fumiyau/python_no_comments_test10000 · Datasets at Hugging Face

f71938dd1d111ba18183cd67737d4ea3ac849931

863

py

Python

Software/Estadística/MCMC/HS/CC+SN_int1/4params/analisis_cadenas_4params.py

matiasleize/tesis_licenciatura

5df6e341314583702b466b8ed7977d410f0ee457

[ "MIT" ]

null

Software/Estadística/MCMC/HS/CC+SN_int1/4params/analisis_cadenas_4params.py

matiasleize/tesis_licenciatura

5df6e341314583702b466b8ed7977d410f0ee457

[ "MIT" ]

null

Software/Estadística/MCMC/HS/CC+SN_int1/4params/analisis_cadenas_4params.py

matiasleize/tesis_licenciatura

5df6e341314583702b466b8ed7977d410f0ee457

[ "MIT" ]

null

import numpy as np from matplotlib import pyplot as plt import emcee import sys import os from pc_path import definir_path path_git, path_datos_global = definir_path() os.chdir(path_git) sys.path.append('./Software/Funcionales/Clases') from funciones_graficador import Graficador #%% Importo las cadenas os.chdir(path_datos_global+'/Resultados_cadenas/') filename = "sample_HS_CC+SN_4params_int1.h5" reader = emcee.backends.HDFBackend(filename) # Algunos valores tau = reader.get_autocorr_time() burnin = int(2 * np.max(tau)) thin = int(0.5 * np.min(tau)) #%% %matplotlib qt5 #burnin=100 #thin=1 analisis = Graficador(reader, ['$M_{abs}$','$\Omega_{m}^{\Lambda CDM}$','b','$H_{0}^{\Lambda CDM}$'],'1 SNIA + CC (HS)') analisis.graficar_contornos(discard=burnin, thin=thin, poster=False,color='r') #%% analisis.graficar_cadenas() analisis.reportar_intervalos()

26.96875

120

0.753187

import numpy as np from matplotlib import pyplot as plt import emcee import sys import os from pc_path import definir_path path_git, path_datos_global = definir_path() os.chdir(path_git) sys.path.append('./Software/Funcionales/Clases') from funciones_graficador import Graficador os.chdir(path_datos_global+'/Resultados_cadenas/') filename = "sample_HS_CC+SN_4params_int1.h5" reader = emcee.backends.HDFBackend(filename) tau = reader.get_autocorr_time() burnin = int(2 * np.max(tau)) thin = int(0.5 * np.min(tau)) %matplotlib qt5 analisis = Graficador(reader, ['$M_{abs}$','$\Omega_{m}^{\Lambda CDM}$','b','$H_{0}^{\Lambda CDM}$'],'1 SNIA + CC (HS)') analisis.graficar_contornos(discard=burnin, thin=thin, poster=False,color='r') analisis.graficar_cadenas() analisis.reportar_intervalos()

false

true

f71939e1d16adffd88e34ce88da8f38f90363eca

2,079

py

Python

scripts/sdk_fetch_coverage_tools.py

PelionIoT/mbed-cloud-sdk-java

cc99c51db43cc9ae36601f20f20b7d8cd7515432

[ "Apache-2.0" ]

7

2017-12-28T11:19:15.000Z

2020-03-23T19:15:31.000Z

scripts/sdk_fetch_coverage_tools.py

PelionIoT/mbed-cloud-sdk-java

cc99c51db43cc9ae36601f20f20b7d8cd7515432

[ "Apache-2.0" ]

99

2018-01-09T23:56:13.000Z

2020-11-03T05:20:55.000Z

scripts/sdk_fetch_coverage_tools.py

PelionIoT/mbed-cloud-sdk-java

cc99c51db43cc9ae36601f20f20b7d8cd7515432

[ "Apache-2.0" ]

5

2018-08-02T06:29:18.000Z

2019-10-23T11:43:59.000Z

#!/usr/bin/python import os import sdk_common # Block in charge of fetching code coverage tools class SDKCoverageToolsFetcher(sdk_common.BuildStepUsingGradle): def __init__(self, logger=None): super(SDKCoverageToolsFetcher, self).__init__('SDK Coverage tools fetch', logger) self.is_code_coverage = self.common_config.get_config().should_perform_code_coverage() self.artifacts_parser = self.common_config.get_config().get_new_artifact_log_parser(self) self.jacoco_cli_name = 'jacococli.jar' def retrieve_folder_location(self, key): if not key: return None self.artifacts_parser.load() return self.clean_path( self.artifacts_parser.get_property(key), False) def check_whether_coverage_result_folder_has_been_created(self): code_coverage_result_dir = self.retrieve_folder_location('SDK_COVERAGE_RESULTS_DIR') return False if not code_coverage_result_dir else os.path.exists(code_coverage_result_dir) def check_whether_tools_have_been_copied(self): code_coverage_tools_dir = self.retrieve_folder_location('SDK_COVERAGE_TOOLS_DIR') return False if not code_coverage_tools_dir else ( os.path.exists(code_coverage_tools_dir) and len( os.listdir(code_coverage_tools_dir)) >= 2) # TODO change if fewer tools are used def has_already_been_run(self): return self.check_whether_coverage_result_folder_has_been_created() and self.check_whether_tools_have_been_copied() def execute(self): self.print_title() try: if self.is_code_coverage: self.log_info("Retrieving code coverage tools") if not self.has_already_been_run(): self.execute_gradle_task("copyCoverageAgent") else: self.log_info("Tools are already present.") except: self.log_error('Failed to retrieving code coverage tools') return False self.log_info("Done.") return True

40.764706

123

0.696489

import os import sdk_common class SDKCoverageToolsFetcher(sdk_common.BuildStepUsingGradle): def __init__(self, logger=None): super(SDKCoverageToolsFetcher, self).__init__('SDK Coverage tools fetch', logger) self.is_code_coverage = self.common_config.get_config().should_perform_code_coverage() self.artifacts_parser = self.common_config.get_config().get_new_artifact_log_parser(self) self.jacoco_cli_name = 'jacococli.jar' def retrieve_folder_location(self, key): if not key: return None self.artifacts_parser.load() return self.clean_path( self.artifacts_parser.get_property(key), False) def check_whether_coverage_result_folder_has_been_created(self): code_coverage_result_dir = self.retrieve_folder_location('SDK_COVERAGE_RESULTS_DIR') return False if not code_coverage_result_dir else os.path.exists(code_coverage_result_dir) def check_whether_tools_have_been_copied(self): code_coverage_tools_dir = self.retrieve_folder_location('SDK_COVERAGE_TOOLS_DIR') return False if not code_coverage_tools_dir else ( os.path.exists(code_coverage_tools_dir) and len( os.listdir(code_coverage_tools_dir)) >= 2) def has_already_been_run(self): return self.check_whether_coverage_result_folder_has_been_created() and self.check_whether_tools_have_been_copied() def execute(self): self.print_title() try: if self.is_code_coverage: self.log_info("Retrieving code coverage tools") if not self.has_already_been_run(): self.execute_gradle_task("copyCoverageAgent") else: self.log_info("Tools are already present.") except: self.log_error('Failed to retrieving code coverage tools') return False self.log_info("Done.") return True

true

f7193a1de09a2338512e1f71556799b0418fb19a

683

py

Python

app/core/migrations/0002_tag.py

bwanarm/recipe-app-api

1204280495547ceb93a59cd2ec2b1c2a82ef187d

[ "MIT" ]

null

app/core/migrations/0002_tag.py

bwanarm/recipe-app-api

1204280495547ceb93a59cd2ec2b1c2a82ef187d

[ "MIT" ]

null

app/core/migrations/0002_tag.py

bwanarm/recipe-app-api

1204280495547ceb93a59cd2ec2b1c2a82ef187d

[ "MIT" ]

null

# Generated by Django 2.1.15 on 2020-07-31 13:10 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('core', '0001_initial'), ] operations = [ migrations.CreateModel( name='Tag', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255)), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]

28.458333

118

0.616398

from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('core', '0001_initial'), ] operations = [ migrations.CreateModel( name='Tag', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=255)), ('user', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to=settings.AUTH_USER_MODEL)), ], ), ]

true

f7193a2229b00e7439ffb31eaf7bc0964fc3bb54

10,877

py

Python

pretrained-model/stt/hubert/conformer-tiny-ctc.py

ishine/malaya-speech

fd34afc7107af1656dff4b3201fa51dda54fde18

[ "MIT" ]

null

pretrained-model/stt/hubert/conformer-tiny-ctc.py

ishine/malaya-speech

fd34afc7107af1656dff4b3201fa51dda54fde18

[ "MIT" ]

null

pretrained-model/stt/hubert/conformer-tiny-ctc.py

ishine/malaya-speech

fd34afc7107af1656dff4b3201fa51dda54fde18

[ "MIT" ]

null

import os os.environ['CUDA_VISIBLE_DEVICES'] = '3' import pyroomacoustics as pra import numpy as np from pydub import AudioSegment from sklearn.utils import shuffle from glob import glob import random import json from malaya_speech.train.model.conformer.model import Model as ConformerModel from malaya_speech.train.model import hubert, ctc import malaya_speech.train as train import malaya_speech.config import malaya_speech.augmentation.waveform as augmentation import malaya_speech import tensorflow as tf import os import string sr = 16000 maxlen = 18 minlen_text = 1 prob_aug = 0.95 unique_vocab = [''] + list(string.ascii_lowercase + string.digits) + [' '] def augment_room(y, scale=1.0): corners = np.array( [[0, 0], [0, 5 * scale], [3 * scale, 5 * scale], [3 * scale, 0]] ).T room = pra.Room.from_corners( corners, fs=sr, materials=pra.Material(0.2, 0.15), ray_tracing=True, air_absorption=True, ) room.extrude(3.5, materials=pra.Material(0.2, 0.15)) room.set_ray_tracing( receiver_radius=0.5, n_rays=1000, energy_thres=1e-5 ) room.add_source([1.5 * scale, 4 * scale, 0.5], signal=y) R = np.array([[1.5 * scale], [0.5 * scale], [0.5]]) room.add_microphone(R) room.simulate() return room.mic_array.signals[0] def random_amplitude_threshold(sample, low=1, high=2, threshold=0.4): y_aug = sample.copy() dyn_change = np.random.uniform(low=low, high=high) y_aug[np.abs(y_aug) >= threshold] = ( y_aug[np.abs(y_aug) >= threshold] * dyn_change ) return np.clip(y_aug, -1, 1) def add_uniform_noise( sample, power=0.01, return_noise=False, scale=False ): y_noise = sample.copy() noise_amp = power * np.random.uniform() * np.amax(y_noise) noise = noise_amp * np.random.normal(size=y_noise.shape[0]) y_noise = y_noise + noise if scale: y_noise = y_noise / (np.max(np.abs(y_noise)) + 1e-9) if return_noise: if scale: noise = noise / (np.max(np.abs(y_noise)) + 1e-9) return y_noise, noise else: return y_noise def calc(signal, add_uniform=True): choice = random.randint(0, 10) print('choice', choice) if choice == 0: x = augmentation.sox_augment_high( signal, min_bass_gain=random.randint(25, 50), reverberance=random.randint(0, 80), hf_damping=10, room_scale=random.randint(0, 50), negate=1, ) if choice == 1: x = augmentation.sox_augment_high( signal, min_bass_gain=random.randint(25, 70), reverberance=random.randint(0, 80), hf_damping=10, room_scale=random.randint(0, 50), negate=0, ) if choice == 2: x = augmentation.sox_augment_low( signal, min_bass_gain=random.randint(5, 30), reverberance=random.randint(0, 80), hf_damping=10, room_scale=random.randint(0, 50), negate=random.randint(0, 1), ) if choice == 3: x = augmentation.sox_augment_combine( signal, min_bass_gain_high=random.randint(25, 70), min_bass_gain_low=random.randint(5, 30), reverberance=random.randint(0, 80), hf_damping=10, room_scale=random.randint(0, 90), ) if choice == 4: x = augmentation.sox_reverb( signal, reverberance=random.randint(10, 80), hf_damping=10, room_scale=random.randint(10, 90), ) if choice == 5: x = random_amplitude_threshold( signal, threshold=random.uniform(0.35, 0.8) ) if choice == 6: x = augmentation.lowpass_filter( signal, sr=sr, cutoff=random.randint(200, 551) ) if choice == 7: x = augmentation.highpass_filter( signal, sr=sr, cutoff=random.randint(551, 1653) ) if choice == 8: x = augmentation.bandpass_filter( signal, sr=sr, cutoff_low=random.randint(200, 551), cutoff_high=random.randint(551, 1653), ) if choice == 9: x = augment_room(signal) if choice == 10: x = signal if choice not in [5] and random.gauss(0.5, 0.14) > 0.6: x = random_amplitude_threshold( x, low=1.0, high=2.0, threshold=random.uniform(0.6, 0.9) ) if random.gauss(0.5, 0.14) > 0.6 and add_uniform: x = add_uniform_noise(x, power=random.uniform(0.005, 0.015)) return x def mp3_to_wav(file, sr=sr): audio = AudioSegment.from_file(file) audio = audio.set_frame_rate(sr).set_channels(1) sample = np.array(audio.get_array_of_samples()) return malaya_speech.astype.int_to_float(sample), sr def generate(file): with open(file) as fopen: dataset = json.load(fopen) audios, cleaned_texts = dataset['X'], dataset['Y'] while True: audios, cleaned_texts = shuffle(audios, cleaned_texts) for i in range(len(audios)): try: if audios[i].endswith('.mp3'): # print('found mp3', audios[i]) wav_data, _ = mp3_to_wav(audios[i]) else: wav_data, _ = malaya_speech.load(audios[i], sr=sr) if len(cleaned_texts[i]) < minlen_text: # print(f'skipped text too short {audios[i]}') continue if (len(wav_data) / sr) > maxlen: continue t = [unique_vocab.index(c) for c in cleaned_texts[i]] yield { 'waveforms': wav_data, 'waveforms_length': [len(wav_data)], 'targets': t, 'targets_length': [len(t)], } except Exception as e: print(e) def get_dataset( file, batch_size=12, shuffle_size=20, thread_count=24, maxlen_feature=1800, ): def get(): dataset = tf.data.Dataset.from_generator( generate, { 'waveforms': tf.float32, 'waveforms_length': tf.int32, 'targets': tf.int32, 'targets_length': tf.int32, }, output_shapes={ 'waveforms': tf.TensorShape([None]), 'waveforms_length': tf.TensorShape([None]), 'targets': tf.TensorShape([None]), 'targets_length': tf.TensorShape([None]), }, args=(file,), ) dataset = dataset.prefetch(tf.contrib.data.AUTOTUNE) dataset = dataset.padded_batch( batch_size, padded_shapes={ 'waveforms': tf.TensorShape([None]), 'waveforms_length': tf.TensorShape([None]), 'targets': tf.TensorShape([None]), 'targets_length': tf.TensorShape([None]), }, padding_values={ 'waveforms': tf.constant(0, dtype=tf.float32), 'waveforms_length': tf.constant(0, dtype=tf.int32), 'targets': tf.constant(0, dtype=tf.int32), 'targets_length': tf.constant(0, dtype=tf.int32), }, ) return dataset return get class Encoder: def __init__(self, config): self.config = config self.encoder = ConformerModel(**self.config) def __call__(self, x, input_mask, training=True): return self.encoder(x, training=training) total_steps = 2000000 def model_fn(features, labels, mode, params): config_conformer = malaya_speech.config.conformer_tiny_encoder_config config_conformer['subsampling']['type'] = 'none' config_conformer['dropout'] = 0.0 encoder = Encoder(config_conformer) cfg = hubert.HuBERTConfig( extractor_mode='layer_norm', dropout=0.0, attention_dropout=0.0, encoder_layerdrop=0.0, dropout_input=0.0, dropout_features=0.0, final_dim=128, ) model = hubert.Model(cfg, encoder, ['pad', 'eos', 'unk'] + [str(i) for i in range(100)]) X = features['waveforms'] X_len = features['waveforms_length'][:, 0] targets = features['targets'] targets_int32 = tf.cast(targets, tf.int32) targets_length = features['targets_length'][:, 0] r = model(X, padding_mask=X_len, features_only=True, mask=False) logits = tf.layers.dense(r['x'], len(unique_vocab) + 1) seq_lens = tf.reduce_sum( tf.cast(tf.logical_not(r['padding_mask']), tf.int32), axis=1 ) mean_error, sum_error, sum_weight = ctc.loss.ctc_loss( logits, seq_lens, targets_int32, targets_length ) loss = mean_error accuracy = ctc.metrics.ctc_sequence_accuracy( logits, seq_lens, targets_int32, targets_length, ) tf.identity(loss, 'train_loss') tf.identity(accuracy, name='train_accuracy') tf.summary.scalar('train_accuracy', accuracy) variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) init_checkpoint = 'hubert-conformer-tiny/model.ckpt-1000000' assignment_map, initialized_variable_names = train.get_assignment_map_from_checkpoint( variables, init_checkpoint ) tf.train.init_from_checkpoint(init_checkpoint, assignment_map) if mode == tf.estimator.ModeKeys.TRAIN: train_op = train.optimizer.adamw.create_optimizer( loss, init_lr=5e-5, num_train_steps=total_steps, num_warmup_steps=100000, end_learning_rate=0.0, weight_decay_rate=0.01, beta_1=0.9, beta_2=0.999, epsilon=1e-6, clip_norm=1.0, ) estimator_spec = tf.estimator.EstimatorSpec( mode=mode, loss=loss, train_op=train_op ) elif mode == tf.estimator.ModeKeys.EVAL: estimator_spec = tf.estimator.EstimatorSpec( mode=tf.estimator.ModeKeys.EVAL, loss=loss, eval_metric_ops={ 'accuracy': ctc.metrics.ctc_sequence_accuracy_estimator( logits, seq_lens, targets_int32, targets_length ) }, ) return estimator_spec train_hooks = [ tf.train.LoggingTensorHook( ['train_accuracy', 'train_loss'], every_n_iter=1 ) ] train_dataset = get_dataset('bahasa-asr-train-combined.json') dev_dataset = get_dataset('bahasa-asr-test.json') train.run_training( train_fn=train_dataset, model_fn=model_fn, model_dir='hubert-conformer-tiny-ctc-char', num_gpus=1, log_step=1, save_checkpoint_step=20000, max_steps=total_steps, eval_fn=dev_dataset, train_hooks=train_hooks, )

30.639437

92

0.590144

import os os.environ['CUDA_VISIBLE_DEVICES'] = '3' import pyroomacoustics as pra import numpy as np from pydub import AudioSegment from sklearn.utils import shuffle from glob import glob import random import json from malaya_speech.train.model.conformer.model import Model as ConformerModel from malaya_speech.train.model import hubert, ctc import malaya_speech.train as train import malaya_speech.config import malaya_speech.augmentation.waveform as augmentation import malaya_speech import tensorflow as tf import os import string sr = 16000 maxlen = 18 minlen_text = 1 prob_aug = 0.95 unique_vocab = [''] + list(string.ascii_lowercase + string.digits) + [' '] def augment_room(y, scale=1.0): corners = np.array( [[0, 0], [0, 5 * scale], [3 * scale, 5 * scale], [3 * scale, 0]] ).T room = pra.Room.from_corners( corners, fs=sr, materials=pra.Material(0.2, 0.15), ray_tracing=True, air_absorption=True, ) room.extrude(3.5, materials=pra.Material(0.2, 0.15)) room.set_ray_tracing( receiver_radius=0.5, n_rays=1000, energy_thres=1e-5 ) room.add_source([1.5 * scale, 4 * scale, 0.5], signal=y) R = np.array([[1.5 * scale], [0.5 * scale], [0.5]]) room.add_microphone(R) room.simulate() return room.mic_array.signals[0] def random_amplitude_threshold(sample, low=1, high=2, threshold=0.4): y_aug = sample.copy() dyn_change = np.random.uniform(low=low, high=high) y_aug[np.abs(y_aug) >= threshold] = ( y_aug[np.abs(y_aug) >= threshold] * dyn_change ) return np.clip(y_aug, -1, 1) def add_uniform_noise( sample, power=0.01, return_noise=False, scale=False ): y_noise = sample.copy() noise_amp = power * np.random.uniform() * np.amax(y_noise) noise = noise_amp * np.random.normal(size=y_noise.shape[0]) y_noise = y_noise + noise if scale: y_noise = y_noise / (np.max(np.abs(y_noise)) + 1e-9) if return_noise: if scale: noise = noise / (np.max(np.abs(y_noise)) + 1e-9) return y_noise, noise else: return y_noise def calc(signal, add_uniform=True): choice = random.randint(0, 10) print('choice', choice) if choice == 0: x = augmentation.sox_augment_high( signal, min_bass_gain=random.randint(25, 50), reverberance=random.randint(0, 80), hf_damping=10, room_scale=random.randint(0, 50), negate=1, ) if choice == 1: x = augmentation.sox_augment_high( signal, min_bass_gain=random.randint(25, 70), reverberance=random.randint(0, 80), hf_damping=10, room_scale=random.randint(0, 50), negate=0, ) if choice == 2: x = augmentation.sox_augment_low( signal, min_bass_gain=random.randint(5, 30), reverberance=random.randint(0, 80), hf_damping=10, room_scale=random.randint(0, 50), negate=random.randint(0, 1), ) if choice == 3: x = augmentation.sox_augment_combine( signal, min_bass_gain_high=random.randint(25, 70), min_bass_gain_low=random.randint(5, 30), reverberance=random.randint(0, 80), hf_damping=10, room_scale=random.randint(0, 90), ) if choice == 4: x = augmentation.sox_reverb( signal, reverberance=random.randint(10, 80), hf_damping=10, room_scale=random.randint(10, 90), ) if choice == 5: x = random_amplitude_threshold( signal, threshold=random.uniform(0.35, 0.8) ) if choice == 6: x = augmentation.lowpass_filter( signal, sr=sr, cutoff=random.randint(200, 551) ) if choice == 7: x = augmentation.highpass_filter( signal, sr=sr, cutoff=random.randint(551, 1653) ) if choice == 8: x = augmentation.bandpass_filter( signal, sr=sr, cutoff_low=random.randint(200, 551), cutoff_high=random.randint(551, 1653), ) if choice == 9: x = augment_room(signal) if choice == 10: x = signal if choice not in [5] and random.gauss(0.5, 0.14) > 0.6: x = random_amplitude_threshold( x, low=1.0, high=2.0, threshold=random.uniform(0.6, 0.9) ) if random.gauss(0.5, 0.14) > 0.6 and add_uniform: x = add_uniform_noise(x, power=random.uniform(0.005, 0.015)) return x def mp3_to_wav(file, sr=sr): audio = AudioSegment.from_file(file) audio = audio.set_frame_rate(sr).set_channels(1) sample = np.array(audio.get_array_of_samples()) return malaya_speech.astype.int_to_float(sample), sr def generate(file): with open(file) as fopen: dataset = json.load(fopen) audios, cleaned_texts = dataset['X'], dataset['Y'] while True: audios, cleaned_texts = shuffle(audios, cleaned_texts) for i in range(len(audios)): try: if audios[i].endswith('.mp3'): wav_data, _ = mp3_to_wav(audios[i]) else: wav_data, _ = malaya_speech.load(audios[i], sr=sr) if len(cleaned_texts[i]) < minlen_text: continue if (len(wav_data) / sr) > maxlen: continue t = [unique_vocab.index(c) for c in cleaned_texts[i]] yield { 'waveforms': wav_data, 'waveforms_length': [len(wav_data)], 'targets': t, 'targets_length': [len(t)], } except Exception as e: print(e) def get_dataset( file, batch_size=12, shuffle_size=20, thread_count=24, maxlen_feature=1800, ): def get(): dataset = tf.data.Dataset.from_generator( generate, { 'waveforms': tf.float32, 'waveforms_length': tf.int32, 'targets': tf.int32, 'targets_length': tf.int32, }, output_shapes={ 'waveforms': tf.TensorShape([None]), 'waveforms_length': tf.TensorShape([None]), 'targets': tf.TensorShape([None]), 'targets_length': tf.TensorShape([None]), }, args=(file,), ) dataset = dataset.prefetch(tf.contrib.data.AUTOTUNE) dataset = dataset.padded_batch( batch_size, padded_shapes={ 'waveforms': tf.TensorShape([None]), 'waveforms_length': tf.TensorShape([None]), 'targets': tf.TensorShape([None]), 'targets_length': tf.TensorShape([None]), }, padding_values={ 'waveforms': tf.constant(0, dtype=tf.float32), 'waveforms_length': tf.constant(0, dtype=tf.int32), 'targets': tf.constant(0, dtype=tf.int32), 'targets_length': tf.constant(0, dtype=tf.int32), }, ) return dataset return get class Encoder: def __init__(self, config): self.config = config self.encoder = ConformerModel(**self.config) def __call__(self, x, input_mask, training=True): return self.encoder(x, training=training) total_steps = 2000000 def model_fn(features, labels, mode, params): config_conformer = malaya_speech.config.conformer_tiny_encoder_config config_conformer['subsampling']['type'] = 'none' config_conformer['dropout'] = 0.0 encoder = Encoder(config_conformer) cfg = hubert.HuBERTConfig( extractor_mode='layer_norm', dropout=0.0, attention_dropout=0.0, encoder_layerdrop=0.0, dropout_input=0.0, dropout_features=0.0, final_dim=128, ) model = hubert.Model(cfg, encoder, ['pad', 'eos', 'unk'] + [str(i) for i in range(100)]) X = features['waveforms'] X_len = features['waveforms_length'][:, 0] targets = features['targets'] targets_int32 = tf.cast(targets, tf.int32) targets_length = features['targets_length'][:, 0] r = model(X, padding_mask=X_len, features_only=True, mask=False) logits = tf.layers.dense(r['x'], len(unique_vocab) + 1) seq_lens = tf.reduce_sum( tf.cast(tf.logical_not(r['padding_mask']), tf.int32), axis=1 ) mean_error, sum_error, sum_weight = ctc.loss.ctc_loss( logits, seq_lens, targets_int32, targets_length ) loss = mean_error accuracy = ctc.metrics.ctc_sequence_accuracy( logits, seq_lens, targets_int32, targets_length, ) tf.identity(loss, 'train_loss') tf.identity(accuracy, name='train_accuracy') tf.summary.scalar('train_accuracy', accuracy) variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES) init_checkpoint = 'hubert-conformer-tiny/model.ckpt-1000000' assignment_map, initialized_variable_names = train.get_assignment_map_from_checkpoint( variables, init_checkpoint ) tf.train.init_from_checkpoint(init_checkpoint, assignment_map) if mode == tf.estimator.ModeKeys.TRAIN: train_op = train.optimizer.adamw.create_optimizer( loss, init_lr=5e-5, num_train_steps=total_steps, num_warmup_steps=100000, end_learning_rate=0.0, weight_decay_rate=0.01, beta_1=0.9, beta_2=0.999, epsilon=1e-6, clip_norm=1.0, ) estimator_spec = tf.estimator.EstimatorSpec( mode=mode, loss=loss, train_op=train_op ) elif mode == tf.estimator.ModeKeys.EVAL: estimator_spec = tf.estimator.EstimatorSpec( mode=tf.estimator.ModeKeys.EVAL, loss=loss, eval_metric_ops={ 'accuracy': ctc.metrics.ctc_sequence_accuracy_estimator( logits, seq_lens, targets_int32, targets_length ) }, ) return estimator_spec train_hooks = [ tf.train.LoggingTensorHook( ['train_accuracy', 'train_loss'], every_n_iter=1 ) ] train_dataset = get_dataset('bahasa-asr-train-combined.json') dev_dataset = get_dataset('bahasa-asr-test.json') train.run_training( train_fn=train_dataset, model_fn=model_fn, model_dir='hubert-conformer-tiny-ctc-char', num_gpus=1, log_step=1, save_checkpoint_step=20000, max_steps=total_steps, eval_fn=dev_dataset, train_hooks=train_hooks, )

true

f7193aa46ca7cccda6fa00809b1c48838617c057

9,046

py

Python

killerbee/dev_telosb.py

Acesonnall/killerbee

354c68bcf21f60910d9f68f62285b977db76fb60

[ "BSD-3-Clause" ]

2

2019-06-16T06:53:46.000Z

2022-02-18T01:05:36.000Z

killerbee/dev_telosb.py

Acesonnall/killerbee

354c68bcf21f60910d9f68f62285b977db76fb60

[ "BSD-3-Clause" ]

1

2019-11-23T17:16:55.000Z

killerbee/dev_telosb.py

Acesonnall/killerbee

354c68bcf21f60910d9f68f62285b977db76fb60

[ "BSD-3-Clause" ]

2

2019-06-15T15:54:36.000Z

2019-06-15T15:55:39.000Z

''' Support for the TelosB / Tmote Sky platforms, and close clones. Utilizes the GoodFET firmware with CCSPI application, and the GoodFET client code. ''' import os import time import struct import time from datetime import datetime, timedelta from kbutils import KBCapabilities, makeFCS from GoodFETCCSPI import GoodFETCCSPI CC2420_REG_SYNC = 0x14 class TELOSB: def __init__(self, dev): ''' Instantiates the KillerBee class for our TelosB/TmoteSky running GoodFET firmware. @type dev: String @param dev: Serial device identifier (ex /dev/ttyUSB0) @return: None @rtype: None ''' self._channel = None self._page = 0 self.handle = None self.dev = dev os.environ["board"] = "telosb" #set enviroment variable for GoodFET code to use self.handle = GoodFETCCSPI() self.handle.serInit(port=self.dev) self.handle.setup() self.__stream_open = False self.capabilities = KBCapabilities() self.__set_capabilities() def close(self): self.handle.serClose() self.handle = None def check_capability(self, capab): return self.capabilities.check(capab) def get_capabilities(self): return self.capabilities.getlist() def __set_capabilities(self): ''' Sets the capability information appropriate for GoodFETCCSPI client and firmware. @rtype: None @return: None ''' self.capabilities.setcapab(KBCapabilities.FREQ_2400, True) self.capabilities.setcapab(KBCapabilities.SNIFF, True) self.capabilities.setcapab(KBCapabilities.SETCHAN, True) self.capabilities.setcapab(KBCapabilities.INJECT, True) self.capabilities.setcapab(KBCapabilities.PHYJAM_REFLEX, True) self.capabilities.setcapab(KBCapabilities.SET_SYNC, True) return # KillerBee expects the driver to implement this function #def get_dev_info(self, dev, bus): def get_dev_info(self): ''' Returns device information in a list identifying the device. @rtype: List @return: List of 3 strings identifying device. ''' return [self.dev, "TelosB/Tmote", ""] # KillerBee expects the driver to implement this function def sniffer_on(self, channel=None, page=0): ''' Turns the sniffer on such that pnext() will start returning observed data. Will set the command mode to Air Capture if it is not already set. @type channel: Integer @param channel: Sets the channel, optional @type page: Integer @param page: Sets the subghz page, not supported on this device @rtype: None ''' self.capabilities.require(KBCapabilities.SNIFF) self.handle.RF_promiscuity(1); self.handle.RF_autocrc(0); if channel != None: self.set_channel(channel, page) self.handle.CC_RFST_RX(); #print "Sniffer started (listening as %010x on %i MHz)" % (self.handle.RF_getsmac(), self.handle.RF_getfreq()/10**6); self.__stream_open = True # KillerBee expects the driver to implement this function def sniffer_off(self): ''' Turns the sniffer off, freeing the hardware for other functions. It is not necessary to call this function before closing the interface with close(). @rtype: None ''' #TODO actually have firmware stop sending us packets! self.__stream_open = False # KillerBee expects the driver to implement this function def set_channel(self, channel, page=0): ''' Sets the radio interface to the specifid channel (limited to 2.4 GHz channels 11-26) @type channel: Integer @param channel: Sets the channel, optional @type page: Integer @param page: Sets the subghz page, not supported on this device @rtype: None ''' self.capabilities.require(KBCapabilities.SETCHAN) if channel >= 11 or channel <= 26: self._channel = channel self.handle.RF_setchan(channel) else: raise Exception('Invalid channel') if page: raise Exception('SubGHz not supported') # KillerBee expects the driver to implement this function def inject(self, packet, channel=None, count=1, delay=0, page=0): ''' Injects the specified packet contents. @type packet: String @param packet: Packet contents to transmit, without FCS. @type channel: Integer @param channel: Sets the channel, optional @type page: Integer @param page: Sets the subghz page, not supported on this device @type count: Integer @param count: Transmits a specified number of frames, def=1 @type delay: Float @param delay: Delay between each frame, def=1 @rtype: None ''' self.capabilities.require(KBCapabilities.INJECT) if len(packet) < 1: raise Exception('Empty packet') if len(packet) > 125: # 127 - 2 to accommodate FCS raise Exception('Packet too long') if channel != None: self.set_channel(channel, page) self.handle.RF_autocrc(1) #let radio add the CRC for pnum in range(0, count): gfready = [ord(x) for x in packet] #convert packet string to GoodFET expected integer format gfready.insert(0, len(gfready)+2) #add a length that leaves room for CRC self.handle.RF_txpacket(gfready) # Sleep was for 1 second but testing by Gianfranco Costamagna suggested lowering to 1/100th of a second time.sleep(0.01) #TODO get rid of completely, and just check CC2420 status # https://github.com/alvarop/msp430-cc2500/blob/master/lib/cc2500/cc2500.c # KillerBee expects the driver to implement this function def pnext(self, timeout=100): ''' Returns a dictionary containing packet data, else None. @type timeout: Integer @param timeout: Timeout to wait for packet reception in usec @rtype: List @return: Returns None is timeout expires and no packet received. When a packet is received, a dictionary is returned with the keys bytes (string of packet bytes), validcrc (boolean if a vaid CRC), rssi (unscaled RSSI), and location (may be set to None). For backwards compatibility, keys for 0,1,2 are provided such that it can be treated as if a list is returned, in the form [ String: packet contents | Bool: Valid CRC | Int: Unscaled RSSI ] ''' if self.__stream_open == False: self.sniffer_on() #start sniffing packet = None; start = datetime.utcnow() while (packet is None and (start + timedelta(microseconds=timeout) > datetime.utcnow())): packet = self.handle.RF_rxpacket() rssi = self.handle.RF_getrssi() #TODO calibrate if packet is None: return None frame = packet[1:] if frame[-2:] == makeFCS(frame[:-2]): validcrc = True else: validcrc = False #Return in a nicer dictionary format, so we don't have to reference by number indicies. #Note that 0,1,2 indicies inserted twice for backwards compatibility. result = {0:frame, 1:validcrc, 2:rssi, 'bytes':frame, 'validcrc':validcrc, 'rssi':rssi, 'location':None} result['dbm'] = rssi - 45 #TODO tune specifically to the Tmote platform (does ext antenna need to different?) result['datetime'] = datetime.utcnow() return result def ping(self, da, panid, sa, channel=None, page=0): ''' Not yet implemented. @return: None @rtype: None ''' raise Exception('Not yet implemented') def jammer_on(self, channel=None, page=0): ''' Not yet implemented. @type channel: Integer @param channel: Sets the channel, optional @type page: Integer @param page: Sets the subghz page, not support on this device @rtype: None ''' self.capabilities.require(KBCapabilities.PHYJAM_REFLEX) self.handle.RF_promiscuity(1) self.handle.RF_autocrc(0) if channel != None: self.set_channel(channel, page) self.handle.CC_RFST_RX() self.handle.RF_reflexjam() def set_sync(self, sync=0xA70F): '''Set the register controlling the 802.15.4 PHY sync byte.''' self.capabilities.require(KBCapabilities.SET_SYNC) if (sync >> 16) > 0: raise Exception("Sync word (%x) must be 2-bytes or less." % sync) return self.handle.poke(CC2420_REG_SYNC, sync) def jammer_off(self, channel=None, page=0): ''' Not yet implemented. @return: None @rtype: None ''' #TODO implement raise Exception('Not yet implemented')

38.168776

452

0.632876

''' Support for the TelosB / Tmote Sky platforms, and close clones. Utilizes the GoodFET firmware with CCSPI application, and the GoodFET client code. ''' import os import time import struct import time from datetime import datetime, timedelta from kbutils import KBCapabilities, makeFCS from GoodFETCCSPI import GoodFETCCSPI CC2420_REG_SYNC = 0x14 class TELOSB: def __init__(self, dev): ''' Instantiates the KillerBee class for our TelosB/TmoteSky running GoodFET firmware. @type dev: String @param dev: Serial device identifier (ex /dev/ttyUSB0) @return: None @rtype: None ''' self._channel = None self._page = 0 self.handle = None self.dev = dev os.environ["board"] = "telosb" self.handle = GoodFETCCSPI() self.handle.serInit(port=self.dev) self.handle.setup() self.__stream_open = False self.capabilities = KBCapabilities() self.__set_capabilities() def close(self): self.handle.serClose() self.handle = None def check_capability(self, capab): return self.capabilities.check(capab) def get_capabilities(self): return self.capabilities.getlist() def __set_capabilities(self): ''' Sets the capability information appropriate for GoodFETCCSPI client and firmware. @rtype: None @return: None ''' self.capabilities.setcapab(KBCapabilities.FREQ_2400, True) self.capabilities.setcapab(KBCapabilities.SNIFF, True) self.capabilities.setcapab(KBCapabilities.SETCHAN, True) self.capabilities.setcapab(KBCapabilities.INJECT, True) self.capabilities.setcapab(KBCapabilities.PHYJAM_REFLEX, True) self.capabilities.setcapab(KBCapabilities.SET_SYNC, True) return def get_dev_info(self): ''' Returns device information in a list identifying the device. @rtype: List @return: List of 3 strings identifying device. ''' return [self.dev, "TelosB/Tmote", ""] def sniffer_on(self, channel=None, page=0): ''' Turns the sniffer on such that pnext() will start returning observed data. Will set the command mode to Air Capture if it is not already set. @type channel: Integer @param channel: Sets the channel, optional @type page: Integer @param page: Sets the subghz page, not supported on this device @rtype: None ''' self.capabilities.require(KBCapabilities.SNIFF) self.handle.RF_promiscuity(1); self.handle.RF_autocrc(0); if channel != None: self.set_channel(channel, page) self.handle.CC_RFST_RX(); self.__stream_open = True def sniffer_off(self): ''' Turns the sniffer off, freeing the hardware for other functions. It is not necessary to call this function before closing the interface with close(). @rtype: None ''' self.__stream_open = False def set_channel(self, channel, page=0): ''' Sets the radio interface to the specifid channel (limited to 2.4 GHz channels 11-26) @type channel: Integer @param channel: Sets the channel, optional @type page: Integer @param page: Sets the subghz page, not supported on this device @rtype: None ''' self.capabilities.require(KBCapabilities.SETCHAN) if channel >= 11 or channel <= 26: self._channel = channel self.handle.RF_setchan(channel) else: raise Exception('Invalid channel') if page: raise Exception('SubGHz not supported') def inject(self, packet, channel=None, count=1, delay=0, page=0): ''' Injects the specified packet contents. @type packet: String @param packet: Packet contents to transmit, without FCS. @type channel: Integer @param channel: Sets the channel, optional @type page: Integer @param page: Sets the subghz page, not supported on this device @type count: Integer @param count: Transmits a specified number of frames, def=1 @type delay: Float @param delay: Delay between each frame, def=1 @rtype: None ''' self.capabilities.require(KBCapabilities.INJECT) if len(packet) < 1: raise Exception('Empty packet') if len(packet) > 125: raise Exception('Packet too long') if channel != None: self.set_channel(channel, page) self.handle.RF_autocrc(1) for pnum in range(0, count): gfready = [ord(x) for x in packet] gfready.insert(0, len(gfready)+2) self.handle.RF_txpacket(gfready) time.sleep(0.01) def pnext(self, timeout=100): ''' Returns a dictionary containing packet data, else None. @type timeout: Integer @param timeout: Timeout to wait for packet reception in usec @rtype: List @return: Returns None is timeout expires and no packet received. When a packet is received, a dictionary is returned with the keys bytes (string of packet bytes), validcrc (boolean if a vaid CRC), rssi (unscaled RSSI), and location (may be set to None). For backwards compatibility, keys for 0,1,2 are provided such that it can be treated as if a list is returned, in the form [ String: packet contents | Bool: Valid CRC | Int: Unscaled RSSI ] ''' if self.__stream_open == False: self.sniffer_on() packet = None; start = datetime.utcnow() while (packet is None and (start + timedelta(microseconds=timeout) > datetime.utcnow())): packet = self.handle.RF_rxpacket() rssi = self.handle.RF_getrssi() if packet is None: return None frame = packet[1:] if frame[-2:] == makeFCS(frame[:-2]): validcrc = True else: validcrc = False #Note that 0,1,2 indicies inserted twice for backwards compatibility. result = {0:frame, 1:validcrc, 2:rssi, 'bytes':frame, 'validcrc':validcrc, 'rssi':rssi, 'location':None} result['dbm'] = rssi - 45 #TODO tune specifically to the Tmote platform (does ext antenna need to different?) result['datetime'] = datetime.utcnow() return result def ping(self, da, panid, sa, channel=None, page=0): ''' Not yet implemented. @return: None @rtype: None ''' raise Exception('Not yet implemented') def jammer_on(self, channel=None, page=0): ''' Not yet implemented. @type channel: Integer @param channel: Sets the channel, optional @type page: Integer @param page: Sets the subghz page, not support on this device @rtype: None ''' self.capabilities.require(KBCapabilities.PHYJAM_REFLEX) self.handle.RF_promiscuity(1) self.handle.RF_autocrc(0) if channel != None: self.set_channel(channel, page) self.handle.CC_RFST_RX() self.handle.RF_reflexjam() def set_sync(self, sync=0xA70F): '''Set the register controlling the 802.15.4 PHY sync byte.''' self.capabilities.require(KBCapabilities.SET_SYNC) if (sync >> 16) > 0: raise Exception("Sync word (%x) must be 2-bytes or less." % sync) return self.handle.poke(CC2420_REG_SYNC, sync) def jammer_off(self, channel=None, page=0): ''' Not yet implemented. @return: None @rtype: None ''' #TODO implement raise Exception('Not yet implemented')

false

true

f7193bb525a1bcd7a4c3765147b5f3469bdd3591

1,555

py

Python

etools/apps/uptime/utils.py

Igelinmist/etools

26ae66a2ad005a7a173253bc9822a770a3115645

[ "BSD-3-Clause" ]

null

etools/apps/uptime/utils.py

Igelinmist/etools

26ae66a2ad005a7a173253bc9822a770a3115645

[ "BSD-3-Clause" ]

null

etools/apps/uptime/utils.py

Igelinmist/etools

26ae66a2ad005a7a173253bc9822a770a3115645

[ "BSD-3-Clause" ]

null

from datetime import timedelta, date def req_date(local_date): if isinstance(local_date, str): d, m, y = local_date.split('.') return '{0}-{1}-{2}'.format(y, m, d) elif isinstance(local_date, date): return local_date.strftime('%Y-%m-%d') else: return local_date def req_timedelta(arg): if isinstance(arg, timedelta): return arg else: if isinstance(arg, str): parts = arg.split(':') try: res = timedelta(hours=int(parts[0]), minutes=int(parts[1])) except ValueError: res = timedelta(0) return res else: return timedelta(0) def yesterday_local(): return (date.today() - timedelta(days=1)).strftime("%d.%m.%Y") def stat_timedelta_for_report(time_delta, round_to_hour=True): if time_delta: sec = time_delta.total_seconds() hours, remainder = divmod(sec, 3600) if round_to_hour: if remainder >= 1800: hours += 1 return str(int(hours)) minutes, remainder = divmod(remainder, 60) return "{0:,d}:{1:02}".format(int(hours), int(minutes)).replace(',',' ') else: return '-' def custom_redirect(url_name, *args, **kwargs): from django.core.urlresolvers import reverse from django.http import HttpResponseRedirect from django.utils.http import urlencode url = reverse(url_name, args=args) params = urlencode(kwargs) return HttpResponseRedirect(url + "?%s" % params)

28.796296

80

0.595498

from datetime import timedelta, date def req_date(local_date): if isinstance(local_date, str): d, m, y = local_date.split('.') return '{0}-{1}-{2}'.format(y, m, d) elif isinstance(local_date, date): return local_date.strftime('%Y-%m-%d') else: return local_date def req_timedelta(arg): if isinstance(arg, timedelta): return arg else: if isinstance(arg, str): parts = arg.split(':') try: res = timedelta(hours=int(parts[0]), minutes=int(parts[1])) except ValueError: res = timedelta(0) return res else: return timedelta(0) def yesterday_local(): return (date.today() - timedelta(days=1)).strftime("%d.%m.%Y") def stat_timedelta_for_report(time_delta, round_to_hour=True): if time_delta: sec = time_delta.total_seconds() hours, remainder = divmod(sec, 3600) if round_to_hour: if remainder >= 1800: hours += 1 return str(int(hours)) minutes, remainder = divmod(remainder, 60) return "{0:,d}:{1:02}".format(int(hours), int(minutes)).replace(',',' ') else: return '-' def custom_redirect(url_name, *args, **kwargs): from django.core.urlresolvers import reverse from django.http import HttpResponseRedirect from django.utils.http import urlencode url = reverse(url_name, args=args) params = urlencode(kwargs) return HttpResponseRedirect(url + "?%s" % params)

true

f7193dc596182608b60c2744dd8a96f97d37ed2c

11,229

py

Python

docs/conf.py

jeromedontdev/discord.py

42bab370a73440fa8af2380211ad92ccb6bf7f46

[ "MIT" ]

13

2020-12-16T06:13:11.000Z

2021-04-15T12:01:38.000Z

docs/conf.py

RootGC/discord.py

8bc489dba8b8c7ca9141e4e7f00a0e916a7c0269

[ "MIT" ]

1

2021-05-23T16:08:10.000Z

docs/conf.py

RootGC/discord.py

8bc489dba8b8c7ca9141e4e7f00a0e916a7c0269

[ "MIT" ]

6

2020-12-16T00:01:24.000Z

2021-02-05T12:32:54.000Z

# # discord.py documentation build configuration file, created by # sphinx-quickstart on Fri Aug 21 05:43:30 2015. # # This file is execfile()d with the current directory set to its # containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys import os import re # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.insert(0, os.path.abspath('..')) sys.path.append(os.path.abspath('extensions')) # -- General configuration ------------------------------------------------ # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'builder', 'sphinx.ext.autodoc', 'sphinx.ext.extlinks', 'sphinx.ext.intersphinx', 'sphinx.ext.napoleon', 'sphinxcontrib_trio', 'details', 'exception_hierarchy', 'attributetable', 'resourcelinks', 'nitpick_file_ignorer', ] autodoc_member_order = 'bysource' autodoc_typehints = 'none' extlinks = { 'issue': ('https://github.com/Rapptz/discord.py/issues/%s', 'GH-'), } # Links used for cross-referencing stuff in other documentation intersphinx_mapping = { 'py': ('https://docs.python.org/3', None), 'aio': ('https://docs.aiohttp.org/en/stable/', None), 'req': ('https://docs.python-requests.org/en/latest/', None) } rst_prolog = """ .. |coro| replace:: This function is a |coroutine_link|_. .. |maybecoro| replace:: This function *could be a* |coroutine_link|_. .. |coroutine_link| replace:: *coroutine* .. _coroutine_link: https://docs.python.org/3/library/asyncio-task.html#coroutine """ # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = 'discord.py' copyright = '2015-present, Rapptz' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '' with open('../discord/__init__.py') as f: version = re.search(r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]', f.read(), re.MULTILINE).group(1) # The full version, including alpha/beta/rc tags. release = version # This assumes a tag is available for final releases branch = 'master' if version.endswith('a') else 'v' + version # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None locale_dirs = ['locale/'] gettext_compact = False # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'friendly' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # Nitpicky mode options nitpick_ignore_files = [ "migrating_to_async", "migrating", "whats_new", ] # -- Options for HTML output ---------------------------------------------- html_experimental_html5_writer = True # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'basic' html_context = { 'discord_invite': 'https://discord.gg/r3sSKJJ', 'discord_extensions': [ ('discord.ext.commands', 'ext/commands'), ('discord.ext.tasks', 'ext/tasks'), ], } resource_links = { 'discord': 'https://discord.gg/r3sSKJJ', 'issues': 'https://github.com/Rapptz/discord.py/issues', 'discussions': 'https://github.com/Rapptz/discord.py/discussions', 'examples': f'https://github.com/Rapptz/discord.py/tree/{branch}/examples', } # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # html_theme_options = { # } # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = './images/discord_py_logo.ico' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' #html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # Now only 'ja' uses this config value #html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. html_search_scorer = '_static/scorer.js' html_js_files = [ 'custom.js', 'settings.js', 'copy.js', 'sidebar.js' ] # Output file base name for HTML help builder. htmlhelp_basename = 'discord.pydoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', # Latex figure (float) alignment #'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ('index', 'discord.py.tex', 'discord.py Documentation', 'Rapptz', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'discord.py', 'discord.py Documentation', ['Rapptz'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'discord.py', 'discord.py Documentation', 'Rapptz', 'discord.py', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False def setup(app): if app.config.language == 'ja': app.config.intersphinx_mapping['py'] = ('https://docs.python.org/ja/3', None) app.config.html_context['discord_invite'] = 'https://discord.gg/nXzj3dg' app.config.resource_links['discord'] = 'https://discord.gg/nXzj3dg'

31.191667

99

0.708612

import sys import os import re sys.path.insert(0, os.path.abspath('..')) sys.path.append(os.path.abspath('extensions')) extensions = [ 'builder', 'sphinx.ext.autodoc', 'sphinx.ext.extlinks', 'sphinx.ext.intersphinx', 'sphinx.ext.napoleon', 'sphinxcontrib_trio', 'details', 'exception_hierarchy', 'attributetable', 'resourcelinks', 'nitpick_file_ignorer', ] autodoc_member_order = 'bysource' autodoc_typehints = 'none' extlinks = { 'issue': ('https://github.com/Rapptz/discord.py/issues/%s', 'GH-'), } intersphinx_mapping = { 'py': ('https://docs.python.org/3', None), 'aio': ('https://docs.aiohttp.org/en/stable/', None), 'req': ('https://docs.python-requests.org/en/latest/', None) } rst_prolog = """ .. |coro| replace:: This function is a |coroutine_link|_. .. |maybecoro| replace:: This function *could be a* |coroutine_link|_. .. |coroutine_link| replace:: *coroutine* .. _coroutine_link: https://docs.python.org/3/library/asyncio-task.html#coroutine """ templates_path = ['_templates'] source_suffix = '.rst' master_doc = 'index' project = 'discord.py' copyright = '2015-present, Rapptz' # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '' with open('../discord/__init__.py') as f: version = re.search(r'^__version__\s*=\s*[\'"]([^\'"]*)[\'"]', f.read(), re.MULTILINE).group(1) # The full version, including alpha/beta/rc tags. release = version # This assumes a tag is available for final releases branch = 'master' if version.endswith('a') else 'v' + version # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = None locale_dirs = ['locale/'] gettext_compact = False # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = ['_build'] # The reST default role (used for this markup: `text`) to use for all # documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. #show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'friendly' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # If true, keep warnings as "system message" paragraphs in the built documents. #keep_warnings = False # Nitpicky mode options nitpick_ignore_files = [ "migrating_to_async", "migrating", "whats_new", ] # -- Options for HTML output ---------------------------------------------- html_experimental_html5_writer = True # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'basic' html_context = { 'discord_invite': 'https://discord.gg/r3sSKJJ', 'discord_extensions': [ ('discord.ext.commands', 'ext/commands'), ('discord.ext.tasks', 'ext/tasks'), ], } resource_links = { 'discord': 'https://discord.gg/r3sSKJJ', 'issues': 'https://github.com/Rapptz/discord.py/issues', 'discussions': 'https://github.com/Rapptz/discord.py/discussions', 'examples': f'https://github.com/Rapptz/discord.py/tree/{branch}/examples', } # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. # html_theme_options = { # } # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # "<project> v<release> documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. #html_logo = None # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. html_favicon = './images/discord_py_logo.ico' # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. #html_extra_path = [] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. #html_domain_indices = True # If false, no index is generated. #html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. #html_show_sourcelink = True # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a <link> tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Language to be used for generating the HTML full-text search index. # Sphinx supports the following languages: # 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' # 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' #html_search_language = 'en' # A dictionary with options for the search language support, empty by default. # Now only 'ja' uses this config value #html_search_options = {'type': 'default'} # The name of a javascript file (relative to the configuration directory) that # implements a search results scorer. If empty, the default will be used. html_search_scorer = '_static/scorer.js' html_js_files = [ 'custom.js', 'settings.js', 'copy.js', 'sidebar.js' ] # Output file base name for HTML help builder. htmlhelp_basename = 'discord.pydoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { # The paper size ('letterpaper' or 'a4paper'). #'papersize': 'letterpaper', # The font size ('10pt', '11pt' or '12pt'). #'pointsize': '10pt', # Additional stuff for the LaTeX preamble. #'preamble': '', # Latex figure (float) alignment #'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ ('index', 'discord.py.tex', 'discord.py Documentation', 'Rapptz', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output --------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'discord.py', 'discord.py Documentation', ['Rapptz'], 1) ] # If true, show URL addresses after external links. #man_show_urls = False # -- Options for Texinfo output ------------------------------------------- # Grouping the document tree into Texinfo files. List of tuples # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ ('index', 'discord.py', 'discord.py Documentation', 'Rapptz', 'discord.py', 'One line description of project.', 'Miscellaneous'), ] # Documents to append as an appendix to all manuals. #texinfo_appendices = [] # If false, no module index is generated. #texinfo_domain_indices = True # How to display URL addresses: 'footnote', 'no', or 'inline'. #texinfo_show_urls = 'footnote' # If true, do not generate a @detailmenu in the "Top" node's menu. #texinfo_no_detailmenu = False def setup(app): if app.config.language == 'ja': app.config.intersphinx_mapping['py'] = ('https://docs.python.org/ja/3', None) app.config.html_context['discord_invite'] = 'https://discord.gg/nXzj3dg' app.config.resource_links['discord'] = 'https://discord.gg/nXzj3dg'

true

f7193e60bdbc11912523b4e6e6233bec11f0c404

11,846

py

Python

synapse/http/proxyagent.py

User-green/synapse

173ddbbe0b220bb28e67575079e1f775d73f967f

[ "Apache-2.0" ]

null

synapse/http/proxyagent.py

User-green/synapse

173ddbbe0b220bb28e67575079e1f775d73f967f

[ "Apache-2.0" ]

null

synapse/http/proxyagent.py

User-green/synapse

173ddbbe0b220bb28e67575079e1f775d73f967f

[ "Apache-2.0" ]

null

# Copyright 2019 The Matrix.org Foundation C.I.C. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import base64 import logging import re from typing import Optional, Tuple from urllib.request import getproxies_environment, proxy_bypass_environment import attr from zope.interface import implementer from twisted.internet import defer from twisted.internet.endpoints import HostnameEndpoint, wrapClientTLS from twisted.python.failure import Failure from twisted.web.client import URI, BrowserLikePolicyForHTTPS, _AgentBase from twisted.web.error import SchemeNotSupported from twisted.web.http_headers import Headers from twisted.web.iweb import IAgent, IPolicyForHTTPS from synapse.http.connectproxyclient import HTTPConnectProxyEndpoint logger = logging.getLogger(__name__) _VALID_URI = re.compile(br"\A[\x21-\x7e]+\Z") @attr.s class ProxyCredentials: username_password = attr.ib(type=bytes) def as_proxy_authorization_value(self) -> bytes: """ Return the value for a Proxy-Authorization header (i.e. 'Basic abdef=='). Returns: A transformation of the authentication string the encoded value for a Proxy-Authorization header. """ # Encode as base64 and prepend the authorization type return b"Basic " + base64.encodebytes(self.username_password) @implementer(IAgent) class ProxyAgent(_AgentBase): """An Agent implementation which will use an HTTP proxy if one was requested Args: reactor: twisted reactor to place outgoing connections. proxy_reactor: twisted reactor to use for connections to the proxy server reactor might have some blacklisting applied (i.e. for DNS queries), but we need unblocked access to the proxy. contextFactory (IPolicyForHTTPS): A factory for TLS contexts, to control the verification parameters of OpenSSL. The default is to use a `BrowserLikePolicyForHTTPS`, so unless you have special requirements you can leave this as-is. connectTimeout (Optional[float]): The amount of time that this Agent will wait for the peer to accept a connection, in seconds. If 'None', HostnameEndpoint's default (30s) will be used. This is used for connections to both proxies and destination servers. bindAddress (bytes): The local address for client sockets to bind to. pool (HTTPConnectionPool|None): connection pool to be used. If None, a non-persistent pool instance will be created. use_proxy (bool): Whether proxy settings should be discovered and used from conventional environment variables. """ def __init__( self, reactor, proxy_reactor=None, contextFactory: Optional[IPolicyForHTTPS] = None, connectTimeout=None, bindAddress=None, pool=None, use_proxy=False, ): contextFactory = contextFactory or BrowserLikePolicyForHTTPS() _AgentBase.__init__(self, reactor, pool) if proxy_reactor is None: self.proxy_reactor = reactor else: self.proxy_reactor = proxy_reactor self._endpoint_kwargs = {} if connectTimeout is not None: self._endpoint_kwargs["timeout"] = connectTimeout if bindAddress is not None: self._endpoint_kwargs["bindAddress"] = bindAddress http_proxy = None https_proxy = None no_proxy = None if use_proxy: proxies = getproxies_environment() http_proxy = proxies["http"].encode() if "http" in proxies else None https_proxy = proxies["https"].encode() if "https" in proxies else None no_proxy = proxies["no"] if "no" in proxies else None # Parse credentials from http and https proxy connection string if present self.http_proxy_creds, http_proxy = parse_username_password(http_proxy) self.https_proxy_creds, https_proxy = parse_username_password(https_proxy) self.http_proxy_endpoint = _http_proxy_endpoint( http_proxy, self.proxy_reactor, **self._endpoint_kwargs ) self.https_proxy_endpoint = _http_proxy_endpoint( https_proxy, self.proxy_reactor, **self._endpoint_kwargs ) self.no_proxy = no_proxy self._policy_for_https = contextFactory self._reactor = reactor def request(self, method, uri, headers=None, bodyProducer=None): """ Issue a request to the server indicated by the given uri. Supports `http` and `https` schemes. An existing connection from the connection pool may be used or a new one may be created. See also: twisted.web.iweb.IAgent.request Args: method (bytes): The request method to use, such as `GET`, `POST`, etc uri (bytes): The location of the resource to request. headers (Headers|None): Extra headers to send with the request bodyProducer (IBodyProducer|None): An object which can generate bytes to make up the body of this request (for example, the properly encoded contents of a file for a file upload). Or, None if the request is to have no body. Returns: Deferred[IResponse]: completes when the header of the response has been received (regardless of the response status code). Can fail with: SchemeNotSupported: if the uri is not http or https twisted.internet.error.TimeoutError if the server we are connecting to (proxy or destination) does not accept a connection before connectTimeout. ... other things too. """ uri = uri.strip() if not _VALID_URI.match(uri): raise ValueError(f"Invalid URI {uri!r}") parsed_uri = URI.fromBytes(uri) pool_key = (parsed_uri.scheme, parsed_uri.host, parsed_uri.port) request_path = parsed_uri.originForm should_skip_proxy = False if self.no_proxy is not None: should_skip_proxy = proxy_bypass_environment( parsed_uri.host.decode(), proxies={"no": self.no_proxy}, ) if ( parsed_uri.scheme == b"http" and self.http_proxy_endpoint and not should_skip_proxy ): # Determine whether we need to set Proxy-Authorization headers if self.http_proxy_creds: # Set a Proxy-Authorization header if headers is None: headers = Headers() headers.addRawHeader( b"Proxy-Authorization", self.http_proxy_creds.as_proxy_authorization_value(), ) # Cache *all* connections under the same key, since we are only # connecting to a single destination, the proxy: pool_key = ("http-proxy", self.http_proxy_endpoint) endpoint = self.http_proxy_endpoint request_path = uri elif ( parsed_uri.scheme == b"https" and self.https_proxy_endpoint and not should_skip_proxy ): connect_headers = Headers() # Determine whether we need to set Proxy-Authorization headers if self.https_proxy_creds: # Set a Proxy-Authorization header connect_headers.addRawHeader( b"Proxy-Authorization", self.https_proxy_creds.as_proxy_authorization_value(), ) endpoint = HTTPConnectProxyEndpoint( self.proxy_reactor, self.https_proxy_endpoint, parsed_uri.host, parsed_uri.port, headers=connect_headers, ) else: # not using a proxy endpoint = HostnameEndpoint( self._reactor, parsed_uri.host, parsed_uri.port, **self._endpoint_kwargs ) logger.debug("Requesting %s via %s", uri, endpoint) if parsed_uri.scheme == b"https": tls_connection_creator = self._policy_for_https.creatorForNetloc( parsed_uri.host, parsed_uri.port ) endpoint = wrapClientTLS(tls_connection_creator, endpoint) elif parsed_uri.scheme == b"http": pass else: return defer.fail( Failure( SchemeNotSupported("Unsupported scheme: %r" % (parsed_uri.scheme,)) ) ) return self._requestWithEndpoint( pool_key, endpoint, method, parsed_uri, headers, bodyProducer, request_path ) def _http_proxy_endpoint(proxy: Optional[bytes], reactor, **kwargs): """Parses an http proxy setting and returns an endpoint for the proxy Args: proxy: the proxy setting in the form: [<username>:<password>@]<host>[:<port>] Note that compared to other apps, this function currently lacks support for specifying a protocol schema (i.e. protocol://...). reactor: reactor to be used to connect to the proxy kwargs: other args to be passed to HostnameEndpoint Returns: interfaces.IStreamClientEndpoint|None: endpoint to use to connect to the proxy, or None """ if proxy is None: return None # Parse the connection string host, port = parse_host_port(proxy, default_port=1080) return HostnameEndpoint(reactor, host, port, **kwargs) def parse_username_password(proxy: bytes) -> Tuple[Optional[ProxyCredentials], bytes]: """ Parses the username and password from a proxy declaration e.g username:password@hostname:port. Args: proxy: The proxy connection string. Returns An instance of ProxyCredentials and the proxy connection string with any credentials stripped, i.e u:p@host:port -> host:port. If no credentials were found, the ProxyCredentials instance is replaced with None. """ if proxy and b"@" in proxy: # We use rsplit here as the password could contain an @ character credentials, proxy_without_credentials = proxy.rsplit(b"@", 1) return ProxyCredentials(credentials), proxy_without_credentials return None, proxy def parse_host_port(hostport: bytes, default_port: int = None) -> Tuple[bytes, int]: """ Parse the hostname and port from a proxy connection byte string. Args: hostport: The proxy connection string. Must be in the form 'host[:port]'. default_port: The default port to return if one is not found in `hostport`. Returns: A tuple containing the hostname and port. Uses `default_port` if one was not found. """ if b":" in hostport: host, port = hostport.rsplit(b":", 1) try: port = int(port) return host, port except ValueError: # the thing after the : wasn't a valid port; presumably this is an # IPv6 address. pass return hostport, default_port

36.674923

92

0.639878

import base64 import logging import re from typing import Optional, Tuple from urllib.request import getproxies_environment, proxy_bypass_environment import attr from zope.interface import implementer from twisted.internet import defer from twisted.internet.endpoints import HostnameEndpoint, wrapClientTLS from twisted.python.failure import Failure from twisted.web.client import URI, BrowserLikePolicyForHTTPS, _AgentBase from twisted.web.error import SchemeNotSupported from twisted.web.http_headers import Headers from twisted.web.iweb import IAgent, IPolicyForHTTPS from synapse.http.connectproxyclient import HTTPConnectProxyEndpoint logger = logging.getLogger(__name__) _VALID_URI = re.compile(br"\A[\x21-\x7e]+\Z") @attr.s class ProxyCredentials: username_password = attr.ib(type=bytes) def as_proxy_authorization_value(self) -> bytes: return b"Basic " + base64.encodebytes(self.username_password) @implementer(IAgent) class ProxyAgent(_AgentBase): def __init__( self, reactor, proxy_reactor=None, contextFactory: Optional[IPolicyForHTTPS] = None, connectTimeout=None, bindAddress=None, pool=None, use_proxy=False, ): contextFactory = contextFactory or BrowserLikePolicyForHTTPS() _AgentBase.__init__(self, reactor, pool) if proxy_reactor is None: self.proxy_reactor = reactor else: self.proxy_reactor = proxy_reactor self._endpoint_kwargs = {} if connectTimeout is not None: self._endpoint_kwargs["timeout"] = connectTimeout if bindAddress is not None: self._endpoint_kwargs["bindAddress"] = bindAddress http_proxy = None https_proxy = None no_proxy = None if use_proxy: proxies = getproxies_environment() http_proxy = proxies["http"].encode() if "http" in proxies else None https_proxy = proxies["https"].encode() if "https" in proxies else None no_proxy = proxies["no"] if "no" in proxies else None self.http_proxy_creds, http_proxy = parse_username_password(http_proxy) self.https_proxy_creds, https_proxy = parse_username_password(https_proxy) self.http_proxy_endpoint = _http_proxy_endpoint( http_proxy, self.proxy_reactor, **self._endpoint_kwargs ) self.https_proxy_endpoint = _http_proxy_endpoint( https_proxy, self.proxy_reactor, **self._endpoint_kwargs ) self.no_proxy = no_proxy self._policy_for_https = contextFactory self._reactor = reactor def request(self, method, uri, headers=None, bodyProducer=None): uri = uri.strip() if not _VALID_URI.match(uri): raise ValueError(f"Invalid URI {uri!r}") parsed_uri = URI.fromBytes(uri) pool_key = (parsed_uri.scheme, parsed_uri.host, parsed_uri.port) request_path = parsed_uri.originForm should_skip_proxy = False if self.no_proxy is not None: should_skip_proxy = proxy_bypass_environment( parsed_uri.host.decode(), proxies={"no": self.no_proxy}, ) if ( parsed_uri.scheme == b"http" and self.http_proxy_endpoint and not should_skip_proxy ): if self.http_proxy_creds: if headers is None: headers = Headers() headers.addRawHeader( b"Proxy-Authorization", self.http_proxy_creds.as_proxy_authorization_value(), ) pool_key = ("http-proxy", self.http_proxy_endpoint) endpoint = self.http_proxy_endpoint request_path = uri elif ( parsed_uri.scheme == b"https" and self.https_proxy_endpoint and not should_skip_proxy ): connect_headers = Headers() if self.https_proxy_creds: connect_headers.addRawHeader( b"Proxy-Authorization", self.https_proxy_creds.as_proxy_authorization_value(), ) endpoint = HTTPConnectProxyEndpoint( self.proxy_reactor, self.https_proxy_endpoint, parsed_uri.host, parsed_uri.port, headers=connect_headers, ) else: endpoint = HostnameEndpoint( self._reactor, parsed_uri.host, parsed_uri.port, **self._endpoint_kwargs ) logger.debug("Requesting %s via %s", uri, endpoint) if parsed_uri.scheme == b"https": tls_connection_creator = self._policy_for_https.creatorForNetloc( parsed_uri.host, parsed_uri.port ) endpoint = wrapClientTLS(tls_connection_creator, endpoint) elif parsed_uri.scheme == b"http": pass else: return defer.fail( Failure( SchemeNotSupported("Unsupported scheme: %r" % (parsed_uri.scheme,)) ) ) return self._requestWithEndpoint( pool_key, endpoint, method, parsed_uri, headers, bodyProducer, request_path ) def _http_proxy_endpoint(proxy: Optional[bytes], reactor, **kwargs): if proxy is None: return None host, port = parse_host_port(proxy, default_port=1080) return HostnameEndpoint(reactor, host, port, **kwargs) def parse_username_password(proxy: bytes) -> Tuple[Optional[ProxyCredentials], bytes]: if proxy and b"@" in proxy: credentials, proxy_without_credentials = proxy.rsplit(b"@", 1) return ProxyCredentials(credentials), proxy_without_credentials return None, proxy def parse_host_port(hostport: bytes, default_port: int = None) -> Tuple[bytes, int]: if b":" in hostport: host, port = hostport.rsplit(b":", 1) try: port = int(port) return host, port except ValueError: # IPv6 address. pass return hostport, default_port

true

f7193e638c0b7630f3bb08df8302e36c5888e4d8

889

py

Python

tensorflow_mri/python/layers/__init__.py

mrphys/tensorflow-mri

46a8929aec4180aba4961f902897e02592f25da6

[ "Apache-2.0" ]

3

2021-07-28T17:22:26.000Z

2022-03-29T15:17:26.000Z

tensorflow_mri/python/layers/__init__.py

mrphys/tensorflow-mri

46a8929aec4180aba4961f902897e02592f25da6

[ "Apache-2.0" ]

1

2021-07-23T01:37:11.000Z

tensorflow_mri/python/layers/__init__.py

mrphys/tensorflow-mri

46a8929aec4180aba4961f902897e02592f25da6

[ "Apache-2.0" ]

null

# Copyright 2021 University College London. 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. # ============================================================================== """Neural network layers.""" from tensorflow_mri.python.layers.conv_blocks import * from tensorflow_mri.python.layers.conv_endec import * from tensorflow_mri.python.layers.preproc_layers import *

44.45

80

0.709786

from tensorflow_mri.python.layers.conv_blocks import * from tensorflow_mri.python.layers.conv_endec import * from tensorflow_mri.python.layers.preproc_layers import *

true

f7193e94de77b2cad9feb7c3c07ac84c618b271a

13,089

py

Python

train.py

fab464654/SSD_on_ActiveVisionDataset

1bc6f0745241d0b45c3f257c6fb09ea0435c993e

[ "MIT" ]

null

train.py

fab464654/SSD_on_ActiveVisionDataset

1bc6f0745241d0b45c3f257c6fb09ea0435c993e

[ "MIT" ]

null

train.py

fab464654/SSD_on_ActiveVisionDataset

1bc6f0745241d0b45c3f257c6fb09ea0435c993e

[ "MIT" ]

null

import time import torch.backends.cudnn as cudnn import torch.optim import torch.utils.data from model import SSD300, MultiBoxLoss from datasets import PascalVOCDataset from utils import * # Data parameters data_folder = 'google_drive/MyDrive/ColabNotebooks/Project/GT' # folder with data files keep_difficult = True # use objects considered difficult to detect? # Model parameters # Not too many here since the SSD300 has a very specific structure n_classes = len(label_map) # number of different types of objects device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Learning parameters checkpoint = "google_drive/MyDrive/checkpointsIeri/checkpoint_ssd300.pth.tar" # path to model checkpoint, None if none batch_size = 9 # batch size iterations = 120000 # number of iterations to train workers = 4 # number of workers for loading data in the DataLoader print_freq = 5 # print training status every __ batches lr = 5e-4 # learning rate decay_lr_at = [80000, 100000] # decay learning rate after these many iterations decay_lr_to = 0.1 # decay learning rate to this fraction of the existing learning rate momentum = 0.9 # momentum weight_decay = 5e-4 # weight decay grad_clip = None # clip if gradients are exploding, which may happen at larger batch sizes (sometimes at 32) - you will recognize it by a sorting error in the MuliBox loss calculation cudnn.benchmark = True def main(): """ Training. """ global start_epoch, label_map, epoch, checkpoint, decay_lr_at # Initialize model or load checkpoint if checkpoint is None: start_epoch = 0 model = SSD300(n_classes=n_classes) # Initialize the optimizer, with twice the default learning rate for biases, as in the original Caffe repo biases = list() not_biases = list() for param_name, param in model.named_parameters(): if param.requires_grad: if param_name.endswith('.bias'): biases.append(param) else: not_biases.append(param) optimizer = torch.optim.SGD(params=[{'params': biases, 'lr': 2 * lr}, {'params': not_biases}], lr=lr, momentum=momentum, weight_decay=weight_decay) else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 print('\nLoaded checkpoint from epoch %d.\n' % start_epoch) model = checkpoint['model'] optimizer = checkpoint['optimizer'] # Move to default device model = model.to(device) criterion = MultiBoxLoss(priors_cxcy=model.priors_cxcy).to(device) #import active_vision_dataset_processing.data_loading import transforms, active_vision_dataset #Include all instances pick_trans = transforms.PickInstances(range(34)) TRAIN_PATH = "./google_drive/MyDrive/ColabNotebooks/Project/trainDataset" train_dataset = active_vision_dataset.AVD(root=TRAIN_PATH, train=True, target_transform=pick_trans, scene_list=['Home_001_1', 'Home_002_1', 'Home_003_1', 'Home_004_1', 'Home_005_1', 'Home_006_1', 'Home_007_1', 'Home_008_1', 'Home_014_1', 'Home_011_1', 'Home_010_1', 'Office_001_1'], fraction_of_no_box=-1) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=active_vision_dataset.collate ) """ #I TRY TO USE THE DEFAULT DATASET LOADER:::::::::::::: # Custom dataloaders train_dataset = PascalVOCDataset(data_folder, split='train', keep_difficult=keep_difficult) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=train_dataset.collate_fn, num_workers=workers, pin_memory=True) # note that we're passing the collate function here """ # Calculate total number of epochs to train and the epochs to decay learning rate at (i.e. convert iterations to epochs) # To convert iterations to epochs, divide iterations by the number of iterations per epoch # The paper trains for 120,000 iterations with a batch size of 32, decays after 80,000 and 100,000 iterations epochs = iterations // (len(train_dataset) // 32) decay_lr_at = [it // (len(train_dataset) // 32) for it in decay_lr_at] # Epochs for epoch in range(start_epoch, epochs): # Decay learning rate at particular epochs if epoch in decay_lr_at: adjust_learning_rate(optimizer, decay_lr_to) # One epoch's training train(train_loader=train_loader, model=model, criterion=criterion, optimizer=optimizer, epoch=epoch) # Save checkpoint save_checkpoint(epoch, model, optimizer) def train(train_loader, model, criterion, optimizer, epoch): """ One epoch's training. :param train_loader: DataLoader for training data :param model: model :param criterion: MultiBox loss :param optimizer: optimizer :param epoch: epoch number """ model.train() # training mode enables dropout batch_time = AverageMeter() # forward prop. + back prop. time data_time = AverageMeter() # data loading time losses = AverageMeter() # loss start = time.time() import numpy as np # Batches for i, (images, labels) in enumerate(train_loader): #CHECK / REMOVE THIS CODE! data_time.update(time.time() - start) #print(len(images)) #print(labels) # Move to default device data = images a = np.asarray(data) #print(a.shape) #a = np.squeeze(a, axis=1) # shape should now be (L, 224, 224, 3) #image = torch.from_numpy(a) #image = image.permute(0,3,1,2) #print(image.shape) #Pre-processing: from torchvision import transforms as transf preprocess = transf.Compose([ transf.ToPILImage(), transf.Resize(300), transf.CenterCrop(300), transf.ToTensor(), transf.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) for j in range(batch_size): if j == 0: input_tensor = preprocess(images[j]) input_tensor = input_tensor.unsqueeze(0) input_batch = input_tensor else: input_tensor = preprocess(images[j]) #print(input_tensor) input_tensor = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model #print(input_tensor.shape) input_batch = torch.cat((input_batch, input_tensor), 0) #print("shape images: ",input_batch.shape) # In the Active Vision Dataset we have this formatting: # [xmin ymin xmax ymax instance_id difficulty] """ From the Tutorial: Since the number of objects in any given image can vary, we can't use a fixed size tensor for storing the bounding boxes for the entire batch of N images. Therefore, ground truth bounding boxes fed to the model must be a list of length N, where each element of the list is a Float tensor of dimensions N_o, 4, where N_o is the number of objects present in that particular image. Therefore, ground truth labels fed to the model must be a list of length N, where each element of the list is a Long tensor of dimensions N_o, where N_o is the number of objects present in that particular image. """ #Prints to test #print(j) box_id_diff = [b for b in labels[j][0]] box = [l[0:4] for l in box_id_diff] #print('before:',box) #To check #Boundary coordinates as requested for k in range(len(box)): box[k][0] = box[k][0]/1920.0 box[k][2] = box[k][2]/1920.0 box[k][1] = box[k][1]/1080.0 box[k][3] = box[k][3]/1080.0 #print('after:',box) #To check box_tensor = torch.FloatTensor(box).to(device) #Done with the parameter in AVD method """ #Check if there are objects in the images if j == 0: start = True if len(box_tensor) > 0: if start == True: box_list = box_tensor start = False elif start == False: box_list = [box_list, box_tensor] #box_list = torch.cat((box_list,box_tensor),0) else: start = True """ #print(box_tensor) #To check if j == 0: box_list = [box_tensor] else: box_list.append(box_tensor) label = [l[4] for l in box_id_diff] label_tensor = torch.LongTensor(label).to(device) if j == 0: label_list = [label_tensor] else: label_list.append(label_tensor) #print(box_id_diff[0][0:4]) """ if len(box_id_diff.size())-1 != 0: if j == 0: box = box_id_diff[0][0:4] print("asad:",box) #box = box.unsqueeze(0) boxes = box else: box = [l[0:4] for l in box_id_diff] #box = box.unsqueeze(0) # create a mini-batch as expected by the model #print(input_tensor.shape) boxes = torch.cat((boxes, box), 0) print("boxes:", boxes) """ #box = torch.split(box_id_diff, 2) #print(box) """ if not labels[j][0]: labels = [] print("coasc") else: labels = [l.to(device) for l in torch.tensor(labels[j][0][4])] """ #print("list of boxes:",box_list) #print("list of labels:", label_list) images = input_batch.to(device) # (batch_size (N), 3, 300, 300) #print(images.shape) boxes = box_list labels = label_list # Forward prop. predicted_locs, predicted_scores = model(images) # (N, 8732, 4), (N, 8732, n_classes) #Prints to check the dimensions #print(predicted_locs.shape) #correct #print(predicted_scores.shape) #correct # Loss loss = criterion(predicted_locs, predicted_scores, boxes, labels) # scalar # Backward prop. optimizer.zero_grad() loss.backward() # Clip gradients, if necessary if grad_clip is not None: clip_gradient(optimizer, grad_clip) # Update model optimizer.step() losses.update(loss.item(), images.size(0)) batch_time.update(time.time() - start) start = time.time() # Print status if i % print_freq == 0: print('Epoch: [{0}][{1}/{2}]\t' 'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(epoch, i, len(train_loader), loss=losses)) """ print('Epoch: [{0}][{1}/{2}]\t' 'Batch Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' 'Data Time {data_time.val:.3f} ({data_time.avg:.3f})\t' 'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(epoch, i, len(train_loader), batch_time=batch_time, data_time=data_time, loss=losses)) """ del predicted_locs, predicted_scores, images, boxes, labels # free some memory since their histories may be stored if __name__ == '__main__': main()

38.049419

184

0.537627

import time import torch.backends.cudnn as cudnn import torch.optim import torch.utils.data from model import SSD300, MultiBoxLoss from datasets import PascalVOCDataset from utils import * data_folder = 'google_drive/MyDrive/ColabNotebooks/Project/GT' keep_difficult = True n_classes = len(label_map) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") checkpoint = "google_drive/MyDrive/checkpointsIeri/checkpoint_ssd300.pth.tar" batch_size = 9 iterations = 120000 workers = 4 print_freq = 5 lr = 5e-4 decay_lr_at = [80000, 100000] decay_lr_to = 0.1 momentum = 0.9 weight_decay = 5e-4 grad_clip = None cudnn.benchmark = True def main(): global start_epoch, label_map, epoch, checkpoint, decay_lr_at if checkpoint is None: start_epoch = 0 model = SSD300(n_classes=n_classes) biases = list() not_biases = list() for param_name, param in model.named_parameters(): if param.requires_grad: if param_name.endswith('.bias'): biases.append(param) else: not_biases.append(param) optimizer = torch.optim.SGD(params=[{'params': biases, 'lr': 2 * lr}, {'params': not_biases}], lr=lr, momentum=momentum, weight_decay=weight_decay) else: checkpoint = torch.load(checkpoint) start_epoch = checkpoint['epoch'] + 1 print('\nLoaded checkpoint from epoch %d.\n' % start_epoch) model = checkpoint['model'] optimizer = checkpoint['optimizer'] model = model.to(device) criterion = MultiBoxLoss(priors_cxcy=model.priors_cxcy).to(device) import transforms, active_vision_dataset pick_trans = transforms.PickInstances(range(34)) TRAIN_PATH = "./google_drive/MyDrive/ColabNotebooks/Project/trainDataset" train_dataset = active_vision_dataset.AVD(root=TRAIN_PATH, train=True, target_transform=pick_trans, scene_list=['Home_001_1', 'Home_002_1', 'Home_003_1', 'Home_004_1', 'Home_005_1', 'Home_006_1', 'Home_007_1', 'Home_008_1', 'Home_014_1', 'Home_011_1', 'Home_010_1', 'Office_001_1'], fraction_of_no_box=-1) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=active_vision_dataset.collate ) epochs = iterations // (len(train_dataset) // 32) decay_lr_at = [it // (len(train_dataset) // 32) for it in decay_lr_at] for epoch in range(start_epoch, epochs): if epoch in decay_lr_at: adjust_learning_rate(optimizer, decay_lr_to) train(train_loader=train_loader, model=model, criterion=criterion, optimizer=optimizer, epoch=epoch) # Save checkpoint save_checkpoint(epoch, model, optimizer) def train(train_loader, model, criterion, optimizer, epoch): model.train() # training mode enables dropout batch_time = AverageMeter() # forward prop. + back prop. time data_time = AverageMeter() # data loading time losses = AverageMeter() # loss start = time.time() import numpy as np # Batches for i, (images, labels) in enumerate(train_loader): #CHECK / REMOVE THIS CODE! data_time.update(time.time() - start) #print(len(images)) #print(labels) # Move to default device data = images a = np.asarray(data) #print(a.shape) #a = np.squeeze(a, axis=1) # shape should now be (L, 224, 224, 3) #image = torch.from_numpy(a) #image = image.permute(0,3,1,2) #print(image.shape) #Pre-processing: from torchvision import transforms as transf preprocess = transf.Compose([ transf.ToPILImage(), transf.Resize(300), transf.CenterCrop(300), transf.ToTensor(), transf.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), ]) for j in range(batch_size): if j == 0: input_tensor = preprocess(images[j]) input_tensor = input_tensor.unsqueeze(0) input_batch = input_tensor else: input_tensor = preprocess(images[j]) #print(input_tensor) input_tensor = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model #print(input_tensor.shape) input_batch = torch.cat((input_batch, input_tensor), 0) #print("shape images: ",input_batch.shape) # In the Active Vision Dataset we have this formatting: # [xmin ymin xmax ymax instance_id difficulty] #Prints to test #print(j) box_id_diff = [b for b in labels[j][0]] box = [l[0:4] for l in box_id_diff] #print('before:',box) #To check #Boundary coordinates as requested for k in range(len(box)): box[k][0] = box[k][0]/1920.0 box[k][2] = box[k][2]/1920.0 box[k][1] = box[k][1]/1080.0 box[k][3] = box[k][3]/1080.0 #print('after:',box) #To check box_tensor = torch.FloatTensor(box).to(device) #Done with the parameter in AVD method #print(box_tensor) #To check if j == 0: box_list = [box_tensor] else: box_list.append(box_tensor) label = [l[4] for l in box_id_diff] label_tensor = torch.LongTensor(label).to(device) if j == 0: label_list = [label_tensor] else: label_list.append(label_tensor) #print(box_id_diff[0][0:4]) #box = torch.split(box_id_diff, 2) #print(box) #print("list of boxes:",box_list) #print("list of labels:", label_list) images = input_batch.to(device) # (batch_size (N), 3, 300, 300) #print(images.shape) boxes = box_list labels = label_list # Forward prop. predicted_locs, predicted_scores = model(images) # (N, 8732, 4), (N, 8732, n_classes) #Prints to check the dimensions #print(predicted_locs.shape) #correct #print(predicted_scores.shape) #correct # Loss loss = criterion(predicted_locs, predicted_scores, boxes, labels) # scalar # Backward prop. optimizer.zero_grad() loss.backward() # Clip gradients, if necessary if grad_clip is not None: clip_gradient(optimizer, grad_clip) # Update model optimizer.step() losses.update(loss.item(), images.size(0)) batch_time.update(time.time() - start) start = time.time() # Print status if i % print_freq == 0: print('Epoch: [{0}][{1}/{2}]\t' 'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(epoch, i, len(train_loader), loss=losses)) del predicted_locs, predicted_scores, images, boxes, labels # free some memory since their histories may be stored if __name__ == '__main__': main()

true

f7193ee3518594b970384543fd7069dcd703cf96

7,181

py

Python

artikcloud/models/aggregates_histogram_response.py

artikcloud/artikcloud-python-dev

683cd8304f031913bcd581d1eb78ee0efbc5c113

[ "Apache-2.0" ]

null

artikcloud/models/aggregates_histogram_response.py

artikcloud/artikcloud-python-dev

683cd8304f031913bcd581d1eb78ee0efbc5c113

[ "Apache-2.0" ]

null

artikcloud/models/aggregates_histogram_response.py

artikcloud/artikcloud-python-dev

683cd8304f031913bcd581d1eb78ee0efbc5c113

[ "Apache-2.0" ]

null

# coding: utf-8 """ ARTIK Cloud API No descripton provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: 2.0.0 Generated by: https://github.com/swagger-api/swagger-codegen.git 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. """ from pprint import pformat from six import iteritems import re class AggregatesHistogramResponse(object): """ NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ def __init__(self, data=None, end_date=None, field=None, interval=None, sdid=None, size=None, start_date=None): """ AggregatesHistogramResponse - a model defined in Swagger :param dict swaggerTypes: The key is attribute name and the value is attribute type. :param dict attributeMap: The key is attribute name and the value is json key in definition. """ self.swagger_types = { 'data': 'list[AggregatesHistogramData]', 'end_date': 'int', 'field': 'str', 'interval': 'str', 'sdid': 'str', 'size': 'int', 'start_date': 'int' } self.attribute_map = { 'data': 'data', 'end_date': 'endDate', 'field': 'field', 'interval': 'interval', 'sdid': 'sdid', 'size': 'size', 'start_date': 'startDate' } self._data = data self._end_date = end_date self._field = field self._interval = interval self._sdid = sdid self._size = size self._start_date = start_date @property def data(self): """ Gets the data of this AggregatesHistogramResponse. :return: The data of this AggregatesHistogramResponse. :rtype: list[AggregatesHistogramData] """ return self._data @data.setter def data(self, data): """ Sets the data of this AggregatesHistogramResponse. :param data: The data of this AggregatesHistogramResponse. :type: list[AggregatesHistogramData] """ self._data = data @property def end_date(self): """ Gets the end_date of this AggregatesHistogramResponse. :return: The end_date of this AggregatesHistogramResponse. :rtype: int """ return self._end_date @end_date.setter def end_date(self, end_date): """ Sets the end_date of this AggregatesHistogramResponse. :param end_date: The end_date of this AggregatesHistogramResponse. :type: int """ self._end_date = end_date @property def field(self): """ Gets the field of this AggregatesHistogramResponse. :return: The field of this AggregatesHistogramResponse. :rtype: str """ return self._field @field.setter def field(self, field): """ Sets the field of this AggregatesHistogramResponse. :param field: The field of this AggregatesHistogramResponse. :type: str """ self._field = field @property def interval(self): """ Gets the interval of this AggregatesHistogramResponse. :return: The interval of this AggregatesHistogramResponse. :rtype: str """ return self._interval @interval.setter def interval(self, interval): """ Sets the interval of this AggregatesHistogramResponse. :param interval: The interval of this AggregatesHistogramResponse. :type: str """ self._interval = interval @property def sdid(self): """ Gets the sdid of this AggregatesHistogramResponse. :return: The sdid of this AggregatesHistogramResponse. :rtype: str """ return self._sdid @sdid.setter def sdid(self, sdid): """ Sets the sdid of this AggregatesHistogramResponse. :param sdid: The sdid of this AggregatesHistogramResponse. :type: str """ self._sdid = sdid @property def size(self): """ Gets the size of this AggregatesHistogramResponse. :return: The size of this AggregatesHistogramResponse. :rtype: int """ return self._size @size.setter def size(self, size): """ Sets the size of this AggregatesHistogramResponse. :param size: The size of this AggregatesHistogramResponse. :type: int """ self._size = size @property def start_date(self): """ Gets the start_date of this AggregatesHistogramResponse. :return: The start_date of this AggregatesHistogramResponse. :rtype: int """ return self._start_date @start_date.setter def start_date(self, start_date): """ Sets the start_date of this AggregatesHistogramResponse. :param start_date: The start_date of this AggregatesHistogramResponse. :type: int """ self._start_date = start_date def to_dict(self): """ Returns the model properties as a dict """ result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """ Returns the string representation of the model """ return pformat(self.to_dict()) def __repr__(self): """ For `print` and `pprint` """ return self.to_str() def __eq__(self, other): """ Returns true if both objects are equal """ return self.__dict__ == other.__dict__ def __ne__(self, other): """ Returns true if both objects are not equal """ return not self == other

25.464539

115

0.576521

from pprint import pformat from six import iteritems import re class AggregatesHistogramResponse(object): def __init__(self, data=None, end_date=None, field=None, interval=None, sdid=None, size=None, start_date=None): self.swagger_types = { 'data': 'list[AggregatesHistogramData]', 'end_date': 'int', 'field': 'str', 'interval': 'str', 'sdid': 'str', 'size': 'int', 'start_date': 'int' } self.attribute_map = { 'data': 'data', 'end_date': 'endDate', 'field': 'field', 'interval': 'interval', 'sdid': 'sdid', 'size': 'size', 'start_date': 'startDate' } self._data = data self._end_date = end_date self._field = field self._interval = interval self._sdid = sdid self._size = size self._start_date = start_date @property def data(self): return self._data @data.setter def data(self, data): self._data = data @property def end_date(self): return self._end_date @end_date.setter def end_date(self, end_date): self._end_date = end_date @property def field(self): return self._field @field.setter def field(self, field): self._field = field @property def interval(self): return self._interval @interval.setter def interval(self, interval): self._interval = interval @property def sdid(self): return self._sdid @sdid.setter def sdid(self, sdid): self._sdid = sdid @property def size(self): return self._size @size.setter def size(self, size): self._size = size @property def start_date(self): return self._start_date @start_date.setter def start_date(self, start_date): self._start_date = start_date def to_dict(self): result = {} for attr, _ in iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): return pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

f7193ef954651cd69d7c79d1330decefaa2e8768

9,116

py

Python

ucsmsdk/mometa/equipment/EquipmentRackEnclosure.py

Kego/ucsmsdk

244f283a5c295cf746110bb96686d079b19927ce

[ "Apache-2.0" ]

78

2015-11-30T14:10:05.000Z

2022-02-13T00:29:08.000Z

ucsmsdk/mometa/equipment/EquipmentRackEnclosure.py

Kego/ucsmsdk

244f283a5c295cf746110bb96686d079b19927ce

[ "Apache-2.0" ]

113

2015-11-20T09:42:46.000Z

2022-03-16T16:53:29.000Z

ucsmsdk/mometa/equipment/EquipmentRackEnclosure.py

Kego/ucsmsdk

244f283a5c295cf746110bb96686d079b19927ce

[ "Apache-2.0" ]

86

2015-12-12T08:22:18.000Z

2022-01-23T03:56:34.000Z

"""This module contains the general information for EquipmentRackEnclosure ManagedObject.""" from ...ucsmo import ManagedObject from ...ucscoremeta import MoPropertyMeta, MoMeta from ...ucsmeta import VersionMeta class EquipmentRackEnclosureConsts: MFG_TIME_NOT_APPLICABLE = "not-applicable" OPERABILITY_ACCESSIBILITY_PROBLEM = "accessibility-problem" OPERABILITY_AUTO_UPGRADE = "auto-upgrade" OPERABILITY_BACKPLANE_PORT_PROBLEM = "backplane-port-problem" OPERABILITY_BIOS_POST_TIMEOUT = "bios-post-timeout" OPERABILITY_CHASSIS_INTRUSION = "chassis-intrusion" OPERABILITY_CHASSIS_LIMIT_EXCEEDED = "chassis-limit-exceeded" OPERABILITY_CONFIG = "config" OPERABILITY_DECOMISSIONING = "decomissioning" OPERABILITY_DEGRADED = "degraded" OPERABILITY_DISABLED = "disabled" OPERABILITY_DISCOVERY = "discovery" OPERABILITY_DISCOVERY_FAILED = "discovery-failed" OPERABILITY_EQUIPMENT_PROBLEM = "equipment-problem" OPERABILITY_FABRIC_CONN_PROBLEM = "fabric-conn-problem" OPERABILITY_FABRIC_UNSUPPORTED_CONN = "fabric-unsupported-conn" OPERABILITY_IDENTIFY = "identify" OPERABILITY_IDENTITY_UNESTABLISHABLE = "identity-unestablishable" OPERABILITY_INOPERABLE = "inoperable" OPERABILITY_LINK_ACTIVATE_BLOCKED = "link-activate-blocked" OPERABILITY_MALFORMED_FRU = "malformed-fru" OPERABILITY_NON_OPTIMAL = "non-optimal" OPERABILITY_NON_OPTIMAL_SEVERE = "non-optimal-severe" OPERABILITY_NOT_SUPPORTED = "not-supported" OPERABILITY_OPERABLE = "operable" OPERABILITY_PEER_COMM_PROBLEM = "peer-comm-problem" OPERABILITY_PERFORMANCE_PROBLEM = "performance-problem" OPERABILITY_POST_FAILURE = "post-failure" OPERABILITY_POWER_PROBLEM = "power-problem" OPERABILITY_POWERED_OFF = "powered-off" OPERABILITY_REMOVED = "removed" OPERABILITY_THERMAL_PROBLEM = "thermal-problem" OPERABILITY_UNKNOWN = "unknown" OPERABILITY_UNSUPPORTED_CONFIG = "unsupported-config" OPERABILITY_UPGRADE_PROBLEM = "upgrade-problem" OPERABILITY_VOLTAGE_PROBLEM = "voltage-problem" PRESENCE_EMPTY = "empty" PRESENCE_EQUIPPED = "equipped" PRESENCE_EQUIPPED_DEPRECATED = "equipped-deprecated" PRESENCE_EQUIPPED_DISC_ERROR = "equipped-disc-error" PRESENCE_EQUIPPED_DISC_IN_PROGRESS = "equipped-disc-in-progress" PRESENCE_EQUIPPED_DISC_NOT_STARTED = "equipped-disc-not-started" PRESENCE_EQUIPPED_DISC_UNKNOWN = "equipped-disc-unknown" PRESENCE_EQUIPPED_IDENTITY_UNESTABLISHABLE = "equipped-identity-unestablishable" PRESENCE_EQUIPPED_NOT_PRIMARY = "equipped-not-primary" PRESENCE_EQUIPPED_SLAVE = "equipped-slave" PRESENCE_EQUIPPED_UNSUPPORTED = "equipped-unsupported" PRESENCE_EQUIPPED_WITH_MALFORMED_FRU = "equipped-with-malformed-fru" PRESENCE_INACCESSIBLE = "inaccessible" PRESENCE_MISMATCH = "mismatch" PRESENCE_MISMATCH_IDENTITY_UNESTABLISHABLE = "mismatch-identity-unestablishable" PRESENCE_MISMATCH_SLAVE = "mismatch-slave" PRESENCE_MISSING = "missing" PRESENCE_MISSING_SLAVE = "missing-slave" PRESENCE_NOT_SUPPORTED = "not-supported" PRESENCE_UNAUTHORIZED = "unauthorized" PRESENCE_UNKNOWN = "unknown" class EquipmentRackEnclosure(ManagedObject): """This is EquipmentRackEnclosure class.""" consts = EquipmentRackEnclosureConsts() naming_props = set(['id']) mo_meta = MoMeta("EquipmentRackEnclosure", "equipmentRackEnclosure", "rack-enclosure-[id]", VersionMeta.Version401a, "InputOutput", 0x3f, [], ["admin", "pn-equipment", "pn-maintenance", "pn-policy"], ['topSystem'], ['equipmentFanModule', 'equipmentPsu', 'equipmentSlotEp'], [None]) prop_meta = { "asset_tag": MoPropertyMeta("asset_tag", "assetTag", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, None, None, r"""[ !#$%&\*\+,\-\./:;\?@\[\]_\{\|\}~a-zA-Z0-9]{0,32}""", [], []), "child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version401a, MoPropertyMeta.INTERNAL, 0x2, None, None, r"""((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}""", [], []), "dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, 0x4, 0, 256, None, [], []), "flt_aggr": MoPropertyMeta("flt_aggr", "fltAggr", "ulong", VersionMeta.Version401a, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), "id": MoPropertyMeta("id", "id", "uint", VersionMeta.Version401a, MoPropertyMeta.NAMING, 0x8, None, None, None, [], []), "mfg_time": MoPropertyMeta("mfg_time", "mfgTime", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, None, None, r"""([0-9]){4}-([0-9]){2}-([0-9]){2}T([0-9]){2}:([0-9]){2}:([0-9]){2}((\.([0-9]){3})){0,1}""", ["not-applicable"], []), "model": MoPropertyMeta("model", "model", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, 0, 510, None, [], []), "oper_qualifier_reason": MoPropertyMeta("oper_qualifier_reason", "operQualifierReason", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, None, None, r"""[ !#$%&\*\+,\-\./:;\?@\[\]_\{\|\}~a-zA-Z0-9]{0,256}""", [], []), "operability": MoPropertyMeta("operability", "operability", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, None, None, None, ["accessibility-problem", "auto-upgrade", "backplane-port-problem", "bios-post-timeout", "chassis-intrusion", "chassis-limit-exceeded", "config", "decomissioning", "degraded", "disabled", "discovery", "discovery-failed", "equipment-problem", "fabric-conn-problem", "fabric-unsupported-conn", "identify", "identity-unestablishable", "inoperable", "link-activate-blocked", "malformed-fru", "non-optimal", "non-optimal-severe", "not-supported", "operable", "peer-comm-problem", "performance-problem", "post-failure", "power-problem", "powered-off", "removed", "thermal-problem", "unknown", "unsupported-config", "upgrade-problem", "voltage-problem"], []), "part_number": MoPropertyMeta("part_number", "partNumber", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, 0, 510, None, [], []), "presence": MoPropertyMeta("presence", "presence", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, None, None, None, ["empty", "equipped", "equipped-deprecated", "equipped-disc-error", "equipped-disc-in-progress", "equipped-disc-not-started", "equipped-disc-unknown", "equipped-identity-unestablishable", "equipped-not-primary", "equipped-slave", "equipped-unsupported", "equipped-with-malformed-fru", "inaccessible", "mismatch", "mismatch-identity-unestablishable", "mismatch-slave", "missing", "missing-slave", "not-supported", "unauthorized", "unknown"], []), "revision": MoPropertyMeta("revision", "revision", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, 0, 510, None, [], []), "rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, 0x10, 0, 256, None, [], []), "sacl": MoPropertyMeta("sacl", "sacl", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, None, None, r"""((none|del|mod|addchild|cascade),){0,4}(none|del|mod|addchild|cascade){0,1}""", [], []), "serial": MoPropertyMeta("serial", "serial", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, 0, 510, None, [], []), "status": MoPropertyMeta("status", "status", "string", VersionMeta.Version401a, MoPropertyMeta.READ_WRITE, 0x20, None, None, r"""((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}""", [], []), "vendor": MoPropertyMeta("vendor", "vendor", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, 0, 510, None, [], []), "vid": MoPropertyMeta("vid", "vid", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, 0, 510, None, [], []), } prop_map = { "assetTag": "asset_tag", "childAction": "child_action", "dn": "dn", "fltAggr": "flt_aggr", "id": "id", "mfgTime": "mfg_time", "model": "model", "operQualifierReason": "oper_qualifier_reason", "operability": "operability", "partNumber": "part_number", "presence": "presence", "revision": "revision", "rn": "rn", "sacl": "sacl", "serial": "serial", "status": "status", "vendor": "vendor", "vid": "vid", } def __init__(self, parent_mo_or_dn, id, **kwargs): self._dirty_mask = 0 self.id = id self.asset_tag = None self.child_action = None self.flt_aggr = None self.mfg_time = None self.model = None self.oper_qualifier_reason = None self.operability = None self.part_number = None self.presence = None self.revision = None self.sacl = None self.serial = None self.status = None self.vendor = None self.vid = None ManagedObject.__init__(self, "EquipmentRackEnclosure", parent_mo_or_dn, **kwargs)

66.057971

805

0.693725

from ...ucsmo import ManagedObject from ...ucscoremeta import MoPropertyMeta, MoMeta from ...ucsmeta import VersionMeta class EquipmentRackEnclosureConsts: MFG_TIME_NOT_APPLICABLE = "not-applicable" OPERABILITY_ACCESSIBILITY_PROBLEM = "accessibility-problem" OPERABILITY_AUTO_UPGRADE = "auto-upgrade" OPERABILITY_BACKPLANE_PORT_PROBLEM = "backplane-port-problem" OPERABILITY_BIOS_POST_TIMEOUT = "bios-post-timeout" OPERABILITY_CHASSIS_INTRUSION = "chassis-intrusion" OPERABILITY_CHASSIS_LIMIT_EXCEEDED = "chassis-limit-exceeded" OPERABILITY_CONFIG = "config" OPERABILITY_DECOMISSIONING = "decomissioning" OPERABILITY_DEGRADED = "degraded" OPERABILITY_DISABLED = "disabled" OPERABILITY_DISCOVERY = "discovery" OPERABILITY_DISCOVERY_FAILED = "discovery-failed" OPERABILITY_EQUIPMENT_PROBLEM = "equipment-problem" OPERABILITY_FABRIC_CONN_PROBLEM = "fabric-conn-problem" OPERABILITY_FABRIC_UNSUPPORTED_CONN = "fabric-unsupported-conn" OPERABILITY_IDENTIFY = "identify" OPERABILITY_IDENTITY_UNESTABLISHABLE = "identity-unestablishable" OPERABILITY_INOPERABLE = "inoperable" OPERABILITY_LINK_ACTIVATE_BLOCKED = "link-activate-blocked" OPERABILITY_MALFORMED_FRU = "malformed-fru" OPERABILITY_NON_OPTIMAL = "non-optimal" OPERABILITY_NON_OPTIMAL_SEVERE = "non-optimal-severe" OPERABILITY_NOT_SUPPORTED = "not-supported" OPERABILITY_OPERABLE = "operable" OPERABILITY_PEER_COMM_PROBLEM = "peer-comm-problem" OPERABILITY_PERFORMANCE_PROBLEM = "performance-problem" OPERABILITY_POST_FAILURE = "post-failure" OPERABILITY_POWER_PROBLEM = "power-problem" OPERABILITY_POWERED_OFF = "powered-off" OPERABILITY_REMOVED = "removed" OPERABILITY_THERMAL_PROBLEM = "thermal-problem" OPERABILITY_UNKNOWN = "unknown" OPERABILITY_UNSUPPORTED_CONFIG = "unsupported-config" OPERABILITY_UPGRADE_PROBLEM = "upgrade-problem" OPERABILITY_VOLTAGE_PROBLEM = "voltage-problem" PRESENCE_EMPTY = "empty" PRESENCE_EQUIPPED = "equipped" PRESENCE_EQUIPPED_DEPRECATED = "equipped-deprecated" PRESENCE_EQUIPPED_DISC_ERROR = "equipped-disc-error" PRESENCE_EQUIPPED_DISC_IN_PROGRESS = "equipped-disc-in-progress" PRESENCE_EQUIPPED_DISC_NOT_STARTED = "equipped-disc-not-started" PRESENCE_EQUIPPED_DISC_UNKNOWN = "equipped-disc-unknown" PRESENCE_EQUIPPED_IDENTITY_UNESTABLISHABLE = "equipped-identity-unestablishable" PRESENCE_EQUIPPED_NOT_PRIMARY = "equipped-not-primary" PRESENCE_EQUIPPED_SLAVE = "equipped-slave" PRESENCE_EQUIPPED_UNSUPPORTED = "equipped-unsupported" PRESENCE_EQUIPPED_WITH_MALFORMED_FRU = "equipped-with-malformed-fru" PRESENCE_INACCESSIBLE = "inaccessible" PRESENCE_MISMATCH = "mismatch" PRESENCE_MISMATCH_IDENTITY_UNESTABLISHABLE = "mismatch-identity-unestablishable" PRESENCE_MISMATCH_SLAVE = "mismatch-slave" PRESENCE_MISSING = "missing" PRESENCE_MISSING_SLAVE = "missing-slave" PRESENCE_NOT_SUPPORTED = "not-supported" PRESENCE_UNAUTHORIZED = "unauthorized" PRESENCE_UNKNOWN = "unknown" class EquipmentRackEnclosure(ManagedObject): consts = EquipmentRackEnclosureConsts() naming_props = set(['id']) mo_meta = MoMeta("EquipmentRackEnclosure", "equipmentRackEnclosure", "rack-enclosure-[id]", VersionMeta.Version401a, "InputOutput", 0x3f, [], ["admin", "pn-equipment", "pn-maintenance", "pn-policy"], ['topSystem'], ['equipmentFanModule', 'equipmentPsu', 'equipmentSlotEp'], [None]) prop_meta = { "asset_tag": MoPropertyMeta("asset_tag", "assetTag", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, None, None, r"""[ !#$%&\*\+,\-\./:;\?@\[\]_\{\|\}~a-zA-Z0-9]{0,32}""", [], []), "child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version401a, MoPropertyMeta.INTERNAL, 0x2, None, None, r"""((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}""", [], []), "dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, 0x4, 0, 256, None, [], []), "flt_aggr": MoPropertyMeta("flt_aggr", "fltAggr", "ulong", VersionMeta.Version401a, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), "id": MoPropertyMeta("id", "id", "uint", VersionMeta.Version401a, MoPropertyMeta.NAMING, 0x8, None, None, None, [], []), "mfg_time": MoPropertyMeta("mfg_time", "mfgTime", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, None, None, r"""([0-9]){4}-([0-9]){2}-([0-9]){2}T([0-9]){2}:([0-9]){2}:([0-9]){2}((\.([0-9]){3})){0,1}""", ["not-applicable"], []), "model": MoPropertyMeta("model", "model", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, 0, 510, None, [], []), "oper_qualifier_reason": MoPropertyMeta("oper_qualifier_reason", "operQualifierReason", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, None, None, r"""[ !#$%&\*\+,\-\./:;\?@\[\]_\{\|\}~a-zA-Z0-9]{0,256}""", [], []), "operability": MoPropertyMeta("operability", "operability", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, None, None, None, ["accessibility-problem", "auto-upgrade", "backplane-port-problem", "bios-post-timeout", "chassis-intrusion", "chassis-limit-exceeded", "config", "decomissioning", "degraded", "disabled", "discovery", "discovery-failed", "equipment-problem", "fabric-conn-problem", "fabric-unsupported-conn", "identify", "identity-unestablishable", "inoperable", "link-activate-blocked", "malformed-fru", "non-optimal", "non-optimal-severe", "not-supported", "operable", "peer-comm-problem", "performance-problem", "post-failure", "power-problem", "powered-off", "removed", "thermal-problem", "unknown", "unsupported-config", "upgrade-problem", "voltage-problem"], []), "part_number": MoPropertyMeta("part_number", "partNumber", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, 0, 510, None, [], []), "presence": MoPropertyMeta("presence", "presence", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, None, None, None, ["empty", "equipped", "equipped-deprecated", "equipped-disc-error", "equipped-disc-in-progress", "equipped-disc-not-started", "equipped-disc-unknown", "equipped-identity-unestablishable", "equipped-not-primary", "equipped-slave", "equipped-unsupported", "equipped-with-malformed-fru", "inaccessible", "mismatch", "mismatch-identity-unestablishable", "mismatch-slave", "missing", "missing-slave", "not-supported", "unauthorized", "unknown"], []), "revision": MoPropertyMeta("revision", "revision", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, 0, 510, None, [], []), "rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, 0x10, 0, 256, None, [], []), "sacl": MoPropertyMeta("sacl", "sacl", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, None, None, r"""((none|del|mod|addchild|cascade),){0,4}(none|del|mod|addchild|cascade){0,1}""", [], []), "serial": MoPropertyMeta("serial", "serial", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, 0, 510, None, [], []), "status": MoPropertyMeta("status", "status", "string", VersionMeta.Version401a, MoPropertyMeta.READ_WRITE, 0x20, None, None, r"""((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}""", [], []), "vendor": MoPropertyMeta("vendor", "vendor", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, 0, 510, None, [], []), "vid": MoPropertyMeta("vid", "vid", "string", VersionMeta.Version401a, MoPropertyMeta.READ_ONLY, None, 0, 510, None, [], []), } prop_map = { "assetTag": "asset_tag", "childAction": "child_action", "dn": "dn", "fltAggr": "flt_aggr", "id": "id", "mfgTime": "mfg_time", "model": "model", "operQualifierReason": "oper_qualifier_reason", "operability": "operability", "partNumber": "part_number", "presence": "presence", "revision": "revision", "rn": "rn", "sacl": "sacl", "serial": "serial", "status": "status", "vendor": "vendor", "vid": "vid", } def __init__(self, parent_mo_or_dn, id, **kwargs): self._dirty_mask = 0 self.id = id self.asset_tag = None self.child_action = None self.flt_aggr = None self.mfg_time = None self.model = None self.oper_qualifier_reason = None self.operability = None self.part_number = None self.presence = None self.revision = None self.sacl = None self.serial = None self.status = None self.vendor = None self.vid = None ManagedObject.__init__(self, "EquipmentRackEnclosure", parent_mo_or_dn, **kwargs)

true

f7193f652b00cfdbac8c192602a1299716aac80a

1,690

py

Python

service/tests/test_auth.py

SWE-AGGERS/reactions_service

eb8e4bcb9f9e69c03a89da82f3c71a3454fc285c

[ "MIT" ]

null

service/tests/test_auth.py

SWE-AGGERS/reactions_service

eb8e4bcb9f9e69c03a89da82f3c71a3454fc285c

[ "MIT" ]

null

service/tests/test_auth.py

SWE-AGGERS/reactions_service

eb8e4bcb9f9e69c03a89da82f3c71a3454fc285c

[ "MIT" ]

null

import json import unittest import mock from service.app import create_app from service.auth import encode_auth_token from service.database import empty_db class TestAuth(unittest.TestCase): def test0(self): user_id = 1 # create token new_token = encode_auth_token(user_id) _app = create_app(debug=True) empty_db(_app) with _app.test_client() as client: with mock.patch('service.views.reactions.exist_story') as exist_story_mock: exist_story_mock.return_value = True reply = client.post('/reactions/1/1/1', headers={'Authorization': 'Bearer ' + new_token}) body = json.loads(str(reply.data, 'utf8')) self.assertEqual(int(body['reaction']), 1) self.assertEqual(body['reply'], 'Reaction created!') self.assertEqual(int(body['story_id']), 1) # wrong token reply = client.post('/reactions/1/1/1', headers={'Authorization': 'Bearer ' + 'a'}) body = json.loads(str(reply.data, 'utf8')) self.assertEqual(int(body['reaction']), 1) self.assertEqual(body['reply'], 'Provide a valid auth token!') self.assertEqual(int(body['story_id']), 1) # wrong token: 'Bearer token malformed!' reply = client.post('/reactions/1/1/1', headers={'Authorization': 'a'}) body = json.loads(str(reply.data, 'utf8')) self.assertEqual(int(body['reaction']), 1) self.assertEqual(body['reply'], 'Bearer token malformed!') self.assertEqual(int(body['story_id']), 1)

40.238095

105

0.586391

import json import unittest import mock from service.app import create_app from service.auth import encode_auth_token from service.database import empty_db class TestAuth(unittest.TestCase): def test0(self): user_id = 1 new_token = encode_auth_token(user_id) _app = create_app(debug=True) empty_db(_app) with _app.test_client() as client: with mock.patch('service.views.reactions.exist_story') as exist_story_mock: exist_story_mock.return_value = True reply = client.post('/reactions/1/1/1', headers={'Authorization': 'Bearer ' + new_token}) body = json.loads(str(reply.data, 'utf8')) self.assertEqual(int(body['reaction']), 1) self.assertEqual(body['reply'], 'Reaction created!') self.assertEqual(int(body['story_id']), 1) reply = client.post('/reactions/1/1/1', headers={'Authorization': 'Bearer ' + 'a'}) body = json.loads(str(reply.data, 'utf8')) self.assertEqual(int(body['reaction']), 1) self.assertEqual(body['reply'], 'Provide a valid auth token!') self.assertEqual(int(body['story_id']), 1) reply = client.post('/reactions/1/1/1', headers={'Authorization': 'a'}) body = json.loads(str(reply.data, 'utf8')) self.assertEqual(int(body['reaction']), 1) self.assertEqual(body['reply'], 'Bearer token malformed!') self.assertEqual(int(body['story_id']), 1)

true

f7193f6e01897e3e8a2c39a33cc6cfcfad2301fb

63,187

py

Python

tests/test_h3.py

SouvikGhosh05/aioquic

da566b8ee616b9c83d51f0f5ad0521393119f40f

[ "BSD-3-Clause" ]

null

tests/test_h3.py

SouvikGhosh05/aioquic

da566b8ee616b9c83d51f0f5ad0521393119f40f

[ "BSD-3-Clause" ]

null

tests/test_h3.py

SouvikGhosh05/aioquic

da566b8ee616b9c83d51f0f5ad0521393119f40f

[ "BSD-3-Clause" ]

null

import binascii import contextlib import copy from unittest import TestCase from aioquic.buffer import Buffer, encode_uint_var from aioquic.h3.connection import ( H3_ALPN, ErrorCode, FrameType, FrameUnexpected, H3Connection, MessageError, Setting, SettingsError, StreamType, encode_frame, encode_settings, parse_settings, validate_push_promise_headers, validate_request_headers, validate_response_headers, validate_trailers, ) from aioquic.h3.events import DataReceived, HeadersReceived, PushPromiseReceived from aioquic.h3.exceptions import NoAvailablePushIDError from aioquic.quic.configuration import QuicConfiguration from aioquic.quic.events import StreamDataReceived from aioquic.quic.logger import QuicLogger from .test_connection import client_and_server, transfer DUMMY_SETTINGS = { Setting.QPACK_MAX_TABLE_CAPACITY: 4096, Setting.QPACK_BLOCKED_STREAMS: 16, Setting.DUMMY: 1, } QUIC_CONFIGURATION_OPTIONS = {"alpn_protocols": H3_ALPN} def h3_client_and_server(options=QUIC_CONFIGURATION_OPTIONS): return client_and_server( client_options=options, server_options=options, ) @contextlib.contextmanager def h3_fake_client_and_server(options=QUIC_CONFIGURATION_OPTIONS): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True, **options) ) quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False, **options) ) # exchange transport parameters quic_client._remote_max_datagram_frame_size = ( quic_server.configuration.max_datagram_frame_size ) quic_server._remote_max_datagram_frame_size = ( quic_client.configuration.max_datagram_frame_size ) yield quic_client, quic_server def h3_transfer(quic_sender, h3_receiver): quic_receiver = h3_receiver._quic if hasattr(quic_sender, "stream_queue"): quic_receiver._events.extend(quic_sender.stream_queue) quic_sender.stream_queue.clear() else: transfer(quic_sender, quic_receiver) # process QUIC events http_events = [] event = quic_receiver.next_event() while event is not None: http_events.extend(h3_receiver.handle_event(event)) event = quic_receiver.next_event() return http_events class FakeQuicConnection: def __init__(self, configuration): self.closed = None self.configuration = configuration self.stream_queue = [] self._events = [] self._next_stream_bidi = 0 if configuration.is_client else 1 self._next_stream_uni = 2 if configuration.is_client else 3 self._quic_logger = QuicLogger().start_trace( is_client=configuration.is_client, odcid=b"" ) self._remote_max_datagram_frame_size = None def close(self, error_code, reason_phrase): self.closed = (error_code, reason_phrase) def get_next_available_stream_id(self, is_unidirectional=False): if is_unidirectional: stream_id = self._next_stream_uni self._next_stream_uni += 4 else: stream_id = self._next_stream_bidi self._next_stream_bidi += 4 return stream_id def next_event(self): try: return self._events.pop(0) except IndexError: return None def send_stream_data(self, stream_id, data, end_stream=False): # chop up data into individual bytes for c in data: self.stream_queue.append( StreamDataReceived( data=bytes([c]), end_stream=False, stream_id=stream_id ) ) if end_stream: self.stream_queue.append( StreamDataReceived(data=b"", end_stream=end_stream, stream_id=stream_id) ) class H3ConnectionTest(TestCase): maxDiff = None def _make_request(self, h3_client, h3_server): quic_client = h3_client._quic quic_server = h3_server._quic # send request stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), (b"x-foo", b"client"), ], ) h3_client.send_data(stream_id=stream_id, data=b"", end_stream=True) # receive request events = h3_transfer(quic_client, h3_server) self.assertEqual( events, [ HeadersReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), (b"x-foo", b"client"), ], stream_id=stream_id, stream_ended=False, ), DataReceived(data=b"", stream_id=stream_id, stream_ended=True), ], ) # send response h3_server.send_headers( stream_id=stream_id, headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), (b"x-foo", b"server"), ], ) h3_server.send_data( stream_id=stream_id, data=b"<html><body>hello</body></html>", end_stream=True, ) # receive response events = h3_transfer(quic_server, h3_client) self.assertEqual( events, [ HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), (b"x-foo", b"server"), ], stream_id=stream_id, stream_ended=False, ), DataReceived( data=b"<html><body>hello</body></html>", stream_id=stream_id, stream_ended=True, ), ], ) def test_handle_control_frame_headers(self): """ We should not receive HEADERS on the control stream. """ quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) self.assertIsNotNone(h3_server.sent_settings) self.assertIsNone(h3_server.received_settings) # receive SETTINGS h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(DUMMY_SETTINGS)), end_stream=False, ) ) self.assertIsNone(quic_server.closed) self.assertIsNotNone(h3_server.sent_settings) self.assertEqual(h3_server.received_settings, DUMMY_SETTINGS) # receive unexpected HEADERS h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_frame(FrameType.HEADERS, b""), end_stream=False, ) ) self.assertEqual( quic_server.closed, (ErrorCode.H3_FRAME_UNEXPECTED, "Invalid frame type on control stream"), ) def test_handle_control_frame_max_push_id_from_client_before_settings(self): """ A server should not receive MAX_PUSH_ID before SETTINGS. """ quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) # receive unexpected MAX_PUSH_ID h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.MAX_PUSH_ID, b""), end_stream=False, ) ) self.assertEqual( quic_server.closed, (ErrorCode.H3_MISSING_SETTINGS, ""), ) def test_handle_control_frame_max_push_id_from_server(self): """ A client should not receive MAX_PUSH_ID on the control stream. """ quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) # receive SETTINGS h3_client.handle_event( StreamDataReceived( stream_id=3, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(DUMMY_SETTINGS)), end_stream=False, ) ) self.assertIsNone(quic_client.closed) # receive unexpected MAX_PUSH_ID h3_client.handle_event( StreamDataReceived( stream_id=3, data=encode_frame(FrameType.MAX_PUSH_ID, b""), end_stream=False, ) ) self.assertEqual( quic_client.closed, (ErrorCode.H3_FRAME_UNEXPECTED, "Servers must not send MAX_PUSH_ID"), ) def test_handle_control_settings_twice(self): """ We should not receive HEADERS on the control stream. """ quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) # receive SETTINGS h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(DUMMY_SETTINGS)), end_stream=False, ) ) self.assertIsNone(quic_server.closed) # receive unexpected SETTINGS h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_frame(FrameType.SETTINGS, encode_settings(DUMMY_SETTINGS)), end_stream=False, ) ) self.assertEqual( quic_server.closed, (ErrorCode.H3_FRAME_UNEXPECTED, "SETTINGS have already been received"), ) def test_handle_control_stream_close(self): """ Closing the control stream is not allowed. """ quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) # receive SETTINGS h3_client.handle_event( StreamDataReceived( stream_id=3, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(DUMMY_SETTINGS)), end_stream=False, ) ) self.assertIsNone(quic_client.closed) # receive unexpected FIN h3_client.handle_event( StreamDataReceived( stream_id=3, data=b"", end_stream=True, ) ) self.assertEqual( quic_client.closed, ( ErrorCode.H3_CLOSED_CRITICAL_STREAM, "Closing control stream is not allowed", ), ) def test_handle_control_stream_duplicate(self): """ We must only receive a single control stream. """ quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) # receive a first control stream h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL), end_stream=False ) ) # receive a second control stream h3_server.handle_event( StreamDataReceived( stream_id=6, data=encode_uint_var(StreamType.CONTROL), end_stream=False ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_STREAM_CREATION_ERROR, "Only one control stream is allowed", ), ) def test_handle_push_frame_wrong_frame_type(self): """ We should not received SETTINGS on a push stream. """ quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=15, data=encode_uint_var(StreamType.PUSH) + encode_uint_var(0) # push ID + encode_frame(FrameType.SETTINGS, b""), end_stream=False, ) ) self.assertEqual( quic_client.closed, (ErrorCode.H3_FRAME_UNEXPECTED, "Invalid frame type on push stream"), ) def test_handle_qpack_decoder_duplicate(self): """ We must only receive a single QPACK decoder stream. """ quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) # receive a first decoder stream h3_client.handle_event( StreamDataReceived( stream_id=11, data=encode_uint_var(StreamType.QPACK_DECODER), end_stream=False, ) ) # receive a second decoder stream h3_client.handle_event( StreamDataReceived( stream_id=15, data=encode_uint_var(StreamType.QPACK_DECODER), end_stream=False, ) ) self.assertEqual( quic_client.closed, ( ErrorCode.H3_STREAM_CREATION_ERROR, "Only one QPACK decoder stream is allowed", ), ) def test_handle_qpack_decoder_stream_error(self): """ Receiving garbage on the QPACK decoder stream triggers an exception. """ quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=11, data=encode_uint_var(StreamType.QPACK_DECODER) + b"\x00", end_stream=False, ) ) self.assertEqual(quic_client.closed, (ErrorCode.QPACK_DECODER_STREAM_ERROR, "")) def test_handle_qpack_encoder_duplicate(self): """ We must only receive a single QPACK encoder stream. """ quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) # receive a first encoder stream h3_client.handle_event( StreamDataReceived( stream_id=11, data=encode_uint_var(StreamType.QPACK_ENCODER), end_stream=False, ) ) # receive a second encoder stream h3_client.handle_event( StreamDataReceived( stream_id=15, data=encode_uint_var(StreamType.QPACK_ENCODER), end_stream=False, ) ) self.assertEqual( quic_client.closed, ( ErrorCode.H3_STREAM_CREATION_ERROR, "Only one QPACK encoder stream is allowed", ), ) def test_handle_qpack_encoder_stream_error(self): """ Receiving garbage on the QPACK encoder stream triggers an exception. """ quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=7, data=encode_uint_var(StreamType.QPACK_ENCODER) + b"\x00", end_stream=False, ) ) self.assertEqual(quic_client.closed, (ErrorCode.QPACK_ENCODER_STREAM_ERROR, "")) def test_handle_request_frame_bad_headers(self): """ We should not receive HEADERS which cannot be decoded. """ quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) h3_server.handle_event( StreamDataReceived( stream_id=0, data=encode_frame(FrameType.HEADERS, b""), end_stream=False ) ) self.assertEqual(quic_server.closed, (ErrorCode.QPACK_DECOMPRESSION_FAILED, "")) def test_handle_request_frame_data_before_headers(self): """ We should not receive DATA before receiving headers. """ quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) h3_server.handle_event( StreamDataReceived( stream_id=0, data=encode_frame(FrameType.DATA, b""), end_stream=False ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_FRAME_UNEXPECTED, "DATA frame is not allowed in this state", ), ) def test_handle_request_frame_headers_after_trailers(self): """ We should not receive HEADERS after receiving trailers. """ with h3_fake_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], ) h3_client.send_headers( stream_id=stream_id, headers=[(b"x-some-trailer", b"foo")], end_stream=True, ) h3_transfer(quic_client, h3_server) h3_server.handle_event( StreamDataReceived( stream_id=0, data=encode_frame(FrameType.HEADERS, b""), end_stream=False, ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_FRAME_UNEXPECTED, "HEADERS frame is not allowed in this state", ), ) def test_handle_request_frame_push_promise_from_client(self): """ A server should not receive PUSH_PROMISE on a request stream. """ quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) h3_server.handle_event( StreamDataReceived( stream_id=0, data=encode_frame(FrameType.PUSH_PROMISE, b""), end_stream=False, ) ) self.assertEqual( quic_server.closed, (ErrorCode.H3_FRAME_UNEXPECTED, "Clients must not send PUSH_PROMISE"), ) def test_handle_request_frame_wrong_frame_type(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) h3_server.handle_event( StreamDataReceived( stream_id=0, data=encode_frame(FrameType.SETTINGS, b""), end_stream=False, ) ) self.assertEqual( quic_server.closed, (ErrorCode.H3_FRAME_UNEXPECTED, "Invalid frame type on request stream"), ) def test_request(self): with h3_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) # make first request self._make_request(h3_client, h3_server) # make second request self._make_request(h3_client, h3_server) # make third request -> dynamic table self._make_request(h3_client, h3_server) def test_request_headers_only(self): with h3_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) # send request stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"HEAD"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), (b"x-foo", b"client"), ], end_stream=True, ) # receive request events = h3_transfer(quic_client, h3_server) self.assertEqual( events, [ HeadersReceived( headers=[ (b":method", b"HEAD"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), (b"x-foo", b"client"), ], stream_id=stream_id, stream_ended=True, ) ], ) # send response h3_server.send_headers( stream_id=stream_id, headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), (b"x-foo", b"server"), ], end_stream=True, ) # receive response events = h3_transfer(quic_server, h3_client) self.assertEqual( events, [ HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), (b"x-foo", b"server"), ], stream_id=stream_id, stream_ended=True, ) ], ) def test_request_fragmented_frame(self): with h3_fake_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) # send request stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), (b"x-foo", b"client"), ], ) h3_client.send_data(stream_id=stream_id, data=b"hello", end_stream=True) # receive request events = h3_transfer(quic_client, h3_server) self.assertEqual( events, [ HeadersReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), (b"x-foo", b"client"), ], stream_id=stream_id, stream_ended=False, ), DataReceived(data=b"h", stream_id=0, stream_ended=False), DataReceived(data=b"e", stream_id=0, stream_ended=False), DataReceived(data=b"l", stream_id=0, stream_ended=False), DataReceived(data=b"l", stream_id=0, stream_ended=False), DataReceived(data=b"o", stream_id=0, stream_ended=False), DataReceived(data=b"", stream_id=0, stream_ended=True), ], ) # send push promise push_stream_id = h3_server.send_push_promise( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/app.txt"), ], ) self.assertEqual(push_stream_id, 15) # send response h3_server.send_headers( stream_id=stream_id, headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), ], end_stream=False, ) h3_server.send_data(stream_id=stream_id, data=b"html", end_stream=True) # fulfill push promise h3_server.send_headers( stream_id=push_stream_id, headers=[(b":status", b"200"), (b"content-type", b"text/plain")], end_stream=False, ) h3_server.send_data(stream_id=push_stream_id, data=b"text", end_stream=True) # receive push promise / reponse events = h3_transfer(quic_server, h3_client) self.assertEqual( events, [ PushPromiseReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/app.txt"), ], push_id=0, stream_id=stream_id, ), HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), ], stream_id=0, stream_ended=False, ), DataReceived(data=b"h", stream_id=0, stream_ended=False), DataReceived(data=b"t", stream_id=0, stream_ended=False), DataReceived(data=b"m", stream_id=0, stream_ended=False), DataReceived(data=b"l", stream_id=0, stream_ended=False), DataReceived(data=b"", stream_id=0, stream_ended=True), HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"text/plain"), ], stream_id=15, stream_ended=False, push_id=0, ), DataReceived( data=b"t", stream_id=15, stream_ended=False, push_id=0 ), DataReceived( data=b"e", stream_id=15, stream_ended=False, push_id=0 ), DataReceived( data=b"x", stream_id=15, stream_ended=False, push_id=0 ), DataReceived( data=b"t", stream_id=15, stream_ended=False, push_id=0 ), DataReceived(data=b"", stream_id=15, stream_ended=True, push_id=0), ], ) def test_request_with_server_push(self): with h3_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) # send request stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], end_stream=True, ) # receive request events = h3_transfer(quic_client, h3_server) self.assertEqual( events, [ HeadersReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], stream_id=stream_id, stream_ended=True, ) ], ) # send push promises push_stream_id_css = h3_server.send_push_promise( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/app.css"), ], ) self.assertEqual(push_stream_id_css, 15) push_stream_id_js = h3_server.send_push_promise( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/app.js"), ], ) self.assertEqual(push_stream_id_js, 19) # send response h3_server.send_headers( stream_id=stream_id, headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), ], end_stream=False, ) h3_server.send_data( stream_id=stream_id, data=b"<html><body>hello</body></html>", end_stream=True, ) # fulfill push promises h3_server.send_headers( stream_id=push_stream_id_css, headers=[(b":status", b"200"), (b"content-type", b"text/css")], end_stream=False, ) h3_server.send_data( stream_id=push_stream_id_css, data=b"body { color: pink }", end_stream=True, ) h3_server.send_headers( stream_id=push_stream_id_js, headers=[ (b":status", b"200"), (b"content-type", b"application/javascript"), ], end_stream=False, ) h3_server.send_data( stream_id=push_stream_id_js, data=b"alert('howdee');", end_stream=True ) # receive push promises, response and push responses events = h3_transfer(quic_server, h3_client) self.assertEqual( events, [ PushPromiseReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/app.css"), ], push_id=0, stream_id=stream_id, ), PushPromiseReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/app.js"), ], push_id=1, stream_id=stream_id, ), HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), ], stream_id=stream_id, stream_ended=False, ), DataReceived( data=b"<html><body>hello</body></html>", stream_id=stream_id, stream_ended=True, ), HeadersReceived( headers=[(b":status", b"200"), (b"content-type", b"text/css")], push_id=0, stream_id=push_stream_id_css, stream_ended=False, ), DataReceived( data=b"body { color: pink }", push_id=0, stream_id=push_stream_id_css, stream_ended=True, ), HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"application/javascript"), ], push_id=1, stream_id=push_stream_id_js, stream_ended=False, ), DataReceived( data=b"alert('howdee');", push_id=1, stream_id=push_stream_id_js, stream_ended=True, ), ], ) def test_request_with_server_push_max_push_id(self): with h3_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) # send request stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], end_stream=True, ) # receive request events = h3_transfer(quic_client, h3_server) self.assertEqual( events, [ HeadersReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], stream_id=stream_id, stream_ended=True, ) ], ) # send push promises for i in range(0, 8): h3_server.send_push_promise( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", "/{}.css".format(i).encode("ascii")), ], ) # send one too many with self.assertRaises(NoAvailablePushIDError): h3_server.send_push_promise( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/8.css"), ], ) def test_send_data_after_trailers(self): """ We should not send DATA after trailers. """ quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], ) h3_client.send_headers( stream_id=stream_id, headers=[(b"x-some-trailer", b"foo")], end_stream=False ) with self.assertRaises(FrameUnexpected): h3_client.send_data(stream_id=stream_id, data=b"hello", end_stream=False) def test_send_data_before_headers(self): """ We should not send DATA before headers. """ quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) stream_id = quic_client.get_next_available_stream_id() with self.assertRaises(FrameUnexpected): h3_client.send_data(stream_id=stream_id, data=b"hello", end_stream=False) def test_send_headers_after_trailers(self): """ We should not send HEADERS after trailers. """ quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], ) h3_client.send_headers( stream_id=stream_id, headers=[(b"x-some-trailer", b"foo")], end_stream=False ) with self.assertRaises(FrameUnexpected): h3_client.send_headers( stream_id=stream_id, headers=[(b"x-other-trailer", b"foo")], end_stream=False, ) def test_blocked_stream(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=3, data=binascii.unhexlify( "0004170150000680020000074064091040bcc0000000faceb00c" ), end_stream=False, ) ) h3_client.handle_event( StreamDataReceived(stream_id=7, data=b"\x02", end_stream=False) ) h3_client.handle_event( StreamDataReceived(stream_id=11, data=b"\x03", end_stream=False) ) h3_client.handle_event( StreamDataReceived( stream_id=0, data=binascii.unhexlify("01040280d910"), end_stream=False ) ) h3_client.handle_event( StreamDataReceived( stream_id=0, data=binascii.unhexlify( "00408d796f752072656163686564206d766673742e6e65742c20726561636820" "746865202f6563686f20656e64706f696e7420666f7220616e206563686f2072" "6573706f6e7365207175657279202f3c6e756d6265723e20656e64706f696e74" "7320666f722061207661726961626c652073697a6520726573706f6e73652077" "6974682072616e646f6d206279746573" ), end_stream=True, ) ) self.assertEqual( h3_client.handle_event( StreamDataReceived( stream_id=7, data=binascii.unhexlify( "3fe101c696d07abe941094cb6d0a08017d403971966e32ca98b46f" ), end_stream=False, ) ), [ HeadersReceived( headers=[ (b":status", b"200"), (b"date", b"Mon, 22 Jul 2019 06:33:33 GMT"), ], stream_id=0, stream_ended=False, ), DataReceived( data=( b"you reached mvfst.net, reach the /echo endpoint for an " b"echo response query /<number> endpoints for a variable " b"size response with random bytes" ), stream_id=0, stream_ended=True, ), ], ) def test_blocked_stream_trailer(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=3, data=binascii.unhexlify( "0004170150000680020000074064091040bcc0000000faceb00c" ), end_stream=False, ) ) h3_client.handle_event( StreamDataReceived(stream_id=7, data=b"\x02", end_stream=False) ) h3_client.handle_event( StreamDataReceived(stream_id=11, data=b"\x03", end_stream=False) ) self.assertEqual( h3_client.handle_event( StreamDataReceived( stream_id=0, data=binascii.unhexlify( "011b0000d95696d07abe941094cb6d0a08017d403971966e32ca98b46f" ), end_stream=False, ) ), [ HeadersReceived( headers=[ (b":status", b"200"), (b"date", b"Mon, 22 Jul 2019 06:33:33 GMT"), ], stream_id=0, stream_ended=False, ) ], ) self.assertEqual( h3_client.handle_event( StreamDataReceived( stream_id=0, data=binascii.unhexlify( "00408d796f752072656163686564206d766673742e6e65742c20726561636820" "746865202f6563686f20656e64706f696e7420666f7220616e206563686f2072" "6573706f6e7365207175657279202f3c6e756d6265723e20656e64706f696e74" "7320666f722061207661726961626c652073697a6520726573706f6e73652077" "6974682072616e646f6d206279746573" ), end_stream=False, ) ), [ DataReceived( data=( b"you reached mvfst.net, reach the /echo endpoint for an " b"echo response query /<number> endpoints for a variable " b"size response with random bytes" ), stream_id=0, stream_ended=False, ) ], ) self.assertEqual( h3_client.handle_event( StreamDataReceived( stream_id=0, data=binascii.unhexlify("0103028010"), end_stream=True ) ), [], ) self.assertEqual( h3_client.handle_event( StreamDataReceived( stream_id=7, data=binascii.unhexlify("6af2b20f49564d833505b38294e7"), end_stream=False, ) ), [ HeadersReceived( headers=[(b"x-some-trailer", b"foo")], stream_id=0, stream_ended=True, push_id=None, ) ], ) def test_uni_stream_grease(self): with h3_client_and_server() as (quic_client, quic_server): h3_server = H3Connection(quic_server) quic_client.send_stream_data( 14, b"\xff\xff\xff\xff\xff\xff\xff\xfeGREASE is the word" ) self.assertEqual(h3_transfer(quic_client, h3_server), []) def test_request_with_trailers(self): with h3_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) # send request with trailers stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], end_stream=False, ) h3_client.send_headers( stream_id=stream_id, headers=[(b"x-some-trailer", b"foo")], end_stream=True, ) # receive request events = h3_transfer(quic_client, h3_server) self.assertEqual( events, [ HeadersReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], stream_id=stream_id, stream_ended=False, ), HeadersReceived( headers=[(b"x-some-trailer", b"foo")], stream_id=stream_id, stream_ended=True, ), ], ) # send response h3_server.send_headers( stream_id=stream_id, headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), ], end_stream=False, ) h3_server.send_data( stream_id=stream_id, data=b"<html><body>hello</body></html>", end_stream=False, ) h3_server.send_headers( stream_id=stream_id, headers=[(b"x-some-trailer", b"bar")], end_stream=True, ) # receive response events = h3_transfer(quic_server, h3_client) self.assertEqual( events, [ HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), ], stream_id=stream_id, stream_ended=False, ), DataReceived( data=b"<html><body>hello</body></html>", stream_id=stream_id, stream_ended=False, ), HeadersReceived( headers=[(b"x-some-trailer", b"bar")], stream_id=stream_id, stream_ended=True, ), ], ) def test_uni_stream_type(self): with h3_client_and_server() as (quic_client, quic_server): h3_server = H3Connection(quic_server) # unknown stream type 9 stream_id = quic_client.get_next_available_stream_id(is_unidirectional=True) self.assertEqual(stream_id, 2) quic_client.send_stream_data(stream_id, b"\x09") self.assertEqual(h3_transfer(quic_client, h3_server), []) self.assertEqual(list(h3_server._stream.keys()), [2]) self.assertEqual(h3_server._stream[2].buffer, b"") self.assertEqual(h3_server._stream[2].stream_type, 9) # unknown stream type 64, one byte at a time stream_id = quic_client.get_next_available_stream_id(is_unidirectional=True) self.assertEqual(stream_id, 6) quic_client.send_stream_data(stream_id, b"\x40") self.assertEqual(h3_transfer(quic_client, h3_server), []) self.assertEqual(list(h3_server._stream.keys()), [2, 6]) self.assertEqual(h3_server._stream[2].buffer, b"") self.assertEqual(h3_server._stream[2].stream_type, 9) self.assertEqual(h3_server._stream[6].buffer, b"\x40") self.assertEqual(h3_server._stream[6].stream_type, None) quic_client.send_stream_data(stream_id, b"\x40") self.assertEqual(h3_transfer(quic_client, h3_server), []) self.assertEqual(list(h3_server._stream.keys()), [2, 6]) self.assertEqual(h3_server._stream[2].buffer, b"") self.assertEqual(h3_server._stream[2].stream_type, 9) self.assertEqual(h3_server._stream[6].buffer, b"") self.assertEqual(h3_server._stream[6].stream_type, 64) def test_validate_settings_h3_datagram_invalid_value(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) # receive SETTINGS with an invalid H3_DATAGRAM value settings = copy.copy(DUMMY_SETTINGS) settings[Setting.H3_DATAGRAM] = 2 h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(settings)), end_stream=False, ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_SETTINGS_ERROR, "H3_DATAGRAM setting must be 0 or 1", ), ) def test_validate_settings_h3_datagram_without_transport_parameter(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) # receive SETTINGS with H3_DATAGRAM=1 but no max_datagram_frame_size TP settings = copy.copy(DUMMY_SETTINGS) settings[Setting.H3_DATAGRAM] = 1 h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(settings)), end_stream=False, ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_SETTINGS_ERROR, "H3_DATAGRAM requires max_datagram_frame_size transport parameter", ), ) def test_validate_settings_enable_connect_protocol_invalid_value(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) # receive SETTINGS with an invalid ENABLE_CONNECT_PROTOCOL value settings = copy.copy(DUMMY_SETTINGS) settings[Setting.ENABLE_CONNECT_PROTOCOL] = 2 h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(settings)), end_stream=False, ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_SETTINGS_ERROR, "ENABLE_CONNECT_PROTOCOL setting must be 0 or 1", ), ) def test_validate_settings_enable_webtransport_invalid_value(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) # receive SETTINGS with an invalid ENABLE_WEBTRANSPORT value settings = copy.copy(DUMMY_SETTINGS) settings[Setting.ENABLE_WEBTRANSPORT] = 2 h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(settings)), end_stream=False, ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_SETTINGS_ERROR, "ENABLE_WEBTRANSPORT setting must be 0 or 1", ), ) def test_validate_settings_enable_webtransport_without_h3_datagram(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) # receive SETTINGS requesting WebTransport, but DATAGRAM was not offered settings = copy.copy(DUMMY_SETTINGS) settings[Setting.ENABLE_WEBTRANSPORT] = 1 h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(settings)), end_stream=False, ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_SETTINGS_ERROR, "ENABLE_WEBTRANSPORT requires H3_DATAGRAM", ), ) class H3ParserTest(TestCase): def test_parse_settings_duplicate_identifier(self): buf = Buffer(capacity=1024) buf.push_uint_var(1) buf.push_uint_var(123) buf.push_uint_var(1) buf.push_uint_var(456) with self.assertRaises(SettingsError) as cm: parse_settings(buf.data) self.assertEqual( cm.exception.reason_phrase, "Setting identifier 0x1 is included twice" ) def test_parse_settings_reserved_identifier(self): buf = Buffer(capacity=1024) buf.push_uint_var(0) buf.push_uint_var(123) with self.assertRaises(SettingsError) as cm: parse_settings(buf.data) self.assertEqual( cm.exception.reason_phrase, "Setting identifier 0x0 is reserved" ) def test_validate_push_promise_headers(self): # OK validate_push_promise_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), (b":authority", b"localhost"), ] ) validate_push_promise_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), (b":authority", b"localhost"), (b"x-foo", b"bar"), ] ) # invalid pseudo-header with self.assertRaises(MessageError) as cm: validate_push_promise_headers([(b":status", b"foo")]) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':status' is not valid" ) # duplicate pseudo-header with self.assertRaises(MessageError) as cm: validate_push_promise_headers( [ (b":method", b"GET"), (b":method", b"POST"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':method' is included twice" ) # pseudo-header after regular headers with self.assertRaises(MessageError) as cm: validate_push_promise_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), (b"x-foo", b"bar"), (b":authority", b"foo"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':authority' is not allowed after regular headers", ) # missing pseudo-headers with self.assertRaises(MessageError) as cm: validate_push_promise_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-headers [b':authority'] are missing", ) def test_validate_request_headers(self): # OK validate_request_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), (b":authority", b"localhost"), ] ) validate_request_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), (b":authority", b"localhost"), (b"x-foo", b"bar"), ] ) # uppercase header with self.assertRaises(MessageError) as cm: validate_request_headers([(b"X-Foo", b"foo")]) self.assertEqual( cm.exception.reason_phrase, "Header b'X-Foo' contains uppercase letters" ) # invalid pseudo-header with self.assertRaises(MessageError) as cm: validate_request_headers([(b":status", b"foo")]) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':status' is not valid" ) # duplicate pseudo-header with self.assertRaises(MessageError) as cm: validate_request_headers( [ (b":method", b"GET"), (b":method", b"POST"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':method' is included twice" ) # pseudo-header after regular headers with self.assertRaises(MessageError) as cm: validate_request_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), (b"x-foo", b"bar"), (b":authority", b"foo"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':authority' is not allowed after regular headers", ) # missing pseudo-headers with self.assertRaises(MessageError) as cm: validate_request_headers([(b":method", b"GET")]) self.assertEqual( cm.exception.reason_phrase, "Pseudo-headers [b':authority'] are missing", ) # empty :authority pseudo-header for http/https for scheme in [b"http", b"https"]: with self.assertRaises(MessageError) as cm: validate_request_headers( [ (b":method", b"GET"), (b":scheme", scheme), (b":authority", b""), (b":path", b"/"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':authority' cannot be empty", ) # empty :path pseudo-header for http/https for scheme in [b"http", b"https"]: with self.assertRaises(MessageError) as cm: validate_request_headers( [ (b":method", b"GET"), (b":scheme", scheme), (b":authority", b"localhost"), (b":path", b""), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':path' cannot be empty" ) def test_validate_response_headers(self): # OK validate_response_headers([(b":status", b"200")]) validate_response_headers( [ (b":status", b"200"), (b"x-foo", b"bar"), ] ) # invalid pseudo-header with self.assertRaises(MessageError) as cm: validate_response_headers([(b":method", b"GET")]) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':method' is not valid" ) # duplicate pseudo-header with self.assertRaises(MessageError) as cm: validate_response_headers( [ (b":status", b"200"), (b":status", b"501"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':status' is included twice" ) def test_validate_trailers(self): # OK validate_trailers([(b"x-foo", b"bar")]) # invalid pseudo-header with self.assertRaises(MessageError) as cm: validate_trailers([(b":status", b"foo")]) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':status' is not valid" ) # pseudo-header after regular headers with self.assertRaises(MessageError) as cm: validate_trailers( [ (b"x-foo", b"bar"), (b":authority", b"foo"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':authority' is not allowed after regular headers", )

34.699066

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0.500593

import binascii import contextlib import copy from unittest import TestCase from aioquic.buffer import Buffer, encode_uint_var from aioquic.h3.connection import ( H3_ALPN, ErrorCode, FrameType, FrameUnexpected, H3Connection, MessageError, Setting, SettingsError, StreamType, encode_frame, encode_settings, parse_settings, validate_push_promise_headers, validate_request_headers, validate_response_headers, validate_trailers, ) from aioquic.h3.events import DataReceived, HeadersReceived, PushPromiseReceived from aioquic.h3.exceptions import NoAvailablePushIDError from aioquic.quic.configuration import QuicConfiguration from aioquic.quic.events import StreamDataReceived from aioquic.quic.logger import QuicLogger from .test_connection import client_and_server, transfer DUMMY_SETTINGS = { Setting.QPACK_MAX_TABLE_CAPACITY: 4096, Setting.QPACK_BLOCKED_STREAMS: 16, Setting.DUMMY: 1, } QUIC_CONFIGURATION_OPTIONS = {"alpn_protocols": H3_ALPN} def h3_client_and_server(options=QUIC_CONFIGURATION_OPTIONS): return client_and_server( client_options=options, server_options=options, ) @contextlib.contextmanager def h3_fake_client_and_server(options=QUIC_CONFIGURATION_OPTIONS): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True, **options) ) quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False, **options) ) quic_client._remote_max_datagram_frame_size = ( quic_server.configuration.max_datagram_frame_size ) quic_server._remote_max_datagram_frame_size = ( quic_client.configuration.max_datagram_frame_size ) yield quic_client, quic_server def h3_transfer(quic_sender, h3_receiver): quic_receiver = h3_receiver._quic if hasattr(quic_sender, "stream_queue"): quic_receiver._events.extend(quic_sender.stream_queue) quic_sender.stream_queue.clear() else: transfer(quic_sender, quic_receiver) http_events = [] event = quic_receiver.next_event() while event is not None: http_events.extend(h3_receiver.handle_event(event)) event = quic_receiver.next_event() return http_events class FakeQuicConnection: def __init__(self, configuration): self.closed = None self.configuration = configuration self.stream_queue = [] self._events = [] self._next_stream_bidi = 0 if configuration.is_client else 1 self._next_stream_uni = 2 if configuration.is_client else 3 self._quic_logger = QuicLogger().start_trace( is_client=configuration.is_client, odcid=b"" ) self._remote_max_datagram_frame_size = None def close(self, error_code, reason_phrase): self.closed = (error_code, reason_phrase) def get_next_available_stream_id(self, is_unidirectional=False): if is_unidirectional: stream_id = self._next_stream_uni self._next_stream_uni += 4 else: stream_id = self._next_stream_bidi self._next_stream_bidi += 4 return stream_id def next_event(self): try: return self._events.pop(0) except IndexError: return None def send_stream_data(self, stream_id, data, end_stream=False): for c in data: self.stream_queue.append( StreamDataReceived( data=bytes([c]), end_stream=False, stream_id=stream_id ) ) if end_stream: self.stream_queue.append( StreamDataReceived(data=b"", end_stream=end_stream, stream_id=stream_id) ) class H3ConnectionTest(TestCase): maxDiff = None def _make_request(self, h3_client, h3_server): quic_client = h3_client._quic quic_server = h3_server._quic stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), (b"x-foo", b"client"), ], ) h3_client.send_data(stream_id=stream_id, data=b"", end_stream=True) events = h3_transfer(quic_client, h3_server) self.assertEqual( events, [ HeadersReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), (b"x-foo", b"client"), ], stream_id=stream_id, stream_ended=False, ), DataReceived(data=b"", stream_id=stream_id, stream_ended=True), ], ) h3_server.send_headers( stream_id=stream_id, headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), (b"x-foo", b"server"), ], ) h3_server.send_data( stream_id=stream_id, data=b"<html><body>hello</body></html>", end_stream=True, ) events = h3_transfer(quic_server, h3_client) self.assertEqual( events, [ HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), (b"x-foo", b"server"), ], stream_id=stream_id, stream_ended=False, ), DataReceived( data=b"<html><body>hello</body></html>", stream_id=stream_id, stream_ended=True, ), ], ) def test_handle_control_frame_headers(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) self.assertIsNotNone(h3_server.sent_settings) self.assertIsNone(h3_server.received_settings) h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(DUMMY_SETTINGS)), end_stream=False, ) ) self.assertIsNone(quic_server.closed) self.assertIsNotNone(h3_server.sent_settings) self.assertEqual(h3_server.received_settings, DUMMY_SETTINGS) h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_frame(FrameType.HEADERS, b""), end_stream=False, ) ) self.assertEqual( quic_server.closed, (ErrorCode.H3_FRAME_UNEXPECTED, "Invalid frame type on control stream"), ) def test_handle_control_frame_max_push_id_from_client_before_settings(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.MAX_PUSH_ID, b""), end_stream=False, ) ) self.assertEqual( quic_server.closed, (ErrorCode.H3_MISSING_SETTINGS, ""), ) def test_handle_control_frame_max_push_id_from_server(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=3, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(DUMMY_SETTINGS)), end_stream=False, ) ) self.assertIsNone(quic_client.closed) h3_client.handle_event( StreamDataReceived( stream_id=3, data=encode_frame(FrameType.MAX_PUSH_ID, b""), end_stream=False, ) ) self.assertEqual( quic_client.closed, (ErrorCode.H3_FRAME_UNEXPECTED, "Servers must not send MAX_PUSH_ID"), ) def test_handle_control_settings_twice(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(DUMMY_SETTINGS)), end_stream=False, ) ) self.assertIsNone(quic_server.closed) h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_frame(FrameType.SETTINGS, encode_settings(DUMMY_SETTINGS)), end_stream=False, ) ) self.assertEqual( quic_server.closed, (ErrorCode.H3_FRAME_UNEXPECTED, "SETTINGS have already been received"), ) def test_handle_control_stream_close(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=3, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(DUMMY_SETTINGS)), end_stream=False, ) ) self.assertIsNone(quic_client.closed) h3_client.handle_event( StreamDataReceived( stream_id=3, data=b"", end_stream=True, ) ) self.assertEqual( quic_client.closed, ( ErrorCode.H3_CLOSED_CRITICAL_STREAM, "Closing control stream is not allowed", ), ) def test_handle_control_stream_duplicate(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL), end_stream=False ) ) h3_server.handle_event( StreamDataReceived( stream_id=6, data=encode_uint_var(StreamType.CONTROL), end_stream=False ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_STREAM_CREATION_ERROR, "Only one control stream is allowed", ), ) def test_handle_push_frame_wrong_frame_type(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=15, data=encode_uint_var(StreamType.PUSH) + encode_uint_var(0) + encode_frame(FrameType.SETTINGS, b""), end_stream=False, ) ) self.assertEqual( quic_client.closed, (ErrorCode.H3_FRAME_UNEXPECTED, "Invalid frame type on push stream"), ) def test_handle_qpack_decoder_duplicate(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=11, data=encode_uint_var(StreamType.QPACK_DECODER), end_stream=False, ) ) h3_client.handle_event( StreamDataReceived( stream_id=15, data=encode_uint_var(StreamType.QPACK_DECODER), end_stream=False, ) ) self.assertEqual( quic_client.closed, ( ErrorCode.H3_STREAM_CREATION_ERROR, "Only one QPACK decoder stream is allowed", ), ) def test_handle_qpack_decoder_stream_error(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=11, data=encode_uint_var(StreamType.QPACK_DECODER) + b"\x00", end_stream=False, ) ) self.assertEqual(quic_client.closed, (ErrorCode.QPACK_DECODER_STREAM_ERROR, "")) def test_handle_qpack_encoder_duplicate(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=11, data=encode_uint_var(StreamType.QPACK_ENCODER), end_stream=False, ) ) h3_client.handle_event( StreamDataReceived( stream_id=15, data=encode_uint_var(StreamType.QPACK_ENCODER), end_stream=False, ) ) self.assertEqual( quic_client.closed, ( ErrorCode.H3_STREAM_CREATION_ERROR, "Only one QPACK encoder stream is allowed", ), ) def test_handle_qpack_encoder_stream_error(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=7, data=encode_uint_var(StreamType.QPACK_ENCODER) + b"\x00", end_stream=False, ) ) self.assertEqual(quic_client.closed, (ErrorCode.QPACK_ENCODER_STREAM_ERROR, "")) def test_handle_request_frame_bad_headers(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) h3_server.handle_event( StreamDataReceived( stream_id=0, data=encode_frame(FrameType.HEADERS, b""), end_stream=False ) ) self.assertEqual(quic_server.closed, (ErrorCode.QPACK_DECOMPRESSION_FAILED, "")) def test_handle_request_frame_data_before_headers(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) h3_server.handle_event( StreamDataReceived( stream_id=0, data=encode_frame(FrameType.DATA, b""), end_stream=False ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_FRAME_UNEXPECTED, "DATA frame is not allowed in this state", ), ) def test_handle_request_frame_headers_after_trailers(self): with h3_fake_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], ) h3_client.send_headers( stream_id=stream_id, headers=[(b"x-some-trailer", b"foo")], end_stream=True, ) h3_transfer(quic_client, h3_server) h3_server.handle_event( StreamDataReceived( stream_id=0, data=encode_frame(FrameType.HEADERS, b""), end_stream=False, ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_FRAME_UNEXPECTED, "HEADERS frame is not allowed in this state", ), ) def test_handle_request_frame_push_promise_from_client(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) h3_server.handle_event( StreamDataReceived( stream_id=0, data=encode_frame(FrameType.PUSH_PROMISE, b""), end_stream=False, ) ) self.assertEqual( quic_server.closed, (ErrorCode.H3_FRAME_UNEXPECTED, "Clients must not send PUSH_PROMISE"), ) def test_handle_request_frame_wrong_frame_type(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) h3_server.handle_event( StreamDataReceived( stream_id=0, data=encode_frame(FrameType.SETTINGS, b""), end_stream=False, ) ) self.assertEqual( quic_server.closed, (ErrorCode.H3_FRAME_UNEXPECTED, "Invalid frame type on request stream"), ) def test_request(self): with h3_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) self._make_request(h3_client, h3_server) self._make_request(h3_client, h3_server) self._make_request(h3_client, h3_server) def test_request_headers_only(self): with h3_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"HEAD"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), (b"x-foo", b"client"), ], end_stream=True, ) events = h3_transfer(quic_client, h3_server) self.assertEqual( events, [ HeadersReceived( headers=[ (b":method", b"HEAD"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), (b"x-foo", b"client"), ], stream_id=stream_id, stream_ended=True, ) ], ) h3_server.send_headers( stream_id=stream_id, headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), (b"x-foo", b"server"), ], end_stream=True, ) events = h3_transfer(quic_server, h3_client) self.assertEqual( events, [ HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), (b"x-foo", b"server"), ], stream_id=stream_id, stream_ended=True, ) ], ) def test_request_fragmented_frame(self): with h3_fake_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), (b"x-foo", b"client"), ], ) h3_client.send_data(stream_id=stream_id, data=b"hello", end_stream=True) events = h3_transfer(quic_client, h3_server) self.assertEqual( events, [ HeadersReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), (b"x-foo", b"client"), ], stream_id=stream_id, stream_ended=False, ), DataReceived(data=b"h", stream_id=0, stream_ended=False), DataReceived(data=b"e", stream_id=0, stream_ended=False), DataReceived(data=b"l", stream_id=0, stream_ended=False), DataReceived(data=b"l", stream_id=0, stream_ended=False), DataReceived(data=b"o", stream_id=0, stream_ended=False), DataReceived(data=b"", stream_id=0, stream_ended=True), ], ) push_stream_id = h3_server.send_push_promise( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/app.txt"), ], ) self.assertEqual(push_stream_id, 15) h3_server.send_headers( stream_id=stream_id, headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), ], end_stream=False, ) h3_server.send_data(stream_id=stream_id, data=b"html", end_stream=True) h3_server.send_headers( stream_id=push_stream_id, headers=[(b":status", b"200"), (b"content-type", b"text/plain")], end_stream=False, ) h3_server.send_data(stream_id=push_stream_id, data=b"text", end_stream=True) events = h3_transfer(quic_server, h3_client) self.assertEqual( events, [ PushPromiseReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/app.txt"), ], push_id=0, stream_id=stream_id, ), HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), ], stream_id=0, stream_ended=False, ), DataReceived(data=b"h", stream_id=0, stream_ended=False), DataReceived(data=b"t", stream_id=0, stream_ended=False), DataReceived(data=b"m", stream_id=0, stream_ended=False), DataReceived(data=b"l", stream_id=0, stream_ended=False), DataReceived(data=b"", stream_id=0, stream_ended=True), HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"text/plain"), ], stream_id=15, stream_ended=False, push_id=0, ), DataReceived( data=b"t", stream_id=15, stream_ended=False, push_id=0 ), DataReceived( data=b"e", stream_id=15, stream_ended=False, push_id=0 ), DataReceived( data=b"x", stream_id=15, stream_ended=False, push_id=0 ), DataReceived( data=b"t", stream_id=15, stream_ended=False, push_id=0 ), DataReceived(data=b"", stream_id=15, stream_ended=True, push_id=0), ], ) def test_request_with_server_push(self): with h3_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], end_stream=True, ) events = h3_transfer(quic_client, h3_server) self.assertEqual( events, [ HeadersReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], stream_id=stream_id, stream_ended=True, ) ], ) push_stream_id_css = h3_server.send_push_promise( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/app.css"), ], ) self.assertEqual(push_stream_id_css, 15) push_stream_id_js = h3_server.send_push_promise( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/app.js"), ], ) self.assertEqual(push_stream_id_js, 19) h3_server.send_headers( stream_id=stream_id, headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), ], end_stream=False, ) h3_server.send_data( stream_id=stream_id, data=b"<html><body>hello</body></html>", end_stream=True, ) h3_server.send_headers( stream_id=push_stream_id_css, headers=[(b":status", b"200"), (b"content-type", b"text/css")], end_stream=False, ) h3_server.send_data( stream_id=push_stream_id_css, data=b"body { color: pink }", end_stream=True, ) h3_server.send_headers( stream_id=push_stream_id_js, headers=[ (b":status", b"200"), (b"content-type", b"application/javascript"), ], end_stream=False, ) h3_server.send_data( stream_id=push_stream_id_js, data=b"alert('howdee');", end_stream=True ) events = h3_transfer(quic_server, h3_client) self.assertEqual( events, [ PushPromiseReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/app.css"), ], push_id=0, stream_id=stream_id, ), PushPromiseReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/app.js"), ], push_id=1, stream_id=stream_id, ), HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), ], stream_id=stream_id, stream_ended=False, ), DataReceived( data=b"<html><body>hello</body></html>", stream_id=stream_id, stream_ended=True, ), HeadersReceived( headers=[(b":status", b"200"), (b"content-type", b"text/css")], push_id=0, stream_id=push_stream_id_css, stream_ended=False, ), DataReceived( data=b"body { color: pink }", push_id=0, stream_id=push_stream_id_css, stream_ended=True, ), HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"application/javascript"), ], push_id=1, stream_id=push_stream_id_js, stream_ended=False, ), DataReceived( data=b"alert('howdee');", push_id=1, stream_id=push_stream_id_js, stream_ended=True, ), ], ) def test_request_with_server_push_max_push_id(self): with h3_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], end_stream=True, ) events = h3_transfer(quic_client, h3_server) self.assertEqual( events, [ HeadersReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], stream_id=stream_id, stream_ended=True, ) ], ) for i in range(0, 8): h3_server.send_push_promise( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", "/{}.css".format(i).encode("ascii")), ], ) with self.assertRaises(NoAvailablePushIDError): h3_server.send_push_promise( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/8.css"), ], ) def test_send_data_after_trailers(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], ) h3_client.send_headers( stream_id=stream_id, headers=[(b"x-some-trailer", b"foo")], end_stream=False ) with self.assertRaises(FrameUnexpected): h3_client.send_data(stream_id=stream_id, data=b"hello", end_stream=False) def test_send_data_before_headers(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) stream_id = quic_client.get_next_available_stream_id() with self.assertRaises(FrameUnexpected): h3_client.send_data(stream_id=stream_id, data=b"hello", end_stream=False) def test_send_headers_after_trailers(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], ) h3_client.send_headers( stream_id=stream_id, headers=[(b"x-some-trailer", b"foo")], end_stream=False ) with self.assertRaises(FrameUnexpected): h3_client.send_headers( stream_id=stream_id, headers=[(b"x-other-trailer", b"foo")], end_stream=False, ) def test_blocked_stream(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=3, data=binascii.unhexlify( "0004170150000680020000074064091040bcc0000000faceb00c" ), end_stream=False, ) ) h3_client.handle_event( StreamDataReceived(stream_id=7, data=b"\x02", end_stream=False) ) h3_client.handle_event( StreamDataReceived(stream_id=11, data=b"\x03", end_stream=False) ) h3_client.handle_event( StreamDataReceived( stream_id=0, data=binascii.unhexlify("01040280d910"), end_stream=False ) ) h3_client.handle_event( StreamDataReceived( stream_id=0, data=binascii.unhexlify( "00408d796f752072656163686564206d766673742e6e65742c20726561636820" "746865202f6563686f20656e64706f696e7420666f7220616e206563686f2072" "6573706f6e7365207175657279202f3c6e756d6265723e20656e64706f696e74" "7320666f722061207661726961626c652073697a6520726573706f6e73652077" "6974682072616e646f6d206279746573" ), end_stream=True, ) ) self.assertEqual( h3_client.handle_event( StreamDataReceived( stream_id=7, data=binascii.unhexlify( "3fe101c696d07abe941094cb6d0a08017d403971966e32ca98b46f" ), end_stream=False, ) ), [ HeadersReceived( headers=[ (b":status", b"200"), (b"date", b"Mon, 22 Jul 2019 06:33:33 GMT"), ], stream_id=0, stream_ended=False, ), DataReceived( data=( b"you reached mvfst.net, reach the /echo endpoint for an " b"echo response query /<number> endpoints for a variable " b"size response with random bytes" ), stream_id=0, stream_ended=True, ), ], ) def test_blocked_stream_trailer(self): quic_client = FakeQuicConnection( configuration=QuicConfiguration(is_client=True) ) h3_client = H3Connection(quic_client) h3_client.handle_event( StreamDataReceived( stream_id=3, data=binascii.unhexlify( "0004170150000680020000074064091040bcc0000000faceb00c" ), end_stream=False, ) ) h3_client.handle_event( StreamDataReceived(stream_id=7, data=b"\x02", end_stream=False) ) h3_client.handle_event( StreamDataReceived(stream_id=11, data=b"\x03", end_stream=False) ) self.assertEqual( h3_client.handle_event( StreamDataReceived( stream_id=0, data=binascii.unhexlify( "011b0000d95696d07abe941094cb6d0a08017d403971966e32ca98b46f" ), end_stream=False, ) ), [ HeadersReceived( headers=[ (b":status", b"200"), (b"date", b"Mon, 22 Jul 2019 06:33:33 GMT"), ], stream_id=0, stream_ended=False, ) ], ) self.assertEqual( h3_client.handle_event( StreamDataReceived( stream_id=0, data=binascii.unhexlify( "00408d796f752072656163686564206d766673742e6e65742c20726561636820" "746865202f6563686f20656e64706f696e7420666f7220616e206563686f2072" "6573706f6e7365207175657279202f3c6e756d6265723e20656e64706f696e74" "7320666f722061207661726961626c652073697a6520726573706f6e73652077" "6974682072616e646f6d206279746573" ), end_stream=False, ) ), [ DataReceived( data=( b"you reached mvfst.net, reach the /echo endpoint for an " b"echo response query /<number> endpoints for a variable " b"size response with random bytes" ), stream_id=0, stream_ended=False, ) ], ) self.assertEqual( h3_client.handle_event( StreamDataReceived( stream_id=0, data=binascii.unhexlify("0103028010"), end_stream=True ) ), [], ) self.assertEqual( h3_client.handle_event( StreamDataReceived( stream_id=7, data=binascii.unhexlify("6af2b20f49564d833505b38294e7"), end_stream=False, ) ), [ HeadersReceived( headers=[(b"x-some-trailer", b"foo")], stream_id=0, stream_ended=True, push_id=None, ) ], ) def test_uni_stream_grease(self): with h3_client_and_server() as (quic_client, quic_server): h3_server = H3Connection(quic_server) quic_client.send_stream_data( 14, b"\xff\xff\xff\xff\xff\xff\xff\xfeGREASE is the word" ) self.assertEqual(h3_transfer(quic_client, h3_server), []) def test_request_with_trailers(self): with h3_client_and_server() as (quic_client, quic_server): h3_client = H3Connection(quic_client) h3_server = H3Connection(quic_server) stream_id = quic_client.get_next_available_stream_id() h3_client.send_headers( stream_id=stream_id, headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], end_stream=False, ) h3_client.send_headers( stream_id=stream_id, headers=[(b"x-some-trailer", b"foo")], end_stream=True, ) events = h3_transfer(quic_client, h3_server) self.assertEqual( events, [ HeadersReceived( headers=[ (b":method", b"GET"), (b":scheme", b"https"), (b":authority", b"localhost"), (b":path", b"/"), ], stream_id=stream_id, stream_ended=False, ), HeadersReceived( headers=[(b"x-some-trailer", b"foo")], stream_id=stream_id, stream_ended=True, ), ], ) h3_server.send_headers( stream_id=stream_id, headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), ], end_stream=False, ) h3_server.send_data( stream_id=stream_id, data=b"<html><body>hello</body></html>", end_stream=False, ) h3_server.send_headers( stream_id=stream_id, headers=[(b"x-some-trailer", b"bar")], end_stream=True, ) events = h3_transfer(quic_server, h3_client) self.assertEqual( events, [ HeadersReceived( headers=[ (b":status", b"200"), (b"content-type", b"text/html; charset=utf-8"), ], stream_id=stream_id, stream_ended=False, ), DataReceived( data=b"<html><body>hello</body></html>", stream_id=stream_id, stream_ended=False, ), HeadersReceived( headers=[(b"x-some-trailer", b"bar")], stream_id=stream_id, stream_ended=True, ), ], ) def test_uni_stream_type(self): with h3_client_and_server() as (quic_client, quic_server): h3_server = H3Connection(quic_server) stream_id = quic_client.get_next_available_stream_id(is_unidirectional=True) self.assertEqual(stream_id, 2) quic_client.send_stream_data(stream_id, b"\x09") self.assertEqual(h3_transfer(quic_client, h3_server), []) self.assertEqual(list(h3_server._stream.keys()), [2]) self.assertEqual(h3_server._stream[2].buffer, b"") self.assertEqual(h3_server._stream[2].stream_type, 9) stream_id = quic_client.get_next_available_stream_id(is_unidirectional=True) self.assertEqual(stream_id, 6) quic_client.send_stream_data(stream_id, b"\x40") self.assertEqual(h3_transfer(quic_client, h3_server), []) self.assertEqual(list(h3_server._stream.keys()), [2, 6]) self.assertEqual(h3_server._stream[2].buffer, b"") self.assertEqual(h3_server._stream[2].stream_type, 9) self.assertEqual(h3_server._stream[6].buffer, b"\x40") self.assertEqual(h3_server._stream[6].stream_type, None) quic_client.send_stream_data(stream_id, b"\x40") self.assertEqual(h3_transfer(quic_client, h3_server), []) self.assertEqual(list(h3_server._stream.keys()), [2, 6]) self.assertEqual(h3_server._stream[2].buffer, b"") self.assertEqual(h3_server._stream[2].stream_type, 9) self.assertEqual(h3_server._stream[6].buffer, b"") self.assertEqual(h3_server._stream[6].stream_type, 64) def test_validate_settings_h3_datagram_invalid_value(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) settings = copy.copy(DUMMY_SETTINGS) settings[Setting.H3_DATAGRAM] = 2 h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(settings)), end_stream=False, ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_SETTINGS_ERROR, "H3_DATAGRAM setting must be 0 or 1", ), ) def test_validate_settings_h3_datagram_without_transport_parameter(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) settings = copy.copy(DUMMY_SETTINGS) settings[Setting.H3_DATAGRAM] = 1 h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(settings)), end_stream=False, ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_SETTINGS_ERROR, "H3_DATAGRAM requires max_datagram_frame_size transport parameter", ), ) def test_validate_settings_enable_connect_protocol_invalid_value(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) settings = copy.copy(DUMMY_SETTINGS) settings[Setting.ENABLE_CONNECT_PROTOCOL] = 2 h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(settings)), end_stream=False, ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_SETTINGS_ERROR, "ENABLE_CONNECT_PROTOCOL setting must be 0 or 1", ), ) def test_validate_settings_enable_webtransport_invalid_value(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) settings = copy.copy(DUMMY_SETTINGS) settings[Setting.ENABLE_WEBTRANSPORT] = 2 h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(settings)), end_stream=False, ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_SETTINGS_ERROR, "ENABLE_WEBTRANSPORT setting must be 0 or 1", ), ) def test_validate_settings_enable_webtransport_without_h3_datagram(self): quic_server = FakeQuicConnection( configuration=QuicConfiguration(is_client=False) ) h3_server = H3Connection(quic_server) settings = copy.copy(DUMMY_SETTINGS) settings[Setting.ENABLE_WEBTRANSPORT] = 1 h3_server.handle_event( StreamDataReceived( stream_id=2, data=encode_uint_var(StreamType.CONTROL) + encode_frame(FrameType.SETTINGS, encode_settings(settings)), end_stream=False, ) ) self.assertEqual( quic_server.closed, ( ErrorCode.H3_SETTINGS_ERROR, "ENABLE_WEBTRANSPORT requires H3_DATAGRAM", ), ) class H3ParserTest(TestCase): def test_parse_settings_duplicate_identifier(self): buf = Buffer(capacity=1024) buf.push_uint_var(1) buf.push_uint_var(123) buf.push_uint_var(1) buf.push_uint_var(456) with self.assertRaises(SettingsError) as cm: parse_settings(buf.data) self.assertEqual( cm.exception.reason_phrase, "Setting identifier 0x1 is included twice" ) def test_parse_settings_reserved_identifier(self): buf = Buffer(capacity=1024) buf.push_uint_var(0) buf.push_uint_var(123) with self.assertRaises(SettingsError) as cm: parse_settings(buf.data) self.assertEqual( cm.exception.reason_phrase, "Setting identifier 0x0 is reserved" ) def test_validate_push_promise_headers(self): validate_push_promise_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), (b":authority", b"localhost"), ] ) validate_push_promise_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), (b":authority", b"localhost"), (b"x-foo", b"bar"), ] ) with self.assertRaises(MessageError) as cm: validate_push_promise_headers([(b":status", b"foo")]) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':status' is not valid" ) with self.assertRaises(MessageError) as cm: validate_push_promise_headers( [ (b":method", b"GET"), (b":method", b"POST"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':method' is included twice" ) with self.assertRaises(MessageError) as cm: validate_push_promise_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), (b"x-foo", b"bar"), (b":authority", b"foo"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':authority' is not allowed after regular headers", ) with self.assertRaises(MessageError) as cm: validate_push_promise_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-headers [b':authority'] are missing", ) def test_validate_request_headers(self): validate_request_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), (b":authority", b"localhost"), ] ) validate_request_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), (b":authority", b"localhost"), (b"x-foo", b"bar"), ] ) with self.assertRaises(MessageError) as cm: validate_request_headers([(b"X-Foo", b"foo")]) self.assertEqual( cm.exception.reason_phrase, "Header b'X-Foo' contains uppercase letters" ) with self.assertRaises(MessageError) as cm: validate_request_headers([(b":status", b"foo")]) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':status' is not valid" ) with self.assertRaises(MessageError) as cm: validate_request_headers( [ (b":method", b"GET"), (b":method", b"POST"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':method' is included twice" ) with self.assertRaises(MessageError) as cm: validate_request_headers( [ (b":method", b"GET"), (b":scheme", b"https"), (b":path", b"/"), (b"x-foo", b"bar"), (b":authority", b"foo"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':authority' is not allowed after regular headers", ) with self.assertRaises(MessageError) as cm: validate_request_headers([(b":method", b"GET")]) self.assertEqual( cm.exception.reason_phrase, "Pseudo-headers [b':authority'] are missing", ) for scheme in [b"http", b"https"]: with self.assertRaises(MessageError) as cm: validate_request_headers( [ (b":method", b"GET"), (b":scheme", scheme), (b":authority", b""), (b":path", b"/"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':authority' cannot be empty", ) for scheme in [b"http", b"https"]: with self.assertRaises(MessageError) as cm: validate_request_headers( [ (b":method", b"GET"), (b":scheme", scheme), (b":authority", b"localhost"), (b":path", b""), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':path' cannot be empty" ) def test_validate_response_headers(self): validate_response_headers([(b":status", b"200")]) validate_response_headers( [ (b":status", b"200"), (b"x-foo", b"bar"), ] ) with self.assertRaises(MessageError) as cm: validate_response_headers([(b":method", b"GET")]) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':method' is not valid" ) with self.assertRaises(MessageError) as cm: validate_response_headers( [ (b":status", b"200"), (b":status", b"501"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':status' is included twice" ) def test_validate_trailers(self): validate_trailers([(b"x-foo", b"bar")]) with self.assertRaises(MessageError) as cm: validate_trailers([(b":status", b"foo")]) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':status' is not valid" ) with self.assertRaises(MessageError) as cm: validate_trailers( [ (b"x-foo", b"bar"), (b":authority", b"foo"), ] ) self.assertEqual( cm.exception.reason_phrase, "Pseudo-header b':authority' is not allowed after regular headers", )

true

f7193fce1fa7b0ba46bf2b1d2e9a114b6453e540

2,901

py

Python

scripts/md_to_html.py

fossabot/granite

7eab82126d0cddb4fdad0c3ba2c6f431eea19cfb

[ "MIT" ]

null

scripts/md_to_html.py

fossabot/granite

7eab82126d0cddb4fdad0c3ba2c6f431eea19cfb

[ "MIT" ]

3

2021-02-06T17:29:31.000Z

2021-05-27T20:48:58.000Z

scripts/md_to_html.py

fossabot/granite

7eab82126d0cddb4fdad0c3ba2c6f431eea19cfb

[ "MIT" ]

2

2021-02-02T23:07:50.000Z

2021-03-27T22:06:27.000Z

#! /usr/bin/env python3 # Script from https://gist.github.com/jiffyclub/5015986 # This script turns Markdown into HTML using the Python markdown library and wraps the result in a # complete HTML document with default Bootstrap styling so that it's immediately printable. # Requires the python libraries jinja2, markdown, and mdx_smartypants. import argparse import sys import jinja2 import markdown # To install dependencies in a virtualenv: # $ py -3 -3 venv .venv # $ .venv/Scripts/activate # $ pip install jinja2 # $ pip install markdown # # To install dependencies on Ubuntu: # $ sudo apt-get install python-jinja2 python-markdown TEMPLATE = """<!DOCTYPE html> <html> <head> <link href="http://netdna.bootstrapcdn.com/twitter-bootstrap/2.3.0/css/bootstrap-combined.min.css" rel="stylesheet"> <style> body { font-family: sans-serif; } code, pre { font-family: monospace; } h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { font-size: inherit; } </style> </head> <body> <div class="container"> {{content}} </div> </body> </html> """ # TEMPLATE = """<!DOCTYPE html> # <html> # <head> # <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> # <meta name="referrer" content="no-referrer" /> # <meta name="referrer" content="unsafe-url" /> # <meta name="referrer" content="origin" /> # <meta name="referrer" content="no-referrer-when-downgrade" /> # <meta name="referrer" content="origin-when-cross-origin" /> # <title>Page Title</title> # <link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet"> # <style> # body { # font-family: Helvetica,Arial,sans-serif; # } # code, pre { # font-family: monospace; # } # </style> # </head> # <body> # <div class="container"> # {{content}} # </div> # </body> # </html> # """ def parse_args(args=None): d = 'Make a complete, styled HTML document from a Markdown file.' parser = argparse.ArgumentParser(description=d) parser.add_argument('mdfile', type=argparse.FileType('r'), nargs='?', default=sys.stdin, help='File to convert. Defaults to stdin.') parser.add_argument('-o', '--out', type=argparse.FileType('w'), default=sys.stdout, help='Output file name. Defaults to stdout.') return parser.parse_args(args) def main(args=None): args = parse_args(args) md = args.mdfile.read() extensions = ['extra', 'smarty'] html = markdown.markdown(md, extensions=extensions, output_format='html5') doc = jinja2.Template(TEMPLATE).render(content=html) args.out.write(doc) if __name__ == '__main__': sys.exit()

29.30303

120

0.612203

# Requires the python libraries jinja2, markdown, and mdx_smartypants. import argparse import sys import jinja2 import markdown # To install dependencies in a virtualenv: # $ py -3 -3 venv .venv # $ .venv/Scripts/activate # $ pip install jinja2 # $ pip install markdown # # To install dependencies on Ubuntu: # $ sudo apt-get install python-jinja2 python-markdown TEMPLATE = """<!DOCTYPE html> <html> <head> <link href="http://netdna.bootstrapcdn.com/twitter-bootstrap/2.3.0/css/bootstrap-combined.min.css" rel="stylesheet"> <style> body { font-family: sans-serif; } code, pre { font-family: monospace; } h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { font-size: inherit; } </style> </head> <body> <div class="container"> {{content}} </div> </body> </html> """ # TEMPLATE = """<!DOCTYPE html> # <html> # <head> # <meta http-equiv="Content-Type" content="text/html; charset=utf-8"> # <meta name="referrer" content="no-referrer" /> # <meta name="referrer" content="unsafe-url" /> # <meta name="referrer" content="origin" /> # <meta name="referrer" content="no-referrer-when-downgrade" /> # <meta name="referrer" content="origin-when-cross-origin" /> # <title>Page Title</title> # <link href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css" rel="stylesheet"> # <style> # body { # font-family: Helvetica,Arial,sans-serif; # } # code, pre { # font-family: monospace; # } # </style> # </head> # <body> # <div class="container"> # {{content}} # </div> # </body> # </html> # """ def parse_args(args=None): d = 'Make a complete, styled HTML document from a Markdown file.' parser = argparse.ArgumentParser(description=d) parser.add_argument('mdfile', type=argparse.FileType('r'), nargs='?', default=sys.stdin, help='File to convert. Defaults to stdin.') parser.add_argument('-o', '--out', type=argparse.FileType('w'), default=sys.stdout, help='Output file name. Defaults to stdout.') return parser.parse_args(args) def main(args=None): args = parse_args(args) md = args.mdfile.read() extensions = ['extra', 'smarty'] html = markdown.markdown(md, extensions=extensions, output_format='html5') doc = jinja2.Template(TEMPLATE).render(content=html) args.out.write(doc) if __name__ == '__main__': sys.exit()

true

f7193fdcc986cc8bca9f6efe5170d075ac0c3ace

7,030

py

Python

dm_control/rl/specs_test.py

1nadequacy/dm_control

a55474768cf0a6d570fe4a376802630027ad5f01

[ "Apache-2.0" ]

null

dm_control/rl/specs_test.py

1nadequacy/dm_control

a55474768cf0a6d570fe4a376802630027ad5f01

[ "Apache-2.0" ]

null

dm_control/rl/specs_test.py

1nadequacy/dm_control

a55474768cf0a6d570fe4a376802630027ad5f01

[ "Apache-2.0" ]

null

# Copyright 2017 The dm_control Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Tests for specs.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function # Internal dependencies. from absl.testing import absltest from dm_control.rl import specs as array_spec import numpy as np import six class ArraySpecTest(absltest.TestCase): def testShapeTypeError(self): with self.assertRaises(TypeError): array_spec.ArraySpec(32, np.int32) def testDtypeTypeError(self): with self.assertRaises(TypeError): array_spec.ArraySpec((1, 2, 3), "32") def testStringDtype(self): array_spec.ArraySpec((1, 2, 3), "int32") def testNumpyDtype(self): array_spec.ArraySpec((1, 2, 3), np.int32) def testDtype(self): spec = array_spec.ArraySpec((1, 2, 3), np.int32) self.assertEqual(np.int32, spec.dtype) def testShape(self): spec = array_spec.ArraySpec([1, 2, 3], np.int32) self.assertEqual((1, 2, 3), spec.shape) def testEqual(self): spec_1 = array_spec.ArraySpec((1, 2, 3), np.int32) spec_2 = array_spec.ArraySpec((1, 2, 3), np.int32) self.assertEqual(spec_1, spec_2) def testNotEqualDifferentShape(self): spec_1 = array_spec.ArraySpec((1, 2, 3), np.int32) spec_2 = array_spec.ArraySpec((1, 3, 3), np.int32) self.assertNotEqual(spec_1, spec_2) def testNotEqualDifferentDtype(self): spec_1 = array_spec.ArraySpec((1, 2, 3), np.int64) spec_2 = array_spec.ArraySpec((1, 2, 3), np.int32) self.assertNotEqual(spec_1, spec_2) def testNotEqualOtherClass(self): spec_1 = array_spec.ArraySpec((1, 2, 3), np.int32) spec_2 = None self.assertNotEqual(spec_1, spec_2) self.assertNotEqual(spec_2, spec_1) spec_2 = () self.assertNotEqual(spec_1, spec_2) self.assertNotEqual(spec_2, spec_1) def testIsUnhashable(self): spec = array_spec.ArraySpec(shape=(1, 2, 3), dtype=np.int32) with self.assertRaisesRegexp(TypeError, "unhashable type"): hash(spec) def testValidateDtype(self): spec = array_spec.ArraySpec((1, 2), np.int32) spec.validate(np.zeros((1, 2), dtype=np.int32)) with self.assertRaises(ValueError): spec.validate(np.zeros((1, 2), dtype=np.float32)) def testValidateShape(self): spec = array_spec.ArraySpec((1, 2), np.int32) spec.validate(np.zeros((1, 2), dtype=np.int32)) with self.assertRaises(ValueError): spec.validate(np.zeros((1, 2, 3), dtype=np.int32)) def testGenerateValue(self): spec = array_spec.ArraySpec((1, 2), np.int32) test_value = spec.generate_value() spec.validate(test_value) class BoundedArraySpecTest(absltest.TestCase): def testInvalidMinimum(self): with six.assertRaisesRegex(self, ValueError, "not compatible"): array_spec.BoundedArraySpec((3, 5), np.uint8, (0, 0, 0), (1, 1)) def testInvalidMaximum(self): with six.assertRaisesRegex(self, ValueError, "not compatible"): array_spec.BoundedArraySpec((3, 5), np.uint8, 0, (1, 1, 1)) def testMinMaxAttributes(self): spec = array_spec.BoundedArraySpec((1, 2, 3), np.float32, 0, (5, 5, 5)) self.assertEqual(type(spec.minimum), np.ndarray) self.assertEqual(type(spec.maximum), np.ndarray) def testNotWriteable(self): spec = array_spec.BoundedArraySpec((1, 2, 3), np.float32, 0, (5, 5, 5)) with six.assertRaisesRegex(self, ValueError, "read-only"): spec.minimum[0] = -1 with six.assertRaisesRegex(self, ValueError, "read-only"): spec.maximum[0] = 100 def testEqualBroadcastingBounds(self): spec_1 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=0.0, maximum=1.0) spec_2 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=[0.0, 0.0], maximum=[1.0, 1.0]) self.assertEqual(spec_1, spec_2) def testNotEqualDifferentMinimum(self): spec_1 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=[0.0, -0.6], maximum=[1.0, 1.0]) spec_2 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=[0.0, 0.0], maximum=[1.0, 1.0]) self.assertNotEqual(spec_1, spec_2) def testNotEqualOtherClass(self): spec_1 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=[0.0, -0.6], maximum=[1.0, 1.0]) spec_2 = array_spec.ArraySpec((1, 2), np.int32) self.assertNotEqual(spec_1, spec_2) self.assertNotEqual(spec_2, spec_1) spec_2 = None self.assertNotEqual(spec_1, spec_2) self.assertNotEqual(spec_2, spec_1) spec_2 = () self.assertNotEqual(spec_1, spec_2) self.assertNotEqual(spec_2, spec_1) def testNotEqualDifferentMaximum(self): spec_1 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=0.0, maximum=2.0) spec_2 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=[0.0, 0.0], maximum=[1.0, 1.0]) self.assertNotEqual(spec_1, spec_2) def testIsUnhashable(self): spec = array_spec.BoundedArraySpec( shape=(1, 2), dtype=np.int32, minimum=0.0, maximum=2.0) with self.assertRaisesRegexp(TypeError, "unhashable type"): hash(spec) def testRepr(self): as_string = repr(array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=101.0, maximum=73.0)) self.assertIn("101", as_string) self.assertIn("73", as_string) def testValidateBounds(self): spec = array_spec.BoundedArraySpec((2, 2), np.int32, minimum=5, maximum=10) spec.validate(np.array([[5, 6], [8, 10]], dtype=np.int32)) with self.assertRaises(ValueError): spec.validate(np.array([[5, 6], [8, 11]], dtype=np.int32)) with self.assertRaises(ValueError): spec.validate(np.array([[4, 6], [8, 10]], dtype=np.int32)) def testGenerateValue(self): spec = array_spec.BoundedArraySpec((2, 2), np.int32, minimum=5, maximum=10) test_value = spec.generate_value() spec.validate(test_value) def testScalarBounds(self): spec = array_spec.BoundedArraySpec((), np.float, minimum=0.0, maximum=1.0) self.assertIsInstance(spec.minimum, np.ndarray) self.assertIsInstance(spec.maximum, np.ndarray) # Sanity check that numpy compares correctly to a scalar for an empty shape. self.assertEqual(0.0, spec.minimum) self.assertEqual(1.0, spec.maximum) # Check that the spec doesn't fail its own input validation. _ = array_spec.BoundedArraySpec( spec.shape, spec.dtype, spec.minimum, spec.maximum) if __name__ == "__main__": absltest.main()

34.975124

80

0.683642

from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl.testing import absltest from dm_control.rl import specs as array_spec import numpy as np import six class ArraySpecTest(absltest.TestCase): def testShapeTypeError(self): with self.assertRaises(TypeError): array_spec.ArraySpec(32, np.int32) def testDtypeTypeError(self): with self.assertRaises(TypeError): array_spec.ArraySpec((1, 2, 3), "32") def testStringDtype(self): array_spec.ArraySpec((1, 2, 3), "int32") def testNumpyDtype(self): array_spec.ArraySpec((1, 2, 3), np.int32) def testDtype(self): spec = array_spec.ArraySpec((1, 2, 3), np.int32) self.assertEqual(np.int32, spec.dtype) def testShape(self): spec = array_spec.ArraySpec([1, 2, 3], np.int32) self.assertEqual((1, 2, 3), spec.shape) def testEqual(self): spec_1 = array_spec.ArraySpec((1, 2, 3), np.int32) spec_2 = array_spec.ArraySpec((1, 2, 3), np.int32) self.assertEqual(spec_1, spec_2) def testNotEqualDifferentShape(self): spec_1 = array_spec.ArraySpec((1, 2, 3), np.int32) spec_2 = array_spec.ArraySpec((1, 3, 3), np.int32) self.assertNotEqual(spec_1, spec_2) def testNotEqualDifferentDtype(self): spec_1 = array_spec.ArraySpec((1, 2, 3), np.int64) spec_2 = array_spec.ArraySpec((1, 2, 3), np.int32) self.assertNotEqual(spec_1, spec_2) def testNotEqualOtherClass(self): spec_1 = array_spec.ArraySpec((1, 2, 3), np.int32) spec_2 = None self.assertNotEqual(spec_1, spec_2) self.assertNotEqual(spec_2, spec_1) spec_2 = () self.assertNotEqual(spec_1, spec_2) self.assertNotEqual(spec_2, spec_1) def testIsUnhashable(self): spec = array_spec.ArraySpec(shape=(1, 2, 3), dtype=np.int32) with self.assertRaisesRegexp(TypeError, "unhashable type"): hash(spec) def testValidateDtype(self): spec = array_spec.ArraySpec((1, 2), np.int32) spec.validate(np.zeros((1, 2), dtype=np.int32)) with self.assertRaises(ValueError): spec.validate(np.zeros((1, 2), dtype=np.float32)) def testValidateShape(self): spec = array_spec.ArraySpec((1, 2), np.int32) spec.validate(np.zeros((1, 2), dtype=np.int32)) with self.assertRaises(ValueError): spec.validate(np.zeros((1, 2, 3), dtype=np.int32)) def testGenerateValue(self): spec = array_spec.ArraySpec((1, 2), np.int32) test_value = spec.generate_value() spec.validate(test_value) class BoundedArraySpecTest(absltest.TestCase): def testInvalidMinimum(self): with six.assertRaisesRegex(self, ValueError, "not compatible"): array_spec.BoundedArraySpec((3, 5), np.uint8, (0, 0, 0), (1, 1)) def testInvalidMaximum(self): with six.assertRaisesRegex(self, ValueError, "not compatible"): array_spec.BoundedArraySpec((3, 5), np.uint8, 0, (1, 1, 1)) def testMinMaxAttributes(self): spec = array_spec.BoundedArraySpec((1, 2, 3), np.float32, 0, (5, 5, 5)) self.assertEqual(type(spec.minimum), np.ndarray) self.assertEqual(type(spec.maximum), np.ndarray) def testNotWriteable(self): spec = array_spec.BoundedArraySpec((1, 2, 3), np.float32, 0, (5, 5, 5)) with six.assertRaisesRegex(self, ValueError, "read-only"): spec.minimum[0] = -1 with six.assertRaisesRegex(self, ValueError, "read-only"): spec.maximum[0] = 100 def testEqualBroadcastingBounds(self): spec_1 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=0.0, maximum=1.0) spec_2 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=[0.0, 0.0], maximum=[1.0, 1.0]) self.assertEqual(spec_1, spec_2) def testNotEqualDifferentMinimum(self): spec_1 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=[0.0, -0.6], maximum=[1.0, 1.0]) spec_2 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=[0.0, 0.0], maximum=[1.0, 1.0]) self.assertNotEqual(spec_1, spec_2) def testNotEqualOtherClass(self): spec_1 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=[0.0, -0.6], maximum=[1.0, 1.0]) spec_2 = array_spec.ArraySpec((1, 2), np.int32) self.assertNotEqual(spec_1, spec_2) self.assertNotEqual(spec_2, spec_1) spec_2 = None self.assertNotEqual(spec_1, spec_2) self.assertNotEqual(spec_2, spec_1) spec_2 = () self.assertNotEqual(spec_1, spec_2) self.assertNotEqual(spec_2, spec_1) def testNotEqualDifferentMaximum(self): spec_1 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=0.0, maximum=2.0) spec_2 = array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=[0.0, 0.0], maximum=[1.0, 1.0]) self.assertNotEqual(spec_1, spec_2) def testIsUnhashable(self): spec = array_spec.BoundedArraySpec( shape=(1, 2), dtype=np.int32, minimum=0.0, maximum=2.0) with self.assertRaisesRegexp(TypeError, "unhashable type"): hash(spec) def testRepr(self): as_string = repr(array_spec.BoundedArraySpec( (1, 2), np.int32, minimum=101.0, maximum=73.0)) self.assertIn("101", as_string) self.assertIn("73", as_string) def testValidateBounds(self): spec = array_spec.BoundedArraySpec((2, 2), np.int32, minimum=5, maximum=10) spec.validate(np.array([[5, 6], [8, 10]], dtype=np.int32)) with self.assertRaises(ValueError): spec.validate(np.array([[5, 6], [8, 11]], dtype=np.int32)) with self.assertRaises(ValueError): spec.validate(np.array([[4, 6], [8, 10]], dtype=np.int32)) def testGenerateValue(self): spec = array_spec.BoundedArraySpec((2, 2), np.int32, minimum=5, maximum=10) test_value = spec.generate_value() spec.validate(test_value) def testScalarBounds(self): spec = array_spec.BoundedArraySpec((), np.float, minimum=0.0, maximum=1.0) self.assertIsInstance(spec.minimum, np.ndarray) self.assertIsInstance(spec.maximum, np.ndarray) self.assertEqual(0.0, spec.minimum) self.assertEqual(1.0, spec.maximum) _ = array_spec.BoundedArraySpec( spec.shape, spec.dtype, spec.minimum, spec.maximum) if __name__ == "__main__": absltest.main()

true

f7193fe758b15f3c5dd4561b3624e25cf35dca18

4,536

py

Python

Model_test.py

JoeTao-097/Multi-REZ-Evalution-for-Breast-Ultrasound-Images

344d64ad2fe9d790c49e8005b3abee219d362278

[ "Apache-2.0" ]

null

Model_test.py

JoeTao-097/Multi-REZ-Evalution-for-Breast-Ultrasound-Images

344d64ad2fe9d790c49e8005b3abee219d362278

[ "Apache-2.0" ]

null

Model_test.py

JoeTao-097/Multi-REZ-Evalution-for-Breast-Ultrasound-Images

344d64ad2fe9d790c49e8005b3abee219d362278

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Aug 2 17:32:52 2021 @author: jiangyt """ from Tools import * from tensorflow import keras from tensorflow.keras.layers import Dense, Activation, Flatten, Dropout, Input, BatchNormalization from tensorflow.keras.layers import Conv2D, MaxPooling2D, add, AveragePooling2D, ZeroPadding2D, GlobalAveragePooling2D from tensorflow.keras.models import Model, Sequential """ Weight Dict """ Weight = {'Resnet50_448':"./model_checkpoints/ResNet50_448_checkpoints/20218131038.h5", 'MobileNet_224':"./model_checkpoints/MobileNet_224_checkpoints/202189956.h5", 'Xception_448':"./model_checkpoints/Xception_448_checkpoints/2021810951.h5", 'EfficientNet_B0_320':"./model_checkpoints/EfficientNetB0_320_checkpoints/2021871045.h5", 'DenseNet121_448':"./model_checkpoints/DenseNet121_448_checkpoints/2021891655.h5"} """ Load model """ df = pd.read_excel('./AI-Physician Comparasion Dataset.xlsx') # df = pd.read_csv('/home/joe/Project/Breast_new/20210805_b_m_Xception_train/df_test_small.csv') """ Eval each model """ for key in Weight.keys(): if key == 'Resnet50_448': from tensorflow.keras.applications.resnet50 import preprocess_input backbone_model= keras.applications.resnet50.ResNet50(include_top=False, weights=None, input_tensor=None, input_shape=(448, 448, 3), pooling=None, classes=2) elif key == 'MobileNet_224': from tensorflow.keras.applications.mobilenet import preprocess_input backbone_model= keras.applications.mobilenet.MobileNet(include_top=False, weights=None, input_tensor=None, input_shape=(224, 224, 3), pooling=None, classes=2) elif key == 'Xception_448': from tensorflow.keras.applications.xception import preprocess_input backbone_model= keras.applications.xception.Xception(include_top=False, weights=None, input_tensor=None, input_shape=(448, 448, 3), pooling=None, classes=2) elif key == 'EfficientNet_B0_320': from tensorflow.keras.applications.efficientnet import preprocess_input backbone_model= keras.applications.efficientnet.EfficientNetB0(include_top=False, weights=None, input_tensor=None, input_shape=(320, 320, 3), pooling=None, classes=2) elif key == 'DenseNet121_448': from tensorflow.keras.applications.densenet import preprocess_input backbone_model = keras.applications.densenet.DenseNet121(include_top=False, weights="imagenet", input_tensor=None, input_shape=(448, 448, 3), pooling=None, classes=2) else: print('Error: No model weight find') test_model = Sequential() test_model.add(backbone_model) test_model.add(GlobalAveragePooling2D()) test_model.add(Dense(2, activation='softmax', name='fc1')) test_model.load_weights(Weight[key]) test_model.summary() y_true = [] y_pred = [] for i in range(len(df)): y_true.append(df['malignancy'][i]) x = Image.open(df['path'][i]) x = np.array(x) x = zero_pad(x,int(key.split('_')[-1])) x = preprocess_input(x) x = x.reshape(1,x.shape[0],x.shape[1],x.shape[2]) y_pred.append(test_model.predict(x)) y_pred = np.array(y_pred) y_pred = y_pred.reshape(y_pred.shape[0],2) y_pred_1 = y_pred[:,1] thresh_0=get_auc(0, np.array(y_true), np.array(y_pred_1), 'Malignancy', plot=False) y_pred_comp_lvl=[1 if y>thresh_0 else 0 for y in y_pred_1] cm_comp=confusion_matrix(y_true, y_pred_comp_lvl) fig, axes = plt.subplots(nrows=2, ncols=2) fig.tight_layout(pad=2, w_pad=2.) fig.set_figheight(8) fig.set_figwidth(7) thresh_0=get_auc(axes[0, 0], np.array(y_true), np.array(y_pred_1), 'Performance of {}'.format(key)) thresh_AP=get_precision_recall(axes[0, 1], np.array(y_true), np.array(y_pred_1), 'Malignancy=0 vs 1') plot_confusion_matrix(axes[1, 0], cm_comp, ["0", "1"], title='Malignancy', normalize=False) plot_confusion_matrix(axes[1, 1], cm_comp, ["0", "1"], title='Malignancy (normalized)') print('f1 score is: {:.3f}'.format(f1_score(y_true, y_pred_comp_lvl)))

47.747368

123

0.646825

from Tools import * from tensorflow import keras from tensorflow.keras.layers import Dense, Activation, Flatten, Dropout, Input, BatchNormalization from tensorflow.keras.layers import Conv2D, MaxPooling2D, add, AveragePooling2D, ZeroPadding2D, GlobalAveragePooling2D from tensorflow.keras.models import Model, Sequential Weight = {'Resnet50_448':"./model_checkpoints/ResNet50_448_checkpoints/20218131038.h5", 'MobileNet_224':"./model_checkpoints/MobileNet_224_checkpoints/202189956.h5", 'Xception_448':"./model_checkpoints/Xception_448_checkpoints/2021810951.h5", 'EfficientNet_B0_320':"./model_checkpoints/EfficientNetB0_320_checkpoints/2021871045.h5", 'DenseNet121_448':"./model_checkpoints/DenseNet121_448_checkpoints/2021891655.h5"} df = pd.read_excel('./AI-Physician Comparasion Dataset.xlsx') for key in Weight.keys(): if key == 'Resnet50_448': from tensorflow.keras.applications.resnet50 import preprocess_input backbone_model= keras.applications.resnet50.ResNet50(include_top=False, weights=None, input_tensor=None, input_shape=(448, 448, 3), pooling=None, classes=2) elif key == 'MobileNet_224': from tensorflow.keras.applications.mobilenet import preprocess_input backbone_model= keras.applications.mobilenet.MobileNet(include_top=False, weights=None, input_tensor=None, input_shape=(224, 224, 3), pooling=None, classes=2) elif key == 'Xception_448': from tensorflow.keras.applications.xception import preprocess_input backbone_model= keras.applications.xception.Xception(include_top=False, weights=None, input_tensor=None, input_shape=(448, 448, 3), pooling=None, classes=2) elif key == 'EfficientNet_B0_320': from tensorflow.keras.applications.efficientnet import preprocess_input backbone_model= keras.applications.efficientnet.EfficientNetB0(include_top=False, weights=None, input_tensor=None, input_shape=(320, 320, 3), pooling=None, classes=2) elif key == 'DenseNet121_448': from tensorflow.keras.applications.densenet import preprocess_input backbone_model = keras.applications.densenet.DenseNet121(include_top=False, weights="imagenet", input_tensor=None, input_shape=(448, 448, 3), pooling=None, classes=2) else: print('Error: No model weight find') test_model = Sequential() test_model.add(backbone_model) test_model.add(GlobalAveragePooling2D()) test_model.add(Dense(2, activation='softmax', name='fc1')) test_model.load_weights(Weight[key]) test_model.summary() y_true = [] y_pred = [] for i in range(len(df)): y_true.append(df['malignancy'][i]) x = Image.open(df['path'][i]) x = np.array(x) x = zero_pad(x,int(key.split('_')[-1])) x = preprocess_input(x) x = x.reshape(1,x.shape[0],x.shape[1],x.shape[2]) y_pred.append(test_model.predict(x)) y_pred = np.array(y_pred) y_pred = y_pred.reshape(y_pred.shape[0],2) y_pred_1 = y_pred[:,1] thresh_0=get_auc(0, np.array(y_true), np.array(y_pred_1), 'Malignancy', plot=False) y_pred_comp_lvl=[1 if y>thresh_0 else 0 for y in y_pred_1] cm_comp=confusion_matrix(y_true, y_pred_comp_lvl) fig, axes = plt.subplots(nrows=2, ncols=2) fig.tight_layout(pad=2, w_pad=2.) fig.set_figheight(8) fig.set_figwidth(7) thresh_0=get_auc(axes[0, 0], np.array(y_true), np.array(y_pred_1), 'Performance of {}'.format(key)) thresh_AP=get_precision_recall(axes[0, 1], np.array(y_true), np.array(y_pred_1), 'Malignancy=0 vs 1') plot_confusion_matrix(axes[1, 0], cm_comp, ["0", "1"], title='Malignancy', normalize=False) plot_confusion_matrix(axes[1, 1], cm_comp, ["0", "1"], title='Malignancy (normalized)') print('f1 score is: {:.3f}'.format(f1_score(y_true, y_pred_comp_lvl)))

true

f7194033eec50c8f02954cf2105d80b049769652

9,394

py

Python

prepare_data.py

Euro2xx/gansformer

83403cdb49e049e3b4d9f3472577f2ee73f7ba64

[ "MIT" ]

1,172

2021-03-02T02:00:44.000Z

2022-03-31T02:46:45.000Z

prepare_data.py

Euro2xx/gansformer

83403cdb49e049e3b4d9f3472577f2ee73f7ba64

[ "MIT" ]

37

2021-03-03T14:11:11.000Z

2022-03-12T15:40:15.000Z

prepare_data.py

Euro2xx/gansformer

83403cdb49e049e3b4d9f3472577f2ee73f7ba64

[ "MIT" ]

138

2021-03-02T06:37:10.000Z

2022-03-30T14:59:09.000Z

# import warnings filter from warnings import simplefilter # ignore all future warnings simplefilter(action = "ignore", category = FutureWarning) import os import sys import tqdm import time import json import glob import gdown import urllib import zipfile import hashlib import argparse import numpy as np from training import misc from dnnlib import EasyDict import dataset_tool catalog = { "ffhq": EasyDict({ "name": "FFHQ", # Dataset name for logging "filename": "ffhq-r08.tfrecords1of1", # Local file name "url": "http://downloads.cs.stanford.edu/nlp/data/dorarad/ffhq-r08.tfrecords1of1", # download URL "md5": "74de4f07dc7bfb07c0ad4471fdac5e67", # MD5 checksum to potentially skip download "ratio": 1, # height/width ratio "size": 13, # download size in GB "shards": 1, # Number of tfrecord shards "img_num": 70000 # Number of images }), "bedrooms": EasyDict({ "name": "LSUN-Bedrooms", # Dataset name for logging "filename": "bedroom_train_lmdb.zip", "url": "http://dl.yf.io/lsun/scenes/bedroom_train_lmdb.zip", "md5": "f2c5d904a82a6295dbdccb322b4b0a99", "dir": "bedroom_train_lmdb", "ratio": 188/256, "size": 43, "shards": 64, "img_num": 3033042, "process": dataset_tool.create_from_lmdb # Function to convert download to tfrecords }), "cityscapes": EasyDict({ "name": "Cityscapes", # Dataset name for logging "filename": "cityscapes.zip", "url": "https://drive.google.com/uc?id=1t9Qhxm0iHFd3k-xTYEbKosSx_DkyoLLJ", "md5": "953d231046275120dc1f73a5aebc9087", "ratio": 0.5, "size": 2, "shards": 16, "img_num": 25000 }), "clevr": EasyDict({ "name": "CLEVR", # Dataset name for logging "filename": "clevr.zip", "url": "https://drive.google.com/uc?id=1lY4JE30yk26v0MWHNpXBOMzltufUcTXj", "md5": "3040bb20a29cd2f0e1e9231aebddf2a1", "size": 6, "ratio": 0.75, "shards": 5, "img_num": 100000 ########################################################################################## # Currently, we download preprocessed TFrecords of CLEVR images with image ratio 0.75. # To process instead the dataset from scratch (with the original image ratio of 320/480), add the following: # "filename": "CLEVR_v1.0.zip", # "size": 18, # "dir": "CLEVR_v1.0/images", # Image directory to process while turning into tfrecords # "url": "https://dl.fbaipublicfiles.com/clevr/CLEVR_v1.0.zip", # "md5": "b11922020e72d0cd9154779b2d3d07d2", # "process": dataset_tool.create_from_imgs # Function to convert download to tfrecords }) } formats_catalog = { "png": lambda tfdir, imgdir, **kwargs: dataset_tool.create_from_imgs(tfdir, imgdir, format = "png", **kwargs), "jpg": lambda tfdir, imgdir, **kwargs: dataset_tool.create_from_imgs(tfdir, imgdir, format = "jpg", **kwargs), "npy": dataset_tool.create_from_npy, "hdf5": dataset_tool.create_from_hdf5, "tfds": dataset_tool.create_from_tfds, "lmdb": dataset_tool.create_from_lmdb } def mkdir(d): if not os.path.exists(d): try: os.makedirs(d) except: pass def verify_md5(filename, md5): print("Verify MD5 for {}...".format(filename)) with open(filename, "rb") as f: new_md5 = hashlib.md5(f.read()).hexdigest() result = md5 == new_md5 if result: print(misc.bold("MD5 matches!")) else: print("MD5 doesn't match. Will redownload the file.") return result def is_unzipped(zip, dir): with zipfile.ZipFile(zip) as zf: archive = zf.namelist() all_exist = all(os.path.exists("{}/{}".format(dir, file)) for file in archive) return all_exist def unzip(zip, dir): with zipfile.ZipFile(zip) as zf: for member in tqdm.tqdm(zf.infolist(), desc = "Extracting "): try: zf.extract(member, dir) except zipfile.error as e: pass def get_path(url, dir = None, path = None): if path is None: path = url.split("/")[-1] if dir is not None: path = "{}/{}".format(dir, path) return path def download_file(url, path, block_sz = 8192): if "drive.google.com" in url: gdown.download(url, path) else: u = urllib.request.urlopen(url) with open(path, "wb") as f: fsize = int(u.info().get_all("Content-Length")[0]) print("Downloading: %s Bytes: %s" % (path, fsize)) curr = 0 while True: buffer = u.read(block_sz) if not buffer: break curr += len(buffer) f.write(buffer) status = r"%10d [%3.2f%%]" % (curr, curr * 100. / fsize) status += chr(8) * (len(status) + 1) print(status, end = "", flush = True) def prepare(tasks, data_dir, shards_num = 1, max_images = None, ratio = 1.0, images_dir = None, format = None): # Options for custom dataset mkdir(data_dir) for task in tasks: # If task not in catalog, create custom task configuration c = catalog.get(task, EasyDict({ "local": True, "name": task, "dir": images_dir, "ratio": ratio, "process": formats_catalog.get(format) })) dirname = "{}/{}".format(data_dir, task) mkdir(dirname) # try: print(misc.bold("Preparing the {} dataset...".format(c.name))) if "local" not in c: fname = "{}/{}".format(dirname, c.filename) download = not ((os.path.exists(fname) and verify_md5(fname, c.md5))) path = get_path(c.url, dirname, path = c.filename) if download: print(misc.bold("Downloading the data ({} GB)...".format(c.size))) download_file(c.url, path) # print(misc.bold("Completed downloading {}".format(c.name))) if path.endswith(".zip"): if not is_unzipped(path, dirname): print(misc.bold("Unzipping {}...".format(path))) unzip(path, dirname) # print(misc.bold("Completed unzipping {}".format(path))) if "process" in c: imgdir = images_dir if "local" in c else ("{}/{}".format(dirname, c.dir)) shards_num = c.shards if max_images is None else shards_num c.process(dirname, imgdir, ratio = c.ratio, shards_num = shards_num, max_imgs = max_images) print(misc.bcolored("Completed preparations for {}!".format(c.name), "blue")) # except: # print(misc.bcolored("Had an error in preparing the {} dataset. Will move on.".format(c.name), "red")) # print(sys.exc_info()) def run_cmdline(argv): parser = argparse.ArgumentParser(prog = argv[0], description = "Download and prepare data for the GANformer.") parser.add_argument("--data-dir", help = "Directory of created dataset", default = "datasets", type = str) parser.add_argument("--shards-num", help = "Number of shards to split each dataset to (optional)", default = 1, type = int) parser.add_argument("--max-images", help = "Maximum number of images to have in the dataset (optional). Use to reduce the produced tfrecords file size", default = None, type = int) # Default tasks parser.add_argument("--clevr", help = "Prepare the CLEVR dataset (6.41GB download, 31GB tfrecords, 100k images)", dest = "tasks", action = "append_const", const = "clevr") parser.add_argument("--bedrooms", help = "Prepare the LSUN-bedrooms dataset (42.8GB download, up to 480GB tfrecords, 3M images)", dest = "tasks", action = "append_const", const = "bedrooms") parser.add_argument("--ffhq", help = "Prepare the FFHQ dataset (13GB download, 13GB tfrecords, 70k images)", dest = "tasks", action = "append_const", const = "ffhq") parser.add_argument("--cityscapes", help = "Prepare the cityscapes dataset (1.8GB download, 8GB tfrecords, 25k images)", dest = "tasks", action = "append_const", const = "cityscapes") # Create a new task with custom images parser.add_argument("--task", help = "New dataset name", type = str, dest = "tasks", action = "append") parser.add_argument("--images-dir", help = "Provide source image directory to convert into tfrecords (will be searched recursively)", default = None, type = str) parser.add_argument("--format", help = "Images format", default = None, choices = ["png", "jpg", "npy", "hdf5", "tfds", "lmdb"], type = str) parser.add_argument("--ratio", help = "Images height/width", default = 1.0, type = float) args = parser.parse_args() if not args.tasks: misc.error("No tasks specified. Please see '-h' for help.") if args.max_images < 50000: misc.log("Warning: max-images is set to {}. We recommend setting it at least to 50,000 to allow statistically correct computation of the FID-50k metric.".format(args.max_images), "red") prepare(**vars(args)) if __name__ == "__main__": run_cmdline(sys.argv)

43.091743

200

0.597828

from warnings import simplefilter simplefilter(action = "ignore", category = FutureWarning) import os import sys import tqdm import time import json import glob import gdown import urllib import zipfile import hashlib import argparse import numpy as np from training import misc from dnnlib import EasyDict import dataset_tool catalog = { "ffhq": EasyDict({ "name": "FFHQ", "filename": "ffhq-r08.tfrecords1of1", "url": "http://downloads.cs.stanford.edu/nlp/data/dorarad/ffhq-r08.tfrecords1of1", "md5": "74de4f07dc7bfb07c0ad4471fdac5e67", "ratio": 1, "size": 13, "shards": 1, "img_num": 70000 }), "bedrooms": EasyDict({ "name": "LSUN-Bedrooms", "filename": "bedroom_train_lmdb.zip", "url": "http://dl.yf.io/lsun/scenes/bedroom_train_lmdb.zip", "md5": "f2c5d904a82a6295dbdccb322b4b0a99", "dir": "bedroom_train_lmdb", "ratio": 188/256, "size": 43, "shards": 64, "img_num": 3033042, "process": dataset_tool.create_from_lmdb }), "cityscapes": EasyDict({ "name": "Cityscapes", "filename": "cityscapes.zip", "url": "https://drive.google.com/uc?id=1t9Qhxm0iHFd3k-xTYEbKosSx_DkyoLLJ", "md5": "953d231046275120dc1f73a5aebc9087", "ratio": 0.5, "size": 2, "shards": 16, "img_num": 25000 }), "clevr": EasyDict({ "name": "CLEVR", "filename": "clevr.zip", "url": "https://drive.google.com/uc?id=1lY4JE30yk26v0MWHNpXBOMzltufUcTXj", "md5": "3040bb20a29cd2f0e1e9231aebddf2a1", "size": 6, "ratio": 0.75, "shards": 5, "img_num": 100000 ".format(c.name), "blue")) # except: # print(misc.bcolored("Had an error in preparing the {} dataset. Will move on.".format(c.name), "red")) # print(sys.exc_info()) def run_cmdline(argv): parser = argparse.ArgumentParser(prog = argv[0], description = "Download and prepare data for the GANformer.") parser.add_argument("--data-dir", help = "Directory of created dataset", default = "datasets", type = str) parser.add_argument("--shards-num", help = "Number of shards to split each dataset to (optional)", default = 1, type = int) parser.add_argument("--max-images", help = "Maximum number of images to have in the dataset (optional). Use to reduce the produced tfrecords file size", default = None, type = int) # Default tasks parser.add_argument("--clevr", help = "Prepare the CLEVR dataset (6.41GB download, 31GB tfrecords, 100k images)", dest = "tasks", action = "append_const", const = "clevr") parser.add_argument("--bedrooms", help = "Prepare the LSUN-bedrooms dataset (42.8GB download, up to 480GB tfrecords, 3M images)", dest = "tasks", action = "append_const", const = "bedrooms") parser.add_argument("--ffhq", help = "Prepare the FFHQ dataset (13GB download, 13GB tfrecords, 70k images)", dest = "tasks", action = "append_const", const = "ffhq") parser.add_argument("--cityscapes", help = "Prepare the cityscapes dataset (1.8GB download, 8GB tfrecords, 25k images)", dest = "tasks", action = "append_const", const = "cityscapes") # Create a new task with custom images parser.add_argument("--task", help = "New dataset name", type = str, dest = "tasks", action = "append") parser.add_argument("--images-dir", help = "Provide source image directory to convert into tfrecords (will be searched recursively)", default = None, type = str) parser.add_argument("--format", help = "Images format", default = None, choices = ["png", "jpg", "npy", "hdf5", "tfds", "lmdb"], type = str) parser.add_argument("--ratio", help = "Images height/width", default = 1.0, type = float) args = parser.parse_args() if not args.tasks: misc.error("No tasks specified. Please see '-h' for help.") if args.max_images < 50000: misc.log("Warning: max-images is set to {}. We recommend setting it at least to 50,000 to allow statistically correct computation of the FID-50k metric.".format(args.max_images), "red") prepare(**vars(args)) if __name__ == "__main__": run_cmdline(sys.argv)

true

f71940e708f2c32d5f339366201b373baa9e265f

5,092

py

Python

venv/Lib/site-packages/pandas/tests/series/indexing/test_get.py

arnoyu-hub/COMP0016miemie

59af664dcf190eab4f93cefb8471908717415fea

[ "MIT" ]

null

venv/Lib/site-packages/pandas/tests/series/indexing/test_get.py

arnoyu-hub/COMP0016miemie

59af664dcf190eab4f93cefb8471908717415fea

[ "MIT" ]

null

venv/Lib/site-packages/pandas/tests/series/indexing/test_get.py

arnoyu-hub/COMP0016miemie

59af664dcf190eab4f93cefb8471908717415fea

[ "MIT" ]

null

import numpy as np import pytest import pandas as pd from pandas import Series import pandas._testing as tm def test_get(): # GH 6383 s = Series( np.array( [ 43, 48, 60, 48, 50, 51, 50, 45, 57, 48, 56, 45, 51, 39, 55, 43, 54, 52, 51, 54, ] ) ) result = s.get(25, 0) expected = 0 assert result == expected s = Series( np.array( [ 43, 48, 60, 48, 50, 51, 50, 45, 57, 48, 56, 45, 51, 39, 55, 43, 54, 52, 51, 54, ] ), index=pd.Float64Index( [ 25.0, 36.0, 49.0, 64.0, 81.0, 100.0, 121.0, 144.0, 169.0, 196.0, 1225.0, 1296.0, 1369.0, 1444.0, 1521.0, 1600.0, 1681.0, 1764.0, 1849.0, 1936.0, ] ), ) result = s.get(25, 0) expected = 43 assert result == expected # GH 7407 # with a boolean accessor df = pd.DataFrame({"i": [0] * 3, "b": [False] * 3}) vc = df.i.value_counts() result = vc.get(99, default="Missing") assert result == "Missing" vc = df.b.value_counts() result = vc.get(False, default="Missing") assert result == 3 result = vc.get(True, default="Missing") assert result == "Missing" def test_get_nan(): # GH 8569 s = pd.Float64Index(range(10)).to_series() assert s.get(np.nan) is None assert s.get(np.nan, default="Missing") == "Missing" def test_get_nan_multiple(): # GH 8569 # ensure that fixing "test_get_nan" above hasn't broken get # with multiple elements s = pd.Float64Index(range(10)).to_series() idx = [2, 30] assert s.get(idx) is None idx = [2, np.nan] assert s.get(idx) is None # GH 17295 - all missing keys idx = [20, 30] assert s.get(idx) is None idx = [np.nan, np.nan] assert s.get(idx) is None def test_get_with_default(): # GH#7725 d0 = ["a", "b", "c", "d"] d1 = np.arange(4, dtype="int64") others = ["e", 10] for data, index in ((d0, d1), (d1, d0)): s = Series(data, index=index) for i, d in zip(index, data): assert s.get(i) == d assert s.get(i, d) == d assert s.get(i, "z") == d for other in others: assert s.get(other, "z") == "z" assert s.get(other, other) == other @pytest.mark.parametrize( "arr", [np.random.randn(10), tm.makeDateIndex(10, name="a").tz_localize(tz="US/Eastern")], ) def test_get2(arr): # TODO: better name, possibly split # GH#21260 ser = Series(arr, index=[2 * i for i in range(len(arr))]) assert ser.get(4) == ser.iloc[2] result = ser.get([4, 6]) expected = ser.iloc[[2, 3]] tm.assert_series_equal(result, expected) result = ser.get(slice(2)) expected = ser.iloc[[0, 1]] tm.assert_series_equal(result, expected) assert ser.get(-1) is None assert ser.get(ser.index.max() + 1) is None ser = Series(arr[:6], index=list("abcdef")) assert ser.get("c") == ser.iloc[2] result = ser.get(slice("b", "d")) expected = ser.iloc[[1, 2, 3]] tm.assert_series_equal(result, expected) result = ser.get("Z") assert result is None assert ser.get(4) == ser.iloc[4] assert ser.get(-1) == ser.iloc[-1] assert ser.get(len(ser)) is None # GH#21257 ser = Series(arr) ser2 = ser[::2] assert ser2.get(1) is None def test_getitem_get(string_series, object_series): for obj in [string_series, object_series]: idx = obj.index[5] assert obj[idx] == obj.get(idx) assert obj[idx] == obj[5] assert string_series.get(-1) == string_series.get(string_series.index[-1]) assert string_series[5] == string_series.get(string_series.index[5]) def test_get_none(): # GH#5652 s1 = Series(dtype=object) s2 = Series(dtype=object, index=list("abc")) for s in [s1, s2]: result = s.get(None) assert result is None

23.683721

88

0.434014

import numpy as np import pytest import pandas as pd from pandas import Series import pandas._testing as tm def test_get(): s = Series( np.array( [ 43, 48, 60, 48, 50, 51, 50, 45, 57, 48, 56, 45, 51, 39, 55, 43, 54, 52, 51, 54, ] ) ) result = s.get(25, 0) expected = 0 assert result == expected s = Series( np.array( [ 43, 48, 60, 48, 50, 51, 50, 45, 57, 48, 56, 45, 51, 39, 55, 43, 54, 52, 51, 54, ] ), index=pd.Float64Index( [ 25.0, 36.0, 49.0, 64.0, 81.0, 100.0, 121.0, 144.0, 169.0, 196.0, 1225.0, 1296.0, 1369.0, 1444.0, 1521.0, 1600.0, 1681.0, 1764.0, 1849.0, 1936.0, ] ), ) result = s.get(25, 0) expected = 43 assert result == expected df = pd.DataFrame({"i": [0] * 3, "b": [False] * 3}) vc = df.i.value_counts() result = vc.get(99, default="Missing") assert result == "Missing" vc = df.b.value_counts() result = vc.get(False, default="Missing") assert result == 3 result = vc.get(True, default="Missing") assert result == "Missing" def test_get_nan(): s = pd.Float64Index(range(10)).to_series() assert s.get(np.nan) is None assert s.get(np.nan, default="Missing") == "Missing" def test_get_nan_multiple(): # with multiple elements s = pd.Float64Index(range(10)).to_series() idx = [2, 30] assert s.get(idx) is None idx = [2, np.nan] assert s.get(idx) is None # GH 17295 - all missing keys idx = [20, 30] assert s.get(idx) is None idx = [np.nan, np.nan] assert s.get(idx) is None def test_get_with_default(): # GH#7725 d0 = ["a", "b", "c", "d"] d1 = np.arange(4, dtype="int64") others = ["e", 10] for data, index in ((d0, d1), (d1, d0)): s = Series(data, index=index) for i, d in zip(index, data): assert s.get(i) == d assert s.get(i, d) == d assert s.get(i, "z") == d for other in others: assert s.get(other, "z") == "z" assert s.get(other, other) == other @pytest.mark.parametrize( "arr", [np.random.randn(10), tm.makeDateIndex(10, name="a").tz_localize(tz="US/Eastern")], ) def test_get2(arr): # TODO: better name, possibly split # GH#21260 ser = Series(arr, index=[2 * i for i in range(len(arr))]) assert ser.get(4) == ser.iloc[2] result = ser.get([4, 6]) expected = ser.iloc[[2, 3]] tm.assert_series_equal(result, expected) result = ser.get(slice(2)) expected = ser.iloc[[0, 1]] tm.assert_series_equal(result, expected) assert ser.get(-1) is None assert ser.get(ser.index.max() + 1) is None ser = Series(arr[:6], index=list("abcdef")) assert ser.get("c") == ser.iloc[2] result = ser.get(slice("b", "d")) expected = ser.iloc[[1, 2, 3]] tm.assert_series_equal(result, expected) result = ser.get("Z") assert result is None assert ser.get(4) == ser.iloc[4] assert ser.get(-1) == ser.iloc[-1] assert ser.get(len(ser)) is None # GH#21257 ser = Series(arr) ser2 = ser[::2] assert ser2.get(1) is None def test_getitem_get(string_series, object_series): for obj in [string_series, object_series]: idx = obj.index[5] assert obj[idx] == obj.get(idx) assert obj[idx] == obj[5] assert string_series.get(-1) == string_series.get(string_series.index[-1]) assert string_series[5] == string_series.get(string_series.index[5]) def test_get_none(): # GH#5652 s1 = Series(dtype=object) s2 = Series(dtype=object, index=list("abc")) for s in [s1, s2]: result = s.get(None) assert result is None

true

f71940f58176a96989ec11a785988a65b24d32a1

1,157

py

Python

venv/Scripts/get_first_company.py

ZUSM0/zynoPYsis

0cbe84d60a9611eac4daed82176477939bf79183

[ "MIT" ]

null

venv/Scripts/get_first_company.py

ZUSM0/zynoPYsis

0cbe84d60a9611eac4daed82176477939bf79183

[ "MIT" ]

null

venv/Scripts/get_first_company.py

ZUSM0/zynoPYsis

0cbe84d60a9611eac4daed82176477939bf79183

[ "MIT" ]

null

#!c:\users\user\pycharmprojects\sinopse de filmes\venv\scripts\python.exe # -*- coding: utf-8 -*- """ get_first_company.py Usage: get_first_company "company name" Search for the given name and print the best matching result. """ import sys # Import the IMDbPY package. try: import imdb except ImportError: print('You need to install the IMDbPY package!') sys.exit(1) if len(sys.argv) != 2: print('Only one argument is required:') print(' %s "company name"' % sys.argv[0]) sys.exit(2) name = sys.argv[1] i = imdb.IMDb() try: # Do the search, and get the results (a list of company objects). results = i.search_company(name) except imdb.IMDbError as e: print("Probably you're not connected to Internet. Complete error report:") print(e) sys.exit(3) if not results: print('No matches for "%s", sorry.' % name) sys.exit(0) # Print only the first result. print(' Best match for "%s"' % name) # This is a company instance. company = results[0] # So far the company object only contains basic information like the # name; retrieve main information: i.update(company) print(company.summary())

21.425926

79

0.684529

import sys try: import imdb except ImportError: print('You need to install the IMDbPY package!') sys.exit(1) if len(sys.argv) != 2: print('Only one argument is required:') print(' %s "company name"' % sys.argv[0]) sys.exit(2) name = sys.argv[1] i = imdb.IMDb() try: results = i.search_company(name) except imdb.IMDbError as e: print("Probably you're not connected to Internet. Complete error report:") print(e) sys.exit(3) if not results: print('No matches for "%s", sorry.' % name) sys.exit(0) # Print only the first result. print(' Best match for "%s"' % name) # This is a company instance. company = results[0] # So far the company object only contains basic information like the # name; retrieve main information: i.update(company) print(company.summary())

true

f7194132aff142671554e00669a4cc1f9a014680

5,299

py

Python

MathExample.py

AdityaSavara/CiteSoft_Py

f3a68666966565d4eb130e457cb11d285b56b4c5

[ "BSD-3-Clause" ]

null

MathExample.py

AdityaSavara/CiteSoft_Py

f3a68666966565d4eb130e457cb11d285b56b4c5

[ "BSD-3-Clause" ]

null

MathExample.py

AdityaSavara/CiteSoft_Py

f3a68666966565d4eb130e457cb11d285b56b4c5

[ "BSD-3-Clause" ]

null

import sys import math try: import CiteSoft except: import os #The below lines are to allow CiteSoftLocal to be called regardless of user's working directory. lenOfFileName = len(os.path.basename(__file__)) #This is the name of **this** file. absPathWithoutFileName = os.path.abspath(__file__)[0:-1*lenOfFileName] sys.path.append(absPathWithoutFileName) import CiteSoftLocal as CiteSoft #Here CiteSoft is used with an example module called "MathExample" #Note that the unique_id should be something truly unique (no other software would use it). #Typically, unique_id is a DOI or a URL. #The author field is typically a list object with names as strings, but can also just be a single string. #Note that there is a function called sqrt which uses the python math module, and uses a *different* citation. software_name = "CiteSoft Math Example" version = "1.0.0" MathExample_unique_id = "https://github.com/AdityaSavara/CiteSoft_py/blob/master/MathExample.py" kwargs = {"version": version, "author": ["Aditya Savara", "CPH"], "url": "https://github.com/AdityaSavara/CiteSoft_py/blob/master/MathExample.py"} #The below line will cause this module's citation to be exported any time the module is imported. #The 'write_immediately = True' causes the checkpoint to be written at the time of export rather than stored. CiteSoft.import_cite(unique_id=MathExample_unique_id, software_name="MathLib Example", write_immediately=True, **kwargs) @CiteSoft.function_call_cite(unique_id=MathExample_unique_id, software_name="CiteSoft Math Example", **kwargs) def add(num1, num2): return num1 + num2 @CiteSoft.function_call_cite(unique_id=MathExample_unique_id, software_name="CiteSoft Math Example", **kwargs) def subtract(num1, num2): return num1 - num2 @CiteSoft.function_call_cite(unique_id=MathExample_unique_id, software_name="CiteSoft Math Example", **kwargs) def multiply(num1, num2): return num1 * num2 @CiteSoft.function_call_cite(unique_id=MathExample_unique_id, software_name="CiteSoft Math Example", **kwargs) def divide(num1, num2): return num1 / num2 @CiteSoft.after_call_compile_consolidated_log() #This will cause the consolidated log to be complied after the mean function is called. #note that we put it after the function_call_cite so that it is a wrapper around that wrapper and occurs second. @CiteSoft.function_call_cite(unique_id=MathExample_unique_id, software_name="MathLib Example", **kwargs) def mean(list_of_num): result = 0 for num in list_of_num: result = add(result, num) result = divide(result, len(list_of_num)) return result math_unique_id = "https://docs.python.org/3/library/math.html" math_software_name = "The Python Library Reference: Mathematical functions" math_version = str(sys.version).split("|")[0] #This is the python version. math_kwargs = {"version": math_version, "author": "Van Rossum, Guido", "cite": "Van Rossum, G. (2020). The Python Library Reference, release 3.8.2. Python Software Foundation.", "url": "https://docs.python.org/3/library/math.html"} @CiteSoft.function_call_cite(unique_id=math_unique_id, software_name=math_software_name, **math_kwargs) def sqrt(num): return math.sqrt(num) @CiteSoft.function_call_cite(MathExample_unique_id, software_name, **kwargs) def sqr(num): return multiply(num, num) @CiteSoft.function_call_cite(MathExample_unique_id, software_name, **kwargs) def sample_variance(list_of_num): meanVal = mean(list_of_num) result = 0 for num in list_of_num: result = add(result, sqr(subtract(num, meanVal))) result = divide(result, (len(list_of_num) - 1)) return result @CiteSoft.function_call_cite(MathExample_unique_id, software_name, **kwargs) def std_dev(list_of_num): return sqrt(sample_variance(list_of_num)) @CiteSoft.after_call_compile_consolidated_log() #This will cause the consolidated log to be complied after the mean function is called. #note that we put it after the function_call_cite so that it is a wrapper around that wrapper and occurs second. @CiteSoft.function_call_cite(MathExample_unique_id, software_name, **kwargs) def cite_me(): #This is just an example of how a package creating dev-user could make a function that other dev-users relying on their package could call at the very end of doing everything, so that no calls to CiteSoft would need to occur during runtime. pass #note that the above lines of code simply add to the file CiteSoftwareCheckPoints #if one wants to create a consolidated log that removes duplicates, one can call a CiteSoft function #This is considered appropriate to do at the end of a complicated program, but is not necessary. #it would have been possible to also use decorators on any of the above functions, like @CiteSoft.after_call_compile_checkpoints_log or @CiteSoft.after_call_compile_consolidated_log. Note that chained/stacked decorators are performed in "first in last out" order, since they are wrappers on wrappers. So if a function has both @CiteSoft.function_call_cite and @after_call_compile_consolidated_log, the @CiteSoft.function_call_cite should be second. def export_citation_checkpoints(filepath=""): if filepath is not "": CiteSoft.compile_checkpoints_log(filepath) else: CiteSoft.compile_checkpoints_log()

58.230769

449

0.780336

import sys import math try: import CiteSoft except: import os lenOfFileName = len(os.path.basename(__file__)) #This is the name of **this** file. absPathWithoutFileName = os.path.abspath(__file__)[0:-1*lenOfFileName] sys.path.append(absPathWithoutFileName) import CiteSoftLocal as CiteSoft #Here CiteSoft is used with an example module called "MathExample" #Note that the unique_id should be something truly unique (no other software would use it). #Typically, unique_id is a DOI or a URL. #The author field is typically a list object with names as strings, but can also just be a single string. #Note that there is a function called sqrt which uses the python math module, and uses a *different* citation. software_name = "CiteSoft Math Example" version = "1.0.0" MathExample_unique_id = "https://github.com/AdityaSavara/CiteSoft_py/blob/master/MathExample.py" kwargs = {"version": version, "author": ["Aditya Savara", "CPH"], "url": "https://github.com/AdityaSavara/CiteSoft_py/blob/master/MathExample.py"} #The below line will cause this module's citation to be exported any time the module is imported. CiteSoft.import_cite(unique_id=MathExample_unique_id, software_name="MathLib Example", write_immediately=True, **kwargs) @CiteSoft.function_call_cite(unique_id=MathExample_unique_id, software_name="CiteSoft Math Example", **kwargs) def add(num1, num2): return num1 + num2 @CiteSoft.function_call_cite(unique_id=MathExample_unique_id, software_name="CiteSoft Math Example", **kwargs) def subtract(num1, num2): return num1 - num2 @CiteSoft.function_call_cite(unique_id=MathExample_unique_id, software_name="CiteSoft Math Example", **kwargs) def multiply(num1, num2): return num1 * num2 @CiteSoft.function_call_cite(unique_id=MathExample_unique_id, software_name="CiteSoft Math Example", **kwargs) def divide(num1, num2): return num1 / num2 @CiteSoft.after_call_compile_consolidated_log() an(list_of_num): result = 0 for num in list_of_num: result = add(result, num) result = divide(result, len(list_of_num)) return result math_unique_id = "https://docs.python.org/3/library/math.html" math_software_name = "The Python Library Reference: Mathematical functions" math_version = str(sys.version).split("|")[0] math_kwargs = {"version": math_version, "author": "Van Rossum, Guido", "cite": "Van Rossum, G. (2020). The Python Library Reference, release 3.8.2. Python Software Foundation.", "url": "https://docs.python.org/3/library/math.html"} @CiteSoft.function_call_cite(unique_id=math_unique_id, software_name=math_software_name, **math_kwargs) def sqrt(num): return math.sqrt(num) @CiteSoft.function_call_cite(MathExample_unique_id, software_name, **kwargs) def sqr(num): return multiply(num, num) @CiteSoft.function_call_cite(MathExample_unique_id, software_name, **kwargs) def sample_variance(list_of_num): meanVal = mean(list_of_num) result = 0 for num in list_of_num: result = add(result, sqr(subtract(num, meanVal))) result = divide(result, (len(list_of_num) - 1)) return result @CiteSoft.function_call_cite(MathExample_unique_id, software_name, **kwargs) def std_dev(list_of_num): return sqrt(sample_variance(list_of_num)) @CiteSoft.after_call_compile_consolidated_log() export_citation_checkpoints(filepath=""): if filepath is not "": CiteSoft.compile_checkpoints_log(filepath) else: CiteSoft.compile_checkpoints_log()

true

f719414c0fab3fa82e996e4adda1253ef5777ac1

141

py

Python

performance_test/torch_deploy/__init__.py

Lionelsy/SQL-Injection-Detection-via-Deep-Learning

5f1958822af98a99172df524eef6e921e6fa6724

[ "MIT" ]

1

2022-01-18T17:08:52.000Z

performance_test/torch_deploy/__init__.py

Lionelsy/SQL-Injection-Detection-via-Deep-Learning

5f1958822af98a99172df524eef6e921e6fa6724

[ "MIT" ]

null

performance_test/torch_deploy/__init__.py

Lionelsy/SQL-Injection-Detection-via-Deep-Learning

5f1958822af98a99172df524eef6e921e6fa6724

[ "MIT" ]

null

# -*- coding: utf-8 -*- # @Time : 2020/12/14 # @Author : Shuyu ZHANG # @FileName: __init__.py # @Software: PyCharm # @Description: Here

17.625

24

0.609929

true

f71941bb492d02c50d6770e3abc0974c97079478

1,354

py

Python

rclpy/services/minimal_client/setup.py

flynneva/examples

16bffa238dfa3ff305f14b1ec75ed41dce634ffb

[ "Apache-2.0" ]

null

rclpy/services/minimal_client/setup.py

flynneva/examples

16bffa238dfa3ff305f14b1ec75ed41dce634ffb

[ "Apache-2.0" ]

null

rclpy/services/minimal_client/setup.py

flynneva/examples

16bffa238dfa3ff305f14b1ec75ed41dce634ffb

[ "Apache-2.0" ]

null

from setuptools import setup package_name = 'examples_rclpy_minimal_client' setup( name=package_name, version='0.9.1', packages=[package_name], data_files=[ ('share/ament_index/resource_index/packages', ['resource/' + package_name]), ('share/' + package_name, ['package.xml']), ], install_requires=['setuptools'], zip_safe=True, author='Mikael Arguedas', author_email='mikael@osrfoundation.org', maintainer='Mikael Arguedas', maintainer_email='mikael@osrfoundation.org', keywords=['ROS'], classifiers=[ 'Intended Audience :: Developers', 'License :: OSI Approved :: Apache Software License', 'Programming Language :: Python', 'Topic :: Software Development', ], description='Examples of minimal service clients using rclpy.', license='Apache License, Version 2.0', tests_require=['pytest'], entry_points={ 'console_scripts': [ 'client = examples_rclpy_minimal_client.client:main', 'client_async = examples_rclpy_minimal_client.client_async:main', 'client_async_member_function =' ' examples_rclpy_minimal_client.client_async_member_function:main', 'client_async_callback = examples_rclpy_minimal_client.client_async_callback:main', ], }, )

33.85

95

0.661004

from setuptools import setup package_name = 'examples_rclpy_minimal_client' setup( name=package_name, version='0.9.1', packages=[package_name], data_files=[ ('share/ament_index/resource_index/packages', ['resource/' + package_name]), ('share/' + package_name, ['package.xml']), ], install_requires=['setuptools'], zip_safe=True, author='Mikael Arguedas', author_email='mikael@osrfoundation.org', maintainer='Mikael Arguedas', maintainer_email='mikael@osrfoundation.org', keywords=['ROS'], classifiers=[ 'Intended Audience :: Developers', 'License :: OSI Approved :: Apache Software License', 'Programming Language :: Python', 'Topic :: Software Development', ], description='Examples of minimal service clients using rclpy.', license='Apache License, Version 2.0', tests_require=['pytest'], entry_points={ 'console_scripts': [ 'client = examples_rclpy_minimal_client.client:main', 'client_async = examples_rclpy_minimal_client.client_async:main', 'client_async_member_function =' ' examples_rclpy_minimal_client.client_async_member_function:main', 'client_async_callback = examples_rclpy_minimal_client.client_async_callback:main', ], }, )

true

f71942098bb86e0175943366277c2cc371a401db

1,735

py

Python

api/migrations/0001_initial.py

psingla1210/django-rest-api

db9fa70e3eeb747399b275e79688dfa4974a00ee

[ "MIT" ]

null

api/migrations/0001_initial.py

psingla1210/django-rest-api

db9fa70e3eeb747399b275e79688dfa4974a00ee

[ "MIT" ]

4

2021-03-19T11:31:00.000Z

2022-02-10T14:07:26.000Z

api/migrations/0001_initial.py

psingla1210/django-rest-api

db9fa70e3eeb747399b275e79688dfa4974a00ee

[ "MIT" ]

null

# Generated by Django 2.2 on 2020-06-21 14:10 from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0011_update_proxy_permissions'), ] operations = [ migrations.CreateModel( name='TwistedUser', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('email', models.CharField(max_length=256, unique=True, verbose_name='email address')), ('name', models.CharField(max_length=256)), ('is_active', models.BooleanField(default=True)), ('is_staff', models.BooleanField(default=False)), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'abstract': False, }, ), ]

51.029412

266

0.641499

from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ('auth', '0011_update_proxy_permissions'), ] operations = [ migrations.CreateModel( name='TwistedUser', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_name='password')), ('last_login', models.DateTimeField(blank=True, null=True, verbose_name='last login')), ('is_superuser', models.BooleanField(default=False, help_text='Designates that this user has all permissions without explicitly assigning them.', verbose_name='superuser status')), ('email', models.CharField(max_length=256, unique=True, verbose_name='email address')), ('name', models.CharField(max_length=256)), ('is_active', models.BooleanField(default=True)), ('is_staff', models.BooleanField(default=False)), ('groups', models.ManyToManyField(blank=True, help_text='The groups this user belongs to. A user will get all permissions granted to each of their groups.', related_name='user_set', related_query_name='user', to='auth.Group', verbose_name='groups')), ('user_permissions', models.ManyToManyField(blank=True, help_text='Specific permissions for this user.', related_name='user_set', related_query_name='user', to='auth.Permission', verbose_name='user permissions')), ], options={ 'abstract': False, }, ), ]

true

f71942734c2781f9110c7944a358f060b7b5ed9c

1,138

py

Python

frappe/utils/connections.py

erpnext-tm/frappe

7b470f28e1cf00b0659c01e06a2d0a4693b28d98

[ "MIT" ]

null

frappe/utils/connections.py

erpnext-tm/frappe

7b470f28e1cf00b0659c01e06a2d0a4693b28d98

[ "MIT" ]

null

frappe/utils/connections.py

erpnext-tm/frappe

7b470f28e1cf00b0659c01e06a2d0a4693b28d98

[ "MIT" ]

null

import socket from six.moves.urllib.parse import urlparse from frappe import get_conf REDIS_KEYS = ("redis_cache", "redis_queue", "redis_socketio") def is_open(ip, port, timeout=10): s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.settimeout(timeout) try: s.connect((ip, int(port))) s.shutdown(socket.SHUT_RDWR) return True except socket.error: return False finally: s.close() def check_database(): config = get_conf() db_type = config.get("db_type", "mariadb") db_host = config.get("db_host", "localhost") db_port = config.get("db_port", 3306 if db_type == "mariadb" else 5432) return {db_type: is_open(db_host, db_port)} def check_redis(redis_services=None): config = get_conf() services = redis_services or REDIS_KEYS status = {} for conn in services: redis_url = urlparse(config.get(conn)).netloc redis_host, redis_port = redis_url.split(":") status[conn] = is_open(redis_host, redis_port) return status def check_connection(redis_services=None): service_status = {} service_status.update(check_database()) service_status.update(check_redis(redis_services)) return service_status

24.212766

72

0.746924

import socket from six.moves.urllib.parse import urlparse from frappe import get_conf REDIS_KEYS = ("redis_cache", "redis_queue", "redis_socketio") def is_open(ip, port, timeout=10): s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) s.settimeout(timeout) try: s.connect((ip, int(port))) s.shutdown(socket.SHUT_RDWR) return True except socket.error: return False finally: s.close() def check_database(): config = get_conf() db_type = config.get("db_type", "mariadb") db_host = config.get("db_host", "localhost") db_port = config.get("db_port", 3306 if db_type == "mariadb" else 5432) return {db_type: is_open(db_host, db_port)} def check_redis(redis_services=None): config = get_conf() services = redis_services or REDIS_KEYS status = {} for conn in services: redis_url = urlparse(config.get(conn)).netloc redis_host, redis_port = redis_url.split(":") status[conn] = is_open(redis_host, redis_port) return status def check_connection(redis_services=None): service_status = {} service_status.update(check_database()) service_status.update(check_redis(redis_services)) return service_status

true

f71943a375acd8972ca01d50dd31b6166bd90ebe

606

py

Python

demo.py

peter-jim/offchain-algorithm

c148fd1e3dffca9a42a4206c516533aae51d1ae1

[ "Apache-2.0" ]

null

demo.py

peter-jim/offchain-algorithm

c148fd1e3dffca9a42a4206c516533aae51d1ae1

[ "Apache-2.0" ]

null

demo.py

peter-jim/offchain-algorithm

c148fd1e3dffca9a42a4206c516533aae51d1ae1

[ "Apache-2.0" ]

1

2021-04-17T06:34:32.000Z

import requests def demo(): ''' 非小号API获取信息 :return btc,eth,eos ... pirce ''' print("start") #接口教程链接 https://github.com/xiaohao2019/API-docs/blob/master/PublicApi_CN.md url = "https://fxhapi.feixiaohao.com/public/v1/ticker/" #传入参数 start=[integer](指定结果集的开始排名) limit=[integer](指定结果集的最大数量) start = "start=" + str(0)+"&" limit = "limit=" + str(10) print(url+"?"+start+limit) try: response = requests.get(url=url+"?"+start+limit) for item in response.json(): print(item) print("获取完毕") except: print('error')

23.307692

79

0.580858

import requests def demo(): print("start") url = "https://fxhapi.feixiaohao.com/public/v1/ticker/" start = "start=" + str(0)+"&" limit = "limit=" + str(10) print(url+"?"+start+limit) try: response = requests.get(url=url+"?"+start+limit) for item in response.json(): print(item) print("获取完毕") except: print('error')

true

f7194440cda8d5fbed1979d6f1691cf0e20dcc13

1,544

py

Python

benchmark/Python/Savina/PingPong.py

Feliix42/lingua-franca

af312ca8d37d9246dcb1d77fdc254a0dbd61b2bc

[ "BSD-2-Clause" ]

1

2020-11-13T02:05:57.000Z

benchmark/Python/Savina/PingPong.py

Feliix42/lingua-franca

af312ca8d37d9246dcb1d77fdc254a0dbd61b2bc

[ "BSD-2-Clause" ]

null

benchmark/Python/Savina/PingPong.py

Feliix42/lingua-franca

af312ca8d37d9246dcb1d77fdc254a0dbd61b2bc

[ "BSD-2-Clause" ]

1

2020-10-20T12:30:38.000Z

from LinguaFrancaBase.constants import * #Useful constants from LinguaFrancaBase.functions import * #Useful helper functions from LinguaFrancaBase.classes import * #Useful classes import sys import copy sys.setrecursionlimit(100000) EXPECTED = 10000 class _Ping: count = 1000000 pingsLeft = count def __init__(self, **kwargs): self.__dict__.update(kwargs) def reaction_function_1(self): self.pingsLeft -= 1 if self.pingsLeft > 0: pingpong_pong_lf.reaction_function_0(self.pingsLeft) else: exit() return 0 class _Pong: expected = 1000000 count = 0 def __init__(self, **kwargs): self.__dict__.update(kwargs) def reaction_function_0(self , receive): self.count += 1 if(self.count == self.expected): exit() pingpong_ping_lf.reaction_function_1() return 0 def reaction_function_1(self ): if self.count != self.expected: sys.stderr.write("Pong expected to receive {:d} inputs, but it received {:d}.\n".format(self.expected, self.count)) exit(1) print("Success.") return 0 # Instantiate classes pingpong_ping_lf = _Ping(bank_index = 0, count=EXPECTED) pingpong_pong_lf = _Pong(bank_index = 0, expected=EXPECTED) # The main function def main(): pingpong_ping_lf.reaction_function_1() # As is customary in Python programs, the main() function # should only be executed if the main module is active. if __name__=="__main__": main()

29.132075

127

0.668394

from LinguaFrancaBase.constants import * from LinguaFrancaBase.functions import * from LinguaFrancaBase.classes import * import sys import copy sys.setrecursionlimit(100000) EXPECTED = 10000 class _Ping: count = 1000000 pingsLeft = count def __init__(self, **kwargs): self.__dict__.update(kwargs) def reaction_function_1(self): self.pingsLeft -= 1 if self.pingsLeft > 0: pingpong_pong_lf.reaction_function_0(self.pingsLeft) else: exit() return 0 class _Pong: expected = 1000000 count = 0 def __init__(self, **kwargs): self.__dict__.update(kwargs) def reaction_function_0(self , receive): self.count += 1 if(self.count == self.expected): exit() pingpong_ping_lf.reaction_function_1() return 0 def reaction_function_1(self ): if self.count != self.expected: sys.stderr.write("Pong expected to receive {:d} inputs, but it received {:d}.\n".format(self.expected, self.count)) exit(1) print("Success.") return 0 pingpong_ping_lf = _Ping(bank_index = 0, count=EXPECTED) pingpong_pong_lf = _Pong(bank_index = 0, expected=EXPECTED) def main(): pingpong_ping_lf.reaction_function_1() if __name__=="__main__": main()

true

f71944995b26a94873f53f48809fb0b8c10684e6

11,354

py

Python

ministack/build.py

lyndon160/ref

122e8315c00784c58285c6ad54bf8fffd39623fa

[ "Apache-2.0" ]

6

2018-10-22T09:43:46.000Z

2021-11-15T11:08:54.000Z

ministack/build.py

lyndon160/REF

122e8315c00784c58285c6ad54bf8fffd39623fa

[ "Apache-2.0" ]

5

2016-01-29T16:51:39.000Z

2020-08-01T16:16:29.000Z

ministack/build.py

lyndon160/REF

122e8315c00784c58285c6ad54bf8fffd39623fa

[ "Apache-2.0" ]

4

2016-01-05T20:41:18.000Z

2019-05-12T10:15:56.000Z

#!/usr/bin/python # # build.py # # chain nova and neuton service requests to build predefined compute topologies # # topology is defined in python language 'spec' files # # by default the program will build the topology requested form a clean slate, # but give the correct option (-c) it will also attempt to complete a build when some elements already exist, e.g. networks or compute instances # Or, if option -d is given it will attempt to delete any VMs # Of option -d is given twice it will also attempt to remove networks named in the spec file # # before commencing work the program will make some basic checks on the spec file, # e.g. checking that the named keypairs, flavors and images exist # ## import time import sys import traceback from socket import gethostbyname from os import environ as env import os import argparse from pprint import pprint import novaclient.client from neutron import Neutron spec_error = False parser = argparse.ArgumentParser() group = parser.add_mutually_exclusive_group() group.add_argument('--auth','-a', action='store_true') group.add_argument('--dryrun','-n', action='store_true') group.add_argument('--build','-b', action='store_true') group.add_argument('--delete', '-d', action='count') group.add_argument('--suspend', '-s', action='store_true') group.add_argument('--resume', '-r', action='store_true') group.add_argument('--complete','-c', action='store_true') parser.add_argument('specfile') args=parser.parse_args() specfile=args.specfile # build is the default action build = not (args.resume or args.suspend or args.complete or args.dryrun or args.delete) if ( not os.access(specfile,os.R_OK)): print "spec file not readable" sys.exit(1) name,extension = os.path.splitext(specfile) if ( extension and extension != ".py"): print "spec file not a python script" sys.exit(1) try: _imp = __import__(name) spec = _imp.spec print "reading template: %s" % spec['name'] except: print "couldn't read the spec file '%s'" % specfile sys.exit(1) if spec['credentials'] and spec['controller']: # print "using OpenStack auth credentials from spec file" credentials = spec['credentials'] auth_url = "http://" + spec['controller'] + ":35357/v2.0" elif (os.environ['OS_USERNAME'] and os.environ['OS_PASSWORD'] and os.environ['OS_AUTH_URL'] and os.environ['OS_TENANT_NAME']): print "using OpenStack auth credentials from environment" credentials = { 'user' : os.environ['OS_USERNAME'], 'password' : os.environ['OS_PASSWORD'], 'project' : os.environ['OS_TENANT_NAME'] } auth_url = os.environ['OS_AUTH_URL'] else: print "Can't find OpenStack auth credentials in environment or spec file, giving up..." sys.exit(1) if args.auth: print "credentials string:\nexport OS_USERNAME=%s OS_PASSWORD=%s OS_TENANT_NAME=%s OS_AUTH_URL=%s" % (credentials['user'],credentials['password'],credentials['project'],auth_url) sys.exit(0) config = {} config['external_network_name'] = spec.get('external network name') #config['external_network_name'] = get(spec['external network name'] config['dns'] = spec['dns'] neutron = Neutron(auth_url, credentials, config) nova = novaclient.client.Client("2", username = credentials['user'], api_key = credentials['password'], project_id = credentials['project'], auth_url = auth_url) servers = nova.servers.list() server_list = {} for server in servers: server_list[server.name] = (server.id,server.status) def server_suspend(name): for s in servers: if s.name == name: (id,status) = server_list[name] if (status == 'ACTIVE'): response = nova.servers.suspend(s) print "suspend %s" % name, response else: print "Can't suspend server %s in state %s " % (name,status) def server_resume(name): for s in servers: if s.name == name: response = nova.servers.resume(s) print "resume %s" % name, response def server_delete(name): for s in servers: if s.name == name: response = nova.servers.delete(s) print "delete %s" % name, response # the net_list somewhat duplicates functionality in the neutron library (net_by_name) # and should probably be removed # however, the possibility of non-unique net names should be considered # before completly removing visibility of net IDs in this code... net_list = {} for net in neutron.networks: net_list[net['name']] = net['id'] def name_to_address(name): global spec_error if "*" == name: return "*" try: address = gethostbyname(name) except: print "Unexpected error looking up hostname '%s':" % name, sys.exc_info()[0] spec_error = True return name if address: return address print stderr,"host lookup failed for '%s'" % name spec_error = True return name def check_keypair(name): try: return not ( nova.keypairs.get(name).deleted ) except novaclient.exceptions.NotFound: return False except: print "Unexpected error:", sys.exc_info()[0] raise if (not args.delete): if check_keypair(spec['keypair']): pass else: print "checking keypair failed" sys.exit(1) net_builder = {} host_builder = {} router_needed = {} if (args.resume or args.suspend): pass elif ( not spec['Networks']): print "warning: no Networks in spec file" # sys.exit(1) else: for net in spec['Networks']: net_name = net['name'] if (not args.delete): if (net_name in net_list): if (args.dryrun or build): spec_error = True print "Build warning - network %s is already defined" % net_name else: print "network %s exists" % net_name else: net_builder[net_name] = net elif (args.delete > 1): if (net_name in net_list): net_builder[net_name] = net else: print "Can't delete non-existent network %s" % net_name if ( not spec['Hosts']): print "Warning - no hosts section in spec file" else: print "processing servers" for host in spec['Hosts']: if (build or args.dryrun or args.complete): print "building host ", host['name'] , host['image'], host['flavor'], host.get('net'), host.get('env') if (host['name'] in server_list): if (args.complete): print "host %s exists" % host['name'] else: spec_error = True print "Build Error - host %s is already defined" % host['name'] else: print "checking host image ", host['image'] image = nova.images.find(name=host['image']) print "checking host flavor ", host['flavor'] flavor = nova.flavors.find(name=host['flavor']) nets = [] try: for net_entry in host.get('net'): # a host network entry defines the network to be used, the assigned IP, and an optional floating IP # the network name is the actual name used in openstack, and must either be defined in the spec file or already exist # there must be a (local) IP (OpenStack insists...) but it can be wildcarded, in which case one will be selected from the pool # the optional third field is for a floating IP - this can be either a domain name or an IP # in either case it will be assigned from the external network range which is defined in the spec file # the first (local) IP may also be a hostname, which is most useful for cases where the attached network is directly routable ip = None fip_id = None if isinstance(net_entry,tuple): name = net_entry[0] if len(net_entry) > 1: ip = name_to_address(net_entry[1]) if len(net_entry) > 2: fip = name_to_address(net_entry[2]) fip_id = neutron.get_floatingip(config['external_network_name'],fip,args.dryrun) elif isinstance(net_entry,basestring): name = net_entry else: # if net_entry is neither a string or a tuple then I am confused.... print "Why me....?" sys.exit(1) if (name in net_builder or name in net_list): nets.append((name,ip,fip_id)) else: print "Build warning - host network %s not defined" % name spec_error = not (args.complete) host_builder[host['name']] = (image,flavor,nets) except: print "this is an unexpected exception!" print(traceback.format_exc()) sys.exit(1) else: if (host['name'] in server_list): print "processing host %s" % host['name'] host_builder[host['name']] = None else: print "not processing host %s (server does not exist)" % host['name'] if (spec_error): print "not building cluster due to spec errors" sys.exit(1) if (args.dryrun): print "dryrun only - not processing cluster" sys.exit(0) def process_networks(): for net in net_builder.values(): net_name = net['name'] if (args.delete): neutron.net_delete(net_list[net_name]) else: net_id = neutron.net_build(net) if (net_id): net_list[net_name] = net_id else: print "error: failed to build network %s" % net_name sys.exit(1) def process_servers(): print "processing servers" if (args.delete): for k in host_builder.keys(): server_delete(k) elif (args.suspend): for k in host_builder.keys(): server_suspend(k) elif (args.resume): for k in host_builder.keys(): server_resume(k) else: for k,(i,f,ns) in host_builder.items(): print "host %s : (%s,%s)" % (k,i,f) nics=[] for (name,ip,fip_id) in ns: id=net_list[name] port_id = neutron.port_build(id,ip) nics.append({'port-id': port_id}) if (fip_id): # floating IP requested neutron.floatingip_bind(port_id,fip_id) instance = nova.servers.create(name=k, image=i, flavor=f, key_name=spec['keypair'], nics=nics, config_drive=True) if (args.delete > 1): process_servers() process_networks() elif (build or args.complete): process_networks() process_servers() else: process_servers()

36.27476

182

0.586577

import time import sys import traceback from socket import gethostbyname from os import environ as env import os import argparse from pprint import pprint import novaclient.client from neutron import Neutron spec_error = False parser = argparse.ArgumentParser() group = parser.add_mutually_exclusive_group() group.add_argument('--auth','-a', action='store_true') group.add_argument('--dryrun','-n', action='store_true') group.add_argument('--build','-b', action='store_true') group.add_argument('--delete', '-d', action='count') group.add_argument('--suspend', '-s', action='store_true') group.add_argument('--resume', '-r', action='store_true') group.add_argument('--complete','-c', action='store_true') parser.add_argument('specfile') args=parser.parse_args() specfile=args.specfile build = not (args.resume or args.suspend or args.complete or args.dryrun or args.delete) if ( not os.access(specfile,os.R_OK)): print "spec file not readable" sys.exit(1) name,extension = os.path.splitext(specfile) if ( extension and extension != ".py"): print "spec file not a python script" sys.exit(1) try: _imp = __import__(name) spec = _imp.spec print "reading template: %s" % spec['name'] except: print "couldn't read the spec file '%s'" % specfile sys.exit(1) if spec['credentials'] and spec['controller']: # print "using OpenStack auth credentials from spec file" credentials = spec['credentials'] auth_url = "http://" + spec['controller'] + ":35357/v2.0" elif (os.environ['OS_USERNAME'] and os.environ['OS_PASSWORD'] and os.environ['OS_AUTH_URL'] and os.environ['OS_TENANT_NAME']): print "using OpenStack auth credentials from environment" credentials = { 'user' : os.environ['OS_USERNAME'], 'password' : os.environ['OS_PASSWORD'], 'project' : os.environ['OS_TENANT_NAME'] } auth_url = os.environ['OS_AUTH_URL'] else: print "Can't find OpenStack auth credentials in environment or spec file, giving up..." sys.exit(1) if args.auth: print "credentials string:\nexport OS_USERNAME=%s OS_PASSWORD=%s OS_TENANT_NAME=%s OS_AUTH_URL=%s" % (credentials['user'],credentials['password'],credentials['project'],auth_url) sys.exit(0) config = {} config['external_network_name'] = spec.get('external network name') config['dns'] = spec['dns'] neutron = Neutron(auth_url, credentials, config) nova = novaclient.client.Client("2", username = credentials['user'], api_key = credentials['password'], project_id = credentials['project'], auth_url = auth_url) servers = nova.servers.list() server_list = {} for server in servers: server_list[server.name] = (server.id,server.status) def server_suspend(name): for s in servers: if s.name == name: (id,status) = server_list[name] if (status == 'ACTIVE'): response = nova.servers.suspend(s) print "suspend %s" % name, response else: print "Can't suspend server %s in state %s " % (name,status) def server_resume(name): for s in servers: if s.name == name: response = nova.servers.resume(s) print "resume %s" % name, response def server_delete(name): for s in servers: if s.name == name: response = nova.servers.delete(s) print "delete %s" % name, response # the net_list somewhat duplicates functionality in the neutron library (net_by_name) # and should probably be removed # however, the possibility of non-unique net names should be considered # before completly removing visibility of net IDs in this code... net_list = {} for net in neutron.networks: net_list[net['name']] = net['id'] def name_to_address(name): global spec_error if "*" == name: return "*" try: address = gethostbyname(name) except: print "Unexpected error looking up hostname '%s':" % name, sys.exc_info()[0] spec_error = True return name if address: return address print stderr,"host lookup failed for '%s'" % name spec_error = True return name def check_keypair(name): try: return not ( nova.keypairs.get(name).deleted ) except novaclient.exceptions.NotFound: return False except: print "Unexpected error:", sys.exc_info()[0] raise if (not args.delete): if check_keypair(spec['keypair']): pass else: print "checking keypair failed" sys.exit(1) net_builder = {} host_builder = {} router_needed = {} if (args.resume or args.suspend): pass elif ( not spec['Networks']): print "warning: no Networks in spec file" # sys.exit(1) else: for net in spec['Networks']: net_name = net['name'] if (not args.delete): if (net_name in net_list): if (args.dryrun or build): spec_error = True print "Build warning - network %s is already defined" % net_name else: print "network %s exists" % net_name else: net_builder[net_name] = net elif (args.delete > 1): if (net_name in net_list): net_builder[net_name] = net else: print "Can't delete non-existent network %s" % net_name if ( not spec['Hosts']): print "Warning - no hosts section in spec file" else: print "processing servers" for host in spec['Hosts']: if (build or args.dryrun or args.complete): print "building host ", host['name'] , host['image'], host['flavor'], host.get('net'), host.get('env') if (host['name'] in server_list): if (args.complete): print "host %s exists" % host['name'] else: spec_error = True print "Build Error - host %s is already defined" % host['name'] else: print "checking host image ", host['image'] image = nova.images.find(name=host['image']) print "checking host flavor ", host['flavor'] flavor = nova.flavors.find(name=host['flavor']) nets = [] try: for net_entry in host.get('net'): ip = None fip_id = None if isinstance(net_entry,tuple): name = net_entry[0] if len(net_entry) > 1: ip = name_to_address(net_entry[1]) if len(net_entry) > 2: fip = name_to_address(net_entry[2]) fip_id = neutron.get_floatingip(config['external_network_name'],fip,args.dryrun) elif isinstance(net_entry,basestring): name = net_entry else: print "Why me....?" sys.exit(1) if (name in net_builder or name in net_list): nets.append((name,ip,fip_id)) else: print "Build warning - host network %s not defined" % name spec_error = not (args.complete) host_builder[host['name']] = (image,flavor,nets) except: print "this is an unexpected exception!" print(traceback.format_exc()) sys.exit(1) else: if (host['name'] in server_list): print "processing host %s" % host['name'] host_builder[host['name']] = None else: print "not processing host %s (server does not exist)" % host['name'] if (spec_error): print "not building cluster due to spec errors" sys.exit(1) if (args.dryrun): print "dryrun only - not processing cluster" sys.exit(0) def process_networks(): for net in net_builder.values(): net_name = net['name'] if (args.delete): neutron.net_delete(net_list[net_name]) else: net_id = neutron.net_build(net) if (net_id): net_list[net_name] = net_id else: print "error: failed to build network %s" % net_name sys.exit(1) def process_servers(): print "processing servers" if (args.delete): for k in host_builder.keys(): server_delete(k) elif (args.suspend): for k in host_builder.keys(): server_suspend(k) elif (args.resume): for k in host_builder.keys(): server_resume(k) else: for k,(i,f,ns) in host_builder.items(): print "host %s : (%s,%s)" % (k,i,f) nics=[] for (name,ip,fip_id) in ns: id=net_list[name] port_id = neutron.port_build(id,ip) nics.append({'port-id': port_id}) if (fip_id): neutron.floatingip_bind(port_id,fip_id) instance = nova.servers.create(name=k, image=i, flavor=f, key_name=spec['keypair'], nics=nics, config_drive=True) if (args.delete > 1): process_servers() process_networks() elif (build or args.complete): process_networks() process_servers() else: process_servers()

false

true

f719453dae93648775b69c46831ebbe5c6081c50

3,822

py

Python

pygasus/storage/sql/query_builder.py

talismud/pygasus

fb01c8bd51003b5a008b572182a96bad86ef769f

[ "BSD-3-Clause" ]

2

2021-11-18T09:35:10.000Z

2021-11-18T14:46:32.000Z

pygasus/storage/sql/query_builder.py

talismud/pygasus

fb01c8bd51003b5a008b572182a96bad86ef769f

[ "BSD-3-Clause" ]

null

pygasus/storage/sql/query_builder.py

talismud/pygasus

fb01c8bd51003b5a008b572182a96bad86ef769f

[ "BSD-3-Clause" ]

null

# Copyright (c) 2021, LE GOFF Vincent # 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 ytranslate 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. """SQLAlchemy query builder.""" from pygasus.storage.query_builder import AbstractQueryBuilder class SQLQueryBuilder(AbstractQueryBuilder): """Query builder for SQLAlchemy.""" def _get_table(self, field): """Return the table for this model.""" model = field.__model__ model_name = getattr( model.__config__, "model_name", model.__name__.lower() ) return self.storage_engine.tables[model_name] def eq(self, field, other): """Compare field to other.""" table = self._get_table(field) return getattr(table.c, field.name) == other def ne(self, field, other): """Compare field to other.""" table = self._get_table(field) return getattr(table.c, field.name) != other def lt(self, field, other): """Compare field to other.""" table = self._get_table(field) return getattr(table.c, field.name) < other def le(self, field, other): """Compare field to other.""" table = self._get_table(field) return getattr(table.c, field.name) <= other def gt(self, field, other): """Compare field to other.""" table = self._get_table(field) return getattr(table.c, field.name) > other def ge(self, field, other): """Compare field to other.""" table = self._get_table(field) return getattr(table.c, field.name) >= other def is_in(self, field, collection): """Filter fields with a value in a collection.""" table = self._get_table(field) return getattr(table.c, field.name).in_(collection) def is_not_in(self, field, collection): """Filter fields with a value not in a collection.""" table = self._get_table(field) return getattr(table.c, field.name).not_in(collection) def has(self, field, value): """Return models with the field having this value (flag).""" table = self._get_table(field) return getattr(table.c, field.name).op("&")(value.value) == value.value def has_not(self, field, value): """Return models without the field having this value (flag).""" table = self._get_table(field) return getattr(table.c, field.name).op("&")(value.value) != value.value

39.8125

79

0.68786

from pygasus.storage.query_builder import AbstractQueryBuilder class SQLQueryBuilder(AbstractQueryBuilder): def _get_table(self, field): model = field.__model__ model_name = getattr( model.__config__, "model_name", model.__name__.lower() ) return self.storage_engine.tables[model_name] def eq(self, field, other): table = self._get_table(field) return getattr(table.c, field.name) == other def ne(self, field, other): table = self._get_table(field) return getattr(table.c, field.name) != other def lt(self, field, other): table = self._get_table(field) return getattr(table.c, field.name) < other def le(self, field, other): table = self._get_table(field) return getattr(table.c, field.name) <= other def gt(self, field, other): table = self._get_table(field) return getattr(table.c, field.name) > other def ge(self, field, other): table = self._get_table(field) return getattr(table.c, field.name) >= other def is_in(self, field, collection): table = self._get_table(field) return getattr(table.c, field.name).in_(collection) def is_not_in(self, field, collection): table = self._get_table(field) return getattr(table.c, field.name).not_in(collection) def has(self, field, value): table = self._get_table(field) return getattr(table.c, field.name).op("&")(value.value) == value.value def has_not(self, field, value): table = self._get_table(field) return getattr(table.c, field.name).op("&")(value.value) != value.value

true

f719456656da103360e5cac1aae1790b2a55d482

20,904

py

Python

octavia/amphorae/drivers/haproxy/rest_api_driver.py

acdc-cloud/openstack-octavia

f68460ddd31f9b09d59fff876f103324078473a6

[ "Apache-2.0" ]

null

octavia/amphorae/drivers/haproxy/rest_api_driver.py

acdc-cloud/openstack-octavia

f68460ddd31f9b09d59fff876f103324078473a6

[ "Apache-2.0" ]

null

octavia/amphorae/drivers/haproxy/rest_api_driver.py

acdc-cloud/openstack-octavia

f68460ddd31f9b09d59fff876f103324078473a6

[ "Apache-2.0" ]

null

# Copyright 2015 Hewlett-Packard Development Company, L.P. # Copyright (c) 2015 Rackspace # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import functools import hashlib import time import warnings from oslo_log import log as logging import requests import simplejson import six from stevedore import driver as stevedore_driver from octavia.amphorae.driver_exceptions import exceptions as driver_except from octavia.amphorae.drivers import driver_base from octavia.amphorae.drivers.haproxy import exceptions as exc from octavia.amphorae.drivers.keepalived import vrrp_rest_driver from octavia.common.config import cfg from octavia.common import constants as consts from octavia.common.jinja.haproxy import jinja_cfg from octavia.common.jinja.lvs import jinja_cfg as jinja_udp_cfg from octavia.common.tls_utils import cert_parser from octavia.common import utils LOG = logging.getLogger(__name__) API_VERSION = consts.API_VERSION OCTAVIA_API_CLIENT = ( "Octavia HaProxy Rest Client/{version} " "(https://wiki.openstack.org/wiki/Octavia)").format(version=API_VERSION) CONF = cfg.CONF class HaproxyAmphoraLoadBalancerDriver( driver_base.AmphoraLoadBalancerDriver, vrrp_rest_driver.KeepalivedAmphoraDriverMixin): def __init__(self): super(HaproxyAmphoraLoadBalancerDriver, self).__init__() self.client = AmphoraAPIClient() self.cert_manager = stevedore_driver.DriverManager( namespace='octavia.cert_manager', name=CONF.certificates.cert_manager, invoke_on_load=True, ).driver self.jinja = jinja_cfg.JinjaTemplater( base_amp_path=CONF.haproxy_amphora.base_path, base_crt_dir=CONF.haproxy_amphora.base_cert_dir, haproxy_template=CONF.haproxy_amphora.haproxy_template, connection_logging=CONF.haproxy_amphora.connection_logging) self.udp_jinja = jinja_udp_cfg.LvsJinjaTemplater() def update_amphora_listeners(self, listeners, amphora_index, amphorae, timeout_dict=None): """Update the amphora with a new configuration. :param listeners: List of listeners to update. :type listener: list :param amphora_id: The ID of the amphora to update :type amphora_id: string :param timeout_dict: Dictionary of timeout values for calls to the amphora. May contain: req_conn_timeout, req_read_timeout, conn_max_retries, conn_retry_interval :returns: None Updates the configuration of the listeners on a single amphora. """ # if the amphora does not yet have listeners, no need to update them. if not listeners: LOG.debug('No listeners found to update.') return amp = amphorae[amphora_index] if amp is None or amp.status == consts.DELETED: return # TODO(johnsom) remove when we don't have a process per listener for listener in listeners: LOG.debug("%s updating listener %s on amphora %s", self.__class__.__name__, listener.id, amp.id) if listener.protocol == 'UDP': # Generate Keepalived LVS configuration from listener object config = self.udp_jinja.build_config(listener=listener) self.client.upload_udp_config(amp, listener.id, config, timeout_dict=timeout_dict) self.client.reload_listener(amp, listener.id, timeout_dict=timeout_dict) else: certs = self._process_tls_certificates(listener) # Generate HaProxy configuration from listener object config = self.jinja.build_config( host_amphora=amp, listener=listener, tls_cert=certs['tls_cert']) self.client.upload_config(amp, listener.id, config, timeout_dict=timeout_dict) self.client.reload_listener(amp, listener.id, timeout_dict=timeout_dict) def _udp_update(self, listener, vip): LOG.debug("Amphora %s keepalivedlvs, updating " "listener %s, vip %s", self.__class__.__name__, listener.protocol_port, vip.ip_address) for amp in listener.load_balancer.amphorae: if amp.status != consts.DELETED: # Generate Keepalived LVS configuration from listener object config = self.udp_jinja.build_config(listener=listener) self.client.upload_udp_config(amp, listener.id, config) self.client.reload_listener(amp, listener.id) def update(self, listener, vip): if listener.protocol == 'UDP': self._udp_update(listener, vip) else: LOG.debug("Amphora %s haproxy, updating listener %s, " "vip %s", self.__class__.__name__, listener.protocol_port, vip.ip_address) # Process listener certificate info certs = self._process_tls_certificates(listener) for amp in listener.load_balancer.amphorae: if amp.status != consts.DELETED: # Generate HaProxy configuration from listener object config = self.jinja.build_config( host_amphora=amp, listener=listener, tls_cert=certs['tls_cert']) self.client.upload_config(amp, listener.id, config) self.client.reload_listener(amp, listener.id) def upload_cert_amp(self, amp, pem): LOG.debug("Amphora %s updating cert in REST driver " "with amphora id %s,", self.__class__.__name__, amp.id) self.client.update_cert_for_rotation(amp, pem) def _apply(self, func, listener=None, amphora=None, *args): if amphora is None: for amp in listener.load_balancer.amphorae: if amp.status != consts.DELETED: func(amp, listener.id, *args) else: if amphora.status != consts.DELETED: func(amphora, listener.id, *args) def stop(self, listener, vip): self._apply(self.client.stop_listener, listener) def start(self, listener, vip, amphora=None): self._apply(self.client.start_listener, listener, amphora) def delete(self, listener, vip): self._apply(self.client.delete_listener, listener) def get_info(self, amphora): return self.client.get_info(amphora) def get_diagnostics(self, amphora): pass def finalize_amphora(self, amphora): pass def post_vip_plug(self, amphora, load_balancer, amphorae_network_config): if amphora.status != consts.DELETED: subnet = amphorae_network_config.get(amphora.id).vip_subnet # NOTE(blogan): using the vrrp port here because that # is what the allowed address pairs network driver sets # this particular port to. This does expose a bit of # tight coupling between the network driver and amphora # driver. We will need to revisit this to try and remove # this tight coupling. # NOTE (johnsom): I am loading the vrrp_ip into the # net_info structure here so that I don't break # compatibility with old amphora agent versions. port = amphorae_network_config.get(amphora.id).vrrp_port LOG.debug("Post-VIP-Plugging with vrrp_ip %s vrrp_port %s", amphora.vrrp_ip, port.id) host_routes = [{'nexthop': hr.nexthop, 'destination': hr.destination} for hr in subnet.host_routes] net_info = {'subnet_cidr': subnet.cidr, 'gateway': subnet.gateway_ip, 'mac_address': port.mac_address, 'vrrp_ip': amphora.vrrp_ip, 'mtu': port.network.mtu, 'host_routes': host_routes} try: self.client.plug_vip(amphora, load_balancer.vip.ip_address, net_info) except exc.Conflict: LOG.warning('VIP with MAC %(mac)s already exists on amphora, ' 'skipping post_vip_plug', {'mac': port.mac_address}) def post_network_plug(self, amphora, port): fixed_ips = [] for fixed_ip in port.fixed_ips: host_routes = [{'nexthop': hr.nexthop, 'destination': hr.destination} for hr in fixed_ip.subnet.host_routes] ip = {'ip_address': fixed_ip.ip_address, 'subnet_cidr': fixed_ip.subnet.cidr, 'host_routes': host_routes} fixed_ips.append(ip) port_info = {'mac_address': port.mac_address, 'fixed_ips': fixed_ips, 'mtu': port.network.mtu} try: self.client.plug_network(amphora, port_info) except exc.Conflict: LOG.warning('Network with MAC %(mac)s already exists on amphora, ' 'skipping post_network_plug', {'mac': port.mac_address}) def _process_tls_certificates(self, listener): """Processes TLS data from the listener. Converts and uploads PEM data to the Amphora API return TLS_CERT and SNI_CERTS """ tls_cert = None sni_certs = [] certs = [] data = cert_parser.load_certificates_data( self.cert_manager, listener) if data['tls_cert'] is not None: tls_cert = data['tls_cert'] certs.append(tls_cert) if data['sni_certs']: sni_certs = data['sni_certs'] certs.extend(sni_certs) for cert in certs: pem = cert_parser.build_pem(cert) md5 = hashlib.md5(pem).hexdigest() # nosec name = '{id}.pem'.format(id=cert.id) self._apply(self._upload_cert, listener, None, pem, md5, name) return {'tls_cert': tls_cert, 'sni_certs': sni_certs} def _upload_cert(self, amp, listener_id, pem, md5, name): try: if self.client.get_cert_md5sum( amp, listener_id, name, ignore=(404,)) == md5: return except exc.NotFound: pass self.client.upload_cert_pem( amp, listener_id, name, pem) # Check a custom hostname class CustomHostNameCheckingAdapter(requests.adapters.HTTPAdapter): def cert_verify(self, conn, url, verify, cert): conn.assert_hostname = self.uuid return super(CustomHostNameCheckingAdapter, self).cert_verify(conn, url, verify, cert) class AmphoraAPIClient(object): def __init__(self): super(AmphoraAPIClient, self).__init__() self.secure = False self.get = functools.partial(self.request, 'get') self.post = functools.partial(self.request, 'post') self.put = functools.partial(self.request, 'put') self.delete = functools.partial(self.request, 'delete') self.head = functools.partial(self.request, 'head') self.start_listener = functools.partial(self._action, consts.AMP_ACTION_START) self.stop_listener = functools.partial(self._action, consts.AMP_ACTION_STOP) self.reload_listener = functools.partial(self._action, consts.AMP_ACTION_RELOAD) self.start_vrrp = functools.partial(self._vrrp_action, consts.AMP_ACTION_START) self.stop_vrrp = functools.partial(self._vrrp_action, consts.AMP_ACTION_STOP) self.reload_vrrp = functools.partial(self._vrrp_action, consts.AMP_ACTION_RELOAD) self.session = requests.Session() self.session.cert = CONF.haproxy_amphora.client_cert self.ssl_adapter = CustomHostNameCheckingAdapter() self.session.mount('https://', self.ssl_adapter) def _base_url(self, ip): if utils.is_ipv6_lla(ip): ip = '[{ip}%{interface}]'.format( ip=ip, interface=CONF.haproxy_amphora.lb_network_interface) elif utils.is_ipv6(ip): ip = '[{ip}]'.format(ip=ip) return "https://{ip}:{port}/{version}/".format( ip=ip, port=CONF.haproxy_amphora.bind_port, version=API_VERSION) def request(self, method, amp, path='/', timeout_dict=None, **kwargs): cfg_ha_amp = CONF.haproxy_amphora if timeout_dict is None: timeout_dict = {} req_conn_timeout = timeout_dict.get( consts.REQ_CONN_TIMEOUT, cfg_ha_amp.rest_request_conn_timeout) req_read_timeout = timeout_dict.get( consts.REQ_READ_TIMEOUT, cfg_ha_amp.rest_request_read_timeout) conn_max_retries = timeout_dict.get( consts.CONN_MAX_RETRIES, cfg_ha_amp.connection_max_retries) conn_retry_interval = timeout_dict.get( consts.CONN_RETRY_INTERVAL, cfg_ha_amp.connection_retry_interval) LOG.debug("request url %s", path) _request = getattr(self.session, method.lower()) _url = self._base_url(amp.lb_network_ip) + path LOG.debug("request url %s", _url) reqargs = { 'verify': CONF.haproxy_amphora.server_ca, 'url': _url, 'timeout': (req_conn_timeout, req_read_timeout), } reqargs.update(kwargs) headers = reqargs.setdefault('headers', {}) headers['User-Agent'] = OCTAVIA_API_CLIENT self.ssl_adapter.uuid = amp.id exception = None # Keep retrying for a in six.moves.xrange(conn_max_retries): try: with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message="A true SSLContext object is not available" ) r = _request(**reqargs) LOG.debug('Connected to amphora. Response: %(resp)s', {'resp': r}) content_type = r.headers.get('content-type', '') # Check the 404 to see if it is just that the network in the # amphora is not yet up, in which case retry. # Otherwise return the response quickly. if r.status_code == 404: LOG.debug('Got a 404 (content-type: %(content_type)s) -- ' 'connection data: %(content)s', {'content_type': content_type, 'content': r.content}) if content_type.find("application/json") == -1: LOG.debug("Amphora agent not ready.") raise requests.ConnectionError try: json_data = r.json().get('details', '') if 'No suitable network interface found' in json_data: LOG.debug("Amphora network interface not found.") raise requests.ConnectionError except simplejson.JSONDecodeError: # if r.json() fails pass # TODO(rm_work) Should we do something? return r except (requests.ConnectionError, requests.Timeout) as e: exception = e LOG.warning("Could not connect to instance. Retrying.") time.sleep(conn_retry_interval) LOG.error("Connection retries (currently set to %(max_retries)s) " "exhausted. The amphora is unavailable. Reason: " "%(exception)s", {'max_retries': conn_max_retries, 'exception': exception}) raise driver_except.TimeOutException() def upload_config(self, amp, listener_id, config, timeout_dict=None): r = self.put( amp, 'listeners/{amphora_id}/{listener_id}/haproxy'.format( amphora_id=amp.id, listener_id=listener_id), timeout_dict, data=config) return exc.check_exception(r) def get_listener_status(self, amp, listener_id): r = self.get( amp, 'listeners/{listener_id}'.format(listener_id=listener_id)) if exc.check_exception(r): return r.json() return None def _action(self, action, amp, listener_id, timeout_dict=None): r = self.put(amp, 'listeners/{listener_id}/{action}'.format( listener_id=listener_id, action=action), timeout_dict=timeout_dict) return exc.check_exception(r) def upload_cert_pem(self, amp, listener_id, pem_filename, pem_file): r = self.put( amp, 'listeners/{listener_id}/certificates/{filename}'.format( listener_id=listener_id, filename=pem_filename), data=pem_file) return exc.check_exception(r) def update_cert_for_rotation(self, amp, pem_file): r = self.put(amp, 'certificate', data=pem_file) return exc.check_exception(r) def get_cert_md5sum(self, amp, listener_id, pem_filename, ignore=tuple()): r = self.get(amp, 'listeners/{listener_id}/certificates/{filename}'.format( listener_id=listener_id, filename=pem_filename)) if exc.check_exception(r, ignore): return r.json().get("md5sum") return None def delete_listener(self, amp, listener_id): r = self.delete( amp, 'listeners/{listener_id}'.format(listener_id=listener_id)) return exc.check_exception(r, (404,)) def get_info(self, amp): r = self.get(amp, "info") if exc.check_exception(r): return r.json() return None def get_details(self, amp): r = self.get(amp, "details") if exc.check_exception(r): return r.json() return None def get_all_listeners(self, amp): r = self.get(amp, "listeners") if exc.check_exception(r): return r.json() return None def delete_cert_pem(self, amp, listener_id, pem_filename): r = self.delete( amp, 'listeners/{listener_id}/certificates/{filename}'.format( listener_id=listener_id, filename=pem_filename)) return exc.check_exception(r, (404,)) def plug_network(self, amp, port): r = self.post(amp, 'plug/network', json=port) return exc.check_exception(r) def plug_vip(self, amp, vip, net_info): r = self.post(amp, 'plug/vip/{vip}'.format(vip=vip), json=net_info) return exc.check_exception(r) def upload_vrrp_config(self, amp, config): r = self.put(amp, 'vrrp/upload', data=config) return exc.check_exception(r) def _vrrp_action(self, action, amp): r = self.put(amp, 'vrrp/{action}'.format(action=action)) return exc.check_exception(r) def get_interface(self, amp, ip_addr, timeout_dict=None): r = self.get(amp, 'interface/{ip_addr}'.format(ip_addr=ip_addr), timeout_dict=timeout_dict) if exc.check_exception(r): return r.json() return None def upload_udp_config(self, amp, listener_id, config, timeout_dict=None): r = self.put( amp, 'listeners/{amphora_id}/{listener_id}/udp_listener'.format( amphora_id=amp.id, listener_id=listener_id), timeout_dict, data=config) return exc.check_exception(r)

41.975904

79

0.591179

import functools import hashlib import time import warnings from oslo_log import log as logging import requests import simplejson import six from stevedore import driver as stevedore_driver from octavia.amphorae.driver_exceptions import exceptions as driver_except from octavia.amphorae.drivers import driver_base from octavia.amphorae.drivers.haproxy import exceptions as exc from octavia.amphorae.drivers.keepalived import vrrp_rest_driver from octavia.common.config import cfg from octavia.common import constants as consts from octavia.common.jinja.haproxy import jinja_cfg from octavia.common.jinja.lvs import jinja_cfg as jinja_udp_cfg from octavia.common.tls_utils import cert_parser from octavia.common import utils LOG = logging.getLogger(__name__) API_VERSION = consts.API_VERSION OCTAVIA_API_CLIENT = ( "Octavia HaProxy Rest Client/{version} " "(https://wiki.openstack.org/wiki/Octavia)").format(version=API_VERSION) CONF = cfg.CONF class HaproxyAmphoraLoadBalancerDriver( driver_base.AmphoraLoadBalancerDriver, vrrp_rest_driver.KeepalivedAmphoraDriverMixin): def __init__(self): super(HaproxyAmphoraLoadBalancerDriver, self).__init__() self.client = AmphoraAPIClient() self.cert_manager = stevedore_driver.DriverManager( namespace='octavia.cert_manager', name=CONF.certificates.cert_manager, invoke_on_load=True, ).driver self.jinja = jinja_cfg.JinjaTemplater( base_amp_path=CONF.haproxy_amphora.base_path, base_crt_dir=CONF.haproxy_amphora.base_cert_dir, haproxy_template=CONF.haproxy_amphora.haproxy_template, connection_logging=CONF.haproxy_amphora.connection_logging) self.udp_jinja = jinja_udp_cfg.LvsJinjaTemplater() def update_amphora_listeners(self, listeners, amphora_index, amphorae, timeout_dict=None): if not listeners: LOG.debug('No listeners found to update.') return amp = amphorae[amphora_index] if amp is None or amp.status == consts.DELETED: return for listener in listeners: LOG.debug("%s updating listener %s on amphora %s", self.__class__.__name__, listener.id, amp.id) if listener.protocol == 'UDP': # Generate Keepalived LVS configuration from listener object config = self.udp_jinja.build_config(listener=listener) self.client.upload_udp_config(amp, listener.id, config, timeout_dict=timeout_dict) self.client.reload_listener(amp, listener.id, timeout_dict=timeout_dict) else: certs = self._process_tls_certificates(listener) # Generate HaProxy configuration from listener object config = self.jinja.build_config( host_amphora=amp, listener=listener, tls_cert=certs['tls_cert']) self.client.upload_config(amp, listener.id, config, timeout_dict=timeout_dict) self.client.reload_listener(amp, listener.id, timeout_dict=timeout_dict) def _udp_update(self, listener, vip): LOG.debug("Amphora %s keepalivedlvs, updating " "listener %s, vip %s", self.__class__.__name__, listener.protocol_port, vip.ip_address) for amp in listener.load_balancer.amphorae: if amp.status != consts.DELETED: # Generate Keepalived LVS configuration from listener object config = self.udp_jinja.build_config(listener=listener) self.client.upload_udp_config(amp, listener.id, config) self.client.reload_listener(amp, listener.id) def update(self, listener, vip): if listener.protocol == 'UDP': self._udp_update(listener, vip) else: LOG.debug("Amphora %s haproxy, updating listener %s, " "vip %s", self.__class__.__name__, listener.protocol_port, vip.ip_address) # Process listener certificate info certs = self._process_tls_certificates(listener) for amp in listener.load_balancer.amphorae: if amp.status != consts.DELETED: # Generate HaProxy configuration from listener object config = self.jinja.build_config( host_amphora=amp, listener=listener, tls_cert=certs['tls_cert']) self.client.upload_config(amp, listener.id, config) self.client.reload_listener(amp, listener.id) def upload_cert_amp(self, amp, pem): LOG.debug("Amphora %s updating cert in REST driver " "with amphora id %s,", self.__class__.__name__, amp.id) self.client.update_cert_for_rotation(amp, pem) def _apply(self, func, listener=None, amphora=None, *args): if amphora is None: for amp in listener.load_balancer.amphorae: if amp.status != consts.DELETED: func(amp, listener.id, *args) else: if amphora.status != consts.DELETED: func(amphora, listener.id, *args) def stop(self, listener, vip): self._apply(self.client.stop_listener, listener) def start(self, listener, vip, amphora=None): self._apply(self.client.start_listener, listener, amphora) def delete(self, listener, vip): self._apply(self.client.delete_listener, listener) def get_info(self, amphora): return self.client.get_info(amphora) def get_diagnostics(self, amphora): pass def finalize_amphora(self, amphora): pass def post_vip_plug(self, amphora, load_balancer, amphorae_network_config): if amphora.status != consts.DELETED: subnet = amphorae_network_config.get(amphora.id).vip_subnet # NOTE(blogan): using the vrrp port here because that # is what the allowed address pairs network driver sets # this particular port to. This does expose a bit of # tight coupling between the network driver and amphora # driver. We will need to revisit this to try and remove # this tight coupling. # NOTE (johnsom): I am loading the vrrp_ip into the # net_info structure here so that I don't break port = amphorae_network_config.get(amphora.id).vrrp_port LOG.debug("Post-VIP-Plugging with vrrp_ip %s vrrp_port %s", amphora.vrrp_ip, port.id) host_routes = [{'nexthop': hr.nexthop, 'destination': hr.destination} for hr in subnet.host_routes] net_info = {'subnet_cidr': subnet.cidr, 'gateway': subnet.gateway_ip, 'mac_address': port.mac_address, 'vrrp_ip': amphora.vrrp_ip, 'mtu': port.network.mtu, 'host_routes': host_routes} try: self.client.plug_vip(amphora, load_balancer.vip.ip_address, net_info) except exc.Conflict: LOG.warning('VIP with MAC %(mac)s already exists on amphora, ' 'skipping post_vip_plug', {'mac': port.mac_address}) def post_network_plug(self, amphora, port): fixed_ips = [] for fixed_ip in port.fixed_ips: host_routes = [{'nexthop': hr.nexthop, 'destination': hr.destination} for hr in fixed_ip.subnet.host_routes] ip = {'ip_address': fixed_ip.ip_address, 'subnet_cidr': fixed_ip.subnet.cidr, 'host_routes': host_routes} fixed_ips.append(ip) port_info = {'mac_address': port.mac_address, 'fixed_ips': fixed_ips, 'mtu': port.network.mtu} try: self.client.plug_network(amphora, port_info) except exc.Conflict: LOG.warning('Network with MAC %(mac)s already exists on amphora, ' 'skipping post_network_plug', {'mac': port.mac_address}) def _process_tls_certificates(self, listener): tls_cert = None sni_certs = [] certs = [] data = cert_parser.load_certificates_data( self.cert_manager, listener) if data['tls_cert'] is not None: tls_cert = data['tls_cert'] certs.append(tls_cert) if data['sni_certs']: sni_certs = data['sni_certs'] certs.extend(sni_certs) for cert in certs: pem = cert_parser.build_pem(cert) md5 = hashlib.md5(pem).hexdigest() name = '{id}.pem'.format(id=cert.id) self._apply(self._upload_cert, listener, None, pem, md5, name) return {'tls_cert': tls_cert, 'sni_certs': sni_certs} def _upload_cert(self, amp, listener_id, pem, md5, name): try: if self.client.get_cert_md5sum( amp, listener_id, name, ignore=(404,)) == md5: return except exc.NotFound: pass self.client.upload_cert_pem( amp, listener_id, name, pem) class CustomHostNameCheckingAdapter(requests.adapters.HTTPAdapter): def cert_verify(self, conn, url, verify, cert): conn.assert_hostname = self.uuid return super(CustomHostNameCheckingAdapter, self).cert_verify(conn, url, verify, cert) class AmphoraAPIClient(object): def __init__(self): super(AmphoraAPIClient, self).__init__() self.secure = False self.get = functools.partial(self.request, 'get') self.post = functools.partial(self.request, 'post') self.put = functools.partial(self.request, 'put') self.delete = functools.partial(self.request, 'delete') self.head = functools.partial(self.request, 'head') self.start_listener = functools.partial(self._action, consts.AMP_ACTION_START) self.stop_listener = functools.partial(self._action, consts.AMP_ACTION_STOP) self.reload_listener = functools.partial(self._action, consts.AMP_ACTION_RELOAD) self.start_vrrp = functools.partial(self._vrrp_action, consts.AMP_ACTION_START) self.stop_vrrp = functools.partial(self._vrrp_action, consts.AMP_ACTION_STOP) self.reload_vrrp = functools.partial(self._vrrp_action, consts.AMP_ACTION_RELOAD) self.session = requests.Session() self.session.cert = CONF.haproxy_amphora.client_cert self.ssl_adapter = CustomHostNameCheckingAdapter() self.session.mount('https://', self.ssl_adapter) def _base_url(self, ip): if utils.is_ipv6_lla(ip): ip = '[{ip}%{interface}]'.format( ip=ip, interface=CONF.haproxy_amphora.lb_network_interface) elif utils.is_ipv6(ip): ip = '[{ip}]'.format(ip=ip) return "https://{ip}:{port}/{version}/".format( ip=ip, port=CONF.haproxy_amphora.bind_port, version=API_VERSION) def request(self, method, amp, path='/', timeout_dict=None, **kwargs): cfg_ha_amp = CONF.haproxy_amphora if timeout_dict is None: timeout_dict = {} req_conn_timeout = timeout_dict.get( consts.REQ_CONN_TIMEOUT, cfg_ha_amp.rest_request_conn_timeout) req_read_timeout = timeout_dict.get( consts.REQ_READ_TIMEOUT, cfg_ha_amp.rest_request_read_timeout) conn_max_retries = timeout_dict.get( consts.CONN_MAX_RETRIES, cfg_ha_amp.connection_max_retries) conn_retry_interval = timeout_dict.get( consts.CONN_RETRY_INTERVAL, cfg_ha_amp.connection_retry_interval) LOG.debug("request url %s", path) _request = getattr(self.session, method.lower()) _url = self._base_url(amp.lb_network_ip) + path LOG.debug("request url %s", _url) reqargs = { 'verify': CONF.haproxy_amphora.server_ca, 'url': _url, 'timeout': (req_conn_timeout, req_read_timeout), } reqargs.update(kwargs) headers = reqargs.setdefault('headers', {}) headers['User-Agent'] = OCTAVIA_API_CLIENT self.ssl_adapter.uuid = amp.id exception = None for a in six.moves.xrange(conn_max_retries): try: with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message="A true SSLContext object is not available" ) r = _request(**reqargs) LOG.debug('Connected to amphora. Response: %(resp)s', {'resp': r}) content_type = r.headers.get('content-type', '') if r.status_code == 404: LOG.debug('Got a 404 (content-type: %(content_type)s) -- ' 'connection data: %(content)s', {'content_type': content_type, 'content': r.content}) if content_type.find("application/json") == -1: LOG.debug("Amphora agent not ready.") raise requests.ConnectionError try: json_data = r.json().get('details', '') if 'No suitable network interface found' in json_data: LOG.debug("Amphora network interface not found.") raise requests.ConnectionError except simplejson.JSONDecodeError: pass return r except (requests.ConnectionError, requests.Timeout) as e: exception = e LOG.warning("Could not connect to instance. Retrying.") time.sleep(conn_retry_interval) LOG.error("Connection retries (currently set to %(max_retries)s) " "exhausted. The amphora is unavailable. Reason: " "%(exception)s", {'max_retries': conn_max_retries, 'exception': exception}) raise driver_except.TimeOutException() def upload_config(self, amp, listener_id, config, timeout_dict=None): r = self.put( amp, 'listeners/{amphora_id}/{listener_id}/haproxy'.format( amphora_id=amp.id, listener_id=listener_id), timeout_dict, data=config) return exc.check_exception(r) def get_listener_status(self, amp, listener_id): r = self.get( amp, 'listeners/{listener_id}'.format(listener_id=listener_id)) if exc.check_exception(r): return r.json() return None def _action(self, action, amp, listener_id, timeout_dict=None): r = self.put(amp, 'listeners/{listener_id}/{action}'.format( listener_id=listener_id, action=action), timeout_dict=timeout_dict) return exc.check_exception(r) def upload_cert_pem(self, amp, listener_id, pem_filename, pem_file): r = self.put( amp, 'listeners/{listener_id}/certificates/{filename}'.format( listener_id=listener_id, filename=pem_filename), data=pem_file) return exc.check_exception(r) def update_cert_for_rotation(self, amp, pem_file): r = self.put(amp, 'certificate', data=pem_file) return exc.check_exception(r) def get_cert_md5sum(self, amp, listener_id, pem_filename, ignore=tuple()): r = self.get(amp, 'listeners/{listener_id}/certificates/{filename}'.format( listener_id=listener_id, filename=pem_filename)) if exc.check_exception(r, ignore): return r.json().get("md5sum") return None def delete_listener(self, amp, listener_id): r = self.delete( amp, 'listeners/{listener_id}'.format(listener_id=listener_id)) return exc.check_exception(r, (404,)) def get_info(self, amp): r = self.get(amp, "info") if exc.check_exception(r): return r.json() return None def get_details(self, amp): r = self.get(amp, "details") if exc.check_exception(r): return r.json() return None def get_all_listeners(self, amp): r = self.get(amp, "listeners") if exc.check_exception(r): return r.json() return None def delete_cert_pem(self, amp, listener_id, pem_filename): r = self.delete( amp, 'listeners/{listener_id}/certificates/{filename}'.format( listener_id=listener_id, filename=pem_filename)) return exc.check_exception(r, (404,)) def plug_network(self, amp, port): r = self.post(amp, 'plug/network', json=port) return exc.check_exception(r) def plug_vip(self, amp, vip, net_info): r = self.post(amp, 'plug/vip/{vip}'.format(vip=vip), json=net_info) return exc.check_exception(r) def upload_vrrp_config(self, amp, config): r = self.put(amp, 'vrrp/upload', data=config) return exc.check_exception(r) def _vrrp_action(self, action, amp): r = self.put(amp, 'vrrp/{action}'.format(action=action)) return exc.check_exception(r) def get_interface(self, amp, ip_addr, timeout_dict=None): r = self.get(amp, 'interface/{ip_addr}'.format(ip_addr=ip_addr), timeout_dict=timeout_dict) if exc.check_exception(r): return r.json() return None def upload_udp_config(self, amp, listener_id, config, timeout_dict=None): r = self.put( amp, 'listeners/{amphora_id}/{listener_id}/udp_listener'.format( amphora_id=amp.id, listener_id=listener_id), timeout_dict, data=config) return exc.check_exception(r)

true

f71945ac8b27591fea8b5cba11d2e49aa0f11109

8,813

py

Python

__init__.py

mastnym/cbpi-SimpleCascadeHysteresis

06e0a5d46868aaf7fef304eaed3308dc9b5ed269

[ "MIT" ]

null

__init__.py

mastnym/cbpi-SimpleCascadeHysteresis

06e0a5d46868aaf7fef304eaed3308dc9b5ed269

[ "MIT" ]

null

__init__.py

mastnym/cbpi-SimpleCascadeHysteresis

06e0a5d46868aaf7fef304eaed3308dc9b5ed269

[ "MIT" ]

null

import time from modules import cbpi from modules.core.controller import KettleController from modules.core.props import Property @cbpi.controller class SimpleCascadeHysteresis(KettleController): """ This hysteresis controls MashTun temp. It creates hysteresis on HLT temp not allowing it to reach much higher values than desired mash tun temp (target). It allows to set offset to target MT temp and temp is held in these values so there is not so much overshooting HLT temp In other words target temp is set in mash tun but is regulated with hystersis in HLT There is also a "safety check" which is the temp of coil/tube in Herms/Rims breweries, which is often much higher than desired target temp. In this plugin, this temp is also switching off the heater with adjustable offset. """ pos_off_desc = "Positive value indicating possibility to go above target temp with actor still switched on. If target is 55 and offset is 1, heater will switch off when reaching 56." neg_off_desc = "Positive value indicating possibility to go below target temp with actor still switched off. If target is 55 and offset is 1, heater will switch back on when reaching 54." coil_sensor_desc = "Safety measurement for preventing overheating in Herms coil or rims tube. Leave blank if you don't have sensor after coil/tube." coil_off_desc = "Positive value indicating, when the heater will switch off if the temp at the end of coil/tube is above the target by this value or more. This helps to prevent rising the temp in HLT too much." a_hyst_sensor = Property.Sensor(label="HLT sensor") b_hysteresis_positive_offset = Property.Number("Positive offset for hysteresis", True, 1, description=pos_off_desc) c_hysteresis_negative_offset = Property.Number("Negative offset for hysteresis", True, 0, description=neg_off_desc) d_on_min = Property.Number("Hysteresis Minimum Time On (s)", True, 60) e_off_min = Property.Number("Hysteresis Minimum Time Off (s)", True, 60) f_coil_tube_sensor = Property.Sensor(label="Sensor after the HERMS coil or RIMS tube", description=coil_sensor_desc) g_coil_positive_offset = Property.Number("Positive offset for coil/tube", True, 1.5, description=coil_off_desc) def stop(self): self.heater_off() super(KettleController, self).stop() def run(self): on_min = abs(float(self.d_on_min)) off_min = abs(float(self.e_off_min)) hyst_pos_offset = abs(float(self.b_hysteresis_positive_offset)) hyst_neg_offset = abs(float(self.c_hysteresis_negative_offset)) coil_pos_offset = abs(float(self.g_coil_positive_offset)) hyst_sensor = int(self.a_hyst_sensor) if not self.f_coil_tube_sensor: coil_sensor = None coil_pos_offset = None else: coil_sensor = int(self.f_coil_tube_sensor) h = HysteresisWithTimeChecksAndSafetySwitch(True, hyst_pos_offset, hyst_neg_offset, on_min, off_min, safety_switch_offset=coil_pos_offset) heater_on = False while self.is_running(): waketime = time.time() + 3 target = self.get_target_temp() current = self.get_temp() # target reached in MT we can switch off no matter what if current >= target: self.heater_off() cbpi.app.logger.info("[%s] Target temp reached" % (waketime)) self.sleep(waketime - time.time()) continue # get control switch temp only if we have control switch control = None if coil_sensor is not None: control = float(cbpi.cache.get("sensors")[coil_sensor].instance.last_value) hyst_temp = float(cbpi.cache.get("sensors")[hyst_sensor].instance.last_value) # Update the hysteresis controller try: heater_on = h.run(hyst_temp, target, control) except TimeIntervalNotPassed as e: self.notify("Hysteresis warning", e.message, type="warning", timeout=1500) if heater_on: self.heater_on(100) cbpi.app.logger.info("[%s] Actor stays ON" % (waketime)) else: self.heater_off() cbpi.app.logger.info("[%s] Actor stays OFF" % (waketime)) # Sleep until update required again if waketime <= time.time() + 0.25: self.notify("Hysteresis Error", "Update interval is too short", type="warning") cbpi.app.logger.info("Hysteresis - Update interval is too short") else: self.sleep(waketime - time.time()) class Hysteresis(object): ROUND = 2 def __init__(self, rising, off_offset, on_offset): self.rising = rising self.off_offset = abs(off_offset) self.on_offset = abs(on_offset) self.action = False def switch_off(self): self.action = False def switch_on(self): self.action = True def round(self, *args): return [round(arg, 2) for arg in args] def run(self, current, target): current, target = self.round(current, target) # Switching off rising eg heating if self.rising and current >= (target + self.off_offset): self.switch_off() # Switching off dropping eg cooling elif not self.rising and current <= (target - self.off_offset): self.switch_off() # switching on rising eg heater elif self.rising and current <= target - self.on_offset: self.switch_on() # Switching on dropping eg cooling elif not self.rising and current >= target + self.on_offset: self.switch_on() return self.action class HysteresisSafetySwitch(object): """ Safety switch is another value which controls the hysteresis and has precedence of current value """ def __init__(self, *args, **kwargs): self.ss_offset = kwargs.pop("safety_switch_offset", None) if self.ss_offset is not None: self.ss_offset = abs(self.ss_offset) super(HysteresisSafetySwitch, self).__init__(*args, **kwargs) def run(self, current, target, control): # not using this switch, run regular hysteresis if self.ss_offset is None or control is None: super(HysteresisSafetySwitch, self).run(current, target) return self.action current, target, control = self.round(current, target, control) if self.rising and control >= target + self.ss_offset: self.switch_off() elif not self.rising and control <= target - self.ss_offset: self.switch_off() else: super(HysteresisSafetySwitch, self).run(current, target) return self.action class HysteresisWithSafetySwitch(HysteresisSafetySwitch, Hysteresis): pass class TimeIntervalNotPassed(Exception): pass class HysteresisWithTimeChecks(Hysteresis): def __init__(self, rising, off_offset, on_offset, minimum_time_on, minimum_time_off): super(HysteresisWithTimeChecks, self).__init__(rising, off_offset, on_offset) self.min_on = minimum_time_on self.min_off = minimum_time_off self.last_switch = None def switch_off(self): # We are off and need to switch off if not self.action: return # last time should not be None when switching off elapsed = time.time() - self.last_switch if elapsed >= self.min_on: self.last_switch = time.time() super(HysteresisWithTimeChecks, self).switch_off() else: raise TimeIntervalNotPassed( "Should be switching off now, but can't because of safety interval set (time since last switch: {}s)".format( round(elapsed, 0))) def switch_on(self): # We are on and need to switch on if self.action: return if self.last_switch is None or time.time() - self.last_switch >= self.min_off: self.last_switch = time.time() super(HysteresisWithTimeChecks, self).switch_on() else: raise TimeIntervalNotPassed( "Should be switching on now, but can't because of safety interval set (time since last switch: {}s).".format( round(time.time() - self.last_switch, 0))) class HysteresisWithTimeChecksAndSafetySwitch(HysteresisSafetySwitch, HysteresisWithTimeChecks): pass

44.510101

214

0.641439

import time from modules import cbpi from modules.core.controller import KettleController from modules.core.props import Property @cbpi.controller class SimpleCascadeHysteresis(KettleController): pos_off_desc = "Positive value indicating possibility to go above target temp with actor still switched on. If target is 55 and offset is 1, heater will switch off when reaching 56." neg_off_desc = "Positive value indicating possibility to go below target temp with actor still switched off. If target is 55 and offset is 1, heater will switch back on when reaching 54." coil_sensor_desc = "Safety measurement for preventing overheating in Herms coil or rims tube. Leave blank if you don't have sensor after coil/tube." coil_off_desc = "Positive value indicating, when the heater will switch off if the temp at the end of coil/tube is above the target by this value or more. This helps to prevent rising the temp in HLT too much." a_hyst_sensor = Property.Sensor(label="HLT sensor") b_hysteresis_positive_offset = Property.Number("Positive offset for hysteresis", True, 1, description=pos_off_desc) c_hysteresis_negative_offset = Property.Number("Negative offset for hysteresis", True, 0, description=neg_off_desc) d_on_min = Property.Number("Hysteresis Minimum Time On (s)", True, 60) e_off_min = Property.Number("Hysteresis Minimum Time Off (s)", True, 60) f_coil_tube_sensor = Property.Sensor(label="Sensor after the HERMS coil or RIMS tube", description=coil_sensor_desc) g_coil_positive_offset = Property.Number("Positive offset for coil/tube", True, 1.5, description=coil_off_desc) def stop(self): self.heater_off() super(KettleController, self).stop() def run(self): on_min = abs(float(self.d_on_min)) off_min = abs(float(self.e_off_min)) hyst_pos_offset = abs(float(self.b_hysteresis_positive_offset)) hyst_neg_offset = abs(float(self.c_hysteresis_negative_offset)) coil_pos_offset = abs(float(self.g_coil_positive_offset)) hyst_sensor = int(self.a_hyst_sensor) if not self.f_coil_tube_sensor: coil_sensor = None coil_pos_offset = None else: coil_sensor = int(self.f_coil_tube_sensor) h = HysteresisWithTimeChecksAndSafetySwitch(True, hyst_pos_offset, hyst_neg_offset, on_min, off_min, safety_switch_offset=coil_pos_offset) heater_on = False while self.is_running(): waketime = time.time() + 3 target = self.get_target_temp() current = self.get_temp() # target reached in MT we can switch off no matter what if current >= target: self.heater_off() cbpi.app.logger.info("[%s] Target temp reached" % (waketime)) self.sleep(waketime - time.time()) continue # get control switch temp only if we have control switch control = None if coil_sensor is not None: control = float(cbpi.cache.get("sensors")[coil_sensor].instance.last_value) hyst_temp = float(cbpi.cache.get("sensors")[hyst_sensor].instance.last_value) # Update the hysteresis controller try: heater_on = h.run(hyst_temp, target, control) except TimeIntervalNotPassed as e: self.notify("Hysteresis warning", e.message, type="warning", timeout=1500) if heater_on: self.heater_on(100) cbpi.app.logger.info("[%s] Actor stays ON" % (waketime)) else: self.heater_off() cbpi.app.logger.info("[%s] Actor stays OFF" % (waketime)) # Sleep until update required again if waketime <= time.time() + 0.25: self.notify("Hysteresis Error", "Update interval is too short", type="warning") cbpi.app.logger.info("Hysteresis - Update interval is too short") else: self.sleep(waketime - time.time()) class Hysteresis(object): ROUND = 2 def __init__(self, rising, off_offset, on_offset): self.rising = rising self.off_offset = abs(off_offset) self.on_offset = abs(on_offset) self.action = False def switch_off(self): self.action = False def switch_on(self): self.action = True def round(self, *args): return [round(arg, 2) for arg in args] def run(self, current, target): current, target = self.round(current, target) # Switching off rising eg heating if self.rising and current >= (target + self.off_offset): self.switch_off() # Switching off dropping eg cooling elif not self.rising and current <= (target - self.off_offset): self.switch_off() # switching on rising eg heater elif self.rising and current <= target - self.on_offset: self.switch_on() # Switching on dropping eg cooling elif not self.rising and current >= target + self.on_offset: self.switch_on() return self.action class HysteresisSafetySwitch(object): def __init__(self, *args, **kwargs): self.ss_offset = kwargs.pop("safety_switch_offset", None) if self.ss_offset is not None: self.ss_offset = abs(self.ss_offset) super(HysteresisSafetySwitch, self).__init__(*args, **kwargs) def run(self, current, target, control): # not using this switch, run regular hysteresis if self.ss_offset is None or control is None: super(HysteresisSafetySwitch, self).run(current, target) return self.action current, target, control = self.round(current, target, control) if self.rising and control >= target + self.ss_offset: self.switch_off() elif not self.rising and control <= target - self.ss_offset: self.switch_off() else: super(HysteresisSafetySwitch, self).run(current, target) return self.action class HysteresisWithSafetySwitch(HysteresisSafetySwitch, Hysteresis): pass class TimeIntervalNotPassed(Exception): pass class HysteresisWithTimeChecks(Hysteresis): def __init__(self, rising, off_offset, on_offset, minimum_time_on, minimum_time_off): super(HysteresisWithTimeChecks, self).__init__(rising, off_offset, on_offset) self.min_on = minimum_time_on self.min_off = minimum_time_off self.last_switch = None def switch_off(self): # We are off and need to switch off if not self.action: return # last time should not be None when switching off elapsed = time.time() - self.last_switch if elapsed >= self.min_on: self.last_switch = time.time() super(HysteresisWithTimeChecks, self).switch_off() else: raise TimeIntervalNotPassed( "Should be switching off now, but can't because of safety interval set (time since last switch: {}s)".format( round(elapsed, 0))) def switch_on(self): if self.action: return if self.last_switch is None or time.time() - self.last_switch >= self.min_off: self.last_switch = time.time() super(HysteresisWithTimeChecks, self).switch_on() else: raise TimeIntervalNotPassed( "Should be switching on now, but can't because of safety interval set (time since last switch: {}s).".format( round(time.time() - self.last_switch, 0))) class HysteresisWithTimeChecksAndSafetySwitch(HysteresisSafetySwitch, HysteresisWithTimeChecks): pass

true

f719467cb68b35eb1744903f1645e3dc36285aae

182

py

Python

Others/joi/joi2021yo1b/a/main.py

KATO-Hiro/AtCoder

cbbdb18e95110b604728a54aed83a6ed6b993fde

[ "CC0-1.0" ]

2

2020-06-12T09:54:23.000Z

2021-05-04T01:34:07.000Z

Others/joi/joi2021yo1b/a/main.py

KATO-Hiro/AtCoder

cbbdb18e95110b604728a54aed83a6ed6b993fde

[ "CC0-1.0" ]

961

2020-06-23T07:26:22.000Z

2022-03-31T21:34:52.000Z

Others/joi/joi2021yo1b/a/main.py

KATO-Hiro/AtCoder

cbbdb18e95110b604728a54aed83a6ed6b993fde

[ "CC0-1.0" ]

null

# -*- coding: utf-8 -*- def main(): a, b, c = map(int, input().split()) if a <= c < b: print(1) else: print(0) if __name__ == "__main__": main()

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def main(): a, b, c = map(int, input().split()) if a <= c < b: print(1) else: print(0) if __name__ == "__main__": main()

true

f719472b0ea2f7adf53faaf80d9dfeb1915076da

286

py

Python

awswrangler/__metadata__.py

Thiago-Dantas/aws-data-wrangler

b13fcd8d169feb3219b4b4fff025dc6089cfe03b

[ "Apache-2.0" ]

1

2021-04-27T12:56:28.000Z

awswrangler/__metadata__.py

Thiago-Dantas/aws-data-wrangler

b13fcd8d169feb3219b4b4fff025dc6089cfe03b

[ "Apache-2.0" ]

63

2021-05-31T08:35:17.000Z

2022-03-28T08:12:04.000Z

awswrangler/__metadata__.py

kukushking/aws-data-wrangler

c91188472f96b222c943b35b3b082c0ba5e54745

[ "Apache-2.0" ]

null

"""Metadata Module. Source repository: https://github.com/awslabs/aws-data-wrangler Documentation: https://aws-data-wrangler.readthedocs.io/ """ __title__: str = "awswrangler" __description__: str = "Pandas on AWS." __version__: str = "2.8.0" __license__: str = "Apache License 2.0"

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__title__: str = "awswrangler" __description__: str = "Pandas on AWS." __version__: str = "2.8.0" __license__: str = "Apache License 2.0"

true

f71949204a3a8f20fdd5c84ef0a61e2047469716

7,418

py

Python

4course/theory_of_pl/course/test.py

soul-catcher/sibsutis

5d7d88ffabbe445052927eb6c6097697df672997

[ "WTFPL" ]

10

2021-08-28T08:44:57.000Z

2022-03-06T16:29:51.000Z

4course/theory_of_pl/course/test.py

soul-catcher/sibsutis

5d7d88ffabbe445052927eb6c6097697df672997

[ "WTFPL" ]

null

4course/theory_of_pl/course/test.py

soul-catcher/sibsutis

5d7d88ffabbe445052927eb6c6097697df672997

[ "WTFPL" ]

6

2021-09-06T07:26:18.000Z

2021-12-16T16:11:10.000Z

import unittest import utils from grammar import Grammar class SplitByCommasTest(unittest.TestCase): def test_simple(self): self.assertEqual(['A', 'B', 'C'], utils.split_by('A, B, C', ',')) def test_without_space(self): self.assertEqual(['A', 'B', 'C'], utils.split_by('A,B,C', ',')) def test_with_chaotic_space(self): self.assertEqual(['A', 'B', 'C'], utils.split_by(' A , B , C ', ',')) def test_without_commas(self): self.assertEqual(['A B C'], utils.split_by('A B C', ',')) def test_zero_length(self): self.assertEqual([], utils.split_by('', ',')) def test_only_spaces(self): self.assertEqual([], utils.split_by(' ', ',')) def test_word(self): self.assertEqual(['test'], utils.split_by('test', ',')) def test_several_words_without_commas(self): self.assertEqual(['one two three'], utils.split_by(' one two three ', ',')) def test_several_words_with_commas(self): self.assertEqual(['one', 'two', 'three'], utils.split_by('one, two,three', ',')) def test_chaotic_commas(self): self.assertEqual(['A', 'B', 'C'], utils.split_by(',,A,,,B,C,', ',')) def test_only_comma(self): self.assertEqual([], utils.split_by(',', ',')) class ParseRulesTest(unittest.TestCase): def test_simple(self): rules = 'A -> aAa' expected = {'A': ['aAa']} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_lambda(self): rules = 'A -> aAa | @' expected = {'A': ['aAa', '']} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_lambda_left(self): rules = 'A -> @ | aAa' expected = {'A': ['', 'aAa']} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_without_spaces(self): rules = 'A->aAa|@' expected = {'A': ['aAa', '']} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_chaotic_spaces(self): rules = ' AB C -> a A a | @ ' expected = {'AB C': ['a A a', '']} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_empty_rules(self): rules = '' expected = {} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_multiline_rules(self): rules = ''' A -> aAa B -> bBb ''' expected = {'A': ['aAa'], 'B': ['bBb']} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_without_arrow(self): with self.assertRaises(utils.WrongRulesException): utils.parse_rules('A aAa', '@') def test_wrong_place_arrow1(self): with self.assertRaises(utils.WrongRulesException): utils.parse_rules('-> A aAa', '@') def test_wrong_place_arrow2(self): with self.assertRaises(utils.WrongRulesException): utils.parse_rules('A aAa -> ', '@') class CanonGrammarTest(unittest.TestCase): def test_find_non_child_free(self): grammar = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'S'], { 'S': ['aAB', 'E'], 'A': ['aA', 'bB'], 'B': ['ACb', 'b'], 'C': ['A', 'bA', 'cC', 'aE'], 'D': ['a', 'c', 'Fb'], 'E': ['cE', 'aE', 'Eb', 'ED', 'FG'], 'F': ['BC', 'EC', 'AC'], 'G': ['Ga', 'Gb'] }, 'S' ) self.assertSetEqual({'E', 'G'}, grammar.find_child_free_non_terms()) def test_remove_rules1(self): grammar = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'S'], { 'S': ['aAB', 'E'], 'A': ['aA', 'bB'], 'B': ['ACb', 'b'], 'C': ['A', 'bA', 'cC', 'aE'], 'D': ['a', 'c', 'Fb'], 'E': ['cE', 'aE', 'Eb', 'ED', 'FG'], 'F': ['BC', 'EC', 'AC'], 'G': ['Ga', 'Gb'] }, 'S' ) grammar_expected = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'D', 'F', 'S'], { 'S': ['aAB'], 'A': ['aA', 'bB'], 'B': ['ACb', 'b'], 'C': ['A', 'bA', 'cC'], 'D': ['a', 'c', 'Fb'], 'F': ['BC', 'AC'], }, 'S' ) self.assertEqual(grammar_expected, grammar.remove_rules({'E', 'G'})) def test_remove_rules2(self): grammar = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'D', 'F', 'S'], { 'S': ['aAB'], 'A': ['aA', 'bB'], 'B': ['ACb', 'b'], 'C': ['A', 'bA', 'cC'], 'D': ['a', 'c', 'Fb'], 'F': ['BC', 'AC'], }, 'S' ) grammar_expected = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'S'], { 'S': ['aAB'], 'A': ['aA', 'bB'], 'B': ['ACb', 'b'], 'C': ['A', 'bA', 'cC'], }, 'S' ) self.assertEqual(grammar_expected, grammar.remove_rules({'D', 'F'})) def test_find_unreachable_rules(self): grammar = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'D', 'F', 'S'], { 'S': ['aAB'], 'A': ['aA', 'bB'], 'B': ['ACb', 'b'], 'C': ['A', 'bA', 'cC'], 'D': ['a', 'c', 'Fb'], 'F': ['BC', 'AC'], }, 'S' ) self.assertSetEqual({'D', 'F'}, grammar.find_unreachable_rules()) def test_remove_empty_rules(self): grammar = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'S'], { 'S': ['AaB', 'aB', 'cC'], 'A': ['AB', 'a', 'b', 'B'], 'B': ['Ba', ''], 'C': ['AB', 'c'] }, 'S' ) grammar_expected = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'S'], { 'S': ['AaB', 'cC', 'aB', 'Aa', 'a', 'c'], 'A': ['AB', 'b', 'a', 'B'], 'B': ['a', 'Ba'], 'C': ['AB', 'c', 'A', 'B'] }, 'S' ) self.assertEqual(grammar_expected, grammar.remove_empty_rules()) def test_remove_chain_rules(self): grammar = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'S'], { 'S': ['AaB', 'cC', 'aB', 'Aa', 'a', 'c'], 'A': ['AB', 'b', 'a', 'B'], 'B': ['a', 'Ba'], 'C': ['AB', 'c', 'A', 'B'] }, 'S' ) grammar_expected = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'S'], { 'S': ['AaB', 'cC', 'aB', 'Aa', 'a', 'c'], 'A': ['AB', 'b', 'a', 'Ba'], 'B': ['a', 'Ba'], 'C': ['AB', 'c', 'a', 'Ba', 'b'] }, 'S' ) self.assertEqual(grammar_expected, grammar.remove_chain_rules()) if __name__ == '__main__': unittest.main()

30.780083

88

0.383392

import unittest import utils from grammar import Grammar class SplitByCommasTest(unittest.TestCase): def test_simple(self): self.assertEqual(['A', 'B', 'C'], utils.split_by('A, B, C', ',')) def test_without_space(self): self.assertEqual(['A', 'B', 'C'], utils.split_by('A,B,C', ',')) def test_with_chaotic_space(self): self.assertEqual(['A', 'B', 'C'], utils.split_by(' A , B , C ', ',')) def test_without_commas(self): self.assertEqual(['A B C'], utils.split_by('A B C', ',')) def test_zero_length(self): self.assertEqual([], utils.split_by('', ',')) def test_only_spaces(self): self.assertEqual([], utils.split_by(' ', ',')) def test_word(self): self.assertEqual(['test'], utils.split_by('test', ',')) def test_several_words_without_commas(self): self.assertEqual(['one two three'], utils.split_by(' one two three ', ',')) def test_several_words_with_commas(self): self.assertEqual(['one', 'two', 'three'], utils.split_by('one, two,three', ',')) def test_chaotic_commas(self): self.assertEqual(['A', 'B', 'C'], utils.split_by(',,A,,,B,C,', ',')) def test_only_comma(self): self.assertEqual([], utils.split_by(',', ',')) class ParseRulesTest(unittest.TestCase): def test_simple(self): rules = 'A -> aAa' expected = {'A': ['aAa']} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_lambda(self): rules = 'A -> aAa | @' expected = {'A': ['aAa', '']} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_lambda_left(self): rules = 'A -> @ | aAa' expected = {'A': ['', 'aAa']} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_without_spaces(self): rules = 'A->aAa|@' expected = {'A': ['aAa', '']} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_chaotic_spaces(self): rules = ' AB C -> a A a | @ ' expected = {'AB C': ['a A a', '']} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_empty_rules(self): rules = '' expected = {} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_multiline_rules(self): rules = ''' A -> aAa B -> bBb ''' expected = {'A': ['aAa'], 'B': ['bBb']} self.assertEqual(expected, utils.parse_rules(rules, '@')) def test_without_arrow(self): with self.assertRaises(utils.WrongRulesException): utils.parse_rules('A aAa', '@') def test_wrong_place_arrow1(self): with self.assertRaises(utils.WrongRulesException): utils.parse_rules('-> A aAa', '@') def test_wrong_place_arrow2(self): with self.assertRaises(utils.WrongRulesException): utils.parse_rules('A aAa -> ', '@') class CanonGrammarTest(unittest.TestCase): def test_find_non_child_free(self): grammar = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'S'], { 'S': ['aAB', 'E'], 'A': ['aA', 'bB'], 'B': ['ACb', 'b'], 'C': ['A', 'bA', 'cC', 'aE'], 'D': ['a', 'c', 'Fb'], 'E': ['cE', 'aE', 'Eb', 'ED', 'FG'], 'F': ['BC', 'EC', 'AC'], 'G': ['Ga', 'Gb'] }, 'S' ) self.assertSetEqual({'E', 'G'}, grammar.find_child_free_non_terms()) def test_remove_rules1(self): grammar = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'S'], { 'S': ['aAB', 'E'], 'A': ['aA', 'bB'], 'B': ['ACb', 'b'], 'C': ['A', 'bA', 'cC', 'aE'], 'D': ['a', 'c', 'Fb'], 'E': ['cE', 'aE', 'Eb', 'ED', 'FG'], 'F': ['BC', 'EC', 'AC'], 'G': ['Ga', 'Gb'] }, 'S' ) grammar_expected = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'D', 'F', 'S'], { 'S': ['aAB'], 'A': ['aA', 'bB'], 'B': ['ACb', 'b'], 'C': ['A', 'bA', 'cC'], 'D': ['a', 'c', 'Fb'], 'F': ['BC', 'AC'], }, 'S' ) self.assertEqual(grammar_expected, grammar.remove_rules({'E', 'G'})) def test_remove_rules2(self): grammar = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'D', 'F', 'S'], { 'S': ['aAB'], 'A': ['aA', 'bB'], 'B': ['ACb', 'b'], 'C': ['A', 'bA', 'cC'], 'D': ['a', 'c', 'Fb'], 'F': ['BC', 'AC'], }, 'S' ) grammar_expected = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'S'], { 'S': ['aAB'], 'A': ['aA', 'bB'], 'B': ['ACb', 'b'], 'C': ['A', 'bA', 'cC'], }, 'S' ) self.assertEqual(grammar_expected, grammar.remove_rules({'D', 'F'})) def test_find_unreachable_rules(self): grammar = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'D', 'F', 'S'], { 'S': ['aAB'], 'A': ['aA', 'bB'], 'B': ['ACb', 'b'], 'C': ['A', 'bA', 'cC'], 'D': ['a', 'c', 'Fb'], 'F': ['BC', 'AC'], }, 'S' ) self.assertSetEqual({'D', 'F'}, grammar.find_unreachable_rules()) def test_remove_empty_rules(self): grammar = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'S'], { 'S': ['AaB', 'aB', 'cC'], 'A': ['AB', 'a', 'b', 'B'], 'B': ['Ba', ''], 'C': ['AB', 'c'] }, 'S' ) grammar_expected = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'S'], { 'S': ['AaB', 'cC', 'aB', 'Aa', 'a', 'c'], 'A': ['AB', 'b', 'a', 'B'], 'B': ['a', 'Ba'], 'C': ['AB', 'c', 'A', 'B'] }, 'S' ) self.assertEqual(grammar_expected, grammar.remove_empty_rules()) def test_remove_chain_rules(self): grammar = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'S'], { 'S': ['AaB', 'cC', 'aB', 'Aa', 'a', 'c'], 'A': ['AB', 'b', 'a', 'B'], 'B': ['a', 'Ba'], 'C': ['AB', 'c', 'A', 'B'] }, 'S' ) grammar_expected = Grammar( ['a', 'b', 'c'], ['A', 'B', 'C', 'S'], { 'S': ['AaB', 'cC', 'aB', 'Aa', 'a', 'c'], 'A': ['AB', 'b', 'a', 'Ba'], 'B': ['a', 'Ba'], 'C': ['AB', 'c', 'a', 'Ba', 'b'] }, 'S' ) self.assertEqual(grammar_expected, grammar.remove_chain_rules()) if __name__ == '__main__': unittest.main()

true

f7194a37fbd1fbc139636447bb1502e8a31ec9fb

1,257

py

Python

backend2/venv/lib/python3.9/site-packages/authlib/jose/rfc7517/_cryptography_key.py

anushkas-bot/cube.js

fc5f66e20a7073fcdb1f279440bcd582c5ccc9da

[ "Cube", "Apache-2.0", "MIT" ]

3,172

2017-11-11T05:54:14.000Z

2022-03-31T23:59:59.000Z

backend2/venv/lib/python3.9/site-packages/authlib/jose/rfc7517/_cryptography_key.py

anushkas-bot/cube.js

fc5f66e20a7073fcdb1f279440bcd582c5ccc9da

[ "Cube", "Apache-2.0", "MIT" ]

397

2017-11-11T02:49:06.000Z

2022-03-31T21:02:37.000Z

backend2/venv/lib/python3.9/site-packages/authlib/jose/rfc7517/_cryptography_key.py

anushkas-bot/cube.js

fc5f66e20a7073fcdb1f279440bcd582c5ccc9da

[ "Cube", "Apache-2.0", "MIT" ]

387

2017-11-18T08:59:56.000Z

2022-03-15T18:37:37.000Z

from cryptography.x509 import load_pem_x509_certificate from cryptography.hazmat.primitives.serialization import ( load_pem_private_key, load_pem_public_key, load_ssh_public_key, ) from cryptography.hazmat.backends import default_backend from authlib.common.encoding import to_bytes def load_pem_key(raw, ssh_type=None, key_type=None, password=None): raw = to_bytes(raw) if ssh_type and raw.startswith(ssh_type): return load_ssh_public_key(raw, backend=default_backend()) if key_type == 'public': return load_pem_public_key(raw, backend=default_backend()) if key_type == 'private' or password is not None: return load_pem_private_key(raw, password=password, backend=default_backend()) if b'PUBLIC' in raw: return load_pem_public_key(raw, backend=default_backend()) if b'PRIVATE' in raw: return load_pem_private_key(raw, password=password, backend=default_backend()) if b'CERTIFICATE' in raw: cert = load_pem_x509_certificate(raw, default_backend()) return cert.public_key() try: return load_pem_private_key(raw, password=password, backend=default_backend()) except ValueError: return load_pem_public_key(raw, backend=default_backend())

35.914286

86

0.747017

from cryptography.x509 import load_pem_x509_certificate from cryptography.hazmat.primitives.serialization import ( load_pem_private_key, load_pem_public_key, load_ssh_public_key, ) from cryptography.hazmat.backends import default_backend from authlib.common.encoding import to_bytes def load_pem_key(raw, ssh_type=None, key_type=None, password=None): raw = to_bytes(raw) if ssh_type and raw.startswith(ssh_type): return load_ssh_public_key(raw, backend=default_backend()) if key_type == 'public': return load_pem_public_key(raw, backend=default_backend()) if key_type == 'private' or password is not None: return load_pem_private_key(raw, password=password, backend=default_backend()) if b'PUBLIC' in raw: return load_pem_public_key(raw, backend=default_backend()) if b'PRIVATE' in raw: return load_pem_private_key(raw, password=password, backend=default_backend()) if b'CERTIFICATE' in raw: cert = load_pem_x509_certificate(raw, default_backend()) return cert.public_key() try: return load_pem_private_key(raw, password=password, backend=default_backend()) except ValueError: return load_pem_public_key(raw, backend=default_backend())

true

f7194ab1a937921f6752fb9ea13a7ad2b345d88f

2,008

py

Python

tests/conftest.py

amosbastian/python-fpl

3db8e0029d7cf07111db61ddee0b37b17b051bcd

[ "MIT" ]

217

2018-01-17T10:03:07.000Z

2022-03-12T06:13:02.000Z

tests/conftest.py

amosbastian/python-fpl

3db8e0029d7cf07111db61ddee0b37b17b051bcd

[ "MIT" ]

84

2018-04-23T09:56:16.000Z

2022-02-11T16:19:58.000Z

tests/conftest.py

amosbastian/python-fpl

3db8e0029d7cf07111db61ddee0b37b17b051bcd

[ "MIT" ]

88

2018-04-21T08:07:16.000Z

2022-02-25T03:43:54.000Z

import aiohttp import pytest import os from fpl import FPL from fpl.models import Fixture, H2HLeague, User, ClassicLeague, Team, Gameweek from tests.test_classic_league import classic_league_data from tests.test_fixture import fixture_data from tests.test_h2h_league import h2h_league_data from tests.test_team import team_data from tests.test_user import user_data from tests.test_gameweek import gameweek_data try: from.temp_env_var import TEMP_ENV_VARS, ENV_VARS_TO_SUSPEND except ImportError: TEMP_ENV_VARS = {} ENV_VARS_TO_SUSPEND = [] @pytest.fixture(scope="session", autouse=True) def tests_setup_and_teardown(): # Will be executed before the first test old_environ = dict(os.environ) os.environ.update(TEMP_ENV_VARS) for env_var in ENV_VARS_TO_SUSPEND: os.environ.pop(env_var, default=None) yield # Will be executed after the last test os.environ.clear() os.environ.update(old_environ) @pytest.fixture() async def fpl(): session = aiohttp.ClientSession() fpl = FPL(session) yield fpl await session.close() @pytest.fixture() async def classic_league(): session = aiohttp.ClientSession() yield ClassicLeague(classic_league_data, session) await session.close() @pytest.fixture() async def gameweek(): return Gameweek(gameweek_data) @pytest.fixture() async def player(fpl): yield await fpl.get_player(345, include_summary=True) @pytest.fixture() async def settings(fpl): yield await fpl.game_settings() @pytest.fixture() async def team(): session = aiohttp.ClientSession() yield Team(team_data, session) await session.close() @pytest.fixture() def fixture(): return Fixture(fixture_data) @pytest.fixture() async def h2h_league(): session = aiohttp.ClientSession() yield H2HLeague(h2h_league_data, session) await session.close() @pytest.fixture() async def user(): session = aiohttp.ClientSession() yield User(user_data, session) await session.close()

23.08046

78

0.744024

import aiohttp import pytest import os from fpl import FPL from fpl.models import Fixture, H2HLeague, User, ClassicLeague, Team, Gameweek from tests.test_classic_league import classic_league_data from tests.test_fixture import fixture_data from tests.test_h2h_league import h2h_league_data from tests.test_team import team_data from tests.test_user import user_data from tests.test_gameweek import gameweek_data try: from.temp_env_var import TEMP_ENV_VARS, ENV_VARS_TO_SUSPEND except ImportError: TEMP_ENV_VARS = {} ENV_VARS_TO_SUSPEND = [] @pytest.fixture(scope="session", autouse=True) def tests_setup_and_teardown(): old_environ = dict(os.environ) os.environ.update(TEMP_ENV_VARS) for env_var in ENV_VARS_TO_SUSPEND: os.environ.pop(env_var, default=None) yield os.environ.clear() os.environ.update(old_environ) @pytest.fixture() async def fpl(): session = aiohttp.ClientSession() fpl = FPL(session) yield fpl await session.close() @pytest.fixture() async def classic_league(): session = aiohttp.ClientSession() yield ClassicLeague(classic_league_data, session) await session.close() @pytest.fixture() async def gameweek(): return Gameweek(gameweek_data) @pytest.fixture() async def player(fpl): yield await fpl.get_player(345, include_summary=True) @pytest.fixture() async def settings(fpl): yield await fpl.game_settings() @pytest.fixture() async def team(): session = aiohttp.ClientSession() yield Team(team_data, session) await session.close() @pytest.fixture() def fixture(): return Fixture(fixture_data) @pytest.fixture() async def h2h_league(): session = aiohttp.ClientSession() yield H2HLeague(h2h_league_data, session) await session.close() @pytest.fixture() async def user(): session = aiohttp.ClientSession() yield User(user_data, session) await session.close()

true

f7194ae6ce49e55ac49b2ee6f18f31f1b76f58c1

35,332

py

Python

test/test_restful.py

ueno/keylime

480c9b107c155e7a20442afe3be929cf5d50fb86

[ "Apache-2.0" ]

null

test/test_restful.py

ueno/keylime

480c9b107c155e7a20442afe3be929cf5d50fb86

[ "Apache-2.0" ]

null

test/test_restful.py

ueno/keylime

480c9b107c155e7a20442afe3be929cf5d50fb86

[ "Apache-2.0" ]

null

#!/usr/bin/python3 ''' SPDX-License-Identifier: Apache-2.0 Copyright 2017 Massachusetts Institute of Technology. NOTE: This unittest is being used as a procedural test. The tests must be run in-order and CANNOT be parallelized! Tests all but two RESTful interfaces: * agent's POST /v2/keys/vkey - Done by CV after the CV's POST /v2/agents/{UUID} command is performed * CV's PUT /v2/agents/{UUID} - POST already bootstraps agent, so PUT is redundant in this test The registrar's PUT vactivate interface is only tested if a vTPM is present! USAGE: Should be run in test directory under root privileges with either command: * python -m unittest -v test_restful * green -vv (with `pip install green`) To run without root privileges, be sure to export KEYLIME_TEST=True For Python Coverage support (pip install coverage), set env COVERAGE_FILE and: * coverage run --parallel-mode test_restful.py ''' import sys import signal import unittest import subprocess import time import os import base64 import threading import shutil import errno from pathlib import Path import dbus import simplejson as json from keylime import config from keylime import tornado_requests from keylime.requests_client import RequestsClient from keylime import tenant from keylime import crypto from keylime.cmd import user_data_encrypt from keylime import secure_mount from keylime.tpm import tpm_main from keylime.tpm import tpm_abstract # Coverage support if "COVERAGE_FILE" in os.environ: FORK_ARGS = ["coverage", "run", "--parallel-mode"] if "COVERAGE_DIR" in os.environ: FORK_ARGS += ["--rcfile=" + os.environ["COVERAGE_DIR"] + "/.coveragerc"] else: FORK_ARGS = ["python3"] # Custom imports PACKAGE_ROOT = Path(__file__).parents[1] KEYLIME_DIR = (f"{PACKAGE_ROOT}/keylime") sys.path.append(KEYLIME_DIR) # Custom imports # PACKAGE_ROOT = Path(__file__).parents[1] # CODE_ROOT = (f"{PACKAGE_ROOT}/keylime/") # sys.path.insert(0, CODE_ROOT) # Will be used to communicate with the TPM tpm_instance = None # cmp depreciated in Python 3, so lets recreate it. def cmp(a, b): return (a > b) - (a < b) # Ensure this is run as root if os.geteuid() != 0 and config.REQUIRE_ROOT: sys.exit("Tests need to be run with root privileges, or set env KEYLIME_TEST=True!") # Force sorting tests alphabetically unittest.TestLoader.sortTestMethodsUsing = lambda _, x, y: cmp(x, y) # Environment to pass to services script_env = os.environ.copy() # Globals to keep track of Keylime components cv_process = None reg_process = None agent_process = None tenant_templ = None # Class-level components that are not static (so can't be added to test class) public_key = None keyblob = None ek_tpm = None aik_tpm = None vtpm = False # Set up mTLS my_cert = config.get('tenant', 'my_cert') my_priv_key = config.get('tenant', 'private_key') cert = (my_cert, my_priv_key) tls_enabled = True # Like os.remove, but ignore file DNE exceptions def fileRemove(path): try: os.remove(path) except OSError as e: # Ignore if file does not exist if e.errno != errno.ENOENT: raise # Boring setup stuff def setUpModule(): try: env = os.environ.copy() env['PATH'] = env['PATH'] + ":/usr/local/bin" # Run init_tpm_server and tpm_serverd (start fresh) its = subprocess.Popen(["init_tpm_server"], shell=False, env=env) its.wait() tsd = subprocess.Popen(["tpm_serverd"], shell=False, env=env) tsd.wait() except Exception as e: print("WARNING: Restarting TPM emulator failed!") # Note: the following is required as abrmd is failing to reconnect to MSSIM, once # MSSIM is killed and restarted. If this is an proved an actual bug and is # fixed upstream, the following dbus restart call can be removed. try: sysbus = dbus.SystemBus() systemd1 = sysbus.get_object('org.freedesktop.systemd1', '/org/freedesktop/systemd1') manager = dbus.Interface(systemd1, 'org.freedesktop.systemd1.Manager') # If the systemd service exists, let's restart it. for service in sysbus.list_names(): if "com.intel.tss2.Tabrmd" in service: print("Found dbus service:", str(service)) try: print("Restarting tpm2-abrmd.service.") manager.RestartUnit('tpm2-abrmd.service', 'fail') except dbus.exceptions.DBusException as e: print(e) except Exception as e: print("Non systemd agent detected, no tpm2-abrmd restart required.") try: # Start with a clean slate for this test fileRemove(config.WORK_DIR + "/tpmdata.yaml") fileRemove(config.WORK_DIR + "/cv_data.sqlite") fileRemove(config.WORK_DIR + "/reg_data.sqlite") shutil.rmtree(config.WORK_DIR + "/cv_ca", True) except Exception as e: print("WARNING: Cleanup of TPM files failed!") # CV must be run first to create CA and certs! launch_cloudverifier() launch_registrar() # launch_cloudagent() # Make the Tenant do a lot of set-up work for us global tenant_templ tenant_templ = tenant.Tenant() tenant_templ.agent_uuid = config.get('cloud_agent', 'agent_uuid') tenant_templ.cloudagent_ip = "localhost" tenant_templ.cloudagent_port = config.get('cloud_agent', 'cloudagent_port') tenant_templ.verifier_ip = config.get('cloud_verifier', 'cloudverifier_ip') tenant_templ.verifier_port = config.get('cloud_verifier', 'cloudverifier_port') tenant_templ.registrar_ip = config.get('registrar', 'registrar_ip') tenant_templ.registrar_boot_port = config.get('registrar', 'registrar_port') tenant_templ.registrar_tls_boot_port = config.get('registrar', 'registrar_tls_port') tenant_templ.registrar_base_url = f'{tenant_templ.registrar_ip}:{tenant_templ.registrar_boot_port}' tenant_templ.registrar_base_tls_url = f'{tenant_templ.registrar_ip}:{tenant_templ.registrar_tls_boot_port}' tenant_templ.agent_base_url = f'{tenant_templ.cloudagent_ip}:{tenant_templ.cloudagent_port}' # Set up TLS my_tls_cert, my_tls_priv_key = tenant_templ.get_tls_context() tenant_templ.cert = (my_tls_cert, my_tls_priv_key) # Destroy everything on teardown def tearDownModule(): # Tear down in reverse order of dependencies kill_cloudagent() kill_cloudverifier() kill_registrar() def launch_cloudverifier(): """Start up the cloud verifier""" global cv_process, script_env, FORK_ARGS if cv_process is None: cv_process = subprocess.Popen("keylime_verifier", shell=False, preexec_fn=os.setsid, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, env=script_env) def initthread(): sys.stdout.write('\033[96m' + "\nCloud Verifier Thread" + '\033[0m') while True: line = cv_process.stdout.readline() if line == b'': break line = line.decode('utf-8') line = line.rstrip(os.linesep) sys.stdout.flush() sys.stdout.write('\n\033[96m' + line + '\033[0m') t = threading.Thread(target=initthread) t.start() time.sleep(30) return True def launch_registrar(): """Start up the registrar""" global reg_process, script_env, FORK_ARGS if reg_process is None: reg_process = subprocess.Popen("keylime_registrar", shell=False, preexec_fn=os.setsid, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, env=script_env) def initthread(): sys.stdout.write('\033[95m' + "\nRegistrar Thread" + '\033[0m') while True: line = reg_process.stdout.readline() if line == b"": break # line = line.rstrip(os.linesep) line = line.decode('utf-8') sys.stdout.flush() sys.stdout.write('\n\033[95m' + line + '\033[0m') t = threading.Thread(target=initthread) t.start() time.sleep(10) return True def launch_cloudagent(): """Start up the cloud agent""" global agent_process, script_env, FORK_ARGS if agent_process is None: agent_process = subprocess.Popen("keylime_agent", shell=False, preexec_fn=os.setsid, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, env=script_env) def initthread(): sys.stdout.write('\033[94m' + "\nCloud Agent Thread" + '\033[0m') while True: line = agent_process.stdout.readline() if line == b'': break # line = line.rstrip(os.linesep) line = line.decode('utf-8') sys.stdout.flush() sys.stdout.write('\n\033[94m' + line + '\033[0m') t = threading.Thread(target=initthread) t.start() time.sleep(10) return True def kill_cloudverifier(): """Kill the cloud verifier""" global cv_process if cv_process is None: return os.killpg(os.getpgid(cv_process.pid), signal.SIGINT) cv_process.wait() cv_process = None def kill_registrar(): """Kill the registrar""" global reg_process if reg_process is None: return os.killpg(os.getpgid(reg_process.pid), signal.SIGINT) reg_process.wait() reg_process = None def kill_cloudagent(): """Kill the cloud agent""" global agent_process if agent_process is None: return os.killpg(os.getpgid(agent_process.pid), signal.SIGINT) agent_process.wait() agent_process = None def services_running(): if reg_process.poll() is None and cv_process.poll() is None: return True return False class TestRestful(unittest.TestCase): # Static class members (won't change between tests) payload = None auth_tag = None tpm_policy = {} vtpm_policy = {} metadata = {} allowlist = {} revocation_key = "" mb_refstate = None K = None U = None V = None api_version = config.API_VERSION cloudagent_ip = None cloudagent_port = None @classmethod def setUpClass(cls): """Prepare the keys and payload to give to the CV""" contents = "random garbage to test as payload" # contents = contents.encode('utf-8') ret = user_data_encrypt.encrypt(contents) cls.K = ret['k'] cls.U = ret['u'] cls.V = ret['v'] cls.payload = ret['ciphertext'] # Set up to register an agent cls.auth_tag = crypto.do_hmac(cls.K, tenant_templ.agent_uuid) # Prepare policies for agent cls.tpm_policy = config.get('tenant', 'tpm_policy') cls.vtpm_policy = config.get('tenant', 'vtpm_policy') cls.tpm_policy = tpm_abstract.TPM_Utilities.readPolicy(cls.tpm_policy) cls.vtpm_policy = tpm_abstract.TPM_Utilities.readPolicy(cls.vtpm_policy) # Allow targeting a specific API version (default latest) cls.api_version = config.API_VERSION def setUp(self): """Nothing to set up before each test""" return def test_000_services(self): """Ensure everyone is running before doing tests""" self.assertTrue(services_running(), "Not all services started successfully!") # Registrar Testset def test_010_reg_agent_post(self): """Test registrar's POST /v2/agents/{UUID} Interface""" global keyblob, vtpm, tpm_instance, ek_tpm, aik_tpm tpm_instance = tpm_main.tpm() # Change CWD for TPM-related operations cwd = os.getcwd() config.ch_dir(config.WORK_DIR, None) _ = secure_mount.mount() # Initialize the TPM with AIK (ekcert, ek_tpm, aik_tpm) = tpm_instance.tpm_init(self_activate=False, config_pw=config.get('cloud_agent', 'tpm_ownerpassword')) vtpm = tpm_instance.is_vtpm() # Handle virtualized and emulated TPMs if ekcert is None: if vtpm: ekcert = 'virtual' elif tpm_instance.is_emulator(): ekcert = 'emulator' # Get back to our original CWD config.ch_dir(cwd, None) data = { 'ekcert': ekcert, 'aik_tpm': aik_tpm, } if ekcert is None or ekcert == 'emulator': data['ek_tpm'] = ek_tpm test_010_reg_agent_post = RequestsClient(tenant_templ.registrar_base_url, tls_enabled=False) response = test_010_reg_agent_post.post( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', data=json.dumps(data), cert="", verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Registrar agent Add return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") self.assertIn("blob", json_response["results"], "Malformed response body!") keyblob = json_response["results"]["blob"] self.assertIsNotNone(keyblob, "Malformed response body!") @unittest.skipIf(vtpm, "Registrar's PUT /v2/agents/{UUID}/activate only for non-vTPMs!") def test_011_reg_agent_activate_put(self): """Test registrar's PUT /v2/agents/{UUID}/activate Interface""" global keyblob self.assertIsNotNone(keyblob, "Required value not set. Previous step may have failed?") key = tpm_instance.activate_identity(keyblob) data = { 'auth_tag': crypto.do_hmac(key, tenant_templ.agent_uuid), } test_011_reg_agent_activate_put = RequestsClient(tenant_templ.registrar_base_url, tls_enabled=False) response = test_011_reg_agent_activate_put.put( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}/activate', data=json.dumps(data), cert="", verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Registrar agent Activate return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") def test_013_reg_agents_get(self): """Test registrar's GET /v2/agents Interface""" test_013_reg_agents_get = RequestsClient(tenant_templ.registrar_base_tls_url, tls_enabled=True) response = test_013_reg_agents_get.get( f'/v{self.api_version}/agents/', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Registrar agent List return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") self.assertIn("uuids", json_response["results"], "Malformed response body!") # We registered exactly one agent so far self.assertEqual(1, len(json_response["results"]["uuids"]), "Incorrect system state!") def test_014_reg_agent_get(self): """Test registrar's GET /v2/agents/{UUID} Interface""" test_014_reg_agent_get = RequestsClient(tenant_templ.registrar_base_tls_url, tls_enabled=True) response = test_014_reg_agent_get.get( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Registrar agent return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") self.assertIn("ek_tpm", json_response["results"], "Malformed response body!") self.assertIn("aik_tpm", json_response["results"], "Malformed response body!") self.assertIn("ekcert", json_response["results"], "Malformed response body!") global aik_tpm aik_tpm = json_response["results"]["aik_tpm"] def test_015_reg_agent_delete(self): """Test registrar's DELETE /v2/agents/{UUID} Interface""" test_015_reg_agent_delete = RequestsClient(tenant_templ.registrar_base_tls_url, tls_enabled=True) response = test_015_reg_agent_delete.delete( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Registrar Delete return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") # Agent Setup Testset def test_020_agent_keys_pubkey_get(self): """Test agent's GET /v2/keys/pubkey Interface""" # We want a real cloud agent to communicate with! launch_cloudagent() time.sleep(10) test_020_agent_keys_pubkey_get = RequestsClient(tenant_templ.agent_base_url, tls_enabled=False) response = test_020_agent_keys_pubkey_get.get( f'/v{self.api_version}/keys/pubkey', cert="", verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Agent pubkey return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") self.assertIn("pubkey", json_response["results"], "Malformed response body!") global public_key public_key = json_response["results"]["pubkey"] self.assertNotEqual(public_key, None, "Malformed response body!") def test_021_reg_agent_get(self): # We need to refresh the aik value we've stored in case it changed self.test_014_reg_agent_get() def test_022_agent_quotes_identity_get(self): """Test agent's GET /v2/quotes/identity Interface""" self.assertIsNotNone(aik_tpm, "Required value not set. Previous step may have failed?") nonce = tpm_abstract.TPM_Utilities.random_password(20) numretries = config.getint('tenant', 'max_retries') while numretries >= 0: test_022_agent_quotes_identity_get = RequestsClient(tenant_templ.agent_base_url, tls_enabled=False) response = test_022_agent_quotes_identity_get.get( f'/v{self.api_version}/quotes/identity?nonce={nonce}', data=None, cert="", verify=False ) if response.status_code == 200: break numretries -= 1 time.sleep(config.getint('tenant', 'retry_interval')) self.assertEqual(response.status_code, 200, "Non-successful Agent identity return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") self.assertIn("quote", json_response["results"], "Malformed response body!") self.assertIn("pubkey", json_response["results"], "Malformed response body!") # Check the quote identity self.assertTrue(tpm_instance.check_quote(tenant_templ.agent_uuid, nonce, json_response["results"]["pubkey"], json_response["results"]["quote"], aik_tpm, hash_alg=json_response["results"]["hash_alg"]), "Invalid quote!") @unittest.skip("Testing of agent's POST /v2/keys/vkey disabled! (spawned CV should do this already)") def test_023_agent_keys_vkey_post(self): """Test agent's POST /v2/keys/vkey Interface""" # CV should do this (during CV POST/PUT test) # Running this test might hide problems with the CV sending the V key global public_key self.assertIsNotNone(self.V, "Required value not set. Previous step may have failed?") self.assertIsNotNone(public_key, "Required value not set. Previous step may have failed?") encrypted_V = crypto.rsa_encrypt(crypto.rsa_import_pubkey(public_key), str(self.V)) b64_encrypted_V = base64.b64encode(encrypted_V) data = {'encrypted_key': b64_encrypted_V} test_023_agent_keys_vkey_post = RequestsClient(tenant_templ.agent_base_url, tls_enabled=False) response = test_023_agent_keys_vkey_post.post( f'/v{self.api_version}/keys/vkey', data=json.dumps(data), cert="", verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Agent vkey post return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") def test_024_agent_keys_ukey_post(self): """Test agents's POST /v2/keys/ukey Interface""" global public_key self.assertIsNotNone(public_key, "Required value not set. Previous step may have failed?") self.assertIsNotNone(self.U, "Required value not set. Previous step may have failed?") self.assertIsNotNone(self.auth_tag, "Required value not set. Previous step may have failed?") self.assertIsNotNone(self.payload, "Required value not set. Previous step may have failed?") encrypted_U = crypto.rsa_encrypt(crypto.rsa_import_pubkey(public_key), self.U) b64_encrypted_u = base64.b64encode(encrypted_U) data = { 'encrypted_key': b64_encrypted_u, 'auth_tag': self.auth_tag, 'payload': self.payload } test_024_agent_keys_ukey_post = RequestsClient(tenant_templ.agent_base_url, tls_enabled=False) response = test_024_agent_keys_ukey_post.post( f'/v{self.api_version}/keys/ukey', data=json.dumps(data), cert="", verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Agent ukey post return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") def test_025_cv_allowlist_post(self): """Test CV's POST /v2/allowlist/{name} Interface""" data = { 'name': 'test-allowlist', 'tpm_policy': json.dumps(self.tpm_policy), 'vtpm_policy': json.dumps(self.vtpm_policy), 'ima_policy': json.dumps(self.allowlist), } cv_client = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = cv_client.post( '/allowlists/test-allowlist', data=json.dumps(data), cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 201, "Non-successful CV allowlist Post return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") def test_026_cv_allowlist_get(self): """Test CV's GET /v2/allowlists/{name} Interface""" cv_client = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = cv_client.get( '/allowlists/test-allowlist', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful CV allowlist Post return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") results = json_response['results'] self.assertEqual(results['name'], 'test-allowlist') self.assertEqual(results['tpm_policy'], json.dumps(self.tpm_policy)) self.assertEqual(results['vtpm_policy'], json.dumps(self.vtpm_policy)) self.assertEqual(results['ima_policy'], json.dumps(self.allowlist)) def test_027_cv_allowlist_delete(self): """Test CV's DELETE /v2/allowlists/{name} Interface""" cv_client = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = cv_client.delete( '/allowlists/test-allowlist', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 204, "Non-successful CV allowlist Delete return code!") # Cloud Verifier Testset def test_030_cv_agent_post(self): """Test CV's POST /v2/agents/{UUID} Interface""" self.assertIsNotNone(self.V, "Required value not set. Previous step may have failed?") b64_v = base64.b64encode(self.V) data = { 'v': b64_v, 'cloudagent_ip': tenant_templ.cloudagent_ip, 'cloudagent_port': tenant_templ.cloudagent_port, 'tpm_policy': json.dumps(self.tpm_policy), 'vtpm_policy': json.dumps(self.vtpm_policy), 'allowlist': json.dumps(self.allowlist), 'ima_sign_verification_keys': '', 'mb_refstate': None, 'metadata': json.dumps(self.metadata), 'revocation_key': self.revocation_key, 'accept_tpm_hash_algs': config.get('tenant', 'accept_tpm_hash_algs').split(','), 'accept_tpm_encryption_algs': config.get('tenant', 'accept_tpm_encryption_algs').split(','), 'accept_tpm_signing_algs': config.get('tenant', 'accept_tpm_signing_algs').split(','), } test_030_cv_agent_post = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = test_030_cv_agent_post.post( f'/agents/{tenant_templ.agent_uuid}', data=json.dumps(data), cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful CV agent Post return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") time.sleep(10) @unittest.skip("Testing of CV's PUT /v2/agents/{UUID} disabled!") def test_031_cv_agent_put(self): """Test CV's PUT /v2/agents/{UUID} Interface""" # TODO: this should actually test PUT functionality (e.g., make agent fail and then PUT back up) test_031_cv_agent_put = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = test_031_cv_agent_put.put( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', data=b'', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful CV agent Post return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") def test_032_cv_agents_get(self): """Test CV's GET /v2/agents Interface""" test_032_cv_agents_get = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = test_032_cv_agents_get.get( f'/v{self.api_version}/agents/', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful CV agent List return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") self.assertIn("uuids", json_response["results"], "Malformed response body!") # Be sure our agent is registered self.assertEqual(1, len(json_response["results"]["uuids"])) def test_033_cv_agent_get(self): """Test CV's GET /v2/agents/{UUID} Interface""" test_033_cv_agent_get = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = test_033_cv_agent_get.get( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful CV agent return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") # Check a few of the important properties are present self.assertIn("operational_state", json_response["results"], "Malformed response body!") self.assertIn("ip", json_response["results"], "Malformed response body!") self.assertIn("port", json_response["results"], "Malformed response body!") def test_034_cv_agent_post_invalid_exclude_list(self): """Test CV's POST /v2/agents/{UUID} Interface""" self.assertIsNotNone(self.V, "Required value not set. Previous step may have failed?") b64_v = base64.b64encode(self.V) # Set unsupported regex in exclude list allowlist = {'exclude': ['*']} data = { 'v': b64_v, 'mb_refstate': None, 'cloudagent_ip': tenant_templ.cloudagent_ip, 'cloudagent_port': tenant_templ.cloudagent_port, 'tpm_policy': json.dumps(self.tpm_policy), 'vtpm_policy': json.dumps(self.vtpm_policy), 'allowlist': json.dumps(allowlist), 'ima_sign_verification_keys': '', 'metadata': json.dumps(self.metadata), 'revocation_key': self.revocation_key, 'accept_tpm_hash_algs': config.get('tenant', 'accept_tpm_hash_algs').split(','), 'accept_tpm_encryption_algs': config.get('tenant', 'accept_tpm_encryption_algs').split(','), 'accept_tpm_signing_algs': config.get('tenant', 'accept_tpm_signing_algs').split(','), } client = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = client.post( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', cert=tenant_templ.cert, data=json.dumps(data), verify=False ) self.assertEqual(response.status_code, 400, "Successful CV agent Post return code!") # Ensure response is well-formed json_response = response.json() self.assertIn("results", json_response, "Malformed response body!") # Agent Poll Testset def test_040_agent_quotes_integrity_get(self): """Test agent's GET /v2/quotes/integrity Interface""" global public_key self.assertIsNotNone(aik_tpm, "Required value not set. Previous step may have failed?") nonce = tpm_abstract.TPM_Utilities.random_password(20) mask = self.tpm_policy["mask"] vmask = self.vtpm_policy["mask"] partial = "1" if public_key is None: partial = "0" test_040_agent_quotes_integrity_get = RequestsClient(tenant_templ.agent_base_url, tls_enabled=False) response = test_040_agent_quotes_integrity_get.get( f'/v{self.api_version}/quotes/integrity?nonce={nonce}&mask={mask}&vmask={vmask}&partial={partial}', cert="", verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Agent Integrity Get return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") self.assertIn("quote", json_response["results"], "Malformed response body!") if public_key is None: self.assertIn("pubkey", json_response["results"], "Malformed response body!") public_key = json_response["results"]["pubkey"] self.assertIn("hash_alg", json_response["results"], "Malformed response body!") quote = json_response["results"]["quote"] hash_alg = json_response["results"]["hash_alg"] validQuote = tpm_instance.check_quote(tenant_templ.agent_uuid, nonce, public_key, quote, aik_tpm, self.tpm_policy, hash_alg=hash_alg) self.assertTrue(validQuote) async def test_041_agent_keys_verify_get(self): """Test agent's GET /v2/keys/verify Interface We use async here to allow function await while key processes""" self.assertIsNotNone(self.K, "Required value not set. Previous step may have failed?") challenge = tpm_abstract.TPM_Utilities.random_password(20) encoded = base64.b64encode(self.K).decode('utf-8') response = tornado_requests.request("GET", "http://%s:%s/keys/verify?challenge=%s" % (self.cloudagent_ip, self.cloudagent_port, challenge)) response = await response self.assertEqual(response.status, 200, "Non-successful Agent verify return code!") json_response = json.loads(response.read().decode()) # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") self.assertIn("hmac", json_response["results"], "Malformed response body!") # Be sure response is valid mac = json_response['results']['hmac'] ex_mac = crypto.do_hmac(encoded, challenge) # ex_mac = crypto.do_hmac(self.K, challenge) self.assertEqual(mac, ex_mac, "Agent failed to validate challenge code!") # CV Cleanup Testset def test_050_cv_agent_delete(self): """Test CV's DELETE /v2/agents/{UUID} Interface""" time.sleep(5) test_050_cv_agent_delete = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = test_050_cv_agent_delete.delete( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 202, "Non-successful CV agent Delete return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") def tearDown(self): """Nothing to bring down after each test""" return @classmethod def tearDownClass(cls): """Nothing to bring down""" return if __name__ == '__main__': unittest.main()

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import sys import signal import unittest import subprocess import time import os import base64 import threading import shutil import errno from pathlib import Path import dbus import simplejson as json from keylime import config from keylime import tornado_requests from keylime.requests_client import RequestsClient from keylime import tenant from keylime import crypto from keylime.cmd import user_data_encrypt from keylime import secure_mount from keylime.tpm import tpm_main from keylime.tpm import tpm_abstract if "COVERAGE_FILE" in os.environ: FORK_ARGS = ["coverage", "run", "--parallel-mode"] if "COVERAGE_DIR" in os.environ: FORK_ARGS += ["--rcfile=" + os.environ["COVERAGE_DIR"] + "/.coveragerc"] else: FORK_ARGS = ["python3"] PACKAGE_ROOT = Path(__file__).parents[1] KEYLIME_DIR = (f"{PACKAGE_ROOT}/keylime") sys.path.append(KEYLIME_DIR) tpm_instance = None def cmp(a, b): return (a > b) - (a < b) if os.geteuid() != 0 and config.REQUIRE_ROOT: sys.exit("Tests need to be run with root privileges, or set env KEYLIME_TEST=True!") unittest.TestLoader.sortTestMethodsUsing = lambda _, x, y: cmp(x, y) script_env = os.environ.copy() cv_process = None reg_process = None agent_process = None tenant_templ = None public_key = None keyblob = None ek_tpm = None aik_tpm = None vtpm = False # Set up mTLS my_cert = config.get('tenant', 'my_cert') my_priv_key = config.get('tenant', 'private_key') cert = (my_cert, my_priv_key) tls_enabled = True # Like os.remove, but ignore file DNE exceptions def fileRemove(path): try: os.remove(path) except OSError as e: # Ignore if file does not exist if e.errno != errno.ENOENT: raise # Boring setup stuff def setUpModule(): try: env = os.environ.copy() env['PATH'] = env['PATH'] + ":/usr/local/bin" # Run init_tpm_server and tpm_serverd (start fresh) its = subprocess.Popen(["init_tpm_server"], shell=False, env=env) its.wait() tsd = subprocess.Popen(["tpm_serverd"], shell=False, env=env) tsd.wait() except Exception as e: print("WARNING: Restarting TPM emulator failed!") # Note: the following is required as abrmd is failing to reconnect to MSSIM, once # MSSIM is killed and restarted. If this is an proved an actual bug and is # fixed upstream, the following dbus restart call can be removed. try: sysbus = dbus.SystemBus() systemd1 = sysbus.get_object('org.freedesktop.systemd1', '/org/freedesktop/systemd1') manager = dbus.Interface(systemd1, 'org.freedesktop.systemd1.Manager') # If the systemd service exists, let's restart it. for service in sysbus.list_names(): if "com.intel.tss2.Tabrmd" in service: print("Found dbus service:", str(service)) try: print("Restarting tpm2-abrmd.service.") manager.RestartUnit('tpm2-abrmd.service', 'fail') except dbus.exceptions.DBusException as e: print(e) except Exception as e: print("Non systemd agent detected, no tpm2-abrmd restart required.") try: fileRemove(config.WORK_DIR + "/tpmdata.yaml") fileRemove(config.WORK_DIR + "/cv_data.sqlite") fileRemove(config.WORK_DIR + "/reg_data.sqlite") shutil.rmtree(config.WORK_DIR + "/cv_ca", True) except Exception as e: print("WARNING: Cleanup of TPM files failed!") launch_cloudverifier() launch_registrar() global tenant_templ tenant_templ = tenant.Tenant() tenant_templ.agent_uuid = config.get('cloud_agent', 'agent_uuid') tenant_templ.cloudagent_ip = "localhost" tenant_templ.cloudagent_port = config.get('cloud_agent', 'cloudagent_port') tenant_templ.verifier_ip = config.get('cloud_verifier', 'cloudverifier_ip') tenant_templ.verifier_port = config.get('cloud_verifier', 'cloudverifier_port') tenant_templ.registrar_ip = config.get('registrar', 'registrar_ip') tenant_templ.registrar_boot_port = config.get('registrar', 'registrar_port') tenant_templ.registrar_tls_boot_port = config.get('registrar', 'registrar_tls_port') tenant_templ.registrar_base_url = f'{tenant_templ.registrar_ip}:{tenant_templ.registrar_boot_port}' tenant_templ.registrar_base_tls_url = f'{tenant_templ.registrar_ip}:{tenant_templ.registrar_tls_boot_port}' tenant_templ.agent_base_url = f'{tenant_templ.cloudagent_ip}:{tenant_templ.cloudagent_port}' my_tls_cert, my_tls_priv_key = tenant_templ.get_tls_context() tenant_templ.cert = (my_tls_cert, my_tls_priv_key) def tearDownModule(): kill_cloudagent() kill_cloudverifier() kill_registrar() def launch_cloudverifier(): global cv_process, script_env, FORK_ARGS if cv_process is None: cv_process = subprocess.Popen("keylime_verifier", shell=False, preexec_fn=os.setsid, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, env=script_env) def initthread(): sys.stdout.write('\033[96m' + "\nCloud Verifier Thread" + '\033[0m') while True: line = cv_process.stdout.readline() if line == b'': break line = line.decode('utf-8') line = line.rstrip(os.linesep) sys.stdout.flush() sys.stdout.write('\n\033[96m' + line + '\033[0m') t = threading.Thread(target=initthread) t.start() time.sleep(30) return True def launch_registrar(): global reg_process, script_env, FORK_ARGS if reg_process is None: reg_process = subprocess.Popen("keylime_registrar", shell=False, preexec_fn=os.setsid, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, env=script_env) def initthread(): sys.stdout.write('\033[95m' + "\nRegistrar Thread" + '\033[0m') while True: line = reg_process.stdout.readline() if line == b"": break line = line.decode('utf-8') sys.stdout.flush() sys.stdout.write('\n\033[95m' + line + '\033[0m') t = threading.Thread(target=initthread) t.start() time.sleep(10) return True def launch_cloudagent(): global agent_process, script_env, FORK_ARGS if agent_process is None: agent_process = subprocess.Popen("keylime_agent", shell=False, preexec_fn=os.setsid, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, env=script_env) def initthread(): sys.stdout.write('\033[94m' + "\nCloud Agent Thread" + '\033[0m') while True: line = agent_process.stdout.readline() if line == b'': break line = line.decode('utf-8') sys.stdout.flush() sys.stdout.write('\n\033[94m' + line + '\033[0m') t = threading.Thread(target=initthread) t.start() time.sleep(10) return True def kill_cloudverifier(): global cv_process if cv_process is None: return os.killpg(os.getpgid(cv_process.pid), signal.SIGINT) cv_process.wait() cv_process = None def kill_registrar(): global reg_process if reg_process is None: return os.killpg(os.getpgid(reg_process.pid), signal.SIGINT) reg_process.wait() reg_process = None def kill_cloudagent(): global agent_process if agent_process is None: return os.killpg(os.getpgid(agent_process.pid), signal.SIGINT) agent_process.wait() agent_process = None def services_running(): if reg_process.poll() is None and cv_process.poll() is None: return True return False class TestRestful(unittest.TestCase): payload = None auth_tag = None tpm_policy = {} vtpm_policy = {} metadata = {} allowlist = {} revocation_key = "" mb_refstate = None K = None U = None V = None api_version = config.API_VERSION cloudagent_ip = None cloudagent_port = None @classmethod def setUpClass(cls): contents = "random garbage to test as payload" # contents = contents.encode('utf-8') ret = user_data_encrypt.encrypt(contents) cls.K = ret['k'] cls.U = ret['u'] cls.V = ret['v'] cls.payload = ret['ciphertext'] # Set up to register an agent cls.auth_tag = crypto.do_hmac(cls.K, tenant_templ.agent_uuid) # Prepare policies for agent cls.tpm_policy = config.get('tenant', 'tpm_policy') cls.vtpm_policy = config.get('tenant', 'vtpm_policy') cls.tpm_policy = tpm_abstract.TPM_Utilities.readPolicy(cls.tpm_policy) cls.vtpm_policy = tpm_abstract.TPM_Utilities.readPolicy(cls.vtpm_policy) # Allow targeting a specific API version (default latest) cls.api_version = config.API_VERSION def setUp(self): return def test_000_services(self): self.assertTrue(services_running(), "Not all services started successfully!") # Registrar Testset def test_010_reg_agent_post(self): global keyblob, vtpm, tpm_instance, ek_tpm, aik_tpm tpm_instance = tpm_main.tpm() # Change CWD for TPM-related operations cwd = os.getcwd() config.ch_dir(config.WORK_DIR, None) _ = secure_mount.mount() # Initialize the TPM with AIK (ekcert, ek_tpm, aik_tpm) = tpm_instance.tpm_init(self_activate=False, config_pw=config.get('cloud_agent', 'tpm_ownerpassword')) vtpm = tpm_instance.is_vtpm() # Handle virtualized and emulated TPMs if ekcert is None: if vtpm: ekcert = 'virtual' elif tpm_instance.is_emulator(): ekcert = 'emulator' # Get back to our original CWD config.ch_dir(cwd, None) data = { 'ekcert': ekcert, 'aik_tpm': aik_tpm, } if ekcert is None or ekcert == 'emulator': data['ek_tpm'] = ek_tpm test_010_reg_agent_post = RequestsClient(tenant_templ.registrar_base_url, tls_enabled=False) response = test_010_reg_agent_post.post( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', data=json.dumps(data), cert="", verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Registrar agent Add return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") self.assertIn("blob", json_response["results"], "Malformed response body!") keyblob = json_response["results"]["blob"] self.assertIsNotNone(keyblob, "Malformed response body!") @unittest.skipIf(vtpm, "Registrar's PUT /v2/agents/{UUID}/activate only for non-vTPMs!") def test_011_reg_agent_activate_put(self): global keyblob self.assertIsNotNone(keyblob, "Required value not set. Previous step may have failed?") key = tpm_instance.activate_identity(keyblob) data = { 'auth_tag': crypto.do_hmac(key, tenant_templ.agent_uuid), } test_011_reg_agent_activate_put = RequestsClient(tenant_templ.registrar_base_url, tls_enabled=False) response = test_011_reg_agent_activate_put.put( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}/activate', data=json.dumps(data), cert="", verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Registrar agent Activate return code!") json_response = response.json() self.assertIn("results", json_response, "Malformed response body!") def test_013_reg_agents_get(self): test_013_reg_agents_get = RequestsClient(tenant_templ.registrar_base_tls_url, tls_enabled=True) response = test_013_reg_agents_get.get( f'/v{self.api_version}/agents/', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Registrar agent List return code!") json_response = response.json() self.assertIn("results", json_response, "Malformed response body!") self.assertIn("uuids", json_response["results"], "Malformed response body!") self.assertEqual(1, len(json_response["results"]["uuids"]), "Incorrect system state!") def test_014_reg_agent_get(self): test_014_reg_agent_get = RequestsClient(tenant_templ.registrar_base_tls_url, tls_enabled=True) response = test_014_reg_agent_get.get( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Registrar agent return code!") json_response = response.json() self.assertIn("results", json_response, "Malformed response body!") self.assertIn("ek_tpm", json_response["results"], "Malformed response body!") self.assertIn("aik_tpm", json_response["results"], "Malformed response body!") self.assertIn("ekcert", json_response["results"], "Malformed response body!") global aik_tpm aik_tpm = json_response["results"]["aik_tpm"] def test_015_reg_agent_delete(self): test_015_reg_agent_delete = RequestsClient(tenant_templ.registrar_base_tls_url, tls_enabled=True) response = test_015_reg_agent_delete.delete( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Registrar Delete return code!") json_response = response.json() self.assertIn("results", json_response, "Malformed response body!") def test_020_agent_keys_pubkey_get(self): launch_cloudagent() time.sleep(10) test_020_agent_keys_pubkey_get = RequestsClient(tenant_templ.agent_base_url, tls_enabled=False) response = test_020_agent_keys_pubkey_get.get( f'/v{self.api_version}/keys/pubkey', cert="", verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Agent pubkey return code!") json_response = response.json() self.assertIn("results", json_response, "Malformed response body!") self.assertIn("pubkey", json_response["results"], "Malformed response body!") global public_key public_key = json_response["results"]["pubkey"] self.assertNotEqual(public_key, None, "Malformed response body!") def test_021_reg_agent_get(self): self.test_014_reg_agent_get() def test_022_agent_quotes_identity_get(self): self.assertIsNotNone(aik_tpm, "Required value not set. Previous step may have failed?") nonce = tpm_abstract.TPM_Utilities.random_password(20) numretries = config.getint('tenant', 'max_retries') while numretries >= 0: test_022_agent_quotes_identity_get = RequestsClient(tenant_templ.agent_base_url, tls_enabled=False) response = test_022_agent_quotes_identity_get.get( f'/v{self.api_version}/quotes/identity?nonce={nonce}', data=None, cert="", verify=False ) if response.status_code == 200: break numretries -= 1 time.sleep(config.getint('tenant', 'retry_interval')) self.assertEqual(response.status_code, 200, "Non-successful Agent identity return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") self.assertIn("quote", json_response["results"], "Malformed response body!") self.assertIn("pubkey", json_response["results"], "Malformed response body!") # Check the quote identity self.assertTrue(tpm_instance.check_quote(tenant_templ.agent_uuid, nonce, json_response["results"]["pubkey"], json_response["results"]["quote"], aik_tpm, hash_alg=json_response["results"]["hash_alg"]), "Invalid quote!") @unittest.skip("Testing of agent's POST /v2/keys/vkey disabled! (spawned CV should do this already)") def test_023_agent_keys_vkey_post(self): global public_key self.assertIsNotNone(self.V, "Required value not set. Previous step may have failed?") self.assertIsNotNone(public_key, "Required value not set. Previous step may have failed?") encrypted_V = crypto.rsa_encrypt(crypto.rsa_import_pubkey(public_key), str(self.V)) b64_encrypted_V = base64.b64encode(encrypted_V) data = {'encrypted_key': b64_encrypted_V} test_023_agent_keys_vkey_post = RequestsClient(tenant_templ.agent_base_url, tls_enabled=False) response = test_023_agent_keys_vkey_post.post( f'/v{self.api_version}/keys/vkey', data=json.dumps(data), cert="", verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Agent vkey post return code!") json_response = response.json() self.assertIn("results", json_response, "Malformed response body!") def test_024_agent_keys_ukey_post(self): global public_key self.assertIsNotNone(public_key, "Required value not set. Previous step may have failed?") self.assertIsNotNone(self.U, "Required value not set. Previous step may have failed?") self.assertIsNotNone(self.auth_tag, "Required value not set. Previous step may have failed?") self.assertIsNotNone(self.payload, "Required value not set. Previous step may have failed?") encrypted_U = crypto.rsa_encrypt(crypto.rsa_import_pubkey(public_key), self.U) b64_encrypted_u = base64.b64encode(encrypted_U) data = { 'encrypted_key': b64_encrypted_u, 'auth_tag': self.auth_tag, 'payload': self.payload } test_024_agent_keys_ukey_post = RequestsClient(tenant_templ.agent_base_url, tls_enabled=False) response = test_024_agent_keys_ukey_post.post( f'/v{self.api_version}/keys/ukey', data=json.dumps(data), cert="", verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Agent ukey post return code!") json_response = response.json() self.assertIn("results", json_response, "Malformed response body!") def test_025_cv_allowlist_post(self): data = { 'name': 'test-allowlist', 'tpm_policy': json.dumps(self.tpm_policy), 'vtpm_policy': json.dumps(self.vtpm_policy), 'ima_policy': json.dumps(self.allowlist), } cv_client = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = cv_client.post( '/allowlists/test-allowlist', data=json.dumps(data), cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 201, "Non-successful CV allowlist Post return code!") json_response = response.json() self.assertIn("results", json_response, "Malformed response body!") def test_026_cv_allowlist_get(self): cv_client = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = cv_client.get( '/allowlists/test-allowlist', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful CV allowlist Post return code!") json_response = response.json() self.assertIn("results", json_response, "Malformed response body!") results = json_response['results'] self.assertEqual(results['name'], 'test-allowlist') self.assertEqual(results['tpm_policy'], json.dumps(self.tpm_policy)) self.assertEqual(results['vtpm_policy'], json.dumps(self.vtpm_policy)) self.assertEqual(results['ima_policy'], json.dumps(self.allowlist)) def test_027_cv_allowlist_delete(self): cv_client = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = cv_client.delete( '/allowlists/test-allowlist', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 204, "Non-successful CV allowlist Delete return code!") def test_030_cv_agent_post(self): self.assertIsNotNone(self.V, "Required value not set. Previous step may have failed?") b64_v = base64.b64encode(self.V) data = { 'v': b64_v, 'cloudagent_ip': tenant_templ.cloudagent_ip, 'cloudagent_port': tenant_templ.cloudagent_port, 'tpm_policy': json.dumps(self.tpm_policy), 'vtpm_policy': json.dumps(self.vtpm_policy), 'allowlist': json.dumps(self.allowlist), 'ima_sign_verification_keys': '', 'mb_refstate': None, 'metadata': json.dumps(self.metadata), 'revocation_key': self.revocation_key, 'accept_tpm_hash_algs': config.get('tenant', 'accept_tpm_hash_algs').split(','), 'accept_tpm_encryption_algs': config.get('tenant', 'accept_tpm_encryption_algs').split(','), 'accept_tpm_signing_algs': config.get('tenant', 'accept_tpm_signing_algs').split(','), } test_030_cv_agent_post = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = test_030_cv_agent_post.post( f'/agents/{tenant_templ.agent_uuid}', data=json.dumps(data), cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful CV agent Post return code!") json_response = response.json() self.assertIn("results", json_response, "Malformed response body!") time.sleep(10) @unittest.skip("Testing of CV's PUT /v2/agents/{UUID} disabled!") def test_031_cv_agent_put(self): # TODO: this should actually test PUT functionality (e.g., make agent fail and then PUT back up) test_031_cv_agent_put = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = test_031_cv_agent_put.put( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', data=b'', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful CV agent Post return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") def test_032_cv_agents_get(self): test_032_cv_agents_get = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = test_032_cv_agents_get.get( f'/v{self.api_version}/agents/', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful CV agent List return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") self.assertIn("uuids", json_response["results"], "Malformed response body!") # Be sure our agent is registered self.assertEqual(1, len(json_response["results"]["uuids"])) def test_033_cv_agent_get(self): test_033_cv_agent_get = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = test_033_cv_agent_get.get( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 200, "Non-successful CV agent return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") # Check a few of the important properties are present self.assertIn("operational_state", json_response["results"], "Malformed response body!") self.assertIn("ip", json_response["results"], "Malformed response body!") self.assertIn("port", json_response["results"], "Malformed response body!") def test_034_cv_agent_post_invalid_exclude_list(self): self.assertIsNotNone(self.V, "Required value not set. Previous step may have failed?") b64_v = base64.b64encode(self.V) # Set unsupported regex in exclude list allowlist = {'exclude': ['*']} data = { 'v': b64_v, 'mb_refstate': None, 'cloudagent_ip': tenant_templ.cloudagent_ip, 'cloudagent_port': tenant_templ.cloudagent_port, 'tpm_policy': json.dumps(self.tpm_policy), 'vtpm_policy': json.dumps(self.vtpm_policy), 'allowlist': json.dumps(allowlist), 'ima_sign_verification_keys': '', 'metadata': json.dumps(self.metadata), 'revocation_key': self.revocation_key, 'accept_tpm_hash_algs': config.get('tenant', 'accept_tpm_hash_algs').split(','), 'accept_tpm_encryption_algs': config.get('tenant', 'accept_tpm_encryption_algs').split(','), 'accept_tpm_signing_algs': config.get('tenant', 'accept_tpm_signing_algs').split(','), } client = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = client.post( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', cert=tenant_templ.cert, data=json.dumps(data), verify=False ) self.assertEqual(response.status_code, 400, "Successful CV agent Post return code!") # Ensure response is well-formed json_response = response.json() self.assertIn("results", json_response, "Malformed response body!") # Agent Poll Testset def test_040_agent_quotes_integrity_get(self): global public_key self.assertIsNotNone(aik_tpm, "Required value not set. Previous step may have failed?") nonce = tpm_abstract.TPM_Utilities.random_password(20) mask = self.tpm_policy["mask"] vmask = self.vtpm_policy["mask"] partial = "1" if public_key is None: partial = "0" test_040_agent_quotes_integrity_get = RequestsClient(tenant_templ.agent_base_url, tls_enabled=False) response = test_040_agent_quotes_integrity_get.get( f'/v{self.api_version}/quotes/integrity?nonce={nonce}&mask={mask}&vmask={vmask}&partial={partial}', cert="", verify=False ) self.assertEqual(response.status_code, 200, "Non-successful Agent Integrity Get return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") self.assertIn("quote", json_response["results"], "Malformed response body!") if public_key is None: self.assertIn("pubkey", json_response["results"], "Malformed response body!") public_key = json_response["results"]["pubkey"] self.assertIn("hash_alg", json_response["results"], "Malformed response body!") quote = json_response["results"]["quote"] hash_alg = json_response["results"]["hash_alg"] validQuote = tpm_instance.check_quote(tenant_templ.agent_uuid, nonce, public_key, quote, aik_tpm, self.tpm_policy, hash_alg=hash_alg) self.assertTrue(validQuote) async def test_041_agent_keys_verify_get(self): self.assertIsNotNone(self.K, "Required value not set. Previous step may have failed?") challenge = tpm_abstract.TPM_Utilities.random_password(20) encoded = base64.b64encode(self.K).decode('utf-8') response = tornado_requests.request("GET", "http://%s:%s/keys/verify?challenge=%s" % (self.cloudagent_ip, self.cloudagent_port, challenge)) response = await response self.assertEqual(response.status, 200, "Non-successful Agent verify return code!") json_response = json.loads(response.read().decode()) # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") self.assertIn("hmac", json_response["results"], "Malformed response body!") # Be sure response is valid mac = json_response['results']['hmac'] ex_mac = crypto.do_hmac(encoded, challenge) # ex_mac = crypto.do_hmac(self.K, challenge) self.assertEqual(mac, ex_mac, "Agent failed to validate challenge code!") # CV Cleanup Testset def test_050_cv_agent_delete(self): time.sleep(5) test_050_cv_agent_delete = RequestsClient(tenant_templ.verifier_base_url, tls_enabled) response = test_050_cv_agent_delete.delete( f'/v{self.api_version}/agents/{tenant_templ.agent_uuid}', cert=tenant_templ.cert, verify=False ) self.assertEqual(response.status_code, 202, "Non-successful CV agent Delete return code!") json_response = response.json() # Ensure response is well-formed self.assertIn("results", json_response, "Malformed response body!") def tearDown(self): return @classmethod def tearDownClass(cls): return if __name__ == '__main__': unittest.main()

true

f7194b8167f283797e8754a97cb5a389e35e14ce

2,442

py

Python

hello_fastapi_project/hello_fastapi/backend/app/alembic/env.py

KimSoungRyoul/PersistenceLayerInPythonApplication

2431553a6cdd913babd546adc6c9376855eb3438

[ "MIT" ]

2

2021-11-01T08:08:13.000Z

2021-11-01T08:11:51.000Z

hello_fastapi_project/hello_fastapi/backend/app/alembic/env.py

KimSoungRyoul/PersistenceLayerInPythonApplication

2431553a6cdd913babd546adc6c9376855eb3438

[ "MIT" ]

null

hello_fastapi_project/hello_fastapi/backend/app/alembic/env.py

KimSoungRyoul/PersistenceLayerInPythonApplication

2431553a6cdd913babd546adc6c9376855eb3438

[ "MIT" ]

null

from __future__ import with_statement import os from alembic import context from sqlalchemy import engine_from_config, pool from logging.config import fileConfig # this is the Alembic Config object, which provides # access to the values within the .ini file in use. config = context.config # Interpret the config file for Python logging. # This line sets up loggers basically. fileConfig(config.config_file_name) # add your model's MetaData object here # for 'autogenerate' support # from myapp import mymodel # target_metadata = mymodel.Base.metadata # target_metadata = None from app.db.base import Base # noqa target_metadata = Base.metadata # other values from the config, defined by the needs of env.py, # can be acquired: # my_important_option = config.get_main_option("my_important_option") # ... etc. def get_url(): user = os.getenv("POSTGRES_USER", "postgres") password = os.getenv("POSTGRES_PASSWORD", "1234") server = os.getenv("POSTGRES_SERVER", "127.0.0.1:5432") db = os.getenv("POSTGRES_DB", "hello_fastapi_db") return f"postgresql://{user}:{password}@{server}/{db}" def run_migrations_offline(): """Run migrations in 'offline' mode. This configures the context with just a URL and not an Engine, though an Engine is acceptable here as well. By skipping the Engine creation we don't even need a DBAPI to be available. Calls to context.execute() here emit the given string to the script output. """ url = get_url() context.configure( url=url, target_metadata=target_metadata, literal_binds=True, compare_type=True ) with context.begin_transaction(): context.run_migrations() def run_migrations_online(): """Run migrations in 'online' mode. In this scenario we need to create an Engine and associate a connection with the context. """ configuration = config.get_section(config.config_ini_section) configuration["sqlalchemy.url"] = get_url() connectable = engine_from_config( configuration, prefix="sqlalchemy.", poolclass=pool.NullPool, ) with connectable.connect() as connection: context.configure( connection=connection, target_metadata=target_metadata, compare_type=True ) with context.begin_transaction(): context.run_migrations() if context.is_offline_mode(): run_migrations_offline() else: run_migrations_online()

27.75

87

0.72154

from __future__ import with_statement import os from alembic import context from sqlalchemy import engine_from_config, pool from logging.config import fileConfig config = context.config fileConfig(config.config_file_name) # for 'autogenerate' support # from myapp import mymodel # target_metadata = mymodel.Base.metadata # target_metadata = None from app.db.base import Base # noqa target_metadata = Base.metadata # other values from the config, defined by the needs of env.py, # can be acquired: # my_important_option = config.get_main_option("my_important_option") # ... etc. def get_url(): user = os.getenv("POSTGRES_USER", "postgres") password = os.getenv("POSTGRES_PASSWORD", "1234") server = os.getenv("POSTGRES_SERVER", "127.0.0.1:5432") db = os.getenv("POSTGRES_DB", "hello_fastapi_db") return f"postgresql://{user}:{password}@{server}/{db}" def run_migrations_offline(): url = get_url() context.configure( url=url, target_metadata=target_metadata, literal_binds=True, compare_type=True ) with context.begin_transaction(): context.run_migrations() def run_migrations_online(): configuration = config.get_section(config.config_ini_section) configuration["sqlalchemy.url"] = get_url() connectable = engine_from_config( configuration, prefix="sqlalchemy.", poolclass=pool.NullPool, ) with connectable.connect() as connection: context.configure( connection=connection, target_metadata=target_metadata, compare_type=True ) with context.begin_transaction(): context.run_migrations() if context.is_offline_mode(): run_migrations_offline() else: run_migrations_online()

true

f7194cecc0e0ff877121d721d5f9416a213d7ec6

129

py

Python

dan_socket/base.py

fredericowu/dan_socket

12ccbd8333b76889f6ee2050c78aba67f0a86533

[ "Apache-2.0" ]

null

dan_socket/base.py

fredericowu/dan_socket

12ccbd8333b76889f6ee2050c78aba67f0a86533

[ "Apache-2.0" ]

null

dan_socket/base.py

fredericowu/dan_socket

12ccbd8333b76889f6ee2050c78aba67f0a86533

[ "Apache-2.0" ]

null

import socket class BaseConnection: PROTOCOL = { "TCP": socket.SOCK_STREAM, "UDP": socket.SOCK_DGRAM }

14.333333

34

0.604651

import socket class BaseConnection: PROTOCOL = { "TCP": socket.SOCK_STREAM, "UDP": socket.SOCK_DGRAM }

true

f7194cede6cfbfe8bf2f8a61911cecb70eada48c

4,031

py

Python

wok/contrib/hooks.py

chrplace/wok

a1368f6c6bc75e0b1b878b315bfd31ac8aefbabb

[ "MIT" ]

38

2015-01-06T03:41:51.000Z

2019-09-18T22:06:28.000Z

wok/contrib/hooks.py

chrplace/wok

a1368f6c6bc75e0b1b878b315bfd31ac8aefbabb

[ "MIT" ]

38

2015-02-12T09:33:24.000Z

2017-06-29T16:52:29.000Z

wok/contrib/hooks.py

chrplace/wok

a1368f6c6bc75e0b1b878b315bfd31ac8aefbabb

[ "MIT" ]

21

2015-01-08T08:46:50.000Z

2020-01-28T23:59:40.000Z

# vim: set fileencoding=utf8 : """Some hooks that might be useful.""" import os import glob import subprocess from StringIO import StringIO import logging from slugify import slugify from wok.exceptions import DependencyException try: from lxml import etree except ImportError: etree = None try: import sass except ImportError: sass = None class HeadingAnchors(object): """ Put some paragraph heading anchors. Serves as a 'page.template.post' wok hook. """ def __init__(self, max_heading=3): if not etree: logging.warning('To use the HeadingAnchors hook, you must install ' 'the library lxml.') return self.max_heading = max_heading logging.info('Loaded hook HeadingAnchors') def __call__(self, config, page): if not etree: return logging.debug('Called hook HeadingAnchors on {0}'.format(page)) parser = etree.HTMLParser() sio_source = StringIO(page.rendered) tree = etree.parse(sio_source, parser) for lvl in range(1, self.max_heading+1): headings = tree.iterfind('//h{0}'.format(lvl)) for heading in headings: if not heading.text: continue logging.debug('[HeadingAnchors] {0} {1}' .format(heading, heading.text)) name = 'heading-{0}'.format(slugify(heading.text)) anchor = etree.Element('a') anchor.set('class', 'heading_anchor') anchor.set('href', '#' + name) anchor.set('title', 'Permalink to this section.') anchor.text = u'¶' heading.append(anchor) heading.set('id', name) sio_destination = StringIO() # Use the extension of the template to determine the type of document if page.template.filename.endswith(".html") or page.filename.endswith(".htm"): logging.debug('[HeadingAnchors] outputting {0} as HTML'.format(page)) tree.write(sio_destination, method='html') else: logging.debug('[HeadingAnchors] outputting {0} as XML'.format(page)) tree.write(sio_destination) page.rendered = sio_destination.getvalue() def compile_sass(config, output_dir): ''' Compile Sass files -> CSS in the output directory. Any .scss or .sass files found in the output directory will be compiled to CSS using Sass. The compiled version of the file will be created in the same directory as the Sass file with the same name and an extension of .css. For example, foo.scss -> foo.css. Serves as a 'site.output.post' wok hook, e.g., your __hooks__.py file might look like this: from wok.contrib.hooks import compile_sass hooks = { 'site.output.post': [compile_sass] } Dependencies: - libsass ''' logging.info('Running hook compile_sass on {0}.'.format(output_dir)) for root, dirs, files in os.walk(output_dir): for f in files: fname, fext = os.path.splitext(f) # Sass partials should not be compiled if not fname.startswith('_') and fext == '.scss' or fext == '.sass': abspath = os.path.abspath(root) sass_src = '{0}/{1}'.format(abspath, f) sass_dest = '{0}/{1}.css'.format(abspath, fname) if sass is None: logging.warning('To use compile_sass hook, you must install ' 'libsass-python package.') return compiled_str = sass.compile(filename=sass_src, output_style='compressed') with open(sass_dest, 'w') as f: f.write(compiled_str) # TODO: Get rid of extra housekeeping by compiling Sass files in # "site.output.pre" hook abspath = os.path.abspath(output_dir) for f in glob.glob(os.path.join(abspath, '**', '*.s[a,c]ss')): os.remove(f)

32.772358

89

0.598115

"""Some hooks that might be useful.""" import os import glob import subprocess from StringIO import StringIO import logging from slugify import slugify from wok.exceptions import DependencyException try: from lxml import etree except ImportError: etree = None try: import sass except ImportError: sass = None class HeadingAnchors(object): """ Put some paragraph heading anchors. Serves as a 'page.template.post' wok hook. """ def __init__(self, max_heading=3): if not etree: logging.warning('To use the HeadingAnchors hook, you must install ' 'the library lxml.') return self.max_heading = max_heading logging.info('Loaded hook HeadingAnchors') def __call__(self, config, page): if not etree: return logging.debug('Called hook HeadingAnchors on {0}'.format(page)) parser = etree.HTMLParser() sio_source = StringIO(page.rendered) tree = etree.parse(sio_source, parser) for lvl in range(1, self.max_heading+1): headings = tree.iterfind('//h{0}'.format(lvl)) for heading in headings: if not heading.text: continue logging.debug('[HeadingAnchors] {0} {1}' .format(heading, heading.text)) name = 'heading-{0}'.format(slugify(heading.text)) anchor = etree.Element('a') anchor.set('class', 'heading_anchor') anchor.set('href', '#' + name) anchor.set('title', 'Permalink to this section.') anchor.text = u'¶' heading.append(anchor) heading.set('id', name) sio_destination = StringIO() if page.template.filename.endswith(".html") or page.filename.endswith(".htm"): logging.debug('[HeadingAnchors] outputting {0} as HTML'.format(page)) tree.write(sio_destination, method='html') else: logging.debug('[HeadingAnchors] outputting {0} as XML'.format(page)) tree.write(sio_destination) page.rendered = sio_destination.getvalue() def compile_sass(config, output_dir): ''' Compile Sass files -> CSS in the output directory. Any .scss or .sass files found in the output directory will be compiled to CSS using Sass. The compiled version of the file will be created in the same directory as the Sass file with the same name and an extension of .css. For example, foo.scss -> foo.css. Serves as a 'site.output.post' wok hook, e.g., your __hooks__.py file might look like this: from wok.contrib.hooks import compile_sass hooks = { 'site.output.post': [compile_sass] } Dependencies: - libsass ''' logging.info('Running hook compile_sass on {0}.'.format(output_dir)) for root, dirs, files in os.walk(output_dir): for f in files: fname, fext = os.path.splitext(f) if not fname.startswith('_') and fext == '.scss' or fext == '.sass': abspath = os.path.abspath(root) sass_src = '{0}/{1}'.format(abspath, f) sass_dest = '{0}/{1}.css'.format(abspath, fname) if sass is None: logging.warning('To use compile_sass hook, you must install ' 'libsass-python package.') return compiled_str = sass.compile(filename=sass_src, output_style='compressed') with open(sass_dest, 'w') as f: f.write(compiled_str) abspath = os.path.abspath(output_dir) for f in glob.glob(os.path.join(abspath, '**', '*.s[a,c]ss')): os.remove(f)

false

true

f7194df8986a3f798477abbd22f0e92a006cfa20

967

py

Python

valid-sudoku/valid-sudoku.py

Atri10/Leet-code---Atri_Patel

49fc59b9147a44ab04a66128fbb2ef259b5f7b7c

[ "MIT" ]

1

2021-10-10T20:21:18.000Z

valid-sudoku/valid-sudoku.py

Atri10/Leet-code---Atri_Patel

49fc59b9147a44ab04a66128fbb2ef259b5f7b7c

[ "MIT" ]

null

valid-sudoku/valid-sudoku.py

Atri10/Leet-code---Atri_Patel

49fc59b9147a44ab04a66128fbb2ef259b5f7b7c

[ "MIT" ]

null

class Solution: def isValidSudoku(self, board: List[List[str]]) -> bool: BZip = list(zip(*board)) def Checkline(li): temp = [i for i in li if i!="."] return len(set(temp))==len(temp) def check_row(board): for i in board: if not Checkline(i):return False return True def check_col(board): for i in BZip: if not Checkline(i):return False return True def square(board): for i in range(0,9,3): for j in range(0,9,3): sqr = [board[x][y] for x in range(i,i+3) for y in range(j,j+3)] if not Checkline(sqr):return False return True def checkmat(): return (check_row(board) and check_col(board) and square(board)) return checkmat()

31.193548

83

0.458118

class Solution: def isValidSudoku(self, board: List[List[str]]) -> bool: BZip = list(zip(*board)) def Checkline(li): temp = [i for i in li if i!="."] return len(set(temp))==len(temp) def check_row(board): for i in board: if not Checkline(i):return False return True def check_col(board): for i in BZip: if not Checkline(i):return False return True def square(board): for i in range(0,9,3): for j in range(0,9,3): sqr = [board[x][y] for x in range(i,i+3) for y in range(j,j+3)] if not Checkline(sqr):return False return True def checkmat(): return (check_row(board) and check_col(board) and square(board)) return checkmat()

true

f7194e480fb8f963b01db5fe2fb064378758697e

11,918

py

Python

instabot/api/api_video.py

danomaj/instabot

769e41587a1aaeb50b89f0bf9d3933123e6ddbcc

[ "Apache-2.0" ]

1

2022-01-23T09:25:50.000Z

instabot/api/api_video.py

danomaj/instabot

769e41587a1aaeb50b89f0bf9d3933123e6ddbcc

[ "Apache-2.0" ]

null

instabot/api/api_video.py

danomaj/instabot

769e41587a1aaeb50b89f0bf9d3933123e6ddbcc

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- import copy import json import os import re import shutil import subprocess import time from requests_toolbelt import MultipartEncoder from . import config def download_video( self, media_id, filename=None, media=False, folder="videos" ): video_urls = [] if not media: self.media_info(media_id) media = self.last_json["items"][0] filename = ( "{}_{}.mp4".format(media["user"]["username"], media_id) if not filename else "{}.mp4".format(filename) ) try: clips = media["video_versions"] video_urls.append(clips[0]["url"]) except KeyError: carousels = media.get("carousel_media", []) for carousel in carousels: video_urls.append(carousel["video_versions"][0]["url"]) except Exception: return False for counter, video_url in enumerate(video_urls): fname = os.path.join(folder, "{}_{}".format(counter, filename)) if os.path.exists(fname): print('File %s is exists, return it' % fname) return os.path.abspath(fname) response = self.session.get(video_url, stream=True) if response.status_code == 200: with open(fname, "wb") as f: response.raw.decode_content = True shutil.copyfileobj(response.raw, f) return os.path.abspath(fname) # leaving here function used by old upload_video, no more used now def get_video_info(filename): res = {} try: terminalResult = subprocess.Popen( ["ffprobe", filename], stdout=subprocess.PIPE, stderr=subprocess.STDOUT ) for x in terminalResult.stdout.readlines(): # Duration: 00:00:59.51, start: 0.000000, bitrate: 435 kb/s m = re.search( r"duration: (\d\d:\d\d:\d\d\.\d\d),", str(x), flags=re.IGNORECASE ) if m is not None: res["duration"] = m.group(1) # Video: h264 (Constrained Baseline) # (avc1 / 0x31637661), yuv420p, 480x268 m = re.search( r"video:\s.*\s(\d+)x(\d+)\s", str(x), flags=re.IGNORECASE ) if m is not None: res["width"] = m.group(1) res["height"] = m.group(2) finally: if "width" not in res: print( "ERROR: 'ffprobe' not found, please install " "'ffprobe' with one of following methods:" ) print(" sudo apt-get install ffmpeg") print("or sudo apt-get install -y libav-tools") return res def upload_video( self, video, caption=None, upload_id=None, thumbnail=None, options={} ): """Upload video to Instagram @param video Path to video file (String) @param caption Media description (String) @param upload_id Unique upload_id (String). When None, then generate automatically @param thumbnail Path to thumbnail for video (String). When None, then thumbnail is generate automatically @param options Object with difference options, e.g. configure_timeout, rename_thumbnail, rename (Dict) Designed to reduce the number of function arguments! This is the simplest request object. @return Object with state of uploading to Instagram (or False) """ options = dict( {"configure_timeout": 15, "rename_thumbnail": True, "rename": True}, **(options or {}) ) if upload_id is None: upload_id = str(int(time.time() * 1000)) video, thumbnail, width, height, duration = resize_video(video, thumbnail) data = { "upload_id": upload_id, "_csrftoken": self.token, "media_type": "2", "_uuid": self.uuid, } m = MultipartEncoder(data, boundary=self.uuid) self.session.headers.update( { "X-IG-Capabilities": "3Q4=", "X-IG-Connection-Type": "WIFI", "Host": "i.instagram.com", "Cookie2": "$Version=1", "Accept-Language": "en-US", "Accept-Encoding": "gzip, deflate", "Content-type": m.content_type, "Connection": "keep-alive", "User-Agent": self.user_agent, } ) response = self.session.post( config.API_URL + "upload/video/", data=m.to_string() ) if response.status_code == 200: body = json.loads(response.text) upload_url = body["video_upload_urls"][3]["url"] upload_job = body["video_upload_urls"][3]["job"] with open(video, "rb") as video_bytes: video_data = video_bytes.read() # solve issue #85 TypeError: # slice indices must be integers or None or have an __index__ method request_size = len(video_data) // 4 last_request_extra = len(video_data) - 3 * request_size headers = copy.deepcopy(self.session.headers) self.session.headers.update( { "X-IG-Capabilities": "3Q4=", "X-IG-Connection-Type": "WIFI", "Cookie2": "$Version=1", "Accept-Language": "en-US", "Accept-Encoding": "gzip, deflate", "Content-type": "application/octet-stream", "Session-ID": upload_id, "Connection": "keep-alive", "Content-Disposition": 'attachment; filename="video.mov"', "job": upload_job, "Host": "upload.instagram.com", "User-Agent": self.user_agent, } ) for i in range(4): start = i * request_size if i == 3: end = i * request_size + last_request_extra else: end = (i + 1) * request_size length = last_request_extra if i == 3 else request_size content_range = "bytes {start}-{end}/{len_video}".format( start=start, end=end - 1, len_video=len(video_data) ).encode("utf-8") self.session.headers.update( { "Content-Length": str(end - start), "Content-Range": content_range } ) response = self.session.post( upload_url, data=video_data[start: start + length] ) self.session.headers = headers configure_timeout = options.get("configure_timeout") if response.status_code == 200: for attempt in range(4): if configure_timeout: time.sleep(configure_timeout) if self.configure_video( upload_id, video, thumbnail, width, height, duration, caption, options=options, ): media = self.last_json.get("media") self.expose() if options.get("rename"): from os import rename rename(video, "{}.REMOVE_ME".format(video)) return media return False def configure_video( self, upload_id, video, thumbnail, width, height, duration, caption="", options={} ): """Post Configure Video (send caption, thumbnail and more to Instagram) @param upload_id Unique upload_id (String). Received from "upload_video" @param video Path to video file (String) @param thumbnail Path to thumbnail for video (String). When None, then thumbnail is generate automatically @param width Width in px (Integer) @param height Height in px (Integer) @param duration Duration in seconds (Integer) @param caption Media description (String) @param options Object with difference options, e.g. configure_timeout, rename_thumbnail, rename (Dict) Designed to reduce the number of function arguments! This is the simplest request object. """ # clipInfo = get_video_info(video) options = {"rename": options.get("rename_thumbnail", True)} self.upload_photo( photo=thumbnail, caption=caption, upload_id=upload_id, from_video=True, options=options, ) data = self.json_data( { "upload_id": upload_id, "source_type": 3, "poster_frame_index": 0, "length": 0.00, "audio_muted": False, "filter_type": 0, "video_result": "deprecated", "clips": { "length": duration, "source_type": "3", "camera_position": "back", }, "extra": {"source_width": width, "source_height": height}, "device": self.device_settings, "caption": caption, } ) return self.send_request("media/configure/?video=1", data) def resize_video(fname, thumbnail=None): from math import ceil try: import moviepy.editor as mp except ImportError as e: print("ERROR: {}".format(e)) print( "Required module `moviepy` not installed\n" "Install with `pip install moviepy` and retry.\n\n" "You may need also:\n" "pip install --upgrade setuptools\n" "pip install numpy --upgrade --ignore-installed" ) return False print("Analizing `{}`".format(fname)) h_lim = {"w": 90.0, "h": 47.0} v_lim = {"w": 4.0, "h": 5.0} d_lim = 60 vid = mp.VideoFileClip(fname) (w, h) = vid.size deg = vid.rotation ratio = w * 1.0 / h * 1.0 print( "FOUND w:{w}, h:{h}, rotation={d}, ratio={r}".format( w=w, h=h, r=ratio, d=deg ) ) if w > h: print("Horizontal video") if ratio > (h_lim["w"] / h_lim["h"]): print("Cropping video") cut = int(ceil((w - h * h_lim["w"] / h_lim["h"]) / 2)) left = cut right = w - cut top = 0 bottom = h vid = vid.crop(x1=left, y1=top, x2=right, y2=bottom) (w, h) = vid.size if w > 1081: print("Resizing video") vid = vid.resize(width=1080) elif w < h: print("Vertical video") if ratio < (v_lim["w"] / v_lim["h"]): print("Cropping video") cut = int(ceil((h - w * v_lim["h"] / v_lim["w"]) / 2)) left = 0 right = w top = cut bottom = h - cut vid = vid.crop(x1=left, y1=top, x2=right, y2=bottom) (w, h) = vid.size if h > 1081: print("Resizing video") vid = vid.resize(height=1080) else: print("Square video") if w > 1081: print("Resizing video") vid = vid.resize(width=1080) (w, h) = vid.size if vid.duration > d_lim: print("Cutting video to {} sec from start".format(d_lim)) vid = vid.subclip(0, d_lim) new_fname = "{}.CONVERTED.mp4".format(fname) print( "Saving new video w:{w} h:{h} to `{f}`".format( w=w, h=h, f=new_fname ) ) vid.write_videofile(new_fname, codec="libx264", audio_codec="aac") if not thumbnail: print("Generating thumbnail...") thumbnail = "{}.jpg".format(fname) vid.save_frame(thumbnail, t=(vid.duration / 2)) return new_fname, thumbnail, w, h, vid.duration

32.831956

78

0.523662

import copy import json import os import re import shutil import subprocess import time from requests_toolbelt import MultipartEncoder from . import config def download_video( self, media_id, filename=None, media=False, folder="videos" ): video_urls = [] if not media: self.media_info(media_id) media = self.last_json["items"][0] filename = ( "{}_{}.mp4".format(media["user"]["username"], media_id) if not filename else "{}.mp4".format(filename) ) try: clips = media["video_versions"] video_urls.append(clips[0]["url"]) except KeyError: carousels = media.get("carousel_media", []) for carousel in carousels: video_urls.append(carousel["video_versions"][0]["url"]) except Exception: return False for counter, video_url in enumerate(video_urls): fname = os.path.join(folder, "{}_{}".format(counter, filename)) if os.path.exists(fname): print('File %s is exists, return it' % fname) return os.path.abspath(fname) response = self.session.get(video_url, stream=True) if response.status_code == 200: with open(fname, "wb") as f: response.raw.decode_content = True shutil.copyfileobj(response.raw, f) return os.path.abspath(fname) def get_video_info(filename): res = {} try: terminalResult = subprocess.Popen( ["ffprobe", filename], stdout=subprocess.PIPE, stderr=subprocess.STDOUT ) for x in terminalResult.stdout.readlines(): m = re.search( r"duration: (\d\d:\d\d:\d\d\.\d\d),", str(x), flags=re.IGNORECASE ) if m is not None: res["duration"] = m.group(1) m = re.search( r"video:\s.*\s(\d+)x(\d+)\s", str(x), flags=re.IGNORECASE ) if m is not None: res["width"] = m.group(1) res["height"] = m.group(2) finally: if "width" not in res: print( "ERROR: 'ffprobe' not found, please install " "'ffprobe' with one of following methods:" ) print(" sudo apt-get install ffmpeg") print("or sudo apt-get install -y libav-tools") return res def upload_video( self, video, caption=None, upload_id=None, thumbnail=None, options={} ): options = dict( {"configure_timeout": 15, "rename_thumbnail": True, "rename": True}, **(options or {}) ) if upload_id is None: upload_id = str(int(time.time() * 1000)) video, thumbnail, width, height, duration = resize_video(video, thumbnail) data = { "upload_id": upload_id, "_csrftoken": self.token, "media_type": "2", "_uuid": self.uuid, } m = MultipartEncoder(data, boundary=self.uuid) self.session.headers.update( { "X-IG-Capabilities": "3Q4=", "X-IG-Connection-Type": "WIFI", "Host": "i.instagram.com", "Cookie2": "$Version=1", "Accept-Language": "en-US", "Accept-Encoding": "gzip, deflate", "Content-type": m.content_type, "Connection": "keep-alive", "User-Agent": self.user_agent, } ) response = self.session.post( config.API_URL + "upload/video/", data=m.to_string() ) if response.status_code == 200: body = json.loads(response.text) upload_url = body["video_upload_urls"][3]["url"] upload_job = body["video_upload_urls"][3]["job"] with open(video, "rb") as video_bytes: video_data = video_bytes.read() request_size = len(video_data) // 4 last_request_extra = len(video_data) - 3 * request_size headers = copy.deepcopy(self.session.headers) self.session.headers.update( { "X-IG-Capabilities": "3Q4=", "X-IG-Connection-Type": "WIFI", "Cookie2": "$Version=1", "Accept-Language": "en-US", "Accept-Encoding": "gzip, deflate", "Content-type": "application/octet-stream", "Session-ID": upload_id, "Connection": "keep-alive", "Content-Disposition": 'attachment; filename="video.mov"', "job": upload_job, "Host": "upload.instagram.com", "User-Agent": self.user_agent, } ) for i in range(4): start = i * request_size if i == 3: end = i * request_size + last_request_extra else: end = (i + 1) * request_size length = last_request_extra if i == 3 else request_size content_range = "bytes {start}-{end}/{len_video}".format( start=start, end=end - 1, len_video=len(video_data) ).encode("utf-8") self.session.headers.update( { "Content-Length": str(end - start), "Content-Range": content_range } ) response = self.session.post( upload_url, data=video_data[start: start + length] ) self.session.headers = headers configure_timeout = options.get("configure_timeout") if response.status_code == 200: for attempt in range(4): if configure_timeout: time.sleep(configure_timeout) if self.configure_video( upload_id, video, thumbnail, width, height, duration, caption, options=options, ): media = self.last_json.get("media") self.expose() if options.get("rename"): from os import rename rename(video, "{}.REMOVE_ME".format(video)) return media return False def configure_video( self, upload_id, video, thumbnail, width, height, duration, caption="", options={} ): options = {"rename": options.get("rename_thumbnail", True)} self.upload_photo( photo=thumbnail, caption=caption, upload_id=upload_id, from_video=True, options=options, ) data = self.json_data( { "upload_id": upload_id, "source_type": 3, "poster_frame_index": 0, "length": 0.00, "audio_muted": False, "filter_type": 0, "video_result": "deprecated", "clips": { "length": duration, "source_type": "3", "camera_position": "back", }, "extra": {"source_width": width, "source_height": height}, "device": self.device_settings, "caption": caption, } ) return self.send_request("media/configure/?video=1", data) def resize_video(fname, thumbnail=None): from math import ceil try: import moviepy.editor as mp except ImportError as e: print("ERROR: {}".format(e)) print( "Required module `moviepy` not installed\n" "Install with `pip install moviepy` and retry.\n\n" "You may need also:\n" "pip install --upgrade setuptools\n" "pip install numpy --upgrade --ignore-installed" ) return False print("Analizing `{}`".format(fname)) h_lim = {"w": 90.0, "h": 47.0} v_lim = {"w": 4.0, "h": 5.0} d_lim = 60 vid = mp.VideoFileClip(fname) (w, h) = vid.size deg = vid.rotation ratio = w * 1.0 / h * 1.0 print( "FOUND w:{w}, h:{h}, rotation={d}, ratio={r}".format( w=w, h=h, r=ratio, d=deg ) ) if w > h: print("Horizontal video") if ratio > (h_lim["w"] / h_lim["h"]): print("Cropping video") cut = int(ceil((w - h * h_lim["w"] / h_lim["h"]) / 2)) left = cut right = w - cut top = 0 bottom = h vid = vid.crop(x1=left, y1=top, x2=right, y2=bottom) (w, h) = vid.size if w > 1081: print("Resizing video") vid = vid.resize(width=1080) elif w < h: print("Vertical video") if ratio < (v_lim["w"] / v_lim["h"]): print("Cropping video") cut = int(ceil((h - w * v_lim["h"] / v_lim["w"]) / 2)) left = 0 right = w top = cut bottom = h - cut vid = vid.crop(x1=left, y1=top, x2=right, y2=bottom) (w, h) = vid.size if h > 1081: print("Resizing video") vid = vid.resize(height=1080) else: print("Square video") if w > 1081: print("Resizing video") vid = vid.resize(width=1080) (w, h) = vid.size if vid.duration > d_lim: print("Cutting video to {} sec from start".format(d_lim)) vid = vid.subclip(0, d_lim) new_fname = "{}.CONVERTED.mp4".format(fname) print( "Saving new video w:{w} h:{h} to `{f}`".format( w=w, h=h, f=new_fname ) ) vid.write_videofile(new_fname, codec="libx264", audio_codec="aac") if not thumbnail: print("Generating thumbnail...") thumbnail = "{}.jpg".format(fname) vid.save_frame(thumbnail, t=(vid.duration / 2)) return new_fname, thumbnail, w, h, vid.duration

true

f7194ea7dd6a75befd86621e8258b956db12dfa2

1,839

py

Python

audiospec.py

MountainRange/mobius_score

fc900ab456b3e3431cfa6d9684b97ec6321d0a23

[ "MIT" ]

null

audiospec.py

MountainRange/mobius_score

fc900ab456b3e3431cfa6d9684b97ec6321d0a23

[ "MIT" ]

null

audiospec.py

MountainRange/mobius_score

fc900ab456b3e3431cfa6d9684b97ec6321d0a23

[ "MIT" ]

null

import numpy as np import librosa from tqdm import tqdm from audiomisc import ks_key from constants import VERTICALCUTOFF, FFT_SIZE, FFT_HOP def stft(x, fft_size, hopsamp): window = np.hanning(fft_size) return np.array([np.fft.rfft(window*x[i:i+fft_size]) for i in range(0, len(x)-fft_size, hopsamp)]) def wav_to_spec(fn): input_signal, sample_rate = librosa.load(fn, sr=44100) stft_mag = np.array([]) split = int(1e6)#int(264600) fft_size = FFT_SIZE hopsamp = fft_size // FFT_HOP for i in tqdm(range(len(input_signal)//split)): temp_signal = input_signal[(split*i):(split*(i+1))] stft_full = stft(temp_signal, fft_size, hopsamp) stft_full = abs(stft_full) if np.max(stft_full) != 0: stft_full = (stft_full - np.mean(stft_full)) / np.std(stft_full) stft_full += abs(np.min(stft_full)) stft_full *= 255.0/np.max(stft_full) if stft_mag.shape[0] != 0: stft_mag = np.concatenate((stft_mag, stft_full)) else: stft_mag = stft_full print("Calculating tempo") tempo, _ = librosa.beat.beat_track(y=input_signal, sr=sample_rate, hop_length=512) print("Calculating music key") chroma = librosa.feature.chroma_stft(y=input_signal, sr=sample_rate) chroma = [sum(x)/len(x) for x in chroma] bestmajor, bestminor = ks_key(chroma) if max(bestmajor) > max(bestminor): key = np.argmax(bestmajor) # C, Db, D, Eb, E, F, F#, G, Ab, A, Bb, B keymap = [0, -5, 2, -3, 4, -1, 6, 1, -4, 3, -2, 5] else: key = np.argmax(bestminor) # c, c#, d, eb, e, f, f#, g, g#, a, bb, b keymap = [-3, 4, -1, -6, 1, -4, 3, -2, 5, 0, -5, 2] return stft_mag[:, :VERTICALCUTOFF].T, tempo, keymap[key]

36.058824

86

0.594889

import numpy as np import librosa from tqdm import tqdm from audiomisc import ks_key from constants import VERTICALCUTOFF, FFT_SIZE, FFT_HOP def stft(x, fft_size, hopsamp): window = np.hanning(fft_size) return np.array([np.fft.rfft(window*x[i:i+fft_size]) for i in range(0, len(x)-fft_size, hopsamp)]) def wav_to_spec(fn): input_signal, sample_rate = librosa.load(fn, sr=44100) stft_mag = np.array([]) split = int(1e6) fft_size = FFT_SIZE hopsamp = fft_size // FFT_HOP for i in tqdm(range(len(input_signal)//split)): temp_signal = input_signal[(split*i):(split*(i+1))] stft_full = stft(temp_signal, fft_size, hopsamp) stft_full = abs(stft_full) if np.max(stft_full) != 0: stft_full = (stft_full - np.mean(stft_full)) / np.std(stft_full) stft_full += abs(np.min(stft_full)) stft_full *= 255.0/np.max(stft_full) if stft_mag.shape[0] != 0: stft_mag = np.concatenate((stft_mag, stft_full)) else: stft_mag = stft_full print("Calculating tempo") tempo, _ = librosa.beat.beat_track(y=input_signal, sr=sample_rate, hop_length=512) print("Calculating music key") chroma = librosa.feature.chroma_stft(y=input_signal, sr=sample_rate) chroma = [sum(x)/len(x) for x in chroma] bestmajor, bestminor = ks_key(chroma) if max(bestmajor) > max(bestminor): key = np.argmax(bestmajor) [0, -5, 2, -3, 4, -1, 6, 1, -4, 3, -2, 5] else: key = np.argmax(bestminor) return stft_mag[:, :VERTICALCUTOFF].T, tempo, keymap[key]

true

f7194ee4f7406065cad234d9d9516182a66c1fdd

4,936

py

Python

catalog/views.py

singdingo/django_local_library

e6928ce96d37e5f233a5eda89dcf63c04a551a2d

[ "MIT" ]

null

catalog/views.py

singdingo/django_local_library

e6928ce96d37e5f233a5eda89dcf63c04a551a2d

[ "MIT" ]

null

catalog/views.py

singdingo/django_local_library

e6928ce96d37e5f233a5eda89dcf63c04a551a2d

[ "MIT" ]

null

from django.contrib.auth.decorators import permission_required from django.shortcuts import render, get_object_or_404 from django.contrib.auth.mixins import LoginRequiredMixin, PermissionRequiredMixin from .models import Book, Author, BookInstance, Genre from django.views import generic from django.views.generic.edit import CreateView, UpdateView, DeleteView from django.http import HttpResponseRedirect from django.urls import reverse, reverse_lazy import datetime from .forms import RenewBookForm def index(request): """ View function for home page of site. """ # Generate counts of some of the main objects num_books = Book.objects.all().count() num_instances = BookInstance.objects.all().count() # Available books (status = 'a') num_instances_available = BookInstance.objects.filter( status__exact='a').count() num_authors = Author.objects.count() # The 'all()' is implied by default. # Number of visits to this view, as counted in the session variable. num_visits = request.session.get('num_visits', 0) request.session['num_visits'] = num_visits + 1 # Render the HTML template index.html with the data in the context variable return render( request, 'index.html', context={ 'num_books': num_books, 'num_instances': num_instances, 'num_instances_available': num_instances_available, 'num_authors': num_authors, 'num_visits': num_visits }, ) class BookListView(generic.ListView): model = Book paginate_by = 2 # Some possible overrides ''' context_object_name = 'my_book_list' # your own name for the list as a template variable template_name = 'books/my_arbitrary_template_name_list.html' # Specify your own template name/location ''' #Another potentially useful override ''' def get_queryset(self): return Book.objects.filter( title__icontains='war')[:5] # Get 5 books containing the title war ''' class BookDetailView(generic.DetailView): model=Book class AuthorListView(generic.ListView): model = Author paginate_by = 10 class AuthorDetailView(generic.DetailView): model = Author class LoanedBooksByUserListView(LoginRequiredMixin, generic.ListView): """ Generic class based view listing books on loan to current user """ model = BookInstance template_name = 'catalog/bookinstance_list_borrowed_user.html' paginate_by = 10 def get_queryset(self): return BookInstance.objects.filter(borrower=self.request.user).filter(status__exact='o').order_by('due_back') class LoanedBooksStaffListview(PermissionRequiredMixin, generic.ListView): """ Generic class back view listing all loanded books (for staff only). """ model = BookInstance template_name = 'catalog/book_instance_list_borrowed_staff.html' paginate_by = 10 # Set permissions permission_required = ('catalog.can_mark_returned',) def get_queryset(self): return BookInstance.objects.filter(status__exact='o').order_by("due_back") @permission_required('catalog.can_mark_returned') def renew_book_librarian(request, pk): """ View function for renewing a specific BookInstance by librarian """ book_inst = get_object_or_404(BookInstance, pk=pk) # If this is a POST request then process the Form data if request.method == 'POST': # Create a form instance and populate it with data from the request (binding): form = RenewBookForm(request.POST) # Check if the form is valid: if form.is_valid(): # process the data in form.cleaned_data as required (here we just write it to the model due_back field) book_inst.due_back = form.cleaned_data['renewal_date'] book_inst.save() # redirect to a new URL: return HttpResponseRedirect(reverse('all-borrowed')) # If this is a GET (or any other method) create the default form. else: proposed_renewal_date = datetime.date.today() + datetime.timedelta( weeks=3) form = RenewBookForm(initial={ 'renewal_date': proposed_renewal_date, }) return render(request, 'catalog/book_renew_librarian.html', { 'form': form, 'bookinst': book_inst }) class AuthorCreate(CreateView): model = Author fields = '__all__' #initial={'date_of_death':'05/01/2018',} class AuthorUpdate(UpdateView): model = Author fields = ['first_name','last_name','date_of_birth','date_of_death'] class AuthorDelete(DeleteView): model = Author success_url = reverse_lazy('authors') class BookCreate(CreateView): model = Book fields = '__all__' class BookUpdate(UpdateView): model = Book fields = '__all__' class BookDelete(DeleteView): model = Book success_url = reverse_lazy('books')

32.261438

117

0.6953

from django.contrib.auth.decorators import permission_required from django.shortcuts import render, get_object_or_404 from django.contrib.auth.mixins import LoginRequiredMixin, PermissionRequiredMixin from .models import Book, Author, BookInstance, Genre from django.views import generic from django.views.generic.edit import CreateView, UpdateView, DeleteView from django.http import HttpResponseRedirect from django.urls import reverse, reverse_lazy import datetime from .forms import RenewBookForm def index(request): num_books = Book.objects.all().count() num_instances = BookInstance.objects.all().count() num_instances_available = BookInstance.objects.filter( status__exact='a').count() num_authors = Author.objects.count() num_visits = request.session.get('num_visits', 0) request.session['num_visits'] = num_visits + 1 return render( request, 'index.html', context={ 'num_books': num_books, 'num_instances': num_instances, 'num_instances_available': num_instances_available, 'num_authors': num_authors, 'num_visits': num_visits }, ) class BookListView(generic.ListView): model = Book paginate_by = 2 class BookDetailView(generic.DetailView): model=Book class AuthorListView(generic.ListView): model = Author paginate_by = 10 class AuthorDetailView(generic.DetailView): model = Author class LoanedBooksByUserListView(LoginRequiredMixin, generic.ListView): model = BookInstance template_name = 'catalog/bookinstance_list_borrowed_user.html' paginate_by = 10 def get_queryset(self): return BookInstance.objects.filter(borrower=self.request.user).filter(status__exact='o').order_by('due_back') class LoanedBooksStaffListview(PermissionRequiredMixin, generic.ListView): model = BookInstance template_name = 'catalog/book_instance_list_borrowed_staff.html' paginate_by = 10 permission_required = ('catalog.can_mark_returned',) def get_queryset(self): return BookInstance.objects.filter(status__exact='o').order_by("due_back") @permission_required('catalog.can_mark_returned') def renew_book_librarian(request, pk): book_inst = get_object_or_404(BookInstance, pk=pk) if request.method == 'POST': form = RenewBookForm(request.POST) if form.is_valid(): book_inst.due_back = form.cleaned_data['renewal_date'] book_inst.save() return HttpResponseRedirect(reverse('all-borrowed')) else: proposed_renewal_date = datetime.date.today() + datetime.timedelta( weeks=3) form = RenewBookForm(initial={ 'renewal_date': proposed_renewal_date, }) return render(request, 'catalog/book_renew_librarian.html', { 'form': form, 'bookinst': book_inst }) class AuthorCreate(CreateView): model = Author fields = '__all__' class AuthorUpdate(UpdateView): model = Author fields = ['first_name','last_name','date_of_birth','date_of_death'] class AuthorDelete(DeleteView): model = Author success_url = reverse_lazy('authors') class BookCreate(CreateView): model = Book fields = '__all__' class BookUpdate(UpdateView): model = Book fields = '__all__' class BookDelete(DeleteView): model = Book success_url = reverse_lazy('books')

true

f7194f009ff4f9095f19defc8b8b945fae4f793a

698

py

Python

PyCharm/Exercicios/Aula17/ex078.py

fabiodarice/Python

15ec1c7428f138be875111ac98ba38cf2eec1a93

[ "MIT" ]

null

PyCharm/Exercicios/Aula17/ex078.py

fabiodarice/Python

15ec1c7428f138be875111ac98ba38cf2eec1a93

[ "MIT" ]

null

PyCharm/Exercicios/Aula17/ex078.py

fabiodarice/Python

15ec1c7428f138be875111ac98ba38cf2eec1a93

[ "MIT" ]

null

# Importação de bibliotecas # Título do programa print('\033[1;34;40mMAIOR E MENOR VALORES NA LISTA\033[m') # Objetos valores = list() # Lógica for c in range(0, 5): valores.append(int(input(f'\033[30mDigite um valor para a Posição {c}:\033[m '))) maior = max(valores) menor = min(valores) print('=-' * 30) print(f'Você digitou os valores {valores}') print(f'O maior valor digitado foi {maior} nas posições ', end='') for pos, num in enumerate(valores): if maior == num: print(f'{pos}...', '', end='') print() print(f'O menor valor digitado foi {menor} nas posições ', end='') for pos, num in enumerate(valores): if menor == num: print(f'{pos}...', '', end='')

22.516129

85

0.636103

print('\033[1;34;40mMAIOR E MENOR VALORES NA LISTA\033[m') valores = list() for c in range(0, 5): valores.append(int(input(f'\033[30mDigite um valor para a Posição {c}:\033[m '))) maior = max(valores) menor = min(valores) print('=-' * 30) print(f'Você digitou os valores {valores}') print(f'O maior valor digitado foi {maior} nas posições ', end='') for pos, num in enumerate(valores): if maior == num: print(f'{pos}...', '', end='') print() print(f'O menor valor digitado foi {menor} nas posições ', end='') for pos, num in enumerate(valores): if menor == num: print(f'{pos}...', '', end='')

true

f7194f875486f67d1eadb26fc5e87f6bfaed4596

6,237

py

Python

detect/image_detector.py

Prasad9/Detect-Flags-SSD

c0d662bde99ed8df33d72bd06d61d5eb869d31a5

[ "MIT" ]

13

2017-11-08T07:09:13.000Z

2022-03-28T07:09:47.000Z

detect/image_detector.py

Prasad9/Detect-Flags-SSD

c0d662bde99ed8df33d72bd06d61d5eb869d31a5

[ "MIT" ]

3

2018-03-08T04:30:19.000Z

2019-01-03T15:47:24.000Z

detect/image_detector.py

Prasad9/Detect-Flags-SSD

c0d662bde99ed8df33d72bd06d61d5eb869d31a5

[ "MIT" ]

5

2018-01-15T15:26:44.000Z

2021-08-18T08:02:51.000Z

from __future__ import print_function import mxnet as mx import numpy as np from timeit import default_timer as timer from dataset.iterator import DetTestImageIter import cv2 class ImageDetector(object): """ SSD detector which hold a detection network and wraps detection API Parameters: ---------- symbol : mx.Symbol detection network Symbol model_prefix : str name prefix of trained model epoch : int load epoch of trained model data_shape : int input data resize shape mean_pixels : tuple of float (mean_r, mean_g, mean_b) batch_size : int run detection with batch size ctx : mx.ctx device to use, if None, use mx.cpu() as default context """ def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \ classes, thresh = 0.6, plot_confidence = True, batch_size=1, ctx=None): self.ctx = ctx if self.ctx is None: self.ctx = mx.cpu() load_symbol, args, auxs = mx.model.load_checkpoint(model_prefix, epoch) if symbol is None: symbol = load_symbol self.mod = mx.mod.Module(symbol, label_names=None, context=ctx) self.data_shape = data_shape self.mod.bind(data_shapes=[('data', (batch_size, 3, data_shape, data_shape))]) self.mod.set_params(args, auxs) self.data_shape = data_shape self.mean_pixels = mean_pixels self.classes = classes self.colors = [] self.fill_random_colors_int() self.thresh = thresh self.plot_confidence = plot_confidence def fill_random_colors(self): import random for i in range(len(self.classes)): self.colors.append((random.random(), random.random(), random.random())) #print(self.colors) def fill_random_colors_int(self): import random for i in range(len(self.classes)): self.colors.append((random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))) #print(self.colors) def detect(self, det_iter, show_timer=False): """ detect all images in iterator Parameters: ---------- det_iter : DetIter iterator for all testing images show_timer : Boolean whether to print out detection exec time Returns: ---------- list of detection results """ num_images = det_iter._size result = [] detections = [] #if not isinstance(det_iter, mx.io.PrefetchingIter): # det_iter = mx.io.PrefetchingIter(det_iter) start = timer() for pred, _, _ in self.mod.iter_predict(det_iter): detections.append(pred[0].asnumpy()) time_elapsed = timer() - start if show_timer: print("Detection time for {} images: {:.4f} sec".format(num_images, time_elapsed)) for output in detections: for i in range(output.shape[0]): det = output[i, :, :] res = det[np.where(det[:, 0] >= 0)[0]] result.append(res) resized_img = det_iter.current_data() return result, resized_img def im_detect(self, img, show_timer=False): """ wrapper for detecting multiple images Parameters: ---------- im_list : list of str image path or list of image paths root_dir : str directory of input images, optional if image path already has full directory information extension : str image extension, eg. ".jpg", optional Returns: ---------- list of detection results in format [det0, det1...], det is in format np.array([id, score, xmin, ymin, xmax, ymax]...) """ im_list = [img] test_iter = DetTestImageIter(im_list, 1, self.data_shape, self.mean_pixels) return self.detect(test_iter, show_timer) def plot_rects(self, img, dets): img_shape = img.shape for i in range(dets.shape[0]): cls_id = int(dets[i, 0]) if cls_id >= 0: score = dets[i, 1] #print('Score is {}, class {}'.format(score, cls_id)) if score > self.thresh: xmin = int(dets[i, 2] * img_shape[1]) ymin = int(dets[i, 3] * img_shape[0]) xmax = int(dets[i, 4] * img_shape[1]) ymax = int(dets[i, 5] * img_shape[0]) cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 4) class_name = self.classes[cls_id] cv2.putText(img, class_name, (xmin, ymin), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 4) #print('Class id = {}, Score = {}, Country = {}, rect = ({}, {}, {}, {})'.format(cls_id, score, class_name, xmin, ymin, xmax, ymax)) def detect_and_visualize_image(self, img, show_timer=False): """ wrapper for im_detect and visualize_detection Parameters: ---------- im_list : list of str or str image path or list of image paths root_dir : str or None directory of input images, optional if image path already has full directory information extension : str or None image extension, eg. ".jpg", optional Returns: ---------- """ dets, resized_img = self.im_detect(img, show_timer=show_timer) resized_img = resized_img.asnumpy() resized_img /= 255.0 for k, det in enumerate(dets): self.plot_rects(resized_img, det) return resized_img def scale_and_plot_rects(self, img, dets): img_shape = img.shape for i in range(dets.shape[0]): cls_id = int(dets[i, 0]) if cls_id >= 0: score = dets[i, 1] #print('Score is {}, class {}'.format(score, cls_id)) if score > self.thresh: xmin = int(dets[i, 2] * img_shape[1]) ymin = int(dets[i, 3] * img_shape[0]) xmax = int(dets[i, 4] * img_shape[1]) ymax = int(dets[i, 5] * img_shape[0]) cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 4) class_name = self.classes[cls_id] cv2.putText(img, class_name, (xmin, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 255), 3) if self.plot_confidence: score_color = (0, 255, 0) if score > 0.5 else (255, 0, 0) cv2.putText(img, '{:.3f}'.format(score), (xmax - 60, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 1, score_color, 1) def detect_and_layover_image(self, img, show_timer=False): """ wrapper for im_detect and visualize_detection Parameters: ---------- im_list : list of str or str image path or list of image paths root_dir : str or None directory of input images, optional if image path already has full directory information extension : str or None image extension, eg. ".jpg", optional Returns: ---------- """ dets, _ = self.im_detect(img, show_timer=show_timer) for k, det in enumerate(dets): self.scale_and_plot_rects(img, det) return img

29.842105

137

0.674683

from __future__ import print_function import mxnet as mx import numpy as np from timeit import default_timer as timer from dataset.iterator import DetTestImageIter import cv2 class ImageDetector(object): def __init__(self, symbol, model_prefix, epoch, data_shape, mean_pixels, \ classes, thresh = 0.6, plot_confidence = True, batch_size=1, ctx=None): self.ctx = ctx if self.ctx is None: self.ctx = mx.cpu() load_symbol, args, auxs = mx.model.load_checkpoint(model_prefix, epoch) if symbol is None: symbol = load_symbol self.mod = mx.mod.Module(symbol, label_names=None, context=ctx) self.data_shape = data_shape self.mod.bind(data_shapes=[('data', (batch_size, 3, data_shape, data_shape))]) self.mod.set_params(args, auxs) self.data_shape = data_shape self.mean_pixels = mean_pixels self.classes = classes self.colors = [] self.fill_random_colors_int() self.thresh = thresh self.plot_confidence = plot_confidence def fill_random_colors(self): import random for i in range(len(self.classes)): self.colors.append((random.random(), random.random(), random.random())) def fill_random_colors_int(self): import random for i in range(len(self.classes)): self.colors.append((random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))) def detect(self, det_iter, show_timer=False): num_images = det_iter._size result = [] detections = [] start = timer() for pred, _, _ in self.mod.iter_predict(det_iter): detections.append(pred[0].asnumpy()) time_elapsed = timer() - start if show_timer: print("Detection time for {} images: {:.4f} sec".format(num_images, time_elapsed)) for output in detections: for i in range(output.shape[0]): det = output[i, :, :] res = det[np.where(det[:, 0] >= 0)[0]] result.append(res) resized_img = det_iter.current_data() return result, resized_img def im_detect(self, img, show_timer=False): im_list = [img] test_iter = DetTestImageIter(im_list, 1, self.data_shape, self.mean_pixels) return self.detect(test_iter, show_timer) def plot_rects(self, img, dets): img_shape = img.shape for i in range(dets.shape[0]): cls_id = int(dets[i, 0]) if cls_id >= 0: score = dets[i, 1] if score > self.thresh: xmin = int(dets[i, 2] * img_shape[1]) ymin = int(dets[i, 3] * img_shape[0]) xmax = int(dets[i, 4] * img_shape[1]) ymax = int(dets[i, 5] * img_shape[0]) cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 4) class_name = self.classes[cls_id] cv2.putText(img, class_name, (xmin, ymin), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 4) def detect_and_visualize_image(self, img, show_timer=False): dets, resized_img = self.im_detect(img, show_timer=show_timer) resized_img = resized_img.asnumpy() resized_img /= 255.0 for k, det in enumerate(dets): self.plot_rects(resized_img, det) return resized_img def scale_and_plot_rects(self, img, dets): img_shape = img.shape for i in range(dets.shape[0]): cls_id = int(dets[i, 0]) if cls_id >= 0: score = dets[i, 1] if score > self.thresh: xmin = int(dets[i, 2] * img_shape[1]) ymin = int(dets[i, 3] * img_shape[0]) xmax = int(dets[i, 4] * img_shape[1]) ymax = int(dets[i, 5] * img_shape[0]) cv2.rectangle(img, (xmin, ymin), (xmax, ymax), self.colors[cls_id], 4) class_name = self.classes[cls_id] cv2.putText(img, class_name, (xmin, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 255), 3) if self.plot_confidence: score_color = (0, 255, 0) if score > 0.5 else (255, 0, 0) cv2.putText(img, '{:.3f}'.format(score), (xmax - 60, ymin - 15), cv2.FONT_HERSHEY_SIMPLEX, 1, score_color, 1) def detect_and_layover_image(self, img, show_timer=False): dets, _ = self.im_detect(img, show_timer=show_timer) for k, det in enumerate(dets): self.scale_and_plot_rects(img, det) return img

true

f7194f9c5decb291e54561f76b15458cea4e4f8b

357

py

Python

aliexpress/api/rest/MarketingRedefiningGetactlist.py

bayborodin/aliexpress-sdk

89935adf46412d8d054fa80a19153971279c4106

[ "MIT" ]

3

2021-03-10T16:46:43.000Z

2022-03-29T15:28:50.000Z

aliexpress/api/rest/MarketingRedefiningGetactlist.py

bayborodin/aliexpress-sdk

89935adf46412d8d054fa80a19153971279c4106

[ "MIT" ]

null

aliexpress/api/rest/MarketingRedefiningGetactlist.py

bayborodin/aliexpress-sdk

89935adf46412d8d054fa80a19153971279c4106

[ "MIT" ]

2

2021-10-30T17:09:34.000Z

2021-11-25T11:50:52.000Z

from aliexpress.api.base import RestApi class AliexpressMarketingRedefiningGetactlistRequest(RestApi): def __init__(self, domain="gw.api.taobao.com", port=80): RestApi.__init__(self, domain, port) self.param_seller_coupon_activity_api_query = None def getapiname(self): return "aliexpress.marketing.redefining.getactlist"

32.454545

62

0.756303

from aliexpress.api.base import RestApi class AliexpressMarketingRedefiningGetactlistRequest(RestApi): def __init__(self, domain="gw.api.taobao.com", port=80): RestApi.__init__(self, domain, port) self.param_seller_coupon_activity_api_query = None def getapiname(self): return "aliexpress.marketing.redefining.getactlist"

true

f7194fa0d3317f35e8c12bcca9423aaf27363280

981

py

Python

tests/test_del_contact.py

aogn/python_train

40131b24633c9771452813872061ca5335edecd8

[ "Apache-2.0" ]

null

tests/test_del_contact.py

aogn/python_train

40131b24633c9771452813872061ca5335edecd8

[ "Apache-2.0" ]

null

tests/test_del_contact.py

aogn/python_train

40131b24633c9771452813872061ca5335edecd8

[ "Apache-2.0" ]

null

from models.contact import Contact import random import allure def test_delete_some_contact(app, db, check_ui): with allure.step('Check contact'): if len(db.get_contact_list()) == 0: app.contact.creation(Contact(first_name="test")) with allure.step('Given a contact list and contact to delete'): old_contacts = db.get_contact_list() contact = random.choice(old_contacts) with allure.step('When I delete a contact %s from the list' % contact): app.contact.delete_contact_by_id(contact.id) with allure.step('Then the new contact list is equal to the old list without the deleted contact'): new_contacts = db.get_contact_list() assert len(old_contacts) - 1 == len(new_contacts) old_contacts.remove(contact) assert old_contacts == new_contacts if check_ui: assert sorted(new_contacts, key=Contact.id_or_max) == sorted(app.group.get_contact_list(), key=Contact.id_or_max)

44.590909

125

0.699286

from models.contact import Contact import random import allure def test_delete_some_contact(app, db, check_ui): with allure.step('Check contact'): if len(db.get_contact_list()) == 0: app.contact.creation(Contact(first_name="test")) with allure.step('Given a contact list and contact to delete'): old_contacts = db.get_contact_list() contact = random.choice(old_contacts) with allure.step('When I delete a contact %s from the list' % contact): app.contact.delete_contact_by_id(contact.id) with allure.step('Then the new contact list is equal to the old list without the deleted contact'): new_contacts = db.get_contact_list() assert len(old_contacts) - 1 == len(new_contacts) old_contacts.remove(contact) assert old_contacts == new_contacts if check_ui: assert sorted(new_contacts, key=Contact.id_or_max) == sorted(app.group.get_contact_list(), key=Contact.id_or_max)

true

f7194fe7656b09b6c529b0342d12157fb1da984f

710

py

Python

tests/apps/minimal2/application.py

blazelibs/blazeweb

b120a6a2e38c8b53da2b73443ff242e2d1438053

[ "BSD-3-Clause" ]

null

tests/apps/minimal2/application.py

blazelibs/blazeweb

b120a6a2e38c8b53da2b73443ff242e2d1438053

[ "BSD-3-Clause" ]

6

2016-11-01T18:42:34.000Z

2020-11-16T16:52:14.000Z

tests/apps/minimal2/application.py

blazelibs/blazeweb

b120a6a2e38c8b53da2b73443ff242e2d1438053

[ "BSD-3-Clause" ]

1

2020-01-22T18:20:46.000Z

from os import path from blazeutils import prependsitedir from blazeweb.application import WSGIApp from blazeweb.middleware import full_wsgi_stack from minimal2.config import settings as settingsmod from blazeweb.scripting import application_entry # make sure our base module gets put on the path try: import minimal2 # noqa except ImportError: prependsitedir(path.dirname(settingsmod.basedir), 'apps') def make_wsgi(profile='Default', use_session=True): app = WSGIApp(settingsmod, profile) if not use_session: app.settings.beaker.enabled = False return full_wsgi_stack(app) def script_entry(): application_entry(make_wsgi) if __name__ == '__main__': script_entry()

25.357143

61

0.773239

from os import path from blazeutils import prependsitedir from blazeweb.application import WSGIApp from blazeweb.middleware import full_wsgi_stack from minimal2.config import settings as settingsmod from blazeweb.scripting import application_entry try: import minimal2 except ImportError: prependsitedir(path.dirname(settingsmod.basedir), 'apps') def make_wsgi(profile='Default', use_session=True): app = WSGIApp(settingsmod, profile) if not use_session: app.settings.beaker.enabled = False return full_wsgi_stack(app) def script_entry(): application_entry(make_wsgi) if __name__ == '__main__': script_entry()

true

f719503e9bb94c5fe728360593ebcf3637d9ab4e

17,459

py

Python

src/graphtastic/clustering.py

richardtjornhammar/graphtastic

1e64d408ffb3e09d5ad068986c847032d5cfdcbd

[ "Apache-2.0" ]

1

2022-02-08T09:53:38.000Z

src/graphtastic/clustering.py

richardtjornhammar/graphtastic

1e64d408ffb3e09d5ad068986c847032d5cfdcbd

[ "Apache-2.0" ]

null

src/graphtastic/clustering.py

richardtjornhammar/graphtastic

1e64d408ffb3e09d5ad068986c847032d5cfdcbd

[ "Apache-2.0" ]

1

2022-03-24T12:37:05.000Z

""" Copyright 2022 RICHARD TJÖRNHAMMAR Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import numpy as np import typing import sys try : from numba import jit bUseNumba = True except ImportError : print ( "ImportError:"," NUMBA. WILL NOT USE IT") bUseNumba = False except OSError: print ( "OSError:"," NUMBA. WILL NOT USE IT") bUseNumba = False # THE FOLLOWING KMEANS ALGORITHM IS THE AUTHOR OWN LOCAL VERSION if bUseNumba : @jit(nopython=True) def seeded_kmeans( dat:np.array, cent:np.array ) : # # PYTHON ADAPTATION OF MY C++ CODE THAT CAN BE FOUND IN # https://github.com/richardtjornhammar/RichTools/blob/master/src/cluster.cc # AROUND LINE 2345 # AGAIN CONSIDER USING THE C++ VERSION SINCE IT IS ALOT FASTER # HERE WE SPEED IT UP USING NUMBA IF THE USER HAS IT INSTALLED AS A MODULE # NN , MM = np.shape ( dat ) KK , LL = np.shape ( cent ) if not LL == MM : print ( 'WARNING DATA FORMAT ERROR. NON COALESCING COORDINATE AXIS' ) labels = [ int(z) for z in np.zeros(NN) ] w = labels counts = np.zeros(KK) tmp_ce = np.zeros(KK*MM).reshape(KK,MM) old_error , error , TOL = 0. , 1. , 1.0E-10 while abs ( error - old_error ) > TOL : old_error = error error = 0. counts = counts * 0. tmp_ce = tmp_ce * 0. # START BC for h in range ( NN ) : min_distance = 1.0E30 for i in range ( KK ) : distance = np.sum( ( dat[h]-cent[i] )**2 ) if distance < min_distance : labels[h] = i min_distance = distance tmp_ce[labels[h]] += dat[ h ] counts[labels[h]] += 1.0 error += min_distance # END BC for i in range ( KK ) : if counts[i]>0: cent[i] = tmp_ce[i]/counts[i] centroids = cent return ( labels , centroids ) else : def seeded_kmeans( dat:np.array, cent:np.array ) : # # SLOW SLUGGISH KMEANS WITH A DUBBLE FOR LOOP # IN PYTHON! WOW! SUCH SPEED! # NN , MM = np.shape ( dat ) KK , LL = np.shape ( cent ) if not LL == MM : print ( 'WARNING DATA FORMAT ERROR. NON COALESCING COORDINATE AXIS' ) labels = [ int(z) for z in np.zeros(NN) ] w = labels counts = np.zeros(KK) tmp_ce = np.zeros(KK*MM).reshape(KK,MM) old_error , error , TOL = 0. , 1. , 1.0E-10 while abs ( error - old_error ) > TOL : old_error = error error = 0. counts = counts * 0. tmp_ce = tmp_ce * 0. # START BC for h in range ( NN ) : min_distance = 1.0E30 for i in range ( KK ) : distance = np.sum( ( dat[h]-cent[i] )**2 ) if distance < min_distance : labels[h] = i min_distance = distance tmp_ce[labels[h]] += dat[ h ] counts[labels[h]] += 1.0 error += min_distance # END BC for i in range ( KK ) : if counts[i]>0: cent[i] = tmp_ce[i]/counts[i] centroids = cent return ( labels , centroids ) if bUseNumba : @jit(nopython=True) def connectivity ( B:np.array , val:float , bVerbose:bool = False ) : description = """ This is a cutoff based clustering algorithm. The intended use is to supply a distance matrix and a cutoff value (then becomes symmetric positive). For a small distance cutoff, you should see all the parts of the system and for a large distance cutoff, you should see the entire system. It has been employed for statistical analysis work as well as the original application where it was employed to segment molecular systems.""" if bVerbose : print ( "CONNECTIVITY CLUSTERING OF ", np.shape(B), " MATRIX" ) # PYTHON ADAPTATION OF MY C++ CODE THAT CAN BE FOUND IN # https://github.com/richardtjornhammar/RichTools/blob/master/src/cluster.cc # AROUND LINE 2277 # CONSIDER COMPILING AND USING THAT AS A MODULE INSTEAD OF THIS SINCE IT IS # A LOT FASTER # FOR A DESCRIPTION READ PAGE 30 (16 INTERNAL NUMBERING) of: # https://kth.diva-portal.org/smash/get/diva2:748464/FULLTEXT01.pdf # # https://github.com/richardtjornhammar/RichTools/blob/master/src/cluster.cc # ADDED TO RICHTOOLS HERE: https://github.com/richardtjornhammar/RichTools/commit/74b35df9c623bf03570707a24eafe828f461ed90#diff-25a6634263c1b1f6fc4697a04e2b9904ea4b042a89af59dc93ec1f5d44848a26 # CONNECTIVITY SEARCH FOR (connectivity) CONNECTIVITY # nr_sq,mr_sq = np.shape(B) if nr_sq != mr_sq : print ( 'ERROR: FAILED' ) N = mr_sq res , nvisi, s, NN, ndx, C = [0], [0], [0], [0], [0], 0 res .append(0) for i in range(N) : nvisi.append(i+1) res.append(0); res.append(0) ndx.append(i) res = res[1:] nvisi = nvisi[1:] ndx = ndx[1:] while ( len(ndx)>0 ) : i = ndx[-1] ; ndx = ndx[:-1] NN = [] if ( nvisi[i]>0 ) : C-=1 for j in range(N) : if ( B[i,j]<=val ) : NN.append(j) while ( len(NN)>0 ) : # back pop_back k = NN[-1]; NN = NN[:-1] nvisi[k] = C for j in range(N): if ( B[j,k]<=val ) : for q in range(N) : if ( nvisi[q] == j+1 ) : NN.append(q) if bVerbose : # VERBOSE print ( "INFO "+str(-1*C) +" clusters" ) Nc = [ 0 for i in range(-1*C) ] for q in range(N) : res[ q*2+1 ] = q; res[ q*2 ] = nvisi[q]-C; Nc [res[q*2]]+= 1; if bVerbose : print ( " "+str(res[q*2])+" "+str(res[2*q+1]) ) if bVerbose : for i in range(-1*C) : print( "CLUSTER " +str(i)+ " HAS " + str(Nc[i]) + " ELEMENTS") return ( Nc , np.array(res[:-1]).reshape(-1,2) ) else : def connectivity ( B:np.array , val:float , bVerbose:bool = False ) : description=""" This is a cutoff based clustering algorithm. The intended use is to supply a distance matrix and a cutoff value (then becomes symmetric positive). For a small distanc> """ if bVerbose : print ( "CONNECTIVITY CLUSTERING OF ", np.shape(B), " MATRIX" ) # PYTHON ADAPTATION OF MY C++ CODE THAT CAN BE FOUND IN # https://github.com/richardtjornhammar/RichTools/blob/master/src/cluster.cc # AROUND LINE 2277 # CONSIDER COMPILING AND USING THAT AS A MODULE INSTEAD OF THIS SINCE IT IS # A LOT FASTER # FOR A DESCRIPTION READ PAGE 30 (16 INTERNAL NUMBERING) of: # https://kth.diva-portal.org/smash/get/diva2:748464/FULLTEXT01.pdf # nr_sq,mr_sq = np.shape(B) if nr_sq != mr_sq : print ( 'ERROR' ) return ( -1 ) N = mr_sq res , nvisi, s, NN, ndx, C = [], [], [], [], [], 0 res .append(0) for i in range(N) : nvisi.append(i+1) res.append(0); res.append(0) ndx.append(i) while ( len(ndx)>0 ) : i = ndx[-1] ; ndx = ndx[:-1] NN = [] if ( nvisi[i]>0 ) : C-=1 for j in range(N) : if ( B[i,j]<=val ) : NN.append(j) while ( len(NN)>0 ) : # back pop_back k = NN[-1]; NN = NN[:-1] nvisi[k] = C for j in range(N): if ( B[j,k]<=val ) : for q in range(N) : if ( nvisi[q] == j+1 ) : NN.append(q) if bVerbose : # VERBOSE print ( "INFO "+str(-1*C) +" clusters" ) Nc = [ 0 for i in range(-1*C) ] for q in range(N) : res[ q*2+1 ] = q; res[ q*2 ] = nvisi[q]-C; Nc [res[q*2]]+= 1; if bVerbose : print ( " "+str(res[q*2])+" "+str(res[2*q+1]) ) if bVerbose: for i in range(-1*C) : print( "CLUSTER " +str(i)+ " HAS " + str(Nc[i]) + " ELEMENTS") return ( Nc , np.array(res[:-1]).reshape(-1,2) ) if bUseNumba : @jit(nopython=True) def connectedness ( distm:np.array , alpha:float , n_connections:int=1 ) -> list : # # AN ALTERNATIVE METHOD # DOES THE SAME THING AS THE CONNECTIVITY CODE IN MY # CLUSTERING MODULE (in src/impetuous/clustering.py ) # OR IN https://github.com/richardtjornhammar/RichTools/blob/master/src/cluster.cc # https://github.com/richardtjornhammar/RichTools/commit/74b35df9c623bf03570707a24eafe828f461ed90#diff-25a6634263c1b1f6fc4697a04e2b9904ea4b042a89af59dc93ec1f5d44848a26 # CONNECTIVITY SEARCH FOR (connectivity) CONNECTIVITY # # THIS ROUTINE RETURNS A LIST BELONGING TO THE CLUSTERS # WITH THE SET OF INDICES THAT MAPS TO THE CLUSTER # if len ( distm.shape ) < 2 : print ( 'PLEASE SUBMIT A SQUARE DISTANCE MATRIX' ) def b2i ( a:list ) -> list : return ( [ i for b,i in zip(a,range(len(a))) if b ] ) def f2i ( a:list,alf:float ) -> list : return ( b2i( a<=alf ) ) L = [] for a in distm : bAdd = True ids = set( f2i(a,alpha) ) for i in range(len(L)) : if len( L[i]&ids ) >= n_connections : L[i] = L[i] | ids bAdd = False break if bAdd and len(ids) >= n_connections : L .append( ids ) return ( L ) else : def connectedness ( distm:np.array , alpha:float , n_connections:int=1 ) -> list : # # AN ALTERNATIVE METHOD # DOES THE SAME THING AS THE CONNECTIVITY CODE IN MY # CLUSTERING MODULE (in src/impetuous/clustering.py ) # OR IN https://github.com/richardtjornhammar/RichTools/blob/master/src/cluster.cc # as of commit https://github.com/richardtjornhammar/RichTools/commit/76201bb07687017ae16a4e57cb1ed9fd8c394f18 2016 # CONNECTIVITY SEARCH FOR (connectivity) CONNECTIVITY # # THIS ROUTINE RETURNS A LIST BELONGING TO THE CLUSTERS # WITH THE SET OF INDICES THAT MAPS TO THE CLUSTER # if len ( distm.shape ) < 2 : print ( 'PLEASE SUBMIT A SQUARE DISTANCE MATRIX' ) def b2i ( a:list ) -> list : return ( [ i for b,i in zip(a,range(len(a))) if b ] ) def f2i ( a:list,alf:float ) -> list : return ( b2i( a<=alf ) ) L = [] for a in distm : bAdd = True ids = set( f2i(a,alpha) ) for i in range(len(L)) : if len( L[i]&ids ) >= n_connections : L[i] = L[i] | ids bAdd = False break if bAdd and len(ids) >= n_connections : L .append( ids ) return ( L ) def dbscan ( coordinates:np.array = None , distance_matrix:np.array = None , eps:float = None, minPts:int = None , bVerbose:bool = False ) -> dict : def absolute_coordinates_to_distance_matrix ( Q:np.array , power:int=2 , bInvPow:bool=False ) -> np.array : # UNUSED FALLBACK DP = np.array( [ np.sum((np.array(p)-np.array(q))**power) for p in Q for q in Q] ).reshape(np.shape(Q)[0],np.shape(Q)[0]) if bInvPow : DP = DP**(1.0/power) return ( DP ) if bVerbose : print ( "THIS IMPLEMENTATION FOR DBSCAN" ) print ( "ASSESSMENT OF NOISE DIFFERS FROM" ) print ( "THE IMPLEMENTATION FOUND IN SKLEARN" ) print ( "ASSUMES LINEAR DISTANCES, NOT SQUARED" ) # # FOR A DESCRIPTION OF THE CONNECTIVITY READ PAGE 30 (16 INTERNAL NUMBERING) of: # https://kth.diva-portal.org/smash/get/diva2:748464/FULLTEXT01.pdf #from impetuous.clustering import absolute_coordinates_to_distance_matrix #from impetuous.clustering import connectivity import operator if not operator.xor( coordinates is None , distance_matrix is None ) : print ( "ONLY SUPPLY A SINGE DATA FRAME OR A DISTANCE MATRIX" ) print ( "dbscan FAILED" ) print ( "DATA MATRICES NEEDS TO BE SPECIFIED WITH \" distance_matrix = ... \" " ) exit(1) if distance_matrix is None : from graphtastic.fit import absolute_coordinates_to_distance_matrix distance_matrix_ = absolute_coordinates_to_distance_matrix ( coordinates ) eps = eps**2.0 else : distance_matrix_ = distance_matrix isNoise = np.sum(distance_matrix_<eps,0)-1 < minPts i_ = 0 for ib in isNoise : if ib : distance_matrix_ [ i_] = ( 1+eps )*10.0 distance_matrix_.T[i_] = ( 1+eps )*10.0 distance_matrix_[i_][i_] = 0. i_ = i_+1 clustercontent , clustercontacts = connectivity ( distance_matrix_ , eps ) return ( {'cluster content': clustercontent, 'clusterid-particleid' : clustercontacts, 'is noise':isNoise} ) def reformat_dbscan_results ( results:dict ) -> dict : if True : clusters = {} for icontent in range(len(results['cluster content'])) : content = results[ 'cluster content' ][ icontent ] for c in results [ 'clusterid-particleid' ] : if c[0] == icontent : if results[ 'is noise' ][c[1]] : icontent=-1 if icontent in clusters: clusters[ icontent ] .append( c[1] ) else : clusters[ icontent ] = [ c[1] ] return ( clusters )

48.497222

462

0.452145

import numpy as np import typing import sys try : from numba import jit bUseNumba = True except ImportError : print ( "ImportError:"," NUMBA. WILL NOT USE IT") bUseNumba = False except OSError: print ( "OSError:"," NUMBA. WILL NOT USE IT") bUseNumba = False if bUseNumba : @jit(nopython=True) def seeded_kmeans( dat:np.array, cent:np.array ) : NN , MM = np.shape ( dat ) KK , LL = np.shape ( cent ) if not LL == MM : print ( 'WARNING DATA FORMAT ERROR. NON COALESCING COORDINATE AXIS' ) labels = [ int(z) for z in np.zeros(NN) ] w = labels counts = np.zeros(KK) tmp_ce = np.zeros(KK*MM).reshape(KK,MM) old_error , error , TOL = 0. , 1. , 1.0E-10 while abs ( error - old_error ) > TOL : old_error = error error = 0. counts = counts * 0. tmp_ce = tmp_ce * 0. for h in range ( NN ) : min_distance = 1.0E30 for i in range ( KK ) : distance = np.sum( ( dat[h]-cent[i] )**2 ) if distance < min_distance : labels[h] = i min_distance = distance tmp_ce[labels[h]] += dat[ h ] counts[labels[h]] += 1.0 error += min_distance for i in range ( KK ) : if counts[i]>0: cent[i] = tmp_ce[i]/counts[i] centroids = cent return ( labels , centroids ) else : def seeded_kmeans( dat:np.array, cent:np.array ) : NN , MM = np.shape ( dat ) KK , LL = np.shape ( cent ) if not LL == MM : print ( 'WARNING DATA FORMAT ERROR. NON COALESCING COORDINATE AXIS' ) labels = [ int(z) for z in np.zeros(NN) ] w = labels counts = np.zeros(KK) tmp_ce = np.zeros(KK*MM).reshape(KK,MM) old_error , error , TOL = 0. , 1. , 1.0E-10 while abs ( error - old_error ) > TOL : old_error = error error = 0. counts = counts * 0. tmp_ce = tmp_ce * 0. for h in range ( NN ) : min_distance = 1.0E30 for i in range ( KK ) : distance = np.sum( ( dat[h]-cent[i] )**2 ) if distance < min_distance : labels[h] = i min_distance = distance tmp_ce[labels[h]] += dat[ h ] counts[labels[h]] += 1.0 error += min_distance for i in range ( KK ) : if counts[i]>0: cent[i] = tmp_ce[i]/counts[i] centroids = cent return ( labels , centroids ) if bUseNumba : @jit(nopython=True) def connectivity ( B:np.array , val:float , bVerbose:bool = False ) : description = """ This is a cutoff based clustering algorithm. The intended use is to supply a distance matrix and a cutoff value (then becomes symmetric positive). For a small distance cutoff, you should see all the parts of the system and for a large distance cutoff, you should see the entire system. It has been employed for statistical analysis work as well as the original application where it was employed to segment molecular systems.""" if bVerbose : print ( "CONNECTIVITY CLUSTERING OF ", np.shape(B), " MATRIX" ) ape(B) if nr_sq != mr_sq : print ( 'ERROR: FAILED' ) N = mr_sq res , nvisi, s, NN, ndx, C = [0], [0], [0], [0], [0], 0 res .append(0) for i in range(N) : nvisi.append(i+1) res.append(0); res.append(0) ndx.append(i) res = res[1:] nvisi = nvisi[1:] ndx = ndx[1:] while ( len(ndx)>0 ) : i = ndx[-1] ; ndx = ndx[:-1] NN = [] if ( nvisi[i]>0 ) : C-=1 for j in range(N) : if ( B[i,j]<=val ) : NN.append(j) while ( len(NN)>0 ) : k = NN[-1]; NN = NN[:-1] nvisi[k] = C for j in range(N): if ( B[j,k]<=val ) : for q in range(N) : if ( nvisi[q] == j+1 ) : NN.append(q) if bVerbose : print ( "INFO "+str(-1*C) +" clusters" ) Nc = [ 0 for i in range(-1*C) ] for q in range(N) : res[ q*2+1 ] = q; res[ q*2 ] = nvisi[q]-C; Nc [res[q*2]]+= 1; if bVerbose : print ( " "+str(res[q*2])+" "+str(res[2*q+1]) ) if bVerbose : for i in range(-1*C) : print( "CLUSTER " +str(i)+ " HAS " + str(Nc[i]) + " ELEMENTS") return ( Nc , np.array(res[:-1]).reshape(-1,2) ) else : def connectivity ( B:np.array , val:float , bVerbose:bool = False ) : description=""" This is a cutoff based clustering algorithm. The intended use is to supply a distance matrix and a cutoff value (then becomes symmetric positive). For a small distanc> """ if bVerbose : print ( "CONNECTIVITY CLUSTERING OF ", np.shape(B), " MATRIX" ) nr_sq,mr_sq = np.shape(B) if nr_sq != mr_sq : print ( 'ERROR' ) return ( -1 ) N = mr_sq res , nvisi, s, NN, ndx, C = [], [], [], [], [], 0 res .append(0) for i in range(N) : nvisi.append(i+1) res.append(0); res.append(0) ndx.append(i) while ( len(ndx)>0 ) : i = ndx[-1] ; ndx = ndx[:-1] NN = [] if ( nvisi[i]>0 ) : C-=1 for j in range(N) : if ( B[i,j]<=val ) : NN.append(j) while ( len(NN)>0 ) : k = NN[-1]; NN = NN[:-1] nvisi[k] = C for j in range(N): if ( B[j,k]<=val ) : for q in range(N) : if ( nvisi[q] == j+1 ) : NN.append(q) if bVerbose : print ( "INFO "+str(-1*C) +" clusters" ) Nc = [ 0 for i in range(-1*C) ] for q in range(N) : res[ q*2+1 ] = q; res[ q*2 ] = nvisi[q]-C; Nc [res[q*2]]+= 1; if bVerbose : print ( " "+str(res[q*2])+" "+str(res[2*q+1]) ) if bVerbose: for i in range(-1*C) : print( "CLUSTER " +str(i)+ " HAS " + str(Nc[i]) + " ELEMENTS") return ( Nc , np.array(res[:-1]).reshape(-1,2) ) if bUseNumba : @jit(nopython=True) def connectedness ( distm:np.array , alpha:float , n_connections:int=1 ) -> list : if len ( distm.shape ) < 2 : print ( 'PLEASE SUBMIT A SQUARE DISTANCE MATRIX' ) def b2i ( a:list ) -> list : return ( [ i for b,i in zip(a,range(len(a))) if b ] ) def f2i ( a:list,alf:float ) -> list : return ( b2i( a<=alf ) ) L = [] for a in distm : bAdd = True ids = set( f2i(a,alpha) ) for i in range(len(L)) : if len( L[i]&ids ) >= n_connections : L[i] = L[i] | ids bAdd = False break if bAdd and len(ids) >= n_connections : L .append( ids ) return ( L ) else : def connectedness ( distm:np.array , alpha:float , n_connections:int=1 ) -> list : if len ( distm.shape ) < 2 : print ( 'PLEASE SUBMIT A SQUARE DISTANCE MATRIX' ) def b2i ( a:list ) -> list : return ( [ i for b,i in zip(a,range(len(a))) if b ] ) def f2i ( a:list,alf:float ) -> list : return ( b2i( a<=alf ) ) L = [] for a in distm : bAdd = True ids = set( f2i(a,alpha) ) for i in range(len(L)) : if len( L[i]&ids ) >= n_connections : L[i] = L[i] | ids bAdd = False break if bAdd and len(ids) >= n_connections : L .append( ids ) return ( L ) def dbscan ( coordinates:np.array = None , distance_matrix:np.array = None , eps:float = None, minPts:int = None , bVerbose:bool = False ) -> dict : def absolute_coordinates_to_distance_matrix ( Q:np.array , power:int=2 , bInvPow:bool=False ) -> np.array : DP = np.array( [ np.sum((np.array(p)-np.array(q))**power) for p in Q for q in Q] ).reshape(np.shape(Q)[0],np.shape(Q)[0]) if bInvPow : DP = DP**(1.0/power) return ( DP ) if bVerbose : print ( "THIS IMPLEMENTATION FOR DBSCAN" ) print ( "ASSESSMENT OF NOISE DIFFERS FROM" ) print ( "THE IMPLEMENTATION FOUND IN SKLEARN" ) print ( "ASSUMES LINEAR DISTANCES, NOT SQUARED" ) import operator if not operator.xor( coordinates is None , distance_matrix is None ) : print ( "ONLY SUPPLY A SINGE DATA FRAME OR A DISTANCE MATRIX" ) print ( "dbscan FAILED" ) print ( "DATA MATRICES NEEDS TO BE SPECIFIED WITH \" distance_matrix = ... \" " ) exit(1) if distance_matrix is None : from graphtastic.fit import absolute_coordinates_to_distance_matrix distance_matrix_ = absolute_coordinates_to_distance_matrix ( coordinates ) eps = eps**2.0 else : distance_matrix_ = distance_matrix isNoise = np.sum(distance_matrix_<eps,0)-1 < minPts i_ = 0 for ib in isNoise : if ib : distance_matrix_ [ i_] = ( 1+eps )*10.0 distance_matrix_.T[i_] = ( 1+eps )*10.0 distance_matrix_[i_][i_] = 0. i_ = i_+1 clustercontent , clustercontacts = connectivity ( distance_matrix_ , eps ) return ( {'cluster content': clustercontent, 'clusterid-particleid' : clustercontacts, 'is noise':isNoise} ) def reformat_dbscan_results ( results:dict ) -> dict : if True : clusters = {} for icontent in range(len(results['cluster content'])) : content = results[ 'cluster content' ][ icontent ] for c in results [ 'clusterid-particleid' ] : if c[0] == icontent : if results[ 'is noise' ][c[1]] : icontent=-1 if icontent in clusters: clusters[ icontent ] .append( c[1] ) else : clusters[ icontent ] = [ c[1] ] return ( clusters )

true

f719505a712591c9db61d06ce1e597d8da79a187

235

py

Python

orb_simulator/lexer/regex_ast/regex_epsilon_node.py

dmguezjaviersnet/IA-Sim-Comp-Project

8165b9546efc45f98091a3774e2dae4f45942048

[ "MIT" ]

1

2022-01-19T22:49:09.000Z

orb_simulator/lexer/regex_ast/regex_epsilon_node.py

dmguezjaviersnet/IA-Sim-Comp-Project

8165b9546efc45f98091a3774e2dae4f45942048

[ "MIT" ]

15

2021-11-10T14:25:02.000Z

2022-02-12T19:17:11.000Z

orb_simulator/lexer/regex_ast/regex_epsilon_node.py

dmguezjaviersnet/IA-Sim-Comp-Project

8165b9546efc45f98091a3774e2dae4f45942048

[ "MIT" ]

null

from lexer.regex_ast.regex_atomic_node import AtomicNode from automaton import Automaton class EpsilonNode(AtomicNode): def eval(self): return Automaton(number_of_states=1, initial_state=0, finalStates=[0], transitions={})

39.166667

94

0.787234

from lexer.regex_ast.regex_atomic_node import AtomicNode from automaton import Automaton class EpsilonNode(AtomicNode): def eval(self): return Automaton(number_of_states=1, initial_state=0, finalStates=[0], transitions={})

true

f71950d1cafe3ade67ae0b9180b0da8119152a85

4,293

py

Python

experiments/steven/disentanglement/pointmass/disentanglement_rig.py

Asap7772/railrl_evalsawyer

baba8ce634d32a48c7dfe4dc03b123e18e96e0a3

[ "MIT" ]

null

experiments/steven/disentanglement/pointmass/disentanglement_rig.py

Asap7772/railrl_evalsawyer

baba8ce634d32a48c7dfe4dc03b123e18e96e0a3

[ "MIT" ]

null

experiments/steven/disentanglement/pointmass/disentanglement_rig.py

Asap7772/railrl_evalsawyer

baba8ce634d32a48c7dfe4dc03b123e18e96e0a3

[ "MIT" ]

null

import os.path as osp import torch.nn.functional as F import multiworld.envs.mujoco as mwmj import rlkit.misc.hyperparameter as hyp from rlkit.launchers.launcher_util import run_experiment from rlkit.launchers.experiments.disentanglement.launcher import \ disentangled_grill_her_twin_sac_experiment from rlkit.torch.vae.conv_vae import imsize48_default_architecture if __name__ == "__main__": variant = dict( env_id='Point2DEnv-Train-Axis-Eval-Everything-Images-v0', qf_kwargs=dict( hidden_sizes=[400, 300], ), policy_kwargs=dict( hidden_sizes=[400, 300], ), encoder_kwargs=dict( hidden_sizes=[400, 300], hidden_activation=F.tanh, ), twin_sac_trainer_kwargs=dict( reward_scale=1, discount=0.99, target_update_period=1, use_automatic_entropy_tuning=True, ), td3_trainer_kwargs=dict( tau=1e-3, ), max_path_length=100, algo_kwargs=dict( batch_size=256, num_epochs=50, num_eval_steps_per_epoch=1000, num_expl_steps_per_train_loop=1000, num_trains_per_train_loop=1000, min_num_steps_before_training=1000, ), replay_buffer_kwargs=dict( fraction_goals_rollout_goals=0.2, fraction_goals_env_goals=0.5, max_size=int(1e6), ob_keys_to_save=[ 'latent_observation', 'latent_desired_goal', 'latent_achieved_goal', 'state_achieved_goal', 'state_desired_goal', 'state_observation', ], goal_keys=['latent_desired_goal', 'state_desired_goal'], ), observation_key='latent_observation', desired_goal_key='latent_desired_goal', achieved_goal_key='latent_achieved_goal', vae_exploration_goal_sampling_mode='env', vae_evaluation_goal_sampling_mode='env', base_env_exploration_goal_sampling_mode='train', base_env_evaluation_goal_sampling_mode='test', vectorized=True, disentangled_qf_kwargs=dict( ), vae_wrapped_env_kwargs=dict( norm_order=1, reward_params=dict( type='vectorized_latent_distance', norm_order=1, ), ), use_vf_to_compute_policy=True, use_special_q_function=True, latent_dim=2, vae_n_vae_training_kwargs=dict( vae_class='spatialVAE', vae_kwargs=dict( input_channels=3, ), vae_trainer_kwargs=dict( lr=1e-3, beta=0, ), vae_train_epochs=50, num_image_examples=30000, vae_architecture=imsize48_default_architecture, ), # vae_path="logs/02-25-disentangle-images-relu/02-25-disentangle-images-relu_2020_02_25_12_59_17_id000--s4248/vae.pkl", save_video=True, save_video_kwargs=dict( save_video_period=10, imsize=48, ), ) search_space = { 'disentangled_qf_kwargs.encode_state': [True], } sweeper = hyp.DeterministicHyperparameterSweeper( search_space, default_parameters=variant, ) n_seeds = 1 mode = 'local' exp_prefix = '{}'.format( __file__.replace('/', '-').replace('_', '-').split('.')[0] ) n_seeds = 2 mode = 'local' exp_prefix = 'disentangle-extrapolate-vectorized-3' for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()): for _ in range(n_seeds): run_experiment( disentangled_grill_her_twin_sac_experiment, exp_prefix=exp_prefix, mode=mode, variant=variant, use_gpu=True, num_exps_per_instance=3, gcp_kwargs=dict( zone='us-east1-c', gpu_kwargs=dict( gpu_model='nvidia-tesla-k80', num_gpu=1, ) ), time_in_mins=int(2.5*24*60), )

32.278195

127

0.575355

import os.path as osp import torch.nn.functional as F import multiworld.envs.mujoco as mwmj import rlkit.misc.hyperparameter as hyp from rlkit.launchers.launcher_util import run_experiment from rlkit.launchers.experiments.disentanglement.launcher import \ disentangled_grill_her_twin_sac_experiment from rlkit.torch.vae.conv_vae import imsize48_default_architecture if __name__ == "__main__": variant = dict( env_id='Point2DEnv-Train-Axis-Eval-Everything-Images-v0', qf_kwargs=dict( hidden_sizes=[400, 300], ), policy_kwargs=dict( hidden_sizes=[400, 300], ), encoder_kwargs=dict( hidden_sizes=[400, 300], hidden_activation=F.tanh, ), twin_sac_trainer_kwargs=dict( reward_scale=1, discount=0.99, target_update_period=1, use_automatic_entropy_tuning=True, ), td3_trainer_kwargs=dict( tau=1e-3, ), max_path_length=100, algo_kwargs=dict( batch_size=256, num_epochs=50, num_eval_steps_per_epoch=1000, num_expl_steps_per_train_loop=1000, num_trains_per_train_loop=1000, min_num_steps_before_training=1000, ), replay_buffer_kwargs=dict( fraction_goals_rollout_goals=0.2, fraction_goals_env_goals=0.5, max_size=int(1e6), ob_keys_to_save=[ 'latent_observation', 'latent_desired_goal', 'latent_achieved_goal', 'state_achieved_goal', 'state_desired_goal', 'state_observation', ], goal_keys=['latent_desired_goal', 'state_desired_goal'], ), observation_key='latent_observation', desired_goal_key='latent_desired_goal', achieved_goal_key='latent_achieved_goal', vae_exploration_goal_sampling_mode='env', vae_evaluation_goal_sampling_mode='env', base_env_exploration_goal_sampling_mode='train', base_env_evaluation_goal_sampling_mode='test', vectorized=True, disentangled_qf_kwargs=dict( ), vae_wrapped_env_kwargs=dict( norm_order=1, reward_params=dict( type='vectorized_latent_distance', norm_order=1, ), ), use_vf_to_compute_policy=True, use_special_q_function=True, latent_dim=2, vae_n_vae_training_kwargs=dict( vae_class='spatialVAE', vae_kwargs=dict( input_channels=3, ), vae_trainer_kwargs=dict( lr=1e-3, beta=0, ), vae_train_epochs=50, num_image_examples=30000, vae_architecture=imsize48_default_architecture, ), save_video=True, save_video_kwargs=dict( save_video_period=10, imsize=48, ), ) search_space = { 'disentangled_qf_kwargs.encode_state': [True], } sweeper = hyp.DeterministicHyperparameterSweeper( search_space, default_parameters=variant, ) n_seeds = 1 mode = 'local' exp_prefix = '{}'.format( __file__.replace('/', '-').replace('_', '-').split('.')[0] ) n_seeds = 2 mode = 'local' exp_prefix = 'disentangle-extrapolate-vectorized-3' for exp_id, variant in enumerate(sweeper.iterate_hyperparameters()): for _ in range(n_seeds): run_experiment( disentangled_grill_her_twin_sac_experiment, exp_prefix=exp_prefix, mode=mode, variant=variant, use_gpu=True, num_exps_per_instance=3, gcp_kwargs=dict( zone='us-east1-c', gpu_kwargs=dict( gpu_model='nvidia-tesla-k80', num_gpu=1, ) ), time_in_mins=int(2.5*24*60), )

true

f71951497f2af63f6a8b59d46f752d982dea0860

8,150

py

Python

python/paddle/fluid/tests/unittests/ir/inference/test_trt_activation_pass.py

TochkaAI/Paddle

481ee79fc92304f33165f7ed0679f16c36862cea

[ "Apache-2.0" ]

3

2021-06-08T14:24:36.000Z

2021-06-08T14:24:38.000Z

python/paddle/fluid/tests/unittests/ir/inference/test_trt_activation_pass.py

chenyanlei1/Paddle

f249a5f05f0f5832279244d88c8cb4eaaad1fbd4

[ "Apache-2.0" ]

1

2021-03-17T07:53:43.000Z

python/paddle/fluid/tests/unittests/ir/inference/test_trt_activation_pass.py

chenyanlei1/Paddle

f249a5f05f0f5832279244d88c8cb4eaaad1fbd4

[ "Apache-2.0" ]

1

2021-06-17T06:52:01.000Z

# Copyright (c) 2020 PaddlePaddle 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. import os import shutil import unittest import numpy as np from inference_pass_test import InferencePassTest import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.core import PassVersionChecker from paddle.fluid.core import AnalysisConfig class TensorRTSubgraphPassActivationTest(InferencePassTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Float32, False, False) def setUp(self): self.setUpTensorRTParam() with fluid.program_guard(self.main_program, self.startup_program): data = fluid.data( name="data", shape=[-1, 6, 64, 64], dtype="float32") act_out = self.append_act(data) out = fluid.layers.batch_norm(act_out, is_test=True) self.feeds = { "data": np.random.random([1, 6, 64, 64]).astype("float32"), } self.fetch_list = [out] def append_act(self, x): return fluid.layers.relu(x) def test_check_output(self): if core.is_compiled_with_cuda(): use_gpu = True if os.path.exists(self.path + "_opt_cache"): shutil.rmtree(self.path + "_opt_cache") if self.trt_parameters.precision == AnalysisConfig.Precision.Float32: self.check_output_with_option(use_gpu) else: self.check_output_with_option(use_gpu, 1e-3) self.assertTrue( PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')) class TensorRTSubgraphPassLeakyReluTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.leaky_relu(x) class TensorRTSubgraphPassRelu6Test(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.relu6(x) class TensorRTSubgraphPassSoftMaxTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.softmax(x) class TensorRTSubgraphPassSigmoidTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.sigmoid(x) class TensorRTSubgraphPassHardSwishTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.hard_swish(x) class TensorRTSubgraphPassHardSigmoidTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.hard_sigmoid(x) class TensorRTSubgraphPassHardSwishPluginTest( TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.hard_swish(x, threshold=4.0, scale=8.0) class TensorRTSubgraphPassClipTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.clip(x, 0, 1) class TensorRTSubgraphPassTanhTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.tanh(x) class TensorRTSubgraphPassSwishTest(TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Float32, True, False) def append_act(self, x): return fluid.layers.swish(x) class TensorRTSubgraphPassSwishFp16SerializeTest( TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Half, True, False) def append_act(self, x): return fluid.layers.swish(x) class TensorRTSubgraphPassDynamicSwishFp16SerializeTest( TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Half, True, False) self.dynamic_shape_params = TensorRTSubgraphPassActivationTest.DynamicShapeParam( { 'data': [1, 6, 8, 8] }, {'data': [1, 6, 512, 512]}, {'data': [1, 6, 256, 256]}, False) def append_act(self, x): return fluid.layers.swish(x) class TensorRTSubgraphPassPreluAllTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.prelu(x, mode='all') class TensorRTSubgraphPassPreluChannelTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.prelu(x, mode='channel') class TensorRTSubgraphPassPreluElementTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.prelu(x, mode='element') class TensorRTSubgraphPassGeluTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.gelu(x) class TensorRTSubgraphPassGeluDynamicTest(TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Float32, False, False) self.dynamic_shape_params = TensorRTSubgraphPassActivationTest.DynamicShapeParam( { 'data': [1, 6, 8, 8] }, {'data': [1, 6, 512, 512]}, {'data': [1, 6, 256, 256]}, False) def append_act(self, x): return fluid.layers.gelu(x) class TensorRTSubgraphPassGeluFp16Test(TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Half, False, False) def append_act(self, x): return fluid.layers.gelu(x) class TensorRTSubgraphPassGeluFp16SerializeTest( TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Half, True, False) def append_act(self, x): return fluid.layers.gelu(x) class TensorRTSubgraphPassGeluFp16DynamicTest( TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Half, False, False) self.dynamic_shape_params = TensorRTSubgraphPassActivationTest.DynamicShapeParam( { 'data': [1, 6, 8, 8] }, {'data': [1, 6, 512, 512]}, {'data': [1, 6, 256, 256]}, False) def append_act(self, x): return fluid.layers.gelu(x) class TensorRTSubgraphPassGeluFp16DynamicSerializeTest( TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Half, True, False) self.dynamic_shape_params = TensorRTSubgraphPassActivationTest.DynamicShapeParam( { 'data': [1, 6, 8, 8] }, {'data': [1, 6, 512, 512]}, {'data': [1, 6, 256, 256]}, False) def append_act(self, x): return fluid.layers.gelu(x) if __name__ == "__main__": unittest.main()

35.58952

89

0.699755

import os import shutil import unittest import numpy as np from inference_pass_test import InferencePassTest import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.core import PassVersionChecker from paddle.fluid.core import AnalysisConfig class TensorRTSubgraphPassActivationTest(InferencePassTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Float32, False, False) def setUp(self): self.setUpTensorRTParam() with fluid.program_guard(self.main_program, self.startup_program): data = fluid.data( name="data", shape=[-1, 6, 64, 64], dtype="float32") act_out = self.append_act(data) out = fluid.layers.batch_norm(act_out, is_test=True) self.feeds = { "data": np.random.random([1, 6, 64, 64]).astype("float32"), } self.fetch_list = [out] def append_act(self, x): return fluid.layers.relu(x) def test_check_output(self): if core.is_compiled_with_cuda(): use_gpu = True if os.path.exists(self.path + "_opt_cache"): shutil.rmtree(self.path + "_opt_cache") if self.trt_parameters.precision == AnalysisConfig.Precision.Float32: self.check_output_with_option(use_gpu) else: self.check_output_with_option(use_gpu, 1e-3) self.assertTrue( PassVersionChecker.IsCompatible('tensorrt_subgraph_pass')) class TensorRTSubgraphPassLeakyReluTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.leaky_relu(x) class TensorRTSubgraphPassRelu6Test(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.relu6(x) class TensorRTSubgraphPassSoftMaxTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.softmax(x) class TensorRTSubgraphPassSigmoidTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.sigmoid(x) class TensorRTSubgraphPassHardSwishTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.hard_swish(x) class TensorRTSubgraphPassHardSigmoidTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.hard_sigmoid(x) class TensorRTSubgraphPassHardSwishPluginTest( TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.hard_swish(x, threshold=4.0, scale=8.0) class TensorRTSubgraphPassClipTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.clip(x, 0, 1) class TensorRTSubgraphPassTanhTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.tanh(x) class TensorRTSubgraphPassSwishTest(TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Float32, True, False) def append_act(self, x): return fluid.layers.swish(x) class TensorRTSubgraphPassSwishFp16SerializeTest( TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Half, True, False) def append_act(self, x): return fluid.layers.swish(x) class TensorRTSubgraphPassDynamicSwishFp16SerializeTest( TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Half, True, False) self.dynamic_shape_params = TensorRTSubgraphPassActivationTest.DynamicShapeParam( { 'data': [1, 6, 8, 8] }, {'data': [1, 6, 512, 512]}, {'data': [1, 6, 256, 256]}, False) def append_act(self, x): return fluid.layers.swish(x) class TensorRTSubgraphPassPreluAllTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.prelu(x, mode='all') class TensorRTSubgraphPassPreluChannelTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.prelu(x, mode='channel') class TensorRTSubgraphPassPreluElementTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.prelu(x, mode='element') class TensorRTSubgraphPassGeluTest(TensorRTSubgraphPassActivationTest): def append_act(self, x): return fluid.layers.gelu(x) class TensorRTSubgraphPassGeluDynamicTest(TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Float32, False, False) self.dynamic_shape_params = TensorRTSubgraphPassActivationTest.DynamicShapeParam( { 'data': [1, 6, 8, 8] }, {'data': [1, 6, 512, 512]}, {'data': [1, 6, 256, 256]}, False) def append_act(self, x): return fluid.layers.gelu(x) class TensorRTSubgraphPassGeluFp16Test(TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Half, False, False) def append_act(self, x): return fluid.layers.gelu(x) class TensorRTSubgraphPassGeluFp16SerializeTest( TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Half, True, False) def append_act(self, x): return fluid.layers.gelu(x) class TensorRTSubgraphPassGeluFp16DynamicTest( TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Half, False, False) self.dynamic_shape_params = TensorRTSubgraphPassActivationTest.DynamicShapeParam( { 'data': [1, 6, 8, 8] }, {'data': [1, 6, 512, 512]}, {'data': [1, 6, 256, 256]}, False) def append_act(self, x): return fluid.layers.gelu(x) class TensorRTSubgraphPassGeluFp16DynamicSerializeTest( TensorRTSubgraphPassActivationTest): def setUpTensorRTParam(self): self.enable_trt = True self.trt_parameters = TensorRTSubgraphPassActivationTest.TensorRTParam( 1 << 30, 32, 0, AnalysisConfig.Precision.Half, True, False) self.dynamic_shape_params = TensorRTSubgraphPassActivationTest.DynamicShapeParam( { 'data': [1, 6, 8, 8] }, {'data': [1, 6, 512, 512]}, {'data': [1, 6, 256, 256]}, False) def append_act(self, x): return fluid.layers.gelu(x) if __name__ == "__main__": unittest.main()

true

f71951e82d394762ce0671379de3793e5bb9983b

1,903

py

Python

UnitTests/test_battery_sensor_features_extractor.py

naveenkambham/big_five_personality_machine_learning

a4d673e7e72287f2448b6a7b2729e5231b4f7ab2

[ "MIT" ]

8

2021-02-22T22:12:32.000Z

2022-03-25T15:18:28.000Z

UnitTests/test_battery_sensor_features_extractor.py

naveenkambham/big_five_personality_machine_learning

a4d673e7e72287f2448b6a7b2729e5231b4f7ab2

[ "MIT" ]

1

2020-12-29T18:59:39.000Z

2021-01-13T17:41:25.000Z

UnitTests/test_battery_sensor_features_extractor.py

naveenkambham/big_five_personality_machine_learning

a4d673e7e72287f2448b6a7b2729e5231b4f7ab2

[ "MIT" ]

4

2021-04-08T11:36:33.000Z

2022-02-18T14:12:47.000Z

""" Developer : Naveen Kambham Description: Unit testing for battery sensor feature extractor code. Majority of the data extraction code has to be tested visually by looking at the plots distributions. """ #Importing the required libraries. import unittest import numpy as np from FeatureExtraction import battery_sensor_features_extractor class BatterySensorTestCase(unittest.TestCase): """ Tests for battery_sensor_features_extractor.py """ def test_TakeMostProbableTimeInStudy(self): """ to test the most probable time functionality :return: """ #case 1 multiple values in each day result= battery_sensor_features_extractor.TakeMostProbableTimeInStudy([1,1,1,1,2,2,3,3,3,3,3,3,3,3],[1,2,0]) self.assertEqual(result,3) # case 2 only one value in a day result = battery_sensor_features_extractor.TakeMostProbableTimeInStudy( [1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3], [1]) self.assertEqual(result, 4) # case 3 only one value in a day and it is not exists in the study times so far seen result = battery_sensor_features_extractor.TakeMostProbableTimeInStudy( [1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3], [0]) self.assertEqual(result, 0) def test_extract(self): """ testing the feature extractor code :return: """ #extracting the features df_battery=battery_sensor_features_extractor.extract(r"/home/naveen/Data/Shed10/Filtered/battery_events.csv") # charging should atleast be greater than 0 self.assertTrue(np.min(df_battery['Battery_Charging_Duration'] >=0)) self.assertTrue(np.min(df_battery['CharginTimeDaily'] >=0) and np.max(df_battery['CharginTimeDaily'] <=24)) if __name__ == '__main__': unittest.main()

38.836735

171

0.656332

import unittest import numpy as np from FeatureExtraction import battery_sensor_features_extractor class BatterySensorTestCase(unittest.TestCase): def test_TakeMostProbableTimeInStudy(self): result= battery_sensor_features_extractor.TakeMostProbableTimeInStudy([1,1,1,1,2,2,3,3,3,3,3,3,3,3],[1,2,0]) self.assertEqual(result,3) result = battery_sensor_features_extractor.TakeMostProbableTimeInStudy( [1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3], [1]) self.assertEqual(result, 4) result = battery_sensor_features_extractor.TakeMostProbableTimeInStudy( [1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3], [0]) self.assertEqual(result, 0) def test_extract(self): df_battery=battery_sensor_features_extractor.extract(r"/home/naveen/Data/Shed10/Filtered/battery_events.csv") self.assertTrue(np.min(df_battery['Battery_Charging_Duration'] >=0)) self.assertTrue(np.min(df_battery['CharginTimeDaily'] >=0) and np.max(df_battery['CharginTimeDaily'] <=24)) if __name__ == '__main__': unittest.main()

true

f7195383ef1c320e1cc9ce4ac28c29823e011af1

2,059

py

Python

graph.py

jacgonzalez/Graph_Art

ea724c3e659aca63107ae9a59cb646f8aba821c6

[ "MIT" ]

null

graph.py

jacgonzalez/Graph_Art

ea724c3e659aca63107ae9a59cb646f8aba821c6

[ "MIT" ]

null

graph.py

jacgonzalez/Graph_Art

ea724c3e659aca63107ae9a59cb646f8aba821c6

[ "MIT" ]

null

import math points = [[4,1], [4,2], [4,3], [3,1], [3,2], [3,3], [2,1], [2,2], [2,3], [1,1], [1,2], [1,3]] def distance(point1, point2): return math.sqrt(((point2[0] - point1[0])**2) + ((point2[1] - point1[1])**2)) def k_neighbors(i, points, k): """ i: index of a point points: list of points k: number of neighbors """ distancias_point_i = [] point_i = points[i] size = len(points) j = 0 while j < size: point_j = points[j] distancias_point_i.append([distance(point_i, point_j), j]) j += 1 distancias_point_i.sort() #print(distancias_point_i) result = [] for m in range(len(distancias_point_i)): result.append(distancias_point_i[m][1]) return result[1:k+1] #print(distance([1,2], [3,4])) print(k_neighbors(3, points, 5)) class Graph(): def __init__(self): self.V = [] self.E = [] def add_vertex(self, info): self.V.append(info) self.E.append([]) def add_edge(self, start, finish): self.E[start].append(finish) def get_vertex(self, i): return self.V[i] def get_neighbors(self, info, k): return k_neighbors(info, points, k) def print(self): print(self.V) print(self.E) """ graph1 = Graph() graph1.add_vertex(0) graph1.add_vertex(1) graph1.add_vertex(2) graph1.add_vertex(3) graph1.add_vertex(4) graph1.add_vertex(5) graph1.add_vertex(6) graph1.add_vertex(7) graph1.add_edge(1, 2) graph1.add_edge(2, 4) graph1.add_edge(3, 1) graph1.add_edge(3, 4) graph1.add_edge(4, 5) graph1.add_edge(4, 7) graph1.add_edge(6, 7) graph1.add_edge(5, 6) graph1.print() """ def create_nn_graph(points, k): nn_graph = Graph() j = 0 size = len(points) while j < size: nn_graph.add_vertex(points[j]) neighbors = k_neighbors(j, points, k) for n in range(len(neighbors)): nn_graph.add_edge(j, neighbors[n]) j += 1 return nn_graph graph = create_nn_graph(points, 3) graph.print()

21.447917

81

0.592521

import math points = [[4,1], [4,2], [4,3], [3,1], [3,2], [3,3], [2,1], [2,2], [2,3], [1,1], [1,2], [1,3]] def distance(point1, point2): return math.sqrt(((point2[0] - point1[0])**2) + ((point2[1] - point1[1])**2)) def k_neighbors(i, points, k): distancias_point_i = [] point_i = points[i] size = len(points) j = 0 while j < size: point_j = points[j] distancias_point_i.append([distance(point_i, point_j), j]) j += 1 distancias_point_i.sort() result = [] for m in range(len(distancias_point_i)): result.append(distancias_point_i[m][1]) return result[1:k+1] print(k_neighbors(3, points, 5)) class Graph(): def __init__(self): self.V = [] self.E = [] def add_vertex(self, info): self.V.append(info) self.E.append([]) def add_edge(self, start, finish): self.E[start].append(finish) def get_vertex(self, i): return self.V[i] def get_neighbors(self, info, k): return k_neighbors(info, points, k) def print(self): print(self.V) print(self.E) def create_nn_graph(points, k): nn_graph = Graph() j = 0 size = len(points) while j < size: nn_graph.add_vertex(points[j]) neighbors = k_neighbors(j, points, k) for n in range(len(neighbors)): nn_graph.add_edge(j, neighbors[n]) j += 1 return nn_graph graph = create_nn_graph(points, 3) graph.print()

true

f71953db54832094263b29e4d88077938efd3aed

5,989

py

Python

test/vanilla/Expected/AcceptanceTests/BodyComplex/bodycomplex/aio/operations/_inheritance_operations.py

amrElroumy/autorest.python

b37af1779f6d53b4fa0d92da62151f8133006f98

[ "MIT" ]

null

test/vanilla/Expected/AcceptanceTests/BodyComplex/bodycomplex/aio/operations/_inheritance_operations.py

amrElroumy/autorest.python

b37af1779f6d53b4fa0d92da62151f8133006f98

[ "MIT" ]

null

test/vanilla/Expected/AcceptanceTests/BodyComplex/bodycomplex/aio/operations/_inheritance_operations.py

amrElroumy/autorest.python

b37af1779f6d53b4fa0d92da62151f8133006f98

[ "MIT" ]

null

# coding=utf-8 # -------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # Code generated by Microsoft (R) AutoRest Code Generator. # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------- from typing import Any, Callable, Dict, Generic, Optional, TypeVar import warnings from azure.core.exceptions import ( ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error, ) from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest from azure.core.tracing.decorator_async import distributed_trace_async from ... import models as _models T = TypeVar("T") ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class InheritanceOperations: """InheritanceOperations async operations. You should not instantiate this class directly. Instead, you should create a Client instance that instantiates it for you and attaches it as an attribute. :ivar models: Alias to model classes used in this operation group. :type models: ~bodycomplex.models :param client: Client for service requests. :param config: Configuration of service client. :param serializer: An object model serializer. :param deserializer: An object model deserializer. """ models = _models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config @distributed_trace_async async def get_valid(self, **kwargs) -> "_models.Siamese": """Get complex types that extend others. :keyword callable cls: A custom type or function that will be passed the direct response :return: Siamese, or the result of cls(response) :rtype: ~bodycomplex.models.Siamese :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType["_models.Siamese"] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) accept = "application/json" # Construct URL url = self.get_valid.metadata["url"] # type: ignore # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters["Accept"] = self._serialize.header("accept", accept, "str") request = self._client.get(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(_models.Error, response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize("Siamese", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_valid.metadata = {"url": "/complex/inheritance/valid"} # type: ignore @distributed_trace_async async def put_valid(self, complex_body: "_models.Siamese", **kwargs) -> None: """Put complex types that extend others. :param complex_body: Please put a siamese with id=2, name="Siameee", color=green, breed=persion, which hates 2 dogs, the 1st one named "Potato" with id=1 and food="tomato", and the 2nd one named "Tomato" with id=-1 and food="french fries". :type complex_body: ~bodycomplex.models.Siamese :keyword callable cls: A custom type or function that will be passed the direct response :return: None, or the result of cls(response) :rtype: None :raises: ~azure.core.exceptions.HttpResponseError """ cls = kwargs.pop("cls", None) # type: ClsType[None] error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" # Construct URL url = self.put_valid.metadata["url"] # type: ignore # Construct parameters query_parameters = {} # type: Dict[str, Any] # Construct headers header_parameters = {} # type: Dict[str, Any] header_parameters["Content-Type"] = self._serialize.header("content_type", content_type, "str") header_parameters["Accept"] = self._serialize.header("accept", accept, "str") body_content_kwargs = {} # type: Dict[str, Any] body_content = self._serialize.body(complex_body, "Siamese") body_content_kwargs["content"] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(_models.Error, response) raise HttpResponseError(response=response, model=error) if cls: return cls(pipeline_response, None, {}) put_valid.metadata = {"url": "/complex/inheritance/valid"} # type: ignore

43.398551

106

0.674236

from typing import Any, Callable, Dict, Generic, Optional, TypeVar import warnings from azure.core.exceptions import ( ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error, ) from azure.core.pipeline import PipelineResponse from azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest from azure.core.tracing.decorator_async import distributed_trace_async from ... import models as _models T = TypeVar("T") ClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]] class InheritanceOperations: models = _models def __init__(self, client, config, serializer, deserializer) -> None: self._client = client self._serialize = serializer self._deserialize = deserializer self._config = config @distributed_trace_async async def get_valid(self, **kwargs) -> "_models.Siamese": cls = kwargs.pop("cls", None) error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) accept = "application/json" url = self.get_valid.metadata["url"] query_parameters = {} header_parameters = {} header_parameters["Accept"] = self._serialize.header("accept", accept, "str") request = self._client.get(url, query_parameters, header_parameters) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(_models.Error, response) raise HttpResponseError(response=response, model=error) deserialized = self._deserialize("Siamese", pipeline_response) if cls: return cls(pipeline_response, deserialized, {}) return deserialized get_valid.metadata = {"url": "/complex/inheritance/valid"} @distributed_trace_async async def put_valid(self, complex_body: "_models.Siamese", **kwargs) -> None: cls = kwargs.pop("cls", None) error_map = {401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError} error_map.update(kwargs.pop("error_map", {})) content_type = kwargs.pop("content_type", "application/json") accept = "application/json" url = self.put_valid.metadata["url"] query_parameters = {} header_parameters = {} header_parameters["Content-Type"] = self._serialize.header("content_type", content_type, "str") header_parameters["Accept"] = self._serialize.header("accept", accept, "str") body_content_kwargs = {} body_content = self._serialize.body(complex_body, "Siamese") body_content_kwargs["content"] = body_content request = self._client.put(url, query_parameters, header_parameters, **body_content_kwargs) pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs) response = pipeline_response.http_response if response.status_code not in [200]: map_error(status_code=response.status_code, response=response, error_map=error_map) error = self._deserialize(_models.Error, response) raise HttpResponseError(response=response, model=error) if cls: return cls(pipeline_response, None, {}) put_valid.metadata = {"url": "/complex/inheritance/valid"}

true

f7195404dfad6b9a9dd9872526043575b12c080b

3,669

py

Python

trafficModel.py

Mobility-simulation/IDM_Program

ac33674373d8560b440562acb5acf82ae5bf4fc6

[ "Unlicense" ]

null

trafficModel.py

Mobility-simulation/IDM_Program

ac33674373d8560b440562acb5acf82ae5bf4fc6

[ "Unlicense" ]

null

trafficModel.py

Mobility-simulation/IDM_Program

ac33674373d8560b440562acb5acf82ae5bf4fc6

[ "Unlicense" ]

null

import tkinter as tk from system.operation import Operation from model.world import World import settings root = tk.Tk() img = tk.PhotoImage(file='png/sports-car.png') root.tk.call('wm', 'iconphoto', root._w, img) menu = tk.Menu(root) root.config(menu=menu) root.protocol("WM_DELETE_WINDOW", lambda: op.terminate(root)) toolbar = tk.Frame(root) function = tk.Frame(toolbar) info = tk.Frame(toolbar) world = World() world.load() buttonGroup = tk.Frame(function) sliderGroup = tk.Frame(function) play = tk.Button(buttonGroup, text="Action") playPNG = tk.PhotoImage(file="png/play-button.png") pausePNG = tk.PhotoImage(file="png/pause.png") play.config(compound=tk.LEFT, image=playPNG, width="70", height="24", bg="#FFFFFF", command=lambda: op.runModel()) play.pack(side=tk.LEFT, padx=2, pady=2) refresh = tk.Button(buttonGroup, text="Reload") refreshPNG = tk.PhotoImage(file="png/refresh-button.png") refresh.config(compound=tk.LEFT, image=refreshPNG, width="70", height="24", bg="#FFFFFF", command=lambda: op.refresh()) refresh.pack(side=tk.LEFT, padx=2, pady=2) debug = tk.Button(buttonGroup, text="Debug") debugPNG = tk.PhotoImage(file="png/debug.png") debug.config(compound=tk.LEFT, image=debugPNG, width="70", height="24", bg="#FFFFFF", command=lambda: op.debugSwitch()) debug.pack(side=tk.LEFT, padx=2, pady=2) gridMap = tk.Button(buttonGroup, text="New Map") mapPNG = tk.PhotoImage(file="png/map.png") gridMap.config(compound=tk.LEFT, image=mapPNG, width="70", height="24", bg="#FFFFFF", command=lambda: op.generateMap()) gridMap.pack(side=tk.LEFT, padx=2, pady=2) timeSliderName = tk.Entry(sliderGroup, width='10') timeSliderName.grid(row=0, column=0) timeSliderName.insert(0, "Time scale") timeSlider = tk.Scale(sliderGroup, from_=settings.setDict["timeMin"], to=settings.setDict["timeMax"], orient=tk.HORIZONTAL, troughcolor="#90C3D4", bg="#FFFFFF") timeSlider.grid(row=1, column=0) carSliderName = tk.Entry(sliderGroup, width='10') carSliderName.grid(row=0, column=1) carSliderName.insert(0, "Cars Number") carSlider = tk.Scale(sliderGroup, from_=settings.setDict["carMin"], to=settings.setDict["carMax"], orient=tk.HORIZONTAL, troughcolor="#90C3D4", bg="#FFFFFF") carSlider.grid(row=1, column=1) systemName = tk.Entry(info, width='10') systemName.grid(row=0, column=0) systemName.insert(0, "System") systemText = tk.Text(info, height=2, width=40) systemText.grid(row=1, column=0) roadName = tk.Entry(info, width='10') roadName.grid(row=0, column=1) roadName.insert(0, "Road info") roadText = tk.Text(info, height=2, width=45) roadText.grid(row=1, column=1) carName = tk.Entry(info, width='10') carName.grid(row=0, column=2) carName.insert(0, "Car info") carText = tk.Text(info, height=2, width=40) carText.grid(row=1, column=2) toolDict = dict() toolDict['playBtn'] = play toolDict['playPNG'] = playPNG toolDict['pausePNG'] = pausePNG toolDict['carText'] = carText toolDict['roadText'] = roadText toolDict['systemText'] = systemText toolDict['carSlider'] = carSlider toolDict['timeSlider'] = timeSlider toolDict['debugBtn'] = debug screen = tk.Frame(root) op = Operation(screen, toolDict, world) op.pack(fill="both", expand=True) buttonGroup.config(bg="#90C3D4") buttonGroup.pack(side=tk.LEFT) sliderGroup.config(bg="#90C3D4") sliderGroup.pack(side=tk.LEFT) function.config(bg="#90C3D4") function.pack(side=tk.LEFT) info.config(bg="#90C3D4") info.pack(side=tk.RIGHT) toolbar.config(bg="#90C3D4") toolbar.pack(side=tk.TOP, fill=tk.X) screen.pack(side=tk.BOTTOM, fill=tk.X) root.mainloop()

29.829268

75

0.706187

import tkinter as tk from system.operation import Operation from model.world import World import settings root = tk.Tk() img = tk.PhotoImage(file='png/sports-car.png') root.tk.call('wm', 'iconphoto', root._w, img) menu = tk.Menu(root) root.config(menu=menu) root.protocol("WM_DELETE_WINDOW", lambda: op.terminate(root)) toolbar = tk.Frame(root) function = tk.Frame(toolbar) info = tk.Frame(toolbar) world = World() world.load() buttonGroup = tk.Frame(function) sliderGroup = tk.Frame(function) play = tk.Button(buttonGroup, text="Action") playPNG = tk.PhotoImage(file="png/play-button.png") pausePNG = tk.PhotoImage(file="png/pause.png") play.config(compound=tk.LEFT, image=playPNG, width="70", height="24", bg="#FFFFFF", command=lambda: op.runModel()) play.pack(side=tk.LEFT, padx=2, pady=2) refresh = tk.Button(buttonGroup, text="Reload") refreshPNG = tk.PhotoImage(file="png/refresh-button.png") refresh.config(compound=tk.LEFT, image=refreshPNG, width="70", height="24", bg="#FFFFFF", command=lambda: op.refresh()) refresh.pack(side=tk.LEFT, padx=2, pady=2) debug = tk.Button(buttonGroup, text="Debug") debugPNG = tk.PhotoImage(file="png/debug.png") debug.config(compound=tk.LEFT, image=debugPNG, width="70", height="24", bg="#FFFFFF", command=lambda: op.debugSwitch()) debug.pack(side=tk.LEFT, padx=2, pady=2) gridMap = tk.Button(buttonGroup, text="New Map") mapPNG = tk.PhotoImage(file="png/map.png") gridMap.config(compound=tk.LEFT, image=mapPNG, width="70", height="24", bg="#FFFFFF", command=lambda: op.generateMap()) gridMap.pack(side=tk.LEFT, padx=2, pady=2) timeSliderName = tk.Entry(sliderGroup, width='10') timeSliderName.grid(row=0, column=0) timeSliderName.insert(0, "Time scale") timeSlider = tk.Scale(sliderGroup, from_=settings.setDict["timeMin"], to=settings.setDict["timeMax"], orient=tk.HORIZONTAL, troughcolor="#90C3D4", bg="#FFFFFF") timeSlider.grid(row=1, column=0) carSliderName = tk.Entry(sliderGroup, width='10') carSliderName.grid(row=0, column=1) carSliderName.insert(0, "Cars Number") carSlider = tk.Scale(sliderGroup, from_=settings.setDict["carMin"], to=settings.setDict["carMax"], orient=tk.HORIZONTAL, troughcolor="#90C3D4", bg="#FFFFFF") carSlider.grid(row=1, column=1) systemName = tk.Entry(info, width='10') systemName.grid(row=0, column=0) systemName.insert(0, "System") systemText = tk.Text(info, height=2, width=40) systemText.grid(row=1, column=0) roadName = tk.Entry(info, width='10') roadName.grid(row=0, column=1) roadName.insert(0, "Road info") roadText = tk.Text(info, height=2, width=45) roadText.grid(row=1, column=1) carName = tk.Entry(info, width='10') carName.grid(row=0, column=2) carName.insert(0, "Car info") carText = tk.Text(info, height=2, width=40) carText.grid(row=1, column=2) toolDict = dict() toolDict['playBtn'] = play toolDict['playPNG'] = playPNG toolDict['pausePNG'] = pausePNG toolDict['carText'] = carText toolDict['roadText'] = roadText toolDict['systemText'] = systemText toolDict['carSlider'] = carSlider toolDict['timeSlider'] = timeSlider toolDict['debugBtn'] = debug screen = tk.Frame(root) op = Operation(screen, toolDict, world) op.pack(fill="both", expand=True) buttonGroup.config(bg="#90C3D4") buttonGroup.pack(side=tk.LEFT) sliderGroup.config(bg="#90C3D4") sliderGroup.pack(side=tk.LEFT) function.config(bg="#90C3D4") function.pack(side=tk.LEFT) info.config(bg="#90C3D4") info.pack(side=tk.RIGHT) toolbar.config(bg="#90C3D4") toolbar.pack(side=tk.TOP, fill=tk.X) screen.pack(side=tk.BOTTOM, fill=tk.X) root.mainloop()

true

f719543be7e7a689ebcb0b8ad3fa69e2a94998d6

3,217

py

Python

setup.py

groupserver/gs.profile.status.send

d33c7ab535565d185a2ef95bf00c92b9ffeb8af7

[ "ZPL-2.1" ]

null

setup.py

groupserver/gs.profile.status.send

d33c7ab535565d185a2ef95bf00c92b9ffeb8af7

[ "ZPL-2.1" ]

null

setup.py

groupserver/gs.profile.status.send

d33c7ab535565d185a2ef95bf00c92b9ffeb8af7

[ "ZPL-2.1" ]

null

# -*- coding: utf-8 -*- ############################################################################ # # Copyright © 2012, 2013, 2014, 2015 OnlineGroups.net and Contributors. # All Rights Reserved. # # This software is subject to the provisions of the Zope Public License, # Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution. # THIS SOFTWARE IS PROVIDED "AS IS" AND ANY AND ALL EXPRESS OR IMPLIED # WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED # WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS # FOR A PARTICULAR PURPOSE. # ############################################################################ import codecs import os import sys from setuptools import setup, find_packages from version import get_version version = get_version() # The argparse library was added to core in Python 2.7 core = ['setuptools', 'blessings', 'gs.config', # Note: without zope-support 'gs.form', ] if sys.version_info > (2, 6): requires = core else: requires = core + ['argparse'] with codecs.open('README.rst', encoding='utf-8') as f: long_description = f.read() with codecs.open(os.path.join("docs", "HISTORY.rst"), encoding='utf-8') as f: long_description += '\n' + f.read() setup( name='gs.profile.status.send', version=version, description="Send the profile-status notifications out", long_description=long_description, classifiers=[ 'Development Status :: 5 - Production/Stable', 'Environment :: Console', "Intended Audience :: Developers", 'License :: OSI Approved :: Zope Public License', "Natural Language :: English", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", 'Topic :: Communications :: Email', 'Topic :: Communications :: Email :: Mailing List Servers', 'Topic :: Communications :: Email :: Mail Transport Agents', "Topic :: Software Development :: Libraries :: Python Modules", ], keywords='groupserver, profile, notification', author='Michael JasonSmith', author_email='mpj17@onlinegroups.net', url='https://github.com/groupserver/gs.profile.status.send/', license='ZPL 2.1', packages=find_packages(exclude=['ez_setup']), namespace_packages=['gs', 'gs.profile', 'gs.profile.status', ], include_package_data=True, zip_safe=False, install_requires=requires, extras_require={'docs': ['Sphinx'], }, entry_points={ 'console_scripts': [ 'sendprofile = gs.profile.status.send.script:main', ], # --=mpj17=-- Entry points are the work of the devil. Some time # you, me and Mr Soldering Iron are going to have a little chat # about how to do things better. }, )

38.297619

76

0.618278

true

f71954b599b64b8d33b4eb0854424d9b156c78cd

64,577

py

Python

test/integration/component/test_redundant_router_network_rules.py

lafferty/cshv3

ee0ff7ac240bd24e19db6bd3fb9869dd087442ba

[ "Apache-2.0" ]

2

2015-05-19T05:04:30.000Z

2016-09-07T00:33:17.000Z

test/integration/component/test_redundant_router_network_rules.py

lafferty/cshv3

ee0ff7ac240bd24e19db6bd3fb9869dd087442ba

[ "Apache-2.0" ]

null

test/integration/component/test_redundant_router_network_rules.py

lafferty/cshv3

ee0ff7ac240bd24e19db6bd3fb9869dd087442ba

[ "Apache-2.0" ]

2

2017-07-07T14:49:03.000Z

2018-07-31T06:38:42.000Z

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from nose.plugins.attrib import attr from marvin.integration.lib.base import * from marvin.integration.lib.utils import * from marvin.integration.lib.common import * #Import Local Modules from marvin.cloudstackTestCase import cloudstackTestCase from marvin.cloudstackAPI import * class Services: """Test Services for customer defects """ def __init__(self): self.services = { "account": { "email": "test@test.com", "firstname": "Test", "lastname": "User", "username": "test", # Random characters are appended for unique # username "password": "password", }, "service_offering": { "name": "Tiny Instance", "displaytext": "Tiny Instance", "cpunumber": 1, "cpuspeed": 100, "memory": 128, }, "disk_offering": { "displaytext": "Small", "name": "Small", "disksize": 1 }, "virtual_machine": { "displayname": "Test VM", "username": "root", "password": "password", "ssh_port": 22, "hypervisor": 'XenServer', "privateport": 22, "publicport": 22, "protocol": 'TCP', }, "static_nat": { "startport": 22, "endport": 22, "protocol": "TCP" }, "network_offering": { "name": 'Network offering-RVR services', "displaytext": 'Network off-RVR services', "guestiptype": 'Isolated', "supportedservices": 'Vpn,Dhcp,Dns,SourceNat,PortForwarding,Firewall,Lb,UserData,StaticNat', "traffictype": 'GUEST', "availability": 'Optional', "serviceProviderList": { "Vpn": 'VirtualRouter', "Dhcp": 'VirtualRouter', "Dns": 'VirtualRouter', "SourceNat": 'VirtualRouter', "PortForwarding": 'VirtualRouter', "Firewall": 'VirtualRouter', "Lb": 'VirtualRouter', "UserData": 'VirtualRouter', "StaticNat": 'VirtualRouter', }, "serviceCapabilityList": { "SourceNat": { "SupportedSourceNatTypes": "peraccount", "RedundantRouter": "true", }, "lb": { "SupportedLbIsolation": "dedicated" }, }, }, "host": { "username": "root", "password": "password", "publicport": 22, }, "network": { "name": "Test Network", "displaytext": "Test Network", }, "lbrule": { "name": "SSH", "alg": "roundrobin", # Algorithm used for load balancing "privateport": 22, "publicport": 22, "openfirewall": True, }, "natrule": { "privateport": 22, "publicport": 22, "protocol": "TCP" }, "natrule_221": { "privateport": 22, "publicport": 221, "protocol": "TCP" }, "fw_rule": { "startport": 1, "endport": 6000, "cidr": '55.55.0.0/11', # Any network (For creating FW rule) "protocol": 'TCP', }, "ostype": 'CentOS 5.3 (64-bit)', "sleep": 60, } class TestRedundantRouterRulesLifeCycle(cloudstackTestCase): @classmethod def setUpClass(cls): cls.api_client = super( TestRedundantRouterRulesLifeCycle, cls ).getClsTestClient().getApiClient() cls.services = Services().services # Get Zone, Domain and templates cls.domain = get_domain(cls.api_client, cls.services) cls.zone = get_zone(cls.api_client, cls.services) cls.template = get_template( cls.api_client, cls.zone.id, cls.services["ostype"] ) cls.services["virtual_machine"]["zoneid"] = cls.zone.id cls.services["virtual_machine"]["template"] = cls.template.id cls.service_offering = ServiceOffering.create( cls.api_client, cls.services["service_offering"] ) cls.network_offering = NetworkOffering.create( cls.api_client, cls.services["network_offering"], conservemode=True ) # Enable Network offering cls.network_offering.update(cls.api_client, state='Enabled') cls._cleanup = [ cls.service_offering, cls.network_offering, ] return @classmethod def tearDownClass(cls): try: #Cleanup resources used cleanup_resources(cls.api_client, cls._cleanup) except Exception as e: raise Exception("Warning: Exception during cleanup : %s" % e) return def setUp(self): self.apiclient = self.testClient.getApiClient() self.dbclient = self.testClient.getDbConnection() self.account = Account.create( self.apiclient, self.services["account"], admin=True, domainid=self.domain.id ) self.cleanup = [] self.cleanup.insert(0, self.account) return def tearDown(self): try: cleanup_resources(self.apiclient, self.cleanup) except Exception as e: self.debug("Warning: Exception during cleanup : %s" % e) #raise Exception("Warning: Exception during cleanup : %s" % e) return @attr(tags=["advanced", "advancedns", "ssh"]) def test_networkRules_afterRebootRouters(self): """Test network rules after master & backup routers rebooted """ # Steps to validate # 1. listNetworks should show the created network in allocated state # 2. listRouters returns no running routers # 3. VMs should be deployed and in Running state # 4. should list MASTER and BACKUP routers # 5. listPublicIpAddresses for networkid should show acquired IP addr # 6. listStaticNats for the network associated # 7. listFirewallRules should show allowed ports open # 8. ssh to succeed to the guestVM # 9. listPublicIpAddresses for networkid should show acquired IP addr # 10. listPortForwardRules to show open ports 221, 222 # 11. ssh should succeed for both ports # 12. listPublicIpAddresses for networkid should show acquired IP addr # 13 and 14. listLoadBalancerRules should show associated VMs for # public IP # 15. ssh should succeed to the user VMs # 16. listRouters should show one Router in MASTER state and Running # 17. ssh should work for PF, FW, and LB ips # 18. listRouters should show both routers MASTER and BACKUP in # Running state # 19. listPortForwardingRules, listFirewallRules, listLoadBalancerRule # should return empty response # 20. listPublicIpAddresses should show now more addresses # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id ) self.debug("Created network with ID: %s" % network.id) networks = Network.list( self.apiclient, id=network.id, listall=True ) self.assertEqual( isinstance(networks, list), True, "List networks should return a valid response for created network" ) nw_response = networks[0] self.debug("Network state: %s" % nw_response.state) self.assertEqual( nw_response.state, "Allocated", "The network should be in allocated state after creation" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( routers, None, "Routers should not be spawned when network is in allocated state" ) self.debug("Deploying VM in account: %s" % self.account.name) # Spawn an instance in that network virtual_machine = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network.id)] ) self.debug("Deployed VM in network: %s" % network.id) vms = VirtualMachine.list( self.apiclient, id=virtual_machine.id, listall=True ) self.assertEqual( isinstance(vms, list), True, "List Vms should return a valid list" ) vm = vms[0] self.assertEqual( vm.state, "Running", "Vm should be in running state after deployment" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) if routers[0].redundantstate == 'MASTER': master_router = routers[0] backup_router = routers[1] else: master_router = routers[1] backup_router = routers[0] self.debug("Associating public IP for network: %s" % network.name) public_ip = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip.ipaddress.ipaddress, network.id )) self.debug("Enabling static NAT for IP: %s" % public_ip.ipaddress.ipaddress) try: static_nat = StaticNATRule.create( self.apiclient, self.services["fw_rule"], ipaddressid=public_ip.ipaddress.id ) static_nat.enable( self.apiclient, ipaddressid=public_ip.ipaddress.id, virtualmachineid=virtual_machine.id ) self.debug("Static NAT enabled for IP: %s" % public_ip.ipaddress.ipaddress) except Exception as e: self.fail("Failed to enable static NAT on IP: %s - %s" % ( public_ip.ipaddress.ipaddress, e)) public_ips = PublicIPAddress.list( self.apiclient, associatednetworkid=network.id, listall=True, isstaticnat=True ) self.assertEqual( isinstance(public_ips, list), True, "List public Ip for network should list the Ip addr" ) self.assertEqual( public_ips[0].ipaddress, public_ip.ipaddress.ipaddress, "List public Ip for network should list the Ip addr" ) self.debug("creating a FW rule on IP: %s" % public_ip.ipaddress.ipaddress) fw_rule = FireWallRule.create( self.apiclient, ipaddressid=public_ip.ipaddress.id, protocol='TCP', cidrlist=[self.services["fw_rule"]["cidr"]], startport=self.services["fw_rule"]["startport"], endport=self.services["fw_rule"]["endport"] ) self.debug("Created a firewall rule on 22 port of IP: %s" % public_ip.ipaddress.ipaddress) self.debug("Associating public IP for network: %s" % network.name) public_ip_2 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_2.ipaddress.ipaddress, network.id )) nat_rule = NATRule.create( self.apiclient, virtual_machine, self.services["natrule_221"], ipaddressid=public_ip_2.ipaddress.id, openfirewall=True ) self.debug("Associating public IP for network: %s" % network.name) public_ip_3 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_3.ipaddress.ipaddress, network.id )) self.debug("Creating LB rule for IP address: %s" % public_ip_3.ipaddress.ipaddress) lb_rule = LoadBalancerRule.create( self.apiclient, self.services["lbrule"], ipaddressid=public_ip_3.ipaddress.id, accountid=self.account.name, networkid=network.id ) self.debug("Adding %s to the LB rule %s" % ( virtual_machine.name, lb_rule.name )) lb_rule.assign(self.apiclient, [virtual_machine]) self.debug("Starting router ID: %s" % master_router.id) for router in routers: try: self.debug("Rebooting router ID: %s" % master_router.id) #Stop the router cmd = rebootRouter.rebootRouterCmd() cmd.id = router.id self.apiclient.rebootRouter(cmd) except Exception as e: self.fail("Failed to reboot router..") self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) for router in routers: self.assertEqual( router.state, "Running", "Router state should be running" ) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip.ipaddress.ipaddress) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_2.ipaddress.ipaddress, reconnect=True, port=self.services["natrule_221"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_3.ipaddress.ipaddress, reconnect=True, port=self.services["lbrule"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) return @attr(tags=["advanced", "advancedns", "ssh"]) def test_applyRules_restartRvRNetwork(self): """Test apply rules after network restart """ # Steps to validate # 1. listNetworks should show the created network in allocated state # 2. listRouters returns no running routers # 3. VMs should be deployed and in Running state # 4. should list MASTER and BACKUP routers # 5. listPublicIpAddresses for networkid should show acquired IP addr # 6. listStaticNats for the network associated # 7. listFirewallRules should show allowed ports open # 8. ssh to succeed to the guestVM # 9. listPublicIpAddresses for networkid should show acquired IP addr # 10. listPortForwardRules to show open ports 221, 222 # 11. ssh should succeed for both ports # 12. listPublicIpAddresses for networkid should show acquired IP addr # 13 and 14. listLoadBalancerRules should show associated VMs for # public IP # 15. ssh should succeed to the user VMs # 16. listRouters should show one Router in MASTER state and Running & # one in BACKUP and Running # 17. ssh should work for PF, FW, and LB ips # 18. listRouters should show one Router in MASTER state and Running & # one in BACKUP and Running # 19. ssh should work for PF, FW, and LB ips # 20. listPortForwardingRules, listFirewallRules, listLoadBalancerRule # should return empty response # 21. listPublicIpAddresses should show now more addresses # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id ) self.debug("Created network with ID: %s" % network.id) networks = Network.list( self.apiclient, id=network.id, listall=True ) self.assertEqual( isinstance(networks, list), True, "List networks should return a valid response for created network" ) nw_response = networks[0] self.debug("Network state: %s" % nw_response.state) self.assertEqual( nw_response.state, "Allocated", "The network should be in allocated state after creation" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( routers, None, "Routers should not be spawned when network is in allocated state" ) self.debug("Deploying VM in account: %s" % self.account.name) # Spawn an instance in that network virtual_machine = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network.id)] ) self.debug("Deployed VM in network: %s" % network.id) vms = VirtualMachine.list( self.apiclient, id=virtual_machine.id, listall=True ) self.assertEqual( isinstance(vms, list), True, "List Vms should return a valid list" ) vm = vms[0] self.assertEqual( vm.state, "Running", "Vm should be in running state after deployment" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) if routers[0].redundantstate == 'MASTER': master_router = routers[0] backup_router = routers[1] else: master_router = routers[1] backup_router = routers[0] self.debug("Associating public IP for network: %s" % network.name) public_ip = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip.ipaddress.ipaddress, network.id )) self.debug("Enabling static NAT for IP: %s" % public_ip.ipaddress.ipaddress) try: static_nat = StaticNATRule.create( self.apiclient, self.services["fw_rule"], ipaddressid=public_ip.ipaddress.id ) static_nat.enable( self.apiclient, ipaddressid=public_ip.ipaddress.id, virtualmachineid=virtual_machine.id ) self.debug("Static NAT enabled for IP: %s" % public_ip.ipaddress.ipaddress) except Exception as e: self.fail("Failed to enable static NAT on IP: %s - %s" % ( public_ip.ipaddress.ipaddress, e)) public_ips = PublicIPAddress.list( self.apiclient, associatednetworkid=network.id, listall=True, isstaticnat=True ) self.assertEqual( isinstance(public_ips, list), True, "List public Ip for network should list the Ip addr" ) self.assertEqual( public_ips[0].ipaddress, public_ip.ipaddress.ipaddress, "List public Ip for network should list the Ip addr" ) self.debug("creating a FW rule on IP: %s" % public_ip.ipaddress.ipaddress) fw_rule = FireWallRule.create( self.apiclient, ipaddressid=public_ip.ipaddress.id, protocol='TCP', cidrlist=[self.services["fw_rule"]["cidr"]], startport=self.services["fw_rule"]["startport"], endport=self.services["fw_rule"]["endport"] ) self.debug("Created a firewall rule on 22 port of IP: %s" % public_ip.ipaddress.ipaddress) self.debug("Associating public IP for network: %s" % network.name) public_ip_2 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_2.ipaddress.ipaddress, network.id )) nat_rule = NATRule.create( self.apiclient, virtual_machine, self.services["natrule_221"], ipaddressid=public_ip_2.ipaddress.id, openfirewall=True ) self.debug("Associating public IP for network: %s" % network.name) public_ip_3 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_3.ipaddress.ipaddress, network.id )) self.debug("Creating LB rule for IP address: %s" % public_ip_3.ipaddress.ipaddress) lb_rule = LoadBalancerRule.create( self.apiclient, self.services["lbrule"], ipaddressid=public_ip_3.ipaddress.id, accountid=self.account.name, networkid=network.id ) self.debug("Adding %s to the LB rule %s" % ( virtual_machine.name, lb_rule.name )) lb_rule.assign(self.apiclient, [virtual_machine]) self.debug("Restarting network ID: %s with cleanup true" % network.id) try: network.restart(self.apiclient, cleanup=True) except Exception as e: self.fail("Failed to cleanup network") self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) for router in routers: self.assertEqual( router.state, "Running", "Router state should be running" ) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip.ipaddress.ipaddress) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_2.ipaddress.ipaddress, reconnect=True, port=self.services["natrule_221"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_3.ipaddress.ipaddress, reconnect=True, port=self.services["lbrule"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Restarting network ID: %s with cleanup false" % network.id) try: network.restart(self.apiclient, cleanup=False) except Exception as e: self.fail("Failed to cleanup network") self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) for router in routers: self.assertEqual( router.state, "Running", "Router state should be running" ) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip.ipaddress.ipaddress) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_2.ipaddress.ipaddress, reconnect=True, port=self.services["natrule_221"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_3.ipaddress.ipaddress, reconnect=True, port=self.services["lbrule"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) return @attr(tags=["advanced", "advancedns", "ssh"]) def test_apply_and__delete_NetworkRulesOnRvR(self): """Test apply and delete network rules on redundant router """ # Steps to validate # 1. listNetworks should show the created network in allocated state # 2. listRouters returns no running routers # 3. VMs should be deployed and in Running state # 4. should list MASTER and BACKUP routers # 5. listPublicIpAddresses for networkid should show acquired IP # 6. listRemoteAccessVpns for the network associated should show the # VPN created # 7. listRemoteAccessVpns for the network associated should return # empty response # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id ) self.debug("Created network with ID: %s" % network.id) networks = Network.list( self.apiclient, id=network.id, listall=True ) self.assertEqual( isinstance(networks, list), True, "List networks should return a valid response for created network" ) nw_response = networks[0] self.debug("Network state: %s" % nw_response.state) self.assertEqual( nw_response.state, "Allocated", "The network should be in allocated state after creation" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( routers, None, "Routers should not be spawned when network is in allocated state" ) self.debug("Deploying VM in account: %s" % self.account.name) # Spawn an instance in that network virtual_machine = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network.id)] ) self.debug("Deployed VM in network: %s" % network.id) vms = VirtualMachine.list( self.apiclient, id=virtual_machine.id, listall=True ) self.assertEqual( isinstance(vms, list), True, "List Vms should return a valid list" ) vm = vms[0] self.assertEqual( vm.state, "Running", "Vm should be in running state after deployment" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) self.debug("Associating public IP for network: %s" % network.name) public_ip = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip.ipaddress.ipaddress, network.id )) self.debug("Enabling static NAT for IP: %s" % public_ip.ipaddress.ipaddress) try: static_nat = StaticNATRule.create( self.apiclient, self.services["fw_rule"], ipaddressid=public_ip.ipaddress.id ) self.debug("Static NAT enabled for IP: %s" % public_ip.ipaddress.ipaddress) static_nat.enable( self.apiclient, ipaddressid=public_ip.ipaddress.id, virtualmachineid=virtual_machine.id ) except Exception as e: self.fail("Failed to enable static NAT on IP: %s - %s" % ( public_ip.ipaddress.ipaddress, e)) public_ips = PublicIPAddress.list( self.apiclient, associatednetworkid=network.id, listall=True, isstaticnat=True ) self.assertEqual( isinstance(public_ips, list), True, "List public Ip for network should list the Ip addr" ) self.assertEqual( public_ips[0].ipaddress, public_ip.ipaddress.ipaddress, "List public Ip for network should list the Ip addr" ) self.debug("creating a FW rule on IP: %s" % public_ip.ipaddress.ipaddress) fw_rule = FireWallRule.create( self.apiclient, ipaddressid=public_ip.ipaddress.id, protocol='TCP', cidrlist=[self.services["fw_rule"]["cidr"]], startport=self.services["fw_rule"]["startport"], endport=self.services["fw_rule"]["endport"] ) self.debug("Created a firewall rule on 22 port of IP: %s" % public_ip.ipaddress.ipaddress) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip.ipaddress.ipaddress) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Associating public IP for network: %s" % network.name) public_ip_2 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_2.ipaddress.ipaddress, network.id )) nat_rule = NATRule.create( self.apiclient, virtual_machine, self.services["natrule_221"], ipaddressid=public_ip_2.ipaddress.id, openfirewall=True ) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_2.ipaddress.ipaddress, reconnect=True, port=self.services["natrule_221"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Associating public IP for network: %s" % network.name) public_ip_3 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_3.ipaddress.ipaddress, network.id )) self.debug("Creating LB rule for IP address: %s" % public_ip_3.ipaddress.ipaddress) lb_rule = LoadBalancerRule.create( self.apiclient, self.services["lbrule"], ipaddressid=public_ip_3.ipaddress.id, accountid=self.account.name, networkid=network.id ) self.debug("Adding %s to the LB rule %s" % ( virtual_machine.name, lb_rule.name )) lb_rule.assign(self.apiclient, [virtual_machine]) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_3.ipaddress.ipaddress, reconnect=True, port=self.services["lbrule"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) return @attr(tags=["advanced", "advancedns", "ssh", "needle"]) def test_applyNetworkRules_MasterDown_deleteNetworkRules(self): """Test apply network rules when master down and delete network rules """ # Steps to validate # 1. listNetworks should show the created network in allocated state # 2. listRouters returns no running routers # 3. VMs should be deployed and in Running state # 4. should list MASTER and BACKUP routers # 5. listPublicIpAddresses for networkid should show acquired IP addr # 6. listStaticNats for the network associated # 7. listFirewallRules should show allowed ports open # 8. ssh to succeed to the guestVM # 9. listPublicIpAddresses for networkid should show acquired IP addr # 10. listPortForwardRules to show open ports 221, 222 # 11. ssh should succeed for both ports # 12. listPublicIpAddresses for networkid should show acquired IP addr # 13 and 14. listLoadBalancerRules should show associated VMs for # public IP # 15. ssh should succeed to the user VMs # 16. listRouters should show one Router in MASTER state and Running # 17. ssh should work for PF, FW, and LB ips # 18. listRouters should show both routers MASTER and BACKUP in # Running state # 19. listPortForwardingRules, listFirewallRules, listLoadBalancerRule # should return empty response # 20. listPublicIpAddresses should show now more addresses # Creating network using the network offering created self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id ) self.debug("Created network with ID: %s" % network.id) networks = Network.list( self.apiclient, id=network.id, listall=True ) self.assertEqual( isinstance(networks, list), True, "List networks should return a valid response for created network" ) nw_response = networks[0] self.debug("Network state: %s" % nw_response.state) self.assertEqual( nw_response.state, "Allocated", "The network should be in allocated state after creation" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( routers, None, "Routers should not be spawned when network is in allocated state" ) self.debug("Deploying VM in account: %s" % self.account.name) # Spawn an instance in that network virtual_machine = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network.id)] ) self.debug("Deployed VM in network: %s" % network.id) vms = VirtualMachine.list( self.apiclient, id=virtual_machine.id, listall=True ) self.assertEqual( isinstance(vms, list), True, "List Vms should return a valid list" ) vm = vms[0] self.assertEqual( vm.state, "Running", "Vm should be in running state after deployment" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) if routers[0].redundantstate == 'MASTER': master_router = routers[0] backup_router = routers[1] else: master_router = routers[1] backup_router = routers[0] self.debug("Stopping router ID: %s" % master_router.id) try: Router.stop(self.apiclient, id=master_router.id) except Exception as e: self.fail("Failed to stop master router..") self.debug("Associating public IP for network: %s" % network.name) public_ip = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip.ipaddress.ipaddress, network.id )) self.debug("Enabling static NAT for IP: %s" % public_ip.ipaddress.ipaddress) try: static_nat = StaticNATRule.create( self.apiclient, self.services["fw_rule"], ipaddressid=public_ip.ipaddress.id ) static_nat.enable( self.apiclient, ipaddressid=public_ip.ipaddress.id, virtualmachineid=virtual_machine.id ) self.debug("Static NAT enabled for IP: %s" % public_ip.ipaddress.ipaddress) except Exception as e: self.fail("Failed to enable static NAT on IP: %s - %s" % ( public_ip.ipaddress.ipaddress, e)) public_ips = PublicIPAddress.list( self.apiclient, associatednetworkid=network.id, listall=True, isstaticnat=True ) self.assertEqual( isinstance(public_ips, list), True, "List public Ip for network should list the Ip addr" ) self.assertEqual( public_ips[0].ipaddress, public_ip.ipaddress.ipaddress, "Public Ip Address in the network created (%s) and listed (%s) do not match" % ( public_ips[0].ipaddress, public_ip.ipaddress.ipaddress) ) self.debug("creating a FW rule on IP: %s" % public_ip.ipaddress.ipaddress) fw_rule = FireWallRule.create( self.apiclient, ipaddressid=public_ip.ipaddress.id, protocol='TCP', cidrlist=[self.services["fw_rule"]["cidr"]], startport=self.services["fw_rule"]["startport"], endport=self.services["fw_rule"]["endport"] ) self.debug("Created a firewall rule on 22 port of IP: %s" % public_ip.ipaddress.ipaddress) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip.ipaddress.ipaddress) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Associating public IP for network: %s" % network.name) public_ip_2 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_2.ipaddress.ipaddress, network.id )) nat_rule = NATRule.create( self.apiclient, virtual_machine, self.services["natrule_221"], ipaddressid=public_ip_2.ipaddress.id, openfirewall=True ) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_2.ipaddress.ipaddress, reconnect=True, port=self.services["natrule_221"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Associating public IP for network: %s" % network.name) public_ip_3 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_3.ipaddress.ipaddress, network.id )) self.debug("Creating LB rule for IP address: %s" % public_ip_3.ipaddress.ipaddress) lb_rule = LoadBalancerRule.create( self.apiclient, self.services["lbrule"], ipaddressid=public_ip_3.ipaddress.id, accountid=self.account.name, networkid=network.id ) self.debug("Adding %s to the LB rule %s" % ( virtual_machine.name, lb_rule.name )) lb_rule.assign(self.apiclient, [virtual_machine]) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_3.ipaddress.ipaddress, reconnect=True, port=self.services["lbrule"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Starting router ID: %s" % master_router.id) try: Router.start(self.apiclient, id=master_router.id) except Exception as e: self.fail("Failed to start master router..") self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) for router in routers: self.assertEqual( router.state, "Running", "Router state should be running" ) return

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from nose.plugins.attrib import attr from marvin.integration.lib.base import * from marvin.integration.lib.utils import * from marvin.integration.lib.common import * from marvin.cloudstackTestCase import cloudstackTestCase from marvin.cloudstackAPI import * class Services: def __init__(self): self.services = { "account": { "email": "test@test.com", "firstname": "Test", "lastname": "User", "username": "test", "password": "password", }, "service_offering": { "name": "Tiny Instance", "displaytext": "Tiny Instance", "cpunumber": 1, "cpuspeed": 100, "memory": 128, }, "disk_offering": { "displaytext": "Small", "name": "Small", "disksize": 1 }, "virtual_machine": { "displayname": "Test VM", "username": "root", "password": "password", "ssh_port": 22, "hypervisor": 'XenServer', "privateport": 22, "publicport": 22, "protocol": 'TCP', }, "static_nat": { "startport": 22, "endport": 22, "protocol": "TCP" }, "network_offering": { "name": 'Network offering-RVR services', "displaytext": 'Network off-RVR services', "guestiptype": 'Isolated', "supportedservices": 'Vpn,Dhcp,Dns,SourceNat,PortForwarding,Firewall,Lb,UserData,StaticNat', "traffictype": 'GUEST', "availability": 'Optional', "serviceProviderList": { "Vpn": 'VirtualRouter', "Dhcp": 'VirtualRouter', "Dns": 'VirtualRouter', "SourceNat": 'VirtualRouter', "PortForwarding": 'VirtualRouter', "Firewall": 'VirtualRouter', "Lb": 'VirtualRouter', "UserData": 'VirtualRouter', "StaticNat": 'VirtualRouter', }, "serviceCapabilityList": { "SourceNat": { "SupportedSourceNatTypes": "peraccount", "RedundantRouter": "true", }, "lb": { "SupportedLbIsolation": "dedicated" }, }, }, "host": { "username": "root", "password": "password", "publicport": 22, }, "network": { "name": "Test Network", "displaytext": "Test Network", }, "lbrule": { "name": "SSH", "alg": "roundrobin", "privateport": 22, "publicport": 22, "openfirewall": True, }, "natrule": { "privateport": 22, "publicport": 22, "protocol": "TCP" }, "natrule_221": { "privateport": 22, "publicport": 221, "protocol": "TCP" }, "fw_rule": { "startport": 1, "endport": 6000, "cidr": '55.55.0.0/11', "protocol": 'TCP', }, "ostype": 'CentOS 5.3 (64-bit)', "sleep": 60, } class TestRedundantRouterRulesLifeCycle(cloudstackTestCase): @classmethod def setUpClass(cls): cls.api_client = super( TestRedundantRouterRulesLifeCycle, cls ).getClsTestClient().getApiClient() cls.services = Services().services cls.domain = get_domain(cls.api_client, cls.services) cls.zone = get_zone(cls.api_client, cls.services) cls.template = get_template( cls.api_client, cls.zone.id, cls.services["ostype"] ) cls.services["virtual_machine"]["zoneid"] = cls.zone.id cls.services["virtual_machine"]["template"] = cls.template.id cls.service_offering = ServiceOffering.create( cls.api_client, cls.services["service_offering"] ) cls.network_offering = NetworkOffering.create( cls.api_client, cls.services["network_offering"], conservemode=True ) cls.network_offering.update(cls.api_client, state='Enabled') cls._cleanup = [ cls.service_offering, cls.network_offering, ] return @classmethod def tearDownClass(cls): try: cleanup_resources(cls.api_client, cls._cleanup) except Exception as e: raise Exception("Warning: Exception during cleanup : %s" % e) return def setUp(self): self.apiclient = self.testClient.getApiClient() self.dbclient = self.testClient.getDbConnection() self.account = Account.create( self.apiclient, self.services["account"], admin=True, domainid=self.domain.id ) self.cleanup = [] self.cleanup.insert(0, self.account) return def tearDown(self): try: cleanup_resources(self.apiclient, self.cleanup) except Exception as e: self.debug("Warning: Exception during cleanup : %s" % e) return @attr(tags=["advanced", "advancedns", "ssh"]) def test_networkRules_afterRebootRouters(self): self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id ) self.debug("Created network with ID: %s" % network.id) networks = Network.list( self.apiclient, id=network.id, listall=True ) self.assertEqual( isinstance(networks, list), True, "List networks should return a valid response for created network" ) nw_response = networks[0] self.debug("Network state: %s" % nw_response.state) self.assertEqual( nw_response.state, "Allocated", "The network should be in allocated state after creation" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( routers, None, "Routers should not be spawned when network is in allocated state" ) self.debug("Deploying VM in account: %s" % self.account.name) virtual_machine = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network.id)] ) self.debug("Deployed VM in network: %s" % network.id) vms = VirtualMachine.list( self.apiclient, id=virtual_machine.id, listall=True ) self.assertEqual( isinstance(vms, list), True, "List Vms should return a valid list" ) vm = vms[0] self.assertEqual( vm.state, "Running", "Vm should be in running state after deployment" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) if routers[0].redundantstate == 'MASTER': master_router = routers[0] backup_router = routers[1] else: master_router = routers[1] backup_router = routers[0] self.debug("Associating public IP for network: %s" % network.name) public_ip = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip.ipaddress.ipaddress, network.id )) self.debug("Enabling static NAT for IP: %s" % public_ip.ipaddress.ipaddress) try: static_nat = StaticNATRule.create( self.apiclient, self.services["fw_rule"], ipaddressid=public_ip.ipaddress.id ) static_nat.enable( self.apiclient, ipaddressid=public_ip.ipaddress.id, virtualmachineid=virtual_machine.id ) self.debug("Static NAT enabled for IP: %s" % public_ip.ipaddress.ipaddress) except Exception as e: self.fail("Failed to enable static NAT on IP: %s - %s" % ( public_ip.ipaddress.ipaddress, e)) public_ips = PublicIPAddress.list( self.apiclient, associatednetworkid=network.id, listall=True, isstaticnat=True ) self.assertEqual( isinstance(public_ips, list), True, "List public Ip for network should list the Ip addr" ) self.assertEqual( public_ips[0].ipaddress, public_ip.ipaddress.ipaddress, "List public Ip for network should list the Ip addr" ) self.debug("creating a FW rule on IP: %s" % public_ip.ipaddress.ipaddress) fw_rule = FireWallRule.create( self.apiclient, ipaddressid=public_ip.ipaddress.id, protocol='TCP', cidrlist=[self.services["fw_rule"]["cidr"]], startport=self.services["fw_rule"]["startport"], endport=self.services["fw_rule"]["endport"] ) self.debug("Created a firewall rule on 22 port of IP: %s" % public_ip.ipaddress.ipaddress) self.debug("Associating public IP for network: %s" % network.name) public_ip_2 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_2.ipaddress.ipaddress, network.id )) nat_rule = NATRule.create( self.apiclient, virtual_machine, self.services["natrule_221"], ipaddressid=public_ip_2.ipaddress.id, openfirewall=True ) self.debug("Associating public IP for network: %s" % network.name) public_ip_3 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_3.ipaddress.ipaddress, network.id )) self.debug("Creating LB rule for IP address: %s" % public_ip_3.ipaddress.ipaddress) lb_rule = LoadBalancerRule.create( self.apiclient, self.services["lbrule"], ipaddressid=public_ip_3.ipaddress.id, accountid=self.account.name, networkid=network.id ) self.debug("Adding %s to the LB rule %s" % ( virtual_machine.name, lb_rule.name )) lb_rule.assign(self.apiclient, [virtual_machine]) self.debug("Starting router ID: %s" % master_router.id) for router in routers: try: self.debug("Rebooting router ID: %s" % master_router.id) cmd = rebootRouter.rebootRouterCmd() cmd.id = router.id self.apiclient.rebootRouter(cmd) except Exception as e: self.fail("Failed to reboot router..") self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) for router in routers: self.assertEqual( router.state, "Running", "Router state should be running" ) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip.ipaddress.ipaddress) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_2.ipaddress.ipaddress, reconnect=True, port=self.services["natrule_221"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_3.ipaddress.ipaddress, reconnect=True, port=self.services["lbrule"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) return @attr(tags=["advanced", "advancedns", "ssh"]) def test_applyRules_restartRvRNetwork(self): self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id ) self.debug("Created network with ID: %s" % network.id) networks = Network.list( self.apiclient, id=network.id, listall=True ) self.assertEqual( isinstance(networks, list), True, "List networks should return a valid response for created network" ) nw_response = networks[0] self.debug("Network state: %s" % nw_response.state) self.assertEqual( nw_response.state, "Allocated", "The network should be in allocated state after creation" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( routers, None, "Routers should not be spawned when network is in allocated state" ) self.debug("Deploying VM in account: %s" % self.account.name) virtual_machine = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network.id)] ) self.debug("Deployed VM in network: %s" % network.id) vms = VirtualMachine.list( self.apiclient, id=virtual_machine.id, listall=True ) self.assertEqual( isinstance(vms, list), True, "List Vms should return a valid list" ) vm = vms[0] self.assertEqual( vm.state, "Running", "Vm should be in running state after deployment" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) if routers[0].redundantstate == 'MASTER': master_router = routers[0] backup_router = routers[1] else: master_router = routers[1] backup_router = routers[0] self.debug("Associating public IP for network: %s" % network.name) public_ip = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip.ipaddress.ipaddress, network.id )) self.debug("Enabling static NAT for IP: %s" % public_ip.ipaddress.ipaddress) try: static_nat = StaticNATRule.create( self.apiclient, self.services["fw_rule"], ipaddressid=public_ip.ipaddress.id ) static_nat.enable( self.apiclient, ipaddressid=public_ip.ipaddress.id, virtualmachineid=virtual_machine.id ) self.debug("Static NAT enabled for IP: %s" % public_ip.ipaddress.ipaddress) except Exception as e: self.fail("Failed to enable static NAT on IP: %s - %s" % ( public_ip.ipaddress.ipaddress, e)) public_ips = PublicIPAddress.list( self.apiclient, associatednetworkid=network.id, listall=True, isstaticnat=True ) self.assertEqual( isinstance(public_ips, list), True, "List public Ip for network should list the Ip addr" ) self.assertEqual( public_ips[0].ipaddress, public_ip.ipaddress.ipaddress, "List public Ip for network should list the Ip addr" ) self.debug("creating a FW rule on IP: %s" % public_ip.ipaddress.ipaddress) fw_rule = FireWallRule.create( self.apiclient, ipaddressid=public_ip.ipaddress.id, protocol='TCP', cidrlist=[self.services["fw_rule"]["cidr"]], startport=self.services["fw_rule"]["startport"], endport=self.services["fw_rule"]["endport"] ) self.debug("Created a firewall rule on 22 port of IP: %s" % public_ip.ipaddress.ipaddress) self.debug("Associating public IP for network: %s" % network.name) public_ip_2 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_2.ipaddress.ipaddress, network.id )) nat_rule = NATRule.create( self.apiclient, virtual_machine, self.services["natrule_221"], ipaddressid=public_ip_2.ipaddress.id, openfirewall=True ) self.debug("Associating public IP for network: %s" % network.name) public_ip_3 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_3.ipaddress.ipaddress, network.id )) self.debug("Creating LB rule for IP address: %s" % public_ip_3.ipaddress.ipaddress) lb_rule = LoadBalancerRule.create( self.apiclient, self.services["lbrule"], ipaddressid=public_ip_3.ipaddress.id, accountid=self.account.name, networkid=network.id ) self.debug("Adding %s to the LB rule %s" % ( virtual_machine.name, lb_rule.name )) lb_rule.assign(self.apiclient, [virtual_machine]) self.debug("Restarting network ID: %s with cleanup true" % network.id) try: network.restart(self.apiclient, cleanup=True) except Exception as e: self.fail("Failed to cleanup network") self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) for router in routers: self.assertEqual( router.state, "Running", "Router state should be running" ) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip.ipaddress.ipaddress) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_2.ipaddress.ipaddress, reconnect=True, port=self.services["natrule_221"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_3.ipaddress.ipaddress, reconnect=True, port=self.services["lbrule"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Restarting network ID: %s with cleanup false" % network.id) try: network.restart(self.apiclient, cleanup=False) except Exception as e: self.fail("Failed to cleanup network") self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) for router in routers: self.assertEqual( router.state, "Running", "Router state should be running" ) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip.ipaddress.ipaddress) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_2.ipaddress.ipaddress, reconnect=True, port=self.services["natrule_221"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_3.ipaddress.ipaddress, reconnect=True, port=self.services["lbrule"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) return @attr(tags=["advanced", "advancedns", "ssh"]) def test_apply_and__delete_NetworkRulesOnRvR(self): self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id ) self.debug("Created network with ID: %s" % network.id) networks = Network.list( self.apiclient, id=network.id, listall=True ) self.assertEqual( isinstance(networks, list), True, "List networks should return a valid response for created network" ) nw_response = networks[0] self.debug("Network state: %s" % nw_response.state) self.assertEqual( nw_response.state, "Allocated", "The network should be in allocated state after creation" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( routers, None, "Routers should not be spawned when network is in allocated state" ) self.debug("Deploying VM in account: %s" % self.account.name) virtual_machine = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network.id)] ) self.debug("Deployed VM in network: %s" % network.id) vms = VirtualMachine.list( self.apiclient, id=virtual_machine.id, listall=True ) self.assertEqual( isinstance(vms, list), True, "List Vms should return a valid list" ) vm = vms[0] self.assertEqual( vm.state, "Running", "Vm should be in running state after deployment" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) self.debug("Associating public IP for network: %s" % network.name) public_ip = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip.ipaddress.ipaddress, network.id )) self.debug("Enabling static NAT for IP: %s" % public_ip.ipaddress.ipaddress) try: static_nat = StaticNATRule.create( self.apiclient, self.services["fw_rule"], ipaddressid=public_ip.ipaddress.id ) self.debug("Static NAT enabled for IP: %s" % public_ip.ipaddress.ipaddress) static_nat.enable( self.apiclient, ipaddressid=public_ip.ipaddress.id, virtualmachineid=virtual_machine.id ) except Exception as e: self.fail("Failed to enable static NAT on IP: %s - %s" % ( public_ip.ipaddress.ipaddress, e)) public_ips = PublicIPAddress.list( self.apiclient, associatednetworkid=network.id, listall=True, isstaticnat=True ) self.assertEqual( isinstance(public_ips, list), True, "List public Ip for network should list the Ip addr" ) self.assertEqual( public_ips[0].ipaddress, public_ip.ipaddress.ipaddress, "List public Ip for network should list the Ip addr" ) self.debug("creating a FW rule on IP: %s" % public_ip.ipaddress.ipaddress) fw_rule = FireWallRule.create( self.apiclient, ipaddressid=public_ip.ipaddress.id, protocol='TCP', cidrlist=[self.services["fw_rule"]["cidr"]], startport=self.services["fw_rule"]["startport"], endport=self.services["fw_rule"]["endport"] ) self.debug("Created a firewall rule on 22 port of IP: %s" % public_ip.ipaddress.ipaddress) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip.ipaddress.ipaddress) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Associating public IP for network: %s" % network.name) public_ip_2 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_2.ipaddress.ipaddress, network.id )) nat_rule = NATRule.create( self.apiclient, virtual_machine, self.services["natrule_221"], ipaddressid=public_ip_2.ipaddress.id, openfirewall=True ) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_2.ipaddress.ipaddress, reconnect=True, port=self.services["natrule_221"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Associating public IP for network: %s" % network.name) public_ip_3 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_3.ipaddress.ipaddress, network.id )) self.debug("Creating LB rule for IP address: %s" % public_ip_3.ipaddress.ipaddress) lb_rule = LoadBalancerRule.create( self.apiclient, self.services["lbrule"], ipaddressid=public_ip_3.ipaddress.id, accountid=self.account.name, networkid=network.id ) self.debug("Adding %s to the LB rule %s" % ( virtual_machine.name, lb_rule.name )) lb_rule.assign(self.apiclient, [virtual_machine]) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_3.ipaddress.ipaddress, reconnect=True, port=self.services["lbrule"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) return @attr(tags=["advanced", "advancedns", "ssh", "needle"]) def test_applyNetworkRules_MasterDown_deleteNetworkRules(self): self.debug("Creating network with network offering: %s" % self.network_offering.id) network = Network.create( self.apiclient, self.services["network"], accountid=self.account.name, domainid=self.account.domainid, networkofferingid=self.network_offering.id, zoneid=self.zone.id ) self.debug("Created network with ID: %s" % network.id) networks = Network.list( self.apiclient, id=network.id, listall=True ) self.assertEqual( isinstance(networks, list), True, "List networks should return a valid response for created network" ) nw_response = networks[0] self.debug("Network state: %s" % nw_response.state) self.assertEqual( nw_response.state, "Allocated", "The network should be in allocated state after creation" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( routers, None, "Routers should not be spawned when network is in allocated state" ) self.debug("Deploying VM in account: %s" % self.account.name) virtual_machine = VirtualMachine.create( self.apiclient, self.services["virtual_machine"], accountid=self.account.name, domainid=self.account.domainid, serviceofferingid=self.service_offering.id, networkids=[str(network.id)] ) self.debug("Deployed VM in network: %s" % network.id) vms = VirtualMachine.list( self.apiclient, id=virtual_machine.id, listall=True ) self.assertEqual( isinstance(vms, list), True, "List Vms should return a valid list" ) vm = vms[0] self.assertEqual( vm.state, "Running", "Vm should be in running state after deployment" ) self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) if routers[0].redundantstate == 'MASTER': master_router = routers[0] backup_router = routers[1] else: master_router = routers[1] backup_router = routers[0] self.debug("Stopping router ID: %s" % master_router.id) try: Router.stop(self.apiclient, id=master_router.id) except Exception as e: self.fail("Failed to stop master router..") self.debug("Associating public IP for network: %s" % network.name) public_ip = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip.ipaddress.ipaddress, network.id )) self.debug("Enabling static NAT for IP: %s" % public_ip.ipaddress.ipaddress) try: static_nat = StaticNATRule.create( self.apiclient, self.services["fw_rule"], ipaddressid=public_ip.ipaddress.id ) static_nat.enable( self.apiclient, ipaddressid=public_ip.ipaddress.id, virtualmachineid=virtual_machine.id ) self.debug("Static NAT enabled for IP: %s" % public_ip.ipaddress.ipaddress) except Exception as e: self.fail("Failed to enable static NAT on IP: %s - %s" % ( public_ip.ipaddress.ipaddress, e)) public_ips = PublicIPAddress.list( self.apiclient, associatednetworkid=network.id, listall=True, isstaticnat=True ) self.assertEqual( isinstance(public_ips, list), True, "List public Ip for network should list the Ip addr" ) self.assertEqual( public_ips[0].ipaddress, public_ip.ipaddress.ipaddress, "Public Ip Address in the network created (%s) and listed (%s) do not match" % ( public_ips[0].ipaddress, public_ip.ipaddress.ipaddress) ) self.debug("creating a FW rule on IP: %s" % public_ip.ipaddress.ipaddress) fw_rule = FireWallRule.create( self.apiclient, ipaddressid=public_ip.ipaddress.id, protocol='TCP', cidrlist=[self.services["fw_rule"]["cidr"]], startport=self.services["fw_rule"]["startport"], endport=self.services["fw_rule"]["endport"] ) self.debug("Created a firewall rule on 22 port of IP: %s" % public_ip.ipaddress.ipaddress) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip.ipaddress.ipaddress) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Associating public IP for network: %s" % network.name) public_ip_2 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_2.ipaddress.ipaddress, network.id )) nat_rule = NATRule.create( self.apiclient, virtual_machine, self.services["natrule_221"], ipaddressid=public_ip_2.ipaddress.id, openfirewall=True ) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_2.ipaddress.ipaddress, reconnect=True, port=self.services["natrule_221"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Associating public IP for network: %s" % network.name) public_ip_3 = PublicIPAddress.create( self.apiclient, accountid=self.account.name, zoneid=self.zone.id, domainid=self.account.domainid, networkid=network.id ) self.debug("Associated %s with network %s" % ( public_ip_3.ipaddress.ipaddress, network.id )) self.debug("Creating LB rule for IP address: %s" % public_ip_3.ipaddress.ipaddress) lb_rule = LoadBalancerRule.create( self.apiclient, self.services["lbrule"], ipaddressid=public_ip_3.ipaddress.id, accountid=self.account.name, networkid=network.id ) self.debug("Adding %s to the LB rule %s" % ( virtual_machine.name, lb_rule.name )) lb_rule.assign(self.apiclient, [virtual_machine]) self.debug("Trying to SSH into the virtual machine") try: virtual_machine.get_ssh_client( ipaddress=public_ip_3.ipaddress.ipaddress, reconnect=True, port=self.services["lbrule"]["publicport"] ) self.debug("SSH to guest VM succeeded") except Exception as e: self.fail("SSH to guest VM failed: %s" % e) self.debug("Starting router ID: %s" % master_router.id) try: Router.start(self.apiclient, id=master_router.id) except Exception as e: self.fail("Failed to start master router..") self.debug("Listing routers for network: %s" % network.name) routers = Router.list( self.apiclient, networkid=network.id, listall=True ) self.assertEqual( isinstance(routers, list), True, "list router should return Master and backup routers" ) self.assertEqual( len(routers), 2, "Length of the list router should be 2 (Backup & master)" ) for router in routers: self.assertEqual( router.state, "Running", "Router state should be running" ) return

true

f719550eb352bbb1095167f47a860d9ae8edd55b

283

py

Python

src/com/python/socket/udp_client.py

Leeo1124/pythonDemo

72e2209c095301a3f1f61edfe03ea69c3c05be40

[ "Apache-2.0" ]

null

src/com/python/socket/udp_client.py

Leeo1124/pythonDemo

72e2209c095301a3f1f61edfe03ea69c3c05be40

[ "Apache-2.0" ]

null

src/com/python/socket/udp_client.py

Leeo1124/pythonDemo

72e2209c095301a3f1f61edfe03ea69c3c05be40

[ "Apache-2.0" ]

null

''' Created on 2016年8月10日 @author: Administrator ''' import socket s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) for data in [b'Michael', b'Tracy', b'Sarah']: # 发送数据: s.sendto(data, ('127.0.0.1', 9999)) # 接收数据: print(s.recv(1024).decode('utf-8')) s.close()

17.6875

52

0.636042

import socket s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) for data in [b'Michael', b'Tracy', b'Sarah']: s.sendto(data, ('127.0.0.1', 9999)) print(s.recv(1024).decode('utf-8')) s.close()

true

f71955e2cbcdd5db22e26670801fd917e9622190

431

py

Python

app/core/migrations/0005_recipe_image.py

JopeAlgorta/django-recipe-api

a92ae3b206682564d147618f83794edaf2c9a785

[ "MIT" ]

null

app/core/migrations/0005_recipe_image.py

JopeAlgorta/django-recipe-api

a92ae3b206682564d147618f83794edaf2c9a785

[ "MIT" ]

null

app/core/migrations/0005_recipe_image.py

JopeAlgorta/django-recipe-api

a92ae3b206682564d147618f83794edaf2c9a785

[ "MIT" ]

null

# Generated by Django 2.2.14 on 2020-07-31 14:42 import core.models from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('core', '0004_recipe'), ] operations = [ migrations.AddField( model_name='recipe', name='image', field=models.ImageField(null=True, upload_to=core.models.recipe_image_file_path), ), ]

21.55

93

0.62181

import core.models from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('core', '0004_recipe'), ] operations = [ migrations.AddField( model_name='recipe', name='image', field=models.ImageField(null=True, upload_to=core.models.recipe_image_file_path), ), ]

true

f71956fe3c17887634434668acacfe3048dd4355

4,815

py

Python

src/mem/ruby/network/garnet/GarnetNetwork.py

NickSica/gem5

87ffe8b4f75f3a6938144e4edc1ba0ba6f3f0610

[ "BSD-3-Clause" ]

null

src/mem/ruby/network/garnet/GarnetNetwork.py

NickSica/gem5

87ffe8b4f75f3a6938144e4edc1ba0ba6f3f0610

[ "BSD-3-Clause" ]

null

src/mem/ruby/network/garnet/GarnetNetwork.py

NickSica/gem5

87ffe8b4f75f3a6938144e4edc1ba0ba6f3f0610

[ "BSD-3-Clause" ]

null

# Copyright (c) 2008 Princeton University # Copyright (c) 2009 Advanced Micro Devices, Inc. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer; # redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution; # neither the name of the copyright holders nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER 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. # # Author: Tushar Krishna # from m5.params import * from m5.proxy import * from m5.objects.Network import RubyNetwork from m5.objects.BasicRouter import BasicRouter from m5.objects.ClockedObject import ClockedObject class GarnetNetwork(RubyNetwork): type = 'GarnetNetwork' cxx_header = "mem/ruby/network/garnet/GarnetNetwork.hh" cxx_class = 'gem5::ruby::garnet::GarnetNetwork' num_rows = Param.Int(0, "number of rows if 2D (mesh/torus/..) topology"); num_cols = Param.Int(0, "number of columns if 2D (mesh/torus..) topology"); z_depth = Param.Int(0, "length of the z-dimension"); num_chiplets_x = Param.Int(0, "number of chiplets in the x-dimension"); num_chiplets_y = Param.Int(0, "number of chiplets in the y-dimension"); nu_chiplets_input = Param.String("", "non-uniform chiplet designation (start col, start row, end col, end row)"); wireless_input = Param.String("", "wireless router designation (if random, then string will define number of wireless antennas to be placed per layer, if user-defined, then string will define exact routers the antennas will be placed on x,y,z,x,y,z..."); wireless_input_pattern = Param.String("", "wireless antenna placement pattern (r=random, u=user-defined)"); wireless_width = Param.Int(0, "width of wireless routers"); wired_width = Param.Int(0, "width of wired routers"); ni_flit_size = Param.UInt32(16, "network interface flit size in bytes") vcs_per_vnet = Param.UInt32(4, "virtual channels per virtual network"); buffers_per_data_vc = Param.UInt32(4, "buffers per data virtual channel"); buffers_per_ctrl_vc = Param.UInt32(1, "buffers per ctrl virtual channel"); routing_algorithm = Param.Int(0, "0: Weight-based Table, 1: XY, 2:XYZ, 3:U_CHIPLETS, 4:NU_CHIPLETS, 5:WIRELESS"); enable_fault_model = Param.Bool(False, "enable network fault model"); fault_model = Param.FaultModel(NULL, "network fault model"); garnet_deadlock_threshold = Param.UInt32(50000, "network-level deadlock threshold") class GarnetNetworkInterface(ClockedObject): type = 'GarnetNetworkInterface' cxx_class = 'gem5::ruby::garnet::NetworkInterface' cxx_header = "mem/ruby/network/garnet/NetworkInterface.hh" id = Param.UInt32("ID in relation to other network interfaces") vcs_per_vnet = Param.UInt32(Parent.vcs_per_vnet, "virtual channels per virtual network") virt_nets = Param.UInt32(Parent.number_of_virtual_networks, "number of virtual networks") garnet_deadlock_threshold = Param.UInt32(Parent.garnet_deadlock_threshold, "network-level deadlock threshold") class GarnetRouter(BasicRouter): type = 'GarnetRouter' cxx_class = 'gem5::ruby::garnet::Router' cxx_header = "mem/ruby/network/garnet/Router.hh" vcs_per_vnet = Param.UInt32(Parent.vcs_per_vnet, "virtual channels per virtual network") virt_nets = Param.UInt32(Parent.number_of_virtual_networks, "number of virtual networks") width = Param.UInt32(Parent.ni_flit_size, "bit width supported by the router")

55.344828

258

0.727726

from m5.params import * from m5.proxy import * from m5.objects.Network import RubyNetwork from m5.objects.BasicRouter import BasicRouter from m5.objects.ClockedObject import ClockedObject class GarnetNetwork(RubyNetwork): type = 'GarnetNetwork' cxx_header = "mem/ruby/network/garnet/GarnetNetwork.hh" cxx_class = 'gem5::ruby::garnet::GarnetNetwork' num_rows = Param.Int(0, "number of rows if 2D (mesh/torus/..) topology"); num_cols = Param.Int(0, "number of columns if 2D (mesh/torus..) topology"); z_depth = Param.Int(0, "length of the z-dimension"); num_chiplets_x = Param.Int(0, "number of chiplets in the x-dimension"); num_chiplets_y = Param.Int(0, "number of chiplets in the y-dimension"); nu_chiplets_input = Param.String("", "non-uniform chiplet designation (start col, start row, end col, end row)"); wireless_input = Param.String("", "wireless router designation (if random, then string will define number of wireless antennas to be placed per layer, if user-defined, then string will define exact routers the antennas will be placed on x,y,z,x,y,z..."); wireless_input_pattern = Param.String("", "wireless antenna placement pattern (r=random, u=user-defined)"); wireless_width = Param.Int(0, "width of wireless routers"); wired_width = Param.Int(0, "width of wired routers"); ni_flit_size = Param.UInt32(16, "network interface flit size in bytes") vcs_per_vnet = Param.UInt32(4, "virtual channels per virtual network"); buffers_per_data_vc = Param.UInt32(4, "buffers per data virtual channel"); buffers_per_ctrl_vc = Param.UInt32(1, "buffers per ctrl virtual channel"); routing_algorithm = Param.Int(0, "0: Weight-based Table, 1: XY, 2:XYZ, 3:U_CHIPLETS, 4:NU_CHIPLETS, 5:WIRELESS"); enable_fault_model = Param.Bool(False, "enable network fault model"); fault_model = Param.FaultModel(NULL, "network fault model"); garnet_deadlock_threshold = Param.UInt32(50000, "network-level deadlock threshold") class GarnetNetworkInterface(ClockedObject): type = 'GarnetNetworkInterface' cxx_class = 'gem5::ruby::garnet::NetworkInterface' cxx_header = "mem/ruby/network/garnet/NetworkInterface.hh" id = Param.UInt32("ID in relation to other network interfaces") vcs_per_vnet = Param.UInt32(Parent.vcs_per_vnet, "virtual channels per virtual network") virt_nets = Param.UInt32(Parent.number_of_virtual_networks, "number of virtual networks") garnet_deadlock_threshold = Param.UInt32(Parent.garnet_deadlock_threshold, "network-level deadlock threshold") class GarnetRouter(BasicRouter): type = 'GarnetRouter' cxx_class = 'gem5::ruby::garnet::Router' cxx_header = "mem/ruby/network/garnet/Router.hh" vcs_per_vnet = Param.UInt32(Parent.vcs_per_vnet, "virtual channels per virtual network") virt_nets = Param.UInt32(Parent.number_of_virtual_networks, "number of virtual networks") width = Param.UInt32(Parent.ni_flit_size, "bit width supported by the router")

true

f71957077b0dab80c0b89a88133f80aa1b7d1e31

23,266

py

Python

data_generators/data_options.py

Zack-Quintana/underdog-devs-ds-a

743c1b977eb52c1ca536df927ab1474949f1bd90

[ "MIT" ]

null

data_generators/data_options.py

Zack-Quintana/underdog-devs-ds-a

743c1b977eb52c1ca536df927ab1474949f1bd90

[ "MIT" ]

null

data_generators/data_options.py

Zack-Quintana/underdog-devs-ds-a

743c1b977eb52c1ca536df927ab1474949f1bd90

[ "MIT" ]

null

import string from itertools import chain from math import ceil, floor from random import randint, choice, random, choices, shuffle male_first_names = ( "Liam", "Noah", "Oliver", "Elijah", "William", "James", "Benjamin", "Lucas", "Henry", "Alexander", "Mason", "Michael", "Ethan", "Daniel", "Jacob", "Logan", "Jackson", "Levi", "Sebastian", "Mateo", "Jack", "Owen", "Theodore", "Aiden", "Samuel", "Joseph", "John", "David", "Wyatt", "Matthew", "Luke", "Asher", "Carter", "Julian", "Grayson", "Leo", "Jayden", "Gabriel", "Isaac", "Lincoln", "Anthony", "Hudson", "Dylan", "Ezra", "Thomas", "Charles", "Christopher", "Jaxon", "Maverick", "Josiah", "Isaiah", "Andrew", "Elias", "Joshua", "Nathan", "Caleb", "Ryan", "Adrian", "Miles", "Eli", "Nolan", "Christian", "Aaron", "Cameron", "Ezekiel", "Colton", "Luca", "Landon", "Hunter", "Jonathan", "Santiago", "Axel", "Easton", "Cooper", "Jeremiah", "Angel", "Roman", "Connor", "Jameson", "Robert", "Greyson", "Jordan", "Ian", "Carson", "Jaxson", "Leonardo", "Nicholas", "Dominic", "Austin", "Everett", "Brooks", "Xavier", "Kai", "Jose", "Parker", "Adam", "Jace", "Wesley", "Kayden", "Silas", "Bennett", "Declan", "Waylon", "Weston", "Evan", "Emmett", "Micah", "Ryder", "Beau", "Damian", "Brayden", "Gael", "Rowan", "Harrison", "Bryson", "Sawyer", "Amir", "Kingston", "Jason", "Giovanni", "Vincent", "Ayden", "Chase", "Myles", "Diego", "Nathaniel", "Legend", "Jonah", "River", "Tyler", "Cole", "Braxton", "George", "Milo", "Zachary", "Ashton", "Luis", "Jasper", "Kaiden", "Adriel", "Gavin", "Bentley", "Calvin", "Zion", "Juan", "Maxwell", "Max", "Ryker", "Carlos", "Emmanuel", "Jayce", "Lorenzo", "Ivan", "Jude", "August", "Kevin", "Malachi", "Elliott", "Rhett", "Archer", "Karter", "Arthur", "Luka", "Elliot", "Thiago", "Brandon", "Camden", "Justin", "Jesus", "Maddox", "King", "Theo", "Enzo", "Matteo", "Emiliano", "Dean", "Hayden", "Finn", "Brody", "Antonio", "Abel", "Alex", "Tristan", "Graham", "Zayden", "Judah", "Xander", "Miguel", "Atlas", "Messiah", "Barrett", "Tucker", "Timothy", "Alan", "Edward", "Leon", "Dawson", "Eric", "Ace", "Victor", "Abraham", "Nicolas", "Jesse", "Charlie", "Patrick", "Walker", "Joel", "Richard", "Beckett", "Blake", "Alejandro", "Avery", "Grant", "Peter", "Oscar", "Matias", "Amari", "Lukas", "Andres", "Arlo", "Colt", "Adonis", "Kyrie", "Steven", "Felix", "Preston", "Marcus", "Holden", "Emilio", "Remington", "Jeremy", "Kaleb", "Brantley", "Bryce", "Mark", "Knox", "Israel", "Phoenix", "Kobe", "Nash", "Griffin", "Caden", "Kenneth", "Kyler", "Hayes", "Jax", "Rafael", "Beckham", "Javier", "Maximus", "Simon", "Paul", "Omar", "Kaden", "Kash", "Lane", "Bryan", "Riley", "Zane", "Louis", "Aidan", "Paxton", "Maximiliano", "Karson", "Cash", "Cayden", "Emerson", "Tobias", "Ronan", "Brian", "Dallas", "Bradley", "Jorge", "Walter", "Josue", "Khalil", "Damien", "Jett", "Kairo", "Zander", "Andre", "Cohen", "Crew", "Hendrix", "Colin", "Chance", "Malakai", "Clayton", "Daxton", "Malcolm", "Lennox", "Martin", "Jaden", "Kayson", "Bodhi", "Francisco", "Cody", "Erick", "Kameron", "Atticus", "Dante", "Jensen", "Cruz", "Finley", "Brady", "Joaquin", "Anderson", "Gunner", "Muhammad", "Zayn", "Derek", "Raymond", "Kyle", "Angelo", "Reid", "Spencer", "Nico", "Jaylen", "Jake", "Prince", "Manuel", "Ali", "Gideon", "Stephen", "Ellis", "Orion", "Rylan", "Eduardo", "Mario", "Rory", "Cristian", "Odin", "Tanner", "Julius", "Callum", "Sean", "Kane", "Ricardo", "Travis", "Wade", "Warren", "Fernando", "Titus", "Leonel", "Edwin", "Cairo", "Corbin", "Dakota", "Ismael", "Colson", "Killian", "Major", "Tate", "Gianni", "Elian", "Remy", "Lawson", "Niko", "Nasir", "Kade", "Armani", "Ezequiel", "Marshall", "Hector", "Desmond", "Kason", "Garrett", "Jared", "Cyrus", "Russell", "Cesar", "Tyson", "Malik", "Donovan", "Jaxton", "Cade", "Romeo", "Nehemiah", "Sergio", "Iker", "Caiden", "Jay", "Pablo", "Devin", "Jeffrey", "Otto", "Kamari", "Ronin", "Johnny", "Clark", "Ari", "Marco", "Edgar", "Bowen", "Jaiden", "Grady", "Zayne", "Sullivan", "Jayceon", "Sterling", "Andy", "Conor", "Raiden", "Royal", "Royce", "Solomon", "Trevor", "Winston", "Emanuel", "Finnegan", "Pedro", "Luciano", "Harvey", "Franklin", "Noel", "Troy", "Princeton", "Johnathan", "Erik", "Fabian", "Oakley", "Rhys", "Porter", "Hugo", "Frank", "Damon", "Kendrick", "Mathias", "Milan", "Peyton", "Wilder", "Callan", "Gregory", "Seth", "Matthias", "Briggs", "Ibrahim", "Roberto", "Conner", "Quinn", "Kashton", "Sage", "Santino", "Kolton", "Alijah", "Dominick", "Zyaire", "Apollo", "Kylo", "Reed", "Philip", "Kian", "Shawn", "Kaison", "Leonidas", "Ayaan", "Lucca", "Memphis", "Ford", "Baylor", "Kyson", "Uriel", "Allen", "Collin", "Ruben", "Archie", "Dalton", "Esteban", "Adan", "Forrest", "Alonzo", "Isaias", "Leland", "Jase", "Dax", "Kasen", "Gage", "Kamden", "Marcos", "Jamison", "Francis", "Hank", "Alexis", "Tripp", "Frederick", "Jonas", "Stetson", "Cassius", "Izaiah", "Eden", "Maximilian", "Rocco", "Tatum", "Keegan", "Aziel", "Moses", "Bruce", "Lewis", "Braylen", "Omari", "Mack", "Augustus", "Enrique", "Armando", "Pierce", "Moises", "Asa", "Shane", "Emmitt", "Soren", "Dorian", "Keanu", "Zaiden", "Raphael", "Deacon", "Abdiel", "Kieran", "Phillip", "Ryland", "Zachariah", "Casey", "Zaire", "Albert", "Baker", "Corey", "Kylan", "Denver", "Gunnar", "Jayson", "Drew", "Callen", "Jasiah", "Drake", "Kannon", "Braylon", "Sonny", "Bo", "Moshe", "Huxley", "Quentin", "Rowen", "Santana", "Cannon", "Kenzo", "Wells", "Julio", "Nikolai", "Conrad", "Jalen", "Makai", "Benson", "Derrick", "Gerardo", "Davis", "Abram", "Mohamed", "Ronald", "Raul", "Arjun", "Dexter", "Kaysen", "Jaime", "Scott", "Lawrence", "Ariel", "Skyler", "Danny", "Roland", "Chandler", "Yusuf", "Samson", "Case", "Zain", "Roy", "Rodrigo", "Sutton", "Boone", "Saint", "Saul", "Jaziel", "Hezekiah", "Alec", "Arturo", "Jamari", "Jaxtyn", "Julien", "Koa", "Reece", "Landen", "Koda", "Darius", "Sylas", "Ares", "Kyree", "Boston", "Keith", "Taylor", "Johan", "Edison", "Sincere", "Watson", "Jerry", "Nikolas", "Quincy", "Shepherd", "Brycen", "Marvin", "Dariel", "Axton", "Donald", "Bodie", "Finnley", "Onyx", "Rayan", "Raylan", "Brixton", "Colby", "Shiloh", "Valentino", "Layton", "Trenton", "Landyn", "Alessandro", "Ahmad", "Gustavo", "Ledger", "Ridge", "Ander", "Ahmed", "Kingsley", "Issac", "Mauricio", "Tony", "Leonard", "Mohammed", "Uriah", "Duke", "Kareem", "Lucian", "Marcelo", "Aarav", "Leandro", "Reign", "Clay", "Kohen", "Dennis", "Samir", "Ermias", "Otis", "Emir", "Nixon", "Ty", "Sam", "Fletcher", "Wilson", "Dustin", "Hamza", "Bryant", "Flynn", "Lionel", "Mohammad", "Cason", "Jamir", "Aden", "Dakari", "Justice", "Dillon", "Layne", "Zaid", "Alden", "Nelson", "Devon", "Titan", "Chris", "Khari", "Zeke", "Noe", "Alberto", "Roger", "Brock", "Rex", "Quinton", "Alvin", "Cullen", "Azariah", "Harlan", "Kellan", "Lennon", "Marcel", "Keaton", "Morgan", "Ricky", "Trey", "Karsyn", "Langston", "Miller", "Chaim", "Salvador", "Amias", "Tadeo", "Curtis", "Lachlan", "Amos", "Anakin", "Krew", "Tomas", "Jefferson", "Yosef", "Bruno", "Korbin", "Augustine", "Cayson", "Mathew", "Vihaan", "Jamie", "Clyde", "Brendan", "Jagger", "Carmelo", "Harry", "Nathanael", "Mitchell", "Darren", "Ray", "Jedidiah", "Jimmy", "Lochlan", "Bellamy", "Eddie", "Rayden", "Reese", "Stanley", "Joe", "Houston", "Douglas", "Vincenzo", "Casen", "Emery", "Joziah", "Leighton", "Marcellus", "Atreus", "Aron", "Hugh", "Musa", "Tommy", "Alfredo", "Junior", "Neil", "Westley", "Banks", "Eliel", "Melvin", "Maximo", "Briar", "Colten", "Lance", "Nova", "Trace", "Axl", "Ramon", "Vicente", "Brennan", "Caspian", "Remi", "Deandre", "Legacy", "Lee", "Valentin", "Ben", "Louie", "Westin", "Wayne", "Benicio", "Grey", "Zayd", "Gatlin", "Mekhi", "Orlando", "Bjorn", "Harley", "Alonso", "Rio", "Aldo", "Byron", "Eliseo", "Ernesto", "Talon", "Thaddeus", "Brecken", "Kace", "Kellen", "Enoch", "Kiaan", "Lian", "Creed", "Rohan", "Callahan", "Jaxxon", "Ocean", "Crosby", "Dash", "Gary", "Mylo", "Ira", "Magnus", "Salem", "Abdullah", "Kye", "Tru", "Forest", "Jon", "Misael", "Madden", "Braden", "Carl", "Hassan", "Emory", "Kristian", "Alaric", "Ambrose", "Dario", "Allan", "Bode", "Boden", "Juelz", "Kristopher", "Genesis", "Idris", "Ameer", "Anders", "Darian", "Kase", "Aryan", "Dane", "Guillermo", "Elisha", "Jakobe", "Thatcher", "Eugene", "Ishaan", "Larry", "Wesson", "Yehuda", "Alvaro", "Bobby", "Bronson", "Dilan", "Kole", "Kyro", "Tristen", "Blaze", "Brayan", "Jadiel", "Kamryn", "Demetrius", "Maurice", "Arian", "Kabir", "Rocky", "Rudy", "Randy", "Rodney", "Yousef", "Felipe", "Robin", "Aydin", "Dior", "Kaiser", "Van", "Brodie", "London", "Eithan", "Stefan", "Ulises", "Camilo", "Branson", "Jakari", "Judson", "Yahir", "Zavier", "Damari", "Jakob", "Jaxx", "Bentlee", "Cain", "Niklaus", "Rey", "Zahir", "Aries", "Blaine", "Kyng", "Castiel", "Henrik", "Joey", "Khalid", "Bear", "Graysen", "Jair", "Kylen", "Darwin", "Alfred", "Ayan", "Kenji", "Zakai", "Avi", "Cory", "Fisher", "Jacoby", "Osiris", "Harlem", "Jamal", "Santos", "Wallace", "Brett", "Fox", "Leif", "Maison", "Reuben", "Adler", "Zev", "Calum", "Kelvin", "Zechariah", "Bridger", "Mccoy", "Seven", "Shepard", "Azrael", "Leroy", "Terry", "Harold", "Mac", "Mordechai", "Ahmir", "Cal", "Franco", "Trent", "Blaise", "Coen", "Dominik", "Marley", "Davion", "Jeremias", "Riggs", "Jones", "Will", "Damir", "Dangelo", "Canaan", "Dion", "Jabari", "Landry", "Salvatore", "Kody", "Hakeem", "Truett", "Gerald", "Lyric", "Gordon", "Jovanni", "Kamdyn", "Alistair", "Cillian", "Foster", "Terrance", "Murphy", "Zyair", "Cedric", "Rome", "Abner", "Colter", "Dayton", "Jad", "Xzavier", "Rene", "Vance", "Duncan", "Frankie", "Bishop", "Davian", "Everest", "Heath", "Jaxen", "Marlon", "Maxton", "Reginald", "Harris", "Jericho", "Keenan", "Korbyn", "Wes", "Eliezer", "Jeffery", "Kalel", "Kylian", "Turner", "Willie", "Rogelio", "Ephraim", ) female_first_names = ( "Olivia", "Emma", "Ava", "Charlotte", "Sophia", "Amelia", "Isabella", "Mia", "Evelyn", "Harper", "Camila", "Gianna", "Abigail", "Luna", "Ella", "Elizabeth", "Sofia", "Emily", "Avery", "Mila", "Scarlett", "Eleanor", "Madison", "Layla", "Penelope", "Aria", "Chloe", "Grace", "Ellie", "Nora", "Hazel", "Zoey", "Riley", "Victoria", "Lily", "Aurora", "Violet", "Nova", "Hannah", "Emilia", "Zoe", "Stella", "Everly", "Isla", "Leah", "Lillian", "Addison", "Willow", "Lucy", "Paisley", "Natalie", "Naomi", "Eliana", "Brooklyn", "Elena", "Aubrey", "Claire", "Ivy", "Kinsley", "Audrey", "Maya", "Genesis", "Skylar", "Bella", "Aaliyah", "Madelyn", "Savannah", "Anna", "Delilah", "Serenity", "Caroline", "Kennedy", "Valentina", "Ruby", "Sophie", "Alice", "Gabriella", "Sadie", "Ariana", "Allison", "Hailey", "Autumn", "Nevaeh", "Natalia", "Quinn", "Josephine", "Sarah", "Cora", "Emery", "Samantha", "Piper", "Leilani", "Eva", "Everleigh", "Madeline", "Lydia", "Jade", "Peyton", "Brielle", "Adeline", "Vivian", "Rylee", "Clara", "Raelynn", "Melanie", "Melody", "Julia", "Athena", "Maria", "Liliana", "Hadley", "Arya", "Rose", "Reagan", "Eliza", "Adalynn", "Kaylee", "Lyla", "Mackenzie", "Alaia", "Isabelle", "Charlie", "Arianna", "Mary", "Remi", "Margaret", "Iris", "Parker", "Ximena", "Eden", "Ayla", "Kylie", "Elliana", "Josie", "Katherine", "Faith", "Alexandra", "Eloise", "Adalyn", "Amaya", "Jasmine", "Amara", "Daisy", "Reese", "Valerie", "Brianna", "Cecilia", "Andrea", "Summer", "Valeria", "Norah", "Ariella", "Esther", "Ashley", "Emerson", "Aubree", "Isabel", "Anastasia", "Ryleigh", "Khloe", "Taylor", "Londyn", "Lucia", "Emersyn", "Callie", "Sienna", "Blakely", "Kehlani", "Genevieve", "Alina", "Bailey", "Juniper", "Maeve", "Molly", "Harmony", "Georgia", "Magnolia", "Catalina", "Freya", "Juliette", "Sloane", "June", "Sara", "Ada", "Kimberly", "River", "Ember", "Juliana", "Aliyah", "Millie", "Brynlee", "Teagan", "Morgan", "Jordyn", "London", "Alaina", "Olive", "Rosalie", "Alyssa", "Ariel", "Finley", "Arabella", "Journee", "Hope", "Leila", "Alana", "Gemma", "Vanessa", "Gracie", "Noelle", "Marley", "Elise", "Presley", "Kamila", "Zara", "Amy", "Kayla", "Payton", "Blake", "Ruth", "Alani", "Annabelle", "Sage", "Aspen", "Laila", "Lila", "Rachel", "Trinity", "Daniela", "Alexa", "Lilly", "Lauren", "Elsie", "Margot", "Adelyn", "Zuri", "Brooke", "Sawyer", "Lilah", "Lola", "Selena", "Mya", "Sydney", "Diana", "Ana", "Vera", "Alayna", "Nyla", "Elaina", "Rebecca", "Angela", "Kali", "Alivia", "Raegan", "Rowan", "Phoebe", "Camilla", "Joanna", "Malia", "Vivienne", "Dakota", "Brooklynn", "Evangeline", "Camille", "Jane", "Nicole", "Catherine", "Jocelyn", "Julianna", "Lena", "Lucille", "Mckenna", "Paige", "Adelaide", "Charlee", "Mariana", "Myla", "Mckenzie", "Tessa", "Miriam", "Oakley", "Kailani", "Alayah", "Amira", "Adaline", "Phoenix", "Milani", "Annie", "Lia", "Angelina", "Harley", "Cali", "Maggie", "Hayden", "Leia", "Fiona", "Briella", "Journey", "Lennon", "Saylor", "Jayla", "Kaia", "Thea", "Adriana", "Mariah", "Juliet", "Oaklynn", "Kiara", "Alexis", "Haven", "Aniyah", "Delaney", "Gracelynn", "Kendall", "Winter", "Lilith", "Logan", "Amiyah", "Evie", "Alexandria", "Gracelyn", "Gabriela", "Sutton", "Harlow", "Madilyn", "Makayla", "Evelynn", "Gia", "Nina", "Amina", "Giselle", "Brynn", "Blair", "Amari", "Octavia", "Michelle", "Talia", "Demi", "Alaya", "Kaylani", "Izabella", "Fatima", "Tatum", "Makenzie", "Lilliana", "Arielle", "Palmer", "Melissa", "Willa", "Samara", "Destiny", "Dahlia", "Celeste", "Ainsley", "Rylie", "Reign", "Laura", "Adelynn", "Gabrielle", "Remington", "Wren", "Brinley", "Amora", "Lainey", "Collins", "Lexi", "Aitana", "Alessandra", "Kenzie", "Raelyn", "Elle", "Everlee", "Haisley", "Hallie", "Wynter", "Daleyza", "Gwendolyn", "Paislee", "Ariyah", "Veronica", "Heidi", "Anaya", "Cataleya", "Kira", "Avianna", "Felicity", "Aylin", "Miracle", "Sabrina", "Lana", "Ophelia", "Elianna", "Royalty", "Madeleine", "Esmeralda", "Joy", "Kalani", "Esme", "Jessica", "Leighton", "Ariah", "Makenna", "Nylah", "Viviana", "Camryn", "Cassidy", "Dream", "Luciana", "Maisie", "Stevie", "Kate", "Lyric", "Daniella", "Alicia", "Daphne", "Frances", "Charli", "Raven", "Paris", "Nayeli", "Serena", "Heaven", "Bianca", "Helen", "Hattie", "Averie", "Mabel", "Selah", "Allie", "Marlee", "Kinley", "Regina", "Carmen", "Jennifer", "Jordan", "Alison", "Stephanie", "Maren", "Kayleigh", "Angel", "Annalise", "Jacqueline", "Braelynn", "Emory", "Rosemary", "Scarlet", "Amanda", "Danielle", "Emelia", "Ryan", "Carolina", "Astrid", "Kensley", "Shiloh", "Maci", "Francesca", "Rory", "Celine", "Kamryn", "Zariah", "Liana", "Poppy", "Maliyah", "Keira", "Skyler", "Noa", "Skye", "Nadia", "Addilyn", "Rosie", "Eve", "Sarai", "Edith", "Jolene", "Maddison", "Meadow", "Charleigh", "Matilda", "Elliott", "Madelynn", "Holly", "Leona", "Azalea", "Katie", "Mira", "Ari", "Kaitlyn", "Danna", "Cameron", "Kyla", "Bristol", "Kora", "Armani", "Nia", "Malani", "Dylan", "Remy", "Maia", "Dior", "Legacy", "Alessia", "Shelby", "Maryam", "Sylvia", "Yaretzi", "Lorelei", "Madilynn", "Abby", "Helena", "Jimena", "Elisa", "Renata", "Amber", "Aviana", "Carter", "Emmy", "Haley", "Alondra", "Elaine", "Erin", "April", "Emely", "Imani", "Kennedi", "Lorelai", "Hanna", "Kelsey", "Aurelia", "Colette", "Jaliyah", "Kylee", "Macie", "Aisha", "Dorothy", "Charley", "Kathryn", "Adelina", "Adley", "Monroe", "Sierra", "Ailani", "Miranda", "Mikayla", "Alejandra", "Amirah", "Jada", "Jazlyn", "Jenna", "Jayleen", "Beatrice", "Kendra", "Lyra", "Nola", "Emberly", "Mckinley", "Myra", "Katalina", "Antonella", "Zelda", "Alanna", "Amaia", "Priscilla", "Briar", "Kaliyah", "Itzel", "Oaklyn", "Alma", "Mallory", "Novah", "Amalia", "Fernanda", "Alia", "Angelica", "Elliot", "Justice", "Mae", "Cecelia", "Gloria", "Ariya", "Virginia", "Cheyenne", "Aleah", "Jemma", "Henley", "Meredith", "Leyla", "Lennox", "Ensley", "Zahra", "Reina", "Frankie", "Lylah", "Nalani", "Reyna", "Saige", "Ivanna", "Aleena", "Emerie", "Ivory", "Leslie", "Alora", "Ashlyn", "Bethany", "Bonnie", "Sasha", "Xiomara", "Salem", "Adrianna", "Dayana", "Clementine", "Karina", "Karsyn", "Emmie", "Julie", "Julieta", "Briana", "Carly", "Macy", "Marie", "Oaklee", "Christina", "Malaysia", "Ellis", "Irene", "Anne", "Anahi", "Mara", "Rhea", "Davina", "Dallas", "Jayda", "Mariam", "Skyla", "Siena", "Elora", "Marilyn", "Jazmin", "Megan", "Rosa", "Savanna", "Allyson", "Milan", "Coraline", "Johanna", "Melany", "Chelsea", "Michaela", "Melina", "Angie", "Cassandra", "Yara", "Kassidy", "Liberty", "Lilian", "Avah", "Anya", "Laney", "Navy", "Opal", "Amani", "Zaylee", "Mina", "Sloan", "Romina", "Ashlynn", "Aliza", "Liv", "Malaya", "Blaire", "Janelle", "Kara", "Analia", "Hadassah", "Hayley", "Karla", "Chaya", "Cadence", "Kyra", "Alena", "Ellianna", "Katelyn", "Kimber", "Laurel", "Lina", "Capri", "Braelyn", "Faye", "Kamiyah", "Kenna", "Louise", "Calliope", "Kaydence", "Nala", "Tiana", "Aileen", "Sunny", "Zariyah", "Milana", "Giuliana", "Eileen", "Elodie", "Rayna", "Monica", "Galilea", "Journi", "Lara", "Marina", "Aliana", "Harmoni", "Jamie", "Holland", "Emmalyn", "Lauryn", "Chanel", "Tinsley", "Jessie", "Lacey", "Elyse", "Janiyah", "Jolie", "Ezra", "Marleigh", "Roselyn", "Lillie", "Louisa", "Madisyn", "Penny", "Kinslee", "Treasure", "Zaniyah", "Estella", "Jaylah", "Khaleesi", "Alexia", "Dulce", "Indie", "Maxine", "Waverly", "Giovanna", "Miley", "Saoirse", "Estrella", "Greta", "Rosalia", "Mylah", "Teresa", "Bridget", "Kelly", "Adalee", "Aubrie", "Lea", "Harlee", "Anika", "Itzayana", "Hana", "Kaisley", "Mikaela", "Naya", "Avalynn", "Margo", "Sevyn", "Florence", "Keilani", "Lyanna", "Joelle", "Kataleya", "Royal", "Averi", "Kallie", "Winnie", "Baylee", "Martha", "Pearl", "Alaiya", "Rayne", "Sylvie", "Brylee", "Jazmine", "Ryann", "Kori", "Noemi", "Haylee", "Julissa", "Celia", "Laylah", "Rebekah", "Rosalee", "Aya", "Bria", "Adele", "Aubrielle", "Tiffany", "Addyson", "Kai", "Bellamy", "Leilany", "Princess", "Chana", "Estelle", "Selene", "Sky", "Dani", "Thalia", "Ellen", "Rivka", "Amelie", "Andi", "Kynlee", "Raina", "Vienna", "Alianna", "Livia", "Madalyn", "Mercy", "Novalee", "Ramona", "Vada", "Berkley", "Gwen", "Persephone", "Milena", "Paula", "Clare", "Kairi", "Linda", "Paulina", "Kamilah", "Amoura", "Hunter", "Isabela", "Karen", "Marianna", "Sariyah", "Theodora", "Annika", "Kyleigh", "Nellie", "Scarlette", "Keyla", "Kailey", "Mavis", "Lilianna", "Rosalyn", "Sariah", "Tori", "Yareli", "Aubriella", "Bexley", "Bailee", "Jianna", "Keily", "Annabella", "Azariah", "Denisse", "Promise", "August", "Hadlee", "Halle", "Fallon", "Oakleigh", "Zaria", "Jaylin", "Paisleigh", "Crystal", "Ila", "Aliya", "Cynthia", "Giana", "Maleah", "Rylan", "Aniya", "Denise", "Emmeline", "Scout", "Simone", "Noah", "Zora", "Meghan", "Landry", "Ainhoa", "Lilyana", "Noor", "Belen", "Brynleigh", "Cleo", "Meilani", "Karter", "Amaris", "Frida", "Iliana", "Violeta", "Addisyn", "Nancy", "Denver", "Leanna", "Braylee", "Kiana", "Wrenley", "Barbara", "Khalani", "Aspyn", "Ellison", "Judith", "Robin", "Valery", "Aila", "Deborah", "Cara", "Clarissa", "Iyla", "Lexie", "Anais", "Kaylie", "Nathalie", "Alisson", "Della", "Addilynn", "Elsa", "Zoya", "Layne", "Marlowe", "Jovie", "Kenia", "Samira", "Jaylee", "Jenesis", "Etta", "Shay", "Amayah", "Avayah", "Egypt", "Flora", "Raquel", "Whitney", "Zola", "Giavanna", "Raya", "Veda", "Halo", "Paloma", "Nataly", "Whitley", "Dalary", "Drew", "Guadalupe", "Kamari", "Esperanza", "Loretta", "Malayah", "Natasha", "Stormi", "Ansley", "Carolyn", "Corinne", "Paola", "Brittany", "Emerald", "Freyja", "Zainab", "Artemis", "Jillian", "Kimora", "Zoie", "Aislinn", "Emmaline", "Ayleen", "Queen", "Jaycee", "Murphy", "Nyomi", "Elina", "Hadleigh", "Marceline", "Marisol", "Yasmin", "Zendaya", "Chandler", "Emani", "Jaelynn", "Kaiya", "Nathalia", "Violette", "Joyce", "Paityn", "Elisabeth", "Emmalynn", "Luella", "Yamileth", "Aarya", "Luisa", "Zhuri", "Araceli", "Harleigh", "Madalynn", "Melani", "Laylani", "Magdalena", "Mazikeen", "Belle", "Kadence", ) last_names = ( "Smith", "Johnson", "Williams", "Brown", "Jones", "Garcia", "Miller", "Davis", "Rodriguez", "Martinez", "Hernandez", "Lopez", "Gonzales", "Wilson", "Anderson", "Thomas", "Taylor", "Moore", "Jackson", "Martin", "Lee", "Perez", "Thompson", "White", "Harris", "Sanchez", "Clark", "Ramirez", "Lewis", "Robinson", "Walker", "Young", "Allen", "King", "Wright", "Scott", "Torres", "Nguyen", "Hill", "Flores", "Green", "Adams", "Nelson", "Baker", "Hall", "Rivera", "Campbell", "Mitchell", "Carter", "Roberts", "Gomez", "Phillips", "Evans", "Turner", "Diaz", "Parker", "Cruz", "Edwards", "Collins", "Reyes", "Stewart", "Morris", "Morales", "Murphy", "Cook", "Rogers", "Gutierrez", "Ortiz", "Morgan", "Cooper", "Peterson", "Bailey", "Reed", "Kelly", "Howard", "Ramos", "Kim", "Cox", "Ward", "Richardson", "Watson", "Brooks", "Chavez", "Wood", "James", "Bennet", "Gray", "Mendoza", "Ruiz", "Hughes", "Price", "Alvarez", "Castillo", "Sanders", "Patel", "Myers", "Long", "Ross", "Foster", "Jimenez", ) skill_levels = ( "Beginner", "Intermediate", "Advanced", "Expert", ) subjects = ( "Web: HTML, CSS, JavaScript", "Data Science: Python", "Android: Java", "iOS: Swift", "Career Development", "General Programming", ) resource_items = ("Laptop", "Books", "Scholarships", "Mental Health Need", "Financial stipends") disability = (True, False) work_status = (True, False) receiving_assistance = (True, False) convictions = ( "Felony", "Misdemeanor", "Infraction", ) feedbacks = ( "Not Recommended, Poor", "Conflicted, Fair", "Recommended, Good", "Highly Recommended, Very Good", "Best, Excellent", ) topics = ( "GCA Help", "Resume Help", "Job Search", "Progress Check" ) def random_first_name(percent_male: int = 50): if randint(1, 100) > percent_male: return choice(female_first_names) else: return choice(male_first_names) def percent_true(percent): return 100 * random() < percent def generate_uuid(n_len: int): n1 = ceil(n_len / 2) n2 = floor(n_len / 2) prefix = choices(string.ascii_letters, k=n1) suffix = map(str, choices(range(0, 9), k=n2)) uuid_list = list(chain(prefix, suffix)) shuffle(uuid_list) uuid = "".join(uuid_list) return uuid

65.538028

80

0.568297

import string from itertools import chain from math import ceil, floor from random import randint, choice, random, choices, shuffle male_first_names = ( "Liam", "Noah", "Oliver", "Elijah", "William", "James", "Benjamin", "Lucas", "Henry", "Alexander", "Mason", "Michael", "Ethan", "Daniel", "Jacob", "Logan", "Jackson", "Levi", "Sebastian", "Mateo", "Jack", "Owen", "Theodore", "Aiden", "Samuel", "Joseph", "John", "David", "Wyatt", "Matthew", "Luke", "Asher", "Carter", "Julian", "Grayson", "Leo", "Jayden", "Gabriel", "Isaac", "Lincoln", "Anthony", "Hudson", "Dylan", "Ezra", "Thomas", "Charles", "Christopher", "Jaxon", "Maverick", "Josiah", "Isaiah", "Andrew", "Elias", "Joshua", "Nathan", "Caleb", "Ryan", "Adrian", "Miles", "Eli", "Nolan", "Christian", "Aaron", "Cameron", "Ezekiel", "Colton", "Luca", "Landon", "Hunter", "Jonathan", "Santiago", "Axel", "Easton", "Cooper", "Jeremiah", "Angel", "Roman", "Connor", "Jameson", "Robert", "Greyson", "Jordan", "Ian", "Carson", "Jaxson", "Leonardo", "Nicholas", "Dominic", "Austin", "Everett", "Brooks", "Xavier", "Kai", "Jose", "Parker", "Adam", "Jace", "Wesley", "Kayden", "Silas", "Bennett", "Declan", "Waylon", "Weston", "Evan", "Emmett", "Micah", "Ryder", "Beau", "Damian", "Brayden", "Gael", "Rowan", "Harrison", "Bryson", "Sawyer", "Amir", "Kingston", "Jason", "Giovanni", "Vincent", "Ayden", "Chase", "Myles", "Diego", "Nathaniel", "Legend", "Jonah", "River", "Tyler", "Cole", "Braxton", "George", "Milo", "Zachary", "Ashton", "Luis", "Jasper", "Kaiden", "Adriel", "Gavin", "Bentley", "Calvin", "Zion", "Juan", "Maxwell", "Max", "Ryker", "Carlos", "Emmanuel", "Jayce", "Lorenzo", "Ivan", "Jude", "August", "Kevin", "Malachi", "Elliott", "Rhett", "Archer", "Karter", "Arthur", "Luka", "Elliot", "Thiago", "Brandon", "Camden", "Justin", "Jesus", "Maddox", "King", "Theo", "Enzo", "Matteo", "Emiliano", "Dean", "Hayden", "Finn", "Brody", "Antonio", "Abel", "Alex", "Tristan", "Graham", "Zayden", "Judah", "Xander", "Miguel", "Atlas", "Messiah", "Barrett", "Tucker", "Timothy", "Alan", "Edward", "Leon", "Dawson", "Eric", "Ace", "Victor", "Abraham", "Nicolas", "Jesse", "Charlie", "Patrick", "Walker", "Joel", "Richard", "Beckett", "Blake", "Alejandro", "Avery", "Grant", "Peter", "Oscar", "Matias", "Amari", "Lukas", "Andres", "Arlo", "Colt", "Adonis", "Kyrie", "Steven", "Felix", "Preston", "Marcus", "Holden", "Emilio", "Remington", "Jeremy", "Kaleb", "Brantley", "Bryce", "Mark", "Knox", "Israel", "Phoenix", "Kobe", "Nash", "Griffin", "Caden", "Kenneth", "Kyler", "Hayes", "Jax", "Rafael", "Beckham", "Javier", "Maximus", "Simon", "Paul", "Omar", "Kaden", "Kash", "Lane", "Bryan", "Riley", "Zane", "Louis", "Aidan", "Paxton", "Maximiliano", "Karson", "Cash", "Cayden", "Emerson", "Tobias", "Ronan", "Brian", "Dallas", "Bradley", "Jorge", "Walter", "Josue", "Khalil", "Damien", "Jett", "Kairo", "Zander", "Andre", "Cohen", "Crew", "Hendrix", "Colin", "Chance", "Malakai", "Clayton", "Daxton", "Malcolm", "Lennox", "Martin", "Jaden", "Kayson", "Bodhi", "Francisco", "Cody", "Erick", "Kameron", "Atticus", "Dante", "Jensen", "Cruz", "Finley", "Brady", "Joaquin", "Anderson", "Gunner", "Muhammad", "Zayn", "Derek", "Raymond", "Kyle", "Angelo", "Reid", "Spencer", "Nico", "Jaylen", "Jake", "Prince", "Manuel", "Ali", "Gideon", "Stephen", "Ellis", "Orion", "Rylan", "Eduardo", "Mario", "Rory", "Cristian", "Odin", "Tanner", "Julius", "Callum", "Sean", "Kane", "Ricardo", "Travis", "Wade", "Warren", "Fernando", "Titus", "Leonel", "Edwin", "Cairo", "Corbin", "Dakota", "Ismael", "Colson", "Killian", "Major", "Tate", "Gianni", "Elian", "Remy", "Lawson", "Niko", "Nasir", "Kade", "Armani", "Ezequiel", "Marshall", "Hector", "Desmond", "Kason", "Garrett", "Jared", "Cyrus", "Russell", "Cesar", "Tyson", "Malik", "Donovan", "Jaxton", "Cade", "Romeo", "Nehemiah", "Sergio", "Iker", "Caiden", "Jay", "Pablo", "Devin", "Jeffrey", "Otto", "Kamari", "Ronin", "Johnny", "Clark", "Ari", "Marco", "Edgar", "Bowen", "Jaiden", "Grady", "Zayne", "Sullivan", "Jayceon", "Sterling", "Andy", "Conor", "Raiden", "Royal", "Royce", "Solomon", "Trevor", "Winston", "Emanuel", "Finnegan", "Pedro", "Luciano", "Harvey", "Franklin", "Noel", "Troy", "Princeton", "Johnathan", "Erik", "Fabian", "Oakley", "Rhys", "Porter", "Hugo", "Frank", "Damon", "Kendrick", "Mathias", "Milan", "Peyton", "Wilder", "Callan", "Gregory", "Seth", "Matthias", "Briggs", "Ibrahim", "Roberto", "Conner", "Quinn", "Kashton", "Sage", "Santino", "Kolton", "Alijah", "Dominick", "Zyaire", "Apollo", "Kylo", "Reed", "Philip", "Kian", "Shawn", "Kaison", "Leonidas", "Ayaan", "Lucca", "Memphis", "Ford", "Baylor", "Kyson", "Uriel", "Allen", "Collin", "Ruben", "Archie", "Dalton", "Esteban", "Adan", "Forrest", "Alonzo", "Isaias", "Leland", "Jase", "Dax", "Kasen", "Gage", "Kamden", "Marcos", "Jamison", "Francis", "Hank", "Alexis", "Tripp", "Frederick", "Jonas", "Stetson", "Cassius", "Izaiah", "Eden", "Maximilian", "Rocco", "Tatum", "Keegan", "Aziel", "Moses", "Bruce", "Lewis", "Braylen", "Omari", "Mack", "Augustus", "Enrique", "Armando", "Pierce", "Moises", "Asa", "Shane", "Emmitt", "Soren", "Dorian", "Keanu", "Zaiden", "Raphael", "Deacon", "Abdiel", "Kieran", "Phillip", "Ryland", "Zachariah", "Casey", "Zaire", "Albert", "Baker", "Corey", "Kylan", "Denver", "Gunnar", "Jayson", "Drew", "Callen", "Jasiah", "Drake", "Kannon", "Braylon", "Sonny", "Bo", "Moshe", "Huxley", "Quentin", "Rowen", "Santana", "Cannon", "Kenzo", "Wells", "Julio", "Nikolai", "Conrad", "Jalen", "Makai", "Benson", "Derrick", "Gerardo", "Davis", "Abram", "Mohamed", "Ronald", "Raul", "Arjun", "Dexter", "Kaysen", "Jaime", "Scott", "Lawrence", "Ariel", "Skyler", "Danny", "Roland", "Chandler", "Yusuf", "Samson", "Case", "Zain", "Roy", "Rodrigo", "Sutton", "Boone", "Saint", "Saul", "Jaziel", "Hezekiah", "Alec", "Arturo", "Jamari", "Jaxtyn", "Julien", "Koa", "Reece", "Landen", "Koda", "Darius", "Sylas", "Ares", "Kyree", "Boston", "Keith", "Taylor", "Johan", "Edison", "Sincere", "Watson", "Jerry", "Nikolas", "Quincy", "Shepherd", "Brycen", "Marvin", "Dariel", "Axton", "Donald", "Bodie", "Finnley", "Onyx", "Rayan", "Raylan", "Brixton", "Colby", "Shiloh", "Valentino", "Layton", "Trenton", "Landyn", "Alessandro", "Ahmad", "Gustavo", "Ledger", "Ridge", "Ander", "Ahmed", "Kingsley", "Issac", "Mauricio", "Tony", "Leonard", "Mohammed", "Uriah", "Duke", "Kareem", "Lucian", "Marcelo", "Aarav", "Leandro", "Reign", "Clay", "Kohen", "Dennis", "Samir", "Ermias", "Otis", "Emir", "Nixon", "Ty", "Sam", "Fletcher", "Wilson", "Dustin", "Hamza", "Bryant", "Flynn", "Lionel", "Mohammad", "Cason", "Jamir", "Aden", "Dakari", "Justice", "Dillon", "Layne", "Zaid", "Alden", "Nelson", "Devon", "Titan", "Chris", "Khari", "Zeke", "Noe", "Alberto", "Roger", "Brock", "Rex", "Quinton", "Alvin", "Cullen", "Azariah", "Harlan", "Kellan", "Lennon", "Marcel", "Keaton", "Morgan", "Ricky", "Trey", "Karsyn", "Langston", "Miller", "Chaim", "Salvador", "Amias", "Tadeo", "Curtis", "Lachlan", "Amos", "Anakin", "Krew", "Tomas", "Jefferson", "Yosef", "Bruno", "Korbin", "Augustine", "Cayson", "Mathew", "Vihaan", "Jamie", "Clyde", "Brendan", "Jagger", "Carmelo", "Harry", "Nathanael", "Mitchell", "Darren", "Ray", "Jedidiah", "Jimmy", "Lochlan", "Bellamy", "Eddie", "Rayden", "Reese", "Stanley", "Joe", "Houston", "Douglas", "Vincenzo", "Casen", "Emery", "Joziah", "Leighton", "Marcellus", "Atreus", "Aron", "Hugh", "Musa", "Tommy", "Alfredo", "Junior", "Neil", "Westley", "Banks", "Eliel", "Melvin", "Maximo", "Briar", "Colten", "Lance", "Nova", "Trace", "Axl", "Ramon", "Vicente", "Brennan", "Caspian", "Remi", "Deandre", "Legacy", "Lee", "Valentin", "Ben", "Louie", "Westin", "Wayne", "Benicio", "Grey", "Zayd", "Gatlin", "Mekhi", "Orlando", "Bjorn", "Harley", "Alonso", "Rio", "Aldo", "Byron", "Eliseo", "Ernesto", "Talon", "Thaddeus", "Brecken", "Kace", "Kellen", "Enoch", "Kiaan", "Lian", "Creed", "Rohan", "Callahan", "Jaxxon", "Ocean", "Crosby", "Dash", "Gary", "Mylo", "Ira", "Magnus", "Salem", "Abdullah", "Kye", "Tru", "Forest", "Jon", "Misael", "Madden", "Braden", "Carl", "Hassan", "Emory", "Kristian", "Alaric", "Ambrose", "Dario", "Allan", "Bode", "Boden", "Juelz", "Kristopher", "Genesis", "Idris", "Ameer", "Anders", "Darian", "Kase", "Aryan", "Dane", "Guillermo", "Elisha", "Jakobe", "Thatcher", "Eugene", "Ishaan", "Larry", "Wesson", "Yehuda", "Alvaro", "Bobby", "Bronson", "Dilan", "Kole", "Kyro", "Tristen", "Blaze", "Brayan", "Jadiel", "Kamryn", "Demetrius", "Maurice", "Arian", "Kabir", "Rocky", "Rudy", "Randy", "Rodney", "Yousef", "Felipe", "Robin", "Aydin", "Dior", "Kaiser", "Van", "Brodie", "London", "Eithan", "Stefan", "Ulises", "Camilo", "Branson", "Jakari", "Judson", "Yahir", "Zavier", "Damari", "Jakob", "Jaxx", "Bentlee", "Cain", "Niklaus", "Rey", "Zahir", "Aries", "Blaine", "Kyng", "Castiel", "Henrik", "Joey", "Khalid", "Bear", "Graysen", "Jair", "Kylen", "Darwin", "Alfred", "Ayan", "Kenji", "Zakai", "Avi", "Cory", "Fisher", "Jacoby", "Osiris", "Harlem", "Jamal", "Santos", "Wallace", "Brett", "Fox", "Leif", "Maison", "Reuben", "Adler", "Zev", "Calum", "Kelvin", "Zechariah", "Bridger", "Mccoy", "Seven", "Shepard", "Azrael", "Leroy", "Terry", "Harold", "Mac", "Mordechai", "Ahmir", "Cal", "Franco", "Trent", "Blaise", "Coen", "Dominik", "Marley", "Davion", "Jeremias", "Riggs", "Jones", "Will", "Damir", "Dangelo", "Canaan", "Dion", "Jabari", "Landry", "Salvatore", "Kody", "Hakeem", "Truett", "Gerald", "Lyric", "Gordon", "Jovanni", "Kamdyn", "Alistair", "Cillian", "Foster", "Terrance", "Murphy", "Zyair", "Cedric", "Rome", "Abner", "Colter", "Dayton", "Jad", "Xzavier", "Rene", "Vance", "Duncan", "Frankie", "Bishop", "Davian", "Everest", "Heath", "Jaxen", "Marlon", "Maxton", "Reginald", "Harris", "Jericho", "Keenan", "Korbyn", "Wes", "Eliezer", "Jeffery", "Kalel", "Kylian", "Turner", "Willie", "Rogelio", "Ephraim", ) female_first_names = ( "Olivia", "Emma", "Ava", "Charlotte", "Sophia", "Amelia", "Isabella", "Mia", "Evelyn", "Harper", "Camila", "Gianna", "Abigail", "Luna", "Ella", "Elizabeth", "Sofia", "Emily", "Avery", "Mila", "Scarlett", "Eleanor", "Madison", "Layla", "Penelope", "Aria", "Chloe", "Grace", "Ellie", "Nora", "Hazel", "Zoey", "Riley", "Victoria", "Lily", "Aurora", "Violet", "Nova", "Hannah", "Emilia", "Zoe", "Stella", "Everly", "Isla", "Leah", "Lillian", "Addison", "Willow", "Lucy", "Paisley", "Natalie", "Naomi", "Eliana", "Brooklyn", "Elena", "Aubrey", "Claire", "Ivy", "Kinsley", "Audrey", "Maya", "Genesis", "Skylar", "Bella", "Aaliyah", "Madelyn", "Savannah", "Anna", "Delilah", "Serenity", "Caroline", "Kennedy", "Valentina", "Ruby", "Sophie", "Alice", "Gabriella", "Sadie", "Ariana", "Allison", "Hailey", "Autumn", "Nevaeh", "Natalia", "Quinn", "Josephine", "Sarah", "Cora", "Emery", "Samantha", "Piper", "Leilani", "Eva", "Everleigh", "Madeline", "Lydia", "Jade", "Peyton", "Brielle", "Adeline", "Vivian", "Rylee", "Clara", "Raelynn", "Melanie", "Melody", "Julia", "Athena", "Maria", "Liliana", "Hadley", "Arya", "Rose", "Reagan", "Eliza", "Adalynn", "Kaylee", "Lyla", "Mackenzie", "Alaia", "Isabelle", "Charlie", "Arianna", "Mary", "Remi", "Margaret", "Iris", "Parker", "Ximena", "Eden", "Ayla", "Kylie", "Elliana", "Josie", "Katherine", "Faith", "Alexandra", "Eloise", "Adalyn", "Amaya", "Jasmine", "Amara", "Daisy", "Reese", "Valerie", "Brianna", "Cecilia", "Andrea", "Summer", "Valeria", "Norah", "Ariella", "Esther", "Ashley", "Emerson", "Aubree", "Isabel", "Anastasia", "Ryleigh", "Khloe", "Taylor", "Londyn", "Lucia", "Emersyn", "Callie", "Sienna", "Blakely", "Kehlani", "Genevieve", "Alina", "Bailey", "Juniper", "Maeve", "Molly", "Harmony", "Georgia", "Magnolia", "Catalina", "Freya", "Juliette", "Sloane", "June", "Sara", "Ada", "Kimberly", "River", "Ember", "Juliana", "Aliyah", "Millie", "Brynlee", "Teagan", "Morgan", "Jordyn", "London", "Alaina", "Olive", "Rosalie", "Alyssa", "Ariel", "Finley", "Arabella", "Journee", "Hope", "Leila", "Alana", "Gemma", "Vanessa", "Gracie", "Noelle", "Marley", "Elise", "Presley", "Kamila", "Zara", "Amy", "Kayla", "Payton", "Blake", "Ruth", "Alani", "Annabelle", "Sage", "Aspen", "Laila", "Lila", "Rachel", "Trinity", "Daniela", "Alexa", "Lilly", "Lauren", "Elsie", "Margot", "Adelyn", "Zuri", "Brooke", "Sawyer", "Lilah", "Lola", "Selena", "Mya", "Sydney", "Diana", "Ana", "Vera", "Alayna", "Nyla", "Elaina", "Rebecca", "Angela", "Kali", "Alivia", "Raegan", "Rowan", "Phoebe", "Camilla", "Joanna", "Malia", "Vivienne", "Dakota", "Brooklynn", "Evangeline", "Camille", "Jane", "Nicole", "Catherine", "Jocelyn", "Julianna", "Lena", "Lucille", "Mckenna", "Paige", "Adelaide", "Charlee", "Mariana", "Myla", "Mckenzie", "Tessa", "Miriam", "Oakley", "Kailani", "Alayah", "Amira", "Adaline", "Phoenix", "Milani", "Annie", "Lia", "Angelina", "Harley", "Cali", "Maggie", "Hayden", "Leia", "Fiona", "Briella", "Journey", "Lennon", "Saylor", "Jayla", "Kaia", "Thea", "Adriana", "Mariah", "Juliet", "Oaklynn", "Kiara", "Alexis", "Haven", "Aniyah", "Delaney", "Gracelynn", "Kendall", "Winter", "Lilith", "Logan", "Amiyah", "Evie", "Alexandria", "Gracelyn", "Gabriela", "Sutton", "Harlow", "Madilyn", "Makayla", "Evelynn", "Gia", "Nina", "Amina", "Giselle", "Brynn", "Blair", "Amari", "Octavia", "Michelle", "Talia", "Demi", "Alaya", "Kaylani", "Izabella", "Fatima", "Tatum", "Makenzie", "Lilliana", "Arielle", "Palmer", "Melissa", "Willa", "Samara", "Destiny", "Dahlia", "Celeste", "Ainsley", "Rylie", "Reign", "Laura", "Adelynn", "Gabrielle", "Remington", "Wren", "Brinley", "Amora", "Lainey", "Collins", "Lexi", "Aitana", "Alessandra", "Kenzie", "Raelyn", "Elle", "Everlee", "Haisley", "Hallie", "Wynter", "Daleyza", "Gwendolyn", "Paislee", "Ariyah", "Veronica", "Heidi", "Anaya", "Cataleya", "Kira", "Avianna", "Felicity", "Aylin", "Miracle", "Sabrina", "Lana", "Ophelia", "Elianna", "Royalty", "Madeleine", "Esmeralda", "Joy", "Kalani", "Esme", "Jessica", "Leighton", "Ariah", "Makenna", "Nylah", "Viviana", "Camryn", "Cassidy", "Dream", "Luciana", "Maisie", "Stevie", "Kate", "Lyric", "Daniella", "Alicia", "Daphne", "Frances", "Charli", "Raven", "Paris", "Nayeli", "Serena", "Heaven", "Bianca", "Helen", "Hattie", "Averie", "Mabel", "Selah", "Allie", "Marlee", "Kinley", "Regina", "Carmen", "Jennifer", "Jordan", "Alison", "Stephanie", "Maren", "Kayleigh", "Angel", "Annalise", "Jacqueline", "Braelynn", "Emory", "Rosemary", "Scarlet", "Amanda", "Danielle", "Emelia", "Ryan", "Carolina", "Astrid", "Kensley", "Shiloh", "Maci", "Francesca", "Rory", "Celine", "Kamryn", "Zariah", "Liana", "Poppy", "Maliyah", "Keira", "Skyler", "Noa", "Skye", "Nadia", "Addilyn", "Rosie", "Eve", "Sarai", "Edith", "Jolene", "Maddison", "Meadow", "Charleigh", "Matilda", "Elliott", "Madelynn", "Holly", "Leona", "Azalea", "Katie", "Mira", "Ari", "Kaitlyn", "Danna", "Cameron", "Kyla", "Bristol", "Kora", "Armani", "Nia", "Malani", "Dylan", "Remy", "Maia", "Dior", "Legacy", "Alessia", "Shelby", "Maryam", "Sylvia", "Yaretzi", "Lorelei", "Madilynn", "Abby", "Helena", "Jimena", "Elisa", "Renata", "Amber", "Aviana", "Carter", "Emmy", "Haley", "Alondra", "Elaine", "Erin", "April", "Emely", "Imani", "Kennedi", "Lorelai", "Hanna", "Kelsey", "Aurelia", "Colette", "Jaliyah", "Kylee", "Macie", "Aisha", "Dorothy", "Charley", "Kathryn", "Adelina", "Adley", "Monroe", "Sierra", "Ailani", "Miranda", "Mikayla", "Alejandra", "Amirah", "Jada", "Jazlyn", "Jenna", "Jayleen", "Beatrice", "Kendra", "Lyra", "Nola", "Emberly", "Mckinley", "Myra", "Katalina", "Antonella", "Zelda", "Alanna", "Amaia", "Priscilla", "Briar", "Kaliyah", "Itzel", "Oaklyn", "Alma", "Mallory", "Novah", "Amalia", "Fernanda", "Alia", "Angelica", "Elliot", "Justice", "Mae", "Cecelia", "Gloria", "Ariya", "Virginia", "Cheyenne", "Aleah", "Jemma", "Henley", "Meredith", "Leyla", "Lennox", "Ensley", "Zahra", "Reina", "Frankie", "Lylah", "Nalani", "Reyna", "Saige", "Ivanna", "Aleena", "Emerie", "Ivory", "Leslie", "Alora", "Ashlyn", "Bethany", "Bonnie", "Sasha", "Xiomara", "Salem", "Adrianna", "Dayana", "Clementine", "Karina", "Karsyn", "Emmie", "Julie", "Julieta", "Briana", "Carly", "Macy", "Marie", "Oaklee", "Christina", "Malaysia", "Ellis", "Irene", "Anne", "Anahi", "Mara", "Rhea", "Davina", "Dallas", "Jayda", "Mariam", "Skyla", "Siena", "Elora", "Marilyn", "Jazmin", "Megan", "Rosa", "Savanna", "Allyson", "Milan", "Coraline", "Johanna", "Melany", "Chelsea", "Michaela", "Melina", "Angie", "Cassandra", "Yara", "Kassidy", "Liberty", "Lilian", "Avah", "Anya", "Laney", "Navy", "Opal", "Amani", "Zaylee", "Mina", "Sloan", "Romina", "Ashlynn", "Aliza", "Liv", "Malaya", "Blaire", "Janelle", "Kara", "Analia", "Hadassah", "Hayley", "Karla", "Chaya", "Cadence", "Kyra", "Alena", "Ellianna", "Katelyn", "Kimber", "Laurel", "Lina", "Capri", "Braelyn", "Faye", "Kamiyah", "Kenna", "Louise", "Calliope", "Kaydence", "Nala", "Tiana", "Aileen", "Sunny", "Zariyah", "Milana", "Giuliana", "Eileen", "Elodie", "Rayna", "Monica", "Galilea", "Journi", "Lara", "Marina", "Aliana", "Harmoni", "Jamie", "Holland", "Emmalyn", "Lauryn", "Chanel", "Tinsley", "Jessie", "Lacey", "Elyse", "Janiyah", "Jolie", "Ezra", "Marleigh", "Roselyn", "Lillie", "Louisa", "Madisyn", "Penny", "Kinslee", "Treasure", "Zaniyah", "Estella", "Jaylah", "Khaleesi", "Alexia", "Dulce", "Indie", "Maxine", "Waverly", "Giovanna", "Miley", "Saoirse", "Estrella", "Greta", "Rosalia", "Mylah", "Teresa", "Bridget", "Kelly", "Adalee", "Aubrie", "Lea", "Harlee", "Anika", "Itzayana", "Hana", "Kaisley", "Mikaela", "Naya", "Avalynn", "Margo", "Sevyn", "Florence", "Keilani", "Lyanna", "Joelle", "Kataleya", "Royal", "Averi", "Kallie", "Winnie", "Baylee", "Martha", "Pearl", "Alaiya", "Rayne", "Sylvie", "Brylee", "Jazmine", "Ryann", "Kori", "Noemi", "Haylee", "Julissa", "Celia", "Laylah", "Rebekah", "Rosalee", "Aya", "Bria", "Adele", "Aubrielle", "Tiffany", "Addyson", "Kai", "Bellamy", "Leilany", "Princess", "Chana", "Estelle", "Selene", "Sky", "Dani", "Thalia", "Ellen", "Rivka", "Amelie", "Andi", "Kynlee", "Raina", "Vienna", "Alianna", "Livia", "Madalyn", "Mercy", "Novalee", "Ramona", "Vada", "Berkley", "Gwen", "Persephone", "Milena", "Paula", "Clare", "Kairi", "Linda", "Paulina", "Kamilah", "Amoura", "Hunter", "Isabela", "Karen", "Marianna", "Sariyah", "Theodora", "Annika", "Kyleigh", "Nellie", "Scarlette", "Keyla", "Kailey", "Mavis", "Lilianna", "Rosalyn", "Sariah", "Tori", "Yareli", "Aubriella", "Bexley", "Bailee", "Jianna", "Keily", "Annabella", "Azariah", "Denisse", "Promise", "August", "Hadlee", "Halle", "Fallon", "Oakleigh", "Zaria", "Jaylin", "Paisleigh", "Crystal", "Ila", "Aliya", "Cynthia", "Giana", "Maleah", "Rylan", "Aniya", "Denise", "Emmeline", "Scout", "Simone", "Noah", "Zora", "Meghan", "Landry", "Ainhoa", "Lilyana", "Noor", "Belen", "Brynleigh", "Cleo", "Meilani", "Karter", "Amaris", "Frida", "Iliana", "Violeta", "Addisyn", "Nancy", "Denver", "Leanna", "Braylee", "Kiana", "Wrenley", "Barbara", "Khalani", "Aspyn", "Ellison", "Judith", "Robin", "Valery", "Aila", "Deborah", "Cara", "Clarissa", "Iyla", "Lexie", "Anais", "Kaylie", "Nathalie", "Alisson", "Della", "Addilynn", "Elsa", "Zoya", "Layne", "Marlowe", "Jovie", "Kenia", "Samira", "Jaylee", "Jenesis", "Etta", "Shay", "Amayah", "Avayah", "Egypt", "Flora", "Raquel", "Whitney", "Zola", "Giavanna", "Raya", "Veda", "Halo", "Paloma", "Nataly", "Whitley", "Dalary", "Drew", "Guadalupe", "Kamari", "Esperanza", "Loretta", "Malayah", "Natasha", "Stormi", "Ansley", "Carolyn", "Corinne", "Paola", "Brittany", "Emerald", "Freyja", "Zainab", "Artemis", "Jillian", "Kimora", "Zoie", "Aislinn", "Emmaline", "Ayleen", "Queen", "Jaycee", "Murphy", "Nyomi", "Elina", "Hadleigh", "Marceline", "Marisol", "Yasmin", "Zendaya", "Chandler", "Emani", "Jaelynn", "Kaiya", "Nathalia", "Violette", "Joyce", "Paityn", "Elisabeth", "Emmalynn", "Luella", "Yamileth", "Aarya", "Luisa", "Zhuri", "Araceli", "Harleigh", "Madalynn", "Melani", "Laylani", "Magdalena", "Mazikeen", "Belle", "Kadence", ) last_names = ( "Smith", "Johnson", "Williams", "Brown", "Jones", "Garcia", "Miller", "Davis", "Rodriguez", "Martinez", "Hernandez", "Lopez", "Gonzales", "Wilson", "Anderson", "Thomas", "Taylor", "Moore", "Jackson", "Martin", "Lee", "Perez", "Thompson", "White", "Harris", "Sanchez", "Clark", "Ramirez", "Lewis", "Robinson", "Walker", "Young", "Allen", "King", "Wright", "Scott", "Torres", "Nguyen", "Hill", "Flores", "Green", "Adams", "Nelson", "Baker", "Hall", "Rivera", "Campbell", "Mitchell", "Carter", "Roberts", "Gomez", "Phillips", "Evans", "Turner", "Diaz", "Parker", "Cruz", "Edwards", "Collins", "Reyes", "Stewart", "Morris", "Morales", "Murphy", "Cook", "Rogers", "Gutierrez", "Ortiz", "Morgan", "Cooper", "Peterson", "Bailey", "Reed", "Kelly", "Howard", "Ramos", "Kim", "Cox", "Ward", "Richardson", "Watson", "Brooks", "Chavez", "Wood", "James", "Bennet", "Gray", "Mendoza", "Ruiz", "Hughes", "Price", "Alvarez", "Castillo", "Sanders", "Patel", "Myers", "Long", "Ross", "Foster", "Jimenez", ) skill_levels = ( "Beginner", "Intermediate", "Advanced", "Expert", ) subjects = ( "Web: HTML, CSS, JavaScript", "Data Science: Python", "Android: Java", "iOS: Swift", "Career Development", "General Programming", ) resource_items = ("Laptop", "Books", "Scholarships", "Mental Health Need", "Financial stipends") disability = (True, False) work_status = (True, False) receiving_assistance = (True, False) convictions = ( "Felony", "Misdemeanor", "Infraction", ) feedbacks = ( "Not Recommended, Poor", "Conflicted, Fair", "Recommended, Good", "Highly Recommended, Very Good", "Best, Excellent", ) topics = ( "GCA Help", "Resume Help", "Job Search", "Progress Check" ) def random_first_name(percent_male: int = 50): if randint(1, 100) > percent_male: return choice(female_first_names) else: return choice(male_first_names) def percent_true(percent): return 100 * random() < percent def generate_uuid(n_len: int): n1 = ceil(n_len / 2) n2 = floor(n_len / 2) prefix = choices(string.ascii_letters, k=n1) suffix = map(str, choices(range(0, 9), k=n2)) uuid_list = list(chain(prefix, suffix)) shuffle(uuid_list) uuid = "".join(uuid_list) return uuid

true

f719579272f6a91654381f1c2c1b0fc8aede760f

1,559

py

Python

channels/streamedit.py

leigh123linux/streamtuner2

43ded3a68bcf3d968a99c849d779fc8c3fb3d8d8

[ "MIT" ]

1

2019-03-03T19:58:01.000Z

channels/streamedit.py

leigh123linux/streamtuner2

43ded3a68bcf3d968a99c849d779fc8c3fb3d8d8

[ "MIT" ]

null

channels/streamedit.py

leigh123linux/streamtuner2

43ded3a68bcf3d968a99c849d779fc8c3fb3d8d8

[ "MIT" ]

null

# api: streamtuner2 # title: Stream entry editor # description: Allows to inspect and modify station/stream entries. # version: 0.6 # type: feature # category: ui # config: - # priority: core # # Editing dialog for stream entries. Available in # the context and main menu. Most useful for # changing bookmarks, or even creating new ones. # from uikit import * import channels from config import * from copy import copy # aux win: stream data editing dialog class streamedit (AuxiliaryWindow): fields = [ "favicon", "format", "genre", "homepage", "playing", "title", "url", "extra" ] # show stream data editing dialog def open(self, mw): self.main.configwin.load_config(self.main.row(), "streamedit_") self.win_streamedit.show_all() # copy widget contents to stream def save(self, w): row = self.main.row() for k in self.fields: if not k in row: row[k] = "" self.main.configwin.save_config(row, "streamedit_") self.main.channel().save() self.cancel(w) # add a new list entry, update window def new(self, w): s = self.main.channel().stations() s.append({"title":"new", "url":"", "format":"audio/mpeg", "genre":"", "listeners":1}); self.main.channel().switch() # update display self.main.channel().gtk_list.get_selection().select_path(str(len(s)-1)); # set cursor to last row self.open(w) # hide window def cancel(self, *w): self.win_streamedit.hide() return True

27.839286

105

0.628608

from uikit import * import channels from config import * from copy import copy class streamedit (AuxiliaryWindow): fields = [ "favicon", "format", "genre", "homepage", "playing", "title", "url", "extra" ] def open(self, mw): self.main.configwin.load_config(self.main.row(), "streamedit_") self.win_streamedit.show_all() def save(self, w): row = self.main.row() for k in self.fields: if not k in row: row[k] = "" self.main.configwin.save_config(row, "streamedit_") self.main.channel().save() self.cancel(w) def new(self, w): s = self.main.channel().stations() s.append({"title":"new", "url":"", "format":"audio/mpeg", "genre":"", "listeners":1}); self.main.channel().switch() self.main.channel().gtk_list.get_selection().select_path(str(len(s)-1)); self.open(w) def cancel(self, *w): self.win_streamedit.hide() return True

true

f71957e4865285d50289aa6cd3aa2a4c44bdc813

260

py

Python

lucky-four.py

omar115/codechef

7634b085bb906e4ef29e6ae08bdbe82add2aa345

[ "MIT" ]

null

lucky-four.py

omar115/codechef

7634b085bb906e4ef29e6ae08bdbe82add2aa345

[ "MIT" ]

null

lucky-four.py

omar115/codechef

7634b085bb906e4ef29e6ae08bdbe82add2aa345

[ "MIT" ]

null

t = int(input()) i=0 while i < t: st = str(input()) length = len(st) j = 0 cnt = 0 while j < length: num = int(st[j]) #print(num) if num == 4: cnt = cnt + 1 j=j+1 print(cnt) i=i+1

16.25

25

0.388462

t = int(input()) i=0 while i < t: st = str(input()) length = len(st) j = 0 cnt = 0 while j < length: num = int(st[j]) if num == 4: cnt = cnt + 1 j=j+1 print(cnt) i=i+1

true

f71958930d922f66399bb7f49e57cab4ab83d335

12,028

py

Python

hfc/protos/peer/collection_pb2.py

roviso/hyberledger-py

908dd597e0822f99cf618f235dd517824ba44bc4

[ "Apache-2.0" ]

389

2016-09-18T11:50:10.000Z

2022-03-29T21:45:40.000Z

hfc/protos/peer/collection_pb2.py

regrlomon/fabric-sdk-py

57ddc125cd0627c602d55b300d3e0ba50600ea9e

[ "Apache-2.0" ]

112

2017-08-18T00:32:21.000Z

2022-02-25T18:55:57.000Z

hfc/protos/peer/collection_pb2.py

regrlomon/fabric-sdk-py

57ddc125cd0627c602d55b300d3e0ba50600ea9e

[ "Apache-2.0" ]

268

2016-10-12T02:56:58.000Z

2022-03-30T09:50:54.000Z

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: hfc/protos/peer/collection.proto """Generated protocol buffer code.""" from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from hfc.protos.common import policies_pb2 as hfc_dot_protos_dot_common_dot_policies__pb2 from hfc.protos.peer import policy_pb2 as hfc_dot_protos_dot_peer_dot_policy__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='hfc/protos/peer/collection.proto', package='protos', syntax='proto3', serialized_options=b'\n\"org.hyperledger.fabric.protos.peerZ,github.com/hyperledger/fabric-protos-go/peer', create_key=_descriptor._internal_create_key, serialized_pb=b'\n hfc/protos/peer/collection.proto\x12\x06protos\x1a hfc/protos/common/policies.proto\x1a\x1chfc/protos/peer/policy.proto\"C\n\x17\x43ollectionConfigPackage\x12(\n\x06\x63onfig\x18\x01 \x03(\x0b\x32\x18.protos.CollectionConfig\"a\n\x10\x43ollectionConfig\x12\x42\n\x18static_collection_config\x18\x01 \x01(\x0b\x32\x1e.protos.StaticCollectionConfigH\x00\x42\t\n\x07payload\"\x9e\x02\n\x16StaticCollectionConfig\x12\x0c\n\x04name\x18\x01 \x01(\t\x12:\n\x12member_orgs_policy\x18\x02 \x01(\x0b\x32\x1e.protos.CollectionPolicyConfig\x12\x1b\n\x13required_peer_count\x18\x03 \x01(\x05\x12\x1a\n\x12maximum_peer_count\x18\x04 \x01(\x05\x12\x15\n\rblock_to_live\x18\x05 \x01(\x04\x12\x18\n\x10member_only_read\x18\x06 \x01(\x08\x12\x19\n\x11member_only_write\x18\x07 \x01(\x08\x12\x35\n\x12\x65ndorsement_policy\x18\x08 \x01(\x0b\x32\x19.protos.ApplicationPolicy\"`\n\x16\x43ollectionPolicyConfig\x12;\n\x10signature_policy\x18\x01 \x01(\x0b\x32\x1f.common.SignaturePolicyEnvelopeH\x00\x42\t\n\x07payloadBR\n\"org.hyperledger.fabric.protos.peerZ,github.com/hyperledger/fabric-protos-go/peerb\x06proto3' , dependencies=[hfc_dot_protos_dot_common_dot_policies__pb2.DESCRIPTOR,hfc_dot_protos_dot_peer_dot_policy__pb2.DESCRIPTOR,]) _COLLECTIONCONFIGPACKAGE = _descriptor.Descriptor( name='CollectionConfigPackage', full_name='protos.CollectionConfigPackage', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='config', full_name='protos.CollectionConfigPackage.config', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=108, serialized_end=175, ) _COLLECTIONCONFIG = _descriptor.Descriptor( name='CollectionConfig', full_name='protos.CollectionConfig', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='static_collection_config', full_name='protos.CollectionConfig.static_collection_config', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='payload', full_name='protos.CollectionConfig.payload', index=0, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), ], serialized_start=177, serialized_end=274, ) _STATICCOLLECTIONCONFIG = _descriptor.Descriptor( name='StaticCollectionConfig', full_name='protos.StaticCollectionConfig', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='name', full_name='protos.StaticCollectionConfig.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='member_orgs_policy', full_name='protos.StaticCollectionConfig.member_orgs_policy', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='required_peer_count', full_name='protos.StaticCollectionConfig.required_peer_count', index=2, number=3, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='maximum_peer_count', full_name='protos.StaticCollectionConfig.maximum_peer_count', index=3, number=4, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='block_to_live', full_name='protos.StaticCollectionConfig.block_to_live', index=4, number=5, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='member_only_read', full_name='protos.StaticCollectionConfig.member_only_read', index=5, number=6, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='member_only_write', full_name='protos.StaticCollectionConfig.member_only_write', index=6, number=7, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='endorsement_policy', full_name='protos.StaticCollectionConfig.endorsement_policy', index=7, number=8, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=277, serialized_end=563, ) _COLLECTIONPOLICYCONFIG = _descriptor.Descriptor( name='CollectionPolicyConfig', full_name='protos.CollectionPolicyConfig', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='signature_policy', full_name='protos.CollectionPolicyConfig.signature_policy', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='payload', full_name='protos.CollectionPolicyConfig.payload', index=0, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), ], serialized_start=565, serialized_end=661, ) _COLLECTIONCONFIGPACKAGE.fields_by_name['config'].message_type = _COLLECTIONCONFIG _COLLECTIONCONFIG.fields_by_name['static_collection_config'].message_type = _STATICCOLLECTIONCONFIG _COLLECTIONCONFIG.oneofs_by_name['payload'].fields.append( _COLLECTIONCONFIG.fields_by_name['static_collection_config']) _COLLECTIONCONFIG.fields_by_name['static_collection_config'].containing_oneof = _COLLECTIONCONFIG.oneofs_by_name['payload'] _STATICCOLLECTIONCONFIG.fields_by_name['member_orgs_policy'].message_type = _COLLECTIONPOLICYCONFIG _STATICCOLLECTIONCONFIG.fields_by_name['endorsement_policy'].message_type = hfc_dot_protos_dot_peer_dot_policy__pb2._APPLICATIONPOLICY _COLLECTIONPOLICYCONFIG.fields_by_name['signature_policy'].message_type = hfc_dot_protos_dot_common_dot_policies__pb2._SIGNATUREPOLICYENVELOPE _COLLECTIONPOLICYCONFIG.oneofs_by_name['payload'].fields.append( _COLLECTIONPOLICYCONFIG.fields_by_name['signature_policy']) _COLLECTIONPOLICYCONFIG.fields_by_name['signature_policy'].containing_oneof = _COLLECTIONPOLICYCONFIG.oneofs_by_name['payload'] DESCRIPTOR.message_types_by_name['CollectionConfigPackage'] = _COLLECTIONCONFIGPACKAGE DESCRIPTOR.message_types_by_name['CollectionConfig'] = _COLLECTIONCONFIG DESCRIPTOR.message_types_by_name['StaticCollectionConfig'] = _STATICCOLLECTIONCONFIG DESCRIPTOR.message_types_by_name['CollectionPolicyConfig'] = _COLLECTIONPOLICYCONFIG _sym_db.RegisterFileDescriptor(DESCRIPTOR) CollectionConfigPackage = _reflection.GeneratedProtocolMessageType('CollectionConfigPackage', (_message.Message,), { 'DESCRIPTOR' : _COLLECTIONCONFIGPACKAGE, '__module__' : 'hfc.protos.peer.collection_pb2' # @@protoc_insertion_point(class_scope:protos.CollectionConfigPackage) }) _sym_db.RegisterMessage(CollectionConfigPackage) CollectionConfig = _reflection.GeneratedProtocolMessageType('CollectionConfig', (_message.Message,), { 'DESCRIPTOR' : _COLLECTIONCONFIG, '__module__' : 'hfc.protos.peer.collection_pb2' # @@protoc_insertion_point(class_scope:protos.CollectionConfig) }) _sym_db.RegisterMessage(CollectionConfig) StaticCollectionConfig = _reflection.GeneratedProtocolMessageType('StaticCollectionConfig', (_message.Message,), { 'DESCRIPTOR' : _STATICCOLLECTIONCONFIG, '__module__' : 'hfc.protos.peer.collection_pb2' # @@protoc_insertion_point(class_scope:protos.StaticCollectionConfig) }) _sym_db.RegisterMessage(StaticCollectionConfig) CollectionPolicyConfig = _reflection.GeneratedProtocolMessageType('CollectionPolicyConfig', (_message.Message,), { 'DESCRIPTOR' : _COLLECTIONPOLICYCONFIG, '__module__' : 'hfc.protos.peer.collection_pb2' # @@protoc_insertion_point(class_scope:protos.CollectionPolicyConfig) }) _sym_db.RegisterMessage(CollectionPolicyConfig) DESCRIPTOR._options = None # @@protoc_insertion_point(module_scope)

45.388679

1,115

0.786415

from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database _sym_db = _symbol_database.Default() from hfc.protos.common import policies_pb2 as hfc_dot_protos_dot_common_dot_policies__pb2 from hfc.protos.peer import policy_pb2 as hfc_dot_protos_dot_peer_dot_policy__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='hfc/protos/peer/collection.proto', package='protos', syntax='proto3', serialized_options=b'\n\"org.hyperledger.fabric.protos.peerZ,github.com/hyperledger/fabric-protos-go/peer', create_key=_descriptor._internal_create_key, serialized_pb=b'\n hfc/protos/peer/collection.proto\x12\x06protos\x1a hfc/protos/common/policies.proto\x1a\x1chfc/protos/peer/policy.proto\"C\n\x17\x43ollectionConfigPackage\x12(\n\x06\x63onfig\x18\x01 \x03(\x0b\x32\x18.protos.CollectionConfig\"a\n\x10\x43ollectionConfig\x12\x42\n\x18static_collection_config\x18\x01 \x01(\x0b\x32\x1e.protos.StaticCollectionConfigH\x00\x42\t\n\x07payload\"\x9e\x02\n\x16StaticCollectionConfig\x12\x0c\n\x04name\x18\x01 \x01(\t\x12:\n\x12member_orgs_policy\x18\x02 \x01(\x0b\x32\x1e.protos.CollectionPolicyConfig\x12\x1b\n\x13required_peer_count\x18\x03 \x01(\x05\x12\x1a\n\x12maximum_peer_count\x18\x04 \x01(\x05\x12\x15\n\rblock_to_live\x18\x05 \x01(\x04\x12\x18\n\x10member_only_read\x18\x06 \x01(\x08\x12\x19\n\x11member_only_write\x18\x07 \x01(\x08\x12\x35\n\x12\x65ndorsement_policy\x18\x08 \x01(\x0b\x32\x19.protos.ApplicationPolicy\"`\n\x16\x43ollectionPolicyConfig\x12;\n\x10signature_policy\x18\x01 \x01(\x0b\x32\x1f.common.SignaturePolicyEnvelopeH\x00\x42\t\n\x07payloadBR\n\"org.hyperledger.fabric.protos.peerZ,github.com/hyperledger/fabric-protos-go/peerb\x06proto3' , dependencies=[hfc_dot_protos_dot_common_dot_policies__pb2.DESCRIPTOR,hfc_dot_protos_dot_peer_dot_policy__pb2.DESCRIPTOR,]) _COLLECTIONCONFIGPACKAGE = _descriptor.Descriptor( name='CollectionConfigPackage', full_name='protos.CollectionConfigPackage', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='config', full_name='protos.CollectionConfigPackage.config', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=108, serialized_end=175, ) _COLLECTIONCONFIG = _descriptor.Descriptor( name='CollectionConfig', full_name='protos.CollectionConfig', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='static_collection_config', full_name='protos.CollectionConfig.static_collection_config', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='payload', full_name='protos.CollectionConfig.payload', index=0, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), ], serialized_start=177, serialized_end=274, ) _STATICCOLLECTIONCONFIG = _descriptor.Descriptor( name='StaticCollectionConfig', full_name='protos.StaticCollectionConfig', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='name', full_name='protos.StaticCollectionConfig.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='member_orgs_policy', full_name='protos.StaticCollectionConfig.member_orgs_policy', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='required_peer_count', full_name='protos.StaticCollectionConfig.required_peer_count', index=2, number=3, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='maximum_peer_count', full_name='protos.StaticCollectionConfig.maximum_peer_count', index=3, number=4, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='block_to_live', full_name='protos.StaticCollectionConfig.block_to_live', index=4, number=5, type=4, cpp_type=4, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='member_only_read', full_name='protos.StaticCollectionConfig.member_only_read', index=5, number=6, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='member_only_write', full_name='protos.StaticCollectionConfig.member_only_write', index=6, number=7, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='endorsement_policy', full_name='protos.StaticCollectionConfig.endorsement_policy', index=7, number=8, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=277, serialized_end=563, ) _COLLECTIONPOLICYCONFIG = _descriptor.Descriptor( name='CollectionPolicyConfig', full_name='protos.CollectionPolicyConfig', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='signature_policy', full_name='protos.CollectionPolicyConfig.signature_policy', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='payload', full_name='protos.CollectionPolicyConfig.payload', index=0, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), ], serialized_start=565, serialized_end=661, ) _COLLECTIONCONFIGPACKAGE.fields_by_name['config'].message_type = _COLLECTIONCONFIG _COLLECTIONCONFIG.fields_by_name['static_collection_config'].message_type = _STATICCOLLECTIONCONFIG _COLLECTIONCONFIG.oneofs_by_name['payload'].fields.append( _COLLECTIONCONFIG.fields_by_name['static_collection_config']) _COLLECTIONCONFIG.fields_by_name['static_collection_config'].containing_oneof = _COLLECTIONCONFIG.oneofs_by_name['payload'] _STATICCOLLECTIONCONFIG.fields_by_name['member_orgs_policy'].message_type = _COLLECTIONPOLICYCONFIG _STATICCOLLECTIONCONFIG.fields_by_name['endorsement_policy'].message_type = hfc_dot_protos_dot_peer_dot_policy__pb2._APPLICATIONPOLICY _COLLECTIONPOLICYCONFIG.fields_by_name['signature_policy'].message_type = hfc_dot_protos_dot_common_dot_policies__pb2._SIGNATUREPOLICYENVELOPE _COLLECTIONPOLICYCONFIG.oneofs_by_name['payload'].fields.append( _COLLECTIONPOLICYCONFIG.fields_by_name['signature_policy']) _COLLECTIONPOLICYCONFIG.fields_by_name['signature_policy'].containing_oneof = _COLLECTIONPOLICYCONFIG.oneofs_by_name['payload'] DESCRIPTOR.message_types_by_name['CollectionConfigPackage'] = _COLLECTIONCONFIGPACKAGE DESCRIPTOR.message_types_by_name['CollectionConfig'] = _COLLECTIONCONFIG DESCRIPTOR.message_types_by_name['StaticCollectionConfig'] = _STATICCOLLECTIONCONFIG DESCRIPTOR.message_types_by_name['CollectionPolicyConfig'] = _COLLECTIONPOLICYCONFIG _sym_db.RegisterFileDescriptor(DESCRIPTOR) CollectionConfigPackage = _reflection.GeneratedProtocolMessageType('CollectionConfigPackage', (_message.Message,), { 'DESCRIPTOR' : _COLLECTIONCONFIGPACKAGE, '__module__' : 'hfc.protos.peer.collection_pb2' }) _sym_db.RegisterMessage(CollectionConfigPackage) CollectionConfig = _reflection.GeneratedProtocolMessageType('CollectionConfig', (_message.Message,), { 'DESCRIPTOR' : _COLLECTIONCONFIG, '__module__' : 'hfc.protos.peer.collection_pb2' }) _sym_db.RegisterMessage(CollectionConfig) StaticCollectionConfig = _reflection.GeneratedProtocolMessageType('StaticCollectionConfig', (_message.Message,), { 'DESCRIPTOR' : _STATICCOLLECTIONCONFIG, '__module__' : 'hfc.protos.peer.collection_pb2' }) _sym_db.RegisterMessage(StaticCollectionConfig) CollectionPolicyConfig = _reflection.GeneratedProtocolMessageType('CollectionPolicyConfig', (_message.Message,), { 'DESCRIPTOR' : _COLLECTIONPOLICYCONFIG, '__module__' : 'hfc.protos.peer.collection_pb2' }) _sym_db.RegisterMessage(CollectionPolicyConfig) DESCRIPTOR._options = None

true

f7195995fd51c5d254684ca38dffb1faa4bb8fd0

594

py

Python

tests/__init__.py

techthiyanes/ml_things

ddeeb16c55cf1d55cf80963217a8d1bffd0913cc

[ "Apache-2.0" ]

153

2020-10-10T05:12:16.000Z

2022-03-17T07:48:42.000Z

tests/__init__.py

techthiyanes/ml_things

ddeeb16c55cf1d55cf80963217a8d1bffd0913cc

[ "Apache-2.0" ]

21

2020-09-15T22:52:43.000Z

2022-02-21T15:27:16.000Z

tests/__init__.py

techthiyanes/ml_things

ddeeb16c55cf1d55cf80963217a8d1bffd0913cc

[ "Apache-2.0" ]

42

2020-10-11T07:33:32.000Z

2022-03-11T01:43:54.000Z

# coding=utf-8 # Copyright 2020 George Mihaila. # # 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.

39.6

74

0.760943

true

f7195a417bd65f657d54400929963ce73b5b19d7

14,584

py

Python

qiskit/quantum_info/operators/measures.py

KOLANICH/qiskit-terra

3947f258ddb31a2b8dd17aff5d2d041d29d74601

[ "Apache-2.0" ]

1

2021-04-28T14:37:16.000Z

qiskit/quantum_info/operators/measures.py

timgates42/qiskit-terra

3947f258ddb31a2b8dd17aff5d2d041d29d74601

[ "Apache-2.0" ]

6

2021-01-17T17:56:08.000Z

2021-04-01T12:40:21.000Z

qiskit/quantum_info/operators/measures.py

timgates42/qiskit-terra

3947f258ddb31a2b8dd17aff5d2d041d29d74601

[ "Apache-2.0" ]

2

2021-03-07T07:58:54.000Z

2021-04-28T03:40:49.000Z

# This code is part of Qiskit. # # (C) Copyright IBM 2017. # # This code is licensed under the Apache License, Version 2.0. You may # obtain a copy of this license in the LICENSE.txt file in the root directory # of this source tree or at http://www.apache.org/licenses/LICENSE-2.0. # # Any modifications or derivative works of this code must retain this # copyright notice, and modified files need to carry a notice indicating # that they have been altered from the originals. # pylint: disable=invalid-name """ A collection of useful quantum information functions for operators. """ import warnings import numpy as np from scipy import sparse from qiskit.exceptions import QiskitError from qiskit.circuit.gate import Gate from qiskit.quantum_info.operators.base_operator import BaseOperator from qiskit.quantum_info.operators.operator import Operator from qiskit.quantum_info.operators.channel.quantum_channel import QuantumChannel from qiskit.quantum_info.operators.channel import Choi, SuperOp from qiskit.quantum_info.states.densitymatrix import DensityMatrix from qiskit.quantum_info.states.measures import state_fidelity try: import cvxpy _HAS_CVX = True except ImportError: _HAS_CVX = False def process_fidelity(channel, target=None, require_cp=True, require_tp=False): r"""Return the process fidelity of a noisy quantum channel. The process fidelity :math:`F_{\text{pro}}(\mathcal{E}, \methcal{F})` between two quantum channels :math:`\mathcal{E}, \mathcal{F}` is given by .. math: F_{\text{pro}}(\mathcal{E}, \mathcal{F}) = F(\rho_{\mathcal{E}}, \rho_{\mathcal{F}}) where :math:`F` is the :func:`~qiskit.quantum_info.state_fidelity`, :math:`\rho_{\mathcal{E}} = \Lambda_{\mathcal{E}} / d` is the normalized :class:`~qiskit.quantum_info.Choi` matrix for the channel :math:`\mathcal{E}`, and :math:`d` is the input dimension of :math:`\mathcal{E}`. When the target channel is unitary this is equivalent to .. math:: F_{\text{pro}}(\mathcal{E}, U) = \frac{Tr[S_U^\dagger S_{\mathcal{E}}]}{d^2} where :math:`S_{\mathcal{E}}, S_{U}` are the :class:`~qiskit.quantum_info.SuperOp` matrices for the *input* quantum channel :math:`\mathcal{E}` and *target* unitary :math:`U` respectively, and :math:`d` is the input dimension of the channel. Args: channel (Operator or QuantumChannel): input quantum channel. target (Operator or QuantumChannel or None): target quantum channel. If `None` target is the identity operator [Default: None]. require_cp (bool): require channel to be completely-positive [Default: True]. require_tp (bool): require channel to be trace-preserving [Default: False]. Returns: float: The process fidelity :math:`F_{\text{pro}}`. Raises: QiskitError: if the channel and target do not have the same dimensions. QiskitError: if the channel and target are not completely-positive (with ``require_cp=True``) or not trace-preserving (with ``require_tp=True``). """ # Format inputs channel = _input_formatter( channel, SuperOp, 'process_fidelity', 'channel') target = _input_formatter( target, Operator, 'process_fidelity', 'target') if target: # Validate dimensions if channel.dim != target.dim: raise QiskitError( 'Input quantum channel and target unitary must have the same ' 'dimensions ({} != {}).'.format(channel.dim, target.dim)) # Validate complete-positivity and trace-preserving for label, chan in [('Input', channel), ('Target', target)]: if isinstance(chan, Operator) and (require_cp or require_tp): is_unitary = chan.is_unitary() # Validate as unitary if require_cp and not is_unitary: raise QiskitError('{} channel is not completely-positive'.format(label)) if require_tp and not is_unitary: raise QiskitError('{} channel is not trace-preserving'.format(label)) elif chan is not None: # Validate as QuantumChannel if require_cp and not chan.is_cp(): raise QiskitError('{} channel is not completely-positive'.format(label)) if require_tp and not chan.is_tp(): raise QiskitError('{} channel is not trace-preserving'.format(label)) if isinstance(target, Operator): # Compute fidelity with unitary target by applying the inverse # to channel and computing fidelity with the identity channel = channel @ target.adjoint() target = None input_dim, _ = channel.dim if target is None: # Compute process fidelity with identity channel if isinstance(channel, Operator): # |Tr[U]/dim| ** 2 fid = np.abs(np.trace(channel.data) / input_dim)**2 else: # Tr[S] / (dim ** 2) fid = np.trace(SuperOp(channel).data) / (input_dim**2) return float(np.real(fid)) # For comparing two non-unitary channels we compute the state fidelity of # the normalized Choi-matrices. This is equivalent to the previous definition # when the target is a unitary channel. state1 = DensityMatrix(Choi(channel).data / input_dim) state2 = DensityMatrix(Choi(target).data / input_dim) return state_fidelity(state1, state2, validate=False) def average_gate_fidelity(channel, target=None, require_cp=True, require_tp=False): r"""Return the average gate fidelity of a noisy quantum channel. The average gate fidelity :math:`F_{\text{ave}}` is given by .. math:: F_{\text{ave}}(\mathcal{E}, U) &= \int d\psi \langle\psi|U^\dagger \mathcal{E}(|\psi\rangle\!\langle\psi|)U|\psi\rangle \\ &= \frac{d F_{\text{pro}}(\mathcal{E}, U) + 1}{d + 1} where :math:`F_{\text{pro}}(\mathcal{E}, U)` is the :meth:`~qiskit.quantum_info.process_fidelity` of the input quantum *channel* :math:`\mathcal{E}` with a *target* unitary :math:`U`, and :math:`d` is the dimension of the *channel*. Args: channel (QuantumChannel or Operator): noisy quantum channel. target (Operator or None): target unitary operator. If `None` target is the identity operator [Default: None]. require_cp (bool): require channel to be completely-positive [Default: True]. require_tp (bool): require channel to be trace-preserving [Default: False]. Returns: float: The average gate fidelity :math:`F_{\text{ave}}`. Raises: QiskitError: if the channel and target do not have the same dimensions, or have different input and output dimensions. QiskitError: if the channel and target or are not completely-positive (with ``require_cp=True``) or not trace-preserving (with ``require_tp=True``). """ # Format inputs channel = _input_formatter( channel, SuperOp, 'average_gate_fidelity', 'channel') target = _input_formatter( target, Operator, 'average_gate_fidelity', 'target') if target is not None: try: target = Operator(target) except QiskitError: raise QiskitError( 'Target channel is not a unitary channel. To compare ' 'two non-unitary channels use the ' '`qiskit.quantum_info.process_fidelity` function instead.') dim, _ = channel.dim f_pro = process_fidelity(channel, target=target, require_cp=require_cp, require_tp=require_tp) return (dim * f_pro + 1) / (dim + 1) def gate_error(channel, target=None, require_cp=True, require_tp=False): r"""Return the gate error of a noisy quantum channel. The gate error :math:`E` is given by the average gate infidelity .. math:: E(\mathcal{E}, U) = 1 - F_{\text{ave}}(\mathcal{E}, U) where :math:`F_{\text{ave}}(\mathcal{E}, U)` is the :meth:`~qiskit.quantum_info.average_gate_fidelity` of the input quantum *channel* :math:`\mathcal{E}` with a *target* unitary :math:`U`. Args: channel (QuantumChannel): noisy quantum channel. target (Operator or None): target unitary operator. If `None` target is the identity operator [Default: None]. require_cp (bool): require channel to be completely-positive [Default: True]. require_tp (bool): require channel to be trace-preserving [Default: False]. Returns: float: The average gate error :math:`E`. Raises: QiskitError: if the channel and target do not have the same dimensions, or have different input and output dimensions. QiskitError: if the channel and target or are not completely-positive (with ``require_cp=True``) or not trace-preserving (with ``require_tp=True``). """ # Format inputs channel = _input_formatter( channel, SuperOp, 'gate_error', 'channel') target = _input_formatter( target, Operator, 'gate_error', 'target') return 1 - average_gate_fidelity( channel, target=target, require_cp=require_cp, require_tp=require_tp) def diamond_norm(choi, **kwargs): r"""Return the diamond norm of the input quantum channel object. This function computes the completely-bounded trace-norm (often referred to as the diamond-norm) of the input quantum channel object using the semidefinite-program from reference [1]. Args: choi(Choi or QuantumChannel): a quantum channel object or Choi-matrix array. kwargs: optional arguments to pass to CVXPY solver. Returns: float: The completely-bounded trace norm :math:`\|\mathcal{E}\|_{\diamond}`. Raises: QiskitError: if CVXPY package cannot be found. Additional Information: The input to this function is typically *not* a CPTP quantum channel, but rather the *difference* between two quantum channels :math:`\|\Delta\mathcal{E}\|_\diamond` where :math:`\Delta\mathcal{E} = \mathcal{E}_1 - \mathcal{E}_2`. Reference: J. Watrous. "Simpler semidefinite programs for completely bounded norms", arXiv:1207.5726 [quant-ph] (2012). .. note:: This function requires the optional CVXPY package to be installed. Any additional kwargs will be passed to the ``cvxpy.solve`` function. See the CVXPY documentation for information on available SDP solvers. """ _cvxpy_check('`diamond_norm`') # Check CVXPY is installed choi = Choi(_input_formatter(choi, Choi, 'diamond_norm', 'choi')) def cvx_bmat(mat_r, mat_i): """Block matrix for embedding complex matrix in reals""" return cvxpy.bmat([[mat_r, -mat_i], [mat_i, mat_r]]) # Dimension of input and output spaces dim_in = choi._input_dim dim_out = choi._output_dim size = dim_in * dim_out # SDP Variables to convert to real valued problem r0_r = cvxpy.Variable((dim_in, dim_in)) r0_i = cvxpy.Variable((dim_in, dim_in)) r0 = cvx_bmat(r0_r, r0_i) r1_r = cvxpy.Variable((dim_in, dim_in)) r1_i = cvxpy.Variable((dim_in, dim_in)) r1 = cvx_bmat(r1_r, r1_i) x_r = cvxpy.Variable((size, size)) x_i = cvxpy.Variable((size, size)) iden = sparse.eye(dim_out) # Watrous uses row-vec convention for his Choi matrix while we use # col-vec. It turns out row-vec convention is requried for CVXPY too # since the cvxpy.kron function must have a constant as its first argument. c_r = cvxpy.bmat([[cvxpy.kron(iden, r0_r), x_r], [x_r.T, cvxpy.kron(iden, r1_r)]]) c_i = cvxpy.bmat([[cvxpy.kron(iden, r0_i), x_i], [-x_i.T, cvxpy.kron(iden, r1_i)]]) c = cvx_bmat(c_r, c_i) # Convert col-vec convention Choi-matrix to row-vec convention and # then take Transpose: Choi_C -> Choi_R.T choi_rt = np.transpose( np.reshape(choi.data, (dim_in, dim_out, dim_in, dim_out)), (3, 2, 1, 0)).reshape(choi.data.shape) choi_rt_r = choi_rt.real choi_rt_i = choi_rt.imag # Constraints cons = [ r0 >> 0, r0_r == r0_r.T, r0_i == - r0_i.T, cvxpy.trace(r0_r) == 1, r1 >> 0, r1_r == r1_r.T, r1_i == - r1_i.T, cvxpy.trace(r1_r) == 1, c >> 0 ] # Objective function obj = cvxpy.Maximize(cvxpy.trace(choi_rt_r @ x_r) + cvxpy.trace(choi_rt_i @ x_i)) prob = cvxpy.Problem(obj, cons) sol = prob.solve(**kwargs) return sol def _cvxpy_check(name): """Check that a supported CVXPY version is installed""" # Check if CVXPY package is installed if not _HAS_CVX: raise QiskitError( 'CVXPY backage is requried for {}. Install' ' with `pip install cvxpy` to use.'.format(name)) # Check CVXPY version version = cvxpy.__version__ if version[0] != '1': raise ImportError( 'Incompatible CVXPY version {} found.' ' Install version >=1.0.'.format(version)) # pylint: disable=too-many-return-statements def _input_formatter(obj, fallback_class, func_name, arg_name): """Formatting function for input conversion""" # Empty input if obj is None: return obj # Channel-like input if isinstance(obj, QuantumChannel): return obj if hasattr(obj, 'to_quantumchannel'): return obj.to_quantumchannel() if hasattr(obj, 'to_channel'): return obj.to_channel() # Unitary-like input if isinstance(obj, (Gate, BaseOperator)): return Operator(obj) if hasattr(obj, 'to_operator'): return obj.to_operator() warnings.warn( 'Passing in a list or Numpy array to `{}` `{}` argument is ' 'deprecated as of 0.17.0 since the matrix representation cannot be inferred ' 'unambiguously. Use a Gate or BaseOperator subclass (eg. Operator, ' 'SuperOp, Choi) object instead.'.format(func_name, arg_name), DeprecationWarning) warnings.warn( 'Treating array input as a {} object'.format(fallback_class.__name__)) return fallback_class(obj)

38.582011

88

0.637959

import warnings import numpy as np from scipy import sparse from qiskit.exceptions import QiskitError from qiskit.circuit.gate import Gate from qiskit.quantum_info.operators.base_operator import BaseOperator from qiskit.quantum_info.operators.operator import Operator from qiskit.quantum_info.operators.channel.quantum_channel import QuantumChannel from qiskit.quantum_info.operators.channel import Choi, SuperOp from qiskit.quantum_info.states.densitymatrix import DensityMatrix from qiskit.quantum_info.states.measures import state_fidelity try: import cvxpy _HAS_CVX = True except ImportError: _HAS_CVX = False def process_fidelity(channel, target=None, require_cp=True, require_tp=False): channel = _input_formatter( channel, SuperOp, 'process_fidelity', 'channel') target = _input_formatter( target, Operator, 'process_fidelity', 'target') if target: if channel.dim != target.dim: raise QiskitError( 'Input quantum channel and target unitary must have the same ' 'dimensions ({} != {}).'.format(channel.dim, target.dim)) for label, chan in [('Input', channel), ('Target', target)]: if isinstance(chan, Operator) and (require_cp or require_tp): is_unitary = chan.is_unitary() if require_cp and not is_unitary: raise QiskitError('{} channel is not completely-positive'.format(label)) if require_tp and not is_unitary: raise QiskitError('{} channel is not trace-preserving'.format(label)) elif chan is not None: if require_cp and not chan.is_cp(): raise QiskitError('{} channel is not completely-positive'.format(label)) if require_tp and not chan.is_tp(): raise QiskitError('{} channel is not trace-preserving'.format(label)) if isinstance(target, Operator): channel = channel @ target.adjoint() target = None input_dim, _ = channel.dim if target is None: if isinstance(channel, Operator): fid = np.abs(np.trace(channel.data) / input_dim)**2 else: fid = np.trace(SuperOp(channel).data) / (input_dim**2) return float(np.real(fid)) state1 = DensityMatrix(Choi(channel).data / input_dim) state2 = DensityMatrix(Choi(target).data / input_dim) return state_fidelity(state1, state2, validate=False) def average_gate_fidelity(channel, target=None, require_cp=True, require_tp=False): channel = _input_formatter( channel, SuperOp, 'average_gate_fidelity', 'channel') target = _input_formatter( target, Operator, 'average_gate_fidelity', 'target') if target is not None: try: target = Operator(target) except QiskitError: raise QiskitError( 'Target channel is not a unitary channel. To compare ' 'two non-unitary channels use the ' '`qiskit.quantum_info.process_fidelity` function instead.') dim, _ = channel.dim f_pro = process_fidelity(channel, target=target, require_cp=require_cp, require_tp=require_tp) return (dim * f_pro + 1) / (dim + 1) def gate_error(channel, target=None, require_cp=True, require_tp=False): channel = _input_formatter( channel, SuperOp, 'gate_error', 'channel') target = _input_formatter( target, Operator, 'gate_error', 'target') return 1 - average_gate_fidelity( channel, target=target, require_cp=require_cp, require_tp=require_tp) def diamond_norm(choi, **kwargs): _cvxpy_check('`diamond_norm`') choi = Choi(_input_formatter(choi, Choi, 'diamond_norm', 'choi')) def cvx_bmat(mat_r, mat_i): return cvxpy.bmat([[mat_r, -mat_i], [mat_i, mat_r]]) dim_in = choi._input_dim dim_out = choi._output_dim size = dim_in * dim_out r0_r = cvxpy.Variable((dim_in, dim_in)) r0_i = cvxpy.Variable((dim_in, dim_in)) r0 = cvx_bmat(r0_r, r0_i) r1_r = cvxpy.Variable((dim_in, dim_in)) r1_i = cvxpy.Variable((dim_in, dim_in)) r1 = cvx_bmat(r1_r, r1_i) x_r = cvxpy.Variable((size, size)) x_i = cvxpy.Variable((size, size)) iden = sparse.eye(dim_out) c_r = cvxpy.bmat([[cvxpy.kron(iden, r0_r), x_r], [x_r.T, cvxpy.kron(iden, r1_r)]]) c_i = cvxpy.bmat([[cvxpy.kron(iden, r0_i), x_i], [-x_i.T, cvxpy.kron(iden, r1_i)]]) c = cvx_bmat(c_r, c_i) choi_rt = np.transpose( np.reshape(choi.data, (dim_in, dim_out, dim_in, dim_out)), (3, 2, 1, 0)).reshape(choi.data.shape) choi_rt_r = choi_rt.real choi_rt_i = choi_rt.imag cons = [ r0 >> 0, r0_r == r0_r.T, r0_i == - r0_i.T, cvxpy.trace(r0_r) == 1, r1 >> 0, r1_r == r1_r.T, r1_i == - r1_i.T, cvxpy.trace(r1_r) == 1, c >> 0 ] obj = cvxpy.Maximize(cvxpy.trace(choi_rt_r @ x_r) + cvxpy.trace(choi_rt_i @ x_i)) prob = cvxpy.Problem(obj, cons) sol = prob.solve(**kwargs) return sol def _cvxpy_check(name): if not _HAS_CVX: raise QiskitError( 'CVXPY backage is requried for {}. Install' ' with `pip install cvxpy` to use.'.format(name)) version = cvxpy.__version__ if version[0] != '1': raise ImportError( 'Incompatible CVXPY version {} found.' ' Install version >=1.0.'.format(version)) def _input_formatter(obj, fallback_class, func_name, arg_name): if obj is None: return obj if isinstance(obj, QuantumChannel): return obj if hasattr(obj, 'to_quantumchannel'): return obj.to_quantumchannel() if hasattr(obj, 'to_channel'): return obj.to_channel() if isinstance(obj, (Gate, BaseOperator)): return Operator(obj) if hasattr(obj, 'to_operator'): return obj.to_operator() warnings.warn( 'Passing in a list or Numpy array to `{}` `{}` argument is ' 'deprecated as of 0.17.0 since the matrix representation cannot be inferred ' 'unambiguously. Use a Gate or BaseOperator subclass (eg. Operator, ' 'SuperOp, Choi) object instead.'.format(func_name, arg_name), DeprecationWarning) warnings.warn( 'Treating array input as a {} object'.format(fallback_class.__name__)) return fallback_class(obj)

true

f7195bbf84421b2fed723996ef1806b0e3e52004

2,465

py

Python

backend/fleet_management/tests/test_crypto.py

OtisRed/pah-fm

68a306fce5593a6f79711fa473a91bc8163b01df

[ "MIT" ]

8

2019-08-09T11:06:16.000Z

2021-10-05T14:56:31.000Z

backend/fleet_management/tests/test_crypto.py

OtisRed/pah-fm

68a306fce5593a6f79711fa473a91bc8163b01df

[ "MIT" ]

382

2018-10-17T19:05:30.000Z

2022-02-10T07:09:45.000Z

backend/fleet_management/tests/test_crypto.py

OtisRed/pah-fm

68a306fce5593a6f79711fa473a91bc8163b01df

[ "MIT" ]

45

2018-10-17T17:04:04.000Z

2021-10-05T14:30:35.000Z

from secrets import randbits from django.conf import settings from rest_framework.test import APISimpleTestCase from fleet_management.crypto import ( sign, verify, inverse_of, is_prime, find_prime, find_p_q_phi, find_pair_of_keys, hash_dict, ) class CryptoTest(APISimpleTestCase): def setUp(self) -> None: self.n_tests = 1000 def test_sign_and_verify(self): for _ in range(1000): message = randbits(settings.RSA_BIT_LENGTH) pub, priv = find_pair_of_keys() signature = sign(message, priv) self.assertTrue(verify(message, signature, pub)) self.assertFalse(verify(message + 1, signature, pub)) def test_inverse_of(self): self.assertEqual(inverse_of(2, 3), 2) self.assertEqual(inverse_of(53, 120), 77) self.assertEqual(inverse_of(1123, 18712), 17379) self.assertEqual(inverse_of(98751, 123719989), 68419280) self.assertEqual( inverse_of(65537, 1034776851837418226012406113933120080), 568411228254986589811047501435713, ) def test_is_prime(self): self.assertTrue(is_prime(2)) self.assertTrue(is_prime(5)) self.assertTrue(is_prime(41)) self.assertTrue(is_prime(97571)) self.assertTrue(is_prime(56790763)) self.assertTrue(is_prime(967901315627)) self.assertFalse(is_prime(1)) self.assertFalse(is_prime(12)) self.assertFalse(is_prime(42)) self.assertFalse(is_prime(2737075)) self.assertFalse(is_prime(273707521121)) def test_find_prime(self): for _ in range(self.n_tests): prime = find_prime(settings.RSA_BIT_LENGTH) self.assertTrue(is_prime(prime)) def test_find_p_q_phi(self): for _ in range(self.n_tests): p, q, phi = find_p_q_phi() my_phi = (p - 1) * (q - 1) self.assertTrue(is_prime(p)) self.assertTrue(is_prime(q)) self.assertEqual(phi, my_phi) def test_hash_dict(self): self.assertEqual(hash_dict({}), 17022) self.assertEqual(hash_dict({1: 1}), 361627) self.assertEqual(hash_dict({1: 1, "asd": "asd"}), 319826) self.assertEqual(hash_dict({1: 1, "asd": "asd", 9: [1, 2, 3]}), 319976) self.assertEqual(hash_dict({1: {2: {3: {4: {5: {}}}}}}), 17022) self.assertEqual(hash_dict({1: {2: {3: {4: {5: "x"}}}}}), 288678)

34.236111

79

0.627586

from secrets import randbits from django.conf import settings from rest_framework.test import APISimpleTestCase from fleet_management.crypto import ( sign, verify, inverse_of, is_prime, find_prime, find_p_q_phi, find_pair_of_keys, hash_dict, ) class CryptoTest(APISimpleTestCase): def setUp(self) -> None: self.n_tests = 1000 def test_sign_and_verify(self): for _ in range(1000): message = randbits(settings.RSA_BIT_LENGTH) pub, priv = find_pair_of_keys() signature = sign(message, priv) self.assertTrue(verify(message, signature, pub)) self.assertFalse(verify(message + 1, signature, pub)) def test_inverse_of(self): self.assertEqual(inverse_of(2, 3), 2) self.assertEqual(inverse_of(53, 120), 77) self.assertEqual(inverse_of(1123, 18712), 17379) self.assertEqual(inverse_of(98751, 123719989), 68419280) self.assertEqual( inverse_of(65537, 1034776851837418226012406113933120080), 568411228254986589811047501435713, ) def test_is_prime(self): self.assertTrue(is_prime(2)) self.assertTrue(is_prime(5)) self.assertTrue(is_prime(41)) self.assertTrue(is_prime(97571)) self.assertTrue(is_prime(56790763)) self.assertTrue(is_prime(967901315627)) self.assertFalse(is_prime(1)) self.assertFalse(is_prime(12)) self.assertFalse(is_prime(42)) self.assertFalse(is_prime(2737075)) self.assertFalse(is_prime(273707521121)) def test_find_prime(self): for _ in range(self.n_tests): prime = find_prime(settings.RSA_BIT_LENGTH) self.assertTrue(is_prime(prime)) def test_find_p_q_phi(self): for _ in range(self.n_tests): p, q, phi = find_p_q_phi() my_phi = (p - 1) * (q - 1) self.assertTrue(is_prime(p)) self.assertTrue(is_prime(q)) self.assertEqual(phi, my_phi) def test_hash_dict(self): self.assertEqual(hash_dict({}), 17022) self.assertEqual(hash_dict({1: 1}), 361627) self.assertEqual(hash_dict({1: 1, "asd": "asd"}), 319826) self.assertEqual(hash_dict({1: 1, "asd": "asd", 9: [1, 2, 3]}), 319976) self.assertEqual(hash_dict({1: {2: {3: {4: {5: {}}}}}}), 17022) self.assertEqual(hash_dict({1: {2: {3: {4: {5: "x"}}}}}), 288678)

true

f7195c0c364fe7bce695c427067417d2cf71be24

3,496

py

Python

bindings/python/ensmallen/datasets/string/pontibacterchinhatensis.py

AnacletoLAB/ensmallen_graph

b2c1b18fb1e5801712852bcc239f239e03076f09

[ "MIT" ]

5

2021-02-17T00:44:45.000Z

2021-08-09T16:41:47.000Z

bindings/python/ensmallen/datasets/string/pontibacterchinhatensis.py

AnacletoLAB/ensmallen_graph

b2c1b18fb1e5801712852bcc239f239e03076f09

[ "MIT" ]

18

2021-01-07T16:47:39.000Z

2021-08-12T21:51:32.000Z

bindings/python/ensmallen/datasets/string/pontibacterchinhatensis.py

AnacletoLAB/ensmallen

b2c1b18fb1e5801712852bcc239f239e03076f09

[ "MIT" ]

3

2021-01-14T02:20:59.000Z

2021-08-04T19:09:52.000Z

""" This file offers the methods to automatically retrieve the graph Pontibacter chinhatensis. The graph is automatically retrieved from the STRING repository. References --------------------- Please cite the following if you use the data: ```bib @article{szklarczyk2019string, title={STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets}, author={Szklarczyk, Damian and Gable, Annika L and Lyon, David and Junge, Alexander and Wyder, Stefan and Huerta-Cepas, Jaime and Simonovic, Milan and Doncheva, Nadezhda T and Morris, John H and Bork, Peer and others}, journal={Nucleic acids research}, volume={47}, number={D1}, pages={D607--D613}, year={2019}, publisher={Oxford University Press} } ``` """ from typing import Dict from ..automatic_graph_retrieval import AutomaticallyRetrievedGraph from ...ensmallen import Graph # pylint: disable=import-error def PontibacterChinhatensis( directed: bool = False, preprocess: bool = True, load_nodes: bool = True, verbose: int = 2, cache: bool = True, cache_path: str = "graphs/string", version: str = "links.v11.5", **additional_graph_kwargs: Dict ) -> Graph: """Return new instance of the Pontibacter chinhatensis graph. The graph is automatically retrieved from the STRING repository. Parameters ------------------- directed: bool = False Wether to load the graph as directed or undirected. By default false. preprocess: bool = True Whether to preprocess the graph to be loaded in optimal time and memory. load_nodes: bool = True, Whether to load the nodes vocabulary or treat the nodes simply as a numeric range. verbose: int = 2, Wether to show loading bars during the retrieval and building of the graph. cache: bool = True Whether to use cache, i.e. download files only once and preprocess them only once. cache_path: str = "graphs" Where to store the downloaded graphs. version: str = "links.v11.5" The version of the graph to retrieve. The available versions are: - homology.v11.5 - physical.links.v11.5 - links.v11.5 additional_graph_kwargs: Dict Additional graph kwargs. Returns ----------------------- Instace of Pontibacter chinhatensis graph. References --------------------- Please cite the following if you use the data: ```bib @article{szklarczyk2019string, title={STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets}, author={Szklarczyk, Damian and Gable, Annika L and Lyon, David and Junge, Alexander and Wyder, Stefan and Huerta-Cepas, Jaime and Simonovic, Milan and Doncheva, Nadezhda T and Morris, John H and Bork, Peer and others}, journal={Nucleic acids research}, volume={47}, number={D1}, pages={D607--D613}, year={2019}, publisher={Oxford University Press} } ``` """ return AutomaticallyRetrievedGraph( graph_name="PontibacterChinhatensis", repository="string", version=version, directed=directed, preprocess=preprocess, load_nodes=load_nodes, verbose=verbose, cache=cache, cache_path=cache_path, additional_graph_kwargs=additional_graph_kwargs )()

33.295238

223

0.67992

from typing import Dict from ..automatic_graph_retrieval import AutomaticallyRetrievedGraph from ...ensmallen import Graph def PontibacterChinhatensis( directed: bool = False, preprocess: bool = True, load_nodes: bool = True, verbose: int = 2, cache: bool = True, cache_path: str = "graphs/string", version: str = "links.v11.5", **additional_graph_kwargs: Dict ) -> Graph: return AutomaticallyRetrievedGraph( graph_name="PontibacterChinhatensis", repository="string", version=version, directed=directed, preprocess=preprocess, load_nodes=load_nodes, verbose=verbose, cache=cache, cache_path=cache_path, additional_graph_kwargs=additional_graph_kwargs )()

true

f7195c158eefd24c981c80cfe2493d7d0991e7c0

1,263

py

Python

jesse/indicators/supersmoother.py

noenfugler/jesse

217a3168620a755c1a9576d9deb27105db7dccf8

[ "MIT" ]

1

2021-03-25T09:25:49.000Z

jesse/indicators/supersmoother.py

noenfugler/jesse

217a3168620a755c1a9576d9deb27105db7dccf8

[ "MIT" ]

null

jesse/indicators/supersmoother.py

noenfugler/jesse

217a3168620a755c1a9576d9deb27105db7dccf8

[ "MIT" ]

1

2021-09-28T16:23:40.000Z

from typing import Union import numpy as np from numba import njit from jesse.helpers import get_candle_source, slice_candles def supersmoother(candles: np.ndarray, period: int = 14, source_type: str = "close", sequential: bool = False) -> Union[ float, np.ndarray]: """ Super Smoother Filter 2pole Butterworth This indicator was described by John F. Ehlers :param candles: np.ndarray :param period: int - default=14 :param source_type: str - default: "close" :param sequential: bool - default=False :return: float | np.ndarray """ candles = slice_candles(candles, sequential) # Accept normal array too. if len(candles.shape) == 1: source = candles else: source = get_candle_source(candles, source_type=source_type) res = supersmoother_fast(source, period) return res if sequential else res[-1] @njit def supersmoother_fast(source, period): a = np.exp(-1.414 * np.pi / period) b = 2 * a * np.cos(1.414 * np.pi / period) newseries = np.copy(source) for i in range(2, source.shape[0]): newseries[i] = (1 + a ** 2 - b) / 2 * (source[i] + source[i - 1]) \ + b * newseries[i - 1] - a ** 2 * newseries[i - 2] return newseries

28.066667

120

0.638163

from typing import Union import numpy as np from numba import njit from jesse.helpers import get_candle_source, slice_candles def supersmoother(candles: np.ndarray, period: int = 14, source_type: str = "close", sequential: bool = False) -> Union[ float, np.ndarray]: candles = slice_candles(candles, sequential) if len(candles.shape) == 1: source = candles else: source = get_candle_source(candles, source_type=source_type) res = supersmoother_fast(source, period) return res if sequential else res[-1] @njit def supersmoother_fast(source, period): a = np.exp(-1.414 * np.pi / period) b = 2 * a * np.cos(1.414 * np.pi / period) newseries = np.copy(source) for i in range(2, source.shape[0]): newseries[i] = (1 + a ** 2 - b) / 2 * (source[i] + source[i - 1]) \ + b * newseries[i - 1] - a ** 2 * newseries[i - 2] return newseries

true

f7195cbfea553513473b2df5d5bf67f1cff230ba

12,078

py

Python

cellrank/pl/_circular_projection.py

WeilerP/cellrank

c8c2b9f6bd2448861fb414435aee7620ca5a0bad

[ "BSD-3-Clause" ]

172

2020-03-19T19:50:53.000Z

2022-03-28T09:36:04.000Z

cellrank/pl/_circular_projection.py

WeilerP/cellrank

c8c2b9f6bd2448861fb414435aee7620ca5a0bad

[ "BSD-3-Clause" ]

702

2020-03-19T08:09:04.000Z

2022-03-30T09:55:14.000Z

cellrank/pl/_circular_projection.py

WeilerP/cellrank

c8c2b9f6bd2448861fb414435aee7620ca5a0bad

[ "BSD-3-Clause" ]

17

2020-04-07T03:11:02.000Z

2022-02-02T20:39:16.000Z

from typing import Any, Tuple, Union, Mapping, Callable, Optional, Sequence from typing_extensions import Literal from enum import auto from types import MappingProxyType from pathlib import Path import scvelo as scv from anndata import AnnData from cellrank import logging as logg from cellrank.tl import Lineage from cellrank._key import Key from scanpy._utils import deprecated_arg_names from cellrank.tl._enum import ModeEnum from cellrank.ul._docs import d from cellrank.pl._utils import _held_karp from cellrank.tl._utils import save_fig, _unique_order_preserving from cellrank.ul._utils import _check_collection from cellrank.tl._lineage import PrimingDegree import numpy as np import pandas as pd from sklearn.metrics import pairwise_distances import matplotlib.pyplot as plt from matplotlib.colors import LogNorm, LinearSegmentedColormap from matplotlib.collections import LineCollection class LineageOrder(ModeEnum): # noqa: D101 DEFAULT = auto() OPTIMAL = auto() class LabelRot(ModeEnum): # noqa: D101 DEFAULT = auto() BEST = auto() Metric_T = Union[str, Callable, np.ndarray, pd.DataFrame] _N = 200 def _get_distances(data: Union[np.ndarray, Lineage], metric: Metric_T) -> np.ndarray: if isinstance(data, Lineage): data = data.X if isinstance(metric, str) or callable(metric): metric = pairwise_distances(data.T, metric=metric) elif isinstance(metric, (pd.DataFrame, np.ndarray)): shape = (data.shape[1], data.shape[1]) if metric.shape != shape: raise ValueError( f"Expected an `numpy.array` or `pandas.DataFrame` of shape `{shape}`, found `{metric.shape}`." ) else: raise TypeError( f"Expected either metric defined by `str`, `callable` or a pairwise distance matrix of type" f" `numpy.ndarray` or `pandas.DataFrame`, found `{type(metric).__name__}`." ) return np.asarray(metric, dtype=np.float64) def _get_optimal_order(data: Lineage, metric: Metric_T) -> Tuple[float, np.ndarray]: """Solve the TSP using dynamic programming.""" return _held_karp(_get_distances(data, metric)) @d.dedent @deprecated_arg_names({"labeldistance": "label_distance", "labelrot": "label_rot"}) def circular_projection( adata: AnnData, keys: Union[str, Sequence[str]], backward: bool = False, lineages: Optional[Union[str, Sequence[str]]] = None, early_cells: Optional[Union[Mapping[str, Sequence[str]], Sequence[str]]] = None, lineage_order: Optional[Literal["default", "optimal"]] = None, metric: Union[str, Callable, np.ndarray, pd.DataFrame] = "correlation", normalize_by_mean: bool = True, ncols: int = 4, space: float = 0.25, use_raw: bool = False, text_kwargs: Mapping[str, Any] = MappingProxyType({}), label_distance: float = 1.25, label_rot: Union[Literal["default", "best"], float] = "best", show_edges: bool = True, key_added: Optional[str] = None, figsize: Optional[Tuple[float, float]] = None, dpi: Optional[int] = None, save: Optional[Union[str, Path]] = None, **kwargs: Any, ): r""" Plot absorption probabilities on a circular embedding as in :cite:`velten:17`. Parameters ---------- %(adata)s keys Keys in :attr:`anndata.AnnData.obs` or :attr:`anndata.AnnData.var_names`. Additional keys are: - `'kl_divergence'` - as in :cite:`velten:17`, computes KL-divergence between the fate probabilities of a cell and the average fate probabilities. See ``early_cells`` for more information. - `'entropy'` - as in :cite:`setty:19`, computes entropy over a cells fate probabilities. %(backward)s lineages Lineages to plot. If `None`, plot all lineages. early_cells Cell ids or a mask marking early cells used to define the average fate probabilities. If `None`, use all cells. Only used when `'kl_divergence'` is in ``keys``. If a :class:`dict`, key specifies a cluster key in :attr:`anndata.AnnData.obs` and the values specify cluster labels containing early cells. lineage_order Can be one of the following: - `None` - it will determined automatically, based on the number of lineages. - `'optimal'` - order lineages optimally by solving the Travelling salesman problem (TSP). Recommended for <= `20` lineages. - `'default'` - use the order as specified by ``lineages``. metric Metric to use when constructing pairwise distance matrix when ``lineage_order = 'optimal'``. For available options, see :func:`sklearn.metrics.pairwise_distances`. normalize_by_mean If `True`, normalize each lineage by its mean probability, as done in :cite:`velten:17`. ncols Number of columns when plotting multiple ``keys``. space Horizontal and vertical space between for :func:`matplotlib.pyplot.subplots_adjust`. use_raw Whether to access :attr:`anndata.AnnData.raw` when there are ``keys`` in :attr:`anndata.AnnData.var_names`. text_kwargs Keyword arguments for :func:`matplotlib.pyplot.text`. label_distance Distance at which the lineage labels will be drawn. label_rot How to rotate the labels. Valid options are: - `'best'` - rotate labels so that they are easily readable. - `'default'` - use :mod:`matplotlib`'s default. - `None` - same as `'default'`. If a :class:`float`, all labels will be rotated by this many degrees. show_edges Whether to show the edges surrounding the simplex. key_added Key in :attr:`anndata.AnnData.obsm` where to add the circular embedding. If `None`, it will be set to `'X_fate_simplex_{fwd,bwd}'`, based on ``backward``. %(plotting)s kwargs Keyword arguments for :func:`scvelo.pl.scatter`. Returns ------- %(just_plots)s Also updates ``adata`` with the following fields: - :attr:`anndata.AnnData.obsm` ``['{key_added}']`` - the circular projection. - :attr:`anndata.AnnData.obs` ``['to_{initial,terminal}_states_{method}']`` - the priming degree, if a method is present in ``keys``. """ if label_distance is not None and label_distance < 0: raise ValueError( f"Expected `label_distance` to be positive, found `{label_distance}`." ) if label_rot is None: label_rot = LabelRot.DEFAULT label_rot = LabelRot(label_rot) suffix = "bwd" if backward else "fwd" if key_added is None: key_added = "X_fate_simplex_" + suffix if isinstance(keys, str): keys = (keys,) keys = _unique_order_preserving(keys) keys_ = _check_collection( adata, keys, "obs", key_name="Observation", raise_exc=False ) + _check_collection( adata, keys, "var_names", key_name="Gene", raise_exc=False, use_raw=use_raw ) haystack = set(PrimingDegree) keys = keys_ + [k for k in keys if k in haystack] keys = _unique_order_preserving(keys) if not len(keys): raise ValueError("No valid keys have been selected.") lineage_key = Key.obsm.abs_probs(backward) if lineage_key not in adata.obsm: raise KeyError(f"Lineages key `{lineage_key!r}` not found in `adata.obsm`.") probs: Lineage = adata.obsm[lineage_key] if isinstance(lineages, str): lineages = (lineages,) elif lineages is None: lineages = probs.names probs = adata.obsm[lineage_key][lineages] n_lin = probs.shape[1] if n_lin < 3: raise ValueError(f"Expected at least `3` lineages, found `{n_lin}`.") X = probs.X.copy() if normalize_by_mean: X /= np.mean(X, axis=0)[None, :] X /= X.sum(1)[:, None] # this happens when cells for sel. lineages sum to 1 (or when the lineage average is 0, which is unlikely) X = np.nan_to_num(X, nan=1.0 / n_lin, copy=False) if lineage_order is None: lineage_order = ( LineageOrder.OPTIMAL if 3 < n_lin <= 20 else LineageOrder.DEFAULT ) logg.debug(f"Set ordering to `{lineage_order}`") lineage_order = LineageOrder(lineage_order) if lineage_order == LineageOrder.OPTIMAL: logg.info(f"Solving TSP for `{n_lin}` states") _, order = _get_optimal_order(X, metric=metric) else: order = np.arange(n_lin) probs = probs[:, order] X = X[:, order] angle_vec = np.linspace(0, 2 * np.pi, n_lin, endpoint=False) angle_vec_sin = np.cos(angle_vec) angle_vec_cos = np.sin(angle_vec) x = np.sum(X * angle_vec_sin, axis=1) y = np.sum(X * angle_vec_cos, axis=1) adata.obsm[key_added] = np.c_[x, y] nrows = int(np.ceil(len(keys) / ncols)) fig, ax = plt.subplots( nrows=nrows, ncols=ncols, figsize=(ncols * 5, nrows * 5) if figsize is None else figsize, dpi=dpi, ) fig.subplots_adjust(wspace=space, hspace=space) axes = np.ravel([ax]) text_kwargs = dict(text_kwargs) text_kwargs["ha"] = "center" text_kwargs["va"] = "center" _i = 0 for _i, (k, ax) in enumerate(zip(keys, axes)): set_lognorm, colorbar = False, kwargs.pop("colorbar", True) try: _ = PrimingDegree(k) logg.debug(f"Calculating priming degree using `method={k}`") val = probs.priming_degree(method=k, early_cells=early_cells) k = f"{lineage_key}_{k}" adata.obs[k] = val except ValueError: pass scv.pl.scatter( adata, basis=key_added, color=k, show=False, ax=ax, use_raw=use_raw, norm=LogNorm() if set_lognorm else None, colorbar=colorbar, **kwargs, ) if colorbar and set_lognorm: cbar = ax.collections[0].colorbar cax = cbar.locator.axis ticks = cax.minor.locator.tick_values(cbar.vmin, cbar.vmax) ticks = [ticks[0], ticks[len(ticks) // 2 + 1], ticks[-1]] cbar.set_ticks(ticks) cbar.set_ticklabels([f"{t:.2f}" for t in ticks]) cbar.update_ticks() patches, texts = ax.pie( np.ones_like(angle_vec), labeldistance=label_distance, rotatelabels=True, labels=probs.names[::-1], startangle=-360 / len(angle_vec) / 2, counterclock=False, textprops=text_kwargs, ) for patch in patches: patch.set_visible(False) # clockwise for color, text in zip(probs.colors[::-1], texts): if isinstance(label_rot, (int, float)): text.set_rotation(label_rot) elif label_rot == LabelRot.BEST: rot = text.get_rotation() text.set_rotation(rot + 90 + (1 - rot // 180) * 180) elif label_rot != LabelRot.DEFAULT: raise NotImplementedError( f"Label rotation `{label_rot}` is not yet implemented." ) text.set_color(color) if not show_edges: continue for i, color in enumerate(probs.colors): next = (i + 1) % n_lin x = 1.04 * np.linspace(angle_vec_sin[i], angle_vec_sin[next], _N) y = 1.04 * np.linspace(angle_vec_cos[i], angle_vec_cos[next], _N) points = np.array([x, y]).T.reshape(-1, 1, 2) segments = np.concatenate([points[:-1], points[1:]], axis=1) cmap = LinearSegmentedColormap.from_list( "abs_prob_cmap", [color, probs.colors[next]], N=_N ) lc = LineCollection(segments, cmap=cmap, zorder=-1) lc.set_array(np.linspace(0, 1, _N)) lc.set_linewidth(2) ax.add_collection(lc) for j in range(_i + 1, len(axes)): axes[j].remove() if save is not None: save_fig(fig, save)

35.946429

119

0.626925

from typing import Any, Tuple, Union, Mapping, Callable, Optional, Sequence from typing_extensions import Literal from enum import auto from types import MappingProxyType from pathlib import Path import scvelo as scv from anndata import AnnData from cellrank import logging as logg from cellrank.tl import Lineage from cellrank._key import Key from scanpy._utils import deprecated_arg_names from cellrank.tl._enum import ModeEnum from cellrank.ul._docs import d from cellrank.pl._utils import _held_karp from cellrank.tl._utils import save_fig, _unique_order_preserving from cellrank.ul._utils import _check_collection from cellrank.tl._lineage import PrimingDegree import numpy as np import pandas as pd from sklearn.metrics import pairwise_distances import matplotlib.pyplot as plt from matplotlib.colors import LogNorm, LinearSegmentedColormap from matplotlib.collections import LineCollection class LineageOrder(ModeEnum): DEFAULT = auto() OPTIMAL = auto() class LabelRot(ModeEnum): DEFAULT = auto() BEST = auto() Metric_T = Union[str, Callable, np.ndarray, pd.DataFrame] _N = 200 def _get_distances(data: Union[np.ndarray, Lineage], metric: Metric_T) -> np.ndarray: if isinstance(data, Lineage): data = data.X if isinstance(metric, str) or callable(metric): metric = pairwise_distances(data.T, metric=metric) elif isinstance(metric, (pd.DataFrame, np.ndarray)): shape = (data.shape[1], data.shape[1]) if metric.shape != shape: raise ValueError( f"Expected an `numpy.array` or `pandas.DataFrame` of shape `{shape}`, found `{metric.shape}`." ) else: raise TypeError( f"Expected either metric defined by `str`, `callable` or a pairwise distance matrix of type" f" `numpy.ndarray` or `pandas.DataFrame`, found `{type(metric).__name__}`." ) return np.asarray(metric, dtype=np.float64) def _get_optimal_order(data: Lineage, metric: Metric_T) -> Tuple[float, np.ndarray]: return _held_karp(_get_distances(data, metric)) @d.dedent @deprecated_arg_names({"labeldistance": "label_distance", "labelrot": "label_rot"}) def circular_projection( adata: AnnData, keys: Union[str, Sequence[str]], backward: bool = False, lineages: Optional[Union[str, Sequence[str]]] = None, early_cells: Optional[Union[Mapping[str, Sequence[str]], Sequence[str]]] = None, lineage_order: Optional[Literal["default", "optimal"]] = None, metric: Union[str, Callable, np.ndarray, pd.DataFrame] = "correlation", normalize_by_mean: bool = True, ncols: int = 4, space: float = 0.25, use_raw: bool = False, text_kwargs: Mapping[str, Any] = MappingProxyType({}), label_distance: float = 1.25, label_rot: Union[Literal["default", "best"], float] = "best", show_edges: bool = True, key_added: Optional[str] = None, figsize: Optional[Tuple[float, float]] = None, dpi: Optional[int] = None, save: Optional[Union[str, Path]] = None, **kwargs: Any, ): if label_distance is not None and label_distance < 0: raise ValueError( f"Expected `label_distance` to be positive, found `{label_distance}`." ) if label_rot is None: label_rot = LabelRot.DEFAULT label_rot = LabelRot(label_rot) suffix = "bwd" if backward else "fwd" if key_added is None: key_added = "X_fate_simplex_" + suffix if isinstance(keys, str): keys = (keys,) keys = _unique_order_preserving(keys) keys_ = _check_collection( adata, keys, "obs", key_name="Observation", raise_exc=False ) + _check_collection( adata, keys, "var_names", key_name="Gene", raise_exc=False, use_raw=use_raw ) haystack = set(PrimingDegree) keys = keys_ + [k for k in keys if k in haystack] keys = _unique_order_preserving(keys) if not len(keys): raise ValueError("No valid keys have been selected.") lineage_key = Key.obsm.abs_probs(backward) if lineage_key not in adata.obsm: raise KeyError(f"Lineages key `{lineage_key!r}` not found in `adata.obsm`.") probs: Lineage = adata.obsm[lineage_key] if isinstance(lineages, str): lineages = (lineages,) elif lineages is None: lineages = probs.names probs = adata.obsm[lineage_key][lineages] n_lin = probs.shape[1] if n_lin < 3: raise ValueError(f"Expected at least `3` lineages, found `{n_lin}`.") X = probs.X.copy() if normalize_by_mean: X /= np.mean(X, axis=0)[None, :] X /= X.sum(1)[:, None] X = np.nan_to_num(X, nan=1.0 / n_lin, copy=False) if lineage_order is None: lineage_order = ( LineageOrder.OPTIMAL if 3 < n_lin <= 20 else LineageOrder.DEFAULT ) logg.debug(f"Set ordering to `{lineage_order}`") lineage_order = LineageOrder(lineage_order) if lineage_order == LineageOrder.OPTIMAL: logg.info(f"Solving TSP for `{n_lin}` states") _, order = _get_optimal_order(X, metric=metric) else: order = np.arange(n_lin) probs = probs[:, order] X = X[:, order] angle_vec = np.linspace(0, 2 * np.pi, n_lin, endpoint=False) angle_vec_sin = np.cos(angle_vec) angle_vec_cos = np.sin(angle_vec) x = np.sum(X * angle_vec_sin, axis=1) y = np.sum(X * angle_vec_cos, axis=1) adata.obsm[key_added] = np.c_[x, y] nrows = int(np.ceil(len(keys) / ncols)) fig, ax = plt.subplots( nrows=nrows, ncols=ncols, figsize=(ncols * 5, nrows * 5) if figsize is None else figsize, dpi=dpi, ) fig.subplots_adjust(wspace=space, hspace=space) axes = np.ravel([ax]) text_kwargs = dict(text_kwargs) text_kwargs["ha"] = "center" text_kwargs["va"] = "center" _i = 0 for _i, (k, ax) in enumerate(zip(keys, axes)): set_lognorm, colorbar = False, kwargs.pop("colorbar", True) try: _ = PrimingDegree(k) logg.debug(f"Calculating priming degree using `method={k}`") val = probs.priming_degree(method=k, early_cells=early_cells) k = f"{lineage_key}_{k}" adata.obs[k] = val except ValueError: pass scv.pl.scatter( adata, basis=key_added, color=k, show=False, ax=ax, use_raw=use_raw, norm=LogNorm() if set_lognorm else None, colorbar=colorbar, **kwargs, ) if colorbar and set_lognorm: cbar = ax.collections[0].colorbar cax = cbar.locator.axis ticks = cax.minor.locator.tick_values(cbar.vmin, cbar.vmax) ticks = [ticks[0], ticks[len(ticks) // 2 + 1], ticks[-1]] cbar.set_ticks(ticks) cbar.set_ticklabels([f"{t:.2f}" for t in ticks]) cbar.update_ticks() patches, texts = ax.pie( np.ones_like(angle_vec), labeldistance=label_distance, rotatelabels=True, labels=probs.names[::-1], startangle=-360 / len(angle_vec) / 2, counterclock=False, textprops=text_kwargs, ) for patch in patches: patch.set_visible(False) for color, text in zip(probs.colors[::-1], texts): if isinstance(label_rot, (int, float)): text.set_rotation(label_rot) elif label_rot == LabelRot.BEST: rot = text.get_rotation() text.set_rotation(rot + 90 + (1 - rot // 180) * 180) elif label_rot != LabelRot.DEFAULT: raise NotImplementedError( f"Label rotation `{label_rot}` is not yet implemented." ) text.set_color(color) if not show_edges: continue for i, color in enumerate(probs.colors): next = (i + 1) % n_lin x = 1.04 * np.linspace(angle_vec_sin[i], angle_vec_sin[next], _N) y = 1.04 * np.linspace(angle_vec_cos[i], angle_vec_cos[next], _N) points = np.array([x, y]).T.reshape(-1, 1, 2) segments = np.concatenate([points[:-1], points[1:]], axis=1) cmap = LinearSegmentedColormap.from_list( "abs_prob_cmap", [color, probs.colors[next]], N=_N ) lc = LineCollection(segments, cmap=cmap, zorder=-1) lc.set_array(np.linspace(0, 1, _N)) lc.set_linewidth(2) ax.add_collection(lc) for j in range(_i + 1, len(axes)): axes[j].remove() if save is not None: save_fig(fig, save)

true

f7195d706b8209cba3d1242687affeb52b7f4d89

10,085

py

Python

app/recipe/tests/test_recipe_api.py

ajayhb/recipe-app

226b1f4cce34412833a943e92d77f1f85775a2fc

[ "MIT" ]

null

app/recipe/tests/test_recipe_api.py

ajayhb/recipe-app

226b1f4cce34412833a943e92d77f1f85775a2fc

[ "MIT" ]

null

app/recipe/tests/test_recipe_api.py

ajayhb/recipe-app

226b1f4cce34412833a943e92d77f1f85775a2fc

[ "MIT" ]

null

import tempfile import os from PIL import Image from django.contrib.auth import get_user_model from django.test import TestCase from django.urls import reverse from decimal import Decimal from rest_framework import status from rest_framework.test import APIClient from core.models import Recipe, Tag, Ingredient from recipe.serializers import RecipeSerializer, RecipeDetailSerializer RECIPES_URL = reverse('recipe:recipe-list') def image_upload_url(recipe_id): """Return url of recipe image upload""" return reverse('recipe:recipe-upload-image', args=[recipe_id]) def detail_url(recipe_id): '''Add recipe_detail url''' return reverse('recipe:recipe-detail', args=[recipe_id]) def sample_tag(user, name='Main Course'): """Create and Return a sample Tag""" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Main Course'): """Create and Return a sample ingredient""" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): '''Create and return a sample recipe''' defaults = { 'title': 'Sample recipe', 'time_minutes': 3, 'price': 30.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) class PublicRecipeApiTests(TestCase): '''Test unauthenticated recipe api access''' def setUp(self): self.client = APIClient() def test_auth_reqd(self): '''Test that authentication is required''' res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): '''Test unauthenticated recipe api access''' def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( 'ajay.b@servify.in', 'testpassword' ) self.client.force_authenticate(self.user) def test_retrieve_recipe(self): '''Test retrieving the list of recipes''' sample_recipe(user=self.user) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipe_limited_to_user(self): '''Test retrieving recipes for user''' user2 = get_user_model().objects.create_user( 'ajay1234@gmail.com', 'password213' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) self.assertEqual(len(res.data), 1) def test_view_recipe_detail(self): '''Test viewing a recipe detail''' recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): '''Test creating recipe''' payload = { 'title': 'Chocolate Cheesecake', 'time_minutes': 30, 'price': 53.34 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) for key in payload.keys(): # Coz we can't do recipe.key directly so getattr() is used # self.assertEqual(payload[key], getattr(recipe, key)) self.assertEqual(recipe.price, Decimal('53.34')) def test_create_recipe_with_tags(self): '''Test creating a recipe with tags''' tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Dessert') payload = { 'title': 'Avocado lime Cheesecake', 'tags': [tag1.id, tag2.id], 'time_minutes': 60, 'price': 353.34 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) tags = recipe.tags.all() self.assertEqual(tags.count(), 2) self.assertIn(tag1, tags) self.assertIn(tag2, tags) def test_create_recipe_for_ingredients(self): '''Test creating recipe with ingredients''' ingredient1 = sample_ingredient(user=self.user, name='Noodles') ingredient2 = sample_ingredient(user=self.user, name='Manchurian') payload = { 'title': 'Manchurian Noodles', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 15, 'price': 133.34 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) def test_partial_update_recipe(self): '''Test updating a recipe with patch''' recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) new_tag = sample_tag(user=self.user, name='Curry') payload = { 'title': 'Manchurian Paneer Noodles', 'tags': [new_tag.id] } url = detail_url(recipe.id) self.client.patch(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) def test_full_update_recipe(self): '''Test updating a recipe with put''' recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Manchurian Paneer Spaghetti', 'time_minutes': 15, 'price': 133.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) class RecipeUploadImageTests(TestCase): def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( 'user@123.com', 'testpass' ) self.client.force_authenticate(self.user) self.recipe = sample_recipe(user=self.user) def tearDown(self): '''Remove the temporary files after test runs''' self.recipe.image.delete() def test_upload_image_to_recipe(self): '''Test for uploading an image to recipe''' url = image_upload_url(self.recipe.id) with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10)) img.save(ntf, format='JPEG') ntf.seek(0) # print(url, ntf) res = self.client.post(url, {'image': ntf}, format='multipart') self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertIn('image', res.data) self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): '''test uploading an invalid image''' url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): '''Test returning recipes with specific tags''' recipe1 = sample_recipe(user=self.user, title='Thai Curry') recipe2 = sample_recipe(user=self.user, title='Dal chawal') tag1 = sample_tag(user=self.user, name='Veggie') tag2 = sample_tag(user=self.user, name='Jain') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Machie') res = self.client.get( RECIPES_URL, {'tags': f'{tag1.id},{tag2.id}'} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) def test_filter_recipes_by_ingredients(self): '''Test returning recipes with specific ingredients''' recipe1 = sample_recipe(user=self.user, title='Thai Curry') recipe2 = sample_recipe(user=self.user, title='Dal chawal') recipe3 = sample_recipe(user=self.user, title='Machie') ingredient1 = sample_ingredient(user=self.user, name='Salt') ingredient2 = sample_ingredient(user=self.user, name='Pakoda') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) res = self.client.get( RECIPES_URL, {'ingredients': f'{ingredient1.id},{ingredient2.id}'} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data)

34.186441

78

0.64115

import tempfile import os from PIL import Image from django.contrib.auth import get_user_model from django.test import TestCase from django.urls import reverse from decimal import Decimal from rest_framework import status from rest_framework.test import APIClient from core.models import Recipe, Tag, Ingredient from recipe.serializers import RecipeSerializer, RecipeDetailSerializer RECIPES_URL = reverse('recipe:recipe-list') def image_upload_url(recipe_id): return reverse('recipe:recipe-upload-image', args=[recipe_id]) def detail_url(recipe_id): return reverse('recipe:recipe-detail', args=[recipe_id]) def sample_tag(user, name='Main Course'): return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Main Course'): return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): defaults = { 'title': 'Sample recipe', 'time_minutes': 3, 'price': 30.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) class PublicRecipeApiTests(TestCase): def setUp(self): self.client = APIClient() def test_auth_reqd(self): res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( 'ajay.b@servify.in', 'testpassword' ) self.client.force_authenticate(self.user) def test_retrieve_recipe(self): sample_recipe(user=self.user) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipe_limited_to_user(self): user2 = get_user_model().objects.create_user( 'ajay1234@gmail.com', 'password213' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) self.assertEqual(len(res.data), 1) def test_view_recipe_detail(self): recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): payload = { 'title': 'Chocolate Cheesecake', 'time_minutes': 30, 'price': 53.34 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) for key in payload.keys(): # self.assertEqual(payload[key], getattr(recipe, key)) self.assertEqual(recipe.price, Decimal('53.34')) def test_create_recipe_with_tags(self): tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Dessert') payload = { 'title': 'Avocado lime Cheesecake', 'tags': [tag1.id, tag2.id], 'time_minutes': 60, 'price': 353.34 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) tags = recipe.tags.all() self.assertEqual(tags.count(), 2) self.assertIn(tag1, tags) self.assertIn(tag2, tags) def test_create_recipe_for_ingredients(self): ingredient1 = sample_ingredient(user=self.user, name='Noodles') ingredient2 = sample_ingredient(user=self.user, name='Manchurian') payload = { 'title': 'Manchurian Noodles', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 15, 'price': 133.34 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) def test_partial_update_recipe(self): recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) new_tag = sample_tag(user=self.user, name='Curry') payload = { 'title': 'Manchurian Paneer Noodles', 'tags': [new_tag.id] } url = detail_url(recipe.id) self.client.patch(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) def test_full_update_recipe(self): recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Manchurian Paneer Spaghetti', 'time_minutes': 15, 'price': 133.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) class RecipeUploadImageTests(TestCase): def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( 'user@123.com', 'testpass' ) self.client.force_authenticate(self.user) self.recipe = sample_recipe(user=self.user) def tearDown(self): self.recipe.image.delete() def test_upload_image_to_recipe(self): url = image_upload_url(self.recipe.id) with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10)) img.save(ntf, format='JPEG') ntf.seek(0) # print(url, ntf) res = self.client.post(url, {'image': ntf}, format='multipart') self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertIn('image', res.data) self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): recipe1 = sample_recipe(user=self.user, title='Thai Curry') recipe2 = sample_recipe(user=self.user, title='Dal chawal') tag1 = sample_tag(user=self.user, name='Veggie') tag2 = sample_tag(user=self.user, name='Jain') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Machie') res = self.client.get( RECIPES_URL, {'tags': f'{tag1.id},{tag2.id}'} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) def test_filter_recipes_by_ingredients(self): recipe1 = sample_recipe(user=self.user, title='Thai Curry') recipe2 = sample_recipe(user=self.user, title='Dal chawal') recipe3 = sample_recipe(user=self.user, title='Machie') ingredient1 = sample_ingredient(user=self.user, name='Salt') ingredient2 = sample_ingredient(user=self.user, name='Pakoda') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) res = self.client.get( RECIPES_URL, {'ingredients': f'{ingredient1.id},{ingredient2.id}'} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data)

true

f7195f13467831480cf8e17b1264da8851ad2e64

21,774

py

Python

quant/platform/kucoin.py

yfjelley/thenextquant

5a2c4324ea390b513632ed2cc64d53314624e4ba

[ "MIT" ]

2

2021-09-22T08:41:55.000Z

2021-11-05T01:45:27.000Z

quant/platform/kucoin.py

mrganer/thenextquant

52fb22f5df20d43cb275a08adad81dc97f25a712

[ "MIT" ]

1

2019-10-25T05:25:28.000Z

quant/platform/kucoin.py

mrganer/thenextquant

52fb22f5df20d43cb275a08adad81dc97f25a712

[ "MIT" ]

4

2019-11-29T03:12:34.000Z

2021-09-19T02:59:29.000Z

# -*- coding:utf-8 -*- """ Kucoin Trade module. https://docs.kucoin.com Author: HuangTao Date: 2019/08/01 Email: huangtao@ifclover.com """ import json import copy import hmac import base64 import hashlib from urllib.parse import urljoin from quant.error import Error from quant.utils import tools from quant.utils import logger from quant.const import KUCOIN from quant.order import Order from quant.asset import Asset, AssetSubscribe from quant.tasks import SingleTask, LoopRunTask from quant.utils.http_client import AsyncHttpRequests from quant.utils.decorator import async_method_locker from quant.order import ORDER_TYPE_LIMIT, ORDER_TYPE_MARKET from quant.order import ORDER_ACTION_BUY, ORDER_ACTION_SELL from quant.order import ORDER_STATUS_SUBMITTED, ORDER_STATUS_PARTIAL_FILLED, ORDER_STATUS_FILLED, \ ORDER_STATUS_CANCELED, ORDER_STATUS_FAILED, ORDER_STATUS_NONE __all__ = ("KucoinRestAPI", "KucoinTrade", ) class KucoinRestAPI: """ Kucoin REST API client. Attributes: host: HTTP request host. access_key: Account"s ACCESS KEY. secret_key: Account"s SECRET KEY. passphrase: API KEY passphrase. """ def __init__(self, host, access_key, secret_key, passphrase): """initialize REST API client.""" self._host = host self._access_key = access_key self._secret_key = secret_key self._passphrase = passphrase async def get_sub_users(self): """Get the user info of all sub-users via this interface. Returns: success: Success results, otherwise it"s None. error: Error information, otherwise it"s None. """ uri = "/api/v1/sub/user" success, error = await self.request("GET", uri, auth=True) return success, error async def get_accounts(self, account_type=None, currency=None): """Get a list of accounts. Args: account_type: Account type, main or trade. currency: Currency name, e.g. BTC, ETH ... Returns: success: Success results, otherwise it"s None. error: Error information, otherwise it"s None. """ uri = "/api/v1/accounts" params = {} if account_type: params["type"] = account_type if currency: params["currency"] = currency success, error = await self.request("GET", uri, params=params, auth=True) return success, error async def get_account(self, account_id): """Information for a single account. Args: account_id: Account id. Returns: success: Success results, otherwise it"s None. error: Error information, otherwise it"s None. """ uri = "/api/v1/accounts/{}".format(account_id) success, error = await self.request("GET", uri, auth=True) return success, error async def create_account(self, account_type, currency): """Create a account. Args: account_type: Account type, main or trade. currency: Currency name, e.g. BTC, ETH ... Returns: success: Success results, otherwise it"s None. error: Error information, otherwise it"s None. """ uri = "/api/v1/accounts" body = { "type": account_type, "currency": currency } success, error = await self.request("POST", uri, body=body, auth=True) return success, error async def create_order(self, client_id, side, symbol, order_type, price, size): """ Add standard order. Args: client_id: Unique order id selected by you to identify your order. side: Trade side, buy or sell. symbol: A valid trading symbol code. e.g. ETH-BTC. order_type: Order type, limit or market (default is limit). price: Price per base currency. size: Amount of base currency to buy or sell. Returns: success: Success results, otherwise it"s None. error: Error information, otherwise it"s None. """ uri = "/api/v1/orders" body = { "clientOid": client_id, "side": side, "symbol": symbol, "type": order_type, "price": price, "size": size } success, error = await self.request("POST", uri, body=body, auth=True) return success, error async def revoke_order(self, order_id): """ Cancel a previously placed order. Args: order_id: Order ID, unique identifier of an order. Returns: success: Success results, otherwise it"s None. error: Error information, otherwise it"s None. """ uri = "/api/v1/orders/{}".format(order_id) success, error = await self.request("DELETE", uri, auth=True) return success, error async def revoke_orders_all(self, symbol=None): """ Attempt to cancel all open orders. The response is a list of ids of the canceled orders. Args: symbol: A valid trading symbol code. e.g. ETH-BTC. Returns: success: Success results, otherwise it"s None. error: Error information, otherwise it"s None. """ uri = "/api/v1/orders" params = {} if symbol: params["symbol"] = symbol success, error = await self.request("DELETE", uri, params=params, auth=True) return success, error async def get_order_list(self, status="active", symbol=None, order_type=None, start=None, end=None): """ Get order information list. Args: status: Only list orders with a specific status, `active` or `done`, default is `active`. symbol: A valid trading symbol code. e.g. ETH-BTC. order_type: Order type, limit, market, limit_stop or market_stop. start: Start time. Unix timestamp calculated in milliseconds will return only items which were created after the start time. end: End time. Unix timestamp calculated in milliseconds will return only items which were created before the end time. Returns: success: Success results, otherwise it"s None. error: Error information, otherwise it"s None. """ uri = "/api/v1/orders" params = {"status": status} if symbol: params["symbol"] = symbol if order_type: params["type"] = order_type if start: params["startAt"] = start if end: params["endAt"] = end success, error = await self.request("GET", uri, params=params, auth=True) return success, error async def get_order_detail(self, order_id): """ Get a single order by order ID. Args: order_id: Order ID, unique identifier of an order. Returns: success: Success results, otherwise it"s None. error: Error information, otherwise it"s None. """ uri = "/api/v1/orders/{}".format(order_id) success, error = await self.request("GET", uri, auth=True) return success, error async def get_websocket_token(self, private=False): """ Get a Websocket token from server. Args: private: If a private token, default is False. Returns: success: Success results, otherwise it"s None. error: Error information, otherwise it"s None. """ if private: uri = "/api/v1/bullet-private" success, error = await self.request("POST", uri, auth=True) else: uri = "/api/v1/bullet-public" success, error = await self.request("POST", uri) return success, error async def get_orderbook(self, symbol, count=20): """ Get orderbook information. Args: symbol: A valid trading symbol code. e.g. ETH-BTC. count: Orderbook length, only support 20 or 100. Returns: success: Success results, otherwise it"s None. error: Error information, otherwise it"s None. """ if count == 20: uri = "/api/v1/market/orderbook/level2_20?symbol={}".format(symbol) else: uri = "/api/v2/market/orderbook/level2_100?symbol={}".format(symbol) success, error = await self.request("GET", uri) return success, error async def request(self, method, uri, params=None, body=None, headers=None, auth=False): """ Do HTTP request. Args: method: HTTP request method. GET, POST, DELETE, PUT. uri: HTTP request uri. params: HTTP query params. body: HTTP request body. headers: HTTP request headers. auth: If this request requires authentication. Returns: success: Success results, otherwise it"s None. error: Error information, otherwise it"s None. """ if params: query = "&".join(["{}={}".format(k, params[k]) for k in sorted(params.keys())]) uri += "?" + query url = urljoin(self._host, uri) if auth: if not headers: headers = {} timestamp = str(tools.get_cur_timestamp_ms()) signature = self._generate_signature(timestamp, method, uri, body) headers["KC-API-KEY"] = self._access_key headers["KC-API-SIGN"] = signature headers["KC-API-TIMESTAMP"] = timestamp headers["KC-API-PASSPHRASE"] = self._passphrase _, success, error = await AsyncHttpRequests.fetch(method, url, data=body, headers=headers, timeout=10) if error: return None, error if success["code"] != "200000": return None, success return success["data"], error def _generate_signature(self, nonce, method, path, data): """Generate the call signature.""" data = json.dumps(data) if data else "" sig_str = "{}{}{}{}".format(nonce, method, path, data) m = hmac.new(self._secret_key.encode("utf-8"), sig_str.encode("utf-8"), hashlib.sha256) return base64.b64encode(m.digest()).decode("utf-8") class KucoinTrade: """ Kucoin Trade module. You can initialize trade object with some attributes in kwargs. Attributes: account: Account name for this trade exchange. strategy: What's name would you want to created for you strategy. symbol: Symbol name for your trade. host: HTTP request host. (default is "https://openapi-v2.kucoin.com") access_key: Account's ACCESS KEY. secret_key: Account's SECRET KEY. passphrase: API KEY passphrase. asset_update_callback: You can use this param to specific a async callback function when you initializing Trade object. `asset_update_callback` is like `async def on_asset_update_callback(asset: Asset): pass` and this callback function will be executed asynchronous when received AssetEvent. order_update_callback: You can use this param to specific a async callback function when you initializing Trade object. `order_update_callback` is like `async def on_order_update_callback(order: Order): pass` and this callback function will be executed asynchronous when some order state updated. init_success_callback: You can use this param to specific a async callback function when you initializing Trade object. `init_success_callback` is like `async def on_init_success_callback(success: bool, error: Error, **kwargs): pass` and this callback function will be executed asynchronous after Trade module object initialized successfully. check_order_interval: The interval time(seconds) for loop run task to check order status. (default is 2 seconds) """ def __init__(self, **kwargs): """Initialize.""" e = None if not kwargs.get("account"): e = Error("param account miss") if not kwargs.get("strategy"): e = Error("param strategy miss") if not kwargs.get("symbol"): e = Error("param symbol miss") if not kwargs.get("host"): kwargs["host"] = "https://openapi-v2.kucoin.com" if not kwargs.get("access_key"): e = Error("param access_key miss") if not kwargs.get("secret_key"): e = Error("param secret_key miss") if not kwargs.get("passphrase"): e = Error("param passphrase miss") if e: logger.error(e, caller=self) if kwargs.get("init_success_callback"): SingleTask.run(kwargs["init_success_callback"], False, e) return self._account = kwargs["account"] self._strategy = kwargs["strategy"] self._platform = KUCOIN self._symbol = kwargs["symbol"] self._host = kwargs["host"] self._access_key = kwargs["access_key"] self._secret_key = kwargs["secret_key"] self._passphrase = kwargs["passphrase"] self._asset_update_callback = kwargs.get("asset_update_callback") self._order_update_callback = kwargs.get("order_update_callback") self._init_success_callback = kwargs.get("init_success_callback") self._check_order_interval = kwargs.get("check_order_interval", 2) self._raw_symbol = self._symbol.replace("/", "-") # Raw symbol name. self._assets = {} # Asset information. e.g. {"BTC": {"free": "1.1", "locked": "2.2", "total": "3.3"}, ... } self._orders = {} # Order details. e.g. {order_no: order-object, ... } # Initialize our REST API client. self._rest_api = KucoinRestAPI(self._host, self._access_key, self._secret_key, self._passphrase) # Create a loop run task to check order status. LoopRunTask.register(self._check_order_update, self._check_order_interval) # Subscribe asset event. if self._asset_update_callback: AssetSubscribe(self._platform, self._account, self.on_event_asset_update) SingleTask.run(self._initialize) @property def assets(self): return copy.copy(self._assets) @property def orders(self): return copy.copy(self._orders) @property def rest_api(self): return self._rest_api async def _initialize(self): """ Initialize. fetch all open order information.""" result, error = await self._rest_api.get_order_list(symbol=self._raw_symbol) if error: e = Error("get open order nos failed: {}".format(error)) logger.error(e, caller=self) if self._init_success_callback: SingleTask.run(self._init_success_callback, False, e) return for item in result["items"]: if item["symbol"] != self._raw_symbol: continue await self._update_order(item) if self._init_success_callback: SingleTask.run(self._init_success_callback, True, None) async def create_order(self, action, price, quantity, order_type=ORDER_TYPE_LIMIT, **kwargs): """ Create an order. Args: action: Trade direction, BUY or SELL. price: Price of order. quantity: The buying or selling quantity. order_type: order type, MARKET or LIMIT. Returns: order_no: Order ID if created successfully, otherwise it's None. error: Error information, otherwise it's None. """ if action == ORDER_ACTION_BUY: action_type = "buy" elif action == ORDER_ACTION_SELL: action_type = "sell" else: return None, "action error" if order_type == ORDER_TYPE_MARKET: order_type_2 = "market" elif order_type == ORDER_TYPE_LIMIT: order_type_2 = "limit" else: return None, "order_type error" client_id = tools.get_uuid1() price = tools.float_to_str(price) quantity = tools.float_to_str(quantity) success, error = await self._rest_api.create_order(client_id, action_type, self._raw_symbol, order_type_2, price, quantity) if error: return None, error order_no = success["orderId"] infos = { "account": self._account, "platform": self._platform, "strategy": self._strategy, "order_no": order_no, "symbol": self._symbol, "action": action, "price": price, "quantity": quantity, "order_type": order_type } order = Order(**infos) self._orders[order_no] = order if self._order_update_callback: SingleTask.run(self._order_update_callback, copy.copy(order)) return order_no, None async def revoke_order(self, *order_nos): """ Revoke (an) order(s). Args: order_nos: Order id list, you can set this param to 0 or multiple items. If you set 0 param, you can cancel all orders for this symbol(initialized in Trade object). If you set 1 param, you can cancel an order. If you set multiple param, you can cancel multiple orders. Do not set param length more than 100. Returns: Success or error, see bellow. """ # If len(order_nos) == 0, you will cancel all orders for this symbol(initialized in Trade object). if len(order_nos) == 0: _, error = await self._rest_api.revoke_orders_all(self._raw_symbol) if error: return False, error return True, None # If len(order_nos) == 1, you will cancel an order. if len(order_nos) == 1: success, error = await self._rest_api.revoke_order(order_nos[0]) if error: return order_nos[0], error else: return order_nos[0], None # If len(order_nos) > 1, you will cancel multiple orders. if len(order_nos) > 1: s, e, = [], [] for order_no in order_nos: success, error = await self._rest_api.revoke_order(order_no) if error: e.append(error) else: s.append(order_no) return s, e async def get_open_order_nos(self): """ Get open order id list. Args: None. Returns: order_nos: Open order id list, otherwise it's None. error: Error information, otherwise it's None. """ result, error = await self._rest_api.get_order_list(symbol=self._raw_symbol) if error: return False, error order_nos = [] for item in result["items"]: if item["symbol"] != self._raw_symbol: continue order_nos.append(item["id"]) return order_nos, None async def _check_order_update(self, *args, **kwargs): """ Loop run task for check order status. """ order_nos = list(self._orders.keys()) if not order_nos: return for order_no in order_nos: success, error = await self._rest_api.get_order_detail(order_no) if error: return await self._update_order(success) @async_method_locker("KucoinTrade.order.locker") async def _update_order(self, order_info): """ Update order object. Args: order_info: Order information. """ if not order_info: return order_no = order_info["id"] size = float(order_info["size"]) deal_size = float(order_info["dealSize"]) order = self._orders.get(order_no) if not order: info = { "platform": self._platform, "account": self._account, "strategy": self._strategy, "order_no": order_no, "action": ORDER_ACTION_BUY if order_info["side"] == "buy" else ORDER_ACTION_SELL, "symbol": self._symbol, "price": order_info["price"], "quantity": order_info["size"], "remain": order_info["size"], "avg_price": order_info["price"] } order = Order(**info) self._orders[order_no] = order if order_info["isActive"]: if size == deal_size: status = ORDER_STATUS_SUBMITTED else: status = ORDER_STATUS_PARTIAL_FILLED else: if size == deal_size: status = ORDER_STATUS_FILLED else: status = ORDER_STATUS_CANCELED if status != order.status: order.status = status order.remain = size - deal_size order.ctime = order_info["createdAt"] order.utime = tools.get_cur_timestamp_ms() SingleTask.run(self._order_update_callback, copy.copy(order)) # Delete order that already completed. if order.status in [ORDER_STATUS_FAILED, ORDER_STATUS_CANCELED, ORDER_STATUS_FILLED]: self._orders.pop(order_no) async def on_event_asset_update(self, asset: Asset): """ Asset update callback. Args: asset: Asset object. """ self._assets = asset SingleTask.run(self._asset_update_callback, asset)

37.412371

133

0.596445

import json import copy import hmac import base64 import hashlib from urllib.parse import urljoin from quant.error import Error from quant.utils import tools from quant.utils import logger from quant.const import KUCOIN from quant.order import Order from quant.asset import Asset, AssetSubscribe from quant.tasks import SingleTask, LoopRunTask from quant.utils.http_client import AsyncHttpRequests from quant.utils.decorator import async_method_locker from quant.order import ORDER_TYPE_LIMIT, ORDER_TYPE_MARKET from quant.order import ORDER_ACTION_BUY, ORDER_ACTION_SELL from quant.order import ORDER_STATUS_SUBMITTED, ORDER_STATUS_PARTIAL_FILLED, ORDER_STATUS_FILLED, \ ORDER_STATUS_CANCELED, ORDER_STATUS_FAILED, ORDER_STATUS_NONE __all__ = ("KucoinRestAPI", "KucoinTrade", ) class KucoinRestAPI: def __init__(self, host, access_key, secret_key, passphrase): self._host = host self._access_key = access_key self._secret_key = secret_key self._passphrase = passphrase async def get_sub_users(self): uri = "/api/v1/sub/user" success, error = await self.request("GET", uri, auth=True) return success, error async def get_accounts(self, account_type=None, currency=None): uri = "/api/v1/accounts" params = {} if account_type: params["type"] = account_type if currency: params["currency"] = currency success, error = await self.request("GET", uri, params=params, auth=True) return success, error async def get_account(self, account_id): uri = "/api/v1/accounts/{}".format(account_id) success, error = await self.request("GET", uri, auth=True) return success, error async def create_account(self, account_type, currency): uri = "/api/v1/accounts" body = { "type": account_type, "currency": currency } success, error = await self.request("POST", uri, body=body, auth=True) return success, error async def create_order(self, client_id, side, symbol, order_type, price, size): uri = "/api/v1/orders" body = { "clientOid": client_id, "side": side, "symbol": symbol, "type": order_type, "price": price, "size": size } success, error = await self.request("POST", uri, body=body, auth=True) return success, error async def revoke_order(self, order_id): uri = "/api/v1/orders/{}".format(order_id) success, error = await self.request("DELETE", uri, auth=True) return success, error async def revoke_orders_all(self, symbol=None): uri = "/api/v1/orders" params = {} if symbol: params["symbol"] = symbol success, error = await self.request("DELETE", uri, params=params, auth=True) return success, error async def get_order_list(self, status="active", symbol=None, order_type=None, start=None, end=None): uri = "/api/v1/orders" params = {"status": status} if symbol: params["symbol"] = symbol if order_type: params["type"] = order_type if start: params["startAt"] = start if end: params["endAt"] = end success, error = await self.request("GET", uri, params=params, auth=True) return success, error async def get_order_detail(self, order_id): uri = "/api/v1/orders/{}".format(order_id) success, error = await self.request("GET", uri, auth=True) return success, error async def get_websocket_token(self, private=False): if private: uri = "/api/v1/bullet-private" success, error = await self.request("POST", uri, auth=True) else: uri = "/api/v1/bullet-public" success, error = await self.request("POST", uri) return success, error async def get_orderbook(self, symbol, count=20): if count == 20: uri = "/api/v1/market/orderbook/level2_20?symbol={}".format(symbol) else: uri = "/api/v2/market/orderbook/level2_100?symbol={}".format(symbol) success, error = await self.request("GET", uri) return success, error async def request(self, method, uri, params=None, body=None, headers=None, auth=False): if params: query = "&".join(["{}={}".format(k, params[k]) for k in sorted(params.keys())]) uri += "?" + query url = urljoin(self._host, uri) if auth: if not headers: headers = {} timestamp = str(tools.get_cur_timestamp_ms()) signature = self._generate_signature(timestamp, method, uri, body) headers["KC-API-KEY"] = self._access_key headers["KC-API-SIGN"] = signature headers["KC-API-TIMESTAMP"] = timestamp headers["KC-API-PASSPHRASE"] = self._passphrase _, success, error = await AsyncHttpRequests.fetch(method, url, data=body, headers=headers, timeout=10) if error: return None, error if success["code"] != "200000": return None, success return success["data"], error def _generate_signature(self, nonce, method, path, data): data = json.dumps(data) if data else "" sig_str = "{}{}{}{}".format(nonce, method, path, data) m = hmac.new(self._secret_key.encode("utf-8"), sig_str.encode("utf-8"), hashlib.sha256) return base64.b64encode(m.digest()).decode("utf-8") class KucoinTrade: def __init__(self, **kwargs): e = None if not kwargs.get("account"): e = Error("param account miss") if not kwargs.get("strategy"): e = Error("param strategy miss") if not kwargs.get("symbol"): e = Error("param symbol miss") if not kwargs.get("host"): kwargs["host"] = "https://openapi-v2.kucoin.com" if not kwargs.get("access_key"): e = Error("param access_key miss") if not kwargs.get("secret_key"): e = Error("param secret_key miss") if not kwargs.get("passphrase"): e = Error("param passphrase miss") if e: logger.error(e, caller=self) if kwargs.get("init_success_callback"): SingleTask.run(kwargs["init_success_callback"], False, e) return self._account = kwargs["account"] self._strategy = kwargs["strategy"] self._platform = KUCOIN self._symbol = kwargs["symbol"] self._host = kwargs["host"] self._access_key = kwargs["access_key"] self._secret_key = kwargs["secret_key"] self._passphrase = kwargs["passphrase"] self._asset_update_callback = kwargs.get("asset_update_callback") self._order_update_callback = kwargs.get("order_update_callback") self._init_success_callback = kwargs.get("init_success_callback") self._check_order_interval = kwargs.get("check_order_interval", 2) self._raw_symbol = self._symbol.replace("/", "-") self._assets = {} self._orders = {} self._rest_api = KucoinRestAPI(self._host, self._access_key, self._secret_key, self._passphrase) LoopRunTask.register(self._check_order_update, self._check_order_interval) if self._asset_update_callback: AssetSubscribe(self._platform, self._account, self.on_event_asset_update) SingleTask.run(self._initialize) @property def assets(self): return copy.copy(self._assets) @property def orders(self): return copy.copy(self._orders) @property def rest_api(self): return self._rest_api async def _initialize(self): result, error = await self._rest_api.get_order_list(symbol=self._raw_symbol) if error: e = Error("get open order nos failed: {}".format(error)) logger.error(e, caller=self) if self._init_success_callback: SingleTask.run(self._init_success_callback, False, e) return for item in result["items"]: if item["symbol"] != self._raw_symbol: continue await self._update_order(item) if self._init_success_callback: SingleTask.run(self._init_success_callback, True, None) async def create_order(self, action, price, quantity, order_type=ORDER_TYPE_LIMIT, **kwargs): if action == ORDER_ACTION_BUY: action_type = "buy" elif action == ORDER_ACTION_SELL: action_type = "sell" else: return None, "action error" if order_type == ORDER_TYPE_MARKET: order_type_2 = "market" elif order_type == ORDER_TYPE_LIMIT: order_type_2 = "limit" else: return None, "order_type error" client_id = tools.get_uuid1() price = tools.float_to_str(price) quantity = tools.float_to_str(quantity) success, error = await self._rest_api.create_order(client_id, action_type, self._raw_symbol, order_type_2, price, quantity) if error: return None, error order_no = success["orderId"] infos = { "account": self._account, "platform": self._platform, "strategy": self._strategy, "order_no": order_no, "symbol": self._symbol, "action": action, "price": price, "quantity": quantity, "order_type": order_type } order = Order(**infos) self._orders[order_no] = order if self._order_update_callback: SingleTask.run(self._order_update_callback, copy.copy(order)) return order_no, None async def revoke_order(self, *order_nos): if len(order_nos) == 0: _, error = await self._rest_api.revoke_orders_all(self._raw_symbol) if error: return False, error return True, None if len(order_nos) == 1: success, error = await self._rest_api.revoke_order(order_nos[0]) if error: return order_nos[0], error else: return order_nos[0], None if len(order_nos) > 1: s, e, = [], [] for order_no in order_nos: success, error = await self._rest_api.revoke_order(order_no) if error: e.append(error) else: s.append(order_no) return s, e async def get_open_order_nos(self): result, error = await self._rest_api.get_order_list(symbol=self._raw_symbol) if error: return False, error order_nos = [] for item in result["items"]: if item["symbol"] != self._raw_symbol: continue order_nos.append(item["id"]) return order_nos, None async def _check_order_update(self, *args, **kwargs): order_nos = list(self._orders.keys()) if not order_nos: return for order_no in order_nos: success, error = await self._rest_api.get_order_detail(order_no) if error: return await self._update_order(success) @async_method_locker("KucoinTrade.order.locker") async def _update_order(self, order_info): if not order_info: return order_no = order_info["id"] size = float(order_info["size"]) deal_size = float(order_info["dealSize"]) order = self._orders.get(order_no) if not order: info = { "platform": self._platform, "account": self._account, "strategy": self._strategy, "order_no": order_no, "action": ORDER_ACTION_BUY if order_info["side"] == "buy" else ORDER_ACTION_SELL, "symbol": self._symbol, "price": order_info["price"], "quantity": order_info["size"], "remain": order_info["size"], "avg_price": order_info["price"] } order = Order(**info) self._orders[order_no] = order if order_info["isActive"]: if size == deal_size: status = ORDER_STATUS_SUBMITTED else: status = ORDER_STATUS_PARTIAL_FILLED else: if size == deal_size: status = ORDER_STATUS_FILLED else: status = ORDER_STATUS_CANCELED if status != order.status: order.status = status order.remain = size - deal_size order.ctime = order_info["createdAt"] order.utime = tools.get_cur_timestamp_ms() SingleTask.run(self._order_update_callback, copy.copy(order)) if order.status in [ORDER_STATUS_FAILED, ORDER_STATUS_CANCELED, ORDER_STATUS_FILLED]: self._orders.pop(order_no) async def on_event_asset_update(self, asset: Asset): self._assets = asset SingleTask.run(self._asset_update_callback, asset)

true

f7195fe4de87239beab23f5be618730dc300a65f

14,645

py

Python

RAdam.py

blnm/RSE

6a3f0dd858ea4b6dafcfb1d97bb979e101d9911c

[ "MIT" ]

40

2020-04-24T01:03:12.000Z

2022-03-20T18:19:30.000Z

RAdam.py

blnm/RSE

6a3f0dd858ea4b6dafcfb1d97bb979e101d9911c

[ "MIT" ]

4

2021-09-09T13:26:09.000Z

2022-03-31T18:37:05.000Z

RAdam.py

blnm/RSE

6a3f0dd858ea4b6dafcfb1d97bb979e101d9911c

[ "MIT" ]

7

2020-11-25T14:26:09.000Z

2022-01-29T10:18:40.000Z

import tensorflow as tf from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops from tensorflow.python.training import optimizer __all__ = ['RAdamOptimizer'] class RAdamOptimizer(optimizer.Optimizer): """RAdam optimizer. According to the paper [On The Variance Of The Adaptive Learning Rate And Beyond](https://arxiv.org/pdf/1908.03265v1.pdf). """ def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-7, L2_decay=0., amsgrad=False, total_steps=0, warmup_proportion=0.1, min_lr=0., use_locking=False, name="RAdam", decay_vars=None, L1_decay=0.0, clip_gradients=False, clip_multiplier=3.0, clip_epsilon=1e-2): r"""Construct a new Adam optimizer. Args: learning_rate: A Tensor or a floating point value. The learning rate. beta1: A float value or a constant float tensor. The exponential decay rate for the 1st moment estimates. beta2: A float value or a constant float tensor. The exponential decay rate for the 2nd moment estimates. epsilon: A small constant for numerical stability. This epsilon is "epsilon hat" in the Kingma and Ba paper (in the formula just before Section 2.1), not the epsilon in Algorithm 1 of the paper. L2_decay: A floating point value. Weight decay for each param. amsgrad: boolean. Whether to apply AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and beyond". total_steps: An integer. Total number of training steps. Enable warmup by setting a positive value. warmup_proportion: A floating point value. The proportion of increasing steps. min_lr: A floating point value. Minimum learning rate after warmup. name: Optional name for the operations created when applying gradients. Defaults to "Adam". @compatibility(eager) When eager execution is enabled, `learning_rate`, `beta_1`, `beta_2`, and `epsilon` can each be a callable that takes no arguments and returns the actual value to use. This can be useful for changing these values across different invocations of optimizer functions. @end_compatibility **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip gradients by value, `decay` is included for backward compatibility to allow time inverse decay of learning rate. `lr` is included for backward compatibility, recommended to use `learning_rate` instead. """ super(RAdamOptimizer, self).__init__(use_locking, name) self._lr = learning_rate self._beta1 = beta1 self._beta2 = beta2 self._epsilon = epsilon self._weight_decay = L2_decay self._L1_decay = L1_decay self._amsgrad = amsgrad self._total_steps = float(total_steps) self._warmup_proportion = warmup_proportion self._min_lr = min_lr self._initial_weight_decay = L2_decay self._initial_total_steps = total_steps self.clip_multiplier = clip_multiplier self.clip_epsilon = clip_epsilon self.clip_gradients = clip_gradients self.clip_multiplier_t = ops.convert_to_tensor(self.clip_multiplier, name="clip_multiplier") self.clip_epsilon_t = ops.convert_to_tensor(self.clip_epsilon, name="clip_epsilon") self._lr_t = None self._step_t = None self._beta1_t = None self._beta2_t = None self._epsilon_t = None self._weight_decay_t = None self._total_steps_t = None self._warmup_proportion_t = None self._min_lr_t = None self.reg_vars = set(decay_vars) if decay_vars is not None else set() def _get_beta_accumulators(self): with ops.init_scope(): if context.executing_eagerly(): graph = None else: graph = ops.get_default_graph() return (self._get_non_slot_variable("step", graph=graph), self._get_non_slot_variable("beta1_power", graph=graph), self._get_non_slot_variable("beta2_power", graph=graph)) def _create_slots_internal(self, var_list): first_var = min(var_list, key=lambda x: x.name) self._create_non_slot_variable(initial_value=1.0, name="step", colocate_with=first_var) self._create_non_slot_variable(initial_value=self._beta1, name="beta1_power", colocate_with=first_var) self._create_non_slot_variable(initial_value=self._beta2, name="beta2_power", colocate_with=first_var) for v in var_list: self._zeros_slot(v, "m", self._name) self._zeros_slot(v, "v", self._name) if self._amsgrad: self._zeros_slot(v, "vhat", self._name) def _prepare(self): lr = self._call_if_callable(self._lr) beta1 = self._call_if_callable(self._beta1) beta2 = self._call_if_callable(self._beta2) epsilon = self._call_if_callable(self._epsilon) weight_decay = self._call_if_callable(self._weight_decay) total_steps = self._call_if_callable(self._total_steps) warmup_proportion = self._call_if_callable(self._warmup_proportion) min_lr = self._call_if_callable(self._min_lr) self._lr_t = ops.convert_to_tensor(lr, name="learning_rate") self._beta1_t = ops.convert_to_tensor(beta1, name="beta1") self._beta2_t = ops.convert_to_tensor(beta2, name="beta2") self._epsilon_t = ops.convert_to_tensor(epsilon, name="epsilon") self._weight_decay_t = ops.convert_to_tensor(weight_decay, name="weight_decay") self._total_steps_t = ops.convert_to_tensor(total_steps, name="total_steps") self._warmup_proportion_t = ops.convert_to_tensor(warmup_proportion, name="warmup_proportion") self._min_lr_t = ops.convert_to_tensor(min_lr, name="min_lr") def apply_gradients(self, grads_and_vars, global_step=None, name=None): tvars = list(zip(*grads_and_vars))[1] self._create_slots_internal(tvars) return super().apply_gradients(grads_and_vars, global_step, name) def _apply_dense(self, grad, var): return self._resource_apply_dense(grad, var) def _resource_apply_dense(self, grad, var): step, beta1_power, beta2_power = self._get_beta_accumulators() beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype) beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype) lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) if self._initial_total_steps > 0: total_steps = math_ops.cast(self._total_steps_t, var.dtype.base_dtype) warmup_proportion = math_ops.cast(self._warmup_proportion_t, var.dtype.base_dtype) min_lr = math_ops.cast(self._min_lr_t, var.dtype.base_dtype) warmup_steps = total_steps * warmup_proportion decay_steps = math_ops.maximum(total_steps - warmup_steps, 1) decay_rate = (min_lr - lr_t) / decay_steps lr_t = tf.where( step <= warmup_steps, lr_t * (step / warmup_steps), lr_t + decay_rate * math_ops.minimum(step - warmup_steps, decay_steps), ) beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype) v = self.get_slot(var, "v") if self.clip_gradients: clipVal = math_ops.sqrt( tf.reduce_sum(v) / (1.0 - beta2_power)) * self.clip_multiplier_t + self.clip_epsilon_t grad = clip_ops.clip_by_norm(grad, clipVal) sma_inf = 2.0 / (1.0 - beta2_t) - 1.0 sma_t = sma_inf - 2.0 * step * beta2_power / (1.0 - beta2_power) m = self.get_slot(var, "m") v_t = state_ops.assign(v, beta2_t * v + (1.0 - beta2_t) * math_ops.square(grad), use_locking=self._use_locking) v_corr_t = math_ops.sqrt(v_t / (1.0 - beta2_power)) + epsilon_t grad_corr = grad / v_corr_t m_t = state_ops.assign(m, beta1_t * m + (1.0 - beta1_t) * grad_corr, use_locking=self._use_locking) m_corr_t = m_t / (1.0 - beta1_power) r_t = math_ops.sqrt((sma_t - 4.0) / (sma_inf - 4.0) * (sma_t - 2.0) / (sma_inf - 2.0) * sma_inf / sma_t) var_t = tf.where(sma_t >= 5.0, r_t * m_corr_t, m_corr_t) if var in self.reg_vars: if self._initial_weight_decay > 0.0: var_t += math_ops.cast(self._weight_decay_t, var.dtype.base_dtype) * var if self._L1_decay > 0.0: var_t += math_ops.cast(self._L1_decay, var.dtype.base_dtype) * math_ops.sign(var) with tf.control_dependencies([var_t]): var_update = state_ops.assign_sub(var, lr_t * var_t, use_locking=self._use_locking) updates = [var_update, m_t, v_t] return control_flow_ops.group(*updates) def _apply_sparse_shared(self, grad, var, indices, scatter_add): step, beta1_power, beta2_power = self._get_beta_accumulators() beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype) beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype) lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) if self._initial_total_steps > 0: total_steps = math_ops.cast(self._total_steps_t, var.dtype.base_dtype) warmup_proportion = math_ops.cast(self._warmup_proportion_t, var.dtype.base_dtype) min_lr = math_ops.cast(self._min_lr_t, var.dtype.base_dtype) warmup_steps = total_steps * warmup_proportion decay_steps = math_ops.maximum(total_steps - warmup_steps, 1) decay_rate = (min_lr - lr_t) / decay_steps lr_t = tf.where( step <= warmup_steps, lr_t * (step / warmup_steps), lr_t + decay_rate * math_ops.minimum(step - warmup_steps, decay_steps), ) beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype) v = self.get_slot(var, "v") if self.clip_gradients: clipVal = math_ops.sqrt( tf.reduce_sum(v) / (1.0 - beta2_power)) * self.clip_multiplier_t + self.clip_epsilon_t grad = clip_ops.clip_by_norm(grad, clipVal) sma_inf = 2.0 / (1.0 - beta2_t) - 1.0 sma_t = sma_inf - 2.0 * step * beta2_power / (1.0 - beta2_power) m = self.get_slot(var, "m") m_scaled_g_values = grad * (1 - beta1_t) m_t = state_ops.assign(m, m * beta1_t, use_locking=self._use_locking) with ops.control_dependencies([m_t]): m_t = scatter_add(m, indices, m_scaled_g_values) m_corr_t = m_t / (1.0 - beta1_power) v_scaled_g_values = (grad * grad) * (1 - beta2_t) v_t = state_ops.assign(v, v * beta2_t, use_locking=self._use_locking) with ops.control_dependencies([v_t]): v_t = scatter_add(v, indices, v_scaled_g_values) if self._amsgrad: vhat = self.get_slot(var, 'vhat') vhat_t = state_ops.assign(vhat, math_ops.maximum(vhat, v_t), use_locking=self._use_locking) v_corr_t = math_ops.sqrt(vhat_t / (1.0 - beta2_power)) + epsilon_t else: v_corr_t = math_ops.sqrt(v_t / (1.0 - beta2_power)) + epsilon_t r_t = math_ops.sqrt((sma_t - 4.0) / (sma_inf - 4.0) * (sma_t - 2.0) / (sma_inf - 2.0) * sma_inf / sma_t) var_t = tf.where(sma_t >= 5.0, r_t * m_corr_t / v_corr_t, m_corr_t) if var in self.reg_vars: if self._initial_weight_decay > 0.0: var_t += math_ops.cast(self._weight_decay_t, var.dtype.base_dtype) * var if self._L1_decay > 0.0: var_t += math_ops.cast(self._L1_decay, var.dtype.base_dtype) * math_ops.sign(var) var_update = state_ops.assign_sub(var, lr_t * var_t, use_locking=self._use_locking) updates = [var_update, m_t, v_t] if self._amsgrad: updates.append(vhat_t) return control_flow_ops.group(*updates) def _apply_sparse(self, grad, var): return self._apply_sparse_shared( grad.values, var, grad.indices, lambda x, i, v: state_ops.scatter_add(x, i, v, use_locking=self._use_locking)) def _resource_scatter_add(self, x, i, v): with ops.control_dependencies([resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_apply_sparse(self, grad, var, indices): return self._apply_sparse_shared(grad, var, indices, self._resource_scatter_add) def _finish(self, update_ops, name_scope): with ops.control_dependencies(update_ops): step, beta1_power, beta2_power = self._get_beta_accumulators() with ops.colocate_with(beta1_power): update_step = step.assign(step + 1.0, use_locking=self._use_locking) update_beta1 = beta1_power.assign(beta1_power * self._beta1_t, use_locking=self._use_locking) update_beta2 = beta2_power.assign(beta2_power * self._beta2_t, use_locking=self._use_locking) return control_flow_ops.group(*update_ops + [update_step, update_beta1, update_beta2], name=name_scope)

49.476351

120

0.629157

import tensorflow as tf from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import state_ops from tensorflow.python.training import optimizer __all__ = ['RAdamOptimizer'] class RAdamOptimizer(optimizer.Optimizer): def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-7, L2_decay=0., amsgrad=False, total_steps=0, warmup_proportion=0.1, min_lr=0., use_locking=False, name="RAdam", decay_vars=None, L1_decay=0.0, clip_gradients=False, clip_multiplier=3.0, clip_epsilon=1e-2): super(RAdamOptimizer, self).__init__(use_locking, name) self._lr = learning_rate self._beta1 = beta1 self._beta2 = beta2 self._epsilon = epsilon self._weight_decay = L2_decay self._L1_decay = L1_decay self._amsgrad = amsgrad self._total_steps = float(total_steps) self._warmup_proportion = warmup_proportion self._min_lr = min_lr self._initial_weight_decay = L2_decay self._initial_total_steps = total_steps self.clip_multiplier = clip_multiplier self.clip_epsilon = clip_epsilon self.clip_gradients = clip_gradients self.clip_multiplier_t = ops.convert_to_tensor(self.clip_multiplier, name="clip_multiplier") self.clip_epsilon_t = ops.convert_to_tensor(self.clip_epsilon, name="clip_epsilon") self._lr_t = None self._step_t = None self._beta1_t = None self._beta2_t = None self._epsilon_t = None self._weight_decay_t = None self._total_steps_t = None self._warmup_proportion_t = None self._min_lr_t = None self.reg_vars = set(decay_vars) if decay_vars is not None else set() def _get_beta_accumulators(self): with ops.init_scope(): if context.executing_eagerly(): graph = None else: graph = ops.get_default_graph() return (self._get_non_slot_variable("step", graph=graph), self._get_non_slot_variable("beta1_power", graph=graph), self._get_non_slot_variable("beta2_power", graph=graph)) def _create_slots_internal(self, var_list): first_var = min(var_list, key=lambda x: x.name) self._create_non_slot_variable(initial_value=1.0, name="step", colocate_with=first_var) self._create_non_slot_variable(initial_value=self._beta1, name="beta1_power", colocate_with=first_var) self._create_non_slot_variable(initial_value=self._beta2, name="beta2_power", colocate_with=first_var) for v in var_list: self._zeros_slot(v, "m", self._name) self._zeros_slot(v, "v", self._name) if self._amsgrad: self._zeros_slot(v, "vhat", self._name) def _prepare(self): lr = self._call_if_callable(self._lr) beta1 = self._call_if_callable(self._beta1) beta2 = self._call_if_callable(self._beta2) epsilon = self._call_if_callable(self._epsilon) weight_decay = self._call_if_callable(self._weight_decay) total_steps = self._call_if_callable(self._total_steps) warmup_proportion = self._call_if_callable(self._warmup_proportion) min_lr = self._call_if_callable(self._min_lr) self._lr_t = ops.convert_to_tensor(lr, name="learning_rate") self._beta1_t = ops.convert_to_tensor(beta1, name="beta1") self._beta2_t = ops.convert_to_tensor(beta2, name="beta2") self._epsilon_t = ops.convert_to_tensor(epsilon, name="epsilon") self._weight_decay_t = ops.convert_to_tensor(weight_decay, name="weight_decay") self._total_steps_t = ops.convert_to_tensor(total_steps, name="total_steps") self._warmup_proportion_t = ops.convert_to_tensor(warmup_proportion, name="warmup_proportion") self._min_lr_t = ops.convert_to_tensor(min_lr, name="min_lr") def apply_gradients(self, grads_and_vars, global_step=None, name=None): tvars = list(zip(*grads_and_vars))[1] self._create_slots_internal(tvars) return super().apply_gradients(grads_and_vars, global_step, name) def _apply_dense(self, grad, var): return self._resource_apply_dense(grad, var) def _resource_apply_dense(self, grad, var): step, beta1_power, beta2_power = self._get_beta_accumulators() beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype) beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype) lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) if self._initial_total_steps > 0: total_steps = math_ops.cast(self._total_steps_t, var.dtype.base_dtype) warmup_proportion = math_ops.cast(self._warmup_proportion_t, var.dtype.base_dtype) min_lr = math_ops.cast(self._min_lr_t, var.dtype.base_dtype) warmup_steps = total_steps * warmup_proportion decay_steps = math_ops.maximum(total_steps - warmup_steps, 1) decay_rate = (min_lr - lr_t) / decay_steps lr_t = tf.where( step <= warmup_steps, lr_t * (step / warmup_steps), lr_t + decay_rate * math_ops.minimum(step - warmup_steps, decay_steps), ) beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype) v = self.get_slot(var, "v") if self.clip_gradients: clipVal = math_ops.sqrt( tf.reduce_sum(v) / (1.0 - beta2_power)) * self.clip_multiplier_t + self.clip_epsilon_t grad = clip_ops.clip_by_norm(grad, clipVal) sma_inf = 2.0 / (1.0 - beta2_t) - 1.0 sma_t = sma_inf - 2.0 * step * beta2_power / (1.0 - beta2_power) m = self.get_slot(var, "m") v_t = state_ops.assign(v, beta2_t * v + (1.0 - beta2_t) * math_ops.square(grad), use_locking=self._use_locking) v_corr_t = math_ops.sqrt(v_t / (1.0 - beta2_power)) + epsilon_t grad_corr = grad / v_corr_t m_t = state_ops.assign(m, beta1_t * m + (1.0 - beta1_t) * grad_corr, use_locking=self._use_locking) m_corr_t = m_t / (1.0 - beta1_power) r_t = math_ops.sqrt((sma_t - 4.0) / (sma_inf - 4.0) * (sma_t - 2.0) / (sma_inf - 2.0) * sma_inf / sma_t) var_t = tf.where(sma_t >= 5.0, r_t * m_corr_t, m_corr_t) if var in self.reg_vars: if self._initial_weight_decay > 0.0: var_t += math_ops.cast(self._weight_decay_t, var.dtype.base_dtype) * var if self._L1_decay > 0.0: var_t += math_ops.cast(self._L1_decay, var.dtype.base_dtype) * math_ops.sign(var) with tf.control_dependencies([var_t]): var_update = state_ops.assign_sub(var, lr_t * var_t, use_locking=self._use_locking) updates = [var_update, m_t, v_t] return control_flow_ops.group(*updates) def _apply_sparse_shared(self, grad, var, indices, scatter_add): step, beta1_power, beta2_power = self._get_beta_accumulators() beta1_power = math_ops.cast(beta1_power, var.dtype.base_dtype) beta2_power = math_ops.cast(beta2_power, var.dtype.base_dtype) lr_t = math_ops.cast(self._lr_t, var.dtype.base_dtype) if self._initial_total_steps > 0: total_steps = math_ops.cast(self._total_steps_t, var.dtype.base_dtype) warmup_proportion = math_ops.cast(self._warmup_proportion_t, var.dtype.base_dtype) min_lr = math_ops.cast(self._min_lr_t, var.dtype.base_dtype) warmup_steps = total_steps * warmup_proportion decay_steps = math_ops.maximum(total_steps - warmup_steps, 1) decay_rate = (min_lr - lr_t) / decay_steps lr_t = tf.where( step <= warmup_steps, lr_t * (step / warmup_steps), lr_t + decay_rate * math_ops.minimum(step - warmup_steps, decay_steps), ) beta1_t = math_ops.cast(self._beta1_t, var.dtype.base_dtype) beta2_t = math_ops.cast(self._beta2_t, var.dtype.base_dtype) epsilon_t = math_ops.cast(self._epsilon_t, var.dtype.base_dtype) v = self.get_slot(var, "v") if self.clip_gradients: clipVal = math_ops.sqrt( tf.reduce_sum(v) / (1.0 - beta2_power)) * self.clip_multiplier_t + self.clip_epsilon_t grad = clip_ops.clip_by_norm(grad, clipVal) sma_inf = 2.0 / (1.0 - beta2_t) - 1.0 sma_t = sma_inf - 2.0 * step * beta2_power / (1.0 - beta2_power) m = self.get_slot(var, "m") m_scaled_g_values = grad * (1 - beta1_t) m_t = state_ops.assign(m, m * beta1_t, use_locking=self._use_locking) with ops.control_dependencies([m_t]): m_t = scatter_add(m, indices, m_scaled_g_values) m_corr_t = m_t / (1.0 - beta1_power) v_scaled_g_values = (grad * grad) * (1 - beta2_t) v_t = state_ops.assign(v, v * beta2_t, use_locking=self._use_locking) with ops.control_dependencies([v_t]): v_t = scatter_add(v, indices, v_scaled_g_values) if self._amsgrad: vhat = self.get_slot(var, 'vhat') vhat_t = state_ops.assign(vhat, math_ops.maximum(vhat, v_t), use_locking=self._use_locking) v_corr_t = math_ops.sqrt(vhat_t / (1.0 - beta2_power)) + epsilon_t else: v_corr_t = math_ops.sqrt(v_t / (1.0 - beta2_power)) + epsilon_t r_t = math_ops.sqrt((sma_t - 4.0) / (sma_inf - 4.0) * (sma_t - 2.0) / (sma_inf - 2.0) * sma_inf / sma_t) var_t = tf.where(sma_t >= 5.0, r_t * m_corr_t / v_corr_t, m_corr_t) if var in self.reg_vars: if self._initial_weight_decay > 0.0: var_t += math_ops.cast(self._weight_decay_t, var.dtype.base_dtype) * var if self._L1_decay > 0.0: var_t += math_ops.cast(self._L1_decay, var.dtype.base_dtype) * math_ops.sign(var) var_update = state_ops.assign_sub(var, lr_t * var_t, use_locking=self._use_locking) updates = [var_update, m_t, v_t] if self._amsgrad: updates.append(vhat_t) return control_flow_ops.group(*updates) def _apply_sparse(self, grad, var): return self._apply_sparse_shared( grad.values, var, grad.indices, lambda x, i, v: state_ops.scatter_add(x, i, v, use_locking=self._use_locking)) def _resource_scatter_add(self, x, i, v): with ops.control_dependencies([resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_apply_sparse(self, grad, var, indices): return self._apply_sparse_shared(grad, var, indices, self._resource_scatter_add) def _finish(self, update_ops, name_scope): with ops.control_dependencies(update_ops): step, beta1_power, beta2_power = self._get_beta_accumulators() with ops.colocate_with(beta1_power): update_step = step.assign(step + 1.0, use_locking=self._use_locking) update_beta1 = beta1_power.assign(beta1_power * self._beta1_t, use_locking=self._use_locking) update_beta2 = beta2_power.assign(beta2_power * self._beta2_t, use_locking=self._use_locking) return control_flow_ops.group(*update_ops + [update_step, update_beta1, update_beta2], name=name_scope)

true

f7196003d3c4be36ba4db4ea82b6856d51483928

18,496

py

Python

btclib/ecc/ssa.py

dginst/btclib

70932afe32167449e369d4e2911b1bf741c0f5d2

[ "MIT" ]

16

2019-01-04T22:21:17.000Z

2020-02-01T10:41:28.000Z

btclib/ecc/ssa.py

dginst/BitcoinBlockchainTechnology

70932afe32167449e369d4e2911b1bf741c0f5d2

[ "MIT" ]

20

2018-05-24T18:47:12.000Z

2018-12-22T09:52:09.000Z

btclib/ecc/ssa.py

dginst/BitcoinBlockchainTechnology

70932afe32167449e369d4e2911b1bf741c0f5d2

[ "MIT" ]

9

2018-05-16T09:53:32.000Z

2019-01-03T13:49:37.000Z

#!/usr/bin/env python3 # Copyright (C) 2017-2022 The btclib developers # # This file is part of btclib. It is subject to the license terms in the # LICENSE file found in the top-level directory of this distribution. # # No part of btclib including this file, may be copied, modified, propagated, # or distributed except according to the terms contained in the LICENSE file. """Elliptic Curve Schnorr Signature Algorithm (ECSSA). This implementation is according to BIP340-Schnorr: https://github.com/bitcoin/bips/blob/master/bip-0340.mediawiki Differently from ECDSA, the BIP340-Schnorr scheme supports messages of size hf_size only. It also uses as public key the x-coordinate (field element) of the curve point associated to the private key 0 < q < n. Therefore, for sepcp256k1 the public key size is 32 bytes. Arguably, the knowledge of q as the discrete logarithm of Q also implies the knowledge of n-q as discrete logarithm of -Q. As such, {q, n-q} can be considered a single private key and {Q, -Q} the associated public key characterized by the shared x_Q. Also, BIP340 advocates its own SHA256 modification as hash function: TaggedHash(tag, x) = SHA256(SHA256(tag)||SHA256(tag)||x) The rationale is to make BIP340 signatures invalid for anything else but Bitcoin and vice versa. TaggedHash is used for both the challenge (with tag 'BIPSchnorr') and the deterministic nonce (with tag 'BIPSchnorrDerive'). To allow for secure batch verification of multiple signatures, BIP340-Schnorr uses a challenge that prevents public key recovery from signature: c = TaggedHash('BIPSchnorr', x_k||x_Q||msg). A custom deterministic algorithm for the ephemeral key (nonce) is used for signing, instead of the RFC6979 standard: nonce = TaggedHash('BIPSchnorrDerive', q||msg) Finally, BIP340-Schnorr adopts a robust [r][s] custom serialization format, instead of the loosely specified ASN.1 DER standard. The signature size is p-size*n-size, where p-size is the field element (curve point coordinate) byte size and n-size is the scalar (curve point multiplication coefficient) byte size. For sepcp256k1 the resulting signature size is 64 bytes. """ import secrets from dataclasses import InitVar, dataclass from hashlib import sha256 from typing import List, Optional, Sequence, Tuple, Type, Union from btclib.alias import BinaryData, HashF, Integer, JacPoint, Octets, Point from btclib.bip32.bip32 import BIP32Key from btclib.ecc.curve import Curve, secp256k1 from btclib.ecc.curve_group import _double_mult, _mult, _multi_mult from btclib.ecc.number_theory import mod_inv from btclib.exceptions import BTClibRuntimeError, BTClibTypeError, BTClibValueError from btclib.hashes import reduce_to_hlen, tagged_hash from btclib.to_prv_key import PrvKey, int_from_prv_key from btclib.to_pub_key import point_from_pub_key from btclib.utils import ( bytes_from_octets, bytesio_from_binarydata, hex_string, int_from_bits, ) @dataclass(frozen=True) class Sig: """BIP340-Schnorr signature. - r is an x-coordinate _field_element_, 0 <= r < ec.p - s is a scalar, 0 <= s < ec.n (yes, for BIP340-Schnorr it can be zero) (ec.p is the field prime, ec.n is the curve order) """ # 32 bytes x-coordinate field element r: int # 32 bytes scalar s: int ec: Curve = secp256k1 check_validity: InitVar[bool] = True def __post_init__(self, check_validity: bool) -> None: if check_validity: self.assert_valid() def assert_valid(self) -> None: # r is a field element, fail if r is not a valid x-coordinate self.ec.y(self.r) # s is a scalar, fail if s is not in [0, n-1] if not 0 <= self.s < self.ec.n: err_msg = "scalar s not in 0..n-1: " err_msg += f"'{hex_string(self.s)}'" if self.s > 0xFFFFFFFF else f"{self.s}" raise BTClibValueError(err_msg) def serialize(self, check_validity: bool = True) -> bytes: if check_validity: self.assert_valid() out = self.r.to_bytes(self.ec.p_size, byteorder="big", signed=False) out += self.s.to_bytes(self.ec.n_size, byteorder="big", signed=False) return out @classmethod def parse(cls: Type["Sig"], data: BinaryData, check_validity: bool = True) -> "Sig": stream = bytesio_from_binarydata(data) ec = secp256k1 r = int.from_bytes(stream.read(ec.p_size), byteorder="big", signed=False) s = int.from_bytes(stream.read(ec.n_size), byteorder="big", signed=False) return cls(r, s, ec, check_validity) # hex-string or bytes representation of an int # 33 or 65 bytes or hex-string # BIP32Key as dict or String # tuple Point BIP340PubKey = Union[Integer, Octets, BIP32Key, Point] def point_from_bip340pub_key(x_Q: BIP340PubKey, ec: Curve = secp256k1) -> Point: """Return a verified-as-valid BIP340 public key as Point tuple. It supports: - BIP32 extended keys (bytes, string, or BIP32KeyData) - SEC Octets (bytes or hex-string, with 02, 03, or 04 prefix) - BIP340 Octets (bytes or hex-string, p-size Point x-coordinate) - native tuple """ # BIP 340 key as integer if isinstance(x_Q, int): return x_Q, ec.y_even(x_Q) # (tuple) Point, (dict or str) BIP32Key, or 33/65 bytes try: x_Q = point_from_pub_key(x_Q, ec)[0] return x_Q, ec.y_even(x_Q) except BTClibValueError: pass # BIP 340 key as bytes or hex-string if isinstance(x_Q, (str, bytes)): Q = bytes_from_octets(x_Q, ec.p_size) x_Q = int.from_bytes(Q, "big", signed=False) return x_Q, ec.y_even(x_Q) raise BTClibTypeError("not a BIP340 public key") def gen_keys_( prv_key: Optional[PrvKey] = None, ec: Curve = secp256k1 ) -> Tuple[int, int, JacPoint]: "Return a BIP340 private/public (int, JacPoint) key-pair." if prv_key is None: q = 1 + secrets.randbelow(ec.n - 1) else: q = int_from_prv_key(prv_key, ec) QJ = _mult(q, ec.GJ, ec) x_Q, y_Q = ec.aff_from_jac(QJ) if y_Q % 2: q = ec.n - q QJ = ec.negate_jac(QJ) return q, x_Q, QJ def gen_keys( prv_key: Optional[PrvKey] = None, ec: Curve = secp256k1 ) -> Tuple[int, int]: "Return a BIP340 private/public (int, int) key-pair." q, x_Q, _ = gen_keys_(prv_key, ec) return q, x_Q def _det_nonce_( msg_hash: bytes, q: int, Q: int, aux: bytes, ec: Curve, hf: HashF ) -> int: # assume the random oracle model for the hash function, # i.e. hash values can be considered uniformly random # Note that in general, taking a uniformly random integer # modulo the curve order n would produce a biased result. # However, if the order n is sufficiently close to 2^hf_len, # then the bias is not observable: # e.g. for secp256k1 and sha256 1-n/2^256 it is about 1.27*2^-128 # # the unbiased implementation is provided here, # which works also for very-low-cardinality test curves randomizer = tagged_hash("BIP0340/aux".encode(), aux, hf) xor = q ^ int.from_bytes(randomizer, "big", signed=False) max_len = max(ec.n_size, hf().digest_size) t = b"".join( [ xor.to_bytes(max_len, byteorder="big", signed=False), Q.to_bytes(ec.p_size, byteorder="big", signed=False), msg_hash, ] ) nonce_tag = "BIP0340/nonce".encode() while True: t = tagged_hash(nonce_tag, t, hf) # The following lines would introduce a bias # nonce = int.from_bytes(t, 'big') % ec.n # nonce = int_from_bits(t, ec.nlen) % ec.n # In general, taking a uniformly random integer (like those # obtained from a hash function in the random oracle model) # modulo the curve order n would produce a biased result. # However, if the order n is sufficiently close to 2^hf_len, # then the bias is not observable: e.g. # for secp256k1 and sha256 1-n/2^256 it is about 1.27*2^-128 nonce = int_from_bits(t, ec.nlen) # candidate nonce if 0 < nonce < ec.n: # acceptable value for nonce return nonce # successful candidate def det_nonce_( msg_hash: Octets, prv_key: PrvKey, aux: Optional[Octets] = None, ec: Curve = secp256k1, hf: HashF = sha256, ) -> int: "Return a BIP340 deterministic ephemeral key (nonce)." # the message msg_hash: a hf_len array hf_len = hf().digest_size msg_hash = bytes_from_octets(msg_hash, hf_len) q, Q = gen_keys(prv_key, ec) # the auxiliary random component aux = secrets.token_bytes(hf_len) if aux is None else bytes_from_octets(aux) return _det_nonce_(msg_hash, q, Q, aux, ec, hf) def challenge_(msg_hash: Octets, x_Q: int, x_K: int, ec: Curve, hf: HashF) -> int: # the message msg_hash: a hf_len array hf_len = hf().digest_size msg_hash = bytes_from_octets(msg_hash, hf_len) t = b"".join( [ x_K.to_bytes(ec.p_size, byteorder="big", signed=False), x_Q.to_bytes(ec.p_size, byteorder="big", signed=False), msg_hash, ] ) t = tagged_hash("BIP0340/challenge".encode(), t, hf) c = int_from_bits(t, ec.nlen) % ec.n if c == 0: raise BTClibRuntimeError("invalid zero challenge") # pragma: no cover return c def _sign_(c: int, q: int, nonce: int, r: int, ec: Curve) -> Sig: # Private function for testing purposes: it allows to explore all # possible value of the challenge c (for low-cardinality curves). # It assume that c is in [1, n-1], while q and nonce are in [1, n-1] if c == 0: # c≠0 required as it multiplies the private key raise BTClibRuntimeError("invalid zero challenge") # s=0 is ok: in verification there is no inverse of s s = (nonce + c * q) % ec.n return Sig(r, s, ec) def sign_( msg_hash: Octets, prv_key: PrvKey, nonce: Optional[PrvKey] = None, ec: Curve = secp256k1, hf: HashF = sha256, ) -> Sig: """Sign a hf_len bytes message according to BIP340 signature algorithm. If the deterministic nonce is not provided, the BIP340 specification (not RFC6979) is used. """ # the message msg_hash: a hf_len array hf_len = hf().digest_size msg_hash = bytes_from_octets(msg_hash, hf_len) # private and public keys q, x_Q = gen_keys(prv_key, ec) # nonce: an integer in the range 1..n-1. if nonce is None: nonce = _det_nonce_(msg_hash, q, x_Q, secrets.token_bytes(hf_len), ec, hf) nonce, x_K = gen_keys(nonce, ec) # the challenge c = challenge_(msg_hash, x_Q, x_K, ec, hf) return _sign_(c, q, nonce, x_K, ec) def sign( msg: Octets, prv_key: PrvKey, nonce: Optional[PrvKey] = None, ec: Curve = secp256k1, hf: HashF = sha256, ) -> Sig: """Sign message according to BIP340 signature algorithm. The message msg is first processed by hf, yielding the value msg_hash = hf(msg), a sequence of bits of length *hf_len*. Normally, hf is chosen such that its output length *hf_len* is roughly equal to *nlen*, the bit-length of the group order *n*, since the overall security of the signature scheme will depend on the smallest of *hf_len* and *nlen*; however, ECSSA supports all combinations of *hf_len* and *nlen*. The BIP340 deterministic nonce (not RFC6979) is used. """ msg_hash = reduce_to_hlen(msg, hf) return sign_(msg_hash, prv_key, nonce, ec, hf) def _assert_as_valid_(c: int, QJ: JacPoint, r: int, s: int, ec: Curve) -> None: # Private function for test/dev purposes # It raises Errors, while verify should always return True or False # Let K = sG - eQ. # in Jacobian coordinates KJ = _double_mult(ec.n - c, QJ, s, ec.GJ, ec) # Fail if infinite(KJ). # Fail if y_K is odd. if ec.y_aff_from_jac(KJ) % 2: raise BTClibRuntimeError("y_K is odd") # Fail if x_K ≠ r if KJ[0] != KJ[2] * KJ[2] * r % ec.p: raise BTClibRuntimeError("signature verification failed") def assert_as_valid_( msg_hash: Octets, Q: BIP340PubKey, sig: Union[Sig, Octets], hf: HashF = sha256 ) -> None: # Private function for test/dev purposes # It raises Errors, while verify should always return True or False if isinstance(sig, Sig): sig.assert_valid() else: sig = Sig.parse(sig) x_Q, y_Q = point_from_bip340pub_key(Q, sig.ec) # Let c = int(hf(bytes(r) || bytes(Q) || msg_hash)) mod n. c = challenge_(msg_hash, x_Q, sig.r, sig.ec, hf) _assert_as_valid_(c, (x_Q, y_Q, 1), sig.r, sig.s, sig.ec) def assert_as_valid( msg: Octets, Q: BIP340PubKey, sig: Union[Sig, Octets], hf: HashF = sha256 ) -> None: msg_hash = reduce_to_hlen(msg, hf) assert_as_valid_(msg_hash, Q, sig, hf) def verify_( msg_hash: Octets, Q: BIP340PubKey, sig: Union[Sig, Octets], hf: HashF = sha256 ) -> bool: "Verify the BIP340 signature of the provided message." # all kind of Exceptions are catched because # verify must always return a bool try: assert_as_valid_(msg_hash, Q, sig, hf) except Exception: # pylint: disable=broad-except return False else: return True def verify( msg: Octets, Q: BIP340PubKey, sig: Union[Sig, Octets], hf: HashF = sha256 ) -> bool: "Verify the BIP340 signature of the provided message." msg_hash = reduce_to_hlen(msg, hf) return verify_(msg_hash, Q, sig, hf) def _recover_pub_key_(c: int, r: int, s: int, ec: Curve) -> int: # Private function provided for testing purposes only. if c == 0: raise BTClibRuntimeError("invalid zero challenge") KJ = r, ec.y_even(r), 1 e1 = mod_inv(c, ec.n) QJ = _double_mult(ec.n - e1, KJ, e1 * s, ec.GJ, ec) # edge case that cannot be reproduced in the test suite if QJ[2] == 0: err_msg = "invalid (INF) key" # pragma: no cover raise BTClibRuntimeError(err_msg) # pragma: no cover return ec.x_aff_from_jac(QJ) def crack_prv_key_( msg_hash1: Octets, sig1: Union[Sig, Octets], msg_hash2: Octets, sig2: Union[Sig, Octets], Q: BIP340PubKey, hf: HashF = sha256, ) -> Tuple[int, int]: if isinstance(sig1, Sig): sig1.assert_valid() else: sig1 = Sig.parse(sig1) if isinstance(sig2, Sig): sig2.assert_valid() else: sig2 = Sig.parse(sig2) ec = sig2.ec if sig1.ec != ec: raise BTClibValueError("not the same curve in signatures") if sig1.r != sig2.r: raise BTClibValueError("not the same r in signatures") if sig1.s == sig2.s: raise BTClibValueError("identical signatures") x_Q = point_from_bip340pub_key(Q, ec)[0] c_1 = challenge_(msg_hash1, x_Q, sig1.r, ec, hf) c_2 = challenge_(msg_hash2, x_Q, sig2.r, ec, hf) q = (sig1.s - sig2.s) * mod_inv(c_2 - c_1, ec.n) % ec.n nonce = (sig1.s + c_1 * q) % ec.n q, _ = gen_keys(q) nonce, _ = gen_keys(nonce) return q, nonce def crack_prv_key( msg1: Octets, sig1: Union[Sig, Octets], msg2: Octets, sig2: Union[Sig, Octets], Q: BIP340PubKey, hf: HashF = sha256, ) -> Tuple[int, int]: msg_hash1 = reduce_to_hlen(msg1, hf) msg_hash2 = reduce_to_hlen(msg2, hf) return crack_prv_key_(msg_hash1, sig1, msg_hash2, sig2, Q, hf) def assert_batch_as_valid_( m_hashes: Sequence[Octets], Qs: Sequence[BIP340PubKey], sigs: Sequence[Sig], hf: HashF = sha256, ) -> None: batch_size = len(Qs) if batch_size == 0: raise BTClibValueError("no signatures provided") if len(m_hashes) != batch_size: err_msg = f"mismatch between number of pub_keys ({batch_size}) " err_msg += f"and number of messages ({len(m_hashes)})" raise BTClibValueError(err_msg) if len(sigs) != batch_size: err_msg = f"mismatch between number of pub_keys ({batch_size}) " err_msg += f"and number of signatures ({len(sigs)})" raise BTClibValueError(err_msg) if batch_size == 1: assert_as_valid_(m_hashes[0], Qs[0], sigs[0], hf) return None ec = sigs[0].ec if any(sig.ec != ec for sig in sigs): raise BTClibValueError("not the same curve for all signatures") t = 0 scalars: List[int] = [] points: List[JacPoint] = [] for i, (msg_hash, Q, sig) in enumerate(zip(m_hashes, Qs, sigs)): msg_hash = bytes_from_octets(msg_hash, hf().digest_size) KJ = sig.r, ec.y_even(sig.r), 1 x_Q, y_Q = point_from_bip340pub_key(Q, ec) QJ = x_Q, y_Q, 1 c = challenge_(msg_hash, x_Q, sig.r, ec, hf) # rand in [1, n-1] # deterministically generated using a CSPRNG seeded by a # cryptographic hash (e.g., SHA256) of all inputs of the # algorithm, or randomly generated independently for each # run of the batch verification algorithm rand = 1 if i == 0 else 1 + secrets.randbelow(ec.n - 1) scalars.append(rand) points.append(KJ) scalars.append(rand * c % ec.n) points.append(QJ) t += rand * sig.s TJ = _mult(t, ec.GJ, ec) RHSJ = _multi_mult(scalars, points, ec) # return T == RHS, checked in Jacobian coordinates RHSZ2 = RHSJ[2] * RHSJ[2] TZ2 = TJ[2] * TJ[2] if (TJ[0] * RHSZ2 % ec.p != RHSJ[0] * TZ2 % ec.p) or ( TJ[1] * RHSZ2 * RHSJ[2] % ec.p != RHSJ[1] * TZ2 * TJ[2] % ec.p ): raise BTClibRuntimeError("signature verification failed") return None def assert_batch_as_valid( ms: Sequence[Octets], Qs: Sequence[BIP340PubKey], sigs: Sequence[Sig], hf: HashF = sha256, ) -> None: m_hashes = [reduce_to_hlen(msg, hf) for msg in ms] return assert_batch_as_valid_(m_hashes, Qs, sigs, hf) def batch_verify_( m_hashes: Sequence[Octets], Qs: Sequence[BIP340PubKey], sigs: Sequence[Sig], hf: HashF = sha256, ) -> bool: # all kind of Exceptions are catched because # verify must always return a bool try: assert_batch_as_valid_(m_hashes, Qs, sigs, hf) except Exception: # pylint: disable=broad-except return False return True def batch_verify( ms: Sequence[Octets], Qs: Sequence[BIP340PubKey], sigs: Sequence[Sig], hf: HashF = sha256, ) -> bool: "Batch verification of BIP340 signatures." m_hashes = [reduce_to_hlen(msg, hf) for msg in ms] return batch_verify_(m_hashes, Qs, sigs, hf)

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import secrets from dataclasses import InitVar, dataclass from hashlib import sha256 from typing import List, Optional, Sequence, Tuple, Type, Union from btclib.alias import BinaryData, HashF, Integer, JacPoint, Octets, Point from btclib.bip32.bip32 import BIP32Key from btclib.ecc.curve import Curve, secp256k1 from btclib.ecc.curve_group import _double_mult, _mult, _multi_mult from btclib.ecc.number_theory import mod_inv from btclib.exceptions import BTClibRuntimeError, BTClibTypeError, BTClibValueError from btclib.hashes import reduce_to_hlen, tagged_hash from btclib.to_prv_key import PrvKey, int_from_prv_key from btclib.to_pub_key import point_from_pub_key from btclib.utils import ( bytes_from_octets, bytesio_from_binarydata, hex_string, int_from_bits, ) @dataclass(frozen=True) class Sig: r: int s: int ec: Curve = secp256k1 check_validity: InitVar[bool] = True def __post_init__(self, check_validity: bool) -> None: if check_validity: self.assert_valid() def assert_valid(self) -> None: self.ec.y(self.r) if not 0 <= self.s < self.ec.n: err_msg = "scalar s not in 0..n-1: " err_msg += f"'{hex_string(self.s)}'" if self.s > 0xFFFFFFFF else f"{self.s}" raise BTClibValueError(err_msg) def serialize(self, check_validity: bool = True) -> bytes: if check_validity: self.assert_valid() out = self.r.to_bytes(self.ec.p_size, byteorder="big", signed=False) out += self.s.to_bytes(self.ec.n_size, byteorder="big", signed=False) return out @classmethod def parse(cls: Type["Sig"], data: BinaryData, check_validity: bool = True) -> "Sig": stream = bytesio_from_binarydata(data) ec = secp256k1 r = int.from_bytes(stream.read(ec.p_size), byteorder="big", signed=False) s = int.from_bytes(stream.read(ec.n_size), byteorder="big", signed=False) return cls(r, s, ec, check_validity) BIP340PubKey = Union[Integer, Octets, BIP32Key, Point] def point_from_bip340pub_key(x_Q: BIP340PubKey, ec: Curve = secp256k1) -> Point: if isinstance(x_Q, int): return x_Q, ec.y_even(x_Q) try: x_Q = point_from_pub_key(x_Q, ec)[0] return x_Q, ec.y_even(x_Q) except BTClibValueError: pass if isinstance(x_Q, (str, bytes)): Q = bytes_from_octets(x_Q, ec.p_size) x_Q = int.from_bytes(Q, "big", signed=False) return x_Q, ec.y_even(x_Q) raise BTClibTypeError("not a BIP340 public key") def gen_keys_( prv_key: Optional[PrvKey] = None, ec: Curve = secp256k1 ) -> Tuple[int, int, JacPoint]: if prv_key is None: q = 1 + secrets.randbelow(ec.n - 1) else: q = int_from_prv_key(prv_key, ec) QJ = _mult(q, ec.GJ, ec) x_Q, y_Q = ec.aff_from_jac(QJ) if y_Q % 2: q = ec.n - q QJ = ec.negate_jac(QJ) return q, x_Q, QJ def gen_keys( prv_key: Optional[PrvKey] = None, ec: Curve = secp256k1 ) -> Tuple[int, int]: q, x_Q, _ = gen_keys_(prv_key, ec) return q, x_Q def _det_nonce_( msg_hash: bytes, q: int, Q: int, aux: bytes, ec: Curve, hf: HashF ) -> int: randomizer = tagged_hash("BIP0340/aux".encode(), aux, hf) xor = q ^ int.from_bytes(randomizer, "big", signed=False) max_len = max(ec.n_size, hf().digest_size) t = b"".join( [ xor.to_bytes(max_len, byteorder="big", signed=False), Q.to_bytes(ec.p_size, byteorder="big", signed=False), msg_hash, ] ) nonce_tag = "BIP0340/nonce".encode() while True: t = tagged_hash(nonce_tag, t, hf) nonce = int_from_bits(t, ec.nlen) if 0 < nonce < ec.n: return nonce def det_nonce_( msg_hash: Octets, prv_key: PrvKey, aux: Optional[Octets] = None, ec: Curve = secp256k1, hf: HashF = sha256, ) -> int: hf_len = hf().digest_size msg_hash = bytes_from_octets(msg_hash, hf_len) q, Q = gen_keys(prv_key, ec) aux = secrets.token_bytes(hf_len) if aux is None else bytes_from_octets(aux) return _det_nonce_(msg_hash, q, Q, aux, ec, hf) def challenge_(msg_hash: Octets, x_Q: int, x_K: int, ec: Curve, hf: HashF) -> int: hf_len = hf().digest_size msg_hash = bytes_from_octets(msg_hash, hf_len) t = b"".join( [ x_K.to_bytes(ec.p_size, byteorder="big", signed=False), x_Q.to_bytes(ec.p_size, byteorder="big", signed=False), msg_hash, ] ) t = tagged_hash("BIP0340/challenge".encode(), t, hf) c = int_from_bits(t, ec.nlen) % ec.n if c == 0: raise BTClibRuntimeError("invalid zero challenge") return c def _sign_(c: int, q: int, nonce: int, r: int, ec: Curve) -> Sig: if c == 0: raise BTClibRuntimeError("invalid zero challenge") s = (nonce + c * q) % ec.n return Sig(r, s, ec) def sign_( msg_hash: Octets, prv_key: PrvKey, nonce: Optional[PrvKey] = None, ec: Curve = secp256k1, hf: HashF = sha256, ) -> Sig: hf_len = hf().digest_size msg_hash = bytes_from_octets(msg_hash, hf_len) q, x_Q = gen_keys(prv_key, ec) if nonce is None: nonce = _det_nonce_(msg_hash, q, x_Q, secrets.token_bytes(hf_len), ec, hf) nonce, x_K = gen_keys(nonce, ec) c = challenge_(msg_hash, x_Q, x_K, ec, hf) return _sign_(c, q, nonce, x_K, ec) def sign( msg: Octets, prv_key: PrvKey, nonce: Optional[PrvKey] = None, ec: Curve = secp256k1, hf: HashF = sha256, ) -> Sig: msg_hash = reduce_to_hlen(msg, hf) return sign_(msg_hash, prv_key, nonce, ec, hf) def _assert_as_valid_(c: int, QJ: JacPoint, r: int, s: int, ec: Curve) -> None: KJ = _double_mult(ec.n - c, QJ, s, ec.GJ, ec) if ec.y_aff_from_jac(KJ) % 2: raise BTClibRuntimeError("y_K is odd") if KJ[0] != KJ[2] * KJ[2] * r % ec.p: raise BTClibRuntimeError("signature verification failed") def assert_as_valid_( msg_hash: Octets, Q: BIP340PubKey, sig: Union[Sig, Octets], hf: HashF = sha256 ) -> None: if isinstance(sig, Sig): sig.assert_valid() else: sig = Sig.parse(sig) x_Q, y_Q = point_from_bip340pub_key(Q, sig.ec) c = challenge_(msg_hash, x_Q, sig.r, sig.ec, hf) _assert_as_valid_(c, (x_Q, y_Q, 1), sig.r, sig.s, sig.ec) def assert_as_valid( msg: Octets, Q: BIP340PubKey, sig: Union[Sig, Octets], hf: HashF = sha256 ) -> None: msg_hash = reduce_to_hlen(msg, hf) assert_as_valid_(msg_hash, Q, sig, hf) def verify_( msg_hash: Octets, Q: BIP340PubKey, sig: Union[Sig, Octets], hf: HashF = sha256 ) -> bool: try: assert_as_valid_(msg_hash, Q, sig, hf) except Exception: return False else: return True def verify( msg: Octets, Q: BIP340PubKey, sig: Union[Sig, Octets], hf: HashF = sha256 ) -> bool: msg_hash = reduce_to_hlen(msg, hf) return verify_(msg_hash, Q, sig, hf) def _recover_pub_key_(c: int, r: int, s: int, ec: Curve) -> int: if c == 0: raise BTClibRuntimeError("invalid zero challenge") KJ = r, ec.y_even(r), 1 e1 = mod_inv(c, ec.n) QJ = _double_mult(ec.n - e1, KJ, e1 * s, ec.GJ, ec) if QJ[2] == 0: err_msg = "invalid (INF) key" raise BTClibRuntimeError(err_msg) return ec.x_aff_from_jac(QJ) def crack_prv_key_( msg_hash1: Octets, sig1: Union[Sig, Octets], msg_hash2: Octets, sig2: Union[Sig, Octets], Q: BIP340PubKey, hf: HashF = sha256, ) -> Tuple[int, int]: if isinstance(sig1, Sig): sig1.assert_valid() else: sig1 = Sig.parse(sig1) if isinstance(sig2, Sig): sig2.assert_valid() else: sig2 = Sig.parse(sig2) ec = sig2.ec if sig1.ec != ec: raise BTClibValueError("not the same curve in signatures") if sig1.r != sig2.r: raise BTClibValueError("not the same r in signatures") if sig1.s == sig2.s: raise BTClibValueError("identical signatures") x_Q = point_from_bip340pub_key(Q, ec)[0] c_1 = challenge_(msg_hash1, x_Q, sig1.r, ec, hf) c_2 = challenge_(msg_hash2, x_Q, sig2.r, ec, hf) q = (sig1.s - sig2.s) * mod_inv(c_2 - c_1, ec.n) % ec.n nonce = (sig1.s + c_1 * q) % ec.n q, _ = gen_keys(q) nonce, _ = gen_keys(nonce) return q, nonce def crack_prv_key( msg1: Octets, sig1: Union[Sig, Octets], msg2: Octets, sig2: Union[Sig, Octets], Q: BIP340PubKey, hf: HashF = sha256, ) -> Tuple[int, int]: msg_hash1 = reduce_to_hlen(msg1, hf) msg_hash2 = reduce_to_hlen(msg2, hf) return crack_prv_key_(msg_hash1, sig1, msg_hash2, sig2, Q, hf) def assert_batch_as_valid_( m_hashes: Sequence[Octets], Qs: Sequence[BIP340PubKey], sigs: Sequence[Sig], hf: HashF = sha256, ) -> None: batch_size = len(Qs) if batch_size == 0: raise BTClibValueError("no signatures provided") if len(m_hashes) != batch_size: err_msg = f"mismatch between number of pub_keys ({batch_size}) " err_msg += f"and number of messages ({len(m_hashes)})" raise BTClibValueError(err_msg) if len(sigs) != batch_size: err_msg = f"mismatch between number of pub_keys ({batch_size}) " err_msg += f"and number of signatures ({len(sigs)})" raise BTClibValueError(err_msg) if batch_size == 1: assert_as_valid_(m_hashes[0], Qs[0], sigs[0], hf) return None ec = sigs[0].ec if any(sig.ec != ec for sig in sigs): raise BTClibValueError("not the same curve for all signatures") t = 0 scalars: List[int] = [] points: List[JacPoint] = [] for i, (msg_hash, Q, sig) in enumerate(zip(m_hashes, Qs, sigs)): msg_hash = bytes_from_octets(msg_hash, hf().digest_size) KJ = sig.r, ec.y_even(sig.r), 1 x_Q, y_Q = point_from_bip340pub_key(Q, ec) QJ = x_Q, y_Q, 1 c = challenge_(msg_hash, x_Q, sig.r, ec, hf) rand = 1 if i == 0 else 1 + secrets.randbelow(ec.n - 1) scalars.append(rand) points.append(KJ) scalars.append(rand * c % ec.n) points.append(QJ) t += rand * sig.s TJ = _mult(t, ec.GJ, ec) RHSJ = _multi_mult(scalars, points, ec) RHSZ2 = RHSJ[2] * RHSJ[2] TZ2 = TJ[2] * TJ[2] if (TJ[0] * RHSZ2 % ec.p != RHSJ[0] * TZ2 % ec.p) or ( TJ[1] * RHSZ2 * RHSJ[2] % ec.p != RHSJ[1] * TZ2 * TJ[2] % ec.p ): raise BTClibRuntimeError("signature verification failed") return None def assert_batch_as_valid( ms: Sequence[Octets], Qs: Sequence[BIP340PubKey], sigs: Sequence[Sig], hf: HashF = sha256, ) -> None: m_hashes = [reduce_to_hlen(msg, hf) for msg in ms] return assert_batch_as_valid_(m_hashes, Qs, sigs, hf) def batch_verify_( m_hashes: Sequence[Octets], Qs: Sequence[BIP340PubKey], sigs: Sequence[Sig], hf: HashF = sha256, ) -> bool: try: assert_batch_as_valid_(m_hashes, Qs, sigs, hf) except Exception: return False return True def batch_verify( ms: Sequence[Octets], Qs: Sequence[BIP340PubKey], sigs: Sequence[Sig], hf: HashF = sha256, ) -> bool: m_hashes = [reduce_to_hlen(msg, hf) for msg in ms] return batch_verify_(m_hashes, Qs, sigs, hf)

true

f71960081cd60ffe81c41f006c1585cf0ab6b33d

763

py

Python

river/metrics/smape.py

brcharron/creme

25290780f6bba0eb030215194e81b120d0219389

[ "BSD-3-Clause" ]

1

2020-12-04T18:56:19.000Z

river/metrics/smape.py

brcharron/creme

25290780f6bba0eb030215194e81b120d0219389

[ "BSD-3-Clause" ]

null

river/metrics/smape.py

brcharron/creme

25290780f6bba0eb030215194e81b120d0219389

[ "BSD-3-Clause" ]

null

from . import base __all__ = ['SMAPE'] class SMAPE(base.MeanMetric, base.RegressionMetric): """Symmetric mean absolute percentage error. Examples -------- >>> from river import metrics >>> y_true = [0, 0.07533, 0.07533, 0.07533, 0.07533, 0.07533, 0.07533, 0.0672, 0.0672] >>> y_pred = [0, 0.102, 0.107, 0.047, 0.1, 0.032, 0.047, 0.108, 0.089] >>> metric = metrics.SMAPE() >>> for yt, yp in zip(y_true, y_pred): ... metric = metric.update(yt, yp) >>> metric SMAPE: 37.869392 """ def _eval(self, y_true, y_pred): den = abs(y_true) + abs(y_pred) if den == 0: return 0. return 2. * abs(y_true - y_pred) / den def get(self): return 100 * super().get()

21.8

90

0.549148

from . import base __all__ = ['SMAPE'] class SMAPE(base.MeanMetric, base.RegressionMetric): def _eval(self, y_true, y_pred): den = abs(y_true) + abs(y_pred) if den == 0: return 0. return 2. * abs(y_true - y_pred) / den def get(self): return 100 * super().get()

true

f7196073162dfb6d958cbb439ef46151f867a863

371,245

py

Python

pandas/core/generic.py

rhshadrach/pandas

8f51c998e84feeac6cb760a9f12baf6948cd5922

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "ECL-2.0", "BSD-3-Clause" ]

1

2021-08-06T14:07:02.000Z

pandas/core/generic.py

jdsurya/pandas

777c0f90c6067c636fcd76ce003a8fbfcc311d7b

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "ECL-2.0", "BSD-3-Clause" ]

null

pandas/core/generic.py

jdsurya/pandas

777c0f90c6067c636fcd76ce003a8fbfcc311d7b

[ "PSF-2.0", "Apache-2.0", "BSD-3-Clause-No-Nuclear-License-2014", "MIT", "ECL-2.0", "BSD-3-Clause" ]

1

2021-02-03T11:02:42.000Z

import collections from datetime import timedelta import functools import gc import json import operator import pickle import re from textwrap import dedent from typing import ( TYPE_CHECKING, Any, Callable, Dict, FrozenSet, Hashable, List, Mapping, Optional, Sequence, Set, Tuple, Type, Union, ) import warnings import weakref import numpy as np from pandas._config import config from pandas._libs import Timestamp, iNaT, lib from pandas._typing import ( Axis, FilePathOrBuffer, FrameOrSeries, JSONSerializable, Label, Level, Renamer, ) from pandas.compat import set_function_name from pandas.compat._optional import import_optional_dependency from pandas.compat.numpy import function as nv from pandas.errors import AbstractMethodError from pandas.util._decorators import ( Appender, Substitution, doc, rewrite_axis_style_signature, ) from pandas.util._validators import ( validate_bool_kwarg, validate_fillna_kwargs, validate_percentile, ) from pandas.core.dtypes.common import ( ensure_int64, ensure_object, ensure_str, is_bool, is_bool_dtype, is_datetime64_any_dtype, is_datetime64tz_dtype, is_dict_like, is_extension_array_dtype, is_float, is_integer, is_list_like, is_number, is_numeric_dtype, is_object_dtype, is_re_compilable, is_scalar, is_timedelta64_dtype, pandas_dtype, ) from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries from pandas.core.dtypes.inference import is_hashable from pandas.core.dtypes.missing import isna, notna import pandas as pd from pandas.core import missing, nanops import pandas.core.algorithms as algos from pandas.core.base import PandasObject, SelectionMixin import pandas.core.common as com from pandas.core.construction import create_series_with_explicit_dtype from pandas.core.indexes.api import ( Index, InvalidIndexError, MultiIndex, RangeIndex, ensure_index, ) from pandas.core.indexes.datetimes import DatetimeIndex from pandas.core.indexes.period import Period, PeriodIndex import pandas.core.indexing as indexing from pandas.core.internals import BlockManager from pandas.core.missing import find_valid_index from pandas.core.ops import _align_method_FRAME from pandas.io.formats import format as fmt from pandas.io.formats.format import DataFrameFormatter, format_percentiles from pandas.io.formats.printing import pprint_thing from pandas.tseries.frequencies import to_offset if TYPE_CHECKING: from pandas.core.resample import Resampler # goal is to be able to define the docs close to function, while still being # able to share _shared_docs: Dict[str, str] = dict() _shared_doc_kwargs = dict( axes="keywords for axes", klass="Series/DataFrame", axes_single_arg="int or labels for object", args_transpose="axes to permute (int or label for object)", optional_by=""" by : str or list of str Name or list of names to sort by""", ) def _single_replace(self, to_replace, method, inplace, limit): """ Replaces values in a Series using the fill method specified when no replacement value is given in the replace method """ if self.ndim != 1: raise TypeError( f"cannot replace {to_replace} with method {method} on a " f"{type(self).__name__}" ) orig_dtype = self.dtype result = self if inplace else self.copy() fill_f = missing.get_fill_func(method) mask = missing.mask_missing(result.values, to_replace) values = fill_f(result.values, limit=limit, mask=mask) if values.dtype == orig_dtype and inplace: return result = pd.Series(values, index=self.index, dtype=self.dtype).__finalize__(self) if inplace: self._update_inplace(result._data) return return result bool_t = bool # Need alias because NDFrame has def bool: class NDFrame(PandasObject, SelectionMixin, indexing.IndexingMixin): """ N-dimensional analogue of DataFrame. Store multi-dimensional in a size-mutable, labeled data structure Parameters ---------- data : BlockManager axes : list copy : bool, default False """ _internal_names: List[str] = [ "_data", "_cacher", "_item_cache", "_cache", "_is_copy", "_subtyp", "_name", "_index", "_default_kind", "_default_fill_value", "_metadata", "__array_struct__", "__array_interface__", ] _internal_names_set: Set[str] = set(_internal_names) _accessors: Set[str] = set() _deprecations: FrozenSet[str] = frozenset(["get_values"]) _metadata: List[str] = [] _is_copy = None _data: BlockManager _attrs: Dict[Optional[Hashable], Any] _typ: str # ---------------------------------------------------------------------- # Constructors def __init__( self, data: BlockManager, copy: bool = False, attrs: Optional[Mapping[Optional[Hashable], Any]] = None, ): # copy kwarg is retained for mypy compat, is not used object.__setattr__(self, "_is_copy", None) object.__setattr__(self, "_data", data) object.__setattr__(self, "_item_cache", {}) if attrs is None: attrs = {} else: attrs = dict(attrs) object.__setattr__(self, "_attrs", attrs) @classmethod def _init_mgr(cls, mgr, axes=None, dtype=None, copy=False): """ passed a manager and a axes dict """ for a, axe in axes.items(): if axe is not None: mgr = mgr.reindex_axis( axe, axis=cls._get_block_manager_axis(a), copy=False ) # make a copy if explicitly requested if copy: mgr = mgr.copy() if dtype is not None: # avoid further copies if we can if len(mgr.blocks) > 1 or mgr.blocks[0].values.dtype != dtype: mgr = mgr.astype(dtype=dtype) return mgr # ---------------------------------------------------------------------- @property def attrs(self) -> Dict[Optional[Hashable], Any]: """ Dictionary of global attributes on this object. .. warning:: attrs is experimental and may change without warning. """ if self._attrs is None: self._attrs = {} return self._attrs @attrs.setter def attrs(self, value: Mapping[Optional[Hashable], Any]) -> None: self._attrs = dict(value) @classmethod def _validate_dtype(cls, dtype): """ validate the passed dtype """ if dtype is not None: dtype = pandas_dtype(dtype) # a compound dtype if dtype.kind == "V": raise NotImplementedError( "compound dtypes are not implemented " f"in the {cls.__name__} constructor" ) return dtype # ---------------------------------------------------------------------- # Construction @property def _constructor(self: FrameOrSeries) -> Type[FrameOrSeries]: """ Used when a manipulation result has the same dimensions as the original. """ raise AbstractMethodError(self) @property def _constructor_sliced(self): """ Used when a manipulation result has one lower dimension(s) as the original, such as DataFrame single columns slicing. """ raise AbstractMethodError(self) @property def _constructor_expanddim(self): """ Used when a manipulation result has one higher dimension as the original, such as Series.to_frame() """ raise NotImplementedError # ---------------------------------------------------------------------- # Axis _AXIS_ALIASES = {"rows": 0} _AXIS_IALIASES = {0: "rows"} _stat_axis_number = 0 _stat_axis_name = "index" _ix = None _AXIS_ORDERS: List[str] _AXIS_NUMBERS: Dict[str, int] _AXIS_NAMES: Dict[int, str] _AXIS_REVERSED: bool _info_axis_number: int _info_axis_name: str _AXIS_LEN: int def _construct_axes_dict(self, axes=None, **kwargs): """Return an axes dictionary for myself.""" d = {a: self._get_axis(a) for a in (axes or self._AXIS_ORDERS)} d.update(kwargs) return d @classmethod def _construct_axes_from_arguments( cls, args, kwargs, require_all: bool = False, sentinel=None ): """ Construct and returns axes if supplied in args/kwargs. If require_all, raise if all axis arguments are not supplied return a tuple of (axes, kwargs). sentinel specifies the default parameter when an axis is not supplied; useful to distinguish when a user explicitly passes None in scenarios where None has special meaning. """ # construct the args args = list(args) for a in cls._AXIS_ORDERS: # look for a argument by position if a not in kwargs: try: kwargs[a] = args.pop(0) except IndexError as err: if require_all: raise TypeError( "not enough/duplicate arguments specified!" ) from err axes = {a: kwargs.pop(a, sentinel) for a in cls._AXIS_ORDERS} return axes, kwargs @classmethod def _get_axis_number(cls, axis): axis = cls._AXIS_ALIASES.get(axis, axis) if is_integer(axis): if axis in cls._AXIS_NAMES: return axis else: try: return cls._AXIS_NUMBERS[axis] except KeyError: pass raise ValueError(f"No axis named {axis} for object type {cls}") @classmethod def _get_axis_name(cls, axis): axis = cls._AXIS_ALIASES.get(axis, axis) if isinstance(axis, str): if axis in cls._AXIS_NUMBERS: return axis else: try: return cls._AXIS_NAMES[axis] except KeyError: pass raise ValueError(f"No axis named {axis} for object type {cls}") def _get_axis(self, axis): name = self._get_axis_name(axis) return getattr(self, name) @classmethod def _get_block_manager_axis(cls, axis): """Map the axis to the block_manager axis.""" axis = cls._get_axis_number(axis) if cls._AXIS_REVERSED: m = cls._AXIS_LEN - 1 return m - axis return axis def _get_axis_resolvers(self, axis: str) -> Dict[str, ABCSeries]: # index or columns axis_index = getattr(self, axis) d = dict() prefix = axis[0] for i, name in enumerate(axis_index.names): if name is not None: key = level = name else: # prefix with 'i' or 'c' depending on the input axis # e.g., you must do ilevel_0 for the 0th level of an unnamed # multiiindex key = f"{prefix}level_{i}" level = i level_values = axis_index.get_level_values(level) s = level_values.to_series() s.index = axis_index d[key] = s # put the index/columns itself in the dict if isinstance(axis_index, MultiIndex): dindex = axis_index else: dindex = axis_index.to_series() d[axis] = dindex return d def _get_index_resolvers(self) -> Dict[str, ABCSeries]: from pandas.core.computation.parsing import clean_column_name d: Dict[str, ABCSeries] = {} for axis_name in self._AXIS_ORDERS: d.update(self._get_axis_resolvers(axis_name)) return {clean_column_name(k): v for k, v in d.items() if not isinstance(k, int)} def _get_cleaned_column_resolvers(self) -> Dict[str, ABCSeries]: """ Return the special character free column resolvers of a dataframe. Column names with special characters are 'cleaned up' so that they can be referred to by backtick quoting. Used in :meth:`DataFrame.eval`. """ from pandas.core.computation.parsing import clean_column_name if isinstance(self, ABCSeries): return {clean_column_name(self.name): self} return { clean_column_name(k): v for k, v in self.items() if not isinstance(k, int) } @property def _info_axis(self): return getattr(self, self._info_axis_name) @property def _stat_axis(self): return getattr(self, self._stat_axis_name) @property def shape(self) -> Tuple[int, ...]: """ Return a tuple of axis dimensions """ return tuple(len(self._get_axis(a)) for a in self._AXIS_ORDERS) @property def axes(self) -> List[Index]: """ Return index label(s) of the internal NDFrame """ # we do it this way because if we have reversed axes, then # the block manager shows then reversed return [self._get_axis(a) for a in self._AXIS_ORDERS] @property def ndim(self) -> int: """ Return an int representing the number of axes / array dimensions. Return 1 if Series. Otherwise return 2 if DataFrame. See Also -------- ndarray.ndim : Number of array dimensions. Examples -------- >>> s = pd.Series({'a': 1, 'b': 2, 'c': 3}) >>> s.ndim 1 >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.ndim 2 """ return self._data.ndim @property def size(self) -> int: """ Return an int representing the number of elements in this object. Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame. See Also -------- ndarray.size : Number of elements in the array. Examples -------- >>> s = pd.Series({'a': 1, 'b': 2, 'c': 3}) >>> s.size 3 >>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]}) >>> df.size 4 """ return np.prod(self.shape) @property def _selected_obj(self: FrameOrSeries) -> FrameOrSeries: """ internal compat with SelectionMixin """ return self @property def _obj_with_exclusions(self: FrameOrSeries) -> FrameOrSeries: """ internal compat with SelectionMixin """ return self def set_axis(self, labels, axis: Axis = 0, inplace: bool = False): """ Assign desired index to given axis. Indexes for%(extended_summary_sub)s row labels can be changed by assigning a list-like or Index. .. versionchanged:: 0.21.0 The signature is now `labels` and `axis`, consistent with the rest of pandas API. Previously, the `axis` and `labels` arguments were respectively the first and second positional arguments. Parameters ---------- labels : list-like, Index The values for the new index. axis : %(axes_single_arg)s, default 0 The axis to update. The value 0 identifies the rows%(axis_description_sub)s. inplace : bool, default False Whether to return a new %(klass)s instance. Returns ------- renamed : %(klass)s or None An object of type %(klass)s if inplace=False, None otherwise. See Also -------- %(klass)s.rename_axis : Alter the name of the index%(see_also_sub)s. """ if inplace: setattr(self, self._get_axis_name(axis), labels) else: obj = self.copy() obj.set_axis(labels, axis=axis, inplace=True) return obj def _set_axis(self, axis: int, labels: Index) -> None: labels = ensure_index(labels) self._data.set_axis(axis, labels) self._clear_item_cache() def swapaxes(self: FrameOrSeries, axis1, axis2, copy=True) -> FrameOrSeries: """ Interchange axes and swap values axes appropriately. Returns ------- y : same as input """ i = self._get_axis_number(axis1) j = self._get_axis_number(axis2) if i == j: if copy: return self.copy() return self mapping = {i: j, j: i} new_axes = (self._get_axis(mapping.get(k, k)) for k in range(self._AXIS_LEN)) new_values = self.values.swapaxes(i, j) if copy: new_values = new_values.copy() return self._constructor(new_values, *new_axes).__finalize__(self) def droplevel(self: FrameOrSeries, level, axis=0) -> FrameOrSeries: """ Return DataFrame with requested index / column level(s) removed. .. versionadded:: 0.24.0 Parameters ---------- level : int, str, or list-like If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels. axis : {0 or 'index', 1 or 'columns'}, default 0 Axis along which the level(s) is removed: * 0 or 'index': remove level(s) in column. * 1 or 'columns': remove level(s) in row. Returns ------- DataFrame DataFrame with requested index / column level(s) removed. Examples -------- >>> df = pd.DataFrame([ ... [1, 2, 3, 4], ... [5, 6, 7, 8], ... [9, 10, 11, 12] ... ]).set_index([0, 1]).rename_axis(['a', 'b']) >>> df.columns = pd.MultiIndex.from_tuples([ ... ('c', 'e'), ('d', 'f') ... ], names=['level_1', 'level_2']) >>> df level_1 c d level_2 e f a b 1 2 3 4 5 6 7 8 9 10 11 12 >>> df.droplevel('a') level_1 c d level_2 e f b 2 3 4 6 7 8 10 11 12 >>> df.droplevel('level_2', axis=1) level_1 c d a b 1 2 3 4 5 6 7 8 9 10 11 12 """ labels = self._get_axis(axis) new_labels = labels.droplevel(level) result = self.set_axis(new_labels, axis=axis, inplace=False) return result def pop(self: FrameOrSeries, item) -> FrameOrSeries: """ Return item and drop from frame. Raise KeyError if not found. Parameters ---------- item : str Label of column to be popped. Returns ------- Series Examples -------- >>> df = pd.DataFrame([('falcon', 'bird', 389.0), ... ('parrot', 'bird', 24.0), ... ('lion', 'mammal', 80.5), ... ('monkey', 'mammal', np.nan)], ... columns=('name', 'class', 'max_speed')) >>> df name class max_speed 0 falcon bird 389.0 1 parrot bird 24.0 2 lion mammal 80.5 3 monkey mammal NaN >>> df.pop('class') 0 bird 1 bird 2 mammal 3 mammal Name: class, dtype: object >>> df name max_speed 0 falcon 389.0 1 parrot 24.0 2 lion 80.5 3 monkey NaN """ result = self[item] del self[item] try: result._reset_cacher() except AttributeError: pass return result def squeeze(self, axis=None): """ Squeeze 1 dimensional axis objects into scalars. Series or DataFrames with a single element are squeezed to a scalar. DataFrames with a single column or a single row are squeezed to a Series. Otherwise the object is unchanged. This method is most useful when you don't know if your object is a Series or DataFrame, but you do know it has just a single column. In that case you can safely call `squeeze` to ensure you have a Series. Parameters ---------- axis : {0 or 'index', 1 or 'columns', None}, default None A specific axis to squeeze. By default, all length-1 axes are squeezed. Returns ------- DataFrame, Series, or scalar The projection after squeezing `axis` or all the axes. See Also -------- Series.iloc : Integer-location based indexing for selecting scalars. DataFrame.iloc : Integer-location based indexing for selecting Series. Series.to_frame : Inverse of DataFrame.squeeze for a single-column DataFrame. Examples -------- >>> primes = pd.Series([2, 3, 5, 7]) Slicing might produce a Series with a single value: >>> even_primes = primes[primes % 2 == 0] >>> even_primes 0 2 dtype: int64 >>> even_primes.squeeze() 2 Squeezing objects with more than one value in every axis does nothing: >>> odd_primes = primes[primes % 2 == 1] >>> odd_primes 1 3 2 5 3 7 dtype: int64 >>> odd_primes.squeeze() 1 3 2 5 3 7 dtype: int64 Squeezing is even more effective when used with DataFrames. >>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b']) >>> df a b 0 1 2 1 3 4 Slicing a single column will produce a DataFrame with the columns having only one value: >>> df_a = df[['a']] >>> df_a a 0 1 1 3 So the columns can be squeezed down, resulting in a Series: >>> df_a.squeeze('columns') 0 1 1 3 Name: a, dtype: int64 Slicing a single row from a single column will produce a single scalar DataFrame: >>> df_0a = df.loc[df.index < 1, ['a']] >>> df_0a a 0 1 Squeezing the rows produces a single scalar Series: >>> df_0a.squeeze('rows') a 1 Name: 0, dtype: int64 Squeezing all axes will project directly into a scalar: >>> df_0a.squeeze() 1 """ axis = self._AXIS_NAMES if axis is None else (self._get_axis_number(axis),) return self.iloc[ tuple( 0 if i in axis and len(a) == 1 else slice(None) for i, a in enumerate(self.axes) ) ] # ---------------------------------------------------------------------- # Rename def rename( self: FrameOrSeries, mapper: Optional[Renamer] = None, *, index: Optional[Renamer] = None, columns: Optional[Renamer] = None, axis: Optional[Axis] = None, copy: bool = True, inplace: bool = False, level: Optional[Level] = None, errors: str = "ignore", ) -> Optional[FrameOrSeries]: """ Alter axes input function or functions. Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don't throw an error. Alternatively, change ``Series.name`` with a scalar value (Series only). Parameters ---------- %(axes)s : scalar, list-like, dict-like or function, optional Scalar or list-like will alter the ``Series.name`` attribute, and raise on DataFrame. dict-like or functions are transformations to apply to that axis' values copy : bool, default True Also copy underlying data. inplace : bool, default False Whether to return a new %(klass)s. If True then value of copy is ignored. level : int or level name, default None In case of a MultiIndex, only rename labels in the specified level. errors : {'ignore', 'raise'}, default 'ignore' If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`, or `columns` contains labels that are not present in the Index being transformed. If 'ignore', existing keys will be renamed and extra keys will be ignored. Returns ------- renamed : %(klass)s (new object) Raises ------ KeyError If any of the labels is not found in the selected axis and "errors='raise'". See Also -------- NDFrame.rename_axis Examples -------- >>> s = pd.Series([1, 2, 3]) >>> s 0 1 1 2 2 3 dtype: int64 >>> s.rename("my_name") # scalar, changes Series.name 0 1 1 2 2 3 Name: my_name, dtype: int64 >>> s.rename(lambda x: x ** 2) # function, changes labels 0 1 1 2 4 3 dtype: int64 >>> s.rename({1: 3, 2: 5}) # mapping, changes labels 0 1 3 2 5 3 dtype: int64 Since ``DataFrame`` doesn't have a ``.name`` attribute, only mapping-type arguments are allowed. >>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) >>> df.rename(2) Traceback (most recent call last): ... TypeError: 'int' object is not callable ``DataFrame.rename`` supports two calling conventions * ``(index=index_mapper, columns=columns_mapper, ...)`` * ``(mapper, axis={'index', 'columns'}, ...)`` We *highly* recommend using keyword arguments to clarify your intent. >>> df.rename(index=str, columns={"A": "a", "B": "c"}) a c 0 1 4 1 2 5 2 3 6 >>> df.rename(index=str, columns={"A": "a", "C": "c"}) a B 0 1 4 1 2 5 2 3 6 Using axis-style parameters >>> df.rename(str.lower, axis='columns') a b 0 1 4 1 2 5 2 3 6 >>> df.rename({1: 2, 2: 4}, axis='index') A B 0 1 4 2 2 5 4 3 6 See the :ref:`user guide <basics.rename>` for more. """ if mapper is None and index is None and columns is None: raise TypeError("must pass an index to rename") if index is not None or columns is not None: if axis is not None: raise TypeError( "Cannot specify both 'axis' and any of 'index' or 'columns'" ) elif mapper is not None: raise TypeError( "Cannot specify both 'mapper' and any of 'index' or 'columns'" ) else: # use the mapper argument if axis and self._get_axis_number(axis) == 1: columns = mapper else: index = mapper result = self if inplace else self.copy(deep=copy) for axis_no, replacements in enumerate((index, columns)): if replacements is None: continue ax = self._get_axis(axis_no) baxis = self._get_block_manager_axis(axis_no) f = com.get_rename_function(replacements) if level is not None: level = ax._get_level_number(level) # GH 13473 if not callable(replacements): indexer = ax.get_indexer_for(replacements) if errors == "raise" and len(indexer[indexer == -1]): missing_labels = [ label for index, label in enumerate(replacements) if indexer[index] == -1 ] raise KeyError(f"{missing_labels} not found in axis") result._data = result._data.rename_axis( f, axis=baxis, copy=copy, level=level ) result._clear_item_cache() if inplace: self._update_inplace(result._data) return None else: return result.__finalize__(self) @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)]) def rename_axis(self, mapper=lib.no_default, **kwargs): """ Set the name of the axis for the index or columns. Parameters ---------- mapper : scalar, list-like, optional Value to set the axis name attribute. index, columns : scalar, list-like, dict-like or function, optional A scalar, list-like, dict-like or functions transformations to apply to that axis' values. Use either ``mapper`` and ``axis`` to specify the axis to target with ``mapper``, or ``index`` and/or ``columns``. .. versionchanged:: 0.24.0 axis : {0 or 'index', 1 or 'columns'}, default 0 The axis to rename. copy : bool, default True Also copy underlying data. inplace : bool, default False Modifies the object directly, instead of creating a new Series or DataFrame. Returns ------- Series, DataFrame, or None The same type as the caller or None if `inplace` is True. See Also -------- Series.rename : Alter Series index labels or name. DataFrame.rename : Alter DataFrame index labels or name. Index.rename : Set new names on index. Notes ----- ``DataFrame.rename_axis`` supports two calling conventions * ``(index=index_mapper, columns=columns_mapper, ...)`` * ``(mapper, axis={'index', 'columns'}, ...)`` The first calling convention will only modify the names of the index and/or the names of the Index object that is the columns. In this case, the parameter ``copy`` is ignored. The second calling convention will modify the names of the the corresponding index if mapper is a list or a scalar. However, if mapper is dict-like or a function, it will use the deprecated behavior of modifying the axis *labels*. We *highly* recommend using keyword arguments to clarify your intent. Examples -------- **Series** >>> s = pd.Series(["dog", "cat", "monkey"]) >>> s 0 dog 1 cat 2 monkey dtype: object >>> s.rename_axis("animal") animal 0 dog 1 cat 2 monkey dtype: object **DataFrame** >>> df = pd.DataFrame({"num_legs": [4, 4, 2], ... "num_arms": [0, 0, 2]}, ... ["dog", "cat", "monkey"]) >>> df num_legs num_arms dog 4 0 cat 4 0 monkey 2 2 >>> df = df.rename_axis("animal") >>> df num_legs num_arms animal dog 4 0 cat 4 0 monkey 2 2 >>> df = df.rename_axis("limbs", axis="columns") >>> df limbs num_legs num_arms animal dog 4 0 cat 4 0 monkey 2 2 **MultiIndex** >>> df.index = pd.MultiIndex.from_product([['mammal'], ... ['dog', 'cat', 'monkey']], ... names=['type', 'name']) >>> df limbs num_legs num_arms type name mammal dog 4 0 cat 4 0 monkey 2 2 >>> df.rename_axis(index={'type': 'class'}) limbs num_legs num_arms class name mammal dog 4 0 cat 4 0 monkey 2 2 >>> df.rename_axis(columns=str.upper) LIMBS num_legs num_arms type name mammal dog 4 0 cat 4 0 monkey 2 2 """ axes, kwargs = self._construct_axes_from_arguments( (), kwargs, sentinel=lib.no_default ) copy = kwargs.pop("copy", True) inplace = kwargs.pop("inplace", False) axis = kwargs.pop("axis", 0) if axis is not None: axis = self._get_axis_number(axis) if kwargs: raise TypeError( "rename_axis() got an unexpected keyword " f'argument "{list(kwargs.keys())[0]}"' ) inplace = validate_bool_kwarg(inplace, "inplace") if mapper is not lib.no_default: # Use v0.23 behavior if a scalar or list non_mapper = is_scalar(mapper) or ( is_list_like(mapper) and not is_dict_like(mapper) ) if non_mapper: return self._set_axis_name(mapper, axis=axis, inplace=inplace) else: raise ValueError("Use `.rename` to alter labels with a mapper.") else: # Use new behavior. Means that index and/or columns # is specified result = self if inplace else self.copy(deep=copy) for axis in range(self._AXIS_LEN): v = axes.get(self._AXIS_NAMES[axis]) if v is lib.no_default: continue non_mapper = is_scalar(v) or (is_list_like(v) and not is_dict_like(v)) if non_mapper: newnames = v else: f = com.get_rename_function(v) curnames = self._get_axis(axis).names newnames = [f(name) for name in curnames] result._set_axis_name(newnames, axis=axis, inplace=True) if not inplace: return result def _set_axis_name(self, name, axis=0, inplace=False): """ Set the name(s) of the axis. Parameters ---------- name : str or list of str Name(s) to set. axis : {0 or 'index', 1 or 'columns'}, default 0 The axis to set the label. The value 0 or 'index' specifies index, and the value 1 or 'columns' specifies columns. inplace : bool, default False If `True`, do operation inplace and return None. .. versionadded:: 0.21.0 Returns ------- Series, DataFrame, or None The same type as the caller or `None` if `inplace` is `True`. See Also -------- DataFrame.rename : Alter the axis labels of :class:`DataFrame`. Series.rename : Alter the index labels or set the index name of :class:`Series`. Index.rename : Set the name of :class:`Index` or :class:`MultiIndex`. Examples -------- >>> df = pd.DataFrame({"num_legs": [4, 4, 2]}, ... ["dog", "cat", "monkey"]) >>> df num_legs dog 4 cat 4 monkey 2 >>> df._set_axis_name("animal") num_legs animal dog 4 cat 4 monkey 2 >>> df.index = pd.MultiIndex.from_product( ... [["mammal"], ['dog', 'cat', 'monkey']]) >>> df._set_axis_name(["type", "name"]) legs type name mammal dog 4 cat 4 monkey 2 """ axis = self._get_axis_number(axis) idx = self._get_axis(axis).set_names(name) inplace = validate_bool_kwarg(inplace, "inplace") renamed = self if inplace else self.copy() renamed.set_axis(idx, axis=axis, inplace=True) if not inplace: return renamed # ---------------------------------------------------------------------- # Comparison Methods def _indexed_same(self, other) -> bool: return all( self._get_axis(a).equals(other._get_axis(a)) for a in self._AXIS_ORDERS ) def equals(self, other): """ Test whether two objects contain the same elements. This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal. The column headers do not need to have the same type, but the elements within the columns must be the same dtype. Parameters ---------- other : Series or DataFrame The other Series or DataFrame to be compared with the first. Returns ------- bool True if all elements are the same in both objects, False otherwise. See Also -------- Series.eq : Compare two Series objects of the same length and return a Series where each element is True if the element in each Series is equal, False otherwise. DataFrame.eq : Compare two DataFrame objects of the same shape and return a DataFrame where each element is True if the respective element in each DataFrame is equal, False otherwise. testing.assert_series_equal : Raises an AssertionError if left and right are not equal. Provides an easy interface to ignore inequality in dtypes, indexes and precision among others. testing.assert_frame_equal : Like assert_series_equal, but targets DataFrames. numpy.array_equal : Return True if two arrays have the same shape and elements, False otherwise. Notes ----- This function requires that the elements have the same dtype as their respective elements in the other Series or DataFrame. However, the column labels do not need to have the same type, as long as they are still considered equal. Examples -------- >>> df = pd.DataFrame({1: [10], 2: [20]}) >>> df 1 2 0 10 20 DataFrames df and exactly_equal have the same types and values for their elements and column labels, which will return True. >>> exactly_equal = pd.DataFrame({1: [10], 2: [20]}) >>> exactly_equal 1 2 0 10 20 >>> df.equals(exactly_equal) True DataFrames df and different_column_type have the same element types and values, but have different types for the column labels, which will still return True. >>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]}) >>> different_column_type 1.0 2.0 0 10 20 >>> df.equals(different_column_type) True DataFrames df and different_data_type have different types for the same values for their elements, and will return False even though their column labels are the same values and types. >>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]}) >>> different_data_type 1 2 0 10.0 20.0 >>> df.equals(different_data_type) False """ if not isinstance(other, self._constructor): return False return self._data.equals(other._data) # ------------------------------------------------------------------------- # Unary Methods def __neg__(self): values = self._values if is_bool_dtype(values): arr = operator.inv(values) elif ( is_numeric_dtype(values) or is_timedelta64_dtype(values) or is_object_dtype(values) ): arr = operator.neg(values) else: raise TypeError(f"Unary negative expects numeric dtype, not {values.dtype}") return self.__array_wrap__(arr) def __pos__(self): values = self._values if is_bool_dtype(values): arr = values elif ( is_numeric_dtype(values) or is_timedelta64_dtype(values) or is_object_dtype(values) ): arr = operator.pos(values) else: raise TypeError(f"Unary plus expects numeric dtype, not {values.dtype}") return self.__array_wrap__(arr) def __invert__(self): if not self.size: # inv fails with 0 len return self new_data = self._data.apply(operator.invert) result = self._constructor(new_data).__finalize__(self) return result def __nonzero__(self): raise ValueError( f"The truth value of a {type(self).__name__} is ambiguous. " "Use a.empty, a.bool(), a.item(), a.any() or a.all()." ) __bool__ = __nonzero__ def bool(self): """ Return the bool of a single element PandasObject. This must be a boolean scalar value, either True or False. Raise a ValueError if the PandasObject does not have exactly 1 element, or that element is not boolean Returns ------- bool Same single boolean value converted to bool type. """ v = self.squeeze() if isinstance(v, (bool, np.bool_)): return bool(v) elif is_scalar(v): raise ValueError( "bool cannot act on a non-boolean single element " f"{type(self).__name__}" ) self.__nonzero__() def __abs__(self: FrameOrSeries) -> FrameOrSeries: return self.abs() def __round__(self: FrameOrSeries, decimals: int = 0) -> FrameOrSeries: return self.round(decimals) # ------------------------------------------------------------------------- # Label or Level Combination Helpers # # A collection of helper methods for DataFrame/Series operations that # accept a combination of column/index labels and levels. All such # operations should utilize/extend these methods when possible so that we # have consistent precedence and validation logic throughout the library. def _is_level_reference(self, key, axis=0): """ Test whether a key is a level reference for a given axis. To be considered a level reference, `key` must be a string that: - (axis=0): Matches the name of an index level and does NOT match a column label. - (axis=1): Matches the name of a column level and does NOT match an index label. Parameters ---------- key : str Potential level name for the given axis axis : int, default 0 Axis that levels are associated with (0 for index, 1 for columns) Returns ------- is_level : bool """ axis = self._get_axis_number(axis) return ( key is not None and is_hashable(key) and key in self.axes[axis].names and not self._is_label_reference(key, axis=axis) ) def _is_label_reference(self, key, axis=0) -> bool_t: """ Test whether a key is a label reference for a given axis. To be considered a label reference, `key` must be a string that: - (axis=0): Matches a column label - (axis=1): Matches an index label Parameters ---------- key: str Potential label name axis: int, default 0 Axis perpendicular to the axis that labels are associated with (0 means search for column labels, 1 means search for index labels) Returns ------- is_label: bool """ axis = self._get_axis_number(axis) other_axes = (ax for ax in range(self._AXIS_LEN) if ax != axis) return ( key is not None and is_hashable(key) and any(key in self.axes[ax] for ax in other_axes) ) def _is_label_or_level_reference(self, key: str, axis: int = 0) -> bool_t: """ Test whether a key is a label or level reference for a given axis. To be considered either a label or a level reference, `key` must be a string that: - (axis=0): Matches a column label or an index level - (axis=1): Matches an index label or a column level Parameters ---------- key: str Potential label or level name axis: int, default 0 Axis that levels are associated with (0 for index, 1 for columns) Returns ------- is_label_or_level: bool """ return self._is_level_reference(key, axis=axis) or self._is_label_reference( key, axis=axis ) def _check_label_or_level_ambiguity(self, key, axis: int = 0) -> None: """ Check whether `key` is ambiguous. By ambiguous, we mean that it matches both a level of the input `axis` and a label of the other axis. Parameters ---------- key: str or object Label or level name. axis: int, default 0 Axis that levels are associated with (0 for index, 1 for columns). Raises ------ ValueError: `key` is ambiguous """ axis = self._get_axis_number(axis) other_axes = (ax for ax in range(self._AXIS_LEN) if ax != axis) if ( key is not None and is_hashable(key) and key in self.axes[axis].names and any(key in self.axes[ax] for ax in other_axes) ): # Build an informative and grammatical warning level_article, level_type = ( ("an", "index") if axis == 0 else ("a", "column") ) label_article, label_type = ( ("a", "column") if axis == 0 else ("an", "index") ) msg = ( f"'{key}' is both {level_article} {level_type} level and " f"{label_article} {label_type} label, which is ambiguous." ) raise ValueError(msg) def _get_label_or_level_values(self, key: str, axis: int = 0) -> np.ndarray: """ Return a 1-D array of values associated with `key`, a label or level from the given `axis`. Retrieval logic: - (axis=0): Return column values if `key` matches a column label. Otherwise return index level values if `key` matches an index level. - (axis=1): Return row values if `key` matches an index label. Otherwise return column level values if 'key' matches a column level Parameters ---------- key: str Label or level name. axis: int, default 0 Axis that levels are associated with (0 for index, 1 for columns) Returns ------- values: np.ndarray Raises ------ KeyError if `key` matches neither a label nor a level ValueError if `key` matches multiple labels FutureWarning if `key` is ambiguous. This will become an ambiguity error in a future version """ axis = self._get_axis_number(axis) other_axes = [ax for ax in range(self._AXIS_LEN) if ax != axis] if self._is_label_reference(key, axis=axis): self._check_label_or_level_ambiguity(key, axis=axis) values = self.xs(key, axis=other_axes[0])._values elif self._is_level_reference(key, axis=axis): values = self.axes[axis].get_level_values(key)._values else: raise KeyError(key) # Check for duplicates if values.ndim > 1: if other_axes and isinstance(self._get_axis(other_axes[0]), MultiIndex): multi_message = ( "\n" "For a multi-index, the label must be a " "tuple with elements corresponding to each level." ) else: multi_message = "" label_axis_name = "column" if axis == 0 else "index" raise ValueError( ( f"The {label_axis_name} label '{key}' " f"is not unique.{multi_message}" ) ) return values def _drop_labels_or_levels(self, keys, axis: int = 0): """ Drop labels and/or levels for the given `axis`. For each key in `keys`: - (axis=0): If key matches a column label then drop the column. Otherwise if key matches an index level then drop the level. - (axis=1): If key matches an index label then drop the row. Otherwise if key matches a column level then drop the level. Parameters ---------- keys: str or list of str labels or levels to drop axis: int, default 0 Axis that levels are associated with (0 for index, 1 for columns) Returns ------- dropped: DataFrame Raises ------ ValueError if any `keys` match neither a label nor a level """ axis = self._get_axis_number(axis) # Validate keys keys = com.maybe_make_list(keys) invalid_keys = [ k for k in keys if not self._is_label_or_level_reference(k, axis=axis) ] if invalid_keys: raise ValueError( ( "The following keys are not valid labels or " f"levels for axis {axis}: {invalid_keys}" ) ) # Compute levels and labels to drop levels_to_drop = [k for k in keys if self._is_level_reference(k, axis=axis)] labels_to_drop = [k for k in keys if not self._is_level_reference(k, axis=axis)] # Perform copy upfront and then use inplace operations below. # This ensures that we always perform exactly one copy. # ``copy`` and/or ``inplace`` options could be added in the future. dropped = self.copy() if axis == 0: # Handle dropping index levels if levels_to_drop: dropped.reset_index(levels_to_drop, drop=True, inplace=True) # Handle dropping columns labels if labels_to_drop: dropped.drop(labels_to_drop, axis=1, inplace=True) else: # Handle dropping column levels if levels_to_drop: if isinstance(dropped.columns, MultiIndex): # Drop the specified levels from the MultiIndex dropped.columns = dropped.columns.droplevel(levels_to_drop) else: # Drop the last level of Index by replacing with # a RangeIndex dropped.columns = RangeIndex(dropped.columns.size) # Handle dropping index labels if labels_to_drop: dropped.drop(labels_to_drop, axis=0, inplace=True) return dropped # ---------------------------------------------------------------------- # Iteration def __hash__(self): raise TypeError( f"{repr(type(self).__name__)} objects are mutable, " f"thus they cannot be hashed" ) def __iter__(self): """ Iterate over info axis. Returns ------- iterator Info axis as iterator. """ return iter(self._info_axis) # can we get a better explanation of this? def keys(self): """ Get the 'info axis' (see Indexing for more). This is index for Series, columns for DataFrame. Returns ------- Index Info axis. """ return self._info_axis def items(self): """ Iterate over (label, values) on info axis This is index for Series and columns for DataFrame. Returns ------- Generator """ for h in self._info_axis: yield h, self[h] @Appender(items.__doc__) def iteritems(self): return self.items() def __len__(self) -> int: """Returns length of info axis""" return len(self._info_axis) def __contains__(self, key) -> bool_t: """True if the key is in the info axis""" return key in self._info_axis @property def empty(self) -> bool_t: """ Indicator whether DataFrame is empty. True if DataFrame is entirely empty (no items), meaning any of the axes are of length 0. Returns ------- bool If DataFrame is empty, return True, if not return False. See Also -------- Series.dropna : Return series without null values. DataFrame.dropna : Return DataFrame with labels on given axis omitted where (all or any) data are missing. Notes ----- If DataFrame contains only NaNs, it is still not considered empty. See the example below. Examples -------- An example of an actual empty DataFrame. Notice the index is empty: >>> df_empty = pd.DataFrame({'A' : []}) >>> df_empty Empty DataFrame Columns: [A] Index: [] >>> df_empty.empty True If we only have NaNs in our DataFrame, it is not considered empty! We will need to drop the NaNs to make the DataFrame empty: >>> df = pd.DataFrame({'A' : [np.nan]}) >>> df A 0 NaN >>> df.empty False >>> df.dropna().empty True """ return any(len(self._get_axis(a)) == 0 for a in self._AXIS_ORDERS) # ---------------------------------------------------------------------- # Array Interface # This is also set in IndexOpsMixin # GH#23114 Ensure ndarray.__op__(DataFrame) returns NotImplemented __array_priority__ = 1000 def __array__(self, dtype=None) -> np.ndarray: return np.asarray(self._values, dtype=dtype) def __array_wrap__(self, result, context=None): result = lib.item_from_zerodim(result) if is_scalar(result): # e.g. we get here with np.ptp(series) # ptp also requires the item_from_zerodim return result d = self._construct_axes_dict(self._AXIS_ORDERS, copy=False) return self._constructor(result, **d).__finalize__(self) # ideally we would define this to avoid the getattr checks, but # is slower # @property # def __array_interface__(self): # """ provide numpy array interface method """ # values = self.values # return dict(typestr=values.dtype.str,shape=values.shape,data=values) # ---------------------------------------------------------------------- # Picklability def __getstate__(self) -> Dict[str, Any]: meta = {k: getattr(self, k, None) for k in self._metadata} return dict( _data=self._data, _typ=self._typ, _metadata=self._metadata, attrs=self.attrs, **meta, ) def __setstate__(self, state): if isinstance(state, BlockManager): self._data = state elif isinstance(state, dict): typ = state.get("_typ") if typ is not None: attrs = state.get("_attrs", {}) object.__setattr__(self, "_attrs", attrs) # set in the order of internal names # to avoid definitional recursion # e.g. say fill_value needing _data to be # defined meta = set(self._internal_names + self._metadata) for k in list(meta): if k in state: v = state[k] object.__setattr__(self, k, v) for k, v in state.items(): if k not in meta: object.__setattr__(self, k, v) else: raise NotImplementedError("Pre-0.12 pickles are no longer supported") elif len(state) == 2: raise NotImplementedError("Pre-0.12 pickles are no longer supported") self._item_cache = {} # ---------------------------------------------------------------------- # Rendering Methods def __repr__(self) -> str: # string representation based upon iterating over self # (since, by definition, `PandasContainers` are iterable) prepr = f"[{','.join(map(pprint_thing, self))}]" return f"{type(self).__name__}({prepr})" def _repr_latex_(self): """ Returns a LaTeX representation for a particular object. Mainly for use with nbconvert (jupyter notebook conversion to pdf). """ if config.get_option("display.latex.repr"): return self.to_latex() else: return None def _repr_data_resource_(self): """ Not a real Jupyter special repr method, but we use the same naming convention. """ if config.get_option("display.html.table_schema"): data = self.head(config.get_option("display.max_rows")) payload = json.loads( data.to_json(orient="table"), object_pairs_hook=collections.OrderedDict ) return payload # ---------------------------------------------------------------------- # I/O Methods _shared_docs[ "to_markdown" ] = """ Print %(klass)s in Markdown-friendly format. .. versionadded:: 1.0.0 Parameters ---------- buf : str, Path or StringIO-like, optional, default None Buffer to write to. If None, the output is returned as a string. mode : str, optional Mode in which file is opened. **kwargs These parameters will be passed to `tabulate`. Returns ------- str %(klass)s in Markdown-friendly format. """ _shared_docs[ "to_excel" ] = """ Write %(klass)s to an Excel sheet. To write a single %(klass)s to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an `ExcelWriter` object with a target file name, and specify a sheet in the file to write to. Multiple sheets may be written to by specifying unique `sheet_name`. With all data written to the file it is necessary to save the changes. Note that creating an `ExcelWriter` object with a file name that already exists will result in the contents of the existing file being erased. Parameters ---------- excel_writer : str or ExcelWriter object File path or existing ExcelWriter. sheet_name : str, default 'Sheet1' Name of sheet which will contain DataFrame. na_rep : str, default '' Missing data representation. float_format : str, optional Format string for floating point numbers. For example ``float_format="%%.2f"`` will format 0.1234 to 0.12. columns : sequence or list of str, optional Columns to write. header : bool or list of str, default True Write out the column names. If a list of string is given it is assumed to be aliases for the column names. index : bool, default True Write row names (index). index_label : str or sequence, optional Column label for index column(s) if desired. If not specified, and `header` and `index` are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. startrow : int, default 0 Upper left cell row to dump data frame. startcol : int, default 0 Upper left cell column to dump data frame. engine : str, optional Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this via the options ``io.excel.xlsx.writer``, ``io.excel.xls.writer``, and ``io.excel.xlsm.writer``. merge_cells : bool, default True Write MultiIndex and Hierarchical Rows as merged cells. encoding : str, optional Encoding of the resulting excel file. Only necessary for xlwt, other writers support unicode natively. inf_rep : str, default 'inf' Representation for infinity (there is no native representation for infinity in Excel). verbose : bool, default True Display more information in the error logs. freeze_panes : tuple of int (length 2), optional Specifies the one-based bottommost row and rightmost column that is to be frozen. See Also -------- to_csv : Write DataFrame to a comma-separated values (csv) file. ExcelWriter : Class for writing DataFrame objects into excel sheets. read_excel : Read an Excel file into a pandas DataFrame. read_csv : Read a comma-separated values (csv) file into DataFrame. Notes ----- For compatibility with :meth:`~DataFrame.to_csv`, to_excel serializes lists and dicts to strings before writing. Once a workbook has been saved it is not possible write further data without rewriting the whole workbook. Examples -------- Create, write to and save a workbook: >>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']], ... index=['row 1', 'row 2'], ... columns=['col 1', 'col 2']) >>> df1.to_excel("output.xlsx") # doctest: +SKIP To specify the sheet name: >>> df1.to_excel("output.xlsx", ... sheet_name='Sheet_name_1') # doctest: +SKIP If you wish to write to more than one sheet in the workbook, it is necessary to specify an ExcelWriter object: >>> df2 = df1.copy() >>> with pd.ExcelWriter('output.xlsx') as writer: # doctest: +SKIP ... df1.to_excel(writer, sheet_name='Sheet_name_1') ... df2.to_excel(writer, sheet_name='Sheet_name_2') ExcelWriter can also be used to append to an existing Excel file: >>> with pd.ExcelWriter('output.xlsx', ... mode='a') as writer: # doctest: +SKIP ... df.to_excel(writer, sheet_name='Sheet_name_3') To set the library that is used to write the Excel file, you can pass the `engine` keyword (the default engine is automatically chosen depending on the file extension): >>> df1.to_excel('output1.xlsx', engine='xlsxwriter') # doctest: +SKIP """ @Appender(_shared_docs["to_excel"] % dict(klass="object")) def to_excel( self, excel_writer, sheet_name="Sheet1", na_rep="", float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep="inf", verbose=True, freeze_panes=None, ) -> None: df = self if isinstance(self, ABCDataFrame) else self.to_frame() from pandas.io.formats.excel import ExcelFormatter formatter = ExcelFormatter( df, na_rep=na_rep, cols=columns, header=header, float_format=float_format, index=index, index_label=index_label, merge_cells=merge_cells, inf_rep=inf_rep, ) formatter.write( excel_writer, sheet_name=sheet_name, startrow=startrow, startcol=startcol, freeze_panes=freeze_panes, engine=engine, ) def to_json( self, path_or_buf: Optional[FilePathOrBuffer] = None, orient: Optional[str] = None, date_format: Optional[str] = None, double_precision: int = 10, force_ascii: bool_t = True, date_unit: str = "ms", default_handler: Optional[Callable[[Any], JSONSerializable]] = None, lines: bool_t = False, compression: Optional[str] = "infer", index: bool_t = True, indent: Optional[int] = None, ) -> Optional[str]: """ Convert the object to a JSON string. Note NaN's and None will be converted to null and datetime objects will be converted to UNIX timestamps. Parameters ---------- path_or_buf : str or file handle, optional File path or object. If not specified, the result is returned as a string. orient : str Indication of expected JSON string format. * Series: - default is 'index' - allowed values are: {'split','records','index','table'}. * DataFrame: - default is 'columns' - allowed values are: {'split', 'records', 'index', 'columns', 'values', 'table'}. * The format of the JSON string: - 'split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]} - 'records' : list like [{column -> value}, ... , {column -> value}] - 'index' : dict like {index -> {column -> value}} - 'columns' : dict like {column -> {index -> value}} - 'values' : just the values array - 'table' : dict like {'schema': {schema}, 'data': {data}} Describing the data, where data component is like ``orient='records'``. .. versionchanged:: 0.20.0 date_format : {None, 'epoch', 'iso'} Type of date conversion. 'epoch' = epoch milliseconds, 'iso' = ISO8601. The default depends on the `orient`. For ``orient='table'``, the default is 'iso'. For all other orients, the default is 'epoch'. double_precision : int, default 10 The number of decimal places to use when encoding floating point values. force_ascii : bool, default True Force encoded string to be ASCII. date_unit : str, default 'ms' (milliseconds) The time unit to encode to, governs timestamp and ISO8601 precision. One of 's', 'ms', 'us', 'ns' for second, millisecond, microsecond, and nanosecond respectively. default_handler : callable, default None Handler to call if object cannot otherwise be converted to a suitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object. lines : bool, default False If 'orient' is 'records' write out line delimited json format. Will throw ValueError if incorrect 'orient' since others are not list like. compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None} A string representing the compression to use in the output file, only used when the first argument is a filename. By default, the compression is inferred from the filename. .. versionadded:: 0.21.0 .. versionchanged:: 0.24.0 'infer' option added and set to default index : bool, default True Whether to include the index values in the JSON string. Not including the index (``index=False``) is only supported when orient is 'split' or 'table'. .. versionadded:: 0.23.0 indent : int, optional Length of whitespace used to indent each record. .. versionadded:: 1.0.0 Returns ------- None or str If path_or_buf is None, returns the resulting json format as a string. Otherwise returns None. See Also -------- read_json : Convert a JSON string to pandas object. Notes ----- The behavior of ``indent=0`` varies from the stdlib, which does not indent the output but does insert newlines. Currently, ``indent=0`` and the default ``indent=None`` are equivalent in pandas, though this may change in a future release. Examples -------- >>> df = pd.DataFrame([['a', 'b'], ['c', 'd']], ... index=['row 1', 'row 2'], ... columns=['col 1', 'col 2']) >>> df.to_json(orient='split') '{"columns":["col 1","col 2"], "index":["row 1","row 2"], "data":[["a","b"],["c","d"]]}' Encoding/decoding a Dataframe using ``'records'`` formatted JSON. Note that index labels are not preserved with this encoding. >>> df.to_json(orient='records') '[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]' Encoding/decoding a Dataframe using ``'index'`` formatted JSON: >>> df.to_json(orient='index') '{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}' Encoding/decoding a Dataframe using ``'columns'`` formatted JSON: >>> df.to_json(orient='columns') '{"col 1":{"row 1":"a","row 2":"c"},"col 2":{"row 1":"b","row 2":"d"}}' Encoding/decoding a Dataframe using ``'values'`` formatted JSON: >>> df.to_json(orient='values') '[["a","b"],["c","d"]]' Encoding with Table Schema >>> df.to_json(orient='table') '{"schema": {"fields": [{"name": "index", "type": "string"}, {"name": "col 1", "type": "string"}, {"name": "col 2", "type": "string"}], "primaryKey": "index", "pandas_version": "0.20.0"}, "data": [{"index": "row 1", "col 1": "a", "col 2": "b"}, {"index": "row 2", "col 1": "c", "col 2": "d"}]}' """ from pandas.io import json if date_format is None and orient == "table": date_format = "iso" elif date_format is None: date_format = "epoch" config.is_nonnegative_int(indent) indent = indent or 0 return json.to_json( path_or_buf=path_or_buf, obj=self, orient=orient, date_format=date_format, double_precision=double_precision, force_ascii=force_ascii, date_unit=date_unit, default_handler=default_handler, lines=lines, compression=compression, index=index, indent=indent, ) def to_hdf( self, path_or_buf, key: str, mode: str = "a", complevel: Optional[int] = None, complib: Optional[str] = None, append: bool_t = False, format: Optional[str] = None, index: bool_t = True, min_itemsize: Optional[Union[int, Dict[str, int]]] = None, nan_rep=None, dropna: Optional[bool_t] = None, data_columns: Optional[List[str]] = None, errors: str = "strict", encoding: str = "UTF-8", ) -> None: """ Write the contained data to an HDF5 file using HDFStore. Hierarchical Data Format (HDF) is self-describing, allowing an application to interpret the structure and contents of a file with no outside information. One HDF file can hold a mix of related objects which can be accessed as a group or as individual objects. In order to add another DataFrame or Series to an existing HDF file please use append mode and a different a key. For more information see the :ref:`user guide <io.hdf5>`. Parameters ---------- path_or_buf : str or pandas.HDFStore File path or HDFStore object. key : str Identifier for the group in the store. mode : {'a', 'w', 'r+'}, default 'a' Mode to open file: - 'w': write, a new file is created (an existing file with the same name would be deleted). - 'a': append, an existing file is opened for reading and writing, and if the file does not exist it is created. - 'r+': similar to 'a', but the file must already exist. complevel : {0-9}, optional Specifies a compression level for data. A value of 0 disables compression. complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib' Specifies the compression library to be used. As of v0.20.2 these additional compressors for Blosc are supported (default if no compressor specified: 'blosc:blosclz'): {'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy', 'blosc:zlib', 'blosc:zstd'}. Specifying a compression library which is not available issues a ValueError. append : bool, default False For Table formats, append the input data to the existing. format : {'fixed', 'table', None}, default 'fixed' Possible values: - 'fixed': Fixed format. Fast writing/reading. Not-appendable, nor searchable. - 'table': Table format. Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data. - If None, pd.get_option('io.hdf.default_format') is checked, followed by fallback to "fixed" errors : str, default 'strict' Specifies how encoding and decoding errors are to be handled. See the errors argument for :func:`open` for a full list of options. encoding : str, default "UTF-8" min_itemsize : dict or int, optional Map column names to minimum string sizes for columns. nan_rep : Any, optional How to represent null values as str. Not allowed with append=True. data_columns : list of columns or True, optional List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See :ref:`io.hdf5-query-data-columns`. Applicable only to format='table'. See Also -------- DataFrame.read_hdf : Read from HDF file. DataFrame.to_parquet : Write a DataFrame to the binary parquet format. DataFrame.to_sql : Write to a sql table. DataFrame.to_feather : Write out feather-format for DataFrames. DataFrame.to_csv : Write out to a csv file. Examples -------- >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}, ... index=['a', 'b', 'c']) >>> df.to_hdf('data.h5', key='df', mode='w') We can add another object to the same file: >>> s = pd.Series([1, 2, 3, 4]) >>> s.to_hdf('data.h5', key='s') Reading from HDF file: >>> pd.read_hdf('data.h5', 'df') A B a 1 4 b 2 5 c 3 6 >>> pd.read_hdf('data.h5', 's') 0 1 1 2 2 3 3 4 dtype: int64 Deleting file with data: >>> import os >>> os.remove('data.h5') """ from pandas.io import pytables pytables.to_hdf( path_or_buf, key, self, mode=mode, complevel=complevel, complib=complib, append=append, format=format, index=index, min_itemsize=min_itemsize, nan_rep=nan_rep, dropna=dropna, data_columns=data_columns, errors=errors, encoding=encoding, ) def to_sql( self, name: str, con, schema=None, if_exists: str = "fail", index: bool_t = True, index_label=None, chunksize=None, dtype=None, method=None, ) -> None: """ Write records stored in a DataFrame to a SQL database. Databases supported by SQLAlchemy [1]_ are supported. Tables can be newly created, appended to, or overwritten. Parameters ---------- name : str Name of SQL table. con : sqlalchemy.engine.Engine or sqlite3.Connection Using SQLAlchemy makes it possible to use any DB supported by that library. Legacy support is provided for sqlite3.Connection objects. The user is responsible for engine disposal and connection closure for the SQLAlchemy connectable See `here \ <https://docs.sqlalchemy.org/en/13/core/connections.html>`_. schema : str, optional Specify the schema (if database flavor supports this). If None, use default schema. if_exists : {'fail', 'replace', 'append'}, default 'fail' How to behave if the table already exists. * fail: Raise a ValueError. * replace: Drop the table before inserting new values. * append: Insert new values to the existing table. index : bool, default True Write DataFrame index as a column. Uses `index_label` as the column name in the table. index_label : str or sequence, default None Column label for index column(s). If None is given (default) and `index` is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. chunksize : int, optional Specify the number of rows in each batch to be written at a time. By default, all rows will be written at once. dtype : dict or scalar, optional Specifying the datatype for columns. If a dictionary is used, the keys should be the column names and the values should be the SQLAlchemy types or strings for the sqlite3 legacy mode. If a scalar is provided, it will be applied to all columns. method : {None, 'multi', callable}, optional Controls the SQL insertion clause used: * None : Uses standard SQL ``INSERT`` clause (one per row). * 'multi': Pass multiple values in a single ``INSERT`` clause. * callable with signature ``(pd_table, conn, keys, data_iter)``. Details and a sample callable implementation can be found in the section :ref:`insert method <io.sql.method>`. .. versionadded:: 0.24.0 Raises ------ ValueError When the table already exists and `if_exists` is 'fail' (the default). See Also -------- read_sql : Read a DataFrame from a table. Notes ----- Timezone aware datetime columns will be written as ``Timestamp with timezone`` type with SQLAlchemy if supported by the database. Otherwise, the datetimes will be stored as timezone unaware timestamps local to the original timezone. .. versionadded:: 0.24.0 References ---------- .. [1] https://docs.sqlalchemy.org .. [2] https://www.python.org/dev/peps/pep-0249/ Examples -------- Create an in-memory SQLite database. >>> from sqlalchemy import create_engine >>> engine = create_engine('sqlite://', echo=False) Create a table from scratch with 3 rows. >>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']}) >>> df name 0 User 1 1 User 2 2 User 3 >>> df.to_sql('users', con=engine) >>> engine.execute("SELECT * FROM users").fetchall() [(0, 'User 1'), (1, 'User 2'), (2, 'User 3')] >>> df1 = pd.DataFrame({'name' : ['User 4', 'User 5']}) >>> df1.to_sql('users', con=engine, if_exists='append') >>> engine.execute("SELECT * FROM users").fetchall() [(0, 'User 1'), (1, 'User 2'), (2, 'User 3'), (0, 'User 4'), (1, 'User 5')] Overwrite the table with just ``df1``. >>> df1.to_sql('users', con=engine, if_exists='replace', ... index_label='id') >>> engine.execute("SELECT * FROM users").fetchall() [(0, 'User 4'), (1, 'User 5')] Specify the dtype (especially useful for integers with missing values). Notice that while pandas is forced to store the data as floating point, the database supports nullable integers. When fetching the data with Python, we get back integer scalars. >>> df = pd.DataFrame({"A": [1, None, 2]}) >>> df A 0 1.0 1 NaN 2 2.0 >>> from sqlalchemy.types import Integer >>> df.to_sql('integers', con=engine, index=False, ... dtype={"A": Integer()}) >>> engine.execute("SELECT * FROM integers").fetchall() [(1,), (None,), (2,)] """ from pandas.io import sql sql.to_sql( self, name, con, schema=schema, if_exists=if_exists, index=index, index_label=index_label, chunksize=chunksize, dtype=dtype, method=method, ) def to_pickle( self, path, compression: Optional[str] = "infer", protocol: int = pickle.HIGHEST_PROTOCOL, ) -> None: """ Pickle (serialize) object to file. Parameters ---------- path : str File path where the pickled object will be stored. compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, \ default 'infer' A string representing the compression to use in the output file. By default, infers from the file extension in specified path. protocol : int Int which indicates which protocol should be used by the pickler, default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible values are 0, 1, 2, 3, 4. A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL. .. [1] https://docs.python.org/3/library/pickle.html. .. versionadded:: 0.21.0. See Also -------- read_pickle : Load pickled pandas object (or any object) from file. DataFrame.to_hdf : Write DataFrame to an HDF5 file. DataFrame.to_sql : Write DataFrame to a SQL database. DataFrame.to_parquet : Write a DataFrame to the binary parquet format. Examples -------- >>> original_df = pd.DataFrame({"foo": range(5), "bar": range(5, 10)}) >>> original_df foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 >>> original_df.to_pickle("./dummy.pkl") >>> unpickled_df = pd.read_pickle("./dummy.pkl") >>> unpickled_df foo bar 0 0 5 1 1 6 2 2 7 3 3 8 4 4 9 >>> import os >>> os.remove("./dummy.pkl") """ from pandas.io.pickle import to_pickle to_pickle(self, path, compression=compression, protocol=protocol) def to_clipboard( self, excel: bool_t = True, sep: Optional[str] = None, **kwargs ) -> None: r""" Copy object to the system clipboard. Write a text representation of object to the system clipboard. This can be pasted into Excel, for example. Parameters ---------- excel : bool, default True Produce output in a csv format for easy pasting into excel. - True, use the provided separator for csv pasting. - False, write a string representation of the object to the clipboard. sep : str, default ``'\t'`` Field delimiter. **kwargs These parameters will be passed to DataFrame.to_csv. See Also -------- DataFrame.to_csv : Write a DataFrame to a comma-separated values (csv) file. read_clipboard : Read text from clipboard and pass to read_table. Notes ----- Requirements for your platform. - Linux : `xclip`, or `xsel` (with `PyQt4` modules) - Windows : none - OS X : none Examples -------- Copy the contents of a DataFrame to the clipboard. >>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C']) >>> df.to_clipboard(sep=',') ... # Wrote the following to the system clipboard: ... # ,A,B,C ... # 0,1,2,3 ... # 1,4,5,6 We can omit the index by passing the keyword `index` and setting it to false. >>> df.to_clipboard(sep=',', index=False) ... # Wrote the following to the system clipboard: ... # A,B,C ... # 1,2,3 ... # 4,5,6 """ from pandas.io import clipboards clipboards.to_clipboard(self, excel=excel, sep=sep, **kwargs) def to_xarray(self): """ Return an xarray object from the pandas object. Returns ------- xarray.DataArray or xarray.Dataset Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series. See Also -------- DataFrame.to_hdf : Write DataFrame to an HDF5 file. DataFrame.to_parquet : Write a DataFrame to the binary parquet format. Notes ----- See the `xarray docs <https://xarray.pydata.org/en/stable/>`__ Examples -------- >>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2), ... ('parrot', 'bird', 24.0, 2), ... ('lion', 'mammal', 80.5, 4), ... ('monkey', 'mammal', np.nan, 4)], ... columns=['name', 'class', 'max_speed', ... 'num_legs']) >>> df name class max_speed num_legs 0 falcon bird 389.0 2 1 parrot bird 24.0 2 2 lion mammal 80.5 4 3 monkey mammal NaN 4 >>> df.to_xarray() <xarray.Dataset> Dimensions: (index: 4) Coordinates: * index (index) int64 0 1 2 3 Data variables: name (index) object 'falcon' 'parrot' 'lion' 'monkey' class (index) object 'bird' 'bird' 'mammal' 'mammal' max_speed (index) float64 389.0 24.0 80.5 nan num_legs (index) int64 2 2 4 4 >>> df['max_speed'].to_xarray() <xarray.DataArray 'max_speed' (index: 4)> array([389. , 24. , 80.5, nan]) Coordinates: * index (index) int64 0 1 2 3 >>> dates = pd.to_datetime(['2018-01-01', '2018-01-01', ... '2018-01-02', '2018-01-02']) >>> df_multiindex = pd.DataFrame({'date': dates, ... 'animal': ['falcon', 'parrot', ... 'falcon', 'parrot'], ... 'speed': [350, 18, 361, 15]}) >>> df_multiindex = df_multiindex.set_index(['date', 'animal']) >>> df_multiindex speed date animal 2018-01-01 falcon 350 parrot 18 2018-01-02 falcon 361 parrot 15 >>> df_multiindex.to_xarray() <xarray.Dataset> Dimensions: (animal: 2, date: 2) Coordinates: * date (date) datetime64[ns] 2018-01-01 2018-01-02 * animal (animal) object 'falcon' 'parrot' Data variables: speed (date, animal) int64 350 18 361 15 """ xarray = import_optional_dependency("xarray") if self.ndim == 1: return xarray.DataArray.from_series(self) else: return xarray.Dataset.from_dataframe(self) @Substitution(returns=fmt.return_docstring) def to_latex( self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep="NaN", formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=False, column_format=None, longtable=None, escape=None, encoding=None, decimal=".", multicolumn=None, multicolumn_format=None, multirow=None, caption=None, label=None, ): r""" Render object to a LaTeX tabular, longtable, or nested table/tabular. Requires ``\usepackage{booktabs}``. The output can be copy/pasted into a main LaTeX document or read from an external file with ``\input{table.tex}``. .. versionchanged:: 0.20.2 Added to Series. .. versionchanged:: 1.0.0 Added caption and label arguments. Parameters ---------- buf : str, Path or StringIO-like, optional, default None Buffer to write to. If None, the output is returned as a string. columns : list of label, optional The subset of columns to write. Writes all columns by default. col_space : int, optional The minimum width of each column. header : bool or list of str, default True Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names. index : bool, default True Write row names (index). na_rep : str, default 'NaN' Missing data representation. formatters : list of functions or dict of {str: function}, optional Formatter functions to apply to columns' elements by position or name. The result of each function must be a unicode string. List must be of length equal to the number of columns. float_format : one-parameter function or str, optional, default None Formatter for floating point numbers. For example ``float_format="%%.2f"`` and ``float_format="{:0.2f}".format`` will both result in 0.1234 being formatted as 0.12. sparsify : bool, optional Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row. By default, the value will be read from the config module. index_names : bool, default True Prints the names of the indexes. bold_rows : bool, default False Make the row labels bold in the output. column_format : str, optional The columns format as specified in `LaTeX table format <https://en.wikibooks.org/wiki/LaTeX/Tables>`__ e.g. 'rcl' for 3 columns. By default, 'l' will be used for all columns except columns of numbers, which default to 'r'. longtable : bool, optional By default, the value will be read from the pandas config module. Use a longtable environment instead of tabular. Requires adding a \usepackage{longtable} to your LaTeX preamble. escape : bool, optional By default, the value will be read from the pandas config module. When set to False prevents from escaping latex special characters in column names. encoding : str, optional A string representing the encoding to use in the output file, defaults to 'utf-8'. decimal : str, default '.' Character recognized as decimal separator, e.g. ',' in Europe. multicolumn : bool, default True Use \multicolumn to enhance MultiIndex columns. The default will be read from the config module. multicolumn_format : str, default 'l' The alignment for multicolumns, similar to `column_format` The default will be read from the config module. multirow : bool, default False Use \multirow to enhance MultiIndex rows. Requires adding a \usepackage{multirow} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module. caption : str, optional The LaTeX caption to be placed inside ``\caption{}`` in the output. .. versionadded:: 1.0.0 label : str, optional The LaTeX label to be placed inside ``\label{}`` in the output. This is used with ``\ref{}`` in the main ``.tex`` file. .. versionadded:: 1.0.0 %(returns)s See Also -------- DataFrame.to_string : Render a DataFrame to a console-friendly tabular output. DataFrame.to_html : Render a DataFrame as an HTML table. Examples -------- >>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'], ... 'mask': ['red', 'purple'], ... 'weapon': ['sai', 'bo staff']}) >>> print(df.to_latex(index=False)) # doctest: +NORMALIZE_WHITESPACE \begin{tabular}{lll} \toprule name & mask & weapon \\ \midrule Raphael & red & sai \\ Donatello & purple & bo staff \\ \bottomrule \end{tabular} """ # Get defaults from the pandas config if self.ndim == 1: self = self.to_frame() if longtable is None: longtable = config.get_option("display.latex.longtable") if escape is None: escape = config.get_option("display.latex.escape") if multicolumn is None: multicolumn = config.get_option("display.latex.multicolumn") if multicolumn_format is None: multicolumn_format = config.get_option("display.latex.multicolumn_format") if multirow is None: multirow = config.get_option("display.latex.multirow") formatter = DataFrameFormatter( self, columns=columns, col_space=col_space, na_rep=na_rep, header=header, index=index, formatters=formatters, float_format=float_format, bold_rows=bold_rows, sparsify=sparsify, index_names=index_names, escape=escape, decimal=decimal, ) return formatter.to_latex( buf=buf, column_format=column_format, longtable=longtable, encoding=encoding, multicolumn=multicolumn, multicolumn_format=multicolumn_format, multirow=multirow, caption=caption, label=label, ) def to_csv( self, path_or_buf: Optional[FilePathOrBuffer] = None, sep: str = ",", na_rep: str = "", float_format: Optional[str] = None, columns: Optional[Sequence[Label]] = None, header: Union[bool_t, List[str]] = True, index: bool_t = True, index_label: Optional[Union[bool_t, str, Sequence[Label]]] = None, mode: str = "w", encoding: Optional[str] = None, compression: Optional[Union[str, Mapping[str, str]]] = "infer", quoting: Optional[int] = None, quotechar: str = '"', line_terminator: Optional[str] = None, chunksize: Optional[int] = None, date_format: Optional[str] = None, doublequote: bool_t = True, escapechar: Optional[str] = None, decimal: Optional[str] = ".", ) -> Optional[str]: r""" Write object to a comma-separated values (csv) file. .. versionchanged:: 0.24.0 The order of arguments for Series was changed. Parameters ---------- path_or_buf : str or file handle, default None File path or object, if None is provided the result is returned as a string. If a file object is passed it should be opened with `newline=''`, disabling universal newlines. .. versionchanged:: 0.24.0 Was previously named "path" for Series. sep : str, default ',' String of length 1. Field delimiter for the output file. na_rep : str, default '' Missing data representation. float_format : str, default None Format string for floating point numbers. columns : sequence, optional Columns to write. header : bool or list of str, default True Write out the column names. If a list of strings is given it is assumed to be aliases for the column names. .. versionchanged:: 0.24.0 Previously defaulted to False for Series. index : bool, default True Write row names (index). index_label : str or sequence, or False, default None Column label for index column(s) if desired. If None is given, and `header` and `index` are True, then the index names are used. A sequence should be given if the object uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R. mode : str Python write mode, default 'w'. encoding : str, optional A string representing the encoding to use in the output file, defaults to 'utf-8'. compression : str or dict, default 'infer' If str, represents compression mode. If dict, value at 'method' is the compression mode. Compression mode may be any of the following possible values: {'infer', 'gzip', 'bz2', 'zip', 'xz', None}. If compression mode is 'infer' and `path_or_buf` is path-like, then detect compression mode from the following extensions: '.gz', '.bz2', '.zip' or '.xz'. (otherwise no compression). If dict given and mode is 'zip' or inferred as 'zip', other entries passed as additional compression options. .. versionchanged:: 1.0.0 May now be a dict with key 'method' as compression mode and other entries as additional compression options if compression mode is 'zip'. quoting : optional constant from csv module Defaults to csv.QUOTE_MINIMAL. If you have set a `float_format` then floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric. quotechar : str, default '\"' String of length 1. Character used to quote fields. line_terminator : str, optional The newline character or character sequence to use in the output file. Defaults to `os.linesep`, which depends on the OS in which this method is called ('\n' for linux, '\r\n' for Windows, i.e.). .. versionchanged:: 0.24.0 chunksize : int or None Rows to write at a time. date_format : str, default None Format string for datetime objects. doublequote : bool, default True Control quoting of `quotechar` inside a field. escapechar : str, default None String of length 1. Character used to escape `sep` and `quotechar` when appropriate. decimal : str, default '.' Character recognized as decimal separator. E.g. use ',' for European data. Returns ------- None or str If path_or_buf is None, returns the resulting csv format as a string. Otherwise returns None. See Also -------- read_csv : Load a CSV file into a DataFrame. to_excel : Write DataFrame to an Excel file. Examples -------- >>> df = pd.DataFrame({'name': ['Raphael', 'Donatello'], ... 'mask': ['red', 'purple'], ... 'weapon': ['sai', 'bo staff']}) >>> df.to_csv(index=False) 'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n' Create 'out.zip' containing 'out.csv' >>> compression_opts = dict(method='zip', ... archive_name='out.csv') # doctest: +SKIP >>> df.to_csv('out.zip', index=False, ... compression=compression_opts) # doctest: +SKIP """ df = self if isinstance(self, ABCDataFrame) else self.to_frame() from pandas.io.formats.csvs import CSVFormatter formatter = CSVFormatter( df, path_or_buf, line_terminator=line_terminator, sep=sep, encoding=encoding, compression=compression, quoting=quoting, na_rep=na_rep, float_format=float_format, cols=columns, header=header, index=index, index_label=index_label, mode=mode, chunksize=chunksize, quotechar=quotechar, date_format=date_format, doublequote=doublequote, escapechar=escapechar, decimal=decimal, ) formatter.save() if path_or_buf is None: return formatter.path_or_buf.getvalue() return None # ---------------------------------------------------------------------- # Lookup Caching def _set_as_cached(self, item, cacher) -> None: """ Set the _cacher attribute on the calling object with a weakref to cacher. """ self._cacher = (item, weakref.ref(cacher)) def _reset_cacher(self) -> None: """ Reset the cacher. """ if hasattr(self, "_cacher"): del self._cacher def _maybe_cache_changed(self, item, value) -> None: """ The object has called back to us saying maybe it has changed. """ self._data.set(item, value) @property def _is_cached(self) -> bool_t: """Return boolean indicating if self is cached or not.""" return getattr(self, "_cacher", None) is not None def _get_cacher(self): """return my cacher or None""" cacher = getattr(self, "_cacher", None) if cacher is not None: cacher = cacher[1]() return cacher def _maybe_update_cacher( self, clear: bool_t = False, verify_is_copy: bool_t = True ) -> None: """ See if we need to update our parent cacher if clear, then clear our cache. Parameters ---------- clear : bool, default False Clear the item cache. verify_is_copy : bool, default True Provide is_copy checks. """ cacher = getattr(self, "_cacher", None) if cacher is not None: ref = cacher[1]() # we are trying to reference a dead referant, hence # a copy if ref is None: del self._cacher else: # Note: we need to call ref._maybe_cache_changed even in the # case where it will raise. (Uh, not clear why) try: ref._maybe_cache_changed(cacher[0], self) except AssertionError: # ref._data.setitem can raise # AssertionError because of shape mismatch pass if verify_is_copy: self._check_setitem_copy(stacklevel=5, t="referant") if clear: self._clear_item_cache() def _clear_item_cache(self) -> None: self._item_cache.clear() # ---------------------------------------------------------------------- # Indexing Methods def take( self: FrameOrSeries, indices, axis=0, is_copy: Optional[bool_t] = None, **kwargs ) -> FrameOrSeries: """ Return the elements in the given *positional* indices along an axis. This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object. Parameters ---------- indices : array-like An array of ints indicating which positions to take. axis : {0 or 'index', 1 or 'columns', None}, default 0 The axis on which to select elements. ``0`` means that we are selecting rows, ``1`` means that we are selecting columns. is_copy : bool Before pandas 1.0, ``is_copy=False`` can be specified to ensure that the return value is an actual copy. Starting with pandas 1.0, ``take`` always returns a copy, and the keyword is therefore deprecated. .. deprecated:: 1.0.0 **kwargs For compatibility with :meth:`numpy.take`. Has no effect on the output. Returns ------- taken : same type as caller An array-like containing the elements taken from the object. See Also -------- DataFrame.loc : Select a subset of a DataFrame by labels. DataFrame.iloc : Select a subset of a DataFrame by positions. numpy.take : Take elements from an array along an axis. Examples -------- >>> df = pd.DataFrame([('falcon', 'bird', 389.0), ... ('parrot', 'bird', 24.0), ... ('lion', 'mammal', 80.5), ... ('monkey', 'mammal', np.nan)], ... columns=['name', 'class', 'max_speed'], ... index=[0, 2, 3, 1]) >>> df name class max_speed 0 falcon bird 389.0 2 parrot bird 24.0 3 lion mammal 80.5 1 monkey mammal NaN Take elements at positions 0 and 3 along the axis 0 (default). Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That's because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3. >>> df.take([0, 3]) name class max_speed 0 falcon bird 389.0 1 monkey mammal NaN Take elements at indices 1 and 2 along the axis 1 (column selection). >>> df.take([1, 2], axis=1) class max_speed 0 bird 389.0 2 bird 24.0 3 mammal 80.5 1 mammal NaN We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists. >>> df.take([-1, -2]) name class max_speed 1 monkey mammal NaN 3 lion mammal 80.5 """ if is_copy is not None: warnings.warn( "is_copy is deprecated and will be removed in a future version. " "'take' always returns a copy, so there is no need to specify this.", FutureWarning, stacklevel=2, ) nv.validate_take(tuple(), kwargs) self._consolidate_inplace() new_data = self._data.take( indices, axis=self._get_block_manager_axis(axis), verify=True ) return self._constructor(new_data).__finalize__(self) def _take_with_is_copy(self: FrameOrSeries, indices, axis=0) -> FrameOrSeries: """ Internal version of the `take` method that sets the `_is_copy` attribute to keep track of the parent dataframe (using in indexing for the SettingWithCopyWarning). See the docstring of `take` for full explanation of the parameters. """ result = self.take(indices=indices, axis=axis) # Maybe set copy if we didn't actually change the index. if not result._get_axis(axis).equals(self._get_axis(axis)): result._set_is_copy(self) return result def xs(self, key, axis=0, level=None, drop_level: bool_t = True): """ Return cross-section from the Series/DataFrame. This method takes a `key` argument to select data at a particular level of a MultiIndex. Parameters ---------- key : label or tuple of label Label contained in the index, or partially in a MultiIndex. axis : {0 or 'index', 1 or 'columns'}, default 0 Axis to retrieve cross-section on. level : object, defaults to first n levels (n=1 or len(key)) In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can be referred by label or position. drop_level : bool, default True If False, returns object with same levels as self. Returns ------- Series or DataFrame Cross-section from the original Series or DataFrame corresponding to the selected index levels. See Also -------- DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array. DataFrame.iloc : Purely integer-location based indexing for selection by position. Notes ----- `xs` can not be used to set values. MultiIndex Slicers is a generic way to get/set values on any level or levels. It is a superset of `xs` functionality, see :ref:`MultiIndex Slicers <advanced.mi_slicers>`. Examples -------- >>> d = {'num_legs': [4, 4, 2, 2], ... 'num_wings': [0, 0, 2, 2], ... 'class': ['mammal', 'mammal', 'mammal', 'bird'], ... 'animal': ['cat', 'dog', 'bat', 'penguin'], ... 'locomotion': ['walks', 'walks', 'flies', 'walks']} >>> df = pd.DataFrame(data=d) >>> df = df.set_index(['class', 'animal', 'locomotion']) >>> df num_legs num_wings class animal locomotion mammal cat walks 4 0 dog walks 4 0 bat flies 2 2 bird penguin walks 2 2 Get values at specified index >>> df.xs('mammal') num_legs num_wings animal locomotion cat walks 4 0 dog walks 4 0 bat flies 2 2 Get values at several indexes >>> df.xs(('mammal', 'dog')) num_legs num_wings locomotion walks 4 0 Get values at specified index and level >>> df.xs('cat', level=1) num_legs num_wings class locomotion mammal walks 4 0 Get values at several indexes and levels >>> df.xs(('bird', 'walks'), ... level=[0, 'locomotion']) num_legs num_wings animal penguin 2 2 Get values at specified column and axis >>> df.xs('num_wings', axis=1) class animal locomotion mammal cat walks 0 dog walks 0 bat flies 2 bird penguin walks 2 Name: num_wings, dtype: int64 """ axis = self._get_axis_number(axis) labels = self._get_axis(axis) if level is not None: loc, new_ax = labels.get_loc_level(key, level=level, drop_level=drop_level) # create the tuple of the indexer _indexer = [slice(None)] * self.ndim _indexer[axis] = loc indexer = tuple(_indexer) result = self.iloc[indexer] setattr(result, result._get_axis_name(axis), new_ax) return result if axis == 1: return self[key] self._consolidate_inplace() index = self.index if isinstance(index, MultiIndex): loc, new_index = self.index.get_loc_level(key, drop_level=drop_level) else: loc = self.index.get_loc(key) if isinstance(loc, np.ndarray): if loc.dtype == np.bool_: (inds,) = loc.nonzero() return self._take_with_is_copy(inds, axis=axis) else: return self._take_with_is_copy(loc, axis=axis) if not is_scalar(loc): new_index = self.index[loc] if is_scalar(loc): # In this case loc should be an integer if self.ndim == 1: # if we encounter an array-like and we only have 1 dim # that means that their are list/ndarrays inside the Series! # so just return them (GH 6394) return self._values[loc] new_values = self._data.fast_xs(loc) result = self._constructor_sliced( new_values, index=self.columns, name=self.index[loc], dtype=new_values.dtype, ) else: result = self.iloc[loc] result.index = new_index # this could be a view # but only in a single-dtyped view sliceable case result._set_is_copy(self, copy=not result._is_view) return result _xs: Callable = xs def __getitem__(self, item): raise AbstractMethodError(self) def _get_item_cache(self, item): """Return the cached item, item represents a label indexer.""" cache = self._item_cache res = cache.get(item) if res is None: values = self._data.get(item) res = self._box_item_values(item, values) cache[item] = res res._set_as_cached(item, self) # for a chain res._is_copy = self._is_copy return res def _box_item_values(self, key, values): raise AbstractMethodError(self) def _slice(self: FrameOrSeries, slobj: slice, axis=0) -> FrameOrSeries: """ Construct a slice of this container. Slicing with this method is *always* positional. """ assert isinstance(slobj, slice), type(slobj) axis = self._get_block_manager_axis(axis) result = self._constructor(self._data.get_slice(slobj, axis=axis)) result = result.__finalize__(self) # this could be a view # but only in a single-dtyped view sliceable case is_copy = axis != 0 or result._is_view result._set_is_copy(self, copy=is_copy) return result def _set_item(self, key, value) -> None: self._data.set(key, value) self._clear_item_cache() def _set_is_copy(self, ref, copy: bool_t = True) -> None: if not copy: self._is_copy = None else: assert ref is not None self._is_copy = weakref.ref(ref) def _check_is_chained_assignment_possible(self) -> bool_t: """ Check if we are a view, have a cacher, and are of mixed type. If so, then force a setitem_copy check. Should be called just near setting a value Will return a boolean if it we are a view and are cached, but a single-dtype meaning that the cacher should be updated following setting. """ if self._is_view and self._is_cached: ref = self._get_cacher() if ref is not None and ref._is_mixed_type: self._check_setitem_copy(stacklevel=4, t="referant", force=True) return True elif self._is_copy: self._check_setitem_copy(stacklevel=4, t="referant") return False def _check_setitem_copy(self, stacklevel=4, t="setting", force=False): """ Parameters ---------- stacklevel : int, default 4 the level to show of the stack when the error is output t : str, the type of setting error force : bool, default False If True, then force showing an error. validate if we are doing a setitem on a chained copy. If you call this function, be sure to set the stacklevel such that the user will see the error *at the level of setting* It is technically possible to figure out that we are setting on a copy even WITH a multi-dtyped pandas object. In other words, some blocks may be views while other are not. Currently _is_view will ALWAYS return False for multi-blocks to avoid having to handle this case. df = DataFrame(np.arange(0,9), columns=['count']) df['group'] = 'b' # This technically need not raise SettingWithCopy if both are view # (which is not # generally guaranteed but is usually True. However, # this is in general not a good practice and we recommend using .loc. df.iloc[0:5]['group'] = 'a' """ # return early if the check is not needed if not (force or self._is_copy): return value = config.get_option("mode.chained_assignment") if value is None: return # see if the copy is not actually referred; if so, then dissolve # the copy weakref if self._is_copy is not None and not isinstance(self._is_copy, str): r = self._is_copy() if not gc.get_referents(r) or r.shape == self.shape: self._is_copy = None return # a custom message if isinstance(self._is_copy, str): t = self._is_copy elif t == "referant": t = ( "\n" "A value is trying to be set on a copy of a slice from a " "DataFrame\n\n" "See the caveats in the documentation: " "https://pandas.pydata.org/pandas-docs/stable/user_guide/" "indexing.html#returning-a-view-versus-a-copy" ) else: t = ( "\n" "A value is trying to be set on a copy of a slice from a " "DataFrame.\n" "Try using .loc[row_indexer,col_indexer] = value " "instead\n\nSee the caveats in the documentation: " "https://pandas.pydata.org/pandas-docs/stable/user_guide/" "indexing.html#returning-a-view-versus-a-copy" ) if value == "raise": raise com.SettingWithCopyError(t) elif value == "warn": warnings.warn(t, com.SettingWithCopyWarning, stacklevel=stacklevel) def __delitem__(self, key) -> None: """ Delete item """ deleted = False maybe_shortcut = False if self.ndim == 2 and isinstance(self.columns, MultiIndex): try: maybe_shortcut = key not in self.columns._engine except TypeError: pass if maybe_shortcut: # Allow shorthand to delete all columns whose first len(key) # elements match key: if not isinstance(key, tuple): key = (key,) for col in self.columns: if isinstance(col, tuple) and col[: len(key)] == key: del self[col] deleted = True if not deleted: # If the above loop ran and didn't delete anything because # there was no match, this call should raise the appropriate # exception: self._data.delete(key) # delete from the caches try: del self._item_cache[key] except KeyError: pass # ---------------------------------------------------------------------- # Unsorted def get(self, key, default=None): """ Get item from object for given key (ex: DataFrame column). Returns default value if not found. Parameters ---------- key : object Returns ------- value : same type as items contained in object """ try: return self[key] except (KeyError, ValueError, IndexError): return default @property def _is_view(self) -> bool_t: """Return boolean indicating if self is view of another array """ return self._data.is_view def reindex_like( self: FrameOrSeries, other, method: Optional[str] = None, copy: bool_t = True, limit=None, tolerance=None, ) -> FrameOrSeries: """ Return an object with matching indices as other object. Conform the object to the same index on all axes. Optional filling logic, placing NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False. Parameters ---------- other : Object of the same data type Its row and column indices are used to define the new indices of this object. method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'} Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index. * None (default): don't fill gaps * pad / ffill: propagate last valid observation forward to next valid * backfill / bfill: use next valid observation to fill gap * nearest: use nearest valid observations to fill gap. copy : bool, default True Return a new object, even if the passed indexes are the same. limit : int, default None Maximum number of consecutive labels to fill for inexact matches. tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index's type. .. versionadded:: 0.21.0 (list-like tolerance) Returns ------- Series or DataFrame Same type as caller, but with changed indices on each axis. See Also -------- DataFrame.set_index : Set row labels. DataFrame.reset_index : Remove row labels or move them to new columns. DataFrame.reindex : Change to new indices or expand indices. Notes ----- Same as calling ``.reindex(index=other.index, columns=other.columns,...)``. Examples -------- >>> df1 = pd.DataFrame([[24.3, 75.7, 'high'], ... [31, 87.8, 'high'], ... [22, 71.6, 'medium'], ... [35, 95, 'medium']], ... columns=['temp_celsius', 'temp_fahrenheit', ... 'windspeed'], ... index=pd.date_range(start='2014-02-12', ... end='2014-02-15', freq='D')) >>> df1 temp_celsius temp_fahrenheit windspeed 2014-02-12 24.3 75.7 high 2014-02-13 31.0 87.8 high 2014-02-14 22.0 71.6 medium 2014-02-15 35.0 95.0 medium >>> df2 = pd.DataFrame([[28, 'low'], ... [30, 'low'], ... [35.1, 'medium']], ... columns=['temp_celsius', 'windspeed'], ... index=pd.DatetimeIndex(['2014-02-12', '2014-02-13', ... '2014-02-15'])) >>> df2 temp_celsius windspeed 2014-02-12 28.0 low 2014-02-13 30.0 low 2014-02-15 35.1 medium >>> df2.reindex_like(df1) temp_celsius temp_fahrenheit windspeed 2014-02-12 28.0 NaN low 2014-02-13 30.0 NaN low 2014-02-14 NaN NaN NaN 2014-02-15 35.1 NaN medium """ d = other._construct_axes_dict( axes=self._AXIS_ORDERS, method=method, copy=copy, limit=limit, tolerance=tolerance, ) return self.reindex(**d) def drop( self, labels=None, axis=0, index=None, columns=None, level=None, inplace: bool_t = False, errors: str = "raise", ): inplace = validate_bool_kwarg(inplace, "inplace") if labels is not None: if index is not None or columns is not None: raise ValueError("Cannot specify both 'labels' and 'index'/'columns'") axis_name = self._get_axis_name(axis) axes = {axis_name: labels} elif index is not None or columns is not None: axes, _ = self._construct_axes_from_arguments((index, columns), {}) else: raise ValueError( "Need to specify at least one of 'labels', 'index' or 'columns'" ) obj = self for axis, labels in axes.items(): if labels is not None: obj = obj._drop_axis(labels, axis, level=level, errors=errors) if inplace: self._update_inplace(obj) else: return obj def _drop_axis( self: FrameOrSeries, labels, axis, level=None, errors: str = "raise" ) -> FrameOrSeries: """ Drop labels from specified axis. Used in the ``drop`` method internally. Parameters ---------- labels : single label or list-like axis : int or axis name level : int or level name, default None For MultiIndex errors : {'ignore', 'raise'}, default 'raise' If 'ignore', suppress error and existing labels are dropped. """ axis = self._get_axis_number(axis) axis_name = self._get_axis_name(axis) axis = self._get_axis(axis) if axis.is_unique: if level is not None: if not isinstance(axis, MultiIndex): raise AssertionError("axis must be a MultiIndex") new_axis = axis.drop(labels, level=level, errors=errors) else: new_axis = axis.drop(labels, errors=errors) result = self.reindex(**{axis_name: new_axis}) # Case for non-unique axis else: labels = ensure_object(com.index_labels_to_array(labels)) if level is not None: if not isinstance(axis, MultiIndex): raise AssertionError("axis must be a MultiIndex") indexer = ~axis.get_level_values(level).isin(labels) # GH 18561 MultiIndex.drop should raise if label is absent if errors == "raise" and indexer.all(): raise KeyError(f"{labels} not found in axis") else: indexer = ~axis.isin(labels) # Check if label doesn't exist along axis labels_missing = (axis.get_indexer_for(labels) == -1).any() if errors == "raise" and labels_missing: raise KeyError(f"{labels} not found in axis") slicer = [slice(None)] * self.ndim slicer[self._get_axis_number(axis_name)] = indexer result = self.loc[tuple(slicer)] return result def _update_inplace(self, result, verify_is_copy: bool_t = True) -> None: """ Replace self internals with result. Parameters ---------- verify_is_copy : bool, default True Provide is_copy checks. """ # NOTE: This does *not* call __finalize__ and that's an explicit # decision that we may revisit in the future. self._reset_cache() self._clear_item_cache() self._data = getattr(result, "_data", result) self._maybe_update_cacher(verify_is_copy=verify_is_copy) def add_prefix(self: FrameOrSeries, prefix: str) -> FrameOrSeries: """ Prefix labels with string `prefix`. For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed. Parameters ---------- prefix : str The string to add before each label. Returns ------- Series or DataFrame New Series or DataFrame with updated labels. See Also -------- Series.add_suffix: Suffix row labels with string `suffix`. DataFrame.add_suffix: Suffix column labels with string `suffix`. Examples -------- >>> s = pd.Series([1, 2, 3, 4]) >>> s 0 1 1 2 2 3 3 4 dtype: int64 >>> s.add_prefix('item_') item_0 1 item_1 2 item_2 3 item_3 4 dtype: int64 >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]}) >>> df A B 0 1 3 1 2 4 2 3 5 3 4 6 >>> df.add_prefix('col_') col_A col_B 0 1 3 1 2 4 2 3 5 3 4 6 """ f = functools.partial("{prefix}{}".format, prefix=prefix) mapper = {self._info_axis_name: f} return self.rename(**mapper) # type: ignore def add_suffix(self: FrameOrSeries, suffix: str) -> FrameOrSeries: """ Suffix labels with string `suffix`. For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed. Parameters ---------- suffix : str The string to add after each label. Returns ------- Series or DataFrame New Series or DataFrame with updated labels. See Also -------- Series.add_prefix: Prefix row labels with string `prefix`. DataFrame.add_prefix: Prefix column labels with string `prefix`. Examples -------- >>> s = pd.Series([1, 2, 3, 4]) >>> s 0 1 1 2 2 3 3 4 dtype: int64 >>> s.add_suffix('_item') 0_item 1 1_item 2 2_item 3 3_item 4 dtype: int64 >>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]}) >>> df A B 0 1 3 1 2 4 2 3 5 3 4 6 >>> df.add_suffix('_col') A_col B_col 0 1 3 1 2 4 2 3 5 3 4 6 """ f = functools.partial("{}{suffix}".format, suffix=suffix) mapper = {self._info_axis_name: f} return self.rename(**mapper) # type: ignore def sort_values( self, axis=0, ascending=True, inplace: bool_t = False, kind: str = "quicksort", na_position: str = "last", ignore_index: bool_t = False, ): """ Sort by the values along either axis. Parameters ----------%(optional_by)s axis : %(axes_single_arg)s, default 0 Axis to be sorted. ascending : bool or list of bool, default True Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by. inplace : bool, default False If True, perform operation in-place. kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort' Choice of sorting algorithm. See also ndarray.np.sort for more information. `mergesort` is the only stable algorithm. For DataFrames, this option is only applied when sorting on a single column or label. na_position : {'first', 'last'}, default 'last' Puts NaNs at the beginning if `first`; `last` puts NaNs at the end. ignore_index : bool, default False If True, the resulting axis will be labeled 0, 1, …, n - 1. .. versionadded:: 1.0.0 Returns ------- sorted_obj : DataFrame or None DataFrame with sorted values if inplace=False, None otherwise. Examples -------- >>> df = pd.DataFrame({ ... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'], ... 'col2': [2, 1, 9, 8, 7, 4], ... 'col3': [0, 1, 9, 4, 2, 3], ... }) >>> df col1 col2 col3 0 A 2 0 1 A 1 1 2 B 9 9 3 NaN 8 4 4 D 7 2 5 C 4 3 Sort by col1 >>> df.sort_values(by=['col1']) col1 col2 col3 0 A 2 0 1 A 1 1 2 B 9 9 5 C 4 3 4 D 7 2 3 NaN 8 4 Sort by multiple columns >>> df.sort_values(by=['col1', 'col2']) col1 col2 col3 1 A 1 1 0 A 2 0 2 B 9 9 5 C 4 3 4 D 7 2 3 NaN 8 4 Sort Descending >>> df.sort_values(by='col1', ascending=False) col1 col2 col3 4 D 7 2 5 C 4 3 2 B 9 9 0 A 2 0 1 A 1 1 3 NaN 8 4 Putting NAs first >>> df.sort_values(by='col1', ascending=False, na_position='first') col1 col2 col3 3 NaN 8 4 4 D 7 2 5 C 4 3 2 B 9 9 0 A 2 0 1 A 1 1 """ raise AbstractMethodError(self) def reindex(self: FrameOrSeries, *args, **kwargs) -> FrameOrSeries: """ Conform %(klass)s to new index with optional filling logic. Places NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and ``copy=False``. Parameters ---------- %(optional_labels)s %(axes)s : array-like, optional New labels / index to conform to, should be specified using keywords. Preferably an Index object to avoid duplicating data. %(optional_axis)s method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'} Method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index. * None (default): don't fill gaps * pad / ffill: Propagate last valid observation forward to next valid. * backfill / bfill: Use next valid observation to fill gap. * nearest: Use nearest valid observations to fill gap. copy : bool, default True Return a new object, even if the passed indexes are the same. level : int or name Broadcast across a level, matching Index values on the passed MultiIndex level. fill_value : scalar, default np.NaN Value to use for missing values. Defaults to NaN, but can be any "compatible" value. limit : int, default None Maximum number of consecutive elements to forward or backward fill. tolerance : optional Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation ``abs(index[indexer] - target) <= tolerance``. Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index's type. .. versionadded:: 0.21.0 (list-like tolerance) Returns ------- %(klass)s with changed index. See Also -------- DataFrame.set_index : Set row labels. DataFrame.reset_index : Remove row labels or move them to new columns. DataFrame.reindex_like : Change to same indices as other DataFrame. Examples -------- ``DataFrame.reindex`` supports two calling conventions * ``(index=index_labels, columns=column_labels, ...)`` * ``(labels, axis={'index', 'columns'}, ...)`` We *highly* recommend using keyword arguments to clarify your intent. Create a dataframe with some fictional data. >>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror'] >>> df = pd.DataFrame({'http_status': [200, 200, 404, 404, 301], ... 'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]}, ... index=index) >>> df http_status response_time Firefox 200 0.04 Chrome 200 0.02 Safari 404 0.07 IE10 404 0.08 Konqueror 301 1.00 Create a new index and reindex the dataframe. By default values in the new index that do not have corresponding records in the dataframe are assigned ``NaN``. >>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10', ... 'Chrome'] >>> df.reindex(new_index) http_status response_time Safari 404.0 0.07 Iceweasel NaN NaN Comodo Dragon NaN NaN IE10 404.0 0.08 Chrome 200.0 0.02 We can fill in the missing values by passing a value to the keyword ``fill_value``. Because the index is not monotonically increasing or decreasing, we cannot use arguments to the keyword ``method`` to fill the ``NaN`` values. >>> df.reindex(new_index, fill_value=0) http_status response_time Safari 404 0.07 Iceweasel 0 0.00 Comodo Dragon 0 0.00 IE10 404 0.08 Chrome 200 0.02 >>> df.reindex(new_index, fill_value='missing') http_status response_time Safari 404 0.07 Iceweasel missing missing Comodo Dragon missing missing IE10 404 0.08 Chrome 200 0.02 We can also reindex the columns. >>> df.reindex(columns=['http_status', 'user_agent']) http_status user_agent Firefox 200 NaN Chrome 200 NaN Safari 404 NaN IE10 404 NaN Konqueror 301 NaN Or we can use "axis-style" keyword arguments >>> df.reindex(['http_status', 'user_agent'], axis="columns") http_status user_agent Firefox 200 NaN Chrome 200 NaN Safari 404 NaN IE10 404 NaN Konqueror 301 NaN To further illustrate the filling functionality in ``reindex``, we will create a dataframe with a monotonically increasing index (for example, a sequence of dates). >>> date_index = pd.date_range('1/1/2010', periods=6, freq='D') >>> df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]}, ... index=date_index) >>> df2 prices 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0 Suppose we decide to expand the dataframe to cover a wider date range. >>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D') >>> df2.reindex(date_index2) prices 2009-12-29 NaN 2009-12-30 NaN 2009-12-31 NaN 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0 2010-01-07 NaN The index entries that did not have a value in the original data frame (for example, '2009-12-29') are by default filled with ``NaN``. If desired, we can fill in the missing values using one of several options. For example, to back-propagate the last valid value to fill the ``NaN`` values, pass ``bfill`` as an argument to the ``method`` keyword. >>> df2.reindex(date_index2, method='bfill') prices 2009-12-29 100.0 2009-12-30 100.0 2009-12-31 100.0 2010-01-01 100.0 2010-01-02 101.0 2010-01-03 NaN 2010-01-04 100.0 2010-01-05 89.0 2010-01-06 88.0 2010-01-07 NaN Please note that the ``NaN`` value present in the original dataframe (at index value 2010-01-03) will not be filled by any of the value propagation schemes. This is because filling while reindexing does not look at dataframe values, but only compares the original and desired indexes. If you do want to fill in the ``NaN`` values present in the original dataframe, use the ``fillna()`` method. See the :ref:`user guide <basics.reindexing>` for more. """ # TODO: Decide if we care about having different examples for different # kinds # construct the args axes, kwargs = self._construct_axes_from_arguments(args, kwargs) method = missing.clean_reindex_fill_method(kwargs.pop("method", None)) level = kwargs.pop("level", None) copy = kwargs.pop("copy", True) limit = kwargs.pop("limit", None) tolerance = kwargs.pop("tolerance", None) fill_value = kwargs.pop("fill_value", None) # Series.reindex doesn't use / need the axis kwarg # We pop and ignore it here, to make writing Series/Frame generic code # easier kwargs.pop("axis", None) if kwargs: raise TypeError( "reindex() got an unexpected keyword " f'argument "{list(kwargs.keys())[0]}"' ) self._consolidate_inplace() # if all axes that are requested to reindex are equal, then only copy # if indicated must have index names equal here as well as values if all( self._get_axis(axis).identical(ax) for axis, ax in axes.items() if ax is not None ): if copy: return self.copy() return self # check if we are a multi reindex if self._needs_reindex_multi(axes, method, level): return self._reindex_multi(axes, copy, fill_value) # perform the reindex on the axes return self._reindex_axes( axes, level, limit, tolerance, method, fill_value, copy ).__finalize__(self) def _reindex_axes( self: FrameOrSeries, axes, level, limit, tolerance, method, fill_value, copy ) -> FrameOrSeries: """Perform the reindex for all the axes.""" obj = self for a in self._AXIS_ORDERS: labels = axes[a] if labels is None: continue ax = self._get_axis(a) new_index, indexer = ax.reindex( labels, level=level, limit=limit, tolerance=tolerance, method=method ) axis = self._get_axis_number(a) obj = obj._reindex_with_indexers( {axis: [new_index, indexer]}, fill_value=fill_value, copy=copy, allow_dups=False, ) return obj def _needs_reindex_multi(self, axes, method, level) -> bool_t: """Check if we do need a multi reindex.""" return ( (com.count_not_none(*axes.values()) == self._AXIS_LEN) and method is None and level is None and not self._is_mixed_type ) def _reindex_multi(self, axes, copy, fill_value): raise AbstractMethodError(self) def _reindex_with_indexers( self: FrameOrSeries, reindexers, fill_value=None, copy: bool_t = False, allow_dups: bool_t = False, ) -> FrameOrSeries: """allow_dups indicates an internal call here """ # reindex doing multiple operations on different axes if indicated new_data = self._data for axis in sorted(reindexers.keys()): index, indexer = reindexers[axis] baxis = self._get_block_manager_axis(axis) if index is None: continue index = ensure_index(index) if indexer is not None: indexer = ensure_int64(indexer) # TODO: speed up on homogeneous DataFrame objects new_data = new_data.reindex_indexer( index, indexer, axis=baxis, fill_value=fill_value, allow_dups=allow_dups, copy=copy, ) if copy and new_data is self._data: new_data = new_data.copy() return self._constructor(new_data).__finalize__(self) def filter( self: FrameOrSeries, items=None, like: Optional[str] = None, regex: Optional[str] = None, axis=None, ) -> FrameOrSeries: """ Subset the dataframe rows or columns according to the specified index labels. Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index. Parameters ---------- items : list-like Keep labels from axis which are in items. like : str Keep labels from axis for which "like in label == True". regex : str (regular expression) Keep labels from axis for which re.search(regex, label) == True. axis : {0 or ‘index’, 1 or ‘columns’, None}, default None The axis to filter on, expressed either as an index (int) or axis name (str). By default this is the info axis, 'index' for Series, 'columns' for DataFrame. Returns ------- same type as input object See Also -------- DataFrame.loc : Access a group of rows and columns by label(s) or a boolean array. Notes ----- The ``items``, ``like``, and ``regex`` parameters are enforced to be mutually exclusive. ``axis`` defaults to the info axis that is used when indexing with ``[]``. Examples -------- >>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])), ... index=['mouse', 'rabbit'], ... columns=['one', 'two', 'three']) >>> # select columns by name >>> df.filter(items=['one', 'three']) one three mouse 1 3 rabbit 4 6 >>> # select columns by regular expression >>> df.filter(regex='e$', axis=1) one three mouse 1 3 rabbit 4 6 >>> # select rows containing 'bbi' >>> df.filter(like='bbi', axis=0) one two three rabbit 4 5 6 """ nkw = com.count_not_none(items, like, regex) if nkw > 1: raise TypeError( "Keyword arguments `items`, `like`, or `regex` " "are mutually exclusive" ) if axis is None: axis = self._info_axis_name labels = self._get_axis(axis) if items is not None: name = self._get_axis_name(axis) return self.reindex(**{name: [r for r in items if r in labels]}) elif like: def f(x): return like in ensure_str(x) values = labels.map(f) return self.loc(axis=axis)[values] elif regex: def f(x): return matcher.search(ensure_str(x)) is not None matcher = re.compile(regex) values = labels.map(f) return self.loc(axis=axis)[values] else: raise TypeError("Must pass either `items`, `like`, or `regex`") def head(self: FrameOrSeries, n: int = 5) -> FrameOrSeries: """ Return the first `n` rows. This function returns the first `n` rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it. For negative values of `n`, this function returns all rows except the last `n` rows, equivalent to ``df[:-n]``. Parameters ---------- n : int, default 5 Number of rows to select. Returns ------- same type as caller The first `n` rows of the caller object. See Also -------- DataFrame.tail: Returns the last `n` rows. Examples -------- >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion', ... 'monkey', 'parrot', 'shark', 'whale', 'zebra']}) >>> df animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey 5 parrot 6 shark 7 whale 8 zebra Viewing the first 5 lines >>> df.head() animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey Viewing the first `n` lines (three in this case) >>> df.head(3) animal 0 alligator 1 bee 2 falcon For negative values of `n` >>> df.head(-3) animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey 5 parrot """ return self.iloc[:n] def tail(self: FrameOrSeries, n: int = 5) -> FrameOrSeries: """ Return the last `n` rows. This function returns last `n` rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows. For negative values of `n`, this function returns all rows except the first `n` rows, equivalent to ``df[n:]``. Parameters ---------- n : int, default 5 Number of rows to select. Returns ------- type of caller The last `n` rows of the caller object. See Also -------- DataFrame.head : The first `n` rows of the caller object. Examples -------- >>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion', ... 'monkey', 'parrot', 'shark', 'whale', 'zebra']}) >>> df animal 0 alligator 1 bee 2 falcon 3 lion 4 monkey 5 parrot 6 shark 7 whale 8 zebra Viewing the last 5 lines >>> df.tail() animal 4 monkey 5 parrot 6 shark 7 whale 8 zebra Viewing the last `n` lines (three in this case) >>> df.tail(3) animal 6 shark 7 whale 8 zebra For negative values of `n` >>> df.tail(-3) animal 3 lion 4 monkey 5 parrot 6 shark 7 whale 8 zebra """ if n == 0: return self.iloc[0:0] return self.iloc[-n:] def sample( self: FrameOrSeries, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None, ) -> FrameOrSeries: """ Return a random sample of items from an axis of object. You can use `random_state` for reproducibility. Parameters ---------- n : int, optional Number of items from axis to return. Cannot be used with `frac`. Default = 1 if `frac` = None. frac : float, optional Fraction of axis items to return. Cannot be used with `n`. replace : bool, default False Allow or disallow sampling of the same row more than once. weights : str or ndarray-like, optional Default 'None' results in equal probability weighting. If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. Infinite values not allowed. random_state : int or numpy.random.RandomState, optional Seed for the random number generator (if int), or numpy RandomState object. axis : {0 or ‘index’, 1 or ‘columns’, None}, default None Axis to sample. Accepts axis number or name. Default is stat axis for given data type (0 for Series and DataFrames). Returns ------- Series or DataFrame A new object of same type as caller containing `n` items randomly sampled from the caller object. See Also -------- numpy.random.choice: Generates a random sample from a given 1-D numpy array. Notes ----- If `frac` > 1, `replacement` should be set to `True`. Examples -------- >>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0], ... 'num_wings': [2, 0, 0, 0], ... 'num_specimen_seen': [10, 2, 1, 8]}, ... index=['falcon', 'dog', 'spider', 'fish']) >>> df num_legs num_wings num_specimen_seen falcon 2 2 10 dog 4 0 2 spider 8 0 1 fish 0 0 8 Extract 3 random elements from the ``Series`` ``df['num_legs']``: Note that we use `random_state` to ensure the reproducibility of the examples. >>> df['num_legs'].sample(n=3, random_state=1) fish 0 spider 8 falcon 2 Name: num_legs, dtype: int64 A random 50% sample of the ``DataFrame`` with replacement: >>> df.sample(frac=0.5, replace=True, random_state=1) num_legs num_wings num_specimen_seen dog 4 0 2 fish 0 0 8 An upsample sample of the ``DataFrame`` with replacement: Note that `replace` parameter has to be `True` for `frac` parameter > 1. >>> df.sample(frac=2, replace=True, random_state=1) num_legs num_wings num_specimen_seen dog 4 0 2 fish 0 0 8 falcon 2 2 10 falcon 2 2 10 fish 0 0 8 dog 4 0 2 fish 0 0 8 dog 4 0 2 Using a DataFrame column as weights. Rows with larger value in the `num_specimen_seen` column are more likely to be sampled. >>> df.sample(n=2, weights='num_specimen_seen', random_state=1) num_legs num_wings num_specimen_seen falcon 2 2 10 fish 0 0 8 """ if axis is None: axis = self._stat_axis_number axis = self._get_axis_number(axis) axis_length = self.shape[axis] # Process random_state argument rs = com.random_state(random_state) # Check weights for compliance if weights is not None: # If a series, align with frame if isinstance(weights, ABCSeries): weights = weights.reindex(self.axes[axis]) # Strings acceptable if a dataframe and axis = 0 if isinstance(weights, str): if isinstance(self, ABCDataFrame): if axis == 0: try: weights = self[weights] except KeyError as err: raise KeyError( "String passed to weights not a valid column" ) from err else: raise ValueError( "Strings can only be passed to " "weights when sampling from rows on " "a DataFrame" ) else: raise ValueError( "Strings cannot be passed as weights " "when sampling from a Series." ) weights = pd.Series(weights, dtype="float64") if len(weights) != axis_length: raise ValueError( "Weights and axis to be sampled must be of same length" ) if (weights == np.inf).any() or (weights == -np.inf).any(): raise ValueError("weight vector may not include `inf` values") if (weights < 0).any(): raise ValueError("weight vector many not include negative values") # If has nan, set to zero. weights = weights.fillna(0) # Renormalize if don't sum to 1 if weights.sum() != 1: if weights.sum() != 0: weights = weights / weights.sum() else: raise ValueError("Invalid weights: weights sum to zero") weights = weights.values # If no frac or n, default to n=1. if n is None and frac is None: n = 1 elif frac is not None and frac > 1 and not replace: raise ValueError( "Replace has to be set to `True` when " "upsampling the population `frac` > 1." ) elif n is not None and frac is None and n % 1 != 0: raise ValueError("Only integers accepted as `n` values") elif n is None and frac is not None: n = int(round(frac * axis_length)) elif n is not None and frac is not None: raise ValueError("Please enter a value for `frac` OR `n`, not both") # Check for negative sizes if n < 0: raise ValueError( "A negative number of rows requested. Please provide positive value." ) locs = rs.choice(axis_length, size=n, replace=replace, p=weights) return self.take(locs, axis=axis) _shared_docs[ "pipe" ] = r""" Apply func(self, \*args, \*\*kwargs). Parameters ---------- func : function Function to apply to the %(klass)s. ``args``, and ``kwargs`` are passed into ``func``. Alternatively a ``(callable, data_keyword)`` tuple where ``data_keyword`` is a string indicating the keyword of ``callable`` that expects the %(klass)s. args : iterable, optional Positional arguments passed into ``func``. kwargs : mapping, optional A dictionary of keyword arguments passed into ``func``. Returns ------- object : the return type of ``func``. See Also -------- DataFrame.apply : Apply a function along input axis of DataFrame. DataFrame.applymap : Apply a function elementwise on a whole DataFrame. Series.map : Apply a mapping correspondence on a :class:`~pandas.Series`. Notes ----- Use ``.pipe`` when chaining together functions that expect Series, DataFrames or GroupBy objects. Instead of writing >>> f(g(h(df), arg1=a), arg2=b, arg3=c) You can write >>> (df.pipe(h) ... .pipe(g, arg1=a) ... .pipe(f, arg2=b, arg3=c) ... ) If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose ``f`` takes its data as ``arg2``: >>> (df.pipe(h) ... .pipe(g, arg1=a) ... .pipe((f, 'arg2'), arg1=a, arg3=c) ... ) """ @Appender(_shared_docs["pipe"] % _shared_doc_kwargs) def pipe(self, func, *args, **kwargs): return com.pipe(self, func, *args, **kwargs) _shared_docs["aggregate"] = dedent( """ Aggregate using one or more operations over the specified axis. %(versionadded)s Parameters ---------- func : function, str, list or dict Function to use for aggregating the data. If a function, must either work when passed a %(klass)s or when passed to %(klass)s.apply. Accepted combinations are: - function - string function name - list of functions and/or function names, e.g. ``[np.sum, 'mean']`` - dict of axis labels -> functions, function names or list of such. %(axis)s *args Positional arguments to pass to `func`. **kwargs Keyword arguments to pass to `func`. Returns ------- scalar, Series or DataFrame The return can be: * scalar : when Series.agg is called with single function * Series : when DataFrame.agg is called with a single function * DataFrame : when DataFrame.agg is called with several functions Return scalar, Series or DataFrame. %(see_also)s Notes ----- `agg` is an alias for `aggregate`. Use the alias. A passed user-defined-function will be passed a Series for evaluation. %(examples)s""" ) _shared_docs[ "transform" ] = """ Call ``func`` on self producing a %(klass)s with transformed values. Produced %(klass)s will have same axis length as self. Parameters ---------- func : function, str, list or dict Function to use for transforming the data. If a function, must either work when passed a %(klass)s or when passed to %(klass)s.apply. Accepted combinations are: - function - string function name - list of functions and/or function names, e.g. ``[np.exp. 'sqrt']`` - dict of axis labels -> functions, function names or list of such. %(axis)s *args Positional arguments to pass to `func`. **kwargs Keyword arguments to pass to `func`. Returns ------- %(klass)s A %(klass)s that must have the same length as self. Raises ------ ValueError : If the returned %(klass)s has a different length than self. See Also -------- %(klass)s.agg : Only perform aggregating type operations. %(klass)s.apply : Invoke function on a %(klass)s. Examples -------- >>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)}) >>> df A B 0 0 1 1 1 2 2 2 3 >>> df.transform(lambda x: x + 1) A B 0 1 2 1 2 3 2 3 4 Even though the resulting %(klass)s must have the same length as the input %(klass)s, it is possible to provide several input functions: >>> s = pd.Series(range(3)) >>> s 0 0 1 1 2 2 dtype: int64 >>> s.transform([np.sqrt, np.exp]) sqrt exp 0 0.000000 1.000000 1 1.000000 2.718282 2 1.414214 7.389056 """ # ---------------------------------------------------------------------- # Attribute access def __finalize__( self: FrameOrSeries, other, method=None, **kwargs ) -> FrameOrSeries: """ Propagate metadata from other to self. Parameters ---------- other : the object from which to get the attributes that we are going to propagate method : optional, a passed method name ; possibly to take different types of propagation actions based on this """ if isinstance(other, NDFrame): for name in other.attrs: self.attrs[name] = other.attrs[name] # For subclasses using _metadata. for name in self._metadata: assert isinstance(name, str) object.__setattr__(self, name, getattr(other, name, None)) return self def __getattr__(self, name: str): """ After regular attribute access, try looking up the name This allows simpler access to columns for interactive use. """ # Note: obj.x will always call obj.__getattribute__('x') prior to # calling obj.__getattr__('x'). if ( name in self._internal_names_set or name in self._metadata or name in self._accessors ): return object.__getattribute__(self, name) else: if self._info_axis._can_hold_identifiers_and_holds_name(name): return self[name] return object.__getattribute__(self, name) def __setattr__(self, name: str, value) -> None: """ After regular attribute access, try setting the name This allows simpler access to columns for interactive use. """ # first try regular attribute access via __getattribute__, so that # e.g. ``obj.x`` and ``obj.x = 4`` will always reference/modify # the same attribute. try: object.__getattribute__(self, name) return object.__setattr__(self, name, value) except AttributeError: pass # if this fails, go on to more involved attribute setting # (note that this matches __getattr__, above). if name in self._internal_names_set: object.__setattr__(self, name, value) elif name in self._metadata: object.__setattr__(self, name, value) else: try: existing = getattr(self, name) if isinstance(existing, Index): object.__setattr__(self, name, value) elif name in self._info_axis: self[name] = value else: object.__setattr__(self, name, value) except (AttributeError, TypeError): if isinstance(self, ABCDataFrame) and (is_list_like(value)): warnings.warn( "Pandas doesn't allow columns to be " "created via a new attribute name - see " "https://pandas.pydata.org/pandas-docs/" "stable/indexing.html#attribute-access", stacklevel=2, ) object.__setattr__(self, name, value) def _dir_additions(self): """ add the string-like attributes from the info_axis. If info_axis is a MultiIndex, it's first level values are used. """ additions = { c for c in self._info_axis.unique(level=0)[:100] if isinstance(c, str) and c.isidentifier() } return super()._dir_additions().union(additions) # ---------------------------------------------------------------------- # Consolidation of internals def _protect_consolidate(self, f): """ Consolidate _data -- if the blocks have changed, then clear the cache """ blocks_before = len(self._data.blocks) result = f() if len(self._data.blocks) != blocks_before: self._clear_item_cache() return result def _consolidate_inplace(self) -> None: """Consolidate data in place and return None""" def f(): self._data = self._data.consolidate() self._protect_consolidate(f) def _consolidate(self, inplace: bool_t = False): """ Compute NDFrame with "consolidated" internals (data of each dtype grouped together in a single ndarray). Parameters ---------- inplace : bool, default False If False return new object, otherwise modify existing object. Returns ------- consolidated : same type as caller """ inplace = validate_bool_kwarg(inplace, "inplace") if inplace: self._consolidate_inplace() else: f = lambda: self._data.consolidate() cons_data = self._protect_consolidate(f) return self._constructor(cons_data).__finalize__(self) @property def _is_mixed_type(self) -> bool_t: f = lambda: self._data.is_mixed_type return self._protect_consolidate(f) @property def _is_numeric_mixed_type(self) -> bool_t: f = lambda: self._data.is_numeric_mixed_type return self._protect_consolidate(f) def _check_inplace_setting(self, value) -> bool_t: """ check whether we allow in-place setting with this type of value """ if self._is_mixed_type: if not self._is_numeric_mixed_type: # allow an actual np.nan thru if is_float(value) and np.isnan(value): return True raise TypeError( "Cannot do inplace boolean setting on " "mixed-types with a non np.nan value" ) return True def _get_numeric_data(self): return self._constructor(self._data.get_numeric_data()).__finalize__(self) def _get_bool_data(self): return self._constructor(self._data.get_bool_data()).__finalize__(self) # ---------------------------------------------------------------------- # Internal Interface Methods @property def values(self) -> np.ndarray: """ Return a Numpy representation of the DataFrame. .. warning:: We recommend using :meth:`DataFrame.to_numpy` instead. Only the values in the DataFrame will be returned, the axes labels will be removed. Returns ------- numpy.ndarray The values of the DataFrame. See Also -------- DataFrame.to_numpy : Recommended alternative to this method. DataFrame.index : Retrieve the index labels. DataFrame.columns : Retrieving the column names. Notes ----- The dtype will be a lower-common-denominator dtype (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. Use this with care if you are not dealing with the blocks. e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8, dtype will be upcast to int32. By :func:`numpy.find_common_type` convention, mixing int64 and uint64 will result in a float64 dtype. Examples -------- A DataFrame where all columns are the same type (e.g., int64) results in an array of the same type. >>> df = pd.DataFrame({'age': [ 3, 29], ... 'height': [94, 170], ... 'weight': [31, 115]}) >>> df age height weight 0 3 94 31 1 29 170 115 >>> df.dtypes age int64 height int64 weight int64 dtype: object >>> df.values array([[ 3, 94, 31], [ 29, 170, 115]], dtype=int64) A DataFrame with mixed type columns(e.g., str/object, int64, float32) results in an ndarray of the broadest type that accommodates these mixed types (e.g., object). >>> df2 = pd.DataFrame([('parrot', 24.0, 'second'), ... ('lion', 80.5, 1), ... ('monkey', np.nan, None)], ... columns=('name', 'max_speed', 'rank')) >>> df2.dtypes name object max_speed float64 rank object dtype: object >>> df2.values array([['parrot', 24.0, 'second'], ['lion', 80.5, 1], ['monkey', nan, None]], dtype=object) """ self._consolidate_inplace() return self._data.as_array(transpose=self._AXIS_REVERSED) @property def _values(self) -> np.ndarray: """internal implementation""" return self.values def _internal_get_values(self) -> np.ndarray: """ Return an ndarray after converting sparse values to dense. This is the same as ``.values`` for non-sparse data. For sparse data contained in a `SparseArray`, the data are first converted to a dense representation. Returns ------- numpy.ndarray Numpy representation of DataFrame. See Also -------- values : Numpy representation of DataFrame. SparseArray : Container for sparse data. """ return self.values @property def dtypes(self): """ Return the dtypes in the DataFrame. This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types are stored with the ``object`` dtype. See :ref:`the User Guide <basics.dtypes>` for more. Returns ------- pandas.Series The data type of each column. Examples -------- >>> df = pd.DataFrame({'float': [1.0], ... 'int': [1], ... 'datetime': [pd.Timestamp('20180310')], ... 'string': ['foo']}) >>> df.dtypes float float64 int int64 datetime datetime64[ns] string object dtype: object """ from pandas import Series return Series(self._data.get_dtypes(), index=self._info_axis, dtype=np.object_) def _to_dict_of_blocks(self, copy: bool_t = True): """ Return a dict of dtype -> Constructor Types that each is a homogeneous dtype. Internal ONLY """ return { k: self._constructor(v).__finalize__(self) for k, v, in self._data.to_dict(copy=copy).items() } def astype( self: FrameOrSeries, dtype, copy: bool_t = True, errors: str = "raise" ) -> FrameOrSeries: """ Cast a pandas object to a specified dtype ``dtype``. Parameters ---------- dtype : data type, or dict of column name -> data type Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, ...}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame's columns to column-specific types. copy : bool, default True Return a copy when ``copy=True`` (be very careful setting ``copy=False`` as changes to values then may propagate to other pandas objects). errors : {'raise', 'ignore'}, default 'raise' Control raising of exceptions on invalid data for provided dtype. - ``raise`` : allow exceptions to be raised - ``ignore`` : suppress exceptions. On error return original object. Returns ------- casted : same type as caller See Also -------- to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type. numpy.ndarray.astype : Cast a numpy array to a specified type. Examples -------- Create a DataFrame: >>> d = {'col1': [1, 2], 'col2': [3, 4]} >>> df = pd.DataFrame(data=d) >>> df.dtypes col1 int64 col2 int64 dtype: object Cast all columns to int32: >>> df.astype('int32').dtypes col1 int32 col2 int32 dtype: object Cast col1 to int32 using a dictionary: >>> df.astype({'col1': 'int32'}).dtypes col1 int32 col2 int64 dtype: object Create a series: >>> ser = pd.Series([1, 2], dtype='int32') >>> ser 0 1 1 2 dtype: int32 >>> ser.astype('int64') 0 1 1 2 dtype: int64 Convert to categorical type: >>> ser.astype('category') 0 1 1 2 dtype: category Categories (2, int64): [1, 2] Convert to ordered categorical type with custom ordering: >>> cat_dtype = pd.api.types.CategoricalDtype( ... categories=[2, 1], ordered=True) >>> ser.astype(cat_dtype) 0 1 1 2 dtype: category Categories (2, int64): [2 < 1] Note that using ``copy=False`` and changing data on a new pandas object may propagate changes: >>> s1 = pd.Series([1, 2]) >>> s2 = s1.astype('int64', copy=False) >>> s2[0] = 10 >>> s1 # note that s1[0] has changed too 0 10 1 2 dtype: int64 """ if is_dict_like(dtype): if self.ndim == 1: # i.e. Series if len(dtype) > 1 or self.name not in dtype: raise KeyError( "Only the Series name can be used for " "the key in Series dtype mappings." ) new_type = dtype[self.name] return self.astype(new_type, copy, errors) for col_name in dtype.keys(): if col_name not in self: raise KeyError( "Only a column name can be used for the " "key in a dtype mappings argument." ) results = [] for col_name, col in self.items(): if col_name in dtype: results.append( col.astype(dtype=dtype[col_name], copy=copy, errors=errors) ) else: results.append(col.copy() if copy else col) elif is_extension_array_dtype(dtype) and self.ndim > 1: # GH 18099/22869: columnwise conversion to extension dtype # GH 24704: use iloc to handle duplicate column names results = [ self.iloc[:, i].astype(dtype, copy=copy) for i in range(len(self.columns)) ] else: # else, only a single dtype is given new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors) return self._constructor(new_data).__finalize__(self) # GH 19920: retain column metadata after concat result = pd.concat(results, axis=1, copy=False) result.columns = self.columns return result def copy(self: FrameOrSeries, deep: bool_t = True) -> FrameOrSeries: """ Make a copy of this object's indices and data. When ``deep=True`` (default), a new object will be created with a copy of the calling object's data and indices. Modifications to the data or indices of the copy will not be reflected in the original object (see notes below). When ``deep=False``, a new object will be created without copying the calling object's data or index (only references to the data and index are copied). Any changes to the data of the original will be reflected in the shallow copy (and vice versa). Parameters ---------- deep : bool, default True Make a deep copy, including a copy of the data and the indices. With ``deep=False`` neither the indices nor the data are copied. Returns ------- copy : Series or DataFrame Object type matches caller. Notes ----- When ``deep=True``, data is copied but actual Python objects will not be copied recursively, only the reference to the object. This is in contrast to `copy.deepcopy` in the Standard Library, which recursively copies object data (see examples below). While ``Index`` objects are copied when ``deep=True``, the underlying numpy array is not copied for performance reasons. Since ``Index`` is immutable, the underlying data can be safely shared and a copy is not needed. Examples -------- >>> s = pd.Series([1, 2], index=["a", "b"]) >>> s a 1 b 2 dtype: int64 >>> s_copy = s.copy() >>> s_copy a 1 b 2 dtype: int64 **Shallow copy versus default (deep) copy:** >>> s = pd.Series([1, 2], index=["a", "b"]) >>> deep = s.copy() >>> shallow = s.copy(deep=False) Shallow copy shares data and index with original. >>> s is shallow False >>> s.values is shallow.values and s.index is shallow.index True Deep copy has own copy of data and index. >>> s is deep False >>> s.values is deep.values or s.index is deep.index False Updates to the data shared by shallow copy and original is reflected in both; deep copy remains unchanged. >>> s[0] = 3 >>> shallow[1] = 4 >>> s a 3 b 4 dtype: int64 >>> shallow a 3 b 4 dtype: int64 >>> deep a 1 b 2 dtype: int64 Note that when copying an object containing Python objects, a deep copy will copy the data, but will not do so recursively. Updating a nested data object will be reflected in the deep copy. >>> s = pd.Series([[1, 2], [3, 4]]) >>> deep = s.copy() >>> s[0][0] = 10 >>> s 0 [10, 2] 1 [3, 4] dtype: object >>> deep 0 [10, 2] 1 [3, 4] dtype: object """ data = self._data.copy(deep=deep) return self._constructor(data).__finalize__(self) def __copy__(self: FrameOrSeries, deep: bool_t = True) -> FrameOrSeries: return self.copy(deep=deep) def __deepcopy__(self: FrameOrSeries, memo=None) -> FrameOrSeries: """ Parameters ---------- memo, default None Standard signature. Unused """ return self.copy(deep=True) def _convert( self: FrameOrSeries, datetime: bool_t = False, numeric: bool_t = False, timedelta: bool_t = False, coerce: bool_t = False, copy: bool_t = True, ) -> FrameOrSeries: """ Attempt to infer better dtype for object columns Parameters ---------- datetime : bool, default False If True, convert to date where possible. numeric : bool, default False If True, attempt to convert to numbers (including strings), with unconvertible values becoming NaN. timedelta : bool, default False If True, convert to timedelta where possible. coerce : bool, default False If True, force conversion with unconvertible values converted to nulls (NaN or NaT). copy : bool, default True If True, return a copy even if no copy is necessary (e.g. no conversion was done). Note: This is meant for internal use, and should not be confused with inplace. Returns ------- converted : same as input object """ validate_bool_kwarg(datetime, "datetime") validate_bool_kwarg(numeric, "numeric") validate_bool_kwarg(timedelta, "timedelta") validate_bool_kwarg(coerce, "coerce") validate_bool_kwarg(copy, "copy") return self._constructor( self._data.convert( datetime=datetime, numeric=numeric, timedelta=timedelta, coerce=coerce, copy=copy, ) ).__finalize__(self) def infer_objects(self: FrameOrSeries) -> FrameOrSeries: """ Attempt to infer better dtypes for object columns. Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction. .. versionadded:: 0.21.0 Returns ------- converted : same type as input object See Also -------- to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to numeric type. convert_dtypes : Convert argument to best possible dtype. Examples -------- >>> df = pd.DataFrame({"A": ["a", 1, 2, 3]}) >>> df = df.iloc[1:] >>> df A 1 1 2 2 3 3 >>> df.dtypes A object dtype: object >>> df.infer_objects().dtypes A int64 dtype: object """ # numeric=False necessary to only soft convert; # python objects will still be converted to # native numpy numeric types return self._constructor( self._data.convert( datetime=True, numeric=False, timedelta=True, coerce=False, copy=True ) ).__finalize__(self) def convert_dtypes( self: FrameOrSeries, infer_objects: bool_t = True, convert_string: bool_t = True, convert_integer: bool_t = True, convert_boolean: bool_t = True, ) -> FrameOrSeries: """ Convert columns to best possible dtypes using dtypes supporting ``pd.NA``. .. versionadded:: 1.0.0 Parameters ---------- infer_objects : bool, default True Whether object dtypes should be converted to the best possible types. convert_string : bool, default True Whether object dtypes should be converted to ``StringDtype()``. convert_integer : bool, default True Whether, if possible, conversion can be done to integer extension types. convert_boolean : bool, defaults True Whether object dtypes should be converted to ``BooleanDtypes()``. Returns ------- Series or DataFrame Copy of input object with new dtype. See Also -------- infer_objects : Infer dtypes of objects. to_datetime : Convert argument to datetime. to_timedelta : Convert argument to timedelta. to_numeric : Convert argument to a numeric type. Notes ----- By default, ``convert_dtypes`` will attempt to convert a Series (or each Series in a DataFrame) to dtypes that support ``pd.NA``. By using the options ``convert_string``, ``convert_integer``, and ``convert_boolean``, it is possible to turn off individual conversions to ``StringDtype``, the integer extension types or ``BooleanDtype``, respectively. For object-dtyped columns, if ``infer_objects`` is ``True``, use the inference rules as during normal Series/DataFrame construction. Then, if possible, convert to ``StringDtype``, ``BooleanDtype`` or an appropriate integer extension type, otherwise leave as ``object``. If the dtype is integer, convert to an appropriate integer extension type. If the dtype is numeric, and consists of all integers, convert to an appropriate integer extension type. In the future, as new dtypes are added that support ``pd.NA``, the results of this method will change to support those new dtypes. Examples -------- >>> df = pd.DataFrame( ... { ... "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")), ... "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")), ... "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")), ... "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")), ... "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")), ... "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")), ... } ... ) Start with a DataFrame with default dtypes. >>> df a b c d e f 0 1 x True h 10.0 NaN 1 2 y False i NaN 100.5 2 3 z NaN NaN 20.0 200.0 >>> df.dtypes a int32 b object c object d object e float64 f float64 dtype: object Convert the DataFrame to use best possible dtypes. >>> dfn = df.convert_dtypes() >>> dfn a b c d e f 0 1 x True h 10 NaN 1 2 y False i <NA> 100.5 2 3 z <NA> <NA> 20 200.0 >>> dfn.dtypes a Int32 b string c boolean d string e Int64 f float64 dtype: object Start with a Series of strings and missing data represented by ``np.nan``. >>> s = pd.Series(["a", "b", np.nan]) >>> s 0 a 1 b 2 NaN dtype: object Obtain a Series with dtype ``StringDtype``. >>> s.convert_dtypes() 0 a 1 b 2 <NA> dtype: string """ if self.ndim == 1: return self._convert_dtypes( infer_objects, convert_string, convert_integer, convert_boolean ) else: results = [ col._convert_dtypes( infer_objects, convert_string, convert_integer, convert_boolean ) for col_name, col in self.items() ] result = pd.concat(results, axis=1, copy=False) return result # ---------------------------------------------------------------------- # Filling NA's @doc(**_shared_doc_kwargs) def fillna( self: FrameOrSeries, value=None, method=None, axis=None, inplace: bool_t = False, limit=None, downcast=None, ) -> Optional[FrameOrSeries]: """ Fill NA/NaN values using the specified method. Parameters ---------- value : scalar, dict, Series, or DataFrame Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list. method : {{'backfill', 'bfill', 'pad', 'ffill', None}}, default None Method to use for filling holes in reindexed Series pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use next valid observation to fill gap. axis : {axes_single_arg} Axis along which to fill missing values. inplace : bool, default False If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame). limit : int, default None If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None. downcast : dict, default is None A dict of item->dtype of what to downcast if possible, or the string 'infer' which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible). Returns ------- {klass} or None Object with missing values filled or None if ``inplace=True``. See Also -------- interpolate : Fill NaN values using interpolation. reindex : Conform object to new index. asfreq : Convert TimeSeries to specified frequency. Examples -------- >>> df = pd.DataFrame([[np.nan, 2, np.nan, 0], ... [3, 4, np.nan, 1], ... [np.nan, np.nan, np.nan, 5], ... [np.nan, 3, np.nan, 4]], ... columns=list('ABCD')) >>> df A B C D 0 NaN 2.0 NaN 0 1 3.0 4.0 NaN 1 2 NaN NaN NaN 5 3 NaN 3.0 NaN 4 Replace all NaN elements with 0s. >>> df.fillna(0) A B C D 0 0.0 2.0 0.0 0 1 3.0 4.0 0.0 1 2 0.0 0.0 0.0 5 3 0.0 3.0 0.0 4 We can also propagate non-null values forward or backward. >>> df.fillna(method='ffill') A B C D 0 NaN 2.0 NaN 0 1 3.0 4.0 NaN 1 2 3.0 4.0 NaN 5 3 3.0 3.0 NaN 4 Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1, 2, and 3 respectively. >>> values = {{'A': 0, 'B': 1, 'C': 2, 'D': 3}} >>> df.fillna(value=values) A B C D 0 0.0 2.0 2.0 0 1 3.0 4.0 2.0 1 2 0.0 1.0 2.0 5 3 0.0 3.0 2.0 4 Only replace the first NaN element. >>> df.fillna(value=values, limit=1) A B C D 0 0.0 2.0 2.0 0 1 3.0 4.0 NaN 1 2 NaN 1.0 NaN 5 3 NaN 3.0 NaN 4 """ inplace = validate_bool_kwarg(inplace, "inplace") value, method = validate_fillna_kwargs(value, method) self._consolidate_inplace() # set the default here, so functions examining the signaure # can detect if something was set (e.g. in groupby) (GH9221) if axis is None: axis = 0 axis = self._get_axis_number(axis) if value is None: if self._is_mixed_type and axis == 1: if inplace: raise NotImplementedError() result = self.T.fillna(method=method, limit=limit).T # need to downcast here because of all of the transposes result._data = result._data.downcast() return result new_data = self._data.interpolate( method=method, axis=axis, limit=limit, inplace=inplace, coerce=True, downcast=downcast, ) else: if len(self._get_axis(axis)) == 0: return self if self.ndim == 1: if isinstance(value, (dict, ABCSeries)): value = create_series_with_explicit_dtype( value, dtype_if_empty=object ) elif not is_list_like(value): pass else: raise TypeError( '"value" parameter must be a scalar, dict ' "or Series, but you passed a " f'"{type(value).__name__}"' ) new_data = self._data.fillna( value=value, limit=limit, inplace=inplace, downcast=downcast ) elif isinstance(value, (dict, ABCSeries)): if axis == 1: raise NotImplementedError( "Currently only can fill " "with dict/Series column " "by column" ) result = self if inplace else self.copy() for k, v in value.items(): if k not in result: continue obj = result[k] obj.fillna(v, limit=limit, inplace=True, downcast=downcast) return result if not inplace else None elif not is_list_like(value): new_data = self._data.fillna( value=value, limit=limit, inplace=inplace, downcast=downcast ) elif isinstance(value, ABCDataFrame) and self.ndim == 2: new_data = self.where(self.notna(), value) else: raise ValueError(f"invalid fill value with a {type(value)}") if inplace: self._update_inplace(new_data) return None else: return self._constructor(new_data).__finalize__(self) def ffill( self: FrameOrSeries, axis=None, inplace: bool_t = False, limit=None, downcast=None, ) -> Optional[FrameOrSeries]: """ Synonym for :meth:`DataFrame.fillna` with ``method='ffill'``. Returns ------- %(klass)s or None Object with missing values filled or None if ``inplace=True``. """ return self.fillna( method="ffill", axis=axis, inplace=inplace, limit=limit, downcast=downcast ) def bfill( self: FrameOrSeries, axis=None, inplace: bool_t = False, limit=None, downcast=None, ) -> Optional[FrameOrSeries]: """ Synonym for :meth:`DataFrame.fillna` with ``method='bfill'``. Returns ------- %(klass)s or None Object with missing values filled or None if ``inplace=True``. """ return self.fillna( method="bfill", axis=axis, inplace=inplace, limit=limit, downcast=downcast ) _shared_docs[ "replace" ] = """ Replace values given in `to_replace` with `value`. Values of the %(klass)s are replaced with other values dynamically. This differs from updating with ``.loc`` or ``.iloc``, which require you to specify a location to update with some value. Parameters ---------- to_replace : str, regex, list, dict, Series, int, float, or None How to find the values that will be replaced. * numeric, str or regex: - numeric: numeric values equal to `to_replace` will be replaced with `value` - str: string exactly matching `to_replace` will be replaced with `value` - regex: regexs matching `to_replace` will be replaced with `value` * list of str, regex, or numeric: - First, if `to_replace` and `value` are both lists, they **must** be the same length. - Second, if ``regex=True`` then all of the strings in **both** lists will be interpreted as regexs otherwise they will match directly. This doesn't matter much for `value` since there are only a few possible substitution regexes you can use. - str, regex and numeric rules apply as above. * dict: - Dicts can be used to specify different replacement values for different existing values. For example, ``{'a': 'b', 'y': 'z'}`` replaces the value 'a' with 'b' and 'y' with 'z'. To use a dict in this way the `value` parameter should be `None`. - For a DataFrame a dict can specify that different values should be replaced in different columns. For example, ``{'a': 1, 'b': 'z'}`` looks for the value 1 in column 'a' and the value 'z' in column 'b' and replaces these values with whatever is specified in `value`. The `value` parameter should not be ``None`` in this case. You can treat this as a special case of passing two lists except that you are specifying the column to search in. - For a DataFrame nested dictionaries, e.g., ``{'a': {'b': np.nan}}``, are read as follows: look in column 'a' for the value 'b' and replace it with NaN. The `value` parameter should be ``None`` to use a nested dict in this way. You can nest regular expressions as well. Note that column names (the top-level dictionary keys in a nested dictionary) **cannot** be regular expressions. * None: - This means that the `regex` argument must be a string, compiled regular expression, or list, dict, ndarray or Series of such elements. If `value` is also ``None`` then this **must** be a nested dictionary or Series. See the examples section for examples of each of these. value : scalar, dict, list, str, regex, default None Value to replace any values matching `to_replace` with. For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed. inplace : bool, default False If True, in place. Note: this will modify any other views on this object (e.g. a column from a DataFrame). Returns the caller if this is True. limit : int, default None Maximum size gap to forward or backward fill. regex : bool or same types as `to_replace`, default False Whether to interpret `to_replace` and/or `value` as regular expressions. If this is ``True`` then `to_replace` *must* be a string. Alternatively, this could be a regular expression or a list, dict, or array of regular expressions in which case `to_replace` must be ``None``. method : {'pad', 'ffill', 'bfill', `None`} The method to use when for replacement, when `to_replace` is a scalar, list or tuple and `value` is ``None``. .. versionchanged:: 0.23.0 Added to DataFrame. Returns ------- %(klass)s Object after replacement. Raises ------ AssertionError * If `regex` is not a ``bool`` and `to_replace` is not ``None``. TypeError * If `to_replace` is not a scalar, array-like, ``dict``, or ``None`` * If `to_replace` is a ``dict`` and `value` is not a ``list``, ``dict``, ``ndarray``, or ``Series`` * If `to_replace` is ``None`` and `regex` is not compilable into a regular expression or is a list, dict, ndarray, or Series. * When replacing multiple ``bool`` or ``datetime64`` objects and the arguments to `to_replace` does not match the type of the value being replaced ValueError * If a ``list`` or an ``ndarray`` is passed to `to_replace` and `value` but they are not the same length. See Also -------- %(klass)s.fillna : Fill NA values. %(klass)s.where : Replace values based on boolean condition. Series.str.replace : Simple string replacement. Notes ----- * Regex substitution is performed under the hood with ``re.sub``. The rules for substitution for ``re.sub`` are the same. * Regular expressions will only substitute on strings, meaning you cannot provide, for example, a regular expression matching floating point numbers and expect the columns in your frame that have a numeric dtype to be matched. However, if those floating point numbers *are* strings, then you can do this. * This method has *a lot* of options. You are encouraged to experiment and play with this method to gain intuition about how it works. * When dict is used as the `to_replace` value, it is like key(s) in the dict are the to_replace part and value(s) in the dict are the value parameter. Examples -------- **Scalar `to_replace` and `value`** >>> s = pd.Series([0, 1, 2, 3, 4]) >>> s.replace(0, 5) 0 5 1 1 2 2 3 3 4 4 dtype: int64 >>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4], ... 'B': [5, 6, 7, 8, 9], ... 'C': ['a', 'b', 'c', 'd', 'e']}) >>> df.replace(0, 5) A B C 0 5 5 a 1 1 6 b 2 2 7 c 3 3 8 d 4 4 9 e **List-like `to_replace`** >>> df.replace([0, 1, 2, 3], 4) A B C 0 4 5 a 1 4 6 b 2 4 7 c 3 4 8 d 4 4 9 e >>> df.replace([0, 1, 2, 3], [4, 3, 2, 1]) A B C 0 4 5 a 1 3 6 b 2 2 7 c 3 1 8 d 4 4 9 e >>> s.replace([1, 2], method='bfill') 0 0 1 3 2 3 3 3 4 4 dtype: int64 **dict-like `to_replace`** >>> df.replace({0: 10, 1: 100}) A B C 0 10 5 a 1 100 6 b 2 2 7 c 3 3 8 d 4 4 9 e >>> df.replace({'A': 0, 'B': 5}, 100) A B C 0 100 100 a 1 1 6 b 2 2 7 c 3 3 8 d 4 4 9 e >>> df.replace({'A': {0: 100, 4: 400}}) A B C 0 100 5 a 1 1 6 b 2 2 7 c 3 3 8 d 4 400 9 e **Regular expression `to_replace`** >>> df = pd.DataFrame({'A': ['bat', 'foo', 'bait'], ... 'B': ['abc', 'bar', 'xyz']}) >>> df.replace(to_replace=r'^ba.$', value='new', regex=True) A B 0 new abc 1 foo new 2 bait xyz >>> df.replace({'A': r'^ba.$'}, {'A': 'new'}, regex=True) A B 0 new abc 1 foo bar 2 bait xyz >>> df.replace(regex=r'^ba.$', value='new') A B 0 new abc 1 foo new 2 bait xyz >>> df.replace(regex={r'^ba.$': 'new', 'foo': 'xyz'}) A B 0 new abc 1 xyz new 2 bait xyz >>> df.replace(regex=[r'^ba.$', 'foo'], value='new') A B 0 new abc 1 new new 2 bait xyz Note that when replacing multiple ``bool`` or ``datetime64`` objects, the data types in the `to_replace` parameter must match the data type of the value being replaced: >>> df = pd.DataFrame({'A': [True, False, True], ... 'B': [False, True, False]}) >>> df.replace({'a string': 'new value', True: False}) # raises Traceback (most recent call last): ... TypeError: Cannot compare types 'ndarray(dtype=bool)' and 'str' This raises a ``TypeError`` because one of the ``dict`` keys is not of the correct type for replacement. Compare the behavior of ``s.replace({'a': None})`` and ``s.replace('a', None)`` to understand the peculiarities of the `to_replace` parameter: >>> s = pd.Series([10, 'a', 'a', 'b', 'a']) When one uses a dict as the `to_replace` value, it is like the value(s) in the dict are equal to the `value` parameter. ``s.replace({'a': None})`` is equivalent to ``s.replace(to_replace={'a': None}, value=None, method=None)``: >>> s.replace({'a': None}) 0 10 1 None 2 None 3 b 4 None dtype: object When ``value=None`` and `to_replace` is a scalar, list or tuple, `replace` uses the method parameter (default 'pad') to do the replacement. So this is why the 'a' values are being replaced by 10 in rows 1 and 2 and 'b' in row 4 in this case. The command ``s.replace('a', None)`` is actually equivalent to ``s.replace(to_replace='a', value=None, method='pad')``: >>> s.replace('a', None) 0 10 1 10 2 10 3 b 4 b dtype: object """ @Appender(_shared_docs["replace"] % _shared_doc_kwargs) def replace( self, to_replace=None, value=None, inplace=False, limit=None, regex=False, method="pad", ): if not ( is_scalar(to_replace) or isinstance(to_replace, pd.Series) or is_re_compilable(to_replace) or is_list_like(to_replace) ): raise TypeError( "Expecting 'to_replace' to be either a scalar, array-like, " "dict or None, got invalid type " f"{repr(type(to_replace).__name__)}" ) inplace = validate_bool_kwarg(inplace, "inplace") if not is_bool(regex) and to_replace is not None: raise AssertionError("'to_replace' must be 'None' if 'regex' is not a bool") self._consolidate_inplace() if value is None: # passing a single value that is scalar like # when value is None (GH5319), for compat if not is_dict_like(to_replace) and not is_dict_like(regex): to_replace = [to_replace] if isinstance(to_replace, (tuple, list)): if isinstance(self, ABCDataFrame): return self.apply( _single_replace, args=(to_replace, method, inplace, limit) ) return _single_replace(self, to_replace, method, inplace, limit) if not is_dict_like(to_replace): if not is_dict_like(regex): raise TypeError( 'If "to_replace" and "value" are both None ' 'and "to_replace" is not a list, then ' "regex must be a mapping" ) to_replace = regex regex = True items = list(to_replace.items()) keys, values = zip(*items) if items else ([], []) are_mappings = [is_dict_like(v) for v in values] if any(are_mappings): if not all(are_mappings): raise TypeError( "If a nested mapping is passed, all values " "of the top level mapping must be mappings" ) # passed a nested dict/Series to_rep_dict = {} value_dict = {} for k, v in items: keys, values = list(zip(*v.items())) or ([], []) to_rep_dict[k] = list(keys) value_dict[k] = list(values) to_replace, value = to_rep_dict, value_dict else: to_replace, value = keys, values return self.replace( to_replace, value, inplace=inplace, limit=limit, regex=regex ) else: # need a non-zero len on all axes if not self.size: return self new_data = self._data if is_dict_like(to_replace): if is_dict_like(value): # {'A' : NA} -> {'A' : 0} res = self if inplace else self.copy() for c, src in to_replace.items(): if c in value and c in self: # object conversion is handled in # series.replace which is called recursively res[c] = res[c].replace( to_replace=src, value=value[c], inplace=False, regex=regex, ) return None if inplace else res # {'A': NA} -> 0 elif not is_list_like(value): keys = [(k, src) for k, src in to_replace.items() if k in self] keys_len = len(keys) - 1 for i, (k, src) in enumerate(keys): convert = i == keys_len new_data = new_data.replace( to_replace=src, value=value, filter=[k], inplace=inplace, regex=regex, convert=convert, ) else: raise TypeError("value argument must be scalar, dict, or Series") elif is_list_like(to_replace): # [NA, ''] -> [0, 'missing'] if is_list_like(value): if len(to_replace) != len(value): raise ValueError( f"Replacement lists must match in length. " f"Expecting {len(to_replace)} got {len(value)} " ) new_data = self._data.replace_list( src_list=to_replace, dest_list=value, inplace=inplace, regex=regex, ) else: # [NA, ''] -> 0 new_data = self._data.replace( to_replace=to_replace, value=value, inplace=inplace, regex=regex ) elif to_replace is None: if not ( is_re_compilable(regex) or is_list_like(regex) or is_dict_like(regex) ): raise TypeError( f"'regex' must be a string or a compiled regular expression " f"or a list or dict of strings or regular expressions, " f"you passed a {repr(type(regex).__name__)}" ) return self.replace( regex, value, inplace=inplace, limit=limit, regex=True ) else: # dest iterable dict-like if is_dict_like(value): # NA -> {'A' : 0, 'B' : -1} new_data = self._data for k, v in value.items(): if k in self: new_data = new_data.replace( to_replace=to_replace, value=v, filter=[k], inplace=inplace, regex=regex, ) elif not is_list_like(value): # NA -> 0 new_data = self._data.replace( to_replace=to_replace, value=value, inplace=inplace, regex=regex ) else: raise TypeError( f'Invalid "to_replace" type: {repr(type(to_replace).__name__)}' ) if inplace: self._update_inplace(new_data) else: return self._constructor(new_data).__finalize__(self) _shared_docs[ "interpolate" ] = """ Please note that only ``method='linear'`` is supported for DataFrame/Series with a MultiIndex. Parameters ---------- method : str, default 'linear' Interpolation technique to use. One of: * 'linear': Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes. * 'time': Works on daily and higher resolution data to interpolate given length of interval. * 'index', 'values': use the actual numerical values of the index. * 'pad': Fill in NaNs using existing values. * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'spline', 'barycentric', 'polynomial': Passed to `scipy.interpolate.interp1d`. These methods use the numerical values of the index. Both 'polynomial' and 'spline' require that you also specify an `order` (int), e.g. ``df.interpolate(method='polynomial', order=5)``. * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima': Wrappers around the SciPy interpolation methods of similar names. See `Notes`. * 'from_derivatives': Refers to `scipy.interpolate.BPoly.from_derivatives` which replaces 'piecewise_polynomial' interpolation method in scipy 0.18. axis : {0 or 'index', 1 or 'columns', None}, default None Axis to interpolate along. limit : int, optional Maximum number of consecutive NaNs to fill. Must be greater than 0. inplace : bool, default False Update the data in place if possible. limit_direction : {'forward', 'backward', 'both'}, default 'forward' If limit is specified, consecutive NaNs will be filled in this direction. limit_area : {`None`, 'inside', 'outside'}, default None If limit is specified, consecutive NaNs will be filled with this restriction. * ``None``: No fill restriction. * 'inside': Only fill NaNs surrounded by valid values (interpolate). * 'outside': Only fill NaNs outside valid values (extrapolate). .. versionadded:: 0.23.0 downcast : optional, 'infer' or None, defaults to None Downcast dtypes if possible. **kwargs Keyword arguments to pass on to the interpolating function. Returns ------- Series or DataFrame Returns the same object type as the caller, interpolated at some or all ``NaN`` values. See Also -------- fillna : Fill missing values using different methods. scipy.interpolate.Akima1DInterpolator : Piecewise cubic polynomials (Akima interpolator). scipy.interpolate.BPoly.from_derivatives : Piecewise polynomial in the Bernstein basis. scipy.interpolate.interp1d : Interpolate a 1-D function. scipy.interpolate.KroghInterpolator : Interpolate polynomial (Krogh interpolator). scipy.interpolate.PchipInterpolator : PCHIP 1-d monotonic cubic interpolation. scipy.interpolate.CubicSpline : Cubic spline data interpolator. Notes ----- The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima' methods are wrappers around the respective SciPy implementations of similar names. These use the actual numerical values of the index. For more information on their behavior, see the `SciPy documentation <https://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__ and `SciPy tutorial <https://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html>`__. Examples -------- Filling in ``NaN`` in a :class:`~pandas.Series` via linear interpolation. >>> s = pd.Series([0, 1, np.nan, 3]) >>> s 0 0.0 1 1.0 2 NaN 3 3.0 dtype: float64 >>> s.interpolate() 0 0.0 1 1.0 2 2.0 3 3.0 dtype: float64 Filling in ``NaN`` in a Series by padding, but filling at most two consecutive ``NaN`` at a time. >>> s = pd.Series([np.nan, "single_one", np.nan, ... "fill_two_more", np.nan, np.nan, np.nan, ... 4.71, np.nan]) >>> s 0 NaN 1 single_one 2 NaN 3 fill_two_more 4 NaN 5 NaN 6 NaN 7 4.71 8 NaN dtype: object >>> s.interpolate(method='pad', limit=2) 0 NaN 1 single_one 2 single_one 3 fill_two_more 4 fill_two_more 5 fill_two_more 6 NaN 7 4.71 8 4.71 dtype: object Filling in ``NaN`` in a Series via polynomial interpolation or splines: Both 'polynomial' and 'spline' methods require that you also specify an ``order`` (int). >>> s = pd.Series([0, 2, np.nan, 8]) >>> s.interpolate(method='polynomial', order=2) 0 0.000000 1 2.000000 2 4.666667 3 8.000000 dtype: float64 Fill the DataFrame forward (that is, going down) along each column using linear interpolation. Note how the last entry in column 'a' is interpolated differently, because there is no entry after it to use for interpolation. Note how the first entry in column 'b' remains ``NaN``, because there is no entry before it to use for interpolation. >>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0), ... (np.nan, 2.0, np.nan, np.nan), ... (2.0, 3.0, np.nan, 9.0), ... (np.nan, 4.0, -4.0, 16.0)], ... columns=list('abcd')) >>> df a b c d 0 0.0 NaN -1.0 1.0 1 NaN 2.0 NaN NaN 2 2.0 3.0 NaN 9.0 3 NaN 4.0 -4.0 16.0 >>> df.interpolate(method='linear', limit_direction='forward', axis=0) a b c d 0 0.0 NaN -1.0 1.0 1 1.0 2.0 -2.0 5.0 2 2.0 3.0 -3.0 9.0 3 2.0 4.0 -4.0 16.0 Using polynomial interpolation. >>> df['d'].interpolate(method='polynomial', order=2) 0 1.0 1 4.0 2 9.0 3 16.0 Name: d, dtype: float64 """ @Appender(_shared_docs["interpolate"] % _shared_doc_kwargs) def interpolate( self, method="linear", axis=0, limit=None, inplace=False, limit_direction="forward", limit_area=None, downcast=None, **kwargs, ): """ Interpolate values according to different methods. """ inplace = validate_bool_kwarg(inplace, "inplace") axis = self._get_axis_number(axis) if axis == 0: ax = self._info_axis_name _maybe_transposed_self = self elif axis == 1: _maybe_transposed_self = self.T ax = 1 ax = _maybe_transposed_self._get_axis_number(ax) if _maybe_transposed_self.ndim == 2: alt_ax = 1 - ax else: alt_ax = ax if isinstance(_maybe_transposed_self.index, MultiIndex) and method != "linear": raise ValueError( "Only `method=linear` interpolation is supported on MultiIndexes." ) if _maybe_transposed_self._data.get_dtype_counts().get("object") == len( _maybe_transposed_self.T ): raise TypeError( "Cannot interpolate with all object-dtype columns " "in the DataFrame. Try setting at least one " "column to a numeric dtype." ) # create/use the index if method == "linear": # prior default index = np.arange(len(_maybe_transposed_self._get_axis(alt_ax))) else: index = _maybe_transposed_self._get_axis(alt_ax) methods = {"index", "values", "nearest", "time"} is_numeric_or_datetime = ( is_numeric_dtype(index) or is_datetime64_any_dtype(index) or is_timedelta64_dtype(index) ) if method not in methods and not is_numeric_or_datetime: raise ValueError( "Index column must be numeric or datetime type when " f"using {method} method other than linear. " "Try setting a numeric or datetime index column before " "interpolating." ) if isna(index).any(): raise NotImplementedError( "Interpolation with NaNs in the index " "has not been implemented. Try filling " "those NaNs before interpolating." ) data = _maybe_transposed_self._data new_data = data.interpolate( method=method, axis=ax, index=index, limit=limit, limit_direction=limit_direction, limit_area=limit_area, inplace=inplace, downcast=downcast, **kwargs, ) if inplace: if axis == 1: new_data = self._constructor(new_data).T._data self._update_inplace(new_data) else: res = self._constructor(new_data).__finalize__(self) if axis == 1: res = res.T return res # ---------------------------------------------------------------------- # Timeseries methods Methods def asof(self, where, subset=None): """ Return the last row(s) without any NaNs before `where`. The last row (for each element in `where`, if list) without any NaN is taken. In case of a :class:`~pandas.DataFrame`, the last row without NaN considering only the subset of columns (if not `None`) If there is no good value, NaN is returned for a Series or a Series of NaN values for a DataFrame Parameters ---------- where : date or array-like of dates Date(s) before which the last row(s) are returned. subset : str or array-like of str, default `None` For DataFrame, if not `None`, only use these columns to check for NaNs. Returns ------- scalar, Series, or DataFrame The return can be: * scalar : when `self` is a Series and `where` is a scalar * Series: when `self` is a Series and `where` is an array-like, or when `self` is a DataFrame and `where` is a scalar * DataFrame : when `self` is a DataFrame and `where` is an array-like Return scalar, Series, or DataFrame. See Also -------- merge_asof : Perform an asof merge. Similar to left join. Notes ----- Dates are assumed to be sorted. Raises if this is not the case. Examples -------- A Series and a scalar `where`. >>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40]) >>> s 10 1.0 20 2.0 30 NaN 40 4.0 dtype: float64 >>> s.asof(20) 2.0 For a sequence `where`, a Series is returned. The first value is NaN, because the first element of `where` is before the first index value. >>> s.asof([5, 20]) 5 NaN 20 2.0 dtype: float64 Missing values are not considered. The following is ``2.0``, not NaN, even though NaN is at the index location for ``30``. >>> s.asof(30) 2.0 Take all columns into consideration >>> df = pd.DataFrame({'a': [10, 20, 30, 40, 50], ... 'b': [None, None, None, None, 500]}, ... index=pd.DatetimeIndex(['2018-02-27 09:01:00', ... '2018-02-27 09:02:00', ... '2018-02-27 09:03:00', ... '2018-02-27 09:04:00', ... '2018-02-27 09:05:00'])) >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30', ... '2018-02-27 09:04:30'])) a b 2018-02-27 09:03:30 NaN NaN 2018-02-27 09:04:30 NaN NaN Take a single column into consideration >>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30', ... '2018-02-27 09:04:30']), ... subset=['a']) a b 2018-02-27 09:03:30 30.0 NaN 2018-02-27 09:04:30 40.0 NaN """ if isinstance(where, str): where = Timestamp(where) if not self.index.is_monotonic: raise ValueError("asof requires a sorted index") is_series = isinstance(self, ABCSeries) if is_series: if subset is not None: raise ValueError("subset is not valid for Series") else: if subset is None: subset = self.columns if not is_list_like(subset): subset = [subset] is_list = is_list_like(where) if not is_list: start = self.index[0] if isinstance(self.index, PeriodIndex): where = Period(where, freq=self.index.freq) if where < start: if not is_series: from pandas import Series return Series(index=self.columns, name=where, dtype=np.float64) return np.nan # It's always much faster to use a *while* loop here for # Series than pre-computing all the NAs. However a # *while* loop is extremely expensive for DataFrame # so we later pre-compute all the NAs and use the same # code path whether *where* is a scalar or list. # See PR: https://github.com/pandas-dev/pandas/pull/14476 if is_series: loc = self.index.searchsorted(where, side="right") if loc > 0: loc -= 1 values = self._values while loc > 0 and isna(values[loc]): loc -= 1 return values[loc] if not isinstance(where, Index): where = Index(where) if is_list else Index([where]) nulls = self.isna() if is_series else self[subset].isna().any(1) if nulls.all(): if is_series: return self._constructor(np.nan, index=where, name=self.name) elif is_list: from pandas import DataFrame return DataFrame(np.nan, index=where, columns=self.columns) else: from pandas import Series return Series(np.nan, index=self.columns, name=where[0]) locs = self.index.asof_locs(where, ~(nulls.values)) # mask the missing missing = locs == -1 data = self.take(locs) data.index = where data.loc[missing] = np.nan return data if is_list else data.iloc[-1] # ---------------------------------------------------------------------- # Action Methods _shared_docs[ "isna" ] = """ Detect missing values. Return a boolean same-sized object indicating if the values are NA. NA values, such as None or :attr:`numpy.NaN`, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings ``''`` or :attr:`numpy.inf` are not considered NA values (unless you set ``pandas.options.mode.use_inf_as_na = True``). Returns ------- %(klass)s Mask of bool values for each element in %(klass)s that indicates whether an element is not an NA value. See Also -------- %(klass)s.isnull : Alias of isna. %(klass)s.notna : Boolean inverse of isna. %(klass)s.dropna : Omit axes labels with missing values. isna : Top-level isna. Examples -------- Show which entries in a DataFrame are NA. >>> df = pd.DataFrame({'age': [5, 6, np.NaN], ... 'born': [pd.NaT, pd.Timestamp('1939-05-27'), ... pd.Timestamp('1940-04-25')], ... 'name': ['Alfred', 'Batman', ''], ... 'toy': [None, 'Batmobile', 'Joker']}) >>> df age born name toy 0 5.0 NaT Alfred None 1 6.0 1939-05-27 Batman Batmobile 2 NaN 1940-04-25 Joker >>> df.isna() age born name toy 0 False True False True 1 False False False False 2 True False False False Show which entries in a Series are NA. >>> ser = pd.Series([5, 6, np.NaN]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64 >>> ser.isna() 0 False 1 False 2 True dtype: bool """ @Appender(_shared_docs["isna"] % _shared_doc_kwargs) def isna(self: FrameOrSeries) -> FrameOrSeries: return isna(self).__finalize__(self) @Appender(_shared_docs["isna"] % _shared_doc_kwargs) def isnull(self: FrameOrSeries) -> FrameOrSeries: return isna(self).__finalize__(self) _shared_docs[ "notna" ] = """ Detect existing (non-missing) values. Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings ``''`` or :attr:`numpy.inf` are not considered NA values (unless you set ``pandas.options.mode.use_inf_as_na = True``). NA values, such as None or :attr:`numpy.NaN`, get mapped to False values. Returns ------- %(klass)s Mask of bool values for each element in %(klass)s that indicates whether an element is not an NA value. See Also -------- %(klass)s.notnull : Alias of notna. %(klass)s.isna : Boolean inverse of notna. %(klass)s.dropna : Omit axes labels with missing values. notna : Top-level notna. Examples -------- Show which entries in a DataFrame are not NA. >>> df = pd.DataFrame({'age': [5, 6, np.NaN], ... 'born': [pd.NaT, pd.Timestamp('1939-05-27'), ... pd.Timestamp('1940-04-25')], ... 'name': ['Alfred', 'Batman', ''], ... 'toy': [None, 'Batmobile', 'Joker']}) >>> df age born name toy 0 5.0 NaT Alfred None 1 6.0 1939-05-27 Batman Batmobile 2 NaN 1940-04-25 Joker >>> df.notna() age born name toy 0 True False True False 1 True True True True 2 False True True True Show which entries in a Series are not NA. >>> ser = pd.Series([5, 6, np.NaN]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64 >>> ser.notna() 0 True 1 True 2 False dtype: bool """ @Appender(_shared_docs["notna"] % _shared_doc_kwargs) def notna(self: FrameOrSeries) -> FrameOrSeries: return notna(self).__finalize__(self) @Appender(_shared_docs["notna"] % _shared_doc_kwargs) def notnull(self: FrameOrSeries) -> FrameOrSeries: return notna(self).__finalize__(self) def _clip_with_scalar(self, lower, upper, inplace: bool_t = False): if (lower is not None and np.any(isna(lower))) or ( upper is not None and np.any(isna(upper)) ): raise ValueError("Cannot use an NA value as a clip threshold") result = self mask = isna(self.values) with np.errstate(all="ignore"): if upper is not None: subset = self.to_numpy() <= upper result = result.where(subset, upper, axis=None, inplace=False) if lower is not None: subset = self.to_numpy() >= lower result = result.where(subset, lower, axis=None, inplace=False) if np.any(mask): result[mask] = np.nan if inplace: self._update_inplace(result) else: return result def _clip_with_one_bound(self, threshold, method, axis, inplace): if axis is not None: axis = self._get_axis_number(axis) # method is self.le for upper bound and self.ge for lower bound if is_scalar(threshold) and is_number(threshold): if method.__name__ == "le": return self._clip_with_scalar(None, threshold, inplace=inplace) return self._clip_with_scalar(threshold, None, inplace=inplace) subset = method(threshold, axis=axis) | isna(self) # GH #15390 # In order for where method to work, the threshold must # be transformed to NDFrame from other array like structure. if (not isinstance(threshold, ABCSeries)) and is_list_like(threshold): if isinstance(self, ABCSeries): threshold = self._constructor(threshold, index=self.index) else: threshold = _align_method_FRAME(self, threshold, axis, flex=None)[1] return self.where(subset, threshold, axis=axis, inplace=inplace) def clip( self: FrameOrSeries, lower=None, upper=None, axis=None, inplace: bool_t = False, *args, **kwargs, ) -> FrameOrSeries: """ Trim values at input threshold(s). Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis. Parameters ---------- lower : float or array_like, default None Minimum threshold value. All values below this threshold will be set to it. upper : float or array_like, default None Maximum threshold value. All values above this threshold will be set to it. axis : int or str axis name, optional Align object with lower and upper along the given axis. inplace : bool, default False Whether to perform the operation in place on the data. .. versionadded:: 0.21.0 *args, **kwargs Additional keywords have no effect but might be accepted for compatibility with numpy. Returns ------- Series or DataFrame Same type as calling object with the values outside the clip boundaries replaced. Examples -------- >>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]} >>> df = pd.DataFrame(data) >>> df col_0 col_1 0 9 -2 1 -3 -7 2 0 6 3 -1 8 4 5 -5 Clips per column using lower and upper thresholds: >>> df.clip(-4, 6) col_0 col_1 0 6 -2 1 -3 -4 2 0 6 3 -1 6 4 5 -4 Clips using specific lower and upper thresholds per column element: >>> t = pd.Series([2, -4, -1, 6, 3]) >>> t 0 2 1 -4 2 -1 3 6 4 3 dtype: int64 >>> df.clip(t, t + 4, axis=0) col_0 col_1 0 6 2 1 -3 -4 2 0 3 3 6 8 4 5 3 """ inplace = validate_bool_kwarg(inplace, "inplace") axis = nv.validate_clip_with_axis(axis, args, kwargs) if axis is not None: axis = self._get_axis_number(axis) # GH 17276 # numpy doesn't like NaN as a clip value # so ignore # GH 19992 # numpy doesn't drop a list-like bound containing NaN if not is_list_like(lower) and np.any(isna(lower)): lower = None if not is_list_like(upper) and np.any(isna(upper)): upper = None # GH 2747 (arguments were reversed) if lower is not None and upper is not None: if is_scalar(lower) and is_scalar(upper): lower, upper = min(lower, upper), max(lower, upper) # fast-path for scalars if (lower is None or (is_scalar(lower) and is_number(lower))) and ( upper is None or (is_scalar(upper) and is_number(upper)) ): return self._clip_with_scalar(lower, upper, inplace=inplace) result = self if lower is not None: result = result._clip_with_one_bound( lower, method=self.ge, axis=axis, inplace=inplace ) if upper is not None: if inplace: result = self result = result._clip_with_one_bound( upper, method=self.le, axis=axis, inplace=inplace ) return result _shared_docs[ "groupby" ] = """ Group %(klass)s using a mapper or by a Series of columns. A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups. Parameters ---------- by : mapping, function, label, or list of labels Used to determine the groups for the groupby. If ``by`` is a function, it's called on each value of the object's index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series' values are first aligned; see ``.align()`` method). If an ndarray is passed, the values are used as-is determine the groups. A label or list of labels may be passed to group by the columns in ``self``. Notice that a tuple is interpreted as a (single) key. axis : {0 or 'index', 1 or 'columns'}, default 0 Split along rows (0) or columns (1). level : int, level name, or sequence of such, default None If the axis is a MultiIndex (hierarchical), group by a particular level or levels. as_index : bool, default True For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively "SQL-style" grouped output. sort : bool, default True Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. group_keys : bool, default True When calling apply, add group keys to index to identify pieces. squeeze : bool, default False Reduce the dimensionality of the return type if possible, otherwise return a consistent type. observed : bool, default False This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers. .. versionadded:: 0.23.0 Returns ------- %(klass)sGroupBy Returns a groupby object that contains information about the groups. See Also -------- resample : Convenience method for frequency conversion and resampling of time series. Notes ----- See the `user guide <https://pandas.pydata.org/pandas-docs/stable/groupby.html>`_ for more. """ def asfreq( self: FrameOrSeries, freq, method=None, how: Optional[str] = None, normalize: bool_t = False, fill_value=None, ) -> FrameOrSeries: """ Convert TimeSeries to specified frequency. Optionally provide filling method to pad/backfill missing values. Returns the original data conformed to a new index with the specified frequency. ``resample`` is more appropriate if an operation, such as summarization, is necessary to represent the data at the new frequency. Parameters ---------- freq : DateOffset or str method : {'backfill'/'bfill', 'pad'/'ffill'}, default None Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present): * 'pad' / 'ffill': propagate last valid observation forward to next valid * 'backfill' / 'bfill': use NEXT valid observation to fill. how : {'start', 'end'}, default end For PeriodIndex only (see PeriodIndex.asfreq). normalize : bool, default False Whether to reset output index to midnight. fill_value : scalar, optional Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present). Returns ------- converted : same type as caller See Also -------- reindex Notes ----- To learn more about the frequency strings, please see `this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__. Examples -------- Start by creating a series with 4 one minute timestamps. >>> index = pd.date_range('1/1/2000', periods=4, freq='T') >>> series = pd.Series([0.0, None, 2.0, 3.0], index=index) >>> df = pd.DataFrame({'s':series}) >>> df s 2000-01-01 00:00:00 0.0 2000-01-01 00:01:00 NaN 2000-01-01 00:02:00 2.0 2000-01-01 00:03:00 3.0 Upsample the series into 30 second bins. >>> df.asfreq(freq='30S') s 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 NaN 2000-01-01 00:01:30 NaN 2000-01-01 00:02:00 2.0 2000-01-01 00:02:30 NaN 2000-01-01 00:03:00 3.0 Upsample again, providing a ``fill value``. >>> df.asfreq(freq='30S', fill_value=9.0) s 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 9.0 2000-01-01 00:01:00 NaN 2000-01-01 00:01:30 9.0 2000-01-01 00:02:00 2.0 2000-01-01 00:02:30 9.0 2000-01-01 00:03:00 3.0 Upsample again, providing a ``method``. >>> df.asfreq(freq='30S', method='bfill') s 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 NaN 2000-01-01 00:01:30 2.0 2000-01-01 00:02:00 2.0 2000-01-01 00:02:30 3.0 2000-01-01 00:03:00 3.0 """ from pandas.core.resample import asfreq return asfreq( self, freq, method=method, how=how, normalize=normalize, fill_value=fill_value, ) def at_time( self: FrameOrSeries, time, asof: bool_t = False, axis=None ) -> FrameOrSeries: """ Select values at particular time of day (e.g., 9:30AM). Parameters ---------- time : datetime.time or str axis : {0 or 'index', 1 or 'columns'}, default 0 .. versionadded:: 0.24.0 Returns ------- Series or DataFrame Raises ------ TypeError If the index is not a :class:`DatetimeIndex` See Also -------- between_time : Select values between particular times of the day. first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_at_time : Get just the index locations for values at particular time of the day. Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='12H') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 00:00:00 1 2018-04-09 12:00:00 2 2018-04-10 00:00:00 3 2018-04-10 12:00:00 4 >>> ts.at_time('12:00') A 2018-04-09 12:00:00 2 2018-04-10 12:00:00 4 """ if axis is None: axis = self._stat_axis_number axis = self._get_axis_number(axis) index = self._get_axis(axis) try: indexer = index.indexer_at_time(time, asof=asof) except AttributeError as err: raise TypeError("Index must be DatetimeIndex") from err return self._take_with_is_copy(indexer, axis=axis) def between_time( self: FrameOrSeries, start_time, end_time, include_start: bool_t = True, include_end: bool_t = True, axis=None, ) -> FrameOrSeries: """ Select values between particular times of the day (e.g., 9:00-9:30 AM). By setting ``start_time`` to be later than ``end_time``, you can get the times that are *not* between the two times. Parameters ---------- start_time : datetime.time or str Initial time as a time filter limit. end_time : datetime.time or str End time as a time filter limit. include_start : bool, default True Whether the start time needs to be included in the result. include_end : bool, default True Whether the end time needs to be included in the result. axis : {0 or 'index', 1 or 'columns'}, default 0 Determine range time on index or columns value. .. versionadded:: 0.24.0 Returns ------- Series or DataFrame Data from the original object filtered to the specified dates range. Raises ------ TypeError If the index is not a :class:`DatetimeIndex` See Also -------- at_time : Select values at a particular time of the day. first : Select initial periods of time series based on a date offset. last : Select final periods of time series based on a date offset. DatetimeIndex.indexer_between_time : Get just the index locations for values between particular times of the day. Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 00:00:00 1 2018-04-10 00:20:00 2 2018-04-11 00:40:00 3 2018-04-12 01:00:00 4 >>> ts.between_time('0:15', '0:45') A 2018-04-10 00:20:00 2 2018-04-11 00:40:00 3 You get the times that are *not* between two times by setting ``start_time`` later than ``end_time``: >>> ts.between_time('0:45', '0:15') A 2018-04-09 00:00:00 1 2018-04-12 01:00:00 4 """ if axis is None: axis = self._stat_axis_number axis = self._get_axis_number(axis) index = self._get_axis(axis) try: indexer = index.indexer_between_time( start_time, end_time, include_start=include_start, include_end=include_end, ) except AttributeError as err: raise TypeError("Index must be DatetimeIndex") from err return self._take_with_is_copy(indexer, axis=axis) def resample( self, rule, axis=0, closed: Optional[str] = None, label: Optional[str] = None, convention: str = "start", kind: Optional[str] = None, loffset=None, base: int = 0, on=None, level=None, ) -> "Resampler": """ Resample time-series data. Convenience method for frequency conversion and resampling of time series. Object must have a datetime-like index (`DatetimeIndex`, `PeriodIndex`, or `TimedeltaIndex`), or pass datetime-like values to the `on` or `level` keyword. Parameters ---------- rule : DateOffset, Timedelta or str The offset string or object representing target conversion. axis : {0 or 'index', 1 or 'columns'}, default 0 Which axis to use for up- or down-sampling. For `Series` this will default to 0, i.e. along the rows. Must be `DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex`. closed : {'right', 'left'}, default None Which side of bin interval is closed. The default is 'left' for all frequency offsets except for 'M', 'A', 'Q', 'BM', 'BA', 'BQ', and 'W' which all have a default of 'right'. label : {'right', 'left'}, default None Which bin edge label to label bucket with. The default is 'left' for all frequency offsets except for 'M', 'A', 'Q', 'BM', 'BA', 'BQ', and 'W' which all have a default of 'right'. convention : {'start', 'end', 's', 'e'}, default 'start' For `PeriodIndex` only, controls whether to use the start or end of `rule`. kind : {'timestamp', 'period'}, optional, default None Pass 'timestamp' to convert the resulting index to a `DateTimeIndex` or 'period' to convert it to a `PeriodIndex`. By default the input representation is retained. loffset : timedelta, default None Adjust the resampled time labels. base : int, default 0 For frequencies that evenly subdivide 1 day, the "origin" of the aggregated intervals. For example, for '5min' frequency, base could range from 0 through 4. Defaults to 0. on : str, optional For a DataFrame, column to use instead of index for resampling. Column must be datetime-like. level : str or int, optional For a MultiIndex, level (name or number) to use for resampling. `level` must be datetime-like. Returns ------- Resampler object See Also -------- groupby : Group by mapping, function, label, or list of labels. Series.resample : Resample a Series. DataFrame.resample: Resample a DataFrame. Notes ----- See the `user guide <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling>`_ for more. To learn more about the offset strings, please see `this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>`__. Examples -------- Start by creating a series with 9 one minute timestamps. >>> index = pd.date_range('1/1/2000', periods=9, freq='T') >>> series = pd.Series(range(9), index=index) >>> series 2000-01-01 00:00:00 0 2000-01-01 00:01:00 1 2000-01-01 00:02:00 2 2000-01-01 00:03:00 3 2000-01-01 00:04:00 4 2000-01-01 00:05:00 5 2000-01-01 00:06:00 6 2000-01-01 00:07:00 7 2000-01-01 00:08:00 8 Freq: T, dtype: int64 Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin. >>> series.resample('3T').sum() 2000-01-01 00:00:00 3 2000-01-01 00:03:00 12 2000-01-01 00:06:00 21 Freq: 3T, dtype: int64 Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed value in the resampled bucket with the label ``2000-01-01 00:03:00`` does not include 3 (if it did, the summed value would be 6, not 3). To include this value close the right side of the bin interval as illustrated in the example below this one. >>> series.resample('3T', label='right').sum() 2000-01-01 00:03:00 3 2000-01-01 00:06:00 12 2000-01-01 00:09:00 21 Freq: 3T, dtype: int64 Downsample the series into 3 minute bins as above, but close the right side of the bin interval. >>> series.resample('3T', label='right', closed='right').sum() 2000-01-01 00:00:00 0 2000-01-01 00:03:00 6 2000-01-01 00:06:00 15 2000-01-01 00:09:00 15 Freq: 3T, dtype: int64 Upsample the series into 30 second bins. >>> series.resample('30S').asfreq()[0:5] # Select first 5 rows 2000-01-01 00:00:00 0.0 2000-01-01 00:00:30 NaN 2000-01-01 00:01:00 1.0 2000-01-01 00:01:30 NaN 2000-01-01 00:02:00 2.0 Freq: 30S, dtype: float64 Upsample the series into 30 second bins and fill the ``NaN`` values using the ``pad`` method. >>> series.resample('30S').pad()[0:5] 2000-01-01 00:00:00 0 2000-01-01 00:00:30 0 2000-01-01 00:01:00 1 2000-01-01 00:01:30 1 2000-01-01 00:02:00 2 Freq: 30S, dtype: int64 Upsample the series into 30 second bins and fill the ``NaN`` values using the ``bfill`` method. >>> series.resample('30S').bfill()[0:5] 2000-01-01 00:00:00 0 2000-01-01 00:00:30 1 2000-01-01 00:01:00 1 2000-01-01 00:01:30 2 2000-01-01 00:02:00 2 Freq: 30S, dtype: int64 Pass a custom function via ``apply`` >>> def custom_resampler(array_like): ... return np.sum(array_like) + 5 ... >>> series.resample('3T').apply(custom_resampler) 2000-01-01 00:00:00 8 2000-01-01 00:03:00 17 2000-01-01 00:06:00 26 Freq: 3T, dtype: int64 For a Series with a PeriodIndex, the keyword `convention` can be used to control whether to use the start or end of `rule`. Resample a year by quarter using 'start' `convention`. Values are assigned to the first quarter of the period. >>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01', ... freq='A', ... periods=2)) >>> s 2012 1 2013 2 Freq: A-DEC, dtype: int64 >>> s.resample('Q', convention='start').asfreq() 2012Q1 1.0 2012Q2 NaN 2012Q3 NaN 2012Q4 NaN 2013Q1 2.0 2013Q2 NaN 2013Q3 NaN 2013Q4 NaN Freq: Q-DEC, dtype: float64 Resample quarters by month using 'end' `convention`. Values are assigned to the last month of the period. >>> q = pd.Series([1, 2, 3, 4], index=pd.period_range('2018-01-01', ... freq='Q', ... periods=4)) >>> q 2018Q1 1 2018Q2 2 2018Q3 3 2018Q4 4 Freq: Q-DEC, dtype: int64 >>> q.resample('M', convention='end').asfreq() 2018-03 1.0 2018-04 NaN 2018-05 NaN 2018-06 2.0 2018-07 NaN 2018-08 NaN 2018-09 3.0 2018-10 NaN 2018-11 NaN 2018-12 4.0 Freq: M, dtype: float64 For DataFrame objects, the keyword `on` can be used to specify the column instead of the index for resampling. >>> d = dict({'price': [10, 11, 9, 13, 14, 18, 17, 19], ... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}) >>> df = pd.DataFrame(d) >>> df['week_starting'] = pd.date_range('01/01/2018', ... periods=8, ... freq='W') >>> df price volume week_starting 0 10 50 2018-01-07 1 11 60 2018-01-14 2 9 40 2018-01-21 3 13 100 2018-01-28 4 14 50 2018-02-04 5 18 100 2018-02-11 6 17 40 2018-02-18 7 19 50 2018-02-25 >>> df.resample('M', on='week_starting').mean() price volume week_starting 2018-01-31 10.75 62.5 2018-02-28 17.00 60.0 For a DataFrame with MultiIndex, the keyword `level` can be used to specify on which level the resampling needs to take place. >>> days = pd.date_range('1/1/2000', periods=4, freq='D') >>> d2 = dict({'price': [10, 11, 9, 13, 14, 18, 17, 19], ... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}) >>> df2 = pd.DataFrame(d2, ... index=pd.MultiIndex.from_product([days, ... ['morning', ... 'afternoon']] ... )) >>> df2 price volume 2000-01-01 morning 10 50 afternoon 11 60 2000-01-02 morning 9 40 afternoon 13 100 2000-01-03 morning 14 50 afternoon 18 100 2000-01-04 morning 17 40 afternoon 19 50 >>> df2.resample('D', level=0).sum() price volume 2000-01-01 21 110 2000-01-02 22 140 2000-01-03 32 150 2000-01-04 36 90 """ from pandas.core.resample import get_resampler axis = self._get_axis_number(axis) return get_resampler( self, freq=rule, label=label, closed=closed, axis=axis, kind=kind, loffset=loffset, convention=convention, base=base, key=on, level=level, ) def first(self: FrameOrSeries, offset) -> FrameOrSeries: """ Method to subset initial periods of time series data based on a date offset. Parameters ---------- offset : str, DateOffset, dateutil.relativedelta Returns ------- subset : same type as caller Raises ------ TypeError If the index is not a :class:`DatetimeIndex` See Also -------- last : Select final periods of time series based on a date offset. at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day. Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='2D') >>> ts = pd.DataFrame({'A': [1,2,3,4]}, index=i) >>> ts A 2018-04-09 1 2018-04-11 2 2018-04-13 3 2018-04-15 4 Get the rows for the first 3 days: >>> ts.first('3D') A 2018-04-09 1 2018-04-11 2 Notice the data for 3 first calender days were returned, not the first 3 days observed in the dataset, and therefore data for 2018-04-13 was not returned. """ if not isinstance(self.index, DatetimeIndex): raise TypeError("'first' only supports a DatetimeIndex index") if len(self.index) == 0: return self offset = to_offset(offset) end_date = end = self.index[0] + offset # Tick-like, e.g. 3 weeks if not offset.is_anchored() and hasattr(offset, "_inc"): if end_date in self.index: end = self.index.searchsorted(end_date, side="left") return self.iloc[:end] return self.loc[:end] def last(self: FrameOrSeries, offset) -> FrameOrSeries: """ Method to subset final periods of time series data based on a date offset. Parameters ---------- offset : str, DateOffset, dateutil.relativedelta Returns ------- subset : same type as caller Raises ------ TypeError If the index is not a :class:`DatetimeIndex` See Also -------- first : Select initial periods of time series based on a date offset. at_time : Select values at a particular time of the day. between_time : Select values between particular times of the day. Examples -------- >>> i = pd.date_range('2018-04-09', periods=4, freq='2D') >>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i) >>> ts A 2018-04-09 1 2018-04-11 2 2018-04-13 3 2018-04-15 4 Get the rows for the last 3 days: >>> ts.last('3D') A 2018-04-13 3 2018-04-15 4 Notice the data for 3 last calender days were returned, not the last 3 observed days in the dataset, and therefore data for 2018-04-11 was not returned. """ if not isinstance(self.index, DatetimeIndex): raise TypeError("'last' only supports a DatetimeIndex index") if len(self.index) == 0: return self offset = to_offset(offset) start_date = self.index[-1] - offset start = self.index.searchsorted(start_date, side="right") return self.iloc[start:] def rank( self: FrameOrSeries, axis=0, method: str = "average", numeric_only: Optional[bool_t] = None, na_option: str = "keep", ascending: bool_t = True, pct: bool_t = False, ) -> FrameOrSeries: """ Compute numerical data ranks (1 through n) along axis. By default, equal values are assigned a rank that is the average of the ranks of those values. Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 Index to direct ranking. method : {'average', 'min', 'max', 'first', 'dense'}, default 'average' How to rank the group of records that have the same value (i.e. ties): * average: average rank of the group * min: lowest rank in the group * max: highest rank in the group * first: ranks assigned in order they appear in the array * dense: like 'min', but rank always increases by 1 between groups. numeric_only : bool, optional For DataFrame objects, rank only numeric columns if set to True. na_option : {'keep', 'top', 'bottom'}, default 'keep' How to rank NaN values: * keep: assign NaN rank to NaN values * top: assign smallest rank to NaN values if ascending * bottom: assign highest rank to NaN values if ascending. ascending : bool, default True Whether or not the elements should be ranked in ascending order. pct : bool, default False Whether or not to display the returned rankings in percentile form. Returns ------- same type as caller Return a Series or DataFrame with data ranks as values. See Also -------- core.groupby.GroupBy.rank : Rank of values within each group. Examples -------- >>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog', ... 'spider', 'snake'], ... 'Number_legs': [4, 2, 4, 8, np.nan]}) >>> df Animal Number_legs 0 cat 4.0 1 penguin 2.0 2 dog 4.0 3 spider 8.0 4 snake NaN The following example shows how the method behaves with the above parameters: * default_rank: this is the default behaviour obtained without using any parameter. * max_rank: setting ``method = 'max'`` the records that have the same values are ranked using the highest rank (e.g.: since 'cat' and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.) * NA_bottom: choosing ``na_option = 'bottom'``, if there are records with NaN values they are placed at the bottom of the ranking. * pct_rank: when setting ``pct = True``, the ranking is expressed as percentile rank. >>> df['default_rank'] = df['Number_legs'].rank() >>> df['max_rank'] = df['Number_legs'].rank(method='max') >>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom') >>> df['pct_rank'] = df['Number_legs'].rank(pct=True) >>> df Animal Number_legs default_rank max_rank NA_bottom pct_rank 0 cat 4.0 2.5 3.0 2.5 0.625 1 penguin 2.0 1.0 1.0 1.0 0.250 2 dog 4.0 2.5 3.0 2.5 0.625 3 spider 8.0 4.0 4.0 4.0 1.000 4 snake NaN NaN NaN 5.0 NaN """ axis = self._get_axis_number(axis) if na_option not in {"keep", "top", "bottom"}: msg = "na_option must be one of 'keep', 'top', or 'bottom'" raise ValueError(msg) def ranker(data): ranks = algos.rank( data.values, axis=axis, method=method, ascending=ascending, na_option=na_option, pct=pct, ) ranks = self._constructor(ranks, **data._construct_axes_dict()) return ranks.__finalize__(self) # if numeric_only is None, and we can't get anything, we try with # numeric_only=True if numeric_only is None: try: return ranker(self) except TypeError: numeric_only = True if numeric_only: data = self._get_numeric_data() else: data = self return ranker(data) _shared_docs[ "align" ] = """ Align two objects on their axes with the specified join method. Join method is specified for each axis Index. Parameters ---------- other : DataFrame or Series join : {'outer', 'inner', 'left', 'right'}, default 'outer' axis : allowed axis of the other object, default None Align on index (0), columns (1), or both (None). level : int or level name, default None Broadcast across a level, matching Index values on the passed MultiIndex level. copy : bool, default True Always returns new objects. If copy=False and no reindexing is required then original objects are returned. fill_value : scalar, default np.NaN Value to use for missing values. Defaults to NaN, but can be any "compatible" value. method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None Method to use for filling holes in reindexed Series: - pad / ffill: propagate last valid observation forward to next valid. - backfill / bfill: use NEXT valid observation to fill gap. limit : int, default None If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None. fill_axis : %(axes_single_arg)s, default 0 Filling axis, method and limit. broadcast_axis : %(axes_single_arg)s, default None Broadcast values along this axis, if aligning two objects of different dimensions. Returns ------- (left, right) : (%(klass)s, type of other) Aligned objects. """ @Appender(_shared_docs["align"] % _shared_doc_kwargs) def align( self, other, join="outer", axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None, ): method = missing.clean_fill_method(method) if broadcast_axis == 1 and self.ndim != other.ndim: if isinstance(self, ABCSeries): # this means other is a DataFrame, and we need to broadcast # self cons = self._constructor_expanddim df = cons( {c: self for c in other.columns}, **other._construct_axes_dict() ) return df._align_frame( other, join=join, axis=axis, level=level, copy=copy, fill_value=fill_value, method=method, limit=limit, fill_axis=fill_axis, ) elif isinstance(other, ABCSeries): # this means self is a DataFrame, and we need to broadcast # other cons = other._constructor_expanddim df = cons( {c: other for c in self.columns}, **self._construct_axes_dict() ) return self._align_frame( df, join=join, axis=axis, level=level, copy=copy, fill_value=fill_value, method=method, limit=limit, fill_axis=fill_axis, ) if axis is not None: axis = self._get_axis_number(axis) if isinstance(other, ABCDataFrame): return self._align_frame( other, join=join, axis=axis, level=level, copy=copy, fill_value=fill_value, method=method, limit=limit, fill_axis=fill_axis, ) elif isinstance(other, ABCSeries): return self._align_series( other, join=join, axis=axis, level=level, copy=copy, fill_value=fill_value, method=method, limit=limit, fill_axis=fill_axis, ) else: # pragma: no cover raise TypeError(f"unsupported type: {type(other)}") def _align_frame( self, other, join="outer", axis=None, level=None, copy: bool_t = True, fill_value=None, method=None, limit=None, fill_axis=0, ): # defaults join_index, join_columns = None, None ilidx, iridx = None, None clidx, cridx = None, None is_series = isinstance(self, ABCSeries) if axis is None or axis == 0: if not self.index.equals(other.index): join_index, ilidx, iridx = self.index.join( other.index, how=join, level=level, return_indexers=True ) if axis is None or axis == 1: if not is_series and not self.columns.equals(other.columns): join_columns, clidx, cridx = self.columns.join( other.columns, how=join, level=level, return_indexers=True ) if is_series: reindexers = {0: [join_index, ilidx]} else: reindexers = {0: [join_index, ilidx], 1: [join_columns, clidx]} left = self._reindex_with_indexers( reindexers, copy=copy, fill_value=fill_value, allow_dups=True ) # other must be always DataFrame right = other._reindex_with_indexers( {0: [join_index, iridx], 1: [join_columns, cridx]}, copy=copy, fill_value=fill_value, allow_dups=True, ) if method is not None: left = self._ensure_type( left.fillna(method=method, axis=fill_axis, limit=limit) ) right = right.fillna(method=method, axis=fill_axis, limit=limit) # if DatetimeIndex have different tz, convert to UTC if is_datetime64tz_dtype(left.index): if left.index.tz != right.index.tz: if join_index is not None: left.index = join_index right.index = join_index return left.__finalize__(self), right.__finalize__(other) def _align_series( self, other, join="outer", axis=None, level=None, copy: bool_t = True, fill_value=None, method=None, limit=None, fill_axis=0, ): is_series = isinstance(self, ABCSeries) # series/series compat, other must always be a Series if is_series: if axis: raise ValueError("cannot align series to a series other than axis 0") # equal if self.index.equals(other.index): join_index, lidx, ridx = None, None, None else: join_index, lidx, ridx = self.index.join( other.index, how=join, level=level, return_indexers=True ) left = self._reindex_indexer(join_index, lidx, copy) right = other._reindex_indexer(join_index, ridx, copy) else: # one has > 1 ndim fdata = self._data if axis == 0: join_index = self.index lidx, ridx = None, None if not self.index.equals(other.index): join_index, lidx, ridx = self.index.join( other.index, how=join, level=level, return_indexers=True ) if lidx is not None: fdata = fdata.reindex_indexer(join_index, lidx, axis=1) elif axis == 1: join_index = self.columns lidx, ridx = None, None if not self.columns.equals(other.index): join_index, lidx, ridx = self.columns.join( other.index, how=join, level=level, return_indexers=True ) if lidx is not None: fdata = fdata.reindex_indexer(join_index, lidx, axis=0) else: raise ValueError("Must specify axis=0 or 1") if copy and fdata is self._data: fdata = fdata.copy() left = self._constructor(fdata) if ridx is None: right = other else: right = other.reindex(join_index, level=level) # fill fill_na = notna(fill_value) or (method is not None) if fill_na: left = left.fillna(fill_value, method=method, limit=limit, axis=fill_axis) right = right.fillna(fill_value, method=method, limit=limit) # if DatetimeIndex have different tz, convert to UTC if is_series or (not is_series and axis == 0): if is_datetime64tz_dtype(left.index): if left.index.tz != right.index.tz: if join_index is not None: left.index = join_index right.index = join_index return left.__finalize__(self), right.__finalize__(other) def _where( self, cond, other=np.nan, inplace=False, axis=None, level=None, errors="raise", try_cast=False, ): """ Equivalent to public method `where`, except that `other` is not applied as a function even if callable. Used in __setitem__. """ inplace = validate_bool_kwarg(inplace, "inplace") # align the cond to same shape as myself cond = com.apply_if_callable(cond, self) if isinstance(cond, NDFrame): cond, _ = cond.align(self, join="right", broadcast_axis=1) else: if not hasattr(cond, "shape"): cond = np.asanyarray(cond) if cond.shape != self.shape: raise ValueError("Array conditional must be same shape as self") cond = self._constructor(cond, **self._construct_axes_dict()) # make sure we are boolean fill_value = bool(inplace) cond = cond.fillna(fill_value) msg = "Boolean array expected for the condition, not {dtype}" if not isinstance(cond, ABCDataFrame): # This is a single-dimensional object. if not is_bool_dtype(cond): raise ValueError(msg.format(dtype=cond.dtype)) elif not cond.empty: for dt in cond.dtypes: if not is_bool_dtype(dt): raise ValueError(msg.format(dtype=dt)) cond = -cond if inplace else cond # try to align with other try_quick = True if hasattr(other, "align"): # align with me if other.ndim <= self.ndim: _, other = self.align( other, join="left", axis=axis, level=level, fill_value=np.nan ) # if we are NOT aligned, raise as we cannot where index if axis is None and not all( other._get_axis(i).equals(ax) for i, ax in enumerate(self.axes) ): raise InvalidIndexError # slice me out of the other else: raise NotImplementedError( "cannot align with a higher dimensional NDFrame" ) if isinstance(other, np.ndarray): if other.shape != self.shape: if self.ndim == 1: icond = cond.values # GH 2745 / GH 4192 # treat like a scalar if len(other) == 1: other = np.array(other[0]) # GH 3235 # match True cond to other elif len(cond[icond]) == len(other): # try to not change dtype at first (if try_quick) if try_quick: new_other = np.asarray(self) new_other = new_other.copy() new_other[icond] = other other = new_other else: raise ValueError( "Length of replacements must equal series length" ) else: raise ValueError( "other must be the same shape as self when an ndarray" ) # we are the same shape, so create an actual object for alignment else: other = self._constructor(other, **self._construct_axes_dict()) if axis is None: axis = 0 if self.ndim == getattr(other, "ndim", 0): align = True else: align = self._get_axis_number(axis) == 1 block_axis = self._get_block_manager_axis(axis) if inplace: # we may have different type blocks come out of putmask, so # reconstruct the block manager self._check_inplace_setting(other) new_data = self._data.putmask( mask=cond, new=other, align=align, inplace=True, axis=block_axis, transpose=self._AXIS_REVERSED, ) self._update_inplace(new_data) else: new_data = self._data.where( other=other, cond=cond, align=align, errors=errors, try_cast=try_cast, axis=block_axis, ) return self._constructor(new_data).__finalize__(self) _shared_docs[ "where" ] = """ Replace values where the condition is %(cond_rev)s. Parameters ---------- cond : bool %(klass)s, array-like, or callable Where `cond` is %(cond)s, keep the original value. Where %(cond_rev)s, replace with corresponding value from `other`. If `cond` is callable, it is computed on the %(klass)s and should return boolean %(klass)s or array. The callable must not change input %(klass)s (though pandas doesn't check it). other : scalar, %(klass)s, or callable Entries where `cond` is %(cond_rev)s are replaced with corresponding value from `other`. If other is callable, it is computed on the %(klass)s and should return scalar or %(klass)s. The callable must not change input %(klass)s (though pandas doesn't check it). inplace : bool, default False Whether to perform the operation in place on the data. axis : int, default None Alignment axis if needed. level : int, default None Alignment level if needed. errors : str, {'raise', 'ignore'}, default 'raise' Note that currently this parameter won't affect the results and will always coerce to a suitable dtype. - 'raise' : allow exceptions to be raised. - 'ignore' : suppress exceptions. On error return original object. try_cast : bool, default False Try to cast the result back to the input type (if possible). Returns ------- Same type as caller See Also -------- :func:`DataFrame.%(name_other)s` : Return an object of same shape as self. Notes ----- The %(name)s method is an application of the if-then idiom. For each element in the calling DataFrame, if ``cond`` is ``%(cond)s`` the element is used; otherwise the corresponding element from the DataFrame ``other`` is used. The signature for :func:`DataFrame.where` differs from :func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to ``np.where(m, df1, df2)``. For further details and examples see the ``%(name)s`` documentation in :ref:`indexing <indexing.where_mask>`. Examples -------- >>> s = pd.Series(range(5)) >>> s.where(s > 0) 0 NaN 1 1.0 2 2.0 3 3.0 4 4.0 dtype: float64 >>> s.mask(s > 0) 0 0.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 >>> s.where(s > 1, 10) 0 10 1 10 2 2 3 3 4 4 dtype: int64 >>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B']) >>> df A B 0 0 1 1 2 3 2 4 5 3 6 7 4 8 9 >>> m = df %% 3 == 0 >>> df.where(m, -df) A B 0 0 -1 1 -2 3 2 -4 -5 3 6 -7 4 -8 9 >>> df.where(m, -df) == np.where(m, df, -df) A B 0 True True 1 True True 2 True True 3 True True 4 True True >>> df.where(m, -df) == df.mask(~m, -df) A B 0 True True 1 True True 2 True True 3 True True 4 True True """ @Appender( _shared_docs["where"] % dict( _shared_doc_kwargs, cond="True", cond_rev="False", name="where", name_other="mask", ) ) def where( self, cond, other=np.nan, inplace=False, axis=None, level=None, errors="raise", try_cast=False, ): other = com.apply_if_callable(other, self) return self._where( cond, other, inplace, axis, level, errors=errors, try_cast=try_cast ) @Appender( _shared_docs["where"] % dict( _shared_doc_kwargs, cond="False", cond_rev="True", name="mask", name_other="where", ) ) def mask( self, cond, other=np.nan, inplace=False, axis=None, level=None, errors="raise", try_cast=False, ): inplace = validate_bool_kwarg(inplace, "inplace") cond = com.apply_if_callable(cond, self) # see gh-21891 if not hasattr(cond, "__invert__"): cond = np.array(cond) return self.where( ~cond, other=other, inplace=inplace, axis=axis, level=level, try_cast=try_cast, errors=errors, ) _shared_docs[ "shift" ] = """ Shift index by desired number of periods with an optional time `freq`. When `freq` is not passed, shift the index without realigning the data. If `freq` is passed (in this case, the index must be date or datetime, or it will raise a `NotImplementedError`), the index will be increased using the periods and the `freq`. Parameters ---------- periods : int Number of periods to shift. Can be positive or negative. freq : DateOffset, tseries.offsets, timedelta, or str, optional Offset to use from the tseries module or time rule (e.g. 'EOM'). If `freq` is specified then the index values are shifted but the data is not realigned. That is, use `freq` if you would like to extend the index when shifting and preserve the original data. axis : {0 or 'index', 1 or 'columns', None}, default None Shift direction. fill_value : object, optional The scalar value to use for newly introduced missing values. the default depends on the dtype of `self`. For numeric data, ``np.nan`` is used. For datetime, timedelta, or period data, etc. :attr:`NaT` is used. For extension dtypes, ``self.dtype.na_value`` is used. .. versionchanged:: 0.24.0 Returns ------- %(klass)s Copy of input object, shifted. See Also -------- Index.shift : Shift values of Index. DatetimeIndex.shift : Shift values of DatetimeIndex. PeriodIndex.shift : Shift values of PeriodIndex. tshift : Shift the time index, using the index's frequency if available. Examples -------- >>> df = pd.DataFrame({'Col1': [10, 20, 15, 30, 45], ... 'Col2': [13, 23, 18, 33, 48], ... 'Col3': [17, 27, 22, 37, 52]}) >>> df.shift(periods=3) Col1 Col2 Col3 0 NaN NaN NaN 1 NaN NaN NaN 2 NaN NaN NaN 3 10.0 13.0 17.0 4 20.0 23.0 27.0 >>> df.shift(periods=1, axis='columns') Col1 Col2 Col3 0 NaN 10.0 13.0 1 NaN 20.0 23.0 2 NaN 15.0 18.0 3 NaN 30.0 33.0 4 NaN 45.0 48.0 >>> df.shift(periods=3, fill_value=0) Col1 Col2 Col3 0 0 0 0 1 0 0 0 2 0 0 0 3 10 13 17 4 20 23 27 """ @Appender(_shared_docs["shift"] % _shared_doc_kwargs) def shift( self: FrameOrSeries, periods=1, freq=None, axis=0, fill_value=None ) -> FrameOrSeries: if periods == 0: return self.copy() block_axis = self._get_block_manager_axis(axis) if freq is None: new_data = self._data.shift( periods=periods, axis=block_axis, fill_value=fill_value ) else: return self.tshift(periods, freq) return self._constructor(new_data).__finalize__(self) def slice_shift(self: FrameOrSeries, periods: int = 1, axis=0) -> FrameOrSeries: """ Equivalent to `shift` without copying data. The shifted data will not include the dropped periods and the shifted axis will be smaller than the original. Parameters ---------- periods : int Number of periods to move, can be positive or negative. Returns ------- shifted : same type as caller Notes ----- While the `slice_shift` is faster than `shift`, you may pay for it later during alignment. """ if periods == 0: return self if periods > 0: vslicer = slice(None, -periods) islicer = slice(periods, None) else: vslicer = slice(-periods, None) islicer = slice(None, periods) new_obj = self._slice(vslicer, axis=axis) shifted_axis = self._get_axis(axis)[islicer] new_obj.set_axis(shifted_axis, axis=axis, inplace=True) return new_obj.__finalize__(self) def tshift( self: FrameOrSeries, periods: int = 1, freq=None, axis=0 ) -> FrameOrSeries: """ Shift the time index, using the index's frequency if available. Parameters ---------- periods : int Number of periods to move, can be positive or negative. freq : DateOffset, timedelta, or str, default None Increment to use from the tseries module or time rule expressed as a string (e.g. 'EOM'). axis : {0 or ‘index’, 1 or ‘columns’, None}, default 0 Corresponds to the axis that contains the Index. Returns ------- shifted : Series/DataFrame Notes ----- If freq is not specified then tries to use the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown """ index = self._get_axis(axis) if freq is None: freq = getattr(index, "freq", None) if freq is None: freq = getattr(index, "inferred_freq", None) if freq is None: msg = "Freq was not given and was not set in the index" raise ValueError(msg) if periods == 0: return self if isinstance(freq, str): freq = to_offset(freq) block_axis = self._get_block_manager_axis(axis) if isinstance(index, PeriodIndex): orig_freq = to_offset(index.freq) if freq == orig_freq: new_data = self._data.copy() new_data.axes[block_axis] = index.shift(periods) elif orig_freq is not None: raise ValueError( f"Given freq {freq.rule_code} does not match " f"PeriodIndex freq {orig_freq.rule_code}" ) else: new_data = self._data.copy() new_data.axes[block_axis] = index.shift(periods, freq) return self._constructor(new_data).__finalize__(self) def truncate( self: FrameOrSeries, before=None, after=None, axis=None, copy: bool_t = True ) -> FrameOrSeries: """ Truncate a Series or DataFrame before and after some index value. This is a useful shorthand for boolean indexing based on index values above or below certain thresholds. Parameters ---------- before : date, str, int Truncate all rows before this index value. after : date, str, int Truncate all rows after this index value. axis : {0 or 'index', 1 or 'columns'}, optional Axis to truncate. Truncates the index (rows) by default. copy : bool, default is True, Return a copy of the truncated section. Returns ------- type of caller The truncated Series or DataFrame. See Also -------- DataFrame.loc : Select a subset of a DataFrame by label. DataFrame.iloc : Select a subset of a DataFrame by position. Notes ----- If the index being truncated contains only datetime values, `before` and `after` may be specified as strings instead of Timestamps. Examples -------- >>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'], ... 'B': ['f', 'g', 'h', 'i', 'j'], ... 'C': ['k', 'l', 'm', 'n', 'o']}, ... index=[1, 2, 3, 4, 5]) >>> df A B C 1 a f k 2 b g l 3 c h m 4 d i n 5 e j o >>> df.truncate(before=2, after=4) A B C 2 b g l 3 c h m 4 d i n The columns of a DataFrame can be truncated. >>> df.truncate(before="A", after="B", axis="columns") A B 1 a f 2 b g 3 c h 4 d i 5 e j For Series, only rows can be truncated. >>> df['A'].truncate(before=2, after=4) 2 b 3 c 4 d Name: A, dtype: object The index values in ``truncate`` can be datetimes or string dates. >>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s') >>> df = pd.DataFrame(index=dates, data={'A': 1}) >>> df.tail() A 2016-01-31 23:59:56 1 2016-01-31 23:59:57 1 2016-01-31 23:59:58 1 2016-01-31 23:59:59 1 2016-02-01 00:00:00 1 >>> df.truncate(before=pd.Timestamp('2016-01-05'), ... after=pd.Timestamp('2016-01-10')).tail() A 2016-01-09 23:59:56 1 2016-01-09 23:59:57 1 2016-01-09 23:59:58 1 2016-01-09 23:59:59 1 2016-01-10 00:00:00 1 Because the index is a DatetimeIndex containing only dates, we can specify `before` and `after` as strings. They will be coerced to Timestamps before truncation. >>> df.truncate('2016-01-05', '2016-01-10').tail() A 2016-01-09 23:59:56 1 2016-01-09 23:59:57 1 2016-01-09 23:59:58 1 2016-01-09 23:59:59 1 2016-01-10 00:00:00 1 Note that ``truncate`` assumes a 0 value for any unspecified time component (midnight). This differs from partial string slicing, which returns any partially matching dates. >>> df.loc['2016-01-05':'2016-01-10', :].tail() A 2016-01-10 23:59:55 1 2016-01-10 23:59:56 1 2016-01-10 23:59:57 1 2016-01-10 23:59:58 1 2016-01-10 23:59:59 1 """ if axis is None: axis = self._stat_axis_number axis = self._get_axis_number(axis) ax = self._get_axis(axis) # GH 17935 # Check that index is sorted if not ax.is_monotonic_increasing and not ax.is_monotonic_decreasing: raise ValueError("truncate requires a sorted index") # if we have a date index, convert to dates, otherwise # treat like a slice if ax.is_all_dates: from pandas.core.tools.datetimes import to_datetime before = to_datetime(before) after = to_datetime(after) if before is not None and after is not None: if before > after: raise ValueError(f"Truncate: {after} must be after {before}") slicer = [slice(None, None)] * self._AXIS_LEN slicer[axis] = slice(before, after) result = self.loc[tuple(slicer)] if isinstance(ax, MultiIndex): setattr(result, self._get_axis_name(axis), ax.truncate(before, after)) if copy: result = result.copy() return result def tz_convert( self: FrameOrSeries, tz, axis=0, level=None, copy: bool_t = True ) -> FrameOrSeries: """ Convert tz-aware axis to target time zone. Parameters ---------- tz : str or tzinfo object axis : the axis to convert level : int, str, default None If axis is a MultiIndex, convert a specific level. Otherwise must be None. copy : bool, default True Also make a copy of the underlying data. Returns ------- %(klass)s Object with time zone converted axis. Raises ------ TypeError If the axis is tz-naive. """ axis = self._get_axis_number(axis) ax = self._get_axis(axis) def _tz_convert(ax, tz): if not hasattr(ax, "tz_convert"): if len(ax) > 0: ax_name = self._get_axis_name(axis) raise TypeError( f"{ax_name} is not a valid DatetimeIndex or PeriodIndex" ) else: ax = DatetimeIndex([], tz=tz) else: ax = ax.tz_convert(tz) return ax # if a level is given it must be a MultiIndex level or # equivalent to the axis name if isinstance(ax, MultiIndex): level = ax._get_level_number(level) new_level = _tz_convert(ax.levels[level], tz) ax = ax.set_levels(new_level, level=level) else: if level not in (None, 0, ax.name): raise ValueError(f"The level {level} is not valid") ax = _tz_convert(ax, tz) result = self._constructor(self._data, copy=copy) result = result.set_axis(ax, axis=axis, inplace=False) return result.__finalize__(self) def tz_localize( self: FrameOrSeries, tz, axis=0, level=None, copy: bool_t = True, ambiguous="raise", nonexistent: str = "raise", ) -> FrameOrSeries: """ Localize tz-naive index of a Series or DataFrame to target time zone. This operation localizes the Index. To localize the values in a timezone-naive Series, use :meth:`Series.dt.tz_localize`. Parameters ---------- tz : str or tzinfo axis : the axis to localize level : int, str, default None If axis ia a MultiIndex, localize a specific level. Otherwise must be None. copy : bool, default True Also make a copy of the underlying data. ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the `ambiguous` parameter dictates how ambiguous times should be handled. - 'infer' will attempt to infer fall dst-transition hours based on order - bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times) - 'NaT' will return NaT where there are ambiguous times - 'raise' will raise an AmbiguousTimeError if there are ambiguous times. nonexistent : str, default 'raise' A nonexistent time does not exist in a particular timezone where clocks moved forward due to DST. Valid values are: - 'shift_forward' will shift the nonexistent time forward to the closest existing time - 'shift_backward' will shift the nonexistent time backward to the closest existing time - 'NaT' will return NaT where there are nonexistent times - timedelta objects will shift nonexistent times by the timedelta - 'raise' will raise an NonExistentTimeError if there are nonexistent times. .. versionadded:: 0.24.0 Returns ------- Series or DataFrame Same type as the input. Raises ------ TypeError If the TimeSeries is tz-aware and tz is not None. Examples -------- Localize local times: >>> s = pd.Series([1], ... index=pd.DatetimeIndex(['2018-09-15 01:30:00'])) >>> s.tz_localize('CET') 2018-09-15 01:30:00+02:00 1 dtype: int64 Be careful with DST changes. When there is sequential data, pandas can infer the DST time: >>> s = pd.Series(range(7), ... index=pd.DatetimeIndex(['2018-10-28 01:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 02:00:00', ... '2018-10-28 02:30:00', ... '2018-10-28 03:00:00', ... '2018-10-28 03:30:00'])) >>> s.tz_localize('CET', ambiguous='infer') 2018-10-28 01:30:00+02:00 0 2018-10-28 02:00:00+02:00 1 2018-10-28 02:30:00+02:00 2 2018-10-28 02:00:00+01:00 3 2018-10-28 02:30:00+01:00 4 2018-10-28 03:00:00+01:00 5 2018-10-28 03:30:00+01:00 6 dtype: int64 In some cases, inferring the DST is impossible. In such cases, you can pass an ndarray to the ambiguous parameter to set the DST explicitly >>> s = pd.Series(range(3), ... index=pd.DatetimeIndex(['2018-10-28 01:20:00', ... '2018-10-28 02:36:00', ... '2018-10-28 03:46:00'])) >>> s.tz_localize('CET', ambiguous=np.array([True, True, False])) 2018-10-28 01:20:00+02:00 0 2018-10-28 02:36:00+02:00 1 2018-10-28 03:46:00+01:00 2 dtype: int64 If the DST transition causes nonexistent times, you can shift these dates forward or backwards with a timedelta object or `'shift_forward'` or `'shift_backwards'`. >>> s = pd.Series(range(2), ... index=pd.DatetimeIndex(['2015-03-29 02:30:00', ... '2015-03-29 03:30:00'])) >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward') 2015-03-29 03:00:00+02:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64 >>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward') 2015-03-29 01:59:59.999999999+01:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64 >>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H')) 2015-03-29 03:30:00+02:00 0 2015-03-29 03:30:00+02:00 1 dtype: int64 """ nonexistent_options = ("raise", "NaT", "shift_forward", "shift_backward") if nonexistent not in nonexistent_options and not isinstance( nonexistent, timedelta ): raise ValueError( "The nonexistent argument must be one of 'raise', " "'NaT', 'shift_forward', 'shift_backward' or " "a timedelta object" ) axis = self._get_axis_number(axis) ax = self._get_axis(axis) def _tz_localize(ax, tz, ambiguous, nonexistent): if not hasattr(ax, "tz_localize"): if len(ax) > 0: ax_name = self._get_axis_name(axis) raise TypeError( f"{ax_name} is not a valid DatetimeIndex or PeriodIndex" ) else: ax = DatetimeIndex([], tz=tz) else: ax = ax.tz_localize(tz, ambiguous=ambiguous, nonexistent=nonexistent) return ax # if a level is given it must be a MultiIndex level or # equivalent to the axis name if isinstance(ax, MultiIndex): level = ax._get_level_number(level) new_level = _tz_localize(ax.levels[level], tz, ambiguous, nonexistent) ax = ax.set_levels(new_level, level=level) else: if level not in (None, 0, ax.name): raise ValueError(f"The level {level} is not valid") ax = _tz_localize(ax, tz, ambiguous, nonexistent) result = self._constructor(self._data, copy=copy) result = result.set_axis(ax, axis=axis, inplace=False) return result.__finalize__(self) # ---------------------------------------------------------------------- # Numeric Methods def abs(self: FrameOrSeries) -> FrameOrSeries: """ Return a Series/DataFrame with absolute numeric value of each element. This function only applies to elements that are all numeric. Returns ------- abs Series/DataFrame containing the absolute value of each element. See Also -------- numpy.absolute : Calculate the absolute value element-wise. Notes ----- For ``complex`` inputs, ``1.2 + 1j``, the absolute value is :math:`\\sqrt{ a^2 + b^2 }`. Examples -------- Absolute numeric values in a Series. >>> s = pd.Series([-1.10, 2, -3.33, 4]) >>> s.abs() 0 1.10 1 2.00 2 3.33 3 4.00 dtype: float64 Absolute numeric values in a Series with complex numbers. >>> s = pd.Series([1.2 + 1j]) >>> s.abs() 0 1.56205 dtype: float64 Absolute numeric values in a Series with a Timedelta element. >>> s = pd.Series([pd.Timedelta('1 days')]) >>> s.abs() 0 1 days dtype: timedelta64[ns] Select rows with data closest to certain value using argsort (from `StackOverflow <https://stackoverflow.com/a/17758115>`__). >>> df = pd.DataFrame({ ... 'a': [4, 5, 6, 7], ... 'b': [10, 20, 30, 40], ... 'c': [100, 50, -30, -50] ... }) >>> df a b c 0 4 10 100 1 5 20 50 2 6 30 -30 3 7 40 -50 >>> df.loc[(df.c - 43).abs().argsort()] a b c 1 5 20 50 0 4 10 100 2 6 30 -30 3 7 40 -50 """ return np.abs(self) def describe( self: FrameOrSeries, percentiles=None, include=None, exclude=None ) -> FrameOrSeries: """ Generate descriptive statistics. Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset's distribution, excluding ``NaN`` values. Analyzes both numeric and object series, as well as ``DataFrame`` column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail. Parameters ---------- percentiles : list-like of numbers, optional The percentiles to include in the output. All should fall between 0 and 1. The default is ``[.25, .5, .75]``, which returns the 25th, 50th, and 75th percentiles. include : 'all', list-like of dtypes or None (default), optional A white list of data types to include in the result. Ignored for ``Series``. Here are the options: - 'all' : All columns of the input will be included in the output. - A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit ``numpy.number``. To limit it instead to object columns submit the ``numpy.object`` data type. Strings can also be used in the style of ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To select pandas categorical columns, use ``'category'`` - None (default) : The result will include all numeric columns. exclude : list-like of dtypes or None (default), optional, A black list of data types to omit from the result. Ignored for ``Series``. Here are the options: - A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit ``numpy.number``. To exclude object columns submit the data type ``numpy.object``. Strings can also be used in the style of ``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To exclude pandas categorical columns, use ``'category'`` - None (default) : The result will exclude nothing. Returns ------- Series or DataFrame Summary statistics of the Series or Dataframe provided. See Also -------- DataFrame.count: Count number of non-NA/null observations. DataFrame.max: Maximum of the values in the object. DataFrame.min: Minimum of the values in the object. DataFrame.mean: Mean of the values. DataFrame.std: Standard deviation of the observations. DataFrame.select_dtypes: Subset of a DataFrame including/excluding columns based on their dtype. Notes ----- For numeric data, the result's index will include ``count``, ``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and upper percentiles. By default the lower percentile is ``25`` and the upper percentile is ``75``. The ``50`` percentile is the same as the median. For object data (e.g. strings or timestamps), the result's index will include ``count``, ``unique``, ``top``, and ``freq``. The ``top`` is the most common value. The ``freq`` is the most common value's frequency. Timestamps also include the ``first`` and ``last`` items. If multiple object values have the highest count, then the ``count`` and ``top`` results will be arbitrarily chosen from among those with the highest count. For mixed data types provided via a ``DataFrame``, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If ``include='all'`` is provided as an option, the result will include a union of attributes of each type. The `include` and `exclude` parameters can be used to limit which columns in a ``DataFrame`` are analyzed for the output. The parameters are ignored when analyzing a ``Series``. Examples -------- Describing a numeric ``Series``. >>> s = pd.Series([1, 2, 3]) >>> s.describe() count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 dtype: float64 Describing a categorical ``Series``. >>> s = pd.Series(['a', 'a', 'b', 'c']) >>> s.describe() count 4 unique 3 top a freq 2 dtype: object Describing a timestamp ``Series``. >>> s = pd.Series([ ... np.datetime64("2000-01-01"), ... np.datetime64("2010-01-01"), ... np.datetime64("2010-01-01") ... ]) >>> s.describe() count 3 unique 2 top 2010-01-01 00:00:00 freq 2 first 2000-01-01 00:00:00 last 2010-01-01 00:00:00 dtype: object Describing a ``DataFrame``. By default only numeric fields are returned. >>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']), ... 'numeric': [1, 2, 3], ... 'object': ['a', 'b', 'c'] ... }) >>> df.describe() numeric count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Describing all columns of a ``DataFrame`` regardless of data type. >>> df.describe(include='all') categorical numeric object count 3 3.0 3 unique 3 NaN 3 top f NaN c freq 1 NaN 1 mean NaN 2.0 NaN std NaN 1.0 NaN min NaN 1.0 NaN 25% NaN 1.5 NaN 50% NaN 2.0 NaN 75% NaN 2.5 NaN max NaN 3.0 NaN Describing a column from a ``DataFrame`` by accessing it as an attribute. >>> df.numeric.describe() count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Name: numeric, dtype: float64 Including only numeric columns in a ``DataFrame`` description. >>> df.describe(include=[np.number]) numeric count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 Including only string columns in a ``DataFrame`` description. >>> df.describe(include=[np.object]) object count 3 unique 3 top c freq 1 Including only categorical columns from a ``DataFrame`` description. >>> df.describe(include=['category']) categorical count 3 unique 3 top f freq 1 Excluding numeric columns from a ``DataFrame`` description. >>> df.describe(exclude=[np.number]) categorical object count 3 3 unique 3 3 top f c freq 1 1 Excluding object columns from a ``DataFrame`` description. >>> df.describe(exclude=[np.object]) categorical numeric count 3 3.0 unique 3 NaN top f NaN freq 1 NaN mean NaN 2.0 std NaN 1.0 min NaN 1.0 25% NaN 1.5 50% NaN 2.0 75% NaN 2.5 max NaN 3.0 """ if self.ndim == 2 and self.columns.size == 0: raise ValueError("Cannot describe a DataFrame without columns") if percentiles is not None: # explicit conversion of `percentiles` to list percentiles = list(percentiles) # get them all to be in [0, 1] validate_percentile(percentiles) # median should always be included if 0.5 not in percentiles: percentiles.append(0.5) percentiles = np.asarray(percentiles) else: percentiles = np.array([0.25, 0.5, 0.75]) # sort and check for duplicates unique_pcts = np.unique(percentiles) if len(unique_pcts) < len(percentiles): raise ValueError("percentiles cannot contain duplicates") percentiles = unique_pcts formatted_percentiles = format_percentiles(percentiles) def describe_numeric_1d(series): stat_index = ( ["count", "mean", "std", "min"] + formatted_percentiles + ["max"] ) d = ( [series.count(), series.mean(), series.std(), series.min()] + series.quantile(percentiles).tolist() + [series.max()] ) return pd.Series(d, index=stat_index, name=series.name) def describe_categorical_1d(data): names = ["count", "unique"] objcounts = data.value_counts() count_unique = len(objcounts[objcounts != 0]) result = [data.count(), count_unique] dtype = None if result[1] > 0: top, freq = objcounts.index[0], objcounts.iloc[0] names += ["top", "freq"] result += [top, freq] # If the DataFrame is empty, set 'top' and 'freq' to None # to maintain output shape consistency else: names += ["top", "freq"] result += [np.nan, np.nan] dtype = "object" return pd.Series(result, index=names, name=data.name, dtype=dtype) def describe_timestamp_1d(data): # GH-30164 stat_index = ["count", "mean", "min"] + formatted_percentiles + ["max"] d = ( [data.count(), data.mean(), data.min()] + data.quantile(percentiles).tolist() + [data.max()] ) return pd.Series(d, index=stat_index, name=data.name) def describe_1d(data): if is_bool_dtype(data): return describe_categorical_1d(data) elif is_numeric_dtype(data): return describe_numeric_1d(data) elif is_datetime64_any_dtype(data): return describe_timestamp_1d(data) elif is_timedelta64_dtype(data): return describe_numeric_1d(data) else: return describe_categorical_1d(data) if self.ndim == 1: return describe_1d(self) elif (include is None) and (exclude is None): # when some numerics are found, keep only numerics data = self.select_dtypes(include=[np.number]) if len(data.columns) == 0: data = self elif include == "all": if exclude is not None: msg = "exclude must be None when include is 'all'" raise ValueError(msg) data = self else: data = self.select_dtypes(include=include, exclude=exclude) ldesc = [describe_1d(s) for _, s in data.items()] # set a convenient order for rows names: List[Label] = [] ldesc_indexes = sorted((x.index for x in ldesc), key=len) for idxnames in ldesc_indexes: for name in idxnames: if name not in names: names.append(name) d = pd.concat([x.reindex(names, copy=False) for x in ldesc], axis=1, sort=False) d.columns = data.columns.copy() return d _shared_docs[ "pct_change" ] = """ Percentage change between the current and a prior element. Computes the percentage change from the immediately previous row by default. This is useful in comparing the percentage of change in a time series of elements. Parameters ---------- periods : int, default 1 Periods to shift for forming percent change. fill_method : str, default 'pad' How to handle NAs before computing percent changes. limit : int, default None The number of consecutive NAs to fill before stopping. freq : DateOffset, timedelta, or str, optional Increment to use from time series API (e.g. 'M' or BDay()). **kwargs Additional keyword arguments are passed into `DataFrame.shift` or `Series.shift`. Returns ------- chg : Series or DataFrame The same type as the calling object. See Also -------- Series.diff : Compute the difference of two elements in a Series. DataFrame.diff : Compute the difference of two elements in a DataFrame. Series.shift : Shift the index by some number of periods. DataFrame.shift : Shift the index by some number of periods. Examples -------- **Series** >>> s = pd.Series([90, 91, 85]) >>> s 0 90 1 91 2 85 dtype: int64 >>> s.pct_change() 0 NaN 1 0.011111 2 -0.065934 dtype: float64 >>> s.pct_change(periods=2) 0 NaN 1 NaN 2 -0.055556 dtype: float64 See the percentage change in a Series where filling NAs with last valid observation forward to next valid. >>> s = pd.Series([90, 91, None, 85]) >>> s 0 90.0 1 91.0 2 NaN 3 85.0 dtype: float64 >>> s.pct_change(fill_method='ffill') 0 NaN 1 0.011111 2 0.000000 3 -0.065934 dtype: float64 **DataFrame** Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01. >>> df = pd.DataFrame({ ... 'FR': [4.0405, 4.0963, 4.3149], ... 'GR': [1.7246, 1.7482, 1.8519], ... 'IT': [804.74, 810.01, 860.13]}, ... index=['1980-01-01', '1980-02-01', '1980-03-01']) >>> df FR GR IT 1980-01-01 4.0405 1.7246 804.74 1980-02-01 4.0963 1.7482 810.01 1980-03-01 4.3149 1.8519 860.13 >>> df.pct_change() FR GR IT 1980-01-01 NaN NaN NaN 1980-02-01 0.013810 0.013684 0.006549 1980-03-01 0.053365 0.059318 0.061876 Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns. >>> df = pd.DataFrame({ ... '2016': [1769950, 30586265], ... '2015': [1500923, 40912316], ... '2014': [1371819, 41403351]}, ... index=['GOOG', 'APPL']) >>> df 2016 2015 2014 GOOG 1769950 1500923 1371819 APPL 30586265 40912316 41403351 >>> df.pct_change(axis='columns') 2016 2015 2014 GOOG NaN -0.151997 -0.086016 APPL NaN 0.337604 0.012002 """ @Appender(_shared_docs["pct_change"] % _shared_doc_kwargs) def pct_change( self: FrameOrSeries, periods=1, fill_method="pad", limit=None, freq=None, **kwargs, ) -> FrameOrSeries: # TODO: Not sure if above is correct - need someone to confirm. axis = self._get_axis_number(kwargs.pop("axis", self._stat_axis_name)) if fill_method is None: data = self else: data = self._ensure_type( self.fillna(method=fill_method, axis=axis, limit=limit) ) rs = data.div(data.shift(periods=periods, freq=freq, axis=axis, **kwargs)) - 1 if freq is not None: # Shift method is implemented differently when freq is not None # We want to restore the original index rs = rs.loc[~rs.index.duplicated()] rs = rs.reindex_like(data) return rs def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwargs): if axis is None: raise ValueError("Must specify 'axis' when aggregating by level.") grouped = self.groupby(level=level, axis=axis, sort=False) if hasattr(grouped, name) and skipna: return getattr(grouped, name)(**kwargs) axis = self._get_axis_number(axis) method = getattr(type(self), name) applyf = lambda x: method(x, axis=axis, skipna=skipna, **kwargs) return grouped.aggregate(applyf) @classmethod def _add_numeric_operations(cls): """ Add the operations to the cls; evaluate the doc strings again """ axis_descr, name1, name2 = _doc_parms(cls) cls.any = _make_logical_function( cls, "any", name1=name1, name2=name2, axis_descr=axis_descr, desc=_any_desc, func=nanops.nanany, see_also=_any_see_also, examples=_any_examples, empty_value=False, ) cls.all = _make_logical_function( cls, "all", name1=name1, name2=name2, axis_descr=axis_descr, desc=_all_desc, func=nanops.nanall, see_also=_all_see_also, examples=_all_examples, empty_value=True, ) @Substitution( desc="Return the mean absolute deviation of the values " "for the requested axis.", name1=name1, name2=name2, axis_descr=axis_descr, min_count="", see_also="", examples="", ) @Appender(_num_doc_mad) def mad(self, axis=None, skipna=None, level=None): if skipna is None: skipna = True if axis is None: axis = self._stat_axis_number if level is not None: return self._agg_by_level("mad", axis=axis, level=level, skipna=skipna) data = self._get_numeric_data() if axis == 0: demeaned = data - data.mean(axis=0) else: demeaned = data.sub(data.mean(axis=1), axis=0) return np.abs(demeaned).mean(axis=axis, skipna=skipna) cls.mad = mad cls.sem = _make_stat_function_ddof( cls, "sem", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return unbiased standard error of the mean over requested " "axis.\n\nNormalized by N-1 by default. This can be changed " "using the ddof argument", func=nanops.nansem, ) cls.var = _make_stat_function_ddof( cls, "var", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return unbiased variance over requested axis.\n\nNormalized by " "N-1 by default. This can be changed using the ddof argument", func=nanops.nanvar, ) cls.std = _make_stat_function_ddof( cls, "std", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return sample standard deviation over requested axis." "\n\nNormalized by N-1 by default. This can be changed using the " "ddof argument", func=nanops.nanstd, ) cls.cummin = _make_cum_function( cls, "cummin", name1=name1, name2=name2, axis_descr=axis_descr, desc="minimum", accum_func=np.minimum.accumulate, accum_func_name="min", mask_a=np.inf, mask_b=np.nan, examples=_cummin_examples, ) cls.cumsum = _make_cum_function( cls, "cumsum", name1=name1, name2=name2, axis_descr=axis_descr, desc="sum", accum_func=np.cumsum, accum_func_name="sum", mask_a=0.0, mask_b=np.nan, examples=_cumsum_examples, ) cls.cumprod = _make_cum_function( cls, "cumprod", name1=name1, name2=name2, axis_descr=axis_descr, desc="product", accum_func=np.cumprod, accum_func_name="prod", mask_a=1.0, mask_b=np.nan, examples=_cumprod_examples, ) cls.cummax = _make_cum_function( cls, "cummax", name1=name1, name2=name2, axis_descr=axis_descr, desc="maximum", accum_func=np.maximum.accumulate, accum_func_name="max", mask_a=-np.inf, mask_b=np.nan, examples=_cummax_examples, ) cls.sum = _make_min_count_stat_function( cls, "sum", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return the sum of the values for the requested axis.\n\n" "This is equivalent to the method ``numpy.sum``.", func=nanops.nansum, see_also=_stat_func_see_also, examples=_sum_examples, ) cls.mean = _make_stat_function( cls, "mean", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return the mean of the values for the requested axis.", func=nanops.nanmean, ) cls.skew = _make_stat_function( cls, "skew", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return unbiased skew over requested axis.\n\nNormalized by N-1.", func=nanops.nanskew, ) cls.kurt = _make_stat_function( cls, "kurt", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return unbiased kurtosis over requested axis.\n\n" "Kurtosis obtained using Fisher's definition of\n" "kurtosis (kurtosis of normal == 0.0). Normalized " "by N-1.", func=nanops.nankurt, ) cls.kurtosis = cls.kurt cls.prod = _make_min_count_stat_function( cls, "prod", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return the product of the values for the requested axis.", func=nanops.nanprod, examples=_prod_examples, ) cls.product = cls.prod cls.median = _make_stat_function( cls, "median", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return the median of the values for the requested axis.", func=nanops.nanmedian, ) cls.max = _make_stat_function( cls, "max", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return the maximum of the values for the requested axis.\n\n" "If you want the *index* of the maximum, use ``idxmax``. This is" "the equivalent of the ``numpy.ndarray`` method ``argmax``.", func=nanops.nanmax, see_also=_stat_func_see_also, examples=_max_examples, ) cls.min = _make_stat_function( cls, "min", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return the minimum of the values for the requested axis.\n\n" "If you want the *index* of the minimum, use ``idxmin``. This is" "the equivalent of the ``numpy.ndarray`` method ``argmin``.", func=nanops.nanmin, see_also=_stat_func_see_also, examples=_min_examples, ) @classmethod def _add_series_or_dataframe_operations(cls): """ Add the series or dataframe only operations to the cls; evaluate the doc strings again. """ from pandas.core.window import EWM, Expanding, Rolling, Window @Appender(Rolling.__doc__) def rolling( self, window, min_periods=None, center=False, win_type=None, on=None, axis=0, closed=None, ): axis = self._get_axis_number(axis) if win_type is not None: return Window( self, window=window, min_periods=min_periods, center=center, win_type=win_type, on=on, axis=axis, closed=closed, ) return Rolling( self, window=window, min_periods=min_periods, center=center, win_type=win_type, on=on, axis=axis, closed=closed, ) cls.rolling = rolling @Appender(Expanding.__doc__) def expanding(self, min_periods=1, center=False, axis=0): axis = self._get_axis_number(axis) return Expanding(self, min_periods=min_periods, center=center, axis=axis) cls.expanding = expanding @Appender(EWM.__doc__) def ewm( self, com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, axis=0, ): axis = self._get_axis_number(axis) return EWM( self, com=com, span=span, halflife=halflife, alpha=alpha, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, axis=axis, ) cls.ewm = ewm @Appender(_shared_docs["transform"] % dict(axis="", **_shared_doc_kwargs)) def transform(self, func, *args, **kwargs): result = self.agg(func, *args, **kwargs) if is_scalar(result) or len(result) != len(self): raise ValueError("transforms cannot produce aggregated results") return result # ---------------------------------------------------------------------- # Misc methods _shared_docs[ "valid_index" ] = """ Return index for %(position)s non-NA/null value. Returns ------- scalar : type of index Notes ----- If all elements are non-NA/null, returns None. Also returns None for empty %(klass)s. """ def _find_valid_index(self, how: str): """ Retrieves the index of the first valid value. Parameters ---------- how : {'first', 'last'} Use this parameter to change between the first or last valid index. Returns ------- idx_first_valid : type of index """ idxpos = find_valid_index(self._values, how) if idxpos is None: return None return self.index[idxpos] @Appender( _shared_docs["valid_index"] % {"position": "first", "klass": "Series/DataFrame"} ) def first_valid_index(self): return self._find_valid_index("first") @Appender( _shared_docs["valid_index"] % {"position": "last", "klass": "Series/DataFrame"} ) def last_valid_index(self): return self._find_valid_index("last") def _doc_parms(cls): """Return a tuple of the doc parms.""" axis_descr = ( f"{{{', '.join(f'{a} ({i})' for i, a in enumerate(cls._AXIS_ORDERS))}}}" ) name = cls._constructor_sliced.__name__ if cls._AXIS_LEN > 1 else "scalar" name2 = cls.__name__ return axis_descr, name, name2 _num_doc = """ %(desc)s Parameters ---------- axis : %(axis_descr)s Axis for the function to be applied on. skipna : bool, default True Exclude NA/null values when computing the result. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a %(name1)s. numeric_only : bool, default None Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series. %(min_count)s\ **kwargs Additional keyword arguments to be passed to the function. Returns ------- %(name1)s or %(name2)s (if level specified)\ %(see_also)s\ %(examples)s """ _num_doc_mad = """ %(desc)s Parameters ---------- axis : %(axis_descr)s Axis for the function to be applied on. skipna : bool, default None Exclude NA/null values when computing the result. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a %(name1)s. Returns ------- %(name1)s or %(name2)s (if level specified)\ %(see_also)s\ %(examples)s """ _num_ddof_doc = """ %(desc)s Parameters ---------- axis : %(axis_descr)s skipna : bool, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a %(name1)s. ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. numeric_only : bool, default None Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series. Returns ------- %(name1)s or %(name2)s (if level specified)\n""" _bool_doc = """ %(desc)s Parameters ---------- axis : {0 or 'index', 1 or 'columns', None}, default 0 Indicate which axis or axes should be reduced. * 0 / 'index' : reduce the index, return a Series whose index is the original column labels. * 1 / 'columns' : reduce the columns, return a Series whose index is the original index. * None : reduce all axes, return a scalar. bool_only : bool, default None Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series. skipna : bool, default True Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be %(empty_value)s, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a %(name1)s. **kwargs : any, default None Additional keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- %(name1)s or %(name2)s If level is specified, then, %(name2)s is returned; otherwise, %(name1)s is returned. %(see_also)s %(examples)s""" _all_desc = """\ Return whether all elements are True, potentially over an axis. Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).""" _all_examples = """\ Examples -------- **Series** >>> pd.Series([True, True]).all() True >>> pd.Series([True, False]).all() False >>> pd.Series([]).all() True >>> pd.Series([np.nan]).all() True >>> pd.Series([np.nan]).all(skipna=False) True **DataFrames** Create a dataframe from a dictionary. >>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]}) >>> df col1 col2 0 True True 1 True False Default behaviour checks if column-wise values all return True. >>> df.all() col1 True col2 False dtype: bool Specify ``axis='columns'`` to check if row-wise values all return True. >>> df.all(axis='columns') 0 True 1 False dtype: bool Or ``axis=None`` for whether every value is True. >>> df.all(axis=None) False """ _all_see_also = """\ See Also -------- Series.all : Return True if all elements are True. DataFrame.any : Return True if one (or more) elements are True. """ _cnum_doc = """ Return cumulative %(desc)s over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative %(desc)s. Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 The index or the name of the axis. 0 is equivalent to None or 'index'. skipna : bool, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA. *args, **kwargs : Additional keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- %(name1)s or %(name2)s See Also -------- core.window.Expanding.%(accum_func_name)s : Similar functionality but ignores ``NaN`` values. %(name2)s.%(accum_func_name)s : Return the %(desc)s over %(name2)s axis. %(name2)s.cummax : Return cumulative maximum over %(name2)s axis. %(name2)s.cummin : Return cumulative minimum over %(name2)s axis. %(name2)s.cumsum : Return cumulative sum over %(name2)s axis. %(name2)s.cumprod : Return cumulative product over %(name2)s axis. %(examples)s""" _cummin_examples = """\ Examples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cummin() 0 2.0 1 NaN 2 2.0 3 -1.0 4 -1.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cummin(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the minimum in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cummin() A B 0 2.0 1.0 1 2.0 NaN 2 1.0 0.0 To iterate over columns and find the minimum in each row, use ``axis=1`` >>> df.cummin(axis=1) A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 """ _cumsum_examples = """\ Examples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cumsum() 0 2.0 1 NaN 2 7.0 3 6.0 4 6.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cumsum(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the sum in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cumsum() A B 0 2.0 1.0 1 5.0 NaN 2 6.0 1.0 To iterate over columns and find the sum in each row, use ``axis=1`` >>> df.cumsum(axis=1) A B 0 2.0 3.0 1 3.0 NaN 2 1.0 1.0 """ _cumprod_examples = """\ Examples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cumprod() 0 2.0 1 NaN 2 10.0 3 -10.0 4 -0.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cumprod(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the product in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cumprod() A B 0 2.0 1.0 1 6.0 NaN 2 6.0 0.0 To iterate over columns and find the product in each row, use ``axis=1`` >>> df.cumprod(axis=1) A B 0 2.0 2.0 1 3.0 NaN 2 1.0 0.0 """ _cummax_examples = """\ Examples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cummax() 0 2.0 1 NaN 2 5.0 3 5.0 4 5.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cummax(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the maximum in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cummax() A B 0 2.0 1.0 1 3.0 NaN 2 3.0 1.0 To iterate over columns and find the maximum in each row, use ``axis=1`` >>> df.cummax(axis=1) A B 0 2.0 2.0 1 3.0 NaN 2 1.0 1.0 """ _any_see_also = """\ See Also -------- numpy.any : Numpy version of this method. Series.any : Return whether any element is True. Series.all : Return whether all elements are True. DataFrame.any : Return whether any element is True over requested axis. DataFrame.all : Return whether all elements are True over requested axis. """ _any_desc = """\ Return whether any element is True, potentially over an axis. Returns False unless there at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).""" _any_examples = """\ Examples -------- **Series** For Series input, the output is a scalar indicating whether any element is True. >>> pd.Series([False, False]).any() False >>> pd.Series([True, False]).any() True >>> pd.Series([]).any() False >>> pd.Series([np.nan]).any() False >>> pd.Series([np.nan]).any(skipna=False) True **DataFrame** Whether each column contains at least one True element (the default). >>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]}) >>> df A B C 0 1 0 0 1 2 2 0 >>> df.any() A True B True C False dtype: bool Aggregating over the columns. >>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]}) >>> df A B 0 True 1 1 False 2 >>> df.any(axis='columns') 0 True 1 True dtype: bool >>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]}) >>> df A B 0 True 1 1 False 0 >>> df.any(axis='columns') 0 True 1 False dtype: bool Aggregating over the entire DataFrame with ``axis=None``. >>> df.any(axis=None) True `any` for an empty DataFrame is an empty Series. >>> pd.DataFrame([]).any() Series([], dtype: bool) """ _shared_docs[ "stat_func_example" ] = """ Examples -------- >>> idx = pd.MultiIndex.from_arrays([ ... ['warm', 'warm', 'cold', 'cold'], ... ['dog', 'falcon', 'fish', 'spider']], ... names=['blooded', 'animal']) >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx) >>> s blooded animal warm dog 4 falcon 2 cold fish 0 spider 8 Name: legs, dtype: int64 >>> s.{stat_func}() {default_output} {verb} using level names, as well as indices. >>> s.{stat_func}(level='blooded') blooded warm {level_output_0} cold {level_output_1} Name: legs, dtype: int64 >>> s.{stat_func}(level=0) blooded warm {level_output_0} cold {level_output_1} Name: legs, dtype: int64""" _sum_examples = _shared_docs["stat_func_example"].format( stat_func="sum", verb="Sum", default_output=14, level_output_0=6, level_output_1=8 ) _sum_examples += """ By default, the sum of an empty or all-NA Series is ``0``. >>> pd.Series([]).sum() # min_count=0 is the default 0.0 This can be controlled with the ``min_count`` parameter. For example, if you'd like the sum of an empty series to be NaN, pass ``min_count=1``. >>> pd.Series([]).sum(min_count=1) nan Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and empty series identically. >>> pd.Series([np.nan]).sum() 0.0 >>> pd.Series([np.nan]).sum(min_count=1) nan""" _max_examples = _shared_docs["stat_func_example"].format( stat_func="max", verb="Max", default_output=8, level_output_0=4, level_output_1=8 ) _min_examples = _shared_docs["stat_func_example"].format( stat_func="min", verb="Min", default_output=0, level_output_0=2, level_output_1=0 ) _stat_func_see_also = """ See Also -------- Series.sum : Return the sum. Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.""" _prod_examples = """ Examples -------- By default, the product of an empty or all-NA Series is ``1`` >>> pd.Series([]).prod() 1.0 This can be controlled with the ``min_count`` parameter >>> pd.Series([]).prod(min_count=1) nan Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and empty series identically. >>> pd.Series([np.nan]).prod() 1.0 >>> pd.Series([np.nan]).prod(min_count=1) nan""" _min_count_stub = """\ min_count : int, default 0 The required number of valid values to perform the operation. If fewer than ``min_count`` non-NA values are present the result will be NA. .. versionadded:: 0.22.0 Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1. """ def _make_min_count_stat_function( cls, name: str, name1: str, name2: str, axis_descr: str, desc: str, func: Callable, see_also: str = "", examples: str = "", ) -> Callable: @Substitution( desc=desc, name1=name1, name2=name2, axis_descr=axis_descr, min_count=_min_count_stub, see_also=see_also, examples=examples, ) @Appender(_num_doc) def stat_func( self, axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs, ): if name == "sum": nv.validate_sum(tuple(), kwargs) elif name == "prod": nv.validate_prod(tuple(), kwargs) else: nv.validate_stat_func(tuple(), kwargs, fname=name) if skipna is None: skipna = True if axis is None: axis = self._stat_axis_number if level is not None: return self._agg_by_level( name, axis=axis, level=level, skipna=skipna, min_count=min_count ) return self._reduce( func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only, min_count=min_count, ) return set_function_name(stat_func, name, cls) def _make_stat_function( cls, name: str, name1: str, name2: str, axis_descr: str, desc: str, func: Callable, see_also: str = "", examples: str = "", ) -> Callable: @Substitution( desc=desc, name1=name1, name2=name2, axis_descr=axis_descr, min_count="", see_also=see_also, examples=examples, ) @Appender(_num_doc) def stat_func( self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs ): if name == "median": nv.validate_median(tuple(), kwargs) else: nv.validate_stat_func(tuple(), kwargs, fname=name) if skipna is None: skipna = True if axis is None: axis = self._stat_axis_number if level is not None: return self._agg_by_level(name, axis=axis, level=level, skipna=skipna) return self._reduce( func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only ) return set_function_name(stat_func, name, cls) def _make_stat_function_ddof( cls, name: str, name1: str, name2: str, axis_descr: str, desc: str, func: Callable ) -> Callable: @Substitution(desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) @Appender(_num_ddof_doc) def stat_func( self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs ): nv.validate_stat_ddof_func(tuple(), kwargs, fname=name) if skipna is None: skipna = True if axis is None: axis = self._stat_axis_number if level is not None: return self._agg_by_level( name, axis=axis, level=level, skipna=skipna, ddof=ddof ) return self._reduce( func, name, axis=axis, numeric_only=numeric_only, skipna=skipna, ddof=ddof ) return set_function_name(stat_func, name, cls) def _make_cum_function( cls, name: str, name1: str, name2: str, axis_descr: str, desc: str, accum_func: Callable, accum_func_name: str, mask_a: float, mask_b: float, examples: str, ) -> Callable: @Substitution( desc=desc, name1=name1, name2=name2, axis_descr=axis_descr, accum_func_name=accum_func_name, examples=examples, ) @Appender(_cnum_doc) def cum_func(self, axis=None, skipna=True, *args, **kwargs): skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name) if axis is None: axis = self._stat_axis_number else: axis = self._get_axis_number(axis) if axis == 1: return cum_func(self.T, axis=0, skipna=skipna, *args, **kwargs).T def na_accum_func(blk_values): # We will be applying this function to block values if blk_values.dtype.kind in ["m", "M"]: # GH#30460, GH#29058 # numpy 1.18 started sorting NaTs at the end instead of beginning, # so we need to work around to maintain backwards-consistency. orig_dtype = blk_values.dtype # We need to define mask before masking NaTs mask = isna(blk_values) if accum_func == np.minimum.accumulate: # Note: the accum_func comparison fails as an "is" comparison y = blk_values.view("i8") y[mask] = np.iinfo(np.int64).max changed = True else: y = blk_values changed = False result = accum_func(y.view("i8"), axis) if skipna: np.putmask(result, mask, iNaT) elif accum_func == np.minimum.accumulate: # Restore NaTs that we masked previously nz = (~np.asarray(mask)).nonzero()[0] if len(nz): # everything up to the first non-na entry stays NaT result[: nz[0]] = iNaT if changed: # restore NaT elements y[mask] = iNaT # TODO: could try/finally for this? if isinstance(blk_values, np.ndarray): result = result.view(orig_dtype) else: # DatetimeArray result = type(blk_values)._from_sequence(result, dtype=orig_dtype) elif skipna and not issubclass( blk_values.dtype.type, (np.integer, np.bool_) ): vals = blk_values.copy().T mask = isna(vals) np.putmask(vals, mask, mask_a) result = accum_func(vals, axis) np.putmask(result, mask, mask_b) else: result = accum_func(blk_values.T, axis) # transpose back for ndarray, not for EA return result.T if hasattr(result, "T") else result result = self._data.apply(na_accum_func) d = self._construct_axes_dict() d["copy"] = False return self._constructor(result, **d).__finalize__(self) return set_function_name(cum_func, name, cls) def _make_logical_function( cls, name: str, name1: str, name2: str, axis_descr: str, desc: str, func: Callable, see_also: str, examples: str, empty_value: bool, ) -> Callable: @Substitution( desc=desc, name1=name1, name2=name2, axis_descr=axis_descr, see_also=see_also, examples=examples, empty_value=empty_value, ) @Appender(_bool_doc) def logical_func(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs): nv.validate_logical_func(tuple(), kwargs, fname=name) if level is not None: if bool_only is not None: raise NotImplementedError( "Option bool_only is not implemented with option level." ) return self._agg_by_level(name, axis=axis, level=level, skipna=skipna) return self._reduce( func, name=name, axis=axis, skipna=skipna, numeric_only=bool_only, filter_type="bool", ) return set_function_name(logical_func, name, cls)

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import collections from datetime import timedelta import functools import gc import json import operator import pickle import re from textwrap import dedent from typing import ( TYPE_CHECKING, Any, Callable, Dict, FrozenSet, Hashable, List, Mapping, Optional, Sequence, Set, Tuple, Type, Union, ) import warnings import weakref import numpy as np from pandas._config import config from pandas._libs import Timestamp, iNaT, lib from pandas._typing import ( Axis, FilePathOrBuffer, FrameOrSeries, JSONSerializable, Label, Level, Renamer, ) from pandas.compat import set_function_name from pandas.compat._optional import import_optional_dependency from pandas.compat.numpy import function as nv from pandas.errors import AbstractMethodError from pandas.util._decorators import ( Appender, Substitution, doc, rewrite_axis_style_signature, ) from pandas.util._validators import ( validate_bool_kwarg, validate_fillna_kwargs, validate_percentile, ) from pandas.core.dtypes.common import ( ensure_int64, ensure_object, ensure_str, is_bool, is_bool_dtype, is_datetime64_any_dtype, is_datetime64tz_dtype, is_dict_like, is_extension_array_dtype, is_float, is_integer, is_list_like, is_number, is_numeric_dtype, is_object_dtype, is_re_compilable, is_scalar, is_timedelta64_dtype, pandas_dtype, ) from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries from pandas.core.dtypes.inference import is_hashable from pandas.core.dtypes.missing import isna, notna import pandas as pd from pandas.core import missing, nanops import pandas.core.algorithms as algos from pandas.core.base import PandasObject, SelectionMixin import pandas.core.common as com from pandas.core.construction import create_series_with_explicit_dtype from pandas.core.indexes.api import ( Index, InvalidIndexError, MultiIndex, RangeIndex, ensure_index, ) from pandas.core.indexes.datetimes import DatetimeIndex from pandas.core.indexes.period import Period, PeriodIndex import pandas.core.indexing as indexing from pandas.core.internals import BlockManager from pandas.core.missing import find_valid_index from pandas.core.ops import _align_method_FRAME from pandas.io.formats import format as fmt from pandas.io.formats.format import DataFrameFormatter, format_percentiles from pandas.io.formats.printing import pprint_thing from pandas.tseries.frequencies import to_offset if TYPE_CHECKING: from pandas.core.resample import Resampler _shared_docs: Dict[str, str] = dict() _shared_doc_kwargs = dict( axes="keywords for axes", klass="Series/DataFrame", axes_single_arg="int or labels for object", args_transpose="axes to permute (int or label for object)", optional_by=""" by : str or list of str Name or list of names to sort by""", ) def _single_replace(self, to_replace, method, inplace, limit): if self.ndim != 1: raise TypeError( f"cannot replace {to_replace} with method {method} on a " f"{type(self).__name__}" ) orig_dtype = self.dtype result = self if inplace else self.copy() fill_f = missing.get_fill_func(method) mask = missing.mask_missing(result.values, to_replace) values = fill_f(result.values, limit=limit, mask=mask) if values.dtype == orig_dtype and inplace: return result = pd.Series(values, index=self.index, dtype=self.dtype).__finalize__(self) if inplace: self._update_inplace(result._data) return return result bool_t = bool class NDFrame(PandasObject, SelectionMixin, indexing.IndexingMixin): _internal_names: List[str] = [ "_data", "_cacher", "_item_cache", "_cache", "_is_copy", "_subtyp", "_name", "_index", "_default_kind", "_default_fill_value", "_metadata", "__array_struct__", "__array_interface__", ] _internal_names_set: Set[str] = set(_internal_names) _accessors: Set[str] = set() _deprecations: FrozenSet[str] = frozenset(["get_values"]) _metadata: List[str] = [] _is_copy = None _data: BlockManager _attrs: Dict[Optional[Hashable], Any] _typ: str def __init__( self, data: BlockManager, copy: bool = False, attrs: Optional[Mapping[Optional[Hashable], Any]] = None, ): object.__setattr__(self, "_is_copy", None) object.__setattr__(self, "_data", data) object.__setattr__(self, "_item_cache", {}) if attrs is None: attrs = {} else: attrs = dict(attrs) object.__setattr__(self, "_attrs", attrs) @classmethod def _init_mgr(cls, mgr, axes=None, dtype=None, copy=False): for a, axe in axes.items(): if axe is not None: mgr = mgr.reindex_axis( axe, axis=cls._get_block_manager_axis(a), copy=False ) if copy: mgr = mgr.copy() if dtype is not None: if len(mgr.blocks) > 1 or mgr.blocks[0].values.dtype != dtype: mgr = mgr.astype(dtype=dtype) return mgr @property def attrs(self) -> Dict[Optional[Hashable], Any]: if self._attrs is None: self._attrs = {} return self._attrs @attrs.setter def attrs(self, value: Mapping[Optional[Hashable], Any]) -> None: self._attrs = dict(value) @classmethod def _validate_dtype(cls, dtype): if dtype is not None: dtype = pandas_dtype(dtype) if dtype.kind == "V": raise NotImplementedError( "compound dtypes are not implemented " f"in the {cls.__name__} constructor" ) return dtype @property def _constructor(self: FrameOrSeries) -> Type[FrameOrSeries]: raise AbstractMethodError(self) @property def _constructor_sliced(self): raise AbstractMethodError(self) @property def _constructor_expanddim(self): raise NotImplementedError _AXIS_ALIASES = {"rows": 0} _AXIS_IALIASES = {0: "rows"} _stat_axis_number = 0 _stat_axis_name = "index" _ix = None _AXIS_ORDERS: List[str] _AXIS_NUMBERS: Dict[str, int] _AXIS_NAMES: Dict[int, str] _AXIS_REVERSED: bool _info_axis_number: int _info_axis_name: str _AXIS_LEN: int def _construct_axes_dict(self, axes=None, **kwargs): d = {a: self._get_axis(a) for a in (axes or self._AXIS_ORDERS)} d.update(kwargs) return d @classmethod def _construct_axes_from_arguments( cls, args, kwargs, require_all: bool = False, sentinel=None ): args = list(args) for a in cls._AXIS_ORDERS: if a not in kwargs: try: kwargs[a] = args.pop(0) except IndexError as err: if require_all: raise TypeError( "not enough/duplicate arguments specified!" ) from err axes = {a: kwargs.pop(a, sentinel) for a in cls._AXIS_ORDERS} return axes, kwargs @classmethod def _get_axis_number(cls, axis): axis = cls._AXIS_ALIASES.get(axis, axis) if is_integer(axis): if axis in cls._AXIS_NAMES: return axis else: try: return cls._AXIS_NUMBERS[axis] except KeyError: pass raise ValueError(f"No axis named {axis} for object type {cls}") @classmethod def _get_axis_name(cls, axis): axis = cls._AXIS_ALIASES.get(axis, axis) if isinstance(axis, str): if axis in cls._AXIS_NUMBERS: return axis else: try: return cls._AXIS_NAMES[axis] except KeyError: pass raise ValueError(f"No axis named {axis} for object type {cls}") def _get_axis(self, axis): name = self._get_axis_name(axis) return getattr(self, name) @classmethod def _get_block_manager_axis(cls, axis): axis = cls._get_axis_number(axis) if cls._AXIS_REVERSED: m = cls._AXIS_LEN - 1 return m - axis return axis def _get_axis_resolvers(self, axis: str) -> Dict[str, ABCSeries]: axis_index = getattr(self, axis) d = dict() prefix = axis[0] for i, name in enumerate(axis_index.names): if name is not None: key = level = name else: key = f"{prefix}level_{i}" level = i level_values = axis_index.get_level_values(level) s = level_values.to_series() s.index = axis_index d[key] = s if isinstance(axis_index, MultiIndex): dindex = axis_index else: dindex = axis_index.to_series() d[axis] = dindex return d def _get_index_resolvers(self) -> Dict[str, ABCSeries]: from pandas.core.computation.parsing import clean_column_name d: Dict[str, ABCSeries] = {} for axis_name in self._AXIS_ORDERS: d.update(self._get_axis_resolvers(axis_name)) return {clean_column_name(k): v for k, v in d.items() if not isinstance(k, int)} def _get_cleaned_column_resolvers(self) -> Dict[str, ABCSeries]: from pandas.core.computation.parsing import clean_column_name if isinstance(self, ABCSeries): return {clean_column_name(self.name): self} return { clean_column_name(k): v for k, v in self.items() if not isinstance(k, int) } @property def _info_axis(self): return getattr(self, self._info_axis_name) @property def _stat_axis(self): return getattr(self, self._stat_axis_name) @property def shape(self) -> Tuple[int, ...]: return tuple(len(self._get_axis(a)) for a in self._AXIS_ORDERS) @property def axes(self) -> List[Index]: return [self._get_axis(a) for a in self._AXIS_ORDERS] @property def ndim(self) -> int: return self._data.ndim @property def size(self) -> int: return np.prod(self.shape) @property def _selected_obj(self: FrameOrSeries) -> FrameOrSeries: return self @property def _obj_with_exclusions(self: FrameOrSeries) -> FrameOrSeries: return self def set_axis(self, labels, axis: Axis = 0, inplace: bool = False): if inplace: setattr(self, self._get_axis_name(axis), labels) else: obj = self.copy() obj.set_axis(labels, axis=axis, inplace=True) return obj def _set_axis(self, axis: int, labels: Index) -> None: labels = ensure_index(labels) self._data.set_axis(axis, labels) self._clear_item_cache() def swapaxes(self: FrameOrSeries, axis1, axis2, copy=True) -> FrameOrSeries: i = self._get_axis_number(axis1) j = self._get_axis_number(axis2) if i == j: if copy: return self.copy() return self mapping = {i: j, j: i} new_axes = (self._get_axis(mapping.get(k, k)) for k in range(self._AXIS_LEN)) new_values = self.values.swapaxes(i, j) if copy: new_values = new_values.copy() return self._constructor(new_values, *new_axes).__finalize__(self) def droplevel(self: FrameOrSeries, level, axis=0) -> FrameOrSeries: labels = self._get_axis(axis) new_labels = labels.droplevel(level) result = self.set_axis(new_labels, axis=axis, inplace=False) return result def pop(self: FrameOrSeries, item) -> FrameOrSeries: result = self[item] del self[item] try: result._reset_cacher() except AttributeError: pass return result def squeeze(self, axis=None): axis = self._AXIS_NAMES if axis is None else (self._get_axis_number(axis),) return self.iloc[ tuple( 0 if i in axis and len(a) == 1 else slice(None) for i, a in enumerate(self.axes) ) ] def rename( self: FrameOrSeries, mapper: Optional[Renamer] = None, *, index: Optional[Renamer] = None, columns: Optional[Renamer] = None, axis: Optional[Axis] = None, copy: bool = True, inplace: bool = False, level: Optional[Level] = None, errors: str = "ignore", ) -> Optional[FrameOrSeries]: if mapper is None and index is None and columns is None: raise TypeError("must pass an index to rename") if index is not None or columns is not None: if axis is not None: raise TypeError( "Cannot specify both 'axis' and any of 'index' or 'columns'" ) elif mapper is not None: raise TypeError( "Cannot specify both 'mapper' and any of 'index' or 'columns'" ) else: if axis and self._get_axis_number(axis) == 1: columns = mapper else: index = mapper result = self if inplace else self.copy(deep=copy) for axis_no, replacements in enumerate((index, columns)): if replacements is None: continue ax = self._get_axis(axis_no) baxis = self._get_block_manager_axis(axis_no) f = com.get_rename_function(replacements) if level is not None: level = ax._get_level_number(level) if not callable(replacements): indexer = ax.get_indexer_for(replacements) if errors == "raise" and len(indexer[indexer == -1]): missing_labels = [ label for index, label in enumerate(replacements) if indexer[index] == -1 ] raise KeyError(f"{missing_labels} not found in axis") result._data = result._data.rename_axis( f, axis=baxis, copy=copy, level=level ) result._clear_item_cache() if inplace: self._update_inplace(result._data) return None else: return result.__finalize__(self) @rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)]) def rename_axis(self, mapper=lib.no_default, **kwargs): axes, kwargs = self._construct_axes_from_arguments( (), kwargs, sentinel=lib.no_default ) copy = kwargs.pop("copy", True) inplace = kwargs.pop("inplace", False) axis = kwargs.pop("axis", 0) if axis is not None: axis = self._get_axis_number(axis) if kwargs: raise TypeError( "rename_axis() got an unexpected keyword " f'argument "{list(kwargs.keys())[0]}"' ) inplace = validate_bool_kwarg(inplace, "inplace") if mapper is not lib.no_default: non_mapper = is_scalar(mapper) or ( is_list_like(mapper) and not is_dict_like(mapper) ) if non_mapper: return self._set_axis_name(mapper, axis=axis, inplace=inplace) else: raise ValueError("Use `.rename` to alter labels with a mapper.") else: result = self if inplace else self.copy(deep=copy) for axis in range(self._AXIS_LEN): v = axes.get(self._AXIS_NAMES[axis]) if v is lib.no_default: continue non_mapper = is_scalar(v) or (is_list_like(v) and not is_dict_like(v)) if non_mapper: newnames = v else: f = com.get_rename_function(v) curnames = self._get_axis(axis).names newnames = [f(name) for name in curnames] result._set_axis_name(newnames, axis=axis, inplace=True) if not inplace: return result def _set_axis_name(self, name, axis=0, inplace=False): axis = self._get_axis_number(axis) idx = self._get_axis(axis).set_names(name) inplace = validate_bool_kwarg(inplace, "inplace") renamed = self if inplace else self.copy() renamed.set_axis(idx, axis=axis, inplace=True) if not inplace: return renamed def _indexed_same(self, other) -> bool: return all( self._get_axis(a).equals(other._get_axis(a)) for a in self._AXIS_ORDERS ) def equals(self, other): if not isinstance(other, self._constructor): return False return self._data.equals(other._data) def __neg__(self): values = self._values if is_bool_dtype(values): arr = operator.inv(values) elif ( is_numeric_dtype(values) or is_timedelta64_dtype(values) or is_object_dtype(values) ): arr = operator.neg(values) else: raise TypeError(f"Unary negative expects numeric dtype, not {values.dtype}") return self.__array_wrap__(arr) def __pos__(self): values = self._values if is_bool_dtype(values): arr = values elif ( is_numeric_dtype(values) or is_timedelta64_dtype(values) or is_object_dtype(values) ): arr = operator.pos(values) else: raise TypeError(f"Unary plus expects numeric dtype, not {values.dtype}") return self.__array_wrap__(arr) def __invert__(self): if not self.size: return self new_data = self._data.apply(operator.invert) result = self._constructor(new_data).__finalize__(self) return result def __nonzero__(self): raise ValueError( f"The truth value of a {type(self).__name__} is ambiguous. " "Use a.empty, a.bool(), a.item(), a.any() or a.all()." ) __bool__ = __nonzero__ def bool(self): v = self.squeeze() if isinstance(v, (bool, np.bool_)): return bool(v) elif is_scalar(v): raise ValueError( "bool cannot act on a non-boolean single element " f"{type(self).__name__}" ) self.__nonzero__() def __abs__(self: FrameOrSeries) -> FrameOrSeries: return self.abs() def __round__(self: FrameOrSeries, decimals: int = 0) -> FrameOrSeries: return self.round(decimals) def _is_level_reference(self, key, axis=0): axis = self._get_axis_number(axis) return ( key is not None and is_hashable(key) and key in self.axes[axis].names and not self._is_label_reference(key, axis=axis) ) def _is_label_reference(self, key, axis=0) -> bool_t: axis = self._get_axis_number(axis) other_axes = (ax for ax in range(self._AXIS_LEN) if ax != axis) return ( key is not None and is_hashable(key) and any(key in self.axes[ax] for ax in other_axes) ) def _is_label_or_level_reference(self, key: str, axis: int = 0) -> bool_t: return self._is_level_reference(key, axis=axis) or self._is_label_reference( key, axis=axis ) def _check_label_or_level_ambiguity(self, key, axis: int = 0) -> None: axis = self._get_axis_number(axis) other_axes = (ax for ax in range(self._AXIS_LEN) if ax != axis) if ( key is not None and is_hashable(key) and key in self.axes[axis].names and any(key in self.axes[ax] for ax in other_axes) ): level_article, level_type = ( ("an", "index") if axis == 0 else ("a", "column") ) label_article, label_type = ( ("a", "column") if axis == 0 else ("an", "index") ) msg = ( f"'{key}' is both {level_article} {level_type} level and " f"{label_article} {label_type} label, which is ambiguous." ) raise ValueError(msg) def _get_label_or_level_values(self, key: str, axis: int = 0) -> np.ndarray: axis = self._get_axis_number(axis) other_axes = [ax for ax in range(self._AXIS_LEN) if ax != axis] if self._is_label_reference(key, axis=axis): self._check_label_or_level_ambiguity(key, axis=axis) values = self.xs(key, axis=other_axes[0])._values elif self._is_level_reference(key, axis=axis): values = self.axes[axis].get_level_values(key)._values else: raise KeyError(key) if values.ndim > 1: if other_axes and isinstance(self._get_axis(other_axes[0]), MultiIndex): multi_message = ( "\n" "For a multi-index, the label must be a " "tuple with elements corresponding to each level." ) else: multi_message = "" label_axis_name = "column" if axis == 0 else "index" raise ValueError( ( f"The {label_axis_name} label '{key}' " f"is not unique.{multi_message}" ) ) return values def _drop_labels_or_levels(self, keys, axis: int = 0): axis = self._get_axis_number(axis) keys = com.maybe_make_list(keys) invalid_keys = [ k for k in keys if not self._is_label_or_level_reference(k, axis=axis) ] if invalid_keys: raise ValueError( ( "The following keys are not valid labels or " f"levels for axis {axis}: {invalid_keys}" ) ) levels_to_drop = [k for k in keys if self._is_level_reference(k, axis=axis)] labels_to_drop = [k for k in keys if not self._is_level_reference(k, axis=axis)] dropped = self.copy() if axis == 0: if levels_to_drop: dropped.reset_index(levels_to_drop, drop=True, inplace=True) if labels_to_drop: dropped.drop(labels_to_drop, axis=1, inplace=True) else: if levels_to_drop: if isinstance(dropped.columns, MultiIndex): dropped.columns = dropped.columns.droplevel(levels_to_drop) else: dropped.columns = RangeIndex(dropped.columns.size) if labels_to_drop: dropped.drop(labels_to_drop, axis=0, inplace=True) return dropped def __hash__(self): raise TypeError( f"{repr(type(self).__name__)} objects are mutable, " f"thus they cannot be hashed" ) def __iter__(self): return iter(self._info_axis) def keys(self): return self._info_axis def items(self): for h in self._info_axis: yield h, self[h] @Appender(items.__doc__) def iteritems(self): return self.items() def __len__(self) -> int: return len(self._info_axis) def __contains__(self, key) -> bool_t: return key in self._info_axis @property def empty(self) -> bool_t: return any(len(self._get_axis(a)) == 0 for a in self._AXIS_ORDERS) None) -> np.ndarray: return np.asarray(self._values, dtype=dtype) def __array_wrap__(self, result, context=None): result = lib.item_from_zerodim(result) if is_scalar(result): return result d = self._construct_axes_dict(self._AXIS_ORDERS, copy=False) return self._constructor(result, **d).__finalize__(self) def __getstate__(self) -> Dict[str, Any]: meta = {k: getattr(self, k, None) for k in self._metadata} return dict( _data=self._data, _typ=self._typ, _metadata=self._metadata, attrs=self.attrs, **meta, ) def __setstate__(self, state): if isinstance(state, BlockManager): self._data = state elif isinstance(state, dict): typ = state.get("_typ") if typ is not None: attrs = state.get("_attrs", {}) object.__setattr__(self, "_attrs", attrs) meta = set(self._internal_names + self._metadata) for k in list(meta): if k in state: v = state[k] object.__setattr__(self, k, v) for k, v in state.items(): if k not in meta: object.__setattr__(self, k, v) else: raise NotImplementedError("Pre-0.12 pickles are no longer supported") elif len(state) == 2: raise NotImplementedError("Pre-0.12 pickles are no longer supported") self._item_cache = {} def __repr__(self) -> str: prepr = f"[{','.join(map(pprint_thing, self))}]" return f"{type(self).__name__}({prepr})" def _repr_latex_(self): if config.get_option("display.latex.repr"): return self.to_latex() else: return None def _repr_data_resource_(self): if config.get_option("display.html.table_schema"): data = self.head(config.get_option("display.max_rows")) payload = json.loads( data.to_json(orient="table"), object_pairs_hook=collections.OrderedDict ) return payload _shared_docs[ "to_markdown" ] = """ Print %(klass)s in Markdown-friendly format. .. versionadded:: 1.0.0 Parameters ---------- buf : str, Path or StringIO-like, optional, default None Buffer to write to. If None, the output is returned as a string. mode : str, optional Mode in which file is opened. **kwargs These parameters will be passed to `tabulate`. Returns ------- str %(klass)s in Markdown-friendly format. """ _shared_docs[ "to_excel" ] = """ Write %(klass)s to an Excel sheet. To write a single %(klass)s to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an `ExcelWriter` object with a target file name, and specify a sheet in the file to write to. Multiple sheets may be written to by specifying unique `sheet_name`. With all data written to the file it is necessary to save the changes. Note that creating an `ExcelWriter` object with a file name that already exists will result in the contents of the existing file being erased. Parameters ---------- excel_writer : str or ExcelWriter object File path or existing ExcelWriter. sheet_name : str, default 'Sheet1' Name of sheet which will contain DataFrame. na_rep : str, default '' Missing data representation. float_format : str, optional Format string for floating point numbers. For example ``float_format="%%.2f"`` will format 0.1234 to 0.12. columns : sequence or list of str, optional Columns to write. header : bool or list of str, default True Write out the column names. If a list of string is given it is assumed to be aliases for the column names. index : bool, default True Write row names (index). index_label : str or sequence, optional Column label for index column(s) if desired. If not specified, and `header` and `index` are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. startrow : int, default 0 Upper left cell row to dump data frame. startcol : int, default 0 Upper left cell column to dump data frame. engine : str, optional Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this via the options ``io.excel.xlsx.writer``, ``io.excel.xls.writer``, and ``io.excel.xlsm.writer``. merge_cells : bool, default True Write MultiIndex and Hierarchical Rows as merged cells. encoding : str, optional Encoding of the resulting excel file. Only necessary for xlwt, other writers support unicode natively. inf_rep : str, default 'inf' Representation for infinity (there is no native representation for infinity in Excel). verbose : bool, default True Display more information in the error logs. freeze_panes : tuple of int (length 2), optional Specifies the one-based bottommost row and rightmost column that is to be frozen. See Also -------- to_csv : Write DataFrame to a comma-separated values (csv) file. ExcelWriter : Class for writing DataFrame objects into excel sheets. read_excel : Read an Excel file into a pandas DataFrame. read_csv : Read a comma-separated values (csv) file into DataFrame. Notes ----- For compatibility with :meth:`~DataFrame.to_csv`, to_excel serializes lists and dicts to strings before writing. Once a workbook has been saved it is not possible write further data without rewriting the whole workbook. Examples -------- Create, write to and save a workbook: >>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']], ... index=['row 1', 'row 2'], ... columns=['col 1', 'col 2']) >>> df1.to_excel("output.xlsx") # doctest: +SKIP To specify the sheet name: >>> df1.to_excel("output.xlsx", ... sheet_name='Sheet_name_1') # doctest: +SKIP If you wish to write to more than one sheet in the workbook, it is necessary to specify an ExcelWriter object: >>> df2 = df1.copy() >>> with pd.ExcelWriter('output.xlsx') as writer: # doctest: +SKIP ... df1.to_excel(writer, sheet_name='Sheet_name_1') ... df2.to_excel(writer, sheet_name='Sheet_name_2') ExcelWriter can also be used to append to an existing Excel file: >>> with pd.ExcelWriter('output.xlsx', ... mode='a') as writer: # doctest: +SKIP ... df.to_excel(writer, sheet_name='Sheet_name_3') To set the library that is used to write the Excel file, you can pass the `engine` keyword (the default engine is automatically chosen depending on the file extension): >>> df1.to_excel('output1.xlsx', engine='xlsxwriter') # doctest: +SKIP """ @Appender(_shared_docs["to_excel"] % dict(klass="object")) def to_excel( self, excel_writer, sheet_name="Sheet1", na_rep="", float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep="inf", verbose=True, freeze_panes=None, ) -> None: df = self if isinstance(self, ABCDataFrame) else self.to_frame() from pandas.io.formats.excel import ExcelFormatter formatter = ExcelFormatter( df, na_rep=na_rep, cols=columns, header=header, float_format=float_format, index=index, index_label=index_label, merge_cells=merge_cells, inf_rep=inf_rep, ) formatter.write( excel_writer, sheet_name=sheet_name, startrow=startrow, startcol=startcol, freeze_panes=freeze_panes, engine=engine, ) def to_json( self, path_or_buf: Optional[FilePathOrBuffer] = None, orient: Optional[str] = None, date_format: Optional[str] = None, double_precision: int = 10, force_ascii: bool_t = True, date_unit: str = "ms", default_handler: Optional[Callable[[Any], JSONSerializable]] = None, lines: bool_t = False, compression: Optional[str] = "infer", index: bool_t = True, indent: Optional[int] = None, ) -> Optional[str]: from pandas.io import json if date_format is None and orient == "table": date_format = "iso" elif date_format is None: date_format = "epoch" config.is_nonnegative_int(indent) indent = indent or 0 return json.to_json( path_or_buf=path_or_buf, obj=self, orient=orient, date_format=date_format, double_precision=double_precision, force_ascii=force_ascii, date_unit=date_unit, default_handler=default_handler, lines=lines, compression=compression, index=index, indent=indent, ) def to_hdf( self, path_or_buf, key: str, mode: str = "a", complevel: Optional[int] = None, complib: Optional[str] = None, append: bool_t = False, format: Optional[str] = None, index: bool_t = True, min_itemsize: Optional[Union[int, Dict[str, int]]] = None, nan_rep=None, dropna: Optional[bool_t] = None, data_columns: Optional[List[str]] = None, errors: str = "strict", encoding: str = "UTF-8", ) -> None: from pandas.io import pytables pytables.to_hdf( path_or_buf, key, self, mode=mode, complevel=complevel, complib=complib, append=append, format=format, index=index, min_itemsize=min_itemsize, nan_rep=nan_rep, dropna=dropna, data_columns=data_columns, errors=errors, encoding=encoding, ) def to_sql( self, name: str, con, schema=None, if_exists: str = "fail", index: bool_t = True, index_label=None, chunksize=None, dtype=None, method=None, ) -> None: from pandas.io import sql sql.to_sql( self, name, con, schema=schema, if_exists=if_exists, index=index, index_label=index_label, chunksize=chunksize, dtype=dtype, method=method, ) def to_pickle( self, path, compression: Optional[str] = "infer", protocol: int = pickle.HIGHEST_PROTOCOL, ) -> None: from pandas.io.pickle import to_pickle to_pickle(self, path, compression=compression, protocol=protocol) def to_clipboard( self, excel: bool_t = True, sep: Optional[str] = None, **kwargs ) -> None: from pandas.io import clipboards clipboards.to_clipboard(self, excel=excel, sep=sep, **kwargs) def to_xarray(self): xarray = import_optional_dependency("xarray") if self.ndim == 1: return xarray.DataArray.from_series(self) else: return xarray.Dataset.from_dataframe(self) @Substitution(returns=fmt.return_docstring) def to_latex( self, buf=None, columns=None, col_space=None, header=True, index=True, na_rep="NaN", formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=False, column_format=None, longtable=None, escape=None, encoding=None, decimal=".", multicolumn=None, multicolumn_format=None, multirow=None, caption=None, label=None, ): if self.ndim == 1: self = self.to_frame() if longtable is None: longtable = config.get_option("display.latex.longtable") if escape is None: escape = config.get_option("display.latex.escape") if multicolumn is None: multicolumn = config.get_option("display.latex.multicolumn") if multicolumn_format is None: multicolumn_format = config.get_option("display.latex.multicolumn_format") if multirow is None: multirow = config.get_option("display.latex.multirow") formatter = DataFrameFormatter( self, columns=columns, col_space=col_space, na_rep=na_rep, header=header, index=index, formatters=formatters, float_format=float_format, bold_rows=bold_rows, sparsify=sparsify, index_names=index_names, escape=escape, decimal=decimal, ) return formatter.to_latex( buf=buf, column_format=column_format, longtable=longtable, encoding=encoding, multicolumn=multicolumn, multicolumn_format=multicolumn_format, multirow=multirow, caption=caption, label=label, ) def to_csv( self, path_or_buf: Optional[FilePathOrBuffer] = None, sep: str = ",", na_rep: str = "", float_format: Optional[str] = None, columns: Optional[Sequence[Label]] = None, header: Union[bool_t, List[str]] = True, index: bool_t = True, index_label: Optional[Union[bool_t, str, Sequence[Label]]] = None, mode: str = "w", encoding: Optional[str] = None, compression: Optional[Union[str, Mapping[str, str]]] = "infer", quoting: Optional[int] = None, quotechar: str = '"', line_terminator: Optional[str] = None, chunksize: Optional[int] = None, date_format: Optional[str] = None, doublequote: bool_t = True, escapechar: Optional[str] = None, decimal: Optional[str] = ".", ) -> Optional[str]: df = self if isinstance(self, ABCDataFrame) else self.to_frame() from pandas.io.formats.csvs import CSVFormatter formatter = CSVFormatter( df, path_or_buf, line_terminator=line_terminator, sep=sep, encoding=encoding, compression=compression, quoting=quoting, na_rep=na_rep, float_format=float_format, cols=columns, header=header, index=index, index_label=index_label, mode=mode, chunksize=chunksize, quotechar=quotechar, date_format=date_format, doublequote=doublequote, escapechar=escapechar, decimal=decimal, ) formatter.save() if path_or_buf is None: return formatter.path_or_buf.getvalue() return None # ---------------------------------------------------------------------- # Lookup Caching def _set_as_cached(self, item, cacher) -> None: self._cacher = (item, weakref.ref(cacher)) def _reset_cacher(self) -> None: if hasattr(self, "_cacher"): del self._cacher def _maybe_cache_changed(self, item, value) -> None: self._data.set(item, value) @property def _is_cached(self) -> bool_t: return getattr(self, "_cacher", None) is not None def _get_cacher(self): cacher = getattr(self, "_cacher", None) if cacher is not None: cacher = cacher[1]() return cacher def _maybe_update_cacher( self, clear: bool_t = False, verify_is_copy: bool_t = True ) -> None: cacher = getattr(self, "_cacher", None) if cacher is not None: ref = cacher[1]() # we are trying to reference a dead referant, hence # a copy if ref is None: del self._cacher else: # Note: we need to call ref._maybe_cache_changed even in the # case where it will raise. (Uh, not clear why) try: ref._maybe_cache_changed(cacher[0], self) except AssertionError: # ref._data.setitem can raise # AssertionError because of shape mismatch pass if verify_is_copy: self._check_setitem_copy(stacklevel=5, t="referant") if clear: self._clear_item_cache() def _clear_item_cache(self) -> None: self._item_cache.clear() # ---------------------------------------------------------------------- # Indexing Methods def take( self: FrameOrSeries, indices, axis=0, is_copy: Optional[bool_t] = None, **kwargs ) -> FrameOrSeries: if is_copy is not None: warnings.warn( "is_copy is deprecated and will be removed in a future version. " "'take' always returns a copy, so there is no need to specify this.", FutureWarning, stacklevel=2, ) nv.validate_take(tuple(), kwargs) self._consolidate_inplace() new_data = self._data.take( indices, axis=self._get_block_manager_axis(axis), verify=True ) return self._constructor(new_data).__finalize__(self) def _take_with_is_copy(self: FrameOrSeries, indices, axis=0) -> FrameOrSeries: result = self.take(indices=indices, axis=axis) # Maybe set copy if we didn't actually change the index. if not result._get_axis(axis).equals(self._get_axis(axis)): result._set_is_copy(self) return result def xs(self, key, axis=0, level=None, drop_level: bool_t = True): axis = self._get_axis_number(axis) labels = self._get_axis(axis) if level is not None: loc, new_ax = labels.get_loc_level(key, level=level, drop_level=drop_level) # create the tuple of the indexer _indexer = [slice(None)] * self.ndim _indexer[axis] = loc indexer = tuple(_indexer) result = self.iloc[indexer] setattr(result, result._get_axis_name(axis), new_ax) return result if axis == 1: return self[key] self._consolidate_inplace() index = self.index if isinstance(index, MultiIndex): loc, new_index = self.index.get_loc_level(key, drop_level=drop_level) else: loc = self.index.get_loc(key) if isinstance(loc, np.ndarray): if loc.dtype == np.bool_: (inds,) = loc.nonzero() return self._take_with_is_copy(inds, axis=axis) else: return self._take_with_is_copy(loc, axis=axis) if not is_scalar(loc): new_index = self.index[loc] if is_scalar(loc): # In this case loc should be an integer if self.ndim == 1: # if we encounter an array-like and we only have 1 dim # that means that their are list/ndarrays inside the Series! # so just return them (GH 6394) return self._values[loc] new_values = self._data.fast_xs(loc) result = self._constructor_sliced( new_values, index=self.columns, name=self.index[loc], dtype=new_values.dtype, ) else: result = self.iloc[loc] result.index = new_index # this could be a view # but only in a single-dtyped view sliceable case result._set_is_copy(self, copy=not result._is_view) return result _xs: Callable = xs def __getitem__(self, item): raise AbstractMethodError(self) def _get_item_cache(self, item): cache = self._item_cache res = cache.get(item) if res is None: values = self._data.get(item) res = self._box_item_values(item, values) cache[item] = res res._set_as_cached(item, self) # for a chain res._is_copy = self._is_copy return res def _box_item_values(self, key, values): raise AbstractMethodError(self) def _slice(self: FrameOrSeries, slobj: slice, axis=0) -> FrameOrSeries: assert isinstance(slobj, slice), type(slobj) axis = self._get_block_manager_axis(axis) result = self._constructor(self._data.get_slice(slobj, axis=axis)) result = result.__finalize__(self) # this could be a view # but only in a single-dtyped view sliceable case is_copy = axis != 0 or result._is_view result._set_is_copy(self, copy=is_copy) return result def _set_item(self, key, value) -> None: self._data.set(key, value) self._clear_item_cache() def _set_is_copy(self, ref, copy: bool_t = True) -> None: if not copy: self._is_copy = None else: assert ref is not None self._is_copy = weakref.ref(ref) def _check_is_chained_assignment_possible(self) -> bool_t: if self._is_view and self._is_cached: ref = self._get_cacher() if ref is not None and ref._is_mixed_type: self._check_setitem_copy(stacklevel=4, t="referant", force=True) return True elif self._is_copy: self._check_setitem_copy(stacklevel=4, t="referant") return False def _check_setitem_copy(self, stacklevel=4, t="setting", force=False): # return early if the check is not needed if not (force or self._is_copy): return value = config.get_option("mode.chained_assignment") if value is None: return # see if the copy is not actually referred; if so, then dissolve # the copy weakref if self._is_copy is not None and not isinstance(self._is_copy, str): r = self._is_copy() if not gc.get_referents(r) or r.shape == self.shape: self._is_copy = None return # a custom message if isinstance(self._is_copy, str): t = self._is_copy elif t == "referant": t = ( "\n" "A value is trying to be set on a copy of a slice from a " "DataFrame\n\n" "See the caveats in the documentation: " "https://pandas.pydata.org/pandas-docs/stable/user_guide/" "indexing.html#returning-a-view-versus-a-copy" ) else: t = ( "\n" "A value is trying to be set on a copy of a slice from a " "DataFrame.\n" "Try using .loc[row_indexer,col_indexer] = value " "instead\n\nSee the caveats in the documentation: " "https://pandas.pydata.org/pandas-docs/stable/user_guide/" "indexing.html#returning-a-view-versus-a-copy" ) if value == "raise": raise com.SettingWithCopyError(t) elif value == "warn": warnings.warn(t, com.SettingWithCopyWarning, stacklevel=stacklevel) def __delitem__(self, key) -> None: deleted = False maybe_shortcut = False if self.ndim == 2 and isinstance(self.columns, MultiIndex): try: maybe_shortcut = key not in self.columns._engine except TypeError: pass if maybe_shortcut: # Allow shorthand to delete all columns whose first len(key) # elements match key: if not isinstance(key, tuple): key = (key,) for col in self.columns: if isinstance(col, tuple) and col[: len(key)] == key: del self[col] deleted = True if not deleted: # If the above loop ran and didn't delete anything because # there was no match, this call should raise the appropriate # exception: self._data.delete(key) # delete from the caches try: del self._item_cache[key] except KeyError: pass # ---------------------------------------------------------------------- # Unsorted def get(self, key, default=None): try: return self[key] except (KeyError, ValueError, IndexError): return default @property def _is_view(self) -> bool_t: return self._data.is_view def reindex_like( self: FrameOrSeries, other, method: Optional[str] = None, copy: bool_t = True, limit=None, tolerance=None, ) -> FrameOrSeries: d = other._construct_axes_dict( axes=self._AXIS_ORDERS, method=method, copy=copy, limit=limit, tolerance=tolerance, ) return self.reindex(**d) def drop( self, labels=None, axis=0, index=None, columns=None, level=None, inplace: bool_t = False, errors: str = "raise", ): inplace = validate_bool_kwarg(inplace, "inplace") if labels is not None: if index is not None or columns is not None: raise ValueError("Cannot specify both 'labels' and 'index'/'columns'") axis_name = self._get_axis_name(axis) axes = {axis_name: labels} elif index is not None or columns is not None: axes, _ = self._construct_axes_from_arguments((index, columns), {}) else: raise ValueError( "Need to specify at least one of 'labels', 'index' or 'columns'" ) obj = self for axis, labels in axes.items(): if labels is not None: obj = obj._drop_axis(labels, axis, level=level, errors=errors) if inplace: self._update_inplace(obj) else: return obj def _drop_axis( self: FrameOrSeries, labels, axis, level=None, errors: str = "raise" ) -> FrameOrSeries: axis = self._get_axis_number(axis) axis_name = self._get_axis_name(axis) axis = self._get_axis(axis) if axis.is_unique: if level is not None: if not isinstance(axis, MultiIndex): raise AssertionError("axis must be a MultiIndex") new_axis = axis.drop(labels, level=level, errors=errors) else: new_axis = axis.drop(labels, errors=errors) result = self.reindex(**{axis_name: new_axis}) # Case for non-unique axis else: labels = ensure_object(com.index_labels_to_array(labels)) if level is not None: if not isinstance(axis, MultiIndex): raise AssertionError("axis must be a MultiIndex") indexer = ~axis.get_level_values(level).isin(labels) # GH 18561 MultiIndex.drop should raise if label is absent if errors == "raise" and indexer.all(): raise KeyError(f"{labels} not found in axis") else: indexer = ~axis.isin(labels) # Check if label doesn't exist along axis labels_missing = (axis.get_indexer_for(labels) == -1).any() if errors == "raise" and labels_missing: raise KeyError(f"{labels} not found in axis") slicer = [slice(None)] * self.ndim slicer[self._get_axis_number(axis_name)] = indexer result = self.loc[tuple(slicer)] return result def _update_inplace(self, result, verify_is_copy: bool_t = True) -> None: # NOTE: This does *not* call __finalize__ and that's an explicit # decision that we may revisit in the future. self._reset_cache() self._clear_item_cache() self._data = getattr(result, "_data", result) self._maybe_update_cacher(verify_is_copy=verify_is_copy) def add_prefix(self: FrameOrSeries, prefix: str) -> FrameOrSeries: f = functools.partial("{prefix}{}".format, prefix=prefix) mapper = {self._info_axis_name: f} return self.rename(**mapper) # type: ignore def add_suffix(self: FrameOrSeries, suffix: str) -> FrameOrSeries: f = functools.partial("{}{suffix}".format, suffix=suffix) mapper = {self._info_axis_name: f} return self.rename(**mapper) # type: ignore def sort_values( self, axis=0, ascending=True, inplace: bool_t = False, kind: str = "quicksort", na_position: str = "last", ignore_index: bool_t = False, ): raise AbstractMethodError(self) def reindex(self: FrameOrSeries, *args, **kwargs) -> FrameOrSeries: # TODO: Decide if we care about having different examples for different # kinds # construct the args axes, kwargs = self._construct_axes_from_arguments(args, kwargs) method = missing.clean_reindex_fill_method(kwargs.pop("method", None)) level = kwargs.pop("level", None) copy = kwargs.pop("copy", True) limit = kwargs.pop("limit", None) tolerance = kwargs.pop("tolerance", None) fill_value = kwargs.pop("fill_value", None) # Series.reindex doesn't use / need the axis kwarg # We pop and ignore it here, to make writing Series/Frame generic code # easier kwargs.pop("axis", None) if kwargs: raise TypeError( "reindex() got an unexpected keyword " f'argument "{list(kwargs.keys())[0]}"' ) self._consolidate_inplace() # if all axes that are requested to reindex are equal, then only copy # if indicated must have index names equal here as well as values if all( self._get_axis(axis).identical(ax) for axis, ax in axes.items() if ax is not None ): if copy: return self.copy() return self # check if we are a multi reindex if self._needs_reindex_multi(axes, method, level): return self._reindex_multi(axes, copy, fill_value) # perform the reindex on the axes return self._reindex_axes( axes, level, limit, tolerance, method, fill_value, copy ).__finalize__(self) def _reindex_axes( self: FrameOrSeries, axes, level, limit, tolerance, method, fill_value, copy ) -> FrameOrSeries: obj = self for a in self._AXIS_ORDERS: labels = axes[a] if labels is None: continue ax = self._get_axis(a) new_index, indexer = ax.reindex( labels, level=level, limit=limit, tolerance=tolerance, method=method ) axis = self._get_axis_number(a) obj = obj._reindex_with_indexers( {axis: [new_index, indexer]}, fill_value=fill_value, copy=copy, allow_dups=False, ) return obj def _needs_reindex_multi(self, axes, method, level) -> bool_t: return ( (com.count_not_none(*axes.values()) == self._AXIS_LEN) and method is None and level is None and not self._is_mixed_type ) def _reindex_multi(self, axes, copy, fill_value): raise AbstractMethodError(self) def _reindex_with_indexers( self: FrameOrSeries, reindexers, fill_value=None, copy: bool_t = False, allow_dups: bool_t = False, ) -> FrameOrSeries: # reindex doing multiple operations on different axes if indicated new_data = self._data for axis in sorted(reindexers.keys()): index, indexer = reindexers[axis] baxis = self._get_block_manager_axis(axis) if index is None: continue index = ensure_index(index) if indexer is not None: indexer = ensure_int64(indexer) # TODO: speed up on homogeneous DataFrame objects new_data = new_data.reindex_indexer( index, indexer, axis=baxis, fill_value=fill_value, allow_dups=allow_dups, copy=copy, ) if copy and new_data is self._data: new_data = new_data.copy() return self._constructor(new_data).__finalize__(self) def filter( self: FrameOrSeries, items=None, like: Optional[str] = None, regex: Optional[str] = None, axis=None, ) -> FrameOrSeries: nkw = com.count_not_none(items, like, regex) if nkw > 1: raise TypeError( "Keyword arguments `items`, `like`, or `regex` " "are mutually exclusive" ) if axis is None: axis = self._info_axis_name labels = self._get_axis(axis) if items is not None: name = self._get_axis_name(axis) return self.reindex(**{name: [r for r in items if r in labels]}) elif like: def f(x): return like in ensure_str(x) values = labels.map(f) return self.loc(axis=axis)[values] elif regex: def f(x): return matcher.search(ensure_str(x)) is not None matcher = re.compile(regex) values = labels.map(f) return self.loc(axis=axis)[values] else: raise TypeError("Must pass either `items`, `like`, or `regex`") def head(self: FrameOrSeries, n: int = 5) -> FrameOrSeries: return self.iloc[:n] def tail(self: FrameOrSeries, n: int = 5) -> FrameOrSeries: if n == 0: return self.iloc[0:0] return self.iloc[-n:] def sample( self: FrameOrSeries, n=None, frac=None, replace=False, weights=None, random_state=None, axis=None, ) -> FrameOrSeries: if axis is None: axis = self._stat_axis_number axis = self._get_axis_number(axis) axis_length = self.shape[axis] # Process random_state argument rs = com.random_state(random_state) # Check weights for compliance if weights is not None: # If a series, align with frame if isinstance(weights, ABCSeries): weights = weights.reindex(self.axes[axis]) # Strings acceptable if a dataframe and axis = 0 if isinstance(weights, str): if isinstance(self, ABCDataFrame): if axis == 0: try: weights = self[weights] except KeyError as err: raise KeyError( "String passed to weights not a valid column" ) from err else: raise ValueError( "Strings can only be passed to " "weights when sampling from rows on " "a DataFrame" ) else: raise ValueError( "Strings cannot be passed as weights " "when sampling from a Series." ) weights = pd.Series(weights, dtype="float64") if len(weights) != axis_length: raise ValueError( "Weights and axis to be sampled must be of same length" ) if (weights == np.inf).any() or (weights == -np.inf).any(): raise ValueError("weight vector may not include `inf` values") if (weights < 0).any(): raise ValueError("weight vector many not include negative values") # If has nan, set to zero. weights = weights.fillna(0) # Renormalize if don't sum to 1 if weights.sum() != 1: if weights.sum() != 0: weights = weights / weights.sum() else: raise ValueError("Invalid weights: weights sum to zero") weights = weights.values # If no frac or n, default to n=1. if n is None and frac is None: n = 1 elif frac is not None and frac > 1 and not replace: raise ValueError( "Replace has to be set to `True` when " "upsampling the population `frac` > 1." ) elif n is not None and frac is None and n % 1 != 0: raise ValueError("Only integers accepted as `n` values") elif n is None and frac is not None: n = int(round(frac * axis_length)) elif n is not None and frac is not None: raise ValueError("Please enter a value for `frac` OR `n`, not both") # Check for negative sizes if n < 0: raise ValueError( "A negative number of rows requested. Please provide positive value." ) locs = rs.choice(axis_length, size=n, replace=replace, p=weights) return self.take(locs, axis=axis) _shared_docs[ "pipe" ] = r""" Apply func(self, \*args, \*\*kwargs). Parameters ---------- func : function Function to apply to the %(klass)s. ``args``, and ``kwargs`` are passed into ``func``. Alternatively a ``(callable, data_keyword)`` tuple where ``data_keyword`` is a string indicating the keyword of ``callable`` that expects the %(klass)s. args : iterable, optional Positional arguments passed into ``func``. kwargs : mapping, optional A dictionary of keyword arguments passed into ``func``. Returns ------- object : the return type of ``func``. See Also -------- DataFrame.apply : Apply a function along input axis of DataFrame. DataFrame.applymap : Apply a function elementwise on a whole DataFrame. Series.map : Apply a mapping correspondence on a :class:`~pandas.Series`. Notes ----- Use ``.pipe`` when chaining together functions that expect Series, DataFrames or GroupBy objects. Instead of writing >>> f(g(h(df), arg1=a), arg2=b, arg3=c) You can write >>> (df.pipe(h) ... .pipe(g, arg1=a) ... .pipe(f, arg2=b, arg3=c) ... ) If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose ``f`` takes its data as ``arg2``: >>> (df.pipe(h) ... .pipe(g, arg1=a) ... .pipe((f, 'arg2'), arg1=a, arg3=c) ... ) """ @Appender(_shared_docs["pipe"] % _shared_doc_kwargs) def pipe(self, func, *args, **kwargs): return com.pipe(self, func, *args, **kwargs) _shared_docs["aggregate"] = dedent( """ Aggregate using one or more operations over the specified axis. %(versionadded)s Parameters ---------- func : function, str, list or dict Function to use for aggregating the data. If a function, must either work when passed a %(klass)s or when passed to %(klass)s.apply. Accepted combinations are: - function - string function name - list of functions and/or function names, e.g. ``[np.sum, 'mean']`` - dict of axis labels -> functions, function names or list of such. %(axis)s *args Positional arguments to pass to `func`. **kwargs Keyword arguments to pass to `func`. Returns ------- scalar, Series or DataFrame The return can be: * scalar : when Series.agg is called with single function * Series : when DataFrame.agg is called with a single function * DataFrame : when DataFrame.agg is called with several functions Return scalar, Series or DataFrame. %(see_also)s Notes ----- `agg` is an alias for `aggregate`. Use the alias. A passed user-defined-function will be passed a Series for evaluation. %(examples)s""" ) _shared_docs[ "transform" ] = """ Call ``func`` on self producing a %(klass)s with transformed values. Produced %(klass)s will have same axis length as self. Parameters ---------- func : function, str, list or dict Function to use for transforming the data. If a function, must either work when passed a %(klass)s or when passed to %(klass)s.apply. Accepted combinations are: - function - string function name - list of functions and/or function names, e.g. ``[np.exp. 'sqrt']`` - dict of axis labels -> functions, function names or list of such. %(axis)s *args Positional arguments to pass to `func`. **kwargs Keyword arguments to pass to `func`. Returns ------- %(klass)s A %(klass)s that must have the same length as self. Raises ------ ValueError : If the returned %(klass)s has a different length than self. See Also -------- %(klass)s.agg : Only perform aggregating type operations. %(klass)s.apply : Invoke function on a %(klass)s. Examples -------- >>> df = pd.DataFrame({'A': range(3), 'B': range(1, 4)}) >>> df A B 0 0 1 1 1 2 2 2 3 >>> df.transform(lambda x: x + 1) A B 0 1 2 1 2 3 2 3 4 Even though the resulting %(klass)s must have the same length as the input %(klass)s, it is possible to provide several input functions: >>> s = pd.Series(range(3)) >>> s 0 0 1 1 2 2 dtype: int64 >>> s.transform([np.sqrt, np.exp]) sqrt exp 0 0.000000 1.000000 1 1.000000 2.718282 2 1.414214 7.389056 """ # ---------------------------------------------------------------------- # Attribute access def __finalize__( self: FrameOrSeries, other, method=None, **kwargs ) -> FrameOrSeries: if isinstance(other, NDFrame): for name in other.attrs: self.attrs[name] = other.attrs[name] # For subclasses using _metadata. for name in self._metadata: assert isinstance(name, str) object.__setattr__(self, name, getattr(other, name, None)) return self def __getattr__(self, name: str): # Note: obj.x will always call obj.__getattribute__('x') prior to # calling obj.__getattr__('x'). if ( name in self._internal_names_set or name in self._metadata or name in self._accessors ): return object.__getattribute__(self, name) else: if self._info_axis._can_hold_identifiers_and_holds_name(name): return self[name] return object.__getattribute__(self, name) def __setattr__(self, name: str, value) -> None: # first try regular attribute access via __getattribute__, so that # e.g. ``obj.x`` and ``obj.x = 4`` will always reference/modify # the same attribute. try: object.__getattribute__(self, name) return object.__setattr__(self, name, value) except AttributeError: pass # if this fails, go on to more involved attribute setting # (note that this matches __getattr__, above). if name in self._internal_names_set: object.__setattr__(self, name, value) elif name in self._metadata: object.__setattr__(self, name, value) else: try: existing = getattr(self, name) if isinstance(existing, Index): object.__setattr__(self, name, value) elif name in self._info_axis: self[name] = value else: object.__setattr__(self, name, value) except (AttributeError, TypeError): if isinstance(self, ABCDataFrame) and (is_list_like(value)): warnings.warn( "Pandas doesn't allow columns to be " "created via a new attribute name - see " "https://pandas.pydata.org/pandas-docs/" "stable/indexing.html#attribute-access", stacklevel=2, ) object.__setattr__(self, name, value) def _dir_additions(self): additions = { c for c in self._info_axis.unique(level=0)[:100] if isinstance(c, str) and c.isidentifier() } return super()._dir_additions().union(additions) # ---------------------------------------------------------------------- # Consolidation of internals def _protect_consolidate(self, f): blocks_before = len(self._data.blocks) result = f() if len(self._data.blocks) != blocks_before: self._clear_item_cache() return result def _consolidate_inplace(self) -> None: def f(): self._data = self._data.consolidate() self._protect_consolidate(f) def _consolidate(self, inplace: bool_t = False): inplace = validate_bool_kwarg(inplace, "inplace") if inplace: self._consolidate_inplace() else: f = lambda: self._data.consolidate() cons_data = self._protect_consolidate(f) return self._constructor(cons_data).__finalize__(self) @property def _is_mixed_type(self) -> bool_t: f = lambda: self._data.is_mixed_type return self._protect_consolidate(f) @property def _is_numeric_mixed_type(self) -> bool_t: f = lambda: self._data.is_numeric_mixed_type return self._protect_consolidate(f) def _check_inplace_setting(self, value) -> bool_t: if self._is_mixed_type: if not self._is_numeric_mixed_type: # allow an actual np.nan thru if is_float(value) and np.isnan(value): return True raise TypeError( "Cannot do inplace boolean setting on " "mixed-types with a non np.nan value" ) return True def _get_numeric_data(self): return self._constructor(self._data.get_numeric_data()).__finalize__(self) def _get_bool_data(self): return self._constructor(self._data.get_bool_data()).__finalize__(self) # ---------------------------------------------------------------------- # Internal Interface Methods @property def values(self) -> np.ndarray: self._consolidate_inplace() return self._data.as_array(transpose=self._AXIS_REVERSED) @property def _values(self) -> np.ndarray: return self.values def _internal_get_values(self) -> np.ndarray: return self.values @property def dtypes(self): from pandas import Series return Series(self._data.get_dtypes(), index=self._info_axis, dtype=np.object_) def _to_dict_of_blocks(self, copy: bool_t = True): return { k: self._constructor(v).__finalize__(self) for k, v, in self._data.to_dict(copy=copy).items() } def astype( self: FrameOrSeries, dtype, copy: bool_t = True, errors: str = "raise" ) -> FrameOrSeries: if is_dict_like(dtype): if self.ndim == 1: # i.e. Series if len(dtype) > 1 or self.name not in dtype: raise KeyError( "Only the Series name can be used for " "the key in Series dtype mappings." ) new_type = dtype[self.name] return self.astype(new_type, copy, errors) for col_name in dtype.keys(): if col_name not in self: raise KeyError( "Only a column name can be used for the " "key in a dtype mappings argument." ) results = [] for col_name, col in self.items(): if col_name in dtype: results.append( col.astype(dtype=dtype[col_name], copy=copy, errors=errors) ) else: results.append(col.copy() if copy else col) elif is_extension_array_dtype(dtype) and self.ndim > 1: # GH 18099/22869: columnwise conversion to extension dtype # GH 24704: use iloc to handle duplicate column names results = [ self.iloc[:, i].astype(dtype, copy=copy) for i in range(len(self.columns)) ] else: # else, only a single dtype is given new_data = self._data.astype(dtype=dtype, copy=copy, errors=errors) return self._constructor(new_data).__finalize__(self) # GH 19920: retain column metadata after concat result = pd.concat(results, axis=1, copy=False) result.columns = self.columns return result def copy(self: FrameOrSeries, deep: bool_t = True) -> FrameOrSeries: data = self._data.copy(deep=deep) return self._constructor(data).__finalize__(self) def __copy__(self: FrameOrSeries, deep: bool_t = True) -> FrameOrSeries: return self.copy(deep=deep) def __deepcopy__(self: FrameOrSeries, memo=None) -> FrameOrSeries: return self.copy(deep=True) def _convert( self: FrameOrSeries, datetime: bool_t = False, numeric: bool_t = False, timedelta: bool_t = False, coerce: bool_t = False, copy: bool_t = True, ) -> FrameOrSeries: validate_bool_kwarg(datetime, "datetime") validate_bool_kwarg(numeric, "numeric") validate_bool_kwarg(timedelta, "timedelta") validate_bool_kwarg(coerce, "coerce") validate_bool_kwarg(copy, "copy") return self._constructor( self._data.convert( datetime=datetime, numeric=numeric, timedelta=timedelta, coerce=coerce, copy=copy, ) ).__finalize__(self) def infer_objects(self: FrameOrSeries) -> FrameOrSeries: # numeric=False necessary to only soft convert; # python objects will still be converted to # native numpy numeric types return self._constructor( self._data.convert( datetime=True, numeric=False, timedelta=True, coerce=False, copy=True ) ).__finalize__(self) def convert_dtypes( self: FrameOrSeries, infer_objects: bool_t = True, convert_string: bool_t = True, convert_integer: bool_t = True, convert_boolean: bool_t = True, ) -> FrameOrSeries: if self.ndim == 1: return self._convert_dtypes( infer_objects, convert_string, convert_integer, convert_boolean ) else: results = [ col._convert_dtypes( infer_objects, convert_string, convert_integer, convert_boolean ) for col_name, col in self.items() ] result = pd.concat(results, axis=1, copy=False) return result # ---------------------------------------------------------------------- # Filling NA's @doc(**_shared_doc_kwargs) def fillna( self: FrameOrSeries, value=None, method=None, axis=None, inplace: bool_t = False, limit=None, downcast=None, ) -> Optional[FrameOrSeries]: inplace = validate_bool_kwarg(inplace, "inplace") value, method = validate_fillna_kwargs(value, method) self._consolidate_inplace() # set the default here, so functions examining the signaure # can detect if something was set (e.g. in groupby) (GH9221) if axis is None: axis = 0 axis = self._get_axis_number(axis) if value is None: if self._is_mixed_type and axis == 1: if inplace: raise NotImplementedError() result = self.T.fillna(method=method, limit=limit).T # need to downcast here because of all of the transposes result._data = result._data.downcast() return result new_data = self._data.interpolate( method=method, axis=axis, limit=limit, inplace=inplace, coerce=True, downcast=downcast, ) else: if len(self._get_axis(axis)) == 0: return self if self.ndim == 1: if isinstance(value, (dict, ABCSeries)): value = create_series_with_explicit_dtype( value, dtype_if_empty=object ) elif not is_list_like(value): pass else: raise TypeError( '"value" parameter must be a scalar, dict ' "or Series, but you passed a " f'"{type(value).__name__}"' ) new_data = self._data.fillna( value=value, limit=limit, inplace=inplace, downcast=downcast ) elif isinstance(value, (dict, ABCSeries)): if axis == 1: raise NotImplementedError( "Currently only can fill " "with dict/Series column " "by column" ) result = self if inplace else self.copy() for k, v in value.items(): if k not in result: continue obj = result[k] obj.fillna(v, limit=limit, inplace=True, downcast=downcast) return result if not inplace else None elif not is_list_like(value): new_data = self._data.fillna( value=value, limit=limit, inplace=inplace, downcast=downcast ) elif isinstance(value, ABCDataFrame) and self.ndim == 2: new_data = self.where(self.notna(), value) else: raise ValueError(f"invalid fill value with a {type(value)}") if inplace: self._update_inplace(new_data) return None else: return self._constructor(new_data).__finalize__(self) def ffill( self: FrameOrSeries, axis=None, inplace: bool_t = False, limit=None, downcast=None, ) -> Optional[FrameOrSeries]: return self.fillna( method="ffill", axis=axis, inplace=inplace, limit=limit, downcast=downcast ) def bfill( self: FrameOrSeries, axis=None, inplace: bool_t = False, limit=None, downcast=None, ) -> Optional[FrameOrSeries]: return self.fillna( method="bfill", axis=axis, inplace=inplace, limit=limit, downcast=downcast ) _shared_docs[ "replace" ] = """ Replace values given in `to_replace` with `value`. Values of the %(klass)s are replaced with other values dynamically. This differs from updating with ``.loc`` or ``.iloc``, which require you to specify a location to update with some value. Parameters ---------- to_replace : str, regex, list, dict, Series, int, float, or None How to find the values that will be replaced. * numeric, str or regex: - numeric: numeric values equal to `to_replace` will be replaced with `value` - str: string exactly matching `to_replace` will be replaced with `value` - regex: regexs matching `to_replace` will be replaced with `value` * list of str, regex, or numeric: - First, if `to_replace` and `value` are both lists, they **must** be the same length. - Second, if ``regex=True`` then all of the strings in **both** lists will be interpreted as regexs otherwise they will match directly. This doesn't matter much for `value` since there are only a few possible substitution regexes you can use. - str, regex and numeric rules apply as above. * dict: - Dicts can be used to specify different replacement values for different existing values. For example, ``{'a': 'b', 'y': 'z'}`` replaces the value 'a' with 'b' and 'y' with 'z'. To use a dict in this way the `value` parameter should be `None`. - For a DataFrame a dict can specify that different values should be replaced in different columns. For example, ``{'a': 1, 'b': 'z'}`` looks for the value 1 in column 'a' and the value 'z' in column 'b' and replaces these values with whatever is specified in `value`. The `value` parameter should not be ``None`` in this case. You can treat this as a special case of passing two lists except that you are specifying the column to search in. - For a DataFrame nested dictionaries, e.g., ``{'a': {'b': np.nan}}``, are read as follows: look in column 'a' for the value 'b' and replace it with NaN. The `value` parameter should be ``None`` to use a nested dict in this way. You can nest regular expressions as well. Note that column names (the top-level dictionary keys in a nested dictionary) **cannot** be regular expressions. * None: - This means that the `regex` argument must be a string, compiled regular expression, or list, dict, ndarray or Series of such elements. If `value` is also ``None`` then this **must** be a nested dictionary or Series. See the examples section for examples of each of these. value : scalar, dict, list, str, regex, default None Value to replace any values matching `to_replace` with. For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed. inplace : bool, default False If True, in place. Note: this will modify any other views on this object (e.g. a column from a DataFrame). Returns the caller if this is True. limit : int, default None Maximum size gap to forward or backward fill. regex : bool or same types as `to_replace`, default False Whether to interpret `to_replace` and/or `value` as regular expressions. If this is ``True`` then `to_replace` *must* be a string. Alternatively, this could be a regular expression or a list, dict, or array of regular expressions in which case `to_replace` must be ``None``. method : {'pad', 'ffill', 'bfill', `None`} The method to use when for replacement, when `to_replace` is a scalar, list or tuple and `value` is ``None``. .. versionchanged:: 0.23.0 Added to DataFrame. Returns ------- %(klass)s Object after replacement. Raises ------ AssertionError * If `regex` is not a ``bool`` and `to_replace` is not ``None``. TypeError * If `to_replace` is not a scalar, array-like, ``dict``, or ``None`` * If `to_replace` is a ``dict`` and `value` is not a ``list``, ``dict``, ``ndarray``, or ``Series`` * If `to_replace` is ``None`` and `regex` is not compilable into a regular expression or is a list, dict, ndarray, or Series. * When replacing multiple ``bool`` or ``datetime64`` objects and the arguments to `to_replace` does not match the type of the value being replaced ValueError * If a ``list`` or an ``ndarray`` is passed to `to_replace` and `value` but they are not the same length. See Also -------- %(klass)s.fillna : Fill NA values. %(klass)s.where : Replace values based on boolean condition. Series.str.replace : Simple string replacement. Notes ----- * Regex substitution is performed under the hood with ``re.sub``. The rules for substitution for ``re.sub`` are the same. * Regular expressions will only substitute on strings, meaning you cannot provide, for example, a regular expression matching floating point numbers and expect the columns in your frame that have a numeric dtype to be matched. However, if those floating point numbers *are* strings, then you can do this. * This method has *a lot* of options. You are encouraged to experiment and play with this method to gain intuition about how it works. * When dict is used as the `to_replace` value, it is like key(s) in the dict are the to_replace part and value(s) in the dict are the value parameter. Examples -------- **Scalar `to_replace` and `value`** >>> s = pd.Series([0, 1, 2, 3, 4]) >>> s.replace(0, 5) 0 5 1 1 2 2 3 3 4 4 dtype: int64 >>> df = pd.DataFrame({'A': [0, 1, 2, 3, 4], ... 'B': [5, 6, 7, 8, 9], ... 'C': ['a', 'b', 'c', 'd', 'e']}) >>> df.replace(0, 5) A B C 0 5 5 a 1 1 6 b 2 2 7 c 3 3 8 d 4 4 9 e **List-like `to_replace`** >>> df.replace([0, 1, 2, 3], 4) A B C 0 4 5 a 1 4 6 b 2 4 7 c 3 4 8 d 4 4 9 e >>> df.replace([0, 1, 2, 3], [4, 3, 2, 1]) A B C 0 4 5 a 1 3 6 b 2 2 7 c 3 1 8 d 4 4 9 e >>> s.replace([1, 2], method='bfill') 0 0 1 3 2 3 3 3 4 4 dtype: int64 **dict-like `to_replace`** >>> df.replace({0: 10, 1: 100}) A B C 0 10 5 a 1 100 6 b 2 2 7 c 3 3 8 d 4 4 9 e >>> df.replace({'A': 0, 'B': 5}, 100) A B C 0 100 100 a 1 1 6 b 2 2 7 c 3 3 8 d 4 4 9 e >>> df.replace({'A': {0: 100, 4: 400}}) A B C 0 100 5 a 1 1 6 b 2 2 7 c 3 3 8 d 4 400 9 e **Regular expression `to_replace`** >>> df = pd.DataFrame({'A': ['bat', 'foo', 'bait'], ... 'B': ['abc', 'bar', 'xyz']}) >>> df.replace(to_replace=r'^ba.$', value='new', regex=True) A B 0 new abc 1 foo new 2 bait xyz >>> df.replace({'A': r'^ba.$'}, {'A': 'new'}, regex=True) A B 0 new abc 1 foo bar 2 bait xyz >>> df.replace(regex=r'^ba.$', value='new') A B 0 new abc 1 foo new 2 bait xyz >>> df.replace(regex={r'^ba.$': 'new', 'foo': 'xyz'}) A B 0 new abc 1 xyz new 2 bait xyz >>> df.replace(regex=[r'^ba.$', 'foo'], value='new') A B 0 new abc 1 new new 2 bait xyz Note that when replacing multiple ``bool`` or ``datetime64`` objects, the data types in the `to_replace` parameter must match the data type of the value being replaced: >>> df = pd.DataFrame({'A': [True, False, True], ... 'B': [False, True, False]}) >>> df.replace({'a string': 'new value', True: False}) # raises Traceback (most recent call last): ... TypeError: Cannot compare types 'ndarray(dtype=bool)' and 'str' This raises a ``TypeError`` because one of the ``dict`` keys is not of the correct type for replacement. Compare the behavior of ``s.replace({'a': None})`` and ``s.replace('a', None)`` to understand the peculiarities of the `to_replace` parameter: >>> s = pd.Series([10, 'a', 'a', 'b', 'a']) When one uses a dict as the `to_replace` value, it is like the value(s) in the dict are equal to the `value` parameter. ``s.replace({'a': None})`` is equivalent to ``s.replace(to_replace={'a': None}, value=None, method=None)``: >>> s.replace({'a': None}) 0 10 1 None 2 None 3 b 4 None dtype: object When ``value=None`` and `to_replace` is a scalar, list or tuple, `replace` uses the method parameter (default 'pad') to do the replacement. So this is why the 'a' values are being replaced by 10 in rows 1 and 2 and 'b' in row 4 in this case. The command ``s.replace('a', None)`` is actually equivalent to ``s.replace(to_replace='a', value=None, method='pad')``: >>> s.replace('a', None) 0 10 1 10 2 10 3 b 4 b dtype: object """ @Appender(_shared_docs["replace"] % _shared_doc_kwargs) def replace( self, to_replace=None, value=None, inplace=False, limit=None, regex=False, method="pad", ): if not ( is_scalar(to_replace) or isinstance(to_replace, pd.Series) or is_re_compilable(to_replace) or is_list_like(to_replace) ): raise TypeError( "Expecting 'to_replace' to be either a scalar, array-like, " "dict or None, got invalid type " f"{repr(type(to_replace).__name__)}" ) inplace = validate_bool_kwarg(inplace, "inplace") if not is_bool(regex) and to_replace is not None: raise AssertionError("'to_replace' must be 'None' if 'regex' is not a bool") self._consolidate_inplace() if value is None: # passing a single value that is scalar like # when value is None (GH5319), for compat if not is_dict_like(to_replace) and not is_dict_like(regex): to_replace = [to_replace] if isinstance(to_replace, (tuple, list)): if isinstance(self, ABCDataFrame): return self.apply( _single_replace, args=(to_replace, method, inplace, limit) ) return _single_replace(self, to_replace, method, inplace, limit) if not is_dict_like(to_replace): if not is_dict_like(regex): raise TypeError( 'If "to_replace" and "value" are both None ' 'and "to_replace" is not a list, then ' "regex must be a mapping" ) to_replace = regex regex = True items = list(to_replace.items()) keys, values = zip(*items) if items else ([], []) are_mappings = [is_dict_like(v) for v in values] if any(are_mappings): if not all(are_mappings): raise TypeError( "If a nested mapping is passed, all values " "of the top level mapping must be mappings" ) # passed a nested dict/Series to_rep_dict = {} value_dict = {} for k, v in items: keys, values = list(zip(*v.items())) or ([], []) to_rep_dict[k] = list(keys) value_dict[k] = list(values) to_replace, value = to_rep_dict, value_dict else: to_replace, value = keys, values return self.replace( to_replace, value, inplace=inplace, limit=limit, regex=regex ) else: # need a non-zero len on all axes if not self.size: return self new_data = self._data if is_dict_like(to_replace): if is_dict_like(value): # {'A' : NA} -> {'A' : 0} res = self if inplace else self.copy() for c, src in to_replace.items(): if c in value and c in self: # object conversion is handled in # series.replace which is called recursively res[c] = res[c].replace( to_replace=src, value=value[c], inplace=False, regex=regex, ) return None if inplace else res # {'A': NA} -> 0 elif not is_list_like(value): keys = [(k, src) for k, src in to_replace.items() if k in self] keys_len = len(keys) - 1 for i, (k, src) in enumerate(keys): convert = i == keys_len new_data = new_data.replace( to_replace=src, value=value, filter=[k], inplace=inplace, regex=regex, convert=convert, ) else: raise TypeError("value argument must be scalar, dict, or Series") elif is_list_like(to_replace): # [NA, ''] -> [0, 'missing'] if is_list_like(value): if len(to_replace) != len(value): raise ValueError( f"Replacement lists must match in length. " f"Expecting {len(to_replace)} got {len(value)} " ) new_data = self._data.replace_list( src_list=to_replace, dest_list=value, inplace=inplace, regex=regex, ) else: # [NA, ''] -> 0 new_data = self._data.replace( to_replace=to_replace, value=value, inplace=inplace, regex=regex ) elif to_replace is None: if not ( is_re_compilable(regex) or is_list_like(regex) or is_dict_like(regex) ): raise TypeError( f"'regex' must be a string or a compiled regular expression " f"or a list or dict of strings or regular expressions, " f"you passed a {repr(type(regex).__name__)}" ) return self.replace( regex, value, inplace=inplace, limit=limit, regex=True ) else: # dest iterable dict-like if is_dict_like(value): # NA -> {'A' : 0, 'B' : -1} new_data = self._data for k, v in value.items(): if k in self: new_data = new_data.replace( to_replace=to_replace, value=v, filter=[k], inplace=inplace, regex=regex, ) elif not is_list_like(value): # NA -> 0 new_data = self._data.replace( to_replace=to_replace, value=value, inplace=inplace, regex=regex ) else: raise TypeError( f'Invalid "to_replace" type: {repr(type(to_replace).__name__)}' ) if inplace: self._update_inplace(new_data) else: return self._constructor(new_data).__finalize__(self) _shared_docs[ "interpolate" ] = """ Please note that only ``method='linear'`` is supported for DataFrame/Series with a MultiIndex. Parameters ---------- method : str, default 'linear' Interpolation technique to use. One of: * 'linear': Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes. * 'time': Works on daily and higher resolution data to interpolate given length of interval. * 'index', 'values': use the actual numerical values of the index. * 'pad': Fill in NaNs using existing values. * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'spline', 'barycentric', 'polynomial': Passed to `scipy.interpolate.interp1d`. These methods use the numerical values of the index. Both 'polynomial' and 'spline' require that you also specify an `order` (int), e.g. ``df.interpolate(method='polynomial', order=5)``. * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima': Wrappers around the SciPy interpolation methods of similar names. See `Notes`. * 'from_derivatives': Refers to `scipy.interpolate.BPoly.from_derivatives` which replaces 'piecewise_polynomial' interpolation method in scipy 0.18. axis : {0 or 'index', 1 or 'columns', None}, default None Axis to interpolate along. limit : int, optional Maximum number of consecutive NaNs to fill. Must be greater than 0. inplace : bool, default False Update the data in place if possible. limit_direction : {'forward', 'backward', 'both'}, default 'forward' If limit is specified, consecutive NaNs will be filled in this direction. limit_area : {`None`, 'inside', 'outside'}, default None If limit is specified, consecutive NaNs will be filled with this restriction. * ``None``: No fill restriction. * 'inside': Only fill NaNs surrounded by valid values (interpolate). * 'outside': Only fill NaNs outside valid values (extrapolate). .. versionadded:: 0.23.0 downcast : optional, 'infer' or None, defaults to None Downcast dtypes if possible. **kwargs Keyword arguments to pass on to the interpolating function. Returns ------- Series or DataFrame Returns the same object type as the caller, interpolated at some or all ``NaN`` values. See Also -------- fillna : Fill missing values using different methods. scipy.interpolate.Akima1DInterpolator : Piecewise cubic polynomials (Akima interpolator). scipy.interpolate.BPoly.from_derivatives : Piecewise polynomial in the Bernstein basis. scipy.interpolate.interp1d : Interpolate a 1-D function. scipy.interpolate.KroghInterpolator : Interpolate polynomial (Krogh interpolator). scipy.interpolate.PchipInterpolator : PCHIP 1-d monotonic cubic interpolation. scipy.interpolate.CubicSpline : Cubic spline data interpolator. Notes ----- The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima' methods are wrappers around the respective SciPy implementations of similar names. These use the actual numerical values of the index. For more information on their behavior, see the `SciPy documentation <https://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__ and `SciPy tutorial <https://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html>`__. Examples -------- Filling in ``NaN`` in a :class:`~pandas.Series` via linear interpolation. >>> s = pd.Series([0, 1, np.nan, 3]) >>> s 0 0.0 1 1.0 2 NaN 3 3.0 dtype: float64 >>> s.interpolate() 0 0.0 1 1.0 2 2.0 3 3.0 dtype: float64 Filling in ``NaN`` in a Series by padding, but filling at most two consecutive ``NaN`` at a time. >>> s = pd.Series([np.nan, "single_one", np.nan, ... "fill_two_more", np.nan, np.nan, np.nan, ... 4.71, np.nan]) >>> s 0 NaN 1 single_one 2 NaN 3 fill_two_more 4 NaN 5 NaN 6 NaN 7 4.71 8 NaN dtype: object >>> s.interpolate(method='pad', limit=2) 0 NaN 1 single_one 2 single_one 3 fill_two_more 4 fill_two_more 5 fill_two_more 6 NaN 7 4.71 8 4.71 dtype: object Filling in ``NaN`` in a Series via polynomial interpolation or splines: Both 'polynomial' and 'spline' methods require that you also specify an ``order`` (int). >>> s = pd.Series([0, 2, np.nan, 8]) >>> s.interpolate(method='polynomial', order=2) 0 0.000000 1 2.000000 2 4.666667 3 8.000000 dtype: float64 Fill the DataFrame forward (that is, going down) along each column using linear interpolation. Note how the last entry in column 'a' is interpolated differently, because there is no entry after it to use for interpolation. Note how the first entry in column 'b' remains ``NaN``, because there is no entry before it to use for interpolation. >>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0), ... (np.nan, 2.0, np.nan, np.nan), ... (2.0, 3.0, np.nan, 9.0), ... (np.nan, 4.0, -4.0, 16.0)], ... columns=list('abcd')) >>> df a b c d 0 0.0 NaN -1.0 1.0 1 NaN 2.0 NaN NaN 2 2.0 3.0 NaN 9.0 3 NaN 4.0 -4.0 16.0 >>> df.interpolate(method='linear', limit_direction='forward', axis=0) a b c d 0 0.0 NaN -1.0 1.0 1 1.0 2.0 -2.0 5.0 2 2.0 3.0 -3.0 9.0 3 2.0 4.0 -4.0 16.0 Using polynomial interpolation. >>> df['d'].interpolate(method='polynomial', order=2) 0 1.0 1 4.0 2 9.0 3 16.0 Name: d, dtype: float64 """ @Appender(_shared_docs["interpolate"] % _shared_doc_kwargs) def interpolate( self, method="linear", axis=0, limit=None, inplace=False, limit_direction="forward", limit_area=None, downcast=None, **kwargs, ): inplace = validate_bool_kwarg(inplace, "inplace") axis = self._get_axis_number(axis) if axis == 0: ax = self._info_axis_name _maybe_transposed_self = self elif axis == 1: _maybe_transposed_self = self.T ax = 1 ax = _maybe_transposed_self._get_axis_number(ax) if _maybe_transposed_self.ndim == 2: alt_ax = 1 - ax else: alt_ax = ax if isinstance(_maybe_transposed_self.index, MultiIndex) and method != "linear": raise ValueError( "Only `method=linear` interpolation is supported on MultiIndexes." ) if _maybe_transposed_self._data.get_dtype_counts().get("object") == len( _maybe_transposed_self.T ): raise TypeError( "Cannot interpolate with all object-dtype columns " "in the DataFrame. Try setting at least one " "column to a numeric dtype." ) # create/use the index if method == "linear": # prior default index = np.arange(len(_maybe_transposed_self._get_axis(alt_ax))) else: index = _maybe_transposed_self._get_axis(alt_ax) methods = {"index", "values", "nearest", "time"} is_numeric_or_datetime = ( is_numeric_dtype(index) or is_datetime64_any_dtype(index) or is_timedelta64_dtype(index) ) if method not in methods and not is_numeric_or_datetime: raise ValueError( "Index column must be numeric or datetime type when " f"using {method} method other than linear. " "Try setting a numeric or datetime index column before " "interpolating." ) if isna(index).any(): raise NotImplementedError( "Interpolation with NaNs in the index " "has not been implemented. Try filling " "those NaNs before interpolating." ) data = _maybe_transposed_self._data new_data = data.interpolate( method=method, axis=ax, index=index, limit=limit, limit_direction=limit_direction, limit_area=limit_area, inplace=inplace, downcast=downcast, **kwargs, ) if inplace: if axis == 1: new_data = self._constructor(new_data).T._data self._update_inplace(new_data) else: res = self._constructor(new_data).__finalize__(self) if axis == 1: res = res.T return res # ---------------------------------------------------------------------- # Timeseries methods Methods def asof(self, where, subset=None): if isinstance(where, str): where = Timestamp(where) if not self.index.is_monotonic: raise ValueError("asof requires a sorted index") is_series = isinstance(self, ABCSeries) if is_series: if subset is not None: raise ValueError("subset is not valid for Series") else: if subset is None: subset = self.columns if not is_list_like(subset): subset = [subset] is_list = is_list_like(where) if not is_list: start = self.index[0] if isinstance(self.index, PeriodIndex): where = Period(where, freq=self.index.freq) if where < start: if not is_series: from pandas import Series return Series(index=self.columns, name=where, dtype=np.float64) return np.nan # It's always much faster to use a *while* loop here for # Series than pre-computing all the NAs. However a # *while* loop is extremely expensive for DataFrame # so we later pre-compute all the NAs and use the same # code path whether *where* is a scalar or list. # See PR: https://github.com/pandas-dev/pandas/pull/14476 if is_series: loc = self.index.searchsorted(where, side="right") if loc > 0: loc -= 1 values = self._values while loc > 0 and isna(values[loc]): loc -= 1 return values[loc] if not isinstance(where, Index): where = Index(where) if is_list else Index([where]) nulls = self.isna() if is_series else self[subset].isna().any(1) if nulls.all(): if is_series: return self._constructor(np.nan, index=where, name=self.name) elif is_list: from pandas import DataFrame return DataFrame(np.nan, index=where, columns=self.columns) else: from pandas import Series return Series(np.nan, index=self.columns, name=where[0]) locs = self.index.asof_locs(where, ~(nulls.values)) # mask the missing missing = locs == -1 data = self.take(locs) data.index = where data.loc[missing] = np.nan return data if is_list else data.iloc[-1] # ---------------------------------------------------------------------- # Action Methods _shared_docs[ "isna" ] = """ Detect missing values. Return a boolean same-sized object indicating if the values are NA. NA values, such as None or :attr:`numpy.NaN`, gets mapped to True values. Everything else gets mapped to False values. Characters such as empty strings ``''`` or :attr:`numpy.inf` are not considered NA values (unless you set ``pandas.options.mode.use_inf_as_na = True``). Returns ------- %(klass)s Mask of bool values for each element in %(klass)s that indicates whether an element is not an NA value. See Also -------- %(klass)s.isnull : Alias of isna. %(klass)s.notna : Boolean inverse of isna. %(klass)s.dropna : Omit axes labels with missing values. isna : Top-level isna. Examples -------- Show which entries in a DataFrame are NA. >>> df = pd.DataFrame({'age': [5, 6, np.NaN], ... 'born': [pd.NaT, pd.Timestamp('1939-05-27'), ... pd.Timestamp('1940-04-25')], ... 'name': ['Alfred', 'Batman', ''], ... 'toy': [None, 'Batmobile', 'Joker']}) >>> df age born name toy 0 5.0 NaT Alfred None 1 6.0 1939-05-27 Batman Batmobile 2 NaN 1940-04-25 Joker >>> df.isna() age born name toy 0 False True False True 1 False False False False 2 True False False False Show which entries in a Series are NA. >>> ser = pd.Series([5, 6, np.NaN]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64 >>> ser.isna() 0 False 1 False 2 True dtype: bool """ @Appender(_shared_docs["isna"] % _shared_doc_kwargs) def isna(self: FrameOrSeries) -> FrameOrSeries: return isna(self).__finalize__(self) @Appender(_shared_docs["isna"] % _shared_doc_kwargs) def isnull(self: FrameOrSeries) -> FrameOrSeries: return isna(self).__finalize__(self) _shared_docs[ "notna" ] = """ Detect existing (non-missing) values. Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings ``''`` or :attr:`numpy.inf` are not considered NA values (unless you set ``pandas.options.mode.use_inf_as_na = True``). NA values, such as None or :attr:`numpy.NaN`, get mapped to False values. Returns ------- %(klass)s Mask of bool values for each element in %(klass)s that indicates whether an element is not an NA value. See Also -------- %(klass)s.notnull : Alias of notna. %(klass)s.isna : Boolean inverse of notna. %(klass)s.dropna : Omit axes labels with missing values. notna : Top-level notna. Examples -------- Show which entries in a DataFrame are not NA. >>> df = pd.DataFrame({'age': [5, 6, np.NaN], ... 'born': [pd.NaT, pd.Timestamp('1939-05-27'), ... pd.Timestamp('1940-04-25')], ... 'name': ['Alfred', 'Batman', ''], ... 'toy': [None, 'Batmobile', 'Joker']}) >>> df age born name toy 0 5.0 NaT Alfred None 1 6.0 1939-05-27 Batman Batmobile 2 NaN 1940-04-25 Joker >>> df.notna() age born name toy 0 True False True False 1 True True True True 2 False True True True Show which entries in a Series are not NA. >>> ser = pd.Series([5, 6, np.NaN]) >>> ser 0 5.0 1 6.0 2 NaN dtype: float64 >>> ser.notna() 0 True 1 True 2 False dtype: bool """ @Appender(_shared_docs["notna"] % _shared_doc_kwargs) def notna(self: FrameOrSeries) -> FrameOrSeries: return notna(self).__finalize__(self) @Appender(_shared_docs["notna"] % _shared_doc_kwargs) def notnull(self: FrameOrSeries) -> FrameOrSeries: return notna(self).__finalize__(self) def _clip_with_scalar(self, lower, upper, inplace: bool_t = False): if (lower is not None and np.any(isna(lower))) or ( upper is not None and np.any(isna(upper)) ): raise ValueError("Cannot use an NA value as a clip threshold") result = self mask = isna(self.values) with np.errstate(all="ignore"): if upper is not None: subset = self.to_numpy() <= upper result = result.where(subset, upper, axis=None, inplace=False) if lower is not None: subset = self.to_numpy() >= lower result = result.where(subset, lower, axis=None, inplace=False) if np.any(mask): result[mask] = np.nan if inplace: self._update_inplace(result) else: return result def _clip_with_one_bound(self, threshold, method, axis, inplace): if axis is not None: axis = self._get_axis_number(axis) # method is self.le for upper bound and self.ge for lower bound if is_scalar(threshold) and is_number(threshold): if method.__name__ == "le": return self._clip_with_scalar(None, threshold, inplace=inplace) return self._clip_with_scalar(threshold, None, inplace=inplace) subset = method(threshold, axis=axis) | isna(self) # GH #15390 # In order for where method to work, the threshold must # be transformed to NDFrame from other array like structure. if (not isinstance(threshold, ABCSeries)) and is_list_like(threshold): if isinstance(self, ABCSeries): threshold = self._constructor(threshold, index=self.index) else: threshold = _align_method_FRAME(self, threshold, axis, flex=None)[1] return self.where(subset, threshold, axis=axis, inplace=inplace) def clip( self: FrameOrSeries, lower=None, upper=None, axis=None, inplace: bool_t = False, *args, **kwargs, ) -> FrameOrSeries: inplace = validate_bool_kwarg(inplace, "inplace") axis = nv.validate_clip_with_axis(axis, args, kwargs) if axis is not None: axis = self._get_axis_number(axis) # GH 17276 # numpy doesn't like NaN as a clip value # so ignore # GH 19992 # numpy doesn't drop a list-like bound containing NaN if not is_list_like(lower) and np.any(isna(lower)): lower = None if not is_list_like(upper) and np.any(isna(upper)): upper = None # GH 2747 (arguments were reversed) if lower is not None and upper is not None: if is_scalar(lower) and is_scalar(upper): lower, upper = min(lower, upper), max(lower, upper) # fast-path for scalars if (lower is None or (is_scalar(lower) and is_number(lower))) and ( upper is None or (is_scalar(upper) and is_number(upper)) ): return self._clip_with_scalar(lower, upper, inplace=inplace) result = self if lower is not None: result = result._clip_with_one_bound( lower, method=self.ge, axis=axis, inplace=inplace ) if upper is not None: if inplace: result = self result = result._clip_with_one_bound( upper, method=self.le, axis=axis, inplace=inplace ) return result _shared_docs[ "groupby" ] = """ Group %(klass)s using a mapper or by a Series of columns. A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups. Parameters ---------- by : mapping, function, label, or list of labels Used to determine the groups for the groupby. If ``by`` is a function, it's called on each value of the object's index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series' values are first aligned; see ``.align()`` method). If an ndarray is passed, the values are used as-is determine the groups. A label or list of labels may be passed to group by the columns in ``self``. Notice that a tuple is interpreted as a (single) key. axis : {0 or 'index', 1 or 'columns'}, default 0 Split along rows (0) or columns (1). level : int, level name, or sequence of such, default None If the axis is a MultiIndex (hierarchical), group by a particular level or levels. as_index : bool, default True For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively "SQL-style" grouped output. sort : bool, default True Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. group_keys : bool, default True When calling apply, add group keys to index to identify pieces. squeeze : bool, default False Reduce the dimensionality of the return type if possible, otherwise return a consistent type. observed : bool, default False This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers. .. versionadded:: 0.23.0 Returns ------- %(klass)sGroupBy Returns a groupby object that contains information about the groups. See Also -------- resample : Convenience method for frequency conversion and resampling of time series. Notes ----- See the `user guide <https://pandas.pydata.org/pandas-docs/stable/groupby.html>`_ for more. """ def asfreq( self: FrameOrSeries, freq, method=None, how: Optional[str] = None, normalize: bool_t = False, fill_value=None, ) -> FrameOrSeries: from pandas.core.resample import asfreq return asfreq( self, freq, method=method, how=how, normalize=normalize, fill_value=fill_value, ) def at_time( self: FrameOrSeries, time, asof: bool_t = False, axis=None ) -> FrameOrSeries: if axis is None: axis = self._stat_axis_number axis = self._get_axis_number(axis) index = self._get_axis(axis) try: indexer = index.indexer_at_time(time, asof=asof) except AttributeError as err: raise TypeError("Index must be DatetimeIndex") from err return self._take_with_is_copy(indexer, axis=axis) def between_time( self: FrameOrSeries, start_time, end_time, include_start: bool_t = True, include_end: bool_t = True, axis=None, ) -> FrameOrSeries: if axis is None: axis = self._stat_axis_number axis = self._get_axis_number(axis) index = self._get_axis(axis) try: indexer = index.indexer_between_time( start_time, end_time, include_start=include_start, include_end=include_end, ) except AttributeError as err: raise TypeError("Index must be DatetimeIndex") from err return self._take_with_is_copy(indexer, axis=axis) def resample( self, rule, axis=0, closed: Optional[str] = None, label: Optional[str] = None, convention: str = "start", kind: Optional[str] = None, loffset=None, base: int = 0, on=None, level=None, ) -> "Resampler": from pandas.core.resample import get_resampler axis = self._get_axis_number(axis) return get_resampler( self, freq=rule, label=label, closed=closed, axis=axis, kind=kind, loffset=loffset, convention=convention, base=base, key=on, level=level, ) def first(self: FrameOrSeries, offset) -> FrameOrSeries: if not isinstance(self.index, DatetimeIndex): raise TypeError("'first' only supports a DatetimeIndex index") if len(self.index) == 0: return self offset = to_offset(offset) end_date = end = self.index[0] + offset # Tick-like, e.g. 3 weeks if not offset.is_anchored() and hasattr(offset, "_inc"): if end_date in self.index: end = self.index.searchsorted(end_date, side="left") return self.iloc[:end] return self.loc[:end] def last(self: FrameOrSeries, offset) -> FrameOrSeries: if not isinstance(self.index, DatetimeIndex): raise TypeError("'last' only supports a DatetimeIndex index") if len(self.index) == 0: return self offset = to_offset(offset) start_date = self.index[-1] - offset start = self.index.searchsorted(start_date, side="right") return self.iloc[start:] def rank( self: FrameOrSeries, axis=0, method: str = "average", numeric_only: Optional[bool_t] = None, na_option: str = "keep", ascending: bool_t = True, pct: bool_t = False, ) -> FrameOrSeries: axis = self._get_axis_number(axis) if na_option not in {"keep", "top", "bottom"}: msg = "na_option must be one of 'keep', 'top', or 'bottom'" raise ValueError(msg) def ranker(data): ranks = algos.rank( data.values, axis=axis, method=method, ascending=ascending, na_option=na_option, pct=pct, ) ranks = self._constructor(ranks, **data._construct_axes_dict()) return ranks.__finalize__(self) # if numeric_only is None, and we can't get anything, we try with # numeric_only=True if numeric_only is None: try: return ranker(self) except TypeError: numeric_only = True if numeric_only: data = self._get_numeric_data() else: data = self return ranker(data) _shared_docs[ "align" ] = """ Align two objects on their axes with the specified join method. Join method is specified for each axis Index. Parameters ---------- other : DataFrame or Series join : {'outer', 'inner', 'left', 'right'}, default 'outer' axis : allowed axis of the other object, default None Align on index (0), columns (1), or both (None). level : int or level name, default None Broadcast across a level, matching Index values on the passed MultiIndex level. copy : bool, default True Always returns new objects. If copy=False and no reindexing is required then original objects are returned. fill_value : scalar, default np.NaN Value to use for missing values. Defaults to NaN, but can be any "compatible" value. method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None Method to use for filling holes in reindexed Series: - pad / ffill: propagate last valid observation forward to next valid. - backfill / bfill: use NEXT valid observation to fill gap. limit : int, default None If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None. fill_axis : %(axes_single_arg)s, default 0 Filling axis, method and limit. broadcast_axis : %(axes_single_arg)s, default None Broadcast values along this axis, if aligning two objects of different dimensions. Returns ------- (left, right) : (%(klass)s, type of other) Aligned objects. """ @Appender(_shared_docs["align"] % _shared_doc_kwargs) def align( self, other, join="outer", axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None, ): method = missing.clean_fill_method(method) if broadcast_axis == 1 and self.ndim != other.ndim: if isinstance(self, ABCSeries): # this means other is a DataFrame, and we need to broadcast # self cons = self._constructor_expanddim df = cons( {c: self for c in other.columns}, **other._construct_axes_dict() ) return df._align_frame( other, join=join, axis=axis, level=level, copy=copy, fill_value=fill_value, method=method, limit=limit, fill_axis=fill_axis, ) elif isinstance(other, ABCSeries): # this means self is a DataFrame, and we need to broadcast # other cons = other._constructor_expanddim df = cons( {c: other for c in self.columns}, **self._construct_axes_dict() ) return self._align_frame( df, join=join, axis=axis, level=level, copy=copy, fill_value=fill_value, method=method, limit=limit, fill_axis=fill_axis, ) if axis is not None: axis = self._get_axis_number(axis) if isinstance(other, ABCDataFrame): return self._align_frame( other, join=join, axis=axis, level=level, copy=copy, fill_value=fill_value, method=method, limit=limit, fill_axis=fill_axis, ) elif isinstance(other, ABCSeries): return self._align_series( other, join=join, axis=axis, level=level, copy=copy, fill_value=fill_value, method=method, limit=limit, fill_axis=fill_axis, ) else: # pragma: no cover raise TypeError(f"unsupported type: {type(other)}") def _align_frame( self, other, join="outer", axis=None, level=None, copy: bool_t = True, fill_value=None, method=None, limit=None, fill_axis=0, ): # defaults join_index, join_columns = None, None ilidx, iridx = None, None clidx, cridx = None, None is_series = isinstance(self, ABCSeries) if axis is None or axis == 0: if not self.index.equals(other.index): join_index, ilidx, iridx = self.index.join( other.index, how=join, level=level, return_indexers=True ) if axis is None or axis == 1: if not is_series and not self.columns.equals(other.columns): join_columns, clidx, cridx = self.columns.join( other.columns, how=join, level=level, return_indexers=True ) if is_series: reindexers = {0: [join_index, ilidx]} else: reindexers = {0: [join_index, ilidx], 1: [join_columns, clidx]} left = self._reindex_with_indexers( reindexers, copy=copy, fill_value=fill_value, allow_dups=True ) # other must be always DataFrame right = other._reindex_with_indexers( {0: [join_index, iridx], 1: [join_columns, cridx]}, copy=copy, fill_value=fill_value, allow_dups=True, ) if method is not None: left = self._ensure_type( left.fillna(method=method, axis=fill_axis, limit=limit) ) right = right.fillna(method=method, axis=fill_axis, limit=limit) # if DatetimeIndex have different tz, convert to UTC if is_datetime64tz_dtype(left.index): if left.index.tz != right.index.tz: if join_index is not None: left.index = join_index right.index = join_index return left.__finalize__(self), right.__finalize__(other) def _align_series( self, other, join="outer", axis=None, level=None, copy: bool_t = True, fill_value=None, method=None, limit=None, fill_axis=0, ): is_series = isinstance(self, ABCSeries) # series/series compat, other must always be a Series if is_series: if axis: raise ValueError("cannot align series to a series other than axis 0") # equal if self.index.equals(other.index): join_index, lidx, ridx = None, None, None else: join_index, lidx, ridx = self.index.join( other.index, how=join, level=level, return_indexers=True ) left = self._reindex_indexer(join_index, lidx, copy) right = other._reindex_indexer(join_index, ridx, copy) else: # one has > 1 ndim fdata = self._data if axis == 0: join_index = self.index lidx, ridx = None, None if not self.index.equals(other.index): join_index, lidx, ridx = self.index.join( other.index, how=join, level=level, return_indexers=True ) if lidx is not None: fdata = fdata.reindex_indexer(join_index, lidx, axis=1) elif axis == 1: join_index = self.columns lidx, ridx = None, None if not self.columns.equals(other.index): join_index, lidx, ridx = self.columns.join( other.index, how=join, level=level, return_indexers=True ) if lidx is not None: fdata = fdata.reindex_indexer(join_index, lidx, axis=0) else: raise ValueError("Must specify axis=0 or 1") if copy and fdata is self._data: fdata = fdata.copy() left = self._constructor(fdata) if ridx is None: right = other else: right = other.reindex(join_index, level=level) # fill fill_na = notna(fill_value) or (method is not None) if fill_na: left = left.fillna(fill_value, method=method, limit=limit, axis=fill_axis) right = right.fillna(fill_value, method=method, limit=limit) # if DatetimeIndex have different tz, convert to UTC if is_series or (not is_series and axis == 0): if is_datetime64tz_dtype(left.index): if left.index.tz != right.index.tz: if join_index is not None: left.index = join_index right.index = join_index return left.__finalize__(self), right.__finalize__(other) def _where( self, cond, other=np.nan, inplace=False, axis=None, level=None, errors="raise", try_cast=False, ): inplace = validate_bool_kwarg(inplace, "inplace") # align the cond to same shape as myself cond = com.apply_if_callable(cond, self) if isinstance(cond, NDFrame): cond, _ = cond.align(self, join="right", broadcast_axis=1) else: if not hasattr(cond, "shape"): cond = np.asanyarray(cond) if cond.shape != self.shape: raise ValueError("Array conditional must be same shape as self") cond = self._constructor(cond, **self._construct_axes_dict()) # make sure we are boolean fill_value = bool(inplace) cond = cond.fillna(fill_value) msg = "Boolean array expected for the condition, not {dtype}" if not isinstance(cond, ABCDataFrame): # This is a single-dimensional object. if not is_bool_dtype(cond): raise ValueError(msg.format(dtype=cond.dtype)) elif not cond.empty: for dt in cond.dtypes: if not is_bool_dtype(dt): raise ValueError(msg.format(dtype=dt)) cond = -cond if inplace else cond # try to align with other try_quick = True if hasattr(other, "align"): # align with me if other.ndim <= self.ndim: _, other = self.align( other, join="left", axis=axis, level=level, fill_value=np.nan ) # if we are NOT aligned, raise as we cannot where index if axis is None and not all( other._get_axis(i).equals(ax) for i, ax in enumerate(self.axes) ): raise InvalidIndexError # slice me out of the other else: raise NotImplementedError( "cannot align with a higher dimensional NDFrame" ) if isinstance(other, np.ndarray): if other.shape != self.shape: if self.ndim == 1: icond = cond.values # GH 2745 / GH 4192 # treat like a scalar if len(other) == 1: other = np.array(other[0]) # GH 3235 # match True cond to other elif len(cond[icond]) == len(other): # try to not change dtype at first (if try_quick) if try_quick: new_other = np.asarray(self) new_other = new_other.copy() new_other[icond] = other other = new_other else: raise ValueError( "Length of replacements must equal series length" ) else: raise ValueError( "other must be the same shape as self when an ndarray" ) # we are the same shape, so create an actual object for alignment else: other = self._constructor(other, **self._construct_axes_dict()) if axis is None: axis = 0 if self.ndim == getattr(other, "ndim", 0): align = True else: align = self._get_axis_number(axis) == 1 block_axis = self._get_block_manager_axis(axis) if inplace: # we may have different type blocks come out of putmask, so # reconstruct the block manager self._check_inplace_setting(other) new_data = self._data.putmask( mask=cond, new=other, align=align, inplace=True, axis=block_axis, transpose=self._AXIS_REVERSED, ) self._update_inplace(new_data) else: new_data = self._data.where( other=other, cond=cond, align=align, errors=errors, try_cast=try_cast, axis=block_axis, ) return self._constructor(new_data).__finalize__(self) _shared_docs[ "where" ] = """ Replace values where the condition is %(cond_rev)s. Parameters ---------- cond : bool %(klass)s, array-like, or callable Where `cond` is %(cond)s, keep the original value. Where %(cond_rev)s, replace with corresponding value from `other`. If `cond` is callable, it is computed on the %(klass)s and should return boolean %(klass)s or array. The callable must not change input %(klass)s (though pandas doesn't check it). other : scalar, %(klass)s, or callable Entries where `cond` is %(cond_rev)s are replaced with corresponding value from `other`. If other is callable, it is computed on the %(klass)s and should return scalar or %(klass)s. The callable must not change input %(klass)s (though pandas doesn't check it). inplace : bool, default False Whether to perform the operation in place on the data. axis : int, default None Alignment axis if needed. level : int, default None Alignment level if needed. errors : str, {'raise', 'ignore'}, default 'raise' Note that currently this parameter won't affect the results and will always coerce to a suitable dtype. - 'raise' : allow exceptions to be raised. - 'ignore' : suppress exceptions. On error return original object. try_cast : bool, default False Try to cast the result back to the input type (if possible). Returns ------- Same type as caller See Also -------- :func:`DataFrame.%(name_other)s` : Return an object of same shape as self. Notes ----- The %(name)s method is an application of the if-then idiom. For each element in the calling DataFrame, if ``cond`` is ``%(cond)s`` the element is used; otherwise the corresponding element from the DataFrame ``other`` is used. The signature for :func:`DataFrame.where` differs from :func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to ``np.where(m, df1, df2)``. For further details and examples see the ``%(name)s`` documentation in :ref:`indexing <indexing.where_mask>`. Examples -------- >>> s = pd.Series(range(5)) >>> s.where(s > 0) 0 NaN 1 1.0 2 2.0 3 3.0 4 4.0 dtype: float64 >>> s.mask(s > 0) 0 0.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 >>> s.where(s > 1, 10) 0 10 1 10 2 2 3 3 4 4 dtype: int64 >>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B']) >>> df A B 0 0 1 1 2 3 2 4 5 3 6 7 4 8 9 >>> m = df %% 3 == 0 >>> df.where(m, -df) A B 0 0 -1 1 -2 3 2 -4 -5 3 6 -7 4 -8 9 >>> df.where(m, -df) == np.where(m, df, -df) A B 0 True True 1 True True 2 True True 3 True True 4 True True >>> df.where(m, -df) == df.mask(~m, -df) A B 0 True True 1 True True 2 True True 3 True True 4 True True """ @Appender( _shared_docs["where"] % dict( _shared_doc_kwargs, cond="True", cond_rev="False", name="where", name_other="mask", ) ) def where( self, cond, other=np.nan, inplace=False, axis=None, level=None, errors="raise", try_cast=False, ): other = com.apply_if_callable(other, self) return self._where( cond, other, inplace, axis, level, errors=errors, try_cast=try_cast ) @Appender( _shared_docs["where"] % dict( _shared_doc_kwargs, cond="False", cond_rev="True", name="mask", name_other="where", ) ) def mask( self, cond, other=np.nan, inplace=False, axis=None, level=None, errors="raise", try_cast=False, ): inplace = validate_bool_kwarg(inplace, "inplace") cond = com.apply_if_callable(cond, self) # see gh-21891 if not hasattr(cond, "__invert__"): cond = np.array(cond) return self.where( ~cond, other=other, inplace=inplace, axis=axis, level=level, try_cast=try_cast, errors=errors, ) _shared_docs[ "shift" ] = """ Shift index by desired number of periods with an optional time `freq`. When `freq` is not passed, shift the index without realigning the data. If `freq` is passed (in this case, the index must be date or datetime, or it will raise a `NotImplementedError`), the index will be increased using the periods and the `freq`. Parameters ---------- periods : int Number of periods to shift. Can be positive or negative. freq : DateOffset, tseries.offsets, timedelta, or str, optional Offset to use from the tseries module or time rule (e.g. 'EOM'). If `freq` is specified then the index values are shifted but the data is not realigned. That is, use `freq` if you would like to extend the index when shifting and preserve the original data. axis : {0 or 'index', 1 or 'columns', None}, default None Shift direction. fill_value : object, optional The scalar value to use for newly introduced missing values. the default depends on the dtype of `self`. For numeric data, ``np.nan`` is used. For datetime, timedelta, or period data, etc. :attr:`NaT` is used. For extension dtypes, ``self.dtype.na_value`` is used. .. versionchanged:: 0.24.0 Returns ------- %(klass)s Copy of input object, shifted. See Also -------- Index.shift : Shift values of Index. DatetimeIndex.shift : Shift values of DatetimeIndex. PeriodIndex.shift : Shift values of PeriodIndex. tshift : Shift the time index, using the index's frequency if available. Examples -------- >>> df = pd.DataFrame({'Col1': [10, 20, 15, 30, 45], ... 'Col2': [13, 23, 18, 33, 48], ... 'Col3': [17, 27, 22, 37, 52]}) >>> df.shift(periods=3) Col1 Col2 Col3 0 NaN NaN NaN 1 NaN NaN NaN 2 NaN NaN NaN 3 10.0 13.0 17.0 4 20.0 23.0 27.0 >>> df.shift(periods=1, axis='columns') Col1 Col2 Col3 0 NaN 10.0 13.0 1 NaN 20.0 23.0 2 NaN 15.0 18.0 3 NaN 30.0 33.0 4 NaN 45.0 48.0 >>> df.shift(periods=3, fill_value=0) Col1 Col2 Col3 0 0 0 0 1 0 0 0 2 0 0 0 3 10 13 17 4 20 23 27 """ @Appender(_shared_docs["shift"] % _shared_doc_kwargs) def shift( self: FrameOrSeries, periods=1, freq=None, axis=0, fill_value=None ) -> FrameOrSeries: if periods == 0: return self.copy() block_axis = self._get_block_manager_axis(axis) if freq is None: new_data = self._data.shift( periods=periods, axis=block_axis, fill_value=fill_value ) else: return self.tshift(periods, freq) return self._constructor(new_data).__finalize__(self) def slice_shift(self: FrameOrSeries, periods: int = 1, axis=0) -> FrameOrSeries: if periods == 0: return self if periods > 0: vslicer = slice(None, -periods) islicer = slice(periods, None) else: vslicer = slice(-periods, None) islicer = slice(None, periods) new_obj = self._slice(vslicer, axis=axis) shifted_axis = self._get_axis(axis)[islicer] new_obj.set_axis(shifted_axis, axis=axis, inplace=True) return new_obj.__finalize__(self) def tshift( self: FrameOrSeries, periods: int = 1, freq=None, axis=0 ) -> FrameOrSeries: index = self._get_axis(axis) if freq is None: freq = getattr(index, "freq", None) if freq is None: freq = getattr(index, "inferred_freq", None) if freq is None: msg = "Freq was not given and was not set in the index" raise ValueError(msg) if periods == 0: return self if isinstance(freq, str): freq = to_offset(freq) block_axis = self._get_block_manager_axis(axis) if isinstance(index, PeriodIndex): orig_freq = to_offset(index.freq) if freq == orig_freq: new_data = self._data.copy() new_data.axes[block_axis] = index.shift(periods) elif orig_freq is not None: raise ValueError( f"Given freq {freq.rule_code} does not match " f"PeriodIndex freq {orig_freq.rule_code}" ) else: new_data = self._data.copy() new_data.axes[block_axis] = index.shift(periods, freq) return self._constructor(new_data).__finalize__(self) def truncate( self: FrameOrSeries, before=None, after=None, axis=None, copy: bool_t = True ) -> FrameOrSeries: if axis is None: axis = self._stat_axis_number axis = self._get_axis_number(axis) ax = self._get_axis(axis) # GH 17935 # Check that index is sorted if not ax.is_monotonic_increasing and not ax.is_monotonic_decreasing: raise ValueError("truncate requires a sorted index") # if we have a date index, convert to dates, otherwise # treat like a slice if ax.is_all_dates: from pandas.core.tools.datetimes import to_datetime before = to_datetime(before) after = to_datetime(after) if before is not None and after is not None: if before > after: raise ValueError(f"Truncate: {after} must be after {before}") slicer = [slice(None, None)] * self._AXIS_LEN slicer[axis] = slice(before, after) result = self.loc[tuple(slicer)] if isinstance(ax, MultiIndex): setattr(result, self._get_axis_name(axis), ax.truncate(before, after)) if copy: result = result.copy() return result def tz_convert( self: FrameOrSeries, tz, axis=0, level=None, copy: bool_t = True ) -> FrameOrSeries: axis = self._get_axis_number(axis) ax = self._get_axis(axis) def _tz_convert(ax, tz): if not hasattr(ax, "tz_convert"): if len(ax) > 0: ax_name = self._get_axis_name(axis) raise TypeError( f"{ax_name} is not a valid DatetimeIndex or PeriodIndex" ) else: ax = DatetimeIndex([], tz=tz) else: ax = ax.tz_convert(tz) return ax # if a level is given it must be a MultiIndex level or # equivalent to the axis name if isinstance(ax, MultiIndex): level = ax._get_level_number(level) new_level = _tz_convert(ax.levels[level], tz) ax = ax.set_levels(new_level, level=level) else: if level not in (None, 0, ax.name): raise ValueError(f"The level {level} is not valid") ax = _tz_convert(ax, tz) result = self._constructor(self._data, copy=copy) result = result.set_axis(ax, axis=axis, inplace=False) return result.__finalize__(self) def tz_localize( self: FrameOrSeries, tz, axis=0, level=None, copy: bool_t = True, ambiguous="raise", nonexistent: str = "raise", ) -> FrameOrSeries: nonexistent_options = ("raise", "NaT", "shift_forward", "shift_backward") if nonexistent not in nonexistent_options and not isinstance( nonexistent, timedelta ): raise ValueError( "The nonexistent argument must be one of 'raise', " "'NaT', 'shift_forward', 'shift_backward' or " "a timedelta object" ) axis = self._get_axis_number(axis) ax = self._get_axis(axis) def _tz_localize(ax, tz, ambiguous, nonexistent): if not hasattr(ax, "tz_localize"): if len(ax) > 0: ax_name = self._get_axis_name(axis) raise TypeError( f"{ax_name} is not a valid DatetimeIndex or PeriodIndex" ) else: ax = DatetimeIndex([], tz=tz) else: ax = ax.tz_localize(tz, ambiguous=ambiguous, nonexistent=nonexistent) return ax # if a level is given it must be a MultiIndex level or # equivalent to the axis name if isinstance(ax, MultiIndex): level = ax._get_level_number(level) new_level = _tz_localize(ax.levels[level], tz, ambiguous, nonexistent) ax = ax.set_levels(new_level, level=level) else: if level not in (None, 0, ax.name): raise ValueError(f"The level {level} is not valid") ax = _tz_localize(ax, tz, ambiguous, nonexistent) result = self._constructor(self._data, copy=copy) result = result.set_axis(ax, axis=axis, inplace=False) return result.__finalize__(self) # ---------------------------------------------------------------------- # Numeric Methods def abs(self: FrameOrSeries) -> FrameOrSeries: return np.abs(self) def describe( self: FrameOrSeries, percentiles=None, include=None, exclude=None ) -> FrameOrSeries: if self.ndim == 2 and self.columns.size == 0: raise ValueError("Cannot describe a DataFrame without columns") if percentiles is not None: # explicit conversion of `percentiles` to list percentiles = list(percentiles) # get them all to be in [0, 1] validate_percentile(percentiles) # median should always be included if 0.5 not in percentiles: percentiles.append(0.5) percentiles = np.asarray(percentiles) else: percentiles = np.array([0.25, 0.5, 0.75]) # sort and check for duplicates unique_pcts = np.unique(percentiles) if len(unique_pcts) < len(percentiles): raise ValueError("percentiles cannot contain duplicates") percentiles = unique_pcts formatted_percentiles = format_percentiles(percentiles) def describe_numeric_1d(series): stat_index = ( ["count", "mean", "std", "min"] + formatted_percentiles + ["max"] ) d = ( [series.count(), series.mean(), series.std(), series.min()] + series.quantile(percentiles).tolist() + [series.max()] ) return pd.Series(d, index=stat_index, name=series.name) def describe_categorical_1d(data): names = ["count", "unique"] objcounts = data.value_counts() count_unique = len(objcounts[objcounts != 0]) result = [data.count(), count_unique] dtype = None if result[1] > 0: top, freq = objcounts.index[0], objcounts.iloc[0] names += ["top", "freq"] result += [top, freq] # If the DataFrame is empty, set 'top' and 'freq' to None # to maintain output shape consistency else: names += ["top", "freq"] result += [np.nan, np.nan] dtype = "object" return pd.Series(result, index=names, name=data.name, dtype=dtype) def describe_timestamp_1d(data): # GH-30164 stat_index = ["count", "mean", "min"] + formatted_percentiles + ["max"] d = ( [data.count(), data.mean(), data.min()] + data.quantile(percentiles).tolist() + [data.max()] ) return pd.Series(d, index=stat_index, name=data.name) def describe_1d(data): if is_bool_dtype(data): return describe_categorical_1d(data) elif is_numeric_dtype(data): return describe_numeric_1d(data) elif is_datetime64_any_dtype(data): return describe_timestamp_1d(data) elif is_timedelta64_dtype(data): return describe_numeric_1d(data) else: return describe_categorical_1d(data) if self.ndim == 1: return describe_1d(self) elif (include is None) and (exclude is None): # when some numerics are found, keep only numerics data = self.select_dtypes(include=[np.number]) if len(data.columns) == 0: data = self elif include == "all": if exclude is not None: msg = "exclude must be None when include is 'all'" raise ValueError(msg) data = self else: data = self.select_dtypes(include=include, exclude=exclude) ldesc = [describe_1d(s) for _, s in data.items()] # set a convenient order for rows names: List[Label] = [] ldesc_indexes = sorted((x.index for x in ldesc), key=len) for idxnames in ldesc_indexes: for name in idxnames: if name not in names: names.append(name) d = pd.concat([x.reindex(names, copy=False) for x in ldesc], axis=1, sort=False) d.columns = data.columns.copy() return d _shared_docs[ "pct_change" ] = """ Percentage change between the current and a prior element. Computes the percentage change from the immediately previous row by default. This is useful in comparing the percentage of change in a time series of elements. Parameters ---------- periods : int, default 1 Periods to shift for forming percent change. fill_method : str, default 'pad' How to handle NAs before computing percent changes. limit : int, default None The number of consecutive NAs to fill before stopping. freq : DateOffset, timedelta, or str, optional Increment to use from time series API (e.g. 'M' or BDay()). **kwargs Additional keyword arguments are passed into `DataFrame.shift` or `Series.shift`. Returns ------- chg : Series or DataFrame The same type as the calling object. See Also -------- Series.diff : Compute the difference of two elements in a Series. DataFrame.diff : Compute the difference of two elements in a DataFrame. Series.shift : Shift the index by some number of periods. DataFrame.shift : Shift the index by some number of periods. Examples -------- **Series** >>> s = pd.Series([90, 91, 85]) >>> s 0 90 1 91 2 85 dtype: int64 >>> s.pct_change() 0 NaN 1 0.011111 2 -0.065934 dtype: float64 >>> s.pct_change(periods=2) 0 NaN 1 NaN 2 -0.055556 dtype: float64 See the percentage change in a Series where filling NAs with last valid observation forward to next valid. >>> s = pd.Series([90, 91, None, 85]) >>> s 0 90.0 1 91.0 2 NaN 3 85.0 dtype: float64 >>> s.pct_change(fill_method='ffill') 0 NaN 1 0.011111 2 0.000000 3 -0.065934 dtype: float64 **DataFrame** Percentage change in French franc, Deutsche Mark, and Italian lira from 1980-01-01 to 1980-03-01. >>> df = pd.DataFrame({ ... 'FR': [4.0405, 4.0963, 4.3149], ... 'GR': [1.7246, 1.7482, 1.8519], ... 'IT': [804.74, 810.01, 860.13]}, ... index=['1980-01-01', '1980-02-01', '1980-03-01']) >>> df FR GR IT 1980-01-01 4.0405 1.7246 804.74 1980-02-01 4.0963 1.7482 810.01 1980-03-01 4.3149 1.8519 860.13 >>> df.pct_change() FR GR IT 1980-01-01 NaN NaN NaN 1980-02-01 0.013810 0.013684 0.006549 1980-03-01 0.053365 0.059318 0.061876 Percentage of change in GOOG and APPL stock volume. Shows computing the percentage change between columns. >>> df = pd.DataFrame({ ... '2016': [1769950, 30586265], ... '2015': [1500923, 40912316], ... '2014': [1371819, 41403351]}, ... index=['GOOG', 'APPL']) >>> df 2016 2015 2014 GOOG 1769950 1500923 1371819 APPL 30586265 40912316 41403351 >>> df.pct_change(axis='columns') 2016 2015 2014 GOOG NaN -0.151997 -0.086016 APPL NaN 0.337604 0.012002 """ @Appender(_shared_docs["pct_change"] % _shared_doc_kwargs) def pct_change( self: FrameOrSeries, periods=1, fill_method="pad", limit=None, freq=None, **kwargs, ) -> FrameOrSeries: # TODO: Not sure if above is correct - need someone to confirm. axis = self._get_axis_number(kwargs.pop("axis", self._stat_axis_name)) if fill_method is None: data = self else: data = self._ensure_type( self.fillna(method=fill_method, axis=axis, limit=limit) ) rs = data.div(data.shift(periods=periods, freq=freq, axis=axis, **kwargs)) - 1 if freq is not None: # Shift method is implemented differently when freq is not None # We want to restore the original index rs = rs.loc[~rs.index.duplicated()] rs = rs.reindex_like(data) return rs def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwargs): if axis is None: raise ValueError("Must specify 'axis' when aggregating by level.") grouped = self.groupby(level=level, axis=axis, sort=False) if hasattr(grouped, name) and skipna: return getattr(grouped, name)(**kwargs) axis = self._get_axis_number(axis) method = getattr(type(self), name) applyf = lambda x: method(x, axis=axis, skipna=skipna, **kwargs) return grouped.aggregate(applyf) @classmethod def _add_numeric_operations(cls): axis_descr, name1, name2 = _doc_parms(cls) cls.any = _make_logical_function( cls, "any", name1=name1, name2=name2, axis_descr=axis_descr, desc=_any_desc, func=nanops.nanany, see_also=_any_see_also, examples=_any_examples, empty_value=False, ) cls.all = _make_logical_function( cls, "all", name1=name1, name2=name2, axis_descr=axis_descr, desc=_all_desc, func=nanops.nanall, see_also=_all_see_also, examples=_all_examples, empty_value=True, ) @Substitution( desc="Return the mean absolute deviation of the values " "for the requested axis.", name1=name1, name2=name2, axis_descr=axis_descr, min_count="", see_also="", examples="", ) @Appender(_num_doc_mad) def mad(self, axis=None, skipna=None, level=None): if skipna is None: skipna = True if axis is None: axis = self._stat_axis_number if level is not None: return self._agg_by_level("mad", axis=axis, level=level, skipna=skipna) data = self._get_numeric_data() if axis == 0: demeaned = data - data.mean(axis=0) else: demeaned = data.sub(data.mean(axis=1), axis=0) return np.abs(demeaned).mean(axis=axis, skipna=skipna) cls.mad = mad cls.sem = _make_stat_function_ddof( cls, "sem", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return unbiased standard error of the mean over requested " "axis.\n\nNormalized by N-1 by default. This can be changed " "using the ddof argument", func=nanops.nansem, ) cls.var = _make_stat_function_ddof( cls, "var", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return unbiased variance over requested axis.\n\nNormalized by " "N-1 by default. This can be changed using the ddof argument", func=nanops.nanvar, ) cls.std = _make_stat_function_ddof( cls, "std", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return sample standard deviation over requested axis." "\n\nNormalized by N-1 by default. This can be changed using the " "ddof argument", func=nanops.nanstd, ) cls.cummin = _make_cum_function( cls, "cummin", name1=name1, name2=name2, axis_descr=axis_descr, desc="minimum", accum_func=np.minimum.accumulate, accum_func_name="min", mask_a=np.inf, mask_b=np.nan, examples=_cummin_examples, ) cls.cumsum = _make_cum_function( cls, "cumsum", name1=name1, name2=name2, axis_descr=axis_descr, desc="sum", accum_func=np.cumsum, accum_func_name="sum", mask_a=0.0, mask_b=np.nan, examples=_cumsum_examples, ) cls.cumprod = _make_cum_function( cls, "cumprod", name1=name1, name2=name2, axis_descr=axis_descr, desc="product", accum_func=np.cumprod, accum_func_name="prod", mask_a=1.0, mask_b=np.nan, examples=_cumprod_examples, ) cls.cummax = _make_cum_function( cls, "cummax", name1=name1, name2=name2, axis_descr=axis_descr, desc="maximum", accum_func=np.maximum.accumulate, accum_func_name="max", mask_a=-np.inf, mask_b=np.nan, examples=_cummax_examples, ) cls.sum = _make_min_count_stat_function( cls, "sum", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return the sum of the values for the requested axis.\n\n" "This is equivalent to the method ``numpy.sum``.", func=nanops.nansum, see_also=_stat_func_see_also, examples=_sum_examples, ) cls.mean = _make_stat_function( cls, "mean", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return the mean of the values for the requested axis.", func=nanops.nanmean, ) cls.skew = _make_stat_function( cls, "skew", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return unbiased skew over requested axis.\n\nNormalized by N-1.", func=nanops.nanskew, ) cls.kurt = _make_stat_function( cls, "kurt", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return unbiased kurtosis over requested axis.\n\n" "Kurtosis obtained using Fisher's definition of\n" "kurtosis (kurtosis of normal == 0.0). Normalized " "by N-1.", func=nanops.nankurt, ) cls.kurtosis = cls.kurt cls.prod = _make_min_count_stat_function( cls, "prod", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return the product of the values for the requested axis.", func=nanops.nanprod, examples=_prod_examples, ) cls.product = cls.prod cls.median = _make_stat_function( cls, "median", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return the median of the values for the requested axis.", func=nanops.nanmedian, ) cls.max = _make_stat_function( cls, "max", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return the maximum of the values for the requested axis.\n\n" "If you want the *index* of the maximum, use ``idxmax``. This is" "the equivalent of the ``numpy.ndarray`` method ``argmax``.", func=nanops.nanmax, see_also=_stat_func_see_also, examples=_max_examples, ) cls.min = _make_stat_function( cls, "min", name1=name1, name2=name2, axis_descr=axis_descr, desc="Return the minimum of the values for the requested axis.\n\n" "If you want the *index* of the minimum, use ``idxmin``. This is" "the equivalent of the ``numpy.ndarray`` method ``argmin``.", func=nanops.nanmin, see_also=_stat_func_see_also, examples=_min_examples, ) @classmethod def _add_series_or_dataframe_operations(cls): from pandas.core.window import EWM, Expanding, Rolling, Window @Appender(Rolling.__doc__) def rolling( self, window, min_periods=None, center=False, win_type=None, on=None, axis=0, closed=None, ): axis = self._get_axis_number(axis) if win_type is not None: return Window( self, window=window, min_periods=min_periods, center=center, win_type=win_type, on=on, axis=axis, closed=closed, ) return Rolling( self, window=window, min_periods=min_periods, center=center, win_type=win_type, on=on, axis=axis, closed=closed, ) cls.rolling = rolling @Appender(Expanding.__doc__) def expanding(self, min_periods=1, center=False, axis=0): axis = self._get_axis_number(axis) return Expanding(self, min_periods=min_periods, center=center, axis=axis) cls.expanding = expanding @Appender(EWM.__doc__) def ewm( self, com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, axis=0, ): axis = self._get_axis_number(axis) return EWM( self, com=com, span=span, halflife=halflife, alpha=alpha, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na, axis=axis, ) cls.ewm = ewm @Appender(_shared_docs["transform"] % dict(axis="", **_shared_doc_kwargs)) def transform(self, func, *args, **kwargs): result = self.agg(func, *args, **kwargs) if is_scalar(result) or len(result) != len(self): raise ValueError("transforms cannot produce aggregated results") return result # ---------------------------------------------------------------------- # Misc methods _shared_docs[ "valid_index" ] = """ Return index for %(position)s non-NA/null value. Returns ------- scalar : type of index Notes ----- If all elements are non-NA/null, returns None. Also returns None for empty %(klass)s. """ def _find_valid_index(self, how: str): idxpos = find_valid_index(self._values, how) if idxpos is None: return None return self.index[idxpos] @Appender( _shared_docs["valid_index"] % {"position": "first", "klass": "Series/DataFrame"} ) def first_valid_index(self): return self._find_valid_index("first") @Appender( _shared_docs["valid_index"] % {"position": "last", "klass": "Series/DataFrame"} ) def last_valid_index(self): return self._find_valid_index("last") def _doc_parms(cls): axis_descr = ( f"{{{', '.join(f'{a} ({i})' for i, a in enumerate(cls._AXIS_ORDERS))}}}" ) name = cls._constructor_sliced.__name__ if cls._AXIS_LEN > 1 else "scalar" name2 = cls.__name__ return axis_descr, name, name2 _num_doc = """ %(desc)s Parameters ---------- axis : %(axis_descr)s Axis for the function to be applied on. skipna : bool, default True Exclude NA/null values when computing the result. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a %(name1)s. numeric_only : bool, default None Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series. %(min_count)s\ **kwargs Additional keyword arguments to be passed to the function. Returns ------- %(name1)s or %(name2)s (if level specified)\ %(see_also)s\ %(examples)s """ _num_doc_mad = """ %(desc)s Parameters ---------- axis : %(axis_descr)s Axis for the function to be applied on. skipna : bool, default None Exclude NA/null values when computing the result. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a %(name1)s. Returns ------- %(name1)s or %(name2)s (if level specified)\ %(see_also)s\ %(examples)s """ _num_ddof_doc = """ %(desc)s Parameters ---------- axis : %(axis_descr)s skipna : bool, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a %(name1)s. ddof : int, default 1 Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements. numeric_only : bool, default None Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series. Returns ------- %(name1)s or %(name2)s (if level specified)\n""" _bool_doc = """ %(desc)s Parameters ---------- axis : {0 or 'index', 1 or 'columns', None}, default 0 Indicate which axis or axes should be reduced. * 0 / 'index' : reduce the index, return a Series whose index is the original column labels. * 1 / 'columns' : reduce the columns, return a Series whose index is the original index. * None : reduce all axes, return a scalar. bool_only : bool, default None Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series. skipna : bool, default True Exclude NA/null values. If the entire row/column is NA and skipna is True, then the result will be %(empty_value)s, as for an empty row/column. If skipna is False, then NA are treated as True, because these are not equal to zero. level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a %(name1)s. **kwargs : any, default None Additional keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- %(name1)s or %(name2)s If level is specified, then, %(name2)s is returned; otherwise, %(name1)s is returned. %(see_also)s %(examples)s""" _all_desc = """\ Return whether all elements are True, potentially over an axis. Returns True unless there at least one element within a series or along a Dataframe axis that is False or equivalent (e.g. zero or empty).""" _all_examples = """\ Examples -------- **Series** >>> pd.Series([True, True]).all() True >>> pd.Series([True, False]).all() False >>> pd.Series([]).all() True >>> pd.Series([np.nan]).all() True >>> pd.Series([np.nan]).all(skipna=False) True **DataFrames** Create a dataframe from a dictionary. >>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]}) >>> df col1 col2 0 True True 1 True False Default behaviour checks if column-wise values all return True. >>> df.all() col1 True col2 False dtype: bool Specify ``axis='columns'`` to check if row-wise values all return True. >>> df.all(axis='columns') 0 True 1 False dtype: bool Or ``axis=None`` for whether every value is True. >>> df.all(axis=None) False """ _all_see_also = """\ See Also -------- Series.all : Return True if all elements are True. DataFrame.any : Return True if one (or more) elements are True. """ _cnum_doc = """ Return cumulative %(desc)s over a DataFrame or Series axis. Returns a DataFrame or Series of the same size containing the cumulative %(desc)s. Parameters ---------- axis : {0 or 'index', 1 or 'columns'}, default 0 The index or the name of the axis. 0 is equivalent to None or 'index'. skipna : bool, default True Exclude NA/null values. If an entire row/column is NA, the result will be NA. *args, **kwargs : Additional keywords have no effect but might be accepted for compatibility with NumPy. Returns ------- %(name1)s or %(name2)s See Also -------- core.window.Expanding.%(accum_func_name)s : Similar functionality but ignores ``NaN`` values. %(name2)s.%(accum_func_name)s : Return the %(desc)s over %(name2)s axis. %(name2)s.cummax : Return cumulative maximum over %(name2)s axis. %(name2)s.cummin : Return cumulative minimum over %(name2)s axis. %(name2)s.cumsum : Return cumulative sum over %(name2)s axis. %(name2)s.cumprod : Return cumulative product over %(name2)s axis. %(examples)s""" _cummin_examples = """\ Examples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cummin() 0 2.0 1 NaN 2 2.0 3 -1.0 4 -1.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cummin(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the minimum in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cummin() A B 0 2.0 1.0 1 2.0 NaN 2 1.0 0.0 To iterate over columns and find the minimum in each row, use ``axis=1`` >>> df.cummin(axis=1) A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 """ _cumsum_examples = """\ Examples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cumsum() 0 2.0 1 NaN 2 7.0 3 6.0 4 6.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cumsum(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the sum in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cumsum() A B 0 2.0 1.0 1 5.0 NaN 2 6.0 1.0 To iterate over columns and find the sum in each row, use ``axis=1`` >>> df.cumsum(axis=1) A B 0 2.0 3.0 1 3.0 NaN 2 1.0 1.0 """ _cumprod_examples = """\ Examples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cumprod() 0 2.0 1 NaN 2 10.0 3 -10.0 4 -0.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cumprod(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the product in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cumprod() A B 0 2.0 1.0 1 6.0 NaN 2 6.0 0.0 To iterate over columns and find the product in each row, use ``axis=1`` >>> df.cumprod(axis=1) A B 0 2.0 2.0 1 3.0 NaN 2 1.0 0.0 """ _cummax_examples = """\ Examples -------- **Series** >>> s = pd.Series([2, np.nan, 5, -1, 0]) >>> s 0 2.0 1 NaN 2 5.0 3 -1.0 4 0.0 dtype: float64 By default, NA values are ignored. >>> s.cummax() 0 2.0 1 NaN 2 5.0 3 5.0 4 5.0 dtype: float64 To include NA values in the operation, use ``skipna=False`` >>> s.cummax(skipna=False) 0 2.0 1 NaN 2 NaN 3 NaN 4 NaN dtype: float64 **DataFrame** >>> df = pd.DataFrame([[2.0, 1.0], ... [3.0, np.nan], ... [1.0, 0.0]], ... columns=list('AB')) >>> df A B 0 2.0 1.0 1 3.0 NaN 2 1.0 0.0 By default, iterates over rows and finds the maximum in each column. This is equivalent to ``axis=None`` or ``axis='index'``. >>> df.cummax() A B 0 2.0 1.0 1 3.0 NaN 2 3.0 1.0 To iterate over columns and find the maximum in each row, use ``axis=1`` >>> df.cummax(axis=1) A B 0 2.0 2.0 1 3.0 NaN 2 1.0 1.0 """ _any_see_also = """\ See Also -------- numpy.any : Numpy version of this method. Series.any : Return whether any element is True. Series.all : Return whether all elements are True. DataFrame.any : Return whether any element is True over requested axis. DataFrame.all : Return whether all elements are True over requested axis. """ _any_desc = """\ Return whether any element is True, potentially over an axis. Returns False unless there at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).""" _any_examples = """\ Examples -------- **Series** For Series input, the output is a scalar indicating whether any element is True. >>> pd.Series([False, False]).any() False >>> pd.Series([True, False]).any() True >>> pd.Series([]).any() False >>> pd.Series([np.nan]).any() False >>> pd.Series([np.nan]).any(skipna=False) True **DataFrame** Whether each column contains at least one True element (the default). >>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]}) >>> df A B C 0 1 0 0 1 2 2 0 >>> df.any() A True B True C False dtype: bool Aggregating over the columns. >>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]}) >>> df A B 0 True 1 1 False 2 >>> df.any(axis='columns') 0 True 1 True dtype: bool >>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]}) >>> df A B 0 True 1 1 False 0 >>> df.any(axis='columns') 0 True 1 False dtype: bool Aggregating over the entire DataFrame with ``axis=None``. >>> df.any(axis=None) True `any` for an empty DataFrame is an empty Series. >>> pd.DataFrame([]).any() Series([], dtype: bool) """ _shared_docs[ "stat_func_example" ] = """ Examples -------- >>> idx = pd.MultiIndex.from_arrays([ ... ['warm', 'warm', 'cold', 'cold'], ... ['dog', 'falcon', 'fish', 'spider']], ... names=['blooded', 'animal']) >>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx) >>> s blooded animal warm dog 4 falcon 2 cold fish 0 spider 8 Name: legs, dtype: int64 >>> s.{stat_func}() {default_output} {verb} using level names, as well as indices. >>> s.{stat_func}(level='blooded') blooded warm {level_output_0} cold {level_output_1} Name: legs, dtype: int64 >>> s.{stat_func}(level=0) blooded warm {level_output_0} cold {level_output_1} Name: legs, dtype: int64""" _sum_examples = _shared_docs["stat_func_example"].format( stat_func="sum", verb="Sum", default_output=14, level_output_0=6, level_output_1=8 ) _sum_examples += """ By default, the sum of an empty or all-NA Series is ``0``. >>> pd.Series([]).sum() # min_count=0 is the default 0.0 This can be controlled with the ``min_count`` parameter. For example, if you'd like the sum of an empty series to be NaN, pass ``min_count=1``. >>> pd.Series([]).sum(min_count=1) nan Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and empty series identically. >>> pd.Series([np.nan]).sum() 0.0 >>> pd.Series([np.nan]).sum(min_count=1) nan""" _max_examples = _shared_docs["stat_func_example"].format( stat_func="max", verb="Max", default_output=8, level_output_0=4, level_output_1=8 ) _min_examples = _shared_docs["stat_func_example"].format( stat_func="min", verb="Min", default_output=0, level_output_0=2, level_output_1=0 ) _stat_func_see_also = """ See Also -------- Series.sum : Return the sum. Series.min : Return the minimum. Series.max : Return the maximum. Series.idxmin : Return the index of the minimum. Series.idxmax : Return the index of the maximum. DataFrame.sum : Return the sum over the requested axis. DataFrame.min : Return the minimum over the requested axis. DataFrame.max : Return the maximum over the requested axis. DataFrame.idxmin : Return the index of the minimum over the requested axis. DataFrame.idxmax : Return the index of the maximum over the requested axis.""" _prod_examples = """ Examples -------- By default, the product of an empty or all-NA Series is ``1`` >>> pd.Series([]).prod() 1.0 This can be controlled with the ``min_count`` parameter >>> pd.Series([]).prod(min_count=1) nan Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and empty series identically. >>> pd.Series([np.nan]).prod() 1.0 >>> pd.Series([np.nan]).prod(min_count=1) nan""" _min_count_stub = """\ min_count : int, default 0 The required number of valid values to perform the operation. If fewer than ``min_count`` non-NA values are present the result will be NA. .. versionadded:: 0.22.0 Added with the default being 0. This means the sum of an all-NA or empty Series is 0, and the product of an all-NA or empty Series is 1. """ def _make_min_count_stat_function( cls, name: str, name1: str, name2: str, axis_descr: str, desc: str, func: Callable, see_also: str = "", examples: str = "", ) -> Callable: @Substitution( desc=desc, name1=name1, name2=name2, axis_descr=axis_descr, min_count=_min_count_stub, see_also=see_also, examples=examples, ) @Appender(_num_doc) def stat_func( self, axis=None, skipna=None, level=None, numeric_only=None, min_count=0, **kwargs, ): if name == "sum": nv.validate_sum(tuple(), kwargs) elif name == "prod": nv.validate_prod(tuple(), kwargs) else: nv.validate_stat_func(tuple(), kwargs, fname=name) if skipna is None: skipna = True if axis is None: axis = self._stat_axis_number if level is not None: return self._agg_by_level( name, axis=axis, level=level, skipna=skipna, min_count=min_count ) return self._reduce( func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only, min_count=min_count, ) return set_function_name(stat_func, name, cls) def _make_stat_function( cls, name: str, name1: str, name2: str, axis_descr: str, desc: str, func: Callable, see_also: str = "", examples: str = "", ) -> Callable: @Substitution( desc=desc, name1=name1, name2=name2, axis_descr=axis_descr, min_count="", see_also=see_also, examples=examples, ) @Appender(_num_doc) def stat_func( self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs ): if name == "median": nv.validate_median(tuple(), kwargs) else: nv.validate_stat_func(tuple(), kwargs, fname=name) if skipna is None: skipna = True if axis is None: axis = self._stat_axis_number if level is not None: return self._agg_by_level(name, axis=axis, level=level, skipna=skipna) return self._reduce( func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only ) return set_function_name(stat_func, name, cls) def _make_stat_function_ddof( cls, name: str, name1: str, name2: str, axis_descr: str, desc: str, func: Callable ) -> Callable: @Substitution(desc=desc, name1=name1, name2=name2, axis_descr=axis_descr) @Appender(_num_ddof_doc) def stat_func( self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs ): nv.validate_stat_ddof_func(tuple(), kwargs, fname=name) if skipna is None: skipna = True if axis is None: axis = self._stat_axis_number if level is not None: return self._agg_by_level( name, axis=axis, level=level, skipna=skipna, ddof=ddof ) return self._reduce( func, name, axis=axis, numeric_only=numeric_only, skipna=skipna, ddof=ddof ) return set_function_name(stat_func, name, cls) def _make_cum_function( cls, name: str, name1: str, name2: str, axis_descr: str, desc: str, accum_func: Callable, accum_func_name: str, mask_a: float, mask_b: float, examples: str, ) -> Callable: @Substitution( desc=desc, name1=name1, name2=name2, axis_descr=axis_descr, accum_func_name=accum_func_name, examples=examples, ) @Appender(_cnum_doc) def cum_func(self, axis=None, skipna=True, *args, **kwargs): skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name) if axis is None: axis = self._stat_axis_number else: axis = self._get_axis_number(axis) if axis == 1: return cum_func(self.T, axis=0, skipna=skipna, *args, **kwargs).T def na_accum_func(blk_values): # We will be applying this function to block values if blk_values.dtype.kind in ["m", "M"]: # GH#30460, GH#29058 # numpy 1.18 started sorting NaTs at the end instead of beginning, # so we need to work around to maintain backwards-consistency. orig_dtype = blk_values.dtype # We need to define mask before masking NaTs mask = isna(blk_values) if accum_func == np.minimum.accumulate: # Note: the accum_func comparison fails as an "is" comparison y = blk_values.view("i8") y[mask] = np.iinfo(np.int64).max changed = True else: y = blk_values changed = False result = accum_func(y.view("i8"), axis) if skipna: np.putmask(result, mask, iNaT) elif accum_func == np.minimum.accumulate: # Restore NaTs that we masked previously nz = (~np.asarray(mask)).nonzero()[0] if len(nz): # everything up to the first non-na entry stays NaT result[: nz[0]] = iNaT if changed: # restore NaT elements y[mask] = iNaT # TODO: could try/finally for this? if isinstance(blk_values, np.ndarray): result = result.view(orig_dtype) else: # DatetimeArray result = type(blk_values)._from_sequence(result, dtype=orig_dtype) elif skipna and not issubclass( blk_values.dtype.type, (np.integer, np.bool_) ): vals = blk_values.copy().T mask = isna(vals) np.putmask(vals, mask, mask_a) result = accum_func(vals, axis) np.putmask(result, mask, mask_b) else: result = accum_func(blk_values.T, axis) # transpose back for ndarray, not for EA return result.T if hasattr(result, "T") else result result = self._data.apply(na_accum_func) d = self._construct_axes_dict() d["copy"] = False return self._constructor(result, **d).__finalize__(self) return set_function_name(cum_func, name, cls) def _make_logical_function( cls, name: str, name1: str, name2: str, axis_descr: str, desc: str, func: Callable, see_also: str, examples: str, empty_value: bool, ) -> Callable: @Substitution( desc=desc, name1=name1, name2=name2, axis_descr=axis_descr, see_also=see_also, examples=examples, empty_value=empty_value, ) @Appender(_bool_doc) def logical_func(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs): nv.validate_logical_func(tuple(), kwargs, fname=name) if level is not None: if bool_only is not None: raise NotImplementedError( "Option bool_only is not implemented with option level." ) return self._agg_by_level(name, axis=axis, level=level, skipna=skipna) return self._reduce( func, name=name, axis=axis, skipna=skipna, numeric_only=bool_only, filter_type="bool", ) return set_function_name(logical_func, name, cls)

true

f719607b2a4e7f5eac2478307ce00dc1f2055669

2,541

py

Python

e3sm_to_cmip/cmor_handlers/zos.py

xylar/e3sm_to_cmip

4fbe8fc91475eae26df839d0cd8062c4b8dc16ae

[ "MIT" ]

7

2018-05-03T12:30:06.000Z

2022-01-20T23:52:02.000Z

e3sm_to_cmip/cmor_handlers/zos.py

xylar/e3sm_to_cmip

4fbe8fc91475eae26df839d0cd8062c4b8dc16ae

[ "MIT" ]

91

2018-05-02T21:11:30.000Z

2022-03-30T20:25:07.000Z

e3sm_to_cmip/cmor_handlers/zos.py

xylar/e3sm_to_cmip

4fbe8fc91475eae26df839d0cd8062c4b8dc16ae

[ "MIT" ]

7

2018-05-15T02:07:34.000Z

2021-06-30T18:20:33.000Z

""" compute Sea Surface Height Above Geoid, zos """ from __future__ import absolute_import, division, print_function import xarray import logging from e3sm_to_cmip import mpas from e3sm_to_cmip.util import print_message # 'MPAS' as a placeholder for raw variables needed RAW_VARIABLES = ['MPASO', 'MPAS_mesh', 'MPAS_map'] # output variable name VAR_NAME = 'zos' VAR_UNITS = 'm' TABLE = 'CMIP6_Omon.json' def handle(infiles, tables, user_input_path, **kwargs): """ Transform MPASO timeMonthly_avg_pressureAdjustedSSH into CMIP.zos Parameters ---------- infiles : dict a dictionary with namelist, mesh and time series file names tables : str path to CMOR tables user_input_path : str path to user input json file Returns ------- varname : str the name of the processed variable after processing is complete """ if kwargs.get('simple'): msg = f"{VAR_NAME} is not supported for simple conversion" print_message(msg) return msg = 'Starting {name}'.format(name=__name__) logging.info(msg) meshFileName = infiles['MPAS_mesh'] mappingFileName = infiles['MPAS_map'] timeSeriesFiles = infiles['MPASO'] dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False) cellMask2D, _ = mpas.get_cell_masks(dsMesh) areaCell = dsMesh.areaCell.where(cellMask2D) variableList = ['timeMonthly_avg_pressureAdjustedSSH', 'xtime_startMonthly', 'xtime_endMonthly'] ds = xarray.Dataset() with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn: ssh = dsIn.timeMonthly_avg_pressureAdjustedSSH.where(cellMask2D) sshAvg = (ssh*areaCell).sum(dim='nCells')/areaCell.sum(dim='nCells') ds[VAR_NAME] = ssh - sshAvg ds = mpas.add_time(ds, dsIn) ds.compute() ds = mpas.remap(ds, mappingFileName) mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean') # create axes axes = [{'table_entry': 'time', 'units': ds.time.units}, {'table_entry': 'latitude', 'units': 'degrees_north', 'coord_vals': ds.lat.values, 'cell_bounds': ds.lat_bnds.values}, {'table_entry': 'longitude', 'units': 'degrees_east', 'coord_vals': ds.lon.values, 'cell_bounds': ds.lon_bnds.values}] try: mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS) except Exception: return "" return VAR_NAME

28.233333

76

0.648564

from __future__ import absolute_import, division, print_function import xarray import logging from e3sm_to_cmip import mpas from e3sm_to_cmip.util import print_message RAW_VARIABLES = ['MPASO', 'MPAS_mesh', 'MPAS_map'] VAR_NAME = 'zos' VAR_UNITS = 'm' TABLE = 'CMIP6_Omon.json' def handle(infiles, tables, user_input_path, **kwargs): if kwargs.get('simple'): msg = f"{VAR_NAME} is not supported for simple conversion" print_message(msg) return msg = 'Starting {name}'.format(name=__name__) logging.info(msg) meshFileName = infiles['MPAS_mesh'] mappingFileName = infiles['MPAS_map'] timeSeriesFiles = infiles['MPASO'] dsMesh = xarray.open_dataset(meshFileName, mask_and_scale=False) cellMask2D, _ = mpas.get_cell_masks(dsMesh) areaCell = dsMesh.areaCell.where(cellMask2D) variableList = ['timeMonthly_avg_pressureAdjustedSSH', 'xtime_startMonthly', 'xtime_endMonthly'] ds = xarray.Dataset() with mpas.open_mfdataset(timeSeriesFiles, variableList) as dsIn: ssh = dsIn.timeMonthly_avg_pressureAdjustedSSH.where(cellMask2D) sshAvg = (ssh*areaCell).sum(dim='nCells')/areaCell.sum(dim='nCells') ds[VAR_NAME] = ssh - sshAvg ds = mpas.add_time(ds, dsIn) ds.compute() ds = mpas.remap(ds, mappingFileName) mpas.setup_cmor(VAR_NAME, tables, user_input_path, component='ocean') axes = [{'table_entry': 'time', 'units': ds.time.units}, {'table_entry': 'latitude', 'units': 'degrees_north', 'coord_vals': ds.lat.values, 'cell_bounds': ds.lat_bnds.values}, {'table_entry': 'longitude', 'units': 'degrees_east', 'coord_vals': ds.lon.values, 'cell_bounds': ds.lon_bnds.values}] try: mpas.write_cmor(axes, ds, VAR_NAME, VAR_UNITS) except Exception: return "" return VAR_NAME

true

f719619a72f865368354a85f0cae0766341013ef

1,087

py

Python

supports/mover-performance-test/run_ssm_mover.py

MajorJason/SSM

3341585165ac10a47ddeed0e1d5e2467db482b99

[ "Apache-2.0" ]

199

2017-04-19T06:37:24.000Z

2022-03-31T12:14:22.000Z

supports/mover-performance-test/run_ssm_mover.py

Dam1029/SSM

d459811728980258f4ebd0b81022620b750863fe

[ "Apache-2.0" ]

1,091

2017-04-14T07:09:55.000Z

2022-01-20T11:15:54.000Z

supports/mover-performance-test/run_ssm_mover.py

Dam1029/SSM

d459811728980258f4ebd0b81022620b750863fe

[ "Apache-2.0" ]

170

2017-04-14T03:45:30.000Z

2022-03-31T12:14:24.000Z

import sys import time from util import * size = sys.argv[1] num = sys.argv[2] #The data dir is named by case. Please see prepare.sh case = size + "_" + num log = sys.argv[3] #Either "allssd" or "alldisk" action = sys.argv[4] if action == "allssd": rid = submit_rule("file: path matches \"/" + case + "/*\"| allssd") elif action == "alldisk": rid = submit_rule("file: path matches \"/" + case + "/*\"| alldisk") start_rule(rid) start_time = time.time() rule = get_rule(rid) last_checked = rule['numChecked'] last_cmdsgen = rule['numCmdsGen'] time.sleep(.1) #Check whether all expected cmdlets have been generated. #The overall cmdlets' num should equal to the test files' num, #if not, wait for more cmdlets to be generated. cids = get_cids_of_rule(rid) while len(cids) < int(num): time.sleep(.1) rule = get_rule(rid) cids = get_cids_of_rule(rid) time.sleep(.1) cids = get_cids_of_rule(rid) wait_cmdlets(cids) end_time = time.time() stop_rule(rid) # append result to log file f = open(log, 'a') f.write(str(int(end_time - start_time)) + "s" + " " + '\n') f.close()

24.155556

72

0.678933

import sys import time from util import * size = sys.argv[1] num = sys.argv[2] case = size + "_" + num log = sys.argv[3] action = sys.argv[4] if action == "allssd": rid = submit_rule("file: path matches \"/" + case + "/*\"| allssd") elif action == "alldisk": rid = submit_rule("file: path matches \"/" + case + "/*\"| alldisk") start_rule(rid) start_time = time.time() rule = get_rule(rid) last_checked = rule['numChecked'] last_cmdsgen = rule['numCmdsGen'] time.sleep(.1) cids = get_cids_of_rule(rid) while len(cids) < int(num): time.sleep(.1) rule = get_rule(rid) cids = get_cids_of_rule(rid) time.sleep(.1) cids = get_cids_of_rule(rid) wait_cmdlets(cids) end_time = time.time() stop_rule(rid) f = open(log, 'a') f.write(str(int(end_time - start_time)) + "s" + " " + '\n') f.close()

true

f71962cca1d8ea20c86aa01378d38c3db9829b67

9,032

py

Python

example_conda_pkg/descriptors.py

dajtmullaj/example_conda_pkg

7c2bf657d14c714608e653d7218fa3cd658a6297

[ "MIT" ]

null

example_conda_pkg/descriptors.py

dajtmullaj/example_conda_pkg

7c2bf657d14c714608e653d7218fa3cd658a6297

[ "MIT" ]

null

example_conda_pkg/descriptors.py

dajtmullaj/example_conda_pkg

7c2bf657d14c714608e653d7218fa3cd658a6297

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sat Oct 3 21:21:19 2020 Project: chemplot (Chemical Space Visualization) Content: Descriptor operation methods @author: murat cihan sorkun """ from rdkit import Chem from rdkit.Chem import AllChem import pandas as pd import math import mordred from mordred import Calculator, descriptors #Dont remove these imports from sklearn.linear_model import Lasso, LogisticRegression from sklearn.feature_selection import SelectFromModel from sklearn.preprocessing import StandardScaler def get_mordred_descriptors(smiles_list): """ Calculates the Mordred descriptors for given smiles list :param smiles_list: List of smiles :type smiles_list: list :returns: The calculated descriptors list for the given smiles :rtype: Dataframe """ return generate_mordred_descriptors(smiles_list, Chem.MolFromSmiles, 'SMILES') def get_mordred_descriptors_from_inchi(inchi_list): """ Calculates the Mordred descriptors for given InChi list :param inchi_list: List of InChi :type inchi_list: list :returns: The calculated descriptors list for the given smiles :rtype: Dataframe """ return generate_mordred_descriptors(inchi_list, Chem.MolFromInchi, 'InChi') def generate_mordred_descriptors(encoding_list, encoding_function, encoding_name): """ Calculates the Mordred descriptors for list of molecules encodings :param smiles_list: List of molecules encodings :type smiles_list: list :returns: The calculated descriptors list for the given molecules encodings :rtype: Dataframe """ calc = mordred.Calculator() calc.register(mordred.AtomCount) #16 calc.register(mordred.RingCount) #139 calc.register(mordred.BondCount) #9 calc.register(mordred.HydrogenBond) #2 calc.register(mordred.CarbonTypes) #10 calc.register(mordred.SLogP) #2 calc.register(mordred.Constitutional) #16 calc.register(mordred.TopoPSA) #2 calc.register(mordred.Weight) #2 calc.register(mordred.Polarizability) #2 calc.register(mordred.McGowanVolume) #1 name_list=[] for desc_name in calc.descriptors: name_list.append(str(desc_name)) descriptors_list=[] erroneous_encodings=[] encodings_none_descriptors=[] for encoding in encoding_list: mol=encoding_function(encoding) if mol is None: descriptors_list.append([None]*len(name_list)) erroneous_encodings.append(encoding) else: mol=Chem.AddHs(mol) calculated_descriptors = calc(mol) for i in range(len(calculated_descriptors._values)): if math.isnan(calculated_descriptors._values[i]): calculated_descriptors._values = [None]*len(name_list) encodings_none_descriptors.append(encoding) break descriptors_list.append(calculated_descriptors._values) if len(erroneous_encodings)>0: print("The following erroneous {} have been found in the data:\n{}.\nThe erroneous {} will be removed from the data.".format(encoding_name, '\n'.join(map(str, erroneous_encodings)), encoding_name)) if len(encodings_none_descriptors)>0: print("For the following {} not all descriptors can be computed:\n{}.\nThese {} will be removed from the data.".format(encoding_name, '\n'.join(map(str, encodings_none_descriptors)), encoding_name)) df_descriptors=pd.DataFrame(descriptors_list,columns=name_list) df_descriptors = df_descriptors.select_dtypes(exclude=['object']) return df_descriptors def select_descriptors_lasso(df_descriptors,target_list, R_select=0.05, C_select=0.05, kind="R"): """ Selects descriptors by LASSO :param df_descriptors: descriptors of molecules :type df_descriptors: Dataframe :param target_list: list of target values :type target_list: list :param R_select: alpha value for Lasso :type R_select: float :param C_select: C value for LogisticRegression :type C_select: float :param kind: kind of target R->Regression C->Classification :type kind: string :returns: The selected descriptors :rtype: Dataframe """ # Remove erroneous data df_descriptors = df_descriptors.assign(target=target_list.values) df_descriptors = df_descriptors.dropna(how='any') target_list = df_descriptors['target'].to_list() df_descriptors = df_descriptors.drop(columns=['target']) df_descriptors_scaled = StandardScaler().fit_transform(df_descriptors) if(kind=="C"): model = LogisticRegression(C=C_select,penalty='l1', solver='liblinear',random_state=1).fit(df_descriptors_scaled, target_list) else: model = Lasso(alpha=R_select,max_iter=10000,random_state=1).fit(df_descriptors_scaled, target_list) selected = SelectFromModel(model, prefit=True) X_new_lasso = selected.transform(df_descriptors) # Get back the kept features as a DataFrame with dropped columns as all 0s selected_features = pd.DataFrame(selected.inverse_transform(X_new_lasso), index=df_descriptors.index, columns=df_descriptors.columns) # Dropped columns have values of all 0s, keep other columns selected_columns_lasso = selected_features.columns[selected_features.var() != 0] selected_data = df_descriptors[selected_columns_lasso] return selected_data, target_list def get_ecfp(smiles_list, target_list, radius=2, nBits=2048): """ Calculates the ECFP fingerprint for given SMILES list :param smiles_list: List of SMILES :type smiles_list: list :param radius: The ECPF fingerprints radius. :type radius: int :param nBits: The number of bits of the fingerprint vector. :type nBits: int :returns: The calculated ECPF fingerprints for the given SMILES :rtype: Dataframe """ return generate_ecfp(smiles_list, Chem.MolFromSmiles, 'SMILES', target_list, radius=2, nBits=2048) def get_ecfp_from_inchi(inchi_list, target_list, radius=2, nBits=2048): """ Calculates the ECFP fingerprint for given InChi list :param inchi_list: List of InChi :type inchi_list: list :param radius: The ECPF fingerprints radius. :type radius: int :param nBits: The number of bits of the fingerprint vector. :type nBits: int :returns: The calculated ECPF fingerprints for the given InChi :rtype: Dataframe """ return generate_ecfp(inchi_list, Chem.MolFromInchi, 'InChi', target_list, radius=2, nBits=2048) def generate_ecfp(encoding_list, encoding_function, encoding_name, target_list, radius=2, nBits=2048): """ Calculates the ECFP fingerprint for given list of molecules encodings :param encoding_list: List of molecules encodings :type encoding_list: list :param encoding_function: Function used to extract the molecules from the encodings :type encoding_function: fun :param radius: The ECPF fingerprints radius. :type radius: int :param nBits: The number of bits of the fingerprint vector. :type nBits: int :returns: The calculated ECPF fingerprints for the given molecules encodings :rtype: Dataframe """ # Generate ECFP fingerprints ecfp_fingerprints=[] erroneous_encodings=[] for encoding in encoding_list: mol=encoding_function(encoding) if mol is None: ecfp_fingerprints.append([None]*nBits) erroneous_encodings.append(encoding) else: mol=Chem.AddHs(mol) list_bits_fingerprint = [] list_bits_fingerprint[:0] = AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits).ToBitString() ecfp_fingerprints.append(list_bits_fingerprint) # Create dataframe of fingerprints df_ecfp_fingerprints = pd.DataFrame(data = ecfp_fingerprints, index = encoding_list) # Remove erroneous data if len(erroneous_encodings)>0: print("The following erroneous {} have been found in the data:\n{}.\nThe erroneous {} will be removed from the data.".format(encoding_name, '\n'.join(map(str, erroneous_encodings)), encoding_name)) if len(target_list)>0: df_ecfp_fingerprints = df_ecfp_fingerprints.assign(target=target_list.values) df_ecfp_fingerprints = df_ecfp_fingerprints.dropna(how='any') if len(target_list)>0: target_list = df_ecfp_fingerprints['target'].to_list() df_ecfp_fingerprints = df_ecfp_fingerprints.drop(columns=['target']) # Remove bit columns with no variablity (all "0" or all "1") df_ecfp_fingerprints = df_ecfp_fingerprints.loc[:, (df_ecfp_fingerprints != 0).any(axis=0)] df_ecfp_fingerprints = df_ecfp_fingerprints.loc[:, (df_ecfp_fingerprints != 1).any(axis=0)] return df_ecfp_fingerprints, target_list

39.441048

206

0.704052

from rdkit import Chem from rdkit.Chem import AllChem import pandas as pd import math import mordred from mordred import Calculator, descriptors from sklearn.linear_model import Lasso, LogisticRegression from sklearn.feature_selection import SelectFromModel from sklearn.preprocessing import StandardScaler def get_mordred_descriptors(smiles_list): return generate_mordred_descriptors(smiles_list, Chem.MolFromSmiles, 'SMILES') def get_mordred_descriptors_from_inchi(inchi_list): return generate_mordred_descriptors(inchi_list, Chem.MolFromInchi, 'InChi') def generate_mordred_descriptors(encoding_list, encoding_function, encoding_name): calc = mordred.Calculator() calc.register(mordred.AtomCount) calc.register(mordred.RingCount) calc.register(mordred.BondCount) calc.register(mordred.HydrogenBond) calc.register(mordred.CarbonTypes) calc.register(mordred.SLogP) calc.register(mordred.Constitutional) calc.register(mordred.TopoPSA) calc.register(mordred.Weight) calc.register(mordred.Polarizability) calc.register(mordred.McGowanVolume) name_list=[] for desc_name in calc.descriptors: name_list.append(str(desc_name)) descriptors_list=[] erroneous_encodings=[] encodings_none_descriptors=[] for encoding in encoding_list: mol=encoding_function(encoding) if mol is None: descriptors_list.append([None]*len(name_list)) erroneous_encodings.append(encoding) else: mol=Chem.AddHs(mol) calculated_descriptors = calc(mol) for i in range(len(calculated_descriptors._values)): if math.isnan(calculated_descriptors._values[i]): calculated_descriptors._values = [None]*len(name_list) encodings_none_descriptors.append(encoding) break descriptors_list.append(calculated_descriptors._values) if len(erroneous_encodings)>0: print("The following erroneous {} have been found in the data:\n{}.\nThe erroneous {} will be removed from the data.".format(encoding_name, '\n'.join(map(str, erroneous_encodings)), encoding_name)) if len(encodings_none_descriptors)>0: print("For the following {} not all descriptors can be computed:\n{}.\nThese {} will be removed from the data.".format(encoding_name, '\n'.join(map(str, encodings_none_descriptors)), encoding_name)) df_descriptors=pd.DataFrame(descriptors_list,columns=name_list) df_descriptors = df_descriptors.select_dtypes(exclude=['object']) return df_descriptors def select_descriptors_lasso(df_descriptors,target_list, R_select=0.05, C_select=0.05, kind="R"): df_descriptors = df_descriptors.assign(target=target_list.values) df_descriptors = df_descriptors.dropna(how='any') target_list = df_descriptors['target'].to_list() df_descriptors = df_descriptors.drop(columns=['target']) df_descriptors_scaled = StandardScaler().fit_transform(df_descriptors) if(kind=="C"): model = LogisticRegression(C=C_select,penalty='l1', solver='liblinear',random_state=1).fit(df_descriptors_scaled, target_list) else: model = Lasso(alpha=R_select,max_iter=10000,random_state=1).fit(df_descriptors_scaled, target_list) selected = SelectFromModel(model, prefit=True) X_new_lasso = selected.transform(df_descriptors) selected_features = pd.DataFrame(selected.inverse_transform(X_new_lasso), index=df_descriptors.index, columns=df_descriptors.columns) selected_columns_lasso = selected_features.columns[selected_features.var() != 0] selected_data = df_descriptors[selected_columns_lasso] return selected_data, target_list def get_ecfp(smiles_list, target_list, radius=2, nBits=2048): return generate_ecfp(smiles_list, Chem.MolFromSmiles, 'SMILES', target_list, radius=2, nBits=2048) def get_ecfp_from_inchi(inchi_list, target_list, radius=2, nBits=2048): return generate_ecfp(inchi_list, Chem.MolFromInchi, 'InChi', target_list, radius=2, nBits=2048) def generate_ecfp(encoding_list, encoding_function, encoding_name, target_list, radius=2, nBits=2048): ecfp_fingerprints=[] erroneous_encodings=[] for encoding in encoding_list: mol=encoding_function(encoding) if mol is None: ecfp_fingerprints.append([None]*nBits) erroneous_encodings.append(encoding) else: mol=Chem.AddHs(mol) list_bits_fingerprint = [] list_bits_fingerprint[:0] = AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits).ToBitString() ecfp_fingerprints.append(list_bits_fingerprint) df_ecfp_fingerprints = pd.DataFrame(data = ecfp_fingerprints, index = encoding_list) if len(erroneous_encodings)>0: print("The following erroneous {} have been found in the data:\n{}.\nThe erroneous {} will be removed from the data.".format(encoding_name, '\n'.join(map(str, erroneous_encodings)), encoding_name)) if len(target_list)>0: df_ecfp_fingerprints = df_ecfp_fingerprints.assign(target=target_list.values) df_ecfp_fingerprints = df_ecfp_fingerprints.dropna(how='any') if len(target_list)>0: target_list = df_ecfp_fingerprints['target'].to_list() df_ecfp_fingerprints = df_ecfp_fingerprints.drop(columns=['target']) df_ecfp_fingerprints = df_ecfp_fingerprints.loc[:, (df_ecfp_fingerprints != 0).any(axis=0)] df_ecfp_fingerprints = df_ecfp_fingerprints.loc[:, (df_ecfp_fingerprints != 1).any(axis=0)] return df_ecfp_fingerprints, target_list

true

f71962fa2355f2d2493a845f387b9126cf69d7d6

69,118

py

Python

neutron/tests/unit/api/v2/test_base.py

mcadariu/neutron

35494af5a25efb8b314941ab85b44923654f6acc

[ "Apache-2.0" ]

1

2018-07-04T07:59:31.000Z

neutron/tests/unit/api/v2/test_base.py

ljzjohnson/neutron

d78664321482c15981a09642985a540195e754e3

[ "Apache-2.0" ]

null

neutron/tests/unit/api/v2/test_base.py

ljzjohnson/neutron

d78664321482c15981a09642985a540195e754e3

[ "Apache-2.0" ]

1

2018-08-28T17:13:16.000Z

# Copyright (c) 2012 OpenStack Foundation. # 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. import os import mock from neutron_lib.api import attributes from neutron_lib.api import converters from neutron_lib.callbacks import registry from neutron_lib import constants from neutron_lib import context from neutron_lib import exceptions as n_exc from neutron_lib import fixture from neutron_lib.plugins import directory from oslo_config import cfg from oslo_db import exception as db_exc from oslo_policy import policy as oslo_policy from oslo_utils import uuidutils import six import six.moves.urllib.parse as urlparse import webob from webob import exc import webtest from neutron.api import api_common from neutron.api import extensions from neutron.api.v2 import base as v2_base from neutron.api.v2 import router from neutron import policy from neutron import quota from neutron.quota import resource_registry from neutron.tests import base from neutron.tests import fake_notifier from neutron.tests import tools from neutron.tests.unit import dummy_plugin from neutron.tests.unit import testlib_api EXTDIR = os.path.join(base.ROOTDIR, 'unit/extensions') _uuid = uuidutils.generate_uuid def _get_path(resource, id=None, action=None, fmt=None, endpoint=None): path = '/%s' % resource if id is not None: path = path + '/%s' % id if action is not None: path = path + '/%s' % action if endpoint is not None: path = path + '/%s' % endpoint if fmt is not None: path = path + '.%s' % fmt return path class APIv2TestBase(base.BaseTestCase): def setUp(self): super(APIv2TestBase, self).setUp() plugin = 'neutron.neutron_plugin_base_v2.NeutronPluginBaseV2' # Ensure existing ExtensionManager is not used extensions.PluginAwareExtensionManager._instance = None # Create the default configurations self.config_parse() # Update the plugin self.setup_coreplugin(plugin, load_plugins=False) self._plugin_patcher = mock.patch(plugin, autospec=True) self.plugin = self._plugin_patcher.start() instance = self.plugin.return_value instance.supported_extension_aliases = ['empty-string-filtering'] instance._NeutronPluginBaseV2__native_pagination_support = True instance._NeutronPluginBaseV2__native_sorting_support = True tools.make_mock_plugin_json_encodable(instance) api = router.APIRouter() self.api = webtest.TestApp(api) quota.QUOTAS._driver = None cfg.CONF.set_override('quota_driver', 'neutron.quota.ConfDriver', group='QUOTAS') # APIRouter initialization resets policy module, re-initializing it policy.init() class _ArgMatcher(object): """An adapter to assist mock assertions, used to custom compare.""" def __init__(self, cmp, obj): self.cmp = cmp self.obj = obj def __eq__(self, other): return self.cmp(self.obj, other) def _list_cmp(l1, l2): return set(l1) == set(l2) class APIv2TestCase(APIv2TestBase): @staticmethod def _get_policy_attrs(attr_info): policy_attrs = {name for (name, info) in attr_info.items() if info.get('required_by_policy')} if 'tenant_id' in policy_attrs: policy_attrs.add('project_id') return sorted(policy_attrs) def _do_field_list(self, resource, base_fields): attr_info = attributes.RESOURCES[resource] policy_attrs = self._get_policy_attrs(attr_info) for name, info in attr_info.items(): if info.get('primary_key'): policy_attrs.append(name) fields = base_fields fields.extend(policy_attrs) return fields def _get_collection_kwargs(self, skipargs=None, **kwargs): skipargs = skipargs or [] args_list = ['filters', 'fields', 'sorts', 'limit', 'marker', 'page_reverse'] args_dict = dict( (arg, mock.ANY) for arg in set(args_list) - set(skipargs)) args_dict.update(kwargs) return args_dict def test_fields(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'fields': 'foo'}) fields = self._do_field_list('networks', ['foo']) kwargs = self._get_collection_kwargs(fields=fields) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_fields_multiple(self): instance = self.plugin.return_value instance.get_networks.return_value = [] fields = self._do_field_list('networks', ['bar', 'foo']) self.api.get(_get_path('networks'), {'fields': ['foo', 'bar']}) kwargs = self._get_collection_kwargs(fields=fields) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_fields_multiple_with_empty(self): instance = self.plugin.return_value instance.get_networks.return_value = [] fields = self._do_field_list('networks', ['foo']) self.api.get(_get_path('networks'), {'fields': ['foo', '']}) kwargs = self._get_collection_kwargs(fields=fields) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_fields_empty(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'fields': ''}) kwargs = self._get_collection_kwargs(fields=[]) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_fields_multiple_empty(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'fields': ['', '']}) kwargs = self._get_collection_kwargs(fields=[]) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': 'bar'}) filters = {'name': ['bar']} kwargs = self._get_collection_kwargs(filters=filters) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_empty(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': ''}) filters = {'name': ['']} kwargs = self._get_collection_kwargs(filters=filters) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_multiple_empty(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': ['', '']}) filters = {'name': ['', '']} kwargs = self._get_collection_kwargs(filters=filters) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_multiple_with_empty(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': ['bar', '']}) filters = {'name': ['bar', '']} kwargs = self._get_collection_kwargs(filters=filters) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_multiple_values(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': ['bar', 'bar2']}) filters = {'name': ['bar', 'bar2']} kwargs = self._get_collection_kwargs(filters=filters) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_multiple(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': 'bar', 'tenant_id': 'bar2'}) filters = {'name': ['bar'], 'tenant_id': ['bar2']} kwargs = self._get_collection_kwargs(filters=filters) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_with_fields(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': 'bar', 'fields': 'foo'}) filters = {'name': ['bar']} fields = self._do_field_list('networks', ['foo']) kwargs = self._get_collection_kwargs(filters=filters, fields=fields) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_with_convert_to(self): instance = self.plugin.return_value instance.get_ports.return_value = [] self.api.get(_get_path('ports'), {'admin_state_up': 'true'}) filters = {'admin_state_up': [True]} kwargs = self._get_collection_kwargs(filters=filters) instance.get_ports.assert_called_once_with(mock.ANY, **kwargs) def test_filters_with_convert_list_to(self): instance = self.plugin.return_value instance.get_ports.return_value = [] self.api.get(_get_path('ports'), {'fixed_ips': ['ip_address=foo', 'subnet_id=bar']}) filters = {'fixed_ips': {'ip_address': ['foo'], 'subnet_id': ['bar']}} kwargs = self._get_collection_kwargs(filters=filters) instance.get_ports.assert_called_once_with(mock.ANY, **kwargs) def test_limit(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'limit': '10'}) kwargs = self._get_collection_kwargs(limit=10) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_limit_with_great_than_max_limit(self): cfg.CONF.set_default('pagination_max_limit', '1000') instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'limit': '1001'}) kwargs = self._get_collection_kwargs(limit=1000) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_limit_with_zero(self): cfg.CONF.set_default('pagination_max_limit', '1000') instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'limit': '0'}) kwargs = self._get_collection_kwargs(limit=1000) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_limit_with_unspecific(self): cfg.CONF.set_default('pagination_max_limit', '1000') instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks')) kwargs = self._get_collection_kwargs(limit=1000) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_limit_with_negative_value(self): cfg.CONF.set_default('pagination_max_limit', '1000') instance = self.plugin.return_value instance.get_networks.return_value = [] res = self.api.get(_get_path('networks'), {'limit': -1}, expect_errors=True) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) def test_limit_with_non_integer(self): instance = self.plugin.return_value instance.get_networks.return_value = [] res = self.api.get(_get_path('networks'), {'limit': 'abc'}, expect_errors=True) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) self.assertIn('abc', res) def test_limit_with_infinite_pagination_max_limit(self): instance = self.plugin.return_value instance.get_networks.return_value = [] cfg.CONF.set_override('pagination_max_limit', 'Infinite') self.api.get(_get_path('networks')) kwargs = self._get_collection_kwargs(limit=None) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_limit_with_negative_pagination_max_limit(self): instance = self.plugin.return_value instance.get_networks.return_value = [] cfg.CONF.set_default('pagination_max_limit', '-1') self.api.get(_get_path('networks')) kwargs = self._get_collection_kwargs(limit=None) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_limit_with_non_integer_pagination_max_limit(self): instance = self.plugin.return_value instance.get_networks.return_value = [] cfg.CONF.set_default('pagination_max_limit', 'abc') self.api.get(_get_path('networks')) kwargs = self._get_collection_kwargs(limit=None) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_marker(self): cfg.CONF.set_override('pagination_max_limit', '1000') instance = self.plugin.return_value instance.get_networks.return_value = [] marker = _uuid() self.api.get(_get_path('networks'), {'marker': marker}) kwargs = self._get_collection_kwargs(limit=1000, marker=marker) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_page_reverse(self): calls = [] instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'page_reverse': 'True'}) kwargs = self._get_collection_kwargs(page_reverse=True) calls.append(mock.call.get_networks(mock.ANY, **kwargs)) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) instance.get_networks.reset_mock() self.api.get(_get_path('networks'), {'page_reverse': 'False'}) kwargs = self._get_collection_kwargs(page_reverse=False) calls.append(mock.call.get_networks(mock.ANY, **kwargs)) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_page_reverse_with_non_bool(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'page_reverse': 'abc'}) kwargs = self._get_collection_kwargs(page_reverse=False) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_page_reverse_with_unspecific(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks')) kwargs = self._get_collection_kwargs(page_reverse=False) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_sort(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'sort_key': ['name', 'admin_state_up'], 'sort_dir': ['desc', 'asc']}) kwargs = self._get_collection_kwargs(sorts=[('name', False), ('admin_state_up', True), ('id', True)]) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_sort_with_primary_key(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'sort_key': ['name', 'admin_state_up', 'id'], 'sort_dir': ['desc', 'asc', 'desc']}) kwargs = self._get_collection_kwargs(sorts=[('name', False), ('admin_state_up', True), ('id', False)]) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_sort_without_direction(self): instance = self.plugin.return_value instance.get_networks.return_value = [] res = self.api.get(_get_path('networks'), {'sort_key': ['name']}, expect_errors=True) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) def test_sort_with_invalid_attribute(self): instance = self.plugin.return_value instance.get_networks.return_value = [] res = self.api.get(_get_path('networks'), {'sort_key': 'abc', 'sort_dir': 'asc'}, expect_errors=True) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) def test_sort_with_invalid_dirs(self): instance = self.plugin.return_value instance.get_networks.return_value = [] res = self.api.get(_get_path('networks'), {'sort_key': 'name', 'sort_dir': 'abc'}, expect_errors=True) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) def test_emulated_sort(self): instance = self.plugin.return_value instance._NeutronPluginBaseV2__native_pagination_support = False instance._NeutronPluginBaseV2__native_sorting_support = False instance.get_networks.return_value = [] api = webtest.TestApp(router.APIRouter()) api.get(_get_path('networks'), {'sort_key': ['name', 'status'], 'sort_dir': ['desc', 'asc']}) kwargs = self._get_collection_kwargs( skipargs=['sorts', 'limit', 'marker', 'page_reverse']) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_emulated_sort_without_sort_field(self): instance = self.plugin.return_value instance._NeutronPluginBaseV2__native_pagination_support = False instance._NeutronPluginBaseV2__native_sorting_support = False instance.get_networks.return_value = [] api = webtest.TestApp(router.APIRouter()) api.get(_get_path('networks'), {'sort_key': ['name', 'status'], 'sort_dir': ['desc', 'asc'], 'fields': ['subnets']}) kwargs = self._get_collection_kwargs( skipargs=['sorts', 'limit', 'marker', 'page_reverse'], fields=_ArgMatcher(_list_cmp, ['name', 'status', 'id', 'subnets', 'shared', 'project_id', 'tenant_id'])) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_emulated_pagination(self): instance = self.plugin.return_value instance._NeutronPluginBaseV2__native_pagination_support = False instance.get_networks.return_value = [] api = webtest.TestApp(router.APIRouter()) api.get(_get_path('networks'), {'limit': 10, 'marker': 'foo', 'page_reverse': False}) kwargs = self._get_collection_kwargs(skipargs=['limit', 'marker', 'page_reverse']) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_native_pagination_without_native_sorting(self): instance = self.plugin.return_value instance._NeutronPluginBaseV2__native_sorting_support = False self.assertRaises(n_exc.Invalid, router.APIRouter) # Note: since all resources use the same controller and validation # logic, we actually get really good coverage from testing just networks. class JSONV2TestCase(APIv2TestBase, testlib_api.WebTestCase): def _test_list(self, req_tenant_id, real_tenant_id): env = {} if req_tenant_id: env = {'neutron.context': context.Context('', req_tenant_id)} input_dict = {'id': uuidutils.generate_uuid(), 'name': 'net1', 'admin_state_up': True, 'status': "ACTIVE", 'tenant_id': real_tenant_id, 'shared': False, 'subnets': []} return_value = [input_dict] instance = self.plugin.return_value instance.get_networks.return_value = return_value res = self.api.get(_get_path('networks', fmt=self.fmt), extra_environ=env) res = self.deserialize(res) self.assertIn('networks', res) if not req_tenant_id or req_tenant_id == real_tenant_id: # expect full list returned self.assertEqual(1, len(res['networks'])) output_dict = res['networks'][0] input_dict['shared'] = False self.assertEqual(len(input_dict), len(output_dict)) for k, v in input_dict.items(): self.assertEqual(v, output_dict[k]) else: # expect no results self.assertEqual(0, len(res['networks'])) def test_list_noauth(self): self._test_list(None, _uuid()) def test_list_keystone(self): tenant_id = _uuid() self._test_list(tenant_id, tenant_id) def test_list_keystone_bad(self): tenant_id = _uuid() self._test_list(tenant_id + "bad", tenant_id) def test_list_pagination(self): id1 = str(_uuid()) id2 = str(_uuid()) input_dict1 = {'id': id1, 'name': 'net1', 'admin_state_up': True, 'status': "ACTIVE", 'tenant_id': '', 'shared': False, 'subnets': []} input_dict2 = {'id': id2, 'name': 'net2', 'admin_state_up': True, 'status': "ACTIVE", 'tenant_id': '', 'shared': False, 'subnets': []} return_value = [input_dict1, input_dict2] instance = self.plugin.return_value instance.get_networks.return_value = return_value params = {'limit': ['2'], 'marker': [str(_uuid())], 'sort_key': ['name'], 'sort_dir': ['asc']} res = self.api.get(_get_path('networks'), params=params).json self.assertEqual(2, len(res['networks'])) self.assertEqual(sorted([id1, id2]), sorted([res['networks'][0]['id'], res['networks'][1]['id']])) self.assertIn('networks_links', res) next_links = [] previous_links = [] for r in res['networks_links']: if r['rel'] == 'next': next_links.append(r) if r['rel'] == 'previous': previous_links.append(r) self.assertEqual(1, len(next_links)) self.assertEqual(1, len(previous_links)) url = urlparse.urlparse(next_links[0]['href']) self.assertEqual(url.path, _get_path('networks')) params['marker'] = [id2] self.assertEqual(params, urlparse.parse_qs(url.query)) url = urlparse.urlparse(previous_links[0]['href']) self.assertEqual(url.path, _get_path('networks')) params['marker'] = [id1] params['page_reverse'] = ['True'] self.assertEqual(params, urlparse.parse_qs(url.query)) def test_list_pagination_with_last_page(self): id = str(_uuid()) input_dict = {'id': id, 'name': 'net1', 'admin_state_up': True, 'status': "ACTIVE", 'tenant_id': '', 'shared': False, 'subnets': []} return_value = [input_dict] instance = self.plugin.return_value instance.get_networks.return_value = return_value params = {'limit': ['2'], 'marker': str(_uuid())} res = self.api.get(_get_path('networks'), params=params).json self.assertEqual(1, len(res['networks'])) self.assertEqual(id, res['networks'][0]['id']) self.assertIn('networks_links', res) previous_links = [] for r in res['networks_links']: self.assertNotEqual(r['rel'], 'next') if r['rel'] == 'previous': previous_links.append(r) self.assertEqual(1, len(previous_links)) url = urlparse.urlparse(previous_links[0]['href']) self.assertEqual(url.path, _get_path('networks')) expect_params = params.copy() expect_params['marker'] = [id] expect_params['page_reverse'] = ['True'] self.assertEqual(expect_params, urlparse.parse_qs(url.query)) def test_list_pagination_with_empty_page(self): return_value = [] instance = self.plugin.return_value instance.get_networks.return_value = return_value params = {'limit': ['2'], 'marker': str(_uuid())} res = self.api.get(_get_path('networks'), params=params).json self.assertEqual([], res['networks']) previous_links = [] if 'networks_links' in res: for r in res['networks_links']: self.assertNotEqual(r['rel'], 'next') if r['rel'] == 'previous': previous_links.append(r) self.assertEqual(1, len(previous_links)) url = urlparse.urlparse(previous_links[0]['href']) self.assertEqual(url.path, _get_path('networks')) expect_params = params.copy() del expect_params['marker'] expect_params['page_reverse'] = ['True'] self.assertEqual(expect_params, urlparse.parse_qs(url.query)) def test_list_pagination_reverse_with_last_page(self): id = str(_uuid()) input_dict = {'id': id, 'name': 'net1', 'admin_state_up': True, 'status': "ACTIVE", 'tenant_id': '', 'shared': False, 'subnets': []} return_value = [input_dict] instance = self.plugin.return_value instance.get_networks.return_value = return_value params = {'limit': ['2'], 'marker': [str(_uuid())], 'page_reverse': ['True']} res = self.api.get(_get_path('networks'), params=params).json self.assertEqual(len(res['networks']), 1) self.assertEqual(id, res['networks'][0]['id']) self.assertIn('networks_links', res) next_links = [] for r in res['networks_links']: self.assertNotEqual(r['rel'], 'previous') if r['rel'] == 'next': next_links.append(r) self.assertEqual(1, len(next_links)) url = urlparse.urlparse(next_links[0]['href']) self.assertEqual(url.path, _get_path('networks')) expected_params = params.copy() del expected_params['page_reverse'] expected_params['marker'] = [id] self.assertEqual(expected_params, urlparse.parse_qs(url.query)) def test_list_pagination_reverse_with_empty_page(self): return_value = [] instance = self.plugin.return_value instance.get_networks.return_value = return_value params = {'limit': ['2'], 'marker': [str(_uuid())], 'page_reverse': ['True']} res = self.api.get(_get_path('networks'), params=params).json self.assertEqual([], res['networks']) next_links = [] if 'networks_links' in res: for r in res['networks_links']: self.assertNotEqual(r['rel'], 'previous') if r['rel'] == 'next': next_links.append(r) self.assertEqual(1, len(next_links)) url = urlparse.urlparse(next_links[0]['href']) self.assertEqual(url.path, _get_path('networks')) expect_params = params.copy() del expect_params['marker'] del expect_params['page_reverse'] self.assertEqual(expect_params, urlparse.parse_qs(url.query)) def test_create(self): net_id = _uuid() data = {'network': {'name': 'net1', 'admin_state_up': True, 'tenant_id': _uuid()}} return_value = {'subnets': [], 'status': "ACTIVE", 'id': net_id} return_value.update(data['network'].copy()) instance = self.plugin.return_value instance.create_network.return_value = return_value instance.get_networks_count.return_value = 0 res = self.api.post(_get_path('networks', fmt=self.fmt), self.serialize(data), content_type='application/' + self.fmt) self.assertEqual(exc.HTTPCreated.code, res.status_int) res = self.deserialize(res) self.assertIn('network', res) net = res['network'] self.assertEqual(net_id, net['id']) self.assertEqual("ACTIVE", net['status']) def test_create_use_defaults(self): net_id = _uuid() tenant_id = _uuid() initial_input = {'network': {'name': 'net1', 'tenant_id': tenant_id, 'project_id': tenant_id}} full_input = {'network': {'admin_state_up': True, 'shared': False}} full_input['network'].update(initial_input['network']) return_value = {'id': net_id, 'status': "ACTIVE"} return_value.update(full_input['network']) instance = self.plugin.return_value instance.create_network.return_value = return_value instance.get_networks_count.return_value = 0 res = self.api.post(_get_path('networks', fmt=self.fmt), self.serialize(initial_input), content_type='application/' + self.fmt) instance.create_network.assert_called_with(mock.ANY, network=full_input) self.assertEqual(exc.HTTPCreated.code, res.status_int) res = self.deserialize(res) self.assertIn('network', res) net = res['network'] self.assertEqual(net_id, net['id']) self.assertTrue(net['admin_state_up']) self.assertEqual("ACTIVE", net['status']) def test_create_no_keystone_env(self): data = {'name': 'net1'} self._test_create_failure_bad_request('networks', data) def test_create_with_keystone_env(self): tenant_id = _uuid() net_id = _uuid() env = {'neutron.context': context.Context('', tenant_id)} # tenant_id should be fetched from env initial_input = {'network': {'name': 'net1'}} full_input = {'network': {'admin_state_up': True, 'shared': False, 'tenant_id': tenant_id, 'project_id': tenant_id}} full_input['network'].update(initial_input['network']) return_value = {'id': net_id, 'status': "ACTIVE"} return_value.update(full_input['network']) instance = self.plugin.return_value instance.create_network.return_value = return_value instance.get_networks_count.return_value = 0 res = self.api.post(_get_path('networks', fmt=self.fmt), self.serialize(initial_input), content_type='application/' + self.fmt, extra_environ=env) instance.create_network.assert_called_with(mock.ANY, network=full_input) self.assertEqual(exc.HTTPCreated.code, res.status_int) def test_create_bad_keystone_tenant(self): tenant_id = _uuid() data = {'network': {'name': 'net1', 'tenant_id': tenant_id}} env = {'neutron.context': context.Context('', tenant_id + "bad")} self._test_create_failure_bad_request('networks', data, extra_environ=env) def test_create_no_body(self): data = {'whoa': None} self._test_create_failure_bad_request('networks', data) def test_create_body_string_not_json(self): data = 'a string' self._test_create_failure_bad_request('networks', data) def test_create_body_boolean_not_json(self): data = True self._test_create_failure_bad_request('networks', data) def test_create_no_resource(self): data = {} self._test_create_failure_bad_request('networks', data) def test_create_missing_attr(self): data = {'port': {'what': 'who', 'tenant_id': _uuid()}} self._test_create_failure_bad_request('ports', data) def test_create_readonly_attr(self): data = {'network': {'name': 'net1', 'tenant_id': _uuid(), 'status': "ACTIVE"}} self._test_create_failure_bad_request('networks', data) def test_create_with_too_long_name(self): data = {'network': {'name': "12345678" * 32, 'admin_state_up': True, 'tenant_id': _uuid()}} res = self.api.post(_get_path('networks', fmt=self.fmt), self.serialize(data), content_type='application/' + self.fmt, expect_errors=True) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) def test_create_bulk(self): data = {'networks': [{'name': 'net1', 'admin_state_up': True, 'tenant_id': _uuid()}, {'name': 'net2', 'admin_state_up': True, 'tenant_id': _uuid()}]} def side_effect(context, network): net = network.copy() net['network'].update({'subnets': []}) return net['network'] instance = self.plugin.return_value instance.create_network.side_effect = side_effect instance.get_networks_count.return_value = 0 res = self.api.post(_get_path('networks', fmt=self.fmt), self.serialize(data), content_type='application/' + self.fmt) self.assertEqual(exc.HTTPCreated.code, res.status_int) def _test_create_failure_bad_request(self, resource, data, **kwargs): res = self.api.post(_get_path(resource, fmt=self.fmt), self.serialize(data), content_type='application/' + self.fmt, expect_errors=True, **kwargs) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) def test_create_bulk_networks_none(self): self._test_create_failure_bad_request('networks', {'networks': None}) def test_create_bulk_networks_empty_list(self): self._test_create_failure_bad_request('networks', {'networks': []}) def test_create_bulk_missing_attr(self): data = {'ports': [{'what': 'who', 'tenant_id': _uuid()}]} self._test_create_failure_bad_request('ports', data) def test_create_bulk_partial_body(self): data = {'ports': [{'device_id': 'device_1', 'tenant_id': _uuid()}, {'tenant_id': _uuid()}]} self._test_create_failure_bad_request('ports', data) def test_create_attr_not_specified(self): net_id = _uuid() tenant_id = _uuid() device_id = _uuid() initial_input = {'port': {'name': '', 'network_id': net_id, 'tenant_id': tenant_id, 'project_id': tenant_id, 'device_id': device_id, 'admin_state_up': True}} full_input = {'port': {'admin_state_up': True, 'mac_address': constants.ATTR_NOT_SPECIFIED, 'fixed_ips': constants.ATTR_NOT_SPECIFIED, 'device_owner': ''}} full_input['port'].update(initial_input['port']) return_value = {'id': _uuid(), 'status': 'ACTIVE', 'admin_state_up': True, 'mac_address': 'ca:fe:de:ad:be:ef', 'device_id': device_id, 'device_owner': ''} return_value.update(initial_input['port']) instance = self.plugin.return_value instance.get_network.return_value = { 'tenant_id': six.text_type(tenant_id) } instance.get_ports_count.return_value = 1 instance.create_port.return_value = return_value res = self.api.post(_get_path('ports', fmt=self.fmt), self.serialize(initial_input), content_type='application/' + self.fmt) instance.create_port.assert_called_with(mock.ANY, port=full_input) self.assertEqual(exc.HTTPCreated.code, res.status_int) res = self.deserialize(res) self.assertIn('port', res) port = res['port'] self.assertEqual(net_id, port['network_id']) self.assertEqual('ca:fe:de:ad:be:ef', port['mac_address']) def test_create_return_extra_attr(self): net_id = _uuid() data = {'network': {'name': 'net1', 'admin_state_up': True, 'tenant_id': _uuid()}} return_value = {'subnets': [], 'status': "ACTIVE", 'id': net_id, 'v2attrs:something': "123"} return_value.update(data['network'].copy()) instance = self.plugin.return_value instance.create_network.return_value = return_value instance.get_networks_count.return_value = 0 res = self.api.post(_get_path('networks', fmt=self.fmt), self.serialize(data), content_type='application/' + self.fmt) self.assertEqual(exc.HTTPCreated.code, res.status_int) res = self.deserialize(res) self.assertIn('network', res) net = res['network'] self.assertEqual(net_id, net['id']) self.assertEqual("ACTIVE", net['status']) self.assertNotIn('v2attrs:something', net) def test_fields(self): return_value = {'name': 'net1', 'admin_state_up': True, 'subnets': []} instance = self.plugin.return_value instance.get_network.return_value = return_value self.api.get(_get_path('networks', id=uuidutils.generate_uuid(), fmt=self.fmt)) def _test_delete(self, req_tenant_id, real_tenant_id, expected_code, expect_errors=False): env = {} if req_tenant_id: env = {'neutron.context': context.Context('', req_tenant_id)} instance = self.plugin.return_value instance.get_network.return_value = {'tenant_id': real_tenant_id, 'shared': False} instance.delete_network.return_value = None res = self.api.delete(_get_path('networks', id=uuidutils.generate_uuid(), fmt=self.fmt), extra_environ=env, expect_errors=expect_errors) self.assertEqual(expected_code, res.status_int) def test_delete_noauth(self): self._test_delete(None, _uuid(), exc.HTTPNoContent.code) def test_delete_keystone(self): tenant_id = _uuid() self._test_delete(tenant_id, tenant_id, exc.HTTPNoContent.code) def test_delete_keystone_bad_tenant(self): tenant_id = _uuid() self._test_delete(tenant_id + "bad", tenant_id, exc.HTTPNotFound.code, expect_errors=True) def _test_get(self, req_tenant_id, real_tenant_id, expected_code, expect_errors=False): env = {} shared = False if req_tenant_id: env = {'neutron.context': context.Context('', req_tenant_id)} if req_tenant_id.endswith('another'): shared = True env['neutron.context'].roles = ['tenant_admin'] data = {'tenant_id': real_tenant_id, 'shared': shared} instance = self.plugin.return_value instance.get_network.return_value = data res = self.api.get(_get_path('networks', id=uuidutils.generate_uuid(), fmt=self.fmt), extra_environ=env, expect_errors=expect_errors) self.assertEqual(expected_code, res.status_int) return res def test_get_noauth(self): self._test_get(None, _uuid(), 200) def test_get_keystone(self): tenant_id = _uuid() self._test_get(tenant_id, tenant_id, 200) def test_get_keystone_bad_tenant(self): tenant_id = _uuid() self._test_get(tenant_id + "bad", tenant_id, exc.HTTPNotFound.code, expect_errors=True) def test_get_keystone_shared_network(self): tenant_id = _uuid() self._test_get(tenant_id + "another", tenant_id, 200) def test_get_keystone_strip_admin_only_attribute(self): tenant_id = _uuid() # Inject rule in policy engine rules = oslo_policy.Rules.from_dict( {'get_network:name': "rule:admin_only"}) policy.set_rules(rules, overwrite=False) res = self._test_get(tenant_id, tenant_id, 200) res = self.deserialize(res) self.assertNotIn('name', res['network']) def _test_update(self, req_tenant_id, real_tenant_id, expected_code, expect_errors=False): env = {} if req_tenant_id: env = {'neutron.context': context.Context('', req_tenant_id)} # leave out 'name' field intentionally data = {'network': {'admin_state_up': True}} return_value = {'subnets': []} return_value.update(data['network'].copy()) instance = self.plugin.return_value instance.get_network.return_value = {'tenant_id': real_tenant_id, 'shared': False} instance.update_network.return_value = return_value res = self.api.put(_get_path('networks', id=uuidutils.generate_uuid(), fmt=self.fmt), self.serialize(data), extra_environ=env, expect_errors=expect_errors) # Ensure id attribute is included in fields returned by GET call # in update procedure. self.assertEqual(1, instance.get_network.call_count) self.assertIn('id', instance.get_network.call_args[1]['fields']) self.assertEqual(res.status_int, expected_code) def test_update_noauth(self): self._test_update(None, _uuid(), 200) def test_update_keystone(self): tenant_id = _uuid() self._test_update(tenant_id, tenant_id, 200) def test_update_keystone_bad_tenant(self): tenant_id = _uuid() self._test_update(tenant_id + "bad", tenant_id, exc.HTTPNotFound.code, expect_errors=True) def test_update_keystone_no_tenant(self): tenant_id = _uuid() self._test_update(tenant_id, None, exc.HTTPNotFound.code, expect_errors=True) def test_update_readonly_field(self): data = {'network': {'status': "NANANA"}} res = self.api.put(_get_path('networks', id=_uuid()), self.serialize(data), content_type='application/' + self.fmt, expect_errors=True) self.assertEqual(400, res.status_int) def test_invalid_attribute_field(self): data = {'network': {'invalid_key1': "foo1", 'invalid_key2': "foo2"}} res = self.api.put(_get_path('networks', id=_uuid()), self.serialize(data), content_type='application/' + self.fmt, expect_errors=True) self.assertEqual(400, res.status_int) def test_retry_on_index(self): instance = self.plugin.return_value instance.get_networks.side_effect = [db_exc.RetryRequest(None), []] api = webtest.TestApp(router.APIRouter()) api.get(_get_path('networks', fmt=self.fmt)) self.assertTrue(instance.get_networks.called) def test_retry_on_show(self): instance = self.plugin.return_value instance.get_network.side_effect = [db_exc.RetryRequest(None), {}] api = webtest.TestApp(router.APIRouter()) api.get(_get_path('networks', _uuid(), fmt=self.fmt)) self.assertTrue(instance.get_network.called) class SubresourceTest(base.BaseTestCase): def setUp(self): super(SubresourceTest, self).setUp() raise self.skipException('this class will be deleted') plugin = 'neutron.tests.unit.api.v2.test_base.TestSubresourcePlugin' extensions.PluginAwareExtensionManager._instance = None self.useFixture(fixture.APIDefinitionFixture()) self.config_parse() self.setup_coreplugin(plugin, load_plugins=False) self._plugin_patcher = mock.patch(plugin, autospec=True) self.plugin = self._plugin_patcher.start() api = router.APIRouter() SUB_RESOURCES = {} RESOURCE_ATTRIBUTE_MAP = {} SUB_RESOURCES[dummy_plugin.RESOURCE_NAME] = { 'collection_name': 'dummies', 'parent': {'collection_name': 'networks', 'member_name': 'network'} } RESOURCE_ATTRIBUTE_MAP['dummies'] = { 'foo': {'allow_post': True, 'allow_put': True, 'validate': {'type:string': None}, 'default': '', 'is_visible': True}, 'tenant_id': {'allow_post': True, 'allow_put': False, 'validate': {'type:string': None}, 'required_by_policy': True, 'is_visible': True} } collection_name = SUB_RESOURCES[ dummy_plugin.RESOURCE_NAME].get('collection_name') resource_name = dummy_plugin.RESOURCE_NAME parent = SUB_RESOURCES[dummy_plugin.RESOURCE_NAME].get('parent') params = RESOURCE_ATTRIBUTE_MAP['dummies'] member_actions = {'mactions': 'GET'} _plugin = directory.get_plugin() controller = v2_base.create_resource(collection_name, resource_name, _plugin, params, member_actions=member_actions, parent=parent, allow_bulk=True, allow_pagination=True, allow_sorting=True) path_prefix = "/%s/{%s_id}/%s" % (parent['collection_name'], parent['member_name'], collection_name) mapper_kwargs = dict(controller=controller, path_prefix=path_prefix) api.map.collection(collection_name, resource_name, **mapper_kwargs) api.map.resource(collection_name, collection_name, controller=controller, parent_resource=parent, member=member_actions) self.api = webtest.TestApp(api) def test_index_sub_resource(self): instance = self.plugin.return_value self.api.get('/networks/id1/dummies') instance.get_network_dummies.assert_called_once_with(mock.ANY, filters=mock.ANY, fields=mock.ANY, network_id='id1') def test_show_sub_resource(self): instance = self.plugin.return_value dummy_id = _uuid() self.api.get('/networks/id1' + _get_path('dummies', id=dummy_id)) instance.get_network_dummy.assert_called_once_with(mock.ANY, dummy_id, network_id='id1', fields=mock.ANY) def test_create_sub_resource(self): instance = self.plugin.return_value tenant_id = _uuid() body = { dummy_plugin.RESOURCE_NAME: { 'foo': 'bar', 'tenant_id': tenant_id, 'project_id': tenant_id } } self.api.post_json('/networks/id1/dummies', body) instance.create_network_dummy.assert_called_once_with(mock.ANY, network_id='id1', dummy=body) def test_update_sub_resource(self): instance = self.plugin.return_value dummy_id = _uuid() body = {dummy_plugin.RESOURCE_NAME: {'foo': 'bar'}} self.api.put_json('/networks/id1' + _get_path('dummies', id=dummy_id), body) instance.update_network_dummy.assert_called_once_with(mock.ANY, dummy_id, network_id='id1', dummy=body) def test_update_subresource_to_none(self): instance = self.plugin.return_value dummy_id = _uuid() body = {dummy_plugin.RESOURCE_NAME: {}} self.api.put_json('/networks/id1' + _get_path('dummies', id=dummy_id), body) instance.update_network_dummy.assert_called_once_with(mock.ANY, dummy_id, network_id='id1', dummy=body) def test_delete_sub_resource(self): instance = self.plugin.return_value dummy_id = _uuid() self.api.delete('/networks/id1' + _get_path('dummies', id=dummy_id)) instance.delete_network_dummy.assert_called_once_with(mock.ANY, dummy_id, network_id='id1') def test_sub_resource_member_actions(self): instance = self.plugin.return_value dummy_id = _uuid() self.api.get('/networks/id1' + _get_path('dummies', id=dummy_id, action='mactions')) instance.mactions.assert_called_once_with(mock.ANY, dummy_id, network_id='id1') # Note: since all resources use the same controller and validation # logic, we actually get really good coverage from testing just networks. class V2Views(base.BaseTestCase): def _view(self, keys, collection, resource): data = dict((key, 'value') for key in keys) data['fake'] = 'value' attr_info = attributes.RESOURCES[collection] controller = v2_base.Controller(None, collection, resource, attr_info) res = controller._view(context.get_admin_context(), data) self.assertNotIn('fake', res) for key in keys: self.assertIn(key, res) def test_network(self): keys = ('id', 'name', 'subnets', 'admin_state_up', 'status', 'tenant_id') self._view(keys, 'networks', 'network') def test_port(self): keys = ('id', 'network_id', 'mac_address', 'fixed_ips', 'device_id', 'admin_state_up', 'tenant_id', 'status') self._view(keys, 'ports', 'port') def test_subnet(self): keys = ('id', 'network_id', 'tenant_id', 'gateway_ip', 'ip_version', 'cidr', 'enable_dhcp') self._view(keys, 'subnets', 'subnet') class NotificationTest(APIv2TestBase): def setUp(self): super(NotificationTest, self).setUp() fake_notifier.reset() def _resource_op_notifier(self, opname, resource, expected_errors=False): initial_input = {resource: {'name': 'myname'}} instance = self.plugin.return_value instance.get_networks.return_value = initial_input instance.get_networks_count.return_value = 0 expected_code = exc.HTTPCreated.code if opname == 'create': initial_input[resource]['tenant_id'] = _uuid() res = self.api.post_json( _get_path('networks'), initial_input, expect_errors=expected_errors) if opname == 'update': res = self.api.put_json( _get_path('networks', id=_uuid()), initial_input, expect_errors=expected_errors) expected_code = exc.HTTPOk.code if opname == 'delete': initial_input[resource]['tenant_id'] = _uuid() res = self.api.delete( _get_path('networks', id=_uuid()), expect_errors=expected_errors) expected_code = exc.HTTPNoContent.code expected_events = ('.'.join([resource, opname, "start"]), '.'.join([resource, opname, "end"])) self.assertEqual(len(expected_events), len(fake_notifier.NOTIFICATIONS)) for msg, event in zip(fake_notifier.NOTIFICATIONS, expected_events): self.assertEqual('INFO', msg['priority']) self.assertEqual(event, msg['event_type']) if opname == 'delete' and event == 'network.delete.end': self.assertIn('payload', msg) resource = msg['payload'] self.assertIn('network_id', resource) self.assertIn('network', resource) self.assertEqual(expected_code, res.status_int) def test_network_create_notifer(self): self._resource_op_notifier('create', 'network') def test_network_delete_notifer(self): self._resource_op_notifier('delete', 'network') def test_network_update_notifer(self): self._resource_op_notifier('update', 'network') class RegistryNotificationTest(APIv2TestBase): def setUp(self): # This test does not have database support so tracking cannot be used cfg.CONF.set_override('track_quota_usage', False, group='QUOTAS') super(RegistryNotificationTest, self).setUp() def _test_registry_notify(self, opname, resource, initial_input=None): instance = self.plugin.return_value instance.get_networks.return_value = initial_input instance.get_networks_count.return_value = 0 expected_code = exc.HTTPCreated.code with mock.patch.object(registry, 'publish') as notify: if opname == 'create': res = self.api.post_json( _get_path('networks'), initial_input) if opname == 'update': res = self.api.put_json( _get_path('networks', id=_uuid()), initial_input) expected_code = exc.HTTPOk.code if opname == 'delete': res = self.api.delete(_get_path('networks', id=_uuid())) expected_code = exc.HTTPNoContent.code self.assertTrue(notify.called) self.assertEqual(expected_code, res.status_int) def test_network_create_registry_notify(self): input = {'network': {'name': 'net', 'tenant_id': _uuid()}} self._test_registry_notify('create', 'network', input) def test_network_delete_registry_notify(self): self._test_registry_notify('delete', 'network') def test_network_update_registry_notify(self): input = {'network': {'name': 'net'}} self._test_registry_notify('update', 'network', input) def test_networks_create_bulk_registry_notify(self): input = {'networks': [{'name': 'net1', 'tenant_id': _uuid()}, {'name': 'net2', 'tenant_id': _uuid()}]} self._test_registry_notify('create', 'network', input) class QuotaTest(APIv2TestBase): def setUp(self): # This test does not have database support so tracking cannot be used cfg.CONF.set_override('track_quota_usage', False, group='QUOTAS') super(QuotaTest, self).setUp() # Use mock to let the API use a different QuotaEngine instance for # unit test in this class. This will ensure resource are registered # again and instantiated with neutron.quota.resource.CountableResource replacement_registry = resource_registry.ResourceRegistry() registry_patcher = mock.patch('neutron.quota.resource_registry.' 'ResourceRegistry.get_instance') mock_registry = registry_patcher.start().return_value mock_registry.get_resource = replacement_registry.get_resource mock_registry.resources = replacement_registry.resources # Register a resource replacement_registry.register_resource_by_name('network') def test_create_network_quota(self): cfg.CONF.set_override('quota_network', 1, group='QUOTAS') initial_input = {'network': {'name': 'net1', 'tenant_id': _uuid()}} full_input = {'network': {'admin_state_up': True, 'subnets': []}} full_input['network'].update(initial_input['network']) instance = self.plugin.return_value instance.get_networks_count.return_value = 1 res = self.api.post_json( _get_path('networks'), initial_input, expect_errors=True) instance.get_networks_count.assert_called_with(mock.ANY, filters=mock.ANY) self.assertIn("Quota exceeded for resources", res.json['NeutronError']['message']) def test_create_network_quota_no_counts(self): cfg.CONF.set_override('quota_network', 1, group='QUOTAS') initial_input = {'network': {'name': 'net1', 'tenant_id': _uuid()}} full_input = {'network': {'admin_state_up': True, 'subnets': []}} full_input['network'].update(initial_input['network']) instance = self.plugin.return_value instance.get_networks_count.side_effect = ( NotImplementedError()) instance.get_networks.return_value = ["foo"] res = self.api.post_json( _get_path('networks'), initial_input, expect_errors=True) instance.get_networks_count.assert_called_with(mock.ANY, filters=mock.ANY) self.assertIn("Quota exceeded for resources", res.json['NeutronError']['message']) def test_create_network_quota_without_limit(self): cfg.CONF.set_override('quota_network', -1, group='QUOTAS') initial_input = {'network': {'name': 'net1', 'tenant_id': _uuid()}} instance = self.plugin.return_value instance.get_networks_count.return_value = 3 res = self.api.post_json( _get_path('networks'), initial_input) self.assertEqual(exc.HTTPCreated.code, res.status_int) class ExtensionTestCase(base.BaseTestCase): def setUp(self): # This test does not have database support so tracking cannot be used cfg.CONF.set_override('track_quota_usage', False, group='QUOTAS') super(ExtensionTestCase, self).setUp() plugin = 'neutron.neutron_plugin_base_v2.NeutronPluginBaseV2' # Ensure existing ExtensionManager is not used extensions.PluginAwareExtensionManager._instance = None self.useFixture(fixture.APIDefinitionFixture()) # Create the default configurations self.config_parse() # Update the plugin and extensions path self.setup_coreplugin(plugin, load_plugins=False) cfg.CONF.set_override('api_extensions_path', EXTDIR) self._plugin_patcher = mock.patch(plugin, autospec=True) self.plugin = self._plugin_patcher.start() # Instantiate mock plugin and enable the V2attributes extension self.plugin.return_value.supported_extension_aliases = ["v2attrs"] api = router.APIRouter() self.api = webtest.TestApp(api) quota.QUOTAS._driver = None cfg.CONF.set_override('quota_driver', 'neutron.quota.ConfDriver', group='QUOTAS') def test_extended_create(self): net_id = _uuid() tenant_id = _uuid() initial_input = {'network': {'name': 'net1', 'tenant_id': tenant_id, 'project_id': tenant_id, 'v2attrs:something_else': "abc"}} data = {'network': {'admin_state_up': True, 'shared': False}} data['network'].update(initial_input['network']) return_value = {'subnets': [], 'status': "ACTIVE", 'id': net_id, 'v2attrs:something': "123"} return_value.update(data['network'].copy()) instance = self.plugin.return_value instance.create_network.return_value = return_value instance.get_networks_count.return_value = 0 res = self.api.post_json(_get_path('networks'), initial_input) instance.create_network.assert_called_with(mock.ANY, network=data) self.assertEqual(exc.HTTPCreated.code, res.status_int) self.assertIn('network', res.json) net = res.json['network'] self.assertEqual(net_id, net['id']) self.assertEqual("ACTIVE", net['status']) self.assertEqual("123", net['v2attrs:something']) self.assertNotIn('v2attrs:something_else', net) class TestSubresourcePlugin(object): def get_network_dummies(self, context, network_id, filters=None, fields=None): return [] def get_network_dummy(self, context, id, network_id, fields=None): return {} def create_network_dummy(self, context, network_id, dummy): return {} def update_network_dummy(self, context, id, network_id, dummy): return {} def delete_network_dummy(self, context, id, network_id): return def mactions(self, context, id, network_id): return class ListArgsTestCase(base.BaseTestCase): def test_list_args(self): path = '/?fields=4&foo=3&fields=2&bar=1' request = webob.Request.blank(path) expect_val = ['2', '4'] actual_val = api_common.list_args(request, 'fields') self.assertEqual(expect_val, sorted(actual_val)) def test_list_args_with_empty(self): path = '/?foo=4&bar=3&baz=2&qux=1' request = webob.Request.blank(path) self.assertEqual([], api_common.list_args(request, 'fields')) class SortingTestCase(base.BaseTestCase): def test_get_sorts(self): path = '/?sort_key=foo&sort_dir=desc&sort_key=bar&sort_dir=asc' request = webob.Request.blank(path) attr_info = {'foo': {'key': 'val'}, 'bar': {'key': 'val'}} expect_val = [('foo', False), ('bar', True)] actual_val = api_common.get_sorts(request, attr_info) self.assertEqual(expect_val, actual_val) def test_get_sorts_with_project_id(self): path = '/?sort_key=project_id&sort_dir=desc' request = webob.Request.blank(path) attr_info = {'tenant_id': {'key': 'val'}} expect_val = [('project_id', False)] actual_val = api_common.get_sorts(request, attr_info) self.assertEqual(expect_val, actual_val) class FiltersTestCase(base.BaseTestCase): def test_all_skip_args(self): path = '/?fields=4&fields=3&fields=2&fields=1' request = webob.Request.blank(path) self.assertEqual({}, api_common.get_filters(request, {}, ["fields"])) @mock.patch('neutron.api.api_common.is_empty_string_filtering_supported', return_value=False) def test_blank_values(self, mock_is_supported): path = '/?foo=&bar=&baz=&qux=' request = webob.Request.blank(path) self.assertEqual({}, api_common.get_filters(request, {})) @mock.patch('neutron.api.api_common.is_empty_string_filtering_supported', return_value=True) def test_blank_values_with_filtering_supported(self, mock_is_supported): path = '/?foo=&bar=&baz=&qux=' request = webob.Request.blank(path) self.assertEqual({'foo': [''], 'bar': [''], 'baz': [''], 'qux': ['']}, api_common.get_filters(request, {})) def test_no_attr_info(self): path = '/?foo=4&bar=3&baz=2&qux=1' request = webob.Request.blank(path) expect_val = {'foo': ['4'], 'bar': ['3'], 'baz': ['2'], 'qux': ['1']} actual_val = api_common.get_filters(request, {}) self.assertEqual(expect_val, actual_val) def test_attr_info_with_project_info_populated(self): path = '/?foo=4&bar=3&baz=2&qux=1' request = webob.Request.blank(path) attr_info = {'tenant_id': {'key': 'val'}} expect_val = {'foo': ['4'], 'bar': ['3'], 'baz': ['2'], 'qux': ['1']} actual_val = api_common.get_filters(request, attr_info) self.assertEqual(expect_val, actual_val) expect_attr_info = {'tenant_id': {'key': 'val'}, 'project_id': {'key': 'val'}} self.assertEqual(expect_attr_info, attr_info) def test_attr_info_without_conversion(self): path = '/?foo=4&bar=3&baz=2&qux=1' request = webob.Request.blank(path) attr_info = {'foo': {'key': 'val'}} expect_val = {'foo': ['4'], 'bar': ['3'], 'baz': ['2'], 'qux': ['1']} actual_val = api_common.get_filters(request, attr_info) self.assertEqual(expect_val, actual_val) def test_attr_info_with_convert_list_to(self): path = '/?foo=key=4&bar=3&foo=key=2&qux=1' request = webob.Request.blank(path) attr_info = { 'foo': { 'convert_list_to': converters.convert_kvp_list_to_dict, } } expect_val = {'foo': {'key': ['2', '4']}, 'bar': ['3'], 'qux': ['1']} actual_val = api_common.get_filters(request, attr_info) self.assertOrderedEqual(expect_val, actual_val) def test_attr_info_with_convert_to(self): path = '/?foo=4&bar=3&baz=2&qux=1' request = webob.Request.blank(path) attr_info = {'foo': {'convert_to': converters.convert_to_int}} expect_val = {'foo': [4], 'bar': ['3'], 'baz': ['2'], 'qux': ['1']} actual_val = api_common.get_filters(request, attr_info) self.assertEqual(expect_val, actual_val) def test_attr_info_with_base_db_attributes(self): path = '/?__contains__=1&__class__=2' request = webob.Request.blank(path) self.assertEqual({}, api_common.get_filters(request, {})) class CreateResourceTestCase(base.BaseTestCase): def test_resource_creation(self): resource = v2_base.create_resource('fakes', 'fake', None, {}) self.assertIsInstance(resource, webob.dec.wsgify)

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import os import mock from neutron_lib.api import attributes from neutron_lib.api import converters from neutron_lib.callbacks import registry from neutron_lib import constants from neutron_lib import context from neutron_lib import exceptions as n_exc from neutron_lib import fixture from neutron_lib.plugins import directory from oslo_config import cfg from oslo_db import exception as db_exc from oslo_policy import policy as oslo_policy from oslo_utils import uuidutils import six import six.moves.urllib.parse as urlparse import webob from webob import exc import webtest from neutron.api import api_common from neutron.api import extensions from neutron.api.v2 import base as v2_base from neutron.api.v2 import router from neutron import policy from neutron import quota from neutron.quota import resource_registry from neutron.tests import base from neutron.tests import fake_notifier from neutron.tests import tools from neutron.tests.unit import dummy_plugin from neutron.tests.unit import testlib_api EXTDIR = os.path.join(base.ROOTDIR, 'unit/extensions') _uuid = uuidutils.generate_uuid def _get_path(resource, id=None, action=None, fmt=None, endpoint=None): path = '/%s' % resource if id is not None: path = path + '/%s' % id if action is not None: path = path + '/%s' % action if endpoint is not None: path = path + '/%s' % endpoint if fmt is not None: path = path + '.%s' % fmt return path class APIv2TestBase(base.BaseTestCase): def setUp(self): super(APIv2TestBase, self).setUp() plugin = 'neutron.neutron_plugin_base_v2.NeutronPluginBaseV2' extensions.PluginAwareExtensionManager._instance = None self.config_parse() self.setup_coreplugin(plugin, load_plugins=False) self._plugin_patcher = mock.patch(plugin, autospec=True) self.plugin = self._plugin_patcher.start() instance = self.plugin.return_value instance.supported_extension_aliases = ['empty-string-filtering'] instance._NeutronPluginBaseV2__native_pagination_support = True instance._NeutronPluginBaseV2__native_sorting_support = True tools.make_mock_plugin_json_encodable(instance) api = router.APIRouter() self.api = webtest.TestApp(api) quota.QUOTAS._driver = None cfg.CONF.set_override('quota_driver', 'neutron.quota.ConfDriver', group='QUOTAS') policy.init() class _ArgMatcher(object): def __init__(self, cmp, obj): self.cmp = cmp self.obj = obj def __eq__(self, other): return self.cmp(self.obj, other) def _list_cmp(l1, l2): return set(l1) == set(l2) class APIv2TestCase(APIv2TestBase): @staticmethod def _get_policy_attrs(attr_info): policy_attrs = {name for (name, info) in attr_info.items() if info.get('required_by_policy')} if 'tenant_id' in policy_attrs: policy_attrs.add('project_id') return sorted(policy_attrs) def _do_field_list(self, resource, base_fields): attr_info = attributes.RESOURCES[resource] policy_attrs = self._get_policy_attrs(attr_info) for name, info in attr_info.items(): if info.get('primary_key'): policy_attrs.append(name) fields = base_fields fields.extend(policy_attrs) return fields def _get_collection_kwargs(self, skipargs=None, **kwargs): skipargs = skipargs or [] args_list = ['filters', 'fields', 'sorts', 'limit', 'marker', 'page_reverse'] args_dict = dict( (arg, mock.ANY) for arg in set(args_list) - set(skipargs)) args_dict.update(kwargs) return args_dict def test_fields(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'fields': 'foo'}) fields = self._do_field_list('networks', ['foo']) kwargs = self._get_collection_kwargs(fields=fields) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_fields_multiple(self): instance = self.plugin.return_value instance.get_networks.return_value = [] fields = self._do_field_list('networks', ['bar', 'foo']) self.api.get(_get_path('networks'), {'fields': ['foo', 'bar']}) kwargs = self._get_collection_kwargs(fields=fields) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_fields_multiple_with_empty(self): instance = self.plugin.return_value instance.get_networks.return_value = [] fields = self._do_field_list('networks', ['foo']) self.api.get(_get_path('networks'), {'fields': ['foo', '']}) kwargs = self._get_collection_kwargs(fields=fields) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_fields_empty(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'fields': ''}) kwargs = self._get_collection_kwargs(fields=[]) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_fields_multiple_empty(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'fields': ['', '']}) kwargs = self._get_collection_kwargs(fields=[]) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': 'bar'}) filters = {'name': ['bar']} kwargs = self._get_collection_kwargs(filters=filters) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_empty(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': ''}) filters = {'name': ['']} kwargs = self._get_collection_kwargs(filters=filters) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_multiple_empty(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': ['', '']}) filters = {'name': ['', '']} kwargs = self._get_collection_kwargs(filters=filters) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_multiple_with_empty(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': ['bar', '']}) filters = {'name': ['bar', '']} kwargs = self._get_collection_kwargs(filters=filters) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_multiple_values(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': ['bar', 'bar2']}) filters = {'name': ['bar', 'bar2']} kwargs = self._get_collection_kwargs(filters=filters) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_multiple(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': 'bar', 'tenant_id': 'bar2'}) filters = {'name': ['bar'], 'tenant_id': ['bar2']} kwargs = self._get_collection_kwargs(filters=filters) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_with_fields(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'name': 'bar', 'fields': 'foo'}) filters = {'name': ['bar']} fields = self._do_field_list('networks', ['foo']) kwargs = self._get_collection_kwargs(filters=filters, fields=fields) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_filters_with_convert_to(self): instance = self.plugin.return_value instance.get_ports.return_value = [] self.api.get(_get_path('ports'), {'admin_state_up': 'true'}) filters = {'admin_state_up': [True]} kwargs = self._get_collection_kwargs(filters=filters) instance.get_ports.assert_called_once_with(mock.ANY, **kwargs) def test_filters_with_convert_list_to(self): instance = self.plugin.return_value instance.get_ports.return_value = [] self.api.get(_get_path('ports'), {'fixed_ips': ['ip_address=foo', 'subnet_id=bar']}) filters = {'fixed_ips': {'ip_address': ['foo'], 'subnet_id': ['bar']}} kwargs = self._get_collection_kwargs(filters=filters) instance.get_ports.assert_called_once_with(mock.ANY, **kwargs) def test_limit(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'limit': '10'}) kwargs = self._get_collection_kwargs(limit=10) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_limit_with_great_than_max_limit(self): cfg.CONF.set_default('pagination_max_limit', '1000') instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'limit': '1001'}) kwargs = self._get_collection_kwargs(limit=1000) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_limit_with_zero(self): cfg.CONF.set_default('pagination_max_limit', '1000') instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'limit': '0'}) kwargs = self._get_collection_kwargs(limit=1000) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_limit_with_unspecific(self): cfg.CONF.set_default('pagination_max_limit', '1000') instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks')) kwargs = self._get_collection_kwargs(limit=1000) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_limit_with_negative_value(self): cfg.CONF.set_default('pagination_max_limit', '1000') instance = self.plugin.return_value instance.get_networks.return_value = [] res = self.api.get(_get_path('networks'), {'limit': -1}, expect_errors=True) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) def test_limit_with_non_integer(self): instance = self.plugin.return_value instance.get_networks.return_value = [] res = self.api.get(_get_path('networks'), {'limit': 'abc'}, expect_errors=True) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) self.assertIn('abc', res) def test_limit_with_infinite_pagination_max_limit(self): instance = self.plugin.return_value instance.get_networks.return_value = [] cfg.CONF.set_override('pagination_max_limit', 'Infinite') self.api.get(_get_path('networks')) kwargs = self._get_collection_kwargs(limit=None) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_limit_with_negative_pagination_max_limit(self): instance = self.plugin.return_value instance.get_networks.return_value = [] cfg.CONF.set_default('pagination_max_limit', '-1') self.api.get(_get_path('networks')) kwargs = self._get_collection_kwargs(limit=None) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_limit_with_non_integer_pagination_max_limit(self): instance = self.plugin.return_value instance.get_networks.return_value = [] cfg.CONF.set_default('pagination_max_limit', 'abc') self.api.get(_get_path('networks')) kwargs = self._get_collection_kwargs(limit=None) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_marker(self): cfg.CONF.set_override('pagination_max_limit', '1000') instance = self.plugin.return_value instance.get_networks.return_value = [] marker = _uuid() self.api.get(_get_path('networks'), {'marker': marker}) kwargs = self._get_collection_kwargs(limit=1000, marker=marker) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_page_reverse(self): calls = [] instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'page_reverse': 'True'}) kwargs = self._get_collection_kwargs(page_reverse=True) calls.append(mock.call.get_networks(mock.ANY, **kwargs)) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) instance.get_networks.reset_mock() self.api.get(_get_path('networks'), {'page_reverse': 'False'}) kwargs = self._get_collection_kwargs(page_reverse=False) calls.append(mock.call.get_networks(mock.ANY, **kwargs)) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_page_reverse_with_non_bool(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'page_reverse': 'abc'}) kwargs = self._get_collection_kwargs(page_reverse=False) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_page_reverse_with_unspecific(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks')) kwargs = self._get_collection_kwargs(page_reverse=False) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_sort(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'sort_key': ['name', 'admin_state_up'], 'sort_dir': ['desc', 'asc']}) kwargs = self._get_collection_kwargs(sorts=[('name', False), ('admin_state_up', True), ('id', True)]) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_sort_with_primary_key(self): instance = self.plugin.return_value instance.get_networks.return_value = [] self.api.get(_get_path('networks'), {'sort_key': ['name', 'admin_state_up', 'id'], 'sort_dir': ['desc', 'asc', 'desc']}) kwargs = self._get_collection_kwargs(sorts=[('name', False), ('admin_state_up', True), ('id', False)]) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_sort_without_direction(self): instance = self.plugin.return_value instance.get_networks.return_value = [] res = self.api.get(_get_path('networks'), {'sort_key': ['name']}, expect_errors=True) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) def test_sort_with_invalid_attribute(self): instance = self.plugin.return_value instance.get_networks.return_value = [] res = self.api.get(_get_path('networks'), {'sort_key': 'abc', 'sort_dir': 'asc'}, expect_errors=True) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) def test_sort_with_invalid_dirs(self): instance = self.plugin.return_value instance.get_networks.return_value = [] res = self.api.get(_get_path('networks'), {'sort_key': 'name', 'sort_dir': 'abc'}, expect_errors=True) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) def test_emulated_sort(self): instance = self.plugin.return_value instance._NeutronPluginBaseV2__native_pagination_support = False instance._NeutronPluginBaseV2__native_sorting_support = False instance.get_networks.return_value = [] api = webtest.TestApp(router.APIRouter()) api.get(_get_path('networks'), {'sort_key': ['name', 'status'], 'sort_dir': ['desc', 'asc']}) kwargs = self._get_collection_kwargs( skipargs=['sorts', 'limit', 'marker', 'page_reverse']) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_emulated_sort_without_sort_field(self): instance = self.plugin.return_value instance._NeutronPluginBaseV2__native_pagination_support = False instance._NeutronPluginBaseV2__native_sorting_support = False instance.get_networks.return_value = [] api = webtest.TestApp(router.APIRouter()) api.get(_get_path('networks'), {'sort_key': ['name', 'status'], 'sort_dir': ['desc', 'asc'], 'fields': ['subnets']}) kwargs = self._get_collection_kwargs( skipargs=['sorts', 'limit', 'marker', 'page_reverse'], fields=_ArgMatcher(_list_cmp, ['name', 'status', 'id', 'subnets', 'shared', 'project_id', 'tenant_id'])) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_emulated_pagination(self): instance = self.plugin.return_value instance._NeutronPluginBaseV2__native_pagination_support = False instance.get_networks.return_value = [] api = webtest.TestApp(router.APIRouter()) api.get(_get_path('networks'), {'limit': 10, 'marker': 'foo', 'page_reverse': False}) kwargs = self._get_collection_kwargs(skipargs=['limit', 'marker', 'page_reverse']) instance.get_networks.assert_called_once_with(mock.ANY, **kwargs) def test_native_pagination_without_native_sorting(self): instance = self.plugin.return_value instance._NeutronPluginBaseV2__native_sorting_support = False self.assertRaises(n_exc.Invalid, router.APIRouter) class JSONV2TestCase(APIv2TestBase, testlib_api.WebTestCase): def _test_list(self, req_tenant_id, real_tenant_id): env = {} if req_tenant_id: env = {'neutron.context': context.Context('', req_tenant_id)} input_dict = {'id': uuidutils.generate_uuid(), 'name': 'net1', 'admin_state_up': True, 'status': "ACTIVE", 'tenant_id': real_tenant_id, 'shared': False, 'subnets': []} return_value = [input_dict] instance = self.plugin.return_value instance.get_networks.return_value = return_value res = self.api.get(_get_path('networks', fmt=self.fmt), extra_environ=env) res = self.deserialize(res) self.assertIn('networks', res) if not req_tenant_id or req_tenant_id == real_tenant_id: self.assertEqual(1, len(res['networks'])) output_dict = res['networks'][0] input_dict['shared'] = False self.assertEqual(len(input_dict), len(output_dict)) for k, v in input_dict.items(): self.assertEqual(v, output_dict[k]) else: self.assertEqual(0, len(res['networks'])) def test_list_noauth(self): self._test_list(None, _uuid()) def test_list_keystone(self): tenant_id = _uuid() self._test_list(tenant_id, tenant_id) def test_list_keystone_bad(self): tenant_id = _uuid() self._test_list(tenant_id + "bad", tenant_id) def test_list_pagination(self): id1 = str(_uuid()) id2 = str(_uuid()) input_dict1 = {'id': id1, 'name': 'net1', 'admin_state_up': True, 'status': "ACTIVE", 'tenant_id': '', 'shared': False, 'subnets': []} input_dict2 = {'id': id2, 'name': 'net2', 'admin_state_up': True, 'status': "ACTIVE", 'tenant_id': '', 'shared': False, 'subnets': []} return_value = [input_dict1, input_dict2] instance = self.plugin.return_value instance.get_networks.return_value = return_value params = {'limit': ['2'], 'marker': [str(_uuid())], 'sort_key': ['name'], 'sort_dir': ['asc']} res = self.api.get(_get_path('networks'), params=params).json self.assertEqual(2, len(res['networks'])) self.assertEqual(sorted([id1, id2]), sorted([res['networks'][0]['id'], res['networks'][1]['id']])) self.assertIn('networks_links', res) next_links = [] previous_links = [] for r in res['networks_links']: if r['rel'] == 'next': next_links.append(r) if r['rel'] == 'previous': previous_links.append(r) self.assertEqual(1, len(next_links)) self.assertEqual(1, len(previous_links)) url = urlparse.urlparse(next_links[0]['href']) self.assertEqual(url.path, _get_path('networks')) params['marker'] = [id2] self.assertEqual(params, urlparse.parse_qs(url.query)) url = urlparse.urlparse(previous_links[0]['href']) self.assertEqual(url.path, _get_path('networks')) params['marker'] = [id1] params['page_reverse'] = ['True'] self.assertEqual(params, urlparse.parse_qs(url.query)) def test_list_pagination_with_last_page(self): id = str(_uuid()) input_dict = {'id': id, 'name': 'net1', 'admin_state_up': True, 'status': "ACTIVE", 'tenant_id': '', 'shared': False, 'subnets': []} return_value = [input_dict] instance = self.plugin.return_value instance.get_networks.return_value = return_value params = {'limit': ['2'], 'marker': str(_uuid())} res = self.api.get(_get_path('networks'), params=params).json self.assertEqual(1, len(res['networks'])) self.assertEqual(id, res['networks'][0]['id']) self.assertIn('networks_links', res) previous_links = [] for r in res['networks_links']: self.assertNotEqual(r['rel'], 'next') if r['rel'] == 'previous': previous_links.append(r) self.assertEqual(1, len(previous_links)) url = urlparse.urlparse(previous_links[0]['href']) self.assertEqual(url.path, _get_path('networks')) expect_params = params.copy() expect_params['marker'] = [id] expect_params['page_reverse'] = ['True'] self.assertEqual(expect_params, urlparse.parse_qs(url.query)) def test_list_pagination_with_empty_page(self): return_value = [] instance = self.plugin.return_value instance.get_networks.return_value = return_value params = {'limit': ['2'], 'marker': str(_uuid())} res = self.api.get(_get_path('networks'), params=params).json self.assertEqual([], res['networks']) previous_links = [] if 'networks_links' in res: for r in res['networks_links']: self.assertNotEqual(r['rel'], 'next') if r['rel'] == 'previous': previous_links.append(r) self.assertEqual(1, len(previous_links)) url = urlparse.urlparse(previous_links[0]['href']) self.assertEqual(url.path, _get_path('networks')) expect_params = params.copy() del expect_params['marker'] expect_params['page_reverse'] = ['True'] self.assertEqual(expect_params, urlparse.parse_qs(url.query)) def test_list_pagination_reverse_with_last_page(self): id = str(_uuid()) input_dict = {'id': id, 'name': 'net1', 'admin_state_up': True, 'status': "ACTIVE", 'tenant_id': '', 'shared': False, 'subnets': []} return_value = [input_dict] instance = self.plugin.return_value instance.get_networks.return_value = return_value params = {'limit': ['2'], 'marker': [str(_uuid())], 'page_reverse': ['True']} res = self.api.get(_get_path('networks'), params=params).json self.assertEqual(len(res['networks']), 1) self.assertEqual(id, res['networks'][0]['id']) self.assertIn('networks_links', res) next_links = [] for r in res['networks_links']: self.assertNotEqual(r['rel'], 'previous') if r['rel'] == 'next': next_links.append(r) self.assertEqual(1, len(next_links)) url = urlparse.urlparse(next_links[0]['href']) self.assertEqual(url.path, _get_path('networks')) expected_params = params.copy() del expected_params['page_reverse'] expected_params['marker'] = [id] self.assertEqual(expected_params, urlparse.parse_qs(url.query)) def test_list_pagination_reverse_with_empty_page(self): return_value = [] instance = self.plugin.return_value instance.get_networks.return_value = return_value params = {'limit': ['2'], 'marker': [str(_uuid())], 'page_reverse': ['True']} res = self.api.get(_get_path('networks'), params=params).json self.assertEqual([], res['networks']) next_links = [] if 'networks_links' in res: for r in res['networks_links']: self.assertNotEqual(r['rel'], 'previous') if r['rel'] == 'next': next_links.append(r) self.assertEqual(1, len(next_links)) url = urlparse.urlparse(next_links[0]['href']) self.assertEqual(url.path, _get_path('networks')) expect_params = params.copy() del expect_params['marker'] del expect_params['page_reverse'] self.assertEqual(expect_params, urlparse.parse_qs(url.query)) def test_create(self): net_id = _uuid() data = {'network': {'name': 'net1', 'admin_state_up': True, 'tenant_id': _uuid()}} return_value = {'subnets': [], 'status': "ACTIVE", 'id': net_id} return_value.update(data['network'].copy()) instance = self.plugin.return_value instance.create_network.return_value = return_value instance.get_networks_count.return_value = 0 res = self.api.post(_get_path('networks', fmt=self.fmt), self.serialize(data), content_type='application/' + self.fmt) self.assertEqual(exc.HTTPCreated.code, res.status_int) res = self.deserialize(res) self.assertIn('network', res) net = res['network'] self.assertEqual(net_id, net['id']) self.assertEqual("ACTIVE", net['status']) def test_create_use_defaults(self): net_id = _uuid() tenant_id = _uuid() initial_input = {'network': {'name': 'net1', 'tenant_id': tenant_id, 'project_id': tenant_id}} full_input = {'network': {'admin_state_up': True, 'shared': False}} full_input['network'].update(initial_input['network']) return_value = {'id': net_id, 'status': "ACTIVE"} return_value.update(full_input['network']) instance = self.plugin.return_value instance.create_network.return_value = return_value instance.get_networks_count.return_value = 0 res = self.api.post(_get_path('networks', fmt=self.fmt), self.serialize(initial_input), content_type='application/' + self.fmt) instance.create_network.assert_called_with(mock.ANY, network=full_input) self.assertEqual(exc.HTTPCreated.code, res.status_int) res = self.deserialize(res) self.assertIn('network', res) net = res['network'] self.assertEqual(net_id, net['id']) self.assertTrue(net['admin_state_up']) self.assertEqual("ACTIVE", net['status']) def test_create_no_keystone_env(self): data = {'name': 'net1'} self._test_create_failure_bad_request('networks', data) def test_create_with_keystone_env(self): tenant_id = _uuid() net_id = _uuid() env = {'neutron.context': context.Context('', tenant_id)} initial_input = {'network': {'name': 'net1'}} full_input = {'network': {'admin_state_up': True, 'shared': False, 'tenant_id': tenant_id, 'project_id': tenant_id}} full_input['network'].update(initial_input['network']) return_value = {'id': net_id, 'status': "ACTIVE"} return_value.update(full_input['network']) instance = self.plugin.return_value instance.create_network.return_value = return_value instance.get_networks_count.return_value = 0 res = self.api.post(_get_path('networks', fmt=self.fmt), self.serialize(initial_input), content_type='application/' + self.fmt, extra_environ=env) instance.create_network.assert_called_with(mock.ANY, network=full_input) self.assertEqual(exc.HTTPCreated.code, res.status_int) def test_create_bad_keystone_tenant(self): tenant_id = _uuid() data = {'network': {'name': 'net1', 'tenant_id': tenant_id}} env = {'neutron.context': context.Context('', tenant_id + "bad")} self._test_create_failure_bad_request('networks', data, extra_environ=env) def test_create_no_body(self): data = {'whoa': None} self._test_create_failure_bad_request('networks', data) def test_create_body_string_not_json(self): data = 'a string' self._test_create_failure_bad_request('networks', data) def test_create_body_boolean_not_json(self): data = True self._test_create_failure_bad_request('networks', data) def test_create_no_resource(self): data = {} self._test_create_failure_bad_request('networks', data) def test_create_missing_attr(self): data = {'port': {'what': 'who', 'tenant_id': _uuid()}} self._test_create_failure_bad_request('ports', data) def test_create_readonly_attr(self): data = {'network': {'name': 'net1', 'tenant_id': _uuid(), 'status': "ACTIVE"}} self._test_create_failure_bad_request('networks', data) def test_create_with_too_long_name(self): data = {'network': {'name': "12345678" * 32, 'admin_state_up': True, 'tenant_id': _uuid()}} res = self.api.post(_get_path('networks', fmt=self.fmt), self.serialize(data), content_type='application/' + self.fmt, expect_errors=True) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) def test_create_bulk(self): data = {'networks': [{'name': 'net1', 'admin_state_up': True, 'tenant_id': _uuid()}, {'name': 'net2', 'admin_state_up': True, 'tenant_id': _uuid()}]} def side_effect(context, network): net = network.copy() net['network'].update({'subnets': []}) return net['network'] instance = self.plugin.return_value instance.create_network.side_effect = side_effect instance.get_networks_count.return_value = 0 res = self.api.post(_get_path('networks', fmt=self.fmt), self.serialize(data), content_type='application/' + self.fmt) self.assertEqual(exc.HTTPCreated.code, res.status_int) def _test_create_failure_bad_request(self, resource, data, **kwargs): res = self.api.post(_get_path(resource, fmt=self.fmt), self.serialize(data), content_type='application/' + self.fmt, expect_errors=True, **kwargs) self.assertEqual(exc.HTTPBadRequest.code, res.status_int) def test_create_bulk_networks_none(self): self._test_create_failure_bad_request('networks', {'networks': None}) def test_create_bulk_networks_empty_list(self): self._test_create_failure_bad_request('networks', {'networks': []}) def test_create_bulk_missing_attr(self): data = {'ports': [{'what': 'who', 'tenant_id': _uuid()}]} self._test_create_failure_bad_request('ports', data) def test_create_bulk_partial_body(self): data = {'ports': [{'device_id': 'device_1', 'tenant_id': _uuid()}, {'tenant_id': _uuid()}]} self._test_create_failure_bad_request('ports', data) def test_create_attr_not_specified(self): net_id = _uuid() tenant_id = _uuid() device_id = _uuid() initial_input = {'port': {'name': '', 'network_id': net_id, 'tenant_id': tenant_id, 'project_id': tenant_id, 'device_id': device_id, 'admin_state_up': True}} full_input = {'port': {'admin_state_up': True, 'mac_address': constants.ATTR_NOT_SPECIFIED, 'fixed_ips': constants.ATTR_NOT_SPECIFIED, 'device_owner': ''}} full_input['port'].update(initial_input['port']) return_value = {'id': _uuid(), 'status': 'ACTIVE', 'admin_state_up': True, 'mac_address': 'ca:fe:de:ad:be:ef', 'device_id': device_id, 'device_owner': ''} return_value.update(initial_input['port']) instance = self.plugin.return_value instance.get_network.return_value = { 'tenant_id': six.text_type(tenant_id) } instance.get_ports_count.return_value = 1 instance.create_port.return_value = return_value res = self.api.post(_get_path('ports', fmt=self.fmt), self.serialize(initial_input), content_type='application/' + self.fmt) instance.create_port.assert_called_with(mock.ANY, port=full_input) self.assertEqual(exc.HTTPCreated.code, res.status_int) res = self.deserialize(res) self.assertIn('port', res) port = res['port'] self.assertEqual(net_id, port['network_id']) self.assertEqual('ca:fe:de:ad:be:ef', port['mac_address']) def test_create_return_extra_attr(self): net_id = _uuid() data = {'network': {'name': 'net1', 'admin_state_up': True, 'tenant_id': _uuid()}} return_value = {'subnets': [], 'status': "ACTIVE", 'id': net_id, 'v2attrs:something': "123"} return_value.update(data['network'].copy()) instance = self.plugin.return_value instance.create_network.return_value = return_value instance.get_networks_count.return_value = 0 res = self.api.post(_get_path('networks', fmt=self.fmt), self.serialize(data), content_type='application/' + self.fmt) self.assertEqual(exc.HTTPCreated.code, res.status_int) res = self.deserialize(res) self.assertIn('network', res) net = res['network'] self.assertEqual(net_id, net['id']) self.assertEqual("ACTIVE", net['status']) self.assertNotIn('v2attrs:something', net) def test_fields(self): return_value = {'name': 'net1', 'admin_state_up': True, 'subnets': []} instance = self.plugin.return_value instance.get_network.return_value = return_value self.api.get(_get_path('networks', id=uuidutils.generate_uuid(), fmt=self.fmt)) def _test_delete(self, req_tenant_id, real_tenant_id, expected_code, expect_errors=False): env = {} if req_tenant_id: env = {'neutron.context': context.Context('', req_tenant_id)} instance = self.plugin.return_value instance.get_network.return_value = {'tenant_id': real_tenant_id, 'shared': False} instance.delete_network.return_value = None res = self.api.delete(_get_path('networks', id=uuidutils.generate_uuid(), fmt=self.fmt), extra_environ=env, expect_errors=expect_errors) self.assertEqual(expected_code, res.status_int) def test_delete_noauth(self): self._test_delete(None, _uuid(), exc.HTTPNoContent.code) def test_delete_keystone(self): tenant_id = _uuid() self._test_delete(tenant_id, tenant_id, exc.HTTPNoContent.code) def test_delete_keystone_bad_tenant(self): tenant_id = _uuid() self._test_delete(tenant_id + "bad", tenant_id, exc.HTTPNotFound.code, expect_errors=True) def _test_get(self, req_tenant_id, real_tenant_id, expected_code, expect_errors=False): env = {} shared = False if req_tenant_id: env = {'neutron.context': context.Context('', req_tenant_id)} if req_tenant_id.endswith('another'): shared = True env['neutron.context'].roles = ['tenant_admin'] data = {'tenant_id': real_tenant_id, 'shared': shared} instance = self.plugin.return_value instance.get_network.return_value = data res = self.api.get(_get_path('networks', id=uuidutils.generate_uuid(), fmt=self.fmt), extra_environ=env, expect_errors=expect_errors) self.assertEqual(expected_code, res.status_int) return res def test_get_noauth(self): self._test_get(None, _uuid(), 200) def test_get_keystone(self): tenant_id = _uuid() self._test_get(tenant_id, tenant_id, 200) def test_get_keystone_bad_tenant(self): tenant_id = _uuid() self._test_get(tenant_id + "bad", tenant_id, exc.HTTPNotFound.code, expect_errors=True) def test_get_keystone_shared_network(self): tenant_id = _uuid() self._test_get(tenant_id + "another", tenant_id, 200) def test_get_keystone_strip_admin_only_attribute(self): tenant_id = _uuid() rules = oslo_policy.Rules.from_dict( {'get_network:name': "rule:admin_only"}) policy.set_rules(rules, overwrite=False) res = self._test_get(tenant_id, tenant_id, 200) res = self.deserialize(res) self.assertNotIn('name', res['network']) def _test_update(self, req_tenant_id, real_tenant_id, expected_code, expect_errors=False): env = {} if req_tenant_id: env = {'neutron.context': context.Context('', req_tenant_id)} data = {'network': {'admin_state_up': True}} return_value = {'subnets': []} return_value.update(data['network'].copy()) instance = self.plugin.return_value instance.get_network.return_value = {'tenant_id': real_tenant_id, 'shared': False} instance.update_network.return_value = return_value res = self.api.put(_get_path('networks', id=uuidutils.generate_uuid(), fmt=self.fmt), self.serialize(data), extra_environ=env, expect_errors=expect_errors) self.assertEqual(1, instance.get_network.call_count) self.assertIn('id', instance.get_network.call_args[1]['fields']) self.assertEqual(res.status_int, expected_code) def test_update_noauth(self): self._test_update(None, _uuid(), 200) def test_update_keystone(self): tenant_id = _uuid() self._test_update(tenant_id, tenant_id, 200) def test_update_keystone_bad_tenant(self): tenant_id = _uuid() self._test_update(tenant_id + "bad", tenant_id, exc.HTTPNotFound.code, expect_errors=True) def test_update_keystone_no_tenant(self): tenant_id = _uuid() self._test_update(tenant_id, None, exc.HTTPNotFound.code, expect_errors=True) def test_update_readonly_field(self): data = {'network': {'status': "NANANA"}} res = self.api.put(_get_path('networks', id=_uuid()), self.serialize(data), content_type='application/' + self.fmt, expect_errors=True) self.assertEqual(400, res.status_int) def test_invalid_attribute_field(self): data = {'network': {'invalid_key1': "foo1", 'invalid_key2': "foo2"}} res = self.api.put(_get_path('networks', id=_uuid()), self.serialize(data), content_type='application/' + self.fmt, expect_errors=True) self.assertEqual(400, res.status_int) def test_retry_on_index(self): instance = self.plugin.return_value instance.get_networks.side_effect = [db_exc.RetryRequest(None), []] api = webtest.TestApp(router.APIRouter()) api.get(_get_path('networks', fmt=self.fmt)) self.assertTrue(instance.get_networks.called) def test_retry_on_show(self): instance = self.plugin.return_value instance.get_network.side_effect = [db_exc.RetryRequest(None), {}] api = webtest.TestApp(router.APIRouter()) api.get(_get_path('networks', _uuid(), fmt=self.fmt)) self.assertTrue(instance.get_network.called) class SubresourceTest(base.BaseTestCase): def setUp(self): super(SubresourceTest, self).setUp() raise self.skipException('this class will be deleted') plugin = 'neutron.tests.unit.api.v2.test_base.TestSubresourcePlugin' extensions.PluginAwareExtensionManager._instance = None self.useFixture(fixture.APIDefinitionFixture()) self.config_parse() self.setup_coreplugin(plugin, load_plugins=False) self._plugin_patcher = mock.patch(plugin, autospec=True) self.plugin = self._plugin_patcher.start() api = router.APIRouter() SUB_RESOURCES = {} RESOURCE_ATTRIBUTE_MAP = {} SUB_RESOURCES[dummy_plugin.RESOURCE_NAME] = { 'collection_name': 'dummies', 'parent': {'collection_name': 'networks', 'member_name': 'network'} } RESOURCE_ATTRIBUTE_MAP['dummies'] = { 'foo': {'allow_post': True, 'allow_put': True, 'validate': {'type:string': None}, 'default': '', 'is_visible': True}, 'tenant_id': {'allow_post': True, 'allow_put': False, 'validate': {'type:string': None}, 'required_by_policy': True, 'is_visible': True} } collection_name = SUB_RESOURCES[ dummy_plugin.RESOURCE_NAME].get('collection_name') resource_name = dummy_plugin.RESOURCE_NAME parent = SUB_RESOURCES[dummy_plugin.RESOURCE_NAME].get('parent') params = RESOURCE_ATTRIBUTE_MAP['dummies'] member_actions = {'mactions': 'GET'} _plugin = directory.get_plugin() controller = v2_base.create_resource(collection_name, resource_name, _plugin, params, member_actions=member_actions, parent=parent, allow_bulk=True, allow_pagination=True, allow_sorting=True) path_prefix = "/%s/{%s_id}/%s" % (parent['collection_name'], parent['member_name'], collection_name) mapper_kwargs = dict(controller=controller, path_prefix=path_prefix) api.map.collection(collection_name, resource_name, **mapper_kwargs) api.map.resource(collection_name, collection_name, controller=controller, parent_resource=parent, member=member_actions) self.api = webtest.TestApp(api) def test_index_sub_resource(self): instance = self.plugin.return_value self.api.get('/networks/id1/dummies') instance.get_network_dummies.assert_called_once_with(mock.ANY, filters=mock.ANY, fields=mock.ANY, network_id='id1') def test_show_sub_resource(self): instance = self.plugin.return_value dummy_id = _uuid() self.api.get('/networks/id1' + _get_path('dummies', id=dummy_id)) instance.get_network_dummy.assert_called_once_with(mock.ANY, dummy_id, network_id='id1', fields=mock.ANY) def test_create_sub_resource(self): instance = self.plugin.return_value tenant_id = _uuid() body = { dummy_plugin.RESOURCE_NAME: { 'foo': 'bar', 'tenant_id': tenant_id, 'project_id': tenant_id } } self.api.post_json('/networks/id1/dummies', body) instance.create_network_dummy.assert_called_once_with(mock.ANY, network_id='id1', dummy=body) def test_update_sub_resource(self): instance = self.plugin.return_value dummy_id = _uuid() body = {dummy_plugin.RESOURCE_NAME: {'foo': 'bar'}} self.api.put_json('/networks/id1' + _get_path('dummies', id=dummy_id), body) instance.update_network_dummy.assert_called_once_with(mock.ANY, dummy_id, network_id='id1', dummy=body) def test_update_subresource_to_none(self): instance = self.plugin.return_value dummy_id = _uuid() body = {dummy_plugin.RESOURCE_NAME: {}} self.api.put_json('/networks/id1' + _get_path('dummies', id=dummy_id), body) instance.update_network_dummy.assert_called_once_with(mock.ANY, dummy_id, network_id='id1', dummy=body) def test_delete_sub_resource(self): instance = self.plugin.return_value dummy_id = _uuid() self.api.delete('/networks/id1' + _get_path('dummies', id=dummy_id)) instance.delete_network_dummy.assert_called_once_with(mock.ANY, dummy_id, network_id='id1') def test_sub_resource_member_actions(self): instance = self.plugin.return_value dummy_id = _uuid() self.api.get('/networks/id1' + _get_path('dummies', id=dummy_id, action='mactions')) instance.mactions.assert_called_once_with(mock.ANY, dummy_id, network_id='id1') class V2Views(base.BaseTestCase): def _view(self, keys, collection, resource): data = dict((key, 'value') for key in keys) data['fake'] = 'value' attr_info = attributes.RESOURCES[collection] controller = v2_base.Controller(None, collection, resource, attr_info) res = controller._view(context.get_admin_context(), data) self.assertNotIn('fake', res) for key in keys: self.assertIn(key, res) def test_network(self): keys = ('id', 'name', 'subnets', 'admin_state_up', 'status', 'tenant_id') self._view(keys, 'networks', 'network') def test_port(self): keys = ('id', 'network_id', 'mac_address', 'fixed_ips', 'device_id', 'admin_state_up', 'tenant_id', 'status') self._view(keys, 'ports', 'port') def test_subnet(self): keys = ('id', 'network_id', 'tenant_id', 'gateway_ip', 'ip_version', 'cidr', 'enable_dhcp') self._view(keys, 'subnets', 'subnet') class NotificationTest(APIv2TestBase): def setUp(self): super(NotificationTest, self).setUp() fake_notifier.reset() def _resource_op_notifier(self, opname, resource, expected_errors=False): initial_input = {resource: {'name': 'myname'}} instance = self.plugin.return_value instance.get_networks.return_value = initial_input instance.get_networks_count.return_value = 0 expected_code = exc.HTTPCreated.code if opname == 'create': initial_input[resource]['tenant_id'] = _uuid() res = self.api.post_json( _get_path('networks'), initial_input, expect_errors=expected_errors) if opname == 'update': res = self.api.put_json( _get_path('networks', id=_uuid()), initial_input, expect_errors=expected_errors) expected_code = exc.HTTPOk.code if opname == 'delete': initial_input[resource]['tenant_id'] = _uuid() res = self.api.delete( _get_path('networks', id=_uuid()), expect_errors=expected_errors) expected_code = exc.HTTPNoContent.code expected_events = ('.'.join([resource, opname, "start"]), '.'.join([resource, opname, "end"])) self.assertEqual(len(expected_events), len(fake_notifier.NOTIFICATIONS)) for msg, event in zip(fake_notifier.NOTIFICATIONS, expected_events): self.assertEqual('INFO', msg['priority']) self.assertEqual(event, msg['event_type']) if opname == 'delete' and event == 'network.delete.end': self.assertIn('payload', msg) resource = msg['payload'] self.assertIn('network_id', resource) self.assertIn('network', resource) self.assertEqual(expected_code, res.status_int) def test_network_create_notifer(self): self._resource_op_notifier('create', 'network') def test_network_delete_notifer(self): self._resource_op_notifier('delete', 'network') def test_network_update_notifer(self): self._resource_op_notifier('update', 'network') class RegistryNotificationTest(APIv2TestBase): def setUp(self): cfg.CONF.set_override('track_quota_usage', False, group='QUOTAS') super(RegistryNotificationTest, self).setUp() def _test_registry_notify(self, opname, resource, initial_input=None): instance = self.plugin.return_value instance.get_networks.return_value = initial_input instance.get_networks_count.return_value = 0 expected_code = exc.HTTPCreated.code with mock.patch.object(registry, 'publish') as notify: if opname == 'create': res = self.api.post_json( _get_path('networks'), initial_input) if opname == 'update': res = self.api.put_json( _get_path('networks', id=_uuid()), initial_input) expected_code = exc.HTTPOk.code if opname == 'delete': res = self.api.delete(_get_path('networks', id=_uuid())) expected_code = exc.HTTPNoContent.code self.assertTrue(notify.called) self.assertEqual(expected_code, res.status_int) def test_network_create_registry_notify(self): input = {'network': {'name': 'net', 'tenant_id': _uuid()}} self._test_registry_notify('create', 'network', input) def test_network_delete_registry_notify(self): self._test_registry_notify('delete', 'network') def test_network_update_registry_notify(self): input = {'network': {'name': 'net'}} self._test_registry_notify('update', 'network', input) def test_networks_create_bulk_registry_notify(self): input = {'networks': [{'name': 'net1', 'tenant_id': _uuid()}, {'name': 'net2', 'tenant_id': _uuid()}]} self._test_registry_notify('create', 'network', input) class QuotaTest(APIv2TestBase): def setUp(self): cfg.CONF.set_override('track_quota_usage', False, group='QUOTAS') super(QuotaTest, self).setUp() replacement_registry = resource_registry.ResourceRegistry() registry_patcher = mock.patch('neutron.quota.resource_registry.' 'ResourceRegistry.get_instance') mock_registry = registry_patcher.start().return_value mock_registry.get_resource = replacement_registry.get_resource mock_registry.resources = replacement_registry.resources replacement_registry.register_resource_by_name('network') def test_create_network_quota(self): cfg.CONF.set_override('quota_network', 1, group='QUOTAS') initial_input = {'network': {'name': 'net1', 'tenant_id': _uuid()}} full_input = {'network': {'admin_state_up': True, 'subnets': []}} full_input['network'].update(initial_input['network']) instance = self.plugin.return_value instance.get_networks_count.return_value = 1 res = self.api.post_json( _get_path('networks'), initial_input, expect_errors=True) instance.get_networks_count.assert_called_with(mock.ANY, filters=mock.ANY) self.assertIn("Quota exceeded for resources", res.json['NeutronError']['message']) def test_create_network_quota_no_counts(self): cfg.CONF.set_override('quota_network', 1, group='QUOTAS') initial_input = {'network': {'name': 'net1', 'tenant_id': _uuid()}} full_input = {'network': {'admin_state_up': True, 'subnets': []}} full_input['network'].update(initial_input['network']) instance = self.plugin.return_value instance.get_networks_count.side_effect = ( NotImplementedError()) instance.get_networks.return_value = ["foo"] res = self.api.post_json( _get_path('networks'), initial_input, expect_errors=True) instance.get_networks_count.assert_called_with(mock.ANY, filters=mock.ANY) self.assertIn("Quota exceeded for resources", res.json['NeutronError']['message']) def test_create_network_quota_without_limit(self): cfg.CONF.set_override('quota_network', -1, group='QUOTAS') initial_input = {'network': {'name': 'net1', 'tenant_id': _uuid()}} instance = self.plugin.return_value instance.get_networks_count.return_value = 3 res = self.api.post_json( _get_path('networks'), initial_input) self.assertEqual(exc.HTTPCreated.code, res.status_int) class ExtensionTestCase(base.BaseTestCase): def setUp(self): cfg.CONF.set_override('track_quota_usage', False, group='QUOTAS') super(ExtensionTestCase, self).setUp() plugin = 'neutron.neutron_plugin_base_v2.NeutronPluginBaseV2' extensions.PluginAwareExtensionManager._instance = None self.useFixture(fixture.APIDefinitionFixture()) self.config_parse() self.setup_coreplugin(plugin, load_plugins=False) cfg.CONF.set_override('api_extensions_path', EXTDIR) self._plugin_patcher = mock.patch(plugin, autospec=True) self.plugin = self._plugin_patcher.start() self.plugin.return_value.supported_extension_aliases = ["v2attrs"] api = router.APIRouter() self.api = webtest.TestApp(api) quota.QUOTAS._driver = None cfg.CONF.set_override('quota_driver', 'neutron.quota.ConfDriver', group='QUOTAS') def test_extended_create(self): net_id = _uuid() tenant_id = _uuid() initial_input = {'network': {'name': 'net1', 'tenant_id': tenant_id, 'project_id': tenant_id, 'v2attrs:something_else': "abc"}} data = {'network': {'admin_state_up': True, 'shared': False}} data['network'].update(initial_input['network']) return_value = {'subnets': [], 'status': "ACTIVE", 'id': net_id, 'v2attrs:something': "123"} return_value.update(data['network'].copy()) instance = self.plugin.return_value instance.create_network.return_value = return_value instance.get_networks_count.return_value = 0 res = self.api.post_json(_get_path('networks'), initial_input) instance.create_network.assert_called_with(mock.ANY, network=data) self.assertEqual(exc.HTTPCreated.code, res.status_int) self.assertIn('network', res.json) net = res.json['network'] self.assertEqual(net_id, net['id']) self.assertEqual("ACTIVE", net['status']) self.assertEqual("123", net['v2attrs:something']) self.assertNotIn('v2attrs:something_else', net) class TestSubresourcePlugin(object): def get_network_dummies(self, context, network_id, filters=None, fields=None): return [] def get_network_dummy(self, context, id, network_id, fields=None): return {} def create_network_dummy(self, context, network_id, dummy): return {} def update_network_dummy(self, context, id, network_id, dummy): return {} def delete_network_dummy(self, context, id, network_id): return def mactions(self, context, id, network_id): return class ListArgsTestCase(base.BaseTestCase): def test_list_args(self): path = '/?fields=4&foo=3&fields=2&bar=1' request = webob.Request.blank(path) expect_val = ['2', '4'] actual_val = api_common.list_args(request, 'fields') self.assertEqual(expect_val, sorted(actual_val)) def test_list_args_with_empty(self): path = '/?foo=4&bar=3&baz=2&qux=1' request = webob.Request.blank(path) self.assertEqual([], api_common.list_args(request, 'fields')) class SortingTestCase(base.BaseTestCase): def test_get_sorts(self): path = '/?sort_key=foo&sort_dir=desc&sort_key=bar&sort_dir=asc' request = webob.Request.blank(path) attr_info = {'foo': {'key': 'val'}, 'bar': {'key': 'val'}} expect_val = [('foo', False), ('bar', True)] actual_val = api_common.get_sorts(request, attr_info) self.assertEqual(expect_val, actual_val) def test_get_sorts_with_project_id(self): path = '/?sort_key=project_id&sort_dir=desc' request = webob.Request.blank(path) attr_info = {'tenant_id': {'key': 'val'}} expect_val = [('project_id', False)] actual_val = api_common.get_sorts(request, attr_info) self.assertEqual(expect_val, actual_val) class FiltersTestCase(base.BaseTestCase): def test_all_skip_args(self): path = '/?fields=4&fields=3&fields=2&fields=1' request = webob.Request.blank(path) self.assertEqual({}, api_common.get_filters(request, {}, ["fields"])) @mock.patch('neutron.api.api_common.is_empty_string_filtering_supported', return_value=False) def test_blank_values(self, mock_is_supported): path = '/?foo=&bar=&baz=&qux=' request = webob.Request.blank(path) self.assertEqual({}, api_common.get_filters(request, {})) @mock.patch('neutron.api.api_common.is_empty_string_filtering_supported', return_value=True) def test_blank_values_with_filtering_supported(self, mock_is_supported): path = '/?foo=&bar=&baz=&qux=' request = webob.Request.blank(path) self.assertEqual({'foo': [''], 'bar': [''], 'baz': [''], 'qux': ['']}, api_common.get_filters(request, {})) def test_no_attr_info(self): path = '/?foo=4&bar=3&baz=2&qux=1' request = webob.Request.blank(path) expect_val = {'foo': ['4'], 'bar': ['3'], 'baz': ['2'], 'qux': ['1']} actual_val = api_common.get_filters(request, {}) self.assertEqual(expect_val, actual_val) def test_attr_info_with_project_info_populated(self): path = '/?foo=4&bar=3&baz=2&qux=1' request = webob.Request.blank(path) attr_info = {'tenant_id': {'key': 'val'}} expect_val = {'foo': ['4'], 'bar': ['3'], 'baz': ['2'], 'qux': ['1']} actual_val = api_common.get_filters(request, attr_info) self.assertEqual(expect_val, actual_val) expect_attr_info = {'tenant_id': {'key': 'val'}, 'project_id': {'key': 'val'}} self.assertEqual(expect_attr_info, attr_info) def test_attr_info_without_conversion(self): path = '/?foo=4&bar=3&baz=2&qux=1' request = webob.Request.blank(path) attr_info = {'foo': {'key': 'val'}} expect_val = {'foo': ['4'], 'bar': ['3'], 'baz': ['2'], 'qux': ['1']} actual_val = api_common.get_filters(request, attr_info) self.assertEqual(expect_val, actual_val) def test_attr_info_with_convert_list_to(self): path = '/?foo=key=4&bar=3&foo=key=2&qux=1' request = webob.Request.blank(path) attr_info = { 'foo': { 'convert_list_to': converters.convert_kvp_list_to_dict, } } expect_val = {'foo': {'key': ['2', '4']}, 'bar': ['3'], 'qux': ['1']} actual_val = api_common.get_filters(request, attr_info) self.assertOrderedEqual(expect_val, actual_val) def test_attr_info_with_convert_to(self): path = '/?foo=4&bar=3&baz=2&qux=1' request = webob.Request.blank(path) attr_info = {'foo': {'convert_to': converters.convert_to_int}} expect_val = {'foo': [4], 'bar': ['3'], 'baz': ['2'], 'qux': ['1']} actual_val = api_common.get_filters(request, attr_info) self.assertEqual(expect_val, actual_val) def test_attr_info_with_base_db_attributes(self): path = '/?__contains__=1&__class__=2' request = webob.Request.blank(path) self.assertEqual({}, api_common.get_filters(request, {})) class CreateResourceTestCase(base.BaseTestCase): def test_resource_creation(self): resource = v2_base.create_resource('fakes', 'fake', None, {}) self.assertIsInstance(resource, webob.dec.wsgify)

true

f719631a516cc8db5629534c7d6fe3350cae0be1

30,892

py

Python

tests/integrate_test/samples/sample_builtin/0_0_5/governance/governance.py

bayeshack2016/icon-service

36cab484d2e41548d7f2f74526f127ee3a4423fc

[ "Apache-2.0" ]

52

2018-08-24T02:28:43.000Z

2021-07-06T04:44:22.000Z

tests/integrate_test/samples/sample_builtin/0_0_5/governance/governance.py

bayeshack2016/icon-service

36cab484d2e41548d7f2f74526f127ee3a4423fc

[ "Apache-2.0" ]

62

2018-09-17T06:59:16.000Z

2021-12-15T06:02:51.000Z

tests/integrate_test/samples/sample_builtin/0_0_5/governance/governance.py

bayeshack2016/icon-service

36cab484d2e41548d7f2f74526f127ee3a4423fc

[ "Apache-2.0" ]

35

2018-09-14T02:42:10.000Z

2022-02-05T10:34:46.000Z

# -*- coding: utf-8 -*- # Copyright 2018 ICON Foundation # # 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. from iconservice import * TAG = 'Governance' DEBUG = False CURRENT = 'current' NEXT = 'next' STATUS = 'status' DEPLOY_TX_HASH = 'deployTxHash' AUDIT_TX_HASH = 'auditTxHash' VALID_STATUS_KEYS = [STATUS, DEPLOY_TX_HASH, AUDIT_TX_HASH] STATUS_PENDING = 'pending' STATUS_ACTIVE = 'active' STATUS_INACTIVE = 'inactive' STATUS_REJECTED = 'rejected' STEP_TYPE_DEFAULT = 'default' STEP_TYPE_CONTRACT_CALL = 'contractCall' STEP_TYPE_CONTRACT_CREATE = 'contractCreate' STEP_TYPE_CONTRACT_UPDATE = 'contractUpdate' STEP_TYPE_CONTRACT_DESTRUCT = 'contractDestruct' STEP_TYPE_CONTRACT_SET = 'contractSet' STEP_TYPE_GET = 'get' STEP_TYPE_SET = 'set' STEP_TYPE_REPLACE = 'replace' STEP_TYPE_DELETE = 'delete' STEP_TYPE_INPUT = 'input' STEP_TYPE_EVENT_LOG = 'eventLog' STEP_TYPE_API_CALL = 'apiCall' INITIAL_STEP_COST_KEYS = [STEP_TYPE_DEFAULT, STEP_TYPE_CONTRACT_CALL, STEP_TYPE_CONTRACT_CREATE, STEP_TYPE_CONTRACT_UPDATE, STEP_TYPE_CONTRACT_DESTRUCT, STEP_TYPE_CONTRACT_SET, STEP_TYPE_GET, STEP_TYPE_SET, STEP_TYPE_REPLACE, STEP_TYPE_DELETE, STEP_TYPE_INPUT, STEP_TYPE_EVENT_LOG, STEP_TYPE_API_CALL] CONTEXT_TYPE_INVOKE = 'invoke' CONTEXT_TYPE_QUERY = 'query' class StepCosts: """ DB for stepCosts management. It is combined DictDB and ArrayDB in order to iterate items. """ _STEP_TYPES = 'step_types' _STEP_COSTS = 'step_costs' def __init__(self, db: IconScoreDatabase): self._step_types = ArrayDB(self._STEP_TYPES, db, value_type=str) self._step_costs = DictDB(self._STEP_COSTS, db, value_type=int) def __setitem__(self, step_type: str, cost: int): if step_type not in self._step_costs: self._step_types.put(step_type) self._step_costs[step_type] = cost def __getitem__(self, step_type: str): return self._step_costs[step_type] def __delitem__(self, step_type: str): # delete does not actually do delete but set zero if step_type in self._step_costs: self._step_costs[step_type] = 0 def __contains__(self, step_type: str): return step_type in self._step_costs def __iter__(self): return self._step_types.__iter__() def __len__(self): return self._step_types.__len__() def items(self): for step_type in self._step_types: yield (step_type, self._step_costs[step_type]) class Governance(IconSystemScoreBase): _SCORE_STATUS = 'score_status' # legacy _AUDITOR_LIST = 'auditor_list' _DEPLOYER_LIST = 'deployer_list' _SCORE_BLACK_LIST = 'score_black_list' _STEP_PRICE = 'step_price' _MAX_STEP_LIMITS = 'max_step_limits' _VERSION = 'version' _IMPORT_WHITE_LIST = 'import_white_list' _IMPORT_WHITE_LIST_KEYS = 'import_white_list_keys' _SERVICE_CONFIG = 'service_config' _AUDIT_STATUS = 'audit_status' _REJECT_STATUS = 'reject_status' _REVISION_CODE = 'revision_code' _REVISION_NAME = 'revision_name' @eventlog(indexed=1) def Accepted(self, txHash: str): pass @eventlog(indexed=1) def Rejected(self, txHash: str, reason: str): pass @eventlog(indexed=1) def StepPriceChanged(self, stepPrice: int): pass @eventlog(indexed=1) def StepCostChanged(self, stepType: str, cost: int): pass @eventlog(indexed=1) def MaxStepLimitChanged(self, contextType: str, value: int): pass @eventlog(indexed=0) def AddImportWhiteListLog(self, addList: str, addCount: int): pass @eventlog(indexed=0) def RemoveImportWhiteListLog(self, removeList: str, removeCount: int): pass @eventlog(indexed=0) def UpdateServiceConfigLog(self, serviceFlag: int): pass @property def import_white_list_cache(self) -> dict: return self._get_import_white_list() @property def service_config(self) -> int: return self._service_config.get() @property def revision_code(self) -> int: return self._revision_code.get() def __init__(self, db: IconScoreDatabase) -> None: super().__init__(db) # self._score_status = DictDB(self._SCORE_STATUS, db, value_type=bytes, depth=3) self._auditor_list = ArrayDB(self._AUDITOR_LIST, db, value_type=Address) self._deployer_list = ArrayDB(self._DEPLOYER_LIST, db, value_type=Address) self._score_black_list = ArrayDB(self._SCORE_BLACK_LIST, db, value_type=Address) self._step_price = VarDB(self._STEP_PRICE, db, value_type=int) self._step_costs = StepCosts(db) self._max_step_limits = DictDB(self._MAX_STEP_LIMITS, db, value_type=int) self._version = VarDB(self._VERSION, db, value_type=str) self._import_white_list = DictDB(self._IMPORT_WHITE_LIST, db, value_type=str) self._import_white_list_keys = ArrayDB(self._IMPORT_WHITE_LIST_KEYS, db, value_type=str) self._service_config = VarDB(self._SERVICE_CONFIG, db, value_type=int) self._audit_status = DictDB(self._AUDIT_STATUS, db, value_type=bytes) self._reject_status = DictDB(self._REJECT_STATUS, db, value_type=bytes) self._revision_code = VarDB(self._REVISION_CODE, db, value_type=int) self._revision_name = VarDB(self._REVISION_NAME, db, value_type=str) def on_install(self, stepPrice: int = 10 ** 10) -> None: super().on_install() # add owner into initial auditor list Logger.debug(f'on_install: owner = "{self.owner}"', TAG) self._auditor_list.put(self.owner) # add owner into initial deployer list self._deployer_list.put(self.owner) # set initial step price self._step_price.set(stepPrice) # set initial step costs self._set_initial_step_costs() # set initial max step limits self._set_initial_max_step_limits() # set initial import white list self._set_initial_import_white_list() # set initial service config self._set_initial_service_config() def on_update(self) -> None: super().on_update() if self.is_less_than_target_version('0.0.2'): self._migrate_v0_0_2() if self.is_less_than_target_version('0.0.3'): self._migrate_v0_0_3() if self.is_less_than_target_version('0.0.4'): self._migrate_v0_0_4() if self.is_less_than_target_version('0.0.5'): self._migrate_v0_0_5() self._version.set('0.0.5') def is_less_than_target_version(self, target_version: str) -> bool: last_version = self._version.get() return self._versions(last_version) < self._versions(target_version) def _migrate_v0_0_2(self): """ This migration updates the step costs and max step limits """ if len(self._step_costs) == 0: # migrates from old DB of step_costs. for step_type in INITIAL_STEP_COST_KEYS: if step_type in self._step_costs: self._step_costs._step_types.put(step_type) self._set_initial_step_costs() self._set_initial_max_step_limits() def _migrate_v0_0_3(self): # set initial import white list self._set_initial_import_white_list() self._set_initial_service_config() self._set_initial_max_step_limits() self._set_initial_revision() def _migrate_v0_0_4(self): pass def _migrate_v0_0_5(self): self._set_initial_revision() @staticmethod def _versions(version: str): parts = [] if version is not None: for part in version.split("."): try: parts.append(int(part)) except ValueError: pass return tuple(parts) @external(readonly=True) def getScoreStatus(self, address: Address) -> dict: # Governance if self.is_builtin_score(address): deploy_info = self.get_deploy_info(address) result = { CURRENT: { STATUS: STATUS_ACTIVE } } if deploy_info.current_tx_hash is not None: result[CURRENT][DEPLOY_TX_HASH] = deploy_info.current_tx_hash return result deploy_info = self.get_deploy_info(address) if deploy_info is None: self.revert('SCORE not found') current_tx_hash = deploy_info.current_tx_hash next_tx_hash = deploy_info.next_tx_hash active = self.is_score_active(address) # install audit if current_tx_hash is None and next_tx_hash and active is False: reject_tx_hash = self._reject_status[next_tx_hash] if reject_tx_hash: result = { NEXT: { STATUS: STATUS_REJECTED, DEPLOY_TX_HASH: next_tx_hash, AUDIT_TX_HASH: reject_tx_hash }} else: result = { NEXT: { STATUS: STATUS_PENDING, DEPLOY_TX_HASH: next_tx_hash }} elif current_tx_hash and next_tx_hash is None and active is True: audit_tx_hash = self._audit_status[current_tx_hash] result = { CURRENT: { STATUS: STATUS_ACTIVE, DEPLOY_TX_HASH: current_tx_hash }} if audit_tx_hash: result[CURRENT][AUDIT_TX_HASH] = audit_tx_hash else: # update audit if current_tx_hash and next_tx_hash and active is True: current_audit_tx_hash = self._audit_status[current_tx_hash] next_reject_tx_hash = self._reject_status[next_tx_hash] if next_reject_tx_hash: result = { CURRENT: { STATUS: STATUS_ACTIVE, DEPLOY_TX_HASH: current_tx_hash, AUDIT_TX_HASH: current_audit_tx_hash }, NEXT: { STATUS: STATUS_REJECTED, DEPLOY_TX_HASH: next_tx_hash, AUDIT_TX_HASH: next_reject_tx_hash }} else: result = { CURRENT: { STATUS: STATUS_ACTIVE, DEPLOY_TX_HASH: current_tx_hash, AUDIT_TX_HASH: current_audit_tx_hash }, NEXT: { STATUS: STATUS_PENDING, DEPLOY_TX_HASH: next_tx_hash }} else: result = {} return result @external(readonly=True) def getStepPrice(self) -> int: return self._step_price.get() @external def setStepPrice(self, stepPrice: int): # only owner can set new step price if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if stepPrice > 0: self._step_price.set(stepPrice) self.StepPriceChanged(stepPrice) @external def acceptScore(self, txHash: bytes): # check message sender Logger.debug(f'acceptScore: msg.sender = "{self.msg.sender}"', TAG) if self.msg.sender not in self._auditor_list: self.revert('Invalid sender: no permission') # check txHash tx_params = self.get_deploy_tx_params(txHash) if tx_params is None: self.revert('Invalid txHash: None') deploy_score_addr = tx_params.score_address deploy_info = self.get_deploy_info(deploy_score_addr) if txHash != deploy_info.next_tx_hash: self.revert('Invalid txHash: mismatch') next_audit_tx_hash = self._audit_status[txHash] if next_audit_tx_hash: self.revert('Invalid txHash: already accepted') next_reject_tx_hash = self._reject_status[txHash] if next_reject_tx_hash: self.revert('Invalid txHash: already rejected') self._deploy(txHash, deploy_score_addr) Logger.debug(f'acceptScore: score_address = "{tx_params.score_address}"', TAG) self._audit_status[txHash] = self.tx.hash self.Accepted('0x' + txHash.hex()) def _deploy(self, tx_hash: bytes, score_addr: Address): owner = self.get_owner(score_addr) tmp_sender = self.msg.sender self.msg.sender = owner try: self._context.deploy(tx_hash) finally: self.msg.sender = tmp_sender @external def rejectScore(self, txHash: bytes, reason: str): # check message sender Logger.debug(f'rejectScore: msg.sender = "{self.msg.sender}"', TAG) if self.msg.sender not in self._auditor_list: self.revert('Invalid sender: no permission') # check txHash tx_params = self.get_deploy_tx_params(txHash) if tx_params is None: self.revert('Invalid txHash') next_audit_tx_hash = self._audit_status[txHash] if next_audit_tx_hash: self.revert('Invalid txHash: already accepted') next_reject_tx_hash = self._reject_status[txHash] if next_reject_tx_hash: self.revert('Invalid txHash: already rejected') Logger.debug(f'rejectScore: score_address = "{tx_params.score_address}", reason = {reason}', TAG) self._reject_status[txHash] = self.tx.hash self.Rejected('0x' + txHash.hex(), reason) @external def addAuditor(self, address: Address): if address.is_contract: self.revert(f'Invalid EOA Address: {address}') # check message sender, only owner can add new auditor if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if address not in self._auditor_list: self._auditor_list.put(address) else: self.revert(f'Invalid address: already auditor') if DEBUG is True: self._print_auditor_list('addAuditor') @external def removeAuditor(self, address: Address): if address.is_contract: self.revert(f'Invalid EOA Address: {address}') if address not in self._auditor_list: self.revert('Invalid address: not in list') # check message sender if self.msg.sender != self.owner: if self.msg.sender != address: self.revert('Invalid sender: not yourself') # get the topmost value top = self._auditor_list.pop() if top != address: for i in range(len(self._auditor_list)): if self._auditor_list[i] == address: self._auditor_list[i] = top if DEBUG is True: self._print_auditor_list('removeAuditor') def _print_auditor_list(self, header: str): Logger.debug(f'{header}: list len = {len(self._auditor_list)}', TAG) for auditor in self._auditor_list: Logger.debug(f' --- {auditor}', TAG) @external def addDeployer(self, address: Address): if address.is_contract: self.revert(f'Invalid EOA Address: {address}') # check message sender, only owner can add new deployer if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if address not in self._deployer_list: self._deployer_list.put(address) else: self.revert(f'Invalid address: already deployer') if DEBUG is True: self._print_deployer_list('addDeployer') @external def removeDeployer(self, address: Address): if address.is_contract: self.revert(f'Invalid EOA Address: {address}') if address not in self._deployer_list: self.revert('Invalid address: not in list') # check message sender if self.msg.sender != self.owner: if self.msg.sender != address: self.revert('Invalid sender: not yourself') # get the topmost value top = self._deployer_list.pop() if top != address: for i in range(len(self._deployer_list)): if self._deployer_list[i] == address: self._deployer_list[i] = top if DEBUG is True: self._print_deployer_list('removeDeployer') @external(readonly=True) def isDeployer(self, address: Address) -> bool: Logger.debug(f'isDeployer address: {address}', TAG) return address in self._deployer_list def _print_deployer_list(self, header: str): Logger.debug(f'{header}: list len = {len(self._deployer_list)}', TAG) for deployer in self._deployer_list: Logger.debug(f' --- {deployer}', TAG) @external def addToScoreBlackList(self, address: Address): if not address.is_contract: self.revert(f'Invalid SCORE Address: {address}') # check message sender, only owner can add new blacklist if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if self.address == address: self.revert("can't add myself") if address not in self._score_black_list: self._score_black_list.put(address) else: self.revert('Invalid address: already SCORE blacklist') if DEBUG is True: self._print_black_list('addScoreToBlackList') @external def removeFromScoreBlackList(self, address: Address): if not address.is_contract: self.revert(f'Invalid SCORE Address: {address}') # check message sender, only owner can remove from blacklist if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if address not in self._score_black_list: self.revert('Invalid address: not in list') # get the topmost value top = self._score_black_list.pop() if top != address: for i in range(len(self._score_black_list)): if self._score_black_list[i] == address: self._score_black_list[i] = top if DEBUG is True: self._print_black_list('removeScoreFromBlackList') @external(readonly=True) def isInScoreBlackList(self, address: Address) -> bool: Logger.debug(f'isInBlackList address: {address}', TAG) return address in self._score_black_list def _print_black_list(self, header: str): Logger.debug(f'{header}: list len = {len(self._score_black_list)}', TAG) for addr in self._score_black_list: Logger.debug(f' --- {addr}', TAG) def _set_initial_step_costs(self): initial_costs = { STEP_TYPE_DEFAULT: 100_000, STEP_TYPE_CONTRACT_CALL: 25_000, STEP_TYPE_CONTRACT_CREATE: 1_000_000_000, STEP_TYPE_CONTRACT_UPDATE: 1_600_000_000, STEP_TYPE_CONTRACT_DESTRUCT: -70_000, STEP_TYPE_CONTRACT_SET: 30_000, STEP_TYPE_GET: 0, STEP_TYPE_SET: 320, STEP_TYPE_REPLACE: 80, STEP_TYPE_DELETE: -240, STEP_TYPE_INPUT: 200, STEP_TYPE_EVENT_LOG: 100, STEP_TYPE_API_CALL: 0 } for key, value in initial_costs.items(): self._step_costs[key] = value def _set_initial_max_step_limits(self): self._max_step_limits[CONTEXT_TYPE_INVOKE] = 2_500_000_000 self._max_step_limits[CONTEXT_TYPE_QUERY] = 50_000_000 def _set_initial_revision(self): self._revision_code.set(2) self._revision_name.set("1.1.0") @external(readonly=True) def getStepCosts(self) -> dict: result = {} for key, value in self._step_costs.items(): result[key] = value return result @external def setStepCost(self, stepType: str, cost: int): # only owner can set new step cost if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if cost < 0: if stepType != STEP_TYPE_CONTRACT_DESTRUCT and \ stepType != STEP_TYPE_DELETE: self.revert(f'Invalid step cost: {stepType}, {cost}') self._step_costs[stepType] = cost self.StepCostChanged(stepType, cost) @external(readonly=True) def getMaxStepLimit(self, contextType: str) -> int: return self._max_step_limits[contextType] @external def setMaxStepLimit(self, contextType: str, value: int): # only owner can set new context type value if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if value < 0: self.revert('Invalid value: negative number') if contextType == CONTEXT_TYPE_INVOKE or contextType == CONTEXT_TYPE_QUERY: self._max_step_limits[contextType] = value self.MaxStepLimitChanged(contextType, value) else: self.revert("Invalid context type") @external(readonly=True) def getVersion(self) -> str: return self._version.get() def _set_initial_import_white_list(self): key = "iconservice" # if iconsevice has no value set ALL if self._import_white_list[key] == "": self._import_white_list[key] = "*" self._import_white_list_keys.put(key) @external def addImportWhiteList(self, importStmt: str): # only owner can add import white list if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') import_stmt_dict = {} try: import_stmt_dict: dict = self._check_import_stmt(importStmt) except Exception as e: self.revert(f'Invalid import statement: {e}') # add to import white list log_entry = [] for key, value in import_stmt_dict.items(): old_value: str = self._import_white_list[key] if old_value == "*": # no need to add continue if len(value) == 0: # set import white list as ALL self._import_white_list[key] = "*" # add to import white list keys if old_value == "": self._import_white_list_keys.put(key) # make added item list for eventlog log_entry.append((key, value)) elif old_value == "": # set import white list self._import_white_list[key] = ','.join(value) # add to import white list keys self._import_white_list_keys.put(key) # make added item list for eventlog log_entry.append((key, value)) else: old_value_list = old_value.split(',') new_value = [] for v in value: if v not in old_value_list: new_value.append(v) # set import white list self._import_white_list[key] = f'{old_value},{",".join(new_value)}' # make added item list for eventlog log_entry.append((key, new_value)) # make eventlog if len(log_entry): self.AddImportWhiteListLog(str(log_entry), len(log_entry)) if DEBUG is True: Logger.debug(f'checking added item ({importStmt}): {self.isInImportWhiteList(importStmt)}') @external def removeImportWhiteList(self, importStmt: str): # only owner can add import white list if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') import_stmt_dict = {} try: import_stmt_dict: dict = self._check_import_stmt(importStmt) except Exception as e: self.revert(f'Invalid import statement: {e}') # remove from import white list log_entry = [] for key, value in import_stmt_dict.items(): old_value: str = self._import_white_list[key] if old_value == "*": if len(value) == 0: # remove import white list self._remove_import_white_list(key) # make added item list for eventlog log_entry.append((key, value)) continue if len(value) == 0: # remove import white list self._remove_import_white_list(key) # make added item list for eventlog log_entry.append((key, value)) # add to import white list keys self._import_white_list_keys.put(key) else: old_value_list = old_value.split(',') remove_value = [] new_value = [] for v in old_value_list: if v in value: remove_value.append(v) else: new_value.append(v) # set import white list if len(new_value): self._import_white_list[key] = f'{",".join(new_value)}' else: self._remove_import_white_list(key) # make added item list for eventlog log_entry.append((key, remove_value)) if len(log_entry): # make eventlog self.AddImportWhiteListLog(str(log_entry), len(log_entry)) if DEBUG is True: Logger.debug(f'checking removed item ({importStmt}): {self.isInImportWhiteList(importStmt)}') @external(readonly=True) def isInImportWhiteList(self, importStmt: str) -> bool: try: import_stmt_dict: dict = self._check_import_stmt(importStmt) except Exception as e: raise ValueError(f'{e}') cache_import_white_list = self._get_import_white_list() for key, value in import_stmt_dict.items(): old_value: list = cache_import_white_list.get(key, None) if old_value is None: return False if old_value[0] == "*": # import white list has ALL. See next key continue if len(value) == 0: # input is ALL return False for v in value: if v not in old_value: return False if DEBUG is True: Logger.debug(f'({importStmt}) is in import white list') return True @staticmethod def _check_import_stmt(import_stmt: str) -> dict: Logger.debug(f'check_import_stmt: {import_stmt}') import_stmt_dict: dict = json_loads(import_stmt.replace("\'", "\"")) for key, value in import_stmt_dict.items(): if not isinstance(key, str): raise TypeError("Key must be of type `str`") if not isinstance(value, list): raise TypeError("Value must be of type `list`") else: for v in value: if not isinstance(v, str): raise TypeError("Element of value must be of type `str`") Logger.debug(f'check_import_stmt_dict: {import_stmt_dict}') return import_stmt_dict def _get_import_white_list(self) -> dict: whitelist = {} for v in self._import_white_list_keys: values: str = self._import_white_list[v] whitelist[v] = values.split(',') return whitelist def _remove_import_white_list(self, key: str): # remove from import white list self._import_white_list.remove(key) # remove from import white list keys top = self._import_white_list_keys.pop() if top != key: for i in range(len(self._import_white_list_keys)): if self._import_white_list_keys[i] == key: self._import_white_list_keys[i] = top def _set_initial_service_config(self): self._service_config.set(self.get_icon_service_flag() | 8) @external def updateServiceConfig(self, serviceFlag: int): # only owner can add import white list if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if serviceFlag < 0: self.revert(f'updateServiceConfig: serviceFlag({serviceFlag}) < 0') max_flag = 0 for flag in IconServiceFlag: max_flag |= flag if serviceFlag > max_flag: self.revert(f'updateServiceConfig: serviceFlag({serviceFlag}) > max_flag({max_flag})') prev_service_config = self._service_config.get() if prev_service_config != serviceFlag: self._service_config.set(serviceFlag) self.UpdateServiceConfigLog(serviceFlag) if DEBUG is True: Logger.debug(f'updateServiceConfig (prev: {prev_service_config} flag: {serviceFlag})') else: if DEBUG is True: Logger.debug(f'updateServiceConfig not update ({serviceFlag})') @external(readonly=True) def getServiceConfig(self) -> dict: table = {} service_flag = self._service_config.get() for flag in IconServiceFlag: if service_flag & flag == flag: table[flag.name] = True else: table[flag.name] = False return table @external def setRevision(self, code: int, name: str): # only owner can add import white list if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') prev_code = self._revision_code.get() if code < prev_code: self.revert(f"can't decrease code") self._revision_code.set(code) self._revision_name.set(name) @external(readonly=True) def getRevision(self) -> dict: return {'code': self._revision_code.get(), 'name': self._revision_name.get()}

36.515366

109

0.605529

from iconservice import * TAG = 'Governance' DEBUG = False CURRENT = 'current' NEXT = 'next' STATUS = 'status' DEPLOY_TX_HASH = 'deployTxHash' AUDIT_TX_HASH = 'auditTxHash' VALID_STATUS_KEYS = [STATUS, DEPLOY_TX_HASH, AUDIT_TX_HASH] STATUS_PENDING = 'pending' STATUS_ACTIVE = 'active' STATUS_INACTIVE = 'inactive' STATUS_REJECTED = 'rejected' STEP_TYPE_DEFAULT = 'default' STEP_TYPE_CONTRACT_CALL = 'contractCall' STEP_TYPE_CONTRACT_CREATE = 'contractCreate' STEP_TYPE_CONTRACT_UPDATE = 'contractUpdate' STEP_TYPE_CONTRACT_DESTRUCT = 'contractDestruct' STEP_TYPE_CONTRACT_SET = 'contractSet' STEP_TYPE_GET = 'get' STEP_TYPE_SET = 'set' STEP_TYPE_REPLACE = 'replace' STEP_TYPE_DELETE = 'delete' STEP_TYPE_INPUT = 'input' STEP_TYPE_EVENT_LOG = 'eventLog' STEP_TYPE_API_CALL = 'apiCall' INITIAL_STEP_COST_KEYS = [STEP_TYPE_DEFAULT, STEP_TYPE_CONTRACT_CALL, STEP_TYPE_CONTRACT_CREATE, STEP_TYPE_CONTRACT_UPDATE, STEP_TYPE_CONTRACT_DESTRUCT, STEP_TYPE_CONTRACT_SET, STEP_TYPE_GET, STEP_TYPE_SET, STEP_TYPE_REPLACE, STEP_TYPE_DELETE, STEP_TYPE_INPUT, STEP_TYPE_EVENT_LOG, STEP_TYPE_API_CALL] CONTEXT_TYPE_INVOKE = 'invoke' CONTEXT_TYPE_QUERY = 'query' class StepCosts: _STEP_TYPES = 'step_types' _STEP_COSTS = 'step_costs' def __init__(self, db: IconScoreDatabase): self._step_types = ArrayDB(self._STEP_TYPES, db, value_type=str) self._step_costs = DictDB(self._STEP_COSTS, db, value_type=int) def __setitem__(self, step_type: str, cost: int): if step_type not in self._step_costs: self._step_types.put(step_type) self._step_costs[step_type] = cost def __getitem__(self, step_type: str): return self._step_costs[step_type] def __delitem__(self, step_type: str): if step_type in self._step_costs: self._step_costs[step_type] = 0 def __contains__(self, step_type: str): return step_type in self._step_costs def __iter__(self): return self._step_types.__iter__() def __len__(self): return self._step_types.__len__() def items(self): for step_type in self._step_types: yield (step_type, self._step_costs[step_type]) class Governance(IconSystemScoreBase): _SCORE_STATUS = 'score_status' _AUDITOR_LIST = 'auditor_list' _DEPLOYER_LIST = 'deployer_list' _SCORE_BLACK_LIST = 'score_black_list' _STEP_PRICE = 'step_price' _MAX_STEP_LIMITS = 'max_step_limits' _VERSION = 'version' _IMPORT_WHITE_LIST = 'import_white_list' _IMPORT_WHITE_LIST_KEYS = 'import_white_list_keys' _SERVICE_CONFIG = 'service_config' _AUDIT_STATUS = 'audit_status' _REJECT_STATUS = 'reject_status' _REVISION_CODE = 'revision_code' _REVISION_NAME = 'revision_name' @eventlog(indexed=1) def Accepted(self, txHash: str): pass @eventlog(indexed=1) def Rejected(self, txHash: str, reason: str): pass @eventlog(indexed=1) def StepPriceChanged(self, stepPrice: int): pass @eventlog(indexed=1) def StepCostChanged(self, stepType: str, cost: int): pass @eventlog(indexed=1) def MaxStepLimitChanged(self, contextType: str, value: int): pass @eventlog(indexed=0) def AddImportWhiteListLog(self, addList: str, addCount: int): pass @eventlog(indexed=0) def RemoveImportWhiteListLog(self, removeList: str, removeCount: int): pass @eventlog(indexed=0) def UpdateServiceConfigLog(self, serviceFlag: int): pass @property def import_white_list_cache(self) -> dict: return self._get_import_white_list() @property def service_config(self) -> int: return self._service_config.get() @property def revision_code(self) -> int: return self._revision_code.get() def __init__(self, db: IconScoreDatabase) -> None: super().__init__(db) self._auditor_list = ArrayDB(self._AUDITOR_LIST, db, value_type=Address) self._deployer_list = ArrayDB(self._DEPLOYER_LIST, db, value_type=Address) self._score_black_list = ArrayDB(self._SCORE_BLACK_LIST, db, value_type=Address) self._step_price = VarDB(self._STEP_PRICE, db, value_type=int) self._step_costs = StepCosts(db) self._max_step_limits = DictDB(self._MAX_STEP_LIMITS, db, value_type=int) self._version = VarDB(self._VERSION, db, value_type=str) self._import_white_list = DictDB(self._IMPORT_WHITE_LIST, db, value_type=str) self._import_white_list_keys = ArrayDB(self._IMPORT_WHITE_LIST_KEYS, db, value_type=str) self._service_config = VarDB(self._SERVICE_CONFIG, db, value_type=int) self._audit_status = DictDB(self._AUDIT_STATUS, db, value_type=bytes) self._reject_status = DictDB(self._REJECT_STATUS, db, value_type=bytes) self._revision_code = VarDB(self._REVISION_CODE, db, value_type=int) self._revision_name = VarDB(self._REVISION_NAME, db, value_type=str) def on_install(self, stepPrice: int = 10 ** 10) -> None: super().on_install() Logger.debug(f'on_install: owner = "{self.owner}"', TAG) self._auditor_list.put(self.owner) self._deployer_list.put(self.owner) self._step_price.set(stepPrice) self._set_initial_step_costs() self._set_initial_max_step_limits() self._set_initial_import_white_list() self._set_initial_service_config() def on_update(self) -> None: super().on_update() if self.is_less_than_target_version('0.0.2'): self._migrate_v0_0_2() if self.is_less_than_target_version('0.0.3'): self._migrate_v0_0_3() if self.is_less_than_target_version('0.0.4'): self._migrate_v0_0_4() if self.is_less_than_target_version('0.0.5'): self._migrate_v0_0_5() self._version.set('0.0.5') def is_less_than_target_version(self, target_version: str) -> bool: last_version = self._version.get() return self._versions(last_version) < self._versions(target_version) def _migrate_v0_0_2(self): if len(self._step_costs) == 0: for step_type in INITIAL_STEP_COST_KEYS: if step_type in self._step_costs: self._step_costs._step_types.put(step_type) self._set_initial_step_costs() self._set_initial_max_step_limits() def _migrate_v0_0_3(self): self._set_initial_import_white_list() self._set_initial_service_config() self._set_initial_max_step_limits() self._set_initial_revision() def _migrate_v0_0_4(self): pass def _migrate_v0_0_5(self): self._set_initial_revision() @staticmethod def _versions(version: str): parts = [] if version is not None: for part in version.split("."): try: parts.append(int(part)) except ValueError: pass return tuple(parts) @external(readonly=True) def getScoreStatus(self, address: Address) -> dict: if self.is_builtin_score(address): deploy_info = self.get_deploy_info(address) result = { CURRENT: { STATUS: STATUS_ACTIVE } } if deploy_info.current_tx_hash is not None: result[CURRENT][DEPLOY_TX_HASH] = deploy_info.current_tx_hash return result deploy_info = self.get_deploy_info(address) if deploy_info is None: self.revert('SCORE not found') current_tx_hash = deploy_info.current_tx_hash next_tx_hash = deploy_info.next_tx_hash active = self.is_score_active(address) if current_tx_hash is None and next_tx_hash and active is False: reject_tx_hash = self._reject_status[next_tx_hash] if reject_tx_hash: result = { NEXT: { STATUS: STATUS_REJECTED, DEPLOY_TX_HASH: next_tx_hash, AUDIT_TX_HASH: reject_tx_hash }} else: result = { NEXT: { STATUS: STATUS_PENDING, DEPLOY_TX_HASH: next_tx_hash }} elif current_tx_hash and next_tx_hash is None and active is True: audit_tx_hash = self._audit_status[current_tx_hash] result = { CURRENT: { STATUS: STATUS_ACTIVE, DEPLOY_TX_HASH: current_tx_hash }} if audit_tx_hash: result[CURRENT][AUDIT_TX_HASH] = audit_tx_hash else: if current_tx_hash and next_tx_hash and active is True: current_audit_tx_hash = self._audit_status[current_tx_hash] next_reject_tx_hash = self._reject_status[next_tx_hash] if next_reject_tx_hash: result = { CURRENT: { STATUS: STATUS_ACTIVE, DEPLOY_TX_HASH: current_tx_hash, AUDIT_TX_HASH: current_audit_tx_hash }, NEXT: { STATUS: STATUS_REJECTED, DEPLOY_TX_HASH: next_tx_hash, AUDIT_TX_HASH: next_reject_tx_hash }} else: result = { CURRENT: { STATUS: STATUS_ACTIVE, DEPLOY_TX_HASH: current_tx_hash, AUDIT_TX_HASH: current_audit_tx_hash }, NEXT: { STATUS: STATUS_PENDING, DEPLOY_TX_HASH: next_tx_hash }} else: result = {} return result @external(readonly=True) def getStepPrice(self) -> int: return self._step_price.get() @external def setStepPrice(self, stepPrice: int): if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if stepPrice > 0: self._step_price.set(stepPrice) self.StepPriceChanged(stepPrice) @external def acceptScore(self, txHash: bytes): Logger.debug(f'acceptScore: msg.sender = "{self.msg.sender}"', TAG) if self.msg.sender not in self._auditor_list: self.revert('Invalid sender: no permission') tx_params = self.get_deploy_tx_params(txHash) if tx_params is None: self.revert('Invalid txHash: None') deploy_score_addr = tx_params.score_address deploy_info = self.get_deploy_info(deploy_score_addr) if txHash != deploy_info.next_tx_hash: self.revert('Invalid txHash: mismatch') next_audit_tx_hash = self._audit_status[txHash] if next_audit_tx_hash: self.revert('Invalid txHash: already accepted') next_reject_tx_hash = self._reject_status[txHash] if next_reject_tx_hash: self.revert('Invalid txHash: already rejected') self._deploy(txHash, deploy_score_addr) Logger.debug(f'acceptScore: score_address = "{tx_params.score_address}"', TAG) self._audit_status[txHash] = self.tx.hash self.Accepted('0x' + txHash.hex()) def _deploy(self, tx_hash: bytes, score_addr: Address): owner = self.get_owner(score_addr) tmp_sender = self.msg.sender self.msg.sender = owner try: self._context.deploy(tx_hash) finally: self.msg.sender = tmp_sender @external def rejectScore(self, txHash: bytes, reason: str): Logger.debug(f'rejectScore: msg.sender = "{self.msg.sender}"', TAG) if self.msg.sender not in self._auditor_list: self.revert('Invalid sender: no permission') tx_params = self.get_deploy_tx_params(txHash) if tx_params is None: self.revert('Invalid txHash') next_audit_tx_hash = self._audit_status[txHash] if next_audit_tx_hash: self.revert('Invalid txHash: already accepted') next_reject_tx_hash = self._reject_status[txHash] if next_reject_tx_hash: self.revert('Invalid txHash: already rejected') Logger.debug(f'rejectScore: score_address = "{tx_params.score_address}", reason = {reason}', TAG) self._reject_status[txHash] = self.tx.hash self.Rejected('0x' + txHash.hex(), reason) @external def addAuditor(self, address: Address): if address.is_contract: self.revert(f'Invalid EOA Address: {address}') if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if address not in self._auditor_list: self._auditor_list.put(address) else: self.revert(f'Invalid address: already auditor') if DEBUG is True: self._print_auditor_list('addAuditor') @external def removeAuditor(self, address: Address): if address.is_contract: self.revert(f'Invalid EOA Address: {address}') if address not in self._auditor_list: self.revert('Invalid address: not in list') if self.msg.sender != self.owner: if self.msg.sender != address: self.revert('Invalid sender: not yourself') top = self._auditor_list.pop() if top != address: for i in range(len(self._auditor_list)): if self._auditor_list[i] == address: self._auditor_list[i] = top if DEBUG is True: self._print_auditor_list('removeAuditor') def _print_auditor_list(self, header: str): Logger.debug(f'{header}: list len = {len(self._auditor_list)}', TAG) for auditor in self._auditor_list: Logger.debug(f' --- {auditor}', TAG) @external def addDeployer(self, address: Address): if address.is_contract: self.revert(f'Invalid EOA Address: {address}') if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if address not in self._deployer_list: self._deployer_list.put(address) else: self.revert(f'Invalid address: already deployer') if DEBUG is True: self._print_deployer_list('addDeployer') @external def removeDeployer(self, address: Address): if address.is_contract: self.revert(f'Invalid EOA Address: {address}') if address not in self._deployer_list: self.revert('Invalid address: not in list') if self.msg.sender != self.owner: if self.msg.sender != address: self.revert('Invalid sender: not yourself') top = self._deployer_list.pop() if top != address: for i in range(len(self._deployer_list)): if self._deployer_list[i] == address: self._deployer_list[i] = top if DEBUG is True: self._print_deployer_list('removeDeployer') @external(readonly=True) def isDeployer(self, address: Address) -> bool: Logger.debug(f'isDeployer address: {address}', TAG) return address in self._deployer_list def _print_deployer_list(self, header: str): Logger.debug(f'{header}: list len = {len(self._deployer_list)}', TAG) for deployer in self._deployer_list: Logger.debug(f' --- {deployer}', TAG) @external def addToScoreBlackList(self, address: Address): if not address.is_contract: self.revert(f'Invalid SCORE Address: {address}') if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if self.address == address: self.revert("can't add myself") if address not in self._score_black_list: self._score_black_list.put(address) else: self.revert('Invalid address: already SCORE blacklist') if DEBUG is True: self._print_black_list('addScoreToBlackList') @external def removeFromScoreBlackList(self, address: Address): if not address.is_contract: self.revert(f'Invalid SCORE Address: {address}') # check message sender, only owner can remove from blacklist if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if address not in self._score_black_list: self.revert('Invalid address: not in list') # get the topmost value top = self._score_black_list.pop() if top != address: for i in range(len(self._score_black_list)): if self._score_black_list[i] == address: self._score_black_list[i] = top if DEBUG is True: self._print_black_list('removeScoreFromBlackList') @external(readonly=True) def isInScoreBlackList(self, address: Address) -> bool: Logger.debug(f'isInBlackList address: {address}', TAG) return address in self._score_black_list def _print_black_list(self, header: str): Logger.debug(f'{header}: list len = {len(self._score_black_list)}', TAG) for addr in self._score_black_list: Logger.debug(f' --- {addr}', TAG) def _set_initial_step_costs(self): initial_costs = { STEP_TYPE_DEFAULT: 100_000, STEP_TYPE_CONTRACT_CALL: 25_000, STEP_TYPE_CONTRACT_CREATE: 1_000_000_000, STEP_TYPE_CONTRACT_UPDATE: 1_600_000_000, STEP_TYPE_CONTRACT_DESTRUCT: -70_000, STEP_TYPE_CONTRACT_SET: 30_000, STEP_TYPE_GET: 0, STEP_TYPE_SET: 320, STEP_TYPE_REPLACE: 80, STEP_TYPE_DELETE: -240, STEP_TYPE_INPUT: 200, STEP_TYPE_EVENT_LOG: 100, STEP_TYPE_API_CALL: 0 } for key, value in initial_costs.items(): self._step_costs[key] = value def _set_initial_max_step_limits(self): self._max_step_limits[CONTEXT_TYPE_INVOKE] = 2_500_000_000 self._max_step_limits[CONTEXT_TYPE_QUERY] = 50_000_000 def _set_initial_revision(self): self._revision_code.set(2) self._revision_name.set("1.1.0") @external(readonly=True) def getStepCosts(self) -> dict: result = {} for key, value in self._step_costs.items(): result[key] = value return result @external def setStepCost(self, stepType: str, cost: int): # only owner can set new step cost if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if cost < 0: if stepType != STEP_TYPE_CONTRACT_DESTRUCT and \ stepType != STEP_TYPE_DELETE: self.revert(f'Invalid step cost: {stepType}, {cost}') self._step_costs[stepType] = cost self.StepCostChanged(stepType, cost) @external(readonly=True) def getMaxStepLimit(self, contextType: str) -> int: return self._max_step_limits[contextType] @external def setMaxStepLimit(self, contextType: str, value: int): # only owner can set new context type value if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if value < 0: self.revert('Invalid value: negative number') if contextType == CONTEXT_TYPE_INVOKE or contextType == CONTEXT_TYPE_QUERY: self._max_step_limits[contextType] = value self.MaxStepLimitChanged(contextType, value) else: self.revert("Invalid context type") @external(readonly=True) def getVersion(self) -> str: return self._version.get() def _set_initial_import_white_list(self): key = "iconservice" # if iconsevice has no value set ALL if self._import_white_list[key] == "": self._import_white_list[key] = "*" self._import_white_list_keys.put(key) @external def addImportWhiteList(self, importStmt: str): # only owner can add import white list if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') import_stmt_dict = {} try: import_stmt_dict: dict = self._check_import_stmt(importStmt) except Exception as e: self.revert(f'Invalid import statement: {e}') # add to import white list log_entry = [] for key, value in import_stmt_dict.items(): old_value: str = self._import_white_list[key] if old_value == "*": # no need to add continue if len(value) == 0: # set import white list as ALL self._import_white_list[key] = "*" # add to import white list keys if old_value == "": self._import_white_list_keys.put(key) # make added item list for eventlog log_entry.append((key, value)) elif old_value == "": # set import white list self._import_white_list[key] = ','.join(value) # add to import white list keys self._import_white_list_keys.put(key) # make added item list for eventlog log_entry.append((key, value)) else: old_value_list = old_value.split(',') new_value = [] for v in value: if v not in old_value_list: new_value.append(v) # set import white list self._import_white_list[key] = f'{old_value},{",".join(new_value)}' # make added item list for eventlog log_entry.append((key, new_value)) # make eventlog if len(log_entry): self.AddImportWhiteListLog(str(log_entry), len(log_entry)) if DEBUG is True: Logger.debug(f'checking added item ({importStmt}): {self.isInImportWhiteList(importStmt)}') @external def removeImportWhiteList(self, importStmt: str): # only owner can add import white list if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') import_stmt_dict = {} try: import_stmt_dict: dict = self._check_import_stmt(importStmt) except Exception as e: self.revert(f'Invalid import statement: {e}') # remove from import white list log_entry = [] for key, value in import_stmt_dict.items(): old_value: str = self._import_white_list[key] if old_value == "*": if len(value) == 0: # remove import white list self._remove_import_white_list(key) # make added item list for eventlog log_entry.append((key, value)) continue if len(value) == 0: # remove import white list self._remove_import_white_list(key) # make added item list for eventlog log_entry.append((key, value)) # add to import white list keys self._import_white_list_keys.put(key) else: old_value_list = old_value.split(',') remove_value = [] new_value = [] for v in old_value_list: if v in value: remove_value.append(v) else: new_value.append(v) # set import white list if len(new_value): self._import_white_list[key] = f'{",".join(new_value)}' else: self._remove_import_white_list(key) # make added item list for eventlog log_entry.append((key, remove_value)) if len(log_entry): # make eventlog self.AddImportWhiteListLog(str(log_entry), len(log_entry)) if DEBUG is True: Logger.debug(f'checking removed item ({importStmt}): {self.isInImportWhiteList(importStmt)}') @external(readonly=True) def isInImportWhiteList(self, importStmt: str) -> bool: try: import_stmt_dict: dict = self._check_import_stmt(importStmt) except Exception as e: raise ValueError(f'{e}') cache_import_white_list = self._get_import_white_list() for key, value in import_stmt_dict.items(): old_value: list = cache_import_white_list.get(key, None) if old_value is None: return False if old_value[0] == "*": # import white list has ALL. See next key continue if len(value) == 0: # input is ALL return False for v in value: if v not in old_value: return False if DEBUG is True: Logger.debug(f'({importStmt}) is in import white list') return True @staticmethod def _check_import_stmt(import_stmt: str) -> dict: Logger.debug(f'check_import_stmt: {import_stmt}') import_stmt_dict: dict = json_loads(import_stmt.replace("\'", "\"")) for key, value in import_stmt_dict.items(): if not isinstance(key, str): raise TypeError("Key must be of type `str`") if not isinstance(value, list): raise TypeError("Value must be of type `list`") else: for v in value: if not isinstance(v, str): raise TypeError("Element of value must be of type `str`") Logger.debug(f'check_import_stmt_dict: {import_stmt_dict}') return import_stmt_dict def _get_import_white_list(self) -> dict: whitelist = {} for v in self._import_white_list_keys: values: str = self._import_white_list[v] whitelist[v] = values.split(',') return whitelist def _remove_import_white_list(self, key: str): # remove from import white list self._import_white_list.remove(key) # remove from import white list keys top = self._import_white_list_keys.pop() if top != key: for i in range(len(self._import_white_list_keys)): if self._import_white_list_keys[i] == key: self._import_white_list_keys[i] = top def _set_initial_service_config(self): self._service_config.set(self.get_icon_service_flag() | 8) @external def updateServiceConfig(self, serviceFlag: int): # only owner can add import white list if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') if serviceFlag < 0: self.revert(f'updateServiceConfig: serviceFlag({serviceFlag}) < 0') max_flag = 0 for flag in IconServiceFlag: max_flag |= flag if serviceFlag > max_flag: self.revert(f'updateServiceConfig: serviceFlag({serviceFlag}) > max_flag({max_flag})') prev_service_config = self._service_config.get() if prev_service_config != serviceFlag: self._service_config.set(serviceFlag) self.UpdateServiceConfigLog(serviceFlag) if DEBUG is True: Logger.debug(f'updateServiceConfig (prev: {prev_service_config} flag: {serviceFlag})') else: if DEBUG is True: Logger.debug(f'updateServiceConfig not update ({serviceFlag})') @external(readonly=True) def getServiceConfig(self) -> dict: table = {} service_flag = self._service_config.get() for flag in IconServiceFlag: if service_flag & flag == flag: table[flag.name] = True else: table[flag.name] = False return table @external def setRevision(self, code: int, name: str): # only owner can add import white list if self.msg.sender != self.owner: self.revert('Invalid sender: not owner') prev_code = self._revision_code.get() if code < prev_code: self.revert(f"can't decrease code") self._revision_code.set(code) self._revision_name.set(name) @external(readonly=True) def getRevision(self) -> dict: return {'code': self._revision_code.get(), 'name': self._revision_name.get()}

true

f719631ce5568ca0573b1aff26b681add708c145

5,186

py

Python

lib/matplotlib/backends/qt_compat.py

pmarshwx/matplotlib

12be528dbf2114f7c25abf60de8100cb2d4494af

[ "MIT", "BSD-3-Clause" ]

null

lib/matplotlib/backends/qt_compat.py

pmarshwx/matplotlib

12be528dbf2114f7c25abf60de8100cb2d4494af

[ "MIT", "BSD-3-Clause" ]

null

lib/matplotlib/backends/qt_compat.py

pmarshwx/matplotlib

12be528dbf2114f7c25abf60de8100cb2d4494af

[ "MIT", "BSD-3-Clause" ]

null

""" A Qt API selector that can be used to switch between PyQt and PySide. """ from __future__ import (absolute_import, division, print_function, unicode_literals) import six import os from matplotlib import rcParams, verbose # Available APIs. QT_API_PYQT = 'PyQt4' # API is not set here; Python 2.x default is V 1 QT_API_PYQTv2 = 'PyQt4v2' # forced to Version 2 API QT_API_PYSIDE = 'PySide' # only supports Version 2 API QT_API_PYQT5 = 'PyQt5' # use PyQt5 API; Version 2 with module shim ETS = dict(pyqt=(QT_API_PYQTv2, 4), pyside=(QT_API_PYSIDE, 4), pyqt5=(QT_API_PYQT5, 5)) # ETS is a dict of env variable to (QT_API, QT_MAJOR_VERSION) # If the ETS QT_API environment variable is set, use it, but only # if the varible if of the same major QT version. Note that # ETS requires the version 2 of PyQt4, which is not the platform # default for Python 2.x. QT_API_ENV = os.environ.get('QT_API') if rcParams['backend'] == 'Qt5Agg': QT_RC_MAJOR_VERSION = 5 else: QT_RC_MAJOR_VERSION = 4 QT_API = None if (QT_API_ENV is not None): try: QT_ENV_MAJOR_VERSION = ETS[QT_API_ENV][1] except KeyError: raise RuntimeError( ('Unrecognized environment variable %r, valid values are:' ' %r, %r or %r' % (QT_API_ENV, 'pyqt', 'pyside', 'pyqt5'))) if QT_ENV_MAJOR_VERSION == QT_RC_MAJOR_VERSION: # Only if backend and env qt major version are # compatible use the env variable. QT_API = ETS[QT_API_ENV][0] if QT_API is None: # No ETS environment or incompatible so use rcParams. if rcParams['backend'] == 'Qt5Agg': QT_API = rcParams['backend.qt5'] else: QT_API = rcParams['backend.qt4'] # We will define an appropriate wrapper for the differing versions # of file dialog. _getSaveFileName = None # Flag to check if sip could be imported _sip_imported = False # Now perform the imports. if QT_API in (QT_API_PYQT, QT_API_PYQTv2, QT_API_PYQT5): try: import sip _sip_imported = True except ImportError: # Try using PySide QT_API = QT_API_PYSIDE cond = ("Could not import sip; falling back on PySide\n" "in place of PyQt4 or PyQt5.\n") verbose.report(cond, 'helpful') if _sip_imported: if QT_API == QT_API_PYQTv2: if QT_API_ENV == 'pyqt': cond = ("Found 'QT_API=pyqt' environment variable. " "Setting PyQt4 API accordingly.\n") else: cond = "PyQt API v2 specified." try: sip.setapi('QString', 2) except: res = 'QString API v2 specification failed. Defaulting to v1.' verbose.report(cond + res, 'helpful') # condition has now been reported, no need to repeat it: cond = "" try: sip.setapi('QVariant', 2) except: res = 'QVariant API v2 specification failed. Defaulting to v1.' verbose.report(cond + res, 'helpful') if QT_API in [QT_API_PYQT, QT_API_PYQTv2]: # PyQt4 API from PyQt4 import QtCore, QtGui try: if sip.getapi("QString") > 1: # Use new getSaveFileNameAndFilter() _getSaveFileName = QtGui.QFileDialog.getSaveFileNameAndFilter else: # Use old getSaveFileName() def _getSaveFileName(*args, **kwargs): return (QtGui.QFileDialog.getSaveFileName(*args, **kwargs), None) except (AttributeError, KeyError): # call to getapi() can fail in older versions of sip def _getSaveFileName(*args, **kwargs): return QtGui.QFileDialog.getSaveFileName(*args, **kwargs), None else: # PyQt5 API from PyQt5 import QtCore, QtGui, QtWidgets _getSaveFileName = QtWidgets.QFileDialog.getSaveFileName # Alias PyQt-specific functions for PySide compatibility. QtCore.Signal = QtCore.pyqtSignal try: QtCore.Slot = QtCore.pyqtSlot except AttributeError: # Not a perfect match but works in simple cases QtCore.Slot = QtCore.pyqtSignature QtCore.Property = QtCore.pyqtProperty __version__ = QtCore.PYQT_VERSION_STR else: # try importing pyside try: from PySide import QtCore, QtGui, __version__, __version_info__ except ImportError: raise ImportError( "Matplotlib qt-based backends require an external PyQt4, PyQt5,\n" "or PySide package to be installed, but it was not found.") if __version_info__ < (1, 0, 3): raise ImportError( "Matplotlib backend_qt4 and backend_qt4agg require PySide >=1.0.3") _getSaveFileName = QtGui.QFileDialog.getSaveFileName # Apply shim to Qt4 APIs to make them look like Qt5 if QT_API in (QT_API_PYQT, QT_API_PYQTv2, QT_API_PYSIDE): '''Import all used QtGui objects into QtWidgets Here I've opted to simple copy QtGui into QtWidgets as that achieves the same result as copying over the objects, and will continue to work if other objects are used. ''' QtWidgets = QtGui

33.895425

79

0.642885

from __future__ import (absolute_import, division, print_function, unicode_literals) import six import os from matplotlib import rcParams, verbose QT_API_PYQT = 'PyQt4' QT_API_PYQTv2 = 'PyQt4v2' QT_API_PYSIDE = 'PySide' QT_API_PYQT5 = 'PyQt5' ETS = dict(pyqt=(QT_API_PYQTv2, 4), pyside=(QT_API_PYSIDE, 4), pyqt5=(QT_API_PYQT5, 5)) QT_API_ENV = os.environ.get('QT_API') if rcParams['backend'] == 'Qt5Agg': QT_RC_MAJOR_VERSION = 5 else: QT_RC_MAJOR_VERSION = 4 QT_API = None if (QT_API_ENV is not None): try: QT_ENV_MAJOR_VERSION = ETS[QT_API_ENV][1] except KeyError: raise RuntimeError( ('Unrecognized environment variable %r, valid values are:' ' %r, %r or %r' % (QT_API_ENV, 'pyqt', 'pyside', 'pyqt5'))) if QT_ENV_MAJOR_VERSION == QT_RC_MAJOR_VERSION: QT_API = ETS[QT_API_ENV][0] if QT_API is None: if rcParams['backend'] == 'Qt5Agg': QT_API = rcParams['backend.qt5'] else: QT_API = rcParams['backend.qt4'] _getSaveFileName = None _sip_imported = False if QT_API in (QT_API_PYQT, QT_API_PYQTv2, QT_API_PYQT5): try: import sip _sip_imported = True except ImportError: QT_API = QT_API_PYSIDE cond = ("Could not import sip; falling back on PySide\n" "in place of PyQt4 or PyQt5.\n") verbose.report(cond, 'helpful') if _sip_imported: if QT_API == QT_API_PYQTv2: if QT_API_ENV == 'pyqt': cond = ("Found 'QT_API=pyqt' environment variable. " "Setting PyQt4 API accordingly.\n") else: cond = "PyQt API v2 specified." try: sip.setapi('QString', 2) except: res = 'QString API v2 specification failed. Defaulting to v1.' verbose.report(cond + res, 'helpful') cond = "" try: sip.setapi('QVariant', 2) except: res = 'QVariant API v2 specification failed. Defaulting to v1.' verbose.report(cond + res, 'helpful') if QT_API in [QT_API_PYQT, QT_API_PYQTv2]: from PyQt4 import QtCore, QtGui try: if sip.getapi("QString") > 1: _getSaveFileName = QtGui.QFileDialog.getSaveFileNameAndFilter else: def _getSaveFileName(*args, **kwargs): return (QtGui.QFileDialog.getSaveFileName(*args, **kwargs), None) except (AttributeError, KeyError): def _getSaveFileName(*args, **kwargs): return QtGui.QFileDialog.getSaveFileName(*args, **kwargs), None else: from PyQt5 import QtCore, QtGui, QtWidgets _getSaveFileName = QtWidgets.QFileDialog.getSaveFileName QtCore.Signal = QtCore.pyqtSignal try: QtCore.Slot = QtCore.pyqtSlot except AttributeError: QtCore.Slot = QtCore.pyqtSignature QtCore.Property = QtCore.pyqtProperty __version__ = QtCore.PYQT_VERSION_STR else: try: from PySide import QtCore, QtGui, __version__, __version_info__ except ImportError: raise ImportError( "Matplotlib qt-based backends require an external PyQt4, PyQt5,\n" "or PySide package to be installed, but it was not found.") if __version_info__ < (1, 0, 3): raise ImportError( "Matplotlib backend_qt4 and backend_qt4agg require PySide >=1.0.3") _getSaveFileName = QtGui.QFileDialog.getSaveFileName if QT_API in (QT_API_PYQT, QT_API_PYQTv2, QT_API_PYSIDE): QtWidgets = QtGui

true

f71963b2a8fde56239fbbd548e6a4f71526ae07c

70,771

py

Python

controllers/req.py

waidyanatha/deprecated.sambro-eden

62e180703a2f16d5f8fcd532335d8287b76a8175

[ "MIT" ]

1

2016-12-22T09:31:22.000Z

controllers/req.py

waidyanatha/deprecated.sambro-eden

62e180703a2f16d5f8fcd532335d8287b76a8175

[ "MIT" ]

null

controllers/req.py

waidyanatha/deprecated.sambro-eden

62e180703a2f16d5f8fcd532335d8287b76a8175

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Request Management """ module = request.controller resourcename = request.function if not settings.has_module(module): raise HTTP(404, body="Module disabled: %s" % module) # ----------------------------------------------------------------------------- def index(): """ Module's Home Page """ return s3db.cms_index(module, alt_function="index_alt") # ----------------------------------------------------------------------------- def index_alt(): """ Module homepage for non-Admin users when no CMS content found """ # Just redirect to the list of Requests redirect(URL(f="req", args=["search"])) # ----------------------------------------------------------------------------- def is_affiliated(): """ Check if User is affiliated to an Organisation @ToDo: Move this elsewhere """ if not auth.is_logged_in(): return False elif s3_has_role(ADMIN): return True else: table = auth.settings.table_user auth_user = db(table.id == auth.user.id).select(table.organisation_id, limitby=(0, 1) ).first() if auth_user and auth_user.organisation_id: return True else: return False # ============================================================================= def create(): """ Redirect to req/create """ redirect(URL(f="req", args="create")) # ----------------------------------------------------------------------------- def marker_fn(record): """ Function to decide which Marker to use for Requests Map @ToDo: Use Symbology """ # Base Icon based on Type type = record.type if type in (1, 8): # Items marker = "asset" elif type == 3: # People marker = "staff" #elif type == 6: # # Food # marker = "food" else: marker = "request" # Colour code by priority priority = record.priority if priority == 3: # High marker = "%s_red" % marker elif priority == 2: # Medium marker = "%s_yellow" % marker #elif priority == 1: # # Low # marker = "%s_yellow" % marker mtable = db.gis_marker marker = db(mtable.name == marker).select(mtable.image, mtable.height, mtable.width, cache=s3db.cache, limitby=(0, 1)).first() return marker # ----------------------------------------------------------------------------- def req(): """ REST Controller for Request Instances """ s3.filter = (s3db.req_req.is_template == False) output = req_controller() return output # ----------------------------------------------------------------------------- def req_template(): """ REST Controller for Request Templates """ # Hide fields which aren't relevant to templates # @ToDo: Need to get this done later after being opened by Types? table = s3db.req_req field = table.is_template field.default = True field.readable = field.writable = False s3.filter = (field == True) if "req_item" in request.args: # List fields for req_item table = s3db.req_req_item list_fields = ["id", "item_id", "item_pack_id", "quantity", "comments", ] s3db.configure("req_req_item", list_fields=list_fields) elif "req_skill" in request.args: # List fields for req_skill table = s3db.req_req_skill list_fields = ["id", "skill_id", "quantity", "comments", ] s3db.configure("req_req_skill", list_fields=list_fields) else: # Main Req fields = ["req_ref", "date", "date_required", "date_required_until", "date_recv", "recv_by_id", "cancel", "commit_status", "transit_status", "fulfil_status", ] for fieldname in fields: field = table[fieldname] field.readable = field.writable = False table.purpose.label = T("Details") list_fields = ["id", "site_id" ] if len(settings.get_req_req_type()) > 1: list_fields.append("type") list_fields.append("priority") list_fields.append("purpose") list_fields.append("comments") s3db.configure("req_req", list_fields=list_fields) # CRUD strings ADD_REQUEST = T("Add Request Template") s3.crud_strings["req_req"] = Storage( title_create = ADD_REQUEST, title_display = T("Request Template Details"), title_list = T("Request Templates"), title_update = T("Edit Request Template"), subtitle_create = ADD_REQUEST, label_list_button = T("List Request Templates"), label_create_button = ADD_REQUEST, label_delete_button = T("Delete Request Template"), msg_record_created = T("Request Template Added"), msg_record_modified = T("Request Template Updated"), msg_record_deleted = T("Request Template Deleted"), msg_list_empty = T("No Request Templates")) output = req_controller() return output # ----------------------------------------------------------------------------- def req_controller(): """ REST Controller """ def prep(r): table = r.table s3.req_prep(r) #if len(settings.get_req_req_type()) == 1: # # Remove type from list_fields # list_fields = s3db.get_config("req_req", "list_fields") # try: # list_fields.remove("type") # except: # # It has already been removed. # # This can happen if the req controller is called # # for a second time, such as when printing reports # pass # s3db.configure("req_req", list_fields=list_fields) type = (r.record and r.record.type) or \ (request.vars.type and int(request.vars.type)) if r.interactive: # Set the req_item site_id (Requested From), called from action buttons on req/req_item_inv_item/x page if "req_item_id" in request.vars and "inv_item_id" in request.vars: iitable = s3db.inv_inv_item inv_item = db(iitable.id == request.vars.inv_item_id).select(iitable.site_id, iitable.item_id, limitby=(0, 1) ).first() site_id = inv_item.site_id item_id = inv_item.item_id # @ToDo: Check Permissions & Avoid DB updates in GETs db(s3db.req_req_item.id == request.vars.req_item_id).update(site_id = site_id) response.confirmation = T("%(item)s requested from %(site)s") % \ {"item": s3db.supply_ItemRepresent()(item_id), "site": s3db.org_SiteRepresent()(site_id) } elif "req.site_id" in r.get_vars: # Called from 'Make new request' button on [siteinstance]/req page table.site_id.default = request.get_vars.get("req.site_id") table.site_id.writable = False if r.http == "POST": del r.get_vars["req.site_id"] table.requester_id.represent = requester_represent # Set Fields and Labels depending on type if type: table.type.default = type # This prevents the type from being edited AFTER it is set table.type.readable = table.type.writable = False crud_strings = settings.get_req_req_crud_strings(type) if crud_strings: s3.crud_strings["req_req"] = crud_strings elif type == 1: s3.crud_strings["req_req"].title_create = T("Make Supplies Request") elif type == 3: s3.crud_strings["req_req"].title_create = T("Make People Request") # Filter the query based on type if s3.filter: s3.filter = s3.filter & \ (table.type == type) else: s3.filter = (table.type == type) # These changes are applied via JS in create forms where type is editable if type == 1: # Item table.date_recv.readable = table.date_recv.writable = True if settings.get_req_items_ask_purpose(): table.purpose.label = T("What the Items will be used for") table.site_id.label = T("Deliver To") table.request_for_id.label = T("Deliver To") table.requester_id.label = T("Site Contact") table.recv_by_id.label = T("Delivered To") elif type == 3: # Person table.date_required_until.readable = table.date_required_until.writable = True table.purpose.label = T("Task Details") table.purpose.comment = DIV(_class="tooltip", _title="%s|%s" % (T("Task Details"), T("Include any special requirements such as equipment which they need to bring."))) table.site_id.label = T("Report To") table.requester_id.label = T("Volunteer Contact") table.request_for_id.label = T("Report To") table.recv_by_id.label = T("Reported To") if r.component: if r.component.name == "document": s3.crud.submit_button = T("Add") #table = r.component.table # @ToDo: Fix for Link Table #table.date.default = r.record.date #if r.record.site_id: # stable = db.org_site # query = (stable.id == r.record.site_id) # site = db(query).select(stable.location_id, # stable.organisation_id, # limitby=(0, 1)).first() # if site: # table.location_id.default = site.location_id # table.organisation_id.default = site.organisation_id elif r.component.name == "req_item": ctable = r.component.table ctable.site_id.writable = ctable.site_id.readable = False s3.req_hide_quantities(ctable) elif r.component.name == "req_skill": s3.req_hide_quantities(r.component.table) elif r.component.alias == "job": s3task.configure_tasktable_crud( function="req_add_from_template", args = [r.id], vars = dict(user_id = auth.user is not None and auth.user.id or 0), period = 86400, # seconds, so 1 day ) db.scheduler_task.timeout.writable = False else: if r.id: table.is_template.readable = table.is_template.writable = False method = r.method if method not in ("map", "read", "search", "update"): # Hide fields which don't make sense in a Create form # - includes one embedded in list_create # - list_fields over-rides, so still visible within list itself s3.req_create_form_mods() if type and settings.get_req_inline_forms(): # Inline Forms s3.req_inline_form(type, method) # Get the default Facility for this user #if settings.has_module("hrm"): # hrtable = s3db.hrm_human_resource # query = (hrtable.person_id == s3_logged_in_person()) # site = db(query).select(hrtable.site_id, # limitby=(0, 1)).first() # if site: # r.table.site_id.default = site.site_id # Use site_id in User Profile if auth.is_logged_in(): if not table.site_id.default: table.site_id.default = auth.user.site_id elif method == "map": # Tell the client to request per-feature markers s3db.configure("req_req", marker_fn=marker_fn) elif method == "update": if settings.get_req_inline_forms(): # Inline Forms s3.req_inline_form(type, method) s3.scripts.append("/%s/static/scripts/S3/s3.req_update.js" % appname) # Prevent Items from being added to closed or cancelled requests if r.record and (r.record.closed or r.record.cancel): s3db.configure("req_req_item", insertable = False) elif r.representation == "plain": # Map Popups pass elif r.representation == "geojson": # Load these models now as they'll be needed when we encode mtable = s3db.gis_marker s3db.configure("req_req", marker_fn=marker_fn) if r.component and r.component.name == "commit": table = r.component.table record = r.record stable = s3db.org_site commit_status = record.commit_status # Commits belonging to this request rsites = [] query = (table.deleted == False)&(table.req_id == record.id) req_sites = db(query).select(table.site_id) for req_site in req_sites: rsites += [req_site.site_id] # All the sites commit_sites = db((stable.deleted == False)).select(stable.id, stable.code) # Sites which have not committed to this request yet site_opts = {} for site in commit_sites: if (site.id not in site_opts) and (site.id not in rsites): site_opts[site.id] = site.code table.site_id.requires = IS_IN_SET(site_opts) if (commit_status == 2) and settings.get_req_restrict_on_complete(): # Restrict from committing to completed requests s3db.configure(table, listadd=False) else: # Allow commitments to be added when doing so as a component s3db.configure(table, listadd = True) if type == 1: # Items # Limit site_id to facilities the user has permissions for auth.permitted_facilities(table=r.table, error_msg=T("You do not have permission for any facility to make a commitment.")) if r.interactive: # Dropdown not Autocomplete itable = s3db.req_commit_item itable.req_item_id.widget = None req_id = r.id s3db.req_commit_item.req_item_id.requires = \ IS_ONE_OF(db, "req_req_item.id", s3db.req_item_represent, orderby = "req_req_item.id", filterby = "req_id", filter_opts = [req_id], sort=True ) s3.jquery_ready.append(''' S3OptionsFilter({ 'triggerName':'req_item_id', 'targetName':'item_pack_id', 'lookupPrefix':'req', 'lookupResource':'req_item_packs', 'lookupKey':'req_item_id', 'lookupField':'id', 'msgNoRecords':i18n.no_packs, 'fncPrep':S3.supply.fncPrepItem, 'fncRepresent':S3.supply.fncRepresentItem })''') # Custom Form s3forms = s3base.s3forms crud_form = s3forms.S3SQLCustomForm( "site_id", "date", "date_available", "committer_id", s3forms.S3SQLInlineComponent( "commit_item", label = T("Items"), fields = ["req_item_id", "item_pack_id", "quantity", "comments" ] ), "comments", ) s3db.configure("req_commit", crud_form=crud_form) # Redirect to the Items tab after creation #s3db.configure(table, # create_next = URL(c="req", f="commit", # args=["[id]", "commit_item"]), # update_next = URL(c="req", f="commit", # args=["[id]", "commit_item"])) elif type == 3: # People # Limit site_id to orgs the user has permissions for # @ToDo: Make this customisable between Site/Org # @ToDo: is_affiliated() auth.permitted_facilities(table=r.table, error_msg=T("You do not have permission for any facility to make a commitment.")) # Limit organisation_id to organisations the user has permissions for #auth.permitted_organisations(table=r.table, redirect_on_error=False) if r.interactive: #table.organisation_id.readable = True #table.organisation_id.writable = True # Custom Form s3forms = s3base.s3forms crud_form = s3forms.S3SQLCustomForm( "site_id", "date", "date_available", "committer_id", s3forms.S3SQLInlineComponent( "commit_skill", label = T("Skills"), fields = ["quantity", "skill_id", "comments" ] ), "comments", ) s3db.configure("req_commit", crud_form=crud_form) # Redirect to the Skills tab after creation #s3db.configure(table, # create_next = URL(c="req", f="commit", # args=["[id]", "commit_skill"]), # update_next = URL(c="req", f="commit", # args=["[id]", "commit_skill"])) else: # Non-Item commits can have an Organisation # Check if user is affiliated to an Organisation if is_affiliated(): # Limit organisation_id to organisations the user has permissions for auth.permitted_organisations(table=r.table, redirect_on_error=False) table.organisation_id.readable = table.organisation_id.writable = True else: # Unaffiliated people can't commit on behalf of others field = r.component.table.committer_id field.writable = False field.comment = None # Non-Item commits shouldn't have a From Inventory # @ToDo: Assets do? (Well, a 'From Site') table.site_id.readable = table.site_id.writable = False #if r.interactive and r.record.type == 3: # People # # Redirect to the Persons tab after creation # s3db.configure(table, # create_next = URL(c="req", f="commit", # args=["[id]", "commit_person"]), # update_next = URL(c="req", f="commit", # args=["[id]", "commit_person"]) # ) else: # Limit site_id to facilities the user has permissions for # @ToDo: Non-Item requests shouldn't be bound to a Facility? auth.permitted_facilities(table=r.table, error_msg=T("You do not have permission for any facility to make a request.")) return True s3.prep = prep # Post-process def postp(r, output): if r.interactive and r.method != "import": if not r.component: s3_action_buttons(r) #s3_action_buttons(r, copyable=True) # if "buttons" in output: # buttons = output["buttons"] # if "delete_btn" in buttons: # delete_btn = buttons["delete_btn"] # delete_btn = DIV(delete_btn, # A(T("Copy Request"), # _href=URL(args=[r.id, "copy"], ##vars={"type":r.record.type} # ), # _class="action-btn")) # output["buttons"]["delete_btn"] = delete_btn if settings.get_req_use_commit(): # This is appropriate to all s3.actions.append( dict(url = URL(c="req", f="req", args=["[id]", "commit_all"]), _class = "action-btn commit-btn", label = str(T("Commit")) ) ) s3.jquery_ready.append( '''S3ConfirmClick('.commit-btn','%s')''' % T("Do you want to commit to this request?")) # This is only appropriate for item requests #query = (r.table.type == 1) #rows = db(query).select(r.table.id) #restrict = [str(row.id) for row in rows] #s3.actions.append( # dict(url = URL(c="req", f="req", # args=["[id]", "req_item"]), # _class = "action-btn", # label = str(T("View Items")), # restrict = restrict # ) # ) # This is only appropriate for people requests #query = (r.table.type == 3) #rows = db(query).select(r.table.id) #restrict = [str(row.id) for row in rows] #s3.actions.append( # dict(url = URL(c="req", f="req", # args=["[id]", "req_skill"]), # _class = "action-btn", # label = str(T("View Skills")), # restrict = restrict # ) # ) s3.actions.append( dict(url = URL(c="req", f="req", args=["[id]", "commit_all", "send"]), _class = "action-btn send-btn", label = str(T("Send")) ) ) s3.jquery_ready.append( '''S3ConfirmClick('.send-btn','%s')''' % T("Are you sure you want to commit to this request and send a shipment?")) else: s3_action_buttons(r) if r.component.name == "req_item" and settings.get_req_prompt_match(): req_item_inv_item_btn = dict(url = URL(c = "req", f = "req_item_inv_item", args = ["[id]"] ), _class = "action-btn", label = str(T("Request from Facility")), ) s3.actions.append(req_item_inv_item_btn) if r.component.name == "commit": if "form" in output: id = r.record.id ctable = s3db.req_commit query = (ctable.deleted == False) & \ (ctable.req_id == id) exists = current.db(query).select(ctable.id, limitby=(0, 1)) if not exists: output["form"] = A(T("Commit All"), _href=URL(args=[id, "commit_all"]), _class="action-btn", _id="commit-btn") s3.jquery_ready.append(''' S3ConfirmClick('#commit-btn','%s')''' % T("Do you want to commit to this request?")) else: s3.actions.append( dict(url = URL(c="req", f="send_commit", args = ["[id]"]), _class = "action-btn send-btn", label = str(T("Prepare Shipment")) ) ) s3.jquery_ready.append( '''S3ConfirmClick('.send-btn','%s')''' % T("Are you sure you want to send this shipment?")) if r.component.alias == "job": s3.actions = [ dict(label=str(T("Open")), _class="action-btn", url=URL(c="req", f="req_template", args=[str(r.id), "job", "[id]"])), dict(label=str(T("Reset")), _class="action-btn", url=URL(c="req", f="req_template", args=[str(r.id), "job", "[id]", "reset"])), dict(label=str(T("Run Now")), _class="action-btn", url=URL(c="req", f="req_template", args=[str(r.id), "job", "[id]", "run"])), ] return output s3.postp = postp output = s3_rest_controller("req", "req", rheader=s3db.req_rheader) return output # ============================================================================= def requester_represent(id, show_link=True): """ Represent a Requester as Name + Tel# """ if not id: return current.messages["NONE"] htable = s3db.hrm_human_resource ptable = s3db.pr_person ctable = s3db.pr_contact query = (htable.id == id) & \ (htable.person_id == ptable.id) left = ctable.on((ctable.pe_id == ptable.pe_id) & \ (ctable.contact_method == "SMS")) row = db(query).select(htable.type, ptable.first_name, ptable.middle_name, ptable.last_name, ctable.value, left=left, limitby=(0, 1)).first() try: hr = row["hrm_human_resource"] except: return current.messages.UNKNOWN_OPT repr = s3_fullname(row.pr_person) if row.pr_contact.value: repr = "%s %s" % (repr, row.pr_contact.value) if show_link: if hr.type == 1: controller = "hrm" group = "staff" else: controller = "vol" group = "volunteer" request.extension = "html" return A(repr, _href = URL(c = controller, f = "person", args = ["contacts"], vars = {"group": group, "human_resource.id": id} ) ) return repr # ============================================================================= def req_item(): """ REST Controller @ToDo: Filter out fulfilled Items? """ if not s3.filter: # Filter out Template Items ritable = s3db.req_req_item rtable = db.req_req s3.filter = (rtable.is_template == False) & \ (rtable.id == ritable.req_id) # Search method search_method = s3db.get_config("req_req_item", "search_method") if not search_method: S3SearchOptionsWidget = s3base.S3SearchOptionsWidget req_item_search = ( S3SearchOptionsWidget( name="req_search_fulfil_status", label=T("Status"), field="req_id$fulfil_status", options = s3.req_status_opts, cols = 3, ), S3SearchOptionsWidget( name="req_search_priority", label=T("Priority"), field="req_id$priority", options = s3.req_priority_opts, cols = 3, ), #S3SearchOptionsWidget( # name="req_search_L1", # field="req_id$site_id$location_id$L1", # location_level="L1", # cols = 3, #), #S3SearchOptionsWidget( # name="req_search_L2", # field="req_id$site_id$location_id$L2", # location_level="L2", # cols = 3, #), S3SearchOptionsWidget( name="req_search_L3", field="req_id$site_id$location_id$L3", location_level="L3", cols = 3, ), S3SearchOptionsWidget( name="req_search_L4", field="req_id$site_id$location_id$L4", location_level="L4", cols = 3, ), ) s3db.configure("req_req_item", search_method = s3base.S3Search(advanced=req_item_search), ) def prep(r): if r.interactive: list_fields = s3db.get_config("req_req_item", "list_fields") list_fields.insert(1, "req_id$site_id") list_fields.insert(1, "req_id$site_id$location_id$L4") list_fields.insert(1, "req_id$site_id$location_id$L3") s3db.configure("req_req_item", insertable = False, list_fields = list_fields, ) s3.crud_strings["req_req_item"].title_list = T("Requested Items") if r.method != None and r.method != "update" and r.method != "read": # Hide fields which don't make sense in a Create form # - includes one embedded in list_create # - list_fields over-rides, so still visible within list itself s3db.req_hide_quantities(r.table) return True s3.prep = prep output = s3_rest_controller("req", "req_item") if settings.get_req_prompt_match(): req_item_inv_item_btn = dict(url = URL(c="req", f="req_item_inv_item", args=["[id]"]), _class = "action-btn", label = str(T("Request from Facility")), ) if s3.actions: s3.actions += [req_item_inv_item_btn] else: s3.actions = [req_item_inv_item_btn] return output # ----------------------------------------------------------------------------- def req_item_packs(): """ Called by S3OptionsFilter to provide the pack options for an Item """ req_item_id = None args = request.args if len(args) == 1 and args[0].isdigit(): req_item_id = args[0] else: for v in request.vars: if "." in v and v.split(".", 1)[1] == "req_item_id": req_item_id = request.vars[v] break table = s3db.supply_item_pack ritable = s3db.req_req_item query = (ritable.id == req_item_id) & \ (ritable.item_id == table.item_id) response.headers["Content-Type"] = "application/json" return db(query).select(table.id, table.name, table.quantity).json() # ----------------------------------------------------------------------------- def req_item_inv_item(): """ Shows the inventory items which match a requested item @ToDo: Make this page a component of req_item """ req_item_id = request.args[0] request.args = [] # ritable = s3db.req_req_item req_item = ritable[req_item_id] rtable = s3db.req_req req = rtable[req_item.req_id] output = {} output["title"] = T("Request Stock from Available Warehouse") output["req_btn"] = A(T("Return to Request"), _href = URL(c="req", f="req", args=[req_item.req_id, "req_item"]), _class = "action-btn" ) output["req_item"] = TABLE( TR( TH( "%s: " % T("Requested By") ), rtable.site_id.represent(req.site_id), TH( "%s: " % T("Item")), ritable.item_id.represent(req_item.item_id), ), TR( TH( "%s: " % T("Requester") ), rtable.requester_id.represent(req.requester_id), TH( "%s: " % T("Quantity")), req_item.quantity, ), TR( TH( "%s: " % T("Date Requested") ), rtable.date.represent(req.date), TH( T("Quantity Committed")), req_item.quantity_commit, ), TR( TH( "%s: " % T("Date Required") ), rtable.date_required.represent(req.date_required), TH( "%s: " % T("Quantity in Transit")), req_item.quantity_transit, ), TR( TH( "%s: " % T("Priority") ), rtable.priority.represent(req.priority), TH( "%s: " % T("Quantity Fulfilled")), req_item.quantity_fulfil, ) ) s3.no_sspag = True # pagination won't work with 2 datatables on one page @todo: test itable = s3db.inv_inv_item # Get list of matching inventory items s3.filter = (itable.item_id == req_item.item_id) # Tweak CRUD String for this context s3.crud_strings["inv_inv_item"].msg_list_empty = T("No Inventories currently have this item in stock") inv_items = s3_rest_controller("inv", "inv_item") output["items"] = inv_items["items"] if current.deployment_settings.get_supply_use_alt_name(): # Get list of alternative inventory items atable = s3db.supply_item_alt query = (atable.item_id == req_item.item_id ) & \ (atable.deleted == False ) alt_item_rows = db(query).select(atable.alt_item_id) alt_item_ids = [alt_item_row.alt_item_id for alt_item_row in alt_item_rows] if alt_item_ids: s3.filter = (itable.item_id.belongs(alt_item_ids)) inv_items_alt = s3_rest_controller("inv", "inv_item") output["items_alt"] = inv_items_alt["items"] else: output["items_alt"] = T("No Inventories currently have suitable alternative items in stock") response.view = "req/req_item_inv_item.html" s3.actions = [dict(url = URL(c = request.controller, f = "req", args = [req_item.req_id, "req_item"], vars = dict(req_item_id = req_item_id, inv_item_id = "[id]") ), _class = "action-btn", label = str(T("Request From")), )] return output # ============================================================================= def req_skill(): """ REST Controller @ToDo: Filter out fulfilled Skills? """ # Filter out Template Items table = s3db.req_req_skill rtable = s3db.req_req s3.filter = (rtable.is_template == False) & \ (rtable.id == table.req_id) # Search method S3SearchOptionsWidget = s3base.S3SearchOptionsWidget req_skill_search = ( S3SearchOptionsWidget( name="req_search_fulfil_status", label=T("Status"), field="req_id$fulfil_status", options = s3.req_status_opts, cols = 3, ), S3SearchOptionsWidget( name="req_search_priority", label=T("Priority"), field="req_id$priority", options = s3.req_priority_opts, cols = 3, ), #S3SearchOptionsWidget( # name="req_search_L1", # field="req_id$site_id$location_id$L1", # location_level="L1", # cols = 3, #), #S3SearchOptionsWidget( # name="req_search_L2", # field="req_id$site_id$location_id$L2", # location_level="L2", # cols = 3, #), S3SearchOptionsWidget( name="req_search_L3", field="req_id$site_id$location_id$L3", location_level="L3", cols = 3, ), S3SearchOptionsWidget( name="req_search_L4", field="req_id$site_id$location_id$L4", location_level="L4", cols = 3, ), ) s3db.configure("req_req_skill", search_method = s3base.S3Search(advanced=req_skill_search), ) def prep(r): if r.interactive: list_fields = s3db.get_config("req_req_skill", "list_fields") list_fields.insert(1, "req_id$site_id") list_fields.insert(1, "req_id$site_id$location_id$L4") list_fields.insert(1, "req_id$site_id$location_id$L3") s3db.configure("req_req_skill", insertable=False, list_fields = list_fields, ) if r.method != "update" and r.method != "read": # Hide fields which don't make sense in a Create form # - includes one embedded in list_create # - list_fields over-rides, so still visible within list itself s3db.req_hide_quantities(r.table) return True s3.prep = prep # Post-process def postp(r, output): if r.interactive: s3.actions = [ dict(url = URL(c="req", f="req", args=["req_skill", "[id]"]), _class = "action-btn", label = str(READ) ) ] return output s3.postp = postp output = s3_rest_controller("req", "req_skill") return output # ============================================================================= def summary_option(): """ REST Controller """ return s3_rest_controller() # ============================================================================= def commit(): """ REST Controller """ # Check if user is affiliated to an Organisation if not is_affiliated(): tablename = "req_commit_person" table = s3db[tablename] # Unaffiliated people can't commit on behalf of others table.person_id.writable = False # & can only make single-person commitments # (This should have happened in the main commitment) s3db.configure(tablename, insertable=False) def prep(r): if r.interactive: # Commitments created through UI should be done via components table = r.table if r.record: s3.crud.submit_button = T("Save Changes") if r.record.type == 1: # Items # Limit site_id to facilities the user has permissions for auth.permitted_facilities(table=table, error_msg=T("You do not have permission for any facility to make a commitment.") ) table.site_id.comment = A(T("Set as default Site"), _id="req_commit_site_id_link", _target="_blank", _href=URL(c="default", f="user", args=["profile"])) jappend = s3.jquery_ready.append jappend(''' $('#req_commit_site_id_link').click(function(){ var site_id=$('#req_commit_site_id').val() if(site_id){ var url = $('#req_commit_site_id_link').attr('href') var exists=url.indexOf('?') if(exists=='-1'){ $('#req_commit_site_id_link').attr('href',url+'?site_id='+site_id) } } return true })''') # Dropdown not Autocomplete itable = s3db.req_commit_item itable.req_item_id.widget = None jappend(''' S3OptionsFilter({ 'triggerName':'req_item_id', 'targetName':'item_pack_id', 'lookupPrefix':'req', 'lookupResource':'req_item_packs', 'lookupKey':'req_item_id', 'lookupField':'id', 'msgNoRecords':i18n.no_packs, 'fncPrep':S3.supply.fncPrepItem, 'fncRepresent':S3.supply.fncRepresentItem })''') # Custom Form s3forms = s3base.s3forms crud_form = s3forms.S3SQLCustomForm( "site_id", "date", "date_available", "committer_id", s3forms.S3SQLInlineComponent( "commit_item", label = T("Items"), fields = ["req_item_id", "item_pack_id", "quantity", "comments" ] ), "comments", ) s3db.configure("req_commit", crud_form=crud_form) elif r.record.type == 3: # People # Limit site_id to sites the user has permissions for auth.permitted_facilities(table=r.table, error_msg=T("You do not have permission for any facility to make a commitment.")) table.site_id.comment = A(T("Set as default Site"), _id="req_commit_site_id_link", _target="_blank", _href=URL(c="default", f="user", args=["profile"])) # Limit organisation_id to organisations the user has permissions for #auth.permitted_organisations(table=r.table, redirect_on_error=False) #table.organisation_id.readable = True #table.organisation_id.writable = True # Custom Form s3forms = s3base.s3forms crud_form = s3forms.S3SQLCustomForm( #"organisation_id", "site_id", "date", "date_available", "committer_id", s3forms.S3SQLInlineComponent( "commit_skill", label = T("People"), fields = ["quantity", "skill_id", "comments" ] ), "comments", ) s3db.configure("req_commit", crud_form=crud_form) else: # Commits to Other requests can have an Organisation # Limit organisation_id to organisations the user has permissions for auth.permitted_organisations(table=r.table, redirect_on_error=False) table.organisation_id.readable = True table.organisation_id.writable = True # Non-Item commits shouldn't have a From Inventory # @ToDo: Assets do? table.site_id.readable = False table.site_id.writable = False if r.component: req_id = r.record.req_id if r.component.name == "commit_item": # Limit commit items to items from the request s3db.req_commit_item.req_item_id.requires = \ IS_ONE_OF(db, "req_req_item.id", s3db.req_item_represent, orderby = "req_req_item.id", filterby = "req_id", filter_opts = [req_id], sort=True ) elif r.component.name == "person": pass # Limit commit skills to skills from the request #db.req_commit_skill.req_skill_id.requires = \ # IS_ONE_OF(db, # "req_req_skill.id", # s3db.req_skill_represent, # orderby = "req_req_skill.id", # filterby = "req_id", # filter_opts = [req_id], # sort=True # ) return True s3.prep = prep def postp(r, output): if r.interactive and r.method != "import": if not r.component: table = r.table record = r.record s3_action_buttons(r) s3.actions.append( dict(url = URL(f = "send_commit", args=["[id]"]), _class = "action-btn send-btn", label = str(T("Prepare Shipment")) ) ) s3.jquery_ready.append( '''S3ConfirmClick('.send-btn','%s')''' % T("Are you sure you want to send this shipment?")) return output s3.postp = postp output = s3_rest_controller(rheader=commit_rheader) return output # ----------------------------------------------------------------------------- def commit_rheader(r): """ Resource Header for Commitments """ if r.representation == "html": record = r.record if record and r.name == "commit": s3_date_represent = s3base.S3DateTime.date_represent tabs = [(T("Edit Details"), None)] type = record.type and int(record.type) table = r.table if type == 1: tabs.append((T("Items"), "commit_item")) #req_record = db.req_req[record.req_id] #req_date = req_record.date rheader = DIV(TABLE(TR(TH("%s: " % table.req_id.label), table.req_id.represent(record.req_id), ), TR(TH("%s: " % T("Committing Warehouse")), s3db.org_site_represent(record.site_id), TH("%s: " % T("Commit Date")), s3_date_represent(record.date), ), TR(TH("%s: " % table.comments.label), TD(record.comments or "", _colspan=3) ), ), ) prepare_btn = A(T("Prepare Shipment"), _href = URL(f = "send_commit", args = [record.id] ), _id = "send_commit", _class = "action-btn" ) s3.rfooter = TAG[""](prepare_btn) # send_btn = A( T("Send Commitment as Shipment"), # _href = URL(f = "send_commit", # args = [record.id] # ), # _id = "send_commit", # _class = "action-btn" # ) # # send_btn_confirm = SCRIPT("S3ConfirmClick('#send_commit', '%s')" % # T("Do you want to send these Committed items?") ) # s3.rfooter = TAG[""](send_btn,send_btn_confirm) #rheader.append(send_btn) #rheader.append(send_btn_confirm) elif type == 3: #tabs.append((T("People"), "commit_person")) tabs.append((T("People"), "commit_skill")) #req_record = db.req_req[record.req_id] #req_date = req_record.date organisation_represent = s3db.org_organisation_represent rheader = DIV(TABLE(TR(TH("%s: " % table.req_id.label), table.req_id.represent(record.req_id), ), TR(TH("%s: " % T("Committing Organization")), organisation_represent(record.organisation_id), TH("%s: " % T("Commit Date")), s3_date_represent(record.date), ), TR(TH("%s: " % table.comments.label), TD(record.comments, _colspan=3) ), ), ) else: # Other (& Assets/Shelter) rheader = DIV(TABLE(TR(TH("%s: " % table.req_id.label), table.req_id.represent(record.req_id), ), TR(TH("%s: " % T("Committing Person")), table.committer_id.represent(record.committer_id), TH("%s: " % T("Commit Date")), s3_date_represent(record.date), ), TR(TH("%s: " % table.comments.label), TD(record.comments or "", _colspan=3) ), ), ) rheader_tabs = s3_rheader_tabs(r, tabs) rheader.append(rheader_tabs) return rheader return None # ============================================================================= def send(): """ RESTful CRUD controller """ s3db.configure("inv_send", listadd=False) return s3db.inv_send_controller() # ============================================================================== def send_commit(): """ Send a Shipment containing all items in a Commitment """ return s3db.req_send_commit() # ----------------------------------------------------------------------------- def send_process(): """ Process a Shipment """ return s3db.inv_send_process() # ============================================================================= def commit_item(): """ REST Controller """ return s3_rest_controller() # ============================================================================= def commit_req(): """ Function to commit items for a Request - i.e. copy data from a req into a commitment arg: req_id vars: site_id """ req_id = request.args[0] site_id = request.vars.get("site_id") table = s3db.req_req r_req = db(table.id == req_id).select(table.type, limitby=(0, 1)).first() # User must have permissions over facility which is sending (prefix, resourcename, id) = s3db.get_instance(s3db.org_site, site_id) if not site_id or not auth.s3_has_permission("update", "%s_%s" % (prefix, resourcename), record_id=id): session.error = T("You do not have permission to make this commitment.") redirect(URL(c="req", f="req", args=[req_id])) # Create a new commit record commit_id = s3db.req_commit.insert(date = request.utcnow, req_id = req_id, site_id = site_id, type = r_req.type ) # Only select items which are in the warehouse ritable = s3db.req_req_item iitable = s3db.inv_inv_item query = (ritable.req_id == req_id) & \ (ritable.quantity_fulfil < ritable.quantity) & \ (iitable.site_id == site_id) & \ (ritable.item_id == iitable.item_id) & \ (ritable.deleted == False) & \ (iitable.deleted == False) req_items = db(query).select(ritable.id, ritable.quantity, ritable.item_pack_id, iitable.item_id, iitable.quantity, iitable.item_pack_id) citable = s3db.req_commit_item for req_item in req_items: req_item_quantity = req_item.req_req_item.quantity * \ req_item.req_req_item.pack_quantity inv_item_quantity = req_item.inv_inv_item.quantity * \ req_item.inv_inv_item.pack_quantity if inv_item_quantity > req_item_quantity: commit_item_quantity = req_item_quantity else: commit_item_quantity = inv_item_quantity commit_item_quantity = commit_item_quantity / req_item.req_req_item.pack_quantity if commit_item_quantity: req_item_id = req_item.req_req_item.id commit_item_id = citable.insert(commit_id = commit_id, req_item_id = req_item_id, item_pack_id = req_item.req_req_item.item_pack_id, quantity = commit_item_quantity ) # Update the req_item.commit_quantity & req.commit_status s3mgr.store_session("req", "commit_item", commit_item_id) form = Storage() form.vars = Storage( req_item_id = req_item_id ) s3db.req_commit_item_onaccept(form) # Redirect to commit redirect(URL(c="req", f="commit", args=[commit_id, "commit_item"])) # ============================================================================= def send_req(): """ Function to send items for a Request. - i.e. copy data from a req into a send arg: req_id vars: site_id """ req_id = request.args[0] site_id = request.vars.get("site_id", None) site_name = s3db.org_site_represent(site_id, show_link=False) ritable = s3db.req_req_item iitable = s3db.inv_inv_item sendtable = s3db.inv_send tracktable = s3db.inv_track_item siptable = s3db.supply_item_pack table = s3db.req_req r_req = db(table.id == req_id).select(table.req_ref, table.requester_id, table.site_id, limitby=(0, 1)).first() # User must have permissions over facility which is sending (prefix, resourcename, id) = s3db.get_instance(db.org_site, site_id) if not site_id or not auth.s3_has_permission("update", "%s_%s" % (prefix, resourcename), record_id=id): session.error = T("You do not have permission to send this shipment.") redirect(URL(c="req", f="req", args = [req_id])) # Create a new send record code = s3db.inv_get_shipping_code("WB", site_id, s3db.inv_send.send_ref ) send_id = sendtable.insert(send_ref = code, req_ref = r_req.req_ref, sender_id = auth.s3_logged_in_person(), site_id = site_id, date = request.utcnow, recipient_id = r_req.requester_id, to_site_id = r_req.site_id, status = s3db.inv_ship_status["IN_PROCESS"], ) # Get the items for this request that have not been fulfilled (in transit) sip_id_field = siptable.id sip_quantity_field = siptable.quantity query = (ritable.req_id == req_id) & \ (ritable.quantity_transit < ritable.quantity) & \ (ritable.deleted == False) & \ (ritable.item_pack_id == sip_id_field) req_items = db(query).select(ritable.id, ritable.quantity, ritable.quantity_transit, ritable.quantity_fulfil, ritable.item_id, sip_quantity_field ) # Loop through each request item and find matched in the site inventory IN_PROCESS = s3db.inv_tracking_status["IN_PROCESS"] insert = tracktable.insert inv_remove = s3db.inv_remove ii_item_id_field = iitable.item_id ii_quantity_field = iitable.quantity ii_expiry_field = iitable.expiry_date ii_purchase_field = iitable.purchase_date iifields = [iitable.id, ii_item_id_field, ii_quantity_field, iitable.item_pack_id, iitable.pack_value, iitable.currency, ii_expiry_field, ii_purchase_field, iitable.bin, iitable.owner_org_id, iitable.supply_org_id, sip_quantity_field, ] bquery = (ii_quantity_field > 0) & \ (iitable.site_id == site_id) & \ (iitable.deleted == False) & \ (iitable.item_pack_id == sip_id_field) orderby = ii_expiry_field | ii_purchase_field no_match = True for ritem in req_items: rim = ritem.req_req_item rim_id = rim.id query = bquery & \ (ii_item_id_field == rim.item_id) inv_items = db(query).select(*iifields, orderby=orderby) if len(inv_items) == 0: break; no_match = False one_match = len(inv_items) == 1 # Get the Quantity Needed quantity_shipped = max(rim.quantity_transit, rim.quantity_fulfil) quantity_needed = (rim.quantity - quantity_shipped) * ritem.supply_item_pack.quantity # Insert the track item records # If there is more than one item match then we select the stock with the oldest expiry date first # then the oldest purchase date first # then a complete batch, if-possible iids = [] append = iids.append for item in inv_items: if not quantity_needed: break iitem = item.inv_inv_item if one_match: # Remove this total from the warehouse stock send_item_quantity = inv_remove(iitem, quantity_needed) quantity_needed -= send_item_quantity append(iitem.id) else: quantity_available = iitem.quantity * item.supply_item_pack.quantity if iitem.expiry_date: # We take first from the oldest expiry date send_item_quantity = min(quantity_needed, quantity_available) # Remove this total from the warehouse stock send_item_quantity = inv_remove(iitem, send_item_quantity) quantity_needed -= send_item_quantity append(iitem.id) elif iitem.purchase_date: # We take first from the oldest purchase date for non-expiring stock send_item_quantity = min(quantity_needed, quantity_available) # Remove this total from the warehouse stock send_item_quantity = inv_remove(iitem, send_item_quantity) quantity_needed -= send_item_quantity append(iitem.id) elif quantity_needed <= quantity_available: # Assign a complete batch together if possible # Remove this total from the warehouse stock send_item_quantity = inv_remove(iitem, quantity_needed) quantity_needed = 0 append(iitem.id) else: # Try again on the second loop, if-necessary continue insert(send_id = send_id, send_inv_item_id = iitem.id, item_id = iitem.item_id, req_item_id = rim_id, item_pack_id = iitem.item_pack_id, quantity = send_item_quantity, status = IN_PROCESS, pack_value = iitem.pack_value, currency = iitem.currency, bin = iitem.bin, expiry_date = iitem.expiry_date, owner_org_id = iitem.owner_org_id, supply_org_id = iitem.supply_org_id, #comments = comment, ) # 2nd pass for item in inv_items: if not quantity_needed: break iitem = item.inv_inv_item if iitem.id in iids: continue # We have no way to know which stock we should take 1st so show all with quantity 0 & let the user decide send_item_quantity = 0 insert(send_id = send_id, send_inv_item_id = iitem.id, item_id = iitem.item_id, req_item_id = rim_id, item_pack_id = iitem.item_pack_id, quantity = send_item_quantity, status = IN_PROCESS, pack_value = iitem.pack_value, currency = iitem.currency, bin = iitem.bin, expiry_date = iitem.expiry_date, owner_org_id = iitem.owner_org_id, supply_org_id = iitem.supply_org_id, #comments = comment, ) if no_match: session.warning = \ T("%(site)s has no items exactly matching this request. There may still be other items in stock which can fulfill this request!") % \ dict(site=site_name) # Redirect to view the list of items in the Send redirect(URL(c = "inv", f = "send", args = [send_id, "track_item"]) ) # ============================================================================= def commit_item_json(): """ """ ctable = s3db.req_commit itable = s3db.req_commit_item stable = s3db.org_site #ctable.date.represent = lambda dt: dt[:10] query = (itable.req_item_id == request.args[0]) & \ (ctable.id == itable.commit_id) & \ (ctable.site_id == stable.id) & \ (itable.deleted == False) records = db(query).select(ctable.id, ctable.date, stable.name, itable.quantity, orderby = db.req_commit.date) json_str = '''[%s,%s''' % (json.dumps(dict(id = str(T("Committed")), quantity = "#")), records.json()[1:]) response.headers["Content-Type"] = "application/json" return json_str # ============================================================================= def fema(): """ Custom Report to list all open requests for items that FEMA can supply @ToDo: Filter to just Sites that FEMA support """ ritable = s3db.req_req_item rtable = db.req_req itable = db.supply_item ictable = db.supply_item_category citable = db.supply_catalog_item query = (ictable.name == "FEMA") & \ (citable.item_category_id == ictable.id) & \ (citable.item_id == itable.id) & \ (itable.deleted != True) fema_items = db(query).select(itable.id) fema_item_ids = [item.id for item in fema_items] REQ_STATUS_COMPLETE = 2 s3.filter = (rtable.deleted != True) & \ (rtable.is_template == False) & \ (rtable.commit_status != REQ_STATUS_COMPLETE) & \ (rtable.transit_status != REQ_STATUS_COMPLETE) & \ (rtable.fulfil_status != REQ_STATUS_COMPLETE) & \ (ritable.req_id == rtable.id) & \ (ritable.quantity > ritable.quantity_commit) & \ (ritable.quantity > ritable.quantity_transit) & \ (ritable.quantity > ritable.quantity_fulfil) & \ (ritable.deleted != True) & \ (ritable.item_id.belongs(fema_item_ids)) # Search method req_item_search = [ s3base.S3SearchOptionsWidget( name="req_search_site", field="req_id$site_id", label = T("Facility"), cols = 3, ), ] s3db.configure("req_req_item", search_method = s3base.S3Search(advanced=req_item_search), ) output = req_item() return output # END =========================================================================

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module = request.controller resourcename = request.function if not settings.has_module(module): raise HTTP(404, body="Module disabled: %s" % module) def index(): return s3db.cms_index(module, alt_function="index_alt") def index_alt(): redirect(URL(f="req", args=["search"])) def is_affiliated(): if not auth.is_logged_in(): return False elif s3_has_role(ADMIN): return True else: table = auth.settings.table_user auth_user = db(table.id == auth.user.id).select(table.organisation_id, limitby=(0, 1) ).first() if auth_user and auth_user.organisation_id: return True else: return False def create(): redirect(URL(f="req", args="create")) def marker_fn(record): type = record.type if type in (1, 8): marker = "asset" elif type == 3: marker = "staff" else: marker = "request" priority = record.priority if priority == 3: marker = "%s_red" % marker elif priority == 2: marker = "%s_yellow" % marker mtable = db.gis_marker marker = db(mtable.name == marker).select(mtable.image, mtable.height, mtable.width, cache=s3db.cache, limitby=(0, 1)).first() return marker def req(): s3.filter = (s3db.req_req.is_template == False) output = req_controller() return output def req_template(): # @ToDo: Need to get this done later after being opened by Types? table = s3db.req_req field = table.is_template field.default = True field.readable = field.writable = False s3.filter = (field == True) if "req_item" in request.args: # List fields for req_item table = s3db.req_req_item list_fields = ["id", "item_id", "item_pack_id", "quantity", "comments", ] s3db.configure("req_req_item", list_fields=list_fields) elif "req_skill" in request.args: # List fields for req_skill table = s3db.req_req_skill list_fields = ["id", "skill_id", "quantity", "comments", ] s3db.configure("req_req_skill", list_fields=list_fields) else: # Main Req fields = ["req_ref", "date", "date_required", "date_required_until", "date_recv", "recv_by_id", "cancel", "commit_status", "transit_status", "fulfil_status", ] for fieldname in fields: field = table[fieldname] field.readable = field.writable = False table.purpose.label = T("Details") list_fields = ["id", "site_id" ] if len(settings.get_req_req_type()) > 1: list_fields.append("type") list_fields.append("priority") list_fields.append("purpose") list_fields.append("comments") s3db.configure("req_req", list_fields=list_fields) # CRUD strings ADD_REQUEST = T("Add Request Template") s3.crud_strings["req_req"] = Storage( title_create = ADD_REQUEST, title_display = T("Request Template Details"), title_list = T("Request Templates"), title_update = T("Edit Request Template"), subtitle_create = ADD_REQUEST, label_list_button = T("List Request Templates"), label_create_button = ADD_REQUEST, label_delete_button = T("Delete Request Template"), msg_record_created = T("Request Template Added"), msg_record_modified = T("Request Template Updated"), msg_record_deleted = T("Request Template Deleted"), msg_list_empty = T("No Request Templates")) output = req_controller() return output # ----------------------------------------------------------------------------- def req_controller(): def prep(r): table = r.table s3.req_prep(r) #if len(settings.get_req_req_type()) == 1: # # Remove type from list_fields # list_fields = s3db.get_config("req_req", "list_fields") # try: # list_fields.remove("type") # except: # # It has already been removed. # # This can happen if the req controller is called # # for a second time, such as when printing reports # pass # s3db.configure("req_req", list_fields=list_fields) type = (r.record and r.record.type) or \ (request.vars.type and int(request.vars.type)) if r.interactive: # Set the req_item site_id (Requested From), called from action buttons on req/req_item_inv_item/x page if "req_item_id" in request.vars and "inv_item_id" in request.vars: iitable = s3db.inv_inv_item inv_item = db(iitable.id == request.vars.inv_item_id).select(iitable.site_id, iitable.item_id, limitby=(0, 1) ).first() site_id = inv_item.site_id item_id = inv_item.item_id # @ToDo: Check Permissions & Avoid DB updates in GETs db(s3db.req_req_item.id == request.vars.req_item_id).update(site_id = site_id) response.confirmation = T("%(item)s requested from %(site)s") % \ {"item": s3db.supply_ItemRepresent()(item_id), "site": s3db.org_SiteRepresent()(site_id) } elif "req.site_id" in r.get_vars: # Called from 'Make new request' button on [siteinstance]/req page table.site_id.default = request.get_vars.get("req.site_id") table.site_id.writable = False if r.http == "POST": del r.get_vars["req.site_id"] table.requester_id.represent = requester_represent # Set Fields and Labels depending on type if type: table.type.default = type # This prevents the type from being edited AFTER it is set table.type.readable = table.type.writable = False crud_strings = settings.get_req_req_crud_strings(type) if crud_strings: s3.crud_strings["req_req"] = crud_strings elif type == 1: s3.crud_strings["req_req"].title_create = T("Make Supplies Request") elif type == 3: s3.crud_strings["req_req"].title_create = T("Make People Request") # Filter the query based on type if s3.filter: s3.filter = s3.filter & \ (table.type == type) else: s3.filter = (table.type == type) # These changes are applied via JS in create forms where type is editable if type == 1: # Item table.date_recv.readable = table.date_recv.writable = True if settings.get_req_items_ask_purpose(): table.purpose.label = T("What the Items will be used for") table.site_id.label = T("Deliver To") table.request_for_id.label = T("Deliver To") table.requester_id.label = T("Site Contact") table.recv_by_id.label = T("Delivered To") elif type == 3: # Person table.date_required_until.readable = table.date_required_until.writable = True table.purpose.label = T("Task Details") table.purpose.comment = DIV(_class="tooltip", _title="%s|%s" % (T("Task Details"), T("Include any special requirements such as equipment which they need to bring."))) table.site_id.label = T("Report To") table.requester_id.label = T("Volunteer Contact") table.request_for_id.label = T("Report To") table.recv_by_id.label = T("Reported To") if r.component: if r.component.name == "document": s3.crud.submit_button = T("Add") #table = r.component.table # @ToDo: Fix for Link Table #table.date.default = r.record.date #if r.record.site_id: # stable = db.org_site # query = (stable.id == r.record.site_id) # site = db(query).select(stable.location_id, # stable.organisation_id, # limitby=(0, 1)).first() # if site: # table.location_id.default = site.location_id # table.organisation_id.default = site.organisation_id elif r.component.name == "req_item": ctable = r.component.table ctable.site_id.writable = ctable.site_id.readable = False s3.req_hide_quantities(ctable) elif r.component.name == "req_skill": s3.req_hide_quantities(r.component.table) elif r.component.alias == "job": s3task.configure_tasktable_crud( function="req_add_from_template", args = [r.id], vars = dict(user_id = auth.user is not None and auth.user.id or 0), period = 86400, # seconds, so 1 day ) db.scheduler_task.timeout.writable = False else: if r.id: table.is_template.readable = table.is_template.writable = False method = r.method if method not in ("map", "read", "search", "update"): # Hide fields which don't make sense in a Create form s3.req_create_form_mods() if type and settings.get_req_inline_forms(): s3.req_inline_form(type, method) if auth.is_logged_in(): if not table.site_id.default: table.site_id.default = auth.user.site_id elif method == "map": s3db.configure("req_req", marker_fn=marker_fn) elif method == "update": if settings.get_req_inline_forms(): s3.req_inline_form(type, method) s3.scripts.append("/%s/static/scripts/S3/s3.req_update.js" % appname) if r.record and (r.record.closed or r.record.cancel): s3db.configure("req_req_item", insertable = False) elif r.representation == "plain": pass elif r.representation == "geojson": mtable = s3db.gis_marker s3db.configure("req_req", marker_fn=marker_fn) if r.component and r.component.name == "commit": table = r.component.table record = r.record stable = s3db.org_site commit_status = record.commit_status # Commits belonging to this request rsites = [] query = (table.deleted == False)&(table.req_id == record.id) req_sites = db(query).select(table.site_id) for req_site in req_sites: rsites += [req_site.site_id] # All the sites commit_sites = db((stable.deleted == False)).select(stable.id, stable.code) # Sites which have not committed to this request yet site_opts = {} for site in commit_sites: if (site.id not in site_opts) and (site.id not in rsites): site_opts[site.id] = site.code table.site_id.requires = IS_IN_SET(site_opts) if (commit_status == 2) and settings.get_req_restrict_on_complete(): # Restrict from committing to completed requests s3db.configure(table, listadd=False) else: # Allow commitments to be added when doing so as a component s3db.configure(table, listadd = True) if type == 1: # Items # Limit site_id to facilities the user has permissions for auth.permitted_facilities(table=r.table, error_msg=T("You do not have permission for any facility to make a commitment.")) if r.interactive: # Dropdown not Autocomplete itable = s3db.req_commit_item itable.req_item_id.widget = None req_id = r.id s3db.req_commit_item.req_item_id.requires = \ IS_ONE_OF(db, "req_req_item.id", s3db.req_item_represent, orderby = "req_req_item.id", filterby = "req_id", filter_opts = [req_id], sort=True ) s3.jquery_ready.append(''' S3OptionsFilter({ 'triggerName':'req_item_id', 'targetName':'item_pack_id', 'lookupPrefix':'req', 'lookupResource':'req_item_packs', 'lookupKey':'req_item_id', 'lookupField':'id', 'msgNoRecords':i18n.no_packs, 'fncPrep':S3.supply.fncPrepItem, 'fncRepresent':S3.supply.fncRepresentItem })''') # Custom Form s3forms = s3base.s3forms crud_form = s3forms.S3SQLCustomForm( "site_id", "date", "date_available", "committer_id", s3forms.S3SQLInlineComponent( "commit_item", label = T("Items"), fields = ["req_item_id", "item_pack_id", "quantity", "comments" ] ), "comments", ) s3db.configure("req_commit", crud_form=crud_form) # Redirect to the Items tab after creation #s3db.configure(table, # create_next = URL(c="req", f="commit", # args=["[id]", "commit_item"]), # update_next = URL(c="req", f="commit", # args=["[id]", "commit_item"])) elif type == 3: # People # Limit site_id to orgs the user has permissions for # @ToDo: Make this customisable between Site/Org # @ToDo: is_affiliated() auth.permitted_facilities(table=r.table, error_msg=T("You do not have permission for any facility to make a commitment.")) # Limit organisation_id to organisations the user has permissions for #auth.permitted_organisations(table=r.table, redirect_on_error=False) if r.interactive: #table.organisation_id.readable = True #table.organisation_id.writable = True # Custom Form s3forms = s3base.s3forms crud_form = s3forms.S3SQLCustomForm( "site_id", "date", "date_available", "committer_id", s3forms.S3SQLInlineComponent( "commit_skill", label = T("Skills"), fields = ["quantity", "skill_id", "comments" ] ), "comments", ) s3db.configure("req_commit", crud_form=crud_form) # Redirect to the Skills tab after creation #s3db.configure(table, # create_next = URL(c="req", f="commit", # args=["[id]", "commit_skill"]), # update_next = URL(c="req", f="commit", # args=["[id]", "commit_skill"])) else: # Non-Item commits can have an Organisation # Check if user is affiliated to an Organisation if is_affiliated(): # Limit organisation_id to organisations the user has permissions for auth.permitted_organisations(table=r.table, redirect_on_error=False) table.organisation_id.readable = table.organisation_id.writable = True else: # Unaffiliated people can't commit on behalf of others field = r.component.table.committer_id field.writable = False field.comment = None # @ToDo: Assets do? (Well, a 'From Site') table.site_id.readable = table.site_id.writable = False #if r.interactive and r.record.type == 3: # People # # Redirect to the Persons tab after creation # s3db.configure(table, # create_next = URL(c="req", f="commit", # args=["[id]", "commit_person"]), # update_next = URL(c="req", f="commit", # args=["[id]", "commit_person"]) # ) else: # Limit site_id to facilities the user has permissions for # @ToDo: Non-Item requests shouldn't be bound to a Facility? auth.permitted_facilities(table=r.table, error_msg=T("You do not have permission for any facility to make a request.")) return True s3.prep = prep def postp(r, output): if r.interactive and r.method != "import": if not r.component: s3_action_buttons(r) if settings.get_req_use_commit(): s3.actions.append( dict(url = URL(c="req", f="req", args=["[id]", "commit_all"]), _class = "action-btn commit-btn", label = str(T("Commit")) ) ) s3.jquery_ready.append( '''S3ConfirmClick('.commit-btn','%s')''' % T("Do you want to commit to this request?")) s3.actions.append( dict(url = URL(c="req", f="req", args=["[id]", "commit_all", "send"]), _class = "action-btn send-btn", label = str(T("Send")) ) ) s3.jquery_ready.append( '''S3ConfirmClick('.send-btn','%s')''' % T("Are you sure you want to commit to this request and send a shipment?")) else: s3_action_buttons(r) if r.component.name == "req_item" and settings.get_req_prompt_match(): req_item_inv_item_btn = dict(url = URL(c = "req", f = "req_item_inv_item", args = ["[id]"] ), _class = "action-btn", label = str(T("Request from Facility")), ) s3.actions.append(req_item_inv_item_btn) if r.component.name == "commit": if "form" in output: id = r.record.id ctable = s3db.req_commit query = (ctable.deleted == False) & \ (ctable.req_id == id) exists = current.db(query).select(ctable.id, limitby=(0, 1)) if not exists: output["form"] = A(T("Commit All"), _href=URL(args=[id, "commit_all"]), _class="action-btn", _id="commit-btn") s3.jquery_ready.append(''' S3ConfirmClick('#commit-btn','%s')''' % T("Do you want to commit to this request?")) else: s3.actions.append( dict(url = URL(c="req", f="send_commit", args = ["[id]"]), _class = "action-btn send-btn", label = str(T("Prepare Shipment")) ) ) s3.jquery_ready.append( '''S3ConfirmClick('.send-btn','%s')''' % T("Are you sure you want to send this shipment?")) if r.component.alias == "job": s3.actions = [ dict(label=str(T("Open")), _class="action-btn", url=URL(c="req", f="req_template", args=[str(r.id), "job", "[id]"])), dict(label=str(T("Reset")), _class="action-btn", url=URL(c="req", f="req_template", args=[str(r.id), "job", "[id]", "reset"])), dict(label=str(T("Run Now")), _class="action-btn", url=URL(c="req", f="req_template", args=[str(r.id), "job", "[id]", "run"])), ] return output s3.postp = postp output = s3_rest_controller("req", "req", rheader=s3db.req_rheader) return output def requester_represent(id, show_link=True): if not id: return current.messages["NONE"] htable = s3db.hrm_human_resource ptable = s3db.pr_person ctable = s3db.pr_contact query = (htable.id == id) & \ (htable.person_id == ptable.id) left = ctable.on((ctable.pe_id == ptable.pe_id) & \ (ctable.contact_method == "SMS")) row = db(query).select(htable.type, ptable.first_name, ptable.middle_name, ptable.last_name, ctable.value, left=left, limitby=(0, 1)).first() try: hr = row["hrm_human_resource"] except: return current.messages.UNKNOWN_OPT repr = s3_fullname(row.pr_person) if row.pr_contact.value: repr = "%s %s" % (repr, row.pr_contact.value) if show_link: if hr.type == 1: controller = "hrm" group = "staff" else: controller = "vol" group = "volunteer" request.extension = "html" return A(repr, _href = URL(c = controller, f = "person", args = ["contacts"], vars = {"group": group, "human_resource.id": id} ) ) return repr def req_item(): if not s3.filter: ritable = s3db.req_req_item rtable = db.req_req s3.filter = (rtable.is_template == False) & \ (rtable.id == ritable.req_id) search_method = s3db.get_config("req_req_item", "search_method") if not search_method: S3SearchOptionsWidget = s3base.S3SearchOptionsWidget req_item_search = ( S3SearchOptionsWidget( name="req_search_fulfil_status", label=T("Status"), field="req_id$fulfil_status", options = s3.req_status_opts, cols = 3, ), S3SearchOptionsWidget( name="req_search_priority", label=T("Priority"), field="req_id$priority", options = s3.req_priority_opts, cols = 3, ), S3SearchOptionsWidget( name="req_search_L3", field="req_id$site_id$location_id$L3", location_level="L3", cols = 3, ), S3SearchOptionsWidget( name="req_search_L4", field="req_id$site_id$location_id$L4", location_level="L4", cols = 3, ), ) s3db.configure("req_req_item", search_method = s3base.S3Search(advanced=req_item_search), ) def prep(r): if r.interactive: list_fields = s3db.get_config("req_req_item", "list_fields") list_fields.insert(1, "req_id$site_id") list_fields.insert(1, "req_id$site_id$location_id$L4") list_fields.insert(1, "req_id$site_id$location_id$L3") s3db.configure("req_req_item", insertable = False, list_fields = list_fields, ) s3.crud_strings["req_req_item"].title_list = T("Requested Items") if r.method != None and r.method != "update" and r.method != "read": # - includes one embedded in list_create # - list_fields over-rides, so still visible within list itself s3db.req_hide_quantities(r.table) return True s3.prep = prep output = s3_rest_controller("req", "req_item") if settings.get_req_prompt_match(): req_item_inv_item_btn = dict(url = URL(c="req", f="req_item_inv_item", args=["[id]"]), _class = "action-btn", label = str(T("Request from Facility")), ) if s3.actions: s3.actions += [req_item_inv_item_btn] else: s3.actions = [req_item_inv_item_btn] return output # ----------------------------------------------------------------------------- def req_item_packs(): req_item_id = None args = request.args if len(args) == 1 and args[0].isdigit(): req_item_id = args[0] else: for v in request.vars: if "." in v and v.split(".", 1)[1] == "req_item_id": req_item_id = request.vars[v] break table = s3db.supply_item_pack ritable = s3db.req_req_item query = (ritable.id == req_item_id) & \ (ritable.item_id == table.item_id) response.headers["Content-Type"] = "application/json" return db(query).select(table.id, table.name, table.quantity).json() # ----------------------------------------------------------------------------- def req_item_inv_item(): req_item_id = request.args[0] request.args = [] # ritable = s3db.req_req_item req_item = ritable[req_item_id] rtable = s3db.req_req req = rtable[req_item.req_id] output = {} output["title"] = T("Request Stock from Available Warehouse") output["req_btn"] = A(T("Return to Request"), _href = URL(c="req", f="req", args=[req_item.req_id, "req_item"]), _class = "action-btn" ) output["req_item"] = TABLE( TR( TH( "%s: " % T("Requested By") ), rtable.site_id.represent(req.site_id), TH( "%s: " % T("Item")), ritable.item_id.represent(req_item.item_id), ), TR( TH( "%s: " % T("Requester") ), rtable.requester_id.represent(req.requester_id), TH( "%s: " % T("Quantity")), req_item.quantity, ), TR( TH( "%s: " % T("Date Requested") ), rtable.date.represent(req.date), TH( T("Quantity Committed")), req_item.quantity_commit, ), TR( TH( "%s: " % T("Date Required") ), rtable.date_required.represent(req.date_required), TH( "%s: " % T("Quantity in Transit")), req_item.quantity_transit, ), TR( TH( "%s: " % T("Priority") ), rtable.priority.represent(req.priority), TH( "%s: " % T("Quantity Fulfilled")), req_item.quantity_fulfil, ) ) s3.no_sspag = True # pagination won't work with 2 datatables on one page @todo: test itable = s3db.inv_inv_item s3.filter = (itable.item_id == req_item.item_id) s3.crud_strings["inv_inv_item"].msg_list_empty = T("No Inventories currently have this item in stock") inv_items = s3_rest_controller("inv", "inv_item") output["items"] = inv_items["items"] if current.deployment_settings.get_supply_use_alt_name(): atable = s3db.supply_item_alt query = (atable.item_id == req_item.item_id ) & \ (atable.deleted == False ) alt_item_rows = db(query).select(atable.alt_item_id) alt_item_ids = [alt_item_row.alt_item_id for alt_item_row in alt_item_rows] if alt_item_ids: s3.filter = (itable.item_id.belongs(alt_item_ids)) inv_items_alt = s3_rest_controller("inv", "inv_item") output["items_alt"] = inv_items_alt["items"] else: output["items_alt"] = T("No Inventories currently have suitable alternative items in stock") response.view = "req/req_item_inv_item.html" s3.actions = [dict(url = URL(c = request.controller, f = "req", args = [req_item.req_id, "req_item"], vars = dict(req_item_id = req_item_id, inv_item_id = "[id]") ), _class = "action-btn", label = str(T("Request From")), )] return output def req_skill(): table = s3db.req_req_skill rtable = s3db.req_req s3.filter = (rtable.is_template == False) & \ (rtable.id == table.req_id) S3SearchOptionsWidget = s3base.S3SearchOptionsWidget req_skill_search = ( S3SearchOptionsWidget( name="req_search_fulfil_status", label=T("Status"), field="req_id$fulfil_status", options = s3.req_status_opts, cols = 3, ), S3SearchOptionsWidget( name="req_search_priority", label=T("Priority"), field="req_id$priority", options = s3.req_priority_opts, cols = 3, ), S3SearchOptionsWidget( name="req_search_L3", field="req_id$site_id$location_id$L3", location_level="L3", cols = 3, ), S3SearchOptionsWidget( name="req_search_L4", field="req_id$site_id$location_id$L4", location_level="L4", cols = 3, ), ) s3db.configure("req_req_skill", search_method = s3base.S3Search(advanced=req_skill_search), ) def prep(r): if r.interactive: list_fields = s3db.get_config("req_req_skill", "list_fields") list_fields.insert(1, "req_id$site_id") list_fields.insert(1, "req_id$site_id$location_id$L4") list_fields.insert(1, "req_id$site_id$location_id$L3") s3db.configure("req_req_skill", insertable=False, list_fields = list_fields, ) if r.method != "update" and r.method != "read": # - includes one embedded in list_create # - list_fields over-rides, so still visible within list itself s3db.req_hide_quantities(r.table) return True s3.prep = prep # Post-process def postp(r, output): if r.interactive: s3.actions = [ dict(url = URL(c="req", f="req", args=["req_skill", "[id]"]), _class = "action-btn", label = str(READ) ) ] return output s3.postp = postp output = s3_rest_controller("req", "req_skill") return output # ============================================================================= def summary_option(): return s3_rest_controller() # ============================================================================= def commit(): # Check if user is affiliated to an Organisation if not is_affiliated(): tablename = "req_commit_person" table = s3db[tablename] # Unaffiliated people can't commit on behalf of others table.person_id.writable = False s3db.configure(tablename, insertable=False) def prep(r): if r.interactive: table = r.table if r.record: s3.crud.submit_button = T("Save Changes") if r.record.type == 1: auth.permitted_facilities(table=table, error_msg=T("You do not have permission for any facility to make a commitment.") ) table.site_id.comment = A(T("Set as default Site"), _id="req_commit_site_id_link", _target="_blank", _href=URL(c="default", f="user", args=["profile"])) jappend = s3.jquery_ready.append jappend(''' $('#req_commit_site_id_link').click(function(){ var site_id=$('#req_commit_site_id').val() if(site_id){ var url = $('#req_commit_site_id_link').attr('href') var exists=url.indexOf('?') if(exists=='-1'){ $('#req_commit_site_id_link').attr('href',url+'?site_id='+site_id) } } return true })''') itable = s3db.req_commit_item itable.req_item_id.widget = None jappend(''' S3OptionsFilter({ 'triggerName':'req_item_id', 'targetName':'item_pack_id', 'lookupPrefix':'req', 'lookupResource':'req_item_packs', 'lookupKey':'req_item_id', 'lookupField':'id', 'msgNoRecords':i18n.no_packs, 'fncPrep':S3.supply.fncPrepItem, 'fncRepresent':S3.supply.fncRepresentItem })''') s3forms = s3base.s3forms crud_form = s3forms.S3SQLCustomForm( "site_id", "date", "date_available", "committer_id", s3forms.S3SQLInlineComponent( "commit_item", label = T("Items"), fields = ["req_item_id", "item_pack_id", "quantity", "comments" ] ), "comments", ) s3db.configure("req_commit", crud_form=crud_form) elif r.record.type == 3: auth.permitted_facilities(table=r.table, error_msg=T("You do not have permission for any facility to make a commitment.")) table.site_id.comment = A(T("Set as default Site"), _id="req_commit_site_id_link", _target="_blank", _href=URL(c="default", f="user", args=["profile"])) s3forms = s3base.s3forms crud_form = s3forms.S3SQLCustomForm( "site_id", "date", "date_available", "committer_id", s3forms.S3SQLInlineComponent( "commit_skill", label = T("People"), fields = ["quantity", "skill_id", "comments" ] ), "comments", ) s3db.configure("req_commit", crud_form=crud_form) else: auth.permitted_organisations(table=r.table, redirect_on_error=False) table.organisation_id.readable = True table.organisation_id.writable = True # @ToDo: Assets do? table.site_id.readable = False table.site_id.writable = False if r.component: req_id = r.record.req_id if r.component.name == "commit_item": # Limit commit items to items from the request s3db.req_commit_item.req_item_id.requires = \ IS_ONE_OF(db, "req_req_item.id", s3db.req_item_represent, orderby = "req_req_item.id", filterby = "req_id", filter_opts = [req_id], sort=True ) elif r.component.name == "person": pass # Limit commit skills to skills from the request #db.req_commit_skill.req_skill_id.requires = \ # IS_ONE_OF(db, # "req_req_skill.id", # s3db.req_skill_represent, # orderby = "req_req_skill.id", # filterby = "req_id", # filter_opts = [req_id], # sort=True # ) return True s3.prep = prep def postp(r, output): if r.interactive and r.method != "import": if not r.component: table = r.table record = r.record s3_action_buttons(r) s3.actions.append( dict(url = URL(f = "send_commit", args=["[id]"]), _class = "action-btn send-btn", label = str(T("Prepare Shipment")) ) ) s3.jquery_ready.append( '''S3ConfirmClick('.send-btn','%s')''' % T("Are you sure you want to send this shipment?")) return output s3.postp = postp output = s3_rest_controller(rheader=commit_rheader) return output # ----------------------------------------------------------------------------- def commit_rheader(r): if r.representation == "html": record = r.record if record and r.name == "commit": s3_date_represent = s3base.S3DateTime.date_represent tabs = [(T("Edit Details"), None)] type = record.type and int(record.type) table = r.table if type == 1: tabs.append((T("Items"), "commit_item")) #req_record = db.req_req[record.req_id] #req_date = req_record.date rheader = DIV(TABLE(TR(TH("%s: " % table.req_id.label), table.req_id.represent(record.req_id), ), TR(TH("%s: " % T("Committing Warehouse")), s3db.org_site_represent(record.site_id), TH("%s: " % T("Commit Date")), s3_date_represent(record.date), ), TR(TH("%s: " % table.comments.label), TD(record.comments or "", _colspan=3) ), ), ) prepare_btn = A(T("Prepare Shipment"), _href = URL(f = "send_commit", args = [record.id] ), _id = "send_commit", _class = "action-btn" ) s3.rfooter = TAG[""](prepare_btn) # send_btn = A( T("Send Commitment as Shipment"), # _href = URL(f = "send_commit", # args = [record.id] # ), # _id = "send_commit", # _class = "action-btn" # ) # # send_btn_confirm = SCRIPT("S3ConfirmClick('#send_commit', '%s')" % # T("Do you want to send these Committed items?") ) # s3.rfooter = TAG[""](send_btn,send_btn_confirm) #rheader.append(send_btn) #rheader.append(send_btn_confirm) elif type == 3: #tabs.append((T("People"), "commit_person")) tabs.append((T("People"), "commit_skill")) #req_record = db.req_req[record.req_id] #req_date = req_record.date organisation_represent = s3db.org_organisation_represent rheader = DIV(TABLE(TR(TH("%s: " % table.req_id.label), table.req_id.represent(record.req_id), ), TR(TH("%s: " % T("Committing Organization")), organisation_represent(record.organisation_id), TH("%s: " % T("Commit Date")), s3_date_represent(record.date), ), TR(TH("%s: " % table.comments.label), TD(record.comments, _colspan=3) ), ), ) else: # Other (& Assets/Shelter) rheader = DIV(TABLE(TR(TH("%s: " % table.req_id.label), table.req_id.represent(record.req_id), ), TR(TH("%s: " % T("Committing Person")), table.committer_id.represent(record.committer_id), TH("%s: " % T("Commit Date")), s3_date_represent(record.date), ), TR(TH("%s: " % table.comments.label), TD(record.comments or "", _colspan=3) ), ), ) rheader_tabs = s3_rheader_tabs(r, tabs) rheader.append(rheader_tabs) return rheader return None # ============================================================================= def send(): s3db.configure("inv_send", listadd=False) return s3db.inv_send_controller() # ============================================================================== def send_commit(): return s3db.req_send_commit() # ----------------------------------------------------------------------------- def send_process(): return s3db.inv_send_process() # ============================================================================= def commit_item(): return s3_rest_controller() # ============================================================================= def commit_req(): req_id = request.args[0] site_id = request.vars.get("site_id") table = s3db.req_req r_req = db(table.id == req_id).select(table.type, limitby=(0, 1)).first() # User must have permissions over facility which is sending (prefix, resourcename, id) = s3db.get_instance(s3db.org_site, site_id) if not site_id or not auth.s3_has_permission("update", "%s_%s" % (prefix, resourcename), record_id=id): session.error = T("You do not have permission to make this commitment.") redirect(URL(c="req", f="req", args=[req_id])) # Create a new commit record commit_id = s3db.req_commit.insert(date = request.utcnow, req_id = req_id, site_id = site_id, type = r_req.type ) # Only select items which are in the warehouse ritable = s3db.req_req_item iitable = s3db.inv_inv_item query = (ritable.req_id == req_id) & \ (ritable.quantity_fulfil < ritable.quantity) & \ (iitable.site_id == site_id) & \ (ritable.item_id == iitable.item_id) & \ (ritable.deleted == False) & \ (iitable.deleted == False) req_items = db(query).select(ritable.id, ritable.quantity, ritable.item_pack_id, iitable.item_id, iitable.quantity, iitable.item_pack_id) citable = s3db.req_commit_item for req_item in req_items: req_item_quantity = req_item.req_req_item.quantity * \ req_item.req_req_item.pack_quantity inv_item_quantity = req_item.inv_inv_item.quantity * \ req_item.inv_inv_item.pack_quantity if inv_item_quantity > req_item_quantity: commit_item_quantity = req_item_quantity else: commit_item_quantity = inv_item_quantity commit_item_quantity = commit_item_quantity / req_item.req_req_item.pack_quantity if commit_item_quantity: req_item_id = req_item.req_req_item.id commit_item_id = citable.insert(commit_id = commit_id, req_item_id = req_item_id, item_pack_id = req_item.req_req_item.item_pack_id, quantity = commit_item_quantity ) # Update the req_item.commit_quantity & req.commit_status s3mgr.store_session("req", "commit_item", commit_item_id) form = Storage() form.vars = Storage( req_item_id = req_item_id ) s3db.req_commit_item_onaccept(form) # Redirect to commit redirect(URL(c="req", f="commit", args=[commit_id, "commit_item"])) # ============================================================================= def send_req(): req_id = request.args[0] site_id = request.vars.get("site_id", None) site_name = s3db.org_site_represent(site_id, show_link=False) ritable = s3db.req_req_item iitable = s3db.inv_inv_item sendtable = s3db.inv_send tracktable = s3db.inv_track_item siptable = s3db.supply_item_pack table = s3db.req_req r_req = db(table.id == req_id).select(table.req_ref, table.requester_id, table.site_id, limitby=(0, 1)).first() # User must have permissions over facility which is sending (prefix, resourcename, id) = s3db.get_instance(db.org_site, site_id) if not site_id or not auth.s3_has_permission("update", "%s_%s" % (prefix, resourcename), record_id=id): session.error = T("You do not have permission to send this shipment.") redirect(URL(c="req", f="req", args = [req_id])) # Create a new send record code = s3db.inv_get_shipping_code("WB", site_id, s3db.inv_send.send_ref ) send_id = sendtable.insert(send_ref = code, req_ref = r_req.req_ref, sender_id = auth.s3_logged_in_person(), site_id = site_id, date = request.utcnow, recipient_id = r_req.requester_id, to_site_id = r_req.site_id, status = s3db.inv_ship_status["IN_PROCESS"], ) # Get the items for this request that have not been fulfilled (in transit) sip_id_field = siptable.id sip_quantity_field = siptable.quantity query = (ritable.req_id == req_id) & \ (ritable.quantity_transit < ritable.quantity) & \ (ritable.deleted == False) & \ (ritable.item_pack_id == sip_id_field) req_items = db(query).select(ritable.id, ritable.quantity, ritable.quantity_transit, ritable.quantity_fulfil, ritable.item_id, sip_quantity_field ) # Loop through each request item and find matched in the site inventory IN_PROCESS = s3db.inv_tracking_status["IN_PROCESS"] insert = tracktable.insert inv_remove = s3db.inv_remove ii_item_id_field = iitable.item_id ii_quantity_field = iitable.quantity ii_expiry_field = iitable.expiry_date ii_purchase_field = iitable.purchase_date iifields = [iitable.id, ii_item_id_field, ii_quantity_field, iitable.item_pack_id, iitable.pack_value, iitable.currency, ii_expiry_field, ii_purchase_field, iitable.bin, iitable.owner_org_id, iitable.supply_org_id, sip_quantity_field, ] bquery = (ii_quantity_field > 0) & \ (iitable.site_id == site_id) & \ (iitable.deleted == False) & \ (iitable.item_pack_id == sip_id_field) orderby = ii_expiry_field | ii_purchase_field no_match = True for ritem in req_items: rim = ritem.req_req_item rim_id = rim.id query = bquery & \ (ii_item_id_field == rim.item_id) inv_items = db(query).select(*iifields, orderby=orderby) if len(inv_items) == 0: break; no_match = False one_match = len(inv_items) == 1 # Get the Quantity Needed quantity_shipped = max(rim.quantity_transit, rim.quantity_fulfil) quantity_needed = (rim.quantity - quantity_shipped) * ritem.supply_item_pack.quantity # Insert the track item records # If there is more than one item match then we select the stock with the oldest expiry date first # then the oldest purchase date first # then a complete batch, if-possible iids = [] append = iids.append for item in inv_items: if not quantity_needed: break iitem = item.inv_inv_item if one_match: # Remove this total from the warehouse stock send_item_quantity = inv_remove(iitem, quantity_needed) quantity_needed -= send_item_quantity append(iitem.id) else: quantity_available = iitem.quantity * item.supply_item_pack.quantity if iitem.expiry_date: # We take first from the oldest expiry date send_item_quantity = min(quantity_needed, quantity_available) # Remove this total from the warehouse stock send_item_quantity = inv_remove(iitem, send_item_quantity) quantity_needed -= send_item_quantity append(iitem.id) elif iitem.purchase_date: # We take first from the oldest purchase date for non-expiring stock send_item_quantity = min(quantity_needed, quantity_available) # Remove this total from the warehouse stock send_item_quantity = inv_remove(iitem, send_item_quantity) quantity_needed -= send_item_quantity append(iitem.id) elif quantity_needed <= quantity_available: # Assign a complete batch together if possible # Remove this total from the warehouse stock send_item_quantity = inv_remove(iitem, quantity_needed) quantity_needed = 0 append(iitem.id) else: # Try again on the second loop, if-necessary continue insert(send_id = send_id, send_inv_item_id = iitem.id, item_id = iitem.item_id, req_item_id = rim_id, item_pack_id = iitem.item_pack_id, quantity = send_item_quantity, status = IN_PROCESS, pack_value = iitem.pack_value, currency = iitem.currency, bin = iitem.bin, expiry_date = iitem.expiry_date, owner_org_id = iitem.owner_org_id, supply_org_id = iitem.supply_org_id, #comments = comment, ) # 2nd pass for item in inv_items: if not quantity_needed: break iitem = item.inv_inv_item if iitem.id in iids: continue # We have no way to know which stock we should take 1st so show all with quantity 0 & let the user decide send_item_quantity = 0 insert(send_id = send_id, send_inv_item_id = iitem.id, item_id = iitem.item_id, req_item_id = rim_id, item_pack_id = iitem.item_pack_id, quantity = send_item_quantity, status = IN_PROCESS, pack_value = iitem.pack_value, currency = iitem.currency, bin = iitem.bin, expiry_date = iitem.expiry_date, owner_org_id = iitem.owner_org_id, supply_org_id = iitem.supply_org_id, #comments = comment, ) if no_match: session.warning = \ T("%(site)s has no items exactly matching this request. There may still be other items in stock which can fulfill this request!") % \ dict(site=site_name) # Redirect to view the list of items in the Send redirect(URL(c = "inv", f = "send", args = [send_id, "track_item"]) ) # ============================================================================= def commit_item_json(): ctable = s3db.req_commit itable = s3db.req_commit_item stable = s3db.org_site #ctable.date.represent = lambda dt: dt[:10] query = (itable.req_item_id == request.args[0]) & \ (ctable.id == itable.commit_id) & \ (ctable.site_id == stable.id) & \ (itable.deleted == False) records = db(query).select(ctable.id, ctable.date, stable.name, itable.quantity, orderby = db.req_commit.date) json_str = '''[%s,%s''' % (json.dumps(dict(id = str(T("Committed")), quantity = "#")), records.json()[1:]) response.headers["Content-Type"] = "application/json" return json_str # ============================================================================= def fema(): ritable = s3db.req_req_item rtable = db.req_req itable = db.supply_item ictable = db.supply_item_category citable = db.supply_catalog_item query = (ictable.name == "FEMA") & \ (citable.item_category_id == ictable.id) & \ (citable.item_id == itable.id) & \ (itable.deleted != True) fema_items = db(query).select(itable.id) fema_item_ids = [item.id for item in fema_items] REQ_STATUS_COMPLETE = 2 s3.filter = (rtable.deleted != True) & \ (rtable.is_template == False) & \ (rtable.commit_status != REQ_STATUS_COMPLETE) & \ (rtable.transit_status != REQ_STATUS_COMPLETE) & \ (rtable.fulfil_status != REQ_STATUS_COMPLETE) & \ (ritable.req_id == rtable.id) & \ (ritable.quantity > ritable.quantity_commit) & \ (ritable.quantity > ritable.quantity_transit) & \ (ritable.quantity > ritable.quantity_fulfil) & \ (ritable.deleted != True) & \ (ritable.item_id.belongs(fema_item_ids)) # Search method req_item_search = [ s3base.S3SearchOptionsWidget( name="req_search_site", field="req_id$site_id", label = T("Facility"), cols = 3, ), ] s3db.configure("req_req_item", search_method = s3base.S3Search(advanced=req_item_search), ) output = req_item() return output # END =========================================================================

true

f7196479819c081e316242e97b6c71d0635143b6

249

py

Python

ACM/NAQ16/G.py

zzh8829/CompetitiveProgramming

36f36b10269b4648ca8be0b08c2c49e96abede25

[ "MIT" ]

1

2017-10-01T00:51:39.000Z

ACM/NAQ16/G.py

zzh8829/CompetitiveProgramming

36f36b10269b4648ca8be0b08c2c49e96abede25

[ "MIT" ]

null

ACM/NAQ16/G.py

zzh8829/CompetitiveProgramming

36f36b10269b4648ca8be0b08c2c49e96abede25

[ "MIT" ]

null

sa = input() la = len(sa) a = int(sa) import math num = 0 i = 0 while True: i += 1 num += math.log10(i) if(math.ceil(num) >= la- 100): print(i, num, la) if(math.ceil(num) == la): print(i) if(math.ceil(num) > la): break

10.375

32

0.522088

sa = input() la = len(sa) a = int(sa) import math num = 0 i = 0 while True: i += 1 num += math.log10(i) if(math.ceil(num) >= la- 100): print(i, num, la) if(math.ceil(num) == la): print(i) if(math.ceil(num) > la): break

true

f719651f1696393c8cda5badd8ce6c3c1ce02286

3,397

py

Python

browser_history/cli.py

RobertWetzler/browser-history

bce5438e8b697e9be70d3747d0b9835c6c1324bc

[ "Apache-2.0" ]

null

browser_history/cli.py

RobertWetzler/browser-history

bce5438e8b697e9be70d3747d0b9835c6c1324bc

[ "Apache-2.0" ]

null

browser_history/cli.py

RobertWetzler/browser-history

bce5438e8b697e9be70d3747d0b9835c6c1324bc

[ "Apache-2.0" ]

null

"""This module defines functions and globals required for the command line interface of browser-history.""" import sys import argparse from browser_history import get_history, generic, browsers, utils # get list of all implemented browser by finding subclasses of generic.Browser AVAILABLE_BROWSERS = ', '.join(b.__name__ for b in generic.Browser.__subclasses__()) AVAILABLE_FORMATS = ', '.join(generic.Outputs.formats) def make_parser(): """Creates an ArgumentParser, configures and returns it. This was made into a separate function to be used with sphinx-argparse :rtype: :py:class:`argparse.ArgumentParser` """ parser_ = argparse.ArgumentParser(description=''' A tool to retrieve history from (almost) any browser on (almost) any platform''', epilog=''' Checkout the GitHub repo https://github.com/pesos/browser-history if you have any issues or want to help contribute''') parser_.add_argument('-b', '--browser', default='all', help=f''' browser to retrieve history from. Should be one of all, {AVAILABLE_BROWSERS}. Default is all (gets history from all browsers).''') parser_.add_argument('-f', '--format', default="csv", help=f''' Format to be used in output. Should be one of {AVAILABLE_FORMATS}. Default is csv''') parser_.add_argument('-o', '--output', default=None, help=''' File where output is to be written. If not provided standard output is used.''') return parser_ parser = make_parser() def main(): """Entrypoint to the command-line interface (CLI) of browser-history. It parses arguments from sys.argv and performs the appropriate actions. """ args = parser.parse_args() if args.browser == 'all': outputs = get_history() else: try: # gets browser class by name (string). selected_browser = args.browser for browser in generic.Browser.__subclasses__(): if browser.__name__.lower() == args.browser.lower(): selected_browser = browser.__name__ break browser_class = getattr(browsers, selected_browser) except AttributeError: utils.logger.error('Browser %s is unavailable. Check --help for available browsers', args.browser) sys.exit(1) try: browser = browser_class().fetch() outputs = browser except AssertionError as e: utils.logger.error(e) sys.exit(1) # Format the output try: formatted = outputs.formatted(args.format) except ValueError as e: utils.logger.error(e) sys.exit(1) if args.output is None: print(formatted) else: filename = args.output with open(filename, 'w') as output_file: output_file.write(formatted)

37.32967

109

0.548719

import sys import argparse from browser_history import get_history, generic, browsers, utils AVAILABLE_BROWSERS = ', '.join(b.__name__ for b in generic.Browser.__subclasses__()) AVAILABLE_FORMATS = ', '.join(generic.Outputs.formats) def make_parser(): parser_ = argparse.ArgumentParser(description=''' A tool to retrieve history from (almost) any browser on (almost) any platform''', epilog=''' Checkout the GitHub repo https://github.com/pesos/browser-history if you have any issues or want to help contribute''') parser_.add_argument('-b', '--browser', default='all', help=f''' browser to retrieve history from. Should be one of all, {AVAILABLE_BROWSERS}. Default is all (gets history from all browsers).''') parser_.add_argument('-f', '--format', default="csv", help=f''' Format to be used in output. Should be one of {AVAILABLE_FORMATS}. Default is csv''') parser_.add_argument('-o', '--output', default=None, help=''' File where output is to be written. If not provided standard output is used.''') return parser_ parser = make_parser() def main(): args = parser.parse_args() if args.browser == 'all': outputs = get_history() else: try: selected_browser = args.browser for browser in generic.Browser.__subclasses__(): if browser.__name__.lower() == args.browser.lower(): selected_browser = browser.__name__ break browser_class = getattr(browsers, selected_browser) except AttributeError: utils.logger.error('Browser %s is unavailable. Check --help for available browsers', args.browser) sys.exit(1) try: browser = browser_class().fetch() outputs = browser except AssertionError as e: utils.logger.error(e) sys.exit(1) try: formatted = outputs.formatted(args.format) except ValueError as e: utils.logger.error(e) sys.exit(1) if args.output is None: print(formatted) else: filename = args.output with open(filename, 'w') as output_file: output_file.write(formatted)

true

f7196672dac355b888cbfce65b0f4b2221ebe267

30,455

py

Python

testing/test_basic.py

yaccz/pytest-twisted

5dc4efc5d335da0172fec02e48076aacef4bf75d

[ "BSD-3-Clause" ]

null

testing/test_basic.py

yaccz/pytest-twisted

5dc4efc5d335da0172fec02e48076aacef4bf75d

[ "BSD-3-Clause" ]

null

testing/test_basic.py

yaccz/pytest-twisted

5dc4efc5d335da0172fec02e48076aacef4bf75d

[ "BSD-3-Clause" ]

null

import sys import textwrap import pytest # https://docs.python.org/3/whatsnew/3.5.html#pep-492-coroutines-with-async-and-await-syntax ASYNC_AWAIT = sys.version_info >= (3, 5) # https://docs.python.org/3/whatsnew/3.6.html#pep-525-asynchronous-generators ASYNC_GENERATORS = sys.version_info >= (3, 6) timeout = 15 # https://github.com/pytest-dev/pytest/issues/6505 def force_plural(name): if name in {"error", "warning"}: return name + "s" return name def assert_outcomes(run_result, outcomes): formatted_output = format_run_result_output_for_assert(run_result) try: result_outcomes = run_result.parseoutcomes() except ValueError: assert False, formatted_output normalized_result_outcomes = { force_plural(name): outcome for name, outcome in result_outcomes.items() if name != "seconds" } assert normalized_result_outcomes == outcomes, formatted_output def format_run_result_output_for_assert(run_result): tpl = """ ---- stdout {} ---- stderr {} ---- """ return textwrap.dedent(tpl).format( run_result.stdout.str(), run_result.stderr.str() ) @pytest.fixture(name="default_conftest", autouse=True) def _default_conftest(testdir): testdir.makeconftest(textwrap.dedent(""" import pytest import pytest_twisted @pytest.hookimpl(tryfirst=True) def pytest_configure(config): pytest_twisted._use_asyncio_selector_if_required(config=config) """)) def skip_if_reactor_not(request, expected_reactor): actual_reactor = request.config.getoption("reactor", "default") if actual_reactor != expected_reactor: pytest.skip( "reactor is {} not {}".format(actual_reactor, expected_reactor), ) def skip_if_no_async_await(): return pytest.mark.skipif( not ASYNC_AWAIT, reason="async/await syntax not supported on Python <3.5", ) def skip_if_no_async_generators(): return pytest.mark.skipif( not ASYNC_GENERATORS, reason="async generators not support on Python <3.6", ) @pytest.fixture def cmd_opts(request): reactor = request.config.getoption("reactor", "default") return ( sys.executable, "-m", "pytest", "-v", "--reactor={}".format(reactor), ) def test_inline_callbacks_in_pytest(): assert hasattr(pytest, 'inlineCallbacks') @pytest.mark.parametrize( 'decorator, should_warn', ( ('pytest.inlineCallbacks', True), ('pytest_twisted.inlineCallbacks', False), ), ) def test_inline_callbacks_in_pytest_deprecation( testdir, cmd_opts, decorator, should_warn, ): import_path, _, _ = decorator.rpartition('.') test_file = """ import {import_path} def test_deprecation(): @{decorator} def f(): yield 42 """.format(import_path=import_path, decorator=decorator) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) expected_outcomes = {"passed": 1} if should_warn: expected_outcomes["warnings"] = 1 assert_outcomes(rr, expected_outcomes) def test_blockon_in_pytest(): assert hasattr(pytest, 'blockon') @pytest.mark.parametrize( 'function, should_warn', ( ('pytest.blockon', True), ('pytest_twisted.blockon', False), ), ) def test_blockon_in_pytest_deprecation( testdir, cmd_opts, function, should_warn, ): import_path, _, _ = function.rpartition('.') test_file = """ import warnings from twisted.internet import reactor, defer import pytest import {import_path} @pytest.fixture def foo(request): d = defer.Deferred() d.callback(None) {function}(d) def test_succeed(foo): pass """.format(import_path=import_path, function=function) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) expected_outcomes = {"passed": 1} if should_warn: expected_outcomes["warnings"] = 1 assert_outcomes(rr, expected_outcomes) def test_fail_later(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer def test_fail(): def doit(): try: 1 / 0 except: d.errback() d = defer.Deferred() reactor.callLater(0.01, doit) return d """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"failed": 1}) def test_succeed_later(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer def test_succeed(): d = defer.Deferred() reactor.callLater(0.01, d.callback, 1) return d """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_non_deferred(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer def test_succeed(): return 42 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_exception(testdir, cmd_opts): test_file = """ def test_more_fail(): raise RuntimeError("foo") """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"failed": 1}) @pytest.fixture( name="empty_optional_call", params=["", "()"], ids=["no call", "empty call"], ) def empty_optional_call_fixture(request): return request.param def test_inlineCallbacks(testdir, cmd_opts, empty_optional_call): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest.fixture(scope="module", params=["fs", "imap", "web"]) def foo(request): return request.param @pytest_twisted.inlineCallbacks{optional_call} def test_succeed(foo): yield defer.succeed(foo) if foo == "web": raise RuntimeError("baz") """.format(optional_call=empty_optional_call) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2, "failed": 1}) @skip_if_no_async_await() def test_async_await(testdir, cmd_opts, empty_optional_call): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest.fixture(scope="module", params=["fs", "imap", "web"]) def foo(request): return request.param @pytest_twisted.ensureDeferred{optional_call} async def test_succeed(foo): await defer.succeed(foo) if foo == "web": raise RuntimeError("baz") """.format(optional_call=empty_optional_call) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2, "failed": 1}) def test_twisted_greenlet(testdir, cmd_opts): test_file = """ import pytest, greenlet MAIN = None @pytest.fixture(scope="session", autouse=True) def set_MAIN(request, twisted_greenlet): global MAIN MAIN = twisted_greenlet def test_MAIN(): assert MAIN is not None assert MAIN is greenlet.getcurrent() """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_blockon_in_fixture(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest.fixture(scope="module", params=["fs", "imap", "web"]) def foo(request): d1, d2 = defer.Deferred(), defer.Deferred() reactor.callLater(0.01, d1.callback, 1) reactor.callLater(0.02, d2.callback, request.param) pytest_twisted.blockon(d1) return d2 @pytest_twisted.inlineCallbacks def test_succeed(foo): x = yield foo if x == "web": raise RuntimeError("baz") """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2, "failed": 1}) @skip_if_no_async_await() def test_blockon_in_fixture_async(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest.fixture(scope="module", params=["fs", "imap", "web"]) def foo(request): d1, d2 = defer.Deferred(), defer.Deferred() reactor.callLater(0.01, d1.callback, 1) reactor.callLater(0.02, d2.callback, request.param) pytest_twisted.blockon(d1) return d2 @pytest_twisted.ensureDeferred async def test_succeed(foo): x = await foo if x == "web": raise RuntimeError("baz") """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2, "failed": 1}) @skip_if_no_async_await() def test_async_fixture(testdir, cmd_opts): pytest_ini_file = """ [pytest] markers = redgreenblue """ testdir.makefile('.ini', pytest=pytest_ini_file) test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_fixture( scope="function", params=["fs", "imap", "web"], ) @pytest.mark.redgreenblue async def foo(request): d1, d2 = defer.Deferred(), defer.Deferred() reactor.callLater(0.01, d1.callback, 1) reactor.callLater(0.02, d2.callback, request.param) await d1 return d2, @pytest_twisted.inlineCallbacks def test_succeed_blue(foo): x = yield foo[0] if x == "web": raise RuntimeError("baz") """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2, "failed": 1}) @skip_if_no_async_await() def test_async_fixture_no_arguments(testdir, cmd_opts, empty_optional_call): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_fixture{optional_call} async def scope(request): return request.scope def test_is_function_scope(scope): assert scope == "function" """.format(optional_call=empty_optional_call) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_ordered_teardown(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted results = [] @pytest.fixture(scope='function') def sync_fixture(): yield 42 results.append(2) @pytest_twisted.async_yield_fixture(scope='function') async def async_fixture(sync_fixture): yield sync_fixture results.append(1) def test_first(async_fixture): assert async_fixture == 42 def test_second(): assert results == [1, 2] """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) @skip_if_no_async_generators() def test_async_yield_fixture_can_await(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest_twisted @pytest_twisted.async_yield_fixture() async def foo(): d1, d2 = defer.Deferred(), defer.Deferred() reactor.callLater(0.01, d1.callback, 1) reactor.callLater(0.02, d2.callback, 2) await d1 # Twisted doesn't allow calling back with a Deferred as a value. # This deferred is being wrapped up in a tuple to sneak through. # https://github.com/twisted/twisted/blob/c0f1394c7bfb04d97c725a353a1f678fa6a1c602/src/twisted/internet/defer.py#L459 yield d2, @pytest_twisted.ensureDeferred async def test(foo): x = await foo[0] assert x == 2 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_failed_test(testdir, cmd_opts): test_file = """ import pytest_twisted @pytest_twisted.async_yield_fixture() async def foo(): yield 92 @pytest_twisted.ensureDeferred async def test(foo): assert False """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) rr.stdout.fnmatch_lines(lines2=["E*assert False"]) assert_outcomes(rr, {"failed": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_test_exception(testdir, cmd_opts): test_file = """ import pytest_twisted class UniqueLocalException(Exception): pass @pytest_twisted.async_yield_fixture() async def foo(): yield 92 @pytest_twisted.ensureDeferred async def test(foo): raise UniqueLocalException("some message") """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) rr.stdout.fnmatch_lines(lines2=["E*.UniqueLocalException: some message*"]) assert_outcomes(rr, {"failed": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_yields_twice(testdir, cmd_opts): test_file = """ import pytest_twisted @pytest_twisted.async_yield_fixture() async def foo(): yield 92 yield 36 @pytest_twisted.ensureDeferred async def test(foo): assert foo == 92 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1, "errors": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_teardown_exception(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted class UniqueLocalException(Exception): pass @pytest_twisted.async_yield_fixture() async def foo(request): yield 13 raise UniqueLocalException("some message") @pytest_twisted.ensureDeferred async def test_succeed(foo): assert foo == 13 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) rr.stdout.fnmatch_lines(lines2=["E*.UniqueLocalException: some message*"]) assert_outcomes(rr, {"passed": 1, "errors": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_no_arguments( testdir, cmd_opts, empty_optional_call, ): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_yield_fixture{optional_call} async def scope(request): yield request.scope def test_is_function_scope(scope): assert scope == "function" """.format(optional_call=empty_optional_call) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_function_scope(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted check_me = 0 @pytest_twisted.async_yield_fixture(scope="function") async def foo(): global check_me if check_me != 0: raise Exception('check_me already modified before fixture run') check_me = 1 yield 42 if check_me != 2: raise Exception( 'check_me not updated properly: {}'.format(check_me), ) check_me = 0 def test_first(foo): global check_me assert check_me == 1 assert foo == 42 check_me = 2 def test_second(foo): global check_me assert check_me == 1 assert foo == 42 check_me = 2 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) @skip_if_no_async_await() def test_async_simple_fixture_in_fixture(testdir, cmd_opts): test_file = """ import itertools from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_fixture(name='four') async def fixture_four(): return 4 @pytest_twisted.async_fixture(name='doublefour') async def fixture_doublefour(four): return 2 * four @pytest_twisted.ensureDeferred async def test_four(four): assert four == 4 @pytest_twisted.ensureDeferred async def test_doublefour(doublefour): assert doublefour == 8 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) @skip_if_no_async_generators() def test_async_yield_simple_fixture_in_fixture(testdir, cmd_opts): test_file = """ import itertools from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_yield_fixture(name='four') async def fixture_four(): yield 4 @pytest_twisted.async_yield_fixture(name='doublefour') async def fixture_doublefour(four): yield 2 * four @pytest_twisted.ensureDeferred async def test_four(four): assert four == 4 @pytest_twisted.ensureDeferred async def test_doublefour(doublefour): assert doublefour == 8 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) @skip_if_no_async_await() @pytest.mark.parametrize('innerasync', [ pytest.param(truth, id='innerasync={}'.format(truth)) for truth in [True, False] ]) def test_async_fixture_in_fixture(testdir, cmd_opts, innerasync): maybe_async = 'async ' if innerasync else '' maybe_await = 'await ' if innerasync else '' test_file = """ import itertools from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_fixture(name='increment') async def fixture_increment(): counts = itertools.count() {maybe_async}def increment(): return next(counts) return increment @pytest_twisted.async_fixture(name='doubleincrement') async def fixture_doubleincrement(increment): {maybe_async}def doubleincrement(): n = {maybe_await}increment() return n * 2 return doubleincrement @pytest_twisted.ensureDeferred async def test_increment(increment): first = {maybe_await}increment() second = {maybe_await}increment() assert (first, second) == (0, 1) @pytest_twisted.ensureDeferred async def test_doubleincrement(doubleincrement): first = {maybe_await}doubleincrement() second = {maybe_await}doubleincrement() assert (first, second) == (0, 2) """.format(maybe_async=maybe_async, maybe_await=maybe_await) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) # assert_outcomes(rr, {"passed": 1}) @skip_if_no_async_generators() @pytest.mark.parametrize('innerasync', [ pytest.param(truth, id='innerasync={}'.format(truth)) for truth in [True, False] ]) def test_async_yield_fixture_in_fixture(testdir, cmd_opts, innerasync): maybe_async = 'async ' if innerasync else '' maybe_await = 'await ' if innerasync else '' test_file = """ import itertools from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_yield_fixture(name='increment') async def fixture_increment(): counts = itertools.count() {maybe_async}def increment(): return next(counts) yield increment @pytest_twisted.async_yield_fixture(name='doubleincrement') async def fixture_doubleincrement(increment): {maybe_async}def doubleincrement(): n = {maybe_await}increment() return n * 2 yield doubleincrement @pytest_twisted.ensureDeferred async def test_increment(increment): first = {maybe_await}increment() second = {maybe_await}increment() assert (first, second) == (0, 1) @pytest_twisted.ensureDeferred async def test_doubleincrement(doubleincrement): first = {maybe_await}doubleincrement() second = {maybe_await}doubleincrement() assert (first, second) == (0, 2) """.format(maybe_async=maybe_async, maybe_await=maybe_await) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) def test_blockon_in_hook(testdir, cmd_opts, request): skip_if_reactor_not(request, "default") conftest_file = """ import pytest_twisted from twisted.internet import reactor, defer def pytest_configure(config): pytest_twisted.init_default_reactor() d1, d2 = defer.Deferred(), defer.Deferred() reactor.callLater(0.01, d1.callback, 1) reactor.callLater(0.02, d2.callback, 1) pytest_twisted.blockon(d1) pytest_twisted.blockon(d2) """ testdir.makeconftest(conftest_file) test_file = """ from twisted.internet import reactor, defer def test_succeed(): d = defer.Deferred() reactor.callLater(0.01, d.callback, 1) return d """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_wrong_reactor(testdir, cmd_opts, request): skip_if_reactor_not(request, "default") conftest_file = """ def pytest_addhooks(): import twisted.internet.reactor twisted.internet.reactor = None """ testdir.makeconftest(conftest_file) test_file = """ def test_succeed(): pass """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert "WrongReactorAlreadyInstalledError" in rr.stderr.str() def test_blockon_in_hook_with_qt5reactor(testdir, cmd_opts, request): skip_if_reactor_not(request, "qt5reactor") conftest_file = """ import pytest_twisted import pytestqt from twisted.internet import defer def pytest_configure(config): pytest_twisted.init_qt5_reactor() d = defer.Deferred() from twisted.internet import reactor reactor.callLater(0.01, d.callback, 1) pytest_twisted.blockon(d) """ testdir.makeconftest(conftest_file) test_file = """ from twisted.internet import reactor, defer def test_succeed(): d = defer.Deferred() reactor.callLater(0.01, d.callback, 1) return d """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_wrong_reactor_with_qt5reactor(testdir, cmd_opts, request): skip_if_reactor_not(request, "qt5reactor") conftest_file = """ def pytest_addhooks(): import twisted.internet.default twisted.internet.default.install() """ testdir.makeconftest(conftest_file) test_file = """ def test_succeed(): pass """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert "WrongReactorAlreadyInstalledError" in rr.stderr.str() def test_pytest_from_reactor_thread(testdir, cmd_opts, request): skip_if_reactor_not(request, "default") test_file = """ import pytest import pytest_twisted from twisted.internet import reactor, defer @pytest.fixture def fix(): d = defer.Deferred() reactor.callLater(0.01, d.callback, 42) return pytest_twisted.blockon(d) def test_simple(fix): assert fix == 42 @pytest_twisted.inlineCallbacks def test_fail(): d = defer.Deferred() reactor.callLater(0.01, d.callback, 1) yield d assert False """ testdir.makepyfile(test_file) runner_file = """ import pytest from twisted.internet import reactor from twisted.internet.defer import inlineCallbacks from twisted.internet.threads import deferToThread codes = [] @inlineCallbacks def main(): try: codes.append((yield deferToThread(pytest.main, ['-k simple']))) codes.append((yield deferToThread(pytest.main, ['-k fail']))) finally: reactor.stop() if __name__ == '__main__': reactor.callLater(0, main) reactor.run() codes == [0, 1] or exit(1) """ testdir.makepyfile(runner=runner_file) # check test file is ok in standalone mode: rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1, "failed": 1}) # test embedded mode: assert testdir.run(sys.executable, "runner.py", timeout=timeout).ret == 0 def test_blockon_in_hook_with_asyncio(testdir, cmd_opts, request): skip_if_reactor_not(request, "asyncio") conftest_file = """ import pytest import pytest_twisted from twisted.internet import defer @pytest.hookimpl(tryfirst=True) def pytest_configure(config): pytest_twisted._use_asyncio_selector_if_required(config=config) pytest_twisted.init_asyncio_reactor() d = defer.Deferred() from twisted.internet import reactor reactor.callLater(0.01, d.callback, 1) pytest_twisted.blockon(d) """ testdir.makeconftest(conftest_file) test_file = """ from twisted.internet import reactor, defer def test_succeed(): d = defer.Deferred() reactor.callLater(0.01, d.callback, 1) return d """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_wrong_reactor_with_asyncio(testdir, cmd_opts, request): skip_if_reactor_not(request, "asyncio") conftest_file = """ import pytest import pytest_twisted @pytest.hookimpl(tryfirst=True) def pytest_configure(config): pytest_twisted._use_asyncio_selector_if_required(config=config) def pytest_addhooks(): import twisted.internet.default twisted.internet.default.install() """ testdir.makeconftest(conftest_file) test_file = """ def test_succeed(): pass """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert "WrongReactorAlreadyInstalledError" in rr.stderr.str() @skip_if_no_async_generators() def test_async_fixture_module_scope(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted check_me = 0 @pytest_twisted.async_yield_fixture(scope="module") async def foo(): global check_me if check_me != 0: raise Exception('check_me already modified before fixture run') check_me = 1 yield 42 if check_me != 3: raise Exception( 'check_me not updated properly: {}'.format(check_me), ) check_me = 0 def test_first(foo): global check_me assert check_me == 1 assert foo == 42 check_me = 2 def test_second(foo): global check_me assert check_me == 2 assert foo == 42 check_me = 3 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) def test_inlinecallbacks_method_with_fixture_gets_self(testdir, cmd_opts): test_file = """ import pytest import pytest_twisted from twisted.internet import defer @pytest.fixture def foo(): return 37 class TestClass: @pytest_twisted.inlineCallbacks def test_self_isinstance(self, foo): d = defer.succeed(None) yield d assert isinstance(self, TestClass) """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts) assert_outcomes(rr, {"passed": 1}) def test_inlinecallbacks_method_with_fixture_gets_fixture(testdir, cmd_opts): test_file = """ import pytest import pytest_twisted from twisted.internet import defer @pytest.fixture def foo(): return 37 class TestClass: @pytest_twisted.inlineCallbacks def test_self_isinstance(self, foo): d = defer.succeed(None) yield d assert foo == 37 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) @skip_if_no_async_await() def test_ensuredeferred_method_with_fixture_gets_self(testdir, cmd_opts): test_file = """ import pytest import pytest_twisted @pytest.fixture def foo(): return 37 class TestClass: @pytest_twisted.ensureDeferred async def test_self_isinstance(self, foo): assert isinstance(self, TestClass) """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) @skip_if_no_async_await() def test_ensuredeferred_method_with_fixture_gets_fixture(testdir, cmd_opts): test_file = """ import pytest import pytest_twisted @pytest.fixture def foo(): return 37 class TestClass: @pytest_twisted.ensureDeferred async def test_self_isinstance(self, foo): assert foo == 37 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_import_pytest_twisted_in_conftest_py_not_a_problem(testdir, cmd_opts): conftest_file = """ import pytest import pytest_twisted @pytest.hookimpl(tryfirst=True) def pytest_configure(config): pytest_twisted._use_asyncio_selector_if_required(config=config) """ testdir.makeconftest(conftest_file) test_file = """ import pytest_twisted def test_succeed(): pass """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1})

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import sys import textwrap import pytest >= (3, 6) timeout = 15 def force_plural(name): if name in {"error", "warning"}: return name + "s" return name def assert_outcomes(run_result, outcomes): formatted_output = format_run_result_output_for_assert(run_result) try: result_outcomes = run_result.parseoutcomes() except ValueError: assert False, formatted_output normalized_result_outcomes = { force_plural(name): outcome for name, outcome in result_outcomes.items() if name != "seconds" } assert normalized_result_outcomes == outcomes, formatted_output def format_run_result_output_for_assert(run_result): tpl = """ ---- stdout {} ---- stderr {} ---- """ return textwrap.dedent(tpl).format( run_result.stdout.str(), run_result.stderr.str() ) @pytest.fixture(name="default_conftest", autouse=True) def _default_conftest(testdir): testdir.makeconftest(textwrap.dedent(""" import pytest import pytest_twisted @pytest.hookimpl(tryfirst=True) def pytest_configure(config): pytest_twisted._use_asyncio_selector_if_required(config=config) """)) def skip_if_reactor_not(request, expected_reactor): actual_reactor = request.config.getoption("reactor", "default") if actual_reactor != expected_reactor: pytest.skip( "reactor is {} not {}".format(actual_reactor, expected_reactor), ) def skip_if_no_async_await(): return pytest.mark.skipif( not ASYNC_AWAIT, reason="async/await syntax not supported on Python <3.5", ) def skip_if_no_async_generators(): return pytest.mark.skipif( not ASYNC_GENERATORS, reason="async generators not support on Python <3.6", ) @pytest.fixture def cmd_opts(request): reactor = request.config.getoption("reactor", "default") return ( sys.executable, "-m", "pytest", "-v", "--reactor={}".format(reactor), ) def test_inline_callbacks_in_pytest(): assert hasattr(pytest, 'inlineCallbacks') @pytest.mark.parametrize( 'decorator, should_warn', ( ('pytest.inlineCallbacks', True), ('pytest_twisted.inlineCallbacks', False), ), ) def test_inline_callbacks_in_pytest_deprecation( testdir, cmd_opts, decorator, should_warn, ): import_path, _, _ = decorator.rpartition('.') test_file = """ import {import_path} def test_deprecation(): @{decorator} def f(): yield 42 """.format(import_path=import_path, decorator=decorator) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) expected_outcomes = {"passed": 1} if should_warn: expected_outcomes["warnings"] = 1 assert_outcomes(rr, expected_outcomes) def test_blockon_in_pytest(): assert hasattr(pytest, 'blockon') @pytest.mark.parametrize( 'function, should_warn', ( ('pytest.blockon', True), ('pytest_twisted.blockon', False), ), ) def test_blockon_in_pytest_deprecation( testdir, cmd_opts, function, should_warn, ): import_path, _, _ = function.rpartition('.') test_file = """ import warnings from twisted.internet import reactor, defer import pytest import {import_path} @pytest.fixture def foo(request): d = defer.Deferred() d.callback(None) {function}(d) def test_succeed(foo): pass """.format(import_path=import_path, function=function) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) expected_outcomes = {"passed": 1} if should_warn: expected_outcomes["warnings"] = 1 assert_outcomes(rr, expected_outcomes) def test_fail_later(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer def test_fail(): def doit(): try: 1 / 0 except: d.errback() d = defer.Deferred() reactor.callLater(0.01, doit) return d """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"failed": 1}) def test_succeed_later(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer def test_succeed(): d = defer.Deferred() reactor.callLater(0.01, d.callback, 1) return d """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_non_deferred(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer def test_succeed(): return 42 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_exception(testdir, cmd_opts): test_file = """ def test_more_fail(): raise RuntimeError("foo") """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"failed": 1}) @pytest.fixture( name="empty_optional_call", params=["", "()"], ids=["no call", "empty call"], ) def empty_optional_call_fixture(request): return request.param def test_inlineCallbacks(testdir, cmd_opts, empty_optional_call): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest.fixture(scope="module", params=["fs", "imap", "web"]) def foo(request): return request.param @pytest_twisted.inlineCallbacks{optional_call} def test_succeed(foo): yield defer.succeed(foo) if foo == "web": raise RuntimeError("baz") """.format(optional_call=empty_optional_call) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2, "failed": 1}) @skip_if_no_async_await() def test_async_await(testdir, cmd_opts, empty_optional_call): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest.fixture(scope="module", params=["fs", "imap", "web"]) def foo(request): return request.param @pytest_twisted.ensureDeferred{optional_call} async def test_succeed(foo): await defer.succeed(foo) if foo == "web": raise RuntimeError("baz") """.format(optional_call=empty_optional_call) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2, "failed": 1}) def test_twisted_greenlet(testdir, cmd_opts): test_file = """ import pytest, greenlet MAIN = None @pytest.fixture(scope="session", autouse=True) def set_MAIN(request, twisted_greenlet): global MAIN MAIN = twisted_greenlet def test_MAIN(): assert MAIN is not None assert MAIN is greenlet.getcurrent() """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_blockon_in_fixture(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest.fixture(scope="module", params=["fs", "imap", "web"]) def foo(request): d1, d2 = defer.Deferred(), defer.Deferred() reactor.callLater(0.01, d1.callback, 1) reactor.callLater(0.02, d2.callback, request.param) pytest_twisted.blockon(d1) return d2 @pytest_twisted.inlineCallbacks def test_succeed(foo): x = yield foo if x == "web": raise RuntimeError("baz") """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2, "failed": 1}) @skip_if_no_async_await() def test_blockon_in_fixture_async(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest.fixture(scope="module", params=["fs", "imap", "web"]) def foo(request): d1, d2 = defer.Deferred(), defer.Deferred() reactor.callLater(0.01, d1.callback, 1) reactor.callLater(0.02, d2.callback, request.param) pytest_twisted.blockon(d1) return d2 @pytest_twisted.ensureDeferred async def test_succeed(foo): x = await foo if x == "web": raise RuntimeError("baz") """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2, "failed": 1}) @skip_if_no_async_await() def test_async_fixture(testdir, cmd_opts): pytest_ini_file = """ [pytest] markers = redgreenblue """ testdir.makefile('.ini', pytest=pytest_ini_file) test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_fixture( scope="function", params=["fs", "imap", "web"], ) @pytest.mark.redgreenblue async def foo(request): d1, d2 = defer.Deferred(), defer.Deferred() reactor.callLater(0.01, d1.callback, 1) reactor.callLater(0.02, d2.callback, request.param) await d1 return d2, @pytest_twisted.inlineCallbacks def test_succeed_blue(foo): x = yield foo[0] if x == "web": raise RuntimeError("baz") """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2, "failed": 1}) @skip_if_no_async_await() def test_async_fixture_no_arguments(testdir, cmd_opts, empty_optional_call): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_fixture{optional_call} async def scope(request): return request.scope def test_is_function_scope(scope): assert scope == "function" """.format(optional_call=empty_optional_call) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_ordered_teardown(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted results = [] @pytest.fixture(scope='function') def sync_fixture(): yield 42 results.append(2) @pytest_twisted.async_yield_fixture(scope='function') async def async_fixture(sync_fixture): yield sync_fixture results.append(1) def test_first(async_fixture): assert async_fixture == 42 def test_second(): assert results == [1, 2] """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) @skip_if_no_async_generators() def test_async_yield_fixture_can_await(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest_twisted @pytest_twisted.async_yield_fixture() async def foo(): d1, d2 = defer.Deferred(), defer.Deferred() reactor.callLater(0.01, d1.callback, 1) reactor.callLater(0.02, d2.callback, 2) await d1 # Twisted doesn't allow calling back with a Deferred as a value. # This deferred is being wrapped up in a tuple to sneak through. # https://github.com/twisted/twisted/blob/c0f1394c7bfb04d97c725a353a1f678fa6a1c602/src/twisted/internet/defer.py#L459 yield d2, @pytest_twisted.ensureDeferred async def test(foo): x = await foo[0] assert x == 2 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_failed_test(testdir, cmd_opts): test_file = """ import pytest_twisted @pytest_twisted.async_yield_fixture() async def foo(): yield 92 @pytest_twisted.ensureDeferred async def test(foo): assert False """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) rr.stdout.fnmatch_lines(lines2=["E*assert False"]) assert_outcomes(rr, {"failed": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_test_exception(testdir, cmd_opts): test_file = """ import pytest_twisted class UniqueLocalException(Exception): pass @pytest_twisted.async_yield_fixture() async def foo(): yield 92 @pytest_twisted.ensureDeferred async def test(foo): raise UniqueLocalException("some message") """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) rr.stdout.fnmatch_lines(lines2=["E*.UniqueLocalException: some message*"]) assert_outcomes(rr, {"failed": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_yields_twice(testdir, cmd_opts): test_file = """ import pytest_twisted @pytest_twisted.async_yield_fixture() async def foo(): yield 92 yield 36 @pytest_twisted.ensureDeferred async def test(foo): assert foo == 92 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1, "errors": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_teardown_exception(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted class UniqueLocalException(Exception): pass @pytest_twisted.async_yield_fixture() async def foo(request): yield 13 raise UniqueLocalException("some message") @pytest_twisted.ensureDeferred async def test_succeed(foo): assert foo == 13 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) rr.stdout.fnmatch_lines(lines2=["E*.UniqueLocalException: some message*"]) assert_outcomes(rr, {"passed": 1, "errors": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_no_arguments( testdir, cmd_opts, empty_optional_call, ): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_yield_fixture{optional_call} async def scope(request): yield request.scope def test_is_function_scope(scope): assert scope == "function" """.format(optional_call=empty_optional_call) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) @skip_if_no_async_generators() def test_async_yield_fixture_function_scope(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted check_me = 0 @pytest_twisted.async_yield_fixture(scope="function") async def foo(): global check_me if check_me != 0: raise Exception('check_me already modified before fixture run') check_me = 1 yield 42 if check_me != 2: raise Exception( 'check_me not updated properly: {}'.format(check_me), ) check_me = 0 def test_first(foo): global check_me assert check_me == 1 assert foo == 42 check_me = 2 def test_second(foo): global check_me assert check_me == 1 assert foo == 42 check_me = 2 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) @skip_if_no_async_await() def test_async_simple_fixture_in_fixture(testdir, cmd_opts): test_file = """ import itertools from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_fixture(name='four') async def fixture_four(): return 4 @pytest_twisted.async_fixture(name='doublefour') async def fixture_doublefour(four): return 2 * four @pytest_twisted.ensureDeferred async def test_four(four): assert four == 4 @pytest_twisted.ensureDeferred async def test_doublefour(doublefour): assert doublefour == 8 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) @skip_if_no_async_generators() def test_async_yield_simple_fixture_in_fixture(testdir, cmd_opts): test_file = """ import itertools from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_yield_fixture(name='four') async def fixture_four(): yield 4 @pytest_twisted.async_yield_fixture(name='doublefour') async def fixture_doublefour(four): yield 2 * four @pytest_twisted.ensureDeferred async def test_four(four): assert four == 4 @pytest_twisted.ensureDeferred async def test_doublefour(doublefour): assert doublefour == 8 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) @skip_if_no_async_await() @pytest.mark.parametrize('innerasync', [ pytest.param(truth, id='innerasync={}'.format(truth)) for truth in [True, False] ]) def test_async_fixture_in_fixture(testdir, cmd_opts, innerasync): maybe_async = 'async ' if innerasync else '' maybe_await = 'await ' if innerasync else '' test_file = """ import itertools from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_fixture(name='increment') async def fixture_increment(): counts = itertools.count() {maybe_async}def increment(): return next(counts) return increment @pytest_twisted.async_fixture(name='doubleincrement') async def fixture_doubleincrement(increment): {maybe_async}def doubleincrement(): n = {maybe_await}increment() return n * 2 return doubleincrement @pytest_twisted.ensureDeferred async def test_increment(increment): first = {maybe_await}increment() second = {maybe_await}increment() assert (first, second) == (0, 1) @pytest_twisted.ensureDeferred async def test_doubleincrement(doubleincrement): first = {maybe_await}doubleincrement() second = {maybe_await}doubleincrement() assert (first, second) == (0, 2) """.format(maybe_async=maybe_async, maybe_await=maybe_await) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) # assert_outcomes(rr, {"passed": 1}) @skip_if_no_async_generators() @pytest.mark.parametrize('innerasync', [ pytest.param(truth, id='innerasync={}'.format(truth)) for truth in [True, False] ]) def test_async_yield_fixture_in_fixture(testdir, cmd_opts, innerasync): maybe_async = 'async ' if innerasync else '' maybe_await = 'await ' if innerasync else '' test_file = """ import itertools from twisted.internet import reactor, defer import pytest import pytest_twisted @pytest_twisted.async_yield_fixture(name='increment') async def fixture_increment(): counts = itertools.count() {maybe_async}def increment(): return next(counts) yield increment @pytest_twisted.async_yield_fixture(name='doubleincrement') async def fixture_doubleincrement(increment): {maybe_async}def doubleincrement(): n = {maybe_await}increment() return n * 2 yield doubleincrement @pytest_twisted.ensureDeferred async def test_increment(increment): first = {maybe_await}increment() second = {maybe_await}increment() assert (first, second) == (0, 1) @pytest_twisted.ensureDeferred async def test_doubleincrement(doubleincrement): first = {maybe_await}doubleincrement() second = {maybe_await}doubleincrement() assert (first, second) == (0, 2) """.format(maybe_async=maybe_async, maybe_await=maybe_await) testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) def test_blockon_in_hook(testdir, cmd_opts, request): skip_if_reactor_not(request, "default") conftest_file = """ import pytest_twisted from twisted.internet import reactor, defer def pytest_configure(config): pytest_twisted.init_default_reactor() d1, d2 = defer.Deferred(), defer.Deferred() reactor.callLater(0.01, d1.callback, 1) reactor.callLater(0.02, d2.callback, 1) pytest_twisted.blockon(d1) pytest_twisted.blockon(d2) """ testdir.makeconftest(conftest_file) test_file = """ from twisted.internet import reactor, defer def test_succeed(): d = defer.Deferred() reactor.callLater(0.01, d.callback, 1) return d """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_wrong_reactor(testdir, cmd_opts, request): skip_if_reactor_not(request, "default") conftest_file = """ def pytest_addhooks(): import twisted.internet.reactor twisted.internet.reactor = None """ testdir.makeconftest(conftest_file) test_file = """ def test_succeed(): pass """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert "WrongReactorAlreadyInstalledError" in rr.stderr.str() def test_blockon_in_hook_with_qt5reactor(testdir, cmd_opts, request): skip_if_reactor_not(request, "qt5reactor") conftest_file = """ import pytest_twisted import pytestqt from twisted.internet import defer def pytest_configure(config): pytest_twisted.init_qt5_reactor() d = defer.Deferred() from twisted.internet import reactor reactor.callLater(0.01, d.callback, 1) pytest_twisted.blockon(d) """ testdir.makeconftest(conftest_file) test_file = """ from twisted.internet import reactor, defer def test_succeed(): d = defer.Deferred() reactor.callLater(0.01, d.callback, 1) return d """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_wrong_reactor_with_qt5reactor(testdir, cmd_opts, request): skip_if_reactor_not(request, "qt5reactor") conftest_file = """ def pytest_addhooks(): import twisted.internet.default twisted.internet.default.install() """ testdir.makeconftest(conftest_file) test_file = """ def test_succeed(): pass """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert "WrongReactorAlreadyInstalledError" in rr.stderr.str() def test_pytest_from_reactor_thread(testdir, cmd_opts, request): skip_if_reactor_not(request, "default") test_file = """ import pytest import pytest_twisted from twisted.internet import reactor, defer @pytest.fixture def fix(): d = defer.Deferred() reactor.callLater(0.01, d.callback, 42) return pytest_twisted.blockon(d) def test_simple(fix): assert fix == 42 @pytest_twisted.inlineCallbacks def test_fail(): d = defer.Deferred() reactor.callLater(0.01, d.callback, 1) yield d assert False """ testdir.makepyfile(test_file) runner_file = """ import pytest from twisted.internet import reactor from twisted.internet.defer import inlineCallbacks from twisted.internet.threads import deferToThread codes = [] @inlineCallbacks def main(): try: codes.append((yield deferToThread(pytest.main, ['-k simple']))) codes.append((yield deferToThread(pytest.main, ['-k fail']))) finally: reactor.stop() if __name__ == '__main__': reactor.callLater(0, main) reactor.run() codes == [0, 1] or exit(1) """ testdir.makepyfile(runner=runner_file) # check test file is ok in standalone mode: rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1, "failed": 1}) # test embedded mode: assert testdir.run(sys.executable, "runner.py", timeout=timeout).ret == 0 def test_blockon_in_hook_with_asyncio(testdir, cmd_opts, request): skip_if_reactor_not(request, "asyncio") conftest_file = """ import pytest import pytest_twisted from twisted.internet import defer @pytest.hookimpl(tryfirst=True) def pytest_configure(config): pytest_twisted._use_asyncio_selector_if_required(config=config) pytest_twisted.init_asyncio_reactor() d = defer.Deferred() from twisted.internet import reactor reactor.callLater(0.01, d.callback, 1) pytest_twisted.blockon(d) """ testdir.makeconftest(conftest_file) test_file = """ from twisted.internet import reactor, defer def test_succeed(): d = defer.Deferred() reactor.callLater(0.01, d.callback, 1) return d """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_wrong_reactor_with_asyncio(testdir, cmd_opts, request): skip_if_reactor_not(request, "asyncio") conftest_file = """ import pytest import pytest_twisted @pytest.hookimpl(tryfirst=True) def pytest_configure(config): pytest_twisted._use_asyncio_selector_if_required(config=config) def pytest_addhooks(): import twisted.internet.default twisted.internet.default.install() """ testdir.makeconftest(conftest_file) test_file = """ def test_succeed(): pass """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert "WrongReactorAlreadyInstalledError" in rr.stderr.str() @skip_if_no_async_generators() def test_async_fixture_module_scope(testdir, cmd_opts): test_file = """ from twisted.internet import reactor, defer import pytest import pytest_twisted check_me = 0 @pytest_twisted.async_yield_fixture(scope="module") async def foo(): global check_me if check_me != 0: raise Exception('check_me already modified before fixture run') check_me = 1 yield 42 if check_me != 3: raise Exception( 'check_me not updated properly: {}'.format(check_me), ) check_me = 0 def test_first(foo): global check_me assert check_me == 1 assert foo == 42 check_me = 2 def test_second(foo): global check_me assert check_me == 2 assert foo == 42 check_me = 3 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 2}) def test_inlinecallbacks_method_with_fixture_gets_self(testdir, cmd_opts): test_file = """ import pytest import pytest_twisted from twisted.internet import defer @pytest.fixture def foo(): return 37 class TestClass: @pytest_twisted.inlineCallbacks def test_self_isinstance(self, foo): d = defer.succeed(None) yield d assert isinstance(self, TestClass) """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts) assert_outcomes(rr, {"passed": 1}) def test_inlinecallbacks_method_with_fixture_gets_fixture(testdir, cmd_opts): test_file = """ import pytest import pytest_twisted from twisted.internet import defer @pytest.fixture def foo(): return 37 class TestClass: @pytest_twisted.inlineCallbacks def test_self_isinstance(self, foo): d = defer.succeed(None) yield d assert foo == 37 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) @skip_if_no_async_await() def test_ensuredeferred_method_with_fixture_gets_self(testdir, cmd_opts): test_file = """ import pytest import pytest_twisted @pytest.fixture def foo(): return 37 class TestClass: @pytest_twisted.ensureDeferred async def test_self_isinstance(self, foo): assert isinstance(self, TestClass) """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) @skip_if_no_async_await() def test_ensuredeferred_method_with_fixture_gets_fixture(testdir, cmd_opts): test_file = """ import pytest import pytest_twisted @pytest.fixture def foo(): return 37 class TestClass: @pytest_twisted.ensureDeferred async def test_self_isinstance(self, foo): assert foo == 37 """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1}) def test_import_pytest_twisted_in_conftest_py_not_a_problem(testdir, cmd_opts): conftest_file = """ import pytest import pytest_twisted @pytest.hookimpl(tryfirst=True) def pytest_configure(config): pytest_twisted._use_asyncio_selector_if_required(config=config) """ testdir.makeconftest(conftest_file) test_file = """ import pytest_twisted def test_succeed(): pass """ testdir.makepyfile(test_file) rr = testdir.run(*cmd_opts, timeout=timeout) assert_outcomes(rr, {"passed": 1})

true

f71966a40eb176b3c19c2ff7f010677d27b381e5

2,799

py

Python

channel-api/tests/integration/conftest.py

xcantera/demo-provide-baseline

985f391973fa6ca0761104b55077fded28f152fc

[ "CC0-1.0" ]

3

2020-11-17T23:19:20.000Z

2021-03-29T15:08:56.000Z

channel-api/tests/integration/conftest.py

xcantera/demo-provide-baseline

985f391973fa6ca0761104b55077fded28f152fc

[ "CC0-1.0" ]

null

channel-api/tests/integration/conftest.py

xcantera/demo-provide-baseline

985f391973fa6ca0761104b55077fded28f152fc

[ "CC0-1.0" ]

1

2020-12-11T00:26:33.000Z

import pytest from http import HTTPStatus import urllib import requests from src import repos from libtrustbridge.utils.conf import env_s3_config, env_queue_config, env NOTIFICATIONS_REPO = env_queue_config('NOTIFICATIONS_REPO') DELIVERY_OUTBOX_REPO = env_queue_config('DELIVERY_OUTBOX_REPO') SUBSCRIPTIONS_REPO = env_s3_config('SUBSCRIPTIONS_REPO') CHANNEL_REPO = env_queue_config('CHANNEL_REPO') ENDPOINT = env('ENDPOINT', default='AU') @pytest.fixture(scope='function') def notifications_repo(): repo = repos.Notifications(NOTIFICATIONS_REPO) repo.WAIT_FOR_MESSAGE_SECONDS = 1 repo._unsafe_method__clear() yield repo @pytest.fixture(scope='function') def delivery_outbox_repo(): repo = repos.DeliveryOutbox(DELIVERY_OUTBOX_REPO) repo.WAIT_FOR_MESSAGE_SECONDS = 1 repo._unsafe_method__clear() yield repo @pytest.fixture(scope='function') def subscriptions_repo(): repo = repos.Subscriptions(SUBSCRIPTIONS_REPO) repo._unsafe_method__clear() yield repo @pytest.fixture(scope='function') def channel_repo(): repo = repos.Channel(CHANNEL_REPO) repo.WAIT_FOR_MESSAGE_SECONDS = 1 repo._unsafe_method__clear() yield repo class CallbackServer: def __init__(self, base_url=None): self.base_url = base_url if base_url.endswith('/') else base_url + '/' def get_callback_record(self, index): url = urllib.parse.urljoin(self.base_url, f'callbacks/{index}') response = requests.get(url) if response.status_code == HTTPStatus.OK: return response.json() elif response.status_code == HTTPStatus.NOT_FOUND: return None else: raise Exception(f'Unexpected response:{response.status_code}') def get_callback_records(self): url = urllib.parse.urljoin(self.base_url, 'callbacks') response = requests.get(url) if response.status_code == HTTPStatus.OK: return response.json() else: raise Exception(f'Unexpected response:{response.status_code}') def clear_callback_records(self): url = urllib.parse.urljoin(self.base_url, 'callbacks') response = requests.delete(url) if response.status_code == HTTPStatus.OK: pass else: raise Exception(f'Unexpected response:{response.status_code}') def valid_callback_url(self, id): return urllib.parse.urljoin(self.base_url, f'callback/valid/{id}') def invalid_callback_url(self, id): return urllib.parse.urljoin(self.base_url, f'callback/invalid/{id}') @pytest.fixture(scope='function') def callback_server(): callback_server = CallbackServer('http://baseline-channel-api-callback-server:11001') callback_server.clear_callback_records() yield callback_server

31.1

89

0.71597

import pytest from http import HTTPStatus import urllib import requests from src import repos from libtrustbridge.utils.conf import env_s3_config, env_queue_config, env NOTIFICATIONS_REPO = env_queue_config('NOTIFICATIONS_REPO') DELIVERY_OUTBOX_REPO = env_queue_config('DELIVERY_OUTBOX_REPO') SUBSCRIPTIONS_REPO = env_s3_config('SUBSCRIPTIONS_REPO') CHANNEL_REPO = env_queue_config('CHANNEL_REPO') ENDPOINT = env('ENDPOINT', default='AU') @pytest.fixture(scope='function') def notifications_repo(): repo = repos.Notifications(NOTIFICATIONS_REPO) repo.WAIT_FOR_MESSAGE_SECONDS = 1 repo._unsafe_method__clear() yield repo @pytest.fixture(scope='function') def delivery_outbox_repo(): repo = repos.DeliveryOutbox(DELIVERY_OUTBOX_REPO) repo.WAIT_FOR_MESSAGE_SECONDS = 1 repo._unsafe_method__clear() yield repo @pytest.fixture(scope='function') def subscriptions_repo(): repo = repos.Subscriptions(SUBSCRIPTIONS_REPO) repo._unsafe_method__clear() yield repo @pytest.fixture(scope='function') def channel_repo(): repo = repos.Channel(CHANNEL_REPO) repo.WAIT_FOR_MESSAGE_SECONDS = 1 repo._unsafe_method__clear() yield repo class CallbackServer: def __init__(self, base_url=None): self.base_url = base_url if base_url.endswith('/') else base_url + '/' def get_callback_record(self, index): url = urllib.parse.urljoin(self.base_url, f'callbacks/{index}') response = requests.get(url) if response.status_code == HTTPStatus.OK: return response.json() elif response.status_code == HTTPStatus.NOT_FOUND: return None else: raise Exception(f'Unexpected response:{response.status_code}') def get_callback_records(self): url = urllib.parse.urljoin(self.base_url, 'callbacks') response = requests.get(url) if response.status_code == HTTPStatus.OK: return response.json() else: raise Exception(f'Unexpected response:{response.status_code}') def clear_callback_records(self): url = urllib.parse.urljoin(self.base_url, 'callbacks') response = requests.delete(url) if response.status_code == HTTPStatus.OK: pass else: raise Exception(f'Unexpected response:{response.status_code}') def valid_callback_url(self, id): return urllib.parse.urljoin(self.base_url, f'callback/valid/{id}') def invalid_callback_url(self, id): return urllib.parse.urljoin(self.base_url, f'callback/invalid/{id}') @pytest.fixture(scope='function') def callback_server(): callback_server = CallbackServer('http://baseline-channel-api-callback-server:11001') callback_server.clear_callback_records() yield callback_server

true

f7196801f8fa58470aa03ad73efa1012011af858

28,195

py

Python

sacla/scripts/backups/sacla3_Chip_Manager_v7BAK.py

beamline-i24/DiamondChips

02fb58a95ad2c1712c41b641eb5f197d688c54c3

[ "Apache-2.0" ]

null

sacla/scripts/backups/sacla3_Chip_Manager_v7BAK.py

beamline-i24/DiamondChips

02fb58a95ad2c1712c41b641eb5f197d688c54c3

[ "Apache-2.0" ]

null

sacla/scripts/backups/sacla3_Chip_Manager_v7BAK.py

beamline-i24/DiamondChips

02fb58a95ad2c1712c41b641eb5f197d688c54c3

[ "Apache-2.0" ]

null

#!/usr/bin/python import pv, os, re, sys import math, time, string import numpy as np from time import sleep from ca import caput, caget import logging as lg import sacla3_Chip_StartUp_v7 as startup import sacla3_Chip_Mapping_v7 as mapping lg.basicConfig(format='%(asctime)s %(levelname)s: \t%(message)s',level=lg.DEBUG, filename='SACLA3v7.log') ############################################## # MANAGER MANAGER MANAGER MANAGER MANAGER # # This version last edited 03Sep2017 by DAS # # Prep for SACLA3 # ############################################## def initialise(): lg.info('INITIALISED') lg.warning('INITIALISED') lg.debug('INITIALISED') caput(pv.me14e_stage_x + '.VMAX', 15) caput(pv.me14e_stage_y + '.VMAX', 15) caput(pv.me14e_stage_z + '.VMAX', 15) caput(pv.me14e_filter + '.VMAX', 15) caput(pv.me14e_stage_x + '.VELO', 15) caput(pv.me14e_stage_y + '.VELO', 15) caput(pv.me14e_stage_z + '.VELO', 15) caput(pv.me14e_filter + '.VELO', 15) caput(pv.me14e_stage_x + '.ACCL', 0.01) caput(pv.me14e_stage_y + '.ACCL', 0.01) caput(pv.me14e_stage_z + '.ACCL', 0.01) caput(pv.me14e_filter + '.ACCL', 0.01) caput(pv.me14e_stage_x + '.HLM', 30) caput(pv.me14e_stage_x + '.LLM', -30) caput(pv.me14e_stage_y + '.HLM', 30) caput(pv.me14e_stage_y + '.LLM', -30) caput(pv.me14e_stage_z + '.HLM', 5.1) caput(pv.me14e_stage_z + '.LLM', -4.1) caput(pv.me14e_filter + '.HLM', 0.1) caput(pv.me14e_filter + '.LLM', -45.0) caput('ME14E-MO-IOC-01:GP1', 0) caput('ME14E-MO-IOC-01:GP2', 0) print 'Clearing' for i in range(3, 100): pvar = 'ME14E-MO-IOC-01:GP' + str(i) val = caput(pvar, 1) sys.stdout.write('.') sys.stdout.flush() print '\nDONT FORGET TO DO THIS: export EPICS_CA_ADDR_LIST=172.23.190.255' print 'DONT FORGET TO DO THIS: export EPICS_CA_AUTO_ADDR_LIST=NO' print 'Initialisation Complete' def write_parameter_file(): print '\n\n', 10*'set', '\n' #param_path = '/dls_sw/i24/scripts/fastchips/parameter_files/' param_path = '/localhome/local/Documents/sacla/parameter_files/' param_fid = 'parameters.txt' print 'Writing Parameter File\n', param_path+param_fid lg.info('Writing Parameter File\n', param_path+param_fid) lg.info('CHIP_MANAGER\twrite_parameter_file:Writing') f = open(param_path + param_fid,'w') chip_name = caget(pv.me14e_chip_name) f.write('chip_name \t%s\n' %chip_name) print 'chip_name:', chip_name #f.write('path \t%s\n' %path) #print 'path:', path protein_name = caget(pv.me14e_filepath) f.write('protein_name \t%s\n' %protein_name) print 'protein_name:', protein_name n_exposures = caget(pv.me14e_gp3) f.write('n_exposures \t%s\n' %n_exposures) print 'n_exposures', n_exposures chip_type = caget(pv.me14e_gp1) #### Hack for sacla3 to bismuth chip type for oxford inner if str(chip_type) =='3': chip_type = '1' f.write('chip_type \t%s\n' %chip_type) print 'chip_type', chip_type map_type = caget(pv.me14e_gp2) f.write('map_type \t%s\n' %map_type) print 'map_type', map_type f.close() print '\n', 10*'set', '\n\n' def define_current_chip(chipid): load_stock_map('clear') """ Not sure what this is for: print 'Setting Mapping Type to Lite' caput(pv.me14e_gp2, 1) """ chip_type = caget(pv.me14e_gp1) print chip_type, chipid if chipid == 'toronto': caput(pv.me14e_gp1, 0) elif chipid == 'oxford': caput(pv.me14e_gp1, 1) elif chipid == 'hamburg': caput(pv.me14e_gp1, 2) elif chipid == 'hamburgfull': caput(pv.me14e_gp1, 2) elif chipid == 'bismuth1': caput(pv.me14e_gp1, 3) elif chipid == 'bismuth2': caput(pv.me14e_gp1, 4) elif chipid == 'regina': caput(pv.me14e_gp1, 5) #param_path = '/dls_sw/i24/scripts/fastchips/parameter_files/' param_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(param_path + chipid + '.pvar', 'r') for line in f.readlines(): s = line.rstrip('\n') print s if line.startswith('#'): continue caput(pv.me14e_pmac_str, s) print param_path + chipid + '.chip' print 10*'Done ' def save_screen_map(): #litemap_path = '/dls_sw/i24/scripts/fastchips/litemaps/' litemap_path = '/localhome/local/Documents/sacla/parameter_files/' print '\n\nSaving', litemap_path + 'currentchip.map' f = open(litemap_path + 'currentchip.map','w') print 'Printing only blocks with block_val == 1' for x in range(1, 82): block_str = 'ME14E-MO-IOC-01:GP%i' %(x+10) block_val = caget(block_str) if block_val == 1: print block_str, block_val line = '%02dstatus P3%02d1 \t%s\n' %(x, x, block_val) f.write(line) f.close() print 10*'Done ' return 0 def upload_parameters(chipid): if chipid == 'toronto': caput(pv.me14e_gp1, 0) width = 9 elif chipid == 'oxford': caput(pv.me14e_gp1, 1) width = 8 elif chipid == 'hamburg': caput(pv.me14e_gp1, 2) width = 3 elif chipid == 'bismuth1': caput(pv.me14e_gp1, 3) width = 1 elif chipid == 'bismuth2': caput(pv.me14e_gp1, 4) width = 7 elif chipid == 'regina': caput(pv.me14e_gp1, 5) width = 7 #litemap_path = '/dls_sw/i24/scripts/fastchips/litemaps/' litemap_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(litemap_path + 'currentchip.map','r') print 'chipid', chipid print width x = 1 for line in f.readlines()[:width**2]: cols = line.split( ) pvar = cols[1] value = cols[2] s = pvar +'='+ value if value != '1': s2 = pvar + ' ' sys.stdout.write(s2) else: sys.stdout.write(s+' ') sys.stdout.flush() if x == width: print x = 1 else: x += 1 caput(pv.me14e_pmac_str, s) sleep(0.02) print print 'Setting Mapping Type to Lite' caput(pv.me14e_gp2, 1) print 10*'Done ' def upload_full(): #fullmap_path = '/dls_sw/i24/scripts/fastchips/fullmaps/' fullmap_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(fullmap_path + 'currentchip.full', 'r').readlines() for x in range(len(f) / 2): pmac_list = [] for i in range(2): pmac_list.append(f.pop(0).rstrip('\n')) writeline = " ".join(pmac_list) print writeline caput(pv.me14e_pmac_str, writeline) sleep(0.02) print 10*'Done ' def load_stock_map(map_choice): print 'Please wait, adjusting lite map' # r33 = [19,18,17,26,31,32,33,24,25] r55 = [9,10,11,12,13,16,27,30,41,40,39,38,37,34,23,20] + r33 r77 = [7,6,5,4,3,2,1,14,15,28,29,42,43,44,45,46,47,48,49,36,35,22,21,8] + r55 # h33 = [3,2,1,6,7,8,9,4,5] x33 = [31,32,33,40,51,50,49,42,41] x55 = [25,24,23,22,21,34,39,52,57,58,59,60,61,48,43,30] + x33 x77 = [11,12,13,14,15,16,17,20,35,38,53,56,71,70,69,68,67,66,65,62,47,44,29,26] + x55 x99 = [9,8,7,6,5,4,3,2,1,18,19,36,37,54,55,72,73,74,75,76,77,78,79,80,81,64,63,46,45,28,27,10] + x77 x44 = [22,21,20,19,30,35,46,45,44,43,38,27,28,29,36,37] x49 = [x+1 for x in range(49)] x66 = [10,11,12,13,14,15,18,31,34,47,50,51,52,53,54,55,42,39,26,23] + x44 x88 = [8,7,6,5,4,3,2,1,16,17,32,33,48,49,64,63,62,61,60,59,58,57,56,41,40,25,24,9] + x66 map_dict = {} map_dict['clear']= [1] # map_dict['r33'] = r33 map_dict['r55'] = r55 map_dict['r77'] = r77 # map_dict['h33'] = h33 # map_dict['x33'] = x33 map_dict['x44'] = x44 map_dict['x49'] = x49 map_dict['x55'] = x55 map_dict['x66'] = x66 map_dict['x77'] = x77 map_dict['x88'] = x88 map_dict['x99'] = x99 print 'Clearing' for i in range(1, 82): pvar = 'ME14E-MO-IOC-01:GP' + str(i + 10) caput(pvar, 0) sys.stdout.write('.') sys.stdout.flush() print '\nmap cleared' print 'loading map_choice', map_choice for i in map_dict[map_choice]: pvar = 'ME14E-MO-IOC-01:GP' + str(i + 10) caput(pvar, 1) print 10*'Done ' def load_lite_map(): load_stock_map('clear') toronto_block_dict = {\ 'A1':'01', 'A2':'02', 'A3':'03', 'A4':'04', 'A5':'05', 'A6':'06','A7':'07', 'A8':'08', 'A9':'09' ,'B1':'18', 'B2':'17', 'B3':'16', 'B4':'15', 'B5':'14', 'B6':'13','B7':'12', 'B8':'11', 'B9':'10' ,'C1':'19', 'C2':'20', 'C3':'21', 'C4':'22', 'C5':'23', 'C6':'24','C7':'25', 'C8':'26', 'C9':'27' ,'D1':'36', 'D2':'35', 'D3':'34', 'D4':'33', 'D5':'32', 'D6':'31','D7':'30', 'D8':'29', 'D9':'28' ,'E1':'37', 'E2':'38', 'E3':'39', 'E4':'40', 'E5':'41', 'E6':'42','E7':'43', 'E8':'44', 'E9':'45' ,'F1':'54', 'F2':'53', 'F3':'52', 'F4':'51', 'F5':'50', 'F6':'49','F7':'48', 'F8':'47', 'F9':'46' ,'G1':'55', 'G2':'56', 'G3':'57', 'G4':'58', 'G5':'59', 'G6':'60','G7':'61', 'G8':'62', 'G9':'63' ,'H1':'72', 'H2':'71', 'H3':'70', 'H4':'69', 'H5':'68', 'H6':'67','H7':'66', 'H8':'65', 'H9':'64' ,'I1':'73', 'I2':'74', 'I3':'75', 'I4':'76', 'I5':'77', 'I6':'78','I7':'79', 'I8':'80', 'I9':'81'} #Oxford_block_dict is wrong (columns and rows need to flip) added in script below to generate it automatically however kept this for backwards compatiability/reference oxford_block_dict = {\ 'A1':'01', 'A2':'02', 'A3':'03', 'A4':'04', 'A5':'05', 'A6':'06','A7':'07', 'A8':'08' ,'B1':'16', 'B2':'15', 'B3':'14', 'B4':'13', 'B5':'12', 'B6':'11','B7':'10', 'B8':'09' ,'C1':'17', 'C2':'18', 'C3':'19', 'C4':'20', 'C5':'21', 'C6':'22','C7':'23', 'C8':'24' ,'D1':'32', 'D2':'31', 'D3':'30', 'D4':'29', 'D5':'28', 'D6':'27','D7':'26', 'D8':'25' ,'E1':'33', 'E2':'34', 'E3':'35', 'E4':'36', 'E5':'37', 'E6':'38','E7':'39', 'E8':'40' ,'F1':'48', 'F2':'47', 'F3':'46', 'F4':'45', 'F5':'44', 'F6':'43','F7':'42', 'F8':'41' ,'G1':'49', 'G2':'50', 'G3':'51', 'G4':'52', 'G5':'53', 'G6':'54','G7':'55', 'G8':'56' ,'H1':'64', 'H2':'63', 'H3':'62', 'H4':'61', 'H5':'60', 'H6':'59','H7':'58', 'H8':'57'} regina_block_dict = {\ 'A1':'01', 'A2':'02', 'A3':'03', 'A4':'04', 'A5':'05', 'A6':'06','A7':'07' ,'B1':'14', 'B2':'13', 'B3':'12', 'B4':'11', 'B5':'10', 'B6':'09','B7':'08' ,'C1':'15', 'C2':'16', 'C3':'17', 'C4':'18', 'C5':'19', 'C6':'20','C7':'21' ,'D1':'28', 'D2':'27', 'D3':'26', 'D4':'25', 'D5':'24', 'D6':'23','D7':'22' ,'E1':'29', 'E2':'30', 'E3':'31', 'E4':'32', 'E5':'33', 'E6':'34','E7':'35' ,'F1':'42', 'F2':'41', 'F3':'40', 'F4':'39', 'F5':'38', 'F6':'37','F7':'36' ,'G1':'43', 'G2':'44', 'G3':'45', 'G4':'46', 'G5':'47', 'G6':'48','G7':'49'} hamburg_block_dict = {\ 'A1':'01', 'A2':'02', 'A3':'03' ,'B1':'06', 'B2':'05', 'B3':'04' ,'C1':'07', 'C2':'08', 'C3':'09'} chip_type = caget(pv.me14e_gp1) if chip_type == 0: print 'Toronto Block Order' block_dict = toronto_block_dict elif chip_type == 1: print 'Oxford Block Order' #block_dict = oxford_block_dict rows = ['A','B','C','D','E','F','G','H'] columns = list(range(1,9)) btn_names = {} flip = True for x, column in enumerate(columns): for y,row in enumerate(rows): i=x*8+y if i%8 == 0 and flip == False: flip = True z = 8 - (y+1) elif i%8 == 0 and flip == True: flip = False z = y elif flip == False: z = y elif flip == True: z = 8 - (y+1) else: print('something is wrong with chip grid creation') break button_name = str(row)+str(column) lab_num = x*8+z label='%02.d'%(lab_num+1) btn_names[button_name] = label #print button_name, btn_names[button_name] block_dict = btn_names elif chip_type == 2: print 'Hamburg Block Order' block_dict = hamburg_block_dict elif chip_type == 5: print 'Regina Block Order' block_dict = regina_block_dict #litemap_path = '/dls_sw/i24/scripts/fastchips/litemaps/' litemap_path = '/localhome/local/Documents/sacla/parameter_files/' litemap_fid = str(caget(pv.me14e_gp5)) + '.lite' print 'opening', litemap_path + litemap_fid f = open(litemap_path + litemap_fid, 'r') print 'please wait, loading LITE map' for line in f.readlines(): entry = line.split() block_name = entry[0] yesno = entry[1] block_num = block_dict[block_name] pvar = 'ME14E-MO-IOC-01:GP' + str(int(block_num) + 10) print block_name, yesno, pvar caput(pvar, yesno) print 10*'Done ' def load_full_map(location ='SACLA'): if location == 'i24': chip_name, visit, sub_dir, n_exposures, chip_type, map_type = startup.scrape_parameter_file(location) else: chip_name, sub_dir, n_exposures, chip_type, map_type = startup.scrape_parameter_file(location) #fullmap_path = '/dls_sw/i24/scripts/fastchips/fullmaps/' fullmap_path = '/localhome/local/Documents/sacla/parameter_files/' fullmap_fid = fullmap_path + str(caget(pv.me14e_gp5)) + '.spec' print 'opening', fullmap_fid mapping.plot_file(fullmap_fid, chip_type) print '\n\n', 10*'PNG ' mapping.convert_chip_to_hex(full_map_fid, chip_type) os.system("cp %s %s" % (fullmap_fid[:-4]+'full', fullmap_path+'currentchip.full')) print 10*'Done ', '\n' def moveto(place): print 5 * (place + ' ') chip_type = caget(pv.me14e_gp1) print 'CHIP TYPE', chip_type if chip_type == 0: print 'Toronto Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': caput(pv.me14e_stage_x, +18.975) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, +21.375) elif chip_type == 1: print 'Oxford Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': caput(pv.me14e_stage_x, 25.40) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 25.40) elif chip_type == 2: print 'Hamburg Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': #caput(pv.me14e_stage_x, +17.16) caput(pv.me14e_stage_x, +24.968) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) #caput(pv.me14e_stage_y, -26.49) caput(pv.me14e_stage_y, +24.968) elif chip_type == 3: print 'Oxford Inner Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': caput(pv.me14e_stage_x, 24.60) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 24.60) elif chip_type == 5: print 'Regina Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': caput(pv.me14e_stage_x, +17.175) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, +17.175) else: print 'Unknown chip_type move' # Non Chip Specific Move if place == 'zero': caput(pv.me14e_pmac_str, '!x0y0z0') elif place == 'yag': caput(pv.me14e_stage_x, 1.0) caput(pv.me14e_stage_y, 1.0) caput(pv.me14e_stage_z, 1.0) elif place == 'load_position': print 'load position' caput(pv.me14e_filter, -25) caput(pv.me14e_stage_x, -25.0) caput(pv.me14e_stage_y, -25.0) caput(pv.me14e_stage_z, 0.0) caput(pv.me14e_pmac_str, 'M512=0 M511=1') #caput(pv.absb_mp_select, 'Robot') #caput(pv.ap1_mp_select, 'Robot') #caput(pv.blight_mp_select, 'Out') #caput(pv.det_z, 1480) elif place == 'collect_position': print 'collect position' caput(pv.me14e_filter, 25) caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) caput(pv.me14e_stage_z, 0.0) caput(pv.me14e_pmac_str, 'M512=0 M511=1') #caput(pv.absb_mp_select, 'Data Collection') #caput(pv.ap1_mp_select, 'In') #caput(pv.blight_mp_select, 'In') elif place == 'lightin': print 'light in' caput(pv.me14e_filter, 25) elif place == 'lightout': print 'light out' caput(pv.me14e_filter, -25) elif place == 'flipperin': ##### nb need M508=100 M509 =150 somewhere caput(pv.me14e_pmac_str, 'M512=0 M511=1') elif place == 'flipperout': caput(pv.me14e_pmac_str, ' M512=1 M511=1') def scrape_mtr_directions(): #param_path = '/dls_sw/i24/scripts/fastchips/parameter_files/' param_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(param_path + 'motor_direction.txt', 'r') mtr1_dir, mtr2_dir, mtr3_dir = 1,1,1 for line in f.readlines(): if line.startswith('mtr1'): mtr1_dir = float(int(line.split('=')[1])) elif line.startswith('mtr2'): mtr2_dir = float(int(line.split('=')[1])) elif line.startswith('mtr3'): mtr3_dir = float(int(line.split('=')[1])) else: continue f.close() return mtr1_dir, mtr2_dir, mtr3_dir def fiducial(point): scale = 10000.0 #param_path = '/dls_sw/i24/scripts/fastchips/parameter_files/' param_path = '/localhome/local/Documents/sacla/parameter_files/' mtr1_dir, mtr2_dir, mtr3_dir = scrape_mtr_directions() rbv_1 = caget(pv.me14e_stage_x + '.RBV') rbv_2 = caget(pv.me14e_stage_y + '.RBV') rbv_3 = caget(pv.me14e_stage_z + '.RBV') raw_1 = caget(pv.me14e_stage_x + '.RRBV') raw_2 = caget(pv.me14e_stage_y + '.RRBV') raw_3 = caget(pv.me14e_stage_z + '.RRBV') """ June 8th 2017 change from this to rbv f_x = (mtr1_dir*raw_1) / scale f_y = (mtr2_dir*raw_2) / scale f_z = (mtr3_dir*raw_3) / scale """ f_x = rbv_1 f_y = rbv_2 f_z = rbv_3 print '\nWriting Fiducial File', 20*('%s ' %point) print 'MTR\tRBV\tRAW\tDirect.\tf_value' print 'MTR1\t%1.4f\t%i\t%i\t%1.4f' % (rbv_1, raw_1, mtr1_dir, f_x) print 'MTR2\t%1.4f\t%i\t%i\t%1.4f' % (rbv_2, raw_2, mtr2_dir, f_y) print 'MTR3\t%1.4f\t%i\t%i\t%1.4f' % (rbv_3, raw_3, mtr3_dir, f_z) print 'Writing Fiducial File', 20*('%s ' %point) f = open(param_path + 'fiducial_%s.txt' %point, 'w') f.write('MTR\tRBV\tRAW\tCorr\tf_value\n') f.write('MTR1\t%1.4f\t%i\t%i\t%1.4f\n' % (rbv_1, raw_1, mtr1_dir, f_x)) f.write('MTR2\t%1.4f\t%i\t%i\t%1.4f\n' % (rbv_2, raw_2, mtr2_dir, f_y)) f.write('MTR3\t%1.4f\t%i\t%i\t%1.4f' % (rbv_3, raw_3, mtr3_dir, f_z)) f.close() print 10*'Done ' def scrape_mtr_fiducials(point): #param_path = '/dls_sw/i24/scripts/fastchips/parameter_files/' param_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(param_path+'fiducial_%i.txt' %point,'r') f_lines = f.readlines()[1:] f_x = float(f_lines[0].rsplit()[4]) f_y = float(f_lines[1].rsplit()[4]) f_z = float(f_lines[2].rsplit()[4]) f.close() return f_x, f_y, f_z def cs_maker(): chip_type = caget(pv.me14e_gp1) fiducial_dict = {} fiducial_dict[0] = [18.975, 21.375] fiducial_dict[1] = [25.400, 25.400] fiducial_dict[2] = [24.968, 24.968] fiducial_dict[3] = [24.600, 24.600] fiducial_dict[4] = [27.500, 27.500] fiducial_dict[5] = [17.175, 17.175] print chip_type, fiducial_dict[chip_type] mtr1_dir, mtr2_dir, mtr3_dir = scrape_mtr_directions() f1_x, f1_y, f1_z = scrape_mtr_fiducials(1) f2_x, f2_y, f2_z = scrape_mtr_fiducials(2) print 'AAAAAAAAAAAAAAAAABBBBBBBBBBBBBB' print 'mtr1 direction', mtr1_dir print 'mtr2 direction', mtr2_dir print 'mtr3 direction', mtr3_dir """ Theory Rx: rotation about X-axis, pitch Ry: rotation about Y-axis, yaw Rz: rotation about Z-axis, roll The order of rotation is Roll->Yaw->Pitch (Rx*Ry*Rz) Rx Ry Rz |1 0 0| | Cy 0 Sy| |Cz -Sz 0| | CyCz -CxSz Sy | |0 Cx -Sx|*| 0 1 0|*|Sz Cz 0| = | SxSyCz+CxSz -SxSySz+CxCz -SxCy| |0 Sx Cx| |-Sy 0 Cy| | 0 0 1| |-CxSyCz+SxSz CxSySz+SxCz CxCy| BELOW iS TEST TEST (CLOCKWISE) Rx Ry Rz |1 0 0| | Cy 0 -Sy| |Cz Sz 0| | CyCz CxSz -Sy | |0 Cx Sx|*| 0 1 0|*|-Sz Cz 0| = | SxSyCz-CxSz SxSySz+CxCz SxCy| |0 -Sx Cx| | Sy 0 Cy| | 0 0 1| | CxSyCz+SxSz CxSySz-SxCz CxCy| """ # Rotation Around Z # # If stages upsidedown (I24) change sign of Sz Sz1 = f1_y / fiducial_dict[chip_type][0] Sz2 = -1 * (f2_x / fiducial_dict[chip_type][1]) Sz = ((Sz1 + Sz2) / 2) Cz = np.sqrt((1 - Sz**2)) print 'Sz1 , %1.4f, %1.4f' % (Sz1, np.degrees(np.arcsin(Sz1))) print 'Sz2 , %1.4f, %1.4f' % (Sz2, np.degrees(np.arcsin(Sz2))) print 'Sz , %1.4f, %1.4f' % (Sz, np.degrees(np.arcsin(Sz))) print 'Cz , %1.4f, %1.4f\n' % (Cz, np.degrees(np.arccos(Cz))) # Rotation Around Y # Sy = f1_z / fiducial_dict[chip_type][0] Cy = np.sqrt((1 - Sy**2)) print 'Sy , %1.4f, %1.4f' % (Sy, np.degrees(np.arcsin(Sy))) print 'Cy , %1.4f, %1.4f\n' % (Cy, np.degrees(np.arccos(Cy))) # Rotation Around X # # If stages upsidedown (I24) change sign of Sx Sx = -1* f2_z / fiducial_dict[chip_type][1] Cx = np.sqrt((1 - Sx**2)) print 'Sx , %1.4f, %1.4f' % (Sx, np.degrees(np.arcsin(Sx))) print 'Cx , %1.4f, %1.4f\n' % (Cx, np.degrees(np.arccos(Cx))) # Crucifix 1: In normal orientation (sat on table facing away) # X=0.0000 , Y=0.0000, Z=0.0001000 (mm/cts for MRES and ERES) #scalex,scaley,scalez = 10010.0, 10000.0, 10000.0 # Crucifix 1: In beamline position (upside down facing away) # X=0.000099896 , Y=0.000099983, Z=0.0001000 (mm/cts for MRES and ERES) scalex, scaley, scalez = 10010.4, 10001.7, 10000.0 # Crucifix 2: In normal orientation (sat on table facing away) # X=0.0000999 , Y=0.00009996, Z=0.0001000 (mm/cts for MRES and ERES) #scalex,scaley,scalez = 10010.0, 10004.0, 10000.0 # Temple 1: In normal orientation (sat on table facing away) # X=0.0000 , Y=0.0000, Z=0.0001000 (mm/cts for MRES and ERES) #scalex,scaley,scalez = 10008.0, 10002.0, 10000.0 #minus signs added Aug17 in lab 30 preparing for sacla #added to y1factor x2factor x1factor = mtr1_dir * scalex * (Cy * Cz) y1factor = mtr2_dir * scaley * (-1. * Cx * Sz) z1factor = mtr3_dir * scalez * Sy x2factor = mtr1_dir * scalex * ((Sx*Sy*Cz) + (Cx*Sz)) y2factor = mtr2_dir * scaley * ((Cx*Cz) - (Sx*Sy*Sz)) z2factor = mtr3_dir * scalez * (-1. * Sx * Cy) x3factor = mtr1_dir * scalex * ((Sx*Sz) - (Cx*Sy*Cz)) y3factor = mtr2_dir * scaley * ((Cx*Sy*Sz) + (Sx*Cz)) z3factor = mtr3_dir * scalez * (Cx* Cy) """ Rx Ry Rz |1 0 0| | Cy 0 Sy| |Cz -Sz 0| | CyCz -CxSz Sy | |0 Cx -Sx|*| 0 1 0|*|Sz Cz 0| = | SxSyCz+CxSz -SxSySz+CxCz -SxCy| |0 Sx Cx| |-Sy 0 Cy| | 0 0 1| |-CxSyCz+SxSz CxSySz+SxCz CxCy| """ # skew is the difference between the Sz1 and Sz2 after rotation is taken out. # this should be measured in situ prior to expriment # In situ is measure by hand using opposite and adjacent RBV after calibration of # scale factors #print 10*'WARNING\n', '\nHave you calculated skew?\n\n', 10*'WARNING\n' # Crucifix 1 on table #skew = -0.187 # Crucifix 1 on beamline #skew = -0.1568 skew = 0.1863 # Crucifix 2 #skew = 0.060 # Temple 1 #skew = 0.02 print 'Skew being used is: %1.4f' %skew s1 = np.degrees(np.arcsin(Sz1)) s2 = np.degrees(np.arcsin(Sz2)) rot = np.degrees(np.arcsin((Sz1+Sz2) / 2)) calc_skew = ((s1-rot) - (s2-rot)) print 's1:%1.4f s2:%1.4f rot:%1.4f' %(s1, s2, rot) print 'Calculated rotation from current fiducials is: %1.4f' %rot print 'Calculated skew from current fiducials is: %1.4f' %calc_skew #skew = calc_skew sinD = np.sin((skew/2) * (np.pi/180)) cosD = np.cos((skew/2) * (np.pi/180)) new_x1factor = (x1factor * cosD) + (y1factor * sinD) new_y1factor = (x1factor * sinD) + (y1factor * cosD) new_x2factor = (x2factor * cosD) + (y2factor * sinD) new_y2factor = (x2factor * sinD) + (y2factor * cosD) cs1 = "#1->%+1.3fX%+1.3fY%+1.3fZ" % (new_x1factor, new_y1factor, z1factor) cs2 = "#2->%+1.3fX%+1.3fY%+1.3fZ" % (new_x2factor, new_y2factor, z2factor) cs3 = "#3->%+1.3fX%+1.3fY%+1.3fZ" % (x3factor, y3factor, z3factor) print '\n'.join([cs1, cs2, cs3]) print 'These should be 1. This is the sum of the squares of the factors divided by their scale' print np.sqrt(x1factor**2 + y1factor**2 + z1factor**2) / scalex print np.sqrt(x2factor**2 + y2factor**2 + z2factor**2) / scaley print np.sqrt(x3factor**2 + y3factor**2 + z3factor**2) / scalez print 'Long wait, please be patient' caput(pv.me14e_pmac_str, '!x0y0z0') sleep(2.5) caput(pv.me14e_pmac_str, '&2') caput(pv.me14e_pmac_str, cs1) caput(pv.me14e_pmac_str, cs2) caput(pv.me14e_pmac_str, cs3) caput(pv.me14e_pmac_str, '!x0y0z0') sleep(0.1) caput(pv.me14e_pmac_str, '#1hmz#2hmz#3hmz') sleep(0.1) print 5*'chip_type',type(chip_type) # NEXT THREE LINES COMMENTED OUT FOR CS TESTS 5 JUNE if str(chip_type) =='1': caput(pv.me14e_pmac_str, '!x0.4y0.4') sleep(0.1) caput(pv.me14e_pmac_str, '#1hmz#2hmz#3hmz') print 10*'CSDone ' else: caput(pv.me14e_pmac_str, '#1hmz#2hmz#3hmz') print 10*'CSDone ' def cs_reset(): cs1 = "#1->%+10000X%+0Y%+0Z" cs2 = "#2->%+0X%+10000Y%+0Z" cs3 = "#3->0X+0Y+10000Z" print '\n'.join([cs1, cs2, cs3]) caput(pv.me14e_pmac_str, '&2') sleep(0.5) caput(pv.me14e_pmac_str, cs1) sleep(0.5) caput(pv.me14e_pmac_str, cs2) sleep(0.5) caput(pv.me14e_pmac_str, cs3) print 10*'CSDone ' def main(args): if args[1] == 'initialise': initialise() elif args[1] == 'pvar_test': chipid = args[2] pvar_test(chipid) elif args[1] == 'moveto': moveto(args[2]) elif args[1] == 'fiducial': fiducial(args[2]) elif args[1] == 'cs_maker': cs_maker() elif args[1] == 'write_parameter_file': write_parameter_file() startup.run() elif args[1] == 'define_current_chip': chipid = args[2] define_current_chip(chipid) elif args[1] == 'load_stock_map': map_choice = args[2] load_stock_map(map_choice) elif args[1] == 'load_lite_map': load_lite_map() elif args[1] == 'load_full_map': load_full_map() elif args[1] == 'save_screen_map': save_screen_map() elif args[1] == 'upload_full': upload_full() elif args[1] == 'upload_parameters': chipid = args[2] upload_parameters(chipid) elif args[1] == 'cs_reset': cs_reset() else: print 'Unknown Command' if __name__ == '__main__': main(sys.argv)

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import pv, os, re, sys import math, time, string import numpy as np from time import sleep from ca import caput, caget import logging as lg import sacla3_Chip_StartUp_v7 as startup import sacla3_Chip_Mapping_v7 as mapping lg.basicConfig(format='%(asctime)s %(levelname)s: \t%(message)s',level=lg.DEBUG, filename='SACLA3v7.log') get(pv.me14e_gp3) f.write('n_exposures \t%s\n' %n_exposures) print 'n_exposures', n_exposures chip_type = caget(pv.me14e_gp1) ) f.write('map_type \t%s\n' %map_type) print 'map_type', map_type f.close() print '\n', 10*'set', '\n\n' def define_current_chip(chipid): load_stock_map('clear') """ Not sure what this is for: print 'Setting Mapping Type to Lite' caput(pv.me14e_gp2, 1) """ chip_type = caget(pv.me14e_gp1) print chip_type, chipid if chipid == 'toronto': caput(pv.me14e_gp1, 0) elif chipid == 'oxford': caput(pv.me14e_gp1, 1) elif chipid == 'hamburg': caput(pv.me14e_gp1, 2) elif chipid == 'hamburgfull': caput(pv.me14e_gp1, 2) elif chipid == 'bismuth1': caput(pv.me14e_gp1, 3) elif chipid == 'bismuth2': caput(pv.me14e_gp1, 4) elif chipid == 'regina': caput(pv.me14e_gp1, 5) param_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(param_path + chipid + '.pvar', 'r') for line in f.readlines(): s = line.rstrip('\n') print s if line.startswith('#'): continue caput(pv.me14e_pmac_str, s) print param_path + chipid + '.chip' print 10*'Done ' def save_screen_map(): litemap_path = '/localhome/local/Documents/sacla/parameter_files/' print '\n\nSaving', litemap_path + 'currentchip.map' f = open(litemap_path + 'currentchip.map','w') print 'Printing only blocks with block_val == 1' for x in range(1, 82): block_str = 'ME14E-MO-IOC-01:GP%i' %(x+10) block_val = caget(block_str) if block_val == 1: print block_str, block_val line = '%02dstatus P3%02d1 \t%s\n' %(x, x, block_val) f.write(line) f.close() print 10*'Done ' return 0 def upload_parameters(chipid): if chipid == 'toronto': caput(pv.me14e_gp1, 0) width = 9 elif chipid == 'oxford': caput(pv.me14e_gp1, 1) width = 8 elif chipid == 'hamburg': caput(pv.me14e_gp1, 2) width = 3 elif chipid == 'bismuth1': caput(pv.me14e_gp1, 3) width = 1 elif chipid == 'bismuth2': caput(pv.me14e_gp1, 4) width = 7 elif chipid == 'regina': caput(pv.me14e_gp1, 5) width = 7 litemap_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(litemap_path + 'currentchip.map','r') print 'chipid', chipid print width x = 1 for line in f.readlines()[:width**2]: cols = line.split( ) pvar = cols[1] value = cols[2] s = pvar +'='+ value if value != '1': s2 = pvar + ' ' sys.stdout.write(s2) else: sys.stdout.write(s+' ') sys.stdout.flush() if x == width: print x = 1 else: x += 1 caput(pv.me14e_pmac_str, s) sleep(0.02) print print 'Setting Mapping Type to Lite' caput(pv.me14e_gp2, 1) print 10*'Done ' def upload_full(): fullmap_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(fullmap_path + 'currentchip.full', 'r').readlines() for x in range(len(f) / 2): pmac_list = [] for i in range(2): pmac_list.append(f.pop(0).rstrip('\n')) writeline = " ".join(pmac_list) print writeline caput(pv.me14e_pmac_str, writeline) sleep(0.02) print 10*'Done ' def load_stock_map(map_choice): print 'Please wait, adjusting lite map' r33 = [19,18,17,26,31,32,33,24,25] r55 = [9,10,11,12,13,16,27,30,41,40,39,38,37,34,23,20] + r33 r77 = [7,6,5,4,3,2,1,14,15,28,29,42,43,44,45,46,47,48,49,36,35,22,21,8] + r55 h33 = [3,2,1,6,7,8,9,4,5] x33 = [31,32,33,40,51,50,49,42,41] x55 = [25,24,23,22,21,34,39,52,57,58,59,60,61,48,43,30] + x33 x77 = [11,12,13,14,15,16,17,20,35,38,53,56,71,70,69,68,67,66,65,62,47,44,29,26] + x55 x99 = [9,8,7,6,5,4,3,2,1,18,19,36,37,54,55,72,73,74,75,76,77,78,79,80,81,64,63,46,45,28,27,10] + x77 x44 = [22,21,20,19,30,35,46,45,44,43,38,27,28,29,36,37] x49 = [x+1 for x in range(49)] x66 = [10,11,12,13,14,15,18,31,34,47,50,51,52,53,54,55,42,39,26,23] + x44 x88 = [8,7,6,5,4,3,2,1,16,17,32,33,48,49,64,63,62,61,60,59,58,57,56,41,40,25,24,9] + x66 map_dict = {} map_dict['clear']= [1] map_dict['r33'] = r33 map_dict['r55'] = r55 map_dict['r77'] = r77 map_dict['h33'] = h33 map_dict['x33'] = x33 map_dict['x44'] = x44 map_dict['x49'] = x49 map_dict['x55'] = x55 map_dict['x66'] = x66 map_dict['x77'] = x77 map_dict['x88'] = x88 map_dict['x99'] = x99 print 'Clearing' for i in range(1, 82): pvar = 'ME14E-MO-IOC-01:GP' + str(i + 10) caput(pvar, 0) sys.stdout.write('.') sys.stdout.flush() print '\nmap cleared' print 'loading map_choice', map_choice for i in map_dict[map_choice]: pvar = 'ME14E-MO-IOC-01:GP' + str(i + 10) caput(pvar, 1) print 10*'Done ' def load_lite_map(): load_stock_map('clear') toronto_block_dict = {\ 'A1':'01', 'A2':'02', 'A3':'03', 'A4':'04', 'A5':'05', 'A6':'06','A7':'07', 'A8':'08', 'A9':'09' ,'B1':'18', 'B2':'17', 'B3':'16', 'B4':'15', 'B5':'14', 'B6':'13','B7':'12', 'B8':'11', 'B9':'10' ,'C1':'19', 'C2':'20', 'C3':'21', 'C4':'22', 'C5':'23', 'C6':'24','C7':'25', 'C8':'26', 'C9':'27' ,'D1':'36', 'D2':'35', 'D3':'34', 'D4':'33', 'D5':'32', 'D6':'31','D7':'30', 'D8':'29', 'D9':'28' ,'E1':'37', 'E2':'38', 'E3':'39', 'E4':'40', 'E5':'41', 'E6':'42','E7':'43', 'E8':'44', 'E9':'45' ,'F1':'54', 'F2':'53', 'F3':'52', 'F4':'51', 'F5':'50', 'F6':'49','F7':'48', 'F8':'47', 'F9':'46' ,'G1':'55', 'G2':'56', 'G3':'57', 'G4':'58', 'G5':'59', 'G6':'60','G7':'61', 'G8':'62', 'G9':'63' ,'H1':'72', 'H2':'71', 'H3':'70', 'H4':'69', 'H5':'68', 'H6':'67','H7':'66', 'H8':'65', 'H9':'64' ,'I1':'73', 'I2':'74', 'I3':'75', 'I4':'76', 'I5':'77', 'I6':'78','I7':'79', 'I8':'80', 'I9':'81'} oxford_block_dict = {\ 'A1':'01', 'A2':'02', 'A3':'03', 'A4':'04', 'A5':'05', 'A6':'06','A7':'07', 'A8':'08' ,'B1':'16', 'B2':'15', 'B3':'14', 'B4':'13', 'B5':'12', 'B6':'11','B7':'10', 'B8':'09' ,'C1':'17', 'C2':'18', 'C3':'19', 'C4':'20', 'C5':'21', 'C6':'22','C7':'23', 'C8':'24' ,'D1':'32', 'D2':'31', 'D3':'30', 'D4':'29', 'D5':'28', 'D6':'27','D7':'26', 'D8':'25' ,'E1':'33', 'E2':'34', 'E3':'35', 'E4':'36', 'E5':'37', 'E6':'38','E7':'39', 'E8':'40' ,'F1':'48', 'F2':'47', 'F3':'46', 'F4':'45', 'F5':'44', 'F6':'43','F7':'42', 'F8':'41' ,'G1':'49', 'G2':'50', 'G3':'51', 'G4':'52', 'G5':'53', 'G6':'54','G7':'55', 'G8':'56' ,'H1':'64', 'H2':'63', 'H3':'62', 'H4':'61', 'H5':'60', 'H6':'59','H7':'58', 'H8':'57'} regina_block_dict = {\ 'A1':'01', 'A2':'02', 'A3':'03', 'A4':'04', 'A5':'05', 'A6':'06','A7':'07' ,'B1':'14', 'B2':'13', 'B3':'12', 'B4':'11', 'B5':'10', 'B6':'09','B7':'08' ,'C1':'15', 'C2':'16', 'C3':'17', 'C4':'18', 'C5':'19', 'C6':'20','C7':'21' ,'D1':'28', 'D2':'27', 'D3':'26', 'D4':'25', 'D5':'24', 'D6':'23','D7':'22' ,'E1':'29', 'E2':'30', 'E3':'31', 'E4':'32', 'E5':'33', 'E6':'34','E7':'35' ,'F1':'42', 'F2':'41', 'F3':'40', 'F4':'39', 'F5':'38', 'F6':'37','F7':'36' ,'G1':'43', 'G2':'44', 'G3':'45', 'G4':'46', 'G5':'47', 'G6':'48','G7':'49'} hamburg_block_dict = {\ 'A1':'01', 'A2':'02', 'A3':'03' ,'B1':'06', 'B2':'05', 'B3':'04' ,'C1':'07', 'C2':'08', 'C3':'09'} chip_type = caget(pv.me14e_gp1) if chip_type == 0: print 'Toronto Block Order' block_dict = toronto_block_dict elif chip_type == 1: print 'Oxford Block Order' rows = ['A','B','C','D','E','F','G','H'] columns = list(range(1,9)) btn_names = {} flip = True for x, column in enumerate(columns): for y,row in enumerate(rows): i=x*8+y if i%8 == 0 and flip == False: flip = True z = 8 - (y+1) elif i%8 == 0 and flip == True: flip = False z = y elif flip == False: z = y elif flip == True: z = 8 - (y+1) else: print('something is wrong with chip grid creation') break button_name = str(row)+str(column) lab_num = x*8+z label='%02.d'%(lab_num+1) btn_names[button_name] = label block_dict = btn_names elif chip_type == 2: print 'Hamburg Block Order' block_dict = hamburg_block_dict elif chip_type == 5: print 'Regina Block Order' block_dict = regina_block_dict litemap_path = '/localhome/local/Documents/sacla/parameter_files/' litemap_fid = str(caget(pv.me14e_gp5)) + '.lite' print 'opening', litemap_path + litemap_fid f = open(litemap_path + litemap_fid, 'r') print 'please wait, loading LITE map' for line in f.readlines(): entry = line.split() block_name = entry[0] yesno = entry[1] block_num = block_dict[block_name] pvar = 'ME14E-MO-IOC-01:GP' + str(int(block_num) + 10) print block_name, yesno, pvar caput(pvar, yesno) print 10*'Done ' def load_full_map(location ='SACLA'): if location == 'i24': chip_name, visit, sub_dir, n_exposures, chip_type, map_type = startup.scrape_parameter_file(location) else: chip_name, sub_dir, n_exposures, chip_type, map_type = startup.scrape_parameter_file(location) fullmap_path = '/localhome/local/Documents/sacla/parameter_files/' fullmap_fid = fullmap_path + str(caget(pv.me14e_gp5)) + '.spec' print 'opening', fullmap_fid mapping.plot_file(fullmap_fid, chip_type) print '\n\n', 10*'PNG ' mapping.convert_chip_to_hex(full_map_fid, chip_type) os.system("cp %s %s" % (fullmap_fid[:-4]+'full', fullmap_path+'currentchip.full')) print 10*'Done ', '\n' def moveto(place): print 5 * (place + ' ') chip_type = caget(pv.me14e_gp1) print 'CHIP TYPE', chip_type if chip_type == 0: print 'Toronto Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': caput(pv.me14e_stage_x, +18.975) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, +21.375) elif chip_type == 1: print 'Oxford Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': caput(pv.me14e_stage_x, 25.40) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 25.40) elif chip_type == 2: print 'Hamburg Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': caput(pv.me14e_stage_x, +24.968) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, +24.968) elif chip_type == 3: print 'Oxford Inner Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': caput(pv.me14e_stage_x, 24.60) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 24.60) elif chip_type == 5: print 'Regina Move' if place == 'origin': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) if place == 'f1': caput(pv.me14e_stage_x, +17.175) caput(pv.me14e_stage_y, 0.0) if place == 'f2': caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, +17.175) else: print 'Unknown chip_type move' if place == 'zero': caput(pv.me14e_pmac_str, '!x0y0z0') elif place == 'yag': caput(pv.me14e_stage_x, 1.0) caput(pv.me14e_stage_y, 1.0) caput(pv.me14e_stage_z, 1.0) elif place == 'load_position': print 'load position' caput(pv.me14e_filter, -25) caput(pv.me14e_stage_x, -25.0) caput(pv.me14e_stage_y, -25.0) caput(pv.me14e_stage_z, 0.0) caput(pv.me14e_pmac_str, 'M512=0 M511=1') elif place == 'collect_position': print 'collect position' caput(pv.me14e_filter, 25) caput(pv.me14e_stage_x, 0.0) caput(pv.me14e_stage_y, 0.0) caput(pv.me14e_stage_z, 0.0) caput(pv.me14e_pmac_str, 'M512=0 M511=1') elif place == 'lightin': print 'light in' caput(pv.me14e_filter, 25) elif place == 'lightout': print 'light out' caput(pv.me14e_filter, -25) elif place == 'flipperin': s(): param_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(param_path + 'motor_direction.txt', 'r') mtr1_dir, mtr2_dir, mtr3_dir = 1,1,1 for line in f.readlines(): if line.startswith('mtr1'): mtr1_dir = float(int(line.split('=')[1])) elif line.startswith('mtr2'): mtr2_dir = float(int(line.split('=')[1])) elif line.startswith('mtr3'): mtr3_dir = float(int(line.split('=')[1])) else: continue f.close() return mtr1_dir, mtr2_dir, mtr3_dir def fiducial(point): scale = 10000.0 param_path = '/localhome/local/Documents/sacla/parameter_files/' mtr1_dir, mtr2_dir, mtr3_dir = scrape_mtr_directions() rbv_1 = caget(pv.me14e_stage_x + '.RBV') rbv_2 = caget(pv.me14e_stage_y + '.RBV') rbv_3 = caget(pv.me14e_stage_z + '.RBV') raw_1 = caget(pv.me14e_stage_x + '.RRBV') raw_2 = caget(pv.me14e_stage_y + '.RRBV') raw_3 = caget(pv.me14e_stage_z + '.RRBV') """ June 8th 2017 change from this to rbv f_x = (mtr1_dir*raw_1) / scale f_y = (mtr2_dir*raw_2) / scale f_z = (mtr3_dir*raw_3) / scale """ f_x = rbv_1 f_y = rbv_2 f_z = rbv_3 print '\nWriting Fiducial File', 20*('%s ' %point) print 'MTR\tRBV\tRAW\tDirect.\tf_value' print 'MTR1\t%1.4f\t%i\t%i\t%1.4f' % (rbv_1, raw_1, mtr1_dir, f_x) print 'MTR2\t%1.4f\t%i\t%i\t%1.4f' % (rbv_2, raw_2, mtr2_dir, f_y) print 'MTR3\t%1.4f\t%i\t%i\t%1.4f' % (rbv_3, raw_3, mtr3_dir, f_z) print 'Writing Fiducial File', 20*('%s ' %point) f = open(param_path + 'fiducial_%s.txt' %point, 'w') f.write('MTR\tRBV\tRAW\tCorr\tf_value\n') f.write('MTR1\t%1.4f\t%i\t%i\t%1.4f\n' % (rbv_1, raw_1, mtr1_dir, f_x)) f.write('MTR2\t%1.4f\t%i\t%i\t%1.4f\n' % (rbv_2, raw_2, mtr2_dir, f_y)) f.write('MTR3\t%1.4f\t%i\t%i\t%1.4f' % (rbv_3, raw_3, mtr3_dir, f_z)) f.close() print 10*'Done ' def scrape_mtr_fiducials(point): param_path = '/localhome/local/Documents/sacla/parameter_files/' f = open(param_path+'fiducial_%i.txt' %point,'r') f_lines = f.readlines()[1:] f_x = float(f_lines[0].rsplit()[4]) f_y = float(f_lines[1].rsplit()[4]) f_z = float(f_lines[2].rsplit()[4]) f.close() return f_x, f_y, f_z def cs_maker(): chip_type = caget(pv.me14e_gp1) fiducial_dict = {} fiducial_dict[0] = [18.975, 21.375] fiducial_dict[1] = [25.400, 25.400] fiducial_dict[2] = [24.968, 24.968] fiducial_dict[3] = [24.600, 24.600] fiducial_dict[4] = [27.500, 27.500] fiducial_dict[5] = [17.175, 17.175] print chip_type, fiducial_dict[chip_type] mtr1_dir, mtr2_dir, mtr3_dir = scrape_mtr_directions() f1_x, f1_y, f1_z = scrape_mtr_fiducials(1) f2_x, f2_y, f2_z = scrape_mtr_fiducials(2) print 'AAAAAAAAAAAAAAAAABBBBBBBBBBBBBB' print 'mtr1 direction', mtr1_dir print 'mtr2 direction', mtr2_dir print 'mtr3 direction', mtr3_dir """ Theory Rx: rotation about X-axis, pitch Ry: rotation about Y-axis, yaw Rz: rotation about Z-axis, roll The order of rotation is Roll->Yaw->Pitch (Rx*Ry*Rz) Rx Ry Rz |1 0 0| | Cy 0 Sy| |Cz -Sz 0| | CyCz -CxSz Sy | |0 Cx -Sx|*| 0 1 0|*|Sz Cz 0| = | SxSyCz+CxSz -SxSySz+CxCz -SxCy| |0 Sx Cx| |-Sy 0 Cy| | 0 0 1| |-CxSyCz+SxSz CxSySz+SxCz CxCy| BELOW iS TEST TEST (CLOCKWISE) Rx Ry Rz |1 0 0| | Cy 0 -Sy| |Cz Sz 0| | CyCz CxSz -Sy | |0 Cx Sx|*| 0 1 0|*|-Sz Cz 0| = | SxSyCz-CxSz SxSySz+CxCz SxCy| |0 -Sx Cx| | Sy 0 Cy| | 0 0 1| | CxSyCz+SxSz CxSySz-SxCz CxCy| """ Sz1 = f1_y / fiducial_dict[chip_type][0] Sz2 = -1 * (f2_x / fiducial_dict[chip_type][1]) Sz = ((Sz1 + Sz2) / 2) Cz = np.sqrt((1 - Sz**2)) print 'Sz1 , %1.4f, %1.4f' % (Sz1, np.degrees(np.arcsin(Sz1))) print 'Sz2 , %1.4f, %1.4f' % (Sz2, np.degrees(np.arcsin(Sz2))) print 'Sz , %1.4f, %1.4f' % (Sz, np.degrees(np.arcsin(Sz))) print 'Cz , %1.4f, %1.4f\n' % (Cz, np.degrees(np.arccos(Cz))) Sy = f1_z / fiducial_dict[chip_type][0] Cy = np.sqrt((1 - Sy**2)) print 'Sy , %1.4f, %1.4f' % (Sy, np.degrees(np.arcsin(Sy))) print 'Cy , %1.4f, %1.4f\n' % (Cy, np.degrees(np.arccos(Cy))) Sx = -1* f2_z / fiducial_dict[chip_type][1] Cx = np.sqrt((1 - Sx**2)) print 'Sx , %1.4f, %1.4f' % (Sx, np.degrees(np.arcsin(Sx))) print 'Cx , %1.4f, %1.4f\n' % (Cx, np.degrees(np.arccos(Cx))) scalex, scaley, scalez = 10010.4, 10001.7, 10000.0 x1factor = mtr1_dir * scalex * (Cy * Cz) y1factor = mtr2_dir * scaley * (-1. * Cx * Sz) z1factor = mtr3_dir * scalez * Sy x2factor = mtr1_dir * scalex * ((Sx*Sy*Cz) + (Cx*Sz)) y2factor = mtr2_dir * scaley * ((Cx*Cz) - (Sx*Sy*Sz)) z2factor = mtr3_dir * scalez * (-1. * Sx * Cy) x3factor = mtr1_dir * scalex * ((Sx*Sz) - (Cx*Sy*Cz)) y3factor = mtr2_dir * scaley * ((Cx*Sy*Sz) + (Sx*Cz)) z3factor = mtr3_dir * scalez * (Cx* Cy) """ Rx Ry Rz |1 0 0| | Cy 0 Sy| |Cz -Sz 0| | CyCz -CxSz Sy | |0 Cx -Sx|*| 0 1 0|*|Sz Cz 0| = | SxSyCz+CxSz -SxSySz+CxCz -SxCy| |0 Sx Cx| |-Sy 0 Cy| | 0 0 1| |-CxSyCz+SxSz CxSySz+SxCz CxCy| """ skew = 0.1863 print 'Skew being used is: %1.4f' %skew s1 = np.degrees(np.arcsin(Sz1)) s2 = np.degrees(np.arcsin(Sz2)) rot = np.degrees(np.arcsin((Sz1+Sz2) / 2)) calc_skew = ((s1-rot) - (s2-rot)) print 's1:%1.4f s2:%1.4f rot:%1.4f' %(s1, s2, rot) print 'Calculated rotation from current fiducials is: %1.4f' %rot print 'Calculated skew from current fiducials is: %1.4f' %calc_skew sinD = np.sin((skew/2) * (np.pi/180)) cosD = np.cos((skew/2) * (np.pi/180)) new_x1factor = (x1factor * cosD) + (y1factor * sinD) new_y1factor = (x1factor * sinD) + (y1factor * cosD) new_x2factor = (x2factor * cosD) + (y2factor * sinD) new_y2factor = (x2factor * sinD) + (y2factor * cosD) cs1 = "#1->%+1.3fX%+1.3fY%+1.3fZ" % (new_x1factor, new_y1factor, z1factor) cs2 = "#2->%+1.3fX%+1.3fY%+1.3fZ" % (new_x2factor, new_y2factor, z2factor) cs3 = "#3->%+1.3fX%+1.3fY%+1.3fZ" % (x3factor, y3factor, z3factor) print '\n'.join([cs1, cs2, cs3]) print 'These should be 1. This is the sum of the squares of the factors divided by their scale' print np.sqrt(x1factor**2 + y1factor**2 + z1factor**2) / scalex print np.sqrt(x2factor**2 + y2factor**2 + z2factor**2) / scaley print np.sqrt(x3factor**2 + y3factor**2 + z3factor**2) / scalez print 'Long wait, please be patient' caput(pv.me14e_pmac_str, '!x0y0z0') sleep(2.5) caput(pv.me14e_pmac_str, '&2') caput(pv.me14e_pmac_str, cs1) caput(pv.me14e_pmac_str, cs2) caput(pv.me14e_pmac_str, cs3) caput(pv.me14e_pmac_str, '!x0y0z0') sleep(0.1) caput(pv.me14e_pmac_str, '#1hmz#2hmz#3hmz') sleep(0.1) print 5*'chip_type',type(chip_type) if str(chip_type) =='1': caput(pv.me14e_pmac_str, '!x0.4y0.4') sleep(0.1) caput(pv.me14e_pmac_str, '#1hmz#2hmz#3hmz') print 10*'CSDone ' else: caput(pv.me14e_pmac_str, '#1hmz#2hmz#3hmz') print 10*'CSDone ' def cs_reset(): cs1 = "#1->%+10000X%+0Y%+0Z" cs2 = "#2->%+0X%+10000Y%+0Z" cs3 = "#3->0X+0Y+10000Z" print '\n'.join([cs1, cs2, cs3]) caput(pv.me14e_pmac_str, '&2') sleep(0.5) caput(pv.me14e_pmac_str, cs1) sleep(0.5) caput(pv.me14e_pmac_str, cs2) sleep(0.5) caput(pv.me14e_pmac_str, cs3) print 10*'CSDone ' def main(args): if args[1] == 'initialise': initialise() elif args[1] == 'pvar_test': chipid = args[2] pvar_test(chipid) elif args[1] == 'moveto': moveto(args[2]) elif args[1] == 'fiducial': fiducial(args[2]) elif args[1] == 'cs_maker': cs_maker() elif args[1] == 'write_parameter_file': write_parameter_file() startup.run() elif args[1] == 'define_current_chip': chipid = args[2] define_current_chip(chipid) elif args[1] == 'load_stock_map': map_choice = args[2] load_stock_map(map_choice) elif args[1] == 'load_lite_map': load_lite_map() elif args[1] == 'load_full_map': load_full_map() elif args[1] == 'save_screen_map': save_screen_map() elif args[1] == 'upload_full': upload_full() elif args[1] == 'upload_parameters': chipid = args[2] upload_parameters(chipid) elif args[1] == 'cs_reset': cs_reset() else: print 'Unknown Command' if __name__ == '__main__': main(sys.argv)

false

true

f71968c2bfbb4980fde3dad9d2991f5150aef9eb

2,841

py

Python

setup.py

blazelibs/blazeweb

b120a6a2e38c8b53da2b73443ff242e2d1438053

[ "BSD-3-Clause" ]

null

setup.py

blazelibs/blazeweb

b120a6a2e38c8b53da2b73443ff242e2d1438053

[ "BSD-3-Clause" ]

6

2016-11-01T18:42:34.000Z

2020-11-16T16:52:14.000Z

setup.py

blazelibs/blazeweb

b120a6a2e38c8b53da2b73443ff242e2d1438053

[ "BSD-3-Clause" ]

1

2020-01-22T18:20:46.000Z

import os try: from setuptools import setup, find_packages except ImportError: from ez_setup import use_setuptools use_setuptools() from setuptools import setup, find_packages # pip install -e .[develop] develop_requires = [ 'WebTest', 'ScriptTest', 'coverage', 'docutils', 'minimock', 'nose', ] cdir = os.path.abspath(os.path.dirname(__file__)) README = open(os.path.join(cdir, 'readme.rst')).read() CHANGELOG = open(os.path.join(cdir, 'changelog.rst')).read() VERSION = open(os.path.join(cdir, 'blazeweb', 'version.txt')).read().strip() required_packages = [ 'Beaker>=1.5', 'BlazeUtils>0.3.7', 'Blinker>=1.0', 'decorator>=3.0.1', 'FormEncode>=1.2', 'html2text>=2.35', 'jinja2>=2.5', 'markdown2>=1.0.1', 'Paste>=1.7', 'PasteScript>=1.7', 'WebHelpers2', 'Werkzeug>=1.0.0', ] try: import json del json except ImportError: required_packages.append('simplejson>=2.1.1') setup( name="BlazeWeb", version=VERSION, description="A light weight WSGI framework with a pluggable architecture", long_description='\n\n'.join((README, CHANGELOG)), author="Randy Syring", author_email="randy.syring@level12.io", url='http://pypi.python.org/pypi/BlazeWeb/', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: Internet :: WWW/HTTP' ], license='BSD', packages=find_packages(exclude=['tests']), include_package_data=True, install_requires=required_packages, extras_require={'develop': develop_requires}, entry_points=""" [console_scripts] bw = blazeweb.scripting:blazeweb_entry [blazeweb.no_app_command] help=paste.script.help:HelpCommand project = blazeweb.commands:ProjectCommand jinja-convert = blazeweb.commands:JinjaConvertCommand [blazeweb.app_command] serve = blazeweb.commands:ServeCommand help = paste.script.help:HelpCommand testrun = blazeweb.commands:TestRunCommand tasks = blazeweb.commands:TasksCommand shell = blazeweb.commands:ShellCommand routes = blazeweb.commands:RoutesCommand static-copy = blazeweb.commands:StaticCopyCommand component-map = blazeweb.commands:ComponentMapCommand [blazeweb.blazeweb_project_template] minimal = blazeweb.paster_tpl:MinimalProjectTemplate bwproject = blazeweb.paster_tpl:ProjectTemplate [nose.plugins] blazeweb_initapp = blazeweb.nose_plugin:InitAppPlugin [pytest11] blazeweb_initapp = blazeweb.pytest_plugin """, zip_safe=False )

28.128713

78

0.67793

import os try: from setuptools import setup, find_packages except ImportError: from ez_setup import use_setuptools use_setuptools() from setuptools import setup, find_packages develop_requires = [ 'WebTest', 'ScriptTest', 'coverage', 'docutils', 'minimock', 'nose', ] cdir = os.path.abspath(os.path.dirname(__file__)) README = open(os.path.join(cdir, 'readme.rst')).read() CHANGELOG = open(os.path.join(cdir, 'changelog.rst')).read() VERSION = open(os.path.join(cdir, 'blazeweb', 'version.txt')).read().strip() required_packages = [ 'Beaker>=1.5', 'BlazeUtils>0.3.7', 'Blinker>=1.0', 'decorator>=3.0.1', 'FormEncode>=1.2', 'html2text>=2.35', 'jinja2>=2.5', 'markdown2>=1.0.1', 'Paste>=1.7', 'PasteScript>=1.7', 'WebHelpers2', 'Werkzeug>=1.0.0', ] try: import json del json except ImportError: required_packages.append('simplejson>=2.1.1') setup( name="BlazeWeb", version=VERSION, description="A light weight WSGI framework with a pluggable architecture", long_description='\n\n'.join((README, CHANGELOG)), author="Randy Syring", author_email="randy.syring@level12.io", url='http://pypi.python.org/pypi/BlazeWeb/', classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Topic :: Internet :: WWW/HTTP' ], license='BSD', packages=find_packages(exclude=['tests']), include_package_data=True, install_requires=required_packages, extras_require={'develop': develop_requires}, entry_points=""" [console_scripts] bw = blazeweb.scripting:blazeweb_entry [blazeweb.no_app_command] help=paste.script.help:HelpCommand project = blazeweb.commands:ProjectCommand jinja-convert = blazeweb.commands:JinjaConvertCommand [blazeweb.app_command] serve = blazeweb.commands:ServeCommand help = paste.script.help:HelpCommand testrun = blazeweb.commands:TestRunCommand tasks = blazeweb.commands:TasksCommand shell = blazeweb.commands:ShellCommand routes = blazeweb.commands:RoutesCommand static-copy = blazeweb.commands:StaticCopyCommand component-map = blazeweb.commands:ComponentMapCommand [blazeweb.blazeweb_project_template] minimal = blazeweb.paster_tpl:MinimalProjectTemplate bwproject = blazeweb.paster_tpl:ProjectTemplate [nose.plugins] blazeweb_initapp = blazeweb.nose_plugin:InitAppPlugin [pytest11] blazeweb_initapp = blazeweb.pytest_plugin """, zip_safe=False )

true

f71969a63ad11dd00ce0c7b25f5d250f148a897c

2,807

py

Python

crossbaker/samples/declarative/signals/pytoqml1/main.py

josephkirk/MeshBaker

e4f75193074cc92d12f953d6cad3a2a599f63ead

[ "MIT" ]

null

crossbaker/samples/declarative/signals/pytoqml1/main.py

josephkirk/MeshBaker

e4f75193074cc92d12f953d6cad3a2a599f63ead

[ "MIT" ]

5

2018-10-09T02:43:14.000Z

2018-10-12T13:00:09.000Z

crossbaker/samples/declarative/signals/pytoqml1/main.py

josephkirk/CrossBaker

e4f75193074cc92d12f953d6cad3a2a599f63ead

[ "MIT" ]

null

#!/usr/bin/python ############################################################################# ## ## Copyright (C) 2016 The Qt Company Ltd. ## Contact: http://www.qt.io/licensing/ ## ## This file is part of the Qt for Python examples of the Qt Toolkit. ## ## $QT_BEGIN_LICENSE:BSD$ ## You may use this file under the terms of the BSD license as follows: ## ## "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 Qt Company Ltd 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 ## OWNER 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." ## ## $QT_END_LICENSE$ ## ############################################################################# from __future__ import print_function import os import sys from PySide2.QtCore import QTimer, QUrl from PySide2.QtGui import QGuiApplication import PySide2.QtQml from PySide2.QtQuick import QQuickView if __name__ == '__main__': app = QGuiApplication(sys.argv) timer = QTimer() timer.start(2000) view = QQuickView() qmlFile = os.path.join(os.path.dirname(__file__), 'view.qml') view.setSource(QUrl.fromLocalFile(os.path.abspath(qmlFile))) if view.status() == QQuickView.Error: sys.exit(-1) root = view.rootObject() timer.timeout.connect(root.updateRotater) view.show() res = app.exec_() # Deleting the view before it goes out of scope is required to make sure all child QML instances # are destroyed in the correct order. del view sys.exit(res)

38.452055

100

0.695048

true

f7196a03c814613734c343483b20f67cde46b40d

260

py

Python

w02-calling-functions/checkpoint-boxes/boxes.py

carloswm85/2021-cs111-programming-with-functions

73cc376e3f0de60aa0150d33ec95568d217096ec

[ "Unlicense" ]

null

w02-calling-functions/checkpoint-boxes/boxes.py

carloswm85/2021-cs111-programming-with-functions

73cc376e3f0de60aa0150d33ec95568d217096ec

[ "Unlicense" ]

null

w02-calling-functions/checkpoint-boxes/boxes.py

carloswm85/2021-cs111-programming-with-functions

73cc376e3f0de60aa0150d33ec95568d217096ec

[ "Unlicense" ]

null

import math items = int(input("Enter the number of items: ")) items_box = int(input("Enter the number of items per box: ")) boxes = math.ceil(items / items_box) print(f"For {items} items, packing {items_box} items in each box, you will need {boxes} boxes.")

32.5

96

0.711538

import math items = int(input("Enter the number of items: ")) items_box = int(input("Enter the number of items per box: ")) boxes = math.ceil(items / items_box) print(f"For {items} items, packing {items_box} items in each box, you will need {boxes} boxes.")

true

f7196aad36071501a72c16f5e95b38ddb5f8950b

902

py

Python

turbo/turbo_encoder.py

DaulPavid/pyturbo

878e0b1b514c043f1b4ea5cd5268b23c0df5192e

[ "MIT" ]

9

2018-10-17T17:02:05.000Z

2022-03-03T18:58:32.000Z

turbo/turbo_encoder.py

akshay230994/pyturbo

878e0b1b514c043f1b4ea5cd5268b23c0df5192e

[ "MIT" ]

2

2018-10-16T16:57:57.000Z

2020-04-14T13:34:40.000Z

turbo/turbo_encoder.py

akshay230994/pyturbo

878e0b1b514c043f1b4ea5cd5268b23c0df5192e

[ "MIT" ]

4

2019-12-23T18:42:29.000Z

2022-01-19T12:08:35.000Z

# # Turbo Encoder # import numpy as np from .rsc import RSC class TurboEncoder: def __init__(self, interleaver): self.interleaver = interleaver self.block_size = len(self.interleaver) self.encoders = 2 * [RSC()] def reset(self): for e in self.encoders: e.reset() def interleave(self, vector): interleaved = np.zeros(self.block_size, dtype=int) for i in range(0, self.block_size): interleaved[i] = vector[self.interleaver[i]] return interleaved def execute(self, vector): output_size = 3 * (len(vector) + len(self.encoders[0].registers)) output = np.zeros(output_size, dtype=int) interleaved = self.interleave(vector) output[1::3], output[::3] = self.encoders[0].execute(vector) output[2::3], _ = self.encoders[1].execute(interleaved) return output

25.055556

73

0.618625

import numpy as np from .rsc import RSC class TurboEncoder: def __init__(self, interleaver): self.interleaver = interleaver self.block_size = len(self.interleaver) self.encoders = 2 * [RSC()] def reset(self): for e in self.encoders: e.reset() def interleave(self, vector): interleaved = np.zeros(self.block_size, dtype=int) for i in range(0, self.block_size): interleaved[i] = vector[self.interleaver[i]] return interleaved def execute(self, vector): output_size = 3 * (len(vector) + len(self.encoders[0].registers)) output = np.zeros(output_size, dtype=int) interleaved = self.interleave(vector) output[1::3], output[::3] = self.encoders[0].execute(vector) output[2::3], _ = self.encoders[1].execute(interleaved) return output

true

f7196b897dd7d74bfa6480c1e1542bf851614cfa

534

py

Python

src/package/scanner.py

buckler-project/armoury

3d4c1bb9e8af190ba95d60d502b39699848d1e62

[ "MIT" ]

1

2019-02-02T06:21:21.000Z

src/package/scanner.py

buckler-project/armoury

3d4c1bb9e8af190ba95d60d502b39699848d1e62

[ "MIT" ]

5

2019-01-28T00:59:15.000Z

2019-01-31T10:35:36.000Z

src/package/scanner.py

buckler-project/armoury

3d4c1bb9e8af190ba95d60d502b39699848d1e62

[ "MIT" ]

null

from package import package parent_path = '.scanners' config_path = 'scanner.yml' class Scanner(package.Package): def __init__(self, url, name, auther): super().__init__(url, name, auther) self.parent_path = parent_path self.config_path = config_path class ScannerFactory(package.PackageFactory): def __init__(self): self.parent_path = parent_path self.config_path = config_path def _generate(self, url, name, auther): return Scanner(url=url, name=name, auther=auther)

26.7

57

0.694757

from package import package parent_path = '.scanners' config_path = 'scanner.yml' class Scanner(package.Package): def __init__(self, url, name, auther): super().__init__(url, name, auther) self.parent_path = parent_path self.config_path = config_path class ScannerFactory(package.PackageFactory): def __init__(self): self.parent_path = parent_path self.config_path = config_path def _generate(self, url, name, auther): return Scanner(url=url, name=name, auther=auther)

true

f7196bd5fe213fbeb54a87c93b46f43e8cb1f118

2,742

py

Python

inference_speed.py

wmcnally/evopose2d

ea05b818044d8d84e9cbbee778bc465be59ebd59

[ "MIT" ]

75

2020-11-18T05:07:42.000Z

2022-03-27T03:25:16.000Z

inference_speed.py

wmcnally/evopose2d

ea05b818044d8d84e9cbbee778bc465be59ebd59

[ "MIT" ]

26

2020-11-29T17:45:44.000Z

2022-03-22T15:30:31.000Z

inference_speed.py

wmcnally/evopose2d

ea05b818044d8d84e9cbbee778bc465be59ebd59

[ "MIT" ]

8

2020-11-25T02:59:53.000Z

2022-03-27T10:53:59.000Z

import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' import tensorflow as tf from dataset.dataloader import load_tfds from time import time import argparse from nets.simple_basline import SimpleBaseline from nets.evopose2d import EvoPose from nets.hrnet import HRNet from utils import detect_hardware def speed_test(strategy, cfg, split='val', n=1000): with strategy.scope(): if cfg.MODEL.TYPE == 'simple_baseline': model = SimpleBaseline(cfg) elif cfg.MODEL.TYPE == 'hrnet': model = HRNet(cfg) elif cfg.MODEL.TYPE == 'evopose': model = EvoPose(cfg) cfg.DATASET.OUTPUT_SHAPE = model.output_shape[1:] ds = load_tfds(cfg, split, det=cfg.VAL.DET, predict_kp=True, drop_remainder=cfg.VAL.DROP_REMAINDER) ds = strategy.experimental_distribute_dataset(ds) @tf.function def predict(imgs, flip=False): if flip: imgs = imgs[:, :, ::-1, :] return model(imgs, training=False) for count, batch in enumerate(ds): if count == 1: # skip first pass ti = time() _, imgs, _, _, scores = batch hms = strategy.run(predict, args=(imgs,)).numpy() if cfg.VAL.FLIP: flip_hms = strategy.run(predict, args=(imgs, True,)).numpy() flip_hms = flip_hms[:, :, ::-1, :] tmp = flip_hms.copy() for i in range(len(cfg.DATASET.KP_FLIP)): flip_hms[:, :, :, i] = tmp[:, :, :, cfg.DATASET.KP_FLIP[i]] # shift to align features flip_hms[:, :, 1:, :] = flip_hms[:, :, 0:-1, :].copy() hms = (hms + flip_hms) / 2. if count == n: break print('FPS: {:.5f}'.format((n * cfg.VAL.BATCH_SIZE) / (time() - ti))) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--cpu', action='store_true') parser.add_argument('--gpu', action='store_true') parser.add_argument('--tpu', default='') parser.add_argument('-c', '--cfg', required=True) # yaml parser.add_argument('-bs', '--batch-size', type=int, default=1) parser.add_argument('-n', type=int, default=1000) args = parser.parse_args() from dataset.coco import cn as cfg cfg.merge_from_file('configs/' + args.cfg) cfg.MODEL.NAME = args.cfg.split('.')[0] cfg.VAL.BATCH_SIZE = args.batch_size if args.cpu: strategy = tf.distribute.OneDeviceStrategy('/CPU:0') elif args.gpu: strategy = tf.distribute.OneDeviceStrategy('/GPU:0') else: tpu, strategy = detect_hardware(args.tpu) tf.config.optimizer.set_experimental_options({'disable_meta_optimizer': True}) speed_test(strategy, cfg, split='val', n=args.n)

32.258824

82

0.610139

import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' import tensorflow as tf from dataset.dataloader import load_tfds from time import time import argparse from nets.simple_basline import SimpleBaseline from nets.evopose2d import EvoPose from nets.hrnet import HRNet from utils import detect_hardware def speed_test(strategy, cfg, split='val', n=1000): with strategy.scope(): if cfg.MODEL.TYPE == 'simple_baseline': model = SimpleBaseline(cfg) elif cfg.MODEL.TYPE == 'hrnet': model = HRNet(cfg) elif cfg.MODEL.TYPE == 'evopose': model = EvoPose(cfg) cfg.DATASET.OUTPUT_SHAPE = model.output_shape[1:] ds = load_tfds(cfg, split, det=cfg.VAL.DET, predict_kp=True, drop_remainder=cfg.VAL.DROP_REMAINDER) ds = strategy.experimental_distribute_dataset(ds) @tf.function def predict(imgs, flip=False): if flip: imgs = imgs[:, :, ::-1, :] return model(imgs, training=False) for count, batch in enumerate(ds): if count == 1: ti = time() _, imgs, _, _, scores = batch hms = strategy.run(predict, args=(imgs,)).numpy() if cfg.VAL.FLIP: flip_hms = strategy.run(predict, args=(imgs, True,)).numpy() flip_hms = flip_hms[:, :, ::-1, :] tmp = flip_hms.copy() for i in range(len(cfg.DATASET.KP_FLIP)): flip_hms[:, :, :, i] = tmp[:, :, :, cfg.DATASET.KP_FLIP[i]] flip_hms[:, :, 1:, :] = flip_hms[:, :, 0:-1, :].copy() hms = (hms + flip_hms) / 2. if count == n: break print('FPS: {:.5f}'.format((n * cfg.VAL.BATCH_SIZE) / (time() - ti))) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--cpu', action='store_true') parser.add_argument('--gpu', action='store_true') parser.add_argument('--tpu', default='') parser.add_argument('-c', '--cfg', required=True) parser.add_argument('-bs', '--batch-size', type=int, default=1) parser.add_argument('-n', type=int, default=1000) args = parser.parse_args() from dataset.coco import cn as cfg cfg.merge_from_file('configs/' + args.cfg) cfg.MODEL.NAME = args.cfg.split('.')[0] cfg.VAL.BATCH_SIZE = args.batch_size if args.cpu: strategy = tf.distribute.OneDeviceStrategy('/CPU:0') elif args.gpu: strategy = tf.distribute.OneDeviceStrategy('/GPU:0') else: tpu, strategy = detect_hardware(args.tpu) tf.config.optimizer.set_experimental_options({'disable_meta_optimizer': True}) speed_test(strategy, cfg, split='val', n=args.n)

true

f7196c255322af385bffc89c3fcffebd8bcec16e

8,295

py

Python

tango/common/util.py

allenai/tango

80c90caefae4ad1c3f8472718ddada912cd8fcf9

[ "Apache-2.0" ]

52

2021-09-24T17:52:34.000Z

2022-03-29T22:55:02.000Z

tango/common/util.py

allenai/tango

80c90caefae4ad1c3f8472718ddada912cd8fcf9

[ "Apache-2.0" ]

90

2021-09-29T04:23:29.000Z

2022-03-31T21:23:02.000Z

tango/common/util.py

allenai/tango

80c90caefae4ad1c3f8472718ddada912cd8fcf9

[ "Apache-2.0" ]

8

2021-11-13T01:56:22.000Z

2022-02-27T03:29:42.000Z

import importlib import pkgutil import signal import string import sys import traceback from contextlib import contextmanager from datetime import datetime, tzinfo from pathlib import Path from typing import Iterable, Optional, Set, Tuple, Union import pytz from .aliases import PathOrStr from .exceptions import SigTermReceived def tango_cache_dir() -> Path: """ Returns a directory suitable for caching things from Tango, defaulting to ``$HOME/.cache/tango``. """ cache_dir = Path.home() / ".cache" / "tango" cache_dir.mkdir(parents=True, exist_ok=True) return cache_dir def _handle_sigterm(sig, frame): raise SigTermReceived def install_sigterm_handler(): signal.signal(signal.SIGTERM, _handle_sigterm) @contextmanager def push_python_path(path: PathOrStr): """ Prepends the given path to `sys.path`. This method is intended to use with `with`, so after its usage, its value willbe removed from `sys.path`. """ # In some environments, such as TC, it fails when sys.path contains a relative path, such as ".". path = Path(path).resolve() path = str(path) sys.path.insert(0, path) try: yield finally: # Better to remove by value, in case `sys.path` was manipulated in between. sys.path.remove(path) _extra_imported_modules: Set[str] = set() def get_extra_imported_modules() -> Set[str]: return _extra_imported_modules def import_extra_module(package_name: str) -> None: global _extra_imported_modules import_module_and_submodules(package_name) _extra_imported_modules.add(package_name) def resolve_module_name(package_name: str) -> Tuple[str, Path]: base_path = Path(".") package_path = Path(package_name) if not package_path.exists(): raise ValueError(f"'{package_path}' looks like a path, but the path does not exist") parent = package_path.parent while parent != parent.parent: if (parent / "__init__.py").is_file(): parent = parent.parent else: base_path = parent break package_name = str(package_path.relative_to(base_path)).replace("/", ".") if package_path.is_file(): if package_path.name == "__init__.py": # If `__init__.py` file, resolve to the parent module. package_name = package_name[: -len(".__init__.py")] elif package_name.endswith(".py"): package_name = package_name[:-3] if not package_name: raise ValueError(f"invalid package path '{package_path}'") return package_name, base_path def import_module_and_submodules(package_name: str, exclude: Optional[Set[str]] = None) -> None: """ Import all submodules under the given package. Primarily useful so that people using tango can specify their own custom packages and have their custom classes get loaded and registered. """ # If `package_name` is in the form of a path, convert to the module format. if "/" in package_name or package_name.endswith(".py"): package_name, base_path = resolve_module_name(package_name) else: base_path = Path(".") if exclude and package_name in exclude: return importlib.invalidate_caches() # For some reason, python doesn't always add this by default to your path, but you pretty much # always want it when using `--include-package`. And if it's already there, adding it again at # the end won't hurt anything. with push_python_path(base_path): # Import at top level module = importlib.import_module(package_name) path = getattr(module, "__path__", []) path_string = "" if not path else path[0] # walk_packages only finds immediate children, so need to recurse. for module_finder, name, _ in pkgutil.walk_packages(path): # Sometimes when you import third-party libraries that are on your path, # `pkgutil.walk_packages` returns those too, so we need to skip them. if path_string and module_finder.path != path_string: # type: ignore[union-attr] continue subpackage = f"{package_name}.{name}" import_module_and_submodules(subpackage, exclude=exclude) def _parse_bool(value: Union[bool, str]) -> bool: if isinstance(value, bool): return value if value in {"1", "true", "True", "TRUE"}: return True return False def _parse_optional_int(value: Optional[str]) -> Optional[int]: if value is not None: return int(value) return None def find_submodules( module: Optional[str] = None, match: Optional[Set[str]] = None, exclude: Optional[Set[str]] = None, recursive: bool = True, ) -> Iterable[str]: """ Find tango submodules. """ from fnmatch import fnmatch root = Path(__file__).parent / ".." if module: if module.startswith("tango."): module = module.replace("tango.", "", 1) for m in module.split("."): root = root / m module = f"tango.{module}" else: module = "tango" for path in root.iterdir(): if path.name[0] in {"_", "."}: continue submodule: str if path.is_dir(): submodule = path.name elif path.suffix == ".py": submodule = path.name[:-3] else: continue submodule = f"{module}.{submodule}" if exclude and any((fnmatch(submodule, pat) for pat in exclude)): continue if match and not any((fnmatch(submodule, pat) for pat in match)): continue yield submodule if recursive and path.is_dir(): yield from find_submodules(submodule, match=match, exclude=exclude) def find_integrations() -> Iterable[str]: """ Find all tango integration modules. """ yield from find_submodules("tango.integrations", recursive=False) SAFE_FILENAME_CHARS = frozenset("-_.%s%s" % (string.ascii_letters, string.digits)) def filename_is_safe(filename: str) -> bool: return all(c in SAFE_FILENAME_CHARS for c in filename) def could_be_class_name(name: str) -> bool: if "." in name and not name.endswith("."): return all([_is_valid_python_name(part) for part in name.split(".")]) else: return False def _is_valid_python_name(name: str) -> bool: return bool(name and name[0].isalpha() and name.replace("_", "").isalnum()) def threaded_generator(g, queue_size: int = 16): """ Puts the generating side of a generator into its own thread Let's say you have a generator that reads records from disk, and something that consumes the generator that spends most of its time in PyTorch. Wouldn't it be great if you could read more records while the PyTorch code runs? If you wrap your record-reading generator with ``threaded_generator(inner)``, that's exactly what happens. The reading code will run in a new thread, while the consuming code runs in the main thread as normal. ``threaded_generator()`` uses a queue to hand off items. :param queue_size: the maximum queue size for hand-offs between the main thread and the generator thread """ from queue import Queue from threading import Thread q: Queue = Queue(maxsize=queue_size) sentinel = object() def fill_queue(): try: for value in g: q.put(value) finally: q.put(sentinel) thread = Thread(name=repr(g), target=fill_queue, daemon=True) thread.start() yield from iter(q.get, sentinel) thread.join() def exception_to_string(e: BaseException) -> str: """ Generates a string that contains an exception plus stack frames based on an exception. This became trivial in Python 3.10, but we need to run on Pytohn 3.7 as well. """ if sys.version_info >= (3, 10): formatted = traceback.format_exception(e) else: formatted = traceback.format_exception(etype=type(e), value=e, tb=e.__traceback__) return "".join(formatted) def utc_now_datetime() -> datetime: return datetime.utcnow().replace(tzinfo=pytz.utc) def local_timezone() -> Optional[tzinfo]: return datetime.now().astimezone().tzinfo

31.067416

108

0.660277

import importlib import pkgutil import signal import string import sys import traceback from contextlib import contextmanager from datetime import datetime, tzinfo from pathlib import Path from typing import Iterable, Optional, Set, Tuple, Union import pytz from .aliases import PathOrStr from .exceptions import SigTermReceived def tango_cache_dir() -> Path: cache_dir = Path.home() / ".cache" / "tango" cache_dir.mkdir(parents=True, exist_ok=True) return cache_dir def _handle_sigterm(sig, frame): raise SigTermReceived def install_sigterm_handler(): signal.signal(signal.SIGTERM, _handle_sigterm) @contextmanager def push_python_path(path: PathOrStr): path = Path(path).resolve() path = str(path) sys.path.insert(0, path) try: yield finally: sys.path.remove(path) _extra_imported_modules: Set[str] = set() def get_extra_imported_modules() -> Set[str]: return _extra_imported_modules def import_extra_module(package_name: str) -> None: global _extra_imported_modules import_module_and_submodules(package_name) _extra_imported_modules.add(package_name) def resolve_module_name(package_name: str) -> Tuple[str, Path]: base_path = Path(".") package_path = Path(package_name) if not package_path.exists(): raise ValueError(f"'{package_path}' looks like a path, but the path does not exist") parent = package_path.parent while parent != parent.parent: if (parent / "__init__.py").is_file(): parent = parent.parent else: base_path = parent break package_name = str(package_path.relative_to(base_path)).replace("/", ".") if package_path.is_file(): if package_path.name == "__init__.py": package_name = package_name[: -len(".__init__.py")] elif package_name.endswith(".py"): package_name = package_name[:-3] if not package_name: raise ValueError(f"invalid package path '{package_path}'") return package_name, base_path def import_module_and_submodules(package_name: str, exclude: Optional[Set[str]] = None) -> None: if "/" in package_name or package_name.endswith(".py"): package_name, base_path = resolve_module_name(package_name) else: base_path = Path(".") if exclude and package_name in exclude: return importlib.invalidate_caches() # always want it when using `--include-package`. And if it's already there, adding it again at with push_python_path(base_path): # Import at top level module = importlib.import_module(package_name) path = getattr(module, "__path__", []) path_string = "" if not path else path[0] # walk_packages only finds immediate children, so need to recurse. for module_finder, name, _ in pkgutil.walk_packages(path): # Sometimes when you import third-party libraries that are on your path, # `pkgutil.walk_packages` returns those too, so we need to skip them. if path_string and module_finder.path != path_string: # type: ignore[union-attr] continue subpackage = f"{package_name}.{name}" import_module_and_submodules(subpackage, exclude=exclude) def _parse_bool(value: Union[bool, str]) -> bool: if isinstance(value, bool): return value if value in {"1", "true", "True", "TRUE"}: return True return False def _parse_optional_int(value: Optional[str]) -> Optional[int]: if value is not None: return int(value) return None def find_submodules( module: Optional[str] = None, match: Optional[Set[str]] = None, exclude: Optional[Set[str]] = None, recursive: bool = True, ) -> Iterable[str]: from fnmatch import fnmatch root = Path(__file__).parent / ".." if module: if module.startswith("tango."): module = module.replace("tango.", "", 1) for m in module.split("."): root = root / m module = f"tango.{module}" else: module = "tango" for path in root.iterdir(): if path.name[0] in {"_", "."}: continue submodule: str if path.is_dir(): submodule = path.name elif path.suffix == ".py": submodule = path.name[:-3] else: continue submodule = f"{module}.{submodule}" if exclude and any((fnmatch(submodule, pat) for pat in exclude)): continue if match and not any((fnmatch(submodule, pat) for pat in match)): continue yield submodule if recursive and path.is_dir(): yield from find_submodules(submodule, match=match, exclude=exclude) def find_integrations() -> Iterable[str]: yield from find_submodules("tango.integrations", recursive=False) SAFE_FILENAME_CHARS = frozenset("-_.%s%s" % (string.ascii_letters, string.digits)) def filename_is_safe(filename: str) -> bool: return all(c in SAFE_FILENAME_CHARS for c in filename) def could_be_class_name(name: str) -> bool: if "." in name and not name.endswith("."): return all([_is_valid_python_name(part) for part in name.split(".")]) else: return False def _is_valid_python_name(name: str) -> bool: return bool(name and name[0].isalpha() and name.replace("_", "").isalnum()) def threaded_generator(g, queue_size: int = 16): from queue import Queue from threading import Thread q: Queue = Queue(maxsize=queue_size) sentinel = object() def fill_queue(): try: for value in g: q.put(value) finally: q.put(sentinel) thread = Thread(name=repr(g), target=fill_queue, daemon=True) thread.start() yield from iter(q.get, sentinel) thread.join() def exception_to_string(e: BaseException) -> str: if sys.version_info >= (3, 10): formatted = traceback.format_exception(e) else: formatted = traceback.format_exception(etype=type(e), value=e, tb=e.__traceback__) return "".join(formatted) def utc_now_datetime() -> datetime: return datetime.utcnow().replace(tzinfo=pytz.utc) def local_timezone() -> Optional[tzinfo]: return datetime.now().astimezone().tzinfo

true

f7196d520e18090ae1a9e39c71b4703d717c0c07

184

py

Python

ddq_1/lang/fol_quant.py

jadnohra/connect

8eb21e6f122898094447bc3d5edb3053d5a2adf2

[ "Unlicense" ]

null

ddq_1/lang/fol_quant.py

jadnohra/connect

8eb21e6f122898094447bc3d5edb3053d5a2adf2

[ "Unlicense" ]

6

2021-03-19T12:06:56.000Z

2022-03-12T00:23:09.000Z

ddq_1/lang/fol_quant.py

jadnohra/connect

8eb21e6f122898094447bc3d5edb3053d5a2adf2

[ "Unlicense" ]

null

''' References: - Symbolic Logic, Copi, p.396 ''' from .fol_lang import Wff, PropVarWff, BinaryWff, PropositionalVariable, NegWff class QuantRule: def applies_to(): pass

18.4

79

0.701087

from .fol_lang import Wff, PropVarWff, BinaryWff, PropositionalVariable, NegWff class QuantRule: def applies_to(): pass

true

f7196e814752cab4ff82754c55fd3672bc8fd585

29,282

py

Python

google/ads/google_ads/v6/proto/resources/ad_group_pb2.py

arammaliachi/google-ads-python

a4fe89567bd43eb784410523a6306b5d1dd9ee67

[ "Apache-2.0" ]

null

google/ads/google_ads/v6/proto/resources/ad_group_pb2.py

arammaliachi/google-ads-python

a4fe89567bd43eb784410523a6306b5d1dd9ee67

[ "Apache-2.0" ]

null

google/ads/google_ads/v6/proto/resources/ad_group_pb2.py

arammaliachi/google-ads-python

a4fe89567bd43eb784410523a6306b5d1dd9ee67

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: google/ads/googleads/v6/resources/ad_group.proto """Generated protocol buffer code.""" from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from google.ads.google_ads.v6.proto.common import custom_parameter_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_common_dot_custom__parameter__pb2 from google.ads.google_ads.v6.proto.common import explorer_auto_optimizer_setting_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_common_dot_explorer__auto__optimizer__setting__pb2 from google.ads.google_ads.v6.proto.common import targeting_setting_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_common_dot_targeting__setting__pb2 from google.ads.google_ads.v6.proto.enums import ad_group_ad_rotation_mode_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__ad__rotation__mode__pb2 from google.ads.google_ads.v6.proto.enums import ad_group_status_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__status__pb2 from google.ads.google_ads.v6.proto.enums import ad_group_type_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__type__pb2 from google.ads.google_ads.v6.proto.enums import bidding_source_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_bidding__source__pb2 from google.ads.google_ads.v6.proto.enums import targeting_dimension_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_targeting__dimension__pb2 from google.api import field_behavior_pb2 as google_dot_api_dot_field__behavior__pb2 from google.api import resource_pb2 as google_dot_api_dot_resource__pb2 from google.api import annotations_pb2 as google_dot_api_dot_annotations__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='google/ads/googleads/v6/resources/ad_group.proto', package='google.ads.googleads.v6.resources', syntax='proto3', serialized_options=b'\n%com.google.ads.googleads.v6.resourcesB\014AdGroupProtoP\001ZJgoogle.golang.org/genproto/googleapis/ads/googleads/v6/resources;resources\242\002\003GAA\252\002!Google.Ads.GoogleAds.V6.Resources\312\002!Google\\Ads\\GoogleAds\\V6\\Resources\352\002%Google::Ads::GoogleAds::V6::Resources', create_key=_descriptor._internal_create_key, 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googleads.googleapis.com/AdGroup\x12.customers/{customer_id}/adGroups/{ad_group_id}B\x05\n\x03_idB\x07\n\x05_nameB\x10\n\x0e_base_ad_groupB\x18\n\x16_tracking_url_templateB\x0b\n\t_campaignB\x11\n\x0f_cpc_bid_microsB\x11\n\x0f_cpm_bid_microsB\x14\n\x12_target_cpa_microsB\x11\n\x0f_cpv_bid_microsB\x14\n\x12_target_cpm_microsB\x0e\n\x0c_target_roasB\x19\n\x17_percent_cpc_bid_microsB\x13\n\x11_final_url_suffixB\x1e\n\x1c_effective_target_cpa_microsB\x18\n\x16_effective_target_roasB\xf9\x01\n%com.google.ads.googleads.v6.resourcesB\x0c\x41\x64GroupProtoP\x01ZJgoogle.golang.org/genproto/googleapis/ads/googleads/v6/resources;resources\xa2\x02\x03GAA\xaa\x02!Google.Ads.GoogleAds.V6.Resources\xca\x02!Google\\Ads\\GoogleAds\\V6\\Resources\xea\x02%Google::Ads::GoogleAds::V6::Resourcesb\x06proto3' , dependencies=[google_dot_ads_dot_googleads_dot_v6_dot_common_dot_custom__parameter__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_common_dot_explorer__auto__optimizer__setting__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_common_dot_targeting__setting__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__ad__rotation__mode__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__status__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__type__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_bidding__source__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_targeting__dimension__pb2.DESCRIPTOR,google_dot_api_dot_field__behavior__pb2.DESCRIPTOR,google_dot_api_dot_resource__pb2.DESCRIPTOR,google_dot_api_dot_annotations__pb2.DESCRIPTOR,]) _ADGROUP = _descriptor.Descriptor( name='AdGroup', full_name='google.ads.googleads.v6.resources.AdGroup', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='resource_name', full_name='google.ads.googleads.v6.resources.AdGroup.resource_name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\005\372A\"\n googleads.googleapis.com/AdGroup', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='id', full_name='google.ads.googleads.v6.resources.AdGroup.id', index=1, number=34, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='google.ads.googleads.v6.resources.AdGroup.name', index=2, number=35, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='status', full_name='google.ads.googleads.v6.resources.AdGroup.status', index=3, number=5, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='type', full_name='google.ads.googleads.v6.resources.AdGroup.type', index=4, number=12, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\005', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='ad_rotation_mode', full_name='google.ads.googleads.v6.resources.AdGroup.ad_rotation_mode', index=5, number=22, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='base_ad_group', full_name='google.ads.googleads.v6.resources.AdGroup.base_ad_group', index=6, number=36, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003\372A\"\n googleads.googleapis.com/AdGroup', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='tracking_url_template', full_name='google.ads.googleads.v6.resources.AdGroup.tracking_url_template', index=7, number=37, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='url_custom_parameters', full_name='google.ads.googleads.v6.resources.AdGroup.url_custom_parameters', index=8, number=6, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='campaign', full_name='google.ads.googleads.v6.resources.AdGroup.campaign', index=9, number=38, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\005\372A#\n!googleads.googleapis.com/Campaign', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cpc_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup.cpc_bid_micros', index=10, number=39, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cpm_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup.cpm_bid_micros', index=11, number=40, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='target_cpa_micros', full_name='google.ads.googleads.v6.resources.AdGroup.target_cpa_micros', index=12, number=41, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cpv_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup.cpv_bid_micros', index=13, number=42, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='target_cpm_micros', full_name='google.ads.googleads.v6.resources.AdGroup.target_cpm_micros', index=14, number=43, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='target_roas', full_name='google.ads.googleads.v6.resources.AdGroup.target_roas', index=15, number=44, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='percent_cpc_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup.percent_cpc_bid_micros', index=16, number=45, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='explorer_auto_optimizer_setting', full_name='google.ads.googleads.v6.resources.AdGroup.explorer_auto_optimizer_setting', index=17, number=21, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='display_custom_bid_dimension', full_name='google.ads.googleads.v6.resources.AdGroup.display_custom_bid_dimension', index=18, number=23, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='final_url_suffix', full_name='google.ads.googleads.v6.resources.AdGroup.final_url_suffix', index=19, number=46, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='targeting_setting', full_name='google.ads.googleads.v6.resources.AdGroup.targeting_setting', index=20, number=25, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='effective_target_cpa_micros', full_name='google.ads.googleads.v6.resources.AdGroup.effective_target_cpa_micros', index=21, number=47, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='effective_target_cpa_source', full_name='google.ads.googleads.v6.resources.AdGroup.effective_target_cpa_source', index=22, number=29, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='effective_target_roas', full_name='google.ads.googleads.v6.resources.AdGroup.effective_target_roas', index=23, number=48, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='effective_target_roas_source', full_name='google.ads.googleads.v6.resources.AdGroup.effective_target_roas_source', index=24, number=32, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='labels', full_name='google.ads.googleads.v6.resources.AdGroup.labels', index=25, number=49, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003\372A\'\n%googleads.googleapis.com/AdGroupLabel', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=b'\352AR\n googleads.googleapis.com/AdGroup\022.customers/{customer_id}/adGroups/{ad_group_id}', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='_id', full_name='google.ads.googleads.v6.resources.AdGroup._id', index=0, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_name', full_name='google.ads.googleads.v6.resources.AdGroup._name', index=1, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_base_ad_group', full_name='google.ads.googleads.v6.resources.AdGroup._base_ad_group', index=2, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_tracking_url_template', full_name='google.ads.googleads.v6.resources.AdGroup._tracking_url_template', index=3, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_campaign', full_name='google.ads.googleads.v6.resources.AdGroup._campaign', index=4, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_cpc_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup._cpc_bid_micros', index=5, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_cpm_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup._cpm_bid_micros', index=6, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_target_cpa_micros', full_name='google.ads.googleads.v6.resources.AdGroup._target_cpa_micros', index=7, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_cpv_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup._cpv_bid_micros', index=8, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_target_cpm_micros', full_name='google.ads.googleads.v6.resources.AdGroup._target_cpm_micros', index=9, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_target_roas', full_name='google.ads.googleads.v6.resources.AdGroup._target_roas', index=10, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_percent_cpc_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup._percent_cpc_bid_micros', index=11, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_final_url_suffix', full_name='google.ads.googleads.v6.resources.AdGroup._final_url_suffix', index=12, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_effective_target_cpa_micros', full_name='google.ads.googleads.v6.resources.AdGroup._effective_target_cpa_micros', index=13, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_effective_target_roas', full_name='google.ads.googleads.v6.resources.AdGroup._effective_target_roas', index=14, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), ], serialized_start=635, serialized_end=2556, ) _ADGROUP.fields_by_name['status'].enum_type = google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__status__pb2._ADGROUPSTATUSENUM_ADGROUPSTATUS _ADGROUP.fields_by_name['type'].enum_type = google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__type__pb2._ADGROUPTYPEENUM_ADGROUPTYPE _ADGROUP.fields_by_name['ad_rotation_mode'].enum_type = google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__ad__rotation__mode__pb2._ADGROUPADROTATIONMODEENUM_ADGROUPADROTATIONMODE _ADGROUP.fields_by_name['url_custom_parameters'].message_type = google_dot_ads_dot_googleads_dot_v6_dot_common_dot_custom__parameter__pb2._CUSTOMPARAMETER _ADGROUP.fields_by_name['explorer_auto_optimizer_setting'].message_type = google_dot_ads_dot_googleads_dot_v6_dot_common_dot_explorer__auto__optimizer__setting__pb2._EXPLORERAUTOOPTIMIZERSETTING _ADGROUP.fields_by_name['display_custom_bid_dimension'].enum_type = google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_targeting__dimension__pb2._TARGETINGDIMENSIONENUM_TARGETINGDIMENSION _ADGROUP.fields_by_name['targeting_setting'].message_type = google_dot_ads_dot_googleads_dot_v6_dot_common_dot_targeting__setting__pb2._TARGETINGSETTING _ADGROUP.fields_by_name['effective_target_cpa_source'].enum_type = google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_bidding__source__pb2._BIDDINGSOURCEENUM_BIDDINGSOURCE _ADGROUP.fields_by_name['effective_target_roas_source'].enum_type = google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_bidding__source__pb2._BIDDINGSOURCEENUM_BIDDINGSOURCE _ADGROUP.oneofs_by_name['_id'].fields.append( _ADGROUP.fields_by_name['id']) _ADGROUP.fields_by_name['id'].containing_oneof = _ADGROUP.oneofs_by_name['_id'] _ADGROUP.oneofs_by_name['_name'].fields.append( _ADGROUP.fields_by_name['name']) _ADGROUP.fields_by_name['name'].containing_oneof = _ADGROUP.oneofs_by_name['_name'] _ADGROUP.oneofs_by_name['_base_ad_group'].fields.append( _ADGROUP.fields_by_name['base_ad_group']) _ADGROUP.fields_by_name['base_ad_group'].containing_oneof = _ADGROUP.oneofs_by_name['_base_ad_group'] _ADGROUP.oneofs_by_name['_tracking_url_template'].fields.append( _ADGROUP.fields_by_name['tracking_url_template']) _ADGROUP.fields_by_name['tracking_url_template'].containing_oneof = _ADGROUP.oneofs_by_name['_tracking_url_template'] _ADGROUP.oneofs_by_name['_campaign'].fields.append( _ADGROUP.fields_by_name['campaign']) _ADGROUP.fields_by_name['campaign'].containing_oneof = _ADGROUP.oneofs_by_name['_campaign'] _ADGROUP.oneofs_by_name['_cpc_bid_micros'].fields.append( _ADGROUP.fields_by_name['cpc_bid_micros']) _ADGROUP.fields_by_name['cpc_bid_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_cpc_bid_micros'] _ADGROUP.oneofs_by_name['_cpm_bid_micros'].fields.append( _ADGROUP.fields_by_name['cpm_bid_micros']) _ADGROUP.fields_by_name['cpm_bid_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_cpm_bid_micros'] _ADGROUP.oneofs_by_name['_target_cpa_micros'].fields.append( _ADGROUP.fields_by_name['target_cpa_micros']) _ADGROUP.fields_by_name['target_cpa_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_target_cpa_micros'] _ADGROUP.oneofs_by_name['_cpv_bid_micros'].fields.append( _ADGROUP.fields_by_name['cpv_bid_micros']) _ADGROUP.fields_by_name['cpv_bid_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_cpv_bid_micros'] _ADGROUP.oneofs_by_name['_target_cpm_micros'].fields.append( _ADGROUP.fields_by_name['target_cpm_micros']) _ADGROUP.fields_by_name['target_cpm_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_target_cpm_micros'] _ADGROUP.oneofs_by_name['_target_roas'].fields.append( _ADGROUP.fields_by_name['target_roas']) _ADGROUP.fields_by_name['target_roas'].containing_oneof = _ADGROUP.oneofs_by_name['_target_roas'] _ADGROUP.oneofs_by_name['_percent_cpc_bid_micros'].fields.append( _ADGROUP.fields_by_name['percent_cpc_bid_micros']) _ADGROUP.fields_by_name['percent_cpc_bid_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_percent_cpc_bid_micros'] _ADGROUP.oneofs_by_name['_final_url_suffix'].fields.append( _ADGROUP.fields_by_name['final_url_suffix']) _ADGROUP.fields_by_name['final_url_suffix'].containing_oneof = _ADGROUP.oneofs_by_name['_final_url_suffix'] _ADGROUP.oneofs_by_name['_effective_target_cpa_micros'].fields.append( _ADGROUP.fields_by_name['effective_target_cpa_micros']) _ADGROUP.fields_by_name['effective_target_cpa_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_effective_target_cpa_micros'] _ADGROUP.oneofs_by_name['_effective_target_roas'].fields.append( _ADGROUP.fields_by_name['effective_target_roas']) _ADGROUP.fields_by_name['effective_target_roas'].containing_oneof = _ADGROUP.oneofs_by_name['_effective_target_roas'] DESCRIPTOR.message_types_by_name['AdGroup'] = _ADGROUP _sym_db.RegisterFileDescriptor(DESCRIPTOR) AdGroup = _reflection.GeneratedProtocolMessageType('AdGroup', (_message.Message,), { 'DESCRIPTOR' : _ADGROUP, '__module__' : 'google.ads.googleads.v6.resources.ad_group_pb2' # @@protoc_insertion_point(class_scope:google.ads.googleads.v6.resources.AdGroup) }) _sym_db.RegisterMessage(AdGroup) DESCRIPTOR._options = None _ADGROUP.fields_by_name['resource_name']._options = None _ADGROUP.fields_by_name['id']._options = None _ADGROUP.fields_by_name['type']._options = None _ADGROUP.fields_by_name['base_ad_group']._options = None _ADGROUP.fields_by_name['campaign']._options = None _ADGROUP.fields_by_name['cpv_bid_micros']._options = None _ADGROUP.fields_by_name['effective_target_cpa_micros']._options = None _ADGROUP.fields_by_name['effective_target_cpa_source']._options = None _ADGROUP.fields_by_name['effective_target_roas']._options = None _ADGROUP.fields_by_name['effective_target_roas_source']._options = None _ADGROUP.fields_by_name['labels']._options = None _ADGROUP._options = None # @@protoc_insertion_point(module_scope)

73.205

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0.800287

from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database _sym_db = _symbol_database.Default() from google.ads.google_ads.v6.proto.common import custom_parameter_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_common_dot_custom__parameter__pb2 from google.ads.google_ads.v6.proto.common import explorer_auto_optimizer_setting_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_common_dot_explorer__auto__optimizer__setting__pb2 from google.ads.google_ads.v6.proto.common import targeting_setting_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_common_dot_targeting__setting__pb2 from google.ads.google_ads.v6.proto.enums import ad_group_ad_rotation_mode_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__ad__rotation__mode__pb2 from google.ads.google_ads.v6.proto.enums import ad_group_status_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__status__pb2 from google.ads.google_ads.v6.proto.enums import ad_group_type_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__type__pb2 from google.ads.google_ads.v6.proto.enums import bidding_source_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_bidding__source__pb2 from google.ads.google_ads.v6.proto.enums import targeting_dimension_pb2 as google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_targeting__dimension__pb2 from google.api import field_behavior_pb2 as google_dot_api_dot_field__behavior__pb2 from google.api import resource_pb2 as google_dot_api_dot_resource__pb2 from google.api import annotations_pb2 as google_dot_api_dot_annotations__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='google/ads/googleads/v6/resources/ad_group.proto', package='google.ads.googleads.v6.resources', syntax='proto3', serialized_options=b'\n%com.google.ads.googleads.v6.resourcesB\014AdGroupProtoP\001ZJgoogle.golang.org/genproto/googleapis/ads/googleads/v6/resources;resources\242\002\003GAA\252\002!Google.Ads.GoogleAds.V6.Resources\312\002!Google\\Ads\\GoogleAds\\V6\\Resources\352\002%Google::Ads::GoogleAds::V6::Resources', create_key=_descriptor._internal_create_key, serialized_pb=b'\n0google/ads/googleads/v6/resources/ad_group.proto\x12!google.ads.googleads.v6.resources\x1a\x35google/ads/googleads/v6/common/custom_parameter.proto\x1a\x44google/ads/googleads/v6/common/explorer_auto_optimizer_setting.proto\x1a\x36google/ads/googleads/v6/common/targeting_setting.proto\x1a=google/ads/googleads/v6/enums/ad_group_ad_rotation_mode.proto\x1a\x33google/ads/googleads/v6/enums/ad_group_status.proto\x1a\x31google/ads/googleads/v6/enums/ad_group_type.proto\x1a\x32google/ads/googleads/v6/enums/bidding_source.proto\x1a\x37google/ads/googleads/v6/enums/targeting_dimension.proto\x1a\x1fgoogle/api/field_behavior.proto\x1a\x19google/api/resource.proto\x1a\x1cgoogle/api/annotations.proto\"\x81\x0f\n\x07\x41\x64Group\x12?\n\rresource_name\x18\x01 \x01(\tB(\xe0\x41\x05\xfa\x41\"\n googleads.googleapis.com/AdGroup\x12\x14\n\x02id\x18\" \x01(\x03\x42\x03\xe0\x41\x03H\x00\x88\x01\x01\x12\x11\n\x04name\x18# \x01(\tH\x01\x88\x01\x01\x12N\n\x06status\x18\x05 \x01(\x0e\x32>.google.ads.googleads.v6.enums.AdGroupStatusEnum.AdGroupStatus\x12M\n\x04type\x18\x0c \x01(\x0e\x32:.google.ads.googleads.v6.enums.AdGroupTypeEnum.AdGroupTypeB\x03\xe0\x41\x05\x12h\n\x10\x61\x64_rotation_mode\x18\x16 \x01(\x0e\x32N.google.ads.googleads.v6.enums.AdGroupAdRotationModeEnum.AdGroupAdRotationMode\x12\x44\n\rbase_ad_group\x18$ \x01(\tB(\xe0\x41\x03\xfa\x41\"\n googleads.googleapis.com/AdGroupH\x02\x88\x01\x01\x12\"\n\x15tracking_url_template\x18% \x01(\tH\x03\x88\x01\x01\x12N\n\x15url_custom_parameters\x18\x06 \x03(\x0b\x32/.google.ads.googleads.v6.common.CustomParameter\x12@\n\x08\x63\x61mpaign\x18& \x01(\tB)\xe0\x41\x05\xfa\x41#\n!googleads.googleapis.com/CampaignH\x04\x88\x01\x01\x12\x1b\n\x0e\x63pc_bid_micros\x18\' \x01(\x03H\x05\x88\x01\x01\x12\x1b\n\x0e\x63pm_bid_micros\x18( \x01(\x03H\x06\x88\x01\x01\x12\x1e\n\x11target_cpa_micros\x18) \x01(\x03H\x07\x88\x01\x01\x12 \n\x0e\x63pv_bid_micros\x18* \x01(\x03\x42\x03\xe0\x41\x03H\x08\x88\x01\x01\x12\x1e\n\x11target_cpm_micros\x18+ \x01(\x03H\t\x88\x01\x01\x12\x18\n\x0btarget_roas\x18, \x01(\x01H\n\x88\x01\x01\x12#\n\x16percent_cpc_bid_micros\x18- \x01(\x03H\x0b\x88\x01\x01\x12\x65\n\x1f\x65xplorer_auto_optimizer_setting\x18\x15 \x01(\x0b\x32<.google.ads.googleads.v6.common.ExplorerAutoOptimizerSetting\x12n\n\x1c\x64isplay_custom_bid_dimension\x18\x17 \x01(\x0e\x32H.google.ads.googleads.v6.enums.TargetingDimensionEnum.TargetingDimension\x12\x1d\n\x10\x66inal_url_suffix\x18. \x01(\tH\x0c\x88\x01\x01\x12K\n\x11targeting_setting\x18\x19 \x01(\x0b\x32\x30.google.ads.googleads.v6.common.TargetingSetting\x12-\n\x1b\x65\x66\x66\x65\x63tive_target_cpa_micros\x18/ \x01(\x03\x42\x03\xe0\x41\x03H\r\x88\x01\x01\x12h\n\x1b\x65\x66\x66\x65\x63tive_target_cpa_source\x18\x1d \x01(\x0e\x32>.google.ads.googleads.v6.enums.BiddingSourceEnum.BiddingSourceB\x03\xe0\x41\x03\x12\'\n\x15\x65\x66\x66\x65\x63tive_target_roas\x18\x30 \x01(\x01\x42\x03\xe0\x41\x03H\x0e\x88\x01\x01\x12i\n\x1c\x65\x66\x66\x65\x63tive_target_roas_source\x18 \x01(\x0e\x32>.google.ads.googleads.v6.enums.BiddingSourceEnum.BiddingSourceB\x03\xe0\x41\x03\x12=\n\x06labels\x18\x31 \x03(\tB-\xe0\x41\x03\xfa\x41\'\n%googleads.googleapis.com/AdGroupLabel:U\xea\x41R\n googleads.googleapis.com/AdGroup\x12.customers/{customer_id}/adGroups/{ad_group_id}B\x05\n\x03_idB\x07\n\x05_nameB\x10\n\x0e_base_ad_groupB\x18\n\x16_tracking_url_templateB\x0b\n\t_campaignB\x11\n\x0f_cpc_bid_microsB\x11\n\x0f_cpm_bid_microsB\x14\n\x12_target_cpa_microsB\x11\n\x0f_cpv_bid_microsB\x14\n\x12_target_cpm_microsB\x0e\n\x0c_target_roasB\x19\n\x17_percent_cpc_bid_microsB\x13\n\x11_final_url_suffixB\x1e\n\x1c_effective_target_cpa_microsB\x18\n\x16_effective_target_roasB\xf9\x01\n%com.google.ads.googleads.v6.resourcesB\x0c\x41\x64GroupProtoP\x01ZJgoogle.golang.org/genproto/googleapis/ads/googleads/v6/resources;resources\xa2\x02\x03GAA\xaa\x02!Google.Ads.GoogleAds.V6.Resources\xca\x02!Google\\Ads\\GoogleAds\\V6\\Resources\xea\x02%Google::Ads::GoogleAds::V6::Resourcesb\x06proto3' , dependencies=[google_dot_ads_dot_googleads_dot_v6_dot_common_dot_custom__parameter__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_common_dot_explorer__auto__optimizer__setting__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_common_dot_targeting__setting__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__ad__rotation__mode__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__status__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__type__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_bidding__source__pb2.DESCRIPTOR,google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_targeting__dimension__pb2.DESCRIPTOR,google_dot_api_dot_field__behavior__pb2.DESCRIPTOR,google_dot_api_dot_resource__pb2.DESCRIPTOR,google_dot_api_dot_annotations__pb2.DESCRIPTOR,]) _ADGROUP = _descriptor.Descriptor( name='AdGroup', full_name='google.ads.googleads.v6.resources.AdGroup', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='resource_name', full_name='google.ads.googleads.v6.resources.AdGroup.resource_name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\005\372A\"\n googleads.googleapis.com/AdGroup', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='id', full_name='google.ads.googleads.v6.resources.AdGroup.id', index=1, number=34, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='google.ads.googleads.v6.resources.AdGroup.name', index=2, number=35, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='status', full_name='google.ads.googleads.v6.resources.AdGroup.status', index=3, number=5, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='type', full_name='google.ads.googleads.v6.resources.AdGroup.type', index=4, number=12, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\005', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='ad_rotation_mode', full_name='google.ads.googleads.v6.resources.AdGroup.ad_rotation_mode', index=5, number=22, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='base_ad_group', full_name='google.ads.googleads.v6.resources.AdGroup.base_ad_group', index=6, number=36, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003\372A\"\n googleads.googleapis.com/AdGroup', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='tracking_url_template', full_name='google.ads.googleads.v6.resources.AdGroup.tracking_url_template', index=7, number=37, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='url_custom_parameters', full_name='google.ads.googleads.v6.resources.AdGroup.url_custom_parameters', index=8, number=6, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='campaign', full_name='google.ads.googleads.v6.resources.AdGroup.campaign', index=9, number=38, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\005\372A#\n!googleads.googleapis.com/Campaign', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cpc_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup.cpc_bid_micros', index=10, number=39, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cpm_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup.cpm_bid_micros', index=11, number=40, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='target_cpa_micros', full_name='google.ads.googleads.v6.resources.AdGroup.target_cpa_micros', index=12, number=41, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='cpv_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup.cpv_bid_micros', index=13, number=42, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='target_cpm_micros', full_name='google.ads.googleads.v6.resources.AdGroup.target_cpm_micros', index=14, number=43, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='target_roas', full_name='google.ads.googleads.v6.resources.AdGroup.target_roas', index=15, number=44, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='percent_cpc_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup.percent_cpc_bid_micros', index=16, number=45, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='explorer_auto_optimizer_setting', full_name='google.ads.googleads.v6.resources.AdGroup.explorer_auto_optimizer_setting', index=17, number=21, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='display_custom_bid_dimension', full_name='google.ads.googleads.v6.resources.AdGroup.display_custom_bid_dimension', index=18, number=23, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='final_url_suffix', full_name='google.ads.googleads.v6.resources.AdGroup.final_url_suffix', index=19, number=46, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='targeting_setting', full_name='google.ads.googleads.v6.resources.AdGroup.targeting_setting', index=20, number=25, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='effective_target_cpa_micros', full_name='google.ads.googleads.v6.resources.AdGroup.effective_target_cpa_micros', index=21, number=47, type=3, cpp_type=2, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='effective_target_cpa_source', full_name='google.ads.googleads.v6.resources.AdGroup.effective_target_cpa_source', index=22, number=29, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='effective_target_roas', full_name='google.ads.googleads.v6.resources.AdGroup.effective_target_roas', index=23, number=48, type=1, cpp_type=5, label=1, has_default_value=False, default_value=float(0), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='effective_target_roas_source', full_name='google.ads.googleads.v6.resources.AdGroup.effective_target_roas_source', index=24, number=32, type=14, cpp_type=8, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='labels', full_name='google.ads.googleads.v6.resources.AdGroup.labels', index=25, number=49, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=b'\340A\003\372A\'\n%googleads.googleapis.com/AdGroupLabel', file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=b'\352AR\n googleads.googleapis.com/AdGroup\022.customers/{customer_id}/adGroups/{ad_group_id}', is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ _descriptor.OneofDescriptor( name='_id', full_name='google.ads.googleads.v6.resources.AdGroup._id', index=0, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_name', full_name='google.ads.googleads.v6.resources.AdGroup._name', index=1, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_base_ad_group', full_name='google.ads.googleads.v6.resources.AdGroup._base_ad_group', index=2, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_tracking_url_template', full_name='google.ads.googleads.v6.resources.AdGroup._tracking_url_template', index=3, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_campaign', full_name='google.ads.googleads.v6.resources.AdGroup._campaign', index=4, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_cpc_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup._cpc_bid_micros', index=5, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_cpm_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup._cpm_bid_micros', index=6, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_target_cpa_micros', full_name='google.ads.googleads.v6.resources.AdGroup._target_cpa_micros', index=7, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_cpv_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup._cpv_bid_micros', index=8, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_target_cpm_micros', full_name='google.ads.googleads.v6.resources.AdGroup._target_cpm_micros', index=9, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_target_roas', full_name='google.ads.googleads.v6.resources.AdGroup._target_roas', index=10, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_percent_cpc_bid_micros', full_name='google.ads.googleads.v6.resources.AdGroup._percent_cpc_bid_micros', index=11, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_final_url_suffix', full_name='google.ads.googleads.v6.resources.AdGroup._final_url_suffix', index=12, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_effective_target_cpa_micros', full_name='google.ads.googleads.v6.resources.AdGroup._effective_target_cpa_micros', index=13, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), _descriptor.OneofDescriptor( name='_effective_target_roas', full_name='google.ads.googleads.v6.resources.AdGroup._effective_target_roas', index=14, containing_type=None, create_key=_descriptor._internal_create_key, fields=[]), ], serialized_start=635, serialized_end=2556, ) _ADGROUP.fields_by_name['status'].enum_type = google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__status__pb2._ADGROUPSTATUSENUM_ADGROUPSTATUS _ADGROUP.fields_by_name['type'].enum_type = google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__type__pb2._ADGROUPTYPEENUM_ADGROUPTYPE _ADGROUP.fields_by_name['ad_rotation_mode'].enum_type = google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_ad__group__ad__rotation__mode__pb2._ADGROUPADROTATIONMODEENUM_ADGROUPADROTATIONMODE _ADGROUP.fields_by_name['url_custom_parameters'].message_type = google_dot_ads_dot_googleads_dot_v6_dot_common_dot_custom__parameter__pb2._CUSTOMPARAMETER _ADGROUP.fields_by_name['explorer_auto_optimizer_setting'].message_type = google_dot_ads_dot_googleads_dot_v6_dot_common_dot_explorer__auto__optimizer__setting__pb2._EXPLORERAUTOOPTIMIZERSETTING _ADGROUP.fields_by_name['display_custom_bid_dimension'].enum_type = google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_targeting__dimension__pb2._TARGETINGDIMENSIONENUM_TARGETINGDIMENSION _ADGROUP.fields_by_name['targeting_setting'].message_type = google_dot_ads_dot_googleads_dot_v6_dot_common_dot_targeting__setting__pb2._TARGETINGSETTING _ADGROUP.fields_by_name['effective_target_cpa_source'].enum_type = google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_bidding__source__pb2._BIDDINGSOURCEENUM_BIDDINGSOURCE _ADGROUP.fields_by_name['effective_target_roas_source'].enum_type = google_dot_ads_dot_googleads_dot_v6_dot_enums_dot_bidding__source__pb2._BIDDINGSOURCEENUM_BIDDINGSOURCE _ADGROUP.oneofs_by_name['_id'].fields.append( _ADGROUP.fields_by_name['id']) _ADGROUP.fields_by_name['id'].containing_oneof = _ADGROUP.oneofs_by_name['_id'] _ADGROUP.oneofs_by_name['_name'].fields.append( _ADGROUP.fields_by_name['name']) _ADGROUP.fields_by_name['name'].containing_oneof = _ADGROUP.oneofs_by_name['_name'] _ADGROUP.oneofs_by_name['_base_ad_group'].fields.append( _ADGROUP.fields_by_name['base_ad_group']) _ADGROUP.fields_by_name['base_ad_group'].containing_oneof = _ADGROUP.oneofs_by_name['_base_ad_group'] _ADGROUP.oneofs_by_name['_tracking_url_template'].fields.append( _ADGROUP.fields_by_name['tracking_url_template']) _ADGROUP.fields_by_name['tracking_url_template'].containing_oneof = _ADGROUP.oneofs_by_name['_tracking_url_template'] _ADGROUP.oneofs_by_name['_campaign'].fields.append( _ADGROUP.fields_by_name['campaign']) _ADGROUP.fields_by_name['campaign'].containing_oneof = _ADGROUP.oneofs_by_name['_campaign'] _ADGROUP.oneofs_by_name['_cpc_bid_micros'].fields.append( _ADGROUP.fields_by_name['cpc_bid_micros']) _ADGROUP.fields_by_name['cpc_bid_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_cpc_bid_micros'] _ADGROUP.oneofs_by_name['_cpm_bid_micros'].fields.append( _ADGROUP.fields_by_name['cpm_bid_micros']) _ADGROUP.fields_by_name['cpm_bid_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_cpm_bid_micros'] _ADGROUP.oneofs_by_name['_target_cpa_micros'].fields.append( _ADGROUP.fields_by_name['target_cpa_micros']) _ADGROUP.fields_by_name['target_cpa_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_target_cpa_micros'] _ADGROUP.oneofs_by_name['_cpv_bid_micros'].fields.append( _ADGROUP.fields_by_name['cpv_bid_micros']) _ADGROUP.fields_by_name['cpv_bid_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_cpv_bid_micros'] _ADGROUP.oneofs_by_name['_target_cpm_micros'].fields.append( _ADGROUP.fields_by_name['target_cpm_micros']) _ADGROUP.fields_by_name['target_cpm_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_target_cpm_micros'] _ADGROUP.oneofs_by_name['_target_roas'].fields.append( _ADGROUP.fields_by_name['target_roas']) _ADGROUP.fields_by_name['target_roas'].containing_oneof = _ADGROUP.oneofs_by_name['_target_roas'] _ADGROUP.oneofs_by_name['_percent_cpc_bid_micros'].fields.append( _ADGROUP.fields_by_name['percent_cpc_bid_micros']) _ADGROUP.fields_by_name['percent_cpc_bid_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_percent_cpc_bid_micros'] _ADGROUP.oneofs_by_name['_final_url_suffix'].fields.append( _ADGROUP.fields_by_name['final_url_suffix']) _ADGROUP.fields_by_name['final_url_suffix'].containing_oneof = _ADGROUP.oneofs_by_name['_final_url_suffix'] _ADGROUP.oneofs_by_name['_effective_target_cpa_micros'].fields.append( _ADGROUP.fields_by_name['effective_target_cpa_micros']) _ADGROUP.fields_by_name['effective_target_cpa_micros'].containing_oneof = _ADGROUP.oneofs_by_name['_effective_target_cpa_micros'] _ADGROUP.oneofs_by_name['_effective_target_roas'].fields.append( _ADGROUP.fields_by_name['effective_target_roas']) _ADGROUP.fields_by_name['effective_target_roas'].containing_oneof = _ADGROUP.oneofs_by_name['_effective_target_roas'] DESCRIPTOR.message_types_by_name['AdGroup'] = _ADGROUP _sym_db.RegisterFileDescriptor(DESCRIPTOR) AdGroup = _reflection.GeneratedProtocolMessageType('AdGroup', (_message.Message,), { 'DESCRIPTOR' : _ADGROUP, '__module__' : 'google.ads.googleads.v6.resources.ad_group_pb2' # @@protoc_insertion_point(class_scope:google.ads.googleads.v6.resources.AdGroup) }) _sym_db.RegisterMessage(AdGroup) DESCRIPTOR._options = None _ADGROUP.fields_by_name['resource_name']._options = None _ADGROUP.fields_by_name['id']._options = None _ADGROUP.fields_by_name['type']._options = None _ADGROUP.fields_by_name['base_ad_group']._options = None _ADGROUP.fields_by_name['campaign']._options = None _ADGROUP.fields_by_name['cpv_bid_micros']._options = None _ADGROUP.fields_by_name['effective_target_cpa_micros']._options = None _ADGROUP.fields_by_name['effective_target_cpa_source']._options = None _ADGROUP.fields_by_name['effective_target_roas']._options = None _ADGROUP.fields_by_name['effective_target_roas_source']._options = None _ADGROUP.fields_by_name['labels']._options = None _ADGROUP._options = None # @@protoc_insertion_point(module_scope)

true

f719701d150a3482167aae75965a980e8a9f516b

2,199

py

Python

backend/wazzle_33192/urls.py

crowdbotics-apps/wazzle-33192

6203ab17b0c80344f1b15d1d5452bfdd2e6559bd

[ "FTL", "AML", "RSA-MD" ]

null

backend/wazzle_33192/urls.py

crowdbotics-apps/wazzle-33192

6203ab17b0c80344f1b15d1d5452bfdd2e6559bd

[ "FTL", "AML", "RSA-MD" ]

null

backend/wazzle_33192/urls.py

crowdbotics-apps/wazzle-33192

6203ab17b0c80344f1b15d1d5452bfdd2e6559bd

[ "FTL", "AML", "RSA-MD" ]

null

"""wazzle_33192 URL Configuration The `urlpatterns` list routes URLs to views. For more information please see: https://docs.djangoproject.com/en/2.2/topics/http/urls/ Examples: Function views 1. Add an import: from my_app import views 2. Add a URL to urlpatterns: path('', views.home, name='home') Class-based views 1. Add an import: from other_app.views import Home 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home') Including another URLconf 1. Import the include() function: from django.urls import include, path 2. Add a URL to urlpatterns: path('blog/', include('blog.urls')) """ from django.contrib import admin from django.urls import path, include, re_path from django.views.generic.base import TemplateView from allauth.account.views import confirm_email from rest_framework import permissions from drf_yasg.views import get_schema_view from drf_yasg import openapi urlpatterns = [ path("", include("home.urls")), path("accounts/", include("allauth.urls")), path("modules/", include("modules.urls")), path("api/v1/", include("home.api.v1.urls")), path("admin/", admin.site.urls), path("users/", include("users.urls", namespace="users")), path("rest-auth/", include("rest_auth.urls")), # Override email confirm to use allauth's HTML view instead of rest_auth's API view path("rest-auth/registration/account-confirm-email/<str:key>/", confirm_email), path("rest-auth/registration/", include("rest_auth.registration.urls")), ] admin.site.site_header = "Wazzle" admin.site.site_title = "Wazzle Admin Portal" admin.site.index_title = "Wazzle Admin" # swagger api_info = openapi.Info( title="Wazzle API", default_version="v1", description="API documentation for Wazzle App", ) schema_view = get_schema_view( api_info, public=True, permission_classes=(permissions.IsAuthenticated,), ) urlpatterns += [ path("api-docs/", schema_view.with_ui("swagger", cache_timeout=0), name="api_docs") ] urlpatterns += [path("", TemplateView.as_view(template_name='index.html'))] urlpatterns += [re_path(r"^(?:.*)/?$", TemplateView.as_view(template_name='index.html'))]

34.904762

87

0.710778

from django.contrib import admin from django.urls import path, include, re_path from django.views.generic.base import TemplateView from allauth.account.views import confirm_email from rest_framework import permissions from drf_yasg.views import get_schema_view from drf_yasg import openapi urlpatterns = [ path("", include("home.urls")), path("accounts/", include("allauth.urls")), path("modules/", include("modules.urls")), path("api/v1/", include("home.api.v1.urls")), path("admin/", admin.site.urls), path("users/", include("users.urls", namespace="users")), path("rest-auth/", include("rest_auth.urls")), path("rest-auth/registration/account-confirm-email/<str:key>/", confirm_email), path("rest-auth/registration/", include("rest_auth.registration.urls")), ] admin.site.site_header = "Wazzle" admin.site.site_title = "Wazzle Admin Portal" admin.site.index_title = "Wazzle Admin" api_info = openapi.Info( title="Wazzle API", default_version="v1", description="API documentation for Wazzle App", ) schema_view = get_schema_view( api_info, public=True, permission_classes=(permissions.IsAuthenticated,), ) urlpatterns += [ path("api-docs/", schema_view.with_ui("swagger", cache_timeout=0), name="api_docs") ] urlpatterns += [path("", TemplateView.as_view(template_name='index.html'))] urlpatterns += [re_path(r"^(?:.*)/?$", TemplateView.as_view(template_name='index.html'))]

true