xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

22f9996e35b6cbbaeea6e8c3929b7498dd603017

4,471

py

Python

ppq/utils/round.py

xiguadong/ppq

6c71adb3c2a8ca95967f101724b5e4b3e6f761ff

[ "Apache-2.0" ]

null

ppq/utils/round.py

xiguadong/ppq

6c71adb3c2a8ca95967f101724b5e4b3e6f761ff

[ "Apache-2.0" ]

null

ppq/utils/round.py

xiguadong/ppq

6c71adb3c2a8ca95967f101724b5e4b3e6f761ff

[ "Apache-2.0" ]

null

from decimal import ROUND_HALF_DOWN, ROUND_HALF_EVEN, ROUND_HALF_UP, Decimal from math import ceil, floor, log2 from typing import Union import torch from ppq.core import RoundingPolicy def ppq_numerical_round(value: float, policy: RoundingPolicy=RoundingPolicy.ROUND_HALF_EVEN) -> int: """ reference: https://en.wikipedia.org/wiki/Rounding decimal defination: - decimal.ROUND_CEILING (towards Infinity) - decimal.ROUND_DOWN (towards zero) - decimal.ROUND_FLOOR (towards -Infinity) - decimal.ROUND_HALF_DOWN (to nearest with ties going towards zero) - decimal.ROUND_HALF_EVEN (to nearest with ties going to nearest even integer) - decimal.ROUND_HALF_UP (to nearest with ties going away from zero) - decimal.ROUND_UP (away from zero) - decimal.ROUND_05UP (away from zero if last digit after rounding towards zero would have been 0 or 5; otherwise towards zero) Args: value (float): [description] policy (RoundingPolicy, optional): [description]. Defaults to RoundingPolicy.ROUND_HALF_EVEN. Raises: ValueError: [description] Returns: int: [description] """ assert isinstance(value, float), 'numerical round only takes effect on float number.' if policy == RoundingPolicy.ROUND_HALF_EVEN: return int(Decimal(value).quantize(exp=Decimal(1), rounding=ROUND_HALF_EVEN)) elif policy == RoundingPolicy.ROUND_HALF_UP: if value > 0: return int(Decimal(value).quantize(exp=Decimal(1), rounding=ROUND_HALF_UP)) else: return int(Decimal(value).quantize(exp=Decimal(1), rounding=ROUND_HALF_DOWN)) elif policy == RoundingPolicy.ROUND_HALF_DOWN: if value > 0: return int(Decimal(value).quantize(exp=Decimal(1), rounding=ROUND_HALF_DOWN)) else: return int(Decimal(value).quantize(exp=Decimal(1), rounding=ROUND_HALF_UP)) elif policy == RoundingPolicy.ROUND_HALF_TOWARDS_ZERO: return ppq_numerical_round(value, RoundingPolicy.ROUND_HALF_DOWN) elif policy == RoundingPolicy.ROUND_HALF_FAR_FORM_ZERO: return ppq_numerical_round(value, RoundingPolicy.ROUND_HALF_UP) elif policy == RoundingPolicy.ROUND_TO_NEAR_INT: if value > 0: return floor(value + 0.5) else: return ceil(value - 0.5) elif policy == RoundingPolicy.ROUND_UP: return ceil(value) else: raise ValueError('Unexpected rounding policy found.') def ppq_tensor_round(value: torch.Tensor, policy:RoundingPolicy=RoundingPolicy.ROUND_HALF_EVEN) -> torch.Tensor: """ reference: https://en.wikipedia.org/wiki/Rounding Args: value (torch.Tensor): [description] policy (RoundingPolicy, optional): [description]. Defaults to RoundingPolicy.ROUND_HALF_EVEN. Raises: ValueError: [description] Returns: torch.Tensor: [description] """ assert isinstance(value, torch.Tensor), 'tensor round only takes effect on torch tensor.' if policy == RoundingPolicy.ROUND_HALF_EVEN: # default rounding policy of torch is ROUND_TO_NEAR_EVEN # try this: print(torch.Tensor([1.5, 2.5, 3.5, 4.5]).round()) # However it may generate unexpected results due to version difference. return value.round() elif policy == RoundingPolicy.ROUND_UP: return value.ceil() elif policy == RoundingPolicy.ROUND_HALF_TOWARDS_ZERO: return torch.sign(value) * torch.ceil(value.abs() - 0.5) elif policy == RoundingPolicy.ROUND_HALF_FAR_FORM_ZERO: return torch.sign(value) * torch.floor(value.abs() + 0.5) elif policy == RoundingPolicy.ROUND_HALF_DOWN: return torch.ceil(value - 0.5) elif policy == RoundingPolicy.ROUND_HALF_UP: return torch.floor(value + 0.5) elif policy == RoundingPolicy.ROUND_TO_NEAR_INT: raise NotImplementedError(f'Torch Tensor can not use this rounding policy({policy}) try ROUND_HALF_EVEN instead.') else: raise ValueError('Unexpected rounding policy found.') def ppq_round_to_power_of_2(value: Union[float, int], policy: RoundingPolicy=RoundingPolicy.ROUND_UP) -> float: if value == 0: return 0 sign = 1 if value >= 0 else -1 assert isinstance(value, float) or isinstance(value, int), \ 'power-of-2 round only takes effect on float or int.' return sign * float(pow(2, ppq_numerical_round(log2(sign * value), policy=policy)))

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22f9fe832c0a98e82946d0744a46553bfba443ca

11,944

py

Python

python/repair/train.py

maropu/scavenger

03a935968f4aa507d4d98c8ca528195b770757d9

[ "Apache-2.0" ]

null

python/repair/train.py

maropu/scavenger

03a935968f4aa507d4d98c8ca528195b770757d9

[ "Apache-2.0" ]

2

2019-12-22T13:29:07.000Z

2020-01-07T11:55:41.000Z

python/repair/train.py

maropu/scavenger

03a935968f4aa507d4d98c8ca528195b770757d9

[ "Apache-2.0" ]

1

2020-10-26T20:07:28.000Z

#!/usr/bin/env python3 # # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You 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 import time import numpy as np # type: ignore[import] import pandas as pd # type: ignore[import] from collections import namedtuple from typing import Any, Dict, List, Optional, Tuple from repair.utils import elapsed_time, get_option_value, setup_logger _logger = setup_logger() # List of internal configurations _option = namedtuple('_option', 'key default_value type_class validator err_msg') _opt_boosting_type = \ _option('model.lgb.boosting_type', 'gbdt', str, lambda v: v in ['gbdt', 'dart', 'goss', 'rf'], "`{}` should be in ['gbdt', 'dart', 'goss', 'rf']") _opt_class_weight = \ _option('model.lgb.class_weight', 'balanced', str, None, None) _opt_learning_rate = \ _option('model.lgb.learning_rate', 0.01, float, lambda v: v > 0.0, '`{}` should be positive') _opt_max_depth = \ _option('model.lgb.max_depth', 7, int, None, None) _opt_max_bin = \ _option('model.lgb.max_bin', 255, int, None, None) _opt_reg_alpha = \ _option('model.lgb.reg_alpha', 0.0, float, lambda v: v >= 0.0, '`{}` should be greater than or equal to 0.0') _opt_min_split_gain = \ _option('model.lgb.min_split_gain', 0.0, float, lambda v: v >= 0.0, '`{}` should be greater than or equal to 0.0') _opt_n_estimators = \ _option('model.lgb.n_estimators', 300, int, lambda v: v > 0, '`{}` should be positive') _opt_importance_type = \ _option('model.lgb.importance_type', 'gain', str, lambda v: v in ['split', 'gain'], "`{}` should be in ['split', 'gain']") _opt_n_splits = \ _option('model.cv.n_splits', 3, int, lambda v: v >= 3, '`{}` should be greater than 2') _opt_timeout = \ _option('model.hp.timeout', 0, int, None, None) _opt_max_evals = \ _option('model.hp.max_evals', 100000000, int, lambda v: v > 0, '`{}` should be positive') _opt_no_progress_loss = \ _option('model.hp.no_progress_loss', 50, int, lambda v: v > 0, '`{}` should be positive') train_option_keys = [ _opt_boosting_type.key, _opt_class_weight.key, _opt_learning_rate.key, _opt_max_depth.key, _opt_max_bin.key, _opt_reg_alpha.key, _opt_min_split_gain.key, _opt_n_estimators.key, _opt_importance_type.key, _opt_n_splits.key, _opt_timeout.key, _opt_max_evals.key, _opt_no_progress_loss.key ] @elapsed_time # type: ignore def _build_lgb_model(X: pd.DataFrame, y: pd.Series, is_discrete: bool, num_class: int, n_jobs: int, opts: Dict[str, str]) -> Tuple[Any, float]: import lightgbm as lgb # type: ignore[import] def _get_option_value(*args) -> Any: # type: ignore return get_option_value(opts, *args) if is_discrete: objective = "binary" if num_class <= 2 else "multiclass" else: objective = "regression" fixed_params = { "boosting_type": _get_option_value(*_opt_boosting_type), "objective": objective, "class_weight": _get_option_value(*_opt_class_weight), "learning_rate": _get_option_value(*_opt_learning_rate), "max_depth": _get_option_value(*_opt_max_depth), "max_bin": _get_option_value(*_opt_max_bin), "reg_alpha": _get_option_value(*_opt_reg_alpha), "min_split_gain": _get_option_value(*_opt_min_split_gain), "n_estimators": _get_option_value(*_opt_n_estimators), "importance_type": _get_option_value(*_opt_importance_type), "random_state": 42, "n_jobs": n_jobs } # Set `num_class` only in the `multiclass` mode if objective == "multiclass": fixed_params["num_class"] = num_class model_class = lgb.LGBMClassifier if is_discrete \ else lgb.LGBMRegressor def _create_model(params: Dict[str, Any]) -> Any: # Some params must be int for k in ["num_leaves", "subsample_freq", "min_child_samples"]: if k in params: params[k] = int(params[k]) p = copy.deepcopy(fixed_params) p.update(params) return model_class(**p) from hyperopt import hp, tpe, Trials # type: ignore[import] from hyperopt.early_stop import no_progress_loss # type: ignore[import] from hyperopt.fmin import fmin # type: ignore[import] from sklearn.model_selection import ( # type: ignore[import] cross_val_score, KFold, StratifiedKFold ) # TODO: Temporality supress `sklearn.model_selection` user's warning import warnings warnings.simplefilter("ignore", UserWarning) # Forcibly disable INFO-level logging in the `hyperopt` module from logging import getLogger, WARN getLogger("hyperopt").setLevel(WARN) param_space = { "num_leaves": hp.quniform("num_leaves", 2, 100, 1), "subsample": hp.uniform("subsample", 0.5, 1.0), "subsample_freq": hp.quniform("subsample_freq", 1, 20, 1), "colsample_bytree": hp.uniform("colsample_bytree", 0.01, 1.0), "min_child_samples": hp.quniform("min_child_samples", 1, 50, 1), "min_child_weight": hp.loguniform("min_child_weight", -3, 1), "reg_lambda": hp.loguniform("reg_lambda", -2, 3) } scorer = "f1_macro" if is_discrete else "neg_mean_squared_error" n_splits = int(_get_option_value(*_opt_n_splits)) cv = StratifiedKFold(n_splits=n_splits, shuffle=True) if is_discrete \ else KFold(n_splits=n_splits, shuffle=True) def _objective(params: Dict[str, Any]) -> float: model = _create_model(params) fit_params: Dict[str, str] = { # TODO: Raises an error if a single regressor is used # "categorical_feature": "auto", } try: # TODO: Replace with `lgb.cv` to remove the `sklearn` dependency scores = cross_val_score( model, X, y, scoring=scorer, cv=cv, fit_params=fit_params, n_jobs=n_jobs) return -scores.mean() # it might throw an exception because `y` contains # previously unseen labels. except Exception as e: _logger.warning(f"{e.__class__}: {e}") return 0.0 def _early_stop_fn() -> Any: no_progress_loss_fn = no_progress_loss(int(_get_option_value(*_opt_no_progress_loss))) timeout = int(_get_option_value(*_opt_timeout)) if timeout <= 0: return no_progress_loss_fn # Set base time for budget mechanism start_time = time.time() def timeout_fn(trials, best_loss=None, iteration_no_progress=0): # type: ignore no_progress_loss, meta = no_progress_loss_fn(trials, best_loss, iteration_no_progress) to = time.time() - start_time > timeout return no_progress_loss or to, meta return timeout_fn try: trials = Trials() max_evals = int(_get_option_value(*_opt_max_evals)) best_params = fmin( fn=_objective, space=param_space, algo=tpe.suggest, trials=trials, max_evals=max_evals, early_stop_fn=_early_stop_fn(), rstate=np.random.RandomState(42), show_progressbar=False, verbose=False) _logger.info("hyperopt: #eval={}/{}".format(len(trials.trials), max_evals)) # Builds a model with `best_params` # TODO: Could we extract constraint rules (e.g., FD and CFD) from built statistical models? model = _create_model(best_params) model.fit(X, y) def _feature_importances() -> List[Any]: f = filter(lambda x: x[1] > 0.0, zip(model.feature_name_, model.feature_importances_)) return list(sorted(f, key=lambda x: x[1], reverse=True)) _logger.debug(f"lightgbm: feature_importances={_feature_importances()}") sorted_lst = sorted(trials.trials, key=lambda x: x['result']['loss']) min_loss = sorted_lst[0]['result']['loss'] return model, -min_loss except Exception as e: _logger.warning(f"Failed to build a stat model because: {e}") return None, 0.0 def build_model(X: pd.DataFrame, y: pd.Series, is_discrete: bool, num_class: int, n_jobs: int, opts: Dict[str, str]) -> Tuple[Any, float]: return _build_lgb_model(X, y, is_discrete, num_class, n_jobs, opts) def compute_class_nrow_stdv(y: pd.Series, is_discrete: bool) -> Optional[float]: from collections import Counter return float(np.std(list(map(lambda x: x[1], Counter(y).items())))) if is_discrete else None def rebalance_training_data(X: pd.DataFrame, y: pd.Series, target: str) -> Tuple[pd.DataFrame, pd.Series]: # Uses median as the number of training rows for each class from collections import Counter prev_nrows = len(X) prev_stdv = compute_class_nrow_stdv(y, is_discrete=True) hist = dict(Counter(y).items()) # type: ignore median = int(np.median([count for key, count in hist.items()])) def _split_data(df: pd.DataFrame) -> Tuple[pd.DataFrame, pd.Series]: X = df[df.columns[df.columns != target]] # type: ignore y = df[target] return X, y # Filters out rows having NaN values for over-sampling X[target] = y X_notna, y_notna = _split_data(X.dropna()) X_na, y_na = _split_data(X[X.isnull().any(axis=1)]) # Over-sampling for training data whose row number is smaller than the median value hist_na = dict(Counter(y_na).items()) # type: ignore smote_targets = [] kn = 5 # `k_neighbors` default value in `SMOTEN` for key, count in hist.items(): if count < median: nna = hist_na[key] if key in hist_na else 0 if count - nna > kn: smote_targets.append((key, median - nna)) else: _logger.warning(f"Over-sampling of '{key}' in y='{target}' failed because the number of the clean rows " f"is too small: {count - nna}") if len(smote_targets) > 0: from imblearn.over_sampling import SMOTEN sampler = SMOTEN(random_state=42, sampling_strategy=dict(smote_targets), k_neighbors=kn) X_notna, y_notna = sampler.fit_resample(X_notna, y_notna) X = pd.concat([X_notna, X_na]) y = pd.concat([y_notna, y_na]) # Under-sampling for training data whose row number is greater than the median value rus_targets = list(map(lambda x: (x[0], median), filter(lambda x: x[1] > median, hist.items()))) if len(rus_targets) > 0: # NOTE: The other smarter implementations can skew samples if there are many rows having NaN values, # so we just use `RandomUnderSampler` here. from imblearn.under_sampling import RandomUnderSampler sampler = RandomUnderSampler(random_state=42, sampling_strategy=dict(rus_targets)) X, y = sampler.fit_resample(X, y) _logger.info("Rebalanced training data (y={}, median={}): #rows={}(stdv={}) -> #rows={}(stdv={})".format( target, median, prev_nrows, prev_stdv, len(X), compute_class_nrow_stdv(y, is_discrete=True))) _logger.debug("class hist: {} => {}".format(hist.items(), Counter(y).items())) return X, y

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0

null

0

1

0

22fa19437d01af6a56a8a1b30127d97248a1bdcd

519

py

Python

howl/roomsensor/urls.py

volzotan/django-howl

3b11c530da95d152844934da09592619b3d4497f

[ "MIT" ]

null

howl/roomsensor/urls.py

volzotan/django-howl

3b11c530da95d152844934da09592619b3d4497f

[ "MIT" ]

null

howl/roomsensor/urls.py

volzotan/django-howl

3b11c530da95d152844934da09592619b3d4497f

[ "MIT" ]

null

from django.conf.urls import patterns, url from roomsensor import views urlpatterns = patterns('', url(r'^$', views.index, name='roomsensor'), # ex: /roomsensor/name/ url(r'^(?P<roomsensor_name>\w+)/$', views.display, name='roomsensor_display'), url(r'^(?P<roomsensor_name>\w+)/read/$', views.read, name='roomsensor_read'), # JSON data for graph creation url(r'^(?P<roomsensor_name>\w+)/rawdata/(?P<datapoints>\d+)/(?P<compression_factor>\d+)/$', views.rawdata, name='roomsensor_rawdata'), )

37.071429

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519

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0.046784

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0.131579

0.175439

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519

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false

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0.25

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null

0

null

0

1

0

22fc97fb3dafaa3d0c68a5549bbe8a39af3d15d4

7,031

py

Python

app.py

kosovojs/wikibooster

70a9d9d7bf41be9fa5e58d40fba216d9b6df008d

[ "MIT" ]

null

app.py

kosovojs/wikibooster

70a9d9d7bf41be9fa5e58d40fba216d9b6df008d

[ "MIT" ]

17

2019-07-08T15:32:18.000Z

2021-01-03T10:30:55.000Z

app.py

kosovojs/wikibooster

70a9d9d7bf41be9fa5e58d40fba216d9b6df008d

[ "MIT" ]

1

2019-08-28T21:23:48.000Z

import flask from flask import Flask from flask import jsonify from flask import request from flask_cors import CORS, cross_origin from flask import render_template import mwoauth import requests_oauthlib import os import yaml import mwapi from tasks.main import Tasks from save import Save from db import DB from typo.fix import TypoFix app = Flask(__name__, static_folder="./frontend/build/static", template_folder="./frontend/build") #app = Flask(__name__) CORS(app) user_agent = 'WikiBooster' __dir__ = os.path.dirname(__file__) configFile = open(os.path.join(__dir__, 'config.yaml')) app.config.update(yaml.safe_load(configFile)) def authenticated_session(domain = 'meta.wikimedia.org'): if 'oauth_access_token' in flask.session: access_token = mwoauth.AccessToken(**flask.session['oauth_access_token']) auth = requests_oauthlib.OAuth1(client_key=app.config['CONSUMER_KEY'], client_secret=app.config['CONSUMER_SECRET'], resource_owner_key=access_token.key, resource_owner_secret=access_token.secret) return mwapi.Session(host='https://'+domain, auth=auth, user_agent=user_agent) else: return None def getUserInfo(domain = 'meta.wikimedia.org'): session = authenticated_session(domain) if not session: return None, None, {'status':'error','message':'not logged in'} try: userinfo = session.get(action='query', meta='userinfo', uiprop=['groups', 'centralids'])['query']['userinfo'] return True, session, {'status':'ok','username':userinfo['name']} except mwapi.errors.APIError as e: if e.code == 'mwoauth-invalid-authorization-invalid-user': # user is viewing a batch for a wiki where they do not have a local user account # treat as anonymous on the local wiki, but query Meta to find out if they’re a steward return None, None, {'status':'error','message':'server error'} else: raise e return None, None, {'status':'error','message':'server error'} @app.route('/', methods=['GET']) def index_page(): return render_template('index.html') #http://127.0.0.1:5000/task/lvwiki/1/Helēna Mārnija @app.route('/task/<wiki>/<name>/<page>', methods=['GET']) def getTaskResult(wiki,name,page): tasks = Tasks(wiki) articleInfo = tasks.getDataForTask(name,page) return jsonify(articleInfo) @app.route('/testing', methods=['GET']) def runTests(): tasks = Tasks('lvwiki') articleInfo = tasks.runTests() return articleInfo @app.route('/wikis', methods=['GET']) def listWikis(): db = DB() wikis = db.getAvailableWikis() return jsonify(wikis) @app.route('/tasks/<wiki>', methods=['GET']) def listJobs(wiki): db = DB() articles = db.getTasksForWiki(wiki) return jsonify(articles) @app.route('/task/<wiki>/<task_id>/articles', methods=['GET']) def listArticles(wiki,task_id): db = DB() articles = db.get_articles_for_task(wiki,task_id) return jsonify(articles) # @app.route('/typo/<wiki>', methods=['GET']) def listTypos(wiki): db = DB() typos = db.getTyposForWiki(wiki) return jsonify(typos) @app.route('/typo/articles', methods=['GET']) def typo_list_for_wiki(): db = DB() wiki = 'lvwiki' typos = db.get_typo_articles(wiki) return jsonify(typos) @app.route('/typo/fix/<article>', methods=['GET']) def fix_typos(article): db = DB() typoFixer = TypoFix() res = typoFixer.getData('lvwiki', article, db) return jsonify(res) @app.route('/rules/<wiki>', methods=['GET']) def listRules(wiki): db = DB() rules = db.getRulesForWiki(wiki) return jsonify(rules) @app.route('/save', methods=['POST']) def doSave(): req = request.get_json() wiki = req['wiki'] domain = "{}.wikipedia.org".format(wiki) userStatus, session, respFromGettingUserInfo = getUserInfo(domain) if not userStatus: return jsonify(respFromGettingUserInfo) # userName = respFromGettingUserInfo['username'] if 'username' in respFromGettingUserInfo else respFromGettingUserInfo['message'] job = req['job'] article = req['article'] result = req['result'] wikitext = req['wikitext'] status = req['status'] handlingSave = Save(session) respFromSave = handlingSave.saveArticle(job,article,result,wikitext,status,userName) return jsonify(respFromSave) @app.route('/save_typo', methods=['POST']) def doSaveTypo(): req = request.get_json() wiki = req['wiki'] domain = "{}.wikipedia.org".format(wiki.replace('wiki','')) userStatus, session, respFromGettingUserInfo = getUserInfo(domain) if not userStatus: return jsonify(respFromGettingUserInfo) userName = respFromGettingUserInfo['username'] if 'username' in respFromGettingUserInfo else respFromGettingUserInfo['message'] active = req['active'] case = req['case'] comment = req['comment'] dumpsearch = req['dumpsearch'] minor = req['minor'] name = req['name'] regex = req['regex'] replace_with = req['replace_with'] search_for = req['search_for'] test_cases = req['test_cases'] whole = req['whole'] id = req['id'] db = DB() typoData = db.saveTypo(active,case,comment,dumpsearch,minor,name,regex,replace_with,search_for,test_cases,whole,wiki,userName,id) return jsonify({'status':'ok', 'info':typoData}) @app.route('/save_rule', methods=['POST']) def saveRule(): req = request.get_json() wiki = req['wiki'] domain = "{}.wikipedia.org".format(wiki.replace('wiki','')) userStatus, session, respFromGettingUserInfo = getUserInfo(domain) if not userStatus: return jsonify(respFromGettingUserInfo) userName = respFromGettingUserInfo['username'] if 'username' in respFromGettingUserInfo else respFromGettingUserInfo['message'] wiki = req['wiki'] rule_name = req['rule_name'] rule_object = req['rule_object'] rule = req['rule'] result = req['result'] id = req['id'] db = DB() db.saveRule(id, wiki, rule_name, rule_object, rule, result) return jsonify({'status':'ok'}) @app.route('/info', methods=['GET']) def user_info(): userStatus, _,respFromGettingUserInfo = getUserInfo() return jsonify(respFromGettingUserInfo) @app.route('/login') def login(): consumer_token = mwoauth.ConsumerToken(app.config['CONSUMER_KEY'], app.config['CONSUMER_SECRET']) redirect, request_token = mwoauth.initiate('https://meta.wikimedia.org/w/index.php', consumer_token, user_agent=user_agent) flask.session['oauth_request_token'] = dict(zip(request_token._fields, request_token)) return flask.redirect(redirect) @app.route('/oauth-callback') def oauth_callback(): consumer_token = mwoauth.ConsumerToken(app.config['CONSUMER_KEY'], app.config['CONSUMER_SECRET']) request_token = mwoauth.RequestToken(**flask.session.pop('oauth_request_token')) access_token = mwoauth.complete('https://meta.wikimedia.org/w/index.php', consumer_token, request_token, flask.request.query_string, user_agent=user_agent) flask.session['oauth_access_token'] = dict(zip(access_token._fields, access_token)) return flask.redirect(flask.url_for('index_page')) @app.route('/logout') def logout(): """Log the user out by clearing their session.""" flask.session.clear() return flask.redirect(flask.url_for('index_page')) if __name__ == '__main__': app.run(debug=True)

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0.729768

911

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5.493963

0.232711

0.027173

0.028571

0.011988

0.314685

0.281718

0.275325

0.247752

0.214985

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0.001926

0.114066

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0.340909

0

null

0

null

0

1

0

22fcb38b78558c9add6900dca954fd92ecf359b7

1,483

py

Python

pre_embed.py

shelleyyyyu/few_shot

0fe54444e820fe3201927e6363682913b6d61028

[ "Apache-2.0" ]

253

2018-08-29T18:59:00.000Z

2022-03-15T04:53:47.000Z

pre_embed.py

shelleyyyyu/few_shot

0fe54444e820fe3201927e6363682913b6d61028

[ "Apache-2.0" ]

18

2018-10-24T09:49:44.000Z

2022-03-31T14:39:37.000Z

pre_embed.py

shelleyyyyu/few_shot

0fe54444e820fe3201927e6363682913b6d61028

[ "Apache-2.0" ]

38

2018-10-17T07:43:25.000Z

2022-03-05T12:20:33.000Z

import numpy as np from collections import defaultdict, Counter import random import json from tqdm import tqdm def transX(dataset): rel2id = json.load(open(dataset + '/relation2ids')) ent2id = json.load(open(dataset + '/ent2ids')) with open('../Fast-TransX/' + dataset + '_base/entity2id.txt', 'w') as g1: num_ents = len(ent2id.keys()) g1.write(str(num_ents) + '\n') for k, v in ent2id.items(): g1.write(k + '\t' + str(v) + '\n') with open('../Fast-TransX/' + dataset + '_base/relation2id.txt', 'w') as g1: num_rels = len(rel2id.keys()) g1.write(str(num_rels) + '\n') for k, v in rel2id.items(): g1.write(k + '\t' + str(v) + '\n') file_name = dataset + '/path_graph' train_triples = [] with open(file_name) as f: lines = f.readlines() for line in tqdm(lines): e1 = line.split('\t')[0] e2 = line.rstrip().split('\t')[2] rel = line.split('\t')[1] train_triples.append([e1,rel,e2]) train_triples.append([e2,rel+'_inv',e1]) with open('../Fast-TransX/' + dataset + '_base/train2id.txt', 'w') as g3: num_triples = len(train_triples) g3.write(str(num_triples) + '\n') for triple in train_triples: e1, rel, e2 = triple g3.write(str(ent2id[e1]) + '\t' + str(ent2id[e2]) + '\t' + str(rel2id[rel]) + '\n') if __name__ == '__main__': transX('Wiki')

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0

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0.268375

1,483

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null

0

null

0

1

0

22fd80b994ca4f5c482661c444d74e7a50232ab0

7,673

py

Python

botc/gamemodes/troublebrewing/FortuneTeller.py

Xinverse/BOTC-Bot

1932c649c81a5a1eab735d7abdee0761c2853940

[ "MIT" ]

1

2020-06-21T17:20:17.000Z

botc/gamemodes/troublebrewing/FortuneTeller.py

BlueLenz/Blood-on-the-Clocktower-Storyteller-Discord-Bot

1932c649c81a5a1eab735d7abdee0761c2853940

[ "MIT" ]

1

2020-07-07T03:47:44.000Z

botc/gamemodes/troublebrewing/FortuneTeller.py

BlueLenz/Blood-on-the-Clocktower-Storyteller-Discord-Bot

1932c649c81a5a1eab735d7abdee0761c2853940

[ "MIT" ]

1

2022-02-18T00:42:19.000Z

"""Contains the Fortune Teller Character class""" import json import random import discord import datetime from botc import Action, ActionTypes, Townsfolk, Character, Storyteller, RedHerring, \ RecurringAction, Category, StatusList from botc.BOTCUtils import GameLogic from ._utils import TroubleBrewing, TBRole import globvars with open('botc/gamemodes/troublebrewing/character_text.json') as json_file: character_text = json.load(json_file)[TBRole.fortuneteller.value.lower()] with open('botutils/bot_text.json') as json_file: bot_text = json.load(json_file) butterfly = bot_text["esthetics"]["butterfly"] with open('botc/game_text.json') as json_file: strings = json.load(json_file) fortune_teller_nightly = strings["gameplay"]["fortune_teller_nightly"] copyrights_str = strings["misc"]["copyrights"] yes = strings["gameplay"]["yes"] no = strings["gameplay"]["no"] good_link = strings["images"]["good"] evil_link = strings["images"]["evil"] class FortuneTeller(Townsfolk, TroubleBrewing, Character, RecurringAction): """Fortune Teller: Each night, choose 2 players: you learn if either is a Demon. There is 1 good player that registers falsely to you. ===== FORTUNE TELLER ===== true_self = fortune teller ego_self = fortune teller social_self = fortune teller commands: - read <player> and <player> initialize setup? -> NO initialize role? -> YES ----- First night START: override first night instruction? -> YES # default is to send instruction string only => Send query for "read" command ----- Regular night START: override regular night instruction? -> YES # default is to send nothing => Send query for "read" command """ def __init__(self): Character.__init__(self) TroubleBrewing.__init__(self) Townsfolk.__init__(self) self._desc_string = character_text["description"] self._examp_string = character_text["examples"] self._instr_string = character_text["instruction"] self._lore_string = character_text["lore"] self._brief_string = character_text["brief"] self._action = character_text["action"] self._art_link = "https://bloodontheclocktower.com/wiki/images/3/3a/Fortune_Teller_Token.png" self._art_link_cropped = "https://imgur.com/23ZXb1y.png" self._wiki_link = "https://bloodontheclocktower.com/wiki/Fortune_Teller" self._role_enum = TBRole.fortuneteller self._emoji = "<:tbfortuneteller:739317350733578280>" def create_n1_instr_str(self): """Create the instruction field on the opening dm card""" # First line is the character instruction string msg = f"{self.emoji} {self.instruction}" addendum = character_text["n1_addendum"] # Some characters have a line of addendum if addendum: with open("botutils/bot_text.json") as json_file: bot_text = json.load(json_file) scroll_emoji = bot_text["esthetics"]["scroll"] msg += f"\n{scroll_emoji} {addendum}" return msg def add_action_field_n1(self, embed_obj): """Send the stats list n1""" msg = self.action msg += globvars.master_state.game.create_sitting_order_stats_string() embed_obj.add_field(name = butterfly + " **「 Your Action 」**", value = msg, inline = False) return embed_obj def exec_init_role(self, setup): """Assign one of the townsfolks or outsiders as a red herring""" possibilities = setup.townsfolks + setup.outsiders chosen = random.choice(possibilities) chosen.add_status_effect(RedHerring(Storyteller(), chosen)) globvars.logging.info(f">>> Fortune Teller [exec_init_role] Set red herring to {str(chosen)}") def has_finished_night_action(self, player): """Return True if fortune teller has submitted the read action""" if player.is_alive(): current_phase_id = globvars.master_state.game._chrono.phase_id received_action = player.action_grid.retrieve_an_action(current_phase_id) return received_action is not None and received_action.action_type == ActionTypes.read return True @GameLogic.requires_two_targets @GameLogic.requires_different_targets @GameLogic.changes_not_allowed async def register_read(self, player, targets): """Read command""" # Must be 2 targets assert len(targets) == 2, "Received a number of targets different than 2 for fortune teller 'read'" action = Action(player, targets, ActionTypes.read, globvars.master_state.game._chrono.phase_id) player.action_grid.register_an_action(action, globvars.master_state.game._chrono.phase_id) msg = butterfly + " " + character_text["feedback"].format(targets[0].game_nametag, targets[1].game_nametag) await player.user.send(msg) async def exec_read(self, fortune_teller_player, read_player_1, read_player_2): """Execute the read action (night ability interaction)""" if fortune_teller_player.is_alive(): # Correct info if not fortune_teller_player.is_droisoned(): response = read_player_1.role.social_self.category == Category.demon or \ read_player_2.role.social_self.category == Category.demon or \ read_player_1.has_status_effect(StatusList.red_herring) or \ read_player_2.has_status_effect(StatusList.red_herring) # Droisoned info else: response = random.choice((True, False)) reply = yes if response else no link = evil_link if response else good_link recipient = fortune_teller_player.user msg = f"***{recipient.name}#{recipient.discriminator}***, the **{self.name}**:" msg += "\n" msg += self.emoji + " " + self.instruction msg += "\n" msg += fortune_teller_nightly.format(reply) embed = discord.Embed(description = msg) embed.set_thumbnail(url = link) embed.set_footer(text = copyrights_str) embed.timestamp = datetime.datetime.utcnow() try: await recipient.send(embed = embed) except discord.Forbidden: pass # If the fortune teller player is dead, then nothing is sent to them else: pass async def process_night_ability(self, player): """Process night actions for the fortune teller character. @player : the Fortune Teller player (Player object) """ phase = globvars.master_state.game._chrono.phase_id action = player.action_grid.retrieve_an_action(phase) # The Fortune teller has submitted an action. We call the execution function immediately if action: assert action.action_type == ActionTypes.read, f"Wrong action type {action} in fortune teller" targets = action.target_player read_player_1 = targets[0] read_player_2 = targets[1] await self.exec_read(player, read_player_1, read_player_2) # The fortune teller has not submitted an action. We will not randomize the action since # the reading ability is a "priviledged" ability else: pass

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115

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898

7,673

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0

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0

null

0

null

0

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0

22fdcdf03da29d4d6e3f5e50e7e03925c3c15cdd

10,849

py

Python

src/schmetterling/build/tests/test_maven.py

bjuvensjo/schmetterling

0cdbfe4f379a081d9d4711dd21866b90983365cf

[ "Apache-2.0" ]

null

src/schmetterling/build/tests/test_maven.py

bjuvensjo/schmetterling

0cdbfe4f379a081d9d4711dd21866b90983365cf

[ "Apache-2.0" ]

null

src/schmetterling/build/tests/test_maven.py

bjuvensjo/schmetterling

0cdbfe4f379a081d9d4711dd21866b90983365cf

[ "Apache-2.0" ]

null

from unittest.mock import call, MagicMock, patch from schmetterling.build.maven import build_multi_modules from schmetterling.build.maven import create_build_result from schmetterling.build.maven import create_command from schmetterling.build.maven import create_multi_modules from schmetterling.build.maven import create_state from schmetterling.build.maven import get_maven_infos from schmetterling.build.maven import get_maven_repos from schmetterling.build.maven import get_multi_modules from schmetterling.build.state import BuildState, Build from schmetterling.setup.state import Repo def test_build_multi_modules(): mm = [ { 'updated': 'updated1', 'pom_dir': 'pom_dir1', 'coordinates': 'coordinates1' }, { 'updated': 'updated2', 'pom_dir': 'pom_dir2', 'coordinates': 'coordinates2' }, ] with patch( 'schmetterling.build.maven.create_command', return_value='create_command') as m_create_command, patch( 'schmetterling.build.maven.run_command') as m_run_command, patch( 'schmetterling.build.maven.create_build_result', return_value=[['success_coordinates'], [ 'failure_coordinates' ]]) as m_create_build_result: assert ( ['success_coordinates', 'success_coordinates'], ['failure_coordinates', 'failure_coordinates'], ) == build_multi_modules(mm, 'repository_dir', 'settings_file', 'logback_file') assert [ call('updated1', 'pom_dir1/mvn.log', 'repository_dir', 'settings_file', 'logback_file'), call('updated2', 'pom_dir2/mvn.log', 'repository_dir', 'settings_file', 'logback_file') ] == m_create_command.mock_calls assert [ call('create_command', cwd='pom_dir1'), call('create_command', cwd='pom_dir2') ] == m_run_command.mock_calls assert [ call('coordinates1', 'updated1', 'pom_dir1/mvn.log'), call('coordinates2', 'updated2', 'pom_dir2/mvn.log') ] == m_create_build_result.mock_calls def test_create_command(): assert str('mvn -Dmaven.repo.local=repository ' '-s settings.xml ' '-DcreateChecksum=true ' '-Dfile.encoding=UTF-8 ' '-Dsun.jnu.encoding=UTF-8 ' '-Dlogback.configurationFile=logback.xml ' '-B -amd -pl mygroup:app.admin,mygroup:app.sign ' 'clean install javadoc:jar source:jar ' '--fail-at-end | tee mvn.log') == create_command( [{ 'artifact_id': 'app.admin', 'group_id': 'mygroup', }, { 'artifact_id': 'app.sign', 'group_id': 'mygroup', }], 'mvn.log', 'repository', 'settings.xml', 'logback.xml') @patch( 'schmetterling.build.maven.get_summary', return_value=(['mygroup:app.admin'], ['app.sign'])) def test_create_build_result(mock_get_summary): assert ( [ { 'artifact_id': 'app.admin', 'group_id': 'mygroup', }, ], [ { 'artifact_id': 'app.sign', 'group_id': 'mygroup', }, { 'artifact_id': 'pipeline.env', 'group_id': 'mygroup', }, ], ) == create_build_result( [ { 'artifact_id': 'app.admin', 'group_id': 'mygroup', }, { 'artifact_id': 'app.sign', 'group_id': 'mygroup', }, { 'artifact_id': 'pipeline.env', 'group_id': 'mygroup', }, { 'artifact_id': 'xml.ws', 'group_id': 'mygroup', }, ], [ { 'artifact_id': 'app.admin', 'group_id': 'mygroup', }, { 'artifact_id': 'app.sign', 'group_id': 'mygroup', }, { 'artifact_id': 'pipeline.env', 'group_id': 'mygroup', }, ], 'mvn.log', ) def test_create_multi_modules(): with patch('schmetterling.build.maven.makedirs') as m, patch( 'schmetterling.build.maven.open') as o: f = MagicMock() o.return_value = MagicMock(__enter__=MagicMock(return_value=f)) create_multi_modules([ { 'pom_dir': 'pd1', 'pom_content': 'pc1' }, { 'pom_dir': 'pd2', 'pom_content': 'pc2' }, ]) assert [call('pd1', exist_ok=True), call('pd2', exist_ok=True)] == m.mock_calls assert [call.write('pc1'), call.write('pc2')] == f.mock_calls def test_create_state(): state = BuildState('schmetterling.build.maven', [ Build('mygroup', 'app.admin', '0.0.1-SNAPSHOT', 'app.admin', Build.SUCCESS, 1), Build('mygroup', 'pipeline-apache-proxy', '1.0.0-SNAPSHOT', 'pipeline-apache-proxy', Build.FAILURE, 1), ]) assert state == create_state( [], [{ 'pom_path': 'app.admin/pom.xml', 'artifact_id': 'app.admin', 'group_id': 'mygroup', 'version': '0.0.1-SNAPSHOT', 'packaging': 'jar' }], [{ 'pom_path': 'pipeline-apache-proxy/pom.xml', 'artifact_id': 'pipeline-apache-proxy', 'group_id': 'mygroup', 'version': '1.0.0-SNAPSHOT', 'packaging': 'jar' }], 1, ) def test_get_maven_info(): with patch('schmetterling.build.maven.get_pom_info', side_effect=lambda x: x): repos = [ MagicMock(status=Repo.STATUS_UPDATED, path='path1'), MagicMock(status=Repo.STATUS_UNCHANGED, path='path2'), ] assert [(True, 'path1/pom.xml'), (False, 'path2/pom.xml')] == get_maven_infos(repos) def test_get_maven_repos(): with patch('schmetterling.build.maven.isinstance', return_value=True): with patch('schmetterling.build.maven.exists', side_effect=[False, True]): m = MagicMock(path='pom_repo', return_value='pom_repo') state = [MagicMock(repos=[ MagicMock(path='non_pom_repo'), m, ])] assert [m] == get_maven_repos(state) def test_get_multi_modules(): with patch('schmetterling.build.maven.get_pom', return_value='pom_content'): assert [] == get_multi_modules([(False, {})], 'build_dir') assert [{ 'coordinates': [{}], 'pom_content': 'pom_content', 'pom_dir': 'build_dir/jar-modules', 'updated': [{}] }] == get_multi_modules([(True, {})], 'build_dir') assert [{ 'coordinates': [{ 'packaging': 'jar' }], 'pom_content': 'pom_content', 'pom_dir': 'build_dir/jar-modules', 'updated': [{ 'packaging': 'jar' }] }] == get_multi_modules([(True, { 'packaging': 'jar' })], 'build_dir') assert [{ 'coordinates': [{ 'artifact_id': 'super-pom', 'packaging': 'pom' }], 'pom_content': 'pom_content', 'pom_dir': 'build_dir/super-pom-modules', 'updated': [{ 'artifact_id': 'super-pom', 'packaging': 'pom' }] }] == get_multi_modules([(True, { 'artifact_id': 'super-pom', 'packaging': 'pom' })], 'build_dir') assert [{ 'coordinates': [{ 'artifact_id': 'pom', 'packaging': 'pom' }], 'pom_content': 'pom_content', 'pom_dir': 'build_dir/pom-pom-modules', 'updated': [{ 'artifact_id': 'pom', 'packaging': 'pom' }] }] == get_multi_modules([(True, { 'artifact_id': 'pom', 'packaging': 'pom' })], 'build_dir') assert [{ 'coordinates': [{ 'artifact_id': 'x', 'packaging': 'x' }], 'pom_content': 'pom_content', 'pom_dir': 'build_dir/other-modules', 'updated': [{ 'artifact_id': 'x', 'packaging': 'x' }] }] == get_multi_modules([(True, { 'artifact_id': 'x', 'packaging': 'x' })], 'build_dir') assert [{ 'coordinates': [{ 'artifact_id': 'war', 'packaging': 'war' }], 'pom_content': 'pom_content', 'pom_dir': 'build_dir/war-modules', 'updated': [{ 'artifact_id': 'war', 'packaging': 'war' }] }] == get_multi_modules([(True, { 'artifact_id': 'war', 'packaging': 'war' })], 'build_dir') assert [{ 'coordinates': [{ 'artifact_id': 'jar1', 'packaging': 'jar' }, { 'artifact_id': 'jar2' }, { 'artifact_id': 'jar3' }], 'pom_content': 'pom_content', 'pom_dir': 'build_dir/jar-modules', 'updated': [{ 'artifact_id': 'jar1', 'packaging': 'jar' }, { 'artifact_id': 'jar2' }] }, { 'coordinates': [{ 'artifact_id': 'war', 'packaging': 'war' }], 'pom_content': 'pom_content', 'pom_dir': 'build_dir/war-modules', 'updated': [{ 'artifact_id': 'war', 'packaging': 'war' }] }] == get_multi_modules([(True, { 'artifact_id': 'jar1', 'packaging': 'jar' }), (True, { 'artifact_id': 'jar2' }), (False, { 'artifact_id': 'jar3' }), (True, { 'artifact_id': 'war', 'packaging': 'war' })], 'build_dir')

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0.461702

940

10,849

5.07234

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0.077601

0.091653

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0.062092

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null

0

null

0

1

0

22fe0847296c50b27120f9c55084e9eba84b2a5a

1,753

py

Python

Copados y Clases/Mastermind_DEBUG.py

FdelMazo/7540rw-Algo1

8900604873195df9e902ead6bcb67723a8b654c8

[ "MIT" ]

1

2021-11-20T18:41:34.000Z

Copados y Clases/Mastermind_DEBUG.py

FdelMazo/7540rw-Algo1

8900604873195df9e902ead6bcb67723a8b654c8

[ "MIT" ]

null

Copados y Clases/Mastermind_DEBUG.py

FdelMazo/7540rw-Algo1

8900604873195df9e902ead6bcb67723a8b654c8

[ "MIT" ]

null

#Sacar las lineas con DEBUG para que el juego funcione import random DIGITOS = 4 def mastermind(): """Funcion principal del juego Mastermind""" print("Bienvenido al Mastermind!") print("Instrucciones: Tenes que adivinar un codigo de {} digitos distintos. Tu cantidad de aciertos son los numeros que estan correctamente posicionados, tu cantidad de coincidencias son los numeros bien elegidos pero mal posicionados. Suerte!".format(DIGITOS)) codigo = elegir_codigo() intentos = 1 propuesta = input("Que codigo propones? (o pone 'Me retiro') ") retirarse = "Me retiro" while propuesta != codigo and propuesta != retirarse: intentos+=1 aciertos, coincidencias = analizar_propuesta(propuesta, codigo) print ("Tu propuesta ({}) tiene {} aciertos y {} coincidencias.".format(propuesta,aciertos,coincidencias)) propuesta = input("Propone otro codigo: ") if propuesta == retirarse: print ("El codigo era: {}".format(codigo)) else: print ("Ganaste! Ganaste en {} intentos".format(intentos)) def elegir_codigo(): """Elige un codigo de DIGITOS digitos al azar""" digitos= ("0","1","2","3","4","5","6","7","8","9") codigo = "" for i in range(DIGITOS): candidato = random.choice(digitos) print("[DEBUG] candidato:", candidato) while candidato in codigo: candidato = random.choice(digitos) codigo = codigo + candidato print("[DEBUG] el codigo va siendo", codigo) return codigo def analizar_propuesta(propuesta, codigo): """Determina aciertos y coincidencias""" aciertos = 0 coincidencias = 0 for i in range(DIGITOS): if propuesta[i] == codigo[i]: aciertos += 1 elif propuesta[i] in codigo: coincidencias += 1 return aciertos,coincidencias mastermind()

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null

0

null

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1

0

22ffabcfd90f7354812821f61ad46409c8d4a120

15,233

py

Python

PyPortal_User_Interface/code.py

RichardA1/Adafruit_Learning_System_Guides

7d06d8a126f357a431384c3af73339cb46f44c19

[ "MIT" ]

1

2022-01-31T21:55:48.000Z

PyPortal_User_Interface/code.py

aadisalimani/Adafruit_Learning_System_Guides

1b18cfcd6d426bf018545fd7b4102a8196c11c16

[ "MIT" ]

null

PyPortal_User_Interface/code.py

aadisalimani/Adafruit_Learning_System_Guides

1b18cfcd6d426bf018545fd7b4102a8196c11c16

[ "MIT" ]

null

import time import board import displayio import busio from analogio import AnalogIn import neopixel import adafruit_adt7410 from adafruit_bitmap_font import bitmap_font from adafruit_display_text.label import Label from adafruit_button import Button import adafruit_touchscreen from adafruit_pyportal import PyPortal # ------------- Inputs and Outputs Setup ------------- # # init. the temperature sensor i2c_bus = busio.I2C(board.SCL, board.SDA) adt = adafruit_adt7410.ADT7410(i2c_bus, address=0x48) adt.high_resolution = True # init. the light sensor light_sensor = AnalogIn(board.LIGHT) pixel = neopixel.NeoPixel(board.NEOPIXEL, 1, brightness=1) WHITE = 0xffffff RED = 0xff0000 YELLOW = 0xffff00 GREEN = 0x00ff00 BLUE = 0x0000ff PURPLE = 0xff00ff BLACK = 0x000000 # ---------- Sound Effects ------------- # soundDemo = '/sounds/sound.wav' soundBeep = '/sounds/beep.wav' soundTab = '/sounds/tab.wav' # ------------- Other Helper Functions------------- # # Helper for cycling through a number set of 1 to x. def numberUP(num, max_val): num += 1 if num <= max_val: return num else: return 1 # ------------- Screen Setup ------------- # pyportal = PyPortal() display = board.DISPLAY display.rotation = 270 # Backlight function # Value between 0 and 1 where 0 is OFF, 0.5 is 50% and 1 is 100% brightness. def set_backlight(val): val = max(0, min(1.0, val)) board.DISPLAY.auto_brightness = False board.DISPLAY.brightness = val # Set the Backlight set_backlight(0.3) # Touchscreen setup # ------Rotate 270: screen_width = 240 screen_height = 320 ts = adafruit_touchscreen.Touchscreen(board.TOUCH_YD, board.TOUCH_YU, board.TOUCH_XR, board.TOUCH_XL, calibration=((5200, 59000), (5800, 57000)), size=(screen_width, screen_height)) # ------------- Display Groups ------------- # splash = displayio.Group(max_size=15) # The Main Display Group view1 = displayio.Group(max_size=15) # Group for View 1 objects view2 = displayio.Group(max_size=15) # Group for View 2 objects view3 = displayio.Group(max_size=15) # Group for View 3 objects def hideLayer(hide_target): try: splash.remove(hide_target) except ValueError: pass def showLayer(show_target): try: time.sleep(0.1) splash.append(show_target) except ValueError: pass # ------------- Setup for Images ------------- # # Display an image until the loop starts pyportal.set_background('/images/loading.bmp') bg_group = displayio.Group(max_size=1) splash.append(bg_group) icon_group = displayio.Group(max_size=1) icon_group.x = 180 icon_group.y = 120 icon_group.scale = 1 view2.append(icon_group) # This will handel switching Images and Icons def set_image(group, filename): """Set the image file for a given goup for display. This is most useful for Icons or image slideshows. :param group: The chosen group :param filename: The filename of the chosen image """ print("Set image to ", filename) if group: group.pop() if not filename: return # we're done, no icon desired image_file = open(filename, "rb") image = displayio.OnDiskBitmap(image_file) try: image_sprite = displayio.TileGrid(image, pixel_shader=displayio.ColorConverter()) except TypeError: image_sprite = displayio.TileGrid(image, pixel_shader=displayio.ColorConverter(), position=(0, 0)) group.append(image_sprite) set_image(bg_group, "/images/BGimage.bmp") # ---------- Text Boxes ------------- # # Set the font and preload letters font = bitmap_font.load_font("/fonts/Helvetica-Bold-16.bdf") font.load_glyphs(b'abcdefghjiklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890- ()') # Default Label styling: TABS_X = 5 TABS_Y = 50 # Text Label Objects feed1_label = Label(font, text="Text Wondow 1", color=0xE39300, max_glyphs=200) feed1_label.x = TABS_X feed1_label.y = TABS_Y view1.append(feed1_label) feed2_label = Label(font, text="Text Wondow 2", color=0xFFFFFF, max_glyphs=200) feed2_label.x = TABS_X feed2_label.y = TABS_Y view2.append(feed2_label) sensors_label = Label(font, text="Data View", color=0x03AD31, max_glyphs=200) sensors_label.x = TABS_X sensors_label.y = TABS_Y view3.append(sensors_label) sensor_data = Label(font, text="Data View", color=0x03AD31, max_glyphs=100) sensor_data.x = TABS_X+15 sensor_data.y = 170 view3.append(sensor_data) text_hight = Label(font, text="M", color=0x03AD31, max_glyphs=10) # return a reformatted string with word wrapping using PyPortal.wrap_nicely def text_box(target, top, string, max_chars): text = pyportal.wrap_nicely(string, max_chars) new_text = "" test = "" for w in text: new_text += '\n'+w test += 'M\n' text_hight.text = test # Odd things happen without this glyph_box = text_hight.bounding_box target.text = "" # Odd things happen without this target.y = int(glyph_box[3]/2)+top target.text = new_text # ---------- Display Buttons ------------- # # Default button styling: BUTTON_HEIGHT = 40 BUTTON_WIDTH = 80 # We want three buttons across the top of the screen TAPS_HEIGHT = 40 TAPS_WIDTH = int(screen_width/3) TAPS_Y = 0 # We want two big buttons at the bottom of the screen BIG_BUTTON_HEIGHT = int(screen_height/3.2) BIG_BUTTON_WIDTH = int(screen_width/2) BIG_BUTTON_Y = int(screen_height-BIG_BUTTON_HEIGHT) # This group will make it easy for us to read a button press later. buttons = [] # Main User Interface Buttons button_view1 = Button(x=0, y=0, width=TAPS_WIDTH, height=TAPS_HEIGHT, label="View1", label_font=font, label_color=0xff7e00, fill_color=0x5c5b5c, outline_color=0x767676, selected_fill=0x1a1a1a, selected_outline=0x2e2e2e, selected_label=0x525252) buttons.append(button_view1) # adding this button to the buttons group button_view2 = Button(x=TAPS_WIDTH, y=0, width=TAPS_WIDTH, height=TAPS_HEIGHT, label="View2", label_font=font, label_color=0xff7e00, fill_color=0x5c5b5c, outline_color=0x767676, selected_fill=0x1a1a1a, selected_outline=0x2e2e2e, selected_label=0x525252) buttons.append(button_view2) # adding this button to the buttons group button_view3 = Button(x=TAPS_WIDTH*2, y=0, width=TAPS_WIDTH, height=TAPS_HEIGHT, label="View3", label_font=font, label_color=0xff7e00, fill_color=0x5c5b5c, outline_color=0x767676, selected_fill=0x1a1a1a, selected_outline=0x2e2e2e, selected_label=0x525252) buttons.append(button_view3) # adding this button to the buttons group button_switch = Button(x=0, y=BIG_BUTTON_Y, width=BIG_BUTTON_WIDTH, height=BIG_BUTTON_HEIGHT, label="Switch", label_font=font, label_color=0xff7e00, fill_color=0x5c5b5c, outline_color=0x767676, selected_fill=0x1a1a1a, selected_outline=0x2e2e2e, selected_label=0x525252) buttons.append(button_switch) # adding this button to the buttons group button_2 = Button(x=BIG_BUTTON_WIDTH, y=BIG_BUTTON_Y, width=BIG_BUTTON_WIDTH, height=BIG_BUTTON_HEIGHT, label="Button", label_font=font, label_color=0xff7e00, fill_color=0x5c5b5c, outline_color=0x767676, selected_fill=0x1a1a1a, selected_outline=0x2e2e2e, selected_label=0x525252) buttons.append(button_2) # adding this button to the buttons group # Add all of the main buttons to the spalsh Group for b in buttons: splash.append(b.group) # Make a button to change the icon image on view2 button_icon = Button(x=150, y=60, width=BUTTON_WIDTH, height=BUTTON_HEIGHT, label="Icon", label_font=font, label_color=0xffffff, fill_color=0x8900ff, outline_color=0xbc55fd, selected_fill=0x5a5a5a, selected_outline=0xff6600, selected_label=0x525252, style=Button.ROUNDRECT) buttons.append(button_icon) # adding this button to the buttons group # Add this button to view2 Group view2.append(button_icon.group) # Make a button to play a sound on view2 button_sound = Button(x=150, y=170, width=BUTTON_WIDTH, height=BUTTON_HEIGHT, label="Sound", label_font=font, label_color=0xffffff, fill_color=0x8900ff, outline_color=0xbc55fd, selected_fill=0x5a5a5a, selected_outline=0xff6600, selected_label=0x525252, style=Button.ROUNDRECT) buttons.append(button_sound) # adding this button to the buttons group # Add this button to view2 Group view3.append(button_sound.group) #pylint: disable=global-statement def switch_view(what_view): global view_live if what_view == 1: hideLayer(view2) hideLayer(view3) button_view1.selected = False button_view2.selected = True button_view3.selected = True showLayer(view1) view_live = 1 print("View1 On") elif what_view == 2: # global icon hideLayer(view1) hideLayer(view3) button_view1.selected = True button_view2.selected = False button_view3.selected = True showLayer(view2) view_live = 2 print("View2 On") else: hideLayer(view1) hideLayer(view2) button_view1.selected = True button_view2.selected = True button_view3.selected = False showLayer(view3) view_live = 3 print("View3 On") #pylint: enable=global-statement # Set veriables and startup states button_view1.selected = False button_view2.selected = True button_view3.selected = True showLayer(view1) hideLayer(view2) hideLayer(view3) view_live = 1 icon = 1 icon_name = "Ruby" button_mode = 1 switch_state = 0 button_switch.label = "OFF" button_switch.selected = True # Update out Labels with display text. text_box(feed1_label, TABS_Y, "The text on this screen is wrapped so that all of it fits nicely into a \ text box that is ### x ###.", 30) text_box(feed1_label, TABS_Y, 'The text on this screen is wrapped so that all of it fits nicely into a \ text box that is {} x {}.' .format(feed1_label.bounding_box[2], feed1_label.bounding_box[3]*2), 30) text_box(feed2_label, TABS_Y, 'Tap on the Icon button to meet a new friend.', 18) text_box(sensors_label, TABS_Y, "This screen can display sensor readings and tap Sound to play a WAV file.", 28) board.DISPLAY.show(splash) # ------------- Code Loop ------------- # while True: touch = ts.touch_point light = light_sensor.value tempC = round(adt.temperature) tempF = tempC * 1.8 + 32 sensor_data.text = 'Touch: {}\nLight: {}\n Temp: {}°F'.format(touch, light, tempF) # ------------- Handle Button Press Detection ------------- # if touch: # Only do this if the screen is touched # loop with buttons using enumerate() to number each button group as i for i, b in enumerate(buttons): if b.contains(touch): # Test each button to see if it was pressed print('button%d pressed' % i) if i == 0 and view_live != 1: # only if view1 is visable pyportal.play_file(soundTab) switch_view(1) while ts.touch_point: pass if i == 1 and view_live != 2: # only if view2 is visable pyportal.play_file(soundTab) switch_view(2) while ts.touch_point: pass if i == 2 and view_live != 3: # only if view3 is visable pyportal.play_file(soundTab) switch_view(3) while ts.touch_point: pass if i == 3: pyportal.play_file(soundBeep) # Toggle switch button type if switch_state == 0: switch_state = 1 b.label = "ON" b.selected = False pixel.fill(WHITE) print("Swich ON") else: switch_state = 0 b.label = "OFF" b.selected = True pixel.fill(BLACK) print("Swich OFF") # for debounce while ts.touch_point: pass print("Swich Pressed") if i == 4: pyportal.play_file(soundBeep) # Momentary button type b.selected = True print('Button Pressed') button_mode = numberUP(button_mode, 5) if button_mode == 1: pixel.fill(RED) elif button_mode == 2: pixel.fill(YELLOW) elif button_mode == 3: pixel.fill(GREEN) elif button_mode == 4: pixel.fill(BLUE) elif button_mode == 5: pixel.fill(PURPLE) switch_state = 1 button_switch.label = "ON" button_switch.selected = False # for debounce while ts.touch_point: pass print("Button released") b.selected = False if i == 5 and view_live == 2: # only if view2 is visable pyportal.play_file(soundBeep) b.selected = True while ts.touch_point: pass print("Icon Button Pressed") icon = numberUP(icon, 3) if icon == 1: icon_name = "Ruby" elif icon == 2: icon_name = "Gus" elif icon == 3: icon_name = "Billie" b.selected = False text_box(feed2_label, TABS_Y, "Every time you tap the Icon button the icon image will \ change. Say hi to {}!".format(icon_name), 18) set_image(icon_group, "/images/"+icon_name+".bmp") if i == 6 and view_live == 3: # only if view3 is visable b.selected = True while ts.touch_point: pass print("Sound Button Pressed") pyportal.play_file(soundDemo) b.selected = False

35.508159

89

0.594433

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4.70446

0.192907

0.011879

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0.014392

0.380126

0.35671

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0.309766

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15,233

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0

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false

0.027864

0.037152

0

0.068111

0.037152

0

null

0

null

0

1

0

fe00cf45d1015948865b349bcd27a15e243e3e66

7,741

py

Python

btse_futures/order.py

yottatix/btse-python

1c5019d0a68dff797afc70c4cc32c1950c28af4e

[ "MIT" ]

null

btse_futures/order.py

yottatix/btse-python

1c5019d0a68dff797afc70c4cc32c1950c28af4e

[ "MIT" ]

null

btse_futures/order.py

yottatix/btse-python

1c5019d0a68dff797afc70c4cc32c1950c28af4e

[ "MIT" ]

null

import json from btse_futures.constants import OrderType, Side, TimeInForce class Order: """ Class to represent a BTSE Order ... Attributes ---------- size : int order quantity or size. e.g. 1 price : float price. e.g. 7000.0 side: str order side. BUY or SELL time_in_force: str time the order is in force. Possible options defined in TimeInForce. e.g. GTC symbol: str instrument symbol. e.g. BTCPFC type: str order type. "LIMIT", "MARKET", or "OCO" txType: str transaction type postOnly: bool Is order post only? reduceOnly: bool Is order reduce only? triggerPrice: float Trigger price. Relevant only for LIMIT and OCO order types stopPrice: float Stop price. trailValue: float Trail value. clOrderId: str User defined order id trigger: str If an order is a stop loss or take profit order, then this parameter determines the trigger price. Available values are: 1. markPrice = Mark Price (Default) and 2. lastPrice = Last transacted Price Documentation: https://www.btse.com/apiexplorer/futures/?shell#tocs_orderformv2 """ def __init__(self, size: int, price: float, side: str, time_in_force: str, symbol: str, type: str, txType: str, postOnly: bool, reduceOnly: bool, triggerPrice: float, stopPrice: float = None, trailValue: float = None, clOrderId: str = None, trigger: str = None) -> None: assert(isinstance(size, int)) assert(isinstance(price, float)) assert(isinstance(side, str)) assert(isinstance(time_in_force, str)) assert(isinstance(symbol, str)) assert(isinstance(type, str)) assert(isinstance(postOnly, bool)) assert(isinstance(reduceOnly, bool)) assert(isinstance(triggerPrice, float)) self.size = size self.price = price self.side = side self.time_in_force = time_in_force self.symbol = symbol self.type = type self.txType = txType self.postOnly = postOnly self.reduceOnly = reduceOnly self.triggerPrice = triggerPrice self.stopPrice = stopPrice self.trailValue = trailValue self.clOrderId = clOrderId self.trigger = trigger @property def quantity(self): return self.size def to_json(self): json_string = json.dumps(self.order_without_none_values()) print(f'json string: {json_string}') return json_string def order_without_none_values(self): order_dict = self.__dict__ for key, value in list(order_dict.items()): if value is None: del order_dict[key] return order_dict class OpenOrder: """ open order endpoint response format https://www.btse.com/apiexplorer/futures/#tocs_positionrespv2_1 Example: -------- `{ "orderType": 0, "price": 6875, "size": 4, "side": "BUY", "filledSize": 3, "orderValue": 20.625, "pegPriceMin": 0, "pegPriceMax": 0, "pegPriceDeviation": 0, "cancelDuration": 0, "timestamp": 1576661434072, "orderID": "string", "stealth": 0.2, "triggerOrder": true, "triggered": true, "triggerPrice": 0, "triggerOriginalPrice": 0, "triggerOrderType": 1001, "triggerTrailingStopDeviation": 0, "triggerStopPrice": 0, "symbol": "string", "trailValue": 0, "clOrderID": "market001", "reduceOnly": true, "orderState": "string" }` """ def __init__(self) -> None: self.orderType = 0 self.price = 0 self.size = 0 self.side = '' self.filledSize = 0 self.orderValue = 0.0 self.pegPriceMin = 0 self.pegPriceMax = 0 self.pegPriceDeviation = 0 self.cancelDuration = 0 self.timestamp = 0 self.orderID = '' self.stealth = 0.0 self.triggerOrder = '' self.triggered = '' self.triggerPrice = 0 self.triggerOriginalPrice = 0 self.triggerOrderType = 0 self.triggerTrailingStopDeviation = 0 self.triggerStopPrice = 0 self.symbol = '' self.trailValue = 0 self.clOrderID = '' self.reduceOnly = '' self.orderState = '' @staticmethod def from_dict(data): open_order = OpenOrder() open_order.orderType = data.get('orderType') open_order.price = data.get('price') open_order.size = data.get('size') open_order.side = data.get('side') open_order.filledSize = data.get('filledSize') open_order.orderValue = data.get('orderValue') open_order.pegPriceMin = data.get('pegPriceMin') open_order.pegPriceMax = data.get('pegPriceMax') open_order.pegPriceDeviation = data.get('pegPriceDeviation') open_order.cancelDuration = data.get('cancelDuration') open_order.timestamp = data.get('timestamp') open_order.orderID = data.get('orderID') open_order.stealth = data.get('stealth') open_order.triggerOrder = data.get('triggerOrder') open_order.triggered = data.get('triggered') open_order.triggerPrice = data.get('triggerPrice') open_order.triggerOriginalPrice = data.get('triggerOriginalPrice') open_order.triggerOrderType = data.get('triggerOrderType') open_order.triggerTrailingStopDeviation = data.get( 'triggerTrailingStopDeviation') open_order.triggerStopPrice = data.get('triggerStopPrice') open_order.symbol = data.get('symbol') open_order.trailValue = data.get('trailValue') open_order.clOrderID = data.get('clOrderID') open_order.reduceOnly = data.get('reduceOnly') open_order.orderState = data.get('orderState') return open_order class OrderResponseV21: """ Order Response V2.1 Documentation -- https://www.btse.com/apiexplorer/futures/?shell#tocs_orderrespv2_1 """ def __init__(self) -> None: self.status = 0 self.symbol = '' self.orderType = 0 self.price = 0.0 self.side = '' self.size = 0 self.orderID = '' self.timestamp = 0 self.triggerPrice = 0.0 self.trigger = '' self.deviation = 0.0 self.stealth = 0.0 self.message = '' self.avgFillPrice = 0.0 self.fillSize = 0.0 self.clOrderID = '' @staticmethod def from_dict(data): order_response_v21 = OrderResponseV21() order_response_v21.status = data.get('status') order_response_v21.symbol = data.get('symbol') order_response_v21.orderType = data.get('orderType') order_response_v21.price = data.get('price') order_response_v21.side = data.get('side') order_response_v21.size = data.get('size') order_response_v21.orderID = data.get('orderID') order_response_v21.timestamp = data.get('timestamp') order_response_v21.triggerPrice = data.get('triggerPrice') order_response_v21.trigger = data.get('trigger') order_response_v21.deviation = data.get('deviation') order_response_v21.stealth = data.get('stealth') order_response_v21.message = data.get('message') order_response_v21.avgFillPrice = data.get('avgFillPrice') order_response_v21.fillSize = data.get('fillSize') order_response_v21.clOrderID = data.get('clOrderID') return order_response_v21

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0.612324

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7,741

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0.062

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0.007407

0

null

0

null

0

1

0

fe00feaeeab5dd9b94bc8b6fc0a0dcbedc801a5d

2,037

py

Python

tests/mock_responses.py

md-reddevil/blinkpy

3c7892385352079227c6251eb88257870bea0bb3

[ "MIT" ]

null

tests/mock_responses.py

md-reddevil/blinkpy

3c7892385352079227c6251eb88257870bea0bb3

[ "MIT" ]

null

tests/mock_responses.py

md-reddevil/blinkpy

3c7892385352079227c6251eb88257870bea0bb3

[ "MIT" ]

null

"""Simple mock responses definitions.""" from blinkpy.helpers.util import BlinkURLHandler import blinkpy.helpers.constants as const LOGIN_RESPONSE = { 'region': {'mock': 'Test'}, 'networks': { '1234': {'name': 'test', 'onboarded': True} }, 'authtoken': {'authtoken': 'foobar123', 'message': 'auth'} } class MockResponse: """Class for mock request response.""" def __init__(self, json_data, status_code, raw_data=None): """Initialize mock get response.""" self.json_data = json_data self.status_code = status_code self.raw_data = raw_data def json(self): """Return json data from get_request.""" return self.json_data @property def raw(self): """Return raw data from get request.""" return self.raw_data def mocked_session_send(*args, **kwargs): """Mock session.""" prepped = args[0] url = prepped.url header = prepped.headers method = prepped.method if method == 'GET': expected_token = LOGIN_RESPONSE['authtoken']['authtoken'] if header['TOKEN_AUTH'] != expected_token: response = {'message': 'Not Authorized', 'code': 400} status = 400 elif url == 'use_bad_response': response = {'foo': 'bar'} status = 200 elif url == 'reauth': response = {'message': 'REAUTH', 'code': 777} status = 777 else: response = {'test': 'foo'} status = 200 elif method == 'POST': if url in (const.LOGIN_URL, const.LOGIN_BACKUP_URL): response = LOGIN_RESPONSE status = 200 elif url == 'http://wrong.url/' or url is None: response = {'message': 'Error', 'code': 404} status = 404 else: response = {'message': 'foo', 'code': 200} status = 200 return MockResponse(response, status) class MockURLHandler(BlinkURLHandler): """Mocks URL Handler in blinkpy module.""" pass

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0.377273

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0.031579

0.049123

0

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0.291605

2,037

71

66

28.690141

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0.078431

false

0.019608

0.039216

0

0.215686

0

null

0

null

0

1

0

fe01b90ce53e119b08e13770e4500dbf262d962f

2,061

py

Python

fits_tools.py

steveschulze/Photometry

3bc4ce457a270962321176d0e3e288b5a96cd34b

[ "BSD-2-Clause" ]

6

2020-03-05T20:58:35.000Z

2022-02-13T20:18:46.000Z

fits_tools.py

steveschulze/Photometry

3bc4ce457a270962321176d0e3e288b5a96cd34b

[ "BSD-2-Clause" ]

1

2020-03-10T00:03:46.000Z

fits_tools.py

steveschulze/Photometry

3bc4ce457a270962321176d0e3e288b5a96cd34b

[ "BSD-2-Clause" ]

1

2020-11-26T10:38:47.000Z

from astropy import coordinates as coord from astropy import wcs from astropy.io import fits from astropy import units as u from misc import bcolors import numpy as np import os def convert_hms_dd(RA, DEC): ''' Convert HMS to DD system ''' if (':' in RA) and (':' in DEC): Coord_dd = coord.SkyCoord(RA, DEC, unit=(u.hour,u.degree), frame='icrs') RA_dd = Coord_dd.ra.deg Dec_dd = Coord_dd.dec.deg elif (not (':' in RA) and not (':' in DEC)) and (('.' in RA) and ('.' in DEC)): RA_dd, Dec_dd = float(RA), float(DEC) else: print(bcolors.FAIL + 'Coordinates have wrong format.' + bcolors.ENDC) sys.exit() return RA_dd, Dec_dd def get_header(FILE, KEYWORD): ''' Get keyword from fits file ''' header = fits.getheader(FILE) return header[KEYWORD] def pix2arcsec(FITS): ''' Get pixel scale ''' hdu = fits.open(FITS) if len(hdu) > 1: header = fits.getheader(FITS, 0) header += fits.getheader(FITS, 1) else: header = fits.getheader(FITS) hdu_wcs = wcs.WCS(header) return np.median(wcs.utils.proj_plane_pixel_scales(hdu_wcs)) * 3600 def sky2xy (FITS, RA=False, DEC=False, CAT=None): ''' Coordinate transformation: sky -> xy ''' if CAT == None: if RA != False and DEC != False: cmd=('sky2xy %s %s %s | grep -v off' %(FITS, RA, DEC)) program_call = os.popen(cmd) xy = [] for line in program_call: xy=np.array(line.strip().split()[-2:]).astype(float) if len(xy) > 0: return xy else: cmd =("more %s | awk '{print $1,$2}' > %s" %(CAT, CAT.replace(CAT.split('.')[-1], 'reg'))) os.system(cmd) cmd = ("sky2xy %s @%s | grep -v off | awk '{print $5, $6}'" %(FITS, CAT.replace(CAT.split('.')[-1], 'reg'))) cat = os.popen(cmd) xy = [] for line in cat: xy.append(list(map(float, line.replace('\n', '').split()))) return np.array(xy) def xy2sky (FITSFILE,X,Y): ''' Coordinate transformation: xy -> sky ''' program_call = os.popen('xy2sky %s %s %s' %(FITSFILE, X, Y)) sky = [] for line in program_call: sky.append(line.strip().split()[:2]) return sky

21.247423

111

0.622028

327

2,061

3.856269

0.314985

0.00793

0.06027

0.054718

0.127676

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0.033307

0

0.013301

0.197477

2,061

96

112

21.46875

0.749093

0.068413

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0

0.094984

0

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false

0

0.127273

0

0.327273

0.054545

0

null

0

null

0

1

0

fe028f3f35a9ad5d36908ec80630b139c6300e3c

2,155

py

Python

test_stbp_snn_eval.py

neurom-iot/n3ml

39c6b50661f293d58b4b37ef613643860724bb24

[ "MIT" ]

11

2019-03-15T17:20:54.000Z

2022-03-01T08:25:36.000Z

test_stbp_snn_eval.py

neurom-iot/n3ml

39c6b50661f293d58b4b37ef613643860724bb24

[ "MIT" ]

7

2019-03-15T16:02:51.000Z

2021-12-03T08:17:06.000Z

test_stbp_snn_eval.py

neurom-iot/n3ml

39c6b50661f293d58b4b37ef613643860724bb24

[ "MIT" ]

9

2019-10-14T12:38:19.000Z

2021-12-02T04:49:28.000Z

import argparse import torch import torch.nn as nn import torchvision import torchvision.transforms as transforms from n3ml.model import DynamicModel_STBP_SNN def validate(val_loader, model, encoder, criterion, opt): model.eval() total_images = 0 num_corrects = 0 total_loss = 0 with torch.no_grad(): for step, (images, labels) in enumerate(val_loader): images = images.cuda() labels = labels.cuda() preds = model(encoder, images, opt.num_steps) labels_ = torch.zeros(torch.numel(labels), 10, device=labels.device) labels_ = labels_.scatter_(1, labels.view(-1, 1), 1) loss = criterion(preds, labels_) num_corrects += torch.argmax(preds, dim=1).eq(labels).sum(dim=0) total_loss += loss.cpu().detach().numpy() * images.size(0) total_images += images.size(0) val_acc = num_corrects.float() / total_images val_loss = total_loss / total_images return val_acc, val_loss def app(opt): print(opt) val_loader = torch.utils.data.DataLoader( torchvision.datasets.MNIST( opt.data, train=False, download=True, transform=torchvision.transforms.Compose([transforms.ToTensor()])), batch_size=opt.batch_size) state_dict = torch.load(opt.pretrained) model = DynamicModel_STBP_SNN(batch_size=opt.batch_size) for m in state_dict['arch']: model.add_module(m[0], m[1]) if torch.cuda.is_available(): model.cuda() encoder = lambda x: (x > torch.rand(x.size(), device=x.device)).float() criterion = nn.MSELoss() acc, loss = validate(val_loader, model, encoder, criterion, opt) print("In test, loss: {} - acc: {}".format(loss, acc)) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--data', default='data') parser.add_argument('--batch_size', default=100, type=int) parser.add_argument('--num_steps', default=15, type=int) parser.add_argument('--pretrained', default='pretrained/stbp_dynamic_acc_9897.pt') app(parser.parse_args())

28.355263

86

0.647332

277

2,155

4.837545

0.371841

0.033582

0.050746

0.032836

0.128358

0.061194

0

0.014934

0.223202

2,155

75

87

28.733333

0.785544

0

0.05522

0.016241

0

1

0.039216

false

0

0.117647

0

0.176471

0.039216

0

null

0

null

0

1

0

fe03d9810588ad4d8d061ca21558f5e026141e64

2,334

py

Python

kaggle_melanoma/schedulers.py

tinve/kaggle_melanoma

6d2d16d62a394fd9cc2498bdf1a19ce60fe047eb

[ "MIT" ]

8

2020-06-01T10:42:40.000Z

2022-02-17T08:42:49.000Z

kaggle_melanoma/schedulers.py

tinve/kaggle_melanoma

6d2d16d62a394fd9cc2498bdf1a19ce60fe047eb

[ "MIT" ]

null

kaggle_melanoma/schedulers.py

tinve/kaggle_melanoma

6d2d16d62a394fd9cc2498bdf1a19ce60fe047eb

[ "MIT" ]

2

2020-06-08T22:34:38.000Z

2022-02-24T03:15:59.000Z

import math from torch.optim.lr_scheduler import _LRScheduler from torch.optim.optimizer import Optimizer class PolyLR(_LRScheduler): """ Sets the learning rate of each parameter group according to poly learning rate policy """ def __init__(self, optimizer, max_iter=90000, power=0.9, last_epoch=-1): self.max_iter = max_iter self.power = power super().__init__(optimizer, last_epoch) def get_lr(self): return [base_lr * (1 - float(self.last_epoch) / self.max_iter) ** self.power for base_lr in self.base_lrs] func_zoo = { "cosine_decay": lambda epoch, step, len_epoch, total_epoch: 0.5 * (math.cos(step * math.pi / (total_epoch * len_epoch)) + 1) } class CosineWarmRestart: def __init__( self, optimizer: Optimizer, func: str = "cosine_decay", warmup: bool = True, warmup_epoch: int = 1, period: int = 10, min_lr: float = 1e-5, low_epoch: int = 1, ): # self.base_lrs = list(map(lambda group: group["lr"], optimizer.param_groups))[0] self.base_lrs = [x["lr"] for x in optimizer.param_groups][0] self.optimizer = optimizer self.warmup = warmup self.warmup_epoch = warmup_epoch self.period = period self.cos_period = period - low_epoch self.low_epoch = low_epoch self.lr_func = func_zoo[func] self.min_lr = min_lr def cosine_step(self, current_epoch: int, global_step: int, len_epoch: int) -> float: if self.warmup and current_epoch < self.warmup_epoch: lr = self.base_lrs * float(1 + global_step) / (self.warmup_epoch * len_epoch) else: lr = self.base_lrs * self.lr_func(current_epoch, global_step, len_epoch, self.cos_period) lr = max(self.min_lr, lr) for param_group in self.optimizer.param_groups: param_group["lr"] = lr return lr def step(self, current_epoch: int, global_step: int, len_epoch: int) -> float: current_epoch = current_epoch % self.period if current_epoch >= self.period - self.low_epoch: global_step = len_epoch * self.cos_period else: global_step = global_step % (self.period * len_epoch) return self.cosine_step(current_epoch, global_step, len_epoch)

35.363636

114

0.641388

322

2,334

4.378882

0.232919

0.057447

0.039007

0.038298

0.186525

0.151064

0.124823

0.073759

0

0.012118

0.257498

2,334

65

115

35.907692

0.8015

0.071123

0

0.04

0

0.013011

0

1

0.1

false

0

0.06

0.02

0.26

0

null

0

null

0

1

0

fe056ef418d151035d2b9bd419b580cf756d0fd1

1,099

py

Python

utils.py

federicosapienza/InboxNotionTelegramBot

031d5e78cd352dfb692b93f3e0b421695f1dc18e

[ "MIT" ]

null

utils.py

federicosapienza/InboxNotionTelegramBot

031d5e78cd352dfb692b93f3e0b421695f1dc18e

[ "MIT" ]

null

utils.py

federicosapienza/InboxNotionTelegramBot

031d5e78cd352dfb692b93f3e0b421695f1dc18e

[ "MIT" ]

null

import json import logging logger = logging.getLogger(__name__) with open('configuration.json') as f: config = json.load(f) TELEGRAM_TOKEN = config["telegram-bot-token"] NOTION_TOKEN = config["notion-token"] NOTION_TABLE_URL = config["inbox_table"]["table_url"] def check_allowed_user(user_id): """ check if allowed user :param user_id: telegram user id :return True if user is valid , False otherwise """ valid_user = config["allowed_user_id"] user_id = str(user_id) return user_id == valid_user def restrict_action(handled_action): """ Wrapper for creating a private bot :param handled_action: the action to perform """ def check_private(update, context): if not (check_allowed_user(update.message.from_user.id)): logging.warning("An unauthorized user attempted to use the bot. username: {}, id: {} .".format( update.message.from_user.username, update.message.from_user.id )) return else: return handled_action(update, context) return check_private

27.475

107

0.674249

143

1,099

4.958042

0.405594

0.076164

0.050776

0.088858

0.06488

0

0.22566

1,099

39

108

28.179487

0.833137

0.165605

0

0.173913

0

1

0.136364

false

0

0.090909

0

0.409091

0

null

0

null

0

1

0

fe068879b9f1513a9f5e49e88200ed64c8fa16f1

12,623

py

Python

cassiopeia/datastores/riotapi/match.py

artemigkh/cassiopeia

fa78cb8f86ea21857916a707d04de6a05498033e

[ "MIT" ]

1

2021-09-07T05:26:21.000Z

cassiopeia/datastores/riotapi/match.py

artemigkh/cassiopeia

fa78cb8f86ea21857916a707d04de6a05498033e

[ "MIT" ]

null

cassiopeia/datastores/riotapi/match.py

artemigkh/cassiopeia

fa78cb8f86ea21857916a707d04de6a05498033e

[ "MIT" ]

1

2016-10-20T11:54:20.000Z

from time import time from typing import Type, TypeVar, MutableMapping, Any, Iterable, Generator, Union import arrow import datetime import math from datapipelines import DataSource, PipelineContext, Query, NotFoundError, validate_query from .common import RiotAPIService, APINotFoundError from ...data import Platform, Season, Queue, SEASON_IDS, QUEUE_IDS from ...dto.match import MatchDto, MatchListDto, TimelineDto from ..uniquekeys import convert_region_to_platform T = TypeVar("T") def _get_current_time(query: MutableMapping[str, Any], context: PipelineContext = None) -> int: return int(time()) * 1000 class MatchAPI(RiotAPIService): @DataSource.dispatch def get(self, type: Type[T], query: MutableMapping[str, Any], context: PipelineContext = None) -> T: pass @DataSource.dispatch def get_many(self, type: Type[T], query: MutableMapping[str, Any], context: PipelineContext = None) -> Iterable[T]: pass _validate_get_match_query = Query. \ has("id").as_(int).also. \ has("platform").as_(Platform) @get.register(MatchDto) @validate_query(_validate_get_match_query, convert_region_to_platform) def get_match(self, query: MutableMapping[str, Any], context: PipelineContext = None) -> MatchDto: url = "https://{platform}.api.riotgames.com/lol/match/v4/matches/{id}".format(platform=query["platform"].value.lower(), id=query["id"]) try: app_limiter, method_limiter = self._get_rate_limiter(query["platform"], "matches/id") data = self._get(url, {}, app_limiter=app_limiter, method_limiter=method_limiter) except APINotFoundError as error: raise NotFoundError(str(error)) from error data["gameId"] = query["id"] data["region"] = query["platform"].region.value for p in data["participantIdentities"]: aid = p.get("player", {}).get("currentAccountId", None) if aid == 0: p["player"]["bot"] = True return MatchDto(data) _validate_get_many_match_query = Query. \ has("ids").as_(Iterable).also. \ has("platform").as_(Platform) @get_many.register(MatchDto) @validate_query(_validate_get_many_match_query, convert_region_to_platform) def get_many_match(self, query: MutableMapping[str, Any], context: PipelineContext = None) -> Generator[MatchDto, None, None]: def generator(): for id in query["ids"]: url = "https://{platform}.api.riotgames.com/lol/match/v4/matches/{id}".format(platform=query["platform"].value.lower(), id=id) try: app_limiter, method_limiter = self._get_rate_limiter(query["platform"], "matches/id") data = self._get(url, {}, app_limiter=app_limiter, method_limiter=method_limiter) except APINotFoundError as error: raise NotFoundError(str(error)) from error for participant in data["participants"]: participant.setdefault("runes", []) for p in data["participantIdentities"]: aid = p.get("player", {}).get("currentAccountId", None) if aid == 0: p["player"]["bot"] = True data["gameId"] = id data["region"] = query["platform"].region.value yield MatchDto(data) return generator() _validate_get_match_list_query = Query. \ has("accountId").as_(str).also. \ has("platform").as_(Platform).also. \ has("beginTime").as_(int).also. \ can_have("endTime").as_(int).also. \ has("beginIndex").as_(int).also. \ has("maxNumberOfMatches").as_(float).also. \ can_have("seasons").as_(Iterable).also. \ can_have("champion.ids").as_(Iterable).also. \ can_have("queues").as_(Iterable) @get.register(MatchListDto) @validate_query(_validate_get_match_list_query, convert_region_to_platform) def get_match_list(self, query: MutableMapping[str, Any], context: PipelineContext = None) -> MatchListDto: params = {} riot_index_interval = 100 riot_date_interval = datetime.timedelta(days=7) begin_time = query["beginTime"] # type: arrow.Arrow end_time = query.get("endTime", arrow.now()) # type: arrow.Arrow if isinstance(begin_time, int): begin_time = arrow.get(begin_time / 1000) if isinstance(end_time, int): end_time = arrow.get(end_time / 1000) def determine_calling_method(begin_time, end_time) -> str: """Returns either "by_date" or "by_index".""" matches_per_date_interval = 10 # This is an assumption seconds_per_day = (60 * 60 * 24) riot_date_interval_in_days = riot_date_interval.total_seconds() / seconds_per_day # in units of days npulls_by_date = (end_time - begin_time).total_seconds() / seconds_per_day / riot_date_interval_in_days npulls_by_index = (arrow.now() - begin_time).total_seconds() / seconds_per_day / riot_date_interval_in_days * matches_per_date_interval / riot_index_interval if math.ceil(npulls_by_date) < math.ceil(npulls_by_index): by = "by_date" else: by = "by_index" return by calling_method = determine_calling_method(begin_time, end_time) if calling_method == "by_date": params["beginTime"] = begin_time.timestamp * 1000 if "endTime" in query: params["endTime"] = min((begin_time + riot_date_interval).timestamp * 1000, query["endTime"]) else: params["endTime"] = (begin_time + riot_date_interval).timestamp * 1000 else: params["beginIndex"] = query["beginIndex"] params["endIndex"] = query["beginIndex"] + min(riot_index_interval, query["maxNumberOfMatches"]) params["endIndex"] = int(params["endIndex"]) if "seasons" in query: seasons = {Season(season) for season in query["seasons"]} params["season"] = {SEASON_IDS[season] for season in seasons} else: seasons = set() if "champion.ids" in query: champions = query["champion.ids"] params["champion"] = champions else: champions = set() if "queues" in query: queues = {Queue(queue) for queue in query["queues"]} params["queue"] = {QUEUE_IDS[queue] for queue in queues} else: queues = set() url = "https://{platform}.api.riotgames.com/lol/match/v4/matchlists/by-account/{accountId}".format(platform=query["platform"].value.lower(), accountId=query["accountId"]) try: app_limiter, method_limiter = self._get_rate_limiter(query["platform"], "matchlists/by-account/accountId") data = self._get(url, params, app_limiter=app_limiter, method_limiter=method_limiter) except APINotFoundError: data = {"matches": []} data["accountId"] = query["accountId"] data["region"] = query["platform"].region.value data["season"] = seasons data["champion"] = champions data["queue"] = queues if calling_method == "by_index": data["beginIndex"] = params["beginIndex"] data["endIndex"] = params["endIndex"] data["maxNumberOfMatches"] = query["maxNumberOfMatches"] else: data["beginTime"] = params["beginTime"] data["endTime"] = params["endTime"] for match in data["matches"]: match["accountId"] = query["accountId"] match["region"] = Platform(match["platformId"]).region.value return MatchListDto(data) _validate_get_many_match_list_query = Query. \ has("accountIds").as_(Iterable).also. \ has("platform").as_(Platform).also. \ can_have("beginTime").as_(int).also. \ can_have("endTime").as_(int).also. \ can_have("beginIndex").as_(int).also. \ can_have("endIndex").as_(int).also. \ can_have("seasons").as_(Iterable).also. \ can_have("champion.ids").as_(Iterable).also. \ can_have("queues").as_(Iterable) @get_many.register(MatchListDto) @validate_query(_validate_get_many_match_list_query, convert_region_to_platform) def get_many_match_list(self, query: MutableMapping[str, Any], context: PipelineContext = None) -> Generator[MatchListDto, None, None]: params = {} if "beginIndex" in query: params["beginIndex"] = query["beginIndex"] if "endIndex" in query: params["endIndex"] = query["endIndex"] if "seasons" in query: seasons = {Season(season) for season in query["seasons"]} params["season"] = {SEASON_IDS[season] for season in seasons} else: seasons = set() if "champion.ids" in query: params["champion"] = {query["champion.ids"]} if "queues" in query: queues = {Queue(queue) for queue in query["queues"]} params["queue"] = {QUEUE_IDS[queue] for queue in queues} else: queues = set() def generator(): for id in query["accountIds"]: url = "https://{platform}.api.riotgames.com/lol/match/v4/matchlists/by-account/{accountId}".format(platform=query["platform"].value.lower(), accountId=id) try: app_limiter, method_limiter = self._get_rate_limiter(query["platform"], "matchlists/by-account/accountId") data = self._get(url, params, app_limiter=app_limiter, method_limiter=method_limiter) except APINotFoundError as error: raise NotFoundError(str(error)) from error data["accountId"] = id data["region"] = query["platform"].region.value if "beginIndex" in query: data["beginIndex"] = query["beginIndex"] if "endIndex" in query: data["endIndex"] = query["endIndex"] if "seasons" in query: data["seasons"] = seasons if "champion.ids" in query: data["champion"] = params["champion"] if "queues" in query: params["queue"] = queues yield MatchListDto(data) return generator() _validate_get_timeline_query = Query. \ has("id").as_(int).also. \ has("platform").as_(Platform) @get.register(TimelineDto) @validate_query(_validate_get_timeline_query, convert_region_to_platform) def get_match_timeline(self, query: MutableMapping[str, Any], context: PipelineContext = None) -> TimelineDto: url = "https://{platform}.api.riotgames.com/lol/match/v4/timelines/by-match/{id}".format(platform=query["platform"].value.lower(), id=query["id"]) try: app_limiter, method_limiter = self._get_rate_limiter(query["platform"], "timelines/by-match/id") data = self._get(url, {}, app_limiter=app_limiter, method_limiter=method_limiter) except APINotFoundError as error: raise NotFoundError(str(error)) from error data["matchId"] = query["id"] data["region"] = query["platform"].region.value return TimelineDto(data) _validate_get_many_timeline_query = Query. \ has("ids").as_(Iterable).also. \ has("platform").as_(Platform) @get_many.register(TimelineDto) @validate_query(_validate_get_many_timeline_query, convert_region_to_platform) def get_many_match_timeline(self, query: MutableMapping[str, Any], context: PipelineContext = None) -> Generator[TimelineDto, None, None]: def generator(): for id in query["ids"]: url = "https://{platform}.api.riotgames.com/lol/match/v4/timelines/by-match/{id}".format(platform=query["platform"].value.lower(), id=id) try: app_limiter, method_limiter = self._get_rate_limiter(query["platform"], "timelines/by-match/id") data = self._get(url, {}, app_limiter=app_limiter, method_limiter=method_limiter) except APINotFoundError as error: raise NotFoundError(str(error)) from error data["matchId"] = id data["region"] = query["platform"].region.value yield TimelineDto(data) return generator()

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0.004367

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0

null

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null

0

1

0

fe073352dbed399802293822986fcaea27535a33

10,374

py

Python

Lib/site-packages/hackedit/vendor/jedi/cache.py

fochoao/cpython

3dc84b260e5bced65ebc2c45c40c8fa65f9b5aa9

[ "bzip2-1.0.6", "0BSD" ]

1

2017-08-19T08:13:28.000Z

node_modules/nuclide/pkg/nuclide-python-rpc/VendorLib/jedi/cache.py

kevingatera/kgatewebapp

f0dbc50b7af2736e1f6c6f96f0a26fc7ff69db20

[ "Unlicense" ]

20

2021-05-03T18:02:23.000Z

2022-03-12T12:01:04.000Z

Lib/site-packages/hackedit/vendor/jedi/cache.py

fochoao/cpython

3dc84b260e5bced65ebc2c45c40c8fa65f9b5aa9

[ "bzip2-1.0.6", "0BSD" ]

null

""" This caching is very important for speed and memory optimizations. There's nothing really spectacular, just some decorators. The following cache types are available: - module caching (`load_parser` and `save_parser`), which uses pickle and is really important to assure low load times of modules like ``numpy``. - ``time_cache`` can be used to cache something for just a limited time span, which can be useful if there's user interaction and the user cannot react faster than a certain time. This module is one of the reasons why |jedi| is not thread-safe. As you can see there are global variables, which are holding the cache information. Some of these variables are being cleaned after every API usage. """ import time import os import sys import json import hashlib import gc import inspect import shutil import re try: import cPickle as pickle except ImportError: import pickle from jedi import settings from jedi import common from jedi import debug _time_caches = {} # for fast_parser, should not be deleted parser_cache = {} class ParserCacheItem(object): def __init__(self, parser, change_time=None): self.parser = parser if change_time is None: change_time = time.time() self.change_time = change_time def clear_time_caches(delete_all=False): """ Jedi caches many things, that should be completed after each completion finishes. :param delete_all: Deletes also the cache that is normally not deleted, like parser cache, which is important for faster parsing. """ global _time_caches if delete_all: for cache in _time_caches.values(): cache.clear() parser_cache.clear() else: # normally just kill the expired entries, not all for tc in _time_caches.values(): # check time_cache for expired entries for key, (t, value) in list(tc.items()): if t < time.time(): # delete expired entries del tc[key] def time_cache(time_add_setting): """ s This decorator works as follows: Call it with a setting and after that use the function with a callable that returns the key. But: This function is only called if the key is not available. After a certain amount of time (`time_add_setting`) the cache is invalid. """ def _temp(key_func): dct = {} _time_caches[time_add_setting] = dct def wrapper(*args, **kwargs): generator = key_func(*args, **kwargs) key = next(generator) try: expiry, value = dct[key] if expiry > time.time(): return value except KeyError: pass value = next(generator) time_add = getattr(settings, time_add_setting) if key is not None: dct[key] = time.time() + time_add, value return value return wrapper return _temp @time_cache("call_signatures_validity") def cache_call_signatures(evaluator, call, source, user_pos): """This function calculates the cache key.""" index = user_pos[0] - 1 lines = common.splitlines(source) before_cursor = lines[index][:user_pos[1]] other_lines = lines[call.start_pos[0]:index] whole = '\n'.join(other_lines + [before_cursor]) before_bracket = re.match(r'.*\(', whole, re.DOTALL) module_path = call.get_parent_until().path yield None if module_path is None else (module_path, before_bracket, call.start_pos) yield evaluator.eval_element(call) def underscore_memoization(func): """ Decorator for methods:: class A(object): def x(self): if self._x: self._x = 10 return self._x Becomes:: class A(object): @underscore_memoization def x(self): return 10 A now has an attribute ``_x`` written by this decorator. """ name = '_' + func.__name__ def wrapper(self): try: return getattr(self, name) except AttributeError: result = func(self) if inspect.isgenerator(result): result = list(result) setattr(self, name, result) return result return wrapper def memoize_method(method): """A normal memoize function.""" def wrapper(self, *args, **kwargs): dct = self.__dict__.setdefault('_memoize_method_dct', {}) key = (args, frozenset(kwargs.items())) try: return dct[key] except KeyError: result = method(self, *args, **kwargs) dct[key] = result return result return wrapper def memoize_function(obj): """ A normal memoize function for memoizing free functions. """ cache = obj.cache = {} def memoizer(*args, **kwargs): key = str(args) + str(kwargs) if key not in cache: cache[key] = obj(*args, **kwargs) return cache[key] return memoizer def cache_star_import(func): @time_cache("star_import_cache_validity") def wrapper(self): yield self.base # The cache key yield func(self) return wrapper def _invalidate_star_import_cache_module(module, only_main=False): """ Important if some new modules are being reparsed """ try: t, modules = _time_caches['star_import_cache_validity'][module] except KeyError: pass else: del _time_caches['star_import_cache_validity'][module] def invalidate_star_import_cache(path): """On success returns True.""" try: parser_cache_item = parser_cache[path] except KeyError: pass else: _invalidate_star_import_cache_module(parser_cache_item.parser.module) def load_parser(path): """ Returns the module or None, if it fails. """ p_time = os.path.getmtime(path) if path else None try: parser_cache_item = parser_cache[path] if not path or p_time <= parser_cache_item.change_time: return parser_cache_item.parser else: # In case there is already a module cached and this module # has to be reparsed, we also need to invalidate the import # caches. _invalidate_star_import_cache_module(parser_cache_item.parser.module) except KeyError: if settings.use_filesystem_cache: return ParserPickling.load_parser(path, p_time) def save_parser(path, parser, pickling=True): try: p_time = None if path is None else os.path.getmtime(path) except OSError: p_time = None pickling = False item = ParserCacheItem(parser, p_time) parser_cache[path] = item if settings.use_filesystem_cache and pickling: ParserPickling.save_parser(path, item) class ParserPickling(object): version = 24 """ Version number (integer) for file system cache. Increment this number when there are any incompatible changes in parser representation classes. For example, the following changes are regarded as incompatible. - Class name is changed. - Class is moved to another module. - Defined slot of the class is changed. """ def __init__(self): self.__index = None self.py_tag = 'cpython-%s%s' % sys.version_info[:2] """ Short name for distinguish Python implementations and versions. It's like `sys.implementation.cache_tag` but for Python < 3.3 we generate something similar. See: http://docs.python.org/3/library/sys.html#sys.implementation .. todo:: Detect interpreter (e.g., PyPy). """ def load_parser(self, path, original_changed_time): try: pickle_changed_time = self._index[path] except KeyError: return None if original_changed_time is not None \ and pickle_changed_time < original_changed_time: # the pickle file is outdated return None with open(self._get_hashed_path(path), 'rb') as f: try: gc.disable() parser_cache_item = pickle.load(f) finally: gc.enable() debug.dbg('pickle loaded: %s', path) parser_cache[path] = parser_cache_item return parser_cache_item.parser def save_parser(self, path, parser_cache_item): self.__index = None try: files = self._index except KeyError: files = {} self._index = files with open(self._get_hashed_path(path), 'wb') as f: pickle.dump(parser_cache_item, f, pickle.HIGHEST_PROTOCOL) files[path] = parser_cache_item.change_time self._flush_index() @property def _index(self): if self.__index is None: try: with open(self._get_path('index.json')) as f: data = json.load(f) except (IOError, ValueError): self.__index = {} else: # 0 means version is not defined (= always delete cache): if data.get('version', 0) != self.version: self.clear_cache() self.__index = {} else: self.__index = data['index'] return self.__index def _remove_old_modules(self): # TODO use change = False if change: self._flush_index(self) self._index # reload index def _flush_index(self): data = {'version': self.version, 'index': self._index} with open(self._get_path('index.json'), 'w') as f: json.dump(data, f) self.__index = None def clear_cache(self): shutil.rmtree(self._cache_directory()) def _get_hashed_path(self, path): return self._get_path('%s.pkl' % hashlib.md5(path.encode("utf-8")).hexdigest()) def _get_path(self, file): dir = self._cache_directory() if not os.path.exists(dir): os.makedirs(dir) return os.path.join(dir, file) def _cache_directory(self): return os.path.join(settings.cache_directory, self.py_tag) # is a singleton ParserPickling = ParserPickling()

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0.365385

0

null

0

null

0

1

0

fe0a261cca22dd0888b296d89b5ce6c47723b470

4,569

py

Python

python-modules/robcoewmrobotconfigurator/robcoewmrobotconfigurator/run.py

yschiebelhut/ewm-cloud-robotics

bdf3a6c13850d266b70168912494300c32d4d803

[ "Apache-2.0" ]

null

python-modules/robcoewmrobotconfigurator/robcoewmrobotconfigurator/run.py

yschiebelhut/ewm-cloud-robotics

bdf3a6c13850d266b70168912494300c32d4d803

[ "Apache-2.0" ]

null

python-modules/robcoewmrobotconfigurator/robcoewmrobotconfigurator/run.py

yschiebelhut/ewm-cloud-robotics

bdf3a6c13850d266b70168912494300c32d4d803

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 # encoding: utf-8 # # Copyright (c) 2019 SAP SE or an SAP affiliate company. All rights reserved. # # This file is part of ewm-cloud-robotics # (see https://github.com/SAP/ewm-cloud-robotics). # # This file is licensed under the Apache Software License, v. 2 except as noted # otherwise in the LICENSE file (https://github.com/SAP/ewm-cloud-robotics/blob/master/LICENSE) # """Run the SAP EWM robot configurator.""" import sys import signal import traceback import logging import time from robcoewmrobotconfigurator.ewm_robot_sync import EWMRobotSync from robcoewmrobotconfigurator.robotconfigcontroller import RobotConfigurationController from robcoewmrobotconfigurator.robco_robot_api import RobCoRobotAPI _LOGGER = logging.getLogger(__name__) class MainLoopController: """Control the main loop.""" def __init__(self): """Construct.""" # Shutdown Handler self.shutdown = False signal.signal(signal.SIGINT, self.exit_gracefully) signal.signal(signal.SIGTERM, self.exit_gracefully) # Sleep handler self.last_time = time.time() def exit_gracefully(self, signum, frame): """Set shutdown flag on SIGTERM and SIGINT.""" self.shutdown = True _LOGGER.info('Closing application because signal %s received', signum) def sleep(self, seconds: float): """Sleep maximum n seconds after the last call.""" timediff = time.time() - self.last_time if timediff < seconds: time.sleep(seconds-timediff) self.last_time = time.time() def run_robotconfigurator(): """Run one instance of the robot configurator.""" # Register handler to control main loop loop_control = MainLoopController() # Create CR watcher instances k8s_rb = RobCoRobotAPI() k8s_rc = RobotConfigurationController() # Create EWM robot syncer instance robotsync = EWMRobotSync(k8s_rc) # Register callback functions k8s_rb.register_callback('ConfigurationController', ['ADDED'], k8s_rc.robco_robot_cb) k8s_rc.register_callback( 'EWMRobotSync', ['ADDED', 'MODIFIED', 'REPROCESS'], robotsync.robotconfiguration_cb) # Start k8s_rb.run() k8s_rc.run(reprocess=True) _LOGGER.info('SAP EWM Robot Configurator started') try: # Looping while K8S watchers are running while loop_control.shutdown is False: # Refresh bearer token when using OAuth if robotsync.odataconfig.authorization == robotsync.odataconfig.AUTH_OAUTH: robotsync.odatahandler.refresh_access_token() # Check if K8S CR handler exception occured for k, exc in k8s_rb.thread_exceptions.items(): _LOGGER.error( 'Uncovered exception in "%s" thread of RobCoRobotAPI. Raising it in main ' 'thread', k) raise exc for k, exc in k8s_rc.thread_exceptions.items(): _LOGGER.error( 'Uncovered exception in "%s" thread of RobotConfigurationController. Raising ' 'it in main thread', k) raise exc # Sleep maximum 1.0 second loop_control.sleep(1.0) except KeyboardInterrupt: _LOGGER.info('Keyboard interrupt - terminating') except SystemExit: _LOGGER.info('System exit - terminating') finally: # Stop K8S CR watchers _LOGGER.info('Stopping K8S CR watchers') k8s_rb.stop_watcher() k8s_rc.stop_watcher() # Shutdown threadpool executor robotsync.executor.shutdown() if __name__ == '__main__': # Create root logger if running as main program ROOT_LOGGER = logging.getLogger() ROOT_LOGGER.setLevel(logging.INFO) # Create console handler and set level to info CH = logging.StreamHandler() CH.setLevel(logging.INFO) # Create formatter FORMATTER = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') # Add formatter to ch CH.setFormatter(FORMATTER) # Add ch to logger ROOT_LOGGER.addHandler(CH) # Run robot master try: run_robotconfigurator() except Exception: # pylint: disable=broad-except EXC_INFO = sys.exc_info() _LOGGER.critical( 'Unexpected error "%s" - "%s" - TRACEBACK: %s', EXC_INFO[0], EXC_INFO[1], traceback.format_exception(*EXC_INFO)) sys.exit('Application terminated with exception: "{}" - "{}"'.format( EXC_INFO[0], EXC_INFO[1]))

33.595588

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null

0

null

0

1

0

fe0d4c9278280b1296bb8358bef8f6502e5d0540

82,820

py

Python

ninjabackend.py

tp-m/meson

2d1aa395e86848ca948d30d83cc5357777e5b490

[ "Apache-2.0" ]

null

ninjabackend.py

tp-m/meson

2d1aa395e86848ca948d30d83cc5357777e5b490

[ "Apache-2.0" ]

null

ninjabackend.py

tp-m/meson

2d1aa395e86848ca948d30d83cc5357777e5b490

[ "Apache-2.0" ]

null

# Copyright 2012-2014 The Meson development team # 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 backends import environment, mesonlib import build import mlog import dependencies from mesonlib import File from meson_install import InstallData from build import InvalidArguments from coredata import MesonException import os, sys, pickle, re import subprocess, shutil if mesonlib.is_windows(): quote_char = '"' execute_wrapper = 'cmd /c' else: quote_char = "'" execute_wrapper = '' def ninja_quote(text): return text.replace(' ', '$ ').replace(':', '$:') class RawFilename(): def __init__(self, fname): self.fname = fname def split(self, c): return self.fname.split(c) def startswith(self, s): return self.fname.startswith(s) class NinjaBuildElement(): def __init__(self, outfilenames, rule, infilenames): if isinstance(outfilenames, str): self.outfilenames = [outfilenames] else: self.outfilenames = outfilenames assert(isinstance(rule, str)) self.rule = rule if isinstance(infilenames, str): self.infilenames = [infilenames] else: self.infilenames = infilenames self.deps = [] self.orderdeps = [] self.elems = [] def add_dep(self, dep): if isinstance(dep, list): self.deps += dep else: self.deps.append(dep) def add_orderdep(self, dep): if isinstance(dep, list): self.orderdeps += dep else: self.orderdeps.append(dep) def add_item(self, name, elems): if isinstance(elems, str): elems = [elems] self.elems.append((name, elems)) def write(self, outfile): line = 'build %s: %s %s' % (' '.join([ninja_quote(i) for i in self.outfilenames]),\ self.rule, ' '.join([ninja_quote(i) for i in self.infilenames])) if len(self.deps) > 0: line += ' | ' + ' '.join([ninja_quote(x) for x in self.deps]) if len(self.orderdeps) > 0: line += ' || ' + ' '.join([ninja_quote(x) for x in self.orderdeps]) line += '\n' # This is the only way I could find to make this work on all # platforms including Windows command shell. Slash is a dir separator # on Windows, too, so all characters are unambiguous and, more importantly, # do not require quoting. line = line.replace('\\', '/') outfile.write(line) for e in self.elems: (name, elems) = e should_quote = True if name == 'DEPFILE' or name == 'DESC' or name == 'pool': should_quote = False line = ' %s = ' % name q_templ = quote_char + "%s" + quote_char noq_templ = "%s" newelems = [] for i in elems: if not should_quote or i == '&&': # Hackety hack hack templ = noq_templ else: templ = q_templ i = i.replace('\\', '\\\\') if quote_char == '"': i = i.replace('"', '\\"') newelems.append(templ % ninja_quote(i)) line += ' '.join(newelems) line += '\n' outfile.write(line) outfile.write('\n') class NinjaBackend(backends.Backend): def __init__(self, build): super().__init__(build) self.source_suffix_in_objs = True self.ninja_filename = 'build.ninja' self.fortran_deps = {} self.all_outputs = {} def check_outputs(self, elem): for n in elem.outfilenames: if n in self.all_outputs: raise MesonException('Multiple producers for Ninja target "%s". Please rename your targets.' % n) self.all_outputs[n] = True def detect_vs_dep_prefix(self, outfile, tempfilename): '''VS writes its dependency in a locale dependent format. Detect the search prefix to use.''' if shutil.which('cl') is None: return outfile outfile.close() open(os.path.join(self.environment.get_scratch_dir(), 'incdetect.c'), 'w').write('''#include<stdio.h> int dummy; ''') pc = subprocess.Popen(['cl', '/showIncludes', '/c', 'incdetect.c'], stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=self.environment.get_scratch_dir()) (stdo, _) = pc.communicate() for line in stdo.split(b'\r\n'): if line.endswith(b'stdio.h'): matchstr = b':'.join(line.split(b':')[0:2]) + b':' binfile = open(tempfilename, 'ab') binfile.write(b'msvc_deps_prefix = ' + matchstr + b'\r\n') binfile.close() return open(tempfilename, 'a') raise MesonException('Could not determine vs dep dependency prefix string.') def generate(self, interp): self.interpreter = interp outfilename = os.path.join(self.environment.get_build_dir(), self.ninja_filename) tempfilename = outfilename + '~' outfile = open(tempfilename, 'w') outfile.write('# This is the build file for project "%s"\n' % self.build.get_project()) outfile.write('# It is autogenerated by the Meson build system.\n') outfile.write('# Do not edit by hand.\n\n') outfile.write('ninja_required_version = 1.5.1\n\n') outfile = self.detect_vs_dep_prefix(outfile, tempfilename) self.generate_rules(outfile) self.generate_phony(outfile) outfile.write('# Build rules for targets\n\n') [self.generate_target(t, outfile) for t in self.build.get_targets().values()] if len(self.build.pot) > 0: outfile.write('# Build rules for localisation.\n\n') self.generate_po(outfile) outfile.write('# Test rules\n\n') self.generate_tests(outfile) outfile.write('# Install rules\n\n') self.generate_install(outfile) if self.environment.coredata.get_builtin_option('coverage'): outfile.write('# Coverage rules\n\n') self.generate_coverage_rules(outfile) outfile.write('# Suffix\n\n') self.generate_ending(outfile) # Only ovewrite the old build file after the new one has been # fully created. outfile.close() os.replace(tempfilename, outfilename) self.generate_compdb() # http://clang.llvm.org/docs/JSONCompilationDatabase.html def generate_compdb(self): ninja_exe = environment.detect_ninja() builddir = self.environment.get_build_dir() jsondb = subprocess.check_output([ninja_exe, '-t', 'compdb', 'c_COMPILER', 'cpp_COMPILER'], cwd=builddir) open(os.path.join(builddir, 'compile_commands.json'), 'wb').write(jsondb) # Get all generated headers. Any source file might need them so # we need to add an order dependency to them. def get_generated_headers(self, target): header_deps = [] for gensource in target.get_generated_sources(): if isinstance(gensource, build.CustomTarget): continue for src in gensource.get_outfilelist(): if self.environment.is_header(src): header_deps.append(os.path.join(self.get_target_private_dir(target), src)) for dep in target.link_targets: if isinstance(dep, (build.StaticLibrary, build.SharedLibrary)): header_deps += self.get_generated_headers(dep) return header_deps def generate_target(self, target, outfile): if isinstance(target, build.CustomTarget): self.generate_custom_target(target, outfile) if isinstance(target, build.RunTarget): self.generate_run_target(target, outfile) name = target.get_id() gen_src_deps = [] if name in self.processed_targets: return if isinstance(target, build.Jar): self.generate_jar_target(target, outfile) return if 'rust' in self.environment.coredata.compilers.keys() and self.has_rust(target): self.generate_rust_target(target, outfile) return if 'cs' in self.environment.coredata.compilers.keys() and self.has_cs(target): self.generate_cs_target(target, outfile) return if 'vala' in self.environment.coredata.compilers.keys() and self.has_vala(target): gen_src_deps += self.generate_vala_compile(target, outfile) if 'swift' in self.environment.coredata.compilers.keys() and self.has_swift(target): self.generate_swift_target(target, outfile) return self.scan_fortran_module_outputs(target) # The following deals with C/C++ compilation. (gen_src, gen_other_deps) = self.process_dep_gens(outfile, target) gen_src_deps += gen_src self.process_target_dependencies(target, outfile) self.generate_custom_generator_rules(target, outfile) outname = self.get_target_filename(target) obj_list = [] use_pch = self.environment.coredata.get_builtin_option('use_pch') is_unity = self.environment.coredata.get_builtin_option('unity') if use_pch and target.has_pch(): pch_objects = self.generate_pch(target, outfile) else: pch_objects = [] header_deps = gen_other_deps unity_src = [] unity_deps = [] # Generated sources that must be built before compiling a Unity target. header_deps += self.get_generated_headers(target) for gensource in target.get_generated_sources(): if isinstance(gensource, build.CustomTarget): for src in gensource.output: src = os.path.join(self.get_target_dir(gensource), src) if self.environment.is_source(src) and not self.environment.is_header(src): if is_unity: unity_deps.append(os.path.join(self.environment.get_build_dir(), RawFilename(src))) else: obj_list.append(self.generate_single_compile(target, outfile, RawFilename(src), True, header_deps)) elif self.environment.is_object(src): obj_list.append(src) elif self.environment.is_library(src): pass else: # Assume anything not specifically a source file is a header. This is because # people generate files with weird suffixes (.inc, .fh) that they then include # in their source files. header_deps.append(RawFilename(src)) else: for src in gensource.get_outfilelist(): if self.environment.is_object(src): obj_list.append(os.path.join(self.get_target_private_dir(target), src)) elif not self.environment.is_header(src): if is_unity: if self.has_dir_part(src): rel_src = src else: rel_src = os.path.join(self.get_target_private_dir(target), src) unity_deps.append(rel_src) abs_src = os.path.join(self.environment.get_build_dir(), rel_src) unity_src.append(abs_src) else: obj_list.append(self.generate_single_compile(target, outfile, src, True, header_deps=header_deps)) src_list = [] for src in gen_src_deps: src_list.append(src) if is_unity: unity_src.append(os.path.join(self.environment.get_build_dir(), src)) header_deps.append(src) else: # Generated targets are ordered deps because the must exist # before the sources compiling them are used. After the first # compile we get precise dependency info from dep files. # This should work in all cases. If it does not, then just # move them from orderdeps to proper deps. if self.environment.is_header(src): header_deps.append(src) else: obj_list.append(self.generate_single_compile(target, outfile, src, True, [], header_deps)) for src in target.get_sources(): if src.endswith('.vala'): continue if not self.environment.is_header(src): src_list.append(src) if is_unity: abs_src = os.path.join(self.environment.get_build_dir(), src.rel_to_builddir(self.build_to_src)) unity_src.append(abs_src) else: obj_list.append(self.generate_single_compile(target, outfile, src, False, [], header_deps)) obj_list += self.flatten_object_list(target) if is_unity: for src in self.generate_unity_files(target, unity_src): obj_list.append(self.generate_single_compile(target, outfile, src, True, unity_deps + header_deps)) linker = self.determine_linker(target, src_list) elem = self.generate_link(target, outfile, outname, obj_list, linker, pch_objects) self.generate_shlib_aliases(target, self.get_target_dir(target)) elem.write(outfile) self.processed_targets[name] = True def process_target_dependencies(self, target, outfile): for t in target.get_dependencies(): tname = t.get_basename() + t.type_suffix() if not tname in self.processed_targets: self.generate_target(t, outfile) def generate_custom_target(self, target, outfile): (srcs, ofilenames, cmd) = self.eval_custom_target_command(target) deps = [] for i in target.get_dependencies(): # FIXME, should not grab element at zero but rather expand all. if isinstance(i, list): i = i[0] fname = i.get_filename() if isinstance(fname, list): fname = fname[0] deps.append(os.path.join(self.get_target_dir(i), fname)) if target.build_always: deps.append('PHONY') elem = NinjaBuildElement(ofilenames, 'CUSTOM_COMMAND', srcs) for i in target.depend_files: if isinstance(i, mesonlib.File): deps.append(i.rel_to_builddir(self.build_to_src)) else: deps.append(os.path.join(self.build_to_src, i)) elem.add_dep(deps) for d in target.extra_depends: tmp = d.get_filename() if not isinstance(tmp, list): tmp = [tmp] for fname in tmp: elem.add_dep(os.path.join(self.get_target_dir(d), fname)) elem.add_item('COMMAND', cmd) elem.add_item('description', 'Generating %s with a custom command.' % target.name) elem.write(outfile) self.check_outputs(elem) self.processed_targets[target.name + target.type_suffix()] = True def generate_run_target(self, target, outfile): runnerscript = os.path.join(self.environment.get_script_dir(), 'commandrunner.py') deps = [] arg_strings = [] for i in target.args: if isinstance(i, str): arg_strings.append(i) elif isinstance(i, (build.BuildTarget, build.CustomTarget)): relfname = self.get_target_filename(i) deps.append(relfname) arg_strings.append(os.path.join(self.environment.get_build_dir(), relfname)) else: mlog.debug(str(i)) raise MesonException('Unreachable code in generate_run_target.') elem = NinjaBuildElement(target.name, 'CUSTOM_COMMAND', deps) cmd = [sys.executable, runnerscript, self.environment.get_source_dir(), self.environment.get_build_dir(), target.subdir] texe = target.command try: texe = texe.held_object except AttributeError: pass if isinstance(texe, build.Executable): abs_exe = os.path.join(self.environment.get_build_dir(), self.get_target_filename(texe)) deps.append(self.get_target_filename(texe)) if self.environment.is_cross_build() \ and self.environment.cross_info.config['binaries'].get('exe_wrapper', None) is not None: cmd += [self.environment.cross_info.config['binaries']['exe_wrapper']] cmd.append(abs_exe) else: cmd.append(target.command) cmd += arg_strings elem.add_item('COMMAND', cmd) elem.add_item('description', 'Running external command %s.' % target.name) elem.add_item('pool', 'console') elem.write(outfile) self.check_outputs(elem) self.processed_targets[target.name + target.type_suffix()] = True def generate_po(self, outfile): for p in self.build.pot: (packagename, languages, subdir) = p input_file = os.path.join(subdir, 'POTFILES') elem = NinjaBuildElement('pot', 'GEN_POT', []) elem.add_item('PACKAGENAME', packagename) elem.add_item('OUTFILE', packagename + '.pot') elem.add_item('FILELIST', os.path.join(self.environment.get_source_dir(), input_file)) elem.add_item('OUTDIR', os.path.join(self.environment.get_source_dir(), subdir)) elem.write(outfile) self.check_outputs(elem) for l in languages: infile = os.path.join(self.environment.get_source_dir(), subdir, l + '.po') outfilename = os.path.join(subdir, l + '.gmo') lelem = NinjaBuildElement(outfilename, 'GEN_GMO', infile) lelem.add_item('INFILE', infile) lelem.add_item('OUTFILE', outfilename) lelem.write(outfile) self.check_outputs(lelem) def generate_coverage_rules(self, outfile): (gcovr_exe, lcov_exe, genhtml_exe) = environment.find_coverage_tools() added_rule = False if gcovr_exe: added_rule = True elem = NinjaBuildElement('coverage-xml', 'CUSTOM_COMMAND', '') elem.add_item('COMMAND', [gcovr_exe, '-x', '-r', self.environment.get_build_dir(),\ '-o', os.path.join(self.environment.get_log_dir(), 'coverage.xml')]) elem.add_item('DESC', 'Generating XML coverage report.') elem.write(outfile) elem = NinjaBuildElement('coverage-text', 'CUSTOM_COMMAND', '') elem.add_item('COMMAND', [gcovr_exe, '-r', self.environment.get_build_dir(),\ '-o', os.path.join(self.environment.get_log_dir(), 'coverage.txt')]) elem.add_item('DESC', 'Generating text coverage report.') elem.write(outfile) self.check_outputs(elem) if lcov_exe and genhtml_exe: added_rule = True phony_elem = NinjaBuildElement('coverage-html', 'phony', 'coveragereport/index.html') phony_elem.write(outfile) elem = NinjaBuildElement('coveragereport/index.html', 'CUSTOM_COMMAND', '') command = [lcov_exe, '--directory', self.environment.get_build_dir(),\ '--capture', '--output-file', 'coverage.info', '--no-checksum',\ '&&', genhtml_exe, '--prefix', self.environment.get_build_dir(),\ '--output-directory', self.environment.get_log_dir(), '--title', 'Code coverage',\ '--legend', '--show-details', 'coverage.info'] elem.add_item('COMMAND', command) elem.add_item('DESC', 'Generating HTML coverage report.') self.check_outputs(elem) elem.write(outfile) if not added_rule: mlog.log(mlog.red('Warning:'), 'coverage requested but neither gcovr nor lcov/genhtml found.') def generate_install(self, outfile): script_root = self.environment.get_script_dir() install_script = os.path.join(script_root, 'meson_install.py') install_data_file = os.path.join(self.environment.get_scratch_dir(), 'install.dat') depfixer = os.path.join(script_root, 'depfixer.py') d = InstallData(self.environment.get_source_dir(), self.environment.get_build_dir(), self.environment.get_prefix(), depfixer) elem = NinjaBuildElement('install', 'CUSTOM_COMMAND', 'PHONY') elem.add_dep('all') elem.add_item('DESC', 'Installing files.') elem.add_item('COMMAND', [sys.executable, install_script, install_data_file]) elem.add_item('pool', 'console') self.generate_depmf_install(d) self.generate_target_install(d) self.generate_header_install(d) self.generate_man_install(d) self.generate_data_install(d) self.generate_po_install(d, elem) self.generate_custom_install_script(d) self.generate_subdir_install(d) elem.write(outfile) self.check_outputs(elem) ofile = open(install_data_file, 'wb') pickle.dump(d, ofile) def generate_po_install(self, d, elem): for p in self.build.pot: (package_name, languages, subdir) = p # FIXME: assumes only one po package per source d.po_package_name = package_name for lang in languages: rel_src = os.path.join(subdir, lang + '.gmo') src_file = os.path.join(self.environment.get_build_dir(), rel_src) d.po.append((src_file, self.environment.coredata.get_builtin_option('localedir'), lang)) elem.add_dep(rel_src) def generate_target_install(self, d): libdir = self.environment.get_libdir() bindir = self.environment.get_bindir() should_strip = self.environment.coredata.get_builtin_option('strip') for t in self.build.get_targets().values(): if t.should_install(): outdir = t.get_custom_install_dir() if outdir is None: if isinstance(t, build.Executable): outdir = bindir else: outdir = libdir i = [self.get_target_filename(t), outdir, t.get_aliaslist(),\ should_strip, t.install_rpath] d.targets.append(i) def generate_custom_install_script(self, d): d.install_scripts = self.build.install_scripts def generate_header_install(self, d): incroot = self.environment.get_includedir() headers = self.build.get_headers() for h in headers: outdir = h.get_custom_install_dir() if outdir is None: outdir = os.path.join(incroot, h.get_install_subdir()) for f in h.get_sources(): abspath = os.path.join(self.environment.get_source_dir(), h.get_source_subdir(), f) i = [abspath, outdir] d.headers.append(i) def generate_man_install(self, d): manroot = self.environment.get_mandir() man = self.build.get_man() for m in man: for f in m.get_sources(): num = f.split('.')[-1] subdir = m.get_custom_install_dir() if subdir is None: subdir = os.path.join(manroot, 'man' + num) srcabs = os.path.join(self.environment.get_source_dir(), m.get_source_subdir(), f) dstabs = os.path.join(subdir, f + '.gz') i = [srcabs, dstabs] d.man.append(i) def generate_data_install(self, d): data = self.build.get_data() for de in data: assert(isinstance(de, build.Data)) subdir = de.install_dir for f in de.sources: if de.in_sourcetree: srcprefix = self.environment.get_source_dir() else: srcprefix = self.environment.get_build_dir() srcabs = os.path.join(srcprefix, de.source_subdir, f) dstabs = os.path.join(subdir, f) i = [srcabs, dstabs] d.data.append(i) def generate_subdir_install(self, d): for sd in self.build.get_install_subdirs(): src_dir = os.path.join(self.environment.get_source_dir(), sd.source_subdir, sd.installable_subdir) dst_dir = os.path.join(self.environment.get_prefix(), sd.install_dir) d.install_subdirs.append([src_dir, dst_dir]) def write_test_suite_targets(self, cmd, outfile): suites = {} for t in self.build.get_tests(): for s in t.suite: suites[s] = True suites = list(suites.keys()) suites.sort() for s in suites: if s == '': visible_name = 'for top level tests' else: visible_name = s elem = NinjaBuildElement('test-' + s, 'CUSTOM_COMMAND', ['all', 'PHONY']) elem.add_item('COMMAND', cmd + ['--suite=' + s]) elem.add_item('DESC', 'Running test suite %s.' % visible_name) elem.add_item('pool', 'console') elem.write(outfile) self.check_outputs(elem) def generate_tests(self, outfile): self.serialise_tests() valgrind = environment.find_valgrind() script_root = self.environment.get_script_dir() test_script = os.path.join(script_root, 'meson_test.py') test_data = os.path.join(self.environment.get_scratch_dir(), 'meson_test_setup.dat') cmd = [sys.executable, test_script, test_data] elem = NinjaBuildElement('test', 'CUSTOM_COMMAND', ['all', 'PHONY']) elem.add_item('COMMAND', cmd) elem.add_item('DESC', 'Running all tests.') elem.add_item('pool', 'console') elem.write(outfile) self.check_outputs(elem) self.write_test_suite_targets(cmd, outfile) if valgrind: velem = NinjaBuildElement('test-valgrind', 'CUSTOM_COMMAND', ['all', 'PHONY']) velem.add_item('COMMAND', cmd + ['--wrapper=' + valgrind]) velem.add_item('DESC', 'Running test suite under Valgrind.') velem.add_item('pool', 'console') velem.write(outfile) self.check_outputs(velem) # And then benchmarks. benchmark_script = os.path.join(script_root, 'meson_benchmark.py') benchmark_data = os.path.join(self.environment.get_scratch_dir(), 'meson_benchmark_setup.dat') cmd = [sys.executable, benchmark_script, benchmark_data] elem = NinjaBuildElement('benchmark', 'CUSTOM_COMMAND', ['all', 'PHONY']) elem.add_item('COMMAND', cmd) elem.add_item('DESC', 'Running benchmark suite.') elem.add_item('pool', 'console') elem.write(outfile) self.check_outputs(elem) def generate_rules(self, outfile): outfile.write('# Rules for compiling.\n\n') self.generate_compile_rules(outfile) outfile.write('# Rules for linking.\n\n') if self.environment.is_cross_build(): self.generate_static_link_rules(True, outfile) self.generate_static_link_rules(False, outfile) self.generate_dynamic_link_rules(outfile) outfile.write('# Other rules\n\n') outfile.write('rule CUSTOM_COMMAND\n') outfile.write(' command = $COMMAND\n') outfile.write(' description = $DESC\n') outfile.write(' restat = 1\n\n') outfile.write('rule REGENERATE_BUILD\n') c = (quote_char + ninja_quote(sys.executable) + quote_char, quote_char + ninja_quote(self.environment.get_build_command()) + quote_char, quote_char + ninja_quote(self.environment.get_source_dir()) + quote_char, quote_char + ninja_quote(self.environment.get_build_dir()) + quote_char) outfile.write(" command = %s %s %s %s --backend ninja secret-handshake\n" % c) outfile.write(' description = Regenerating build files\n') outfile.write(' generator = 1\n\n') if len(self.build.pot) > 0: self.generate_gettext_rules(outfile) outfile.write('\n') def generate_gettext_rules(self, outfile): rule = 'rule GEN_POT\n' command = " command = xgettext --package-name=$PACKAGENAME -p $OUTDIR -f $FILELIST -D '%s' -k_ -o $OUTFILE\n" % \ self.environment.get_source_dir() desc = " description = Creating pot file for package $PACKAGENAME.\n" outfile.write(rule) outfile.write(command) outfile.write(desc) outfile.write('\n') rule = 'rule GEN_GMO\n' command = ' command = msgfmt $INFILE -o $OUTFILE\n' desc = ' description = Generating gmo file $OUTFILE\n' outfile.write(rule) outfile.write(command) outfile.write(desc) outfile.write('\n') def generate_phony(self, outfile): outfile.write('# Phony build target, always out of date\n') outfile.write('build PHONY: phony\n') outfile.write('\n') def generate_jar_target(self, target, outfile): fname = target.get_filename() subdir = target.get_subdir() outname_rel = os.path.join(self.get_target_dir(target), fname) src_list = target.get_sources() class_list = [] compiler = self.get_compiler_for_source(src_list[0]) assert(compiler.get_language() == 'java') c = 'c' m = '' e = '' f = 'f' main_class = target.get_main_class() if main_class != '': e = 'e' for src in src_list: plain_class_path = self.generate_single_java_compile(src, target, compiler, outfile) class_list.append(plain_class_path) class_dep_list = [os.path.join(self.get_target_private_dir(target), i) for i in class_list] jar_rule = 'java_LINKER' commands = [c+m+e+f] if e != '': commands.append(main_class) commands.append(self.get_target_filename(target)) for cls in class_list: commands += ['-C', self.get_target_private_dir(target), cls] elem = NinjaBuildElement(outname_rel, jar_rule, []) elem.add_dep(class_dep_list) elem.add_item('ARGS', commands) elem.write(outfile) self.check_outputs(elem) def generate_cs_resource_tasks(self, target, outfile): args = [] deps = [] for r in target.resources: rel_sourcefile = os.path.join(self.build_to_src, target.subdir, r) if r.endswith('.resources'): a = '-resource:' + rel_sourcefile elif r.endswith('.txt') or r.endswith('.resx'): ofilebase = os.path.splitext(os.path.basename(r))[0] + '.resources' ofilename = os.path.join(self.get_target_private_dir(target), ofilebase) elem = NinjaBuildElement(ofilename, "CUSTOM_COMMAND", rel_sourcefile) elem.add_item('COMMAND', ['resgen', rel_sourcefile, ofilename]) elem.add_item('DESC', 'Compiling resource %s.' % rel_sourcefile) elem.write(outfile) self.check_outputs(elem) deps.append(ofilename) a = '-resource:' + ofilename else: raise InvalidArguments('Unknown resource file %s.' % r) args.append(a) return (args, deps) def generate_cs_target(self, target, outfile): buildtype = self.environment.coredata.get_builtin_option('buildtype') fname = target.get_filename() outname_rel = os.path.join(self.get_target_dir(target), fname) src_list = target.get_sources() compiler = self.get_compiler_for_source(src_list[0]) assert(compiler.get_language() == 'cs') rel_srcs = [s.rel_to_builddir(self.build_to_src) for s in src_list] deps = [] commands = target.extra_args.get('cs', []) commands += compiler.get_buildtype_args(buildtype) if isinstance(target, build.Executable): commands.append('-target:exe') elif isinstance(target, build.SharedLibrary): commands.append('-target:library') else: raise MesonException('Unknown C# target type.') (resource_args, resource_deps) = self.generate_cs_resource_tasks(target, outfile) commands += resource_args deps += resource_deps commands += compiler.get_output_args(outname_rel) for l in target.link_targets: lname = os.path.join(self.get_target_dir(l), l.get_filename()) commands += compiler.get_link_args(lname) deps.append(lname) if '-g' in commands: outputs = [outname_rel, outname_rel + '.mdb'] else: outputs = [outname_rel] elem = NinjaBuildElement(outputs, 'cs_COMPILER', rel_srcs) elem.add_dep(deps) elem.add_item('ARGS', commands) self.check_outputs(elem) elem.write(outfile) def generate_single_java_compile(self, src, target, compiler, outfile): args = [] args += compiler.get_buildtype_args(self.environment.coredata.get_builtin_option('buildtype')) args += compiler.get_output_args(self.get_target_private_dir(target)) for i in target.include_dirs: for idir in i.get_incdirs(): args += ['-sourcepath', os.path.join(self.build_to_src, i.curdir, idir)] rel_src = src.rel_to_builddir(self.build_to_src) plain_class_path = src.fname[:-4] + 'class' rel_obj = os.path.join(self.get_target_private_dir(target), plain_class_path) element = NinjaBuildElement(rel_obj, compiler.get_language() + '_COMPILER', rel_src) element.add_item('ARGS', args) element.write(outfile) self.check_outputs(element) return plain_class_path def generate_java_link(self, outfile): rule = 'rule java_LINKER\n' command = ' command = jar $ARGS\n' description = ' description = Creating jar $out.\n' outfile.write(rule) outfile.write(command) outfile.write(description) outfile.write('\n') def split_vala_sources(self, sources): src = [] vapi_src = [] for s in sources: if s.endswith('.vapi'): vapi_src.append(s) else: src.append(s) return (src, vapi_src) def determine_dep_vapis(self, target): result = [] for dep in target.link_targets: for i in dep.sources: if hasattr(i, 'fname'): i = i.fname if i.endswith('vala'): vapiname = os.path.splitext(os.path.split(i)[1])[0] + '.vapi' fullname = os.path.join(self.get_target_private_dir(dep), vapiname) result.append(fullname) break return result def generate_vala_compile(self, target, outfile): """Vala is compiled into C. Set up all necessary build steps here.""" valac = self.environment.coredata.compilers['vala'] (src, vapi_src) = self.split_vala_sources(target.get_sources()) vapi_src = [x.rel_to_builddir(self.build_to_src) for x in vapi_src] extra_dep_files = [] vala_input_files = [] for s in src: if s.endswith('.vala'): vala_input_files.append(s.rel_to_builddir(self.build_to_src)) namebase = os.path.splitext(os.path.split(vala_input_files[0])[1])[0] hname = namebase + '.h' vapiname = namebase + '.vapi' outputs = [vapiname] args = ['-d', self.get_target_private_dir(target)] args += ['-C']#, '-o', cname] if not isinstance(target, build.Executable): outputs.append(hname) args += ['-H', hname] args += ['--vapi=' + vapiname] for src in vala_input_files: namebase = os.path.splitext(os.path.split(src)[1])[0] + '.c' outputs.append(namebase) if self.environment.coredata.get_builtin_option('werror'): args += valac.get_werror_args() for d in target.external_deps: if isinstance(d, dependencies.PkgConfigDependency): if d.name == 'glib-2.0' and d.version_requirement is not None \ and d.version_requirement.startswith(('>=', '==')): args += ['--target-glib', d.version_requirement[2:]] args += ['--pkg', d.name] extra_args = [] for a in target.extra_args.get('vala', []): if isinstance(a, File): relname = a.rel_to_builddir(self.build_to_src) extra_dep_files.append(relname) extra_args.append(relname) else: extra_args.append(a) dependency_vapis = self.determine_dep_vapis(target) extra_dep_files += dependency_vapis args += extra_args args += dependency_vapis outputs = [os.path.join(self.get_target_private_dir(target), x) for x in outputs] element = NinjaBuildElement(outputs, valac.get_language() + '_COMPILER', vala_input_files + vapi_src) element.add_item('ARGS', args) element.add_dep(extra_dep_files) element.write(outfile) self.check_outputs(element) return outputs def generate_rust_target(self, target, outfile): rustc = self.environment.coredata.compilers['rust'] relsrc = [] for i in target.get_sources(): if not rustc.can_compile(i): raise InvalidArguments('Rust target %s contains a non-rust source file.' % target.get_basename()) relsrc.append(i.rel_to_builddir(self.build_to_src)) target_name = os.path.join(target.subdir, target.get_filename()) args = ['--crate-type'] if isinstance(target, build.Executable): cratetype = 'bin' elif isinstance(target, build.SharedLibrary): cratetype = 'rlib' elif isinstance(target, build.StaticLibrary): cratetype = 'rlib' else: raise InvalidArguments('Unknown target type for rustc.') args.append(cratetype) args += rustc.get_buildtype_args(self.environment.coredata.get_builtin_option('buildtype')) depfile = target.name + '.d' args += ['--out-dir', target.subdir] args += ['--emit', 'dep-info', '--emit', 'link'] orderdeps = [os.path.join(t.subdir, t.get_filename()) for t in target.link_targets] linkdirs = {} for d in target.link_targets: linkdirs[d.subdir] = True for d in linkdirs.keys(): if d == '': d = '.' args += ['-L', d] element = NinjaBuildElement(target_name, 'rust_COMPILER', relsrc) if len(orderdeps) > 0: element.add_orderdep(orderdeps) element.add_item('ARGS', args) element.add_item('targetdep', depfile) element.add_item('cratetype', cratetype) element.write(outfile) self.check_outputs(element) def swift_module_file_name(self, target): return os.path.join(self.get_target_private_dir(target), self.target_swift_modulename(target) + '.swiftmodule') def target_swift_modulename(self, target): return target.name def is_swift_target(self, target): for s in target.sources: if s.endswith('swift'): return True return False def determine_swift_dep_modules(self, target): result = [] for l in target.link_targets: if self.is_swift_target(l): result.append(self.swift_module_file_name(l)) return result def determine_swift_dep_dirs(self, target): result = [] for l in target.link_targets: result.append(self.get_target_private_dir_abs(l)) return result def get_swift_link_deps(self, target): result = [] for l in target.link_targets: result.append(self.get_target_filename(l)) return result def split_swift_generated_sources(self, target): all_srcs = [] for genlist in target.get_generated_sources(): if isinstance(genlist, build.CustomTarget): for ifile in genlist.get_filename(): rel = os.path.join(self.get_target_dir(genlist), ifile) all_srcs.append(rel) else: for ifile in genlist.get_outfilelist(): rel = os.path.join(self.get_target_private_dir(target), ifile) all_srcs.append(rel) srcs = [] others = [] for i in all_srcs: if i.endswith('.swift'): srcs.append(i) else: others.append(i) return (srcs, others) def generate_swift_target(self, target, outfile): module_name = self.target_swift_modulename(target) swiftc = self.environment.coredata.compilers['swift'] abssrc = [] abs_headers = [] header_imports = [] for i in target.get_sources(): if swiftc.can_compile(i): relsrc = i.rel_to_builddir(self.build_to_src) abss = os.path.normpath(os.path.join(self.environment.get_build_dir(), relsrc)) abssrc.append(abss) elif self.environment.is_header(i): relh = i.rel_to_builddir(self.build_to_src) absh = os.path.normpath(os.path.join(self.environment.get_build_dir(), relh)) abs_headers.append(absh) header_imports += swiftc.get_header_import_args(absh) else: raise InvalidArguments('Swift target %s contains a non-swift source file.' % target.get_basename()) os.makedirs(self.get_target_private_dir_abs(target), exist_ok=True) compile_args = swiftc.get_compile_only_args() compile_args += swiftc.get_module_args(module_name) link_args = swiftc.get_output_args(os.path.join(self.environment.get_build_dir(), self.get_target_filename(target))) rundir = self.get_target_private_dir(target) out_module_name = self.swift_module_file_name(target) in_module_files = self.determine_swift_dep_modules(target) abs_module_dirs = self.determine_swift_dep_dirs(target) module_includes = [] for x in abs_module_dirs: module_includes += swiftc.get_include_args(x) link_deps = self.get_swift_link_deps(target) abs_link_deps = [os.path.join(self.environment.get_build_dir(), x) for x in link_deps] (rel_generated, _) = self.split_swift_generated_sources(target) abs_generated = [os.path.join(self.environment.get_build_dir(), x) for x in rel_generated] # We need absolute paths because swiftc needs to be invoked in a subdir # and this is the easiest way about it. objects = [] # Relative to swift invocation dir rel_objects = [] # Relative to build.ninja for i in abssrc + abs_generated: base = os.path.split(i)[1] oname = os.path.splitext(base)[0] + '.o' objects.append(oname) rel_objects.append(os.path.join(self.get_target_private_dir(target), oname)) # Swiftc does not seem to be able to emit objects and module files in one go. elem = NinjaBuildElement(rel_objects, 'swift_COMPILER', abssrc) elem.add_dep(in_module_files + rel_generated) elem.add_dep(abs_headers) elem.add_item('ARGS', compile_args + header_imports + abs_generated + module_includes) elem.add_item('RUNDIR', rundir) elem.write(outfile) self.check_outputs(elem) elem = NinjaBuildElement(out_module_name, 'swift_COMPILER', abssrc) elem.add_dep(in_module_files + rel_generated) elem.add_item('ARGS', compile_args + abs_generated + module_includes + swiftc.get_mod_gen_args()) elem.add_item('RUNDIR', rundir) elem.write(outfile) self.check_outputs(elem) if isinstance(target, build.StaticLibrary): elem = self.generate_link(target, outfile, self.get_target_filename(target), rel_objects, self.build.static_linker) elem.write(outfile) elif isinstance(target, build.Executable): elem = NinjaBuildElement(self.get_target_filename(target), 'swift_COMPILER', []) elem.add_dep(rel_objects) elem.add_dep(link_deps) elem.add_item('ARGS', link_args + swiftc.get_std_exe_link_args() + objects + abs_link_deps) elem.add_item('RUNDIR', rundir) elem.write(outfile) self.check_outputs(elem) else: raise MesonException('Swift supports only executable and static library targets.') def generate_static_link_rules(self, is_cross, outfile): if self.build.has_language('java'): if not is_cross: self.generate_java_link(outfile) if is_cross: if self.environment.cross_info.need_cross_compiler(): static_linker = self.build.static_cross_linker else: static_linker = self.build.static_linker crstr = '_CROSS' else: static_linker = self.build.static_linker crstr = '' if static_linker is None: return rule = 'rule STATIC%s_LINKER\n' % crstr if mesonlib.is_windows(): command_templ = ''' command = %s @$out.rsp rspfile = $out.rsp rspfile_content = $LINK_ARGS %s $in ''' else: command_templ = ' command = %s $LINK_ARGS %s $in\n' command = command_templ %\ (' '.join(static_linker.get_exelist()), ' '.join(static_linker.get_output_args('$out'))) description = ' description = Static linking library $out\n\n' outfile.write(rule) outfile.write(command) outfile.write(description) def generate_dynamic_link_rules(self, outfile): ctypes = [(self.build.compilers, False)] if self.environment.is_cross_build(): if self.environment.cross_info.need_cross_compiler(): ctypes.append((self.build.cross_compilers, True)) else: # Native compiler masquerades as the cross compiler. ctypes.append((self.build.compilers, True)) else: ctypes.append((self.build.cross_compilers, True)) for (complist, is_cross) in ctypes: for compiler in complist: langname = compiler.get_language() if langname == 'java' or langname == 'vala' or\ langname == 'rust' or langname == 'cs': continue crstr = '' cross_args = [] if is_cross: crstr = '_CROSS' try: cross_args = self.environment.cross_info.config['properties'][langname + '_link_args'] except KeyError: pass rule = 'rule %s%s_LINKER\n' % (langname, crstr) if mesonlib.is_windows(): command_template = ''' command = %s @$out.rsp rspfile = $out.rsp rspfile_content = %s $ARGS %s $in $LINK_ARGS $aliasing ''' else: command_template = ' command = %s %s $ARGS %s $in $LINK_ARGS $aliasing\n' command = command_template % \ (' '.join(compiler.get_linker_exelist()),\ ' '.join(cross_args),\ ' '.join(compiler.get_linker_output_args('$out'))) description = ' description = Linking target $out' outfile.write(rule) outfile.write(command) outfile.write(description) outfile.write('\n') scriptdir = self.environment.get_script_dir() outfile.write('\n') symrule = 'rule SHSYM\n' symcmd = ' command = "%s" "%s" %s %s $CROSS\n' % (ninja_quote(sys.executable), ninja_quote(os.path.join(scriptdir, 'symbolextractor.py')), '$in', '$out') synstat = ' restat = 1\n' syndesc = ' description = Generating symbol file $out.\n' outfile.write(symrule) outfile.write(symcmd) outfile.write(synstat) outfile.write(syndesc) outfile.write('\n') def generate_java_compile_rule(self, compiler, outfile): rule = 'rule %s_COMPILER\n' % compiler.get_language() invoc = ' '.join([ninja_quote(i) for i in compiler.get_exelist()]) command = ' command = %s $ARGS $in\n' % invoc description = ' description = Compiling Java object $in.\n' outfile.write(rule) outfile.write(command) outfile.write(description) outfile.write('\n') def generate_cs_compile_rule(self, compiler, outfile): rule = 'rule %s_COMPILER\n' % compiler.get_language() invoc = ' '.join([ninja_quote(i) for i in compiler.get_exelist()]) command = ' command = %s $ARGS $in\n' % invoc description = ' description = Compiling cs target $out.\n' outfile.write(rule) outfile.write(command) outfile.write(description) outfile.write('\n') def generate_vala_compile_rules(self, compiler, outfile): rule = 'rule %s_COMPILER\n' % compiler.get_language() invoc = ' '.join([ninja_quote(i) for i in compiler.get_exelist()]) command = ' command = %s $ARGS $in\n' % invoc description = ' description = Compiling Vala source $in.\n' restat = ' restat = 1\n' # ValaC does this always to take advantage of it. outfile.write(rule) outfile.write(command) outfile.write(description) outfile.write(restat) outfile.write('\n') def generate_rust_compile_rules(self, compiler, outfile): rule = 'rule %s_COMPILER\n' % compiler.get_language() invoc = ' '.join([ninja_quote(i) for i in compiler.get_exelist()]) command = ' command = %s $ARGS $in\n' % invoc description = ' description = Compiling Rust source $in.\n' depfile = ' depfile = $targetdep\n' depstyle = ' deps = gcc\n' outfile.write(rule) outfile.write(command) outfile.write(description) outfile.write(depfile) outfile.write(depstyle) outfile.write('\n') def generate_swift_compile_rules(self, compiler, outfile): rule = 'rule %s_COMPILER\n' % compiler.get_language() full_exe = [sys.executable, os.path.join(self.environment.get_script_dir(), 'dirchanger.py'), '$RUNDIR'] + compiler.get_exelist() invoc = ' '.join([ninja_quote(i) for i in full_exe]) command = ' command = %s $ARGS $in\n' % invoc description = ' description = Compiling Swift source $in.\n' outfile.write(rule) outfile.write(command) outfile.write(description) outfile.write('\n') def generate_fortran_dep_hack(self, outfile): if mesonlib.is_windows(): cmd = 'cmd /C ""' else: cmd = 'true' template = '''# Workaround for these issues: # https://groups.google.com/forum/#!topic/ninja-build/j-2RfBIOd_8 # https://gcc.gnu.org/bugzilla/show_bug.cgi?id=47485 rule FORTRAN_DEP_HACK command = %s description = Dep hack restat = 1 ''' outfile.write(template % cmd) def generate_compile_rule_for(self, langname, compiler, qstr, is_cross, outfile): if langname == 'java': if not is_cross: self.generate_java_compile_rule(compiler, outfile) return if langname == 'cs': if not is_cross: self.generate_cs_compile_rule(compiler, outfile) return if langname == 'vala': if not is_cross: self.generate_vala_compile_rules(compiler, outfile) return if langname == 'rust': if not is_cross: self.generate_rust_compile_rules(compiler, outfile) return if langname == 'swift': if not is_cross: self.generate_swift_compile_rules(compiler, outfile) return if langname == 'fortran': self.generate_fortran_dep_hack(outfile) if is_cross: crstr = '_CROSS' else: crstr = '' rule = 'rule %s%s_COMPILER\n' % (langname, crstr) depargs = compiler.get_dependency_gen_args('$out', '$DEPFILE') quoted_depargs = [] for d in depargs: if d != '$out' and d != '$in': d = qstr % d quoted_depargs.append(d) cross_args = [] if is_cross: try: cross_args = self.environment.cross_info.config['properties'][langname + '_args'] except KeyError: pass if mesonlib.is_windows(): command_template = ''' command = %s @$out.rsp rspfile = $out.rsp rspfile_content = %s $ARGS %s %s %s $in ''' else: command_template = ' command = %s %s $ARGS %s %s %s $in\n' command = command_template % \ (' '.join(compiler.get_exelist()),\ ' '.join(cross_args), ' '.join(quoted_depargs),\ ' '.join(compiler.get_output_args('$out')),\ ' '.join(compiler.get_compile_only_args())) description = ' description = Compiling %s object $out\n' % langname if compiler.get_id() == 'msvc': deps = ' deps = msvc\n' else: deps = ' deps = gcc\n' deps += ' depfile = $DEPFILE\n' outfile.write(rule) outfile.write(command) outfile.write(deps) outfile.write(description) outfile.write('\n') def generate_pch_rule_for(self, langname, compiler, qstr, is_cross, outfile): if langname != 'c' and langname != 'cpp': return if is_cross: crstr = '_CROSS' else: crstr = '' rule = 'rule %s%s_PCH\n' % (langname, crstr) depargs = compiler.get_dependency_gen_args('$out', '$DEPFILE') cross_args = [] if is_cross: try: cross_args = self.environment.cross_info.config['properties'][langname + '_args'] except KeyError: pass quoted_depargs = [] for d in depargs: if d != '$out' and d != '$in': d = qstr % d quoted_depargs.append(d) if compiler.get_id() == 'msvc': output = '' else: output = ' '.join(compiler.get_output_args('$out')) command = " command = %s %s $ARGS %s %s %s $in\n" % \ (' '.join(compiler.get_exelist()),\ ' '.join(cross_args),\ ' '.join(quoted_depargs),\ output,\ ' '.join(compiler.get_compile_only_args())) description = ' description = Precompiling header %s\n' % '$in' if compiler.get_id() == 'msvc': deps = ' deps = msvc\n' else: deps = ' deps = gcc\n' deps += ' depfile = $DEPFILE\n' outfile.write(rule) outfile.write(command) outfile.write(deps) outfile.write(description) outfile.write('\n') def generate_compile_rules(self, outfile): qstr = quote_char + "%s" + quote_char for compiler in self.build.compilers: langname = compiler.get_language() self.generate_compile_rule_for(langname, compiler, qstr, False, outfile) self.generate_pch_rule_for(langname, compiler, qstr, False, outfile) if self.environment.is_cross_build(): # In case we are going a target-only build, make the native compilers # masquerade as cross compilers. if self.environment.cross_info.need_cross_compiler(): cclist = self.build.cross_compilers else: cclist = self.build.compilers for compiler in cclist: langname = compiler.get_language() self.generate_compile_rule_for(langname, compiler, qstr, True, outfile) self.generate_pch_rule_for(langname, compiler, qstr, True, outfile) outfile.write('\n') def replace_outputs(self, args, private_dir, output_list): newargs = [] regex = re.compile('@OUTPUT(\d+)@') for arg in args: m = regex.search(arg) while m is not None: index = int(m.group(1)) src = '@OUTPUT%d@' % index arg = arg.replace(src, os.path.join(private_dir, output_list[index])) m = regex.search(arg) newargs.append(arg) return newargs def generate_custom_generator_rules(self, target, outfile): for genlist in target.get_generated_sources(): if isinstance(genlist, build.CustomTarget): continue # Customtarget has already written its output rules generator = genlist.get_generator() exe = generator.get_exe() exe_arr = self.exe_object_to_cmd_array(exe) infilelist = genlist.get_infilelist() outfilelist = genlist.get_outfilelist() base_args = generator.get_arglist() extra_dependencies = [os.path.join(self.build_to_src, i) for i in genlist.extra_depends] for i in range(len(infilelist)): if len(generator.outputs) == 1: sole_output = os.path.join(self.get_target_private_dir(target), outfilelist[i]) else: sole_output = '' curfile = infilelist[i] infilename = os.path.join(self.build_to_src, curfile) outfiles = genlist.get_outputs_for(curfile) outfiles = [os.path.join(self.get_target_private_dir(target), of) for of in outfiles] args = [x.replace("@INPUT@", infilename).replace('@OUTPUT@', sole_output)\ for x in base_args] args = self.replace_outputs(args, self.get_target_private_dir(target), outfilelist) # We have consumed output files, so drop them from the list of remaining outputs. if sole_output == '': outfilelist = outfilelist[len(generator.outputs):] relout = self.get_target_private_dir(target) args = [x.replace("@SOURCE_DIR@", self.build_to_src).replace("@BUILD_DIR@", relout) for x in args] final_args = [] for a in args: if a == '@EXTRA_ARGS@': final_args += genlist.get_extra_args() else: final_args.append(a) cmdlist = exe_arr + final_args elem = NinjaBuildElement(outfiles, 'CUSTOM_COMMAND', infilename) if len(extra_dependencies) > 0: elem.add_dep(extra_dependencies) elem.add_item('DESC', 'Generating $out') if isinstance(exe, build.BuildTarget): elem.add_dep(self.get_target_filename(exe)) elem.add_item('COMMAND', cmdlist) elem.write(outfile) self.check_outputs(elem) def scan_fortran_module_outputs(self, target): compiler = None for c in self.build.compilers: if c.get_language() == 'fortran': compiler = c break if compiler is None: self.fortran_deps[target.get_basename()] = {} return modre = re.compile(r"\s*module\s+(\w+)", re.IGNORECASE) module_files = {} for s in target.get_sources(): # FIXME, does not work for generated Fortran sources, # but those are really rare. I hope. if not compiler.can_compile(s): continue for line in open(os.path.join(self.environment.get_source_dir(), s.subdir, s.fname)): modmatch = modre.match(line) if modmatch is not None: modname = modmatch.group(1) if modname.lower() == 'procedure': # MODULE PROCEDURE construct continue if modname in module_files: raise InvalidArguments('Namespace collision: module %s defined in two files %s and %s.' % (modname, module_files[modname], s)) module_files[modname] = s self.fortran_deps[target.get_basename()] = module_files def get_fortran_deps(self, compiler, src, target): mod_files = [] usere = re.compile(r"\s*use\s+(\w+)", re.IGNORECASE) dirname = self.get_target_private_dir(target) tdeps= self.fortran_deps[target.get_basename()] for line in open(src): usematch = usere.match(line) if usematch is not None: usename = usematch.group(1) if usename not in tdeps: # The module is not provided by any source file. This is due to # a) missing file/typo/etc # b) using a module provided by the compiler, such as OpenMP # There's no easy way to tell which is which (that I know of) # so just ignore this and go on. Ideally we would print a # warning message to the user but this is a common occurrance, # which would lead to lots of distracting noise. continue mod_source_file = tdeps[usename] # Check if a source uses a module it exports itself. # Potential bug if multiple targets have a file with # the same name. if mod_source_file.fname == os.path.split(src)[1]: continue mod_name = compiler.module_name_to_filename(usematch.group(1)) mod_files.append(os.path.join(dirname, mod_name)) return mod_files def generate_single_compile(self, target, outfile, src, is_generated=False, header_deps=[], order_deps=[]): if(isinstance(src, str) and src.endswith('.h')): raise RuntimeError('Fug') if isinstance(src, RawFilename) and src.fname.endswith('.h'): raise RuntimeError('Fug') extra_orderdeps = [] compiler = self.get_compiler_for_source(src) commands = self.generate_basic_compiler_args(target, compiler) commands += compiler.get_include_args(self.get_target_private_dir(target), False) curdir = target.get_subdir() tmppath = os.path.normpath(os.path.join(self.build_to_src, curdir)) commands += compiler.get_include_args(tmppath, False) if curdir == '': curdir = '.' commands += compiler.get_include_args(curdir, False) for d in target.external_deps: if d.need_threads(): commands += compiler.thread_flags() break if isinstance(src, RawFilename): rel_src = src.fname elif is_generated: if self.has_dir_part(src): rel_src = src else: rel_src = os.path.join(self.get_target_private_dir(target), src) abs_src = os.path.join(self.environment.get_source_dir(), rel_src) else: if isinstance(src, File): rel_src = src.rel_to_builddir(self.build_to_src) else: raise build.InvalidArguments('Invalid source type.') abs_src = os.path.join(self.environment.get_build_dir(), rel_src) if isinstance(src, RawFilename): src_filename = src.fname elif isinstance(src, File): src_filename = src.fname elif os.path.isabs(src): src_filename = os.path.basename(src) else: src_filename = src obj_basename = src_filename.replace('/', '_').replace('\\', '_') rel_obj = os.path.join(self.get_target_private_dir(target), obj_basename) rel_obj += '.' + self.environment.get_object_suffix() dep_file = compiler.depfile_for_object(rel_obj) if self.environment.coredata.get_builtin_option('use_pch'): pchlist = target.get_pch(compiler.language) else: pchlist = [] if len(pchlist) == 0: pch_dep = [] else: arr = [] i = os.path.join(self.get_target_private_dir(target), compiler.get_pch_name(pchlist[0])) arr.append(i) pch_dep = arr for i in target.get_include_dirs(): basedir = i.get_curdir() for d in i.get_incdirs(): expdir = os.path.join(basedir, d) srctreedir = os.path.join(self.build_to_src, expdir) bargs = compiler.get_include_args(expdir, i.is_system) sargs = compiler.get_include_args(srctreedir, i.is_system) commands += bargs commands += sargs for d in i.get_extra_build_dirs(): commands += compiler.get_include_args(d, i.is_system) custom_target_include_dirs = [] for i in target.generated: if isinstance(i, build.CustomTarget): idir = self.get_target_dir(i) if idir not in custom_target_include_dirs: custom_target_include_dirs.append(idir) for i in custom_target_include_dirs: commands+= compiler.get_include_args(i, False) if self.environment.coredata.get_builtin_option('use_pch'): commands += self.get_pch_include_args(compiler, target) crstr = '' if target.is_cross: crstr = '_CROSS' compiler_name = '%s%s_COMPILER' % (compiler.get_language(), crstr) extra_deps = [] if compiler.get_language() == 'fortran': extra_deps += self.get_fortran_deps(compiler, abs_src, target) # Dependency hack. Remove once multiple outputs in Ninja is fixed: # https://groups.google.com/forum/#!topic/ninja-build/j-2RfBIOd_8 for modname, srcfile in self.fortran_deps[target.get_basename()].items(): modfile = os.path.join(self.get_target_private_dir(target), compiler.module_name_to_filename(modname)) if srcfile == src: depelem = NinjaBuildElement(modfile, 'FORTRAN_DEP_HACK', rel_obj) depelem.write(outfile) self.check_outputs(depelem) commands += compiler.get_module_outdir_args(self.get_target_private_dir(target)) element = NinjaBuildElement(rel_obj, compiler_name, rel_src) for d in header_deps: if isinstance(d, RawFilename): d = d.fname elif not self.has_dir_part(d): d = os.path.join(self.get_target_private_dir(target), d) element.add_dep(d) for d in extra_deps: element.add_dep(d) for d in order_deps: if isinstance(d, RawFilename): d = d.fname elif not self.has_dir_part(d): d = os.path.join(self.get_target_private_dir(target), d) element.add_orderdep(d) element.add_orderdep(pch_dep) element.add_orderdep(extra_orderdeps) for i in self.get_fortran_orderdeps(target, compiler): element.add_orderdep(i) element.add_item('DEPFILE', dep_file) element.add_item('ARGS', commands) element.write(outfile) self.check_outputs(element) return rel_obj def has_dir_part(self, fname): return '/' in fname or '\\' in fname # Fortran is a bit weird (again). When you link against a library, just compiling a source file # requires the mod files that are output when single files are built. To do this right we would need to # scan all inputs and write out explicit deps for each file. That is stoo slow and too much effort so # instead just have an ordered dependendy on the library. This ensures all required mod files are created. # The real deps are then detected via dep file generation from the compiler. This breaks on compilers that # produce incorrect dep files but such is life. def get_fortran_orderdeps(self, target, compiler): if compiler.language != 'fortran': return [] return [os.path.join(self.get_target_dir(lt), lt.get_filename()) for lt in target.link_targets] def generate_msvc_pch_command(self, target, compiler, pch): if len(pch) != 2: raise RuntimeError('MSVC requires one header and one source to produce precompiled headers.') header = pch[0] source = pch[1] pchname = compiler.get_pch_name(header) dst = os.path.join(self.get_target_private_dir(target), pchname) commands = [] commands += self.generate_basic_compiler_args(target, compiler) just_name = os.path.split(header)[1] (objname, pch_args) = compiler.gen_pch_args(just_name, source, dst) commands += pch_args dep = dst + '.' + compiler.get_depfile_suffix() return (commands, dep, dst, [objname]) def generate_gcc_pch_command(self, target, compiler, pch): commands = [] commands += self.generate_basic_compiler_args(target, compiler) dst = os.path.join(self.get_target_private_dir(target), os.path.split(pch)[-1] + '.' + compiler.get_pch_suffix()) dep = dst + '.' + compiler.get_depfile_suffix() return (commands, dep, dst, []) # Gcc does not create an object file during pch generation. def generate_pch(self, target, outfile): cstr = '' pch_objects = [] if target.is_cross: cstr = '_CROSS' for lang in ['c', 'cpp']: pch = target.get_pch(lang) if len(pch) == 0: continue if '/' not in pch[0] or '/' not in pch[-1]: raise build.InvalidArguments('Precompiled header of "%s" must not be in the same directory as source, please put it in a subdirectory.' % target.get_basename()) compiler = self.get_compiler_for_lang(lang) if compiler.id == 'msvc': src = os.path.join(self.build_to_src, target.get_source_subdir(), pch[-1]) (commands, dep, dst, objs) = self.generate_msvc_pch_command(target, compiler, pch) extradep = os.path.join(self.build_to_src, target.get_source_subdir(), pch[0]) else: src = os.path.join(self.build_to_src, target.get_source_subdir(), pch[0]) (commands, dep, dst, objs) = self.generate_gcc_pch_command(target, compiler, pch[0]) extradep = None pch_objects += objs rulename = compiler.get_language() + cstr + '_PCH' elem = NinjaBuildElement(dst, rulename, src) if extradep is not None: elem.add_dep(extradep) elem.add_item('ARGS', commands) elem.add_item('DEPFILE', dep) elem.write(outfile) self.check_outputs(elem) return pch_objects def generate_shsym(self, outfile, target): target_name = self.get_target_filename(target) targetdir = self.get_target_private_dir(target) symname = os.path.join(targetdir, target_name + '.symbols') elem = NinjaBuildElement(symname, 'SHSYM', target_name) if self.environment.is_cross_build() and self.environment.cross_info.need_cross_compiler(): elem.add_item('CROSS', '--cross-host=' + self.environment.cross_info.config['host_machine']['system']) elem.write(outfile) self.check_outputs(elem) def generate_link(self, target, outfile, outname, obj_list, linker, extra_args=[]): if isinstance(target, build.StaticLibrary): linker_base = 'STATIC' else: linker_base = linker.get_language() # Fixme. if isinstance(target, build.SharedLibrary): self.generate_shsym(outfile, target) crstr = '' if target.is_cross: crstr = '_CROSS' linker_rule = linker_base + crstr + '_LINKER' abspath = os.path.join(self.environment.get_build_dir(), target.subdir) commands = [] commands += linker.get_linker_always_args() commands += linker.get_buildtype_linker_args(self.environment.coredata.get_builtin_option('buildtype')) commands += linker.get_option_link_args(self.environment.coredata.compiler_options) if not(isinstance(target, build.StaticLibrary)): commands += self.environment.coredata.external_link_args[linker.get_language()] if isinstance(target, build.Executable): commands += linker.get_std_exe_link_args() elif isinstance(target, build.SharedLibrary): commands += linker.get_std_shared_lib_link_args() commands += linker.get_pic_args() if hasattr(target, 'soversion'): soversion = target.soversion else: soversion = None commands += linker.get_soname_args(target.name, abspath, soversion) elif isinstance(target, build.StaticLibrary): commands += linker.get_std_link_args() else: raise RuntimeError('Unknown build target type.') # Link arguments of static libraries are not put in the command line of # the library. They are instead appended to the command line where # the static library is used. if linker_base == 'STATIC': dependencies = [] else: dependencies = target.get_dependencies() commands += self.build_target_link_arguments(linker, dependencies) for d in target.external_deps: if d.need_threads(): commands += linker.thread_link_flags() if not isinstance(target, build.StaticLibrary): commands += target.link_args # External deps must be last because target link libraries may depend on them. if not(isinstance(target, build.StaticLibrary)): for dep in target.get_external_deps(): commands += dep.get_link_args() for d in target.get_dependencies(): if isinstance(d, build.StaticLibrary): for dep in d.get_external_deps(): commands += dep.get_link_args() commands += linker.build_rpath_args(self.environment.get_build_dir(),\ self.determine_rpath_dirs(target), target.install_rpath) if self.environment.coredata.get_builtin_option('coverage'): commands += linker.get_coverage_link_args() custom_target_libraries = self.get_custom_target_provided_libraries(target) commands += extra_args commands += custom_target_libraries commands = linker.unixtype_flags_to_native(commands) dep_targets = [self.get_dependency_filename(t) for t in dependencies] dep_targets += [os.path.join(self.environment.source_dir, target.subdir, t) for t in target.link_depends] elem = NinjaBuildElement(outname, linker_rule, obj_list) elem.add_dep(dep_targets + custom_target_libraries) elem.add_item('LINK_ARGS', commands) self.check_outputs(elem) return elem def get_custom_target_provided_libraries(self, target): libs = [] for t in target.get_generated_sources(): if not isinstance(t, build.CustomTarget): continue for f in t.output: if self.environment.is_library(f): libs.append(os.path.join(self.get_target_dir(t), f)) return libs def determine_rpath_dirs(self, target): link_deps = target.get_all_link_deps() result = [] for ld in link_deps: prospective = self.get_target_dir(ld) if not prospective in result: result.append(prospective) return result def get_dependency_filename(self, t): if isinstance(t, build.SharedLibrary): return os.path.join(self.get_target_private_dir(t), self.get_target_filename(t) + '.symbols') return self.get_target_filename(t) def generate_shlib_aliases(self, target, outdir): basename = target.get_filename() aliases = target.get_aliaslist() if not mesonlib.is_windows(): for alias in aliases: aliasfile = os.path.join(self.environment.get_build_dir(), outdir, alias) try: os.remove(aliasfile) except Exception: pass os.symlink(basename, aliasfile) else: mlog.debug("Library versioning disabled because host does not support symlinks.") def generate_gcov_clean(self, outfile): gcno_elem = NinjaBuildElement('clean-gcno', 'CUSTOM_COMMAND', 'PHONY') script_root = self.environment.get_script_dir() clean_script = os.path.join(script_root, 'delwithsuffix.py') gcno_elem.add_item('COMMAND', [sys.executable, clean_script, '.', 'gcno']) gcno_elem.add_item('description', 'Deleting gcno files') gcno_elem.write(outfile) self.check_outputs(gcno_elem) gcda_elem = NinjaBuildElement('clean-gcda', 'CUSTOM_COMMAND', 'PHONY') script_root = self.environment.get_script_dir() clean_script = os.path.join(script_root, 'delwithsuffix.py') gcda_elem.add_item('COMMAND', [sys.executable, clean_script, '.', 'gcda']) gcda_elem.add_item('description', 'Deleting gcda files') gcda_elem.write(outfile) self.check_outputs(gcda_elem) def is_compilable_file(self, filename): if filename.endswith('.cpp') or\ filename.endswith('.c') or\ filename.endswith('.cxx') or\ filename.endswith('.cc') or\ filename.endswith('.C'): return True return False def process_dep_gens(self, outfile, target): src_deps = [] other_deps = [] for rule in self.dep_rules.values(): srcs = target.get_original_kwargs().get(rule.src_keyword, []) if isinstance(srcs, str): srcs = [srcs] for src in srcs: plainname = os.path.split(src)[1] basename = plainname.split('.')[0] outname = rule.name_templ.replace('@BASENAME@', basename).replace('@PLAINNAME@', plainname) outfilename = os.path.join(self.get_target_private_dir(target), outname) infilename = os.path.join(self.build_to_src, target.get_source_subdir(), src) elem = NinjaBuildElement(outfilename, rule.name, infilename) elem.write(outfile) self.check_outputs(elem) if self.is_compilable_file(outfilename): src_deps.append(outfilename) else: other_deps.append(outfilename) return (src_deps, other_deps) def generate_ending(self, outfile): targetlist = [self.get_target_filename(t) for t in self.build.get_targets().values()\ if not isinstance(t, build.RunTarget)] elem = NinjaBuildElement('all', 'phony', targetlist) elem.write(outfile) self.check_outputs(elem) default = 'default all\n\n' outfile.write(default) ninja_command = environment.detect_ninja() if ninja_command is None: raise MesonException('Could not detect ninja command') elem = NinjaBuildElement('clean', 'CUSTOM_COMMAND', 'PHONY') elem.add_item('COMMAND', [ninja_command, '-t', 'clean']) elem.add_item('description', 'Cleaning') if self.environment.coredata.get_builtin_option('coverage'): self.generate_gcov_clean(outfile) elem.add_dep('clean-gcda') elem.add_dep('clean-gcno') elem.write(outfile) self.check_outputs(elem) deps = self.get_regen_filelist() elem = NinjaBuildElement('build.ninja', 'REGENERATE_BUILD', deps) elem.add_item('pool', 'console') elem.write(outfile) elem = NinjaBuildElement(deps, 'phony', '') elem.write(outfile) self.check_outputs(elem)

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fe12421e5a8c03bfd1fbb0c021c5255e880a14d5

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py

Python

tools/third_party/iniconfig/testing/test_iniconfig.py

meyerweb/wpt

f04261533819893c71289614c03434c06856c13e

[ "BSD-3-Clause" ]

2,479

2018-05-28T14:51:29.000Z

2022-03-30T14:41:18.000Z

tools/third_party/iniconfig/testing/test_iniconfig.py

meyerweb/wpt

f04261533819893c71289614c03434c06856c13e

[ "BSD-3-Clause" ]

7,642

2018-05-28T09:38:03.000Z

2022-03-31T20:55:48.000Z

tools/third_party/iniconfig/testing/test_iniconfig.py

meyerweb/wpt

f04261533819893c71289614c03434c06856c13e

[ "BSD-3-Clause" ]

1,303

2018-05-29T14:50:02.000Z

2022-03-30T17:30:42.000Z

import py import pytest from iniconfig import IniConfig, ParseError, __all__ as ALL from iniconfig import iscommentline from textwrap import dedent check_tokens = { 'section': ( '[section]', [(0, 'section', None, None)] ), 'value': ( 'value = 1', [(0, None, 'value', '1')] ), 'value in section': ( '[section]\nvalue=1', [(0, 'section', None, None), (1, 'section', 'value', '1')] ), 'value with continuation': ( 'names =\n Alice\n Bob', [(0, None, 'names', 'Alice\nBob')] ), 'value with aligned continuation': ( 'names = Alice\n' ' Bob', [(0, None, 'names', 'Alice\nBob')] ), 'blank line': ( '[section]\n\nvalue=1', [(0, 'section', None, None), (2, 'section', 'value', '1')] ), 'comment': ( '# comment', [] ), 'comment on value': ( 'value = 1', [(0, None, 'value', '1')] ), 'comment on section': ( '[section] #comment', [(0, 'section', None, None)] ), 'comment2': ( '; comment', [] ), 'comment2 on section': ( '[section] ;comment', [(0, 'section', None, None)] ), 'pseudo section syntax in value': ( 'name = value []', [(0, None, 'name', 'value []')] ), 'assignment in value': ( 'value = x = 3', [(0, None, 'value', 'x = 3')] ), 'use of colon for name-values': ( 'name: y', [(0, None, 'name', 'y')] ), 'use of colon without space': ( 'value:y=5', [(0, None, 'value', 'y=5')] ), 'equality gets precedence': ( 'value=xyz:5', [(0, None, 'value', 'xyz:5')] ), } @pytest.fixture(params=sorted(check_tokens)) def input_expected(request): return check_tokens[request.param] @pytest.fixture def input(input_expected): return input_expected[0] @pytest.fixture def expected(input_expected): return input_expected[1] def parse(input): # only for testing purposes - _parse() does not use state except path ini = object.__new__(IniConfig) ini.path = "sample" return ini._parse(input.splitlines(True)) def parse_a_error(input): return py.test.raises(ParseError, parse, input) def test_tokenize(input, expected): parsed = parse(input) assert parsed == expected def test_parse_empty(): parsed = parse("") assert not parsed ini = IniConfig("sample", "") assert not ini.sections def test_ParseError(): e = ParseError("filename", 0, "hello") assert str(e) == "filename:1: hello" def test_continuation_needs_perceeding_token(): excinfo = parse_a_error(' Foo') assert excinfo.value.lineno == 0 def test_continuation_cant_be_after_section(): excinfo = parse_a_error('[section]\n Foo') assert excinfo.value.lineno == 1 def test_section_cant_be_empty(): excinfo = parse_a_error('[]') assert excinfo.value.lineno == 0 @py.test.mark.parametrize('line', [ '!!', ]) def test_error_on_weird_lines(line): parse_a_error(line) def test_iniconfig_from_file(tmpdir): path = tmpdir/'test.txt' path.write('[metadata]\nname=1') config = IniConfig(path=path) assert list(config.sections) == ['metadata'] config = IniConfig(path, "[diff]") assert list(config.sections) == ['diff'] with pytest.raises(TypeError): IniConfig(data=path.read()) def test_iniconfig_section_first(tmpdir): with pytest.raises(ParseError) as excinfo: IniConfig("x", data='name=1') assert excinfo.value.msg == "no section header defined" def test_iniconig_section_duplicate_fails(): with pytest.raises(ParseError) as excinfo: IniConfig("x", data='[section]\n[section]') assert 'duplicate section' in str(excinfo.value) def test_iniconfig_duplicate_key_fails(): with pytest.raises(ParseError) as excinfo: IniConfig("x", data='[section]\nname = Alice\nname = bob') assert 'duplicate name' in str(excinfo.value) def test_iniconfig_lineof(): config = IniConfig("x.ini", data=( '[section]\n' 'value = 1\n' '[section2]\n' '# comment\n' 'value =2' )) assert config.lineof('missing') is None assert config.lineof('section') == 1 assert config.lineof('section2') == 3 assert config.lineof('section', 'value') == 2 assert config.lineof('section2', 'value') == 5 assert config['section'].lineof('value') == 2 assert config['section2'].lineof('value') == 5 def test_iniconfig_get_convert(): config = IniConfig("x", data='[section]\nint = 1\nfloat = 1.1') assert config.get('section', 'int') == '1' assert config.get('section', 'int', convert=int) == 1 def test_iniconfig_get_missing(): config = IniConfig("x", data='[section]\nint = 1\nfloat = 1.1') assert config.get('section', 'missing', default=1) == 1 assert config.get('section', 'missing') is None def test_section_get(): config = IniConfig("x", data='[section]\nvalue=1') section = config['section'] assert section.get('value', convert=int) == 1 assert section.get('value', 1) == "1" assert section.get('missing', 2) == 2 def test_missing_section(): config = IniConfig("x", data='[section]\nvalue=1') with pytest.raises(KeyError): config["other"] def test_section_getitem(): config = IniConfig("x", data='[section]\nvalue=1') assert config['section']['value'] == '1' assert config['section']['value'] == '1' def test_section_iter(): config = IniConfig("x", data='[section]\nvalue=1') names = list(config['section']) assert names == ['value'] items = list(config['section'].items()) assert items == [('value', '1')] def test_config_iter(): config = IniConfig("x.ini", data=dedent(''' [section1] value=1 [section2] value=2 ''')) l = list(config) assert len(l) == 2 assert l[0].name == 'section1' assert l[0]['value'] == '1' assert l[1].name == 'section2' assert l[1]['value'] == '2' def test_config_contains(): config = IniConfig("x.ini", data=dedent(''' [section1] value=1 [section2] value=2 ''')) assert 'xyz' not in config assert 'section1' in config assert 'section2' in config def test_iter_file_order(): config = IniConfig("x.ini", data=""" [section2] #cpython dict ordered before section value = 1 value2 = 2 # dict ordered before value [section] a = 1 b = 2 """) l = list(config) secnames = [x.name for x in l] assert secnames == ['section2', 'section'] assert list(config['section2']) == ['value', 'value2'] assert list(config['section']) == ['a', 'b'] def test_example_pypirc(): config = IniConfig("pypirc", data=dedent(''' [distutils] index-servers = pypi other [pypi] repository: <repository-url> username: <username> password: <password> [other] repository: http://example.com/pypi username: <username> password: <password> ''')) distutils, pypi, other = list(config) assert distutils["index-servers"] == "pypi\nother" assert pypi['repository'] == '<repository-url>' assert pypi['username'] == '<username>' assert pypi['password'] == '<password>' assert ['repository', 'username', 'password'] == list(other) def test_api_import(): assert ALL == ['IniConfig', 'ParseError'] @pytest.mark.parametrize("line", [ "#qwe", " #qwe", ";qwe", " ;qwe", ]) def test_iscommentline_true(line): assert iscommentline(line)

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null

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null

0

1

0

fe13276650bb177fc42299abc71b473c1a0414dc

3,586

py

Python

jskparser/jskparser/util.py

natebragg/java-sketch

f5ac26f2cc46ae4556f9a61c55afd37f55c961ff

[ "MIT" ]

15

2015-12-15T18:33:50.000Z

2021-09-29T11:48:54.000Z

jskparser/jskparser/util.py

natebragg/java-sketch

f5ac26f2cc46ae4556f9a61c55afd37f55c961ff

[ "MIT" ]

11

2015-11-16T22:14:58.000Z

2021-09-23T05:28:40.000Z

jskparser/jskparser/util.py

natebragg/java-sketch

f5ac26f2cc46ae4556f9a61c55afd37f55c961ff

[ "MIT" ]

8

2015-11-16T21:50:08.000Z

2021-03-23T15:15:34.000Z

import os from subprocess import call from . import glob2 pwd = os.path.dirname(__file__) def get_files_from_path(path, ext): # use set to remove duplicate files. weird...but it happens if os.path.isfile(path): return set([os.path.abspath(path)]) else: # i.e., folder files = glob2.glob(os.path.abspath(os.path.join(path, "**/*.{}".format(ext)))) return set(sorted(files)) # to guarantee the order of files read """ handling javajskparser AST """ def toAST(files, ext, add_libs): prg_files = [] for f in files: prg_files.extend(get_files_from_path(f, "java")) if not prg_files: exit('jskparser.util: File(s) not found!') java_in = os.path.abspath(os.path.join(pwd, '../tests/ir_asts/API.java')) json_out = os.path.abspath(os.path.join(pwd, '../tests/ir_asts/java.json')) if add_libs: obj_path = os.path.abspath(os.path.join(pwd, '../../model/lang/Object.java')) str_path = os.path.abspath(os.path.join(pwd, '../../model/lang/String.java')) num_path = os.path.abspath(os.path.join(pwd, '../../model/lang/Number.java')) int_path = os.path.abspath(os.path.join(pwd, '../../model/lang/Integer.java')) char_path = os.path.abspath(os.path.join(pwd, '../../model/lang/Character.java')) itbl_path = os.path.abspath(os.path.join(pwd, '../../model/lang/Iterable.java')) iter_path = os.path.abspath(os.path.join(pwd, '../../model/util/Iterator.java')) arr_path = os.path.abspath(os.path.join(pwd, '../../model/util/Arrays.java')) list_path = os.path.abspath(os.path.join(pwd, '../../model/util/List.java')) alist_path = os.path.abspath(os.path.join(pwd, '../../model/util/ArrayList.java')) llist_path = os.path.abspath(os.path.join(pwd, '../../model/util/LinkedList.java')) hmap_path = os.path.abspath(os.path.join(pwd, '../../model/util/HashMap.java')) hset_path = os.path.abspath(os.path.join(pwd, '../../model/util/HashSet.java')) if obj_path not in prg_files: prg_files.append(obj_path) if str_path not in prg_files: prg_files.append(str_path) if num_path not in prg_files: prg_files.append(num_path) if int_path not in prg_files: prg_files.append(int_path) if char_path not in prg_files: prg_files.append(char_path) if itbl_path not in prg_files: prg_files.append(itbl_path) if iter_path not in prg_files: prg_files.append(iter_path) if arr_path not in prg_files: prg_files.append(arr_path) if list_path not in prg_files: prg_files.append(list_path) if alist_path not in prg_files: prg_files.append(alist_path) if llist_path not in prg_files: prg_files.append(llist_path) if hmap_path not in prg_files: prg_files.append(hmap_path) if hset_path not in prg_files: prg_files.append(hset_path) api = "" for fname in prg_files: with open(fname, 'r') as fd: api += fd.read() with open(java_in, 'w') as fd: fd.write(api) # this classpath stuff seems awful. Jsonify is hardcoded, passing a # single string to subprocess.call is platform dependant, and shell=True # can be a security vulnerability (if allowed to take user input). # This just got a whole lot nastier cmd = 'cd ' + pwd + '/..; /usr/bin/java -cp .:javaparser/javaparser-core/target/classes:$HOME/.m2/repository/com/cedarsoftware/json-io/4.3.0/json-io-4.3.0.jar jskparser.Jsonify ' + java_in + ' ' + json_out ret = call(cmd, shell=True) if ret != 0: exit('Problem parsing.') return json_out

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fe133101724c39453da53bbd1a90715fd62fd7e1

24,301

py

Python

fiftyone/core/patches.py

SNeugber/fiftyone

a50be47bbbf189e4bbdcd631b93c4c9cbf41c6b7

[ "Apache-2.0" ]

null

fiftyone/core/patches.py

SNeugber/fiftyone

a50be47bbbf189e4bbdcd631b93c4c9cbf41c6b7

[ "Apache-2.0" ]

null

fiftyone/core/patches.py

SNeugber/fiftyone

a50be47bbbf189e4bbdcd631b93c4c9cbf41c6b7

[ "Apache-2.0" ]

null

""" Patches views. | Copyright 2017-2021, Voxel51, Inc. | `voxel51.com <https://voxel51.com/>`_ | """ from copy import deepcopy import eta.core.utils as etau import fiftyone.core.aggregations as foa import fiftyone.core.dataset as fod import fiftyone.core.fields as fof import fiftyone.core.labels as fol import fiftyone.core.media as fom import fiftyone.core.sample as fos import fiftyone.core.view as fov _SINGLE_TYPES_MAP = { fol.Detections: fol.Detection, fol.Polylines: fol.Polyline, } _PATCHES_TYPES = (fol.Detections, fol.Polylines) _NO_MATCH_ID = "" class _PatchView(fos.SampleView): @property def _sample_id(self): return self._doc.sample_id def save(self): super().save() self._view._sync_source_sample(self) class PatchView(_PatchView): """A patch in a :class:`PatchesView`. :class:`PatchView` instances should not be created manually; they are generated by iterating over :class:`PatchesView` instances. Args: doc: a :class:`fiftyone.core.odm.DatasetSampleDocument` view: the :class:`PatchesView` that the patch belongs to selected_fields (None): a set of field names that this view is restricted to excluded_fields (None): a set of field names that are excluded from this view filtered_fields (None): a set of field names of list fields that are filtered in this view """ pass class EvaluationPatchView(_PatchView): """A patch in an :class:`EvaluationPatchesView`. :class:`EvaluationPatchView` instances should not be created manually; they are generated by iterating over :class:`EvaluationPatchesView` instances. Args: doc: a :class:`fiftyone.core.odm.DatasetSampleDocument` view: the :class:`EvaluationPatchesView` that the patch belongs to selected_fields (None): a set of field names that this view is restricted to excluded_fields (None): a set of field names that are excluded from this view filtered_fields (None): a set of field names of list fields that are filtered in this view """ pass class _PatchesView(fov.DatasetView): def __init__( self, source_collection, patches_stage, patches_dataset, _stages=None ): if _stages is None: _stages = [] self._source_collection = source_collection self._patches_stage = patches_stage self._patches_dataset = patches_dataset self.__stages = _stages def __copy__(self): return self.__class__( self._source_collection, deepcopy(self._patches_stage), self._patches_dataset, _stages=deepcopy(self.__stages), ) @property def _base_view(self): return self.__class__( self._source_collection, self._patches_stage, self._patches_dataset, ) @property def _dataset(self): return self._patches_dataset @property def _root_dataset(self): return self._source_collection._root_dataset @property def _stages(self): return self.__stages @property def _all_stages(self): return ( self._source_collection.view()._all_stages + [self._patches_stage] + self.__stages ) @property def _label_fields(self): raise NotImplementedError("subclass must implement _label_fields") @property def _element_str(self): return "patch" @property def _elements_str(self): return "patches" @property def name(self): return self.dataset_name + "-patches" @classmethod def _get_default_sample_fields( cls, include_private=False, use_db_fields=False ): fields = super()._get_default_sample_fields( include_private=include_private, use_db_fields=use_db_fields ) if use_db_fields: return fields + ("_sample_id",) return fields + ("sample_id",) def set_values(self, field_name, *args, **kwargs): field = field_name.split(".", 1)[0] must_sync = field in self._label_fields # The `set_values()` operation could change the contents of this view, # so we first record the sample IDs that need to be synced if must_sync and self._stages: ids = self.values("_id") else: ids = None super().set_values(field_name, *args, **kwargs) if must_sync: self._sync_source_field(field, ids=ids) def save(self, fields=None): """Overwrites the object patches in the source dataset with the contents of the view. If this view contains any additional fields that were not extracted from the source dataset, these fields are not saved. .. warning:: This will permanently delete any omitted, filtered, or otherwise modified patches from the source dataset. Args: fields (None): an optional field or list of fields to save. If specified, only these fields are overwritten """ if etau.is_str(fields): fields = [fields] super().save(fields=fields) if fields is None: fields = self._label_fields else: fields = [l for l in fields if l in self._label_fields] # # IMPORTANT: we sync the contents of `_patches_dataset`, not `self` # here because the `save()` call above updated the dataset, which means # this view may no longer have the same contents (e.g., if `skip()` is # involved) # self._sync_source_root(fields) def reload(self): self._root_dataset.reload() # # Regenerate the patches dataset # # This assumes that calling `load_view()` when the current patches # dataset has been deleted will cause a new one to be generated # self._patches_dataset.delete() _view = self._patches_stage.load_view(self._source_collection) self._patches_dataset = _view._patches_dataset def _sync_source_sample(self, sample): for field in self._label_fields: self._sync_source_sample_field(sample, field) def _sync_source_sample_field(self, sample, field): label_type = self._patches_dataset._get_label_field_type(field) is_list_field = issubclass(label_type, fol._LABEL_LIST_FIELDS) doc = sample._doc.field_to_mongo(field) if is_list_field: doc = doc[label_type._LABEL_LIST_FIELD] self._source_collection._set_labels_by_id( field, [sample.sample_id], [doc] ) def _sync_source_field(self, field, ids=None): _, label_path = self._patches_dataset._get_label_field_path(field) if ids is not None: view = self._patches_dataset.mongo( [{"$match": {"_id": {"$in": ids}}}] ) else: view = self._patches_dataset sample_ids, docs = view.aggregate( [foa.Values("sample_id"), foa.Values(label_path, _raw=True)] ) self._source_collection._set_labels_by_id(field, sample_ids, docs) def _sync_source_root(self, fields): for field in fields: self._sync_source_root_field(field) def _sync_source_root_field(self, field): _, id_path = self._get_label_field_path(field, "id") label_path = id_path.rsplit(".", 1)[0] # # Sync label updates # sample_ids, docs, label_ids = self._patches_dataset.aggregate( [ foa.Values("sample_id"), foa.Values(label_path, _raw=True), foa.Values(id_path, unwind=True), ] ) self._source_collection._set_labels_by_id(field, sample_ids, docs) # # Sync label deletions # _, src_id_path = self._source_collection._get_label_field_path( field, "id" ) src_ids = self._source_collection.values(src_id_path, unwind=True) delete_ids = set(src_ids) - set(label_ids) if delete_ids: self._source_collection._dataset.delete_labels( ids=delete_ids, fields=field ) def _get_ids_map(self, field): label_type = self._patches_dataset._get_label_field_type(field) is_list_field = issubclass(label_type, fol._LABEL_LIST_FIELDS) _, id_path = self._get_label_field_path(field, "id") sample_ids, label_ids = self.values(["id", id_path]) ids_map = {} if is_list_field: for sample_id, _label_ids in zip(sample_ids, label_ids): if not _label_ids: continue for label_id in _label_ids: ids_map[label_id] = sample_id else: for sample_id, label_id in zip(sample_ids, label_ids): if not label_id: continue ids_map[label_id] = sample_id return ids_map class PatchesView(_PatchesView): """A :class:`fiftyone.core.view.DatasetView` of patches from a :class:`fiftyone.core.dataset.Dataset`. Patches views contain an ordered collection of patch samples, each of which contains a subset of a sample of the parent dataset corresponding to a single object or logical grouping of of objects. Patches retrieved from patches views are returned as :class:`PatchView` objects. Args: source_collection: the :class:`fiftyone.core.collections.SampleCollection` from which this view was created patches_stage: the :class:`fiftyone.core.stages.ToPatches` stage that defines how the patches were extracted patches_dataset: the :class:`fiftyone.core.dataset.Dataset` that serves the patches in this view """ _SAMPLE_CLS = PatchView def __init__( self, source_collection, patches_stage, patches_dataset, _stages=None ): super().__init__( source_collection, patches_stage, patches_dataset, _stages=_stages ) self._patches_field = patches_stage.field @property def _label_fields(self): return [self._patches_field] @property def patches_field(self): """The field from which the patches in this view were extracted.""" return self._patches_field class EvaluationPatchesView(_PatchesView): """A :class:`fiftyone.core.view.DatasetView` containing evaluation patches from a :class:`fiftyone.core.dataset.Dataset`. Evalation patches views contain an ordered collection of evaluation examples, each of which contains the ground truth and/or predicted labels for a true positive, false positive, or false negative example from an evaluation run on the underlying dataset. Patches retrieved from patches views are returned as :class:`EvaluationPatchView` objects. Args: source_collection: the :class:`fiftyone.core.collections.SampleCollection` from which this view was created patches_stage: the :class:`fiftyone.core.stages.ToEvaluationPatches` stage that defines how the patches were extracted patches_dataset: the :class:`fiftyone.core.dataset.Dataset` that serves the patches in this view """ _SAMPLE_CLS = EvaluationPatchView def __init__( self, source_collection, patches_stage, patches_dataset, _stages=None ): super().__init__( source_collection, patches_stage, patches_dataset, _stages=_stages ) eval_key = patches_stage.eval_key eval_info = source_collection.get_evaluation_info(eval_key) self._gt_field = eval_info.config.gt_field self._pred_field = eval_info.config.pred_field @property def _label_fields(self): return [self._gt_field, self._pred_field] @property def gt_field(self): """The ground truth field for the evaluation patches in this view.""" return self._gt_field @property def pred_field(self): """The predictions field for the evaluation patches in this view.""" return self._pred_field def make_patches_dataset( sample_collection, field, keep_label_lists=False, name=None ): """Creates a dataset that contains one sample per object patch in the specified field of the collection. Fields other than ``field`` and the default sample fields will not be included in the returned dataset. A ``sample_id`` field will be added that records the sample ID from which each patch was taken. Args: sample_collection: a :class:`fiftyone.core.collections.SampleCollection` field: the patches field, which must be of type :class:`fiftyone.core.labels.Detections` or :class:`fiftyone.core.labels.Polylines` keep_label_lists (False): whether to store the patches in label list fields of the same type as the input collection rather than using their single label variants name (None): a name for the returned dataset Returns: a :class:`fiftyone.core.dataset.Dataset` """ if keep_label_lists: field_type = sample_collection._get_label_field_type(field) else: field_type = _get_single_label_field_type(sample_collection, field) dataset = fod.Dataset(name, _patches=True) dataset.media_type = fom.IMAGE dataset.add_sample_field( "sample_id", fof.ObjectIdField, db_field="_sample_id" ) dataset.add_sample_field( field, fof.EmbeddedDocumentField, embedded_doc_type=field_type ) patches_view = _make_patches_view( sample_collection, field, keep_label_lists=keep_label_lists ) _write_samples(dataset, patches_view) return dataset def _get_single_label_field_type(sample_collection, field): label_type = sample_collection._get_label_field_type(field) if label_type not in _SINGLE_TYPES_MAP: raise ValueError("Unsupported label field type %s" % label_type) return _SINGLE_TYPES_MAP[label_type] def make_evaluation_dataset(sample_collection, eval_key, name=None): """Creates a dataset based on the results of the evaluation with the given key that contains one sample for each true positive, false positive, and false negative example in the input collection, respectively. True positive examples will result in samples with both their ground truth and predicted fields populated, while false positive/negative examples will only have one of their corresponding predicted/ground truth fields populated, respectively. If multiple predictions are matched to a ground truth object (e.g., if the evaluation protocol includes a crowd attribute), then all matched predictions will be stored in the single sample along with the ground truth object. The returned dataset will also have top-level ``type`` and ``iou`` fields populated based on the evaluation results for that example, as well as a ``sample_id`` field recording the sample ID of the example, and a ``crowd`` field if the evaluation protocol defines a crowd attribute. .. note:: The returned dataset will contain patches for the contents of the input collection, which may differ from the view on which the ``eval_key`` evaluation was performed. This may exclude some labels that were evaluated and/or include labels that were not evaluated. If you would like to see patches for the exact view on which an evaluation was performed, first call :meth:`load_evaluation_view() <fiftyone.core.collections.SampleCollection.load_evaluation_view>` to load the view and then convert to patches. Args: sample_collection: a :class:`fiftyone.core.collections.SampleCollection` eval_key: an evaluation key that corresponds to the evaluation of ground truth/predicted fields that are of type :class:`fiftyone.core.labels.Detections` or :class:`fiftyone.core.labels.Polylines` name (None): a name for the returned dataset Returns: a :class:`fiftyone.core.dataset.Dataset` """ # Parse evaluation info eval_info = sample_collection.get_evaluation_info(eval_key) pred_field = eval_info.config.pred_field gt_field = eval_info.config.gt_field if hasattr(eval_info.config, "iscrowd"): crowd_attr = eval_info.config.iscrowd else: crowd_attr = None pred_type = sample_collection._get_label_field_type(pred_field) gt_type = sample_collection._get_label_field_type(gt_field) # Setup dataset with correct schema dataset = fod.Dataset(name, _patches=True) dataset.media_type = fom.IMAGE dataset.add_sample_field( pred_field, fof.EmbeddedDocumentField, embedded_doc_type=pred_type ) dataset.add_sample_field( gt_field, fof.EmbeddedDocumentField, embedded_doc_type=gt_type ) dataset.add_sample_field( "sample_id", fof.ObjectIdField, db_field="_sample_id" ) dataset.add_sample_field("type", fof.StringField) dataset.add_sample_field("iou", fof.FloatField) if crowd_attr is not None: dataset.add_sample_field("crowd", fof.BooleanField) # Add ground truth patches gt_view = _make_eval_view( sample_collection, eval_key, gt_field, crowd_attr=crowd_attr ) _write_samples(dataset, gt_view) # Merge matched predictions _merge_matched_labels(dataset, sample_collection, eval_key, pred_field) # Add unmatched predictions unmatched_pred_view = _make_eval_view( sample_collection, eval_key, pred_field, skip_matched=True ) _add_samples(dataset, unmatched_pred_view) return dataset def _make_patches_view(sample_collection, field, keep_label_lists=False): if sample_collection._is_frames: raise ValueError( "Creating patches views into frame views is not yet supported" ) if sample_collection._is_frame_field(field): raise ValueError( "Frame label patches cannot be directly extracted; you must first " "convert your video dataset to frames via `to_frames()`" ) label_type = sample_collection._get_label_field_type(field) if issubclass(label_type, _PATCHES_TYPES): list_field = field + "." + label_type._LABEL_LIST_FIELD else: raise ValueError( "Invalid label field type %s. Extracting patches is only " "supported for the following types: %s" % (label_type, _PATCHES_TYPES) ) pipeline = [ { "$project": { "_id": True, "_sample_id": "$_id", "_media_type": True, "filepath": True, "metadata": True, "tags": True, field + "._cls": True, list_field: True, } }, {"$unwind": "$" + list_field}, {"$set": {"_rand": {"$rand": {}}}}, {"$set": {"_id": "$" + list_field + "._id"}}, ] if keep_label_lists: pipeline.append({"$set": {list_field: ["$" + list_field]}}) else: pipeline.append({"$set": {field: "$" + list_field}}) return sample_collection.mongo(pipeline) def _make_eval_view( sample_collection, eval_key, field, skip_matched=False, crowd_attr=None ): eval_type = field + "." + eval_key eval_id = field + "." + eval_key + "_id" eval_iou = field + "." + eval_key + "_iou" view = _make_patches_view(sample_collection, field) if skip_matched: view = view.mongo( [ { "$match": { "$expr": { "$or": [ {"$eq": ["$" + eval_id, _NO_MATCH_ID]}, {"$not": {"$gt": ["$" + eval_id, None]}}, ] } } } ] ) view = view.mongo( [{"$set": {"type": "$" + eval_type, "iou": "$" + eval_iou}}] ) if crowd_attr is not None: crowd_path1 = "$" + field + "." + crowd_attr # @todo remove Attributes usage crowd_path2 = "$" + field + ".attributes." + crowd_attr + ".value" view = view.mongo( [ { "$set": { "crowd": { "$cond": { "if": {"$gt": [crowd_path1, None]}, "then": {"$toBool": crowd_path1}, "else": { "$cond": { "if": {"$gt": [crowd_path2, None]}, "then": {"$toBool": crowd_path2}, "else": None, } }, } } } } ] ) return _upgrade_labels(view, field) def _upgrade_labels(view, field): tmp_field = "_" + field label_type = view._get_label_field_type(field) return view.mongo( [ {"$set": {tmp_field: "$" + field}}, {"$unset": field}, { "$set": { field: { "_cls": label_type.__name__, label_type._LABEL_LIST_FIELD: ["$" + tmp_field], } } }, {"$unset": tmp_field}, ] ) def _merge_matched_labels(dataset, src_collection, eval_key, field): field_type = src_collection._get_label_field_type(field) list_field = field + "." + field_type._LABEL_LIST_FIELD eval_id = eval_key + "_id" eval_field = list_field + "." + eval_id pipeline = src_collection._pipeline(detach_frames=True) pipeline.extend( [ {"$project": {list_field: True}}, {"$unwind": "$" + list_field}, { "$match": { "$expr": { "$and": [ {"$gt": ["$" + eval_field, None]}, {"$ne": ["$" + eval_field, _NO_MATCH_ID]}, ] } } }, { "$group": { "_id": {"$toObjectId": "$" + eval_field}, "_labels": {"$push": "$" + list_field}, } }, { "$project": { field: { "_cls": field_type.__name__, field_type._LABEL_LIST_FIELD: "$_labels", } }, }, { "$merge": { "into": dataset._sample_collection_name, "on": "_id", "whenMatched": "merge", "whenNotMatched": "discard", } }, ] ) src_collection._dataset._aggregate(pipeline=pipeline, attach_frames=False) def _write_samples(dataset, src_collection): pipeline = src_collection._pipeline(detach_frames=True) pipeline.append({"$out": dataset._sample_collection_name}) src_collection._dataset._aggregate(pipeline=pipeline, attach_frames=False) def _add_samples(dataset, src_collection): pipeline = src_collection._pipeline(detach_frames=True) pipeline.append( { "$merge": { "into": dataset._sample_collection_name, "on": "_id", "whenMatched": "keepExisting", "whenNotMatched": "insert", } } ) src_collection._dataset._aggregate(pipeline=pipeline, attach_frames=False)

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32.229443

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0.004505

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0.18018

0

null

0

null

0

1

0

fe14a23d28223212d47c4b4e15846d9b001de45c

6,153

py

Python

src/zope/app/debug/debug.py

zopefoundation/zope.app.debug

4f31e98f6a633f089bf132dd55cb3ead0270887b

[ "ZPL-2.1" ]

null

src/zope/app/debug/debug.py

zopefoundation/zope.app.debug

4f31e98f6a633f089bf132dd55cb3ead0270887b

[ "ZPL-2.1" ]

2

2017-05-08T10:46:20.000Z

2021-02-02T07:16:49.000Z

src/zope/app/debug/debug.py

zopefoundation/zope.app.debug

4f31e98f6a633f089bf132dd55cb3ead0270887b

[ "ZPL-2.1" ]

1

2015-04-03T07:36:10.000Z

############################################################################## # # Copyright (c) 2002 Zope Foundation and Contributors. # All Rights Reserved. # # This software is subject to the provisions of the Zope Public License, # Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution. # THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED # WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS # FOR A PARTICULAR PURPOSE. # ############################################################################## """Code to initialize the application server """ from __future__ import print_function __docformat__ = 'restructuredtext' import base64 import time import sys from pdb import Pdb from io import BytesIO from zope.publisher.publish import publish as _publish, debug_call from zope.publisher.browser import TestRequest, setDefaultSkin from zope.app.publication.browser import BrowserPublication from zope.app.appsetup import config, database try: from time import process_time as time_process_time # pragma: PY3 except ImportError: from time import clock as time_process_time # pragma: PY2 try: import urllib.parse as urllib # pragma: PY3 except ImportError: import urllib # pragma: PY2 try: text_type = unicode # pragma: PY2 except NameError: text_type = str # pragma: PY3 class Debugger(object): pdb = Pdb def __init__(self, db=None, config_file=None, stdout=None): if db is None and config_file is None: db = 'Data.fs' config_file = 'site.zcml' if config_file is not None: config(config_file) self.db = database(db) self.stdout = stdout @classmethod def fromDatabase(cls, db): inst = cls.__new__(cls) inst.db = db return inst def root(self): """Get the top-level application object The object returned is connected to an open database connection. """ from zope.app.publication.zopepublication import ZopePublication return self.db.open().root()[ZopePublication.root_name] def _request(self, path='/', stdin='', basic=None, environment=None, form=None, request=None, publication=BrowserPublication): """Create a request """ env = {} if isinstance(stdin, text_type): stdin = stdin.encode("utf-8") if isinstance(stdin, bytes): stdin = BytesIO(stdin) p = path.split('?') if len(p) == 1: env['PATH_INFO'] = p[0] elif len(p) == 2: env['PATH_INFO'], env['QUERY_STRING'] = p else: raise ValueError("Too many ?s in path", path) env['PATH_INFO'] = urllib.unquote(env['PATH_INFO']) if environment is not None: env.update(environment) if basic: basic_bytes = basic.encode('ascii') if not isinstance( basic, bytes) else basic basic64_bytes = base64.b64encode(basic_bytes) basic64 = basic64_bytes.decode('ascii').strip() env['HTTP_AUTHORIZATION'] = "Basic %s" % basic64 pub = publication(self.db) if request is not None: request = request(stdin, env) else: request = TestRequest(stdin, env) setDefaultSkin(request) request.setPublication(pub) if form: request.form.update(form) return request def publish(self, path='/', stdin='', *args, **kw): t, pt = time.time(), time_process_time() request = self._request(path, stdin, *args, **kw) # agroszer: 2008.feb.1.: if a retry occurs in the publisher, # the response will be LOST, so we must accept the returned request request = _publish(request) getStatus = getattr(request.response, 'getStatus', lambda: None) headers = sorted(request.response.getHeaders()) print( 'Status %s\r\n%s\r\n\r\n%s' % ( request.response.getStatusString(), '\r\n'.join([("%s: %s" % h) for h in headers]), request.response.consumeBody(), ), file=self.stdout or sys.stdout) return time.time() - t, time_process_time() - pt, getStatus() def run(self, *args, **kw): t, pt = time.time(), time_process_time() request = self._request(*args, **kw) # agroszer: 2008.feb.1.: if a retry occurs in the publisher, # the response will be LOST, so we must accept the returned request request = _publish(request, handle_errors=False) getStatus = getattr(request.response, 'getStatus', lambda: None) return time.time() - t, time_process_time() - pt, getStatus() def debug(self, *args, **kw): out = self.stdout or sys.stdout class ZopePdb(self.Pdb): done_pub = False done_ob = False def do_pub(self, arg): if self.done_pub: print('pub already done.', file=out) return self.do_s('') self.do_s('') self.do_c('') self.done_pub = True def do_ob(self, arg): if self.done_ob: print('ob already done.', file=out) return self.do_pub('') self.do_c('') self.done_ob = True dbg = ZopePdb() request = self._request(*args, **kw) fbreak(dbg, _publish) fbreak(dbg, debug_call) print('* Type c<cr> to jump to published object call.', file=out) dbg.runcall(_publish, request) return dbg def getlineno(code): return code.co_firstlineno def fbreak(db, meth): try: meth = meth.__func__ except AttributeError: pass code = meth.__code__ lineno = getlineno(code) filename = code.co_filename db.set_break(filename, lineno)

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78

0.57988

725

6,153

4.804138

0.310345

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0.171117

0.125179

0

0.009238

0.296278

6,153

205

79

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0

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1

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false

0.007463

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0.007463

0.298507

0.037313

0

null

0

null

0

1

0

fe1507ff94aad4e4172a286172e136314812d8b6

1,855

py

Python

transfer_learning.py

terryli710/SIIM-ACR-Pneumothorax-Classification

8b278a9885b71c919d7064b2df42863b53f7adf3

[ "MIT" ]

null

transfer_learning.py

terryli710/SIIM-ACR-Pneumothorax-Classification

8b278a9885b71c919d7064b2df42863b53f7adf3

[ "MIT" ]

null

transfer_learning.py

terryli710/SIIM-ACR-Pneumothorax-Classification

8b278a9885b71c919d7064b2df42863b53f7adf3

[ "MIT" ]

1

2020-05-14T06:16:12.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Mon May 18 22:42:54 2020 @author: mike """ import numpy as np import tensorflow as tf from tensorflow import keras from sklearn.model_selection import train_test_split from tensorflow.keras.applications import VGG16 from tensorflow.keras import layers from sklearn.preprocessing import OneHotEncoder from skimage.transform import resize import matplotlib.pyplot as plt train_data = np.load("train_data.npy") x_data = np.zeros((210,204,204,3)) y_data = np.zeros(210) for i in range(210): img = train_data[i,1:].reshape(1024,1024) img_resized = resize(img,(204,204)) y_data[i] = train_data[i,0] x_data[i,:,:,0] = img_resized.astype(int) x_data[i,:,:,1] = img_resized.astype(int) x_data[i,:,:,2] = img_resized.astype(int) x_train, x_test, y_train, y_test = train_test_split( x_data, y_data, test_size=0.2, random_state=42) y_train = OneHotEncoder().fit_transform(y_train.reshape(-1,1)).toarray() y_test = OneHotEncoder().fit_transform(y_test.reshape(-1,1)).toarray() base_model = VGG16(include_top=False, weights='vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5', input_shape=(204, 204, 3)) base_model.trainable = False inputs = tf.keras.Input(shape=(204, 204, 3)) x = base_model(inputs) x = tf.keras.layers.Flatten()(x) x = tf.keras.layers.Dense(256, activation='relu')(x) x = tf.keras.layers.Dense(64, activation='relu')(x) outputs = tf.keras.layers.Dense(2, activation='softmax')(x) model = keras.Model(inputs, outputs) model.summary() model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.001),loss="binary_crossentropy",metrics=["accuracy"]) model.fit(x_train, y_train, batch_size=16, epochs=5) pred = model.predict(x_train) score = model.evaluate(x_test, y_test, verbose=0) print(score[0],score[1])

26.884058

117

0.725067

301

1,855

4.292359

0.38206

0.02322

0.040248

0.044118

0.111455

0.069659

0.0387

0

0.057917

0.125067

1,855

69

118

26.884058

0.738139

0.050674

0

0.059897

0.027952

0

1

0

false

0

0.230769

0

0.230769

0.025641

0

null

0

null

0

1

0

fe15525a101c45bc65c1049e9b6ece9e4cd29f69

2,158

py

Python

core/tests/test_polyflow/test_workflows/test_hyperband.py

erexer/polyaxon

be14dae1ed56d568983388736bcdaf27a7baa4a4

[ "Apache-2.0" ]

null

core/tests/test_polyflow/test_workflows/test_hyperband.py

erexer/polyaxon

be14dae1ed56d568983388736bcdaf27a7baa4a4

[ "Apache-2.0" ]

null

core/tests/test_polyflow/test_workflows/test_hyperband.py

erexer/polyaxon

be14dae1ed56d568983388736bcdaf27a7baa4a4

[ "Apache-2.0" ]

null

#!/usr/bin/python # # Copyright 2018-2020 Polyaxon, 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 pytest from marshmallow.exceptions import ValidationError from tests.utils import BaseTestCase, assert_equal_dict from polyaxon.polyflow.matrix import V1Hyperband from polyaxon.polyflow.optimization import V1Optimization, V1OptimizationMetric @pytest.mark.workflow_mark class TestWorkflowV1Hyperbands(BaseTestCase): def test_hyperband_config(self): config_dict = { "kind": "hyperband", "maxIterations": 10, "eta": 3, "resource": {"name": "steps", "type": "int"}, "resume": False, "metric": V1OptimizationMetric( name="loss", optimization=V1Optimization.MINIMIZE ).to_dict(), "params": {"lr": {"kind": "choice", "value": [[0.1], [0.9]]}}, } config = V1Hyperband.from_dict(config_dict) assert_equal_dict(config.to_dict(), config_dict) # Raises for negative values config_dict["maxIterations"] = 0 with self.assertRaises(ValidationError): V1Hyperband.from_dict(config_dict) config_dict["maxIterations"] = -0.5 with self.assertRaises(ValidationError): V1Hyperband.from_dict(config_dict) config_dict["maxIterations"] = 3 # Add numRuns percent config_dict["eta"] = -0.5 with self.assertRaises(ValidationError): V1Hyperband.from_dict(config_dict) config_dict["eta"] = 2.9 config = V1Hyperband.from_dict(config_dict) assert_equal_dict(config.to_dict(), config_dict)

35.377049

79

0.672845

252

2,158

5.642857

0.484127

0.091421

0.098453

0.087904

0.279887

0

0.022754

0.226135

2,158

60

80

35.966667

0.828743

0.287766

0

0.294118

0

0.090132

0

0.176471

1

0.029412

false

0

0.147059

0

0.205882

0

null

0

null

0

1

0

fe185aaa73619017a36f547b25642264993ebd15

1,820

py

Python

clickhouse_sqlalchemy/drivers/reflection.py

Fozar/clickhouse-sqlalchemy

88fd630856655cc470430b365dce7e85516abf62

[ "MIT" ]

null

clickhouse_sqlalchemy/drivers/reflection.py

Fozar/clickhouse-sqlalchemy

88fd630856655cc470430b365dce7e85516abf62

[ "MIT" ]

null

clickhouse_sqlalchemy/drivers/reflection.py

Fozar/clickhouse-sqlalchemy

88fd630856655cc470430b365dce7e85516abf62

[ "MIT" ]

null

from sqlalchemy.engine import reflection from clickhouse_sqlalchemy import Table, engines class ClickHouseInspector(reflection.Inspector): def reflect_table(self, table, *args, **kwargs): # This check is necessary to support direct instantiation of # `clickhouse_sqlalchemy.Table` and then reflection of it. if not isinstance(table, Table): table.metadata.remove(table) ch_table = Table._make_from_standard( table, _extend_on=kwargs.get('_extend_on') ) else: ch_table = table super(ClickHouseInspector, self).reflect_table( ch_table, *args, **kwargs ) with self._operation_context() as conn: schema = conn.schema_for_object(ch_table) self._reflect_engine(ch_table.name, schema, ch_table) def _reflect_engine(self, table_name, schema, table): should_reflect = ( self.dialect.supports_engine_reflection and self.dialect.engine_reflection ) if not should_reflect: return engine_cls_by_name = {e.__name__: e for e in engines.__all__} e = self.get_engine(table_name, schema=table.schema) if not e: raise ValueError("Cannot find engine for table '%s'" % table_name) engine_cls = engine_cls_by_name.get(e['engine']) if engine_cls is not None: engine = engine_cls.reflect(table, **e) engine._set_parent(table) else: table.engine = None def get_engine(self, table_name, schema=None, **kw): with self._operation_context() as conn: return self.dialect.get_engine( conn, table_name, schema=schema, info_cache=self.info_cache, **kw )

33.703704

78

0.621978

215

1,820

4.981395

0.325581

0.039216

0.070028

0.044818

0.102708

0.056022

0

0.295055

1,820

53

79

34.339623

0.834762

0.063187

0

0.1

0

0.02879

0

1

0.075

false

0

0.05

0

0.2

0

null

0

null

0

1

0

fe190819e431106bd53c08a681b3911ad9502e88

6,289

py

Python

src/runner.py

samirsahoo007/Naive-Bayes-and-Decision-Tree-Classifiers

619c5c0b17438d1014f7ca7e4ce13cc44c45de3c

[ "MIT" ]

1

2020-11-17T16:09:13.000Z

src/runner.py

samirsahoo007/Naive-Bayes-and-Decision-Tree-Classifiers

619c5c0b17438d1014f7ca7e4ce13cc44c45de3c

[ "MIT" ]

null

src/runner.py

samirsahoo007/Naive-Bayes-and-Decision-Tree-Classifiers

619c5c0b17438d1014f7ca7e4ce13cc44c45de3c

[ "MIT" ]

4

2019-07-05T02:03:02.000Z

2022-01-21T22:12:16.000Z

# -*- coding: utf-8 -*- # """*********************************************************************************************""" # FileName [ runner.py ] # Synopsis [ main program that runs the 'Naive Bayes' and 'Decision Tree' training / testing ] # Author [ Ting-Wei Liu (Andi611) ] # Copyright [ Copyleft(c), NTUEE, NTU, Taiwan ] """*********************************************************************************************""" ############### # IMPORTATION # ############### import os import csv import argparse import numpy as np from data_loader import data_loader from classifiers import naive_bayes_runner from classifiers import decision_tree_runner ################## # CONFIGURATIONS # ################## def get_config(): parser = argparse.ArgumentParser(description='descrip_msg') classifier = parser.add_argument_group('classifier') classifier.add_argument('--classifier', type=str, default='', help='classifier to be specified by user') classifier.add_argument('--naive_bayes', action='store_true', help='enable Naive Bayes classification mode') classifier.add_argument('--decision_tree', action='store_true', help='enable Decision Tree classification mode') mode_args = parser.add_argument_group('mode') mode_args.add_argument('--search_opt', action='store_true', help='search for optimal parameters for classifiers') mode_args.add_argument('--run_all', action='store_true', help='run all distribution assumption for the Naive Bayes classifier') mode_args.add_argument('--visualize_tree', action='store_true', help='plot and visualize the Decision Tree classifier') data_args = parser.add_argument_group('data') data_args.add_argument('--data_news', action='store_true', help='Training and testing on the News dataset') data_args.add_argument('--data_mushroom', action='store_true', help='Training and testing on the Mushroom dataset') data_args.add_argument('--data_income', action='store_true', help='Training and testing on the Income dataset') path_args = parser.add_argument_group('train_path') path_args.add_argument('--train_path', type=str, default='', help='training path to be specified by user') path_args.add_argument('--train_path_news', type=str, default='../data/news/news_train.csv', help='path to the News training dataset') path_args.add_argument('--train_path_mushroom', type=str, default='../data/mushroom/mushroom_train.csv', help='path to the Mushroom training dataset') path_args.add_argument('--train_path_income', type=str, default='../data/income/income_train.csv', help='path to the Income training dataset') path_args = parser.add_argument_group('test_path') path_args.add_argument('--test_path', type=str, default='', help='testing path to be specified by user') path_args.add_argument('--test_path_news', type=str, default='../data/news/news_test.csv', help='path to the News testing dataset') path_args.add_argument('--test_path_mushroom', type=str, default='../data/mushroom/mushroom_test.csv', help='path to the Mushroom testing dataset') path_args.add_argument('--test_path_income', type=str, default='../data/income/income_test.csv', help='path to the Income testing dataset') path_args = parser.add_argument_group('output_path') path_args.add_argument('--output_path', type=str, default='../result/output.csv', help='path to save model prediction') args = parser.parse_args() args = error_handling(args) return args ################## # ERROR HANDLING # ################## def error_handling(args): if args.classifier != '': args.naive_bayes = True if args.classifier == 'N' else False args.decision_tree = True if args.classifier == 'D' else False if args.naive_bayes and args.decision_tree == True: raise AssertionError('Please choose one classifier at once, or specify the correct classifier!') if args.search_opt and args.run_all and args.visualize_tree == True: raise AssertionError('Please choose one mode at a time!') if args.data_news and args.data_mushroom and args.income == True: raise AssertionError('Please choose one and at least one dataset at a time!') if args.train_path != '' and args.test_path != '': if not os.path.isfile(args.train_path) or not os.path.isfile(args.test_path): raise AssertionError('The given file path is invalid!') if args.data_news: args.train_path_news = args.train_path args.test_path_news = args.test_path elif args.data_mushroom: args.train_path_mushroom = args.train_path args.test_path_mushroom = args.test_path elif args.data_income: args.train_path_income = args.train_path args.test_path_income = args.test_path else: raise AssertionError('Must choose a dataset!') return args ################# # OUTPUT WRITER # ################# def output_writer(path, result): with open(path, 'w') as f: file = csv.writer(f, delimiter=',', quotechar='\r') for item in result: file.writerow([int(item)]) print('Results have been successfully saved to: %s' % (path)) return True ######## # MAIN # ######## """ main function """ def main(): args = get_config() loader = data_loader(args) #---fetch data---# if args.data_news: train_x, train_y, test_x, test_y = loader.fetch_news() MODEL = 'NEWS' elif args.data_mushroom: train_x, train_y, test_x, test_y = loader.fetch_mushroom() MODEL = 'MUSHROOM' elif args.data_income: train_x, train_y, test_x, test_y = loader.fetch_income() # -> test_y == None MODEL = 'INCOME' ############### # NAIVE BAYES # ############### if args.naive_bayes: #---construct model---# naive_bayes = naive_bayes_runner(MODEL, train_x, train_y, test_x, test_y) #---modes---# if args.search_opt: naive_bayes.search_alpha() elif args.run_all: naive_bayes.run_best_all() else: pred_y = naive_bayes.run_best() output_writer(args.output_path, pred_y) ################# # DECISION TREE # ################# if args.decision_tree: #---construct model---# decision_tree = decision_tree_runner(MODEL, train_x, train_y, test_x, test_y) #---modes---# if args.search_opt: decision_tree.search_max_depth() elif args.visualize_tree: decision_tree.visualize() else: pred_y = decision_tree.run_best() output_writer(args.output_path, pred_y) if __name__ == '__main__': main()

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null

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null

0

1

0

fe195c652a959304ac79843bfd7f33439351fd89

7,393

py

Python

igibson/metrics/agent.py

Nick-AhSen/iGibson

c6854f11eec5d935fa3ef3d6d4852c6571beab4b

[ "MIT" ]

null

igibson/metrics/agent.py

Nick-AhSen/iGibson

c6854f11eec5d935fa3ef3d6d4852c6571beab4b

[ "MIT" ]

null

igibson/metrics/agent.py

Nick-AhSen/iGibson

c6854f11eec5d935fa3ef3d6d4852c6571beab4b

[ "MIT" ]

null

import copy import numpy as np import pybullet as p from igibson.metrics.metric_base import MetricBase class BehaviorRobotMetric(MetricBase): def __init__(self): self.initialized = False self.state_cache = {} self.next_state_cache = {} self.agent_pos = {part: [] for part in ["left_hand", "right_hand", "body"]} self.agent_grasping = {part: [] for part in ["left_hand", "right_hand"]} self.agent_local_pos = {part: [] for part in ["left_hand", "right_hand"]} self.agent_reset = {part: [] for part in ["left_hand", "right_hand", "body"]} self.delta_agent_work = {part: [] for part in ["left_hand", "right_hand", "body"]} self.delta_agent_distance = {part: [] for part in ["left_hand", "right_hand", "body"]} self.delta_agent_grasp_distance = {part: [] for part in ["left_hand", "right_hand"]} self.clip = 0.2 def step_callback(self, igbhvr_act_inst, _): robot = igbhvr_act_inst.simulator.robots[0] agent_work = {part: 0 for part in ["left_hand", "right_hand", "body"]} agent_distance = {part: 0 for part in ["left_hand", "right_hand", "body"]} for part in ["left_hand", "right_hand", "body"]: self.next_state_cache[part] = { "position": np.array(p.getBasePositionAndOrientation(robot.parts[part].get_body_id())[0]), } if not self.initialized: self.state_cache = copy.deepcopy(self.next_state_cache) self.initialized = True if robot.action[19] > 0 and robot.action[27] > 0: self.agent_reset["left_hand"].append(True) self.agent_reset["right_hand"].append(True) self.agent_reset["body"].append(True) if robot.action[19] > 0: self.agent_reset["left_hand"].append(True) self.agent_reset["right_hand"].append(False) self.agent_reset["body"].append(True) elif robot.action[27] > 0: self.agent_reset["left_hand"].append(False) self.agent_reset["right_hand"].append(True) self.agent_reset["body"].append(True) else: self.agent_reset["left_hand"].append(False) self.agent_reset["right_hand"].append(False) self.agent_reset["body"].append(False) for part in self.state_cache: delta_pos = np.linalg.norm(self.next_state_cache[part]["position"] - self.state_cache[part]["position"]) self.agent_pos[part].append(list(self.state_cache[part]["position"])) # Exclude agent teleports delta_pos = np.clip(delta_pos, -self.clip, self.clip) if robot.parts[part].movement_cid is None: force = 0 work = 0 else: force = p.getConstraintState(robot.parts[part].movement_cid) work = np.abs((delta_pos * np.linalg.norm(force))) distance = np.abs(delta_pos) if part in ["left_hand", "right_hand"]: self.agent_local_pos[part].append(list(robot.parts[part].get_local_position_orientation()[0])) if part in ["left_hand", "right_hand"] and ( len(p.getContactPoints(robot.parts[part].get_body_id())) > 0 or robot.parts[part].object_in_hand is not None ): self.delta_agent_grasp_distance[part].append(distance) self.agent_grasping[part].append(True) elif part in ["left_hand", "right_hand"]: self.delta_agent_grasp_distance[part].append(0) self.agent_grasping[part].append(False) agent_work[part] = work agent_distance[part] = distance self.delta_agent_work[part].append(work) self.delta_agent_distance[part].append(distance) self.state_cache = copy.deepcopy(self.next_state_cache) def gather_results(self): return { "agent_distance": { "timestep": self.delta_agent_distance, }, "grasp_distance": { "timestep": self.delta_agent_grasp_distance, }, "work": { "timestep": self.delta_agent_work, }, "pos": { "timestep": self.agent_pos, }, "local_pos": { "timestep": self.agent_local_pos, }, "grasping": { "timestep": self.agent_grasping, }, "reset": { "timestep": self.agent_reset, }, } class FetchRobotMetric(MetricBase): def __init__(self): self.initialized = False self.state_cache = {} self.next_state_cache = {} self.agent_pos = {part: [] for part in ["gripper", "body"]} self.agent_grasping = {part: [] for part in ["gripper"]} self.agent_local_pos = {part: [] for part in ["gripper"]} self.delta_agent_distance = {part: [] for part in ["gripper", "body"]} self.delta_agent_grasp_distance = {part: [] for part in ["gripper"]} self.clip = 0.2 def step_callback(self, igbhvr_act_inst, _): robot = igbhvr_act_inst.simulator.robots[0] agent_distance = {part: 0 for part in self.agent_pos} self.next_state_cache = { "gripper": {"position": robot.get_end_effector_position()}, "body": {"position": robot.get_position()}, } if not self.initialized: self.state_cache = copy.deepcopy(self.next_state_cache) self.initialized = True self.agent_pos["body"].append(list(self.state_cache["body"]["position"])) delta_pos = np.linalg.norm( np.array(self.next_state_cache["body"]["position"]) - self.state_cache["body"]["position"] ) distance = np.abs(delta_pos) self.delta_agent_distance["body"].append(distance) self.agent_pos["gripper"].append(list(self.state_cache["gripper"]["position"])) delta_pos = np.linalg.norm( self.next_state_cache["gripper"]["position"] - self.state_cache["gripper"]["position"] ) gripper_distance = np.abs(delta_pos) self.delta_agent_distance["gripper"].append(gripper_distance) self.agent_local_pos["gripper"].append(list(robot.get_relative_eef_position())) contacts = p.getContactPoints(bodyA=robot.robot_ids[0], linkIndexA=robot.eef_link_id) if len(contacts) > 0: self.delta_agent_grasp_distance["gripper"].append(gripper_distance) self.agent_grasping["gripper"].append(True) else: self.delta_agent_grasp_distance["gripper"].append(0) self.agent_grasping["gripper"].append(False) self.state_cache = copy.deepcopy(self.next_state_cache) def gather_results(self): return { "agent_distance": { "timestep": self.delta_agent_distance, }, "grasp_distance": { "timestep": self.delta_agent_grasp_distance, }, "pos": { "timestep": self.agent_pos, }, "local_pos": { "timestep": self.agent_local_pos, }, "grasping": { "timestep": self.agent_grasping, }, }

38.305699

116

0.581631

857

7,393

4.75846

0.115519

0.079451

0.061795

0.04463

0.78102

0.670181

0.626533

0.537764

0.510299

0.456106

0

0.005694

0.287299

7,393

192

117

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0.768267

0.003111

0

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1

0.03871

false

0

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0.090323

0

null

0

null

0

1

0

fe1c00d5c2481798d64766027364e0e668d8c7bc

59,866

py

Python

src/ttkbootstrap/dialogs/dialogs.py

MrJaatt/ttkbootstrap

4e837d64859e5a230ef0500faddbb2c384f5b9d4

[ "MIT" ]

1

2022-01-28T09:37:32.000Z

src/ttkbootstrap/dialogs/dialogs.py

MrJaatt/ttkbootstrap

4e837d64859e5a230ef0500faddbb2c384f5b9d4

[ "MIT" ]

null

src/ttkbootstrap/dialogs/dialogs.py

MrJaatt/ttkbootstrap

4e837d64859e5a230ef0500faddbb2c384f5b9d4

[ "MIT" ]

null

""" This module contains various base dialog base classes that can be used to create custom dialogs for the end user. These classes serve as the basis for the pre-defined static helper methods in the `Messagebox`, and `Querybox` container classes. """ import calendar import textwrap from datetime import datetime from tkinter import font import ttkbootstrap as ttk from ttkbootstrap import utility from ttkbootstrap.icons import Icon from ttkbootstrap.constants import * from tkinter import BaseWidget from ttkbootstrap.localization import MessageCatalog class Dialog(BaseWidget): """A simple dialog base class.""" def __init__(self, parent=None, title="", alert=False): """ Parameters: parent (Widget): Makes the window the logical parent of the message box. The messagebox is displayed on top of its parent window. title (str): The string displayed as the title of the message box. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. alert (bool): Ring the display's bell when the dialog is shown. """ BaseWidget._setup(self, parent, {}) self._winsys = self.master.tk.call("tk", "windowingsystem") self._toplevel = None self._title = title or " " self._result = None self._alert = alert self._initial_focus = None def _locate(self): toplevel = self._toplevel master = toplevel.master screen_height = toplevel.winfo_screenheight() screen_width = toplevel.winfo_screenwidth() toplevel.update_idletasks() if master.winfo_viewable(): m_width = master.winfo_width() m_height = master.winfo_height() m_x = master.winfo_rootx() m_y = master.winfo_rooty() else: m_width = screen_width m_height = screen_height m_x = m_y = 0 w_width = toplevel.winfo_reqwidth() w_height = toplevel.winfo_reqheight() x = int(m_x + (m_width - w_width) * 0.45) y = int(m_y + (m_height - w_height) * 0.3) if x + w_width > screen_width: x = screen_width - w_width elif x < 0: x = 0 if y + w_height > screen_height: y = screen_height - w_height elif y < 0: y = 0 toplevel.geometry(f"+{x}+{y}") def show(self): """Show the popup dialog""" self._result = None self.build() self._locate() self._toplevel.deiconify() if self._alert: self._toplevel.bell() if self._initial_focus: self._initial_focus.focus_force() self._toplevel.grab_set() self._toplevel.wait_window() def create_body(self, master): """Create the dialog body. This method should be overridden and is called by the `build` method. Set the `self._initial_focus` for the widget that should receive the initial focus. Parameters: master (Widget): The parent widget. """ raise NotImplementedError def create_buttonbox(self, master): """Create the dialog button box. This method should be overridden and is called by the `build` method. Set the `self._initial_focus` for the button that should receive the intial focus. Parameters: master (Widget): The parent widget. """ raise NotImplementedError def build(self): """Build the dialog from settings""" # setup toplevel based on widowing system if self._winsys == "win32": self._toplevel = ttk.Toplevel( transient=self.master, title=self._title, resizable=(0, 0), minsize=(250, 15), iconify=True, ) else: self._toplevel = ttk.Toplevel( transient=self.master, title=self._title, resizable=(0, 0), windowtype="dialog", iconify=True, ) self._toplevel.withdraw() # reset the iconify state # bind <Escape> event to window close self._toplevel.bind("<Escape>", lambda _: self._toplevel.destroy()) # set position of popup from parent window #self._locate() # create widgets self.create_body(self._toplevel) self.create_buttonbox(self._toplevel) # update the window before showing self._toplevel.update_idletasks() @property def result(self): """Returns the result of the dialog.""" return self._result class MessageDialog(Dialog): """A simple modal dialog class that can be used to build simple message dialogs. Displays a message and a set of buttons. Each of the buttons in the message window is identified by a unique symbolic name. After the message window is popped up, the message box awaits for the user to select one of the buttons. Then it returns the symbolic name of the selected button. Use a `Toplevel` widget for more advanced modal dialog designs. """ def __init__( self, message, title=" ", buttons=None, command=None, width=50, parent=None, alert=False, default=None, padding=(20, 20), icon=None, **kwargs ): """ Parameters: message (str): A message to display in the message box. title (str): The string displayed as the title of the message box. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. buttons (List[str]): A list of buttons to appear at the bottom of the popup messagebox. The buttons can be a list of strings which will define the symbolic name and the button text. `['OK', 'Cancel']`. Alternatively, you can assign a bootstyle to each button by using the colon to separate the button text and the bootstyle. If no colon is found, then the style is set to 'primary' by default. `['OK:success','Cancel:danger']`. command (Tuple[Callable, str]): The function to invoke when the user closes the dialog. The actual command is a tuple that consists of the function to call and the symbolic name of the button that closes the dialog. width (int): The maximum number of characters per line in the message. If the text stretches beyond the limit, the line will break at the word. parent (Widget): Makes the window the logical parent of the message box. The messagebox is displayed on top of its parent window. alert (bool): Ring the display's bell when the dialog is shown. default (str): The symbolic name of the default button. The default button is invoked when the the <Return> key is pressed. If no default is provided, the right-most button in the button list will be set as the default., padding (Union[int, Tuple[int]]): The amount of space between the border and the widget contents. icon (str): An image path, path-like object or image data to be displayed to the left of the text. **kwargs (Dict): Other optional keyword arguments. Example: ```python root = tk.Tk() md = MessageDialog("Displays a message with buttons.") md.show() ``` """ super().__init__(parent, title, alert) self._message = message self._command = command self._width = width self._alert = alert self._default = (default,) self._padding = padding self._icon = icon self._localize = kwargs.get('localize') if buttons is None: self._buttons = [ f"{MessageCatalog.translate('Cancel')}:secondary", f"{MessageCatalog.translate('OK')}:primary" ] else: self._buttons = buttons def create_body(self, master): """Overrides the parent method; adds the message section.""" container = ttk.Frame(master, padding=self._padding) if self._icon: try: # assume this is image data self._img = ttk.PhotoImage(data=self._icon) icon_lbl = ttk.Label(container, image=self._img) icon_lbl.pack(side=LEFT, padx=5) except: try: # assume this is a file path self._img = ttk.PhotoImage(file=self._icon) icon_lbl = ttk.Label(container, image=self._img) icon_lbl.pack(side=LEFT, padx=5) except: # icon is neither data nor a valid file path print('MessageDialog icon is invalid') if self._message: for msg in self._message.split("\n"): message = "\n".join(textwrap.wrap(msg, width=self._width)) message_label = ttk.Label(container, text=message) message_label.pack(pady=(0, 3), fill=X, anchor=N) container.pack(fill=X, expand=True) def create_buttonbox(self, master): """Overrides the parent method; adds the message buttonbox""" frame = ttk.Frame(master, padding=(5, 5)) button_list = [] for i, button in enumerate(self._buttons[::-1]): cnf = button.split(":") if len(cnf) == 2: text, bootstyle = cnf else: text = cnf[0] bootstyle = "secondary" if self._localize == True: text = MessageCatalog.translate(text) btn = ttk.Button(frame, bootstyle=bootstyle, text=text) btn.bind("<Return>", lambda _: btn.invoke()) btn.configure(command=lambda b=btn: self.on_button_press(b)) btn.pack(padx=2, side=RIGHT) btn.lower() # set focus traversal left-to-right button_list.append(btn) if self._default is not None and text == self._default: self._initial_focus = btn elif self._default is None and i == 0: self._initial_focus = btn # bind default button to return key press and set focus self._toplevel.bind("<Return>", lambda _, b=btn: b.invoke()) self._toplevel.bind("<KP_Enter>", lambda _, b=btn: b.invoke()) ttk.Separator(self._toplevel).pack(fill=X) frame.pack(side=BOTTOM, fill=X, anchor=S) if not self._initial_focus: self._initial_focus = button_list[0] def on_button_press(self, button): """Save result, destroy the toplevel, and execute command.""" self._result = button["text"] command = self._command if command is not None: command() self._toplevel.destroy() def show(self): """Create and display the popup messagebox.""" super().show() class QueryDialog(Dialog): """A simple modal dialog class that can be used to build simple data input dialogs. Displays a prompt, and input box, and a set of buttons. Additional data manipulation can be performed on the user input post-hoc by overriding the `apply` method. Use a `Toplevel` widget for more advanced modal dialog designs. """ def __init__( self, prompt, title=" ", initialvalue="", minvalue=None, maxvalue=None, width=65, datatype=str, padding=(20, 20), parent=None, ): """ Parameters: prompt (str): A message to display in the message box above the entry widget. title (str): The string displayed as the title of the message box. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. initialvalue (Any): The initial value in the entry widget. minvalue (Any): The minimum allowed value. Only valid for int and float data types. maxvalue (Any): The maximum allowed value. Only valid for int and float data types. width (int): The maximum number of characters per line in the message. If the text stretches beyond the limit, the line will break at the word. parent (Widget): Makes the window the logical parent of the message box. The messagebox is displayed on top of its parent window. padding (Union[int, Tuple[int]]): The amount of space between the border and the widget contents. datatype (Union[int, str, float]): The data type used to validate the entry value. """ super().__init__(parent, title) self._prompt = prompt self._initialvalue = initialvalue self._minvalue = minvalue self._maxvalue = maxvalue self._width = width self._datatype = datatype self._padding = padding self._result = None def create_body(self, master): """Overrides the parent method; adds the message and input section.""" frame = ttk.Frame(master, padding=self._padding) if self._prompt: for p in self._prompt.split("\n"): prompt = "\n".join(textwrap.wrap(p, width=self._width)) prompt_label = ttk.Label(frame, text=prompt) prompt_label.pack(pady=(0, 5), fill=X, anchor=N) entry = ttk.Entry(master=frame) entry.insert(END, self._initialvalue) entry.pack(pady=(0, 5), fill=X) entry.bind("<Return>", self.on_submit) entry.bind("<KP_Enter>", self.on_submit) entry.bind("<Escape>", self.on_cancel) frame.pack(fill=X, expand=True) self._initial_focus = entry def create_buttonbox(self, master): """Overrides the parent method; adds the message buttonbox""" frame = ttk.Frame(master, padding=(5, 10)) submit = ttk.Button( master=frame, bootstyle="primary", text=MessageCatalog.translate("Submit"), command=self.on_submit, ) submit.pack(padx=5, side=RIGHT) submit.lower() # set focus traversal left-to-right cancel = ttk.Button( master=frame, bootstyle="secondary", text=MessageCatalog.translate("Cancel"), command=self.on_cancel, ) cancel.pack(padx=5, side=RIGHT) cancel.lower() # set focus traversal left-to-right ttk.Separator(self._toplevel).pack(fill=X) frame.pack(side=BOTTOM, fill=X, anchor=S) def on_submit(self, *_): """Save result, destroy the toplevel, and apply any post-hoc data manipulations.""" self._result = self._initial_focus.get() valid_result = self.validate() if not valid_result: return # keep toplevel open for valid response self._toplevel.destroy() self.apply() def on_cancel(self, *_): """Close the toplevel and return empty.""" self._toplevel.destroy() return def validate(self): """Validate the data This method is called automatically to validate the data before the dialog is destroyed. Can be subclassed and overridden. """ # no default checks required for string data types if self._datatype not in [float, int, complex]: return True # convert result to appropriate data type try: self._result = self._datatype(self._result) except ValueError: msg = MessageCatalog.translate('Should be of data type') Messagebox.ok( message=f"{msg} `{self._datatype}`", title=MessageCatalog.translate("Invalid data type"), ) return False # max value range if self._maxvalue is not None: if self._result > self._maxvalue: msg = MessageCatalog.translate('Number cannot be greater than') Messagebox.ok( message=f"{msg} {self._maxvalue}", title=MessageCatalog.translate("Out of range"), ) return False # min value range if self._minvalue is not None: if self._result < self._minvalue: msg = MessageCatalog.translate('Number cannot be less than') Messagebox.ok( message=f"{msg} {self._minvalue}", title=MessageCatalog.translate("Out of range"), ) return False # valid result return True def apply(self): """Process the data. This method is called automatically to process the data after the dialog is destroyed. By default, it does nothing. """ pass # override class DatePickerDialog: """A dialog that displays a calendar popup and returns the selected date as a datetime object. The current date is displayed by default unless the `startdate` parameter is provided. The month can be changed by clicking the chevrons to the left and right of the month-year title. Left-click the arrow to move the calendar by one month. Right-click the arrow to move the calendar by one year. Right-click the title to reset the calendar to the start date. The starting weekday can be changed with the `firstweekday` parameter for geographies that do not start the calendar on Sunday, which is the default. The widget grabs focus and all screen events until released. If you want to cancel a date selection, click the 'X' button at the top-right corner of the widget. The bootstyle api may be used to change the style of the widget. The available colors include -> primary, secondary, success, info, warning, danger, light, dark. ![](../../assets/dialogs/date-picker-dialog.png) """ def __init__( self, parent=None, title=" ", firstweekday=6, startdate=None, bootstyle=PRIMARY, ): """ Parameters: parent (Widget): The parent widget; the popup will appear to the bottom-right of the parent widget. If no parent is provided, the widget is centered on the screen. title (str): The text that appears on the titlebar. firstweekday (int): Specifies the first day of the week. 0=Monday, 1=Tuesday, etc... startdate (datetime): The date to be in focus when the widget is displayed. bootstyle (str): The following colors can be used to change the color of the title and hover / pressed color -> primary, secondary, info, warning, success, danger, light, dark. """ self.parent = parent self.root = ttk.Toplevel( title=title, transient=self.parent, resizable=(False, False), topmost=True, minsize=(226, 1), iconify=True ) self.firstweekday = firstweekday self.startdate = startdate or datetime.today().date() self.bootstyle = bootstyle or PRIMARY self.date_selected = self.startdate self.date = startdate or self.date_selected self.calendar = calendar.Calendar(firstweekday=firstweekday) self.titlevar = ttk.StringVar() self.datevar = ttk.IntVar() self._setup_calendar() self.root.grab_set() self.root.wait_window() def _setup_calendar(self): """Setup the calendar widget""" # create the widget containers self.frm_calendar = ttk.Frame( master=self.root, padding=0, borderwidth=0, relief=FLAT ) self.frm_calendar.pack(fill=BOTH, expand=YES) self.frm_title = ttk.Frame(self.frm_calendar, padding=(3, 3)) self.frm_title.pack(fill=X) self.frm_header = ttk.Frame(self.frm_calendar, bootstyle=SECONDARY) self.frm_header.pack(fill=X) # setup the toplevel widget self.root.withdraw() # reset the iconify state self.frm_calendar.update_idletasks() # actualize geometry # create visual components self._draw_titlebar() self._draw_calendar() # make toplevel visible self._set_window_position() self.root.deiconify() def _update_widget_bootstyle(self): self.frm_title.configure(bootstyle=self.bootstyle) self.title.configure(bootstyle=f"{self.bootstyle}-inverse") self.prev_period.configure(style=f"Chevron.{self.bootstyle}.TButton") self.next_period.configure(style=f"Chevron.{self.bootstyle}.TButton") def _draw_calendar(self): self._update_widget_bootstyle() self._set_title() self._current_month_days() self.frm_dates = ttk.Frame(self.frm_calendar) self.frm_dates.pack(fill=BOTH, expand=YES) for row, weekday_list in enumerate(self.monthdays): for col, day in enumerate(weekday_list): self.frm_dates.columnconfigure(col, weight=1) if day == 0: ttk.Label( master=self.frm_dates, text=self.monthdates[row][col].day, anchor=CENTER, padding=5, bootstyle=SECONDARY, ).grid(row=row, column=col, sticky=NSEW) else: if all( [ day == self.date_selected.day, self.date.month == self.date_selected.month, self.date.year == self.date_selected.year, ] ): day_style = "secondary-toolbutton" else: day_style = f"{self.bootstyle}-calendar" def selected(x=row, y=col): self._on_date_selected(x, y) btn = ttk.Radiobutton( master=self.frm_dates, variable=self.datevar, value=day, text=day, bootstyle=day_style, padding=5, command=selected, ) btn.grid(row=row, column=col, sticky=NSEW) def _draw_titlebar(self): """Draw the calendar title bar which includes the month title and the buttons that increment and decrement the selected month. In addition to the previous and next MONTH commands that are assigned to the button press, a "right-click" event is assigned to each button that causes the calendar to move to the previous and next YEAR. """ # create and pack the title and action buttons self.prev_period = ttk.Button( master=self.frm_title, text="«", command=self.on_prev_month ) self.prev_period.pack(side=LEFT) self.title = ttk.Label( master=self.frm_title, textvariable=self.titlevar, anchor=CENTER, font="-weight bold", ) self.title.pack(side=LEFT, fill=X, expand=YES) self.next_period = ttk.Button( master=self.frm_title, text="»", command=self.on_next_month, ) self.next_period.pack(side=LEFT) # bind "year" callbacks to action buttons self.prev_period.bind("<Button-3>", self.on_prev_year, "+") self.next_period.bind("<Button-3>", self.on_next_year, "+") self.title.bind("<Button-1>", self.on_reset_date) # create and pack days of the week header for col in self._header_columns(): ttk.Label( master=self.frm_header, text=col, anchor=CENTER, padding=5, bootstyle=(SECONDARY, INVERSE), ).pack(side=LEFT, fill=X, expand=YES) def _set_title(self): _titledate = f'{self.date.strftime("%B %Y")}' self.titlevar.set(value=_titledate) def _current_month_days(self): """Fetch the day numbers and dates for all days in the current month. `monthdays` is a list of days as integers, and `monthdates` is a list of `datetime` objects. """ self.monthdays = self.calendar.monthdayscalendar( year=self.date.year, month=self.date.month ) self.monthdates = self.calendar.monthdatescalendar( year=self.date.year, month=self.date.month ) def _header_columns(self): """Create and return a list of weekdays to be used as a header in the calendar. The order of the weekdays is based on the `firstweekday` property. Returns: List[str]: A list of weekday column names for the calendar header. """ weekdays = [MessageCatalog.translate("Mo"), MessageCatalog.translate("Tu"), MessageCatalog.translate("We"), MessageCatalog.translate("Th"), MessageCatalog.translate("Fr"), MessageCatalog.translate("Sa"), MessageCatalog.translate("Su")] header = weekdays[self.firstweekday :] + weekdays[: self.firstweekday] return header def _on_date_selected(self, row, col): """Callback for selecting a date. An index is assigned to each date button that corresponds to the dates in the `monthdates` matrix. When the user clicks a button to select a date, the index from this button is used to lookup the date value of the button based on the row and column index reference. This value is saved in the `date_selected` property and the `Toplevel` is destroyed. Parameters: index (Tuple[int, int]): A row and column index of the date selected; to be found in the `monthdates` matrix. Returns: datetime: The date selected """ self.date_selected = self.monthdates[row][col] self.root.destroy() def _selection_callback(func): """Calls the decorated `func` and redraws the calendar.""" def inner(self, *args): func(self, *args) self.frm_dates.destroy() self._draw_calendar() return inner @_selection_callback def on_next_month(self): """Increment the calendar data to the next month""" year, month = self._nextmonth(self.date.year, self.date.month) self.date = datetime(year=year, month=month, day=1).date() @_selection_callback def on_next_year(self, *_): """Increment the calendar data to the next year""" year = self.date.year + 1 month = self.date.month self.date = datetime(year=year, month=month, day=1).date() @_selection_callback def on_prev_month(self): """Decrement the calendar to the previous year""" year, month = self._prevmonth(self.date.year, self.date.month) self.date = datetime(year=year, month=month, day=1).date() @_selection_callback def on_prev_year(self, *_): year = self.date.year - 1 month = self.date.month self.date = datetime(year=year, month=month, day=1).date() @_selection_callback def on_reset_date(self, *_): """Set the calendar to the start date""" self.date = self.startdate def _set_window_position(self): """Move the window the to bottom-right of the parent widget, or to the middle of the screen if no parent is provided. """ width = self.root.winfo_reqwidth() height = self.root.winfo_reqheight() if self.parent: xpos = self.parent.winfo_rootx() + self.parent.winfo_width() ypos = self.parent.winfo_rooty() + self.parent.winfo_height() self.root.geometry(f"+{xpos}+{ypos}") else: xpos = self.root.winfo_screenwidth() // 2 - width ypos = self.root.winfo_screenheight() // 2 - height self.root.geometry(f"+{xpos}+{ypos}") @staticmethod def _nextmonth(year, month): if month == 12: return year+1, 1 else: return year, month+1 @staticmethod def _prevmonth(year, month): if month == 1: return year-1, 12 else: return year, month-1 class FontDialog(Dialog): """A dialog that displays a variety of options for choosing a font. This dialog constructs and returns a `Font` object based on the options selected by the user. The initial font is based on OS settings and will vary. The font object is returned when the **Ok** button is pressed and can be passed to any widget that accepts a _font_ configuration option. ![](../../assets/dialogs/querybox-get-font.png) """ def __init__(self, title="Font Selector", parent=None): title = MessageCatalog.translate(title) super().__init__(parent=parent, title=title) self._style = ttk.Style() self._default = font.nametofont("TkDefaultFont") self._actual = self._default.actual() self._size = ttk.Variable(value=self._actual["size"]) self._family = ttk.Variable(value=self._actual["family"]) self._slant = ttk.Variable(value=self._actual["slant"]) self._weight = ttk.Variable(value=self._actual["weight"]) self._overstrike = ttk.Variable(value=self._actual["overstrike"]) self._underline = ttk.Variable(value=self._actual["underline"]) self._preview_font = font.Font() self._slant.trace_add("write", self._update_font_preview) self._weight.trace_add("write", self._update_font_preview) self._overstrike.trace_add("write", self._update_font_preview) self._underline.trace_add("write", self._update_font_preview) _headingfont = font.nametofont("TkHeadingFont") _headingfont.configure(weight="bold") self._update_font_preview() self._families = set([self._family.get()]) for f in font.families(): if all([f, not f.startswith("@"), "emoji" not in f.lower()]): self._families.add(f) def create_body(self, master): width = utility.scale_size(master, 600) height = utility.scale_size(master, 500) self._toplevel.geometry(f"{width}x{height}") family_size_frame = ttk.Frame(master, padding=10) family_size_frame.pack(fill=X, anchor=N) self._initial_focus = self._font_families_selector(family_size_frame) self._font_size_selector(family_size_frame) self._font_options_selectors(master, padding=10) self._font_preview(master, padding=10) def create_buttonbox(self, master): container = ttk.Frame(master, padding=(5, 10)) container.pack(fill=X) ok_btn = ttk.Button( master=container, bootstyle="primary", text=MessageCatalog.translate("OK"), command=self._on_submit, ) ok_btn.pack(side=RIGHT, padx=5) ok_btn.bind("<Return>", lambda _: ok_btn.invoke()) cancel_btn = ttk.Button( master=container, bootstyle="secondary", text=MessageCatalog.translate("Cancel"), command=self._on_cancel, ) cancel_btn.pack(side=RIGHT, padx=5) cancel_btn.bind("<Return>", lambda _: cancel_btn.invoke()) def _font_families_selector(self, master): container = ttk.Frame(master) container.pack(fill=BOTH, expand=YES, side=LEFT) header = ttk.Label(container, text=MessageCatalog.translate("Family"), font="TkHeadingFont") header.pack(fill=X, pady=(0, 2), anchor=N) listbox = ttk.Treeview( master=container, height=5, show="", columns=[0], ) listbox.column(0, width=utility.scale_size(listbox, 250)) listbox.pack(side=LEFT, fill=BOTH, expand=YES) listbox_vbar = ttk.Scrollbar( container, command=listbox.yview, orient=VERTICAL, bootstyle="rounded", ) listbox_vbar.pack(side=RIGHT, fill=Y) listbox.configure(yscrollcommand=listbox_vbar.set) for f in self._families: listbox.insert("", iid=f, index=END, tags=[f], values=[f]) listbox.tag_configure(f, font=(f, self._size.get())) iid = self._family.get() listbox.selection_set(iid) # select default value listbox.see(iid) # ensure default is visible listbox.bind( "<<TreeviewSelect>>", lambda e: self._on_select_font_family(e) ) return listbox def _font_size_selector(self, master): container = ttk.Frame(master) container.pack(side=LEFT, fill=Y, padx=(10, 0)) header = ttk.Label(container, text=MessageCatalog.translate("Size"), font="TkHeadingFont") header.pack(fill=X, pady=(0, 2), anchor=N) sizes_listbox = ttk.Treeview(container, height=7, columns=[0], show="") sizes_listbox.column(0, width=utility.scale_size(sizes_listbox, 24)) sizes = [*range(8, 13), *range(13, 30, 2), 36, 48, 72] for s in sizes: sizes_listbox.insert("", iid=s, index=END, values=[s]) iid = self._size.get() sizes_listbox.selection_set(iid) sizes_listbox.see(iid) sizes_listbox.bind( "<<TreeviewSelect>>", lambda e: self._on_select_font_size(e) ) sizes_listbox_vbar = ttk.Scrollbar( master=container, orient=VERTICAL, command=sizes_listbox.yview, bootstyle="round", ) sizes_listbox.configure(yscrollcommand=sizes_listbox_vbar.set) sizes_listbox.pack(side=LEFT, fill=Y, expand=YES, anchor=N) sizes_listbox_vbar.pack(side=LEFT, fill=Y, expand=YES) def _font_options_selectors(self, master, padding: int): container = ttk.Frame(master, padding=padding) container.pack(fill=X, padx=2, pady=2, anchor=N) weight_lframe = ttk.Labelframe(container, text=MessageCatalog.translate("Weight"), padding=5) weight_lframe.pack(side=LEFT, fill=X, expand=YES) opt_normal = ttk.Radiobutton( master=weight_lframe, text=MessageCatalog.translate("normal"), value="normal", variable=self._weight, ) opt_normal.invoke() opt_normal.pack(side=LEFT, padx=5, pady=5) opt_bold = ttk.Radiobutton( master=weight_lframe, text=MessageCatalog.translate("bold"), value="bold", variable=self._weight, ) opt_bold.pack(side=LEFT, padx=5, pady=5) slant_lframe = ttk.Labelframe(container, text=MessageCatalog.translate("Slant"), padding=5) slant_lframe.pack(side=LEFT, fill=X, padx=10, expand=YES) opt_roman = ttk.Radiobutton( master=slant_lframe, text=MessageCatalog.translate("roman"), value="roman", variable=self._slant, ) opt_roman.invoke() opt_roman.pack(side=LEFT, padx=5, pady=5) opt_italic = ttk.Radiobutton( master=slant_lframe, text=MessageCatalog.translate("italic"), value="italic", variable=self._slant, ) opt_italic.pack(side=LEFT, padx=5, pady=5) effects_lframe = ttk.Labelframe(container, text=MessageCatalog.translate("Effects"), padding=5) effects_lframe.pack(side=LEFT, padx=(2, 0), fill=X, expand=YES) opt_underline = ttk.Checkbutton( master=effects_lframe, text=MessageCatalog.translate("underline"), variable=self._underline ) opt_underline.pack(side=LEFT, padx=5, pady=5) opt_overstrike = ttk.Checkbutton( master=effects_lframe, text=MessageCatalog.translate("overstrike"), variable=self._overstrike ) opt_overstrike.pack(side=LEFT, padx=5, pady=5) def _font_preview(self, master, padding: int): container = ttk.Frame(master, padding=padding) container.pack(fill=BOTH, expand=YES, anchor=N) header = ttk.Label(container, text=MessageCatalog.translate("Preview"), font="TkHeadingFont") header.pack(fill=X, pady=2, anchor=N) content = MessageCatalog.translate("The quick brown fox jumps over the lazy dog.") self._preview_text = ttk.Text( master=container, height=3, font=self._preview_font, highlightbackground=self._style.colors.primary, ) self._preview_text.insert(END, content) self._preview_text.pack(fill=BOTH, expand=YES) container.pack_propagate(False) def _on_select_font_family(self, e): tree: ttk.Treeview = self._toplevel.nametowidget(e.widget) fontfamily = tree.selection()[0] self._family.set(value=fontfamily) self._update_font_preview() def _on_select_font_size(self, e): tree: ttk.Treeview = self._toplevel.nametowidget(e.widget) fontsize = tree.selection()[0] self._size.set(value=fontsize) self._update_font_preview() def _on_submit(self) -> font.Font: self._toplevel.destroy() return self.result def _on_cancel(self): self._toplevel.destroy() def _update_font_preview(self, *_): family = self._family.get() size = self._size.get() slant = self._slant.get() overstrike = self._overstrike.get() underline = self._underline.get() self._preview_font.config( family=family, size=size, slant=slant, overstrike=overstrike, underline=underline, ) try: self._preview_text.configure(font=self._preview_font) except: pass self._result = self._preview_font class Messagebox: """This class contains various static methods that show popups with a message to the end user with various arrangments of buttons and alert options.""" @staticmethod def show_info(message, title=" ", parent=None, **kwargs): """Display a modal dialog box with an OK button and an INFO icon. ![](../../assets/dialogs/messagebox-show-info.png) Parameters: message (str): A message to display in the message box. title (str): The string displayed as the title of the messagebox. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. parent (Union[Window, Toplevel]): Makes the window the logical parent of the message box. The message box is displayed on top of its parent window. **kwargs (Dict): Other optional keyword arguments. """ sd = MessageDialog( message=message, title=title, parent=parent, buttons=["OK:primary"], icon=Icon.info, localize=True ) sd.show() @staticmethod def show_warning(message, title=" ", parent=None, **kwargs): """Display a modal dialog box with an OK button and a warning icon. Also will ring the display bell. ![](../../assets/dialogs/messagebox-show-warning.png) Parameters: message (str): A message to display in the message box. title (str): The string displayed as the title of the messagebox. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. parent (Union[Window, Toplevel]): Makes the window the logical parent of the message box. The message box is displayed on top of its parent window. **kwargs (Dict): Other optional keyword arguments. """ sd = MessageDialog( message=message, title=title, parent=parent, buttons=["OK:primary"], icon=Icon.warning, alert=True, localize=True, **kwargs, ) sd.show() @staticmethod def show_error(message, title=" ", parent=None, **kwargs): """Display a modal dialog box with an OK button and an error icon. Also will ring the display bell. ![](../../assets/dialogs/messagebox-show-error.png) Parameters: message (str): A message to display in the message box. title (str): The string displayed as the title of the messagebox. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. parent (Union[Window, Toplevel]): Makes the window the logical parent of the message box. The message box is displayed on top of its parent window. **kwargs (Dict): Other optional keyword arguments. """ sd = MessageDialog( message=message, title=title, parent=parent, buttons=["OK:primary"], icon=Icon.error, alert=True, localize=True, **kwargs, ) sd.show() @staticmethod def show_question( message, title=" ", parent=None, buttons=["No:secondary", "Yes:primary"], **kwargs, ): """Display a modal dialog box with yes, no buttons and a question icon. Also will ring the display bell. You may also change the button scheme using the `buttons` parameter. ![](../../assets/dialogs/messagebox-show-question.png) Parameters: message (str): A message to display in the message box. title (str): The string displayed as the title of the messagebox. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. parent (Union[Window, Toplevel]): Makes the window the logical parent of the message box. The message box is displayed on top of its parent window. buttons (List[str]): A list of buttons to appear at the bottom of the popup messagebox. The buttons can be a list of strings which will define the symbolic name and the button text. `['OK', 'Cancel']`. Alternatively, you can assign a bootstyle to each button by using the colon to separate the button text and the bootstyle. If no colon is found, then the style is set to 'primary' by default. `['Yes:success','No:danger']`. **kwargs (Dict): Other optional keyword arguments. Returns: Union[str, None]: The symbolic name of the button pressed, or None if the window is closed without pressing a button. """ sd = MessageDialog( message=message, title=title, parent=parent, buttons=buttons, icon=Icon.question, alert=True, localize=True, **kwargs, ) sd.show() return sd.result @staticmethod def ok(message, title=" ", alert=False, parent=None, **kwargs): """Display a modal dialog box with an OK button and and optional bell alert. ![](../../assets/dialogs/messagebox-ok.png) Parameters: message (str): A message to display in the message box. title (str): The string displayed as the title of the messagebox. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. alert (bool): Specified whether to ring the display bell. parent (Union[Window, Toplevel]): Makes the window the logical parent of the message box. The message box is displayed on top of its parent window. **kwargs (Dict): Other optional keyword arguments. """ sd = MessageDialog( title=title, message=message, parent=parent, alert=alert, buttons=["OK:primary"], localize=True, **kwargs, ) sd.show() @staticmethod def okcancel(message, title=" ", alert=False, parent=None, **kwargs): """Displays a modal dialog box with OK and Cancel buttons and return the symbolic name of the button pressed. ![](../../assets/dialogs/messagebox-ok-cancel.png) Parameters: message (str): A message to display in the message box. title (str): The string displayed as the title of the messagebox. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. alert (bool): Specified whether to ring the display bell. parent (Union[Window, Toplevel]): Makes the window the logical parent of the message box. The message box is displayed on top of its parent window. **kwargs (Dict): Other optional keyword arguments. Returns: Union[str, None]: The symbolic name of the button pressed, or None if the window is closed without pressing a button. """ sd = MessageDialog( title=title, message=message, parent=parent, alert=alert, localize=True, **kwargs ) sd.show() return sd.result @staticmethod def yesno(message, title=" ", alert=False, parent=None, **kwargs): """Display a modal dialog box with YES and NO buttons and return the symbolic name of the button pressed. ![](../../assets/dialogs/messagebox-yes-no.png) Parameters: message (str): A message to display in the message box. title (str): The string displayed as the title of the messagebox. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. alert (bool): Specified whether to ring the display bell. parent (Union[Window, Toplevel]): Makes the window the logical parent of the message box. The message box is displayed on top of its parent window. **kwargs (Dict): Other optional keyword arguments. Returns: Union[str, None]: The symbolic name of the button pressed, or None if the window is closed without pressing a button. """ sd = MessageDialog( title=title, message=message, parent=parent, buttons=["No", "Yes:primary"], alert=alert, localize=True, **kwargs, ) sd.show() return sd.result @staticmethod def yesnocancel(message, title=" ", alert=False, parent=None, **kwargs): """Display a modal dialog box with YES, NO, and Cancel buttons, and return the symbolic name of the button pressed. ![](../../assets/dialogs/messagebox-yes-no-cancel.png) Parameters: message (str): A message to display in the message box. title (str): The string displayed as the title of the messagebox. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. alert (bool): Specified whether to ring the display bell. parent (Union[Window, Toplevel]): Makes the window the logical parent of the message box. The message box is displayed on top of its parent window. **kwargs (Dict): Optional keyword arguments. Returns: Union[str, None]: The symbolic name of the button pressed, or None if the window is closed without pressing a button. """ sd = MessageDialog( title=title, message=message, parent=parent, alert=alert, buttons=["Cancel", "No", "Yes:primary"], localize=True, **kwargs, ) sd.show() return sd.result @staticmethod def retrycancel(message, title=" ", alert=False, parent=None, **kwargs): """Display a modal dialog box with RETRY and Cancel buttons; returns the symbolic name of the button pressed. ![](../../assets/dialogs/messagebox-retry-cancel.png) Parameters: message (str): A message to display in the message box. title (str): The string displayed as the title of the messagebox. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. alert (bool): Specified whether to ring the display bell. parent (Union[Window, Toplevel]): Makes the window the logical parent of the message box. The message box is displayed on top of its parent window. **kwargs (Dict): Other optional keyword arguments. Returns: Union[str, None]: The symbolic name of the button pressed, or None if the window is closed without pressing a button. """ sd = MessageDialog( title=title, message=message, parent=parent, alert=alert, buttons=["Cancel", "Retry:primary"], localize=True, **kwargs, ) sd.show() return sd.result class Querybox: """This class contains various static methods that request data from the end user.""" @staticmethod def get_color( parent=None, title="Color Chooser", initialcolor=None, ): """Show a color picker and return the select color when the user pressed OK. ![](../../assets/dialogs/querybox-get-color.png) Parameters: parent (Widget): The parent widget. title (str): Optional text that appears on the titlebar. initialcolor (str): The initial color to display in the 'Current' color frame. Returns: Tuple[rgb, hsl, hex] The selected color in various colors models. """ from ttkbootstrap.dialogs.colorchooser import ColorChooserDialog cd = ColorChooserDialog(parent, title, initialcolor) cd.show() return cd.result @staticmethod def get_date( parent=None, title=" ", firstweekday=6, startdate=None, bootstyle="primary", ): """Shows a calendar popup and returns the selection. ![](../../assets/dialogs/querybox-get-date.png) Parameters: parent (Widget): The parent widget; the popup will appear to the bottom-right of the parent widget. If no parent is provided, the widget is centered on the screen. title (str): The text that appears on the popup titlebar. firstweekday (int): Specifies the first day of the week. `0` is Monday, `6` is Sunday (the default). startdate (datetime): The date to be in focus when the widget is displayed; bootstyle (str): The following colors can be used to change the color of the title and hover / pressed color -> primary, secondary, info, warning, success, danger, light, dark. Returns: datetime: The date selected; the current date if no date is selected. """ chooser = DatePickerDialog( parent=parent, title=title, firstweekday=firstweekday, startdate=startdate, bootstyle=bootstyle, ) return chooser.date_selected @staticmethod def get_string( prompt="", title=" ", initialvalue=None, parent=None, **kwargs ): """Request a string type input from the user. ![](../../assets/dialogs/querybox-get-string.png) Parameters: prompt (str): A message to display in the message box above the entry widget. title (str): The string displayed as the title of the message box. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. initialvalue (Any): The initial value in the entry widget. parent (Widget): Makes the window the logical parent of the message box. The messagebox is displayed on top of its parent window. **kwargs (Dict): Other optional keyword arguments. Returns: str: The string value of the entry widget. """ initialvalue = initialvalue or "" dialog = QueryDialog( prompt, title, initialvalue, parent=parent, **kwargs ) dialog.show() return dialog._result @staticmethod def get_integer( prompt="", title=" ", initialvalue=None, minvalue=None, maxvalue=None, parent=None, **kwargs, ): """Request an integer type input from the user. ![](../../assets/dialogs/querybox-get-integer.png) Parameters: prompt (str): A message to display in the message box above the entry widget. title (str): The string displayed as the title of the message box. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. initialvalue (int): The initial value in the entry widget. minvalue (int): The minimum allowed value. maxvalue (int): The maximum allowed value. parent (Widget): Makes the window the logical parent of the message box. The messagebox is displayed on top of its parent window. **kwargs (Dict): Other optional keyword arguments. Returns: int: The integer value of the entry widget. """ initialvalue = initialvalue or "" dialog = QueryDialog( prompt, title, initialvalue, minvalue, maxvalue, datatype=int, parent=parent, **kwargs, ) dialog.show() return dialog._result @staticmethod def get_float( prompt="", title=" ", initialvalue=None, minvalue=None, maxvalue=None, parent=None, **kwargs, ): """Request a float type input from the user. ![](../../assets/dialogs/querybox-get-float.png) Parameters: prompt (str): A message to display in the message box above the entry widget. title (str): The string displayed as the title of the message box. This option is ignored on Mac OS X, where platform guidelines forbid the use of a title on this kind of dialog. initialvalue (float): The initial value in the entry widget. minvalue (float): The minimum allowed value. maxvalue (float): The maximum allowed value. parent (Widget): Makes the window the logical parent of the message box. The messagebox is displayed on top of its parent window. **kwargs (Dict): Other optional keyword arguments. Returns: float: The float value of the entry widget. """ initialvalue = initialvalue or "" dialog = QueryDialog( prompt, title, initialvalue, minvalue, maxvalue, datatype=float, parent=parent, **kwargs, ) dialog.show() return dialog._result @staticmethod def get_font(parent=None, **kwargs): """Request a customized font ![](../../assets/dialogs/querybox-get-font.png) Parameters: parent (Widget): Makes the window the logical parent of the dialog box. The dialog is displayed on top of its parent window. **kwargs (Dict): Other keyword arguments. Returns: Font: A font object. """ dialog = FontDialog(parent=parent, **kwargs) dialog.show() return dialog.result

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null

0

null

0

1

0

a3a6ae7f4fab920589a878c0b0e9e7fa6a88c26a

2,504

py

Python

Google-Play-Store-App-Rating/code.py

venky4121994/ga-learner-dsmp-repo

1bef03489931eece0d5ecb9ce0501dfeb558dc59

[ "MIT" ]

null

Google-Play-Store-App-Rating/code.py

venky4121994/ga-learner-dsmp-repo

1bef03489931eece0d5ecb9ce0501dfeb558dc59

[ "MIT" ]

null

Google-Play-Store-App-Rating/code.py

venky4121994/ga-learner-dsmp-repo

1bef03489931eece0d5ecb9ce0501dfeb558dc59

[ "MIT" ]

null

# -------------- #Importing header files import pandas as pd import matplotlib.pyplot as plt import seaborn as sns #Code starts here data = pd.read_csv(path) data.hist(['Rating']) data = data[data['Rating']<=5] data.hist(['Rating']) #Code ends here # -------------- # code starts here total_null = data.isnull().sum() percent_null = (total_null/data.isnull().count()) missing_data = pd.concat([total_null,percent_null],keys=['Total','Percent'],axis=1) print(missing_data) data.dropna(inplace=True) total_null_1 = data.isnull().sum() percent_null_1 = (total_null_1/data.isnull().count()) missing_data_1 = pd.concat([total_null_1,percent_null_1],keys=['Total','Percent'],axis=1) print(missing_data_1) # code ends here # -------------- #Code starts here plt.figure(figsize=(10,20)) catplot = sns.catplot(x = "Category", y = "Rating", data=data, kind="box",height=10) catplot.set_xticklabels(rotation=90) plt.title('Rating vs Category [BoxPlot]',size = 20) #Code ends here # -------------- #Importing header files from sklearn.preprocessing import MinMaxScaler, LabelEncoder #Code starts here print(data['Installs']) data['Installs'] = data['Installs'].str.replace('+','') data['Installs'] = data['Installs'].str.replace(',','') data['Installs'] = data['Installs'].astype('int32') le = LabelEncoder() data['Installs'] = le.fit_transform(data['Installs']) graph = sns.regplot(data['Installs'],data['Rating'],data=data) graph.set_title('Rating vs Installs [Boxplot]') plt.show() #Code ends here # -------------- #Code starts here print(data['Price'].value_counts()) data['Price'] = data['Price'].str.replace('$','') data['Price'] = data['Price'].astype('float32') graph2 = sns.regplot(data['Price'],data['Rating'],data=data) graph2.set_title('Rating vs Price [RegPlot]') #Code ends here # -------------- #Code starts here print(len(data['Genres'].unique()), "genres") data['Genres'] = data['Genres'].str.split(';').str[0] gr_mean = data[['Genres','Rating']].groupby(['Genres'],as_index=False).mean() print(gr_mean.describe()) gr_mean=gr_mean.sort_values('Rating') print(gr_mean.head(1)) print(gr_mean.head(1)) #Code ends here # -------------- #Code starts here data['Last Updated'] = pd.to_datetime(data['Last Updated']) data['Last Updated Days'] = (data['Last Updated'].max()-data['Last Updated']).dt.days plt.figure(figsize = (10,10)) sns.regplot(x="Last Updated Days", y="Rating",color='lightpink',data=data) plt.title('Rating vs Last Updated [Regplot]',size =20) #Code ends here

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null

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a3a6e52033cd00d1b8f29b49e45d1f519baff3e9

6,597

py

Python

converters/brat2iob.py

Banguiskode/nerds

366420b2ec57bf790562de62a79f4973cbd6b3ed

[ "BSD-3-Clause" ]

15

2019-12-05T18:40:22.000Z

2021-02-20T05:34:50.000Z

converters/brat2iob.py

Banguiskode/nerds

366420b2ec57bf790562de62a79f4973cbd6b3ed

[ "BSD-3-Clause" ]

null

converters/brat2iob.py

Banguiskode/nerds

366420b2ec57bf790562de62a79f4973cbd6b3ed

[ "BSD-3-Clause" ]

4

2019-12-30T13:03:05.000Z

2021-02-16T13:08:09.000Z

import argparse import operator import os import re import shutil import spacy import tempfile from nerds.utils import spans_to_tokens, get_logger def segment_text_to_sentences(text_file, sentence_splitter): """ Segment text into sentences. Text is provided by BRAT in .txt file. Args: text_file (str): the full path to the BRAT .txt file. sentence_splitter (spacy LM): SpaCy EN language model. Returns: sentences (list((int, int, str))): list of sentence spans. Spans are triples of (start_offset, end_offset, text), where offset is relative to the text. """ sentences = [] ftext = open(text_file, "r") for line in ftext: splits = sentence_splitter(line.strip()) for sent in splits.sents: sentences.append((sent.start_char, sent.end_char, sent.text)) ftext.close() return sentences def parse_text_annotations(ann_file): """ Parses BRAT annotations provided in the .ann file and converts them to annotation spans of (start_position, end_position, entity_class). Args: ann_file (str): full path to the BRAT .ann file. Returns: annotations (list((int, int, str))): list of annotation spans. Spans are triples of (start_offset, end_offset, entity_class) where offset is relative to the text. """ annots = [] fann = open(ann_file, "r") for line in fann: cols = re.split(r"\s+", line.strip()) if not cols[0].startswith("T"): continue annots.append((int(cols[2]), int(cols[3]), cols[1])) fann.close() return annots def apply_annotations(sentences, annotations, tokenizer): """ Apply annotation spans to the sentence spans to create a list of tokens and tags. Args: sentences (list((int, int, str))): list of sentence spans. annotations (list((int, int, str))): list of annotation spans. tokenizer (spacy LM): SpaCy EN language model. Returns: tokens_tags_list (list((list(str), list(str)))): list of list of token tag pairs. Each list of token-tag pairs corresponds to a single sentence. """ tokens_tags_list = [] for sent_start, sent_end, sent_text in sentences: sent_annots = [a for a in annotations if a[0] >= sent_start and a[1] <= sent_end] # convert document offsets to sentence offsets sent_annots = [(s[0] - sent_start, s[1] - sent_start, s[2]) for s in sent_annots] tokens, tags = spans_to_tokens(sent_text, sent_annots, tokenizer) tokens_tags_list.append(zip(tokens, tags)) return tokens_tags_list def convert_brat_to_iob(input_dir, output_file, nlp): """ Convenience Convertor function. Args: input_dir (str): the directory where the BRAT .txt and .ann files are located. output_file (str): the full path name of file to write output in IOB format to. nlp (SpaCy LM): reference to the SpaCy EN model. Returns: None. """ fout = open(output_file, "w") for text_file in os.listdir(input_dir): # only process .txt and .ann pairs in specified directory if not text_file.endswith(".txt"): continue annot_file = text_file[:-4] + ".ann" if not os.path.exists(os.path.join(input_dir, annot_file)): # do not process file if no corresponding .ann file continue # process file pair logger.info("Processing file: {:s}".format(text_file)) sentences = segment_text_to_sentences(os.path.join(input_dir, text_file), nlp) annotations = parse_text_annotations(os.path.join(input_dir, annot_file)) tokens_tags_list = apply_annotations(sentences, annotations, nlp) for tokens_tags in tokens_tags_list: for token, tag in tokens_tags: fout.write("{:s}\t{:s}\n".format(token, tag)) fout.write("\n") fout.close() def do_self_test(nlp): """ Simple self-test with small dataset to prove that this works okay. """ text = "Pierre Vinken, 61 years old, will join the board as a nonexecutive director, Nov. 29. Mr. Vinken is chairman of Elsevier N.V., the Dutch publishing group." annotations = [ "T1 PER 0 13 Pierre Vinken", "T2 PER 86 96 Mr. Vinken", "T3 DATE 15 27 61 years old", "T4 DATE 77 84 Nov. 29", "T5 ORG 112 125 Elsevier N.V.", "T6 NORP 131 136 Dutch" ] input_dir = tempfile.mkdtemp(dir="/tmp") ftext = open(os.path.join(input_dir, "test.txt"), "w") ftext.write(text) ftext.close() fann = open(os.path.join(input_dir, "test.ann"), "w") for line in annotations: fann.write(line + "\n") fann.close() output_file = os.path.join(input_dir, "test.iob") convert_brat_to_iob(input_dir, output_file, nlp) fout = open(output_file, "r") for line in fout: logger.warn(line.strip()) shutil.rmtree(input_dir) ################################ main ################################ # # usage: brat2iob.py [-h] [-i INPUT_DIR] [-o OUTPUT_FILE] [-t] # Script to convert BRAT annotations to IOB (NERDS) format. # optional arguments: # -h, --help show this help message and exit # -i INPUT_DIR, --input_dir INPUT_DIR # Directory to store BRAT .txt and .ann files. # -o OUTPUT_FILE, --output_file OUTPUT_FILE # Output file to write IOB output to. # -t, --test Runs self test. ###################################################################### parser = argparse.ArgumentParser( description="Script to convert BRAT annotations to IOB (NERDS) format.") parser.add_argument("-i", "--input_dir", help="Directory to store BRAT .txt and .ann files.") parser.add_argument("-o", "--output_file", help="Output file to write IOB output to.") parser.add_argument("-t", "--test", help="Runs self test.", action="store_true") args = parser.parse_args() logger = get_logger() input_dir = args.input_dir output_file = args.output_file self_test = args.test nlp = spacy.load("en") if self_test: logger.info("Executing self test...") do_self_test(nlp) else: logger.info("Reading BRAT .txt and .ann files from: {:s}".format(input_dir)) logger.info("Writing IOB tokens/tags to file: {:s}".format(output_file)) convert_brat_to_iob(input_dir, output_file, nlp)

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0.042499

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null

0

null

0

1

0

a3a738f0c10019d9229ed8e9b93898831920170d

2,503

py

Python

kraken/lib/util.py

zjsteyn/kraken

eaa9f4290db5425ddf80d0aebfa3944713558ab5

[ "Apache-2.0" ]

1

2022-02-03T14:41:58.000Z

kraken/lib/util.py

ephenum/kraken

47be8f7ddcb7c7ad63bfc5636df1976a4e84a5f0

[ "Apache-2.0" ]

null

kraken/lib/util.py

ephenum/kraken

47be8f7ddcb7c7ad63bfc5636df1976a4e84a5f0

[ "Apache-2.0" ]

1

2022-01-19T10:53:20.000Z

""" Ocropus's magic PIL-numpy array conversion routines. They express slightly different behavior from PIL.Image.toarray(). """ import unicodedata import numpy as np from PIL import Image __all__ = ['pil2array', 'array2pil'] def pil2array(im: Image.Image, alpha: int = 0) -> np.array: if im.mode == '1': return np.array(im.convert('L')) return np.array(im) def array2pil(a: np.array) -> Image: if a.dtype == np.dtype("B"): if a.ndim == 2: return Image.frombytes("L", (a.shape[1], a.shape[0]), a.tostring()) elif a.ndim == 3: return Image.frombytes("RGB", (a.shape[1], a.shape[0]), a.tostring()) else: raise Exception("bad image rank") elif a.dtype == np.dtype('float32'): return Image.frombytes("F", (a.shape[1], a.shape[0]), a.tostring()) else: raise Exception("unknown image type") def is_bitonal(im: Image.Image) -> bool: """ Tests a PIL.Image for bitonality. Args: im (PIL.Image.Image): Image to test Returns: True if the image contains only two different color values. False otherwise. """ return im.getcolors(2) is not None and len(im.getcolors(2)) == 2 def get_im_str(im: Image.Image) -> str: return im.filename if hasattr(im, 'filename') else str(im) def is_printable(char: str) -> bool: """ Determines if a chode point is printable/visible when printed. Args: char (str): Input code point. Returns: True if printable, False otherwise. """ letters = ('LC', 'Ll', 'Lm', 'Lo', 'Lt', 'Lu') numbers = ('Nd', 'Nl', 'No') punctuation = ('Pc', 'Pd', 'Pe', 'Pf', 'Pi', 'Po', 'Ps') symbol = ('Sc', 'Sk', 'Sm', 'So') printable = letters + numbers + punctuation + symbol return unicodedata.category(char) in printable def make_printable(char: str) -> str: """ Takes a Unicode code point and return a printable representation of it. Args: char (str): Input code point Returns: Either the original code point, the name of the code point if it is a combining mark, whitespace etc., or the hex code if it is a control symbol. """ if not char or is_printable(char): return char elif unicodedata.category(char) in ('Cc', 'Cs', 'Co'): return '0x{:x}'.format(ord(char)) else: return unicodedata.name(char)

27.811111

77

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false

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null

0

null

0

1

0

a3a7f40bcb06653665d3b8d30577d4282cd0f05f

2,877

py

Python

analysis/calculate_holding_amount.py

hao44le/ico_top_holder_analysis

aeeab01c90e4446b424c52c33a68ccb814123121

[ "MIT" ]

538

2018-07-04T21:14:52.000Z

2022-03-26T15:16:08.000Z

analysis/calculate_holding_amount.py

hao44le/ico_top_holder_analysis

aeeab01c90e4446b424c52c33a68ccb814123121

[ "MIT" ]

4

2018-07-08T22:11:32.000Z

2021-12-13T19:48:38.000Z

analysis/calculate_holding_amount.py

hao44le/ico_top_holder_analysis

aeeab01c90e4446b424c52c33a68ccb814123121

[ "MIT" ]

52

2018-07-05T12:07:37.000Z

2021-04-05T23:34:20.000Z

import sys sys.path.insert(0,'..') from data.whale_data import exchnage_accounts from data.html_helper import check_if_address_name_exists from data.whale_eth_tx_data import * from data.whale_token_tx_data import identify_investor_type_token holding_account = "holding_account" deposit_account = 'deposit_account' withdraw_account = "withdraw_account" in_type = "IN" out_type = "OUT" all_acc_types = dict() for acc in exchnage_accounts: all_acc_types[acc] = exchange_type def update_y_array(X,y,timestamp,amount): target_index = 0 for i in range(len(X)): x_time = X[i] if timestamp < x_time: target_index = i break for i in range(target_index,len(y)): y[i] += amount return y def perform_bfs_on_accounts(out_txs,top_holder_type,acc,m_type='OUT'): print("\t"+m_type) unique_out = set() for out in out_txs: unique_out.add(out[3]) unique_out = list(unique_out)[:5] for out in unique_out: print("\t"+out) if out not in all_acc_types: investor_type = identify_investor_type(out) if investor_type == affliate_type: investor_type = identify_investor_type_token(out) print("\t\t{}".format(investor_type)) else: investor_type = all_acc_types[out] if investor_type == exchange_type: top_holder_type[acc] = deposit_account if m_type == "OUT" else withdraw_account all_acc_types[out] = investor_type if acc not in top_holder_type: top_holder_type[acc] = holding_account return top_holder_type def calculate_holding_amount(X,escape_accounts,txs): top_holder_type = dict() for acc in txs: tx = txs[acc] if acc in escape_accounts: continue #如果当前账户从来没有向外打过token,ignore out_txs = [item for item in tx if item[2] == 'OUT'] if len(out_txs) == 0: print("\tholding account") top_holder_type[acc] = holding_account continue # build all traxe Y: holding_amount, deposit_amount, withdraw_amount amount_trace_y = [0] * len(X) for holder in txs: if holder in escape_accounts: continue if holder not in top_holder_type: print("{} not identified! ".format(holder)) continue holder_type = top_holder_type[holder] holder_txs = txs[holder] print("{} {}".format(holder,holder_type)) for tx in holder_txs: [timestamp,from_a,tx_type,to_a,amount] = tx if holder_type == holding_account: if tx_type == in_type: amount_trace_y = update_y_array(X,amount_trace_y,timestamp,amount) else: amount_trace_y = update_y_array(X,amount_trace_y,timestamp,-amount) return amount_trace_y

29.96875

91

0.642336

400

2,877

4.2975

0.2075

0.069808

0.068063

0.037231

0.178592

0.095404

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0

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0.271116

2,877

95

92

30.284211

0.816404

0.032325

0

0.108108

0

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0

1

0.040541

false

0

0.067568

0

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0

null

0

null

0

1

0

a3a80291d5fdb7e2a418a7fbbb6542744e0db4d2

66,926

py

Python

textbox/trainer/trainer.py

JBoRu/TextBox-1

0dcbaa153acc507e3d55075312d7ca5d23146e03

[ "MIT" ]

1

2021-08-12T01:08:09.000Z

textbox/trainer/trainer.py

JBoRu/TextBox-1

0dcbaa153acc507e3d55075312d7ca5d23146e03

[ "MIT" ]

null

textbox/trainer/trainer.py

JBoRu/TextBox-1

0dcbaa153acc507e3d55075312d7ca5d23146e03

[ "MIT" ]

null

# @Time : 2020/11/14 # @Author : Junyi Li, Gaole He # @Email : lijunyi@ruc.edu.cn # UPDATE: # @Time : 2020/12/2, 2020/11/27, 2020/12/3, 2020/12/26 # @Author : Jinhao Jiang, Xiaoxuan Hu, Tianyi Tang, Jinhao Jiang # @Email : jiangjinhao@std.uestc.edu.cn, huxiaoxuan@ruc.edu.cn, steventang@ruc.edu.cn, jiangjinhao@std.uestc.edu.cn r""" textbox.trainer.trainer ################################ """ import os import torch import torch.optim as optim import numpy as np import matplotlib.pyplot as plt import copy import math from torch.utils.data import DataLoader from time import time from logging import getLogger from textbox.module.Optimizer.optim import ScheduledOptim from textbox.evaluator import NgramEvaluator, TranslationEvaluator, SummarizationEvaluator from textbox.utils import ensure_dir, early_stopping class AbstractTrainer(object): r"""Trainer Class is used to manage the training and evaluation processes of text generation system models. AbstractTrainer is an abstract class in which the fit() and evaluate() method should be implemented according to different training and evaluation strategies. """ def __init__(self, config, model): self.config = config self.model = model def fit(self, train_data): r"""Train the model based on the train data. """ raise NotImplementedError('Method [next] should be implemented.') def evaluate(self, eval_data): r"""Evaluate the model based on the eval data. """ raise NotImplementedError('Method [next] should be implemented.') class Trainer(AbstractTrainer): r"""The basic Trainer for basic training and evaluation strategies in text generation systems. This class defines common functions for training and evaluation processes of most text generation system models, including fit(), evalute(), resume_checkpoint() and some other features helpful for model training and evaluation. Generally speaking, this class can serve most text generation system models, If the training process of the model is to simply optimize a single loss without involving any complex training strategies, such as adversarial learning, pre-training and so on. Initializing the Trainer needs two parameters: `config` and `model`. `config` records the parameters information for controlling training and evaluation, such as `learning_rate`, `epochs`, `eval_step` and so on. More information can be found in [placeholder]. `model` is the instantiated object of a Model Class. """ def __init__(self, config, model): super(Trainer, self).__init__(config, model) self.logger = getLogger() self.learner = config['learner'] self.learning_rate = config['learning_rate'] self.epochs = config['epochs'] self.eval_step = min(config['eval_step'], self.epochs) self.stopping_step = config['stopping_step'] self.test_batch_size = config['eval_batch_size'] self.device = config['device'] self.embedding_size = config['embedding_size'] self.warmup_steps = config['warmup_steps'] self.checkpoint_dir = config['checkpoint_dir'] ensure_dir(self.checkpoint_dir) saved_model_file = self.config['filename'] + '.pth' self.saved_model_file = os.path.join(self.checkpoint_dir, saved_model_file) self.generated_text_dir = config['generated_text_dir'] ensure_dir(self.generated_text_dir) saved_text_file = self.config['filename'] + '.txt' self.saved_text_file = os.path.join(self.generated_text_dir, saved_text_file) self.start_epoch = 0 self.cur_step = 0 self.best_valid_score = 100000000 self.best_valid_result = None self.train_loss_dict = dict() self.optimizer = self._build_optimizer() self.task_type = config['task_type'].lower() if self.task_type == "translation": self.evaluator = TranslationEvaluator(config) elif self.task_type == "summarization": self.evaluator = SummarizationEvaluator(config) else: self.evaluator = NgramEvaluator(config) self.item_tensor = None self.tot_item_num = None self.iid_field = config['ITEM_ID_FIELD'] def _build_optimizer(self): r"""Init the Optimizer Returns: torch.optim: the optimizer """ if self.learner.lower() == 'adam': optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate) elif self.learner.lower() == 'sgd': optimizer = optim.SGD(self.model.parameters(), lr=self.learning_rate) elif self.learner.lower() == 'adagrad': optimizer = optim.Adagrad(self.model.parameters(), lr=self.learning_rate) elif self.learner.lower() == 'rmsprop': optimizer = optim.RMSprop(self.model.parameters(), lr=self.learning_rate) elif self.learner.lower() == 'schedule': optimizer = ScheduledOptim(optim.Adam(self.model.parameters(), betas=(0.9, 0.98), eps=1e-09), self.learning_rate, self.embedding_size, self.warmup_steps) else: self.logger.warning('Received unrecognized optimizer, set default Adam optimizer') optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate) return optimizer def _train_epoch(self, train_data, epoch_idx): r"""Train the model in an epoch Args: train_data (DataLoader): the train data epoch_idx (int): the current epoch id Returns: float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a tuple which includes the sum of loss in each part. """ self.model.train() total_loss = None for batch_idx, data in enumerate(train_data): self.optimizer.zero_grad() losses = self.model.calculate_loss(data, epoch_idx=epoch_idx) if isinstance(losses, tuple): loss = sum(losses) loss_tuple = tuple(per_loss.item() for per_loss in losses) total_loss = loss_tuple if total_loss is None else tuple(map(sum, zip(total_loss, loss_tuple))) else: loss = losses total_loss = losses.item() if total_loss is None else total_loss + losses.item() self._check_nan(loss) loss.backward() self.optimizer.step() train_loss = total_loss / len(train_data) return train_loss def _valid_epoch(self, valid_data): r"""Valid the model with valid data Args: valid_data (DataLoader): the valid data Returns: float: valid score dict: valid result """ self.model.eval() total_loss = None for batch_idx, data in enumerate(valid_data): losses = self.model.calculate_loss(data) if isinstance(losses, tuple): loss = sum(losses) loss_tuple = tuple(per_loss.item() for per_loss in losses) total_loss = loss_tuple if total_loss is None else tuple(map(sum, zip(total_loss, loss_tuple))) else: loss = losses total_loss = losses.item() if total_loss is None else total_loss + losses.item() self._check_nan(loss) valid_loss = total_loss / len(valid_data) ppl = np.exp(valid_loss) return valid_loss, ppl def _save_checkpoint(self, epoch): r"""Store the model parameters information and training information. Args: epoch (int): the current epoch id """ state = { 'config': self.config, 'epoch': epoch, 'cur_step': self.cur_step, 'best_valid_score': self.best_valid_score, 'state_dict': self.model.state_dict(), 'optimizer': self.optimizer.state_dict(), } torch.save(state, self.saved_model_file) def _save_generated_text(self, generated_corpus): r"""Store the generated text by our model. Args: corpus (list of string list): """ with open(self.saved_text_file, 'w') as fin: for tokens in generated_corpus: fin.write(' '.join(tokens) + '\n') def resume_checkpoint(self, resume_file): r"""Load the model parameters information and training information. Args: resume_file (file): the checkpoint file """ resume_file = str(resume_file) checkpoint = torch.load(resume_file) self.start_epoch = checkpoint['epoch'] + 1 self.cur_step = checkpoint['cur_step'] self.best_valid_score = checkpoint['best_valid_score'] # load architecture params from checkpoint if checkpoint['config']['model'].lower() != self.config['model'].lower(): self.logger.warning('Architecture configuration given in config file is different from that of checkpoint. ' 'This may yield an exception while state_dict is being loaded.') self.model.load_state_dict(checkpoint['state_dict']) # load optimizer state from checkpoint only when optimizer type is not changed self.optimizer.load_state_dict(checkpoint['optimizer']) message_output = 'Checkpoint loaded. Resume training from epoch {}'.format(self.start_epoch) self.logger.info(message_output) def _check_nan(self, loss): if torch.isnan(loss): raise ValueError('Training loss is nan') def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses, train_info=""): train_loss_output = "epoch %d %straining [time: %.2fs, " % (epoch_idx, train_info, e_time - s_time) if isinstance(losses, tuple): for idx, loss in enumerate(losses): train_loss_output += 'train_loss%d: %.4f, ' % (idx + 1, loss) train_loss_output = train_loss_output[:-2] else: train_loss_output += "train loss: %.4f" % losses return train_loss_output + ']' def fit(self, train_data, valid_data=None, verbose=True, saved=True): r"""Train the model based on the train data and the valid data. Args: train_data (DataLoader): the train data valid_data (DataLoader, optional): the valid data, default: None. If it's None, the early_stopping is invalid. verbose (bool, optional): whether to write training and evaluation information to logger, default: True saved (bool, optional): whether to save the model parameters, default: True Returns: (float, dict): best valid score and best valid result. If valid_data is None, it returns (-1, None) """ for epoch_idx in range(self.start_epoch, self.epochs): # train training_start_time = time() train_loss = self._train_epoch(train_data, epoch_idx) self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss training_end_time = time() self._save_checkpoint(epoch_idx) train_loss_output = \ self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss) if verbose: self.logger.info(train_loss_output) # eval if self.eval_step <= 0 or not valid_data: if saved: self._save_checkpoint(epoch_idx) update_output = 'Saving current: %s' % self.saved_model_file if verbose: self.logger.info(update_output) continue if (epoch_idx + 1) % self.eval_step == 0: valid_start_time = time() with torch.no_grad(): valid_score, valid_result = self._valid_epoch(valid_data) # valid_loss, ppl self.best_valid_score, self.cur_step, stop_flag, update_flag = early_stopping( valid_score, self.best_valid_score, self.cur_step, max_step=self.stopping_step, bigger=False) # better model are supposed to provide smaller perplexity and loss valid_end_time = time() valid_score_output = "epoch %d evaluating [time: %.2fs, valid_loss: %f]" % \ (epoch_idx, valid_end_time - valid_start_time, valid_score) valid_result_output = 'valid ppl: {}'.format(valid_result) if verbose: self.logger.info(valid_score_output) self.logger.info(valid_result_output) if update_flag: if saved: self._save_checkpoint(epoch_idx) update_output = 'Saving current best: %s' % self.saved_model_file if verbose: self.logger.info(update_output) self.best_valid_result = valid_result if stop_flag: stop_output = 'Finished training, best eval result in epoch %d' % \ (epoch_idx - self.cur_step * self.eval_step) if verbose: self.logger.info(stop_output) break return self.best_valid_score, self.best_valid_result def _evaluate_nll_test(self, eval_data): r"""Calculate the negative log-likelihood of the eval_data. Args: eval_data (DataLoader): the eval data. Returns: Float: NLL_test of the eval data. """ total_loss = 0 for epoch_idx, eval_batch in enumerate(eval_data): nll_test = self.model.calculate_nll_test(eval_batch, epoch_idx) total_loss += float(nll_test) return total_loss / len(eval_data) @torch.no_grad() def evaluate(self, eval_data, load_best_model=True, model_file=None): r"""Evaluate the model based on the eval data. Args: eval_data (DataLoader): the eval data load_best_model (bool, optional): whether load the best model in the training process, default: True. It should be set True, if users want to test the model after training. model_file (str, optional): the saved model file, default: None. If users want to test the previously trained model file, they can set this parameter. Returns: dict: eval result, key is the eval metric and value in the corresponding metric value """ if load_best_model: if model_file: checkpoint_file = model_file else: checkpoint_file = self.saved_model_file checkpoint = torch.load(checkpoint_file) self.model.load_state_dict(checkpoint['state_dict']) message_output = 'Loading model structure and parameters from {}'.format(checkpoint_file) self.logger.info(message_output) self.model.eval() with torch.no_grad(): generate_corpus = self.model.generate(eval_data) self._save_generated_text(generate_corpus) reference_corpus = eval_data.get_reference() result = self.evaluator.evaluate(generate_corpus, reference_corpus) result['nll_test'] = self._evaluate_nll_test(eval_data) return result def plot_train_loss(self, show=True, save_path=None): r"""Plot the train loss in each epoch Args: show (bool, optional): whether to show this figure, default: True save_path (str, optional): the data path to save the figure, default: None. If it's None, it will not be saved. """ epochs = list(self.train_loss_dict.keys()) epochs.sort() values = [float(self.train_loss_dict[epoch]) for epoch in epochs] plt.plot(epochs, values) plt.xticks(epochs) plt.xlabel('Epoch') plt.ylabel('Loss') if show: plt.show() if save_path: plt.savefig(save_path) class UnconditionalTrainer(Trainer): r"""UnconditionalTrainer is designed for RNN, which is a typical unconditional generator. """ def __init__(self, config, model): super(UnconditionalTrainer, self).__init__(config, model) class GANTrainer(Trainer): r"""GANTrainer is designed for GAN, which is a generative adversarial net method. """ def __init__(self, config, model): super(GANTrainer, self).__init__(config, model) self.optimizer = None self.g_optimizer = self._build_module_optimizer(self.model.generator) self.d_optimizer = self._build_module_optimizer(self.model.discriminator) self.grad_clip = config['grad_clip'] self.g_pretraining_epochs = config['g_pretraining_epochs'] self.d_pretraining_epochs = config['d_pretraining_epochs'] self.d_sample_num = config['d_sample_num'] self.d_sample_training_epochs = config['d_sample_training_epochs'] self.adversarail_training_epochs = config['adversarail_training_epochs'] self.adversarail_d_epochs = config['adversarail_d_epochs'] self.g_pretraining_loss_dict = dict() self.d_pretraining_loss_dict = dict() self.max_length = config['max_seq_length'] + 2 self.pad_idx = model.pad_idx def _build_module_optimizer(self, module): r"""Init the Module Optimizer Args: module (torch.nn.Mudule): Mudule class of torch.nn needed optimizer Returns: torch.optim: the optimizer """ if self.learner.lower() == 'adam': optimizer = optim.Adam(module.parameters(), lr=self.learning_rate) elif self.learner.lower() == 'sgd': optimizer = optim.SGD(module.parameters(), lr=self.learning_rate) elif self.learner.lower() == 'adagrad': optimizer = optim.Adagrad(module.parameters(), lr=self.learning_rate) elif self.learner.lower() == 'rmsprop': optimizer = optim.RMSprop(module.parameters(), lr=self.learning_rate) else: self.logger.warning('Received unrecognized optimizer, set default Adam optimizer') optimizer = optim.Adam(module.parameters(), lr=self.learning_rate) return optimizer def _optimize_step(self, losses, total_loss, model, opt): r"""The opt uses the cliped losses to conduct an optimize step to optimize model and sum up losses to the total_loss. Args: losses (torch.Tensor or tuple): The loss to be backward. total_loss (Float): Total loss in an epoch. model (torch.nn.Mudule): The model to be optimized. opt (torch.optim): The optimizer of the model. Returns: torch.Tensor or tuple: Total loss in an epoch, shape: []. """ if isinstance(losses, tuple): loss = sum(losses) loss_tuple = tuple(per_loss.item() for per_loss in losses) total_loss = loss_tuple if total_loss is None else tuple(map(sum, zip(total_loss, loss_tuple))) else: loss = losses total_loss = losses.item() if total_loss is None else total_loss + losses.item() self._check_nan(loss) opt.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), self.grad_clip) opt.step() return total_loss def _save_checkpoint(self, epoch): state = { 'config': self.config, 'epoch': epoch, 'cur_step': self.cur_step, 'best_valid_score': self.best_valid_score, 'state_dict': self.model.state_dict() } torch.save(state, self.saved_model_file) def _add_pad(self, data): r"""Pad the data to the max length of corpus. Args: data (torch.Tensor): The data to be padded, shape: [batch_size, max_batch_length]. Returns: torch.Tensor: The padded data, shape: [batch_size, max_seq_length]. """ batch_size = data.shape[0] padded_data = torch.full((batch_size, self.max_length), self.pad_idx, dtype=torch.long, device=self.device) padded_data[:, : data.shape[1]] = data return padded_data def _get_real_data(self, train_data): r"""Get the target text index of the corpus train_datas. Args: train_data (DataLoader): the train data. Returns: torch.Tensor: The target text index, shape: [batch_size, max_batch_length]. """ real_datas = [] for corpus in train_data: real_data = corpus['target_idx'] real_data = self._add_pad(real_data) real_datas.append(real_data) real_datas = torch.cat(real_datas, dim=0) return real_datas def _g_train_epoch(self, train_data, epoch_idx): r"""Train the generator module in an epoch Args: train_data (DataLoader): the train data epoch_idx (int): the current epoch id Returns: float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a tuple which includes the sum of loss in each part. """ self.model.generator.train() total_loss = None for batch_idx, data in enumerate(train_data): losses = self.model.calculate_g_train_loss(data, epoch_idx=epoch_idx) total_loss = self._optimize_step(losses, total_loss, self.model.generator, self.g_optimizer) total_loss = [l / len(train_data) for l in total_loss] if isinstance(total_loss, tuple) else total_loss / len( train_data) total_loss = tuple(total_loss) if isinstance(total_loss, list) else total_loss return total_loss def _d_train_epoch(self, train_data, epoch_idx): r"""Train the discriminator module in an epoch Args: train_data (DataLoader): the train data epoch_idx (int): the current epoch id Returns: float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a tuple which includes the sum of loss in each part. """ self.model.discriminator.train() total_loss = None real_data = self._get_real_data(train_data) real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) fake_data = self.model.sample(self.d_sample_num) fake_dataloader = DataLoader(fake_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) for _ in range(self.d_sample_training_epochs): # d_epoch for real_data, fake_data in zip(real_dataloader, fake_dataloader): losses = self.model.calculate_d_train_loss(real_data, fake_data, epoch_idx=epoch_idx) total_loss = self._optimize_step(losses, total_loss, self.model.discriminator, self.d_optimizer) return total_loss / min(len(real_dataloader), len(fake_dataloader)) / self.d_sample_training_epochs def _adversarial_train_epoch(self, train_data, epoch_idx): r"""Adversarial training in an epoch Args: train_data (DataLoader): the train data epoch_idx (int): the current epoch id Returns: float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a tuple which includes the sum of loss in each part. """ self.model.generator.train() total_loss = None losses = self.model.calculate_g_adversarial_loss(epoch_idx=epoch_idx) total_loss = self._optimize_step(losses, total_loss, self.model.generator, self.g_optimizer) for epoch_idx in range(self.adversarail_d_epochs): self._d_train_epoch(train_data, epoch_idx=epoch_idx) return total_loss def fit(self, train_data, valid_data=None, verbose=True, saved=True): # generator pretraining if verbose: self.logger.info("Start generator pretraining...") for epoch_idx in range(self.g_pretraining_epochs): training_start_time = time() train_loss = self._g_train_epoch(train_data, epoch_idx) self.g_pretraining_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss training_end_time = time() train_loss_output = \ self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss, "generator pre") if verbose: self.logger.info(train_loss_output) if verbose: self.logger.info("End generator pretraining...") # discriminator pretraining if verbose: self.logger.info("Start discriminator pretraining...") for epoch_idx in range(self.d_pretraining_epochs): training_start_time = time() train_loss = self._d_train_epoch(train_data, epoch_idx) self.d_pretraining_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss training_end_time = time() train_loss_output = \ self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss, "discriminator pre") if verbose: self.logger.info(train_loss_output) if verbose: self.logger.info("End discriminator pretraining...") # adversarial training if verbose: self.logger.info("Start adversarial training...") for epoch_idx in range(self.adversarail_training_epochs): training_start_time = time() train_loss = self._adversarial_train_epoch(train_data, epoch_idx) self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss training_end_time = time() train_loss_output = \ self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss) if verbose: self.logger.info(train_loss_output) if verbose: self.logger.info("End adversarial pretraining...") self._save_checkpoint(self.adversarail_training_epochs) return -1, None class TextGANTrainer(GANTrainer): r"""TextGANTrainer is designed for TextGAN. """ def __init__(self, config, model): super(TextGANTrainer, self).__init__(config, model) self.adversarail_g_epochs = config['adversarail_g_epochs'] def _d_train_epoch(self, train_data, epoch_idx): self.model.discriminator.train() total_loss = None real_data = self._get_real_data(train_data) real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) for _ in range(self.d_sample_training_epochs): for idx, real_data in enumerate(real_dataloader): fake_data, z = self.model.sample() losses = self.model.calculate_d_train_loss(real_data, fake_data, z, epoch_idx=epoch_idx) total_loss = self._optimize_step(losses, total_loss, self.model.discriminator, self.d_optimizer) if (idx * self.model.batch_size >= self.d_sample_num): break return total_loss / min(len(real_dataloader), self.d_sample_num // self.model.batch_size) / self.d_sample_training_epochs def _adversarial_train_epoch(self, train_data, epoch_idx): self.model.generator.train() total_loss = None real_data = self._get_real_data(train_data) real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) for idx, real_data in enumerate(real_dataloader): if (idx == self.adversarail_g_epochs): break losses = self.model.calculate_g_adversarial_loss(real_data, epoch_idx=epoch_idx) total_loss = self._optimize_step(losses, total_loss, self.model.generator, self.g_optimizer) for epoch_idx in range(self.adversarail_d_epochs): self._d_train_epoch(train_data, epoch_idx=epoch_idx) return total_loss / min(len(real_dataloader), self.adversarail_g_epochs) class RankGANTrainer(GANTrainer): r"""RankGANTrainer is designed for RankGAN. """ def __init__(self, config, model): super(RankGANTrainer, self).__init__(config, model) def _d_train_epoch(self, train_data, epoch_idx): r"""Train the discriminator module in an epoch Args: train_data (DataLoader): the train data epoch_idx (int): the current epoch id Returns: float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a tuple which includes the sum of loss in each part. """ self.model.discriminator.train() total_loss = None real_data = self._get_real_data(train_data) real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) fake_data = self.model.sample(self.d_sample_num) fake_dataloader = DataLoader(fake_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) ref_index = np.random.randint(0, real_data.shape[0], size=self.model.ref_size) ref_data = real_data[ref_index] # ref_size * l for _ in range(self.d_sample_training_epochs): for real_data, fake_data in zip(real_dataloader, fake_dataloader): losses = self.model.calculate_d_train_loss(real_data, fake_data, ref_data, epoch_idx=epoch_idx) total_loss = self._optimize_step(losses, total_loss, self.model.discriminator, self.d_optimizer) return total_loss / min(len(real_dataloader), len(fake_dataloader)) / self.d_sample_training_epochs def _adversarial_train_epoch(self, train_data, epoch_idx): r"""Adversarial training in an epoch Args: train_data (DataLoader): the train data epoch_idx (int): the current epoch id Returns: float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a tuple which includes the sum of loss in each part. """ self.model.generator.train() total_loss = None real_data = self._get_real_data(train_data) ref_index = np.random.randint(0, real_data.shape[0], size=self.model.ref_size) ref_data = real_data[ref_index] # ref_size * l losses = self.model.calculate_g_adversarial_loss(ref_data, epoch_idx=epoch_idx) total_loss = self._optimize_step(losses, total_loss, self.model.generator, self.g_optimizer) d_loss = 0 for epoch_idx in range(self.adversarail_d_epochs): d_loss += self._d_train_epoch(train_data, epoch_idx=epoch_idx) d_loss = d_loss / self.adversarail_d_epochs return total_loss class ConditionalTrainer(Trainer): r"""ConditionalTrainer is designed for seq2seq testing, which is a typically used setting. """ def __init__(self, config, model): super(ConditionalTrainer, self).__init__(config, model) @torch.no_grad() def evaluate(self, eval_data, load_best_model=True, model_file=None): r"""Evaluate the model based on the eval data. Args: eval_data (DataLoader): the eval data load_best_model (bool, optional): whether load the best model in the training process, default: True. It should be set True, if users want to test the model after training. model_file (str, optional): the saved model file, default: None. If users want to test the previously trained model file, they can set this parameter. Returns: dict: eval result, key is the eval metric and value in the corresponding metric value """ if load_best_model: if model_file: checkpoint_file = model_file else: checkpoint_file = self.saved_model_file checkpoint = torch.load(checkpoint_file) self.model.load_state_dict(checkpoint['state_dict']) message_output = 'Loading model structure and parameters from {}'.format(checkpoint_file) self.logger.info(message_output) self.model.eval() generate_corpus = self.model.generate(eval_data) self._save_generated_text(generate_corpus) reference_corpus = eval_data.get_reference() result = self.evaluator.evaluate(generate_corpus, reference_corpus) return result class MaskGANTrainer(GANTrainer): r""" Trainer specifically designed for MaskGAN training process. """ def __init__(self, config, model): super(MaskGANTrainer, self).__init__(config, model) self.max_length = config["max_seq_length"] self.eos_token_idx = model.eos_idx self.adversarail_c_epochs = config['adversarail_c_epochs'] self.g_mask_pretraining_epochs = config['g_mask_pretraining_epochs'] self.g_lr = config['gen_learning_rate'] self.d_lr = config['dis_learning_rate'] self.c_lr = config['critic_learning_rate'] self.g_optimizer = self._build_module_optimizer_(self.model.generator, self.g_lr) self.d_optimizer = self._build_module_optimizer_(self.model.discriminator, self.d_lr) self.c_optimizer = self._build_module_optimizer_(self.model.discriminator.critic_fc_linear, self.c_lr) self.pre_lm_weight = config["pre_lm_weight"] self.pretrain_lm_epochs = config["pretrain_lm_epochs"] self.checkp = config['checkp'] def _build_module_optimizer_(self, module, lr): r""" Init the Module Optimizer with specified learning rate Returns: torch.optim: the optimizer """ if self.learner.lower() == 'adam': optimizer = optim.Adam(module.parameters(), lr) elif self.learner.lower() == 'sgd': optimizer = optim.SGD(module.parameters(), lr) elif self.learner.lower() == 'adagrad': optimizer = optim.Adagrad(module.parameters(), lr) elif self.learner.lower() == 'rmsprop': optimizer = optim.RMSprop(module.parameters(), lr) else: self.logger.warning('Received unrecognized optimizer, set default Adam optimizer') optimizer = optim.Adam(module.parameters(), lr) return optimizer def _optimize_step(self, losses, total_loss, model, opt, retain_graph=False): r""" Add retain_graph option """ if isinstance(losses, tuple): loss = sum(losses) loss_tuple = tuple(per_loss.item() for per_loss in losses) total_loss = loss_tuple if total_loss is None else tuple(map(sum, zip(total_loss, loss_tuple))) else: loss = losses total_loss = losses.item() if total_loss is None else total_loss + losses.item() self._check_nan(loss) opt.zero_grad() loss.backward(retain_graph=retain_graph) torch.nn.utils.clip_grad_norm_(model.parameters(), self.grad_clip) opt.step() return total_loss def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses, train_info=""): r""" Specified for maskgan output """ train_loss_output = "%straining [time: %.2fs, " % (train_info, e_time - s_time) if isinstance(losses, dict): for key, loss in losses.items(): train_loss_output += '%s: %.4f, ' % (key, loss) train_loss_output = train_loss_output[:-2] else: train_loss_output += "train loss: %.4f" % losses return train_loss_output + ']' def pretrain_lm(self, train_data, valid_data, verbose): r""" Pretrain rnn-based Language Model with teacher forcing mechanism """ def lm_forward(data): r""" One iteration of LM forward """ input = data[:, :-1] # bs * self.max_len - 1 target = data[:, 1:] bs, seq_len = target.size() lengths = torch.tensor([seq_len] * bs) target_present = torch.ones_like(input).byte() device = target.device lengths = lengths.cuda(device) # pretaining encoder_outputs = pre_train_lm(input, lengths, target, target_present, pretrain=True) logit = pre_train_lm.vocab_linear(encoder_outputs) logit = logit.permute([0, 2, 1]) lossf = torch.nn.CrossEntropyLoss() loss = lossf(logit, target) return loss pre_train_lm = self.model.generator lm_opt = self._build_module_optimizer_(pre_train_lm, lr=0.001) for epoch in range(self.pretrain_lm_epochs): total_loss = None real_data = self._get_real_data(train_data) # bs * self.max_len real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) for batch_idx, data in enumerate(real_dataloader): loss = lm_forward(data) total_loss = self._optimize_step(loss, total_loss, pre_train_lm, lm_opt) total_loss = total_loss / len(real_dataloader) if verbose: self.logger.info("Epoch {}/{} of LM pretraining loss: {} ".format(epoch+1, self.pretrain_lm_epochs, total_loss)) ppl = 0.0 if (epoch+1) % 1 == 0: pre_train_lm.eval() validate_data = self._get_real_data(valid_data) # bs * self.max_len validate_dataloader = DataLoader(validate_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) ppl = 0.0 for batch_idx, data in enumerate(validate_dataloader): cross_entropy_loss = lm_forward(data) ppl += math.exp(cross_entropy_loss.item()) ppl = ppl / len(validate_dataloader) pre_train_lm.train() if verbose: self.logger.info("Epoch {}/{} of LM pretraining PPL: {}...".format(epoch + 1, self.pretrain_lm_epochs, ppl)) if ppl < 110: state_dict = { 'embedder': pre_train_lm.embedder, 'encoder': pre_train_lm.encoder.encoder, 'vocab_linear': pre_train_lm.vocab_linear } self.pre_lm_weight = "saved/pretrain_lm_weight" + str(epoch+1) + ".pkl" torch.save(state_dict, self.pre_lm_weight) if verbose: self.logger.info("End LM pretraining. PPL: {}".format(ppl)) self.logger.info("Weigth saved in {}".format(self.pre_lm_weight)) return pre_train_lm, ppl def _g_train_epoch(self, train_data, epoch_idx): self.model.generator.train() total_loss = None real_data = self._get_real_data(train_data) # bs * self.max_len real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) for batch_idx, data in enumerate(real_dataloader): loss = self.model.calculate_g_train_loss(data, epoch_idx=epoch_idx) total_loss = self._optimize_step(loss, total_loss, self.model.generator, self.g_optimizer) total_loss = total_loss / len(real_dataloader) return total_loss def _get_validate_ppl(self, validate_data, epoch_idx): self.model.generator.eval() ppl = 0.0 validate_data = self._get_real_data(validate_data) # bs * self.max_len validate_dataloader = DataLoader(validate_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) for batch_idx, data in enumerate(validate_dataloader): loss = self.model.calculate_g_train_loss(data, epoch_idx=epoch_idx, validate=True) ppl += math.exp(loss.item()) ppl = ppl / len(validate_dataloader) self.model.generator.train() return ppl def _d_train_epoch(self, train_data, epoch_idx): self.model.discriminator.train() total_loss = None real_data = self._get_real_data(train_data) real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) for batch_idx, data in enumerate(real_dataloader): losses = self.model.calculate_d_train_loss(data, epoch_idx=epoch_idx) total_loss = self._optimize_step(losses, total_loss, self.model.discriminator, self.d_optimizer) return total_loss / len(real_dataloader) def _adversarial_train_epoch(self, train_data, epoch_idx): r""" Specified for MaskGAN adversarial training """ dis_total_loss = None gen_total_loss = None critic_total_loss = None g_num = 0.0 d_num = 0.0 real_data = self._get_real_data(train_data) real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) dis_train_data = copy.deepcopy(real_dataloader) gen_train_data = copy.deepcopy(real_dataloader) c_train_data = copy.deepcopy(real_dataloader) dis_train_data = iter(dis_train_data) gen_train_data = iter(gen_train_data) _ = next(dis_train_data) # have one offset for g_x in gen_train_data: g_num += 1 for _ in range(3): d_num += 1 try: d_x = next(dis_train_data) except StopIteration: del dis_train_data dis_train_data = copy.deepcopy(real_dataloader) dis_train_data = iter(dis_train_data) d_x = next(dis_train_data) losses = self.model.calculate_d_train_loss(d_x, epoch_idx=_) dis_total_loss = self._optimize_step(losses, dis_total_loss, self.model.discriminator, self.d_optimizer) gen_losses, critic_losses = self.model.calculate_g_adversarial_loss(g_x, epoch_idx=g_num) gen_total_loss = self._optimize_step(gen_losses, gen_total_loss, self.model.generator, self.g_optimizer) critic_total_loss = self._optimize_step(critic_losses, critic_total_loss, self.model.discriminator.critic_fc_linear, self.c_optimizer) return {"dis_loss": dis_total_loss / d_num, "gen_loss": gen_total_loss / g_num, "critic_loss": critic_total_loss / g_num} def _evaluate_nll_test(self, eval_data): total_loss = 0 real_data = self._get_real_data(eval_data) real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) for batch_idx, data in enumerate(real_dataloader): nll_test = self.model.calculate_nll_test(data, batch_idx) total_loss += float(nll_test) return total_loss / len(eval_data) def _add_eos(self, data, length): batch_size, pad_seq_len = data.size() padded_data = torch.full((batch_size, self.max_length), self.eos_token_idx, dtype=torch.long, device=self.device) for i in range(batch_size): l = int(length[i].cpu().data) if l == self.max_length+2: padded_data[i, :] = data[i, 1:l-1] else: padded_data[i, 0:l-1] = data[i, 1:l] return padded_data def _get_real_data(self, train_data): real_datas = [] for corpus in train_data: real_data = corpus['target_idx'] # bs*batch_max_seq_len length = corpus['target_length'] real_data = self._add_eos(real_data, length) real_datas.append(real_data) real_datas = torch.cat(real_datas, dim=0) return real_datas def _save_checkpoint(self, epoch, postfix=None): state = { 'config': self.config, 'epoch': epoch, 'cur_step': self.cur_step, 'best_valid_score': self.best_valid_score, 'state_dict': self.model.state_dict(), 'g_opt': self.g_optimizer.state_dict(), 'd_opt': self.d_optimizer.state_dict(), 'c_opt':self.c_optimizer.state_dict() } if postfix is not None: path = self.saved_model_file + "_" + str(epoch) + "_" + postfix torch.save(state, path) return path else: torch.save(state, self.saved_model_file) def _load_generated_text(self): r""" Load the generated text by our model to log. """ with open(self.saved_text_file, 'r') as fin: samples = [] for i in range(5): text = fin.readline() samples.append(text) return samples def fit(self, train_data, valid_data=None, verbose=True, saved=True): # generator pretraining if self.checkp is not None: checkpoint = torch.load(self.checkp) self.model.load_state_dict(checkpoint['state_dict']) self.d_optimizer.load_state_dict(checkpoint["d_opt"]) self.g_optimizer.load_state_dict(checkpoint["g_opt"]) epoch_check = checkpoint['epoch'] if verbose: self.logger.info("Load checkpoint file from: {}".format(self.checkp)) else: if self.pre_lm_weight is None: if verbose: self.logger.info("Start LM pretraining...") pretrain_lm, ppl = self.pretrain_lm(train_data, valid_data, verbose) pretrain_lm = torch.load(self.pre_lm_weight) embedder = pretrain_lm['embedder'].state_dict() lstm = pretrain_lm['encoder'].state_dict() vocab_linear = pretrain_lm['vocab_linear'].state_dict() self.model.generator.embedder.load_state_dict(embedder) self.model.generator.encoder.encoder.load_state_dict(lstm) self.model.generator.decoder.decoder.load_state_dict(lstm) self.model.generator.vocab_linear.load_state_dict(vocab_linear) self.model.discriminator.encoder.encoder.load_state_dict(lstm) self.model.discriminator.decoder.decoder.load_state_dict(lstm) if verbose: self.logger.info("Load pretrained LM weight") else: pretrain_lm = torch.load(self.pre_lm_weight) embedder = pretrain_lm['embedder'].state_dict() lstm = pretrain_lm['encoder'].state_dict() vocab_linear = pretrain_lm['vocab_linear'].state_dict() self.model.generator.embedder.load_state_dict(embedder) self.model.generator.encoder.encoder.load_state_dict(lstm) self.model.generator.decoder.decoder.load_state_dict(lstm) self.model.generator.vocab_linear.load_state_dict(vocab_linear) self.model.discriminator.encoder.encoder.load_state_dict(lstm) self.model.discriminator.decoder.decoder.load_state_dict(lstm) if verbose: self.logger.info("Load pretrained LM weight from: {}".format(self.pre_lm_weight)) if verbose: self.logger.info("Start generator mask pretraining...") for epoch_idx in range(self.g_mask_pretraining_epochs): training_start_time = time() train_loss = self._g_train_epoch(train_data, epoch_idx) self.g_pretraining_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss training_end_time = time() train_loss_output = \ self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss, "generator pre") if verbose: self.logger.info(train_loss_output) ppl = self._get_validate_ppl(valid_data, epoch_idx) if verbose: self.logger.info( "Epoch {}/{} of mask pretraining PPL: {}...".format(epoch_idx + 1, self.g_mask_pretraining_epochs, ppl)) if ppl <= 90: if verbose: path = self._save_checkpoint(epoch_idx + 1, postfix="pretrain_gen") self.logger.info(">>>> [Pretrain Gen] PPL: {} save weight in {}".format(ppl, path)) self.logger.info("End generator mask pretraining...") break if (epoch_idx) % 10 == 0: self.logger.info(">>>> [Pretrain Gen] Save pretrain gen check in epoch %d ..." % (epoch_idx + 1)) path = self._save_checkpoint(epoch_idx + 1, postfix="pretrain_gen") self.model.eval() test_result = self.evaluate(valid_data, model_file=path) self.model.train() sample = self._load_generated_text() tmp = "\n" for i, s in enumerate(sample): tmp += str(i) tmp += ": " tmp += s.strip() tmp += "\n" self.logger.info('>>>> [Pretrain Gen] test result: {}'.format(test_result)) self.logger.info('>>>> [Pretrain Gen] test result samples: {}'.format(tmp)) # discriminator pretraining if verbose: self.logger.info("Start discriminator pretraining...") for epoch_idx in range(self.d_pretraining_epochs): training_start_time = time() train_loss = self._d_train_epoch(train_data, epoch_idx) self.d_pretraining_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss training_end_time = time() train_loss_output = \ self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss, "discriminator pre") if verbose: self.logger.info(train_loss_output) if verbose: self.logger.info("End discriminator pretraining...") # adversarial training if verbose: self.logger.info("Start adversarial training...") for epoch_idx in range(self.adversarail_training_epochs): training_start_time = time() train_loss = self._adversarial_train_epoch(train_data, epoch_idx) self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss training_end_time = time() train_loss_output = \ self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss) if verbose: self.logger.info(train_loss_output) if (epoch_idx+1) % 10 == 0: path = self._save_checkpoint((epoch_idx + 1), postfix="adv_train") self.model.eval() test_result = self.evaluate(valid_data, model_file=path) self.model.train() sample = self._load_generated_text() tmp = "\n" for i, s in enumerate(sample): tmp += str(i) tmp += ": " tmp += s.strip() tmp += "\n" self.logger.info('>>>>>> [Adv] test result: {}'.format(test_result)) self.logger.info('>>>>>> [Adv] test result samples: {}'.format(tmp)) if verbose: self.logger.info("End adversarial pretraining...") self._save_checkpoint(self.adversarail_training_epochs) return -1, None class LeakGANTrainer(GANTrainer): r"""Specified for leakgan trainer """ def __init__(self, config, model): super(LeakGANTrainer, self).__init__(config, model) self.interleaved_pretrain_epoch = config['interleaved_pretrain_epoch'] self.adversarail_g_epochs = config['adversarail_g_epochs'] gen_lr = config['generator_lr'] # 0.001 dis_lr = config['discriminator_lr'] # 0.00005 self.g_optimizer = self._build_module_optimizer_(self.model.generator, gen_lr) # (manager_opt, worker_opt) self.d_optimizer = self._build_module_optimizer_(self.model.discriminator, dis_lr) self.iters_num = config['iter_num'] self.end_idx = model.end_idx def _build_module_optimizer_(self, module, learing_rate): r"""Specified for leakgan """ multi_flag = False if module._get_name() == 'LeakGANGenerator': manager_params, worker_params = module.split_params() multi_flag = True if self.learner.lower() == 'adam': if multi_flag: manager_opt = optim.Adam(manager_params, lr=learing_rate) worker_opt = optim.Adam(worker_params, lr=learing_rate) else: optimizer = optim.Adam(module.parameters(), lr=learing_rate) elif self.learner.lower() == 'sgd': if multi_flag: manager_opt = optim.SGD(manager_params, lr=learing_rate) worker_opt = optim.SGD(worker_params, lr=learing_rate) else: optimizer = optim.SGD(module.parameters(), lr=learing_rate) elif self.learner.lower() == 'adagrad': if multi_flag: manager_opt = optim.Adagrad(manager_params, lr=learing_rate) worker_opt = optim.Adagrad(worker_params, lr=learing_rate) else: optimizer = optim.Adagrad(module.parameters(), lr=learing_rate) elif self.learner.lower() == 'rmsprop': if multi_flag: manager_opt = optim.RMSprop(manager_params, lr=learing_rate) worker_opt = optim.RMSprop(worker_params, lr=learing_rate) else: optimizer = optim.RMSprop(module.parameters(), lr=learing_rate) else: self.logger.warning('Received unrecognized optimizer, set default Adam optimizer') if multi_flag: manager_opt = optim.Adam(manager_params, lr=learing_rate) worker_opt = optim.Adam(worker_params, lr=learing_rate) else: optimizer = optim.Adam(module.parameters(), lr=learing_rate) if multi_flag: return (manager_opt, worker_opt) else: return optimizer def _optimize_step(self, losses, total_loss, model, opt): r"""Specified for leakgan optimize """ if isinstance(losses, tuple): loss = sum(losses) loss_tuple = tuple(per_loss.item() for per_loss in losses) total_loss = loss_tuple if total_loss is None else tuple(map(sum, zip(total_loss, loss_tuple))) else: loss = losses total_loss = losses.item() if total_loss is None else total_loss + losses.item() self._check_nan(loss) if isinstance(losses, tuple): for i, (o, loss) in enumerate(zip(opt, losses)): o.zero_grad() loss.backward(retain_graph=True if i < len(opt) - 1 else False) torch.nn.utils.clip_grad_norm_(model.parameters(), self.grad_clip) o.step() else: opt.zero_grad() losses.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), self.grad_clip) opt.step() return total_loss def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses, train_info=""): r"""Specified for leakgan output format """ train_loss_output = "%straining [time: %.2fs, " % (train_info, e_time - s_time) if isinstance(losses, dict): for key, loss in losses.items(): train_loss_output += '%s: %.4f, ' % (key, loss) train_loss_output = train_loss_output[:-2] else: train_loss_output += "train loss: %.4f" % losses return train_loss_output + ']' def _add_eos(self, data, length): batch_size = data.shape[0] padded_data = torch.full((batch_size, self.max_length), self.end_idx, dtype=torch.long, device=self.device) for i in range(batch_size): len = length[i].cpu().data padded_data[i, :len] = data[i, :len] return padded_data def _get_real_data(self, train_data): r"""Specified for leakgan which use eos_idx pad not pad_idx """ real_datas = [] for corpus in train_data: real_data = corpus['target_idx'] length = corpus['target_length'] real_data = self._add_eos(real_data, length) real_datas.append(real_data) real_datas = torch.cat(real_datas, dim=0) return real_datas def _adversarial_train_epoch(self, train_data, epoch_idx): r"""Specified for leakgan adversarial training """ self.model.generator.train() total_g_loss = None total_d_loss = 0 total_d_acc = 0 adv_mana_loss = 0 adv_work_loss = 0 adv_d_loss = 0 for e in range(self.adversarail_g_epochs): losses = self.model.calculate_g_adversarial_loss(epoch_idx=e) total_g_loss = self._optimize_step(losses, total_g_loss, self.model.generator, self.g_optimizer) adv_mana_loss, adv_work_loss = total_g_loss adv_mana_loss = adv_mana_loss / self.adversarail_g_epochs adv_work_loss = adv_work_loss / self.adversarail_g_epochs for e in range(self.adversarail_d_epochs): loss_dict = self._d_train_epoch(train_data, epoch_idx=epoch_idx) total_d_loss = total_d_loss + loss_dict['total_loss'] total_d_acc = total_d_acc + loss_dict['train_acc'] adv_d_loss = total_d_loss / self.adversarail_d_epochs adv_c_loss = total_d_acc / self.adversarail_d_epochs return {"mana_loss": adv_mana_loss, "work_loss": adv_work_loss, "dis_loss": adv_d_loss, "train_acc": adv_c_loss} def _g_train_epoch(self, train_data, epoch_idx): total_loss = None real_data = self._get_real_data(train_data) real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) for batch_idx, data in enumerate(real_dataloader): # interaction = interaction.to(self.device) losses = self.model.calculate_g_train_loss(data, epoch_idx=epoch_idx) total_loss = self._optimize_step(losses, total_loss, self.model.generator, self.g_optimizer) total_loss = [l / len(real_dataloader) for l in total_loss] if isinstance(total_loss, tuple) else total_loss / len( train_data) mana_loss, work_loss = total_loss return {"mana_loss": mana_loss, "work_loss": work_loss} def _d_train_epoch(self, train_data, epoch_idx): total_loss = None total_acc = 0 real_data = self._get_real_data(train_data) real_dataloader = DataLoader(real_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) # not need sample self.d_sample_num numbers becauese only train discriminator 5 batch d_sample_num = (self.d_sample_training_epochs + 1) * self.model.batch_size fake_data = self.model.sample(d_sample_num) fake_dataloader = DataLoader(fake_data, batch_size=self.model.batch_size, shuffle=True, drop_last=True) idx = 0 for real_data, fake_data in zip(real_dataloader, fake_dataloader): # self.model.discriminator.eval() # pretraining not use dropout if idx == self.d_sample_training_epochs: break losses, acc = self.model.calculate_d_train_loss(real_data, fake_data, epoch_idx=epoch_idx) total_loss = self._optimize_step(losses, total_loss, self.model.discriminator, self.d_optimizer) total_acc = total_acc + acc idx += 1 total_loss = total_loss / self.d_sample_training_epochs total_acc = total_acc / self.d_sample_training_epochs return {"total_loss": total_loss, "train_acc": total_acc} def fit(self, train_data, valid_data=None, verbose=True, saved=True): # pretraining if verbose: self.logger.info(">> Start pretraining") # generator pretraining for epoch_idx in range(self.g_pretraining_epochs): # 80 if verbose: self.logger.info(">>>> [Pretrain Gen] Start %d / %d epochs generator pretraining" % ( epoch_idx + 1, self.g_pretraining_epochs)) training_start_time = time() train_loss = self._g_train_epoch(train_data, epoch_idx) training_end_time = time() train_loss_output = \ self._generate_train_loss_output(epoch_idx + 1, training_start_time, training_end_time, train_loss, "generator pre") train_loss_output = ">>>> " + train_loss_output if verbose: self.logger.info(train_loss_output) # discriminator pretraining for epoch_idx in range(self.d_pretraining_epochs): # 5 if verbose: self.logger.info(">>>> [Pretrain Dis]Start %d / %d epochs discriminator pretraining..." % ( epoch_idx + 1, self.d_pretraining_epochs)) training_start_time = time() train_loss = self._d_train_epoch(train_data, epoch_idx) training_end_time = time() train_loss_output = \ self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss, "discriminator pre") train_loss_output = ">>>> " + train_loss_output if verbose: self.logger.info(train_loss_output) if verbose: self.logger.info(">> End pretraining") # adversarial training if verbose: self.logger.info(">> Start adversarial training") for epoch in range(int(self.iters_num / self.adversarail_training_epochs)): if verbose: self.logger.info(">>>> [Adv] Start epoch %d / 10 interleaved adversarial training" % (epoch + 1)) for epoch_idx in range(self.adversarail_training_epochs): if verbose: self.logger.info(">>>>>> [Adv] Start epoch %d / %d adversarial training" % ( epoch_idx + 1, self.adversarail_training_epochs)) training_start_time = time() train_loss = self._adversarial_train_epoch(train_data, epoch_idx) # self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss training_end_time = time() train_loss_output = \ self._generate_train_loss_output((epoch_idx + 1), training_start_time, training_end_time, train_loss, train_info="adv ") train_loss_output = ">>>>>> " + train_loss_output if verbose: self.logger.info(train_loss_output) # gen pretrain for epoch_idx in range(5): if verbose: self.logger.info(">>>>>> [Adv] Start epoch %d / 5 pretrain generator" % (epoch_idx + 1)) training_start_time = time() train_loss = self._g_train_epoch(train_data, epoch_idx) training_end_time = time() train_loss_output = \ self._generate_train_loss_output((epoch_idx + 1), training_start_time, training_end_time, train_loss, "adv generator pre") train_loss_output = ">>>>>> " + train_loss_output if verbose: self.logger.info(train_loss_output) # dis pretrain for epoch_idx in range(5): # d_steps if verbose: self.logger.info(">>>>>> [Adv] Start epoch %d / 5 pretrain discriminator" % (epoch_idx + 1)) training_start_time = time() train_loss = self._d_train_epoch(train_data, epoch_idx) training_end_time = time() train_loss_output = \ self._generate_train_loss_output((epoch_idx + 1), training_start_time, training_end_time, train_loss, "adv discriminator pre") train_loss_output = ">>>>>> " + train_loss_output if verbose: self.logger.info(train_loss_output) self._save_checkpoint(self.adversarail_training_epochs) return -1, None

45.997251

146

0.621836

8,279

66,926

4.749245

0.062206

0.032046

0.02556

0.021262

0.725909

0.678527

0.645439

0.614538

0.576668

0.559628

0

0.003928

0.288587

66,926

1,454

147

46.028886

0.821894

0.146311

0

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0

0.067128

0.00226

0

1

0.057602

false

0

0.012276

0

0.120869

0

null

0

null

0

1

0

a3a8234ec61d7794c6426793212657ac24a62f4a

649

py

Python

rsserpent/plugins/builtin/__init__.py

EurusEurus/RSSerpent

fd7aaf67b80b2b48c14b1a3efe733374b0012338

[ "MIT" ]

null

rsserpent/plugins/builtin/__init__.py

EurusEurus/RSSerpent

fd7aaf67b80b2b48c14b1a3efe733374b0012338

[ "MIT" ]

null

rsserpent/plugins/builtin/__init__.py

EurusEurus/RSSerpent

fd7aaf67b80b2b48c14b1a3efe733374b0012338

[ "MIT" ]

null

from ...models import Persona, Plugin from . import example, example_cache, example_ratelimit, example_with_args plugin = Plugin( name="rsserpent-plugin-builtin", author=Persona( name="queensferryme", link="https://github.com/queensferryme", email="queensferry.me@gmail.com", ), repository="https://github.com/RSSerpent/RSSerpent", prefix="/_", routers={ example.path: example.provider, example_cache.path: example_cache.provider, example_ratelimit.path: example_ratelimit.provider, example_with_args.path: example_with_args.provider, }, ) __all__ = ("plugin",)

28.217391

74

0.682589

69

649

6.173913

0.42029

0.103286

0.105634

0

0.191063

649

22

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29.5

0.811429

0

0.214176

0.07396

0

1

0

false

0

0.105263

0

0.105263

0

null

0

null

0

1

0

a3a86ac522e7ca59c54af2df1492f75fd0ad7b3e

2,859

py

Python

data_processing/process_xls.py

luisroel91/libdib_assesment

c969cfecbce1243b457961ffafe5caaea7bb5149

[ "MIT" ]

null

data_processing/process_xls.py

luisroel91/libdib_assesment

c969cfecbce1243b457961ffafe5caaea7bb5149

[ "MIT" ]

null

data_processing/process_xls.py

luisroel91/libdib_assesment

c969cfecbce1243b457961ffafe5caaea7bb5149

[ "MIT" ]

null

import pandas as pd # Define our header col_names = [ "year", "num_males_with_income", "male_median_income_curr_dollars", "male_median_income_2019_dollars", "num_females_with_income", "female_median_income_curr_dollars", "female_median_income_2019_dollars", ] # Load Asian census data XLS, skipping all headers dfa = pd.read_excel( r'p08a.xlsx', skiprows=8, # Make sure PD doesn't use header row for our DF header=None, # Define col names names=col_names, ) # Load White census data XLS, skipping all headers dfw = pd.read_excel( r'p08w.xlsx', skiprows=8, # Make sure PD doesn't use header row for our DF header=None, # Define cold names names=col_names ) # Splinter off rows into age group DFs for both sets of data dfa1524 = dfa.iloc[:20] dfa2534 = dfa.iloc[25:45] dfa3544 = dfa.iloc[50:70] dfa4554 = dfa.iloc[75:95] dfa5564 = dfa.iloc[100:120] dfa6574 = dfa.iloc[125:145] dfa75 = dfa.iloc[150:170] dfw1524 = dfw.iloc[:20] dfw2534 = dfw.iloc[25:45] dfw3544 = dfw.iloc[50:70] dfw4554 = dfw.iloc[75:95] dfw5564 = dfw.iloc[100:120] dfw6574 = dfw.iloc[125:145] dfw75 = dfw.iloc[150:170] # Add Age Range col to each DF dfa1524.insert(0, 'age_range', '15-24') dfa2534.insert(0, 'age_range', '25-34') dfa3544.insert(0, 'age_range', '35-44') dfa4554.insert(0, 'age_range', '45-54') dfa5564.insert(0, 'age_range', '55-64') dfa6574.insert(0, 'age_range', '65-74') dfa75.insert(0, 'age_range', 'Over 75') dfw1524.insert(0, 'age_range', '15-24') dfw2534.insert(0, 'age_range', '25-34') dfw3544.insert(0, 'age_range', '35-44') dfw4554.insert(0, 'age_range', '45-54') dfw5564.insert(0, 'age_range', '55-64') dfw6574.insert(0, 'age_range', '65-74') dfw75.insert(0, 'age_range', 'Over 75') # Stack cleaned DF's vertically dfa = pd.concat([ dfa1524, dfa2534, dfa3544, dfa4554, dfa5564, dfa6574, dfa75 ], axis=0) dfw = pd.concat([ dfw1524, dfw2534, dfw3544, dfw4554, dfw5564, dfw6574, dfw75 ], axis=0) # Add Race col dfa.insert(0, 'race', 'asian') dfw.insert(0, 'race', 'white') # Clean garbage chars in Year col using regex dfa['year'] = dfa['year'].replace(to_replace=r'(\s$\d+$)', value='', regex=True) dfw['year'] = dfw['year'].replace(to_replace=r'(\s$\d+$)', value='', regex=True) # Stack our cleaned + normalized data into a single DF df = pd.concat([ dfa, dfw ], axis=0) # Convert the DF col types to conform to our CensusRecord model df = df.astype({ "race": str, "age_range": str, "year": int, "num_males_with_income": int, "male_median_income_curr_dollars": float, "male_median_income_2019_dollars": float, "num_females_with_income": int, "female_median_income_curr_dollars": float, "female_median_income_2019_dollars": float, }) # Pickle the DF df.to_pickle("./res.pkl")

24.646552

82

0.671913

450

2,859

4.12

0.306667

0.06904

0.075512

0.113269

0.420712

0.289105

0.110032

0

0.132291

0.172438

2,859

115

83

24.86087

0.65131

0.191675

0

0.079545

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0.282353

0.149891

0

1

0

false

0

0.011364

0

0.011364

0

null

0

null

0

1

0

a3aa7d175c4008d278417caf82ba36b9fb655fda

520

py

Python

Section_1/Exercise_16.py

Szymon-Budziak/WDI_exercises_solutions

51ffc9ec8b3cd6809bd55e98ecb8aed759c2d460

[ "MIT" ]

null

Section_1/Exercise_16.py

Szymon-Budziak/WDI_exercises_solutions

51ffc9ec8b3cd6809bd55e98ecb8aed759c2d460

[ "MIT" ]

null

Section_1/Exercise_16.py

Szymon-Budziak/WDI_exercises_solutions

51ffc9ec8b3cd6809bd55e98ecb8aed759c2d460

[ "MIT" ]

1

2021-11-21T09:38:33.000Z

""" Dany jest ciąg określony wzorem: A[n+1] = (A[n] % 2) ∗ (3 ∗ A[n] + 1) + (1 − A[n] % 2) ∗ A[n] / 2. Startując z dowolnej liczby naturalnej > 1 ciąg ten osiąga wartość 1. Napisać program, który znajdzie wyraz początkowy z przedziału 2-10000 dla którego wartość 1 jest osiągalna po największej liczbie kroków. """ a0 = 2 m = 1 for a0 in range(2, 10000): n = 0 while a0 != 1: a0 = (((a0 % 2) * (3 * a0 + 1)) + ((1 - (a0 % 2)) * (a0 / 2))) n += 1 if n > m: m = n a0 += 1 print(m)

27.368421

98

0.542308

94

520

3.042553

0.468085

0.034965

0.031469

0.027972

0

0.117166

0.294231

520

18

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0.651226

0.588462

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1

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false

0

0.090909

0

null

0

null

0

1

0

a3ad80bfdfa53d706abcbf25b9e00b65302a112a

1,480

py

Python

AndroidSpider/spider_main.py

lidenghong1/SmallReptileTraining

a1bfb81c9969edfb7554acc50370c0cb036da690

[ "MIT" ]

1

2018-05-10T01:52:37.000Z

AndroidSpider/spider_main.py

lidenghong1/SmallReptileTraining

a1bfb81c9969edfb7554acc50370c0cb036da690

[ "MIT" ]

null

AndroidSpider/spider_main.py

lidenghong1/SmallReptileTraining

a1bfb81c9969edfb7554acc50370c0cb036da690

[ "MIT" ]

null

from AndroidSpider import url_manager, html_downloader, html_parser, html_output ''' 爬取百度百科 Android 关键词相关词及简介并输出为一个HTML tab网页 Extra module: BeautifulSoup ''' class SpiderMain(object): def __init__(self): self.urls = url_manager.UrlManager() self.downloader = html_downloader.HtmlDownLoader() self.parser = html_parser.HtmlParser() self.out_put = html_output.HtmlOutput() def craw(self, root_url): count = 1 self.urls.add_new_url(root_url) while self.urls.has_new_url(): try: new_url = self.urls.get_new_url() print("craw %d : %s" % (count, new_url)) headers = { "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.100 Safari/537.36" } html_content = self.downloader.download(new_url, retry_count=2, headers=headers) new_urls, new_data = self.parser.parse(new_url, html_content, "utf-8") self.urls.add_new_urls(new_urls) self.out_put.collect_data(new_data) if count >= 30: break count = count + 1 except Exception as e: print("craw failed!\n"+str(e)) self.out_put.output_html() if __name__ == "__main__": rootUrl = "http://baike.baidu.com/item/Android" objSpider = SpiderMain() objSpider.craw(rootUrl)

36.097561

141

0.597297

180

1,480

4.65

0.505556

0.050179

0.035842

0.033453

0

0.032567

0.294595

1,480

40

142

37

0.769157

0

0.032258

0.134711

0

1

0.064516

false

0

0.032258

0

0.129032

0.064516

0

null

0

null

0

1

0

a3ae0fed36bd78447d3c9b110c995da7eb0ec44e

517

py

Python

trompace/mutations/__init__.py

trompamusic/ce-queries-template

cc5ae69d0e76623bfd72e9453f569f6624bf7c3b

[ "Apache-2.0" ]

1

2020-06-18T15:43:18.000Z

trompace/mutations/__init__.py

trompamusic/ce-queries-template

cc5ae69d0e76623bfd72e9453f569f6624bf7c3b

[ "Apache-2.0" ]

60

2019-12-17T11:08:28.000Z

2021-03-02T16:19:41.000Z

trompace/mutations/__init__.py

trompamusic/trompace-client

cc5ae69d0e76623bfd72e9453f569f6624bf7c3b

[ "Apache-2.0" ]

null

MUTATION = '''mutation {{ {mutation} }}''' def _verify_additional_type(additionaltype): """Check that the input to additionaltype is a list of strings. If it is empty, raise ValueError If it is a string, convert it to a list of strings.""" if additionaltype is None: return None if isinstance(additionaltype, str): additionaltype = [additionaltype] if len(additionaltype) == 0: raise ValueError("additionaltype must be a non-empty list") return additionaltype

28.722222

67

0.68472

64

517

5.484375

0.5

0.091168

0.039886

0.079772

0.091168

0

0.002513

0.230174

517

17

68

30.411765

0.879397

0.280464

0

0.184358

0

1

0.090909

false

0

0.272727

0

null

0

null

0

1

0

a3ae4f1aada9f0b92aa00f9f17807bd4f8c072c1

951

py

Python

Web_App/infrastructure/infra.py

CapitalOneDevExchangeHackathon/Financial-Fitness

54a2203d6b3d96687d822247b040613b644874f2

[ "MIT" ]

null

Web_App/infrastructure/infra.py

CapitalOneDevExchangeHackathon/Financial-Fitness

54a2203d6b3d96687d822247b040613b644874f2

[ "MIT" ]

null

Web_App/infrastructure/infra.py

CapitalOneDevExchangeHackathon/Financial-Fitness

54a2203d6b3d96687d822247b040613b644874f2

[ "MIT" ]

null

import boto import boto3 from config import Config dynamodb = boto3.resource('dynamodb', aws_access_key_id=Config.AWS_KEY, aws_secret_access_key=Config.AWS_SECRET_KEY, region_name=Config.REGION) table = dynamodb.Table('user_details') tables = boto3.resource('dynamodb', aws_access_key_id=Config.AWS_KEY, aws_secret_access_key=Config.AWS_SECRET_KEY, region_name=Config.REGION).Table('user_details') print(tables.creation_date_time) def main(): print("29.7604267") def insert_into_db(user): print(user.lastname) try: table.put_item( Item={ 'pin': user.pin, 'firstname': user.firstname, 'lastname': user.lastname, } ) except Exception as E: print(E) return False return True if __name__ == "__main__": main()

22.116279

119

0.589905

108

951

4.87037

0.398148

0.068441

0.079848

0.091255

0.418251

0

0.018433

0.315457

951

42

120

22.642857

0.789555

0

0.082105

0

1

0.068966

false

0

0.103448

0

0.241379

0.137931

0

null

0

null

0

1

0

a3b0debd51a02674a2485fcb5fa43dc82bc97eff

2,751

py

Python

SelfTests.py

TeaPackCZ/RobotZed

7ac8bfb14a6c2e5887f8fed299ad87b384701c54

[ "MIT" ]

null

SelfTests.py

TeaPackCZ/RobotZed

7ac8bfb14a6c2e5887f8fed299ad87b384701c54

[ "MIT" ]

null

SelfTests.py

TeaPackCZ/RobotZed

7ac8bfb14a6c2e5887f8fed299ad87b384701c54

[ "MIT" ]

null

import os import unittest from Logger import Logger class TestLogger(unittest.TestCase): def test_file_handling(self): testLog = Logger("testLog") ## Check if program can create and open file self.assertTrue(testLog.opened) returns = testLog.close() ## Check if logger correctly signs bool OPENED and returns ## 0 as succes. self.assertFalse(testLog.opened) self.assertEqual(returns,0) returns = testLog.close() ## Check if logger returns 1 when trying to close already ## closed file self.assertEqual(returns,1) ## Do cleanup: os.remove(testLog.name) def test_logging(self): testLog = Logger("testLog") testPhrase = "TestLine\r\n" testLog.save_line(testPhrase) testLog.close() logfile = open(testLog.name) content = logfile.read() logfile.close() saved = content.split(" : ") ## Check if saved data corresponds self.assertEqual(saved[1],testPhrase) ## cleanup os.remove(testLog.name) from gpsNavigation import gpsModule,gpsPoint class TestGPSNavigation(unittest.TestCase): def test_gps_angles(self): gpsMod = gpsModule() A = gpsPoint(10,10) B = gpsPoint(10.1,10.1) distance, azimut = gpsMod.GPSData.getDirAndDist(A,B) self.assertEqual(distance,15623.0) self.assertEqual(azimut,45.0) B = gpsPoint(10.0,10.1) distance, azimut = gpsMod.GPSData.getDirAndDist(A,B) self.assertEqual(distance,10963.0) self.assertEqual(azimut,90.0) B = gpsPoint(9.9,10.1) distance, azimut = gpsMod.GPSData.getDirAndDist(A,B) self.assertEqual(distance,15625.0) self.assertEqual(azimut,135.0) B = gpsPoint(9.9,10.0) distance, azimut = gpsMod.GPSData.getDirAndDist(A,B) self.assertEqual(distance,11132.0) self.assertEqual(azimut,180.0) B = gpsPoint(9.9,9.9) distance, azimut = gpsMod.GPSData.getDirAndDist(A,B) self.assertEqual(distance,15625.0) self.assertEqual(azimut,225.0) B = gpsPoint(10.0,9.9) distance, azimut = gpsMod.GPSData.getDirAndDist(A,B) self.assertEqual(distance,10963.0) self.assertEqual(azimut,270.0) B = gpsPoint(10.1,9.9) distance, azimut = gpsMod.GPSData.getDirAndDist(A,B) self.assertEqual(distance,15623.0) self.assertEqual(azimut,315.0) B = gpsPoint(10.1,10.0) distance, azimut = gpsMod.GPSData.getDirAndDist(A,B) self.assertEqual(distance,11132.0) self.assertEqual(azimut,0) if __name__ == '__main__': unittest.main()

31.988372

66

0.624137

332

2,751

5.129518

0.253012

0.167352

0.093952

0.126835

0.571345

0.496183

0.44451

0

0.06213

0.262814

2,751

85

67

32.364706

0.777613

0.08615

0

0.349206

0

0.014818

0

0.333333

1

0.047619

false

0

0.063492

0

0.142857

0

null

0

null

0

1

0

a3b19235edf240100e043436d336caa4a2f88321

1,986

py

Python

manga_py/parser.py

Abijithkrishna/manga-py

03b142ecb944ef37a36e5095ffa580209021e3b0

[ "MIT" ]

null

manga_py/parser.py

Abijithkrishna/manga-py

03b142ecb944ef37a36e5095ffa580209021e3b0

[ "MIT" ]

null

manga_py/parser.py

Abijithkrishna/manga-py

03b142ecb944ef37a36e5095ffa580209021e3b0

[ "MIT" ]

null

from logging import warning from requests import get from .info import Info from .provider import Provider from .providers import get_provider class Parser: def __init__(self, args: dict): self.params = args def init_provider( self, chapter_progress: callable = None, global_progress: callable = None, log: callable = None, quest: callable = None, info: Info = None, quest_password: callable = None, ): original_url = self.params.get('url', '') provider_url = self.params.get('force_provider', None) provider = get_provider(provider_url or original_url) if isinstance(provider, bool): raise AttributeError('Provider not found') # update url (if redirect) self.provider = provider(info) # type: Provider self.provider.original_url = original_url real_url = self.check_url(original_url) if self.provider.allow_auto_change_url(): if real_url != original_url: warning('Manga url changed! New url: {}'.format(real_url)) self.params['url'] = real_url self.provider.quiet = self.params.get('quiet', False) self.provider.set_chapter_progress_callback(chapter_progress) self.provider.set_global_progress_callback(global_progress) self.provider.set_log_callback(log) self.provider.set_quest_callback(quest) self.provider.set_quest_password_callback(quest_password) def start(self): self.provider.process(self.params['url'], self.params) def check_url(self, url): proxy = self.params.get('proxy', None) proxies = { 'http': proxy, 'https': proxy, } if proxy else None with get(url, stream=True, proxies=proxies) as response: _url = response.url if url != _url: url = _url return url

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null

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null

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1

0

a3b29bffdf2e36c45f804f1c4fc3a56bbdcb9b59

1,127

py

Python

customers/views.py

sindhumadhadi09/CustomerMgmt

db8b27ad6ceb8050843dc33509dc2b6c2ed2c1e2

[ "MIT" ]

null

customers/views.py

sindhumadhadi09/CustomerMgmt

db8b27ad6ceb8050843dc33509dc2b6c2ed2c1e2

[ "MIT" ]

null

customers/views.py

sindhumadhadi09/CustomerMgmt

db8b27ad6ceb8050843dc33509dc2b6c2ed2c1e2

[ "MIT" ]

null

from django.shortcuts import get_object_or_404, render from django.http import HttpResponseRedirect from django.urls import reverse from django.views import generic from django.utils import timezone from .models import Customer class IndexView(generic.ListView): template_name = 'customers/index.html' context_object_name = 'customers_list' def get_queryset(self): return Customer.objects.all() class CustomerView(generic.TemplateView): template_name = 'customers/detail.html' def add_customer(request): customer = Customer() customer.customer_firstname = request.POST['fname'] customer.customer_lastname = request.POST['lname'] customer.customer_address = request.POST['address'] customer.customer_city = request.POST['city'] customer.customer_zipcode = request.POST['zip'] customer.customer_state = request.POST['state'] customer.save() return HttpResponseRedirect(reverse('customers:index')) def delete_customer(request, customer_id): p = Customer.objects.get(pk=customer_id) p.delete() return HttpResponseRedirect(reverse('customers:index'))

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59

0.759539

133

1,127

6.293233

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1,127

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0.111111

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0.62963

0

null

0

null

0

1

0

a3b315d5551d6efa8a8b5d2f47e368467747b831

3,512

py

Python

butterfree/configs/db/metastore_config.py

fossabot/butterfree

8a7da8c540b51c6560b2825cb926c40a351f202b

[ "Apache-2.0" ]

null

butterfree/configs/db/metastore_config.py

fossabot/butterfree

8a7da8c540b51c6560b2825cb926c40a351f202b

[ "Apache-2.0" ]

null

butterfree/configs/db/metastore_config.py

fossabot/butterfree

8a7da8c540b51c6560b2825cb926c40a351f202b

[ "Apache-2.0" ]

null

"""Holds configurations to read and write with Spark to AWS S3.""" import os from typing import Any, Dict, List, Optional from pyspark.sql import DataFrame from butterfree.configs import environment from butterfree.configs.db import AbstractWriteConfig from butterfree.dataframe_service import extract_partition_values class MetastoreConfig(AbstractWriteConfig): """Configuration for Spark metastore database stored. By default the configuration is for AWS S3. Attributes: path: database root location. mode: writing mode used be writers. format_: expected stored file format. file_system: file schema uri, like: s3a, file. """ def __init__( self, path: str = None, mode: str = None, format_: str = None, file_system: str = None, ): self.path = path self.mode = mode self.format_ = format_ self.file_system = file_system @property def path(self) -> Optional[str]: """Bucket name.""" return self.__path @path.setter def path(self, value: str) -> None: self.__path = value or environment.get_variable("FEATURE_STORE_S3_BUCKET") @property def format_(self) -> Optional[str]: """Expected stored file format.""" return self.__format @format_.setter def format_(self, value: str) -> None: self.__format = value or "parquet" @property def mode(self) -> Optional[str]: """Writing mode used be writers.""" return self.__mode @mode.setter def mode(self, value: str) -> None: self.__mode = value or "overwrite" @property def file_system(self) -> Optional[str]: """Writing mode used be writers.""" return self.__file_system @file_system.setter def file_system(self, value: str) -> None: self.__file_system = value or "s3a" def get_options(self, key: str) -> Dict[Optional[str], Optional[str]]: """Get options for Metastore. Options will be a dictionary with the write and read configuration for Spark Metastore. Args: key: path to save data into Metastore. Returns: Options configuration for Metastore. """ return { "mode": self.mode, "format_": self.format_, "path": os.path.join(f"{self.file_system}://{self.path}/", key), } def get_path_with_partitions(self, key: str, dataframe: DataFrame) -> List: """Get options for AWS S3 from partitioned parquet file. Options will be a dictionary with the write and read configuration for Spark to AWS S3. Args: key: path to save data into AWS S3 bucket. dataframe: spark dataframe containing data from a feature set. Returns: A list of string for file-system backed data sources. """ path_list = [] dataframe_values = extract_partition_values( dataframe, partition_columns=["year", "month", "day"] ) for row in dataframe_values: path_list.append( f"{self.file_system}://{self.path}/{key}/year={row['year']}/" f"month={row['month']}/day={row['day']}" ) return path_list def translate(self, schema: List[Dict[str, Any]]) -> List[Dict[str, Any]]: """Translate feature set spark schema to the corresponding database.""" pass

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null

0

null

0

1

0

a3b384657bc7cd2ab9ee0a1d8b09ee80039ad894

2,401

py

Python

examples/2-objects.py

johanngan/special_relativity

cd372c7460d2c0d4040c81bc1bd0090086dba735

[ "MIT" ]

4

2020-08-19T04:56:40.000Z

2022-02-07T22:09:45.000Z

examples/2-objects.py

johanngan/special_relativity

cd372c7460d2c0d4040c81bc1bd0090086dba735

[ "MIT" ]

null

examples/2-objects.py

johanngan/special_relativity

cd372c7460d2c0d4040c81bc1bd0090086dba735

[ "MIT" ]

null

#!/usr/bin/env python3 import sys sys.path.append('..') import specrel.geom as geom import specrel.spacetime.physical as phy import specrel.visualize as vis # Shared parameters include_grid = True include_legend = True tlim = (0, 2) xlim = (-2, 2) # A stationary point object stationary = phy.MovingObject(0, draw_options={'label': '$v = 0$'}) ## Alternate: # direction = (1, 0) # point = (0, 0) # stationary = geom.Line(direction, point, draw_options={'label': '$v = 0$'}) title='Stationary object' p = vis.stplot(stationary, title=title, tlim=tlim, xlim=xlim, grid=include_grid, legend=include_legend) p.save('2-objects_stationary_point.png') p.show() # A stationary point object, animated anim = vis.stanimate(stationary, title=title, tlim=tlim, xlim=xlim, grid=include_grid, legend=include_legend) anim.save('2-objects_stationary_point_anim.mp4') anim.show() # A stationary point object, animated with worldline anim = vis.stanimate_with_worldline(stationary, title=title, tlim=tlim, xlim=xlim, grid=include_grid, legend=include_legend, legend_loc='upper right') anim.save('2-objects_stationary_point_anim_worldline.mp4') anim.show() # A bunch of moving point objects, animated moving = phy.MovingObject(0, velocity=1/2, draw_options={'color': 'red', 'label': '$v = c/2$'}) light = phy.MovingObject(0, velocity=1, draw_options={'color': 'gold', 'label': '$v = c$'}) ftl = phy.MovingObject(0, velocity=3/2, draw_options={'color': 'cyan', 'label': '$v = 3c/2$'}) objects = geom.Collection([stationary, moving, light, ftl]) title = 'Various objects' anim = vis.stanimate_with_worldline(objects, title=title, current_time_color='magenta', tlim=tlim, xlim=xlim, grid=include_grid, legend=include_legend, legend_loc='upper left') anim.save('2-objects_moving_points.mp4') anim.show() # A moving meterstick meterstick = phy.MovingObject(-1/2, length=1, velocity=1/2, draw_options={'label': 'Meterstick'}) # # Alternate: # direction = (1, 1/2) # left = geom.Line(direction, (0, -1/2)) # right = geom.Line(direction, (0, 1/2)) # meterstick = geom.Ribbon(left, right, draw_options={'label': 'Meterstick'}) title = 'Moving meterstick ($v = c/2$)' anim = vis.stanimate_with_worldline(meterstick, title=title, tlim=tlim, xlim=xlim, grid=include_grid, legend=include_legend, legend_loc='upper left') anim.save('2-objects_moving_meterstick.mp4') anim.show()

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0

0.022243

0.11995

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0.088889

0

null

0

null

0

1

0

a3b4e8143896f099b74b0a3738681f49e357493f

4,049

py

Python

tests/sentry/auth/test_helper.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

[ "BSD-3-Clause" ]

null

tests/sentry/auth/test_helper.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

[ "BSD-3-Clause" ]

null

tests/sentry/auth/test_helper.py

pierredup/sentry

0145e4b3bc0e775bf3482fe65f5e1a689d0dbb80

[ "BSD-3-Clause" ]

null

from __future__ import absolute_import from six.moves.urllib.parse import urlencode from django.test import RequestFactory from django.contrib.auth.models import AnonymousUser from sentry.auth.helper import handle_new_user from sentry.models import AuthProvider, InviteStatus, OrganizationMember from sentry.testutils import TestCase from sentry.utils.compat import mock class HandleNewUserTest(TestCase): @mock.patch("sentry.analytics.record") def test_simple(self, mock_record): provider = "dummy" request = RequestFactory().post("/auth/sso/") request.user = AnonymousUser() auth_provider = AuthProvider.objects.create( organization=self.organization, provider=provider ) identity = {"id": "1234", "email": "test@example.com", "name": "Morty"} auth_identity = handle_new_user(auth_provider, self.organization, request, identity) user = auth_identity.user assert user.email == identity["email"] assert OrganizationMember.objects.filter(organization=self.organization, user=user).exists() signup_record = [r for r in mock_record.call_args_list if r[0][0] == "user.signup"] assert signup_record == [ mock.call( "user.signup", user_id=user.id, source="sso", provider=provider, referrer="in-app" ) ] def test_associated_existing_member_invite_by_email(self): request = RequestFactory().post("/auth/sso/") request.user = AnonymousUser() provider = AuthProvider.objects.create(organization=self.organization, provider="dummy") identity = {"id": "1234", "email": "test@example.com", "name": "Morty"} member = OrganizationMember.objects.create( organization=self.organization, email=identity["email"] ) auth_identity = handle_new_user(provider, self.organization, request, identity) assigned_member = OrganizationMember.objects.get( organization=self.organization, user=auth_identity.user ) assert assigned_member.id == member.id def test_associated_existing_member_invite_request(self): request = RequestFactory().post("/auth/sso/") request.user = AnonymousUser() provider = AuthProvider.objects.create(organization=self.organization, provider="dummy") identity = {"id": "1234", "email": "test@example.com", "name": "Morty"} member = self.create_member( organization=self.organization, email=identity["email"], invite_status=InviteStatus.REQUESTED_TO_BE_INVITED.value, ) auth_identity = handle_new_user(provider, self.organization, request, identity) assert OrganizationMember.objects.filter( organization=self.organization, user=auth_identity.user, invite_status=InviteStatus.APPROVED.value, ).exists() assert not OrganizationMember.objects.filter(id=member.id).exists() def test_associate_pending_invite(self): provider = AuthProvider.objects.create(organization=self.organization, provider="dummy") identity = {"id": "1234", "email": "test@example.com", "name": "Morty"} # The org member invite should have a non matching email, but the # member id and token will match from the cookie, allowing association member = OrganizationMember.objects.create( organization=self.organization, email="different.email@example.com", token="abc" ) request = RequestFactory().post("/auth/sso/") request.user = AnonymousUser() request.COOKIES["pending-invite"] = urlencode( {"memberId": member.id, "token": member.token, "url": ""} ) auth_identity = handle_new_user(provider, self.organization, request, identity) assigned_member = OrganizationMember.objects.get( organization=self.organization, user=auth_identity.user ) assert assigned_member.id == member.id

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0

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4,049

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101

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0.175676

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null

0

null

0

1

0

a3b57d8c1a4088165ce4f67e6fb27850615f9653

4,583

py

Python

density_model_torch_custom.py

piotrwinkler/breast_density_classifier

4d47dd98bb0a839cea8b9aef242f5af5db84f06f

[ "BSD-2-Clause" ]

null

density_model_torch_custom.py

piotrwinkler/breast_density_classifier

4d47dd98bb0a839cea8b9aef242f5af5db84f06f

[ "BSD-2-Clause" ]

null

density_model_torch_custom.py

piotrwinkler/breast_density_classifier

4d47dd98bb0a839cea8b9aef242f5af5db84f06f

[ "BSD-2-Clause" ]

null

import argparse import glob import os import numpy as np import torch from sklearn.metrics import accuracy_score import models_torch as models import utils EXPERIMENT_DATA_DIR = "/tmp/mgr" def inference(parameters, verbose=True) -> int: # resolve device device = torch.device( "cuda:{}".format(parameters["gpu_number"]) if parameters["device_type"] == "gpu" else "cpu" ) # load input images datum_l_cc = utils.load_images(parameters['image_path'], 'L-CC') datum_r_cc = utils.load_images(parameters['image_path'], 'R-CC') datum_l_mlo = utils.load_images(parameters['image_path'], 'L-MLO') datum_r_mlo = utils.load_images(parameters['image_path'], 'R-MLO') # construct models and prepare data if parameters["model_type"] == 'cnn': model = models.BaselineBreastModel(device, nodropout_probability=1.0, gaussian_noise_std=0.0).to(device) model.load_state_dict(torch.load(parameters["model_path"])) x = { "L-CC": torch.Tensor(datum_l_cc).permute(0, 3, 1, 2).to(device), "L-MLO": torch.Tensor(datum_l_mlo).permute(0, 3, 1, 2).to(device), "R-CC": torch.Tensor(datum_r_cc).permute(0, 3, 1, 2).to(device), "R-MLO": torch.Tensor(datum_r_mlo).permute(0, 3, 1, 2).to(device), } elif parameters["model_type"] == 'histogram': model = models.BaselineHistogramModel(num_bins=parameters["bins_histogram"]).to(device) model.load_state_dict(torch.load(parameters["model_path"])) x = torch.Tensor(utils.histogram_features_generator([ datum_l_cc, datum_r_cc, datum_l_mlo, datum_r_mlo ], parameters)).to(device) else: raise RuntimeError(parameters["model_type"]) # run prediction with torch.no_grad(): prediction_density = model(x).cpu().numpy() if verbose: # nicely prints out the predictions print('Density prediction:\n' '\tAlmost entirely fatty (0):\t\t\t' + str(prediction_density[0, 0]) + '\n' '\tScattered areas of fibroglandular density (1):\t' + str(prediction_density[0, 1]) + '\n' '\tHeterogeneously dense (2):\t\t\t' + str(prediction_density[0, 2]) + '\n' '\tExtremely dense (3):\t\t\t\t' + str(prediction_density[0, 3]) + '\n') return np.argmax(prediction_density[0])+1 # return density in scope 1 to 4 if __name__ == "__main__": parser = argparse.ArgumentParser(description='Run Inference') parser.add_argument('model_type') parser.add_argument('--bins-histogram', default=50) parser.add_argument('--model-path', default=None) parser.add_argument('--device-type', default="cpu") # parser.add_argument('--image-path', default="images/") args = parser.parse_args() parameters_ = { "model_type": args.model_type, "bins_histogram": args.bins_histogram, "model_path": args.model_path, "device_type": args.device_type, # "image_path": args.image_path, } if parameters_["model_path"] is None: if args.model_type == "histogram": parameters_["model_path"] = "saved_models/BreastDensity_BaselineHistogramModel/model.p" if args.model_type == "cnn": parameters_["model_path"] = "saved_models/BreastDensity_BaselineBreastModel/model.p" predicted_values = [] real_values = [] predicted_values_two_classes = [] real_values_two_classes = [] two_classes_mapping = {1: 0, 2: 0, 3: 1, 4: 1} for dir in glob.glob(f"{EXPERIMENT_DATA_DIR}/*/"): parameters_["image_path"] = dir predicted_density = inference(parameters_) with open(os.path.join(dir, "density.txt")) as file: real_density = int(file.read()) print(f"Predicted density: {predicted_density}") print(f"Real density: {real_density}\n") print(f"Predicted density (2 cls): {two_classes_mapping[predicted_density]}") print(f"Real density (2 cls): {two_classes_mapping[real_density]}\n") predicted_values.append(predicted_density) real_values.append(real_density) predicted_values_two_classes.append(two_classes_mapping[predicted_density]) real_values_two_classes.append(two_classes_mapping[real_density]) print(f"Total accuracy: {accuracy_score(real_values, predicted_values)}") print(f"Total accuracy two classes: {accuracy_score(real_values_two_classes, predicted_values_two_classes)}") """ python density_model_torch_custom.py histogram python density_model_torch_custom.py cnn """

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0.085366

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null

0

null

0

1

0

a3b664d11a53af7fe489af747c1768858a1613a2

4,878

py

Python

esmvaltool/diag_scripts/ensclus/ens_anom.py

yifatdzigan/ESMValTool

83320b0e0b24ddde965599961bb80428e180a731

[ "Apache-2.0" ]

148

2017-02-07T13:16:03.000Z

2022-03-26T02:21:56.000Z

esmvaltool/diag_scripts/ensclus/ens_anom.py

yifatdzigan/ESMValTool

83320b0e0b24ddde965599961bb80428e180a731

[ "Apache-2.0" ]

2,026

2017-02-03T12:57:13.000Z

2022-03-31T15:11:51.000Z

esmvaltool/diag_scripts/ensclus/ens_anom.py

yifatdzigan/ESMValTool

83320b0e0b24ddde965599961bb80428e180a731

[ "Apache-2.0" ]

113

2017-01-27T13:10:19.000Z

2022-02-03T13:42:11.000Z

"""Computation of ensemble anomalies based on a desired value.""" import os import numpy as np from scipy import stats # User-defined packages from read_netcdf import read_iris, save_n_2d_fields from sel_season_area import sel_area, sel_season def ens_anom(filenames, dir_output, name_outputs, varname, numens, season, area, extreme): """Ensemble anomalies. Computation of the ensemble anomalies based on the desired value from the input variable (it can be the percentile, mean, maximum, standard deviation or trend) OUTPUT: NetCDF files of ensemble mean of climatology, selected value and anomaly maps. """ print('The name of the output files will be <variable>_{0}.txt' .format(name_outputs)) print('Number of ensemble members: {0}'.format(numens)) outfiles = [] # Reading the netCDF file of 3Dfield, for all the ensemble members var_ens = [] for ens in range(numens): ifile = filenames[ens] # print('ENSEMBLE MEMBER %s' %ens) var, varunits, lat, lon, dates, _ = read_iris(ifile) # Convertion from kg m-2 s-1 to mm/day if varunits == 'kg m-2 s-1': var = var * 86400 # there are 86400 seconds in a day varunits = 'mm/day' # Selecting a season (DJF,DJFM,NDJFM,JJA) var_season, _ = sel_season(var, dates, season) # Selecting only [latS-latN, lonW-lonE] box region var_area, lat_area, lon_area = sel_area(lat, lon, var_season, area) var_ens.append(var_area) if varunits == 'kg m-2 s-1': print('\nPrecipitation rate units were converted from kg m-2 s-1 ' 'to mm/day') print('The variable is {0} ({1})'.format(varname, varunits)) print('Original var shape: (time x lat x lon)={0}'.format(var.shape)) print('var shape after selecting season {0} and area {1}: ' '(time x lat x lon)={2}'.format(season, area, var_area.shape)) if extreme == 'mean': # Compute the time mean over the entire period, for each ens member varextreme_ens = [np.nanmean(var_ens[i], axis=0) for i in range(numens)] elif len(extreme.split("_")) == 2: # Compute the chosen percentile over the period, for each ens member quant = int(extreme.partition("th")[0]) varextreme_ens = [np.nanpercentile(var_ens[i], quant, axis=0) for i in range(numens)] elif extreme == 'maximum': # Compute the maximum value over the period, for each ensemble member varextreme_ens = [np.nanmax(var_ens[i], axis=0) for i in range(numens)] elif extreme == 'std': # Compute the standard deviation over the period, for each ens member varextreme_ens = [np.nanstd(var_ens[i], axis=0) for i in range(numens)] elif extreme == 'trend': # Compute the linear trend over the period, for each ensemble member trendmap = np.empty((var_ens[0].shape[1], var_ens[0].shape[2])) trendmap_ens = [] for i in range(numens): for jla in range(var_ens[0].shape[1]): for jlo in range(var_ens[0].shape[2]): slope, _, _, _, _ = \ stats.linregress(range(var_ens[0].shape[0]), var_ens[i][:, jla, jlo]) trendmap[jla, jlo] = slope trendmap_ens.append(trendmap.copy()) varextreme_ens = trendmap_ens varextreme_ens_np = np.array(varextreme_ens) print('Anomalies are computed with respect to the {0}'.format(extreme)) # Compute and save the anomalies with respect to the ensemble ens_anomalies = varextreme_ens_np - np.nanmean(varextreme_ens_np, axis=0) varsave = 'ens_anomalies' ofile = os.path.join(dir_output, 'ens_anomalies_{0}.nc' .format(name_outputs)) # print(ofile) print('ens_anomalies shape: (numens x lat x lon)={0}' .format(ens_anomalies.shape)) save_n_2d_fields(lat_area, lon_area, ens_anomalies, varsave, varunits, ofile) outfiles.append(ofile) # Compute and save the climatology vartimemean_ens = [np.mean(var_ens[i], axis=0) for i in range(numens)] ens_climatologies = np.array(vartimemean_ens) varsave = 'ens_climatologies' ofile = os.path.join(dir_output, 'ens_climatologies_{0}.nc' .format(name_outputs)) save_n_2d_fields(lat_area, lon_area, ens_climatologies, varsave, varunits, ofile) outfiles.append(ofile) ens_extreme = varextreme_ens_np varsave = 'ens_extreme' ofile = os.path.join(dir_output, 'ens_extreme_{0}.nc'.format(name_outputs)) save_n_2d_fields(lat_area, lon_area, ens_extreme, varsave, varunits, ofile) outfiles.append(ofile) return outfiles

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0

null

0

null

0

1

0

a3b714ec9b000678e3e81df98484d9da903f0406

24,074

py

Python

pytition/petition/models.py

Te-k/Pytition

16ebce01b491b72ed387709d9b705f7cb0d5476f

[ "BSD-3-Clause" ]

null

pytition/petition/models.py

Te-k/Pytition

16ebce01b491b72ed387709d9b705f7cb0d5476f

[ "BSD-3-Clause" ]

null

pytition/petition/models.py

Te-k/Pytition

16ebce01b491b72ed387709d9b705f7cb0d5476f

[ "BSD-3-Clause" ]

null

from django.db import models from django.utils.html import mark_safe, strip_tags from django.utils.text import slugify from django.utils.translation import ugettext as _ from django.utils.translation import ugettext_lazy from django.core.exceptions import ValidationError from django.db.models.signals import post_save, post_delete from django.dispatch import receiver from django.conf import settings from django.contrib.auth.hashers import get_hasher from django.db import transaction from django.urls import reverse from django.db.models import Q from tinymce import models as tinymce_models from colorfield.fields import ColorField import html class Petition(models.Model): NO = "no gradient" RIGHT = "to right" BOTTOM = "to bottom" BOTTOM_RIGHT = "to bottom right" BOTTOM_LEFT = "to bottom left" LINEAR_GRADIENT_CHOICES = ( (NO, "no gradient"), (RIGHT, "to right"), (BOTTOM, "to bottom"), (BOTTOM_RIGHT, "to bottom right"), (BOTTOM_LEFT, "to bottom left") ) MAIL = "MAIL" POST = "POST" GET = "GET" NEWSLETTER_SUBSCRIBE_METHOD_CHOICES = ( (MAIL, "MAIL"), (POST, "POST"), (GET, "GET") ) title = models.TextField(verbose_name=ugettext_lazy("Title")) text = tinymce_models.HTMLField(blank=True) side_text = tinymce_models.HTMLField(blank=True) target = models.IntegerField(default=500) linear_gradient_direction = models.CharField(choices=LINEAR_GRADIENT_CHOICES, max_length=15, default=NO, blank=True) gradient_from = ColorField(blank=True) gradient_to = ColorField(blank=True) bgcolor = ColorField(blank=True) footer_text = tinymce_models.HTMLField(blank=True) footer_links = tinymce_models.HTMLField(blank=True) twitter_description = models.CharField(max_length=200, blank=True) twitter_image = models.CharField(max_length=500, blank=True) has_newsletter = models.BooleanField(default=False) newsletter_subscribe_http_data = models.TextField(blank=True) newsletter_subscribe_http_mailfield = models.CharField(max_length=100, blank=True) newsletter_subscribe_http_url = models.CharField(max_length=1000, blank=True) newsletter_subscribe_mail_subject = models.CharField(max_length=1000, blank=True) newsletter_subscribe_mail_from = models.CharField(max_length=500, blank=True) newsletter_subscribe_mail_to = models.CharField(max_length=500, blank=True) newsletter_subscribe_method = models.CharField(choices=NEWSLETTER_SUBSCRIBE_METHOD_CHOICES, max_length=4, default=MAIL) newsletter_subscribe_mail_smtp_host = models.CharField(max_length=100, default='localhost', blank=True) newsletter_subscribe_mail_smtp_port = models.IntegerField(default=25, blank=True) newsletter_subscribe_mail_smtp_user = models.CharField(max_length=200, blank=True) newsletter_subscribe_mail_smtp_password = models.CharField(max_length=200, blank=True) newsletter_subscribe_mail_smtp_tls = models.BooleanField(default=False) newsletter_subscribe_mail_smtp_starttls = models.BooleanField(default=False) org_twitter_handle = models.CharField(max_length=20, blank=True) published = models.BooleanField(default=False) newsletter_text = models.CharField(max_length=1000, blank=True) sign_form_footer = models.TextField(blank=True) confirmation_email_sender = models.CharField(max_length=100, blank=True) confirmation_email_smtp_host = models.CharField(max_length=100, default='localhost', blank=True) confirmation_email_smtp_port = models.IntegerField(default=25, blank=True) confirmation_email_smtp_user = models.CharField(max_length=200, blank=True) confirmation_email_smtp_password = models.CharField(max_length=200, blank=True) confirmation_email_smtp_tls = models.BooleanField(default=False) confirmation_email_smtp_starttls = models.BooleanField(default=False) use_custom_email_settings = models.BooleanField(default=False) salt = models.TextField(blank=True) slugs = models.ManyToManyField('SlugModel', blank=True, through='SlugOwnership') def prepopulate_from_template(self, template): for field in self._meta.fields: if hasattr(self, field.name) and hasattr(template, field.name): template_value = getattr(template, field.name) if template_value is not None and template_value != "": setattr(self, field.name, template_value) def save(self, *args, **kwargs): super().save(*args, **kwargs) if not self.salt: hasher = get_hasher() self.salt = hasher.salt().decode('utf-8') super().save() def slugify(self): if self.slugs.count() == 0: slugtext = slugify(self.raw_title) # let's search for slug collisions filters = {'slugs__slug': slugtext} if self.organization_set.count() > 0: org = self.organization_set.first() filters.update({'organization__name': org.name}) else: user = self.pytitionuser_set.first() filters.update({'pytitionuser__user__username': user.user.username}) results = Petition.objects.filter(**filters) if results.count() > 0: raise ValueError(_("This slug is already used by another petition from this organization/user")) slug = SlugModel(slug=slugify(slugtext)) slug.save() self.slugs.add(slug) self.save() @classmethod def by_id(cls, id): try: return Petition.objects.get(pk=id) except Petition.DoesNotExist: return None def get_signature_number(self, confirmed=None): signatures = self.signature_set if confirmed is not None: signatures = signatures.filter(confirmed=confirmed) return signatures.count() def already_signed(self, email): signature_number = Signature.objects.filter(petition = self.id)\ .filter(confirmed = True).filter(email = email).count() return signature_number > 0 def confirm_signature(self, conf_hash): signature = Signature.objects.filter(petition=self.id).get(confirmation_hash=conf_hash) if signature: # Now confirm the signature corresponding to this hash signature.confirm() signature.save() return _("Thank you for confirming your signature!") else: return None def add_slug(self, slugtext): with transaction.atomic(): slugtext = slugify(slugtext) slug = SlugModel.objects.create(slug=slugtext) if self.owner_type == "org": SlugOwnership.objects.create(slug=slug, petition=self, organization=self.owner) elif self.owner_type == "user": SlugOwnership.objects.create(slug=slug, petition=self, user=self.owner) else: raise ValueError(_("This petition has no owner, cannot add slug!")) def del_slug(self, slug): slug.delete() def publish(self): self.published = True self.save() def unpublish(self): self.published = False self.save() @property def owner_type(self): if self.organization_set.count() > 0: return "org" elif self.pytitionuser_set.count() > 0: return "user" else: return "no_owner" @property def owner(self): if self.organization_set.count() > 0: return self.organization_set.first() elif self.pytitionuser_set.count() > 0: return self.pytitionuser_set.first() else: return None @property def signature_number(self): return self.get_signature_number(True) @property def raw_twitter_description(self): return html.unescape(mark_safe(strip_tags(self.twitter_description))) @property def raw_text(self): return html.unescape(mark_safe(strip_tags(self.text))) @property def raw_title(self): return html.unescape(mark_safe(strip_tags(self.title).strip())) def __str__(self): return self.raw_title def __repr__(self): return self.raw_title @property def url(self): slugs = self.slugs.all() if len(slugs) == 0: # If there is no slug, ugly url return reverse('detail', kwargs={'petition_id': self.id}) else: if self.organization_set.count() > 0: # This petition is owned by an Organization org = self.organization_set.first() return reverse("slug_show_petition", kwargs={"orgslugname": org.slugname, "petitionname": slugs[0]}) elif self.pytitionuser_set.count() > 0: # This petition is owned by a PytitionUser user = self.pytitionuser_set.first() return reverse("slug_show_petition", kwargs={"username": user.user.username, "petitionname": slugs[0]}) else: # This is a BUG! raise ValueError(_("This petition is buggy. Sorry about that!")) class SlugOwnership(models.Model): petition = models.ForeignKey(Petition, on_delete=models.CASCADE) slug = models.ForeignKey('SlugModel', on_delete=models.CASCADE) user = models.ForeignKey('PytitionUser', on_delete=models.CASCADE, blank=True, null=True, default=None) organization = models.ForeignKey('Organization', on_delete=models.CASCADE, blank=True, null=True, default=None) class Meta: constraints = [ models.UniqueConstraint(fields=['slug', 'organization'], name="unique_slugnameperorg", condition=Q(user=None)), models.UniqueConstraint(fields=['slug', 'user'], name="unique_slugnameperuser", condition=Q(organization=None)), ] class Signature(models.Model): first_name = models.CharField(max_length=50, verbose_name=ugettext_lazy("First name")) last_name = models.CharField(max_length=50, verbose_name=ugettext_lazy("Last name")) phone = models.CharField(max_length=20, blank=True, verbose_name=ugettext_lazy("Phone number")) email = models.EmailField(verbose_name=ugettext_lazy("Email address")) confirmation_hash = models.CharField(max_length=128) confirmed = models.BooleanField(default=False, verbose_name=ugettext_lazy("Confirmed")) petition = models.ForeignKey(Petition, on_delete=models.CASCADE, verbose_name=ugettext_lazy("Petition")) subscribed_to_mailinglist = models.BooleanField(default=False, verbose_name=ugettext_lazy("Subscribed to mailing list")) date = models.DateTimeField(blank=True, auto_now_add=True, verbose_name=ugettext_lazy("Date")) ipaddress = models.TextField(blank=True, null=True) def clean(self): if self.petition.already_signed(self.email): if self.petition.signature_set.filter(email = self.email).get(confirmed = True).id != self.id: raise ValidationError(_("You already signed the petition")) def save(self, *args, **kwargs): self.clean() if self.confirmed: # invalidating other signatures from same email Signature.objects.filter(petition=self.petition).filter(email=self.email)\ .exclude(id=self.id).delete() super().save(*args, **kwargs) def confirm(self): self.confirmed = True def __str__(self): return html.unescape("[{}:{}] {} {}".format(self.petition.id, "OK" if self.confirmed else "..", self.first_name, self.last_name)) def __repr__(self): return html.unescape("[{}:{}] {} {}".format(self.petition.id, "OK" if self.confirmed else "..", self.first_name, self.last_name)) class PetitionTemplate(models.Model): NO = "no gradient" RIGHT = "to right" BOTTOM = "to bottom" BOTTOM_RIGHT = "to bottom right" BOTTOM_LEFT = "to bottom left" LINEAR_GRADIENT_CHOICES = ( (NO, "no gradient"), (RIGHT, "to right"), (BOTTOM, "to bottom"), (BOTTOM_RIGHT, "to bottom right"), (BOTTOM_LEFT, "to bottom left") ) MAIL = "MAIL" POST = "POST" GET = "GET" NEWSLETTER_SUBSCRIBE_METHOD_CHOICES = ( (MAIL, "MAIL"), (POST, "POST"), (GET, "GET") ) name = models.CharField(max_length=50, verbose_name=ugettext_lazy("Name"), db_index=True) text = tinymce_models.HTMLField(blank=True) side_text = tinymce_models.HTMLField(blank=True) target = models.IntegerField(blank=True, null=True) linear_gradient_direction = models.CharField(choices=LINEAR_GRADIENT_CHOICES, max_length=15, default=NO, blank=True) gradient_from = ColorField(blank=True) gradient_to = ColorField(blank=True) bgcolor = ColorField(blank=True) footer_text = tinymce_models.HTMLField(blank=True) footer_links = tinymce_models.HTMLField(blank=True) twitter_description = models.CharField(max_length=200, blank=True) twitter_image = models.CharField(max_length=500, blank=True) has_newsletter = models.BooleanField(default=False) newsletter_subscribe_http_data = models.TextField(blank=True) newsletter_subscribe_http_mailfield = models.CharField(max_length=100, blank=True) newsletter_subscribe_http_url = models.CharField(max_length=1000, blank=True) newsletter_subscribe_mail_subject = models.CharField(max_length=1000, blank=True) newsletter_subscribe_mail_from = models.EmailField(max_length=500, blank=True) newsletter_subscribe_mail_to = models.EmailField(max_length=500, blank=True) newsletter_subscribe_method = models.CharField(choices=NEWSLETTER_SUBSCRIBE_METHOD_CHOICES, max_length=4, default=MAIL) newsletter_subscribe_mail_smtp_host = models.CharField(max_length=100, default='localhost', blank=True) newsletter_subscribe_mail_smtp_port = models.IntegerField(default=25) newsletter_subscribe_mail_smtp_user = models.CharField(max_length=200, blank=True) newsletter_subscribe_mail_smtp_password = models.CharField(max_length=200, blank=True) newsletter_subscribe_mail_smtp_tls = models.BooleanField(default=False) newsletter_subscribe_mail_smtp_starttls = models.BooleanField(default=False) org_twitter_handle = models.CharField(max_length=20, blank=True) newsletter_text = models.CharField(max_length=1000, blank=True) sign_form_footer = models.TextField(blank=True) confirmation_email_sender = models.EmailField(max_length=100, blank=True) confirmation_email_smtp_host = models.CharField(max_length=100, default='localhost', blank=True) confirmation_email_smtp_port = models.IntegerField(default=25, blank=True) confirmation_email_smtp_user = models.CharField(max_length=200, blank=True) confirmation_email_smtp_password = models.CharField(max_length=200, blank=True) confirmation_email_smtp_tls = models.BooleanField(default=False) confirmation_email_smtp_starttls = models.BooleanField(default=False) use_custom_email_settings = models.BooleanField(default=False) def __str__(self): return self.name def __repr__(self): return self.name class Meta: index_together = ["id", ] class SlugModel(models.Model): slug = models.SlugField(max_length=200) class Meta: constraints = [ models.UniqueConstraint(fields=['slug'], name='unique_slugname') ] def __str__(self): return self.slug def __repr__(self): return self.slug class Organization(models.Model): name = models.CharField(max_length=200, verbose_name=ugettext_lazy("Name"), unique=True) petition_templates = models.ManyToManyField(PetitionTemplate, through='TemplateOwnership', through_fields=['organization', 'template'], blank=True, verbose_name=ugettext_lazy("Petition templates")) petitions = models.ManyToManyField(Petition, blank=True, verbose_name=ugettext_lazy("Petitions")) default_template = models.ForeignKey(PetitionTemplate, blank=True, null=True, related_name='+', verbose_name=ugettext_lazy("Default petition template"), to_field='id', on_delete=models.SET_NULL) slugname = models.SlugField(max_length=200, unique=True) def drop(self): with transaction.atomic(): petitions = list(self.petitions.all()) templates = list(self.petition_templates.all()) self.delete() for petition in petitions: petition.delete() for template in templates: template.delete() def add_member(self, member): member.organizations.add(self) permission = Permission.objects.create(organization=self) permission.save() member.permissions.add(permission) member.save() def __str__(self): return self.name def __repr__(self): return self.name def save(self, *args, **kwargs): if not self.slugname: self.slugname = slugify(self.name) super(Organization, self).save(*args, **kwargs) @property def kind(self): return "org" @property def fullname(self): return self.name def save(self, *args, **kwargs): self.slugname = slugify(self.name) super(Organization, self).save(*args, **kwargs) class Permission(models.Model): organization = models.ForeignKey(Organization, on_delete=models.CASCADE, verbose_name=ugettext_lazy("Organization related to these permissions")) can_add_members = models.BooleanField(default=False) can_remove_members = models.BooleanField(default=False) can_create_petitions = models.BooleanField(default=False) can_modify_petitions = models.BooleanField(default=False) can_delete_petitions = models.BooleanField(default=False) can_create_templates = models.BooleanField(default=False) can_modify_templates = models.BooleanField(default=False) can_delete_templates = models.BooleanField(default=False) can_view_signatures = models.BooleanField(default=False) can_modify_signatures = models.BooleanField(default=False) can_delete_signatures = models.BooleanField(default=False) can_modify_permissions = models.BooleanField(default=False) def set_all(self, value): self.can_add_members = value self.can_remove_members = value self.can_create_petitions = value self.can_modify_petitions = value self.can_delete_petitions = value self.can_create_templates = value self.can_modify_templates = value self.can_delete_templates = value self.can_view_signatures = value self.can_modify_signatures = value self.can_delete_signatures = value self.can_modify_permissions = value self.save() def __str__(self): ret = "{orgname} : ".format(orgname=self.organization.name) if self.user.count() > 0: ret = ret + "{username}".format(username=self.user.all()[0].name) else: ret = ret + "None" return ret def __repr__(self): return self.__str__() class PytitionUser(models.Model): petitions = models.ManyToManyField(Petition, blank=True) organizations = models.ManyToManyField(Organization, related_name="members", blank=True) user = models.OneToOneField(settings.AUTH_USER_MODEL, on_delete=models.CASCADE, related_name="pytitionuser") permissions = models.ManyToManyField(Permission, related_name="user", blank=True) invitations = models.ManyToManyField(Organization, related_name="invited", blank=True) petition_templates = models.ManyToManyField(PetitionTemplate, blank=True, through='TemplateOwnership', through_fields=['user', 'template'], verbose_name=ugettext_lazy("Petition templates")) default_template = models.ForeignKey(PetitionTemplate, blank=True, null=True, related_name='+', verbose_name=ugettext_lazy("Default petition template"), to_field='id', on_delete=models.SET_NULL) def has_right(self, right, petition=None, org=None): if petition: if petition in self.petitions.all(): return True try: if not org: org = Organization.objects.get(petitions=petition, members=self) permissions = self.permissions.get(organization=org) return getattr(permissions, right) except: return False if org: try: permissions = self.permissions.get(organization=org) return getattr(permissions, right) except: return False return False def drop(self): with transaction.atomic(): orgs = list(self.organizations.all()) petitions = list(self.petitions.all()) templates = list(self.petition_templates.all()) self.delete() for org in orgs: if org.members.count() == 0: org.drop() for petition in petitions: petition.delete() for template in templates: template.delete() @property def is_authenticated(self): return self.user.is_authenticated @property def name(self): return self.username @property def username(self): return self.user.username @property def get_full_name(self): return self.user.get_full_name() @property def fullname(self): return self.get_full_name @property def kind(self): return "user" def __str__(self): return self.get_full_name def __repr__(self): return self.get_full_name @receiver(post_save, sender=settings.AUTH_USER_MODEL) def create_user_profile(sender, instance, created, **kwargs): if created: PytitionUser.objects.create(user=instance) @receiver(post_save, sender=settings.AUTH_USER_MODEL) def save_user_profile(sender, instance, **kwargs): instance.pytitionuser.save() @receiver(post_save, sender=Organization) def save_user_profile(sender, instance, **kwargs): if not instance.slugname: slugtext = slugify(instance.name) instance.slugname = slugtext instance.save() @receiver(post_delete, sender=PytitionUser) def post_delete_user(sender, instance, *args, **kwargs): if instance.user: # just in case user is not specified instance.user.delete() class TemplateOwnership(models.Model): user = models.ForeignKey(PytitionUser, blank=True, null=True, on_delete=models.CASCADE) organization = models.ForeignKey(Organization, blank=True, null=True, on_delete=models.CASCADE) template = models.ForeignKey(PetitionTemplate, to_field='id', on_delete=models.CASCADE) def clean(self): if self.user is None and self.organization is None: raise ValidationError(_("The template needs to be owned by a User or an Organization." "It cannot hang around alone by itself.")) #class Meta: # unique_together = (("user", "template"), ("organization", "template"))

40.734349

124

0.662748

2,715

24,074

5.673665

0.108287

0.043236

0.040898

0.054531

0.610556

0.560439

0.481823

0.450597

0.407492

0.386783

0

0.008396

0.238058

24,074

590

125

40.80339

0.83138

0.015785

0

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0

0.057805

0.002998

0

1

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false

0.008282

0.033126

0.05176

0.559006

0

null

0

null

0

1

0

a3b72847ef50516acce4d8d4114c3432f306c66d

4,026

py

Python

bin/socialhistory.py

JohnShullTopDev/generating-traning-data-for-healthcare-machine-learningcare-

d0ffb26e1b99204a796df905b50c8caf01417f69

[ "Apache-2.0" ]

1

2019-11-11T11:21:08.000Z

bin/socialhistory.py

JohnShullTopDev/generating-traning-data-for-healthcare-machine-learningcare-

d0ffb26e1b99204a796df905b50c8caf01417f69

[ "Apache-2.0" ]

null

bin/socialhistory.py

JohnShullTopDev/generating-traning-data-for-healthcare-machine-learningcare-

d0ffb26e1b99204a796df905b50c8caf01417f69

[ "Apache-2.0" ]

1

2020-01-28T03:48:14.000Z

import csv from testdata import SOCIALHISTORY_FILE from testdata import rndDate from patient import Patient SMOKINGCODES = { '428041000124106': 'Current some day smoker', '266919005' : 'Never smoker', '449868002' : 'Current every day smoker', '266927001' : 'Unknown if ever smoked', '8517006' : 'Former smoker' } class SocialHistory(object): """Create instances of SocialHistory; also maintains socialHistory by patient id""" socialHistories = {} # Dictionary of socialHistory by patient ID @classmethod def load(cls): """Loads patient SocialHistory""" # Loop through socialHistories and build patient socialHistory lists: histories = csv.reader(open(SOCIALHISTORY_FILE, 'U'), dialect='excel-tab') header = next(histories) for history in histories: cls(dict(zip(header, history))) # Create a socialHistory instance def __init__(self, p): self.pid = p['PID'] self.id = p['ID'] self.smokingStatusCode = p['SMOKINGSTATUSCODE'] self.smokingStatusText = SMOKINGCODES[self.smokingStatusCode] # Append socialHistory to the patient's socialHistory list: if self.pid in self.__class__.socialHistories: raise "Found >1 socialHistory for a patient" else: self.__class__.socialHistories[self.pid] = self def toJSON(self, prefix=""): if prefix: prefix += "-" patient = Patient.mpi[self.pid] return { "request": { "method": "PUT", "url": "Observation/" + prefix + "smokingstatus-" + self.id }, "resource": { "id": prefix + "smokingstatus-" + self.id, "resourceType": "Observation", "status": "final", "identifier": [ { "use" : "official", "system": "http://www.bmc.nl/zorgportal/identifiers/observations", "value" : prefix + self.id } ], "text": { "status": "generated", "div": '<div xmlns="http://www.w3.org/1999/xhtml">' + 'Tobacco smoking status: %s</div>'%self.smokingStatusText }, "performer": [ { "reference": "Practitioner/" + prefix + "Practitioner-" + patient.gp } ], "effectiveDateTime": rndDate(2016).isoformat(), "code": { "coding": [ { "system" : "http://loinc.org", "code" : "72166-2", "display": "Tobacco smoking status" } ], "text": "Tobacco smoking status" }, "subject": { "reference": "Patient/" + prefix + self.pid }, "category": [ { "coding": [ { "system" : "http://hl7.org/fhir/observation-category", "code" : "social-history", "display": "Social History" } ], "text": "Social History" } ], "valueCodeableConcept": { "coding": [ { "system" : "http://snomed.info/sct", "code" : self.smokingStatusCode, "display": self.smokingStatusText } ], "text": self.smokingStatusText } } }

35.946429

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null

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null

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0

a3b9cafed89d7582e18fd4f82c78858c2882f5b3

1,453

py

Python

lib/spack/spack/test/cache_fetch.py

LiamBindle/spack

e90d5ad6cfff2ba3de7b537d6511adccd9d5fcf1

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2,360

2017-11-06T08:47:01.000Z

2022-03-31T14:45:33.000Z

lib/spack/spack/test/cache_fetch.py

LiamBindle/spack

e90d5ad6cfff2ba3de7b537d6511adccd9d5fcf1

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

13,838

2017-11-04T07:49:45.000Z

2022-03-31T23:38:39.000Z

lib/spack/spack/test/cache_fetch.py

LiamBindle/spack

e90d5ad6cfff2ba3de7b537d6511adccd9d5fcf1

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

1,793

2017-11-04T07:45:50.000Z

2022-03-30T14:31:53.000Z

# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) import os import pytest from llnl.util.filesystem import mkdirp, touch import spack.config from spack.fetch_strategy import CacheURLFetchStrategy, NoCacheError from spack.stage import Stage @pytest.mark.parametrize('_fetch_method', ['curl', 'urllib']) def test_fetch_missing_cache(tmpdir, _fetch_method): """Ensure raise a missing cache file.""" testpath = str(tmpdir) with spack.config.override('config:url_fetch_method', _fetch_method): fetcher = CacheURLFetchStrategy(url='file:///not-a-real-cache-file') with Stage(fetcher, path=testpath): with pytest.raises(NoCacheError, match=r'No cache'): fetcher.fetch() @pytest.mark.parametrize('_fetch_method', ['curl', 'urllib']) def test_fetch(tmpdir, _fetch_method): """Ensure a fetch after expanding is effectively a no-op.""" testpath = str(tmpdir) cache = os.path.join(testpath, 'cache.tar.gz') touch(cache) url = 'file:///{0}'.format(cache) with spack.config.override('config:url_fetch_method', _fetch_method): fetcher = CacheURLFetchStrategy(url=url) with Stage(fetcher, path=testpath) as stage: source_path = stage.source_path mkdirp(source_path) fetcher.fetch()

35.439024

76

0.705437

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1,453

5.410811

0.432432

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0.051948

0.333666

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0

0.307692

0

null

0

null

0

1

0

a3bac2f51025032288427c9fc39e3497207cc25d

2,201

py

Python

temp_range_sql.py

hanhanwu/Hanhan-Spark-Python

a04c33100742acffa2ad11d1937ea05c44688427

[ "MIT" ]

45

2016-03-18T07:57:53.000Z

2022-03-20T07:14:15.000Z

temp_range_sql.py

hanhanwu/Hanhan-Spark-Python

a04c33100742acffa2ad11d1937ea05c44688427

[ "MIT" ]

null

temp_range_sql.py

hanhanwu/Hanhan-Spark-Python

a04c33100742acffa2ad11d1937ea05c44688427

[ "MIT" ]

16

2016-07-07T16:47:46.000Z

2020-05-04T17:38:40.000Z

__author__ = 'hanhanw' import sys from pyspark import SparkConf, SparkContext from pyspark.sql.context import SQLContext from pyspark.sql.types import StructType, StructField, StringType, DoubleType conf = SparkConf().setAppName("temp range sql") sc = SparkContext(conf=conf) sqlContext = SQLContext(sc) assert sc.version >= '1.5.1' inputs1 = sys.argv[1] output = sys.argv[2] def get_range(recordings): recordings.registerTempTable('Recordings') dfrange = sqlContext.sql(""" SELECT r1.DateTime, r1.StationID, (r1.DataValue-r2.DataValue) AS Range FROM (SELECT StationID, DateTime, Observation, DataValue FROM Recordings WHERE Observation='TMAX') r1 JOIN (SELECT StationID, DateTime, Observation, DataValue FROM Recordings WHERE Observation='TMIN') r2 ON (r1.StationID = r2.StationID AND r1.DateTime = r2.DateTime) """) dfrange.registerTempTable('RangeTable') df_maxrange = sqlContext.sql(""" SELECT DateTime, MAX(Range) AS MaxRange FROM RangeTable GROUP BY DateTime """) df_maxrange.registerTempTable('MaxRange') df_result = sqlContext.sql(""" SELECT t1.DateTime as DateTime, t1.StationID as StationID, t2.MaxRange as MaxRange FROM RangeTable t1 JOIN MaxRange t2 ON (t1.DateTime = t2.DateTime AND t1.Range = t2.MaxRange) """) return df_result def main(): temp_schema = StructType([ StructField('StationID', StringType(), False), StructField('DateTime', StringType(), False), StructField('Observation', StringType(), False), StructField('DataValue', DoubleType(), False), StructField('MFlag', StringType(), True), StructField('QFlag', StringType(), True), StructField('SFlag', StringType(), True), StructField('OBSTime', StringType(), True), ]) df = sqlContext.read.format('com.databricks.spark.csv').options(header='false').load(inputs1, schema=temp_schema) df = df.filter(df.QFlag == '') dfrange = get_range(df) result = dfrange.rdd.map(lambda r: str(r.DateTime)+' '+str(r.StationID)+' '+str(r.MaxRange)) outdata = result.sortBy(lambda r: r[0]).coalesce(1) outdata.saveAsTextFile(output) if __name__ == "__main__": main()

31.898551

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5.902724

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0.044825

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2,201

68

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false

0

0.074074

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0.12963

0

null

0

null

0

1

0

a3bca9436abafd191ec47379ebb1db10a4043237

11,326

py

Python

desktop/core/ext-py/openpyxl-2.3.0-b2/openpyxl/drawing/shape.py

kokosing/hue

2307f5379a35aae9be871e836432e6f45138b3d9

[ "Apache-2.0" ]

3

2018-01-29T14:16:02.000Z

2019-02-05T21:33:05.000Z

desktop/core/ext-py/openpyxl-2.3.0-b2/openpyxl/drawing/shape.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

4

2021-03-11T04:02:00.000Z

2022-03-27T08:31:56.000Z

desktop/core/ext-py/openpyxl-2.3.0-b2/openpyxl/drawing/shape.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

2

2019-12-05T17:24:36.000Z

2021-11-22T21:21:32.000Z

from __future__ import absolute_import # Copyright (c) 2010-2015 openpyxl from openpyxl.styles.colors import Color, BLACK, WHITE from openpyxl.utils.units import ( pixels_to_EMU, EMU_to_pixels, short_color, ) from openpyxl.compat import deprecated from openpyxl.xml.functions import Element, SubElement, tostring from openpyxl.xml.constants import ( DRAWING_NS, SHEET_DRAWING_NS, CHART_NS, CHART_DRAWING_NS, PKG_REL_NS ) from openpyxl.compat.strings import safe_string class Shape(object): """ a drawing inside a chart coordiantes are specified by the user in the axis units """ MARGIN_LEFT = 6 + 13 + 1 MARGIN_BOTTOM = 17 + 11 FONT_WIDTH = 7 FONT_HEIGHT = 8 ROUND_RECT = 'roundRect' RECT = 'rect' # other shapes to define : ''' "line" "lineInv" "triangle" "rtTriangle" "diamond" "parallelogram" "trapezoid" "nonIsoscelesTrapezoid" "pentagon" "hexagon" "heptagon" "octagon" "decagon" "dodecagon" "star4" "star5" "star6" "star7" "star8" "star10" "star12" "star16" "star24" "star32" "roundRect" "round1Rect" "round2SameRect" "round2DiagRect" "snipRoundRect" "snip1Rect" "snip2SameRect" "snip2DiagRect" "plaque" "ellipse" "teardrop" "homePlate" "chevron" "pieWedge" "pie" "blockArc" "donut" "noSmoking" "rightArrow" "leftArrow" "upArrow" "downArrow" "stripedRightArrow" "notchedRightArrow" "bentUpArrow" "leftRightArrow" "upDownArrow" "leftUpArrow" "leftRightUpArrow" "quadArrow" "leftArrowCallout" "rightArrowCallout" "upArrowCallout" "downArrowCallout" "leftRightArrowCallout" "upDownArrowCallout" "quadArrowCallout" "bentArrow" "uturnArrow" "circularArrow" "leftCircularArrow" "leftRightCircularArrow" "curvedRightArrow" "curvedLeftArrow" "curvedUpArrow" "curvedDownArrow" "swooshArrow" "cube" "can" "lightningBolt" "heart" "sun" "moon" "smileyFace" "irregularSeal1" "irregularSeal2" "foldedCorner" "bevel" "frame" "halfFrame" "corner" "diagStripe" "chord" "arc" "leftBracket" "rightBracket" "leftBrace" "rightBrace" "bracketPair" "bracePair" "straightConnector1" "bentConnector2" "bentConnector3" "bentConnector4" "bentConnector5" "curvedConnector2" "curvedConnector3" "curvedConnector4" "curvedConnector5" "callout1" "callout2" "callout3" "accentCallout1" "accentCallout2" "accentCallout3" "borderCallout1" "borderCallout2" "borderCallout3" "accentBorderCallout1" "accentBorderCallout2" "accentBorderCallout3" "wedgeRectCallout" "wedgeRoundRectCallout" "wedgeEllipseCallout" "cloudCallout" "cloud" "ribbon" "ribbon2" "ellipseRibbon" "ellipseRibbon2" "leftRightRibbon" "verticalScroll" "horizontalScroll" "wave" "doubleWave" "plus" "flowChartProcess" "flowChartDecision" "flowChartInputOutput" "flowChartPredefinedProcess" "flowChartInternalStorage" "flowChartDocument" "flowChartMultidocument" "flowChartTerminator" "flowChartPreparation" "flowChartManualInput" "flowChartManualOperation" "flowChartConnector" "flowChartPunchedCard" "flowChartPunchedTape" "flowChartSummingJunction" "flowChartOr" "flowChartCollate" "flowChartSort" "flowChartExtract" "flowChartMerge" "flowChartOfflineStorage" "flowChartOnlineStorage" "flowChartMagneticTape" "flowChartMagneticDisk" "flowChartMagneticDrum" "flowChartDisplay" "flowChartDelay" "flowChartAlternateProcess" "flowChartOffpageConnector" "actionButtonBlank" "actionButtonHome" "actionButtonHelp" "actionButtonInformation" "actionButtonForwardNext" "actionButtonBackPrevious" "actionButtonEnd" "actionButtonBeginning" "actionButtonReturn" "actionButtonDocument" "actionButtonSound" "actionButtonMovie" "gear6" "gear9" "funnel" "mathPlus" "mathMinus" "mathMultiply" "mathDivide" "mathEqual" "mathNotEqual" "cornerTabs" "squareTabs" "plaqueTabs" "chartX" "chartStar" "chartPlus" ''' @deprecated("Chart Drawings need a complete rewrite") def __init__(self, chart, coordinates=((0, 0), (1, 1)), text=None, scheme="accent1"): self.chart = chart self.coordinates = coordinates # in axis units self.text = text self.scheme = scheme self.style = Shape.RECT self.border_width = 0 self.border_color = BLACK # "F3B3C5" self.color = WHITE self.text_color = BLACK @property def border_color(self): return self._border_color @border_color.setter def border_color(self, color): self._border_color = short_color(color) @property def color(self): return self._color @color.setter def color(self, color): self._color = short_color(color) @property def text_color(self): return self._text_color @text_color.setter def text_color(self, color): self._text_color = short_color(color) @property def border_width(self): return self._border_width @border_width.setter def border_width(self, w): self._border_width = w @property def coordinates(self): """Return coordindates in axis units""" return self._coordinates @coordinates.setter def coordinates(self, coords): """ set shape coordinates in percentages (left, top, right, bottom) """ # this needs refactoring to reflect changes in charts self.axis_coordinates = coords (x1, y1), (x2, y2) = coords # bottom left, top right drawing_width = pixels_to_EMU(self.chart.drawing.width) drawing_height = pixels_to_EMU(self.chart.drawing.height) plot_width = drawing_width * self.chart.width plot_height = drawing_height * self.chart.height margin_left = self.chart._get_margin_left() * drawing_width xunit = plot_width / self.chart.get_x_units() margin_top = self.chart._get_margin_top() * drawing_height yunit = self.chart.get_y_units() x_start = (margin_left + (float(x1) * xunit)) / drawing_width y_start = ((margin_top + plot_height - (float(y1) * yunit)) / drawing_height) x_end = (margin_left + (float(x2) * xunit)) / drawing_width y_end = ((margin_top + plot_height - (float(y2) * yunit)) / drawing_height) # allow user to specify y's in whatever order # excel expect y_end to be lower if y_end < y_start: y_end, y_start = y_start, y_end self._coordinates = ( self._norm_pct(x_start), self._norm_pct(y_start), self._norm_pct(x_end), self._norm_pct(y_end) ) @staticmethod def _norm_pct(pct): """ force shapes to appear by truncating too large sizes """ if pct > 1: return 1 elif pct < 0: return 0 return pct class ShapeWriter(object): """ one file per shape """ def __init__(self, shapes): self._shapes = shapes def write(self, shape_id): root = Element('{%s}userShapes' % CHART_NS) for shape in self._shapes: anchor = SubElement(root, '{%s}relSizeAnchor' % CHART_DRAWING_NS) xstart, ystart, xend, yend = shape.coordinates _from = SubElement(anchor, '{%s}from' % CHART_DRAWING_NS) SubElement(_from, '{%s}x' % CHART_DRAWING_NS).text = str(xstart) SubElement(_from, '{%s}y' % CHART_DRAWING_NS).text = str(ystart) _to = SubElement(anchor, '{%s}to' % CHART_DRAWING_NS) SubElement(_to, '{%s}x' % CHART_DRAWING_NS).text = str(xend) SubElement(_to, '{%s}y' % CHART_DRAWING_NS).text = str(yend) sp = SubElement(anchor, '{%s}sp' % CHART_DRAWING_NS, {'macro':'', 'textlink':''}) nvspr = SubElement(sp, '{%s}nvSpPr' % CHART_DRAWING_NS) SubElement(nvspr, '{%s}cNvPr' % CHART_DRAWING_NS, {'id':str(shape_id), 'name':'shape %s' % shape_id}) SubElement(nvspr, '{%s}cNvSpPr' % CHART_DRAWING_NS) sppr = SubElement(sp, '{%s}spPr' % CHART_DRAWING_NS) frm = SubElement(sppr, '{%s}xfrm' % DRAWING_NS,) # no transformation SubElement(frm, '{%s}off' % DRAWING_NS, {'x':'0', 'y':'0'}) SubElement(frm, '{%s}ext' % DRAWING_NS, {'cx':'0', 'cy':'0'}) prstgeom = SubElement(sppr, '{%s}prstGeom' % DRAWING_NS, {'prst':str(shape.style)}) SubElement(prstgeom, '{%s}avLst' % DRAWING_NS) fill = SubElement(sppr, '{%s}solidFill' % DRAWING_NS, ) SubElement(fill, '{%s}srgbClr' % DRAWING_NS, {'val':shape.color}) border = SubElement(sppr, '{%s}ln' % DRAWING_NS, {'w':str(shape._border_width)}) sf = SubElement(border, '{%s}solidFill' % DRAWING_NS) SubElement(sf, '{%s}srgbClr' % DRAWING_NS, {'val':shape.border_color}) self._write_style(sp) self._write_text(sp, shape) shape_id += 1 return tostring(root) def _write_text(self, node, shape): """ write text in the shape """ tx_body = SubElement(node, '{%s}txBody' % CHART_DRAWING_NS) SubElement(tx_body, '{%s}bodyPr' % DRAWING_NS, {'vertOverflow':'clip'}) SubElement(tx_body, '{%s}lstStyle' % DRAWING_NS) p = SubElement(tx_body, '{%s}p' % DRAWING_NS) if shape.text: r = SubElement(p, '{%s}r' % DRAWING_NS) rpr = SubElement(r, '{%s}rPr' % DRAWING_NS, {'lang':'en-US'}) fill = SubElement(rpr, '{%s}solidFill' % DRAWING_NS) SubElement(fill, '{%s}srgbClr' % DRAWING_NS, {'val':shape.text_color}) SubElement(r, '{%s}t' % DRAWING_NS).text = shape.text else: SubElement(p, '{%s}endParaRPr' % DRAWING_NS, {'lang':'en-US'}) def _write_style(self, node): """ write style theme """ style = SubElement(node, '{%s}style' % CHART_DRAWING_NS) ln_ref = SubElement(style, '{%s}lnRef' % DRAWING_NS, {'idx':'2'}) scheme_clr = SubElement(ln_ref, '{%s}schemeClr' % DRAWING_NS, {'val':'accent1'}) SubElement(scheme_clr, '{%s}shade' % DRAWING_NS, {'val':'50000'}) fill_ref = SubElement(style, '{%s}fillRef' % DRAWING_NS, {'idx':'1'}) SubElement(fill_ref, '{%s}schemeClr' % DRAWING_NS, {'val':'accent1'}) effect_ref = SubElement(style, '{%s}effectRef' % DRAWING_NS, {'idx':'0'}) SubElement(effect_ref, '{%s}schemeClr' % DRAWING_NS, {'val':'accent1'}) font_ref = SubElement(style, '{%s}fontRef' % DRAWING_NS, {'idx':'minor'}) SubElement(font_ref, '{%s}schemeClr' % DRAWING_NS, {'val':'lt1'})

27.160671

113

0.607099

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11,326

5.922598

0.349644

0.060838

0.031546

0.014421

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0.045666

0.017425

0

0.012571

0.269557

11,326

416

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27.225962

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false

0

0.044304

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0.253165

0

null

0

null

0

1

0

a3bd2daadf5e4d9e5163b4a0fc7578b8fb655779

3,118

py

Python

scripts/VCF/FILTER/subset_vcf.py

elowy01/igsr_analysis

ffea4885227c2299f886a4f41e70b6e1f6bb43da

[ "Apache-2.0" ]

3

2018-04-20T15:04:34.000Z

2022-03-30T06:36:02.000Z

scripts/VCF/FILTER/subset_vcf.py

elowy01/igsr_analysis

ffea4885227c2299f886a4f41e70b6e1f6bb43da

[ "Apache-2.0" ]

7

2019-06-06T09:22:20.000Z

2021-11-23T17:41:52.000Z

scripts/VCF/FILTER/subset_vcf.py

elowy01/igsr_analysis

ffea4885227c2299f886a4f41e70b6e1f6bb43da

[ "Apache-2.0" ]

5

2017-11-02T11:17:35.000Z

2021-12-11T19:34:09.000Z

from VcfQC import VcfQC from ReseqTrackDB import File from ReseqTrackDB import ReseqTrackDB import argparse import os import logging import datetime #get command line arguments parser = argparse.ArgumentParser(description='Script to subset a VCF by excluding the variants within the regions defined by a BED file') ''' Reseqtrack DB connection parameters ''' parser.add_argument('--hostname', type=str, required=True, help='Hostname for ReseqTrack DB' ) parser.add_argument('--username', type=str, required=True, help='User for ReseqTrack DB' ) parser.add_argument('--port', type=int, required=True, help='Port number in the ReseqTrack DB' ) parser.add_argument('--pwd', type=str, help='PWD for the ReseqTrack DB' ) parser.add_argument('--db', type=str, required=True, help='DB name in the ReseqTrack DB' ) parser.add_argument('--type', type=str, required=True, help='Type of the new VCF file' ) parser.add_argument('--vcftools_folder', type=str, required=True, help='Folder containing the VCFtools binary' ) parser.add_argument('--bgzip_folder', type=str, required=True, help='Folder containing the bgzip binary') parser.add_argument('--filename', type=str, required=True, help='Name (without the fullpath) of the VCF file that will be analysed. It assumes that the filename format is for example lc_bams.gatk.xxxx.vcf.gz, where lc_bams is the analysis group and gatk is the method used' ) parser.add_argument('--bed', type=str, required=True, help='BED file containing the coordinates to exclude' ) parser.add_argument('--outsuffix', type=str, required=True, help='Suffix for vcf output file. i.e. no_cms or no_offtarget' ) parser.add_argument('--outdir', type=str, required=True, help='Directory used to put the output files.' ) args = parser.parse_args() if __name__ == '__main__': if os.path.isdir(args.outdir) == False: raise Exception("Output dir does not exist: %s"%args.outdir) hostname=args.hostname username=args.username db=args.db port=args.port pwd=args.pwd reseqdb = ReseqTrackDB(host=hostname,user=username,port=port,pwd=pwd,db=db) file=reseqdb.fetch_file_by_filename(args.filename) #constructing the out filename now = datetime.datetime.now().strftime('%Y%m%d') bits= os.path.basename(file.name).split('.') outprefix=bits[0]+"."+bits[1]+"."+args.outsuffix+"."+now log_filename="subset_vcf_%s.log"% outprefix logger = logging.getLogger("subset_vcf") logger.setLevel(logging.INFO) # create the logging file handler fh = logging.FileHandler(log_filename) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') fh.setFormatter(formatter) # add handler to logger object logger.addHandler(fh) logger.info("Program started") vcfQC = VcfQC(vcf=file.path,bgzip_folder=args.bgzip_folder,vcftools_folder=args.vcftools_folder) vcffile=vcfQC.subset_vcf(bed=args.bed,outprefix=outprefix,outdir=args.outdir,create_index=True) f=File(path=vcffile,type=args.type,host_id=1,withdrawn=0) f.store(reseqdb,do_md5=True) logger.info("Done!.")

41.026316

275

0.735407

453

3,118

4.966887

0.335541

0.048

0.090667

0.084444

0.197333

0.115556

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0.148936

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null

0

null

0

1

0

a3bef41781bb732a7cb06f991f90aba75666a0ca

4,276

py

Python

nova/tests/unit/conductor/tasks/test_migrate.py

badock/nova-tidb

4c4591f2cd887fdc22828e12f0c297c051bbd912

[ "Apache-2.0" ]

null

nova/tests/unit/conductor/tasks/test_migrate.py

badock/nova-tidb

4c4591f2cd887fdc22828e12f0c297c051bbd912

[ "Apache-2.0" ]

null

nova/tests/unit/conductor/tasks/test_migrate.py

badock/nova-tidb

4c4591f2cd887fdc22828e12f0c297c051bbd912

[ "Apache-2.0" ]

null

# 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 mock from nova.compute import rpcapi as compute_rpcapi from nova.conductor.tasks import migrate from nova import objects from nova.scheduler import client as scheduler_client from nova.scheduler import utils as scheduler_utils from nova import test from nova.tests.unit.conductor.test_conductor import FakeContext from nova.tests.unit import fake_flavor from nova.tests.unit import fake_instance class MigrationTaskTestCase(test.NoDBTestCase): def setUp(self): super(MigrationTaskTestCase, self).setUp() self.user_id = 'fake' self.project_id = 'fake' self.context = FakeContext(self.user_id, self.project_id) self.flavor = fake_flavor.fake_flavor_obj(self.context) self.flavor.extra_specs = {'extra_specs': 'fake'} inst = fake_instance.fake_db_instance(image_ref='image_ref', instance_type=self.flavor) inst_object = objects.Instance( flavor=self.flavor, numa_topology=None, pci_requests=None, system_metadata={'image_hw_disk_bus': 'scsi'}) self.instance = objects.Instance._from_db_object( self.context, inst_object, inst, []) self.request_spec = objects.RequestSpec(image=objects.ImageMeta()) self.hosts = [dict(host='host1', nodename=None, limits={})] self.filter_properties = {'limits': {}, 'retry': {'num_attempts': 1, 'hosts': [['host1', None]]}} self.reservations = [] self.clean_shutdown = True def _generate_task(self): return migrate.MigrationTask(self.context, self.instance, self.flavor, self.request_spec, self.reservations, self.clean_shutdown, compute_rpcapi.ComputeAPI(), scheduler_client.SchedulerClient()) @mock.patch.object(objects.RequestSpec, 'from_components') @mock.patch.object(scheduler_utils, 'setup_instance_group') @mock.patch.object(scheduler_client.SchedulerClient, 'select_destinations') @mock.patch.object(compute_rpcapi.ComputeAPI, 'prep_resize') @mock.patch.object(objects.Quotas, 'from_reservations') def test_execute(self, quotas_mock, prep_resize_mock, sel_dest_mock, sig_mock, request_spec_from_components): sel_dest_mock.return_value = self.hosts task = self._generate_task() request_spec_from_components.return_value = self.request_spec legacy_request_spec = self.request_spec.to_legacy_request_spec_dict() task.execute() quotas_mock.assert_called_once_with(self.context, self.reservations, instance=self.instance) sig_mock.assert_called_once_with(self.context, legacy_request_spec, self.filter_properties) task.scheduler_client.select_destinations.assert_called_once_with( self.context, self.request_spec) prep_resize_mock.assert_called_once_with( self.context, self.instance, legacy_request_spec['image'], self.flavor, self.hosts[0]['host'], self.reservations, request_spec=legacy_request_spec, filter_properties=self.filter_properties, node=self.hosts[0]['nodename'], clean_shutdown=self.clean_shutdown) self.assertFalse(quotas_mock.return_value.rollback.called) def test_rollback(self): task = self._generate_task() task.quotas = mock.MagicMock() task.rollback() task.quotas.rollback.assert_called_once_with()

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0

0.002786

0.244621

4,276

89

80

48.044944

0.844272

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0

0.086957

1

0.057971

false

0

0.144928

0.014493

0.231884

0

null

0

null

0

1

0

a3c289b2ddb7ec4ef9412f5ae94e7553200e0202

4,668

py

Python

mojoco trivial/mujocoSim/UR5/simple_example/Mujoco_py_example.py

garlicbutter/Jonathan-Tom

c1696f0a94da46911b3566a3d4f49791e877373f

[ "MIT" ]

2

2021-10-05T04:31:19.000Z

2021-10-05T04:31:26.000Z

mojoco trivial/mujocoSim/UR5/simple_example/Mujoco_py_example.py

garlicbutter/Tom-Jonathan

c1696f0a94da46911b3566a3d4f49791e877373f

[ "MIT" ]

null

mojoco trivial/mujocoSim/UR5/simple_example/Mujoco_py_example.py

garlicbutter/Tom-Jonathan

c1696f0a94da46911b3566a3d4f49791e877373f

[ "MIT" ]

null

import numpy as np import mujoco_py as mj from mujoco_py_renderer import SimulationError, XMLError, MujocoPyRenderer from mujoco_py import (MjSim, load_model_from_xml,functions, load_model_from_path, MjSimState, ignore_mujoco_warnings, load_model_from_mjb) from matplotlib import pyplot as plt import time xml = """ <mujoco model="example"> <compiler coordinate="global"/> <default> <geom rgba=".8 .6 .4 1"/> </default> <asset> <texture type="skybox" builtin="gradient" rgb1="1 1 1" rgb2=".6 .8 1" width="256" height="256"/> </asset> <worldbody> <light pos="0 1 1" dir="0 -1 -1" diffuse="1 1 1"/> <geom name="floor" pos="0 0 0" rgba="0.8 0.9 0.8 1" size="10 10 10" type="plane"/> <body> <site name="world" size="0.1" pos="0 0 0" /> <geom name="first_pole" type="capsule" fromto="0 0 0 0 0 0.5" size="0.04"/> <joint name='a' type="hinge" pos="0 0 0" axis="0 0 1" /> <body name="second_pole"> <inertial pos="0 0 0" mass="0.00000001" diaginertia="1e-008 1e-008 1e-008" /> <geom type="capsule" fromto="0 0 0.5 0.5 0 0.5" size="0.04" name="second_pole"/> <joint name='b' type="hinge" pos="0 0 0.5" axis="0 1 0"/> <body name='third_pole'> <inertial pos="0 0 0" mass="0.00000001" diaginertia="1e-008 1e-008 1e-008" /> <geom type="capsule" fromto="0.5 0 0.5 1 0 0.5" size="0.04" name="third_pole"/> <joint name='c' type="hinge" pos="0.5 0 0.5" axis="0 1 0"/> <site name="target" size="0.1" pos="1 0 0.5" /> <body name="mass"> <inertial pos="1 0 0.5" mass="1e-2" diaginertia="1e-008 1e-008 1e-008" /> <geom type="sphere" pos="1 0 0.5" size="0.2" name="mass"/> </body> </body> </body> </body> </worldbody> <actuator> <motor joint="a"/> <motor joint="b"/> <motor joint="c"/> </actuator> </mujoco> """ model = load_model_from_xml(xml) sim = MjSim(model) viewer = MujocoPyRenderer(sim) sim.reset() # After reset jacobians are all zeros sim.forward() target_jacp = np.zeros(3 * sim.model.nv) target_jacr= np.zeros(3 * sim.model.nv) F=np.array([0,0,-9.81*1e-2,0,0,0]).T #np.testing.assert_allclose(target_jacp, np.zeros(3 * sim.model.nv)) # After first forward, jacobians are real #sim.forward() K_diag=2000 C_diag=100 A_diag=1e-3 K=np.identity(3)*K_diag C=np.identity(3)*C_diag A=np.identity(3)*A_diag #K_diag=0.3 #C_diag=0.05 for i in range(3): K[i, i]=K_diag C[i,i]=C_diag A[i, i] = A_diag x_intial=sim.data.site_xpos[1] print(x_intial) x_desired=np.array([0,1,0.3]) v_intial=sim.data.site_xvelp[1] v_desired=np.array([0,0,0]) a_desired=np.array([0,0,0]) a_intial=np.array([0,0,0]) dt=sim.model.opt.timestep #sim.data.get_site_jacp('target', jacp=target_jacp) # Should be unchanged after steps (zero action) graph=[] for _ in range(100000): F[:3]=np.dot(K,x_desired-x_intial)+np.dot(C,v_desired-v_intial)+np.dot(A,a_desired-a_intial) H = np.zeros(sim.model.nv* sim.model.nv) functions.mj_fullM(sim.model, H, sim.data.qM) sim.data.get_site_jacp('target', jacp=target_jacp) sim.data.get_site_jacr('target', jacr=target_jacr) J_L = target_jacp.reshape((3, sim.model.nv)) J_A = target_jacr.reshape((3, sim.model.nv)) J = np.concatenate((J_L, J_A), axis=0) H_L =np.dot(np.linalg.pinv(J_L.T),np.dot(H.reshape(sim.model.nv, sim.model.nv), np.linalg.pinv(J_L))) H_all=np.dot(np.linalg.pinv(J.T),np.dot(H.reshape(sim.model.nv, sim.model.nv), np.linalg.pinv(J))) #F_a=np.dot(A,0.3-sim.data.qacc) #action = np.dot(J_L.T, np.dot(H_L, F[:3]))+sim.data.qfrc_bias action = sim.data.qfrc_bias+np.dot(H.reshape(3,3),np.dot(J_L.T,F[:3])) #print(action) #action = np.dot(J.T, F) sim.data.ctrl[:] = action sim.step() sim.forward() #print(np.max(action)) #print(sim.data.qacc) viewer.render() x_intial = sim.data.site_xpos[1] a_intial=(v_intial-sim.data.site_xvelp[1])/dt print(a_intial) v_intial = sim.data.site_xvelp[1] normal=np.linalg.norm(x_intial-x_desired) #print(normal) if normal<0.1: print("in") if x_desired[0]==0: x_desired = np.array([-1, 0, 0.5]) elif x_desired[0]==1: x_desired = np.array([0, 1, 0.3]) elif x_desired[0] == -1: x_desired = np.array([1, 0, 0.5]) graph.append(np.abs(x_intial-x_desired)) # sim.forward() print("the desired is {} and the intial is{}".format(x_desired,x_intial)) plt.plot(graph) plt.show()

29.923077

105

0.610111

835

4,668

3.291018

0.201198

0.028384

0.016376

0.0131

0.356623

0.338428

0.268559

0.212518

0.1754

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4,668

156

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0

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0.054545

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0

null

0

null

0

1

0

a3c726cfaf4ab3b53d1df8bd6d6c24aef693e3ab

5,066

py

Python

fedml_api/standalone/federated_sgan/fedssgan_api.py

arj119/FedML

5b7c098659f3e61f9e44583965300d8d0829f7a8

[ "Apache-2.0" ]

null

fedml_api/standalone/federated_sgan/fedssgan_api.py

arj119/FedML

5b7c098659f3e61f9e44583965300d8d0829f7a8

[ "Apache-2.0" ]

null

fedml_api/standalone/federated_sgan/fedssgan_api.py

arj119/FedML

5b7c098659f3e61f9e44583965300d8d0829f7a8

[ "Apache-2.0" ]

null

import copy import logging import random from typing import List, Tuple import numpy as np import torch import wandb from torch.utils.data import ConcatDataset from fedml_api.standalone.fedavg.my_model_trainer import MyModelTrainer from fedml_api.standalone.federated_sgan.ac_gan_model_trainer import ACGANModelTrainer from fedml_api.standalone.federated_sgan.client import FedSSGANClient from fedml_api.standalone.federated_sgan.model_trainer import FedSSGANModelTrainer from fedml_api.standalone.utils.HeterogeneousModelBaseTrainerAPI import HeterogeneousModelBaseTrainerAPI class FedSSGANAPI(HeterogeneousModelBaseTrainerAPI): def __init__(self, dataset, device, args, adapter_model, client_models: List[Tuple[torch.nn.Module, int]]): """ Args: dataset: Dataset presplit into data loaders device: Device to run training on args: Additional args client_models: List of client models and their frequency participating (assuming a stateful algorithm for simplicity) """ super().__init__(dataset, device, args) self.global_model = MyModelTrainer(adapter_model) self._setup_clients(self.train_data_local_num_dict, self.train_data_local_dict, self.test_data_local_dict, client_models) self._plot_client_training_data_distribution() def _setup_clients(self, train_data_local_num_dict, train_data_local_dict, test_data_local_dict, client_models): logging.info("############setup_clients (START)#############") c_idx = 0 for local_model, freq in client_models: for i in range(freq): model_trainer = ACGANModelTrainer( copy.deepcopy(self.global_model.model), copy.deepcopy(local_model) ) c = FedSSGANClient(c_idx, train_data_local_dict[c_idx], test_data_local_dict[c_idx], train_data_local_num_dict[c_idx], self.test_global, self.args, self.device, model_trainer) c_idx += 1 self.client_list.append(c) logging.info("############setup_clients (END)#############") def train(self): logging.info('\n###############Pre-Training clients#############\n') for i, c in enumerate(self.client_list): logging.info(f'Pre=training client: {i}') c.pre_train() logging.info('###############Pre-Training clients (END)###########\n') unlabelled_synthesised_data = None w_global = self.global_model.get_model_params() for round_idx in range(self.args.comm_round): logging.info("################Communication round : {}".format(round_idx)) w_locals = [] synthesised_data_locals = [] client_synthesised_data_lens = {'round': round_idx} client: FedSSGANClient for idx, client in enumerate(self.client_list): # Update client synthetic datasets # client.set_synthetic_dataset(unlabelled_synthesised_data) # Local round w = client.train(copy.deepcopy(w_global), round_idx) # self.logger.info("local weights = " + str(w)) w_locals.append((client.get_sample_number(), copy.deepcopy(w))) # synthetic_data = client.generate_synthetic_dataset() # if synthetic_data is not None: # synthesised_data_locals.append(synthetic_data) # client_synthesised_data_lens[f'Client_{idx}: Synthetic Dataset Size'] = len(synthetic_data) # else: # client_synthesised_data_lens[f'Client_{idx}: Synthetic Dataset Size'] = 0 # # if len(synthesised_data_locals) > 0: # unlabelled_synthesised_data = ConcatDataset(synthesised_data_locals) # logging.info(f'\n Synthetic Unlabelled Dataset Size: {len(unlabelled_synthesised_data)}\n') # client_synthesised_data_lens['Total Synthetic Dataset Size'] = len(unlabelled_synthesised_data) # else: # unlabelled_synthesised_data = None # client_synthesised_data_lens['Total Synthetic Dataset Size'] = 0 # wandb.log(client_synthesised_data_lens) # update global weights w_global = self._aggregate(w_locals) self.global_model.set_model_params(w_global) # test results # at last round if round_idx == self.args.comm_round - 1: self._local_test_on_all_clients(round_idx) # per {frequency_of_the_test} round elif round_idx % self.args.frequency_of_the_test == 0: if self.args.dataset.startswith("stackoverflow"): self._local_test_on_validation_set(round_idx) else: self._local_test_on_all_clients(round_idx)

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0

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0

null

0

null

0

1

0

a3c78b4ed55d10de069695bce6f3d899ee02cc99

20,932

py

Python

pytorch-word2vec-master/csv.py

arjun-sai-krishnan/tamil-morpho-embeddings

a33bcb427d635dba3b1857f26ea7ab287e1a44c5

[ "MIT" ]

2

2021-04-11T18:25:16.000Z

2022-03-16T03:48:52.000Z

pytorch-word2vec-master/csv.py

arjun-sai-krishnan/tamil-morpho-embeddings

a33bcb427d635dba3b1857f26ea7ab287e1a44c5

[ "MIT" ]

null

pytorch-word2vec-master/csv.py

arjun-sai-krishnan/tamil-morpho-embeddings

a33bcb427d635dba3b1857f26ea7ab287e1a44c5

[ "MIT" ]

null

#!/usr/bin/env python3 import argparse from collections import Counter import pdb import pickle import re import sys import time import numpy as np import torch import torch.nn as nn from torch.autograd import Variable from torch import optim import torch.nn.functional as F import torch.multiprocessing as mp import data_producer from multiprocessing import set_start_method parser = argparse.ArgumentParser() parser.add_argument("--train", type=str, default="", help="training file") parser.add_argument("--vocab", type=str, default="", help="vocab pickle file") parser.add_argument("--save", type=str, default="csv.pth.tar", help="saved model filename") parser.add_argument("--size", type=int, default=300, help="word embedding dimension") parser.add_argument("--window", type=int, default=5, help="context window size") parser.add_argument("--sample", type=float, default=1e-5, help="subsample threshold") parser.add_argument("--negative", type=int, default=10, help="number of negative samples") parser.add_argument("--delta", type=float, default=0.15, help="create new sense for a type if similarity lower than this value.") parser.add_argument("--min_count", type=int, default=5, help="minimum frequency of a word") parser.add_argument("--processes", type=int, default=4, help="number of processes") parser.add_argument("--num_workers", type=int, default=6, help="number of workers for data processsing") parser.add_argument("--iter", type=int, default=3, help="number of iterations") parser.add_argument("--lr", type=float, default=-1.0, help="initial learning rate") parser.add_argument("--batch_size", type=int, default=100, help="(max) batch size") parser.add_argument("--cuda", action='store_true', default=False, help="enable cuda") parser.add_argument("--multi_proto", action='store_true', default=False, help="True: multi-prototype, False:single-prototype") MAX_SENT_LEN = 1000 # Build the vocabulary. def file_split(f, delim=' \t\n', bufsize=1024): prev = '' while True: s = f.read(bufsize) if not s: break tokens = re.split('['+delim+']{1,}', s) if len(tokens) > 1: yield prev + tokens[0] prev = tokens[-1] for x in tokens[1:-1]: yield x else: prev += s if prev: yield prev def build_vocab(args): vocab = Counter() word_count = 0 for word in file_split(open(args.train)): vocab[word] += 1 word_count += 1 if word_count % 10000 == 0: sys.stdout.write('%d\r' % len(vocab)) freq = {k:v for k,v in vocab.items() if v >= args.min_count} word_count = sum([freq[k] for k in freq]) word_list = sorted(freq, key=freq.get, reverse=True) word2idx = {} for i,w in enumerate(word_list): word2idx[w] = i print("Vocab size: %ld" % len(word2idx)) print("Words in train file: %ld" % word_count) vars(args)['vocab_size'] = len(word2idx) vars(args)['train_words'] = word_count return word2idx, word_list, freq class CSV(nn.Module): def __init__(self, args): super(CSV, self).__init__() self.global_embs = nn.Embedding(args.vocab_size+1, args.size, padding_idx=args.vocab_size, sparse=True) self.sense_embs = nn.Embedding(args.vocab_size*5, args.size, sparse=True) self.ctx_weight = torch.nn.Parameter(torch.ones(2*args.window, args.size)) self.word2sense = [ [i] for i in range(args.vocab_size) ] ''' word2sense = np.zeros((args.vocab_size, 5), dtype='int32') for i in range(args.vocab_size): word2sense[i, 0] = i self.word2sense = torch.nn.Parameter(torch.from_numpy(word2sense).int()) self.word_sense_cnts = torch.nn.Parameter(torch.ones((args.vocab_size,)).int()) ''' self.global_embs.weight.data.uniform_(-0.5/args.size, 0.5/args.size) self.sense_embs.weight.data.uniform_(-0.5/args.size, 0.5/args.size) self.n_senses = args.vocab_size self.sense_capacity = args.vocab_size*5 self.batch_size = args.batch_size self.size = args.size self.window = args.window self.negative = args.negative self.pad_idx = args.vocab_size def get_context_feats(self, ctx_type_indices): ctx_type_embs = self.global_embs(ctx_type_indices) return torch.sum(ctx_type_embs * self.ctx_weight, 1).cpu().data.numpy() def get_possible_sense_embs(self, type_indices, cuda=True): sense_indices = [] sense2idx = {} for type_id in type_indices: for s_id in self.word2sense[type_id]: if s_id not in sense2idx: sense2idx[s_id] = len(sense_indices) sense_indices.append( s_id ) sense_indices = np.array(sense_indices) if cuda: sense_embs = self.sense_embs(Variable(torch.LongTensor(sense_indices).cuda())) return sense2idx, sense_embs.cpu().data.numpy() else: sense_embs = self.sense_embs(Variable(torch.LongTensor(sense_indices))) return sense2idx, sense_embs.data.numpy() def forward(self, data): ctx_type_indices = data[:, 0:2*self.window] pos_sense_idx = data[:, 2*self.window+1] neg_sense_indices = data[:, 2*self.window+2:2*self.window+2+self.negative] neg_mask = data[:, 2*self.window+2+self.negative:].float() ctx_type_embs = self.global_embs(ctx_type_indices) pos_sense_embs = self.sense_embs(pos_sense_idx) neg_sense_embs = self.sense_embs(neg_sense_indices) ctx_feats = torch.sum(ctx_type_embs * self.ctx_weight, 1, keepdim=True) # Neg Log Likelihood pos_ips = torch.sum(ctx_feats[:,0,:] * pos_sense_embs, 1) pos_loss = torch.sum( -F.logsigmoid(torch.clamp(pos_ips,max=10,min=-10))) neg_ips = torch.bmm(neg_sense_embs, ctx_feats.permute(0,2,1))[:,:,0] neg_loss = torch.sum( -F.logsigmoid(torch.clamp(-neg_ips,max=10,min=-10)) * neg_mask ) return pos_loss + neg_loss # Initialize model. def init_net(args): if args.lr == -1.0: vars(args)['lr'] = 0.05 return CSV(args) def save_model(filename, model, args, word2idx): torch.save({ 'word2idx':word2idx, 'args':args, #'word2sense': model.word2sense, 'n_senses': model.n_senses, 'params': model.state_dict() }, filename) def load_model(filename): checkpoint = torch.load(filename) word2idx = checkpoint['word2idx'] args = checkpoint['args'] model = CSV(args) if args.cuda: model.cuda() model.global_embs.weight.data = checkpoint['params']['global_embs.weight'] model.sense_embs.weight.data = checkpoint['params']['sense_embs.weight'] model.ctx_weight.data = checkpoint['params']['ctx_weight'] model.word2sense = checkpoint['word2sense'] #model.word2sense.data = checkpoint['params']['word2sense'] #model.word_sense_cnts.data = checkpoint['params']['word_sense_cnts'] model.n_senses = checkpoint['n_senses'] return model, word2idx # Training def train_process_sent_producer(p_id, data_queue, word_count_actual, word_list, word2idx, freq, args): n_proc = 1 if args.stage == 2 else args.processes N = 1 if args.stage == 2 else args.iter neg = 0 if args.stage == 2 else args.negative if args.negative > 0: table_ptr_val = data_producer.init_unigram_table(word_list, freq, args.train_words) train_file = open(args.train) file_pos = args.file_size * p_id // n_proc train_file.seek(file_pos, 0) while True: try: train_file.read(1) except UnicodeDecodeError: file_pos -= 1 train_file.seek(file_pos, 0) else: train_file.seek(file_pos, 0) break batch_count = 0 batch_placeholder = np.zeros((args.batch_size, 2*args.window+2+2*neg), 'int64') for it in range(N): train_file.seek(file_pos, 0) last_word_cnt = 0 word_cnt = 0 sentence = [] prev = '' eof = False while True: if eof or train_file.tell() > file_pos + args.file_size / n_proc: break while True: s = train_file.read(1) if not s: eof = True break elif s == ' ' or s == '\t': if prev in word2idx: sentence.append(prev) prev = '' if len(sentence) >= MAX_SENT_LEN: break elif s == '\n': if prev in word2idx: sentence.append(prev) prev = '' break else: prev += s if len(sentence) > 0: # subsampling sent_id = [] if args.sample != 0: sent_len = len(sentence) i = 0 while i < sent_len: word = sentence[i] f = freq[word] / args.train_words pb = (np.sqrt(f / args.sample) + 1) * args.sample / f; if pb > np.random.random_sample(): sent_id.append( word2idx[word] ) i += 1 if len(sent_id) < 2: word_cnt += len(sentence) sentence.clear() continue next_random = (2**24) * np.random.randint(0, 2**24) + np.random.randint(0, 2**24) chunk = data_producer.cbow_producer(sent_id, len(sent_id), table_ptr_val, args.window, neg, args.vocab_size, args.batch_size, next_random) chunk_pos = 0 while chunk_pos < chunk.shape[0]: remain_space = args.batch_size - batch_count remain_chunk = chunk.shape[0] - chunk_pos if remain_chunk < remain_space: take_from_chunk = remain_chunk else: take_from_chunk = remain_space batch_placeholder[batch_count:batch_count+take_from_chunk, :] = chunk[chunk_pos:chunk_pos+take_from_chunk, :] batch_count += take_from_chunk if batch_count == args.batch_size: data_queue.put(batch_placeholder) batch_count = 0 chunk_pos += take_from_chunk word_cnt += len(sentence) if word_cnt - last_word_cnt > 10000: with word_count_actual.get_lock(): word_count_actual.value += word_cnt - last_word_cnt last_word_cnt = word_cnt sentence.clear() with word_count_actual.get_lock(): word_count_actual.value += word_cnt - last_word_cnt print(p_id, it, file_pos, train_file.tell(), args.file_size) if batch_count > 0: data_queue.put(batch_placeholder[:batch_count,:]) data_queue.put(None) print(p_id, file_pos, train_file.tell(), args.file_size) def train_process(p_id, word_count_actual, word2idx, word_list, freq, args, model): data_queue = mp.SimpleQueue() lr = args.lr #optimizer = optim.SGD(filter(lambda p: p.requires_grad, model.parameters()), lr=lr) optimizer = optim.Adagrad(filter(lambda p: p.requires_grad, model.parameters()), lr=lr) t = mp.Process(target=train_process_sent_producer, args=(p_id, data_queue, word_count_actual, word_list, word2idx, freq, args)) t.start() #n_iter = 1 if args.stage == 2 else args.iter n_iter = args.iter # get from data_queue and feed to model prev_word_cnt = 0 while True: chunk = data_queue.get() if chunk is None: break else: # lr anneal & output if word_count_actual.value - prev_word_cnt > 10000: #if args.lr_anneal: # lr = args.lr * (1 - word_count_actual.value / (n_iter * args.train_words)) # if lr < 0.0001 * args.lr: # lr = 0.0001 * args.lr # for param_group in optimizer.param_groups: # param_group['lr'] = lr #sys.stdout.write("\rAlpha: %0.8f, Progess: %0.2f, Words/sec: %f, word_cnt: %d" % (lr, word_count_actual.value / (n_iter * args.train_words) * 100, word_count_actual.value / (time.monotonic() - args.t_start), word_count_actual.value)) sys.stdout.write("\rProgess: %0.2f, Words/sec: %f, word_cnt: %d" % (word_count_actual.value / (n_iter * args.train_words) * 100, word_count_actual.value / (time.monotonic() - args.t_start), word_count_actual.value)) sys.stdout.flush() prev_word_cnt = word_count_actual.value if args.stage == 1: if args.cuda: data = Variable(torch.LongTensor(chunk).cuda(), requires_grad=False) else: data = Variable(torch.LongTensor(chunk), requires_grad=False) optimizer.zero_grad() loss = model(data) loss.backward() optimizer.step() model.global_embs.weight.data[args.vocab_size].fill_(0) elif args.stage == 3: if args.cuda: data = Variable(torch.LongTensor(chunk).cuda(), requires_grad=False) else: data = Variable(torch.LongTensor(chunk), requires_grad=False) #type_ids = chunk[:, 2*args.window+1:2*args.window+2+2*args.negative] type_ids = chunk[:, 2*args.window+1:2*args.window+2+args.negative] type_ids = np.reshape(type_ids, (type_ids.shape[0] * type_ids.shape[1])) sense2idx, sense_embs = model.get_possible_sense_embs(type_ids.tolist()) # get type_idx from chunk, and do sense selection here. context_feats = model.get_context_feats(data[:, :2*args.window]) chunk = data_producer.select_sense(chunk, context_feats, sense2idx, sense_embs, model.word2sense, chunk.shape[0], args.size, args.window, args.negative) if args.cuda: data = Variable(torch.LongTensor(chunk).cuda(), requires_grad=False) else: data = Variable(torch.LongTensor(chunk), requires_grad=False) optimizer.zero_grad() loss = model(data) loss.backward() optimizer.step() model.global_embs.weight.data[args.vocab_size].fill_(0) t.join() def train_process_stage2(p_id, word_count_actual, word2idx, word_list, freq, args, model): data_queue = mp.SimpleQueue() sense_embs = model.sense_embs.weight.data.numpy() counter_list = np.zeros((model.sense_capacity), dtype='float32') t = mp.Process(target=train_process_sent_producer, args=(p_id, data_queue, word_count_actual, word_list, word2idx, freq, args)) t.start() n_iter = 1 # get from data_queue and feed to model prev_word_cnt = 0 while True: chunk = data_queue.get() if chunk is None: break else: if word_count_actual.value - prev_word_cnt > 10000: sys.stdout.write("\rProgess: %0.2f, Words/sec: %f, word_cnt: %d" % (word_count_actual.value / (n_iter * args.train_words) * 100, word_count_actual.value / (time.monotonic() - args.t_start), word_count_actual.value)) sys.stdout.flush() prev_word_cnt = word_count_actual.value if args.cuda: data = Variable(torch.LongTensor(chunk).cuda(), requires_grad=False) else: data = Variable(torch.LongTensor(chunk), requires_grad=False) context_feats = model.get_context_feats(data[:, :2*args.window]) # update sense_embs create_cnt = data_producer.create_n_update_sense(chunk[:, 2*args.window+1], context_feats, sense_embs, model.word2sense, counter_list, chunk.shape[0], args.size, args.delta, model.n_senses) model.n_senses += create_cnt #if model.n_senses + args.batch_size > model.sense_capacity: # new_capacity = model.sense_capacity * 3 // 2 # counter_list = np.concatenate( (counter_list, np.ones((new_capacity - model.sense_capacity),dtype='float32')), axis=0) # zero = np.zeros((new_capacity - model.sense_capacity, args.size), 'float32') # sense_embs = np.concatenate((sense_embs, zero), 0) # model.sense_capacity = new_capacity # print("\nexapnded sense_embs: %d" % model.n_senses) t.join() sense_embs[:model.n_senses, :] = sense_embs[:model.n_senses, :] / counter_list[:model.n_senses, None] if __name__ == '__main__': set_start_method('forkserver') args = parser.parse_args() print("Starting training using file %s" % args.train) train_file = open(args.train) train_file.seek(0, 2) vars(args)['file_size'] = train_file.tell() word_count_actual = mp.Value('L', 0) if args.vocab == '': word2idx, word_list, freq = build_vocab(args) else: with open(args.vocab, 'rb') as f: word2idx, word_list, freq, pos2idx, dep2id = pickle.load(f) word_count = sum([freq[k] for k in freq]) vars(args)['vocab_size'] = len(word2idx) vars(args)['train_words'] = word_count print("Vocab size: %ld" % len(word2idx)) print("Words in train file: %ld" % word_count) model = init_net(args) model.share_memory() if args.cuda: model.cuda() # stage 1, learn robust context representation. vars(args)['stage'] = 1 print("Stage 1") vars(args)['lr_anneal'] = True vars(args)['t_start'] = time.monotonic() processes = [] for p_id in range(args.processes): p = mp.Process(target=train_process, args=(p_id, word_count_actual, word2idx, word_list, freq, args, model)) p.start() processes.append(p) for p in processes: p.join() del processes print("\nStage 1, ", time.monotonic() - args.t_start, " secs ", word_count_actual.value) filename = args.save if not filename.endswith('.pth.tar'): filename += '.stage1.pth.tar' save_model(filename, model, args, word2idx) if args.multi_proto: # stage 2, create new sense in a non-parametric way. # Freeze model paramters except sense_embs, and use only 1 process to prevent race condition old_batch_size = vars(args)['batch_size'] model.global_embs.requires_grad = False model.ctx_weight.requires_grad = False model.sense_embs = model.sense_embs.cpu() vars(args)['stage'] = 2 vars(args)['batch_size'] = 5000 print("\nStage 2") word_count_actual.value = 0 vars(args)['t_start'] = time.monotonic() train_process_stage2(0, word_count_actual, word2idx, word_list, freq, args, model) if args.cuda: model.cuda() print("\nStage 2, ", time.monotonic() - args.t_start, " secs") print("Current # of senses: %d" % model.n_senses) pdb.set_trace() filename = args.save if not filename.endswith('.pth.tar'): filename += '.stage2.pth.tar' save_model(filename, model, args, word2idx) # stage 3, no more sense creation. vars(args)['lr'] = args.lr * 0.01 vars(args)['batch_size'] = old_batch_size model.global_embs.requires_grad = True model.ctx_weight.requires_grad = True vars(args)['stage'] = 3 print("\nBegin stage 3") word_count_actual.value = 0 vars(args)['t_start'] = time.monotonic() processes = [] for p_id in range(args.processes): p = mp.Process(target=train_process, args=(p_id, word_count_actual, word2idx, word_list, freq, args, model)) p.start() processes.append(p) for p in processes: p.join() print("\nStage 3, ", time.monotonic() - args.t_start, " secs") # save model filename = args.save if not filename.endswith('.pth.tar'): filename += '.stage3.pth.tar' save_model(filename, model, args, word2idx) print("")

40.487427

250

0.591821

2,703

20,932

4.376619

0.128746

0.02967

0.038039

0.032122

0.471344

0.391801

0.352663

0.332798

0.295773

0.265596

0

0.01997

0.289509

20,932

516

251

40.565891

0.775484

0.089002

0

0.375321

0

0.066652

0.001184

0

1

0.030848

false

0

0.041131

0

0.092545

0.03856

0

null

0

null

0

1

0

a3c8721ad82d9b0c4f4bbb5e4ea027824401f22d

339

py

Python

Ogrenciler/Varol/buyuksayi.py

ProEgitim/Python-Dersleri-BEM

b25e9fdb1fa3026925a46b2fcbcba348726b775c

[ "MIT" ]

1

2021-04-18T17:35:22.000Z

Ogrenciler/Varol/buyuksayi.py

waroi/Python-Dersleri-BEM

b25e9fdb1fa3026925a46b2fcbcba348726b775c

[ "MIT" ]

null

Ogrenciler/Varol/buyuksayi.py

waroi/Python-Dersleri-BEM

b25e9fdb1fa3026925a46b2fcbcba348726b775c

[ "MIT" ]

2

2021-04-18T18:22:26.000Z

2021-04-24T17:16:19.000Z

sayi1 = int(input("1. Sayı: ")) sayi2 = int(input("2. Sayı: ")) sayi3 = int(input("3. Sayı: ")) sayi4 = int(input("4. Sayı: ")) sayi5 = int(input("5. Sayı: ")) sayilar=[]; sayilar.append(sayi1) sayilar.append(sayi2) sayilar.append(sayi3) sayilar.append(sayi4) sayilar.append(sayi5) sayilar.sort() print("En büyük sayimiz..",sayilar[-1])

21.1875

39

0.663717

49

339

4.591837

0.408163

0.177778

0

0.05298

0.109145

339

15

40

22.6

0.692053

0

0.186944

0

1

0

false

0

0.076923

0

null

0

null

0

1

0

a3c959da81854ccd184aefdeb715f7df8413b8b8

8,899

py

Python

baselines/deepq/build_graph_mfec.py

MouseHu/emdqn

ba907e959f21dd0b5a17117accccae9c82a79a3b

[ "MIT" ]

null

baselines/deepq/build_graph_mfec.py

MouseHu/emdqn

ba907e959f21dd0b5a17117accccae9c82a79a3b

[ "MIT" ]

null

baselines/deepq/build_graph_mfec.py

MouseHu/emdqn

ba907e959f21dd0b5a17117accccae9c82a79a3b

[ "MIT" ]

1

2021-04-26T13:55:47.000Z

"""Deep Q learning graph The functions in this file can are used to create the following functions: ======= act ======== Function to chose an action given an observation Parameters ---------- observation: object Observation that can be feed into the output of make_obs_ph stochastic: bool if set to False all the actions are always deterministic (default False) update_eps_ph: float update epsilon a new value, if negative not update happens (default: no update) Returns ------- Tensor of dtype tf.int64 and shape (BATCH_SIZE,) with an action to be performed for every element of the batch. ======= train ======= Function that takes a transition (s,a,r,s') and optimizes Bellman equation's error: td_error = Q(s,a) - (r + gamma * max_a' Q(s', a')) loss = huber_loss[td_error] Parameters ---------- obs_t: object a batch of observations action: np.array actions that were selected upon seeing obs_t. dtype must be int32 and shape must be (batch_size,) reward: np.array immediate reward attained after executing those actions dtype must be float32 and shape must be (batch_size,) obs_tp1: object observations that followed obs_t done: np.array 1 if obs_t was the last observation in the episode and 0 otherwise obs_tp1 gets ignored, but must be of the valid shape. dtype must be float32 and shape must be (batch_size,) weight: np.array imporance weights for every element of the batch (gradient is multiplied by the importance weight) dtype must be float32 and shape must be (batch_size,) Returns ------- td_error: np.array a list of differences between Q(s,a) and the target in Bellman's equation. dtype is float32 and shape is (batch_size,) ======= update_target ======== copy the parameters from optimized Q function to the target Q function. In Q learning we actually optimize the following error: Q(s,a) - (r + gamma * max_a' Q'(s', a')) Where Q' is lagging behind Q to stablize the learning. For example for Atari Q' is set to Q once every 10000 updates training steps. """ import tensorflow as tf import baselines.common.tf_util as U import numpy as np def build_act_mf(make_obs_ph, q_func, z_noise, num_actions, scope="deepq", reuse=None): with tf.variable_scope(scope, reuse=reuse): observations_ph = U.ensure_tf_input(make_obs_ph("observation")) q, q_deterministic, v_mean, v_logvar, z_mean, z_logvar, recon_obs = q_func(observations_ph.get(), z_noise, num_actions, scope="q_func", reuse=tf.AUTO_REUSE) act = U.function(inputs=[observations_ph,z_noise], outputs=[z_mean, z_logvar]) return act def build_train_mf(make_obs_ph, q_func, num_actions, optimizer, grad_norm_clipping=None, gamma=1.0, scope="mfec", alpha=1.0, beta=1.0, theta=1.0, latent_dim=32, ib=True, reuse=None): """Creates the train function: Parameters ---------- make_obs_ph: str -> tf.placeholder or TfInput a function that takes a name and creates a placeholder of input with that name q_func: (tf.Variable, int, str, bool) -> tf.Variable the model that takes the following inputs: observation_in: object the output of observation placeholder num_actions: int number of actions scope: str reuse: bool should be passed to outer variable scope and returns a tensor of shape (batch_size, num_actions) with values of every action. num_actions: int number of actions reuse: bool whether or not to reuse the graph variables optimizer: tf.train.Optimizer optimizer to use for the Q-learning objective. grad_norm_clipping: float or None clip gradient norms to this value. If None no clipping is performed. gamma: float discount rate. double_q: bool if true will use Double Q Learning (https://arxiv.org/abs/1509.06461). In general it is a good idea to keep it enabled. scope: str or VariableScope optional scope for variable_scope. reuse: bool or None whether or not the variables should be reused. To be able to reuse the scope must be given. Returns ------- act: (tf.Variable, bool, float) -> tf.Variable function to select and action given observation. ` See the top of the file for details. train: (object, np.array, np.array, object, np.array, np.array) -> np.array optimize the error in Bellman's equation. ` See the top of the file for details. update_target: () -> () copy the parameters from optimized Q function to the target Q function. ` See the top of the file for details. debug: {str: function} a bunch of functions to print debug data like q_values. """ act_noise = tf.placeholder(tf.float32, [None, latent_dim], name="act_noise") act_f = build_act_mf(make_obs_ph, q_func, act_noise, num_actions, scope=scope, reuse=reuse) with tf.variable_scope(scope, reuse=reuse): # set up placeholders # EMDQN obs_vae_input = U.ensure_tf_input(make_obs_ph("obs_vae")) z_noise_vae = tf.placeholder(tf.float32, [None, latent_dim], name="z_noise_vae") inputs = [obs_vae_input,z_noise_vae] if ib: qec_input = tf.placeholder(tf.float32, [None], name='qec') inputs.append(qec_input) outputs = [] q_vae, q_deterministic_vae, v_mean_vae, v_logvar_vae, z_mean_vae, z_logvar_vae, recon_obs = q_func(obs_vae_input.get(), z_noise_vae, num_actions, scope="q_func", reuse=True) q_func_vars = U.scope_vars(U.absolute_scope_name("q_func")) encoder_loss = -1 + z_mean_vae ** 2 + tf.exp(z_logvar_vae) - z_logvar_vae total_loss = tf.reduce_mean(beta * encoder_loss) decoder_loss = tf.keras.losses.binary_crossentropy(tf.reshape(recon_obs, [-1]), tf.reshape( tf.dtypes.cast(obs_vae_input._placeholder, tf.float32), [-1])) print("here", z_mean_vae.shape, z_logvar_vae.shape, encoder_loss.shape, decoder_loss.shape) vae_loss = beta * encoder_loss + theta * decoder_loss outputs.append(encoder_loss) outputs.append(decoder_loss) outputs.append(vae_loss) total_loss += tf.reduce_mean(theta * decoder_loss) if ib: ib_loss = (v_mean_vae - tf.stop_gradient(tf.expand_dims(qec_input, 1))) ** 2 / tf.exp( v_logvar_vae) + v_logvar_vae print("here2", v_mean_vae.shape, tf.expand_dims(qec_input, 1).shape, v_logvar_vae.shape, ib_loss.shape) total_ib_loss = alpha * ib_loss + beta * encoder_loss outputs.append(total_ib_loss) total_loss += tf.reduce_mean(alpha * ib_loss) if grad_norm_clipping is not None: optimize_expr = U.minimize_and_clip(optimizer, total_loss, var_list=q_func_vars, clip_val=grad_norm_clipping) else: optimize_expr = optimizer.minimize(total_loss, var_list=q_func_vars) # Create callable functions # EMDQN total_loss_summary = tf.summary.scalar("total loss", total_loss) z_var_summary = tf.summary.scalar("z_var", tf.reduce_mean(tf.exp(z_logvar_vae))) encoder_loss_summary = tf.summary.scalar("encoder loss", tf.reduce_mean(encoder_loss)) decoder_loss_summary = tf.summary.scalar("decoder loss", tf.reduce_mean(decoder_loss)) summaries = [total_loss_summary, z_var_summary, encoder_loss_summary, decoder_loss_summary] if ib: ib_loss_summary = tf.summary.scalar("ib loss", tf.reduce_mean(ib_loss)) total_ib_loss_summary = tf.summary.scalar("total ib loss", tf.reduce_mean(total_ib_loss)) summaries.append(ib_loss_summary) summaries.append(total_ib_loss_summary) summary = tf.summary.merge(summaries) outputs.append(summary) train = U.function( inputs=inputs, outputs=[total_loss,summary], updates=[optimize_expr] ) return act_f, train

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a3cadf1c1469dc28d63f965c32ff3b98b7eb9d52

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py

Python

src/salgan_dhf1k/train_bce.py

juanjo3ns/SalGAN2

ac52af743b94961cdb44c5d89774b72fc8acfd3e

[ "MIT" ]

null

src/salgan_dhf1k/train_bce.py

juanjo3ns/SalGAN2

ac52af743b94961cdb44c5d89774b72fc8acfd3e

[ "MIT" ]

null

src/salgan_dhf1k/train_bce.py

juanjo3ns/SalGAN2

ac52af743b94961cdb44c5d89774b72fc8acfd3e

[ "MIT" ]

null

import os from dataloader.datasetDHF1K import DHF1K from torch.utils.data import DataLoader from utils.salgan_utils import save_model, get_lr_optimizer from utils.sendTelegram import send from utils.printer import param_print from utils.salgan_generator import create_model, add_bn from evaluation.fast_evaluation import compute_metrics import numpy as np import torch from torch.nn import AvgPool2d from torch.nn.modules.loss import BCELoss import torch.backends.cudnn as cudnn from torch.optim import SGD, Adam from torch.optim.lr_scheduler import ReduceLROnPlateau, StepLR from time import time from IPython import embed from tensorboard_logger import configure, log_value, log_histogram TRAIN = 'train' VAL = 'val' TEST = 'test' def add_layer_weights(vgg_weights): # Mean of RGB weights of first layer with size [64,1,3,3] layer1 = vgg_weights['0.weight'] mean_rgb = layer1.mean(dim=1,keepdim=True) vgg_weights['0.weight'] = torch.cat([layer1.cuda(),mean_rgb.cuda()],1) # We could do it easily accessing to the weights trought model[0].weight and change dimension 1, but as we # already have the 4th channel we'd be doing the mean of all of the channels, inicializing it in the wrong way. return vgg_weights def train_eval(mode, model, optimizer, dataloader): if mode == TRAIN: N = len(ds_train)/batch_size model.train() else: N = len(ds_validate)/batch_size model.eval() total_loss = [] #iterate epoch... #iterate epoch... for i, X in enumerate(dataloader[mode]): inputs = X[0].cuda() # noramlize saliency maps values between [0,1] gt_maps = X[1].cuda()/255 embed() predictions = model.forward(inputs).squeeze() # reduce size for loss reduce_size = AvgPool2d((4,4)) pred_ = reduce_size(predictions) gt_maps_ = reduce_size(gt_maps) pred_ = pred_.view(pred_.size()[0], -1) gt_maps_ = gt_maps_.view(gt_maps_.size()[0], -1) loss = bce_loss(pred_, gt_maps_) # make actual step update if mode==TRAIN: # compute gradients loss.backward() # step optimizer optimizer.step() # reset grads for next step optimizer.zero_grad() print("\t{}/{} loss:{}".format(i, int(N), loss.item()), end="\r") total_loss.append(loss.item()) total_loss=np.mean(total_loss) return total_loss if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument("--path_out", default='sal_dhf1k_adamdepthcoordaugm2_frombestsaldepth', type=str, help="""set output path for the trained model""") parser.add_argument("--batch_size", default=12, type=int, help="""Set batch size""") parser.add_argument("--n_epochs", default=10, type=int, help="""Set total number of epochs""") parser.add_argument("--depth", default=False, type=bool, help="""Enable 4th channel with depth""") parser.add_argument("--augment", default=False, type=bool, help="""Enable data augmentation""") parser.add_argument("--coord", default=False, type=bool, help="""Enable coordconv""") parser.add_argument("--flow", default=False, type=bool, help="""Enable opticalflow""") parser.add_argument("--lr", type=float, default=0.00001, help="""Learning rate for training""") parser.add_argument("--patience", type=int, default=3, help="""Patience for learning rate scheduler (default 10)""") args = parser.parse_args() # set output path ========================================================== path_out = '../trained_models/batch12_/' + args.path_out if not os.path.exists(path_out): # create output path os.makedirs(path_out) # create output for models path_models = os.path.join(path_out, 'models') if not os.path.exists(path_models): os.makedirs(path_models) # tensorboard configure("{}".format(path_out), flush_secs=5) # data ===================================================================== batch_size = args.batch_size n_epochs = args.n_epochs lr = args.lr DEPTH = args.depth AUGMENT = args.augment COORD = args.coord FLOW = args.flow # Datasets for DHF1K ds_train = DHF1K(mode=TRAIN, transformation=True, depth=DEPTH, d_augm=AUGMENT, coord=COORD) ds_validate = DHF1K(mode=VAL, transformation=False, depth=DEPTH, d_augm=False, coord=COORD) # Dataloaders dataloader = { TRAIN: DataLoader(ds_train, batch_size=batch_size, shuffle=True, num_workers=2), VAL: DataLoader(ds_validate, batch_size=batch_size, shuffle=False, num_workers=2) } # POSSIBILITY OF CHOOSING GPU torch.cuda.set_device(1) # MODEL INITIALIZATION print("Init model...") vgg_weights = torch.load('../trained_models/salgan_baseline.pt')['state_dict'] model = create_model(3) # if DEPTH and COORD: # model = create_model(6) # for i in range(0,3): # vgg_weights = add_layer_weights(vgg_weights) # elif DEPTH: # model = create_model(4) # add_layer_weights(vgg_weights) # elif COORD: # model = create_model(5) # for i in range(0,2): # vgg_weights = add_layer_weights(vgg_weights) # else: model = create_model(3) # Instead of adding manually the layer of new weights, we could use strict=False model.load_state_dict(vgg_weights) # Add batch normalization to current model if needed model = add_bn(model) model.train() model.cuda() cudnn.benchmark = True # NOT WORKING UNMOUNTED DISK # If we have the two GPU's available we are going to use both # if torch.cuda.device_count() > 1: # print("Using ", torch.cuda.device_count(), "GPUs!") # model = torch.nn.DataParallel(model) # LOSS FUNCTION bce_loss = BCELoss() # FINE-TUNE WHOLE NETWORK OR JUST DECODER => uncomment / or different lr for each part # decoder_parameters = [] # base_params = [] # for i, (a, p) in enumerate(model.named_parameters()): # embed() # if i>25: # # print(i, a, p.shape) # decoder_parameters.append(p) # else: # base_params.append(p) # If you wanna train just the decoder put this # p.requires_grad = False # ADAM OPTIMIZER optimizer = Adam(model.parameters(), lr = lr, weight_decay=0.000001) # STOCHASTIC GRADIENT DESCENT OPTIMIZER # optimizer = SGD(model.parameters(), # lr = 0.00001, # momentum=0.9, # weight_decay=0.00001, # nesterov=True) # NUMBER OF TOTAL PARAMETERS # pytorch_total_params = sum(p.numel() for p in model.parameters()) # NUMBER OF TRAINABLE PARAMETERS trainable_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) print("Trainable parameters: ", trainable_parameters) send("Trainable parameters: " + str(trainable_parameters)) send("Experiment: " + args.path_out) # PRINT TABLE OF PARAMETERS param_print([path_out,"",DEPTH,AUGMENT,COORD,FLOW,batch_size,lr,n_epochs, trainable_parameters]) # set learning rate scheduler # ReduceLROnPlateau( # optimizer, # mode (str) 'min':lr es reduira quan la metrica no es redueixi mes, 'max' al contrari, # factor (float) factor de reduccio de la lr, # patience (int) num epochs sense millora a partir dels quals es redueix lr, # verbose (bool), # ) # scheduler = ReduceLROnPlateau(optimizer, # 'min', # patience=args.patience, # verbose=True) scheduler = StepLR(optimizer, step_size=3, gamma=0.1) best_loss=9999999 # main loop training ======================================================= for id_epoch in range(n_epochs): for mode in [VAL, TRAIN]: # select dataloader data_iterator = dataloader[mode] # # # saliency metrics # if mode ==VAL: # print("Evaluating metrics....") # # only do 100 images from validation # metrics = compute_metrics(model, 100, DEPTH, COORD) # # # log metric values # for metric in metrics.keys(): # log_value("Metrics/{}".format(metric), # metrics[metric], id_epoch) # # # get epoch loss # print("--> {} epoch {}".format(mode, id_epoch)) epoch_loss = train_eval(mode, model, optimizer, dataloader) lr = list(get_lr_optimizer(optimizer))[0] print("-----------") print("Done! {} epoch {} loss {} lr {}".format(mode, id_epoch, epoch_loss, lr)) send("{} epoch {}/{} loss {}".format(mode, id_epoch, n_epochs, epoch_loss)) print("\n") # record loss log_value("loss/{}".format(mode), epoch_loss, id_epoch) log_value("lr/{}".format(mode), lr, id_epoch) # for v in model.state_dict(): # log_histogram("Layer {}".format(v), model.state_dict()[v], id_epoch) if (id_epoch%2)==0: save_model(model, optimizer, id_epoch, path_out, name_model='{:03d}'.format(id_epoch)) # store model if val loss improves if mode==VAL: if best_loss > epoch_loss: # update loss best_loss = epoch_loss save_model(model, optimizer, id_epoch, path_out, name_model='best') # scheduler.step(epoch_loss) scheduler.step()

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null

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a3cc11867421204e587bf63f6a7dd58a6716ea01

2,030

py

Python

infapy/v3/agentService.py

infapy/infapy

0cb11310130be70ce1b647aa5ede929c1eb9b2ce

[ "Apache-2.0" ]

null

infapy/v3/agentService.py

infapy/infapy

0cb11310130be70ce1b647aa5ede929c1eb9b2ce

[ "Apache-2.0" ]

null

infapy/v3/agentService.py

infapy/infapy

0cb11310130be70ce1b647aa5ede929c1eb9b2ce

[ "Apache-2.0" ]

1

2021-09-23T10:31:56.000Z

# Copyright (c) 2021-Present (Prashanth Pradeep) # 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 requests as re import infapy from infapy.exceptions import InvalidDetailsProvided class AgentService(): def __init__(self,v3,v3BaseURL,v3SessionID): self._v3 = v3 self._v3BaseURL = v3BaseURL self._v3SessionID = v3SessionID def updateAgentService(self,serviceName, serviceAction, agentId): url=self._v3BaseURL + "/public/core/v3/agent/service" headers = {'Content-Type': "application/json", 'Accept': "application/json","INFA-SESSION-ID":self._v3SessionID} body = { 'serviceName':serviceName, 'serviceAction':serviceAction, 'agentId':agentId} infapy.log.info("agentService API URL - " + url) infapy.log.info("API Headers: " + str(headers)) infapy.log.info("Body: " + str(body)) try: response = re.post(url=url, json=body, headers=headers) data = response.json() infapy.log.debug(str(data)) try: if ("error" in data): infapy.log.error("Please validate the details passed") infapy.log.error(str(data)) raise InvalidDetailsProvided except Exception as e: infapy.log.exception(e) raise except Exception as e: infapy.log.exception(e) raise infapy.log.info(data["message"]) return data

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a3cd937793e2d0c588285b6a5f1e77f851ebcc85

5,703

py

Python

home_application/views.py

pengwow/test-demo

9d5c460b534d93d84f39ae24db82aa101027d199

[ "Apache-2.0" ]

null

home_application/views.py

pengwow/test-demo

9d5c460b534d93d84f39ae24db82aa101027d199

[ "Apache-2.0" ]

4

2020-02-12T01:47:04.000Z

2021-06-10T21:34:36.000Z

home_application/views.py

pengwow/test-demo

9d5c460b534d93d84f39ae24db82aa101027d199

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Tencent is pleased to support the open source community by making 蓝鲸智云(BlueKing) available. Copyright (C) 2017 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT 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. """ from common.mymako import render_mako_context, render_json from blueking.component.shortcuts import get_client_by_request from django.views.decorators.csrf import csrf_exempt from models import TEST, HostDisk, ScriptExecInfo import json import base64 def home(request): """ 首页 """ # yewu = [ # {'id': 1, "name": u"业务1"}, # {'id': 2, "name": u"业务2"}, # {'id': 3, "name": u"业务3"}, # ] # 从环境配置获取APP信息，从request获取当前用户信息 client = get_client_by_request(request) kwargs = {} result = client.cc.search_business(kwargs) print(result) yewu = result['data']['info'] return render_mako_context(request, '/home_application/home.html', { "yewu": yewu, "AAA": u"业务列表" }) def submit_template(request): """ 首页 """ print(request.body) return render_json({"1111111": "dddddddddd"}) def dev_guide(request): """ 开发指引 """ return render_mako_context(request, '/home_application/dev_guide.html') def contactus(request): """ 联系我们 """ return render_mako_context(request, '/home_application/contact.html') def tijiao(request): data = json.loads(request.body) print(type(data)) sss = TEST(**data) sss.save() return render_json({"DATA": "AAAAAAAA"}) def host_disk(request): host_list = HostDisk.objects.all() re_list = list() for item in host_list: temp_dict = dict() temp_dict['os'] = item.os temp_dict['host_ip'] = item.host_ip temp_dict['host_name'] = item.host_name temp_dict['host_path'] = item.host_path temp_dict['create_time'] = item.create_time re_list.append(temp_dict) print(re_list) return render_mako_context(request, '/home_application/host_disk.html', {'host_all': re_list} ) def host_tijiao(request): data = request.body print(type(data)) data = json.loads(data) host = HostDisk(**data) host.save() return render_json({"status": "OK"}) def host_script(request): # 根据作业id查询日志 data = ScriptExecInfo.objects.all() client = get_client_by_request(request) script_all = list() for item in data: temp_dict = dict() kwargs = {} kwargs['bk_biz_id'] = item.bk_biz_id kwargs['job_instance_id'] = item.job_instance_id result = client.job.get_job_instance_log(kwargs) log_content = result['data'][0]['step_results'][0]['ip_logs'][0]['log_content'] temp_dict['host_ip'] = item.host_ip temp_dict['log_content'] = log_content temp_dict['script_content'] = item.script_content temp_dict['create_time'] = item.create_time script_all.append(temp_dict) return render_mako_context(request, '/home_application/host_script.html', {'script_all': script_all}, ) def script_tijiao(request): try: print(request.user.username) except Exception as e: print(str(e)) data = json.loads(request.body) client = get_client_by_request(request) kwargs = {} result = client.cc.search_business(kwargs) bk_biz_id = result['data']['info'][0]['bk_biz_id'] script_content = base64.b64encode(data['script_content']) kwargs = dict() kwargs['bk_biz_id'] = bk_biz_id kwargs['script_content'] = script_content kwargs["account"] = "root" kwargs['ip_list'] = [{'bk_cloud_id': 0, "ip": data['host_ip']}] result = client.job.fast_execute_script(kwargs) script_dict = dict() script_dict["host_ip"] = data['host_ip'] script_dict["script_content"] = data['script_content'] script_dict["job_instance_id"] = result['data']['job_instance_id'] script_dict['bk_biz_id'] = bk_biz_id scriptexecinfo = ScriptExecInfo(**script_dict) scriptexecinfo.save() return render_json({"status": "OK"}) # ####################其他 def other(request): return render_mako_context(request, '/home_application/other.html') @csrf_exempt # 注意：需要添加此装饰器 def upload_file(request): # 接收的为文件列表，需要遍历操作 files = request.FILES for item in files: _file = files.get(item) print(_file.name) print(_file.size) with open('./' + str(_file.name), 'wb') as fd: fd.write(_file.file.read()) return render_json({"status": "OK"}) def download_file(request): """ 文件下载 :param request: :return: 文件response """ from django.http import FileResponse # 接收文件名请求 file_name = request.GET.get('filename') fd = open('./' + file_name, 'rb') response = FileResponse(fd) response['Content-Type'] = 'application/octet-stream' response['Content-Disposition'] = 'attachment;filename="%s"' % file_name return response

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a3cdf292bfc1d114fbf7d5d60cd7d8fcf12221e7

455

py

Python

Chapter 6/09 - The built-in multiprocessing module/basic_multiprocessing.py

moseskim/Expert-Python-Programming-Fourth-Edition

5160f974deb2365597b7be9cc032f24bfa13471a

[ "MIT" ]

null

Chapter 6/09 - The built-in multiprocessing module/basic_multiprocessing.py

moseskim/Expert-Python-Programming-Fourth-Edition

5160f974deb2365597b7be9cc032f24bfa13471a

[ "MIT" ]

null

Chapter 6/09 - The built-in multiprocessing module/basic_multiprocessing.py

moseskim/Expert-Python-Programming-Fourth-Edition

5160f974deb2365597b7be9cc032f24bfa13471a

[ "MIT" ]

null

""" "멀티프로세싱"절 예시 `multiprocessing` 모듈을 이용해 새로운 프로세스들을 생성하는 방법을 설명한다. """ from multiprocessing import Process import os def work(identifier): print(f'Hey, I am the process ' f'{identifier}, pid: {os.getpid()}') def main(): processes = [Process(target=work, args=(number,)) for number in range(5)] for process in processes: process.start() while processes: processes.pop().join() if __name__ == "__main__": main()

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0.208791

455

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0.333333

0.083333

0

null

0

null

0

1

0

a3ce427e7608fff21718948d99c9396b801b2425

670

py

Python

sweeper/cloud/localhost/manager.py

dominoFire/sweeper

26c5497b81c8d0c50671f8ab75c1cf5c4c8191c9

[ "MIT" ]

null

sweeper/cloud/localhost/manager.py

dominoFire/sweeper

26c5497b81c8d0c50671f8ab75c1cf5c4c8191c9

[ "MIT" ]

null

sweeper/cloud/localhost/manager.py

dominoFire/sweeper

26c5497b81c8d0c50671f8ab75c1cf5c4c8191c9

[ "MIT" ]

null

__author__ = '@dominofire' import os from sweeper.cloud import resource_config_combinations from sweeper.cloud.localhost import resource_config_factory as config_factory from sweeper.resource import Resource def possible_configs(num): configs = config_factory.list_configs() combs = resource_config_combinations(num, configs) return combs def create_resource(name, config_object): res = Resource(config_object, name, 'localhost', None, None) return res def mount_distributed_file_system(name, vm_resources): vm_first = vm_resources[0] vm_first.execute_command('mkdir ./fileshare') return os.path.join(os.getcwd(), 'fileshare')

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77

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5.732558

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0.11359

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0

0.625

0

null

0

null

0

1

0

a3ced405166d997be98745f69fe1f51cd0fcd9c9

3,193

py

Python

tfx/orchestration/experimental/core/service_jobs_test.py

BACtaki/tfx

29db845200beccbb0ffa1e1e1a091e314a3a470f

[ "Apache-2.0" ]

1,813

2019-02-04T17:17:30.000Z

2022-03-29T13:39:30.000Z

tfx/orchestration/experimental/core/service_jobs_test.py

BACtaki/tfx

29db845200beccbb0ffa1e1e1a091e314a3a470f

[ "Apache-2.0" ]

2,710

2019-02-14T00:41:00.000Z

2022-03-31T07:23:00.000Z

tfx/orchestration/experimental/core/service_jobs_test.py

BACtaki/tfx

29db845200beccbb0ffa1e1e1a091e314a3a470f

[ "Apache-2.0" ]

731

2019-02-04T17:59:18.000Z

2022-03-31T06:45:51.000Z

# Copyright 2021 Google LLC. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for tfx.orchestration.experimental.core.service_jobs.""" from absl.testing.absltest import mock import tensorflow as tf from tfx.orchestration.experimental.core import service_jobs from tfx.orchestration.experimental.core import test_utils class ExceptionHandlingServiceJobManagerWrapperTest(test_utils.TfxTest): def setUp(self): super().setUp() self._mock_service_job_manager = mock.create_autospec( service_jobs.ServiceJobManager, instance=True) self._mock_service_job_manager.ensure_node_services.return_value = ( service_jobs.ServiceStatus.SUCCESS) self._mock_service_job_manager.stop_node_services.return_value = True self._mock_service_job_manager.is_pure_service_node.return_value = True self._mock_service_job_manager.is_mixed_service_node.return_value = False self._wrapper = service_jobs.ExceptionHandlingServiceJobManagerWrapper( self._mock_service_job_manager) def test_calls_forwarded_to_underlying_instance(self): self.assertEqual(service_jobs.ServiceStatus.SUCCESS, self._wrapper.ensure_node_services(mock.Mock(), 'node1')) self.assertTrue(self._wrapper.stop_node_services(mock.Mock(), 'node2')) self.assertTrue(self._wrapper.is_pure_service_node(mock.Mock(), 'node3')) self.assertFalse(self._wrapper.is_mixed_service_node(mock.Mock(), 'node4')) self._mock_service_job_manager.ensure_node_services.assert_called_once_with( mock.ANY, 'node1') self._mock_service_job_manager.stop_node_services.assert_called_once_with( mock.ANY, 'node2') self._mock_service_job_manager.is_pure_service_node.assert_called_once_with( mock.ANY, 'node3') self._mock_service_job_manager.is_mixed_service_node.assert_called_once_with( mock.ANY, 'node4') def test_ensure_node_services_exception_handling(self): self._mock_service_job_manager.ensure_node_services.side_effect = RuntimeError( 'test error') self.assertEqual(service_jobs.ServiceStatus.FAILED, self._wrapper.ensure_node_services(mock.Mock(), 'node1')) self._mock_service_job_manager.ensure_node_services.assert_called_once_with( mock.ANY, 'node1') def test_stop_node_services_exception_handling(self): self._mock_service_job_manager.stop_node_services.side_effect = RuntimeError( 'test error') self.assertFalse(self._wrapper.stop_node_services(mock.Mock(), 'node2')) self._mock_service_job_manager.stop_node_services.assert_called_once_with( mock.ANY, 'node2') if __name__ == '__main__': tf.test.main()

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0

null

0

null

0

1

0

a3d03f04854e2e542f97a3c9c4b2caeaa5e05045

17,041

py

Python

dragonn/models.py

kundajelab/dragonn

431e7c6b94a82972ac0fc3ef76d76e9ce8ba67fc

[ "MIT" ]

251

2016-06-20T20:18:27.000Z

2022-03-03T23:31:38.000Z

dragonn/models.py

kundajelab/dragonn

431e7c6b94a82972ac0fc3ef76d76e9ce8ba67fc

[ "MIT" ]

39

2016-07-01T20:40:59.000Z

2022-02-09T23:30:24.000Z

dragonn/models.py

kundajelab/dragonn

431e7c6b94a82972ac0fc3ef76d76e9ce8ba67fc

[ "MIT" ]

89

2016-06-09T17:59:21.000Z

2021-12-20T03:00:09.000Z

from __future__ import absolute_import, division, print_function import matplotlib import numpy as np import os import subprocess import sys import tempfile matplotlib.use('pdf') import matplotlib.pyplot as plt from abc import abstractmethod, ABCMeta from dragonn.metrics import ClassificationResult from sklearn.svm import SVC as scikit_SVC from sklearn.tree import DecisionTreeClassifier as scikit_DecisionTree from sklearn.ensemble import RandomForestClassifier from keras.models import load_model from dragonn.runtime_metrics import * from dragonn.custom_losses import * import warnings warnings.filterwarnings('ignore') def load_dragonn_model(model_string): custom_objects={"recall":recall, "sensitivity":recall, "specificity":specificity, "fpr":fpr, "fnr":fnr, "fdr":fdr, "precision":precision, "f1":f1, "spearman_corr":spearman_corr, "ambig_binary_crossentropy":ambig_binary_crossentropy, "ambig_mean_squared_error":ambig_mean_squared_error} model=load_model(model_string,custom_objects=custom_objects) return model class Model(object): __metaclass__ = ABCMeta @abstractmethod def __init__(self, **hyperparameters): pass @abstractmethod def train(self, X, y, validation_data): pass @abstractmethod def predict(self, X): pass def test(self, X, y): return ClassificationResult(y, self.predict(X)) def score(self, X, y, metric): return self.test(X, y)[metric] class SequenceDNN(Model): """ Sequence DNN models. Parameters ---------- seq_length : int, optional length of input sequence. keras_model : instance of keras.models.Sequential, optional seq_length or keras_model must be specified. num_tasks : int, optional number of tasks. Default: 1. num_filters : list[int] | tuple[int] number of convolutional filters in each layer. Default: (15,). conv_width : list[int] | tuple[int] width of each layer's convolutional filters. Default: (15,). pool_width : int width of max pooling after the last layer. Default: 35. L1 : float strength of L1 penalty. dropout : float dropout probability in every convolutional layer. Default: 0. verbose: int Verbosity level during training. Valida values: 0, 1, 2. Returns ------- Compiled DNN model. """ def __init__(self, seq_length=None, keras_model=None, use_RNN=False, num_tasks=1, num_filters=(15, 15, 15), conv_width=(15, 15, 15), pool_width=35, GRU_size=35, TDD_size=15, L1=0, dropout=0.0, num_epochs=100, verbose=1): from keras.models import Sequential from keras.layers.core import ( Activation, Dense, Dropout, Flatten, Permute, Reshape ) from keras.layers.convolutional import Convolution2D, MaxPooling2D from keras.layers.recurrent import GRU from keras.regularizers import l1 self.num_tasks = num_tasks self.num_epochs = num_epochs self.verbose = verbose self.train_metrics = [] self.valid_metrics = [] if keras_model is not None and seq_length is None: self.model = keras_model self.num_tasks = keras_model.layers[-1].output_shape[-1] elif seq_length is not None and keras_model is None: self.model = Sequential() assert len(num_filters) == len(conv_width) for i, (nb_filter, nb_col) in enumerate(zip(num_filters, conv_width)): conv_height = 4 if i == 0 else 1 self.model.add(Convolution2D( nb_filter=nb_filter, nb_row=conv_height, nb_col=nb_col, activation='linear', init='he_normal', input_shape=(1, 4, seq_length), W_regularizer=l1(L1), b_regularizer=l1(L1))) self.model.add(Activation('relu')) self.model.add(Dropout(dropout)) self.model.add(MaxPooling2D(pool_size=(1, pool_width))) if use_RNN: num_max_pool_outputs = self.model.layers[-1].output_shape[-1] self.model.add(Reshape((num_filters[-1], num_max_pool_outputs))) self.model.add(Permute((2, 1))) self.model.add(GRU(GRU_size, return_sequences=True)) self.model.add(TimeDistributedDense(TDD_size, activation='relu')) self.model.add(Flatten()) self.model.add(Dense(output_dim=self.num_tasks)) self.model.add(Activation('sigmoid')) self.model.compile(optimizer='adam', loss='binary_crossentropy') else: raise ValueError("Exactly one of seq_length or keras_model must be specified!") def train(self, X, y, validation_data, early_stopping_metric='Loss', early_stopping_patience=5, save_best_model_to_prefix=None): if y.dtype != bool: assert set(np.unique(y)) == {0, 1} y = y.astype(bool) multitask = y.shape[1] > 1 if not multitask: num_positives = y.sum() num_sequences = len(y) num_negatives = num_sequences - num_positives if self.verbose >= 1: print('Training model (* indicates new best result)...') X_valid, y_valid = validation_data early_stopping_wait = 0 best_metric = np.inf if early_stopping_metric == 'Loss' else -np.inf for epoch in range(1, self.num_epochs + 1): self.model.fit(X, y, batch_size=128, nb_epoch=1, class_weight={True: num_sequences / num_positives, False: num_sequences / num_negatives} if not multitask else None, verbose=self.verbose >= 2) epoch_train_metrics = self.test(X, y) epoch_valid_metrics = self.test(X_valid, y_valid) self.train_metrics.append(epoch_train_metrics) self.valid_metrics.append(epoch_valid_metrics) if self.verbose >= 1: print('Epoch {}:'.format(epoch)) print('Train {}'.format(epoch_train_metrics)) print('Valid {}'.format(epoch_valid_metrics), end='') current_metric = epoch_valid_metrics[early_stopping_metric].mean() if (early_stopping_metric == 'Loss') == (current_metric <= best_metric): if self.verbose >= 1: print(' *') best_metric = current_metric best_epoch = epoch early_stopping_wait = 0 if save_best_model_to_prefix is not None: self.save(save_best_model_to_prefix) else: if self.verbose >= 1: print() if early_stopping_wait >= early_stopping_patience: break early_stopping_wait += 1 if self.verbose >= 1: print('Finished training after {} epochs.'.format(epoch)) if save_best_model_to_prefix is not None: print("The best model's architecture and weights (from epoch {0}) " 'were saved to {1}.arch.json and {1}.weights.h5'.format( best_epoch, save_best_model_to_prefix)) def predict(self, X): return self.model.predict(X, batch_size=128, verbose=False) def get_sequence_filters(self): """ Returns 3D array of 2D sequence filters. """ return self.model.layers[0].get_weights()[0].squeeze(axis=1) @staticmethod def _plot_scores(X, output_directory, peak_width, score_func, score_name): from dragonn.plot import plot_bases_on_ax scores = score_func(X).squeeze(axis=2) # (num_task, num_samples, num_bases, sequence_length) try: os.makedirs(output_directory) except OSError: pass num_tasks = len(scores) for task_index, task_scores in enumerate(scores): for sequence_index, sequence_scores in enumerate(task_scores): # sequence_scores is num_bases x sequence_length basewise_max_sequence_scores = sequence_scores.max(axis=0) plt.clf() figure, (top_axis, bottom_axis) = plt.subplots(2) top_axis.plot(range(1, len(basewise_max_sequence_scores) + 1), basewise_max_sequence_scores) top_axis.set_title('{} scores (motif highlighted)'.format(score_name)) peak_position = basewise_max_sequence_scores.argmax() top_axis.axvspan(peak_position - peak_width, peak_position + peak_width, color='grey', alpha=0.1) peak_sequence_scores = sequence_scores[:, peak_position - peak_width : peak_position + peak_width].T # Set non-max letter_heights to zero letter_heights = np.zeros_like(peak_sequence_scores) letter_heights[np.arange(len(letter_heights)), peak_sequence_scores.argmax(axis=1)] = \ basewise_max_sequence_scores[peak_position - peak_width : peak_position + peak_width] plot_bases_on_ax(letter_heights, bottom_axis) bottom_axis.set_xticklabels(tuple(map( str, np.arange(peak_position - peak_width, peak_position + peak_width + 1)))) bottom_axis.tick_params(axis='x', labelsize='small') plt.xlabel('Position') plt.ylabel('Score') plt.savefig(os.path.join(output_directory, 'sequence_{}{}'.format( sequence_index, '_task_{}'.format(task_index) if num_tasks > 1 else ''))) plt.close() def plot_deeplift(self, X, output_directory, peak_width=10): self._plot_scores(X, output_directory, peak_width, score_func=self.deeplift, score_name='DeepLift') def plot_in_silico_mutagenesis(self, X, output_directory, peak_width=10): self._plot_scores(X, output_directory, peak_width, score_func=self.in_silico_mutagenesis, score_name='ISM') def plot_architecture(self, output_file): from dragonn.visualize_util import plot as plot_keras_model plot_keras_model(self.model, output_file, show_shape=True) def save(self, save_best_model_to_prefix): arch_fname = save_best_model_to_prefix + '.arch.json' weights_fname = save_best_model_to_prefix + '.weights.h5' open(arch_fname, 'w').write(self.model.to_json()) self.model.save_weights(weights_fname, overwrite=True) @staticmethod def load(model_hdf5_fname=None, arch_fname=None, weights_fname=None): if model_hdf5_fname!=None: from keras.models import load_model sequence_dnn=SequenceDNN(keras_model=load_model(model_hdf5_fname)) else: from keras.models import model_from_json model_json_string = open(arch_fname).read() sequence_dnn = SequenceDNN(keras_model=model_from_json(model_json_string)) if weights_fname is not None: sequence_dnn.model.load_weights(weights_fname) return sequence_dnn class MotifScoreRNN(Model): def __init__(self, input_shape, gru_size=10, tdd_size=4): from keras.models import Sequential from keras.layers.core import ( Activation, Dense, Flatten, TimeDistributedDense ) from keras.layers.recurrent import GRU self.model = Sequential() self.model.add(GRU(gru_size, return_sequences=True, input_shape=input_shape)) if tdd_size is not None: self.model.add(TimeDistributedDense(tdd_size)) self.model.add(Flatten()) self.model.add(Dense(1)) self.model.add(Activation('sigmoid')) print('Compiling model...') self.model.compile(optimizer='adam', loss='binary_crossentropy') def train(self, X, y, validation_data): from keras.callbacks import EarlyStopping print('Training model...') multitask = y.shape[1] > 1 if not multitask: num_positives = y.sum() num_sequences = len(y) num_negatives = num_sequences - num_positives self.model.fit( X, y, batch_size=128, nb_epoch=100, validation_data=validation_data, class_weight={True: num_sequences / num_positives, False: num_sequences / num_negatives} if not multitask else None, callbacks=[EarlyStopping(monitor='val_loss', patience=10)], verbose=True) def predict(self, X): return self.model.predict(X, batch_size=128, verbose=False) class gkmSVM(Model): def __init__(self, prefix='./gkmSVM', word_length=11, mismatches=3, C=1, threads=1, cache_memory=100, verbosity=4): self.word_length = word_length self.mismatches = mismatches self.C = C self.threads = threads self.prefix = '_'.join(map(str, (prefix, word_length, mismatches, C))) options_list = zip( ['-l', '-d', '-c', '-T', '-m', '-v'], map(str, (word_length, mismatches, C, threads, cache_memory, verbosity))) self.options = ' '.join([' '.join(option) for option in options_list]) @property def model_file(self): model_fname = '{}.model.txt'.format(self.prefix) return model_fname if os.path.isfile(model_fname) else None @staticmethod def encode_sequence_into_fasta_file(sequence_iterator, ofname): """writes sequences into fasta file """ with open(ofname, "w") as wf: for i, seq in enumerate(sequence_iterator): print('>{}'.format(i), file=wf) print(seq, file=wf) def train(self, X, y, validation_data=None): """ Trains gkm-svm, saves model file. """ y = y.squeeze() pos_sequence = X[y] neg_sequence = X[~y] pos_fname = "%s.pos_seq.fa" % self.prefix neg_fname = "%s.neg_seq.fa" % self.prefix # create temporary fasta files self.encode_sequence_into_fasta_file(pos_sequence, pos_fname) self.encode_sequence_into_fasta_file(neg_sequence, neg_fname) # run command command = ' '.join( ('gkmtrain', self.options, pos_fname, neg_fname, self.prefix)) process = subprocess.Popen(command, stdout=subprocess.PIPE, shell=True) process.wait() # wait for it to finish # remove fasta files os.system("rm %s" % pos_fname) os.system("rm %s" % neg_fname) def predict(self, X): if self.model_file is None: raise RuntimeError("GkmSvm hasn't been trained!") # write test fasta file test_fname = "%s.test.fa" % self.prefix self.encode_sequence_into_fasta_file(X, test_fname) # test gkmsvm temp_ofp = tempfile.NamedTemporaryFile() threads_option = '-T %s' % (str(self.threads)) command = ' '.join(['gkmpredict', test_fname, self.model_file, temp_ofp.name, threads_option]) process = subprocess.Popen(command, shell=True) process.wait() # wait for it to finish os.system("rm %s" % test_fname) # remove fasta file # get classification results temp_ofp.seek(0) y = np.array([line.split()[-1] for line in temp_ofp], dtype=float) temp_ofp.close() return np.expand_dims(y, 1) class SVC(Model): def __init__(self): self.classifier = scikit_SVC(probability=True, kernel='linear') def train(self, X, y, validation_data=None): self.classifier.fit(X, y) def predict(self, X): return self.classifier.predict_proba(X)[:, 1:] class DecisionTree(Model): def __init__(self): self.classifier = scikit_DecisionTree() def train(self, X, y, validation_data=None): self.classifier.fit(X, y) def predict(self, X): predictions = np.asarray(self.classifier.predict_proba(X))[..., 1] if len(predictions.shape) == 2: # multitask predictions = predictions.T else: # single-task predictions = np.expand_dims(predictions, 1) return predictions class RandomForest(DecisionTree): def __init__(self): self.classifier = RandomForestClassifier(n_estimators=100)

41.261501

101

0.607007

2,039

17,041

4.835213

0.192251

0.030125

0.019475

0.012172

0.311086

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0.19292

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0.140379

0.113602

0

0.012492

0.295347

17,041

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1

0.096273

false

0.012422

0.093168

0.015528

0.248447

0.040373

0

null

0

null

0

1

0

a3d07703df62a187a4037e7b46931b65c218c987

3,921

py

Python

dgt/inference/forward_inference.py

fractalego/dgt

6781b9445d93c4a1680ab3d5636803c81062cc67

[ "MIT" ]

3

2021-07-26T02:07:15.000Z

2021-12-21T22:36:15.000Z

dgt/inference/forward_inference.py

fractalego/dgt

6781b9445d93c4a1680ab3d5636803c81062cc67

[ "MIT" ]

null

dgt/inference/forward_inference.py

fractalego/dgt

6781b9445d93c4a1680ab3d5636803c81062cc67

[ "MIT" ]

null

import logging import random from dgt.graph.graph_matcher import GraphWeightedMatch from dgt.utils import graph_iterations _logger = logging.getLogger(__name__) def find_weight_between(s, first, last): try: start = s.index(first) + len(first) end = s.index(last, start) return s[start:end] except ValueError: return 1 def clean_between(s, first, last): try: start = s.index(first) + len(first) end = s.index(last, start) new_s = s[:start - 1] + s[end + 1:] return new_s except ValueError: return s def eliminate_spaces(line): line = line.replace(' ', '') line = line.replace('\t', '') line = line.replace('\n', '') return line class UniqueNamesModifier: def apply(self, g): from ..auxiliary import get_random_name substitution_dict = {} for v in g.vs: random_name = get_random_name() old_name = v['name'] new_name = old_name + random_name v['name'] = new_name substitution_dict[old_name] = new_name try: for v in g.vs: referring_name = v['refers_to'] if referring_name: v['refers_to'] = substitution_dict[referring_name] except Exception as e: _logger.warning("Exception while substituting refers_to ID: " + str(e)) for e in g.es: e['name'] += get_random_name() class BaseForwardInference: def compute(self): return None class ForwardInference(BaseForwardInference): _unique = UniqueNamesModifier() def __init__(self, data, knowledge, permutation_shift, max_depth=1): self.permutations = permutation_shift self.data = data self.knowledge = knowledge self._max_depth = max_depth self.permutation_shift = permutation_shift def __apply_clause_to_graph(self, rule, data, i): drs = data.copy() drs.visit(self._unique) w = 1 iterations = graph_iterations(drs._g) if not iterations: return drs, 0 drs._g = iterations[self.permutations[i] % len(iterations)] if not rule.gradient: weighted_match = GraphWeightedMatch(rule.get_hypothesis(), self.knowledge._metric, self.knowledge._relations_metric) w = drs.visit(weighted_match) is_match = drs.visit(rule) if not is_match: return drs, 0 return drs, w def _compute_step(self, data_tuple, i): """ Applies all the rules to a drs :return: all the variants of the drs after a rule match as a pair (<NEW_DRS>, <WEIGHT>) """ data = data_tuple[0] prior_w = data_tuple[1] clauses = self.knowledge.ask_rule(data) results = [] for clause_tuple in clauses: rule = clause_tuple[0] rule_weight = rule.weight prior_rules = list(data_tuple[2]) if rule in prior_rules: # A rule can be used only once per path continue drs, w = self.__apply_clause_to_graph(rule, data, i) if w > 0: prior_rules.append(rule) prior_rules.append(drs) results.append((drs, prior_w * w * rule_weight, prior_rules)) return results def compute(self): results = [] to_process = [(self.data, 1, [self.data])] for i in range(self._max_depth): new_results = [] for data_tuple in to_process: new_results += self._compute_step(data_tuple, i) if not new_results: break to_process = sorted(new_results, key=lambda x: -x[1]) results += to_process results = sorted(results, key=lambda x: -x[1]) return results

30.632813

95

0.579444

479

3,921

4.524008

0.263048

0.024919

0.020766

0.01569

0.118136

0.074758

0.057222

0

0.005693

0.327978

3,921

127

96

30.874016

0.816698

0.040041

0

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0.0209

0

1

0.089109

false

0

0.049505

0.009901

0.287129

0

null

0

null

0

1

0

a3d0a689ffb0010c1b8ab3fafb0b2e1dd2c2562d

1,528

py

Python

serverPythonClient/client.py

ikekilinc/dnnSuperBinoculars

b0fc584b1d449961bdbab37cf9d72c0b466f197f

[ "MIT" ]

null

serverPythonClient/client.py

ikekilinc/dnnSuperBinoculars

b0fc584b1d449961bdbab37cf9d72c0b466f197f

[ "MIT" ]

null

serverPythonClient/client.py

ikekilinc/dnnSuperBinoculars

b0fc584b1d449961bdbab37cf9d72c0b466f197f

[ "MIT" ]

null

import argparse import cv2 import common # from .utils.cropAtCenter import cropImageCenter # from cropAtCenter import cropImageCenter from gabriel_client.websocket_client import WebsocketClient from gabriel_client.opencv_adapter import OpencvAdapter DEFAULT_SERVER_HOST = '128.2.212.50' DEFAULT_ZOOM_FACTOR = 10 def preprocess(frame): # return frame print(type(frame), frame.shape) width, height = frame.shape[1], frame.shape[0] left = int(width/2 * (1 - 1/DEFAULT_ZOOM_FACTOR)) top = int(height/2 * (1 - 1/DEFAULT_ZOOM_FACTOR)) right = int(width/2 * (1 + 1/DEFAULT_ZOOM_FACTOR)) bottom = int(height/2 * (1 + 1/DEFAULT_ZOOM_FACTOR)) cropped_frame = frame[top:bottom, left:right] return cropped_frame def produce_extras(): return None def consume_frame(frame, _): cv2.imshow('Image from server', frame) cv2.waitKey(1) def main(): common.configure_logging() parser = argparse.ArgumentParser() parser.add_argument( 'source_name', nargs='?', default=common.DEFAULT_SOURCE_NAME) parser.add_argument('server_host', nargs='?', default=DEFAULT_SERVER_HOST) args = parser.parse_args() capture = cv2.VideoCapture(0) opencv_adapter = OpencvAdapter( preprocess, produce_extras, consume_frame, capture, args.source_name) client = WebsocketClient( args.server_host, common.WEBSOCKET_PORT, opencv_adapter.get_producer_wrappers(), opencv_adapter.consumer) client.launch() if __name__ == '__main__': main()

25.898305

78

0.719241

193

1,528

5.440415

0.362694

0.052381

0.080952

0.038095

0.108571

0

0.024564

0.174084

1,528

58

79

26.344828

0.807448

0.066099

0

0.042867

0

1

0.108108

false

0

0.135135

0.027027

0.297297

0.027027

0

null

0

null

0

1

0

a3d10c9654ae4266e8db0dc3b63e312a5537bc75

719

py

Python

src/DeepCard.API/batch.py

SharsDela/BankCardRecognize

ce80589bc5a5afaba2b97b1ccab35354fb99b548

[ "Apache-2.0" ]

7

2019-09-01T13:36:52.000Z

2021-05-20T19:38:40.000Z

src/DeepCard.API/batch.py

SharsDela/BankCardRecognize

ce80589bc5a5afaba2b97b1ccab35354fb99b548

[ "Apache-2.0" ]

1

2019-09-01T13:37:50.000Z

2020-09-18T10:35:20.000Z

src/DeepCard.API/batch.py

SharsDela/BankCardRecognize

ce80589bc5a5afaba2b97b1ccab35354fb99b548

[ "Apache-2.0" ]

2

2020-02-03T01:57:36.000Z

2020-03-05T11:19:14.000Z

from api import get_result import os import shutil from glob import glob from PIL import Image if __name__ == '__main__': image_files = glob('./test_images/*.*') result_dir = './test_results' if os.path.exists(result_dir): shutil.rmtree(result_dir) os.mkdir(result_dir) txt_file = os.path.join(result_dir, 'result.txt') txt_f = open(txt_file, 'w') for image_file in sorted(image_files): if ".gitkeep" in image_files: continue print("Finded file", image_file, end=" ") result = get_result(Image.open(image_file)) print(":", result) txt_f.write(image_file.split('/')[-1].split('.')[0] + ':' + result + '\n') txt_f.close()

28.76

82

0.623088

100

719

4.19

0.42

0.107399

0

0.003604

0.228095

719

25

83

28.76

0.751351

0

0.105556

0

1

0

false

0

0.238095

0

0.238095

0.095238

0

null

0

null

0

1

0

a3d15d48b5db9739108b6ecc4d1923cf6d0d654b

4,106

py

Python

CIM14/ENTSOE/Equipment/Core/Curve.py

MaximeBaudette/PyCIM

d68ee5ccfc1d32d44c5cd09fb173142fb5ff4f14

[ "MIT" ]

58

2015-04-22T10:41:03.000Z

2022-03-29T16:04:34.000Z

CIM14/ENTSOE/Equipment/Core/Curve.py

MaximeBaudette/PyCIM

d68ee5ccfc1d32d44c5cd09fb173142fb5ff4f14

[ "MIT" ]

12

2015-08-26T03:57:23.000Z

2020-12-11T20:14:42.000Z

CIM14/ENTSOE/Equipment/Core/Curve.py

MaximeBaudette/PyCIM

d68ee5ccfc1d32d44c5cd09fb173142fb5ff4f14

[ "MIT" ]

35

2015-01-10T12:21:03.000Z

2020-09-09T08:18:16.000Z

# Copyright (C) 2010-2011 Richard Lincoln # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to # deal in the Software without restriction, including without limitation the # rights to use, copy, modify, merge, publish, distribute, sublicense, and/or # sell copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING # FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS # IN THE SOFTWARE. from CIM14.ENTSOE.Equipment.Core.IdentifiedObject import IdentifiedObject class Curve(IdentifiedObject): """A multi-purpose curve or functional relationship between an independent variable (X-axis) and dependent (Y-axis) variables. """ def __init__(self, y1Unit="A", curveStyle="straightLineYValues", xUnit="A", CurveDatas=None, *args, **kw_args): """Initialises a new 'Curve' instance. @param y1Unit: The Y1-axis units of measure. Values are: "A", "rad", "none", "g", "W/Hz", "V", "m2", "VA", "VArh", "N", "Pa", "VAh", "F", "H", "Hz-1", "W/s", "J", "m", "S", "min", "deg", "J/s", "s", "Wh", "m3", "oC", "V/VAr", "s-1", "h", "W", "ohm", "Hz", "VAr", "kg/J" @param curveStyle: The style or shape of the curve. Values are: "straightLineYValues", "rampYValue", "constantYValue", "formula" @param xUnit: The X-axis units of measure. Values are: "A", "rad", "none", "g", "W/Hz", "V", "m2", "VA", "VArh", "N", "Pa", "VAh", "F", "H", "Hz-1", "W/s", "J", "m", "S", "min", "deg", "J/s", "s", "Wh", "m3", "oC", "V/VAr", "s-1", "h", "W", "ohm", "Hz", "VAr", "kg/J" @param CurveDatas: The point data values that define a curve """ #: The Y1-axis units of measure. Values are: "A", "rad", "none", "g", "W/Hz", "V", "m2", "VA", "VArh", "N", "Pa", "VAh", "F", "H", "Hz-1", "W/s", "J", "m", "S", "min", "deg", "J/s", "s", "Wh", "m3", "oC", "V/VAr", "s-1", "h", "W", "ohm", "Hz", "VAr", "kg/J" self.y1Unit = y1Unit #: The style or shape of the curve. Values are: "straightLineYValues", "rampYValue", "constantYValue", "formula" self.curveStyle = curveStyle #: The X-axis units of measure. Values are: "A", "rad", "none", "g", "W/Hz", "V", "m2", "VA", "VArh", "N", "Pa", "VAh", "F", "H", "Hz-1", "W/s", "J", "m", "S", "min", "deg", "J/s", "s", "Wh", "m3", "oC", "V/VAr", "s-1", "h", "W", "ohm", "Hz", "VAr", "kg/J" self.xUnit = xUnit self._CurveDatas = [] self.CurveDatas = [] if CurveDatas is None else CurveDatas super(Curve, self).__init__(*args, **kw_args) _attrs = ["y1Unit", "curveStyle", "xUnit"] _attr_types = {"y1Unit": str, "curveStyle": str, "xUnit": str} _defaults = {"y1Unit": "A", "curveStyle": "straightLineYValues", "xUnit": "A"} _enums = {"y1Unit": "UnitSymbol", "curveStyle": "CurveStyle", "xUnit": "UnitSymbol"} _refs = ["CurveDatas"] _many_refs = ["CurveDatas"] def getCurveDatas(self): """The point data values that define a curve """ return self._CurveDatas def setCurveDatas(self, value): for x in self._CurveDatas: x.Curve = None for y in value: y._Curve = self self._CurveDatas = value CurveDatas = property(getCurveDatas, setCurveDatas) def addCurveDatas(self, *CurveDatas): for obj in CurveDatas: obj.Curve = self def removeCurveDatas(self, *CurveDatas): for obj in CurveDatas: obj.Curve = None

52.641026

277

0.614467

574

4,106

4.355401

0.329268

0.0352

0.0176

0.0288

0.3312

0.2976

0.2384

0

0.011057

0.207014

4,106

77

278

53.324675

0.756757

0.640039

0

0.066667

0

0.125804

0

1

0.166667

false

0

0.033333

0

0.5

0

null

0

null

0

1

0

a3d2324b7f134c8871f8f82a96cc6abc0a30b3ea

2,432

py

Python

fluent/syntax/errors.py

unclenachoduh/python-fluent

1d15bdc94a37ecb488a80aefcdd37b8cb5535f73

[ "Apache-2.0" ]

null

fluent/syntax/errors.py

unclenachoduh/python-fluent

1d15bdc94a37ecb488a80aefcdd37b8cb5535f73

[ "Apache-2.0" ]

null

fluent/syntax/errors.py

unclenachoduh/python-fluent

1d15bdc94a37ecb488a80aefcdd37b8cb5535f73

[ "Apache-2.0" ]

null

from __future__ import unicode_literals class ParseError(Exception): def __init__(self, code, *args): self.code = code self.args = args self.message = get_error_message(code, args) def get_error_message(code, args): if code == 'E00001': return 'Generic error' if code == 'E0002': return 'Expected an entry start' if code == 'E0003': return 'Expected token: "{}"'.format(args[0]) if code == 'E0004': return 'Expected a character from range: "{}"'.format(args[0]) if code == 'E0005': msg = 'Expected message "{}" to have a value or attributes' return msg.format(args[0]) if code == 'E0006': msg = 'Expected term "{}" to have a value' return msg.format(args[0]) if code == 'E0007': return 'Keyword cannot end with a whitespace' if code == 'E0008': return 'The callee has to be a simple, upper-case identifier' if code == 'E0009': return 'The key has to be a simple identifier' if code == 'E0010': return 'Expected one of the variants to be marked as default (*)' if code == 'E0011': return 'Expected at least one variant after "->"' if code == 'E0012': return 'Expected value' if code == 'E0013': return 'Expected variant key' if code == 'E0014': return 'Expected literal' if code == 'E0015': return 'Only one variant can be marked as default (*)' if code == 'E0016': return 'Message references cannot be used as selectors' if code == 'E0017': return 'Variants cannot be used as selectors' if code == 'E0018': return 'Attributes of messages cannot be used as selectors' if code == 'E0019': return 'Attributes of terms cannot be used as placeables' if code == 'E0020': return 'Unterminated string expression' if code == 'E0021': return 'Positional arguments must not follow named arguments' if code == 'E0022': return 'Named arguments must be unique' if code == 'E0023': return 'VariantLists are only allowed inside of other VariantLists.' if code == 'E0024': return 'Cannot access variants of a message.' if code == 'E0025': return 'Unknown escape sequence: {}'.format(args[0]) if code == 'E0026': return 'Invalid Unicode escape sequence: {}'.format(args[0]) return code

36.298507

76

0.60773

307

2,432

4.771987

0.374593

0.106485

0.045051

0.044369

0.238225

0.12628

0.094881

0

0.063356

0.279605

2,432

66

77

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0

0.032258

0

0.441612

0

1

0.032258

false

0

0.016129

0

0.5

0

null

0

null

0

1

0

a3d2e1e0e46f7f6e0817c75f138edaf65c103137

14,084

py

Python

twitterinfrastructure/CH-Data-Public.py

jacob-heglund/socialsensing-jh

fd6d2d749f40fee46bee749ff868212bf117a747

[ "BSD-2-Clause", "MIT" ]

null

twitterinfrastructure/CH-Data-Public.py

jacob-heglund/socialsensing-jh

fd6d2d749f40fee46bee749ff868212bf117a747

[ "BSD-2-Clause", "MIT" ]

null

twitterinfrastructure/CH-Data-Public.py

jacob-heglund/socialsensing-jh

fd6d2d749f40fee46bee749ff868212bf117a747

[ "BSD-2-Clause", "MIT" ]

null

''' Created on Mar 22, 2018 Edited on Jan 11, 2019 @author: npvance2 @author: curtisd2 Variables that will need to be edited/personalized: monitorID in Variables() (line 27) projectStartDate in Variables() (line 28) projectEndDate in Variables() (line 29) authToken in getAuthToken() (line 49) consumer_key in twitterAPI() (line 62) consumer_secret in twitterAPI() (line 63) access_token in twitterAPI() (line 64) access_secret in twitterAPI() (line 65) ''' from datetime import date, timedelta import urllib.request import json import csv import tweepy from tweepy import OAuthHandler def Variables(): monitorID = "9926183772" # The numerical ID for your Crimson Hexagon monitor startDate = "yyyy-mm-dd" # Date must be in yyyy-mm-dd format endDate = "yyyy-mm-dd" # Date must be in yyyy-mm-dd format variableMap = {} variableMap['monitorID'] = monitorID variableMap['startDate'] = startDate variableMap['endDate'] = endDate return variableMap def getURL(): #provides URL for Crimson API urlStart = "https://api.crimsonhexagon.com/api" return urlStart ########### # # You'll need to generate your own Crimson API key/token from here: # https://apidocs.crimsonhexagon.com/reference # ########### def getAuthToken(): #provides auth token needed to access Crimson API authToken = '' authToken = "&auth="+authToken return authToken ########### # # You'll need to add your own Twitter API keys here. # Instructions on generating API keys: https://developer.twitter.com/en/docs/basics/authentication/guides/access-tokens.html # API reference guide: https://developer.twitter.com/en/docs/api-reference-index.html # ########### def twitterAPI(): #Provides access keys for Twitter API consumer_key = '2S1Z7Giq0oOf3w0R0sJUPnLFx' consumer_secret = '9IPOE8dqWzUPseAPHeNxTTv1jAr9BNj8mF2ryw8DIud8Ot8VCe' access_token = '998275516892409858-hQ1pk5wKg1YyxUrbiFkuFHKHqztPMNE' access_secret = 'gsXqGx1gU93HkKNDupTPt56ZnAmmalsaSNBUuoBToraBw' if (consumer_key == '') or (consumer_secret =='') or (access_token =='') or (access_secret ==''): print("Not all Twitter keys have been entered, please add them to the script and try again") auth = OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_secret) api = tweepy.API(auth, wait_on_rate_limit=True, wait_on_rate_limit_notify=True) return api def getTwitterURL(): #provides URL for Twitter api urlStart = "https://api.twitter.com/1.1/statuses/lookup.json?id=" return urlStart def DatePull(startdate, enddate): listArray = [] startdate = date(int(startdate[0:4]), int(startdate[5:7]), int(startdate[8:10])) enddate = date(int(enddate[0:4]), int(enddate[5:7]), int(enddate[8:10])) while startdate <= enddate: listArray.append(str(startdate)) startdate += timedelta(days=1) return listArray def main(): monitorID = Variables()['monitorID'] projectStartDate = Variables()['startDate'] projectEndDate = Variables()['endDate'] fPath = "Monitor-"+monitorID+'-from-'+projectStartDate+'-to-'+projectEndDate+'.csv' lineArray = DatePull(projectStartDate, projectEndDate) print("------------------------------") print("MonitorID is "+monitorID) print(lineArray[0],lineArray[-1]) with open(fPath, 'w', newline = '', encoding = 'utf-8') as f: writer = csv.writer(f) header = ["PostType","PostDate","PostTime","URL","TweetID","Contents","RetweetCount","FavoriteCount","Location","Language","Sentiment","NeutralScore","PositiveScore","NegativeScore","Followers","Friends","Author","AuthorGender","AuthorTweets"] writer.writerow(header) for i in range(len(lineArray)-1): print(lineArray[i]) startDate = lineArray[i] endDate = lineArray[i+1] dates = "&start="+startDate+"&end="+endDate #Combines start and end date into format needed for API call urlStart = getURL() #Gets URL authToken = getAuthToken() #Gets auth token endpoint = "/monitor/posts?id="; #endpoint needed for this query extendLimit = "&extendLimit=true" #extends call number from 500 to 10,000 fullContents = "&fullContents=true" #Brings back full contents for Blog and Tumblr posts which are usually truncated around search keywords. This can occasionally disrupt CSV formatting. urlData = urlStart+endpoint+monitorID+authToken+dates+extendLimit+fullContents #Combines all API calls parts into full URL webURL = urllib.request.urlopen(urlData) if (webURL.getcode() == 200): with open(fPath, 'a', newline='', encoding='utf-8') as f: writer = csv.writer(f) data = webURL.read().decode('utf8') theJSON = json.loads(data) postDates = [] #These initialize the attributes of the final output postTimes = [] urls = [] contents = [] authors = [] authorGenders = [] locations = [] languages = [] postTypes = [] sentiments = [] neutralScore = [] positiveScore = [] negativeScore = [] tweetIDs = [] followers = [] friends = [] retweetCounts = [] favoritesCount = [] statusesCount = [] tweetCount = 0 tempTweetIDs = [] api = twitterAPI() c = 0 for i in theJSON["posts"]: postDates.append("") postTimes.append("") if ('date' in i): #identifies date posted tempDate = str(i["date"]) dateTime = tempDate.split("T") postDates[c] = dateTime[0] postTimes[c] = dateTime[1] urls.append(i["url"]) contents.append("") if ('contents' in i): #identifies post contents contents[c] = i["contents"].replace(",","").replace("\n"," ") #replaces commas and new lines to facilitate CSV formatting, this occasionally missed new lines in some blog posts which I'm working to fix authors.append("") if ('author' in i): #identifies author authors[c] = i["author"].replace(",","") authorGenders.append("") if ('authorGender' in i): #identifies author gender authorGenders[c] = i["authorGender"] locations.append("") if ('location' in i): #identifies location locations[c] = i["location"].replace(",","") languages.append("") if ('language' in i): #identifies language specified in the author's profile languages[c] = i["language"] postTypes.append(i["type"]) #identifies the type of post, i.e. Twitter, Tumblr, Blog tweetIDs.append("") followers.append("") friends.append("") retweetCounts.append("") favoritesCount.append("") statusesCount.append("") if postTypes[c] == "Twitter": #if the post type is Twitter it goes through more processing tweetCount = tweetCount + 1 #counts number of tweets tweetSplit = urls[c].split("status/") #splits URL to get tweetID tweetIDs[c] = tweetSplit[1] tempTweetIDs.append(tweetIDs[c]) if tweetCount == 100: #the max number of TweetIDs in one API call is 100 so a call is run every 100 tweets identified tweepys = api.statuses_lookup(id_=tempTweetIDs) #call to Twitter API for tweet in tweepys: tempID = tweet.id_str #finds tweetsID postMatch = 0 for idMatch in tweetIDs: if idMatch==tempID: #matches tweetID in Twitter API call to tweetID stored from Crimson API tempDate = str(tweet.created_at).replace(" "," ") #These all fill the matching Crimson attributes to those found in the Twitter API dateTime = tempDate.split(" ") postDates[postMatch] = dateTime[0] postTimes[postMatch] = dateTime[1] contents[postMatch] = tweet.text.replace(",","") authors[postMatch] = tweet.author.screen_name followers[postMatch] = str(tweet.author.followers_count) friends[postMatch] = str(tweet.author.friends_count) retweetCounts[postMatch] = str(tweet.retweet_count) favoritesCount[postMatch] = str(tweet.favorite_count) statusesCount[postMatch] = str(tweet.author.statuses_count) postMatch = postMatch + 1 tweetCount = 0 #clears tweet count for a new 100 tempTweetIDs = [] #clears tweetIDs for next call sentiments.append("") neutralScore.append("") positiveScore.append("") negativeScore.append("") if ('categoryScores' in i): #finds sentiment value and matching attribute for l in i["categoryScores"]: catName = l["categoryName"] if catName == "Basic Neutral": neutralScore[c] = l["score"] elif catName =="Basic Positive": positiveScore[c] = l["score"] elif catName == "Basic Negative": negativeScore[c] = l["score"] if neutralScore[c] > positiveScore[c] and neutralScore[c] > negativeScore[c]: sentiments[c] = "Basic Neutral" if positiveScore[c] > neutralScore[c] and positiveScore[c] > negativeScore[c]: sentiments[c] = "Basic Positive" if negativeScore[c] > positiveScore[c] and negativeScore[c] > neutralScore[c]: sentiments[c] = "Basic Negative" c = c + 1 if len(tempTweetIDs) != 0: #after loop the Twitter API call must run one more time to clean up all the tweets since the last 100 try: tweepys = api.statuses_lookup(id_=tempTweetIDs) for tweet in tweepys: tempID = tweet.id_str postMatch = 0 for idMatch in tweetIDs: if idMatch==tempID: tempDate = str(tweet.created_at).replace(" "," ") dateTime = tempDate.split(" ") postDates[postMatch] = dateTime[0] postTimes[postMatch] = dateTime[1] contents[postMatch] = tweet.text.replace(",","") authors[postMatch] = tweet.author.screen_name followers[postMatch] = str(tweet.author.followers_count) friends[postMatch] = str(tweet.author.friends_count) retweetCounts[postMatch] = str(tweet.retweet_count) favoritesCount[postMatch] = str(tweet.favorite_count) statusesCount[postMatch] = str(tweet.author.statuses_count) postMatch = postMatch + 1 tweetCount = 0 except: print("Tweepy error: skipping cleanup") pC = 0 for pDate in postDates: #iterates through the word lists and prints matching posts to CSV csvRow=[postTypes[pC], pDate, postTimes[pC], urls[pC], str(tweetIDs[pC]), contents[pC].replace("\n"," "), retweetCounts[pC], favoritesCount[pC], locations[pC], languages[pC], sentiments[pC], str(neutralScore[pC]), str(positiveScore[pC]), str(negativeScore[pC]), followers[pC], friends[pC], authors[pC], authorGenders[pC], statusesCount[pC]] writer.writerow(csvRow) pC = pC + 1 else: print("Server Error, No Data" + str(webURL.getcode())) #displays error if Crimson URL fails if __name__ == '__main__': main()

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0.274092

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0.196556

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0

0.017683

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null

0

null

0

1

0

a3d3652391aca6bc7ecc488069329c58736eb71f

1,286

py

Python

roles/slurm/files/startnode.py

danhnguyen48/slurm-elastic-computing

0793cf23677169a6d9dceea0793118bc00c0913e

[ "MIT" ]

null

roles/slurm/files/startnode.py

danhnguyen48/slurm-elastic-computing

0793cf23677169a6d9dceea0793118bc00c0913e

[ "MIT" ]

null

roles/slurm/files/startnode.py

danhnguyen48/slurm-elastic-computing

0793cf23677169a6d9dceea0793118bc00c0913e

[ "MIT" ]

null

#! /opt/cloud_sdk/bin/python import asyncio import logging import subprocess import sys import citc_cloud def handle_exception(exc_type, exc_value, exc_traceback): if issubclass(exc_type, KeyboardInterrupt): sys.__excepthook__(exc_type, exc_value, exc_traceback) return log.critical("Uncaught exception", exc_info=(exc_type, exc_value, exc_traceback)) async def main() -> None: nodespace = citc_cloud.get_nodespace() keys_file = "/home/slurm/opc_authorized_keys" with open(keys_file) as kf: ssh_keys = kf.read() hosts = subprocess.run(["scontrol", "show", "hostnames", sys.argv[1]], stdout=subprocess.PIPE).stdout.decode().split() await asyncio.gather(*( citc_cloud.start_node( log, host, nodespace, ssh_keys) for host in hosts )) sys.excepthook = handle_exception if __name__ == "__main__": log = logging.getLogger("startnode") log.setLevel(logging.INFO) handler = logging.FileHandler('/var/log/slurm/elastic.log') formatter = logging.Formatter('%(asctime)s %(name)-10s %(levelname)-8s %(message)s') handler.setFormatter(formatter) log.addHandler(handler) loop = asyncio.get_event_loop() try: loop.run_until_complete(main()) finally: loop.close()

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1,286

48

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0.212121

0

null

0

null

0

1

0

a3d474d2b653dcd5a9578ce3979ff7a04e191213

2,300

py

Python

tests/pyre/components/component_class_registration_model.py

BryanRiel/pyre

179359634a7091979cced427b6133dd0ec4726ea

[ "BSD-3-Clause" ]

null

tests/pyre/components/component_class_registration_model.py

BryanRiel/pyre

179359634a7091979cced427b6133dd0ec4726ea

[ "BSD-3-Clause" ]

null

tests/pyre/components/component_class_registration_model.py

BryanRiel/pyre

179359634a7091979cced427b6133dd0ec4726ea

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # michael a.g. aïvázis # orthologue # (c) 1998-2018 all rights reserved # """ Verify that component registration interacts correctly with the pyre configurator model """ # access # print(" -- importing pyre") import pyre # print(" -- done") def declare(): # declare a protocol class protocol(pyre.protocol): """a protocol""" # properties p1 = pyre.properties.str() p2 = pyre.properties.str() # behavior @pyre.provides def do(self): """behave""" # declare a component class component(pyre.component, family="test", implements=protocol): """a component""" # traits p1 = pyre.properties.str(default="p1") p2 = pyre.properties.str(default="p2") @pyre.export def do(self): """behave""" return "component" return component def test(): # and the model model = pyre.executive.nameserver # model.dump(pattern='test') # print(" -- making some configuration changes") # add an assignment model['test.p1'] = 'step 1' # an alias model.alias(alias='p1', target='test.p1') # and a reference to the alias model['ref'] = '{p1}' # check that they point to the same slot assert model.retrieve(name='p1') == model.retrieve(name='test.p1') # save the nodes ref = model.retrieve(name='ref') step_0 = model.retrieve(name='test.p1') # now declare the component and its protocol # print(" -- declaring components") component = declare() # print(" -- done") # model.dump(pattern='') assert component.p1 == 'step 1' assert component.p2 == 'p2' # check that the model is as we expect # model.dump() assert model['test.p1'] == component.p1 assert model['test.p2'] == component.p2 # how about the alias and the reference? assert model['ref'] == component.p1 assert model['p1'] == component.p1 # make a late registration to what is now the component trait model['test.p2'] = 'step 2' # model.dump(pattern='test') # and check assert component.p1 == 'step 1' assert component.p2 == 'step 2' return # main if __name__ == "__main__": test() # end of file

22.772277

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2,300

4.837456

0.367491

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0.049671

0.027757

0.090577

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0

0.02472

0.261304

2,300

100

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1

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false

0

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0

0.4

0

null

0

null

0

1

0

a3d571f1fc3a63903055bc9efe42eada3f2c5310

3,699

py

Python

apps/ignite/views.py

Mozilla-GitHub-Standards/93f18f14efcf5fdfc0e04f9bf247f66baf46663f37b1d2087ab8d850abc90803

4e374b4d52dfb9039ebe543e7f27682189022307

[ "BSD-3-Clause" ]

2

2015-04-06T15:20:29.000Z

2016-12-30T12:25:11.000Z

apps/ignite/views.py

Mozilla-GitHub-Standards/93f18f14efcf5fdfc0e04f9bf247f66baf46663f37b1d2087ab8d850abc90803

4e374b4d52dfb9039ebe543e7f27682189022307

[ "BSD-3-Clause" ]

2

2019-02-17T17:38:02.000Z

2019-03-28T03:49:16.000Z

apps/ignite/views.py

Mozilla-GitHub-Standards/93f18f14efcf5fdfc0e04f9bf247f66baf46663f37b1d2087ab8d850abc90803

4e374b4d52dfb9039ebe543e7f27682189022307

[ "BSD-3-Clause" ]

1

2019-03-28T03:49:18.000Z

from django.shortcuts import get_object_or_404 import jingo import waffle from django.contrib.auth.models import User from challenges.models import Submission, Category from projects.models import Project from blogs.models import BlogEntry from events.models import Event def splash(request, project, slug, template_name='ignite/splash.html'): """Show an individual project challenge.""" project = get_object_or_404(Project, slug=project) challenge = get_object_or_404(project.challenge_set, slug=slug) num_blogs = 3 # have we announced the winners yet - switch template if waffle.switch_is_active('announce_winners'): template_name = 'ignite/homepage-winners.html' num_blogs = 5 blogs = BlogEntry.objects.filter( page='splash' ).order_by("-updated",)[:num_blogs] # if the dev challenge is open we want to only show dev entries if request.development.is_open: entries = (Submission.objects.visible() .filter(phase__challenge=challenge) .filter(phase__name="Development") .order_by("?")) num_entries = len(entries) entries_from = 'apps' if num_entries < 5: entries = (Submission.objects.visible() .filter(phase__challenge=challenge) .filter(phase__name="Ideation") .order_by("?")) entries_from = 'ideas' else: entries = (Submission.objects.visible() .filter(phase__challenge=challenge) .filter(phase__name="Ideation") .order_by("?")) entries_from = 'ideas' event_list = Event.objects.get_featured()[:5] return jingo.render(request, template_name, { 'challenge': challenge, 'project': project, 'phases': list(enumerate(challenge.phases.all(), start=1)), 'entries': entries[:5], 'categories': Category.objects.all(), 'blogs': blogs, 'event_list': event_list, 'entries_from': entries_from, }) def about(request, project, slug, template_name='ignite/about.html'): if waffle.switch_is_active('announce_winners'): template_name = 'ignite/about-winners.html' return jingo.render(request, template_name) def judges(request, project, slug, template_name='challenges/all_judges.html'): """ List all judges we have in the system """ profiles = [] for judge in User.objects.filter(groups__name='Judges'): profile = judge.get_profile() # we only want to show featured profiles if profile.featured == True: profiles.append(profile) return jingo.render(request, 'ignite/judges.html', { 'profiles': profiles }) def terms(request, project, slug, template_name='static/terms_conditions.html'): return jingo.render(request, template_name, {}) def terms_development(request, project, slug, template_name='static/terms_conditions_development.html'): return jingo.render(request, template_name, {}) def fail(request, template_name='404.html'): return jingo.render(request, template_name, {}, status=404) def app_fail(request, template_name='500.html'): return jingo.render(request, template_name, {}, status=500) def action_unavailable_response(request, message=None, template_name="action_unavailable.html"): """Generic page for unavailable actions""" context = {'message': message} return jingo.render(request, template_name, context, status=403)

36.99

104

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412

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5.550971

0.262136

0.0892

0.07477

0.083953

0.40927

0.377787

0.314823

0.17359

0

0.010842

0.25196

3,699

99

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0.815685

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0

0.260274

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0

null

0

null

0

1

0

a3d6b9ef2efd18b552dbe05895fafd84b7430c25

17,209

py

Python

bdlb/diabetic_retinopathy_diagnosis/benchmark.py

Sairam954/bdl-benchmarks

6fbc855ca51403ad8f64b6be30ed92f6118c6cae

[ "Apache-2.0" ]

666

2019-06-14T17:14:05.000Z

2022-03-24T10:48:47.000Z

bdlb/diabetic_retinopathy_diagnosis/benchmark.py

Sairam954/bdl-benchmarks

6fbc855ca51403ad8f64b6be30ed92f6118c6cae

[ "Apache-2.0" ]

12

2019-06-26T16:54:14.000Z

2020-08-18T13:16:01.000Z

bdlb/diabetic_retinopathy_diagnosis/benchmark.py

Sairam954/bdl-benchmarks

6fbc855ca51403ad8f64b6be30ed92f6118c6cae

[ "Apache-2.0" ]

97

2019-06-14T20:30:39.000Z

2022-02-05T08:33:49.000Z

# Copyright 2019 BDL Benchmarks 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. # ============================================================================== """Diabetic retinopathy diagnosis BDL Benchmark.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import os from typing import Callable from typing import Dict from typing import Optional from typing import Sequence from typing import Text from typing import Tuple from typing import Union import numpy as np import pandas as pd import tensorflow as tf from absl import logging from ..core import transforms from ..core.benchmark import Benchmark from ..core.benchmark import BenchmarkInfo from ..core.benchmark import DataSplits from ..core.constants import DATA_DIR from ..core.levels import Level tfk = tf.keras _DIABETIC_RETINOPATHY_DIAGNOSIS_DATA_DIR = os.path.join( DATA_DIR, "downloads", "manual", "diabetic_retinopathy_diagnosis") class DiabeticRetinopathyDiagnosisBecnhmark(Benchmark): """Diabetic retinopathy diagnosis benchmark class.""" def __init__( self, level: Union[Text, Level], batch_size: int = 64, data_dir: Optional[Text] = None, download_and_prepare: bool = False, ): """Constructs a benchmark object. Args: level: `Level` or `str, downstream task level. batch_size: (optional) `int`, number of datapoints per mini-batch. data_dir: (optional) `str`, path to parent data directory. download_and_prepare: (optional) `bool`, if the data is not available it downloads and preprocesses it. """ self.__level = level if isinstance(level, Level) else Level.from_str(level) try: self.__ds = self.load(level=level, batch_size=batch_size, data_dir=data_dir or DATA_DIR) except AssertionError: if not download_and_prepare: raise else: logging.info( "Data not found, `DiabeticRetinopathyDiagnosisBecnhmark.download_and_prepare()`" " is now running...") self.download_and_prepare() @classmethod def evaluate( cls, estimator: Callable[[np.ndarray], Tuple[np.ndarray, np.ndarray]], dataset: tf.data.Dataset, output_dir: Optional[Text] = None, name: Optional[Text] = None, ) -> Dict[Text, float]: """Evaluates an `estimator` on the `mode` benchmark dataset. Args: estimator: `lambda x: mu_x, uncertainty_x`, an uncertainty estimation function, which returns `mean_x` and predictive `uncertainty_x`. dataset: `tf.data.Dataset`, on which dataset to performance evaluation. output_dir: (optional) `str`, directory to save figures. name: (optional) `str`, the name of the method. """ import inspect import tqdm import tensorflow_datasets as tfds from sklearn.metrics import roc_auc_score from sklearn.metrics import accuracy_score import matplotlib.pyplot as plt # Containers used for caching performance evaluation y_true = list() y_pred = list() y_uncertainty = list() # Convert to NumPy iterator if necessary ds = dataset if inspect.isgenerator(dataset) else tfds.as_numpy(dataset) for x, y in tqdm.tqdm(ds): # Sample from probabilistic model mean, uncertainty = estimator(x) # Cache predictions y_true.append(y) y_pred.append(mean) y_uncertainty.append(uncertainty) # Use vectorized NumPy containers y_true = np.concatenate(y_true).flatten() y_pred = np.concatenate(y_pred).flatten() y_uncertainty = np.concatenate(y_uncertainty).flatten() fractions = np.asarray([0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) # Metrics for evaluation metrics = zip(["accuracy", "auc"], cls.metrics()) return { metric: cls._evaluate_metric( y_true, y_pred, y_uncertainty, fractions, lambda y_true, y_pred: metric_fn(y_true, y_pred).numpy(), name, ) for (metric, metric_fn) in metrics } @staticmethod def _evaluate_metric( y_true: np.ndarray, y_pred: np.ndarray, y_uncertainty: np.ndarray, fractions: Sequence[float], metric_fn: Callable[[np.ndarray, np.ndarray], float], name=None, ) -> pd.DataFrame: """Evaluate model predictive distribution on `metric_fn` at data retain `fractions`. Args: y_true: `numpy.ndarray`, the ground truth labels, with shape [N]. y_pred: `numpy.ndarray`, the model predictions, with shape [N]. y_uncertainty: `numpy.ndarray`, the model uncertainties, with shape [N]. fractions: `iterable`, the percentages of data to retain for calculating `metric_fn`. metric_fn: `lambda(y_true, y_pred) -> float`, a metric function that provides a score given ground truths and predictions. name: (optional) `str`, the name of the method. Returns: A `pandas.DataFrame` with columns ["retained_data", "mean", "std"], that summarizes the scores at different data retained fractions. """ N = y_true.shape[0] # Sorts indexes by ascending uncertainty I_uncertainties = np.argsort(y_uncertainty) # Score containers mean = np.empty_like(fractions) # TODO(filangel): do bootstrap sampling and estimate standard error std = np.zeros_like(fractions) for i, frac in enumerate(fractions): # Keep only the %-frac of lowest uncertainties I = np.zeros(N, dtype=bool) I[I_uncertainties[:int(N * frac)]] = True mean[i] = metric_fn(y_true[I], y_pred[I]) # Store df = pd.DataFrame(dict(retained_data=fractions, mean=mean, std=std)) df.name = name return df @property def datasets(self) -> tf.data.Dataset: """Pointer to the processed datasets.""" return self.__ds @property def info(self) -> BenchmarkInfo: """Text description of the benchmark.""" return BenchmarkInfo(description="", urls="", setup="", citation="") @property def level(self) -> Level: """The downstream task level.""" return self.__level @staticmethod def loss() -> tfk.losses.Loss: """Loss used for training binary classifiers.""" return tfk.losses.BinaryCrossentropy() @staticmethod def metrics() -> tfk.metrics.Metric: """Evaluation metrics used for monitoring training.""" return [tfk.metrics.BinaryAccuracy(), tfk.metrics.AUC()] @staticmethod def class_weight() -> Sequence[float]: """Class weights used for rebalancing the dataset, by skewing the `loss` accordingly.""" return [1.0, 4.0] @classmethod def load( cls, level: Union[Text, Level] = "realworld", batch_size: int = 64, data_dir: Optional[Text] = None, as_numpy: bool = False, ) -> DataSplits: """Loads the datasets for the benchmark. Args: level: `Level` or `str, downstream task level. batch_size: (optional) `int`, number of datapoints per mini-batch. data_dir: (optional) `str`, path to parent data directory. as_numpy: (optional) `bool`, if True returns python generators with `numpy.ndarray` outputs. Returns: A namedtuple with properties: * train: `tf.data.Dataset`, train dataset. * validation: `tf.data.Dataset`, validation dataset. * test: `tf.data.Dataset`, test dataset. """ import tensorflow_datasets as tfds from .tfds_adapter import DiabeticRetinopathyDiagnosis # Fetch datasets try: ds_train, ds_validation, ds_test = DiabeticRetinopathyDiagnosis( data_dir=data_dir or DATA_DIR, config=level).as_dataset(split=["train", "validation", "test"], shuffle_files=True, batch_size=batch_size) except AssertionError as ae: raise AssertionError( str(ae) + " Run DiabeticRetinopathyDiagnosisBecnhmark.download_and_prepare()" " first and then retry.") # Parse task level level = level if isinstance(level, Level) else Level.from_str(level) # Dataset tranformations transforms_train, transforms_eval = cls._preprocessors() # Apply transformations ds_train = ds_train.map(transforms_train, num_parallel_calls=tf.data.experimental.AUTOTUNE) ds_validation = ds_validation.map( transforms_eval, num_parallel_calls=tf.data.experimental.AUTOTUNE) ds_test = ds_test.map(transforms_eval, num_parallel_calls=tf.data.experimental.AUTOTUNE) # Prefetches datasets to memory ds_train = ds_train.prefetch(tf.data.experimental.AUTOTUNE) ds_validation = ds_validation.prefetch(tf.data.experimental.AUTOTUNE) ds_test = ds_test.prefetch(tf.data.experimental.AUTOTUNE) if as_numpy: # Convert to NumPy iterators ds_train = tfds.as_numpy(ds_train) ds_validation = tfds.as_numpy(ds_validation) ds_test = tfds.as_numpy(ds_test) return DataSplits(ds_train, ds_validation, ds_test) @classmethod def download_and_prepare(cls, levels=None) -> None: """Downloads dataset from Kaggle, extracts zip files and processes it using `tensorflow_datasets`. Args: levels: (optional) `iterable` of `str`, specifies which levels from {'medium', 'realworld'} to prepare, if None it prepares all the levels. Raises: OSError: if `~/.kaggle/kaggle.json` is not set up. """ # Disable GPU for data download, extraction and preparation import os os.environ["CUDA_VISIBLE_DEVICES"] = "-1" cls._download() # cls._extract() #cls._prepare(levels) @staticmethod def _download() -> None: """Downloads data from Kaggle using `tensorflow_datasets`. Raises: OSError: if `~/.kaggle/kaggle.json` is not set up. """ import subprocess as sp import tensorflow_datasets as tfds # Append `/home/$USER/.local/bin` to path os.environ["PATH"] += ":/home/{}/.local/bin/".format(os.environ["USER"]) # Download all files from Kaggle drd = tfds.download.kaggle.KaggleCompetitionDownloader( "diabetic-retinopathy-detection") try: for dfile in drd.competition_files: drd.download_file(dfile, output_dir=_DIABETIC_RETINOPATHY_DIAGNOSIS_DATA_DIR) except sp.CalledProcessError as cpe: raise OSError( str(cpe) + "." + " Make sure you have ~/.kaggle/kaggle.json setup, fetched from the Kaggle website" " https://www.kaggle.com/<username>/account -> 'Create New API Key'." " Also accept the dataset license by going to" " https://www.kaggle.com/c/diabetic-retinopathy-detection/rules" " and look for the button 'I Understand and Accept' (make sure when reloading the" " page that the button does not pop up again).") @staticmethod def _extract() -> None: """Extracts zip files downloaded from Kaggle.""" import glob import tqdm import zipfile import tempfile # Extract train and test original images for split in ["train", "test"]: # Extract "<split>.zip.00*"" files to "<split>" with tempfile.NamedTemporaryFile() as tmp: # Concatenate "<split>.zip.00*" to "<split>.zip" for fname in tqdm.tqdm( sorted( glob.glob( os.path.join(_DIABETIC_RETINOPATHY_DIAGNOSIS_DATA_DIR, "{split}.zip.00*".format(split=split))))): # Unzip "<split>.zip" to "<split>" with open(fname, "rb") as ztmp: tmp.write(ztmp.read()) with zipfile.ZipFile(tmp) as zfile: for image in tqdm.tqdm(iterable=zfile.namelist(), total=len(zfile.namelist())): zfile.extract(member=image, path=_DIABETIC_RETINOPATHY_DIAGNOSIS_DATA_DIR) # Delete "<split>.zip.00*" files for splitzip in os.listdir(_DIABETIC_RETINOPATHY_DIAGNOSIS_DATA_DIR): if "{split}.zip.00".format(split=split) in splitzip: os.remove( os.path.join(_DIABETIC_RETINOPATHY_DIAGNOSIS_DATA_DIR, splitzip)) # Extract "sample.zip", "trainLabels.csv.zip" for fname in ["sample", "trainLabels.csv"]: zfname = os.path.join(_DIABETIC_RETINOPATHY_DIAGNOSIS_DATA_DIR, "{fname}.zip".format(fname=fname)) with zipfile.ZipFile(zfname) as zfile: zfile.extractall(_DIABETIC_RETINOPATHY_DIAGNOSIS_DATA_DIR) os.remove(zfname) @staticmethod def _prepare(levels=None) -> None: """Generates the TFRecord objects for medium and realworld experiments.""" import multiprocessing from absl import logging from .tfds_adapter import DiabeticRetinopathyDiagnosis # Hangle each level individually for level in levels or ["medium", "realworld"]: dtask = DiabeticRetinopathyDiagnosis(data_dir=DATA_DIR, config=level) logging.debug("=== Preparing TFRecords for {} ===".format(level)) dtask.download_and_prepare() @classmethod def _preprocessors(cls) -> Tuple[transforms.Transform, transforms.Transform]: """Applies transformations to the raw data.""" import tensorflow_datasets as tfds # Transformation hyperparameters mean = np.asarray([0.42606387, 0.29752496, 0.21309826]) stddev = np.asarray([0.27662534, 0.20280295, 0.1687619]) class Parse(transforms.Transform): """Parses datapoints from raw `tf.data.Dataset`.""" def __call__(self, x, y=None): """Returns `as_supervised` tuple.""" return x["image"], x["label"] class CastX(transforms.Transform): """Casts image to `dtype`.""" def __init__(self, dtype): """Constructs a type caster.""" self.dtype = dtype def __call__(self, x, y): """Returns casted image (to `dtype`) and its (unchanged) label as tuple.""" return tf.cast(x, self.dtype), y class To01X(transforms.Transform): """Rescales image to [min, max]=[0, 1].""" def __call__(self, x, y): """Returns rescaled image and its (unchanged) label as tuple.""" return x / 255.0, y # Get augmentation schemes [augmentation_config, no_augmentation_config] = cls._ImageDataGenerator_config() # Transformations for train dataset transforms_train = transforms.Compose([ Parse(), CastX(tf.float32), To01X(), transforms.Normalize(mean, stddev), # TODO(filangel): hangle batch with ImageDataGenerator # transforms.RandomAugment(**augmentation_config), ]) # Transformations for validation/test dataset transforms_eval = transforms.Compose([ Parse(), CastX(tf.float32), To01X(), transforms.Normalize(mean, stddev), # TODO(filangel): hangle batch with ImageDataGenerator # transforms.RandomAugment(**no_augmentation_config), ]) return transforms_train, transforms_eval @staticmethod def _ImageDataGenerator_config(): """Returns the configs for the `tensorflow.keras.preprocessing.image.ImageDataGenerator`, used for the random augmentation of the dataset, following the implementation of https://github.com/chleibig/disease-detection/blob/f3401b26aa9b832ff77afe93 e3faa342f7d088e5/scripts/inspect_data_augmentation.py.""" augmentation_config = dict( featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=180.0, width_shift_range=0.05, height_shift_range=0.05, shear_range=0., zoom_range=0.10, channel_shift_range=0., fill_mode="constant", cval=0., horizontal_flip=True, vertical_flip=True, data_format="channels_last", ) no_augmentation_config = dict( featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=0.0, width_shift_range=0.0, height_shift_range=0.0, shear_range=0., zoom_range=0.0, channel_shift_range=0., fill_mode="nearest", cval=0., horizontal_flip=False, vertical_flip=False, data_format="channels_last", ) return augmentation_config, no_augmentation_config

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null

0

null

0

1

0

a3d75ce424bf88d4d06c99b804df0f846b952cac

1,873

py

Python

vivisect/storage/mpfile.py

vEpiphyte/vivisect

14947a53c6781175f0aa83d49cc16c524a2e23a3

[ "ECL-2.0", "Apache-2.0" ]

null

vivisect/storage/mpfile.py

vEpiphyte/vivisect

14947a53c6781175f0aa83d49cc16c524a2e23a3

[ "ECL-2.0", "Apache-2.0" ]

null

vivisect/storage/mpfile.py

vEpiphyte/vivisect

14947a53c6781175f0aa83d49cc16c524a2e23a3

[ "ECL-2.0", "Apache-2.0" ]

null

import base64 import logging import msgpack logger = logging.getLogger(__name__) loadargs = {'use_list': False, 'raw': False} if msgpack.version < (1, 0, 0): loadargs['encoding'] = 'utf-8' else: loadargs['strict_map_key'] = False VSIG = b'MSGVIV'.ljust(8, b'\x00') def vivEventsAppendFile(filename, events): with open(filename, 'ab') as f: for event in events: if event[0] == 20: mape = base64.b64encode(event[1][3]) event = (event[0], (event[1][0], event[1][1], event[1][2], mape)) msgpack.pack(event, f, use_bin_type=False) def saveWorkspaceChanges(vw, filename): events = vw.exportWorkspaceChanges() vivEventsAppendFile(filename, events) def vivEventsToFile(filename, events): with open(filename, 'wb') as f: msgpack.pack(VSIG, f, use_bin_type=False) for event in events: if event[0] == 20: mape = base64.b64encode(event[1][3]) event = (event[0], (event[1][0], event[1][1], event[1][2], mape)) msgpack.pack(event, f, use_bin_type=False) def saveWorkspace(vw, filename): events = vw.exportWorkspace() vivEventsToFile(filename, events) def vivEventsFromFile(filename): events = [] with open(filename, 'rb') as f: unpacker = msgpack.Unpacker(f, **loadargs) siggy = next(unpacker) if siggy.encode('utf-8') != VSIG: logger.warning('Invalid file signature of %s', str(siggy)) return for event in unpacker: if event[0] == 20: mape = base64.b64decode(event[1][3]) event = (event[0], (event[1][0], event[1][1], event[1][2], mape)) events.append(event) return events def loadWorkspace(vw, filename): events = vivEventsFromFile(filename) vw.importWorkspace(events)

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null

0

null

0

1

0

a3d771361889efe007b26f62c7cd92ffc6f656a2

3,832

py

Python

pytest_pgsql/plugin.py

mathiasose/pytest-pgsql

5e076db146699c3b683b49e4a31323c4c23054de

[ "BSD-3-Clause" ]

null

pytest_pgsql/plugin.py

mathiasose/pytest-pgsql

5e076db146699c3b683b49e4a31323c4c23054de

[ "BSD-3-Clause" ]

null

pytest_pgsql/plugin.py

mathiasose/pytest-pgsql

5e076db146699c3b683b49e4a31323c4c23054de

[ "BSD-3-Clause" ]

null

"""This forms the core of the pytest plugin.""" import pytest import testing.postgresql from pytest_pgsql import database from pytest_pgsql import ext def pytest_addoption(parser): """Add configuration options for pytest_pgsql.""" parser.addoption( '--pg-extensions', action='store', default='', help="A comma-separated list of PostgreSQL extensions to install at " "the beginning of the session for use by all tests. Example: " "--pg-extensions=uuid-ossp,pg_tgrm,pgcrypto") parser.addoption( '--pg-work-mem', type=int, default=32, help='Set the value of the `work_mem` setting, in megabytes. ' '`pytest_pgsql` defaults to 32. Adjusting this up or down can ' 'help performance; see the Postgres documentation for more details.') parser.addoption( '--pg-conf-opt', action='append', help='Add a key=value line that will be appended to postgresql.conf') @pytest.fixture(scope='session') def database_uri(request): """A fixture giving the connection URI of the session-wide test database.""" # Note: due to the nature of the variable configs, the command line options # must be tested manually. work_mem = request.config.getoption('--pg-work-mem') if work_mem < 0: # pragma: no cover pytest.exit('ERROR: --pg-work-mem value must be >= 0. Got: %d' % work_mem) return elif work_mem == 0: # pragma: no cover # Disable memory tweak and use the server default. work_mem_setting = '' else: # User wants to change the working memory setting. work_mem_setting = '-c work_mem=%dMB ' % work_mem conf_opts = request.config.getoption('--pg-conf-opt') if conf_opts: conf_opts_string = ' -c ' + ' -c '.join(conf_opts) else: conf_opts_string = '' # pylint: disable=bad-continuation,deprecated-method with testing.postgresql.Postgresql( postgres_args='-c TimeZone=UTC ' '-c fsync=off ' '-c synchronous_commit=off ' '-c full_page_writes=off ' + work_mem_setting + '-c checkpoint_timeout=30min ' '-c bgwriter_delay=10000ms' + conf_opts_string) as pgdb: yield pgdb.url() #: A SQLAlchemy engine shared by the transacted and non-transacted database fixtures. #: #: .. seealso:: `pytest_pgsql.ext.create_engine_fixture` # pylint: disable=invalid-name pg_engine = ext.create_engine_fixture('pg_engine', scope='session') # pylint: enable=invalid-name @pytest.fixture(scope='session') def database_snapshot(pg_engine): """Create one database snapshot for the session. The database will be restored to this state after each test. .. note :: This is an implementation detail and should not be used directly except by derived fixtures. """ return database.create_database_snapshot(pg_engine) # pylint: disable=invalid-name #: Create a test database instance and cleans up after each test finishes. #: #: You should prefer the `transacted_postgresql_db` fixture unless your test #: cannot be run in a single transaction. The `transacted_postgresql_db` fixture #: leads to faster tests since it doesn't tear down the entire database between #: each test. postgresql_db = \ database.PostgreSQLTestDB.create_fixture('postgresql_db') #: Create a test database instance that rolls back the current transaction after #: each test finishes, verifying its integrity before returning. #: #: Read the warning in the main documentation page before using this fixture. transacted_postgresql_db = \ database.TransactedPostgreSQLTestDB.create_fixture('transacted_postgresql_db') # pylint: enable=invalid-name

35.813084

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0.675626

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0

null

0

null

0

1

0

a3d816c8c07445ebc9580d3703129a46fcf2cc64

737

py

Python

power_data_to_sat_passes/date_utils.py

abrahamneben/orbcomm_beam_mapping

71b3e7d6e4214db0a6f4e68ebeeb7d7f846f5004

[ "MIT" ]

1

2019-04-10T02:50:19.000Z

power_data_to_sat_passes/date_utils.py

abrahamneben/orbcomm_beam_mapping

71b3e7d6e4214db0a6f4e68ebeeb7d7f846f5004

[ "MIT" ]

null

power_data_to_sat_passes/date_utils.py

abrahamneben/orbcomm_beam_mapping

71b3e7d6e4214db0a6f4e68ebeeb7d7f846f5004

[ "MIT" ]

null

# written by abraham on aug 24 def dyear2date(dyear): year = int(dyear) month_lengths = [31,28,31,30,31,30,31,31,30,31,30,31] days_before_months = [0,31,59,90,120,151,181,212,243,273,304,334] days_into_year_f = (dyear-year)*365 days_into_year_i = int(days_into_year_f) for i in range(12): if days_before_months[i] < days_into_year_f < (days_before_months[i]+month_lengths[i]): month = i+1 break date = days_into_year_i - days_before_months[month-1] hours_f = (days_into_year_f-days_into_year_i)*24 hours_i = int(hours_f) minutes_f = (hours_f-hours_i)*60 minutes_i = int(minutes_f) seconds_i = int((minutes_f-minutes_i)*60) return "%02d/%02d/%d %02d:%02d:%02d" % (month,date,year,hours_i,minutes_i,seconds_i)

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0.109756

0

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737

26

90

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0

0.03819

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false

0

0.117647

0

null

0

null

0

1

0

a3d8391391013bac7dd77afd2eebf78925078f05

752

py

Python

app/base/count_lines.py

sourcery-ai-bot/personal-expenses-accounting

55e76744a06fd502d119f57427cd7a0bfaf68fe1

[ "MIT" ]

5

2020-02-21T16:26:21.000Z

2021-08-05T09:34:28.000Z

app/base/count_lines.py

sourcery-ai-bot/personal-expenses-accounting

55e76744a06fd502d119f57427cd7a0bfaf68fe1

[ "MIT" ]

11

2020-06-26T09:05:04.000Z

2022-01-24T20:35:07.000Z

app/base/count_lines.py

sourcery-ai-bot/personal-expenses-accounting

55e76744a06fd502d119f57427cd7a0bfaf68fe1

[ "MIT" ]

1

2021-06-25T09:42:08.000Z

import glob from os import walk exclude_folders = [ 'node_modules', 'ios', 'android', '__pycache__' ] exclude_files = [ 'json', 'txt', 'traineddata', 'lstmf', 'yml', 'md' 'log', 'env', 'gitignore', 'dockerignore' ] # get all files in directory dirr = '/home/viktor/Documents/personal-expenses-accounting/app/services/web_service/' folders = glob.glob(dirr + '/**/', recursive=True) # only app related directories directories = [] for folder in folders: current_folder = folder.split('/')[-2] if current_folder not in exclude_folders: files = glob.glob(folder + '*') print(files) directories.append(folder) # num_lines = sum(1 for line in open('myfile.txt'))

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87

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0.061135

0

0.003448

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false

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0.034483

0

null

0

null

0

1

0

a3d89936d8b1b9966571e7248379800a7bb8190c

17,617

py

Python

charmhelpers/contrib/charmsupport/nrpe.py

nobuto-m/charm-helpers

4cffc05ace43234d34b040cccdde3460f68cb673

[ "Apache-2.0" ]

null

charmhelpers/contrib/charmsupport/nrpe.py

nobuto-m/charm-helpers

4cffc05ace43234d34b040cccdde3460f68cb673

[ "Apache-2.0" ]

1

2019-09-04T12:17:17.000Z

charmhelpers/contrib/charmsupport/nrpe.py

nobuto-m/charm-helpers

4cffc05ace43234d34b040cccdde3460f68cb673

[ "Apache-2.0" ]

null

# Copyright 2014-2015 Canonical Limited. # # 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. """Compatibility with the nrpe-external-master charm""" # Copyright 2012 Canonical Ltd. # # Authors: # Matthew Wedgwood <matthew.wedgwood@canonical.com> import subprocess import pwd import grp import os import glob import shutil import re import shlex import yaml from charmhelpers.core.hookenv import ( config, hook_name, local_unit, log, relation_get, relation_ids, relation_set, relations_of_type, ) from charmhelpers.core.host import service from charmhelpers.core import host # This module adds compatibility with the nrpe-external-master and plain nrpe # subordinate charms. To use it in your charm: # # 1. Update metadata.yaml # # provides: # (...) # nrpe-external-master: # interface: nrpe-external-master # scope: container # # and/or # # provides: # (...) # local-monitors: # interface: local-monitors # scope: container # # 2. Add the following to config.yaml # # nagios_context: # default: "juju" # type: string # description: | # Used by the nrpe subordinate charms. # A string that will be prepended to instance name to set the host name # in nagios. So for instance the hostname would be something like: # juju-myservice-0 # If you're running multiple environments with the same services in them # this allows you to differentiate between them. # nagios_servicegroups: # default: "" # type: string # description: | # A comma-separated list of nagios servicegroups. # If left empty, the nagios_context will be used as the servicegroup # # 3. Add custom checks (Nagios plugins) to files/nrpe-external-master # # 4. Update your hooks.py with something like this: # # from charmsupport.nrpe import NRPE # (...) # def update_nrpe_config(): # nrpe_compat = NRPE() # nrpe_compat.add_check( # shortname = "myservice", # description = "Check MyService", # check_cmd = "check_http -w 2 -c 10 http://localhost" # ) # nrpe_compat.add_check( # "myservice_other", # "Check for widget failures", # check_cmd = "/srv/myapp/scripts/widget_check" # ) # nrpe_compat.write() # # def config_changed(): # (...) # update_nrpe_config() # # def nrpe_external_master_relation_changed(): # update_nrpe_config() # # def local_monitors_relation_changed(): # update_nrpe_config() # # 4.a If your charm is a subordinate charm set primary=False # # from charmsupport.nrpe import NRPE # (...) # def update_nrpe_config(): # nrpe_compat = NRPE(primary=False) # # 5. ln -s hooks.py nrpe-external-master-relation-changed # ln -s hooks.py local-monitors-relation-changed class CheckException(Exception): pass class Check(object): shortname_re = '[A-Za-z0-9-_.@]+$' service_template = (""" #--------------------------------------------------- # This file is Juju managed #--------------------------------------------------- define service {{ use active-service host_name {nagios_hostname} service_description {nagios_hostname}[{shortname}] """ """{description} check_command check_nrpe!{command} servicegroups {nagios_servicegroup} }} """) def __init__(self, shortname, description, check_cmd): super(Check, self).__init__() # XXX: could be better to calculate this from the service name if not re.match(self.shortname_re, shortname): raise CheckException("shortname must match {}".format( Check.shortname_re)) self.shortname = shortname self.command = "check_{}".format(shortname) # Note: a set of invalid characters is defined by the # Nagios server config # The default is: illegal_object_name_chars=`~!$%^&*"|'<>?,()= self.description = description self.check_cmd = self._locate_cmd(check_cmd) def _get_check_filename(self): return os.path.join(NRPE.nrpe_confdir, '{}.cfg'.format(self.command)) def _get_service_filename(self, hostname): return os.path.join(NRPE.nagios_exportdir, 'service__{}_{}.cfg'.format(hostname, self.command)) def _locate_cmd(self, check_cmd): search_path = ( '/usr/lib/nagios/plugins', '/usr/local/lib/nagios/plugins', ) parts = shlex.split(check_cmd) for path in search_path: if os.path.exists(os.path.join(path, parts[0])): command = os.path.join(path, parts[0]) if len(parts) > 1: command += " " + " ".join(parts[1:]) return command log('Check command not found: {}'.format(parts[0])) return '' def _remove_service_files(self): if not os.path.exists(NRPE.nagios_exportdir): return for f in os.listdir(NRPE.nagios_exportdir): if f.endswith('_{}.cfg'.format(self.command)): os.remove(os.path.join(NRPE.nagios_exportdir, f)) def remove(self, hostname): nrpe_check_file = self._get_check_filename() if os.path.exists(nrpe_check_file): os.remove(nrpe_check_file) self._remove_service_files() def write(self, nagios_context, hostname, nagios_servicegroups): nrpe_check_file = self._get_check_filename() with open(nrpe_check_file, 'w') as nrpe_check_config: nrpe_check_config.write("# check {}\n".format(self.shortname)) if nagios_servicegroups: nrpe_check_config.write( "# The following header was added automatically by juju\n") nrpe_check_config.write( "# Modifying it will affect nagios monitoring and alerting\n") nrpe_check_config.write( "# servicegroups: {}\n".format(nagios_servicegroups)) nrpe_check_config.write("command[{}]={}\n".format( self.command, self.check_cmd)) if not os.path.exists(NRPE.nagios_exportdir): log('Not writing service config as {} is not accessible'.format( NRPE.nagios_exportdir)) else: self.write_service_config(nagios_context, hostname, nagios_servicegroups) def write_service_config(self, nagios_context, hostname, nagios_servicegroups): self._remove_service_files() templ_vars = { 'nagios_hostname': hostname, 'nagios_servicegroup': nagios_servicegroups, 'description': self.description, 'shortname': self.shortname, 'command': self.command, } nrpe_service_text = Check.service_template.format(**templ_vars) nrpe_service_file = self._get_service_filename(hostname) with open(nrpe_service_file, 'w') as nrpe_service_config: nrpe_service_config.write(str(nrpe_service_text)) def run(self): subprocess.call(self.check_cmd) class NRPE(object): nagios_logdir = '/var/log/nagios' nagios_exportdir = '/var/lib/nagios/export' nrpe_confdir = '/etc/nagios/nrpe.d' homedir = '/var/lib/nagios' # home dir provided by nagios-nrpe-server def __init__(self, hostname=None, primary=True): super(NRPE, self).__init__() self.config = config() self.primary = primary self.nagios_context = self.config['nagios_context'] if 'nagios_servicegroups' in self.config and self.config['nagios_servicegroups']: self.nagios_servicegroups = self.config['nagios_servicegroups'] else: self.nagios_servicegroups = self.nagios_context self.unit_name = local_unit().replace('/', '-') if hostname: self.hostname = hostname else: nagios_hostname = get_nagios_hostname() if nagios_hostname: self.hostname = nagios_hostname else: self.hostname = "{}-{}".format(self.nagios_context, self.unit_name) self.checks = [] # Iff in an nrpe-external-master relation hook, set primary status relation = relation_ids('nrpe-external-master') if relation: log("Setting charm primary status {}".format(primary)) for rid in relation: relation_set(relation_id=rid, relation_settings={'primary': self.primary}) self.remove_check_queue = set() def add_check(self, *args, **kwargs): shortname = None if kwargs.get('shortname') is None: if len(args) > 0: shortname = args[0] else: shortname = kwargs['shortname'] self.checks.append(Check(*args, **kwargs)) try: self.remove_check_queue.remove(shortname) except KeyError: pass def remove_check(self, *args, **kwargs): if kwargs.get('shortname') is None: raise ValueError('shortname of check must be specified') # Use sensible defaults if they're not specified - these are not # actually used during removal, but they're required for constructing # the Check object; check_disk is chosen because it's part of the # nagios-plugins-basic package. if kwargs.get('check_cmd') is None: kwargs['check_cmd'] = 'check_disk' if kwargs.get('description') is None: kwargs['description'] = '' check = Check(*args, **kwargs) check.remove(self.hostname) self.remove_check_queue.add(kwargs['shortname']) def write(self): try: nagios_uid = pwd.getpwnam('nagios').pw_uid nagios_gid = grp.getgrnam('nagios').gr_gid except Exception: log("Nagios user not set up, nrpe checks not updated") return if not os.path.exists(NRPE.nagios_logdir): os.mkdir(NRPE.nagios_logdir) os.chown(NRPE.nagios_logdir, nagios_uid, nagios_gid) nrpe_monitors = {} monitors = {"monitors": {"remote": {"nrpe": nrpe_monitors}}} for nrpecheck in self.checks: nrpecheck.write(self.nagios_context, self.hostname, self.nagios_servicegroups) nrpe_monitors[nrpecheck.shortname] = { "command": nrpecheck.command, } # update-status hooks are configured to firing every 5 minutes by # default. When nagios-nrpe-server is restarted, the nagios server # reports checks failing causing unnecessary alerts. Let's not restart # on update-status hooks. if not hook_name() == 'update-status': service('restart', 'nagios-nrpe-server') monitor_ids = relation_ids("local-monitors") + \ relation_ids("nrpe-external-master") for rid in monitor_ids: reldata = relation_get(unit=local_unit(), rid=rid) if 'monitors' in reldata: # update the existing set of monitors with the new data old_monitors = yaml.safe_load(reldata['monitors']) old_nrpe_monitors = old_monitors['monitors']['remote']['nrpe'] # remove keys that are in the remove_check_queue old_nrpe_monitors = {k: v for k, v in old_nrpe_monitors.items() if k not in self.remove_check_queue} # update/add nrpe_monitors old_nrpe_monitors.update(nrpe_monitors) old_monitors['monitors']['remote']['nrpe'] = old_nrpe_monitors # write back to the relation relation_set(relation_id=rid, monitors=yaml.dump(old_monitors)) else: # write a brand new set of monitors, as no existing ones. relation_set(relation_id=rid, monitors=yaml.dump(monitors)) self.remove_check_queue.clear() def get_nagios_hostcontext(relation_name='nrpe-external-master'): """ Query relation with nrpe subordinate, return the nagios_host_context :param str relation_name: Name of relation nrpe sub joined to """ for rel in relations_of_type(relation_name): if 'nagios_host_context' in rel: return rel['nagios_host_context'] def get_nagios_hostname(relation_name='nrpe-external-master'): """ Query relation with nrpe subordinate, return the nagios_hostname :param str relation_name: Name of relation nrpe sub joined to """ for rel in relations_of_type(relation_name): if 'nagios_hostname' in rel: return rel['nagios_hostname'] def get_nagios_unit_name(relation_name='nrpe-external-master'): """ Return the nagios unit name prepended with host_context if needed :param str relation_name: Name of relation nrpe sub joined to """ host_context = get_nagios_hostcontext(relation_name) if host_context: unit = "%s:%s" % (host_context, local_unit()) else: unit = local_unit() return unit def add_init_service_checks(nrpe, services, unit_name, immediate_check=True): """ Add checks for each service in list :param NRPE nrpe: NRPE object to add check to :param list services: List of services to check :param str unit_name: Unit name to use in check description :param bool immediate_check: For sysv init, run the service check immediately """ for svc in services: # Don't add a check for these services from neutron-gateway if svc in ['ext-port', 'os-charm-phy-nic-mtu']: next upstart_init = '/etc/init/%s.conf' % svc sysv_init = '/etc/init.d/%s' % svc if host.init_is_systemd(): nrpe.add_check( shortname=svc, description='process check {%s}' % unit_name, check_cmd='check_systemd.py %s' % svc ) elif os.path.exists(upstart_init): nrpe.add_check( shortname=svc, description='process check {%s}' % unit_name, check_cmd='check_upstart_job %s' % svc ) elif os.path.exists(sysv_init): cronpath = '/etc/cron.d/nagios-service-check-%s' % svc checkpath = '%s/service-check-%s.txt' % (nrpe.homedir, svc) croncmd = ( '/usr/local/lib/nagios/plugins/check_exit_status.pl ' '-e -s /etc/init.d/%s status' % svc ) cron_file = '*/5 * * * * root %s > %s\n' % (croncmd, checkpath) f = open(cronpath, 'w') f.write(cron_file) f.close() nrpe.add_check( shortname=svc, description='service check {%s}' % unit_name, check_cmd='check_status_file.py -f %s' % checkpath, ) # if /var/lib/nagios doesn't exist open(checkpath, 'w') will fail # (LP: #1670223). if immediate_check and os.path.isdir(nrpe.homedir): f = open(checkpath, 'w') subprocess.call( croncmd.split(), stdout=f, stderr=subprocess.STDOUT ) f.close() os.chmod(checkpath, 0o644) def copy_nrpe_checks(nrpe_files_dir=None): """ Copy the nrpe checks into place """ NAGIOS_PLUGINS = '/usr/local/lib/nagios/plugins' if nrpe_files_dir is None: # determine if "charmhelpers" is in CHARMDIR or CHARMDIR/hooks for segment in ['.', 'hooks']: nrpe_files_dir = os.path.abspath(os.path.join( os.getenv('CHARM_DIR'), segment, 'charmhelpers', 'contrib', 'openstack', 'files')) if os.path.isdir(nrpe_files_dir): break else: raise RuntimeError("Couldn't find charmhelpers directory") if not os.path.exists(NAGIOS_PLUGINS): os.makedirs(NAGIOS_PLUGINS) for fname in glob.glob(os.path.join(nrpe_files_dir, "check_*")): if os.path.isfile(fname): shutil.copy2(fname, os.path.join(NAGIOS_PLUGINS, os.path.basename(fname))) def add_haproxy_checks(nrpe, unit_name): """ Add checks for each service in list :param NRPE nrpe: NRPE object to add check to :param str unit_name: Unit name to use in check description """ nrpe.add_check( shortname='haproxy_servers', description='Check HAProxy {%s}' % unit_name, check_cmd='check_haproxy.sh') nrpe.add_check( shortname='haproxy_queue', description='Check HAProxy queue depth {%s}' % unit_name, check_cmd='check_haproxy_queue_depth.sh')

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0.173145

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null

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null

0

1

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a3d9e6ada4265efd73113dc71c68649cc06c25fa

13,250

py

Python

venv/Lib/site-packages/proglog/proglog.py

mintzer/pupillometry-rf-back

cfa86fa984a49dce0123798f8de5b838c02e10d5

[ "CC-BY-4.0" ]

83

2017-08-14T02:20:38.000Z

2022-03-01T20:32:03.000Z

venv/lib/python3.7/site-packages/proglog/proglog.py

haideraltahan/CropMe

75a111b9d3b2c50c6f2a9a36d21432053f02284d

[ "MIT" ]

20

2021-05-03T18:02:23.000Z

2022-03-12T12:01:04.000Z

venv/lib/python3.7/site-packages/proglog/proglog.py

haideraltahan/CropMe

75a111b9d3b2c50c6f2a9a36d21432053f02284d

[ "MIT" ]

6

2018-10-23T08:12:26.000Z

2021-02-14T13:53:13.000Z

"""Implements the generic progress logger class, and the ProgressBar class. """ from tqdm import tqdm, tqdm_notebook from collections import OrderedDict import time SETTINGS = { 'notebook': False } def notebook(turn='on'): SETTINGS['notebook'] = True if (turn == 'on') else False def troncate_string(s, max_length=25): return s if (len(s) < max_length) else (s[:max_length] + "...") class ProgressLogger: """Generic class for progress loggers. A progress logger contains a "state" dictionnary. Parameters ---------- init_state Dictionnary representing the initial state. """ def __init__(self, init_state=None): self.state = {} self.stored = {} self.logs = [] self.log_indent = 0 if init_state is not None: self.state.update(init_state) def log(self, message): self.logs.append((' ' * self.log_indent) + message) def dump_logs(self, filepath=None): if filepath is not None: with open(filepath, 'a') as f: f.write("\n".join(self.logs)) else: return "\n".join(self.logs) def callback(self, **kw): """Execute something after the state has been updated by the given state elements. This default callback does nothing, overwrite it by subclassing """ pass def store(self, **kw): """Store objects in the logger and trigger ``self.store_callback``. This works exactly like ``logger()``, but the later is meant for simple data objects (text, numbers) that will be sent over the network or written to a file. The ``store`` method expects rather large objects which are not necessarily serializable, and will be used eg to draw plots on the fly. """ self.stored.update(kw) self.store_callback(**kw) def store_callback(self, **kw): """Execute something after the store has been updated by the given state elements. This default callback does nothing, overwrite it by subclassing """ pass def iter(self, **kw): """Iterate through a list while updating the state. Examples -------- >>> for username in logger.iter(user=['tom', 'tim', 'lea']: >>> # At every loop, logger.state['user'] is updated >>> print (username) """ for field, iterable in kw.items(): for it in iterable: self(**{field: it}) yield it def __call__(self, **kw): self.state.update(kw) self.callback(**kw) class ProgressBarLogger(ProgressLogger): """Generic class for progress loggers. A progress logger contains a "state" dictionnary Parameters ---------- init_state Initial state of the logger bars Either None (will be initialized with no bar) or a list/tuple of bar names (``['main', 'sub']``) which will be initialized with index -1 and no total, or a dictionary (possibly ordered) of bars, of the form ``{bar_1: {title: 'bar1', index: 2, total:23}, bar_2: {...}}`` ignored_bars Either None (newly met bars will be added) or a list of blacklisted bar names, or ``'all_others'`` to signify that all bar names not already in ``self.bars`` will be ignored. """ bar_indent = 2 def __init__(self, init_state=None, bars=None, ignored_bars=None, logged_bars='all', min_time_interval=0, ignore_bars_under=0): ProgressLogger.__init__(self, init_state) if bars is None: bars = OrderedDict() elif isinstance(bars, (list, tuple)): bars = OrderedDict([ (b, dict(title=b, index=-1, total=None, message=None, indent=0)) for b in bars ]) if isinstance(ignored_bars, (list, tuple)): ignored_bars = set(ignored_bars) self.ignored_bars = ignored_bars self.logged_bars = logged_bars self.state['bars'] = bars self.min_time_interval = min_time_interval self.ignore_bars_under = ignore_bars_under @property def bars(self): """Return ``self.state['bars'].``""" return self.state['bars'] def bar_is_ignored(self, bar): if self.ignored_bars is None: return False elif self.ignored_bars == 'all_others': return (bar not in self.bars) else: return bar in self.ignored_bars def bar_is_logged(self, bar): if (not self.logged_bars): return False elif self.logged_bars == 'all': return True else: return bar in self.logged_bars def iterable_is_too_short(self, iterable): length = len(iterable) if hasattr(iterable, '__len__') else None return (length is not None) and (length < self.ignore_bars_under) def iter_bar(self, bar_prefix='', **kw): """Iterate through a list while updating a state bar. Examples -------- >>> for username in logger.iter_bar(user=['tom', 'tim', 'lea']): >>> # At every loop, logger.state['bars']['user'] is updated >>> # to {index: i, total: 3, title:'user'} >>> print (username) """ if 'bar_message' in kw: bar_message = kw.pop('bar_message') else: bar_message = None bar, iterable = kw.popitem() if self.bar_is_ignored(bar) or self.iterable_is_too_short(iterable): return iterable bar = bar_prefix + bar if hasattr(iterable, '__len__'): self(**{bar + '__total': len(iterable)}) def new_iterable(): last_time = time.time() i = 0 # necessary in case the iterator is empty for i, it in enumerate(iterable): now_time = time.time() if (i == 0) or (now_time - last_time > self.min_time_interval): if bar_message is not None: self(**{bar + '__message': bar_message(it)}) self(**{bar + '__index': i}) last_time = now_time yield it if self.bars[bar]['index'] != i: self(**{bar + '__index': i}) self(**{bar + '__index': i + 1}) return new_iterable() def bars_callback(self, bar, attr, value, old_value=None): """Execute a custom action after the progress bars are updated. Parameters ---------- bar Name/ID of the bar to be modified. attr Attribute of the bar attribute to be modified value New value of the attribute old_value Previous value of this bar's attribute. This default callback does nothing, overwrite it by subclassing. """ pass def __call__(self, **kw): items = sorted(kw.items(), key=lambda kv: not kv[0].endswith('total')) for key, value in items: if '__' in key: bar, attr = key.split('__') if self.bar_is_ignored(bar): continue kw.pop(key) if bar not in self.bars: self.bars[bar] = dict(title=bar, index=-1, total=None, message=None) old_value = self.bars[bar][attr] if self.bar_is_logged(bar): new_bar = (attr == 'index') and (value < old_value) if (attr == 'total') or (new_bar): self.bars[bar]['indent'] = self.log_indent else: self.log_indent = self.bars[bar]['indent'] self.log("[%s] %s: %s" % (bar, attr, value)) self.log_indent += self.bar_indent self.bars[bar][attr] = value self.bars_callback(bar, attr, value, old_value) self.state.update(kw) self.callback(**kw) class TqdmProgressBarLogger(ProgressBarLogger): """Tqdm-powered progress bar for console or Notebooks. Parameters ---------- init_state Initial state of the logger bars Either None (will be initialized with no bar) or a list/tuple of bar names (``['main', 'sub']``) which will be initialized with index -1 and no total, or a dictionary (possibly ordered) of bars, of the form ``{bar_1: {title: 'bar1', index: 2, total:23}, bar_2: {...}}`` ignored_bars Either None (newly met bars will be added) or a list of blacklisted bar names, or ``'all_others'`` to signify that all bar names not already in ``self.bars`` will be ignored. leave_bars notebook True will make the bars look nice (HTML) in the jupyter notebook. It is advised to leave to 'default' as the default can be globally set from inside a notebook with ``import proglog; proglog.notebook_mode()``. print_messages If True, every ``logger(message='something')`` will print a message in the console / notebook """ def __init__(self, init_state=None, bars=None, leave_bars=False, ignored_bars=None, logged_bars='all', notebook='default', print_messages=True, min_time_interval=0, ignore_bars_under=0): ProgressBarLogger.__init__(self, init_state=init_state, bars=bars, ignored_bars=ignored_bars, logged_bars=logged_bars, ignore_bars_under=ignore_bars_under, min_time_interval=min_time_interval) self.leave_bars = leave_bars self.tqdm_bars = OrderedDict([ (bar, None) for bar in self.bars ]) if notebook == 'default': notebook = SETTINGS['notebook'] self.notebook = notebook self.print_messages = print_messages self.tqdm = (tqdm_notebook if self.notebook else tqdm) def new_tqdm_bar(self, bar): """Create a new tqdm bar, possibly replacing an existing one.""" if (bar in self.tqdm_bars) and (self.tqdm_bars[bar] is not None): self.close_tqdm_bar(bar) infos = self.bars[bar] self.tqdm_bars[bar] = self.tqdm( total=infos['total'], desc=infos['title'], postfix=dict(now=troncate_string(str(infos['message']))), leave=self.leave_bars ) def close_tqdm_bar(self, bar): """Close and erase the tqdm bar""" self.tqdm_bars[bar].close() if not self.notebook: self.tqdm_bars[bar] = None def bars_callback(self, bar, attr, value, old_value): if (bar not in self.tqdm_bars) or (self.tqdm_bars[bar] is None): self.new_tqdm_bar(bar) if attr == 'index': if value >= old_value: total = self.bars[bar]['total'] if total and (value >= total): self.close_tqdm_bar(bar) else: self.tqdm_bars[bar].update(value - old_value) else: self.new_tqdm_bar(bar) self.tqdm_bars[bar].update(value + 1) elif attr == 'message': self.tqdm_bars[bar].set_postfix(now=troncate_string(str(value))) self.tqdm_bars[bar].update(0) def callback(self, **kw): if self.print_messages and ('message' in kw) and kw['message']: if self.notebook: print(kw['message']) else: self.tqdm.write(kw['message']) class RqWorkerProgressLogger: def __init__(self, job): self.job = job if 'progress_data' not in self.job.meta: self.job.meta['progress_data'] = {} self.job.save() def callback(self, **kw): self.job.meta['progress_data'] = self.state self.job.save() class RqWorkerBarLogger(RqWorkerProgressLogger, ProgressBarLogger): def __init__(self, job, init_state=None, bars=None, ignored_bars=(), logged_bars='all', min_time_interval=0): RqWorkerProgressLogger.__init__(self, job) ProgressBarLogger.__init__(self, init_state=init_state, bars=bars, ignored_bars=ignored_bars, logged_bars=logged_bars, min_time_interval=min_time_interval) class MuteProgressBarLogger(ProgressBarLogger): def bar_is_ignored(self, bar): return True def default_bar_logger(logger, bars=None, ignored_bars=None, logged_bars='all', min_time_interval=0, ignore_bars_under=0): if logger == 'bar': return TqdmProgressBarLogger( bars=bars, ignored_bars=ignored_bars, logged_bars=logged_bars, min_time_interval=min_time_interval, ignore_bars_under=ignore_bars_under ) elif logger is None: return MuteProgressBarLogger() else: return logger

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false

0.013453

0.008969

0.246637

0.017937

0

null

0

null

0

1

0

a3d9f04c9618b248a5e94c0c7319362fccd10a9f

665

py

Python

gdsfactory/tests/test_component_from_yaml_bezier.py

jorgepadilla19/gdsfactory

68e1c18257a75d4418279851baea417c8899a165

[ "MIT" ]

42

2020-05-25T09:33:45.000Z

2022-03-29T03:41:19.000Z

gdsfactory/tests/test_component_from_yaml_bezier.py

jorgepadilla19/gdsfactory

68e1c18257a75d4418279851baea417c8899a165

[ "MIT" ]

133

2020-05-28T18:29:04.000Z

2022-03-31T22:21:42.000Z

gdsfactory/tests/test_component_from_yaml_bezier.py

jorgepadilla19/gdsfactory

68e1c18257a75d4418279851baea417c8899a165

[ "MIT" ]

17

2020-06-30T07:07:50.000Z

2022-03-17T15:45:27.000Z

import gdsfactory as gf from gdsfactory.component import Component yaml = """ name: test_component_yaml_without_cell instances: mmi: component: mmi1x2 bend: component: bend_s connections: bend,o1: mmi,o2 """ def test_component_from_yaml_without_cell() -> Component: """bezier does not have cell""" c = gf.read.from_yaml(yaml) assert c.name == "test_component_yaml_without_cell", c.name assert len(c.get_dependencies()) == 2, len(c.get_dependencies()) assert len(c.ports) == 0, len(c.ports) return c if __name__ == "__main__": c = test_component_from_yaml_without_cell() print(c.name) c.show()

20.151515

68

0.682707

93

665

4.55914

0.408602

0.122642

0.141509

0.099057

0.301887

0

0.011299

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665

32

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false

0

0.086957

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0.043478

0

null

0

null

0

1

0

a3db4ad6c588be26e30297068925d6bff9a900d1

5,616

py

Python

Tests/Methods/Machine/test_Magnet_Type_11_meth.py

Superomeg4/pyleecan

2b695b5f39e77475a07aa0ea89489fb0a9659337

[ "Apache-2.0" ]

2

2020-06-29T13:48:37.000Z

2021-06-15T07:34:05.000Z

Tests/Methods/Machine/test_Magnet_Type_11_meth.py

Superomeg4/pyleecan

2b695b5f39e77475a07aa0ea89489fb0a9659337

[ "Apache-2.0" ]

null

Tests/Methods/Machine/test_Magnet_Type_11_meth.py

Superomeg4/pyleecan

2b695b5f39e77475a07aa0ea89489fb0a9659337

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ @date Created on Thu Dec 18 13:56:33 2014 @copyright (C) 2014-2015 EOMYS ENGINEERING. @author pierre_b """ from unittest import TestCase from ddt import ddt, data from pyleecan.Classes.Arc1 import Arc1 from pyleecan.Classes.Segment import Segment from pyleecan.Classes.MagnetType11 import MagnetType11 from pyleecan.Classes.LamSlotMag import LamSlotMag from pyleecan.Classes.SlotMPolar import SlotMPolar from numpy import pi, exp, angle, array from pyleecan.Methods.Machine.Magnet.comp_surface import comp_surface Mag11_test = list() # Internal Slot surface lam = LamSlotMag(is_internal=True, Rext=0.5) lam.slot = SlotMPolar(H0=0, W0=pi / 4, Zs=4) lam.slot.magnet = [MagnetType11(Hmag=1, Wmag=pi / 4)] Mag11_test.append({"test_obj": lam, "S_exp": 0.78539616, "Ao": pi / 4, "H_exp": 1}) # Internal Slot inset lam = LamSlotMag(is_internal=True, Rext=0.5) lam.slot = SlotMPolar(H0=40e-3, W0=pi / 4, Zs=4) lam.slot.magnet = [MagnetType11(Hmag=20e-3, Wmag=pi / 4)] Mag11_test.append({"test_obj": lam, "S_exp": 7.3827e-3, "Ao": pi / 4, "H_exp": 20e-3}) # Outward Slot inset lam = LamSlotMag(is_internal=False, Rext=0.1325) lam.slot = SlotMPolar(H0=5e-3, W0=pi / 10, Zs=8) lam.slot.magnet = [MagnetType11(Hmag=8e-3, Wmag=pi / 12)] Mag11_test.append({"test_obj": lam, "S_exp": 2.09439e-6, "Ao": pi / 12, "H_exp": 8e-3}) # For AlmostEqual DELTA = 1e-4 @ddt class test_Magnet_Type_11_meth(TestCase): """unittest for MagnetType11 methods """ @data(*Mag11_test) def test_comp_surface(self, test_dict): """Check that the computation of the surface is correct """ test_obj = test_dict["test_obj"] result = test_obj.slot.magnet[0].comp_surface() a = result b = test_dict["S_exp"] msg = "Return " + str(a) + " expected " + str(b) self.assertAlmostEqual((a - b) / a, 0, delta=DELTA, msg=msg) # Compare numerical and analytical results b = comp_surface(test_obj.slot.magnet[0]) msg = "Analytical: " + str(a) + " Numerical " + str(b) self.assertAlmostEqual((a - b) / a, 0, delta=DELTA, msg=msg) @data(*Mag11_test) def test_comp_height(self, test_dict): """Check that the computation of the height is correct """ test_obj = test_dict["test_obj"] result = test_obj.slot.magnet[0].comp_height() a = result b = test_dict["H_exp"] msg = "Return " + str(a) + " expected " + str(b) self.assertAlmostEqual((a - b) / a, 0, delta=DELTA, msg=msg) @data(*Mag11_test) def test_comp_angle_op(self, test_dict): """Check that the computation of the opening angle is correct """ test_obj = test_dict["test_obj"] result = test_obj.slot.magnet[0].comp_angle_opening() a = result b = test_dict["Ao"] msg = "Return " + str(a) + " expected " + str(b) self.assertAlmostEqual((a - b) / a, 0, delta=DELTA, msg=msg) def test_build_geometry_out(self): """check that curve_list is correct (outwards magnet)""" lam = LamSlotMag( Rint=40e-3, Rext=90e-3, is_internal=False, is_stator=False, L1=0.45, Nrvd=1, Wrvd=0.05, ) magnet = [MagnetType11(Wmag=pi / 10, Hmag=0.2)] lam.slot = SlotMPolar(Zs=8, W0=pi / 10, H0=0.2, magnet=magnet) test_obj = lam.slot.magnet[0] Z1 = (40e-3 + 0.2) * exp(-1j * pi / 10 / 2) Z2 = (40e-3 + 0.2) * exp(1j * pi / 10 / 2) Z = abs(Z1) Z3 = (Z - 0.2) * exp(1j * angle(Z1)) Z4 = (Z - 0.2) * exp(1j * angle(Z2)) # # Creation of curve curve_list = list() curve_list.append(Segment(Z1, Z3)) curve_list.append(Arc1(Z3, Z4, abs(Z3))) curve_list.append(Segment(Z4, Z2)) curve_list.append(Arc1(Z2, Z1, -abs(Z2))) surface = test_obj.build_geometry() result = surface[0].get_lines() for i in range(0, len(result)): a = result[i].begin b = curve_list[i].begin self.assertAlmostEqual((a - b) / a, 0, delta=DELTA) a = result[i].end b = curve_list[i].end self.assertAlmostEqual((a - b) / a, 0, delta=DELTA) def test_build_geometry_in(self): """check that curve_list is correct (inwards magnet)""" lam = LamSlotMag( Rint=40e-1, Rext=90e-1, is_internal=True, is_stator=False, L1=0.45, Nrvd=1, Wrvd=0.05, ) magnet = [MagnetType11(Wmag=pi / 10, Hmag=0.2)] lam.slot = SlotMPolar(Zs=8, W0=pi / 10, H0=0.2, magnet=magnet) test_obj = lam.slot.magnet[0] Z1 = (90e-1 - 0.2) * exp(-1j * pi / 10 / 2) Z2 = (90e-1 - 0.2) * exp(1j * pi / 10 / 2) Z = abs(Z1) Z3 = (Z + 0.2) * exp(1j * angle(Z1)) Z4 = (Z + 0.2) * exp(1j * angle(Z2)) # # Creation of curve curve_list = list() curve_list.append(Segment(Z1, Z3)) curve_list.append(Arc1(Z3, Z4, abs(Z3))) curve_list.append(Segment(Z4, Z2)) curve_list.append(Arc1(Z2, Z1, -abs(Z2))) surface = test_obj.build_geometry() result = surface[0].get_lines() for i in range(0, len(result)): a = result[i].begin b = curve_list[i].begin self.assertAlmostEqual((a - b) / a, 0, delta=DELTA) a = result[i].end b = curve_list[i].end self.assertAlmostEqual((a - b) / a, 0, delta=DELTA)

33.035294

87

0.579238

821

5,616

3.848965

0.177832

0.037658

0.055696

0.058228

0.704747

0.652532

0.631962

0.612342

0.601899

0.551266

0

0.069008

0.274929

5,616

169

88

33.230769

0.707024

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false

0

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0

0.12931

0

null

0

null

0

1

0

a3db7f4c59462c81c92a9534466aa08adc11bb16

4,600

py

Python

tomo_encoders/tasks/void_mapping.py

arshadzahangirchowdhury/TomoEncoders

9c2b15fd515d864079f198546821faee5d78df17

[ "BSD-3-Clause" ]

null

tomo_encoders/tasks/void_mapping.py

arshadzahangirchowdhury/TomoEncoders

9c2b15fd515d864079f198546821faee5d78df17

[ "BSD-3-Clause" ]

null

tomo_encoders/tasks/void_mapping.py

arshadzahangirchowdhury/TomoEncoders

9c2b15fd515d864079f198546821faee5d78df17

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ """ from operator import mod from tomo_encoders.misc.voxel_processing import modified_autocontrast, TimerGPU from tomo_encoders.reconstruction.recon import recon_patches_3d import cupy as cp import numpy as np from skimage.filters import threshold_otsu from tomo_encoders import Grid def get_values_cyl_mask(vol, mask_fac): vol_shape = vol.shape assert vol_shape[1] == vol_shape[2], "must be a tomographic volume where shape y = shape x" shape_yx = vol_shape[1] shape_z = vol_shape[0] rad = int(mask_fac*shape_yx/2) pts = cp.arange(-int(shape_yx//2), int(cp.ceil(shape_yx//2))) yy, xx = cp.meshgrid(pts, pts, indexing = 'ij') circ = (cp.sqrt(yy**2 + xx**2) < rad).astype(cp.uint8) # inside is positive circ = circ[cp.newaxis, ...] cyl = cp.repeat(circ, shape_z, axis = 0) return vol[cyl > 0] def cylindrical_mask(out_vol, mask_fac, mask_val = 0): vol_shape = out_vol.shape assert vol_shape[1] == vol_shape[2], "must be a tomographic volume where shape y = shape x" shape_yx = vol_shape[1] shape_z = vol_shape[0] rad = int(mask_fac*shape_yx/2) pts = cp.arange(-int(shape_yx//2), int(cp.ceil(shape_yx//2))) yy, xx = cp.meshgrid(pts, pts, indexing = 'ij') circ = (cp.sqrt(yy**2 + xx**2) < rad).astype(cp.uint8) # inside is positive circ = circ[cp.newaxis, ...] cyl = cp.repeat(circ, shape_z, axis = 0) out_vol[cyl == 0] = mask_val return def segment_otsu(vol, s = 0.05): '''segment volume with otsu''' timer = TimerGPU() timer.tic() tmp_values = vol[::4,::4,::4].get() # rec_min_max = modified_autocontrast(tmp_values, s = s, normalize_sampling_factor=1) thresh = cp.float32(threshold_otsu(tmp_values.reshape(-1))) vol = (vol < thresh).astype(cp.uint8) timer.toc("otsu thresholding") return vol def edge_map(Y): ''' this algorithm was inspired by: https://github.com/tomochallenge/tomochallenge_utils/blob/master/foam_phantom_utils.py ''' msk = cp.zeros_like(Y) tmp = Y[:-1]!=Y[1:] msk[:-1][tmp] = 1 msk[1:][tmp] = 1 tmp = Y[:,:-1]!=Y[:,1:] msk[:,:-1][tmp] = 1 msk[:,1:][tmp] = 1 tmp = Y[:,:,:-1]!=Y[:,:,1:] msk[:,:,:-1][tmp] = 1 msk[:,:,1:][tmp] = 1 return msk > 0 def guess_surface(V_bin, b, wd): # find patches on surface wdb = int(wd//b) p3d = Grid(V_bin.shape, width = wdb) x = p3d.extract(V_bin) is_surf = (np.std(x, axis = (1,2,3)) > 0.0) is_ones = (np.sum(x, axis = (1,2,3))/(wdb**3) == 1) is_zeros = (np.sum(x, axis = (1,2,3))/(wdb**3) == 0) p3d = p3d.rescale(b) p3d_surf = p3d.filter_by_condition(is_surf) p3d_ones = p3d.filter_by_condition(is_ones) p3d_zeros = p3d.filter_by_condition(is_zeros) eff = len(p3d_surf)*(wd**3)/np.prod(p3d_surf.vol_shape) print(f"\tSTAT: r value: {eff*100.0:.2f}") return p3d_surf, p3d_ones, p3d_zeros def process_patches(projs, theta, center, fe, p_surf, min_max, TIMEIT = False): # SCHEME 1: integrate reconstruction and segmention (segments data on gpu itself) # st_proc = cp.cuda.Event(); end_proc = cp.cuda.Event(); st_proc.record() # x_surf, p_surf = recon_patches_3d(projs, theta, center, p_surf, \ # apply_fbp = True, segmenter = fe, \ # segmenter_batch_size = 256) # end_proc.record(); end_proc.synchronize(); t_surf = cp.cuda.get_elapsed_time(st_proc,end_proc) # SCHEME 2: reconstruct and segment separately (copies rec data from gpu to cpu) st_rec = cp.cuda.Event(); end_rec = cp.cuda.Event(); st_rec.record() x_surf, p_surf = recon_patches_3d(projs, theta, center, p_surf, \ apply_fbp =True) end_rec.record(); end_rec.synchronize(); t_rec = cp.cuda.get_elapsed_time(st_rec,end_rec) st_seg = cp.cuda.Event(); end_seg = cp.cuda.Event(); st_seg.record() x_surf = np.clip(x_surf, *min_max) x_surf = fe.predict_patches("segmenter", x_surf[...,np.newaxis], 256, None, min_max = min_max)[...,0] end_seg.record(); end_seg.synchronize(); t_seg = cp.cuda.get_elapsed_time(st_seg,end_seg) print(f'\tTIME: local reconstruction - {t_rec/1000.0:.2f} secs') print(f'\tTIME: local segmentation - {t_seg/1000.0:.2f} secs') print(f'\tSTAT: total patches in neighborhood: {len(p_surf)}') if TIMEIT: return x_surf, p_surf, t_rec, t_seg else: return x_surf, p_surf

35.384615

122

0.62087

728

4,600

3.721154

0.273352

0.038391

0.017719

0.370986

0.331857

0.294943

0.282392

0

0.032773

0.223913

4,600

129

123

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0

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0

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0

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1

0.073171

false

0

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0

0.243902

0.04878

0

null

0

null

0

1

0

a3dd7fa87a5a13e38a56d66d0de7938491e30d3e

793

py

Python

TuShare/view/sh_margins.py

lwh2015/TuShare

f244e05e5cf208e18e6237d3b81f71f0d3c1394a

[ "MIT" ]

1

2018-09-26T08:34:02.000Z

TuShare/view/sh_margins.py

lwh2015/TuShare

f244e05e5cf208e18e6237d3b81f71f0d3c1394a

[ "MIT" ]

null

TuShare/view/sh_margins.py

lwh2015/TuShare

f244e05e5cf208e18e6237d3b81f71f0d3c1394a

[ "MIT" ]

null

# -*- coding: UTF-8 -*- import json from django.http import HttpResponse from django.views.decorators.csrf import csrf_exempt import tushare as ts from .publiceClass import DateEncoder @csrf_exempt def sh_margins(request): try: start = request.POST.get('start','')#选填 end = request.POST.get('end','')#选填 data = ts.sh_margins(start,end) res = {'columns':[ '信用交易日期', '本日融资余额(元)', '本日融资买入额(元)', '本日融券余量', '本日融券余量金额(元)', '本日融券卖出量', '本日融资融券余额(元)' ],'data':json.loads(json.dumps(data.values,cls=DateEncoder))} except(BaseException): return HttpResponse(BaseException) else: return HttpResponse(json.dumps(res),content_type="application/json")

26.433333

76

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88

793

5.318182

0.568182

0.042735

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0

0.001712

0.263556

793

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27.344828

0.799658

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false

0

0.208333

0

0.333333

0

null

0

null

0

1

0

a3de1f30f9f2a9d6efbf703fb8df76e65a62d871

1,181

py

Python

intermediate/classes/camera.py

robertob45/learning-python

7407f7d9e513792150eb2b65ebc644b5f8632c56

[ "MIT" ]

null

intermediate/classes/camera.py

robertob45/learning-python

7407f7d9e513792150eb2b65ebc644b5f8632c56

[ "MIT" ]

null

intermediate/classes/camera.py

robertob45/learning-python

7407f7d9e513792150eb2b65ebc644b5f8632c56

[ "MIT" ]

null

class Camera: """docstring for .""" def __init__(self, brand, sensor, lens, battery): self.brand = brand self.sensor = sensor self.lens = lens self.battery = battery def __str__(self): return self.brand + ' ' + self.sensor + ' ' + self.lens + ' ' + self.battery def focus(self): print('Focusing using', self.lens, '...') print('') def frame(self): print('Move until your subject is in the desired position') print('.') print('.') print('.') def flash(self, flash_use): if flash_use == 's': print('Shooting with flash...') else: print('Shooting without flash...') print('') def format(self, save_format): if save_format == 'jpg': print('Saving in: ' + save_format) elif save_format == 'raw': print('Saving in: ' + save_format) else: print('No valid format to save') def take_picture(self, save_format, flash_use): print('Say cheese!') self.focus() self.frame() self.flash(flash_use) self.format(save_format)

27.465116

84

0.531753

132

1,181

4.606061

0.363636

0.115132

0.049342

0.055921

0.075658

0

0.329382

1,181

42

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false

0

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0.371429

0

null

0

null

0

1

0

a3de3ec0c21d41a610e2d90e04c28f83ca0ba4c2

7,332

py

Python

dbaas/tsuru/tests/test_service_add.py

didindinn/database-as-a-service

747de31ff8546f7874ddd654af860e130afd17a0

[ "BSD-3-Clause" ]

null

dbaas/tsuru/tests/test_service_add.py

didindinn/database-as-a-service

747de31ff8546f7874ddd654af860e130afd17a0

[ "BSD-3-Clause" ]

null

dbaas/tsuru/tests/test_service_add.py

didindinn/database-as-a-service

747de31ff8546f7874ddd654af860e130afd17a0

[ "BSD-3-Clause" ]

null

from mock import patch, MagicMock from django.contrib.auth.models import User from django.test import TestCase from django.core.urlresolvers import reverse from django.utils.datastructures import MultiValueDictKeyError from account.models import Role, Team, Organization from physical.tests.factory import EnvironmentFactory, PlanFactory from physical.models import Plan class ValidationTestCase(TestCase): """HTTP test cases for the tsuru Service Add. This class focuses on validations of POST """ USERNAME = "fake_user" PASSWORD = "123456" def setUp(self): self.role = Role.objects.get_or_create(name="fake_role")[0] self.organization = Organization.objects.get_or_create( name='fake_organization' )[0] self.team = Team.objects.get_or_create( name="fake_team", role=self.role, organization=self.organization)[0] self.superuser = User.objects.create_superuser( self.USERNAME, email="{}@admin.com".format(self.USERNAME), password=self.PASSWORD ) self.team.users.add(self.superuser) self.client.login(username=self.USERNAME, password=self.PASSWORD) self.env = 'dev' self.environment = EnvironmentFactory.create(name=self.env) self.url = reverse('tsuru:service-add', args=(self.env,)) self.name = 'fake_database' self.user = '{}@admin.com'.format(self.USERNAME) self.description = 'fake desc' self.plan = PlanFactory(name='fake_plan', provider=Plan.CLOUDSTACK) self.plan.environments.add(self.environment) self.plan_name = 'fake-plan-dev' def tearDown(self): self.client.logout() def _assert_resp(self, resp, msg): self.assertEqual(resp.status_code, 400) self.assertEqual(resp.content, msg) def test_name_not_in_payload(self): with self.assertRaises(MultiValueDictKeyError): self.client.post(self.url, {}) def test_user_not_in_payload(self): with self.assertRaises(MultiValueDictKeyError): self.client.post( self.url, {'name': self.name} ) def test_team_not_in_payload(self): with self.assertRaises(MultiValueDictKeyError): self.client.post( self.url, {'name': self.name, 'user': self.user} ) def test_description_fail(self): resp = self.client.post( self.url, {'name': self.name, 'user': self.user, 'team': self.team} ) self._assert_resp(resp, '"A description must be provided."') def test_name_fail(self): resp = self.client.post( self.url, { 'name': '99invalid-name', 'user': self.user, 'description': self.description, 'team': self.team } ) self._assert_resp( resp, '"Your database name must match /^[a-z][a-z0-9_]+$/ ."' ) @patch('tsuru.views.Database.objects.get', new=MagicMock()) def test_database_found(self): resp = self.client.post( self.url, { 'name': self.name, 'user': self.user, 'description': self.description, 'team': self.team } ) self._assert_resp( resp, '"There is already a database called fake_database in dev."' ) @patch( 'tsuru.views.database_name_evironment_constraint', new=MagicMock(return_value=True) ) def test_already_exist_database_with_name(self): resp = self.client.post( self.url, { 'name': self.name, 'user': self.user, 'description': self.description, 'team': self.team } ) self._assert_resp( resp, '"fake_database already exists in env dev!"' ) def test_user_not_found(self): resp = self.client.post( self.url, { 'name': self.name, 'user': 'another_user@not_found.com', 'description': self.description, 'team': self.team } ) self._assert_resp( resp, '"User does not exist."' ) def test_team_not_found(self): resp = self.client.post( self.url, { 'name': self.name, 'user': 'another_user@not_found.com', 'description': self.description, 'team': 'team_not_found' } ) self._assert_resp( resp, '"User does not exist."' ) def test_env_not_found(self): self.url = self.url.replace( '/{}/'.format(self.env), '/env_not_found/' ) resp = self.client.post( self.url, { 'name': self.name, 'user': self.user, 'description': self.description, 'team': self.team.name } ) self._assert_resp( resp, '"Environment does not exist."' ) @patch( 'tsuru.views.Team.count_databases_in_use', new=MagicMock(return_value=99) ) def test_allocation_limit(self): resp = self.client.post( self.url, { 'name': self.name, 'user': self.user, 'description': self.description, 'team': self.team.name } ) self._assert_resp( resp, ('"The database alocation limit of 2 has been exceeded for the ' 'selected team: fake_team"') ) def test_plan_not_on_payload(self): resp = self.client.post( self.url, { 'name': self.name, 'user': self.user, 'description': self.description, 'team': self.team.name } ) self._assert_resp( resp, '"Plan was not found"' ) def test_plan_not_found(self): resp = self.client.post( self.url, { 'name': self.name, 'user': self.user, 'description': self.description, 'team': self.team.name, 'plan': 'not found' } ) self._assert_resp( resp, '"Plan was not found"' ) @patch('notification.tasks.TaskRegister.create_task', new=MagicMock()) @patch('notification.tasks.create_database_with_retry') def test_call_database_create(self, create_database_mock): resp = self.client.post( self.url, { 'name': self.name, 'user': self.user, 'description': self.description, 'team': self.team.name, 'plan': self.plan_name } ) self.assertTrue(create_database_mock.called) self.assertEqual(resp.status_code, 201)

30.17284

76

0.524686

740

7,332

5.048649

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0.038544

0.052463

0.067452

0.492773

0.466542

0.422109

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0.401231

0.390257

0

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7,332

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77

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null

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null

0

1

0

a3e06ae8cd6e0aabca5915c1a17ae312a2a03a30

734

py

Python

gryphon/data/template_scaffolding/template/setup.py

ow-gryphon/gryphon

0b34f2f61a50af46b9d1ec1d3c15d53cf4055dd5

[ "MIT" ]

null

gryphon/data/template_scaffolding/template/setup.py

ow-gryphon/gryphon

0b34f2f61a50af46b9d1ec1d3c15d53cf4055dd5

[ "MIT" ]

1

2022-03-08T14:54:26.000Z

2022-03-08T15:02:52.000Z

gryphon/data/template_scaffolding/template/setup.py

ow-gryphon/gryphon

0b34f2f61a50af46b9d1ec1d3c15d53cf4055dd5

[ "MIT" ]

null

import json import setuptools with open("template/README.md", "r") as fh: long_description = fh.read() with open('requirements.txt') as fr: requirements = fr.read().strip().split('\n') with open('metadata.json') as fr: metadata = json.load(fr) setuptools.setup( name="", # Name of the repository version="0.0.1", author=metadata.get("author", ""), author_email=metadata.get("author_email", ""), description=metadata.get("description", ""), long_description=long_description, long_description_content_type="text/markdown", url="", # Repository URL or externally maintained page packages=setuptools.find_packages(), python_requires='>=3.6', install_requires=requirements, )

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0.121457

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734

25

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0

0.095238

0

null

0

null

0

1

0

a3e0ad9312af3accd64fc327daefc5bf89405ae4

6,558

py

Python

train_base3.py

Mhaiyang/iccv

04a8ee52c2323d7ff5cdf03c0be1466e8180d2eb

[ "MIT" ]

2

2019-01-10T03:44:03.000Z

2019-05-24T08:50:14.000Z

train_base3.py

Mhaiyang/iccv

04a8ee52c2323d7ff5cdf03c0be1466e8180d2eb

[ "MIT" ]

null

train_base3.py

Mhaiyang/iccv

04a8ee52c2323d7ff5cdf03c0be1466e8180d2eb

[ "MIT" ]

null

""" @Time : 201/21/19 10:41 @Author : TaylorMei @Email : mhy845879017@gmail.com @Project : iccv @File : train_base3.py @Function: """ import datetime import os import torch from torch import nn from torch import optim from torch.autograd import Variable from torch.backends import cudnn from torch.utils.data import DataLoader from torchvision import transforms from tensorboardX import SummaryWriter from tqdm import tqdm import joint_transforms from config import msd_training_root from config import backbone_path from dataset import ImageFolder from misc import AvgMeter, check_mkdir from model.base3 import BASE3 import loss as L cudnn.benchmark = True device_ids = [2] ckpt_path = './ckpt' exp_name = 'BASE3' args = { 'epoch_num': 100, 'train_batch_size': 14, 'last_epoch': 0, 'lr': 5e-3, 'lr_decay': 0.9, 'weight_decay': 5e-4, 'momentum': 0.9, 'snapshot': '', 'scale': 384, 'save_point': [60, 80, 90], 'add_graph': True, 'poly_train': True, 'optimizer': 'SGD' } # Path. check_mkdir(ckpt_path) check_mkdir(os.path.join(ckpt_path, exp_name)) vis_path = os.path.join(ckpt_path, exp_name, 'log') check_mkdir(vis_path) log_path = os.path.join(ckpt_path, exp_name, str(datetime.datetime.now()) + '.txt') writer = SummaryWriter(log_dir=vis_path, comment=exp_name) # Transform Data. joint_transform = joint_transforms.Compose([ joint_transforms.RandomRotate(), joint_transforms.Resize((args['scale'], args['scale'])) ]) img_transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) # maybe can optimized. ]) target_transform = transforms.ToTensor() # Prepare Data Set. train_set = ImageFolder(msd_training_root, joint_transform, img_transform, target_transform) print("Train set: {}".format(train_set.__len__())) train_loader = DataLoader(train_set, batch_size=args['train_batch_size'], num_workers=0, shuffle=True) def main(): print(args) print(exp_name) net = BASE3(backbone_path).cuda(device_ids[0]).train() if args['add_graph']: writer.add_graph(net, input_to_model=torch.rand( args['train_batch_size'], 3, args['scale'], args['scale']).cuda(device_ids[0])) if args['optimizer'] == 'Adam': print("Adam") optimizer = optim.Adam([ {'params': [param for name, param in net.named_parameters() if name[-4:] == 'bias'], 'lr': 2 * args['lr']}, {'params': [param for name, param in net.named_parameters() if name[-4:] != 'bias'], 'lr': 1 * args['lr'], 'weight_decay': args['weight_decay']} ]) else: print("SGD") optimizer = optim.SGD([ {'params': [param for name, param in net.named_parameters() if name[-4:] == 'bias'], 'lr': 2 * args['lr']}, {'params': [param for name, param in net.named_parameters() if name[-4:] != 'bias'], 'lr': 1 * args['lr'], 'weight_decay': args['weight_decay']} ], momentum=args['momentum']) if len(args['snapshot']) > 0: print('Training Resumes From \'%s\'' % args['snapshot']) net.load_state_dict(torch.load(os.path.join(ckpt_path, exp_name, args['snapshot'] + '.pth'))) net = nn.DataParallel(net, device_ids=device_ids) print("Using {} GPU(s) to Train.".format(len(device_ids))) open(log_path, 'w').write(str(args) + '\n\n') train(net, optimizer) writer.close() def train(net, optimizer): curr_iter = 1 for epoch in range(args['last_epoch'] + 1, args['last_epoch'] + 1 + args['epoch_num']): loss_4_record, loss_3_record, loss_2_record, loss_1_record, \ loss_f_record, loss_record = AvgMeter(), AvgMeter(), AvgMeter(), AvgMeter(), AvgMeter(), AvgMeter() train_iterator = tqdm(train_loader, total=len(train_loader)) for data in train_iterator: if args['poly_train']: base_lr = args['lr'] * (1 - float(curr_iter) / (args['epoch_num'] * len(train_loader))) ** args[ 'lr_decay'] optimizer.param_groups[0]['lr'] = 2 * base_lr optimizer.param_groups[1]['lr'] = 1 * base_lr inputs, labels = data batch_size = inputs.size(0) inputs = Variable(inputs).cuda(device_ids[0]) labels = Variable(labels).cuda(device_ids[0]) optimizer.zero_grad() predict_4, predict_3, predict_2, predict_1, predict_f = net(inputs) loss_4 = L.lovasz_hinge(predict_4, labels) loss_3 = L.lovasz_hinge(predict_3, labels) loss_2 = L.lovasz_hinge(predict_2, labels) loss_1 = L.lovasz_hinge(predict_1, labels) loss_f = L.lovasz_hinge(predict_f, labels) loss = loss_4 + loss_3 + loss_2 + loss_1 + loss_f loss.backward() optimizer.step() loss_record.update(loss.data, batch_size) loss_4_record.update(loss_4.data, batch_size) loss_3_record.update(loss_3.data, batch_size) loss_2_record.update(loss_2.data, batch_size) loss_1_record.update(loss_1.data, batch_size) loss_f_record.update(loss_f.data, batch_size) if curr_iter % 50 == 0: writer.add_scalar('loss', loss, curr_iter) writer.add_scalar('loss_4', loss_4, curr_iter) writer.add_scalar('loss_3', loss_3, curr_iter) writer.add_scalar('loss_2', loss_2, curr_iter) writer.add_scalar('loss_1', loss_1, curr_iter) writer.add_scalar('loss_f', loss_f, curr_iter) log = '[%3d], [%6d], [%.6f], [%.5f], [L4: %.5f], [L3: %.5f], [L2: %.5f], [L1: %.5f], [Lf: %.5f]' % \ (epoch, curr_iter, base_lr, loss_record.avg, loss_4_record.avg, loss_3_record.avg, loss_2_record.avg, loss_1_record.avg, loss_f_record.avg) train_iterator.set_description(log) open(log_path, 'a').write(log + '\n') curr_iter += 1 if epoch in args['save_point']: net.cpu() torch.save(net.module.state_dict(), os.path.join(ckpt_path, exp_name, '%d.pth' % epoch)) net.cuda(device_ids[0]) if epoch >= args['epoch_num']: net.cpu() torch.save(net.module.state_dict(), os.path.join(ckpt_path, exp_name, '%d.pth' % epoch)) print("Optimization Have Done!") return if __name__ == '__main__': main()

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6,558

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0.223714

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0.023772

0.021944

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0

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6,558

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0.734013

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0

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false

0

0.126761

0

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0.056338

0

null

0

null

0

1

0

a3e11b8d66ab1bd3a621bca6d89f7a077e4198d7

3,584

py

Python

teacher/views.py

itteamforslp/safelife_project

53af23dec0d19acf7227a43a16d7aedad443e90d

[ "MIT" ]

null

teacher/views.py

itteamforslp/safelife_project

53af23dec0d19acf7227a43a16d7aedad443e90d

[ "MIT" ]

4

2021-04-08T20:11:37.000Z

2021-09-22T19:37:57.000Z

safelife/safelife_project/teacher/views.py

CSUS-Scrumbags/safelife

2de7f83f637fae930b1176af796f4cc6f0519c86

[ "MIT" ]

null

from django.shortcuts import render from django.http import HttpResponse from django.contrib.auth.decorators import login_required from django.views.decorators.csrf import csrf_exempt from django.template import loader from django.db import connection from django.http import HttpResponseRedirect import datetime from django.http import JsonResponse from administrator.models import Course, CourseTeacher, CourseStudent, Student from django.core.exceptions import PermissionDenied def teacher_only(function): #"""Limit view to teacher only.""" def _inner(request, *args, **kwargs): if not request.user.is_staff == False | request.user.is_superuser: raise PermissionDenied return function(request, *args, **kwargs) return _inner @login_required(login_url = '/users') @teacher_only def home(request): current_user = request.user.id teacher_current_courses = Course.objects.select_related().raw('SELECT * ' 'FROM course_teachers as CT, courses as C ' 'WHERE CT.teachers_id = %s AND C.course_id = CT.course_id AND C.is_complete = 0 ', [current_user]) currentdate = datetime.datetime.today().strftime('%Y-%m-%d') with connection.cursor() as cursor: cursor.execute('SELECT CL.course_id, CL.date ' 'FROM classes as CL, course_teachers as CT ' 'WHERE CT.teachers_id = %s AND CL.date >= %s ' 'AND CT.course_id = CL.course_id ' 'GROUP BY CL.course_id ', [current_user, currentdate]) next_class_date = cursor.fetchall() with connection.cursor() as cursor: cursor.execute('SELECT CS.course_id, COUNT(CS.students_id) ' 'FROM course_teachers as CT, course_students as CS ' 'WHERE CT.teachers_id = %s AND CT.course_id = CS.course_id ' 'GROUP BY CS.course_id ', [current_user]) teacher_student_count = cursor.fetchall() with connection.cursor() as cursor: cursor.execute('SELECT C.course_id, C.notes ' 'FROM course_teachers as CT, courses as C ' 'WHERE CT.teachers_id = %s AND C.course_id = CT.course_id ' 'GROUP BY CT.course_id ', [current_user]) teacher_course_notes = cursor.fetchall() template = loader.get_template('teacher/dashboard.html') context = { 'teacher_current_courses': teacher_current_courses, 'teacher_student_count': teacher_student_count, 'next_class_date': next_class_date, 'teacher_course_notes': teacher_course_notes } # Render the template to the user return HttpResponse(template.render(context, request)) @csrf_exempt def update_course_notes(request): # Get the student name that was passed from the web page courseNotes = request.POST.get('courseNotes') courseId = request.POST.get('courseId') # Create a cursor to execute raw SQL queries. with connection.cursor() as cursor: cursor.execute('UPDATE courses ' 'SET notes = %s ' 'WHERE course_id = %s', [courseNotes, courseId]) # Render the response to the user

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0.058168

0.024237

0.034901

0.239457

0.191953

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0.151721

0.128938

0

0.000412

0.323382

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79

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0

1

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false

0

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0

0.3

0

null

0

null

0

1

0

a3e2215b6ec560d3033ce187558d53690b59cd03

33,955

py

Python

pywikibot/site/_datasite.py

xqt/pwb

9a4fe27138f32952e533256195849d05855df0b0

[ "MIT" ]

null

pywikibot/site/_datasite.py

xqt/pwb

9a4fe27138f32952e533256195849d05855df0b0

[ "MIT" ]

1

2021-12-08T16:29:41.000Z

pywikibot/site/_datasite.py

xqt/pwb

9a4fe27138f32952e533256195849d05855df0b0

[ "MIT" ]

2

2022-01-04T04:10:38.000Z

2022-01-04T04:18:18.000Z

"""Objects representing API interface to Wikibase site.""" # # (C) Pywikibot team, 2012-2022 # # Distributed under the terms of the MIT license. # import datetime import json import uuid from contextlib import suppress from typing import Optional from warnings import warn import pywikibot from pywikibot.data import api from pywikibot.exceptions import ( APIError, EntityTypeUnknownError, IsRedirectPageError, NoPageError, NoWikibaseEntityError, ) from pywikibot.site._apisite import APISite from pywikibot.site._decorators import need_extension, need_right, need_version from pywikibot.tools import itergroup, merge_unique_dicts, remove_last_args __all__ = ('DataSite', ) class DataSite(APISite): """Wikibase data capable site.""" def __init__(self, *args, **kwargs) -> None: """Initializer.""" super().__init__(*args, **kwargs) self._item_namespace = None self._property_namespace = None self._type_to_class = { 'item': pywikibot.ItemPage, 'property': pywikibot.PropertyPage, 'mediainfo': pywikibot.MediaInfo, 'lexeme': pywikibot.LexemePage, 'form': pywikibot.LexemeForm, 'sense': pywikibot.LexemeSense, } def _cache_entity_namespaces(self) -> None: """Find namespaces for each known wikibase entity type.""" self._entity_namespaces = {} for entity_type in self._type_to_class: for namespace in self.namespaces.values(): if not hasattr(namespace, 'defaultcontentmodel'): continue content_model = namespace.defaultcontentmodel if content_model == ('wikibase-' + entity_type): self._entity_namespaces[entity_type] = namespace break def get_namespace_for_entity_type(self, entity_type): """ Return namespace for given entity type. :return: corresponding namespace :rtype: Namespace """ if not hasattr(self, '_entity_namespaces'): self._cache_entity_namespaces() if entity_type in self._entity_namespaces: return self._entity_namespaces[entity_type] raise EntityTypeUnknownError( '{!r} does not support entity type "{}" ' "or it doesn't have its own namespace" .format(self, entity_type)) @property def item_namespace(self): """ Return namespace for items. :return: item namespace :rtype: Namespace """ if self._item_namespace is None: self._item_namespace = self.get_namespace_for_entity_type('item') return self._item_namespace @property def property_namespace(self): """ Return namespace for properties. :return: property namespace :rtype: Namespace """ if self._property_namespace is None: self._property_namespace = self.get_namespace_for_entity_type( 'property') return self._property_namespace def get_entity_for_entity_id(self, entity_id): """ Return a new instance for given entity id. :raises pywikibot.exceptions.NoWikibaseEntityError: there is no entity with the id :return: a WikibaseEntity subclass :rtype: WikibaseEntity """ for cls in self._type_to_class.values(): if cls.is_valid_id(entity_id): return cls(self, entity_id) entity = pywikibot.page.WikibaseEntity(self, entity_id) raise NoWikibaseEntityError(entity) @property @need_version('1.28-wmf.3') def sparql_endpoint(self): """ Return the sparql endpoint url, if any has been set. :return: sparql endpoint url :rtype: str|None """ return self.siteinfo['general'].get('wikibase-sparql') @property @need_version('1.28-wmf.23') def concept_base_uri(self): """ Return the base uri for concepts/entities. :return: concept base uri :rtype: str """ return self.siteinfo['general']['wikibase-conceptbaseuri'] def geo_shape_repository(self): """Return Site object for the geo-shapes repository e.g. commons.""" url = self.siteinfo['general'].get('wikibase-geoshapestoragebaseurl') if url: return pywikibot.Site(url=url, user=self.username()) return None def tabular_data_repository(self): """Return Site object for the tabular-datas repository e.g. commons.""" url = self.siteinfo['general'].get( 'wikibase-tabulardatastoragebaseurl') if url: return pywikibot.Site(url=url, user=self.username()) return None def loadcontent(self, identification, *props): """ Fetch the current content of a Wikibase item. This is called loadcontent since wbgetentities does not support fetching old revisions. Eventually this will get replaced by an actual loadrevisions. :param identification: Parameters used to identify the page(s) :type identification: dict :param props: the optional properties to fetch. """ params = merge_unique_dicts(identification, action='wbgetentities', # TODO: When props is empty it results in # an empty string ('&props=') but it should # result in a missing entry. props=props if props else False) req = self.simple_request(**params) data = req.submit() if 'success' not in data: raise APIError(data['errors'], '') return data['entities'] def preload_entities(self, pagelist, groupsize: int = 50): """ Yield subclasses of WikibaseEntity's with content prefilled. Note that pages will be iterated in a different order than in the underlying pagelist. :param pagelist: an iterable that yields either WikibaseEntity objects, or Page objects linked to an ItemPage. :param groupsize: how many pages to query at a time """ if not hasattr(self, '_entity_namespaces'): self._cache_entity_namespaces() for sublist in itergroup(pagelist, groupsize): req = {'ids': [], 'titles': [], 'sites': []} for p in sublist: if isinstance(p, pywikibot.page.WikibaseEntity): ident = p._defined_by() for key in ident: req[key].append(ident[key]) else: if p.site == self and p.namespace() in ( self._entity_namespaces.values()): req['ids'].append(p.title(with_ns=False)) else: assert p.site.has_data_repository, \ 'Site must have a data repository' req['sites'].append(p.site.dbName()) req['titles'].append(p._link._text) req = self.simple_request(action='wbgetentities', **req) data = req.submit() for entity in data['entities']: if 'missing' in data['entities'][entity]: continue cls = self._type_to_class[data['entities'][entity]['type']] page = cls(self, entity) # No api call is made because item._content is given page._content = data['entities'][entity] with suppress(IsRedirectPageError): page.get() # cannot provide get_redirect=True (T145971) yield page def getPropertyType(self, prop): """ Obtain the type of a property. This is used specifically because we can cache the value for a much longer time (near infinite). """ params = {'action': 'wbgetentities', 'ids': prop.getID(), 'props': 'datatype'} expiry = datetime.timedelta(days=365 * 100) # Store it for 100 years req = self._request(expiry=expiry, parameters=params) data = req.submit() # the IDs returned from the API can be upper or lowercase, depending # on the version. See bug T55894 for more information. try: dtype = data['entities'][prop.getID()]['datatype'] except KeyError: dtype = data['entities'][prop.getID().lower()]['datatype'] return dtype @need_right('edit') def editEntity(self, entity, data, bot: bool = True, **kwargs): """ Edit entity. Note: This method is unable to create entities other than 'item' if dict with API parameters was passed to 'entity' parameter. :param entity: Page to edit, or dict with API parameters to use for entity identification :type entity: WikibaseEntity or dict :param data: data updates :type data: dict :param bot: Whether to mark the edit as a bot edit :return: New entity data :rtype: dict """ # this changes the reference to a new object data = dict(data) if isinstance(entity, pywikibot.page.WikibaseEntity): params = entity._defined_by(singular=True) if 'id' in params and params['id'] == '-1': del params['id'] if not params: params['new'] = entity.entity_type data_for_new_entity = entity.get_data_for_new_entity() data.update(data_for_new_entity) else: if 'id' in entity and entity['id'] == '-1': del entity['id'] params = dict(entity) if not params: # If no identification was provided params['new'] = 'item' params['action'] = 'wbeditentity' if bot: params['bot'] = 1 if 'baserevid' in kwargs and kwargs['baserevid']: params['baserevid'] = kwargs['baserevid'] params['token'] = self.tokens['edit'] for arg in kwargs: if arg in ['clear', 'summary']: params[arg] = kwargs[arg] elif arg != 'baserevid': warn('Unknown wbeditentity parameter {} ignored'.format(arg), UserWarning, 2) params['data'] = json.dumps(data) req = self.simple_request(**params) return req.submit() @need_right('edit') def addClaim(self, entity, claim, bot: bool = True, summary=None) -> None: """ Add a claim. :param entity: Entity to modify :type entity: WikibaseEntity :param claim: Claim to be added :type claim: pywikibot.Claim :param bot: Whether to mark the edit as a bot edit :param summary: Edit summary :type summary: str """ claim.snak = entity.getID() + '$' + str(uuid.uuid4()) params = {'action': 'wbsetclaim', 'claim': json.dumps(claim.toJSON()), 'baserevid': entity.latest_revision_id, 'summary': summary, 'token': self.tokens['edit'], 'bot': bot, } req = self.simple_request(**params) data = req.submit() # Update the item if claim.getID() in entity.claims: entity.claims[claim.getID()].append(claim) else: entity.claims[claim.getID()] = [claim] entity.latest_revision_id = data['pageinfo']['lastrevid'] @need_right('edit') def changeClaimTarget(self, claim, snaktype: str = 'value', bot: bool = True, summary=None): """ Set the claim target to the value of the provided claim target. :param claim: The source of the claim target value :type claim: pywikibot.Claim :param snaktype: An optional snaktype ('value', 'novalue' or 'somevalue'). Default: 'value' :param bot: Whether to mark the edit as a bot edit :param summary: Edit summary :type summary: str """ if claim.isReference or claim.isQualifier: raise NotImplementedError if not claim.snak: # We need to already have the snak value raise NoPageError(claim) params = {'action': 'wbsetclaimvalue', 'claim': claim.snak, 'snaktype': snaktype, 'summary': summary, 'bot': bot, 'token': self.tokens['edit']} if snaktype == 'value': params['value'] = json.dumps(claim._formatValue()) params['baserevid'] = claim.on_item.latest_revision_id req = self.simple_request(**params) return req.submit() @need_right('edit') def save_claim(self, claim, summary=None, bot: bool = True): """ Save the whole claim to the wikibase site. :param claim: The claim to save :type claim: pywikibot.Claim :param bot: Whether to mark the edit as a bot edit :param summary: Edit summary :type summary: str """ if claim.isReference or claim.isQualifier: raise NotImplementedError if not claim.snak: # We need to already have the snak value raise NoPageError(claim) params = {'action': 'wbsetclaim', 'claim': json.dumps(claim.toJSON()), 'token': self.tokens['edit'], 'baserevid': claim.on_item.latest_revision_id, 'summary': summary, 'bot': bot, } req = self.simple_request(**params) data = req.submit() claim.on_item.latest_revision_id = data['pageinfo']['lastrevid'] return data @need_right('edit') @remove_last_args(['baserevid']) # since 7.0.0 def editSource(self, claim, source, new: bool = False, bot: bool = True, summary: Optional[str] = None): """Create/Edit a source. .. versionchanged:: 7.0 deprecated `baserevid` parameter was removed :param claim: A Claim object to add the source to :type claim: pywikibot.Claim :param source: A Claim object to be used as a source :type source: pywikibot.Claim :param new: Whether to create a new one if the "source" already exists :param bot: Whether to mark the edit as a bot edit :param summary: Edit summary """ if claim.isReference or claim.isQualifier: raise ValueError('The claim cannot have a source.') params = {'action': 'wbsetreference', 'statement': claim.snak, 'baserevid': claim.on_item.latest_revision_id, 'summary': summary, 'bot': bot, 'token': self.tokens['edit']} # build up the snak if isinstance(source, list): sources = source else: sources = [source] snak = {} for sourceclaim in sources: datavalue = sourceclaim._formatDataValue() valuesnaks = snak.get(sourceclaim.getID(), []) valuesnaks.append({ 'snaktype': 'value', 'property': sourceclaim.getID(), 'datavalue': datavalue, }) snak[sourceclaim.getID()] = valuesnaks # set the hash if the source should be changed. # if present, all claims of one source have the same hash if not new and hasattr(sourceclaim, 'hash'): params['reference'] = sourceclaim.hash params['snaks'] = json.dumps(snak) req = self.simple_request(**params) return req.submit() @need_right('edit') @remove_last_args(['baserevid']) # since 7.0.0 def editQualifier(self, claim, qualifier, new: bool = False, bot: bool = True, summary: Optional[str] = None): """Create/Edit a qualifier. .. versionchanged:: 7.0 deprecated `baserevid` parameter was removed :param claim: A Claim object to add the qualifier to :type claim: pywikibot.Claim :param qualifier: A Claim object to be used as a qualifier :type qualifier: pywikibot.Claim :param new: Whether to create a new one if the "qualifier" already exists :param bot: Whether to mark the edit as a bot edit :param summary: Edit summary """ if claim.isReference or claim.isQualifier: raise ValueError('The claim cannot have a qualifier.') params = {'action': 'wbsetqualifier', 'claim': claim.snak, 'baserevid': claim.on_item.latest_revision_id, 'summary': summary, 'bot': bot} if (not new and hasattr(qualifier, 'hash') and qualifier.hash is not None): params['snakhash'] = qualifier.hash params['token'] = self.tokens['edit'] # build up the snak if qualifier.getSnakType() == 'value': params['value'] = json.dumps(qualifier._formatValue()) params['snaktype'] = qualifier.getSnakType() params['property'] = qualifier.getID() req = self.simple_request(**params) return req.submit() @need_right('edit') @remove_last_args(['baserevid']) # since 7.0.0 def removeClaims(self, claims, bot: bool = True, summary: Optional[str] = None): """Remove claims. .. versionchanged:: 7.0 deprecated `baserevid` parameter was removed :param claims: Claims to be removed :type claims: List[pywikibot.Claim] :param bot: Whether to mark the edit as a bot edit :type bot: bool :param summary: Edit summary :type summary: str """ # Check on_item for all additional claims items = {claim.on_item for claim in claims if claim.on_item} assert len(items) == 1 baserevid = items.pop().latest_revision_id params = { 'action': 'wbremoveclaims', 'baserevid': baserevid, 'summary': summary, 'bot': bot, 'claim': '|'.join(claim.snak for claim in claims), 'token': self.tokens['edit'], } req = self.simple_request(**params) return req.submit() @need_right('edit') @remove_last_args(['baserevid']) # since 7.0.0 def removeSources(self, claim, sources, bot: bool = True, summary: Optional[str] = None): """Remove sources. .. versionchanged:: 7.0 deprecated `baserevid` parameter was removed :param claim: A Claim object to remove the sources from :type claim: pywikibot.Claim :param sources: A list of Claim objects that are sources :type sources: list :param bot: Whether to mark the edit as a bot edit :param summary: Edit summary """ params = { 'action': 'wbremovereferences', 'baserevid': claim.on_item.latest_revision_id, 'summary': summary, 'bot': bot, 'statement': claim.snak, 'references': '|'.join(source.hash for source in sources), 'token': self.tokens['edit'], } req = self.simple_request(**params) return req.submit() @need_right('edit') @remove_last_args(['baserevid']) # since 7.0.0 def remove_qualifiers(self, claim, qualifiers, bot: bool = True, summary: Optional[str] = None): """Remove qualifiers. .. versionchanged:: 7.0 deprecated `baserevid` parameter was removed :param claim: A Claim object to remove the qualifier from :type claim: pywikibot.Claim :param qualifiers: Claim objects currently used as a qualifiers :type qualifiers: List[pywikibot.Claim] :param bot: Whether to mark the edit as a bot edit :param summary: Edit summary """ params = { 'action': 'wbremovequalifiers', 'claim': claim.snak, 'baserevid': claim.on_item.latest_revision_id, 'summary': summary, 'bot': bot, 'qualifiers': [qualifier.hash for qualifier in qualifiers], 'token': self.tokens['edit'] } req = self.simple_request(**params) return req.submit() @need_right('edit') def linkTitles(self, page1, page2, bot: bool = True): """ Link two pages together. :param page1: First page to link :type page1: pywikibot.Page :param page2: Second page to link :type page2: pywikibot.Page :param bot: Whether to mark the edit as a bot edit :return: dict API output :rtype: dict """ params = { 'action': 'wblinktitles', 'tosite': page1.site.dbName(), 'totitle': page1.title(), 'fromsite': page2.site.dbName(), 'fromtitle': page2.title(), 'token': self.tokens['edit'] } if bot: params['bot'] = 1 req = self.simple_request(**params) return req.submit() @need_right('item-merge') def mergeItems(self, from_item, to_item, ignore_conflicts=None, summary=None, bot: bool = True): """ Merge two items together. :param from_item: Item to merge from :type from_item: pywikibot.ItemPage :param to_item: Item to merge into :type to_item: pywikibot.ItemPage :param ignore_conflicts: Which type of conflicts ('description', 'sitelink', and 'statement') should be ignored :type ignore_conflicts: list of str :param summary: Edit summary :type summary: str :param bot: Whether to mark the edit as a bot edit :return: dict API output :rtype: dict """ params = { 'action': 'wbmergeitems', 'fromid': from_item.getID(), 'toid': to_item.getID(), 'ignoreconflicts': ignore_conflicts, 'token': self.tokens['edit'], 'summary': summary, } if bot: params['bot'] = 1 req = self.simple_request(**params) return req.submit() @need_right('item-merge') @need_extension('WikibaseLexeme') def mergeLexemes(self, from_lexeme, to_lexeme, summary=None, *, bot: bool = True) -> dict: """ Merge two lexemes together. :param from_lexeme: Lexeme to merge from :type from_lexeme: pywikibot.LexemePage :param to_lexeme: Lexeme to merge into :type to_lexeme: pywikibot.LexemePage :param summary: Edit summary :type summary: str :keyword bot: Whether to mark the edit as a bot edit :return: dict API output """ params = { 'action': 'wblmergelexemes', 'source': from_lexeme.getID(), 'target': to_lexeme.getID(), 'token': self.tokens['edit'], 'summary': summary, } if bot: params['bot'] = 1 req = self.simple_request(**params) data = req.submit() return data @need_right('item-redirect') def set_redirect_target(self, from_item, to_item, bot: bool = True): """ Make a redirect to another item. :param to_item: title of target item. :type to_item: pywikibot.ItemPage :param from_item: Title of the item to be redirected. :type from_item: pywikibot.ItemPage :param bot: Whether to mark the edit as a bot edit """ params = { 'action': 'wbcreateredirect', 'from': from_item.getID(), 'to': to_item.getID(), 'token': self.tokens['edit'], 'bot': bot, } req = self.simple_request(**params) return req.submit() def search_entities(self, search: str, language: str, total: Optional[int] = None, **kwargs): """ Search for pages or properties that contain the given text. :param search: Text to find. :param language: Language to search in. :param total: Maximum number of pages to retrieve in total, or None in case of no limit. :return: 'search' list from API output. :rtype: Generator """ lang_codes = self._paraminfo.parameter('wbsearchentities', 'language')['type'] if language not in lang_codes: raise ValueError('Data site used does not support provided ' 'language.') if 'site' in kwargs: if kwargs['site'].sitename != self.sitename: raise ValueError('The site given in the kwargs is different.') warn('search_entities should not get a site via kwargs.', UserWarning, 2) del kwargs['site'] parameters = dict(search=search, language=language, **kwargs) gen = self._generator(api.APIGenerator, type_arg='wbsearchentities', data_name='search', total=total, parameters=parameters) return gen @need_right('edit') def _wbset_action(self, itemdef, action: str, action_data, **kwargs) -> dict: """ Execute wbset{action} on a Wikibase entity. Supported actions are: wbsetaliases, wbsetdescription, wbsetlabel and wbsetsitelink :param itemdef: Entity to modify or create :type itemdef: str, WikibaseEntity or Page connected to such item :param action: wbset{action} to perform: 'wbsetaliases', 'wbsetdescription', 'wbsetlabel', 'wbsetsitelink' :param action_data: data to be used in API request, see API help :type action_data: SiteLink or dict wbsetaliases: dict shall have the following structure: {'language': value (str), 'add': list of language codes (str), 'remove': list of language codes (str), 'set' list of language codes (str) } 'add' and 'remove' are alternative to 'set' wbsetdescription and wbsetlabel: dict shall have keys 'language', 'value' wbsetsitelink: dict shall have keys 'linksite', 'linktitle' and optionally 'badges' :keyword bot: Whether to mark the edit as a bot edit, default is True :type bot: bool :keyword tags: Change tags to apply with the edit :type tags: list of str :return: query result :raises AssertionError, TypeError """ def format_sitelink(sitelink): """Convert SiteLink to a dict accepted by wbsetsitelink API.""" if isinstance(sitelink, pywikibot.page.SiteLink): _dict = { 'linksite': sitelink._sitekey, 'linktitle': sitelink._rawtitle, 'badges': '|'.join([b.title() for b in sitelink.badges]), } else: _dict = sitelink return _dict def prepare_data(action, data): """Prepare data as expected by API.""" if action == 'wbsetaliases': res = data keys = set(res) assert keys < {'language', 'add', 'remove', 'set'} assert 'language' in keys assert ({'add', 'remove', 'set'} & keys) assert ({'add', 'set'} >= keys) assert ({'remove', 'set'} >= keys) elif action in ('wbsetlabel', 'wbsetdescription'): res = data keys = set(res) assert keys == {'language', 'value'} elif action == 'wbsetsitelink': res = format_sitelink(data) keys = set(res) assert keys >= {'linksite'} assert keys <= {'linksite', 'linktitle', 'badges'} else: raise ValueError('Something has gone wrong ...') return res # Supported actions assert action in ('wbsetaliases', 'wbsetdescription', 'wbsetlabel', 'wbsetsitelink'), \ 'action {} not supported.'.format(action) # prefer ID over (site, title) if isinstance(itemdef, str): itemdef = self.get_entity_for_entity_id(itemdef) elif isinstance(itemdef, pywikibot.Page): itemdef = pywikibot.ItemPage.fromPage(itemdef, lazy_load=True) elif not isinstance(itemdef, pywikibot.page.WikibaseEntity): raise TypeError('itemdef shall be str, WikibaseEntity or Page') params = itemdef._defined_by(singular=True) # TODO: support 'new' baserevid = kwargs.pop( 'baserevid', itemdef.latest_revision_id if 'id' in params else 0 ) params.update( {'baserevid': baserevid, 'action': action, 'token': self.tokens['edit'], 'bot': kwargs.pop('bot', True), }) params.update(prepare_data(action, action_data)) for arg in kwargs: if arg in ['summary', 'tags']: params[arg] = kwargs[arg] else: warn('Unknown parameter {} for action {}, ignored' .format(arg, action), UserWarning, 2) req = self.simple_request(**params) data = req.submit() return data def wbsetaliases(self, itemdef, aliases, **kwargs): """ Set aliases for a single Wikibase entity. See self._wbset_action() for parameters """ return self._wbset_action(itemdef, 'wbsetaliases', aliases, **kwargs) def wbsetdescription(self, itemdef, description, **kwargs): """ Set description for a single Wikibase entity. See self._wbset_action() """ return self._wbset_action(itemdef, 'wbsetdescription', description, **kwargs) def wbsetlabel(self, itemdef, label, **kwargs): """ Set label for a single Wikibase entity. See self._wbset_action() for parameters """ return self._wbset_action(itemdef, 'wbsetlabel', label, **kwargs) def wbsetsitelink(self, itemdef, sitelink, **kwargs): """ Set, remove or modify a sitelink on a Wikibase item. See self._wbset_action() for parameters """ return self._wbset_action(itemdef, 'wbsetsitelink', sitelink, **kwargs) @need_right('edit') @need_extension('WikibaseLexeme') def add_form(self, lexeme, form, *, bot: bool = True, baserevid=None) -> dict: """ Add a form. :param lexeme: Lexeme to modify :type lexeme: pywikibot.LexemePage :param form: Form to be added :type form: pywikibot.LexemeForm :keyword bot: Whether to mark the edit as a bot edit :keyword baserevid: Base revision id override, used to detect conflicts. :type baserevid: long """ params = { 'action': 'wbladdform', 'lexemeId': lexeme.getID(), 'data': json.dumps(form.toJSON()), 'bot': bot, 'token': self.tokens['edit'], } if baserevid: params['baserevid'] = baserevid req = self.simple_request(**params) data = req.submit() return data @need_right('edit') @need_extension('WikibaseLexeme') def remove_form(self, form, *, bot: bool = True, baserevid=None) -> dict: """ Remove a form. :param form: Form to be removed :type form: pywikibot.LexemeForm :keyword bot: Whether to mark the edit as a bot edit :keyword baserevid: Base revision id override, used to detect conflicts. :type baserevid: long """ params = { 'action': 'wblremoveform', 'id': form.getID(), 'bot': bot, 'token': self.tokens['edit'], } if baserevid: params['baserevid'] = baserevid req = self.simple_request(**params) data = req.submit() return data @need_right('edit') @need_extension('WikibaseLexeme') def edit_form_elements(self, form, data, *, bot: bool = True, baserevid=None) -> dict: """ Edit lexeme form elements. :param form: Form :type form: pywikibot.LexemeForm :param data: data updates :type data: dict :keyword bot: Whether to mark the edit as a bot edit :keyword baserevid: Base revision id override, used to detect conflicts. :type baserevid: long :return: New form data """ params = { 'action': 'wbleditformelements', 'formId': form.getID(), 'data': json.dumps(data), 'bot': bot, 'token': self.tokens['edit'], } if baserevid: params['baserevid'] = baserevid req = self.simple_request(**params) data = req.submit() return data

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null

0

null

0

1

0

a3e3eb5e33cc147796a90e6e65542a513c75576b

1,210

py

Python

app.py

MisaelVillaverde/fourier-calculator

fd50cd292e333c1a9d75e93962a0aaa0985ecef9

[ "MIT" ]

null

app.py

MisaelVillaverde/fourier-calculator

fd50cd292e333c1a9d75e93962a0aaa0985ecef9

[ "MIT" ]

1

2021-11-07T04:40:13.000Z

app.py

MisaelVillaverde/fourier-calculator

fd50cd292e333c1a9d75e93962a0aaa0985ecef9

[ "MIT" ]

null

from flask import Flask from flask import render_template, request from flask import jsonify import requests import json app = Flask(__name__) @app.route("/symbo",methods=['POST']) def symbo(): #import pdb; pdb.set_trace() session = requests.session() token = session.get("https://es.symbolab.com/solver/step-by-step/x%5E%7B2%7D?or=input").cookies.get_dict()["sy2.pub.token"] query = request.json["expression"] #response = json.loads(session.get(f"https://es.symbolab.com/pub_api/steps?subscribed=true&origin=input&language=es&query=%5Cint+tcos%5Cleft(nt%5Cright)dt+&referer=https%3A%2F%2Fes.symbolab.com%2Fsolver%2Fstep-by-step%2F%255Cint_%257B%2520%257Dtcos%255Cleft(nt%255Cright)dt%2520%3For%3Dinput&plotRequest=PlotOptional&page=step-by-step",headers={ response = json.loads(session.get(f"https://es.symbolab.com/pub_api/steps?subscribed=true&origin=input&language=es&query={query}",headers={ "x-requested-with":"XMLHttpRequest", "authorization":f"Bearer {token}" }).content) return { "dym":response["dym"], "solutions":response["solutions"] } @app.route('/') def hello(): return render_template('index.html') app.run(debug=True)

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null

0

1

0

a3e55a939b6d954bcaed4fd506083967468d2eb3

1,584

py

Python

my_code/Chapter_2.py

kalona/Spark-The-Definitive-Guide

0b495c4710b2030aa59d5a7f4053ee0a8345d0d8

[ "Apache-2.0" ]

2

2022-01-02T14:24:29.000Z

2022-01-02T15:54:47.000Z

my_code/Chapter_2.py

kalona/Spark-The-Definitive-Guide

0b495c4710b2030aa59d5a7f4053ee0a8345d0d8

[ "Apache-2.0" ]

null

my_code/Chapter_2.py

kalona/Spark-The-Definitive-Guide

0b495c4710b2030aa59d5a7f4053ee0a8345d0d8

[ "Apache-2.0" ]

null

from pyspark.sql import SparkSession # spark = SparkSession.builder.master("local[*]").getOrCreate() spark = SparkSession.builder.getOrCreate() file_path = "C:\home_work\local_github\Spark-The-Definitive-Guide\data\/flight-data\csv\/2015-summary.csv" # COMMAND ---------- # COMMAND ---------- flightData2015 = spark\ .read\ .option("inferSchema", "true")\ .option("header", "true")\ .csv("./data/flight-data/csv/2015-summary.csv") # COMMAND ---------- flightData2015.createOrReplaceTempView("flight_data_2015") # COMMAND ---------- sqlWay = spark.sql(""" SELECT DEST_COUNTRY_NAME, count(1) FROM flight_data_2015 GROUP BY DEST_COUNTRY_NAME """) dataFrameWay = flightData2015\ .groupBy("DEST_COUNTRY_NAME")\ .count() sqlWay.explain() dataFrameWay.explain() # COMMAND ---------- from pyspark.sql.functions import max, col # flightData2015.select(max(col("count"))).show(1) # COMMAND ---------- maxSql = spark.sql(""" SELECT DEST_COUNTRY_NAME, sum(count) as destination_total FROM flight_data_2015 GROUP BY DEST_COUNTRY_NAME ORDER BY sum(count) DESC LIMIT 5 """) maxSql.show() # COMMAND ---------- from pyspark.sql.functions import desc flightData2015\ .groupBy("DEST_COUNTRY_NAME")\ .sum("count")\ .withColumnRenamed("sum(count)", "destination_total")\ .sort(desc("destination_total"))\ .limit(5)\ .show() # COMMAND ---------- flightData2015\ .groupBy("DEST_COUNTRY_NAME")\ .sum("count")\ .withColumnRenamed("sum(count)", "destination_total")\ .sort(desc("destination_total"))\ .limit(5)\ .explain() # COMMAND ----------

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null

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null

0

1

0

a3e58f9e7062eea97241b4b05b8e709ab53b50c3

7,508

py

Python

tests/test_intake_postgres.py

ContinuumIO/intake-postgres

fda7f7b2b6255544ea7ffd365a4ac8b2655fd226

[ "BSD-2-Clause" ]

2

2018-11-26T00:14:10.000Z

2018-12-21T01:52:44.000Z

tests/test_intake_postgres.py

ContinuumIO/intake-postgres

fda7f7b2b6255544ea7ffd365a4ac8b2655fd226

[ "BSD-2-Clause" ]

1

2018-12-20T08:41:05.000Z

2018-12-21T15:00:08.000Z

tests/test_intake_postgres.py

ContinuumIO/intake-postgres

fda7f7b2b6255544ea7ffd365a4ac8b2655fd226

[ "BSD-2-Clause" ]

3

2018-12-19T08:34:14.000Z

2019-01-24T07:58:32.000Z

import os import pickle import pytest import pandas as pd from shapely import wkt from intake_postgres import PostgresSource from intake import open_catalog from .util import verify_datasource_interface TEST_DATA_DIR = 'tests' TEST_DATA = [ ('sample1', 'sample1.csv'), ('sample2_1', 'sample2_1.csv'), ('sample2_2', 'sample2_2.csv'), ] TEST_GIS_DATA = [ ('points', 'sample_points.psql'), ('multipoints', 'sample_multipoints.psql'), ('lines', 'sample_lines.psql'), ('multilines', 'sample_multilines.psql'), ('polygons', 'sample_polygons.psql'), ('multipolygons', 'sample_multipolygons.psql'), # ('triangles', 'sample_triangles.psql'), ] TEST_TEMPLATE_DATA = [ 'jinja2_params_with_env', ] @pytest.fixture(scope='module') def engine(): """Start docker container for PostgreSQL database, yield a tuple (engine, metadata), and cleanup connection afterward.""" from .util import start_postgres, stop_postgres from sqlalchemy import create_engine stop_postgres(let_fail=True) local_port = start_postgres() uri = 'postgresql://postgres@localhost:{}/postgres'.format(local_port) engine = create_engine(uri) for table_name, csv_fname in TEST_DATA: csv_fpath = os.path.join(TEST_DATA_DIR, csv_fname) df = pd.read_csv(csv_fpath) df.to_sql(table_name, engine, index=False) for table_name, psql_fname in TEST_GIS_DATA: psql_fpath = os.path.join(TEST_DATA_DIR, psql_fname) with engine.connect() as conn: with open(psql_fpath, 'r') as fp: cmds = fp.read().strip().split(';') for cmd in cmds: if cmd.strip(): conn.execute(' '.join(cmd.split())) try: yield engine finally: stop_postgres() @pytest.mark.parametrize('table_name,_', TEST_DATA) def test_open(engine, table_name, _): d = PostgresSource(str(engine.url), 'select * from '+table_name) assert d.container == 'dataframe' assert d.description is None verify_datasource_interface(d) @pytest.mark.parametrize('table_name,csv_fpath', TEST_DATA) def test_discover(engine, table_name, csv_fpath): expected_df = pd.read_csv(os.path.join(TEST_DATA_DIR, csv_fpath)) source = PostgresSource(str(engine.url), 'select * from '+table_name) info = source.discover() dt = {k: str(v) for k, v in expected_df.dtypes.to_dict().items()} assert info['dtype'] == dt assert info['shape'] == (None, 3) assert info['npartitions'] == 1 @pytest.mark.parametrize('table_name,csv_fpath', TEST_DATA) def test_read(engine, table_name, csv_fpath): expected_df = pd.read_csv(os.path.join(TEST_DATA_DIR, csv_fpath)) source = PostgresSource(str(engine.url), 'select * from '+table_name) df = source.read() assert expected_df.equals(df) @pytest.mark.parametrize('table_name,csv_fpath', TEST_DATA) def test_discover_after_read(engine, table_name, csv_fpath): """Assert that after reading the dataframe, discover() shows more accurate information. """ expected_df = pd.read_csv(os.path.join(TEST_DATA_DIR, csv_fpath)) source = PostgresSource(str(engine.url), 'select * from '+table_name) info = source.discover() dt = {k: str(v) for k, v in expected_df.dtypes.to_dict().items()} assert info['dtype'] == dt assert info['shape'] == (None, 3) assert info['npartitions'] == 1 df = source.read() assert expected_df.equals(df) info = source.discover() assert info['dtype'] == dt assert info['shape'] == (4, 3) assert info['npartitions'] == 1 assert expected_df.equals(df) @pytest.mark.parametrize('table_name,csv_fpath', TEST_DATA) def test_close(engine, table_name, csv_fpath): expected_df = pd.read_csv(os.path.join(TEST_DATA_DIR, csv_fpath)) source = PostgresSource(str(engine.url), 'select * from '+table_name) source.close() # Can reopen after close df = source.read() assert expected_df.equals(df) @pytest.mark.parametrize('table_name,csv_fpath', TEST_DATA) def test_pickle(engine, table_name, csv_fpath): source = PostgresSource(str(engine.url), 'select * from '+table_name) pickled_source = pickle.dumps(source) source_clone = pickle.loads(pickled_source) expected_df = source.read() df = source_clone.read() assert expected_df.equals(df) @pytest.mark.parametrize('table_name,_1', TEST_DATA) def test_catalog(engine, table_name, _1): catalog_fpath = os.path.join(TEST_DATA_DIR, 'catalog1.yml') catalog = open_catalog(catalog_fpath) ds_name = table_name.rsplit('_idx', 1)[0] src = catalog[ds_name] pgsrc = src.get() pgsrc._uri = str(engine.url) assert src.describe()['container'] == 'dataframe' assert src.describe_open()['plugin'] == 'postgres' assert src.describe_open()['args']['sql_expr'][:6] in ('select', 'SELECT') metadata = pgsrc.discover() assert metadata['npartitions'] == 1 expected_df = pd.read_sql_query(pgsrc._sql_expr, engine) df = pgsrc.read() assert expected_df.equals(df) pgsrc.close() def test_catalog_join(engine): catalog_fpath = os.path.join(TEST_DATA_DIR, 'catalog1.yml') catalog = open_catalog(catalog_fpath) ds_name = 'sample2' src = catalog[ds_name] pgsrc = src.get() pgsrc._uri = str(engine.url) assert src.describe()['container'] == 'dataframe' assert src.describe_open()['plugin'] == 'postgres' assert src.describe_open()['args']['sql_expr'][:6] in ('select', 'SELECT') metadata = pgsrc.discover() assert metadata['npartitions'] == 1 expected_df = pd.read_sql_query(pgsrc._sql_expr, engine) df = pgsrc.read() assert expected_df.equals(df) pgsrc.close() @pytest.mark.parametrize('table_name,_1', TEST_GIS_DATA) def test_postgis_data(engine, table_name, _1): from sqlalchemy import MetaData catalog_fpath = os.path.join(TEST_DATA_DIR, 'catalog1.yml') catalog = open_catalog(catalog_fpath) ds_name = table_name src = catalog[ds_name] pgsrc = src.get() pgsrc._uri = str(engine.url) assert src.describe()['container'] == 'dataframe' assert src.describe_open()['plugin'] == 'postgres' assert src.describe_open()['args']['sql_expr'][:6] in ('select', 'SELECT') metadata = pgsrc.discover() assert metadata['npartitions'] == 1 meta = MetaData() meta.reflect(bind=engine) col_exprs = ['ST_AsText({0}) as {0}'.format(col.name) for col in meta.tables[table_name].columns] _query = pgsrc._sql_expr.replace('*', ', '.join(col_exprs)) expected_df = pd.read_sql_query(_query, engine).applymap( lambda geom: str(wkt.loads(geom)) ) df = pgsrc.read().applymap(lambda geom: str(wkt.loads(geom))) assert expected_df.equals(df) pgsrc.close() @pytest.mark.parametrize('ds_name', TEST_TEMPLATE_DATA) def test_jinja2(engine, ds_name): catalog_fpath = os.path.join(TEST_DATA_DIR, 'catalog1.yml') catalog = open_catalog(catalog_fpath) src = catalog[ds_name] pgsrc = src.get() pgsrc._uri = str(engine.url) assert src.describe()['container'] == 'dataframe' assert src.describe_open()['plugin'] == 'postgres' assert src.describe_open()['args']['sql_expr'][:6] in ('select', 'SELECT') metadata = pgsrc.discover() assert metadata['npartitions'] == 1 expected_df = pd.read_sql_query(pgsrc._sql_expr, engine) df = pgsrc.read() assert expected_df.equals(df) pgsrc.close()

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a3e6e2cb9c18b7306bf960a8fcbaf212c1159394

351

py

Python

Module_3/testImage.py

dks1018/CoffeeShopCoding

13ac1700673c86c601eb2758570920620a956e4c

[ "ADSL" ]

null

Module_3/testImage.py

dks1018/CoffeeShopCoding

13ac1700673c86c601eb2758570920620a956e4c

[ "ADSL" ]

null

Module_3/testImage.py

dks1018/CoffeeShopCoding

13ac1700673c86c601eb2758570920620a956e4c

[ "ADSL" ]

null

# file = open('C:\\Users\\dks10\\OneDrive\\Desktop\\Projects\\Code\\Python\\PythonCrypto\\Module_3\\eye.png', 'rb') file = open('encrypt_eye.png', 'rb') image = file.read() file.close() image = bytearray(image) key = 48 for index, value in enumerate(image): image[index] = value^key file = open('2eye.png','wb') file.write(image) file.close()

21.9375

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null

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1

0

a3e8a92c23b5ddc471c49e37f3c8dc3fb274d2ab

1,702

py

Python

ledfxcontroller/effects/temporal.py

Aircoookie/LedFx

95628fc237497dd89aaf30fdbf88f780f3330166

[ "MIT" ]

17

2018-08-31T05:51:09.000Z

2022-02-12T15:41:33.000Z

ledfxcontroller/effects/temporal.py

Aircoookie/LedFx

95628fc237497dd89aaf30fdbf88f780f3330166

[ "MIT" ]

null

ledfxcontroller/effects/temporal.py

Aircoookie/LedFx

95628fc237497dd89aaf30fdbf88f780f3330166

[ "MIT" ]

5

2019-07-15T22:12:45.000Z

2022-02-05T10:50:44.000Z

import time import logging from ledfxcontroller.effects import Effect from threading import Thread import voluptuous as vol _LOGGER = logging.getLogger(__name__) DEFAULT_RATE = 1.0 / 60.0 @Effect.no_registration class TemporalEffect(Effect): _thread_active = False _thread = None CONFIG_SCHEMA = vol.Schema({ vol.Required('speed', default = 1.0): float }) def thread_function(self): while self._thread_active: startTime = time.time() # Treat the return value of the effect loop as a speed modifier # such that effects that are nartually faster or slower can have # a consistent feel. sleepInterval = self.effect_loop() if sleepInterval is None: sleepInterval = 1.0 sleepInterval = sleepInterval * DEFAULT_RATE # Calculate the time to sleep accounting for potential heavy # frame assembly operations timeToSleep = (sleepInterval / self._config['speed']) - (time.time() - startTime) if timeToSleep > 0: time.sleep(timeToSleep) def effect_loop(self): """ Triggered periodically based on the effect speed and any additional effect modifiers """ pass def activate(self, pixel_count): super().activate(pixel_count) self._thread_active = True self._thread = Thread(target = self.thread_function) self._thread.start() def deactivate(self): if self._thread_active: self._thread_active = False self._thread.join() self._thread = None super().deactivate()

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0

null

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null

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0

a3ec4aae5421f3c1473f18af462a1b949c04b4de

1,796

py

Python

utils.py

LuChang-CS/sherbet

d1061aca108eab8e0ccbd2202460e25261fdf1d5

[ "Apache-2.0" ]

2

2022-01-26T05:38:04.000Z

2022-03-20T08:54:18.000Z

utils.py

LuChang-CS/sherbet

d1061aca108eab8e0ccbd2202460e25261fdf1d5

[ "Apache-2.0" ]

null

utils.py

LuChang-CS/sherbet

d1061aca108eab8e0ccbd2202460e25261fdf1d5

[ "Apache-2.0" ]

null

import numpy as np class DataGenerator: def __init__(self, inputs, shuffle=True, batch_size=32): assert len(inputs) > 0 self.inputs = inputs self.idx = np.arange(len(inputs[0])) self.shuffle = shuffle self.batch_size = batch_size self.on_epoch_end() def data_length(self): return len(self.idx) def __len__(self): n = self.data_length() len_ = n // self.batch_size return len_ if n % self.batch_size == 0 else len_ + 1 def __getitem__(self, index): start = index * self.batch_size end = start + self.batch_size index = self.idx[start:end] data = [] for x in self.inputs: data.append(x[index]) return data def on_epoch_end(self): if self.shuffle: np.random.shuffle(self.idx) def set_batch_size(self, batch_size): self.batch_size = batch_size def lr_decay(total_epoch, init_lr, split_val): lr_map = [init_lr] * total_epoch if len(split_val) > 0: assert split_val[0][0] > 1 assert split_val[-1][0] <= total_epoch current_split_index = 0 current_lr = init_lr next_epoch, next_lr = split_val[current_split_index] for i in range(total_epoch): if i < next_epoch - 1: lr_map[i] = current_lr else: current_lr = next_lr lr_map[i] = current_lr current_split_index += 1 if current_split_index >= len(split_val): next_epoch = total_epoch + 1 else: next_epoch, next_lr = split_val[current_split_index] def lr_schedule_fn(epoch, lr): return lr_map[epoch] return lr_schedule_fn

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0.3

0

null

0

null

0

1

0

a3ec779913e7a7957725c231bcea5cdaa55dcfbf

810

py

Python

Version1_STI.py

sudhanshu55/Speech_to_Image

7a047725b3167cfcb7a68004b3c35b2ece75fde4

[ "MIT" ]

null

Version1_STI.py

sudhanshu55/Speech_to_Image

7a047725b3167cfcb7a68004b3c35b2ece75fde4

[ "MIT" ]

null

Version1_STI.py

sudhanshu55/Speech_to_Image

7a047725b3167cfcb7a68004b3c35b2ece75fde4

[ "MIT" ]

null

from nltk.tokenize import sent_tokenize, word_tokenize from nltk.corpus import stopwords import speech_recognition as sr import nltk from google_images_download import google_images_download response = google_images_download.googleimagesdownload() r = sr.Recognizer() with sr.Microphone() as source: print("Say something!") audio = r.listen(source) data = r.recognize_google(audio).encode("utf-8") print (data) stopWords = set(stopwords.words('english')) words = word_tokenize(data) wordsFiltered = [] for w in words: if w not in stopWords: wordsFiltered.append(w) into_string = str(wordsFiltered) print(into_string) arguments = {"keywords":into_string,"limit":2,"print_urls":True} #creating list of arguments response.download(arguments) #passing the arguments to the function

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