outputs_shape) # Handle the bias and activation function. return self._bias_activation(outputs) def call(self, inputs): if self._can_run_fast_1x1(inputs): return self._call_fast_1x1(inputs) return super(Conv2D, self).call(inputs) class EvalCkptDriver(object): """A driver for running eval inference. Attributes: model_name: str. Model name to eval. batch_size: int. Eval batch size. image_size: int. Input image size, determined by model name. num_classes: int. Number of classes, default to 1000 for ImageNet. include_background_label: whether to include extra background label. advprop_preprocessing: whether to use advprop preprocessing. """ def __init__(self, model_name, batch_size=1, image_size=224, num_classes=1000, include_background_label=False, advprop_preprocessing=False): """Initialize internal variables.""" self.model_name = model_name self.batch_size = batch_size self.num_classes = num_classes self.include_background_label = include_background_label self.image_size = image_size self.advprop_preprocessing = advprop_preprocessing def restore_model(self, sess, ckpt_dir, enable_ema=True, export_ckpt=None): """Restore variables from checkpoint dir.""" sess.run(tf.global_variables_initializer()) checkpoint = tf.train.latest_checkpoint(ckpt_dir) if enable_ema: ema = tf.train.ExponentialMovingAverage(decay=0.0) ema_vars = get_ema_vars() var_dict = ema.variables_to_restore(ema_vars) ema_assign_op = ema.apply(ema_vars) else: var_dict = get_ema_vars() ema_assign_op = None tf.train.get_or_create_global_step() sess.run(tf.global_variables_initializer()) saver = tf.train.Saver(var_dict, max_to_keep=1) saver.restore(sess, checkpoint) if export_ckpt: if ema_assign_op is not None: sess.run(ema_assign_op) saver = tf.train.Saver(max_to_keep=1, save_relative_paths=True) saver.save(sess, export_ckpt) def build_model(self, features, is_training): """Build model with input features.""" del features, is_training raise ValueError('Must be implemented by subclasses.') def get_preprocess_fn(self): raise ValueError('Must be implemented by subclsses.') def build_dataset(self, filenames, labels, is_training): """Build input dataset.""" batch_drop_remainder = False if 'condconv' in self.model_name and not is_training: # CondConv layers can only be called with known batch dimension. Thus, we # must drop all remaining examples that do not make up one full batch. # To ensure all examples are evaluated, use a batch size that evenly # divides the number of files. batch_drop_remainder = True num_files = len(filenames) if num_files % self.batch_size != 0: tf.logging.warn('Remaining examples in last batch are not being ' 'evaluated.') filenames = tf.constant(filenames) labels = tf.constant(labels) dataset = tf.data.Dataset.from_tensor_slices((filenames, labels)) def _parse_function(filename, label): image_string = tf.read_file(filename) preprocess_fn = self.get_preprocess_fn() image_decoded = preprocess_fn( image_string, is_training, image_size=self.image_size) image = tf.cast(image_decoded, tf.float32) return image, label dataset = dataset.map(_parse_function) dataset = dataset.batch(self.batch_size, drop_remainder=batch_drop_remainder) iterator = dataset.make_one_shot_iterator() images, labels = iterator.get_next() return images, labels def run_inference(self, ckpt_dir, image_files, labels, enable_ema=True, export_ckpt=None): """Build and run inference on the target images and labels.""" label_offset = 1 if self.include_background_label else 0 with tf.Graph().as_default(), tf.Session() as sess: images, labels = self.build_dataset(image_files, labels, False) probs = self.build_model(images, is_training=False) if isinstance(probs, tuple): probs = probs[0] self.restore_model(sess, ckpt_dir, enable_ema, export_ckpt) prediction_idx = [] prediction_prob = [] for _ in range(len(image_files) // self.batch_size): out_probs = sess.run(probs) idx = np.argsort(out_probs)[::-1] prediction_idx.append(idx[:5] - label_offset) prediction_prob.append([out_probs[pid] for pid in idx[:5]]) # Return the top 5 predictions (idx and prob) for each image. return prediction_idx, prediction_prob def eval_example_images(self, ckpt_dir, image_files, labels_map_file, enable_ema=True, export_ckpt=None): """Eval a list of example images. Args: ckpt_dir: str. Checkpoint directory path. image_files: List[str]. A list of image file paths. labels_map_file: str. The labels map file path. enable_ema: enable expotential moving average. export_ckpt: export ckpt folder. Returns: A tuple (pred_idx, and pred_prob), where pred_idx is the top 5 prediction index and pred_prob is the top 5 prediction probability. """ classes = json.loads(tf.gfile.Open(labels_map_file).read()) pred_idx, pred_prob = self.run_inference( ckpt_dir, image_files, [0] * len(image_files), enable_ema, export_ckpt) for i in range(len(image_files)): print('predicted class for image {}: '.format(image_files[i])) for j, idx in enumerate(pred_idx[i]): print(' -> top_{} ({:4.2f}%): {} '.format(j, pred_prob[i][j] * 100, classes[str(idx)])) return pred_idx, pred_prob def eval_imagenet(self, ckpt_dir, imagenet_eval_glob, imagenet_eval_label, num_images, enable_ema, export_ckpt): """Eval ImageNet images and report top1/top5 accuracy. Args: ckpt_dir: str. Checkpoint directory path. imagenet_eval_glob: str. File path glob for all eval images. imagenet_eval_label: str. File path for eval label. num_images: int. Number of images to eval: -1 means eval the whole dataset. enable_ema: enable expotential moving average. export_ckpt: export checkpoint folder. Returns: A tuple (top1, top5) for top1 and top5 accuracy. """ imagenet_val_labels = [int(i) for i in tf.gfile.GFile(imagenet_eval_label)] imagenet_filenames = sorted(tf.gfile.Glob(imagenet_eval_glob)) if num_images < 0: num_images = len(imagenet_filenames) image_files = imagenet_filenames[:num_images] labels = imagenet_val_labels[:num_images] pred_idx, _ = self.run_inference( ckpt_dir, image_files, labels, enable_ema, export_ckpt) top1_cnt, top5_cnt = 0.0, 0.0 for i, label in enumerate(labels): top1_cnt += label in pred_idx[i][:1] top5_cnt += label in pred_idx[i][:5] if i % 100 == 0: print('Step {}: top1_acc = {:4.2f}% top5_acc = {:4.2f}%'.format( i, 100 * top1_cnt / (i + 1), 100 * top5_cnt / (i + 1))) sys.stdout.flush() top1, top5 = 100 * top1_cnt / num_images, 100 * top5_cnt / num_images print('Final: top1_acc = {:4.2f}% top5_acc = {:4.2f}%'.format(top1, top5)) return top1, top5	nilq/baby-python	python
from django import forms from django.http import QueryDict from django.utils.translation import ugettext_lazy as _ from panoptes.analysis import FilteredSessions from panoptes.analysis.fields import LensChoiceField, WeekdayChoiceField from panoptes.core.fields import LocationField from panoptes.core.models import Session import datetime class SessionFilterForm(forms.Form): """A form for filtering session data based on user bounds.""" location = LocationField(label=_("location")) lens = LensChoiceField(label=_("data view")) start = forms.DateField(label=_("start date"), required=False) end = forms.DateField(label=_("end date"), required=False) start_time = forms.TimeField(label=_("start time"), required=False) end_time = forms.TimeField(label=_("end time"), required=False) weekdays = WeekdayChoiceField(label=_("weekdays"), required=False) x_detail = forms.CharField(label=_("x-value detail"), required=False, widget=forms.HiddenInput) y_detail = forms.CharField(label=_("y-value detail"), required=False, widget=forms.HiddenInput) def __init__(self, args, kwargs): """Accept a 'profile' kwarg that provides default data.""" profile = kwargs.pop('profile', None) if profile: today = datetime.date.today() post = QueryDict("") post = post.copy() post.update({ 'location': profile.default_location.pk, 'lens': profile.default_lens.slug, 'start': self._parsable_date(today - datetime.timedelta(days=profile.default_recent_days)), 'end': self._parsable_date(today) }) args = (post,) super(SessionFilterForm, self).__init__(args, **kwargs) def _parsable_date(self, date): """Return the given date as a parsable string.""" return date.strftime("%m/%d/%Y") def clean(self): """Perform extra validation and resolution of data. This adds an `x_detail` key to the cleaned data containing the resolved x-value whose details should be shown, and also makes sure that the dates and times are coherent. """ cleaned_data = self.cleaned_data today = datetime.date.today() # If a start date is provided but the end date is left blank, end on the # current date if cleaned_data.get('start',None) and not cleaned_data.get('end', None): cleaned_data['end'] = today # If an end date is provided and no start date is given, start at the first # date on which sessions were recorded, or a year ago, if no sessions exist if cleaned_data.get('end', None) and not cleaned_data.get('start', None): cleaned_data['start'] = Session.objects.first_session_date_for_location(cleaned_data['location']) # If the date bounds are left blank, default to viewing the past week if not cleaned_data.get('start', None) and not cleaned_data.get('end', None): cleaned_data['start'] = today - datetime.timedelta(weeks=1) cleaned_data['end'] = today # Have empty time filters use the opening or closing time of the location if not cleaned_data.get('start_time', None): cleaned_data['start_time'] = cleaned_data['location'].earliest_opening if not cleaned_data.get('end_time', None): cleaned_data['end_time'] = cleaned_data['location'].latest_closing # Make sure that the start and end dates and times are properly ordered if cleaned_data['start'] > cleaned_data['end']: raise forms.ValidationError(_("The start must come before the end date")) if cleaned_data['start_time'] > cleaned_data['end_time']: raise forms.ValidationError(_("The start time must come before the end time")) # Resolve the x- and y-value details if possible if cleaned_data.get('x_detail', None): x_axis = cleaned_data['lens'].x_axis() cleaned_data['x_detail'] = x_axis.deserialize_value(cleaned_data['x_detail']) if cleaned_data.get('y_detail', None): y_axis = cleaned_data['lens'].y_axis() cleaned_data['y_detail'] = y_axis.deserialize_value(cleaned_data['y_detail']) cleaned_data['x_detail'] = cleaned_data['x_detail'] or None cleaned_data['y_detail'] = cleaned_data['y_detail'] or None return cleaned_data def as_filtered_sessions(self): """ If the form was successfully validated, return a FilteredSessions instance built from the form's cleaned data. """ data = self.cleaned_data filtered_sessions = FilteredSessions( location=data['location'], start_date=data.get('start', None), end_date=data.get('end', None), start_time=data.get('start_time', None), end_time=data.get('end_time', None), weekdays=data.get('weekdays', []), x_detail=data.get('x_detail', None)) lens = data.get('lens', None) if lens: filtered_sessions.set_axes(lens.x_axis, lens.y_axis) return filtered_sessions	nilq/baby-python	python
import os from typing import Any import torch.optim as optim import yaml from aim.sdk.utils import generate_run_hash from deep_compression.losses import ( BatchChannelDecorrelationLoss, RateDistortionLoss, ) def create_criterion(conf): if conf.name == "RateDistortionLoss": return RateDistortionLoss( lmbda=conf.lambda_, target_bpp=conf.get("target_bpp", None), ) if conf.name == "BatchChannelDecorrelationLoss": return BatchChannelDecorrelationLoss( lmbda=conf.lambda_, lmbda_corr=conf.lambda_corr, top_k_corr=conf.top_k_corr, ) raise ValueError("Unknown criterion.") def configure_optimizers(net, conf): """Separate parameters for the main optimizer and the auxiliary optimizer. Return two optimizers""" parameters = { n for n, p in net.named_parameters() if not n.endswith(".quantiles") and p.requires_grad } aux_parameters = { n for n, p in net.named_parameters() if n.endswith(".quantiles") and p.requires_grad } # Make sure we don't have an intersection of parameters params_dict = dict(net.named_parameters()) inter_params = parameters & aux_parameters union_params = parameters \| aux_parameters assert len(inter_params) == 0 assert len(union_params) - len(params_dict.keys()) == 0 optimizer = optim.Adam( (params_dict[n] for n in sorted(parameters)), lr=conf.learning_rate, ) aux_optimizer = optim.Adam( (params_dict[n] for n in sorted(aux_parameters)), lr=conf.aux_learning_rate, ) return {"net": optimizer, "aux": aux_optimizer} def configure_logs(logdir: str) -> dict[str, Any]: filename = os.path.join(logdir, "info.yaml") try: with open(filename) as f: config = yaml.safe_load(f) except FileNotFoundError: config = {} config["run_hash"] = generate_run_hash() os.makedirs(logdir, exist_ok=True) with open(filename, "w") as f: yaml.safe_dump(config, f) return config	nilq/baby-python	python
# -- coding: utf-8 -- # maya import pymel.core as pm from maya.app.general.mayaMixin import MayaQDockWidget from maya.app.general.mayaMixin import MayaQWidgetDockableMixin # Built-in from functools import partial import os import sys import json import shutil import subprocess # mbox from . import naming_rules_ui as name_ui from . import custom_step_ui as custom_step_ui from . import root_settings_ui as root_ui from . import block_settings_ui as block_ui from . import joint_names_ui as joint_name_ui from mbox.lego import naming, lib # mgear from mgear.core import pyqt, string from mgear.vendor.Qt import QtCore, QtWidgets, QtGui from mgear.anim_picker.gui import MAYA_OVERRIDE_COLOR ROOT_TYPE = "mbox_guide_root" BLOCK_TYPE = "mbox_guide_block" class RootMainTabUI(QtWidgets.QDialog, root_ui.Ui_Form): def __init__(self, parent=None): super(RootMainTabUI, self).__init__(parent) self.setupUi(self) class RootCustomStepTabUI(QtWidgets.QDialog, custom_step_ui.Ui_Form): def __init__(self, parent=None): super(RootCustomStepTabUI, self).__init__(parent) self.setupUi(self) class RootNameTabUI(QtWidgets.QDialog, name_ui.Ui_Form): def __init__(self, parent=None): super(RootNameTabUI, self).__init__(parent) self.setupUi(self) class HelperSlots: def __init__(self): self._network = None # def update_host_ui(self, l_edit, target_attr): guide = lib.get_component_guide(pm.selected(type="transform")[0]) if guide: network = guide[0].message.outputs(type="network")[0] l_edit.setText(guide[0].name()) self._network.attr(target_attr).set("{},{}".format(guide[0].name(), network.attr("oid").get())) else: if l_edit.text(): l_edit.clear() self._network.attr(target_attr).set("") pm.displayWarning("") def update_line_edit(self, l_edit, target_attr): name = string.removeInvalidCharacter(l_edit.text()) l_edit.setText(name) self._network.attr(target_attr).set(name) def update_line_edit2(self, l_edit, target_attr): # nomralize the text to be Maya naming compatible # replace invalid characters with "_" name = string.normalize2(l_edit.text()) l_edit.setText(name) self._network.attr(target_attr).set(name) def update_text_edit(self, l_edit, target_attr): self._network.attr(target_attr).set(l_edit.toPlainText()) def update_line_edit_path(self, l_edit, target_attr): self._network.attr(target_attr).set(l_edit.text()) def update_name_rule_line_edit(self, l_edit, target_attr): # nomralize the text to be Maya naming compatible # replace invalid characters with "_" name = naming.normalize_name_rule(l_edit.text()) l_edit.setText(name) self._network.attr(target_attr).set(name) self.naming_rule_validator(l_edit) def naming_rule_validator(self, l_edit, log=True): Palette = QtGui.QPalette() if not naming.name_rule_validator(l_edit.text(), naming.NAMING_RULE_TOKENS, log=log): Palette.setBrush(QtGui.QPalette.Text, self.red_brush) else: Palette.setBrush(QtGui.QPalette.Text, self.white_down_brush) l_edit.setPalette(Palette) def add_item_to_list_widget(self, list_widget, target_attr=None): items = pm.selected() items_list = [i.text() for i in list_widget.findItems( "", QtCore.Qt.MatchContains)] # Quick clean the first empty item if items_list and not items_list[0]: list_widget.takeItem(0) for item in items: if len(item.name().split("\|")) != 1: pm.displayWarning("Not valid obj: %s, name is not unique." % item.name()) continue if item.name() not in items_list: if item.hasAttr("is_guide_component") or item.hasAttr("is_guide_root"): list_widget.addItem(item.name()) else: pm.displayWarning( "The object: %s, is not a valid" " reference, Please select only guide componet" " roots and guide locators." % item.name()) else: pm.displayWarning("The object: %s, is already in the list." % item.name()) if target_attr: self.update_list_attr(list_widget, target_attr) def remove_selected_from_list_widget(self, list_widget, target_attr=None): for item in list_widget.selectedItems(): list_widget.takeItem(list_widget.row(item)) if target_attr: self.update_list_attr(list_widget, target_attr) def move_from_list_widget_to_list_widget(self, source_list_widget, target_list_widget, target_attr_list_widget, target_attr=None): # Quick clean the first empty item items_list = [i.text() for i in target_attr_list_widget.findItems( "", QtCore.Qt.MatchContains)] if items_list and not items_list[0]: target_attr_list_widget.takeItem(0) for item in source_list_widget.selectedItems(): target_list_widget.addItem(item.text()) source_list_widget.takeItem(source_list_widget.row(item)) if target_attr: self.update_list_attr(target_attr_list_widget, target_attr) def copy_from_list_widget(self, source_list_widget, target_list_widget, target_attr=None): target_list_widget.clear() items_list = [i.text() for i in source_list_widget.findItems( "", QtCore.Qt.MatchContains)] for item in items_list: target_list_widget.addItem(item) if target_attr: self.update_list_attr(source_list_widget, target_attr) def update_list_attr(self, source_list_widget, target_attr): """Update the string attribute with values separated by commas""" new_value = ",".join([i.text() for i in source_list_widget.findItems( "", QtCore.Qt.MatchContains)]) self._network.attr(target_attr).set(new_value) def update_component_name(self): with pm.UndoChunk(): side_set = ["center", "left", "right"] line_name = self.main_tab.name_lineEdit.text() new_name = string.normalize2(line_name) if line_name != new_name: self.main_tab.name_lineEdit.setText(new_name) return side_index = self.main_tab.side_comboBox.currentIndex() new_side = side_set[side_index] index = self.main_tab.componentIndex_spinBox.value() blueprint = lib.blueprint_from_guide(self._guide.getParent(generations=-1)) block = blueprint.find_block_with_oid(self._network.attr("oid").get()) new_index = blueprint.solve_index(new_name, new_side, index, block) rename_check = False if self._network.attr("comp_name").get() != new_name \ or self._network.attr("comp_side").get(asString=True) != new_side \ or self._network.attr("comp_index").get() != new_index: rename_check = True if self._network.attr("comp_name").get() == new_name \ and self._network.attr("comp_side").get(asString=True) == new_side \ and self._network.attr("comp_index").get() == index: return if rename_check: block["comp_name"] = new_name block["comp_side"] = new_side block["comp_index"] = new_index block.to_network() block.update_guide() if self._network.attr("comp_index").get() != self.main_tab.componentIndex_spinBox.value(): self.main_tab.componentIndex_spinBox.setValue(self._network.attr("comp_index").get()) def update_connector(self, source_widget, items_list, args): self._network.attr("connector").set(items_list[source_widget.currentIndex()]) def populate_check(self, target_widget, source_attr, args): if self._network.attr(source_attr).get(): target_widget.setCheckState(QtCore.Qt.Checked) else: target_widget.setCheckState(QtCore.Qt.Unchecked) def update_check(self, source_widget, target_attr, args): self._network.attr(target_attr).set(source_widget.isChecked()) def update_spin_box(self, source_widget, target_attr, args): self._network.attr(target_attr).set(source_widget.value()) return True def update_slider(self, source_widget, target_attr, args): self._network.attr(target_attr).set(float(source_widget.value()) / 100) def update_combo_box(self, source_widget, target_attr, args): self._network.attr(target_attr).set(source_widget.currentIndex()) def update_control_shape(self, source_widget, ctl_list, target_attr, args): current_index = source_widget.currentIndex() self._network.attr(target_attr).set(ctl_list[current_index]) def update_index_color_widgets( self, source_widget, target_attr, color_widget, args): self.update_spin_box(source_widget, target_attr) self.update_widget_style_sheet( color_widget, (i / 255.0 for i in MAYA_OVERRIDE_COLOR[source_widget.value()])) def update_rgb_color_widgets(self, button_widget, rgb, slider_widget): self.update_widget_style_sheet(button_widget, rgb) slider_widget.blockSignals(True) slider_widget.setValue(sorted(rgb)[2] * 255) slider_widget.blockSignals(False) def update_widget_style_sheet(self, source_widget, rgb): color = ', '.join(str(i * 255) for i in pm.colorManagementConvert(toDisplaySpace=rgb)) source_widget.setStyleSheet( "* {background-color: rgb(" + color + ")}") def rgb_slider_value_changed(self, button_widget, target_attr, value): rgb = self._network.attr(target_attr).get() hsv_value = sorted(rgb)[2] if hsv_value: new_rgb = tuple(i / (hsv_value / 1.0) * (value / 255.0) for i in rgb) else: new_rgb = tuple((1.0 * (value / 255.0), 1.0 * (value / 255.0), 1.0 * (value / 255.0))) self.update_widget_style_sheet(button_widget, new_rgb) self._network.attr(target_attr).set(new_rgb) def rgb_color_editor(self, source_widget, target_attr, slider_widget, args): pm.colorEditor(rgb=self._network.attr(target_attr).get()) if pm.colorEditor(query=True, result=True): rgb = pm.colorEditor(query=True, rgb=True) self._network.attr(target_attr).set(rgb) self.update_rgb_color_widgets(source_widget, rgb, slider_widget) def toggle_rgb_index_widgets(self, check_box, idx_widgets, rgb_widgets, target_attr, checked): show_widgets, hide_widgets = ( rgb_widgets, idx_widgets) if checked else ( idx_widgets, rgb_widgets) for widget in show_widgets: widget.show() for widget in hide_widgets: widget.hide() self.update_check(check_box, target_attr) def set_profile(self): pm.select(self._network, r=True) pm.runtime.GraphEditor() def get_cs_file_fullpath(self, cs_data): filepath = cs_data.split("\|")[-1][1:] if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): fullpath = os.path.join( os.environ.get( "MBOX_CUSTOM_STEP_PATH", ""), filepath) else: fullpath = filepath return fullpath def edit_file(self, widgetList): try: cs_data = widgetList.selectedItems()[0].text() fullpath = self.get_cs_file_fullpath(cs_data) if fullpath: if sys.platform.startswith('darwin'): subprocess.call(('open', fullpath)) elif os.name == 'nt': os.startfile(fullpath) elif os.name == 'posix': subprocess.call(('xdg-open', fullpath)) else: pm.displayWarning("Please select one item from the list") except Exception: pm.displayError("The step can't be find or does't exists") def format_info(self, data): data_parts = data.split("\|") cs_name = data_parts[0] if cs_name.startswith(""): cs_status = "Deactivated" cs_name = cs_name[1:] else: cs_status = "Active" cs_fullpath = self.get_cs_file_fullpath(data) if "_shared" in data: cs_shared_owner = self.shared_owner(cs_fullpath) cs_shared_status = "Shared" else: cs_shared_status = "Local" cs_shared_owner = "None" info = '<html><head/><body><p><span style=" font-weight:600;">\ {0}</span></p><p>------------------</p><p><span style=" \ font-weight:600;">Status</span>: {1}</p><p><span style=" \ font-weight:600;">Shared Status:</span> {2}</p><p><span \ style=" font-weight:600;">Shared Owner:</span> \ {3}</p><p><span style=" font-weight:600;">Full Path</span>: \ {4}</p></body></html>'.format(cs_name, cs_status, cs_shared_status, cs_shared_owner, cs_fullpath) return info def shared_owner(self, cs_fullpath): scan_dir = os.path.abspath(os.path.join(cs_fullpath, os.pardir)) while not scan_dir.endswith("_shared"): scan_dir = os.path.abspath(os.path.join(scan_dir, os.pardir)) # escape infinite loop if scan_dir == '/': break scan_dir = os.path.abspath(os.path.join(scan_dir, os.pardir)) return os.path.split(scan_dir)[1] @classmethod def get_steps_dict(self, itemsList): stepsDict = {} stepsDict["itemsList"] = itemsList for item in itemsList: step = open(item, "r") data = step.read() stepsDict[item] = data step.close() return stepsDict @classmethod def runStep(self, stepPath, customStepDic): try: with pm.UndoChunk(): pm.displayInfo( "EXEC: Executing custom step: %s" % stepPath) # use forward slash for OS compatibility if sys.platform.startswith('darwin'): stepPath = stepPath.replace('\\', '/') fileName = os.path.split(stepPath)[1].split(".")[0] if os.environ.get(MGEAR_SHIFTER_CUSTOMSTEP_KEY, ""): runPath = os.path.join( os.environ.get( MGEAR_SHIFTER_CUSTOMSTEP_KEY, ""), stepPath) else: runPath = stepPath customStep = imp.load_source(fileName, runPath) if hasattr(customStep, "CustomShifterStep"): argspec = inspect.getargspec( customStep.CustomShifterStep.__init__) if "stored_dict" in argspec.args: cs = customStep.CustomShifterStep(customStepDic) cs.setup() cs.run() else: cs = customStep.CustomShifterStep() cs.run(customStepDic) customStepDic[cs.name] = cs pm.displayInfo( "SUCCEED: Custom Shifter Step Class: %s. " "Succeed!!" % stepPath) else: pm.displayInfo( "SUCCEED: Custom Step simple script: %s. " "Succeed!!" % stepPath) except Exception as ex: template = "An exception of type {0} occurred. " "Arguments:\n{1!r}" message = template.format(type(ex).__name__, ex.args) pm.displayError(message) pm.displayError(traceback.format_exc()) cont = pm.confirmBox( "FAIL: Custom Step Fail", "The step:%s has failed. Continue with next step?" % stepPath + "\n\n" + message + "\n\n" + traceback.format_exc(), "Continue", "Stop Build", "Try Again!") if cont == "Stop Build": # stop Build return True elif cont == "Try Again!": try: # just in case there is nothing to undo pm.undo() except Exception: pass pm.displayInfo("Trying again! : {}".format(stepPath)) inception = self.runStep(stepPath, customStepDic) if inception: # stops build from the recursion loop. return True else: return False def run_manual_step(self, widgetList): selItems = widgetList.selectedItems() for item in selItems: self.runStep(item.text().split("\|")[-1][1:], customStepDic={}) def close_settings(self): self.close() pyqt.deleteInstances(self, MayaQDockWidget) class RootSettings(MayaQWidgetDockableMixin, QtWidgets.QDialog, HelperSlots): green_brush = QtGui.QColor(0, 160, 0) red_brush = QtGui.QColor(180, 0, 0) white_brush = QtGui.QColor(255, 255, 255) white_down_brush = QtGui.QColor(160, 160, 160) orange_brush = QtGui.QColor(240, 160, 0) def __init__(self): self.toolName = ROOT_TYPE # Delete old instances of the componet settings window. pyqt.deleteInstances(self, MayaQDockWidget) # super(self.__class__, self).__init__(parent=parent) super(RootSettings, self).__init__() # the inspectSettings function set the current selection to the # component root before open the settings dialog self._network = pm.selected(type="transform")[0].message.outputs(type="network")[0] self.main_tab = RootMainTabUI() self.custom_step_tab = RootCustomStepTabUI() self.naming_rule_tab = RootNameTabUI() self.mayaMainWindow = pyqt.maya_main_window() self.setObjectName(self.toolName) self.setWindowFlags(QtCore.Qt.Window) self.setWindowTitle(ROOT_TYPE) self.resize(500, 615) self.create_controls() self.populate_controls() self.create_layout() self.create_connections() self.setAttribute(QtCore.Qt.WA_DeleteOnClose, True) # hover info self.pre_cs = self.custom_step_tab.preCustomStep_listWidget self.pre_cs.setMouseTracking(True) self.pre_cs.entered.connect(self.pre_info) self.post_cs = self.custom_step_tab.postCustomStep_listWidget self.post_cs.setMouseTracking(True) self.post_cs.entered.connect(self.post_info) def pre_info(self, index): self.hover_info_item_entered(self.pre_cs, index) def post_info(self, index): self.hover_info_item_entered(self.post_cs, index) def hover_info_item_entered(self, view, index): if index.isValid(): info_data = self.format_info(index.data()) QtWidgets.QToolTip.showText( QtGui.QCursor.pos(), info_data, view.viewport(), view.visualRect(index)) def create_controls(self): """Create the controls for the component base""" self.tabs = QtWidgets.QTabWidget() self.tabs.setObjectName("settings_tab") # Close Button self.close_button = QtWidgets.QPushButton("Close") def populate_controls(self): """Populate the controls values from the custom attributes of the component. """ # populate tab self.tabs.insertTab(0, self.main_tab, "Guide Settings") self.tabs.insertTab(1, self.custom_step_tab, "Custom Steps") self.tabs.insertTab(2, self.naming_rule_tab, "Naming Rules") # populate main settings self.main_tab.rigName_lineEdit.setText( self._network.attr("name").get()) self.main_tab.mode_comboBox.setCurrentIndex( self._network.attr("process").get()) self.main_tab.step_comboBox.setCurrentIndex( self._network.attr("step").get()) # self.populateCheck( # self.main_tab.proxyChannels_checkBox, "proxyChannels") self.populate_check(self.main_tab.worldCtl_checkBox, "world_ctl") self.main_tab.worldCtl_lineEdit.setText( self._network.attr("world_ctl_name").get()) # self.populateCheck( # self.main_tab.classicChannelNames_checkBox, # "classicChannelNames") # self.populateCheck( # self.main_tab.attrPrefix_checkBox, # "attrPrefixName") # self.populateCheck( # self.main_tab.importSkin_checkBox, "importSkin") # self.main_tab.skin_lineEdit.setText( # self._network.attr("skin").get()) # self.populateCheck( # self.main_tab.dataCollector_checkBox, "data_collector") # self.main_tab.dataCollectorPath_lineEdit.setText( # self._network.attr("data_collector_path").get()) self.populate_check( self.main_tab.jointRig_checkBox, "joint_rig") self.populate_check( self.main_tab.force_uniScale_checkBox, "force_uni_scale") self.populate_check( self.main_tab.connect_joints_checkBox, "connect_joints") # self.populateAvailableSynopticTabs() # for item in self._network.attr("synoptic").get().split(","): # self.main_tab.rigTabs_listWidget.addItem(item) tap = self.main_tab index_widgets = ((tap.L_color_fk_spinBox, tap.L_color_fk_label, "l_color_fk"), (tap.L_color_ik_spinBox, tap.L_color_ik_label, "l_color_ik"), (tap.C_color_fk_spinBox, tap.C_color_fk_label, "c_color_fk"), (tap.C_color_ik_spinBox, tap.C_color_ik_label, "c_color_ik"), (tap.R_color_fk_spinBox, tap.R_color_fk_label, "r_color_fk"), (tap.R_color_ik_spinBox, tap.R_color_ik_label, "r_color_ik")) rgb_widgets = ((tap.L_RGB_fk_pushButton, tap.L_RGB_fk_slider, "l_RGB_fk"), (tap.L_RGB_ik_pushButton, tap.L_RGB_ik_slider, "l_RGB_ik"), (tap.C_RGB_fk_pushButton, tap.C_RGB_fk_slider, "c_RGB_fk"), (tap.C_RGB_ik_pushButton, tap.C_RGB_ik_slider, "c_RGB_ik"), (tap.R_RGB_fk_pushButton, tap.R_RGB_fk_slider, "r_RGB_fk"), (tap.R_RGB_ik_pushButton, tap.R_RGB_ik_slider, "r_RGB_ik")) for spinBox, label, source_attr in index_widgets: color_index = self._network.attr(source_attr).get() spinBox.setValue(color_index) self.update_widget_style_sheet( label, [i / 255.0 for i in MAYA_OVERRIDE_COLOR[color_index]]) for button, slider, source_attr in rgb_widgets: self.update_rgb_color_widgets( button, self._network.attr(source_attr).get(), slider) # forceing the size of the color buttons/label to keep ui clean for widget in tuple(i[0] for i in rgb_widgets) + tuple( i[1] for i in index_widgets): widget.setFixedSize(pyqt.dpi_scale(30), pyqt.dpi_scale(20)) self.populate_check(tap.useRGB_checkBox, "use_RGB_color") self.toggle_rgb_index_widgets(tap.useRGB_checkBox, (w for i in index_widgets for w in i[:2]), (w for i in rgb_widgets for w in i[:2]), "use_RGB_color", tap.useRGB_checkBox.checkState()) tap.notes_textEdit.setText(self._network.attr("notes").get()) # pupulate custom steps sttings self.populate_check( self.custom_step_tab.preCustomStep_checkBox, "run_pre_custom_step") for item in self._network.attr("pre_custom_step").get().split(","): self.custom_step_tab.preCustomStep_listWidget.addItem(item) self.refresh_status_color(self.custom_step_tab.preCustomStep_listWidget) self.populate_check( self.custom_step_tab.postCustomStep_checkBox, "run_post_custom_step") for item in self._network.attr("post_custom_step").get().split(","): self.custom_step_tab.postCustomStep_listWidget.addItem(item) self.refresh_status_color(self.custom_step_tab.postCustomStep_listWidget) self.populate_naming_controls() def populate_naming_controls(self): # populate name settings self.naming_rule_tab.ctl_name_rule_lineEdit.setText( self._network.attr("ctl_name_rule").get()) self.naming_rule_validator( self.naming_rule_tab.ctl_name_rule_lineEdit) self.naming_rule_tab.joint_name_rule_lineEdit.setText( self._network.attr("joint_name_rule").get()) self.naming_rule_validator( self.naming_rule_tab.joint_name_rule_lineEdit) self.naming_rule_tab.side_left_name_lineEdit.setText( self._network.attr("ctl_left_name").get()) self.naming_rule_tab.side_right_name_lineEdit.setText( self._network.attr("ctl_right_name").get()) self.naming_rule_tab.side_center_name_lineEdit.setText( self._network.attr("ctl_center_name").get()) self.naming_rule_tab.side_joint_left_name_lineEdit.setText( self._network.attr("joint_left_name").get()) self.naming_rule_tab.side_joint_right_name_lineEdit.setText( self._network.attr("joint_right_name").get()) self.naming_rule_tab.side_joint_center_name_lineEdit.setText( self._network.attr("joint_center_name").get()) self.naming_rule_tab.ctl_name_ext_lineEdit.setText( self._network.attr("ctl_name_ext").get()) self.naming_rule_tab.joint_name_ext_lineEdit.setText( self._network.attr("joint_name_ext").get()) self.naming_rule_tab.ctl_des_letter_case_comboBox.setCurrentIndex( self._network.attr("ctl_description_letter_case").get()) self.naming_rule_tab.joint_des_letter_case_comboBox.setCurrentIndex( self._network.attr("joint_description_letter_case").get()) self.naming_rule_tab.ctl_padding_spinBox.setValue( self._network.attr("ctl_index_padding").get()) self.naming_rule_tab.joint_padding_spinBox.setValue( self._network.attr("joint_index_padding").get()) def create_layout(self): """ Create the layout for the component base settings """ self.settings_layout = QtWidgets.QVBoxLayout() self.settings_layout.addWidget(self.tabs) self.settings_layout.addWidget(self.close_button) self.setLayout(self.settings_layout) def create_connections(self): """Create the slots connections to the controls functions""" self.close_button.clicked.connect(self.close_settings) # Setting Tab tap = self.main_tab tap.rigName_lineEdit.editingFinished.connect( partial(self.update_line_edit, tap.rigName_lineEdit, "name")) tap.mode_comboBox.currentIndexChanged.connect( partial(self.update_combo_box, tap.mode_comboBox, "process")) tap.step_comboBox.currentIndexChanged.connect( partial(self.update_combo_box, tap.step_comboBox, "step")) # tap.proxyChannels_checkBox.stateChanged.connect( # partial(self.update_check, # tap.proxyChannels_checkBox, # "proxyChannels")) tap.worldCtl_checkBox.stateChanged.connect( partial(self.update_check, tap.worldCtl_checkBox, "world_ctl")) tap.worldCtl_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.worldCtl_lineEdit, "world_ctl_name")) # tap.classicChannelNames_checkBox.stateChanged.connect( # partial(self.updateCheck, # tap.classicChannelNames_checkBox, # "classicChannelNames")) # tap.attrPrefix_checkBox.stateChanged.connect( # partial(self.updateCheck, # tap.attrPrefix_checkBox, # "attrPrefixName")) # tap.dataCollector_checkBox.stateChanged.connect( # partial(self.updateCheck, # tap.dataCollector_checkBox, # "data_collector")) # tap.dataCollectorPath_lineEdit.editingFinished.connect( # partial(self.updateLineEditPath, # tap.dataCollectorPath_lineEdit, # "data_collector_path")) tap.jointRig_checkBox.stateChanged.connect( partial(self.update_check, tap.jointRig_checkBox, "joint_rig")) tap.force_uniScale_checkBox.stateChanged.connect( partial(self.update_check, tap.force_uniScale_checkBox, "force_uni_scale")) tap.connect_joints_checkBox.stateChanged.connect( partial(self.update_check, tap.connect_joints_checkBox, "connect_joints")) # tap.addTab_pushButton.clicked.connect( # partial(self.moveFromListWidget2ListWidget, # tap.available_listWidget, # tap.rigTabs_listWidget, # tap.rigTabs_listWidget, # "synoptic")) # tap.removeTab_pushButton.clicked.connect( # partial(self.moveFromListWidget2ListWidget, # tap.rigTabs_listWidget, # tap.available_listWidget, # tap.rigTabs_listWidget, # "synoptic")) # tap.loadSkinPath_pushButton.clicked.connect( # self.skinLoad) # tap.dataCollectorPath_pushButton.clicked.connect( # self.data_collector_path) # tap.rigTabs_listWidget.installEventFilter(self) # colors connections index_widgets = ((tap.L_color_fk_spinBox, tap.L_color_fk_label, "l_color_fk"), (tap.L_color_ik_spinBox, tap.L_color_ik_label, "l_color_ik"), (tap.C_color_fk_spinBox, tap.C_color_fk_label, "c_color_fk"), (tap.C_color_ik_spinBox, tap.C_color_ik_label, "c_color_ik"), (tap.R_color_fk_spinBox, tap.R_color_fk_label, "r_color_fk"), (tap.R_color_ik_spinBox, tap.R_color_ik_label, "r_color_ik")) rgb_widgets = ((tap.L_RGB_fk_pushButton, tap.L_RGB_fk_slider, "l_RGB_fk"), (tap.L_RGB_ik_pushButton, tap.L_RGB_ik_slider, "l_RGB_ik"), (tap.C_RGB_fk_pushButton, tap.C_RGB_fk_slider, "c_RGB_fk"), (tap.C_RGB_ik_pushButton, tap.C_RGB_ik_slider, "c_RGB_ik"), (tap.R_RGB_fk_pushButton, tap.R_RGB_fk_slider, "r_RGB_fk"), (tap.R_RGB_ik_pushButton, tap.R_RGB_ik_slider, "r_RGB_ik")) for spinBox, label, source_attr in index_widgets: spinBox.valueChanged.connect( partial(self.update_index_color_widgets, spinBox, source_attr, label)) for button, slider, source_attr in rgb_widgets: button.clicked.connect( partial(self.rgb_color_editor, button, source_attr, slider)) slider.valueChanged.connect( partial(self.rgb_slider_value_changed, button, source_attr)) tap.useRGB_checkBox.stateChanged.connect( partial(self.toggle_rgb_index_widgets, tap.useRGB_checkBox, tuple(w for i in index_widgets for w in i[:2]), tuple(w for i in rgb_widgets for w in i[:2]), "use_RGB_color")) tap.notes_textEdit.textChanged.connect( partial(self.update_text_edit, tap.notes_textEdit, "notes")) # custom Step Tab csTap = self.custom_step_tab csTap.preCustomStep_checkBox.stateChanged.connect( partial(self.update_check, csTap.preCustomStep_checkBox, "run_pre_custom_step")) csTap.preCustomStepAdd_pushButton.clicked.connect( self.add_custom_step) csTap.preCustomStepNew_pushButton.clicked.connect( self.new_custom_step) csTap.preCustomStepDuplicate_pushButton.clicked.connect( self.duplicate_custom_step) csTap.preCustomStepExport_pushButton.clicked.connect( self.export_custom_step) csTap.preCustomStepImport_pushButton.clicked.connect( self.import_custom_step) csTap.preCustomStepRemove_pushButton.clicked.connect( partial(self.remove_selected_from_list_widget, csTap.preCustomStep_listWidget, "pre_custom_step")) csTap.preCustomStep_listWidget.installEventFilter(self) csTap.preCustomStepRun_pushButton.clicked.connect( partial(self.run_manual_step, csTap.preCustomStep_listWidget)) csTap.preCustomStepEdit_pushButton.clicked.connect( partial(self.edit_file, csTap.preCustomStep_listWidget)) csTap.postCustomStep_checkBox.stateChanged.connect( partial(self.update_check, csTap.postCustomStep_checkBox, "run_post_custom_step")) csTap.postCustomStepAdd_pushButton.clicked.connect( partial(self.add_custom_step, False)) csTap.postCustomStepNew_pushButton.clicked.connect( partial(self.new_custom_step, False)) csTap.postCustomStepDuplicate_pushButton.clicked.connect( partial(self.duplicate_custom_step, False)) csTap.postCustomStepExport_pushButton.clicked.connect( partial(self.export_custom_step, False)) csTap.postCustomStepImport_pushButton.clicked.connect( partial(self.import_custom_step, False)) csTap.postCustomStepRemove_pushButton.clicked.connect( partial(self.remove_selected_from_list_widget, csTap.postCustomStep_listWidget, "post_custom_step")) csTap.postCustomStep_listWidget.installEventFilter(self) csTap.postCustomStepRun_pushButton.clicked.connect( partial(self.run_manual_step, csTap.postCustomStep_listWidget)) csTap.postCustomStepEdit_pushButton.clicked.connect( partial(self.edit_file, csTap.postCustomStep_listWidget)) # right click menus csTap.preCustomStep_listWidget.setContextMenuPolicy( QtCore.Qt.CustomContextMenu) csTap.preCustomStep_listWidget.customContextMenuRequested.connect( self.pre_custom_step_menu) csTap.postCustomStep_listWidget.setContextMenuPolicy( QtCore.Qt.CustomContextMenu) csTap.postCustomStep_listWidget.customContextMenuRequested.connect( self.post_custom_step_menu) # search hightlight csTap.preSearch_lineEdit.textChanged.connect( self.pre_highlight_search) csTap.postSearch_lineEdit.textChanged.connect( self.post_highlight_search) # Naming Tab tap = self.naming_rule_tab # names rules tap.ctl_name_rule_lineEdit.editingFinished.connect( partial(self.update_name_rule_line_edit, tap.ctl_name_rule_lineEdit, "ctl_name_rule")) tap.joint_name_rule_lineEdit.editingFinished.connect( partial(self.update_name_rule_line_edit, tap.joint_name_rule_lineEdit, "joint_name_rule")) # sides names tap.side_left_name_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.side_left_name_lineEdit, "ctl_left_name")) tap.side_right_name_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.side_right_name_lineEdit, "ctl_right_name")) tap.side_center_name_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.side_center_name_lineEdit, "ctl_center_name")) tap.side_joint_left_name_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.side_joint_left_name_lineEdit, "joint_left_name")) tap.side_joint_right_name_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.side_joint_right_name_lineEdit, "joint_right_name")) tap.side_joint_center_name_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.side_joint_center_name_lineEdit, "joint_center_name")) # names extensions tap.ctl_name_ext_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.ctl_name_ext_lineEdit, "ctl_name_ext")) tap.joint_name_ext_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.joint_name_ext_lineEdit, "joint_name_ext")) # description letter case tap.ctl_des_letter_case_comboBox.currentIndexChanged.connect( partial(self.update_combo_box, tap.ctl_des_letter_case_comboBox, "ctl_description_letter_case")) tap.joint_des_letter_case_comboBox.currentIndexChanged.connect( partial(self.update_combo_box, tap.joint_des_letter_case_comboBox, "joint_description_letter_case")) # reset naming rules tap.reset_ctl_name_rule_pushButton.clicked.connect( partial(self.reset_naming_rule, tap.ctl_name_rule_lineEdit, "ctl_name_rule")) tap.reset_joint_name_rule_pushButton.clicked.connect( partial(self.reset_naming_rule, tap.joint_name_rule_lineEdit, "joint_name_rule")) # reset naming sides tap.reset_side_name_pushButton.clicked.connect( self.reset_naming_sides) tap.reset_joint_side_name_pushButton.clicked.connect( self.reset_joint_naming_sides) # reset naming extension tap.reset_name_ext_pushButton.clicked.connect( self.reset_naming_extension) # index padding tap.ctl_padding_spinBox.valueChanged.connect( partial(self.update_spin_box, tap.ctl_padding_spinBox, "ctl_index_padding")) tap.joint_padding_spinBox.valueChanged.connect( partial(self.update_spin_box, tap.joint_padding_spinBox, "joint_index_padding")) # import name configuration tap.load_naming_configuration_pushButton.clicked.connect( self.import_name_config) # export name configuration tap.save_naming_configuration_pushButton.clicked.connect( self.export_name_config) def eventFilter(self, sender, event): if event.type() == QtCore.QEvent.ChildRemoved: # if sender == self.main_tab.rigTabs_listWidget: # self.updateListAttr(sender, "synoptic") if sender == self.custom_step_tab.preCustomStep_listWidget: self.update_list_attr(sender, "pre_custom_step") elif sender == self.custom_step_tab.postCustomStep_listWidget: self.update_list_attr(sender, "post_custom_step") return True else: return QtWidgets.QDialog.eventFilter(self, sender, event) # Slots ######################################################## def export_name_config(self, file_path=None): # set focus to the save button to ensure all values are updated # if the cursor stay in other lineEdit since the edition is not # finished will not take the last edition self.naming_rule_tab.save_naming_configuration_pushButton.setFocus( QtCore.Qt.MouseFocusReason) config = dict() config["ctl_name_rule"] = self._network.attr( "ctl_name_rule").get() config["joint_name_rule"] = self._network.attr( "joint_name_rule").get() config["ctl_left_name"] = self._network.attr( "ctl_left_name").get() config["ctl_right_name"] = self._network.attr( "ctl_right_name").get() config["ctl_center_name"] = self._network.attr( "ctl_center_name").get() config["joint_left_name"] = self._network.attr( "joint_left_name").get() config["joint_right_name"] = self._network.attr( "joint_right_name").get() config["joint_center_name"] = self._network.attr( "joint_center_name").get() config["ctl_name_ext"] = self._network.attr( "ctl_name_ext").get() config["joint_name_ext"] = self._network.attr( "joint_name_ext").get() config["ctl_description_letter_case"] = self._network.attr( "ctl_description_letter_case").get() config["joint_description_letter_case"] = self._network.attr( "joint_description_letter_case").get() config["ctl_index_padding"] = self._network.attr( "ctl_index_padding").get() config["joint_index_padding"] = self._network.attr( "joint_index_padding").get() if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") else: startDir = pm.workspace(q=True, rootDirectory=True) data_string = json.dumps(config, indent=4, sort_keys=True) if not file_path: file_path = pm.fileDialog2( fileMode=0, startingDirectory=startDir, fileFilter='Naming Configuration .naming (%s)' % ".naming") if not file_path: return if not isinstance(file_path, str): file_path = file_path[0] f = open(file_path, 'w') f.write(data_string) f.close() def import_name_config(self, file_path=None): if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") else: startDir = pm.workspace(q=True, rootDirectory=True) if not file_path: file_path = pm.fileDialog2( fileMode=1, startingDirectory=startDir, fileFilter='Naming Configuration .naming (%s)' % ".naming") if not file_path: return if not isinstance(file_path, str): file_path = file_path[0] config = json.load(open(file_path)) for key in config.keys(): self._network.attr(key).set(config[key]) self.populate_naming_controls() def reset_naming_rule(self, rule_lineEdit, target_attr): rule_lineEdit.setText(naming.DEFAULT_NAMING_RULE) self.update_name_rule_line_edit(rule_lineEdit, target_attr) def reset_naming_sides(self): self.naming_rule_tab.side_left_name_lineEdit.setText( naming.DEFAULT_SIDE_L_NAME) self.naming_rule_tab.side_right_name_lineEdit.setText( naming.DEFAULT_SIDE_R_NAME) self.naming_rule_tab.side_center_name_lineEdit.setText( naming.DEFAULT_SIDE_C_NAME) self._network.attr("ctl_left_name").set(naming.DEFAULT_SIDE_L_NAME) self._network.attr("ctl_right_name").set(naming.DEFAULT_SIDE_R_NAME) self._network.attr("ctl_center_name").set(naming.DEFAULT_SIDE_C_NAME) def reset_joint_naming_sides(self): self.naming_rule_tab.side_joint_left_name_lineEdit.setText( naming.DEFAULT_JOINT_SIDE_L_NAME) self.naming_rule_tab.side_joint_right_name_lineEdit.setText( naming.DEFAULT_JOINT_SIDE_R_NAME) self.naming_rule_tab.side_joint_center_name_lineEdit.setText( naming.DEFAULT_JOINT_SIDE_C_NAME) self._network.attr("joint_left_name").set( naming.DEFAULT_JOINT_SIDE_L_NAME) self._network.attr("joint_right_name").set( naming.DEFAULT_JOINT_SIDE_R_NAME) self._network.attr("joint_center_name").set( naming.DEFAULT_JOINT_SIDE_C_NAME) def reset_naming_extension(self): self.naming_rule_tab.ctl_name_ext_lineEdit.setText( naming.DEFAULT_CTL_EXT_NAME) self.naming_rule_tab.joint_name_ext_lineEdit.setText( naming.DEFAULT_JOINT_EXT_NAME) self._network.attr("ctl_name_ext").set(naming.DEFAULT_CTL_EXT_NAME) self._network.attr("joint_name_ext").set(naming.DEFAULT_JOINT_EXT_NAME) # def populateAvailableSynopticTabs(self): # # import mgear.shifter as shifter # defPath = os.environ.get("MGEAR_SYNOPTIC_PATH", None) # if not defPath or not os.path.isdir(defPath): # defPath = shifter.SYNOPTIC_PATH # # # Sanity check for folder existence. # if not os.path.isdir(defPath): # return # # tabsDirectories = [name for name in os.listdir(defPath) if # os.path.isdir(os.path.join(defPath, name))] # # Quick clean the first empty item # if tabsDirectories and not tabsDirectories[0]: # self.main_tab.available_listWidget.takeItem(0) # # itemsList = self._network.attr("synoptic").get().split(",") # for tab in sorted(tabsDirectories): # if tab not in itemsList: # self.main_tab.available_listWidget.addItem(tab) # # def skinLoad(self, args): # startDir = self._network.attr("skin").get() # filePath = pm.fileDialog2( # fileMode=1, # startingDirectory=startDir, # okc="Apply", # fileFilter='mGear skin (%s)' % skin.FILE_EXT) # if not filePath: # return # if not isinstance(filePath, str): # filePath = filePath[0] # # self._network.attr("skin").set(filePath) # self.main_tab.skin_lineEdit.setText(filePath) # # def _data_collector_path(self, args): # ext_filter = 'Shifter Collected data ({})'.format(DATA_COLLECTOR_EXT) # filePath = pm.fileDialog2( # fileMode=0, # fileFilter=ext_filter) # if not filePath: # return # if not isinstance(filePath, str): # filePath = filePath[0] # # return filePath # # def data_collector_path(self, args): # filePath = self._data_collector_path() # # if filePath: # self._network.attr("data_collector_path").set(filePath) # self.main_tab.dataCollectorPath_lineEdit.setText(filePath) def add_custom_step(self, pre=True, args): """Add a new custom step Arguments: pre (bool, optional): If true adds the steps to the pre step list args: Maya's Dummy Returns: None: None """ if pre: stepAttr = "pre_custom_step" stepWidget = self.custom_step_tab.preCustomStep_listWidget else: stepAttr = "post_custom_step" stepWidget = self.custom_step_tab.postCustomStep_listWidget # Check if we have a custom env for the custom steps initial folder if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") else: startDir = self._network.attr(stepAttr).get() filePath = pm.fileDialog2( fileMode=1, startingDirectory=startDir, okc="Add", fileFilter='Custom Step .py (.py)') if not filePath: return if not isinstance(filePath, str): filePath = filePath[0] # Quick clean the first empty item itemsList = [i.text() for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] if itemsList and not itemsList[0]: stepWidget.takeItem(0) if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): filePath = os.path.abspath(filePath) baseReplace = os.path.abspath(os.environ.get( "MBOX_CUSTOM_STEP_PATH", "")) filePath = filePath.replace(baseReplace, "")[1:] fileName = os.path.split(filePath)[1].split(".")[0] stepWidget.addItem(fileName + " \| " + filePath) self.updateListAttr(stepWidget, stepAttr) self.refresh_status_color(stepWidget) def new_custom_step(self, pre=True, args): """Creates a new custom step Arguments: pre (bool, optional): If true adds the steps to the pre step list args: Maya's Dummy Returns: None: None """ if pre: stepAttr = "pre_custom_step" stepWidget = self.custom_step_tab.preCustomStep_listWidget else: stepAttr = "post_custom_step" stepWidget = self.custom_step_tab.postCustomStep_listWidget # Check if we have a custom env for the custom steps initial folder if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") else: startDir = self._network.attr(stepAttr).get() filePath = pm.fileDialog2( fileMode=0, startingDirectory=startDir, okc="New", fileFilter='Custom Step .py (.py)') if not filePath: return if not isinstance(filePath, str): filePath = filePath[0] n, e = os.path.splitext(filePath) stepName = os.path.split(n)[-1] # raw custome step string rawString = r'''import mbox.lego.lib as lib class CustomStep(lib.{pre_post}): """Custom Step description """ def process(self): """Run method. Returns: None: None """ return'''.format(pre_post="PreScript" if pre else "PostScript") f = open(filePath, 'w') f.write(rawString + "\n") f.close() # Quick clean the first empty item itemsList = [i.text() for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] if itemsList and not itemsList[0]: stepWidget.takeItem(0) if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): filePath = os.path.abspath(filePath) baseReplace = os.path.abspath(os.environ.get( "MBOX_CUSTOM_STEP_PATH", "")) filePath = filePath.replace(baseReplace, "")[1:] fileName = os.path.split(filePath)[1].split(".")[0] stepWidget.addItem(fileName + " \| " + filePath) self.update_list_attr(stepWidget, stepAttr) self.refresh_status_color(stepWidget) def duplicate_custom_step(self, pre=True, args): """Duplicate the selected step Arguments: pre (bool, optional): If true adds the steps to the pre step list args: Maya's Dummy Returns: None: None """ if pre: stepAttr = "pre_custom_step" stepWidget = self.custom_step_tab.preCustomStep_listWidget else: stepAttr = "post_custom_step" stepWidget = self.custom_step_tab.postCustomStep_listWidget # Check if we have a custom env for the custom steps initial folder if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") else: startDir = self._network.attr(stepAttr).get() if stepWidget.selectedItems(): sourcePath = stepWidget.selectedItems()[0].text().split( "\|")[-1][1:] filePath = pm.fileDialog2( fileMode=0, startingDirectory=startDir, okc="New", fileFilter='Custom Step .py (.py)') if not filePath: return if not isinstance(filePath, str): filePath = filePath[0] if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): sourcePath = os.path.join(startDir, sourcePath) shutil.copy(sourcePath, filePath) # Quick clean the first empty item itemsList = [i.text() for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] if itemsList and not itemsList[0]: stepWidget.takeItem(0) if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): filePath = os.path.abspath(filePath) baseReplace = os.path.abspath(os.environ.get( "MBOX_CUSTOM_STEP_PATH", "")) filePath = filePath.replace(baseReplace, "")[1:] fileName = os.path.split(filePath)[1].split(".")[0] stepWidget.addItem(fileName + " \| " + filePath) self.update_list_attr(stepWidget, stepAttr) self.refresh_status_color(stepWidget) def export_custom_step(self, pre=True, args): """Export custom steps to a json file Arguments: pre (bool, optional): If true takes the steps from the pre step list args: Maya's Dummy Returns: None: None """ if pre: stepWidget = self.custom_step_tab.preCustomStep_listWidget else: stepWidget = self.custom_step_tab.postCustomStep_listWidget # Quick clean the first empty item itemsList = [i.text() for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] if itemsList and not itemsList[0]: stepWidget.takeItem(0) # Check if we have a custom env for the custom steps initial folder if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") itemsList = [os.path.join(startDir, i.text().split("\|")[-1][1:]) for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] else: itemsList = [i.text().split("\|")[-1][1:] for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] if itemsList: startDir = os.path.split(itemsList[-1])[0] else: pm.displayWarning("No custom steps to export.") return stepsDict = self.get_steps_dict(itemsList) data_string = json.dumps(stepsDict, indent=4, sort_keys=True) filePath = pm.fileDialog2( fileMode=0, startingDirectory=startDir, fileFilter='Lego Custom Steps .lcs (%s)' % ".lcs") if not filePath: return if not isinstance(filePath, str): filePath = filePath[0] f = open(filePath, 'w') f.write(data_string) f.close() def import_custom_step(self, pre=True, args): """Import custom steps from a json file Arguments: pre (bool, optional): If true import to pre steps list args: Maya's Dummy Returns: None: None """ if pre: stepAttr = "pre_custom_step" stepWidget = self.custom_step_tab.preCustomStep_listWidget else: stepAttr = "post_custom_step" stepWidget = self.custom_step_tab.postCustomStep_listWidget # option import only paths or unpack steps option = pm.confirmDialog( title='Lego Custom Step Import Style', message='Do you want to import only the path or' ' unpack and import?', button=['Only Path', 'Unpack', 'Cancel'], defaultButton='Only Path', cancelButton='Cancel', dismissString='Cancel') if option in ['Only Path', 'Unpack']: if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") else: startDir = pm.workspace(q=True, rootDirectory=True) filePath = pm.fileDialog2( fileMode=1, startingDirectory=startDir, fileFilter='Shifter Custom Steps .scs (%s)' % ".scs") if not filePath: return if not isinstance(filePath, str): filePath = filePath[0] stepDict = json.load(open(filePath)) stepsList = [] if option == 'Only Path': for item in stepDict["itemsList"]: stepsList.append(item) elif option == 'Unpack': unPackDir = pm.fileDialog2( fileMode=2, startingDirectory=startDir) if not filePath: return if not isinstance(unPackDir, str): unPackDir = unPackDir[0] for item in stepDict["itemsList"]: fileName = os.path.split(item)[1] fileNewPath = os.path.join(unPackDir, fileName) stepsList.append(fileNewPath) f = open(fileNewPath, 'w') f.write(stepDict[item]) f.close() if option in ['Only Path', 'Unpack']: for item in stepsList: # Quick clean the first empty item itemsList = [i.text() for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] if itemsList and not itemsList[0]: stepWidget.takeItem(0) if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): item = os.path.abspath(item) baseReplace = os.path.abspath(os.environ.get( "MBOX_CUSTOM_STEP_PATH", "")) item = item.replace(baseReplace, "")[1:] fileName = os.path.split(item)[1].split(".")[0] stepWidget.addItem(fileName + " \| " + item) self.update_list_attr(stepWidget, stepAttr) def _custom_step_menu(self, cs_listWidget, stepAttr, QPos): "right click context menu for custom step" currentSelection = cs_listWidget.currentItem() if currentSelection is None: return self.csMenu = QtWidgets.QMenu() parentPosition = cs_listWidget.mapToGlobal(QtCore.QPoint(0, 0)) menu_item_01 = self.csMenu.addAction("Toggle Custom Step") self.csMenu.addSeparator() menu_item_02 = self.csMenu.addAction("Turn OFF Selected") menu_item_03 = self.csMenu.addAction("Turn ON Selected") self.csMenu.addSeparator() menu_item_04 = self.csMenu.addAction("Turn OFF All") menu_item_05 = self.csMenu.addAction("Turn ON All") menu_item_01.triggered.connect(partial(self.toggle_status_custom_step, cs_listWidget, stepAttr)) menu_item_02.triggered.connect(partial(self.set_status_custom_step, cs_listWidget, stepAttr, False)) menu_item_03.triggered.connect(partial(self.set_status_custom_step, cs_listWidget, stepAttr, True)) menu_item_04.triggered.connect(partial(self.set_status_custom_step, cs_listWidget, stepAttr, False, False)) menu_item_05.triggered.connect(partial(self.set_status_custom_step, cs_listWidget, stepAttr, True, False)) self.csMenu.move(parentPosition + QPos) self.csMenu.show() def pre_custom_step_menu(self, QPos): self._custom_step_menu(self.custom_step_tab.preCustomStep_listWidget, "pre_custom_step", QPos) def post_custom_step_menu(self, QPos): self._custom_step_menu(self.custom_step_tab.postCustomStep_listWidget, "post_custom_step", QPos) def toggle_status_custom_step(self, cs_listWidget, stepAttr): items = cs_listWidget.selectedItems() for item in items: if item.text().startswith(""): item.setText(item.text()[1:]) item.setForeground(self.white_down_brush) else: item.setText("" + item.text()) item.setForeground(self.red_brush) self.update_list_attr(cs_listWidget, stepAttr) self.refresh_status_color(cs_listWidget) def set_status_custom_step( self, cs_listWidget, stepAttr, status=True, selected=True): if selected: items = cs_listWidget.selectedItems() else: items = self.get_all_items(cs_listWidget) for item in items: off = item.text().startswith("") if status and off: item.setText(item.text()[1:]) elif not status and not off: item.setText("" + item.text()) self.set_status_color(item) self.update_list_attr(cs_listWidget, stepAttr) self.refresh_status_color(cs_listWidget) def get_all_items(self, cs_listWidget): return [cs_listWidget.item(i) for i in range(cs_listWidget.count())] def set_status_color(self, item): if item.text().startswith("*"): item.setForeground(self.red_brush) elif "_shared" in item.text(): item.setForeground(self.green_brush) else: item.setForeground(self.white_down_brush) def refresh_status_color(self, cs_listWidget): items = self.get_all_items(cs_listWidget) for i in items: self.set_status_color(i) # Highligter filter def _highlight_search(self, cs_listWidget, searchText): items = self.get_all_items(cs_listWidget) for i in items: if searchText and searchText.lower() in i.text().lower(): i.setBackground(QtGui.QColor(128, 128, 128, 255)) else: i.setBackground(QtGui.QColor(255, 255, 255, 0)) def pre_highlight_search(self): searchText = self.custom_step_tab.preSearch_lineEdit.text() self._highlight_search(self.custom_step_tab.preCustomStep_listWidget, searchText) def post_highlight_search(self): searchText = self.custom_step_tab.postSearch_lineEdit.text() self._highlight_search(self.custom_step_tab.postCustomStep_listWidget, searchText) class BlockMainTabUI(QtWidgets.QDialog, block_ui.Ui_Form): def __init__(self): super(BlockMainTabUI, self).__init__() self.setupUi(self) class BlockSettings(QtWidgets.QDialog, HelperSlots): valueChanged = QtCore.Signal(int) def __init__(self, parent=None): super(BlockSettings, self).__init__() # the inspectSettings function set the current selection to the # component root before open the settings dialog self._guide = lib.get_component_guide(pm.selected(type="transform")[0])[0] self._network = self._guide.message.outputs(type="network")[0] self.main_tab = BlockMainTabUI() self.create_controls() self.populate_controls() self.create_layout() self.create_connections() self.setAttribute(QtCore.Qt.WA_DeleteOnClose, True) def create_controls(self): """ Create the controls for the component base """ self.tabs = QtWidgets.QTabWidget() self.tabs.setObjectName("block_settings_tab") # Close Button self.close_button = QtWidgets.QPushButton("Close") def populate_controls(self): """Populate Controls attribute values Populate the controls values from the custom attributes of the component. """ # populate tab self.tabs.insertTab(0, self.main_tab, "Main Settings") # populate main settings self.main_tab.name_lineEdit.setText( self._network.attr("comp_name").get()) sideSet = ["center", "left", "right"] sideIndex = sideSet.index(self._network.attr("comp_side").get(asString=True)) self.main_tab.side_comboBox.setCurrentIndex(sideIndex) self.main_tab.componentIndex_spinBox.setValue( self._network.attr("comp_index").get()) # if self._network.attr("useIndex").get(): # self.main_tab.useJointIndex_checkBox.setCheckState( # QtCore.Qt.Checked) # else: # self.main_tab.useJointIndex_checkBox.setCheckState( # QtCore.Qt.Unchecked) # self.main_tab.parentJointIndex_spinBox.setValue( # self._network.attr("parentJointIndex").get()) self.main_tab.host_lineEdit.setText( self._network.attr("ui_host").get().split(",")[0]) # self.main_tab.subGroup_lineEdit.setText( # self._network.attr("ctlGrp").get()) # self.main_tab.joint_offset_x_doubleSpinBox.setValue( # self._network.attr("joint_rot_offset_x").get()) # self.main_tab.joint_offset_y_doubleSpinBox.setValue( # self._network.attr("joint_rot_offset_y").get()) # self.main_tab.joint_offset_z_doubleSpinBox.setValue( # self._network.attr("joint_rot_offset_z").get()) # testing adding custom color per component self.main_tab.overrideColors_checkBox.setCheckState( QtCore.Qt.Checked if self._network.attr("override_color").get() else QtCore.Qt.Unchecked) self.main_tab.useRGB_checkBox.setCheckState( QtCore.Qt.Checked if self._network.attr("use_RGB_color").get() else QtCore.Qt.Unchecked) tab = self.main_tab index_widgets = ((tab.color_fk_spinBox, tab.color_fk_label, "color_fk"), (tab.color_ik_spinBox, tab.color_ik_label, "color_ik")) rgb_widgets = ((tab.RGB_fk_pushButton, tab.RGB_fk_slider, "RGB_fk"), (tab.RGB_ik_pushButton, tab.RGB_ik_slider, "RGB_ik")) for spinBox, label, source_attr in index_widgets: color_index = self._network.attr(source_attr).get() spinBox.setValue(color_index) self.update_widget_style_sheet( label, [i / 255.0 for i in MAYA_OVERRIDE_COLOR[color_index]]) for button, slider, source_attr in rgb_widgets: self.update_rgb_color_widgets( button, self._network.attr(source_attr).get(), slider) # forceing the size of the color buttons/label to keep ui clean for widget in tuple(i[0] for i in rgb_widgets) + tuple( i[1] for i in index_widgets): widget.setFixedSize(pyqt.dpi_scale(30), pyqt.dpi_scale(20)) self.toggle_rgb_index_widgets(tab.useRGB_checkBox, (w for i in index_widgets for w in i[:2]), (w for i in rgb_widgets for w in i[:2]), "use_RGB_color", tab.useRGB_checkBox.checkState()) self.refresh_controls() def refresh_controls(self): joint_names = [name.strip() for name in self._network.attr("joint_names").get().split(",")] if any(joint_names): summary = "<b>({0} set)</b>".format(sum(map(bool, joint_names))) else: summary = "(None)" self.main_tab.jointNames_label.setText("Joint Names " + summary) def create_layout(self): """ Create the layout for the component base settings """ return def create_connections(self): """ Create the slots connections to the controls functions """ self.close_button.clicked.connect(self.close_settings) self.main_tab.name_lineEdit.editingFinished.connect( self.update_component_name) self.main_tab.side_comboBox.currentIndexChanged.connect( self.update_component_name) self.main_tab.componentIndex_spinBox.valueChanged.connect( self.update_component_name) # self.main_tab.useJointIndex_checkBox.stateChanged.connect( # partial(self.update_check, # self.main_tab.useJointIndex_checkBox, # "useIndex")) # self.main_tab.parentJointIndex_spinBox.valueChanged.connect( # partial(self.update_spin_box, # self.main_tab.parentJointIndex_spinBox, # "parentJointIndex")) self.main_tab.host_pushButton.clicked.connect( partial(self.update_host_ui, self.main_tab.host_lineEdit, "ui_host")) # self.main_tab.subGroup_lineEdit.editingFinished.connect( # partial(self.update_line_edit, # self.main_tab.subGroup_lineEdit, # "ctlGrp")) self.main_tab.jointNames_pushButton.clicked.connect( self.joint_names_dialog) # self.main_tab.joint_offset_x_doubleSpinBox.valueChanged.connect( # partial(self.update_spin_box, # self.main_tab.joint_offset_x_doubleSpinBox, # "joint_rot_offset_x")) # self.main_tab.joint_offset_y_doubleSpinBox.valueChanged.connect( # partial(self.update_spin_box, # self.main_tab.joint_offset_y_doubleSpinBox, # "joint_rot_offset_y")) # self.main_tab.joint_offset_z_doubleSpinBox.valueChanged.connect( # partial(self.update_spin_box, # self.main_tab.joint_offset_z_doubleSpinBox, # "joint_rot_offset_z")) tab = self.main_tab index_widgets = ((tab.color_fk_spinBox, tab.color_fk_label, "color_fk"), (tab.color_ik_spinBox, tab.color_ik_label, "color_ik")) rgb_widgets = ((tab.RGB_fk_pushButton, tab.RGB_fk_slider, "RGB_fk"), (tab.RGB_ik_pushButton, tab.RGB_ik_slider, "RGB_ik")) for spinBox, label, source_attr in index_widgets: spinBox.valueChanged.connect( partial(self.update_index_color_widgets, spinBox, source_attr, label)) for button, slider, source_attr in rgb_widgets: button.clicked.connect( partial(self.rgb_color_editor, button, source_attr, slider)) slider.valueChanged.connect( partial(self.rgb_slider_value_changed, button, source_attr)) tab.useRGB_checkBox.stateChanged.connect( partial(self.toggle_rgb_index_widgets, tab.useRGB_checkBox, tuple(w for i in index_widgets for w in i[:2]), tuple(w for i in rgb_widgets for w in i[:2]), "use_RGB_color")) tab.overrideColors_checkBox.stateChanged.connect( partial(self.update_check, tab.overrideColors_checkBox, "override_color")) def joint_names_dialog(self): dialog = JointNames(self._network, self) dialog.setWindowTitle(self.windowTitle()) dialog.attributeChanged.connect(self.refresh_controls) dialog.show() class JointNames(QtWidgets.QDialog, joint_name_ui.Ui_Form): attributeChanged = QtCore.Signal() def __init__(self, network, parent=None): super(JointNames, self).__init__(parent) self._network = network self.setupUi(self) self.populate_controls() self.apply_names() self.create_connections() def populate_controls(self): jointNames = self._network.attr("joint_names").get().split(",") if jointNames[-1]: jointNames.append("") self.jointNamesList.clearContents() self.jointNamesList.setRowCount(0) for i, name in enumerate(jointNames): self.jointNamesList.insertRow(i) item = QtWidgets.QTableWidgetItem(name.strip()) self.jointNamesList.setItem(i, 0, item) def create_connections(self): self.jointNamesList.cellChanged.connect(self.update_name) self.jointNamesList.itemActivated.connect(self.jointNamesList.editItem) self.add_pushButton.clicked.connect(self.add) self.remove_pushButton.clicked.connect(self.remove) self.removeAll_pushButton.clicked.connect(self.remove_all) self.moveUp_pushButton.clicked.connect(lambda: self.move(-1)) self.moveDown_pushButton.clicked.connect(lambda: self.move(1)) def apply_names(self): jointNames = [] for i in range(self.jointNamesList.rowCount()): item = self.jointNamesList.item(i, 0) jointNames.append(item.text()) value = ",".join(jointNames[0:-1]) self._network.attr("joint_names").set(value) self.jointNamesList.setVerticalHeaderLabels( [str(i) for i in range(len(jointNames))]) self.attributeChanged.emit() def add(self): row = max(0, self.jointNamesList.currentRow() or 0) self.jointNamesList.insertRow(row) item = QtWidgets.QTableWidgetItem("") self.jointNamesList.setItem(row, 0, item) self.jointNamesList.setCurrentCell(row, 0) self.apply_names() def remove(self): row = self.jointNamesList.currentRow() if row + 1 < self.jointNamesList.rowCount() > 1: self.jointNamesList.removeRow(row) self.apply_names() self.jointNamesList.setCurrentCell(row, 0) def remove_all(self): self.jointNamesList.clearContents() self.jointNamesList.setRowCount(0) self.jointNamesList.insertRow(0) self.jointNamesList.setItem(0, 0, QtWidgets.QTableWidgetItem("")) self.jointNamesList.setCurrentCell(0, 0) self.apply_names() def move(self, step): row = self.jointNamesList.currentRow() if row + step < 0: return item1 = self.jointNamesList.item(row, 0).text() item2 = self.jointNamesList.item(row + step, 0).text() self.jointNamesList.item(row, 0).setText(item2) self.jointNamesList.item(row + step, 0).setText(item1) self.jointNamesList.setCurrentCell(row + step, 0) def update_name(self, row, column): item = self.jointNamesList.item(row, column) if row == self.jointNamesList.rowCount() - 1 and item.text(): self.jointNamesList.insertRow(row + 1) self.jointNamesList.setItem( row + 1, 0, QtWidgets.QTableWidgetItem("")) self.apply_names() self.jointNamesList.setCurrentCell(row + 1, 0) self.jointNamesList.editItem(self.jointNamesList.currentItem()) def keyPressEvent(self): pass	nilq/baby-python	python
from django.shortcuts import render # Create your views here. def main(request): title = 'Travel Freely!' content = { 'title': title, } return render(request, 'mainsite/index.html', context=content)	nilq/baby-python	python
from empire.python.typings import * from empire.fs.file_system import FileSystem from empire.archive.archive_types import ArchiveTypes from empire.archive.abstract_compression import AbstractCompression from empire.archive.abstract_archive import AbstractArchive from empire.archive.zip_ar import Zip from empire.archive.gzip_ar import GZIP from empire.archive.lzma_ar import LZMA from empire.archive.bzip_ar import BZIP from empire.archive.tarbz_ar import TAR_BZ from empire.archive.targz_ar import TAR_GZ from empire.archive.tarxz_ar import TAR_XZ from empire.util.log import * COMPRESSION_TYPE_TO_IMPL: Final[Dict[int, Type[AbstractCompression]]] = { ArchiveTypes.ZIP: Zip, ArchiveTypes.GZIP: GZIP, ArchiveTypes.LZMA: LZMA, ArchiveTypes.BZIP: BZIP } TAR_TYPE_TO_IMPL: Final[Dict[int, Type[AbstractCompression]]] = { ArchiveTypes.TAR_XZ: TAR_XZ, ArchiveTypes.TAR_BZ: TAR_BZ, ArchiveTypes.TAR_GZ: TAR_GZ } MIME_TYPE_TO_IMPL: Final[Dict[str, Type[AbstractCompression]]] = { 'application/x-bzip2': BZIP, 'application/x-bzip': BZIP, 'application/x-gzip': GZIP, 'application/x-compressed': Zip, 'application/x-zip-compressed': Zip, 'application/zip': Zip, 'application/x-xz': LZMA, 'application/x-lzma': LZMA } def get_class_for_file(file_path: str) -> Union[Type[AbstractCompression], Type[AbstractArchive], None]: if '.tar' in file_path: compresser: Type[AbstractCompression] = MIME_TYPE_TO_IMPL[FileSystem.get_mime_from_file(file_path)] if compresser == LZMA: return TAR_XZ elif compresser == BZIP: return TAR_BZ elif compresser == GZIP: return TAR_GZ else: Log.severe('Unable to determine valid class', __file__, get_function_name(), file=file_path) return None else: return MIME_TYPE_TO_IMPL[FileSystem.get_mime_from_file(file_path)]	nilq/baby-python	python
"""API urls.""" from rest_framework import routers from . import viewsets router = routers.SimpleRouter() router.register(r"email-providers", viewsets.EmailProviderViewSet) router.register(r"migrations", viewsets.MigrationViewSet) urlpatterns = router.urls	nilq/baby-python	python
''' Leia 3 valores de ponto flutuante A, B e C e ordene-os em ordem decrescente, de modo que o lado A representa o maior dos 3 lados. A seguir, determine o tipo de triângulo que estes três lados formam, com base nos seguintes casos, sempre escrevendo uma mensagem adequada: - se A ≥ B+C, apresente a mensagem: NAO FORMA TRIANGULO - se A2 = B2 + C2, apresente a mensagem: TRIANGULO RETANGULO - se A2 > B2 + C2, apresente a mensagem: TRIANGULO OBTUSANGULO - se A2 < B2 + C2, apresente a mensagem: TRIANGULO ACUTANGULO - se os três lados forem iguais, apresente a mensagem: TRIANGULO EQUILATERO - se apenas dois dos lados forem iguais, apresente a mensagem: TRIANGULO ISOSCELES Input A entrada contem três valores de ponto flutuante de dupla precisão A (0 < A) , B (0 < B) e C (0 < C). Output Imprima todas as classificações do triângulo especificado na entrada. \| Input Sample \| Output Samples \| \| ------------ \| ---------------------- \| \| 7.0 5.0 7.0 \| TRIANGULO ACUTANGULO \| \| \| TRIANGULO ISOSCELES \| \| ------------ \| ---------------------- \| \| 6.0 6.0 10.0 \| TRIANGULO OBTUSANGULO \| \| \| TRIANGULO ISOSCELES \| \| ------------ \| ---------------------- \| \| 6.0 6.0 6.0 \| TRIANGULO ACUTANGULO \| \| \| TRIANGULO EQUILATERO \| \| ------------ \| ---------------------- \| \| 5.0 7.0 2.0 \| NAO FORMA TRIANGULO \| \| ------------ \| ---------------------- \| \| 6.0 8.0 10.0 \| TRIANGULO RETANGULO \| ''' triang = input().split() n1 = float(triang[0]) n2 = float(triang[1]) n3 = float(triang[2]) result = n1, n2, n3 ordem = sorted(result, reverse=True) a = ordem[0] b = ordem[1] c = ordem[2] ''' #debug print("n1 = {}".format(n1)) print("n2 = {}".format(n2)) print("n3 = {}".format(n3)) print("result = {}".format(result)) print("ordem = {}".format(ordem)) print("A = {}".format(a)) print("B = {}".format(b)) print("C = {}".format(c)) ''' tag = True if a >= (b + c): tag = False print("NAO FORMA TRIANGULO") if (a 2) == (b 2) + (c 2) and tag == True: print("TRIANGULO RETANGULO") if (a 2) > (b 2) + (c 2) and tag == True: print("TRIANGULO OBTUSANGULO") if (a 2) < (b 2) + (c ** 2) and tag == True: print("TRIANGULO ACUTANGULO") if a == b and a == c and b == a and b == c and c == a and c == b and tag == True: print("TRIANGULO EQUILATERO") if a == b and a != c or a == c and a != b or b == a and b!= c or b == c and b != a or c == b and c != a or c == a and c != b and tag == True: print("TRIANGULO ISOSCELES")	nilq/baby-python	python
from esteira.pipeline.stage import Stage from pathlib import Path BASE_DIR = Path(__file__).parent.absolute() def test_instance(): class TestShell(Stage): script = [ 'echo "hello world"' ] test = TestShell(BASE_DIR) test.run()	nilq/baby-python	python
from __future__ import division import sys import math import random import time import webbrowser as wb import keyboard as kb import pyautogui from collections import deque from pyglet import image from pyglet.gl import * from pyglet.graphics import TextureGroup from pyglet.window import key, mouse from playsound import playsound TICKS_PER_SEC = 60 # Size of sectors used to ease block loading. SECTOR_SIZE = 16 WALKING_SPEED = 3 FLYING_SPEED = 15 GRAVITY = 20.0 MAX_JUMP_HEIGHT = 1.0 # About the height of a block. # To derive the formula for calculating jump speed, first solve # v_t = v_0 + a * t # for the time at which you achieve maximum height, where a is the acceleration # due to gravity and v_t = 0. This gives: # t = - v_0 / a # Use t and the desired MAX_JUMP_HEIGHT to solve for v_0 (jump speed) in # s = s_0 + v_0 * t + (a * t^2) / 2 JUMP_SPEED = math.sqrt(2 * GRAVITY * MAX_JUMP_HEIGHT) TERMINAL_VELOCITY = 50 PLAYER_HEIGHT = 2 LIVES = 10 if sys.version_info[0] >= 3: xrange = range def cube_vertices(x, y, z, n): """ Return the vertices of the cube at position x, y, z with size 2n. """ return [ x-n,y+n,z-n, x-n,y+n,z+n, x+n,y+n,z+n, x+n,y+n,z-n, # top x-n,y-n,z-n, x+n,y-n,z-n, x+n,y-n,z+n, x-n,y-n,z+n, # bottom x-n,y-n,z-n, x-n,y-n,z+n, x-n,y+n,z+n, x-n,y+n,z-n, # left x+n,y-n,z+n, x+n,y-n,z-n, x+n,y+n,z-n, x+n,y+n,z+n, # right x-n,y-n,z+n, x+n,y-n,z+n, x+n,y+n,z+n, x-n,y+n,z+n, # front x+n,y-n,z-n, x-n,y-n,z-n, x-n,y+n,z-n, x+n,y+n,z-n, # back ] def tex_coord(x, y, n=4): """ Return the bounding vertices of the texture square. """ m = 1.0 / n dx = x m dy = y * m return dx, dy, dx + m, dy, dx + m, dy + m, dx, dy + m def tex_coords(top, bottom, side): """ Return a list of the texture squares for the top, bottom and side. """ top = tex_coord(top) bottom = tex_coord(bottom) side = tex_coord(side) result = [] result.extend(top) result.extend(bottom) result.extend(side 4) return result def crouch(): WALKING_SPEED = 0.5 TEXTURE_PATH = 'texture.png' GRASS = tex_coords((1, 0), (0, 1), (0, 0)) SAND = tex_coords((1, 1), (1, 1), (1, 1)) BRICK = tex_coords((2, 0), (2, 0), (2, 0)) STONE = tex_coords((2, 1), (2, 1), (2, 1)) DIRT = tex_coords((0, 1), (0, 1), (0, 1)) BOOKSHELF = tex_coords((1, 2), (1, 2), (0, 2)) SNOW = tex_coords((2, 2), (2, 2), (2, 2)) WOOD = tex_coords((3, 0), (3, 0), (3, 1)) LEAVES = tex_coords((3, 2), (3, 2), (3, 2)) FACES = [ ( 0, 1, 0), ( 0,-1, 0), (-1, 0, 0), ( 1, 0, 0), ( 0, 0, 1), ( 0, 0,-1), ] def normalize(position): """ Accepts `position` of arbitrary precision and returns the block containing that position. Parameters ---------- position : tuple of len 3 Returns ------- block_position : tuple of ints of len 3 """ x, y, z = position x, y, z = (int(round(x)), int(round(y)), int(round(z))) return (x, y, z) def sectorize(position): """ Returns a tuple representing the sector for the given `position`. Parameters ---------- position : tuple of len 3 Returns ------- sector : tuple of len 3 """ x, y, z = normalize(position) x, y, z = x // SECTOR_SIZE, y // SECTOR_SIZE, z // SECTOR_SIZE return (x, 0, z) class Model(object): def __init__(self): # A Batch is a collection of vertex lists for batched rendering. self.batch = pyglet.graphics.Batch() # A TextureGroup manages an OpenGL texture. self.group = TextureGroup(image.load(TEXTURE_PATH).get_texture()) # A mapping from position to the texture of the block at that position. # This defines all the blocks that are currently in the world. self.world = {} # Same mapping as `world` but only contains blocks that are shown. self.shown = {} # Mapping from position to a pyglet `VertextList` for all shown blocks. self._shown = {} # Mapping from sector to a list of positions inside that sector. self.sectors = {} # Simple function queue implementation. The queue is populated with # _show_block() and _hide_block() calls self.queue = deque() self._initialize() def _initialize(self): """ Initialize the world by placing all the blocks. """ n = 80 # 1/2 width and height of world s = 1 # step size y = 0 # initial y height for x in xrange(-n, n + 1, s): for z in xrange(-n, n + 1, s): # create a layer stone an grass everywhere. self.add_block((x, y - 1, z), GRASS, immediate=False) self.add_block((x, y - 2, z), DIRT, immediate=False) self.add_block((x, y - 3, z), DIRT, immediate=False) self.add_block((x, y - 4, z), DIRT, immediate=False) self.add_block((x, y - 5, z), STONE, immediate=False) # generate the hills randomly o = n - 10 for _ in xrange(120): a = random.randint(-o, o) # x position of the hill b = random.randint(-o, o) # z position of the hill c = -1 # base of the hill h = random.randint(1, 9) # height of the hill s = random.randint(4, 8) # 2 * s is the side length of the hill d = 1 # how quickly to taper off the hills t = random.choice([STONE]) tz = random.choice([SNOW]) fk = random.choice([DIRT]) for y in xrange(c, c + h): for x in xrange(a - s, a + s + 1): for z in xrange(b - s, b + s + 1): if (x - a) 2 + (z - b) 2 > (s + 1) 2: continue if (x - 0) 2 + (z - 0) 2 < 5 2: continue if y == 10: self.add_block((x, y, z), tz, immediate=False) if y >= 4: self.add_block((x, y, z), tz, immediate=False) elif y < 4: self.add_block((x, y, z), t, immediate=False) s -= d # decrement side lenth so hills taper off # generate trees X = random.randint(-80, 80) Z = random.randint(-80, 80) H = random.randint(4, 8) B = random.choice([WOOD]) C = 0 w = WOOD for x in range(0, C): self.add_block((X, H, Z), w, immediate=False) if C == H: continue def hit_test(self, position, vector, max_distance=8): """ Line of sight search from current position. If a block is intersected it is returned, along with the block previously in the line of sight. If no block is found, return None, None. Parameters ---------- position : tuple of len 3 The (x, y, z) position to check visibility from. vector : tuple of len 3 The line of sight vector. max_distance : int How many blocks away to search for a hit. """ m = 8 x, y, z = position dx, dy, dz = vector previous = None for _ in xrange(max_distance * m): key = normalize((x, y, z)) if key != previous and key in self.world: return key, previous previous = key x, y, z = x + dx / m, y + dy / m, z + dz / m return None, None def exposed(self, position): """ Returns False is given `position` is surrounded on all 6 sides by blocks, True otherwise. """ x, y, z = position for dx, dy, dz in FACES: if (x + dx, y + dy, z + dz) not in self.world: return True return False def add_block(self, position, texture, immediate=True): """ Add a block with the given `texture` and `position` to the world. Parameters ---------- position : tuple of len 3 The (x, y, z) position of the block to add. texture : list of len 3 The coordinates of the texture squares. Use `tex_coords()` to generate. immediate : bool Whether or not to draw the block immediately. """ if position in self.world: self.remove_block(position, immediate) self.world[position] = texture self.sectors.setdefault(sectorize(position), []).append(position) if immediate: if self.exposed(position): self.show_block(position) self.check_neighbors(position) def remove_block(self, position, immediate=True): """ Remove the block at the given `position`. Parameters ---------- position : tuple of len 3 The (x, y, z) position of the block to remove. immediate : bool Whether or not to immediately remove block from canvas. """ del self.world[position] self.sectors[sectorize(position)].remove(position) if immediate: if position in self.shown: self.hide_block(position) self.check_neighbors(position) def check_neighbors(self, position): """ Check all blocks surrounding `position` and ensure their visual state is current. This means hiding blocks that are not exposed and ensuring that all exposed blocks are shown. Usually used after a block is added or removed. """ x, y, z = position for dx, dy, dz in FACES: key = (x + dx, y + dy, z + dz) if key not in self.world: continue if self.exposed(key): if key not in self.shown: self.show_block(key) else: if key in self.shown: self.hide_block(key) def show_block(self, position, immediate=True): """ Show the block at the given `position`. This method assumes the block has already been added with add_block() Parameters ---------- position : tuple of len 3 The (x, y, z) position of the block to show. immediate : bool Whether or not to show the block immediately. """ texture = self.world[position] self.shown[position] = texture if immediate: self._show_block(position, texture) else: self._enqueue(self._show_block, position, texture) def _show_block(self, position, texture): """ Private implementation of the `show_block()` method. Parameters ---------- position : tuple of len 3 The (x, y, z) position of the block to show. texture : list of len 3 The coordinates of the texture squares. Use `tex_coords()` to generate. """ x, y, z = position vertex_data = cube_vertices(x, y, z, 0.5) texture_data = list(texture) # create vertex list # FIXME Maybe `add_indexed()` should be used instead self._shown[position] = self.batch.add(24, GL_QUADS, self.group, ('v3f/static', vertex_data), ('t2f/static', texture_data)) def hide_block(self, position, immediate=True): """ Hide the block at the given `position`. Hiding does not remove the block from the world. Parameters ---------- position : tuple of len 3 The (x, y, z) position of the block to hide. immediate : bool Whether or not to immediately remove the block from the canvas. """ self.shown.pop(position) if immediate: self._hide_block(position) else: self._enqueue(self._hide_block, position) def _hide_block(self, position): """ Private implementation of the 'hide_block()` method. """ self._shown.pop(position).delete() def show_sector(self, sector): """ Ensure all blocks in the given sector that should be shown are drawn to the canvas. """ for position in self.sectors.get(sector, []): if position not in self.shown and self.exposed(position): self.show_block(position, False) def hide_sector(self, sector): """ Ensure all blocks in the given sector that should be hidden are removed from the canvas. """ for position in self.sectors.get(sector, []): if position in self.shown: self.hide_block(position, False) def change_sectors(self, before, after): """ Move from sector `before` to sector `after`. A sector is a contiguous x, y sub-region of world. Sectors are used to speed up world rendering. """ before_set = set() after_set = set() pad = 4 for dx in xrange(-pad, pad + 1): for dy in [0]: # xrange(-pad, pad + 1): for dz in xrange(-pad, pad + 1): if dx 2 + dy 2 + dz 2 > (pad + 1) 2: continue if before: x, y, z = before before_set.add((x + dx, y + dy, z + dz)) if after: x, y, z = after after_set.add((x + dx, y + dy, z + dz)) show = after_set - before_set hide = before_set - after_set for sector in show: self.show_sector(sector) for sector in hide: self.hide_sector(sector) def _enqueue(self, func, args): """ Add `func` to the internal queue. """ self.queue.append((func, args)) def _dequeue(self): """ Pop the top function from the internal queue and call it. """ func, args = self.queue.popleft() func(args) def process_queue(self): """ Process the entire queue while taking periodic breaks. This allows the game loop to run smoothly. The queue contains calls to _show_block() and _hide_block() so this method should be called if add_block() or remove_block() was called with immediate=False """ start = time.clock() while self.queue and time.clock() - start < 1.0 / TICKS_PER_SEC: self._dequeue() def process_entire_queue(self): """ Process the entire queue with no breaks. """ while self.queue: self._dequeue() class Window(pyglet.window.Window): def __init__(self, args, kwargs): super(Window, self).__init__(args, *kwargs) # Whether or not the window exclusively captures the mouse. self.exclusive = False # When flying gravity has no effect and speed is increased. self.flying = False # Strafing is moving lateral to the direction you are facing, # e.g. moving to the left or right while continuing to face forward. # # First element is -1 when moving forward, 1 when moving back, and 0 # otherwise. The second element is -1 when moving left, 1 when moving # right, and 0 otherwise. self.strafe = [0, 0] # Current (x, y, z) position in the world, specified with floats. Note # that, perhaps unlike in math class, the y-axis is the vertical axis. self.position = (0, 0, 0) # First element is rotation of the player in the x-z plane (ground # plane) measured from the z-axis down. The second is the rotation # angle from the ground plane up. Rotation is in degrees. # # The vertical plane rotation ranges from -90 (looking straight down) to # 90 (looking straight up). The horizontal rotation range is unbounded. self.rotation = (0, 0) # Which sector the player is currently in. self.sector = None # The crosshairs at the center of the screen. self.reticle = None # Velocity in the y (upward) direction. self.dy = 0 # A list of blocks the player can place. Hit num keys to cycle. self.inventory = [BRICK, GRASS, SAND, BOOKSHELF, WOOD, SNOW, LEAVES, DIRT, STONE] # The current block the user can place. Hit num keys to cycle. self.block = self.inventory[0] # Convenience list of num keys. self.num_keys = [ key._1, key._2, key._3, key._4, key._5, key._6, key._7, key._8, key._9, key._0] # Instance of the model that handles the world. self.model = Model() # The label that is displayed in the top left of the canvas. self.label = pyglet.text.Label('', font_name='Arial', font_size=18, x=10, y=self.height - 10, anchor_x='left', anchor_y='top', color=(0, 0, 0, 255)) # This call schedules the `update()` method to be called # TICKS_PER_SEC. This is the main game event loop. pyglet.clock.schedule_interval(self.update, 1.0 / TICKS_PER_SEC) def set_exclusive_mouse(self, exclusive): """ If `exclusive` is True, the game will capture the mouse, if False the game will ignore the mouse. """ super(Window, self).set_exclusive_mouse(exclusive) self.exclusive = exclusive def get_sight_vector(self): """ Returns the current line of sight vector indicating the direction the player is looking. """ x, y = self.rotation # y ranges from -90 to 90, or -pi/2 to pi/2, so m ranges from 0 to 1 and # is 1 when looking ahead parallel to the ground and 0 when looking # straight up or down. m = math.cos(math.radians(y)) # dy ranges from -1 to 1 and is -1 when looking straight down and 1 when # looking straight up. dy = math.sin(math.radians(y)) dx = math.cos(math.radians(x - 90)) m dz = math.sin(math.radians(x - 90)) * m return (dx, dy, dz) def get_motion_vector(self): """ Returns the current motion vector indicating the velocity of the player. Returns ------- vector : tuple of len 3 Tuple containing the velocity in x, y, and z respectively. """ if any(self.strafe): x, y = self.rotation strafe = math.degrees(math.atan2(self.strafe)) y_angle = math.radians(y) x_angle = math.radians(x + strafe) if self.flying: m = math.cos(y_angle) dy = math.sin(y_angle) if self.strafe[1]: # Moving left or right. dy = 0.0 m = 1 if self.strafe[0] > 0: # Moving backwards. dy = -1 # When you are flying up or down, you have less left and right # motion. dx = math.cos(x_angle) * m dz = math.sin(x_angle) * m else: dy = 0.0 dx = math.cos(x_angle) dz = math.sin(x_angle) else: dy = 0.0 dx = 0.0 dz = 0.0 return (dx, dy, dz) def update(self, dt): """ This method is scheduled to be called repeatedly by the pyglet clock. Parameters ---------- dt : float The change in time since the last call. """ self.model.process_queue() sector = sectorize(self.position) if sector != self.sector: self.model.change_sectors(self.sector, sector) if self.sector is None: self.model.process_entire_queue() self.sector = sector m = 8 dt = min(dt, 0.2) for _ in xrange(m): self._update(dt / m) def _update(self, dt): """ Private implementation of the `update()` method. This is where most of the motion logic lives, along with gravity and collision detection. Parameters ---------- dt : float The change in time since the last call. """ # walking speed = FLYING_SPEED if self.flying else WALKING_SPEED d = dt * speed # distance covered this tick. dx, dy, dz = self.get_motion_vector() # New position in space, before accounting for gravity. dx, dy, dz = dx * d, dy * d, dz * d # gravity if not self.flying: # Update your vertical speed: if you are falling, speed up until you # hit terminal velocity; if you are jumping, slow down until you # start falling. self.dy -= dt * GRAVITY self.dy = max(self.dy, -TERMINAL_VELOCITY) dy += self.dy * dt # collisions x, y, z = self.position x, y, z = self.collide((x + dx, y + dy, z + dz), PLAYER_HEIGHT) self.position = (x, y, z) def collide(self, position, height): """ Checks to see if the player at the given `position` and `height` is colliding with any blocks in the world. Parameters ---------- position : tuple of len 3 The (x, y, z) position to check for collisions at. height : int or float The height of the player. Returns ------- position : tuple of len 3 The new position of the player taking into account collisions. """ # How much overlap with a dimension of a surrounding block you need to # have to count as a collision. If 0, touching terrain at all counts as # a collision. If .49, you sink into the ground, as if walking through # tall grass. If >= .5, you'll fall through the ground. pad = 0.25 p = list(position) np = normalize(position) for face in FACES: # check all surrounding blocks for i in xrange(3): # check each dimension independently if not face[i]: continue # How much overlap you have with this dimension. d = (p[i] - np[i]) * face[i] if d < pad: continue for dy in xrange(height): # check each height op = list(np) op[1] -= dy op[i] += face[i] if tuple(op) not in self.model.world: continue p[i] -= (d - pad) * face[i] if face == (0, -1, 0) or face == (0, 1, 0): # You are colliding with the ground or ceiling, so stop # falling / rising. self.dy = 0 break return tuple(p) def on_mouse_press(self, x, y, button, modifiers): """ Called when a mouse button is pressed. See pyglet docs for button amd modifier mappings. Parameters ---------- x, y : int The coordinates of the mouse click. Always center of the screen if the mouse is captured. button : int Number representing mouse button that was clicked. 1 = left button, 4 = right button. modifiers : int Number representing any modifying keys that were pressed when the mouse button was clicked. """ if self.exclusive: vector = self.get_sight_vector() block, previous = self.model.hit_test(self.position, vector) if (button == mouse.RIGHT) or \ ((button == mouse.LEFT) and (modifiers & key.MOD_CTRL)): # ON OSX, control + left click = right click. if previous: self.model.add_block(previous, self.block) elif button == pyglet.window.mouse.LEFT and block: texture = self.model.world[block] else: self.set_exclusive_mouse(True) def on_mouse_motion(self, x, y, dx, dy): """ Called when the player moves the mouse. Parameters ---------- x, y : int The coordinates of the mouse click. Always center of the screen if the mouse is captured. dx, dy : float The movement of the mouse. """ if self.exclusive: m = 0.15 x, y = self.rotation x, y = x + dx * m, y + dy * m y = max(-90, min(90, y)) self.rotation = (x, y) def on_key_press(self, symbol, modifiers): """ Called when the player presses a key. See pyglet docs for key mappings. Parameters ---------- symbol : int Number representing the key that was pressed. modifiers : int Number representing any modifying keys that were pressed. """ if symbol == key.W: self.strafe[0] -= 1 elif symbol == key.S: self.strafe[0] += 1 elif symbol == key.A: self.strafe[1] -= 1 elif symbol == key.D: self.strafe[1] += 1 elif symbol == key.L: crouch() elif symbol == key.SPACE: if self.dy == 0: self.dy = JUMP_SPEED elif symbol == key.ESCAPE: self.set_exclusive_mouse(False) elif symbol == key.TAB: self.flying = not self.flying elif symbol in self.num_keys: index = (symbol - self.num_keys[0]) % len(self.inventory) self.block = self.inventory[index] def on_key_release(self, symbol, modifiers): """ Called when the player releases a key. See pyglet docs for key mappings. Parameters ---------- symbol : int Number representing the key that was pressed. modifiers : int Number representing any modifying keys that were pressed. """ if symbol == key.W: self.strafe[0] += 1 elif symbol == key.S: self.strafe[0] -= 1 elif symbol == key.A: self.strafe[1] += 1 elif symbol == key.D: self.strafe[1] -= 1 def on_resize(self, width, height): """ Called when the window is resized to a new `width` and `height`. """ # label self.label.y = height - 10 # reticle if self.reticle: self.reticle.delete() x, y = self.width // 2, self.height // 2 n = 10 self.reticle = pyglet.graphics.vertex_list(4, ('v2i', (x - n, y, x + n, y, x, y - n, x, y + n)) ) def set_2d(self): """ Configure OpenGL to draw in 2d. """ width, height = self.get_size() glDisable(GL_DEPTH_TEST) glViewport(0, 0, width, height) glMatrixMode(GL_PROJECTION) glLoadIdentity() glOrtho(0, width, 0, height, -1, 1) glMatrixMode(GL_MODELVIEW) glLoadIdentity() def set_3d(self): """ Configure OpenGL to draw in 3d. """ width, height = self.get_size() glEnable(GL_DEPTH_TEST) glViewport(0, 0, width, height) glMatrixMode(GL_PROJECTION) glLoadIdentity() gluPerspective(65.0, width / float(height), 0.1, 60.0) glMatrixMode(GL_MODELVIEW) glLoadIdentity() x, y = self.rotation glRotatef(x, 0, 1, 0) glRotatef(-y, math.cos(math.radians(x)), 0, math.sin(math.radians(x))) x, y, z = self.position glTranslatef(-x, -y, -z) def on_draw(self): """ Called by pyglet to draw the canvas. """ self.clear() self.set_3d() glColor3d(1, 1, 1) self.model.batch.draw() self.draw_focused_block() self.set_2d() self.draw_label() self.draw_reticle() def draw_focused_block(self): """ Draw black edges around the block that is currently under the crosshairs. """ vector = self.get_sight_vector() block = self.model.hit_test(self.position, vector)[0] if block: x, y, z = block vertex_data = cube_vertices(x, y, z, 0.51) glColor3d(0, 0, 0) glPolygonMode(GL_FRONT_AND_BACK, GL_LINE) pyglet.graphics.draw(24, GL_QUADS, ('v3f/static', vertex_data)) glPolygonMode(GL_FRONT_AND_BACK, GL_FILL) def draw_label(self): """ Draw the label in the top left of the screen. """ x, y, z = self.position self.label.text = '%02d (%.2f, %.2f, %.2f) %d / %d' % ( pyglet.clock.get_fps(), x, y, z, len(self.model._shown), len(self.model.world)) self.label.draw() self.label.text = 'JetAdven 0.04. Work in progress' def draw_reticle(self): """ Draw the crosshairs in the center of the screen. """ glColor3d(0, 0, 0) self.reticle.draw(GL_LINES) def setup_fog(): """ Configure the OpenGL fog properties. """ # Enable fog. Fog "blends a fog color with each rasterized pixel fragment's # post-texturing color." glEnable(GL_FOG) # Set the fog color. glFogfv(GL_FOG_COLOR, (GLfloat * 4)(0.5, 0.69, 1.0, 1)) # Say we have no preference between rendering speed and quality. glHint(GL_FOG_HINT, GL_DONT_CARE) # Specify the equation used to compute the blending factor. glFogi(GL_FOG_MODE, GL_LINEAR) # How close and far away fog starts and ends. The closer the start and end, # the denser the fog in the fog range. glFogf(GL_FOG_START, 20.0) glFogf(GL_FOG_END, 60.0) def music(): music = pyglet.resource.media('gamemusic.mp3') music.play() def setup(): """ Basic OpenGL configuration. """ # Set the color of "clear", i.e. the sky, in rgba. (will add day night cycle) glClearColor(0.5, 0.69, 1.0, 1) #def daynight(): #time.sleep(100) #glClearColor(0.2, 0.2, 0.2, 1) #time.sleep(100) #d#/aynight() # Enable culling (not rendering) of back-facing facets -- facets that aren't # visible to you. glEnable(GL_CULL_FACE) # Set the texture minification/magnification function to GL_NEAREST (nearest # in Manhattan distance) to the specified texture coordinates. GL_NEAREST # "is generally faster than GL_LINEAR, but it can produce textured images # with sharper edges because the transition between texture elements is not # as smooth." # glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_NEAREST) # glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_NEAREST) # setup_fog() def main(): window = Window(width=25002, height=15002, caption='JetAdven 0.04. not a clone of minecraft :)', resizable=True) # Hide the mouse cursor and prevent the mouse from leaving the window. window.set_exclusive_mouse(True) setup() pyglet.app.run() music() if __name__ == '__main__': main()	nilq/baby-python	python
from logging import getLogger from hornet import models from .common import ClientCommand logger = getLogger(__name__) class Command(ClientCommand): def add_arguments(self, parser): parser.add_argument("member_id", type=int) def handle(self, member_id, args, *kwargs): try: member = models.Member.objects.get(pk=member_id) except models.Member.DoesNotExist: self.stderr.write("Unknown member") return result = self.client.list_message(member) for message in result: print(" ", message) self.stderr.write("Total messages: %s" % len(result))	nilq/baby-python	python
# Copyright DataStax, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import hashlib import logging from base64 import urlsafe_b64encode from collections import namedtuple from datetime import datetime, tzinfo, timedelta try: from itertools import ifilterfalse as filterfalse except ImportError: from itertools import filterfalse from adelphi.anonymize import anonymize_keyspace from adelphi.exceptions import KeyspaceSelectionException from adelphi.store import build_keyspace_objects log = logging.getLogger('adelphi') try: from datetime import timezone utc = timezone.utc except ImportError: class UTC(tzinfo): def utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return "UTC" def dst(self, dt): return timedelta(0) utc = UTC() KsTuple = namedtuple('KsTuple',['ks_id', 'ks_obj']) class BaseExporter: # unique_everseen from itertools recipes def __unique(self, iterable, key=None): "List unique elements, preserving order. Remember all elements ever seen." # unique_everseen('AAAABBBCCDAABBB') --> A B C D # unique_everseen('ABBCcAD', str.lower) --> A B C D seen = set() seen_add = seen.add if key is None: for element in filterfalse(seen.__contains__, iterable): seen_add(element) yield element else: for element in iterable: k = key(element) if k not in seen: seen_add(k) yield element def build_keyspace_id(self, ks): m = hashlib.sha256() m.update(ks.name.encode("utf-8")) # Leverage the urlsafe base64 encoding defined in RFC 4648, section 5 to provide an ID which can # safely be used for filenames as well return urlsafe_b64encode(m.digest()).decode('ascii') def get_keyspaces(self, cluster, props): keyspace_names = props["keyspace-names"] metadata = cluster.metadata keyspaces = build_keyspace_objects(keyspace_names, metadata) if len(keyspaces) == 0: raise KeyspaceSelectionException("Unable to select a keyspace from specified keyspace names") log.info("Processing the following keyspaces: %s", ','.join((ks.name for ks in keyspaces))) # anonymize_keyspace mutates keyspace state so we must trap keyspace_id before we (possibly) call it ids = {ks.name : self.build_keyspace_id(ks) for ks in keyspaces} # Create a tuple to represent this keyspace. Note that we must perform anonymization as part of this # operation because we need the keyspace name before anonymization to access the correct ID from the # dict above. def make_tuple(ks): orig_name = ks.name if props['anonymize']: anonymize_keyspace(ks) return KsTuple(ids[orig_name], ks) return {t.ks_obj.name : t for t in [make_tuple(ks) for ks in keyspaces]} def get_cluster_metadata(self, cluster): hosts = cluster.metadata.all_hosts() unique_dcs = self.__unique((host.datacenter for host in hosts)) unique_cass_vers = self.__unique((host.release_version for host in hosts)) return {"host_count": len(hosts), "dc_count": sum(1 for _ in unique_dcs), "cassandra_versions": ",".join(unique_cass_vers)} def get_common_metadata(self, cluster, props): metadata = {k : props[k] for k in ["purpose", "maturity"] if k in props} metadata.update(self.get_cluster_metadata(cluster)) metadata["creation_timestamp"] = datetime.now(utc).isoformat() return metadata # Remaining methods in this class represent default impls of methods for subclasses def export_all(self): return self.export_schema() # Note assumption of keyspace and keyspace_id as attrs def each_keyspace(self, ks_fn): ks_fn(self.keyspace, self.keyspace_id) # Functions below assume self.metadata as a dict def export_metadata_dict(self): return {k : self.metadata[k] for k in self.metadata.keys() if self.metadata[k]} def add_metadata(self, k, v): """Note that this function sets a metadata value for the entire exporter. If you need something keyspace-specific you're probably better off just adding it to the exported metadata directory.""" self.metadata[k] = v	nilq/baby-python	python
import uuid import hashlib import prettytable from keystoneclient import exceptions # Decorator for cli-args def arg(args, kwargs): def _decorator(func): # Because of the sematics of decorator composition if we just append # to the options list positional options will appear to be backwards. func.__dict__.setdefault('arguments', []).insert(0, (args, kwargs)) return func return _decorator def pretty_choice_list(l): return ', '.join("'%s'" % i for i in l) def print_list(objs, fields, formatters={}): pt = prettytable.PrettyTable([f for f in fields], caching=False) pt.aligns = ['l' for f in fields] for o in objs: row = [] for field in fields: if field in formatters: row.append(formatters[field](o)) else: field_name = field.lower().replace(' ', '_') data = getattr(o, field_name, '') if data is None: data = '' row.append(data) pt.add_row(row) print pt.get_string(sortby=fields[0]) def _word_wrap(string, max_length=0): """wrap long strings to be no longer then max_length""" if max_length <= 0: return string return '\n'.join([string[i:i + max_length] for i in range(0, len(string), max_length)]) def print_dict(d, wrap=0): """pretty table prints dictionaries. Wrap values to max_length wrap if wrap>0 """ pt = prettytable.PrettyTable(['Property', 'Value'], caching=False) pt.aligns = ['l', 'l'] for (prop, value) in d.iteritems(): if value is None: value = '' value = _word_wrap(value, max_length=wrap) pt.add_row([prop, value]) print pt.get_string(sortby='Property') def find_resource(manager, name_or_id): """Helper for the _find_ methods.""" # first try to get entity as integer id try: if isinstance(name_or_id, int) or name_or_id.isdigit(): return manager.get(int(name_or_id)) except exceptions.NotFound: pass # now try to get entity as uuid try: uuid.UUID(str(name_or_id)) return manager.get(name_or_id) except (ValueError, exceptions.NotFound): pass # finally try to find entity by name try: return manager.find(name=name_or_id) except exceptions.NotFound: msg = ("No %s with a name or ID of '%s' exists." % (manager.resource_class.__name__.lower(), name_or_id)) raise exceptions.CommandError(msg) def unauthenticated(f): """Adds 'unauthenticated' attribute to decorated function. Usage:: @unauthenticated def mymethod(f): ... """ f.unauthenticated = True return f def isunauthenticated(f): """ Checks to see if the function is marked as not requiring authentication with the @unauthenticated decorator. Returns True if decorator is set to True, False otherwise. """ return getattr(f, 'unauthenticated', False) def string_to_bool(arg): if isinstance(arg, bool): return arg return arg.strip().lower() in ('t', 'true', 'yes', '1') def hash_signed_token(signed_text): hash_ = hashlib.md5() hash_.update(signed_text) return hash_.hexdigest()	nilq/baby-python	python
import multiprocessing print(multiprocessing.cpu_count(), "núcleos") # conta a quantidade de núcleos disponpives no sistema # processamento senquancial import threading # módulo para a a contrução de threads import urllib.request # módulo para a requição da url import time # módulo para tratar o tempo # função criada para realização do dowload das imagens def downloadImangens(imagepath, fileName): print("Realizando Dowload......", imagepath) urllib.request.urlretrieve(imagepath, fileName) # Realiza a requisição para pagina da Web t0 = time.time() # armazena o tempo inicial da execução for i in range (10): imageName = "imagens/image-" +str(i) +".jpg" # coloca o nome em cada uma das imagens baixadas downloadImangens("http://lorempixel.com.br/400/200/sports", imageName) # aplica o download da imagem t1 = time.time() # tempo final após a execução totalTime = t1 - t0 # diferença de tempo entre o valor inicial de execução e o final print("Tempo tiotal de execuções {}".format(totalTime))	nilq/baby-python	python
class Solution: def strStr(self, haystack, needle): """ :type haystack: str :type needle: str :rtype: int """ lenh = len(haystack) lenn = len(needle) for i in range(lenh-lenn+1): if haystack[i:i+lenn] == needle: return i return -1 # return haystack.find(needle)	nilq/baby-python	python
###################################################################### # controller - deals with the UI concerns # 1. navigation # 2. preparing data elements in ui way for the screens # # It will not be referring to the business domain objects # - it will use the bl component to deal with the business logic ###################################################################### import flask import sys import datetime import traceback from flask import send_file from core.constants import _DATE_STR_DISPLAY_FORMAT_ from factory import XManFactory from core.timer import Timer # all app level variables __version__=1.0 __author__='Ramakrishnan Jayachandran' __appname__='XMAN (eXpense MANager) v1.0' # Flask initialisation app = flask.Flask( __name__ ) ####################################### ## This section contains all the code ## related to just navigation to other ## pages in the system ####################################### # This is the index page or the home page for the App @app.route( '/', methods = [ 'GET'] ) def index_page() -> str: with Timer( 'index_page') as stime: summary = getExpenseSummary() return flask.render_template( 'index.html', the_title=__appname__, summary=summary ) # redirection to input screen for expense - and build neccessary objects for it @app.route( '/expense_input', methods = [ 'GET' ] ) def expense_input() -> str : with Timer( 'expense_input' ) as start_time: summary = getExpenseSummary() # constants for accessing tuple with some readability _EXPENSE_TYPES_ : int = 0 _PEOPLE_ : int = 1 _STORES_ : int = 2 _PAYMENT_MODE_ : int = 3 ui_objects : tuple = factory_object.getBusinessLayer().prepareExpenseInput() ## TODO: add code here to navigate to expense_input page return flask.render_template( 'expense_input.html', the_title=__appname__ , summary=summary, \ short_names=ui_objects[ _PEOPLE_ ], store_names=ui_objects[ _STORES_ ], \ payment_types=ui_objects[ _PAYMENT_MODE_], expense_types=ui_objects[ _EXPENSE_TYPES_ ] ) # expense category redirection to the input screen @app.route( '/expense_category_input', methods = [ 'GET' ] ) def expense_category_input() -> str : summary = getExpenseSummary() return flask.render_template( 'expense_category_input.html', the_title=__appname__, summary=summary ) @app.route( '/store_input', methods = [ 'GET' ] ) def store_input() -> str : summary = getExpenseSummary() return flask.render_template( 'store_input.html', the_title=__appname__, summary=summary ) @app.route( '/payment_type_input', methods = [ 'GET' ] ) def payment_type_input() -> str : summary = getExpenseSummary() return flask.render_template( 'payment_type_input.html', the_title=__appname__, summary=summary ) @app.route( '/person_input', methods = [ 'GET' ] ) def person_input() -> str : summary = getExpenseSummary() return flask.render_template( 'person_input.html', the_title=__appname__, summary=summary ) ####################################### ## This section contains all the code ## related to just backend operations ## and then subsequent navigations ####################################### # All Add flows go here ... @app.route( '/expense_add', methods=['POST'] ) def add_expense() -> str : summary = getExpenseSummary() try: factory_object.getBusinessLayer().addExpense( flask.request.form[ 'exp_type' ], flask.request.form[ 'exp_detail' ], datetime.datetime.strptime( flask.request.form[ 'exp_date' ], '%Y-%m-%d' ) , float( flask.request.form[ 'exp_amount' ]), flask.request.form[ 'payment_type' ], flask.request.form[ 'store_name' ], flask.request.form[ 'short_name' ]) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed adding expense information', \ error_action = 'Please reenter the expense data and try again',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/expense_category_add', methods=['POST'] ) def add_expense_category() -> str : print( 'add_expense_category') summary = getExpenseSummary() expense_type : str = flask.request.form[ 'expense_type' ] expense_detail : str = flask.request.form[ 'expense_type_detail' ] try: factory_object.getBusinessLayer().addExpenseCategory( expense_type, expense_detail ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed adding expense category', \ error_action = 'Please reenter the Expense category - make sure it is not a duplicate', summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/store_add', methods=['POST'] ) def add_store() -> str : summary = getExpenseSummary() try: factory_object.getBusinessLayer().addStore( flask.request.form[ 'store_name' ], flask.request.form[ 'store_detail' ], flask.request.form[ 'home_delivery' ] ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed adding store data', \ error_action = 'Please reenter the Store data and make sure it is not a duplicate',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/payment_type_add', methods=['POST'] ) def add_payment_type() -> str : summary = getExpenseSummary() try: factory_object.getBusinessLayer().addPaymentType( flask.request.form[ 'payment_mode' ], flask.request.form[ 'payment_mode_detail' ] ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed adding payment type data', \ error_action = 'Please reenter the payment type data and make sure it is not a duplicate',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/person_add', methods=['POST'] ) def add_person() -> str : summary = getExpenseSummary() try: factory_object.getBusinessLayer().addPerson( flask.request.form[ 'person_first_name' ], flask.request.form[ 'person_last_name' ], flask.request.form[ 'person_short_name' ] ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed adding person data', \ error_action = 'Please reenter the person data and make sure short name it is not a duplicate',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) # All list flows go here ... @app.route( '/expenses_list', methods = [ 'GET'] ) def list_expenses() -> str : print( 'list_expenses' ) summary = getExpenseSummary() expenses : list = factory_object.getBusinessLayer().listExpenses() ui_header = [ 'ID', 'Expense Detail', 'Expense Date', 'Amount', 'Spent by', 'Store', 'Expense Type', 'Payment mode' ] ui_data : list = [ (e.getId(), e.getExpenseDetail(), e.getExpenseDate().strftime( _DATE_STR_DISPLAY_FORMAT_ ) , e.getExpenseAmount(), \ e.getPerson().getShortName(), e.getStore().getStoreName(), \ e.getExpenseCategory().getExpenseType(), e.getPaymentType().getPaymentMode()) for e in expenses ] # Generate the csv file for future use csv_rows = [] csv_rows.append( ui_header ) for row in ui_data: csv_rows.append( [ c for c in row ]) factory_object.getCSVGenerator().generateFile( 'all_expenses.csv', csv_rows ) return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module='expense', download=True ) @app.route( '/expense_categories_list', methods = [ 'GET'] ) def list_expense_categories() -> str : print( 'list_expense_categories' ) summary = getExpenseSummary() expense_categories : list = factory_object.getBusinessLayer().listExpenseCategories() ui_header = ( 'Id', 'Expense Type', 'Expense Detail' ) ui_data : list = [ ( ec.getId(), ec.getExpenseType(), ec.getExpenseDetail()) for ec in expense_categories ] mode = flask.request.args.get( 'mode' ) if mode == 'popup': return flask.render_template( 'list_data_popup.html', the_title=__appname__, \ the_header = ui_header, summary=summary, the_data = ui_data, module = None ) else: return flask.render_template( 'list_data_page.html', the_title=__appname__, summary=summary, \ the_header = ui_header, the_data = ui_data, module = 'expense_category' ) @app.route( '/stores_list', methods = [ 'GET'] ) def list_stores() -> str : summary = getExpenseSummary() stores : list = factory_object.getBusinessLayer().listStores() ui_header = ('ID', 'Store Name', 'Store Detail', 'Home Delivery ?' ) ui_data : list = [ (st.getId(), st.getStoreName(), st.getStoreDetail(), ('Y' if st.getHomeDelivery() else 'N') ) for st in stores ] mode = flask.request.args.get( 'mode' ) if mode == 'popup': return flask.render_template( 'list_data_popup.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module=None) else: return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module='store' ) @app.route( '/payment_type_list', methods = [ 'GET'] ) def list_payment_types() -> str : summary = getExpenseSummary() payment_modes : list = factory_object.getBusinessLayer().listPaymentTypes() ui_header = ('ID', 'Payment Mode', 'Payment Mode Detail' ) ui_data : list = [ (p.getId(), p.getPaymentMode(), p.getPaymentModeDetail() ) for p in payment_modes ] mode = flask.request.args.get( 'mode' ) if mode == 'popup': return flask.render_template( 'list_data_popup.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module=None) else: return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module='payment_type' ) @app.route( '/person_list', methods = [ 'GET'] ) def list_person() -> str : summary = getExpenseSummary() people : list = factory_object.getBusinessLayer().listPeople() ui_header = ('ID', 'First Name', 'Last Name', 'Short Name' ) ui_data : list = [ (p.getId(), p.getFirstName(), p.getLastName(), p.getShortName() ) for p in people ] mode = flask.request.args.get( 'mode' ) if mode == 'popup': return flask.render_template( 'list_data_popup.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module=None) else: return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module='person' ) # All delete flows go here ... @app.route( '/expense_delete', methods = [ 'GET' ] ) def delete_expense() -> str : print( 'delete_expense') summary = getExpenseSummary() Id = flask.request.args.get( 'Id' ) try: factory_object.getBusinessLayer().deleteExpense( Id ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed deleting expense', \ error_action = 'Please retry or check the log for details',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/expense_category_delete', methods = [ 'GET' ] ) def delete_expense_category() -> str : print( 'delete_expense_category') summary = getExpenseSummary() Id = flask.request.args.get( 'Id' ) try: factory_object.getBusinessLayer().deleteExpenseCategory( Id ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed deleting expense category', \ error_action = 'Please retry or check the log for details',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/store_delete', methods=['GET'] ) def delete_store() -> str: summary = getExpenseSummary() Id = flask.request.args.get( 'Id' ) try: factory_object.getBusinessLayer().deleteStore( Id ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed deleting store data', \ error_action = 'Please retry or check the log for details',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/payment_type_delete', methods=['GET'] ) def delete_payment_type() -> str: summary = getExpenseSummary() Id = flask.request.args.get( 'Id' ) try: factory_object.getBusinessLayer().deletePaymentType( Id ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed deleting payment type data', \ error_action = 'Please retry or check the log for details',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/person_delete', methods=['GET'] ) def delete_person() -> str: summary = getExpenseSummary() Id = flask.request.args.get( 'Id' ) try: factory_object.getBusinessLayer().deletePerson( Id ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed deleting person data', \ error_action = 'Please retry or check the log for details',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) # All report flows go here ... @app.route( '/expense_month_summary_list', methods = [ 'GET' ] ) def list_expenses_monthly_summary() -> str: summary = getExpenseSummary() report = factory_object.getReportingLayer().listMonthwiseSummary() return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = report[ 0 ], the_data = report[ 1 ], module=None ) @app.route( '/expense_month_category_summary_list', methods = [ 'GET' ] ) def list_expenses_monthly_category_summary() -> str: summary = getExpenseSummary() report = factory_object.getReportingLayer().listMonthwiseCategorySummary() return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = report[ 0 ], the_data = report[ 1 ], module=None ) @app.route( '/expense_month_person_summary_list', methods = [ 'GET' ] ) def list_expenses_monthly_person_summary() -> str: summary = getExpenseSummary() report = factory_object.getReportingLayer().listMonthwisePersonSummary() return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = report[ 0 ], the_data = report[ 1 ], module=None ) @app.route( '/expense_month_paytype_summary_list', methods = [ 'GET' ] ) def list_expenses_monthly_paytype_summary() -> str: summary = getExpenseSummary() report = factory_object.getReportingLayer().listMonthwisePaymentTypeSummary() return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = report[ 0 ], the_data = report[ 1 ], module=None ) # download links @app.route( '/download_expenses', methods = [ 'GET' ] ) def download_expense_list() -> str: return send_file( factory_object.getCSVGenerator().getFilenameWithPath( 'all_expenses.csv'), mimetype='text/csv' ) # other utility methods go here ... def getExpenseSummary(): current_month_string = datetime.datetime.now().strftime( '%Y/%m' ) result = factory_object.getBusinessLayer().getExpenseSummary(current_month_string ) return result # Main code ... if len( sys.argv ) > 2: factory_object = XManFactory() dbargs = { 'dbtype' : sys.argv[ 1 ], 'username' : sys.argv[ 2 ], 'password' : sys.argv[ 3 ], 'hostname' : sys.argv[ 4 ] , 'dbname' : sys.argv[ 5 ] } factory_object.createObjects( dbargs ) app.run(debug=True) else: print( 'Invalid usage python3 controller.py <DB-String>' )	nilq/baby-python	python
import requests from env import QuerybookSettings from lib.notify.base_notifier import BaseNotifier class SlackNotifier(BaseNotifier): def __init__(self, token=None): self.token = ( token if token is not None else QuerybookSettings.QUERYBOOK_SLACK_TOKEN ) @property def notifier_name(self): return "slack" @property def notifier_format(self): return "plaintext" def notify(self, user, message): to = f"@{user.username}" url = "https://slack.com/api/chat.postMessage" headers = {"Authorization": "Bearer {}".format(self.token)} text = self._convert_markdown(message) data = { "text": text, "channel": to, } requests.post(url, json=data, headers=headers, timeout=30)	nilq/baby-python	python
# # Copyright 2012 eNovance <licensing@enovance.com> # Copyright 2012 Red Hat, Inc # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import copy from oslo_log import log from oslo_utils import timeutils import ceilometer from ceilometer.compute import pollsters from ceilometer.compute.pollsters import util from ceilometer.compute import util as compute_util from ceilometer.compute.virt import inspector as virt_inspector from ceilometer.i18n import _, _LW from ceilometer import sample LOG = log.getLogger(__name__) class _Base(pollsters.BaseComputePollster): NET_USAGE_MESSAGE = ' '.join(["NETWORK USAGE:", "%s %s:", "read-bytes=%d", "write-bytes=%d"]) @staticmethod def make_vnic_sample(instance, name, type, unit, volume, vnic_data): metadata = copy.copy(vnic_data) resource_metadata = dict(zip(metadata._fields, metadata)) resource_metadata['instance_id'] = instance.id resource_metadata['instance_type'] = (instance.flavor['id'] if instance.flavor else None) compute_util.add_reserved_user_metadata(instance.metadata, resource_metadata) if vnic_data.fref is not None: rid = vnic_data.fref else: instance_name = util.instance_name(instance) rid = "%s-%s-%s" % (instance_name, instance.id, vnic_data.name) return sample.Sample( name=name, type=type, unit=unit, volume=volume, user_id=instance.user_id, project_id=instance.tenant_id, resource_id=rid, timestamp=timeutils.isotime(), resource_metadata=resource_metadata ) CACHE_KEY_VNIC = 'vnics' def _get_vnic_info(self, inspector, instance): return inspector.inspect_vnics(instance) @staticmethod def _get_rx_info(info): return info.rx_bytes @staticmethod def _get_tx_info(info): return info.tx_bytes def _get_vnics_for_instance(self, cache, inspector, instance): i_cache = cache.setdefault(self.CACHE_KEY_VNIC, {}) if instance.id not in i_cache: i_cache[instance.id] = list( self._get_vnic_info(inspector, instance) ) return i_cache[instance.id] def get_samples(self, manager, cache, resources): self._inspection_duration = self._record_poll_time() for instance in resources: instance_name = util.instance_name(instance) LOG.debug(_('checking net info for instance %s'), instance.id) try: vnics = self._get_vnics_for_instance( cache, self.inspector, instance, ) for vnic, info in vnics: LOG.debug(self.NET_USAGE_MESSAGE, instance_name, vnic.name, self._get_rx_info(info), self._get_tx_info(info)) yield self._get_sample(instance, vnic, info) except virt_inspector.InstanceNotFoundException as err: # Instance was deleted while getting samples. Ignore it. LOG.debug(_('Exception while getting samples %s'), err) except virt_inspector.InstanceShutOffException as e: LOG.warn(_LW('Instance %(instance_id)s was shut off while ' 'getting samples of %(pollster)s: %(exc)s'), {'instance_id': instance.id, 'pollster': self.__class__.__name__, 'exc': e}) except ceilometer.NotImplementedError: # Selected inspector does not implement this pollster. LOG.debug(_('%(inspector)s does not provide data for ' ' %(pollster)s'), {'inspector': self.inspector.__class__.__name__, 'pollster': self.__class__.__name__}) except Exception as err: LOG.exception(_('Ignoring instance %(name)s: %(error)s'), {'name': instance_name, 'error': err}) class _RateBase(_Base): NET_USAGE_MESSAGE = ' '.join(["NETWORK RATE:", "%s %s:", "read-bytes-rate=%d", "write-bytes-rate=%d"]) CACHE_KEY_VNIC = 'vnic-rates' def _get_vnic_info(self, inspector, instance): return inspector.inspect_vnic_rates(instance, self._inspection_duration) @staticmethod def _get_rx_info(info): return info.rx_bytes_rate @staticmethod def _get_tx_info(info): return info.tx_bytes_rate class IncomingBytesPollster(_Base): def _get_sample(self, instance, vnic, info): return self.make_vnic_sample( instance, name='network.incoming.bytes', type=sample.TYPE_CUMULATIVE, unit='B', volume=info.rx_bytes, vnic_data=vnic, ) class IncomingPacketsPollster(_Base): def _get_sample(self, instance, vnic, info): return self.make_vnic_sample( instance, name='network.incoming.packets', type=sample.TYPE_CUMULATIVE, unit='packet', volume=info.rx_packets, vnic_data=vnic, ) class OutgoingBytesPollster(_Base): def _get_sample(self, instance, vnic, info): return self.make_vnic_sample( instance, name='network.outgoing.bytes', type=sample.TYPE_CUMULATIVE, unit='B', volume=info.tx_bytes, vnic_data=vnic, ) class OutgoingPacketsPollster(_Base): def _get_sample(self, instance, vnic, info): return self.make_vnic_sample( instance, name='network.outgoing.packets', type=sample.TYPE_CUMULATIVE, unit='packet', volume=info.tx_packets, vnic_data=vnic, ) class IncomingBytesRatePollster(_RateBase): def _get_sample(self, instance, vnic, info): return self.make_vnic_sample( instance, name='network.incoming.bytes.rate', type=sample.TYPE_GAUGE, unit='B/s', volume=info.rx_bytes_rate, vnic_data=vnic, ) class OutgoingBytesRatePollster(_RateBase): def _get_sample(self, instance, vnic, info): return self.make_vnic_sample( instance, name='network.outgoing.bytes.rate', type=sample.TYPE_GAUGE, unit='B/s', volume=info.tx_bytes_rate, vnic_data=vnic, )	nilq/baby-python	python
""" Module docstring """ from copy import deepcopy from uuid import uuid4 from os import mkdir import numpy as np from scipy.integrate import solve_ivp class OmicsGenerator: """ Handles all omics generation. This class is used to specify omics generation parameters and generate synthetic data. Typical workfolow is: Initialize generator -> set interactions -> set interventions -> generate synthetic data Attributes: ----------- nodes: List of nodes. Args: ----- time_points: Integer. How many total time points to generate. Not to be confused with downsampling coefficient (applied later). nodes: List of strings. (Unique) node names for each node. node_sizes: List of ints. Node sizes for each node. discard_first: Integer. How many initial time points to discard. Setting higher discard_first values generally ensures samples closer to equilibrium. init_full: Boolean. If True, initializes all interactions, growth rates,and initial abundances at random. silent: Boolean. If True, suppresses all print statements. kwargs: C, d, sigma, rho for AT-Normal matrix Returns: -------- OmicsGenerator object. Raises: ------- TODO """ def __init__( self, node_sizes : list = None, nodes : list = None, time_points : int = 100, discard_first : int = 0, init_full : bool = False, silent : bool = False, kwargs) -> None: """ Initializes generator. See docstring for class. """ # Require node sizes if node_sizes == None: raise Exception("Must specify at least one node size.") # Better handling for single-node systems if isinstance(nodes, str): nodes = [nodes] if isinstance(node_sizes, int): node_sizes = [node_sizes] # Give default node names if node_sizes is not None and nodes is None: nodes = [f"n{i}" for i in range(len(node_sizes))] elif len(nodes) != len(node_sizes): raise Exception(f"Node lengths and node sizes do not match: {len(nodes)} != {len(node_sizes)}") self._interactions = [] self._interventions = [] self._time_points = time_points + discard_first self._T = np.array(range(self._time_points)) self._namespace = set() self._discard_first = discard_first self._silent = silent # Process nodes self.nodes = [] for node_name, size in zip(nodes, node_sizes): self.add_node(node_name, size) if init_full: self._init_full(*kwargs) if not self._silent: print("Initialized") class _OmicsNode: """ PRIVATE METHOD. Call with self.add_node() instead. A class for omics nodes. Contains pointers to interactions, interventions. Attributes: ----------- inbound: A dict of (node name, matrix) tuples representing matrix interactions of the type Ax --> y, where y is another node. Maintained by self.add_interaction(). outbound: A dict of (node name, matrix) tuples representing matrix interactions of the type Ay --> x, where y is another node. Maintained by self.add_interaction(). interventions: A list of interventions which affect this node. Maintained by self.add_intervention(). Args: ----- name: String. The node name. Must be unique. size: Integer: How many elements does this node have? initial_value: A vector of initial abundances for node elements. Length must be equal to size. Generally not called on initialization - use self.add_initial_value() instead. growth_rates: Intrinsic growth/death rates for node elements. Length must be equal to size. Generally not called on initialization - use self.add_initial_value() with 'growth_rate = True' instead. names: List of strings for naming node dimensions. log_noise: Boolean. If True, noise will be added to log-relative abundances. True by default. verbose: Boolean. If False, suppresses print statements. Returns: -------- _OmicsNode object. Raises: ------- None (fails silently, use add_node() instead.) """ def __init__( self, name : str, size : int, initial_value : np.ndarray, growth_rates : np.ndarray, names : list, log_noise : bool, verbose : bool = True) -> None: """ Initializes node. See docstring for class. """ self.name = name self.size = size self.initial_value = initial_value self.growth_rates = growth_rates self.log_noise = log_noise self.outbound = {} self.inbound = {} self.interventions = [] self.names = names if verbose: print(f"Node '{name}' initialized") def __str__(self): return f"{self.name}\t{self.size}" class _OmicsInteraction: """ PRIVATE METHOD. Call with self.add_interaction() instead. A class for omics interactions. This has the general form of an m x n matrix representing interactions between one set (e.g. taxa) and another set (e.g. other taxa, metabolites, whatever) Attributes: ----------- nrows: Number of rows (e.g. taxa) in matrix. ncols: Number of columns (e.g. metabolites) in matrix. Args: ----- name: String. A name for this interaction. Must be unique. outbound_node: Node from which the edge originates inbound_node: Node at which the edge terminates matrix: A matrix-type object with interactions lag: Integer. How much delay to put into dependencies. For instance, a lag of 1 on an interaction means we compute Ax_t = y_(t+1) verbose: Boolean. If False, suppresses print statements. Returns: -------- _OmicsInteraction object. Raises: ------- None (fails silently, use add_interaction() instead). """ def __init__( self, name : str, outbound_node : None, inbound_node : None, matrix : np.ndarray, lag : int, verbose : bool = True) -> None: """ Initializes interaction. See docstring for class. """ self.name = name self.outbound_node = outbound_node self.inbound_node = inbound_node self.matrix = np.array(matrix) self.lag = lag self.nrows = matrix.shape[0] # e.g. number of taxa self.ncols = matrix.shape[1] # e.g. number of metabolites if verbose: print(f"Interaction '{name}' added") def __str__(self): return f"{self.name}:\t({self.outbound_node.name})-->({self.inbound_node.name})\tLag: {self.lag}" class _OmicsIntervention: """ PRIVATE METHOD. Call with self.add_intervention() instead. A class for omics interventions. This has the general form of an n-length matrix which describes the reactions of some set (e.g. taxa) to this particular intervention. Args: ----- name: String. A name for our intervention. Only used for printing and other bookkeeping. vector: A vector-type object with reactions to the intervention. node_name: String. Name of node affected by this intervention/matrix. U: An indicator vector which is 1 for time points when the intervention is active, 0 otherwise. affects_abundance: Boolean. If True, intervention vector will be applied directly to the abundance vector rather than growth rates. verbose: Boolean. If False, suppresses print statements. Returns: -------- _OmicsIntevention object. Raises: ------- None (fails silently, use add_intervention() instead). """ def __init__( self, name : str, vector : np.ndarray, node_name : str, U : np.ndarray, affects_abundance : bool, verbose : bool = True) -> None: """ Initializes an intervention. See docstring for class. """ self.name = name self.vector = vector self.node_name = node_name self.U = np.array(U) self.affects_abundance = affects_abundance if verbose: print(f"Intervention '{name}' added") return def __str__(self): end = "" if self.affects_abundance: end = "\taffects abundance" return f"{self.name}\t{self.node_name}{end}" def add_node( self, name : str, size : int, initial_value : np.ndarray = None, growth_rates : np.ndarray = None, names : list = None, log_noise : bool = True, verbose : bool = True) -> None: """ Adds nodes to generator object. Args: ----- name: String. Used to identify node. Must be unique. size: Length of vector associated with a time point of this node. For instance, for a metagenomics node, this would correspond to the number of taxa. initial_value: Value of this node at t = 0. Must be same length as node size. growth_rates: Element-wise growth/death rates for this node. Must be same length as node size. names: Optional. List of names for each node element. Used for printing/saving data. log_noise: Boolean. If True, noise will be added to log-relative abundance.If False, noise will be added to relative abundances. verbose: Boolean. If False, suppresses print statements. Returns: -------- None (modifies generator in place). Raises: ------- ValueError: One or more of [initial_value, growth_rates, names] are the wrong size. """ # Check sizes of inputs agree for param_name in ["initial_value", "growth_rates", "names"]: param = eval(param_name) if param is not None and len(param) != size: raise ValueError(f"{param_name} is wrong size: {len(param)} != {size}") # Check namespace if name in self._namespace: raise Exception(f"Name {name} already in use. Please use a unique name") # Check verbosity if self._silent: verbose = False # Generate node and append to object node = self._OmicsNode( name, size, initial_value, growth_rates, names, log_noise, verbose ) self.nodes.append(node) self._namespace.add(name) def add_interaction( self, name : str, outbound_node_name : str, inbound_node_name : str, matrix : np.ndarray, lag : int = 0, verbose : bool = True) -> None: """ Adds interactions to generator object. Edges look like this: Graphical: (OUTBOUND NODE)--->(INBOUND NODE) Linear algebra: [inbound] = [matrix] @ [outbound] + [...] Args: ----- name: String. A name for this interaction. outbound_node_name: String. Name of node from which the edge originates inbound_node_name: String. Name of node at which the edge terminates matrix: A matrix-type object with interactions lag: Integer. How much delay to put into dependencies. For instance, a lag of 1 on an interaction means we compute Ax_t = y_(t+1) verbose: Boolean. If False, suppresses print statements. Returns: -------- None (modifies generator in place). Raises: ------- TODO """ # Check namespace if name in self._namespace: raise Exception(f"Name {name} already in use. Please use a unique name") # Check verbosity if self._silent: verbose = False # Get nodes outbound_node = self.get(outbound_node_name, "node") if outbound_node is None: raise Exception("Outbound node is invalid") inbound_node = self.get(inbound_node_name, "node") if inbound_node is None: raise Exception("Inbound node is invalid") # Check that matrix dimensions match if matrix.shape[1] != inbound_node.size: raise ValueError(f"Matrix shape[1] = {matrix.shape[1]} != {inbound_node.size} (size of inbound node '{inbound_node.name}')") if matrix.shape[0] != outbound_node.size: raise ValueError(f"Matrix shape[0] = {matrix.shape[0]} != {outbound_node.size} (size of outbound node '{outbound_node.name}')") interaction = self._OmicsInteraction( name, outbound_node, inbound_node, matrix, lag, verbose ) self._interactions.append(interaction) # Append to nodes outbound_node.inbound[inbound_node_name] = interaction inbound_node.outbound[outbound_node_name] = interaction self._namespace.add(name) def add_intervention( self, name : str, node_name : str, vector : np.ndarray, affects_abundance : bool = False, U : np.ndarray = None, start : int = None, end : int = None, verbose : bool = True) -> None: """ Adds an intervention to generator. Must have either U or (start, end) set to specify timeframe. Args: ----- name: String. A name for our intervention. Only used for printing and other bookkeeping. node_name: String. Name of node affected by this intervention/matrix. vector: A vector-type object detailing, elementwise, the reactions of each node coordinate to an intervention. affects_abundance: Boolean. If True, intervention vector will be applied directly to the abundance vector rather than to growth rates. U: An indicator vector which is 1 for time pointswhen the intervention is active, 0 otherwise. start: First time point when interaction begins. Use only for interactions of the form 01+0. Otherwise, use U variable instead. end: Last node when interaction is active. Use only for interactions of the form 01+0. Otherwise, use U variable instaed. verbose: Boolean. If False, suppresses print statements. Returns: -------- None (modifies generator in place). Raises: ------- TODO """ # Check namespace if name in self._namespace: raise Exception(f"Name {name} already in use. Please use a unique name") # Check U vector is correct length if U is not None: if len(U) != self._time_points: raise Exception(f"U vector is different size from number of time points: {len(U)} != {self._time_points}") # Check verbosity if self._silent: verbose = False # Process node node = self.get(node_name, "node") if node is None: raise Exception("Invalid node! Please try again") # A bunch of control flow to make a boolean vector called U if U is not None: pass # explicit U vectors are best elif start is None or end is None: raise Exception("Need to supply a (start,end) pair or a U vector") else: U = np.array([0] self._time_points) U[start:end] = 1 # Make the intervention and add it to self intervention = self._OmicsIntervention( name, vector, node_name, U, affects_abundance, verbose ) if len(intervention.U) == self._time_points: self._interventions.append(intervention) else: raise Exception("Intervention vector is not the same length at time vector") # Modify node accordingly node.interventions.append(intervention) self._namespace.add(name) def set_initial_value( self, node_name : str, values : np.ndarray, growth_rate : bool = False, verbose : bool = True) -> None: """ Sets a node value or growth rate. Args: ----- node_name: Name of node being altered values: Vector. Initial values for node. Must be same length as node size. growth_rate: Boolean. If True, affects the growth_rate parameter of the node. Otherwise, affects initial values of node. verbose: Boolean. If False, suppresses print statements. Returns: -------- None (modifies generator in place). Raises: ------- TODO """ node = self.get(node_name, "node") # Check node exists if node is None: raise Exception(f"Invalid node name: {node_name} does not exist") # Check dimensions match if len(values) != node.size: raise Exception(f"Size mismatch with node size: {len(values)} != {node.size}") # Set values if not growth_rate: node.initial_value = values elif growth_rate: node.growth_rates = values # Print output if verbose and not self._silent: if not growth_rate: print(f"Added x0 vector to node {node_name}") elif growth_rate: print(f"Added growth rates to node {node_name}") def get( self, name : str, node_type : str in ["node", "interaction", "intervention"] = None) -> "generator element": """ Gets a (node/interaction/intervention) by name. Args: ----- name: String. Name of node/interaction/intervention. type: String. One of ["node", "interaction", "intervention"]. Specifies the type of generator element to look for. Returns: -------- _OmicsNode, _OmicsInteraction, _OmicsIntervention, or None. Raises: ------- None """ if node_type in (None, "node"): for node in self.nodes: if node.name == name: return node if node_type in (None, "interaction"): for interaction in self._interactions: if interaction.name == name: return interaction if node_type in (None, "intervention"): for intervention in self._interventions: if intervention.name == name: return intervention return None def remove( self, name : str, verbose : bool = True) -> None: """ Removes a node, intervention, or interaction from the generator by name. Args: ----- name: A string specifying the (unique) name of the element to be removed. Returns: -------- None (modifies generator in place). Raises: ------- TODO """ obj = self.get(name) if obj is None: raise Exception(f"Cannot find object named {name} to remove") if isinstance(obj, self._OmicsNode): for interaction in reversed(self._interactions): # reversed so we can remove interactions as we go if obj in (interaction.inbound_node, interaction.outbound_node): self._interactions.remove(interaction) for intervention in reversed(self._interventions): if intervention.node_name == name: self._interventions.remove(intervention) for node in self.nodes: node.inbound.pop(name, None) node.outbound.pop(name, None) self.nodes.remove(obj) if verbose: print(f"Removed node '{name}'") elif isinstance(obj, self._OmicsInteraction): # Remove interaction from inbound node obj.inbound_node.outbound.pop(obj.outbound_node.name, None) # Remove interaction from outbound node obj.outbound_node.inbound.pop(obj.inbound_node.name, None) # Remove interaction from list self._interactions.remove(obj) if verbose: print(f"Removed interaction '{name}'") elif isinstance(obj, self._OmicsIntervention): node = self.get(obj.node_name) node.interventions.remove(obj) self._interventions.remove(obj) if verbose: print(f"Removed intervention '{name}'") else: raise Exception(f"Cannot remove '{name}': unknown type. Is the name correct?") self._namespace.remove(name) def generate( self, noise_var : float = 1e-2, n_reads : int = 1e5, dt : float = 1e-2, downsample : int = 1) -> (dict, dict, dict): """ Generates a single timecourse of synthetic data. Args: ----- noise_var: Float. variance parameter for gaussian noise term. n_reads: Integer. Number of reads to draw from the unsampled distribution. dt: Float. time step size which gets passed to IVP solver downsample: Integer. fraction of outputs to keep (1/n). By default, keeps all samples. downsample=4 means every 4th sample is kept, etc. Downsample is deprecated. Simply modify "dt" instead. Returns: -------- The following three dicts (in order): //======================================================\\ \|\|Name: Sampling: Normalization: Number of samples:\|\| \|\|======================================================\|\| \|\|Z unsampled unnormalized full \|\| \|\|X unsampled normalized downsampled \|\| \|\|Y sampled normalized downsampled \|\| \\======================================================// Each Z/X/Y dict contains (node, timecourse) pairs. The timecourse is a numpy array with shape (number of time points, node size). Raises: ------- TODO """ # Sanity checks for node in self.nodes: if node.initial_value is None: raise ValueError(f"Node '{node.name}' has no x0 vector") if node.growth_rates is None: raise ValueError(f"Node '{node.name}' has no growth rate set") def _grad_fn( node : None, X : list, growth_rates : np.ndarray, t : int) -> None: """ This gets passed to the solver. It's just the vector f used in GLV calculations. """ # Interactions: interaction_coef = np.zeros(node.size) for node_name in node.outbound: interaction = node.outbound[node_name] # Adjust for lag idx = -1 - interaction.lag try: # Get interaction matrix M = interaction.matrix # Get last value (modulo lag term) of node abundance y = X[node_name][idx] # f += yM (GLV equation) interaction_coef += y @ M except IndexError: # Happens when lag is larger than number of values already generated pass # Interventions: intervention_coef = np.zeros(node.size) for intervention in node.interventions: if not intervention.affects_abundance: intervention_coef += intervention.vector.dot(intervention.U[t]) # Self xt = X[node.name][-1] # The function itself: def fn(t, x): return xt * (growth_rates + interaction_coef + intervention_coef) return fn # Initialization steps Z = {} # Latent absolute abundances X = {} # Probability distribution/normalized abundances Y = {} # Sampled abundances for node in self.nodes: Z[node.name] = [node.initial_value] # Generalized Lotka-Volterra steps, plus bells and whistles for t in range(self._time_points - 1): Z_temp = {} # Use this so that all values are updated at once for node in self.nodes: # Get values from dicts z = Z[node.name] g = node.growth_rates # Initialize values Zprev = np.copy(z[-1]) # last time point, X_(t-1) # Pass to solver # TODO: possible to do this all in one shot rather than looping? grad = _grad_fn(node, Z, g, t) ivp = solve_ivp(grad, (0,dt), Zprev, method="RK45") Zt = ivp.y[:,-1] # Tweak abundances on a per-node basis # TODO: Maybe this would be better if it were size-adjusted? for intervention in node.interventions: if intervention.affects_abundance == True: Zt += intervention.vector * intervention.U[t] # Add biological noise: noise = np.random.normal(scale=noise_var, size=node.size) # No noise for missing taxa noise = noise * (Zt > 0) # Equivalent to log->add noise->exp if node.log_noise == True: Zt = np.exp(noise) else: Zt += noise # Push to results Zt = np.clip(Zt, 0, None) Z_temp[node.name] = Zt # Push all values for this time point to X at once for key in Z_temp: Z[key] += [Z_temp[key]] # Simulate sampling noise for node in self.nodes: z = np.array(Z[node.name]) # Save latent state x = z.copy() # Discard first couple elements (ensure values are near attractor) x = x[self._discard_first:] # Take every nth element # Negative coefficient ensures we sample from the end x = x[::-downsample] # Need to un-reverse the data now x = x[::-1] # Relative abundances x = np.apply_along_axis(lambda a: a/sum(a), 1, x) # y = y / np.sum(y, axis=1).reshape(-1,1) # Draw samples y = [] for idx in range(x.shape[0]): try: Yt = np.random.multinomial(n_reads, x[idx]) / n_reads y += [Yt] except ValueError: # TODO: circle back and figure out what was breaking this # print("ERROR: check self._weird for more info") # self._weird = X[node.name][idx] # debugging variable y += [np.zeros(node.size)] # Push to output X[node.name] = x Y[node.name] = np.array(y) Z[node.name] = z return Z, X, Y def generate_multiple( self, n : int, extinct_fraction : float = 0, generate_args) -> (list, list, list): """ Generates several timecourses of synthetic data. This is essentially a wrapper around a loop of generate() calls, with the added element of reinitializing individuals. The extinct_fraction parameter gives some degree of control over re-initialization. Args: ----- n: Integer. Number of individuals for whom to generate synthetic data timecourses. extinct_fraction: Float in [0, 1) range. Fraction of abundances that should be extinct for each individual. Additional args (same as generate()): ------------------------------------- noise_var: Float. variance parameter for gaussian noise term. n_reads: Integer. Number of reads to draw from the unsampled distribution. dt: Float. time step size which gets passed to IVP solver downsample: Integer. fraction of outputs to keep (1/n). By default, keeps all samples. downsample=4 means every 4th sample is kept, etc. Downsample is deprecated. Simply modify "dt" instead. Returns: -------- The following three arrays (in order): //======================================================\\ \|\|Name: Sampling: Normalization: Number of samples:\|\| \|\|======================================================\|\| \|\|Z unsampled unnormalized full \|\| \|\|X unsampled normalized downsampled \|\| \|\|Y sampled normalized downsampled \|\| \\======================================================// Each Z/X/Y array contains n dicts, each of which contains (node, timecourse) pairs. The timecourse is a numpy array with shape (number of time points, node size). Raises: ------- TODO """ # Initialize: old_nodes = self.nodes # store old initial values out_X = [] out_Y = [] out_Z = [] # Generation loop for i in range(n): # Set new initial values for each node for node in self.nodes: # TODO: allow passing of any function to generate this abundances = np.random.exponential(size=node.size) np.random.binomial(1, 1-extinct_fraction, size=node.size) self.set_initial_value(node.name, abundances, verbose=False) Z,X,Y = self.generate(generate_args) out_X.append(X) out_Y.append(Y) out_Z.append(Z) # return nodes to old values self.nodes = old_nodes return out_Z, out_X, out_Y def _allesina_tang_normal_matrix( self, n : int, C : float, d : float, sigma : float, rho : float) -> np.ndarray: """ Generates an Allesina-Tang normal matrix. Inspired by https://stefanoallesina.github.io/Sao_Paulo_School/intro.html#multi-species-dynamics. How this works: --------------- 1. Creates covariance matrix has the following form: 1 rho rho ... rho 1 rho ... rho rho 1 ... ... (you get the idea) 2. Draws multivariate normal pairs from this covariance matrix 3. Populates non-diagonal entries of matrix with drawn pairs 4. Symmetrically sparsifies matrix, keeping only ~C% of entries 5. Sets diagonals of matrix to -d Args: ----- n: Integer. Number of rows/columns in square matrix. C: Float in (0,1]: Sparsity parameter. Higher C = less sparse. d: Float. Negative self-interaction size. sigma: Float. Variance used to generate multivariate normal covariance matrix. rho: Float in [-1, 1]. Correlation term of covariance matrix. Higher rho = positive connectance = mutualism = harder to stabilize. Lower rho = predator-prey--type relationships = easier to stabilize. Returns: -------- A matrix M that can be used as an interaction matrix. Raises: ------- None (fails silently). """ # sample coefficients mu = np.zeros(2) cov = sigma 2 * np.array([[1, rho], [rho, 1]]) n_samples = int(n * (n-1) / 2) pairs = np.random.multivariate_normal(mu, cov, n_samples) # completely filled matrix M = np.ndarray((n, n)) M[np.triu_indices(n, 1)] = pairs[:,0] M = M.transpose() M[np.triu_indices(n, 1)] = pairs[:,1] # winnow down connections = np.random.rand(n, n) <= C connections = connections * 1 # binarize connections[np.tril_indices(n,1)] = 0 connections += connections.transpose() # symmetric M = connections # set negative self-interactions M[np.diag_indices(n)] = -d return M def _set_interactions( self, C : float = 0.5, d : float = None, sigma : float = 1, rho : float = -0.4) -> None: """ Sets all interaction matrices from one big AT-normal matrix Args: ----- C: Float in (0,1]: Sparsity parameter. Higher C = less sparse. d: Float. Negative self-interaction size. sigma: Float. Variance used to generate multivariate normal covariance matrix. rho: Float in [-1, 1]. Correlation term of covariance matrix. Higher rho = positive connectance = mutualism = harder to stabilize. Lower rho = predator-prey--type relationships = easier to stabilize. Returns: -------- None (modifies generator in place). Raises: ------- None (fails silently). """ # Generate master matrix sizes = [node.size for node in self.nodes] n = np.sum(sizes) # Solve for a stable value of d if d is not provided if d is None: d = sigma np.sqrt(n * C) + 1 m0 = self._allesina_tang_normal_matrix(n, C, d, sigma, rho) # Carve up master matrix i = 0 # row for node1 in self.nodes: j = 0 # col for node2 in self.nodes: m_ij = m0[i:i + node1.size, j:j + node2.size] self.add_interaction( f"{node1.name}->{node2.name}", node1.name, node2.name, m_ij ) if not self._silent: print(f"set m:({node1.name})->({node2.name}): {i}:{i + node1.size} {j}:{j + node2.size}") j += node2.size i += node1.size def _init_full( self, dist : None = np.random.exponential, kwargs) -> None: """ A fully random initialization of all generator parameters. Args: ----- dist: A function to draw initial distributions (e.g. np.random.exponential, np.random.rand, etc) Returns: -------- None (modifies generator in place) Raises: ------- None """ # TODO: make use of dist argument self._set_interactions(kwargs) for node in self.nodes: self.set_initial_value( node.name, np.random.exponential(size=node.size) ) self.set_initial_value( node.name, 2 * (0.5 - np.random.rand(node.size)), growth_rate=True ) def case_control( self, participants : int, case_frac : float, node_name: str, effect_size : float, generate_args) -> (list, list, list, list, list, list): """ Generates synthetic case and control timecourses. Args: ----- participants: Integer. The total number of participants in the study. case_frac: Float in [0,1]. Fraction of total participants belonging to the case group. node_name: String. Name of node to which the intervention is applied. effect_size: Float. Magnitude of intervention. kwargs: Arguments that get passed to generate_multiple(). Returns: -------- Z_control: Z-list like generate_multiple() for control group. X_control: X-list like generate_multiple() for control group. Y_control: Y-list like generate_multiple() for control group. Z_case: Z-list like generate_multiple() for case group. X_case: X-list like generate_multiple() for case group. Y_case: Y-list like generate_multiple() for case group. Raises: ------- TODO """ # inferred settings n_cases = int(participants * case_frac) n_controls = int(participants * (1-case_frac)) # get control values x_control, y_control, z_control = self.generate_multiple(n_controls, *generate_args) # get case values case_gen = self.copy() node_size = self.get(node_name).size case_gen.add_intervention( name='CASE', node_name=node_name, vector=effect_size (0.5-np.random.rand(node_size)), start=0, end=self._time_points ) z_case, x_case, y_case = case_gen.generate_multiple(n_cases, **generate_args) return z_control, x_control, y_control, z_case, x_case, y_case def copy(self) -> None: """ Makes a deep copy of generator. Args: ----- None Returns: -------- OmicsGenerator copy Raises: ------- None """ return deepcopy(self) def _save_single(self, data : "generator output", path : str = None, delim : str = "\t", ext : str = ".tsv") -> None: """ Helper function. Saves a single timecourse. """ for node in data: data_t = data[node].transpose() names = self.get(node).names if names is None: names = [f"{node}_{x}" for x in range(data_t.shape[0])] sample_names = [f"S_{x}" for x in range(data_t.shape[1])] header = f"{delim}{delim.join(sample_names)}" # blank top-left cell data_joined = np.column_stack([names, data_t]) np.savetxt( f"{path}{node}.{ext}", data_joined, fmt="%-12s", delimiter=delim, header=header, ) def save(self, data : "generator output", output_path : str = ".", prefix : str = "", delim : str = "\t", ext : str = "tsv") -> None: """ Saves generator outputs (single or multiple timecourses) as a text file/files. Args: ----- data: An output from the self.generate(), self.generate_multiple(), or self.case_control() method. Expected to be a dict or a list of dicts. path: String. Where to save outputs. prefix: String. Name to append to beginning of filenames. delim: String. Delimiter character. ext: String. Filename extension for saved timecourses. Returns: -------- None. Saves output to disk (as .tsv files by default) Raises: ------- TODO """ # Path handling save_id = uuid4() if output_path is None: output_path = f"./{save_id}" try: mkdir(output_path) except FileExistsError as e: raise FileExistsError("f{output_path} already exists.") from e # re-raise error # Multiple outputs if isinstance(data, list): for idx, individual in enumerate(data): if not self._silent: print(f"\tSaving individual {idx} in directory {output_path}/{idx}/") # Check correct nested datatypes if not isinstance(individual, dict): raise Exception(f"Wrong datatype: submitted list of {type(individual)}, expected list of dicts.") mkdir(f"{output_path}/{idx}") self._save_single(individual, f"{output_path}/{idx}/{prefix}{idx}", delim, ext) # Single output elif isinstance(data, dict): self._save_single(data, f"{output_path}/{prefix}", delim, ext) def __str__(self): # TODO: Rewrite this more cleanly with f-strings out = "\n=========================GENERATOR=========================\n\nTime_points:\t" out += str(self._time_points) out += "\n\nNodes:\n\t" out += "\n\t".join([ str(x) for x in self.nodes ] ) out += "\n\nInteractions:\n\t" out += "\n\t".join([ str(x) for x in self._interactions ] ) out += "\n\nInterventions:\n\t" out += "\n\t".join([ str(x) for x in self._interventions ] ) return out	nilq/baby-python	python
#!/usr/bin/python3 # -- coding: UTF-8 -- """ This script defines the function to do the irq related analysis """ import csv import struct from config import TSC_FREQ TSC_BEGIN = 0 TSC_END = 0 VMEXIT_ENTRY = 0x10000 LIST_EVENTS = { 'VMEXIT_EXTERNAL_INTERRUPT': VMEXIT_ENTRY + 0x00000001, } IRQ_EXITS = {} # 4 * 64bit per trace entry TRCREC = "QQQQ" def parse_trace(ifile): """parse the trace data file Args: ifile: input trace data file Return: None """ fd = open(ifile, 'rb') while True: global TSC_BEGIN, TSC_END try: line = fd.read(struct.calcsize(TRCREC)) if not line: break (tsc, event, vec, d2) = struct.unpack(TRCREC, line) event = event & 0xffffffffffff if TSC_BEGIN == 0: TSC_BEGIN = tsc TSC_END = tsc for key in LIST_EVENTS.keys(): if event == LIST_EVENTS.get(key): if vec in IRQ_EXITS.keys(): IRQ_EXITS[vec] += 1 else: IRQ_EXITS[vec] = 1 except struct.error: sys.exit() def generate_report(ofile, freq): """ generate analysis report Args: ofile: output report freq: TSC frequency of the device trace data from Return: None """ global TSC_BEGIN, TSC_END csv_name = ofile + '.csv' try: with open(csv_name, 'a') as filep: f_csv = csv.writer(filep) rt_cycle = TSC_END - TSC_BEGIN assert rt_cycle != 0, "Total run time in cycle is 0, \ TSC end %d, TSC begin %d" \ % (TSC_END, TSC_BEGIN) rt_sec = float(rt_cycle) / (float(freq) * 1000 * 1000) print ("\nVector \t\tCount \tNR_Exit/Sec") f_csv.writerow(['Vector', 'NR_Exit', 'NR_Exit/Sec']) for e in IRQ_EXITS.keys(): pct = float(IRQ_EXITS[e]) / rt_sec print ("0x%08x \t %d \t%.2f" % (e, IRQ_EXITS[e], pct)) f_csv.writerow([e, IRQ_EXITS[e], '%.2f' % pct]) except IOError as err: print ("Output File Error: " + str(err)) def analyze_irq(ifile, ofile): """do the vm exits analysis Args: ifile: input trace data file ofile: output report file Return: None """ print("IRQ analysis started... \n\tinput file: %s\n" "\toutput file: %s.csv" % (ifile, ofile)) parse_trace(ifile) # save report to the output file generate_report(ofile, TSC_FREQ)	nilq/baby-python	python
def Widget(self): return self	nilq/baby-python	python
import unittest import torch from torchdrug import data, layers class GraphSamplerTest(unittest.TestCase): def setUp(self): self.num_node = 10 self.input_dim = 5 self.output_dim = 7 adjacency = torch.rand(self.num_node, self.num_node) threshold = adjacency.flatten().kthvalue((self.num_node - 3) * self.num_node)[0] adjacency = adjacency * (adjacency > threshold) self.graph = data.Graph.from_dense(adjacency).cuda() self.input = torch.rand(self.num_node, self.input_dim).cuda() def test_sampler(self): conv = layers.GraphConv(self.input_dim, self.output_dim, activation=None).cuda() readout = layers.SumReadout().cuda() sampler = layers.NodeSampler(ratio=0.8).cuda() results = [] for i in range(2000): graph = sampler(self.graph) node_feature = conv(graph, self.input) result = readout(graph, node_feature) results.append(result) result = torch.stack(results).mean(dim=0) node_feature = conv(self.graph, self.input) truth = readout(self.graph, node_feature) self.assertTrue(torch.allclose(result, truth, rtol=5e-2, atol=5e-2), "Found bias in node sampler") sampler = layers.EdgeSampler(ratio=0.8).cuda() results = [] for i in range(2000): graph = sampler(self.graph) node_feature = conv(graph, self.input) result = readout(graph, node_feature) results.append(result) result = torch.stack(results).mean(dim=0) node_feature = conv(self.graph, self.input) truth = readout(self.graph, node_feature) self.assertTrue(torch.allclose(result, truth, rtol=5e-2, atol=5e-2), "Found bias in edge sampler") if __name__ == "__main__": unittest.main()	nilq/baby-python	python
""" Number 1. Integer 2. Floating point 3. Octal & Hexadecimal 1) Octal a = 0o828 a = 0O828 2) Hexadecimal a = 0x828 4. Operate +, -, , / pow : * mod : // remainder : % Contents Source : https://wikidocs.net/12 """	nilq/baby-python	python
from sys import argv script, first, second = argv print "This script is called: ", script print "The first variable is: ", first print "The second variable is: ", second	nilq/baby-python	python
# Generated by Django 3.2.4 on 2021-06-15 22:49 import django.db.models.deletion from django.db import migrations, models class Migration(migrations.Migration): dependencies = [("rules", "0001_initial")] operations = [ migrations.CreateModel( name="Ordinance", fields=[ ("id", models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name="ID")), ("created_at", models.DateTimeField(auto_now_add=True)), ("modified_at", models.DateTimeField(auto_now=True)), ("ordinance", models.CharField(max_length=25)), ("slug", models.SlugField(unique=True)), ], options={"abstract": False}, ), migrations.AlterField( model_name="rule", name="ordinance", field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name="ordinance", to="rules.rulegroup" ), ), migrations.AlterField( model_name="rule", name="rule_group", field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name="rule_group", to="rules.rulegroup" ), ), ]	nilq/baby-python	python
from Crypto.PublicKey import RSA from Crypto import Random #This one is important since it has the default function in RSA.generate() to generate random bytes! from Crypto.Cipher import PKCS1_OAEP import base64 #I'm leaving this function so that you understand how it works from encryption => decryption def rsa_encrypt_decrypt(): #Generating RSA key pair key = RSA.generate(2048) #Extracting private_key private_key = key.export_key('PEM') #Extracting public_key public_key = key.publickey().exportKey('PEM') #Get the message to send message = input('\nPlease enter your message for RSA encryption and decryption: ') #Encode the message message = str.encode(message) #Import the public key in order to use it for encryption rsa_public_key = RSA.importKey(public_key) #PKCS#1 OAEP is an asymmetric cipher based on RSA and the OAEP padding rsa_public_key = PKCS1_OAEP.new(rsa_public_key) #Finally encryption encrypted_message = rsa_public_key.encrypt(message) #Base64 encoding so that we can store it easily on DB/Server encrypted_message = base64.b64encode(encrypted_message) print('\nYour encrypted message is : ', encrypted_message) #DECRYPTION #Import private key rsa_private_key = RSA.importKey(private_key) #Apply the same magic trick again using PKCS1 OAEP rsa_private_key = PKCS1_OAEP.new(rsa_private_key) #Base64 decoding before decrypting, otherwise it would be incorrect, it's logical right? :) encrypted_message = base64.b64decode(encrypted_message) decrypted_message = rsa_private_key.decrypt(encrypted_message) print('\nYour message after decryption is : ', decrypted_message) #THESE FUNCTIONS ARE THE ONES WE GONNA USE IN OUR FINAL APP #How are we gonna get the public/private keys, I think that those are stored on the server #So server will be able to get the proper key pair using users id maybe? or certificate? #For the encrypt fct: sender calls it then sends the encrypted message to server along with the receiver's address def rsa_encrypt(message, receiver_public_key): message = str.encode(message) rsa_public_key = RSA.importKey(receiver_public_key) rsa_public_key = PKCS1_OAEP.new(rsa_public_key) encrypted_message = rsa_public_key.encrypt(message) encrypted_message = base64.b64encode(encrypted_message) return encrypted_message #LOGICALLY, the server now has the encrypted message and will distribute it to the receiver #For the decrypt fct: receiver calls it using his private key to get the initial message def rsa_decrypt(encrypted_message, receiver_private_key): rsa_private_key = RSA.importKey(receiver_private_key) rsa_private_key = PKCS1_OAEP.new(rsa_private_key) encrypted_message = base64.b64decode(encrypted_message) decrypted_message = rsa_private_key.decrypt(encrypted_message) return decrypted_message #FOR TESTING! SINCE WE DON'T HAVE RSA KEY PAIRS LOCALLY #rsa_encrypt_decrypt() # get rsa key from file def get_rsa_key(filepath): with open(filepath, mode='rb') as private_file: priv_key_data = private_file.read() private_key = RSA.importKey(priv_key_data) #print(private_key.export_key()) return private_key	nilq/baby-python	python
# -- coding: utf-8 -- from model.contact import Contact from fixture.application import Application import pytest from model.contact import Contact def test_add_contact(app): app.open_home_page() app.contact.add(Contact(firstname="dsf", dlename="gdfg", lastname="ew", nickname="gdf", title="wer", company="dg", address="dg", home="dg", mobile="43", work="sdg", fax="213", email="243", email2="234", email3="245", homepage="fsdf", address2="dsf", phone2="sg", notes="sfghh")) app.return_home_page() def tearDown(self): self.app.destroy()	nilq/baby-python	python
############################################################################## # Copyright (c) 2013-2018, Lawrence Livermore National Security, LLC. # Produced at the Lawrence Livermore National Laboratory. # # This file is part of Spack. # Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved. # LLNL-CODE-647188 # # For details, see https://github.com/spack/spack # Please also see the NOTICE and LICENSE files for our notice and the LGPL. # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License (as # published by the Free Software Foundation) version 2.1, February 1999. # # 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 terms and # conditions of the GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ############################################################################## from spack import * class PyCookiecutter(PythonPackage): """A command-line utility that creates projects from cookiecutters (project templates). E.g. Python package projects, jQuery plugin projects.""" homepage = "https://cookiecutter.readthedocs.io/en/latest/" url = "https://github.com/audreyr/cookiecutter/archive/1.6.0.tar.gz" version('1.6.0', sha256='0c9018699b556b83d7c37b27fe0cc17485b90b6e1f47365b3cdddf77f6ca9d36') depends_on('py-setuptools', type='build') depends_on('py-future') depends_on('py-binaryornot') depends_on('py-jinja2') depends_on('py-click') depends_on('py-whichcraft') depends_on('py-poyo') depends_on('py-jinja2-time') depends_on('py-requests')	nilq/baby-python	python
from django.db import models from cloudinary.models import CloudinaryField class Image(models.Model): short_title = models.CharField(max_length=20) file = CloudinaryField('image', default="https://cdn.pixabay.com/photo/2016/06/16/03/49/befall-the-earth-quote-1460570_960_720.jpg") timeStamp = models.DateTimeField(auto_now_add=True) def __str__(self): return self.short_title	nilq/baby-python	python
def zigzag(n): '''zigzag rows''' def compare(xy): x, y = xy return (x + y, -y if (x + y) % 2 else y) xs = range(n) return {index: n for n, index in enumerate(sorted( ((x, y) for x in xs for y in xs), key=compare ))} def printzz(myarray): '''show zigzag rows as lines''' n = int(len(myarray) ** 0.5 + 0.5) xs = range(n) print('\n'.join( [''.join("%3i" % myarray[(x, y)] for x in xs) for y in xs] )) printzz(zigzag(6))	nilq/baby-python	python
import unittest import requests import time from vaurienclient import Client from vaurien.util import start_proxy, stop_proxy from vaurien.tests.support import start_simplehttp_server _PROXY = 'http://localhost:8000' # we should provide a way to set an option # for all behaviors at once # _OPTIONS = ['--behavior-delay-sleep', '1'] class TestHttpProxy(unittest.TestCase): def setUp(self): self._proxy_pid = start_proxy(options=_OPTIONS, log_level='error', log_output='/dev/null', protocol='http') self._web = start_simplehttp_server() time.sleep(.3) try: if self._web.poll(): raise ValueError("Could not start the proxy") self.client = Client() assert self.client.get_behavior() == 'dummy' except Exception: self.tearDown() raise def tearDown(self): stop_proxy(self._proxy_pid) self._web.terminate() def test_proxy(self): # let's do a few simple request first to make sure the proxy works self.assertEqual(self.client.get_behavior(), 'dummy') times = [] for i in range(10): start = time.time() try: res = requests.get(_PROXY) finally: times.append(time.time() - start) self.assertEqual(res.status_code, 200) fastest = min(times) # now let's try the various behaviors with self.client.with_behavior('blackout'): # oh look we broke it self.assertRaises(requests.ConnectionError, requests.get, _PROXY) self.assertEqual(self.client.get_behavior(), 'blackout') with self.client.with_behavior('delay'): # should work but be slower start = time.time() try: res = requests.get(_PROXY) finally: duration = time.time() - start self.assertEqual(res.status_code, 200) self.assertTrue(duration > fastest + 1) # we should be back to normal self.assertEqual(self.client.get_behavior(), 'dummy') res = requests.get(_PROXY) self.assertEqual(res.status_code, 200)	nilq/baby-python	python
import os import unittest2 as unittest import json import sys from sendgrid import SendGridClient, Mail class TestSendGrid(unittest.TestCase): def setUp(self): self.sg = SendGridClient(os.getenv('SG_USER'), os.getenv('SG_PWD')) @unittest.skipUnless(sys.version_info < (3, 0), 'only for python2') def test_unicode_recipients(self): recipients = [unicode('test@test.com'), unicode('guy@man.com')] m = Mail(to=recipients, subject='testing', html='awesome', from_email='from@test.com') mock = {'to[]': ['test@test.com', 'guy@man.com']} result = self.sg._build_body(m) self.assertEqual(result['to[]'], mock['to[]']) def test_send(self): m = Mail() m.add_to('John, Doe <john@email.com>') m.set_subject('test') m.set_html('WIN') m.set_text('WIN') m.set_from('doe@email.com') m.add_substitution('subKey', 'subValue') m.add_section('testSection', 'sectionValue') m.add_category('testCategory') m.add_unique_arg('testUnique', 'uniqueValue') m.add_filter('testFilter', 'filter', 'filterValue') m.add_attachment_stream('testFile', 'fileValue') url = self.sg._build_body(m) url.pop('api_key', None) url.pop('api_user', None) url.pop('date', None) test_url = json.loads(''' { "to[]": ["john@email.com"], "toname[]": ["John Doe"], "html": "WIN", "text": "WIN", "subject": "test", "files[testFile]": "fileValue", "from": "doe@email.com" } ''') test_url['x-smtpapi'] = json.dumps(json.loads(''' { "sub": { "subKey": ["subValue"] }, "section": { "testSection":"sectionValue" }, "category": ["testCategory"], "unique_args": { "testUnique":"uniqueValue" }, "filters": { "testFilter": { "settings": { "filter": "filterValue" } } } } ''')) self.assertEqual(url, test_url) if __name__ == '__main__': unittest.main()	nilq/baby-python	python
import os import torch from typing import Dict from catalyst.dl.fp16 import Fp16Wrap, copy_params, copy_grads from catalyst.dl.state import RunnerState from catalyst.dl.utils import UtilsFactory from catalyst.rl.registry import GRAD_CLIPPERS from .core import Callback from .utils import get_optimizer_momentum, scheduler_step class CheckpointCallback(Callback): """ Checkpoint callback to save/restore your model/criterion/optimizer/metrics. """ def __init__( self, save_n_best: int = 3, resume: str = None ): """ :param save_n_best: number of best checkpoint to keep :param resume: path to checkpoint to load and initialize runner state """ self.save_n_best = save_n_best self.resume = resume self.top_best_metrics = [] self._keys_from_state = ["resume"] @staticmethod def load_checkpoint(, filename, state): if os.path.isfile(filename): print("=> loading checkpoint \"{}\"".format(filename)) checkpoint = UtilsFactory.load_checkpoint(filename) state.epoch = checkpoint["epoch"] UtilsFactory.unpack_checkpoint( checkpoint, model=state.model, criterion=state.criterion, optimizer=state.optimizer, scheduler=state.scheduler ) print( "loaded checkpoint \"{}\" (epoch {})".format( filename, checkpoint["epoch"] ) ) else: raise Exception("no checkpoint found at \"{}\"".format(filename)) def save_checkpoint( self, logdir, checkpoint, is_best, save_n_best=5, main_metric="loss", minimize_metric=True ): suffix = f"{checkpoint['stage']}.{checkpoint['epoch']}" filepath = UtilsFactory.save_checkpoint( logdir=f"{logdir}/checkpoints/", checkpoint=checkpoint, suffix=suffix, is_best=is_best, is_last=True ) checkpoint_metric = checkpoint["valid_metrics"][main_metric] self.top_best_metrics.append((filepath, checkpoint_metric)) self.top_best_metrics = sorted( self.top_best_metrics, key=lambda x: x[1], reverse=not minimize_metric ) if len(self.top_best_metrics) > save_n_best: last_item = self.top_best_metrics.pop(-1) last_filepath = last_item[0] os.remove(last_filepath) def pack_checkpoint(self, kwargs): return UtilsFactory.pack_checkpoint(kwargs) def on_stage_start(self, state): for key in self._keys_from_state: value = getattr(state, key, None) if value is not None: setattr(self, key, value) if self.resume is not None: self.load_checkpoint(filename=self.resume, state=state) def on_epoch_end(self, state: RunnerState): if state.stage.startswith("infer"): return checkpoint = self.pack_checkpoint( model=state.model, criterion=state.criterion, optimizer=state.optimizer, scheduler=state.scheduler, epoch_metrics=dict(state.metrics.epoch_values), valid_metrics=dict(state.metrics.valid_values), stage=state.stage, epoch=state.epoch ) self.save_checkpoint( logdir=state.logdir, checkpoint=checkpoint, is_best=state.metrics.is_best, save_n_best=self.save_n_best, main_metric=state.main_metric, minimize_metric=state.minimize_metric ) def on_stage_end(self, state): print("Top best models:") top_best_metrics_str = "\n".join( [ "{filepath}\t{metric:3.4f}".format( filepath=filepath, metric=metric ) for filepath, metric in self.top_best_metrics ] ) print(top_best_metrics_str) class OptimizerCallback(Callback): """ Optimizer callback, abstraction over optimizer step. """ def __init__( self, grad_clip_params: Dict = None, fp16_grad_scale: float = 128.0, accumulation_steps: int = 1, optimizer_key: str = None, loss_key: str = None ): """ @TODO: docs """ grad_clip_params = grad_clip_params or {} self.grad_clip_fn = GRAD_CLIPPERS.get_from_params(grad_clip_params) self.fp16 = False self.fp16_grad_scale = fp16_grad_scale self.accumulation_steps = accumulation_steps self.optimizer_key = optimizer_key self.loss_key = loss_key self._optimizer_wd = 0 self._accumulation_counter = 0 def on_stage_start(self, state: RunnerState): self.fp16 = isinstance(state.model, Fp16Wrap) optimizer = state.get_key( key="optimizer", inner_key=self.optimizer_key ) assert optimizer is not None lr = optimizer.defaults["lr"] momentum = get_optimizer_momentum(optimizer) state.set_key(lr, "lr", inner_key=self.optimizer_key) state.set_key(momentum, "momentum", inner_key=self.optimizer_key) def on_epoch_start(self, state): optimizer = state.get_key( key="optimizer", inner_key=self.optimizer_key ) self._optimizer_wd = optimizer.param_groups[0].get("weight_decay", 0.0) optimizer.param_groups[0]["weight_decay"] = 0.0 @staticmethod def grad_step(, optimizer, optimizer_wd=0, grad_clip_fn=None): for group in optimizer.param_groups: if optimizer_wd > 0: for param in group["params"]: param.data = param.data.add( -optimizer_wd * group["lr"], param.data ) if grad_clip_fn is not None: grad_clip_fn(group["params"]) optimizer.step() def on_batch_end(self, state): if not state.need_backward: return self._accumulation_counter += 1 if not self.fp16: model = state.model optimizer = state.get_key( key="optimizer", inner_key=self.optimizer_key ) loss = state.get_key(key="loss", inner_key=self.loss_key) loss.backward() if (self._accumulation_counter + 1) % self.accumulation_steps == 0: self.grad_step( optimizer=optimizer, optimizer_wd=self._optimizer_wd, grad_clip_fn=self.grad_clip_fn ) model.zero_grad() self._accumulation_counter = 0 else: model = state.model model.zero_grad() optimizer = state.get_key( key="optimizer", inner_key=self.optimizer_key ) loss = state.get_key(key="loss", inner_key=self.optimizer_key) scaled_loss = self.fp16_grad_scale * loss.float() scaled_loss.backward() master_params = list(optimizer.param_groups[0]["params"]) model_params = list( filter(lambda p: p.requires_grad, model.parameters()) ) copy_grads(source=model_params, target=master_params) for param in master_params: param.grad.data.mul_(1. / self.fp16_grad_scale) self.grad_step( optimizer=optimizer, optimizer_wd=self._optimizer_wd, grad_clip_fn=self.grad_clip_fn ) copy_params(source=master_params, target=model_params) torch.cuda.synchronize() def on_epoch_end(self, state): optimizer = state.get_key( key="optimizer", inner_key=self.optimizer_key ) optimizer.param_groups[0]["weight_decay"] = self._optimizer_wd class SchedulerCallback(Callback): def __init__( self, scheduler_key: str = None, mode: str = "epoch", reduce_metric: str = "loss" ): self.scheduler_key = scheduler_key self.mode = mode self.reduce_metric = reduce_metric def step(self, state): scheduler = state.get_key( key="scheduler", inner_key=self.scheduler_key ) lr, momentum = scheduler_step( scheduler=scheduler, valid_metric=state.metrics.valid_values.get( self.reduce_metric, None) ) state.set_key(lr, key="lr", inner_key=self.scheduler_key) state.set_key(momentum, key="momentum", inner_key=self.scheduler_key) def on_stage_start(self, state): scheduler = state.get_key( key="scheduler", inner_key=self.scheduler_key ) assert scheduler is not None def on_batch_end(self, state): if self.mode == "batch": self.step(state=state) def on_epoch_end(self, state): if self.mode == "epoch": self.step(state=state) class LossCallback(Callback): def __init__(self, input_key: str = "targets", output_key: str = "logits"): self.input_key = input_key self.output_key = output_key def on_stage_start(self, state): assert state.criterion is not None def on_batch_end(self, state): state.loss = state.criterion( state.output[self.output_key], state.input[self.input_key] ) class EarlyStoppingCallback(Callback): def __init__( self, patience: int, metric: str = "loss", minimize: bool = True, min_delta: float = 1e-6 ): self.best_score = None self.metric = metric self.patience = patience self.num_bad_epochs = 0 self.is_better = None if minimize: self.is_better = lambda score, best: score <= (best - min_delta) else: self.is_better = lambda score, best: score >= (best - min_delta) def on_epoch_end(self, state: RunnerState) -> None: if state.stage.startswith("infer"): return score = state.metrics.valid_values[self.metric] if self.best_score is None: self.best_score = score if self.is_better(score, self.best_score): self.num_bad_epochs = 0 self.best_score = score else: self.num_bad_epochs += 1 if self.num_bad_epochs >= self.patience: print(f"Early stop at {state.epoch} epoch") state.early_stop = True	nilq/baby-python	python
# O(N + M) time and space def sum_swap(a, b): a_sum = 0 a_s = {} b_sum = 0 b_s = {} for i, n in enumerate(a): a_sum += n a_s[n] = i for i, n in enumerate(b): b_sum += n b_s[n] = i diff = (a_sum - b_sum + 1) // 2 for i, n in enumerate(a): if n - diff in b_s: return i, b_s[n - diff] return None	nilq/baby-python	python
from django import template register = template.Library() @register.inclusion_tag('registration/error_messages.html') def error_messages(errors): return {'errors': errors}	nilq/baby-python	python
if __name__ == "__main__": user_inpu = int(input()) user_list = list(map(int, input().split())) user_list = set(user_list) n = int(input()) for _ in range(n): user_input = input().split() if user_input[0] == 'intersection_update': new_list = list(map(int, input().split())) user_list.intersection_update(new_list) elif user_input[0] == 'symmetric_difference_update': new_list2 = list(map(int, input().split())) user_list.symmetric_difference_update(new_list2) elif user_input[0] == 'difference_update': new_list3 = list(map(int, input().split())) user_list.difference_update(new_list3) elif user_input[0] == 'update': new_list4 = list(map(int, input().split())) user_list.update(new_list4) else: print('Something gone wrong!') a = sum(user_list) print(a)	nilq/baby-python	python
import serial, struct, traceback, sys from rhum.rhumlogging import get_logger from rhum.drivers.driver import Driver from rhum.drivers.enocean.messages.message import EnOceanMessage from rhum.drivers.enocean.messages.response.VersionMessage import VersionMessage from rhum.drivers.enocean.constants import PacketType, CommonCommandType, ResponseType from rhum.utils.crc8 import CRC8Utils import logging from rhum.drivers.enocean.messages.typingmessage import TypingMessage class EnOceanDriver(Driver): _logger = get_logger('rhum.driver.enocean.EnOceanDriver') def __init__(self, port='/dev/ttyAMA0', callback=None): super(EnOceanDriver, self).__init__(callback) # Initialize serial port self.__buffer = [] self.__port = port self._logger.debug('initialize connection to '.format(port)) self.__connection = serial.Serial(self.__port, 57600, timeout=0) def stop(self): Driver.stop(self) self.__connection.close() self._logger.info('EnOcean Driver on {0} stopped'.format(self.__port)) def run(self): self._logger.info('EnOcean Driver started on {0}'.format(self.__port)) while not self._stop.is_set(): # Read chars from serial port as hex numbers try: msg = self.parse() __type, __datas, __opts = msg._get() msg = TypingMessage.transform(__type, __datas, __opts) self._logger.info(msg) except serial.SerialException: self._logger.error('Serial port exception! (device disconnected or multiple access on port?)') break except Exception: exc_type, exc_value, exc_traceback = sys.exc_info() lines = traceback.format_exception(exc_type, exc_value, exc_traceback) for line in lines: self._logger.error(line) def test(self): msg = EnOceanMessage(PacketType.COMMON_COMMAND.value, [CommonCommandType.CD_R_VERSION.value]) buffer = msg.build() self._logger.debug('EnOcean Driver message {0}'.format(buffer)) self._logger.debug(self.__connection.isOpen()) #for index in range(len(buffer)): #byte by byte tx buffer = bytes(buffer) self._logger.debug('writing byte {0}'.format(buffer)) self.__connection.write(buffer) try: self._logger.debug('ask for parsing data') msg = self.parse() msg = VersionMessage(msg._get()[0], msg._get()[1], msg._get()[2]) self._logger.info('EnOcean Test Message (Version)') self._logger.info(msg) if msg.isResponse() and msg.getReturnCode() == ResponseType.RET_OK: return True except Exception: exc_type, exc_value, exc_traceback = sys.exc_info() lines = traceback.format_exception(exc_type, exc_value, exc_traceback) for line in lines: self._logger.error(line) self.__connection.close() return False def parse(self): Driver.parse(self) self._logger.debug('parsing data') msg = self._getSerialData() if isinstance(msg, EnOceanMessage): return msg raise Exception('No message parsed') def _getSerialData(self): self._logger.debug('searching for sync byte') s = 0 while s != b'\x55': if self.__connection.inWaiting() != 0: s = self.__connection.read(1) self._logger.debug('sync byte found') while self.__connection.inWaiting() < 5: () header = self.__connection.read(4) #read header fields headerCRC = self.__connection.read(1)[0] #read header crc field self._logger.debug('header reading : {0} and crc : {1}'.format(header, headerCRC)) if (CRC8Utils.calc(header) == headerCRC): self._logger.debug('header CRC OK') data_length, opt_length, msgType = struct.unpack("!HBB", header) self._logger.debug('data_length {0}; opt_length {1}; msg_type {2}'.format( data_length, opt_length, msgType )) totalDataLength = data_length + opt_length while self.__connection.inWaiting() < totalDataLength+1: () datas = self.__connection.read(data_length) opts = self.__connection.read(opt_length) dataCRC = self.__connection.read(1) self._logger.debug('datas {0}; opts {1}; dataCRC {2}'.format( datas, opts, dataCRC )) if(self._logger.isEnabledFor(logging.DEBUG)): msg = header msg += bytes({headerCRC}) msg += datas msg += opts msg += dataCRC self._logger.debug(msg) if (CRC8Utils.calc(datas+opts) == dataCRC[0]): return EnOceanMessage(msgType, datas, opts) return "Data CRC Failed" return "Header CRC Failed"	nilq/baby-python	python
from tkinter import Frame, Label, Button, messagebox, filedialog as fd from tkinter.constants import DISABLED, E, NORMAL, RAISED, SUNKEN, X import pandas import requests from threading import Thread import json from messages import messages from utils import config from ibuki import Ibuki class TopFrame(Frame): def __init__(self, parent): super().__init__(parent, highlightcolor='black', highlightthickness=2, padx=10, pady=10) self.btn_select_input = Button(self, text='Select input file and upload', width=22, bg='yellow', fg='blue', font=10, cursor='hand2', command=self.select_file) self.btn_select_input.grid(row=0, column=0) btn_view = Button(self, text='Extended warranty view', width=18, bg='yellow', fg='blue', font=10, padx=10, cursor='hand2', command=self.view_extended_warranty_customers) btn_view.grid(row=0, column=1) btn_send_sms = Button(self, text='Send SMS', width=10, bg='yellow', fg='red', font=10, padx=10, cursor='hand2', command=self.send_sms) btn_send_sms.grid(row=0, column=2, sticky=E) self.columnconfigure(2, weight=4) self.columnconfigure(1, weight=2) def select_file(self): filetypes = ( ('excel files', '.xlsx'), ('All files', '.*') ) try: select_folder = config.selectFolder or './' filename = fd.askopenfilename( title='Open customer data', initialdir=select_folder, filetypes=filetypes ) data = self.get_json(filename) self.enable_disable_button(self.btn_select_input, False) s = Thread(target=self.upload_data, args=(data,)) s.start() except(Exception) as error: messagebox.showerror( 'Error', error or messages.get('errSelectingFile')) self.enable_disable_button(self.btn_select_input, True) def get_json(self, filename): df = pandas.read_excel(filename, converters={'Purchased Date': str, 'Serial No': str}, header=1, usecols=['ASC Code', 'Customer Group', 'Job ID', 'Warranty Type', 'Warranty Category', 'Service Type', 'Product category name', 'Product sub category name', 'Set Model', 'Model Name', 'Serial No', 'Purchased Date', 'Customer Name', 'Mobile No', 'Postal Code', 'Address' ]) json_str = df.to_json(orient='index') js = json_str.encode('ascii', "ignore").decode() js = js.replace(u'\\ufeff', '').replace('\\/', '').replace("\'", '') jsn = json.loads(js) temp_data = [value for key, value in jsn.items()] filtered = filter( lambda value: ('TV' in value.get( 'Product category name', '').upper()) and (value.get('Purchased Date', None) is not None) and (value.get('Purchased Date', '').strip() != ''), temp_data) data = [item for item in filtered] return(data) def upload_data(self, data): try: upload_endpoint = config.uploadEndPoint requests.post(upload_endpoint, json=data) messagebox.showinfo("Success", messages['infoUploadSuccess']) self.enable_disable_button(self.btn_select_input, True) except(Exception) as error: messagebox.showerror('Error', error or 'Upload error') self.enable_disable_button(self.btn_select_input, True) def enable_disable_button(self, btn, isEnabled): btn.configure(relief=RAISED if isEnabled else SUNKEN) btn.configure(state=NORMAL if isEnabled else DISABLED) def view_extended_warranty_customers(self): Ibuki.emit('VIEW-EXTENDED-WARRANTY-CUSTOMERS', None) def send_sms(self): Ibuki.emit('SEND-SMS', None) def init_top_frame(root): try: frame_top = TopFrame(root) frame_top.pack(fill=X, padx=10, pady=10) except(Exception) as error: messagebox.showerror('Error', error or messages.get('errGeneric'))	nilq/baby-python	python
import os import torch, pickle from torch import nn import torch.nn.functional as F from dataloader import get_transform, get_dataset from model import get_model from utils import get_dirname_from_args # how are we going to name our checkpoint file def get_ckpt_path(args, epoch, loss): ckpt_name = get_dirname_from_args(args) # inside the ckpt path ckpt_path = args.ckpt_path / ckpt_name # if you are creating checkpoint file for the first time args.ckpt_path.mkdir(exist_ok=True) ckpt_path.mkdir(exist_ok=True) # checkpoint name is named after the loss and epoch loss = '{:.4f}'.format(loss) ckpt_path = ckpt_path / 'loss_{}_epoch_{}.pickle'.format(loss, epoch) # return the path name/address return ckpt_path # saving checkpoint file based on current status def save_ckpt(args, epoch, loss, model): # since checkpoint file is named based on epoch and loss, we state which epoch is being saved print('saving epoch {}'.format(epoch)) dt = { 'args': args, 'epoch': epoch, 'loss': loss, 'model': model.state.dict(), } ckpt_path = get_ckpt_path(args, epoch, loss) # name checkpoint file based on epoch and loss print("Saving checkpoint {}".format(ckpt_path)) # what checkpoint in what epoch torch.save(dt, str(ckpt_path)) # get a model from checkpoint file def get_model_ckpt(args): # if there is a model specified to be fetched ckpt_available = args.ckpt_name is not None if ckpt_available: name = '{}'.format(args.ckpt_name) # add * behind the name name = '{}'.format(name) if not name.endswith('') else name # now every name has * behind it ckpt_paths = sorted(args.ckpt_path.glob(name), reverse=False) assert len(ckpt_paths>0), "no ckpt candidate for {}".format(args.ckpt_path / args.ckpt_name) # full address is ckpt_path / ckpt_name ckpt_path = ckpt_paths[0] print("loading from {}".format(ckpt_path)) # load model from ckpt_path # 1. first update the arguments args.update(dt['args']) # 2. get model based on the arguments model = get_model(args) if ckpt_available: model.load_state_dict(dt['model']) # load other state in the model return args, model, ckpt_available	nilq/baby-python	python
import smtplib import datetime from email.mime.text import MIMEText from flask import current_app def notify(notifyType, message, all=True): # Only notify if less than 3 notifications in the past 24 hours sendNotification = True now = datetime.datetime.now() if current_app.config.get(notifyType) is None: # Create and track this notify type current_app.config[notifyType] = (now, 1) else: oneDayAgo = now - datetime.timedelta(days=1) previousNotification = current_app.config.get(notifyType) if previousNotification[0] > oneDayAgo and previousNotification[1] >= 3: # If last notify was newer than 1 day ago and there have been 3 notifications sendNotification = False elif previousNotification[0] > oneDayAgo and previousNotification[1] < 3: # If last notify was newer than 1 day ago and there less than 3 notifications current_app.config[notifyType] = ( now, previousNotification[1] + 1) else: # Last notification was more than 1 day ago start over current_app.config[notifyType] = (now, 1) if sendNotification: sender = current_app.config.get('SMTP_EMAIL') recipients = current_app.config.get('ALL_NOTIFY') if all else current_app.config.get('PRIMARY_NOTIFY') # Build email header msg = MIMEText(message) msg['Subject'] = 'Arduino Water Control Temperature Alert' msg['From'] = sender msg['To'] = ', '.join(recipients) server = smtplib.SMTP_SSL( current_app.config.get('SMTP_DOMAIN'), port=current_app.config.get('SMTP_PORT')) server.login(sender, current_app.config.get('SMTP_PASSWORD')) server.sendmail(sender, recipients, msg.as_string()) server.quit()	nilq/baby-python	python

from typing import Any, Dict, List, Optional from pydantic import BaseModel, Field class RecordSearch(BaseModel): """ Dao search Attributes: ----------- query: The elasticsearch search query portion aggregations: The elasticsearch search aggregations """ query: Optional[Dict[str, Any]] aggregations: Optional[Dict[str, Any]] class RecordSearchResults(BaseModel): """ Dao search results Attributes: ----------- total: int The total of query results records: List[T] List of records retrieved for the pagination configuration aggregations: Optional[Dict[str, Dict[str, Any]]] The query aggregations grouped by task. Optional words: Optional[Dict[str, int]] The words cloud aggregations metadata: Optional[Dict[str, int]] Metadata fields aggregations """ total: int records: List[Dict[str, Any]] aggregations: Optional[Dict[str, Dict[str, Any]]] = Field(default_factory=dict) words: Optional[Dict[str, int]] = None metadata: Optional[Dict[str, int]] = None

nilq/baby-python

python

from typing import Tuple, Callable from .template import Processor from .concat import BatchConcat, BatchPool from .denoise import Dada2SingleEnd, Dada2PairedEnd from .importing import ImportSingleEndFastq, ImportPairedEndFastq from .trimming import BatchTrimGalorePairedEnd, BatchTrimGaloreSingleEnd class GenerateASVPairedEnd(Processor): fq_dir: str fq1_suffix: str fq2_suffix: str clip_r1_5_prime: int clip_r2_5_prime: int trimmed_fq_dir: str feature_sequence_qza: str feature_table_qza: str def main( self, fq_dir: str, fq1_suffix: str, fq2_suffix: str, clip_r1_5_prime: int, clip_r2_5_prime: int) -> Tuple[str, str]: return self.feature_table_qza, self.feature_sequence_qza def trimming(self): self.trimmed_fq_dir = BatchTrimGalorePairedEnd(self.settings).main( fq_dir=self.fq_dir, fq1_suffix=self.fq1_suffix, fq2_suffix=self.fq2_suffix, clip_r1_5_prime=self.clip_r1_5_prime, clip_r2_5_prime=self.clip_r2_5_prime) class GenerateASVConcatPairedEnd(GenerateASVPairedEnd): concat_fq_dir: str fq_suffix: str single_end_seq_qza: str def main( self, fq_dir: str, fq1_suffix: str, fq2_suffix: str, clip_r1_5_prime: int, clip_r2_5_prime: int) -> Tuple[str, str]: self.fq_dir = fq_dir self.fq1_suffix = fq1_suffix self.fq2_suffix = fq2_suffix self.clip_r1_5_prime = clip_r1_5_prime self.clip_r2_5_prime = clip_r2_5_prime self.trimming() self.concat() self.importing() self.denoise() return self.feature_table_qza, self.feature_sequence_qza def concat(self): self.concat_fq_dir, self.fq_suffix = BatchConcat(self.settings).main( fq_dir=self.trimmed_fq_dir, fq1_suffix=self.fq1_suffix, fq2_suffix=self.fq2_suffix) def importing(self): self.single_end_seq_qza = ImportSingleEndFastq(self.settings).main( fq_dir=self.concat_fq_dir, fq_suffix=self.fq_suffix) def denoise(self): self.feature_table_qza, self.feature_sequence_qza = Dada2SingleEnd(self.settings).main( demultiplexed_seq_qza=self.single_end_seq_qza) class GenerateASVMergePairedEnd(GenerateASVPairedEnd): paired_end_seq_qza: str def main( self, fq_dir: str, fq1_suffix: str, fq2_suffix: str, clip_r1_5_prime: int, clip_r2_5_prime: int) -> Tuple[str, str]: self.fq_dir = fq_dir self.fq1_suffix = fq1_suffix self.fq2_suffix = fq2_suffix self.clip_r1_5_prime = clip_r1_5_prime self.clip_r2_5_prime = clip_r2_5_prime self.trimming() self.importing() self.denoise() return self.feature_table_qza, self.feature_sequence_qza def importing(self): self.paired_end_seq_qza = ImportPairedEndFastq(self.settings).main( fq_dir=self.trimmed_fq_dir, fq1_suffix=self.fq1_suffix, fq2_suffix=self.fq2_suffix) def denoise(self): self.feature_table_qza, self.feature_sequence_qza = Dada2PairedEnd(self.settings).main( demultiplexed_seq_qza=self.paired_end_seq_qza) class GenerateASVPoolPairedEnd(GenerateASVPairedEnd): pooled_fq_dir: str fq_suffix: str single_end_seq_qza: str def main( self, fq_dir: str, fq1_suffix: str, fq2_suffix: str, clip_r1_5_prime: int, clip_r2_5_prime: int) -> Tuple[str, str]: self.fq_dir = fq_dir self.fq1_suffix = fq1_suffix self.fq2_suffix = fq2_suffix self.clip_r1_5_prime = clip_r1_5_prime self.clip_r2_5_prime = clip_r2_5_prime self.trimming() self.pool() self.importing() self.denoise() return self.feature_table_qza, self.feature_sequence_qza def pool(self): self.pooled_fq_dir, self.fq_suffix = BatchPool(self.settings).main( fq_dir=self.trimmed_fq_dir, fq1_suffix=self.fq1_suffix, fq2_suffix=self.fq2_suffix) def importing(self): self.single_end_seq_qza = ImportSingleEndFastq(self.settings).main( fq_dir=self.pooled_fq_dir, fq_suffix=self.fq_suffix) def denoise(self): self.feature_table_qza, self.feature_sequence_qza = Dada2SingleEnd(self.settings).main( demultiplexed_seq_qza=self.single_end_seq_qza) class FactoryGenerateASVPairedEnd(Processor): MODE_TO_CLASS = { 'concat': GenerateASVConcatPairedEnd, 'merge': GenerateASVMergePairedEnd, 'pool': GenerateASVPoolPairedEnd } def main(self, paired_end_mode: str) -> Callable: assert paired_end_mode in self.MODE_TO_CLASS.keys(), \ f'"{paired_end_mode}" is not a valid mode for GenerateASV' _Class = self.MODE_TO_CLASS[paired_end_mode] return _Class(self.settings).main class GenerateASVSingleEnd(Processor): fq_dir: str fq_suffix: str clip_5_prime: int trimmed_fq_dir: str single_end_seq_qza: str feature_sequence_qza: str feature_table_qza: str def main( self, fq_dir: str, fq_suffix: str, clip_5_prime: int) -> Tuple[str, str]: self.fq_dir = fq_dir self.fq_suffix = fq_suffix self.clip_5_prime = clip_5_prime self.trimming() self.importing() self.denoise() return self.feature_table_qza, self.feature_sequence_qza def trimming(self): self.trimmed_fq_dir = BatchTrimGaloreSingleEnd(self.settings).main( fq_dir=self.fq_dir, fq_suffix=self.fq_suffix, clip_5_prime=self.clip_5_prime) def importing(self): self.single_end_seq_qza = ImportSingleEndFastq(self.settings).main( fq_dir=self.trimmed_fq_dir, fq_suffix=self.fq_suffix) def denoise(self): self.feature_table_qza, self.feature_sequence_qza = Dada2SingleEnd(self.settings).main( demultiplexed_seq_qza=self.single_end_seq_qza)

nilq/baby-python

python

import personalnames.titles as titles import bisect # noinspection PyTypeChecker def gen_initials(lastname, firstname, formats, title=None, post_nominal=None, no_ws=False): """ Generate the name formats with initials. :param lastname: person's lastname :param firstname: person's firstname :param title: person's title :param post_nominal: suffix, e.g. 'junior', or 'esq.' :param formats: list of formats ['firstnamelastname', 'lastnamefirstname'] :param no_ws: add a form with no whitespace. :return: de-duplicated list of names with initials. """ # Normalise whitespace to single space lastname = normalise_whitespace(lastname) parts = normalise_whitespace(firstname).split() forms = [] for x in range(1, len(parts) + 1): initials = [part[0:1] + "." for part in parts[0:x]] initials += parts[x:] if "firstnamelastname" in formats: forms.append(" ".join([" ".join(initials), lastname])) if title: forms.append(" ".join([title, " ".join(initials), lastname])) if "lastnamefirstname" in formats: forms.append(", ".join([lastname, " ".join(initials)])) if title: forms.append(", ".join([lastname, title + " " + " ".join(initials)])) for x in range(1, len(parts) + 1): initials = [part[0:1] + "." for part in parts[1:x]] initials += parts[x:] if "firstnamelastname" in formats: forms.append(" ".join([parts[0], " ".join(initials), lastname])) if title: forms.append(" ".join([title, parts[0], " ".join(initials), lastname])) if "lastnamefirstname" in formats: forms.append(lastname + ", " + " ".join([parts[0], " ".join(initials)])) if title: forms.append( lastname + ", " + " ".join([title, parts[0], " ".join(initials)]) ) if post_nominal: forms.extend([x + ", " + post_nominal for x in forms[:]]) if no_ws: forms.extend([removewhitespace(x) for x in forms[:]]) return list(set(forms)) def parse_titles(parts): title_parts = [] suffix_parts = [] nominal_parts = [] for part in parts: if part.lower() in titles.prefixes: title_parts.append(part) elif part.lower() in titles.suffixes: suffix_parts.append(part) else: nominal_parts.append(part) return title_parts, nominal_parts, suffix_parts def name_split(name, split_char=","): """ Split a name into a list of name parts (not categorised, just an ordered list). Retain commas for later use in splitting the list into surname and forename parts. :param name: string for personal name :param split_char: character to split on (default to comma) :return: list of strings, including commas. """ name_list = [] split_split = name.split(split_char) for split_item in split_split[:-1]: [name_list.append(normalise_whitespace(x)) for x in split_item.split()] name_list.append(split_char) [name_list.append(normalise_whitespace(x)) for x in split_split[-1].split()] return name_list def name_parts(name, split_c=","): """ TO DO: handle case with multiple commas (if this is a genuine case) :param name: :param split_c: :return: """ n_parts = name_split(name, split_char=split_c) title, personal_name, suffix = parse_titles(n_parts) if personal_name[-1] == split_c: # case where multiple commas in name, or only comma is a post-nominal, e.g. Esq. n = personal_name[:-1] else: n = personal_name if split_c in n: lastname = whitespace_list( n[: bisect.bisect(n, split_c) - 1] ) firstname = whitespace_list( n[bisect.bisect(n, split_c):] ) else: firstname = whitespace_list(n[:-1]) lastname = whitespace_list([n[-1]]) title = whitespace_list(title) suffix = whitespace_list(suffix) return title, firstname, lastname, suffix def name_initials(name, name_formats=None, non_ws=False): """ Generate a set of initials from a name provided as a string. :param name: string, e.g. Dr. Martin Luther King :param name_formats: list of formats for the name. :param non_ws: no whitespace form :return: list of formats including initials """ if name_formats is None: name_formats = ["firstnamelastname", "lastnamefirstname"] honorific, forename, surname, suffix = name_parts(name) initials = gen_initials( lastname=surname, firstname=forename, title=honorific, post_nominal=suffix, formats=name_formats, no_ws=non_ws ) return [normalise_whitespace(x) for x in initials] def whitespace_list(text_list): return normalise_whitespace(" ".join(text_list)) def normalise_whitespace(text): """ Normalise the whitespace in the string :param text: string :return: string with whitespace normalised to single space """ return " ".join(text.strip().split()) def removewhitespace(text): """ Remove the whitespace in the string :param text: string :return: string with no whitespace """ return "".join(text.strip().split())

nilq/baby-python

python

from typing import List, Union def is_valid(sides: List[Union[float,int]]) -> bool: [x, y, z] = sides return x > 0 and y > 0 and z > 0 and x + y > z def equilateral(sides: List[Union[float,int]]) -> bool: sides.sort() return is_valid(sides) and sides.count(sides[0]) == 3 def isosceles(sides: List[Union[float,int]]) -> bool: sides.sort() return is_valid(sides) and sides[0] == sides[1] or sides[1] == sides[2] def scalene(sides: List[Union[float,int]]) -> bool: sides.sort() return is_valid(sides) and sides[0] != sides[1] and sides[1] != sides[2]

nilq/baby-python

python

import numpy as np import scipy.sparse as sparse import scipy.sparse.linalg as linalg from .mesh import UniformMesh from .laminate_model import LaminateModel from .laminate_dof import LaminateDOF class LaminateFEM(object): def __init__(self, material, cantilever): self.cantilever = cantilever self.mesh = UniformMesh(cantilever.topology) self.dof = LaminateDOF(self.mesh) self.model = LaminateModel(material, cantilever.a, cantilever.b) self.a = cantilever.a self.b = cantilever.b self.assemble() def get_mass_matrix(self, free=False): muu = self.muu.tocsr() if free is False: return muu return muu[self.dof.free_dofs, :][:, self.dof.free_dofs] def get_stiffness_matrix(self, free=False): kuu = self.kuu.tocsr() if free is False: return kuu return kuu[self.dof.free_dofs, :][:, self.dof.free_dofs] def get_piezoelectric_matrix(self, free=False): kuv = self.kuv.tocsr() if free is False: return kuv return kuv[self.dof.free_dofs, :] def get_capacitance_matrix(self): return self.kvv def modal_analysis(self, n_modes): """The return value (w) are the eigenvalues and the return value (v) are the eigenvectors. """ m = self.muu.tocsc()[self.dof.free_dofs, :][:, self.dof.free_dofs] k = self.kuu.tocsc()[self.dof.free_dofs, :][:, self.dof.free_dofs] w, v = linalg.eigsh(k, k=n_modes, M=m, sigma=0, which='LM') vall = np.zeros((self.dof.n_mdof, n_modes)) vall[self.dof.free_dofs, :] = v return w, v, vall def assemble(self): """The mass, stiffness, piezoelectric, and capacitance matricies are assembled in this function. """ muue = self.model.get_mass_element() kuue = self.model.get_stiffness_element() kuve = self.model.get_piezoelectric_element() kvve = self.model.get_capacitance_element() nm, ne = kuve.shape k_num = nm * nm * self.mesh.n_elem p_num = nm * ne * self.mesh.n_elem c_num = ne * ne * self.mesh.n_elem k_index = list(np.ndindex(nm, nm)) p_index = list(np.ndindex(nm, ne)) c_index = list(np.ndindex(ne, ne)) k_row = np.zeros(k_num) k_col = np.zeros(k_num) k_val = np.zeros(k_num) m_val = np.zeros(k_num) p_row = np.zeros(p_num) p_col = np.zeros(p_num) p_val = np.zeros(p_num) c_row = np.zeros(c_num) c_col = np.zeros(c_num) c_val = np.zeros(c_num) k_ntriplet = 0 p_ntriplet = 0 c_ntriplet = 0 for ni, e in enumerate(self.dof.dof_elements): for ii, jj in k_index: k_row[k_ntriplet] = e.mechanical_dof[ii] k_col[k_ntriplet] = e.mechanical_dof[jj] k_val[k_ntriplet] = kuue[ii, jj] m_val[k_ntriplet] = muue[ii, jj] k_ntriplet += 1 for ii, jj in p_index: p_row[p_ntriplet] = e.mechanical_dof[ii] p_col[p_ntriplet] = e.electrical_dof[jj] p_val[p_ntriplet] = kuve[ii, jj] p_ntriplet += 1 for ii, jj in c_index: c_row[c_ntriplet] = e.electrical_dof[ii] c_col[c_ntriplet] = e.electrical_dof[jj] c_val[c_ntriplet] = kvve[ii, jj] c_ntriplet += 1 muu_shape = (self.dof.n_mdof, self.dof.n_mdof) kuu_shape = (self.dof.n_mdof, self.dof.n_mdof) kuv_shape = (self.dof.n_mdof, self.dof.n_edof) kvv_shape = (self.dof.n_edof, self.dof.n_edof) self.muu = sparse.coo_matrix((m_val, (k_row, k_col)), shape=muu_shape) self.kuu = sparse.coo_matrix((k_val, (k_row, k_col)), shape=kuu_shape) self.kuv = sparse.coo_matrix((p_val, (p_row, p_col)), shape=kuv_shape) self.kvv = sparse.coo_matrix((c_val, (c_row, c_col)), shape=kvv_shape)

nilq/baby-python

python

"""Falcon benchmarks""" from bench import main # NOQA

nilq/baby-python

python

import os from pathlib import Path import pyspark.sql.types as st from pyspark.sql.types import Row from pyspark.ml.regression import GBTRegressor from pyspark.sql import DataFrame, SparkSession spark = SparkSession.builder \ .appName("karl02") \ .getOrCreate() datadir: str = os.getenv("DATADIR") if datadir is None: raise ValueError("Environment variable DATADIR must be defined") print(f"datadir = '{datadir}'") schema = st.StructType([ st.StructField('year', st.IntegerType(), True), st.StructField('month', st.IntegerType(), True), st.StructField('dn', st.IntegerType(), True), st.StructField('wday', st.IntegerType(), True), st.StructField('snap', st.IntegerType(), True), st.StructField('dept_id', st.StringType(), True), st.StructField('item_id', st.StringType(), True), st.StructField('store_id', st.StringType(), True), st.StructField('sales', st.DoubleType(), True), st.StructField('flag_ram', st.IntegerType(), True), st.StructField('Sales_Pred', st.DoubleType(), True) ]) p = str(Path(datadir, "Sales5_Ab2011_InklPred.csv")) print(f"Reading: '{p}'") train: DataFrame = spark.read.csv(p, header='true', schema=schema) rows = train.rdd.take(5) for r in rows: dn = r["sales"] d = r.asDict() v = list(d.values()) print(v) print(type(v)) print("------------------------- R E A D Y --------------------------------") def train(df: DataFrame): def astraining(row: Row) -> Row: df = row.asDict() del df['Sales_Pred'] del df['sales'] sales = row.asDict()['sales'] return Row(label=sales, features=list(df.values())) t3 = train.rdd \ .filter(lambda r: r["sales"] is not None) \ .map(astraining) gbt = GBTRegressor(maxIter=10) df = spark.createDataFrame(t3) df.show() gbt.fit(df) print("----------- after fit ------------")

nilq/baby-python

python

############################################################################### # Imports ############################################################################### from layer import Layer import numpy as np class HiddenLayer(Layer): def setDownstreamSum(self, w, delta): """Sum the product of w and delta for the next layer Needed for calculating delta for this layer Parameters ---------- w : np.ndarray Matrix of weight values for the next layer delta : np.ndarray Matrix of delta values for the next layer """ self.downstream_sum = np.matmul(w[:,:-1].transpose(), delta) def setDelta(self): """Calculate delta for the hidden layer """ # Derivative of sigmoid using last forward pass output_der = self.y * (1 - self.y) self.delta = output_der * self.downstream_sum if __name__ == '__main__': print('Warning: Tests for this file are deprecated')

nilq/baby-python

python

# -*- coding: utf-8 -*- ''' @Time : 2021/8/30 @Author : Yanyuxiang @Email : yanyuxiangtoday@163.com @FileName: send_message.py @Software: PyCharm ''' import itchat def main(): itchat.auto_login() friends = itchat.get_friends(update=True) # itchat.send('这是来自python程序的一条消息', toUserName='filehelper') return if __name__ == '__main__': main()

nilq/baby-python

python

from . import scheduler from app.utils.refresh_mat_views import refresh_all_mat_views from app.utils.constants import COUNTRIES # 5/9 = 5am, 2pm, and 11pm # https://cron.help/#0_5/9_*_*_* @scheduler.task("cron", minute="0", hour="5") def run_task_ALL(): with scheduler.app.app_context(): from app.service.routes import call_loader for country in COUNTRIES: call_loader( country=country, search=dict(ad_reached_countries=[country], ad_active_status="ALL"), ) @scheduler.task("cron", minute="35", hour="*") def refresh_views(): with scheduler.app.app_context(): refresh_all_mat_views(False)

nilq/baby-python

python

import torch from torch.utils.data import DataLoader from torch import optim from torch.optim.lr_scheduler import StepLR from snf.layers.flowsequential import FlowSequential from snf.layers.selfnorm import SelfNormConv, SelfNormFC from snf.train.losses import NegativeGaussianLoss from snf.train.experiment import Experiment def create_model(data_size, layer='conv'): layers = [] c_in = data_size[0] h = data_size[1] w = data_size[2] if layer == 'fc': size = c_in * h * w layers.append(SelfNormFC(size, size, bias=True, sym_recon_grad=False, only_R_recon=False)) model = FlowSequential(NegativeGaussianLoss(size=(size,)), *layers) elif layer == 'conv': layers.append(SelfNormConv(c_in, c_in, (3,3), bias=True, stride=1, padding=1, sym_recon_grad=False, only_R_recon=False)) model = FlowSequential(NegativeGaussianLoss(size=data_size), *layers) return model def load_data(batch_size=100, im_size=(1,28,28), n_train=60_000, n_val=10_0000, n_test=10_000): trainx = torch.randn(n_train, *im_size) testx = torch.randn(n_test, *im_size) trainy = torch.zeros(n_train) testy = torch.zeros(n_test) trainvalset = torch.utils.data.TensorDataset(trainx, trainy) testset = torch.utils.data.TensorDataset(testx, testy) trainset = torch.utils.data.Subset(trainvalset, range(0, n_train - n_val)) valset = torch.utils.data.Subset(trainvalset, range(n_train - n_val, n_train)) train_loader = DataLoader(trainset, batch_size=batch_size) val_loader = DataLoader(valset, batch_size=batch_size) test_loader = DataLoader(testset, batch_size=batch_size) return train_loader, val_loader, test_loader def run_timing_experiment(name, snf_name, config, sz, m, results): train_loader, val_loader, test_loader = load_data(batch_size=config['batch_size'], im_size=sz, n_train=50_000, n_val=100, n_test=100) model = create_model(data_size=sz, layer=m).to('cuda') optimizer = optim.Adam(model.parameters(), lr=config['lr'], betas=(0.9, 0.999)) scheduler = StepLR(optimizer, step_size=1, gamma=1.0) experiment = Experiment(model, train_loader, val_loader, test_loader, optimizer, scheduler, **config) experiment.run() mean_time = experiment.summary['Batch Time Mean'] std_time = experiment.summary['Batch Time Std'] print(f"{name}: {mean_time} +/- {std_time}") results[f'{m} {snf_name}']['n_params'].append(sz[0] * sz[1] * sz[2]) results[f'{m} {snf_name}']['mean'].append(mean_time) results[f'{m} {snf_name}']['std'].append(std_time) return results def main(): image_sizes = [(1, x*32, 1) for x in range(1, 130, 3)] model_type = ['fc', 'conv'] self_normalized = [True, False] name = 'Timing Experiment ' results = {} for m in model_type: for snf in self_normalized: if snf: snf_name = 'SNF' else: snf_name = 'Reg' results[f'{m} {snf_name}'] = { 'n_params': [], 'mean': [], 'std': [] } for sz in image_sizes: name = f'Timing Experiment {m} {snf_name} {sz}' config = { 'name': name, 'eval_epochs': 1, 'sample_epochs': 1000, 'log_interval': 10000, 'lr': 1e-4, 'batch_size': 128, 'modified_grad': snf, 'add_recon_grad': snf, 'sym_recon_grad': False, 'only_R_recon': False, 'actnorm': False, 'split_prior': False, 'activation': 'None', 'log_timing': True, 'epochs': 10 } results = run_timing_experiment(name, snf_name, config, sz, m, results) print(results[f'{m} {snf_name}']) print(results) print(results) print(results)

nilq/baby-python

python

from VcfNormalize import VcfNormalize import argparse import os #get command line arguments parser = argparse.ArgumentParser(description='Script to run GATK VariantsToAllelicPrimitives in order to decompose MNPs into more basic/primitive alleles') parser.add_argument('--gatk_folder', type=str, required=True, help='Folder containing GATK jar file' ) parser.add_argument('--bgzip_folder', type=str, required=True, help='Folder containing bgzip' ) parser.add_argument('--vcf', type=str, required=True, help='Path to the VCF file that will be analysed' ) parser.add_argument('--outprefix', type=str, required=True, help='Prefix for output file' ) parser.add_argument('--reference', type=str, required=True, help='Path to the reference Fasta file' ) parser.add_argument('--compress', type=str, required=False, help='Compress the output file' ) args = parser.parse_args() if __name__ == '__main__': vcfallprim = VcfNormalize(vcf=args.vcf,gatk_folder=args.gatk_folder,bgzip_folder=args.bgzip_folder) vcfallprim.run_gatk_VariantsToAllelicPrimitives(outprefix=args.outprefix,reference=args.reference,compress=args.compress)

nilq/baby-python

python

# This file was automatically created by FeynRules 2.3.36 # Mathematica version: 11.3.0 for Linux x86 (64-bit) (March 7, 2018) # Date: Wed 24 Feb 2021 15:52:48 from object_library import all_couplings, Coupling from function_library import complexconjugate, re, im, csc, sec, acsc, asec, cot GC_1 = Coupling(name = 'GC_1', value = '-(ee*complex(0,1))/3.', order = {'QED':1}) GC_2 = Coupling(name = 'GC_2', value = '(2*ee*complex(0,1))/3.', order = {'QED':1}) GC_3 = Coupling(name = 'GC_3', value = '-(ee*complex(0,1))', order = {'QED':1}) GC_4 = Coupling(name = 'GC_4', value = 'ee*complex(0,1)', order = {'QED':1}) GC_5 = Coupling(name = 'GC_5', value = 'ee**2*complex(0,1)', order = {'QED':2}) GC_6 = Coupling(name = 'GC_6', value = '2*ee**2*complex(0,1)', order = {'QED':2}) GC_7 = Coupling(name = 'GC_7', value = '-ee**2/(2.*cw)', order = {'QED':2}) GC_8 = Coupling(name = 'GC_8', value = 'ee**2/(2.*cw)', order = {'QED':2}) GC_9 = Coupling(name = 'GC_9', value = '-(cab*ee**2*complex(0,1))/(2.*cw)', order = {'QED':2}) GC_10 = Coupling(name = 'GC_10', value = '-(cphi*ee**2)/(2.*cw)', order = {'QED':2}) GC_11 = Coupling(name = 'GC_11', value = '(cphi*ee**2)/(2.*cw)', order = {'QED':2}) GC_12 = Coupling(name = 'GC_12', value = '-(cab*cphi*ee**2*complex(0,1))/(2.*cw)', order = {'QED':2}) GC_13 = Coupling(name = 'GC_13', value = 'cphi*fl1x2*complex(0,1) - cphi*fl2x1*complex(0,1)', order = {'ZEE':1}) GC_14 = Coupling(name = 'GC_14', value = '-(cphi*fl1x2*complex(0,1)) + cphi*fl2x1*complex(0,1)', order = {'ZEE':1}) GC_15 = Coupling(name = 'GC_15', value = 'cphi*fl1x3*complex(0,1) - cphi*fl3x1*complex(0,1)', order = {'ZEE':1}) GC_16 = Coupling(name = 'GC_16', value = '-(cphi*fl1x3*complex(0,1)) + cphi*fl3x1*complex(0,1)', order = {'ZEE':1}) GC_17 = Coupling(name = 'GC_17', value = 'cphi*fl2x3*complex(0,1) - cphi*fl3x2*complex(0,1)', order = {'ZEE':1}) GC_18 = Coupling(name = 'GC_18', value = '-(cphi*fl2x3*complex(0,1)) + cphi*fl3x2*complex(0,1)', order = {'ZEE':1}) GC_19 = Coupling(name = 'GC_19', value = '-G', order = {'QCD':1}) GC_20 = Coupling(name = 'GC_20', value = 'complex(0,1)*G', order = {'QCD':1}) GC_21 = Coupling(name = 'GC_21', value = 'complex(0,1)*G**2', order = {'QCD':2}) GC_22 = Coupling(name = 'GC_22', value = '-(complex(0,1)*I1a11)', order = {'QED':1}) GC_23 = Coupling(name = 'GC_23', value = '-(complex(0,1)*I1a12)', order = {'QED':1}) GC_24 = Coupling(name = 'GC_24', value = '-(complex(0,1)*I1a13)', order = {'QED':1}) GC_25 = Coupling(name = 'GC_25', value = '-(complex(0,1)*I1a21)', order = {'QED':1}) GC_26 = Coupling(name = 'GC_26', value = '-(complex(0,1)*I1a22)', order = {'QED':1}) GC_27 = Coupling(name = 'GC_27', value = '-(complex(0,1)*I1a23)', order = {'QED':1}) GC_28 = Coupling(name = 'GC_28', value = '-(complex(0,1)*I1a31)', order = {'QED':1}) GC_29 = Coupling(name = 'GC_29', value = '-(complex(0,1)*I1a32)', order = {'QED':1}) GC_30 = Coupling(name = 'GC_30', value = '-(complex(0,1)*I1a33)', order = {'QED':1}) GC_31 = Coupling(name = 'GC_31', value = 'complex(0,1)*I2a11', order = {'QED':1}) GC_32 = Coupling(name = 'GC_32', value = 'complex(0,1)*I2a12', order = {'QED':1}) GC_33 = Coupling(name = 'GC_33', value = 'complex(0,1)*I2a13', order = {'QED':1}) GC_34 = Coupling(name = 'GC_34', value = 'complex(0,1)*I2a21', order = {'QED':1}) GC_35 = Coupling(name = 'GC_35', value = 'complex(0,1)*I2a22', order = {'QED':1}) GC_36 = Coupling(name = 'GC_36', value = 'complex(0,1)*I2a23', order = {'QED':1}) GC_37 = Coupling(name = 'GC_37', value = 'complex(0,1)*I2a31', order = {'QED':1}) GC_38 = Coupling(name = 'GC_38', value = 'complex(0,1)*I2a32', order = {'QED':1}) GC_39 = Coupling(name = 'GC_39', value = 'complex(0,1)*I2a33', order = {'QED':1}) GC_40 = Coupling(name = 'GC_40', value = 'complex(0,1)*I3a11', order = {'QED':1}) GC_41 = Coupling(name = 'GC_41', value = 'complex(0,1)*I3a12', order = {'QED':1}) GC_42 = Coupling(name = 'GC_42', value = 'complex(0,1)*I3a13', order = {'QED':1}) GC_43 = Coupling(name = 'GC_43', value = 'complex(0,1)*I3a21', order = {'QED':1}) GC_44 = Coupling(name = 'GC_44', value = 'complex(0,1)*I3a22', order = {'QED':1}) GC_45 = Coupling(name = 'GC_45', value = 'complex(0,1)*I3a23', order = {'QED':1}) GC_46 = Coupling(name = 'GC_46', value = 'complex(0,1)*I3a31', order = {'QED':1}) GC_47 = Coupling(name = 'GC_47', value = 'complex(0,1)*I3a32', order = {'QED':1}) GC_48 = Coupling(name = 'GC_48', value = 'complex(0,1)*I3a33', order = {'QED':1}) GC_49 = Coupling(name = 'GC_49', value = '-(complex(0,1)*I4a11)', order = {'QED':1}) GC_50 = Coupling(name = 'GC_50', value = '-(complex(0,1)*I4a12)', order = {'QED':1}) GC_51 = Coupling(name = 'GC_51', value = '-(complex(0,1)*I4a13)', order = {'QED':1}) GC_52 = Coupling(name = 'GC_52', value = '-(complex(0,1)*I4a21)', order = {'QED':1}) GC_53 = Coupling(name = 'GC_53', value = '-(complex(0,1)*I4a22)', order = {'QED':1}) GC_54 = Coupling(name = 'GC_54', value = '-(complex(0,1)*I4a23)', order = {'QED':1}) GC_55 = Coupling(name = 'GC_55', value = '-(complex(0,1)*I4a31)', order = {'QED':1}) GC_56 = Coupling(name = 'GC_56', value = '-(complex(0,1)*I4a32)', order = {'QED':1}) GC_57 = Coupling(name = 'GC_57', value = '-(complex(0,1)*I4a33)', order = {'QED':1}) GC_58 = Coupling(name = 'GC_58', value = '-(complex(0,1)*lam1)', order = {'QED':2}) GC_59 = Coupling(name = 'GC_59', value = '-2*complex(0,1)*lam1', order = {'QED':2}) GC_60 = Coupling(name = 'GC_60', value = '-3*complex(0,1)*lam1', order = {'QED':2}) GC_61 = Coupling(name = 'GC_61', value = '-3*complex(0,1)*lam2', order = {'QED':2}) GC_62 = Coupling(name = 'GC_62', value = '-(complex(0,1)*lam3)', order = {'QED':2}) GC_63 = Coupling(name = 'GC_63', value = '-(complex(0,1)*lam3) - complex(0,1)*lam4 - complex(0,1)*lam5', order = {'QED':2}) GC_64 = Coupling(name = 'GC_64', value = '-2*cphi**2*complex(0,1)*lam5', order = {'QED':2}) GC_65 = Coupling(name = 'GC_65', value = '-(cphi*complex(0,1)*lam4)/2. - (cphi*complex(0,1)*lam5)/2.', order = {'QED':2}) GC_66 = Coupling(name = 'GC_66', value = '(cab*cphi*lam4)/2. - (cab*cphi*lam5)/2.', order = {'QED':2}) GC_67 = Coupling(name = 'GC_67', value = '-(cab*cphi*lam4)/2. + (cab*cphi*lam5)/2.', order = {'QED':2}) GC_68 = Coupling(name = 'GC_68', value = '-(complex(0,1)*lam6)', order = {'QED':2}) GC_69 = Coupling(name = 'GC_69', value = '-3*complex(0,1)*lam6', order = {'QED':2}) GC_70 = Coupling(name = 'GC_70', value = '-(cphi*complex(0,1)*lam6)', order = {'QED':2}) GC_71 = Coupling(name = 'GC_71', value = '-2*cphi*complex(0,1)*lam6', order = {'QED':2}) GC_72 = Coupling(name = 'GC_72', value = '-3*complex(0,1)*lam7', order = {'QED':2}) GC_73 = Coupling(name = 'GC_73', value = '-(cphi*complex(0,1)*lam7)', order = {'QED':2}) GC_74 = Coupling(name = 'GC_74', value = '(ee**2*complex(0,1)*sab)/(2.*cw)', order = {'QED':2}) GC_75 = Coupling(name = 'GC_75', value = '-(cphi*ee**2*complex(0,1)*sab)/(2.*cw)', order = {'QED':2}) GC_76 = Coupling(name = 'GC_76', value = '(cphi*lam4*sab)/2. - (cphi*lam5*sab)/2.', order = {'QED':2}) GC_77 = Coupling(name = 'GC_77', value = '-(cphi*lam4*sab)/2. + (cphi*lam5*sab)/2.', order = {'QED':2}) GC_78 = Coupling(name = 'GC_78', value = '-(cab**2*complex(0,1)*lam3) + 2*cab*complex(0,1)*lam6*sab - complex(0,1)*lam1*sab**2', order = {'QED':2}) GC_79 = Coupling(name = 'GC_79', value = '-(cab**2*complex(0,1)*lam3) - cab**2*complex(0,1)*lam4 + cab**2*complex(0,1)*lam5 + 2*cab*complex(0,1)*lam6*sab - complex(0,1)*lam1*sab**2', order = {'QED':2}) GC_80 = Coupling(name = 'GC_80', value = '-(cab**2*complex(0,1)*lam3) - cab**2*complex(0,1)*lam4 + cab**2*complex(0,1)*lam5 - 2*cab*complex(0,1)*lam7*sab - complex(0,1)*lam2*sab**2', order = {'QED':2}) GC_81 = Coupling(name = 'GC_81', value = '-(cab**2*complex(0,1)*lam1) - 2*cab*complex(0,1)*lam6*sab - complex(0,1)*lam3*sab**2', order = {'QED':2}) GC_82 = Coupling(name = 'GC_82', value = '-(cab**2*complex(0,1)*lam5) + cab*complex(0,1)*lam6*sab - cab*complex(0,1)*lam7*sab + complex(0,1)*lam5*sab**2', order = {'QED':2}) GC_83 = Coupling(name = 'GC_83', value = '-(cab**2*complex(0,1)*lam1) - 2*cab*complex(0,1)*lam6*sab - complex(0,1)*lam3*sab**2 - complex(0,1)*lam4*sab**2 + complex(0,1)*lam5*sab**2', order = {'QED':2}) GC_84 = Coupling(name = 'GC_84', value = '-(cab**2*complex(0,1)*lam2) + 2*cab*complex(0,1)*lam7*sab - complex(0,1)*lam3*sab**2 - complex(0,1)*lam4*sab**2 + complex(0,1)*lam5*sab**2', order = {'QED':2}) GC_85 = Coupling(name = 'GC_85', value = '-(cab**2*cphi*complex(0,1)*lam4)/2. - (cab**2*cphi*complex(0,1)*lam5)/2. + cab*cphi*complex(0,1)*lam6*sab - cab*cphi*complex(0,1)*lam7*sab + (cphi*complex(0,1)*lam4*sab**2)/2. + (cphi*complex(0,1)*lam5*sab**2)/2.', order = {'QED':2}) GC_86 = Coupling(name = 'GC_86', value = '-(cab**2*complex(0,1)*lam7) + 2*cab*complex(0,1)*lam5*sab - complex(0,1)*lam6*sab**2', order = {'QED':2}) GC_87 = Coupling(name = 'GC_87', value = '-(cab**2*complex(0,1)*lam6) + cab*complex(0,1)*lam1*sab - cab*complex(0,1)*lam3*sab + complex(0,1)*lam6*sab**2', order = {'QED':2}) GC_88 = Coupling(name = 'GC_88', value = '-(cab**2*complex(0,1)*lam6) + cab*complex(0,1)*lam1*sab - cab*complex(0,1)*lam3*sab - cab*complex(0,1)*lam4*sab + cab*complex(0,1)*lam5*sab + complex(0,1)*lam6*sab**2', order = {'QED':2}) GC_89 = Coupling(name = 'GC_89', value = '-(cab**2*cphi*complex(0,1)*lam7) + cab*cphi*complex(0,1)*lam4*sab + cab*cphi*complex(0,1)*lam5*sab - cphi*complex(0,1)*lam6*sab**2', order = {'QED':2}) GC_90 = Coupling(name = 'GC_90', value = '-(cab**2*complex(0,1)*lam6) - 2*cab*complex(0,1)*lam5*sab - complex(0,1)*lam7*sab**2', order = {'QED':2}) GC_91 = Coupling(name = 'GC_91', value = '-(cab**2*complex(0,1)*lam7) - cab*complex(0,1)*lam2*sab + cab*complex(0,1)*lam3*sab + cab*complex(0,1)*lam4*sab - cab*complex(0,1)*lam5*sab + complex(0,1)*lam7*sab**2', order = {'QED':2}) GC_92 = Coupling(name = 'GC_92', value = '-(cab**2*cphi*complex(0,1)*lam6) - cab*cphi*complex(0,1)*lam4*sab - cab*cphi*complex(0,1)*lam5*sab - cphi*complex(0,1)*lam7*sab**2', order = {'QED':2}) GC_93 = Coupling(name = 'GC_93', value = '-3*cab**4*complex(0,1)*lam2 + 12*cab**3*complex(0,1)*lam7*sab - 6*cab**2*complex(0,1)*lam3*sab**2 - 6*cab**2*complex(0,1)*lam4*sab**2 - 6*cab**2*complex(0,1)*lam5*sab**2 + 12*cab*complex(0,1)*lam6*sab**3 - 3*complex(0,1)*lam1*sab**4', order = {'QED':2}) GC_94 = Coupling(name = 'GC_94', value = '-3*cab**4*complex(0,1)*lam1 - 12*cab**3*complex(0,1)*lam6*sab - 6*cab**2*complex(0,1)*lam3*sab**2 - 6*cab**2*complex(0,1)*lam4*sab**2 - 6*cab**2*complex(0,1)*lam5*sab**2 - 12*cab*complex(0,1)*lam7*sab**3 - 3*complex(0,1)*lam2*sab**4', order = {'QED':2}) GC_95 = Coupling(name = 'GC_95', value = '-(cab**4*complex(0,1)*lam3) - cab**4*complex(0,1)*lam4 - cab**4*complex(0,1)*lam5 + 6*cab**3*complex(0,1)*lam6*sab - 6*cab**3*complex(0,1)*lam7*sab - 3*cab**2*complex(0,1)*lam1*sab**2 - 3*cab**2*complex(0,1)*lam2*sab**2 + 4*cab**2*complex(0,1)*lam3*sab**2 + 4*cab**2*complex(0,1)*lam4*sab**2 + 4*cab**2*complex(0,1)*lam5*sab**2 - 6*cab*complex(0,1)*lam6*sab**3 + 6*cab*complex(0,1)*lam7*sab**3 - complex(0,1)*lam3*sab**4 - complex(0,1)*lam4*sab**4 - complex(0,1)*lam5*sab**4', order = {'QED':2}) GC_96 = Coupling(name = 'GC_96', value = '-3*cab**4*complex(0,1)*lam7 - 3*cab**3*complex(0,1)*lam2*sab + 3*cab**3*complex(0,1)*lam3*sab + 3*cab**3*complex(0,1)*lam4*sab + 3*cab**3*complex(0,1)*lam5*sab - 9*cab**2*complex(0,1)*lam6*sab**2 + 9*cab**2*complex(0,1)*lam7*sab**2 + 3*cab*complex(0,1)*lam1*sab**3 - 3*cab*complex(0,1)*lam3*sab**3 - 3*cab*complex(0,1)*lam4*sab**3 - 3*cab*complex(0,1)*lam5*sab**3 + 3*complex(0,1)*lam6*sab**4', order = {'QED':2}) GC_97 = Coupling(name = 'GC_97', value = '-3*cab**4*complex(0,1)*lam6 + 3*cab**3*complex(0,1)*lam1*sab - 3*cab**3*complex(0,1)*lam3*sab - 3*cab**3*complex(0,1)*lam4*sab - 3*cab**3*complex(0,1)*lam5*sab + 9*cab**2*complex(0,1)*lam6*sab**2 - 9*cab**2*complex(0,1)*lam7*sab**2 - 3*cab*complex(0,1)*lam2*sab**3 + 3*cab*complex(0,1)*lam3*sab**3 + 3*cab*complex(0,1)*lam4*sab**3 + 3*cab*complex(0,1)*lam5*sab**3 + 3*complex(0,1)*lam7*sab**4', order = {'QED':2}) GC_98 = Coupling(name = 'GC_98', value = '-(ee**2*sphi)/(2.*cw)', order = {'QED':2}) GC_99 = Coupling(name = 'GC_99', value = '(ee**2*sphi)/(2.*cw)', order = {'QED':2}) GC_100 = Coupling(name = 'GC_100', value = '-(cab*ee**2*complex(0,1)*sphi)/(2.*cw)', order = {'QED':2}) GC_101 = Coupling(name = 'GC_101', value = '-2*cphi*complex(0,1)*lam5*sphi', order = {'QED':2}) GC_102 = Coupling(name = 'GC_102', value = '-(complex(0,1)*lam6*sphi)', order = {'QED':2}) GC_103 = Coupling(name = 'GC_103', value = '-2*complex(0,1)*lam6*sphi', order = {'QED':2}) GC_104 = Coupling(name = 'GC_104', value = '-(complex(0,1)*lam7*sphi)', order = {'QED':2}) GC_105 = Coupling(name = 'GC_105', value = '-(ee**2*complex(0,1)*sab*sphi)/(2.*cw)', order = {'QED':2}) GC_106 = Coupling(name = 'GC_106', value = '-2*complex(0,1)*lam5*sphi**2', order = {'QED':2}) GC_107 = Coupling(name = 'GC_107', value = 'fl1x2*complex(0,1)*sphi - fl2x1*complex(0,1)*sphi', order = {'ZEE':1}) GC_108 = Coupling(name = 'GC_108', value = '-(fl1x2*complex(0,1)*sphi) + fl2x1*complex(0,1)*sphi', order = {'ZEE':1}) GC_109 = Coupling(name = 'GC_109', value = 'fl1x3*complex(0,1)*sphi - fl3x1*complex(0,1)*sphi', order = {'ZEE':1}) GC_110 = Coupling(name = 'GC_110', value = '-(fl1x3*complex(0,1)*sphi) + fl3x1*complex(0,1)*sphi', order = {'ZEE':1}) GC_111 = Coupling(name = 'GC_111', value = 'fl2x3*complex(0,1)*sphi - fl3x2*complex(0,1)*sphi', order = {'ZEE':1}) GC_112 = Coupling(name = 'GC_112', value = '-(fl2x3*complex(0,1)*sphi) + fl3x2*complex(0,1)*sphi', order = {'ZEE':1}) GC_113 = Coupling(name = 'GC_113', value = '-(complex(0,1)*lam4*sphi)/2. - (complex(0,1)*lam5*sphi)/2.', order = {'QED':2}) GC_114 = Coupling(name = 'GC_114', value = '(cab*lam4*sphi)/2. - (cab*lam5*sphi)/2.', order = {'QED':2}) GC_115 = Coupling(name = 'GC_115', value = '-(cab*lam4*sphi)/2. + (cab*lam5*sphi)/2.', order = {'QED':2}) GC_116 = Coupling(name = 'GC_116', value = 'cphi*complex(0,1)*lam10*sphi - cphi*complex(0,1)*lam7*sphi', order = {'QED':2}) GC_117 = Coupling(name = 'GC_117', value = '2*cphi**2*complex(0,1)*lam10*sphi - 2*cphi**2*complex(0,1)*lam7*sphi', order = {'QED':2}) GC_118 = Coupling(name = 'GC_118', value = '-(cphi*complex(0,1)*lam3*sphi) + cphi*complex(0,1)*lam8*sphi', order = {'QED':2}) GC_119 = Coupling(name = 'GC_119', value = '-(cphi*complex(0,1)*lam3*sphi) - cphi*complex(0,1)*lam4*sphi + cphi*complex(0,1)*lam8*sphi', order = {'QED':2}) GC_120 = Coupling(name = 'GC_120', value = '-(cphi*complex(0,1)*lam2*sphi) + cphi*complex(0,1)*lam9*sphi', order = {'QED':2}) GC_121 = Coupling(name = 'GC_121', value = '(lam4*sab*sphi)/2. - (lam5*sab*sphi)/2.', order = {'QED':2}) GC_122 = Coupling(name = 'GC_122', value = '-(lam4*sab*sphi)/2. + (lam5*sab*sphi)/2.', order = {'QED':2}) GC_123 = Coupling(name = 'GC_123', value = '-(cab**2*complex(0,1)*lam4*sphi)/2. - (cab**2*complex(0,1)*lam5*sphi)/2. + cab*complex(0,1)*lam6*sab*sphi - cab*complex(0,1)*lam7*sab*sphi + (complex(0,1)*lam4*sab**2*sphi)/2. + (complex(0,1)*lam5*sab**2*sphi)/2.', order = {'QED':2}) GC_124 = Coupling(name = 'GC_124', value = '-(cab**2*complex(0,1)*lam7*sphi) + cab*complex(0,1)*lam4*sab*sphi + cab*complex(0,1)*lam5*sab*sphi - complex(0,1)*lam6*sab**2*sphi', order = {'QED':2}) GC_125 = Coupling(name = 'GC_125', value = '-(cab**2*complex(0,1)*lam6*sphi) - cab*complex(0,1)*lam4*sab*sphi - cab*complex(0,1)*lam5*sab*sphi - complex(0,1)*lam7*sab**2*sphi', order = {'QED':2}) GC_126 = Coupling(name = 'GC_126', value = 'cab**2*cphi*complex(0,1)*lam10*sphi - cab**2*cphi*complex(0,1)*lam7*sphi - cab*cphi*complex(0,1)*lam2*sab*sphi + cab*cphi*complex(0,1)*lam3*sab*sphi - cab*cphi*complex(0,1)*lam8*sab*sphi + cab*cphi*complex(0,1)*lam9*sab*sphi - cphi*complex(0,1)*lam10*sab**2*sphi + cphi*complex(0,1)*lam7*sab**2*sphi', order = {'QED':2}) GC_127 = Coupling(name = 'GC_127', value = '-(cab**2*cphi*complex(0,1)*lam2*sphi) + cab**2*cphi*complex(0,1)*lam9*sphi - 2*cab*cphi*complex(0,1)*lam10*sab*sphi + 2*cab*cphi*complex(0,1)*lam7*sab*sphi - cphi*complex(0,1)*lam3*sab**2*sphi + cphi*complex(0,1)*lam8*sab**2*sphi', order = {'QED':2}) GC_128 = Coupling(name = 'GC_128', value = '-(cab**2*cphi*complex(0,1)*lam3*sphi) + cab**2*cphi*complex(0,1)*lam8*sphi + 2*cab*cphi*complex(0,1)*lam10*sab*sphi - 2*cab*cphi*complex(0,1)*lam7*sab*sphi - cphi*complex(0,1)*lam2*sab**2*sphi + cphi*complex(0,1)*lam9*sab**2*sphi', order = {'QED':2}) GC_129 = Coupling(name = 'GC_129', value = '-(cphi**2*ee*complex(0,1)) - ee*complex(0,1)*sphi**2', order = {'QED':1}) GC_130 = Coupling(name = 'GC_130', value = '2*cphi**2*ee**2*complex(0,1) + 2*ee**2*complex(0,1)*sphi**2', order = {'QED':2}) GC_131 = Coupling(name = 'GC_131', value = '-(cphi**2*complex(0,1)*lam7) - complex(0,1)*lam10*sphi**2', order = {'QED':2}) GC_132 = Coupling(name = 'GC_132', value = '-2*cphi**3*complex(0,1)*lam7 - 2*cphi*complex(0,1)*lam10*sphi**2', order = {'QED':2}) GC_133 = Coupling(name = 'GC_133', value = '-(cphi**2*complex(0,1)*lam9) - complex(0,1)*lam2*sphi**2', order = {'QED':2}) GC_134 = Coupling(name = 'GC_134', value = '-(cphi**2*complex(0,1)*lam8) - complex(0,1)*lam3*sphi**2', order = {'QED':2}) GC_135 = Coupling(name = 'GC_135', value = '-(cphi**2*complex(0,1)*lam8) - complex(0,1)*lam3*sphi**2 - complex(0,1)*lam4*sphi**2', order = {'QED':2}) GC_136 = Coupling(name = 'GC_136', value = '-(cphi**2*complex(0,1)*lam10) - complex(0,1)*lam7*sphi**2', order = {'QED':2}) GC_137 = Coupling(name = 'GC_137', value = '-(cphi**3*complex(0,1)*lam10) + cphi*complex(0,1)*lam10*sphi**2 - 2*cphi*complex(0,1)*lam7*sphi**2', order = {'QED':2}) GC_138 = Coupling(name = 'GC_138', value = '2*cphi*complex(0,1)*lam10*sphi**2 - 2*cphi*complex(0,1)*lam7*sphi**2', order = {'QED':2}) GC_139 = Coupling(name = 'GC_139', value = '-(cphi**2*complex(0,1)*lam3) - complex(0,1)*lam8*sphi**2', order = {'QED':2}) GC_140 = Coupling(name = 'GC_140', value = '-(cphi**2*complex(0,1)*lam3) - cphi**2*complex(0,1)*lam4 - complex(0,1)*lam8*sphi**2', order = {'QED':2}) GC_141 = Coupling(name = 'GC_141', value = '-(cphi**2*complex(0,1)*lam2) - complex(0,1)*lam9*sphi**2', order = {'QED':2}) GC_142 = Coupling(name = 'GC_142', value = '-2*cphi**2*complex(0,1)*lam2*sphi**2 + 4*cphi**2*complex(0,1)*lam9*sphi**2 - 4*cphi**2*complex(0,1)*lameta*sphi**2', order = {'QED':2}) GC_143 = Coupling(name = 'GC_143', value = '-((cphi**2*muzee)/cmath.sqrt(2)) - (muzee*sphi**2)/cmath.sqrt(2)', order = {'QED':1}) GC_144 = Coupling(name = 'GC_144', value = '(cphi**2*muzee)/cmath.sqrt(2) + (muzee*sphi**2)/cmath.sqrt(2)', order = {'QED':1}) GC_145 = Coupling(name = 'GC_145', value = '-(cab**2*cphi**2*complex(0,1)*lam7) - cab*cphi**2*complex(0,1)*lam2*sab + cab*cphi**2*complex(0,1)*lam3*sab + cphi**2*complex(0,1)*lam7*sab**2 - cab**2*complex(0,1)*lam10*sphi**2 + cab*complex(0,1)*lam8*sab*sphi**2 - cab*complex(0,1)*lam9*sab*sphi**2 + complex(0,1)*lam10*sab**2*sphi**2', order = {'QED':2}) GC_146 = Coupling(name = 'GC_146', value = '-(cab**2*cphi**2*complex(0,1)*lam8) - 2*cab*cphi**2*complex(0,1)*lam10*sab - cphi**2*complex(0,1)*lam9*sab**2 - cab**2*complex(0,1)*lam3*sphi**2 - 2*cab*complex(0,1)*lam7*sab*sphi**2 - complex(0,1)*lam2*sab**2*sphi**2', order = {'QED':2}) GC_147 = Coupling(name = 'GC_147', value = '-(cab**2*cphi**2*complex(0,1)*lam9) + 2*cab*cphi**2*complex(0,1)*lam10*sab - cphi**2*complex(0,1)*lam8*sab**2 - cab**2*complex(0,1)*lam2*sphi**2 + 2*cab*complex(0,1)*lam7*sab*sphi**2 - complex(0,1)*lam3*sab**2*sphi**2', order = {'QED':2}) GC_148 = Coupling(name = 'GC_148', value = '-(cab**2*cphi**2*complex(0,1)*lam10) + cab*cphi**2*complex(0,1)*lam8*sab - cab*cphi**2*complex(0,1)*lam9*sab + cphi**2*complex(0,1)*lam10*sab**2 - cab**2*complex(0,1)*lam7*sphi**2 - cab*complex(0,1)*lam2*sab*sphi**2 + cab*complex(0,1)*lam3*sab*sphi**2 + complex(0,1)*lam7*sab**2*sphi**2', order = {'QED':2}) GC_149 = Coupling(name = 'GC_149', value = '-(cab**2*cphi**2*complex(0,1)*lam2) + 2*cab*cphi**2*complex(0,1)*lam7*sab - cphi**2*complex(0,1)*lam3*sab**2 - cab**2*complex(0,1)*lam9*sphi**2 + 2*cab*complex(0,1)*lam10*sab*sphi**2 - complex(0,1)*lam8*sab**2*sphi**2', order = {'QED':2}) GC_150 = Coupling(name = 'GC_150', value = '-(cab**2*cphi**2*complex(0,1)*lam3) - 2*cab*cphi**2*complex(0,1)*lam7*sab - cphi**2*complex(0,1)*lam2*sab**2 - cab**2*complex(0,1)*lam8*sphi**2 - 2*cab*complex(0,1)*lam10*sab*sphi**2 - complex(0,1)*lam9*sab**2*sphi**2', order = {'QED':2}) GC_151 = Coupling(name = 'GC_151', value = 'cphi**2*complex(0,1)*lam10*sphi - 2*cphi**2*complex(0,1)*lam7*sphi - complex(0,1)*lam10*sphi**3', order = {'QED':2}) GC_152 = Coupling(name = 'GC_152', value = '-2*cphi**2*complex(0,1)*lam10*sphi - 2*complex(0,1)*lam7*sphi**3', order = {'QED':2}) GC_153 = Coupling(name = 'GC_153', value = '-2*cphi**3*complex(0,1)*lam9*sphi + 4*cphi**3*complex(0,1)*lameta*sphi - 2*cphi*complex(0,1)*lam2*sphi**3 + 2*cphi*complex(0,1)*lam9*sphi**3', order = {'QED':2}) GC_154 = Coupling(name = 'GC_154', value = '-2*cphi**3*complex(0,1)*lam2*sphi + 2*cphi**3*complex(0,1)*lam9*sphi - 2*cphi*complex(0,1)*lam9*sphi**3 + 4*cphi*complex(0,1)*lameta*sphi**3', order = {'QED':2}) GC_155 = Coupling(name = 'GC_155', value = '-4*cphi**4*complex(0,1)*lameta - 4*cphi**2*complex(0,1)*lam9*sphi**2 - 2*complex(0,1)*lam2*sphi**4', order = {'QED':2}) GC_156 = Coupling(name = 'GC_156', value = '-(cphi**4*complex(0,1)*lam9) - 2*cphi**2*complex(0,1)*lam2*sphi**2 + 2*cphi**2*complex(0,1)*lam9*sphi**2 - 4*cphi**2*complex(0,1)*lameta*sphi**2 - complex(0,1)*lam9*sphi**4', order = {'QED':2}) GC_157 = Coupling(name = 'GC_157', value = '-2*cphi**4*complex(0,1)*lam2 - 4*cphi**2*complex(0,1)*lam9*sphi**2 - 4*complex(0,1)*lameta*sphi**4', order = {'QED':2}) GC_158 = Coupling(name = 'GC_158', value = '(cab**2*ee**2*complex(0,1))/(2.*sw**2) + (ee**2*complex(0,1)*sab**2)/(2.*sw**2)', order = {'QED':2}) GC_159 = Coupling(name = 'GC_159', value = '(ee**2*complex(0,1))/(2.*sw**2)', order = {'QED':2}) GC_160 = Coupling(name = 'GC_160', value = '-((ee**2*complex(0,1))/sw**2)', order = {'QED':2}) GC_161 = Coupling(name = 'GC_161', value = '(cphi**2*ee**2*complex(0,1))/(2.*sw**2)', order = {'QED':2}) GC_162 = Coupling(name = 'GC_162', value = '(cw**2*ee**2*complex(0,1))/sw**2', order = {'QED':2}) GC_163 = Coupling(name = 'GC_163', value = '(cphi*ee**2*complex(0,1)*sphi)/(2.*sw**2)', order = {'QED':2}) GC_164 = Coupling(name = 'GC_164', value = '(ee**2*complex(0,1)*sphi**2)/(2.*sw**2)', order = {'QED':2}) GC_165 = Coupling(name = 'GC_165', value = 'ee/(2.*sw)', order = {'QED':1}) GC_166 = Coupling(name = 'GC_166', value = '(ee*complex(0,1))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_167 = Coupling(name = 'GC_167', value = '-(cab*ee*complex(0,1))/(2.*sw)', order = {'QED':1}) GC_168 = Coupling(name = 'GC_168', value = '(cab*ee*complex(0,1))/(2.*sw)', order = {'QED':1}) GC_169 = Coupling(name = 'GC_169', value = '(CKM1x1*ee*complex(0,1))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_170 = Coupling(name = 'GC_170', value = '(CKM1x2*ee*complex(0,1))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_171 = Coupling(name = 'GC_171', value = '(CKM1x3*ee*complex(0,1))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_172 = Coupling(name = 'GC_172', value = '(CKM2x1*ee*complex(0,1))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_173 = Coupling(name = 'GC_173', value = '(CKM2x2*ee*complex(0,1))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_174 = Coupling(name = 'GC_174', value = '(CKM2x3*ee*complex(0,1))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_175 = Coupling(name = 'GC_175', value = '(CKM3x1*ee*complex(0,1))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_176 = Coupling(name = 'GC_176', value = '(CKM3x2*ee*complex(0,1))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_177 = Coupling(name = 'GC_177', value = '(CKM3x3*ee*complex(0,1))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_178 = Coupling(name = 'GC_178', value = '(cphi*ee)/(2.*sw)', order = {'QED':1}) GC_179 = Coupling(name = 'GC_179', value = '-(cab*cphi*ee*complex(0,1))/(2.*sw)', order = {'QED':1}) GC_180 = Coupling(name = 'GC_180', value = '(cab*cphi*ee*complex(0,1))/(2.*sw)', order = {'QED':1}) GC_181 = Coupling(name = 'GC_181', value = '-((cw*ee*complex(0,1))/sw)', order = {'QED':1}) GC_182 = Coupling(name = 'GC_182', value = '(cw*ee*complex(0,1))/sw', order = {'QED':1}) GC_183 = Coupling(name = 'GC_183', value = '-ee**2/(2.*sw)', order = {'QED':2}) GC_184 = Coupling(name = 'GC_184', value = 'ee**2/(2.*sw)', order = {'QED':2}) GC_185 = Coupling(name = 'GC_185', value = '(cab*ee**2*complex(0,1))/(2.*sw)', order = {'QED':2}) GC_186 = Coupling(name = 'GC_186', value = '-(cphi*ee**2)/(2.*sw)', order = {'QED':2}) GC_187 = Coupling(name = 'GC_187', value = '(cphi*ee**2)/(2.*sw)', order = {'QED':2}) GC_188 = Coupling(name = 'GC_188', value = '(cab*cphi*ee**2*complex(0,1))/(2.*sw)', order = {'QED':2}) GC_189 = Coupling(name = 'GC_189', value = '(-2*cw*ee**2*complex(0,1))/sw', order = {'QED':2}) GC_190 = Coupling(name = 'GC_190', value = '-(ee*complex(0,1)*sab)/(2.*sw)', order = {'QED':1}) GC_191 = Coupling(name = 'GC_191', value = '(ee*complex(0,1)*sab)/(2.*sw)', order = {'QED':1}) GC_192 = Coupling(name = 'GC_192', value = '-(cphi*ee*complex(0,1)*sab)/(2.*sw)', order = {'QED':1}) GC_193 = Coupling(name = 'GC_193', value = '(cphi*ee*complex(0,1)*sab)/(2.*sw)', order = {'QED':1}) GC_194 = Coupling(name = 'GC_194', value = '-(ee**2*complex(0,1)*sab)/(2.*sw)', order = {'QED':2}) GC_195 = Coupling(name = 'GC_195', value = '(cphi*ee**2*complex(0,1)*sab)/(2.*sw)', order = {'QED':2}) GC_196 = Coupling(name = 'GC_196', value = '(ee*sphi)/(2.*sw)', order = {'QED':1}) GC_197 = Coupling(name = 'GC_197', value = '-(cab*ee*complex(0,1)*sphi)/(2.*sw)', order = {'QED':1}) GC_198 = Coupling(name = 'GC_198', value = '(cab*ee*complex(0,1)*sphi)/(2.*sw)', order = {'QED':1}) GC_199 = Coupling(name = 'GC_199', value = '-(ee**2*sphi)/(2.*sw)', order = {'QED':2}) GC_200 = Coupling(name = 'GC_200', value = '(ee**2*sphi)/(2.*sw)', order = {'QED':2}) GC_201 = Coupling(name = 'GC_201', value = '(cab*ee**2*complex(0,1)*sphi)/(2.*sw)', order = {'QED':2}) GC_202 = Coupling(name = 'GC_202', value = '-(ee*complex(0,1)*sab*sphi)/(2.*sw)', order = {'QED':1}) GC_203 = Coupling(name = 'GC_203', value = '(ee*complex(0,1)*sab*sphi)/(2.*sw)', order = {'QED':1}) GC_204 = Coupling(name = 'GC_204', value = '(ee**2*complex(0,1)*sab*sphi)/(2.*sw)', order = {'QED':2}) GC_205 = Coupling(name = 'GC_205', value = '(ee*complex(0,1)*sw)/(3.*cw)', order = {'QED':1}) GC_206 = Coupling(name = 'GC_206', value = '(-2*ee*complex(0,1)*sw)/(3.*cw)', order = {'QED':1}) GC_207 = Coupling(name = 'GC_207', value = '(ee*complex(0,1)*sw)/cw', order = {'QED':1}) GC_208 = Coupling(name = 'GC_208', value = '-(cw*ee*complex(0,1))/(2.*sw) - (ee*complex(0,1)*sw)/(6.*cw)', order = {'QED':1}) GC_209 = Coupling(name = 'GC_209', value = '(cw*ee*complex(0,1))/(2.*sw) - (ee*complex(0,1)*sw)/(6.*cw)', order = {'QED':1}) GC_210 = Coupling(name = 'GC_210', value = '-(cw*ee*complex(0,1))/(2.*sw) + (ee*complex(0,1)*sw)/(2.*cw)', order = {'QED':1}) GC_211 = Coupling(name = 'GC_211', value = '(cw*ee*complex(0,1))/(2.*sw) + (ee*complex(0,1)*sw)/(2.*cw)', order = {'QED':1}) GC_212 = Coupling(name = 'GC_212', value = '-(cab*cw*ee)/(2.*sw) - (cab*ee*sw)/(2.*cw)', order = {'QED':1}) GC_213 = Coupling(name = 'GC_213', value = '(cw*ee**2*complex(0,1))/sw - (ee**2*complex(0,1)*sw)/cw', order = {'QED':2}) GC_214 = Coupling(name = 'GC_214', value = '-(cw*ee*sab)/(2.*sw) - (ee*sab*sw)/(2.*cw)', order = {'QED':1}) GC_215 = Coupling(name = 'GC_215', value = '(cw*ee*sab)/(2.*sw) + (ee*sab*sw)/(2.*cw)', order = {'QED':1}) GC_216 = Coupling(name = 'GC_216', value = '-(cphi*cw*ee*complex(0,1)*sphi)/(2.*sw) - (cphi*ee*complex(0,1)*sphi*sw)/(2.*cw)', order = {'QED':1}) GC_217 = Coupling(name = 'GC_217', value = '(cphi*cw*ee*complex(0,1)*sphi)/(2.*sw) + (cphi*ee*complex(0,1)*sphi*sw)/(2.*cw)', order = {'QED':1}) GC_218 = Coupling(name = 'GC_218', value = '(cphi*cw*ee**2*complex(0,1)*sphi)/sw + (cphi*ee**2*complex(0,1)*sphi*sw)/cw', order = {'QED':2}) GC_219 = Coupling(name = 'GC_219', value = '-(cw*ee*complex(0,1)*sphi**2)/(2.*sw) + (cphi**2*ee*complex(0,1)*sw)/cw + (ee*complex(0,1)*sphi**2*sw)/(2.*cw)', order = {'QED':1}) GC_220 = Coupling(name = 'GC_220', value = '-(cphi**2*cw*ee*complex(0,1))/(2.*sw) + (cphi**2*ee*complex(0,1)*sw)/(2.*cw) + (ee*complex(0,1)*sphi**2*sw)/cw', order = {'QED':1}) GC_221 = Coupling(name = 'GC_221', value = '(cw*ee**2*complex(0,1)*sphi**2)/sw - (2*cphi**2*ee**2*complex(0,1)*sw)/cw - (ee**2*complex(0,1)*sphi**2*sw)/cw', order = {'QED':2}) GC_222 = Coupling(name = 'GC_222', value = '(cphi**2*cw*ee**2*complex(0,1))/sw - (cphi**2*ee**2*complex(0,1)*sw)/cw - (2*ee**2*complex(0,1)*sphi**2*sw)/cw', order = {'QED':2}) GC_223 = Coupling(name = 'GC_223', value = '-(ee**2*complex(0,1)) + (cw**2*ee**2*complex(0,1))/(2.*sw**2) + (ee**2*complex(0,1)*sw**2)/(2.*cw**2)', order = {'QED':2}) GC_224 = Coupling(name = 'GC_224', value = 'ee**2*complex(0,1) + (cw**2*ee**2*complex(0,1))/(2.*sw**2) + (ee**2*complex(0,1)*sw**2)/(2.*cw**2)', order = {'QED':2}) GC_225 = Coupling(name = 'GC_225', value = 'cab**2*ee**2*complex(0,1) + ee**2*complex(0,1)*sab**2 + (cab**2*cw**2*ee**2*complex(0,1))/(2.*sw**2) + (cw**2*ee**2*complex(0,1)*sab**2)/(2.*sw**2) + (cab**2*ee**2*complex(0,1)*sw**2)/(2.*cw**2) + (ee**2*complex(0,1)*sab**2*sw**2)/(2.*cw**2)', order = {'QED':2}) GC_226 = Coupling(name = 'GC_226', value = '-(cphi*ee**2*complex(0,1)*sphi) + (cphi*cw**2*ee**2*complex(0,1)*sphi)/(2.*sw**2) - (3*cphi*ee**2*complex(0,1)*sphi*sw**2)/(2.*cw**2)', order = {'QED':2}) GC_227 = Coupling(name = 'GC_227', value = '-(ee**2*complex(0,1)*sphi**2) + (cw**2*ee**2*complex(0,1)*sphi**2)/(2.*sw**2) + (2*cphi**2*ee**2*complex(0,1)*sw**2)/cw**2 + (ee**2*complex(0,1)*sphi**2*sw**2)/(2.*cw**2)', order = {'QED':2}) GC_228 = Coupling(name = 'GC_228', value = '-(cphi**2*ee**2*complex(0,1)) + (cphi**2*cw**2*ee**2*complex(0,1))/(2.*sw**2) + (cphi**2*ee**2*complex(0,1)*sw**2)/(2.*cw**2) + (2*ee**2*complex(0,1)*sphi**2*sw**2)/cw**2', order = {'QED':2}) GC_229 = Coupling(name = 'GC_229', value = '-(ee**2*complex(0,1)*vev)/(2.*cw)', order = {'QED':1}) GC_230 = Coupling(name = 'GC_230', value = '(cab*ee**2*complex(0,1)*vev)/(2.*sw**2)', order = {'QED':1}) GC_231 = Coupling(name = 'GC_231', value = '-(ee**2*complex(0,1)*sab*vev)/(2.*sw**2)', order = {'QED':1}) GC_232 = Coupling(name = 'GC_232', value = '-(ee**2*vev)/(2.*sw)', order = {'QED':1}) GC_233 = Coupling(name = 'GC_233', value = '(ee**2*complex(0,1)*vev)/(2.*sw)', order = {'QED':1}) GC_234 = Coupling(name = 'GC_234', value = '(ee**2*vev)/(2.*sw)', order = {'QED':1}) GC_235 = Coupling(name = 'GC_235', value = '(cphi*lam4*vev)/2. - (cphi*lam5*vev)/2. - (muzee*sphi)/cmath.sqrt(2)', order = {'QED':1}) GC_236 = Coupling(name = 'GC_236', value = '-(cphi*lam4*vev)/2. + (cphi*lam5*vev)/2. + (muzee*sphi)/cmath.sqrt(2)', order = {'QED':1}) GC_237 = Coupling(name = 'GC_237', value = '-(cab*complex(0,1)*lam6*vev) + complex(0,1)*lam1*sab*vev', order = {'QED':1}) GC_238 = Coupling(name = 'GC_238', value = '-(cab*complex(0,1)*lam6*vev) - complex(0,1)*lam5*sab*vev', order = {'QED':1}) GC_239 = Coupling(name = 'GC_239', value = '-(cab*complex(0,1)*lam7*vev) + complex(0,1)*lam3*sab*vev + complex(0,1)*lam4*sab*vev - complex(0,1)*lam5*sab*vev', order = {'QED':1}) GC_240 = Coupling(name = 'GC_240', value = '-(cab*cphi*complex(0,1)*lam6*vev) - (cphi*complex(0,1)*lam4*sab*vev)/2. - (cphi*complex(0,1)*lam5*sab*vev)/2. + (complex(0,1)*muzee*sab*sphi)/cmath.sqrt(2)', order = {'QED':1}) GC_241 = Coupling(name = 'GC_241', value = '-(cab*complex(0,1)*lam1*vev) - complex(0,1)*lam6*sab*vev', order = {'QED':1}) GC_242 = Coupling(name = 'GC_242', value = '-(cab*complex(0,1)*lam5*vev) + complex(0,1)*lam6*sab*vev', order = {'QED':1}) GC_243 = Coupling(name = 'GC_243', value = '-(cab*cphi*complex(0,1)*lam4*vev)/2. - (cab*cphi*complex(0,1)*lam5*vev)/2. + cphi*complex(0,1)*lam6*sab*vev + (cab*complex(0,1)*muzee*sphi)/cmath.sqrt(2)', order = {'QED':1}) GC_244 = Coupling(name = 'GC_244', value = '-(cab*complex(0,1)*lam3*vev) - cab*complex(0,1)*lam4*vev + cab*complex(0,1)*lam5*vev - complex(0,1)*lam7*sab*vev', order = {'QED':1}) GC_245 = Coupling(name = 'GC_245', value = '-3*cab**3*complex(0,1)*lam7*vev + 3*cab**2*complex(0,1)*lam3*sab*vev + 3*cab**2*complex(0,1)*lam4*sab*vev + 3*cab**2*complex(0,1)*lam5*sab*vev - 9*cab*complex(0,1)*lam6*sab**2*vev + 3*complex(0,1)*lam1*sab**3*vev', order = {'QED':1}) GC_246 = Coupling(name = 'GC_246', value = '-3*cab**3*complex(0,1)*lam6*vev + 3*cab**2*complex(0,1)*lam1*sab*vev - 2*cab**2*complex(0,1)*lam3*sab*vev - 2*cab**2*complex(0,1)*lam4*sab*vev - 2*cab**2*complex(0,1)*lam5*sab*vev + 6*cab*complex(0,1)*lam6*sab**2*vev - 3*cab*complex(0,1)*lam7*sab**2*vev + complex(0,1)*lam3*sab**3*vev + complex(0,1)*lam4*sab**3*vev + complex(0,1)*lam5*sab**3*vev', order = {'QED':1}) GC_247 = Coupling(name = 'GC_247', value = '-(cab**3*complex(0,1)*lam3*vev) - cab**3*complex(0,1)*lam4*vev - cab**3*complex(0,1)*lam5*vev + 6*cab**2*complex(0,1)*lam6*sab*vev - 3*cab**2*complex(0,1)*lam7*sab*vev - 3*cab*complex(0,1)*lam1*sab**2*vev + 2*cab*complex(0,1)*lam3*sab**2*vev + 2*cab*complex(0,1)*lam4*sab**2*vev + 2*cab*complex(0,1)*lam5*sab**2*vev - 3*complex(0,1)*lam6*sab**3*vev', order = {'QED':1}) GC_248 = Coupling(name = 'GC_248', value = '-3*cab**3*complex(0,1)*lam1*vev - 9*cab**2*complex(0,1)*lam6*sab*vev - 3*cab*complex(0,1)*lam3*sab**2*vev - 3*cab*complex(0,1)*lam4*sab**2*vev - 3*cab*complex(0,1)*lam5*sab**2*vev - 3*complex(0,1)*lam7*sab**3*vev', order = {'QED':1}) GC_249 = Coupling(name = 'GC_249', value = '(lam4*sphi*vev)/2. - (lam5*sphi*vev)/2. + (cphi*muzee)/cmath.sqrt(2)', order = {'QED':1}) GC_250 = Coupling(name = 'GC_250', value = '-(lam4*sphi*vev)/2. + (lam5*sphi*vev)/2. - (cphi*muzee)/cmath.sqrt(2)', order = {'QED':1}) GC_251 = Coupling(name = 'GC_251', value = '-(cab*complex(0,1)*lam6*sphi*vev) - (complex(0,1)*lam4*sab*sphi*vev)/2. - (complex(0,1)*lam5*sab*sphi*vev)/2. - (cphi*complex(0,1)*muzee*sab)/cmath.sqrt(2)', order = {'QED':1}) GC_252 = Coupling(name = 'GC_252', value = '-(cab*complex(0,1)*lam4*sphi*vev)/2. - (cab*complex(0,1)*lam5*sphi*vev)/2. + complex(0,1)*lam6*sab*sphi*vev - (cab*cphi*complex(0,1)*muzee)/cmath.sqrt(2)', order = {'QED':1}) GC_253 = Coupling(name = 'GC_253', value = '-(cab*cphi*complex(0,1)*lam3*sphi*vev) + cab*cphi*complex(0,1)*lam8*sphi*vev + cphi*complex(0,1)*lam10*sab*sphi*vev - cphi*complex(0,1)*lam7*sab*sphi*vev + (cab*cphi**2*complex(0,1)*muzee)/cmath.sqrt(2) - (cab*complex(0,1)*muzee*sphi**2)/cmath.sqrt(2)', order = {'QED':1}) GC_254 = Coupling(name = 'GC_254', value = 'cab*cphi*complex(0,1)*lam10*sphi*vev - cab*cphi*complex(0,1)*lam7*sphi*vev + cphi*complex(0,1)*lam3*sab*sphi*vev - cphi*complex(0,1)*lam8*sab*sphi*vev - (cphi**2*complex(0,1)*muzee*sab)/cmath.sqrt(2) + (complex(0,1)*muzee*sab*sphi**2)/cmath.sqrt(2)', order = {'QED':1}) GC_255 = Coupling(name = 'GC_255', value = '-(cab*cphi**2*complex(0,1)*lam3*vev) - cphi**2*complex(0,1)*lam7*sab*vev - cab*complex(0,1)*lam8*sphi**2*vev - complex(0,1)*lam10*sab*sphi**2*vev - cab*cphi*complex(0,1)*muzee*sphi*cmath.sqrt(2)', order = {'QED':1}) GC_256 = Coupling(name = 'GC_256', value = '-(cab*cphi**2*complex(0,1)*lam10*vev) + cphi**2*complex(0,1)*lam8*sab*vev - cab*complex(0,1)*lam7*sphi**2*vev + complex(0,1)*lam3*sab*sphi**2*vev - cphi*complex(0,1)*muzee*sab*sphi*cmath.sqrt(2)', order = {'QED':1}) GC_257 = Coupling(name = 'GC_257', value = '-(cab*cphi**2*complex(0,1)*lam8*vev) - cphi**2*complex(0,1)*lam10*sab*vev - cab*complex(0,1)*lam3*sphi**2*vev - complex(0,1)*lam7*sab*sphi**2*vev + cab*cphi*complex(0,1)*muzee*sphi*cmath.sqrt(2)', order = {'QED':1}) GC_258 = Coupling(name = 'GC_258', value = '-(cab*cphi**2*complex(0,1)*lam7*vev) + cphi**2*complex(0,1)*lam3*sab*vev - cab*complex(0,1)*lam10*sphi**2*vev + complex(0,1)*lam8*sab*sphi**2*vev + cphi*complex(0,1)*muzee*sab*sphi*cmath.sqrt(2)', order = {'QED':1}) GC_259 = Coupling(name = 'GC_259', value = '-(ee**2*vev)/(4.*cw) - (cw*ee**2*vev)/(4.*sw**2)', order = {'QED':1}) GC_260 = Coupling(name = 'GC_260', value = '(ee**2*vev)/(4.*cw) - (cw*ee**2*vev)/(4.*sw**2)', order = {'QED':1}) GC_261 = Coupling(name = 'GC_261', value = '-(ee**2*vev)/(4.*cw) + (cw*ee**2*vev)/(4.*sw**2)', order = {'QED':1}) GC_262 = Coupling(name = 'GC_262', value = '(ee**2*vev)/(4.*cw) + (cw*ee**2*vev)/(4.*sw**2)', order = {'QED':1}) GC_263 = Coupling(name = 'GC_263', value = 'cab*ee**2*complex(0,1)*vev + (cab*cw**2*ee**2*complex(0,1)*vev)/(2.*sw**2) + (cab*ee**2*complex(0,1)*sw**2*vev)/(2.*cw**2)', order = {'QED':1}) GC_264 = Coupling(name = 'GC_264', value = '-(ee**2*complex(0,1)*sab*vev) - (cw**2*ee**2*complex(0,1)*sab*vev)/(2.*sw**2) - (ee**2*complex(0,1)*sab*sw**2*vev)/(2.*cw**2)', order = {'QED':1}) GC_265 = Coupling(name = 'GC_265', value = '-(yb/cmath.sqrt(2))', order = {'QED':1}) GC_266 = Coupling(name = 'GC_266', value = '-((cab*complex(0,1)*yb)/cmath.sqrt(2))', order = {'QED':1}) GC_267 = Coupling(name = 'GC_267', value = '(complex(0,1)*sab*yb)/cmath.sqrt(2)', order = {'QED':1}) GC_268 = Coupling(name = 'GC_268', value = '-(yc/cmath.sqrt(2))', order = {'QED':1}) GC_269 = Coupling(name = 'GC_269', value = 'yc/cmath.sqrt(2)', order = {'QED':1}) GC_270 = Coupling(name = 'GC_270', value = '-((cab*complex(0,1)*yc)/cmath.sqrt(2))', order = {'QED':1}) GC_271 = Coupling(name = 'GC_271', value = '(complex(0,1)*sab*yc)/cmath.sqrt(2)', order = {'QED':1}) GC_272 = Coupling(name = 'GC_272', value = '-(ydo/cmath.sqrt(2))', order = {'QED':1}) GC_273 = Coupling(name = 'GC_273', value = '-((cab*complex(0,1)*ydo)/cmath.sqrt(2))', order = {'QED':1}) GC_274 = Coupling(name = 'GC_274', value = '(complex(0,1)*sab*ydo)/cmath.sqrt(2)', order = {'QED':1}) GC_275 = Coupling(name = 'GC_275', value = '-(complex(0,1)*ye)', order = {'QED':1}) GC_276 = Coupling(name = 'GC_276', value = '-(ye/cmath.sqrt(2))', order = {'QED':1}) GC_277 = Coupling(name = 'GC_277', value = 'ye/cmath.sqrt(2)', order = {'QED':1}) GC_278 = Coupling(name = 'GC_278', value = '-((cab*complex(0,1)*ye)/cmath.sqrt(2))', order = {'QED':1}) GC_279 = Coupling(name = 'GC_279', value = '(complex(0,1)*sab*ye)/cmath.sqrt(2)', order = {'QED':1}) GC_280 = Coupling(name = 'GC_280', value = '-(complex(0,1)*ym)', order = {'QED':1}) GC_281 = Coupling(name = 'GC_281', value = '-(ym/cmath.sqrt(2))', order = {'QED':1}) GC_282 = Coupling(name = 'GC_282', value = 'ym/cmath.sqrt(2)', order = {'QED':1}) GC_283 = Coupling(name = 'GC_283', value = '-((cab*complex(0,1)*ym)/cmath.sqrt(2))', order = {'QED':1}) GC_284 = Coupling(name = 'GC_284', value = '(complex(0,1)*sab*ym)/cmath.sqrt(2)', order = {'QED':1}) GC_285 = Coupling(name = 'GC_285', value = '-(ys/cmath.sqrt(2))', order = {'QED':1}) GC_286 = Coupling(name = 'GC_286', value = '-((cab*complex(0,1)*ys)/cmath.sqrt(2))', order = {'QED':1}) GC_287 = Coupling(name = 'GC_287', value = '(complex(0,1)*sab*ys)/cmath.sqrt(2)', order = {'QED':1}) GC_288 = Coupling(name = 'GC_288', value = '-(yt/cmath.sqrt(2))', order = {'QED':1}) GC_289 = Coupling(name = 'GC_289', value = 'yt/cmath.sqrt(2)', order = {'QED':1}) GC_290 = Coupling(name = 'GC_290', value = '-((cab*complex(0,1)*yt)/cmath.sqrt(2))', order = {'QED':1}) GC_291 = Coupling(name = 'GC_291', value = '(complex(0,1)*sab*yt)/cmath.sqrt(2)', order = {'QED':1}) GC_292 = Coupling(name = 'GC_292', value = '-(complex(0,1)*ytau)', order = {'QED':1}) GC_293 = Coupling(name = 'GC_293', value = '-(ytau/cmath.sqrt(2))', order = {'QED':1}) GC_294 = Coupling(name = 'GC_294', value = 'ytau/cmath.sqrt(2)', order = {'QED':1}) GC_295 = Coupling(name = 'GC_295', value = '-((cab*complex(0,1)*ytau)/cmath.sqrt(2))', order = {'QED':1}) GC_296 = Coupling(name = 'GC_296', value = '(complex(0,1)*sab*ytau)/cmath.sqrt(2)', order = {'QED':1}) GC_297 = Coupling(name = 'GC_297', value = '-(yup/cmath.sqrt(2))', order = {'QED':1}) GC_298 = Coupling(name = 'GC_298', value = 'yup/cmath.sqrt(2)', order = {'QED':1}) GC_299 = Coupling(name = 'GC_299', value = '-((cab*complex(0,1)*yup)/cmath.sqrt(2))', order = {'QED':1}) GC_300 = Coupling(name = 'GC_300', value = '(complex(0,1)*sab*yup)/cmath.sqrt(2)', order = {'QED':1}) GC_301 = Coupling(name = 'GC_301', value = '-(yzee/cmath.sqrt(2))', order = {'ZEE':1}) GC_302 = Coupling(name = 'GC_302', value = 'yzee/cmath.sqrt(2)', order = {'ZEE':1}) GC_303 = Coupling(name = 'GC_303', value = '-((cab*complex(0,1)*yzee)/cmath.sqrt(2))', order = {'ZEE':1}) GC_304 = Coupling(name = 'GC_304', value = '-(cphi*complex(0,1)*yzee)', order = {'ZEE':1}) GC_305 = Coupling(name = 'GC_305', value = '-((complex(0,1)*sab*yzee)/cmath.sqrt(2))', order = {'ZEE':1}) GC_306 = Coupling(name = 'GC_306', value = '-(complex(0,1)*sphi*yzee)', order = {'ZEE':1}) GC_307 = Coupling(name = 'GC_307', value = '-(yzem/cmath.sqrt(2))', order = {'ZEE':1}) GC_308 = Coupling(name = 'GC_308', value = 'yzem/cmath.sqrt(2)', order = {'ZEE':1}) GC_309 = Coupling(name = 'GC_309', value = '-((cab*complex(0,1)*yzem)/cmath.sqrt(2))', order = {'ZEE':1}) GC_310 = Coupling(name = 'GC_310', value = '-(cphi*complex(0,1)*yzem)', order = {'ZEE':1}) GC_311 = Coupling(name = 'GC_311', value = '-((complex(0,1)*sab*yzem)/cmath.sqrt(2))', order = {'ZEE':1}) GC_312 = Coupling(name = 'GC_312', value = '-(complex(0,1)*sphi*yzem)', order = {'ZEE':1}) GC_313 = Coupling(name = 'GC_313', value = '-(yzet/cmath.sqrt(2))', order = {'ZEE':1}) GC_314 = Coupling(name = 'GC_314', value = 'yzet/cmath.sqrt(2)', order = {'ZEE':1}) GC_315 = Coupling(name = 'GC_315', value = '-((cab*complex(0,1)*yzet)/cmath.sqrt(2))', order = {'ZEE':1}) GC_316 = Coupling(name = 'GC_316', value = '-(cphi*complex(0,1)*yzet)', order = {'ZEE':1}) GC_317 = Coupling(name = 'GC_317', value = '-((complex(0,1)*sab*yzet)/cmath.sqrt(2))', order = {'ZEE':1}) GC_318 = Coupling(name = 'GC_318', value = '-(complex(0,1)*sphi*yzet)', order = {'ZEE':1}) GC_319 = Coupling(name = 'GC_319', value = '-(yzme/cmath.sqrt(2))', order = {'ZEE':1}) GC_320 = Coupling(name = 'GC_320', value = 'yzme/cmath.sqrt(2)', order = {'ZEE':1}) GC_321 = Coupling(name = 'GC_321', value = '-((cab*complex(0,1)*yzme)/cmath.sqrt(2))', order = {'ZEE':1}) GC_322 = Coupling(name = 'GC_322', value = '-(cphi*complex(0,1)*yzme)', order = {'ZEE':1}) GC_323 = Coupling(name = 'GC_323', value = '-((complex(0,1)*sab*yzme)/cmath.sqrt(2))', order = {'ZEE':1}) GC_324 = Coupling(name = 'GC_324', value = '-(complex(0,1)*sphi*yzme)', order = {'ZEE':1}) GC_325 = Coupling(name = 'GC_325', value = '-(yzmm/cmath.sqrt(2))', order = {'ZEE':1}) GC_326 = Coupling(name = 'GC_326', value = 'yzmm/cmath.sqrt(2)', order = {'ZEE':1}) GC_327 = Coupling(name = 'GC_327', value = '-((cab*complex(0,1)*yzmm)/cmath.sqrt(2))', order = {'ZEE':1}) GC_328 = Coupling(name = 'GC_328', value = '-(cphi*complex(0,1)*yzmm)', order = {'ZEE':1}) GC_329 = Coupling(name = 'GC_329', value = '-((complex(0,1)*sab*yzmm)/cmath.sqrt(2))', order = {'ZEE':1}) GC_330 = Coupling(name = 'GC_330', value = '-(complex(0,1)*sphi*yzmm)', order = {'ZEE':1}) GC_331 = Coupling(name = 'GC_331', value = '-(yzmt/cmath.sqrt(2))', order = {'ZEE':1}) GC_332 = Coupling(name = 'GC_332', value = 'yzmt/cmath.sqrt(2)', order = {'ZEE':1}) GC_333 = Coupling(name = 'GC_333', value = '-((cab*complex(0,1)*yzmt)/cmath.sqrt(2))', order = {'ZEE':1}) GC_334 = Coupling(name = 'GC_334', value = '-(cphi*complex(0,1)*yzmt)', order = {'ZEE':1}) GC_335 = Coupling(name = 'GC_335', value = '-((complex(0,1)*sab*yzmt)/cmath.sqrt(2))', order = {'ZEE':1}) GC_336 = Coupling(name = 'GC_336', value = '-(complex(0,1)*sphi*yzmt)', order = {'ZEE':1}) GC_337 = Coupling(name = 'GC_337', value = '-(yzte/cmath.sqrt(2))', order = {'ZEE':1}) GC_338 = Coupling(name = 'GC_338', value = 'yzte/cmath.sqrt(2)', order = {'ZEE':1}) GC_339 = Coupling(name = 'GC_339', value = '-((cab*complex(0,1)*yzte)/cmath.sqrt(2))', order = {'ZEE':1}) GC_340 = Coupling(name = 'GC_340', value = '-(cphi*complex(0,1)*yzte)', order = {'ZEE':1}) GC_341 = Coupling(name = 'GC_341', value = '-((complex(0,1)*sab*yzte)/cmath.sqrt(2))', order = {'ZEE':1}) GC_342 = Coupling(name = 'GC_342', value = '-(complex(0,1)*sphi*yzte)', order = {'ZEE':1}) GC_343 = Coupling(name = 'GC_343', value = '-(yztm/cmath.sqrt(2))', order = {'ZEE':1}) GC_344 = Coupling(name = 'GC_344', value = 'yztm/cmath.sqrt(2)', order = {'ZEE':1}) GC_345 = Coupling(name = 'GC_345', value = '-((cab*complex(0,1)*yztm)/cmath.sqrt(2))', order = {'ZEE':1}) GC_346 = Coupling(name = 'GC_346', value = '-(cphi*complex(0,1)*yztm)', order = {'ZEE':1}) GC_347 = Coupling(name = 'GC_347', value = '-((complex(0,1)*sab*yztm)/cmath.sqrt(2))', order = {'ZEE':1}) GC_348 = Coupling(name = 'GC_348', value = '-(complex(0,1)*sphi*yztm)', order = {'ZEE':1}) GC_349 = Coupling(name = 'GC_349', value = '-(yztt/cmath.sqrt(2))', order = {'ZEE':1}) GC_350 = Coupling(name = 'GC_350', value = 'yztt/cmath.sqrt(2)', order = {'ZEE':1}) GC_351 = Coupling(name = 'GC_351', value = '-((cab*complex(0,1)*yztt)/cmath.sqrt(2))', order = {'ZEE':1}) GC_352 = Coupling(name = 'GC_352', value = '-(cphi*complex(0,1)*yztt)', order = {'ZEE':1}) GC_353 = Coupling(name = 'GC_353', value = '-((complex(0,1)*sab*yztt)/cmath.sqrt(2))', order = {'ZEE':1}) GC_354 = Coupling(name = 'GC_354', value = '-(complex(0,1)*sphi*yztt)', order = {'ZEE':1}) GC_355 = Coupling(name = 'GC_355', value = '(ee*complex(0,1)*complexconjugate(CKM1x1))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_356 = Coupling(name = 'GC_356', value = '(ee*complex(0,1)*complexconjugate(CKM1x2))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_357 = Coupling(name = 'GC_357', value = '(ee*complex(0,1)*complexconjugate(CKM1x3))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_358 = Coupling(name = 'GC_358', value = '(ee*complex(0,1)*complexconjugate(CKM2x1))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_359 = Coupling(name = 'GC_359', value = '(ee*complex(0,1)*complexconjugate(CKM2x2))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_360 = Coupling(name = 'GC_360', value = '(ee*complex(0,1)*complexconjugate(CKM2x3))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_361 = Coupling(name = 'GC_361', value = '(ee*complex(0,1)*complexconjugate(CKM3x1))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_362 = Coupling(name = 'GC_362', value = '(ee*complex(0,1)*complexconjugate(CKM3x2))/(sw*cmath.sqrt(2))', order = {'QED':1}) GC_363 = Coupling(name = 'GC_363', value = '(ee*complex(0,1)*complexconjugate(CKM3x3))/(sw*cmath.sqrt(2))', order = {'QED':1})

nilq/baby-python

python

import AnimatedProp from direct.actor import Actor from direct.interval.IntervalGlobal import * class HQPeriscopeAnimatedProp(AnimatedProp.AnimatedProp): def __init__(self, node): AnimatedProp.AnimatedProp.__init__(self, node) parent = node.getParent() self.periscope = Actor.Actor(node, copy=0) self.periscope.reparentTo(parent) self.periscope.loadAnims({'anim': 'phase_3.5/models/props/HQ_periscope-chan'}) self.periscope.pose('anim', 0) self.node = self.periscope self.track = Sequence(Wait(2.0), self.periscope.actorInterval('anim', startFrame=0, endFrame=40), Wait(0.7), self.periscope.actorInterval('anim', startFrame=40, endFrame=90), Wait(0.7), self.periscope.actorInterval('anim', startFrame=91, endFrame=121), Wait(0.7), self.periscope.actorInterval('anim', startFrame=121, endFrame=91), Wait(0.7), self.periscope.actorInterval('anim', startFrame=90, endFrame=40), Wait(0.7), self.periscope.actorInterval('anim', startFrame=40, endFrame=90), Wait(0.7), self.periscope.actorInterval('anim', startFrame=91, endFrame=121), Wait(0.5), self.periscope.actorInterval('anim', startFrame=121, endFrame=148), Wait(3.0), name=self.uniqueName('HQPeriscope')) def delete(self): AnimatedProp.AnimatedProp.delete(self) self.node.cleanup() del self.node del self.periscope del self.track def enter(self): AnimatedProp.AnimatedProp.enter(self) self.track.loop() def exit(self): AnimatedProp.AnimatedProp.exit(self) self.track.finish()

nilq/baby-python

python

from threading import Lock, Thread from time import sleep class Ingresso: def __init__(self, estoque): self.estoque = estoque self.lock = Lock() def comprar(self, quantidade): self.lock.acquire() if self.estoque < quantidade: print("-Não temos ingresso suficientes.") self.lock.release() return sleep(1) self.estoque -= quantidade print( f"-Você comprou {quantidade} de ingresso(s), restando {self.estoque} no estoque." ) self.lock.release() if __name__ == "__main__": ingresso = Ingresso(10) for i in range(1, 20): t = Thread(target=ingresso.comprar, args=(i,)) t.start()

nilq/baby-python

python

from functools import partial, wraps from slm_lab import ROOT_DIR from slm_lab.lib import logger, util import os import pydash as ps import torch import torch.nn as nn logger = logger.get_logger(__name__) class NoOpLRScheduler: '''Symbolic LRScheduler class for API consistency''' def __init__(self, optim): self.optim = optim def step(self, epoch=None): pass def get_lr(self): return self.optim.defaults['lr'] def build_fc_model(dims, activation=None): '''Build a full-connected model by interleaving nn.Linear and activation_fn''' assert len(dims) >= 2, 'dims need to at least contain input, output' # shift dims and make pairs of (in, out) dims per layer dim_pairs = list(zip(dims[:-1], dims[1:])) layers = [] for in_d, out_d in dim_pairs: layers.append(nn.Linear(in_d, out_d)) if activation is not None: layers.append(get_activation_fn(activation)) model = nn.Sequential(*layers) return model def get_nn_name(uncased_name): '''Helper to get the proper name in PyTorch nn given a case-insensitive name''' for nn_name in nn.__dict__: if uncased_name.lower() == nn_name.lower(): return nn_name raise ValueError(f'Name {uncased_name} not found in {nn.__dict__}') def get_activation_fn(activation): '''Helper to generate activation function layers for net''' activation = activation or 'relu' ActivationClass = getattr(nn, get_nn_name(activation)) return ActivationClass() def get_loss_fn(cls, loss_spec): '''Helper to parse loss param and construct loss_fn for net''' LossClass = getattr(nn, get_nn_name(loss_spec['name'])) loss_spec = ps.omit(loss_spec, 'name') loss_fn = LossClass(**loss_spec) return loss_fn def get_lr_scheduler(cls, lr_scheduler_spec): '''Helper to parse lr_scheduler param and construct Pytorch optim.lr_scheduler''' if ps.is_empty(lr_scheduler_spec): lr_scheduler = NoOpLRScheduler(cls.optim) elif lr_scheduler_spec['name'] == 'LinearToZero': LRSchedulerClass = getattr(torch.optim.lr_scheduler, 'LambdaLR') total_t = float(lr_scheduler_spec['total_t']) lr_scheduler = LRSchedulerClass(cls.optim, lr_lambda=lambda x: 1 - x / total_t) else: LRSchedulerClass = getattr(torch.optim.lr_scheduler, lr_scheduler_spec['name']) lr_scheduler_spec = ps.omit(lr_scheduler_spec, 'name') lr_scheduler = LRSchedulerClass(cls.optim, **lr_scheduler_spec) return lr_scheduler def get_optim(cls, optim_spec): '''Helper to parse optim param and construct optim for net''' OptimClass = getattr(torch.optim, optim_spec['name']) optim_spec = ps.omit(optim_spec, 'name') optim = OptimClass(cls.parameters(), **optim_spec) return optim def get_policy_out_dim(body): '''Helper method to construct the policy network out_dim for a body according to is_discrete, action_type''' action_dim = body.action_dim if body.is_discrete: if body.action_type == 'multi_discrete': assert ps.is_list(action_dim), action_dim policy_out_dim = action_dim else: assert ps.is_integer(action_dim), action_dim policy_out_dim = action_dim else: if body.action_type == 'multi_continuous': assert ps.is_list(action_dim), action_dim raise NotImplementedError('multi_continuous not supported yet') else: assert ps.is_integer(action_dim), action_dim if action_dim == 1: policy_out_dim = 2 # singleton stay as int else: # TODO change this to one slicable layer for efficiency policy_out_dim = action_dim * [2] return policy_out_dim def get_out_dim(body, add_critic=False): '''Construct the NetClass out_dim for a body according to is_discrete, action_type, and whether to add a critic unit''' policy_out_dim = get_policy_out_dim(body) if add_critic: if ps.is_list(policy_out_dim): out_dim = policy_out_dim + [1] else: out_dim = [policy_out_dim, 1] else: out_dim = policy_out_dim return out_dim def init_layers(net, init_fn): if init_fn is None: return nonlinearity = get_nn_name(net.hid_layers_activation).lower() if nonlinearity == 'leakyrelu': nonlinearity = 'leaky_relu' if init_fn == 'xavier_uniform_': try: gain = nn.init.calculate_gain(nonlinearity) except ValueError: gain = 1 init_fn = partial(nn.init.xavier_uniform_, gain=gain) elif 'kaiming' in init_fn: assert nonlinearity in ['relu', 'leaky_relu'], f'Kaiming initialization not supported for {nonlinearity}' init_fn = nn.init.__dict__[init_fn] init_fn = partial(init_fn, nonlinearity=nonlinearity) else: init_fn = nn.init.__dict__[init_fn] net.apply(partial(init_parameters, init_fn=init_fn)) def init_parameters(module, init_fn): ''' Initializes module's weights using init_fn, which is the name of function from from nn.init Initializes module's biases to either 0.01 or 0.0, depending on module The only exception is BatchNorm layers, for which we use uniform initialization ''' bias_init = 0.0 classname = util.get_class_name(module) if 'BatchNorm' in classname: init_fn(module.weight) nn.init.constant_(module.bias, bias_init) elif 'GRU' in classname: for name, param in module.named_parameters(): if 'weight' in name: init_fn(param) elif 'bias' in name: nn.init.constant_(param, 0.0) elif 'Linear' in classname or ('Conv' in classname and 'Net' not in classname): init_fn(module.weight) nn.init.constant_(module.bias, bias_init) # params methods def save(net, model_path): '''Save model weights to path''' torch.save(net.state_dict(), util.smart_path(model_path)) logger.info(f'Saved model to {model_path}') def save_algorithm(algorithm, ckpt=None): '''Save all the nets for an algorithm''' agent = algorithm.agent net_names = algorithm.net_names prepath = util.get_prepath(agent.spec, agent.info_space, unit='session') if ckpt is not None: prepath = f'{prepath}_ckpt-{ckpt}' logger.info(f'Saving algorithm {util.get_class_name(algorithm)} nets {net_names}') for net_name in net_names: net = getattr(algorithm, net_name) model_path = f'{prepath}_{net_name}_model.pth' save(net, model_path) optim_path = f'{prepath}_{net_name}_optim.pth' save(net.optim, optim_path) def load(net, model_path): '''Save model weights from a path into a net module''' device = None if torch.cuda.is_available() else 'cpu' net.load_state_dict(torch.load(util.smart_path(model_path), map_location=device)) logger.info(f'Loaded model from {model_path}') def load_algorithm(algorithm): '''Save all the nets for an algorithm''' agent = algorithm.agent net_names = algorithm.net_names if util.in_eval_lab_modes(): # load specific model in eval mode prepath = agent.info_space.eval_model_prepath else: prepath = util.get_prepath(agent.spec, agent.info_space, unit='session') logger.info(f'Loading algorithm {util.get_class_name(algorithm)} nets {net_names}') for net_name in net_names: net = getattr(algorithm, net_name) model_path = f'{prepath}_{net_name}_model.pth' load(net, model_path) optim_path = f'{prepath}_{net_name}_optim.pth' load(net.optim, optim_path) def copy(src_net, tar_net): '''Copy model weights from src to target''' tar_net.load_state_dict(src_net.state_dict()) def polyak_update(src_net, tar_net, old_ratio=0.5): ''' Polyak weight update to update a target tar_net, retain old weights by its ratio, i.e. target <- old_ratio * source + (1 - old_ratio) * target ''' for src_param, tar_param in zip(src_net.parameters(), tar_net.parameters()): tar_param.data.copy_(old_ratio * src_param.data + (1.0 - old_ratio) * tar_param.data) def to_check_training_step(): '''Condition for running assert_trained''' return os.environ.get('PY_ENV') == 'test' or util.get_lab_mode() == 'dev' def dev_check_training_step(fn): ''' Decorator to check if net.training_step actually updates the network weights properly Triggers only if to_check_training_step is True (dev/test mode) @example @net_util.dev_check_training_step def training_step(self, ...): ... ''' @wraps(fn) def check_fn(*args, **kwargs): if not to_check_training_step(): return fn(*args, **kwargs) net = args[0] # first arg self # get pre-update parameters to compare pre_params = [param.clone() for param in net.parameters()] # run training_step, get loss loss = fn(*args, **kwargs) # get post-update parameters to compare post_params = [param.clone() for param in net.parameters()] if loss == 0.0: # if loss is 0, there should be no updates # TODO if without momentum, parameters should not change too for p_name, param in net.named_parameters(): assert param.grad.norm() == 0 else: # check parameter updates try: assert not all(torch.equal(w1, w2) for w1, w2 in zip(pre_params, post_params)), f'Model parameter is not updated in training_step(), check if your tensor is detached from graph. Loss: {loss:g}' logger.info(f'Model parameter is updated in training_step(). Loss: {loss: g}') except Exception as e: logger.error(e) if os.environ.get('PY_ENV') == 'test': # raise error if in unit test raise(e) # check grad norms min_norm, max_norm = 0.0, 1e5 for p_name, param in net.named_parameters(): try: grad_norm = param.grad.norm() assert min_norm < grad_norm < max_norm, f'Gradient norm for {p_name} is {grad_norm:g}, fails the extreme value check {min_norm} < grad_norm < {max_norm}. Loss: {loss:g}. Check your network and loss computation.' logger.info(f'Gradient norm for {p_name} is {grad_norm:g}; passes value check.') except Exception as e: logger.warn(e) logger.debug('Passed network parameter update check.') # store grad norms for debugging net.store_grad_norms() return loss return check_fn def get_grad_norms(algorithm): '''Gather all the net's grad norms of an algorithm for debugging''' grad_norms = [] for net_name in algorithm.net_names: net = getattr(algorithm, net_name) if net.grad_norms is not None: grad_norms.extend(net.grad_norms) return grad_norms

nilq/baby-python

python

""" Minimize the Himmelblau function. http://en.wikipedia.org/wiki/Himmelblau%27s_function """ import numpy import minhelper def himmelblau(X): """ This R^2 -> R^1 function should be compatible with algopy. http://en.wikipedia.org/wiki/Himmelblau%27s_function This function has four local minima where the value of the function is 0. """ x = X[0] y = X[1] a = x*x + y - 11 b = x + y*y - 7 return a*a + b*b def main(): target = [3, 2] easy_init = [3.1, 2.1] hard_init = [-0.27, -0.9] minhelper.show_minimization_results( himmelblau, target, easy_init, hard_init) if __name__ == '__main__': main()

nilq/baby-python

python

import os import tempfile from unittest import TestCase from pubmed_bpe_tokeniser import PubmedBPETokenisor class TestPubmedBPETokenisor(TestCase): def test_train(self): # Arrange data_file = os.path.join(os.path.dirname(__file__), "data", "sample_pubmed.json") sut = PubmedBPETokenisor(vocab_size=300) tempdir = tempfile.mkdtemp() output_file_json = os.path.join(tempdir, "vocab.json") # Act sut.train([data_file], output_file_json) # Assert self.assertTrue(os.path.getsize(output_file_json) > 100, "Expected the vocab file size {} to be greater than 100")

nilq/baby-python

python

# # -*- coding: utf-8 -*- # # Copyright (c) 2020 Intel Corporation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # import os from common.base_model_init import BaseModelInitializer, set_env_var class ModelInitializer(BaseModelInitializer): # SSD-MobileNet BFloat16 inference model initialization args = None custom_args = [] def __init__(self, args, custom_args=[], platform_util=None): super(ModelInitializer, self).__init__(args, custom_args, platform_util) # Set the num_inter_threads and num_intra_threads # if user did not provide then default value based on platform will be set self.set_num_inter_intra_threads(self.args.num_inter_threads, self.args.num_intra_threads) # Set KMP env vars, if they haven't already been set config_file_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "config.json") self.set_kmp_vars(config_file_path) benchmark_script = os.path.join(self.args.intelai_models, self.args.mode, "infer_detections.py") self.command_prefix = self.get_command_prefix(self.args.socket_id) \ + "{} {}".format(self.python_exe, benchmark_script) set_env_var("OMP_NUM_THREADS", self.args.num_intra_threads) self.command_prefix += " -g {0}".format(self.args.input_graph) self.command_prefix += " -i 1000" self.command_prefix += " -w 200" self.command_prefix += " -a {0}".format(self.args.num_intra_threads) self.command_prefix += " -e {0}".format(self.args.num_inter_threads) self.command_prefix += " -p {0}".format(self.args.precision) if self.args.data_location: self.command_prefix += " -d {0}".format(self.args.data_location) if self.args.accuracy_only: self.command_prefix += " -r" assert self.args.data_location, "accuracy must provide the data." else: # Did not support multi-batch accuracy check. self.command_prefix += " -b {0}".format(self.args.batch_size) def run(self): # Run script from the tensorflow models research directory self.run_command(self.command_prefix)

nilq/baby-python

python

import os from ament_index_python.packages import get_package_share_directory from launch import LaunchDescription from launch.actions import IncludeLaunchDescription from launch.launch_description_sources import PythonLaunchDescriptionSource def generate_launch_description(): # # launch 海龟节点<正常版> # turtlesim_world_1 = IncludeLaunchDescription( # PythonLaunchDescriptionSource([os.path.join( # get_package_share_directory('launch_tutorial'), 'launch'), # '/turtlesim_world_1.launch.py']) # ) # # launch 海龟节点 <YAML> # turtlesim_world_2 = IncludeLaunchDescription( # PythonLaunchDescriptionSource([os.path.join( # get_package_share_directory('launch_tutorial'), 'launch'), # '/turtlesim_world_2.launch.py']) # ) # launch 海龟节点 <YAML> 使用wildcards 通配符 /**: turtlesim_world_3 = IncludeLaunchDescription( PythonLaunchDescriptionSource([os.path.join( get_package_share_directory('launch_tutorial'), 'launch'), '/turtlesim_world_3.launch.py']) ) # broadcaster_listener_nodes = IncludeLaunchDescription( # PythonLaunchDescriptionSource([os.path.join( # get_package_share_directory('launch_tutorial'), 'launch'), # '/broadcaster_listener.launch.py']), # launch_arguments={'target_frame': 'carrot1'}.items(), # ) # mimic_node = IncludeLaunchDescription( # PythonLaunchDescriptionSource([os.path.join( # get_package_share_directory('launch_tutorial'), 'launch'), # '/mimic.launch.py']) # ) # fixed_frame_node = IncludeLaunchDescription( # PythonLaunchDescriptionSource([os.path.join( # get_package_share_directory('launch_tutorial'), 'launch'), # '/fixed_broadcaster.launch.py']) # ) # rviz_node = IncludeLaunchDescription( # PythonLaunchDescriptionSource([os.path.join( # get_package_share_directory('launch_tutorial'), 'launch'), # '/turtlesim_rviz.launch.py']) # ) return LaunchDescription([ # turtlesim_world_1, # turtlesim_world_2, turtlesim_world_3, # broadcaster_listener_nodes, # mimic_node, # fixed_frame_node, # rviz_node ])

nilq/baby-python

python

from setuptools import setup,find_packages import lixtools setup( name='lixtools', description="""software tools for data collection/processing at LiX""", version=lixtools.__version__, author='Lin Yang', author_email='lyang@bnl.gov', license="BSD-3-Clause", url="https://github.com/NSLS-II-LIX/lixtools", packages=find_packages(), package_data={'': ['plate_label_template.html', 'template_report.ipynb']}, include_package_data=True, install_requires=['py4xs', 'numpy', 'pandas', 'python-barcode', 'matplotlib', 'pillow', 'openpyxl>=3', 'qrcode'], python_requires='>=3.6', classifiers=[ "Intended Audience :: Science/Research", "Topic :: Scientific/Engineering :: Information Analysis", "Development Status :: 3 - Alpha", "Programming Language :: Python :: 3.6", ], keywords='x-ray scattering', )

nilq/baby-python

python

''' * 'show system status' ''' import re from genie.metaparser import MetaParser from genie.metaparser.util.schemaengine import Optional # =========================================== # Schema for 'show system status' # =========================================== class ShowSystemStatusSchema(MetaParser): """ Schema for "show system status" """ schema = { 'boot_loader_version': str, 'build': str, 'chassis_serial_number': str, 'commit_pending': str, 'configuration_template': str, Optional('engineering_signed'): bool, Optional('controller_compatibility'): str, Optional('cpu_allocation'): { Optional('total'): int, Optional('control'): int, Optional('data'): int }, 'cpu_reported_reboot': str, 'cpu_states': { 'idle': float, 'system': float, 'user': float }, 'current_time': str, 'disk_usage': { 'avail_mega': int, 'filesystem': str, 'mounted_on': str, 'size_mega': int, 'use_pc': int, 'used_mega': int }, Optional('vmanage_storage_usage'): { Optional('filesystem'): str, Optional('size_mega'): int, Optional('used_mega'): int, Optional('avail_mega'): int, Optional('use_pc'): int, Optional('mounted_on'): str }, 'last_reboot': str, Optional('load_average'): { Optional('minute_1'): float, Optional('minute_15'): float, Optional('minute_5'): float }, 'memory_usage': { 'buffers_kilo': int, 'cache_kilo': int, 'free_kilo': int, 'total_kilo': int, 'used_kilo': int }, Optional('hypervisor_type'):str, Optional('cloud_hosted_instance'):str, 'model_name': str, 'personality': str, 'processes': int, 'services': str, 'system_fips_state': str, 'system_logging_disk': str, 'system_logging_host': str, 'system_state': str, 'system_uptime': str, Optional('device_role'): str, Optional('testbed_mode'): str, 'version': str, 'vmanaged': str } # =========================================== # Parser for 'show system status' # =========================================== class ShowSystemStatus(ShowSystemStatusSchema): """ Parser for "show system status" """ cli_command = "show system status" def cli(self, output=None): if output is None: output = self.device.execute(self.cli_command) parsed_dict = {} # System logging to host is disabled # System logging to disk is enabled p1 = re.compile(r'^System\s+logging\s+to\s+(?P<type>\w+)\s+is\s+(?P<value>enabled|disabled)$') # CPU allocation: 4 total, 1 control, 3 data # CPU allocation: 16 total p2 = re.compile(r'^CPU\s+allocation:\s+(?P<total>\d+)\s+total(,\s+(?P<control>\d+)\s+control)?(,\s+(?P<data>\d+)\s+data)?$') # CPU states: 1.25% user, 5.26% system, 93.48% idle p3 = re.compile(r'^CPU\s+states:\s+(?P<user>[\d\.]+)\%\s+user,\s+(?P<system>[\d\.]+)\%\s+system,\s+(?P<idle>[\d\.]+)\%\s+idle$') # Load average: 1 minute: 3.20, 5 minutes: 3.13, 15 minutes: 3.10 p4 = re.compile(r'^Load\s+average:\s+1\s+minute:\s+(?P<minute_1>[\d\.]+),\s+5\s+minutes:\s+(?P<minute_5>[\d\.]+),\s+15\s+minutes:\s+(?P<minute_15>[\d\.]+)$') # Engineering Signed True p5 = re.compile(r'^Engineering +Signed +(?P<value>True|False)$') # Memory usage: 1907024K total, 1462908K used, 444116K free p6 = re.compile(r'^Memory\s+usage:\s+(?P<total_kilo>\d+)K\s+total,\s+(?P<used_kilo>\d+)K\s+used,\s+(?P<free_kilo>\d+)K\s+free$') # 0K buffers, 0K cache p7 = re.compile(r'^(?P<buffers_kilo>\d+)K\s+buffers,\s+(?P<cache_kilo>\d+)K\s+cache$') # Disk usage: Filesystem Size Used Avail Use % Mounted on # vManage storage usage: Filesystem Size Used Avail Use% Mounted on p8 = re.compile(r'^(?P<usage_dict>.+usage):\s+Filesystem\s+Size\s+Used\s+Avail\s+Use\s*%\s+Mounted\s+on$') # /dev/root 7615M 447M 6741M 6% / # /dev/disk/by-label/fs-bootflash 11039M 1240M 9219M 12% /bootflash # /dev/bootflash1 28748M 2031M 25257M 7% /bootflash p9 = re.compile(r'^(?P<filesystem>.+\S)\s+(?P<size_mega>\d+)M\s+(?P<used_mega>\d+)M\s+(?P<avail_mega>\d+)M\s+(?P<use_pc>\d+)\%\s+(?P<mounted_on>.+)$') # Controller Compatibility: 20.3 # Version: 99.99.999-4567 # Build: 4567 # System state: GREEN. All daemons up # System FIPS state: Enabled # Testbed mode: Enabled # Hypervisor Type: None # Cloud Hosted Instance: false # Last reboot: Initiated by user - activate 99.99.999-4567. # CPU-reported reboot: Not Applicable # Boot loader version: Not applicable # System uptime: 0 days 21 hrs 35 min 28 sec # Current time: Thu Aug 06 02:49:25 PDT 2020 # Processes: 250 total # Personality: vedge # Model name: vedge-cloud # Services: None # vManaged: true # Commit pending: false # Configuration template: CLItemplate_srp_vedge # Chassis serial number: None p10 = re.compile(r'^(?P<key>.*):\s+(?P<value>.*)$') for line in output.splitlines(): line = line.strip() # System logging to host is disabled # System logging to disk is enabled m1 = p1.match(line) if m1: group = m1.groupdict() parsed_dict['system_logging_' + group['type']] = group['value'] continue # CPU allocation: 4 total, 1 control, 3 data # CPU allocation: 16 total m2 = p2.match(line) if m2: group = m2.groupdict() group = {key: int(group[key]) for key in group if group[key]} parsed_dict.update({'cpu_allocation': group}) continue # CPU states: 1.25% user, 5.26% system, 93.48% idle m3 = p3.match(line) if m3: group = m3.groupdict() for keys in group: group[keys] = float(group[keys]) parsed_dict.update({'cpu_states': group}) continue # Load average: 1 minute: 3.20, 5 minutes: 3.13, 15 minutes: 3.10 m4 = p4.match(line) if m4: group = m4.groupdict() for keys in group: group[keys] = float(group[keys]) parsed_dict.update({'load_average': group}) continue # Engineering Signed True m5 = p5.match(line) if m5: group = m5.groupdict() group = bool(group['value']) parsed_dict.update({'engineering_signed': group}) continue # Memory usage: 1907024K total, 1462908K used, 444116K free m6 = p6.match(line) if m6: group = m6.groupdict() parsed_dict.update({'memory_usage': { key:int(group[key]) for key in group }}) continue # 0K buffers, 0K cache m7 = p7.match(line) if m7: group = m7.groupdict() parsed_dict['memory_usage'].update({ key:int(group[key]) for key in group }) continue # Disk usage: Filesystem Size Used Avail Use % Mounted on # vManage storage usage: Filesystem Size Used Avail Use% Mounted on m8 = p8.match(line) if m8: group = m8.groupdict() usage_dict_name = group['usage_dict'].replace(' ', '_').lower() usage_dict = parsed_dict.setdefault(usage_dict_name, {}) continue # /dev/sda 503966M 6162M 472203M 1% /opt/data # /dev/bootflash1 28748M 2031M 25257M 7% /bootflash m9 = p9.match(line) if m9: group = m9.groupdict() usage_dict.update({'filesystem': group.pop('filesystem')}) usage_dict.update({'mounted_on': group.pop('mounted_on')}) usage_dict.update({ key: int(group[key]) for key in group }) continue # Controller Compatibility: 20.3 # Version: 99.99.999-4567 # Build: 4567 # System state: GREEN. All daemons up # System FIPS state: Enabled # Testbed mode: Enabled # Hypervisor Type: None # Cloud Hosted Instance: false # Last reboot: Initiated by user - activate 99.99.999-4567. # CPU-reported reboot: Not Applicable # Boot loader version: Not applicable # System uptime: 0 days 21 hrs 35 min 28 sec # Current time: Thu Aug 06 02:49:25 PDT 2020 # Processes: 250 total # Personality: vedge # Model name: vedge-cloud # Services: None # vManaged: true # Commit pending: false # Configuration template: CLItemplate_srp_vedge # Chassis serial number: None m10 = p10.match(line) if m10: group = m10.groupdict() key = group['key'].replace('-', '_').replace(' ','_').replace(':','').lower() if key == 'processes': group['value'] = int(group['value'].replace('total','')) parsed_dict.update({key: (group['value'])}) continue return parsed_dict

nilq/baby-python

python

from django.conf.urls import patterns, include, url from tastypie.api import Api from tastypie_evostream.api import StreamResource tastypie_evostream_api = Api() tastypie_evostream_api.register(StreamResource()) urlpatterns = patterns( '', url(r'', include(tastypie_evostream_api.urls)), )

nilq/baby-python

python

# -*- coding: UTF-8 -*- from django.db import models from django.contrib.contenttypes import fields from django.contrib.contenttypes.models import ContentType # from south.modelsinspector import add_introspection_rules # from tagging.models import Tag # from tagging_autocomplete.models import TagAutocompleteField from taggit_autosuggest.managers import TaggableManager from django.contrib.auth.models import User #from contrapartes.models import Usuarios # from thumbs import ImageWithThumbsField from sorl.thumbnail import ImageField from utils import * import datetime # from south.modelsinspector import add_introspection_rules from ckeditor_uploader.fields import RichTextUploadingField # add_introspection_rules ([], ["^ckeditor\.fields\.RichTextField"]) # add_introspection_rules ([], ["^tagging_autocomplete\.models\.TagAutocompleteField"]) # Create your models here. class Imagen(models.Model): ''' Modelo generico para subir imagenes en todos los demas app :)''' content_type = models.ForeignKey(ContentType,on_delete=models.DO_NOTHING) object_id = models.IntegerField(db_index=True) content_object = fields.GenericForeignKey('content_type', 'object_id') nombre_img = models.CharField("Nombre",max_length=200, null=True, blank=True) foto = ImageField("Foto",upload_to=get_file_path,null=True, blank=True) tags_img = TaggableManager("Tags",help_text='Separar elementos con "," ', blank=True) fileDir = 'fotos/' class Meta: verbose_name_plural = "Imágenes" def __str__(self): return self.nombre_img class Documentos(models.Model): ''' Modelo generico para subir los documentos en distintos app''' content_type = models.ForeignKey(ContentType,on_delete=models.DO_NOTHING) object_id = models.IntegerField(db_index=True) content_object = fields.GenericForeignKey('content_type', 'object_id') nombre_doc = models.CharField("Nombre",max_length=200, null=True, blank=True) adjunto = models.FileField("Adjunto",upload_to=get_file_path, null=True, blank=True) tags_doc = TaggableManager("Tags",help_text='Separar elementos con "," ', blank=True) fileDir = 'documentos/' class Meta: verbose_name_plural = "Documentos" def __str__(self): return self.nombre_doc class Videos(models.Model): ''' Modelo generico para subir videos en todos los app''' content_type = models.ForeignKey(ContentType,on_delete=models.DO_NOTHING) object_id = models.IntegerField(db_index=True) content_object = fields.GenericForeignKey('content_type', 'object_id') nombre_video = models.CharField(max_length=200, null=True, blank=True) url = models.URLField(null=True, blank=True) tags_vid = TaggableManager(help_text='Separar elementos con "," ', blank=True) class Meta: verbose_name_plural = "Videos" def __str__(self): return self.nombre_video class Audios(models.Model): '''' Modelo generico para subir audios en todos los demas app ''' content_type = models.ForeignKey(ContentType,on_delete=models.DO_NOTHING) object_id = models.IntegerField(db_index=True) content_object = fields.GenericForeignKey('content_type', 'object_id') nombre_audio = models.CharField(max_length=200, null=True, blank=True) audio = models.FileField(upload_to=get_file_path, null=True, blank=True) tags_aud = TaggableManager(help_text='Separar elementos con "," ', blank=True) fileDir = 'audios/' class Meta: verbose_name_plural = "Audios" def __str__(self): return self.nombre_audio class Foros(models.Model): nombre = models.CharField(max_length=200) creacion = models.DateField(auto_now_add=True) apertura = models.DateField('Apertura y recepción de aportes') cierre = models.DateField('Cierre de aportes') fecha_skype = models.DateField('Propuesta de reunión skype') memoria = models.DateField('Propuesta entrega de memoria') contenido = RichTextUploadingField() contraparte = models.ForeignKey(User,on_delete=models.DO_NOTHING) #documentos = fields.GenericRelation(Documentos) #fotos = fields.GenericRelation(Imagen) #video = fields.GenericRelation(Videos) #audio = fields.GenericRelation(Audios) correo_enviado = models.BooleanField(editable=False) class Meta: verbose_name_plural = "Foros" ordering = ['-creacion'] def __str__(self): return self.nombre def __documento__(self): lista = [] for obj in self.documentos.all(): lista.append(obj) return lista def __fotos__(self): lista = [] for obj in self.fotos.all(): lista.append(obj) return lista def __video__(self): lista = [] for obj in self.video.all(): lista.append(obj) return lista def __audio__(self): lista = [] for obj in self.audio.all(): lista.append(obj) return lista def get_absolute_url(self): return "/foros/ver/%d" % (self.id) class Aportes(models.Model): foro = models.ForeignKey(Foros,on_delete=models.CASCADE) fecha = models.DateField(auto_now_add=True) contenido = RichTextUploadingField() user = models.ForeignKey(User,on_delete=models.DO_NOTHING) adjuntos = fields.GenericRelation(Documentos) fotos = fields.GenericRelation(Imagen) video = fields.GenericRelation(Videos) audio = fields.GenericRelation(Audios) class Meta: verbose_name_plural = "Aportes" def __str__(self): return self.foro.nombre def __documento__(self): lista = [] for obj in self.adjuntos.all(): lista.append(obj) return lista def __fotos__(self): lista = [] for obj in self.fotos.all(): lista.append(obj) return lista def __video__(self): lista = [] for obj in self.video.all(): lista.append(obj) return lista def __audio__(self): lista = [] for obj in self.audio.all(): lista.append(obj) return lista class Comentarios(models.Model): fecha = models.DateField(auto_now_add=True) usuario = models.ForeignKey(User,on_delete=models.DO_NOTHING) comentario = RichTextUploadingField() aporte = models.ForeignKey(Aportes,on_delete=models.CASCADE) class Meta: verbose_name_plural = "Comentarios" def __str__(self): return self.usuario.username

nilq/baby-python

python

# coding: utf-8 ''' ------------------------------------------------------------------------------ Copyright 2015 - 2017 Esri 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. ------------------------------------------------------------------------------ UnitTestUtiliites.py -------------------------------------------------- requirments: * ArcGIS Desktop 10.X+ or ArcGIS Pro 1.X+ * Python 2.7 or Python 3.4 author: ArcGIS Solutions company: Esri ================================================== description: Basic methods used in unit tests ================================================== history: 10/06/2015 - JH - original coding 10/23/2015 - MF - mods for tests ================================================== ''' import arcpy import os import sys import traceback import platform import logging import Configuration import datetime def getLoggerName(): ''' get unique log file name ''' if Configuration.DEBUG == True: print("UnitTestUtilities - getLoggerName") seq = 0 name = nameFromDate(seq) #add +=1 to seq until name doesn't exist as a path while os.path.exists(os.path.join(Configuration.logPath, name)): seq += 1 name = nameFromDate(seq) #logFilePath = os.path.join(Configuration.logPath, name) return name def getCurrentDateTimeForLogFile(): ''' Get current date/time string as: YYYY-MM-DD_HH-MM-SS ''' return datetime.datetime.now().strftime("%Y-%B-%d_%H-%M-%S") def getCurrentDateTime(): ''' Get current date/time string as: DD/MM/YYYY HH:MM:SS ''' return datetime.datetime.now().strftime("%d/%B/%Y %H:%M:%S") def nameFromDate(seq): ''' Make log file name''' return 'SGT_' + str(getCurrentDateTimeForLogFile()) + '_seq' + str(seq) + '.log' def makeFileFromPath(filePath): ''' make a file object from a path to that file if it doesn't already exist ''' if not checkExists(filePath): try: fd = open(filePath, 'a') fd.close() except: print("Can't make file for some reason.") return filePath def makeFolderFromPath(folderPath): ''' make a folder(s) from a path if it doesn't already exist ''' if not checkExists(folderPath): try: os.makedirs(folderPath) except: print("Can't make the folder for some reason.") return folderPath def initializeLogger(name, logLevel = logging.DEBUG): ''' get and return named logger ''' if Configuration.DEBUG == True: print("UnitTestUtilities - initializeLogger") # Check if the path to the log files exists, and create if not if not os.path.exists(Configuration.logPath): dummy = makeFolderFromPath(Configuration.logPath) # get a unique log file name if we don't have a name already if name == None or name == "": name = getLoggerName() logFile = os.path.join(Configuration.logPath, name) Configuration.LoggerFile = logFile # if the log file does NOT exist, create it if not os.path.exists(logFile): logFile = makeFileFromPath(logFile) logger = logging.getLogger(name) logger.setLevel(logLevel) logFormatter = logging.Formatter('%(levelname)s: %(asctime)s %(message)s') fileHandler = logging.FileHandler(logFile) fileHandler.setFormatter(logFormatter) logger.addHandler(fileHandler) consoleHandler = logging.StreamHandler(sys.stdout) consoleHandler.setFormatter(logFormatter) logger.addHandler(consoleHandler) return logger def setUpLogFileHeader(): ''' Add a header to log file when initialized ''' Configuration.Logger.debug("UnitTestUtilities - setUpLogFileHeader") Configuration.Logger.info("------------ Begin Tests ------------------") Configuration.Logger.info("Platform: {0}".format(platform.platform())) Configuration.Logger.info("Python Version {0}".format(sys.version)) agsInstallInfo = arcpy.GetInstallInfo() Configuration.Logger.info("Product: {0}, Version: {1}, Installed on: {2}, Build: {3}.".format(agsInstallInfo['ProductName'], \ agsInstallInfo['Version'], agsInstallInfo['InstallDate'], agsInstallInfo['BuildNumber'])) Configuration.Logger.info("-------------------------------------------") def checkArcPy(): ''' sanity check that ArcPy is working ''' if Configuration.DEBUG == True: print("UnitTestUtilities - checkArcPy") arcpy.AddMessage("ArcPy works") def checkExists(p): ''' Python check for existence ''' if Configuration.DEBUG == True: print("UnitTestUtilities - checkExists") return os.path.exists(p) def createScratch(scratchPath): ''' create scratch geodatabase ''' if Configuration.DEBUG == True: print("UnitTestUtilities - createScratch") scratchName = 'scratch.gdb' scratchGDB = os.path.join(scratchPath, scratchName) if checkExists(scratchGDB): print("Scratch already exists") return scratchGDB try: if Configuration.DEBUG == True: print("Creating scratch geodatabase...") arcpy.CreateFileGDB_management(scratchPath, scratchName) if Configuration.DEBUG == True: print("Created scratch gdb.") except: print("Problem creating scratch.gdb") return scratchGDB def deleteScratch(scratchPath): ''' delete scratch geodatabase ''' if Configuration.DEBUG == True: print("UnitTestUtilities - deleteScratch") try: arcpy.Delete_management(scratchPath) if Configuration.DEBUG == True: print("Deleted scratch gdb.") except: print("scratch.gdb delete failed") return def checkFilePaths(paths): ''' check file/folder paths exist ''' if Configuration.DEBUG == True: print("UnitTestUtilities - checkFilePaths") for path2check in paths: if os.path.exists(path2check): if Configuration.DEBUG == True: print("Valid Path: " + path2check) else: raise Exception('Bad Path: ' + str(path2check)) def checkGeoObjects(objects): ''' check geospatial stuff exists ''' if Configuration.DEBUG == True: print("UnitTestUtilities - checkGeoObjects") for object2Check in objects: #TODO: Shouldn't we be using arcpy.Exists()? desc = arcpy.Describe(object2Check) if desc == None: print("--> Invalid Object: " + str(object2Check)) arcpy.AddError("Bad Input") raise Exception('Bad Input') else: if Configuration.DEBUG == True: print("Valid Object: " + desc.Name) def handleArcPyError(): ''' Basic GP error handling, errors printed to console and logger ''' if Configuration.DEBUG == True: print("UnitTestUtilities - handleArcPyError") # Get the arcpy error messages msgs = arcpy.GetMessages() arcpy.AddError(msgs) print(msgs) Configuration.Logger.error(msgs) raise Exception('ArcPy Error') def handleGeneralError(exception = None): ''' Basic error handler, errors printed to console and logger ''' if Configuration.DEBUG == True: print("UnitTestUtilities - handleGeneralError") if isinstance(exception, Exception): print(str(exception)) Configuration.Logger.error(str(exception)) # Get the traceback object tb = sys.exc_info()[2] tbinfo = traceback.format_tb(tb)[0] # Concatenate information together concerning the error into a message string pymsg = "PYTHON ERRORS:\nTraceback info:\n" + tbinfo + "\nError Info:\n" + str(sys.exc_info()[1]) msgs = "ArcPy ERRORS:\n" + arcpy.GetMessages() + "\n" # Print Python error messages for use in Python / Python Window print(pymsg + "\n") Configuration.Logger.error(pymsg) print(msgs) Configuration.Logger.error(msgs) if isinstance(exception, Exception): raise exception else: raise Exception('General Error') def geoObjectsExist(objects): ''' Return true if all of the input list of geo-objects exist, false otherwise ''' allExist = True for obj in objects: if not arcpy.Exists(obj): allExist = False return allExist def folderPathsExist(paths): ''' Return true if all input paths exist, false otherwise ''' allExist = True for p in paths: if not os.path.exists(p): allExist = False return allExist def deleteIfExists(dataset): ''' Delete the input dataset if it exists ''' if (arcpy.Exists(dataset)): arcpy.Delete_management(dataset) arcpy.AddMessage("deleted dataset: " + dataset)

nilq/baby-python

python

from flask_restful import Resource, reqparse, request from flask_restful import fields, marshal_with, marshal from sqlalchemy.exc import IntegrityError from sqlalchemy import or_, and_, text from flask_jwt_extended import jwt_required from models.keyword import Keyword from app import db from utils.util import max_res from helpers.keywords_resource_helper import * class KeywordsResource(Resource): @jwt_required def get(self, keyword_id=None): if keyword_id: keyword = Keyword.find_by_id(keyword_id) return max_res(marshal(keyword, keyword_fields)) else: conditions = [] args = keyword_query_parser.parse_args() page = args['page'] per_page = args['pagesize'] if args['orderby'] not in sortable_fields: return max_res('', code=500, errmsg='排序非法字段') sort = args['orderby'] if args['desc']>0: sort = args['orderby'] + ' desc' conditions = make_conditions(conditions,args) # 在这里添加更多的条件查询例如 # if args['name'] is not None: # conditions.append(Keyword.name.like('%'+args['name']+'%')) if conditions is []: pagination = Keyword.query.order_by(text(sort)).paginate(page, per_page, error_out=False) else: pagination = Keyword.query.filter(*conditions).order_by(text(sort)).paginate(page, per_page, error_out=False) paginate = { 'total':pagination.total, 'pageSize': pagination.per_page, 'current': pagination.page } print(pagination.items) return max_res(marshal({ 'pagination': paginate, 'list': [marshal(u, keyword_fields) for u in pagination.items] }, keyword_list_fields)) @jwt_required def post(self): args = keyword_post_parser.parse_args() keyword = Keyword(**args) try: keyword.add() except IntegrityError: return max_res('', code=401, errmsg='名称重复') return max_res(marshal(keyword, keyword_fields)) def put(self, keyword_id=None): keyword = Keyword.find_by_id(keyword_id) args = keyword_update_parser.parse_args() keyword = update_all_fields(args, keyword) #可以在这里继续添加需要更新的字段如 # if args['name']: # o.name = args['name'] # db.session.commit() try: keyword.update() except Exception as e: return max_res('',500, 'Failed to modify.') return max_res(marshal(keyword, keyword_fields)) def delete(self, keyword_id=None): keyword = Keyword.find_by_id(keyword_id) try: keyword.delete() except Exception as e: return max_res('',500, 'The record has already deleted.') return max_res('The keyword has been deleted.')

nilq/baby-python

python

# -*- coding: utf-8 -*- import unittest from iemlav.lib.log_monitor.server_log.parser.apache import ApacheParser from iemlav.lib.log_monitor.server_log.server_logger import ServerLogger try: # if python 3.x.x from unittest.mock import patch except ImportError: # python 2.x.x from mock import patch class TestApacheParser(unittest.TestCase): """ Test class for SecureTea Server Log Apache Log Parser. """ def setUp(self): """ Setup class for TestApacheParser. """ # Initialize Apache object self.apache_obj = ApacheParser(window=30, path="random_path") # Mock log data self.data = ['83.149.9.216 - - [14/Jun/2019:10:30:00 +0000] ' \ '"GET /presentations/logstash-monitorama-2013/images/kibana-dashboard3.png HTTP/1.1" ' \ '400 171717 "http://semicomplete.com/presentations/logstash-monitorama-2013/" ' \ '"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) AppleWebKit/537.36 (KHTML, like Gecko) '\ 'Chrome/32.0.1700.77 Safari/537.36'] # Mock parsed log data self.parsed_dict = {'83.149.9.216': { 'ep_time': [1560508200], 'get': ['/presentations/logstash-monitorama-2013/images/kibana-dashboard3.png HTTP/1.1'], 'status_code': [400], 'ua': ['Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_1) ' \ 'AppleWebKit/537.36 (KHTML, like Gecko) Chrome/32.0.1700.77 Safari/537.36'], 'count': 1, 'unique_get': ['/presentations/logstash-monitorama-2013/images/kibana-dashboard3.png HTTP/1.1'] } } @patch.object(ApacheParser, "check_within_window") @patch.object(ServerLogger, "log") @patch("iemlav.lib.log_monitor.server_log.parser.apache.utils") def test_parse(self, mck_utils, mock_log, mck_window): """ Test parse. """ mck_utils.open_file.return_value = self.data mck_window.return_value = True mck_utils.get_epoch_time.return_value = 1560508200 # Check if the parsing is correct self.assertEqual(self.apache_obj.parse(), self.parsed_dict) @patch("iemlav.lib.log_monitor.server_log.parser.apache.time") def test_check_within_window(self, mock_time): """ Test check_within_window. """ # Case 1: When time difference is less than window mock_time.time.return_value = 1560508200 res = self.apache_obj.check_within_window(1560508200) self.assertTrue(res) # Case 2: When time difference is greater than window res = self.apache_obj.check_within_window(1557916100) self.assertFalse(res) def test_update_dict(self): """ Test update_dict. """ self.apache_obj.update_dict( ip="1.1.1.1", ep_time=1500, get="/random/get/req", status_code=200, user_agent="random-user-agent" ) temp_dict = {'ep_time': [1500], 'get': ['/random/get/req'], 'status_code': [200], 'ua': ['random-user-agent'], 'count': 1, 'unique_get': ['/random/get/req']} # Check if the key exists self.assertTrue(self.apache_obj.apache_dict.get("1.1.1.1")) # Check if the updated dict is correct self.assertEqual(self.apache_obj.apache_dict["1.1.1.1"], temp_dict)

nilq/baby-python

python

# Copyright (c) 2019 fortiss GmbH # # This software is released under the MIT License. # https://opensource.org/licenses/MIT from bark.world.agent import * from bark.models.behavior import * from bark.world import * from bark.world.map import * from modules.runtime.commons.parameters import ParameterServer from modules.runtime.commons.xodr_parser import XodrParser import copy class Scenario: def __init__(self, agent_list=None, eval_agent_ids=None, map_file_name=None, json_params=None, map_interface=None): self._agent_list = agent_list or [] self._eval_agent_ids = eval_agent_ids or [] self._map_file_name = map_file_name self._json_params = json_params self._map_interface = map_interface def get_world_state(self): """get initial world state of scenario to start simulation from here Returns: [bark.world.World] """ return self._build_world_state() def copy(self): return Scenario(agent_list=copy.deepcopy(self._agent_list), eval_agent_ids=self._eval_agent_ids.copy(), map_file_name=self._map_file_name, json_params=self._json_params.copy(), map_interface=self._map_interface) def _build_world_state(self): param_server = ParameterServer(json=self._json_params) world = World(param_server) if self._map_interface is None: world = self.setup_map(world, self._map_file_name) else: world.set_map(self._map_interface) for agent in self._agent_list: world.add_agent(agent) return world def __getstate__(self): odict = self.__dict__.copy() print(odict['_map_interface']) del odict['_map_interface'] print(odict) return odict def __setstate__(self, sdict): sdict['_map_interface'] = None self.__dict__.update(sdict) def setup_map(self, world, _map_file_name): if not _map_file_name: return world xodr_parser = XodrParser(_map_file_name ) map_interface = MapInterface() map_interface.set_open_drive_map(xodr_parser.map) self._map_interface = map_interface world.set_map(map_interface) return world

nilq/baby-python

python

import os import pytest from h2_conf import HttpdConf def setup_data(env): s100 = "012345678901234567890123456789012345678901234567890123456789012345678901234567890123456789012345678\n" with open(os.path.join(env.gen_dir, "data-1k"), 'w') as f: for i in range(10): f.write(s100) # The trailer tests depend on "nghttp" as no other client seems to be able to send those # rare things. class TestStore: @pytest.fixture(autouse=True, scope='class') def _class_scope(self, env): setup_data(env) HttpdConf(env).add_vhost_cgi(h2proxy_self=True).install() assert env.apache_restart() == 0 # check if the server survives a trailer or two def test_202_01(self, env): url = env.mkurl("https", "cgi", "/echo.py") fpath = os.path.join(env.gen_dir, "data-1k") r = env.nghttp().upload(url, fpath, options=["--trailer", "test: 1"]) assert 300 > r.response["status"] assert 1000 == len(r.response["body"]) r = env.nghttp().upload(url, fpath, options=["--trailer", "test: 1b", "--trailer", "XXX: test"]) assert 300 > r.response["status"] assert 1000 == len(r.response["body"]) # check if the server survives a trailer without content-length def test_202_02(self, env): url = env.mkurl("https", "cgi", "/echo.py") fpath = os.path.join(env.gen_dir, "data-1k") r = env.nghttp().upload(url, fpath, options=["--trailer", "test: 2", "--no-content-length"]) assert 300 > r.response["status"] assert 1000 == len(r.response["body"]) # check if echoing request headers in response from GET works def test_202_03(self, env): url = env.mkurl("https", "cgi", "/echohd.py?name=X") r = env.nghttp().get(url, options=["--header", "X: 3"]) assert 300 > r.response["status"] assert b"X: 3\n" == r.response["body"] # check if echoing request headers in response from POST works def test_202_03b(self, env): url = env.mkurl("https", "cgi", "/echohd.py?name=X") r = env.nghttp().post_name(url, "Y", options=["--header", "X: 3b"]) assert 300 > r.response["status"] assert b"X: 3b\n" == r.response["body"] # check if echoing request headers in response from POST works, but trailers are not seen # This is the way CGI invocation works. def test_202_04(self, env): url = env.mkurl("https", "cgi", "/echohd.py?name=X") r = env.nghttp().post_name(url, "Y", options=["--header", "X: 4a", "--trailer", "X: 4b"]) assert 300 > r.response["status"] assert b"X: 4a\n" == r.response["body"]

nilq/baby-python

python

# -*- coding: utf-8 -*- from mimetypes import MimeTypes from hashlib import md5 def list_of(_list, _class): """ Chequea que la lista _list contenga elementos del mismo tipo, desciptos en _class. Args: - _list: - list(). - Lista de elementos sobre la que se desea trabajar. - El argumento solo acepta objetos de class list. - _class: - Clase esperada en los elemntos de la lista. - admite que se chequee cualquier tipo, incuso, NoneType. Returns: - bool(). - True: La lista posee todos sus elementos de la clase _class - False: Al menos uno de los elementos no es de clase _class """ if not isinstance(_list, list): raise TypeError('check_list_type() solo acepta type(_list)==list') return not False in [isinstance(element, _class) for element in _list] def get_mimetype(_filename=None): """ Retorna Mime Type de un archivo (_filename). Args: ==== - _filename: Str(). path al archivo que se desea chequear. Require: ======= - Python Builtin lib: mimetypes. Returns: ======= - Str(). MimeType. Exito. - None: Fallo. """ try: mime = MimeTypes() return mime.guess_type(_filename)[0] except TypeError: pass except IOError: pass def build_hash(_filename): """ Crear hash de recurso. Args: ==== - _filename: Str(). Archivo sobre el cual se desea calcular HASH. Return: ====== - Exito: Str() MD5-Hash. - Fallo: None. """ hash_md5 = md5() try: with open(_filename, "rb") as f: for chunk in iter(lambda: f.read(4096), b""): hash_md5.update(chunk) return hash_md5.hexdigest() except IOError: pass

nilq/baby-python

python

import unittest import random from hypothesis import given, settings, assume, Verbosity, strategies as st from src.poker.app.card import Deck, Card, Suit, Value from src.poker.app.hand import Hand, Play, Result, calculate_play_hand DeckStrategy = st.builds(Deck) NaiveHandStrategy = st.builds(Hand, st.sets( st.builds(Card, st.sampled_from(Suit), st.sampled_from(Value)) , max_size = 5 , min_size = 5)) @st.composite def three_of_a_kind_in_hand(draw) -> Hand: d = draw(DeckStrategy) r = draw(st.randoms()) #1 sample = r.choice(list(d.deck)) cards = set([sample]) #2 sample = Card(sample.suit.next(), sample.val.next()) cards.add(sample) #3 sample = Card(sample.suit.next(), sample.val.next()) cards.add(sample) #4 sample = Card(sample.suit.next(), sample.val) cards.add(sample) #5 sample = Card(sample.suit.next(), sample.val) cards.add(sample) return Hand(cards) @st.composite def full_house_in_hand(draw) -> Hand: d = draw(DeckStrategy) r = draw(st.randoms()) #1 sample = r.choice(list(d.deck)) cards = set([sample]) #2 sample = Card(sample.suit.next(), sample.val) cards.add(sample) #3 sample = Card(sample.suit.next(), sample.val.next()) cards.add(sample) #4 sample = Card(sample.suit.next(), sample.val) cards.add(sample) #5 sample = Card(sample.suit.next(), sample.val) cards.add(sample) return Hand(cards) @st.composite def straight_in_hand(draw) -> Hand: blacklist = {Value.JACK, Value.QUEEN, Value.KING} d = draw(DeckStrategy) r = draw(st.randoms()) sample = r.choice(list(d.deck)) assume(not sample.val in blacklist) # while v in blacklist: # v = random.choice(list(Value)) cards = set([sample]) for _ in range(4): sample = Card(sample.suit.next(), sample.val.next()) cards.add(sample) return Hand(cards) class PokerTest(unittest.TestCase): @given(d=DeckStrategy, n_gets=st.integers(min_value=0, max_value=55), m_sets=st.integers(min_value=0, max_value=55)) #@settings(verbosity=Verbosity.verbose) def test_deck_gets_and_sets(self, d: Deck, n_gets, m_sets) -> None: """ Tests if the deck class takes and returns properly cards """ withdraws = list() for _ in range(n_gets+1): card = d.get_random_card() if card: withdraws.append(card) for _ in range(m_sets+1): if withdraws: card = random.choice(withdraws) withdraws.remove(card) d.set_card(card) self.assertEqual(len(withdraws) + len(d.deck), Deck.TOTAL_CARDS) @given(hand=NaiveHandStrategy) @settings(max_examples=150) def test_hand_plays_value(self, hand: Hand) -> None: calculate_play_hand(hand) assert hand.value > 0 and len(hand.cards) == 5 @given(hand=three_of_a_kind_in_hand()) def test_three_of_a_kind(self, hand: Hand) -> None: calculate_play_hand(hand) self.assertEqual(hand.play, Play.THREE_OF_A_KIND) @given(hand=full_house_in_hand()) def test_full_house(self, hand: Hand) -> None: calculate_play_hand(hand) self.assertEqual(hand.play, Play.FULL_HOUSE) @given(hand=straight_in_hand()) def test_straight(self, hand: Hand) -> None: calculate_play_hand(hand) self.assertEqual(hand.play, Play.STRAIGHT) @given(hand1=st.one_of(full_house_in_hand(), straight_in_hand()), hand2=st.one_of(three_of_a_kind_in_hand())) #@settings(verbosity=Verbosity.verbose) def test_two_hands(self, hand1: Hand, hand2: Hand) -> None: calculate_play_hand(hand1) calculate_play_hand(hand2) self.assertEqual(Result.WIN, hand1.compare(hand2)) if __name__ == "__main__": unittest.main()

nilq/baby-python

python

import numpy as np # Reshaping arrays: # Reshaping means changing the shape of an array. # The shape of an array is the number of elements in each dimension. # By reshaping we can add or remove dimensions or change number of elements in each dimension. # **Note: The product of the number of elements inside the Reshape must be equal to the number of elements of the array arr = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5]) print(arr.reshape(4, 4, 2)) print(arr.reshape(2, 2, 2, 4)) copy_or_view = arr.reshape(4, 8) print(copy_or_view.base) print(arr.reshape(2, 4, -1)) arr = np.array([ [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]], [[1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 5]] ]) print(arr.reshape(-1))

nilq/baby-python

python

import re j_format = { "j": "000010", } i_format = { 'beq': "000100", 'bne': None, 'addi': "001000", 'addiu': None, 'andi': None, 'ori': None, 'slti': None, 'sltiu': None, 'lui': None, 'lw': "100011", 'sw': "101011", } r_format = { 'add': "100000", 'addu': None, 'sub': "100010", 'subu': None, 'and': "100100", 'or': "100101", 'xor': "100110", 'slt': "101010", 'sltu': None, 'sll': "101001", 'srl': None, 'jr': None } reg_name = { '$zero': 1, '$at': 1, '$v': 2, '$a': 4, '$t': 8, '$s': 8, '$tt': 2, '$k': 2, '$gp': 1, '$sp': 1, '$fp': 1, '$ra': 1 } def reg_init(): registers = {} n = 0 for key, value in reg_name.items(): for i in range(value): b = bin(n)[2:].zfill(5) if key == "$zero": registers[f"{key}"] = b elif key == "$tt": registers[f"$t{i + 8}"] = b else: registers[f"{key}{i}"] = b n += 1 return registers def check_dependency(ins, regs): if ins[1] in regs: return True return False def twos_comp(binary): binary = binary[::-1] new_b = "" flag = False for i in range(len(binary)): if flag: new_b += '0' if binary[i] == '1' else '1' else: if binary[i] == '1': flag = True new_b += binary[i] return new_b[::-1] def compile_asm(lines, registers): # for beq, if R -> NOP, if LW -> NOP*2 instructions = [] labels = {} add = 0 for line in lines: if line[-1] == ':': labels[line[:-1]] = add continue ins = re.findall("^[a-z]+", line) if ins[0] != 'j': regs = re.findall(" [a-zA-Z0-9]+|\$[a-z]+[0-9]|[0-9]+|\$zero|-[0-9]+", line) else: regs = [line.split(" ")[1]] if ins[0] == 'beq' and check_dependency(instructions[-1], regs): if instructions[-1][0] == 'lw': instructions.append(['nop']) instructions.append(['nop']) add += 2 elif r_format.get(instructions[-1][0]): instructions.append(['nop']) add += 1 elif instructions[-1][0] in list(i_format.keys())[2:9] and i_format.get(instructions[-1][0]): instructions.append(['nop']) add += 1 add += 1 instructions.append(ins + regs) binary = [] for add, ins in enumerate(instructions): b = [] if ins[0] == 'nop': b.append('0' * 32) elif ins[0] in i_format: b.append(i_format[ins[0]]) im, reg = (ins[2], ins[3]) if ins[0] in ['lw', 'sw'] else (ins[3], ins[2]) im = im.strip() b.append(registers[reg]) b.append(registers[ins[1]]) if im.isnumeric() or (im[0] == '-' and im[1:].isnumeric()): immediate = int(im) while ins[0] == "beq" and instructions[immediate][0] == "nop": immediate += 1 if immediate < 0: b.append(twos_comp(bin(immediate)[2:].zfill(16))) else: b.append(bin(immediate)[2:].zfill(16)) else: r_ad = labels[im.strip()] - add - 1 while instructions[add + 1 + r_ad][0] == "nop": r_ad += 1 if r_ad < 0: r_ad_bin = twos_comp(bin(r_ad)[2:].zfill(16)) else: r_ad_bin = bin(r_ad)[2:].zfill(16) b.append(r_ad_bin) elif ins[0] in r_format: b.append("000000") # OPCODE if ins[0] == "sll" or ins[0] == "srl": b.append(registers[ins[2]]) # RT b.append("00000") # RS b.append(registers[ins[1]]) # RD shamt = bin(int(ins[3]))[2:].zfill(5) b.append(shamt) # SHAMT else: b.append(registers[ins[2]]) # RS b.append(registers[ins[3]]) # RT b.append(registers[ins[1]]) # RD b.append("00000") # SHAMT b.append(r_format[ins[0]]) # FUNCT elif ins[0] in j_format: b.append(j_format[ins[0]]) if ins[1].isnumeric(): ad = int(ins[1]) while instructions[ad][0] == "nop": ad += 1 b.append(bin(ad)[2:].zfill(26)) else: ad = labels[ins[1]] while instructions[ad][0] == "nop": ad += 1 b.append(bin(ad)[2:].zfill(26)) binary.append("".join(b)) return binary def compiler(file_name): registers = reg_init() lines = open(file_name).read().split('\n') return compile_asm(lines, registers) # compiler("p.art")

nilq/baby-python

python

import random from collections import defaultdict import numpy as np from maddpg.common.utils_common import zip_map class ReplayBuffer(object): def __init__(self, size): """Create Prioritized Replay buffer. Parameters ---------- size: int Max number of transitions to store in the buffer. When the buffer overflows the old memories are dropped. """ self._storage = [] self._maxsize = int(size) self._next_idx = 0 def __len__(self): return len(self._storage) def clear(self): self._storage = [] self._next_idx = 0 def add(self, obs_t, action, reward, obs_tp1, done): data = (obs_t, action, reward, obs_tp1, done) if self._next_idx >= len(self._storage): self._storage.append(data) else: self._storage[self._next_idx] = data self._next_idx = (self._next_idx + 1) % self._maxsize def _encode_sample(self, idxes): obses_t = defaultdict(list) actions = defaultdict(list) rewards = defaultdict(list) obses_tp1 = defaultdict(list) dones = defaultdict(list) for i in idxes: data = self._storage[i] obs_t, action, reward, obs_tp1, done = data for key, (obs_t, action, reward, obs_tp1, done) in zip_map(*data): obses_t[key].append(obs_t) actions[key].append(action) rewards[key].append(reward) obses_tp1[key].append(obs_tp1) dones[key].append(done) return obses_t, actions, rewards, obses_tp1, dones def make_index(self, batch_size): return [random.randint(0, len(self._storage) - 1) for _ in range(batch_size)] def make_latest_index(self, batch_size): idx = [(self._next_idx - 1 - i) % self._maxsize for i in range(batch_size)] np.random.shuffle(idx) return idx def sample_index(self, idxes): return self._encode_sample(idxes) def sample(self, batch_size): """Sample a batch of experiences. Parameters ---------- batch_size: int How many transitions to sample. Returns ------- obs_batch: np.array batch of observations act_batch: np.array batch of actions executed given obs_batch rew_batch: np.array rewards received as results of executing act_batch next_obs_batch: np.array next set of observations seen after executing act_batch done_mask: np.array done_mask[i] = 1 if executing act_batch[i] resulted in the end of an episode and 0 otherwise. """ if batch_size > 0: idxes = self.make_index(batch_size) else: idxes = range(0, len(self._storage)) return self._encode_sample(idxes) def collect(self): return self.sample(-1)

nilq/baby-python

python

from arbre_binaire_jeu import * #------------------------------------------------------------------------------- # DM MISSION # # Objectif : Construire un jeu à partir d'un texte préconstruit # # Contrainte : utiliser un arbre binaire #------------------------------------------------------------------------------- # Phrases préconstruites phrases = [None] * 18 phrases[0] = """21/10/2024 - 03h30, New York, un quartier mal famé et mal éclairé. Vous êtes au pied de l'immeuble auquel vous a mené votre enquête. 25 étages à vue de nez. Même au coeur de la nuit, on voit qu'il aurait besoin au minimum d'un sacré coup de peinture ; on le donnerait pour abandonné si on ne percevait pas ça et là de faibles rayons de lumière. Tout est calme alentour, un silence à couper au couteau. D'après mon informateur, le fils du Président, retenu prisonnier par le Gang des Ignares, est situé tout en haut au dernier étage. Il est probablement sous surveillance. Pour le libérer, il va falloir être discret...""" phrases[1] = "Damnation, je ne peux plus avancer! Il va falloir tenter la voie des airs, ça va être compliqué ..." phrases[2] = "La porte d'entrée n'offre pas de résistance, je traverse le hall vers les accès aux étages." phrases[3] = "Quelle malchance, l'ascenseur n' a plus d 'électricité. je vois un boîtier avec des fils qui dépassent." phrases[4] = "L'escalier est fermé par une énorme grille. Il y a un boîtier avec un code à taper." phrases[5] = "Ca y est, l'ascenseur fonctionne. Voilà, je suis dedans et je monte." phrases[6] = "Ca y est, la grille s'ouvre. Prenons l' escalier. A moi les 25 étages...pfff !" phrases[7] = """Ascension terminée, me voici à pied d'oeuvre! Je découvre un couloir. Il y a une porte latérale ouverte, ce doit être celle du gardien. La porte fermée au fond doit être celle du prisonnier""" phrases[8] = "Sacrebleu ! J 'ai foutu en l'air cette boîte rouillée qui s'est appelée un jour ascenseur. Voyons l'escalier..." phrases[9] = "Enfer, le code ne marche pas, la grille est bloquée l'escalier est inaccessible. Voyons l'ascenseur..." phrases[10] = "C'est la catastrophe, je ne peux pas monter, j' abandonne la mission en attendant de trouver un autre moyen" phrases[11] = "Malédiction, le couloir est allumé, je vais me faire repérer, à moins qu' il dorme" phrases[12] = "Le couloir est dans l'obscurité, pas de lumière, je vais me glisser dans l'ombre, il ne me verra pas" phrases[13] = "Pas de bruit sauf une légère respiration, le surveillant doit dormir, je tente ma chance" phrases[14] = "Des bruits de table et de chaise, le surveillant est apparemment bien éveillé." phrases[15] = """Ouf, j'ai pu passer le couloir sans encombre. J'ouvre la porte au fond. Le prisonnier tourne la tête lentement vers moi et me lance un regard ébahi. Je prend la pose et je lui lance un « Salut fiston, ton sauveur est arrivé! »""" phrases[16] = """Un jet de lumière, le gardien braque sur moi un gros flingue, un sourire mauvais éclaire son visage ; manifestement il m'attendait c'est un piège !""" phrases[17] = "C'est trop risqué pour l' instant, je reviendrai dans quelques heures." questions = [None] * 6 questions[0] = "La porte de l'immeuble est-elle ouverte (taper 1) ou verrouillée (taper 0) ? " questions[1] = "Choisissez vous de prendre l'ascenseur (taper 1) ou l'escalier (taper 0)? " questions[2] = "Branchez vous le fil vert avec noir (taper 1) ou le rouge avec le noir (taper 0)? " questions[3] = "Choisissez vous le code 1111 (taper 1) ou le code 9999 (taper 0)? " questions[4] = "Le couloir en face est-il éclairé (taper 1) ou dans le noir (taper 0)? " questions[5] = "Entendez vous quelqu'un qui s'agite dans la pièce de surveillance (taper 1) ou est elle silencieuse (taper 0) ? " # déroulement de l'histoire (aide) ''' porte ouverte ou fermée ? porte ouverte, choix ascenseur ou escalier porte fermée, fin de l' histoire ascenseur, fil vert ou rouge ascenseur marche, arrivée en haut, question sur lumière couloir ascenseur en panne, test escalier couloir allumé, bruit ? trop risqué sauvé couloir éteint, bruit ? sauvé piège escalier, 1111 ou 9999 ? escalier marche, arrivée en haut, question sur lumière couloir ... escalier foutu, test ascenseur escalier bloqué, mission reportée ''' arbre_jeu = Noeud((phrases[0] + questions[0]), Noeud(phrases[1], None, None ), Noeud((phrases[2] + questions[1]), Noeud((phrases[3] + questions[2]), Noeud((phrases[8] + phrases[4] + questions[3]), "Fin Bis", None ), Noeud((phrases[6] + phrases[7] + questions[4]), Noeud((phrases[11] + questions[5]), Noeud((phrases[13] + phrases[16]), # Fin None, None ) ), Noeud((phrases[12] + phrases[15])) ) ), Noeud((phrases[4] + phrases[3]), Noeud, Noeud((phrases[6] + phrases[7] + questions[4]) Noeud((phrases[11] + questions[5]), Noeud((phrases[13] + phrases[16]), # Fin None, None ) ), Noeud((phrases[12] + phrases[15])) ) ), ) ) affichage(arbre_jeu) print("....!! FIN !!...") #------------------------------------------------------------------------------- # QUESTIONS # # 1. a) Sur papier, construire l'arbre en notant comme étiquette de noeud les phrases et la question éventuelle associées # b) Déterminer la taille et la hauteur de l'arbre. Combien comporte-t-il de feuilles ? # 2. A l'aide du module arbre_binaire importé, constuire l'arbre précédent en python. # 3. Ecrire une fonction qui parcours l'arbre comme dans en affichant le texte dans la console et les questions # sous forme d'input pour lequel le joueur répond 0 ou 1. # 4. Tester le jeu.

nilq/baby-python

python

# coding: utf-8 from olo import funcs from olo.funcs import COUNT, SUM, AVG, MAX, DISTINCT from .base import TestCase, Foo, Bar, Dummy from .fixture import is_pg from .utils import ( patched_execute, no_pk ) attrs = dict( name='foo', tags=['a', 'b', 'c'], password='password', payload={ 'abc': ['1', 2, 3], 'def': [4, '5', 6] } ) class TestCachedQuery(TestCase): def test_fallback(self): bar = Bar.create(name='a', xixi='a', age=1) with patched_execute as execute: bar = Bar.cq.filter(age=MAX(Bar.cq('age'))).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq.filter(age=MAX(Bar.cq('age'))).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq.filter(Bar.age > 0).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq.filter(Bar.age > 0).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq('age').filter(Bar.age > 0).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq('age').filter(Bar.age > 0).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) def test_first(self): with patched_execute as execute: bar = Bar.cq.filter(xixi='a', age=1).first() self.assertIsNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq.filter(xixi='a', age=1).first() self.assertIsNone(bar) self.assertFalse(execute.called) bar = Bar.create(name='a', xixi='a', age=1) with patched_execute as execute: bar = Bar.cq.filter(xixi='a', age=1).first() self.assertIsNotNone(bar) self.assertTrue(execute.called) with patched_execute as execute: bar = Bar.cq.filter(xixi='a', age=1).first() self.assertIsNotNone(bar) self.assertFalse(execute.called) def test_all(self): with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).all() self.assertEqual(bars, []) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).all() self.assertEqual(bars, []) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(10).all() self.assertEqual(bars, []) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(bars, []) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.limit(10).all() self.assertEqual(bars, []) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.limit(11).all() self.assertEqual(bars, []) self.assertFalse(execute.called) bar = Bar.create(name='a', xixi='a', age=1) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) self.assertEqual(execute.call_count, 2) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.limit(10).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) bar.update(name='a+') with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) self.assertEqual(execute.call_count, 2) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) bar.update(name='a') with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) self.assertEqual(execute.call_count, 2) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) bar.update(word='1') with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) self.assertEqual(execute.call_count, 1) self.assertEqual(bars[0].word, bar.word) bar.update(word='2') Bar.cache.get(bar.name) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) self.assertEqual(bars[0].word, bar.word) bar.update(xixi='b') with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 0) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='a', age=1).limit(11).all() self.assertEqual(len(bars), 0) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).limit(11).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) bar.update(word='a') bar = Bar.create(name='b', xixi='b', age=1, word='b') bar = Bar.create(name='c', xixi='b', age=1, word='c') bar = Bar.create(name='d', xixi='b', age=1, word='d') with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).limit(11).all() self.assertEqual(len(bars), 4) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).limit(11).all() self.assertEqual(len(bars), 4) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).limit(2).all() self.assertEqual(len(bars), 2) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cache.gets_by(xixi='b', age=1, start=3, limit=2) self.assertEqual(len(bars), 1) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( '-name' ).limit(3).all() self.assertEqual(len(bars), 3) self.assertEqual(['d', 'c', 'b'], list(map(lambda x: x.name, bars))) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( '-name' ).limit(3).all() self.assertEqual(len(bars), 3) self.assertEqual(['d', 'c', 'b'], list(map(lambda x: x.name, bars))) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( 'name' ).limit(3).all() self.assertEqual(len(bars), 3) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( 'name' ).limit(3).all() self.assertEqual(len(bars), 3) self.assertEqual(['a', 'b', 'c'], list(map(lambda x: x.name, bars))) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( '-age', 'word' ).offset(3).limit(2).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( '-age', 'word' ).offset(3).limit(2).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) _bar = bars[0] _bar.update(xixi='c') with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( '-age', 'word' ).offset(2).limit(2).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) _bar.update(xixi='b') with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( 'word', 'age' ).offset(3).limit(2).all() self.assertEqual(len(bars), 1) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( 'word', 'age' ).offset(3).limit(2).all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) Bar.create(name='e', xixi='b', age=1, word='e') Bar.create(name='f', xixi='b', age=1, word='f') with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( 'word', 'age' ).offset(3).limit(2).all() self.assertEqual(len(bars), 2) self.assertTrue(execute.called) with patched_execute as execute: bars = Bar.cq.filter(xixi='b', age=1).order_by( 'word', 'age' ).offset(3).limit(2).all() self.assertEqual(len(bars), 2) self.assertFalse(execute.called) with patched_execute as execute: bars = Bar.cq.filter(name='e').all() self.assertEqual(len(bars), 1) self.assertFalse(execute.called) Foo.create(name='1', age=1) Foo.create(name='2', age=1) Foo.create(name='3', age=2) with no_pk(Foo): Foo.cq.filter(age=1).limit(3).all() foos = Foo.cq.filter(age=3).limit(3).all() self.assertEqual(foos, []) def test_count_by(self): with patched_execute as execute: c = Bar.cq.filter(xixi='a', age=1).count() self.assertEqual(c, 0) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='a', age=1).count() self.assertEqual(c, 0) self.assertFalse(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 0) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 0) self.assertFalse(execute.called) with patched_execute as execute: c = Bar.cq.filter().count() self.assertEqual(c, 0) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter().count() self.assertEqual(c, 0) self.assertFalse(execute.called) with patched_execute as execute: c = Bar.cq.filter(name='a').count() self.assertEqual(c, 0) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(name='a').count() self.assertEqual(c, 0) self.assertFalse(execute.called) with patched_execute as execute: c = Bar.cq.filter(word='a').count() self.assertEqual(c, 0) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(word='a').count() self.assertEqual(c, 0) self.assertTrue(execute.called) Bar.create(name='a', xixi='b', age=1) with patched_execute as execute: c = Bar.cq.filter(xixi='a', age=1).count() self.assertEqual(c, 0) self.assertFalse(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 1) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter().count() self.assertEqual(c, 1) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(name='a').count() self.assertEqual(c, 1) self.assertTrue(execute.called) Bar.create(name='b', xixi='a', age=1) with patched_execute as execute: c = Bar.cq.filter(xixi='a', age=1).count() self.assertEqual(c, 1) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 1) self.assertFalse(execute.called) bar = Bar.create(name='c', xixi='b', age=1) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 2) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 2) self.assertFalse(execute.called) bar.update(xixi='c') with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 1) self.assertTrue(execute.called) with patched_execute as execute: c = Bar.cq.filter(xixi='b', age=1).count() self.assertEqual(c, 1) self.assertFalse(execute.called) def test_order_by(self): Dummy.create(name='foo0', age=3) Dummy.create(name='foo2', age=6) Dummy.create(name='foo2', age=7) Dummy.create(name='foo3', age=4) Dummy.create(name='foo4', age=2) rv = Dummy.cq('age').order_by('age').all() self.assertEqual(rv, [2, 3, 4, 6, 7]) rv = Dummy.cq('age').order_by(Dummy.age).all() self.assertEqual(rv, [2, 3, 4, 6, 7]) rv = Dummy.cq('age').order_by(Dummy.age.desc()).all() self.assertEqual(rv, [7, 6, 4, 3, 2]) age = Dummy.age.alias('a') rv = Dummy.cq(age).order_by(age).all() self.assertEqual(rv, [2, 3, 4, 6, 7]) rv = Dummy.cq(age).order_by(age.desc()).all() self.assertEqual(rv, [7, 6, 4, 3, 2]) rv = Dummy.cq(age).order_by(Dummy.id.asc(), Dummy.age.desc()).all() self.assertEqual(rv, [3, 6, 7, 4, 2]) rv = Dummy.cq(age).order_by(Dummy.age.in_([2, 4]).desc(), Dummy.id.desc()).all() # noqa self.assertEqual(rv, [2, 4, 7, 6, 3]) rv = Dummy.cq(age).order_by(Dummy.age.in_([2, 4]).desc()).order_by(Dummy.id.desc()).all() # noqa self.assertEqual(rv, [2, 4, 7, 6, 3]) def test_group_by(self): Dummy.create(name='foo0', age=1) Dummy.create(name='foo2', age=2) Dummy.create(name='foo2', age=2) Dummy.create(name='foo3', age=3) Dummy.create(name='foo4', age=3) rv = Dummy.cq('age', funcs.COUNT(1)).group_by('age').order_by('age').all() self.assertEqual(rv, [(1, 1), (2, 2), (3, 2)]) rv = Dummy.cq('name', 'age').group_by('name', 'age').order_by('age').all() self.assertEqual(rv, [('foo0', 1), ('foo2', 2), ('foo3', 3), ('foo4', 3)]) rv = Dummy.cq('name', 'age').group_by('name').group_by('age').order_by('age').all() self.assertEqual(rv, [('foo0', 1), ('foo2', 2), ('foo3', 3), ('foo4', 3)]) def test_having(self): # FIXME(PG) if is_pg: return Dummy.create(name='foo0', age=1) Dummy.create(name='foo2', age=2) Dummy.create(name='foo2', age=2) Dummy.create(name='foo3', age=3) Dummy.create(name='foo4', age=3) Dummy.create(name='foo5', age=3) c = COUNT(1).alias('c') rv = Dummy.cq('age', c).group_by( 'age' ).having(c > 2).all() self.assertEqual(rv, [(3, 3)]) def test_join(self): Dummy.create(name='dummy0', age=3) Dummy.create(name='dummy1', age=6) Dummy.create(name='dummy2', age=9) Foo.create(name='foo0', age=1) Foo.create(name='foo1', age=2) Foo.create(name='foo2', age=3) Foo.create(name='foo3', age=3) Foo.create(name='foo4', age=6) Foo.create(name='foo5', age=6) Foo.create(name='foo6', age=6) q = Foo.cq.join(Dummy).on(Foo.age == Dummy.age) res = q.all() self.assertEqual(len(res), 5) self.assertEqual({x.name for x in res}, { 'foo2', 'foo3', 'foo4', 'foo5', 'foo6' }) q = Dummy.cq.join(Foo).on(Foo.age == Dummy.age) res = q.all() self.assertEqual(len(res), 5) self.assertEqual({x.name for x in res}, { 'dummy0', 'dummy0', 'dummy1', 'dummy1', 'dummy1' }) q = Dummy.cq.join(Foo).on(Foo.age == Dummy.age, Dummy.age == 6) res = q.all() self.assertEqual(len(res), 3) self.assertEqual({x.name for x in res}, { 'dummy1', 'dummy1', 'dummy1' }) q = Dummy.cq(DISTINCT(Dummy.id)).join(Foo).on( Foo.age == Dummy.age ).order_by( Foo.id.desc(), Dummy.age.desc() ) res = q.all() self.assertEqual(res, [2, 1]) q = Dummy.cq(DISTINCT(Dummy.id)).left_join(Foo).on( Foo.age == Dummy.age ).order_by( Foo.id.desc(), Dummy.age.desc() ) res = q.all() if is_pg: self.assertEqual(res, [3, 2, 1]) else: self.assertEqual(res, [2, 1, 3]) q = Dummy.cq(DISTINCT(Dummy.id)).right_join(Foo).on( Foo.age == Dummy.age ).order_by( Foo.id.desc(), Dummy.age.desc() ) res = q.all() self.assertEqual(res, [2, 1, None]) def test_sum(self): Dummy.create(name='foo0', age=1) Dummy.create(name='foo2', age=2) Dummy.create(name='foo3', age=3) rv = Dummy.cq(SUM(Dummy.age)).first() self.assertEqual(rv, 6) def test_avg(self): Dummy.create(name='foo0', age=1) Dummy.create(name='foo2', age=2) Dummy.create(name='foo3', age=3) rv = Dummy.cq(AVG(Dummy.age)).first() self.assertEqual(rv, 2)

nilq/baby-python

python

from setuptools import setup setup( name='german_transliterate', version='0.1.3', author='repodiac', author_email='spamornot@gmx.net', packages=['german_transliterate'], url='http://github.com/repodiac/german_transliterate', license='CC-BY-4.0 License', description='german_transliterate can clean and transliterate (i.e. normalize) German text including abbreviations, numbers, timestamps etc.', long_description=open('README.md', encoding="UTF-8").read(), install_requires=[ "num2words", ], )

nilq/baby-python

python

# This file is part of Sequana software # # Copyright (c) 2016-2021 - Sequana Development Team # # # Distributed under the terms of the 3-clause BSD license. # The full license is in the LICENSE file, distributed with this software. # # website: https://github.com/sequana/sequana # documentation: http://sequana.readthedocs.io # ############################################################################## import os import sys import shutil from easydev import execute, TempFile, md5 from sequana.lazy import pandas as pd from sequana.lazy import pylab from sequana.lazy import numpy as np from sequana.misc import wget from sequana import sequana_config_path from colormap import Colormap import colorlog logger = colorlog.getLogger(__name__) __all__ = [ "KrakenResults", "KrakenPipeline", "KrakenAnalysis", "KrakenDownload", "KrakenSequential", "KrakenDB", ] class KrakenDB: """Class to handle a kraken DB""" def __init__(self, filename): if isinstance(filename, KrakenDB): filename = filename.path if os.path.exists(filename) is False: possible_path = sequana_config_path + "/kraken2_dbs/" + filename if os.path.exists(possible_path) is True: self.path = possible_path else: msg = f"{filename} not found locally or in {sequana_config_path}." raise IOError(msg) else: self.path = os.path.abspath(filename) self.name = os.path.basename(self.path) def _get_database_version(self): if os.path.exists(self.path + os.sep + "hash.k2d"): return "kraken2" else: # pragma: no cover logger.error( "Sequana supports kraken2 only. Looks like an invalid kraken database directory" ) version = property(_get_database_version) def __repr__(self): return self.name class KrakenResults(object): """Translate Kraken results into a Krona-compatible file If you run a kraken analysis with :class:`KrakenAnalysis`, you will end up with a file e.g. named kraken.out (by default). You could use kraken-translate but then you need extra parsing to convert into a Krona-compatible file. Here, we take the output from kraken and directly transform it to a krona-compatible file. kraken2 uses the --use-names that needs extra parsing. :: k = KrakenResults("kraken.out") k.kraken_to_krona() Then format expected looks like:: C HISEQ:426:C5T65ACXX:5:2301:18719:16377 1 203 1:71 A:31 1:71 C HISEQ:426:C5T65ACXX:5:2301:21238:16397 1 202 1:71 A:31 1:71 Where each row corresponds to one read. :: "562:13 561:4 A:31 0:1 562:3" would indicate that: the first 13 k-mers mapped to taxonomy ID #562 the next 4 k-mers mapped to taxonomy ID #561 the next 31 k-mers contained an ambiguous nucleotide the next k-mer was not in the database the last 3 k-mers mapped to taxonomy ID #562 For kraken2, format is slighlty different since it depends on paired or not. If paired, :: C read1 2697049 151|151 2697049:117 |:| 0:1 2697049:116 See kraken documentation for details. .. note:: a taxon of ID 1 (root) means that the read is classified but in differen domain. https://github.com/DerrickWood/kraken/issues/100 .. note:: This takes care of fetching taxons and the corresponding lineages from online web services. """ def __init__(self, filename="kraken.out", verbose=True): """.. rubric:: **constructor** :param filename: the input from KrakenAnalysis class """ self.filename = filename on_rtd = os.environ.get("READTHEDOCS", None) == "True" if on_rtd is False: from sequana.taxonomy import Taxonomy self.tax = Taxonomy(verbose=verbose) self.tax.download_taxonomic_file() # make sure it is available locally else: # pragma: no cover class Taxonomy(object): # pragma: no cover from sequana import sequana_data # must be local df = pd.read_csv(sequana_data("test_taxon_rtd.csv"), index_col=0) def get_lineage_and_rank(self, x): # Note that we add the name as well here ranks = [ "kingdom", "phylum", "class", "order", "family", "genus", "species", "name", ] return [(self.df.loc[x][rank], rank) for rank in ranks] self.tax = Taxonomy() if filename: # This initialise the data self._parse_data() self._data_created = False def get_taxonomy_db(self, ids): """Retrieve taxons given a list of taxons :param list ids: list of taxons as strings or integers. Could also be a single string or a single integer :return: a dataframe .. note:: the first call first loads all taxons in memory and takes a few seconds but subsequent calls are much faster """ # filter the lineage to keep only information from one of the main rank # that is superkingdom, kingdom, phylum, class, order, family, genus and # species ranks = ("kingdom", "phylum", "class", "order", "family", "genus", "species") if isinstance(ids, int): ids = [ids] if len(ids) == 0: return pd.DataFrame() if isinstance(ids, list) is False: ids = [ids] lineage = [self.tax.get_lineage_and_rank(x) for x in ids] # Now, we filter each lineage to keep only relevant ranks # There are a few caveats though as explained hereafter # we merge the kingdom and superkingdom and subkingdom results = [] for i, this in enumerate(lineage): default = dict.fromkeys(ranks, " ") for entry in this: if entry[1] in ranks: default[entry[1]] = entry[0] # if there is a superkingdom, overwrite the kingdom for entry in this: if entry[1] == "superkingdom": default["kingdom"] = entry[0] if default["kingdom"] == " ": for entry in this: if entry[1] == "subkingdom": default["kingdom"] = entry[0] # in theory, we have now populated all ranks; # Yet, there are several special cases (need examples): # 1. all ranks are filled: perfect # 2. some ranks are empty: we fill them with a space. # 3. all ranks are empty: # a. this is the root # b. this may be expected. e.g for an artifical sequence # c. all ranks below species are empty --> this is probably # what we will get e.g. for plasmids # case 3.b if set([x[1] for x in this]) == {"no rank", "species"}: # we can ignore the root and keep the others # if we end up with more than 6 entries, this is annoying # let us put a warning for now. count = 0 for x in this: if x[1] == "no rank" and x[0] != "root": default[ranks[count]] = x[0] count += 1 if count > 6: logger.warning("too many no_rank in taxon{}".format(ids[i])) break # for the name, we take the last entry, which is suppose to be the # scientific name found, so the scientific name of the taxon itself. # Note that this is not alwyas the species rank name # For instance for the taxon 2509511, the ID correspond to # a subgenus of Sarbecovirus and has no species entry. last_name, last_rank = this[-1] if last_rank not in ["species", "no rank"]: default["name"] = f"{last_rank}:{last_name}" else: default["name"] = "" results.append(default) df = pd.DataFrame.from_records(results) df.index = ids df = df[list(ranks) + ["name"]] df.index = df.index.astype(int) return df def _parse_data(self): taxonomy = {} logger.info("Reading kraken data from {}".format(self.filename)) columns = ["status", "taxon", "length"] # we select only col 0,2,3 to save memory, which is required on very # large files try: # each call to concat in the for loop below # will take time and increase with chunk position. # for 15M reads, this has a big cost. So chunksize set to 1M # is better than 1000 and still reasonable in memory reader = pd.read_csv( self.filename, sep="\t", header=None, usecols=[0, 2, 3], chunksize=1000000, ) except pd.errors.EmptyDataError: # pragma: no cover logger.warning("Empty files. 100%% unclassified ?") self.unclassified = "?" # size of the input data set self.classified = 0 self._df = pd.DataFrame([], columns=columns) self._taxons = self._df.taxon return except pd.errors.ParserError: # raise NotImplementedError # this section is for the case # #only_classified_output when there is no found classified read raise NotImplementedError for chunk in reader: try: self._df self._df = pd.concat([self._df, chunk]) except AttributeError: self._df = chunk self._df.columns = columns count = sum(self._df.taxon == 1) percentage = count / len(self._df) * 100 if percentage >= 1: logger.warning( "Found {} taxons of classified reads with root ID (1) ({} %)".format( count, round(percentage, 2) ) ) # This gives the list of taxons as index and their amount # above, we select only columns 0, 2, 3 the column are still labelled # 0, 2, 3 in the df self._taxons = self._df.groupby("taxon").size() try: self._taxons.drop(0, inplace=True) except: pass # 0 may not be there self._taxons.sort_values(ascending=False, inplace=True) category = self.df.groupby("status").size() if "C" in category.index: self.classified = category["C"] else: self.classified = 0 if "U" in category.index: self.unclassified = category["U"] else: self.unclassified = 0 logger.debug(self.taxons.iloc[0:10]) def _get_taxons(self): try: return self._taxons except: self._parse_data() return self._taxons taxons = property(_get_taxons) def _get_df(self): try: return self._df except: self._parse_data() return self._df df = property(_get_df) def _get_df_with_taxon(self, dbname): df = self.get_taxonomy_db([int(x) for x in self.taxons.index]) df["count"] = self.taxons.values df.reset_index(inplace=True) newrow = len(df) df.loc[newrow] = "Unclassified" df.loc[newrow, "count"] = self.unclassified df.loc[newrow, "index"] = -1 df.rename(columns={"index": "taxon"}, inplace=True) df["percentage"] = df["count"] / df["count"].sum() * 100 starter = ["taxon", "count", "percentage"] df = df[starter + [x for x in df.columns if x not in starter]] df.sort_values(by="percentage", inplace=True, ascending=False) return df def kraken_to_csv(self, filename, dbname): df = self._get_df_with_taxon(dbname) df.to_csv(filename, index=False) return df def kraken_to_json(self, filename, dbname): df = self._get_df_with_taxon(dbname) try: df.to_json(filename, indent=4, orient="records") except: df.to_json(filename, orient="records") return df def kraken_to_krona(self, output_filename=None, nofile=False): """ :return: status: True is everything went fine otherwise False """ if output_filename is None: output_filename = self.filename + ".summary" taxon_to_find = list(self.taxons.index) if len(taxon_to_find) == 0: logger.warning( "No reads were identified. You will need a more complete database" ) self.output_filename = output_filename with open(output_filename, "w") as fout: fout.write("%s\t%s" % (self.unclassified, "Unclassified")) return False if len(taxon_to_find) == 0: return False df = self.get_taxonomy_db(taxon_to_find) self.lineage = [";".join(this) for this in df[df.columns[0:-1]].values] self.scnames = list(df["name"].values) # do we need a cast ? # Now save the file self.output_filename = output_filename with open(output_filename, "w") as fout: for i, this in enumerate(self.lineage): taxon = taxon_to_find[i] count = self.taxons.loc[taxon] line = str(count) + "\t" + "\t".join(this.split(";")) line += " " + self.scnames[i] fout.write(line + "\n") try: fout.write("%s\t%s" % (self.unclassified, "Unclassified")) except: pass # unclassified may not exists if all classified self._data_created = True return True def plot2(self, kind="pie", fontsize=12): """This is the simplified static krona-like plot included in HTML reports""" import matplotlib.pyplot as plt taxons = self.taxons.copy() if len(self.taxons.index) == 0: return None df = self.get_taxonomy_db(list(self.taxons.index)) self.dd = df if self.unclassified > 0: df.loc[-1] = ["Unclassified"] * 8 taxons[-1] = self.unclassified df["ratio"] = taxons / taxons.sum() * 100 data_class = df.groupby(["kingdom", "class"]).sum() data_species = df.groupby(["kingdom", "species"]).sum() X = [] Y = [] Z = [] labels = [] zlabels, ztaxons = [], [] kingdom_colors = [] inner_colors = [] inner_labels = [] species_colors = [] taxons = df["species"].reset_index().set_index("species") for kingdom in data_class.index.levels[0]: # kingdom info X.append(data_class.loc[kingdom].ratio.sum()) # class info y = list(data_class.loc[kingdom].ratio.values) temp = data_class.loc[kingdom] y1 = temp.query("ratio>=0.5") y2 = temp.query("ratio<0.5") y = list(y1.ratio.values) + list(y2.ratio.values) inner_labels += list(y1.ratio.index) + [""] * len(y2.ratio) Y.extend(y) # species info temp = data_species.loc[kingdom] z1 = temp.query("ratio>=0.5") z2 = temp.query("ratio<0.5") z = list(z1.ratio.values) + list(z2.ratio.values) zlabels += list(z1.ratio.index) + [""] * len(z2.ratio) Z.extend(z) if kingdom.strip(): labels.append(kingdom) else: labels.append("undefined/unknown taxon") if kingdom == "Eukaryota": this_cmap = plt.cm.Purples elif kingdom == "Unclassified": this_cmap = plt.cm.Greys elif kingdom == "Bacteria": this_cmap = plt.cm.Reds elif kingdom == "Viruses": this_cmap = plt.cm.Greens elif kingdom == "Archaea": this_cmap = Colormap().cmap_linear("yellow", "yellow", "orange") else: this_cmap = Colormap().cmap_linear( "light gray", "gray(w3c)", "dark gray" ) kingdom_colors.append(this_cmap(0.8)) inner_colors.extend(this_cmap(np.linspace(0.6, 0.2, len(y)))) species_colors.extend(this_cmap(np.linspace(0.6, 0.2, len(z)))) fig, ax = pylab.subplots(figsize=(9.5, 7)) size = 0.2 pct_distance = 0 w1, l1 = ax.pie( X, radius=1 - 2 * size, colors=kingdom_colors, wedgeprops=dict(width=size, edgecolor="w"), labels=labels, labeldistance=0.4, ) w2, l2 = ax.pie( Y, radius=1 - size, colors=inner_colors, labels=[x.replace("Unclassified", "") for x in inner_labels], wedgeprops=dict(width=size, edgecolor="w"), labeldistance=0.65, ) # labels can be long. Let us cut them zlabels2 = [] for this in zlabels: if len(this) > 30: zlabels2.append(this[0:30] + "...") else: zlabels2.append(this) w3, l3 = ax.pie( Z, radius=1, colors=species_colors, labels=[x.replace("Unclassified", "") for x in zlabels2], wedgeprops=dict(width=size, edgecolor="w"), labeldistance=0.9, ) ax.set(aspect="equal") pylab.subplots_adjust(right=1, left=0, bottom=0, top=1) pylab.legend(labels, title="kingdom", loc="upper right", fontsize=fontsize) import webbrowser mapper = {k: v for k, v in zip(zlabels, Z)} def on_pick(event): wedge = event.artist label = wedge.get_label() if mapper[label] > 1: taxon = taxons.loc[label, "index"] webbrowser.open( "https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?id={}".format( taxon ) ) else: wedge.set_color("white") for wedge in w3: wedge.set_picker(True) fig.canvas.mpl_connect("pick_event", on_pick) # this is used to check that everything was okay in the rules return df def plot( self, kind="pie", cmap="tab20c", threshold=1, radius=0.9, textcolor="red", delete_krona_file=False, **kargs, ): """A simple non-interactive plot of taxons :return: None if no taxon were found and a dataframe otherwise A Krona Javascript output is also available in :meth:`kraken_to_krona` .. plot:: :include-source: from sequana import KrakenResults, sequana_data test_file = sequana_data("kraken.out", "doc") k = KrakenResults(test_file) df = k.plot(kind='pie') .. seealso:: to generate the data see :class:`KrakenPipeline` or the standalone application **sequana_taxonomy**. .. todo:: For a future release, we could use this kind of plot https://stackoverflow.com/questions/57720935/how-to-use-correct-cmap-colors-in-nested-pie-chart-in-matplotlib """ if len(self._df) == 0: return if self._data_created == False: status = self.kraken_to_krona() if kind not in ["barh", "pie"]: logger.error("kind parameter: Only barh and pie are supported") return # This may have already been called but maybe not. This is not time # consuming, so we call it again here if len(self.taxons.index) == 0: return None df = self.get_taxonomy_db(list(self.taxons.index)) if self.unclassified > 0: df.loc[-1] = ["Unclassified"] * 8 data = self.taxons.copy() # we add the unclassified only if needed if self.unclassified > 0: data.loc[-1] = self.unclassified data = data / data.sum() * 100 assert threshold > 0 and threshold < 100 # everything below the threshold (1) is gather together and summarised # into 'others' others = data[data < threshold].sum() data = data[data >= threshold] names = df.loc[data.index]["name"] data.index = names.values if others > 0: data.loc["others"] = others try: data.sort_values(inplace=True) except: data.sort(inplace=True) pylab.figure(figsize=(10, 8)) pylab.clf() self.dd = data if kind == "pie": ax = data.plot( kind=kind, cmap=cmap, autopct="%1.1f%%", radius=radius, **kargs ) pylab.ylabel(" ") for text in ax.texts: # large, x-small, small, None, x-large, medium, xx-small, # smaller, xx-large, larger text.set_size("small") text.set_color(textcolor) for wedge in ax.patches: wedge.set_linewidth(1) wedge.set_edgecolor("k") self.ax = ax elif kind == "barh": ax = data.plot(kind=kind, **kargs) pylab.xlabel(" percentage ") if delete_krona_file: os.remove(self.filename + ".summary") return data def to_js(self, output="krona.html"): if self._data_created == False: status = self.kraken_to_krona() execute("ktImportText %s -o %s" % (self.output_filename, output)) def boxplot_classified_vs_read_length(self): """Show distribution of the read length grouped by classified or not""" # if paired and kraken2, there are | in length to separate both reads. # to simplify, if this is the case, we will just take the first read # length for now. df = self.df.copy() try: # kraken2 df.length = df.length.apply(lambda x: int(x.split("|")[0])) except: pass df[["status", "length"]].groupby("status").boxplot() return df def histo_classified_vs_read_length(self): """Show distribution of the read length grouped by classified or not""" # if paired and kraken2, there are | in length to separate both reads. # to simplify, if this is the case, we will just take the first read # length for now. df = self.df.copy() if "|" in str(df.length.values[0]): df.length = df.length.apply(lambda x: int(x.split("|")[0])) df = df[["status", "length"]] M = df["length"].max() df.hist(by="status", sharey=True, bins=pylab.linspace(0, M, int(M / 5))) axes = pylab.gcf().get_axes() axes[0].set_xlabel("read length") axes[1].set_xlabel("read length") axes[1].grid(True) axes[0].grid(True) return df class KrakenPipeline(object): """Used by the standalone application sequana_taxonomy This runs Kraken on a set of FastQ files, transform the results in a format compatible for Krona, and creates a Krona HTML report. :: from sequana import KrakenPipeline kt = KrakenPipeline(["R1.fastq.gz", "R2.fastq.gz"], database="krakendb") kt.run() kt.show() .. warning:: We do not provide Kraken database within sequana. You may either download a database from https://ccb.jhu.edu/software/kraken/ or use this class to download a toy example that will be stored in e.g .config/sequana under Unix platforms. See :class:`KrakenDownload`. .. seealso:: We provide a standalone application of this class, which is called sequana_taxonomy and can be used within a command shell. """ def __init__( self, fastq, database, threads=4, output_directory="kraken", dbname=None, confidence=0, ): """.. rubric:: Constructor :param fastq: either a fastq filename or a list of 2 fastq filenames :param database: the path to a valid Kraken database :param threads: number of threads to be used by Kraken :param output_directory: output filename of the Krona HTML page :param dbname: Description: internally, once Kraken has performed an analysis, reads are associated to a taxon (or not). We then find the correponding lineage and scientific names to be stored within a Krona formatted file. KtImportTex is then used to create the Krona page. """ # Set and create output directory self.output_directory = output_directory try: os.makedirs(output_directory) except FileExistsError: pass self.database = database self.ka = KrakenAnalysis(fastq, database, threads, confidence=confidence) if dbname is None: self.dbname = os.path.basename(database) else: self.dbname = dbname def run( self, output_filename_classified=None, output_filename_unclassified=None, only_classified_output=False, ): """Run the analysis using Kraken and create the Krona output .. todo:: reuse the KrakenResults code to simplify this method. """ # Run Kraken (KrakenAnalysis) kraken_results = self.output_directory + os.sep + "kraken.out" self.ka.run( output_filename=kraken_results, output_filename_unclassified=output_filename_unclassified, output_filename_classified=output_filename_classified, only_classified_output=only_classified_output, ) # Translate kraken output to a format understood by Krona and save png # image self.kr = KrakenResults(kraken_results, verbose=False) # we save the pie chart try: self.kr.plot2(kind="pie") except Exception as err: logger.warning(err) self.kr.plot(kind="pie") pylab.savefig(self.output_directory + os.sep + "kraken.png") # we save information about the unclassified reads (length) try: self.kr.boxplot_classified_vs_read_length() pylab.savefig(self.output_directory + os.sep + "boxplot_read_length.png") except Exception as err: logger.warning("boxplot read length could not be computed") try: self.kr.histo_classified_vs_read_length() pylab.savefig(self.output_directory + os.sep + "hist_read_length.png") except Exception as err: logger.warning("hist read length could not be computed") prefix = self.output_directory + os.sep self.kr.kraken_to_json(prefix + "kraken.json", self.dbname) self.kr.kraken_to_csv(prefix + "kraken.csv", self.dbname) # Transform to Krona HTML from snakemake import shell kraken_html = self.output_directory + os.sep + "kraken.html" status = self.kr.kraken_to_krona(output_filename=prefix + "kraken.out.summary") if status is True: shell( "ktImportText %s -o %s" % (prefix + "kraken.out.summary", kraken_html) ) else: shell("touch {}".format(kraken_html)) # finally a summary database = KrakenDB(self.database) summary = {"database": [database.name]} summary[database.name] = {"C": int(self.kr.classified)} summary["U"] = int(self.kr.unclassified) summary["total"] = int(self.kr.unclassified + self.kr.classified) # redundant but useful and compatible with sequential approach summary["unclassified"] = int(self.kr.unclassified) summary["classified"] = int(self.kr.classified) return summary def show(self): """Opens the filename defined in the constructor""" from easydev import onweb onweb(self.output) class KrakenAnalysis(object): """Run kraken on a set of FastQ files In order to run a Kraken analysis, we firtst need a local database. We provide a Toy example. The ToyDB is downloadable as follows ( you will need to run the following code only once):: from sequana import KrakenDownload kd = KrakenDownload() kd.download_kraken_toydb() .. seealso:: :class:`KrakenDownload` for more databases The path to the database is required to run the analysis. It has been stored in the directory ./config/sequana/kraken_toydb under Linux platforms The following code should be platform independent:: import os from sequana import sequana_config_path database = sequana_config_path + os.sep + "kraken_toydb") Finally, we can run the analysis on the toy data set:: from sequana import sequana_data data = sequana_data("Hm2_GTGAAA_L005_R1_001.fastq.gz", "data") ka = KrakenAnalysis(data, database=database) ka.run() This creates a file named *kraken.out*. It can be interpreted with :class:`KrakenResults` """ def __init__(self, fastq, database, threads=4, confidence=0): """.. rubric:: Constructor :param fastq: either a fastq filename or a list of 2 fastq filenames :param database: the path to a valid Kraken database :param threads: number of threads to be used by Kraken :param confidence: parameter used by kraken2 :param return: """ self.database = KrakenDB(database) self.threads = threads self.confidence = confidence # Fastq input if isinstance(fastq, str): self.paired = False self.fastq = [fastq] elif isinstance(fastq, list): if len(fastq) == 2: self.paired = True elif len(fastq) == 1: self.paired = False else: raise IOError(("You must provide 1 or 2 files")) self.fastq = fastq else: raise ValueError("Expected a fastq filename or list of 2 fastq filenames") def run( self, output_filename=None, output_filename_classified=None, output_filename_unclassified=None, only_classified_output=False, ): """Performs the kraken analysis :param str output_filename: if not provided, a temporary file is used and stored in :attr:`kraken_output`. :param str output_filename_classified: not compressed :param str output_filename_unclassified: not compressed """ if self.database.version != "kraken2": logger.error(f"input database is not valid kraken2 database") sys.exit(1) if output_filename is None: self.kraken_output = TempFile().name else: self.kraken_output = output_filename dirname = os.path.dirname(output_filename) if os.path.exists(dirname) is False: os.makedirs(dirname) # make sure the required output directories exist: # and that the output filenames ends in .fastq if output_filename_classified: assert output_filename_classified.endswith(".fastq") dirname = os.path.dirname(output_filename_classified) if os.path.exists(dirname) is False: os.makedirs(dirname) if output_filename_unclassified: assert output_filename_unclassified.endswith(".fastq") dirname = os.path.dirname(output_filename_unclassified) if os.path.exists(dirname) is False: os.makedirs(dirname) params = { "database": self.database.path, "thread": self.threads, "file1": self.fastq[0], "kraken_output": self.kraken_output, "output_filename_unclassified": output_filename_unclassified, "output_filename_classified": output_filename_classified, } if self.paired: params["file2"] = self.fastq[1] command = f"kraken2 --confidence {self.confidence}" command += f" {params['file1']}" if self.paired: command += f" {params['file2']} --paired" command += f" --db {params['database']} " command += f" --threads {params['thread']} " command += f" --output {params['kraken_output']} " # If N is number of reads unclassified 3 cases depending on out-fmt # choice # case1 --paired and out-fmt legacy saved fasta R1 and R2 together on N lines # case2 --paired and out-fmt interleaved saved fasta R1 and R2 alternatively on 2N lines # case3 --paired and out-fmt paired saved R1 on N lines. Where is R2 ???? # Note, that there is always one single file. So, the only way for # kraken to know that this new files (used as input) is paired, is to # use --paired. # In any case, this new file looks like an R1-only file. Indeed, if # interleaved, all data inside the file, if legacy, The R1 and R2 are # separated by N but a unique sequence. If --out-fmt is paired, this is # annoying. Indeed, half of the data is lost. # So, if now input is # case1, we cannot provide --paired # case2 we cannot either, so how are R1 and R2 taken care of ? # besides, if provided, the interleaved input is seen as single ended. # Indeed, if provided, --out-fmt cannot be interleaved since krakne1 # complains that input is not paired. # case3, only R1 so we cannot use --paired # if kraken2, there is no --out-fmt option, so output is always a fastq # with either R1 only or two output files. # If we omit the --paired options, the 2 input R1 and R2 are considered # as 2 different unrelated samples # if we use --paired we now must have # in the file name, and then # the two files are created if self.database.version == "kraken2": if output_filename_unclassified: command += " --unclassified-out %(output_filename_unclassified)s " if output_filename_classified: command += " --classified-out %(output_filename_classified)s " command = command % params logger.debug(command) from snakemake import shell shell(command) if only_classified_output: # kraken2 has no classified_output option. we mimic it here below # just to get a temporary filename fout = TempFile() outname = fout.name newfile = open(outname, "w") with open(output_filename, "r") as fin: for line in fin.readlines(): if line.startswith("C"): newfile.write(line) newfile.close() shutil.move(outname, output_filename) # a simple utility function try: from itertools import izip_longest except: from itertools import zip_longest as izip_longest def grouper(iterable): args = [iter(iterable)] * 8 return izip_longest(*args) class KrakenSequential(object): """Kraken Sequential Analysis This runs Kraken on a FastQ file with multiple k-mer databases in a sequencial way way. Unclassified sequences with the first database are input for the second, and so on. The input may be a single FastQ file or paired, gzipped or not. FastA are also accepted. """ def __init__( self, filename_fastq, fof_databases, threads=1, output_directory="./kraken_sequential/", keep_temp_files=False, output_filename_unclassified=None, output_filename_classified=None, force=False, confidence=0, ): """.. rubric:: **constructor** :param filename_fastq: FastQ file to analyse :param fof_databases: file that contains a list of databases paths (one per line). The order is important. Note that you may also provide a list of datab ase paths. :param threads: number of threads to be used by Kraken :param output_directory: name of the output directory :param keep_temp_files: bool, if True, will keep intermediate files from each Kraken analysis, and save html report at each step :param bool force: if the output directory already exists, the instanciation fails so that the existing data is not overrwritten. If you wish to overwrite the existing directory, set this parameter to iTrue. """ self.filename_fastq = filename_fastq self.confidence = confidence # input databases may be stored in a file if isinstance(fof_databases, str) and os.path.exists(fof_databases): with open(fof_databases, "r") as fof: self.databases = [ absolute_path.split("\n")[0] for absolute_path in fof.readlines() ] # or simply provided as a list elif isinstance(fof_databases, list): self.databases = fof_databases[:] else: raise TypeError( "input databases must be a list of valid kraken2 " "databases or a file (see documebntation)" ) self.databases = [KrakenDB(x) for x in self.databases] for d in self.databases: if d.version != "kraken2": logger.error(f"input database {d} is not valid kraken2 ") sys.exit(1) self.threads = threads self.output_directory = output_directory self.keep_temp_files = keep_temp_files # check if the output directory already exist try: os.mkdir(output_directory) except OSError: if os.path.isdir(output_directory) and force is False: logger.error("Output directory %s already exists" % output_directory) raise Exception elif force is True: logger.warning( "Output directory %s already exists. You may " "overwrite existing results" % output_directory ) # list of input fastq files if isinstance(filename_fastq, list) and len(filename_fastq) in [1, 2]: self.inputs = filename_fastq[:] elif isinstance(filename_fastq, str): self.inputs = [filename_fastq] else: msg = "input file must be a string or list of 2 filenames" msg += "\nYou provided {}".format(filename_fastq) raise TypeError(msg) if len(self.inputs) == 1: self.paired = False elif len(self.inputs) == 2: self.paired = True self.unclassified_output = output_filename_unclassified self.classified_output = output_filename_classified def _run_one_analysis(self, iteration): """Run one analysis""" db = self.databases[iteration] logger.info("Analysing data using database {}".format(db)) # a convenient alias _pathto = lambda x: self.output_directory + x # the output is saved in this file if self.paired: # if paired, kraken2 expect a # and then will create 2 files (1 and 2 # ) # Note that kraken adds a _ before the # (1,2) so no need to add one output_filename_unclassified = _pathto("unclassified_%d#.fastq" % iteration) file_fastq_unclass = [ _pathto("unclassified_%d_1.fastq" % iteration), _pathto("unclassified_%d_2.fastq" % iteration), ] else: output_filename_unclassified = _pathto("unclassified_%d.fastq" % iteration) file_fastq_unclass = _pathto("unclassified_%d.fastq" % iteration) if iteration == 0: inputs = self.inputs else: inputs = self._list_kraken_input[iteration - 1] # if this is the last iteration (even if iteration is zero), save # classified and unclassified in the final kraken results. if iteration == len(self.databases) - 1: only_classified_output = False else: only_classified_output = True file_kraken_out = self.output_directory + "/kraken_{}.out".format(iteration) # The analysis itself analysis = KrakenAnalysis(inputs, db, self.threads, confidence=self.confidence) analysis.run( output_filename=file_kraken_out, output_filename_unclassified=output_filename_unclassified, only_classified_output=only_classified_output, ) # save input/output files. self._list_kraken_input.append(file_fastq_unclass) self._list_kraken_output.append(file_kraken_out) def run(self, dbname="multiple", output_prefix="kraken_final"): """Run the sequential analysis :param dbname: :param output_prefix: :return: dictionary summarizing the databases names and classified/unclassied This method does not return anything creates a set of files: - kraken_final.out - krona_final.html - kraken.png (pie plot of the classified/unclassified reads) .. note:: the databases are run in the order provided in the constructor. """ # list of all output to merge at the end self._list_kraken_output = [] self._list_kraken_input = [] # Iteration over the databases for iteration in range(len(self.databases)): # The analysis itself status = self._run_one_analysis(iteration) last_unclassified = self._list_kraken_input[-1] # If everything was classified, we can stop here if isinstance(last_unclassified, str): stat = os.stat(last_unclassified) if stat.st_size == 0: break elif isinstance(last_unclassified, list): stat = os.stat(last_unclassified[0]) if stat.st_size == 0: break # concatenate all kraken output files file_output_final = self.output_directory + os.sep + "%s.out" % output_prefix with open(file_output_final, "w") as outfile: for fname in self._list_kraken_output: with open(fname) as infile: for line in infile: outfile.write(line) logger.info("Analysing final results") result = KrakenResults(file_output_final, verbose=False) try: result.histo_classified_vs_read_length() pylab.savefig(self.output_directory + os.sep + "hist_read_length.png") except Exception as err: logger.warning("hist read length could not be computed") try: result.boxplot_classified_vs_read_length() pylab.savefig(self.output_directory + os.sep + "boxplot_read_length.png") except Exception as err: logger.warning("hist read length could not be computed") # TODO: this looks similar to the code in KrakenPipeline. could be factorised result.to_js("%s%s%s.html" % (self.output_directory, os.sep, output_prefix)) try: result.plot2(kind="pie") except Exception as err: logger.warning(err) result.plot(kind="pie") pylab.savefig(self.output_directory + os.sep + "kraken.png") prefix = self.output_directory + os.sep result.kraken_to_json(prefix + "kraken.json", dbname) result.kraken_to_csv(prefix + "kraken.csv", dbname) # remove kraken intermediate files (including unclassified files) if self.unclassified_output: # Just cp the last unclassified file try: # single-end data (one file) shutil.copy2(self._list_kraken_input[-1], self.unclassified_output) except: for i, x in enumerate(self._list_kraken_input[-1]): shutil.copy2(x, self.unclassified_output.replace("#", str(i + 1))) if self.classified_output: # Just cp the last classified file shutil.copy2(self._list_kraken_input[-1], self.classified_output) summary = {"databases": [x.name for x in self.databases]} total = 0 classified = 0 for f_temp, db in zip(self._list_kraken_output, self.databases): # In theory, the first N-1 DB returns only classified (C) read # and the last one contains both try: df = pd.read_csv(f_temp, sep="\t", header=None, usecols=[0]) C = sum(df[0] == "C") U = sum(df[0] == "U") except pd.errors.EmptyDataError: # if no read classified, C = 0 U = 0 total += U total += C classified += C summary[db.name] = {"C": C} if U != 0: # the last one summary["unclassified"] = U summary["total"] = total summary["classified"] = classified if not self.keep_temp_files: # kraken_0.out for f_temp in self._list_kraken_output: os.remove(f_temp) # unclassified for f_temp in self._list_kraken_input: if isinstance(f_temp, str): os.remove(f_temp) elif isinstance(f_temp, list): for this in f_temp: os.remove(this) return summary class KrakenDownload(object): """Utility to download Kraken DB and place them in a local directory :: from sequana import KrakenDownload kd = KrakenDownload() kd.download('toydb') """ def __init__(self, output_dir=None): if output_dir is None: self.output_dir = f"{sequana_config_path}{os.sep}kraken2_dbs" else: self.output_dir = output_dir def download(self, name, verbose=True): if name == "toydb": self._download_kraken2_toydb(verbose=verbose) else: raise ValueError("name must be 'toydb' for now") def _download_kraken2_toydb(self, verbose=True): """Download the kraken DB toy example from sequana_data into .config/sequana directory Checks the md5 checksums. About 32Mb of data """ base = f"{self.output_dir}{os.sep}toydb" try: os.makedirs(base) except FileExistsError: pass baseurl = "https://github.com/sequana/data/raw/master/" # download only if required logger.info("Downloading the database into %s" % base) md5sums = [ "31f4b20f9e5c6beb9e1444805264a6e5", "733f7587f9c0c7339666d5906ec6fcd3", "7bb56a0f035b27839fb5c18590b79263", ] filenames = ["hash.k2d", "opts.k2d", "taxo.k2d"] for filename, md5sum in zip(filenames, md5sums): url = baseurl + f"kraken2_toydb/{filename}" filename = base + os.sep + filename if os.path.exists(filename) and md5(filename) == md5sum: logger.warning(f"{filename} already present with good md5sum") else: logger.info(f"Downloading {url}") wget(url, filename)

nilq/baby-python

python

import planckStyle as s g = s.getSubplotPlotter() g.settings.legend_fontsize -= 3.5 g.settings.lineM = ['-g', '-r', '-b', '-k', '--r', '--b'] pol = ['TT', 'TE', 'EE', 'TTTEEE'] dataroots = [ getattr(s, 'defdata_' + p) for p in pol] dataroots += [dataroots[1].replace('lowEB', 'lowTEB'), dataroots[2].replace('lowEB', 'lowTEB')] for par, marker in zip(['', 'nnu', 'mnu', 'Alens', 'r', 'yhe', 'nrun'], [None, 3.046, 0.06, 1, None, 0.2449, 0]): g.newPlot() base = 'base_' if par: base += par + '_' roots = [base + dat for dat in dataroots] labels = [s.datalabel[r] for r in dataroots] g.settings.legend_frac_subplot_margin = 0.15 plotpars = [ 'zrei', 'H0', 'omegabh2', 'thetastar', 'A', 'tau', 'omegam', 'omegach2', 'ns', 'sigma8'] if par: plotpars[0] = par g.plots_1d(roots, plotpars, nx=5, legend_ncol=len(roots), legend_labels=labels, share_y=True, markers=[marker]) g.export(tag=par)

nilq/baby-python

python

#!/usr/bin/env python # -*- coding: utf-8 -*- """Python KISS Module Test Constants.""" __author__ = 'Greg Albrecht W2GMD <oss@undef.net>' # NOQA pylint: disable=R0801 __copyright__ = 'Copyright 2017 Greg Albrecht and Contributors' # NOQA pylint: disable=R0801 __license__ = 'Apache License, Version 2.0' # NOQA pylint: disable=R0801 PANGRAM = 'the quick brown fox jumps over the lazy dog' ALPHABET = PANGRAM.replace(' ', '') NUMBERS = ''.join([str(x) for x in range(0, 10)]) POSITIVE_NUMBERS = NUMBERS[1:] ALPHANUM = ''.join([ALPHABET, NUMBERS]) TEST_FRAMES = 'tests/test_frames.log' TEST_FRAME = ( '82a0a4b0646860ae648e9a88406cae92888a62406303f021333734352e3735' '4e4931323232382e303557235732474d442d3620496e6e65722053756e73657' '42c2053462069476174652f4469676970656174657220687474703a2f2f7732' '676d642e6f7267')

nilq/baby-python

python

from toolz import get from functools import partial pairs = [(1, 2) for i in range(100000)] def test_get(): first = partial(get, 0) for p in pairs: first(p)

nilq/baby-python

python

import torch import torch.nn as nn import torch.optim as optim import pytorch_lightning as pl from network.dla import MOC_DLA from network.resnet import MOC_ResNet from trainer.losses import MOCLoss from MOC_utils.model import load_coco_pretrained_model backbone = { 'dla': MOC_DLA, 'resnet': MOC_ResNet } def fill_fc_weights(layers): for m in layers.modules(): if isinstance(m, nn.Conv2d): if m.bias is not None: nn.init.constant_(m.bias, 0) class MOC_Branch(nn.Module): def __init__(self, input_channel, arch, head_conv, branch_info, K): super(MOC_Branch, self).__init__() assert head_conv > 0 wh_head_conv = 64 if arch == 'resnet' else head_conv self.hm = nn.Sequential( nn.Conv2d(K * input_channel, head_conv, kernel_size=3, padding=1, bias=True), nn.ReLU(inplace=True), nn.Conv2d(head_conv, branch_info['hm'], kernel_size=1, stride=1, padding=0, bias=True)) self.hm[-1].bias.data.fill_(-2.19) self.mov = nn.Sequential( nn.Conv2d(K * input_channel, head_conv, kernel_size=3, padding=1, bias=True), nn.ReLU(inplace=True), nn.Conv2d(head_conv, branch_info['mov'], kernel_size=1, stride=1, padding=0, bias=True)) fill_fc_weights(self.mov) self.wh = nn.Sequential( nn.Conv2d(input_channel, wh_head_conv, kernel_size=3, padding=1, bias=True), nn.ReLU(inplace=True), nn.Conv2d(wh_head_conv, branch_info['wh'] // K, kernel_size=1, stride=1, padding=0, bias=True)) fill_fc_weights(self.wh) def forward(self, input_chunk): output = {} output_wh = [] for feature in input_chunk: output_wh.append(self.wh(feature)) input_chunk = torch.cat(input_chunk, dim=1) output_wh = torch.cat(output_wh, dim=1) output['hm'] = self.hm(input_chunk) output['mov'] = self.mov(input_chunk) output['wh'] = output_wh return output class MOC_Net(pl.LightningModule): def __init__(self, arch, num_classes, head_conv=256, K=7, **kwargs): super().__init__() self.save_hyperparameters() num_layers = int(arch[arch.find('_') + 1:]) if '_' in arch else 0 arch = arch[:arch.find('_')] if '_' in arch else arch branch_info = {'hm': num_classes, 'mov': 2 * K, 'wh': 2 * K} self.K = K self.backbone = backbone[arch](num_layers) self.branch = MOC_Branch(self.backbone.output_channel, arch, head_conv, branch_info, K) # Define the loss function self.loss = MOCLoss() def forward(self, x): chunk = [self.backbone(x[i]) for i in range(self.K)] return [self.branch(chunk)] def configure_optimizers(self): if self.hparams.optimizer == 'sgd': return optim.SGD(self.parameters(), self.hparams.lr, momentum = 0.9) elif self.hparams.optimizer == 'adam': return optim.Adam(self.parameters(), self.hparams.lr) elif self.hparams.optimizer == 'adamax': return optim.Adamax(self.parameters(), self.hparams.lr) def run_epoch(self, phase, batch, batch_idx): assert len(batch['input']) == self.K output = self(batch['input'])[0] loss, loss_stats = self.loss(output, batch) self.log(f'{phase}_loss', loss, prog_bar=True, logger=True) self.log(f'{phase}_loss_hm', loss_stats['loss_hm'], prog_bar=True, logger=True) self.log(f'{phase}_loss_mov', loss_stats['loss_mov'], prog_bar=True, logger=True) self.log(f'{phase}_loss_wh', loss_stats['loss_wh'], prog_bar=True, logger=True) return loss.mean() def training_step(self, batch, batch_idx): return self.run_epoch("train", batch, batch_idx) def validation_step(self, batch, batch_idx): self.run_epoch("val", batch, batch_idx) def test_step(self, batch, batch_idx): self.run_epoch("test", batch, batch_idx) if __name__ == '__main__': num_classes = 24 K = 7 arch = 'resnet_18' head_conv = 256 model = MOC_Net(arch, num_classes, head_conv, K, lr=0.001, optimizer='adam') model = load_coco_pretrained_model(model, arch, print_log=False) input_shape = (1, 3, 288, 288) x = [torch.randn(input_shape)] * K # y = model.backbone(x) #1, 64, 72, 72 y = model(x) # print(len(y)) print(y[0].keys()) hm = y[0]['hm'] mov = y[0]['mov'] wh = y[0]['wh'] print(hm.shape) print(mov.shape) print(wh.shape) print(model.hparams) model.configure_optimizers()

nilq/baby-python

python

import os from django.http import HttpResponse from django.template import Context, RequestContext, loader def ajax_aware_render(request, template_list, context=None, **kwargs): """ Render a template, using a different one automatically for AJAX requests. :param template_list: Either a template name or a list of template names. :param context: Optional extra context to pass to the template. For AJAX requests, the template list is altered to look for alternate templates first and the ``is_ajax`` context variable is set to ``True``. For example, if ``template_list`` was set to ``['custom/login.html', 'login.html']``, then an AJAX request will change this to:: ['custom/login.ajax.html', 'login.ajax.html', 'custom/login.html', 'login.html'] """ if not isinstance(context, Context): context = RequestContext(request, context) if isinstance(template_list, basestring): template_list = [template_list] if request.is_ajax(): ajax_template_list = [] for name in template_list: ajax_template_list.append('%s.ajax%s' % os.path.splitext(name)) template_list = ajax_template_list + list(template_list) context['is_ajax'] = True context['current_url'] = request.get_full_path() template = loader.select_template(template_list) return HttpResponse(template.render(context), **kwargs)

nilq/baby-python

python

import logging import collections import time import six from six.moves import http_client from flask import url_for, g, jsonify from flask.views import MethodView import marshmallow as ma from flask_restx import reqparse from flask_smorest import Blueprint, abort from drift.core.extensions.urlregistry import Endpoints from driftbase.models.db import CorePlayer, Counter, CounterEntry from driftbase.utils import get_all_counters, get_counter from driftbase.players import get_playergroup_ids log = logging.getLogger(__name__) bp = Blueprint("counters", __name__, url_prefix="/counters", description="Counters") endpoints = Endpoints() NUM_RESULTS = 100 def drift_init_extension(app, api, **kwargs): api.register_blueprint(bp) endpoints.init_app(app) @bp.route('/', endpoint='list') class CountersApi(MethodView): get_args = reqparse.RequestParser() def get(self): """ Get a list of all 'leaderboards' """ all_counters = g.db.query(Counter).order_by(Counter.name).distinct() ret = [] for s in all_counters: ret.append({ "name": s.name, "label": s.label, "counter_id": s.counter_id, "url": url_for("counters.entry", counter_id=s.counter_id, _external=True) }) return jsonify(ret), http_client.OK, {'Cache-Control': "max_age=60"} @bp.route('/<int:counter_id>', endpoint='entry') class CounterApi(MethodView): get_args = reqparse.RequestParser() get_args.add_argument("num", type=int, default=NUM_RESULTS) get_args.add_argument("include", type=int, action='append') # TODO: Sunset this in favour of player_group get_args.add_argument("player_id", type=int, action='append') get_args.add_argument("player_group", type=str) get_args.add_argument("reverse", type=bool) #@namespace.expect(get_args) def get(self, counter_id): start_time = time.time() args = self.get_args.parse_args() num = args.get("num") or NUM_RESULTS counter = get_counter(counter_id) if not counter: abort(404) filter_player_ids = [] reverse = not not args.reverse if args.player_id: filter_player_ids = args.player_id query = g.db.query(CounterEntry, CorePlayer) query = query.filter(CounterEntry.counter_id == counter_id, CounterEntry.period == "total", CounterEntry.player_id == CorePlayer.player_id, CorePlayer.status == "active", CorePlayer.player_name != u"",) if filter_player_ids: query = query.filter(CounterEntry.player_id.in_(filter_player_ids)) if args.player_group: filter_player_ids = get_playergroup_ids(args.player_group) query = query.filter(CounterEntry.player_id.in_(filter_player_ids)) if reverse: query = query.order_by(CounterEntry.value) else: query = query.order_by(-CounterEntry.value) query = query.limit(num) rows = query.all() counter_totals = collections.defaultdict(list) counter_names = {} if args.include: all_counters = get_all_counters() # inline other counters for the players player_ids = [r[0].player_id for r in rows] counter_rows = g.db.query(CounterEntry.player_id, CounterEntry.counter_id, CounterEntry.value) \ .filter(CounterEntry.period == "total", CounterEntry.player_id.in_(player_ids), CounterEntry.counter_id.in_(args.include)) \ .all() for r in counter_rows: this_player_id = r[0] this_counter_id = r[1] this_value = r[2] # find the name of this counter. We cache this locally for performance try: counter_name = counter_names[this_counter_id] except KeyError: c = all_counters.get(six.text_type(this_counter_id), {}) name = c.get("name", this_counter_id) counter_names[this_counter_id] = name counter_name = name entry = { "name": counter_name, "counter_id": this_counter_id, "counter_url": url_for("player_counters.entry", player_id=this_player_id, counter_id=this_counter_id, _external=True), "total": this_value } counter_totals[r.player_id].append(entry) ret = [] for i, row in enumerate(rows): player_id = row[0].player_id entry = { "name": counter["name"], "counter_id": counter_id, "player_id": player_id, "player_name": row[1].player_name, "player_url": url_for("players.entry", player_id=player_id, _external=True), "counter_url": url_for("player_counters.entry", player_id=player_id, counter_id=row[0].counter_id, _external=True), "total": row[0].value, "position": i + 1, "include": counter_totals.get(player_id, {}) } ret.append(entry) log.info("Returning counters in %.2fsec", time.time() - start_time) return jsonify(ret), http_client.OK, {'Cache-Control': "max_age=60"} @endpoints.register def endpoint_info(current_user): ret = {} ret["counters"] = url_for("counters.list", _external=True) return ret

nilq/baby-python

python

#!/usr/bin/env python """ Recursively find and replace text in files under a specific folder with preview of changed data in dry-run mode ============ Example Usage --------------- **See what is going to change (dry run):** > flip all dates from 2017-12-31 to 31-12-2017 find_replace.py --dir project/myfolder --search-regex "\d{4}-\d{2}-\d{2}" --replace-regex "\3-\2-\1" --dry-run **Do actual replacement:** find_replace.py --dir project/myfolder --search-regex "\d{4}-\d{2}-\d{2}" --replace-regex "\3-\2-\1" **Do actual replacement and create backup files:** find_replace.py --dir project/myfolder --search-regex "\d{4}-\d{2}-\d{2}" --replace-regex "\3-\2-\1" --create-backup **Same action as previous command with short-hand syntax:** find_replace.py -d project/myfolder -s "\d{4}-\d{2}-\d{2}" -r "\3-\2-\1" -b Output of `find_replace.py -h`: usage: find-replace-in-files-regex.py [-h] [--dir DIR] --search-regex SEARCH_REGEX --replace-regex REPLACE_REGEX [--glob GLOB] [--dry-run] [--create-backup] [--verbose] [--print-parent-folder] USAGE: find-replace-in-files-regex.py -d [my_folder] -s <search_regex> -r <replace_regex> -g [glob_pattern] """ from __future__ import print_function import os import fnmatch import sys import shutil import re import argparse class Colors: Default = "\033[39m" Black = "\033[30m" Red = "\033[31m" Green = "\033[32m" Yellow = "\033[33m" Blue = "\033[34m" Magenta = "\033[35m" Cyan = "\033[36m" LightGray = "\033[37m" DarkGray = "\033[90m" LightRed = "\033[91m" LightGreen = "\033[92m" LightYellow = "\033[93m" LightBlue = "\033[94m" LightMagenta = "\033[95m" LightCyan = "\033[96m" White = "\033[97m" NoColor = "\033[0m" def find_replace(cfg): search_pattern = re.compile(cfg.search_regex) if cfg.dry_run: print('THIS IS A DRY RUN -- NO FILES WILL BE CHANGED!') for path, dirs, files in os.walk(os.path.abspath(cfg.dir)): for filename in fnmatch.filter(files, cfg.glob): if cfg.print_parent_folder: pardir = os.path.normpath(os.path.join(path, '..')) pardir = os.path.split(pardir)[-1] print('[%s]' % pardir) full_path = os.path.join(path, filename) # backup original file if cfg.create_backup: backup_path = full_path + '.bak' while os.path.exists(backup_path): backup_path += '.bak' print('DBG: creating backup', backup_path) shutil.copyfile(full_path, backup_path) if os.path.islink(full_path): print("{}File {} is a symlink. Skipping{}".format(Colors.Red, full_path, Colors.NoColor)) continue with open(full_path) as f: old_text = f.read() all_matches = search_pattern.findall(old_text) if all_matches: print('{}Found {} match(es) in file {}{}'.format(Colors.LightMagenta, len(all_matches), filename, Colors.NoColor)) new_text = search_pattern.sub(cfg.replace_regex, old_text) if not cfg.dry_run: with open(full_path, "w") as f: print('DBG: replacing in file', full_path) f.write(new_text) # else: # for idx, matches in enumerate(all_matches): # print("Match #{}: {}".format(idx, matches)) if cfg.verbose or cfg.dry_run: colorized_old = search_pattern.sub(Colors.LightBlue + r"\g<0>" + Colors.NoColor, old_text) colorized_old = '\n'.join(['\t' + line.strip() for line in colorized_old.split('\n') if Colors.LightBlue in line]) colorized = search_pattern.sub(Colors.Green + cfg.replace_regex + Colors.NoColor, old_text) colorized = '\n'.join(['\t' + line.strip() for line in colorized.split('\n') if Colors.Green in line]) print("{}BEFORE:{}\n{}".format(Colors.White, Colors.NoColor, colorized_old)) print("{}AFTER :{}\n{}".format(Colors.Yellow, Colors.NoColor, colorized)) elif cfg.list_non_matching: print('File {} does not contain search regex "{}"'.format(filename, cfg.search_regex)) if __name__ == '__main__': parser = argparse.ArgumentParser(description='''DESCRIPTION: Find and replace recursively from the given folder using regular expressions''', formatter_class=argparse.RawDescriptionHelpFormatter, epilog='''USAGE: {0} -d [my_folder] -s <search_regex> -r <replace_regex> -g [glob_pattern] '''.format(os.path.basename(sys.argv[0]))) parser.add_argument('--dir', '-d', help='folder to search in; by default current folder', default='.') parser.add_argument('--search-regex', '-s', help='search regex', required=True) parser.add_argument('--replace-regex', '-r', help='replacement regex', required=True) parser.add_argument('--glob', '-g', help='glob pattern, i.e. *.html', default="*.*") parser.add_argument('--dry-run', '-dr', action='store_true', help="don't replace anything just show what is going to be done", default=False) parser.add_argument('--create-backup', '-b', action='store_true', help='Create backup files', default=False) parser.add_argument('--verbose', '-v', action='store_true', help="Show files which don't match the search regex", default=False) parser.add_argument('--print-parent-folder', '-p', action='store_true', help="Show the parent info for debug", default=False) parser.add_argument('--list-non-matching', '-n', action='store_true', help="Supress colors", default=False) config = parser.parse_args(sys.argv[1:]) find_replace(config)

nilq/baby-python

python

# ___________________________________________________________________________ # # EGRET: Electrical Grid Research and Engineering Tools # Copyright 2019 National Technology & Engineering Solutions of Sandia, LLC # (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. # Government retains certain rights in this software. # This software is distributed under the Revised BSD License. # ___________________________________________________________________________ """ This is the logging configuration for EGRET. The documentation below is primarily for EGRET developers. Examples ======== To use the logger in your code, add the following after your import .. code-block:: python import logging logger = logging.getLogger('egret.path.to.module') Then, you can use the standard logging functions .. code-block:: python logger.debug('message') logger.info('message') logger.warning('message') logger.error('message') logger.critical('message') Note that by default, any message that has a logging level of warning or higher (warning, error, critical) will be logged. To log an exception and capture the stack trace .. code-block:: python try: c = a / b except Exception as e: logging.error("Exception occurred", exc_info=True) """ import sys import logging log_format = '%(message)s' # configure the root logger for egret logger = logging.getLogger('egret') logger.setLevel(logging.INFO) console_handler = logging.StreamHandler(sys.stdout) fmtr = logging.Formatter(log_format) console_handler.setFormatter(fmtr) logger.addHandler(console_handler)

nilq/baby-python

python

import os import cv2 import numpy as np import random classnames = ["no weather degradation", "fog", "rain", "snow"] modes = ["train", "val", "test"] for classname in classnames: input_path = "./jhucrowd+weather dataset/{}".format(classname) images = os.listdir(input_path) random.shuffle(images) N = len(images) tot_train = int(N * 0.7) tot_val = int(N * 0.1) tot_test = int(N * 0.2) r = N - (tot_train + tot_val + tot_test) tot_train = tot_train + r start_index_train = 0 start_index_val = tot_train start_index_test = tot_train + tot_val for i_img, img_name in enumerate(images): if i_img < start_index_val: mode = modes[0] elif i_img < start_index_test and i_img >= start_index_val: mode = modes[1] else: mode = modes[2] output_path = "./preprocessed_data/{}/{}".format(mode, classname) print(os.path.join(output_path, img_name)) image = cv2.imread(os.path.join(input_path, img_name)) cv2.imwrite(os.path.join(output_path, img_name), image)

nilq/baby-python

python

#!/usr/bin/env python import sys import time import random mn,mx,count = map(int,sys.argv[1:4]) seed = sys.argv[4] if len(sys.argv) > 4 else time.time() random.seed(seed) print 'x,y' for i in xrange(count): print ','.join(map(str,[random.randint(mn,mx),random.randint(mn,mx)]))

nilq/baby-python

python

# -*- coding: utf-8 -*- from __future__ import unicode_literals from django.utils.encoding import python_2_unicode_compatible from django.db import models # Create your models here. @python_2_unicode_compatible class SummaryNote(models.Model): title = models.CharField(max_length=60) content = models.TextField() def __str__ (self): return self.title def __repr__ (self): return '<SummaryNote %s>' % self.title

nilq/baby-python

python

# Generated by Django 3.0.8 on 2020-07-29 13:57 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('wagtailimages', '0022_uploadedimage'), ('events', '0003_auto_20200725_2158'), ] operations = [ migrations.AddField( model_name='eventtype', name='list_image', field=models.ForeignKey(blank=True, help_text='This image will be displayed above the event on the front page', null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.Image'), ), ]

nilq/baby-python

python

"""A CLI utility that aggregates configuration sources into a JSON object.""" import json import logging import os import typing import cleo import structlog import toml import pitstop import pitstop.backends.base import pitstop.strategies import pitstop.strategies.base import pitstop.types __all__ = ('app', 'main') app = cleo.Application("pitstop", pitstop.__version__, complete=True) def load_strategy( path: str, strategy_name: typing.Optional[str] = None ) -> pitstop.strategies.base.BaseStrategy: """Load a configuration strategy from a pitstop configuration file.""" filename = os.path.basename(path) with open(path, 'r') as f: config = toml.loads(f.read()) if filename == 'pyproject.toml': config = config['tool']['pitstop'] return pitstop.strategies.strategy_factory(config, strategy_name) def main() -> None: """``pitstop`` entrypoint.""" shared_processors = [ structlog.stdlib.add_logger_name, structlog.stdlib.add_log_level, structlog.stdlib.PositionalArgumentsFormatter(), structlog.processors.TimeStamper(fmt='%Y-%m-%d %H:%M:%S'), structlog.processors.StackInfoRenderer(), structlog.processors.format_exc_info, structlog.processors.UnicodeDecoder(), ] structlog.configure( processors=shared_processors + [structlog.stdlib.ProcessorFormatter.wrap_for_formatter], context_class=dict, logger_factory=structlog.stdlib.LoggerFactory(), wrapper_class=structlog.stdlib.BoundLogger, cache_logger_on_first_use=True, ) formatter = structlog.stdlib.ProcessorFormatter( processor=structlog.dev.ConsoleRenderer(), foreign_pre_chain=shared_processors, ) handler = logging.StreamHandler() handler.setFormatter(formatter) root_logger = logging.getLogger() root_logger.addHandler(handler) app.add(ResolveCommand()) app.run() class BaseCommand(cleo.Command): """Base :class:`cleo.Command`.""" def handle(self) -> None: """Perform shared CLI application setup. All CLI commands should subclass :class:`BaseCommand` and call :func:`super` when overriding this method. """ verbosity = self.output.get_verbosity() if verbosity == cleo.Output.VERBOSITY_QUIET: level = logging.FATAL elif verbosity == cleo.Output.VERBOSITY_NORMAL: level = logging.WARN elif verbosity <= cleo.Output.VERBOSITY_VERBOSE: level = logging.INFO elif verbosity <= cleo.Output.VERBOSITY_DEBUG: level = logging.DEBUG root_logger = logging.getLogger() root_logger.setLevel(level) class ResolveCommand(BaseCommand): """ Resolve all backend sources and output resolved configuration. resolve {config? : pitstop configuration file} {--s|strategy=v1 : pitstop strategy version} {--c|compact : enable compact output} """ def handle(self) -> None: # noqa: D102 super().handle() config = self.argument('config') strategy = self.option('strategy') if config is None: config = 'pyproject.toml' strategy = load_strategy(config, strategy_name=strategy) config = strategy.resolve() self.line( json.dumps(config, indent=None if self.option('compact') else 4) ) if __name__ == '__main__': main()

nilq/baby-python

python

from django.shortcuts import render, get_object_or_404 from django.http import HttpResponseRedirect, JsonResponse from django.contrib.auth.decorators import login_required from django.core.urlresolvers import reverse from django.template import loader, Context from pure_pagination import Paginator, EmptyPage, PageNotAnInteger from .decorators import valid_character_selected from .forms import FilterJournalForm from apps.apies.forms import ApiForm from apps.characters.models import CharacterApi, CharacterJournal import utils @login_required def characters(request): api_form = ApiForm(request.POST or None, user=request.user) if request.POST and api_form.is_valid(): api_form.save(request.user) api_form = ApiForm(user=request.user) characters = CharacterApi.objects.filter(api__user=request.user) if request.user.groups.filter( name="moderator" ).exists() or request.user.is_superuser: members = CharacterApi.objects.exclude(api__user=request.user) return render( request, "characters/characters.html", { "api_form": api_form, "characters": characters, "members": members } ) return render( request, "characters/characters.html", { "api_form": api_form, "characters": characters } ) @login_required def select_character(request, pk): if request.user.groups.filter( name="moderator" ).exists() or request.user.is_superuser: character = get_object_or_404(CharacterApi, pk=pk) request.session['moderator'] = True else: character = get_object_or_404( CharacterApi, pk=pk, api__user=request.user ) request.session['moderator'] = False request.session['charpk'] = character.pk request.session['access'] = character.api.access() return HttpResponseRedirect(reverse("character_sheet")) @login_required @valid_character_selected def character_sheet(request): character = get_object_or_404(CharacterApi, pk=request.session['charpk']) cache_key = character.sheet_cache_key() result = utils.connection.get_cache(cache_key) if not result: #or time to live is to long character.sheet_set_cache_job() #sheet, employment = character.character_sheet() #account = character.api.account_status() #in_training = character.skill_in_training() # "employment": employment, # "in_training": in_training, # "sheet": sheet, # "account": account, #"character": character, return render( request, "characters/sheet.html", ) @login_required @valid_character_selected def character_sheet_data(request): character = get_object_or_404(CharacterApi, pk=request.session['charpk']) cache_key = character.sheet_cache_key() result = utils.connection.get_cache(cache_key) if result: #render template sheet, employment = character.character_sheet() paid_until = character.api.account_status() in_training = None #character.skill_in_training() context = Context( { "employment": employment, "in_training": in_training, "sheet": sheet, "paid_until": paid_until, "character": character, } ) template = loader.get_template('characters/sheet_content.html') content = template.render(context) refresh_timer = 60 * 10 * 1000 else: content = """""" refrsh_timer = 0.3 return JsonResponse( { "content": content, "refresh_timer": refresh_timer, } ) @login_required @valid_character_selected def character_skills(request): character = get_object_or_404(CharacterApi, pk=request.session['charpk']) if not character.api.access_to("CharacterSheet"): return HttpResponseRedirect(reverse("characters")) skills = character.trained_skills() queue = character.skill_queue() return render( request, "characters/character_skills.html", { "character": character, "skills": skills, "queue": queue, } ) @login_required @valid_character_selected def character_journal(request): character = get_object_or_404(CharacterApi, pk=request.session['charpk']) if not character.api.access_to("WalletJournal"): return HttpResponseRedirect(reverse("characters")) all_transactions = character.wallet_journal() filter_form = FilterJournalForm( request.POST or None, characterapi=character ) paginator = Paginator( all_transactions, 50, request=request ) page = request.GET.get('page', 1) try: transactions = paginator.page(page) except PageNotAnInteger: transactions = paginator.page(1) except EmptyPage: transactions = paginator.page(paginator.num_pages) chart_list = CharacterJournal.monthly_balance(character) return render( request, "characters/wallet_journal.html", { "character": character, "transactions": transactions, "chart_list": chart_list, "filter_form": filter_form, } )

nilq/baby-python

python

import os import unittest from bs4 import BeautifulSoup from parser import Parser class ParserTestCase(unittest.TestCase): def setUp(self): pass def test_item_info_images(self): base_url = "https://www.akusherstvo.ru" page_url = "/catalog/50666-avtokreslo-rant-star/" page_mock_url = base_url + page_url dump_folder = "test" parser = Parser(base_url, dump_folder) page = self.get_page_mock(parser, page_mock_url) page_url = "/catalog/36172-carmela/" item_info = parser.get_item_info(page, page_url) more_photos = item_info["more_photos"] color_photos = item_info["color_photos"] self.assertEqual(len(more_photos), 4) self.assertEqual(len(color_photos), 4) self.assertEqual(any([ "_b." in photo_url for photo_url in color_photos]), False, "all paths should be without and postfix") self.assertEqual(any([ "_s." in photo_url for photo_url in more_photos]), False, "all paths should be without and postfix") def get_page_mock(self, parser, url): normalized_url = url.replace("/", "_") full_path = "./test_data/mock_{}.html".format(normalized_url) if os.path.exists(full_path): with open(full_path, "r") as f: raw_text = f.read() page = BeautifulSoup(raw_text, features="html5lib") else: page = parser.get_bs(url, codec="cp1251") os.makedirs("./test_data", exist_ok=True) with open(full_path, "w") as f: f.write(str(page)) return page if __name__ == '__main__': unittest.main()

nilq/baby-python

python

from modules import engine from modules import out @engine.prepare_and_clean def execute(key = None): out.log('These are all configuration settings.') config_vars = engine.get_config(key) if key is None: for k in config_vars: out.log(k + ' = ' + str(config_vars[k])) else: out.log(key + ' = ' + config_vars) def help(): out.log("This command will print all the variables, that are set in the engines environment that look like config variables.", 'help')

nilq/baby-python

python

# 2D dataset loaders import data.data_hcp as data_hcp import data.data_abide as data_abide import data.data_nci as data_nci import data.data_promise as data_promise import data.data_pirad_erc as data_pirad_erc import data.data_mnms as data_mnms import data.data_wmh as data_wmh import data.data_scgm as data_scgm # other imports import logging import config.system_paths as sys_config import numpy as np # ================================================================== # TRAINING DATA LOADER # ================================================================== def load_test_data(dataset, image_size, target_resolution, cv_fold_num = 1): # ================================================================ # NCI # ================================================================ if dataset in ['RUNMC', 'BMC']: logging.info('Reading NCI - ' + dataset + ' images...') logging.info('Data root directory: ' + sys_config.orig_data_root_nci) data_pros = data_nci.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_nci, preprocessing_folder = sys_config.preproc_folder_nci, size = image_size, target_resolution = target_resolution, force_overwrite = False, sub_dataset = dataset, cv_fold_num = cv_fold_num) imtr = data_pros['images_train'] gttr = data_pros['labels_train'] orig_data_res_x = data_pros['px_train'][:] orig_data_res_y = data_pros['py_train'][:] orig_data_res_z = data_pros['pz_train'][:] orig_data_siz_x = data_pros['nx_train'][:] orig_data_siz_y = data_pros['ny_train'][:] orig_data_siz_z = data_pros['nz_train'][:] num_train_subjects = orig_data_siz_z.shape[0] imvl = data_pros['images_validation'] gtvl = data_pros['labels_validation'] orig_data_siz_z_val = data_pros['nz_validation'][:] num_val_subjects = orig_data_siz_z_val.shape[0] elif dataset in ['UCL', 'BIDMC', 'HK']: logging.info('Reading' + dataset + ' images...') logging.info('Data root directory: ' + sys_config.orig_data_root_promise) data_pros = data_promise.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_promise, preprocessing_folder = sys_config.preproc_folder_promise, size = image_size, target_resolution = target_resolution, force_overwrite = False, sub_dataset = dataset, cv_fold_num = cv_fold_num) imtr = data_pros['images_train'] gttr = data_pros['labels_train'] orig_data_res_x = data_pros['px_train'][:] orig_data_res_y = data_pros['py_train'][:] orig_data_res_z = data_pros['pz_train'][:] orig_data_siz_x = data_pros['nx_train'][:] orig_data_siz_y = data_pros['ny_train'][:] orig_data_siz_z = data_pros['nz_train'][:] num_train_subjects = orig_data_siz_z.shape[0] imvl = data_pros['images_validation'] gtvl = data_pros['labels_validation'] orig_data_siz_z_val = data_pros['nz_validation'][:] num_val_subjects = orig_data_siz_z_val.shape[0] elif dataset in ['USZ']: logging.info('Reading PIRAD_ERC images...') logging.info('Data root directory: ' + sys_config.orig_data_root_pirad_erc) data_pros_train = data_pirad_erc.load_data(input_folder = sys_config.orig_data_root_pirad_erc, preproc_folder = sys_config.preproc_folder_pirad_erc, idx_start = 40, idx_end = 68, size = image_size, target_resolution = target_resolution, labeller = 'ek', force_overwrite = False) imtr = data_pros_train['images'] gttr = data_pros_train['labels'] orig_data_res_x = data_pros_train['px'][:] orig_data_res_y = data_pros_train['py'][:] orig_data_res_z = data_pros_train['pz'][:] orig_data_siz_x = data_pros_train['nx'][:] orig_data_siz_y = data_pros_train['ny'][:] orig_data_siz_z = data_pros_train['nz'][:] num_train_subjects = orig_data_siz_z.shape[0] data_pros_val = data_pirad_erc.load_data(input_folder = sys_config.orig_data_root_pirad_erc, preproc_folder = sys_config.preproc_folder_pirad_erc, idx_start = 20, idx_end = 40, size = image_size, target_resolution = target_resolution, labeller = 'ek', force_overwrite = False) imvl = data_pros_val['images'] gtvl = data_pros_val['labels'] orig_data_siz_z_val = data_pros_val['nz'][:] num_val_subjects = orig_data_siz_z_val.shape[0] # ================================================================ # CARDIAC (MNMS) # ================================================================ elif dataset in ['HVHD', 'CSF', 'UHE']: logging.info('Reading MNMS - ' + dataset + ' images...') logging.info('Data root directory: ' + sys_config.orig_data_root_mnms) data_cardiac = data_mnms.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_mnms, preprocessing_folder = sys_config.preproc_folder_mnms, size = image_size, target_resolution = target_resolution, force_overwrite = False, sub_dataset = dataset) imtr = data_cardiac['images_train'] gttr = data_cardiac['labels_train'] orig_data_res_x = data_cardiac['px_train'][:] orig_data_res_y = data_cardiac['py_train'][:] orig_data_res_z = data_cardiac['pz_train'][:] orig_data_siz_x = data_cardiac['nx_train'][:] orig_data_siz_y = data_cardiac['ny_train'][:] orig_data_siz_z = data_cardiac['nz_train'][:] num_train_subjects = orig_data_siz_z.shape[0] imvl = data_cardiac['images_validation'] gtvl = data_cardiac['labels_validation'] orig_data_siz_z_val = data_cardiac['nz_validation'][:] num_val_subjects = orig_data_siz_z_val.shape[0] # ================================================================ # Brain lesions (WMH) # ================================================================ elif dataset in ['UMC', 'NUHS']: data_brain_lesions = data_wmh.load_and_maybe_process_data(sys_config.orig_data_root_wmh, sys_config.preproc_folder_wmh, image_size, target_resolution, force_overwrite=False, sub_dataset = dataset, cv_fold_number = cv_fold_num, protocol = 'FLAIR') imtr = data_brain_lesions['images_train'] gttr = data_brain_lesions['labels_train'] orig_data_res_x = data_brain_lesions['px_train'][:] orig_data_res_y = data_brain_lesions['py_train'][:] orig_data_res_z = data_brain_lesions['pz_train'][:] orig_data_siz_x = data_brain_lesions['nx_train'][:] orig_data_siz_y = data_brain_lesions['ny_train'][:] orig_data_siz_z = data_brain_lesions['nz_train'][:] num_train_subjects = orig_data_siz_z.shape[0] imvl = data_brain_lesions['images_validation'] gtvl = data_brain_lesions['labels_validation'] orig_data_siz_z_val = data_brain_lesions['nz_validation'][:] num_val_subjects = orig_data_siz_z_val.shape[0] elif dataset in ['site1', 'site2', 'site3', 'site4']: data_gm = data_scgm.load_and_maybe_process_data(sys_config.orig_data_root_scgm, sys_config.preproc_folder_scgm, image_size, target_resolution, force_overwrite=False, sub_dataset = dataset, cv_fold_number = cv_fold_num) imtr = data_gm['images_train'] gttr = data_gm['labels_train'] orig_data_res_x = data_gm['px_train'][:] orig_data_res_y = data_gm['py_train'][:] orig_data_res_z = data_gm['pz_train'][:] orig_data_siz_x = data_gm['nx_train'][:] orig_data_siz_y = data_gm['ny_train'][:] orig_data_siz_z = data_gm['nz_train'][:] num_train_subjects = orig_data_siz_z.shape[0] imvl = data_gm['images_validation'] gtvl = data_gm['labels_validation'] orig_data_siz_z_val = data_gm['nz_validation'][:] num_val_subjects = orig_data_siz_z_val.shape[0] # ================================================================ # HCP T1 / T2 # ================================================================ elif dataset in ['HCPT1', 'HCPT2']: logging.info('Reading ' + str(dataset) + ' images...') logging.info('Data root directory: ' + sys_config.orig_data_root_hcp) data_brain_train = data_hcp.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_hcp, preprocessing_folder = sys_config.preproc_folder_hcp, idx_start = 0, idx_end = 20, protocol = dataset[-2:], size = image_size, depth = 256, target_resolution = target_resolution) imtr = data_brain_train['images'] gttr = data_brain_train['labels'] orig_data_res_x = data_brain_train['px'][:] orig_data_res_y = data_brain_train['py'][:] orig_data_res_z = data_brain_train['pz'][:] orig_data_siz_x = data_brain_train['nx'][:] orig_data_siz_y = data_brain_train['ny'][:] orig_data_siz_z = data_brain_train['nz'][:] num_train_subjects = orig_data_siz_z.shape[0] data_brain_val = data_hcp.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_hcp, preprocessing_folder = sys_config.preproc_folder_hcp, idx_start = 20, idx_end = 25, protocol = dataset[-2:], size = image_size, depth = 256, target_resolution = target_resolution) imvl = data_brain_val['images'] gtvl = data_brain_val['labels'] orig_data_siz_z_val = data_brain_val['nz'][:] num_val_subjects = orig_data_siz_z_val.shape[0] elif dataset in ['CALTECH']: logging.info('Reading CALTECH images...') logging.info('Data root directory: ' + sys_config.orig_data_root_abide + 'CALTECH/') data_brain_train = data_abide.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_abide, preprocessing_folder = sys_config.preproc_folder_abide, site_name = 'CALTECH', idx_start = 0, idx_end = 10, protocol = 'T1', size = image_size, depth = 256, target_resolution = target_resolution) imtr = data_brain_train['images'] gttr = data_brain_train['labels'] orig_data_res_x = data_brain_train['px'][:] orig_data_res_y = data_brain_train['py'][:] orig_data_res_z = data_brain_train['pz'][:] orig_data_siz_x = data_brain_train['nx'][:] orig_data_siz_y = data_brain_train['ny'][:] orig_data_siz_z = data_brain_train['nz'][:] num_train_subjects = orig_data_siz_z.shape[0] data_brain_val = data_abide.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_abide, preprocessing_folder = sys_config.preproc_folder_abide, site_name = 'CALTECH', idx_start = 10, idx_end = 15, protocol = 'T1', size = image_size, depth = 256, target_resolution = target_resolution) imvl = data_brain_val['images'] gtvl = data_brain_val['labels'] orig_data_siz_z_val = data_brain_val['nz'][:] num_val_subjects = orig_data_siz_z_val.shape[0] return (imtr, # 0 gttr, # 1 orig_data_res_x, # 2 orig_data_res_y, # 3 orig_data_res_z, # 4 orig_data_siz_x, # 5 orig_data_siz_y, # 6 orig_data_siz_z, # 7 num_train_subjects, # 8 imvl, # 9 gtvl, # 10 orig_data_siz_z_val, # 11 num_val_subjects) # 12 # ================================================================== # TEST DATA LOADER # ================================================================== def load_testing_data(test_dataset, cv_fold_num, image_size, target_resolution, image_depth): # ================================================================ # PROMISE # ================================================================ if test_dataset in ['UCL', 'BIDMC', 'HK']: data_pros = data_promise.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_promise, preprocessing_folder = sys_config.preproc_folder_promise, size = image_size, target_resolution = target_resolution, force_overwrite = False, sub_dataset = test_dataset, cv_fold_num = cv_fold_num) imts = data_pros['images_test'] gtts = data_pros['labels_test'] orig_data_res_x = data_pros['px_test'][:] orig_data_res_y = data_pros['py_test'][:] orig_data_res_z = data_pros['pz_test'][:] orig_data_siz_x = data_pros['nx_test'][:] orig_data_siz_y = data_pros['ny_test'][:] orig_data_siz_z = data_pros['nz_test'][:] name_test_subjects = data_pros['patnames_test'] num_test_subjects = orig_data_siz_z.shape[0] ids = np.arange(num_test_subjects) # ================================================================ # USZ # ================================================================ elif test_dataset == 'USZ': image_depth = 32 z_resolution = 2.5 idx_start = 0 idx_end = 20 data_pros = data_pirad_erc.load_data(input_folder = sys_config.orig_data_root_pirad_erc, preproc_folder = sys_config.preproc_folder_pirad_erc, idx_start = idx_start, idx_end = idx_end, size = image_size, target_resolution = target_resolution, labeller = 'ek') imts = data_pros['images'] gtts = data_pros['labels'] orig_data_res_x = data_pros['px'][:] orig_data_res_y = data_pros['py'][:] orig_data_res_z = data_pros['pz'][:] orig_data_siz_x = data_pros['nx'][:] orig_data_siz_y = data_pros['ny'][:] orig_data_siz_z = data_pros['nz'][:] name_test_subjects = data_pros['patnames'] num_test_subjects = 10 # orig_data_siz_z.shape[0] ids = np.arange(idx_start, idx_end) # ================================================================ # NCI # ================================================================ elif test_dataset in ['BMC', 'RUNMC']: logging.info('Reading ' + test_dataset + ' images...') logging.info('Data root directory: ' + sys_config.orig_data_root_nci) data_pros = data_nci.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_nci, preprocessing_folder = sys_config.preproc_folder_nci, size = image_size, target_resolution = target_resolution, force_overwrite = False, sub_dataset = test_dataset, cv_fold_num = cv_fold_num) imts = data_pros['images_test'] gtts = data_pros['labels_test'] orig_data_res_x = data_pros['px_test'][:] orig_data_res_y = data_pros['py_test'][:] orig_data_res_z = data_pros['pz_test'][:] orig_data_siz_x = data_pros['nx_test'][:] orig_data_siz_y = data_pros['ny_test'][:] orig_data_siz_z = data_pros['nz_test'][:] name_test_subjects = data_pros['patnames_test'] num_test_subjects = orig_data_siz_z.shape[0] ids = np.arange(num_test_subjects) # ================================================================ # CARDIAC (MNMS) # ================================================================ elif test_dataset == 'HVHD' or test_dataset == 'CSF' or test_dataset == 'UHE': logging.info('Reading MNMS - ' + test_dataset + ' images...') logging.info('Data root directory: ' + sys_config.orig_data_root_mnms) data_cardiac = data_mnms.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_mnms, preprocessing_folder = sys_config.preproc_folder_mnms, size = image_size, target_resolution = target_resolution, force_overwrite = False, sub_dataset = test_dataset) imts = data_cardiac['images_test'] gtts = data_cardiac['labels_test'] orig_data_res_x = data_cardiac['px_test'][:] orig_data_res_y = data_cardiac['py_test'][:] orig_data_res_z = data_cardiac['pz_test'][:] orig_data_siz_x = data_cardiac['nx_test'][:] orig_data_siz_y = data_cardiac['ny_test'][:] orig_data_siz_z = data_cardiac['nz_test'][:] name_test_subjects = data_cardiac['patnames_test'] num_test_subjects = orig_data_siz_z.shape[0] ids = np.arange(num_test_subjects) # ================================================================ # Brain lesions (WMH) # ================================================================ elif test_dataset == 'UMC' or test_dataset == 'NUHS': data_brain_lesions = data_wmh.load_and_maybe_process_data(sys_config.orig_data_root_wmh, sys_config.preproc_folder_wmh, image_size, target_resolution, force_overwrite=False, sub_dataset = test_dataset, cv_fold_number = cv_fold_num, protocol = 'FLAIR') imts = data_brain_lesions['images_test'] gtts = data_brain_lesions['labels_test'] orig_data_res_x = data_brain_lesions['px_test'][:] orig_data_res_y = data_brain_lesions['py_test'][:] orig_data_res_z = data_brain_lesions['pz_test'][:] orig_data_siz_x = data_brain_lesions['nx_test'][:] orig_data_siz_y = data_brain_lesions['ny_test'][:] orig_data_siz_z = data_brain_lesions['nz_test'][:] name_test_subjects = data_brain_lesions['patnames_test'] num_test_subjects = orig_data_siz_z.shape[0] ids = np.arange(num_test_subjects) # ================================================================ # SPINE # ================================================================ elif test_dataset == 'site1' or test_dataset == 'site2' or test_dataset == 'site3' or test_dataset == 'site4': data_spine = data_scgm.load_and_maybe_process_data(sys_config.orig_data_root_scgm, sys_config.preproc_folder_scgm, image_size, target_resolution, force_overwrite=False, sub_dataset = test_dataset, cv_fold_number = cv_fold_num) imts = data_spine['images_test'] gtts = data_spine['labels_test'] orig_data_res_x = data_spine['px_test'][:] orig_data_res_y = data_spine['py_test'][:] orig_data_res_z = data_spine['pz_test'][:] orig_data_siz_x = data_spine['nx_test'][:] orig_data_siz_y = data_spine['ny_test'][:] orig_data_siz_z = data_spine['nz_test'][:] name_test_subjects = data_spine['patnames_test'] num_test_subjects = orig_data_siz_z.shape[0] ids = np.arange(num_test_subjects) # ================================================================ # HCP T1 # ================================================================ elif test_dataset == 'HCPT1': logging.info('Reading HCPT1 images...') logging.info('Data root directory: ' + sys_config.orig_data_root_hcp) idx_start = 50 idx_end = 70 data_brain = data_hcp.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_hcp, preprocessing_folder = sys_config.preproc_folder_hcp, idx_start = idx_start, idx_end = idx_end, protocol = 'T1', size = image_size, depth = image_depth, target_resolution = target_resolution) imts = data_brain['images'] gtts = data_brain['labels'] orig_data_res_x = data_brain['px'][:] orig_data_res_y = data_brain['py'][:] orig_data_res_z = data_brain['pz'][:] orig_data_siz_x = data_brain['nx'][:] orig_data_siz_y = data_brain['ny'][:] orig_data_siz_z = data_brain['nz'][:] name_test_subjects = data_brain['patnames'] num_test_subjects = 10 # imts.shape[0] // image_depth ids = np.arange(idx_start, idx_end) # ================================================================ # HCP T2 # ================================================================ elif test_dataset == 'HCPT2': logging.info('Reading HCPT2 images...') logging.info('Data root directory: ' + sys_config.orig_data_root_hcp) idx_start = 50 idx_end = 70 data_brain = data_hcp.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_hcp, preprocessing_folder = sys_config.preproc_folder_hcp, idx_start = idx_start, idx_end = idx_end, protocol = 'T2', size = image_size, depth = image_depth, target_resolution = target_resolution) imts = data_brain['images'] gtts = data_brain['labels'] orig_data_res_x = data_brain['px'][:] orig_data_res_y = data_brain['py'][:] orig_data_res_z = data_brain['pz'][:] orig_data_siz_x = data_brain['nx'][:] orig_data_siz_y = data_brain['ny'][:] orig_data_siz_z = data_brain['nz'][:] name_test_subjects = data_brain['patnames'] num_test_subjects = 10 # imts.shape[0] // image_depth ids = np.arange(idx_start, idx_end) # ================================================================ # ABIDE CALTECH T1 # ================================================================ elif test_dataset == 'CALTECH': logging.info('Reading CALTECH images...') logging.info('Data root directory: ' + sys_config.orig_data_root_abide + 'CALTECH/') idx_start = 16 idx_end = 36 data_brain = data_abide.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_abide, preprocessing_folder = sys_config.preproc_folder_abide, site_name = 'CALTECH', idx_start = idx_start, idx_end = idx_end, protocol = 'T1', size = image_size, depth = image_depth, target_resolution = target_resolution) imts = data_brain['images'] gtts = data_brain['labels'] orig_data_res_x = data_brain['px'][:] orig_data_res_y = data_brain['py'][:] orig_data_res_z = data_brain['pz'][:] orig_data_siz_x = data_brain['nx'][:] orig_data_siz_y = data_brain['ny'][:] orig_data_siz_z = data_brain['nz'][:] name_test_subjects = data_brain['patnames'] num_test_subjects = 10 # imts.shape[0] // image_depth ids = np.arange(idx_start, idx_end) # ================================================================ # ABIDE STANFORD T1 # ================================================================ elif test_dataset == 'STANFORD': logging.info('Reading STANFORD images...') logging.info('Data root directory: ' + sys_config.orig_data_root_abide + 'STANFORD/') idx_start = 16 idx_end = 36 data_brain = data_abide.load_and_maybe_process_data(input_folder = sys_config.orig_data_root_abide, preprocessing_folder = sys_config.preproc_folder_abide, site_name = 'STANFORD', idx_start = idx_start, idx_end = idx_end, protocol = 'T1', size = image_size, depth = image_depth, target_resolution = target_resolution) imts = data_brain['images'] gtts = data_brain['labels'] orig_data_res_x = data_brain['px'][:] orig_data_res_y = data_brain['py'][:] orig_data_res_z = data_brain['pz'][:] orig_data_siz_x = data_brain['nx'][:] orig_data_siz_y = data_brain['ny'][:] orig_data_siz_z = data_brain['nz'][:] name_test_subjects = data_brain['patnames'] num_test_subjects = 10 # imts.shape[0] // image_depth ids = np.arange(idx_start, idx_end) return (imts, # 0 gtts, # 1 orig_data_res_x, # 2 orig_data_res_y, # 3 orig_data_res_z, # 4 orig_data_siz_x, # 5 orig_data_siz_y, # 6 orig_data_siz_z, # 7 name_test_subjects, # 8 num_test_subjects, # 9 ids) # 10 # ================================================================ # ================================================================ def load_testing_data_wo_preproc(test_dataset_name, ids, sub_num, subject_name, image_depth): if test_dataset_name == 'HCPT1': # image will be normalized to [0,1] image_orig, labels_orig = data_hcp.load_without_size_preprocessing(input_folder = sys_config.orig_data_root_hcp, idx = ids[sub_num], protocol = 'T1', preprocessing_folder = sys_config.preproc_folder_hcp, depth = image_depth) elif test_dataset_name == 'HCPT2': # image will be normalized to [0,1] image_orig, labels_orig = data_hcp.load_without_size_preprocessing(input_folder = sys_config.orig_data_root_hcp, idx = ids[sub_num], protocol = 'T2', preprocessing_folder = sys_config.preproc_folder_hcp, depth = image_depth) elif test_dataset_name == 'CALTECH': # image will be normalized to [0,1] image_orig, labels_orig = data_abide.load_without_size_preprocessing(input_folder = sys_config.orig_data_root_abide, site_name = 'CALTECH', idx = ids[sub_num], depth = image_depth) elif test_dataset_name == 'STANFORD': # image will be normalized to [0,1] image_orig, labels_orig = data_abide.load_without_size_preprocessing(input_folder = sys_config.orig_data_root_abide, site_name = 'STANFORD', idx = ids[sub_num], depth = image_depth) elif test_dataset_name in ['BMC', 'RUNMC']: # image will be normalized to [0,1] image_orig, labels_orig = data_nci.load_without_size_preprocessing(sys_config.orig_data_root_nci, sys_config.preproc_folder_nci, test_dataset_name, cv_fold_num=1, train_test='test', idx=ids[sub_num]) elif test_dataset_name == 'USZ': # image will be normalized to [0,1] image_orig, labels_orig = data_pirad_erc.load_without_size_preprocessing(sys_config.orig_data_root_pirad_erc, subject_name, labeller='ek') elif test_dataset_name in ['UCL', 'BIDMC', 'HK']: # image will be normalized to [0,1] image_orig, labels_orig = data_promise.load_without_size_preprocessing(sys_config.preproc_folder_promise, subject_name[4:6]) elif test_dataset_name in ['CSF', 'UHE', 'HVHD']: # image will be normalized to [0,1] image_orig, labels_orig = data_mnms.load_without_size_preprocessing(sys_config.preproc_folder_mnms, subject_name) elif test_dataset_name in ['VU', 'UMC', 'NUHS']: # image will be normalized to [0,1] image_orig, labels_orig = data_wmh.load_without_size_preprocessing(sys_config.orig_data_root_wmh, test_dataset_name, subject_name, 'FLAIR') elif test_dataset_name in ['site1', 'site2', 'site3', 'site4']: # image will be normalized to [0,1] image_orig, labels_orig = data_scgm.load_without_size_preprocessing(sys_config.orig_data_root_scgm, sys_config.preproc_folder_scgm, test_dataset_name, subject_name) return image_orig, labels_orig def load_and_maybe_process_data(input_folder, preprocessing_folder, size, target_resolution, force_overwrite=False, sub_dataset = 'RUNMC', # RUNMC / BMC cv_fold_num = 1): #size_str = '_'.join([str(i) for i in size]) #res_str = '_'.join([str(i) for i in target_resolution]) data_file_name = 'data_2d_size_%s_res_%s_cv_fold_%d_%s.hdf5' % (size, target_resolution, cv_fold_num, sub_dataset) data_file_path = os.path.join(preprocessing_folder, data_file_name) utils.makefolder(preprocessing_folder) if not os.path.exists(data_file_path) or force_overwrite: logging.info('This configuration of mode, size and target resolution has not yet been preprocessed') logging.info('Preprocessing now!') prepare_data(input_folder, preprocessing_folder, data_file_path, size, target_resolution, sub_dataset, cv_fold_num) else: logging.info('Already preprocessed this configuration. Loading now!') return h5py.File(data_file_path, 'r') # =============================================================== # function to read a single subjects image and labels without any pre-processing # =============================================================== def load_without_size_preprocessing(input_folder, preprocessing_folder, sub_dataset, cv_fold_num, train_test, idx): # ======================= # ======================= if sub_dataset == 'RUNMC': image_folder = input_folder + 'Images/Prostate-3T/' folder_base = 'Prostate3T' elif sub_dataset == 'BMC': image_folder = input_folder + 'Images/PROSTATE-DIAGNOSIS/' folder_base = 'ProstateDx' # ======================= # ======================= folder_list = get_patient_folders(image_folder, folder_base, sub_dataset, cv_fold_num) folder = folder_list[train_test][idx] patname = folder_base + '-' + str(folder.split('-')[-2]) + '-' + str(folder.split('-')[-1]) nifti_img_path = preprocessing_folder + 'Individual_NIFTI/' + patname # ============ # read the image and normalize the image to be between 0 and 1 # ============ image = utils.load_nii(img_path = nifti_img_path + '_img_n4.nii.gz')[0] image = utils.normalise_image(image, norm_type='div_by_max') # ================== # read the label file # ================== label = utils.load_nii(img_path = nifti_img_path + '_lbl.nii.gz')[0] return image, label

nilq/baby-python

python

# -*- coding: utf-8 -*- from sqlalchemy.ext.hybrid import hybrid_property from . import db, bcrypt from datetime import datetime class User(db.Model): __tablename__ = 'users' id = db.Column(db.Integer, primary_key=True, autoincrement=True) username = db.Column(db.String(32), index=True, unique=True) email = db.Column(db.String(64), unique=True) _password = db.Column(db.String(64)) reg_time = db.Column(db.DateTime, default=datetime.utcnow) last_login = db.Column(db.DateTime) @property def is_authenticated(self): return True @property def is_active(self): return True @property def is_anonymous(self): return False def get_id(self): try: return unicode(self.id) except NameError: return str(self.id) @hybrid_property def password(self): return self._password @password.setter def _set_password(self, plaintext): self._password = bcrypt.generate_password_hash(plaintext) def is_correct_password(self, plaintext): if bcrypt.check_password_hash(self._password, plaintext): return True return False def __repr__(self): return '<User %r>' % self.username class Entry(db.Model): __tablename__ = 'entries' id = db.Column(db.Integer, primary_key=True, autoincrement=True) good = db.Column(db.String(64)) bad = db.Column(db.String(64)) def __repr__(self): return '<Entry %r & %r>' % (self.good, self.bad) class Hobby(db.Model): __tablename__ = 'hobbies' id = db.Column(db.Integer, primary_key=True, autoincrement=True) hobby = db.Column(db.String(12)) hb_entries = db.relationship('HBEntry', backref='hobby', lazy='dynamic') class HBEntry(db.Model): __tablename__ = 'hbentries' id = db.Column(db.Integer, primary_key=True, autoincrement=True) hb_id = db.Column(db.Integer, db.ForeignKey('hobbies.id')) good = db.Column(db.String(64)) bad = db.Column(db.String(64)) def __repr__(self): return '<Entry %r & %r>' % (self.good, self.bad)

nilq/baby-python

python

""" Stingy OLX ad message forwarder: check for new message(s) and send them to your email @author yohanes.gultom@gmail.com """ from stingy_olx import StingyOLX import re import argparse import smtplib email_tpl = '''From: {0}\r\nTo: {1}\r\nSubject: {2}\r\nMIME-Version: 1.0\r\nContent-Type: text/html\r\n\r\n {3} ''' message_group_tpl = ''' <a href="{}">{}</a> {} ''' message_tpl = ''' <div style="padding-bottom:5px"> {} ({}) <div>{}</div> </div> ''' def send_email(smtp_config, to, body): server_ssl = smtplib.SMTP_SSL(smtp_config['server'], smtp_config['port']) server_ssl.ehlo() server_ssl.login(smtp_config['username'], smtp_config['password']) email = email_tpl.format( smtp_config['from'], to, smtp_config['subject'], body, ) server_ssl.sendmail(smtp_config['from'], to, email) server_ssl.close() print('Email sent') def build_email(ads): """ Build HTML email format based on template and ad messages """ email = [] for ad in ads: html_messages = [] for msg in ad['messages']: html_messages.append(message_tpl.format(msg['sender'], msg['time'], msg['body'])) email.append(message_group_tpl.format(ad['url'], ad['title'], '\n'.join(html_messages))) return '\n'.join(email) def main(): parser = argparse.ArgumentParser() parser.add_argument("olx_username", help="OLX username") parser.add_argument("olx_password", help="OLX password") parser.add_argument("smtp_username", help="SMTP username") parser.add_argument("smtp_password", help="SMTP password") parser.add_argument("email_to", help="Email recipient") parser.add_argument("-s", "--smtp_server", help="SMTP server", default="smtp.gmail.com") parser.add_argument("-p", "--smtp_port", help="SMTP port", type=int, default=465) args = parser.parse_args() smtp_config = { 'username': args.smtp_username, 'password': args.smtp_password, 'server': args.smtp_server, 'port': args.smtp_port, 'from': 'Yohanes Gultom', 'subject': 'Pesan baru di olx.co.id' } olx = StingyOLX() olx.login(args.olx_username, args.olx_password) ads = olx.check_unread_message() if ads: email = build_email(ads) send_email(smtp_config, args.email_to, email) olx.logout() if __name__ == '__main__': main()

nilq/baby-python

python

# encoding: utf-8 # module pandas._libs.reduction # from C:\Python27\lib\site-packages\pandas\_libs\reduction.pyd # by generator 1.147 # no doc # imports import __builtin__ as __builtins__ # <module '__builtin__' (built-in)> import numpy as np # C:\Python27\lib\site-packages\numpy\__init__.pyc from pandas._libs.lib import maybe_convert_objects import distutils.version as __distutils_version # functions def apply_frame_axis0(*args, **kwargs): # real signature unknown pass def reduce(*args, **kwargs): # real signature unknown """ Parameters ----------- arr : NDFrame object f : function axis : integer axis dummy : type of reduced output (series) labels : Index or None """ pass def __pyx_unpickle_Reducer(*args, **kwargs): # real signature unknown pass def __pyx_unpickle_SeriesBinGrouper(*args, **kwargs): # real signature unknown pass def __pyx_unpickle_SeriesGrouper(*args, **kwargs): # real signature unknown pass def __pyx_unpickle_Slider(*args, **kwargs): # real signature unknown pass # classes class BlockSlider(object): """ Only capable of sliding on axis=0 """ def move(self, *args, **kwargs): # real signature unknown pass def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(S, *more): # real signature unknown; restored from __doc__ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """ pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass blocks = property(lambda self: object(), lambda self, v: None, lambda self: None) # default dummy = property(lambda self: object(), lambda self, v: None, lambda self: None) # default frame = property(lambda self: object(), lambda self, v: None, lambda self: None) # default idx_slider = property(lambda self: object(), lambda self, v: None, lambda self: None) # default index = property(lambda self: object(), lambda self, v: None, lambda self: None) # default nblocks = property(lambda self: object(), lambda self, v: None, lambda self: None) # default __pyx_vtable__ = None # (!) real value is '<capsule object NULL at 0x0000000006A57CC0>' class InvalidApply(Exception): # no doc def __init__(self, *args, **kwargs): # real signature unknown pass __weakref__ = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """list of weak references to the object (if defined)""" __qualname__ = 'InvalidApply' class LooseVersion(__distutils_version.Version): """ Version numbering for anarchists and software realists. Implements the standard interface for version number classes as described above. A version number consists of a series of numbers, separated by either periods or strings of letters. When comparing version numbers, the numeric components will be compared numerically, and the alphabetic components lexically. The following are all valid version numbers, in no particular order: 1.5.1 1.5.2b2 161 3.10a 8.02 3.4j 1996.07.12 3.2.pl0 3.1.1.6 2g6 11g 0.960923 2.2beta29 1.13++ 5.5.kw 2.0b1pl0 In fact, there is no such thing as an invalid version number under this scheme; the rules for comparison are simple and predictable, but may not always give the results you want (for some definition of "want"). """ def parse(self, *args, **kwargs): # real signature unknown pass def __cmp__(self, *args, **kwargs): # real signature unknown pass def __init__(self, *args, **kwargs): # real signature unknown pass def __repr__(self, *args, **kwargs): # real signature unknown pass def __str__(self, *args, **kwargs): # real signature unknown pass component_re = None # (!) real value is '<_sre.SRE_Pattern object at 0x0000000003C98470>' class Reducer(object): """ Performs generic reduction operation on a C or Fortran-contiguous ndarray while avoiding ndarray construction overhead """ def get_result(self, *args, **kwargs): # real signature unknown pass def _check_dummy(self, *args, **kwargs): # real signature unknown pass def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(S, *more): # real signature unknown; restored from __doc__ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """ pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass class SeriesBinGrouper(object): """ Performs grouping operation according to bin edges, rather than labels """ def get_result(self, *args, **kwargs): # real signature unknown pass def _check_dummy(self, *args, **kwargs): # real signature unknown pass def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(S, *more): # real signature unknown; restored from __doc__ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """ pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass arr = property(lambda self: object(), lambda self, v: None, lambda self: None) # default bins = property(lambda self: object(), lambda self, v: None, lambda self: None) # default dummy_arr = property(lambda self: object(), lambda self, v: None, lambda self: None) # default dummy_index = property(lambda self: object(), lambda self, v: None, lambda self: None) # default f = property(lambda self: object(), lambda self, v: None, lambda self: None) # default index = property(lambda self: object(), lambda self, v: None, lambda self: None) # default ityp = property(lambda self: object(), lambda self, v: None, lambda self: None) # default name = property(lambda self: object(), lambda self, v: None, lambda self: None) # default typ = property(lambda self: object(), lambda self, v: None, lambda self: None) # default values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default class SeriesGrouper(object): """ Performs generic grouping operation while avoiding ndarray construction overhead """ def get_result(self, *args, **kwargs): # real signature unknown pass def _check_dummy(self, *args, **kwargs): # real signature unknown pass def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(S, *more): # real signature unknown; restored from __doc__ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """ pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass arr = property(lambda self: object(), lambda self, v: None, lambda self: None) # default dummy_arr = property(lambda self: object(), lambda self, v: None, lambda self: None) # default dummy_index = property(lambda self: object(), lambda self, v: None, lambda self: None) # default f = property(lambda self: object(), lambda self, v: None, lambda self: None) # default index = property(lambda self: object(), lambda self, v: None, lambda self: None) # default ityp = property(lambda self: object(), lambda self, v: None, lambda self: None) # default labels = property(lambda self: object(), lambda self, v: None, lambda self: None) # default name = property(lambda self: object(), lambda self, v: None, lambda self: None) # default typ = property(lambda self: object(), lambda self, v: None, lambda self: None) # default values = property(lambda self: object(), lambda self, v: None, lambda self: None) # default class Slider(object): """ Only handles contiguous data for now """ def advance(self, *args, **kwargs): # real signature unknown pass def reset(self, *args, **kwargs): # real signature unknown pass def set_length(self, *args, **kwargs): # real signature unknown pass def __init__(self, *args, **kwargs): # real signature unknown pass @staticmethod # known case of __new__ def __new__(S, *more): # real signature unknown; restored from __doc__ """ T.__new__(S, ...) -> a new object with type S, a subtype of T """ pass def __reduce__(self, *args, **kwargs): # real signature unknown pass def __setstate__(self, *args, **kwargs): # real signature unknown pass __pyx_vtable__ = None # (!) real value is '<capsule object NULL at 0x0000000006A57C60>' # variables with complex values __test__ = {}

nilq/baby-python

python

""" Base threading server class """ from threading import Thread class ThreadServer: def __init__(self): self.server_thread = None self.running = False def start(self, *args, **kwargs): if self.running: return self.running = True self.server_thread = Thread(target=self.run, args=args, kwargs=kwargs) self.server_thread.start() def stop(self): self.running = False def run(self): """ Server main function """ pass class StaticServer: def start(self, *args, **kwargs): pass def stop(self): pass

nilq/baby-python

python

from dynaconf import FlaskDynaconf flask_dynaconf = FlaskDynaconf() def init_app(app, **config): flask_dynaconf.init_app(app, **config) app.config.load_extensions()

nilq/baby-python

python

# Copyright 2019 The TensorFlow Authors. 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. # ============================================================================== """Model utilities.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import json import os import sys from absl import logging import numpy as np import tensorflow.compat.v1 as tf class BatchNormalization(tf.layers.BatchNormalization): """Fixed default name of BatchNormalization to match TpuBatchNormalization.""" def __init__(self, name='tpu_batch_normalization', **kwargs): super(BatchNormalization, self).__init__(name=name, **kwargs) def drop_connect(inputs, is_training, survival_prob): """Drop the entire conv with given survival probability.""" # "Deep Networks with Stochastic Depth", https://arxiv.org/pdf/1603.09382.pdf if not is_training: return inputs # Compute tensor. batch_size = tf.shape(inputs)[0] random_tensor = survival_prob random_tensor += tf.random_uniform([batch_size, 1, 1, 1], dtype=inputs.dtype) binary_tensor = tf.floor(random_tensor) # Unlike conventional way that multiply survival_prob at test time, here we # divide survival_prob at training time, such that no addition compute is # needed at test time. output = tf.div(inputs, survival_prob) * binary_tensor return output def get_ema_vars(): """Get all exponential moving average (ema) variables.""" ema_vars = tf.trainable_variables() + tf.get_collection('moving_vars') for v in tf.global_variables(): # We maintain mva for batch norm moving mean and variance as well. if 'moving_mean' in v.name or 'moving_variance' in v.name: ema_vars.append(v) return list(set(ema_vars)) class DepthwiseConv2D(tf.keras.layers.DepthwiseConv2D, tf.layers.Layer): """Wrap keras DepthwiseConv2D to tf.layers.""" pass class Conv2D(tf.layers.Conv2D): """Wrapper for Conv2D with specialization for fast inference.""" def _bias_activation(self, outputs): if self.use_bias: outputs = tf.nn.bias_add(outputs, self.bias, data_format='NCHW') if self.activation is not None: return self.activation(outputs) return outputs def _can_run_fast_1x1(self, inputs): batch_size = inputs.shape.as_list()[0] return (self.data_format == 'channels_first' and batch_size == 1 and self.kernel_size == (1, 1)) def _call_fast_1x1(self, inputs): # Compute the 1x1 convolution as a matmul. inputs_shape = tf.shape(inputs) flat_inputs = tf.reshape(inputs, [inputs_shape[1], -1]) flat_outputs = tf.matmul( tf.squeeze(self.kernel), flat_inputs, transpose_a=True) outputs_shape = tf.concat([[1, self.filters], inputs_shape[2:]], axis=0) outputs = tf.reshape(flat_outputs, outputs_shape) # Handle the bias and activation function. return self._bias_activation(outputs) def call(self, inputs): if self._can_run_fast_1x1(inputs): return self._call_fast_1x1(inputs) return super(Conv2D, self).call(inputs) class EvalCkptDriver(object): """A driver for running eval inference. Attributes: model_name: str. Model name to eval. batch_size: int. Eval batch size. image_size: int. Input image size, determined by model name. num_classes: int. Number of classes, default to 1000 for ImageNet. include_background_label: whether to include extra background label. advprop_preprocessing: whether to use advprop preprocessing. """ def __init__(self, model_name, batch_size=1, image_size=224, num_classes=1000, include_background_label=False, advprop_preprocessing=False): """Initialize internal variables.""" self.model_name = model_name self.batch_size = batch_size self.num_classes = num_classes self.include_background_label = include_background_label self.image_size = image_size self.advprop_preprocessing = advprop_preprocessing def restore_model(self, sess, ckpt_dir, enable_ema=True, export_ckpt=None): """Restore variables from checkpoint dir.""" sess.run(tf.global_variables_initializer()) checkpoint = tf.train.latest_checkpoint(ckpt_dir) if enable_ema: ema = tf.train.ExponentialMovingAverage(decay=0.0) ema_vars = get_ema_vars() var_dict = ema.variables_to_restore(ema_vars) ema_assign_op = ema.apply(ema_vars) else: var_dict = get_ema_vars() ema_assign_op = None tf.train.get_or_create_global_step() sess.run(tf.global_variables_initializer()) saver = tf.train.Saver(var_dict, max_to_keep=1) saver.restore(sess, checkpoint) if export_ckpt: if ema_assign_op is not None: sess.run(ema_assign_op) saver = tf.train.Saver(max_to_keep=1, save_relative_paths=True) saver.save(sess, export_ckpt) def build_model(self, features, is_training): """Build model with input features.""" del features, is_training raise ValueError('Must be implemented by subclasses.') def get_preprocess_fn(self): raise ValueError('Must be implemented by subclsses.') def build_dataset(self, filenames, labels, is_training): """Build input dataset.""" batch_drop_remainder = False if 'condconv' in self.model_name and not is_training: # CondConv layers can only be called with known batch dimension. Thus, we # must drop all remaining examples that do not make up one full batch. # To ensure all examples are evaluated, use a batch size that evenly # divides the number of files. batch_drop_remainder = True num_files = len(filenames) if num_files % self.batch_size != 0: tf.logging.warn('Remaining examples in last batch are not being ' 'evaluated.') filenames = tf.constant(filenames) labels = tf.constant(labels) dataset = tf.data.Dataset.from_tensor_slices((filenames, labels)) def _parse_function(filename, label): image_string = tf.read_file(filename) preprocess_fn = self.get_preprocess_fn() image_decoded = preprocess_fn( image_string, is_training, image_size=self.image_size) image = tf.cast(image_decoded, tf.float32) return image, label dataset = dataset.map(_parse_function) dataset = dataset.batch(self.batch_size, drop_remainder=batch_drop_remainder) iterator = dataset.make_one_shot_iterator() images, labels = iterator.get_next() return images, labels def run_inference(self, ckpt_dir, image_files, labels, enable_ema=True, export_ckpt=None): """Build and run inference on the target images and labels.""" label_offset = 1 if self.include_background_label else 0 with tf.Graph().as_default(), tf.Session() as sess: images, labels = self.build_dataset(image_files, labels, False) probs = self.build_model(images, is_training=False) if isinstance(probs, tuple): probs = probs[0] self.restore_model(sess, ckpt_dir, enable_ema, export_ckpt) prediction_idx = [] prediction_prob = [] for _ in range(len(image_files) // self.batch_size): out_probs = sess.run(probs) idx = np.argsort(out_probs)[::-1] prediction_idx.append(idx[:5] - label_offset) prediction_prob.append([out_probs[pid] for pid in idx[:5]]) # Return the top 5 predictions (idx and prob) for each image. return prediction_idx, prediction_prob def eval_example_images(self, ckpt_dir, image_files, labels_map_file, enable_ema=True, export_ckpt=None): """Eval a list of example images. Args: ckpt_dir: str. Checkpoint directory path. image_files: List[str]. A list of image file paths. labels_map_file: str. The labels map file path. enable_ema: enable expotential moving average. export_ckpt: export ckpt folder. Returns: A tuple (pred_idx, and pred_prob), where pred_idx is the top 5 prediction index and pred_prob is the top 5 prediction probability. """ classes = json.loads(tf.gfile.Open(labels_map_file).read()) pred_idx, pred_prob = self.run_inference( ckpt_dir, image_files, [0] * len(image_files), enable_ema, export_ckpt) for i in range(len(image_files)): print('predicted class for image {}: '.format(image_files[i])) for j, idx in enumerate(pred_idx[i]): print(' -> top_{} ({:4.2f}%): {} '.format(j, pred_prob[i][j] * 100, classes[str(idx)])) return pred_idx, pred_prob def eval_imagenet(self, ckpt_dir, imagenet_eval_glob, imagenet_eval_label, num_images, enable_ema, export_ckpt): """Eval ImageNet images and report top1/top5 accuracy. Args: ckpt_dir: str. Checkpoint directory path. imagenet_eval_glob: str. File path glob for all eval images. imagenet_eval_label: str. File path for eval label. num_images: int. Number of images to eval: -1 means eval the whole dataset. enable_ema: enable expotential moving average. export_ckpt: export checkpoint folder. Returns: A tuple (top1, top5) for top1 and top5 accuracy. """ imagenet_val_labels = [int(i) for i in tf.gfile.GFile(imagenet_eval_label)] imagenet_filenames = sorted(tf.gfile.Glob(imagenet_eval_glob)) if num_images < 0: num_images = len(imagenet_filenames) image_files = imagenet_filenames[:num_images] labels = imagenet_val_labels[:num_images] pred_idx, _ = self.run_inference( ckpt_dir, image_files, labels, enable_ema, export_ckpt) top1_cnt, top5_cnt = 0.0, 0.0 for i, label in enumerate(labels): top1_cnt += label in pred_idx[i][:1] top5_cnt += label in pred_idx[i][:5] if i % 100 == 0: print('Step {}: top1_acc = {:4.2f}% top5_acc = {:4.2f}%'.format( i, 100 * top1_cnt / (i + 1), 100 * top5_cnt / (i + 1))) sys.stdout.flush() top1, top5 = 100 * top1_cnt / num_images, 100 * top5_cnt / num_images print('Final: top1_acc = {:4.2f}% top5_acc = {:4.2f}%'.format(top1, top5)) return top1, top5

nilq/baby-python

python

from django import forms from django.http import QueryDict from django.utils.translation import ugettext_lazy as _ from panoptes.analysis import FilteredSessions from panoptes.analysis.fields import LensChoiceField, WeekdayChoiceField from panoptes.core.fields import LocationField from panoptes.core.models import Session import datetime class SessionFilterForm(forms.Form): """A form for filtering session data based on user bounds.""" location = LocationField(label=_("location")) lens = LensChoiceField(label=_("data view")) start = forms.DateField(label=_("start date"), required=False) end = forms.DateField(label=_("end date"), required=False) start_time = forms.TimeField(label=_("start time"), required=False) end_time = forms.TimeField(label=_("end time"), required=False) weekdays = WeekdayChoiceField(label=_("weekdays"), required=False) x_detail = forms.CharField(label=_("x-value detail"), required=False, widget=forms.HiddenInput) y_detail = forms.CharField(label=_("y-value detail"), required=False, widget=forms.HiddenInput) def __init__(self, *args, **kwargs): """Accept a 'profile' kwarg that provides default data.""" profile = kwargs.pop('profile', None) if profile: today = datetime.date.today() post = QueryDict("") post = post.copy() post.update({ 'location': profile.default_location.pk, 'lens': profile.default_lens.slug, 'start': self._parsable_date(today - datetime.timedelta(days=profile.default_recent_days)), 'end': self._parsable_date(today) }) args = (post,) super(SessionFilterForm, self).__init__(*args, **kwargs) def _parsable_date(self, date): """Return the given date as a parsable string.""" return date.strftime("%m/%d/%Y") def clean(self): """Perform extra validation and resolution of data. This adds an `x_detail` key to the cleaned data containing the resolved x-value whose details should be shown, and also makes sure that the dates and times are coherent. """ cleaned_data = self.cleaned_data today = datetime.date.today() # If a start date is provided but the end date is left blank, end on the # current date if cleaned_data.get('start',None) and not cleaned_data.get('end', None): cleaned_data['end'] = today # If an end date is provided and no start date is given, start at the first # date on which sessions were recorded, or a year ago, if no sessions exist if cleaned_data.get('end', None) and not cleaned_data.get('start', None): cleaned_data['start'] = Session.objects.first_session_date_for_location(cleaned_data['location']) # If the date bounds are left blank, default to viewing the past week if not cleaned_data.get('start', None) and not cleaned_data.get('end', None): cleaned_data['start'] = today - datetime.timedelta(weeks=1) cleaned_data['end'] = today # Have empty time filters use the opening or closing time of the location if not cleaned_data.get('start_time', None): cleaned_data['start_time'] = cleaned_data['location'].earliest_opening if not cleaned_data.get('end_time', None): cleaned_data['end_time'] = cleaned_data['location'].latest_closing # Make sure that the start and end dates and times are properly ordered if cleaned_data['start'] > cleaned_data['end']: raise forms.ValidationError(_("The start must come before the end date")) if cleaned_data['start_time'] > cleaned_data['end_time']: raise forms.ValidationError(_("The start time must come before the end time")) # Resolve the x- and y-value details if possible if cleaned_data.get('x_detail', None): x_axis = cleaned_data['lens'].x_axis() cleaned_data['x_detail'] = x_axis.deserialize_value(cleaned_data['x_detail']) if cleaned_data.get('y_detail', None): y_axis = cleaned_data['lens'].y_axis() cleaned_data['y_detail'] = y_axis.deserialize_value(cleaned_data['y_detail']) cleaned_data['x_detail'] = cleaned_data['x_detail'] or None cleaned_data['y_detail'] = cleaned_data['y_detail'] or None return cleaned_data def as_filtered_sessions(self): """ If the form was successfully validated, return a FilteredSessions instance built from the form's cleaned data. """ data = self.cleaned_data filtered_sessions = FilteredSessions( location=data['location'], start_date=data.get('start', None), end_date=data.get('end', None), start_time=data.get('start_time', None), end_time=data.get('end_time', None), weekdays=data.get('weekdays', []), x_detail=data.get('x_detail', None)) lens = data.get('lens', None) if lens: filtered_sessions.set_axes(lens.x_axis, lens.y_axis) return filtered_sessions

nilq/baby-python

python

import os from typing import Any import torch.optim as optim import yaml from aim.sdk.utils import generate_run_hash from deep_compression.losses import ( BatchChannelDecorrelationLoss, RateDistortionLoss, ) def create_criterion(conf): if conf.name == "RateDistortionLoss": return RateDistortionLoss( lmbda=conf.lambda_, target_bpp=conf.get("target_bpp", None), ) if conf.name == "BatchChannelDecorrelationLoss": return BatchChannelDecorrelationLoss( lmbda=conf.lambda_, lmbda_corr=conf.lambda_corr, top_k_corr=conf.top_k_corr, ) raise ValueError("Unknown criterion.") def configure_optimizers(net, conf): """Separate parameters for the main optimizer and the auxiliary optimizer. Return two optimizers""" parameters = { n for n, p in net.named_parameters() if not n.endswith(".quantiles") and p.requires_grad } aux_parameters = { n for n, p in net.named_parameters() if n.endswith(".quantiles") and p.requires_grad } # Make sure we don't have an intersection of parameters params_dict = dict(net.named_parameters()) inter_params = parameters & aux_parameters union_params = parameters | aux_parameters assert len(inter_params) == 0 assert len(union_params) - len(params_dict.keys()) == 0 optimizer = optim.Adam( (params_dict[n] for n in sorted(parameters)), lr=conf.learning_rate, ) aux_optimizer = optim.Adam( (params_dict[n] for n in sorted(aux_parameters)), lr=conf.aux_learning_rate, ) return {"net": optimizer, "aux": aux_optimizer} def configure_logs(logdir: str) -> dict[str, Any]: filename = os.path.join(logdir, "info.yaml") try: with open(filename) as f: config = yaml.safe_load(f) except FileNotFoundError: config = {} config["run_hash"] = generate_run_hash() os.makedirs(logdir, exist_ok=True) with open(filename, "w") as f: yaml.safe_dump(config, f) return config

nilq/baby-python

python

# -*- coding: utf-8 -*- # maya import pymel.core as pm from maya.app.general.mayaMixin import MayaQDockWidget from maya.app.general.mayaMixin import MayaQWidgetDockableMixin # Built-in from functools import partial import os import sys import json import shutil import subprocess # mbox from . import naming_rules_ui as name_ui from . import custom_step_ui as custom_step_ui from . import root_settings_ui as root_ui from . import block_settings_ui as block_ui from . import joint_names_ui as joint_name_ui from mbox.lego import naming, lib # mgear from mgear.core import pyqt, string from mgear.vendor.Qt import QtCore, QtWidgets, QtGui from mgear.anim_picker.gui import MAYA_OVERRIDE_COLOR ROOT_TYPE = "mbox_guide_root" BLOCK_TYPE = "mbox_guide_block" class RootMainTabUI(QtWidgets.QDialog, root_ui.Ui_Form): def __init__(self, parent=None): super(RootMainTabUI, self).__init__(parent) self.setupUi(self) class RootCustomStepTabUI(QtWidgets.QDialog, custom_step_ui.Ui_Form): def __init__(self, parent=None): super(RootCustomStepTabUI, self).__init__(parent) self.setupUi(self) class RootNameTabUI(QtWidgets.QDialog, name_ui.Ui_Form): def __init__(self, parent=None): super(RootNameTabUI, self).__init__(parent) self.setupUi(self) class HelperSlots: def __init__(self): self._network = None # def update_host_ui(self, l_edit, target_attr): guide = lib.get_component_guide(pm.selected(type="transform")[0]) if guide: network = guide[0].message.outputs(type="network")[0] l_edit.setText(guide[0].name()) self._network.attr(target_attr).set("{},{}".format(guide[0].name(), network.attr("oid").get())) else: if l_edit.text(): l_edit.clear() self._network.attr(target_attr).set("") pm.displayWarning("") def update_line_edit(self, l_edit, target_attr): name = string.removeInvalidCharacter(l_edit.text()) l_edit.setText(name) self._network.attr(target_attr).set(name) def update_line_edit2(self, l_edit, target_attr): # nomralize the text to be Maya naming compatible # replace invalid characters with "_" name = string.normalize2(l_edit.text()) l_edit.setText(name) self._network.attr(target_attr).set(name) def update_text_edit(self, l_edit, target_attr): self._network.attr(target_attr).set(l_edit.toPlainText()) def update_line_edit_path(self, l_edit, target_attr): self._network.attr(target_attr).set(l_edit.text()) def update_name_rule_line_edit(self, l_edit, target_attr): # nomralize the text to be Maya naming compatible # replace invalid characters with "_" name = naming.normalize_name_rule(l_edit.text()) l_edit.setText(name) self._network.attr(target_attr).set(name) self.naming_rule_validator(l_edit) def naming_rule_validator(self, l_edit, log=True): Palette = QtGui.QPalette() if not naming.name_rule_validator(l_edit.text(), naming.NAMING_RULE_TOKENS, log=log): Palette.setBrush(QtGui.QPalette.Text, self.red_brush) else: Palette.setBrush(QtGui.QPalette.Text, self.white_down_brush) l_edit.setPalette(Palette) def add_item_to_list_widget(self, list_widget, target_attr=None): items = pm.selected() items_list = [i.text() for i in list_widget.findItems( "", QtCore.Qt.MatchContains)] # Quick clean the first empty item if items_list and not items_list[0]: list_widget.takeItem(0) for item in items: if len(item.name().split("|")) != 1: pm.displayWarning("Not valid obj: %s, name is not unique." % item.name()) continue if item.name() not in items_list: if item.hasAttr("is_guide_component") or item.hasAttr("is_guide_root"): list_widget.addItem(item.name()) else: pm.displayWarning( "The object: %s, is not a valid" " reference, Please select only guide componet" " roots and guide locators." % item.name()) else: pm.displayWarning("The object: %s, is already in the list." % item.name()) if target_attr: self.update_list_attr(list_widget, target_attr) def remove_selected_from_list_widget(self, list_widget, target_attr=None): for item in list_widget.selectedItems(): list_widget.takeItem(list_widget.row(item)) if target_attr: self.update_list_attr(list_widget, target_attr) def move_from_list_widget_to_list_widget(self, source_list_widget, target_list_widget, target_attr_list_widget, target_attr=None): # Quick clean the first empty item items_list = [i.text() for i in target_attr_list_widget.findItems( "", QtCore.Qt.MatchContains)] if items_list and not items_list[0]: target_attr_list_widget.takeItem(0) for item in source_list_widget.selectedItems(): target_list_widget.addItem(item.text()) source_list_widget.takeItem(source_list_widget.row(item)) if target_attr: self.update_list_attr(target_attr_list_widget, target_attr) def copy_from_list_widget(self, source_list_widget, target_list_widget, target_attr=None): target_list_widget.clear() items_list = [i.text() for i in source_list_widget.findItems( "", QtCore.Qt.MatchContains)] for item in items_list: target_list_widget.addItem(item) if target_attr: self.update_list_attr(source_list_widget, target_attr) def update_list_attr(self, source_list_widget, target_attr): """Update the string attribute with values separated by commas""" new_value = ",".join([i.text() for i in source_list_widget.findItems( "", QtCore.Qt.MatchContains)]) self._network.attr(target_attr).set(new_value) def update_component_name(self): with pm.UndoChunk(): side_set = ["center", "left", "right"] line_name = self.main_tab.name_lineEdit.text() new_name = string.normalize2(line_name) if line_name != new_name: self.main_tab.name_lineEdit.setText(new_name) return side_index = self.main_tab.side_comboBox.currentIndex() new_side = side_set[side_index] index = self.main_tab.componentIndex_spinBox.value() blueprint = lib.blueprint_from_guide(self._guide.getParent(generations=-1)) block = blueprint.find_block_with_oid(self._network.attr("oid").get()) new_index = blueprint.solve_index(new_name, new_side, index, block) rename_check = False if self._network.attr("comp_name").get() != new_name \ or self._network.attr("comp_side").get(asString=True) != new_side \ or self._network.attr("comp_index").get() != new_index: rename_check = True if self._network.attr("comp_name").get() == new_name \ and self._network.attr("comp_side").get(asString=True) == new_side \ and self._network.attr("comp_index").get() == index: return if rename_check: block["comp_name"] = new_name block["comp_side"] = new_side block["comp_index"] = new_index block.to_network() block.update_guide() if self._network.attr("comp_index").get() != self.main_tab.componentIndex_spinBox.value(): self.main_tab.componentIndex_spinBox.setValue(self._network.attr("comp_index").get()) def update_connector(self, source_widget, items_list, *args): self._network.attr("connector").set(items_list[source_widget.currentIndex()]) def populate_check(self, target_widget, source_attr, *args): if self._network.attr(source_attr).get(): target_widget.setCheckState(QtCore.Qt.Checked) else: target_widget.setCheckState(QtCore.Qt.Unchecked) def update_check(self, source_widget, target_attr, *args): self._network.attr(target_attr).set(source_widget.isChecked()) def update_spin_box(self, source_widget, target_attr, *args): self._network.attr(target_attr).set(source_widget.value()) return True def update_slider(self, source_widget, target_attr, *args): self._network.attr(target_attr).set(float(source_widget.value()) / 100) def update_combo_box(self, source_widget, target_attr, *args): self._network.attr(target_attr).set(source_widget.currentIndex()) def update_control_shape(self, source_widget, ctl_list, target_attr, *args): current_index = source_widget.currentIndex() self._network.attr(target_attr).set(ctl_list[current_index]) def update_index_color_widgets( self, source_widget, target_attr, color_widget, *args): self.update_spin_box(source_widget, target_attr) self.update_widget_style_sheet( color_widget, (i / 255.0 for i in MAYA_OVERRIDE_COLOR[source_widget.value()])) def update_rgb_color_widgets(self, button_widget, rgb, slider_widget): self.update_widget_style_sheet(button_widget, rgb) slider_widget.blockSignals(True) slider_widget.setValue(sorted(rgb)[2] * 255) slider_widget.blockSignals(False) def update_widget_style_sheet(self, source_widget, rgb): color = ', '.join(str(i * 255) for i in pm.colorManagementConvert(toDisplaySpace=rgb)) source_widget.setStyleSheet( "* {background-color: rgb(" + color + ")}") def rgb_slider_value_changed(self, button_widget, target_attr, value): rgb = self._network.attr(target_attr).get() hsv_value = sorted(rgb)[2] if hsv_value: new_rgb = tuple(i / (hsv_value / 1.0) * (value / 255.0) for i in rgb) else: new_rgb = tuple((1.0 * (value / 255.0), 1.0 * (value / 255.0), 1.0 * (value / 255.0))) self.update_widget_style_sheet(button_widget, new_rgb) self._network.attr(target_attr).set(new_rgb) def rgb_color_editor(self, source_widget, target_attr, slider_widget, *args): pm.colorEditor(rgb=self._network.attr(target_attr).get()) if pm.colorEditor(query=True, result=True): rgb = pm.colorEditor(query=True, rgb=True) self._network.attr(target_attr).set(rgb) self.update_rgb_color_widgets(source_widget, rgb, slider_widget) def toggle_rgb_index_widgets(self, check_box, idx_widgets, rgb_widgets, target_attr, checked): show_widgets, hide_widgets = ( rgb_widgets, idx_widgets) if checked else ( idx_widgets, rgb_widgets) for widget in show_widgets: widget.show() for widget in hide_widgets: widget.hide() self.update_check(check_box, target_attr) def set_profile(self): pm.select(self._network, r=True) pm.runtime.GraphEditor() def get_cs_file_fullpath(self, cs_data): filepath = cs_data.split("|")[-1][1:] if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): fullpath = os.path.join( os.environ.get( "MBOX_CUSTOM_STEP_PATH", ""), filepath) else: fullpath = filepath return fullpath def edit_file(self, widgetList): try: cs_data = widgetList.selectedItems()[0].text() fullpath = self.get_cs_file_fullpath(cs_data) if fullpath: if sys.platform.startswith('darwin'): subprocess.call(('open', fullpath)) elif os.name == 'nt': os.startfile(fullpath) elif os.name == 'posix': subprocess.call(('xdg-open', fullpath)) else: pm.displayWarning("Please select one item from the list") except Exception: pm.displayError("The step can't be find or does't exists") def format_info(self, data): data_parts = data.split("|") cs_name = data_parts[0] if cs_name.startswith("*"): cs_status = "Deactivated" cs_name = cs_name[1:] else: cs_status = "Active" cs_fullpath = self.get_cs_file_fullpath(data) if "_shared" in data: cs_shared_owner = self.shared_owner(cs_fullpath) cs_shared_status = "Shared" else: cs_shared_status = "Local" cs_shared_owner = "None" info = '<html><head/><body>\ {0}------------------Status: {1}Shared Status: {2}Shared Owner: \ {3}Full Path: \ {4}</body></html>'.format(cs_name, cs_status, cs_shared_status, cs_shared_owner, cs_fullpath) return info def shared_owner(self, cs_fullpath): scan_dir = os.path.abspath(os.path.join(cs_fullpath, os.pardir)) while not scan_dir.endswith("_shared"): scan_dir = os.path.abspath(os.path.join(scan_dir, os.pardir)) # escape infinite loop if scan_dir == '/': break scan_dir = os.path.abspath(os.path.join(scan_dir, os.pardir)) return os.path.split(scan_dir)[1] @classmethod def get_steps_dict(self, itemsList): stepsDict = {} stepsDict["itemsList"] = itemsList for item in itemsList: step = open(item, "r") data = step.read() stepsDict[item] = data step.close() return stepsDict @classmethod def runStep(self, stepPath, customStepDic): try: with pm.UndoChunk(): pm.displayInfo( "EXEC: Executing custom step: %s" % stepPath) # use forward slash for OS compatibility if sys.platform.startswith('darwin'): stepPath = stepPath.replace('\\', '/') fileName = os.path.split(stepPath)[1].split(".")[0] if os.environ.get(MGEAR_SHIFTER_CUSTOMSTEP_KEY, ""): runPath = os.path.join( os.environ.get( MGEAR_SHIFTER_CUSTOMSTEP_KEY, ""), stepPath) else: runPath = stepPath customStep = imp.load_source(fileName, runPath) if hasattr(customStep, "CustomShifterStep"): argspec = inspect.getargspec( customStep.CustomShifterStep.__init__) if "stored_dict" in argspec.args: cs = customStep.CustomShifterStep(customStepDic) cs.setup() cs.run() else: cs = customStep.CustomShifterStep() cs.run(customStepDic) customStepDic[cs.name] = cs pm.displayInfo( "SUCCEED: Custom Shifter Step Class: %s. " "Succeed!!" % stepPath) else: pm.displayInfo( "SUCCEED: Custom Step simple script: %s. " "Succeed!!" % stepPath) except Exception as ex: template = "An exception of type {0} occurred. " "Arguments:\n{1!r}" message = template.format(type(ex).__name__, ex.args) pm.displayError(message) pm.displayError(traceback.format_exc()) cont = pm.confirmBox( "FAIL: Custom Step Fail", "The step:%s has failed. Continue with next step?" % stepPath + "\n\n" + message + "\n\n" + traceback.format_exc(), "Continue", "Stop Build", "Try Again!") if cont == "Stop Build": # stop Build return True elif cont == "Try Again!": try: # just in case there is nothing to undo pm.undo() except Exception: pass pm.displayInfo("Trying again! : {}".format(stepPath)) inception = self.runStep(stepPath, customStepDic) if inception: # stops build from the recursion loop. return True else: return False def run_manual_step(self, widgetList): selItems = widgetList.selectedItems() for item in selItems: self.runStep(item.text().split("|")[-1][1:], customStepDic={}) def close_settings(self): self.close() pyqt.deleteInstances(self, MayaQDockWidget) class RootSettings(MayaQWidgetDockableMixin, QtWidgets.QDialog, HelperSlots): green_brush = QtGui.QColor(0, 160, 0) red_brush = QtGui.QColor(180, 0, 0) white_brush = QtGui.QColor(255, 255, 255) white_down_brush = QtGui.QColor(160, 160, 160) orange_brush = QtGui.QColor(240, 160, 0) def __init__(self): self.toolName = ROOT_TYPE # Delete old instances of the componet settings window. pyqt.deleteInstances(self, MayaQDockWidget) # super(self.__class__, self).__init__(parent=parent) super(RootSettings, self).__init__() # the inspectSettings function set the current selection to the # component root before open the settings dialog self._network = pm.selected(type="transform")[0].message.outputs(type="network")[0] self.main_tab = RootMainTabUI() self.custom_step_tab = RootCustomStepTabUI() self.naming_rule_tab = RootNameTabUI() self.mayaMainWindow = pyqt.maya_main_window() self.setObjectName(self.toolName) self.setWindowFlags(QtCore.Qt.Window) self.setWindowTitle(ROOT_TYPE) self.resize(500, 615) self.create_controls() self.populate_controls() self.create_layout() self.create_connections() self.setAttribute(QtCore.Qt.WA_DeleteOnClose, True) # hover info self.pre_cs = self.custom_step_tab.preCustomStep_listWidget self.pre_cs.setMouseTracking(True) self.pre_cs.entered.connect(self.pre_info) self.post_cs = self.custom_step_tab.postCustomStep_listWidget self.post_cs.setMouseTracking(True) self.post_cs.entered.connect(self.post_info) def pre_info(self, index): self.hover_info_item_entered(self.pre_cs, index) def post_info(self, index): self.hover_info_item_entered(self.post_cs, index) def hover_info_item_entered(self, view, index): if index.isValid(): info_data = self.format_info(index.data()) QtWidgets.QToolTip.showText( QtGui.QCursor.pos(), info_data, view.viewport(), view.visualRect(index)) def create_controls(self): """Create the controls for the component base""" self.tabs = QtWidgets.QTabWidget() self.tabs.setObjectName("settings_tab") # Close Button self.close_button = QtWidgets.QPushButton("Close") def populate_controls(self): """Populate the controls values from the custom attributes of the component. """ # populate tab self.tabs.insertTab(0, self.main_tab, "Guide Settings") self.tabs.insertTab(1, self.custom_step_tab, "Custom Steps") self.tabs.insertTab(2, self.naming_rule_tab, "Naming Rules") # populate main settings self.main_tab.rigName_lineEdit.setText( self._network.attr("name").get()) self.main_tab.mode_comboBox.setCurrentIndex( self._network.attr("process").get()) self.main_tab.step_comboBox.setCurrentIndex( self._network.attr("step").get()) # self.populateCheck( # self.main_tab.proxyChannels_checkBox, "proxyChannels") self.populate_check(self.main_tab.worldCtl_checkBox, "world_ctl") self.main_tab.worldCtl_lineEdit.setText( self._network.attr("world_ctl_name").get()) # self.populateCheck( # self.main_tab.classicChannelNames_checkBox, # "classicChannelNames") # self.populateCheck( # self.main_tab.attrPrefix_checkBox, # "attrPrefixName") # self.populateCheck( # self.main_tab.importSkin_checkBox, "importSkin") # self.main_tab.skin_lineEdit.setText( # self._network.attr("skin").get()) # self.populateCheck( # self.main_tab.dataCollector_checkBox, "data_collector") # self.main_tab.dataCollectorPath_lineEdit.setText( # self._network.attr("data_collector_path").get()) self.populate_check( self.main_tab.jointRig_checkBox, "joint_rig") self.populate_check( self.main_tab.force_uniScale_checkBox, "force_uni_scale") self.populate_check( self.main_tab.connect_joints_checkBox, "connect_joints") # self.populateAvailableSynopticTabs() # for item in self._network.attr("synoptic").get().split(","): # self.main_tab.rigTabs_listWidget.addItem(item) tap = self.main_tab index_widgets = ((tap.L_color_fk_spinBox, tap.L_color_fk_label, "l_color_fk"), (tap.L_color_ik_spinBox, tap.L_color_ik_label, "l_color_ik"), (tap.C_color_fk_spinBox, tap.C_color_fk_label, "c_color_fk"), (tap.C_color_ik_spinBox, tap.C_color_ik_label, "c_color_ik"), (tap.R_color_fk_spinBox, tap.R_color_fk_label, "r_color_fk"), (tap.R_color_ik_spinBox, tap.R_color_ik_label, "r_color_ik")) rgb_widgets = ((tap.L_RGB_fk_pushButton, tap.L_RGB_fk_slider, "l_RGB_fk"), (tap.L_RGB_ik_pushButton, tap.L_RGB_ik_slider, "l_RGB_ik"), (tap.C_RGB_fk_pushButton, tap.C_RGB_fk_slider, "c_RGB_fk"), (tap.C_RGB_ik_pushButton, tap.C_RGB_ik_slider, "c_RGB_ik"), (tap.R_RGB_fk_pushButton, tap.R_RGB_fk_slider, "r_RGB_fk"), (tap.R_RGB_ik_pushButton, tap.R_RGB_ik_slider, "r_RGB_ik")) for spinBox, label, source_attr in index_widgets: color_index = self._network.attr(source_attr).get() spinBox.setValue(color_index) self.update_widget_style_sheet( label, [i / 255.0 for i in MAYA_OVERRIDE_COLOR[color_index]]) for button, slider, source_attr in rgb_widgets: self.update_rgb_color_widgets( button, self._network.attr(source_attr).get(), slider) # forceing the size of the color buttons/label to keep ui clean for widget in tuple(i[0] for i in rgb_widgets) + tuple( i[1] for i in index_widgets): widget.setFixedSize(pyqt.dpi_scale(30), pyqt.dpi_scale(20)) self.populate_check(tap.useRGB_checkBox, "use_RGB_color") self.toggle_rgb_index_widgets(tap.useRGB_checkBox, (w for i in index_widgets for w in i[:2]), (w for i in rgb_widgets for w in i[:2]), "use_RGB_color", tap.useRGB_checkBox.checkState()) tap.notes_textEdit.setText(self._network.attr("notes").get()) # pupulate custom steps sttings self.populate_check( self.custom_step_tab.preCustomStep_checkBox, "run_pre_custom_step") for item in self._network.attr("pre_custom_step").get().split(","): self.custom_step_tab.preCustomStep_listWidget.addItem(item) self.refresh_status_color(self.custom_step_tab.preCustomStep_listWidget) self.populate_check( self.custom_step_tab.postCustomStep_checkBox, "run_post_custom_step") for item in self._network.attr("post_custom_step").get().split(","): self.custom_step_tab.postCustomStep_listWidget.addItem(item) self.refresh_status_color(self.custom_step_tab.postCustomStep_listWidget) self.populate_naming_controls() def populate_naming_controls(self): # populate name settings self.naming_rule_tab.ctl_name_rule_lineEdit.setText( self._network.attr("ctl_name_rule").get()) self.naming_rule_validator( self.naming_rule_tab.ctl_name_rule_lineEdit) self.naming_rule_tab.joint_name_rule_lineEdit.setText( self._network.attr("joint_name_rule").get()) self.naming_rule_validator( self.naming_rule_tab.joint_name_rule_lineEdit) self.naming_rule_tab.side_left_name_lineEdit.setText( self._network.attr("ctl_left_name").get()) self.naming_rule_tab.side_right_name_lineEdit.setText( self._network.attr("ctl_right_name").get()) self.naming_rule_tab.side_center_name_lineEdit.setText( self._network.attr("ctl_center_name").get()) self.naming_rule_tab.side_joint_left_name_lineEdit.setText( self._network.attr("joint_left_name").get()) self.naming_rule_tab.side_joint_right_name_lineEdit.setText( self._network.attr("joint_right_name").get()) self.naming_rule_tab.side_joint_center_name_lineEdit.setText( self._network.attr("joint_center_name").get()) self.naming_rule_tab.ctl_name_ext_lineEdit.setText( self._network.attr("ctl_name_ext").get()) self.naming_rule_tab.joint_name_ext_lineEdit.setText( self._network.attr("joint_name_ext").get()) self.naming_rule_tab.ctl_des_letter_case_comboBox.setCurrentIndex( self._network.attr("ctl_description_letter_case").get()) self.naming_rule_tab.joint_des_letter_case_comboBox.setCurrentIndex( self._network.attr("joint_description_letter_case").get()) self.naming_rule_tab.ctl_padding_spinBox.setValue( self._network.attr("ctl_index_padding").get()) self.naming_rule_tab.joint_padding_spinBox.setValue( self._network.attr("joint_index_padding").get()) def create_layout(self): """ Create the layout for the component base settings """ self.settings_layout = QtWidgets.QVBoxLayout() self.settings_layout.addWidget(self.tabs) self.settings_layout.addWidget(self.close_button) self.setLayout(self.settings_layout) def create_connections(self): """Create the slots connections to the controls functions""" self.close_button.clicked.connect(self.close_settings) # Setting Tab tap = self.main_tab tap.rigName_lineEdit.editingFinished.connect( partial(self.update_line_edit, tap.rigName_lineEdit, "name")) tap.mode_comboBox.currentIndexChanged.connect( partial(self.update_combo_box, tap.mode_comboBox, "process")) tap.step_comboBox.currentIndexChanged.connect( partial(self.update_combo_box, tap.step_comboBox, "step")) # tap.proxyChannels_checkBox.stateChanged.connect( # partial(self.update_check, # tap.proxyChannels_checkBox, # "proxyChannels")) tap.worldCtl_checkBox.stateChanged.connect( partial(self.update_check, tap.worldCtl_checkBox, "world_ctl")) tap.worldCtl_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.worldCtl_lineEdit, "world_ctl_name")) # tap.classicChannelNames_checkBox.stateChanged.connect( # partial(self.updateCheck, # tap.classicChannelNames_checkBox, # "classicChannelNames")) # tap.attrPrefix_checkBox.stateChanged.connect( # partial(self.updateCheck, # tap.attrPrefix_checkBox, # "attrPrefixName")) # tap.dataCollector_checkBox.stateChanged.connect( # partial(self.updateCheck, # tap.dataCollector_checkBox, # "data_collector")) # tap.dataCollectorPath_lineEdit.editingFinished.connect( # partial(self.updateLineEditPath, # tap.dataCollectorPath_lineEdit, # "data_collector_path")) tap.jointRig_checkBox.stateChanged.connect( partial(self.update_check, tap.jointRig_checkBox, "joint_rig")) tap.force_uniScale_checkBox.stateChanged.connect( partial(self.update_check, tap.force_uniScale_checkBox, "force_uni_scale")) tap.connect_joints_checkBox.stateChanged.connect( partial(self.update_check, tap.connect_joints_checkBox, "connect_joints")) # tap.addTab_pushButton.clicked.connect( # partial(self.moveFromListWidget2ListWidget, # tap.available_listWidget, # tap.rigTabs_listWidget, # tap.rigTabs_listWidget, # "synoptic")) # tap.removeTab_pushButton.clicked.connect( # partial(self.moveFromListWidget2ListWidget, # tap.rigTabs_listWidget, # tap.available_listWidget, # tap.rigTabs_listWidget, # "synoptic")) # tap.loadSkinPath_pushButton.clicked.connect( # self.skinLoad) # tap.dataCollectorPath_pushButton.clicked.connect( # self.data_collector_path) # tap.rigTabs_listWidget.installEventFilter(self) # colors connections index_widgets = ((tap.L_color_fk_spinBox, tap.L_color_fk_label, "l_color_fk"), (tap.L_color_ik_spinBox, tap.L_color_ik_label, "l_color_ik"), (tap.C_color_fk_spinBox, tap.C_color_fk_label, "c_color_fk"), (tap.C_color_ik_spinBox, tap.C_color_ik_label, "c_color_ik"), (tap.R_color_fk_spinBox, tap.R_color_fk_label, "r_color_fk"), (tap.R_color_ik_spinBox, tap.R_color_ik_label, "r_color_ik")) rgb_widgets = ((tap.L_RGB_fk_pushButton, tap.L_RGB_fk_slider, "l_RGB_fk"), (tap.L_RGB_ik_pushButton, tap.L_RGB_ik_slider, "l_RGB_ik"), (tap.C_RGB_fk_pushButton, tap.C_RGB_fk_slider, "c_RGB_fk"), (tap.C_RGB_ik_pushButton, tap.C_RGB_ik_slider, "c_RGB_ik"), (tap.R_RGB_fk_pushButton, tap.R_RGB_fk_slider, "r_RGB_fk"), (tap.R_RGB_ik_pushButton, tap.R_RGB_ik_slider, "r_RGB_ik")) for spinBox, label, source_attr in index_widgets: spinBox.valueChanged.connect( partial(self.update_index_color_widgets, spinBox, source_attr, label)) for button, slider, source_attr in rgb_widgets: button.clicked.connect( partial(self.rgb_color_editor, button, source_attr, slider)) slider.valueChanged.connect( partial(self.rgb_slider_value_changed, button, source_attr)) tap.useRGB_checkBox.stateChanged.connect( partial(self.toggle_rgb_index_widgets, tap.useRGB_checkBox, tuple(w for i in index_widgets for w in i[:2]), tuple(w for i in rgb_widgets for w in i[:2]), "use_RGB_color")) tap.notes_textEdit.textChanged.connect( partial(self.update_text_edit, tap.notes_textEdit, "notes")) # custom Step Tab csTap = self.custom_step_tab csTap.preCustomStep_checkBox.stateChanged.connect( partial(self.update_check, csTap.preCustomStep_checkBox, "run_pre_custom_step")) csTap.preCustomStepAdd_pushButton.clicked.connect( self.add_custom_step) csTap.preCustomStepNew_pushButton.clicked.connect( self.new_custom_step) csTap.preCustomStepDuplicate_pushButton.clicked.connect( self.duplicate_custom_step) csTap.preCustomStepExport_pushButton.clicked.connect( self.export_custom_step) csTap.preCustomStepImport_pushButton.clicked.connect( self.import_custom_step) csTap.preCustomStepRemove_pushButton.clicked.connect( partial(self.remove_selected_from_list_widget, csTap.preCustomStep_listWidget, "pre_custom_step")) csTap.preCustomStep_listWidget.installEventFilter(self) csTap.preCustomStepRun_pushButton.clicked.connect( partial(self.run_manual_step, csTap.preCustomStep_listWidget)) csTap.preCustomStepEdit_pushButton.clicked.connect( partial(self.edit_file, csTap.preCustomStep_listWidget)) csTap.postCustomStep_checkBox.stateChanged.connect( partial(self.update_check, csTap.postCustomStep_checkBox, "run_post_custom_step")) csTap.postCustomStepAdd_pushButton.clicked.connect( partial(self.add_custom_step, False)) csTap.postCustomStepNew_pushButton.clicked.connect( partial(self.new_custom_step, False)) csTap.postCustomStepDuplicate_pushButton.clicked.connect( partial(self.duplicate_custom_step, False)) csTap.postCustomStepExport_pushButton.clicked.connect( partial(self.export_custom_step, False)) csTap.postCustomStepImport_pushButton.clicked.connect( partial(self.import_custom_step, False)) csTap.postCustomStepRemove_pushButton.clicked.connect( partial(self.remove_selected_from_list_widget, csTap.postCustomStep_listWidget, "post_custom_step")) csTap.postCustomStep_listWidget.installEventFilter(self) csTap.postCustomStepRun_pushButton.clicked.connect( partial(self.run_manual_step, csTap.postCustomStep_listWidget)) csTap.postCustomStepEdit_pushButton.clicked.connect( partial(self.edit_file, csTap.postCustomStep_listWidget)) # right click menus csTap.preCustomStep_listWidget.setContextMenuPolicy( QtCore.Qt.CustomContextMenu) csTap.preCustomStep_listWidget.customContextMenuRequested.connect( self.pre_custom_step_menu) csTap.postCustomStep_listWidget.setContextMenuPolicy( QtCore.Qt.CustomContextMenu) csTap.postCustomStep_listWidget.customContextMenuRequested.connect( self.post_custom_step_menu) # search hightlight csTap.preSearch_lineEdit.textChanged.connect( self.pre_highlight_search) csTap.postSearch_lineEdit.textChanged.connect( self.post_highlight_search) # Naming Tab tap = self.naming_rule_tab # names rules tap.ctl_name_rule_lineEdit.editingFinished.connect( partial(self.update_name_rule_line_edit, tap.ctl_name_rule_lineEdit, "ctl_name_rule")) tap.joint_name_rule_lineEdit.editingFinished.connect( partial(self.update_name_rule_line_edit, tap.joint_name_rule_lineEdit, "joint_name_rule")) # sides names tap.side_left_name_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.side_left_name_lineEdit, "ctl_left_name")) tap.side_right_name_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.side_right_name_lineEdit, "ctl_right_name")) tap.side_center_name_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.side_center_name_lineEdit, "ctl_center_name")) tap.side_joint_left_name_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.side_joint_left_name_lineEdit, "joint_left_name")) tap.side_joint_right_name_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.side_joint_right_name_lineEdit, "joint_right_name")) tap.side_joint_center_name_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.side_joint_center_name_lineEdit, "joint_center_name")) # names extensions tap.ctl_name_ext_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.ctl_name_ext_lineEdit, "ctl_name_ext")) tap.joint_name_ext_lineEdit.editingFinished.connect( partial(self.update_line_edit2, tap.joint_name_ext_lineEdit, "joint_name_ext")) # description letter case tap.ctl_des_letter_case_comboBox.currentIndexChanged.connect( partial(self.update_combo_box, tap.ctl_des_letter_case_comboBox, "ctl_description_letter_case")) tap.joint_des_letter_case_comboBox.currentIndexChanged.connect( partial(self.update_combo_box, tap.joint_des_letter_case_comboBox, "joint_description_letter_case")) # reset naming rules tap.reset_ctl_name_rule_pushButton.clicked.connect( partial(self.reset_naming_rule, tap.ctl_name_rule_lineEdit, "ctl_name_rule")) tap.reset_joint_name_rule_pushButton.clicked.connect( partial(self.reset_naming_rule, tap.joint_name_rule_lineEdit, "joint_name_rule")) # reset naming sides tap.reset_side_name_pushButton.clicked.connect( self.reset_naming_sides) tap.reset_joint_side_name_pushButton.clicked.connect( self.reset_joint_naming_sides) # reset naming extension tap.reset_name_ext_pushButton.clicked.connect( self.reset_naming_extension) # index padding tap.ctl_padding_spinBox.valueChanged.connect( partial(self.update_spin_box, tap.ctl_padding_spinBox, "ctl_index_padding")) tap.joint_padding_spinBox.valueChanged.connect( partial(self.update_spin_box, tap.joint_padding_spinBox, "joint_index_padding")) # import name configuration tap.load_naming_configuration_pushButton.clicked.connect( self.import_name_config) # export name configuration tap.save_naming_configuration_pushButton.clicked.connect( self.export_name_config) def eventFilter(self, sender, event): if event.type() == QtCore.QEvent.ChildRemoved: # if sender == self.main_tab.rigTabs_listWidget: # self.updateListAttr(sender, "synoptic") if sender == self.custom_step_tab.preCustomStep_listWidget: self.update_list_attr(sender, "pre_custom_step") elif sender == self.custom_step_tab.postCustomStep_listWidget: self.update_list_attr(sender, "post_custom_step") return True else: return QtWidgets.QDialog.eventFilter(self, sender, event) # Slots ######################################################## def export_name_config(self, file_path=None): # set focus to the save button to ensure all values are updated # if the cursor stay in other lineEdit since the edition is not # finished will not take the last edition self.naming_rule_tab.save_naming_configuration_pushButton.setFocus( QtCore.Qt.MouseFocusReason) config = dict() config["ctl_name_rule"] = self._network.attr( "ctl_name_rule").get() config["joint_name_rule"] = self._network.attr( "joint_name_rule").get() config["ctl_left_name"] = self._network.attr( "ctl_left_name").get() config["ctl_right_name"] = self._network.attr( "ctl_right_name").get() config["ctl_center_name"] = self._network.attr( "ctl_center_name").get() config["joint_left_name"] = self._network.attr( "joint_left_name").get() config["joint_right_name"] = self._network.attr( "joint_right_name").get() config["joint_center_name"] = self._network.attr( "joint_center_name").get() config["ctl_name_ext"] = self._network.attr( "ctl_name_ext").get() config["joint_name_ext"] = self._network.attr( "joint_name_ext").get() config["ctl_description_letter_case"] = self._network.attr( "ctl_description_letter_case").get() config["joint_description_letter_case"] = self._network.attr( "joint_description_letter_case").get() config["ctl_index_padding"] = self._network.attr( "ctl_index_padding").get() config["joint_index_padding"] = self._network.attr( "joint_index_padding").get() if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") else: startDir = pm.workspace(q=True, rootDirectory=True) data_string = json.dumps(config, indent=4, sort_keys=True) if not file_path: file_path = pm.fileDialog2( fileMode=0, startingDirectory=startDir, fileFilter='Naming Configuration .naming (*%s)' % ".naming") if not file_path: return if not isinstance(file_path, str): file_path = file_path[0] f = open(file_path, 'w') f.write(data_string) f.close() def import_name_config(self, file_path=None): if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") else: startDir = pm.workspace(q=True, rootDirectory=True) if not file_path: file_path = pm.fileDialog2( fileMode=1, startingDirectory=startDir, fileFilter='Naming Configuration .naming (*%s)' % ".naming") if not file_path: return if not isinstance(file_path, str): file_path = file_path[0] config = json.load(open(file_path)) for key in config.keys(): self._network.attr(key).set(config[key]) self.populate_naming_controls() def reset_naming_rule(self, rule_lineEdit, target_attr): rule_lineEdit.setText(naming.DEFAULT_NAMING_RULE) self.update_name_rule_line_edit(rule_lineEdit, target_attr) def reset_naming_sides(self): self.naming_rule_tab.side_left_name_lineEdit.setText( naming.DEFAULT_SIDE_L_NAME) self.naming_rule_tab.side_right_name_lineEdit.setText( naming.DEFAULT_SIDE_R_NAME) self.naming_rule_tab.side_center_name_lineEdit.setText( naming.DEFAULT_SIDE_C_NAME) self._network.attr("ctl_left_name").set(naming.DEFAULT_SIDE_L_NAME) self._network.attr("ctl_right_name").set(naming.DEFAULT_SIDE_R_NAME) self._network.attr("ctl_center_name").set(naming.DEFAULT_SIDE_C_NAME) def reset_joint_naming_sides(self): self.naming_rule_tab.side_joint_left_name_lineEdit.setText( naming.DEFAULT_JOINT_SIDE_L_NAME) self.naming_rule_tab.side_joint_right_name_lineEdit.setText( naming.DEFAULT_JOINT_SIDE_R_NAME) self.naming_rule_tab.side_joint_center_name_lineEdit.setText( naming.DEFAULT_JOINT_SIDE_C_NAME) self._network.attr("joint_left_name").set( naming.DEFAULT_JOINT_SIDE_L_NAME) self._network.attr("joint_right_name").set( naming.DEFAULT_JOINT_SIDE_R_NAME) self._network.attr("joint_center_name").set( naming.DEFAULT_JOINT_SIDE_C_NAME) def reset_naming_extension(self): self.naming_rule_tab.ctl_name_ext_lineEdit.setText( naming.DEFAULT_CTL_EXT_NAME) self.naming_rule_tab.joint_name_ext_lineEdit.setText( naming.DEFAULT_JOINT_EXT_NAME) self._network.attr("ctl_name_ext").set(naming.DEFAULT_CTL_EXT_NAME) self._network.attr("joint_name_ext").set(naming.DEFAULT_JOINT_EXT_NAME) # def populateAvailableSynopticTabs(self): # # import mgear.shifter as shifter # defPath = os.environ.get("MGEAR_SYNOPTIC_PATH", None) # if not defPath or not os.path.isdir(defPath): # defPath = shifter.SYNOPTIC_PATH # # # Sanity check for folder existence. # if not os.path.isdir(defPath): # return # # tabsDirectories = [name for name in os.listdir(defPath) if # os.path.isdir(os.path.join(defPath, name))] # # Quick clean the first empty item # if tabsDirectories and not tabsDirectories[0]: # self.main_tab.available_listWidget.takeItem(0) # # itemsList = self._network.attr("synoptic").get().split(",") # for tab in sorted(tabsDirectories): # if tab not in itemsList: # self.main_tab.available_listWidget.addItem(tab) # # def skinLoad(self, *args): # startDir = self._network.attr("skin").get() # filePath = pm.fileDialog2( # fileMode=1, # startingDirectory=startDir, # okc="Apply", # fileFilter='mGear skin (*%s)' % skin.FILE_EXT) # if not filePath: # return # if not isinstance(filePath, str): # filePath = filePath[0] # # self._network.attr("skin").set(filePath) # self.main_tab.skin_lineEdit.setText(filePath) # # def _data_collector_path(self, *args): # ext_filter = 'Shifter Collected data (*{})'.format(DATA_COLLECTOR_EXT) # filePath = pm.fileDialog2( # fileMode=0, # fileFilter=ext_filter) # if not filePath: # return # if not isinstance(filePath, str): # filePath = filePath[0] # # return filePath # # def data_collector_path(self, *args): # filePath = self._data_collector_path() # # if filePath: # self._network.attr("data_collector_path").set(filePath) # self.main_tab.dataCollectorPath_lineEdit.setText(filePath) def add_custom_step(self, pre=True, *args): """Add a new custom step Arguments: pre (bool, optional): If true adds the steps to the pre step list *args: Maya's Dummy Returns: None: None """ if pre: stepAttr = "pre_custom_step" stepWidget = self.custom_step_tab.preCustomStep_listWidget else: stepAttr = "post_custom_step" stepWidget = self.custom_step_tab.postCustomStep_listWidget # Check if we have a custom env for the custom steps initial folder if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") else: startDir = self._network.attr(stepAttr).get() filePath = pm.fileDialog2( fileMode=1, startingDirectory=startDir, okc="Add", fileFilter='Custom Step .py (*.py)') if not filePath: return if not isinstance(filePath, str): filePath = filePath[0] # Quick clean the first empty item itemsList = [i.text() for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] if itemsList and not itemsList[0]: stepWidget.takeItem(0) if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): filePath = os.path.abspath(filePath) baseReplace = os.path.abspath(os.environ.get( "MBOX_CUSTOM_STEP_PATH", "")) filePath = filePath.replace(baseReplace, "")[1:] fileName = os.path.split(filePath)[1].split(".")[0] stepWidget.addItem(fileName + " | " + filePath) self.updateListAttr(stepWidget, stepAttr) self.refresh_status_color(stepWidget) def new_custom_step(self, pre=True, *args): """Creates a new custom step Arguments: pre (bool, optional): If true adds the steps to the pre step list *args: Maya's Dummy Returns: None: None """ if pre: stepAttr = "pre_custom_step" stepWidget = self.custom_step_tab.preCustomStep_listWidget else: stepAttr = "post_custom_step" stepWidget = self.custom_step_tab.postCustomStep_listWidget # Check if we have a custom env for the custom steps initial folder if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") else: startDir = self._network.attr(stepAttr).get() filePath = pm.fileDialog2( fileMode=0, startingDirectory=startDir, okc="New", fileFilter='Custom Step .py (*.py)') if not filePath: return if not isinstance(filePath, str): filePath = filePath[0] n, e = os.path.splitext(filePath) stepName = os.path.split(n)[-1] # raw custome step string rawString = r'''import mbox.lego.lib as lib class CustomStep(lib.{pre_post}): """Custom Step description """ def process(self): """Run method. Returns: None: None """ return'''.format(pre_post="PreScript" if pre else "PostScript") f = open(filePath, 'w') f.write(rawString + "\n") f.close() # Quick clean the first empty item itemsList = [i.text() for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] if itemsList and not itemsList[0]: stepWidget.takeItem(0) if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): filePath = os.path.abspath(filePath) baseReplace = os.path.abspath(os.environ.get( "MBOX_CUSTOM_STEP_PATH", "")) filePath = filePath.replace(baseReplace, "")[1:] fileName = os.path.split(filePath)[1].split(".")[0] stepWidget.addItem(fileName + " | " + filePath) self.update_list_attr(stepWidget, stepAttr) self.refresh_status_color(stepWidget) def duplicate_custom_step(self, pre=True, *args): """Duplicate the selected step Arguments: pre (bool, optional): If true adds the steps to the pre step list *args: Maya's Dummy Returns: None: None """ if pre: stepAttr = "pre_custom_step" stepWidget = self.custom_step_tab.preCustomStep_listWidget else: stepAttr = "post_custom_step" stepWidget = self.custom_step_tab.postCustomStep_listWidget # Check if we have a custom env for the custom steps initial folder if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") else: startDir = self._network.attr(stepAttr).get() if stepWidget.selectedItems(): sourcePath = stepWidget.selectedItems()[0].text().split( "|")[-1][1:] filePath = pm.fileDialog2( fileMode=0, startingDirectory=startDir, okc="New", fileFilter='Custom Step .py (*.py)') if not filePath: return if not isinstance(filePath, str): filePath = filePath[0] if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): sourcePath = os.path.join(startDir, sourcePath) shutil.copy(sourcePath, filePath) # Quick clean the first empty item itemsList = [i.text() for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] if itemsList and not itemsList[0]: stepWidget.takeItem(0) if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): filePath = os.path.abspath(filePath) baseReplace = os.path.abspath(os.environ.get( "MBOX_CUSTOM_STEP_PATH", "")) filePath = filePath.replace(baseReplace, "")[1:] fileName = os.path.split(filePath)[1].split(".")[0] stepWidget.addItem(fileName + " | " + filePath) self.update_list_attr(stepWidget, stepAttr) self.refresh_status_color(stepWidget) def export_custom_step(self, pre=True, *args): """Export custom steps to a json file Arguments: pre (bool, optional): If true takes the steps from the pre step list *args: Maya's Dummy Returns: None: None """ if pre: stepWidget = self.custom_step_tab.preCustomStep_listWidget else: stepWidget = self.custom_step_tab.postCustomStep_listWidget # Quick clean the first empty item itemsList = [i.text() for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] if itemsList and not itemsList[0]: stepWidget.takeItem(0) # Check if we have a custom env for the custom steps initial folder if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") itemsList = [os.path.join(startDir, i.text().split("|")[-1][1:]) for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] else: itemsList = [i.text().split("|")[-1][1:] for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] if itemsList: startDir = os.path.split(itemsList[-1])[0] else: pm.displayWarning("No custom steps to export.") return stepsDict = self.get_steps_dict(itemsList) data_string = json.dumps(stepsDict, indent=4, sort_keys=True) filePath = pm.fileDialog2( fileMode=0, startingDirectory=startDir, fileFilter='Lego Custom Steps .lcs (*%s)' % ".lcs") if not filePath: return if not isinstance(filePath, str): filePath = filePath[0] f = open(filePath, 'w') f.write(data_string) f.close() def import_custom_step(self, pre=True, *args): """Import custom steps from a json file Arguments: pre (bool, optional): If true import to pre steps list *args: Maya's Dummy Returns: None: None """ if pre: stepAttr = "pre_custom_step" stepWidget = self.custom_step_tab.preCustomStep_listWidget else: stepAttr = "post_custom_step" stepWidget = self.custom_step_tab.postCustomStep_listWidget # option import only paths or unpack steps option = pm.confirmDialog( title='Lego Custom Step Import Style', message='Do you want to import only the path or' ' unpack and import?', button=['Only Path', 'Unpack', 'Cancel'], defaultButton='Only Path', cancelButton='Cancel', dismissString='Cancel') if option in ['Only Path', 'Unpack']: if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): startDir = os.environ.get("MBOX_CUSTOM_STEP_PATH", "") else: startDir = pm.workspace(q=True, rootDirectory=True) filePath = pm.fileDialog2( fileMode=1, startingDirectory=startDir, fileFilter='Shifter Custom Steps .scs (*%s)' % ".scs") if not filePath: return if not isinstance(filePath, str): filePath = filePath[0] stepDict = json.load(open(filePath)) stepsList = [] if option == 'Only Path': for item in stepDict["itemsList"]: stepsList.append(item) elif option == 'Unpack': unPackDir = pm.fileDialog2( fileMode=2, startingDirectory=startDir) if not filePath: return if not isinstance(unPackDir, str): unPackDir = unPackDir[0] for item in stepDict["itemsList"]: fileName = os.path.split(item)[1] fileNewPath = os.path.join(unPackDir, fileName) stepsList.append(fileNewPath) f = open(fileNewPath, 'w') f.write(stepDict[item]) f.close() if option in ['Only Path', 'Unpack']: for item in stepsList: # Quick clean the first empty item itemsList = [i.text() for i in stepWidget.findItems( "", QtCore.Qt.MatchContains)] if itemsList and not itemsList[0]: stepWidget.takeItem(0) if os.environ.get("MBOX_CUSTOM_STEP_PATH", ""): item = os.path.abspath(item) baseReplace = os.path.abspath(os.environ.get( "MBOX_CUSTOM_STEP_PATH", "")) item = item.replace(baseReplace, "")[1:] fileName = os.path.split(item)[1].split(".")[0] stepWidget.addItem(fileName + " | " + item) self.update_list_attr(stepWidget, stepAttr) def _custom_step_menu(self, cs_listWidget, stepAttr, QPos): "right click context menu for custom step" currentSelection = cs_listWidget.currentItem() if currentSelection is None: return self.csMenu = QtWidgets.QMenu() parentPosition = cs_listWidget.mapToGlobal(QtCore.QPoint(0, 0)) menu_item_01 = self.csMenu.addAction("Toggle Custom Step") self.csMenu.addSeparator() menu_item_02 = self.csMenu.addAction("Turn OFF Selected") menu_item_03 = self.csMenu.addAction("Turn ON Selected") self.csMenu.addSeparator() menu_item_04 = self.csMenu.addAction("Turn OFF All") menu_item_05 = self.csMenu.addAction("Turn ON All") menu_item_01.triggered.connect(partial(self.toggle_status_custom_step, cs_listWidget, stepAttr)) menu_item_02.triggered.connect(partial(self.set_status_custom_step, cs_listWidget, stepAttr, False)) menu_item_03.triggered.connect(partial(self.set_status_custom_step, cs_listWidget, stepAttr, True)) menu_item_04.triggered.connect(partial(self.set_status_custom_step, cs_listWidget, stepAttr, False, False)) menu_item_05.triggered.connect(partial(self.set_status_custom_step, cs_listWidget, stepAttr, True, False)) self.csMenu.move(parentPosition + QPos) self.csMenu.show() def pre_custom_step_menu(self, QPos): self._custom_step_menu(self.custom_step_tab.preCustomStep_listWidget, "pre_custom_step", QPos) def post_custom_step_menu(self, QPos): self._custom_step_menu(self.custom_step_tab.postCustomStep_listWidget, "post_custom_step", QPos) def toggle_status_custom_step(self, cs_listWidget, stepAttr): items = cs_listWidget.selectedItems() for item in items: if item.text().startswith("*"): item.setText(item.text()[1:]) item.setForeground(self.white_down_brush) else: item.setText("*" + item.text()) item.setForeground(self.red_brush) self.update_list_attr(cs_listWidget, stepAttr) self.refresh_status_color(cs_listWidget) def set_status_custom_step( self, cs_listWidget, stepAttr, status=True, selected=True): if selected: items = cs_listWidget.selectedItems() else: items = self.get_all_items(cs_listWidget) for item in items: off = item.text().startswith("*") if status and off: item.setText(item.text()[1:]) elif not status and not off: item.setText("*" + item.text()) self.set_status_color(item) self.update_list_attr(cs_listWidget, stepAttr) self.refresh_status_color(cs_listWidget) def get_all_items(self, cs_listWidget): return [cs_listWidget.item(i) for i in range(cs_listWidget.count())] def set_status_color(self, item): if item.text().startswith("*"): item.setForeground(self.red_brush) elif "_shared" in item.text(): item.setForeground(self.green_brush) else: item.setForeground(self.white_down_brush) def refresh_status_color(self, cs_listWidget): items = self.get_all_items(cs_listWidget) for i in items: self.set_status_color(i) # Highligter filter def _highlight_search(self, cs_listWidget, searchText): items = self.get_all_items(cs_listWidget) for i in items: if searchText and searchText.lower() in i.text().lower(): i.setBackground(QtGui.QColor(128, 128, 128, 255)) else: i.setBackground(QtGui.QColor(255, 255, 255, 0)) def pre_highlight_search(self): searchText = self.custom_step_tab.preSearch_lineEdit.text() self._highlight_search(self.custom_step_tab.preCustomStep_listWidget, searchText) def post_highlight_search(self): searchText = self.custom_step_tab.postSearch_lineEdit.text() self._highlight_search(self.custom_step_tab.postCustomStep_listWidget, searchText) class BlockMainTabUI(QtWidgets.QDialog, block_ui.Ui_Form): def __init__(self): super(BlockMainTabUI, self).__init__() self.setupUi(self) class BlockSettings(QtWidgets.QDialog, HelperSlots): valueChanged = QtCore.Signal(int) def __init__(self, parent=None): super(BlockSettings, self).__init__() # the inspectSettings function set the current selection to the # component root before open the settings dialog self._guide = lib.get_component_guide(pm.selected(type="transform")[0])[0] self._network = self._guide.message.outputs(type="network")[0] self.main_tab = BlockMainTabUI() self.create_controls() self.populate_controls() self.create_layout() self.create_connections() self.setAttribute(QtCore.Qt.WA_DeleteOnClose, True) def create_controls(self): """ Create the controls for the component base """ self.tabs = QtWidgets.QTabWidget() self.tabs.setObjectName("block_settings_tab") # Close Button self.close_button = QtWidgets.QPushButton("Close") def populate_controls(self): """Populate Controls attribute values Populate the controls values from the custom attributes of the component. """ # populate tab self.tabs.insertTab(0, self.main_tab, "Main Settings") # populate main settings self.main_tab.name_lineEdit.setText( self._network.attr("comp_name").get()) sideSet = ["center", "left", "right"] sideIndex = sideSet.index(self._network.attr("comp_side").get(asString=True)) self.main_tab.side_comboBox.setCurrentIndex(sideIndex) self.main_tab.componentIndex_spinBox.setValue( self._network.attr("comp_index").get()) # if self._network.attr("useIndex").get(): # self.main_tab.useJointIndex_checkBox.setCheckState( # QtCore.Qt.Checked) # else: # self.main_tab.useJointIndex_checkBox.setCheckState( # QtCore.Qt.Unchecked) # self.main_tab.parentJointIndex_spinBox.setValue( # self._network.attr("parentJointIndex").get()) self.main_tab.host_lineEdit.setText( self._network.attr("ui_host").get().split(",")[0]) # self.main_tab.subGroup_lineEdit.setText( # self._network.attr("ctlGrp").get()) # self.main_tab.joint_offset_x_doubleSpinBox.setValue( # self._network.attr("joint_rot_offset_x").get()) # self.main_tab.joint_offset_y_doubleSpinBox.setValue( # self._network.attr("joint_rot_offset_y").get()) # self.main_tab.joint_offset_z_doubleSpinBox.setValue( # self._network.attr("joint_rot_offset_z").get()) # testing adding custom color per component self.main_tab.overrideColors_checkBox.setCheckState( QtCore.Qt.Checked if self._network.attr("override_color").get() else QtCore.Qt.Unchecked) self.main_tab.useRGB_checkBox.setCheckState( QtCore.Qt.Checked if self._network.attr("use_RGB_color").get() else QtCore.Qt.Unchecked) tab = self.main_tab index_widgets = ((tab.color_fk_spinBox, tab.color_fk_label, "color_fk"), (tab.color_ik_spinBox, tab.color_ik_label, "color_ik")) rgb_widgets = ((tab.RGB_fk_pushButton, tab.RGB_fk_slider, "RGB_fk"), (tab.RGB_ik_pushButton, tab.RGB_ik_slider, "RGB_ik")) for spinBox, label, source_attr in index_widgets: color_index = self._network.attr(source_attr).get() spinBox.setValue(color_index) self.update_widget_style_sheet( label, [i / 255.0 for i in MAYA_OVERRIDE_COLOR[color_index]]) for button, slider, source_attr in rgb_widgets: self.update_rgb_color_widgets( button, self._network.attr(source_attr).get(), slider) # forceing the size of the color buttons/label to keep ui clean for widget in tuple(i[0] for i in rgb_widgets) + tuple( i[1] for i in index_widgets): widget.setFixedSize(pyqt.dpi_scale(30), pyqt.dpi_scale(20)) self.toggle_rgb_index_widgets(tab.useRGB_checkBox, (w for i in index_widgets for w in i[:2]), (w for i in rgb_widgets for w in i[:2]), "use_RGB_color", tab.useRGB_checkBox.checkState()) self.refresh_controls() def refresh_controls(self): joint_names = [name.strip() for name in self._network.attr("joint_names").get().split(",")] if any(joint_names): summary = "({0} set)".format(sum(map(bool, joint_names))) else: summary = "(None)" self.main_tab.jointNames_label.setText("Joint Names " + summary) def create_layout(self): """ Create the layout for the component base settings """ return def create_connections(self): """ Create the slots connections to the controls functions """ self.close_button.clicked.connect(self.close_settings) self.main_tab.name_lineEdit.editingFinished.connect( self.update_component_name) self.main_tab.side_comboBox.currentIndexChanged.connect( self.update_component_name) self.main_tab.componentIndex_spinBox.valueChanged.connect( self.update_component_name) # self.main_tab.useJointIndex_checkBox.stateChanged.connect( # partial(self.update_check, # self.main_tab.useJointIndex_checkBox, # "useIndex")) # self.main_tab.parentJointIndex_spinBox.valueChanged.connect( # partial(self.update_spin_box, # self.main_tab.parentJointIndex_spinBox, # "parentJointIndex")) self.main_tab.host_pushButton.clicked.connect( partial(self.update_host_ui, self.main_tab.host_lineEdit, "ui_host")) # self.main_tab.subGroup_lineEdit.editingFinished.connect( # partial(self.update_line_edit, # self.main_tab.subGroup_lineEdit, # "ctlGrp")) self.main_tab.jointNames_pushButton.clicked.connect( self.joint_names_dialog) # self.main_tab.joint_offset_x_doubleSpinBox.valueChanged.connect( # partial(self.update_spin_box, # self.main_tab.joint_offset_x_doubleSpinBox, # "joint_rot_offset_x")) # self.main_tab.joint_offset_y_doubleSpinBox.valueChanged.connect( # partial(self.update_spin_box, # self.main_tab.joint_offset_y_doubleSpinBox, # "joint_rot_offset_y")) # self.main_tab.joint_offset_z_doubleSpinBox.valueChanged.connect( # partial(self.update_spin_box, # self.main_tab.joint_offset_z_doubleSpinBox, # "joint_rot_offset_z")) tab = self.main_tab index_widgets = ((tab.color_fk_spinBox, tab.color_fk_label, "color_fk"), (tab.color_ik_spinBox, tab.color_ik_label, "color_ik")) rgb_widgets = ((tab.RGB_fk_pushButton, tab.RGB_fk_slider, "RGB_fk"), (tab.RGB_ik_pushButton, tab.RGB_ik_slider, "RGB_ik")) for spinBox, label, source_attr in index_widgets: spinBox.valueChanged.connect( partial(self.update_index_color_widgets, spinBox, source_attr, label)) for button, slider, source_attr in rgb_widgets: button.clicked.connect( partial(self.rgb_color_editor, button, source_attr, slider)) slider.valueChanged.connect( partial(self.rgb_slider_value_changed, button, source_attr)) tab.useRGB_checkBox.stateChanged.connect( partial(self.toggle_rgb_index_widgets, tab.useRGB_checkBox, tuple(w for i in index_widgets for w in i[:2]), tuple(w for i in rgb_widgets for w in i[:2]), "use_RGB_color")) tab.overrideColors_checkBox.stateChanged.connect( partial(self.update_check, tab.overrideColors_checkBox, "override_color")) def joint_names_dialog(self): dialog = JointNames(self._network, self) dialog.setWindowTitle(self.windowTitle()) dialog.attributeChanged.connect(self.refresh_controls) dialog.show() class JointNames(QtWidgets.QDialog, joint_name_ui.Ui_Form): attributeChanged = QtCore.Signal() def __init__(self, network, parent=None): super(JointNames, self).__init__(parent) self._network = network self.setupUi(self) self.populate_controls() self.apply_names() self.create_connections() def populate_controls(self): jointNames = self._network.attr("joint_names").get().split(",") if jointNames[-1]: jointNames.append("") self.jointNamesList.clearContents() self.jointNamesList.setRowCount(0) for i, name in enumerate(jointNames): self.jointNamesList.insertRow(i) item = QtWidgets.QTableWidgetItem(name.strip()) self.jointNamesList.setItem(i, 0, item) def create_connections(self): self.jointNamesList.cellChanged.connect(self.update_name) self.jointNamesList.itemActivated.connect(self.jointNamesList.editItem) self.add_pushButton.clicked.connect(self.add) self.remove_pushButton.clicked.connect(self.remove) self.removeAll_pushButton.clicked.connect(self.remove_all) self.moveUp_pushButton.clicked.connect(lambda: self.move(-1)) self.moveDown_pushButton.clicked.connect(lambda: self.move(1)) def apply_names(self): jointNames = [] for i in range(self.jointNamesList.rowCount()): item = self.jointNamesList.item(i, 0) jointNames.append(item.text()) value = ",".join(jointNames[0:-1]) self._network.attr("joint_names").set(value) self.jointNamesList.setVerticalHeaderLabels( [str(i) for i in range(len(jointNames))]) self.attributeChanged.emit() def add(self): row = max(0, self.jointNamesList.currentRow() or 0) self.jointNamesList.insertRow(row) item = QtWidgets.QTableWidgetItem("") self.jointNamesList.setItem(row, 0, item) self.jointNamesList.setCurrentCell(row, 0) self.apply_names() def remove(self): row = self.jointNamesList.currentRow() if row + 1 < self.jointNamesList.rowCount() > 1: self.jointNamesList.removeRow(row) self.apply_names() self.jointNamesList.setCurrentCell(row, 0) def remove_all(self): self.jointNamesList.clearContents() self.jointNamesList.setRowCount(0) self.jointNamesList.insertRow(0) self.jointNamesList.setItem(0, 0, QtWidgets.QTableWidgetItem("")) self.jointNamesList.setCurrentCell(0, 0) self.apply_names() def move(self, step): row = self.jointNamesList.currentRow() if row + step < 0: return item1 = self.jointNamesList.item(row, 0).text() item2 = self.jointNamesList.item(row + step, 0).text() self.jointNamesList.item(row, 0).setText(item2) self.jointNamesList.item(row + step, 0).setText(item1) self.jointNamesList.setCurrentCell(row + step, 0) def update_name(self, row, column): item = self.jointNamesList.item(row, column) if row == self.jointNamesList.rowCount() - 1 and item.text(): self.jointNamesList.insertRow(row + 1) self.jointNamesList.setItem( row + 1, 0, QtWidgets.QTableWidgetItem("")) self.apply_names() self.jointNamesList.setCurrentCell(row + 1, 0) self.jointNamesList.editItem(self.jointNamesList.currentItem()) def keyPressEvent(self): pass

nilq/baby-python

python

from django.shortcuts import render # Create your views here. def main(request): title = 'Travel Freely!' content = { 'title': title, } return render(request, 'mainsite/index.html', context=content)

nilq/baby-python

python

from empire.python.typings import * from empire.fs.file_system import FileSystem from empire.archive.archive_types import ArchiveTypes from empire.archive.abstract_compression import AbstractCompression from empire.archive.abstract_archive import AbstractArchive from empire.archive.zip_ar import Zip from empire.archive.gzip_ar import GZIP from empire.archive.lzma_ar import LZMA from empire.archive.bzip_ar import BZIP from empire.archive.tarbz_ar import TAR_BZ from empire.archive.targz_ar import TAR_GZ from empire.archive.tarxz_ar import TAR_XZ from empire.util.log import * COMPRESSION_TYPE_TO_IMPL: Final[Dict[int, Type[AbstractCompression]]] = { ArchiveTypes.ZIP: Zip, ArchiveTypes.GZIP: GZIP, ArchiveTypes.LZMA: LZMA, ArchiveTypes.BZIP: BZIP } TAR_TYPE_TO_IMPL: Final[Dict[int, Type[AbstractCompression]]] = { ArchiveTypes.TAR_XZ: TAR_XZ, ArchiveTypes.TAR_BZ: TAR_BZ, ArchiveTypes.TAR_GZ: TAR_GZ } MIME_TYPE_TO_IMPL: Final[Dict[str, Type[AbstractCompression]]] = { 'application/x-bzip2': BZIP, 'application/x-bzip': BZIP, 'application/x-gzip': GZIP, 'application/x-compressed': Zip, 'application/x-zip-compressed': Zip, 'application/zip': Zip, 'application/x-xz': LZMA, 'application/x-lzma': LZMA } def get_class_for_file(file_path: str) -> Union[Type[AbstractCompression], Type[AbstractArchive], None]: if '.tar' in file_path: compresser: Type[AbstractCompression] = MIME_TYPE_TO_IMPL[FileSystem.get_mime_from_file(file_path)] if compresser == LZMA: return TAR_XZ elif compresser == BZIP: return TAR_BZ elif compresser == GZIP: return TAR_GZ else: Log.severe('Unable to determine valid class', __file__, get_function_name(), file=file_path) return None else: return MIME_TYPE_TO_IMPL[FileSystem.get_mime_from_file(file_path)]

nilq/baby-python

python

"""API urls.""" from rest_framework import routers from . import viewsets router = routers.SimpleRouter() router.register(r"email-providers", viewsets.EmailProviderViewSet) router.register(r"migrations", viewsets.MigrationViewSet) urlpatterns = router.urls

nilq/baby-python

python

''' Leia 3 valores de ponto flutuante A, B e C e ordene-os em ordem decrescente, de modo que o lado A representa o maior dos 3 lados. A seguir, determine o tipo de triângulo que estes três lados formam, com base nos seguintes casos, sempre escrevendo uma mensagem adequada: - se A ≥ B+C, apresente a mensagem: NAO FORMA TRIANGULO - se A2 = B2 + C2, apresente a mensagem: TRIANGULO RETANGULO - se A2 > B2 + C2, apresente a mensagem: TRIANGULO OBTUSANGULO - se A2 < B2 + C2, apresente a mensagem: TRIANGULO ACUTANGULO - se os três lados forem iguais, apresente a mensagem: TRIANGULO EQUILATERO - se apenas dois dos lados forem iguais, apresente a mensagem: TRIANGULO ISOSCELES **Input** A entrada contem três valores de ponto flutuante de dupla precisão A (0 < A) , B (0 < B) e C (0 < C). **Output** Imprima todas as classificações do triângulo especificado na entrada. | Input Sample | Output Samples | | ------------ | ---------------------- | | 7.0 5.0 7.0 | TRIANGULO ACUTANGULO | | | TRIANGULO ISOSCELES | | ------------ | ---------------------- | | 6.0 6.0 10.0 | TRIANGULO OBTUSANGULO | | | TRIANGULO ISOSCELES | | ------------ | ---------------------- | | 6.0 6.0 6.0 | TRIANGULO ACUTANGULO | | | TRIANGULO EQUILATERO | | ------------ | ---------------------- | | 5.0 7.0 2.0 | NAO FORMA TRIANGULO | | ------------ | ---------------------- | | 6.0 8.0 10.0 | TRIANGULO RETANGULO | ''' triang = input().split() n1 = float(triang[0]) n2 = float(triang[1]) n3 = float(triang[2]) result = n1, n2, n3 ordem = sorted(result, reverse=True) a = ordem[0] b = ordem[1] c = ordem[2] ''' #debug print("n1 = {}".format(n1)) print("n2 = {}".format(n2)) print("n3 = {}".format(n3)) print("result = {}".format(result)) print("ordem = {}".format(ordem)) print("A = {}".format(a)) print("B = {}".format(b)) print("C = {}".format(c)) ''' tag = True if a >= (b + c): tag = False print("NAO FORMA TRIANGULO") if (a ** 2) == (b ** 2) + (c ** 2) and tag == True: print("TRIANGULO RETANGULO") if (a ** 2) > (b ** 2) + (c ** 2) and tag == True: print("TRIANGULO OBTUSANGULO") if (a ** 2) < (b ** 2) + (c ** 2) and tag == True: print("TRIANGULO ACUTANGULO") if a == b and a == c and b == a and b == c and c == a and c == b and tag == True: print("TRIANGULO EQUILATERO") if a == b and a != c or a == c and a != b or b == a and b!= c or b == c and b != a or c == b and c != a or c == a and c != b and tag == True: print("TRIANGULO ISOSCELES")

nilq/baby-python

python

from esteira.pipeline.stage import Stage from pathlib import Path BASE_DIR = Path(__file__).parent.absolute() def test_instance(): class TestShell(Stage): script = [ 'echo "hello world"' ] test = TestShell(BASE_DIR) test.run()

nilq/baby-python

python

from __future__ import division import sys import math import random import time import webbrowser as wb import keyboard as kb import pyautogui from collections import deque from pyglet import image from pyglet.gl import * from pyglet.graphics import TextureGroup from pyglet.window import key, mouse from playsound import playsound TICKS_PER_SEC = 60 # Size of sectors used to ease block loading. SECTOR_SIZE = 16 WALKING_SPEED = 3 FLYING_SPEED = 15 GRAVITY = 20.0 MAX_JUMP_HEIGHT = 1.0 # About the height of a block. # To derive the formula for calculating jump speed, first solve # v_t = v_0 + a * t # for the time at which you achieve maximum height, where a is the acceleration # due to gravity and v_t = 0. This gives: # t = - v_0 / a # Use t and the desired MAX_JUMP_HEIGHT to solve for v_0 (jump speed) in # s = s_0 + v_0 * t + (a * t^2) / 2 JUMP_SPEED = math.sqrt(2 * GRAVITY * MAX_JUMP_HEIGHT) TERMINAL_VELOCITY = 50 PLAYER_HEIGHT = 2 LIVES = 10 if sys.version_info[0] >= 3: xrange = range def cube_vertices(x, y, z, n): """ Return the vertices of the cube at position x, y, z with size 2*n. """ return [ x-n,y+n,z-n, x-n,y+n,z+n, x+n,y+n,z+n, x+n,y+n,z-n, # top x-n,y-n,z-n, x+n,y-n,z-n, x+n,y-n,z+n, x-n,y-n,z+n, # bottom x-n,y-n,z-n, x-n,y-n,z+n, x-n,y+n,z+n, x-n,y+n,z-n, # left x+n,y-n,z+n, x+n,y-n,z-n, x+n,y+n,z-n, x+n,y+n,z+n, # right x-n,y-n,z+n, x+n,y-n,z+n, x+n,y+n,z+n, x-n,y+n,z+n, # front x+n,y-n,z-n, x-n,y-n,z-n, x-n,y+n,z-n, x+n,y+n,z-n, # back ] def tex_coord(x, y, n=4): """ Return the bounding vertices of the texture square. """ m = 1.0 / n dx = x * m dy = y * m return dx, dy, dx + m, dy, dx + m, dy + m, dx, dy + m def tex_coords(top, bottom, side): """ Return a list of the texture squares for the top, bottom and side. """ top = tex_coord(*top) bottom = tex_coord(*bottom) side = tex_coord(*side) result = [] result.extend(top) result.extend(bottom) result.extend(side * 4) return result def crouch(): WALKING_SPEED = 0.5 TEXTURE_PATH = 'texture.png' GRASS = tex_coords((1, 0), (0, 1), (0, 0)) SAND = tex_coords((1, 1), (1, 1), (1, 1)) BRICK = tex_coords((2, 0), (2, 0), (2, 0)) STONE = tex_coords((2, 1), (2, 1), (2, 1)) DIRT = tex_coords((0, 1), (0, 1), (0, 1)) BOOKSHELF = tex_coords((1, 2), (1, 2), (0, 2)) SNOW = tex_coords((2, 2), (2, 2), (2, 2)) WOOD = tex_coords((3, 0), (3, 0), (3, 1)) LEAVES = tex_coords((3, 2), (3, 2), (3, 2)) FACES = [ ( 0, 1, 0), ( 0,-1, 0), (-1, 0, 0), ( 1, 0, 0), ( 0, 0, 1), ( 0, 0,-1), ] def normalize(position): """ Accepts `position` of arbitrary precision and returns the block containing that position. Parameters ---------- position : tuple of len 3 Returns ------- block_position : tuple of ints of len 3 """ x, y, z = position x, y, z = (int(round(x)), int(round(y)), int(round(z))) return (x, y, z) def sectorize(position): """ Returns a tuple representing the sector for the given `position`. Parameters ---------- position : tuple of len 3 Returns ------- sector : tuple of len 3 """ x, y, z = normalize(position) x, y, z = x // SECTOR_SIZE, y // SECTOR_SIZE, z // SECTOR_SIZE return (x, 0, z) class Model(object): def __init__(self): # A Batch is a collection of vertex lists for batched rendering. self.batch = pyglet.graphics.Batch() # A TextureGroup manages an OpenGL texture. self.group = TextureGroup(image.load(TEXTURE_PATH).get_texture()) # A mapping from position to the texture of the block at that position. # This defines all the blocks that are currently in the world. self.world = {} # Same mapping as `world` but only contains blocks that are shown. self.shown = {} # Mapping from position to a pyglet `VertextList` for all shown blocks. self._shown = {} # Mapping from sector to a list of positions inside that sector. self.sectors = {} # Simple function queue implementation. The queue is populated with # _show_block() and _hide_block() calls self.queue = deque() self._initialize() def _initialize(self): """ Initialize the world by placing all the blocks. """ n = 80 # 1/2 width and height of world s = 1 # step size y = 0 # initial y height for x in xrange(-n, n + 1, s): for z in xrange(-n, n + 1, s): # create a layer stone an grass everywhere. self.add_block((x, y - 1, z), GRASS, immediate=False) self.add_block((x, y - 2, z), DIRT, immediate=False) self.add_block((x, y - 3, z), DIRT, immediate=False) self.add_block((x, y - 4, z), DIRT, immediate=False) self.add_block((x, y - 5, z), STONE, immediate=False) # generate the hills randomly o = n - 10 for _ in xrange(120): a = random.randint(-o, o) # x position of the hill b = random.randint(-o, o) # z position of the hill c = -1 # base of the hill h = random.randint(1, 9) # height of the hill s = random.randint(4, 8) # 2 * s is the side length of the hill d = 1 # how quickly to taper off the hills t = random.choice([STONE]) tz = random.choice([SNOW]) fk = random.choice([DIRT]) for y in xrange(c, c + h): for x in xrange(a - s, a + s + 1): for z in xrange(b - s, b + s + 1): if (x - a) ** 2 + (z - b) ** 2 > (s + 1) ** 2: continue if (x - 0) ** 2 + (z - 0) ** 2 < 5 ** 2: continue if y == 10: self.add_block((x, y, z), tz, immediate=False) if y >= 4: self.add_block((x, y, z), tz, immediate=False) elif y < 4: self.add_block((x, y, z), t, immediate=False) s -= d # decrement side lenth so hills taper off # generate trees X = random.randint(-80, 80) Z = random.randint(-80, 80) H = random.randint(4, 8) B = random.choice([WOOD]) C = 0 w = WOOD for x in range(0, C): self.add_block((X, H, Z), w, immediate=False) if C == H: continue def hit_test(self, position, vector, max_distance=8): """ Line of sight search from current position. If a block is intersected it is returned, along with the block previously in the line of sight. If no block is found, return None, None. Parameters ---------- position : tuple of len 3 The (x, y, z) position to check visibility from. vector : tuple of len 3 The line of sight vector. max_distance : int How many blocks away to search for a hit. """ m = 8 x, y, z = position dx, dy, dz = vector previous = None for _ in xrange(max_distance * m): key = normalize((x, y, z)) if key != previous and key in self.world: return key, previous previous = key x, y, z = x + dx / m, y + dy / m, z + dz / m return None, None def exposed(self, position): """ Returns False is given `position` is surrounded on all 6 sides by blocks, True otherwise. """ x, y, z = position for dx, dy, dz in FACES: if (x + dx, y + dy, z + dz) not in self.world: return True return False def add_block(self, position, texture, immediate=True): """ Add a block with the given `texture` and `position` to the world. Parameters ---------- position : tuple of len 3 The (x, y, z) position of the block to add. texture : list of len 3 The coordinates of the texture squares. Use `tex_coords()` to generate. immediate : bool Whether or not to draw the block immediately. """ if position in self.world: self.remove_block(position, immediate) self.world[position] = texture self.sectors.setdefault(sectorize(position), []).append(position) if immediate: if self.exposed(position): self.show_block(position) self.check_neighbors(position) def remove_block(self, position, immediate=True): """ Remove the block at the given `position`. Parameters ---------- position : tuple of len 3 The (x, y, z) position of the block to remove. immediate : bool Whether or not to immediately remove block from canvas. """ del self.world[position] self.sectors[sectorize(position)].remove(position) if immediate: if position in self.shown: self.hide_block(position) self.check_neighbors(position) def check_neighbors(self, position): """ Check all blocks surrounding `position` and ensure their visual state is current. This means hiding blocks that are not exposed and ensuring that all exposed blocks are shown. Usually used after a block is added or removed. """ x, y, z = position for dx, dy, dz in FACES: key = (x + dx, y + dy, z + dz) if key not in self.world: continue if self.exposed(key): if key not in self.shown: self.show_block(key) else: if key in self.shown: self.hide_block(key) def show_block(self, position, immediate=True): """ Show the block at the given `position`. This method assumes the block has already been added with add_block() Parameters ---------- position : tuple of len 3 The (x, y, z) position of the block to show. immediate : bool Whether or not to show the block immediately. """ texture = self.world[position] self.shown[position] = texture if immediate: self._show_block(position, texture) else: self._enqueue(self._show_block, position, texture) def _show_block(self, position, texture): """ Private implementation of the `show_block()` method. Parameters ---------- position : tuple of len 3 The (x, y, z) position of the block to show. texture : list of len 3 The coordinates of the texture squares. Use `tex_coords()` to generate. """ x, y, z = position vertex_data = cube_vertices(x, y, z, 0.5) texture_data = list(texture) # create vertex list # FIXME Maybe `add_indexed()` should be used instead self._shown[position] = self.batch.add(24, GL_QUADS, self.group, ('v3f/static', vertex_data), ('t2f/static', texture_data)) def hide_block(self, position, immediate=True): """ Hide the block at the given `position`. Hiding does not remove the block from the world. Parameters ---------- position : tuple of len 3 The (x, y, z) position of the block to hide. immediate : bool Whether or not to immediately remove the block from the canvas. """ self.shown.pop(position) if immediate: self._hide_block(position) else: self._enqueue(self._hide_block, position) def _hide_block(self, position): """ Private implementation of the 'hide_block()` method. """ self._shown.pop(position).delete() def show_sector(self, sector): """ Ensure all blocks in the given sector that should be shown are drawn to the canvas. """ for position in self.sectors.get(sector, []): if position not in self.shown and self.exposed(position): self.show_block(position, False) def hide_sector(self, sector): """ Ensure all blocks in the given sector that should be hidden are removed from the canvas. """ for position in self.sectors.get(sector, []): if position in self.shown: self.hide_block(position, False) def change_sectors(self, before, after): """ Move from sector `before` to sector `after`. A sector is a contiguous x, y sub-region of world. Sectors are used to speed up world rendering. """ before_set = set() after_set = set() pad = 4 for dx in xrange(-pad, pad + 1): for dy in [0]: # xrange(-pad, pad + 1): for dz in xrange(-pad, pad + 1): if dx ** 2 + dy ** 2 + dz ** 2 > (pad + 1) ** 2: continue if before: x, y, z = before before_set.add((x + dx, y + dy, z + dz)) if after: x, y, z = after after_set.add((x + dx, y + dy, z + dz)) show = after_set - before_set hide = before_set - after_set for sector in show: self.show_sector(sector) for sector in hide: self.hide_sector(sector) def _enqueue(self, func, *args): """ Add `func` to the internal queue. """ self.queue.append((func, args)) def _dequeue(self): """ Pop the top function from the internal queue and call it. """ func, args = self.queue.popleft() func(*args) def process_queue(self): """ Process the entire queue while taking periodic breaks. This allows the game loop to run smoothly. The queue contains calls to _show_block() and _hide_block() so this method should be called if add_block() or remove_block() was called with immediate=False """ start = time.clock() while self.queue and time.clock() - start < 1.0 / TICKS_PER_SEC: self._dequeue() def process_entire_queue(self): """ Process the entire queue with no breaks. """ while self.queue: self._dequeue() class Window(pyglet.window.Window): def __init__(self, *args, **kwargs): super(Window, self).__init__(*args, **kwargs) # Whether or not the window exclusively captures the mouse. self.exclusive = False # When flying gravity has no effect and speed is increased. self.flying = False # Strafing is moving lateral to the direction you are facing, # e.g. moving to the left or right while continuing to face forward. # # First element is -1 when moving forward, 1 when moving back, and 0 # otherwise. The second element is -1 when moving left, 1 when moving # right, and 0 otherwise. self.strafe = [0, 0] # Current (x, y, z) position in the world, specified with floats. Note # that, perhaps unlike in math class, the y-axis is the vertical axis. self.position = (0, 0, 0) # First element is rotation of the player in the x-z plane (ground # plane) measured from the z-axis down. The second is the rotation # angle from the ground plane up. Rotation is in degrees. # # The vertical plane rotation ranges from -90 (looking straight down) to # 90 (looking straight up). The horizontal rotation range is unbounded. self.rotation = (0, 0) # Which sector the player is currently in. self.sector = None # The crosshairs at the center of the screen. self.reticle = None # Velocity in the y (upward) direction. self.dy = 0 # A list of blocks the player can place. Hit num keys to cycle. self.inventory = [BRICK, GRASS, SAND, BOOKSHELF, WOOD, SNOW, LEAVES, DIRT, STONE] # The current block the user can place. Hit num keys to cycle. self.block = self.inventory[0] # Convenience list of num keys. self.num_keys = [ key._1, key._2, key._3, key._4, key._5, key._6, key._7, key._8, key._9, key._0] # Instance of the model that handles the world. self.model = Model() # The label that is displayed in the top left of the canvas. self.label = pyglet.text.Label('', font_name='Arial', font_size=18, x=10, y=self.height - 10, anchor_x='left', anchor_y='top', color=(0, 0, 0, 255)) # This call schedules the `update()` method to be called # TICKS_PER_SEC. This is the main game event loop. pyglet.clock.schedule_interval(self.update, 1.0 / TICKS_PER_SEC) def set_exclusive_mouse(self, exclusive): """ If `exclusive` is True, the game will capture the mouse, if False the game will ignore the mouse. """ super(Window, self).set_exclusive_mouse(exclusive) self.exclusive = exclusive def get_sight_vector(self): """ Returns the current line of sight vector indicating the direction the player is looking. """ x, y = self.rotation # y ranges from -90 to 90, or -pi/2 to pi/2, so m ranges from 0 to 1 and # is 1 when looking ahead parallel to the ground and 0 when looking # straight up or down. m = math.cos(math.radians(y)) # dy ranges from -1 to 1 and is -1 when looking straight down and 1 when # looking straight up. dy = math.sin(math.radians(y)) dx = math.cos(math.radians(x - 90)) * m dz = math.sin(math.radians(x - 90)) * m return (dx, dy, dz) def get_motion_vector(self): """ Returns the current motion vector indicating the velocity of the player. Returns ------- vector : tuple of len 3 Tuple containing the velocity in x, y, and z respectively. """ if any(self.strafe): x, y = self.rotation strafe = math.degrees(math.atan2(*self.strafe)) y_angle = math.radians(y) x_angle = math.radians(x + strafe) if self.flying: m = math.cos(y_angle) dy = math.sin(y_angle) if self.strafe[1]: # Moving left or right. dy = 0.0 m = 1 if self.strafe[0] > 0: # Moving backwards. dy *= -1 # When you are flying up or down, you have less left and right # motion. dx = math.cos(x_angle) * m dz = math.sin(x_angle) * m else: dy = 0.0 dx = math.cos(x_angle) dz = math.sin(x_angle) else: dy = 0.0 dx = 0.0 dz = 0.0 return (dx, dy, dz) def update(self, dt): """ This method is scheduled to be called repeatedly by the pyglet clock. Parameters ---------- dt : float The change in time since the last call. """ self.model.process_queue() sector = sectorize(self.position) if sector != self.sector: self.model.change_sectors(self.sector, sector) if self.sector is None: self.model.process_entire_queue() self.sector = sector m = 8 dt = min(dt, 0.2) for _ in xrange(m): self._update(dt / m) def _update(self, dt): """ Private implementation of the `update()` method. This is where most of the motion logic lives, along with gravity and collision detection. Parameters ---------- dt : float The change in time since the last call. """ # walking speed = FLYING_SPEED if self.flying else WALKING_SPEED d = dt * speed # distance covered this tick. dx, dy, dz = self.get_motion_vector() # New position in space, before accounting for gravity. dx, dy, dz = dx * d, dy * d, dz * d # gravity if not self.flying: # Update your vertical speed: if you are falling, speed up until you # hit terminal velocity; if you are jumping, slow down until you # start falling. self.dy -= dt * GRAVITY self.dy = max(self.dy, -TERMINAL_VELOCITY) dy += self.dy * dt # collisions x, y, z = self.position x, y, z = self.collide((x + dx, y + dy, z + dz), PLAYER_HEIGHT) self.position = (x, y, z) def collide(self, position, height): """ Checks to see if the player at the given `position` and `height` is colliding with any blocks in the world. Parameters ---------- position : tuple of len 3 The (x, y, z) position to check for collisions at. height : int or float The height of the player. Returns ------- position : tuple of len 3 The new position of the player taking into account collisions. """ # How much overlap with a dimension of a surrounding block you need to # have to count as a collision. If 0, touching terrain at all counts as # a collision. If .49, you sink into the ground, as if walking through # tall grass. If >= .5, you'll fall through the ground. pad = 0.25 p = list(position) np = normalize(position) for face in FACES: # check all surrounding blocks for i in xrange(3): # check each dimension independently if not face[i]: continue # How much overlap you have with this dimension. d = (p[i] - np[i]) * face[i] if d < pad: continue for dy in xrange(height): # check each height op = list(np) op[1] -= dy op[i] += face[i] if tuple(op) not in self.model.world: continue p[i] -= (d - pad) * face[i] if face == (0, -1, 0) or face == (0, 1, 0): # You are colliding with the ground or ceiling, so stop # falling / rising. self.dy = 0 break return tuple(p) def on_mouse_press(self, x, y, button, modifiers): """ Called when a mouse button is pressed. See pyglet docs for button amd modifier mappings. Parameters ---------- x, y : int The coordinates of the mouse click. Always center of the screen if the mouse is captured. button : int Number representing mouse button that was clicked. 1 = left button, 4 = right button. modifiers : int Number representing any modifying keys that were pressed when the mouse button was clicked. """ if self.exclusive: vector = self.get_sight_vector() block, previous = self.model.hit_test(self.position, vector) if (button == mouse.RIGHT) or \ ((button == mouse.LEFT) and (modifiers & key.MOD_CTRL)): # ON OSX, control + left click = right click. if previous: self.model.add_block(previous, self.block) elif button == pyglet.window.mouse.LEFT and block: texture = self.model.world[block] else: self.set_exclusive_mouse(True) def on_mouse_motion(self, x, y, dx, dy): """ Called when the player moves the mouse. Parameters ---------- x, y : int The coordinates of the mouse click. Always center of the screen if the mouse is captured. dx, dy : float The movement of the mouse. """ if self.exclusive: m = 0.15 x, y = self.rotation x, y = x + dx * m, y + dy * m y = max(-90, min(90, y)) self.rotation = (x, y) def on_key_press(self, symbol, modifiers): """ Called when the player presses a key. See pyglet docs for key mappings. Parameters ---------- symbol : int Number representing the key that was pressed. modifiers : int Number representing any modifying keys that were pressed. """ if symbol == key.W: self.strafe[0] -= 1 elif symbol == key.S: self.strafe[0] += 1 elif symbol == key.A: self.strafe[1] -= 1 elif symbol == key.D: self.strafe[1] += 1 elif symbol == key.L: crouch() elif symbol == key.SPACE: if self.dy == 0: self.dy = JUMP_SPEED elif symbol == key.ESCAPE: self.set_exclusive_mouse(False) elif symbol == key.TAB: self.flying = not self.flying elif symbol in self.num_keys: index = (symbol - self.num_keys[0]) % len(self.inventory) self.block = self.inventory[index] def on_key_release(self, symbol, modifiers): """ Called when the player releases a key. See pyglet docs for key mappings. Parameters ---------- symbol : int Number representing the key that was pressed. modifiers : int Number representing any modifying keys that were pressed. """ if symbol == key.W: self.strafe[0] += 1 elif symbol == key.S: self.strafe[0] -= 1 elif symbol == key.A: self.strafe[1] += 1 elif symbol == key.D: self.strafe[1] -= 1 def on_resize(self, width, height): """ Called when the window is resized to a new `width` and `height`. """ # label self.label.y = height - 10 # reticle if self.reticle: self.reticle.delete() x, y = self.width // 2, self.height // 2 n = 10 self.reticle = pyglet.graphics.vertex_list(4, ('v2i', (x - n, y, x + n, y, x, y - n, x, y + n)) ) def set_2d(self): """ Configure OpenGL to draw in 2d. """ width, height = self.get_size() glDisable(GL_DEPTH_TEST) glViewport(0, 0, width, height) glMatrixMode(GL_PROJECTION) glLoadIdentity() glOrtho(0, width, 0, height, -1, 1) glMatrixMode(GL_MODELVIEW) glLoadIdentity() def set_3d(self): """ Configure OpenGL to draw in 3d. """ width, height = self.get_size() glEnable(GL_DEPTH_TEST) glViewport(0, 0, width, height) glMatrixMode(GL_PROJECTION) glLoadIdentity() gluPerspective(65.0, width / float(height), 0.1, 60.0) glMatrixMode(GL_MODELVIEW) glLoadIdentity() x, y = self.rotation glRotatef(x, 0, 1, 0) glRotatef(-y, math.cos(math.radians(x)), 0, math.sin(math.radians(x))) x, y, z = self.position glTranslatef(-x, -y, -z) def on_draw(self): """ Called by pyglet to draw the canvas. """ self.clear() self.set_3d() glColor3d(1, 1, 1) self.model.batch.draw() self.draw_focused_block() self.set_2d() self.draw_label() self.draw_reticle() def draw_focused_block(self): """ Draw black edges around the block that is currently under the crosshairs. """ vector = self.get_sight_vector() block = self.model.hit_test(self.position, vector)[0] if block: x, y, z = block vertex_data = cube_vertices(x, y, z, 0.51) glColor3d(0, 0, 0) glPolygonMode(GL_FRONT_AND_BACK, GL_LINE) pyglet.graphics.draw(24, GL_QUADS, ('v3f/static', vertex_data)) glPolygonMode(GL_FRONT_AND_BACK, GL_FILL) def draw_label(self): """ Draw the label in the top left of the screen. """ x, y, z = self.position self.label.text = '%02d (%.2f, %.2f, %.2f) %d / %d' % ( pyglet.clock.get_fps(), x, y, z, len(self.model._shown), len(self.model.world)) self.label.draw() self.label.text = 'JetAdven 0.04. Work in progress' def draw_reticle(self): """ Draw the crosshairs in the center of the screen. """ glColor3d(0, 0, 0) self.reticle.draw(GL_LINES) def setup_fog(): """ Configure the OpenGL fog properties. """ # Enable fog. Fog "blends a fog color with each rasterized pixel fragment's # post-texturing color." glEnable(GL_FOG) # Set the fog color. glFogfv(GL_FOG_COLOR, (GLfloat * 4)(0.5, 0.69, 1.0, 1)) # Say we have no preference between rendering speed and quality. glHint(GL_FOG_HINT, GL_DONT_CARE) # Specify the equation used to compute the blending factor. glFogi(GL_FOG_MODE, GL_LINEAR) # How close and far away fog starts and ends. The closer the start and end, # the denser the fog in the fog range. glFogf(GL_FOG_START, 20.0) glFogf(GL_FOG_END, 60.0) def music(): music = pyglet.resource.media('gamemusic.mp3') music.play() def setup(): """ Basic OpenGL configuration. """ # Set the color of "clear", i.e. the sky, in rgba. (will add day night cycle) glClearColor(0.5, 0.69, 1.0, 1) #def daynight(): #time.sleep(100) #glClearColor(0.2, 0.2, 0.2, 1) #time.sleep(100) #d#/aynight() # Enable culling (not rendering) of back-facing facets -- facets that aren't # visible to you. glEnable(GL_CULL_FACE) # Set the texture minification/magnification function to GL_NEAREST (nearest # in Manhattan distance) to the specified texture coordinates. GL_NEAREST # "is generally faster than GL_LINEAR, but it can produce textured images # with sharper edges because the transition between texture elements is not # as smooth." # glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_NEAREST) # glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_NEAREST) # setup_fog() def main(): window = Window(width=2500*2, height=1500*2, caption='JetAdven 0.04. not a clone of minecraft :)', resizable=True) # Hide the mouse cursor and prevent the mouse from leaving the window. window.set_exclusive_mouse(True) setup() pyglet.app.run() music() if __name__ == '__main__': main()

nilq/baby-python

python

from logging import getLogger from hornet import models from .common import ClientCommand logger = getLogger(__name__) class Command(ClientCommand): def add_arguments(self, parser): parser.add_argument("member_id", type=int) def handle(self, member_id, *args, **kwargs): try: member = models.Member.objects.get(pk=member_id) except models.Member.DoesNotExist: self.stderr.write("Unknown member") return result = self.client.list_message(member) for message in result: print(" ", message) self.stderr.write("Total messages: %s" % len(result))

nilq/baby-python

python

# Copyright DataStax, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import hashlib import logging from base64 import urlsafe_b64encode from collections import namedtuple from datetime import datetime, tzinfo, timedelta try: from itertools import ifilterfalse as filterfalse except ImportError: from itertools import filterfalse from adelphi.anonymize import anonymize_keyspace from adelphi.exceptions import KeyspaceSelectionException from adelphi.store import build_keyspace_objects log = logging.getLogger('adelphi') try: from datetime import timezone utc = timezone.utc except ImportError: class UTC(tzinfo): def utcoffset(self, dt): return timedelta(0) def tzname(self, dt): return "UTC" def dst(self, dt): return timedelta(0) utc = UTC() KsTuple = namedtuple('KsTuple',['ks_id', 'ks_obj']) class BaseExporter: # unique_everseen from itertools recipes def __unique(self, iterable, key=None): "List unique elements, preserving order. Remember all elements ever seen." # unique_everseen('AAAABBBCCDAABBB') --> A B C D # unique_everseen('ABBCcAD', str.lower) --> A B C D seen = set() seen_add = seen.add if key is None: for element in filterfalse(seen.__contains__, iterable): seen_add(element) yield element else: for element in iterable: k = key(element) if k not in seen: seen_add(k) yield element def build_keyspace_id(self, ks): m = hashlib.sha256() m.update(ks.name.encode("utf-8")) # Leverage the urlsafe base64 encoding defined in RFC 4648, section 5 to provide an ID which can # safely be used for filenames as well return urlsafe_b64encode(m.digest()).decode('ascii') def get_keyspaces(self, cluster, props): keyspace_names = props["keyspace-names"] metadata = cluster.metadata keyspaces = build_keyspace_objects(keyspace_names, metadata) if len(keyspaces) == 0: raise KeyspaceSelectionException("Unable to select a keyspace from specified keyspace names") log.info("Processing the following keyspaces: %s", ','.join((ks.name for ks in keyspaces))) # anonymize_keyspace mutates keyspace state so we must trap keyspace_id before we (possibly) call it ids = {ks.name : self.build_keyspace_id(ks) for ks in keyspaces} # Create a tuple to represent this keyspace. Note that we must perform anonymization as part of this # operation because we need the keyspace name before anonymization to access the correct ID from the # dict above. def make_tuple(ks): orig_name = ks.name if props['anonymize']: anonymize_keyspace(ks) return KsTuple(ids[orig_name], ks) return {t.ks_obj.name : t for t in [make_tuple(ks) for ks in keyspaces]} def get_cluster_metadata(self, cluster): hosts = cluster.metadata.all_hosts() unique_dcs = self.__unique((host.datacenter for host in hosts)) unique_cass_vers = self.__unique((host.release_version for host in hosts)) return {"host_count": len(hosts), "dc_count": sum(1 for _ in unique_dcs), "cassandra_versions": ",".join(unique_cass_vers)} def get_common_metadata(self, cluster, props): metadata = {k : props[k] for k in ["purpose", "maturity"] if k in props} metadata.update(self.get_cluster_metadata(cluster)) metadata["creation_timestamp"] = datetime.now(utc).isoformat() return metadata # Remaining methods in this class represent default impls of methods for subclasses def export_all(self): return self.export_schema() # Note assumption of keyspace and keyspace_id as attrs def each_keyspace(self, ks_fn): ks_fn(self.keyspace, self.keyspace_id) # Functions below assume self.metadata as a dict def export_metadata_dict(self): return {k : self.metadata[k] for k in self.metadata.keys() if self.metadata[k]} def add_metadata(self, k, v): """Note that this function sets a metadata value for the entire exporter. If you need something keyspace-specific you're probably better off just adding it to the exported metadata directory.""" self.metadata[k] = v

nilq/baby-python

python

import uuid import hashlib import prettytable from keystoneclient import exceptions # Decorator for cli-args def arg(*args, **kwargs): def _decorator(func): # Because of the sematics of decorator composition if we just append # to the options list positional options will appear to be backwards. func.__dict__.setdefault('arguments', []).insert(0, (args, kwargs)) return func return _decorator def pretty_choice_list(l): return ', '.join("'%s'" % i for i in l) def print_list(objs, fields, formatters={}): pt = prettytable.PrettyTable([f for f in fields], caching=False) pt.aligns = ['l' for f in fields] for o in objs: row = [] for field in fields: if field in formatters: row.append(formatters[field](o)) else: field_name = field.lower().replace(' ', '_') data = getattr(o, field_name, '') if data is None: data = '' row.append(data) pt.add_row(row) print pt.get_string(sortby=fields[0]) def _word_wrap(string, max_length=0): """wrap long strings to be no longer then max_length""" if max_length <= 0: return string return '\n'.join([string[i:i + max_length] for i in range(0, len(string), max_length)]) def print_dict(d, wrap=0): """pretty table prints dictionaries. Wrap values to max_length wrap if wrap>0 """ pt = prettytable.PrettyTable(['Property', 'Value'], caching=False) pt.aligns = ['l', 'l'] for (prop, value) in d.iteritems(): if value is None: value = '' value = _word_wrap(value, max_length=wrap) pt.add_row([prop, value]) print pt.get_string(sortby='Property') def find_resource(manager, name_or_id): """Helper for the _find_* methods.""" # first try to get entity as integer id try: if isinstance(name_or_id, int) or name_or_id.isdigit(): return manager.get(int(name_or_id)) except exceptions.NotFound: pass # now try to get entity as uuid try: uuid.UUID(str(name_or_id)) return manager.get(name_or_id) except (ValueError, exceptions.NotFound): pass # finally try to find entity by name try: return manager.find(name=name_or_id) except exceptions.NotFound: msg = ("No %s with a name or ID of '%s' exists." % (manager.resource_class.__name__.lower(), name_or_id)) raise exceptions.CommandError(msg) def unauthenticated(f): """Adds 'unauthenticated' attribute to decorated function. Usage:: @unauthenticated def mymethod(f): ... """ f.unauthenticated = True return f def isunauthenticated(f): """ Checks to see if the function is marked as not requiring authentication with the @unauthenticated decorator. Returns True if decorator is set to True, False otherwise. """ return getattr(f, 'unauthenticated', False) def string_to_bool(arg): if isinstance(arg, bool): return arg return arg.strip().lower() in ('t', 'true', 'yes', '1') def hash_signed_token(signed_text): hash_ = hashlib.md5() hash_.update(signed_text) return hash_.hexdigest()

nilq/baby-python

python

import multiprocessing print(multiprocessing.cpu_count(), "núcleos") # conta a quantidade de núcleos disponpives no sistema # processamento senquancial import threading # módulo para a a contrução de threads import urllib.request # módulo para a requição da url import time # módulo para tratar o tempo # função criada para realização do dowload das imagens def downloadImangens(imagepath, fileName): print("Realizando Dowload......", imagepath) urllib.request.urlretrieve(imagepath, fileName) # Realiza a requisição para pagina da Web t0 = time.time() # armazena o tempo inicial da execução for i in range (10): imageName = "imagens/image-" +str(i) +".jpg" # coloca o nome em cada uma das imagens baixadas downloadImangens("http://lorempixel.com.br/400/200/sports", imageName) # aplica o download da imagem t1 = time.time() # tempo final após a execução totalTime = t1 - t0 # diferença de tempo entre o valor inicial de execução e o final print("Tempo tiotal de execuções {}".format(totalTime))

nilq/baby-python

python

class Solution: def strStr(self, haystack, needle): """ :type haystack: str :type needle: str :rtype: int """ lenh = len(haystack) lenn = len(needle) for i in range(lenh-lenn+1): if haystack[i:i+lenn] == needle: return i return -1 # return haystack.find(needle)

nilq/baby-python

python

###################################################################### # controller - deals with the UI concerns # 1. navigation # 2. preparing data elements in ui way for the screens # # It will not be referring to the business domain objects # - it will use the bl component to deal with the business logic ###################################################################### import flask import sys import datetime import traceback from flask import send_file from core.constants import _DATE_STR_DISPLAY_FORMAT_ from factory import XManFactory from core.timer import Timer # all app level variables __version__=1.0 __author__='Ramakrishnan Jayachandran' __appname__='XMAN (eXpense MANager) v1.0' # Flask initialisation app = flask.Flask( __name__ ) ####################################### ## This section contains all the code ## related to just navigation to other ## pages in the system ####################################### # This is the index page or the home page for the App @app.route( '/', methods = [ 'GET'] ) def index_page() -> str: with Timer( 'index_page') as stime: summary = getExpenseSummary() return flask.render_template( 'index.html', the_title=__appname__, summary=summary ) # redirection to input screen for expense - and build neccessary objects for it @app.route( '/expense_input', methods = [ 'GET' ] ) def expense_input() -> str : with Timer( 'expense_input' ) as start_time: summary = getExpenseSummary() # constants for accessing tuple with some readability _EXPENSE_TYPES_ : int = 0 _PEOPLE_ : int = 1 _STORES_ : int = 2 _PAYMENT_MODE_ : int = 3 ui_objects : tuple = factory_object.getBusinessLayer().prepareExpenseInput() ## TODO: add code here to navigate to expense_input page return flask.render_template( 'expense_input.html', the_title=__appname__ , summary=summary, \ short_names=ui_objects[ _PEOPLE_ ], store_names=ui_objects[ _STORES_ ], \ payment_types=ui_objects[ _PAYMENT_MODE_], expense_types=ui_objects[ _EXPENSE_TYPES_ ] ) # expense category redirection to the input screen @app.route( '/expense_category_input', methods = [ 'GET' ] ) def expense_category_input() -> str : summary = getExpenseSummary() return flask.render_template( 'expense_category_input.html', the_title=__appname__, summary=summary ) @app.route( '/store_input', methods = [ 'GET' ] ) def store_input() -> str : summary = getExpenseSummary() return flask.render_template( 'store_input.html', the_title=__appname__, summary=summary ) @app.route( '/payment_type_input', methods = [ 'GET' ] ) def payment_type_input() -> str : summary = getExpenseSummary() return flask.render_template( 'payment_type_input.html', the_title=__appname__, summary=summary ) @app.route( '/person_input', methods = [ 'GET' ] ) def person_input() -> str : summary = getExpenseSummary() return flask.render_template( 'person_input.html', the_title=__appname__, summary=summary ) ####################################### ## This section contains all the code ## related to just backend operations ## and then subsequent navigations ####################################### # All Add flows go here ... @app.route( '/expense_add', methods=['POST'] ) def add_expense() -> str : summary = getExpenseSummary() try: factory_object.getBusinessLayer().addExpense( flask.request.form[ 'exp_type' ], flask.request.form[ 'exp_detail' ], datetime.datetime.strptime( flask.request.form[ 'exp_date' ], '%Y-%m-%d' ) , float( flask.request.form[ 'exp_amount' ]), flask.request.form[ 'payment_type' ], flask.request.form[ 'store_name' ], flask.request.form[ 'short_name' ]) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed adding expense information', \ error_action = 'Please reenter the expense data and try again',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/expense_category_add', methods=['POST'] ) def add_expense_category() -> str : print( 'add_expense_category') summary = getExpenseSummary() expense_type : str = flask.request.form[ 'expense_type' ] expense_detail : str = flask.request.form[ 'expense_type_detail' ] try: factory_object.getBusinessLayer().addExpenseCategory( expense_type, expense_detail ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed adding expense category', \ error_action = 'Please reenter the Expense category - make sure it is not a duplicate', summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/store_add', methods=['POST'] ) def add_store() -> str : summary = getExpenseSummary() try: factory_object.getBusinessLayer().addStore( flask.request.form[ 'store_name' ], flask.request.form[ 'store_detail' ], flask.request.form[ 'home_delivery' ] ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed adding store data', \ error_action = 'Please reenter the Store data and make sure it is not a duplicate',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/payment_type_add', methods=['POST'] ) def add_payment_type() -> str : summary = getExpenseSummary() try: factory_object.getBusinessLayer().addPaymentType( flask.request.form[ 'payment_mode' ], flask.request.form[ 'payment_mode_detail' ] ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed adding payment type data', \ error_action = 'Please reenter the payment type data and make sure it is not a duplicate',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/person_add', methods=['POST'] ) def add_person() -> str : summary = getExpenseSummary() try: factory_object.getBusinessLayer().addPerson( flask.request.form[ 'person_first_name' ], flask.request.form[ 'person_last_name' ], flask.request.form[ 'person_short_name' ] ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed adding person data', \ error_action = 'Please reenter the person data and make sure short name it is not a duplicate',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) # All list flows go here ... @app.route( '/expenses_list', methods = [ 'GET'] ) def list_expenses() -> str : print( 'list_expenses' ) summary = getExpenseSummary() expenses : list = factory_object.getBusinessLayer().listExpenses() ui_header = [ 'ID', 'Expense Detail', 'Expense Date', 'Amount', 'Spent by', 'Store', 'Expense Type', 'Payment mode' ] ui_data : list = [ (e.getId(), e.getExpenseDetail(), e.getExpenseDate().strftime( _DATE_STR_DISPLAY_FORMAT_ ) , e.getExpenseAmount(), \ e.getPerson().getShortName(), e.getStore().getStoreName(), \ e.getExpenseCategory().getExpenseType(), e.getPaymentType().getPaymentMode()) for e in expenses ] # Generate the csv file for future use csv_rows = [] csv_rows.append( ui_header ) for row in ui_data: csv_rows.append( [ c for c in row ]) factory_object.getCSVGenerator().generateFile( 'all_expenses.csv', csv_rows ) return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module='expense', download=True ) @app.route( '/expense_categories_list', methods = [ 'GET'] ) def list_expense_categories() -> str : print( 'list_expense_categories' ) summary = getExpenseSummary() expense_categories : list = factory_object.getBusinessLayer().listExpenseCategories() ui_header = ( 'Id', 'Expense Type', 'Expense Detail' ) ui_data : list = [ ( ec.getId(), ec.getExpenseType(), ec.getExpenseDetail()) for ec in expense_categories ] mode = flask.request.args.get( 'mode' ) if mode == 'popup': return flask.render_template( 'list_data_popup.html', the_title=__appname__, \ the_header = ui_header, summary=summary, the_data = ui_data, module = None ) else: return flask.render_template( 'list_data_page.html', the_title=__appname__, summary=summary, \ the_header = ui_header, the_data = ui_data, module = 'expense_category' ) @app.route( '/stores_list', methods = [ 'GET'] ) def list_stores() -> str : summary = getExpenseSummary() stores : list = factory_object.getBusinessLayer().listStores() ui_header = ('ID', 'Store Name', 'Store Detail', 'Home Delivery ?' ) ui_data : list = [ (st.getId(), st.getStoreName(), st.getStoreDetail(), ('Y' if st.getHomeDelivery() else 'N') ) for st in stores ] mode = flask.request.args.get( 'mode' ) if mode == 'popup': return flask.render_template( 'list_data_popup.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module=None) else: return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module='store' ) @app.route( '/payment_type_list', methods = [ 'GET'] ) def list_payment_types() -> str : summary = getExpenseSummary() payment_modes : list = factory_object.getBusinessLayer().listPaymentTypes() ui_header = ('ID', 'Payment Mode', 'Payment Mode Detail' ) ui_data : list = [ (p.getId(), p.getPaymentMode(), p.getPaymentModeDetail() ) for p in payment_modes ] mode = flask.request.args.get( 'mode' ) if mode == 'popup': return flask.render_template( 'list_data_popup.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module=None) else: return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module='payment_type' ) @app.route( '/person_list', methods = [ 'GET'] ) def list_person() -> str : summary = getExpenseSummary() people : list = factory_object.getBusinessLayer().listPeople() ui_header = ('ID', 'First Name', 'Last Name', 'Short Name' ) ui_data : list = [ (p.getId(), p.getFirstName(), p.getLastName(), p.getShortName() ) for p in people ] mode = flask.request.args.get( 'mode' ) if mode == 'popup': return flask.render_template( 'list_data_popup.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module=None) else: return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = ui_header, the_data = ui_data, module='person' ) # All delete flows go here ... @app.route( '/expense_delete', methods = [ 'GET' ] ) def delete_expense() -> str : print( 'delete_expense') summary = getExpenseSummary() Id = flask.request.args.get( 'Id' ) try: factory_object.getBusinessLayer().deleteExpense( Id ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed deleting expense', \ error_action = 'Please retry or check the log for details',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/expense_category_delete', methods = [ 'GET' ] ) def delete_expense_category() -> str : print( 'delete_expense_category') summary = getExpenseSummary() Id = flask.request.args.get( 'Id' ) try: factory_object.getBusinessLayer().deleteExpenseCategory( Id ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed deleting expense category', \ error_action = 'Please retry or check the log for details',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/store_delete', methods=['GET'] ) def delete_store() -> str: summary = getExpenseSummary() Id = flask.request.args.get( 'Id' ) try: factory_object.getBusinessLayer().deleteStore( Id ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed deleting store data', \ error_action = 'Please retry or check the log for details',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/payment_type_delete', methods=['GET'] ) def delete_payment_type() -> str: summary = getExpenseSummary() Id = flask.request.args.get( 'Id' ) try: factory_object.getBusinessLayer().deletePaymentType( Id ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed deleting payment type data', \ error_action = 'Please retry or check the log for details',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) @app.route( '/person_delete', methods=['GET'] ) def delete_person() -> str: summary = getExpenseSummary() Id = flask.request.args.get( 'Id' ) try: factory_object.getBusinessLayer().deletePerson( Id ) except: traceback.print_exc() return flask.render_template( 'error_page.html', \ error_cause='Failed deleting person data', \ error_action = 'Please retry or check the log for details',\ summary=summary ) else: return flask.render_template('data_saved.html', the_title= __appname__, summary=summary ) # All report flows go here ... @app.route( '/expense_month_summary_list', methods = [ 'GET' ] ) def list_expenses_monthly_summary() -> str: summary = getExpenseSummary() report = factory_object.getReportingLayer().listMonthwiseSummary() return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = report[ 0 ], the_data = report[ 1 ], module=None ) @app.route( '/expense_month_category_summary_list', methods = [ 'GET' ] ) def list_expenses_monthly_category_summary() -> str: summary = getExpenseSummary() report = factory_object.getReportingLayer().listMonthwiseCategorySummary() return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = report[ 0 ], the_data = report[ 1 ], module=None ) @app.route( '/expense_month_person_summary_list', methods = [ 'GET' ] ) def list_expenses_monthly_person_summary() -> str: summary = getExpenseSummary() report = factory_object.getReportingLayer().listMonthwisePersonSummary() return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = report[ 0 ], the_data = report[ 1 ], module=None ) @app.route( '/expense_month_paytype_summary_list', methods = [ 'GET' ] ) def list_expenses_monthly_paytype_summary() -> str: summary = getExpenseSummary() report = factory_object.getReportingLayer().listMonthwisePaymentTypeSummary() return flask.render_template( 'list_data_page.html', the_title=__appname__, \ summary=summary, the_header = report[ 0 ], the_data = report[ 1 ], module=None ) # download links @app.route( '/download_expenses', methods = [ 'GET' ] ) def download_expense_list() -> str: return send_file( factory_object.getCSVGenerator().getFilenameWithPath( 'all_expenses.csv'), mimetype='text/csv' ) # other utility methods go here ... def getExpenseSummary(): current_month_string = datetime.datetime.now().strftime( '%Y/%m' ) result = factory_object.getBusinessLayer().getExpenseSummary(current_month_string ) return result # Main code ... if len( sys.argv ) > 2: factory_object = XManFactory() dbargs = { 'dbtype' : sys.argv[ 1 ], 'username' : sys.argv[ 2 ], 'password' : sys.argv[ 3 ], 'hostname' : sys.argv[ 4 ] , 'dbname' : sys.argv[ 5 ] } factory_object.createObjects( dbargs ) app.run(debug=True) else: print( 'Invalid usage python3 controller.py <DB-String>' )

nilq/baby-python

python

import requests from env import QuerybookSettings from lib.notify.base_notifier import BaseNotifier class SlackNotifier(BaseNotifier): def __init__(self, token=None): self.token = ( token if token is not None else QuerybookSettings.QUERYBOOK_SLACK_TOKEN ) @property def notifier_name(self): return "slack" @property def notifier_format(self): return "plaintext" def notify(self, user, message): to = f"@{user.username}" url = "https://slack.com/api/chat.postMessage" headers = {"Authorization": "Bearer {}".format(self.token)} text = self._convert_markdown(message) data = { "text": text, "channel": to, } requests.post(url, json=data, headers=headers, timeout=30)

nilq/baby-python

python

# # Copyright 2012 eNovance <licensing@enovance.com> # Copyright 2012 Red Hat, Inc # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import copy from oslo_log import log from oslo_utils import timeutils import ceilometer from ceilometer.compute import pollsters from ceilometer.compute.pollsters import util from ceilometer.compute import util as compute_util from ceilometer.compute.virt import inspector as virt_inspector from ceilometer.i18n import _, _LW from ceilometer import sample LOG = log.getLogger(__name__) class _Base(pollsters.BaseComputePollster): NET_USAGE_MESSAGE = ' '.join(["NETWORK USAGE:", "%s %s:", "read-bytes=%d", "write-bytes=%d"]) @staticmethod def make_vnic_sample(instance, name, type, unit, volume, vnic_data): metadata = copy.copy(vnic_data) resource_metadata = dict(zip(metadata._fields, metadata)) resource_metadata['instance_id'] = instance.id resource_metadata['instance_type'] = (instance.flavor['id'] if instance.flavor else None) compute_util.add_reserved_user_metadata(instance.metadata, resource_metadata) if vnic_data.fref is not None: rid = vnic_data.fref else: instance_name = util.instance_name(instance) rid = "%s-%s-%s" % (instance_name, instance.id, vnic_data.name) return sample.Sample( name=name, type=type, unit=unit, volume=volume, user_id=instance.user_id, project_id=instance.tenant_id, resource_id=rid, timestamp=timeutils.isotime(), resource_metadata=resource_metadata ) CACHE_KEY_VNIC = 'vnics' def _get_vnic_info(self, inspector, instance): return inspector.inspect_vnics(instance) @staticmethod def _get_rx_info(info): return info.rx_bytes @staticmethod def _get_tx_info(info): return info.tx_bytes def _get_vnics_for_instance(self, cache, inspector, instance): i_cache = cache.setdefault(self.CACHE_KEY_VNIC, {}) if instance.id not in i_cache: i_cache[instance.id] = list( self._get_vnic_info(inspector, instance) ) return i_cache[instance.id] def get_samples(self, manager, cache, resources): self._inspection_duration = self._record_poll_time() for instance in resources: instance_name = util.instance_name(instance) LOG.debug(_('checking net info for instance %s'), instance.id) try: vnics = self._get_vnics_for_instance( cache, self.inspector, instance, ) for vnic, info in vnics: LOG.debug(self.NET_USAGE_MESSAGE, instance_name, vnic.name, self._get_rx_info(info), self._get_tx_info(info)) yield self._get_sample(instance, vnic, info) except virt_inspector.InstanceNotFoundException as err: # Instance was deleted while getting samples. Ignore it. LOG.debug(_('Exception while getting samples %s'), err) except virt_inspector.InstanceShutOffException as e: LOG.warn(_LW('Instance %(instance_id)s was shut off while ' 'getting samples of %(pollster)s: %(exc)s'), {'instance_id': instance.id, 'pollster': self.__class__.__name__, 'exc': e}) except ceilometer.NotImplementedError: # Selected inspector does not implement this pollster. LOG.debug(_('%(inspector)s does not provide data for ' ' %(pollster)s'), {'inspector': self.inspector.__class__.__name__, 'pollster': self.__class__.__name__}) except Exception as err: LOG.exception(_('Ignoring instance %(name)s: %(error)s'), {'name': instance_name, 'error': err}) class _RateBase(_Base): NET_USAGE_MESSAGE = ' '.join(["NETWORK RATE:", "%s %s:", "read-bytes-rate=%d", "write-bytes-rate=%d"]) CACHE_KEY_VNIC = 'vnic-rates' def _get_vnic_info(self, inspector, instance): return inspector.inspect_vnic_rates(instance, self._inspection_duration) @staticmethod def _get_rx_info(info): return info.rx_bytes_rate @staticmethod def _get_tx_info(info): return info.tx_bytes_rate class IncomingBytesPollster(_Base): def _get_sample(self, instance, vnic, info): return self.make_vnic_sample( instance, name='network.incoming.bytes', type=sample.TYPE_CUMULATIVE, unit='B', volume=info.rx_bytes, vnic_data=vnic, ) class IncomingPacketsPollster(_Base): def _get_sample(self, instance, vnic, info): return self.make_vnic_sample( instance, name='network.incoming.packets', type=sample.TYPE_CUMULATIVE, unit='packet', volume=info.rx_packets, vnic_data=vnic, ) class OutgoingBytesPollster(_Base): def _get_sample(self, instance, vnic, info): return self.make_vnic_sample( instance, name='network.outgoing.bytes', type=sample.TYPE_CUMULATIVE, unit='B', volume=info.tx_bytes, vnic_data=vnic, ) class OutgoingPacketsPollster(_Base): def _get_sample(self, instance, vnic, info): return self.make_vnic_sample( instance, name='network.outgoing.packets', type=sample.TYPE_CUMULATIVE, unit='packet', volume=info.tx_packets, vnic_data=vnic, ) class IncomingBytesRatePollster(_RateBase): def _get_sample(self, instance, vnic, info): return self.make_vnic_sample( instance, name='network.incoming.bytes.rate', type=sample.TYPE_GAUGE, unit='B/s', volume=info.rx_bytes_rate, vnic_data=vnic, ) class OutgoingBytesRatePollster(_RateBase): def _get_sample(self, instance, vnic, info): return self.make_vnic_sample( instance, name='network.outgoing.bytes.rate', type=sample.TYPE_GAUGE, unit='B/s', volume=info.tx_bytes_rate, vnic_data=vnic, )

nilq/baby-python

python

""" Module docstring """ from copy import deepcopy from uuid import uuid4 from os import mkdir import numpy as np from scipy.integrate import solve_ivp class OmicsGenerator: """ Handles all omics generation. This class is used to specify omics generation parameters and generate synthetic data. Typical workfolow is: Initialize generator -> set interactions -> set interventions -> generate synthetic data Attributes: ----------- nodes: List of nodes. Args: ----- time_points: Integer. How many total time points to generate. Not to be confused with downsampling coefficient (applied later). nodes: List of strings. (Unique) node names for each node. node_sizes: List of ints. Node sizes for each node. discard_first: Integer. How many initial time points to discard. Setting higher discard_first values generally ensures samples closer to equilibrium. init_full: Boolean. If True, initializes all interactions, growth rates,and initial abundances at random. silent: Boolean. If True, suppresses all print statements. **kwargs: C, d, sigma, rho for AT-Normal matrix Returns: -------- OmicsGenerator object. Raises: ------- TODO """ def __init__( self, node_sizes : list = None, nodes : list = None, time_points : int = 100, discard_first : int = 0, init_full : bool = False, silent : bool = False, **kwargs) -> None: """ Initializes generator. See docstring for class. """ # Require node sizes if node_sizes == None: raise Exception("Must specify at least one node size.") # Better handling for single-node systems if isinstance(nodes, str): nodes = [nodes] if isinstance(node_sizes, int): node_sizes = [node_sizes] # Give default node names if node_sizes is not None and nodes is None: nodes = [f"n{i}" for i in range(len(node_sizes))] elif len(nodes) != len(node_sizes): raise Exception(f"Node lengths and node sizes do not match: {len(nodes)} != {len(node_sizes)}") self._interactions = [] self._interventions = [] self._time_points = time_points + discard_first self._T = np.array(range(self._time_points)) self._namespace = set() self._discard_first = discard_first self._silent = silent # Process nodes self.nodes = [] for node_name, size in zip(nodes, node_sizes): self.add_node(node_name, size) if init_full: self._init_full(**kwargs) if not self._silent: print("Initialized") class _OmicsNode: """ PRIVATE METHOD. Call with self.add_node() instead. A class for omics nodes. Contains pointers to interactions, interventions. Attributes: ----------- inbound: A dict of (node name, matrix) tuples representing matrix interactions of the type Ax --> y, where y is another node. Maintained by self.add_interaction(). outbound: A dict of (node name, matrix) tuples representing matrix interactions of the type Ay --> x, where y is another node. Maintained by self.add_interaction(). interventions: A list of interventions which affect this node. Maintained by self.add_intervention(). Args: ----- name: String. The node name. Must be unique. size: Integer: How many elements does this node have? initial_value: A vector of initial abundances for node elements. Length must be equal to size. Generally not called on initialization - use self.add_initial_value() instead. growth_rates: Intrinsic growth/death rates for node elements. Length must be equal to size. Generally not called on initialization - use self.add_initial_value() with 'growth_rate = True' instead. names: List of strings for naming node dimensions. log_noise: Boolean. If True, noise will be added to log-relative abundances. True by default. verbose: Boolean. If False, suppresses print statements. Returns: -------- _OmicsNode object. Raises: ------- None (fails silently, use add_node() instead.) """ def __init__( self, name : str, size : int, initial_value : np.ndarray, growth_rates : np.ndarray, names : list, log_noise : bool, verbose : bool = True) -> None: """ Initializes node. See docstring for class. """ self.name = name self.size = size self.initial_value = initial_value self.growth_rates = growth_rates self.log_noise = log_noise self.outbound = {} self.inbound = {} self.interventions = [] self.names = names if verbose: print(f"Node '{name}' initialized") def __str__(self): return f"{self.name}\t{self.size}" class _OmicsInteraction: """ PRIVATE METHOD. Call with self.add_interaction() instead. A class for omics interactions. This has the general form of an m x n matrix representing interactions between one set (e.g. taxa) and another set (e.g. other taxa, metabolites, whatever) Attributes: ----------- nrows: Number of rows (e.g. taxa) in matrix. ncols: Number of columns (e.g. metabolites) in matrix. Args: ----- name: String. A name for this interaction. Must be unique. outbound_node: Node from which the edge originates inbound_node: Node at which the edge terminates matrix: A matrix-type object with interactions lag: Integer. How much delay to put into dependencies. For instance, a lag of 1 on an interaction means we compute Ax_t = y_(t+1) verbose: Boolean. If False, suppresses print statements. Returns: -------- _OmicsInteraction object. Raises: ------- None (fails silently, use add_interaction() instead). """ def __init__( self, name : str, outbound_node : None, inbound_node : None, matrix : np.ndarray, lag : int, verbose : bool = True) -> None: """ Initializes interaction. See docstring for class. """ self.name = name self.outbound_node = outbound_node self.inbound_node = inbound_node self.matrix = np.array(matrix) self.lag = lag self.nrows = matrix.shape[0] # e.g. number of taxa self.ncols = matrix.shape[1] # e.g. number of metabolites if verbose: print(f"Interaction '{name}' added") def __str__(self): return f"{self.name}:\t({self.outbound_node.name})-->({self.inbound_node.name})\tLag: {self.lag}" class _OmicsIntervention: """ PRIVATE METHOD. Call with self.add_intervention() instead. A class for omics interventions. This has the general form of an n-length matrix which describes the reactions of some set (e.g. taxa) to this particular intervention. Args: ----- name: String. A name for our intervention. Only used for printing and other bookkeeping. vector: A vector-type object with reactions to the intervention. node_name: String. Name of node affected by this intervention/matrix. U: An indicator vector which is 1 for time points when the intervention is active, 0 otherwise. affects_abundance: Boolean. If True, intervention vector will be applied directly to the abundance vector rather than growth rates. verbose: Boolean. If False, suppresses print statements. Returns: -------- _OmicsIntevention object. Raises: ------- None (fails silently, use add_intervention() instead). """ def __init__( self, name : str, vector : np.ndarray, node_name : str, U : np.ndarray, affects_abundance : bool, verbose : bool = True) -> None: """ Initializes an intervention. See docstring for class. """ self.name = name self.vector = vector self.node_name = node_name self.U = np.array(U) self.affects_abundance = affects_abundance if verbose: print(f"Intervention '{name}' added") return def __str__(self): end = "" if self.affects_abundance: end = "\taffects abundance" return f"{self.name}\t{self.node_name}{end}" def add_node( self, name : str, size : int, initial_value : np.ndarray = None, growth_rates : np.ndarray = None, names : list = None, log_noise : bool = True, verbose : bool = True) -> None: """ Adds nodes to generator object. Args: ----- name: String. Used to identify node. Must be unique. size: Length of vector associated with a time point of this node. For instance, for a metagenomics node, this would correspond to the number of taxa. initial_value: Value of this node at t = 0. Must be same length as node size. growth_rates: Element-wise growth/death rates for this node. Must be same length as node size. names: Optional. List of names for each node element. Used for printing/saving data. log_noise: Boolean. If True, noise will be added to log-relative abundance.If False, noise will be added to relative abundances. verbose: Boolean. If False, suppresses print statements. Returns: -------- None (modifies generator in place). Raises: ------- ValueError: One or more of [initial_value, growth_rates, names] are the wrong size. """ # Check sizes of inputs agree for param_name in ["initial_value", "growth_rates", "names"]: param = eval(param_name) if param is not None and len(param) != size: raise ValueError(f"{param_name} is wrong size: {len(param)} != {size}") # Check namespace if name in self._namespace: raise Exception(f"Name {name} already in use. Please use a unique name") # Check verbosity if self._silent: verbose = False # Generate node and append to object node = self._OmicsNode( name, size, initial_value, growth_rates, names, log_noise, verbose ) self.nodes.append(node) self._namespace.add(name) def add_interaction( self, name : str, outbound_node_name : str, inbound_node_name : str, matrix : np.ndarray, lag : int = 0, verbose : bool = True) -> None: """ Adds interactions to generator object. Edges look like this: Graphical: (OUTBOUND NODE)--->(INBOUND NODE) Linear algebra: [inbound] = [matrix] @ [outbound] + [...] Args: ----- name: String. A name for this interaction. outbound_node_name: String. Name of node from which the edge originates inbound_node_name: String. Name of node at which the edge terminates matrix: A matrix-type object with interactions lag: Integer. How much delay to put into dependencies. For instance, a lag of 1 on an interaction means we compute Ax_t = y_(t+1) verbose: Boolean. If False, suppresses print statements. Returns: -------- None (modifies generator in place). Raises: ------- TODO """ # Check namespace if name in self._namespace: raise Exception(f"Name {name} already in use. Please use a unique name") # Check verbosity if self._silent: verbose = False # Get nodes outbound_node = self.get(outbound_node_name, "node") if outbound_node is None: raise Exception("Outbound node is invalid") inbound_node = self.get(inbound_node_name, "node") if inbound_node is None: raise Exception("Inbound node is invalid") # Check that matrix dimensions match if matrix.shape[1] != inbound_node.size: raise ValueError(f"Matrix shape[1] = {matrix.shape[1]} != {inbound_node.size} (size of inbound node '{inbound_node.name}')") if matrix.shape[0] != outbound_node.size: raise ValueError(f"Matrix shape[0] = {matrix.shape[0]} != {outbound_node.size} (size of outbound node '{outbound_node.name}')") interaction = self._OmicsInteraction( name, outbound_node, inbound_node, matrix, lag, verbose ) self._interactions.append(interaction) # Append to nodes outbound_node.inbound[inbound_node_name] = interaction inbound_node.outbound[outbound_node_name] = interaction self._namespace.add(name) def add_intervention( self, name : str, node_name : str, vector : np.ndarray, affects_abundance : bool = False, U : np.ndarray = None, start : int = None, end : int = None, verbose : bool = True) -> None: """ Adds an intervention to generator. Must have either U or (start, end) set to specify timeframe. Args: ----- name: String. A name for our intervention. Only used for printing and other bookkeeping. node_name: String. Name of node affected by this intervention/matrix. vector: A vector-type object detailing, elementwise, the reactions of each node coordinate to an intervention. affects_abundance: Boolean. If True, intervention vector will be applied directly to the abundance vector rather than to growth rates. U: An indicator vector which is 1 for time pointswhen the intervention is active, 0 otherwise. start: First time point when interaction begins. Use only for interactions of the form 0*1+0*. Otherwise, use U variable instead. end: Last node when interaction is active. Use only for interactions of the form 0*1+0*. Otherwise, use U variable instaed. verbose: Boolean. If False, suppresses print statements. Returns: -------- None (modifies generator in place). Raises: ------- TODO """ # Check namespace if name in self._namespace: raise Exception(f"Name {name} already in use. Please use a unique name") # Check U vector is correct length if U is not None: if len(U) != self._time_points: raise Exception(f"U vector is different size from number of time points: {len(U)} != {self._time_points}") # Check verbosity if self._silent: verbose = False # Process node node = self.get(node_name, "node") if node is None: raise Exception("Invalid node! Please try again") # A bunch of control flow to make a boolean vector called U if U is not None: pass # explicit U vectors are best elif start is None or end is None: raise Exception("Need to supply a (start,end) pair or a U vector") else: U = np.array([0] * self._time_points) U[start:end] = 1 # Make the intervention and add it to self intervention = self._OmicsIntervention( name, vector, node_name, U, affects_abundance, verbose ) if len(intervention.U) == self._time_points: self._interventions.append(intervention) else: raise Exception("Intervention vector is not the same length at time vector") # Modify node accordingly node.interventions.append(intervention) self._namespace.add(name) def set_initial_value( self, node_name : str, values : np.ndarray, growth_rate : bool = False, verbose : bool = True) -> None: """ Sets a node value or growth rate. Args: ----- node_name: Name of node being altered values: Vector. Initial values for node. Must be same length as node size. growth_rate: Boolean. If True, affects the growth_rate parameter of the node. Otherwise, affects initial values of node. verbose: Boolean. If False, suppresses print statements. Returns: -------- None (modifies generator in place). Raises: ------- TODO """ node = self.get(node_name, "node") # Check node exists if node is None: raise Exception(f"Invalid node name: {node_name} does not exist") # Check dimensions match if len(values) != node.size: raise Exception(f"Size mismatch with node size: {len(values)} != {node.size}") # Set values if not growth_rate: node.initial_value = values elif growth_rate: node.growth_rates = values # Print output if verbose and not self._silent: if not growth_rate: print(f"Added x0 vector to node {node_name}") elif growth_rate: print(f"Added growth rates to node {node_name}") def get( self, name : str, node_type : str in ["node", "interaction", "intervention"] = None) -> "generator element": """ Gets a (node/interaction/intervention) by name. Args: ----- name: String. Name of node/interaction/intervention. type: String. One of ["node", "interaction", "intervention"]. Specifies the type of generator element to look for. Returns: -------- _OmicsNode, _OmicsInteraction, _OmicsIntervention, or None. Raises: ------- None """ if node_type in (None, "node"): for node in self.nodes: if node.name == name: return node if node_type in (None, "interaction"): for interaction in self._interactions: if interaction.name == name: return interaction if node_type in (None, "intervention"): for intervention in self._interventions: if intervention.name == name: return intervention return None def remove( self, name : str, verbose : bool = True) -> None: """ Removes a node, intervention, or interaction from the generator by name. Args: ----- name: A string specifying the (unique) name of the element to be removed. Returns: -------- None (modifies generator in place). Raises: ------- TODO """ obj = self.get(name) if obj is None: raise Exception(f"Cannot find object named {name} to remove") if isinstance(obj, self._OmicsNode): for interaction in reversed(self._interactions): # reversed so we can remove interactions as we go if obj in (interaction.inbound_node, interaction.outbound_node): self._interactions.remove(interaction) for intervention in reversed(self._interventions): if intervention.node_name == name: self._interventions.remove(intervention) for node in self.nodes: node.inbound.pop(name, None) node.outbound.pop(name, None) self.nodes.remove(obj) if verbose: print(f"Removed node '{name}'") elif isinstance(obj, self._OmicsInteraction): # Remove interaction from inbound node obj.inbound_node.outbound.pop(obj.outbound_node.name, None) # Remove interaction from outbound node obj.outbound_node.inbound.pop(obj.inbound_node.name, None) # Remove interaction from list self._interactions.remove(obj) if verbose: print(f"Removed interaction '{name}'") elif isinstance(obj, self._OmicsIntervention): node = self.get(obj.node_name) node.interventions.remove(obj) self._interventions.remove(obj) if verbose: print(f"Removed intervention '{name}'") else: raise Exception(f"Cannot remove '{name}': unknown type. Is the name correct?") self._namespace.remove(name) def generate( self, noise_var : float = 1e-2, n_reads : int = 1e5, dt : float = 1e-2, downsample : int = 1) -> (dict, dict, dict): """ Generates a single timecourse of synthetic data. Args: ----- noise_var: Float. variance parameter for gaussian noise term. n_reads: Integer. Number of reads to draw from the unsampled distribution. dt: Float. time step size which gets passed to IVP solver downsample: Integer. fraction of outputs to keep (1/n). By default, keeps all samples. downsample=4 means every 4th sample is kept, etc. Downsample is deprecated. Simply modify "dt" instead. Returns: -------- The following three dicts (in order): //======================================================\\ ||Name: Sampling: Normalization: Number of samples:|| ||======================================================|| ||Z unsampled unnormalized full || ||X unsampled normalized downsampled || ||Y sampled normalized downsampled || \\======================================================// Each Z/X/Y dict contains (node, timecourse) pairs. The timecourse is a numpy array with shape (number of time points, node size). Raises: ------- TODO """ # Sanity checks for node in self.nodes: if node.initial_value is None: raise ValueError(f"Node '{node.name}' has no x0 vector") if node.growth_rates is None: raise ValueError(f"Node '{node.name}' has no growth rate set") def _grad_fn( node : None, X : list, growth_rates : np.ndarray, t : int) -> None: """ This gets passed to the solver. It's just the vector f used in GLV calculations. """ # Interactions: interaction_coef = np.zeros(node.size) for node_name in node.outbound: interaction = node.outbound[node_name] # Adjust for lag idx = -1 - interaction.lag try: # Get interaction matrix M = interaction.matrix # Get last value (modulo lag term) of node abundance y = X[node_name][idx] # f += yM (GLV equation) interaction_coef += y @ M except IndexError: # Happens when lag is larger than number of values already generated pass # Interventions: intervention_coef = np.zeros(node.size) for intervention in node.interventions: if not intervention.affects_abundance: intervention_coef += intervention.vector.dot(intervention.U[t]) # Self xt = X[node.name][-1] # The function itself: def fn(t, x): return xt * (growth_rates + interaction_coef + intervention_coef) return fn # Initialization steps Z = {} # Latent absolute abundances X = {} # Probability distribution/normalized abundances Y = {} # Sampled abundances for node in self.nodes: Z[node.name] = [node.initial_value] # Generalized Lotka-Volterra steps, plus bells and whistles for t in range(self._time_points - 1): Z_temp = {} # Use this so that all values are updated at once for node in self.nodes: # Get values from dicts z = Z[node.name] g = node.growth_rates # Initialize values Zprev = np.copy(z[-1]) # last time point, X_(t-1) # Pass to solver # TODO: possible to do this all in one shot rather than looping? grad = _grad_fn(node, Z, g, t) ivp = solve_ivp(grad, (0,dt), Zprev, method="RK45") Zt = ivp.y[:,-1] # Tweak abundances on a per-node basis # TODO: Maybe this would be better if it were size-adjusted? for intervention in node.interventions: if intervention.affects_abundance == True: Zt += intervention.vector * intervention.U[t] # Add biological noise: noise = np.random.normal(scale=noise_var, size=node.size) # No noise for missing taxa noise = noise * (Zt > 0) # Equivalent to log->add noise->exp if node.log_noise == True: Zt *= np.exp(noise) else: Zt += noise # Push to results Zt = np.clip(Zt, 0, None) Z_temp[node.name] = Zt # Push all values for this time point to X at once for key in Z_temp: Z[key] += [Z_temp[key]] # Simulate sampling noise for node in self.nodes: z = np.array(Z[node.name]) # Save latent state x = z.copy() # Discard first couple elements (ensure values are near attractor) x = x[self._discard_first:] # Take every nth element # Negative coefficient ensures we sample from the end x = x[::-downsample] # Need to un-reverse the data now x = x[::-1] # Relative abundances x = np.apply_along_axis(lambda a: a/sum(a), 1, x) # y = y / np.sum(y, axis=1).reshape(-1,1) # Draw samples y = [] for idx in range(x.shape[0]): try: Yt = np.random.multinomial(n_reads, x[idx]) / n_reads y += [Yt] except ValueError: # TODO: circle back and figure out what was breaking this # print("ERROR: check self._weird for more info") # self._weird = X[node.name][idx] # debugging variable y += [np.zeros(node.size)] # Push to output X[node.name] = x Y[node.name] = np.array(y) Z[node.name] = z return Z, X, Y def generate_multiple( self, n : int, extinct_fraction : float = 0, **generate_args) -> (list, list, list): """ Generates several timecourses of synthetic data. This is essentially a wrapper around a loop of generate() calls, with the added element of reinitializing individuals. The extinct_fraction parameter gives some degree of control over re-initialization. Args: ----- n: Integer. Number of individuals for whom to generate synthetic data timecourses. extinct_fraction: Float in [0, 1) range. Fraction of abundances that should be extinct for each individual. Additional args (same as generate()): ------------------------------------- noise_var: Float. variance parameter for gaussian noise term. n_reads: Integer. Number of reads to draw from the unsampled distribution. dt: Float. time step size which gets passed to IVP solver downsample: Integer. fraction of outputs to keep (1/n). By default, keeps all samples. downsample=4 means every 4th sample is kept, etc. Downsample is deprecated. Simply modify "dt" instead. Returns: -------- The following three arrays (in order): //======================================================\\ ||Name: Sampling: Normalization: Number of samples:|| ||======================================================|| ||Z unsampled unnormalized full || ||X unsampled normalized downsampled || ||Y sampled normalized downsampled || \\======================================================// Each Z/X/Y array contains n dicts, each of which contains (node, timecourse) pairs. The timecourse is a numpy array with shape (number of time points, node size). Raises: ------- TODO """ # Initialize: old_nodes = self.nodes # store old initial values out_X = [] out_Y = [] out_Z = [] # Generation loop for i in range(n): # Set new initial values for each node for node in self.nodes: # TODO: allow passing of any function to generate this abundances = np.random.exponential(size=node.size) * np.random.binomial(1, 1-extinct_fraction, size=node.size) self.set_initial_value(node.name, abundances, verbose=False) Z,X,Y = self.generate(**generate_args) out_X.append(X) out_Y.append(Y) out_Z.append(Z) # return nodes to old values self.nodes = old_nodes return out_Z, out_X, out_Y def _allesina_tang_normal_matrix( self, n : int, C : float, d : float, sigma : float, rho : float) -> np.ndarray: """ Generates an Allesina-Tang normal matrix. Inspired by https://stefanoallesina.github.io/Sao_Paulo_School/intro.html#multi-species-dynamics. How this works: --------------- 1. Creates covariance matrix has the following form: 1 rho rho ... rho 1 rho ... rho rho 1 ... ... (you get the idea) 2. Draws multivariate normal pairs from this covariance matrix 3. Populates non-diagonal entries of matrix with drawn pairs 4. Symmetrically sparsifies matrix, keeping only ~C% of entries 5. Sets diagonals of matrix to -d Args: ----- n: Integer. Number of rows/columns in square matrix. C: Float in (0,1]: Sparsity parameter. Higher C = less sparse. d: Float. Negative self-interaction size. sigma: Float. Variance used to generate multivariate normal covariance matrix. rho: Float in [-1, 1]. Correlation term of covariance matrix. Higher rho = positive connectance = mutualism = harder to stabilize. Lower rho = predator-prey--type relationships = easier to stabilize. Returns: -------- A matrix M that can be used as an interaction matrix. Raises: ------- None (fails silently). """ # sample coefficients mu = np.zeros(2) cov = sigma ** 2 * np.array([[1, rho], [rho, 1]]) n_samples = int(n * (n-1) / 2) pairs = np.random.multivariate_normal(mu, cov, n_samples) # completely filled matrix M = np.ndarray((n, n)) M[np.triu_indices(n, 1)] = pairs[:,0] M = M.transpose() M[np.triu_indices(n, 1)] = pairs[:,1] # winnow down connections = np.random.rand(n, n) <= C connections = connections * 1 # binarize connections[np.tril_indices(n,1)] = 0 connections += connections.transpose() # symmetric M *= connections # set negative self-interactions M[np.diag_indices(n)] = -d return M def _set_interactions( self, C : float = 0.5, d : float = None, sigma : float = 1, rho : float = -0.4) -> None: """ Sets all interaction matrices from one big AT-normal matrix Args: ----- C: Float in (0,1]: Sparsity parameter. Higher C = less sparse. d: Float. Negative self-interaction size. sigma: Float. Variance used to generate multivariate normal covariance matrix. rho: Float in [-1, 1]. Correlation term of covariance matrix. Higher rho = positive connectance = mutualism = harder to stabilize. Lower rho = predator-prey--type relationships = easier to stabilize. Returns: -------- None (modifies generator in place). Raises: ------- None (fails silently). """ # Generate master matrix sizes = [node.size for node in self.nodes] n = np.sum(sizes) # Solve for a stable value of d if d is not provided if d is None: d = sigma * np.sqrt(n * C) + 1 m0 = self._allesina_tang_normal_matrix(n, C, d, sigma, rho) # Carve up master matrix i = 0 # row for node1 in self.nodes: j = 0 # col for node2 in self.nodes: m_ij = m0[i:i + node1.size, j:j + node2.size] self.add_interaction( f"{node1.name}->{node2.name}", node1.name, node2.name, m_ij ) if not self._silent: print(f"set m:({node1.name})->({node2.name}): {i}:{i + node1.size} {j}:{j + node2.size}") j += node2.size i += node1.size def _init_full( self, dist : None = np.random.exponential, **kwargs) -> None: """ A fully random initialization of all generator parameters. Args: ----- dist: A function to draw initial distributions (e.g. np.random.exponential, np.random.rand, etc) Returns: -------- None (modifies generator in place) Raises: ------- None """ # TODO: make use of dist argument self._set_interactions(**kwargs) for node in self.nodes: self.set_initial_value( node.name, np.random.exponential(size=node.size) ) self.set_initial_value( node.name, 2 * (0.5 - np.random.rand(node.size)), growth_rate=True ) def case_control( self, participants : int, case_frac : float, node_name: str, effect_size : float, **generate_args) -> (list, list, list, list, list, list): """ Generates synthetic case and control timecourses. Args: ----- participants: Integer. The total number of participants in the study. case_frac: Float in [0,1]. Fraction of total participants belonging to the case group. node_name: String. Name of node to which the intervention is applied. effect_size: Float. Magnitude of intervention. **kwargs: Arguments that get passed to generate_multiple(). Returns: -------- Z_control: Z-list like generate_multiple() for control group. X_control: X-list like generate_multiple() for control group. Y_control: Y-list like generate_multiple() for control group. Z_case: Z-list like generate_multiple() for case group. X_case: X-list like generate_multiple() for case group. Y_case: Y-list like generate_multiple() for case group. Raises: ------- TODO """ # inferred settings n_cases = int(participants * case_frac) n_controls = int(participants * (1-case_frac)) # get control values x_control, y_control, z_control = self.generate_multiple(n_controls, **generate_args) # get case values case_gen = self.copy() node_size = self.get(node_name).size case_gen.add_intervention( name='CASE', node_name=node_name, vector=effect_size * (0.5-np.random.rand(node_size)), start=0, end=self._time_points ) z_case, x_case, y_case = case_gen.generate_multiple(n_cases, **generate_args) return z_control, x_control, y_control, z_case, x_case, y_case def copy(self) -> None: """ Makes a deep copy of generator. Args: ----- None Returns: -------- OmicsGenerator copy Raises: ------- None """ return deepcopy(self) def _save_single(self, data : "generator output", path : str = None, delim : str = "\t", ext : str = ".tsv") -> None: """ Helper function. Saves a single timecourse. """ for node in data: data_t = data[node].transpose() names = self.get(node).names if names is None: names = [f"{node}_{x}" for x in range(data_t.shape[0])] sample_names = [f"S_{x}" for x in range(data_t.shape[1])] header = f"{delim}{delim.join(sample_names)}" # blank top-left cell data_joined = np.column_stack([names, data_t]) np.savetxt( f"{path}{node}.{ext}", data_joined, fmt="%-12s", delimiter=delim, header=header, ) def save(self, data : "generator output", output_path : str = ".", prefix : str = "", delim : str = "\t", ext : str = "tsv") -> None: """ Saves generator outputs (single or multiple timecourses) as a text file/files. Args: ----- data: An output from the self.generate(), self.generate_multiple(), or self.case_control() method. Expected to be a dict or a list of dicts. path: String. Where to save outputs. prefix: String. Name to append to beginning of filenames. delim: String. Delimiter character. ext: String. Filename extension for saved timecourses. Returns: -------- None. Saves output to disk (as .tsv files by default) Raises: ------- TODO """ # Path handling save_id = uuid4() if output_path is None: output_path = f"./{save_id}" try: mkdir(output_path) except FileExistsError as e: raise FileExistsError("f{output_path} already exists.") from e # re-raise error # Multiple outputs if isinstance(data, list): for idx, individual in enumerate(data): if not self._silent: print(f"\tSaving individual {idx} in directory {output_path}/{idx}/") # Check correct nested datatypes if not isinstance(individual, dict): raise Exception(f"Wrong datatype: submitted list of {type(individual)}, expected list of dicts.") mkdir(f"{output_path}/{idx}") self._save_single(individual, f"{output_path}/{idx}/{prefix}{idx}", delim, ext) # Single output elif isinstance(data, dict): self._save_single(data, f"{output_path}/{prefix}", delim, ext) def __str__(self): # TODO: Rewrite this more cleanly with f-strings out = "\n=========================GENERATOR=========================\n\nTime_points:\t" out += str(self._time_points) out += "\n\nNodes:\n\t" out += "\n\t".join([ str(x) for x in self.nodes ] ) out += "\n\nInteractions:\n\t" out += "\n\t".join([ str(x) for x in self._interactions ] ) out += "\n\nInterventions:\n\t" out += "\n\t".join([ str(x) for x in self._interventions ] ) return out

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#!/usr/bin/python3 # -*- coding: UTF-8 -*- """ This script defines the function to do the irq related analysis """ import csv import struct from config import TSC_FREQ TSC_BEGIN = 0 TSC_END = 0 VMEXIT_ENTRY = 0x10000 LIST_EVENTS = { 'VMEXIT_EXTERNAL_INTERRUPT': VMEXIT_ENTRY + 0x00000001, } IRQ_EXITS = {} # 4 * 64bit per trace entry TRCREC = "QQQQ" def parse_trace(ifile): """parse the trace data file Args: ifile: input trace data file Return: None """ fd = open(ifile, 'rb') while True: global TSC_BEGIN, TSC_END try: line = fd.read(struct.calcsize(TRCREC)) if not line: break (tsc, event, vec, d2) = struct.unpack(TRCREC, line) event = event & 0xffffffffffff if TSC_BEGIN == 0: TSC_BEGIN = tsc TSC_END = tsc for key in LIST_EVENTS.keys(): if event == LIST_EVENTS.get(key): if vec in IRQ_EXITS.keys(): IRQ_EXITS[vec] += 1 else: IRQ_EXITS[vec] = 1 except struct.error: sys.exit() def generate_report(ofile, freq): """ generate analysis report Args: ofile: output report freq: TSC frequency of the device trace data from Return: None """ global TSC_BEGIN, TSC_END csv_name = ofile + '.csv' try: with open(csv_name, 'a') as filep: f_csv = csv.writer(filep) rt_cycle = TSC_END - TSC_BEGIN assert rt_cycle != 0, "Total run time in cycle is 0, \ TSC end %d, TSC begin %d" \ % (TSC_END, TSC_BEGIN) rt_sec = float(rt_cycle) / (float(freq) * 1000 * 1000) print ("\nVector \t\tCount \tNR_Exit/Sec") f_csv.writerow(['Vector', 'NR_Exit', 'NR_Exit/Sec']) for e in IRQ_EXITS.keys(): pct = float(IRQ_EXITS[e]) / rt_sec print ("0x%08x \t %d \t%.2f" % (e, IRQ_EXITS[e], pct)) f_csv.writerow([e, IRQ_EXITS[e], '%.2f' % pct]) except IOError as err: print ("Output File Error: " + str(err)) def analyze_irq(ifile, ofile): """do the vm exits analysis Args: ifile: input trace data file ofile: output report file Return: None """ print("IRQ analysis started... \n\tinput file: %s\n" "\toutput file: %s.csv" % (ifile, ofile)) parse_trace(ifile) # save report to the output file generate_report(ofile, TSC_FREQ)

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def Widget(self): return self

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import unittest import torch from torchdrug import data, layers class GraphSamplerTest(unittest.TestCase): def setUp(self): self.num_node = 10 self.input_dim = 5 self.output_dim = 7 adjacency = torch.rand(self.num_node, self.num_node) threshold = adjacency.flatten().kthvalue((self.num_node - 3) * self.num_node)[0] adjacency = adjacency * (adjacency > threshold) self.graph = data.Graph.from_dense(adjacency).cuda() self.input = torch.rand(self.num_node, self.input_dim).cuda() def test_sampler(self): conv = layers.GraphConv(self.input_dim, self.output_dim, activation=None).cuda() readout = layers.SumReadout().cuda() sampler = layers.NodeSampler(ratio=0.8).cuda() results = [] for i in range(2000): graph = sampler(self.graph) node_feature = conv(graph, self.input) result = readout(graph, node_feature) results.append(result) result = torch.stack(results).mean(dim=0) node_feature = conv(self.graph, self.input) truth = readout(self.graph, node_feature) self.assertTrue(torch.allclose(result, truth, rtol=5e-2, atol=5e-2), "Found bias in node sampler") sampler = layers.EdgeSampler(ratio=0.8).cuda() results = [] for i in range(2000): graph = sampler(self.graph) node_feature = conv(graph, self.input) result = readout(graph, node_feature) results.append(result) result = torch.stack(results).mean(dim=0) node_feature = conv(self.graph, self.input) truth = readout(self.graph, node_feature) self.assertTrue(torch.allclose(result, truth, rtol=5e-2, atol=5e-2), "Found bias in edge sampler") if __name__ == "__main__": unittest.main()

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""" Number 1. Integer 2. Floating point 3. Octal & Hexadecimal 1) Octal a = 0o828 a = 0O828 2) Hexadecimal a = 0x828 4. Operate +, -, *, / pow : ** mod : // remainder : % Contents Source : https://wikidocs.net/12 """

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from sys import argv script, first, second = argv print "This script is called: ", script print "The first variable is: ", first print "The second variable is: ", second

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python

# Generated by Django 3.2.4 on 2021-06-15 22:49 import django.db.models.deletion from django.db import migrations, models class Migration(migrations.Migration): dependencies = [("rules", "0001_initial")] operations = [ migrations.CreateModel( name="Ordinance", fields=[ ("id", models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name="ID")), ("created_at", models.DateTimeField(auto_now_add=True)), ("modified_at", models.DateTimeField(auto_now=True)), ("ordinance", models.CharField(max_length=25)), ("slug", models.SlugField(unique=True)), ], options={"abstract": False}, ), migrations.AlterField( model_name="rule", name="ordinance", field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name="ordinance", to="rules.rulegroup" ), ), migrations.AlterField( model_name="rule", name="rule_group", field=models.ForeignKey( on_delete=django.db.models.deletion.CASCADE, related_name="rule_group", to="rules.rulegroup" ), ), ]

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python

from Crypto.PublicKey import RSA from Crypto import Random #This one is important since it has the default function in RSA.generate() to generate random bytes! from Crypto.Cipher import PKCS1_OAEP import base64 #I'm leaving this function so that you understand how it works from encryption => decryption def rsa_encrypt_decrypt(): #Generating RSA key pair key = RSA.generate(2048) #Extracting private_key private_key = key.export_key('PEM') #Extracting public_key public_key = key.publickey().exportKey('PEM') #Get the message to send message = input('\nPlease enter your message for RSA encryption and decryption: ') #Encode the message message = str.encode(message) #Import the public key in order to use it for encryption rsa_public_key = RSA.importKey(public_key) #PKCS#1 OAEP is an asymmetric cipher based on RSA and the OAEP padding rsa_public_key = PKCS1_OAEP.new(rsa_public_key) #Finally encryption encrypted_message = rsa_public_key.encrypt(message) #Base64 encoding so that we can store it easily on DB/Server encrypted_message = base64.b64encode(encrypted_message) print('\nYour encrypted message is : ', encrypted_message) #DECRYPTION #Import private key rsa_private_key = RSA.importKey(private_key) #Apply the same magic trick again using PKCS1 OAEP rsa_private_key = PKCS1_OAEP.new(rsa_private_key) #Base64 decoding before decrypting, otherwise it would be incorrect, it's logical right? :) encrypted_message = base64.b64decode(encrypted_message) decrypted_message = rsa_private_key.decrypt(encrypted_message) print('\nYour message after decryption is : ', decrypted_message) #THESE FUNCTIONS ARE THE ONES WE GONNA USE IN OUR FINAL APP #How are we gonna get the public/private keys, I think that those are stored on the server #So server will be able to get the proper key pair using users id maybe? or certificate? #For the encrypt fct: sender calls it then sends the encrypted message to server along with the receiver's address def rsa_encrypt(message, receiver_public_key): message = str.encode(message) rsa_public_key = RSA.importKey(receiver_public_key) rsa_public_key = PKCS1_OAEP.new(rsa_public_key) encrypted_message = rsa_public_key.encrypt(message) encrypted_message = base64.b64encode(encrypted_message) return encrypted_message #LOGICALLY, the server now has the encrypted message and will distribute it to the receiver #For the decrypt fct: receiver calls it using his private key to get the initial message def rsa_decrypt(encrypted_message, receiver_private_key): rsa_private_key = RSA.importKey(receiver_private_key) rsa_private_key = PKCS1_OAEP.new(rsa_private_key) encrypted_message = base64.b64decode(encrypted_message) decrypted_message = rsa_private_key.decrypt(encrypted_message) return decrypted_message #FOR TESTING! SINCE WE DON'T HAVE RSA KEY PAIRS LOCALLY #rsa_encrypt_decrypt() # get rsa key from file def get_rsa_key(filepath): with open(filepath, mode='rb') as private_file: priv_key_data = private_file.read() private_key = RSA.importKey(priv_key_data) #print(private_key.export_key()) return private_key

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# -*- coding: utf-8 -*- from model.contact import Contact from fixture.application import Application import pytest from model.contact import Contact def test_add_contact(app): app.open_home_page() app.contact.add(Contact(firstname="dsf", dlename="gdfg", lastname="ew", nickname="gdf", title="wer", company="dg", address="dg", home="dg", mobile="43", work="sdg", fax="213", email="243", email2="234", email3="245", homepage="fsdf", address2="dsf", phone2="sg", notes="sfghh")) app.return_home_page() def tearDown(self): self.app.destroy()

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############################################################################## # Copyright (c) 2013-2018, Lawrence Livermore National Security, LLC. # Produced at the Lawrence Livermore National Laboratory. # # This file is part of Spack. # Created by Todd Gamblin, tgamblin@llnl.gov, All rights reserved. # LLNL-CODE-647188 # # For details, see https://github.com/spack/spack # Please also see the NOTICE and LICENSE files for our notice and the LGPL. # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU Lesser General Public License (as # published by the Free Software Foundation) version 2.1, February 1999. # # 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 terms and # conditions of the GNU Lesser General Public License for more details. # # You should have received a copy of the GNU Lesser General Public # License along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA ############################################################################## from spack import * class PyCookiecutter(PythonPackage): """A command-line utility that creates projects from cookiecutters (project templates). E.g. Python package projects, jQuery plugin projects.""" homepage = "https://cookiecutter.readthedocs.io/en/latest/" url = "https://github.com/audreyr/cookiecutter/archive/1.6.0.tar.gz" version('1.6.0', sha256='0c9018699b556b83d7c37b27fe0cc17485b90b6e1f47365b3cdddf77f6ca9d36') depends_on('py-setuptools', type='build') depends_on('py-future') depends_on('py-binaryornot') depends_on('py-jinja2') depends_on('py-click') depends_on('py-whichcraft') depends_on('py-poyo') depends_on('py-jinja2-time') depends_on('py-requests')

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from django.db import models from cloudinary.models import CloudinaryField class Image(models.Model): short_title = models.CharField(max_length=20) file = CloudinaryField('image', default="https://cdn.pixabay.com/photo/2016/06/16/03/49/befall-the-earth-quote-1460570_960_720.jpg") timeStamp = models.DateTimeField(auto_now_add=True) def __str__(self): return self.short_title

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def zigzag(n): '''zigzag rows''' def compare(xy): x, y = xy return (x + y, -y if (x + y) % 2 else y) xs = range(n) return {index: n for n, index in enumerate(sorted( ((x, y) for x in xs for y in xs), key=compare ))} def printzz(myarray): '''show zigzag rows as lines''' n = int(len(myarray) ** 0.5 + 0.5) xs = range(n) print('\n'.join( [''.join("%3i" % myarray[(x, y)] for x in xs) for y in xs] )) printzz(zigzag(6))

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python

import unittest import requests import time from vaurienclient import Client from vaurien.util import start_proxy, stop_proxy from vaurien.tests.support import start_simplehttp_server _PROXY = 'http://localhost:8000' # we should provide a way to set an option # for all behaviors at once # _OPTIONS = ['--behavior-delay-sleep', '1'] class TestHttpProxy(unittest.TestCase): def setUp(self): self._proxy_pid = start_proxy(options=_OPTIONS, log_level='error', log_output='/dev/null', protocol='http') self._web = start_simplehttp_server() time.sleep(.3) try: if self._web.poll(): raise ValueError("Could not start the proxy") self.client = Client() assert self.client.get_behavior() == 'dummy' except Exception: self.tearDown() raise def tearDown(self): stop_proxy(self._proxy_pid) self._web.terminate() def test_proxy(self): # let's do a few simple request first to make sure the proxy works self.assertEqual(self.client.get_behavior(), 'dummy') times = [] for i in range(10): start = time.time() try: res = requests.get(_PROXY) finally: times.append(time.time() - start) self.assertEqual(res.status_code, 200) fastest = min(times) # now let's try the various behaviors with self.client.with_behavior('blackout'): # oh look we broke it self.assertRaises(requests.ConnectionError, requests.get, _PROXY) self.assertEqual(self.client.get_behavior(), 'blackout') with self.client.with_behavior('delay'): # should work but be slower start = time.time() try: res = requests.get(_PROXY) finally: duration = time.time() - start self.assertEqual(res.status_code, 200) self.assertTrue(duration > fastest + 1) # we should be back to normal self.assertEqual(self.client.get_behavior(), 'dummy') res = requests.get(_PROXY) self.assertEqual(res.status_code, 200)

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python

import os import unittest2 as unittest import json import sys from sendgrid import SendGridClient, Mail class TestSendGrid(unittest.TestCase): def setUp(self): self.sg = SendGridClient(os.getenv('SG_USER'), os.getenv('SG_PWD')) @unittest.skipUnless(sys.version_info < (3, 0), 'only for python2') def test_unicode_recipients(self): recipients = [unicode('test@test.com'), unicode('guy@man.com')] m = Mail(to=recipients, subject='testing', html='awesome', from_email='from@test.com') mock = {'to[]': ['test@test.com', 'guy@man.com']} result = self.sg._build_body(m) self.assertEqual(result['to[]'], mock['to[]']) def test_send(self): m = Mail() m.add_to('John, Doe <john@email.com>') m.set_subject('test') m.set_html('WIN') m.set_text('WIN') m.set_from('doe@email.com') m.add_substitution('subKey', 'subValue') m.add_section('testSection', 'sectionValue') m.add_category('testCategory') m.add_unique_arg('testUnique', 'uniqueValue') m.add_filter('testFilter', 'filter', 'filterValue') m.add_attachment_stream('testFile', 'fileValue') url = self.sg._build_body(m) url.pop('api_key', None) url.pop('api_user', None) url.pop('date', None) test_url = json.loads(''' { "to[]": ["john@email.com"], "toname[]": ["John Doe"], "html": "WIN", "text": "WIN", "subject": "test", "files[testFile]": "fileValue", "from": "doe@email.com" } ''') test_url['x-smtpapi'] = json.dumps(json.loads(''' { "sub": { "subKey": ["subValue"] }, "section": { "testSection":"sectionValue" }, "category": ["testCategory"], "unique_args": { "testUnique":"uniqueValue" }, "filters": { "testFilter": { "settings": { "filter": "filterValue" } } } } ''')) self.assertEqual(url, test_url) if __name__ == '__main__': unittest.main()

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python

import os import torch from typing import Dict from catalyst.dl.fp16 import Fp16Wrap, copy_params, copy_grads from catalyst.dl.state import RunnerState from catalyst.dl.utils import UtilsFactory from catalyst.rl.registry import GRAD_CLIPPERS from .core import Callback from .utils import get_optimizer_momentum, scheduler_step class CheckpointCallback(Callback): """ Checkpoint callback to save/restore your model/criterion/optimizer/metrics. """ def __init__( self, save_n_best: int = 3, resume: str = None ): """ :param save_n_best: number of best checkpoint to keep :param resume: path to checkpoint to load and initialize runner state """ self.save_n_best = save_n_best self.resume = resume self.top_best_metrics = [] self._keys_from_state = ["resume"] @staticmethod def load_checkpoint(*, filename, state): if os.path.isfile(filename): print("=> loading checkpoint \"{}\"".format(filename)) checkpoint = UtilsFactory.load_checkpoint(filename) state.epoch = checkpoint["epoch"] UtilsFactory.unpack_checkpoint( checkpoint, model=state.model, criterion=state.criterion, optimizer=state.optimizer, scheduler=state.scheduler ) print( "loaded checkpoint \"{}\" (epoch {})".format( filename, checkpoint["epoch"] ) ) else: raise Exception("no checkpoint found at \"{}\"".format(filename)) def save_checkpoint( self, logdir, checkpoint, is_best, save_n_best=5, main_metric="loss", minimize_metric=True ): suffix = f"{checkpoint['stage']}.{checkpoint['epoch']}" filepath = UtilsFactory.save_checkpoint( logdir=f"{logdir}/checkpoints/", checkpoint=checkpoint, suffix=suffix, is_best=is_best, is_last=True ) checkpoint_metric = checkpoint["valid_metrics"][main_metric] self.top_best_metrics.append((filepath, checkpoint_metric)) self.top_best_metrics = sorted( self.top_best_metrics, key=lambda x: x[1], reverse=not minimize_metric ) if len(self.top_best_metrics) > save_n_best: last_item = self.top_best_metrics.pop(-1) last_filepath = last_item[0] os.remove(last_filepath) def pack_checkpoint(self, **kwargs): return UtilsFactory.pack_checkpoint(**kwargs) def on_stage_start(self, state): for key in self._keys_from_state: value = getattr(state, key, None) if value is not None: setattr(self, key, value) if self.resume is not None: self.load_checkpoint(filename=self.resume, state=state) def on_epoch_end(self, state: RunnerState): if state.stage.startswith("infer"): return checkpoint = self.pack_checkpoint( model=state.model, criterion=state.criterion, optimizer=state.optimizer, scheduler=state.scheduler, epoch_metrics=dict(state.metrics.epoch_values), valid_metrics=dict(state.metrics.valid_values), stage=state.stage, epoch=state.epoch ) self.save_checkpoint( logdir=state.logdir, checkpoint=checkpoint, is_best=state.metrics.is_best, save_n_best=self.save_n_best, main_metric=state.main_metric, minimize_metric=state.minimize_metric ) def on_stage_end(self, state): print("Top best models:") top_best_metrics_str = "\n".join( [ "{filepath}\t{metric:3.4f}".format( filepath=filepath, metric=metric ) for filepath, metric in self.top_best_metrics ] ) print(top_best_metrics_str) class OptimizerCallback(Callback): """ Optimizer callback, abstraction over optimizer step. """ def __init__( self, grad_clip_params: Dict = None, fp16_grad_scale: float = 128.0, accumulation_steps: int = 1, optimizer_key: str = None, loss_key: str = None ): """ @TODO: docs """ grad_clip_params = grad_clip_params or {} self.grad_clip_fn = GRAD_CLIPPERS.get_from_params(**grad_clip_params) self.fp16 = False self.fp16_grad_scale = fp16_grad_scale self.accumulation_steps = accumulation_steps self.optimizer_key = optimizer_key self.loss_key = loss_key self._optimizer_wd = 0 self._accumulation_counter = 0 def on_stage_start(self, state: RunnerState): self.fp16 = isinstance(state.model, Fp16Wrap) optimizer = state.get_key( key="optimizer", inner_key=self.optimizer_key ) assert optimizer is not None lr = optimizer.defaults["lr"] momentum = get_optimizer_momentum(optimizer) state.set_key(lr, "lr", inner_key=self.optimizer_key) state.set_key(momentum, "momentum", inner_key=self.optimizer_key) def on_epoch_start(self, state): optimizer = state.get_key( key="optimizer", inner_key=self.optimizer_key ) self._optimizer_wd = optimizer.param_groups[0].get("weight_decay", 0.0) optimizer.param_groups[0]["weight_decay"] = 0.0 @staticmethod def grad_step(*, optimizer, optimizer_wd=0, grad_clip_fn=None): for group in optimizer.param_groups: if optimizer_wd > 0: for param in group["params"]: param.data = param.data.add( -optimizer_wd * group["lr"], param.data ) if grad_clip_fn is not None: grad_clip_fn(group["params"]) optimizer.step() def on_batch_end(self, state): if not state.need_backward: return self._accumulation_counter += 1 if not self.fp16: model = state.model optimizer = state.get_key( key="optimizer", inner_key=self.optimizer_key ) loss = state.get_key(key="loss", inner_key=self.loss_key) loss.backward() if (self._accumulation_counter + 1) % self.accumulation_steps == 0: self.grad_step( optimizer=optimizer, optimizer_wd=self._optimizer_wd, grad_clip_fn=self.grad_clip_fn ) model.zero_grad() self._accumulation_counter = 0 else: model = state.model model.zero_grad() optimizer = state.get_key( key="optimizer", inner_key=self.optimizer_key ) loss = state.get_key(key="loss", inner_key=self.optimizer_key) scaled_loss = self.fp16_grad_scale * loss.float() scaled_loss.backward() master_params = list(optimizer.param_groups[0]["params"]) model_params = list( filter(lambda p: p.requires_grad, model.parameters()) ) copy_grads(source=model_params, target=master_params) for param in master_params: param.grad.data.mul_(1. / self.fp16_grad_scale) self.grad_step( optimizer=optimizer, optimizer_wd=self._optimizer_wd, grad_clip_fn=self.grad_clip_fn ) copy_params(source=master_params, target=model_params) torch.cuda.synchronize() def on_epoch_end(self, state): optimizer = state.get_key( key="optimizer", inner_key=self.optimizer_key ) optimizer.param_groups[0]["weight_decay"] = self._optimizer_wd class SchedulerCallback(Callback): def __init__( self, scheduler_key: str = None, mode: str = "epoch", reduce_metric: str = "loss" ): self.scheduler_key = scheduler_key self.mode = mode self.reduce_metric = reduce_metric def step(self, state): scheduler = state.get_key( key="scheduler", inner_key=self.scheduler_key ) lr, momentum = scheduler_step( scheduler=scheduler, valid_metric=state.metrics.valid_values.get( self.reduce_metric, None) ) state.set_key(lr, key="lr", inner_key=self.scheduler_key) state.set_key(momentum, key="momentum", inner_key=self.scheduler_key) def on_stage_start(self, state): scheduler = state.get_key( key="scheduler", inner_key=self.scheduler_key ) assert scheduler is not None def on_batch_end(self, state): if self.mode == "batch": self.step(state=state) def on_epoch_end(self, state): if self.mode == "epoch": self.step(state=state) class LossCallback(Callback): def __init__(self, input_key: str = "targets", output_key: str = "logits"): self.input_key = input_key self.output_key = output_key def on_stage_start(self, state): assert state.criterion is not None def on_batch_end(self, state): state.loss = state.criterion( state.output[self.output_key], state.input[self.input_key] ) class EarlyStoppingCallback(Callback): def __init__( self, patience: int, metric: str = "loss", minimize: bool = True, min_delta: float = 1e-6 ): self.best_score = None self.metric = metric self.patience = patience self.num_bad_epochs = 0 self.is_better = None if minimize: self.is_better = lambda score, best: score <= (best - min_delta) else: self.is_better = lambda score, best: score >= (best - min_delta) def on_epoch_end(self, state: RunnerState) -> None: if state.stage.startswith("infer"): return score = state.metrics.valid_values[self.metric] if self.best_score is None: self.best_score = score if self.is_better(score, self.best_score): self.num_bad_epochs = 0 self.best_score = score else: self.num_bad_epochs += 1 if self.num_bad_epochs >= self.patience: print(f"Early stop at {state.epoch} epoch") state.early_stop = True

nilq/baby-python

python

# O(N + M) time and space def sum_swap(a, b): a_sum = 0 a_s = {} b_sum = 0 b_s = {} for i, n in enumerate(a): a_sum += n a_s[n] = i for i, n in enumerate(b): b_sum += n b_s[n] = i diff = (a_sum - b_sum + 1) // 2 for i, n in enumerate(a): if n - diff in b_s: return i, b_s[n - diff] return None

nilq/baby-python

python

from django import template register = template.Library() @register.inclusion_tag('registration/error_messages.html') def error_messages(errors): return {'errors': errors}

nilq/baby-python

python

if __name__ == "__main__": user_inpu = int(input()) user_list = list(map(int, input().split())) user_list = set(user_list) n = int(input()) for _ in range(n): user_input = input().split() if user_input[0] == 'intersection_update': new_list = list(map(int, input().split())) user_list.intersection_update(new_list) elif user_input[0] == 'symmetric_difference_update': new_list2 = list(map(int, input().split())) user_list.symmetric_difference_update(new_list2) elif user_input[0] == 'difference_update': new_list3 = list(map(int, input().split())) user_list.difference_update(new_list3) elif user_input[0] == 'update': new_list4 = list(map(int, input().split())) user_list.update(new_list4) else: print('Something gone wrong!') a = sum(user_list) print(a)

nilq/baby-python

python

import serial, struct, traceback, sys from rhum.rhumlogging import get_logger from rhum.drivers.driver import Driver from rhum.drivers.enocean.messages.message import EnOceanMessage from rhum.drivers.enocean.messages.response.VersionMessage import VersionMessage from rhum.drivers.enocean.constants import PacketType, CommonCommandType, ResponseType from rhum.utils.crc8 import CRC8Utils import logging from rhum.drivers.enocean.messages.typingmessage import TypingMessage class EnOceanDriver(Driver): _logger = get_logger('rhum.driver.enocean.EnOceanDriver') def __init__(self, port='/dev/ttyAMA0', callback=None): super(EnOceanDriver, self).__init__(callback) # Initialize serial port self.__buffer = [] self.__port = port self._logger.debug('initialize connection to '.format(port)) self.__connection = serial.Serial(self.__port, 57600, timeout=0) def stop(self): Driver.stop(self) self.__connection.close() self._logger.info('EnOcean Driver on {0} stopped'.format(self.__port)) def run(self): self._logger.info('EnOcean Driver started on {0}'.format(self.__port)) while not self._stop.is_set(): # Read chars from serial port as hex numbers try: msg = self.parse() __type, __datas, __opts = msg._get() msg = TypingMessage.transform(__type, __datas, __opts) self._logger.info(msg) except serial.SerialException: self._logger.error('Serial port exception! (device disconnected or multiple access on port?)') break except Exception: exc_type, exc_value, exc_traceback = sys.exc_info() lines = traceback.format_exception(exc_type, exc_value, exc_traceback) for line in lines: self._logger.error(line) def test(self): msg = EnOceanMessage(PacketType.COMMON_COMMAND.value, [CommonCommandType.CD_R_VERSION.value]) buffer = msg.build() self._logger.debug('EnOcean Driver message {0}'.format(buffer)) self._logger.debug(self.__connection.isOpen()) #for index in range(len(buffer)): #byte by byte tx buffer = bytes(buffer) self._logger.debug('writing byte {0}'.format(buffer)) self.__connection.write(buffer) try: self._logger.debug('ask for parsing data') msg = self.parse() msg = VersionMessage(msg._get()[0], msg._get()[1], msg._get()[2]) self._logger.info('EnOcean Test Message (Version)') self._logger.info(msg) if msg.isResponse() and msg.getReturnCode() == ResponseType.RET_OK: return True except Exception: exc_type, exc_value, exc_traceback = sys.exc_info() lines = traceback.format_exception(exc_type, exc_value, exc_traceback) for line in lines: self._logger.error(line) self.__connection.close() return False def parse(self): Driver.parse(self) self._logger.debug('parsing data') msg = self._getSerialData() if isinstance(msg, EnOceanMessage): return msg raise Exception('No message parsed') def _getSerialData(self): self._logger.debug('searching for sync byte') s = 0 while s != b'\x55': if self.__connection.inWaiting() != 0: s = self.__connection.read(1) self._logger.debug('sync byte found') while self.__connection.inWaiting() < 5: () header = self.__connection.read(4) #read header fields headerCRC = self.__connection.read(1)[0] #read header crc field self._logger.debug('header reading : {0} and crc : {1}'.format(header, headerCRC)) if (CRC8Utils.calc(header) == headerCRC): self._logger.debug('header CRC OK') data_length, opt_length, msgType = struct.unpack("!HBB", header) self._logger.debug('data_length {0}; opt_length {1}; msg_type {2}'.format( data_length, opt_length, msgType )) totalDataLength = data_length + opt_length while self.__connection.inWaiting() < totalDataLength+1: () datas = self.__connection.read(data_length) opts = self.__connection.read(opt_length) dataCRC = self.__connection.read(1) self._logger.debug('datas {0}; opts {1}; dataCRC {2}'.format( datas, opts, dataCRC )) if(self._logger.isEnabledFor(logging.DEBUG)): msg = header msg += bytes({headerCRC}) msg += datas msg += opts msg += dataCRC self._logger.debug(msg) if (CRC8Utils.calc(datas+opts) == dataCRC[0]): return EnOceanMessage(msgType, datas, opts) return "Data CRC Failed" return "Header CRC Failed"

nilq/baby-python

python

from tkinter import Frame, Label, Button, messagebox, filedialog as fd from tkinter.constants import DISABLED, E, NORMAL, RAISED, SUNKEN, X import pandas import requests from threading import Thread import json from messages import messages from utils import config from ibuki import Ibuki class TopFrame(Frame): def __init__(self, parent): super().__init__(parent, highlightcolor='black', highlightthickness=2, padx=10, pady=10) self.btn_select_input = Button(self, text='Select input file and upload', width=22, bg='yellow', fg='blue', font=10, cursor='hand2', command=self.select_file) self.btn_select_input.grid(row=0, column=0) btn_view = Button(self, text='Extended warranty view', width=18, bg='yellow', fg='blue', font=10, padx=10, cursor='hand2', command=self.view_extended_warranty_customers) btn_view.grid(row=0, column=1) btn_send_sms = Button(self, text='Send SMS', width=10, bg='yellow', fg='red', font=10, padx=10, cursor='hand2', command=self.send_sms) btn_send_sms.grid(row=0, column=2, sticky=E) self.columnconfigure(2, weight=4) self.columnconfigure(1, weight=2) def select_file(self): filetypes = ( ('excel files', '*.xlsx'), ('All files', '*.*') ) try: select_folder = config.selectFolder or './' filename = fd.askopenfilename( title='Open customer data', initialdir=select_folder, filetypes=filetypes ) data = self.get_json(filename) self.enable_disable_button(self.btn_select_input, False) s = Thread(target=self.upload_data, args=(data,)) s.start() except(Exception) as error: messagebox.showerror( 'Error', error or messages.get('errSelectingFile')) self.enable_disable_button(self.btn_select_input, True) def get_json(self, filename): df = pandas.read_excel(filename, converters={'Purchased Date': str, 'Serial No': str}, header=1, usecols=['ASC Code', 'Customer Group', 'Job ID', 'Warranty Type', 'Warranty Category', 'Service Type', 'Product category name', 'Product sub category name', 'Set Model', 'Model Name', 'Serial No', 'Purchased Date', 'Customer Name', 'Mobile No', 'Postal Code', 'Address' ]) json_str = df.to_json(orient='index') js = json_str.encode('ascii', "ignore").decode() js = js.replace(u'\\ufeff', '').replace('\\/', '').replace("\'", '') jsn = json.loads(js) temp_data = [value for key, value in jsn.items()] filtered = filter( lambda value: ('TV' in value.get( 'Product category name', '').upper()) and (value.get('Purchased Date', None) is not None) and (value.get('Purchased Date', '').strip() != ''), temp_data) data = [item for item in filtered] return(data) def upload_data(self, data): try: upload_endpoint = config.uploadEndPoint requests.post(upload_endpoint, json=data) messagebox.showinfo("Success", messages['infoUploadSuccess']) self.enable_disable_button(self.btn_select_input, True) except(Exception) as error: messagebox.showerror('Error', error or 'Upload error') self.enable_disable_button(self.btn_select_input, True) def enable_disable_button(self, btn, isEnabled): btn.configure(relief=RAISED if isEnabled else SUNKEN) btn.configure(state=NORMAL if isEnabled else DISABLED) def view_extended_warranty_customers(self): Ibuki.emit('VIEW-EXTENDED-WARRANTY-CUSTOMERS', None) def send_sms(self): Ibuki.emit('SEND-SMS', None) def init_top_frame(root): try: frame_top = TopFrame(root) frame_top.pack(fill=X, padx=10, pady=10) except(Exception) as error: messagebox.showerror('Error', error or messages.get('errGeneric'))

nilq/baby-python

python

import os import torch, pickle from torch import nn import torch.nn.functional as F from dataloader import get_transform, get_dataset from model import get_model from utils import get_dirname_from_args # how are we going to name our checkpoint file def get_ckpt_path(args, epoch, loss): ckpt_name = get_dirname_from_args(args) # inside the ckpt path ckpt_path = args.ckpt_path / ckpt_name # if you are creating checkpoint file for the first time args.ckpt_path.mkdir(exist_ok=True) ckpt_path.mkdir(exist_ok=True) # checkpoint name is named after the loss and epoch loss = '{:.4f}'.format(loss) ckpt_path = ckpt_path / 'loss_{}_epoch_{}.pickle'.format(loss, epoch) # return the path name/address return ckpt_path # saving checkpoint file based on current status def save_ckpt(args, epoch, loss, model): # since checkpoint file is named based on epoch and loss, we state which epoch is being saved print('saving epoch {}'.format(epoch)) dt = { 'args': args, 'epoch': epoch, 'loss': loss, 'model': model.state.dict(), } ckpt_path = get_ckpt_path(args, epoch, loss) # name checkpoint file based on epoch and loss print("Saving checkpoint {}".format(ckpt_path)) # what checkpoint in what epoch torch.save(dt, str(ckpt_path)) # get a model from checkpoint file def get_model_ckpt(args): # if there is a model specified to be fetched ckpt_available = args.ckpt_name is not None if ckpt_available: name = '{}'.format(args.ckpt_name) # add * behind the name name = '{}*'.format(name) if not name.endswith('*') else name # now every name has * behind it ckpt_paths = sorted(args.ckpt_path.glob(name), reverse=False) assert len(ckpt_paths>0), "no ckpt candidate for {}".format(args.ckpt_path / args.ckpt_name) # full address is ckpt_path / ckpt_name ckpt_path = ckpt_paths[0] print("loading from {}".format(ckpt_path)) # load model from ckpt_path # 1. first update the arguments args.update(dt['args']) # 2. get model based on the arguments model = get_model(args) if ckpt_available: model.load_state_dict(dt['model']) # load other state in the model return args, model, ckpt_available

nilq/baby-python

python

import smtplib import datetime from email.mime.text import MIMEText from flask import current_app def notify(notifyType, message, all=True): # Only notify if less than 3 notifications in the past 24 hours sendNotification = True now = datetime.datetime.now() if current_app.config.get(notifyType) is None: # Create and track this notify type current_app.config[notifyType] = (now, 1) else: oneDayAgo = now - datetime.timedelta(days=1) previousNotification = current_app.config.get(notifyType) if previousNotification[0] > oneDayAgo and previousNotification[1] >= 3: # If last notify was newer than 1 day ago and there have been 3 notifications sendNotification = False elif previousNotification[0] > oneDayAgo and previousNotification[1] < 3: # If last notify was newer than 1 day ago and there less than 3 notifications current_app.config[notifyType] = ( now, previousNotification[1] + 1) else: # Last notification was more than 1 day ago start over current_app.config[notifyType] = (now, 1) if sendNotification: sender = current_app.config.get('SMTP_EMAIL') recipients = current_app.config.get('ALL_NOTIFY') if all else current_app.config.get('PRIMARY_NOTIFY') # Build email header msg = MIMEText(message) msg['Subject'] = 'Arduino Water Control Temperature Alert' msg['From'] = sender msg['To'] = ', '.join(recipients) server = smtplib.SMTP_SSL( current_app.config.get('SMTP_DOMAIN'), port=current_app.config.get('SMTP_PORT')) server.login(sender, current_app.config.get('SMTP_PASSWORD')) server.sendmail(sender, recipients, msg.as_string()) server.quit()

nilq/baby-python

python