fumiyau/python_no_comments_test10000 · Datasets at Hugging Face

f7174cab91de04cda9ea71e938b27612f1038cb9

11,857

py

Python

tests/test_stochatreat_assignment.py

RoyalTS/stochatreat

6e638e748b8638b64a185229f78967cf864cd45e

[ "MIT" ]

null

tests/test_stochatreat_assignment.py

RoyalTS/stochatreat

6e638e748b8638b64a185229f78967cf864cd45e

[ "MIT" ]

13

2019-07-11T13:13:39.000Z

2019-07-19T14:26:57.000Z

tests/test_stochatreat_assignment.py

RoyalTS/stochatreat

6e638e748b8638b64a185229f78967cf864cd45e

[ "MIT" ]

null

import pytest from math import gcd import numpy as np import pandas as pd from stochatreat import stochatreat from stochatreat import get_lcm_prob_denominators ################################################################################ # fixtures ################################################################################ @pytest.fixture(params=[10_000, 100_000]) def df(request): N = request.param df = pd.DataFrame( data={ "id": np.arange(N), "dummy": [1] * N, "stratum1": np.random.randint(1, 100, size=N), "stratum2": np.random.randint(0, 2, size=N), } ) return df # a set of treatment assignment probabilities to throw at many tests standard_probs = [[0.1, 0.9], [1/3, 2/3], [0.5, 0.5], [2/3, 1/3], [0.9, 0.1]] # a set of stratum column combinations from the above df fixture to throw at # many tests standard_stratum_cols = [ ["dummy"], ["stratum1"], ["stratum1", "stratum2"], ] # a DataFrame and treatment assignment probabilities under which there will be # no misfits @pytest.fixture def df_no_misfits(): N = 1_000 stratum_size = 10 df = pd.DataFrame( data={ "id": np.arange(N), "stratum": np.repeat( np.arange(N / stratum_size), repeats=stratum_size ) } ) return df probs_no_misfits =[ [0.1, 0.9], [0.5, 0.5], [0.9, 0.1], ] ################################################################################ # overall treatment assignment proportions ################################################################################ @pytest.mark.parametrize("n_treats", [2, 3, 4, 5, 10]) @pytest.mark.parametrize("stratum_cols", standard_stratum_cols) def test_stochatreat_no_probs(n_treats, stratum_cols, df): """ Tests that overall treatment assignment proportions across all strata are as intended with equal treatment assignment probabilities -- relies on the Law of Large Numbers, not deterministic """ treats = stochatreat( data=df, stratum_cols=stratum_cols, treats=n_treats, idx_col="id", random_state=42 ) treatment_shares = treats.groupby('treat')['id'].size() / treats.shape[0] np.testing.assert_almost_equal( treatment_shares, np.array([1 / n_treats] * n_treats), decimal=2 ) @pytest.mark.parametrize("probs", standard_probs) @pytest.mark.parametrize("stratum_cols", standard_stratum_cols) def test_stochatreat_probs(probs, stratum_cols, df): """ Tests that overall treatment assignment proportions across all strata are as intended with unequal treatment assignment probabilities -- relies on the Law of Large Numbers, not deterministic """ treats = stochatreat( data=df, stratum_cols=stratum_cols, treats=len(probs), idx_col="id", probs=probs, random_state=42, ) treatment_shares = treats.groupby('treat')['id'].size() / treats.shape[0] np.testing.assert_almost_equal( treatment_shares, np.array(probs), decimal=2 ) @pytest.mark.parametrize("probs", probs_no_misfits) def test_stochatreat_no_misfits(probs, df_no_misfits): """ Tests that overall treatment assignment proportions across all strata are as intended when strata are such that there are no misfits """ treats = stochatreat( data=df_no_misfits, stratum_cols=["stratum"], treats=len(probs), idx_col="id", probs=probs, random_state=42, ) treatment_shares = treats.groupby('treat')['id'].size() / treats.shape[0] np.testing.assert_almost_equal( treatment_shares, np.array(probs), decimal=2 ) @pytest.mark.parametrize("probs", standard_probs) def test_stochatreat_only_misfits(probs): """ Tests that overall treatment assignment proportions across all strata are as intended when strata are such that there are only misfits and the number of units is sufficiently large -- relies on the Law of Large Numbers, not deterministic """ N = 10_000 df = pd.DataFrame( data={ "id": np.arange(N), "stratum": np.arange(N), } ) treats = stochatreat( data=df, stratum_cols=["stratum"], treats=len(probs), idx_col="id", probs=probs, random_state=42, ) treatment_shares = treats.groupby('treat')['id'].size() / treats.shape[0] np.testing.assert_almost_equal( treatment_shares, np.array(probs), decimal=2 ) ################################################################################ # within-stratum treatment assignments ################################################################################ def get_within_strata_counts(treats): """Helper function to compute the treatment shares within strata""" treatment_counts = (treats .groupby(["stratum_id", "treat"])[["id"]] .count() .rename(columns={"id": "treat_count"}) .reset_index() ) stratum_counts = (treats .groupby(["stratum_id"])[["id"]] .count() .rename(columns={"id": "stratum_count"}) .reset_index() ) counts = pd.merge( treatment_counts, stratum_counts, on="stratum_id", how="left" ) return counts def compute_count_diff(treats, probs): """ Helper function to compute the treatment counts within strata and line them up with required counts, and returns the different treatment counts aggregated at the stratum level as well as the dataframe with the different counts used in some tests """ counts = get_within_strata_counts(treats) required_props = pd.DataFrame( {"required_prop": probs, "treat": range(len(probs))} ) comp = pd.merge( counts, required_props, on="treat", how="left" ) comp["desired_counts"] = comp["stratum_count"] * comp["required_prop"] comp["count_diff"] = (comp["treat_count"] - comp["desired_counts"]).abs() return comp @pytest.mark.parametrize("n_treats", [2, 3, 4, 5, 10]) @pytest.mark.parametrize("stratum_cols", standard_stratum_cols) def test_stochatreat_within_strata_no_probs(n_treats, stratum_cols, df): """ Tests that within strata treatment assignment counts are only as far from the required counts as misfit assignment randomization allows with equal treatment assignment probabilities but a differing number of treatments """ probs = n_treats * [1 / n_treats] lcm_prob_denominators = n_treats treats = stochatreat( data=df, stratum_cols=stratum_cols, treats=n_treats, idx_col="id", random_state=42 ) comp = compute_count_diff(treats, probs) assert_msg = """The counts differences exceed the bound that misfit allocation should not exceed""" assert (comp["count_diff"] < lcm_prob_denominators).all(), assert_msg @pytest.mark.parametrize("probs", standard_probs) @pytest.mark.parametrize("stratum_cols", standard_stratum_cols) def test_stochatreat_within_strata_probs(probs, stratum_cols, df): """ Tests that within strata treatment assignment counts are only as far from the required counts as misfit assignment randomization allows with two treatments but unequal treatment assignment probabilities """ lcm_prob_denominators = get_lcm_prob_denominators(probs) treats = stochatreat( data=df, stratum_cols=stratum_cols, treats=len(probs), idx_col="id", probs=probs, random_state=42, ) comp = compute_count_diff(treats, probs) assert_msg = """The counts differences exceed the bound that misfit allocation should not exceed""" assert (comp["count_diff"] < lcm_prob_denominators).all(), assert_msg @pytest.mark.parametrize("probs", probs_no_misfits) def test_stochatreat_within_strata_no_misfits(probs, df_no_misfits): """ Tests that within strata treatment assignment counts are exactly equal to the required counts when strata are such that there are no misfits """ treats = stochatreat( data=df_no_misfits, stratum_cols=["stratum"], treats=len(probs), idx_col="id", probs=probs, random_state=42, ) comp = compute_count_diff(treats, probs) assert_msg = "The required proportions are not reached without misfits" assert (comp["count_diff"] == 0).all(), assert_msg @pytest.mark.parametrize("probs", standard_probs) @pytest.mark.parametrize("stratum_cols", standard_stratum_cols) def test_stochatreat_global_strategy(probs, stratum_cols, df): treats = stochatreat( data=df, stratum_cols=stratum_cols, treats=len(probs), idx_col="id", probs=probs, random_state=42, misfit_strategy="global" ) comp = compute_count_diff(treats, probs) stratum_count_diff = comp.groupby(["stratum_id"])["count_diff"].sum() assert_msg = "There is more than one stratum with misfits" assert (stratum_count_diff != 0).sum() <= 1, assert_msg @pytest.mark.parametrize("misfit_strategy", ["global", "stratum"]) @pytest.mark.parametrize("stratum_cols", standard_stratum_cols) def test_stochatreat_stratum_ids(df, misfit_strategy, stratum_cols): """Tests that the function returns the right number of stratum ids""" treats = stochatreat( data=df, stratum_cols=stratum_cols, treats=2, idx_col="id", random_state=42, misfit_strategy=misfit_strategy, ) n_unique_strata = len(df[stratum_cols].drop_duplicates()) n_unique_stratum_ids = len(treats["stratum_id"].drop_duplicates()) if misfit_strategy == "global": # depending on whether there are misfits assert ( (n_unique_stratum_ids == n_unique_strata) or (n_unique_stratum_ids - 1 == n_unique_strata) ) else: assert n_unique_stratum_ids == n_unique_strata @pytest.mark.parametrize("stratum_cols", standard_stratum_cols) @pytest.mark.parametrize("misfit_strategy", ["global", "stratum"]) def test_stochatreat_random_state(df, stratum_cols, misfit_strategy): """ Tests that the results are the same on two consecutive calls with the same random state """ random_state = 42 treats = [] for _ in range(2): treatments_i = stochatreat( data=df, stratum_cols=stratum_cols, treats=2, idx_col="id", random_state=random_state, misfit_strategy=misfit_strategy, ) treats.append(treatments_i) pd.testing.assert_series_equal( treats[0]["treat"], treats[1]["treat"] ) @pytest.mark.parametrize("stratum_cols", standard_stratum_cols) @pytest.mark.parametrize("misfit_strategy", ["global", "stratum"]) def test_stochatreat_shuffle_data(df, stratum_cols, misfit_strategy): """ Tests that the mapping between idx_col and the assignments is the same on two consecutive calls with the same random state and shuffled data points """ random_state = 42 treats = [] for _ in range(2): treatments_i = stochatreat( data=df, stratum_cols=stratum_cols, treats=2, idx_col="id", random_state=random_state, misfit_strategy=misfit_strategy, ) treatments_i = treatments_i.sort_values("id") treats.append(treatments_i) df = df.sample(len(df), random_state=random_state) pd.testing.assert_series_equal( treats[0]["treat"], treats[1]["treat"] )

30.017722

80

0.627899

import pytest from math import gcd import numpy as np import pandas as pd from stochatreat import stochatreat from stochatreat import get_lcm_prob_denominators

true

f7174cd8c1d5c09c4cbcb9df7c5490a9c0982657

1,305

py

Python

source/ship.py

seveirbian/Plane-game

96c5377e72d3dfb1c5720a1769e9db8e89624ed5

[ "MIT" ]

1

2018-06-12T08:56:52.000Z

source/ship.py

seveirbian/Plane-game

96c5377e72d3dfb1c5720a1769e9db8e89624ed5

[ "MIT" ]

null

source/ship.py

seveirbian/Plane-game

96c5377e72d3dfb1c5720a1769e9db8e89624ed5

[ "MIT" ]

null

import pygame from pygame.sprite import Sprite class Ship(Sprite): def __init__(self, ai_settings, screen): super().__init__() '''初始化飞船并设置其初始位置''' self.screen = screen self.ai_settings = ai_settings # 加载飞船图像并获取其外接矩形 self.image = pygame.image.load('../images/ship.png') self.rect = self.image.get_rect() self.screen_rect = screen.get_rect() # 将每艘新飞船放在屏幕底部中央 self.rect.centerx = self.screen_rect.centerx self.rect.bottom = self.screen_rect.bottom # 在飞船的属性center中存储小数值 self.center = float(self.rect.centerx) # 移动标志 self.moving_right = False self.moving_left = False def blitme(self): '''在指定位置绘制飞船''' self.screen.blit(self.image, self.rect) def update(self): '''根据移动标志调整飞船的位置''' # 更新飞船的center值，而不是rect if self.moving_right and (self.rect.right < self.screen_rect.right): self.center += self.ai_settings.ship_speed_factor if self.moving_left and (self.rect.left > 0): self.center -= self.ai_settings.ship_speed_factor # 根据self.center值跟新rect对象 self.rect.centerx = self.center def center_ship(self): '''让飞船在屏幕上居中''' self.center = self.screen_rect.centerx

29

76

0.622989

import pygame from pygame.sprite import Sprite class Ship(Sprite): def __init__(self, ai_settings, screen): super().__init__() self.screen = screen self.ai_settings = ai_settings self.image = pygame.image.load('../images/ship.png') self.rect = self.image.get_rect() self.screen_rect = screen.get_rect() self.rect.centerx = self.screen_rect.centerx self.rect.bottom = self.screen_rect.bottom self.center = float(self.rect.centerx) self.moving_right = False self.moving_left = False def blitme(self): self.screen.blit(self.image, self.rect) def update(self): if self.moving_right and (self.rect.right < self.screen_rect.right): self.center += self.ai_settings.ship_speed_factor if self.moving_left and (self.rect.left > 0): self.center -= self.ai_settings.ship_speed_factor self.rect.centerx = self.center def center_ship(self): self.center = self.screen_rect.centerx

true

f7174db2dec152988a0f2418273a7a9086488a8b

4,836

py

Python

pynubank/nubank.py

FlavioMoreiraTec/nubank-flavio

224f483a7c7644116657c4f9e0929010ed511aa4

[ "MIT" ]

null

pynubank/nubank.py

FlavioMoreiraTec/nubank-flavio

224f483a7c7644116657c4f9e0929010ed511aa4

[ "MIT" ]

null

pynubank/nubank.py

FlavioMoreiraTec/nubank-flavio

224f483a7c7644116657c4f9e0929010ed511aa4

[ "MIT" ]

null

import json import os import uuid from typing import Tuple import requests from qrcode import QRCode from requests import Response PAYMENT_EVENT_TYPES = ( 'TransferOutEvent', 'TransferInEvent', 'TransferOutReversalEvent', 'BarcodePaymentEvent', 'DebitPurchaseEvent', 'DebitPurchaseReversalEvent', ) class NuException(Exception): def __init__(self, status_code, response, url): super().__init__(f'The request made failed with HTTP status code {status_code}') self.url = url self.status_code = status_code self.response = response class Nubank: DISCOVERY_URL = 'https://prod-s0-webapp-proxy.nubank.com.br/api/discovery' DISCOVERY_APP_URL = 'https://prod-s0-webapp-proxy.nubank.com.br/api/app/discovery' auth_url = None feed_url = None proxy_list_url = None proxy_list_app_url = None query_url = None bills_url = None def __init__(self): self.headers = { 'Content-Type': 'application/json', 'X-Correlation-Id': 'WEB-APP.pewW9', 'User-Agent': 'pynubank Client - https://github.com/andreroggeri/pynubank', } self._update_proxy_urls() self.auth_url = self.proxy_list_url['login'] @staticmethod def _get_query(query_name): root = os.path.abspath(os.path.dirname(__file__)) gql_file = query_name + '.gql' path = os.path.join(root, 'queries', gql_file) with open(path) as gql: return gql.read() def _update_proxy_urls(self): request = requests.get(self.DISCOVERY_URL, headers=self.headers) self.proxy_list_url = json.loads(request.content.decode('utf-8')) request = requests.get(self.DISCOVERY_APP_URL, headers=self.headers) self.proxy_list_app_url = json.loads(request.content.decode('utf-8')) def _make_graphql_request(self, graphql_object): body = { 'query': self._get_query(graphql_object) } response = requests.post(self.query_url, json=body, headers=self.headers) return self._handle_response(response) def _password_auth(self, cpf: str, password: str): payload = { "grant_type": "password", "login": cpf, "password": password, "client_id": "other.conta", "client_secret": "yQPeLzoHuJzlMMSAjC-LgNUJdUecx8XO" } response = requests.post(self.auth_url, json=payload, headers=self.headers) data = self._handle_response(response) return data def _handle_response(self, response: Response) -> dict: if response.status_code != 200: raise NuException(response.status_code, response.json(), response.url) return response.json() def get_qr_code(self) -> Tuple[str, QRCode]: content = str(uuid.uuid4()) qr = QRCode() qr.add_data(content) return content, qr def authenticate_with_qr_code(self, cpf: str, password, uuid: str): auth_data = self._password_auth(cpf, password) self.headers['Authorization'] = f'Bearer {auth_data["access_token"]}' payload = { 'qr_code_id': uuid, 'type': 'login-webapp' } response = requests.post(self.proxy_list_app_url['lift'], json=payload, headers=self.headers) auth_data = self._handle_response(response) self.headers['Authorization'] = f'Bearer {auth_data["access_token"]}' self.feed_url = auth_data['_links']['events']['href'] self.query_url = auth_data['_links']['ghostflame']['href'] self.bills_url = auth_data['_links']['bills_summary']['href'] def get_card_feed(self): request = requests.get(self.feed_url, headers=self.headers) return json.loads(request.content.decode('utf-8')) def get_card_statements(self): feed = self.get_card_feed() return list(filter(lambda x: x['category'] == 'transaction', feed['events'])) def get_bills(self): request = requests.get(self.bills_url, headers=self.headers) return json.loads(request.content.decode('utf-8'))['bills'] def get_bill_details(self, bill): request = requests.get(bill['_links']['self']['href'], headers=self.headers) return json.loads(request.content.decode('utf-8')) def get_account_feed(self): data = self._make_graphql_request('account_feed') return data['data']['viewer']['savingsAccount']['feed'] def get_account_statements(self): feed = self.get_account_feed() return list(filter(lambda x: x['__typename'] in PAYMENT_EVENT_TYPES, feed)) def get_account_balance(self): data = self._make_graphql_request('account_balance') return data['data']['viewer']['savingsAccount']['currentSavingsBalance']['netAmount']

35.29927

101

0.651778

import json import os import uuid from typing import Tuple import requests from qrcode import QRCode from requests import Response PAYMENT_EVENT_TYPES = ( 'TransferOutEvent', 'TransferInEvent', 'TransferOutReversalEvent', 'BarcodePaymentEvent', 'DebitPurchaseEvent', 'DebitPurchaseReversalEvent', ) class NuException(Exception): def __init__(self, status_code, response, url): super().__init__(f'The request made failed with HTTP status code {status_code}') self.url = url self.status_code = status_code self.response = response class Nubank: DISCOVERY_URL = 'https://prod-s0-webapp-proxy.nubank.com.br/api/discovery' DISCOVERY_APP_URL = 'https://prod-s0-webapp-proxy.nubank.com.br/api/app/discovery' auth_url = None feed_url = None proxy_list_url = None proxy_list_app_url = None query_url = None bills_url = None def __init__(self): self.headers = { 'Content-Type': 'application/json', 'X-Correlation-Id': 'WEB-APP.pewW9', 'User-Agent': 'pynubank Client - https://github.com/andreroggeri/pynubank', } self._update_proxy_urls() self.auth_url = self.proxy_list_url['login'] @staticmethod def _get_query(query_name): root = os.path.abspath(os.path.dirname(__file__)) gql_file = query_name + '.gql' path = os.path.join(root, 'queries', gql_file) with open(path) as gql: return gql.read() def _update_proxy_urls(self): request = requests.get(self.DISCOVERY_URL, headers=self.headers) self.proxy_list_url = json.loads(request.content.decode('utf-8')) request = requests.get(self.DISCOVERY_APP_URL, headers=self.headers) self.proxy_list_app_url = json.loads(request.content.decode('utf-8')) def _make_graphql_request(self, graphql_object): body = { 'query': self._get_query(graphql_object) } response = requests.post(self.query_url, json=body, headers=self.headers) return self._handle_response(response) def _password_auth(self, cpf: str, password: str): payload = { "grant_type": "password", "login": cpf, "password": password, "client_id": "other.conta", "client_secret": "yQPeLzoHuJzlMMSAjC-LgNUJdUecx8XO" } response = requests.post(self.auth_url, json=payload, headers=self.headers) data = self._handle_response(response) return data def _handle_response(self, response: Response) -> dict: if response.status_code != 200: raise NuException(response.status_code, response.json(), response.url) return response.json() def get_qr_code(self) -> Tuple[str, QRCode]: content = str(uuid.uuid4()) qr = QRCode() qr.add_data(content) return content, qr def authenticate_with_qr_code(self, cpf: str, password, uuid: str): auth_data = self._password_auth(cpf, password) self.headers['Authorization'] = f'Bearer {auth_data["access_token"]}' payload = { 'qr_code_id': uuid, 'type': 'login-webapp' } response = requests.post(self.proxy_list_app_url['lift'], json=payload, headers=self.headers) auth_data = self._handle_response(response) self.headers['Authorization'] = f'Bearer {auth_data["access_token"]}' self.feed_url = auth_data['_links']['events']['href'] self.query_url = auth_data['_links']['ghostflame']['href'] self.bills_url = auth_data['_links']['bills_summary']['href'] def get_card_feed(self): request = requests.get(self.feed_url, headers=self.headers) return json.loads(request.content.decode('utf-8')) def get_card_statements(self): feed = self.get_card_feed() return list(filter(lambda x: x['category'] == 'transaction', feed['events'])) def get_bills(self): request = requests.get(self.bills_url, headers=self.headers) return json.loads(request.content.decode('utf-8'))['bills'] def get_bill_details(self, bill): request = requests.get(bill['_links']['self']['href'], headers=self.headers) return json.loads(request.content.decode('utf-8')) def get_account_feed(self): data = self._make_graphql_request('account_feed') return data['data']['viewer']['savingsAccount']['feed'] def get_account_statements(self): feed = self.get_account_feed() return list(filter(lambda x: x['__typename'] in PAYMENT_EVENT_TYPES, feed)) def get_account_balance(self): data = self._make_graphql_request('account_balance') return data['data']['viewer']['savingsAccount']['currentSavingsBalance']['netAmount']

true

f7174f069101d37e1152c091948b84f7ddc5aa8d

313

py

Python

py_merge/mergeexample.py

mutazag/misc

dfef362cdd835ef4efd1f2d02e13ff5297ccfc0f

[ "MIT" ]

null

py_merge/mergeexample.py

mutazag/misc

dfef362cdd835ef4efd1f2d02e13ff5297ccfc0f

[ "MIT" ]

null

py_merge/mergeexample.py

mutazag/misc

dfef362cdd835ef4efd1f2d02e13ff5297ccfc0f

[ "MIT" ]

null

#%% import pandas as pd #%% df1 = pd.read_csv('df1.csv', index_col=0) # %% df2 = pd.read_csv('df2.csv', index_col=0) # %% df3 = pd.read_csv('df3.csv', index_col=0) # %% df1.merge(df2, on='proj_id').merge(df3, on='doc_id') # %% df1.merge(df2, on='proj_id', how='left').merge(df3, on='doc_id', how='left') # %%

18.411765

76

0.603834

import pandas as pd df1 = pd.read_csv('df1.csv', index_col=0) df2 = pd.read_csv('df2.csv', index_col=0) df3 = pd.read_csv('df3.csv', index_col=0) df1.merge(df2, on='proj_id').merge(df3, on='doc_id') df1.merge(df2, on='proj_id', how='left').merge(df3, on='doc_id', how='left')

true

f7175023ba297508308f5f971d92777633745cb2

1,542

py

Python

tests/python/gaia-ui-tests/gaiatest/tests/functional/settings/test_settings_media_storage.py

BReduardokramer/gaia

c00302cdcd435ab193e8365917cfc6abac9e4f2e

[ "Apache-2.0" ]

1

2021-11-09T00:27:34.000Z

tests/python/gaia-ui-tests/gaiatest/tests/functional/settings/test_settings_media_storage.py

Delphine/gaia

df92f0ebd89efbc63570a61e70c4304c17b8b555

[ "Apache-2.0" ]

null

tests/python/gaia-ui-tests/gaiatest/tests/functional/settings/test_settings_media_storage.py

Delphine/gaia

df92f0ebd89efbc63570a61e70c4304c17b8b555

[ "Apache-2.0" ]

null

# This Source Code Form is subject to the terms of the Mozilla Public # License, v. 2.0. If a copy of the MPL was not distributed with this # file, You can obtain one at http://mozilla.org/MPL/2.0/. from gaiatest import GaiaTestCase from gaiatest.apps.settings.app import Settings class TestSettingsMediaStorage(GaiaTestCase): def test_settings_media_storage(self): settings = Settings(self.marionette) settings.launch() media_storage_settings = settings.open_media_storage_settings() # Check that no media is on the device self.assertEqual(media_storage_settings.music_size, '0 B') self.assertEqual(media_storage_settings.pictures_size, '0 B') self.assertEqual(media_storage_settings.movies_size, '0 B') # Close the settings application self.apps.kill(settings.app) # Push media to the device self.push_resource('VID_0001.3gp', destination='DCIM/100MZLLA') self.push_resource('IMG_0001.jpg', destination='DCIM/100MZLLA') self.push_resource('MUS_0001.mp3', destination='DCIM/100MZLLA') # Access 'Media storage' in Settings settings.launch() media_storage_settings = settings.open_media_storage_settings() # Check that media storage has updated to reflect the newly pushed media self.assertEqual(media_storage_settings.music_size, '120 KB') self.assertEqual(media_storage_settings.pictures_size, '348 KB') self.assertEqual(media_storage_settings.movies_size, '120 KB')

40.578947

80

0.72179

from gaiatest import GaiaTestCase from gaiatest.apps.settings.app import Settings class TestSettingsMediaStorage(GaiaTestCase): def test_settings_media_storage(self): settings = Settings(self.marionette) settings.launch() media_storage_settings = settings.open_media_storage_settings() self.assertEqual(media_storage_settings.music_size, '0 B') self.assertEqual(media_storage_settings.pictures_size, '0 B') self.assertEqual(media_storage_settings.movies_size, '0 B') self.apps.kill(settings.app) self.push_resource('VID_0001.3gp', destination='DCIM/100MZLLA') self.push_resource('IMG_0001.jpg', destination='DCIM/100MZLLA') self.push_resource('MUS_0001.mp3', destination='DCIM/100MZLLA') settings.launch() media_storage_settings = settings.open_media_storage_settings() self.assertEqual(media_storage_settings.music_size, '120 KB') self.assertEqual(media_storage_settings.pictures_size, '348 KB') self.assertEqual(media_storage_settings.movies_size, '120 KB')

true

f71752ad85213d316ee14113c2e19d7243632bd1

17,836

py

Python

features/eolearn/features/radiometric_normalization.py

mohammadrezabk/eo-learn

8de3cfd64e74c1e4832e585954cdbf0ee9676eb3

[ "MIT" ]

null

features/eolearn/features/radiometric_normalization.py

mohammadrezabk/eo-learn

8de3cfd64e74c1e4832e585954cdbf0ee9676eb3

[ "MIT" ]

null

features/eolearn/features/radiometric_normalization.py

mohammadrezabk/eo-learn

8de3cfd64e74c1e4832e585954cdbf0ee9676eb3

[ "MIT" ]

null

""" Module for radiometric normalization Credits: Copyright (c) 2018-2019 Johannes Schmid (GeoVille) Copyright (c) 2017-2019 Matej Aleksandrov, Matic Lubej, Devis Peresutti (Sinergise) This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. """ import numpy as np from eolearn.core import EOTask, FeatureType class ReferenceScenes(EOTask): """ Creates a layer of reference scenes which have the highest fraction of valid pixels. The number of reference scenes is limited to a definable number. Contributor: Johannes Schmid, GeoVille Information Systems GmbH, 2018 :param feature: Name of the eopatch data layer. Needs to be of the FeatureType "DATA". :type feature: (FeatureType, str) or (FeatureType, str, str) :param valid_fraction_feature: Name of the layer containing the valid fraction obtained with the EOTask 'AddValidDataFraction'. Needs to be of the FeatureType "SCALAR". :type valid_fraction_feature: (FeatureType, str) :param max_scene_number: Maximum number of reference scenes taken for the creation of the composite. By default, the maximum number of scenes equals the number of time frames :type max_scene_number: int """ def __init__(self, feature, valid_fraction_feature, max_scene_number=None): self.feature = self._parse_features(feature, new_names=True, default_feature_type=FeatureType.DATA, rename_function='{}_REFERENCE'.format) self.valid_fraction_feature = self._parse_features(valid_fraction_feature, default_feature_type=FeatureType.SCALAR) self.number = max_scene_number def execute(self, eopatch): feature_type, feature_name, new_feature_name = next(self.feature(eopatch)) valid_fraction_feature_type, valid_fraction_feature_name = next(self.valid_fraction_feature(eopatch)) valid_frac = list(eopatch[valid_fraction_feature_type][valid_fraction_feature_name].flatten()) data = eopatch[feature_type][feature_name] number = data.shape[0] if self.number is None else self.number eopatch[feature_type][new_feature_name] = np.array([data[x] for _, x in sorted(zip(valid_frac, range(data.shape[0])), reverse=True) if x <= number-1]) return eopatch class BaseCompositing(EOTask): """ Base class to create a composite of reference scenes Contributor: Johannes Schmid, GeoVille Information Systems GmbH, 2018 :param feature: Feature holding the input time-series. Default type is FeatureType.DATA :type feature: (FeatureType, str) :param feature_composite: Type and name of output composite image. Default type is FeatureType.DATA_TIMELESS :type feature_composite: (FeatureType, str) :param percentile: Percentile along the time dimension used for compositing. Methods use different percentiles :type percentile: int or list :param max_index: Value used to flag indices with NaNs. Could be integer or NaN. Default is 255 :type max_index: int or NaN :param interpolation: Method used to compute percentile. Allowed values are {'geoville', 'linear', 'lower', 'higher', 'midpoint', 'nearest'}. 'geoville' interpolation performs a custom implementation, while the other methods use the numpy `percentile` function. Default is 'lower' :type interpolation: str :param no_data_value: Value in the composite assigned to non valid data points. Default is NaN :type no_data_value: float or NaN """ def __init__(self, feature, feature_composite, percentile=None, max_index=255, interpolation='lower', no_data_value=np.nan): self.feature = self._parse_features(feature, default_feature_type=FeatureType.DATA, rename_function='{}_COMPOSITE'.format) self.composite_type, self.composite_name = next( self._parse_features(feature_composite, default_feature_type=FeatureType.DATA_TIMELESS)()) self.percentile = percentile self.max_index = max_index self.interpolation = interpolation self._index_by_percentile = self._geoville_index_by_percentile \ if self.interpolation.lower() == 'geoville' else self._numpy_index_by_percentile self.no_data_value = no_data_value def _numpy_index_by_percentile(self, data, percentile): """ Calculate percentile of numpy stack and return the index of the chosen pixel. numpy percentile function is used with one of the following interpolations {'linear', 'lower', 'higher', 'midpoint', 'nearest'} """ data_perc_low = np.nanpercentile(data, percentile, axis=0, interpolation=self.interpolation) indices = np.empty(data_perc_low.shape, dtype=np.uint8) indices[:] = np.nan abs_diff = np.where(np.isnan(data_perc_low), np.inf, abs(data - data_perc_low)) indices = np.where(np.isnan(data_perc_low), self.max_index, np.nanargmin(abs_diff, axis=0)) return indices def _geoville_index_by_percentile(self, data, percentile): """ Calculate percentile of numpy stack and return the index of the chosen pixel. """ # no_obs = bn.allnan(arr_tmp["data"], axis=0) data_tmp = np.array(data, copy=True) valid_obs = np.sum(np.isfinite(data_tmp), axis=0) # replace NaN with maximum max_val = np.nanmax(data_tmp) + 1 data_tmp[np.isnan(data_tmp)] = max_val # sort - former NaNs will move to the end ind_tmp = np.argsort(data_tmp, kind="mergesort", axis=0) # desired position as well as floor and ceiling of it k_arr = (valid_obs - 1) * (percentile / 100.0) k_arr = np.where(k_arr < 0, 0, k_arr) f_arr = np.floor(k_arr + 0.5) f_arr = f_arr.astype(int) # get floor value of reference band and index band ind = f_arr.astype("int16") y_val, x_val = ind_tmp.shape[1], ind_tmp.shape[2] y_val, x_val = np.ogrid[0:y_val, 0:x_val] idx = np.where(valid_obs == 0, self.max_index, ind_tmp[ind, y_val, x_val]) return idx def _get_reference_band(self, data): """ Extract reference band from input 4D data according to compositing method :param data: 4D array from which to extract reference band (e.g. blue, maxNDVI, ..) :type data: numpy array :return: 3D array containing reference band according to compositing method """ raise NotImplementedError def _get_indices(self, data): """ Compute indices along temporal dimension corresponding to the sought percentile :param data: Input 3D array holding the reference band :type data: numpy array :return: 2D array holding the temporal index corresponding to percentile """ indices = self._index_by_percentile(data, self.percentile) return indices def execute(self, eopatch): """ Compute composite array merging temporal frames according to the compositing method :param eopatch: eopatch holding time-series :return: eopatch with composite image of time-series """ feature_type, feature_name = next(self.feature(eopatch)) data = eopatch[feature_type][feature_name].copy() # compute band according to compositing method (e.g. blue, maxNDVI, maxNDWI) reference_bands = self._get_reference_band(data) # find temporal indices corresponding to pre-defined percentile indices = self._get_indices(reference_bands) # compute composite image selecting values along temporal dimension corresponding to percentile indices composite_image = np.empty((data.shape[1:]), np.float32) composite_image[:] = self.no_data_value for scene_id, scene in enumerate(data): composite_image = np.where(np.dstack([indices]) == scene_id, scene, composite_image) eopatch[self.composite_type][self.composite_name] = composite_image return eopatch class BlueCompositing(BaseCompositing): """ Blue band compositing method - blue (25th percentile of the blue band) :param blue_idx: Index of blue band in `feature` array :type blue_idx: int """ def __init__(self, feature, feature_composite, blue_idx, interpolation='lower'): super().__init__(feature, feature_composite, percentile=25, interpolation=interpolation) self.blue_idx = blue_idx if not isinstance(blue_idx, int): raise ValueError('Incorrect value of blue band index specified') def _get_reference_band(self, data): """ Extract the blue band from time-series :param data: 4D array from which to extract the blue reference band :type data: numpy array :return: 3D array containing the blue reference band """ return data[..., self.blue_idx].astype("float32") class HOTCompositing(BaseCompositing): """ HOT compositing method - HOT (Index using bands blue and red) The HOT index is defined as per Zhu, Z., & Woodcock, C. E. (2012). "Object-based cloud and cloud shadow detection in Landsat imagery." Remote Sensing of Environment, 118, 83-94. :param blue_idx: Index of blue band in `feature` array :type blue_idx: int :param red_idx: Index of red band in `feature` array :type red_idx: int """ def __init__(self, feature, feature_composite, blue_idx, red_idx, interpolation='lower'): super().__init__(feature, feature_composite, percentile=25, interpolation=interpolation) self.blue_idx = blue_idx self.red_idx = red_idx if not isinstance(blue_idx, int) or not isinstance(red_idx, int): raise ValueError('Incorrect values of blue and red band indices specified') def _get_reference_band(self, data): """ Extract the HOT band from time-series :param data: 4D array from which to extract the HOT reference band :type data: numpy array :return: 3D array containing the HOT reference band """ return data[..., self.blue_idx] - 0.5 * data[..., self.red_idx] - 0.08 class MaxNDVICompositing(BaseCompositing): """ maxNDVI compositing method - maxNDVI (temporal maximum of NDVI) :param red_idx: Index of red band in `feature` array :type red_idx: int :param nir_idx: Index of NIR band in `feature` array :type nir_idx: int """ def __init__(self, feature, feature_composite, red_idx, nir_idx, interpolation='lower'): super().__init__(feature, feature_composite, percentile=[0, 100], interpolation=interpolation) self.red_idx = red_idx self.nir_idx = nir_idx if not isinstance(nir_idx, int) or not isinstance(red_idx, int): raise ValueError('Incorrect values of red and NIR band indices specified') def _get_reference_band(self, data): """ Extract the NDVI band from time-series :param data: 4D array from which to compute the NDVI reference band :type data: numpy array :return: 3D array containing the NDVI reference band """ nir = data[..., self.nir_idx].astype("float32") red = data[..., self.red_idx].astype("float32") return (nir - red) / (nir + red) def _get_indices(self, data): median = np.nanmedian(data, axis=0) indices_min = self._index_by_percentile(data, self.percentile[0]) indices_max = self._index_by_percentile(data, self.percentile[1]) indices = np.where(median < -0.05, indices_min, indices_max) return indices class MaxNDWICompositing(BaseCompositing): """ maxNDWI compositing method - maxNDWI (temporal maximum of NDWI) :param nir_idx: Index of NIR band in `feature` array :type nir_idx: int :param swir1_idx: Index of SWIR1 band in `feature` array :type swir1_idx: int """ def __init__(self, feature, feature_composite, nir_idx, swir1_idx, interpolation='lower'): super().__init__(feature, feature_composite, percentile=100, interpolation=interpolation) self.nir_idx = nir_idx self.swir1_idx = swir1_idx if not isinstance(nir_idx, int) or not isinstance(swir1_idx, int): raise ValueError('Incorrect values of NIR and SWIR1 band indices specified') def _get_reference_band(self, data): """ Extract the NDWI band from time-series :param data: 4D array from which to compute the NDWI reference band :type data: numpy array :return: 3D array containing the NDWI reference band """ nir = data[..., self.nir_idx].astype("float32") swir1 = data[..., self.swir1_idx].astype("float32") return (nir - swir1) / (nir + swir1) class MaxRatioCompositing(BaseCompositing): """ maxRatio compositing method - maxRatio (temporal maximum of a ratio using bands blue, NIR and SWIR) :param blue_idx: Index of blue band in `feature` array :type blue_idx: int :param nir_idx: Index of NIR band in `feature` array :type nir_idx: int :param swir1_idx: Index of SWIR1 band in `feature` array :type swir1_idx: int """ def __init__(self, feature, feature_composite, blue_idx, nir_idx, swir1_idx, interpolation='lower'): super().__init__(feature, feature_composite, percentile=100, interpolation=interpolation) self.blue_idx = blue_idx self.nir_idx = nir_idx self.swir1_idx = swir1_idx if not isinstance(blue_idx, int) or not isinstance(nir_idx, int) or not isinstance(swir1_idx, int): raise ValueError('Incorrect values for either blue, NIR or SWIR1 band indices specified') def _get_reference_band(self, data): """ Extract the max-ratio band from time-series The max-ratio is defined as max(NIR,SWIR1)/BLUE :param data: 4D array from which to compute the max-ratio reference band :type data: numpy array :return: 3D array containing the max-ratio reference band """ blue = data[..., self.blue_idx].astype("float32") nir = data[..., self.nir_idx].astype("float32") swir1 = data[..., self.swir1_idx].astype("float32") return np.nanmax(np.array([nir, swir1]), axis=0) / blue class HistogramMatching(EOTask): """ Histogram match of each band of each scene within a time-series with respect to the corresponding band of a reference composite. Contributor: Johannes Schmid, GeoVille Information Systems GmbH, 2018 :param feature: Name of the eopatch data layer that will undergo a histogram match. Should be of the FeatureType "DATA". :type feature: (FeatureType, str) or (FeatureType, str, str) :param reference: Name of the eopatch data layer that represents the reference for the histogram match. Should be of the FeatureType "DATA_TIMELESS". :type reference: (FeatureType, str) """ def __init__(self, feature, reference): self.feature = self._parse_features(feature, new_names=True, default_feature_type=FeatureType.DATA, rename_function='{}_NORMALISED'.format) self.reference = self._parse_features(reference, default_feature_type=FeatureType.DATA_TIMELESS) def execute(self, eopatch): """ Perform histogram matching of the time-series with respect to a reference scene :param eopatch: eopatch holding the time-series and reference data :type eopatch: EOPatch :return: The same eopatch instance with the normalised time-series """ feature_type, feature_name, new_feature_name = next(self.feature(eopatch)) reference_type, reference_name = next(self.reference(eopatch)) reference_scene = eopatch[reference_type][reference_name] # check if band dimension matches if reference_scene.shape[-1] != eopatch[feature_type][feature_name].shape[-1]: raise ValueError('Time-series and reference scene must have corresponding bands') eopatch[feature_type][new_feature_name] = np.zeros_like(eopatch[feature_type][feature_name]) for source_id, source in enumerate(eopatch[feature_type][feature_name]): # mask-out same invalid pixels src_masked = np.where(np.isnan(reference_scene), np.nan, source) ref_masked = np.where(np.isnan(source), np.nan, reference_scene) # compute statistics std_ref = np.nanstd(ref_masked, axis=(0, 1), dtype=np.float64) std_src = np.nanstd(src_masked, axis=(0, 1), dtype=np.float64) mean_ref = np.nanmean(ref_masked, axis=(0, 1), dtype=np.float64) mean_src = np.nanmean(src_masked, axis=(0, 1), dtype=np.float64) # normalise values eopatch[feature_type][new_feature_name][source_id] = \ source * (std_ref / std_src) + (mean_ref - (mean_src * (std_ref / std_src))) return eopatch

46.569191

120

0.655808

import numpy as np from eolearn.core import EOTask, FeatureType class ReferenceScenes(EOTask): def __init__(self, feature, valid_fraction_feature, max_scene_number=None): self.feature = self._parse_features(feature, new_names=True, default_feature_type=FeatureType.DATA, rename_function='{}_REFERENCE'.format) self.valid_fraction_feature = self._parse_features(valid_fraction_feature, default_feature_type=FeatureType.SCALAR) self.number = max_scene_number def execute(self, eopatch): feature_type, feature_name, new_feature_name = next(self.feature(eopatch)) valid_fraction_feature_type, valid_fraction_feature_name = next(self.valid_fraction_feature(eopatch)) valid_frac = list(eopatch[valid_fraction_feature_type][valid_fraction_feature_name].flatten()) data = eopatch[feature_type][feature_name] number = data.shape[0] if self.number is None else self.number eopatch[feature_type][new_feature_name] = np.array([data[x] for _, x in sorted(zip(valid_frac, range(data.shape[0])), reverse=True) if x <= number-1]) return eopatch class BaseCompositing(EOTask): def __init__(self, feature, feature_composite, percentile=None, max_index=255, interpolation='lower', no_data_value=np.nan): self.feature = self._parse_features(feature, default_feature_type=FeatureType.DATA, rename_function='{}_COMPOSITE'.format) self.composite_type, self.composite_name = next( self._parse_features(feature_composite, default_feature_type=FeatureType.DATA_TIMELESS)()) self.percentile = percentile self.max_index = max_index self.interpolation = interpolation self._index_by_percentile = self._geoville_index_by_percentile \ if self.interpolation.lower() == 'geoville' else self._numpy_index_by_percentile self.no_data_value = no_data_value def _numpy_index_by_percentile(self, data, percentile): data_perc_low = np.nanpercentile(data, percentile, axis=0, interpolation=self.interpolation) indices = np.empty(data_perc_low.shape, dtype=np.uint8) indices[:] = np.nan abs_diff = np.where(np.isnan(data_perc_low), np.inf, abs(data - data_perc_low)) indices = np.where(np.isnan(data_perc_low), self.max_index, np.nanargmin(abs_diff, axis=0)) return indices def _geoville_index_by_percentile(self, data, percentile): data_tmp = np.array(data, copy=True) valid_obs = np.sum(np.isfinite(data_tmp), axis=0) max_val = np.nanmax(data_tmp) + 1 data_tmp[np.isnan(data_tmp)] = max_val ind_tmp = np.argsort(data_tmp, kind="mergesort", axis=0) k_arr = (valid_obs - 1) * (percentile / 100.0) k_arr = np.where(k_arr < 0, 0, k_arr) f_arr = np.floor(k_arr + 0.5) f_arr = f_arr.astype(int) ind = f_arr.astype("int16") y_val, x_val = ind_tmp.shape[1], ind_tmp.shape[2] y_val, x_val = np.ogrid[0:y_val, 0:x_val] idx = np.where(valid_obs == 0, self.max_index, ind_tmp[ind, y_val, x_val]) return idx def _get_reference_band(self, data): raise NotImplementedError def _get_indices(self, data): indices = self._index_by_percentile(data, self.percentile) return indices def execute(self, eopatch): feature_type, feature_name = next(self.feature(eopatch)) data = eopatch[feature_type][feature_name].copy() reference_bands = self._get_reference_band(data) indices = self._get_indices(reference_bands) composite_image = np.empty((data.shape[1:]), np.float32) composite_image[:] = self.no_data_value for scene_id, scene in enumerate(data): composite_image = np.where(np.dstack([indices]) == scene_id, scene, composite_image) eopatch[self.composite_type][self.composite_name] = composite_image return eopatch class BlueCompositing(BaseCompositing): def __init__(self, feature, feature_composite, blue_idx, interpolation='lower'): super().__init__(feature, feature_composite, percentile=25, interpolation=interpolation) self.blue_idx = blue_idx if not isinstance(blue_idx, int): raise ValueError('Incorrect value of blue band index specified') def _get_reference_band(self, data): return data[..., self.blue_idx].astype("float32") class HOTCompositing(BaseCompositing): def __init__(self, feature, feature_composite, blue_idx, red_idx, interpolation='lower'): super().__init__(feature, feature_composite, percentile=25, interpolation=interpolation) self.blue_idx = blue_idx self.red_idx = red_idx if not isinstance(blue_idx, int) or not isinstance(red_idx, int): raise ValueError('Incorrect values of blue and red band indices specified') def _get_reference_band(self, data): return data[..., self.blue_idx] - 0.5 * data[..., self.red_idx] - 0.08 class MaxNDVICompositing(BaseCompositing): def __init__(self, feature, feature_composite, red_idx, nir_idx, interpolation='lower'): super().__init__(feature, feature_composite, percentile=[0, 100], interpolation=interpolation) self.red_idx = red_idx self.nir_idx = nir_idx if not isinstance(nir_idx, int) or not isinstance(red_idx, int): raise ValueError('Incorrect values of red and NIR band indices specified') def _get_reference_band(self, data): nir = data[..., self.nir_idx].astype("float32") red = data[..., self.red_idx].astype("float32") return (nir - red) / (nir + red) def _get_indices(self, data): median = np.nanmedian(data, axis=0) indices_min = self._index_by_percentile(data, self.percentile[0]) indices_max = self._index_by_percentile(data, self.percentile[1]) indices = np.where(median < -0.05, indices_min, indices_max) return indices class MaxNDWICompositing(BaseCompositing): def __init__(self, feature, feature_composite, nir_idx, swir1_idx, interpolation='lower'): super().__init__(feature, feature_composite, percentile=100, interpolation=interpolation) self.nir_idx = nir_idx self.swir1_idx = swir1_idx if not isinstance(nir_idx, int) or not isinstance(swir1_idx, int): raise ValueError('Incorrect values of NIR and SWIR1 band indices specified') def _get_reference_band(self, data): nir = data[..., self.nir_idx].astype("float32") swir1 = data[..., self.swir1_idx].astype("float32") return (nir - swir1) / (nir + swir1) class MaxRatioCompositing(BaseCompositing): def __init__(self, feature, feature_composite, blue_idx, nir_idx, swir1_idx, interpolation='lower'): super().__init__(feature, feature_composite, percentile=100, interpolation=interpolation) self.blue_idx = blue_idx self.nir_idx = nir_idx self.swir1_idx = swir1_idx if not isinstance(blue_idx, int) or not isinstance(nir_idx, int) or not isinstance(swir1_idx, int): raise ValueError('Incorrect values for either blue, NIR or SWIR1 band indices specified') def _get_reference_band(self, data): blue = data[..., self.blue_idx].astype("float32") nir = data[..., self.nir_idx].astype("float32") swir1 = data[..., self.swir1_idx].astype("float32") return np.nanmax(np.array([nir, swir1]), axis=0) / blue class HistogramMatching(EOTask): def __init__(self, feature, reference): self.feature = self._parse_features(feature, new_names=True, default_feature_type=FeatureType.DATA, rename_function='{}_NORMALISED'.format) self.reference = self._parse_features(reference, default_feature_type=FeatureType.DATA_TIMELESS) def execute(self, eopatch): feature_type, feature_name, new_feature_name = next(self.feature(eopatch)) reference_type, reference_name = next(self.reference(eopatch)) reference_scene = eopatch[reference_type][reference_name] if reference_scene.shape[-1] != eopatch[feature_type][feature_name].shape[-1]: raise ValueError('Time-series and reference scene must have corresponding bands') eopatch[feature_type][new_feature_name] = np.zeros_like(eopatch[feature_type][feature_name]) for source_id, source in enumerate(eopatch[feature_type][feature_name]): src_masked = np.where(np.isnan(reference_scene), np.nan, source) ref_masked = np.where(np.isnan(source), np.nan, reference_scene) std_ref = np.nanstd(ref_masked, axis=(0, 1), dtype=np.float64) std_src = np.nanstd(src_masked, axis=(0, 1), dtype=np.float64) mean_ref = np.nanmean(ref_masked, axis=(0, 1), dtype=np.float64) mean_src = np.nanmean(src_masked, axis=(0, 1), dtype=np.float64) eopatch[feature_type][new_feature_name][source_id] = \ source * (std_ref / std_src) + (mean_ref - (mean_src * (std_ref / std_src))) return eopatch

true

f71754673dd76b5b137364e722d76f8cba4d6ce8

3,160

py

Python

pypureclient/flasharray/FA_2_11/models/software_bundle_response.py

Flav-STOR-WL/py-pure-client

03b889c997d90380ac5d6380ca5d5432792d3e89

[ "BSD-2-Clause" ]

14

2018-12-07T18:30:27.000Z

2022-02-22T09:12:33.000Z

pypureclient/flasharray/FA_2_11/models/software_bundle_response.py

Flav-STOR-WL/py-pure-client

03b889c997d90380ac5d6380ca5d5432792d3e89

[ "BSD-2-Clause" ]

28

2019-09-17T21:03:52.000Z

2022-03-29T22:07:35.000Z

pypureclient/flasharray/FA_2_11/models/software_bundle_response.py

Flav-STOR-WL/py-pure-client

03b889c997d90380ac5d6380ca5d5432792d3e89

[ "BSD-2-Clause" ]

15

2020-06-11T15:50:08.000Z

2022-03-21T09:27:25.000Z

# coding: utf-8 """ FlashArray REST API No description provided (generated by Swagger Codegen https://github.com/swagger-api/swagger-codegen) OpenAPI spec version: 2.11 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re import six import typing from ....properties import Property if typing.TYPE_CHECKING: from pypureclient.flasharray.FA_2_11 import models class SoftwareBundleResponse(object): """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'items': 'list[SoftwareBundle]' } attribute_map = { 'items': 'items' } required_args = { } def __init__( self, items=None, # type: List[models.SoftwareBundle] ): """ Keyword args: items (list[SoftwareBundle]) """ if items is not None: self.items = items def __setattr__(self, key, value): if key not in self.attribute_map: raise KeyError("Invalid key `{}` for `SoftwareBundleResponse`".format(key)) self.__dict__[key] = value def __getattribute__(self, item): value = object.__getattribute__(self, item) if isinstance(value, Property): raise AttributeError else: return value def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): if hasattr(self, attr): 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 if issubclass(SoftwareBundleResponse, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.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""" if not isinstance(other, SoftwareBundleResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

28.214286

105

0.549051

import pprint import re import six import typing from ....properties import Property if typing.TYPE_CHECKING: from pypureclient.flasharray.FA_2_11 import models class SoftwareBundleResponse(object): swagger_types = { 'items': 'list[SoftwareBundle]' } attribute_map = { 'items': 'items' } required_args = { } def __init__( self, items=None, ): if items is not None: self.items = items def __setattr__(self, key, value): if key not in self.attribute_map: raise KeyError("Invalid key `{}` for `SoftwareBundleResponse`".format(key)) self.__dict__[key] = value def __getattribute__(self, item): value = object.__getattribute__(self, item) if isinstance(value, Property): raise AttributeError else: return value def to_dict(self): result = {} for attr, _ in six.iteritems(self.swagger_types): if hasattr(self, attr): 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 if issubclass(SoftwareBundleResponse, dict): for key, value in self.items(): result[key] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, SoftwareBundleResponse): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

f7175555296b859086cc2c753888bdfe21cb502e

936

py

Python

deoldify/save.py

TaktakTaktouk/DeOldify

0ff6139bb09d0abdf535a724f05bdad3ec04dcc1

[ "MIT" ]

14,898

2018-11-01T14:48:38.000Z

2022-03-31T16:28:38.000Z

deoldify/save.py

TaktakTaktouk/DeOldify

0ff6139bb09d0abdf535a724f05bdad3ec04dcc1

[ "MIT" ]

376

2018-11-02T18:22:23.000Z

2022-03-24T21:29:19.000Z

deoldify/save.py

TaktakTaktouk/DeOldify

0ff6139bb09d0abdf535a724f05bdad3ec04dcc1

[ "MIT" ]

2,250

2018-11-02T15:45:39.000Z

2022-03-28T17:08:23.000Z

from fastai.basic_train import Learner, LearnerCallback from fastai.vision.gan import GANLearner class GANSaveCallback(LearnerCallback): """A `LearnerCallback` that saves history of metrics while training `learn` into CSV `filename`.""" def __init__( self, learn: GANLearner, learn_gen: Learner, filename: str, save_iters: int = 1000, ): super().__init__(learn) self.learn_gen = learn_gen self.filename = filename self.save_iters = save_iters def on_batch_end(self, iteration: int, epoch: int, **kwargs) -> None: if iteration == 0: return if iteration % self.save_iters == 0: self._save_gen_learner(iteration=iteration, epoch=epoch) def _save_gen_learner(self, iteration: int, epoch: int): filename = '{}_{}_{}'.format(self.filename, epoch, iteration) self.learn_gen.save(filename)

31.2

103

0.645299

from fastai.basic_train import Learner, LearnerCallback from fastai.vision.gan import GANLearner class GANSaveCallback(LearnerCallback): def __init__( self, learn: GANLearner, learn_gen: Learner, filename: str, save_iters: int = 1000, ): super().__init__(learn) self.learn_gen = learn_gen self.filename = filename self.save_iters = save_iters def on_batch_end(self, iteration: int, epoch: int, **kwargs) -> None: if iteration == 0: return if iteration % self.save_iters == 0: self._save_gen_learner(iteration=iteration, epoch=epoch) def _save_gen_learner(self, iteration: int, epoch: int): filename = '{}_{}_{}'.format(self.filename, epoch, iteration) self.learn_gen.save(filename)

true

f71756f9227d14924ce1c8f11117e55a80ba40c0

1,904

py

Python

tests/test_args.py

rauljim/passgen

ca55c08b1ab0439d598dc045982b6971bfee1629

[ "MIT" ]

null

tests/test_args.py

rauljim/passgen

ca55c08b1ab0439d598dc045982b6971bfee1629

[ "MIT" ]

null

tests/test_args.py

rauljim/passgen

ca55c08b1ab0439d598dc045982b6971bfee1629

[ "MIT" ]

null

from passgen import args def test_num_words(): mock_argv = ['passgen', '-n', '22'] options = args.get_cli_options(mock_argv) assert 22 == options.num_words mock_argv = ['passgen', '--num-words', '33'] options = args.get_cli_options(mock_argv) assert 33 == options.num_words mock_argv = ['passgen'] options = args.get_cli_options(mock_argv) assert args.DEFAULT_NUM_WORDS == options.num_words def test_count(): mock_argv = ['passgen', '-c', '22'] options = args.get_cli_options(mock_argv) assert 22 == options.count mock_argv = ['passgen', '--count', '33'] options = args.get_cli_options(mock_argv) assert 33 == options.count mock_argv = ['passgen'] options = args.get_cli_options(mock_argv) assert args.DEFAULT_COUNT == options.count mock_argv = ['passgen', '--count', '-1'] # negative value ignored options = args.get_cli_options(mock_argv) assert args.DEFAULT_COUNT == options.count def test_min_chars(): mock_argv = ['passgen', '--min-chars', '33'] options = args.get_cli_options(mock_argv) assert 33 == options.min_chars mock_argv = ['passgen'] options = args.get_cli_options(mock_argv) assert args.DEFAULT_MIN_CHARS == options.min_chars def test_max_chars(): mock_argv = ['passgen', '--max-chars', '33'] options = args.get_cli_options(mock_argv) assert 33 == options.max_chars mock_argv = ['passgen'] options = args.get_cli_options(mock_argv) assert args.DEFAULT_MAX_CHARS == options.max_chars def test_conflicting_min_max_chars(): mock_argv = ['passgen', '--min-chars', '9999', '--max-chars', '11'] options = args.get_cli_options(mock_argv) assert 9999 == options.min_chars assert args.DEFAULT_MAX_CHARS == options.max_chars def test_get_defaults(): options = args.get_default_options() assert args.DEFAULT_COUNT == options.count

32.271186

71

0.688025

from passgen import args def test_num_words(): mock_argv = ['passgen', '-n', '22'] options = args.get_cli_options(mock_argv) assert 22 == options.num_words mock_argv = ['passgen', '--num-words', '33'] options = args.get_cli_options(mock_argv) assert 33 == options.num_words mock_argv = ['passgen'] options = args.get_cli_options(mock_argv) assert args.DEFAULT_NUM_WORDS == options.num_words def test_count(): mock_argv = ['passgen', '-c', '22'] options = args.get_cli_options(mock_argv) assert 22 == options.count mock_argv = ['passgen', '--count', '33'] options = args.get_cli_options(mock_argv) assert 33 == options.count mock_argv = ['passgen'] options = args.get_cli_options(mock_argv) assert args.DEFAULT_COUNT == options.count mock_argv = ['passgen', '--count', '-1'] options = args.get_cli_options(mock_argv) assert args.DEFAULT_COUNT == options.count def test_min_chars(): mock_argv = ['passgen', '--min-chars', '33'] options = args.get_cli_options(mock_argv) assert 33 == options.min_chars mock_argv = ['passgen'] options = args.get_cli_options(mock_argv) assert args.DEFAULT_MIN_CHARS == options.min_chars def test_max_chars(): mock_argv = ['passgen', '--max-chars', '33'] options = args.get_cli_options(mock_argv) assert 33 == options.max_chars mock_argv = ['passgen'] options = args.get_cli_options(mock_argv) assert args.DEFAULT_MAX_CHARS == options.max_chars def test_conflicting_min_max_chars(): mock_argv = ['passgen', '--min-chars', '9999', '--max-chars', '11'] options = args.get_cli_options(mock_argv) assert 9999 == options.min_chars assert args.DEFAULT_MAX_CHARS == options.max_chars def test_get_defaults(): options = args.get_default_options() assert args.DEFAULT_COUNT == options.count

true

f717576ebe1b232b2fdba0695ea262b2ae5063cc

2,568

py

Python

src/base/base_train.py

MohamedAli1995/Cifar-100-Classifier

924704a81ce13062825a88b90b80e8ac2ba45d63

[ "MIT" ]

2

2019-05-12T16:11:20.000Z

2020-04-10T22:39:57.000Z

src/base/base_train.py

MohamedAli1995/Cifar-100-Classifier

924704a81ce13062825a88b90b80e8ac2ba45d63

[ "MIT" ]

null

src/base/base_train.py

MohamedAli1995/Cifar-100-Classifier

924704a81ce13062825a88b90b80e8ac2ba45d63

[ "MIT" ]

null

import tensorflow as tf class BaseTrain: """Standard base_train-class for easy multiple-inheritance. It is responsible for defining the functions to be implemented with any child. Attributes: sess: Tensorflow session to use. model: Model to be trained. data: Data_loader object to interact with dataset. config: Config object to store data related to training, testing and validation. logger: Logger object to use tensorboard. """ def __init__(self, sess, model, data, config, logger): self.model = model self.config = config self.sess = sess self.data = data self.logger = logger if not self.config.pretrain: # If not pretrain then initialize variables. self.init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) self.sess.run(self.init) def train(self): """Train the model for the number of epochs in config.num_epochs. Calls validate_epoch if config.use_val is set to true and per config.val_per_epoch. Returns: """ for cur_epoch in range(self.model.cur_epoch_tensor.eval(self.sess), self.config.num_epochs + 1, 1): self.data.prepare_new_epoch_data() self.train_epoch() if self.config.use_val and ( cur_epoch % self.config.val_per_epoch == 0 or cur_epoch == self.config.num_epochs): self.validate_epoch() self.sess.run(self.model.increment_cur_epoch_tensor) def train_epoch(self): """Implements the logic of training_epoch: -Loop over the batches of the training data and call the train step for each. -Add any summaries you want using the summary """ raise NotImplemented def train_step(self): """Implements the logic of the train step: -Run the tensorflow session -Returns: Any of the metrics needs to be summarized. """ raise NotImplementedError def validate_epoch(self): """Implements the logic of validation_epoch: -Loop over the batches of the validation data and call the validate step for each. -Add any summaries you want using the summary """ raise NotImplemented def validate_step(self): """Implements the logic of the validate step: -Run the tensorflow session -Returns: Any of the metrics needs to be summarized. """ raise NotImplemented

36.169014

115

0.640576

import tensorflow as tf class BaseTrain: def __init__(self, sess, model, data, config, logger): self.model = model self.config = config self.sess = sess self.data = data self.logger = logger if not self.config.pretrain: self.init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer()) self.sess.run(self.init) def train(self): for cur_epoch in range(self.model.cur_epoch_tensor.eval(self.sess), self.config.num_epochs + 1, 1): self.data.prepare_new_epoch_data() self.train_epoch() if self.config.use_val and ( cur_epoch % self.config.val_per_epoch == 0 or cur_epoch == self.config.num_epochs): self.validate_epoch() self.sess.run(self.model.increment_cur_epoch_tensor) def train_epoch(self): raise NotImplemented def train_step(self): raise NotImplementedError def validate_epoch(self): raise NotImplemented def validate_step(self): raise NotImplemented

true

f7175782c5dfa546dd124965a86fa50da687c7ac

1,267

py

Python

SIGNUS/app/api/signus_v1/post.py

837477/SIGNUS

cd395dfd45d2c36d09ec9a8069e6e52e19f058e8

[ "MIT" ]

null

SIGNUS/app/api/signus_v1/post.py

837477/SIGNUS

cd395dfd45d2c36d09ec9a8069e6e52e19f058e8

[ "MIT" ]

null

SIGNUS/app/api/signus_v1/post.py

837477/SIGNUS

cd395dfd45d2c36d09ec9a8069e6e52e19f058e8

[ "MIT" ]

null

''' SIGNUS V1 post API ''' from flask import g from app.api.signus_v1 import signus_v1 as api from app.api.decorators import timer, login_required, login_optional from app.controllers.post import (post_like, post_unlike, post_view) @api.route("/post/like/<string:post_oid>", methods=["PATCH"]) @timer @login_required def signus_v1_post_like(post_oid): ''' 게시글 좋아요 ''' return { "msg": "success", "result": post_like(g.mongo_cur, post_oid, g.user) } @api.route("/post/unlike/<string:post_oid>", methods=["PATCH"]) @timer @login_required def signus_v1_post_unlike(post_oid): ''' 게시글 좋아요 취소 ''' return { "msg": "success", "result": post_unlike(g.mongo_cur, post_oid, g.user) } @api.route("/post/view/<string:post_oid>", methods=["PATCH"]) @timer @login_optional def signus_v1_post_view(post_oid): ''' 게시글 조회수 ''' if 'user' in g: result = post_view(g.mongo_cur, post_oid, g.user) else: result = post_view(g.mongo_cur, post_oid) return { "msg": "success", "result": result }

24.843137

68

0.556433

from flask import g from app.api.signus_v1 import signus_v1 as api from app.api.decorators import timer, login_required, login_optional from app.controllers.post import (post_like, post_unlike, post_view) @api.route("/post/like/<string:post_oid>", methods=["PATCH"]) @timer @login_required def signus_v1_post_like(post_oid): return { "msg": "success", "result": post_like(g.mongo_cur, post_oid, g.user) } @api.route("/post/unlike/<string:post_oid>", methods=["PATCH"]) @timer @login_required def signus_v1_post_unlike(post_oid): return { "msg": "success", "result": post_unlike(g.mongo_cur, post_oid, g.user) } @api.route("/post/view/<string:post_oid>", methods=["PATCH"]) @timer @login_optional def signus_v1_post_view(post_oid): if 'user' in g: result = post_view(g.mongo_cur, post_oid, g.user) else: result = post_view(g.mongo_cur, post_oid) return { "msg": "success", "result": result }

true

f71759ee5d329c4385a20b4d6bd880bfb741c347

1,398

py

Python

migrations/versions/f0a99f6b5e5e_.py

CSCfi/pebbles

24b32e8fc538cc8095fda62c892a8221346c2bce

[ "MIT" ]

4

2017-05-11T14:50:32.000Z

2020-01-10T09:02:27.000Z

migrations/versions/f0a99f6b5e5e_.py

CSCfi/pebbles

24b32e8fc538cc8095fda62c892a8221346c2bce

[ "MIT" ]

145

2017-04-07T11:01:58.000Z

2019-12-11T15:30:23.000Z

migrations/versions/f0a99f6b5e5e_.py

CSCfi/pebbles

24b32e8fc538cc8095fda62c892a8221346c2bce

[ "MIT" ]

3

2017-10-25T12:36:16.000Z

2018-04-26T08:49:34.000Z

"""empty message Revision ID: f0a99f6b5e5e Revises: he536vdwh29f Create Date: 2019-05-31 15:57:36.032393 """ # revision identifiers, used by Alembic. revision = 'f0a99f6b5e5e' down_revision = 'he536vdwh29f' from alembic import op import sqlalchemy as sa def upgrade(): ### commands auto generated by Alembic - please adjust! ### op.create_table('instance_tokens', sa.Column('token', sa.String(length=32), nullable=False), sa.Column('instance_id', sa.String(length=32), nullable=True), sa.Column('expires_on', sa.DateTime(), nullable=True), sa.ForeignKeyConstraint(['instance_id'], ['instances.id'], name=op.f('fk_instance_tokens_instance_id_instances')), sa.PrimaryKeyConstraint('token', name=op.f('pk_instance_tokens')) ) # op.create_unique_constraint(op.f('uq_users_email_id'), 'users', ['email_id']) # op.create_unique_constraint(op.f('uq_users_eppn'), 'users', ['eppn']) # op.drop_constraint(u'uq_users_email', 'users', type_='unique') ### end Alembic commands ### def downgrade(): ### commands auto generated by Alembic - please adjust! ### # op.create_unique_constraint(u'uq_users_email', 'users', ['eppn']) # op.drop_constraint(op.f('uq_users_eppn'), 'users', type_='unique') # op.drop_constraint(op.f('uq_users_email_id'), 'users', type_='unique') op.drop_table('instance_tokens') ### end Alembic commands ###

35.846154

118

0.700286

revision = 'f0a99f6b5e5e' down_revision = 'he536vdwh29f' from alembic import op import sqlalchemy as sa def upgrade(): ance_id', sa.String(length=32), nullable=True), sa.Column('expires_on', sa.DateTime(), nullable=True), sa.ForeignKeyConstraint(['instance_id'], ['instances.id'], name=op.f('fk_instance_tokens_instance_id_instances')), sa.PrimaryKeyConstraint('token', name=op.f('pk_instance_tokens')) )

true

f71759fb13c6dffdc6c632f41e1c01f82a06b50a

103,166

py

Python

python/pyarrow/tests/test_parquet.py

sparkma/arrow

62fd703a4ef0abbecb02397a06a630a9dee382d9

[ "Apache-2.0" ]

null

python/pyarrow/tests/test_parquet.py

sparkma/arrow

62fd703a4ef0abbecb02397a06a630a9dee382d9

[ "Apache-2.0" ]

null

python/pyarrow/tests/test_parquet.py

sparkma/arrow

62fd703a4ef0abbecb02397a06a630a9dee382d9

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # 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 collections import OrderedDict import datetime import decimal import io import json import os import six import pickle import pytest import numpy as np import pyarrow as pa from pyarrow.compat import guid, u, BytesIO, unichar, PY2 from pyarrow.pandas_compat import _pandas_api from pyarrow.tests import util from pyarrow.filesystem import LocalFileSystem, FileSystem try: import pyarrow.parquet as pq except ImportError: pq = None try: import pandas as pd import pandas.util.testing as tm from .pandas_examples import dataframe_with_arrays, dataframe_with_lists except ImportError: pd = tm = None # Marks all of the tests in this module # Ignore these with pytest ... -m 'not parquet' pytestmark = pytest.mark.parquet @pytest.fixture(scope='module') def datadir(datadir): return datadir / 'parquet' def _write_table(table, path, **kwargs): # So we see the ImportError somewhere import pyarrow.parquet as pq if _pandas_api.is_data_frame(table): table = pa.Table.from_pandas(table) pq.write_table(table, path, **kwargs) return table def _read_table(*args, **kwargs): return pq.read_table(*args, **kwargs) def _roundtrip_table(table, read_table_kwargs=None, write_table_kwargs=None): read_table_kwargs = read_table_kwargs or {} write_table_kwargs = write_table_kwargs or {} buf = io.BytesIO() _write_table(table, buf, **write_table_kwargs) buf.seek(0) return _read_table(buf, **read_table_kwargs) def _check_roundtrip(table, expected=None, read_table_kwargs=None, **write_table_kwargs): if expected is None: expected = table read_table_kwargs = read_table_kwargs or {} # intentionally check twice result = _roundtrip_table(table, read_table_kwargs=read_table_kwargs, write_table_kwargs=write_table_kwargs) assert result.equals(expected) result = _roundtrip_table(result, read_table_kwargs=read_table_kwargs, write_table_kwargs=write_table_kwargs) assert result.equals(expected) def _roundtrip_pandas_dataframe(df, write_kwargs): table = pa.Table.from_pandas(df) buf = io.BytesIO() _write_table(table, buf, **write_kwargs) buf.seek(0) table1 = _read_table(buf) return table1.to_pandas() @pytest.mark.parametrize('dtype', [int, float]) def test_single_pylist_column_roundtrip(tempdir, dtype): filename = tempdir / 'single_{}_column.parquet'.format(dtype.__name__) data = [pa.array(list(map(dtype, range(5))))] table = pa.Table.from_arrays(data, names=['a']) _write_table(table, filename) table_read = _read_table(filename) for i in range(table.num_columns): col_written = table[i] col_read = table_read[i] assert table.field(i).name == table_read.field(i).name assert col_read.num_chunks == 1 data_written = col_written.chunk(0) data_read = col_read.chunk(0) assert data_written.equals(data_read) def alltypes_sample(size=10000, seed=0, categorical=False): np.random.seed(seed) arrays = { 'uint8': np.arange(size, dtype=np.uint8), 'uint16': np.arange(size, dtype=np.uint16), 'uint32': np.arange(size, dtype=np.uint32), 'uint64': np.arange(size, dtype=np.uint64), 'int8': np.arange(size, dtype=np.int16), 'int16': np.arange(size, dtype=np.int16), 'int32': np.arange(size, dtype=np.int32), 'int64': np.arange(size, dtype=np.int64), 'float32': np.arange(size, dtype=np.float32), 'float64': np.arange(size, dtype=np.float64), 'bool': np.random.randn(size) > 0, # TODO(wesm): Test other timestamp resolutions now that arrow supports # them 'datetime': np.arange("2016-01-01T00:00:00.001", size, dtype='datetime64[ms]'), 'str': pd.Series([str(x) for x in range(size)]), 'empty_str': [''] * size, 'str_with_nulls': [None] + [str(x) for x in range(size - 2)] + [None], 'null': [None] * size, 'null_list': [None] * 2 + [[None] * (x % 4) for x in range(size - 2)], } if categorical: arrays['str_category'] = arrays['str'].astype('category') return pd.DataFrame(arrays) @pytest.mark.pandas @pytest.mark.parametrize('chunk_size', [None, 1000]) def test_pandas_parquet_2_0_roundtrip(tempdir, chunk_size): df = alltypes_sample(size=10000, categorical=True) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df) assert arrow_table.schema.pandas_metadata is not None _write_table(arrow_table, filename, version="2.0", coerce_timestamps='ms', chunk_size=chunk_size) table_read = pq.read_pandas(filename) assert table_read.schema.pandas_metadata is not None assert arrow_table.schema.metadata == table_read.schema.metadata df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) def test_set_data_page_size(): arr = pa.array([1, 2, 3] * 1000000) t = pa.Table.from_arrays([arr], names=['f0']) # 128K, 256K, 512K page_sizes = [2 << 16, 2 << 17, 2 << 18] for target_page_size in page_sizes: _check_roundtrip(t, data_page_size=target_page_size) @pytest.mark.pandas def test_chunked_table_write(): # ARROW-232 df = alltypes_sample(size=10) batch = pa.RecordBatch.from_pandas(df) table = pa.Table.from_batches([batch] * 3) _check_roundtrip(table, version='2.0') df, _ = dataframe_with_lists() batch = pa.RecordBatch.from_pandas(df) table = pa.Table.from_batches([batch] * 3) _check_roundtrip(table, version='2.0') @pytest.mark.pandas def test_no_memory_map(tempdir): df = alltypes_sample(size=10) table = pa.Table.from_pandas(df) _check_roundtrip(table, read_table_kwargs={'memory_map': False}, version='2.0') filename = str(tempdir / 'tmp_file') with open(filename, 'wb') as f: _write_table(table, f, version='2.0') table_read = pq.read_pandas(filename, memory_map=False) assert table_read.equals(table) def test_special_chars_filename(tempdir): table = pa.Table.from_arrays([pa.array([42])], ["ints"]) filename = "foo # bar" path = tempdir / filename assert not path.exists() _write_table(table, str(path)) assert path.exists() table_read = _read_table(str(path)) assert table_read.equals(table) @pytest.mark.pandas def test_empty_table_roundtrip(): df = alltypes_sample(size=10) # Create a non-empty table to infer the types correctly, then slice to 0 table = pa.Table.from_pandas(df) table = pa.Table.from_arrays( [col.chunk(0)[:0] for col in table.itercolumns()], names=table.schema.names) assert table.schema.field_by_name('null').type == pa.null() assert table.schema.field_by_name('null_list').type == pa.list_(pa.null()) _check_roundtrip(table, version='2.0') @pytest.mark.pandas def test_empty_table_no_columns(): df = pd.DataFrame() empty = pa.Table.from_pandas(df, preserve_index=False) _check_roundtrip(empty) def test_empty_lists_table_roundtrip(): # ARROW-2744: Shouldn't crash when writing an array of empty lists arr = pa.array([[], []], type=pa.list_(pa.int32())) table = pa.Table.from_arrays([arr], ["A"]) _check_roundtrip(table) @pytest.mark.pandas def test_pandas_parquet_datetime_tz(): s = pd.Series([datetime.datetime(2017, 9, 6)]) s = s.dt.tz_localize('utc') s.index = s # Both a column and an index to hit both use cases df = pd.DataFrame({'tz_aware': s, 'tz_eastern': s.dt.tz_convert('US/Eastern')}, index=s) f = BytesIO() arrow_table = pa.Table.from_pandas(df) _write_table(arrow_table, f, coerce_timestamps='ms') f.seek(0) table_read = pq.read_pandas(f) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas @pytest.mark.skipif(six.PY2, reason='datetime.timezone is available since ' 'python version 3.2') def test_datetime_timezone_tzinfo(): value = datetime.datetime(2018, 1, 1, 1, 23, 45, tzinfo=datetime.timezone.utc) df = pd.DataFrame({'foo': [value]}) _roundtrip_pandas_dataframe(df, write_kwargs={}) @pytest.mark.pandas def test_pandas_parquet_custom_metadata(tempdir): df = alltypes_sample(size=10000) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df) assert b'pandas' in arrow_table.schema.metadata _write_table(arrow_table, filename, version='2.0', coerce_timestamps='ms') metadata = pq.read_metadata(filename).metadata assert b'pandas' in metadata js = json.loads(metadata[b'pandas'].decode('utf8')) assert js['index_columns'] == [{'kind': 'range', 'name': None, 'start': 0, 'stop': 10000, 'step': 1}] @pytest.mark.pandas def test_pandas_parquet_column_multiindex(tempdir): df = alltypes_sample(size=10) df.columns = pd.MultiIndex.from_tuples( list(zip(df.columns, df.columns[::-1])), names=['level_1', 'level_2'] ) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df) assert arrow_table.schema.pandas_metadata is not None _write_table(arrow_table, filename, version='2.0', coerce_timestamps='ms') table_read = pq.read_pandas(filename) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_pandas_parquet_2_0_roundtrip_read_pandas_no_index_written(tempdir): df = alltypes_sample(size=10000) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df, preserve_index=False) js = arrow_table.schema.pandas_metadata assert not js['index_columns'] # ARROW-2170 # While index_columns should be empty, columns needs to be filled still. assert js['columns'] _write_table(arrow_table, filename, version='2.0', coerce_timestamps='ms') table_read = pq.read_pandas(filename) js = table_read.schema.pandas_metadata assert not js['index_columns'] assert arrow_table.schema.metadata == table_read.schema.metadata df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_pandas_parquet_1_0_roundtrip(tempdir): size = 10000 np.random.seed(0) df = pd.DataFrame({ 'uint8': np.arange(size, dtype=np.uint8), 'uint16': np.arange(size, dtype=np.uint16), 'uint32': np.arange(size, dtype=np.uint32), 'uint64': np.arange(size, dtype=np.uint64), 'int8': np.arange(size, dtype=np.int16), 'int16': np.arange(size, dtype=np.int16), 'int32': np.arange(size, dtype=np.int32), 'int64': np.arange(size, dtype=np.int64), 'float32': np.arange(size, dtype=np.float32), 'float64': np.arange(size, dtype=np.float64), 'bool': np.random.randn(size) > 0, 'str': [str(x) for x in range(size)], 'str_with_nulls': [None] + [str(x) for x in range(size - 2)] + [None], 'empty_str': [''] * size }) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df) _write_table(arrow_table, filename, version='1.0') table_read = _read_table(filename) df_read = table_read.to_pandas() # We pass uint32_t as int64_t if we write Parquet version 1.0 df['uint32'] = df['uint32'].values.astype(np.int64) tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_multiple_path_types(tempdir): # Test compatibility with PEP 519 path-like objects path = tempdir / 'zzz.parquet' df = pd.DataFrame({'x': np.arange(10, dtype=np.int64)}) _write_table(df, path) table_read = _read_table(path) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) # Test compatibility with plain string paths path = str(tempdir) + 'zzz.parquet' df = pd.DataFrame({'x': np.arange(10, dtype=np.int64)}) _write_table(df, path) table_read = _read_table(path) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_pandas_column_selection(tempdir): size = 10000 np.random.seed(0) df = pd.DataFrame({ 'uint8': np.arange(size, dtype=np.uint8), 'uint16': np.arange(size, dtype=np.uint16) }) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df) _write_table(arrow_table, filename) table_read = _read_table(filename, columns=['uint8']) df_read = table_read.to_pandas() tm.assert_frame_equal(df[['uint8']], df_read) # ARROW-4267: Selection of duplicate columns still leads to these columns # being read uniquely. table_read = _read_table(filename, columns=['uint8', 'uint8']) df_read = table_read.to_pandas() tm.assert_frame_equal(df[['uint8']], df_read) def _random_integers(size, dtype): # We do not generate integers outside the int64 range platform_int_info = np.iinfo('int_') iinfo = np.iinfo(dtype) return np.random.randint(max(iinfo.min, platform_int_info.min), min(iinfo.max, platform_int_info.max), size=size).astype(dtype) def _test_dataframe(size=10000, seed=0): np.random.seed(seed) df = pd.DataFrame({ 'uint8': _random_integers(size, np.uint8), 'uint16': _random_integers(size, np.uint16), 'uint32': _random_integers(size, np.uint32), 'uint64': _random_integers(size, np.uint64), 'int8': _random_integers(size, np.int8), 'int16': _random_integers(size, np.int16), 'int32': _random_integers(size, np.int32), 'int64': _random_integers(size, np.int64), 'float32': np.random.randn(size).astype(np.float32), 'float64': np.arange(size, dtype=np.float64), 'bool': np.random.randn(size) > 0, 'strings': [tm.rands(10) for i in range(size)], 'all_none': [None] * size, 'all_none_category': [None] * size }) # TODO(PARQUET-1015) # df['all_none_category'] = df['all_none_category'].astype('category') return df @pytest.mark.pandas def test_pandas_parquet_native_file_roundtrip(tempdir): df = _test_dataframe(10000) arrow_table = pa.Table.from_pandas(df) imos = pa.BufferOutputStream() _write_table(arrow_table, imos, version="2.0") buf = imos.getvalue() reader = pa.BufferReader(buf) df_read = _read_table(reader).to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_parquet_incremental_file_build(tempdir): df = _test_dataframe(100) df['unique_id'] = 0 arrow_table = pa.Table.from_pandas(df, preserve_index=False) out = pa.BufferOutputStream() writer = pq.ParquetWriter(out, arrow_table.schema, version='2.0') frames = [] for i in range(10): df['unique_id'] = i arrow_table = pa.Table.from_pandas(df, preserve_index=False) writer.write_table(arrow_table) frames.append(df.copy()) writer.close() buf = out.getvalue() result = _read_table(pa.BufferReader(buf)) expected = pd.concat(frames, ignore_index=True) tm.assert_frame_equal(result.to_pandas(), expected) @pytest.mark.pandas def test_read_pandas_column_subset(tempdir): df = _test_dataframe(10000) arrow_table = pa.Table.from_pandas(df) imos = pa.BufferOutputStream() _write_table(arrow_table, imos, version="2.0") buf = imos.getvalue() reader = pa.BufferReader(buf) df_read = pq.read_pandas(reader, columns=['strings', 'uint8']).to_pandas() tm.assert_frame_equal(df[['strings', 'uint8']], df_read) @pytest.mark.pandas def test_pandas_parquet_empty_roundtrip(tempdir): df = _test_dataframe(0) arrow_table = pa.Table.from_pandas(df) imos = pa.BufferOutputStream() _write_table(arrow_table, imos, version="2.0") buf = imos.getvalue() reader = pa.BufferReader(buf) df_read = _read_table(reader).to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_pandas_parquet_pyfile_roundtrip(tempdir): filename = tempdir / 'pandas_pyfile_roundtrip.parquet' size = 5 df = pd.DataFrame({ 'int64': np.arange(size, dtype=np.int64), 'float32': np.arange(size, dtype=np.float32), 'float64': np.arange(size, dtype=np.float64), 'bool': np.random.randn(size) > 0, 'strings': ['foo', 'bar', None, 'baz', 'qux'] }) arrow_table = pa.Table.from_pandas(df) with filename.open('wb') as f: _write_table(arrow_table, f, version="1.0") data = io.BytesIO(filename.read_bytes()) table_read = _read_table(data) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_pandas_parquet_configuration_options(tempdir): size = 10000 np.random.seed(0) df = pd.DataFrame({ 'uint8': np.arange(size, dtype=np.uint8), 'uint16': np.arange(size, dtype=np.uint16), 'uint32': np.arange(size, dtype=np.uint32), 'uint64': np.arange(size, dtype=np.uint64), 'int8': np.arange(size, dtype=np.int16), 'int16': np.arange(size, dtype=np.int16), 'int32': np.arange(size, dtype=np.int32), 'int64': np.arange(size, dtype=np.int64), 'float32': np.arange(size, dtype=np.float32), 'float64': np.arange(size, dtype=np.float64), 'bool': np.random.randn(size) > 0 }) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df) for use_dictionary in [True, False]: _write_table(arrow_table, filename, version='2.0', use_dictionary=use_dictionary) table_read = _read_table(filename) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) for write_statistics in [True, False]: _write_table(arrow_table, filename, version='2.0', write_statistics=write_statistics) table_read = _read_table(filename) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) for compression in ['NONE', 'SNAPPY', 'GZIP', 'LZ4', 'ZSTD']: _write_table(arrow_table, filename, version='2.0', compression=compression) table_read = _read_table(filename) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) def make_sample_file(table_or_df): if isinstance(table_or_df, pa.Table): a_table = table_or_df else: a_table = pa.Table.from_pandas(table_or_df) buf = io.BytesIO() _write_table(a_table, buf, compression='SNAPPY', version='2.0', coerce_timestamps='ms') buf.seek(0) return pq.ParquetFile(buf) @pytest.mark.pandas def test_parquet_metadata_api(): df = alltypes_sample(size=10000) df = df.reindex(columns=sorted(df.columns)) df.index = np.random.randint(0, 1000000, size=len(df)) fileh = make_sample_file(df) ncols = len(df.columns) # Series of sniff tests meta = fileh.metadata repr(meta) assert meta.num_rows == len(df) assert meta.num_columns == ncols + 1 # +1 for index assert meta.num_row_groups == 1 assert meta.format_version == '2.0' assert 'parquet-cpp' in meta.created_by assert isinstance(meta.serialized_size, int) assert isinstance(meta.metadata, dict) # Schema schema = fileh.schema assert meta.schema is schema assert len(schema) == ncols + 1 # +1 for index repr(schema) col = schema[0] repr(col) assert col.name == df.columns[0] assert col.max_definition_level == 1 assert col.max_repetition_level == 0 assert col.max_repetition_level == 0 assert col.physical_type == 'BOOLEAN' assert col.converted_type == 'NONE' with pytest.raises(IndexError): schema[ncols + 1] # +1 for index with pytest.raises(IndexError): schema[-1] # Row group for rg in range(meta.num_row_groups): rg_meta = meta.row_group(rg) assert isinstance(rg_meta, pq.RowGroupMetaData) repr(rg_meta) for col in range(rg_meta.num_columns): col_meta = rg_meta.column(col) assert isinstance(col_meta, pq.ColumnChunkMetaData) repr(col_meta) with pytest.raises(IndexError): meta.row_group(-1) with pytest.raises(IndexError): meta.row_group(meta.num_row_groups + 1) rg_meta = meta.row_group(0) assert rg_meta.num_rows == len(df) assert rg_meta.num_columns == ncols + 1 # +1 for index assert rg_meta.total_byte_size > 0 with pytest.raises(IndexError): col_meta = rg_meta.column(-1) with pytest.raises(IndexError): col_meta = rg_meta.column(ncols + 2) col_meta = rg_meta.column(0) assert col_meta.file_offset > 0 assert col_meta.file_path == '' # created from BytesIO assert col_meta.physical_type == 'BOOLEAN' assert col_meta.num_values == 10000 assert col_meta.path_in_schema == 'bool' assert col_meta.is_stats_set is True assert isinstance(col_meta.statistics, pq.Statistics) assert col_meta.compression == 'SNAPPY' assert col_meta.encodings == ('PLAIN', 'RLE') assert col_meta.has_dictionary_page is False assert col_meta.dictionary_page_offset is None assert col_meta.data_page_offset > 0 assert col_meta.total_compressed_size > 0 assert col_meta.total_uncompressed_size > 0 with pytest.raises(NotImplementedError): col_meta.has_index_page with pytest.raises(NotImplementedError): col_meta.index_page_offset @pytest.mark.pandas @pytest.mark.parametrize( ( 'data', 'type', 'physical_type', 'min_value', 'max_value', 'null_count', 'num_values', 'distinct_count' ), [ ([1, 2, 2, None, 4], pa.uint8(), 'INT32', 1, 4, 1, 4, 0), ([1, 2, 2, None, 4], pa.uint16(), 'INT32', 1, 4, 1, 4, 0), ([1, 2, 2, None, 4], pa.uint32(), 'INT32', 1, 4, 1, 4, 0), ([1, 2, 2, None, 4], pa.uint64(), 'INT64', 1, 4, 1, 4, 0), ([-1, 2, 2, None, 4], pa.int8(), 'INT32', -1, 4, 1, 4, 0), ([-1, 2, 2, None, 4], pa.int16(), 'INT32', -1, 4, 1, 4, 0), ([-1, 2, 2, None, 4], pa.int32(), 'INT32', -1, 4, 1, 4, 0), ([-1, 2, 2, None, 4], pa.int64(), 'INT64', -1, 4, 1, 4, 0), ( [-1.1, 2.2, 2.3, None, 4.4], pa.float32(), 'FLOAT', -1.1, 4.4, 1, 4, 0 ), ( [-1.1, 2.2, 2.3, None, 4.4], pa.float64(), 'DOUBLE', -1.1, 4.4, 1, 4, 0 ), ( [u'', u'b', unichar(1000), None, u'aaa'], pa.binary(), 'BYTE_ARRAY', b'', unichar(1000).encode('utf-8'), 1, 4, 0 ), ( [True, False, False, True, True], pa.bool_(), 'BOOLEAN', False, True, 0, 5, 0 ), ( [b'\x00', b'b', b'12', None, b'aaa'], pa.binary(), 'BYTE_ARRAY', b'\x00', b'b', 1, 4, 0 ), ] ) def test_parquet_column_statistics_api(data, type, physical_type, min_value, max_value, null_count, num_values, distinct_count): df = pd.DataFrame({'data': data}) schema = pa.schema([pa.field('data', type)]) table = pa.Table.from_pandas(df, schema=schema, safe=False) fileh = make_sample_file(table) meta = fileh.metadata rg_meta = meta.row_group(0) col_meta = rg_meta.column(0) stat = col_meta.statistics assert stat.has_min_max assert _close(type, stat.min, min_value) assert _close(type, stat.max, max_value) assert stat.null_count == null_count assert stat.num_values == num_values # TODO(kszucs) until parquet-cpp API doesn't expose HasDistinctCount # method, missing distinct_count is represented as zero instead of None assert stat.distinct_count == distinct_count assert stat.physical_type == physical_type def _close(type, left, right): if type == pa.float32(): return abs(left - right) < 1E-7 elif type == pa.float64(): return abs(left - right) < 1E-13 else: return left == right def test_statistics_convert_logical_types(tempdir): # ARROW-5166, ARROW-4139 # (min, max, type) cases = [(10, 11164359321221007157, pa.uint64()), (10, 4294967295, pa.uint32()), (u"ähnlich", u"öffentlich", pa.utf8()), (datetime.time(10, 30, 0, 1000), datetime.time(15, 30, 0, 1000), pa.time32('ms')), (datetime.time(10, 30, 0, 1000), datetime.time(15, 30, 0, 1000), pa.time64('us')), (datetime.datetime(2019, 6, 24, 0, 0, 0, 1000), datetime.datetime(2019, 6, 25, 0, 0, 0, 1000), pa.timestamp('ms')), (datetime.datetime(2019, 6, 24, 0, 0, 0, 1000), datetime.datetime(2019, 6, 25, 0, 0, 0, 1000), pa.timestamp('us'))] for i, (min_val, max_val, typ) in enumerate(cases): t = pa.Table.from_arrays([pa.array([min_val, max_val], type=typ)], ['col']) path = str(tempdir / ('example{}.parquet'.format(i))) pq.write_table(t, path, version='2.0') pf = pq.ParquetFile(path) stats = pf.metadata.row_group(0).column(0).statistics assert stats.min == min_val assert stats.max == max_val def test_parquet_write_disable_statistics(tempdir): table = pa.Table.from_pydict( {'a': pa.array([1, 2, 3]), 'b': pa.array(['a', 'b', 'c'])}) _write_table(table, tempdir / 'data.parquet') meta = pq.read_metadata(tempdir / 'data.parquet') for col in [0, 1]: cc = meta.row_group(0).column(col) assert cc.is_stats_set is True assert cc.statistics is not None _write_table(table, tempdir / 'data2.parquet', write_statistics=False) meta = pq.read_metadata(tempdir / 'data2.parquet') for col in [0, 1]: cc = meta.row_group(0).column(col) assert cc.is_stats_set is False assert cc.statistics is None _write_table(table, tempdir / 'data3.parquet', write_statistics=['a']) meta = pq.read_metadata(tempdir / 'data3.parquet') cc_a = meta.row_group(0).column(0) assert cc_a.is_stats_set is True assert cc_a.statistics is not None cc_b = meta.row_group(0).column(1) assert cc_b.is_stats_set is False assert cc_b.statistics is None @pytest.mark.pandas def test_compare_schemas(): df = alltypes_sample(size=10000) fileh = make_sample_file(df) fileh2 = make_sample_file(df) fileh3 = make_sample_file(df[df.columns[::2]]) # ParquetSchema assert isinstance(fileh.schema, pq.ParquetSchema) assert fileh.schema.equals(fileh.schema) assert fileh.schema == fileh.schema assert fileh.schema.equals(fileh2.schema) assert fileh.schema == fileh2.schema assert fileh.schema != 'arbitrary object' assert not fileh.schema.equals(fileh3.schema) assert fileh.schema != fileh3.schema # ColumnSchema assert isinstance(fileh.schema[0], pq.ColumnSchema) assert fileh.schema[0].equals(fileh.schema[0]) assert fileh.schema[0] == fileh.schema[0] assert not fileh.schema[0].equals(fileh.schema[1]) assert fileh.schema[0] != fileh.schema[1] assert fileh.schema[0] != 'arbitrary object' def test_validate_schema_write_table(tempdir): # ARROW-2926 simple_fields = [ pa.field('POS', pa.uint32()), pa.field('desc', pa.string()) ] simple_schema = pa.schema(simple_fields) # simple_table schema does not match simple_schema simple_from_array = [pa.array([1]), pa.array(['bla'])] simple_table = pa.Table.from_arrays(simple_from_array, ['POS', 'desc']) path = tempdir / 'simple_validate_schema.parquet' with pq.ParquetWriter(path, simple_schema, version='2.0', compression='snappy', flavor='spark') as w: with pytest.raises(ValueError): w.write_table(simple_table) @pytest.mark.pandas def test_column_of_arrays(tempdir): df, schema = dataframe_with_arrays() filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df, schema=schema) _write_table(arrow_table, filename, version="2.0", coerce_timestamps='ms') table_read = _read_table(filename) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_coerce_timestamps(tempdir): from collections import OrderedDict # ARROW-622 arrays = OrderedDict() fields = [pa.field('datetime64', pa.list_(pa.timestamp('ms')))] arrays['datetime64'] = [ np.array(['2007-07-13T01:23:34.123456789', None, '2010-08-13T05:46:57.437699912'], dtype='datetime64[ms]'), None, None, np.array(['2007-07-13T02', None, '2010-08-13T05:46:57.437699912'], dtype='datetime64[ms]'), ] df = pd.DataFrame(arrays) schema = pa.schema(fields) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df, schema=schema) _write_table(arrow_table, filename, version="2.0", coerce_timestamps='us') table_read = _read_table(filename) df_read = table_read.to_pandas() df_expected = df.copy() for i, x in enumerate(df_expected['datetime64']): if isinstance(x, np.ndarray): df_expected['datetime64'][i] = x.astype('M8[us]') tm.assert_frame_equal(df_expected, df_read) with pytest.raises(ValueError): _write_table(arrow_table, filename, version='2.0', coerce_timestamps='unknown') @pytest.mark.pandas def test_coerce_timestamps_truncated(tempdir): """ ARROW-2555: Test that we can truncate timestamps when coercing if explicitly allowed. """ dt_us = datetime.datetime(year=2017, month=1, day=1, hour=1, minute=1, second=1, microsecond=1) dt_ms = datetime.datetime(year=2017, month=1, day=1, hour=1, minute=1, second=1) fields_us = [pa.field('datetime64', pa.timestamp('us'))] arrays_us = {'datetime64': [dt_us, dt_ms]} df_us = pd.DataFrame(arrays_us) schema_us = pa.schema(fields_us) filename = tempdir / 'pandas_truncated.parquet' table_us = pa.Table.from_pandas(df_us, schema=schema_us) _write_table(table_us, filename, version="2.0", coerce_timestamps='ms', allow_truncated_timestamps=True) table_ms = _read_table(filename) df_ms = table_ms.to_pandas() arrays_expected = {'datetime64': [dt_ms, dt_ms]} df_expected = pd.DataFrame(arrays_expected) tm.assert_frame_equal(df_expected, df_ms) @pytest.mark.pandas def test_column_of_lists(tempdir): df, schema = dataframe_with_lists(parquet_compatible=True) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df, schema=schema) _write_table(arrow_table, filename, version='2.0') table_read = _read_table(filename) df_read = table_read.to_pandas() if PY2: # assert_frame_equal fails when comparing datetime.date and # np.datetime64, even with check_datetimelike_compat=True so # convert the values to np.datetime64 instead for col in ['date32[day]_list', 'date64[ms]_list']: df[col] = df[col].apply( lambda x: list(map(np.datetime64, x)) if x else x ) tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_date_time_types(tempdir): t1 = pa.date32() data1 = np.array([17259, 17260, 17261], dtype='int32') a1 = pa.array(data1, type=t1) t2 = pa.date64() data2 = data1.astype('int64') * 86400000 a2 = pa.array(data2, type=t2) t3 = pa.timestamp('us') start = pd.Timestamp('2001-01-01').value / 1000 data3 = np.array([start, start + 1, start + 2], dtype='int64') a3 = pa.array(data3, type=t3) t4 = pa.time32('ms') data4 = np.arange(3, dtype='i4') a4 = pa.array(data4, type=t4) t5 = pa.time64('us') a5 = pa.array(data4.astype('int64'), type=t5) t6 = pa.time32('s') a6 = pa.array(data4, type=t6) ex_t6 = pa.time32('ms') ex_a6 = pa.array(data4 * 1000, type=ex_t6) t7 = pa.timestamp('ns') start = pd.Timestamp('2001-01-01').value data7 = np.array([start, start + 1000, start + 2000], dtype='int64') a7 = pa.array(data7, type=t7) table = pa.Table.from_arrays([a1, a2, a3, a4, a5, a6, a7], ['date32', 'date64', 'timestamp[us]', 'time32[s]', 'time64[us]', 'time32_from64[s]', 'timestamp[ns]']) # date64 as date32 # time32[s] to time32[ms] expected = pa.Table.from_arrays([a1, a1, a3, a4, a5, ex_a6, a7], ['date32', 'date64', 'timestamp[us]', 'time32[s]', 'time64[us]', 'time32_from64[s]', 'timestamp[ns]']) _check_roundtrip(table, expected=expected, version='2.0') t0 = pa.timestamp('ms') data0 = np.arange(4, dtype='int64') a0 = pa.array(data0, type=t0) t1 = pa.timestamp('us') data1 = np.arange(4, dtype='int64') a1 = pa.array(data1, type=t1) t2 = pa.timestamp('ns') data2 = np.arange(4, dtype='int64') a2 = pa.array(data2, type=t2) table = pa.Table.from_arrays([a0, a1, a2], ['ts[ms]', 'ts[us]', 'ts[ns]']) expected = pa.Table.from_arrays([a0, a1, a2], ['ts[ms]', 'ts[us]', 'ts[ns]']) # int64 for all timestamps supported by default filename = tempdir / 'int64_timestamps.parquet' _write_table(table, filename, version='2.0') parquet_schema = pq.ParquetFile(filename).schema for i in range(3): assert parquet_schema.column(i).physical_type == 'INT64' read_table = _read_table(filename) assert read_table.equals(expected) t0_ns = pa.timestamp('ns') data0_ns = np.array(data0 * 1000000, dtype='int64') a0_ns = pa.array(data0_ns, type=t0_ns) t1_ns = pa.timestamp('ns') data1_ns = np.array(data1 * 1000, dtype='int64') a1_ns = pa.array(data1_ns, type=t1_ns) expected = pa.Table.from_arrays([a0_ns, a1_ns, a2], ['ts[ms]', 'ts[us]', 'ts[ns]']) # int96 nanosecond timestamps produced upon request filename = tempdir / 'explicit_int96_timestamps.parquet' _write_table(table, filename, version='2.0', use_deprecated_int96_timestamps=True) parquet_schema = pq.ParquetFile(filename).schema for i in range(3): assert parquet_schema.column(i).physical_type == 'INT96' read_table = _read_table(filename) assert read_table.equals(expected) # int96 nanosecond timestamps implied by flavor 'spark' filename = tempdir / 'spark_int96_timestamps.parquet' _write_table(table, filename, version='2.0', flavor='spark') parquet_schema = pq.ParquetFile(filename).schema for i in range(3): assert parquet_schema.column(i).physical_type == 'INT96' read_table = _read_table(filename) assert read_table.equals(expected) def test_timestamp_restore_timezone(): # ARROW-5888, restore timezone from serialized metadata ty = pa.timestamp('ms', tz='America/New_York') arr = pa.array([1, 2, 3], type=ty) t = pa.table([arr], names=['f0']) _check_roundtrip(t) @pytest.mark.pandas def test_list_of_datetime_time_roundtrip(): # ARROW-4135 times = pd.to_datetime(['09:00', '09:30', '10:00', '10:30', '11:00', '11:30', '12:00']) df = pd.DataFrame({'time': [times.time]}) _roundtrip_pandas_dataframe(df, write_kwargs={}) @pytest.mark.pandas def test_parquet_version_timestamp_differences(): i_s = pd.Timestamp('2010-01-01').value / 1000000000 # := 1262304000 d_s = np.arange(i_s, i_s + 10, 1, dtype='int64') d_ms = d_s * 1000 d_us = d_ms * 1000 d_ns = d_us * 1000 a_s = pa.array(d_s, type=pa.timestamp('s')) a_ms = pa.array(d_ms, type=pa.timestamp('ms')) a_us = pa.array(d_us, type=pa.timestamp('us')) a_ns = pa.array(d_ns, type=pa.timestamp('ns')) names = ['ts:s', 'ts:ms', 'ts:us', 'ts:ns'] table = pa.Table.from_arrays([a_s, a_ms, a_us, a_ns], names) # Using Parquet version 1.0, seconds should be coerced to milliseconds # and nanoseconds should be coerced to microseconds by default expected = pa.Table.from_arrays([a_ms, a_ms, a_us, a_us], names) _check_roundtrip(table, expected) # Using Parquet version 2.0, seconds should be coerced to milliseconds # and nanoseconds should be retained by default expected = pa.Table.from_arrays([a_ms, a_ms, a_us, a_ns], names) _check_roundtrip(table, expected, version='2.0') # Using Parquet version 1.0, coercing to milliseconds or microseconds # is allowed expected = pa.Table.from_arrays([a_ms, a_ms, a_ms, a_ms], names) _check_roundtrip(table, expected, coerce_timestamps='ms') # Using Parquet version 2.0, coercing to milliseconds or microseconds # is allowed expected = pa.Table.from_arrays([a_us, a_us, a_us, a_us], names) _check_roundtrip(table, expected, version='2.0', coerce_timestamps='us') # TODO: after pyarrow allows coerce_timestamps='ns', tests like the # following should pass ... # Using Parquet version 1.0, coercing to nanoseconds is not allowed # expected = None # with pytest.raises(NotImplementedError): # _roundtrip_table(table, coerce_timestamps='ns') # Using Parquet version 2.0, coercing to nanoseconds is allowed # expected = pa.Table.from_arrays([a_ns, a_ns, a_ns, a_ns], names) # _check_roundtrip(table, expected, version='2.0', coerce_timestamps='ns') # For either Parquet version, coercing to nanoseconds is allowed # if Int96 storage is used expected = pa.Table.from_arrays([a_ns, a_ns, a_ns, a_ns], names) _check_roundtrip(table, expected, use_deprecated_int96_timestamps=True) _check_roundtrip(table, expected, version='2.0', use_deprecated_int96_timestamps=True) def test_large_list_records(): # This was fixed in PARQUET-1100 list_lengths = np.random.randint(0, 500, size=50) list_lengths[::10] = 0 list_values = [list(map(int, np.random.randint(0, 100, size=x))) if i % 8 else None for i, x in enumerate(list_lengths)] a1 = pa.array(list_values) table = pa.Table.from_arrays([a1], ['int_lists']) _check_roundtrip(table) def test_sanitized_spark_field_names(): a0 = pa.array([0, 1, 2, 3, 4]) name = 'prohib; ,\t{}' table = pa.Table.from_arrays([a0], [name]) result = _roundtrip_table(table, write_table_kwargs={'flavor': 'spark'}) expected_name = 'prohib______' assert result.schema[0].name == expected_name @pytest.mark.pandas def test_spark_flavor_preserves_pandas_metadata(): df = _test_dataframe(size=100) df.index = np.arange(0, 10 * len(df), 10) df.index.name = 'foo' result = _roundtrip_pandas_dataframe(df, {'version': '2.0', 'flavor': 'spark'}) tm.assert_frame_equal(result, df) def test_fixed_size_binary(): t0 = pa.binary(10) data = [b'fooooooooo', None, b'barooooooo', b'quxooooooo'] a0 = pa.array(data, type=t0) table = pa.Table.from_arrays([a0], ['binary[10]']) _check_roundtrip(table) @pytest.mark.pandas def test_multithreaded_read(): df = alltypes_sample(size=10000) table = pa.Table.from_pandas(df) buf = io.BytesIO() _write_table(table, buf, compression='SNAPPY', version='2.0') buf.seek(0) table1 = _read_table(buf, use_threads=True) buf.seek(0) table2 = _read_table(buf, use_threads=False) assert table1.equals(table2) @pytest.mark.pandas def test_min_chunksize(): data = pd.DataFrame([np.arange(4)], columns=['A', 'B', 'C', 'D']) table = pa.Table.from_pandas(data.reset_index()) buf = io.BytesIO() _write_table(table, buf, chunk_size=-1) buf.seek(0) result = _read_table(buf) assert result.equals(table) with pytest.raises(ValueError): _write_table(table, buf, chunk_size=0) @pytest.mark.pandas def test_pass_separate_metadata(): # ARROW-471 df = alltypes_sample(size=10000) a_table = pa.Table.from_pandas(df) buf = io.BytesIO() _write_table(a_table, buf, compression='snappy', version='2.0') buf.seek(0) metadata = pq.read_metadata(buf) buf.seek(0) fileh = pq.ParquetFile(buf, metadata=metadata) tm.assert_frame_equal(df, fileh.read().to_pandas()) @pytest.mark.pandas def test_read_single_row_group(): # ARROW-471 N, K = 10000, 4 df = alltypes_sample(size=N) a_table = pa.Table.from_pandas(df) buf = io.BytesIO() _write_table(a_table, buf, row_group_size=N / K, compression='snappy', version='2.0') buf.seek(0) pf = pq.ParquetFile(buf) assert pf.num_row_groups == K row_groups = [pf.read_row_group(i) for i in range(K)] result = pa.concat_tables(row_groups) tm.assert_frame_equal(df, result.to_pandas()) @pytest.mark.pandas def test_read_single_row_group_with_column_subset(): N, K = 10000, 4 df = alltypes_sample(size=N) a_table = pa.Table.from_pandas(df) buf = io.BytesIO() _write_table(a_table, buf, row_group_size=N / K, compression='snappy', version='2.0') buf.seek(0) pf = pq.ParquetFile(buf) cols = list(df.columns[:2]) row_groups = [pf.read_row_group(i, columns=cols) for i in range(K)] result = pa.concat_tables(row_groups) tm.assert_frame_equal(df[cols], result.to_pandas()) # ARROW-4267: Selection of duplicate columns still leads to these columns # being read uniquely. row_groups = [pf.read_row_group(i, columns=cols + cols) for i in range(K)] result = pa.concat_tables(row_groups) tm.assert_frame_equal(df[cols], result.to_pandas()) @pytest.mark.pandas def test_scan_contents(): N, K = 10000, 4 df = alltypes_sample(size=N) a_table = pa.Table.from_pandas(df) buf = io.BytesIO() _write_table(a_table, buf, row_group_size=N / K, compression='snappy', version='2.0') buf.seek(0) pf = pq.ParquetFile(buf) assert pf.scan_contents() == 10000 assert pf.scan_contents(df.columns[:4]) == 10000 @pytest.mark.pandas def test_parquet_piece_read(tempdir): df = _test_dataframe(1000) table = pa.Table.from_pandas(df) path = tempdir / 'parquet_piece_read.parquet' _write_table(table, path, version='2.0') piece1 = pq.ParquetDatasetPiece(path) result = piece1.read() assert result.equals(table) @pytest.mark.pandas def test_parquet_piece_open_and_get_metadata(tempdir): df = _test_dataframe(100) table = pa.Table.from_pandas(df) path = tempdir / 'parquet_piece_read.parquet' _write_table(table, path, version='2.0') piece = pq.ParquetDatasetPiece(path) table1 = piece.read() assert isinstance(table1, pa.Table) meta1 = piece.get_metadata() assert isinstance(meta1, pq.FileMetaData) assert table == table1 def test_parquet_piece_basics(): path = '/baz.parq' piece1 = pq.ParquetDatasetPiece(path) piece2 = pq.ParquetDatasetPiece(path, row_group=1) piece3 = pq.ParquetDatasetPiece( path, row_group=1, partition_keys=[('foo', 0), ('bar', 1)]) assert str(piece1) == path assert str(piece2) == '/baz.parq | row_group=1' assert str(piece3) == 'partition[foo=0, bar=1] /baz.parq | row_group=1' assert piece1 == piece1 assert piece2 == piece2 assert piece3 == piece3 assert piece1 != piece3 def test_partition_set_dictionary_type(): set1 = pq.PartitionSet('key1', [u('foo'), u('bar'), u('baz')]) set2 = pq.PartitionSet('key2', [2007, 2008, 2009]) assert isinstance(set1.dictionary, pa.StringArray) assert isinstance(set2.dictionary, pa.IntegerArray) set3 = pq.PartitionSet('key2', [datetime.datetime(2007, 1, 1)]) with pytest.raises(TypeError): set3.dictionary @pytest.mark.pandas def test_read_partitioned_directory(tempdir): fs = LocalFileSystem.get_instance() _partition_test_for_filesystem(fs, tempdir) @pytest.mark.pandas def test_create_parquet_dataset_multi_threaded(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir _partition_test_for_filesystem(fs, base_path) manifest = pq.ParquetManifest(base_path, filesystem=fs, metadata_nthreads=1) dataset = pq.ParquetDataset(base_path, filesystem=fs, metadata_nthreads=16) assert len(dataset.pieces) > 0 partitions = dataset.partitions assert len(partitions.partition_names) > 0 assert partitions.partition_names == manifest.partitions.partition_names assert len(partitions.levels) == len(manifest.partitions.levels) @pytest.mark.pandas def test_equivalency(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir integer_keys = [0, 1] string_keys = ['a', 'b', 'c'] boolean_keys = [True, False] partition_spec = [ ['integer', integer_keys], ['string', string_keys], ['boolean', boolean_keys] ] df = pd.DataFrame({ 'integer': np.array(integer_keys, dtype='i4').repeat(15), 'string': np.tile(np.tile(np.array(string_keys, dtype=object), 5), 2), 'boolean': np.tile(np.tile(np.array(boolean_keys, dtype='bool'), 5), 3), }, columns=['integer', 'string', 'boolean']) _generate_partition_directories(fs, base_path, partition_spec, df) # Old filters syntax: # integer == 1 AND string != b AND boolean == True dataset = pq.ParquetDataset( base_path, filesystem=fs, filters=[('integer', '=', 1), ('string', '!=', 'b'), ('boolean', '==', True)] ) table = dataset.read() result_df = (table.to_pandas().reset_index(drop=True)) assert 0 not in result_df['integer'].values assert 'b' not in result_df['string'].values assert False not in result_df['boolean'].values # filters in disjunctive normal form: # (integer == 1 AND string != b AND boolean == True) OR # (integer == 2 AND boolean == False) # TODO(ARROW-3388): boolean columns are reconstructed as string filters = [ [ ('integer', '=', 1), ('string', '!=', 'b'), ('boolean', '==', 'True') ], [('integer', '=', 0), ('boolean', '==', 'False')] ] dataset = pq.ParquetDataset(base_path, filesystem=fs, filters=filters) table = dataset.read() result_df = table.to_pandas().reset_index(drop=True) # Check that all rows in the DF fulfill the filter # Pandas 0.23.x has problems with indexing constant memoryviews in # categoricals. Thus we need to make an explicity copy here with np.array. df_filter_1 = (np.array(result_df['integer']) == 1) \ & (np.array(result_df['string']) != 'b') \ & (np.array(result_df['boolean']) == 'True') df_filter_2 = (np.array(result_df['integer']) == 0) \ & (np.array(result_df['boolean']) == 'False') assert df_filter_1.sum() > 0 assert df_filter_2.sum() > 0 assert result_df.shape[0] == (df_filter_1.sum() + df_filter_2.sum()) # Check for \0 in predicate values. Until they are correctly implemented # in ARROW-3391, they would otherwise lead to weird results with the # current code. with pytest.raises(NotImplementedError): filters = [[('string', '==', b'1\0a')]] pq.ParquetDataset(base_path, filesystem=fs, filters=filters) with pytest.raises(NotImplementedError): filters = [[('string', '==', u'1\0a')]] pq.ParquetDataset(base_path, filesystem=fs, filters=filters) @pytest.mark.pandas def test_cutoff_exclusive_integer(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir integer_keys = [0, 1, 2, 3, 4] partition_spec = [ ['integers', integer_keys], ] N = 5 df = pd.DataFrame({ 'index': np.arange(N), 'integers': np.array(integer_keys, dtype='i4'), }, columns=['index', 'integers']) _generate_partition_directories(fs, base_path, partition_spec, df) dataset = pq.ParquetDataset( base_path, filesystem=fs, filters=[ ('integers', '<', 4), ('integers', '>', 1), ] ) table = dataset.read() result_df = (table.to_pandas() .sort_values(by='index') .reset_index(drop=True)) result_list = [x for x in map(int, result_df['integers'].values)] assert result_list == [2, 3] @pytest.mark.pandas @pytest.mark.xfail( raises=TypeError, reason='Loss of type information in creation of categoricals.' ) def test_cutoff_exclusive_datetime(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir date_keys = [ datetime.date(2018, 4, 9), datetime.date(2018, 4, 10), datetime.date(2018, 4, 11), datetime.date(2018, 4, 12), datetime.date(2018, 4, 13) ] partition_spec = [ ['dates', date_keys] ] N = 5 df = pd.DataFrame({ 'index': np.arange(N), 'dates': np.array(date_keys, dtype='datetime64'), }, columns=['index', 'dates']) _generate_partition_directories(fs, base_path, partition_spec, df) dataset = pq.ParquetDataset( base_path, filesystem=fs, filters=[ ('dates', '<', "2018-04-12"), ('dates', '>', "2018-04-10") ] ) table = dataset.read() result_df = (table.to_pandas() .sort_values(by='index') .reset_index(drop=True)) expected = pd.Categorical( np.array([datetime.date(2018, 4, 11)], dtype='datetime64'), categories=np.array(date_keys, dtype='datetime64')) assert result_df['dates'].values == expected @pytest.mark.pandas def test_inclusive_integer(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir integer_keys = [0, 1, 2, 3, 4] partition_spec = [ ['integers', integer_keys], ] N = 5 df = pd.DataFrame({ 'index': np.arange(N), 'integers': np.array(integer_keys, dtype='i4'), }, columns=['index', 'integers']) _generate_partition_directories(fs, base_path, partition_spec, df) dataset = pq.ParquetDataset( base_path, filesystem=fs, filters=[ ('integers', '<=', 3), ('integers', '>=', 2), ] ) table = dataset.read() result_df = (table.to_pandas() .sort_values(by='index') .reset_index(drop=True)) result_list = [int(x) for x in map(int, result_df['integers'].values)] assert result_list == [2, 3] @pytest.mark.pandas def test_inclusive_set(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir integer_keys = [0, 1] string_keys = ['a', 'b', 'c'] boolean_keys = [True, False] partition_spec = [ ['integer', integer_keys], ['string', string_keys], ['boolean', boolean_keys] ] df = pd.DataFrame({ 'integer': np.array(integer_keys, dtype='i4').repeat(15), 'string': np.tile(np.tile(np.array(string_keys, dtype=object), 5), 2), 'boolean': np.tile(np.tile(np.array(boolean_keys, dtype='bool'), 5), 3), }, columns=['integer', 'string', 'boolean']) _generate_partition_directories(fs, base_path, partition_spec, df) dataset = pq.ParquetDataset( base_path, filesystem=fs, filters=[('integer', 'in', {1}), ('string', 'in', {'a', 'b'}), ('boolean', 'in', {True})] ) table = dataset.read() result_df = (table.to_pandas().reset_index(drop=True)) assert 0 not in result_df['integer'].values assert 'c' not in result_df['string'].values assert False not in result_df['boolean'].values @pytest.mark.pandas def test_invalid_pred_op(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir integer_keys = [0, 1, 2, 3, 4] partition_spec = [ ['integers', integer_keys], ] N = 5 df = pd.DataFrame({ 'index': np.arange(N), 'integers': np.array(integer_keys, dtype='i4'), }, columns=['index', 'integers']) _generate_partition_directories(fs, base_path, partition_spec, df) with pytest.raises(ValueError): pq.ParquetDataset(base_path, filesystem=fs, filters=[ ('integers', '=<', 3), ]) with pytest.raises(ValueError): pq.ParquetDataset(base_path, filesystem=fs, filters=[ ('integers', 'in', set()), ]) with pytest.raises(ValueError): pq.ParquetDataset(base_path, filesystem=fs, filters=[ ('integers', '!=', {3}), ]) @pytest.mark.pandas def test_filters_read_table(tempdir): # test that filters keyword is passed through in read_table fs = LocalFileSystem.get_instance() base_path = tempdir integer_keys = [0, 1, 2, 3, 4] partition_spec = [ ['integers', integer_keys], ] N = 5 df = pd.DataFrame({ 'index': np.arange(N), 'integers': np.array(integer_keys, dtype='i4'), }, columns=['index', 'integers']) _generate_partition_directories(fs, base_path, partition_spec, df) table = pq.read_table( base_path, filesystem=fs, filters=[('integers', '<', 3)]) assert table.num_rows == 3 table = pq.read_table( base_path, filesystem=fs, filters=[[('integers', '<', 3)]]) assert table.num_rows == 3 table = pq.read_pandas( base_path, filters=[('integers', '<', 3)]) assert table.num_rows == 3 @pytest.yield_fixture def s3_example(): access_key = os.environ['PYARROW_TEST_S3_ACCESS_KEY'] secret_key = os.environ['PYARROW_TEST_S3_SECRET_KEY'] bucket_name = os.environ['PYARROW_TEST_S3_BUCKET'] import s3fs fs = s3fs.S3FileSystem(key=access_key, secret=secret_key) test_dir = guid() bucket_uri = 's3://{0}/{1}'.format(bucket_name, test_dir) fs.mkdir(bucket_uri) yield fs, bucket_uri fs.rm(bucket_uri, recursive=True) @pytest.mark.pandas @pytest.mark.s3 def test_read_partitioned_directory_s3fs(s3_example): from pyarrow.filesystem import S3FSWrapper fs, bucket_uri = s3_example wrapper = S3FSWrapper(fs) _partition_test_for_filesystem(wrapper, bucket_uri) # Check that we can auto-wrap dataset = pq.ParquetDataset(bucket_uri, filesystem=fs) dataset.read() def _partition_test_for_filesystem(fs, base_path): foo_keys = [0, 1] bar_keys = ['a', 'b', 'c'] partition_spec = [ ['foo', foo_keys], ['bar', bar_keys] ] N = 30 df = pd.DataFrame({ 'index': np.arange(N), 'foo': np.array(foo_keys, dtype='i4').repeat(15), 'bar': np.tile(np.tile(np.array(bar_keys, dtype=object), 5), 2), 'values': np.random.randn(N) }, columns=['index', 'foo', 'bar', 'values']) _generate_partition_directories(fs, base_path, partition_spec, df) dataset = pq.ParquetDataset(base_path, filesystem=fs) table = dataset.read() result_df = (table.to_pandas() .sort_values(by='index') .reset_index(drop=True)) expected_df = (df.sort_values(by='index') .reset_index(drop=True) .reindex(columns=result_df.columns)) expected_df['foo'] = pd.Categorical(df['foo'], categories=foo_keys) expected_df['bar'] = pd.Categorical(df['bar'], categories=bar_keys) assert (result_df.columns == ['index', 'values', 'foo', 'bar']).all() tm.assert_frame_equal(result_df, expected_df) def _generate_partition_directories(fs, base_dir, partition_spec, df): # partition_spec : list of lists, e.g. [['foo', [0, 1, 2], # ['bar', ['a', 'b', 'c']] # part_table : a pyarrow.Table to write to each partition DEPTH = len(partition_spec) def _visit_level(base_dir, level, part_keys): name, values = partition_spec[level] for value in values: this_part_keys = part_keys + [(name, value)] level_dir = base_dir / '{0}={1}'.format(name, value) fs.mkdir(level_dir) if level == DEPTH - 1: # Generate example data file_path = level_dir / guid() filtered_df = _filter_partition(df, this_part_keys) part_table = pa.Table.from_pandas(filtered_df) with fs.open(file_path, 'wb') as f: _write_table(part_table, f) assert fs.exists(file_path) (level_dir / '_SUCCESS').touch() else: _visit_level(level_dir, level + 1, this_part_keys) (level_dir / '_SUCCESS').touch() _visit_level(base_dir, 0, []) def _test_read_common_metadata_files(fs, base_path): N = 100 df = pd.DataFrame({ 'index': np.arange(N), 'values': np.random.randn(N) }, columns=['index', 'values']) base_path = str(base_path) data_path = os.path.join(base_path, 'data.parquet') table = pa.Table.from_pandas(df) with fs.open(data_path, 'wb') as f: _write_table(table, f) metadata_path = os.path.join(base_path, '_common_metadata') with fs.open(metadata_path, 'wb') as f: pq.write_metadata(table.schema, f) dataset = pq.ParquetDataset(base_path, filesystem=fs) assert dataset.common_metadata_path == str(metadata_path) with fs.open(data_path) as f: common_schema = pq.read_metadata(f).schema assert dataset.schema.equals(common_schema) # handle list of one directory dataset2 = pq.ParquetDataset([base_path], filesystem=fs) assert dataset2.schema.equals(dataset.schema) @pytest.mark.pandas def test_read_common_metadata_files(tempdir): fs = LocalFileSystem.get_instance() _test_read_common_metadata_files(fs, tempdir) @pytest.mark.pandas def test_read_metadata_files(tempdir): fs = LocalFileSystem.get_instance() N = 100 df = pd.DataFrame({ 'index': np.arange(N), 'values': np.random.randn(N) }, columns=['index', 'values']) data_path = tempdir / 'data.parquet' table = pa.Table.from_pandas(df) with fs.open(data_path, 'wb') as f: _write_table(table, f) metadata_path = tempdir / '_metadata' with fs.open(metadata_path, 'wb') as f: pq.write_metadata(table.schema, f) dataset = pq.ParquetDataset(tempdir, filesystem=fs) assert dataset.metadata_path == str(metadata_path) with fs.open(data_path) as f: metadata_schema = pq.read_metadata(f).schema assert dataset.schema.equals(metadata_schema) @pytest.mark.pandas def test_read_schema(tempdir): N = 100 df = pd.DataFrame({ 'index': np.arange(N), 'values': np.random.randn(N) }, columns=['index', 'values']) data_path = tempdir / 'test.parquet' table = pa.Table.from_pandas(df) _write_table(table, data_path) read1 = pq.read_schema(data_path) read2 = pq.read_schema(data_path, memory_map=True) assert table.schema.equals(read1, check_metadata=False) assert table.schema.equals(read2, check_metadata=False) assert table.schema.metadata[b'pandas'] == read1.metadata[b'pandas'] def _filter_partition(df, part_keys): predicate = np.ones(len(df), dtype=bool) to_drop = [] for name, value in part_keys: to_drop.append(name) # to avoid pandas warning if isinstance(value, (datetime.date, datetime.datetime)): value = pd.Timestamp(value) predicate &= df[name] == value return df[predicate].drop(to_drop, axis=1) @pytest.mark.pandas def test_read_multiple_files(tempdir): nfiles = 10 size = 5 dirpath = tempdir / guid() dirpath.mkdir() test_data = [] paths = [] for i in range(nfiles): df = _test_dataframe(size, seed=i) # Hack so that we don't have a dtype cast in v1 files df['uint32'] = df['uint32'].astype(np.int64) path = dirpath / '{}.parquet'.format(i) table = pa.Table.from_pandas(df) _write_table(table, path) test_data.append(table) paths.append(path) # Write a _SUCCESS.crc file (dirpath / '_SUCCESS.crc').touch() def read_multiple_files(paths, columns=None, use_threads=True, **kwargs): dataset = pq.ParquetDataset(paths, **kwargs) return dataset.read(columns=columns, use_threads=use_threads) result = read_multiple_files(paths) expected = pa.concat_tables(test_data) assert result.equals(expected) # Read with provided metadata metadata = pq.read_metadata(paths[0]) result2 = read_multiple_files(paths, metadata=metadata) assert result2.equals(expected) result3 = pa.localfs.read_parquet(dirpath, schema=metadata.schema) assert result3.equals(expected) # Read column subset to_read = [0, 2, 6, result.num_columns - 1] col_names = [result.field(i).name for i in to_read] out = pa.localfs.read_parquet(dirpath, columns=col_names) expected = pa.Table.from_arrays([result.column(i) for i in to_read], names=col_names, metadata=result.schema.metadata) assert out.equals(expected) # Read with multiple threads pa.localfs.read_parquet(dirpath, use_threads=True) # Test failure modes with non-uniform metadata bad_apple = _test_dataframe(size, seed=i).iloc[:, :4] bad_apple_path = tempdir / '{}.parquet'.format(guid()) t = pa.Table.from_pandas(bad_apple) _write_table(t, bad_apple_path) bad_meta = pq.read_metadata(bad_apple_path) with pytest.raises(ValueError): read_multiple_files(paths + [bad_apple_path]) with pytest.raises(ValueError): read_multiple_files(paths, metadata=bad_meta) mixed_paths = [bad_apple_path, paths[0]] with pytest.raises(ValueError): read_multiple_files(mixed_paths, schema=bad_meta.schema) with pytest.raises(ValueError): read_multiple_files(mixed_paths) @pytest.mark.pandas def test_dataset_read_pandas(tempdir): nfiles = 5 size = 5 dirpath = tempdir / guid() dirpath.mkdir() test_data = [] frames = [] paths = [] for i in range(nfiles): df = _test_dataframe(size, seed=i) df.index = np.arange(i * size, (i + 1) * size) df.index.name = 'index' path = dirpath / '{}.parquet'.format(i) table = pa.Table.from_pandas(df) _write_table(table, path) test_data.append(table) frames.append(df) paths.append(path) dataset = pq.ParquetDataset(dirpath) columns = ['uint8', 'strings'] result = dataset.read_pandas(columns=columns).to_pandas() expected = pd.concat([x[columns] for x in frames]) tm.assert_frame_equal(result, expected) @pytest.mark.pandas def test_dataset_no_memory_map(tempdir): # ARROW-2627: Check that we can use ParquetDataset without memory-mapping dirpath = tempdir / guid() dirpath.mkdir() df = _test_dataframe(10, seed=0) path = dirpath / '{}.parquet'.format(0) table = pa.Table.from_pandas(df) _write_table(table, path, version='2.0') # TODO(wesm): Not sure how to easily check that memory mapping is _not_ # used. Mocking is not especially easy for pa.memory_map dataset = pq.ParquetDataset(dirpath, memory_map=False) assert dataset.pieces[0].read().equals(table) @pytest.mark.pandas @pytest.mark.parametrize('preserve_index', [True, False, None]) def test_dataset_read_pandas_common_metadata(tempdir, preserve_index): # ARROW-1103 nfiles = 5 size = 5 dirpath = tempdir / guid() dirpath.mkdir() test_data = [] frames = [] paths = [] for i in range(nfiles): df = _test_dataframe(size, seed=i) df.index = pd.Index(np.arange(i * size, (i + 1) * size), name='index') path = dirpath / '{}.parquet'.format(i) table = pa.Table.from_pandas(df, preserve_index=preserve_index) # Obliterate metadata table = table.replace_schema_metadata(None) assert table.schema.metadata is None _write_table(table, path) test_data.append(table) frames.append(df) paths.append(path) # Write _metadata common file table_for_metadata = pa.Table.from_pandas( df, preserve_index=preserve_index ) pq.write_metadata(table_for_metadata.schema, dirpath / '_metadata') dataset = pq.ParquetDataset(dirpath) columns = ['uint8', 'strings'] result = dataset.read_pandas(columns=columns).to_pandas() expected = pd.concat([x[columns] for x in frames]) expected.index.name = ( df.index.name if preserve_index is not False else None) tm.assert_frame_equal(result, expected) def _make_example_multifile_dataset(base_path, nfiles=10, file_nrows=5): test_data = [] paths = [] for i in range(nfiles): df = _test_dataframe(file_nrows, seed=i) path = base_path / '{}.parquet'.format(i) test_data.append(_write_table(df, path)) paths.append(path) return paths @pytest.mark.pandas def test_ignore_private_directories(tempdir): dirpath = tempdir / guid() dirpath.mkdir() paths = _make_example_multifile_dataset(dirpath, nfiles=10, file_nrows=5) # private directory (dirpath / '_impala_staging').mkdir() dataset = pq.ParquetDataset(dirpath) assert set(map(str, paths)) == set(x.path for x in dataset.pieces) @pytest.mark.pandas def test_ignore_hidden_files_dot(tempdir): dirpath = tempdir / guid() dirpath.mkdir() paths = _make_example_multifile_dataset(dirpath, nfiles=10, file_nrows=5) with (dirpath / '.DS_Store').open('wb') as f: f.write(b'gibberish') with (dirpath / '.private').open('wb') as f: f.write(b'gibberish') dataset = pq.ParquetDataset(dirpath) assert set(map(str, paths)) == set(x.path for x in dataset.pieces) @pytest.mark.pandas def test_ignore_hidden_files_underscore(tempdir): dirpath = tempdir / guid() dirpath.mkdir() paths = _make_example_multifile_dataset(dirpath, nfiles=10, file_nrows=5) with (dirpath / '_committed_123').open('wb') as f: f.write(b'abcd') with (dirpath / '_started_321').open('wb') as f: f.write(b'abcd') dataset = pq.ParquetDataset(dirpath) assert set(map(str, paths)) == set(x.path for x in dataset.pieces) @pytest.mark.pandas def test_multiindex_duplicate_values(tempdir): num_rows = 3 numbers = list(range(num_rows)) index = pd.MultiIndex.from_arrays( [['foo', 'foo', 'bar'], numbers], names=['foobar', 'some_numbers'], ) df = pd.DataFrame({'numbers': numbers}, index=index) table = pa.Table.from_pandas(df) filename = tempdir / 'dup_multi_index_levels.parquet' _write_table(table, filename) result_table = _read_table(filename) assert table.equals(result_table) result_df = result_table.to_pandas() tm.assert_frame_equal(result_df, df) @pytest.mark.pandas def test_write_error_deletes_incomplete_file(tempdir): # ARROW-1285 df = pd.DataFrame({'a': list('abc'), 'b': list(range(1, 4)), 'c': np.arange(3, 6).astype('u1'), 'd': np.arange(4.0, 7.0, dtype='float64'), 'e': [True, False, True], 'f': pd.Categorical(list('abc')), 'g': pd.date_range('20130101', periods=3), 'h': pd.date_range('20130101', periods=3, tz='US/Eastern'), 'i': pd.date_range('20130101', periods=3, freq='ns')}) pdf = pa.Table.from_pandas(df) filename = tempdir / 'tmp_file' try: _write_table(pdf, filename) except pa.ArrowException: pass assert not filename.exists() @pytest.mark.pandas def test_noncoerced_nanoseconds_written_without_exception(tempdir): # ARROW-1957: the Parquet version 2.0 writer preserves Arrow # nanosecond timestamps by default n = 9 df = pd.DataFrame({'x': range(n)}, index=pd.DatetimeIndex(start='2017-01-01', freq='1n', periods=n)) tb = pa.Table.from_pandas(df) filename = tempdir / 'written.parquet' try: pq.write_table(tb, filename, version='2.0') except Exception: pass assert filename.exists() recovered_table = pq.read_table(filename) assert tb.equals(recovered_table) # Loss of data thru coercion (without explicit override) still an error filename = tempdir / 'not_written.parquet' with pytest.raises(ValueError): pq.write_table(tb, filename, coerce_timestamps='ms', version='2.0') def test_read_non_existent_file(tempdir): path = 'non-existent-file.parquet' try: pq.read_table(path) except Exception as e: assert path in e.args[0] def test_read_table_doesnt_warn(datadir): with pytest.warns(None) as record: pq.read_table(datadir / 'v0.7.1.parquet') assert len(record) == 0 def _test_write_to_dataset_with_partitions(base_path, filesystem=None, schema=None, index_name=None): # ARROW-1400 output_df = pd.DataFrame({'group1': list('aaabbbbccc'), 'group2': list('eefeffgeee'), 'num': list(range(10)), 'nan': [pd.np.nan] * 10, 'date': np.arange('2017-01-01', '2017-01-11', dtype='datetime64[D]')}) cols = output_df.columns.tolist() partition_by = ['group1', 'group2'] output_table = pa.Table.from_pandas(output_df, schema=schema, safe=False, preserve_index=False) pq.write_to_dataset(output_table, base_path, partition_by, filesystem=filesystem) metadata_path = os.path.join(base_path, '_common_metadata') if filesystem is not None: with filesystem.open(metadata_path, 'wb') as f: pq.write_metadata(output_table.schema, f) else: pq.write_metadata(output_table.schema, metadata_path) # ARROW-2891: Ensure the output_schema is preserved when writing a # partitioned dataset dataset = pq.ParquetDataset(base_path, filesystem=filesystem, validate_schema=True) # ARROW-2209: Ensure the dataset schema also includes the partition columns dataset_cols = set(dataset.schema.to_arrow_schema().names) assert dataset_cols == set(output_table.schema.names) input_table = dataset.read() input_df = input_table.to_pandas() # Read data back in and compare with original DataFrame # Partitioned columns added to the end of the DataFrame when read input_df_cols = input_df.columns.tolist() assert partition_by == input_df_cols[-1 * len(partition_by):] # Partitioned columns become 'categorical' dtypes input_df = input_df[cols] for col in partition_by: output_df[col] = output_df[col].astype('category') assert output_df.equals(input_df) def _test_write_to_dataset_no_partitions(base_path, filesystem=None): # ARROW-1400 output_df = pd.DataFrame({'group1': list('aaabbbbccc'), 'group2': list('eefeffgeee'), 'num': list(range(10)), 'date': np.arange('2017-01-01', '2017-01-11', dtype='datetime64[D]')}) cols = output_df.columns.tolist() output_table = pa.Table.from_pandas(output_df) if filesystem is None: filesystem = LocalFileSystem.get_instance() # Without partitions, append files to root_path n = 5 for i in range(n): pq.write_to_dataset(output_table, base_path, filesystem=filesystem) output_files = [file for file in filesystem.ls(base_path) if file.endswith(".parquet")] assert len(output_files) == n # Deduplicated incoming DataFrame should match # original outgoing Dataframe input_table = pq.ParquetDataset(base_path, filesystem=filesystem).read() input_df = input_table.to_pandas() input_df = input_df.drop_duplicates() input_df = input_df[cols] assert output_df.equals(input_df) @pytest.mark.pandas def test_write_to_dataset_with_partitions(tempdir): _test_write_to_dataset_with_partitions(str(tempdir)) @pytest.mark.pandas def test_write_to_dataset_with_partitions_and_schema(tempdir): schema = pa.schema([pa.field('group1', type=pa.string()), pa.field('group2', type=pa.string()), pa.field('num', type=pa.int64()), pa.field('nan', type=pa.int32()), pa.field('date', type=pa.timestamp(unit='us'))]) _test_write_to_dataset_with_partitions(str(tempdir), schema=schema) @pytest.mark.pandas def test_write_to_dataset_with_partitions_and_index_name(tempdir): _test_write_to_dataset_with_partitions(str(tempdir), index_name='index_name') @pytest.mark.pandas def test_write_to_dataset_no_partitions(tempdir): _test_write_to_dataset_no_partitions(str(tempdir)) @pytest.mark.pandas def test_write_to_dataset_with_partitions_and_custom_filenames(tempdir): output_df = pd.DataFrame({'group1': list('aaabbbbccc'), 'group2': list('eefeffgeee'), 'num': list(range(10)), 'nan': [pd.np.nan] * 10, 'date': np.arange('2017-01-01', '2017-01-11', dtype='datetime64[D]')}) partition_by = ['group1', 'group2'] output_table = pa.Table.from_pandas(output_df) path = str(tempdir) def partition_filename_callback(keys): return "{0}-{1}.parquet".format(*keys) pq.write_to_dataset(output_table, path, partition_by, partition_filename_callback) dataset = pq.ParquetDataset(path) # ARROW-3538: Ensure partition filenames match the given pattern # defined in the local function partition_filename_callback expected_basenames = [ 'a-e.parquet', 'a-f.parquet', 'b-e.parquet', 'b-f.parquet', 'b-g.parquet', 'c-e.parquet' ] output_basenames = [os.path.basename(p.path) for p in dataset.pieces] assert sorted(expected_basenames) == sorted(output_basenames) @pytest.mark.large_memory def test_large_table_int32_overflow(): size = np.iinfo('int32').max + 1 arr = np.ones(size, dtype='uint8') parr = pa.array(arr, type=pa.uint8()) table = pa.Table.from_arrays([parr], names=['one']) f = io.BytesIO() _write_table(table, f) def _simple_table_roundtrip(table): stream = pa.BufferOutputStream() _write_table(table, stream) buf = stream.getvalue() return _read_table(buf) @pytest.mark.pandas @pytest.mark.large_memory def test_binary_array_overflow_to_chunked(): # ARROW-3762 # 2^31 + 1 bytes values = [b'x'] + [ b'x' * (1 << 20) ] * 2 * (1 << 10) df = pd.DataFrame({'byte_col': values}) tbl = pa.Table.from_pandas(df, preserve_index=False) read_tbl = _simple_table_roundtrip(tbl) col0_data = read_tbl[0] assert isinstance(col0_data, pa.ChunkedArray) # Split up into 2GB chunks assert col0_data.num_chunks == 2 assert tbl.equals(read_tbl) @pytest.mark.pandas @pytest.mark.large_memory def test_list_of_binary_large_cell(): # ARROW-4688 data = [] # TODO(wesm): handle chunked children # 2^31 - 1 bytes in a single cell # data.append([b'x' * (1 << 20)] * 2047 + [b'x' * ((1 << 20) - 1)]) # A little under 2GB in cell each containing approximately 10MB each data.extend([[b'x' * 1000000] * 10] * 214) arr = pa.array(data) table = pa.Table.from_arrays([arr], ['chunky_cells']) read_table = _simple_table_roundtrip(table) assert table.equals(read_table) @pytest.mark.pandas def test_index_column_name_duplicate(tempdir): data = { 'close': { pd.Timestamp('2017-06-30 01:31:00'): 154.99958999999998, pd.Timestamp('2017-06-30 01:32:00'): 154.99958999999998, }, 'time': { pd.Timestamp('2017-06-30 01:31:00'): pd.Timestamp( '2017-06-30 01:31:00' ), pd.Timestamp('2017-06-30 01:32:00'): pd.Timestamp( '2017-06-30 01:32:00' ), } } path = str(tempdir / 'data.parquet') dfx = pd.DataFrame(data).set_index('time', drop=False) tdfx = pa.Table.from_pandas(dfx) _write_table(tdfx, path) arrow_table = _read_table(path) result_df = arrow_table.to_pandas() tm.assert_frame_equal(result_df, dfx) @pytest.mark.pandas def test_parquet_nested_convenience(tempdir): # ARROW-1684 df = pd.DataFrame({ 'a': [[1, 2, 3], None, [4, 5], []], 'b': [[1.], None, None, [6., 7.]], }) path = str(tempdir / 'nested_convenience.parquet') table = pa.Table.from_pandas(df, preserve_index=False) _write_table(table, path) read = pq.read_table(path, columns=['a']) tm.assert_frame_equal(read.to_pandas(), df[['a']]) read = pq.read_table(path, columns=['a', 'b']) tm.assert_frame_equal(read.to_pandas(), df) @pytest.mark.pandas def test_backwards_compatible_index_naming(datadir): expected_string = b"""\ carat cut color clarity depth table price x y z 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75 0.24 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48 0.24 Very Good I VVS1 62.3 57.0 336 3.95 3.98 2.47 0.26 Very Good H SI1 61.9 55.0 337 4.07 4.11 2.53 0.22 Fair E VS2 65.1 61.0 337 3.87 3.78 2.49 0.23 Very Good H VS1 59.4 61.0 338 4.00 4.05 2.39""" expected = pd.read_csv(io.BytesIO(expected_string), sep=r'\s{2,}', index_col=None, header=0, engine='python') table = _read_table(datadir / 'v0.7.1.parquet') result = table.to_pandas() tm.assert_frame_equal(result, expected) @pytest.mark.pandas def test_backwards_compatible_index_multi_level_named(datadir): expected_string = b"""\ carat cut color clarity depth table price x y z 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75 0.24 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48 0.24 Very Good I VVS1 62.3 57.0 336 3.95 3.98 2.47 0.26 Very Good H SI1 61.9 55.0 337 4.07 4.11 2.53 0.22 Fair E VS2 65.1 61.0 337 3.87 3.78 2.49 0.23 Very Good H VS1 59.4 61.0 338 4.00 4.05 2.39""" expected = pd.read_csv( io.BytesIO(expected_string), sep=r'\s{2,}', index_col=['cut', 'color', 'clarity'], header=0, engine='python' ).sort_index() table = _read_table(datadir / 'v0.7.1.all-named-index.parquet') result = table.to_pandas() tm.assert_frame_equal(result, expected) @pytest.mark.pandas def test_backwards_compatible_index_multi_level_some_named(datadir): expected_string = b"""\ carat cut color clarity depth table price x y z 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75 0.24 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48 0.24 Very Good I VVS1 62.3 57.0 336 3.95 3.98 2.47 0.26 Very Good H SI1 61.9 55.0 337 4.07 4.11 2.53 0.22 Fair E VS2 65.1 61.0 337 3.87 3.78 2.49 0.23 Very Good H VS1 59.4 61.0 338 4.00 4.05 2.39""" expected = pd.read_csv( io.BytesIO(expected_string), sep=r'\s{2,}', index_col=['cut', 'color', 'clarity'], header=0, engine='python' ).sort_index() expected.index = expected.index.set_names(['cut', None, 'clarity']) table = _read_table(datadir / 'v0.7.1.some-named-index.parquet') result = table.to_pandas() tm.assert_frame_equal(result, expected) @pytest.mark.pandas def test_backwards_compatible_column_metadata_handling(datadir): expected = pd.DataFrame( {'a': [1, 2, 3], 'b': [.1, .2, .3], 'c': pd.date_range("2017-01-01", periods=3, tz='Europe/Brussels')}) expected.index = pd.MultiIndex.from_arrays( [['a', 'b', 'c'], pd.date_range("2017-01-01", periods=3, tz='Europe/Brussels')], names=['index', None]) path = datadir / 'v0.7.1.column-metadata-handling.parquet' table = _read_table(path) result = table.to_pandas() tm.assert_frame_equal(result, expected) table = _read_table(path, columns=['a']) result = table.to_pandas() tm.assert_frame_equal(result, expected[['a']].reset_index(drop=True)) def _make_dataset_for_pickling(tempdir, N=100): path = tempdir / 'data.parquet' fs = LocalFileSystem.get_instance() df = pd.DataFrame({ 'index': np.arange(N), 'values': np.random.randn(N) }, columns=['index', 'values']) table = pa.Table.from_pandas(df) num_groups = 3 with pq.ParquetWriter(path, table.schema) as writer: for i in range(num_groups): writer.write_table(table) reader = pq.ParquetFile(path) assert reader.metadata.num_row_groups == num_groups metadata_path = tempdir / '_metadata' with fs.open(metadata_path, 'wb') as f: pq.write_metadata(table.schema, f) dataset = pq.ParquetDataset(tempdir, filesystem=fs) assert dataset.metadata_path == str(metadata_path) return dataset @pytest.mark.pandas @pytest.mark.parametrize('pickler', [ pytest.param(pickle, id='builtin'), pytest.param(pytest.importorskip('cloudpickle'), id='cloudpickle') ]) def test_pickle_dataset(tempdir, datadir, pickler): def is_pickleable(obj): return obj == pickler.loads(pickler.dumps(obj)) dataset = _make_dataset_for_pickling(tempdir) assert is_pickleable(dataset) assert is_pickleable(dataset.metadata) assert is_pickleable(dataset.metadata.schema) assert len(dataset.metadata.schema) for column in dataset.metadata.schema: assert is_pickleable(column) for piece in dataset.pieces: assert is_pickleable(piece) metadata = piece.get_metadata() assert metadata.num_row_groups for i in range(metadata.num_row_groups): assert is_pickleable(metadata.row_group(i)) @pytest.mark.pandas def test_decimal_roundtrip(tempdir): num_values = 10 columns = {} for precision in range(1, 39): for scale in range(0, precision + 1): with util.random_seed(0): random_decimal_values = [ util.randdecimal(precision, scale) for _ in range(num_values) ] column_name = ('dec_precision_{:d}_scale_{:d}' .format(precision, scale)) columns[column_name] = random_decimal_values expected = pd.DataFrame(columns) filename = tempdir / 'decimals.parquet' string_filename = str(filename) table = pa.Table.from_pandas(expected) _write_table(table, string_filename) result_table = _read_table(string_filename) result = result_table.to_pandas() tm.assert_frame_equal(result, expected) @pytest.mark.pandas @pytest.mark.xfail( raises=pa.ArrowException, reason='Parquet does not support negative scale' ) def test_decimal_roundtrip_negative_scale(tempdir): expected = pd.DataFrame({'decimal_num': [decimal.Decimal('1.23E4')]}) filename = tempdir / 'decimals.parquet' string_filename = str(filename) t = pa.Table.from_pandas(expected) _write_table(t, string_filename) result_table = _read_table(string_filename) result = result_table.to_pandas() tm.assert_frame_equal(result, expected) @pytest.mark.pandas def test_parquet_writer_context_obj(tempdir): df = _test_dataframe(100) df['unique_id'] = 0 arrow_table = pa.Table.from_pandas(df, preserve_index=False) out = pa.BufferOutputStream() with pq.ParquetWriter(out, arrow_table.schema, version='2.0') as writer: frames = [] for i in range(10): df['unique_id'] = i arrow_table = pa.Table.from_pandas(df, preserve_index=False) writer.write_table(arrow_table) frames.append(df.copy()) buf = out.getvalue() result = _read_table(pa.BufferReader(buf)) expected = pd.concat(frames, ignore_index=True) tm.assert_frame_equal(result.to_pandas(), expected) @pytest.mark.pandas def test_parquet_writer_context_obj_with_exception(tempdir): df = _test_dataframe(100) df['unique_id'] = 0 arrow_table = pa.Table.from_pandas(df, preserve_index=False) out = pa.BufferOutputStream() error_text = 'Artificial Error' try: with pq.ParquetWriter(out, arrow_table.schema, version='2.0') as writer: frames = [] for i in range(10): df['unique_id'] = i arrow_table = pa.Table.from_pandas(df, preserve_index=False) writer.write_table(arrow_table) frames.append(df.copy()) if i == 5: raise ValueError(error_text) except Exception as e: assert str(e) == error_text buf = out.getvalue() result = _read_table(pa.BufferReader(buf)) expected = pd.concat(frames, ignore_index=True) tm.assert_frame_equal(result.to_pandas(), expected) @pytest.mark.pandas def test_zlib_compression_bug(): # ARROW-3514: "zlib deflate failed, output buffer too small" table = pa.Table.from_arrays([pa.array(['abc', 'def'])], ['some_col']) f = io.BytesIO() pq.write_table(table, f, compression='gzip') f.seek(0) roundtrip = pq.read_table(f) tm.assert_frame_equal(roundtrip.to_pandas(), table.to_pandas()) @pytest.mark.pandas def test_merging_parquet_tables_with_different_pandas_metadata(tempdir): # ARROW-3728: Merging Parquet Files - Pandas Meta in Schema Mismatch schema = pa.schema([ pa.field('int', pa.int16()), pa.field('float', pa.float32()), pa.field('string', pa.string()) ]) df1 = pd.DataFrame({ 'int': np.arange(3, dtype=np.uint8), 'float': np.arange(3, dtype=np.float32), 'string': ['ABBA', 'EDDA', 'ACDC'] }) df2 = pd.DataFrame({ 'int': [4, 5], 'float': [1.1, None], 'string': [None, None] }) table1 = pa.Table.from_pandas(df1, schema=schema, preserve_index=False) table2 = pa.Table.from_pandas(df2, schema=schema, preserve_index=False) assert not table1.schema.equals(table2.schema) assert table1.schema.equals(table2.schema, check_metadata=False) writer = pq.ParquetWriter(tempdir / 'merged.parquet', schema=schema) writer.write_table(table1) writer.write_table(table2) def test_empty_row_groups(tempdir): # ARROW-3020 table = pa.Table.from_arrays([pa.array([], type='int32')], ['f0']) path = tempdir / 'empty_row_groups.parquet' num_groups = 3 with pq.ParquetWriter(path, table.schema) as writer: for i in range(num_groups): writer.write_table(table) reader = pq.ParquetFile(path) assert reader.metadata.num_row_groups == num_groups for i in range(num_groups): assert reader.read_row_group(i).equals(table) @pytest.mark.pandas def test_parquet_writer_with_caller_provided_filesystem(): out = pa.BufferOutputStream() class CustomFS(FileSystem): def __init__(self): self.path = None self.mode = None def open(self, path, mode='rb'): self.path = path self.mode = mode return out fs = CustomFS() fname = 'expected_fname.parquet' df = _test_dataframe(100) table = pa.Table.from_pandas(df, preserve_index=False) with pq.ParquetWriter(fname, table.schema, filesystem=fs, version='2.0') \ as writer: writer.write_table(table) assert fs.path == fname assert fs.mode == 'wb' assert out.closed buf = out.getvalue() table_read = _read_table(pa.BufferReader(buf)) df_read = table_read.to_pandas() tm.assert_frame_equal(df_read, df) # Should raise ValueError when filesystem is passed with file-like object with pytest.raises(ValueError) as err_info: pq.ParquetWriter(pa.BufferOutputStream(), table.schema, filesystem=fs) expected_msg = ("filesystem passed but where is file-like, so" " there is nothing to open with filesystem.") assert str(err_info) == expected_msg def test_writing_empty_lists(): # ARROW-2591: [Python] Segmentation fault issue in pq.write_table arr1 = pa.array([[], []], pa.list_(pa.int32())) table = pa.Table.from_arrays([arr1], ['list(int32)']) _check_roundtrip(table) def test_write_nested_zero_length_array_chunk_failure(): # Bug report in ARROW-3792 cols = OrderedDict( int32=pa.int32(), list_string=pa.list_(pa.string()) ) data = [[], [OrderedDict(int32=1, list_string=('G',)), ]] # This produces a table with a column like # <Column name='list_string' type=ListType(list<item: string>)> # [ # [], # [ # [ # "G" # ] # ] # ] # # Each column is a ChunkedArray with 2 elements my_arrays = [pa.array(batch, type=pa.struct(cols)).flatten() for batch in data] my_batches = [pa.RecordBatch.from_arrays(batch, pa.schema(cols)) for batch in my_arrays] tbl = pa.Table.from_batches(my_batches, pa.schema(cols)) _check_roundtrip(tbl) @pytest.mark.pandas def test_partitioned_dataset(tempdir): # ARROW-3208: Segmentation fault when reading a Parquet partitioned dataset # to a Parquet file path = tempdir / "ARROW-3208" df = pd.DataFrame({ 'one': [-1, 10, 2.5, 100, 1000, 1, 29.2], 'two': [-1, 10, 2, 100, 1000, 1, 11], 'three': [0, 0, 0, 0, 0, 0, 0] }) table = pa.Table.from_pandas(df) pq.write_to_dataset(table, root_path=str(path), partition_cols=['one', 'two']) table = pq.ParquetDataset(path).read() pq.write_table(table, path / "output.parquet") def test_read_column_invalid_index(): table = pa.table([pa.array([4, 5]), pa.array(["foo", "bar"])], names=['ints', 'strs']) bio = pa.BufferOutputStream() pq.write_table(table, bio) f = pq.ParquetFile(bio.getvalue()) assert f.reader.read_column(0).to_pylist() == [4, 5] assert f.reader.read_column(1).to_pylist() == ["foo", "bar"] for index in (-1, 2): with pytest.raises((ValueError, IndexError)): f.reader.read_column(index) def test_direct_read_dictionary(): # ARROW-3325 repeats = 10 nunique = 5 data = [ [tm.rands(10) for i in range(nunique)] * repeats, ] table = pa.table(data, names=['f0']) bio = pa.BufferOutputStream() pq.write_table(table, bio) contents = bio.getvalue() result = pq.read_table(pa.BufferReader(contents), read_dictionary=['f0']) # Compute dictionary-encoded subfield expected = pa.table([table[0].dictionary_encode()], names=['f0']) assert result.equals(expected) def test_dataset_read_dictionary(tempdir): path = tempdir / "ARROW-3325-dataset" t1 = pa.table([[tm.rands(10) for i in range(5)] * 10], names=['f0']) t2 = pa.table([[tm.rands(10) for i in range(5)] * 10], names=['f0']) pq.write_to_dataset(t1, root_path=str(path)) pq.write_to_dataset(t2, root_path=str(path)) result = pq.ParquetDataset(path, read_dictionary=['f0']).read() # The order of the chunks is non-deterministic ex_chunks = [t1[0].chunk(0).dictionary_encode(), t2[0].chunk(0).dictionary_encode()] assert result[0].num_chunks == 2 c0, c1 = result[0].chunk(0), result[0].chunk(1) if c0.equals(ex_chunks[0]): assert c1.equals(ex_chunks[1]) else: assert c0.equals(ex_chunks[1]) assert c1.equals(ex_chunks[0]) def test_direct_read_dictionary_subfield(): repeats = 10 nunique = 5 data = [ [[tm.rands(10)] for i in range(nunique)] * repeats, ] table = pa.table(data, names=['f0']) bio = pa.BufferOutputStream() pq.write_table(table, bio) contents = bio.getvalue() result = pq.read_table(pa.BufferReader(contents), read_dictionary=['f0.list.item']) arr = pa.array(data[0]) values_as_dict = arr.values.dictionary_encode() inner_indices = values_as_dict.indices.cast('int32') new_values = pa.DictionaryArray.from_arrays(inner_indices, values_as_dict.dictionary) offsets = pa.array(range(51), type='int32') expected_arr = pa.ListArray.from_arrays(offsets, new_values) expected = pa.table([expected_arr], names=['f0']) assert result.equals(expected) assert result[0].num_chunks == 1 @pytest.mark.pandas def test_dataset_metadata(tempdir): path = tempdir / "ARROW-1983-dataset" # create and write a test dataset df = pd.DataFrame({ 'one': [1, 2, 3], 'two': [-1, -2, -3], 'three': [[1, 2], [2, 3], [3, 4]], }) table = pa.Table.from_pandas(df) metadata_list = [] pq.write_to_dataset(table, root_path=str(path), partition_cols=['one', 'two'], metadata_collector=metadata_list) # open the dataset and collect metadata from pieces: dataset = pq.ParquetDataset(path) metadata_list2 = [p.get_metadata() for p in dataset.pieces] # compare metadata list content: assert len(metadata_list) == len(metadata_list2) for md, md2 in zip(metadata_list, metadata_list2): d = md.to_dict() d2 = md2.to_dict() # serialized_size is initialized in the reader: assert d.pop('serialized_size') == 0 assert d2.pop('serialized_size') > 0 assert d == d2 def test_parquet_file_too_small(tempdir): path = str(tempdir / "test.parquet") with pytest.raises(pa.ArrowIOError, match='size is 0 bytes'): with open(path, 'wb') as f: pass pq.read_table(path) with pytest.raises(pa.ArrowIOError, match='size is 4 bytes'): with open(path, 'wb') as f: f.write(b'ffff') pq.read_table(path) @pytest.mark.pandas def test_categorical_index_survives_roundtrip(): # ARROW-3652, addressed by ARROW-3246 df = pd.DataFrame([['a', 'b'], ['c', 'd']], columns=['c1', 'c2']) df['c1'] = df['c1'].astype('category') df = df.set_index(['c1']) table = pa.Table.from_pandas(df) bos = pa.BufferOutputStream() pq.write_table(table, bos) ref_df = pq.read_pandas(bos.getvalue()).to_pandas() assert isinstance(ref_df.index, pd.CategoricalIndex) assert ref_df.index.equals(df.index) def test_dictionary_array_automatically_read(): # ARROW-3246 # Make a large dictionary, a little over 4MB of data dict_length = 4000 dict_values = pa.array([('x' * 1000 + '_{}'.format(i)) for i in range(dict_length)]) num_chunks = 10 chunk_size = 100 chunks = [] for i in range(num_chunks): indices = np.random.randint(0, dict_length, size=chunk_size).astype(np.int32) chunks.append(pa.DictionaryArray.from_arrays(pa.array(indices), dict_values)) table = pa.table([pa.chunked_array(chunks)], names=['f0']) bio = pa.BufferOutputStream() pq.write_table(table, bio) contents = bio.getvalue() result = pq.read_table(pa.BufferReader(contents)) assert result.equals(table) # The only key in the metadata was the Arrow schema key assert result.schema.metadata is None @pytest.mark.pandas def test_pandas_categorical_na_type_row_groups(): # ARROW-5085 df = pd.DataFrame({"col": [None] * 100, "int": [1.0] * 100}) df_category = df.astype({"col": "category", "int": "category"}) table = pa.Table.from_pandas(df) table_cat = pa.Table.from_pandas(df_category) buf = pa.BufferOutputStream() # it works pq.write_table(table_cat, buf, version="2.0", chunk_size=10) result = pq.read_table(buf.getvalue()) # Result is non-categorical assert result[0].equals(table[0]) assert result[1].equals(table[1]) @pytest.mark.pandas def test_pandas_categorical_roundtrip(): # ARROW-5480, this was enabled by ARROW-3246 # Have one of the categories unobserved and include a null (-1) codes = np.array([2, 0, 0, 2, 0, -1, 2], dtype='int32') categories = ['foo', 'bar', 'baz'] df = pd.DataFrame({'x': pd.Categorical.from_codes( codes, categories=categories)}) buf = pa.BufferOutputStream() pq.write_table(pa.table(df), buf) result = pq.read_table(buf.getvalue()).to_pandas() assert result.x.dtype == 'category' assert (result.x.cat.categories == categories).all() tm.assert_frame_equal(result, df) @pytest.mark.pandas def test_multi_dataset_metadata(tempdir): filenames = ["ARROW-1983-dataset.0", "ARROW-1983-dataset.1"] metapath = str(tempdir / "_metadata") # create a test dataset df = pd.DataFrame({ 'one': [1, 2, 3], 'two': [-1, -2, -3], 'three': [[1, 2], [2, 3], [3, 4]], }) table = pa.Table.from_pandas(df) # write dataset twice and collect/merge metadata _meta = None for filename in filenames: meta = [] pq.write_table(table, str(tempdir / filename), metadata_collector=meta) meta[0].set_file_path(filename) if _meta is None: _meta = meta[0] else: _meta.append_row_groups(meta[0]) # Write merged metadata-only file with open(metapath, "wb") as f: _meta.write_metadata_file(f) # Read back the metadata meta = pq.read_metadata(metapath) md = meta.to_dict() _md = _meta.to_dict() for key in _md: if key != 'serialized_size': assert _md[key] == md[key] assert _md['num_columns'] == 3 assert _md['num_rows'] == 6 assert _md['num_row_groups'] == 2 assert _md['serialized_size'] == 0 assert md['serialized_size'] > 0 @pytest.mark.pandas def test_filter_before_validate_schema(tempdir): # ARROW-4076 apply filter before schema validation # to avoid checking unneeded schemas # create partitioned dataset with mismatching schemas which would # otherwise raise if first validation all schemas dir1 = tempdir / 'A=0' dir1.mkdir() table1 = pa.Table.from_pandas(pd.DataFrame({'B': [1, 2, 3]})) pq.write_table(table1, dir1 / 'data.parquet') dir2 = tempdir / 'A=1' dir2.mkdir() table2 = pa.Table.from_pandas(pd.DataFrame({'B': ['a', 'b', 'c']})) pq.write_table(table2, dir2 / 'data.parquet') # read single file using filter table = pq.read_table(tempdir, filters=[[('A', '==', 0)]]) assert table.column('B').equals(pa.chunked_array([[1, 2, 3]]))

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from collections import OrderedDict import datetime import decimal import io import json import os import six import pickle import pytest import numpy as np import pyarrow as pa from pyarrow.compat import guid, u, BytesIO, unichar, PY2 from pyarrow.pandas_compat import _pandas_api from pyarrow.tests import util from pyarrow.filesystem import LocalFileSystem, FileSystem try: import pyarrow.parquet as pq except ImportError: pq = None try: import pandas as pd import pandas.util.testing as tm from .pandas_examples import dataframe_with_arrays, dataframe_with_lists except ImportError: pd = tm = None pytestmark = pytest.mark.parquet @pytest.fixture(scope='module') def datadir(datadir): return datadir / 'parquet' def _write_table(table, path, **kwargs): import pyarrow.parquet as pq if _pandas_api.is_data_frame(table): table = pa.Table.from_pandas(table) pq.write_table(table, path, **kwargs) return table def _read_table(*args, **kwargs): return pq.read_table(*args, **kwargs) def _roundtrip_table(table, read_table_kwargs=None, write_table_kwargs=None): read_table_kwargs = read_table_kwargs or {} write_table_kwargs = write_table_kwargs or {} buf = io.BytesIO() _write_table(table, buf, **write_table_kwargs) buf.seek(0) return _read_table(buf, **read_table_kwargs) def _check_roundtrip(table, expected=None, read_table_kwargs=None, **write_table_kwargs): if expected is None: expected = table read_table_kwargs = read_table_kwargs or {} result = _roundtrip_table(table, read_table_kwargs=read_table_kwargs, write_table_kwargs=write_table_kwargs) assert result.equals(expected) result = _roundtrip_table(result, read_table_kwargs=read_table_kwargs, write_table_kwargs=write_table_kwargs) assert result.equals(expected) def _roundtrip_pandas_dataframe(df, write_kwargs): table = pa.Table.from_pandas(df) buf = io.BytesIO() _write_table(table, buf, **write_kwargs) buf.seek(0) table1 = _read_table(buf) return table1.to_pandas() @pytest.mark.parametrize('dtype', [int, float]) def test_single_pylist_column_roundtrip(tempdir, dtype): filename = tempdir / 'single_{}_column.parquet'.format(dtype.__name__) data = [pa.array(list(map(dtype, range(5))))] table = pa.Table.from_arrays(data, names=['a']) _write_table(table, filename) table_read = _read_table(filename) for i in range(table.num_columns): col_written = table[i] col_read = table_read[i] assert table.field(i).name == table_read.field(i).name assert col_read.num_chunks == 1 data_written = col_written.chunk(0) data_read = col_read.chunk(0) assert data_written.equals(data_read) def alltypes_sample(size=10000, seed=0, categorical=False): np.random.seed(seed) arrays = { 'uint8': np.arange(size, dtype=np.uint8), 'uint16': np.arange(size, dtype=np.uint16), 'uint32': np.arange(size, dtype=np.uint32), 'uint64': np.arange(size, dtype=np.uint64), 'int8': np.arange(size, dtype=np.int16), 'int16': np.arange(size, dtype=np.int16), 'int32': np.arange(size, dtype=np.int32), 'int64': np.arange(size, dtype=np.int64), 'float32': np.arange(size, dtype=np.float32), 'float64': np.arange(size, dtype=np.float64), 'bool': np.random.randn(size) > 0, 'datetime': np.arange("2016-01-01T00:00:00.001", size, dtype='datetime64[ms]'), 'str': pd.Series([str(x) for x in range(size)]), 'empty_str': [''] * size, 'str_with_nulls': [None] + [str(x) for x in range(size - 2)] + [None], 'null': [None] * size, 'null_list': [None] * 2 + [[None] * (x % 4) for x in range(size - 2)], } if categorical: arrays['str_category'] = arrays['str'].astype('category') return pd.DataFrame(arrays) @pytest.mark.pandas @pytest.mark.parametrize('chunk_size', [None, 1000]) def test_pandas_parquet_2_0_roundtrip(tempdir, chunk_size): df = alltypes_sample(size=10000, categorical=True) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df) assert arrow_table.schema.pandas_metadata is not None _write_table(arrow_table, filename, version="2.0", coerce_timestamps='ms', chunk_size=chunk_size) table_read = pq.read_pandas(filename) assert table_read.schema.pandas_metadata is not None assert arrow_table.schema.metadata == table_read.schema.metadata df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) def test_set_data_page_size(): arr = pa.array([1, 2, 3] * 1000000) t = pa.Table.from_arrays([arr], names=['f0']) page_sizes = [2 << 16, 2 << 17, 2 << 18] for target_page_size in page_sizes: _check_roundtrip(t, data_page_size=target_page_size) @pytest.mark.pandas def test_chunked_table_write(): df = alltypes_sample(size=10) batch = pa.RecordBatch.from_pandas(df) table = pa.Table.from_batches([batch] * 3) _check_roundtrip(table, version='2.0') df, _ = dataframe_with_lists() batch = pa.RecordBatch.from_pandas(df) table = pa.Table.from_batches([batch] * 3) _check_roundtrip(table, version='2.0') @pytest.mark.pandas def test_no_memory_map(tempdir): df = alltypes_sample(size=10) table = pa.Table.from_pandas(df) _check_roundtrip(table, read_table_kwargs={'memory_map': False}, version='2.0') filename = str(tempdir / 'tmp_file') with open(filename, 'wb') as f: _write_table(table, f, version='2.0') table_read = pq.read_pandas(filename, memory_map=False) assert table_read.equals(table) def test_special_chars_filename(tempdir): table = pa.Table.from_arrays([pa.array([42])], ["ints"]) filename = "foo # bar" path = tempdir / filename assert not path.exists() _write_table(table, str(path)) assert path.exists() table_read = _read_table(str(path)) assert table_read.equals(table) @pytest.mark.pandas def test_empty_table_roundtrip(): df = alltypes_sample(size=10) table = pa.Table.from_pandas(df) table = pa.Table.from_arrays( [col.chunk(0)[:0] for col in table.itercolumns()], names=table.schema.names) assert table.schema.field_by_name('null').type == pa.null() assert table.schema.field_by_name('null_list').type == pa.list_(pa.null()) _check_roundtrip(table, version='2.0') @pytest.mark.pandas def test_empty_table_no_columns(): df = pd.DataFrame() empty = pa.Table.from_pandas(df, preserve_index=False) _check_roundtrip(empty) def test_empty_lists_table_roundtrip(): arr = pa.array([[], []], type=pa.list_(pa.int32())) table = pa.Table.from_arrays([arr], ["A"]) _check_roundtrip(table) @pytest.mark.pandas def test_pandas_parquet_datetime_tz(): s = pd.Series([datetime.datetime(2017, 9, 6)]) s = s.dt.tz_localize('utc') s.index = s # Both a column and an index to hit both use cases df = pd.DataFrame({'tz_aware': s, 'tz_eastern': s.dt.tz_convert('US/Eastern')}, index=s) f = BytesIO() arrow_table = pa.Table.from_pandas(df) _write_table(arrow_table, f, coerce_timestamps='ms') f.seek(0) table_read = pq.read_pandas(f) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas @pytest.mark.skipif(six.PY2, reason='datetime.timezone is available since ' 'python version 3.2') def test_datetime_timezone_tzinfo(): value = datetime.datetime(2018, 1, 1, 1, 23, 45, tzinfo=datetime.timezone.utc) df = pd.DataFrame({'foo': [value]}) _roundtrip_pandas_dataframe(df, write_kwargs={}) @pytest.mark.pandas def test_pandas_parquet_custom_metadata(tempdir): df = alltypes_sample(size=10000) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df) assert b'pandas' in arrow_table.schema.metadata _write_table(arrow_table, filename, version='2.0', coerce_timestamps='ms') metadata = pq.read_metadata(filename).metadata assert b'pandas' in metadata js = json.loads(metadata[b'pandas'].decode('utf8')) assert js['index_columns'] == [{'kind': 'range', 'name': None, 'start': 0, 'stop': 10000, 'step': 1}] @pytest.mark.pandas def test_pandas_parquet_column_multiindex(tempdir): df = alltypes_sample(size=10) df.columns = pd.MultiIndex.from_tuples( list(zip(df.columns, df.columns[::-1])), names=['level_1', 'level_2'] ) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df) assert arrow_table.schema.pandas_metadata is not None _write_table(arrow_table, filename, version='2.0', coerce_timestamps='ms') table_read = pq.read_pandas(filename) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_pandas_parquet_2_0_roundtrip_read_pandas_no_index_written(tempdir): df = alltypes_sample(size=10000) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df, preserve_index=False) js = arrow_table.schema.pandas_metadata assert not js['index_columns'] # ARROW-2170 # While index_columns should be empty, columns needs to be filled still. assert js['columns'] _write_table(arrow_table, filename, version='2.0', coerce_timestamps='ms') table_read = pq.read_pandas(filename) js = table_read.schema.pandas_metadata assert not js['index_columns'] assert arrow_table.schema.metadata == table_read.schema.metadata df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_pandas_parquet_1_0_roundtrip(tempdir): size = 10000 np.random.seed(0) df = pd.DataFrame({ 'uint8': np.arange(size, dtype=np.uint8), 'uint16': np.arange(size, dtype=np.uint16), 'uint32': np.arange(size, dtype=np.uint32), 'uint64': np.arange(size, dtype=np.uint64), 'int8': np.arange(size, dtype=np.int16), 'int16': np.arange(size, dtype=np.int16), 'int32': np.arange(size, dtype=np.int32), 'int64': np.arange(size, dtype=np.int64), 'float32': np.arange(size, dtype=np.float32), 'float64': np.arange(size, dtype=np.float64), 'bool': np.random.randn(size) > 0, 'str': [str(x) for x in range(size)], 'str_with_nulls': [None] + [str(x) for x in range(size - 2)] + [None], 'empty_str': [''] * size }) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df) _write_table(arrow_table, filename, version='1.0') table_read = _read_table(filename) df_read = table_read.to_pandas() # We pass uint32_t as int64_t if we write Parquet version 1.0 df['uint32'] = df['uint32'].values.astype(np.int64) tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_multiple_path_types(tempdir): # Test compatibility with PEP 519 path-like objects path = tempdir / 'zzz.parquet' df = pd.DataFrame({'x': np.arange(10, dtype=np.int64)}) _write_table(df, path) table_read = _read_table(path) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) # Test compatibility with plain string paths path = str(tempdir) + 'zzz.parquet' df = pd.DataFrame({'x': np.arange(10, dtype=np.int64)}) _write_table(df, path) table_read = _read_table(path) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_pandas_column_selection(tempdir): size = 10000 np.random.seed(0) df = pd.DataFrame({ 'uint8': np.arange(size, dtype=np.uint8), 'uint16': np.arange(size, dtype=np.uint16) }) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df) _write_table(arrow_table, filename) table_read = _read_table(filename, columns=['uint8']) df_read = table_read.to_pandas() tm.assert_frame_equal(df[['uint8']], df_read) # ARROW-4267: Selection of duplicate columns still leads to these columns # being read uniquely. table_read = _read_table(filename, columns=['uint8', 'uint8']) df_read = table_read.to_pandas() tm.assert_frame_equal(df[['uint8']], df_read) def _random_integers(size, dtype): # We do not generate integers outside the int64 range platform_int_info = np.iinfo('int_') iinfo = np.iinfo(dtype) return np.random.randint(max(iinfo.min, platform_int_info.min), min(iinfo.max, platform_int_info.max), size=size).astype(dtype) def _test_dataframe(size=10000, seed=0): np.random.seed(seed) df = pd.DataFrame({ 'uint8': _random_integers(size, np.uint8), 'uint16': _random_integers(size, np.uint16), 'uint32': _random_integers(size, np.uint32), 'uint64': _random_integers(size, np.uint64), 'int8': _random_integers(size, np.int8), 'int16': _random_integers(size, np.int16), 'int32': _random_integers(size, np.int32), 'int64': _random_integers(size, np.int64), 'float32': np.random.randn(size).astype(np.float32), 'float64': np.arange(size, dtype=np.float64), 'bool': np.random.randn(size) > 0, 'strings': [tm.rands(10) for i in range(size)], 'all_none': [None] * size, 'all_none_category': [None] * size }) # TODO(PARQUET-1015) # df['all_none_category'] = df['all_none_category'].astype('category') return df @pytest.mark.pandas def test_pandas_parquet_native_file_roundtrip(tempdir): df = _test_dataframe(10000) arrow_table = pa.Table.from_pandas(df) imos = pa.BufferOutputStream() _write_table(arrow_table, imos, version="2.0") buf = imos.getvalue() reader = pa.BufferReader(buf) df_read = _read_table(reader).to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_parquet_incremental_file_build(tempdir): df = _test_dataframe(100) df['unique_id'] = 0 arrow_table = pa.Table.from_pandas(df, preserve_index=False) out = pa.BufferOutputStream() writer = pq.ParquetWriter(out, arrow_table.schema, version='2.0') frames = [] for i in range(10): df['unique_id'] = i arrow_table = pa.Table.from_pandas(df, preserve_index=False) writer.write_table(arrow_table) frames.append(df.copy()) writer.close() buf = out.getvalue() result = _read_table(pa.BufferReader(buf)) expected = pd.concat(frames, ignore_index=True) tm.assert_frame_equal(result.to_pandas(), expected) @pytest.mark.pandas def test_read_pandas_column_subset(tempdir): df = _test_dataframe(10000) arrow_table = pa.Table.from_pandas(df) imos = pa.BufferOutputStream() _write_table(arrow_table, imos, version="2.0") buf = imos.getvalue() reader = pa.BufferReader(buf) df_read = pq.read_pandas(reader, columns=['strings', 'uint8']).to_pandas() tm.assert_frame_equal(df[['strings', 'uint8']], df_read) @pytest.mark.pandas def test_pandas_parquet_empty_roundtrip(tempdir): df = _test_dataframe(0) arrow_table = pa.Table.from_pandas(df) imos = pa.BufferOutputStream() _write_table(arrow_table, imos, version="2.0") buf = imos.getvalue() reader = pa.BufferReader(buf) df_read = _read_table(reader).to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_pandas_parquet_pyfile_roundtrip(tempdir): filename = tempdir / 'pandas_pyfile_roundtrip.parquet' size = 5 df = pd.DataFrame({ 'int64': np.arange(size, dtype=np.int64), 'float32': np.arange(size, dtype=np.float32), 'float64': np.arange(size, dtype=np.float64), 'bool': np.random.randn(size) > 0, 'strings': ['foo', 'bar', None, 'baz', 'qux'] }) arrow_table = pa.Table.from_pandas(df) with filename.open('wb') as f: _write_table(arrow_table, f, version="1.0") data = io.BytesIO(filename.read_bytes()) table_read = _read_table(data) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_pandas_parquet_configuration_options(tempdir): size = 10000 np.random.seed(0) df = pd.DataFrame({ 'uint8': np.arange(size, dtype=np.uint8), 'uint16': np.arange(size, dtype=np.uint16), 'uint32': np.arange(size, dtype=np.uint32), 'uint64': np.arange(size, dtype=np.uint64), 'int8': np.arange(size, dtype=np.int16), 'int16': np.arange(size, dtype=np.int16), 'int32': np.arange(size, dtype=np.int32), 'int64': np.arange(size, dtype=np.int64), 'float32': np.arange(size, dtype=np.float32), 'float64': np.arange(size, dtype=np.float64), 'bool': np.random.randn(size) > 0 }) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df) for use_dictionary in [True, False]: _write_table(arrow_table, filename, version='2.0', use_dictionary=use_dictionary) table_read = _read_table(filename) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) for write_statistics in [True, False]: _write_table(arrow_table, filename, version='2.0', write_statistics=write_statistics) table_read = _read_table(filename) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) for compression in ['NONE', 'SNAPPY', 'GZIP', 'LZ4', 'ZSTD']: _write_table(arrow_table, filename, version='2.0', compression=compression) table_read = _read_table(filename) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) def make_sample_file(table_or_df): if isinstance(table_or_df, pa.Table): a_table = table_or_df else: a_table = pa.Table.from_pandas(table_or_df) buf = io.BytesIO() _write_table(a_table, buf, compression='SNAPPY', version='2.0', coerce_timestamps='ms') buf.seek(0) return pq.ParquetFile(buf) @pytest.mark.pandas def test_parquet_metadata_api(): df = alltypes_sample(size=10000) df = df.reindex(columns=sorted(df.columns)) df.index = np.random.randint(0, 1000000, size=len(df)) fileh = make_sample_file(df) ncols = len(df.columns) # Series of sniff tests meta = fileh.metadata repr(meta) assert meta.num_rows == len(df) assert meta.num_columns == ncols + 1 # +1 for index assert meta.num_row_groups == 1 assert meta.format_version == '2.0' assert 'parquet-cpp' in meta.created_by assert isinstance(meta.serialized_size, int) assert isinstance(meta.metadata, dict) # Schema schema = fileh.schema assert meta.schema is schema assert len(schema) == ncols + 1 # +1 for index repr(schema) col = schema[0] repr(col) assert col.name == df.columns[0] assert col.max_definition_level == 1 assert col.max_repetition_level == 0 assert col.max_repetition_level == 0 assert col.physical_type == 'BOOLEAN' assert col.converted_type == 'NONE' with pytest.raises(IndexError): schema[ncols + 1] # +1 for index with pytest.raises(IndexError): schema[-1] # Row group for rg in range(meta.num_row_groups): rg_meta = meta.row_group(rg) assert isinstance(rg_meta, pq.RowGroupMetaData) repr(rg_meta) for col in range(rg_meta.num_columns): col_meta = rg_meta.column(col) assert isinstance(col_meta, pq.ColumnChunkMetaData) repr(col_meta) with pytest.raises(IndexError): meta.row_group(-1) with pytest.raises(IndexError): meta.row_group(meta.num_row_groups + 1) rg_meta = meta.row_group(0) assert rg_meta.num_rows == len(df) assert rg_meta.num_columns == ncols + 1 # +1 for index assert rg_meta.total_byte_size > 0 with pytest.raises(IndexError): col_meta = rg_meta.column(-1) with pytest.raises(IndexError): col_meta = rg_meta.column(ncols + 2) col_meta = rg_meta.column(0) assert col_meta.file_offset > 0 assert col_meta.file_path == '' # created from BytesIO assert col_meta.physical_type == 'BOOLEAN' assert col_meta.num_values == 10000 assert col_meta.path_in_schema == 'bool' assert col_meta.is_stats_set is True assert isinstance(col_meta.statistics, pq.Statistics) assert col_meta.compression == 'SNAPPY' assert col_meta.encodings == ('PLAIN', 'RLE') assert col_meta.has_dictionary_page is False assert col_meta.dictionary_page_offset is None assert col_meta.data_page_offset > 0 assert col_meta.total_compressed_size > 0 assert col_meta.total_uncompressed_size > 0 with pytest.raises(NotImplementedError): col_meta.has_index_page with pytest.raises(NotImplementedError): col_meta.index_page_offset @pytest.mark.pandas @pytest.mark.parametrize( ( 'data', 'type', 'physical_type', 'min_value', 'max_value', 'null_count', 'num_values', 'distinct_count' ), [ ([1, 2, 2, None, 4], pa.uint8(), 'INT32', 1, 4, 1, 4, 0), ([1, 2, 2, None, 4], pa.uint16(), 'INT32', 1, 4, 1, 4, 0), ([1, 2, 2, None, 4], pa.uint32(), 'INT32', 1, 4, 1, 4, 0), ([1, 2, 2, None, 4], pa.uint64(), 'INT64', 1, 4, 1, 4, 0), ([-1, 2, 2, None, 4], pa.int8(), 'INT32', -1, 4, 1, 4, 0), ([-1, 2, 2, None, 4], pa.int16(), 'INT32', -1, 4, 1, 4, 0), ([-1, 2, 2, None, 4], pa.int32(), 'INT32', -1, 4, 1, 4, 0), ([-1, 2, 2, None, 4], pa.int64(), 'INT64', -1, 4, 1, 4, 0), ( [-1.1, 2.2, 2.3, None, 4.4], pa.float32(), 'FLOAT', -1.1, 4.4, 1, 4, 0 ), ( [-1.1, 2.2, 2.3, None, 4.4], pa.float64(), 'DOUBLE', -1.1, 4.4, 1, 4, 0 ), ( [u'', u'b', unichar(1000), None, u'aaa'], pa.binary(), 'BYTE_ARRAY', b'', unichar(1000).encode('utf-8'), 1, 4, 0 ), ( [True, False, False, True, True], pa.bool_(), 'BOOLEAN', False, True, 0, 5, 0 ), ( [b'\x00', b'b', b'12', None, b'aaa'], pa.binary(), 'BYTE_ARRAY', b'\x00', b'b', 1, 4, 0 ), ] ) def test_parquet_column_statistics_api(data, type, physical_type, min_value, max_value, null_count, num_values, distinct_count): df = pd.DataFrame({'data': data}) schema = pa.schema([pa.field('data', type)]) table = pa.Table.from_pandas(df, schema=schema, safe=False) fileh = make_sample_file(table) meta = fileh.metadata rg_meta = meta.row_group(0) col_meta = rg_meta.column(0) stat = col_meta.statistics assert stat.has_min_max assert _close(type, stat.min, min_value) assert _close(type, stat.max, max_value) assert stat.null_count == null_count assert stat.num_values == num_values # TODO(kszucs) until parquet-cpp API doesn't expose HasDistinctCount assert stat.distinct_count == distinct_count assert stat.physical_type == physical_type def _close(type, left, right): if type == pa.float32(): return abs(left - right) < 1E-7 elif type == pa.float64(): return abs(left - right) < 1E-13 else: return left == right def test_statistics_convert_logical_types(tempdir): cases = [(10, 11164359321221007157, pa.uint64()), (10, 4294967295, pa.uint32()), (u"ähnlich", u"öffentlich", pa.utf8()), (datetime.time(10, 30, 0, 1000), datetime.time(15, 30, 0, 1000), pa.time32('ms')), (datetime.time(10, 30, 0, 1000), datetime.time(15, 30, 0, 1000), pa.time64('us')), (datetime.datetime(2019, 6, 24, 0, 0, 0, 1000), datetime.datetime(2019, 6, 25, 0, 0, 0, 1000), pa.timestamp('ms')), (datetime.datetime(2019, 6, 24, 0, 0, 0, 1000), datetime.datetime(2019, 6, 25, 0, 0, 0, 1000), pa.timestamp('us'))] for i, (min_val, max_val, typ) in enumerate(cases): t = pa.Table.from_arrays([pa.array([min_val, max_val], type=typ)], ['col']) path = str(tempdir / ('example{}.parquet'.format(i))) pq.write_table(t, path, version='2.0') pf = pq.ParquetFile(path) stats = pf.metadata.row_group(0).column(0).statistics assert stats.min == min_val assert stats.max == max_val def test_parquet_write_disable_statistics(tempdir): table = pa.Table.from_pydict( {'a': pa.array([1, 2, 3]), 'b': pa.array(['a', 'b', 'c'])}) _write_table(table, tempdir / 'data.parquet') meta = pq.read_metadata(tempdir / 'data.parquet') for col in [0, 1]: cc = meta.row_group(0).column(col) assert cc.is_stats_set is True assert cc.statistics is not None _write_table(table, tempdir / 'data2.parquet', write_statistics=False) meta = pq.read_metadata(tempdir / 'data2.parquet') for col in [0, 1]: cc = meta.row_group(0).column(col) assert cc.is_stats_set is False assert cc.statistics is None _write_table(table, tempdir / 'data3.parquet', write_statistics=['a']) meta = pq.read_metadata(tempdir / 'data3.parquet') cc_a = meta.row_group(0).column(0) assert cc_a.is_stats_set is True assert cc_a.statistics is not None cc_b = meta.row_group(0).column(1) assert cc_b.is_stats_set is False assert cc_b.statistics is None @pytest.mark.pandas def test_compare_schemas(): df = alltypes_sample(size=10000) fileh = make_sample_file(df) fileh2 = make_sample_file(df) fileh3 = make_sample_file(df[df.columns[::2]]) assert isinstance(fileh.schema, pq.ParquetSchema) assert fileh.schema.equals(fileh.schema) assert fileh.schema == fileh.schema assert fileh.schema.equals(fileh2.schema) assert fileh.schema == fileh2.schema assert fileh.schema != 'arbitrary object' assert not fileh.schema.equals(fileh3.schema) assert fileh.schema != fileh3.schema assert isinstance(fileh.schema[0], pq.ColumnSchema) assert fileh.schema[0].equals(fileh.schema[0]) assert fileh.schema[0] == fileh.schema[0] assert not fileh.schema[0].equals(fileh.schema[1]) assert fileh.schema[0] != fileh.schema[1] assert fileh.schema[0] != 'arbitrary object' def test_validate_schema_write_table(tempdir): simple_fields = [ pa.field('POS', pa.uint32()), pa.field('desc', pa.string()) ] simple_schema = pa.schema(simple_fields) simple_from_array = [pa.array([1]), pa.array(['bla'])] simple_table = pa.Table.from_arrays(simple_from_array, ['POS', 'desc']) path = tempdir / 'simple_validate_schema.parquet' with pq.ParquetWriter(path, simple_schema, version='2.0', compression='snappy', flavor='spark') as w: with pytest.raises(ValueError): w.write_table(simple_table) @pytest.mark.pandas def test_column_of_arrays(tempdir): df, schema = dataframe_with_arrays() filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df, schema=schema) _write_table(arrow_table, filename, version="2.0", coerce_timestamps='ms') table_read = _read_table(filename) df_read = table_read.to_pandas() tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_coerce_timestamps(tempdir): from collections import OrderedDict arrays = OrderedDict() fields = [pa.field('datetime64', pa.list_(pa.timestamp('ms')))] arrays['datetime64'] = [ np.array(['2007-07-13T01:23:34.123456789', None, '2010-08-13T05:46:57.437699912'], dtype='datetime64[ms]'), None, None, np.array(['2007-07-13T02', None, '2010-08-13T05:46:57.437699912'], dtype='datetime64[ms]'), ] df = pd.DataFrame(arrays) schema = pa.schema(fields) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df, schema=schema) _write_table(arrow_table, filename, version="2.0", coerce_timestamps='us') table_read = _read_table(filename) df_read = table_read.to_pandas() df_expected = df.copy() for i, x in enumerate(df_expected['datetime64']): if isinstance(x, np.ndarray): df_expected['datetime64'][i] = x.astype('M8[us]') tm.assert_frame_equal(df_expected, df_read) with pytest.raises(ValueError): _write_table(arrow_table, filename, version='2.0', coerce_timestamps='unknown') @pytest.mark.pandas def test_coerce_timestamps_truncated(tempdir): dt_us = datetime.datetime(year=2017, month=1, day=1, hour=1, minute=1, second=1, microsecond=1) dt_ms = datetime.datetime(year=2017, month=1, day=1, hour=1, minute=1, second=1) fields_us = [pa.field('datetime64', pa.timestamp('us'))] arrays_us = {'datetime64': [dt_us, dt_ms]} df_us = pd.DataFrame(arrays_us) schema_us = pa.schema(fields_us) filename = tempdir / 'pandas_truncated.parquet' table_us = pa.Table.from_pandas(df_us, schema=schema_us) _write_table(table_us, filename, version="2.0", coerce_timestamps='ms', allow_truncated_timestamps=True) table_ms = _read_table(filename) df_ms = table_ms.to_pandas() arrays_expected = {'datetime64': [dt_ms, dt_ms]} df_expected = pd.DataFrame(arrays_expected) tm.assert_frame_equal(df_expected, df_ms) @pytest.mark.pandas def test_column_of_lists(tempdir): df, schema = dataframe_with_lists(parquet_compatible=True) filename = tempdir / 'pandas_roundtrip.parquet' arrow_table = pa.Table.from_pandas(df, schema=schema) _write_table(arrow_table, filename, version='2.0') table_read = _read_table(filename) df_read = table_read.to_pandas() if PY2: for col in ['date32[day]_list', 'date64[ms]_list']: df[col] = df[col].apply( lambda x: list(map(np.datetime64, x)) if x else x ) tm.assert_frame_equal(df, df_read) @pytest.mark.pandas def test_date_time_types(tempdir): t1 = pa.date32() data1 = np.array([17259, 17260, 17261], dtype='int32') a1 = pa.array(data1, type=t1) t2 = pa.date64() data2 = data1.astype('int64') * 86400000 a2 = pa.array(data2, type=t2) t3 = pa.timestamp('us') start = pd.Timestamp('2001-01-01').value / 1000 data3 = np.array([start, start + 1, start + 2], dtype='int64') a3 = pa.array(data3, type=t3) t4 = pa.time32('ms') data4 = np.arange(3, dtype='i4') a4 = pa.array(data4, type=t4) t5 = pa.time64('us') a5 = pa.array(data4.astype('int64'), type=t5) t6 = pa.time32('s') a6 = pa.array(data4, type=t6) ex_t6 = pa.time32('ms') ex_a6 = pa.array(data4 * 1000, type=ex_t6) t7 = pa.timestamp('ns') start = pd.Timestamp('2001-01-01').value data7 = np.array([start, start + 1000, start + 2000], dtype='int64') a7 = pa.array(data7, type=t7) table = pa.Table.from_arrays([a1, a2, a3, a4, a5, a6, a7], ['date32', 'date64', 'timestamp[us]', 'time32[s]', 'time64[us]', 'time32_from64[s]', 'timestamp[ns]']) expected = pa.Table.from_arrays([a1, a1, a3, a4, a5, ex_a6, a7], ['date32', 'date64', 'timestamp[us]', 'time32[s]', 'time64[us]', 'time32_from64[s]', 'timestamp[ns]']) _check_roundtrip(table, expected=expected, version='2.0') t0 = pa.timestamp('ms') data0 = np.arange(4, dtype='int64') a0 = pa.array(data0, type=t0) t1 = pa.timestamp('us') data1 = np.arange(4, dtype='int64') a1 = pa.array(data1, type=t1) t2 = pa.timestamp('ns') data2 = np.arange(4, dtype='int64') a2 = pa.array(data2, type=t2) table = pa.Table.from_arrays([a0, a1, a2], ['ts[ms]', 'ts[us]', 'ts[ns]']) expected = pa.Table.from_arrays([a0, a1, a2], ['ts[ms]', 'ts[us]', 'ts[ns]']) filename = tempdir / 'int64_timestamps.parquet' _write_table(table, filename, version='2.0') parquet_schema = pq.ParquetFile(filename).schema for i in range(3): assert parquet_schema.column(i).physical_type == 'INT64' read_table = _read_table(filename) assert read_table.equals(expected) t0_ns = pa.timestamp('ns') data0_ns = np.array(data0 * 1000000, dtype='int64') a0_ns = pa.array(data0_ns, type=t0_ns) t1_ns = pa.timestamp('ns') data1_ns = np.array(data1 * 1000, dtype='int64') a1_ns = pa.array(data1_ns, type=t1_ns) expected = pa.Table.from_arrays([a0_ns, a1_ns, a2], ['ts[ms]', 'ts[us]', 'ts[ns]']) filename = tempdir / 'explicit_int96_timestamps.parquet' _write_table(table, filename, version='2.0', use_deprecated_int96_timestamps=True) parquet_schema = pq.ParquetFile(filename).schema for i in range(3): assert parquet_schema.column(i).physical_type == 'INT96' read_table = _read_table(filename) assert read_table.equals(expected) filename = tempdir / 'spark_int96_timestamps.parquet' _write_table(table, filename, version='2.0', flavor='spark') parquet_schema = pq.ParquetFile(filename).schema for i in range(3): assert parquet_schema.column(i).physical_type == 'INT96' read_table = _read_table(filename) assert read_table.equals(expected) def test_timestamp_restore_timezone(): ty = pa.timestamp('ms', tz='America/New_York') arr = pa.array([1, 2, 3], type=ty) t = pa.table([arr], names=['f0']) _check_roundtrip(t) @pytest.mark.pandas def test_list_of_datetime_time_roundtrip(): times = pd.to_datetime(['09:00', '09:30', '10:00', '10:30', '11:00', '11:30', '12:00']) df = pd.DataFrame({'time': [times.time]}) _roundtrip_pandas_dataframe(df, write_kwargs={}) @pytest.mark.pandas def test_parquet_version_timestamp_differences(): i_s = pd.Timestamp('2010-01-01').value / 1000000000 d_s = np.arange(i_s, i_s + 10, 1, dtype='int64') d_ms = d_s * 1000 d_us = d_ms * 1000 d_ns = d_us * 1000 a_s = pa.array(d_s, type=pa.timestamp('s')) a_ms = pa.array(d_ms, type=pa.timestamp('ms')) a_us = pa.array(d_us, type=pa.timestamp('us')) a_ns = pa.array(d_ns, type=pa.timestamp('ns')) names = ['ts:s', 'ts:ms', 'ts:us', 'ts:ns'] table = pa.Table.from_arrays([a_s, a_ms, a_us, a_ns], names) expected = pa.Table.from_arrays([a_ms, a_ms, a_us, a_us], names) _check_roundtrip(table, expected) expected = pa.Table.from_arrays([a_ms, a_ms, a_us, a_ns], names) _check_roundtrip(table, expected, version='2.0') expected = pa.Table.from_arrays([a_ms, a_ms, a_ms, a_ms], names) _check_roundtrip(table, expected, coerce_timestamps='ms') expected = pa.Table.from_arrays([a_us, a_us, a_us, a_us], names) _check_roundtrip(table, expected, version='2.0', coerce_timestamps='us') expected = pa.Table.from_arrays([a_ns, a_ns, a_ns, a_ns], names) _check_roundtrip(table, expected, use_deprecated_int96_timestamps=True) _check_roundtrip(table, expected, version='2.0', use_deprecated_int96_timestamps=True) def test_large_list_records(): list_lengths = np.random.randint(0, 500, size=50) list_lengths[::10] = 0 list_values = [list(map(int, np.random.randint(0, 100, size=x))) if i % 8 else None for i, x in enumerate(list_lengths)] a1 = pa.array(list_values) table = pa.Table.from_arrays([a1], ['int_lists']) _check_roundtrip(table) def test_sanitized_spark_field_names(): a0 = pa.array([0, 1, 2, 3, 4]) name = 'prohib; ,\t{}' table = pa.Table.from_arrays([a0], [name]) result = _roundtrip_table(table, write_table_kwargs={'flavor': 'spark'}) expected_name = 'prohib______' assert result.schema[0].name == expected_name @pytest.mark.pandas def test_spark_flavor_preserves_pandas_metadata(): df = _test_dataframe(size=100) df.index = np.arange(0, 10 * len(df), 10) df.index.name = 'foo' result = _roundtrip_pandas_dataframe(df, {'version': '2.0', 'flavor': 'spark'}) tm.assert_frame_equal(result, df) def test_fixed_size_binary(): t0 = pa.binary(10) data = [b'fooooooooo', None, b'barooooooo', b'quxooooooo'] a0 = pa.array(data, type=t0) table = pa.Table.from_arrays([a0], ['binary[10]']) _check_roundtrip(table) @pytest.mark.pandas def test_multithreaded_read(): df = alltypes_sample(size=10000) table = pa.Table.from_pandas(df) buf = io.BytesIO() _write_table(table, buf, compression='SNAPPY', version='2.0') buf.seek(0) table1 = _read_table(buf, use_threads=True) buf.seek(0) table2 = _read_table(buf, use_threads=False) assert table1.equals(table2) @pytest.mark.pandas def test_min_chunksize(): data = pd.DataFrame([np.arange(4)], columns=['A', 'B', 'C', 'D']) table = pa.Table.from_pandas(data.reset_index()) buf = io.BytesIO() _write_table(table, buf, chunk_size=-1) buf.seek(0) result = _read_table(buf) assert result.equals(table) with pytest.raises(ValueError): _write_table(table, buf, chunk_size=0) @pytest.mark.pandas def test_pass_separate_metadata(): df = alltypes_sample(size=10000) a_table = pa.Table.from_pandas(df) buf = io.BytesIO() _write_table(a_table, buf, compression='snappy', version='2.0') buf.seek(0) metadata = pq.read_metadata(buf) buf.seek(0) fileh = pq.ParquetFile(buf, metadata=metadata) tm.assert_frame_equal(df, fileh.read().to_pandas()) @pytest.mark.pandas def test_read_single_row_group(): N, K = 10000, 4 df = alltypes_sample(size=N) a_table = pa.Table.from_pandas(df) buf = io.BytesIO() _write_table(a_table, buf, row_group_size=N / K, compression='snappy', version='2.0') buf.seek(0) pf = pq.ParquetFile(buf) assert pf.num_row_groups == K row_groups = [pf.read_row_group(i) for i in range(K)] result = pa.concat_tables(row_groups) tm.assert_frame_equal(df, result.to_pandas()) @pytest.mark.pandas def test_read_single_row_group_with_column_subset(): N, K = 10000, 4 df = alltypes_sample(size=N) a_table = pa.Table.from_pandas(df) buf = io.BytesIO() _write_table(a_table, buf, row_group_size=N / K, compression='snappy', version='2.0') buf.seek(0) pf = pq.ParquetFile(buf) cols = list(df.columns[:2]) row_groups = [pf.read_row_group(i, columns=cols) for i in range(K)] result = pa.concat_tables(row_groups) tm.assert_frame_equal(df[cols], result.to_pandas()) row_groups = [pf.read_row_group(i, columns=cols + cols) for i in range(K)] result = pa.concat_tables(row_groups) tm.assert_frame_equal(df[cols], result.to_pandas()) @pytest.mark.pandas def test_scan_contents(): N, K = 10000, 4 df = alltypes_sample(size=N) a_table = pa.Table.from_pandas(df) buf = io.BytesIO() _write_table(a_table, buf, row_group_size=N / K, compression='snappy', version='2.0') buf.seek(0) pf = pq.ParquetFile(buf) assert pf.scan_contents() == 10000 assert pf.scan_contents(df.columns[:4]) == 10000 @pytest.mark.pandas def test_parquet_piece_read(tempdir): df = _test_dataframe(1000) table = pa.Table.from_pandas(df) path = tempdir / 'parquet_piece_read.parquet' _write_table(table, path, version='2.0') piece1 = pq.ParquetDatasetPiece(path) result = piece1.read() assert result.equals(table) @pytest.mark.pandas def test_parquet_piece_open_and_get_metadata(tempdir): df = _test_dataframe(100) table = pa.Table.from_pandas(df) path = tempdir / 'parquet_piece_read.parquet' _write_table(table, path, version='2.0') piece = pq.ParquetDatasetPiece(path) table1 = piece.read() assert isinstance(table1, pa.Table) meta1 = piece.get_metadata() assert isinstance(meta1, pq.FileMetaData) assert table == table1 def test_parquet_piece_basics(): path = '/baz.parq' piece1 = pq.ParquetDatasetPiece(path) piece2 = pq.ParquetDatasetPiece(path, row_group=1) piece3 = pq.ParquetDatasetPiece( path, row_group=1, partition_keys=[('foo', 0), ('bar', 1)]) assert str(piece1) == path assert str(piece2) == '/baz.parq | row_group=1' assert str(piece3) == 'partition[foo=0, bar=1] /baz.parq | row_group=1' assert piece1 == piece1 assert piece2 == piece2 assert piece3 == piece3 assert piece1 != piece3 def test_partition_set_dictionary_type(): set1 = pq.PartitionSet('key1', [u('foo'), u('bar'), u('baz')]) set2 = pq.PartitionSet('key2', [2007, 2008, 2009]) assert isinstance(set1.dictionary, pa.StringArray) assert isinstance(set2.dictionary, pa.IntegerArray) set3 = pq.PartitionSet('key2', [datetime.datetime(2007, 1, 1)]) with pytest.raises(TypeError): set3.dictionary @pytest.mark.pandas def test_read_partitioned_directory(tempdir): fs = LocalFileSystem.get_instance() _partition_test_for_filesystem(fs, tempdir) @pytest.mark.pandas def test_create_parquet_dataset_multi_threaded(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir _partition_test_for_filesystem(fs, base_path) manifest = pq.ParquetManifest(base_path, filesystem=fs, metadata_nthreads=1) dataset = pq.ParquetDataset(base_path, filesystem=fs, metadata_nthreads=16) assert len(dataset.pieces) > 0 partitions = dataset.partitions assert len(partitions.partition_names) > 0 assert partitions.partition_names == manifest.partitions.partition_names assert len(partitions.levels) == len(manifest.partitions.levels) @pytest.mark.pandas def test_equivalency(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir integer_keys = [0, 1] string_keys = ['a', 'b', 'c'] boolean_keys = [True, False] partition_spec = [ ['integer', integer_keys], ['string', string_keys], ['boolean', boolean_keys] ] df = pd.DataFrame({ 'integer': np.array(integer_keys, dtype='i4').repeat(15), 'string': np.tile(np.tile(np.array(string_keys, dtype=object), 5), 2), 'boolean': np.tile(np.tile(np.array(boolean_keys, dtype='bool'), 5), 3), }, columns=['integer', 'string', 'boolean']) _generate_partition_directories(fs, base_path, partition_spec, df) dataset = pq.ParquetDataset( base_path, filesystem=fs, filters=[('integer', '=', 1), ('string', '!=', 'b'), ('boolean', '==', True)] ) table = dataset.read() result_df = (table.to_pandas().reset_index(drop=True)) assert 0 not in result_df['integer'].values assert 'b' not in result_df['string'].values assert False not in result_df['boolean'].values filters = [ [ ('integer', '=', 1), ('string', '!=', 'b'), ('boolean', '==', 'True') ], [('integer', '=', 0), ('boolean', '==', 'False')] ] dataset = pq.ParquetDataset(base_path, filesystem=fs, filters=filters) table = dataset.read() result_df = table.to_pandas().reset_index(drop=True) df_filter_1 = (np.array(result_df['integer']) == 1) \ & (np.array(result_df['string']) != 'b') \ & (np.array(result_df['boolean']) == 'True') df_filter_2 = (np.array(result_df['integer']) == 0) \ & (np.array(result_df['boolean']) == 'False') assert df_filter_1.sum() > 0 assert df_filter_2.sum() > 0 assert result_df.shape[0] == (df_filter_1.sum() + df_filter_2.sum()) with pytest.raises(NotImplementedError): filters = [[('string', '==', b'1\0a')]] pq.ParquetDataset(base_path, filesystem=fs, filters=filters) with pytest.raises(NotImplementedError): filters = [[('string', '==', u'1\0a')]] pq.ParquetDataset(base_path, filesystem=fs, filters=filters) @pytest.mark.pandas def test_cutoff_exclusive_integer(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir integer_keys = [0, 1, 2, 3, 4] partition_spec = [ ['integers', integer_keys], ] N = 5 df = pd.DataFrame({ 'index': np.arange(N), 'integers': np.array(integer_keys, dtype='i4'), }, columns=['index', 'integers']) _generate_partition_directories(fs, base_path, partition_spec, df) dataset = pq.ParquetDataset( base_path, filesystem=fs, filters=[ ('integers', '<', 4), ('integers', '>', 1), ] ) table = dataset.read() result_df = (table.to_pandas() .sort_values(by='index') .reset_index(drop=True)) result_list = [x for x in map(int, result_df['integers'].values)] assert result_list == [2, 3] @pytest.mark.pandas @pytest.mark.xfail( raises=TypeError, reason='Loss of type information in creation of categoricals.' ) def test_cutoff_exclusive_datetime(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir date_keys = [ datetime.date(2018, 4, 9), datetime.date(2018, 4, 10), datetime.date(2018, 4, 11), datetime.date(2018, 4, 12), datetime.date(2018, 4, 13) ] partition_spec = [ ['dates', date_keys] ] N = 5 df = pd.DataFrame({ 'index': np.arange(N), 'dates': np.array(date_keys, dtype='datetime64'), }, columns=['index', 'dates']) _generate_partition_directories(fs, base_path, partition_spec, df) dataset = pq.ParquetDataset( base_path, filesystem=fs, filters=[ ('dates', '<', "2018-04-12"), ('dates', '>', "2018-04-10") ] ) table = dataset.read() result_df = (table.to_pandas() .sort_values(by='index') .reset_index(drop=True)) expected = pd.Categorical( np.array([datetime.date(2018, 4, 11)], dtype='datetime64'), categories=np.array(date_keys, dtype='datetime64')) assert result_df['dates'].values == expected @pytest.mark.pandas def test_inclusive_integer(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir integer_keys = [0, 1, 2, 3, 4] partition_spec = [ ['integers', integer_keys], ] N = 5 df = pd.DataFrame({ 'index': np.arange(N), 'integers': np.array(integer_keys, dtype='i4'), }, columns=['index', 'integers']) _generate_partition_directories(fs, base_path, partition_spec, df) dataset = pq.ParquetDataset( base_path, filesystem=fs, filters=[ ('integers', '<=', 3), ('integers', '>=', 2), ] ) table = dataset.read() result_df = (table.to_pandas() .sort_values(by='index') .reset_index(drop=True)) result_list = [int(x) for x in map(int, result_df['integers'].values)] assert result_list == [2, 3] @pytest.mark.pandas def test_inclusive_set(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir integer_keys = [0, 1] string_keys = ['a', 'b', 'c'] boolean_keys = [True, False] partition_spec = [ ['integer', integer_keys], ['string', string_keys], ['boolean', boolean_keys] ] df = pd.DataFrame({ 'integer': np.array(integer_keys, dtype='i4').repeat(15), 'string': np.tile(np.tile(np.array(string_keys, dtype=object), 5), 2), 'boolean': np.tile(np.tile(np.array(boolean_keys, dtype='bool'), 5), 3), }, columns=['integer', 'string', 'boolean']) _generate_partition_directories(fs, base_path, partition_spec, df) dataset = pq.ParquetDataset( base_path, filesystem=fs, filters=[('integer', 'in', {1}), ('string', 'in', {'a', 'b'}), ('boolean', 'in', {True})] ) table = dataset.read() result_df = (table.to_pandas().reset_index(drop=True)) assert 0 not in result_df['integer'].values assert 'c' not in result_df['string'].values assert False not in result_df['boolean'].values @pytest.mark.pandas def test_invalid_pred_op(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir integer_keys = [0, 1, 2, 3, 4] partition_spec = [ ['integers', integer_keys], ] N = 5 df = pd.DataFrame({ 'index': np.arange(N), 'integers': np.array(integer_keys, dtype='i4'), }, columns=['index', 'integers']) _generate_partition_directories(fs, base_path, partition_spec, df) with pytest.raises(ValueError): pq.ParquetDataset(base_path, filesystem=fs, filters=[ ('integers', '=<', 3), ]) with pytest.raises(ValueError): pq.ParquetDataset(base_path, filesystem=fs, filters=[ ('integers', 'in', set()), ]) with pytest.raises(ValueError): pq.ParquetDataset(base_path, filesystem=fs, filters=[ ('integers', '!=', {3}), ]) @pytest.mark.pandas def test_filters_read_table(tempdir): fs = LocalFileSystem.get_instance() base_path = tempdir integer_keys = [0, 1, 2, 3, 4] partition_spec = [ ['integers', integer_keys], ] N = 5 df = pd.DataFrame({ 'index': np.arange(N), 'integers': np.array(integer_keys, dtype='i4'), }, columns=['index', 'integers']) _generate_partition_directories(fs, base_path, partition_spec, df) table = pq.read_table( base_path, filesystem=fs, filters=[('integers', '<', 3)]) assert table.num_rows == 3 table = pq.read_table( base_path, filesystem=fs, filters=[[('integers', '<', 3)]]) assert table.num_rows == 3 table = pq.read_pandas( base_path, filters=[('integers', '<', 3)]) assert table.num_rows == 3 @pytest.yield_fixture def s3_example(): access_key = os.environ['PYARROW_TEST_S3_ACCESS_KEY'] secret_key = os.environ['PYARROW_TEST_S3_SECRET_KEY'] bucket_name = os.environ['PYARROW_TEST_S3_BUCKET'] import s3fs fs = s3fs.S3FileSystem(key=access_key, secret=secret_key) test_dir = guid() bucket_uri = 's3://{0}/{1}'.format(bucket_name, test_dir) fs.mkdir(bucket_uri) yield fs, bucket_uri fs.rm(bucket_uri, recursive=True) @pytest.mark.pandas @pytest.mark.s3 def test_read_partitioned_directory_s3fs(s3_example): from pyarrow.filesystem import S3FSWrapper fs, bucket_uri = s3_example wrapper = S3FSWrapper(fs) _partition_test_for_filesystem(wrapper, bucket_uri) dataset = pq.ParquetDataset(bucket_uri, filesystem=fs) dataset.read() def _partition_test_for_filesystem(fs, base_path): foo_keys = [0, 1] bar_keys = ['a', 'b', 'c'] partition_spec = [ ['foo', foo_keys], ['bar', bar_keys] ] N = 30 df = pd.DataFrame({ 'index': np.arange(N), 'foo': np.array(foo_keys, dtype='i4').repeat(15), 'bar': np.tile(np.tile(np.array(bar_keys, dtype=object), 5), 2), 'values': np.random.randn(N) }, columns=['index', 'foo', 'bar', 'values']) _generate_partition_directories(fs, base_path, partition_spec, df) dataset = pq.ParquetDataset(base_path, filesystem=fs) table = dataset.read() result_df = (table.to_pandas() .sort_values(by='index') .reset_index(drop=True)) expected_df = (df.sort_values(by='index') .reset_index(drop=True) .reindex(columns=result_df.columns)) expected_df['foo'] = pd.Categorical(df['foo'], categories=foo_keys) expected_df['bar'] = pd.Categorical(df['bar'], categories=bar_keys) assert (result_df.columns == ['index', 'values', 'foo', 'bar']).all() tm.assert_frame_equal(result_df, expected_df) def _generate_partition_directories(fs, base_dir, partition_spec, df): DEPTH = len(partition_spec) def _visit_level(base_dir, level, part_keys): name, values = partition_spec[level] for value in values: this_part_keys = part_keys + [(name, value)] level_dir = base_dir / '{0}={1}'.format(name, value) fs.mkdir(level_dir) if level == DEPTH - 1: file_path = level_dir / guid() filtered_df = _filter_partition(df, this_part_keys) part_table = pa.Table.from_pandas(filtered_df) with fs.open(file_path, 'wb') as f: _write_table(part_table, f) assert fs.exists(file_path) (level_dir / '_SUCCESS').touch() else: _visit_level(level_dir, level + 1, this_part_keys) (level_dir / '_SUCCESS').touch() _visit_level(base_dir, 0, []) def _test_read_common_metadata_files(fs, base_path): N = 100 df = pd.DataFrame({ 'index': np.arange(N), 'values': np.random.randn(N) }, columns=['index', 'values']) base_path = str(base_path) data_path = os.path.join(base_path, 'data.parquet') table = pa.Table.from_pandas(df) with fs.open(data_path, 'wb') as f: _write_table(table, f) metadata_path = os.path.join(base_path, '_common_metadata') with fs.open(metadata_path, 'wb') as f: pq.write_metadata(table.schema, f) dataset = pq.ParquetDataset(base_path, filesystem=fs) assert dataset.common_metadata_path == str(metadata_path) with fs.open(data_path) as f: common_schema = pq.read_metadata(f).schema assert dataset.schema.equals(common_schema) dataset2 = pq.ParquetDataset([base_path], filesystem=fs) assert dataset2.schema.equals(dataset.schema) @pytest.mark.pandas def test_read_common_metadata_files(tempdir): fs = LocalFileSystem.get_instance() _test_read_common_metadata_files(fs, tempdir) @pytest.mark.pandas def test_read_metadata_files(tempdir): fs = LocalFileSystem.get_instance() N = 100 df = pd.DataFrame({ 'index': np.arange(N), 'values': np.random.randn(N) }, columns=['index', 'values']) data_path = tempdir / 'data.parquet' table = pa.Table.from_pandas(df) with fs.open(data_path, 'wb') as f: _write_table(table, f) metadata_path = tempdir / '_metadata' with fs.open(metadata_path, 'wb') as f: pq.write_metadata(table.schema, f) dataset = pq.ParquetDataset(tempdir, filesystem=fs) assert dataset.metadata_path == str(metadata_path) with fs.open(data_path) as f: metadata_schema = pq.read_metadata(f).schema assert dataset.schema.equals(metadata_schema) @pytest.mark.pandas def test_read_schema(tempdir): N = 100 df = pd.DataFrame({ 'index': np.arange(N), 'values': np.random.randn(N) }, columns=['index', 'values']) data_path = tempdir / 'test.parquet' table = pa.Table.from_pandas(df) _write_table(table, data_path) read1 = pq.read_schema(data_path) read2 = pq.read_schema(data_path, memory_map=True) assert table.schema.equals(read1, check_metadata=False) assert table.schema.equals(read2, check_metadata=False) assert table.schema.metadata[b'pandas'] == read1.metadata[b'pandas'] def _filter_partition(df, part_keys): predicate = np.ones(len(df), dtype=bool) to_drop = [] for name, value in part_keys: to_drop.append(name) if isinstance(value, (datetime.date, datetime.datetime)): value = pd.Timestamp(value) predicate &= df[name] == value return df[predicate].drop(to_drop, axis=1) @pytest.mark.pandas def test_read_multiple_files(tempdir): nfiles = 10 size = 5 dirpath = tempdir / guid() dirpath.mkdir() test_data = [] paths = [] for i in range(nfiles): df = _test_dataframe(size, seed=i) df['uint32'] = df['uint32'].astype(np.int64) path = dirpath / '{}.parquet'.format(i) table = pa.Table.from_pandas(df) _write_table(table, path) test_data.append(table) paths.append(path) # Write a _SUCCESS.crc file (dirpath / '_SUCCESS.crc').touch() def read_multiple_files(paths, columns=None, use_threads=True, **kwargs): dataset = pq.ParquetDataset(paths, **kwargs) return dataset.read(columns=columns, use_threads=use_threads) result = read_multiple_files(paths) expected = pa.concat_tables(test_data) assert result.equals(expected) # Read with provided metadata metadata = pq.read_metadata(paths[0]) result2 = read_multiple_files(paths, metadata=metadata) assert result2.equals(expected) result3 = pa.localfs.read_parquet(dirpath, schema=metadata.schema) assert result3.equals(expected) # Read column subset to_read = [0, 2, 6, result.num_columns - 1] col_names = [result.field(i).name for i in to_read] out = pa.localfs.read_parquet(dirpath, columns=col_names) expected = pa.Table.from_arrays([result.column(i) for i in to_read], names=col_names, metadata=result.schema.metadata) assert out.equals(expected) # Read with multiple threads pa.localfs.read_parquet(dirpath, use_threads=True) # Test failure modes with non-uniform metadata bad_apple = _test_dataframe(size, seed=i).iloc[:, :4] bad_apple_path = tempdir / '{}.parquet'.format(guid()) t = pa.Table.from_pandas(bad_apple) _write_table(t, bad_apple_path) bad_meta = pq.read_metadata(bad_apple_path) with pytest.raises(ValueError): read_multiple_files(paths + [bad_apple_path]) with pytest.raises(ValueError): read_multiple_files(paths, metadata=bad_meta) mixed_paths = [bad_apple_path, paths[0]] with pytest.raises(ValueError): read_multiple_files(mixed_paths, schema=bad_meta.schema) with pytest.raises(ValueError): read_multiple_files(mixed_paths) @pytest.mark.pandas def test_dataset_read_pandas(tempdir): nfiles = 5 size = 5 dirpath = tempdir / guid() dirpath.mkdir() test_data = [] frames = [] paths = [] for i in range(nfiles): df = _test_dataframe(size, seed=i) df.index = np.arange(i * size, (i + 1) * size) df.index.name = 'index' path = dirpath / '{}.parquet'.format(i) table = pa.Table.from_pandas(df) _write_table(table, path) test_data.append(table) frames.append(df) paths.append(path) dataset = pq.ParquetDataset(dirpath) columns = ['uint8', 'strings'] result = dataset.read_pandas(columns=columns).to_pandas() expected = pd.concat([x[columns] for x in frames]) tm.assert_frame_equal(result, expected) @pytest.mark.pandas def test_dataset_no_memory_map(tempdir): # ARROW-2627: Check that we can use ParquetDataset without memory-mapping dirpath = tempdir / guid() dirpath.mkdir() df = _test_dataframe(10, seed=0) path = dirpath / '{}.parquet'.format(0) table = pa.Table.from_pandas(df) _write_table(table, path, version='2.0') # TODO(wesm): Not sure how to easily check that memory mapping is _not_ # used. Mocking is not especially easy for pa.memory_map dataset = pq.ParquetDataset(dirpath, memory_map=False) assert dataset.pieces[0].read().equals(table) @pytest.mark.pandas @pytest.mark.parametrize('preserve_index', [True, False, None]) def test_dataset_read_pandas_common_metadata(tempdir, preserve_index): # ARROW-1103 nfiles = 5 size = 5 dirpath = tempdir / guid() dirpath.mkdir() test_data = [] frames = [] paths = [] for i in range(nfiles): df = _test_dataframe(size, seed=i) df.index = pd.Index(np.arange(i * size, (i + 1) * size), name='index') path = dirpath / '{}.parquet'.format(i) table = pa.Table.from_pandas(df, preserve_index=preserve_index) # Obliterate metadata table = table.replace_schema_metadata(None) assert table.schema.metadata is None _write_table(table, path) test_data.append(table) frames.append(df) paths.append(path) # Write _metadata common file table_for_metadata = pa.Table.from_pandas( df, preserve_index=preserve_index ) pq.write_metadata(table_for_metadata.schema, dirpath / '_metadata') dataset = pq.ParquetDataset(dirpath) columns = ['uint8', 'strings'] result = dataset.read_pandas(columns=columns).to_pandas() expected = pd.concat([x[columns] for x in frames]) expected.index.name = ( df.index.name if preserve_index is not False else None) tm.assert_frame_equal(result, expected) def _make_example_multifile_dataset(base_path, nfiles=10, file_nrows=5): test_data = [] paths = [] for i in range(nfiles): df = _test_dataframe(file_nrows, seed=i) path = base_path / '{}.parquet'.format(i) test_data.append(_write_table(df, path)) paths.append(path) return paths @pytest.mark.pandas def test_ignore_private_directories(tempdir): dirpath = tempdir / guid() dirpath.mkdir() paths = _make_example_multifile_dataset(dirpath, nfiles=10, file_nrows=5) # private directory (dirpath / '_impala_staging').mkdir() dataset = pq.ParquetDataset(dirpath) assert set(map(str, paths)) == set(x.path for x in dataset.pieces) @pytest.mark.pandas def test_ignore_hidden_files_dot(tempdir): dirpath = tempdir / guid() dirpath.mkdir() paths = _make_example_multifile_dataset(dirpath, nfiles=10, file_nrows=5) with (dirpath / '.DS_Store').open('wb') as f: f.write(b'gibberish') with (dirpath / '.private').open('wb') as f: f.write(b'gibberish') dataset = pq.ParquetDataset(dirpath) assert set(map(str, paths)) == set(x.path for x in dataset.pieces) @pytest.mark.pandas def test_ignore_hidden_files_underscore(tempdir): dirpath = tempdir / guid() dirpath.mkdir() paths = _make_example_multifile_dataset(dirpath, nfiles=10, file_nrows=5) with (dirpath / '_committed_123').open('wb') as f: f.write(b'abcd') with (dirpath / '_started_321').open('wb') as f: f.write(b'abcd') dataset = pq.ParquetDataset(dirpath) assert set(map(str, paths)) == set(x.path for x in dataset.pieces) @pytest.mark.pandas def test_multiindex_duplicate_values(tempdir): num_rows = 3 numbers = list(range(num_rows)) index = pd.MultiIndex.from_arrays( [['foo', 'foo', 'bar'], numbers], names=['foobar', 'some_numbers'], ) df = pd.DataFrame({'numbers': numbers}, index=index) table = pa.Table.from_pandas(df) filename = tempdir / 'dup_multi_index_levels.parquet' _write_table(table, filename) result_table = _read_table(filename) assert table.equals(result_table) result_df = result_table.to_pandas() tm.assert_frame_equal(result_df, df) @pytest.mark.pandas def test_write_error_deletes_incomplete_file(tempdir): # ARROW-1285 df = pd.DataFrame({'a': list('abc'), 'b': list(range(1, 4)), 'c': np.arange(3, 6).astype('u1'), 'd': np.arange(4.0, 7.0, dtype='float64'), 'e': [True, False, True], 'f': pd.Categorical(list('abc')), 'g': pd.date_range('20130101', periods=3), 'h': pd.date_range('20130101', periods=3, tz='US/Eastern'), 'i': pd.date_range('20130101', periods=3, freq='ns')}) pdf = pa.Table.from_pandas(df) filename = tempdir / 'tmp_file' try: _write_table(pdf, filename) except pa.ArrowException: pass assert not filename.exists() @pytest.mark.pandas def test_noncoerced_nanoseconds_written_without_exception(tempdir): # ARROW-1957: the Parquet version 2.0 writer preserves Arrow # nanosecond timestamps by default n = 9 df = pd.DataFrame({'x': range(n)}, index=pd.DatetimeIndex(start='2017-01-01', freq='1n', periods=n)) tb = pa.Table.from_pandas(df) filename = tempdir / 'written.parquet' try: pq.write_table(tb, filename, version='2.0') except Exception: pass assert filename.exists() recovered_table = pq.read_table(filename) assert tb.equals(recovered_table) # Loss of data thru coercion (without explicit override) still an error filename = tempdir / 'not_written.parquet' with pytest.raises(ValueError): pq.write_table(tb, filename, coerce_timestamps='ms', version='2.0') def test_read_non_existent_file(tempdir): path = 'non-existent-file.parquet' try: pq.read_table(path) except Exception as e: assert path in e.args[0] def test_read_table_doesnt_warn(datadir): with pytest.warns(None) as record: pq.read_table(datadir / 'v0.7.1.parquet') assert len(record) == 0 def _test_write_to_dataset_with_partitions(base_path, filesystem=None, schema=None, index_name=None): # ARROW-1400 output_df = pd.DataFrame({'group1': list('aaabbbbccc'), 'group2': list('eefeffgeee'), 'num': list(range(10)), 'nan': [pd.np.nan] * 10, 'date': np.arange('2017-01-01', '2017-01-11', dtype='datetime64[D]')}) cols = output_df.columns.tolist() partition_by = ['group1', 'group2'] output_table = pa.Table.from_pandas(output_df, schema=schema, safe=False, preserve_index=False) pq.write_to_dataset(output_table, base_path, partition_by, filesystem=filesystem) metadata_path = os.path.join(base_path, '_common_metadata') if filesystem is not None: with filesystem.open(metadata_path, 'wb') as f: pq.write_metadata(output_table.schema, f) else: pq.write_metadata(output_table.schema, metadata_path) # ARROW-2891: Ensure the output_schema is preserved when writing a # partitioned dataset dataset = pq.ParquetDataset(base_path, filesystem=filesystem, validate_schema=True) # ARROW-2209: Ensure the dataset schema also includes the partition columns dataset_cols = set(dataset.schema.to_arrow_schema().names) assert dataset_cols == set(output_table.schema.names) input_table = dataset.read() input_df = input_table.to_pandas() # Read data back in and compare with original DataFrame # Partitioned columns added to the end of the DataFrame when read input_df_cols = input_df.columns.tolist() assert partition_by == input_df_cols[-1 * len(partition_by):] # Partitioned columns become 'categorical' dtypes input_df = input_df[cols] for col in partition_by: output_df[col] = output_df[col].astype('category') assert output_df.equals(input_df) def _test_write_to_dataset_no_partitions(base_path, filesystem=None): # ARROW-1400 output_df = pd.DataFrame({'group1': list('aaabbbbccc'), 'group2': list('eefeffgeee'), 'num': list(range(10)), 'date': np.arange('2017-01-01', '2017-01-11', dtype='datetime64[D]')}) cols = output_df.columns.tolist() output_table = pa.Table.from_pandas(output_df) if filesystem is None: filesystem = LocalFileSystem.get_instance() # Without partitions, append files to root_path n = 5 for i in range(n): pq.write_to_dataset(output_table, base_path, filesystem=filesystem) output_files = [file for file in filesystem.ls(base_path) if file.endswith(".parquet")] assert len(output_files) == n # Deduplicated incoming DataFrame should match # original outgoing Dataframe input_table = pq.ParquetDataset(base_path, filesystem=filesystem).read() input_df = input_table.to_pandas() input_df = input_df.drop_duplicates() input_df = input_df[cols] assert output_df.equals(input_df) @pytest.mark.pandas def test_write_to_dataset_with_partitions(tempdir): _test_write_to_dataset_with_partitions(str(tempdir)) @pytest.mark.pandas def test_write_to_dataset_with_partitions_and_schema(tempdir): schema = pa.schema([pa.field('group1', type=pa.string()), pa.field('group2', type=pa.string()), pa.field('num', type=pa.int64()), pa.field('nan', type=pa.int32()), pa.field('date', type=pa.timestamp(unit='us'))]) _test_write_to_dataset_with_partitions(str(tempdir), schema=schema) @pytest.mark.pandas def test_write_to_dataset_with_partitions_and_index_name(tempdir): _test_write_to_dataset_with_partitions(str(tempdir), index_name='index_name') @pytest.mark.pandas def test_write_to_dataset_no_partitions(tempdir): _test_write_to_dataset_no_partitions(str(tempdir)) @pytest.mark.pandas def test_write_to_dataset_with_partitions_and_custom_filenames(tempdir): output_df = pd.DataFrame({'group1': list('aaabbbbccc'), 'group2': list('eefeffgeee'), 'num': list(range(10)), 'nan': [pd.np.nan] * 10, 'date': np.arange('2017-01-01', '2017-01-11', dtype='datetime64[D]')}) partition_by = ['group1', 'group2'] output_table = pa.Table.from_pandas(output_df) path = str(tempdir) def partition_filename_callback(keys): return "{0}-{1}.parquet".format(*keys) pq.write_to_dataset(output_table, path, partition_by, partition_filename_callback) dataset = pq.ParquetDataset(path) # ARROW-3538: Ensure partition filenames match the given pattern # defined in the local function partition_filename_callback expected_basenames = [ 'a-e.parquet', 'a-f.parquet', 'b-e.parquet', 'b-f.parquet', 'b-g.parquet', 'c-e.parquet' ] output_basenames = [os.path.basename(p.path) for p in dataset.pieces] assert sorted(expected_basenames) == sorted(output_basenames) @pytest.mark.large_memory def test_large_table_int32_overflow(): size = np.iinfo('int32').max + 1 arr = np.ones(size, dtype='uint8') parr = pa.array(arr, type=pa.uint8()) table = pa.Table.from_arrays([parr], names=['one']) f = io.BytesIO() _write_table(table, f) def _simple_table_roundtrip(table): stream = pa.BufferOutputStream() _write_table(table, stream) buf = stream.getvalue() return _read_table(buf) @pytest.mark.pandas @pytest.mark.large_memory def test_binary_array_overflow_to_chunked(): # ARROW-3762 # 2^31 + 1 bytes values = [b'x'] + [ b'x' * (1 << 20) ] * 2 * (1 << 10) df = pd.DataFrame({'byte_col': values}) tbl = pa.Table.from_pandas(df, preserve_index=False) read_tbl = _simple_table_roundtrip(tbl) col0_data = read_tbl[0] assert isinstance(col0_data, pa.ChunkedArray) # Split up into 2GB chunks assert col0_data.num_chunks == 2 assert tbl.equals(read_tbl) @pytest.mark.pandas @pytest.mark.large_memory def test_list_of_binary_large_cell(): # ARROW-4688 data = [] # TODO(wesm): handle chunked children # 2^31 - 1 bytes in a single cell # data.append([b'x' * (1 << 20)] * 2047 + [b'x' * ((1 << 20) - 1)]) # A little under 2GB in cell each containing approximately 10MB each data.extend([[b'x' * 1000000] * 10] * 214) arr = pa.array(data) table = pa.Table.from_arrays([arr], ['chunky_cells']) read_table = _simple_table_roundtrip(table) assert table.equals(read_table) @pytest.mark.pandas def test_index_column_name_duplicate(tempdir): data = { 'close': { pd.Timestamp('2017-06-30 01:31:00'): 154.99958999999998, pd.Timestamp('2017-06-30 01:32:00'): 154.99958999999998, }, 'time': { pd.Timestamp('2017-06-30 01:31:00'): pd.Timestamp( '2017-06-30 01:31:00' ), pd.Timestamp('2017-06-30 01:32:00'): pd.Timestamp( '2017-06-30 01:32:00' ), } } path = str(tempdir / 'data.parquet') dfx = pd.DataFrame(data).set_index('time', drop=False) tdfx = pa.Table.from_pandas(dfx) _write_table(tdfx, path) arrow_table = _read_table(path) result_df = arrow_table.to_pandas() tm.assert_frame_equal(result_df, dfx) @pytest.mark.pandas def test_parquet_nested_convenience(tempdir): # ARROW-1684 df = pd.DataFrame({ 'a': [[1, 2, 3], None, [4, 5], []], 'b': [[1.], None, None, [6., 7.]], }) path = str(tempdir / 'nested_convenience.parquet') table = pa.Table.from_pandas(df, preserve_index=False) _write_table(table, path) read = pq.read_table(path, columns=['a']) tm.assert_frame_equal(read.to_pandas(), df[['a']]) read = pq.read_table(path, columns=['a', 'b']) tm.assert_frame_equal(read.to_pandas(), df) @pytest.mark.pandas def test_backwards_compatible_index_naming(datadir): expected_string = b"""\ carat cut color clarity depth table price x y z 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75 0.24 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48 0.24 Very Good I VVS1 62.3 57.0 336 3.95 3.98 2.47 0.26 Very Good H SI1 61.9 55.0 337 4.07 4.11 2.53 0.22 Fair E VS2 65.1 61.0 337 3.87 3.78 2.49 0.23 Very Good H VS1 59.4 61.0 338 4.00 4.05 2.39""" expected = pd.read_csv(io.BytesIO(expected_string), sep=r'\s{2,}', index_col=None, header=0, engine='python') table = _read_table(datadir / 'v0.7.1.parquet') result = table.to_pandas() tm.assert_frame_equal(result, expected) @pytest.mark.pandas def test_backwards_compatible_index_multi_level_named(datadir): expected_string = b"""\ carat cut color clarity depth table price x y z 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75 0.24 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48 0.24 Very Good I VVS1 62.3 57.0 336 3.95 3.98 2.47 0.26 Very Good H SI1 61.9 55.0 337 4.07 4.11 2.53 0.22 Fair E VS2 65.1 61.0 337 3.87 3.78 2.49 0.23 Very Good H VS1 59.4 61.0 338 4.00 4.05 2.39""" expected = pd.read_csv( io.BytesIO(expected_string), sep=r'\s{2,}', index_col=['cut', 'color', 'clarity'], header=0, engine='python' ).sort_index() table = _read_table(datadir / 'v0.7.1.all-named-index.parquet') result = table.to_pandas() tm.assert_frame_equal(result, expected) @pytest.mark.pandas def test_backwards_compatible_index_multi_level_some_named(datadir): expected_string = b"""\ carat cut color clarity depth table price x y z 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75 0.24 Very Good J VVS2 62.8 57.0 336 3.94 3.96 2.48 0.24 Very Good I VVS1 62.3 57.0 336 3.95 3.98 2.47 0.26 Very Good H SI1 61.9 55.0 337 4.07 4.11 2.53 0.22 Fair E VS2 65.1 61.0 337 3.87 3.78 2.49 0.23 Very Good H VS1 59.4 61.0 338 4.00 4.05 2.39""" expected = pd.read_csv( io.BytesIO(expected_string), sep=r'\s{2,}', index_col=['cut', 'color', 'clarity'], header=0, engine='python' ).sort_index() expected.index = expected.index.set_names(['cut', None, 'clarity']) table = _read_table(datadir / 'v0.7.1.some-named-index.parquet') result = table.to_pandas() tm.assert_frame_equal(result, expected) @pytest.mark.pandas def test_backwards_compatible_column_metadata_handling(datadir): expected = pd.DataFrame( {'a': [1, 2, 3], 'b': [.1, .2, .3], 'c': pd.date_range("2017-01-01", periods=3, tz='Europe/Brussels')}) expected.index = pd.MultiIndex.from_arrays( [['a', 'b', 'c'], pd.date_range("2017-01-01", periods=3, tz='Europe/Brussels')], names=['index', None]) path = datadir / 'v0.7.1.column-metadata-handling.parquet' table = _read_table(path) result = table.to_pandas() tm.assert_frame_equal(result, expected) table = _read_table(path, columns=['a']) result = table.to_pandas() tm.assert_frame_equal(result, expected[['a']].reset_index(drop=True)) def _make_dataset_for_pickling(tempdir, N=100): path = tempdir / 'data.parquet' fs = LocalFileSystem.get_instance() df = pd.DataFrame({ 'index': np.arange(N), 'values': np.random.randn(N) }, columns=['index', 'values']) table = pa.Table.from_pandas(df) num_groups = 3 with pq.ParquetWriter(path, table.schema) as writer: for i in range(num_groups): writer.write_table(table) reader = pq.ParquetFile(path) assert reader.metadata.num_row_groups == num_groups metadata_path = tempdir / '_metadata' with fs.open(metadata_path, 'wb') as f: pq.write_metadata(table.schema, f) dataset = pq.ParquetDataset(tempdir, filesystem=fs) assert dataset.metadata_path == str(metadata_path) return dataset @pytest.mark.pandas @pytest.mark.parametrize('pickler', [ pytest.param(pickle, id='builtin'), pytest.param(pytest.importorskip('cloudpickle'), id='cloudpickle') ]) def test_pickle_dataset(tempdir, datadir, pickler): def is_pickleable(obj): return obj == pickler.loads(pickler.dumps(obj)) dataset = _make_dataset_for_pickling(tempdir) assert is_pickleable(dataset) assert is_pickleable(dataset.metadata) assert is_pickleable(dataset.metadata.schema) assert len(dataset.metadata.schema) for column in dataset.metadata.schema: assert is_pickleable(column) for piece in dataset.pieces: assert is_pickleable(piece) metadata = piece.get_metadata() assert metadata.num_row_groups for i in range(metadata.num_row_groups): assert is_pickleable(metadata.row_group(i)) @pytest.mark.pandas def test_decimal_roundtrip(tempdir): num_values = 10 columns = {} for precision in range(1, 39): for scale in range(0, precision + 1): with util.random_seed(0): random_decimal_values = [ util.randdecimal(precision, scale) for _ in range(num_values) ] column_name = ('dec_precision_{:d}_scale_{:d}' .format(precision, scale)) columns[column_name] = random_decimal_values expected = pd.DataFrame(columns) filename = tempdir / 'decimals.parquet' string_filename = str(filename) table = pa.Table.from_pandas(expected) _write_table(table, string_filename) result_table = _read_table(string_filename) result = result_table.to_pandas() tm.assert_frame_equal(result, expected) @pytest.mark.pandas @pytest.mark.xfail( raises=pa.ArrowException, reason='Parquet does not support negative scale' ) def test_decimal_roundtrip_negative_scale(tempdir): expected = pd.DataFrame({'decimal_num': [decimal.Decimal('1.23E4')]}) filename = tempdir / 'decimals.parquet' string_filename = str(filename) t = pa.Table.from_pandas(expected) _write_table(t, string_filename) result_table = _read_table(string_filename) result = result_table.to_pandas() tm.assert_frame_equal(result, expected) @pytest.mark.pandas def test_parquet_writer_context_obj(tempdir): df = _test_dataframe(100) df['unique_id'] = 0 arrow_table = pa.Table.from_pandas(df, preserve_index=False) out = pa.BufferOutputStream() with pq.ParquetWriter(out, arrow_table.schema, version='2.0') as writer: frames = [] for i in range(10): df['unique_id'] = i arrow_table = pa.Table.from_pandas(df, preserve_index=False) writer.write_table(arrow_table) frames.append(df.copy()) buf = out.getvalue() result = _read_table(pa.BufferReader(buf)) expected = pd.concat(frames, ignore_index=True) tm.assert_frame_equal(result.to_pandas(), expected) @pytest.mark.pandas def test_parquet_writer_context_obj_with_exception(tempdir): df = _test_dataframe(100) df['unique_id'] = 0 arrow_table = pa.Table.from_pandas(df, preserve_index=False) out = pa.BufferOutputStream() error_text = 'Artificial Error' try: with pq.ParquetWriter(out, arrow_table.schema, version='2.0') as writer: frames = [] for i in range(10): df['unique_id'] = i arrow_table = pa.Table.from_pandas(df, preserve_index=False) writer.write_table(arrow_table) frames.append(df.copy()) if i == 5: raise ValueError(error_text) except Exception as e: assert str(e) == error_text buf = out.getvalue() result = _read_table(pa.BufferReader(buf)) expected = pd.concat(frames, ignore_index=True) tm.assert_frame_equal(result.to_pandas(), expected) @pytest.mark.pandas def test_zlib_compression_bug(): # ARROW-3514: "zlib deflate failed, output buffer too small" table = pa.Table.from_arrays([pa.array(['abc', 'def'])], ['some_col']) f = io.BytesIO() pq.write_table(table, f, compression='gzip') f.seek(0) roundtrip = pq.read_table(f) tm.assert_frame_equal(roundtrip.to_pandas(), table.to_pandas()) @pytest.mark.pandas def test_merging_parquet_tables_with_different_pandas_metadata(tempdir): # ARROW-3728: Merging Parquet Files - Pandas Meta in Schema Mismatch schema = pa.schema([ pa.field('int', pa.int16()), pa.field('float', pa.float32()), pa.field('string', pa.string()) ]) df1 = pd.DataFrame({ 'int': np.arange(3, dtype=np.uint8), 'float': np.arange(3, dtype=np.float32), 'string': ['ABBA', 'EDDA', 'ACDC'] }) df2 = pd.DataFrame({ 'int': [4, 5], 'float': [1.1, None], 'string': [None, None] }) table1 = pa.Table.from_pandas(df1, schema=schema, preserve_index=False) table2 = pa.Table.from_pandas(df2, schema=schema, preserve_index=False) assert not table1.schema.equals(table2.schema) assert table1.schema.equals(table2.schema, check_metadata=False) writer = pq.ParquetWriter(tempdir / 'merged.parquet', schema=schema) writer.write_table(table1) writer.write_table(table2) def test_empty_row_groups(tempdir): # ARROW-3020 table = pa.Table.from_arrays([pa.array([], type='int32')], ['f0']) path = tempdir / 'empty_row_groups.parquet' num_groups = 3 with pq.ParquetWriter(path, table.schema) as writer: for i in range(num_groups): writer.write_table(table) reader = pq.ParquetFile(path) assert reader.metadata.num_row_groups == num_groups for i in range(num_groups): assert reader.read_row_group(i).equals(table) @pytest.mark.pandas def test_parquet_writer_with_caller_provided_filesystem(): out = pa.BufferOutputStream() class CustomFS(FileSystem): def __init__(self): self.path = None self.mode = None def open(self, path, mode='rb'): self.path = path self.mode = mode return out fs = CustomFS() fname = 'expected_fname.parquet' df = _test_dataframe(100) table = pa.Table.from_pandas(df, preserve_index=False) with pq.ParquetWriter(fname, table.schema, filesystem=fs, version='2.0') \ as writer: writer.write_table(table) assert fs.path == fname assert fs.mode == 'wb' assert out.closed buf = out.getvalue() table_read = _read_table(pa.BufferReader(buf)) df_read = table_read.to_pandas() tm.assert_frame_equal(df_read, df) # Should raise ValueError when filesystem is passed with file-like object with pytest.raises(ValueError) as err_info: pq.ParquetWriter(pa.BufferOutputStream(), table.schema, filesystem=fs) expected_msg = ("filesystem passed but where is file-like, so" " there is nothing to open with filesystem.") assert str(err_info) == expected_msg def test_writing_empty_lists(): # ARROW-2591: [Python] Segmentation fault issue in pq.write_table arr1 = pa.array([[], []], pa.list_(pa.int32())) table = pa.Table.from_arrays([arr1], ['list(int32)']) _check_roundtrip(table) def test_write_nested_zero_length_array_chunk_failure(): # Bug report in ARROW-3792 cols = OrderedDict( int32=pa.int32(), list_string=pa.list_(pa.string()) ) data = [[], [OrderedDict(int32=1, list_string=('G',)), ]] # This produces a table with a column like # <Column name='list_string' type=ListType(list<item: string>)> # [ # [], # [ # [ # "G" # ] # ] # ] # # Each column is a ChunkedArray with 2 elements my_arrays = [pa.array(batch, type=pa.struct(cols)).flatten() for batch in data] my_batches = [pa.RecordBatch.from_arrays(batch, pa.schema(cols)) for batch in my_arrays] tbl = pa.Table.from_batches(my_batches, pa.schema(cols)) _check_roundtrip(tbl) @pytest.mark.pandas def test_partitioned_dataset(tempdir): # ARROW-3208: Segmentation fault when reading a Parquet partitioned dataset # to a Parquet file path = tempdir / "ARROW-3208" df = pd.DataFrame({ 'one': [-1, 10, 2.5, 100, 1000, 1, 29.2], 'two': [-1, 10, 2, 100, 1000, 1, 11], 'three': [0, 0, 0, 0, 0, 0, 0] }) table = pa.Table.from_pandas(df) pq.write_to_dataset(table, root_path=str(path), partition_cols=['one', 'two']) table = pq.ParquetDataset(path).read() pq.write_table(table, path / "output.parquet") def test_read_column_invalid_index(): table = pa.table([pa.array([4, 5]), pa.array(["foo", "bar"])], names=['ints', 'strs']) bio = pa.BufferOutputStream() pq.write_table(table, bio) f = pq.ParquetFile(bio.getvalue()) assert f.reader.read_column(0).to_pylist() == [4, 5] assert f.reader.read_column(1).to_pylist() == ["foo", "bar"] for index in (-1, 2): with pytest.raises((ValueError, IndexError)): f.reader.read_column(index) def test_direct_read_dictionary(): # ARROW-3325 repeats = 10 nunique = 5 data = [ [tm.rands(10) for i in range(nunique)] * repeats, ] table = pa.table(data, names=['f0']) bio = pa.BufferOutputStream() pq.write_table(table, bio) contents = bio.getvalue() result = pq.read_table(pa.BufferReader(contents), read_dictionary=['f0']) # Compute dictionary-encoded subfield expected = pa.table([table[0].dictionary_encode()], names=['f0']) assert result.equals(expected) def test_dataset_read_dictionary(tempdir): path = tempdir / "ARROW-3325-dataset" t1 = pa.table([[tm.rands(10) for i in range(5)] * 10], names=['f0']) t2 = pa.table([[tm.rands(10) for i in range(5)] * 10], names=['f0']) pq.write_to_dataset(t1, root_path=str(path)) pq.write_to_dataset(t2, root_path=str(path)) result = pq.ParquetDataset(path, read_dictionary=['f0']).read() # The order of the chunks is non-deterministic ex_chunks = [t1[0].chunk(0).dictionary_encode(), t2[0].chunk(0).dictionary_encode()] assert result[0].num_chunks == 2 c0, c1 = result[0].chunk(0), result[0].chunk(1) if c0.equals(ex_chunks[0]): assert c1.equals(ex_chunks[1]) else: assert c0.equals(ex_chunks[1]) assert c1.equals(ex_chunks[0]) def test_direct_read_dictionary_subfield(): repeats = 10 nunique = 5 data = [ [[tm.rands(10)] for i in range(nunique)] * repeats, ] table = pa.table(data, names=['f0']) bio = pa.BufferOutputStream() pq.write_table(table, bio) contents = bio.getvalue() result = pq.read_table(pa.BufferReader(contents), read_dictionary=['f0.list.item']) arr = pa.array(data[0]) values_as_dict = arr.values.dictionary_encode() inner_indices = values_as_dict.indices.cast('int32') new_values = pa.DictionaryArray.from_arrays(inner_indices, values_as_dict.dictionary) offsets = pa.array(range(51), type='int32') expected_arr = pa.ListArray.from_arrays(offsets, new_values) expected = pa.table([expected_arr], names=['f0']) assert result.equals(expected) assert result[0].num_chunks == 1 @pytest.mark.pandas def test_dataset_metadata(tempdir): path = tempdir / "ARROW-1983-dataset" # create and write a test dataset df = pd.DataFrame({ 'one': [1, 2, 3], 'two': [-1, -2, -3], 'three': [[1, 2], [2, 3], [3, 4]], }) table = pa.Table.from_pandas(df) metadata_list = [] pq.write_to_dataset(table, root_path=str(path), partition_cols=['one', 'two'], metadata_collector=metadata_list) # open the dataset and collect metadata from pieces: dataset = pq.ParquetDataset(path) metadata_list2 = [p.get_metadata() for p in dataset.pieces] # compare metadata list content: assert len(metadata_list) == len(metadata_list2) for md, md2 in zip(metadata_list, metadata_list2): d = md.to_dict() d2 = md2.to_dict() # serialized_size is initialized in the reader: assert d.pop('serialized_size') == 0 assert d2.pop('serialized_size') > 0 assert d == d2 def test_parquet_file_too_small(tempdir): path = str(tempdir / "test.parquet") with pytest.raises(pa.ArrowIOError, match='size is 0 bytes'): with open(path, 'wb') as f: pass pq.read_table(path) with pytest.raises(pa.ArrowIOError, match='size is 4 bytes'): with open(path, 'wb') as f: f.write(b'ffff') pq.read_table(path) @pytest.mark.pandas def test_categorical_index_survives_roundtrip(): # ARROW-3652, addressed by ARROW-3246 df = pd.DataFrame([['a', 'b'], ['c', 'd']], columns=['c1', 'c2']) df['c1'] = df['c1'].astype('category') df = df.set_index(['c1']) table = pa.Table.from_pandas(df) bos = pa.BufferOutputStream() pq.write_table(table, bos) ref_df = pq.read_pandas(bos.getvalue()).to_pandas() assert isinstance(ref_df.index, pd.CategoricalIndex) assert ref_df.index.equals(df.index) def test_dictionary_array_automatically_read(): # ARROW-3246 # Make a large dictionary, a little over 4MB of data dict_length = 4000 dict_values = pa.array([('x' * 1000 + '_{}'.format(i)) for i in range(dict_length)]) num_chunks = 10 chunk_size = 100 chunks = [] for i in range(num_chunks): indices = np.random.randint(0, dict_length, size=chunk_size).astype(np.int32) chunks.append(pa.DictionaryArray.from_arrays(pa.array(indices), dict_values)) table = pa.table([pa.chunked_array(chunks)], names=['f0']) bio = pa.BufferOutputStream() pq.write_table(table, bio) contents = bio.getvalue() result = pq.read_table(pa.BufferReader(contents)) assert result.equals(table) # The only key in the metadata was the Arrow schema key assert result.schema.metadata is None @pytest.mark.pandas def test_pandas_categorical_na_type_row_groups(): # ARROW-5085 df = pd.DataFrame({"col": [None] * 100, "int": [1.0] * 100}) df_category = df.astype({"col": "category", "int": "category"}) table = pa.Table.from_pandas(df) table_cat = pa.Table.from_pandas(df_category) buf = pa.BufferOutputStream() # it works pq.write_table(table_cat, buf, version="2.0", chunk_size=10) result = pq.read_table(buf.getvalue()) # Result is non-categorical assert result[0].equals(table[0]) assert result[1].equals(table[1]) @pytest.mark.pandas def test_pandas_categorical_roundtrip(): # ARROW-5480, this was enabled by ARROW-3246 # Have one of the categories unobserved and include a null (-1) codes = np.array([2, 0, 0, 2, 0, -1, 2], dtype='int32') categories = ['foo', 'bar', 'baz'] df = pd.DataFrame({'x': pd.Categorical.from_codes( codes, categories=categories)}) buf = pa.BufferOutputStream() pq.write_table(pa.table(df), buf) result = pq.read_table(buf.getvalue()).to_pandas() assert result.x.dtype == 'category' assert (result.x.cat.categories == categories).all() tm.assert_frame_equal(result, df) @pytest.mark.pandas def test_multi_dataset_metadata(tempdir): filenames = ["ARROW-1983-dataset.0", "ARROW-1983-dataset.1"] metapath = str(tempdir / "_metadata") # create a test dataset df = pd.DataFrame({ 'one': [1, 2, 3], 'two': [-1, -2, -3], 'three': [[1, 2], [2, 3], [3, 4]], }) table = pa.Table.from_pandas(df) # write dataset twice and collect/merge metadata _meta = None for filename in filenames: meta = [] pq.write_table(table, str(tempdir / filename), metadata_collector=meta) meta[0].set_file_path(filename) if _meta is None: _meta = meta[0] else: _meta.append_row_groups(meta[0]) # Write merged metadata-only file with open(metapath, "wb") as f: _meta.write_metadata_file(f) # Read back the metadata meta = pq.read_metadata(metapath) md = meta.to_dict() _md = _meta.to_dict() for key in _md: if key != 'serialized_size': assert _md[key] == md[key] assert _md['num_columns'] == 3 assert _md['num_rows'] == 6 assert _md['num_row_groups'] == 2 assert _md['serialized_size'] == 0 assert md['serialized_size'] > 0 @pytest.mark.pandas def test_filter_before_validate_schema(tempdir): # ARROW-4076 apply filter before schema validation # to avoid checking unneeded schemas # create partitioned dataset with mismatching schemas which would # otherwise raise if first validation all schemas dir1 = tempdir / 'A=0' dir1.mkdir() table1 = pa.Table.from_pandas(pd.DataFrame({'B': [1, 2, 3]})) pq.write_table(table1, dir1 / 'data.parquet') dir2 = tempdir / 'A=1' dir2.mkdir() table2 = pa.Table.from_pandas(pd.DataFrame({'B': ['a', 'b', 'c']})) pq.write_table(table2, dir2 / 'data.parquet') # read single file using filter table = pq.read_table(tempdir, filters=[[('A', '==', 0)]]) assert table.column('B').equals(pa.chunked_array([[1, 2, 3]]))

true

f7175c38c11862b31ec72a419e525c555b34bcf3

16,570

py

Python

batch/processor_BondLedger_JP.py

BoostryJP/ibet-Issuer

efc599f8784be06588cf3ad8f239d36f24fdf3fa

[ "Apache-2.0" ]

1

2021-06-16T03:38:07.000Z

batch/processor_BondLedger_JP.py

BoostryJP/ibet-Issuer

efc599f8784be06588cf3ad8f239d36f24fdf3fa

[ "Apache-2.0" ]

17

2021-04-26T03:28:40.000Z

2021-11-24T07:15:55.000Z

batch/processor_BondLedger_JP.py

BoostryJP/ibet-Issuer

efc599f8784be06588cf3ad8f239d36f24fdf3fa

[ "Apache-2.0" ]

1

2021-05-30T14:09:11.000Z

""" Copyright BOOSTRY Co., Ltd. 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. SPDX-License-Identifier: Apache-2.0 """ import base64 from Crypto.Cipher import PKCS1_OAEP from Crypto.PublicKey import RSA from datetime import ( datetime, timezone, timedelta ) import json import os import sys import time from eth_utils import to_checksum_address from sqlalchemy import ( create_engine, func ) from sqlalchemy.orm import ( sessionmaker, scoped_session ) from web3 import Web3 from web3.middleware import geth_poa_middleware path = os.path.join(os.path.dirname(__file__), '../') sys.path.append(path) from app.utils import ContractUtils from app.models import ( Token, UTXO, BondLedger, BondLedgerBlockNumber, Issuer, CorporateBondLedgerTemplate, PersonalInfo as PersonalInfoModel ) from config import Config import log process_name = "PROCESSOR-BondLedger" LOG = log.get_logger(process_name=process_name) web3 = Web3(Web3.HTTPProvider(Config.WEB3_HTTP_PROVIDER)) web3.middleware_onion.inject(geth_poa_middleware, layer=0) engine = create_engine(Config.SQLALCHEMY_DATABASE_URI, echo=False) db_session = scoped_session(sessionmaker()) db_session.configure(bind=engine) JST = timezone(timedelta(hours=+9), "JST") class Sinks: def __init__(self): self.sinks = [] def register(self, sink): self.sinks.append(sink) def on_utxo(self, *args, **kwargs): for sink in self.sinks: sink.on_utxo(*args, **kwargs) def on_bond_ledger(self, *args, **kwargs): for sink in self.sinks: sink.on_bond_ledger(*args, **kwargs) def flush(self, *args, **kwargs): for sink in self.sinks: sink.flush(*args, **kwargs) class DBSink: def __init__(self, db): self.db = db def on_utxo(self, spent: bool, transaction_hash: str, account_address: str, token_address: str, amount: int, block_timestamp: datetime, transaction_date_jst: str): if spent is False: LOG.debug(f"Append UTXO: account_address={account_address}, token_address={token_address}, amount={amount}") utxo = self.db.query(UTXO). \ filter(UTXO.transaction_hash == transaction_hash). \ first() if utxo is None: utxo = UTXO() utxo.transaction_hash = transaction_hash utxo.account_address = account_address utxo.token_address = token_address utxo.amount = amount utxo.block_timestamp = block_timestamp utxo.transaction_date_jst = transaction_date_jst self.db.add(utxo) else: LOG.debug(f"Spend UTXO: account_address={account_address}, token_address={token_address}, amount={amount}") utxo_list = self.db.query(UTXO). \ filter(UTXO.account_address == account_address). \ filter(UTXO.token_address == token_address). \ filter(UTXO.amount > 0). \ order_by(UTXO.block_timestamp). \ all() spend_amount = amount for utxo in utxo_list: utxo_amount = utxo.amount if spend_amount <= 0: pass elif utxo.amount <= spend_amount: utxo.amount = 0 spend_amount = spend_amount - utxo_amount self.db.merge(utxo) else: utxo.amount = utxo_amount - spend_amount spend_amount = 0 self.db.merge(utxo) def on_bond_ledger(self, token): ######################################### # 原簿作成日 ######################################### created_date = datetime.utcnow().replace(tzinfo=timezone.utc).astimezone(JST).strftime("%Y/%m/%d") ######################################### # 社債の情報 ######################################### ledger_template = self.db.query(CorporateBondLedgerTemplate). \ filter(CorporateBondLedgerTemplate.token_address == token.address). \ first() if ledger_template is not None: bond_description = { "社債名称": ledger_template.bond_name, "社債の説明": ledger_template.bond_description, "社債の総額": ledger_template.total_amount, "各社債の金額": ledger_template.face_value, "払込情報": { "払込金額": ledger_template.payment_amount, "払込日": ledger_template.payment_date, "払込状況": ledger_template.payment_status }, "社債の種類": ledger_template.bond_type } else: bond_description = { "社債名称": "", "社債の説明": "", "社債の総額": None, "各社債の金額": None, "払込情報": { "払込金額": None, "払込日": "", "払込状況": None }, "社債の種類": "" } ######################################### # 原簿管理人 ######################################### if ledger_template is not None: ledger_admin = { "氏名または名称": ledger_template.ledger_admin_name, "住所": ledger_template.ledger_admin_address, "事務取扱場所": ledger_template.ledger_admin_location } else: ledger_admin = { "氏名または名称": "", "住所": "", "事務取扱場所": "" } ######################################### # 債権者情報 ######################################### issuer_address = token.functions.owner().call() face_value = token.functions.faceValue().call() utxo_list = self.db.query(UTXO.account_address, UTXO.token_address, func.sum(UTXO.amount), UTXO.transaction_date_jst). \ filter(UTXO.token_address == token.address). \ filter(UTXO.amount > 0). \ group_by(UTXO.account_address, UTXO.token_address, UTXO.transaction_date_jst). \ all() creditors = [] for utxo in utxo_list: account_address = utxo[0] amount = utxo[2] transaction_date_jst = utxo[3] # 初期値設定 details = { "アカウントアドレス": account_address, "氏名または名称": "", "住所": "", "社債金額": face_value * amount, "取得日": transaction_date_jst, "金銭以外の財産給付情報": { "財産の価格": "-", "給付日": "-" }, "債権相殺情報": { "相殺する債権額": "-", "相殺日": "-" }, "質権情報": { "質権者の氏名または名称": "-", "質権者の住所": "-", "質権の目的である債券": "-" }, "備考": "-" } # 個人情報取得 personal_info_json = self.__get_personalinfo_from_db( account_address=account_address, issuer_address=issuer_address ) if personal_info_json is None: # DBに情報が登録されていない場合はコントラクトから情報を取得する personal_info_contract_address = token.functions.personalInfoAddress().call() personal_info_json = self.__get_personalinfo_from_contract( account_address=account_address, issuer_address=issuer_address, personal_info_contract_address=personal_info_contract_address ) if personal_info_json is not None: name = personal_info_json.get("name", "") # 氏名 address = personal_info_json.get("address", "") # 住所 else: name = "" address = "" # 保有者情報設定 details["氏名または名称"] = name details["住所"] = address creditors.append(details) # 原簿保管 ledger = { "社債原簿作成日": created_date, "社債情報": bond_description, "社債原簿管理人": ledger_admin, "社債権者": creditors } bond_ledger = BondLedger( token_address=token.address, ledger=json.dumps(ledger, ensure_ascii=False).encode() ) self.db.add(bond_ledger) def __get_personalinfo_from_db(self, account_address: str, issuer_address: str): """個人情報取得（DB） :param account_address: アカウントアドレス :param issuer_address: 発行体アドレス :return: 個人情報JSON """ # 個人情報取得 personal_info_record = self.db.query(PersonalInfoModel). \ filter(PersonalInfoModel.account_address == to_checksum_address(account_address)). \ filter(PersonalInfoModel.issuer_address == to_checksum_address(issuer_address)). \ first() if personal_info_record is not None: personal_info_json = personal_info_record.personal_info else: personal_info_json = None return personal_info_json def __get_personalinfo_from_contract(self, account_address: str, issuer_address: str, personal_info_contract_address: str): """個人情報取得（コントラクト） :param account_address: アカウントアドレス :param issuer_address: 発行体アドレス :param personal_info_contract_address: 個人情報コントラクトアドレス :return: 個人情報JSON """ personal_info_json = None try: issuer = self.db.query(Issuer).filter(Issuer.eth_account == issuer_address).first() personal_info_contract = ContractUtils.get_contract('PersonalInfo', personal_info_contract_address) cipher = None try: key = RSA.importKey(issuer.encrypted_rsa_private_key, Config.RSA_PASSWORD) cipher = PKCS1_OAEP.new(key) except Exception as err: LOG.error(f"Cannot open the private key: {err}") # 暗号化個人情報取得 personal_info = personal_info_contract.functions. \ personal_info(account_address, issuer_address). \ call() encrypted_personal_info = personal_info[2] if encrypted_personal_info != '' and cipher is not None: # 情報が空の場合、デフォルト値を設定 # 個人情報復号化 ciphertext = base64.decodebytes(encrypted_personal_info.encode('utf-8')) # NOTE: # JavaScriptでRSA暗号化する際に、先頭が0x00の場合は00を削った状態でデータが連携される。 # そのままdecryptすると、ValueError（Ciphertext with incorrect length）になるため、 # 先頭に再度00を加えて、decryptを行う。 if len(ciphertext) == 1279: hex_fixed = "00" + ciphertext.hex() ciphertext = base64.b16decode(hex_fixed.upper()) message = cipher.decrypt(ciphertext) personal_info_json = json.loads(message) except Exception as err: LOG.error(f"Failed to decrypt: {err} : account_address = {account_address}") return personal_info_json def flush(self): self.db.commit() class Processor: def __init__(self, db, sink): self.sink = sink self.db = db self.token_list = [] def process(self): self.__refresh_token_list() ledger_block_number = self.__get_ledger_blocknumber() latest_block = web3.eth.blockNumber if ledger_block_number >= latest_block: LOG.debug("skip process") pass else: LOG.debug("syncing from={}, to={}".format(ledger_block_number + 1, latest_block)) for token in self.token_list: event_triggered = self.__create_utxo(token, ledger_block_number + 1, latest_block) if event_triggered: # UTXOの更新イベントが発生している場合 self.__create_ledger(token) self.__set_ledger_blocknumber(latest_block) self.sink.flush() def __refresh_token_list(self): """発行済トークンの直近化 :return: None """ self.token_list = [] issued_tokens = self.db.query(Token). \ filter(Token.template_id == Config.TEMPLATE_ID_SB). \ all() for issued_token in issued_tokens: if issued_token.token_address is not None: abi = json.loads(issued_token.abi.replace("'", '"').replace('True', 'true').replace('False', 'false')) token_contract = web3.eth.contract( address=issued_token.token_address, abi=abi ) self.token_list.append(token_contract) def __get_ledger_blocknumber(self): block_number = self.db.query(BondLedgerBlockNumber).first() if block_number is None: return 0 else: return block_number.latest_block_number def __set_ledger_blocknumber(self, block_number: int): """latest block number の設定 :param block_number: 設定するblockNumber :return: None """ ledger_block = self.db.query(BondLedgerBlockNumber).first() if ledger_block is None: ledger_block = BondLedgerBlockNumber() ledger_block.latest_block_number = block_number else: ledger_block.latest_block_number = block_number self.db.merge(ledger_block) def __create_utxo(self, token, from_block: int, to_block: int) -> bool: """UTXO作成（Transferイベント発生時） :param token: token contract :param from_block: from block number :param to_block: to block number :return: event_triggered イベント発生 """ event_triggered = False events = token.events.Transfer.getLogs( fromBlock=from_block, toBlock=to_block ) for event in events: event_triggered = True transaction_hash = event["transactionHash"].hex() args = event["args"] from_account = args.get("from", Config.ZERO_ADDRESS) to_account = args.get("to", Config.ZERO_ADDRESS) amount = args.get("value") block_timestamp = datetime.fromtimestamp( web3.eth.getBlock(event['blockNumber'])['timestamp'] ) block_timestamp_jst = block_timestamp.replace(tzinfo=timezone.utc). \ astimezone(JST) transaction_date_jst = block_timestamp_jst.strftime("%Y/%m/%d") if amount is not None and amount <= sys.maxsize: # UTXOの更新（from account） self.sink.on_utxo( spent=True, transaction_hash=transaction_hash, token_address=token.address, account_address=from_account, amount=amount, block_timestamp=block_timestamp, transaction_date_jst=transaction_date_jst ) # UTXOの更新（to account） self.sink.on_utxo( spent=False, transaction_hash=transaction_hash, token_address=token.address, account_address=to_account, amount=amount, block_timestamp=block_timestamp, transaction_date_jst=transaction_date_jst ) return event_triggered def __create_ledger(self, token): """原簿作成 :param token: token contract """ self.sink.on_bond_ledger(token=token) sinks = Sinks() sinks.register(DBSink(db_session)) processor = Processor(db=db_session, sink=sinks) LOG.info("Service started successfully") while True: try: processor.process() LOG.debug("processed") except Exception as ex: LOG.exception(ex) # 1分間隔で実行 time.sleep(Config.INTERVAL_PROCESSOR_BOND_LEDGER_JP)

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120

0.559626

import base64 from Crypto.Cipher import PKCS1_OAEP from Crypto.PublicKey import RSA from datetime import ( datetime, timezone, timedelta ) import json import os import sys import time from eth_utils import to_checksum_address from sqlalchemy import ( create_engine, func ) from sqlalchemy.orm import ( sessionmaker, scoped_session ) from web3 import Web3 from web3.middleware import geth_poa_middleware path = os.path.join(os.path.dirname(__file__), '../') sys.path.append(path) from app.utils import ContractUtils from app.models import ( Token, UTXO, BondLedger, BondLedgerBlockNumber, Issuer, CorporateBondLedgerTemplate, PersonalInfo as PersonalInfoModel ) from config import Config import log process_name = "PROCESSOR-BondLedger" LOG = log.get_logger(process_name=process_name) web3 = Web3(Web3.HTTPProvider(Config.WEB3_HTTP_PROVIDER)) web3.middleware_onion.inject(geth_poa_middleware, layer=0) engine = create_engine(Config.SQLALCHEMY_DATABASE_URI, echo=False) db_session = scoped_session(sessionmaker()) db_session.configure(bind=engine) JST = timezone(timedelta(hours=+9), "JST") class Sinks: def __init__(self): self.sinks = [] def register(self, sink): self.sinks.append(sink) def on_utxo(self, *args, **kwargs): for sink in self.sinks: sink.on_utxo(*args, **kwargs) def on_bond_ledger(self, *args, **kwargs): for sink in self.sinks: sink.on_bond_ledger(*args, **kwargs) def flush(self, *args, **kwargs): for sink in self.sinks: sink.flush(*args, **kwargs) class DBSink: def __init__(self, db): self.db = db def on_utxo(self, spent: bool, transaction_hash: str, account_address: str, token_address: str, amount: int, block_timestamp: datetime, transaction_date_jst: str): if spent is False: LOG.debug(f"Append UTXO: account_address={account_address}, token_address={token_address}, amount={amount}") utxo = self.db.query(UTXO). \ filter(UTXO.transaction_hash == transaction_hash). \ first() if utxo is None: utxo = UTXO() utxo.transaction_hash = transaction_hash utxo.account_address = account_address utxo.token_address = token_address utxo.amount = amount utxo.block_timestamp = block_timestamp utxo.transaction_date_jst = transaction_date_jst self.db.add(utxo) else: LOG.debug(f"Spend UTXO: account_address={account_address}, token_address={token_address}, amount={amount}") utxo_list = self.db.query(UTXO). \ filter(UTXO.account_address == account_address). \ filter(UTXO.token_address == token_address). \ filter(UTXO.amount > 0). \ order_by(UTXO.block_timestamp). \ all() spend_amount = amount for utxo in utxo_list: utxo_amount = utxo.amount if spend_amount <= 0: pass elif utxo.amount <= spend_amount: utxo.amount = 0 spend_amount = spend_amount - utxo_amount self.db.merge(utxo) else: utxo.amount = utxo_amount - spend_amount spend_amount = 0 self.db.merge(utxo) def on_bond_ledger(self, token): info_json def flush(self): self.db.commit() class Processor: def __init__(self, db, sink): self.sink = sink self.db = db self.token_list = [] def process(self): self.__refresh_token_list() ledger_block_number = self.__get_ledger_blocknumber() latest_block = web3.eth.blockNumber if ledger_block_number >= latest_block: LOG.debug("skip process") pass else: LOG.debug("syncing from={}, to={}".format(ledger_block_number + 1, latest_block)) for token in self.token_list: event_triggered = self.__create_utxo(token, ledger_block_number + 1, latest_block) if event_triggered: self.__create_ledger(token) self.__set_ledger_blocknumber(latest_block) self.sink.flush() def __refresh_token_list(self): self.token_list = [] issued_tokens = self.db.query(Token). \ filter(Token.template_id == Config.TEMPLATE_ID_SB). \ all() for issued_token in issued_tokens: if issued_token.token_address is not None: abi = json.loads(issued_token.abi.replace("'", '"').replace('True', 'true').replace('False', 'false')) token_contract = web3.eth.contract( address=issued_token.token_address, abi=abi ) self.token_list.append(token_contract) def __get_ledger_blocknumber(self): block_number = self.db.query(BondLedgerBlockNumber).first() if block_number is None: return 0 else: return block_number.latest_block_number def __set_ledger_blocknumber(self, block_number: int): ledger_block = self.db.query(BondLedgerBlockNumber).first() if ledger_block is None: ledger_block = BondLedgerBlockNumber() ledger_block.latest_block_number = block_number else: ledger_block.latest_block_number = block_number self.db.merge(ledger_block) def __create_utxo(self, token, from_block: int, to_block: int) -> bool: event_triggered = False events = token.events.Transfer.getLogs( fromBlock=from_block, toBlock=to_block ) for event in events: event_triggered = True transaction_hash = event["transactionHash"].hex() args = event["args"] from_account = args.get("from", Config.ZERO_ADDRESS) to_account = args.get("to", Config.ZERO_ADDRESS) amount = args.get("value") block_timestamp = datetime.fromtimestamp( web3.eth.getBlock(event['blockNumber'])['timestamp'] ) block_timestamp_jst = block_timestamp.replace(tzinfo=timezone.utc). \ astimezone(JST) transaction_date_jst = block_timestamp_jst.strftime("%Y/%m/%d") if amount is not None and amount <= sys.maxsize: # UTXOの更新（from account） self.sink.on_utxo( spent=True, transaction_hash=transaction_hash, token_address=token.address, account_address=from_account, amount=amount, block_timestamp=block_timestamp, transaction_date_jst=transaction_date_jst ) # UTXOの更新（to account） self.sink.on_utxo( spent=False, transaction_hash=transaction_hash, token_address=token.address, account_address=to_account, amount=amount, block_timestamp=block_timestamp, transaction_date_jst=transaction_date_jst ) return event_triggered def __create_ledger(self, token): self.sink.on_bond_ledger(token=token) sinks = Sinks() sinks.register(DBSink(db_session)) processor = Processor(db=db_session, sink=sinks) LOG.info("Service started successfully") while True: try: processor.process() LOG.debug("processed") except Exception as ex: LOG.exception(ex) # 1分間隔で実行 time.sleep(Config.INTERVAL_PROCESSOR_BOND_LEDGER_JP)

true

f7175d22337c8b28777d73b05a950345e75f3ce4

1,993

py

Python

app/forms.py

TimothyBenger/top_lists

b3b5895a3a3c525e81fe167eb7d7ba46cfcbd785

[ "MIT" ]

null

app/forms.py

TimothyBenger/top_lists

b3b5895a3a3c525e81fe167eb7d7ba46cfcbd785

[ "MIT" ]

null

app/forms.py

TimothyBenger/top_lists

b3b5895a3a3c525e81fe167eb7d7ba46cfcbd785

[ "MIT" ]

null

from flask_wtf import FlaskForm from wtforms import StringField, PasswordField, BooleanField, SubmitField, IntegerField from wtforms.validators import ValidationError, DataRequired, Email, EqualTo from app.models import User class LoginForm(FlaskForm): username = StringField('Username', validators=[DataRequired()]) password = PasswordField('Password', validators=[DataRequired()]) remember_me = BooleanField('Remember Me') submit = SubmitField('Sign In') class RegistrationForm(FlaskForm): username = StringField('Username', validators=[DataRequired()]) email = StringField('Email', validators=[DataRequired(), Email()]) password = PasswordField('Password', validators=[DataRequired()]) password2 = PasswordField( 'Repeat Password', validators=[DataRequired(), EqualTo('password')]) submit = SubmitField('Register') def validate_username(self, username): user = User.query.filter_by(username=username.data).first() if user is not None: raise ValidationError('Please use a different username.') def validate_email(self, email): user = User.query.filter_by(email=email.data).first() if user is not None: raise ValidationError('Please use a different email address.') class EditForm(FlaskForm): title1 = StringField('Title', validators=[DataRequired()]) author1 = StringField('Author', validators=[DataRequired()]) title2 = StringField('Title', validators=[DataRequired()]) author2 = StringField('Author', validators=[DataRequired()]) title3 = StringField('Title', validators=[DataRequired()]) author3 = StringField('Author', validators=[DataRequired()]) title4 = StringField('Title', validators=[DataRequired()]) author4 = StringField('Author', validators=[DataRequired()]) title5 = StringField('Title', validators=[DataRequired()]) author5 = StringField('Author', validators=[DataRequired()]) submit = SubmitField('Submit changes')

44.288889

87

0.714501

from flask_wtf import FlaskForm from wtforms import StringField, PasswordField, BooleanField, SubmitField, IntegerField from wtforms.validators import ValidationError, DataRequired, Email, EqualTo from app.models import User class LoginForm(FlaskForm): username = StringField('Username', validators=[DataRequired()]) password = PasswordField('Password', validators=[DataRequired()]) remember_me = BooleanField('Remember Me') submit = SubmitField('Sign In') class RegistrationForm(FlaskForm): username = StringField('Username', validators=[DataRequired()]) email = StringField('Email', validators=[DataRequired(), Email()]) password = PasswordField('Password', validators=[DataRequired()]) password2 = PasswordField( 'Repeat Password', validators=[DataRequired(), EqualTo('password')]) submit = SubmitField('Register') def validate_username(self, username): user = User.query.filter_by(username=username.data).first() if user is not None: raise ValidationError('Please use a different username.') def validate_email(self, email): user = User.query.filter_by(email=email.data).first() if user is not None: raise ValidationError('Please use a different email address.') class EditForm(FlaskForm): title1 = StringField('Title', validators=[DataRequired()]) author1 = StringField('Author', validators=[DataRequired()]) title2 = StringField('Title', validators=[DataRequired()]) author2 = StringField('Author', validators=[DataRequired()]) title3 = StringField('Title', validators=[DataRequired()]) author3 = StringField('Author', validators=[DataRequired()]) title4 = StringField('Title', validators=[DataRequired()]) author4 = StringField('Author', validators=[DataRequired()]) title5 = StringField('Title', validators=[DataRequired()]) author5 = StringField('Author', validators=[DataRequired()]) submit = SubmitField('Submit changes')

true

f7175d5c72e50e87ef42937e0544adccadf5efa8

452

py

Python

lidi/signup/migrations/0002_user_conf_link.py

campovski/lidi

9699e62e70e679970816e29ca7618c9ed0146c7e

[ "Apache-2.0" ]

null

lidi/signup/migrations/0002_user_conf_link.py

campovski/lidi

9699e62e70e679970816e29ca7618c9ed0146c7e

[ "Apache-2.0" ]

21

2017-06-03T14:16:14.000Z

2018-05-29T07:28:27.000Z

lidi/signup/migrations/0002_user_conf_link.py

campovski/lidi

9699e62e70e679970816e29ca7618c9ed0146c7e

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.11.1 on 2017-06-02 18:17 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('signup', '0001_initial'), ] operations = [ migrations.AddField( model_name='user', name='conf_link', field=models.CharField(default=b'', max_length=200), ), ]

21.52381

64

0.606195

from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('signup', '0001_initial'), ] operations = [ migrations.AddField( model_name='user', name='conf_link', field=models.CharField(default=b'', max_length=200), ), ]

true

f7175d75f291571b61c64c017b95e8592d28ea76

424

py

Python

app/moviestore/migrations/0007_alter_movie_user_charge.py

GeorgiosDolias/Movie-Store-REST-API

3a07301e4574071d6edb00d1a8b2c266c1fc8ff1

[ "MIT" ]

null

app/moviestore/migrations/0007_alter_movie_user_charge.py

GeorgiosDolias/Movie-Store-REST-API

3a07301e4574071d6edb00d1a8b2c266c1fc8ff1

[ "MIT" ]

null

app/moviestore/migrations/0007_alter_movie_user_charge.py

GeorgiosDolias/Movie-Store-REST-API

3a07301e4574071d6edb00d1a8b2c266c1fc8ff1

[ "MIT" ]

null

# Generated by Django 3.2.8 on 2021-11-05 00:25 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('moviestore', '0006_movie_user_charge'), ] operations = [ migrations.AlterField( model_name='movie', name='user_charge', field=models.DecimalField(blank=True, decimal_places=2, max_digits=5), ), ]

22.315789

82

0.620283

from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('moviestore', '0006_movie_user_charge'), ] operations = [ migrations.AlterField( model_name='movie', name='user_charge', field=models.DecimalField(blank=True, decimal_places=2, max_digits=5), ), ]

true

f7175f33e2db2534a03ad6bfb8c47d7e1b04f568

486

py

Python

main/strings/slice/words.py

catalinprescure/python-pages

93df3b22df2cfa269127e803a1b6c6a34bae6745

[ "MIT" ]

null

main/strings/slice/words.py

catalinprescure/python-pages

93df3b22df2cfa269127e803a1b6c6a34bae6745

[ "MIT" ]

null

main/strings/slice/words.py

catalinprescure/python-pages

93df3b22df2cfa269127e803a1b6c6a34bae6745

[ "MIT" ]

1

2021-12-24T15:58:32.000Z

# Open file and search through words list # Return number of words with no e in them import os file = os.path.dirname(__file__) + "/words.txt" rows = open(file) def has_no_e(word): for letter in word: if letter == "e": return False return True W = [] # words E = [] # word with no e for row in rows: word = row.strip() W.append(word) if (has_no_e(word)): E.append(word) print("W: " + repr(len(W))) print("E: " + repr(len(E)))

18.692308

47

0.584362

import os file = os.path.dirname(__file__) + "/words.txt" rows = open(file) def has_no_e(word): for letter in word: if letter == "e": return False return True W = [] E = [] for row in rows: word = row.strip() W.append(word) if (has_no_e(word)): E.append(word) print("W: " + repr(len(W))) print("E: " + repr(len(E)))

true

f717606f6302397f6adbcd4ea5ca8a1e1e665802

374

py

Python

setup.py

adamjoshuagray/PredictItPy

4006cb00f38c256765b556b3476c286470555533

[ "MIT" ]

null

setup.py

adamjoshuagray/PredictItPy

4006cb00f38c256765b556b3476c286470555533

[ "MIT" ]

1

2016-11-08T10:02:31.000Z

setup.py

adamjoshuagray/PredictItPy

4006cb00f38c256765b556b3476c286470555533

[ "MIT" ]

1

2019-07-04T10:53:41.000Z

from setuptools import setup setup(name='predictitpy', version='0.2', py_modules=['predictitpy'], description='A very light wrapper around the PredictIt.org market data api.', url='https://github.com/adamjoshuagray/predictitpy', author='Adam J. Gray', author_email='adam.joshua.gray@gmail.com', license='MIT', zip_safe=False)

34

83

0.671123

from setuptools import setup setup(name='predictitpy', version='0.2', py_modules=['predictitpy'], description='A very light wrapper around the PredictIt.org market data api.', url='https://github.com/adamjoshuagray/predictitpy', author='Adam J. Gray', author_email='adam.joshua.gray@gmail.com', license='MIT', zip_safe=False)

true

f71763968b7433700606cc9fe3afcf0b874429db

129,676

py

Python

salt/ext/tornado/web.py

yuriks/salt

d2a5bd8adddb98ec1718d79384aa13b4f37e8028

[ "Apache-2.0", "MIT" ]

1

2020-03-31T22:51:16.000Z

salt/ext/tornado/web.py

yuriks/salt

d2a5bd8adddb98ec1718d79384aa13b4f37e8028

[ "Apache-2.0", "MIT" ]

null

salt/ext/tornado/web.py

yuriks/salt

d2a5bd8adddb98ec1718d79384aa13b4f37e8028

[ "Apache-2.0", "MIT" ]

1

2021-09-30T07:00:01.000Z

#!/usr/bin/env python # # Copyright 2009 Facebook # # 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. """``tornado.web`` provides a simple web framework with asynchronous features that allow it to scale to large numbers of open connections, making it ideal for `long polling <http://en.wikipedia.org/wiki/Push_technology#Long_polling>`_. Here is a simple "Hello, world" example app: .. testcode:: import tornado.ioloop import tornado.web class MainHandler(tornado.web.RequestHandler): def get(self): self.write("Hello, world") if __name__ == "__main__": application = tornado.web.Application([ (r"/", MainHandler), ]) application.listen(8888) tornado.ioloop.IOLoop.current().start() .. testoutput:: :hide: See the :doc:`guide` for additional information. Thread-safety notes ------------------- In general, methods on `RequestHandler` and elsewhere in Tornado are not thread-safe. In particular, methods such as `~RequestHandler.write()`, `~RequestHandler.finish()`, and `~RequestHandler.flush()` must only be called from the main thread. If you use multiple threads it is important to use `.IOLoop.add_callback` to transfer control back to the main thread before finishing the request. """ # pylint: skip-file from __future__ import absolute_import, division, print_function import base64 import binascii import datetime import email.utils import functools import gzip import hashlib import hmac import mimetypes import numbers import os.path import re import stat import sys import threading import time import salt.ext.tornado as tornado import traceback import types from inspect import isclass from io import BytesIO from salt.ext.tornado.concurrent import Future from salt.ext.tornado import escape from salt.ext.tornado import gen from salt.ext.tornado import httputil from salt.ext.tornado import iostream from salt.ext.tornado import locale from salt.ext.tornado.log import access_log, app_log, gen_log from salt.ext.tornado import stack_context from salt.ext.tornado import template from salt.ext.tornado.escape import utf8, _unicode from salt.ext.tornado.routing import (AnyMatches, DefaultHostMatches, HostMatches, ReversibleRouter, Rule, ReversibleRuleRouter, URLSpec) from salt.ext.tornado.util import (ObjectDict, raise_exc_info, unicode_type, _websocket_mask, PY3) url = URLSpec if PY3: import http.cookies as Cookie import urllib.parse as urlparse from urllib.parse import urlencode else: import Cookie import urlparse from urllib import urlencode try: import typing # noqa # The following types are accepted by RequestHandler.set_header # and related methods. _HeaderTypes = typing.Union[bytes, unicode_type, numbers.Integral, datetime.datetime] except ImportError: pass MIN_SUPPORTED_SIGNED_VALUE_VERSION = 1 """The oldest signed value version supported by this version of Tornado. Signed values older than this version cannot be decoded. .. versionadded:: 3.2.1 """ MAX_SUPPORTED_SIGNED_VALUE_VERSION = 2 """The newest signed value version supported by this version of Tornado. Signed values newer than this version cannot be decoded. .. versionadded:: 3.2.1 """ DEFAULT_SIGNED_VALUE_VERSION = 2 """The signed value version produced by `.RequestHandler.create_signed_value`. May be overridden by passing a ``version`` keyword argument. .. versionadded:: 3.2.1 """ DEFAULT_SIGNED_VALUE_MIN_VERSION = 1 """The oldest signed value accepted by `.RequestHandler.get_secure_cookie`. May be overridden by passing a ``min_version`` keyword argument. .. versionadded:: 3.2.1 """ class RequestHandler(object): """Base class for HTTP request handlers. Subclasses must define at least one of the methods defined in the "Entry points" section below. """ SUPPORTED_METHODS = ("GET", "HEAD", "POST", "DELETE", "PATCH", "PUT", "OPTIONS") _template_loaders = {} # type: typing.Dict[str, template.BaseLoader] _template_loader_lock = threading.Lock() _remove_control_chars_regex = re.compile(r"[\x00-\x08\x0e-\x1f]") def __init__(self, application, request, **kwargs): super(RequestHandler, self).__init__() self.application = application self.request = request self._headers_written = False self._finished = False self._auto_finish = True self._transforms = None # will be set in _execute self._prepared_future = None self._headers = None # type: httputil.HTTPHeaders self.path_args = None self.path_kwargs = None self.ui = ObjectDict((n, self._ui_method(m)) for n, m in application.ui_methods.items()) # UIModules are available as both `modules` and `_tt_modules` in the # template namespace. Historically only `modules` was available # but could be clobbered by user additions to the namespace. # The template {% module %} directive looks in `_tt_modules` to avoid # possible conflicts. self.ui["_tt_modules"] = _UIModuleNamespace(self, application.ui_modules) self.ui["modules"] = self.ui["_tt_modules"] self.clear() self.request.connection.set_close_callback(self.on_connection_close) self.initialize(**kwargs) def initialize(self): """Hook for subclass initialization. Called for each request. A dictionary passed as the third argument of a url spec will be supplied as keyword arguments to initialize(). Example:: class ProfileHandler(RequestHandler): def initialize(self, database): self.database = database def get(self, username): ... app = Application([ (r'/user/(.*)', ProfileHandler, dict(database=database)), ]) """ pass @property def settings(self): """An alias for `self.application.settings <Application.settings>`.""" return self.application.settings def head(self, *args, **kwargs): raise HTTPError(405) def get(self, *args, **kwargs): raise HTTPError(405) def post(self, *args, **kwargs): raise HTTPError(405) def delete(self, *args, **kwargs): raise HTTPError(405) def patch(self, *args, **kwargs): raise HTTPError(405) def put(self, *args, **kwargs): raise HTTPError(405) def options(self, *args, **kwargs): raise HTTPError(405) def prepare(self): """Called at the beginning of a request before `get`/`post`/etc. Override this method to perform common initialization regardless of the request method. Asynchronous support: Decorate this method with `.gen.coroutine` or `.return_future` to make it asynchronous (the `asynchronous` decorator cannot be used on `prepare`). If this method returns a `.Future` execution will not proceed until the `.Future` is done. .. versionadded:: 3.1 Asynchronous support. """ pass def on_finish(self): """Called after the end of a request. Override this method to perform cleanup, logging, etc. This method is a counterpart to `prepare`. ``on_finish`` may not produce any output, as it is called after the response has been sent to the client. """ pass def on_connection_close(self): """Called in async handlers if the client closed the connection. Override this to clean up resources associated with long-lived connections. Note that this method is called only if the connection was closed during asynchronous processing; if you need to do cleanup after every request override `on_finish` instead. Proxies may keep a connection open for a time (perhaps indefinitely) after the client has gone away, so this method may not be called promptly after the end user closes their connection. """ if _has_stream_request_body(self.__class__): if not self.request.body.done(): self.request.body.set_exception(iostream.StreamClosedError()) self.request.body.exception() def clear(self): """Resets all headers and content for this response.""" self._headers = httputil.HTTPHeaders({ "Server": "TornadoServer/%s" % tornado.version, "Content-Type": "text/html; charset=UTF-8", "Date": httputil.format_timestamp(time.time()), }) self.set_default_headers() self._write_buffer = [] self._status_code = 200 self._reason = httputil.responses[200] def set_default_headers(self): """Override this to set HTTP headers at the beginning of the request. For example, this is the place to set a custom ``Server`` header. Note that setting such headers in the normal flow of request processing may not do what you want, since headers may be reset during error handling. """ pass def set_status(self, status_code, reason=None): """Sets the status code for our response. :arg int status_code: Response status code. If ``reason`` is ``None``, it must be present in `httplib.responses <http.client.responses>`. :arg string reason: Human-readable reason phrase describing the status code. If ``None``, it will be filled in from `httplib.responses <http.client.responses>`. """ self._status_code = status_code if reason is not None: self._reason = escape.native_str(reason) else: try: self._reason = httputil.responses[status_code] except KeyError: raise ValueError("unknown status code %d" % status_code) def get_status(self): """Returns the status code for our response.""" return self._status_code def set_header(self, name, value): # type: (str, _HeaderTypes) -> None """Sets the given response header name and value. If a datetime is given, we automatically format it according to the HTTP specification. If the value is not a string, we convert it to a string. All header values are then encoded as UTF-8. """ self._headers[name] = self._convert_header_value(value) def add_header(self, name, value): # type: (str, _HeaderTypes) -> None """Adds the given response header and value. Unlike `set_header`, `add_header` may be called multiple times to return multiple values for the same header. """ self._headers.add(name, self._convert_header_value(value)) def clear_header(self, name): """Clears an outgoing header, undoing a previous `set_header` call. Note that this method does not apply to multi-valued headers set by `add_header`. """ if name in self._headers: del self._headers[name] _INVALID_HEADER_CHAR_RE = re.compile(r"[\x00-\x1f]") def _convert_header_value(self, value): # type: (_HeaderTypes) -> str # Convert the input value to a str. This type check is a bit # subtle: The bytes case only executes on python 3, and the # unicode case only executes on python 2, because the other # cases are covered by the first match for str. if isinstance(value, str): retval = value elif isinstance(value, bytes): # py3 # Non-ascii characters in headers are not well supported, # but if you pass bytes, use latin1 so they pass through as-is. retval = value.decode('latin1') elif isinstance(value, unicode_type): # py2 # TODO: This is inconsistent with the use of latin1 above, # but it's been that way for a long time. Should it change? retval = escape.utf8(value) elif isinstance(value, numbers.Integral): # return immediately since we know the converted value will be safe return str(value) elif isinstance(value, datetime.datetime): return httputil.format_timestamp(value) else: raise TypeError("Unsupported header value %r" % value) # If \n is allowed into the header, it is possible to inject # additional headers or split the request. if RequestHandler._INVALID_HEADER_CHAR_RE.search(retval): raise ValueError("Unsafe header value %r", retval) return retval _ARG_DEFAULT = object() def get_argument(self, name, default=_ARG_DEFAULT, strip=True): """Returns the value of the argument with the given name. If default is not provided, the argument is considered to be required, and we raise a `MissingArgumentError` if it is missing. If the argument appears in the url more than once, we return the last value. The returned value is always unicode. """ return self._get_argument(name, default, self.request.arguments, strip) def get_arguments(self, name, strip=True): """Returns a list of the arguments with the given name. If the argument is not present, returns an empty list. The returned values are always unicode. """ # Make sure `get_arguments` isn't accidentally being called with a # positional argument that's assumed to be a default (like in # `get_argument`.) assert isinstance(strip, bool) return self._get_arguments(name, self.request.arguments, strip) def get_body_argument(self, name, default=_ARG_DEFAULT, strip=True): """Returns the value of the argument with the given name from the request body. If default is not provided, the argument is considered to be required, and we raise a `MissingArgumentError` if it is missing. If the argument appears in the url more than once, we return the last value. The returned value is always unicode. .. versionadded:: 3.2 """ return self._get_argument(name, default, self.request.body_arguments, strip) def get_body_arguments(self, name, strip=True): """Returns a list of the body arguments with the given name. If the argument is not present, returns an empty list. The returned values are always unicode. .. versionadded:: 3.2 """ return self._get_arguments(name, self.request.body_arguments, strip) def get_query_argument(self, name, default=_ARG_DEFAULT, strip=True): """Returns the value of the argument with the given name from the request query string. If default is not provided, the argument is considered to be required, and we raise a `MissingArgumentError` if it is missing. If the argument appears in the url more than once, we return the last value. The returned value is always unicode. .. versionadded:: 3.2 """ return self._get_argument(name, default, self.request.query_arguments, strip) def get_query_arguments(self, name, strip=True): """Returns a list of the query arguments with the given name. If the argument is not present, returns an empty list. The returned values are always unicode. .. versionadded:: 3.2 """ return self._get_arguments(name, self.request.query_arguments, strip) def _get_argument(self, name, default, source, strip=True): args = self._get_arguments(name, source, strip=strip) if not args: if default is self._ARG_DEFAULT: raise MissingArgumentError(name) return default return args[-1] def _get_arguments(self, name, source, strip=True): values = [] for v in source.get(name, []): v = self.decode_argument(v, name=name) if isinstance(v, unicode_type): # Get rid of any weird control chars (unless decoding gave # us bytes, in which case leave it alone) v = RequestHandler._remove_control_chars_regex.sub(" ", v) if strip: v = v.strip() values.append(v) return values def decode_argument(self, value, name=None): """Decodes an argument from the request. The argument has been percent-decoded and is now a byte string. By default, this method decodes the argument as utf-8 and returns a unicode string, but this may be overridden in subclasses. This method is used as a filter for both `get_argument()` and for values extracted from the url and passed to `get()`/`post()`/etc. The name of the argument is provided if known, but may be None (e.g. for unnamed groups in the url regex). """ try: return _unicode(value) except UnicodeDecodeError: raise HTTPError(400, "Invalid unicode in %s: %r" % (name or "url", value[:40])) @property def cookies(self): """An alias for `self.request.cookies <.httputil.HTTPServerRequest.cookies>`.""" return self.request.cookies def get_cookie(self, name, default=None): """Gets the value of the cookie with the given name, else default.""" if self.request.cookies is not None and name in self.request.cookies: return self.request.cookies[name].value return default def set_cookie(self, name, value, domain=None, expires=None, path="/", expires_days=None, **kwargs): """Sets the given cookie name/value with the given options. Additional keyword arguments are set on the Cookie.Morsel directly. See https://docs.python.org/2/library/cookie.html#Cookie.Morsel for available attributes. """ # The cookie library only accepts type str, in both python 2 and 3 name = escape.native_str(name) value = escape.native_str(value) if re.search(r"[\x00-\x20]", name + value): # Don't let us accidentally inject bad stuff raise ValueError("Invalid cookie %r: %r" % (name, value)) if not hasattr(self, "_new_cookie"): self._new_cookie = Cookie.SimpleCookie() if name in self._new_cookie: del self._new_cookie[name] self._new_cookie[name] = value morsel = self._new_cookie[name] if domain: morsel["domain"] = domain if expires_days is not None and not expires: expires = datetime.datetime.utcnow() + datetime.timedelta( days=expires_days) if expires: morsel["expires"] = httputil.format_timestamp(expires) if path: morsel["path"] = path for k, v in kwargs.items(): if k == 'max_age': k = 'max-age' # skip falsy values for httponly and secure flags because # SimpleCookie sets them regardless if k in ['httponly', 'secure'] and not v: continue morsel[k] = v def clear_cookie(self, name, path="/", domain=None): """Deletes the cookie with the given name. Due to limitations of the cookie protocol, you must pass the same path and domain to clear a cookie as were used when that cookie was set (but there is no way to find out on the server side which values were used for a given cookie). """ expires = datetime.datetime.utcnow() - datetime.timedelta(days=365) self.set_cookie(name, value="", path=path, expires=expires, domain=domain) def clear_all_cookies(self, path="/", domain=None): """Deletes all the cookies the user sent with this request. See `clear_cookie` for more information on the path and domain parameters. .. versionchanged:: 3.2 Added the ``path`` and ``domain`` parameters. """ for name in self.request.cookies: self.clear_cookie(name, path=path, domain=domain) def set_secure_cookie(self, name, value, expires_days=30, version=None, **kwargs): """Signs and timestamps a cookie so it cannot be forged. You must specify the ``cookie_secret`` setting in your Application to use this method. It should be a long, random sequence of bytes to be used as the HMAC secret for the signature. To read a cookie set with this method, use `get_secure_cookie()`. Note that the ``expires_days`` parameter sets the lifetime of the cookie in the browser, but is independent of the ``max_age_days`` parameter to `get_secure_cookie`. Secure cookies may contain arbitrary byte values, not just unicode strings (unlike regular cookies) .. versionchanged:: 3.2.1 Added the ``version`` argument. Introduced cookie version 2 and made it the default. """ self.set_cookie(name, self.create_signed_value(name, value, version=version), expires_days=expires_days, **kwargs) def create_signed_value(self, name, value, version=None): """Signs and timestamps a string so it cannot be forged. Normally used via set_secure_cookie, but provided as a separate method for non-cookie uses. To decode a value not stored as a cookie use the optional value argument to get_secure_cookie. .. versionchanged:: 3.2.1 Added the ``version`` argument. Introduced cookie version 2 and made it the default. """ self.require_setting("cookie_secret", "secure cookies") secret = self.application.settings["cookie_secret"] key_version = None if isinstance(secret, dict): if self.application.settings.get("key_version") is None: raise Exception("key_version setting must be used for secret_key dicts") key_version = self.application.settings["key_version"] return create_signed_value(secret, name, value, version=version, key_version=key_version) def get_secure_cookie(self, name, value=None, max_age_days=31, min_version=None): """Returns the given signed cookie if it validates, or None. The decoded cookie value is returned as a byte string (unlike `get_cookie`). .. versionchanged:: 3.2.1 Added the ``min_version`` argument. Introduced cookie version 2; both versions 1 and 2 are accepted by default. """ self.require_setting("cookie_secret", "secure cookies") if value is None: value = self.get_cookie(name) return decode_signed_value(self.application.settings["cookie_secret"], name, value, max_age_days=max_age_days, min_version=min_version) def get_secure_cookie_key_version(self, name, value=None): """Returns the signing key version of the secure cookie. The version is returned as int. """ self.require_setting("cookie_secret", "secure cookies") if value is None: value = self.get_cookie(name) return get_signature_key_version(value) def redirect(self, url, permanent=False, status=None): """Sends a redirect to the given (optionally relative) URL. If the ``status`` argument is specified, that value is used as the HTTP status code; otherwise either 301 (permanent) or 302 (temporary) is chosen based on the ``permanent`` argument. The default is 302 (temporary). """ if self._headers_written: raise Exception("Cannot redirect after headers have been written") if status is None: status = 301 if permanent else 302 else: assert isinstance(status, int) and 300 <= status <= 399 self.set_status(status) self.set_header("Location", utf8(url)) self.finish() def write(self, chunk): """Writes the given chunk to the output buffer. To write the output to the network, use the flush() method below. If the given chunk is a dictionary, we write it as JSON and set the Content-Type of the response to be ``application/json``. (if you want to send JSON as a different ``Content-Type``, call set_header *after* calling write()). Note that lists are not converted to JSON because of a potential cross-site security vulnerability. All JSON output should be wrapped in a dictionary. More details at http://haacked.com/archive/2009/06/25/json-hijacking.aspx/ and https://github.com/facebook/tornado/issues/1009 """ if self._finished: raise RuntimeError("Cannot write() after finish()") if not isinstance(chunk, (bytes, unicode_type, dict)): message = "write() only accepts bytes, unicode, and dict objects" if isinstance(chunk, list): message += ". Lists not accepted for security reasons; see http://www.tornadoweb.org/en/stable/web.html#tornado.web.RequestHandler.write" raise TypeError(message) if isinstance(chunk, dict): chunk = escape.json_encode(chunk) self.set_header("Content-Type", "application/json; charset=UTF-8") chunk = utf8(chunk) self._write_buffer.append(chunk) def render(self, template_name, **kwargs): """Renders the template with the given arguments as the response.""" if self._finished: raise RuntimeError("Cannot render() after finish()") html = self.render_string(template_name, **kwargs) # Insert the additional JS and CSS added by the modules on the page js_embed = [] js_files = [] css_embed = [] css_files = [] html_heads = [] html_bodies = [] for module in getattr(self, "_active_modules", {}).values(): embed_part = module.embedded_javascript() if embed_part: js_embed.append(utf8(embed_part)) file_part = module.javascript_files() if file_part: if isinstance(file_part, (unicode_type, bytes)): js_files.append(file_part) else: js_files.extend(file_part) embed_part = module.embedded_css() if embed_part: css_embed.append(utf8(embed_part)) file_part = module.css_files() if file_part: if isinstance(file_part, (unicode_type, bytes)): css_files.append(file_part) else: css_files.extend(file_part) head_part = module.html_head() if head_part: html_heads.append(utf8(head_part)) body_part = module.html_body() if body_part: html_bodies.append(utf8(body_part)) if js_files: # Maintain order of JavaScript files given by modules js = self.render_linked_js(js_files) sloc = html.rindex(b'</body>') html = html[:sloc] + utf8(js) + b'\n' + html[sloc:] if js_embed: js = self.render_embed_js(js_embed) sloc = html.rindex(b'</body>') html = html[:sloc] + js + b'\n' + html[sloc:] if css_files: css = self.render_linked_css(css_files) hloc = html.index(b'</head>') html = html[:hloc] + utf8(css) + b'\n' + html[hloc:] if css_embed: css = self.render_embed_css(css_embed) hloc = html.index(b'</head>') html = html[:hloc] + css + b'\n' + html[hloc:] if html_heads: hloc = html.index(b'</head>') html = html[:hloc] + b''.join(html_heads) + b'\n' + html[hloc:] if html_bodies: hloc = html.index(b'</body>') html = html[:hloc] + b''.join(html_bodies) + b'\n' + html[hloc:] self.finish(html) def render_linked_js(self, js_files): """Default method used to render the final js links for the rendered webpage. Override this method in a sub-classed controller to change the output. """ paths = [] unique_paths = set() for path in js_files: if not is_absolute(path): path = self.static_url(path) if path not in unique_paths: paths.append(path) unique_paths.add(path) return ''.join('<script src="' + escape.xhtml_escape(p) + '" type="text/javascript"></script>' for p in paths) def render_embed_js(self, js_embed): """Default method used to render the final embedded js for the rendered webpage. Override this method in a sub-classed controller to change the output. """ return b'<script type="text/javascript">\n//<![CDATA[\n' + \ b'\n'.join(js_embed) + b'\n//]]>\n</script>' def render_linked_css(self, css_files): """Default method used to render the final css links for the rendered webpage. Override this method in a sub-classed controller to change the output. """ paths = [] unique_paths = set() for path in css_files: if not is_absolute(path): path = self.static_url(path) if path not in unique_paths: paths.append(path) unique_paths.add(path) return ''.join('<link href="' + escape.xhtml_escape(p) + '" ' 'type="text/css" rel="stylesheet"/>' for p in paths) def render_embed_css(self, css_embed): """Default method used to render the final embedded css for the rendered webpage. Override this method in a sub-classed controller to change the output. """ return b'<style type="text/css">\n' + b'\n'.join(css_embed) + \ b'\n</style>' def render_string(self, template_name, **kwargs): """Generate the given template with the given arguments. We return the generated byte string (in utf8). To generate and write a template as a response, use render() above. """ # If no template_path is specified, use the path of the calling file template_path = self.get_template_path() if not template_path: frame = sys._getframe(0) web_file = frame.f_code.co_filename while frame.f_code.co_filename == web_file: frame = frame.f_back template_path = os.path.dirname(frame.f_code.co_filename) with RequestHandler._template_loader_lock: if template_path not in RequestHandler._template_loaders: loader = self.create_template_loader(template_path) RequestHandler._template_loaders[template_path] = loader else: loader = RequestHandler._template_loaders[template_path] t = loader.load(template_name) namespace = self.get_template_namespace() namespace.update(kwargs) return t.generate(**namespace) def get_template_namespace(self): """Returns a dictionary to be used as the default template namespace. May be overridden by subclasses to add or modify values. The results of this method will be combined with additional defaults in the `tornado.template` module and keyword arguments to `render` or `render_string`. """ namespace = dict( handler=self, request=self.request, current_user=self.current_user, locale=self.locale, _=self.locale.translate, pgettext=self.locale.pgettext, static_url=self.static_url, xsrf_form_html=self.xsrf_form_html, reverse_url=self.reverse_url ) namespace.update(self.ui) return namespace def create_template_loader(self, template_path): """Returns a new template loader for the given path. May be overridden by subclasses. By default returns a directory-based loader on the given path, using the ``autoescape`` and ``template_whitespace`` application settings. If a ``template_loader`` application setting is supplied, uses that instead. """ settings = self.application.settings if "template_loader" in settings: return settings["template_loader"] kwargs = {} if "autoescape" in settings: # autoescape=None means "no escaping", so we have to be sure # to only pass this kwarg if the user asked for it. kwargs["autoescape"] = settings["autoescape"] if "template_whitespace" in settings: kwargs["whitespace"] = settings["template_whitespace"] return template.Loader(template_path, **kwargs) def flush(self, include_footers=False, callback=None): """Flushes the current output buffer to the network. The ``callback`` argument, if given, can be used for flow control: it will be run when all flushed data has been written to the socket. Note that only one flush callback can be outstanding at a time; if another flush occurs before the previous flush's callback has been run, the previous callback will be discarded. .. versionchanged:: 4.0 Now returns a `.Future` if no callback is given. """ chunk = b"".join(self._write_buffer) self._write_buffer = [] if not self._headers_written: self._headers_written = True for transform in self._transforms: self._status_code, self._headers, chunk = \ transform.transform_first_chunk( self._status_code, self._headers, chunk, include_footers) # Ignore the chunk and only write the headers for HEAD requests if self.request.method == "HEAD": chunk = None # Finalize the cookie headers (which have been stored in a side # object so an outgoing cookie could be overwritten before it # is sent). if hasattr(self, "_new_cookie"): for cookie in self._new_cookie.values(): self.add_header("Set-Cookie", cookie.OutputString(None)) start_line = httputil.ResponseStartLine('', self._status_code, self._reason) return self.request.connection.write_headers( start_line, self._headers, chunk, callback=callback) else: for transform in self._transforms: chunk = transform.transform_chunk(chunk, include_footers) # Ignore the chunk and only write the headers for HEAD requests if self.request.method != "HEAD": return self.request.connection.write(chunk, callback=callback) else: future = Future() future.set_result(None) return future def finish(self, chunk=None): """Finishes this response, ending the HTTP request.""" if self._finished: raise RuntimeError("finish() called twice") if chunk is not None: self.write(chunk) # Automatically support ETags and add the Content-Length header if # we have not flushed any content yet. if not self._headers_written: if (self._status_code == 200 and self.request.method in ("GET", "HEAD") and "Etag" not in self._headers): self.set_etag_header() if self.check_etag_header(): self._write_buffer = [] self.set_status(304) if (self._status_code in (204, 304) or (self._status_code >= 100 and self._status_code < 200)): assert not self._write_buffer, "Cannot send body with %s" % self._status_code self._clear_headers_for_304() elif "Content-Length" not in self._headers: content_length = sum(len(part) for part in self._write_buffer) self.set_header("Content-Length", content_length) if hasattr(self.request, "connection"): # Now that the request is finished, clear the callback we # set on the HTTPConnection (which would otherwise prevent the # garbage collection of the RequestHandler when there # are keepalive connections) self.request.connection.set_close_callback(None) self.flush(include_footers=True) self.request.finish() self._log() self._finished = True self.on_finish() self._break_cycles() def _break_cycles(self): # Break up a reference cycle between this handler and the # _ui_module closures to allow for faster GC on CPython. self.ui = None def send_error(self, status_code=500, **kwargs): """Sends the given HTTP error code to the browser. If `flush()` has already been called, it is not possible to send an error, so this method will simply terminate the response. If output has been written but not yet flushed, it will be discarded and replaced with the error page. Override `write_error()` to customize the error page that is returned. Additional keyword arguments are passed through to `write_error`. """ if self._headers_written: gen_log.error("Cannot send error response after headers written") if not self._finished: # If we get an error between writing headers and finishing, # we are unlikely to be able to finish due to a # Content-Length mismatch. Try anyway to release the # socket. try: self.finish() except Exception: gen_log.error("Failed to flush partial response", exc_info=True) return self.clear() reason = kwargs.get('reason') if 'exc_info' in kwargs: exception = kwargs['exc_info'][1] if isinstance(exception, HTTPError) and exception.reason: reason = exception.reason self.set_status(status_code, reason=reason) try: self.write_error(status_code, **kwargs) except Exception: app_log.error("Uncaught exception in write_error", exc_info=True) if not self._finished: self.finish() def write_error(self, status_code, **kwargs): """Override to implement custom error pages. ``write_error`` may call `write`, `render`, `set_header`, etc to produce output as usual. If this error was caused by an uncaught exception (including HTTPError), an ``exc_info`` triple will be available as ``kwargs["exc_info"]``. Note that this exception may not be the "current" exception for purposes of methods like ``sys.exc_info()`` or ``traceback.format_exc``. """ if self.settings.get("serve_traceback") and "exc_info" in kwargs: # in debug mode, try to send a traceback self.set_header('Content-Type', 'text/plain') for line in traceback.format_exception(*kwargs["exc_info"]): self.write(line) self.finish() else: self.finish("<html><title>%(code)d: %(message)s</title>" "<body>%(code)d: %(message)s</body></html>" % { "code": status_code, "message": self._reason, }) @property def locale(self): """The locale for the current session. Determined by either `get_user_locale`, which you can override to set the locale based on, e.g., a user preference stored in a database, or `get_browser_locale`, which uses the ``Accept-Language`` header. .. versionchanged: 4.1 Added a property setter. """ if not hasattr(self, "_locale"): self._locale = self.get_user_locale() if not self._locale: self._locale = self.get_browser_locale() assert self._locale return self._locale @locale.setter def locale(self, value): self._locale = value def get_user_locale(self): """Override to determine the locale from the authenticated user. If None is returned, we fall back to `get_browser_locale()`. This method should return a `tornado.locale.Locale` object, most likely obtained via a call like ``tornado.locale.get("en")`` """ return None def get_browser_locale(self, default="en_US"): """Determines the user's locale from ``Accept-Language`` header. See http://www.w3.org/Protocols/rfc2616/rfc2616-sec14.html#sec14.4 """ if "Accept-Language" in self.request.headers: languages = self.request.headers["Accept-Language"].split(",") locales = [] for language in languages: parts = language.strip().split(";") if len(parts) > 1 and parts[1].startswith("q="): try: score = float(parts[1][2:]) except (ValueError, TypeError): score = 0.0 else: score = 1.0 locales.append((parts[0], score)) if locales: locales.sort(key=lambda pair: pair[1], reverse=True) codes = [l[0] for l in locales] return locale.get(*codes) return locale.get(default) @property def current_user(self): """The authenticated user for this request. This is set in one of two ways: * A subclass may override `get_current_user()`, which will be called automatically the first time ``self.current_user`` is accessed. `get_current_user()` will only be called once per request, and is cached for future access:: def get_current_user(self): user_cookie = self.get_secure_cookie("user") if user_cookie: return json.loads(user_cookie) return None * It may be set as a normal variable, typically from an overridden `prepare()`:: @gen.coroutine def prepare(self): user_id_cookie = self.get_secure_cookie("user_id") if user_id_cookie: self.current_user = yield load_user(user_id_cookie) Note that `prepare()` may be a coroutine while `get_current_user()` may not, so the latter form is necessary if loading the user requires asynchronous operations. The user object may be any type of the application's choosing. """ if not hasattr(self, "_current_user"): self._current_user = self.get_current_user() return self._current_user @current_user.setter def current_user(self, value): self._current_user = value def get_current_user(self): """Override to determine the current user from, e.g., a cookie. This method may not be a coroutine. """ return None def get_login_url(self): """Override to customize the login URL based on the request. By default, we use the ``login_url`` application setting. """ self.require_setting("login_url", "@tornado.web.authenticated") return self.application.settings["login_url"] def get_template_path(self): """Override to customize template path for each handler. By default, we use the ``template_path`` application setting. Return None to load templates relative to the calling file. """ return self.application.settings.get("template_path") @property def xsrf_token(self): """The XSRF-prevention token for the current user/session. To prevent cross-site request forgery, we set an '_xsrf' cookie and include the same '_xsrf' value as an argument with all POST requests. If the two do not match, we reject the form submission as a potential forgery. See http://en.wikipedia.org/wiki/Cross-site_request_forgery .. versionchanged:: 3.2.2 The xsrf token will now be have a random mask applied in every request, which makes it safe to include the token in pages that are compressed. See http://breachattack.com for more information on the issue fixed by this change. Old (version 1) cookies will be converted to version 2 when this method is called unless the ``xsrf_cookie_version`` `Application` setting is set to 1. .. versionchanged:: 4.3 The ``xsrf_cookie_kwargs`` `Application` setting may be used to supply additional cookie options (which will be passed directly to `set_cookie`). For example, ``xsrf_cookie_kwargs=dict(httponly=True, secure=True)`` will set the ``secure`` and ``httponly`` flags on the ``_xsrf`` cookie. """ if not hasattr(self, "_xsrf_token"): version, token, timestamp = self._get_raw_xsrf_token() output_version = self.settings.get("xsrf_cookie_version", 2) cookie_kwargs = self.settings.get("xsrf_cookie_kwargs", {}) if output_version == 1: self._xsrf_token = binascii.b2a_hex(token) elif output_version == 2: mask = os.urandom(4) self._xsrf_token = b"|".join([ b"2", binascii.b2a_hex(mask), binascii.b2a_hex(_websocket_mask(mask, token)), utf8(str(int(timestamp)))]) else: raise ValueError("unknown xsrf cookie version %d", output_version) if version is None: expires_days = 30 if self.current_user else None self.set_cookie("_xsrf", self._xsrf_token, expires_days=expires_days, **cookie_kwargs) return self._xsrf_token def _get_raw_xsrf_token(self): """Read or generate the xsrf token in its raw form. The raw_xsrf_token is a tuple containing: * version: the version of the cookie from which this token was read, or None if we generated a new token in this request. * token: the raw token data; random (non-ascii) bytes. * timestamp: the time this token was generated (will not be accurate for version 1 cookies) """ if not hasattr(self, '_raw_xsrf_token'): cookie = self.get_cookie("_xsrf") if cookie: version, token, timestamp = self._decode_xsrf_token(cookie) else: version, token, timestamp = None, None, None if token is None: version = None token = os.urandom(16) timestamp = time.time() self._raw_xsrf_token = (version, token, timestamp) return self._raw_xsrf_token def _decode_xsrf_token(self, cookie): """Convert a cookie string into a the tuple form returned by _get_raw_xsrf_token. """ try: m = _signed_value_version_re.match(utf8(cookie)) if m: version = int(m.group(1)) if version == 2: _, mask, masked_token, timestamp = cookie.split("|") mask = binascii.a2b_hex(utf8(mask)) token = _websocket_mask( mask, binascii.a2b_hex(utf8(masked_token))) timestamp = int(timestamp) return version, token, timestamp else: # Treat unknown versions as not present instead of failing. raise Exception("Unknown xsrf cookie version") else: version = 1 try: token = binascii.a2b_hex(utf8(cookie)) except (binascii.Error, TypeError): token = utf8(cookie) # We don't have a usable timestamp in older versions. timestamp = int(time.time()) return (version, token, timestamp) except Exception: # Catch exceptions and return nothing instead of failing. gen_log.debug("Uncaught exception in _decode_xsrf_token", exc_info=True) return None, None, None def check_xsrf_cookie(self): """Verifies that the ``_xsrf`` cookie matches the ``_xsrf`` argument. To prevent cross-site request forgery, we set an ``_xsrf`` cookie and include the same value as a non-cookie field with all ``POST`` requests. If the two do not match, we reject the form submission as a potential forgery. The ``_xsrf`` value may be set as either a form field named ``_xsrf`` or in a custom HTTP header named ``X-XSRFToken`` or ``X-CSRFToken`` (the latter is accepted for compatibility with Django). See http://en.wikipedia.org/wiki/Cross-site_request_forgery Prior to release 1.1.1, this check was ignored if the HTTP header ``X-Requested-With: XMLHTTPRequest`` was present. This exception has been shown to be insecure and has been removed. For more information please see http://www.djangoproject.com/weblog/2011/feb/08/security/ http://weblog.rubyonrails.org/2011/2/8/csrf-protection-bypass-in-ruby-on-rails .. versionchanged:: 3.2.2 Added support for cookie version 2. Both versions 1 and 2 are supported. """ token = (self.get_argument("_xsrf", None) or self.request.headers.get("X-Xsrftoken") or self.request.headers.get("X-Csrftoken")) if not token: raise HTTPError(403, "'_xsrf' argument missing from POST") _, token, _ = self._decode_xsrf_token(token) _, expected_token, _ = self._get_raw_xsrf_token() if not token: raise HTTPError(403, "'_xsrf' argument has invalid format") if not _time_independent_equals(utf8(token), utf8(expected_token)): raise HTTPError(403, "XSRF cookie does not match POST argument") def xsrf_form_html(self): """An HTML ``<input/>`` element to be included with all POST forms. It defines the ``_xsrf`` input value, which we check on all POST requests to prevent cross-site request forgery. If you have set the ``xsrf_cookies`` application setting, you must include this HTML within all of your HTML forms. In a template, this method should be called with ``{% module xsrf_form_html() %}`` See `check_xsrf_cookie()` above for more information. """ return '<input type="hidden" name="_xsrf" value="' + \ escape.xhtml_escape(self.xsrf_token) + '"/>' def static_url(self, path, include_host=None, **kwargs): """Returns a static URL for the given relative static file path. This method requires you set the ``static_path`` setting in your application (which specifies the root directory of your static files). This method returns a versioned url (by default appending ``?v=<signature>``), which allows the static files to be cached indefinitely. This can be disabled by passing ``include_version=False`` (in the default implementation; other static file implementations are not required to support this, but they may support other options). By default this method returns URLs relative to the current host, but if ``include_host`` is true the URL returned will be absolute. If this handler has an ``include_host`` attribute, that value will be used as the default for all `static_url` calls that do not pass ``include_host`` as a keyword argument. """ self.require_setting("static_path", "static_url") get_url = self.settings.get("static_handler_class", StaticFileHandler).make_static_url if include_host is None: include_host = getattr(self, "include_host", False) if include_host: base = self.request.protocol + "://" + self.request.host else: base = "" return base + get_url(self.settings, path, **kwargs) def require_setting(self, name, feature="this feature"): """Raises an exception if the given app setting is not defined.""" if not self.application.settings.get(name): raise Exception("You must define the '%s' setting in your " "application to use %s" % (name, feature)) def reverse_url(self, name, *args): """Alias for `Application.reverse_url`.""" return self.application.reverse_url(name, *args) def compute_etag(self): """Computes the etag header to be used for this request. By default uses a hash of the content written so far. May be overridden to provide custom etag implementations, or may return None to disable tornado's default etag support. """ hasher = hashlib.sha1() for part in self._write_buffer: hasher.update(part) return '"%s"' % hasher.hexdigest() def set_etag_header(self): """Sets the response's Etag header using ``self.compute_etag()``. Note: no header will be set if ``compute_etag()`` returns ``None``. This method is called automatically when the request is finished. """ etag = self.compute_etag() if etag is not None: self.set_header("Etag", etag) def check_etag_header(self): """Checks the ``Etag`` header against requests's ``If-None-Match``. Returns ``True`` if the request's Etag matches and a 304 should be returned. For example:: self.set_etag_header() if self.check_etag_header(): self.set_status(304) return This method is called automatically when the request is finished, but may be called earlier for applications that override `compute_etag` and want to do an early check for ``If-None-Match`` before completing the request. The ``Etag`` header should be set (perhaps with `set_etag_header`) before calling this method. """ computed_etag = utf8(self._headers.get("Etag", "")) # Find all weak and strong etag values from If-None-Match header # because RFC 7232 allows multiple etag values in a single header. etags = re.findall( br'\*|(?:W/)?"[^"]*"', utf8(self.request.headers.get("If-None-Match", "")) ) if not computed_etag or not etags: return False match = False if etags[0] == b'*': match = True else: # Use a weak comparison when comparing entity-tags. def val(x): return x[2:] if x.startswith(b'W/') else x for etag in etags: if val(etag) == val(computed_etag): match = True break return match def _stack_context_handle_exception(self, type, value, traceback): try: # For historical reasons _handle_request_exception only takes # the exception value instead of the full triple, # so re-raise the exception to ensure that it's in # sys.exc_info() raise_exc_info((type, value, traceback)) except Exception: self._handle_request_exception(value) return True @gen.coroutine def _execute(self, transforms, *args, **kwargs): """Executes this request with the given output transforms.""" self._transforms = transforms try: if self.request.method not in self.SUPPORTED_METHODS: raise HTTPError(405) self.path_args = [self.decode_argument(arg) for arg in args] self.path_kwargs = dict((k, self.decode_argument(v, name=k)) for (k, v) in kwargs.items()) # If XSRF cookies are turned on, reject form submissions without # the proper cookie if self.request.method not in ("GET", "HEAD", "OPTIONS") and \ self.application.settings.get("xsrf_cookies"): self.check_xsrf_cookie() result = self.prepare() if result is not None: result = yield result if self._prepared_future is not None: # Tell the Application we've finished with prepare() # and are ready for the body to arrive. self._prepared_future.set_result(None) if self._finished: return if _has_stream_request_body(self.__class__): # In streaming mode request.body is a Future that signals # the body has been completely received. The Future has no # result; the data has been passed to self.data_received # instead. try: yield self.request.body except iostream.StreamClosedError: return method = getattr(self, self.request.method.lower()) result = method(*self.path_args, **self.path_kwargs) if result is not None: result = yield result if self._auto_finish and not self._finished: self.finish() except Exception as e: try: self._handle_request_exception(e) except Exception: app_log.error("Exception in exception handler", exc_info=True) if (self._prepared_future is not None and not self._prepared_future.done()): # In case we failed before setting _prepared_future, do it # now (to unblock the HTTP server). Note that this is not # in a finally block to avoid GC issues prior to Python 3.4. self._prepared_future.set_result(None) def data_received(self, chunk): """Implement this method to handle streamed request data. Requires the `.stream_request_body` decorator. """ raise NotImplementedError() def _log(self): """Logs the current request. Sort of deprecated since this functionality was moved to the Application, but left in place for the benefit of existing apps that have overridden this method. """ self.application.log_request(self) def _request_summary(self): return "%s %s (%s)" % (self.request.method, self.request.uri, self.request.remote_ip) def _handle_request_exception(self, e): if isinstance(e, Finish): # Not an error; just finish the request without logging. if not self._finished: self.finish(*e.args) return try: self.log_exception(*sys.exc_info()) except Exception: # An error here should still get a best-effort send_error() # to avoid leaking the connection. app_log.error("Error in exception logger", exc_info=True) if self._finished: # Extra errors after the request has been finished should # be logged, but there is no reason to continue to try and # send a response. return if isinstance(e, HTTPError): if e.status_code not in httputil.responses and not e.reason: gen_log.error("Bad HTTP status code: %d", e.status_code) self.send_error(500, exc_info=sys.exc_info()) else: self.send_error(e.status_code, exc_info=sys.exc_info()) else: self.send_error(500, exc_info=sys.exc_info()) def log_exception(self, typ, value, tb): """Override to customize logging of uncaught exceptions. By default logs instances of `HTTPError` as warnings without stack traces (on the ``tornado.general`` logger), and all other exceptions as errors with stack traces (on the ``tornado.application`` logger). .. versionadded:: 3.1 """ if isinstance(value, HTTPError): if value.log_message: format = "%d %s: " + value.log_message args = ([value.status_code, self._request_summary()] + list(value.args)) gen_log.warning(format, *args) else: app_log.error("Uncaught exception %s\n%r", self._request_summary(), self.request, exc_info=(typ, value, tb)) def _ui_module(self, name, module): def render(*args, **kwargs): if not hasattr(self, "_active_modules"): self._active_modules = {} if name not in self._active_modules: self._active_modules[name] = module(self) rendered = self._active_modules[name].render(*args, **kwargs) return rendered return render def _ui_method(self, method): return lambda *args, **kwargs: method(self, *args, **kwargs) def _clear_headers_for_304(self): # 304 responses should not contain entity headers (defined in # http://www.w3.org/Protocols/rfc2616/rfc2616-sec7.html#sec7.1) # not explicitly allowed by # http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html#sec10.3.5 headers = ["Allow", "Content-Encoding", "Content-Language", "Content-Length", "Content-MD5", "Content-Range", "Content-Type", "Last-Modified"] for h in headers: self.clear_header(h) def asynchronous(method): """Wrap request handler methods with this if they are asynchronous. This decorator is for callback-style asynchronous methods; for coroutines, use the ``@gen.coroutine`` decorator without ``@asynchronous``. (It is legal for legacy reasons to use the two decorators together provided ``@asynchronous`` is first, but ``@asynchronous`` will be ignored in this case) This decorator should only be applied to the :ref:`HTTP verb methods <verbs>`; its behavior is undefined for any other method. This decorator does not *make* a method asynchronous; it tells the framework that the method *is* asynchronous. For this decorator to be useful the method must (at least sometimes) do something asynchronous. If this decorator is given, the response is not finished when the method returns. It is up to the request handler to call `self.finish() <RequestHandler.finish>` to finish the HTTP request. Without this decorator, the request is automatically finished when the ``get()`` or ``post()`` method returns. Example: .. testcode:: class MyRequestHandler(RequestHandler): @asynchronous def get(self): http = httpclient.AsyncHTTPClient() http.fetch("http://friendfeed.com/", self._on_download) def _on_download(self, response): self.write("Downloaded!") self.finish() .. testoutput:: :hide: .. versionchanged:: 3.1 The ability to use ``@gen.coroutine`` without ``@asynchronous``. .. versionchanged:: 4.3 Returning anything but ``None`` or a yieldable object from a method decorated with ``@asynchronous`` is an error. Such return values were previously ignored silently. """ # Delay the IOLoop import because it's not available on app engine. from salt.ext.tornado.ioloop import IOLoop @functools.wraps(method) def wrapper(self, *args, **kwargs): self._auto_finish = False with stack_context.ExceptionStackContext( self._stack_context_handle_exception): result = method(self, *args, **kwargs) if result is not None: result = gen.convert_yielded(result) # If @asynchronous is used with @gen.coroutine, (but # not @gen.engine), we can automatically finish the # request when the future resolves. Additionally, # the Future will swallow any exceptions so we need # to throw them back out to the stack context to finish # the request. def future_complete(f): f.result() if not self._finished: self.finish() IOLoop.current().add_future(result, future_complete) # Once we have done this, hide the Future from our # caller (i.e. RequestHandler._when_complete), which # would otherwise set up its own callback and # exception handler (resulting in exceptions being # logged twice). return None return result return wrapper def stream_request_body(cls): """Apply to `RequestHandler` subclasses to enable streaming body support. This decorator implies the following changes: * `.HTTPServerRequest.body` is undefined, and body arguments will not be included in `RequestHandler.get_argument`. * `RequestHandler.prepare` is called when the request headers have been read instead of after the entire body has been read. * The subclass must define a method ``data_received(self, data):``, which will be called zero or more times as data is available. Note that if the request has an empty body, ``data_received`` may not be called. * ``prepare`` and ``data_received`` may return Futures (such as via ``@gen.coroutine``, in which case the next method will not be called until those futures have completed. * The regular HTTP method (``post``, ``put``, etc) will be called after the entire body has been read. See the `file receiver demo <https://github.com/tornadoweb/tornado/tree/master/demos/file_upload/>`_ for example usage. """ if not issubclass(cls, RequestHandler): raise TypeError("expected subclass of RequestHandler, got %r", cls) cls._stream_request_body = True return cls def _has_stream_request_body(cls): if not issubclass(cls, RequestHandler): raise TypeError("expected subclass of RequestHandler, got %r", cls) return getattr(cls, '_stream_request_body', False) def removeslash(method): """Use this decorator to remove trailing slashes from the request path. For example, a request to ``/foo/`` would redirect to ``/foo`` with this decorator. Your request handler mapping should use a regular expression like ``r'/foo/*'`` in conjunction with using the decorator. """ @functools.wraps(method) def wrapper(self, *args, **kwargs): if self.request.path.endswith("/"): if self.request.method in ("GET", "HEAD"): uri = self.request.path.rstrip("/") if uri: # don't try to redirect '/' to '' if self.request.query: uri += "?" + self.request.query self.redirect(uri, permanent=True) return else: raise HTTPError(404) return method(self, *args, **kwargs) return wrapper def addslash(method): """Use this decorator to add a missing trailing slash to the request path. For example, a request to ``/foo`` would redirect to ``/foo/`` with this decorator. Your request handler mapping should use a regular expression like ``r'/foo/?'`` in conjunction with using the decorator. """ @functools.wraps(method) def wrapper(self, *args, **kwargs): if not self.request.path.endswith("/"): if self.request.method in ("GET", "HEAD"): uri = self.request.path + "/" if self.request.query: uri += "?" + self.request.query self.redirect(uri, permanent=True) return raise HTTPError(404) return method(self, *args, **kwargs) return wrapper class _ApplicationRouter(ReversibleRuleRouter): """Routing implementation used internally by `Application`. Provides a binding between `Application` and `RequestHandler`. This implementation extends `~.routing.ReversibleRuleRouter` in a couple of ways: * it allows to use `RequestHandler` subclasses as `~.routing.Rule` target and * it allows to use a list/tuple of rules as `~.routing.Rule` target. ``process_rule`` implementation will substitute this list with an appropriate `_ApplicationRouter` instance. """ def __init__(self, application, rules=None): assert isinstance(application, Application) self.application = application super(_ApplicationRouter, self).__init__(rules) def process_rule(self, rule): rule = super(_ApplicationRouter, self).process_rule(rule) if isinstance(rule.target, (list, tuple)): rule.target = _ApplicationRouter(self.application, rule.target) return rule def get_target_delegate(self, target, request, **target_params): if isclass(target) and issubclass(target, RequestHandler): return self.application.get_handler_delegate(request, target, **target_params) return super(_ApplicationRouter, self).get_target_delegate(target, request, **target_params) class Application(ReversibleRouter): """A collection of request handlers that make up a web application. Instances of this class are callable and can be passed directly to HTTPServer to serve the application:: application = web.Application([ (r"/", MainPageHandler), ]) http_server = httpserver.HTTPServer(application) http_server.listen(8080) ioloop.IOLoop.current().start() The constructor for this class takes in a list of `~.routing.Rule` objects or tuples of values corresponding to the arguments of `~.routing.Rule` constructor: ``(matcher, target, [target_kwargs], [name])``, the values in square brackets being optional. The default matcher is `~.routing.PathMatches`, so ``(regexp, target)`` tuples can also be used instead of ``(PathMatches(regexp), target)``. A common routing target is a `RequestHandler` subclass, but you can also use lists of rules as a target, which create a nested routing configuration:: application = web.Application([ (HostMatches("example.com"), [ (r"/", MainPageHandler), (r"/feed", FeedHandler), ]), ]) In addition to this you can use nested `~.routing.Router` instances, `~.httputil.HTTPMessageDelegate` subclasses and callables as routing targets (see `~.routing` module docs for more information). When we receive requests, we iterate over the list in order and instantiate an instance of the first request class whose regexp matches the request path. The request class can be specified as either a class object or a (fully-qualified) name. A dictionary may be passed as the third element (``target_kwargs``) of the tuple, which will be used as keyword arguments to the handler's constructor and `~RequestHandler.initialize` method. This pattern is used for the `StaticFileHandler` in this example (note that a `StaticFileHandler` can be installed automatically with the static_path setting described below):: application = web.Application([ (r"/static/(.*)", web.StaticFileHandler, {"path": "/var/www"}), ]) We support virtual hosts with the `add_handlers` method, which takes in a host regular expression as the first argument:: application.add_handlers(r"www\.myhost\.com", [ (r"/article/([0-9]+)", ArticleHandler), ]) If there's no match for the current request's host, then ``default_host`` parameter value is matched against host regular expressions. You can serve static files by sending the ``static_path`` setting as a keyword argument. We will serve those files from the ``/static/`` URI (this is configurable with the ``static_url_prefix`` setting), and we will serve ``/favicon.ico`` and ``/robots.txt`` from the same directory. A custom subclass of `StaticFileHandler` can be specified with the ``static_handler_class`` setting. .. versionchanged:: 4.5 Integration with the new `tornado.routing` module. """ def __init__(self, handlers=None, default_host=None, transforms=None, **settings): if transforms is None: self.transforms = [] if settings.get("compress_response") or settings.get("gzip"): self.transforms.append(GZipContentEncoding) else: self.transforms = transforms self.default_host = default_host self.settings = settings self.ui_modules = {'linkify': _linkify, 'xsrf_form_html': _xsrf_form_html, 'Template': TemplateModule, } self.ui_methods = {} self._load_ui_modules(settings.get("ui_modules", {})) self._load_ui_methods(settings.get("ui_methods", {})) if self.settings.get("static_path"): path = self.settings["static_path"] handlers = list(handlers or []) static_url_prefix = settings.get("static_url_prefix", "/static/") static_handler_class = settings.get("static_handler_class", StaticFileHandler) static_handler_args = settings.get("static_handler_args", {}) static_handler_args['path'] = path for pattern in [re.escape(static_url_prefix) + r"(.*)", r"/(favicon\.ico)", r"/(robots\.txt)"]: handlers.insert(0, (pattern, static_handler_class, static_handler_args)) if self.settings.get('debug'): self.settings.setdefault('autoreload', True) self.settings.setdefault('compiled_template_cache', False) self.settings.setdefault('static_hash_cache', False) self.settings.setdefault('serve_traceback', True) self.wildcard_router = _ApplicationRouter(self, handlers) self.default_router = _ApplicationRouter(self, [ Rule(AnyMatches(), self.wildcard_router) ]) # Automatically reload modified modules if self.settings.get('autoreload'): from salt.ext.tornado import autoreload autoreload.start() def listen(self, port, address="", **kwargs): """Starts an HTTP server for this application on the given port. This is a convenience alias for creating an `.HTTPServer` object and calling its listen method. Keyword arguments not supported by `HTTPServer.listen <.TCPServer.listen>` are passed to the `.HTTPServer` constructor. For advanced uses (e.g. multi-process mode), do not use this method; create an `.HTTPServer` and call its `.TCPServer.bind`/`.TCPServer.start` methods directly. Note that after calling this method you still need to call ``IOLoop.current().start()`` to start the server. Returns the `.HTTPServer` object. .. versionchanged:: 4.3 Now returns the `.HTTPServer` object. """ # import is here rather than top level because HTTPServer # is not importable on appengine from salt.ext.tornado.httpserver import HTTPServer server = HTTPServer(self, **kwargs) server.listen(port, address) return server def add_handlers(self, host_pattern, host_handlers): """Appends the given handlers to our handler list. Host patterns are processed sequentially in the order they were added. All matching patterns will be considered. """ host_matcher = HostMatches(host_pattern) rule = Rule(host_matcher, _ApplicationRouter(self, host_handlers)) self.default_router.rules.insert(-1, rule) if self.default_host is not None: self.wildcard_router.add_rules([( DefaultHostMatches(self, host_matcher.host_pattern), host_handlers )]) def add_transform(self, transform_class): self.transforms.append(transform_class) def _load_ui_methods(self, methods): if isinstance(methods, types.ModuleType): self._load_ui_methods(dict((n, getattr(methods, n)) for n in dir(methods))) elif isinstance(methods, list): for m in methods: self._load_ui_methods(m) else: for name, fn in methods.items(): if not name.startswith("_") and hasattr(fn, "__call__") \ and name[0].lower() == name[0]: self.ui_methods[name] = fn def _load_ui_modules(self, modules): if isinstance(modules, types.ModuleType): self._load_ui_modules(dict((n, getattr(modules, n)) for n in dir(modules))) elif isinstance(modules, list): for m in modules: self._load_ui_modules(m) else: assert isinstance(modules, dict) for name, cls in modules.items(): try: if issubclass(cls, UIModule): self.ui_modules[name] = cls except TypeError: pass def __call__(self, request): # Legacy HTTPServer interface dispatcher = self.find_handler(request) return dispatcher.execute() def find_handler(self, request, **kwargs): route = self.default_router.find_handler(request) if route is not None: return route if self.settings.get('default_handler_class'): return self.get_handler_delegate( request, self.settings['default_handler_class'], self.settings.get('default_handler_args', {})) return self.get_handler_delegate( request, ErrorHandler, {'status_code': 404}) def get_handler_delegate(self, request, target_class, target_kwargs=None, path_args=None, path_kwargs=None): """Returns `~.httputil.HTTPMessageDelegate` that can serve a request for application and `RequestHandler` subclass. :arg httputil.HTTPServerRequest request: current HTTP request. :arg RequestHandler target_class: a `RequestHandler` class. :arg dict target_kwargs: keyword arguments for ``target_class`` constructor. :arg list path_args: positional arguments for ``target_class`` HTTP method that will be executed while handling a request (``get``, ``post`` or any other). :arg dict path_kwargs: keyword arguments for ``target_class`` HTTP method. """ return _HandlerDelegate( self, request, target_class, target_kwargs, path_args, path_kwargs) def reverse_url(self, name, *args): """Returns a URL path for handler named ``name`` The handler must be added to the application as a named `URLSpec`. Args will be substituted for capturing groups in the `URLSpec` regex. They will be converted to strings if necessary, encoded as utf8, and url-escaped. """ reversed_url = self.default_router.reverse_url(name, *args) if reversed_url is not None: return reversed_url raise KeyError("%s not found in named urls" % name) def log_request(self, handler): """Writes a completed HTTP request to the logs. By default writes to the python root logger. To change this behavior either subclass Application and override this method, or pass a function in the application settings dictionary as ``log_function``. """ if "log_function" in self.settings: self.settings["log_function"](handler) return if handler.get_status() < 400: log_method = access_log.info elif handler.get_status() < 500: log_method = access_log.warning else: log_method = access_log.error request_time = 1000.0 * handler.request.request_time() log_method("%d %s %.2fms", handler.get_status(), handler._request_summary(), request_time) class _HandlerDelegate(httputil.HTTPMessageDelegate): def __init__(self, application, request, handler_class, handler_kwargs, path_args, path_kwargs): self.application = application self.connection = request.connection self.request = request self.handler_class = handler_class self.handler_kwargs = handler_kwargs or {} self.path_args = path_args or [] self.path_kwargs = path_kwargs or {} self.chunks = [] self.stream_request_body = _has_stream_request_body(self.handler_class) def headers_received(self, start_line, headers): if self.stream_request_body: self.request.body = Future() return self.execute() def data_received(self, data): if self.stream_request_body: return self.handler.data_received(data) else: self.chunks.append(data) def finish(self): if self.stream_request_body: self.request.body.set_result(None) else: self.request.body = b''.join(self.chunks) self.request._parse_body() self.execute() def on_connection_close(self): if self.stream_request_body: self.handler.on_connection_close() else: self.chunks = None def execute(self): # If template cache is disabled (usually in the debug mode), # re-compile templates and reload static files on every # request so you don't need to restart to see changes if not self.application.settings.get("compiled_template_cache", True): with RequestHandler._template_loader_lock: for loader in RequestHandler._template_loaders.values(): loader.reset() if not self.application.settings.get('static_hash_cache', True): StaticFileHandler.reset() self.handler = self.handler_class(self.application, self.request, **self.handler_kwargs) transforms = [t(self.request) for t in self.application.transforms] if self.stream_request_body: self.handler._prepared_future = Future() # Note that if an exception escapes handler._execute it will be # trapped in the Future it returns (which we are ignoring here, # leaving it to be logged when the Future is GC'd). # However, that shouldn't happen because _execute has a blanket # except handler, and we cannot easily access the IOLoop here to # call add_future (because of the requirement to remain compatible # with WSGI) self.handler._execute(transforms, *self.path_args, **self.path_kwargs) # If we are streaming the request body, then execute() is finished # when the handler has prepared to receive the body. If not, # it doesn't matter when execute() finishes (so we return None) return self.handler._prepared_future class HTTPError(Exception): """An exception that will turn into an HTTP error response. Raising an `HTTPError` is a convenient alternative to calling `RequestHandler.send_error` since it automatically ends the current function. To customize the response sent with an `HTTPError`, override `RequestHandler.write_error`. :arg int status_code: HTTP status code. Must be listed in `httplib.responses <http.client.responses>` unless the ``reason`` keyword argument is given. :arg string log_message: Message to be written to the log for this error (will not be shown to the user unless the `Application` is in debug mode). May contain ``%s``-style placeholders, which will be filled in with remaining positional parameters. :arg string reason: Keyword-only argument. The HTTP "reason" phrase to pass in the status line along with ``status_code``. Normally determined automatically from ``status_code``, but can be used to use a non-standard numeric code. """ def __init__(self, status_code=500, log_message=None, *args, **kwargs): self.status_code = status_code self.log_message = log_message self.args = args self.reason = kwargs.get('reason', None) if log_message and not args: self.log_message = log_message.replace('%', '%%') def __str__(self): message = "HTTP %d: %s" % ( self.status_code, self.reason or httputil.responses.get(self.status_code, 'Unknown')) if self.log_message: return message + " (" + (self.log_message % self.args) + ")" else: return message class Finish(Exception): """An exception that ends the request without producing an error response. When `Finish` is raised in a `RequestHandler`, the request will end (calling `RequestHandler.finish` if it hasn't already been called), but the error-handling methods (including `RequestHandler.write_error`) will not be called. If `Finish()` was created with no arguments, the pending response will be sent as-is. If `Finish()` was given an argument, that argument will be passed to `RequestHandler.finish()`. This can be a more convenient way to implement custom error pages than overriding ``write_error`` (especially in library code):: if self.current_user is None: self.set_status(401) self.set_header('WWW-Authenticate', 'Basic realm="something"') raise Finish() .. versionchanged:: 4.3 Arguments passed to ``Finish()`` will be passed on to `RequestHandler.finish`. """ pass class MissingArgumentError(HTTPError): """Exception raised by `RequestHandler.get_argument`. This is a subclass of `HTTPError`, so if it is uncaught a 400 response code will be used instead of 500 (and a stack trace will not be logged). .. versionadded:: 3.1 """ def __init__(self, arg_name): super(MissingArgumentError, self).__init__( 400, 'Missing argument %s' % arg_name) self.arg_name = arg_name class ErrorHandler(RequestHandler): """Generates an error response with ``status_code`` for all requests.""" def initialize(self, status_code): self.set_status(status_code) def prepare(self): raise HTTPError(self._status_code) def check_xsrf_cookie(self): # POSTs to an ErrorHandler don't actually have side effects, # so we don't need to check the xsrf token. This allows POSTs # to the wrong url to return a 404 instead of 403. pass class RedirectHandler(RequestHandler): """Redirects the client to the given URL for all GET requests. You should provide the keyword argument ``url`` to the handler, e.g.:: application = web.Application([ (r"/oldpath", web.RedirectHandler, {"url": "/newpath"}), ]) `RedirectHandler` supports regular expression substitutions. E.g., to swap the first and second parts of a path while preserving the remainder:: application = web.Application([ (r"/(.*?)/(.*?)/(.*)", web.RedirectHandler, {"url": "/{1}/{0}/{2}"}), ]) The final URL is formatted with `str.format` and the substrings that match the capturing groups. In the above example, a request to "/a/b/c" would be formatted like:: str.format("/{1}/{0}/{2}", "a", "b", "c") # -> "/b/a/c" Use Python's :ref:`format string syntax <formatstrings>` to customize how values are substituted. .. versionchanged:: 4.5 Added support for substitutions into the destination URL. """ def initialize(self, url, permanent=True): self._url = url self._permanent = permanent def get(self, *args): self.redirect(self._url.format(*args), permanent=self._permanent) class StaticFileHandler(RequestHandler): """A simple handler that can serve static content from a directory. A `StaticFileHandler` is configured automatically if you pass the ``static_path`` keyword argument to `Application`. This handler can be customized with the ``static_url_prefix``, ``static_handler_class``, and ``static_handler_args`` settings. To map an additional path to this handler for a static data directory you would add a line to your application like:: application = web.Application([ (r"/content/(.*)", web.StaticFileHandler, {"path": "/var/www"}), ]) The handler constructor requires a ``path`` argument, which specifies the local root directory of the content to be served. Note that a capture group in the regex is required to parse the value for the ``path`` argument to the get() method (different than the constructor argument above); see `URLSpec` for details. To serve a file like ``index.html`` automatically when a directory is requested, set ``static_handler_args=dict(default_filename="index.html")`` in your application settings, or add ``default_filename`` as an initializer argument for your ``StaticFileHandler``. To maximize the effectiveness of browser caching, this class supports versioned urls (by default using the argument ``?v=``). If a version is given, we instruct the browser to cache this file indefinitely. `make_static_url` (also available as `RequestHandler.static_url`) can be used to construct a versioned url. This handler is intended primarily for use in development and light-duty file serving; for heavy traffic it will be more efficient to use a dedicated static file server (such as nginx or Apache). We support the HTTP ``Accept-Ranges`` mechanism to return partial content (because some browsers require this functionality to be present to seek in HTML5 audio or video). **Subclassing notes** This class is designed to be extensible by subclassing, but because of the way static urls are generated with class methods rather than instance methods, the inheritance patterns are somewhat unusual. Be sure to use the ``@classmethod`` decorator when overriding a class method. Instance methods may use the attributes ``self.path`` ``self.absolute_path``, and ``self.modified``. Subclasses should only override methods discussed in this section; overriding other methods is error-prone. Overriding ``StaticFileHandler.get`` is particularly problematic due to the tight coupling with ``compute_etag`` and other methods. To change the way static urls are generated (e.g. to match the behavior of another server or CDN), override `make_static_url`, `parse_url_path`, `get_cache_time`, and/or `get_version`. To replace all interaction with the filesystem (e.g. to serve static content from a database), override `get_content`, `get_content_size`, `get_modified_time`, `get_absolute_path`, and `validate_absolute_path`. .. versionchanged:: 3.1 Many of the methods for subclasses were added in Tornado 3.1. """ CACHE_MAX_AGE = 86400 * 365 * 10 # 10 years _static_hashes = {} # type: typing.Dict _lock = threading.Lock() # protects _static_hashes def initialize(self, path, default_filename=None): self.root = path self.default_filename = default_filename @classmethod def reset(cls): with cls._lock: cls._static_hashes = {} def head(self, path): return self.get(path, include_body=False) @gen.coroutine def get(self, path, include_body=True): # Set up our path instance variables. self.path = self.parse_url_path(path) del path # make sure we don't refer to path instead of self.path again absolute_path = self.get_absolute_path(self.root, self.path) self.absolute_path = self.validate_absolute_path( self.root, absolute_path) if self.absolute_path is None: return self.modified = self.get_modified_time() self.set_headers() if self.should_return_304(): self.set_status(304) return request_range = None range_header = self.request.headers.get("Range") if range_header: # As per RFC 2616 14.16, if an invalid Range header is specified, # the request will be treated as if the header didn't exist. request_range = httputil._parse_request_range(range_header) size = self.get_content_size() if request_range: start, end = request_range if (start is not None and start >= size) or end == 0: # As per RFC 2616 14.35.1, a range is not satisfiable only: if # the first requested byte is equal to or greater than the # content, or when a suffix with length 0 is specified self.set_status(416) # Range Not Satisfiable self.set_header("Content-Type", "text/plain") self.set_header("Content-Range", "bytes */%s" % (size, )) return if start is not None and start < 0: start += size if end is not None and end > size: # Clients sometimes blindly use a large range to limit their # download size; cap the endpoint at the actual file size. end = size # Note: only return HTTP 206 if less than the entire range has been # requested. Not only is this semantically correct, but Chrome # refuses to play audio if it gets an HTTP 206 in response to # ``Range: bytes=0-``. if size != (end or size) - (start or 0): self.set_status(206) # Partial Content self.set_header("Content-Range", httputil._get_content_range(start, end, size)) else: start = end = None if start is not None and end is not None: content_length = end - start elif end is not None: content_length = end elif start is not None: content_length = size - start else: content_length = size self.set_header("Content-Length", content_length) if include_body: content = self.get_content(self.absolute_path, start, end) if isinstance(content, bytes): content = [content] for chunk in content: try: self.write(chunk) yield self.flush() except iostream.StreamClosedError: return else: assert self.request.method == "HEAD" def compute_etag(self): """Sets the ``Etag`` header based on static url version. This allows efficient ``If-None-Match`` checks against cached versions, and sends the correct ``Etag`` for a partial response (i.e. the same ``Etag`` as the full file). .. versionadded:: 3.1 """ version_hash = self._get_cached_version(self.absolute_path) if not version_hash: return None return '"%s"' % (version_hash, ) def set_headers(self): """Sets the content and caching headers on the response. .. versionadded:: 3.1 """ self.set_header("Accept-Ranges", "bytes") self.set_etag_header() if self.modified is not None: self.set_header("Last-Modified", self.modified) content_type = self.get_content_type() if content_type: self.set_header("Content-Type", content_type) cache_time = self.get_cache_time(self.path, self.modified, content_type) if cache_time > 0: self.set_header("Expires", datetime.datetime.utcnow() + datetime.timedelta(seconds=cache_time)) self.set_header("Cache-Control", "max-age=" + str(cache_time)) self.set_extra_headers(self.path) def should_return_304(self): """Returns True if the headers indicate that we should return 304. .. versionadded:: 3.1 """ if self.check_etag_header(): return True # Check the If-Modified-Since, and don't send the result if the # content has not been modified ims_value = self.request.headers.get("If-Modified-Since") if ims_value is not None: date_tuple = email.utils.parsedate(ims_value) if date_tuple is not None: if_since = datetime.datetime(*date_tuple[:6]) if if_since >= self.modified: return True return False @classmethod def get_absolute_path(cls, root, path): """Returns the absolute location of ``path`` relative to ``root``. ``root`` is the path configured for this `StaticFileHandler` (in most cases the ``static_path`` `Application` setting). This class method may be overridden in subclasses. By default it returns a filesystem path, but other strings may be used as long as they are unique and understood by the subclass's overridden `get_content`. .. versionadded:: 3.1 """ abspath = os.path.abspath(os.path.join(root, path)) return abspath def validate_absolute_path(self, root, absolute_path): """Validate and return the absolute path. ``root`` is the configured path for the `StaticFileHandler`, and ``path`` is the result of `get_absolute_path` This is an instance method called during request processing, so it may raise `HTTPError` or use methods like `RequestHandler.redirect` (return None after redirecting to halt further processing). This is where 404 errors for missing files are generated. This method may modify the path before returning it, but note that any such modifications will not be understood by `make_static_url`. In instance methods, this method's result is available as ``self.absolute_path``. .. versionadded:: 3.1 """ # os.path.abspath strips a trailing /. # We must add it back to `root` so that we only match files # in a directory named `root` instead of files starting with # that prefix. root = os.path.abspath(root) if not root.endswith(os.path.sep): # abspath always removes a trailing slash, except when # root is '/'. This is an unusual case, but several projects # have independently discovered this technique to disable # Tornado's path validation and (hopefully) do their own, # so we need to support it. root += os.path.sep # The trailing slash also needs to be temporarily added back # the requested path so a request to root/ will match. if not (absolute_path + os.path.sep).startswith(root): raise HTTPError(403, "%s is not in root static directory", self.path) if (os.path.isdir(absolute_path) and self.default_filename is not None): # need to look at the request.path here for when path is empty # but there is some prefix to the path that was already # trimmed by the routing if not self.request.path.endswith("/"): self.redirect(self.request.path + "/", permanent=True) return absolute_path = os.path.join(absolute_path, self.default_filename) if not os.path.exists(absolute_path): raise HTTPError(404) if not os.path.isfile(absolute_path): raise HTTPError(403, "%s is not a file", self.path) return absolute_path @classmethod def get_content(cls, abspath, start=None, end=None): """Retrieve the content of the requested resource which is located at the given absolute path. This class method may be overridden by subclasses. Note that its signature is different from other overridable class methods (no ``settings`` argument); this is deliberate to ensure that ``abspath`` is able to stand on its own as a cache key. This method should either return a byte string or an iterator of byte strings. The latter is preferred for large files as it helps reduce memory fragmentation. .. versionadded:: 3.1 """ with open(abspath, "rb") as file: if start is not None: file.seek(start) if end is not None: remaining = end - (start or 0) else: remaining = None while True: chunk_size = 64 * 1024 if remaining is not None and remaining < chunk_size: chunk_size = remaining chunk = file.read(chunk_size) if chunk: if remaining is not None: remaining -= len(chunk) yield chunk else: if remaining is not None: assert remaining == 0 return @classmethod def get_content_version(cls, abspath): """Returns a version string for the resource at the given path. This class method may be overridden by subclasses. The default implementation is a hash of the file's contents. .. versionadded:: 3.1 """ data = cls.get_content(abspath) hasher = hashlib.md5() if isinstance(data, bytes): hasher.update(data) else: for chunk in data: hasher.update(chunk) return hasher.hexdigest() def _stat(self): if not hasattr(self, '_stat_result'): self._stat_result = os.stat(self.absolute_path) return self._stat_result def get_content_size(self): """Retrieve the total size of the resource at the given path. This method may be overridden by subclasses. .. versionadded:: 3.1 .. versionchanged:: 4.0 This method is now always called, instead of only when partial results are requested. """ stat_result = self._stat() return stat_result[stat.ST_SIZE] def get_modified_time(self): """Returns the time that ``self.absolute_path`` was last modified. May be overridden in subclasses. Should return a `~datetime.datetime` object or None. .. versionadded:: 3.1 """ stat_result = self._stat() modified = datetime.datetime.utcfromtimestamp( stat_result[stat.ST_MTIME]) return modified def get_content_type(self): """Returns the ``Content-Type`` header to be used for this request. .. versionadded:: 3.1 """ mime_type, encoding = mimetypes.guess_type(self.absolute_path) # per RFC 6713, use the appropriate type for a gzip compressed file if encoding == "gzip": return "application/gzip" # As of 2015-07-21 there is no bzip2 encoding defined at # http://www.iana.org/assignments/media-types/media-types.xhtml # So for that (and any other encoding), use octet-stream. elif encoding is not None: return "application/octet-stream" elif mime_type is not None: return mime_type # if mime_type not detected, use application/octet-stream else: return "application/octet-stream" def set_extra_headers(self, path): """For subclass to add extra headers to the response""" pass def get_cache_time(self, path, modified, mime_type): """Override to customize cache control behavior. Return a positive number of seconds to make the result cacheable for that amount of time or 0 to mark resource as cacheable for an unspecified amount of time (subject to browser heuristics). By default returns cache expiry of 10 years for resources requested with ``v`` argument. """ return self.CACHE_MAX_AGE if "v" in self.request.arguments else 0 @classmethod def make_static_url(cls, settings, path, include_version=True): """Constructs a versioned url for the given path. This method may be overridden in subclasses (but note that it is a class method rather than an instance method). Subclasses are only required to implement the signature ``make_static_url(cls, settings, path)``; other keyword arguments may be passed through `~RequestHandler.static_url` but are not standard. ``settings`` is the `Application.settings` dictionary. ``path`` is the static path being requested. The url returned should be relative to the current host. ``include_version`` determines whether the generated URL should include the query string containing the version hash of the file corresponding to the given ``path``. """ url = settings.get('static_url_prefix', '/static/') + path if not include_version: return url version_hash = cls.get_version(settings, path) if not version_hash: return url return '%s?v=%s' % (url, version_hash) def parse_url_path(self, url_path): """Converts a static URL path into a filesystem path. ``url_path`` is the path component of the URL with ``static_url_prefix`` removed. The return value should be filesystem path relative to ``static_path``. This is the inverse of `make_static_url`. """ if os.path.sep != "/": url_path = url_path.replace("/", os.path.sep) return url_path @classmethod def get_version(cls, settings, path): """Generate the version string to be used in static URLs. ``settings`` is the `Application.settings` dictionary and ``path`` is the relative location of the requested asset on the filesystem. The returned value should be a string, or ``None`` if no version could be determined. .. versionchanged:: 3.1 This method was previously recommended for subclasses to override; `get_content_version` is now preferred as it allows the base class to handle caching of the result. """ abs_path = cls.get_absolute_path(settings['static_path'], path) return cls._get_cached_version(abs_path) @classmethod def _get_cached_version(cls, abs_path): with cls._lock: hashes = cls._static_hashes if abs_path not in hashes: try: hashes[abs_path] = cls.get_content_version(abs_path) except Exception: gen_log.error("Could not open static file %r", abs_path) hashes[abs_path] = None hsh = hashes.get(abs_path) if hsh: return hsh return None class FallbackHandler(RequestHandler): """A `RequestHandler` that wraps another HTTP server callback. The fallback is a callable object that accepts an `~.httputil.HTTPServerRequest`, such as an `Application` or `tornado.wsgi.WSGIContainer`. This is most useful to use both Tornado ``RequestHandlers`` and WSGI in the same server. Typical usage:: wsgi_app = tornado.wsgi.WSGIContainer( django.core.handlers.wsgi.WSGIHandler()) application = tornado.web.Application([ (r"/foo", FooHandler), (r".*", FallbackHandler, dict(fallback=wsgi_app), ]) """ def initialize(self, fallback): self.fallback = fallback def prepare(self): self.fallback(self.request) self._finished = True class OutputTransform(object): """A transform modifies the result of an HTTP request (e.g., GZip encoding) Applications are not expected to create their own OutputTransforms or interact with them directly; the framework chooses which transforms (if any) to apply. """ def __init__(self, request): pass def transform_first_chunk(self, status_code, headers, chunk, finishing): # type: (int, httputil.HTTPHeaders, bytes, bool) -> typing.Tuple[int, httputil.HTTPHeaders, bytes] return status_code, headers, chunk def transform_chunk(self, chunk, finishing): return chunk class GZipContentEncoding(OutputTransform): """Applies the gzip content encoding to the response. See http://www.w3.org/Protocols/rfc2616/rfc2616-sec14.html#sec14.11 .. versionchanged:: 4.0 Now compresses all mime types beginning with ``text/``, instead of just a whitelist. (the whitelist is still used for certain non-text mime types). """ # Whitelist of compressible mime types (in addition to any types # beginning with "text/"). CONTENT_TYPES = set(["application/javascript", "application/x-javascript", "application/xml", "application/atom+xml", "application/json", "application/xhtml+xml", "image/svg+xml"]) # Python's GzipFile defaults to level 9, while most other gzip # tools (including gzip itself) default to 6, which is probably a # better CPU/size tradeoff. GZIP_LEVEL = 6 # Responses that are too short are unlikely to benefit from gzipping # after considering the "Content-Encoding: gzip" header and the header # inside the gzip encoding. # Note that responses written in multiple chunks will be compressed # regardless of size. MIN_LENGTH = 1024 def __init__(self, request): self._gzipping = "gzip" in request.headers.get("Accept-Encoding", "") def _compressible_type(self, ctype): return ctype.startswith('text/') or ctype in self.CONTENT_TYPES def transform_first_chunk(self, status_code, headers, chunk, finishing): # type: (int, httputil.HTTPHeaders, bytes, bool) -> typing.Tuple[int, httputil.HTTPHeaders, bytes] # TODO: can/should this type be inherited from the superclass? if 'Vary' in headers: headers['Vary'] += ', Accept-Encoding' else: headers['Vary'] = 'Accept-Encoding' if self._gzipping: ctype = _unicode(headers.get("Content-Type", "")).split(";")[0] self._gzipping = self._compressible_type(ctype) and \ (not finishing or len(chunk) >= self.MIN_LENGTH) and \ ("Content-Encoding" not in headers) if self._gzipping: headers["Content-Encoding"] = "gzip" self._gzip_value = BytesIO() self._gzip_file = gzip.GzipFile(mode="w", fileobj=self._gzip_value, compresslevel=self.GZIP_LEVEL) chunk = self.transform_chunk(chunk, finishing) if "Content-Length" in headers: # The original content length is no longer correct. # If this is the last (and only) chunk, we can set the new # content-length; otherwise we remove it and fall back to # chunked encoding. if finishing: headers["Content-Length"] = str(len(chunk)) else: del headers["Content-Length"] return status_code, headers, chunk def transform_chunk(self, chunk, finishing): if self._gzipping: self._gzip_file.write(chunk) if finishing: self._gzip_file.close() else: self._gzip_file.flush() chunk = self._gzip_value.getvalue() self._gzip_value.truncate(0) self._gzip_value.seek(0) return chunk def authenticated(method): """Decorate methods with this to require that the user be logged in. If the user is not logged in, they will be redirected to the configured `login url <RequestHandler.get_login_url>`. If you configure a login url with a query parameter, Tornado will assume you know what you're doing and use it as-is. If not, it will add a `next` parameter so the login page knows where to send you once you're logged in. """ @functools.wraps(method) def wrapper(self, *args, **kwargs): if not self.current_user: if self.request.method in ("GET", "HEAD"): url = self.get_login_url() if "?" not in url: if urlparse.urlsplit(url).scheme: # if login url is absolute, make next absolute too next_url = self.request.full_url() else: next_url = self.request.uri url += "?" + urlencode(dict(next=next_url)) self.redirect(url) return raise HTTPError(403) return method(self, *args, **kwargs) return wrapper class UIModule(object): """A re-usable, modular UI unit on a page. UI modules often execute additional queries, and they can include additional CSS and JavaScript that will be included in the output page, which is automatically inserted on page render. Subclasses of UIModule must override the `render` method. """ def __init__(self, handler): self.handler = handler self.request = handler.request self.ui = handler.ui self.locale = handler.locale @property def current_user(self): return self.handler.current_user def render(self, *args, **kwargs): """Override in subclasses to return this module's output.""" raise NotImplementedError() def embedded_javascript(self): """Override to return a JavaScript string to be embedded in the page.""" return None def javascript_files(self): """Override to return a list of JavaScript files needed by this module. If the return values are relative paths, they will be passed to `RequestHandler.static_url`; otherwise they will be used as-is. """ return None def embedded_css(self): """Override to return a CSS string that will be embedded in the page.""" return None def css_files(self): """Override to returns a list of CSS files required by this module. If the return values are relative paths, they will be passed to `RequestHandler.static_url`; otherwise they will be used as-is. """ return None def html_head(self): """Override to return an HTML string that will be put in the <head/> element. """ return None def html_body(self): """Override to return an HTML string that will be put at the end of the <body/> element. """ return None def render_string(self, path, **kwargs): """Renders a template and returns it as a string.""" return self.handler.render_string(path, **kwargs) class _linkify(UIModule): def render(self, text, **kwargs): return escape.linkify(text, **kwargs) class _xsrf_form_html(UIModule): def render(self): return self.handler.xsrf_form_html() class TemplateModule(UIModule): """UIModule that simply renders the given template. {% module Template("foo.html") %} is similar to {% include "foo.html" %}, but the module version gets its own namespace (with kwargs passed to Template()) instead of inheriting the outer template's namespace. Templates rendered through this module also get access to UIModule's automatic javascript/css features. Simply call set_resources inside the template and give it keyword arguments corresponding to the methods on UIModule: {{ set_resources(js_files=static_url("my.js")) }} Note that these resources are output once per template file, not once per instantiation of the template, so they must not depend on any arguments to the template. """ def __init__(self, handler): super(TemplateModule, self).__init__(handler) # keep resources in both a list and a dict to preserve order self._resource_list = [] self._resource_dict = {} def render(self, path, **kwargs): def set_resources(**kwargs): if path not in self._resource_dict: self._resource_list.append(kwargs) self._resource_dict[path] = kwargs else: if self._resource_dict[path] != kwargs: raise ValueError("set_resources called with different " "resources for the same template") return "" return self.render_string(path, set_resources=set_resources, **kwargs) def _get_resources(self, key): return (r[key] for r in self._resource_list if key in r) def embedded_javascript(self): return "\n".join(self._get_resources("embedded_javascript")) def javascript_files(self): result = [] for f in self._get_resources("javascript_files"): if isinstance(f, (unicode_type, bytes)): result.append(f) else: result.extend(f) return result def embedded_css(self): return "\n".join(self._get_resources("embedded_css")) def css_files(self): result = [] for f in self._get_resources("css_files"): if isinstance(f, (unicode_type, bytes)): result.append(f) else: result.extend(f) return result def html_head(self): return "".join(self._get_resources("html_head")) def html_body(self): return "".join(self._get_resources("html_body")) class _UIModuleNamespace(object): """Lazy namespace which creates UIModule proxies bound to a handler.""" def __init__(self, handler, ui_modules): self.handler = handler self.ui_modules = ui_modules def __getitem__(self, key): return self.handler._ui_module(key, self.ui_modules[key]) def __getattr__(self, key): try: return self[key] except KeyError as e: raise AttributeError(str(e)) if hasattr(hmac, 'compare_digest'): # python 3.3 _time_independent_equals = hmac.compare_digest else: def _time_independent_equals(a, b): if len(a) != len(b): return False result = 0 if isinstance(a[0], int): # python3 byte strings for x, y in zip(a, b): result |= x ^ y else: # python2 for x, y in zip(a, b): result |= ord(x) ^ ord(y) return result == 0 def create_signed_value(secret, name, value, version=None, clock=None, key_version=None): if version is None: version = DEFAULT_SIGNED_VALUE_VERSION if clock is None: clock = time.time timestamp = utf8(str(int(clock()))) value = base64.b64encode(utf8(value)) if version == 1: signature = _create_signature_v1(secret, name, value, timestamp) value = b"|".join([value, timestamp, signature]) return value elif version == 2: # The v2 format consists of a version number and a series of # length-prefixed fields "%d:%s", the last of which is a # signature, all separated by pipes. All numbers are in # decimal format with no leading zeros. The signature is an # HMAC-SHA256 of the whole string up to that point, including # the final pipe. # # The fields are: # - format version (i.e. 2; no length prefix) # - key version (integer, default is 0) # - timestamp (integer seconds since epoch) # - name (not encoded; assumed to be ~alphanumeric) # - value (base64-encoded) # - signature (hex-encoded; no length prefix) def format_field(s): return utf8("%d:" % len(s)) + utf8(s) to_sign = b"|".join([ b"2", format_field(str(key_version or 0)), format_field(timestamp), format_field(name), format_field(value), b'']) if isinstance(secret, dict): assert key_version is not None, 'Key version must be set when sign key dict is used' assert version >= 2, 'Version must be at least 2 for key version support' secret = secret[key_version] signature = _create_signature_v2(secret, to_sign) return to_sign + signature else: raise ValueError("Unsupported version %d" % version) # A leading version number in decimal # with no leading zeros, followed by a pipe. _signed_value_version_re = re.compile(br"^([1-9][0-9]*)\|(.*)$") def _get_version(value): # Figures out what version value is. Version 1 did not include an # explicit version field and started with arbitrary base64 data, # which makes this tricky. m = _signed_value_version_re.match(value) if m is None: version = 1 else: try: version = int(m.group(1)) if version > 999: # Certain payloads from the version-less v1 format may # be parsed as valid integers. Due to base64 padding # restrictions, this can only happen for numbers whose # length is a multiple of 4, so we can treat all # numbers up to 999 as versions, and for the rest we # fall back to v1 format. version = 1 except ValueError: version = 1 return version def decode_signed_value(secret, name, value, max_age_days=31, clock=None, min_version=None): if clock is None: clock = time.time if min_version is None: min_version = DEFAULT_SIGNED_VALUE_MIN_VERSION if min_version > 2: raise ValueError("Unsupported min_version %d" % min_version) if not value: return None value = utf8(value) version = _get_version(value) if version < min_version: return None if version == 1: return _decode_signed_value_v1(secret, name, value, max_age_days, clock) elif version == 2: return _decode_signed_value_v2(secret, name, value, max_age_days, clock) else: return None def _decode_signed_value_v1(secret, name, value, max_age_days, clock): parts = utf8(value).split(b"|") if len(parts) != 3: return None signature = _create_signature_v1(secret, name, parts[0], parts[1]) if not _time_independent_equals(parts[2], signature): gen_log.warning("Invalid cookie signature %r", value) return None timestamp = int(parts[1]) if timestamp < clock() - max_age_days * 86400: gen_log.warning("Expired cookie %r", value) return None if timestamp > clock() + 31 * 86400: # _cookie_signature does not hash a delimiter between the # parts of the cookie, so an attacker could transfer trailing # digits from the payload to the timestamp without altering the # signature. For backwards compatibility, sanity-check timestamp # here instead of modifying _cookie_signature. gen_log.warning("Cookie timestamp in future; possible tampering %r", value) return None if parts[1].startswith(b"0"): gen_log.warning("Tampered cookie %r", value) return None try: return base64.b64decode(parts[0]) except Exception: return None def _decode_fields_v2(value): def _consume_field(s): length, _, rest = s.partition(b':') n = int(length) field_value = rest[:n] # In python 3, indexing bytes returns small integers; we must # use a slice to get a byte string as in python 2. if rest[n:n + 1] != b'|': raise ValueError("malformed v2 signed value field") rest = rest[n + 1:] return field_value, rest rest = value[2:] # remove version number key_version, rest = _consume_field(rest) timestamp, rest = _consume_field(rest) name_field, rest = _consume_field(rest) value_field, passed_sig = _consume_field(rest) return int(key_version), timestamp, name_field, value_field, passed_sig def _decode_signed_value_v2(secret, name, value, max_age_days, clock): try: key_version, timestamp, name_field, value_field, passed_sig = _decode_fields_v2(value) except ValueError: return None signed_string = value[:-len(passed_sig)] if isinstance(secret, dict): try: secret = secret[key_version] except KeyError: return None expected_sig = _create_signature_v2(secret, signed_string) if not _time_independent_equals(passed_sig, expected_sig): return None if name_field != utf8(name): return None timestamp = int(timestamp) if timestamp < clock() - max_age_days * 86400: # The signature has expired. return None try: return base64.b64decode(value_field) except Exception: return None def get_signature_key_version(value): value = utf8(value) version = _get_version(value) if version < 2: return None try: key_version, _, _, _, _ = _decode_fields_v2(value) except ValueError: return None return key_version def _create_signature_v1(secret, *parts): hash = hmac.new(utf8(secret), digestmod=hashlib.sha1) for part in parts: hash.update(utf8(part)) return utf8(hash.hexdigest()) def _create_signature_v2(secret, s): hash = hmac.new(utf8(secret), digestmod=hashlib.sha256) hash.update(utf8(s)) return utf8(hash.hexdigest()) def is_absolute(path): return any(path.startswith(x) for x in ["/", "http:", "https:"])

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from __future__ import absolute_import, division, print_function import base64 import binascii import datetime import email.utils import functools import gzip import hashlib import hmac import mimetypes import numbers import os.path import re import stat import sys import threading import time import salt.ext.tornado as tornado import traceback import types from inspect import isclass from io import BytesIO from salt.ext.tornado.concurrent import Future from salt.ext.tornado import escape from salt.ext.tornado import gen from salt.ext.tornado import httputil from salt.ext.tornado import iostream from salt.ext.tornado import locale from salt.ext.tornado.log import access_log, app_log, gen_log from salt.ext.tornado import stack_context from salt.ext.tornado import template from salt.ext.tornado.escape import utf8, _unicode from salt.ext.tornado.routing import (AnyMatches, DefaultHostMatches, HostMatches, ReversibleRouter, Rule, ReversibleRuleRouter, URLSpec) from salt.ext.tornado.util import (ObjectDict, raise_exc_info, unicode_type, _websocket_mask, PY3) url = URLSpec if PY3: import http.cookies as Cookie import urllib.parse as urlparse from urllib.parse import urlencode else: import Cookie import urlparse from urllib import urlencode try: import typing _HeaderTypes = typing.Union[bytes, unicode_type, numbers.Integral, datetime.datetime] except ImportError: pass MIN_SUPPORTED_SIGNED_VALUE_VERSION = 1 MAX_SUPPORTED_SIGNED_VALUE_VERSION = 2 DEFAULT_SIGNED_VALUE_VERSION = 2 DEFAULT_SIGNED_VALUE_MIN_VERSION = 1 class RequestHandler(object): SUPPORTED_METHODS = ("GET", "HEAD", "POST", "DELETE", "PATCH", "PUT", "OPTIONS") _template_loaders = {} _template_loader_lock = threading.Lock() _remove_control_chars_regex = re.compile(r"[\x00-\x08\x0e-\x1f]") def __init__(self, application, request, **kwargs): super(RequestHandler, self).__init__() self.application = application self.request = request self._headers_written = False self._finished = False self._auto_finish = True self._transforms = None self._prepared_future = None self._headers = None self.path_args = None self.path_kwargs = None self.ui = ObjectDict((n, self._ui_method(m)) for n, m in application.ui_methods.items()) self.ui["_tt_modules"] = _UIModuleNamespace(self, application.ui_modules) self.ui["modules"] = self.ui["_tt_modules"] self.clear() self.request.connection.set_close_callback(self.on_connection_close) self.initialize(**kwargs) def initialize(self): pass @property def settings(self): return self.application.settings def head(self, *args, **kwargs): raise HTTPError(405) def get(self, *args, **kwargs): raise HTTPError(405) def post(self, *args, **kwargs): raise HTTPError(405) def delete(self, *args, **kwargs): raise HTTPError(405) def patch(self, *args, **kwargs): raise HTTPError(405) def put(self, *args, **kwargs): raise HTTPError(405) def options(self, *args, **kwargs): raise HTTPError(405) def prepare(self): pass def on_finish(self): pass def on_connection_close(self): if _has_stream_request_body(self.__class__): if not self.request.body.done(): self.request.body.set_exception(iostream.StreamClosedError()) self.request.body.exception() def clear(self): self._headers = httputil.HTTPHeaders({ "Server": "TornadoServer/%s" % tornado.version, "Content-Type": "text/html; charset=UTF-8", "Date": httputil.format_timestamp(time.time()), }) self.set_default_headers() self._write_buffer = [] self._status_code = 200 self._reason = httputil.responses[200] def set_default_headers(self): pass def set_status(self, status_code, reason=None): self._status_code = status_code if reason is not None: self._reason = escape.native_str(reason) else: try: self._reason = httputil.responses[status_code] except KeyError: raise ValueError("unknown status code %d" % status_code) def get_status(self): return self._status_code def set_header(self, name, value): self._headers[name] = self._convert_header_value(value) def add_header(self, name, value): self._headers.add(name, self._convert_header_value(value)) def clear_header(self, name): if name in self._headers: del self._headers[name] _INVALID_HEADER_CHAR_RE = re.compile(r"[\x00-\x1f]") def _convert_header_value(self, value): if isinstance(value, str): retval = value elif isinstance(value, bytes): retval = value.decode('latin1') elif isinstance(value, unicode_type): retval = escape.utf8(value) elif isinstance(value, numbers.Integral): # return immediately since we know the converted value will be safe return str(value) elif isinstance(value, datetime.datetime): return httputil.format_timestamp(value) else: raise TypeError("Unsupported header value %r" % value) # If \n is allowed into the header, it is possible to inject # additional headers or split the request. if RequestHandler._INVALID_HEADER_CHAR_RE.search(retval): raise ValueError("Unsafe header value %r", retval) return retval _ARG_DEFAULT = object() def get_argument(self, name, default=_ARG_DEFAULT, strip=True): return self._get_argument(name, default, self.request.arguments, strip) def get_arguments(self, name, strip=True): # Make sure `get_arguments` isn't accidentally being called with a # `get_argument`.) assert isinstance(strip, bool) return self._get_arguments(name, self.request.arguments, strip) def get_body_argument(self, name, default=_ARG_DEFAULT, strip=True): return self._get_argument(name, default, self.request.body_arguments, strip) def get_body_arguments(self, name, strip=True): return self._get_arguments(name, self.request.body_arguments, strip) def get_query_argument(self, name, default=_ARG_DEFAULT, strip=True): return self._get_argument(name, default, self.request.query_arguments, strip) def get_query_arguments(self, name, strip=True): return self._get_arguments(name, self.request.query_arguments, strip) def _get_argument(self, name, default, source, strip=True): args = self._get_arguments(name, source, strip=strip) if not args: if default is self._ARG_DEFAULT: raise MissingArgumentError(name) return default return args[-1] def _get_arguments(self, name, source, strip=True): values = [] for v in source.get(name, []): v = self.decode_argument(v, name=name) if isinstance(v, unicode_type): # Get rid of any weird control chars (unless decoding gave # us bytes, in which case leave it alone) v = RequestHandler._remove_control_chars_regex.sub(" ", v) if strip: v = v.strip() values.append(v) return values def decode_argument(self, value, name=None): try: return _unicode(value) except UnicodeDecodeError: raise HTTPError(400, "Invalid unicode in %s: %r" % (name or "url", value[:40])) @property def cookies(self): return self.request.cookies def get_cookie(self, name, default=None): if self.request.cookies is not None and name in self.request.cookies: return self.request.cookies[name].value return default def set_cookie(self, name, value, domain=None, expires=None, path="/", expires_days=None, **kwargs): # The cookie library only accepts type str, in both python 2 and 3 name = escape.native_str(name) value = escape.native_str(value) if re.search(r"[\x00-\x20]", name + value): # Don't let us accidentally inject bad stuff raise ValueError("Invalid cookie %r: %r" % (name, value)) if not hasattr(self, "_new_cookie"): self._new_cookie = Cookie.SimpleCookie() if name in self._new_cookie: del self._new_cookie[name] self._new_cookie[name] = value morsel = self._new_cookie[name] if domain: morsel["domain"] = domain if expires_days is not None and not expires: expires = datetime.datetime.utcnow() + datetime.timedelta( days=expires_days) if expires: morsel["expires"] = httputil.format_timestamp(expires) if path: morsel["path"] = path for k, v in kwargs.items(): if k == 'max_age': k = 'max-age' if k in ['httponly', 'secure'] and not v: continue morsel[k] = v def clear_cookie(self, name, path="/", domain=None): expires = datetime.datetime.utcnow() - datetime.timedelta(days=365) self.set_cookie(name, value="", path=path, expires=expires, domain=domain) def clear_all_cookies(self, path="/", domain=None): for name in self.request.cookies: self.clear_cookie(name, path=path, domain=domain) def set_secure_cookie(self, name, value, expires_days=30, version=None, **kwargs): self.set_cookie(name, self.create_signed_value(name, value, version=version), expires_days=expires_days, **kwargs) def create_signed_value(self, name, value, version=None): self.require_setting("cookie_secret", "secure cookies") secret = self.application.settings["cookie_secret"] key_version = None if isinstance(secret, dict): if self.application.settings.get("key_version") is None: raise Exception("key_version setting must be used for secret_key dicts") key_version = self.application.settings["key_version"] return create_signed_value(secret, name, value, version=version, key_version=key_version) def get_secure_cookie(self, name, value=None, max_age_days=31, min_version=None): self.require_setting("cookie_secret", "secure cookies") if value is None: value = self.get_cookie(name) return decode_signed_value(self.application.settings["cookie_secret"], name, value, max_age_days=max_age_days, min_version=min_version) def get_secure_cookie_key_version(self, name, value=None): self.require_setting("cookie_secret", "secure cookies") if value is None: value = self.get_cookie(name) return get_signature_key_version(value) def redirect(self, url, permanent=False, status=None): if self._headers_written: raise Exception("Cannot redirect after headers have been written") if status is None: status = 301 if permanent else 302 else: assert isinstance(status, int) and 300 <= status <= 399 self.set_status(status) self.set_header("Location", utf8(url)) self.finish() def write(self, chunk): if self._finished: raise RuntimeError("Cannot write() after finish()") if not isinstance(chunk, (bytes, unicode_type, dict)): message = "write() only accepts bytes, unicode, and dict objects" if isinstance(chunk, list): message += ". Lists not accepted for security reasons; see http://www.tornadoweb.org/en/stable/web.html#tornado.web.RequestHandler.write" raise TypeError(message) if isinstance(chunk, dict): chunk = escape.json_encode(chunk) self.set_header("Content-Type", "application/json; charset=UTF-8") chunk = utf8(chunk) self._write_buffer.append(chunk) def render(self, template_name, **kwargs): if self._finished: raise RuntimeError("Cannot render() after finish()") html = self.render_string(template_name, **kwargs) js_embed = [] js_files = [] css_embed = [] css_files = [] html_heads = [] html_bodies = [] for module in getattr(self, "_active_modules", {}).values(): embed_part = module.embedded_javascript() if embed_part: js_embed.append(utf8(embed_part)) file_part = module.javascript_files() if file_part: if isinstance(file_part, (unicode_type, bytes)): js_files.append(file_part) else: js_files.extend(file_part) embed_part = module.embedded_css() if embed_part: css_embed.append(utf8(embed_part)) file_part = module.css_files() if file_part: if isinstance(file_part, (unicode_type, bytes)): css_files.append(file_part) else: css_files.extend(file_part) head_part = module.html_head() if head_part: html_heads.append(utf8(head_part)) body_part = module.html_body() if body_part: html_bodies.append(utf8(body_part)) if js_files: js = self.render_linked_js(js_files) sloc = html.rindex(b'</body>') html = html[:sloc] + utf8(js) + b'\n' + html[sloc:] if js_embed: js = self.render_embed_js(js_embed) sloc = html.rindex(b'</body>') html = html[:sloc] + js + b'\n' + html[sloc:] if css_files: css = self.render_linked_css(css_files) hloc = html.index(b'</head>') html = html[:hloc] + utf8(css) + b'\n' + html[hloc:] if css_embed: css = self.render_embed_css(css_embed) hloc = html.index(b'</head>') html = html[:hloc] + css + b'\n' + html[hloc:] if html_heads: hloc = html.index(b'</head>') html = html[:hloc] + b''.join(html_heads) + b'\n' + html[hloc:] if html_bodies: hloc = html.index(b'</body>') html = html[:hloc] + b''.join(html_bodies) + b'\n' + html[hloc:] self.finish(html) def render_linked_js(self, js_files): paths = [] unique_paths = set() for path in js_files: if not is_absolute(path): path = self.static_url(path) if path not in unique_paths: paths.append(path) unique_paths.add(path) return ''.join('<script src="' + escape.xhtml_escape(p) + '" type="text/javascript"></script>' for p in paths) def render_embed_js(self, js_embed): return b'<script type="text/javascript">\n//<![CDATA[\n' + \ b'\n'.join(js_embed) + b'\n//]]>\n</script>' def render_linked_css(self, css_files): paths = [] unique_paths = set() for path in css_files: if not is_absolute(path): path = self.static_url(path) if path not in unique_paths: paths.append(path) unique_paths.add(path) return ''.join('<link href="' + escape.xhtml_escape(p) + '" ' 'type="text/css" rel="stylesheet"/>' for p in paths) def render_embed_css(self, css_embed): return b'<style type="text/css">\n' + b'\n'.join(css_embed) + \ b'\n</style>' def render_string(self, template_name, **kwargs): template_path = self.get_template_path() if not template_path: frame = sys._getframe(0) web_file = frame.f_code.co_filename while frame.f_code.co_filename == web_file: frame = frame.f_back template_path = os.path.dirname(frame.f_code.co_filename) with RequestHandler._template_loader_lock: if template_path not in RequestHandler._template_loaders: loader = self.create_template_loader(template_path) RequestHandler._template_loaders[template_path] = loader else: loader = RequestHandler._template_loaders[template_path] t = loader.load(template_name) namespace = self.get_template_namespace() namespace.update(kwargs) return t.generate(**namespace) def get_template_namespace(self): namespace = dict( handler=self, request=self.request, current_user=self.current_user, locale=self.locale, _=self.locale.translate, pgettext=self.locale.pgettext, static_url=self.static_url, xsrf_form_html=self.xsrf_form_html, reverse_url=self.reverse_url ) namespace.update(self.ui) return namespace def create_template_loader(self, template_path): settings = self.application.settings if "template_loader" in settings: return settings["template_loader"] kwargs = {} if "autoescape" in settings: kwargs["autoescape"] = settings["autoescape"] if "template_whitespace" in settings: kwargs["whitespace"] = settings["template_whitespace"] return template.Loader(template_path, **kwargs) def flush(self, include_footers=False, callback=None): chunk = b"".join(self._write_buffer) self._write_buffer = [] if not self._headers_written: self._headers_written = True for transform in self._transforms: self._status_code, self._headers, chunk = \ transform.transform_first_chunk( self._status_code, self._headers, chunk, include_footers) if self.request.method == "HEAD": chunk = None if hasattr(self, "_new_cookie"): for cookie in self._new_cookie.values(): self.add_header("Set-Cookie", cookie.OutputString(None)) start_line = httputil.ResponseStartLine('', self._status_code, self._reason) return self.request.connection.write_headers( start_line, self._headers, chunk, callback=callback) else: for transform in self._transforms: chunk = transform.transform_chunk(chunk, include_footers) if self.request.method != "HEAD": return self.request.connection.write(chunk, callback=callback) else: future = Future() future.set_result(None) return future def finish(self, chunk=None): if self._finished: raise RuntimeError("finish() called twice") if chunk is not None: self.write(chunk) if not self._headers_written: if (self._status_code == 200 and self.request.method in ("GET", "HEAD") and "Etag" not in self._headers): self.set_etag_header() if self.check_etag_header(): self._write_buffer = [] self.set_status(304) if (self._status_code in (204, 304) or (self._status_code >= 100 and self._status_code < 200)): assert not self._write_buffer, "Cannot send body with %s" % self._status_code self._clear_headers_for_304() elif "Content-Length" not in self._headers: content_length = sum(len(part) for part in self._write_buffer) self.set_header("Content-Length", content_length) if hasattr(self.request, "connection"): self.request.connection.set_close_callback(None) self.flush(include_footers=True) self.request.finish() self._log() self._finished = True self.on_finish() self._break_cycles() def _break_cycles(self): self.ui = None def send_error(self, status_code=500, **kwargs): if self._headers_written: gen_log.error("Cannot send error response after headers written") if not self._finished: try: self.finish() except Exception: gen_log.error("Failed to flush partial response", exc_info=True) return self.clear() reason = kwargs.get('reason') if 'exc_info' in kwargs: exception = kwargs['exc_info'][1] if isinstance(exception, HTTPError) and exception.reason: reason = exception.reason self.set_status(status_code, reason=reason) try: self.write_error(status_code, **kwargs) except Exception: app_log.error("Uncaught exception in write_error", exc_info=True) if not self._finished: self.finish() def write_error(self, status_code, **kwargs): if self.settings.get("serve_traceback") and "exc_info" in kwargs: self.set_header('Content-Type', 'text/plain') for line in traceback.format_exception(*kwargs["exc_info"]): self.write(line) self.finish() else: self.finish("<html><title>%(code)d: %(message)s</title>" "<body>%(code)d: %(message)s</body></html>" % { "code": status_code, "message": self._reason, }) @property def locale(self): if not hasattr(self, "_locale"): self._locale = self.get_user_locale() if not self._locale: self._locale = self.get_browser_locale() assert self._locale return self._locale @locale.setter def locale(self, value): self._locale = value def get_user_locale(self): return None def get_browser_locale(self, default="en_US"): if "Accept-Language" in self.request.headers: languages = self.request.headers["Accept-Language"].split(",") locales = [] for language in languages: parts = language.strip().split(";") if len(parts) > 1 and parts[1].startswith("q="): try: score = float(parts[1][2:]) except (ValueError, TypeError): score = 0.0 else: score = 1.0 locales.append((parts[0], score)) if locales: locales.sort(key=lambda pair: pair[1], reverse=True) codes = [l[0] for l in locales] return locale.get(*codes) return locale.get(default) @property def current_user(self): if not hasattr(self, "_current_user"): self._current_user = self.get_current_user() return self._current_user @current_user.setter def current_user(self, value): self._current_user = value def get_current_user(self): return None def get_login_url(self): self.require_setting("login_url", "@tornado.web.authenticated") return self.application.settings["login_url"] def get_template_path(self): return self.application.settings.get("template_path") @property def xsrf_token(self): if not hasattr(self, "_xsrf_token"): version, token, timestamp = self._get_raw_xsrf_token() output_version = self.settings.get("xsrf_cookie_version", 2) cookie_kwargs = self.settings.get("xsrf_cookie_kwargs", {}) if output_version == 1: self._xsrf_token = binascii.b2a_hex(token) elif output_version == 2: mask = os.urandom(4) self._xsrf_token = b"|".join([ b"2", binascii.b2a_hex(mask), binascii.b2a_hex(_websocket_mask(mask, token)), utf8(str(int(timestamp)))]) else: raise ValueError("unknown xsrf cookie version %d", output_version) if version is None: expires_days = 30 if self.current_user else None self.set_cookie("_xsrf", self._xsrf_token, expires_days=expires_days, **cookie_kwargs) return self._xsrf_token def _get_raw_xsrf_token(self): if not hasattr(self, '_raw_xsrf_token'): cookie = self.get_cookie("_xsrf") if cookie: version, token, timestamp = self._decode_xsrf_token(cookie) else: version, token, timestamp = None, None, None if token is None: version = None token = os.urandom(16) timestamp = time.time() self._raw_xsrf_token = (version, token, timestamp) return self._raw_xsrf_token def _decode_xsrf_token(self, cookie): try: m = _signed_value_version_re.match(utf8(cookie)) if m: version = int(m.group(1)) if version == 2: _, mask, masked_token, timestamp = cookie.split("|") mask = binascii.a2b_hex(utf8(mask)) token = _websocket_mask( mask, binascii.a2b_hex(utf8(masked_token))) timestamp = int(timestamp) return version, token, timestamp else: raise Exception("Unknown xsrf cookie version") else: version = 1 try: token = binascii.a2b_hex(utf8(cookie)) except (binascii.Error, TypeError): token = utf8(cookie) timestamp = int(time.time()) return (version, token, timestamp) except Exception: # Catch exceptions and return nothing instead of failing. gen_log.debug("Uncaught exception in _decode_xsrf_token", exc_info=True) return None, None, None def check_xsrf_cookie(self): token = (self.get_argument("_xsrf", None) or self.request.headers.get("X-Xsrftoken") or self.request.headers.get("X-Csrftoken")) if not token: raise HTTPError(403, "'_xsrf' argument missing from POST") _, token, _ = self._decode_xsrf_token(token) _, expected_token, _ = self._get_raw_xsrf_token() if not token: raise HTTPError(403, "'_xsrf' argument has invalid format") if not _time_independent_equals(utf8(token), utf8(expected_token)): raise HTTPError(403, "XSRF cookie does not match POST argument") def xsrf_form_html(self): return '<input type="hidden" name="_xsrf" value="' + \ escape.xhtml_escape(self.xsrf_token) + '"/>' def static_url(self, path, include_host=None, **kwargs): self.require_setting("static_path", "static_url") get_url = self.settings.get("static_handler_class", StaticFileHandler).make_static_url if include_host is None: include_host = getattr(self, "include_host", False) if include_host: base = self.request.protocol + "://" + self.request.host else: base = "" return base + get_url(self.settings, path, **kwargs) def require_setting(self, name, feature="this feature"): if not self.application.settings.get(name): raise Exception("You must define the '%s' setting in your " "application to use %s" % (name, feature)) def reverse_url(self, name, *args): return self.application.reverse_url(name, *args) def compute_etag(self): hasher = hashlib.sha1() for part in self._write_buffer: hasher.update(part) return '"%s"' % hasher.hexdigest() def set_etag_header(self): etag = self.compute_etag() if etag is not None: self.set_header("Etag", etag) def check_etag_header(self): computed_etag = utf8(self._headers.get("Etag", "")) # Find all weak and strong etag values from If-None-Match header # because RFC 7232 allows multiple etag values in a single header. etags = re.findall( br'\*|(?:W/)?"[^"]*"', utf8(self.request.headers.get("If-None-Match", "")) ) if not computed_etag or not etags: return False match = False if etags[0] == b'*': match = True else: # Use a weak comparison when comparing entity-tags. def val(x): return x[2:] if x.startswith(b'W/') else x for etag in etags: if val(etag) == val(computed_etag): match = True break return match def _stack_context_handle_exception(self, type, value, traceback): try: # For historical reasons _handle_request_exception only takes # the exception value instead of the full triple, # so re-raise the exception to ensure that it's in # sys.exc_info() raise_exc_info((type, value, traceback)) except Exception: self._handle_request_exception(value) return True @gen.coroutine def _execute(self, transforms, *args, **kwargs): self._transforms = transforms try: if self.request.method not in self.SUPPORTED_METHODS: raise HTTPError(405) self.path_args = [self.decode_argument(arg) for arg in args] self.path_kwargs = dict((k, self.decode_argument(v, name=k)) for (k, v) in kwargs.items()) # If XSRF cookies are turned on, reject form submissions without # the proper cookie if self.request.method not in ("GET", "HEAD", "OPTIONS") and \ self.application.settings.get("xsrf_cookies"): self.check_xsrf_cookie() result = self.prepare() if result is not None: result = yield result if self._prepared_future is not None: # Tell the Application we've finished with prepare() # and are ready for the body to arrive. self._prepared_future.set_result(None) if self._finished: return if _has_stream_request_body(self.__class__): # In streaming mode request.body is a Future that signals # the body has been completely received. The Future has no # result; the data has been passed to self.data_received # instead. try: yield self.request.body except iostream.StreamClosedError: return method = getattr(self, self.request.method.lower()) result = method(*self.path_args, **self.path_kwargs) if result is not None: result = yield result if self._auto_finish and not self._finished: self.finish() except Exception as e: try: self._handle_request_exception(e) except Exception: app_log.error("Exception in exception handler", exc_info=True) if (self._prepared_future is not None and not self._prepared_future.done()): # In case we failed before setting _prepared_future, do it # now (to unblock the HTTP server). Note that this is not # in a finally block to avoid GC issues prior to Python 3.4. self._prepared_future.set_result(None) def data_received(self, chunk): raise NotImplementedError() def _log(self): self.application.log_request(self) def _request_summary(self): return "%s %s (%s)" % (self.request.method, self.request.uri, self.request.remote_ip) def _handle_request_exception(self, e): if isinstance(e, Finish): # Not an error; just finish the request without logging. if not self._finished: self.finish(*e.args) return try: self.log_exception(*sys.exc_info()) except Exception: # An error here should still get a best-effort send_error() # to avoid leaking the connection. app_log.error("Error in exception logger", exc_info=True) if self._finished: # Extra errors after the request has been finished should # be logged, but there is no reason to continue to try and # send a response. return if isinstance(e, HTTPError): if e.status_code not in httputil.responses and not e.reason: gen_log.error("Bad HTTP status code: %d", e.status_code) self.send_error(500, exc_info=sys.exc_info()) else: self.send_error(e.status_code, exc_info=sys.exc_info()) else: self.send_error(500, exc_info=sys.exc_info()) def log_exception(self, typ, value, tb): if isinstance(value, HTTPError): if value.log_message: format = "%d %s: " + value.log_message args = ([value.status_code, self._request_summary()] + list(value.args)) gen_log.warning(format, *args) else: app_log.error("Uncaught exception %s\n%r", self._request_summary(), self.request, exc_info=(typ, value, tb)) def _ui_module(self, name, module): def render(*args, **kwargs): if not hasattr(self, "_active_modules"): self._active_modules = {} if name not in self._active_modules: self._active_modules[name] = module(self) rendered = self._active_modules[name].render(*args, **kwargs) return rendered return render def _ui_method(self, method): return lambda *args, **kwargs: method(self, *args, **kwargs) def _clear_headers_for_304(self): # 304 responses should not contain entity headers (defined in # http://www.w3.org/Protocols/rfc2616/rfc2616-sec7.html#sec7.1) # not explicitly allowed by # http://www.w3.org/Protocols/rfc2616/rfc2616-sec10.html#sec10.3.5 headers = ["Allow", "Content-Encoding", "Content-Language", "Content-Length", "Content-MD5", "Content-Range", "Content-Type", "Last-Modified"] for h in headers: self.clear_header(h) def asynchronous(method): # Delay the IOLoop import because it's not available on app engine. from salt.ext.tornado.ioloop import IOLoop @functools.wraps(method) def wrapper(self, *args, **kwargs): self._auto_finish = False with stack_context.ExceptionStackContext( self._stack_context_handle_exception): result = method(self, *args, **kwargs) if result is not None: result = gen.convert_yielded(result) # If @asynchronous is used with @gen.coroutine, (but # not @gen.engine), we can automatically finish the # request when the future resolves. Additionally, # the Future will swallow any exceptions so we need # to throw them back out to the stack context to finish # the request. def future_complete(f): f.result() if not self._finished: self.finish() IOLoop.current().add_future(result, future_complete) # Once we have done this, hide the Future from our # caller (i.e. RequestHandler._when_complete), which # would otherwise set up its own callback and # exception handler (resulting in exceptions being # logged twice). return None return result return wrapper def stream_request_body(cls): if not issubclass(cls, RequestHandler): raise TypeError("expected subclass of RequestHandler, got %r", cls) cls._stream_request_body = True return cls def _has_stream_request_body(cls): if not issubclass(cls, RequestHandler): raise TypeError("expected subclass of RequestHandler, got %r", cls) return getattr(cls, '_stream_request_body', False) def removeslash(method): @functools.wraps(method) def wrapper(self, *args, **kwargs): if self.request.path.endswith("/"): if self.request.method in ("GET", "HEAD"): uri = self.request.path.rstrip("/") if uri: # don't try to redirect '/' to '' if self.request.query: uri += "?" + self.request.query self.redirect(uri, permanent=True) return else: raise HTTPError(404) return method(self, *args, **kwargs) return wrapper def addslash(method): @functools.wraps(method) def wrapper(self, *args, **kwargs): if not self.request.path.endswith("/"): if self.request.method in ("GET", "HEAD"): uri = self.request.path + "/" if self.request.query: uri += "?" + self.request.query self.redirect(uri, permanent=True) return raise HTTPError(404) return method(self, *args, **kwargs) return wrapper class _ApplicationRouter(ReversibleRuleRouter): def __init__(self, application, rules=None): assert isinstance(application, Application) self.application = application super(_ApplicationRouter, self).__init__(rules) def process_rule(self, rule): rule = super(_ApplicationRouter, self).process_rule(rule) if isinstance(rule.target, (list, tuple)): rule.target = _ApplicationRouter(self.application, rule.target) return rule def get_target_delegate(self, target, request, **target_params): if isclass(target) and issubclass(target, RequestHandler): return self.application.get_handler_delegate(request, target, **target_params) return super(_ApplicationRouter, self).get_target_delegate(target, request, **target_params) class Application(ReversibleRouter): def __init__(self, handlers=None, default_host=None, transforms=None, **settings): if transforms is None: self.transforms = [] if settings.get("compress_response") or settings.get("gzip"): self.transforms.append(GZipContentEncoding) else: self.transforms = transforms self.default_host = default_host self.settings = settings self.ui_modules = {'linkify': _linkify, 'xsrf_form_html': _xsrf_form_html, 'Template': TemplateModule, } self.ui_methods = {} self._load_ui_modules(settings.get("ui_modules", {})) self._load_ui_methods(settings.get("ui_methods", {})) if self.settings.get("static_path"): path = self.settings["static_path"] handlers = list(handlers or []) static_url_prefix = settings.get("static_url_prefix", "/static/") static_handler_class = settings.get("static_handler_class", StaticFileHandler) static_handler_args = settings.get("static_handler_args", {}) static_handler_args['path'] = path for pattern in [re.escape(static_url_prefix) + r"(.*)", r"/(favicon\.ico)", r"/(robots\.txt)"]: handlers.insert(0, (pattern, static_handler_class, static_handler_args)) if self.settings.get('debug'): self.settings.setdefault('autoreload', True) self.settings.setdefault('compiled_template_cache', False) self.settings.setdefault('static_hash_cache', False) self.settings.setdefault('serve_traceback', True) self.wildcard_router = _ApplicationRouter(self, handlers) self.default_router = _ApplicationRouter(self, [ Rule(AnyMatches(), self.wildcard_router) ]) # Automatically reload modified modules if self.settings.get('autoreload'): from salt.ext.tornado import autoreload autoreload.start() def listen(self, port, address="", **kwargs): # import is here rather than top level because HTTPServer # is not importable on appengine from salt.ext.tornado.httpserver import HTTPServer server = HTTPServer(self, **kwargs) server.listen(port, address) return server def add_handlers(self, host_pattern, host_handlers): host_matcher = HostMatches(host_pattern) rule = Rule(host_matcher, _ApplicationRouter(self, host_handlers)) self.default_router.rules.insert(-1, rule) if self.default_host is not None: self.wildcard_router.add_rules([( DefaultHostMatches(self, host_matcher.host_pattern), host_handlers )]) def add_transform(self, transform_class): self.transforms.append(transform_class) def _load_ui_methods(self, methods): if isinstance(methods, types.ModuleType): self._load_ui_methods(dict((n, getattr(methods, n)) for n in dir(methods))) elif isinstance(methods, list): for m in methods: self._load_ui_methods(m) else: for name, fn in methods.items(): if not name.startswith("_") and hasattr(fn, "__call__") \ and name[0].lower() == name[0]: self.ui_methods[name] = fn def _load_ui_modules(self, modules): if isinstance(modules, types.ModuleType): self._load_ui_modules(dict((n, getattr(modules, n)) for n in dir(modules))) elif isinstance(modules, list): for m in modules: self._load_ui_modules(m) else: assert isinstance(modules, dict) for name, cls in modules.items(): try: if issubclass(cls, UIModule): self.ui_modules[name] = cls except TypeError: pass def __call__(self, request): # Legacy HTTPServer interface dispatcher = self.find_handler(request) return dispatcher.execute() def find_handler(self, request, **kwargs): route = self.default_router.find_handler(request) if route is not None: return route if self.settings.get('default_handler_class'): return self.get_handler_delegate( request, self.settings['default_handler_class'], self.settings.get('default_handler_args', {})) return self.get_handler_delegate( request, ErrorHandler, {'status_code': 404}) def get_handler_delegate(self, request, target_class, target_kwargs=None, path_args=None, path_kwargs=None): return _HandlerDelegate( self, request, target_class, target_kwargs, path_args, path_kwargs) def reverse_url(self, name, *args): reversed_url = self.default_router.reverse_url(name, *args) if reversed_url is not None: return reversed_url raise KeyError("%s not found in named urls" % name) def log_request(self, handler): if "log_function" in self.settings: self.settings["log_function"](handler) return if handler.get_status() < 400: log_method = access_log.info elif handler.get_status() < 500: log_method = access_log.warning else: log_method = access_log.error request_time = 1000.0 * handler.request.request_time() log_method("%d %s %.2fms", handler.get_status(), handler._request_summary(), request_time) class _HandlerDelegate(httputil.HTTPMessageDelegate): def __init__(self, application, request, handler_class, handler_kwargs, path_args, path_kwargs): self.application = application self.connection = request.connection self.request = request self.handler_class = handler_class self.handler_kwargs = handler_kwargs or {} self.path_args = path_args or [] self.path_kwargs = path_kwargs or {} self.chunks = [] self.stream_request_body = _has_stream_request_body(self.handler_class) def headers_received(self, start_line, headers): if self.stream_request_body: self.request.body = Future() return self.execute() def data_received(self, data): if self.stream_request_body: return self.handler.data_received(data) else: self.chunks.append(data) def finish(self): if self.stream_request_body: self.request.body.set_result(None) else: self.request.body = b''.join(self.chunks) self.request._parse_body() self.execute() def on_connection_close(self): if self.stream_request_body: self.handler.on_connection_close() else: self.chunks = None def execute(self): # If template cache is disabled (usually in the debug mode), # re-compile templates and reload static files on every # request so you don't need to restart to see changes if not self.application.settings.get("compiled_template_cache", True): with RequestHandler._template_loader_lock: for loader in RequestHandler._template_loaders.values(): loader.reset() if not self.application.settings.get('static_hash_cache', True): StaticFileHandler.reset() self.handler = self.handler_class(self.application, self.request, **self.handler_kwargs) transforms = [t(self.request) for t in self.application.transforms] if self.stream_request_body: self.handler._prepared_future = Future() # Note that if an exception escapes handler._execute it will be # trapped in the Future it returns (which we are ignoring here, # leaving it to be logged when the Future is GC'd). # However, that shouldn't happen because _execute has a blanket # except handler, and we cannot easily access the IOLoop here to # call add_future (because of the requirement to remain compatible # with WSGI) self.handler._execute(transforms, *self.path_args, **self.path_kwargs) # If we are streaming the request body, then execute() is finished # when the handler has prepared to receive the body. If not, # it doesn't matter when execute() finishes (so we return None) return self.handler._prepared_future class HTTPError(Exception): def __init__(self, status_code=500, log_message=None, *args, **kwargs): self.status_code = status_code self.log_message = log_message self.args = args self.reason = kwargs.get('reason', None) if log_message and not args: self.log_message = log_message.replace('%', '%%') def __str__(self): message = "HTTP %d: %s" % ( self.status_code, self.reason or httputil.responses.get(self.status_code, 'Unknown')) if self.log_message: return message + " (" + (self.log_message % self.args) + ")" else: return message class Finish(Exception): pass class MissingArgumentError(HTTPError): def __init__(self, arg_name): super(MissingArgumentError, self).__init__( 400, 'Missing argument %s' % arg_name) self.arg_name = arg_name class ErrorHandler(RequestHandler): def initialize(self, status_code): self.set_status(status_code) def prepare(self): raise HTTPError(self._status_code) def check_xsrf_cookie(self): # POSTs to an ErrorHandler don't actually have side effects, # so we don't need to check the xsrf token. This allows POSTs # to the wrong url to return a 404 instead of 403. pass class RedirectHandler(RequestHandler): def initialize(self, url, permanent=True): self._url = url self._permanent = permanent def get(self, *args): self.redirect(self._url.format(*args), permanent=self._permanent) class StaticFileHandler(RequestHandler): CACHE_MAX_AGE = 86400 * 365 * 10 # 10 years _static_hashes = {} # type: typing.Dict _lock = threading.Lock() # protects _static_hashes def initialize(self, path, default_filename=None): self.root = path self.default_filename = default_filename @classmethod def reset(cls): with cls._lock: cls._static_hashes = {} def head(self, path): return self.get(path, include_body=False) @gen.coroutine def get(self, path, include_body=True): # Set up our path instance variables. self.path = self.parse_url_path(path) del path # make sure we don't refer to path instead of self.path again absolute_path = self.get_absolute_path(self.root, self.path) self.absolute_path = self.validate_absolute_path( self.root, absolute_path) if self.absolute_path is None: return self.modified = self.get_modified_time() self.set_headers() if self.should_return_304(): self.set_status(304) return request_range = None range_header = self.request.headers.get("Range") if range_header: # As per RFC 2616 14.16, if an invalid Range header is specified, # the request will be treated as if the header didn't exist. request_range = httputil._parse_request_range(range_header) size = self.get_content_size() if request_range: start, end = request_range if (start is not None and start >= size) or end == 0: # As per RFC 2616 14.35.1, a range is not satisfiable only: if # the first requested byte is equal to or greater than the # content, or when a suffix with length 0 is specified self.set_status(416) # Range Not Satisfiable self.set_header("Content-Type", "text/plain") self.set_header("Content-Range", "bytes */%s" % (size, )) return if start is not None and start < 0: start += size if end is not None and end > size: # Clients sometimes blindly use a large range to limit their # download size; cap the endpoint at the actual file size. end = size # Note: only return HTTP 206 if less than the entire range has been # requested. Not only is this semantically correct, but Chrome # refuses to play audio if it gets an HTTP 206 in response to # ``Range: bytes=0-``. if size != (end or size) - (start or 0): self.set_status(206) # Partial Content self.set_header("Content-Range", httputil._get_content_range(start, end, size)) else: start = end = None if start is not None and end is not None: content_length = end - start elif end is not None: content_length = end elif start is not None: content_length = size - start else: content_length = size self.set_header("Content-Length", content_length) if include_body: content = self.get_content(self.absolute_path, start, end) if isinstance(content, bytes): content = [content] for chunk in content: try: self.write(chunk) yield self.flush() except iostream.StreamClosedError: return else: assert self.request.method == "HEAD" def compute_etag(self): version_hash = self._get_cached_version(self.absolute_path) if not version_hash: return None return '"%s"' % (version_hash, ) def set_headers(self): self.set_header("Accept-Ranges", "bytes") self.set_etag_header() if self.modified is not None: self.set_header("Last-Modified", self.modified) content_type = self.get_content_type() if content_type: self.set_header("Content-Type", content_type) cache_time = self.get_cache_time(self.path, self.modified, content_type) if cache_time > 0: self.set_header("Expires", datetime.datetime.utcnow() + datetime.timedelta(seconds=cache_time)) self.set_header("Cache-Control", "max-age=" + str(cache_time)) self.set_extra_headers(self.path) def should_return_304(self): if self.check_etag_header(): return True # Check the If-Modified-Since, and don't send the result if the # content has not been modified ims_value = self.request.headers.get("If-Modified-Since") if ims_value is not None: date_tuple = email.utils.parsedate(ims_value) if date_tuple is not None: if_since = datetime.datetime(*date_tuple[:6]) if if_since >= self.modified: return True return False @classmethod def get_absolute_path(cls, root, path): abspath = os.path.abspath(os.path.join(root, path)) return abspath def validate_absolute_path(self, root, absolute_path): # os.path.abspath strips a trailing /. # We must add it back to `root` so that we only match files # in a directory named `root` instead of files starting with # that prefix. root = os.path.abspath(root) if not root.endswith(os.path.sep): # abspath always removes a trailing slash, except when # root is '/'. This is an unusual case, but several projects # have independently discovered this technique to disable # Tornado's path validation and (hopefully) do their own, # so we need to support it. root += os.path.sep # The trailing slash also needs to be temporarily added back # the requested path so a request to root/ will match. if not (absolute_path + os.path.sep).startswith(root): raise HTTPError(403, "%s is not in root static directory", self.path) if (os.path.isdir(absolute_path) and self.default_filename is not None): # need to look at the request.path here for when path is empty # but there is some prefix to the path that was already # trimmed by the routing if not self.request.path.endswith("/"): self.redirect(self.request.path + "/", permanent=True) return absolute_path = os.path.join(absolute_path, self.default_filename) if not os.path.exists(absolute_path): raise HTTPError(404) if not os.path.isfile(absolute_path): raise HTTPError(403, "%s is not a file", self.path) return absolute_path @classmethod def get_content(cls, abspath, start=None, end=None): with open(abspath, "rb") as file: if start is not None: file.seek(start) if end is not None: remaining = end - (start or 0) else: remaining = None while True: chunk_size = 64 * 1024 if remaining is not None and remaining < chunk_size: chunk_size = remaining chunk = file.read(chunk_size) if chunk: if remaining is not None: remaining -= len(chunk) yield chunk else: if remaining is not None: assert remaining == 0 return @classmethod def get_content_version(cls, abspath): data = cls.get_content(abspath) hasher = hashlib.md5() if isinstance(data, bytes): hasher.update(data) else: for chunk in data: hasher.update(chunk) return hasher.hexdigest() def _stat(self): if not hasattr(self, '_stat_result'): self._stat_result = os.stat(self.absolute_path) return self._stat_result def get_content_size(self): stat_result = self._stat() return stat_result[stat.ST_SIZE] def get_modified_time(self): stat_result = self._stat() modified = datetime.datetime.utcfromtimestamp( stat_result[stat.ST_MTIME]) return modified def get_content_type(self): mime_type, encoding = mimetypes.guess_type(self.absolute_path) # per RFC 6713, use the appropriate type for a gzip compressed file if encoding == "gzip": return "application/gzip" # As of 2015-07-21 there is no bzip2 encoding defined at # http://www.iana.org/assignments/media-types/media-types.xhtml # So for that (and any other encoding), use octet-stream. elif encoding is not None: return "application/octet-stream" elif mime_type is not None: return mime_type # if mime_type not detected, use application/octet-stream else: return "application/octet-stream" def set_extra_headers(self, path): pass def get_cache_time(self, path, modified, mime_type): return self.CACHE_MAX_AGE if "v" in self.request.arguments else 0 @classmethod def make_static_url(cls, settings, path, include_version=True): url = settings.get('static_url_prefix', '/static/') + path if not include_version: return url version_hash = cls.get_version(settings, path) if not version_hash: return url return '%s?v=%s' % (url, version_hash) def parse_url_path(self, url_path): if os.path.sep != "/": url_path = url_path.replace("/", os.path.sep) return url_path @classmethod def get_version(cls, settings, path): abs_path = cls.get_absolute_path(settings['static_path'], path) return cls._get_cached_version(abs_path) @classmethod def _get_cached_version(cls, abs_path): with cls._lock: hashes = cls._static_hashes if abs_path not in hashes: try: hashes[abs_path] = cls.get_content_version(abs_path) except Exception: gen_log.error("Could not open static file %r", abs_path) hashes[abs_path] = None hsh = hashes.get(abs_path) if hsh: return hsh return None class FallbackHandler(RequestHandler): def initialize(self, fallback): self.fallback = fallback def prepare(self): self.fallback(self.request) self._finished = True class OutputTransform(object): def __init__(self, request): pass def transform_first_chunk(self, status_code, headers, chunk, finishing): # type: (int, httputil.HTTPHeaders, bytes, bool) -> typing.Tuple[int, httputil.HTTPHeaders, bytes] return status_code, headers, chunk def transform_chunk(self, chunk, finishing): return chunk class GZipContentEncoding(OutputTransform): # Whitelist of compressible mime types (in addition to any types # beginning with "text/"). CONTENT_TYPES = set(["application/javascript", "application/x-javascript", "application/xml", "application/atom+xml", "application/json", "application/xhtml+xml", "image/svg+xml"]) # Python's GzipFile defaults to level 9, while most other gzip # tools (including gzip itself) default to 6, which is probably a # better CPU/size tradeoff. GZIP_LEVEL = 6 # Responses that are too short are unlikely to benefit from gzipping # after considering the "Content-Encoding: gzip" header and the header # inside the gzip encoding. # Note that responses written in multiple chunks will be compressed # regardless of size. MIN_LENGTH = 1024 def __init__(self, request): self._gzipping = "gzip" in request.headers.get("Accept-Encoding", "") def _compressible_type(self, ctype): return ctype.startswith('text/') or ctype in self.CONTENT_TYPES def transform_first_chunk(self, status_code, headers, chunk, finishing): # type: (int, httputil.HTTPHeaders, bytes, bool) -> typing.Tuple[int, httputil.HTTPHeaders, bytes] # TODO: can/should this type be inherited from the superclass? if 'Vary' in headers: headers['Vary'] += ', Accept-Encoding' else: headers['Vary'] = 'Accept-Encoding' if self._gzipping: ctype = _unicode(headers.get("Content-Type", "")).split(";")[0] self._gzipping = self._compressible_type(ctype) and \ (not finishing or len(chunk) >= self.MIN_LENGTH) and \ ("Content-Encoding" not in headers) if self._gzipping: headers["Content-Encoding"] = "gzip" self._gzip_value = BytesIO() self._gzip_file = gzip.GzipFile(mode="w", fileobj=self._gzip_value, compresslevel=self.GZIP_LEVEL) chunk = self.transform_chunk(chunk, finishing) if "Content-Length" in headers: # The original content length is no longer correct. # If this is the last (and only) chunk, we can set the new # content-length; otherwise we remove it and fall back to # chunked encoding. if finishing: headers["Content-Length"] = str(len(chunk)) else: del headers["Content-Length"] return status_code, headers, chunk def transform_chunk(self, chunk, finishing): if self._gzipping: self._gzip_file.write(chunk) if finishing: self._gzip_file.close() else: self._gzip_file.flush() chunk = self._gzip_value.getvalue() self._gzip_value.truncate(0) self._gzip_value.seek(0) return chunk def authenticated(method): @functools.wraps(method) def wrapper(self, *args, **kwargs): if not self.current_user: if self.request.method in ("GET", "HEAD"): url = self.get_login_url() if "?" not in url: if urlparse.urlsplit(url).scheme: # if login url is absolute, make next absolute too next_url = self.request.full_url() else: next_url = self.request.uri url += "?" + urlencode(dict(next=next_url)) self.redirect(url) return raise HTTPError(403) return method(self, *args, **kwargs) return wrapper class UIModule(object): def __init__(self, handler): self.handler = handler self.request = handler.request self.ui = handler.ui self.locale = handler.locale @property def current_user(self): return self.handler.current_user def render(self, *args, **kwargs): raise NotImplementedError() def embedded_javascript(self): return None def javascript_files(self): return None def embedded_css(self): return None def css_files(self): return None def html_head(self): return None def html_body(self): return None def render_string(self, path, **kwargs): return self.handler.render_string(path, **kwargs) class _linkify(UIModule): def render(self, text, **kwargs): return escape.linkify(text, **kwargs) class _xsrf_form_html(UIModule): def render(self): return self.handler.xsrf_form_html() class TemplateModule(UIModule): def __init__(self, handler): super(TemplateModule, self).__init__(handler) # keep resources in both a list and a dict to preserve order self._resource_list = [] self._resource_dict = {} def render(self, path, **kwargs): def set_resources(**kwargs): if path not in self._resource_dict: self._resource_list.append(kwargs) self._resource_dict[path] = kwargs else: if self._resource_dict[path] != kwargs: raise ValueError("set_resources called with different " "resources for the same template") return "" return self.render_string(path, set_resources=set_resources, **kwargs) def _get_resources(self, key): return (r[key] for r in self._resource_list if key in r) def embedded_javascript(self): return "\n".join(self._get_resources("embedded_javascript")) def javascript_files(self): result = [] for f in self._get_resources("javascript_files"): if isinstance(f, (unicode_type, bytes)): result.append(f) else: result.extend(f) return result def embedded_css(self): return "\n".join(self._get_resources("embedded_css")) def css_files(self): result = [] for f in self._get_resources("css_files"): if isinstance(f, (unicode_type, bytes)): result.append(f) else: result.extend(f) return result def html_head(self): return "".join(self._get_resources("html_head")) def html_body(self): return "".join(self._get_resources("html_body")) class _UIModuleNamespace(object): def __init__(self, handler, ui_modules): self.handler = handler self.ui_modules = ui_modules def __getitem__(self, key): return self.handler._ui_module(key, self.ui_modules[key]) def __getattr__(self, key): try: return self[key] except KeyError as e: raise AttributeError(str(e)) if hasattr(hmac, 'compare_digest'): # python 3.3 _time_independent_equals = hmac.compare_digest else: def _time_independent_equals(a, b): if len(a) != len(b): return False result = 0 if isinstance(a[0], int): # python3 byte strings for x, y in zip(a, b): result |= x ^ y else: # python2 for x, y in zip(a, b): result |= ord(x) ^ ord(y) return result == 0 def create_signed_value(secret, name, value, version=None, clock=None, key_version=None): if version is None: version = DEFAULT_SIGNED_VALUE_VERSION if clock is None: clock = time.time timestamp = utf8(str(int(clock()))) value = base64.b64encode(utf8(value)) if version == 1: signature = _create_signature_v1(secret, name, value, timestamp) value = b"|".join([value, timestamp, signature]) return value elif version == 2: # The v2 format consists of a version number and a series of # length-prefixed fields "%d:%s", the last of which is a # signature, all separated by pipes. All numbers are in # decimal format with no leading zeros. The signature is an # HMAC-SHA256 of the whole string up to that point, including # the final pipe. # # The fields are: # - format version (i.e. 2; no length prefix) # - key version (integer, default is 0) # - timestamp (integer seconds since epoch) # - name (not encoded; assumed to be ~alphanumeric) # - value (base64-encoded) # - signature (hex-encoded; no length prefix) def format_field(s): return utf8("%d:" % len(s)) + utf8(s) to_sign = b"|".join([ b"2", format_field(str(key_version or 0)), format_field(timestamp), format_field(name), format_field(value), b'']) if isinstance(secret, dict): assert key_version is not None, 'Key version must be set when sign key dict is used' assert version >= 2, 'Version must be at least 2 for key version support' secret = secret[key_version] signature = _create_signature_v2(secret, to_sign) return to_sign + signature else: raise ValueError("Unsupported version %d" % version) # A leading version number in decimal # with no leading zeros, followed by a pipe. _signed_value_version_re = re.compile(br"^([1-9][0-9]*)\|(.*)$") def _get_version(value): # Figures out what version value is. Version 1 did not include an # explicit version field and started with arbitrary base64 data, # which makes this tricky. m = _signed_value_version_re.match(value) if m is None: version = 1 else: try: version = int(m.group(1)) if version > 999: # Certain payloads from the version-less v1 format may # be parsed as valid integers. Due to base64 padding # restrictions, this can only happen for numbers whose # length is a multiple of 4, so we can treat all # numbers up to 999 as versions, and for the rest we # fall back to v1 format. version = 1 except ValueError: version = 1 return version def decode_signed_value(secret, name, value, max_age_days=31, clock=None, min_version=None): if clock is None: clock = time.time if min_version is None: min_version = DEFAULT_SIGNED_VALUE_MIN_VERSION if min_version > 2: raise ValueError("Unsupported min_version %d" % min_version) if not value: return None value = utf8(value) version = _get_version(value) if version < min_version: return None if version == 1: return _decode_signed_value_v1(secret, name, value, max_age_days, clock) elif version == 2: return _decode_signed_value_v2(secret, name, value, max_age_days, clock) else: return None def _decode_signed_value_v1(secret, name, value, max_age_days, clock): parts = utf8(value).split(b"|") if len(parts) != 3: return None signature = _create_signature_v1(secret, name, parts[0], parts[1]) if not _time_independent_equals(parts[2], signature): gen_log.warning("Invalid cookie signature %r", value) return None timestamp = int(parts[1]) if timestamp < clock() - max_age_days * 86400: gen_log.warning("Expired cookie %r", value) return None if timestamp > clock() + 31 * 86400: # _cookie_signature does not hash a delimiter between the # parts of the cookie, so an attacker could transfer trailing # digits from the payload to the timestamp without altering the # signature. For backwards compatibility, sanity-check timestamp # here instead of modifying _cookie_signature. gen_log.warning("Cookie timestamp in future; possible tampering %r", value) return None if parts[1].startswith(b"0"): gen_log.warning("Tampered cookie %r", value) return None try: return base64.b64decode(parts[0]) except Exception: return None def _decode_fields_v2(value): def _consume_field(s): length, _, rest = s.partition(b':') n = int(length) field_value = rest[:n] # In python 3, indexing bytes returns small integers; we must # use a slice to get a byte string as in python 2. if rest[n:n + 1] != b'|': raise ValueError("malformed v2 signed value field") rest = rest[n + 1:] return field_value, rest rest = value[2:] # remove version number key_version, rest = _consume_field(rest) timestamp, rest = _consume_field(rest) name_field, rest = _consume_field(rest) value_field, passed_sig = _consume_field(rest) return int(key_version), timestamp, name_field, value_field, passed_sig def _decode_signed_value_v2(secret, name, value, max_age_days, clock): try: key_version, timestamp, name_field, value_field, passed_sig = _decode_fields_v2(value) except ValueError: return None signed_string = value[:-len(passed_sig)] if isinstance(secret, dict): try: secret = secret[key_version] except KeyError: return None expected_sig = _create_signature_v2(secret, signed_string) if not _time_independent_equals(passed_sig, expected_sig): return None if name_field != utf8(name): return None timestamp = int(timestamp) if timestamp < clock() - max_age_days * 86400: # The signature has expired. return None try: return base64.b64decode(value_field) except Exception: return None def get_signature_key_version(value): value = utf8(value) version = _get_version(value) if version < 2: return None try: key_version, _, _, _, _ = _decode_fields_v2(value) except ValueError: return None return key_version def _create_signature_v1(secret, *parts): hash = hmac.new(utf8(secret), digestmod=hashlib.sha1) for part in parts: hash.update(utf8(part)) return utf8(hash.hexdigest()) def _create_signature_v2(secret, s): hash = hmac.new(utf8(secret), digestmod=hashlib.sha256) hash.update(utf8(s)) return utf8(hash.hexdigest()) def is_absolute(path): return any(path.startswith(x) for x in ["/", "http:", "https:"])

true

f717643a746a127d6805e12ee1e2871ece59b391

231

py

Python

replacefs/colors.py

yoarch/replace

5255810c019141f7de03b96c26a9b732d2218597

[ "MIT" ]

null

replacefs/colors.py

yoarch/replace

5255810c019141f7de03b96c26a9b732d2218597

[ "MIT" ]

null

replacefs/colors.py

yoarch/replace

5255810c019141f7de03b96c26a9b732d2218597

[ "MIT" ]

null

#!/usr/bin/env python RED = '\033[38;5;196;1m' ORANGE = '\033[38;5;202;1m' WHITE = '\033[1;37m' BLUE = '\033[1;34m' BASE_C = '\033[0m' GREEN = '\033[38;5;40;1m' PURPLE = '\033[38;5;135;1m' GREY = '\033[1;30m' YELLOW = '\033[1;33m'

21

27

0.588745

RED = '\033[38;5;196;1m' ORANGE = '\033[38;5;202;1m' WHITE = '\033[1;37m' BLUE = '\033[1;34m' BASE_C = '\033[0m' GREEN = '\033[38;5;40;1m' PURPLE = '\033[38;5;135;1m' GREY = '\033[1;30m' YELLOW = '\033[1;33m'

true

f7176531240df0f77d476afe1bbca902bbc7bc3d

2,864

py

Python

src/assets/sd_vaccine_plots/main.py

drvinceknight/amwoss

8b0bf80f0a06dc5cf9bfeef4b9f9e174ccadf06d

[ "MIT" ]

1

2022-03-21T21:35:44.000Z

src/assets/sd_vaccine_plots/main.py

drvinceknight/amwoss

8b0bf80f0a06dc5cf9bfeef4b9f9e174ccadf06d

[ "MIT" ]

71

2019-11-18T11:00:25.000Z

2021-10-21T22:49:40.000Z

src/assets/sd_vaccine_plots/main.py

drvinceknight/amwoss

8b0bf80f0a06dc5cf9bfeef4b9f9e174ccadf06d

[ "MIT" ]

1

2020-01-15T12:00:49.000Z

from scipy.integrate import solve_ivp import matplotlib.pyplot as plt plt.style.use('seaborn-whitegrid') def derivatives(t, y, vaccine_rate, birth_rate=0.01): """Defines the system of differential equations that describe the epidemiology model. Args: t: a positive float y: a tuple of three integers vaccine_rate: a positive float <= 1 birth_rate: a positive float <= 1 Returns: A tuple containing dS, dI, and dR """ infection_rate = 0.3 recovery_rate = 0.02 death_rate = 0.01 S, I, R = y N = S + I + R dSdt = ( -((infection_rate * S * I) / N) + ((1 - vaccine_rate) * birth_rate * N) - (death_rate * S) ) dIdt = ( ((infection_rate * S * I) / N) - (recovery_rate * I) - (death_rate * I) ) dRdt = ( (recovery_rate * I) - (death_rate * R) + (vaccine_rate * birth_rate * N) ) return dSdt, dIdt, dRdt def integrate_ode( derivative_function, t_span, y0=(2999, 1, 0), vaccine_rate=0.85, birth_rate=0.01, ): """Numerically solve the system of differential equations. Args: derivative_function: a function returning a tuple of three floats t_span: endpoints oif the time range to integrate over y0: a tuple of three integers (default: (2999, 1, 0)) vaccine_rate: a positive float <= 1 (default: 0.85) birth_rate: a positive float <= 1 (default: 0.01) Returns: A tuple of three arrays """ sol = solve_ivp( derivative_function, t_span, y0, args=(vaccine_rate, birth_rate), ) ts, S, I, R = sol.t, sol.y[0], sol.y[1], sol.y[2] return ts, S, I, R t_span = [0, 730] t, S, I, R = integrate_ode(derivatives, t_span, vaccine_rate=0.0) fig, ax = plt.subplots(1, figsize=(10, 5)) ax.plot(t, S, label='Susceptible', c='black', linestyle='solid', linewidth=1.75) ax.plot(t, I, label='Infected', c='black', linestyle='dotted', linewidth=1.75) ax.plot(t, R, label='Recovered', c='black', linestyle='dashed', linewidth=1.75) ax.legend(fontsize=14, frameon=True, ncol=3, bbox_to_anchor=(0.85, 1.13)) ax.set_xlabel('Time', fontsize=14) ax.set_ylabel('People', fontsize=14) fig.savefig("plot_no_vaccine.pdf") t, S, I, R = integrate_ode(derivatives, t_span, vaccine_rate=0.85) fig, ax = plt.subplots(1, figsize=(10, 5)) ax.plot(t, S, label='Susceptible', c='black', linestyle='solid', linewidth=1.75) ax.plot(t, I, label='Infected', c='black', linestyle='dotted', linewidth=1.75) ax.plot(t, R, label='Recovered', c='black', linestyle='dashed', linewidth=1.75) ax.legend(fontsize=14, frameon=True, ncol=3, bbox_to_anchor=(0.85, 1.13)) ax.set_xlabel('Time', fontsize=14) ax.set_ylabel('People', fontsize=14) fig.savefig("plot_with_vaccine.pdf")

31.822222

80

0.62081

from scipy.integrate import solve_ivp import matplotlib.pyplot as plt plt.style.use('seaborn-whitegrid') def derivatives(t, y, vaccine_rate, birth_rate=0.01): infection_rate = 0.3 recovery_rate = 0.02 death_rate = 0.01 S, I, R = y N = S + I + R dSdt = ( -((infection_rate * S * I) / N) + ((1 - vaccine_rate) * birth_rate * N) - (death_rate * S) ) dIdt = ( ((infection_rate * S * I) / N) - (recovery_rate * I) - (death_rate * I) ) dRdt = ( (recovery_rate * I) - (death_rate * R) + (vaccine_rate * birth_rate * N) ) return dSdt, dIdt, dRdt def integrate_ode( derivative_function, t_span, y0=(2999, 1, 0), vaccine_rate=0.85, birth_rate=0.01, ): sol = solve_ivp( derivative_function, t_span, y0, args=(vaccine_rate, birth_rate), ) ts, S, I, R = sol.t, sol.y[0], sol.y[1], sol.y[2] return ts, S, I, R t_span = [0, 730] t, S, I, R = integrate_ode(derivatives, t_span, vaccine_rate=0.0) fig, ax = plt.subplots(1, figsize=(10, 5)) ax.plot(t, S, label='Susceptible', c='black', linestyle='solid', linewidth=1.75) ax.plot(t, I, label='Infected', c='black', linestyle='dotted', linewidth=1.75) ax.plot(t, R, label='Recovered', c='black', linestyle='dashed', linewidth=1.75) ax.legend(fontsize=14, frameon=True, ncol=3, bbox_to_anchor=(0.85, 1.13)) ax.set_xlabel('Time', fontsize=14) ax.set_ylabel('People', fontsize=14) fig.savefig("plot_no_vaccine.pdf") t, S, I, R = integrate_ode(derivatives, t_span, vaccine_rate=0.85) fig, ax = plt.subplots(1, figsize=(10, 5)) ax.plot(t, S, label='Susceptible', c='black', linestyle='solid', linewidth=1.75) ax.plot(t, I, label='Infected', c='black', linestyle='dotted', linewidth=1.75) ax.plot(t, R, label='Recovered', c='black', linestyle='dashed', linewidth=1.75) ax.legend(fontsize=14, frameon=True, ncol=3, bbox_to_anchor=(0.85, 1.13)) ax.set_xlabel('Time', fontsize=14) ax.set_ylabel('People', fontsize=14) fig.savefig("plot_with_vaccine.pdf")

true

f717657e62b89fc8eff399b8be1e18a4646e310b

8,981

py

Python

alipay/aop/api/request/AlipayOpenServicemarketOrderCreateRequest.py

articuly/alipay-sdk-python-all

0259cd28eca0f219b97dac7f41c2458441d5e7a6

[ "Apache-2.0" ]

null

alipay/aop/api/request/AlipayOpenServicemarketOrderCreateRequest.py

articuly/alipay-sdk-python-all

0259cd28eca0f219b97dac7f41c2458441d5e7a6

[ "Apache-2.0" ]

null

alipay/aop/api/request/AlipayOpenServicemarketOrderCreateRequest.py

articuly/alipay-sdk-python-all

0259cd28eca0f219b97dac7f41c2458441d5e7a6

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- import simplejson as json from alipay.aop.api.FileItem import FileItem from alipay.aop.api.constant.ParamConstants import * class AlipayOpenServicemarketOrderCreateRequest(object): def __init__(self, biz_model=None): self._biz_model = biz_model self._app_category_ids = None self._app_desc = None self._app_english_name = None self._app_name = None self._app_slogan = None self._merchandise_id = None self._merchant_pid = None self._out_biz_no = None self._service_email = None self._service_phone = None self._app_logo = None self._version = "1.0" self._terminal_type = None self._terminal_info = None self._prod_code = None self._notify_url = None self._return_url = None self._udf_params = None self._need_encrypt = False @property def biz_model(self): return self._biz_model @biz_model.setter def biz_model(self, value): self._biz_model = value @property def app_category_ids(self): return self._app_category_ids @app_category_ids.setter def app_category_ids(self, value): self._app_category_ids = value @property def app_desc(self): return self._app_desc @app_desc.setter def app_desc(self, value): self._app_desc = value @property def app_english_name(self): return self._app_english_name @app_english_name.setter def app_english_name(self, value): self._app_english_name = value @property def app_name(self): return self._app_name @app_name.setter def app_name(self, value): self._app_name = value @property def app_slogan(self): return self._app_slogan @app_slogan.setter def app_slogan(self, value): self._app_slogan = value @property def merchandise_id(self): return self._merchandise_id @merchandise_id.setter def merchandise_id(self, value): self._merchandise_id = value @property def merchant_pid(self): return self._merchant_pid @merchant_pid.setter def merchant_pid(self, value): self._merchant_pid = value @property def out_biz_no(self): return self._out_biz_no @out_biz_no.setter def out_biz_no(self, value): self._out_biz_no = value @property def service_email(self): return self._service_email @service_email.setter def service_email(self, value): self._service_email = value @property def service_phone(self): return self._service_phone @service_phone.setter def service_phone(self, value): self._service_phone = value @property def app_logo(self): return self._app_logo @app_logo.setter def app_logo(self, value): if not isinstance(value, FileItem): return self._app_logo = value @property def version(self): return self._version @version.setter def version(self, value): self._version = value @property def terminal_type(self): return self._terminal_type @terminal_type.setter def terminal_type(self, value): self._terminal_type = value @property def terminal_info(self): return self._terminal_info @terminal_info.setter def terminal_info(self, value): self._terminal_info = value @property def prod_code(self): return self._prod_code @prod_code.setter def prod_code(self, value): self._prod_code = value @property def notify_url(self): return self._notify_url @notify_url.setter def notify_url(self, value): self._notify_url = value @property def return_url(self): return self._return_url @return_url.setter def return_url(self, value): self._return_url = value @property def udf_params(self): return self._udf_params @udf_params.setter def udf_params(self, value): if not isinstance(value, dict): return self._udf_params = value @property def need_encrypt(self): return self._need_encrypt @need_encrypt.setter def need_encrypt(self, value): self._need_encrypt = value def add_other_text_param(self, key, value): if not self.udf_params: self.udf_params = dict() self.udf_params[key] = value def get_params(self): params = dict() params[P_METHOD] = 'alipay.open.servicemarket.order.create' params[P_VERSION] = self.version if self.biz_model: params[P_BIZ_CONTENT] = json.dumps(obj=self.biz_model.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) if self.app_category_ids: if hasattr(self.app_category_ids, 'to_alipay_dict'): params['app_category_ids'] = json.dumps(obj=self.app_category_ids.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['app_category_ids'] = self.app_category_ids if self.app_desc: if hasattr(self.app_desc, 'to_alipay_dict'): params['app_desc'] = json.dumps(obj=self.app_desc.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['app_desc'] = self.app_desc if self.app_english_name: if hasattr(self.app_english_name, 'to_alipay_dict'): params['app_english_name'] = json.dumps(obj=self.app_english_name.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['app_english_name'] = self.app_english_name if self.app_name: if hasattr(self.app_name, 'to_alipay_dict'): params['app_name'] = json.dumps(obj=self.app_name.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['app_name'] = self.app_name if self.app_slogan: if hasattr(self.app_slogan, 'to_alipay_dict'): params['app_slogan'] = json.dumps(obj=self.app_slogan.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['app_slogan'] = self.app_slogan if self.merchandise_id: if hasattr(self.merchandise_id, 'to_alipay_dict'): params['merchandise_id'] = json.dumps(obj=self.merchandise_id.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['merchandise_id'] = self.merchandise_id if self.merchant_pid: if hasattr(self.merchant_pid, 'to_alipay_dict'): params['merchant_pid'] = json.dumps(obj=self.merchant_pid.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['merchant_pid'] = self.merchant_pid if self.out_biz_no: if hasattr(self.out_biz_no, 'to_alipay_dict'): params['out_biz_no'] = json.dumps(obj=self.out_biz_no.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['out_biz_no'] = self.out_biz_no if self.service_email: if hasattr(self.service_email, 'to_alipay_dict'): params['service_email'] = json.dumps(obj=self.service_email.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['service_email'] = self.service_email if self.service_phone: if hasattr(self.service_phone, 'to_alipay_dict'): params['service_phone'] = json.dumps(obj=self.service_phone.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['service_phone'] = self.service_phone if self.terminal_type: params['terminal_type'] = self.terminal_type if self.terminal_info: params['terminal_info'] = self.terminal_info if self.prod_code: params['prod_code'] = self.prod_code if self.notify_url: params['notify_url'] = self.notify_url if self.return_url: params['return_url'] = self.return_url if self.udf_params: params.update(self.udf_params) return params def get_multipart_params(self): multipart_params = dict() if self.app_logo: multipart_params['app_logo'] = self.app_logo return multipart_params

33.262963

176

0.635119

import simplejson as json from alipay.aop.api.FileItem import FileItem from alipay.aop.api.constant.ParamConstants import * class AlipayOpenServicemarketOrderCreateRequest(object): def __init__(self, biz_model=None): self._biz_model = biz_model self._app_category_ids = None self._app_desc = None self._app_english_name = None self._app_name = None self._app_slogan = None self._merchandise_id = None self._merchant_pid = None self._out_biz_no = None self._service_email = None self._service_phone = None self._app_logo = None self._version = "1.0" self._terminal_type = None self._terminal_info = None self._prod_code = None self._notify_url = None self._return_url = None self._udf_params = None self._need_encrypt = False @property def biz_model(self): return self._biz_model @biz_model.setter def biz_model(self, value): self._biz_model = value @property def app_category_ids(self): return self._app_category_ids @app_category_ids.setter def app_category_ids(self, value): self._app_category_ids = value @property def app_desc(self): return self._app_desc @app_desc.setter def app_desc(self, value): self._app_desc = value @property def app_english_name(self): return self._app_english_name @app_english_name.setter def app_english_name(self, value): self._app_english_name = value @property def app_name(self): return self._app_name @app_name.setter def app_name(self, value): self._app_name = value @property def app_slogan(self): return self._app_slogan @app_slogan.setter def app_slogan(self, value): self._app_slogan = value @property def merchandise_id(self): return self._merchandise_id @merchandise_id.setter def merchandise_id(self, value): self._merchandise_id = value @property def merchant_pid(self): return self._merchant_pid @merchant_pid.setter def merchant_pid(self, value): self._merchant_pid = value @property def out_biz_no(self): return self._out_biz_no @out_biz_no.setter def out_biz_no(self, value): self._out_biz_no = value @property def service_email(self): return self._service_email @service_email.setter def service_email(self, value): self._service_email = value @property def service_phone(self): return self._service_phone @service_phone.setter def service_phone(self, value): self._service_phone = value @property def app_logo(self): return self._app_logo @app_logo.setter def app_logo(self, value): if not isinstance(value, FileItem): return self._app_logo = value @property def version(self): return self._version @version.setter def version(self, value): self._version = value @property def terminal_type(self): return self._terminal_type @terminal_type.setter def terminal_type(self, value): self._terminal_type = value @property def terminal_info(self): return self._terminal_info @terminal_info.setter def terminal_info(self, value): self._terminal_info = value @property def prod_code(self): return self._prod_code @prod_code.setter def prod_code(self, value): self._prod_code = value @property def notify_url(self): return self._notify_url @notify_url.setter def notify_url(self, value): self._notify_url = value @property def return_url(self): return self._return_url @return_url.setter def return_url(self, value): self._return_url = value @property def udf_params(self): return self._udf_params @udf_params.setter def udf_params(self, value): if not isinstance(value, dict): return self._udf_params = value @property def need_encrypt(self): return self._need_encrypt @need_encrypt.setter def need_encrypt(self, value): self._need_encrypt = value def add_other_text_param(self, key, value): if not self.udf_params: self.udf_params = dict() self.udf_params[key] = value def get_params(self): params = dict() params[P_METHOD] = 'alipay.open.servicemarket.order.create' params[P_VERSION] = self.version if self.biz_model: params[P_BIZ_CONTENT] = json.dumps(obj=self.biz_model.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) if self.app_category_ids: if hasattr(self.app_category_ids, 'to_alipay_dict'): params['app_category_ids'] = json.dumps(obj=self.app_category_ids.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['app_category_ids'] = self.app_category_ids if self.app_desc: if hasattr(self.app_desc, 'to_alipay_dict'): params['app_desc'] = json.dumps(obj=self.app_desc.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['app_desc'] = self.app_desc if self.app_english_name: if hasattr(self.app_english_name, 'to_alipay_dict'): params['app_english_name'] = json.dumps(obj=self.app_english_name.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['app_english_name'] = self.app_english_name if self.app_name: if hasattr(self.app_name, 'to_alipay_dict'): params['app_name'] = json.dumps(obj=self.app_name.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['app_name'] = self.app_name if self.app_slogan: if hasattr(self.app_slogan, 'to_alipay_dict'): params['app_slogan'] = json.dumps(obj=self.app_slogan.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['app_slogan'] = self.app_slogan if self.merchandise_id: if hasattr(self.merchandise_id, 'to_alipay_dict'): params['merchandise_id'] = json.dumps(obj=self.merchandise_id.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['merchandise_id'] = self.merchandise_id if self.merchant_pid: if hasattr(self.merchant_pid, 'to_alipay_dict'): params['merchant_pid'] = json.dumps(obj=self.merchant_pid.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['merchant_pid'] = self.merchant_pid if self.out_biz_no: if hasattr(self.out_biz_no, 'to_alipay_dict'): params['out_biz_no'] = json.dumps(obj=self.out_biz_no.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['out_biz_no'] = self.out_biz_no if self.service_email: if hasattr(self.service_email, 'to_alipay_dict'): params['service_email'] = json.dumps(obj=self.service_email.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['service_email'] = self.service_email if self.service_phone: if hasattr(self.service_phone, 'to_alipay_dict'): params['service_phone'] = json.dumps(obj=self.service_phone.to_alipay_dict(), use_decimal=True, ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['service_phone'] = self.service_phone if self.terminal_type: params['terminal_type'] = self.terminal_type if self.terminal_info: params['terminal_info'] = self.terminal_info if self.prod_code: params['prod_code'] = self.prod_code if self.notify_url: params['notify_url'] = self.notify_url if self.return_url: params['return_url'] = self.return_url if self.udf_params: params.update(self.udf_params) return params def get_multipart_params(self): multipart_params = dict() if self.app_logo: multipart_params['app_logo'] = self.app_logo return multipart_params

true

f71767ca2c739f1474e2622061bcec8545048d82

18,990

py

Python

plugins/modules/oci_opsi_resource_forecast_trend_facts.py

sagar2938/oci-ansible-collection

5b8ce583a0d5d0aabf14494d61aea4649e18d1e6

[ "Apache-2.0" ]

null

plugins/modules/oci_opsi_resource_forecast_trend_facts.py

sagar2938/oci-ansible-collection

5b8ce583a0d5d0aabf14494d61aea4649e18d1e6

[ "Apache-2.0" ]

null

plugins/modules/oci_opsi_resource_forecast_trend_facts.py

sagar2938/oci-ansible-collection

5b8ce583a0d5d0aabf14494d61aea4649e18d1e6

[ "Apache-2.0" ]

null

#!/usr/bin/python # Copyright (c) 2020, 2021 Oracle and/or its affiliates. # This software is made available to you under the terms of the GPL 3.0 license or the Apache 2.0 license. # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) # Apache License v2.0 # See LICENSE.TXT for details. # GENERATED FILE - DO NOT EDIT - MANUAL CHANGES WILL BE OVERWRITTEN from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_opsi_resource_forecast_trend_facts short_description: Fetches details about a ResourceForecastTrend resource in Oracle Cloud Infrastructure description: - Fetches details about a ResourceForecastTrend resource in Oracle Cloud Infrastructure - Get Forecast predictions for CPU and Storage resources since a time in the past. If compartmentIdInSubtree is specified, aggregates resources in a compartment and in all sub-compartments. version_added: "2.9.0" author: Oracle (@oracle) options: compartment_id: description: - The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the compartment. type: str required: true resource_metric: description: - Filter by resource metric. Supported values are CPU , STORAGE, MEMORY and IO. type: str required: true analysis_time_interval: description: - Specify time period in ISO 8601 format with respect to current time. Default is last 30 days represented by P30D. If timeInterval is specified, then timeIntervalStart and timeIntervalEnd will be ignored. Examples P90D (last 90 days), P4W (last 4 weeks), P2M (last 2 months), P1Y (last 12 months), . Maximum value allowed is 25 months prior to current time (P25M). type: str time_interval_start: description: - Analysis start time in UTC in ISO 8601 format(inclusive). Example 2019-10-30T00:00:00Z (yyyy-MM-ddThh:mm:ssZ). The minimum allowed value is 2 years prior to the current day. timeIntervalStart and timeIntervalEnd parameters are used together. If analysisTimeInterval is specified, this parameter is ignored. type: str time_interval_end: description: - Analysis end time in UTC in ISO 8601 format(exclusive). Example 2019-10-30T00:00:00Z (yyyy-MM-ddThh:mm:ssZ). timeIntervalStart and timeIntervalEnd are used together. If timeIntervalEnd is not specified, current time is used as timeIntervalEnd. type: str database_type: description: - Filter by one or more database type. Possible values are ADW-S, ATP-S, ADW-D, ATP-D, EXTERNAL-PDB, EXTERNAL-NONCDB. type: list elements: str choices: - "ADW-S" - "ATP-S" - "ADW-D" - "ATP-D" - "EXTERNAL-PDB" - "EXTERNAL-NONCDB" database_id: description: - Optional list of database L(OCIDs,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the associated DBaaS entity. type: list elements: str id: description: - Optional list of database insight resource L(OCIDs,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm). type: list elements: str exadata_insight_id: description: - Optional list of exadata insight resource L(OCIDs,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm). type: list elements: str cdb_name: description: - Filter by one or more cdb name. type: list elements: str statistic: description: - Choose the type of statistic metric data to be used for forecasting. type: str choices: - "AVG" - "MAX" forecast_days: description: - Number of days used for utilization forecast analysis. type: int forecast_model: description: - "Choose algorithm model for the forecasting. Possible values: - LINEAR: Uses linear regression algorithm for forecasting. - ML_AUTO: Automatically detects best algorithm to use for forecasting. - ML_NO_AUTO: Automatically detects seasonality of the data for forecasting using linear or seasonal algorithm." type: str choices: - "LINEAR" - "ML_AUTO" - "ML_NO_AUTO" utilization_level: description: - "Filter by utilization level by the following buckets: - HIGH_UTILIZATION: DBs with utilization greater or equal than 75. - LOW_UTILIZATION: DBs with utilization lower than 25. - MEDIUM_HIGH_UTILIZATION: DBs with utilization greater or equal than 50 but lower than 75. - MEDIUM_LOW_UTILIZATION: DBs with utilization greater or equal than 25 but lower than 50." type: str choices: - "HIGH_UTILIZATION" - "LOW_UTILIZATION" - "MEDIUM_HIGH_UTILIZATION" - "MEDIUM_LOW_UTILIZATION" confidence: description: - This parameter is used to change data's confidence level, this data is ingested by the forecast algorithm. Confidence is the probability of an interval to contain the expected population parameter. Manipulation of this value will lead to different results. If not set, default confidence value is 95%. type: int host_name: description: - Filter by one or more hostname. type: list elements: str tablespace_name: description: - Tablespace name for a database type: str is_database_instance_level_metrics: description: - Flag to indicate if database instance level metrics should be returned. The flag is ignored when a host name filter is not applied. When a hostname filter is applied this flag will determine whether to return metrics for the instances located on the specified host or for the whole database which contains an instance on this host. type: bool defined_tag_equals: description: - "A list of tag filters to apply. Only resources with a defined tag matching the value will be returned. Each item in the list has the format \\"{namespace}.{tagName}.{value}\\". All inputs are case-insensitive. Multiple values for the same key (i.e. same namespace and tag name) are interpreted as \\"OR\\". Values for different keys (i.e. different namespaces, different tag names, or both) are interpreted as \\"AND\\"." type: list elements: str freeform_tag_equals: description: - "A list of tag filters to apply. Only resources with a freeform tag matching the value will be returned. The key for each tag is \\"{tagName}.{value}\\". All inputs are case-insensitive. Multiple values for the same tag name are interpreted as \\"OR\\". Values for different tag names are interpreted as \\"AND\\"." type: list elements: str defined_tag_exists: description: - "A list of tag existence filters to apply. Only resources for which the specified defined tags exist will be returned. Each item in the list has the format \\"{namespace}.{tagName}.true\\" (for checking existence of a defined tag) or \\"{namespace}.true\\". All inputs are case-insensitive. Currently, only existence (\\"true\\" at the end) is supported. Absence (\\"false\\" at the end) is not supported. Multiple values for the same key (i.e. same namespace and tag name) are interpreted as \\"OR\\". Values for different keys (i.e. different namespaces, different tag names, or both) are interpreted as \\"AND\\"." type: list elements: str freeform_tag_exists: description: - "A list of tag existence filters to apply. Only resources for which the specified freeform tags exist the value will be returned. The key for each tag is \\"{tagName}.true\\". All inputs are case-insensitive. Currently, only existence (\\"true\\" at the end) is supported. Absence (\\"false\\" at the end) is not supported. Multiple values for different tag names are interpreted as \\"AND\\"." type: list elements: str compartment_id_in_subtree: description: - A flag to search all resources within a given compartment and all sub-compartments. type: bool extends_documentation_fragment: [ oracle.oci.oracle ] """ EXAMPLES = """ - name: Get a specific resource_forecast_trend oci_opsi_resource_forecast_trend_facts: # required compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx" resource_metric: resource_metric_example # optional analysis_time_interval: analysis_time_interval_example time_interval_start: 2013-10-20T19:20:30+01:00 time_interval_end: 2013-10-20T19:20:30+01:00 database_type: [ "$p.getValue()" ] database_id: [ "$p.getValue()" ] id: [ "$p.getValue()" ] exadata_insight_id: [ "$p.getValue()" ] cdb_name: [ "$p.getValue()" ] statistic: AVG forecast_days: 56 forecast_model: LINEAR utilization_level: HIGH_UTILIZATION confidence: 56 host_name: [ "$p.getValue()" ] tablespace_name: tablespace_name_example is_database_instance_level_metrics: true defined_tag_equals: [ "$p.getValue()" ] freeform_tag_equals: [ "$p.getValue()" ] defined_tag_exists: [ "$p.getValue()" ] freeform_tag_exists: [ "$p.getValue()" ] compartment_id_in_subtree: true """ RETURN = """ resource_forecast_trend: description: - ResourceForecastTrend resource returned: on success type: complex contains: time_interval_start: description: - The start timestamp that was passed into the request. returned: on success type: str sample: "2020-12-06T00:00:00.000Z" time_interval_end: description: - The end timestamp that was passed into the request. returned: on success type: str sample: "2020-12-06T00:00:00.000Z" resource_metric: description: - "Defines the type of resource metric (example: CPU, STORAGE)" returned: on success type: str sample: STORAGE usage_unit: description: - Displays usage unit ( CORES, GB) returned: on success type: str sample: CORES pattern: description: - Time series patterns used in the forecasting. returned: on success type: str sample: LINEAR tablespace_name: description: - The name of tablespace. returned: on success type: str sample: tablespace_name_example historical_data: description: - Time series data used for the forecast analysis. returned: on success type: complex contains: end_timestamp: description: - The timestamp in which the current sampling period ends in RFC 3339 format. returned: on success type: str sample: "2020-05-01T00:00:00.000Z" usage: description: - Total amount used of the resource metric type (CPU, STORAGE). returned: on success type: float sample: 34.5 projected_data: description: - Time series data result of the forecasting analysis. returned: on success type: complex contains: end_timestamp: description: - The timestamp in which the current sampling period ends in RFC 3339 format. returned: on success type: str sample: "2020-05-01T00:00:00.000Z" usage: description: - Total amount used of the resource metric type (CPU, STORAGE). returned: on success type: float sample: 34.5 high_value: description: - Upper uncertainty bound of the current usage value. returned: on success type: float sample: 1.2 low_value: description: - Lower uncertainty bound of the current usage value. returned: on success type: float sample: 1.2 sample: { "time_interval_start": "2020-12-06T00:00:00.000Z", "time_interval_end": "2020-12-06T00:00:00.000Z", "resource_metric": "STORAGE", "usage_unit": "CORES", "pattern": "LINEAR", "tablespace_name": "tablespace_name_example", "historical_data": [{ "end_timestamp": "2020-05-01T00:00:00.000Z", "usage": 34.5 }], "projected_data": [{ "end_timestamp": "2020-05-01T00:00:00.000Z", "usage": 34.5, "high_value": 1.2, "low_value": 1.2 }] } """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceFactsHelperBase, get_custom_class, ) try: from oci.opsi import OperationsInsightsClient HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class ResourceForecastTrendFactsHelperGen(OCIResourceFactsHelperBase): """Supported operations: get""" def get_required_params_for_get(self): return [ "compartment_id", "resource_metric", ] def get_resource(self): optional_get_method_params = [ "analysis_time_interval", "time_interval_start", "time_interval_end", "database_type", "database_id", "id", "exadata_insight_id", "cdb_name", "statistic", "forecast_days", "forecast_model", "utilization_level", "confidence", "host_name", "tablespace_name", "is_database_instance_level_metrics", "defined_tag_equals", "freeform_tag_equals", "defined_tag_exists", "freeform_tag_exists", "compartment_id_in_subtree", ] optional_kwargs = dict( (param, self.module.params[param]) for param in optional_get_method_params if self.module.params.get(param) is not None ) return oci_common_utils.call_with_backoff( self.client.summarize_database_insight_resource_forecast_trend, compartment_id=self.module.params.get("compartment_id"), resource_metric=self.module.params.get("resource_metric"), **optional_kwargs ) ResourceForecastTrendFactsHelperCustom = get_custom_class( "ResourceForecastTrendFactsHelperCustom" ) class ResourceFactsHelper( ResourceForecastTrendFactsHelperCustom, ResourceForecastTrendFactsHelperGen ): pass def main(): module_args = oci_common_utils.get_common_arg_spec() module_args.update( dict( compartment_id=dict(type="str", required=True), resource_metric=dict(type="str", required=True), analysis_time_interval=dict(type="str"), time_interval_start=dict(type="str"), time_interval_end=dict(type="str"), database_type=dict( type="list", elements="str", choices=[ "ADW-S", "ATP-S", "ADW-D", "ATP-D", "EXTERNAL-PDB", "EXTERNAL-NONCDB", ], ), database_id=dict(type="list", elements="str"), id=dict(type="list", elements="str"), exadata_insight_id=dict(type="list", elements="str"), cdb_name=dict(type="list", elements="str"), statistic=dict(type="str", choices=["AVG", "MAX"]), forecast_days=dict(type="int"), forecast_model=dict( type="str", choices=["LINEAR", "ML_AUTO", "ML_NO_AUTO"] ), utilization_level=dict( type="str", choices=[ "HIGH_UTILIZATION", "LOW_UTILIZATION", "MEDIUM_HIGH_UTILIZATION", "MEDIUM_LOW_UTILIZATION", ], ), confidence=dict(type="int"), host_name=dict(type="list", elements="str"), tablespace_name=dict(type="str"), is_database_instance_level_metrics=dict(type="bool"), defined_tag_equals=dict(type="list", elements="str"), freeform_tag_equals=dict(type="list", elements="str"), defined_tag_exists=dict(type="list", elements="str"), freeform_tag_exists=dict(type="list", elements="str"), compartment_id_in_subtree=dict(type="bool"), ) ) module = AnsibleModule(argument_spec=module_args) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_facts_helper = ResourceFactsHelper( module=module, resource_type="resource_forecast_trend", service_client_class=OperationsInsightsClient, namespace="opsi", ) result = [] if resource_facts_helper.is_get(): result = resource_facts_helper.get() else: resource_facts_helper.fail() module.exit_json(resource_forecast_trend=result) if __name__ == "__main__": main()

38.99384

157

0.597999

from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = { "metadata_version": "1.1", "status": ["preview"], "supported_by": "community", } DOCUMENTATION = """ --- module: oci_opsi_resource_forecast_trend_facts short_description: Fetches details about a ResourceForecastTrend resource in Oracle Cloud Infrastructure description: - Fetches details about a ResourceForecastTrend resource in Oracle Cloud Infrastructure - Get Forecast predictions for CPU and Storage resources since a time in the past. If compartmentIdInSubtree is specified, aggregates resources in a compartment and in all sub-compartments. version_added: "2.9.0" author: Oracle (@oracle) options: compartment_id: description: - The L(OCID,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the compartment. type: str required: true resource_metric: description: - Filter by resource metric. Supported values are CPU , STORAGE, MEMORY and IO. type: str required: true analysis_time_interval: description: - Specify time period in ISO 8601 format with respect to current time. Default is last 30 days represented by P30D. If timeInterval is specified, then timeIntervalStart and timeIntervalEnd will be ignored. Examples P90D (last 90 days), P4W (last 4 weeks), P2M (last 2 months), P1Y (last 12 months), . Maximum value allowed is 25 months prior to current time (P25M). type: str time_interval_start: description: - Analysis start time in UTC in ISO 8601 format(inclusive). Example 2019-10-30T00:00:00Z (yyyy-MM-ddThh:mm:ssZ). The minimum allowed value is 2 years prior to the current day. timeIntervalStart and timeIntervalEnd parameters are used together. If analysisTimeInterval is specified, this parameter is ignored. type: str time_interval_end: description: - Analysis end time in UTC in ISO 8601 format(exclusive). Example 2019-10-30T00:00:00Z (yyyy-MM-ddThh:mm:ssZ). timeIntervalStart and timeIntervalEnd are used together. If timeIntervalEnd is not specified, current time is used as timeIntervalEnd. type: str database_type: description: - Filter by one or more database type. Possible values are ADW-S, ATP-S, ADW-D, ATP-D, EXTERNAL-PDB, EXTERNAL-NONCDB. type: list elements: str choices: - "ADW-S" - "ATP-S" - "ADW-D" - "ATP-D" - "EXTERNAL-PDB" - "EXTERNAL-NONCDB" database_id: description: - Optional list of database L(OCIDs,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the associated DBaaS entity. type: list elements: str id: description: - Optional list of database insight resource L(OCIDs,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm). type: list elements: str exadata_insight_id: description: - Optional list of exadata insight resource L(OCIDs,https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm). type: list elements: str cdb_name: description: - Filter by one or more cdb name. type: list elements: str statistic: description: - Choose the type of statistic metric data to be used for forecasting. type: str choices: - "AVG" - "MAX" forecast_days: description: - Number of days used for utilization forecast analysis. type: int forecast_model: description: - "Choose algorithm model for the forecasting. Possible values: - LINEAR: Uses linear regression algorithm for forecasting. - ML_AUTO: Automatically detects best algorithm to use for forecasting. - ML_NO_AUTO: Automatically detects seasonality of the data for forecasting using linear or seasonal algorithm." type: str choices: - "LINEAR" - "ML_AUTO" - "ML_NO_AUTO" utilization_level: description: - "Filter by utilization level by the following buckets: - HIGH_UTILIZATION: DBs with utilization greater or equal than 75. - LOW_UTILIZATION: DBs with utilization lower than 25. - MEDIUM_HIGH_UTILIZATION: DBs with utilization greater or equal than 50 but lower than 75. - MEDIUM_LOW_UTILIZATION: DBs with utilization greater or equal than 25 but lower than 50." type: str choices: - "HIGH_UTILIZATION" - "LOW_UTILIZATION" - "MEDIUM_HIGH_UTILIZATION" - "MEDIUM_LOW_UTILIZATION" confidence: description: - This parameter is used to change data's confidence level, this data is ingested by the forecast algorithm. Confidence is the probability of an interval to contain the expected population parameter. Manipulation of this value will lead to different results. If not set, default confidence value is 95%. type: int host_name: description: - Filter by one or more hostname. type: list elements: str tablespace_name: description: - Tablespace name for a database type: str is_database_instance_level_metrics: description: - Flag to indicate if database instance level metrics should be returned. The flag is ignored when a host name filter is not applied. When a hostname filter is applied this flag will determine whether to return metrics for the instances located on the specified host or for the whole database which contains an instance on this host. type: bool defined_tag_equals: description: - "A list of tag filters to apply. Only resources with a defined tag matching the value will be returned. Each item in the list has the format \\"{namespace}.{tagName}.{value}\\". All inputs are case-insensitive. Multiple values for the same key (i.e. same namespace and tag name) are interpreted as \\"OR\\". Values for different keys (i.e. different namespaces, different tag names, or both) are interpreted as \\"AND\\"." type: list elements: str freeform_tag_equals: description: - "A list of tag filters to apply. Only resources with a freeform tag matching the value will be returned. The key for each tag is \\"{tagName}.{value}\\". All inputs are case-insensitive. Multiple values for the same tag name are interpreted as \\"OR\\". Values for different tag names are interpreted as \\"AND\\"." type: list elements: str defined_tag_exists: description: - "A list of tag existence filters to apply. Only resources for which the specified defined tags exist will be returned. Each item in the list has the format \\"{namespace}.{tagName}.true\\" (for checking existence of a defined tag) or \\"{namespace}.true\\". All inputs are case-insensitive. Currently, only existence (\\"true\\" at the end) is supported. Absence (\\"false\\" at the end) is not supported. Multiple values for the same key (i.e. same namespace and tag name) are interpreted as \\"OR\\". Values for different keys (i.e. different namespaces, different tag names, or both) are interpreted as \\"AND\\"." type: list elements: str freeform_tag_exists: description: - "A list of tag existence filters to apply. Only resources for which the specified freeform tags exist the value will be returned. The key for each tag is \\"{tagName}.true\\". All inputs are case-insensitive. Currently, only existence (\\"true\\" at the end) is supported. Absence (\\"false\\" at the end) is not supported. Multiple values for different tag names are interpreted as \\"AND\\"." type: list elements: str compartment_id_in_subtree: description: - A flag to search all resources within a given compartment and all sub-compartments. type: bool extends_documentation_fragment: [ oracle.oci.oracle ] """ EXAMPLES = """ - name: Get a specific resource_forecast_trend oci_opsi_resource_forecast_trend_facts: # required compartment_id: "ocid1.compartment.oc1..xxxxxxEXAMPLExxxxxx" resource_metric: resource_metric_example # optional analysis_time_interval: analysis_time_interval_example time_interval_start: 2013-10-20T19:20:30+01:00 time_interval_end: 2013-10-20T19:20:30+01:00 database_type: [ "$p.getValue()" ] database_id: [ "$p.getValue()" ] id: [ "$p.getValue()" ] exadata_insight_id: [ "$p.getValue()" ] cdb_name: [ "$p.getValue()" ] statistic: AVG forecast_days: 56 forecast_model: LINEAR utilization_level: HIGH_UTILIZATION confidence: 56 host_name: [ "$p.getValue()" ] tablespace_name: tablespace_name_example is_database_instance_level_metrics: true defined_tag_equals: [ "$p.getValue()" ] freeform_tag_equals: [ "$p.getValue()" ] defined_tag_exists: [ "$p.getValue()" ] freeform_tag_exists: [ "$p.getValue()" ] compartment_id_in_subtree: true """ RETURN = """ resource_forecast_trend: description: - ResourceForecastTrend resource returned: on success type: complex contains: time_interval_start: description: - The start timestamp that was passed into the request. returned: on success type: str sample: "2020-12-06T00:00:00.000Z" time_interval_end: description: - The end timestamp that was passed into the request. returned: on success type: str sample: "2020-12-06T00:00:00.000Z" resource_metric: description: - "Defines the type of resource metric (example: CPU, STORAGE)" returned: on success type: str sample: STORAGE usage_unit: description: - Displays usage unit ( CORES, GB) returned: on success type: str sample: CORES pattern: description: - Time series patterns used in the forecasting. returned: on success type: str sample: LINEAR tablespace_name: description: - The name of tablespace. returned: on success type: str sample: tablespace_name_example historical_data: description: - Time series data used for the forecast analysis. returned: on success type: complex contains: end_timestamp: description: - The timestamp in which the current sampling period ends in RFC 3339 format. returned: on success type: str sample: "2020-05-01T00:00:00.000Z" usage: description: - Total amount used of the resource metric type (CPU, STORAGE). returned: on success type: float sample: 34.5 projected_data: description: - Time series data result of the forecasting analysis. returned: on success type: complex contains: end_timestamp: description: - The timestamp in which the current sampling period ends in RFC 3339 format. returned: on success type: str sample: "2020-05-01T00:00:00.000Z" usage: description: - Total amount used of the resource metric type (CPU, STORAGE). returned: on success type: float sample: 34.5 high_value: description: - Upper uncertainty bound of the current usage value. returned: on success type: float sample: 1.2 low_value: description: - Lower uncertainty bound of the current usage value. returned: on success type: float sample: 1.2 sample: { "time_interval_start": "2020-12-06T00:00:00.000Z", "time_interval_end": "2020-12-06T00:00:00.000Z", "resource_metric": "STORAGE", "usage_unit": "CORES", "pattern": "LINEAR", "tablespace_name": "tablespace_name_example", "historical_data": [{ "end_timestamp": "2020-05-01T00:00:00.000Z", "usage": 34.5 }], "projected_data": [{ "end_timestamp": "2020-05-01T00:00:00.000Z", "usage": 34.5, "high_value": 1.2, "low_value": 1.2 }] } """ from ansible.module_utils.basic import AnsibleModule from ansible_collections.oracle.oci.plugins.module_utils import oci_common_utils from ansible_collections.oracle.oci.plugins.module_utils.oci_resource_utils import ( OCIResourceFactsHelperBase, get_custom_class, ) try: from oci.opsi import OperationsInsightsClient HAS_OCI_PY_SDK = True except ImportError: HAS_OCI_PY_SDK = False class ResourceForecastTrendFactsHelperGen(OCIResourceFactsHelperBase): def get_required_params_for_get(self): return [ "compartment_id", "resource_metric", ] def get_resource(self): optional_get_method_params = [ "analysis_time_interval", "time_interval_start", "time_interval_end", "database_type", "database_id", "id", "exadata_insight_id", "cdb_name", "statistic", "forecast_days", "forecast_model", "utilization_level", "confidence", "host_name", "tablespace_name", "is_database_instance_level_metrics", "defined_tag_equals", "freeform_tag_equals", "defined_tag_exists", "freeform_tag_exists", "compartment_id_in_subtree", ] optional_kwargs = dict( (param, self.module.params[param]) for param in optional_get_method_params if self.module.params.get(param) is not None ) return oci_common_utils.call_with_backoff( self.client.summarize_database_insight_resource_forecast_trend, compartment_id=self.module.params.get("compartment_id"), resource_metric=self.module.params.get("resource_metric"), **optional_kwargs ) ResourceForecastTrendFactsHelperCustom = get_custom_class( "ResourceForecastTrendFactsHelperCustom" ) class ResourceFactsHelper( ResourceForecastTrendFactsHelperCustom, ResourceForecastTrendFactsHelperGen ): pass def main(): module_args = oci_common_utils.get_common_arg_spec() module_args.update( dict( compartment_id=dict(type="str", required=True), resource_metric=dict(type="str", required=True), analysis_time_interval=dict(type="str"), time_interval_start=dict(type="str"), time_interval_end=dict(type="str"), database_type=dict( type="list", elements="str", choices=[ "ADW-S", "ATP-S", "ADW-D", "ATP-D", "EXTERNAL-PDB", "EXTERNAL-NONCDB", ], ), database_id=dict(type="list", elements="str"), id=dict(type="list", elements="str"), exadata_insight_id=dict(type="list", elements="str"), cdb_name=dict(type="list", elements="str"), statistic=dict(type="str", choices=["AVG", "MAX"]), forecast_days=dict(type="int"), forecast_model=dict( type="str", choices=["LINEAR", "ML_AUTO", "ML_NO_AUTO"] ), utilization_level=dict( type="str", choices=[ "HIGH_UTILIZATION", "LOW_UTILIZATION", "MEDIUM_HIGH_UTILIZATION", "MEDIUM_LOW_UTILIZATION", ], ), confidence=dict(type="int"), host_name=dict(type="list", elements="str"), tablespace_name=dict(type="str"), is_database_instance_level_metrics=dict(type="bool"), defined_tag_equals=dict(type="list", elements="str"), freeform_tag_equals=dict(type="list", elements="str"), defined_tag_exists=dict(type="list", elements="str"), freeform_tag_exists=dict(type="list", elements="str"), compartment_id_in_subtree=dict(type="bool"), ) ) module = AnsibleModule(argument_spec=module_args) if not HAS_OCI_PY_SDK: module.fail_json(msg="oci python sdk required for this module.") resource_facts_helper = ResourceFactsHelper( module=module, resource_type="resource_forecast_trend", service_client_class=OperationsInsightsClient, namespace="opsi", ) result = [] if resource_facts_helper.is_get(): result = resource_facts_helper.get() else: resource_facts_helper.fail() module.exit_json(resource_forecast_trend=result) if __name__ == "__main__": main()

true

f71767d27ceb9c0a11fa4ade0519de1ef382cf0f

1,385

py

Python

2020/src/day6.py

vionion/advent-of-code-2019

c3389694a42e9b1d978f82c6fb42925465799734

[ "MIT" ]

null

2020/src/day6.py

vionion/advent-of-code-2019

c3389694a42e9b1d978f82c6fb42925465799734

[ "MIT" ]

null

2020/src/day6.py

vionion/advent-of-code-2019

c3389694a42e9b1d978f82c6fb42925465799734

[ "MIT" ]

null

from typing import List, Set from io_utils import read_input_file def day6_1(): input_list = read_input_file("day6.txt", input_type=str) answers = get_all_yes_answers_per_group(input_list) amount_yes_answers = 0 for answers_per_group in answers: amount_yes_answers += len(answers_per_group) return amount_yes_answers def get_all_yes_answers_per_group(input_list): answers: List[Set[str]] = [] i = 0 for line in input_list: if line == "\n": i += 1 else: if i == len(answers): answers.append(set()) line = line.strip() for char in line: answers[i].add(char) return answers def get_common_yes_answers_per_group(input_list): answers: List[Set[str]] = [] i = 0 for line in input_list: if line == "\n": i += 1 else: line = line.strip() if i == len(answers): answers.append(set(line)) else: answers[i] = answers[i].intersection(line) return answers def day6_2(): input_list = read_input_file("day6.txt", input_type=str) answers = get_common_yes_answers_per_group(input_list) amount_yes_answers = 0 for answers_per_group in answers: amount_yes_answers += len(answers_per_group) return amount_yes_answers

26.634615

60

0.61083

from typing import List, Set from io_utils import read_input_file def day6_1(): input_list = read_input_file("day6.txt", input_type=str) answers = get_all_yes_answers_per_group(input_list) amount_yes_answers = 0 for answers_per_group in answers: amount_yes_answers += len(answers_per_group) return amount_yes_answers def get_all_yes_answers_per_group(input_list): answers: List[Set[str]] = [] i = 0 for line in input_list: if line == "\n": i += 1 else: if i == len(answers): answers.append(set()) line = line.strip() for char in line: answers[i].add(char) return answers def get_common_yes_answers_per_group(input_list): answers: List[Set[str]] = [] i = 0 for line in input_list: if line == "\n": i += 1 else: line = line.strip() if i == len(answers): answers.append(set(line)) else: answers[i] = answers[i].intersection(line) return answers def day6_2(): input_list = read_input_file("day6.txt", input_type=str) answers = get_common_yes_answers_per_group(input_list) amount_yes_answers = 0 for answers_per_group in answers: amount_yes_answers += len(answers_per_group) return amount_yes_answers

true

f71768dfa296b62f40248813c15aa926044590df

360

py

Python

votes/migrations/0002_auto_20190529_0721.py

isidaruk/eurovision_project

976743e66a2fed17c0513f17a9a7d35850e9cde5

[ "MIT" ]

null

votes/migrations/0002_auto_20190529_0721.py

isidaruk/eurovision_project

976743e66a2fed17c0513f17a9a7d35850e9cde5

[ "MIT" ]

8

2020-02-12T00:23:27.000Z

2022-03-08T21:10:13.000Z

votes/migrations/0002_auto_20190529_0721.py

isidaruk/eurovision_project

976743e66a2fed17c0513f17a9a7d35850e9cde5

[ "MIT" ]

null

# Generated by Django 2.2.1 on 2019-05-29 07:21 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('votes', '0001_initial'), ] operations = [ migrations.RenameField( model_name='vote', old_name='to_country', new_name='to_participant', ), ]

18.947368

47

0.580556

from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('votes', '0001_initial'), ] operations = [ migrations.RenameField( model_name='vote', old_name='to_country', new_name='to_participant', ), ]

true

f717690a322e1a696f5c7c83ea215426620aa34e

673

py

Python

src/data/456.py

NULLCT/LOMC

79a16474a8f21310e0fb47e536d527dd5dc6d655

[ "MIT" ]

null

src/data/456.py

NULLCT/LOMC

79a16474a8f21310e0fb47e536d527dd5dc6d655

[ "MIT" ]

null

src/data/456.py

NULLCT/LOMC

79a16474a8f21310e0fb47e536d527dd5dc6d655

[ "MIT" ]

null

from collections import deque n, m = map(int, input().split()) graph = [list() for _ in range(n)] for _ in range(n - 1): u, k = [int(x) for x in input().split()] # uは頂点番号、kは隣接頂点の個数 u, k = u - 1, k - 1 graph[u].append(k) graph[k].append(u) # 無向グラフ dist = [-1] * n #距離 dist[0] = 0 #startは0 q = deque() q.append(0) #startは0 while q: #qが空になるまで v = q.popleft() for x in graph[v]: if dist[x] != -1: #更新 continue dist[x] = 1 - dist[v] q.append(x) for i in range(m): c, d = map(int, input().split()) c, d = c - 1, d - 1 if dist[c] != dist[d]: print("Road") else: print("Town")

21.03125

64

0.499257

from collections import deque n, m = map(int, input().split()) graph = [list() for _ in range(n)] for _ in range(n - 1): u, k = [int(x) for x in input().split()] u, k = u - 1, k - 1 graph[u].append(k) graph[k].append(u) dist = [-1] * n dist[0] = 0 q = deque() q.append(0) while q: v = q.popleft() for x in graph[v]: if dist[x] != -1: continue dist[x] = 1 - dist[v] q.append(x) for i in range(m): c, d = map(int, input().split()) c, d = c - 1, d - 1 if dist[c] != dist[d]: print("Road") else: print("Town")

true

f71769abced2b28f75a37afaba30e87febaaf7f8

2,143

py

Python

Tracker/update.py

nordwind80/BT-Tracker

558c15b399871c1ca11d0c4ae1eb598e3060931e

[ "MIT" ]

1

2019-05-05T06:46:27.000Z

Tracker/update.py

nordwind80/BT-Tracker

558c15b399871c1ca11d0c4ae1eb598e3060931e

[ "MIT" ]

null

Tracker/update.py

nordwind80/BT-Tracker

558c15b399871c1ca11d0c4ae1eb598e3060931e

[ "MIT" ]

null

#!/usr/bin/env python3 # # Author: eaglewings # E-Mail: ZWFnbGV3aW5ncy55aUBnbWFpbC5jb20= # Created Time: 2019-04-17 15:17 # Last Modified: # Description: # - Project: BT Trackers Updater # - File Name: update.py # - Trackers Updater import os import re from typing import NoReturn class Filer(object): def __init__(self): self._aria2_path = "/.aria2/" self._file_name = "aria2.conf" self._file_path = f"{self._get_home}{self._aria2_path}" @property def _get_home(self) -> str: """ Return User $HOME path. :return: str """ return os.path.expanduser("~") @property def get_path(self) -> str: """ Return Aria2 config file path. :return: str """ return f"{self._file_path}{self._file_name}" def _create_dir(self) -> NoReturn: try: os.mkdir(f"{self._file_path}") except FileExistsError as why: print(f"Create directory failed. {why}") def check_dirctory(self) -> bool: """ Find Aria2 directory, If not create it. If mkdir fail, raise FileExistsError error. :return: NoReturn """ if os.path.exists(f"{self._file_path}"): return True else: self._create_dir() return False class Updater(object): def __init__(self, path: str, trackers: str): self._path = path self._trackers = trackers def start(self) -> NoReturn: check = False with open(self._path, "r+") as file: lines = file.readlines() file.seek(0) file.truncate() for line in lines: if re.search(r"bt-tracker=.*", line): line = line.replace(line, f"bt-tracker={self._trackers}\n") file.write(line) check = True else: file.write(line) else: if check: return else: file.write(f"bt-tracker={self._trackers}\n")

26.45679

95

0.527765

import os import re from typing import NoReturn class Filer(object): def __init__(self): self._aria2_path = "/.aria2/" self._file_name = "aria2.conf" self._file_path = f"{self._get_home}{self._aria2_path}" @property def _get_home(self) -> str: return os.path.expanduser("~") @property def get_path(self) -> str: return f"{self._file_path}{self._file_name}" def _create_dir(self) -> NoReturn: try: os.mkdir(f"{self._file_path}") except FileExistsError as why: print(f"Create directory failed. {why}") def check_dirctory(self) -> bool: if os.path.exists(f"{self._file_path}"): return True else: self._create_dir() return False class Updater(object): def __init__(self, path: str, trackers: str): self._path = path self._trackers = trackers def start(self) -> NoReturn: check = False with open(self._path, "r+") as file: lines = file.readlines() file.seek(0) file.truncate() for line in lines: if re.search(r"bt-tracker=.*", line): line = line.replace(line, f"bt-tracker={self._trackers}\n") file.write(line) check = True else: file.write(line) else: if check: return else: file.write(f"bt-tracker={self._trackers}\n")

true

f7176bc8b9827c81ae4f1e4df6897e9563ad218f

2,628

py

Python

source/boundaryconds.py

agstub/sglake-detectability

5556250a59d7f500bcee86899dd9a497a368faca

[ "MIT" ]

1

2021-05-27T12:24:35.000Z

source/boundaryconds.py

ldeo-glaciology/sglake-detectability

5556250a59d7f500bcee86899dd9a497a368faca

[ "MIT" ]

null

source/boundaryconds.py

ldeo-glaciology/sglake-detectability

5556250a59d7f500bcee86899dd9a497a368faca

[ "MIT" ]

null

#------------------------------------------------------------------------------- # This file contains functions that: # (1) define the boundaries (ice-air,ice-water,ice-bed) of the mesh, AND... # (2) mark the boundaries of the mesh #------------------------------------------------------------------------------- from params import tol,Lngth,Hght from geometry import bed import numpy as np from dolfin import * #------------------------------------------------------------------------------- # Define SubDomains for ice-water boundary, ice-bed boundary, inflow (x=0) and # outflow (x=Length of domain). The parameter 'tol' is a minimal water depth # used to distinguish the ice-water and ice-bed surfaces. class WaterBoundary(SubDomain): # Ice-water boundary. # This boundary is marked first and all of the irrelevant portions are # overwritten by the other boundary markers. def inside(self, x, on_boundary): return (on_boundary and (x[1]<0.5*Hght)) class BedBoundary(SubDomain): # Ice-bed boundary away from the lake; the portions near the lake are overwritten # by BasinBoundary. # Lifting of ice from the bed *is not* allowed on this boundary. def inside(self, x, on_boundary): return (on_boundary and ((x[1]-bed(x[0]))<=tol)) class LeftBoundary(SubDomain): # Left boundary def inside(self, x, on_boundary): return (on_boundary and np.abs(x[0])<tol) class RightBoundary(SubDomain): # Right boundary def inside(self, x, on_boundary): return (on_boundary and np.abs(x[0]-Lngth)<tol) #------------------------------------------------------------------------------- def mark_boundary(mesh): # Assign markers to each boundary segment (except the upper surface). # This is used at each time step to update the markers. # # Boundary marker numbering convention: # 1 - Left boundary # 2 - Right boundary # 3 - Ice-bed boundary # 4 - Ice-water boundary # # This function returns these markers, which are used to define the # boundary integrals and dirichlet conditions. boundary_markers = MeshFunction('size_t', mesh,dim=1) boundary_markers.set_all(0) # Mark ice-water boundary bdryWater = WaterBoundary() bdryWater.mark(boundary_markers, 4) # Mark ice-bed boundary away from lake bdryBed = BedBoundary() bdryBed.mark(boundary_markers, 3) # Mark inflow boundary bdryLeft = LeftBoundary() bdryLeft.mark(boundary_markers, 1) # Mark outflow boundary bdryRight = RightBoundary() bdryRight.mark(boundary_markers, 2) return boundary_markers

35.04

85

0.614916

from params import tol,Lngth,Hght from geometry import bed import numpy as np from dolfin import * class WaterBoundary(SubDomain): def inside(self, x, on_boundary): return (on_boundary and (x[1]<0.5*Hght)) class BedBoundary(SubDomain): def inside(self, x, on_boundary): return (on_boundary and ((x[1]-bed(x[0]))<=tol)) class LeftBoundary(SubDomain): def inside(self, x, on_boundary): return (on_boundary and np.abs(x[0])<tol) class RightBoundary(SubDomain): def inside(self, x, on_boundary): return (on_boundary and np.abs(x[0]-Lngth)<tol) def mark_boundary(mesh): boundary_markers = MeshFunction('size_t', mesh,dim=1) boundary_markers.set_all(0) bdryWater = WaterBoundary() bdryWater.mark(boundary_markers, 4) bdryBed = BedBoundary() bdryBed.mark(boundary_markers, 3) bdryLeft = LeftBoundary() bdryLeft.mark(boundary_markers, 1) bdryRight = RightBoundary() bdryRight.mark(boundary_markers, 2) return boundary_markers

true

f7176c29d3c8975aef635b4e3270b662412a46af

1,825

py

Python

data_mining/dataset/main.py

basantbhandari/LaptopPricePrediction

086cfaf99b7c625345d5d383ba7f7e2109821c43

[ "MIT" ]

null

data_mining/dataset/main.py

basantbhandari/LaptopPricePrediction

086cfaf99b7c625345d5d383ba7f7e2109821c43

[ "MIT" ]

null

data_mining/dataset/main.py

basantbhandari/LaptopPricePrediction

086cfaf99b7c625345d5d383ba7f7e2109821c43

[ "MIT" ]

null

print("Scrape the dataset from...") # import the necessary library from bs4 import BeautifulSoup import requests import pandas as pd # Request to website and download HTML contents url='https://www.gadgetbytenepal.com/category/laptop-price-in-nepal/' # write data in a file. file1 = open("alldata.txt","w") req=requests.get(url) htmlcontent=req.content # print(content) # Format the downloadable content soup=BeautifulSoup(htmlcontent, 'html.parser') # print(soup.prettify()) desired_content = soup.find(class_='td-category-description') print("############################################################") # print(desired_content.prettify()) print("############################################################") data_header = desired_content.find_all('h2') print("############################################################") #print(data_header) print("############################################################") print("############################################################") #for item in data_header: #print(item.get_text()) print("############################################################") data_items = desired_content.find_all('div', class_ = 'su-table su-table-alternate') print("############################################################") # print(data_items) print("############################################################") print("############################################################") i=0 for item in data_items: print("############################################################") # print(item) eachrow = item.find_all('tr') for tabledata in eachrow: print(tabledata.get_text()) file1.writelines(tabledata.get_text()) i=i+1 print("\n",i) print("############################################################") file1.close()

19.623656

84

0.447671

print("Scrape the dataset from...") from bs4 import BeautifulSoup import requests import pandas as pd url='https://www.gadgetbytenepal.com/category/laptop-price-in-nepal/' file1 = open("alldata.txt","w") req=requests.get(url) htmlcontent=req.content soup=BeautifulSoup(htmlcontent, 'html.parser') desired_content = soup.find(class_='td-category-description') print("############################################################") print("############################################################") data_header = desired_content.find_all('h2') print("############################################################") print("############################################################") print("############################################################") print("############################################################") data_items = desired_content.find_all('div', class_ = 'su-table su-table-alternate') print("############################################################") print("############################################################") print("############################################################") i=0 for item in data_items: print("############################################################") eachrow = item.find_all('tr') for tabledata in eachrow: print(tabledata.get_text()) file1.writelines(tabledata.get_text()) i=i+1 print("\n",i) print("############################################################") file1.close()

true

f7176c890702cb23e4dd9472f56d2732e8d22b76

7,927

py

Python

tests_python/tests_008/test_basic.py

arvidj/tezos

9d9e75425ebd603e9e6b9158d573424cd74e9a30

[ "MIT" ]

null

tests_python/tests_008/test_basic.py

arvidj/tezos

9d9e75425ebd603e9e6b9158d573424cd74e9a30

[ "MIT" ]

null

tests_python/tests_008/test_basic.py

arvidj/tezos

9d9e75425ebd603e9e6b9158d573424cd74e9a30

[ "MIT" ]

null

from os import path import pytest from client.client import Client from tools import utils from tools.paths import ACCOUNT_PATH from tools.utils import assert_run_failure from .contract_paths import CONTRACT_PATH TRANSFER_ARGS = ['--burn-cap', '0.257'] @pytest.mark.incremental class TestRawContext: def test_delegates(self, client: Client): path = '/chains/main/blocks/head/context/raw/bytes/delegates/?depth=3' res = client.rpc('get', path) expected = { "ed25519": { "02": {"29": None}, "a9": {"ce": None}, "c5": {"5c": None}, "da": {"c9": None}, "e7": {"67": None}, } } assert res == expected def test_no_service_1(self, client: Client): path = '/chains/main/blocks/head/context/raw/bytes/non-existent' with assert_run_failure('No service found at this URL'): client.rpc('get', path) def test_no_service_2(self, client: Client): path = ( '/chains/main/blocks/head/context/raw/bytes/' 'non-existent?depth=-1' ) expected = 'Command failed: Extraction depth -1 is invalid' with assert_run_failure(expected): client.rpc('get', path) def test_no_service_3(self, client: Client): path = '/chains/main/blocks/head/context/raw/bytes/non-existent?depth=0' with assert_run_failure('No service found at this URL'): client.rpc('get', path) def test_bake(self, client: Client): utils.bake(client, 'bootstrap4') def test_gen_keys(self, client: Client, session): session['keys'] = ['foo', 'bar', 'boo'] sigs = [None, 'secp256k1', 'ed25519'] for key, sig in zip(session['keys'], sigs): args = [] if sig is None else ['--sig', sig] client.gen_key(key, args) def test_transfers(self, client: Client, session): client.transfer(1000, 'bootstrap1', session['keys'][0], TRANSFER_ARGS) utils.bake(client) client.transfer(2000, 'bootstrap1', session['keys'][1], TRANSFER_ARGS) utils.bake(client) client.transfer(3000, 'bootstrap1', session['keys'][2], TRANSFER_ARGS) utils.bake(client) def test_balances(self, client: Client, session): assert client.get_balance(session['keys'][0]) == 1000 assert client.get_balance(session['keys'][1]) == 2000 assert client.get_balance(session['keys'][2]) == 3000 def test_transfer_bar_foo(self, client: Client, session): client.transfer( 1000, session['keys'][1], session['keys'][0], ['--fee', '0', '--force-low-fee'], ) utils.bake(client) def test_balances_bar_foo(self, client: Client, session): assert client.get_balance(session['keys'][0]) == 2000 assert client.get_balance(session['keys'][1]) == 1000 def test_transfer_foo_bar(self, client: Client, session): client.transfer( 1000, session['keys'][0], session['keys'][1], ['--fee', '0.05'] ) utils.bake(client) def test_balances_foo_bar(self, client: Client, session): assert client.get_balance(session['keys'][0]) == 999.95 assert client.get_balance(session['keys'][1]) == 2000 def test_transfer_failure(self, client: Client, session): with pytest.raises(Exception): client.transfer(999.95, session['keys'][0], session['keys'][1]) def test_originate_contract_noop(self, client: Client): contract = path.join(CONTRACT_PATH, 'opcodes', 'noop.tz') client.remember('noop', contract) client.typecheck(contract) client.originate( 'noop', 1000, 'bootstrap1', contract, ['--burn-cap', '0.295'] ) utils.bake(client) def test_transfer_to_noop(self, client: Client): client.transfer(10, 'bootstrap1', 'noop', ['--arg', 'Unit']) utils.bake(client) def test_contract_hardlimit(self, client: Client): contract = path.join(CONTRACT_PATH, 'mini_scenarios', 'hardlimit.tz') client.originate( 'hardlimit', 1000, 'bootstrap1', contract, ['--init', '3', '--burn-cap', '0.341'], ) utils.bake(client) client.transfer(10, 'bootstrap1', 'hardlimit', ['--arg', 'Unit']) utils.bake(client) client.transfer(10, 'bootstrap1', 'hardlimit', ['--arg', 'Unit']) utils.bake(client) def test_transfers_bootstraps5_bootstrap1(self, client: Client): assert client.get_balance('bootstrap5') == 4000000 client.transfer( 400000, 'bootstrap5', 'bootstrap1', ['--fee', '0', '--force-low-fee'], ) utils.bake(client) client.transfer( 400000, 'bootstrap1', 'bootstrap5', ['--fee', '0', '--force-low-fee'], ) utils.bake(client) assert client.get_balance('bootstrap5') == 4000000 def test_activate_accounts(self, client: Client, session): account = f"{ACCOUNT_PATH}/king_commitment.json" session['keys'] += ['king', 'queen'] client.activate_account(session['keys'][3], account) utils.bake(client) account = f"{ACCOUNT_PATH}/queen_commitment.json" client.activate_account(session['keys'][4], account) utils.bake(client) assert client.get_balance(session['keys'][3]) == 23932454.669343 assert client.get_balance(session['keys'][4]) == 72954577.464032 def test_transfer_king_queen(self, client: Client, session): keys = session['keys'] client.transfer(10, keys[3], keys[4], TRANSFER_ARGS) utils.bake(client) def test_duplicate_alias(self, client: Client): client.add_address("baz", "foo", force=True) show_foo = client.show_address("foo", show_secret=True) assert show_foo.secret_key is not None class TestRememberContract: @pytest.mark.parametrize( "contract_name,non_originated_contract_address", [ ("test", "KT1BuEZtb68c1Q4yjtckcNjGELqWt56Xyesc"), ("test-2", "KT1TZCh8fmUbuDqFxetPWC2fsQanAHzLx4W9"), ], ) def test_non_originated_contract_no_forcing_not_saved_before( self, client, contract_name, non_originated_contract_address, ): client.remember_contract(contract_name, non_originated_contract_address) # As it is always the same client, the contracts have been saved # before @pytest.mark.parametrize( "contract_name,non_originated_contract_address", [ ("test", "KT1BuEZtb68c1Q4yjtckcNjGELqWt56Xyesc"), ("test-2", "KT1TZCh8fmUbuDqFxetPWC2fsQanAHzLx4W9"), ], ) def test_non_originated_contract_with_forcing_and_saved_before( self, client, contract_name, non_originated_contract_address, ): client.remember_contract( contract_name, non_originated_contract_address, force=True ) # As it is always the same client, the contracts have been saved # before @pytest.mark.parametrize( "contract_name,non_originated_contract_address", [ ("test", "KT1BuEZtb68c1Q4yjtckcNjGELqWt56Xyesc"), ("test-2", "KT1TZCh8fmUbuDqFxetPWC2fsQanAHzLx4W9"), ], ) def test_non_originated_contract_no_forcing_and_saved_before( self, client, contract_name, non_originated_contract_address, ): expected_error = f"The contract alias {contract_name} already exists" with assert_run_failure(expected_error): client.remember_contract( contract_name, non_originated_contract_address, force=False )

35.707207

80

0.608931

from os import path import pytest from client.client import Client from tools import utils from tools.paths import ACCOUNT_PATH from tools.utils import assert_run_failure from .contract_paths import CONTRACT_PATH TRANSFER_ARGS = ['--burn-cap', '0.257'] @pytest.mark.incremental class TestRawContext: def test_delegates(self, client: Client): path = '/chains/main/blocks/head/context/raw/bytes/delegates/?depth=3' res = client.rpc('get', path) expected = { "ed25519": { "02": {"29": None}, "a9": {"ce": None}, "c5": {"5c": None}, "da": {"c9": None}, "e7": {"67": None}, } } assert res == expected def test_no_service_1(self, client: Client): path = '/chains/main/blocks/head/context/raw/bytes/non-existent' with assert_run_failure('No service found at this URL'): client.rpc('get', path) def test_no_service_2(self, client: Client): path = ( '/chains/main/blocks/head/context/raw/bytes/' 'non-existent?depth=-1' ) expected = 'Command failed: Extraction depth -1 is invalid' with assert_run_failure(expected): client.rpc('get', path) def test_no_service_3(self, client: Client): path = '/chains/main/blocks/head/context/raw/bytes/non-existent?depth=0' with assert_run_failure('No service found at this URL'): client.rpc('get', path) def test_bake(self, client: Client): utils.bake(client, 'bootstrap4') def test_gen_keys(self, client: Client, session): session['keys'] = ['foo', 'bar', 'boo'] sigs = [None, 'secp256k1', 'ed25519'] for key, sig in zip(session['keys'], sigs): args = [] if sig is None else ['--sig', sig] client.gen_key(key, args) def test_transfers(self, client: Client, session): client.transfer(1000, 'bootstrap1', session['keys'][0], TRANSFER_ARGS) utils.bake(client) client.transfer(2000, 'bootstrap1', session['keys'][1], TRANSFER_ARGS) utils.bake(client) client.transfer(3000, 'bootstrap1', session['keys'][2], TRANSFER_ARGS) utils.bake(client) def test_balances(self, client: Client, session): assert client.get_balance(session['keys'][0]) == 1000 assert client.get_balance(session['keys'][1]) == 2000 assert client.get_balance(session['keys'][2]) == 3000 def test_transfer_bar_foo(self, client: Client, session): client.transfer( 1000, session['keys'][1], session['keys'][0], ['--fee', '0', '--force-low-fee'], ) utils.bake(client) def test_balances_bar_foo(self, client: Client, session): assert client.get_balance(session['keys'][0]) == 2000 assert client.get_balance(session['keys'][1]) == 1000 def test_transfer_foo_bar(self, client: Client, session): client.transfer( 1000, session['keys'][0], session['keys'][1], ['--fee', '0.05'] ) utils.bake(client) def test_balances_foo_bar(self, client: Client, session): assert client.get_balance(session['keys'][0]) == 999.95 assert client.get_balance(session['keys'][1]) == 2000 def test_transfer_failure(self, client: Client, session): with pytest.raises(Exception): client.transfer(999.95, session['keys'][0], session['keys'][1]) def test_originate_contract_noop(self, client: Client): contract = path.join(CONTRACT_PATH, 'opcodes', 'noop.tz') client.remember('noop', contract) client.typecheck(contract) client.originate( 'noop', 1000, 'bootstrap1', contract, ['--burn-cap', '0.295'] ) utils.bake(client) def test_transfer_to_noop(self, client: Client): client.transfer(10, 'bootstrap1', 'noop', ['--arg', 'Unit']) utils.bake(client) def test_contract_hardlimit(self, client: Client): contract = path.join(CONTRACT_PATH, 'mini_scenarios', 'hardlimit.tz') client.originate( 'hardlimit', 1000, 'bootstrap1', contract, ['--init', '3', '--burn-cap', '0.341'], ) utils.bake(client) client.transfer(10, 'bootstrap1', 'hardlimit', ['--arg', 'Unit']) utils.bake(client) client.transfer(10, 'bootstrap1', 'hardlimit', ['--arg', 'Unit']) utils.bake(client) def test_transfers_bootstraps5_bootstrap1(self, client: Client): assert client.get_balance('bootstrap5') == 4000000 client.transfer( 400000, 'bootstrap5', 'bootstrap1', ['--fee', '0', '--force-low-fee'], ) utils.bake(client) client.transfer( 400000, 'bootstrap1', 'bootstrap5', ['--fee', '0', '--force-low-fee'], ) utils.bake(client) assert client.get_balance('bootstrap5') == 4000000 def test_activate_accounts(self, client: Client, session): account = f"{ACCOUNT_PATH}/king_commitment.json" session['keys'] += ['king', 'queen'] client.activate_account(session['keys'][3], account) utils.bake(client) account = f"{ACCOUNT_PATH}/queen_commitment.json" client.activate_account(session['keys'][4], account) utils.bake(client) assert client.get_balance(session['keys'][3]) == 23932454.669343 assert client.get_balance(session['keys'][4]) == 72954577.464032 def test_transfer_king_queen(self, client: Client, session): keys = session['keys'] client.transfer(10, keys[3], keys[4], TRANSFER_ARGS) utils.bake(client) def test_duplicate_alias(self, client: Client): client.add_address("baz", "foo", force=True) show_foo = client.show_address("foo", show_secret=True) assert show_foo.secret_key is not None class TestRememberContract: @pytest.mark.parametrize( "contract_name,non_originated_contract_address", [ ("test", "KT1BuEZtb68c1Q4yjtckcNjGELqWt56Xyesc"), ("test-2", "KT1TZCh8fmUbuDqFxetPWC2fsQanAHzLx4W9"), ], ) def test_non_originated_contract_no_forcing_not_saved_before( self, client, contract_name, non_originated_contract_address, ): client.remember_contract(contract_name, non_originated_contract_address) @pytest.mark.parametrize( "contract_name,non_originated_contract_address", [ ("test", "KT1BuEZtb68c1Q4yjtckcNjGELqWt56Xyesc"), ("test-2", "KT1TZCh8fmUbuDqFxetPWC2fsQanAHzLx4W9"), ], ) def test_non_originated_contract_with_forcing_and_saved_before( self, client, contract_name, non_originated_contract_address, ): client.remember_contract( contract_name, non_originated_contract_address, force=True ) @pytest.mark.parametrize( "contract_name,non_originated_contract_address", [ ("test", "KT1BuEZtb68c1Q4yjtckcNjGELqWt56Xyesc"), ("test-2", "KT1TZCh8fmUbuDqFxetPWC2fsQanAHzLx4W9"), ], ) def test_non_originated_contract_no_forcing_and_saved_before( self, client, contract_name, non_originated_contract_address, ): expected_error = f"The contract alias {contract_name} already exists" with assert_run_failure(expected_error): client.remember_contract( contract_name, non_originated_contract_address, force=False )

true

f7176e98b7a02c6157bf7280dc45d7ede12e9f2b

2,915

py

Python

Bio/Geo/Record.py

bneron/biopython

2c52e57661c8f6cdf4a191850b2f6871f8582af7

[ "PostgreSQL" ]

1

2019-07-29T02:53:51.000Z

Bio/Geo/Record.py

bneron/biopython

2c52e57661c8f6cdf4a191850b2f6871f8582af7

[ "PostgreSQL" ]

1

2021-09-11T14:30:32.000Z

Bio/Geo/Record.py

bneron/biopython

2c52e57661c8f6cdf4a191850b2f6871f8582af7

[ "PostgreSQL" ]

2

2016-12-19T02:27:46.000Z

2019-07-29T02:53:54.000Z

# Copyright 2001 by Katharine Lindner. All rights reserved. # Copyright 2006 by PeterC. All rights reserved. # This code is part of the Biopython distribution and governed by its # license. Please see the LICENSE file that should have been included # as part of this package. """Hold GEO data in a straightforward format. classes: o Record - All of the information in an GEO record. See http://www.ncbi.nlm.nih.gov/geo/ """ from __future__ import print_function class Record(object): """Hold GEO information in a format similar to the original record. The Record class is meant to make data easy to get to when you are just interested in looking at GEO data. Attributes: entity_type entity_id entity_attributes col_defs table_rows """ def __init__(self): self.entity_type = '' self.entity_id = '' self.entity_attributes = {} self.col_defs = {} self.table_rows = [] def __str__(self): output = '' output += 'GEO Type: %s\n' % self.entity_type output += 'GEO Id: %s\n' % self.entity_id att_keys = sorted(self.entity_attributes) for key in att_keys: contents = self.entity_attributes[key] if isinstance(contents, list): for item in contents: try: output += '%s: %s\n' % (key, item[:40]) output += out_block(item[40:]) except: pass elif isinstance(contents, str): output += '%s: %s\n' % (key, contents[:40]) output += out_block(contents[40:]) else: print(contents) output += '%s: %s\n' % (key, contents[:40]) output += out_block(contents[40:]) col_keys = sorted(self.col_defs) output += 'Column Header Definitions\n' for key in col_keys: val = self.col_defs[key] output += ' %s: %s\n' % (key, val[:40]) output += out_block(val[40:], ' ') # May have to display VERY large tables, # so only show the first 20 lines of data MAX_ROWS = 20 + 1 # include header in count for row in self.table_rows[0:MAX_ROWS]: output += '%s: ' % self.table_rows.index(row) for col in row: output += '%s\t' % col output += '\n' if len(self.table_rows) > MAX_ROWS: output += '...\n' row = self.table_rows[-1] output += '%s: ' % self.table_rows.index(row) for col in row: output += '%s\t' % col output += '\n' return output def out_block(text, prefix=''): output = '' for j in range(0, len(text), 80): output += '%s%s\n' % (prefix, text[j:j + 80]) output += '\n' return output

32.032967

71

0.540995

from __future__ import print_function class Record(object): def __init__(self): self.entity_type = '' self.entity_id = '' self.entity_attributes = {} self.col_defs = {} self.table_rows = [] def __str__(self): output = '' output += 'GEO Type: %s\n' % self.entity_type output += 'GEO Id: %s\n' % self.entity_id att_keys = sorted(self.entity_attributes) for key in att_keys: contents = self.entity_attributes[key] if isinstance(contents, list): for item in contents: try: output += '%s: %s\n' % (key, item[:40]) output += out_block(item[40:]) except: pass elif isinstance(contents, str): output += '%s: %s\n' % (key, contents[:40]) output += out_block(contents[40:]) else: print(contents) output += '%s: %s\n' % (key, contents[:40]) output += out_block(contents[40:]) col_keys = sorted(self.col_defs) output += 'Column Header Definitions\n' for key in col_keys: val = self.col_defs[key] output += ' %s: %s\n' % (key, val[:40]) output += out_block(val[40:], ' ') MAX_ROWS = 20 + 1 for row in self.table_rows[0:MAX_ROWS]: output += '%s: ' % self.table_rows.index(row) for col in row: output += '%s\t' % col output += '\n' if len(self.table_rows) > MAX_ROWS: output += '...\n' row = self.table_rows[-1] output += '%s: ' % self.table_rows.index(row) for col in row: output += '%s\t' % col output += '\n' return output def out_block(text, prefix=''): output = '' for j in range(0, len(text), 80): output += '%s%s\n' % (prefix, text[j:j + 80]) output += '\n' return output

true

f7176eb2dd972a167ede03275af13333e556edee

829

py

Python

setup.py

brandjon/iast

23961536c3bfb5d8fce39c28214ea88b8072450c

[ "PSF-2.0" ]

11

2015-01-04T08:40:09.000Z

2021-03-24T03:56:34.000Z

setup.py

brandjon/iast

23961536c3bfb5d8fce39c28214ea88b8072450c

[ "PSF-2.0" ]

null

setup.py

brandjon/iast

23961536c3bfb5d8fce39c28214ea88b8072450c

[ "PSF-2.0" ]

null

from setuptools import setup setup( name = 'iAST', version = '0.2.1', url = 'https://github.com/brandjon/iast', author = 'Jon Brandvein', author_email = 'jon.brandvein@gmail.com', license = 'MIT License', description = 'A library for defining and manipulating ASTs', classifiers = [ 'Development Status :: 3 - Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3', 'Topic :: Software Development :: Libraries :: Python Modules', ], packages = ['iast', 'iast.asdl', 'iast.python'], package_data = {'iast.asdl': ['*.asdl']}, test_suite = 'tests', install_requires = ['simplestruct >=0.2.1'], )

29.607143

71

0.546441

from setuptools import setup setup( name = 'iAST', version = '0.2.1', url = 'https://github.com/brandjon/iast', author = 'Jon Brandvein', author_email = 'jon.brandvein@gmail.com', license = 'MIT License', description = 'A library for defining and manipulating ASTs', classifiers = [ 'Development Status :: 3 - Alpha', 'Intended Audience :: Developers', 'License :: OSI Approved :: MIT License', 'Programming Language :: Python :: 3', 'Topic :: Software Development :: Libraries :: Python Modules', ], packages = ['iast', 'iast.asdl', 'iast.python'], package_data = {'iast.asdl': ['*.asdl']}, test_suite = 'tests', install_requires = ['simplestruct >=0.2.1'], )

true

f717706b17d80b336156b8f3eb5d703f5a5b7596

3,082

py

Python

airbyte-integrations/connectors/source-paypal-transaction/bin/fixture_helper.py

luizgribeiro/airbyte

71a96f5417b678c39b34e2e92234d8a51529e086

[ "MIT" ]

2

2021-08-04T03:17:38.000Z

2021-11-15T10:16:08.000Z

airbyte-integrations/connectors/source-paypal-transaction/bin/fixture_helper.py

luizgribeiro/airbyte

71a96f5417b678c39b34e2e92234d8a51529e086

[ "MIT" ]

52

2021-06-11T12:39:05.000Z

2022-03-30T04:59:35.000Z

airbyte-integrations/connectors/source-paypal-transaction/bin/fixture_helper.py

luizgribeiro/airbyte

71a96f5417b678c39b34e2e92234d8a51529e086

[ "MIT" ]

2

2021-12-14T17:15:40.000Z

2021-12-14T17:18:03.000Z

# # MIT License # # Copyright (c) 2020 Airbyte # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # import logging from pprint import pprint # %% import requests logging.basicConfig(level=logging.DEBUG) # %% specification = { "client_id": "REPLACE_ME", "secret": "REPLACE_ME", "start_date": "2021-06-01T00:00:00+00:00", "end_date": "2021-06-30T00:00:00+00:00", "is_sandbox": True, } # %% READ <client_id> and <secret> client_id = specification.get("client_id") secret = specification.get("secret") # %% GET API_TOKEN token_refresh_endpoint = "https://api-m.sandbox.paypal.com/v1/oauth2/token" data = "grant_type=client_credentials" headers = { "Accept": "application/json", "Accept-Language": "en_US", } response = requests.request( method="POST", url=token_refresh_endpoint, data=data, headers=headers, auth=(client_id, secret), ) response_json = response.json() print(response_json) API_TOKEN = response_json["access_token"] # CREATE TRANSACTIONS # for i in range(1000): # create_response = requests.post( # "https://api-m.sandbox.paypal.com/v2/checkout/orders", # headers={'content-type': 'application/json', 'authorization': f'Bearer {API_TOKEN}', "prefer": "return=representation"}, # json={ # "intent": "CAPTURE", # "purchase_units": [ # { # "amount": { # "currency_code": "USD", # "value": f"{float(i)}" # } # } # ] # } # ) # # print(create_response.json()) # %% LIST TRANSACTIONS url = "https://api-m.sandbox.paypal.com/v1/reporting/transactions" params = { "start_date": "2021-06-20T00:00:00+00:00", "end_date": "2021-07-10T07:19:45Z", "fields": "all", "page_size": "100", "page": "1", } headers = { "Authorization": f"Bearer {API_TOKEN}", "Content-Type": "application/json", } response = requests.get( url, headers=headers, params=params, ) pprint(response.json())

28.018182

130

0.658014

import logging from pprint import pprint import requests logging.basicConfig(level=logging.DEBUG) specification = { "client_id": "REPLACE_ME", "secret": "REPLACE_ME", "start_date": "2021-06-01T00:00:00+00:00", "end_date": "2021-06-30T00:00:00+00:00", "is_sandbox": True, } client_id = specification.get("client_id") secret = specification.get("secret") token_refresh_endpoint = "https://api-m.sandbox.paypal.com/v1/oauth2/token" data = "grant_type=client_credentials" headers = { "Accept": "application/json", "Accept-Language": "en_US", } response = requests.request( method="POST", url=token_refresh_endpoint, data=data, headers=headers, auth=(client_id, secret), ) response_json = response.json() print(response_json) API_TOKEN = response_json["access_token"] url = "https://api-m.sandbox.paypal.com/v1/reporting/transactions" params = { "start_date": "2021-06-20T00:00:00+00:00", "end_date": "2021-07-10T07:19:45Z", "fields": "all", "page_size": "100", "page": "1", } headers = { "Authorization": f"Bearer {API_TOKEN}", "Content-Type": "application/json", } response = requests.get( url, headers=headers, params=params, ) pprint(response.json())

true

f71770a671bd24aaba89e7db742a94eb08713f1f

2,095

py

Python

test/main_test.py

vaughn-johnson/talkspace-public-api

20eca278e8ac651f610d6afaff8fdc3fce2918fc

[ "MIT" ]

null

test/main_test.py

vaughn-johnson/talkspace-public-api

20eca278e8ac651f610d6afaff8fdc3fce2918fc

[ "MIT" ]

6

2020-11-19T04:25:05.000Z

2020-11-20T20:53:32.000Z

test/main_test.py

vaughn-johnson/talkspace-public-api

20eca278e8ac651f610d6afaff8fdc3fce2918fc

[ "MIT" ]

null

from unittest.mock import MagicMock, Mock, patch from .mock_mongo import MockPyMongo import json import sys import os ### TEST SETUP ### EXPECTED_DATA_FRAME_FILENAME = os.path.join(os.path.dirname(__file__), 'expected_response.json') EXPECTED_DATA_FRAME = open(EXPECTED_DATA_FRAME_FILENAME).read() sys.modules['pymongo'] = MockPyMongo cloud_mock = MagicMock() sys.modules['google.cloud'] = cloud_mock ################## from src.main import _get_data, _refresh_data # noqa: E402 def test_snapshot(): expected = _pretty_print(EXPECTED_DATA_FRAME) observed = _pretty_print(f'{_get_data().to_json()}') assert expected == observed def test_cold_cache_json(): _blob().return_value.exists = lambda: False _blob().return_value.download_as_string.return_value = '{}' find = MockPyMongo.MongoClient().talkspace.messages.find find.reset_mock() _refresh_data('json') find.assert_called_once() def test_cold_cache_csv(): _blob().return_value.exists = lambda: False _blob().return_value.download_as_string.return_value = '{}' find = MockPyMongo.MongoClient().talkspace.messages.find find.reset_mock() _refresh_data('csv') find.assert_called_once() def test_warm_cache_json(): _blob().return_value.exists = lambda: True _blob().return_value.download_as_string.return_value = '{}' find = MockPyMongo.MongoClient().talkspace.messages.find find.reset_mock() _refresh_data('json') find.assert_not_called() def test_warm_cache_csv(): _blob().return_value.exists = lambda: True _blob().return_value.download_as_string.return_value = '{}' find = MockPyMongo.MongoClient().talkspace.messages.find find.reset_mock() _refresh_data('csv') find.assert_not_called() def _pretty_print(json_string): json.dumps( json.loads(json_string), indent=2, sort_keys=True ) def _blob(): return cloud_mock.storage\ .Client.return_value\ .bucket.return_value\ .blob

26.858974

70

0.686396

from unittest.mock import MagicMock, Mock, patch from .mock_mongo import MockPyMongo import json import sys import os .join(os.path.dirname(__file__), 'expected_response.json') EXPECTED_DATA_FRAME = open(EXPECTED_DATA_FRAME_FILENAME).read() sys.modules['pymongo'] = MockPyMongo cloud_mock = MagicMock() sys.modules['google.cloud'] = cloud_mock {_get_data().to_json()}') assert expected == observed def test_cold_cache_json(): _blob().return_value.exists = lambda: False _blob().return_value.download_as_string.return_value = '{}' find = MockPyMongo.MongoClient().talkspace.messages.find find.reset_mock() _refresh_data('json') find.assert_called_once() def test_cold_cache_csv(): _blob().return_value.exists = lambda: False _blob().return_value.download_as_string.return_value = '{}' find = MockPyMongo.MongoClient().talkspace.messages.find find.reset_mock() _refresh_data('csv') find.assert_called_once() def test_warm_cache_json(): _blob().return_value.exists = lambda: True _blob().return_value.download_as_string.return_value = '{}' find = MockPyMongo.MongoClient().talkspace.messages.find find.reset_mock() _refresh_data('json') find.assert_not_called() def test_warm_cache_csv(): _blob().return_value.exists = lambda: True _blob().return_value.download_as_string.return_value = '{}' find = MockPyMongo.MongoClient().talkspace.messages.find find.reset_mock() _refresh_data('csv') find.assert_not_called() def _pretty_print(json_string): json.dumps( json.loads(json_string), indent=2, sort_keys=True ) def _blob(): return cloud_mock.storage\ .Client.return_value\ .bucket.return_value\ .blob

true

f7177131572435ea6057a138ae45fca472fca8a1

50,438

py

Python

train/comms/pt/comms.py

caogao/param

9de2602c894df264a004c352ee16abc14f93da76

[ "MIT" ]

null

train/comms/pt/comms.py

caogao/param

9de2602c894df264a004c352ee16abc14f93da76

[ "MIT" ]

null

train/comms/pt/comms.py

caogao/param

9de2602c894df264a004c352ee16abc14f93da76

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. from __future__ import absolute_import, division, print_function, unicode_literals import argparse import logging import time import comms_utils import numpy as np # pytorch import torch from comms_utils import paramCommsBench, ensureTensorFlush ### TODO: add these to class variables? supportedCollectives = [ "reduce", "all_reduce", "all_to_all", "all_to_allv", "all_gather", "broadcast", "reduce_scatter", "reduce_scatter_base", "all_gather_base", "incast", "multicast", ] # , "scatter", "gather"] pt2ptPatterns = [ "one2one", "pairwise", ] logger = logging.getLogger(__name__) class MultilineFormatter(argparse.ArgumentDefaultsHelpFormatter): def _split_lines(self, text, width): if text.startswith("R|"): return text[2:].splitlines() # this is the RawTextHelpFormatter._split_lines return argparse.ArgumentDefaultsHelpFormatter._split_lines(self, text, width) # define the collective benchmark class commsCollBench(paramCommsBench): def __init__(self): super().__init__(supportedNwstacks=["pytorch-dist", "pytorch-xla-tpu"]) # def readCollArgs(self, parser): def readArgs(self, parser): # read the common/basic arguments super().readArgs(parser) parser.add_argument( "--w", type=int, default=5, help="number of warmup iterations" ) # number of warmup-iterations parser.add_argument( "--n", type=int, default=5, help="number of iterations" ) # number of iterations # experiment related parameters parser.add_argument( "--mode", type=str, default="comms", help="benchmark mode", choices=["comms", "compute", "dlrm", "comms-compute"], ) # alternative is DLRM mode or comm-compute mode parser.add_argument( "--b", type=str, default="8", help="minimum size, in bytes, to start with" ) # COMMS mode, begin the sweep at. parser.add_argument( "--e", type=str, default="64", help="maximum size, in bytes, to end at" ) # COMMS mode, end the sweep at. parser.add_argument( "--f", type=int, default=2, help="multiplication factor between sizes" ) # COMMS mode, multiplication factor. parser.add_argument( "--collective", type=str, default="all_reduce", help="Collective operation to be evaluated", choices=supportedCollectives, ) # collective op to benchmark # For comm-compute or compute mode parser.add_argument( "--kernel", type=str, default="gemm", help="Compute kernel, used for comms-compute or compute mode", choices=["gemm", "emb_lookup"], ) # Compute kernel: "gemm" parser.add_argument( "--num-compute", type=int, default=100, help="one collective for every NUM_COMPUTE compute kernels", ) # Launch one coll for every n compute kernels # For GEMM parser.add_argument( "--mm-dim", type=int, default=100, help="dimension size for GEMM compute kernel", ) # Matrix multiplication dim n, A[n,n] * B [n,n] # For emb lookup parser.add_argument( "--emb-dim", type=int, default=128, help="dimension size for Embedding table compute kernel", ) # Embedding table dimension parser.add_argument( "--num-embs", type=int, default=100000, help="Embedding table hash size for Embedding table compute kernel", ) # Embedding table hash size parser.add_argument( "--avg-len", type=int, default=28, help="Average lookup operations per sample", ) # Average #lookup per sample parser.add_argument( "--batch-size", type=int, default=512, help="number of samples reading the table concurrently", ) # #Samples reading the table concurrently parser.add_argument( "--root", type=int, default=0, help="root process for reduce benchmark" ) # root process for reduce and bcast (and gather, scatter, etc., if support in the future) # TODO: check the correctness of root, should be between 0 to [world_size -1] parser.add_argument( "--src-ranks", type=str, nargs="?", help="R|src ranks for many-to-one incast pattern or pt2pt.\n" "List of ranks separated by comma or a range specified by start:end.\n" "Pt2pt one2one should set only one rank.\n" "The default value of incast includes all ranks, pt2pt includes rank 0.", ) # optional: group of src ranks in many-to-one incast or pt2pt parser.add_argument( "--dst-ranks", type=str, nargs="?", help="R|dst ranks for one-to-many multicast pattern or pt2pt.\n" "List of ranks separated by comma or a range specified by start:end.\n" "Pt2pt one2one should set only one rank\n" "The default value of multicast includes all ranks, pt2pt includes rank 1.", ) # optional: group of dst ranks in one-to-many multicast or pt2pt parser.add_argument( "--pair", action="store_true", default=False, help="Toggle to enable collective pair mode", ) parser.add_argument( "--collective-pair", type=str, default="all_reduce", help="Collective pair operation to be evaluated", choices=supportedCollectives, ) # collective op to pair with the other collective, --collective should be non-empty parser.add_argument( "--overlap-pair-pgs", action="store_true", default=False, help="Toggle to enable overlapping collective pair with two pgs", ) # overlap collective pair with two pgs parser.add_argument( "--pt2pt", type=str, default=None, help="point to point pattern", choices=pt2ptPatterns, ) # point to point mode parser.add_argument( "--window", type=int, default=100, help="window size for pt2pt throughput test", ) # optional: point to point throughput test window size return parser.parse_known_args() def checkArgs(self, args): super().checkArgs(args) if args.pt2pt is not None: args.collective = "pt2pt" if args.pt2pt not in pt2ptPatterns: logger.error( f"Specified pt2pt pattern: {args.pt2pt} is not one of the supported pt2pt patterns: {str(pt2ptPatterns)}" ) comms_utils.gracefulExit() args.b = comms_utils.parsesize(args.b) args.e = comms_utils.parsesize(args.e) args.dtype = self.dtypeMap[args.data_type] if args.b < 1: logger.warning( f"Starting size (--b {args.b}) should be greater than 1 byte...fix and continue" ) args.b = 1 if args.e < args.b: logger.warning( f"the begin-size (--b {args.b}) is larger than the end-size (--e {args.e})" ) if args.device == "cpu" and args.backend == "nccl": raise ValueError(f"NCCL is not supported for device type {args.device}") if args.c == 1 and args.z == 0 and args.collective in ("all_reduce", "reduce", "reduce_scatter"): logger.warning( f"Data validation is not supported for {args.collective} in non-blocking mode, disabled and continue" ) args.c = 0 # run a few sanity checks if args.bitwidth < 32: if args.device != "cuda": logger.error( f"collective quantization may not be fully supported for {args.device}" ) comms_utils.checkQuantArgs( args.collective, args.dtype, args.b, args.quant_a2a_embedding_dim, args.z, ) def runColl(self, comm_fn=None, compute_fn=None, comm_fn_pair=None): self.backendFuncs.complete_accel_ops(self.collectiveArgs, initOp=True) self.backendFuncs.sync_barrier(self.collectiveArgs, desc="runColl_begin") elapsedTimeNS = 0.0 is_blocking = not self.collectiveArgs.asyncOp enable_comms = False if (comm_fn is None or comm_fn == self.backendFuncs.noop) else True enable_compute = False if (compute_fn is None or compute_fn == self.backendFuncs.noop) else True enable_comms_pair = False if (comm_fn_pair is None or comm_fn_pair == self.backendFuncs.noop) else True # for comms pair mode, force async comms for overlapping evaluation if enable_comms_pair: self.collectiveArgs.asyncOp = True for nIter in range( self.collectiveArgs.numWarmupIters + self.collectiveArgs.numIters ): if nIter == self.collectiveArgs.numWarmupIters: # Flush non-blocking ops to ensure warmup is really complete self.backendFuncs.complete_accel_ops(self.collectiveArgs) ensureTensorFlush(self.collectiveArgs.opTensor) if enable_comms_pair: ensureTensorFlush(self.collectiveArgs.opTensor_pair) # Start measuring time after warmup iterations elapsedTimeNS = 0.0 self.collectiveArgs.quant_time.reset() self.collectiveArgs.dequant_time.reset() # reset tensor values for data validation check if enable_comms: self.setTensorVal(self.collectiveArgs.opTensor) # for blocking mode, do barrier before starting collective if is_blocking: self.backendFuncs.sync_barrier(self.collectiveArgs) start = time.monotonic() # available only in py3 self.collectiveArgs.group = self.backendFuncs.get_next_group() comm_fn(self.collectiveArgs) # post another collecitve if on comms pair mode, otherwise it's noop self.collectiveArgs.group = self.backendFuncs.get_next_group() comm_fn_pair(self.collectiveArgs, pair=enable_comms_pair) if enable_compute: for _ in range(self.collectiveArgs.numComputePerColl): # TODO: investigate the cache effect # Flush the cache # _ = torch.rand(6 * 1024 * 1024 // 4).float() * 2 # V100 6MB L2 cache compute_fn(self.collectiveArgs) if is_blocking: # should be sychronous, wait for the collective self.backendFuncs.complete_accel_ops(self.collectiveArgs) # Measuring time. elapsedTimeNS += ( time.monotonic() - start ) * 1e9 # keeping time in NS, helps in divising data by nanosecond start = time.monotonic() # available only in py3 self.backendFuncs.complete_accel_ops(self.collectiveArgs) end = time.monotonic() # available only in py3 ensureTensorFlush(self.collectiveArgs.opTensor) if enable_comms_pair: ensureTensorFlush(self.collectiveArgs.opTensor_pair) elapsedTimeNS += ( end - start ) * 1e9 # keeping time in NS, helps in divising data by nanoseconds memSize = self.backendFuncs.get_mem_size(self.collectiveArgs) avgIterNS, algBW = comms_utils.getAlgBW( elapsedTimeNS, memSize, self.collectiveArgs.numIters ) busBW = self.backendFuncs.getBusBW( self.collectiveArgs.collective, algBW, self.collectiveArgs, ) if enable_comms_pair: memSize_pair = self.backendFuncs.get_mem_size( self.collectiveArgs, pair=enable_comms_pair ) memSize += memSize_pair _, algBW_pair = comms_utils.getAlgBW( elapsedTimeNS, memSize_pair, self.collectiveArgs.numIters ) algBW += algBW_pair busBW += self.backendFuncs.getBusBW( self.collectiveArgs.collective_pair, algBW_pair, self.collectiveArgs, ) self.backendFuncs.sync_barrier(self.collectiveArgs, desc="runColl_end") results = { "timeUS": avgIterNS / 1e3, "algBW": algBW, "busBW": busBW, "memSize": memSize, } return results def runPt2Pt(self): self.backendFuncs.complete_accel_ops(self.collectiveArgs, initOp=True) # warm-up memSize = self.backendFuncs.get_mem_size(self.collectiveArgs) self.getPingLatency(self.collectiveArgs.numWarmupIters) self.getPingPongLatency(self.collectiveArgs.numWarmupIters) self.getUniBW(self.collectiveArgs.numWarmupIters, memSize) self.getBiBW(self.collectiveArgs.numWarmupIters, memSize) self.backendFuncs.sync_barrier(self.collectiveArgs, "runpt2pt_begin") # pt2pt benchmark pingPerIterNS = self.getPingLatency(self.collectiveArgs.numIters) pingPongPerIterNS = self.getPingPongLatency(self.collectiveArgs.numIters) avgUniBW = self.getUniBW(self.collectiveArgs.numIters, memSize) avgBiBW = self.getBiBW(self.collectiveArgs.numIters, memSize) self.backendFuncs.sync_barrier(self.collectiveArgs, "runpt2pt") results = { "pingPerIterNS": pingPerIterNS, "pingPongPerIterNS": pingPongPerIterNS, "avgUniBW": avgUniBW, "avgBiBW": avgBiBW, "memSize": memSize, } return results def getPingLatency(self, numIters): logger.debug( "STATUS: begin ping test with src_ranks=%s, dst_ranks=%s." % (self.collectiveArgs.src_ranks, self.collectiveArgs.dst_ranks) ) self.collectiveArgs.asyncOp = False # get one-way latency pingLatencyNS = [] for _ in range(numIters): self.backendFuncs.sync_barrier(self.collectiveArgs) start = time.monotonic() if self.collectiveArgs.global_rank in self.collectiveArgs.src_ranks: idx = self.collectiveArgs.src_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.send( self.collectiveArgs, self.collectiveArgs.dst_ranks[idx] ) elif self.collectiveArgs.global_rank in self.collectiveArgs.dst_ranks: idx = self.collectiveArgs.dst_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.recv( self.collectiveArgs, self.collectiveArgs.src_ranks[idx] ) self.backendFuncs.complete_accel_ops(self.collectiveArgs) pingLatencyNS.append( (time.monotonic() - start) * 1e9 ) # keeping time in NS, helps in divising data by nanosecond logger.debug("STATUS: end ping test.") return pingLatencyNS def getPingPongLatency(self, numIters): logger.debug( "STATUS: begin ping-pong with src_ranks=%s, dst_ranks=%s." % (self.collectiveArgs.src_ranks, self.collectiveArgs.dst_ranks) ) self.collectiveArgs.asyncOp = False # get round-trip latency pingPongLatencyNS = [] for _ in range(numIters): self.backendFuncs.sync_barrier(self.collectiveArgs) start = time.monotonic() if self.collectiveArgs.global_rank in self.collectiveArgs.src_ranks: idx = self.collectiveArgs.src_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.send( self.collectiveArgs, self.collectiveArgs.dst_ranks[idx] ) self.backendFuncs.recv( self.collectiveArgs, self.collectiveArgs.dst_ranks[idx] ) elif self.collectiveArgs.global_rank in self.collectiveArgs.dst_ranks: idx = self.collectiveArgs.dst_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.recv( self.collectiveArgs, self.collectiveArgs.src_ranks[idx] ) self.backendFuncs.send( self.collectiveArgs, self.collectiveArgs.src_ranks[idx] ) self.backendFuncs.complete_accel_ops(self.collectiveArgs) pingPongLatencyNS.append( (time.monotonic() - start) * 1e9 ) # keeping time in NS, helps in divising data by nanosecond logger.debug("STATUS: end ping-pong test.") return pingPongLatencyNS def getUniBW(self, numIters, memSize): logger.debug( "STATUS: begin UniBW test with src_ranks=%s, dst_ranks=%s." % (self.collectiveArgs.src_ranks, self.collectiveArgs.dst_ranks) ) self.collectiveArgs.asyncOp = True # get unidirectional bandwidth uniLatencyNS = [] for _ in range(numIters): self.backendFuncs.sync_barrier(self.collectiveArgs) start = time.monotonic() for w in range(self.collectiveArgs.window): if self.collectiveArgs.global_rank in self.collectiveArgs.src_ranks: idx = self.collectiveArgs.src_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.isend( self.collectiveArgs, self.collectiveArgs.dst_ranks[idx], tag=w ) elif self.collectiveArgs.global_rank in self.collectiveArgs.dst_ranks: idx = self.collectiveArgs.dst_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.irecv( self.collectiveArgs, self.collectiveArgs.src_ranks[idx], tag=w ) self.backendFuncs.complete_accel_ops(self.collectiveArgs) uniLatencyNS.append( (time.monotonic() - start) * 1e9 ) # keeping time in NS, helps in divising data by nanosecond uniLatencyNS = [lat / self.collectiveArgs.window for lat in uniLatencyNS] uniLatencyNS = np.mean(np.array(uniLatencyNS)) _, avgUniBW = comms_utils.getAlgBW(uniLatencyNS, memSize, 1) logger.debug("STATUS: end UniBW test.") return avgUniBW def getBiBW(self, numIters, memSize): logger.debug( "STATUS: begin BiBW test with src_ranks=%s, dst_ranks=%s." % (self.collectiveArgs.src_ranks, self.collectiveArgs.dst_ranks) ) self.collectiveArgs.asyncOp = True # get bidirectional bandwidth biLatencyNS = [] for _ in range(numIters): self.backendFuncs.sync_barrier(self.collectiveArgs) start = time.monotonic() for w in range(self.collectiveArgs.window): if self.collectiveArgs.global_rank in self.collectiveArgs.src_ranks: idx = self.collectiveArgs.src_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.isend( self.collectiveArgs, self.collectiveArgs.dst_ranks[idx], tag=w ) self.backendFuncs.irecv( self.collectiveArgs, self.collectiveArgs.dst_ranks[idx], tag=w + self.collectiveArgs.window, ) elif self.collectiveArgs.global_rank in self.collectiveArgs.dst_ranks: idx = self.collectiveArgs.dst_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.irecv( self.collectiveArgs, self.collectiveArgs.src_ranks[idx], tag=w ) self.backendFuncs.isend( self.collectiveArgs, self.collectiveArgs.src_ranks[idx], tag=w + self.collectiveArgs.window, ) self.backendFuncs.complete_accel_ops(self.collectiveArgs) biLatencyNS.append( (time.monotonic() - start) * 1e9 ) # keeping time in NS, helps in divising data by nanosecond biLatencyNS = [lat / self.collectiveArgs.window for lat in biLatencyNS] biLatencyNS = np.mean(np.array(biLatencyNS)) _, avgBiBW = comms_utils.getAlgBW(biLatencyNS, 2 * memSize, 1) logger.debug("STATUS: end UniBW test.") return avgBiBW def checkPt2PtRanks(self): # set default values if not self.collectiveArgs.src_ranks: self.collectiveArgs.src_ranks = [0] if not self.collectiveArgs.dst_ranks: self.collectiveArgs.dst_ranks = [1] # sanity check if self.collectiveArgs.pt2pt == "one2one": if ( len(self.collectiveArgs.src_ranks) > 1 or len(self.collectiveArgs.dst_ranks) > 1 ): if self.global_rank == 0: logger.error( "One2one Pt2Pt requires only a single rank is specified in src_ranks and dst_ranks! " ) comms_utils.gracefulExit() elif self.collectiveArgs.pt2pt == "pairwise": # pairwise pt2pt requires identical number of ranks in src_ranks and dst_ranks. if len(self.collectiveArgs.src_ranks) != len(self.collectiveArgs.dst_ranks): if self.global_rank == 0: logger.error( "Pairwise Pt2Pt requires identical number of members in src_ranks and dst_ranks! " ) comms_utils.gracefulExit() # pairwise pt2pt does not allow same rank to exist in both groups if bool( set(self.collectiveArgs.src_ranks).intersection( self.collectiveArgs.dst_ranks ) ): if self.global_rank == 0: logger.error( "Pairwise Pt2Pt requires distinct members in src_ranks and dst_ranks! " ) comms_utils.gracefulExit() if self.global_rank == 0: print( f"\t collective={self.collectiveArgs.collective}\t{self.collectiveArgs.pt2pt}, src_ranks={self.collectiveArgs.src_ranks}, dst_ranks={self.collectiveArgs.dst_ranks}" ) def checkCollectiveRanks(self): if self.collectiveArgs.collective == "incast": # incast: set default value and exclude root if not self.collectiveArgs.src_ranks: self.collectiveArgs.src_ranks = [*range(self.comm_size)] if self.collectiveArgs.srcOrDst in self.collectiveArgs.src_ranks: self.collectiveArgs.src_ranks.remove(self.collectiveArgs.srcOrDst) elif self.collectiveArgs.collective == "multicast": # multicast: set default value and exclude root if not self.collectiveArgs.dst_ranks: self.collectiveArgs.dst_ranks = [*range(self.comm_size)] if self.collectiveArgs.srcOrDst in self.collectiveArgs.dst_ranks: self.collectiveArgs.dst_ranks.remove(self.collectiveArgs.srcOrDst) if self.global_rank == 0: print( f"\t collective={self.collectiveArgs.collective}, src_ranks={self.collectiveArgs.src_ranks}, dst_ranks={self.collectiveArgs.dst_ranks}" ) def initCollectiveArgs(self, commsParams): # lint was complaining that benchTime was too complex! ( local_rank, global_rank, world_size, group, curDevice, curHwDevice, ) = comms_utils.get_rank_details( self.backendFuncs ) # Getting ranks from backednFuncs object, since we cannot use MPI (e.g.: TPU) to launch all the processes. self.backendFuncs.sayHello() # Informs us where each process is running. groups = self.backendFuncs.get_groups() num_pgs = len(groups) self.comm_size = world_size self.global_rank = global_rank comms_utils.fixBeginSize( commsParams, world_size ) # Ensuring that all-reduce and all-to-all has atleast one member per rank. allSizes = comms_utils.getSizes( commsParams.beginSize, commsParams.endSize, commsParams.stepFactor ) # Given the begin-size, end-size, step-factor what are the message sizes to iterate on. if global_rank == 0: print( f"[Rank {global_rank:>3}] allSizes: {allSizes} local_rank: {local_rank} element_size: {commsParams.element_size}" ) self.collectiveArgs.group = group self.collectiveArgs.groups = groups self.collectiveArgs.num_pgs = num_pgs self.collectiveArgs.device = curDevice self.collectiveArgs.world_size = world_size self.collectiveArgs.numIters = commsParams.numIters self.collectiveArgs.numWarmupIters = commsParams.numWarmupIters self.collectiveArgs.global_rank = global_rank self.collectiveArgs.backendFuncs = self.backendFuncs self.collectiveArgs.collective = commsParams.collective op = self.backendFuncs.get_reduce_op("sum") self.collectiveArgs.op = op self.collectiveArgs.srcOrDst = commsParams.srcOrDst self.collectiveArgs.src_ranks = commsParams.src_ranks self.collectiveArgs.dst_ranks = commsParams.dst_ranks self.collectiveArgs.pair = commsParams.pair self.collectiveArgs.collective_pair = commsParams.collective_pair self.collectiveArgs.pt2pt = commsParams.pt2pt self.collectiveArgs.window = commsParams.window self.collectiveArgs.asyncOp = False if commsParams.blockingFlag == 1 else True if commsParams.bitwidth < 32: comms_utils.initQuantCommCtx(self.collectiveArgs, commsParams) if self.collectiveArgs.collective == "pt2pt": self.checkPt2PtRanks() else: self.checkCollectiveRanks() computeFunc = self.backendFuncs.noop if ( commsParams.mode != "comms" ): # Compute mode related initialization if not in comms-only mode if commsParams.kernel == "gemm": computeFunc = self.backendFuncs.gemm mm_dim = commsParams.mm_dim in1 = np.random.rand(mm_dim, mm_dim) MMin1 = torch.FloatTensor(in1).to(curDevice) in2 = np.random.rand(mm_dim, mm_dim) MMin2 = torch.FloatTensor(in2).to(curDevice) in3 = np.random.rand(mm_dim, mm_dim) MMin3 = torch.FloatTensor(in3).to(curDevice) MMout = self.backendFuncs.alloc_empty( [mm_dim, mm_dim], commsParams.dtype, curDevice ) self.collectiveArgs.MMout = MMout self.collectiveArgs.MMin1 = MMin1 self.collectiveArgs.MMin2 = MMin2 self.collectiveArgs.MMin3 = MMin3 self.collectiveArgs.numComputePerColl = commsParams.num_compute elif commsParams.kernel == "emb_lookup": computeFunc = self.backendFuncs.emb_lookup emb_dim = commsParams.emb_dim num_embeddings = commsParams.num_embs avg_length = commsParams.avg_len batch_size = commsParams.batch_size print( f"emb_dim {emb_dim} num_embs {num_embeddings} avg_len {avg_length} bs {batch_size}" ) self.collectiveArgs.EmbWeights = self.backendFuncs.alloc_empty( [num_embeddings, emb_dim], torch.double, curDevice ) self.collectiveArgs.TableOffsets = torch.LongTensor( [0, num_embeddings] ).to(curDevice) self.collectiveArgs.Indices = torch.LongTensor( np.random.randint(0, num_embeddings - 1, avg_length * batch_size) ).to(curDevice) lengths = np.ones((1, batch_size)) * avg_length flat_lengths = lengths.flatten() self.collectiveArgs.Offsets = torch.LongTensor( [0] + np.cumsum(flat_lengths).tolist() ).to(curDevice) self.collectiveArgs.LookupOut = self.backendFuncs.alloc_empty( [batch_size, emb_dim], torch.double, curDevice ) self.collectiveArgs.AvgLengths = avg_length self.collectiveArgs.numComputePerColl = commsParams.num_compute return ( local_rank, global_rank, world_size, group, curDevice, curHwDevice, allSizes, computeFunc, ) def gatherBenchTime(self, collectiveArgs, commsParams, timeUsElapsedList): # Push the list to device, then do an all-gather. timeElapsedTensor = torch.tensor( timeUsElapsedList, device=self.backendFuncs.get_device() ) collectiveArgs.opTensor = None if commsParams.backend != "xla": timeList = list(torch.ones( (self.comm_size,) + timeElapsedTensor.shape, dtype=timeElapsedTensor.dtype, device=timeElapsedTensor.device, ).unbind(0)) collectiveArgs.opTensor = timeList collectiveArgs.ipTensor = timeElapsedTensor collectiveArgs.asyncOp = False collectiveArgs.dataSize = ( timeElapsedTensor.nelement() * timeElapsedTensor.element_size() ) collectiveArgs.numElements = timeElapsedTensor.nelement() # use allgather as all process group should support it self.backendFuncs.all_gather(collectiveArgs) self.backendFuncs.complete_accel_ops(collectiveArgs) return timeList def printPreamble(self, commsParams): logger.debug(f"\tcommsParams: {str(commsParams.__dict__)}") header = "\n\tCOMMS-RES" if self.collectiveArgs.collective == "pt2pt": header += "{:>15}{:>20}{:>10}{:>10}{:>25}{:>10}{:>10}{:>15}{:>15}{:>18}{:>18}".format( "size (B)", "pingLatency(us):p50", "p75", "p95", "pingPongLatency(us):p50", "p75", "p95", "avgUniBW(GB/s)", "avgBiBW(GB/s)", "totalUniBW(GB/s)", "totalBiBW(GB/s)", ) else: if commsParams.bitwidth < 32: header += "-QUANT\t{:>15}{:>18}{:>25}{:>15}{:>15}{:>15}".format( "size (B)", "nElementsPerRank", "P95 Latency(us): Quant", "Comms", "De-Quant", "Overall", ) elif not self.collectiveArgs.pair: header += ( "{:>15}{:>18}{:>18}{:>12}{:>12}{:>12}{:>12}{:>15}{:>12}".format( "size (B)", "nElementsPerRank", "Latency(us):p50", "p75", "p95", "Min", "Max", "AlgBW(GB/s)", "BusBW(GB/s)", ) ) else: header += "{:>15}{:>18}{:>22}{:>18}{:>12}{:>12}{:>12}{:>12}{:>15}{:>12}".format( "total-size (B)", "nElementsPerRank", "nElementsPairPerRank", "Latency(us):p50", "p75", "p95", "Min", "Max", "AlgBW(GB/s)", "BusBW(GB/s)", ) print(header) def reportBenchTimeCollWithQuant( self, commsParams, results, tensorList, quantTimeTensorList, dequantTimeTensorList, ): if commsParams.backend == "xla": latencyAcrossRanks = torch.transpose(tensorList.view(-1, 1), 0, 1)[0] latencyAcrossRanks = latencyAcrossRanks.cpu().detach().numpy() # quant tensor quantLatencyAcrossRanks = torch.transpose( quantTimeTensorList.view(-1, 1), 0, 1 )[0] quantLatencyAcrossRanks = quantLatencyAcrossRanks.cpu().detach().numpy() # dequant tensor dequantLatencyAcrossRanks = torch.transpose( dequantTimeTensorList.view(-1, 1), 0, 1 )[0] dequantLatencyAcrossRanks = dequantLatencyAcrossRanks.cpu().detach().numpy() else: if isinstance(tensorList, list): tensorList = [t.cpu().detach().numpy() for t in tensorList] latencyAcrossRanks = np.array(tensorList) # quant tensor quantLatencyAcrossRanks = np.array(quantTimeTensorList) # dequant tensor dequantLatencyAcrossRanks = np.array(dequantTimeTensorList) p95 = np.percentile(latencyAcrossRanks, 95) quant_p95 = np.percentile(quantLatencyAcrossRanks, 95) dequant_p95 = np.percentile(dequantLatencyAcrossRanks, 95) print( "\tCOMMS-RES-QUANT\t{:>15}{:>18}{:>25}{:>15}{:>15}{:>15}".format( results["memSize"], str("%d" % (results["numElements"])), str("%.1f" % (quant_p95)), str("%.1f" % (p95 - quant_p95 - dequant_p95)), str("%.1f" % (dequant_p95)), str("%.1f" % (p95)), # str("%.3f" % (algBW)), # str("%.3f" % (busBW)), ) ) def reportBenchTime( self, commsParams, results, tensorList, quantTimeTensorList, dequantTimeTensorList, ): # convernt num_elements to # of elements per rank if commsParams.collective in ("all_to_all", "all_to_allv"): results["numElements"] = int( results["numElements"] // commsParams.comms_world_info.world_size ) if commsParams.collective == "pt2pt": self.reportBenchTimePt2Pt(commsParams, tensorList, results) elif commsParams.bitwidth < 32: self.reportBenchTimeCollWithQuant( commsParams, results, tensorList, quantTimeTensorList, dequantTimeTensorList, ) else: self.reportBenchTimeColl(commsParams, results, tensorList) def reportBenchTimeColl(self, commsParams, results, tensorList): if commsParams.backend == "xla": latencyAcrossRanks = torch.transpose(tensorList.view(-1, 1), 0, 1)[0] latencyAcrossRanks = latencyAcrossRanks.cpu().detach().numpy() else: if isinstance(tensorList, list): tensorList = [t.cpu().detach().numpy() for t in tensorList] latencyAcrossRanks = np.array(tensorList) logger.debug(f"Latency across all ranks: {latencyAcrossRanks}") # Include only communicating ranks if self.collectiveArgs.collective == "multicast": commRanks = [self.collectiveArgs.srcOrDst] + self.collectiveArgs.dst_ranks elif self.collectiveArgs.collective == "incast": commRanks = [self.collectiveArgs.srcOrDst] + self.collectiveArgs.src_ranks else: commRanks = range(self.collectiveArgs.world_size) latencyAcrossCommRanks = latencyAcrossRanks[commRanks] logger.debug( "Latency across communicating ranks (%s): %s" % (commRanks, latencyAcrossCommRanks) ) p50 = np.percentile(latencyAcrossCommRanks, 50) p75 = np.percentile(latencyAcrossCommRanks, 75) p95 = np.percentile(latencyAcrossCommRanks, 95) minlat = np.amin(latencyAcrossCommRanks) maxlat = np.amax(latencyAcrossCommRanks) # adjust busBW busBW = results["busBW"] * (commsParams.bitwidth / 32.0) if not self.collectiveArgs.pair: print( "\tCOMMS-RES{:>15}{:>18}{:>18}{:>12}{:>12}{:>12}{:>12}{:>15}{:>12}".format( results["memSize"], str("%d" % (results["numElements"])), str("%.1f" % (p50)), str("%.1f" % (p75)), str("%.1f" % (p95)), str("%.1f" % (minlat)), str("%.1f" % (maxlat)), str("%.3f" % (results["algBW"])), str("%.3f" % (busBW)), ) ) else: # convernt to # of elements per rank if commsParams.collective_pair in ("all_to_all", "all_to_allv"): results["numElements_pair"] = int( results["numElements_pair"] // commsParams.comms_world_info.world_size ) print( "\tCOMMS-RES{:>15}{:>18}{:>22}{:>18}{:>12}{:>12}{:>12}{:>12}{:>15}{:>12}".format( results["memSize"], str("%d" % (results["numElements"])), str("%d" % (results["numElements_pair"])), str("%.1f" % (p50)), str("%.1f" % (p75)), str("%.1f" % (p95)), str("%.1f" % (minlat)), str("%.1f" % (maxlat)), str("%.3f" % (results["algBW"])), str("%.3f" % (busBW)), ) ) def reportBenchTimePt2Pt(self, commsParams, resultsAcrossRanks, results): pingLatencyAcrossRanks = [] pingPongLatencyAcrossRanks = [] uniBWAcrossRanks = [] biBWAcrossRanks = [] # idx = 0 for curRankTensor in resultsAcrossRanks: pingLatencyAcrossRanks.append(curRankTensor[0].item()) pingPongLatencyAcrossRanks.append(curRankTensor[1].item()) uniBWAcrossRanks.append(curRankTensor[2].item()) biBWAcrossRanks.append(curRankTensor[3].item()) pingLatencyAcrossRanks = np.array(pingLatencyAcrossRanks) pingPongLatencyAcrossRanks = np.array(pingPongLatencyAcrossRanks) uniBWAcrossRanks = np.array(uniBWAcrossRanks) biBWAcrossRanks = np.array(biBWAcrossRanks) # Include only communicating ranks commRanks = self.collectiveArgs.src_ranks + self.collectiveArgs.dst_ranks pingLatencyAcrossCommRanks = pingLatencyAcrossRanks[commRanks] pingPongLatencyAcrossCommRanks = pingPongLatencyAcrossRanks[commRanks] uniBWAcrossCommRanks = uniBWAcrossRanks[commRanks] biBWAcrossCommRanks = biBWAcrossRanks[commRanks] logger.debug( "Ping latency across communicating ranks (%s): %s" % (commRanks, pingLatencyAcrossCommRanks) ) logger.debug( "PingPong latency across communicating ranks (%s): %s" % (commRanks, pingPongLatencyAcrossCommRanks) ) logger.debug( "UniBW across all communicating ranks (%s): %s" % (commRanks, uniBWAcrossCommRanks) ) logger.debug( "BiBW across all communicating ranks (%s): %s" % (commRanks, biBWAcrossCommRanks) ) avgUniBW = np.mean(uniBWAcrossCommRanks) avgBiBW = np.mean(biBWAcrossCommRanks) totalUniBW = np.sum(uniBWAcrossCommRanks) / 2 totalBiBW = np.sum(biBWAcrossCommRanks) / 2 ping_p50 = np.percentile(pingLatencyAcrossCommRanks, 50) ping_p75 = np.percentile(pingLatencyAcrossCommRanks, 75) ping_p95 = np.percentile(pingLatencyAcrossCommRanks, 95) ping_pong_p50 = np.percentile(pingPongLatencyAcrossCommRanks, 50) ping_pong_p75 = np.percentile(pingPongLatencyAcrossCommRanks, 75) ping_pong_p95 = np.percentile(pingPongLatencyAcrossCommRanks, 95) print( "\tCOMMS-RES{:>15}{:>20}{:>10}{:>10}{:>25}{:>10}{:>10}{:>15}{:>15}{:>18}{:>18}".format( results["memSize"], str("%.1f" % (ping_p50)), str("%.1f" % (ping_p75)), str("%.1f" % (ping_p95)), str("%.1f" % (ping_pong_p50)), str("%.1f" % (ping_pong_p75)), str("%.1f" % (ping_pong_p95)), str("%.3f" % (avgUniBW)), str("%.3f" % (avgBiBW)), str("%.3f" % (totalUniBW)), str("%.3f" % (totalBiBW)), ) ) def benchTime(self, index, commsParams, backendFuncs): # Get NW stack specific parameters ( local_rank, global_rank, world_size, group, curDevice, curHwDevice, allSizes, computeFunc, ) = self.initCollectiveArgs(commsParams) backendFuncs.sync_barrier(self.collectiveArgs) if global_rank == 0: self.printPreamble(commsParams) for curSize in allSizes: results = {} timeUsElapsedList = [] quantTimeElapsedList = [] dequantTimeElapsedList = [] numElements = int(curSize // commsParams.element_size) collectiveFunc = self.backendFuncs.noop collectiveFunc_pair = self.backendFuncs.noop if ( commsParams.mode != "compute" ): # comms specific initializations if not in compute-only mode # set corresponding function pointers if commsParams.collective != "pt2pt": collectiveFunc = backendFuncs.collectiveFunc[commsParams.collective] ( self.collectiveArgs.ipTensor, self.collectiveArgs.opTensor, ) = self.prepComm( curComm={ "in_msg_size": numElements, "out_msg_size": numElements, "world_size": world_size, }, commsParams=commsParams, ) # Setup the arguments. self.collectiveArgs.dataSize = curSize self.collectiveArgs.numElements = numElements self.collectiveArgs.waitObj = [] results["numElements"] = numElements if ( commsParams.pair and commsParams.mode != "compute" ): # comms-pair specific initializations if not in compute-only mode: # set corresponding function pointers collectiveFunc_pair = backendFuncs.collectiveFunc[ commsParams.collective_pair ] # TODO: allow user to set specific size # Setup the arguments. self.collectiveArgs.dataSize_pair = curSize self.collectiveArgs.numElements_pair = int( self.collectiveArgs.dataSize_pair // commsParams.element_size ) results["numElements_pair"] = self.collectiveArgs.numElements_pair ( self.collectiveArgs.ipTensor_pair, self.collectiveArgs.opTensor_pair, ) = self.prepComm( curComm={ "in_msg_size": self.collectiveArgs.numElements_pair, "out_msg_size": self.collectiveArgs.numElements_pair, "world_size": world_size, }, commsParams=commsParams, ) # self.collectiveArgs has all the information on the experiment. if commsParams.collective == "pt2pt": results.update(self.runPt2Pt()) timeUsElapsedList = [ np.mean(np.array(results["pingPerIterNS"])) / 1e3, np.mean(np.array(results["pingPongPerIterNS"])) / 1e3, results["avgUniBW"], results["avgBiBW"], ] # time in US if ( global_rank in self.collectiveArgs.src_ranks or global_rank in self.collectiveArgs.dst_ranks ): logger.debug(timeUsElapsedList) else: results.update( self.runColl( comm_fn=collectiveFunc, compute_fn=computeFunc, comm_fn_pair=collectiveFunc_pair, ) ) timeUsElapsedList = [results["timeUS"]] # perfom data validation check on the final opTensor if commsParams.dcheck == 1: self.dcheck(commsParams, curSize, self.collectiveArgs.opTensor) backendFuncs.clear_memory(self.collectiveArgs) # gather quantization overhead if enabled if commsParams.bitwidth < 32: # calculate average (de-)quantization overhead results["quantTimeUS"] = ( self.collectiveArgs.quant_time.getTimeUS() / self.collectiveArgs.numIters ) results["dequantTimeUS"] = ( self.collectiveArgs.dequant_time.getTimeUS() / self.collectiveArgs.numIters ) quantTimeElapsedList.append(results["quantTimeUS"]) dequantTimeElapsedList.append(results["dequantTimeUS"]) logger.debug(quantTimeElapsedList) quantTimeElapsedList = self.gatherBenchTime( self.collectiveArgs, commsParams, quantTimeElapsedList ) dequantTimeElapsedList = self.gatherBenchTime( self.collectiveArgs, commsParams, dequantTimeElapsedList ) # gather and report performance to stdout tensorList = self.gatherBenchTime( self.collectiveArgs, commsParams, timeUsElapsedList ) if global_rank == 0: self.reportBenchTime( commsParams, results, tensorList, quantTimeElapsedList, dequantTimeElapsedList, ) self.backendFuncs.sync_barrier( self.collectiveArgs, desc=f"curSize_{curSize}" ) comms_utils.clearQuantCommCtx(self.collectiveArgs) # wait rank 0 reports results to avoid other ranks mess up the output self.backendFuncs.sync_barrier(self.collectiveArgs, "benchtime") def runBench(self, comms_world_info, commsParams): # Init the desired backend if commsParams.nw_stack == "pytorch-dist": from pytorch_dist_backend import PyTorchDistBackend backendObj = PyTorchDistBackend(comms_world_info, commsParams) elif commsParams.nw_stack == "pytorch-xla-tpu": from pytorch_tpu_backend import PyTorchTPUBackend backendObj = PyTorchTPUBackend(comms_world_info, commsParams) else: logger.error("Unsupported NW stack! ") comms_utils.gracefulExit() self.backendFuncs = backendObj try: backendObj.benchmark_comms() except ValueError as ve: if commsParams.backend == "ucc": logger.critical("PyTorch UCC not implemented? {}".format(repr(ve))) raise def main(): collBenchObj = commsCollBench() ### parse arguments ### parser = argparse.ArgumentParser( description="PARAM-Comm Benchmark", formatter_class=MultilineFormatter, ) args, leftovers = collBenchObj.readArgs(parser) collBenchObj.checkArgs(args) comms_env_params = comms_utils.read_comms_env_vars() if comms_env_params["global_rank"] == 0: print("\t MPI environment: %s " % (str(comms_env_params))) print( "\t backend: %s nw-stack: %s mode: %s args.b: %d args.e: %d args.f: %d args.z: %s args.master_ip: %s " % ( args.backend, args.nw_stack, args.mode, args.b, args.e, args.f, args.z, args.master_ip, ) ) element_size = torch.ones([1], dtype=args.dtype).element_size() comms_world_info = comms_utils.comms_world_info_holder( args.master_ip, args.master_port, args.num_tpu_cores, comms_env_params ) commsParams = comms_utils.commsParamsHolder( args, comms_world_info, element_size, collBenchObj.benchTime ) if args.pair and args.overlap_pair_pgs: commsParams.num_pgs = 2 collBenchObj.runBench(comms_world_info, commsParams) if __name__ == "__main__": main()

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from __future__ import absolute_import, division, print_function, unicode_literals import argparse import logging import time import comms_utils import numpy as np import torch from comms_utils import paramCommsBench, ensureTensorFlush "all_to_allv", "all_gather", "broadcast", "reduce_scatter", "reduce_scatter_base", "all_gather_base", "incast", "multicast", ] pt2ptPatterns = [ "one2one", "pairwise", ] logger = logging.getLogger(__name__) class MultilineFormatter(argparse.ArgumentDefaultsHelpFormatter): def _split_lines(self, text, width): if text.startswith("R|"): return text[2:].splitlines() return argparse.ArgumentDefaultsHelpFormatter._split_lines(self, text, width) class commsCollBench(paramCommsBench): def __init__(self): super().__init__(supportedNwstacks=["pytorch-dist", "pytorch-xla-tpu"]) def readArgs(self, parser): super().readArgs(parser) parser.add_argument( "--w", type=int, default=5, help="number of warmup iterations" ) parser.add_argument( "--n", type=int, default=5, help="number of iterations" ) parser.add_argument( "--mode", type=str, default="comms", help="benchmark mode", choices=["comms", "compute", "dlrm", "comms-compute"], ) parser.add_argument( "--b", type=str, default="8", help="minimum size, in bytes, to start with" ) parser.add_argument( "--e", type=str, default="64", help="maximum size, in bytes, to end at" ) parser.add_argument( "--f", type=int, default=2, help="multiplication factor between sizes" ) parser.add_argument( "--collective", type=str, default="all_reduce", help="Collective operation to be evaluated", choices=supportedCollectives, ) parser.add_argument( "--kernel", type=str, default="gemm", help="Compute kernel, used for comms-compute or compute mode", choices=["gemm", "emb_lookup"], ) parser.add_argument( "--num-compute", type=int, default=100, help="one collective for every NUM_COMPUTE compute kernels", ) parser.add_argument( "--mm-dim", type=int, default=100, help="dimension size for GEMM compute kernel", ) parser.add_argument( "--emb-dim", type=int, default=128, help="dimension size for Embedding table compute kernel", ) parser.add_argument( "--num-embs", type=int, default=100000, help="Embedding table hash size for Embedding table compute kernel", ) parser.add_argument( "--avg-len", type=int, default=28, help="Average lookup operations per sample", ) d_argument( "--batch-size", type=int, default=512, help="number of samples reading the table concurrently", ) "--root", type=int, default=0, help="root process for reduce benchmark" ) parser.add_argument( "--src-ranks", type=str, nargs="?", help="R|src ranks for many-to-one incast pattern or pt2pt.\n" "List of ranks separated by comma or a range specified by start:end.\n" "Pt2pt one2one should set only one rank.\n" "The default value of incast includes all ranks, pt2pt includes rank 0.", ) parser.add_argument( "--dst-ranks", type=str, nargs="?", help="R|dst ranks for one-to-many multicast pattern or pt2pt.\n" "List of ranks separated by comma or a range specified by start:end.\n" "Pt2pt one2one should set only one rank\n" "The default value of multicast includes all ranks, pt2pt includes rank 1.", ) parser.add_argument( "--pair", action="store_true", default=False, help="Toggle to enable collective pair mode", ) parser.add_argument( "--collective-pair", type=str, default="all_reduce", help="Collective pair operation to be evaluated", choices=supportedCollectives, ) parser.add_argument( "--overlap-pair-pgs", action="store_true", default=False, help="Toggle to enable overlapping collective pair with two pgs", ) parser.add_argument( "--pt2pt", type=str, default=None, help="point to point pattern", choices=pt2ptPatterns, ) parser.add_argument( "--window", type=int, default=100, help="window size for pt2pt throughput test", ) return parser.parse_known_args() def checkArgs(self, args): super().checkArgs(args) if args.pt2pt is not None: args.collective = "pt2pt" if args.pt2pt not in pt2ptPatterns: logger.error( f"Specified pt2pt pattern: {args.pt2pt} is not one of the supported pt2pt patterns: {str(pt2ptPatterns)}" ) comms_utils.gracefulExit() args.b = comms_utils.parsesize(args.b) args.e = comms_utils.parsesize(args.e) args.dtype = self.dtypeMap[args.data_type] if args.b < 1: logger.warning( f"Starting size (--b {args.b}) should be greater than 1 byte...fix and continue" ) args.b = 1 if args.e < args.b: logger.warning( f"the begin-size (--b {args.b}) is larger than the end-size (--e {args.e})" ) if args.device == "cpu" and args.backend == "nccl": raise ValueError(f"NCCL is not supported for device type {args.device}") if args.c == 1 and args.z == 0 and args.collective in ("all_reduce", "reduce", "reduce_scatter"): logger.warning( f"Data validation is not supported for {args.collective} in non-blocking mode, disabled and continue" ) args.c = 0 if args.bitwidth < 32: if args.device != "cuda": logger.error( f"collective quantization may not be fully supported for {args.device}" ) comms_utils.checkQuantArgs( args.collective, args.dtype, args.b, args.quant_a2a_embedding_dim, args.z, ) def runColl(self, comm_fn=None, compute_fn=None, comm_fn_pair=None): self.backendFuncs.complete_accel_ops(self.collectiveArgs, initOp=True) self.backendFuncs.sync_barrier(self.collectiveArgs, desc="runColl_begin") elapsedTimeNS = 0.0 is_blocking = not self.collectiveArgs.asyncOp enable_comms = False if (comm_fn is None or comm_fn == self.backendFuncs.noop) else True enable_compute = False if (compute_fn is None or compute_fn == self.backendFuncs.noop) else True enable_comms_pair = False if (comm_fn_pair is None or comm_fn_pair == self.backendFuncs.noop) else True if enable_comms_pair: self.collectiveArgs.asyncOp = True for nIter in range( self.collectiveArgs.numWarmupIters + self.collectiveArgs.numIters ): if nIter == self.collectiveArgs.numWarmupIters: self.backendFuncs.complete_accel_ops(self.collectiveArgs) ensureTensorFlush(self.collectiveArgs.opTensor) if enable_comms_pair: ensureTensorFlush(self.collectiveArgs.opTensor_pair) elapsedTimeNS = 0.0 self.collectiveArgs.quant_time.reset() self.collectiveArgs.dequant_time.reset() if enable_comms: self.setTensorVal(self.collectiveArgs.opTensor) if is_blocking: self.backendFuncs.sync_barrier(self.collectiveArgs) start = time.monotonic() self.collectiveArgs.group = self.backendFuncs.get_next_group() comm_fn(self.collectiveArgs) self.collectiveArgs.group = self.backendFuncs.get_next_group() comm_fn_pair(self.collectiveArgs, pair=enable_comms_pair) if enable_compute: for _ in range(self.collectiveArgs.numComputePerColl): # TODO: investigate the cache effect # Flush the cache # _ = torch.rand(6 * 1024 * 1024 // 4).float() * 2 # V100 6MB L2 cache compute_fn(self.collectiveArgs) if is_blocking: # should be sychronous, wait for the collective self.backendFuncs.complete_accel_ops(self.collectiveArgs) # Measuring time. elapsedTimeNS += ( time.monotonic() - start ) * 1e9 # keeping time in NS, helps in divising data by nanosecond start = time.monotonic() # available only in py3 self.backendFuncs.complete_accel_ops(self.collectiveArgs) end = time.monotonic() # available only in py3 ensureTensorFlush(self.collectiveArgs.opTensor) if enable_comms_pair: ensureTensorFlush(self.collectiveArgs.opTensor_pair) elapsedTimeNS += ( end - start ) * 1e9 # keeping time in NS, helps in divising data by nanoseconds memSize = self.backendFuncs.get_mem_size(self.collectiveArgs) avgIterNS, algBW = comms_utils.getAlgBW( elapsedTimeNS, memSize, self.collectiveArgs.numIters ) busBW = self.backendFuncs.getBusBW( self.collectiveArgs.collective, algBW, self.collectiveArgs, ) if enable_comms_pair: memSize_pair = self.backendFuncs.get_mem_size( self.collectiveArgs, pair=enable_comms_pair ) memSize += memSize_pair _, algBW_pair = comms_utils.getAlgBW( elapsedTimeNS, memSize_pair, self.collectiveArgs.numIters ) algBW += algBW_pair busBW += self.backendFuncs.getBusBW( self.collectiveArgs.collective_pair, algBW_pair, self.collectiveArgs, ) self.backendFuncs.sync_barrier(self.collectiveArgs, desc="runColl_end") results = { "timeUS": avgIterNS / 1e3, "algBW": algBW, "busBW": busBW, "memSize": memSize, } return results def runPt2Pt(self): self.backendFuncs.complete_accel_ops(self.collectiveArgs, initOp=True) # warm-up memSize = self.backendFuncs.get_mem_size(self.collectiveArgs) self.getPingLatency(self.collectiveArgs.numWarmupIters) self.getPingPongLatency(self.collectiveArgs.numWarmupIters) self.getUniBW(self.collectiveArgs.numWarmupIters, memSize) self.getBiBW(self.collectiveArgs.numWarmupIters, memSize) self.backendFuncs.sync_barrier(self.collectiveArgs, "runpt2pt_begin") # pt2pt benchmark pingPerIterNS = self.getPingLatency(self.collectiveArgs.numIters) pingPongPerIterNS = self.getPingPongLatency(self.collectiveArgs.numIters) avgUniBW = self.getUniBW(self.collectiveArgs.numIters, memSize) avgBiBW = self.getBiBW(self.collectiveArgs.numIters, memSize) self.backendFuncs.sync_barrier(self.collectiveArgs, "runpt2pt") results = { "pingPerIterNS": pingPerIterNS, "pingPongPerIterNS": pingPongPerIterNS, "avgUniBW": avgUniBW, "avgBiBW": avgBiBW, "memSize": memSize, } return results def getPingLatency(self, numIters): logger.debug( "STATUS: begin ping test with src_ranks=%s, dst_ranks=%s." % (self.collectiveArgs.src_ranks, self.collectiveArgs.dst_ranks) ) self.collectiveArgs.asyncOp = False # get one-way latency pingLatencyNS = [] for _ in range(numIters): self.backendFuncs.sync_barrier(self.collectiveArgs) start = time.monotonic() if self.collectiveArgs.global_rank in self.collectiveArgs.src_ranks: idx = self.collectiveArgs.src_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.send( self.collectiveArgs, self.collectiveArgs.dst_ranks[idx] ) elif self.collectiveArgs.global_rank in self.collectiveArgs.dst_ranks: idx = self.collectiveArgs.dst_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.recv( self.collectiveArgs, self.collectiveArgs.src_ranks[idx] ) self.backendFuncs.complete_accel_ops(self.collectiveArgs) pingLatencyNS.append( (time.monotonic() - start) * 1e9 ) # keeping time in NS, helps in divising data by nanosecond logger.debug("STATUS: end ping test.") return pingLatencyNS def getPingPongLatency(self, numIters): logger.debug( "STATUS: begin ping-pong with src_ranks=%s, dst_ranks=%s." % (self.collectiveArgs.src_ranks, self.collectiveArgs.dst_ranks) ) self.collectiveArgs.asyncOp = False # get round-trip latency pingPongLatencyNS = [] for _ in range(numIters): self.backendFuncs.sync_barrier(self.collectiveArgs) start = time.monotonic() if self.collectiveArgs.global_rank in self.collectiveArgs.src_ranks: idx = self.collectiveArgs.src_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.send( self.collectiveArgs, self.collectiveArgs.dst_ranks[idx] ) self.backendFuncs.recv( self.collectiveArgs, self.collectiveArgs.dst_ranks[idx] ) elif self.collectiveArgs.global_rank in self.collectiveArgs.dst_ranks: idx = self.collectiveArgs.dst_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.recv( self.collectiveArgs, self.collectiveArgs.src_ranks[idx] ) self.backendFuncs.send( self.collectiveArgs, self.collectiveArgs.src_ranks[idx] ) self.backendFuncs.complete_accel_ops(self.collectiveArgs) pingPongLatencyNS.append( (time.monotonic() - start) * 1e9 ) # keeping time in NS, helps in divising data by nanosecond logger.debug("STATUS: end ping-pong test.") return pingPongLatencyNS def getUniBW(self, numIters, memSize): logger.debug( "STATUS: begin UniBW test with src_ranks=%s, dst_ranks=%s." % (self.collectiveArgs.src_ranks, self.collectiveArgs.dst_ranks) ) self.collectiveArgs.asyncOp = True # get unidirectional bandwidth uniLatencyNS = [] for _ in range(numIters): self.backendFuncs.sync_barrier(self.collectiveArgs) start = time.monotonic() for w in range(self.collectiveArgs.window): if self.collectiveArgs.global_rank in self.collectiveArgs.src_ranks: idx = self.collectiveArgs.src_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.isend( self.collectiveArgs, self.collectiveArgs.dst_ranks[idx], tag=w ) elif self.collectiveArgs.global_rank in self.collectiveArgs.dst_ranks: idx = self.collectiveArgs.dst_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.irecv( self.collectiveArgs, self.collectiveArgs.src_ranks[idx], tag=w ) self.backendFuncs.complete_accel_ops(self.collectiveArgs) uniLatencyNS.append( (time.monotonic() - start) * 1e9 ) # keeping time in NS, helps in divising data by nanosecond uniLatencyNS = [lat / self.collectiveArgs.window for lat in uniLatencyNS] uniLatencyNS = np.mean(np.array(uniLatencyNS)) _, avgUniBW = comms_utils.getAlgBW(uniLatencyNS, memSize, 1) logger.debug("STATUS: end UniBW test.") return avgUniBW def getBiBW(self, numIters, memSize): logger.debug( "STATUS: begin BiBW test with src_ranks=%s, dst_ranks=%s." % (self.collectiveArgs.src_ranks, self.collectiveArgs.dst_ranks) ) self.collectiveArgs.asyncOp = True # get bidirectional bandwidth biLatencyNS = [] for _ in range(numIters): self.backendFuncs.sync_barrier(self.collectiveArgs) start = time.monotonic() for w in range(self.collectiveArgs.window): if self.collectiveArgs.global_rank in self.collectiveArgs.src_ranks: idx = self.collectiveArgs.src_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.isend( self.collectiveArgs, self.collectiveArgs.dst_ranks[idx], tag=w ) self.backendFuncs.irecv( self.collectiveArgs, self.collectiveArgs.dst_ranks[idx], tag=w + self.collectiveArgs.window, ) elif self.collectiveArgs.global_rank in self.collectiveArgs.dst_ranks: idx = self.collectiveArgs.dst_ranks.index( self.collectiveArgs.global_rank ) self.backendFuncs.irecv( self.collectiveArgs, self.collectiveArgs.src_ranks[idx], tag=w ) self.backendFuncs.isend( self.collectiveArgs, self.collectiveArgs.src_ranks[idx], tag=w + self.collectiveArgs.window, ) self.backendFuncs.complete_accel_ops(self.collectiveArgs) biLatencyNS.append( (time.monotonic() - start) * 1e9 ) # keeping time in NS, helps in divising data by nanosecond biLatencyNS = [lat / self.collectiveArgs.window for lat in biLatencyNS] biLatencyNS = np.mean(np.array(biLatencyNS)) _, avgBiBW = comms_utils.getAlgBW(biLatencyNS, 2 * memSize, 1) logger.debug("STATUS: end UniBW test.") return avgBiBW def checkPt2PtRanks(self): # set default values if not self.collectiveArgs.src_ranks: self.collectiveArgs.src_ranks = [0] if not self.collectiveArgs.dst_ranks: self.collectiveArgs.dst_ranks = [1] # sanity check if self.collectiveArgs.pt2pt == "one2one": if ( len(self.collectiveArgs.src_ranks) > 1 or len(self.collectiveArgs.dst_ranks) > 1 ): if self.global_rank == 0: logger.error( "One2one Pt2Pt requires only a single rank is specified in src_ranks and dst_ranks! " ) comms_utils.gracefulExit() elif self.collectiveArgs.pt2pt == "pairwise": # pairwise pt2pt requires identical number of ranks in src_ranks and dst_ranks. if len(self.collectiveArgs.src_ranks) != len(self.collectiveArgs.dst_ranks): if self.global_rank == 0: logger.error( "Pairwise Pt2Pt requires identical number of members in src_ranks and dst_ranks! " ) comms_utils.gracefulExit() # pairwise pt2pt does not allow same rank to exist in both groups if bool( set(self.collectiveArgs.src_ranks).intersection( self.collectiveArgs.dst_ranks ) ): if self.global_rank == 0: logger.error( "Pairwise Pt2Pt requires distinct members in src_ranks and dst_ranks! " ) comms_utils.gracefulExit() if self.global_rank == 0: print( f"\t collective={self.collectiveArgs.collective}\t{self.collectiveArgs.pt2pt}, src_ranks={self.collectiveArgs.src_ranks}, dst_ranks={self.collectiveArgs.dst_ranks}" ) def checkCollectiveRanks(self): if self.collectiveArgs.collective == "incast": # incast: set default value and exclude root if not self.collectiveArgs.src_ranks: self.collectiveArgs.src_ranks = [*range(self.comm_size)] if self.collectiveArgs.srcOrDst in self.collectiveArgs.src_ranks: self.collectiveArgs.src_ranks.remove(self.collectiveArgs.srcOrDst) elif self.collectiveArgs.collective == "multicast": # multicast: set default value and exclude root if not self.collectiveArgs.dst_ranks: self.collectiveArgs.dst_ranks = [*range(self.comm_size)] if self.collectiveArgs.srcOrDst in self.collectiveArgs.dst_ranks: self.collectiveArgs.dst_ranks.remove(self.collectiveArgs.srcOrDst) if self.global_rank == 0: print( f"\t collective={self.collectiveArgs.collective}, src_ranks={self.collectiveArgs.src_ranks}, dst_ranks={self.collectiveArgs.dst_ranks}" ) def initCollectiveArgs(self, commsParams): # lint was complaining that benchTime was too complex! ( local_rank, global_rank, world_size, group, curDevice, curHwDevice, ) = comms_utils.get_rank_details( self.backendFuncs ) # Getting ranks from backednFuncs object, since we cannot use MPI (e.g.: TPU) to launch all the processes. self.backendFuncs.sayHello() # Informs us where each process is running. groups = self.backendFuncs.get_groups() num_pgs = len(groups) self.comm_size = world_size self.global_rank = global_rank comms_utils.fixBeginSize( commsParams, world_size ) # Ensuring that all-reduce and all-to-all has atleast one member per rank. allSizes = comms_utils.getSizes( commsParams.beginSize, commsParams.endSize, commsParams.stepFactor ) # Given the begin-size, end-size, step-factor what are the message sizes to iterate on. if global_rank == 0: print( f"[Rank {global_rank:>3}] allSizes: {allSizes} local_rank: {local_rank} element_size: {commsParams.element_size}" ) self.collectiveArgs.group = group self.collectiveArgs.groups = groups self.collectiveArgs.num_pgs = num_pgs self.collectiveArgs.device = curDevice self.collectiveArgs.world_size = world_size self.collectiveArgs.numIters = commsParams.numIters self.collectiveArgs.numWarmupIters = commsParams.numWarmupIters self.collectiveArgs.global_rank = global_rank self.collectiveArgs.backendFuncs = self.backendFuncs self.collectiveArgs.collective = commsParams.collective op = self.backendFuncs.get_reduce_op("sum") self.collectiveArgs.op = op self.collectiveArgs.srcOrDst = commsParams.srcOrDst self.collectiveArgs.src_ranks = commsParams.src_ranks self.collectiveArgs.dst_ranks = commsParams.dst_ranks self.collectiveArgs.pair = commsParams.pair self.collectiveArgs.collective_pair = commsParams.collective_pair self.collectiveArgs.pt2pt = commsParams.pt2pt self.collectiveArgs.window = commsParams.window self.collectiveArgs.asyncOp = False if commsParams.blockingFlag == 1 else True if commsParams.bitwidth < 32: comms_utils.initQuantCommCtx(self.collectiveArgs, commsParams) if self.collectiveArgs.collective == "pt2pt": self.checkPt2PtRanks() else: self.checkCollectiveRanks() computeFunc = self.backendFuncs.noop if ( commsParams.mode != "comms" ): # Compute mode related initialization if not in comms-only mode if commsParams.kernel == "gemm": computeFunc = self.backendFuncs.gemm mm_dim = commsParams.mm_dim in1 = np.random.rand(mm_dim, mm_dim) MMin1 = torch.FloatTensor(in1).to(curDevice) in2 = np.random.rand(mm_dim, mm_dim) MMin2 = torch.FloatTensor(in2).to(curDevice) in3 = np.random.rand(mm_dim, mm_dim) MMin3 = torch.FloatTensor(in3).to(curDevice) MMout = self.backendFuncs.alloc_empty( [mm_dim, mm_dim], commsParams.dtype, curDevice ) self.collectiveArgs.MMout = MMout self.collectiveArgs.MMin1 = MMin1 self.collectiveArgs.MMin2 = MMin2 self.collectiveArgs.MMin3 = MMin3 self.collectiveArgs.numComputePerColl = commsParams.num_compute elif commsParams.kernel == "emb_lookup": computeFunc = self.backendFuncs.emb_lookup emb_dim = commsParams.emb_dim num_embeddings = commsParams.num_embs avg_length = commsParams.avg_len batch_size = commsParams.batch_size print( f"emb_dim {emb_dim} num_embs {num_embeddings} avg_len {avg_length} bs {batch_size}" ) self.collectiveArgs.EmbWeights = self.backendFuncs.alloc_empty( [num_embeddings, emb_dim], torch.double, curDevice ) self.collectiveArgs.TableOffsets = torch.LongTensor( [0, num_embeddings] ).to(curDevice) self.collectiveArgs.Indices = torch.LongTensor( np.random.randint(0, num_embeddings - 1, avg_length * batch_size) ).to(curDevice) lengths = np.ones((1, batch_size)) * avg_length flat_lengths = lengths.flatten() self.collectiveArgs.Offsets = torch.LongTensor( [0] + np.cumsum(flat_lengths).tolist() ).to(curDevice) self.collectiveArgs.LookupOut = self.backendFuncs.alloc_empty( [batch_size, emb_dim], torch.double, curDevice ) self.collectiveArgs.AvgLengths = avg_length self.collectiveArgs.numComputePerColl = commsParams.num_compute return ( local_rank, global_rank, world_size, group, curDevice, curHwDevice, allSizes, computeFunc, ) def gatherBenchTime(self, collectiveArgs, commsParams, timeUsElapsedList): # Push the list to device, then do an all-gather. timeElapsedTensor = torch.tensor( timeUsElapsedList, device=self.backendFuncs.get_device() ) collectiveArgs.opTensor = None if commsParams.backend != "xla": timeList = list(torch.ones( (self.comm_size,) + timeElapsedTensor.shape, dtype=timeElapsedTensor.dtype, device=timeElapsedTensor.device, ).unbind(0)) collectiveArgs.opTensor = timeList collectiveArgs.ipTensor = timeElapsedTensor collectiveArgs.asyncOp = False collectiveArgs.dataSize = ( timeElapsedTensor.nelement() * timeElapsedTensor.element_size() ) collectiveArgs.numElements = timeElapsedTensor.nelement() # use allgather as all process group should support it self.backendFuncs.all_gather(collectiveArgs) self.backendFuncs.complete_accel_ops(collectiveArgs) return timeList def printPreamble(self, commsParams): logger.debug(f"\tcommsParams: {str(commsParams.__dict__)}") header = "\n\tCOMMS-RES" if self.collectiveArgs.collective == "pt2pt": header += "{:>15}{:>20}{:>10}{:>10}{:>25}{:>10}{:>10}{:>15}{:>15}{:>18}{:>18}".format( "size (B)", "pingLatency(us):p50", "p75", "p95", "pingPongLatency(us):p50", "p75", "p95", "avgUniBW(GB/s)", "avgBiBW(GB/s)", "totalUniBW(GB/s)", "totalBiBW(GB/s)", ) else: if commsParams.bitwidth < 32: header += "-QUANT\t{:>15}{:>18}{:>25}{:>15}{:>15}{:>15}".format( "size (B)", "nElementsPerRank", "P95 Latency(us): Quant", "Comms", "De-Quant", "Overall", ) elif not self.collectiveArgs.pair: header += ( "{:>15}{:>18}{:>18}{:>12}{:>12}{:>12}{:>12}{:>15}{:>12}".format( "size (B)", "nElementsPerRank", "Latency(us):p50", "p75", "p95", "Min", "Max", "AlgBW(GB/s)", "BusBW(GB/s)", ) ) else: header += "{:>15}{:>18}{:>22}{:>18}{:>12}{:>12}{:>12}{:>12}{:>15}{:>12}".format( "total-size (B)", "nElementsPerRank", "nElementsPairPerRank", "Latency(us):p50", "p75", "p95", "Min", "Max", "AlgBW(GB/s)", "BusBW(GB/s)", ) print(header) def reportBenchTimeCollWithQuant( self, commsParams, results, tensorList, quantTimeTensorList, dequantTimeTensorList, ): if commsParams.backend == "xla": latencyAcrossRanks = torch.transpose(tensorList.view(-1, 1), 0, 1)[0] latencyAcrossRanks = latencyAcrossRanks.cpu().detach().numpy() # quant tensor quantLatencyAcrossRanks = torch.transpose( quantTimeTensorList.view(-1, 1), 0, 1 )[0] quantLatencyAcrossRanks = quantLatencyAcrossRanks.cpu().detach().numpy() # dequant tensor dequantLatencyAcrossRanks = torch.transpose( dequantTimeTensorList.view(-1, 1), 0, 1 )[0] dequantLatencyAcrossRanks = dequantLatencyAcrossRanks.cpu().detach().numpy() else: if isinstance(tensorList, list): tensorList = [t.cpu().detach().numpy() for t in tensorList] latencyAcrossRanks = np.array(tensorList) # quant tensor quantLatencyAcrossRanks = np.array(quantTimeTensorList) # dequant tensor dequantLatencyAcrossRanks = np.array(dequantTimeTensorList) p95 = np.percentile(latencyAcrossRanks, 95) quant_p95 = np.percentile(quantLatencyAcrossRanks, 95) dequant_p95 = np.percentile(dequantLatencyAcrossRanks, 95) print( "\tCOMMS-RES-QUANT\t{:>15}{:>18}{:>25}{:>15}{:>15}{:>15}".format( results["memSize"], str("%d" % (results["numElements"])), str("%.1f" % (quant_p95)), str("%.1f" % (p95 - quant_p95 - dequant_p95)), str("%.1f" % (dequant_p95)), str("%.1f" % (p95)), # str("%.3f" % (algBW)), # str("%.3f" % (busBW)), ) ) def reportBenchTime( self, commsParams, results, tensorList, quantTimeTensorList, dequantTimeTensorList, ): # convernt num_elements to # of elements per rank if commsParams.collective in ("all_to_all", "all_to_allv"): results["numElements"] = int( results["numElements"] // commsParams.comms_world_info.world_size ) if commsParams.collective == "pt2pt": self.reportBenchTimePt2Pt(commsParams, tensorList, results) elif commsParams.bitwidth < 32: self.reportBenchTimeCollWithQuant( commsParams, results, tensorList, quantTimeTensorList, dequantTimeTensorList, ) else: self.reportBenchTimeColl(commsParams, results, tensorList) def reportBenchTimeColl(self, commsParams, results, tensorList): if commsParams.backend == "xla": latencyAcrossRanks = torch.transpose(tensorList.view(-1, 1), 0, 1)[0] latencyAcrossRanks = latencyAcrossRanks.cpu().detach().numpy() else: if isinstance(tensorList, list): tensorList = [t.cpu().detach().numpy() for t in tensorList] latencyAcrossRanks = np.array(tensorList) logger.debug(f"Latency across all ranks: {latencyAcrossRanks}") # Include only communicating ranks if self.collectiveArgs.collective == "multicast": commRanks = [self.collectiveArgs.srcOrDst] + self.collectiveArgs.dst_ranks elif self.collectiveArgs.collective == "incast": commRanks = [self.collectiveArgs.srcOrDst] + self.collectiveArgs.src_ranks else: commRanks = range(self.collectiveArgs.world_size) latencyAcrossCommRanks = latencyAcrossRanks[commRanks] logger.debug( "Latency across communicating ranks (%s): %s" % (commRanks, latencyAcrossCommRanks) ) p50 = np.percentile(latencyAcrossCommRanks, 50) p75 = np.percentile(latencyAcrossCommRanks, 75) p95 = np.percentile(latencyAcrossCommRanks, 95) minlat = np.amin(latencyAcrossCommRanks) maxlat = np.amax(latencyAcrossCommRanks) # adjust busBW busBW = results["busBW"] * (commsParams.bitwidth / 32.0) if not self.collectiveArgs.pair: print( "\tCOMMS-RES{:>15}{:>18}{:>18}{:>12}{:>12}{:>12}{:>12}{:>15}{:>12}".format( results["memSize"], str("%d" % (results["numElements"])), str("%.1f" % (p50)), str("%.1f" % (p75)), str("%.1f" % (p95)), str("%.1f" % (minlat)), str("%.1f" % (maxlat)), str("%.3f" % (results["algBW"])), str("%.3f" % (busBW)), ) ) else: # convernt to # of elements per rank if commsParams.collective_pair in ("all_to_all", "all_to_allv"): results["numElements_pair"] = int( results["numElements_pair"] // commsParams.comms_world_info.world_size ) print( "\tCOMMS-RES{:>15}{:>18}{:>22}{:>18}{:>12}{:>12}{:>12}{:>12}{:>15}{:>12}".format( results["memSize"], str("%d" % (results["numElements"])), str("%d" % (results["numElements_pair"])), str("%.1f" % (p50)), str("%.1f" % (p75)), str("%.1f" % (p95)), str("%.1f" % (minlat)), str("%.1f" % (maxlat)), str("%.3f" % (results["algBW"])), str("%.3f" % (busBW)), ) ) def reportBenchTimePt2Pt(self, commsParams, resultsAcrossRanks, results): pingLatencyAcrossRanks = [] pingPongLatencyAcrossRanks = [] uniBWAcrossRanks = [] biBWAcrossRanks = [] # idx = 0 for curRankTensor in resultsAcrossRanks: pingLatencyAcrossRanks.append(curRankTensor[0].item()) pingPongLatencyAcrossRanks.append(curRankTensor[1].item()) uniBWAcrossRanks.append(curRankTensor[2].item()) biBWAcrossRanks.append(curRankTensor[3].item()) pingLatencyAcrossRanks = np.array(pingLatencyAcrossRanks) pingPongLatencyAcrossRanks = np.array(pingPongLatencyAcrossRanks) uniBWAcrossRanks = np.array(uniBWAcrossRanks) biBWAcrossRanks = np.array(biBWAcrossRanks) # Include only communicating ranks commRanks = self.collectiveArgs.src_ranks + self.collectiveArgs.dst_ranks pingLatencyAcrossCommRanks = pingLatencyAcrossRanks[commRanks] pingPongLatencyAcrossCommRanks = pingPongLatencyAcrossRanks[commRanks] uniBWAcrossCommRanks = uniBWAcrossRanks[commRanks] biBWAcrossCommRanks = biBWAcrossRanks[commRanks] logger.debug( "Ping latency across communicating ranks (%s): %s" % (commRanks, pingLatencyAcrossCommRanks) ) logger.debug( "PingPong latency across communicating ranks (%s): %s" % (commRanks, pingPongLatencyAcrossCommRanks) ) logger.debug( "UniBW across all communicating ranks (%s): %s" % (commRanks, uniBWAcrossCommRanks) ) logger.debug( "BiBW across all communicating ranks (%s): %s" % (commRanks, biBWAcrossCommRanks) ) avgUniBW = np.mean(uniBWAcrossCommRanks) avgBiBW = np.mean(biBWAcrossCommRanks) totalUniBW = np.sum(uniBWAcrossCommRanks) / 2 totalBiBW = np.sum(biBWAcrossCommRanks) / 2 ping_p50 = np.percentile(pingLatencyAcrossCommRanks, 50) ping_p75 = np.percentile(pingLatencyAcrossCommRanks, 75) ping_p95 = np.percentile(pingLatencyAcrossCommRanks, 95) ping_pong_p50 = np.percentile(pingPongLatencyAcrossCommRanks, 50) ping_pong_p75 = np.percentile(pingPongLatencyAcrossCommRanks, 75) ping_pong_p95 = np.percentile(pingPongLatencyAcrossCommRanks, 95) print( "\tCOMMS-RES{:>15}{:>20}{:>10}{:>10}{:>25}{:>10}{:>10}{:>15}{:>15}{:>18}{:>18}".format( results["memSize"], str("%.1f" % (ping_p50)), str("%.1f" % (ping_p75)), str("%.1f" % (ping_p95)), str("%.1f" % (ping_pong_p50)), str("%.1f" % (ping_pong_p75)), str("%.1f" % (ping_pong_p95)), str("%.3f" % (avgUniBW)), str("%.3f" % (avgBiBW)), str("%.3f" % (totalUniBW)), str("%.3f" % (totalBiBW)), ) ) def benchTime(self, index, commsParams, backendFuncs): # Get NW stack specific parameters ( local_rank, global_rank, world_size, group, curDevice, curHwDevice, allSizes, computeFunc, ) = self.initCollectiveArgs(commsParams) backendFuncs.sync_barrier(self.collectiveArgs) if global_rank == 0: self.printPreamble(commsParams) for curSize in allSizes: results = {} timeUsElapsedList = [] quantTimeElapsedList = [] dequantTimeElapsedList = [] numElements = int(curSize // commsParams.element_size) collectiveFunc = self.backendFuncs.noop collectiveFunc_pair = self.backendFuncs.noop if ( commsParams.mode != "compute" ): # comms specific initializations if not in compute-only mode # set corresponding function pointers if commsParams.collective != "pt2pt": collectiveFunc = backendFuncs.collectiveFunc[commsParams.collective] ( self.collectiveArgs.ipTensor, self.collectiveArgs.opTensor, ) = self.prepComm( curComm={ "in_msg_size": numElements, "out_msg_size": numElements, "world_size": world_size, }, commsParams=commsParams, ) # Setup the arguments. self.collectiveArgs.dataSize = curSize self.collectiveArgs.numElements = numElements self.collectiveArgs.waitObj = [] results["numElements"] = numElements if ( commsParams.pair and commsParams.mode != "compute" ): # comms-pair specific initializations if not in compute-only mode: # set corresponding function pointers collectiveFunc_pair = backendFuncs.collectiveFunc[ commsParams.collective_pair ] # TODO: allow user to set specific size # Setup the arguments. self.collectiveArgs.dataSize_pair = curSize self.collectiveArgs.numElements_pair = int( self.collectiveArgs.dataSize_pair // commsParams.element_size ) results["numElements_pair"] = self.collectiveArgs.numElements_pair ( self.collectiveArgs.ipTensor_pair, self.collectiveArgs.opTensor_pair, ) = self.prepComm( curComm={ "in_msg_size": self.collectiveArgs.numElements_pair, "out_msg_size": self.collectiveArgs.numElements_pair, "world_size": world_size, }, commsParams=commsParams, ) # self.collectiveArgs has all the information on the experiment. if commsParams.collective == "pt2pt": results.update(self.runPt2Pt()) timeUsElapsedList = [ np.mean(np.array(results["pingPerIterNS"])) / 1e3, np.mean(np.array(results["pingPongPerIterNS"])) / 1e3, results["avgUniBW"], results["avgBiBW"], ] # time in US if ( global_rank in self.collectiveArgs.src_ranks or global_rank in self.collectiveArgs.dst_ranks ): logger.debug(timeUsElapsedList) else: results.update( self.runColl( comm_fn=collectiveFunc, compute_fn=computeFunc, comm_fn_pair=collectiveFunc_pair, ) ) timeUsElapsedList = [results["timeUS"]] # perfom data validation check on the final opTensor if commsParams.dcheck == 1: self.dcheck(commsParams, curSize, self.collectiveArgs.opTensor) backendFuncs.clear_memory(self.collectiveArgs) # gather quantization overhead if enabled if commsParams.bitwidth < 32: # calculate average (de-)quantization overhead results["quantTimeUS"] = ( self.collectiveArgs.quant_time.getTimeUS() / self.collectiveArgs.numIters ) results["dequantTimeUS"] = ( self.collectiveArgs.dequant_time.getTimeUS() / self.collectiveArgs.numIters ) quantTimeElapsedList.append(results["quantTimeUS"]) dequantTimeElapsedList.append(results["dequantTimeUS"]) logger.debug(quantTimeElapsedList) quantTimeElapsedList = self.gatherBenchTime( self.collectiveArgs, commsParams, quantTimeElapsedList ) dequantTimeElapsedList = self.gatherBenchTime( self.collectiveArgs, commsParams, dequantTimeElapsedList ) # gather and report performance to stdout tensorList = self.gatherBenchTime( self.collectiveArgs, commsParams, timeUsElapsedList ) if global_rank == 0: self.reportBenchTime( commsParams, results, tensorList, quantTimeElapsedList, dequantTimeElapsedList, ) self.backendFuncs.sync_barrier( self.collectiveArgs, desc=f"curSize_{curSize}" ) comms_utils.clearQuantCommCtx(self.collectiveArgs) # wait rank 0 reports results to avoid other ranks mess up the output self.backendFuncs.sync_barrier(self.collectiveArgs, "benchtime") def runBench(self, comms_world_info, commsParams): # Init the desired backend if commsParams.nw_stack == "pytorch-dist": from pytorch_dist_backend import PyTorchDistBackend backendObj = PyTorchDistBackend(comms_world_info, commsParams) elif commsParams.nw_stack == "pytorch-xla-tpu": from pytorch_tpu_backend import PyTorchTPUBackend backendObj = PyTorchTPUBackend(comms_world_info, commsParams) else: logger.error("Unsupported NW stack! ") comms_utils.gracefulExit() self.backendFuncs = backendObj try: backendObj.benchmark_comms() except ValueError as ve: if commsParams.backend == "ucc": logger.critical("PyTorch UCC not implemented? {}".format(repr(ve))) raise def main(): collBenchObj = commsCollBench() ### parse arguments ### parser = argparse.ArgumentParser( description="PARAM-Comm Benchmark", formatter_class=MultilineFormatter, ) args, leftovers = collBenchObj.readArgs(parser) collBenchObj.checkArgs(args) comms_env_params = comms_utils.read_comms_env_vars() if comms_env_params["global_rank"] == 0: print("\t MPI environment: %s " % (str(comms_env_params))) print( "\t backend: %s nw-stack: %s mode: %s args.b: %d args.e: %d args.f: %d args.z: %s args.master_ip: %s " % ( args.backend, args.nw_stack, args.mode, args.b, args.e, args.f, args.z, args.master_ip, ) ) element_size = torch.ones([1], dtype=args.dtype).element_size() comms_world_info = comms_utils.comms_world_info_holder( args.master_ip, args.master_port, args.num_tpu_cores, comms_env_params ) commsParams = comms_utils.commsParamsHolder( args, comms_world_info, element_size, collBenchObj.benchTime ) if args.pair and args.overlap_pair_pgs: commsParams.num_pgs = 2 collBenchObj.runBench(comms_world_info, commsParams) if __name__ == "__main__": main()

true

f717719b096d0afc9e9656a8a3d67b6ed3d90cd0

3,338

py

Python

mime/agent/script_agent_augmented.py

rjgpinel/mime-release

26a850c4ba5b702b86d068995614163338fb01df

[ "MIT" ]

null

mime/agent/script_agent_augmented.py

rjgpinel/mime-release

26a850c4ba5b702b86d068995614163338fb01df

[ "MIT" ]

null

mime/agent/script_agent_augmented.py

rjgpinel/mime-release

26a850c4ba5b702b86d068995614163338fb01df

[ "MIT" ]

null

import itertools import types import numpy as np import torch import click import gym import time import yaml from robos2r.model import build_model from .agent import Agent from .script_agent import ScriptAgent, make_noised from .utils import Rate from PIL import Image from pathlib import Path from einops import rearrange from torchvision import transforms as T @click.command(help="script_agent env_name [options]") @click.argument("env_name", type=str) @click.option("-s", "--seed", default=0, help="seed") @click.option("-t", "--times-repeat", default=1, help="times to repeat the script") @click.option("-n", "--add-noise", is_flag=True, help="adding noise to actions or not") @click.option( "-sc", "--skill-collection/--no-skill-collection", is_flag=True, help="whether to show the skills collection", ) def main(env_name, seed, times_repeat, add_noise, skill_collection): print("Loading Augmentor model...") diffaug_model_path = "/home/rgarciap/Remote/models/diffs2r_new/resnet_adam_lr_1e-3_lraug0.01_bs_64_L8/" diffaug_model_path = Path(diffaug_model_path) diffaug_cfg_path = diffaug_model_path / "config.yml" with open(str(diffaug_cfg_path), "rb") as f: diffaug_cfg = yaml.load(f, Loader=yaml.FullLoader) model_cfg = dict( name="diffaug", reg_output_size=3, aug_pipeline=diffaug_cfg["aug_pipeline"], multi=diffaug_cfg["multi_pipeline"], num_layers=diffaug_cfg["num_layers"], gumbel=diffaug_cfg["gumbel"], backbone_name=diffaug_cfg["backbone_name"], ) diffaug_model = build_model(model_cfg) diffaug_ckp_path = diffaug_model_path / "best_checkpoint.pth" checkpoint = torch.load(str(diffaug_ckp_path), map_location="cpu") diffaug_model.load_state_dict(checkpoint["model"]) augmentor = diffaug_model.augmentor augmentor.to("cpu") augmentor.eval() print("Model loaded") env = gym.make(env_name) scene = env.unwrapped.scene scene.renders(True) if skill_collection: scene.skill_data_collection = True env.seed(seed) for _ in range(times_repeat): obs = env.reset() agent = ScriptAgent(env) import matplotlib.pyplot as plt done = False i = 0 rate = Rate(scene.dt) action = agent.get_action() if add_noise: make_noised(action) frames = [] j = 0 while not done and action is not None: obs, reward, done, info = env.step(action) im = T.ToTensor()(obs["rgb0"]).unsqueeze(0) mask = torch.tensor(obs["mask0"]).unsqueeze(0) im, mask = augmentor((im, mask)) im = rearrange(im.detach().detach().squeeze(0).numpy(), "c h w -> h w c") im = Image.fromarray((im * 255).astype(np.uint8)) im.save(f"0/output{j}.jpeg") j += 1 action = agent.get_action() if add_noise and action is not None: make_noised(action) if action is None: info["failure_message"] = "End of Script." if not info["success"]: click.secho( "Failure Seed {}: {}".format(seed, info["failure_message"]), fg="red" ) print("Success", info["success"]) if __name__ == "__main__": main()

31.490566

107

0.641402

import itertools import types import numpy as np import torch import click import gym import time import yaml from robos2r.model import build_model from .agent import Agent from .script_agent import ScriptAgent, make_noised from .utils import Rate from PIL import Image from pathlib import Path from einops import rearrange from torchvision import transforms as T @click.command(help="script_agent env_name [options]") @click.argument("env_name", type=str) @click.option("-s", "--seed", default=0, help="seed") @click.option("-t", "--times-repeat", default=1, help="times to repeat the script") @click.option("-n", "--add-noise", is_flag=True, help="adding noise to actions or not") @click.option( "-sc", "--skill-collection/--no-skill-collection", is_flag=True, help="whether to show the skills collection", ) def main(env_name, seed, times_repeat, add_noise, skill_collection): print("Loading Augmentor model...") diffaug_model_path = "/home/rgarciap/Remote/models/diffs2r_new/resnet_adam_lr_1e-3_lraug0.01_bs_64_L8/" diffaug_model_path = Path(diffaug_model_path) diffaug_cfg_path = diffaug_model_path / "config.yml" with open(str(diffaug_cfg_path), "rb") as f: diffaug_cfg = yaml.load(f, Loader=yaml.FullLoader) model_cfg = dict( name="diffaug", reg_output_size=3, aug_pipeline=diffaug_cfg["aug_pipeline"], multi=diffaug_cfg["multi_pipeline"], num_layers=diffaug_cfg["num_layers"], gumbel=diffaug_cfg["gumbel"], backbone_name=diffaug_cfg["backbone_name"], ) diffaug_model = build_model(model_cfg) diffaug_ckp_path = diffaug_model_path / "best_checkpoint.pth" checkpoint = torch.load(str(diffaug_ckp_path), map_location="cpu") diffaug_model.load_state_dict(checkpoint["model"]) augmentor = diffaug_model.augmentor augmentor.to("cpu") augmentor.eval() print("Model loaded") env = gym.make(env_name) scene = env.unwrapped.scene scene.renders(True) if skill_collection: scene.skill_data_collection = True env.seed(seed) for _ in range(times_repeat): obs = env.reset() agent = ScriptAgent(env) import matplotlib.pyplot as plt done = False i = 0 rate = Rate(scene.dt) action = agent.get_action() if add_noise: make_noised(action) frames = [] j = 0 while not done and action is not None: obs, reward, done, info = env.step(action) im = T.ToTensor()(obs["rgb0"]).unsqueeze(0) mask = torch.tensor(obs["mask0"]).unsqueeze(0) im, mask = augmentor((im, mask)) im = rearrange(im.detach().detach().squeeze(0).numpy(), "c h w -> h w c") im = Image.fromarray((im * 255).astype(np.uint8)) im.save(f"0/output{j}.jpeg") j += 1 action = agent.get_action() if add_noise and action is not None: make_noised(action) if action is None: info["failure_message"] = "End of Script." if not info["success"]: click.secho( "Failure Seed {}: {}".format(seed, info["failure_message"]), fg="red" ) print("Success", info["success"]) if __name__ == "__main__": main()

true

f71771ce6064be5fc44ff24790cce8db6106923c

8,762

py

Python

benchmark_runner/common/clouds/shared/s3/s3_operations.py

kpouget/benchmark-runner

eecdb57d12f8c17268800632722af8fe8046185a

[ "Apache-2.0" ]

10

2021-07-21T21:44:20.000Z

2022-02-24T22:01:13.000Z

benchmark_runner/common/clouds/shared/s3/s3_operations.py

kpouget/benchmark-runner

eecdb57d12f8c17268800632722af8fe8046185a

[ "Apache-2.0" ]

83

2021-07-20T14:37:44.000Z

2022-03-24T13:48:04.000Z

benchmark_runner/common/clouds/shared/s3/s3_operations.py

kpouget/benchmark-runner

eecdb57d12f8c17268800632722af8fe8046185a

[ "Apache-2.0" ]

6

2021-07-14T21:12:48.000Z

2022-02-15T12:48:27.000Z

import os import boto3 import typeguard from botocore.exceptions import ClientError from os import listdir from os.path import isfile, join from benchmark_runner.common.clouds.shared.s3.s3_operations_exceptions import S3FileNotUploaded, S3FileNotDownloaded, S3FileNotDeleted, S3KeyNotCreated, S3FileNotExist, S3FailedCreatePresingedURL from benchmark_runner.main.environment_variables import environment_variables class S3Operations: """ This class is responsible for S3 operations """ def __init__(self, region_name: str = '', endpoint_url: str = None, aws_access_key_id: str = None, aws_secret_access_key: str = None): # environment variables self.__environment_variables_dict = environment_variables.environment_variables_dict # must add region for pytest if region_name: self.__region = region_name self.__endpoint_url = endpoint_url self.__aws_access_key_id = aws_access_key_id self.__aws_secret_access_key = aws_secret_access_key else: self.__region = self.__environment_variables_dict.get('region_name', '') # must be None for pytest self.__endpoint_url = self.__environment_variables_dict.get('endpoint_url', None) self.__aws_access_key_id = self.__environment_variables_dict.get('access_key_id', '') self.__aws_secret_access_key = self.__environment_variables_dict.get('secret_access_key', '') self.__s3_client = boto3.client(service_name='s3', region_name=self.__region, endpoint_url=self.__endpoint_url, aws_access_key_id=self.__aws_access_key_id, aws_secret_access_key=self.__aws_secret_access_key) @typeguard.typechecked def upload_file(self, file_name_path: str, bucket: str, key: str, upload_file: str): """ This method upload file to s3 :param file_name_path:'/home/user/test.txt' :param bucket:'benchmark' :param key:'test-data' :param upload_file:'test.txt' :return: """ try: self.__s3_client.upload_file(Filename=file_name_path, Bucket=bucket, Key=f'{key}/{upload_file}', ExtraArgs={'ServerSideEncryption': 'AES256'}) except ClientError: raise except Exception: raise S3FileNotUploaded @typeguard.typechecked def download_file(self, bucket: str, key: str, download_file: str, file_name_path: str): """ This method download file from s3 :param bucket:'benchmark' :param key:'logs/ec2-idle/2021/01/19/18' :param download_file: 'test.txt' :param file_name_path:'D:\\Performance\\Projects\\py-image-service\\data\\rt_results\\test.txt' :return: """ try: if download_file: self.__s3_client.download_file(Bucket=bucket, Key=f'{key}/{download_file}', Filename=file_name_path) else: self.__s3_client.download_file(Bucket=bucket, Key=key, Filename=file_name_path) except ClientError: raise except Exception: raise S3FileNotDownloaded @typeguard.typechecked def delete_file(self, bucket: str, key: str, file_name: str): """ This method delete file from s3 :param bucket:'benchmark' :param key:'test-data' :param file_name: 'test.txt' :return: """ try: self.__s3_client.delete_object(Bucket=bucket, Key=f'{key}/{file_name}') except ClientError: raise except Exception: raise S3FileNotDeleted @typeguard.typechecked def delete_folder(self, bucket: str, key: str): """ This method delete folder from s3 :param bucket:'benchmark' :param key:'framework/test' :return: """ try: objects_to_delete = self.__s3_client.list_objects(Bucket=bucket, Prefix=key) delete_keys = { 'Objects': [{'Key': k} for k in [obj['Key'] for obj in objects_to_delete.get('Contents', [])]]} if delete_keys['Objects']: self.__s3_client.delete_objects(Bucket=bucket, Delete=delete_keys) except ClientError: raise except Exception: raise S3FileNotDeleted @typeguard.typechecked def create_folder(self, bucket: str, key: str): """ This method download file from s3 :param bucket:'benchmark' :param key:'framework/test' :return: """ try: self.__s3_client.put_object(Bucket=bucket, Key=key) except ClientError: raise except Exception: raise S3KeyNotCreated @typeguard.typechecked def file_exist(self, bucket: str, key: str, file_name: str): """ This method check if file exist :param bucket:'benchmark' :param key:'framework/test' :param file_name:'file.txt' :return: """ try: response = self.__s3_client.list_objects_v2(Bucket=bucket, Prefix=key) if response.get('Contents'): for item in response['Contents']: if file_name in item['Key']: return True return False # Todo add custom error except ClientError: raise except Exception: raise S3FileNotExist @typeguard.typechecked def upload_objects(self, local_source: str, s3_target: str): """ This method upload local data folder to s3 target path :param local_source: local data folder i.e. '/home/user/' :param s3_target: target s3 path i.e. 'data_store/calc_image_data/' :return: """ try: if '/' in s3_target: targets = s3_target.split('/') bucket = targets[0] key = '/'.join(targets[1:]) else: bucket = s3_target key = '' files = [f for f in listdir(local_source) if isfile(join(local_source, f))] for file in files: filename = os.path.join(local_source, file) self.upload_file(file_name_path=filename, bucket=bucket, key=key, upload_file=file) except ClientError as err: raise except Exception: raise S3FileNotUploaded @typeguard.typechecked def download_objects(self, s3_target: str, local_source: str): """ This method download from s3 target to local data folder :param local_source: local data folder i.e. '/home/user/ :param s3_target: target s3 path i.e. 'data_store/calc_image_data/' :return: """ files = [] try: if '/' in s3_target: targets = s3_target.split('/') bucket = targets[0] key = '/'.join(targets[1:]) else: bucket = s3_target key = '' response = self.__s3_client.list_objects_v2(Bucket=bucket, Prefix=key) if response.get('Contents'): for item in response['Contents']: if item['Key'].split('/')[-1]: files.append(item['Key'].split('/')[-1]) else: files.append(item['Key']) for file in files: file_name = os.path.join(local_source, file) self.download_file(bucket=bucket, key=key, download_file=file, file_name_path=file_name) except ClientError as err: raise except Exception: raise S3FileNotDownloaded @typeguard.typechecked def generate_presigned_url(self, bucket: str, key: str, file_name: str): """ This method generate presigned url for specific uploaded object, default 7 days :param bucket:'benchmark' :param key:'logs/test-data' :param file_name:'file.txt' :return: """ try: return self.__s3_client.generate_presigned_url('get_object', Params={'Bucket': bucket, 'Key': f'{key}/{file_name}'}, ExpiresIn=604800) # Todo add custom error except ClientError: raise except Exception: raise S3FailedCreatePresingedURL

37.127119

195

0.57738

import os import boto3 import typeguard from botocore.exceptions import ClientError from os import listdir from os.path import isfile, join from benchmark_runner.common.clouds.shared.s3.s3_operations_exceptions import S3FileNotUploaded, S3FileNotDownloaded, S3FileNotDeleted, S3KeyNotCreated, S3FileNotExist, S3FailedCreatePresingedURL from benchmark_runner.main.environment_variables import environment_variables class S3Operations: def __init__(self, region_name: str = '', endpoint_url: str = None, aws_access_key_id: str = None, aws_secret_access_key: str = None): self.__environment_variables_dict = environment_variables.environment_variables_dict if region_name: self.__region = region_name self.__endpoint_url = endpoint_url self.__aws_access_key_id = aws_access_key_id self.__aws_secret_access_key = aws_secret_access_key else: self.__region = self.__environment_variables_dict.get('region_name', '') self.__endpoint_url = self.__environment_variables_dict.get('endpoint_url', None) self.__aws_access_key_id = self.__environment_variables_dict.get('access_key_id', '') self.__aws_secret_access_key = self.__environment_variables_dict.get('secret_access_key', '') self.__s3_client = boto3.client(service_name='s3', region_name=self.__region, endpoint_url=self.__endpoint_url, aws_access_key_id=self.__aws_access_key_id, aws_secret_access_key=self.__aws_secret_access_key) @typeguard.typechecked def upload_file(self, file_name_path: str, bucket: str, key: str, upload_file: str): try: self.__s3_client.upload_file(Filename=file_name_path, Bucket=bucket, Key=f'{key}/{upload_file}', ExtraArgs={'ServerSideEncryption': 'AES256'}) except ClientError: raise except Exception: raise S3FileNotUploaded @typeguard.typechecked def download_file(self, bucket: str, key: str, download_file: str, file_name_path: str): try: if download_file: self.__s3_client.download_file(Bucket=bucket, Key=f'{key}/{download_file}', Filename=file_name_path) else: self.__s3_client.download_file(Bucket=bucket, Key=key, Filename=file_name_path) except ClientError: raise except Exception: raise S3FileNotDownloaded @typeguard.typechecked def delete_file(self, bucket: str, key: str, file_name: str): try: self.__s3_client.delete_object(Bucket=bucket, Key=f'{key}/{file_name}') except ClientError: raise except Exception: raise S3FileNotDeleted @typeguard.typechecked def delete_folder(self, bucket: str, key: str): try: objects_to_delete = self.__s3_client.list_objects(Bucket=bucket, Prefix=key) delete_keys = { 'Objects': [{'Key': k} for k in [obj['Key'] for obj in objects_to_delete.get('Contents', [])]]} if delete_keys['Objects']: self.__s3_client.delete_objects(Bucket=bucket, Delete=delete_keys) except ClientError: raise except Exception: raise S3FileNotDeleted @typeguard.typechecked def create_folder(self, bucket: str, key: str): try: self.__s3_client.put_object(Bucket=bucket, Key=key) except ClientError: raise except Exception: raise S3KeyNotCreated @typeguard.typechecked def file_exist(self, bucket: str, key: str, file_name: str): try: response = self.__s3_client.list_objects_v2(Bucket=bucket, Prefix=key) if response.get('Contents'): for item in response['Contents']: if file_name in item['Key']: return True return False except ClientError: raise except Exception: raise S3FileNotExist @typeguard.typechecked def upload_objects(self, local_source: str, s3_target: str): try: if '/' in s3_target: targets = s3_target.split('/') bucket = targets[0] key = '/'.join(targets[1:]) else: bucket = s3_target key = '' files = [f for f in listdir(local_source) if isfile(join(local_source, f))] for file in files: filename = os.path.join(local_source, file) self.upload_file(file_name_path=filename, bucket=bucket, key=key, upload_file=file) except ClientError as err: raise except Exception: raise S3FileNotUploaded @typeguard.typechecked def download_objects(self, s3_target: str, local_source: str): files = [] try: if '/' in s3_target: targets = s3_target.split('/') bucket = targets[0] key = '/'.join(targets[1:]) else: bucket = s3_target key = '' response = self.__s3_client.list_objects_v2(Bucket=bucket, Prefix=key) if response.get('Contents'): for item in response['Contents']: if item['Key'].split('/')[-1]: files.append(item['Key'].split('/')[-1]) else: files.append(item['Key']) for file in files: file_name = os.path.join(local_source, file) self.download_file(bucket=bucket, key=key, download_file=file, file_name_path=file_name) except ClientError as err: raise except Exception: raise S3FileNotDownloaded @typeguard.typechecked def generate_presigned_url(self, bucket: str, key: str, file_name: str): try: return self.__s3_client.generate_presigned_url('get_object', Params={'Bucket': bucket, 'Key': f'{key}/{file_name}'}, ExpiresIn=604800) except ClientError: raise except Exception: raise S3FailedCreatePresingedURL

true

f71772584a21b6b069b86c34169c1b8debf5cb25

476

py

Python

erinn/python/models/__init__.py

swcjack6931677/ERINN

a4f3d0ad213515bc86e2a18575537d6affd472ac

[ "MIT" ]

null

erinn/python/models/__init__.py

swcjack6931677/ERINN

a4f3d0ad213515bc86e2a18575537d6affd472ac

[ "MIT" ]

null

erinn/python/models/__init__.py

swcjack6931677/ERINN

a4f3d0ad213515bc86e2a18575537d6affd472ac

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import absolute_import from __future__ import division from __future__ import print_function from . import CNN from . import CNN1D from . import CNN1D_Rx from . import CNN1D_Tx from . import DFN # Globally-importable models. from .CNN import get_cnn_relu from .CNN1D import get_cnn1d_relu from .CNN1D_Rx import get_cnn1d_rx_relu from .CNN1D_Tx import get_cnn1d_tx from .CNN1D_Tx import get_cnn1d_tx_relu from .DFN import get_dfn_relu

25.052632

39

0.813025

from __future__ import absolute_import from __future__ import division from __future__ import print_function from . import CNN from . import CNN1D from . import CNN1D_Rx from . import CNN1D_Tx from . import DFN from .CNN import get_cnn_relu from .CNN1D import get_cnn1d_relu from .CNN1D_Rx import get_cnn1d_rx_relu from .CNN1D_Tx import get_cnn1d_tx from .CNN1D_Tx import get_cnn1d_tx_relu from .DFN import get_dfn_relu

true

f717729d6712f5bbd8b1c9f44dde5e6c1bc8107e

411

py

Python

server/config.py

ahnaf-zamil/flask-react-session-authenticaton-tutorial

88c454af9932435d0bd9ad1c16718beb6fc0e1c1

[ "MIT" ]

1

2021-11-01T10:46:16.000Z

server/config.py

ahnaf-zamil/flask-react-session-authenticaton-tutorial

88c454af9932435d0bd9ad1c16718beb6fc0e1c1

[ "MIT" ]

null

server/config.py

ahnaf-zamil/flask-react-session-authenticaton-tutorial

88c454af9932435d0bd9ad1c16718beb6fc0e1c1

[ "MIT" ]

null

from dotenv import load_dotenv import os import redis load_dotenv() class ApplicationConfig: SECRET_KEY = os.environ["SECRET_KEY"] SQLALCHEMY_TRACK_MODIFICATIONS = False SQLALCHEMY_ECHO = True SQLALCHEMY_DATABASE_URI = r"sqlite:///./db.sqlite" SESSION_TYPE = "redis" SESSION_PERMANENT = False SESSION_USE_SIGNER = True SESSION_REDIS = redis.from_url("redis://127.0.0.1:6379")

24.176471

60

0.734793

from dotenv import load_dotenv import os import redis load_dotenv() class ApplicationConfig: SECRET_KEY = os.environ["SECRET_KEY"] SQLALCHEMY_TRACK_MODIFICATIONS = False SQLALCHEMY_ECHO = True SQLALCHEMY_DATABASE_URI = r"sqlite:///./db.sqlite" SESSION_TYPE = "redis" SESSION_PERMANENT = False SESSION_USE_SIGNER = True SESSION_REDIS = redis.from_url("redis://127.0.0.1:6379")

true

f71772cc3a746eb0432c3f9d0877b4f980c06a5f

1,344

py

Python

Embed.py

zhengxiawu/Transformer

8cad013913254ea4e06c4a8d460d9f2cf42df086

[ "Apache-2.0" ]

null

Embed.py

zhengxiawu/Transformer

8cad013913254ea4e06c4a8d460d9f2cf42df086

[ "Apache-2.0" ]

null

Embed.py

zhengxiawu/Transformer

8cad013913254ea4e06c4a8d460d9f2cf42df086

[ "Apache-2.0" ]

null

import torch import torch.nn as nn import math from torch.autograd import Variable class Embedder(nn.Module): def __init__(self, vocab_size, d_model): super().__init__() self.d_model = d_model self.embed = nn.Embedding(vocab_size, d_model) def forward(self, x): return self.embed(x) class PositionalEncoder(nn.Module): def __init__(self, d_model, max_seq_len=200, dropout=0.1): super().__init__() self.d_model = d_model self.dropout = nn.Dropout(dropout) # create constant 'pe' matrix with values dependant on # pos and i pe = torch.zeros(max_seq_len, d_model) for pos in range(max_seq_len): for i in range(0, d_model, 2): pe[pos, i] = \ math.sin(pos / (10000 ** ((2 * i)/d_model))) pe[pos, i + 1] = \ math.cos(pos / (10000 ** ((2 * (i + 1))/d_model))) pe = pe.unsqueeze(0) self.register_buffer('pe', pe) def forward(self, x): # make embeddings relatively larger x = x * math.sqrt(self.d_model) # add constant to embedding seq_len = x.size(1) pe = Variable(self.pe[:, :seq_len], requires_grad=False) if x.is_cuda: pe.cuda() x = x + pe return self.dropout(x)

30.545455

70

0.563244

import torch import torch.nn as nn import math from torch.autograd import Variable class Embedder(nn.Module): def __init__(self, vocab_size, d_model): super().__init__() self.d_model = d_model self.embed = nn.Embedding(vocab_size, d_model) def forward(self, x): return self.embed(x) class PositionalEncoder(nn.Module): def __init__(self, d_model, max_seq_len=200, dropout=0.1): super().__init__() self.d_model = d_model self.dropout = nn.Dropout(dropout) pe = torch.zeros(max_seq_len, d_model) for pos in range(max_seq_len): for i in range(0, d_model, 2): pe[pos, i] = \ math.sin(pos / (10000 ** ((2 * i)/d_model))) pe[pos, i + 1] = \ math.cos(pos / (10000 ** ((2 * (i + 1))/d_model))) pe = pe.unsqueeze(0) self.register_buffer('pe', pe) def forward(self, x): x = x * math.sqrt(self.d_model) seq_len = x.size(1) pe = Variable(self.pe[:, :seq_len], requires_grad=False) if x.is_cuda: pe.cuda() x = x + pe return self.dropout(x)

true

f71772d3409ea5f7a2383df7d2159cca0dc08add

8,729

py

Python

rl/train_a2c_mc.py

ds4dm/GraphRL

b5b1519f6dd92b401625d51add9ae5829004a30b

[ "MIT" ]

2

2021-02-26T18:51:01.000Z

2021-07-12T05:20:18.000Z

rl/train_a2c_mc.py

pandat8/GraphRL

b5b1519f6dd92b401625d51add9ae5829004a30b

[ "MIT" ]

3

2019-05-09T20:59:10.000Z

2020-05-13T14:03:50.000Z

rl/train_a2c_mc.py

pandat8/GraphRL

b5b1519f6dd92b401625d51add9ae5829004a30b

[ "MIT" ]

3

2018-08-13T20:43:29.000Z

2020-05-13T14:00:57.000Z

import torch import torch.optim as optm import torch.nn.functional as F import numpy as np from torch.autograd import Variable from torch.utils.data import Dataset, DataLoader from data.graph import Graph from collections import namedtuple SavedAction = namedtuple('SavedAction', ['log_prob', 'value_current']) # Mont Carlo methods class TrainModel_MC: def __init__(self, model, train_dataset, val_dataset, max_grad_norm=2, use_cuda=False): self.model = model self.train_dataset = train_dataset self.val_dataset = val_dataset self.max_grad_norm = max_grad_norm self.use_cuda = use_cuda self.train_loader = DataLoader(train_dataset, shuffle=True, num_workers=1, batch_size=1, collate_fn=lambda x: x) self.val_loader = DataLoader(val_dataset, shuffle=True, num_workers=1, batch_size=1, collate_fn=lambda x: x) self.epochs = 0 self.beta = 0.9 self.eps = np.finfo(np.float32).eps.item() def train_and_validate(self, n_epochs, lr_actor, lr_critic, gamma=0.99, use_critic=True): self.actor_optim = optm.Adam(self.model.actor.parameters(), lr=lr_actor) print(use_critic) if use_critic: self.critic_optim = optm.Adam(self.model.critic.parameters(), lr=lr_critic) self.critic_loss_criterion = torch.nn.MSELoss() else: baseline = torch.zeros(1) if self.use_cuda: baseline = baseline.cuda() for epoch in range(1): n_graphs_proceed = 0 for X in self.train_loader: for x in X: self.model.train() ratio_gcn2mind = [] ratio_gcn2rand = [] for epoch in range(n_epochs): rewards_mindegree = 0 # number of added edges rewards_random = 0 x_mind = Graph(x.M) x_rand = Graph(x.M) x_rl = Graph(x.M) # loop for training while eliminating a graph iteratively for i in range(x.n - 2): # baseline1: compute return of min degree if i % 100 == 0: print('iterations {}'.format(i)) node_mind, d_min = x_mind.min_degree(x_mind.M) rewards_mindegree += x_mind.eliminate_node(node_mind, reduce=True) # baseline2: compute return of random rewards_random += x_rand.eliminate_node(np.random.randint(low=0, high=x_rand.n), reduce=True) # call actor-critic model action, log_prob, reward, value_current, value_next, x_rl = self.model(x_rl) # forward propagation,action: node selected, reward: nb edges added self.model.rewards.append(reward) self.model.actions.append(action) self.model.saved_actions.append(SavedAction(log_prob, value_current)) R = 0 actor_losses = [] critic_losses = [] returns = [] # compute sampled return for each step for r in self.model.rewards[::-1]: R = r + gamma * R returns.insert(0, R) returns = torch.tensor(returns) returns = (returns - returns.mean()) / (returns.std() + self.eps) saved_actions = self.model.saved_actions # compute cummulated loss of actor and critic of one graph for (log_prob, value_current), R in zip(saved_actions, returns): if use_critic: advantage = R - value_current critic_losses.append(-value_current* advantage) # critic_losses.append(self.critic_loss_criterion(value_current, torch.Tensor([R.detach()]))) else: advantage = R - baseline actor_losses.append(log_prob * advantage.detach()) # the return here is discounted nb of added edges, # hence, it actually represents loss # step update of actor self.actor_optim.zero_grad() actor_loss = torch.stack(actor_losses).sum() actor_loss.backward(retain_graph=True) self.actor_optim.step() # step update of critic if use_critic: self.critic_optim.zero_grad() critic_closs = torch.stack(critic_losses).sum() critic_closs.backward() self.critic_optim.step() else: baseline = baseline.detach() rewards_gcn = sum(self.model.rewards) _ratio_gcn2mind = rewards_gcn / rewards_mindegree _ratio_gcn2rand = rewards_gcn / rewards_random print('graph {:04d}'.format(n_graphs_proceed), 'epoch {:04d}'.format(epoch), 'gcn2mind ratio {}'.format(_ratio_gcn2mind), 'value {}'.format(saved_actions[0].value_current), 'R {}'.format(returns[0])) print('graph {:04d}'.format(n_graphs_proceed), 'epoch {:04d}'.format(epoch), 'gcn2rand ratio {}'.format(_ratio_gcn2rand)) ratio_gcn2mind.append(_ratio_gcn2mind) ratio_gcn2rand.append(_ratio_gcn2rand) del self.model.rewards[:] del self.model.actions[:] del self.model.saved_actions[:] ratio_gcn2mind = np.array(ratio_gcn2mind).reshape(-1) ratio_gcn2rand = np.array(ratio_gcn2rand).reshape(-1) min_ratio_gcn2mind = np.min(ratio_gcn2mind) max_ratio_gcn2mind = np.max(ratio_gcn2mind) av_ratio_gcn2mind = np.sum(ratio_gcn2mind)/ n_epochs min_ratio_gcn2rand = np.min(ratio_gcn2rand) max_ratio_gcn2rand = np.max(ratio_gcn2rand) av_ratio_gcn2rand = np.sum(ratio_gcn2rand) / n_epochs print('graph {:04d}'.format(n_graphs_proceed), 'gcn2mind{:04d}', 'min_ratio {}'.format(min_ratio_gcn2mind), 'max_ratio {}'.format(max_ratio_gcn2mind), 'av_ratio {}'.format(av_ratio_gcn2mind)) print('graph {:04d}'.format(n_graphs_proceed), 'gcn2rand{:04d}', 'min_ratio {}'.format(min_ratio_gcn2rand), 'max_ratio {}'.format(max_ratio_gcn2rand), 'av_ratio {}'.format(av_ratio_gcn2rand), 'nb graph proceeded {}'.format(n_graphs_proceed)) n_graphs_proceed += len(X) # ratio_gcn2mind = np.array(ratio_gcn2mind).reshape(-1) # ratio_gcn2rand = np.array(ratio_gcn2rand).reshape(-1) # # total_ratio_gcn2mind = np.sum(ratio_gcn2mind) # total_ratio_gcn2rand = np.sum(ratio_gcn2rand) # # min_ratio_gcn2mind = np.min(ratio_gcn2mind) # max_ratio_gcn2mind = np.max(ratio_gcn2mind) # av_ratio_gcn2mind = total_ratio_gcn2mind / n_graphs_proceed # # min_ratio_gcn2rand = np.min(ratio_gcn2rand) # max_ratio_gcn2rand = np.max(ratio_gcn2rand) # av_ratio_gcn2rand = total_ratio_gcn2rand / n_graphs_proceed # # print('epoch {:04d}'.format(epoch), 'gcn2mind{:04d}', # 'min_ratio {}'.format(min_ratio_gcn2mind), # 'max_ratio {}'.format(max_ratio_gcn2mind), # 'av_ratio {}'.format(av_ratio_gcn2mind)) # print('epoch {:04d}'.format(epoch), 'gcn2rand{:04d}', # 'min_ratio {}'.format(min_ratio_gcn2rand), # 'max_ratio {}'.format(max_ratio_gcn2rand), # 'av_ratio {}'.format(av_ratio_gcn2rand), # 'nb graph proceeded {}'.format(n_graphs_proceed))

47.961538

172

0.520907

import torch import torch.optim as optm import torch.nn.functional as F import numpy as np from torch.autograd import Variable from torch.utils.data import Dataset, DataLoader from data.graph import Graph from collections import namedtuple SavedAction = namedtuple('SavedAction', ['log_prob', 'value_current']) class TrainModel_MC: def __init__(self, model, train_dataset, val_dataset, max_grad_norm=2, use_cuda=False): self.model = model self.train_dataset = train_dataset self.val_dataset = val_dataset self.max_grad_norm = max_grad_norm self.use_cuda = use_cuda self.train_loader = DataLoader(train_dataset, shuffle=True, num_workers=1, batch_size=1, collate_fn=lambda x: x) self.val_loader = DataLoader(val_dataset, shuffle=True, num_workers=1, batch_size=1, collate_fn=lambda x: x) self.epochs = 0 self.beta = 0.9 self.eps = np.finfo(np.float32).eps.item() def train_and_validate(self, n_epochs, lr_actor, lr_critic, gamma=0.99, use_critic=True): self.actor_optim = optm.Adam(self.model.actor.parameters(), lr=lr_actor) print(use_critic) if use_critic: self.critic_optim = optm.Adam(self.model.critic.parameters(), lr=lr_critic) self.critic_loss_criterion = torch.nn.MSELoss() else: baseline = torch.zeros(1) if self.use_cuda: baseline = baseline.cuda() for epoch in range(1): n_graphs_proceed = 0 for X in self.train_loader: for x in X: self.model.train() ratio_gcn2mind = [] ratio_gcn2rand = [] for epoch in range(n_epochs): rewards_mindegree = 0 rewards_random = 0 x_mind = Graph(x.M) x_rand = Graph(x.M) x_rl = Graph(x.M) for i in range(x.n - 2): if i % 100 == 0: print('iterations {}'.format(i)) node_mind, d_min = x_mind.min_degree(x_mind.M) rewards_mindegree += x_mind.eliminate_node(node_mind, reduce=True) rewards_random += x_rand.eliminate_node(np.random.randint(low=0, high=x_rand.n), reduce=True) action, log_prob, reward, value_current, value_next, x_rl = self.model(x_rl) self.model.rewards.append(reward) self.model.actions.append(action) self.model.saved_actions.append(SavedAction(log_prob, value_current)) R = 0 actor_losses = [] critic_losses = [] returns = [] for r in self.model.rewards[::-1]: R = r + gamma * R returns.insert(0, R) returns = torch.tensor(returns) returns = (returns - returns.mean()) / (returns.std() + self.eps) saved_actions = self.model.saved_actions for (log_prob, value_current), R in zip(saved_actions, returns): if use_critic: advantage = R - value_current critic_losses.append(-value_current* advantage) else: advantage = R - baseline actor_losses.append(log_prob * advantage.detach()) self.actor_optim.zero_grad() actor_loss = torch.stack(actor_losses).sum() actor_loss.backward(retain_graph=True) self.actor_optim.step() if use_critic: self.critic_optim.zero_grad() critic_closs = torch.stack(critic_losses).sum() critic_closs.backward() self.critic_optim.step() else: baseline = baseline.detach() rewards_gcn = sum(self.model.rewards) _ratio_gcn2mind = rewards_gcn / rewards_mindegree _ratio_gcn2rand = rewards_gcn / rewards_random print('graph {:04d}'.format(n_graphs_proceed), 'epoch {:04d}'.format(epoch), 'gcn2mind ratio {}'.format(_ratio_gcn2mind), 'value {}'.format(saved_actions[0].value_current), 'R {}'.format(returns[0])) print('graph {:04d}'.format(n_graphs_proceed), 'epoch {:04d}'.format(epoch), 'gcn2rand ratio {}'.format(_ratio_gcn2rand)) ratio_gcn2mind.append(_ratio_gcn2mind) ratio_gcn2rand.append(_ratio_gcn2rand) del self.model.rewards[:] del self.model.actions[:] del self.model.saved_actions[:] ratio_gcn2mind = np.array(ratio_gcn2mind).reshape(-1) ratio_gcn2rand = np.array(ratio_gcn2rand).reshape(-1) min_ratio_gcn2mind = np.min(ratio_gcn2mind) max_ratio_gcn2mind = np.max(ratio_gcn2mind) av_ratio_gcn2mind = np.sum(ratio_gcn2mind)/ n_epochs min_ratio_gcn2rand = np.min(ratio_gcn2rand) max_ratio_gcn2rand = np.max(ratio_gcn2rand) av_ratio_gcn2rand = np.sum(ratio_gcn2rand) / n_epochs print('graph {:04d}'.format(n_graphs_proceed), 'gcn2mind{:04d}', 'min_ratio {}'.format(min_ratio_gcn2mind), 'max_ratio {}'.format(max_ratio_gcn2mind), 'av_ratio {}'.format(av_ratio_gcn2mind)) print('graph {:04d}'.format(n_graphs_proceed), 'gcn2rand{:04d}', 'min_ratio {}'.format(min_ratio_gcn2rand), 'max_ratio {}'.format(max_ratio_gcn2rand), 'av_ratio {}'.format(av_ratio_gcn2rand), 'nb graph proceeded {}'.format(n_graphs_proceed)) n_graphs_proceed += len(X)

true

f71774d3bb18a2b3e70569ab8a10dcf140e26e81

5,989

py

Python

submit_data.py

dimagi/submission_api_example

266eb36c6ef6b331ea894298cbacfd2752410f80

[ "Apache-2.0" ]

null

submit_data.py

dimagi/submission_api_example

266eb36c6ef6b331ea894298cbacfd2752410f80

[ "Apache-2.0" ]

1

2021-12-06T20:29:54.000Z

2021-12-13T20:32:37.000Z

submit_data.py

dimagi/submission_api_example

266eb36c6ef6b331ea894298cbacfd2752410f80

[ "Apache-2.0" ]

1

2021-12-06T20:27:00.000Z

#!/usr/bin/env python3 """ An example script to send data to CommCare using the Submission API Usage: $ export CCHQ_PROJECT_SPACE=my-project-space $ export CCHQ_CASE_TYPE=person $ export CCHQ_USERNAME=user@example.com $ export CCHQ_PASSWORD=MijByG_se3EcKr.t $ export CCHQ_USER_ID=c0ffeeeeeb574eb8b5d5036c9a61a483 $ export CCHQ_OWNER_ID=c0ffeeeee1e34b12bb5da0dc838e8406 $ ./submit_data.py sample_data.csv """ # (Optional) Configure the following settings with your values # An XML namespace to identify your XForm submission FORM_XMLNS = 'http://example.com/submission-api-example-form/' # A string to identify the origin of your data DEVICE_ID = "submission_api_example" # End of configurable settings import csv import os import sys import uuid from dataclasses import dataclass from datetime import datetime, timezone from http.client import responses as http_responses from typing import Any, Iterable, List, Optional, Tuple from xml.etree import ElementTree as ET import requests from jinja2 import Template COMMCARE_URL = 'https://www.commcarehq.org/' @dataclass class CaseProperty: name: str value: Any @dataclass class Case: id: str # A UUID. Generated if not given in the data. name: str # Required type: str # A name for the case type. e.g. "person" or "site" modified_on: str # Generated if not given. e.g. "2020-06-08T18:41:33.207Z" owner_id: str # ID of the user or location that cases must be assigned to properties: List[CaseProperty] # All other given data server_modified_on: Optional[str] def main(filename): """ Sends data to CommCare HQ using the Submission API. """ data = get_data(filename) cases = as_cases(data) xform_str = render_xform(cases) success, message = submit_xform(xform_str) return success, message def get_data(csv_filename) -> Iterable[dict]: """ Reads data in CSV format from the given filename, and yields it as dictionaries. """ with open(csv_filename) as csv_file: reader = csv.DictReader(csv_file) yield from reader def as_cases(data: Iterable[dict]) -> Iterable[Case]: """ Casts dictionaries as Case instances """ reserved = ('id', 'name', 'case_type', 'modified_on', 'server_modified_on') for dict_ in data: properties = [CaseProperty(name=key, value=value) for key, value in dict_.items() if key not in reserved] yield Case( id=dict_.get('id', str(uuid.uuid4())), name=dict_['name'], type=os.environ['CCHQ_CASE_TYPE'], modified_on=dict_.get('modified_on', now_utc()), owner_id=os.environ['CCHQ_OWNER_ID'], server_modified_on=dict_.get('server_modified_on'), properties=properties, ) def render_xform(cases: Iterable[Case]) -> str: context = { 'form_xmlns': FORM_XMLNS, 'device_id': DEVICE_ID, 'now_utc': now_utc(), 'cchq_username': os.environ['CCHQ_USERNAME'], 'cchq_user_id': os.environ['CCHQ_USER_ID'], 'submission_id': uuid.uuid4().hex, 'cases': list(cases), } with open('xform.xml.j2') as template_file: template = Template(template_file.read()) xform = template.render(**context) return xform def submit_xform(xform: str) -> Tuple[bool, str]: """ Submits the given XForm to CommCare. Returns (True, success_message) on success, or (False, failure_message) on failure. """ url = join_url(COMMCARE_URL, f'/a/{os.environ["CCHQ_PROJECT_SPACE"]}/receiver/api/') auth = (os.environ['CCHQ_USERNAME'], os.environ['CCHQ_PASSWORD']) headers = {'Content-Type': 'text/html; charset=UTF-8'} response = requests.post(url, xform.encode('utf-8'), headers=headers, auth=auth) if not 200 <= response.status_code < 300: return False, http_responses[response.status_code] return parse_response(response.text) def parse_response(text: str) -> Tuple[bool, str]: """ Parses a CommCare HQ Submission API response. Returns (True, success_message) on success, or (False, failure_message) on failure. >>> text = ''' ... <OpenRosaResponse xmlns="http://openrosa.org/http/response"> ... <message nature="submit_success"> √ </message> ... </OpenRosaResponse> ... ''' >>> parse_response(text) (True, ' √ ') """ xml = ET.XML(text) message = xml.find('{http://openrosa.org/http/response}message') success = message.attrib['nature'] == 'submit_success' return success, message.text def join_url(base_url: str, endpoint: str) -> str: """ Returns ``base_url`` + ``endpoint`` with the right forward slashes. >>> join_url('https://example.com/', '/api/foo') 'https://example.com/api/foo' >>> join_url('https://example.com', 'api/foo') 'https://example.com/api/foo' """ return '/'.join((base_url.rstrip('/'), endpoint.lstrip('/'))) def now_utc() -> str: """ Returns a UTC timestamp in ISO-8601 format with the offset as "Z". e.g. "2020-06-08T18:41:33.207Z" """ now = datetime.now(tz=timezone.utc) now_iso = now.isoformat(timespec='milliseconds') now_iso_z = now_iso.replace('+00:00', 'Z') return now_iso_z def missing_env_vars(): env_vars = ( 'CCHQ_PROJECT_SPACE', 'CCHQ_CASE_TYPE', 'CCHQ_USERNAME', 'CCHQ_PASSWORD', 'CCHQ_USER_ID', 'CCHQ_OWNER_ID', ) return [env_var for env_var in env_vars if env_var not in os.environ] if __name__ == '__main__': if len(sys.argv) != 2: print(__doc__) sys.exit() if missing := missing_env_vars(): print('Missing environment variables:', ', '.join(missing)) sys.exit(1) success, message = main(sys.argv[1]) print(message) if not success: sys.exit(1)

29.357843

79

0.646185

FORM_XMLNS = 'http://example.com/submission-api-example-form/' DEVICE_ID = "submission_api_example" import csv import os import sys import uuid from dataclasses import dataclass from datetime import datetime, timezone from http.client import responses as http_responses from typing import Any, Iterable, List, Optional, Tuple from xml.etree import ElementTree as ET import requests from jinja2 import Template COMMCARE_URL = 'https://www.commcarehq.org/' @dataclass class CaseProperty: name: str value: Any @dataclass class Case: id: str name: str type: str modified_on: str owner_id: str properties: List[CaseProperty] server_modified_on: Optional[str] def main(filename): data = get_data(filename) cases = as_cases(data) xform_str = render_xform(cases) success, message = submit_xform(xform_str) return success, message def get_data(csv_filename) -> Iterable[dict]: with open(csv_filename) as csv_file: reader = csv.DictReader(csv_file) yield from reader def as_cases(data: Iterable[dict]) -> Iterable[Case]: reserved = ('id', 'name', 'case_type', 'modified_on', 'server_modified_on') for dict_ in data: properties = [CaseProperty(name=key, value=value) for key, value in dict_.items() if key not in reserved] yield Case( id=dict_.get('id', str(uuid.uuid4())), name=dict_['name'], type=os.environ['CCHQ_CASE_TYPE'], modified_on=dict_.get('modified_on', now_utc()), owner_id=os.environ['CCHQ_OWNER_ID'], server_modified_on=dict_.get('server_modified_on'), properties=properties, ) def render_xform(cases: Iterable[Case]) -> str: context = { 'form_xmlns': FORM_XMLNS, 'device_id': DEVICE_ID, 'now_utc': now_utc(), 'cchq_username': os.environ['CCHQ_USERNAME'], 'cchq_user_id': os.environ['CCHQ_USER_ID'], 'submission_id': uuid.uuid4().hex, 'cases': list(cases), } with open('xform.xml.j2') as template_file: template = Template(template_file.read()) xform = template.render(**context) return xform def submit_xform(xform: str) -> Tuple[bool, str]: url = join_url(COMMCARE_URL, f'/a/{os.environ["CCHQ_PROJECT_SPACE"]}/receiver/api/') auth = (os.environ['CCHQ_USERNAME'], os.environ['CCHQ_PASSWORD']) headers = {'Content-Type': 'text/html; charset=UTF-8'} response = requests.post(url, xform.encode('utf-8'), headers=headers, auth=auth) if not 200 <= response.status_code < 300: return False, http_responses[response.status_code] return parse_response(response.text) def parse_response(text: str) -> Tuple[bool, str]: xml = ET.XML(text) message = xml.find('{http://openrosa.org/http/response}message') success = message.attrib['nature'] == 'submit_success' return success, message.text def join_url(base_url: str, endpoint: str) -> str: return '/'.join((base_url.rstrip('/'), endpoint.lstrip('/'))) def now_utc() -> str: now = datetime.now(tz=timezone.utc) now_iso = now.isoformat(timespec='milliseconds') now_iso_z = now_iso.replace('+00:00', 'Z') return now_iso_z def missing_env_vars(): env_vars = ( 'CCHQ_PROJECT_SPACE', 'CCHQ_CASE_TYPE', 'CCHQ_USERNAME', 'CCHQ_PASSWORD', 'CCHQ_USER_ID', 'CCHQ_OWNER_ID', ) return [env_var for env_var in env_vars if env_var not in os.environ] if __name__ == '__main__': if len(sys.argv) != 2: print(__doc__) sys.exit() if missing := missing_env_vars(): print('Missing environment variables:', ', '.join(missing)) sys.exit(1) success, message = main(sys.argv[1]) print(message) if not success: sys.exit(1)

true

f717755be13370d96e8eff2b66e83a3b18716be8

515

py

Python

setup.py

Krozark/meteofrance-py

7328a857022f263d1609c939851f612c5ed13d08

[ "MIT" ]

null

setup.py

Krozark/meteofrance-py

7328a857022f263d1609c939851f612c5ed13d08

[ "MIT" ]

null

setup.py

Krozark/meteofrance-py

7328a857022f263d1609c939851f612c5ed13d08

[ "MIT" ]

null

from setuptools import setup setup( name='krozark-meteofrance', version='0.3.9', description = 'Meteo-France weather forecast', author = 'victorcerutti', author_email = 'maxime.barbier1991+meteofrance@gmail.com', url = 'https://github.com/Krozark/meteofrance-py', packages=['meteofrance',], install_requires=[ 'requests', 'beautifulsoup4', 'pytz' ], license='MIT', long_description='Extract Meteo-France current weather and 1 hour rain forecast', )

27.105263

85

0.664078

from setuptools import setup setup( name='krozark-meteofrance', version='0.3.9', description = 'Meteo-France weather forecast', author = 'victorcerutti', author_email = 'maxime.barbier1991+meteofrance@gmail.com', url = 'https://github.com/Krozark/meteofrance-py', packages=['meteofrance',], install_requires=[ 'requests', 'beautifulsoup4', 'pytz' ], license='MIT', long_description='Extract Meteo-France current weather and 1 hour rain forecast', )

true

f7177676b64b016a2006776e619b093446b0ff41

5,353

py

Python

test/language/choice_types/python/UInt64ParamChoiceTest.py

PeachOS/zserio

ea01f6906c125a6baab7e8ed865eeb08cd46c37c

[ "BSD-3-Clause" ]

2

2019-02-06T17:50:24.000Z

2019-11-20T16:51:34.000Z

test/language/choice_types/python/UInt64ParamChoiceTest.py

PeachOS/zserio

ea01f6906c125a6baab7e8ed865eeb08cd46c37c

[ "BSD-3-Clause" ]

1

2019-11-25T16:25:51.000Z

2019-11-25T18:09:39.000Z

test/language/choice_types/python/UInt64ParamChoiceTest.py

PeachOS/zserio

ea01f6906c125a6baab7e8ed865eeb08cd46c37c

[ "BSD-3-Clause" ]

null

import unittest import zserio from testutils import getZserioApi class UInt64ParamChoiceTest(unittest.TestCase): @classmethod def setUpClass(cls): cls.api = getZserioApi(__file__, "choice_types.zs").uint64_param_choice def testSelectorConstructor(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) self.assertEqual(self.VARIANT_A_SELECTOR, uint64ParamChoice.getSelector()) def testFromReader(self): selector = self.VARIANT_B_SELECTOR value = 234 writer = zserio.BitStreamWriter() UInt64ParamChoiceTest._writeUInt64ParamChoiceToStream(writer, selector, value) reader = zserio.BitStreamReader(writer.getByteArray()) uint64ParamChoice = self.api.UInt64ParamChoice.fromReader(reader, selector) self.assertEqual(selector, uint64ParamChoice.getSelector()) self.assertEqual(value, uint64ParamChoice.getB()) def testEq(self): uint64ParamChoice1 = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) uint64ParamChoice2 = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) self.assertTrue(uint64ParamChoice1 == uint64ParamChoice2) value = 99 uint64ParamChoice1.setA(value) self.assertFalse(uint64ParamChoice1 == uint64ParamChoice2) uint64ParamChoice2.setA(value) self.assertTrue(uint64ParamChoice1 == uint64ParamChoice2) diffValue = value + 1 uint64ParamChoice2.setA(diffValue) self.assertFalse(uint64ParamChoice1 == uint64ParamChoice2) def testHash(self): uint64ParamChoice1 = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) uint64ParamChoice2 = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) self.assertEqual(hash(uint64ParamChoice1), hash(uint64ParamChoice2)) value = 99 uint64ParamChoice1.setA(value) self.assertTrue(hash(uint64ParamChoice1) != hash(uint64ParamChoice2)) uint64ParamChoice2.setA(value) self.assertEqual(hash(uint64ParamChoice1), hash(uint64ParamChoice2)) diffValue = value + 1 uint64ParamChoice2.setA(diffValue) self.assertTrue(hash(uint64ParamChoice1) != hash(uint64ParamChoice2)) def testGetSelector(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_C_SELECTOR) self.assertEqual(self.VARIANT_C_SELECTOR, uint64ParamChoice.getSelector()) def testGetSetA(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) value = 99 uint64ParamChoice.setA(value) self.assertEqual(value, uint64ParamChoice.getA()) def testGetSetB(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) value = 234 uint64ParamChoice.setB(value) self.assertEqual(value, uint64ParamChoice.getB()) def testGetSetC(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_C_SELECTOR) value = 23456 uint64ParamChoice.setC(value) self.assertEqual(value, uint64ParamChoice.getC()) def testBitSizeOf(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) self.assertEqual(8, uint64ParamChoice.bitSizeOf()) uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) self.assertEqual(16, uint64ParamChoice.bitSizeOf()) def testInitializeOffsets(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) bitPosition = 1 self.assertEqual(9, uint64ParamChoice.initializeOffsets(bitPosition)) uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) self.assertEqual(17, uint64ParamChoice.initializeOffsets(bitPosition)) def testReadWrite(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) byteValue = 99 uint64ParamChoice.setA(byteValue) writer = zserio.BitStreamWriter() uint64ParamChoice.write(writer) readUInt64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) reader = zserio.BitStreamReader(writer.getByteArray()) readUInt64ParamChoice.read(reader) self.assertEqual(byteValue, readUInt64ParamChoice.getA()) self.assertEqual(uint64ParamChoice, readUInt64ParamChoice) uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) shortValue = 234 uint64ParamChoice.setB(shortValue) writer = zserio.BitStreamWriter() uint64ParamChoice.write(writer) readUInt64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) reader = zserio.BitStreamReader(writer.getByteArray()) readUInt64ParamChoice.read(reader) self.assertEqual(shortValue, readUInt64ParamChoice.getB()) self.assertEqual(uint64ParamChoice, readUInt64ParamChoice) @staticmethod def _writeUInt64ParamChoiceToStream(writer, selector, value): if selector == 1: writer.writeSignedBits(value, 8) elif selector in (2, 3, 4): writer.writeSignedBits(value, 16) elif selector in (5, 6): pass else: writer.writeSignedBits(value, 32) VARIANT_A_SELECTOR = 1 VARIANT_B_SELECTOR = 2 VARIANT_C_SELECTOR = 7

40.862595

86

0.721838

import unittest import zserio from testutils import getZserioApi class UInt64ParamChoiceTest(unittest.TestCase): @classmethod def setUpClass(cls): cls.api = getZserioApi(__file__, "choice_types.zs").uint64_param_choice def testSelectorConstructor(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) self.assertEqual(self.VARIANT_A_SELECTOR, uint64ParamChoice.getSelector()) def testFromReader(self): selector = self.VARIANT_B_SELECTOR value = 234 writer = zserio.BitStreamWriter() UInt64ParamChoiceTest._writeUInt64ParamChoiceToStream(writer, selector, value) reader = zserio.BitStreamReader(writer.getByteArray()) uint64ParamChoice = self.api.UInt64ParamChoice.fromReader(reader, selector) self.assertEqual(selector, uint64ParamChoice.getSelector()) self.assertEqual(value, uint64ParamChoice.getB()) def testEq(self): uint64ParamChoice1 = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) uint64ParamChoice2 = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) self.assertTrue(uint64ParamChoice1 == uint64ParamChoice2) value = 99 uint64ParamChoice1.setA(value) self.assertFalse(uint64ParamChoice1 == uint64ParamChoice2) uint64ParamChoice2.setA(value) self.assertTrue(uint64ParamChoice1 == uint64ParamChoice2) diffValue = value + 1 uint64ParamChoice2.setA(diffValue) self.assertFalse(uint64ParamChoice1 == uint64ParamChoice2) def testHash(self): uint64ParamChoice1 = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) uint64ParamChoice2 = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) self.assertEqual(hash(uint64ParamChoice1), hash(uint64ParamChoice2)) value = 99 uint64ParamChoice1.setA(value) self.assertTrue(hash(uint64ParamChoice1) != hash(uint64ParamChoice2)) uint64ParamChoice2.setA(value) self.assertEqual(hash(uint64ParamChoice1), hash(uint64ParamChoice2)) diffValue = value + 1 uint64ParamChoice2.setA(diffValue) self.assertTrue(hash(uint64ParamChoice1) != hash(uint64ParamChoice2)) def testGetSelector(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_C_SELECTOR) self.assertEqual(self.VARIANT_C_SELECTOR, uint64ParamChoice.getSelector()) def testGetSetA(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) value = 99 uint64ParamChoice.setA(value) self.assertEqual(value, uint64ParamChoice.getA()) def testGetSetB(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) value = 234 uint64ParamChoice.setB(value) self.assertEqual(value, uint64ParamChoice.getB()) def testGetSetC(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_C_SELECTOR) value = 23456 uint64ParamChoice.setC(value) self.assertEqual(value, uint64ParamChoice.getC()) def testBitSizeOf(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) self.assertEqual(8, uint64ParamChoice.bitSizeOf()) uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) self.assertEqual(16, uint64ParamChoice.bitSizeOf()) def testInitializeOffsets(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) bitPosition = 1 self.assertEqual(9, uint64ParamChoice.initializeOffsets(bitPosition)) uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) self.assertEqual(17, uint64ParamChoice.initializeOffsets(bitPosition)) def testReadWrite(self): uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) byteValue = 99 uint64ParamChoice.setA(byteValue) writer = zserio.BitStreamWriter() uint64ParamChoice.write(writer) readUInt64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_A_SELECTOR) reader = zserio.BitStreamReader(writer.getByteArray()) readUInt64ParamChoice.read(reader) self.assertEqual(byteValue, readUInt64ParamChoice.getA()) self.assertEqual(uint64ParamChoice, readUInt64ParamChoice) uint64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) shortValue = 234 uint64ParamChoice.setB(shortValue) writer = zserio.BitStreamWriter() uint64ParamChoice.write(writer) readUInt64ParamChoice = self.api.UInt64ParamChoice(self.VARIANT_B_SELECTOR) reader = zserio.BitStreamReader(writer.getByteArray()) readUInt64ParamChoice.read(reader) self.assertEqual(shortValue, readUInt64ParamChoice.getB()) self.assertEqual(uint64ParamChoice, readUInt64ParamChoice) @staticmethod def _writeUInt64ParamChoiceToStream(writer, selector, value): if selector == 1: writer.writeSignedBits(value, 8) elif selector in (2, 3, 4): writer.writeSignedBits(value, 16) elif selector in (5, 6): pass else: writer.writeSignedBits(value, 32) VARIANT_A_SELECTOR = 1 VARIANT_B_SELECTOR = 2 VARIANT_C_SELECTOR = 7

true

f717772bd33c93521c158ce38b78042fe52c2ff5

52

py

Python

subjects/__init__.py

ankit0tech/Research-Productivity-Tool

c08e39daaaa8dfa08f5eb2607986d9f6bf9f02fa

[ "MIT" ]

1

2021-12-12T04:54:05.000Z

subjects/__init__.py

ankit0tech/Research-Productivity-Tool

c08e39daaaa8dfa08f5eb2607986d9f6bf9f02fa

[ "MIT" ]

null

subjects/__init__.py

ankit0tech/Research-Productivity-Tool

c08e39daaaa8dfa08f5eb2607986d9f6bf9f02fa

[ "MIT" ]

null

default_app_config = 'subjects.apps.SubjectsConfig'

26

51

0.846154

default_app_config = 'subjects.apps.SubjectsConfig'

true

f71777438e6b24d7bdde702a1788bd674bd9b0a3

589

py

Python

Appium_learning/02_ChangeApp.py

yeyuning1/AutoTT

1ce88e9e73d71fa11d4d8ad12bd6741aa71f97d2

[ "MIT" ]

null

Appium_learning/02_ChangeApp.py

yeyuning1/AutoTT

1ce88e9e73d71fa11d4d8ad12bd6741aa71f97d2

[ "MIT" ]

1

2021-06-02T00:24:41.000Z

Appium_learning/02_ChangeApp.py

yeyuning1/AutoTT

1ce88e9e73d71fa11d4d8ad12bd6741aa71f97d2

[ "MIT" ]

null

import time from appium import webdriver from Appium_learning import app_settings driver = webdriver.Remote('http://localhost:4723/wd/hub', app_settings.desired_caps) print(driver.current_package) print(driver.current_activity) print(driver.context) time.sleep(5) # adb shell dumpsys window windows | findstr(grep) mFocusedApp driver.start_activity('com.android.messaging', '.ui.conversationlist.ConversationListActivity') print(driver.current_package) print(driver.current_activity) print(driver.context) time.sleep(5) # driver.quit() 销毁 driver 驱动对象 --> stop_client driver.close_app()

31

95

0.814941

import time from appium import webdriver from Appium_learning import app_settings driver = webdriver.Remote('http://localhost:4723/wd/hub', app_settings.desired_caps) print(driver.current_package) print(driver.current_activity) print(driver.context) time.sleep(5) driver.start_activity('com.android.messaging', '.ui.conversationlist.ConversationListActivity') print(driver.current_package) print(driver.current_activity) print(driver.context) time.sleep(5) driver.close_app()

true

f717798fa86a8765f50f6a661d1e837315188e97

2,151

py

Python

setup.py

deliri/ChatterBot

8d95c43371bf8b7b1a1c44f77827b239bf38dc4e

[ "BSD-3-Clause" ]

1

2021-03-06T00:28:20.000Z

setup.py

deliri/ChatterBot

8d95c43371bf8b7b1a1c44f77827b239bf38dc4e

[ "BSD-3-Clause" ]

null

setup.py

deliri/ChatterBot

8d95c43371bf8b7b1a1c44f77827b239bf38dc4e

[ "BSD-3-Clause" ]

2

2017-05-30T02:18:30.000Z

2021-02-21T18:15:25.000Z

#!/usr/bin/env python """ ChatterBot setup file. """ from setuptools import setup # Dynamically retrieve the version information from the chatterbot module CHATTERBOT = __import__('chatterbot') VERSION = CHATTERBOT.__version__ AUTHOR = CHATTERBOT.__author__ AUTHOR_EMAIL = CHATTERBOT.__email__ URL = CHATTERBOT.__url__ DESCRIPTION = CHATTERBOT.__doc__ with open('requirements.txt') as requirements: REQUIREMENTS = requirements.readlines() setup( name='ChatterBot', version=VERSION, url=URL, download_url='{}/tarball/{}'.format(URL, VERSION), setup_requires=['setuptools-markdown'], long_description_markdown_filename='readme.md', description=DESCRIPTION, author=AUTHOR, author_email=AUTHOR_EMAIL, packages=[ 'chatterbot', 'chatterbot.input', 'chatterbot.output', 'chatterbot.storage', 'chatterbot.logic', 'chatterbot.corpus', 'chatterbot.conversation', 'chatterbot.ext', 'chatterbot.ext.django_chatterbot', 'chatterbot.ext.django_chatterbot.migrations', 'chatterbot.ext.django_chatterbot.management', 'chatterbot.ext.django_chatterbot.management.commands' ], package_dir={'chatterbot': 'chatterbot'}, include_package_data=True, install_requires=REQUIREMENTS, license='BSD', zip_safe=False, platforms=['any'], keywords=['ChatterBot', 'chatbot', 'chat', 'bot'], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Environment :: Console', 'Environment :: Web Environment', 'Operating System :: OS Independent', 'Topic :: Software Development :: Libraries :: Python Modules', 'Topic :: Communications :: Chat', 'Topic :: Internet', 'Programming Language :: Python', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', ], test_suite='tests', tests_require=['mock'] )

31.173913

73

0.653185

from setuptools import setup CHATTERBOT = __import__('chatterbot') VERSION = CHATTERBOT.__version__ AUTHOR = CHATTERBOT.__author__ AUTHOR_EMAIL = CHATTERBOT.__email__ URL = CHATTERBOT.__url__ DESCRIPTION = CHATTERBOT.__doc__ with open('requirements.txt') as requirements: REQUIREMENTS = requirements.readlines() setup( name='ChatterBot', version=VERSION, url=URL, download_url='{}/tarball/{}'.format(URL, VERSION), setup_requires=['setuptools-markdown'], long_description_markdown_filename='readme.md', description=DESCRIPTION, author=AUTHOR, author_email=AUTHOR_EMAIL, packages=[ 'chatterbot', 'chatterbot.input', 'chatterbot.output', 'chatterbot.storage', 'chatterbot.logic', 'chatterbot.corpus', 'chatterbot.conversation', 'chatterbot.ext', 'chatterbot.ext.django_chatterbot', 'chatterbot.ext.django_chatterbot.migrations', 'chatterbot.ext.django_chatterbot.management', 'chatterbot.ext.django_chatterbot.management.commands' ], package_dir={'chatterbot': 'chatterbot'}, include_package_data=True, install_requires=REQUIREMENTS, license='BSD', zip_safe=False, platforms=['any'], keywords=['ChatterBot', 'chatbot', 'chat', 'bot'], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Environment :: Console', 'Environment :: Web Environment', 'Operating System :: OS Independent', 'Topic :: Software Development :: Libraries :: Python Modules', 'Topic :: Communications :: Chat', 'Topic :: Internet', 'Programming Language :: Python', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', ], test_suite='tests', tests_require=['mock'] )

true

f71779abb101df0998a79dffc26322edc971e6e8

10,423

py

Python

seleniumbase/core/jqc_helper.py

mdmintz/seleniumspot

f5c225aa4fcd0b4124fc990e3892c36736290ce8

[ "MIT" ]

1

2015-06-17T10:16:26.000Z

seleniumbase/core/jqc_helper.py

mdmintz/seleniumspot

f5c225aa4fcd0b4124fc990e3892c36736290ce8

[ "MIT" ]

null

seleniumbase/core/jqc_helper.py

mdmintz/seleniumspot

f5c225aa4fcd0b4124fc990e3892c36736290ce8

[ "MIT" ]

null

""" This module contains methods for opening jquery-confirm boxes. These helper methods SHOULD NOT be called directly from tests. """ from seleniumbase.fixtures import constants from seleniumbase.fixtures import js_utils form_code = """'<form align="center" action="" class="jqc_form">' + '<div class="form-group">' + '<input style="font-size:20px; background-color: #f8fdfd; ' + ' width: 84%%; border: 1px solid blue; ' + ' box-shadow:inset 0 0 2px 2px #f4fafa;"' + ' type="text" class="jqc_input" />' + '</div>' + '</form>'""" def jquery_confirm_button_dialog(driver, message, buttons, options=None): js_utils.activate_jquery_confirm(driver) # These defaults will be overwritten later if set theme = constants.JqueryConfirm.DEFAULT_THEME border_color = constants.JqueryConfirm.DEFAULT_COLOR width = constants.JqueryConfirm.DEFAULT_WIDTH if options: for option in options: if option[0].lower() == "theme": theme = option[1] elif option[0].lower() == "color": border_color = option[1] elif option[0].lower() == "width": width = option[1] else: raise Exception('Unknown option: "%s"' % option[0]) if not message: message = "" key_row = "" if len(buttons) == 1: # There's only one button as an option key_row = "keys: ['enter', 'y', '1']," # Shortcut: "Enter","Y","1" b_html = """button_%s: { btnClass: 'btn-%s', text: '<b>%s</b>', %s action: function(){ jqc_status = '%s'; $jqc_status = jqc_status; jconfirm.lastButtonText = jqc_status; } },""" all_buttons = "" btn_count = 0 for button in buttons: btn_count += 1 text = button[0] text = js_utils.escape_quotes_if_needed(text) if len(buttons) > 1 and text.lower() == "yes": key_row = "keys: ['y']," if btn_count < 10: key_row = "keys: ['y', '%s']," % btn_count elif len(buttons) > 1 and text.lower() == "no": key_row = "keys: ['n']," if btn_count < 10: key_row = "keys: ['n', '%s']," % btn_count elif len(buttons) > 1: if btn_count < 10: key_row = "keys: ['%s']," % btn_count color = button[1] if not color: color = "blue" new_button = b_html % (btn_count, color, text, key_row, text) all_buttons += new_button content = '<div></div><font color="#0066ee">%s</font>' % (message) content = js_utils.escape_quotes_if_needed(content) overlay_opacity = "0.32" if theme.lower() == "supervan": overlay_opacity = "0.56" if theme.lower() == "bootstrap": overlay_opacity = "0.64" if theme.lower() == "modern": overlay_opacity = "0.5" if theme.lower() == "material": overlay_opacity = "0.4" jqcd = """jconfirm({ boxWidth: '%s', useBootstrap: false, containerFluid: true, bgOpacity: %s, type: '%s', theme: '%s', animationBounce: 1, typeAnimated: true, animation: 'scale', draggable: true, dragWindowGap: 1, container: 'body', title: '%s', content: '<div></div>', buttons: { %s } });""" % ( width, overlay_opacity, border_color, theme, content, all_buttons, ) driver.execute_script(jqcd) def jquery_confirm_text_dialog(driver, message, button=None, options=None): js_utils.activate_jquery_confirm(driver) # These defaults will be overwritten later if set theme = constants.JqueryConfirm.DEFAULT_THEME border_color = constants.JqueryConfirm.DEFAULT_COLOR width = constants.JqueryConfirm.DEFAULT_WIDTH if not message: message = "" if button: if not type(button) is list and not type(button) is tuple: raise Exception('"button" should be a (text, color) tuple!') if len(button) != 2: raise Exception('"button" should be a (text, color) tuple!') else: button = ("Submit", "blue") if options: for option in options: if option[0].lower() == "theme": theme = option[1] elif option[0].lower() == "color": border_color = option[1] elif option[0].lower() == "width": width = option[1] else: raise Exception('Unknown option: "%s"' % option[0]) btn_text = button[0] btn_color = button[1] if not btn_color: btn_color = "blue" content = '<div></div><font color="#0066ee">%s</font>' % (message) content = js_utils.escape_quotes_if_needed(content) overlay_opacity = "0.32" if theme.lower() == "supervan": overlay_opacity = "0.56" if theme.lower() == "bootstrap": overlay_opacity = "0.64" if theme.lower() == "modern": overlay_opacity = "0.5" if theme.lower() == "material": overlay_opacity = "0.4" jqcd = """jconfirm({ boxWidth: '%s', useBootstrap: false, containerFluid: true, bgOpacity: %s, type: '%s', theme: '%s', animationBounce: 1, typeAnimated: true, animation: 'scale', draggable: true, dragWindowGap: 1, container: 'body', title: '%s', content: '<div></div>' + %s, buttons: { formSubmit: { btnClass: 'btn-%s', text: '%s', action: function () { jqc_input = this.$content.find('.jqc_input').val(); $jqc_input = this.$content.find('.jqc_input').val(); jconfirm.lastInputText = jqc_input; $jqc_status = '%s'; // There is only one button }, }, }, onContentReady: function () { var jc = this; this.$content.find('form.jqc_form').on('submit', function (e) { // User submits the form by pressing "Enter" in the field e.preventDefault(); jc.$$formSubmit.trigger('click'); // Click the button }); } });""" % ( width, overlay_opacity, border_color, theme, content, form_code, btn_color, btn_text, btn_text, ) driver.execute_script(jqcd) def jquery_confirm_full_dialog(driver, message, buttons, options=None): js_utils.activate_jquery_confirm(driver) # These defaults will be overwritten later if set theme = constants.JqueryConfirm.DEFAULT_THEME border_color = constants.JqueryConfirm.DEFAULT_COLOR width = constants.JqueryConfirm.DEFAULT_WIDTH if not message: message = "" btn_count = 0 b_html = """button_%s: { btnClass: 'btn-%s', text: '%s', action: function(){ jqc_input = this.$content.find('.jqc_input').val(); $jqc_input = this.$content.find('.jqc_input').val(); jconfirm.lastInputText = jqc_input; $jqc_status = '%s'; } },""" b1_html = """formSubmit: { btnClass: 'btn-%s', text: '%s', action: function(){ jqc_input = this.$content.find('.jqc_input').val(); $jqc_input = this.$content.find('.jqc_input').val(); jconfirm.lastInputText = jqc_input; jqc_status = '%s'; $jqc_status = jqc_status; jconfirm.lastButtonText = jqc_status; } },""" one_button_trigger = "" if len(buttons) == 1: # If there's only one button, allow form submit with "Enter/Return" one_button_trigger = "jc.$$formSubmit.trigger('click');" all_buttons = "" for button in buttons: text = button[0] text = js_utils.escape_quotes_if_needed(text) color = button[1] if not color: color = "blue" btn_count += 1 if len(buttons) == 1: new_button = b1_html % (color, text, text) else: new_button = b_html % (btn_count, color, text, text) all_buttons += new_button if options: for option in options: if option[0].lower() == "theme": theme = option[1] elif option[0].lower() == "color": border_color = option[1] elif option[0].lower() == "width": width = option[1] else: raise Exception('Unknown option: "%s"' % option[0]) content = '<div></div><font color="#0066ee">%s</font>' % (message) content = js_utils.escape_quotes_if_needed(content) overlay_opacity = "0.32" if theme.lower() == "supervan": overlay_opacity = "0.56" if theme.lower() == "bootstrap": overlay_opacity = "0.64" if theme.lower() == "modern": overlay_opacity = "0.5" if theme.lower() == "material": overlay_opacity = "0.4" jqcd = """jconfirm({ boxWidth: '%s', useBootstrap: false, containerFluid: true, bgOpacity: %s, type: '%s', theme: '%s', animationBounce: 1, typeAnimated: true, animation: 'scale', draggable: true, dragWindowGap: 1, container: 'body', title: '%s', content: '<div></div>' + %s, buttons: { %s }, onContentReady: function () { var jc = this; this.$content.find('form.jqc_form').on('submit', function (e) { // User submits the form by pressing "Enter" in the field e.preventDefault(); %s }); } });""" % ( width, overlay_opacity, border_color, theme, content, form_code, all_buttons, one_button_trigger, ) driver.execute_script(jqcd)

33.300319

75

0.515399

from seleniumbase.fixtures import constants from seleniumbase.fixtures import js_utils form_code = """'<form align="center" action="" class="jqc_form">' + '<div class="form-group">' + '<input style="font-size:20px; background-color: #f8fdfd; ' + ' width: 84%%; border: 1px solid blue; ' + ' box-shadow:inset 0 0 2px 2px #f4fafa;"' + ' type="text" class="jqc_input" />' + '</div>' + '</form>'""" def jquery_confirm_button_dialog(driver, message, buttons, options=None): js_utils.activate_jquery_confirm(driver) theme = constants.JqueryConfirm.DEFAULT_THEME border_color = constants.JqueryConfirm.DEFAULT_COLOR width = constants.JqueryConfirm.DEFAULT_WIDTH if options: for option in options: if option[0].lower() == "theme": theme = option[1] elif option[0].lower() == "color": border_color = option[1] elif option[0].lower() == "width": width = option[1] else: raise Exception('Unknown option: "%s"' % option[0]) if not message: message = "" key_row = "" if len(buttons) == 1: key_row = "keys: ['enter', 'y', '1']," # Shortcut: "Enter","Y","1" b_html = """button_%s: { btnClass: 'btn-%s', text: '<b>%s</b>', %s action: function(){ jqc_status = '%s'; $jqc_status = jqc_status; jconfirm.lastButtonText = jqc_status; } },""" all_buttons = "" btn_count = 0 for button in buttons: btn_count += 1 text = button[0] text = js_utils.escape_quotes_if_needed(text) if len(buttons) > 1 and text.lower() == "yes": key_row = "keys: ['y']," if btn_count < 10: key_row = "keys: ['y', '%s']," % btn_count elif len(buttons) > 1 and text.lower() == "no": key_row = "keys: ['n']," if btn_count < 10: key_row = "keys: ['n', '%s']," % btn_count elif len(buttons) > 1: if btn_count < 10: key_row = "keys: ['%s']," % btn_count color = button[1] if not color: color = "blue" new_button = b_html % (btn_count, color, text, key_row, text) all_buttons += new_button content = '<div></div><font color="#0066ee">%s</font>' % (message) content = js_utils.escape_quotes_if_needed(content) overlay_opacity = "0.32" if theme.lower() == "supervan": overlay_opacity = "0.56" if theme.lower() == "bootstrap": overlay_opacity = "0.64" if theme.lower() == "modern": overlay_opacity = "0.5" if theme.lower() == "material": overlay_opacity = "0.4" jqcd = """jconfirm({ boxWidth: '%s', useBootstrap: false, containerFluid: true, bgOpacity: %s, type: '%s', theme: '%s', animationBounce: 1, typeAnimated: true, animation: 'scale', draggable: true, dragWindowGap: 1, container: 'body', title: '%s', content: '<div></div>', buttons: { %s } });""" % ( width, overlay_opacity, border_color, theme, content, all_buttons, ) driver.execute_script(jqcd) def jquery_confirm_text_dialog(driver, message, button=None, options=None): js_utils.activate_jquery_confirm(driver) # These defaults will be overwritten later if set theme = constants.JqueryConfirm.DEFAULT_THEME border_color = constants.JqueryConfirm.DEFAULT_COLOR width = constants.JqueryConfirm.DEFAULT_WIDTH if not message: message = "" if button: if not type(button) is list and not type(button) is tuple: raise Exception('"button" should be a (text, color) tuple!') if len(button) != 2: raise Exception('"button" should be a (text, color) tuple!') else: button = ("Submit", "blue") if options: for option in options: if option[0].lower() == "theme": theme = option[1] elif option[0].lower() == "color": border_color = option[1] elif option[0].lower() == "width": width = option[1] else: raise Exception('Unknown option: "%s"' % option[0]) btn_text = button[0] btn_color = button[1] if not btn_color: btn_color = "blue" content = '<div></div><font color="#0066ee">%s</font>' % (message) content = js_utils.escape_quotes_if_needed(content) overlay_opacity = "0.32" if theme.lower() == "supervan": overlay_opacity = "0.56" if theme.lower() == "bootstrap": overlay_opacity = "0.64" if theme.lower() == "modern": overlay_opacity = "0.5" if theme.lower() == "material": overlay_opacity = "0.4" jqcd = """jconfirm({ boxWidth: '%s', useBootstrap: false, containerFluid: true, bgOpacity: %s, type: '%s', theme: '%s', animationBounce: 1, typeAnimated: true, animation: 'scale', draggable: true, dragWindowGap: 1, container: 'body', title: '%s', content: '<div></div>' + %s, buttons: { formSubmit: { btnClass: 'btn-%s', text: '%s', action: function () { jqc_input = this.$content.find('.jqc_input').val(); $jqc_input = this.$content.find('.jqc_input').val(); jconfirm.lastInputText = jqc_input; $jqc_status = '%s'; // There is only one button }, }, }, onContentReady: function () { var jc = this; this.$content.find('form.jqc_form').on('submit', function (e) { // User submits the form by pressing "Enter" in the field e.preventDefault(); jc.$$formSubmit.trigger('click'); // Click the button }); } });""" % ( width, overlay_opacity, border_color, theme, content, form_code, btn_color, btn_text, btn_text, ) driver.execute_script(jqcd) def jquery_confirm_full_dialog(driver, message, buttons, options=None): js_utils.activate_jquery_confirm(driver) # These defaults will be overwritten later if set theme = constants.JqueryConfirm.DEFAULT_THEME border_color = constants.JqueryConfirm.DEFAULT_COLOR width = constants.JqueryConfirm.DEFAULT_WIDTH if not message: message = "" btn_count = 0 b_html = """button_%s: { btnClass: 'btn-%s', text: '%s', action: function(){ jqc_input = this.$content.find('.jqc_input').val(); $jqc_input = this.$content.find('.jqc_input').val(); jconfirm.lastInputText = jqc_input; $jqc_status = '%s'; } },""" b1_html = """formSubmit: { btnClass: 'btn-%s', text: '%s', action: function(){ jqc_input = this.$content.find('.jqc_input').val(); $jqc_input = this.$content.find('.jqc_input').val(); jconfirm.lastInputText = jqc_input; jqc_status = '%s'; $jqc_status = jqc_status; jconfirm.lastButtonText = jqc_status; } },""" one_button_trigger = "" if len(buttons) == 1: # If there's only one button, allow form submit with "Enter/Return" one_button_trigger = "jc.$$formSubmit.trigger('click');" all_buttons = "" for button in buttons: text = button[0] text = js_utils.escape_quotes_if_needed(text) color = button[1] if not color: color = "blue" btn_count += 1 if len(buttons) == 1: new_button = b1_html % (color, text, text) else: new_button = b_html % (btn_count, color, text, text) all_buttons += new_button if options: for option in options: if option[0].lower() == "theme": theme = option[1] elif option[0].lower() == "color": border_color = option[1] elif option[0].lower() == "width": width = option[1] else: raise Exception('Unknown option: "%s"' % option[0]) content = '<div></div><font color="#0066ee">%s</font>' % (message) content = js_utils.escape_quotes_if_needed(content) overlay_opacity = "0.32" if theme.lower() == "supervan": overlay_opacity = "0.56" if theme.lower() == "bootstrap": overlay_opacity = "0.64" if theme.lower() == "modern": overlay_opacity = "0.5" if theme.lower() == "material": overlay_opacity = "0.4" jqcd = """jconfirm({ boxWidth: '%s', useBootstrap: false, containerFluid: true, bgOpacity: %s, type: '%s', theme: '%s', animationBounce: 1, typeAnimated: true, animation: 'scale', draggable: true, dragWindowGap: 1, container: 'body', title: '%s', content: '<div></div>' + %s, buttons: { %s }, onContentReady: function () { var jc = this; this.$content.find('form.jqc_form').on('submit', function (e) { // User submits the form by pressing "Enter" in the field e.preventDefault(); %s }); } });""" % ( width, overlay_opacity, border_color, theme, content, form_code, all_buttons, one_button_trigger, ) driver.execute_script(jqcd)

true

f71779ec19d93ec24d41da7b598913f8c5798de5

1,316

py

Python

ShowProcess.py

4a5g0030/line_follow

570e65fb62803f7f5062402a45654809b01b7aaa

[ "MIT" ]

1

2019-06-19T18:32:28.000Z

ShowProcess.py

4a5g0030/line_follow

570e65fb62803f7f5062402a45654809b01b7aaa

[ "MIT" ]

null

ShowProcess.py

4a5g0030/line_follow

570e65fb62803f7f5062402a45654809b01b7aaa

[ "MIT" ]

null

import time import sys class ShowProcess(): # """ # 显示处理进度的类 # 调用该类相关函数即可实现处理进度的显示 # """ i = 0 # 当前的处理进度 max_steps = 0 # 总共需要处理的次数 max_arrow = 50 #进度条的长度 infoDone = 'done' # 初始化函数，需要知道总共的处理次数 def __init__(self, max_steps, infoDone = 'Done'): self.max_steps = max_steps self.i = 0 self.infoDone = infoDone # 显示函数，根据当前的处理进度i显示进度 # 效果为[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>]100.00% def show_process(self, i=None): if i is not None: self.i = i else: self.i += 1 num_arrow = int(self.i * self.max_arrow / self.max_steps) #计算显示多少个'>' num_line = self.max_arrow - num_arrow #计算显示多少个'-' percent = self.i * 100.0 / self.max_steps #计算完成进度，格式为xx.xx% process_bar = '[' + '>' * num_arrow + '-' * num_line + ']'\ + '%.2f' % percent + '%' + '\r' #带输出的字符串，'\r'表示不换行回到最左边 sys.stdout.write(process_bar) #这两句打印字符到终端 sys.stdout.flush() if self.i >= self.max_steps: self.close() def close(self): print('') print(self.infoDone) self.i = 0 # ———————————————— # 版权声明：本文为CSDN博主「持久决心」的原创文章，遵循CC 4.0 by-sa版权协议，转载请附上原文出处链接及本声明。 # 原文链接：https://blog.csdn.net/u013832707/article/details/73608504

29.909091

77

0.534195

import time import sys class ShowProcess(): # 显示处理进度的类 # 调用该类相关函数即可实现处理进度的显示 # """ i = 0 max_steps = 0 max_arrow = 50 infoDone = 'done' def __init__(self, max_steps, infoDone = 'Done'): self.max_steps = max_steps self.i = 0 self.infoDone = infoDone def show_process(self, i=None): if i is not None: self.i = i else: self.i += 1 num_arrow = int(self.i * self.max_arrow / self.max_steps) num_line = self.max_arrow - num_arrow percent = self.i * 100.0 / self.max_steps process_bar = '[' + '>' * num_arrow + '-' * num_line + ']'\ + '%.2f' % percent + '%' + '\r' sys.stdout.write(process_bar) sys.stdout.flush() if self.i >= self.max_steps: self.close() def close(self): print('') print(self.infoDone) self.i = 0

true

f7177a2b9fd9f213e95853d3176e200b98b80f37

1,793

py

Python

unitest/test_supermesh.py

JeremieMelo/ADEPT

f79f518197798735cb684b373e11cdcc8a80d872

[ "MIT" ]

5

2022-02-26T09:14:47.000Z

2022-03-20T22:57:06.000Z

unitest/test_supermesh.py

JeremieMelo/ADEPT

f79f518197798735cb684b373e11cdcc8a80d872

[ "MIT" ]

null

unitest/test_supermesh.py

JeremieMelo/ADEPT

f79f518197798735cb684b373e11cdcc8a80d872

[ "MIT" ]

null

''' Description: Author: Jiaqi Gu (jqgu@utexas.edu) Date: 2021-09-27 23:48:01 LastEditors: Jiaqi Gu (jqgu@utexas.edu) LastEditTime: 2022-02-26 02:22:52 ''' import torch from core.models.layers.super_mesh import super_layer_name_dict def test(): device=torch.device("cuda:0") p, q, k = 2, 2, 4 x = torch.eye(k, dtype=torch.cfloat, device=device).unsqueeze(0).repeat(q,1,1).permute(1,0,2).contiguous() sigma = torch.ones(p,q,k, device=device) # x [bs, q, k] arch = dict( n_waveguides=k, n_front_share_waveguides=k, n_front_share_ops=k, n_blocks=4, n_layers_per_block=2, n_front_share_blocks=2, share_ps="row_col", interleave_dc=True, ) sample_arch = [ k//3,1, k//2,1, k//2,1, k//2,1, 4 ] layer = super_layer_name_dict["ps_dc_cr"](arch, device=device) super_ps_layers = layer.build_ps_layser(grid_dim_x=q, grid_dim_y=p) for m in super_ps_layers: # m.reset_parameters(alg="identity") m.reset_parameters(alg="uniform") layer.set_sample_arch(sample_arch) print(layer) layer.set_identity_cr() layer.build_sampling_coefficients() layer.set_gumbel_temperature(0.1) layer.set_aux_skip_path(0) layer.build_arch_mask() U,V = layer.get_UV(super_ps_layers, q, p) print(U, U.size()) print(U[0,0].conj().t().matmul(U[0,0])) print(V) print(V[0,0].conj().t().matmul(V[0,0])) weight = layer.get_weight_matrix(super_ps_layers, sigma) print(weight) weight.sum().backward() print(super_ps_layers[0].weight.grad.norm(p=2)) print(layer.super_layers_all[0].weight.grad.norm(p=2)) print(layer.super_layers_all[1].weight.grad.norm(p=2)) if __name__ == "__main__": test()

28.460317

110

0.644172

import torch from core.models.layers.super_mesh import super_layer_name_dict def test(): device=torch.device("cuda:0") p, q, k = 2, 2, 4 x = torch.eye(k, dtype=torch.cfloat, device=device).unsqueeze(0).repeat(q,1,1).permute(1,0,2).contiguous() sigma = torch.ones(p,q,k, device=device) arch = dict( n_waveguides=k, n_front_share_waveguides=k, n_front_share_ops=k, n_blocks=4, n_layers_per_block=2, n_front_share_blocks=2, share_ps="row_col", interleave_dc=True, ) sample_arch = [ k//3,1, k//2,1, k//2,1, k//2,1, 4 ] layer = super_layer_name_dict["ps_dc_cr"](arch, device=device) super_ps_layers = layer.build_ps_layser(grid_dim_x=q, grid_dim_y=p) for m in super_ps_layers: m.reset_parameters(alg="uniform") layer.set_sample_arch(sample_arch) print(layer) layer.set_identity_cr() layer.build_sampling_coefficients() layer.set_gumbel_temperature(0.1) layer.set_aux_skip_path(0) layer.build_arch_mask() U,V = layer.get_UV(super_ps_layers, q, p) print(U, U.size()) print(U[0,0].conj().t().matmul(U[0,0])) print(V) print(V[0,0].conj().t().matmul(V[0,0])) weight = layer.get_weight_matrix(super_ps_layers, sigma) print(weight) weight.sum().backward() print(super_ps_layers[0].weight.grad.norm(p=2)) print(layer.super_layers_all[0].weight.grad.norm(p=2)) print(layer.super_layers_all[1].weight.grad.norm(p=2)) if __name__ == "__main__": test()

true

f7177a7169c09bf58f9be260fccfe1d0276b2e83

712

py

Python

parsons/google/utitities.py

Tomiiwa/parsons

3886327c197e357ba5342603d8409774a541333b

[ "Apache-2.0" ]

3

2019-09-05T16:57:15.000Z

2019-10-01T19:56:58.000Z

parsons/google/utitities.py

Tomiiwa/parsons

3886327c197e357ba5342603d8409774a541333b

[ "Apache-2.0" ]

22

2019-09-03T13:23:37.000Z

2019-10-03T20:32:48.000Z

parsons/google/utitities.py

Tomiiwa/parsons

3886327c197e357ba5342603d8409774a541333b

[ "Apache-2.0" ]

2

2019-09-01T18:30:10.000Z

2019-10-03T20:07:46.000Z

from parsons.utilities import files from parsons.utilities import check_env import json import os def setup_google_application_credentials(app_creds, env_var_name='GOOGLE_APPLICATION_CREDENTIALS'): # Detect if app_creds is a dict, path string or json string, and if it is a # json string, then convert it to a temporary file. Then set the # environmental variable. credentials = check_env.check(env_var_name, app_creds) try: if (type(credentials) is dict): credentials = json.dumps(credentials) creds_path = files.string_to_temp_file(credentials, suffix='.json') except ValueError: creds_path = credentials os.environ[env_var_name] = creds_path

35.6

99

0.738764

from parsons.utilities import files from parsons.utilities import check_env import json import os def setup_google_application_credentials(app_creds, env_var_name='GOOGLE_APPLICATION_CREDENTIALS'): credentials = check_env.check(env_var_name, app_creds) try: if (type(credentials) is dict): credentials = json.dumps(credentials) creds_path = files.string_to_temp_file(credentials, suffix='.json') except ValueError: creds_path = credentials os.environ[env_var_name] = creds_path

true

f7177a79f201b728ccb90ed68b5736930baa2a1a

4,126

py

Python

xsimlab/ipython.py

jvail/xarray-simlab

3e8cb81775868e3e7c6495489ba351567e0d7e42

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

48

2017-06-19T16:31:37.000Z

2021-04-26T04:42:48.000Z

xsimlab/ipython.py

jvail/xarray-simlab

3e8cb81775868e3e7c6495489ba351567e0d7e42

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

108

2017-06-26T12:22:10.000Z

2021-03-09T08:57:02.000Z

xsimlab/ipython.py

jvail/xarray-simlab

3e8cb81775868e3e7c6495489ba351567e0d7e42

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

10

2017-08-11T04:56:20.000Z

2021-03-01T16:46:55.000Z

import textwrap from IPython.core.magic import Magics, magics_class, line_magic from IPython.core import magic_arguments import attr from .formatting import format_var_dims from .model import Model from .utils import variables_dict setup_template = """ import xsimlab as xs ds_in = xs.create_setup( model={model}, clocks={{}}, input_vars={{ {in_vars} }}, output_vars={{}} ) """ def format_var_comment(var, verbose=0): comment = "" if verbose: var_desc = var.metadata["description"] if var_desc: comment += textwrap.fill( var_desc, width=86, initial_indent="# ", subsequent_indent="# " ) else: comment += "# ---" comment += "\n" if verbose > 1: var_dims = format_var_dims(var) if var_dims: comment += f"# dimensions: {var_dims}\n" if var.metadata["static"]: comment += f"# static: main clock dimension not supported\n" if verbose > 2: var_attrs = var.metadata.get("attrs", False) if var_attrs: for k, v in var_attrs.items(): comment += f"# {k}: {v}\n" return comment def format_input_vars( model, skip_default=False, default=False, verbose=0, nested=False ): lines = [] for pn, vnames in model.input_vars_dict.items(): plines = [] for vn in vnames: var = variables_dict(type(model[pn]))[vn] if skip_default and var.default is not attr.NOTHING: continue if default and var.default is not attr.NOTHING: default_val = f"{var.default!r}" else: default_val = "" comment = format_var_comment(var, verbose=verbose) if nested: plines.append(comment + f"'{vn}': {default_val},") else: lines.append(comment + f"'{pn}__{vn}': {default_val},") if nested and plines: pfmt = textwrap.indent("\n".join(plines), " " * 4) lines.append(f"'{pn}': {{\n{pfmt}\n}},") return textwrap.indent("\n".join(lines), " " * 8)[8:] @magics_class class SimulationMagics(Magics): @line_magic @magic_arguments.magic_arguments() @magic_arguments.argument("model", help="xsimlab.Model object") @magic_arguments.argument( "-s", "--skip-default", action="store_true", default=False, help="Don't add input variables that have default values", ) @magic_arguments.argument( "-d", "--default", action="store_true", default=False, help="Add input variables default values, if any (ignored if --skip-default)", ) @magic_arguments.argument( "-v", "--verbose", action="count", default=0, help="Increase verbosity (i.e., add more input variables info as comments)", ) @magic_arguments.argument( "-n", "--nested", action="store_true", default=False, help="Group input variables by process", ) def create_setup(self, line=""): """Pre-fill the current cell with a new simulation setup.""" args = magic_arguments.parse_argstring(self.create_setup, line) model_obj = self.shell.user_ns.get(args.model) if model_obj is None: raise KeyError(f"Model '{args.model}' not defined or not imported") elif not isinstance(model_obj, Model): raise TypeError(f"'{args.model}' is not a xsimlab.Model object") rendered = setup_template.format( model=args.model, in_vars=format_input_vars( model_obj, skip_default=args.skip_default, default=args.default, verbose=args.verbose, nested=args.nested, ), ) content = f"# %create_setup {line}" + rendered self.shell.set_next_input(content, replace=True) def load_ipython_extension(ipython): ipython.register_magics(SimulationMagics)

27.506667

86

0.574649

import textwrap from IPython.core.magic import Magics, magics_class, line_magic from IPython.core import magic_arguments import attr from .formatting import format_var_dims from .model import Model from .utils import variables_dict setup_template = """ import xsimlab as xs ds_in = xs.create_setup( model={model}, clocks={{}}, input_vars={{ {in_vars} }}, output_vars={{}} ) """ def format_var_comment(var, verbose=0): comment = "" if verbose: var_desc = var.metadata["description"] if var_desc: comment += textwrap.fill( var_desc, width=86, initial_indent="# ", subsequent_indent="# " ) else: comment += "# ---" comment += "\n" if verbose > 1: var_dims = format_var_dims(var) if var_dims: comment += f"# dimensions: {var_dims}\n" if var.metadata["static"]: comment += f"# static: main clock dimension not supported\n" if verbose > 2: var_attrs = var.metadata.get("attrs", False) if var_attrs: for k, v in var_attrs.items(): comment += f"# {k}: {v}\n" return comment def format_input_vars( model, skip_default=False, default=False, verbose=0, nested=False ): lines = [] for pn, vnames in model.input_vars_dict.items(): plines = [] for vn in vnames: var = variables_dict(type(model[pn]))[vn] if skip_default and var.default is not attr.NOTHING: continue if default and var.default is not attr.NOTHING: default_val = f"{var.default!r}" else: default_val = "" comment = format_var_comment(var, verbose=verbose) if nested: plines.append(comment + f"'{vn}': {default_val},") else: lines.append(comment + f"'{pn}__{vn}': {default_val},") if nested and plines: pfmt = textwrap.indent("\n".join(plines), " " * 4) lines.append(f"'{pn}': {{\n{pfmt}\n}},") return textwrap.indent("\n".join(lines), " " * 8)[8:] @magics_class class SimulationMagics(Magics): @line_magic @magic_arguments.magic_arguments() @magic_arguments.argument("model", help="xsimlab.Model object") @magic_arguments.argument( "-s", "--skip-default", action="store_true", default=False, help="Don't add input variables that have default values", ) @magic_arguments.argument( "-d", "--default", action="store_true", default=False, help="Add input variables default values, if any (ignored if --skip-default)", ) @magic_arguments.argument( "-v", "--verbose", action="count", default=0, help="Increase verbosity (i.e., add more input variables info as comments)", ) @magic_arguments.argument( "-n", "--nested", action="store_true", default=False, help="Group input variables by process", ) def create_setup(self, line=""): args = magic_arguments.parse_argstring(self.create_setup, line) model_obj = self.shell.user_ns.get(args.model) if model_obj is None: raise KeyError(f"Model '{args.model}' not defined or not imported") elif not isinstance(model_obj, Model): raise TypeError(f"'{args.model}' is not a xsimlab.Model object") rendered = setup_template.format( model=args.model, in_vars=format_input_vars( model_obj, skip_default=args.skip_default, default=args.default, verbose=args.verbose, nested=args.nested, ), ) content = f"# %create_setup {line}" + rendered self.shell.set_next_input(content, replace=True) def load_ipython_extension(ipython): ipython.register_magics(SimulationMagics)

true

f7177b1e4046a2c4d8c4f139594073d0ad624f46

752

py

Python

api/tournaments/migrations/0002_auto_20190804_1830.py

individuo7/wololo-tournaments-api

5be6284064373e99346d39c78844e454c41c501d

[ "MIT" ]

2

2019-12-09T10:19:36.000Z

2020-01-11T11:48:41.000Z

api/tournaments/migrations/0002_auto_20190804_1830.py

individuo7/wololo-tournaments-api

5be6284064373e99346d39c78844e454c41c501d

[ "MIT" ]

null

api/tournaments/migrations/0002_auto_20190804_1830.py

individuo7/wololo-tournaments-api

5be6284064373e99346d39c78844e454c41c501d

[ "MIT" ]

null

# Generated by Django 2.2.3 on 2019-08-04 18:30 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('tournaments', '0001_initial'), ] operations = [ migrations.AlterField( model_name='game', name='tournament', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='games', to='tournaments.Tournament'), ), migrations.AlterField( model_name='playergame', name='team', field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to='tournaments.Team'), preserve_default=False, ), ]

28.923077

132

0.630319

from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('tournaments', '0001_initial'), ] operations = [ migrations.AlterField( model_name='game', name='tournament', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, related_name='games', to='tournaments.Tournament'), ), migrations.AlterField( model_name='playergame', name='team', field=models.ForeignKey(default=1, on_delete=django.db.models.deletion.CASCADE, to='tournaments.Team'), preserve_default=False, ), ]

true

f7177be77bf1953daf64f669f1516a9413569e6f

1,785

py

Python

model/contact.py

dorotan/pythontraining

13cd9d5d8b0c772951e9caf98166118e7ffa387c

[ "Apache-2.0" ]

null

model/contact.py

dorotan/pythontraining

13cd9d5d8b0c772951e9caf98166118e7ffa387c

[ "Apache-2.0" ]

null

model/contact.py

dorotan/pythontraining

13cd9d5d8b0c772951e9caf98166118e7ffa387c

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- __author__ = 'dorota' from sys import maxsize class Contact: def __init__(self, first_name=None, middle_name=None, last_name=None, nickname=None, title=None, company=None, address=None, home_number=None, mobile_number=None, work_number=None, fax=None, first_email=None, second_email=None, third_email=None, wwwpage=None, birth_year=None, anniversary_year=None, second_address=None, second_private_number=None, notes=None, id= None, all_phones_from_homepage=None): self.first_name = first_name self.middle_name = middle_name self.last_name = last_name self.nickname = nickname self.title = title self.company = company self.address = address self.home_number = home_number self.mobile_number = mobile_number self.work_number = work_number self.fax = fax self.first_email = first_email self.second_email = second_email self.third_email = third_email self.wwwpage = wwwpage self.birth_year = birth_year self.anniversary_year = anniversary_year self.second_address = second_address self.second_private_number = second_private_number self.notes = notes self.id = id self.all_phones_from_homepage=all_phones_from_homepage def __repr__(self): return "%s:%s %s" % (self.id, self.first_name, self.last_name) def __eq__(self, other): return (self.id is None or other.id is None or self.id == other.id)\ and self.first_name == other.first_name and self.last_name == other.last_name def id_or_max(con): if con.id: return int(con.id) else: return maxsize

38.804348

119

0.656022

__author__ = 'dorota' from sys import maxsize class Contact: def __init__(self, first_name=None, middle_name=None, last_name=None, nickname=None, title=None, company=None, address=None, home_number=None, mobile_number=None, work_number=None, fax=None, first_email=None, second_email=None, third_email=None, wwwpage=None, birth_year=None, anniversary_year=None, second_address=None, second_private_number=None, notes=None, id= None, all_phones_from_homepage=None): self.first_name = first_name self.middle_name = middle_name self.last_name = last_name self.nickname = nickname self.title = title self.company = company self.address = address self.home_number = home_number self.mobile_number = mobile_number self.work_number = work_number self.fax = fax self.first_email = first_email self.second_email = second_email self.third_email = third_email self.wwwpage = wwwpage self.birth_year = birth_year self.anniversary_year = anniversary_year self.second_address = second_address self.second_private_number = second_private_number self.notes = notes self.id = id self.all_phones_from_homepage=all_phones_from_homepage def __repr__(self): return "%s:%s %s" % (self.id, self.first_name, self.last_name) def __eq__(self, other): return (self.id is None or other.id is None or self.id == other.id)\ and self.first_name == other.first_name and self.last_name == other.last_name def id_or_max(con): if con.id: return int(con.id) else: return maxsize

true

f7177c158a0506efce3af2ecad52f923c731c8ea

1,903

py

Python

inference_exploration/cpu/main.py

nbortolotti/tflite-tpu-experiences

8f613e059335d1d90886282f005261917fd9cfd3

[ "Apache-2.0" ]

1

2019-12-06T12:58:33.000Z

inference_exploration/cpu/main.py

nbortolotti/tflite-tpu-experiences

8f613e059335d1d90886282f005261917fd9cfd3

[ "Apache-2.0" ]

9

2020-10-12T13:57:32.000Z

2021-09-16T19:38:26.000Z

inference_exploration/cpu/main.py

nbortolotti/tflite-tpu-experiences

8f613e059335d1d90886282f005261917fd9cfd3

[ "Apache-2.0" ]

null

import os import numpy as np import PIL.Image as Image import matplotlib.pylab as plt import time import tensorflow as tf import tensorflow_hub as hub from tensorflow.keras import layers os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' def image_analysis(classifier, image_shape, img_array): result = classifier.predict(img_array[np.newaxis, ...]) # result.shape predicted_class = np.argmax(result[0], axis=-1) return predicted_class def main(): classifier_url = "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4" image_shape = (224, 224) classifier = tf.keras.Sequential([ hub.KerasLayer(classifier_url, input_shape=image_shape + (3,)) ]) img_file = tf.keras.utils.get_file('image.jpg', 'https://storage.googleapis.com/demostration_images/2.jpg') img = Image.open(img_file).resize(image_shape) img_array = np.array(img) / 255.0 # img_array.shape predicted_class = image_analysis(classifier, image_shape, img_array) labels_path = tf.keras.utils.get_file('ImageNetLabels.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt') imagenet_labels = np.array(open(labels_path).read().splitlines()) # # plt.imshow(img_array) # plt.axis('off') # predicted_class_name = imagenet_labels[predicted_class] # _ = plt.title("Prediction: " + predicted_class_name.title()) # plt.show() for _ in range(5): inferenceTime(img_array, classifier) # explore time to do the inference def inferenceTime(image, mClassifier): start = time.time() result = mClassifier.predict(image[np.newaxis, ...]) end = time.time() print((end - start)*1000) #milliseconds # predicted_class = np.argmax(result[0], axis=-1) # predicted_class_name = mLabels[predicted_class] if __name__ == '__main__': main()

30.206349

123

0.695218

import os import numpy as np import PIL.Image as Image import matplotlib.pylab as plt import time import tensorflow as tf import tensorflow_hub as hub from tensorflow.keras import layers os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' def image_analysis(classifier, image_shape, img_array): result = classifier.predict(img_array[np.newaxis, ...]) predicted_class = np.argmax(result[0], axis=-1) return predicted_class def main(): classifier_url = "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4" image_shape = (224, 224) classifier = tf.keras.Sequential([ hub.KerasLayer(classifier_url, input_shape=image_shape + (3,)) ]) img_file = tf.keras.utils.get_file('image.jpg', 'https://storage.googleapis.com/demostration_images/2.jpg') img = Image.open(img_file).resize(image_shape) img_array = np.array(img) / 255.0 predicted_class = image_analysis(classifier, image_shape, img_array) labels_path = tf.keras.utils.get_file('ImageNetLabels.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt') imagenet_labels = np.array(open(labels_path).read().splitlines()) for _ in range(5): inferenceTime(img_array, classifier) def inferenceTime(image, mClassifier): start = time.time() result = mClassifier.predict(image[np.newaxis, ...]) end = time.time() print((end - start)*1000) if __name__ == '__main__': main()

true

f7177d19acc9ab604ff8ca8dd4f7629ce32e4671

203

py

Python

EOC/prototype/data/datasets/__init__.py

double-fire-0/SystemNoise

ab042dd54371482a18117eb13f816a7472e51590

[ "Apache-2.0" ]

null

EOC/prototype/data/datasets/__init__.py

double-fire-0/SystemNoise

ab042dd54371482a18117eb13f816a7472e51590

[ "Apache-2.0" ]

null

EOC/prototype/data/datasets/__init__.py

double-fire-0/SystemNoise

ab042dd54371482a18117eb13f816a7472e51590

[ "Apache-2.0" ]

null

from .imagenet_dataset import ImageNetDataset, RankedImageNetDataset, DecoderResizeImageNetDataset # noqa from .custom_dataset import CustomDataset # noqa from .imagnetc import ImageNet_C_Dataset

33.833333

106

0.842365

from .imagenet_dataset import ImageNetDataset, RankedImageNetDataset, DecoderResizeImageNetDataset from .custom_dataset import CustomDataset from .imagnetc import ImageNet_C_Dataset

true

f7177d37c526cd723adac7c722303a77bd48abdf

418

py

Python

blog/migrations/0006_auto_20220427_1014.py

ali-abbaszade/mysite

9ef1b1211bd827c178f279e69ddbf4c229c539fa

[ "MIT" ]

null

blog/migrations/0006_auto_20220427_1014.py

ali-abbaszade/mysite

9ef1b1211bd827c178f279e69ddbf4c229c539fa

[ "MIT" ]

null

blog/migrations/0006_auto_20220427_1014.py

ali-abbaszade/mysite

9ef1b1211bd827c178f279e69ddbf4c229c539fa

[ "MIT" ]

null

# Generated by Django 3.2.12 on 2022-04-27 05:44 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('blog', '0005_auto_20220427_1002'), ] operations = [ migrations.RemoveField( model_name='post', name='category', ), migrations.DeleteModel( name='Category', ), ]

19.904762

49

0.538278

from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('blog', '0005_auto_20220427_1002'), ] operations = [ migrations.RemoveField( model_name='post', name='category', ), migrations.DeleteModel( name='Category', ), ]

true

f7177e32b5e25c3c506bb440f649164a5758e294

46,139

py

Python

manila/share/drivers/generic.py

vponomaryov/manila

ffe135a5b35a0964179f0dc148d569037f26a929

[ "Apache-2.0" ]

null

manila/share/drivers/generic.py

vponomaryov/manila

ffe135a5b35a0964179f0dc148d569037f26a929

[ "Apache-2.0" ]

null

manila/share/drivers/generic.py

vponomaryov/manila

ffe135a5b35a0964179f0dc148d569037f26a929

[ "Apache-2.0" ]

null

# Copyright (c) 2014 NetApp, Inc. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """Generic Driver for shares.""" import os import time from oslo_concurrency import processutils from oslo_config import cfg from oslo_log import log from oslo_utils import importutils from oslo_utils import units import retrying import six from manila.common import constants as const from manila import compute from manila import context from manila import exception from manila.i18n import _, _LE, _LI, _LW from manila.share import driver from manila.share.drivers import service_instance from manila import utils from manila import volume LOG = log.getLogger(__name__) share_opts = [ cfg.StrOpt('smb_template_config_path', default='$state_path/smb.conf', help="Path to smb config."), cfg.StrOpt('volume_name_template', default='manila-share-%s', help="Volume name template."), cfg.StrOpt('volume_snapshot_name_template', default='manila-snapshot-%s', help="Volume snapshot name template."), cfg.StrOpt('share_mount_path', default='/shares', help="Parent path in service instance where shares " "will be mounted."), cfg.IntOpt('max_time_to_create_volume', default=180, help="Maximum time to wait for creating cinder volume."), cfg.IntOpt('max_time_to_extend_volume', default=180, help="Maximum time to wait for extending cinder volume."), cfg.IntOpt('max_time_to_attach', default=120, help="Maximum time to wait for attaching cinder volume."), cfg.StrOpt('service_instance_smb_config_path', default='$share_mount_path/smb.conf', help="Path to SMB config in service instance."), cfg.ListOpt('share_helpers', default=[ 'CIFS=manila.share.drivers.helpers.CIFSHelperIPAccess', 'NFS=manila.share.drivers.helpers.NFSHelper', ], help='Specify list of share export helpers.'), cfg.StrOpt('share_volume_fstype', default='ext4', choices=['ext4', 'ext3'], help='Filesystem type of the share volume.'), cfg.StrOpt('cinder_volume_type', help='Name or id of cinder volume type which will be used ' 'for all volumes created by driver.'), ] CONF = cfg.CONF CONF.register_opts(share_opts) # NOTE(u_glide): These constants refer to the column number in the "df" output BLOCK_DEVICE_SIZE_INDEX = 1 USED_SPACE_INDEX = 2 def ensure_server(f): def wrap(self, context, *args, **kwargs): server = kwargs.get('share_server') if not self.driver_handles_share_servers: if not server: server = self.service_instance_manager.get_common_server() kwargs['share_server'] = server else: raise exception.ManilaException( _("Share server handling is not available. " "But 'share_server' was provided. '%s'. " "Share network should not be used.") % server.get('id')) elif not server: raise exception.ManilaException( _("Share server handling is enabled. But 'share_server' " "is not provided. Make sure you used 'share_network'.")) if not server.get('backend_details'): raise exception.ManilaException( _("Share server '%s' does not have backend details.") % server['id']) if not self.service_instance_manager.ensure_service_instance( context, server['backend_details']): raise exception.ServiceInstanceUnavailable() return f(self, context, *args, **kwargs) return wrap class GenericShareDriver(driver.ExecuteMixin, driver.ShareDriver): """Executes commands relating to Shares.""" def __init__(self, *args, **kwargs): """Do initialization.""" super(GenericShareDriver, self).__init__( [False, True], *args, **kwargs) self.admin_context = context.get_admin_context() self.configuration.append_config_values(share_opts) self._helpers = {} self.backend_name = self.configuration.safe_get( 'share_backend_name') or "Cinder_Volumes" self.ssh_connections = {} self._setup_service_instance_manager() self.private_storage = kwargs.get('private_storage') def _setup_service_instance_manager(self): self.service_instance_manager = ( service_instance.ServiceInstanceManager( driver_config=self.configuration)) def _ssh_exec(self, server, command, check_exit_code=True): connection = self.ssh_connections.get(server['instance_id']) ssh_conn_timeout = self.configuration.ssh_conn_timeout if not connection: ssh_pool = utils.SSHPool(server['ip'], 22, ssh_conn_timeout, server['username'], server.get('password'), server.get('pk_path'), max_size=1) ssh = ssh_pool.create() self.ssh_connections[server['instance_id']] = (ssh_pool, ssh) else: ssh_pool, ssh = connection if not ssh.get_transport().is_active(): ssh_pool.remove(ssh) ssh = ssh_pool.create() self.ssh_connections[server['instance_id']] = (ssh_pool, ssh) # (aovchinnikov): ssh_execute does not behave well when passed # parameters with spaces. wrap = lambda token: "\"" + token + "\"" command = [wrap(tkn) if tkn.count(' ') else tkn for tkn in command] return processutils.ssh_execute(ssh, ' '.join(command), check_exit_code=check_exit_code) def check_for_setup_error(self): """Returns an error if prerequisites aren't met.""" def do_setup(self, context): """Any initialization the generic driver does while starting.""" super(GenericShareDriver, self).do_setup(context) self.compute_api = compute.API() self.volume_api = volume.API() self._setup_helpers() common_sv_available = False share_server = None sv_fetch_retry_interval = 5 while not (common_sv_available or self.driver_handles_share_servers): try: # Verify availability of common server share_server = ( self.service_instance_manager.get_common_server()) common_sv_available = self._is_share_server_active( context, share_server) except Exception as ex: LOG.error(ex) if not common_sv_available: time.sleep(sv_fetch_retry_interval) LOG.warning(_LW("Waiting for the common service VM to become " "available. " "Driver is currently uninitialized. " "Share server: %(share_server)s " "Retry interval: %(retry_interval)s"), dict(share_server=share_server, retry_interval=sv_fetch_retry_interval)) def _setup_helpers(self): """Initializes protocol-specific NAS drivers.""" helpers = self.configuration.share_helpers if helpers: for helper_str in helpers: share_proto, __, import_str = helper_str.partition('=') helper = importutils.import_class(import_str) self._helpers[share_proto.upper()] = helper( self._execute, self._ssh_exec, self.configuration) else: raise exception.ManilaException( "No protocol helpers selected for Generic Driver. " "Please specify using config option 'share_helpers'.") @ensure_server def create_share(self, context, share, share_server=None): """Creates share.""" return self._create_share( context, share, snapshot=None, share_server=share_server, ) def _create_share(self, context, share, snapshot, share_server=None): helper = self._get_helper(share) server_details = share_server['backend_details'] volume = self._allocate_container( self.admin_context, share, snapshot=snapshot) volume = self._attach_volume( self.admin_context, share, server_details['instance_id'], volume) if not snapshot: self._format_device(server_details, volume) self._mount_device(share, server_details, volume) export_locations = helper.create_exports( server_details, share['name']) return export_locations @utils.retry(exception.ProcessExecutionError, backoff_rate=1) def _is_device_file_available(self, server_details, volume): """Checks whether the device file is available""" command = ['sudo', 'test', '-b', volume['mountpoint']] self._ssh_exec(server_details, command) def _format_device(self, server_details, volume): """Formats device attached to the service vm.""" self._is_device_file_available(server_details, volume) command = ['sudo', 'mkfs.%s' % self.configuration.share_volume_fstype, volume['mountpoint']] self._ssh_exec(server_details, command) def _is_device_mounted(self, mount_path, server_details, volume=None): """Checks whether volume already mounted or not.""" log_data = { 'mount_path': mount_path, 'server_id': server_details['instance_id'], } if volume and volume.get('mountpoint', ''): log_data['volume_id'] = volume['id'] log_data['dev_mount_path'] = volume['mountpoint'] msg = ("Checking whether volume '%(volume_id)s' with mountpoint " "'%(dev_mount_path)s' is mounted on mount path '%(mount_p" "ath)s' on server '%(server_id)s' or not." % log_data) else: msg = ("Checking whether mount path '%(mount_path)s' exists on " "server '%(server_id)s' or not." % log_data) LOG.debug(msg) mounts_list_cmd = ['sudo', 'mount'] output, __ = self._ssh_exec(server_details, mounts_list_cmd) mounts = output.split('\n') for mount in mounts: mount_elements = mount.split(' ') if (len(mount_elements) > 2 and mount_path == mount_elements[2]): if volume: # Mount goes with device path and mount path if (volume.get('mountpoint', '') == mount_elements[0]): return True else: # Unmount goes only by mount path return True return False def _sync_mount_temp_and_perm_files(self, server_details): """Sync temporary and permanent files for mounted filesystems.""" try: self._ssh_exec( server_details, ['sudo', 'cp', const.MOUNT_FILE_TEMP, const.MOUNT_FILE], ) except exception.ProcessExecutionError as e: LOG.error(_LE("Failed to sync mount files on server '%s'."), server_details['instance_id']) raise exception.ShareBackendException(msg=six.text_type(e)) try: # Remount it to avoid postponed point of failure self._ssh_exec(server_details, ['sudo', 'mount', '-a']) except exception.ProcessExecutionError as e: LOG.error(_LE("Failed to mount all shares on server '%s'."), server_details['instance_id']) raise exception.ShareBackendException(msg=six.text_type(e)) def _mount_device(self, share, server_details, volume): """Mounts block device to the directory on service vm. Mounts attached and formatted block device to the directory if not mounted yet. """ @utils.synchronized('generic_driver_mounts_' '%s' % server_details['instance_id']) def _mount_device_with_lock(): mount_path = self._get_mount_path(share) device_path = volume['mountpoint'] log_data = { 'dev': device_path, 'path': mount_path, 'server': server_details['instance_id'], } try: if not self._is_device_mounted(mount_path, server_details, volume): LOG.debug("Mounting '%(dev)s' to path '%(path)s' on " "server '%(server)s'.", log_data) mount_cmd = ( 'sudo', 'mkdir', '-p', mount_path, '&&', 'sudo', 'mount', device_path, mount_path, '&&', 'sudo', 'chmod', '777', mount_path, '&&', 'sudo', 'umount', mount_path, # NOTE(vponomaryov): 'tune2fs' is required to make # filesystem of share created from snapshot have # unique ID, in case of LVM volumes, by default, # it will have the same UUID as source volume one. # 'tune2fs' command can be executed only when device # is not mounted and also, in current case, it takes # effect only after it was mounted. Closes #1645751 '&&', 'sudo', 'tune2fs', '-U', 'random', device_path, '&&', 'sudo', 'mount', device_path, mount_path, ) self._ssh_exec(server_details, mount_cmd) # Add mount permanently self._sync_mount_temp_and_perm_files(server_details) else: LOG.warning(_LW("Mount point '%(path)s' already exists on " "server '%(server)s'."), log_data) except exception.ProcessExecutionError as e: raise exception.ShareBackendException(msg=six.text_type(e)) return _mount_device_with_lock() @utils.retry(exception.ProcessExecutionError) def _unmount_device(self, share, server_details): """Unmounts block device from directory on service vm.""" @utils.synchronized('generic_driver_mounts_' '%s' % server_details['instance_id']) def _unmount_device_with_lock(): mount_path = self._get_mount_path(share) log_data = { 'path': mount_path, 'server': server_details['instance_id'], } if self._is_device_mounted(mount_path, server_details): LOG.debug("Unmounting path '%(path)s' on server " "'%(server)s'.", log_data) unmount_cmd = ['sudo', 'umount', mount_path, '&&', 'sudo', 'rmdir', mount_path] self._ssh_exec(server_details, unmount_cmd) # Remove mount permanently self._sync_mount_temp_and_perm_files(server_details) else: LOG.warning(_LW("Mount point '%(path)s' does not exist on " "server '%(server)s'."), log_data) return _unmount_device_with_lock() def _get_mount_path(self, share): """Returns the path to use for mount device in service vm.""" return os.path.join(self.configuration.share_mount_path, share['name']) def _attach_volume(self, context, share, instance_id, volume): """Attaches cinder volume to service vm.""" @utils.synchronized( "generic_driver_attach_detach_%s" % instance_id, external=True) def do_attach(volume): if volume['status'] == 'in-use': attached_volumes = [vol.id for vol in self.compute_api.instance_volumes_list( self.admin_context, instance_id)] if volume['id'] in attached_volumes: return volume else: raise exception.ManilaException( _('Volume %s is already attached to another instance') % volume['id']) @retrying.retry(stop_max_attempt_number=3, wait_fixed=2000, retry_on_exception=lambda exc: True) def attach_volume(): self.compute_api.instance_volume_attach( self.admin_context, instance_id, volume['id']) attach_volume() t = time.time() while time.time() - t < self.configuration.max_time_to_attach: volume = self.volume_api.get(context, volume['id']) if volume['status'] == 'in-use': return volume elif volume['status'] != 'attaching': raise exception.ManilaException( _('Failed to attach volume %s') % volume['id']) time.sleep(1) else: err_msg = { 'volume_id': volume['id'], 'max_time': self.configuration.max_time_to_attach } raise exception.ManilaException( _('Volume %(volume_id)s has not been attached in ' '%(max_time)ss. Giving up.') % err_msg) return do_attach(volume) def _get_volume_name(self, share_id): return self.configuration.volume_name_template % share_id def _get_volume(self, context, share_id): """Finds volume, associated to the specific share.""" volume_id = self.private_storage.get(share_id, 'volume_id') if volume_id is not None: return self.volume_api.get(context, volume_id) else: # Fallback to legacy method return self._get_volume_legacy(context, share_id) def _get_volume_legacy(self, context, share_id): # NOTE(u_glide): this method is deprecated and will be removed in # future versions volume_name = self._get_volume_name(share_id) search_opts = {'name': volume_name} if context.is_admin: search_opts['all_tenants'] = True volumes_list = self.volume_api.get_all(context, search_opts) if len(volumes_list) == 1: return volumes_list[0] elif len(volumes_list) > 1: LOG.error( _LE("Expected only one volume in volume list with name " "'%(name)s', but got more than one in a result - " "'%(result)s'."), { 'name': volume_name, 'result': volumes_list}) raise exception.ManilaException( _("Error. Ambiguous volumes for name '%s'") % volume_name) return None def _get_volume_snapshot(self, context, snapshot_id): """Find volume snapshot associated to the specific share snapshot.""" volume_snapshot_id = self.private_storage.get( snapshot_id, 'volume_snapshot_id') if volume_snapshot_id is not None: return self.volume_api.get_snapshot(context, volume_snapshot_id) else: # Fallback to legacy method return self._get_volume_snapshot_legacy(context, snapshot_id) def _get_volume_snapshot_legacy(self, context, snapshot_id): # NOTE(u_glide): this method is deprecated and will be removed in # future versions volume_snapshot_name = ( self.configuration.volume_snapshot_name_template % snapshot_id) volume_snapshot_list = self.volume_api.get_all_snapshots( context, {'name': volume_snapshot_name}) volume_snapshot = None if len(volume_snapshot_list) == 1: volume_snapshot = volume_snapshot_list[0] elif len(volume_snapshot_list) > 1: LOG.error( _LE("Expected only one volume snapshot in list with name " "'%(name)s', but got more than one in a result - " "'%(result)s'."), { 'name': volume_snapshot_name, 'result': volume_snapshot_list}) raise exception.ManilaException( _('Error. Ambiguous volume snaphots')) return volume_snapshot def _detach_volume(self, context, share, server_details): """Detaches cinder volume from service vm.""" instance_id = server_details['instance_id'] @utils.synchronized( "generic_driver_attach_detach_%s" % instance_id, external=True) def do_detach(): attached_volumes = [vol.id for vol in self.compute_api.instance_volumes_list( self.admin_context, instance_id)] try: volume = self._get_volume(context, share['id']) except exception.VolumeNotFound: LOG.warning(_LW("Volume not found for share %s. " "Possibly already deleted."), share['id']) volume = None if volume and volume['id'] in attached_volumes: self.compute_api.instance_volume_detach( self.admin_context, instance_id, volume['id'] ) t = time.time() while time.time() - t < self.configuration.max_time_to_attach: volume = self.volume_api.get(context, volume['id']) if volume['status'] in (const.STATUS_AVAILABLE, const.STATUS_ERROR): break time.sleep(1) else: err_msg = { 'volume_id': volume['id'], 'max_time': self.configuration.max_time_to_attach } raise exception.ManilaException( _('Volume %(volume_id)s has not been detached in ' '%(max_time)ss. Giving up.') % err_msg) do_detach() def _allocate_container(self, context, share, snapshot=None): """Creates cinder volume, associated to share by name.""" volume_snapshot = None if snapshot: volume_snapshot = self._get_volume_snapshot(context, snapshot['id']) volume = self.volume_api.create( context, share['size'], self.configuration.volume_name_template % share['id'], '', snapshot=volume_snapshot, volume_type=self.configuration.cinder_volume_type, availability_zone=share['availability_zone']) self.private_storage.update( share['id'], {'volume_id': volume['id']}) msg_error = _('Failed to create volume') msg_timeout = ( _('Volume has not been created in %ss. Giving up') % self.configuration.max_time_to_create_volume ) return self._wait_for_available_volume( volume, self.configuration.max_time_to_create_volume, msg_error=msg_error, msg_timeout=msg_timeout ) def _wait_for_available_volume(self, volume, timeout, msg_error, msg_timeout, expected_size=None): t = time.time() while time.time() - t < timeout: if volume['status'] == const.STATUS_AVAILABLE: if expected_size and volume['size'] != expected_size: LOG.debug("The volume %(vol_id)s is available but the " "volume size does not match the expected size. " "A volume resize operation may be pending. " "Expected size: %(expected_size)s, " "Actual size: %(volume_size)s.", dict(vol_id=volume['id'], expected_size=expected_size, volume_size=volume['size'])) else: break elif 'error' in volume['status'].lower(): raise exception.ManilaException(msg_error) time.sleep(1) volume = self.volume_api.get(self.admin_context, volume['id']) else: raise exception.ManilaException(msg_timeout) return volume def _deallocate_container(self, context, share): """Deletes cinder volume.""" try: volume = self._get_volume(context, share['id']) except exception.VolumeNotFound: LOG.info(_LI("Volume not found. Already deleted?")) volume = None if volume: if volume['status'] == 'in-use': raise exception.ManilaException( _('Volume is still in use and ' 'cannot be deleted now.')) self.volume_api.delete(context, volume['id']) t = time.time() while (time.time() - t < self.configuration.max_time_to_create_volume): try: volume = self.volume_api.get(context, volume['id']) except exception.VolumeNotFound: LOG.debug('Volume was deleted successfully') break time.sleep(1) else: raise exception.ManilaException( _('Volume have not been ' 'deleted in %ss. Giving up') % self.configuration.max_time_to_create_volume) def _update_share_stats(self): """Retrieve stats info from share volume group.""" data = dict( share_backend_name=self.backend_name, storage_protocol='NFS_CIFS', reserved_percentage=self.configuration.reserved_share_percentage, ) super(GenericShareDriver, self)._update_share_stats(data) @ensure_server def create_share_from_snapshot(self, context, share, snapshot, share_server=None): """Is called to create share from snapshot.""" return self._create_share( context, share, snapshot=snapshot, share_server=share_server, ) @ensure_server def extend_share(self, share, new_size, share_server=None): server_details = share_server['backend_details'] helper = self._get_helper(share) helper.disable_access_for_maintenance(server_details, share['name']) self._unmount_device(share, server_details) volume = self._get_volume(self.admin_context, share['id']) if int(new_size) > volume['size']: self._detach_volume(self.admin_context, share, server_details) volume = self._extend_volume(self.admin_context, volume, new_size) volume = self._attach_volume( self.admin_context, share, server_details['instance_id'], volume) self._resize_filesystem(server_details, volume) self._mount_device(share, server_details, volume) helper.restore_access_after_maintenance(server_details, share['name']) def _extend_volume(self, context, volume, new_size): self.volume_api.extend(context, volume['id'], new_size) msg_error = _('Failed to extend volume %s') % volume['id'] msg_timeout = ( _('Volume has not been extended in %ss. Giving up') % self.configuration.max_time_to_extend_volume ) return self._wait_for_available_volume( volume, self.configuration.max_time_to_extend_volume, msg_error=msg_error, msg_timeout=msg_timeout, expected_size=new_size ) @ensure_server def shrink_share(self, share, new_size, share_server=None): server_details = share_server['backend_details'] helper = self._get_helper(share) export_location = share['export_locations'][0]['path'] mount_path = helper.get_share_path_by_export_location( server_details, export_location) consumed_space = self._get_consumed_space(mount_path, server_details) LOG.debug("Consumed space on share: %s", consumed_space) if consumed_space >= new_size: raise exception.ShareShrinkingPossibleDataLoss( share_id=share['id']) volume = self._get_volume(self.admin_context, share['id']) helper.disable_access_for_maintenance(server_details, share['name']) self._unmount_device(share, server_details) try: self._resize_filesystem(server_details, volume, new_size=new_size) except exception.Invalid: raise exception.ShareShrinkingPossibleDataLoss( share_id=share['id']) except Exception as e: msg = _("Cannot shrink share: %s") % six.text_type(e) raise exception.Invalid(msg) finally: self._mount_device(share, server_details, volume) helper.restore_access_after_maintenance(server_details, share['name']) def _resize_filesystem(self, server_details, volume, new_size=None): """Resize filesystem of provided volume.""" check_command = ['sudo', 'fsck', '-pf', volume['mountpoint']] self._ssh_exec(server_details, check_command) command = ['sudo', 'resize2fs', volume['mountpoint']] if new_size: command.append("%sG" % six.text_type(new_size)) try: self._ssh_exec(server_details, command) except processutils.ProcessExecutionError as e: if e.stderr.find('New size smaller than minimum') != -1: msg = (_("Invalid 'new_size' provided: %s") % six.text_type(new_size)) raise exception.Invalid(msg) else: msg = _("Cannot resize file-system: %s") % six.text_type(e) raise exception.ManilaException(msg) def _is_share_server_active(self, context, share_server): """Check if the share server is active.""" has_active_share_server = ( share_server and share_server.get('backend_details') and self.service_instance_manager.ensure_service_instance( context, share_server['backend_details'])) return has_active_share_server def delete_share(self, context, share, share_server=None): """Deletes share.""" helper = self._get_helper(share) if not self.driver_handles_share_servers: share_server = self.service_instance_manager.get_common_server() if self._is_share_server_active(context, share_server): helper.remove_exports( share_server['backend_details'], share['name']) self._unmount_device(share, share_server['backend_details']) self._detach_volume(self.admin_context, share, share_server['backend_details']) # Note(jun): It is an intended breakage to deal with the cases # with any reason that caused absence of Nova instances. self._deallocate_container(self.admin_context, share) self.private_storage.delete(share['id']) def create_snapshot(self, context, snapshot, share_server=None): """Creates a snapshot.""" model_update = {} volume = self._get_volume(self.admin_context, snapshot['share_id']) volume_snapshot_name = (self.configuration. volume_snapshot_name_template % snapshot['id']) volume_snapshot = self.volume_api.create_snapshot_force( self.admin_context, volume['id'], volume_snapshot_name, '') t = time.time() while time.time() - t < self.configuration.max_time_to_create_volume: if volume_snapshot['status'] == const.STATUS_AVAILABLE: break if volume_snapshot['status'] == const.STATUS_ERROR: raise exception.ManilaException(_('Failed to create volume ' 'snapshot')) time.sleep(1) volume_snapshot = self.volume_api.get_snapshot( self.admin_context, volume_snapshot['id']) # NOTE(xyang): We should look at whether we still need to save # volume_snapshot_id in private_storage later, now that is saved # in provider_location. self.private_storage.update( snapshot['id'], {'volume_snapshot_id': volume_snapshot['id']}) # NOTE(xyang): Need to update provider_location in the db so # that it can be used in manage/unmanage snapshot tempest tests. model_update['provider_location'] = volume_snapshot['id'] else: raise exception.ManilaException( _('Volume snapshot have not been ' 'created in %ss. Giving up') % self.configuration.max_time_to_create_volume) return model_update def delete_snapshot(self, context, snapshot, share_server=None): """Deletes a snapshot.""" volume_snapshot = self._get_volume_snapshot(self.admin_context, snapshot['id']) if volume_snapshot is None: return self.volume_api.delete_snapshot(self.admin_context, volume_snapshot['id']) t = time.time() while time.time() - t < self.configuration.max_time_to_create_volume: try: snapshot = self.volume_api.get_snapshot(self.admin_context, volume_snapshot['id']) except exception.VolumeSnapshotNotFound: LOG.debug('Volume snapshot was deleted successfully') self.private_storage.delete(snapshot['id']) break time.sleep(1) else: raise exception.ManilaException( _('Volume snapshot have not been ' 'deleted in %ss. Giving up') % self.configuration.max_time_to_create_volume) @ensure_server def ensure_share(self, context, share, share_server=None): """Ensure that storage are mounted and exported.""" helper = self._get_helper(share) volume = self._get_volume(context, share['id']) # NOTE(vponomaryov): volume can be None for managed shares if volume: volume = self._attach_volume( context, share, share_server['backend_details']['instance_id'], volume) self._mount_device(share, share_server['backend_details'], volume) helper.create_exports( share_server['backend_details'], share['name'], recreate=True) @ensure_server def update_access(self, context, share, access_rules, add_rules, delete_rules, share_server=None): """Update access rules for given share. This driver has two different behaviors according to parameters: 1. Recovery after error - 'access_rules' contains all access_rules, 'add_rules' and 'delete_rules' shall be empty. Previously existing access rules are cleared and then added back according to 'access_rules'. 2. Adding/Deleting of several access rules - 'access_rules' contains all access_rules, 'add_rules' and 'delete_rules' contain rules which should be added/deleted. Rules in 'access_rules' are ignored and only rules from 'add_rules' and 'delete_rules' are applied. :param context: Current context :param share: Share model with share data. :param access_rules: All access rules for given share :param add_rules: Empty List or List of access rules which should be added. access_rules already contains these rules. :param delete_rules: Empty List or List of access rules which should be removed. access_rules doesn't contain these rules. :param share_server: None or Share server model """ self._get_helper(share).update_access(share_server['backend_details'], share['name'], access_rules, add_rules=add_rules, delete_rules=delete_rules) def _get_helper(self, share): helper = self._helpers.get(share['share_proto']) if helper: return helper else: raise exception.InvalidShare( reason="Wrong, unsupported or disabled protocol") def get_network_allocations_number(self): """Get number of network interfaces to be created.""" # NOTE(vponomaryov): Generic driver does not need allocations, because # Nova will handle it. It is valid for all multitenant drivers, that # use service instance provided by Nova. return 0 def _setup_server(self, network_info, metadata=None): msg = "Creating share server '%s'." LOG.debug(msg % network_info['server_id']) server = self.service_instance_manager.set_up_service_instance( self.admin_context, network_info) for helper in self._helpers.values(): helper.init_helper(server) return server def _teardown_server(self, server_details, security_services=None): instance_id = server_details.get("instance_id") LOG.debug("Removing share infrastructure for service instance '%s'.", instance_id) self.service_instance_manager.delete_service_instance( self.admin_context, server_details) def manage_existing(self, share, driver_options): """Manage existing share to manila. Generic driver accepts only one driver_option 'volume_id'. If an administrator provides this option, then appropriate Cinder volume will be managed by Manila as well. :param share: share data :param driver_options: Empty dict or dict with 'volume_id' option. :return: dict with share size, example: {'size': 1} """ helper = self._get_helper(share) share_server = self.service_instance_manager.get_common_server() server_details = share_server['backend_details'] old_export_location = share['export_locations'][0]['path'] mount_path = helper.get_share_path_by_export_location( share_server['backend_details'], old_export_location) LOG.debug("Manage: mount path = %s", mount_path) mounted = self._is_device_mounted(mount_path, server_details) LOG.debug("Manage: is share mounted = %s", mounted) if not mounted: msg = _("Provided share %s is not mounted.") % share['id'] raise exception.ManageInvalidShare(reason=msg) def get_volume(): if 'volume_id' in driver_options: try: return self.volume_api.get( self.admin_context, driver_options['volume_id']) except exception.VolumeNotFound as e: raise exception.ManageInvalidShare(reason=six.text_type(e)) # NOTE(vponomaryov): Manila can only combine volume name by itself, # nowhere to get volume ID from. Return None since Cinder volume # names are not unique or fixed, hence, they can not be used for # sure. return None share_volume = get_volume() if share_volume: instance_volumes = self.compute_api.instance_volumes_list( self.admin_context, server_details['instance_id']) attached_volumes = [vol.id for vol in instance_volumes] LOG.debug('Manage: attached volumes = %s', six.text_type(attached_volumes)) if share_volume['id'] not in attached_volumes: msg = _("Provided volume %s is not attached " "to service instance.") % share_volume['id'] raise exception.ManageInvalidShare(reason=msg) linked_volume_name = self._get_volume_name(share['id']) if share_volume['name'] != linked_volume_name: LOG.debug('Manage: volume_id = %s' % share_volume['id']) self.volume_api.update(self.admin_context, share_volume['id'], {'name': linked_volume_name}) self.private_storage.update( share['id'], {'volume_id': share_volume['id']}) share_size = share_volume['size'] else: share_size = self._get_mounted_share_size( mount_path, share_server['backend_details']) export_locations = helper.get_exports_for_share( server_details, old_export_location) return {'size': share_size, 'export_locations': export_locations} def manage_existing_snapshot(self, snapshot, driver_options): """Manage existing share snapshot with manila. :param snapshot: Snapshot data :param driver_options: Not used by the Generic driver currently :return: dict with share snapshot size, example: {'size': 1} """ model_update = {} volume_snapshot = None snapshot_size = snapshot.get('share_size', 0) provider_location = snapshot.get('provider_location') try: volume_snapshot = self.volume_api.get_snapshot( self.admin_context, provider_location) except exception.VolumeSnapshotNotFound as e: raise exception.ManageInvalidShareSnapshot( reason=six.text_type(e)) if volume_snapshot: snapshot_size = volume_snapshot['size'] # NOTE(xyang): volume_snapshot_id is saved in private_storage # in create_snapshot, so saving it here too for consistency. # We should look at whether we still need to save it in # private_storage later. self.private_storage.update( snapshot['id'], {'volume_snapshot_id': volume_snapshot['id']}) # NOTE(xyang): provider_location is used to map a Manila snapshot # to its name on the storage backend and prevent managing of the # same snapshot twice. model_update['provider_location'] = volume_snapshot['id'] model_update['size'] = snapshot_size return model_update def unmanage_snapshot(self, snapshot): """Unmanage share snapshot with manila.""" self.private_storage.delete(snapshot['id']) def _get_mount_stats_by_index(self, mount_path, server_details, index, block_size='G'): """Get mount stats using df shell command. :param mount_path: Share path on share server :param server_details: Share server connection details :param index: Data index in df command output: BLOCK_DEVICE_SIZE_INDEX - Size of block device USED_SPACE_INDEX - Used space :param block_size: size of block (example: G, M, Mib, etc) :returns: value of provided index """ share_size_cmd = ['df', '-PB%s' % block_size, mount_path] output, __ = self._ssh_exec(server_details, share_size_cmd) lines = output.split('\n') return int(lines[1].split()[index][:-1]) def _get_mounted_share_size(self, mount_path, server_details): try: size = self._get_mount_stats_by_index( mount_path, server_details, BLOCK_DEVICE_SIZE_INDEX) except Exception as e: msg = _("Cannot calculate size of share %(path)s : %(error)s") % { 'path': mount_path, 'error': six.text_type(e) } raise exception.ManageInvalidShare(reason=msg) return size def _get_consumed_space(self, mount_path, server_details): try: size = self._get_mount_stats_by_index( mount_path, server_details, USED_SPACE_INDEX, block_size='M') size /= float(units.Ki) except Exception as e: msg = _("Cannot calculate consumed space on share " "%(path)s : %(error)s") % { 'path': mount_path, 'error': six.text_type(e) } raise exception.InvalidShare(reason=msg) return size

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import os import time from oslo_concurrency import processutils from oslo_config import cfg from oslo_log import log from oslo_utils import importutils from oslo_utils import units import retrying import six from manila.common import constants as const from manila import compute from manila import context from manila import exception from manila.i18n import _, _LE, _LI, _LW from manila.share import driver from manila.share.drivers import service_instance from manila import utils from manila import volume LOG = log.getLogger(__name__) share_opts = [ cfg.StrOpt('smb_template_config_path', default='$state_path/smb.conf', help="Path to smb config."), cfg.StrOpt('volume_name_template', default='manila-share-%s', help="Volume name template."), cfg.StrOpt('volume_snapshot_name_template', default='manila-snapshot-%s', help="Volume snapshot name template."), cfg.StrOpt('share_mount_path', default='/shares', help="Parent path in service instance where shares " "will be mounted."), cfg.IntOpt('max_time_to_create_volume', default=180, help="Maximum time to wait for creating cinder volume."), cfg.IntOpt('max_time_to_extend_volume', default=180, help="Maximum time to wait for extending cinder volume."), cfg.IntOpt('max_time_to_attach', default=120, help="Maximum time to wait for attaching cinder volume."), cfg.StrOpt('service_instance_smb_config_path', default='$share_mount_path/smb.conf', help="Path to SMB config in service instance."), cfg.ListOpt('share_helpers', default=[ 'CIFS=manila.share.drivers.helpers.CIFSHelperIPAccess', 'NFS=manila.share.drivers.helpers.NFSHelper', ], help='Specify list of share export helpers.'), cfg.StrOpt('share_volume_fstype', default='ext4', choices=['ext4', 'ext3'], help='Filesystem type of the share volume.'), cfg.StrOpt('cinder_volume_type', help='Name or id of cinder volume type which will be used ' 'for all volumes created by driver.'), ] CONF = cfg.CONF CONF.register_opts(share_opts) BLOCK_DEVICE_SIZE_INDEX = 1 USED_SPACE_INDEX = 2 def ensure_server(f): def wrap(self, context, *args, **kwargs): server = kwargs.get('share_server') if not self.driver_handles_share_servers: if not server: server = self.service_instance_manager.get_common_server() kwargs['share_server'] = server else: raise exception.ManilaException( _("Share server handling is not available. " "But 'share_server' was provided. '%s'. " "Share network should not be used.") % server.get('id')) elif not server: raise exception.ManilaException( _("Share server handling is enabled. But 'share_server' " "is not provided. Make sure you used 'share_network'.")) if not server.get('backend_details'): raise exception.ManilaException( _("Share server '%s' does not have backend details.") % server['id']) if not self.service_instance_manager.ensure_service_instance( context, server['backend_details']): raise exception.ServiceInstanceUnavailable() return f(self, context, *args, **kwargs) return wrap class GenericShareDriver(driver.ExecuteMixin, driver.ShareDriver): def __init__(self, *args, **kwargs): super(GenericShareDriver, self).__init__( [False, True], *args, **kwargs) self.admin_context = context.get_admin_context() self.configuration.append_config_values(share_opts) self._helpers = {} self.backend_name = self.configuration.safe_get( 'share_backend_name') or "Cinder_Volumes" self.ssh_connections = {} self._setup_service_instance_manager() self.private_storage = kwargs.get('private_storage') def _setup_service_instance_manager(self): self.service_instance_manager = ( service_instance.ServiceInstanceManager( driver_config=self.configuration)) def _ssh_exec(self, server, command, check_exit_code=True): connection = self.ssh_connections.get(server['instance_id']) ssh_conn_timeout = self.configuration.ssh_conn_timeout if not connection: ssh_pool = utils.SSHPool(server['ip'], 22, ssh_conn_timeout, server['username'], server.get('password'), server.get('pk_path'), max_size=1) ssh = ssh_pool.create() self.ssh_connections[server['instance_id']] = (ssh_pool, ssh) else: ssh_pool, ssh = connection if not ssh.get_transport().is_active(): ssh_pool.remove(ssh) ssh = ssh_pool.create() self.ssh_connections[server['instance_id']] = (ssh_pool, ssh) wrap = lambda token: "\"" + token + "\"" command = [wrap(tkn) if tkn.count(' ') else tkn for tkn in command] return processutils.ssh_execute(ssh, ' '.join(command), check_exit_code=check_exit_code) def check_for_setup_error(self): def do_setup(self, context): super(GenericShareDriver, self).do_setup(context) self.compute_api = compute.API() self.volume_api = volume.API() self._setup_helpers() common_sv_available = False share_server = None sv_fetch_retry_interval = 5 while not (common_sv_available or self.driver_handles_share_servers): try: share_server = ( self.service_instance_manager.get_common_server()) common_sv_available = self._is_share_server_active( context, share_server) except Exception as ex: LOG.error(ex) if not common_sv_available: time.sleep(sv_fetch_retry_interval) LOG.warning(_LW("Waiting for the common service VM to become " "available. " "Driver is currently uninitialized. " "Share server: %(share_server)s " "Retry interval: %(retry_interval)s"), dict(share_server=share_server, retry_interval=sv_fetch_retry_interval)) def _setup_helpers(self): helpers = self.configuration.share_helpers if helpers: for helper_str in helpers: share_proto, __, import_str = helper_str.partition('=') helper = importutils.import_class(import_str) self._helpers[share_proto.upper()] = helper( self._execute, self._ssh_exec, self.configuration) else: raise exception.ManilaException( "No protocol helpers selected for Generic Driver. " "Please specify using config option 'share_helpers'.") @ensure_server def create_share(self, context, share, share_server=None): return self._create_share( context, share, snapshot=None, share_server=share_server, ) def _create_share(self, context, share, snapshot, share_server=None): helper = self._get_helper(share) server_details = share_server['backend_details'] volume = self._allocate_container( self.admin_context, share, snapshot=snapshot) volume = self._attach_volume( self.admin_context, share, server_details['instance_id'], volume) if not snapshot: self._format_device(server_details, volume) self._mount_device(share, server_details, volume) export_locations = helper.create_exports( server_details, share['name']) return export_locations @utils.retry(exception.ProcessExecutionError, backoff_rate=1) def _is_device_file_available(self, server_details, volume): command = ['sudo', 'test', '-b', volume['mountpoint']] self._ssh_exec(server_details, command) def _format_device(self, server_details, volume): self._is_device_file_available(server_details, volume) command = ['sudo', 'mkfs.%s' % self.configuration.share_volume_fstype, volume['mountpoint']] self._ssh_exec(server_details, command) def _is_device_mounted(self, mount_path, server_details, volume=None): log_data = { 'mount_path': mount_path, 'server_id': server_details['instance_id'], } if volume and volume.get('mountpoint', ''): log_data['volume_id'] = volume['id'] log_data['dev_mount_path'] = volume['mountpoint'] msg = ("Checking whether volume '%(volume_id)s' with mountpoint " "'%(dev_mount_path)s' is mounted on mount path '%(mount_p" "ath)s' on server '%(server_id)s' or not." % log_data) else: msg = ("Checking whether mount path '%(mount_path)s' exists on " "server '%(server_id)s' or not." % log_data) LOG.debug(msg) mounts_list_cmd = ['sudo', 'mount'] output, __ = self._ssh_exec(server_details, mounts_list_cmd) mounts = output.split('\n') for mount in mounts: mount_elements = mount.split(' ') if (len(mount_elements) > 2 and mount_path == mount_elements[2]): if volume: if (volume.get('mountpoint', '') == mount_elements[0]): return True else: return True return False def _sync_mount_temp_and_perm_files(self, server_details): try: self._ssh_exec( server_details, ['sudo', 'cp', const.MOUNT_FILE_TEMP, const.MOUNT_FILE], ) except exception.ProcessExecutionError as e: LOG.error(_LE("Failed to sync mount files on server '%s'."), server_details['instance_id']) raise exception.ShareBackendException(msg=six.text_type(e)) try: self._ssh_exec(server_details, ['sudo', 'mount', '-a']) except exception.ProcessExecutionError as e: LOG.error(_LE("Failed to mount all shares on server '%s'."), server_details['instance_id']) raise exception.ShareBackendException(msg=six.text_type(e)) def _mount_device(self, share, server_details, volume): @utils.synchronized('generic_driver_mounts_' '%s' % server_details['instance_id']) def _mount_device_with_lock(): mount_path = self._get_mount_path(share) device_path = volume['mountpoint'] log_data = { 'dev': device_path, 'path': mount_path, 'server': server_details['instance_id'], } try: if not self._is_device_mounted(mount_path, server_details, volume): LOG.debug("Mounting '%(dev)s' to path '%(path)s' on " "server '%(server)s'.", log_data) mount_cmd = ( 'sudo', 'mkdir', '-p', mount_path, '&&', 'sudo', 'mount', device_path, mount_path, '&&', 'sudo', 'chmod', '777', mount_path, '&&', 'sudo', 'umount', mount_path, '&&', 'sudo', 'tune2fs', '-U', 'random', device_path, '&&', 'sudo', 'mount', device_path, mount_path, ) self._ssh_exec(server_details, mount_cmd) self._sync_mount_temp_and_perm_files(server_details) else: LOG.warning(_LW("Mount point '%(path)s' already exists on " "server '%(server)s'."), log_data) except exception.ProcessExecutionError as e: raise exception.ShareBackendException(msg=six.text_type(e)) return _mount_device_with_lock() @utils.retry(exception.ProcessExecutionError) def _unmount_device(self, share, server_details): @utils.synchronized('generic_driver_mounts_' '%s' % server_details['instance_id']) def _unmount_device_with_lock(): mount_path = self._get_mount_path(share) log_data = { 'path': mount_path, 'server': server_details['instance_id'], } if self._is_device_mounted(mount_path, server_details): LOG.debug("Unmounting path '%(path)s' on server " "'%(server)s'.", log_data) unmount_cmd = ['sudo', 'umount', mount_path, '&&', 'sudo', 'rmdir', mount_path] self._ssh_exec(server_details, unmount_cmd) self._sync_mount_temp_and_perm_files(server_details) else: LOG.warning(_LW("Mount point '%(path)s' does not exist on " "server '%(server)s'."), log_data) return _unmount_device_with_lock() def _get_mount_path(self, share): return os.path.join(self.configuration.share_mount_path, share['name']) def _attach_volume(self, context, share, instance_id, volume): @utils.synchronized( "generic_driver_attach_detach_%s" % instance_id, external=True) def do_attach(volume): if volume['status'] == 'in-use': attached_volumes = [vol.id for vol in self.compute_api.instance_volumes_list( self.admin_context, instance_id)] if volume['id'] in attached_volumes: return volume else: raise exception.ManilaException( _('Volume %s is already attached to another instance') % volume['id']) @retrying.retry(stop_max_attempt_number=3, wait_fixed=2000, retry_on_exception=lambda exc: True) def attach_volume(): self.compute_api.instance_volume_attach( self.admin_context, instance_id, volume['id']) attach_volume() t = time.time() while time.time() - t < self.configuration.max_time_to_attach: volume = self.volume_api.get(context, volume['id']) if volume['status'] == 'in-use': return volume elif volume['status'] != 'attaching': raise exception.ManilaException( _('Failed to attach volume %s') % volume['id']) time.sleep(1) else: err_msg = { 'volume_id': volume['id'], 'max_time': self.configuration.max_time_to_attach } raise exception.ManilaException( _('Volume %(volume_id)s has not been attached in ' '%(max_time)ss. Giving up.') % err_msg) return do_attach(volume) def _get_volume_name(self, share_id): return self.configuration.volume_name_template % share_id def _get_volume(self, context, share_id): volume_id = self.private_storage.get(share_id, 'volume_id') if volume_id is not None: return self.volume_api.get(context, volume_id) else: return self._get_volume_legacy(context, share_id) def _get_volume_legacy(self, context, share_id): volume_name = self._get_volume_name(share_id) search_opts = {'name': volume_name} if context.is_admin: search_opts['all_tenants'] = True volumes_list = self.volume_api.get_all(context, search_opts) if len(volumes_list) == 1: return volumes_list[0] elif len(volumes_list) > 1: LOG.error( _LE("Expected only one volume in volume list with name " "'%(name)s', but got more than one in a result - " "'%(result)s'."), { 'name': volume_name, 'result': volumes_list}) raise exception.ManilaException( _("Error. Ambiguous volumes for name '%s'") % volume_name) return None def _get_volume_snapshot(self, context, snapshot_id): volume_snapshot_id = self.private_storage.get( snapshot_id, 'volume_snapshot_id') if volume_snapshot_id is not None: return self.volume_api.get_snapshot(context, volume_snapshot_id) else: return self._get_volume_snapshot_legacy(context, snapshot_id) def _get_volume_snapshot_legacy(self, context, snapshot_id): volume_snapshot_name = ( self.configuration.volume_snapshot_name_template % snapshot_id) volume_snapshot_list = self.volume_api.get_all_snapshots( context, {'name': volume_snapshot_name}) volume_snapshot = None if len(volume_snapshot_list) == 1: volume_snapshot = volume_snapshot_list[0] elif len(volume_snapshot_list) > 1: LOG.error( _LE("Expected only one volume snapshot in list with name " "'%(name)s', but got more than one in a result - " "'%(result)s'."), { 'name': volume_snapshot_name, 'result': volume_snapshot_list}) raise exception.ManilaException( _('Error. Ambiguous volume snaphots')) return volume_snapshot def _detach_volume(self, context, share, server_details): instance_id = server_details['instance_id'] @utils.synchronized( "generic_driver_attach_detach_%s" % instance_id, external=True) def do_detach(): attached_volumes = [vol.id for vol in self.compute_api.instance_volumes_list( self.admin_context, instance_id)] try: volume = self._get_volume(context, share['id']) except exception.VolumeNotFound: LOG.warning(_LW("Volume not found for share %s. " "Possibly already deleted."), share['id']) volume = None if volume and volume['id'] in attached_volumes: self.compute_api.instance_volume_detach( self.admin_context, instance_id, volume['id'] ) t = time.time() while time.time() - t < self.configuration.max_time_to_attach: volume = self.volume_api.get(context, volume['id']) if volume['status'] in (const.STATUS_AVAILABLE, const.STATUS_ERROR): break time.sleep(1) else: err_msg = { 'volume_id': volume['id'], 'max_time': self.configuration.max_time_to_attach } raise exception.ManilaException( _('Volume %(volume_id)s has not been detached in ' '%(max_time)ss. Giving up.') % err_msg) do_detach() def _allocate_container(self, context, share, snapshot=None): volume_snapshot = None if snapshot: volume_snapshot = self._get_volume_snapshot(context, snapshot['id']) volume = self.volume_api.create( context, share['size'], self.configuration.volume_name_template % share['id'], '', snapshot=volume_snapshot, volume_type=self.configuration.cinder_volume_type, availability_zone=share['availability_zone']) self.private_storage.update( share['id'], {'volume_id': volume['id']}) msg_error = _('Failed to create volume') msg_timeout = ( _('Volume has not been created in %ss. Giving up') % self.configuration.max_time_to_create_volume ) return self._wait_for_available_volume( volume, self.configuration.max_time_to_create_volume, msg_error=msg_error, msg_timeout=msg_timeout ) def _wait_for_available_volume(self, volume, timeout, msg_error, msg_timeout, expected_size=None): t = time.time() while time.time() - t < timeout: if volume['status'] == const.STATUS_AVAILABLE: if expected_size and volume['size'] != expected_size: LOG.debug("The volume %(vol_id)s is available but the " "volume size does not match the expected size. " "A volume resize operation may be pending. " "Expected size: %(expected_size)s, " "Actual size: %(volume_size)s.", dict(vol_id=volume['id'], expected_size=expected_size, volume_size=volume['size'])) else: break elif 'error' in volume['status'].lower(): raise exception.ManilaException(msg_error) time.sleep(1) volume = self.volume_api.get(self.admin_context, volume['id']) else: raise exception.ManilaException(msg_timeout) return volume def _deallocate_container(self, context, share): try: volume = self._get_volume(context, share['id']) except exception.VolumeNotFound: LOG.info(_LI("Volume not found. Already deleted?")) volume = None if volume: if volume['status'] == 'in-use': raise exception.ManilaException( _('Volume is still in use and ' 'cannot be deleted now.')) self.volume_api.delete(context, volume['id']) t = time.time() while (time.time() - t < self.configuration.max_time_to_create_volume): try: volume = self.volume_api.get(context, volume['id']) except exception.VolumeNotFound: LOG.debug('Volume was deleted successfully') break time.sleep(1) else: raise exception.ManilaException( _('Volume have not been ' 'deleted in %ss. Giving up') % self.configuration.max_time_to_create_volume) def _update_share_stats(self): data = dict( share_backend_name=self.backend_name, storage_protocol='NFS_CIFS', reserved_percentage=self.configuration.reserved_share_percentage, ) super(GenericShareDriver, self)._update_share_stats(data) @ensure_server def create_share_from_snapshot(self, context, share, snapshot, share_server=None): return self._create_share( context, share, snapshot=snapshot, share_server=share_server, ) @ensure_server def extend_share(self, share, new_size, share_server=None): server_details = share_server['backend_details'] helper = self._get_helper(share) helper.disable_access_for_maintenance(server_details, share['name']) self._unmount_device(share, server_details) volume = self._get_volume(self.admin_context, share['id']) if int(new_size) > volume['size']: self._detach_volume(self.admin_context, share, server_details) volume = self._extend_volume(self.admin_context, volume, new_size) volume = self._attach_volume( self.admin_context, share, server_details['instance_id'], volume) self._resize_filesystem(server_details, volume) self._mount_device(share, server_details, volume) helper.restore_access_after_maintenance(server_details, share['name']) def _extend_volume(self, context, volume, new_size): self.volume_api.extend(context, volume['id'], new_size) msg_error = _('Failed to extend volume %s') % volume['id'] msg_timeout = ( _('Volume has not been extended in %ss. Giving up') % self.configuration.max_time_to_extend_volume ) return self._wait_for_available_volume( volume, self.configuration.max_time_to_extend_volume, msg_error=msg_error, msg_timeout=msg_timeout, expected_size=new_size ) @ensure_server def shrink_share(self, share, new_size, share_server=None): server_details = share_server['backend_details'] helper = self._get_helper(share) export_location = share['export_locations'][0]['path'] mount_path = helper.get_share_path_by_export_location( server_details, export_location) consumed_space = self._get_consumed_space(mount_path, server_details) LOG.debug("Consumed space on share: %s", consumed_space) if consumed_space >= new_size: raise exception.ShareShrinkingPossibleDataLoss( share_id=share['id']) volume = self._get_volume(self.admin_context, share['id']) helper.disable_access_for_maintenance(server_details, share['name']) self._unmount_device(share, server_details) try: self._resize_filesystem(server_details, volume, new_size=new_size) except exception.Invalid: raise exception.ShareShrinkingPossibleDataLoss( share_id=share['id']) except Exception as e: msg = _("Cannot shrink share: %s") % six.text_type(e) raise exception.Invalid(msg) finally: self._mount_device(share, server_details, volume) helper.restore_access_after_maintenance(server_details, share['name']) def _resize_filesystem(self, server_details, volume, new_size=None): check_command = ['sudo', 'fsck', '-pf', volume['mountpoint']] self._ssh_exec(server_details, check_command) command = ['sudo', 'resize2fs', volume['mountpoint']] if new_size: command.append("%sG" % six.text_type(new_size)) try: self._ssh_exec(server_details, command) except processutils.ProcessExecutionError as e: if e.stderr.find('New size smaller than minimum') != -1: msg = (_("Invalid 'new_size' provided: %s") % six.text_type(new_size)) raise exception.Invalid(msg) else: msg = _("Cannot resize file-system: %s") % six.text_type(e) raise exception.ManilaException(msg) def _is_share_server_active(self, context, share_server): has_active_share_server = ( share_server and share_server.get('backend_details') and self.service_instance_manager.ensure_service_instance( context, share_server['backend_details'])) return has_active_share_server def delete_share(self, context, share, share_server=None): helper = self._get_helper(share) if not self.driver_handles_share_servers: share_server = self.service_instance_manager.get_common_server() if self._is_share_server_active(context, share_server): helper.remove_exports( share_server['backend_details'], share['name']) self._unmount_device(share, share_server['backend_details']) self._detach_volume(self.admin_context, share, share_server['backend_details']) self._deallocate_container(self.admin_context, share) self.private_storage.delete(share['id']) def create_snapshot(self, context, snapshot, share_server=None): model_update = {} volume = self._get_volume(self.admin_context, snapshot['share_id']) volume_snapshot_name = (self.configuration. volume_snapshot_name_template % snapshot['id']) volume_snapshot = self.volume_api.create_snapshot_force( self.admin_context, volume['id'], volume_snapshot_name, '') t = time.time() while time.time() - t < self.configuration.max_time_to_create_volume: if volume_snapshot['status'] == const.STATUS_AVAILABLE: break if volume_snapshot['status'] == const.STATUS_ERROR: raise exception.ManilaException(_('Failed to create volume ' 'snapshot')) time.sleep(1) volume_snapshot = self.volume_api.get_snapshot( self.admin_context, volume_snapshot['id']) self.private_storage.update( snapshot['id'], {'volume_snapshot_id': volume_snapshot['id']}) model_update['provider_location'] = volume_snapshot['id'] else: raise exception.ManilaException( _('Volume snapshot have not been ' 'created in %ss. Giving up') % self.configuration.max_time_to_create_volume) return model_update def delete_snapshot(self, context, snapshot, share_server=None): volume_snapshot = self._get_volume_snapshot(self.admin_context, snapshot['id']) if volume_snapshot is None: return self.volume_api.delete_snapshot(self.admin_context, volume_snapshot['id']) t = time.time() while time.time() - t < self.configuration.max_time_to_create_volume: try: snapshot = self.volume_api.get_snapshot(self.admin_context, volume_snapshot['id']) except exception.VolumeSnapshotNotFound: LOG.debug('Volume snapshot was deleted successfully') self.private_storage.delete(snapshot['id']) break time.sleep(1) else: raise exception.ManilaException( _('Volume snapshot have not been ' 'deleted in %ss. Giving up') % self.configuration.max_time_to_create_volume) @ensure_server def ensure_share(self, context, share, share_server=None): helper = self._get_helper(share) volume = self._get_volume(context, share['id']) if volume: volume = self._attach_volume( context, share, share_server['backend_details']['instance_id'], volume) self._mount_device(share, share_server['backend_details'], volume) helper.create_exports( share_server['backend_details'], share['name'], recreate=True) @ensure_server def update_access(self, context, share, access_rules, add_rules, delete_rules, share_server=None): self._get_helper(share).update_access(share_server['backend_details'], share['name'], access_rules, add_rules=add_rules, delete_rules=delete_rules) def _get_helper(self, share): helper = self._helpers.get(share['share_proto']) if helper: return helper else: raise exception.InvalidShare( reason="Wrong, unsupported or disabled protocol") def get_network_allocations_number(self): return 0 def _setup_server(self, network_info, metadata=None): msg = "Creating share server '%s'." LOG.debug(msg % network_info['server_id']) server = self.service_instance_manager.set_up_service_instance( self.admin_context, network_info) for helper in self._helpers.values(): helper.init_helper(server) return server def _teardown_server(self, server_details, security_services=None): instance_id = server_details.get("instance_id") LOG.debug("Removing share infrastructure for service instance '%s'.", instance_id) self.service_instance_manager.delete_service_instance( self.admin_context, server_details) def manage_existing(self, share, driver_options): helper = self._get_helper(share) share_server = self.service_instance_manager.get_common_server() server_details = share_server['backend_details'] old_export_location = share['export_locations'][0]['path'] mount_path = helper.get_share_path_by_export_location( share_server['backend_details'], old_export_location) LOG.debug("Manage: mount path = %s", mount_path) mounted = self._is_device_mounted(mount_path, server_details) LOG.debug("Manage: is share mounted = %s", mounted) if not mounted: msg = _("Provided share %s is not mounted.") % share['id'] raise exception.ManageInvalidShare(reason=msg) def get_volume(): if 'volume_id' in driver_options: try: return self.volume_api.get( self.admin_context, driver_options['volume_id']) except exception.VolumeNotFound as e: raise exception.ManageInvalidShare(reason=six.text_type(e)) return None share_volume = get_volume() if share_volume: instance_volumes = self.compute_api.instance_volumes_list( self.admin_context, server_details['instance_id']) attached_volumes = [vol.id for vol in instance_volumes] LOG.debug('Manage: attached volumes = %s', six.text_type(attached_volumes)) if share_volume['id'] not in attached_volumes: msg = _("Provided volume %s is not attached " "to service instance.") % share_volume['id'] raise exception.ManageInvalidShare(reason=msg) linked_volume_name = self._get_volume_name(share['id']) if share_volume['name'] != linked_volume_name: LOG.debug('Manage: volume_id = %s' % share_volume['id']) self.volume_api.update(self.admin_context, share_volume['id'], {'name': linked_volume_name}) self.private_storage.update( share['id'], {'volume_id': share_volume['id']}) share_size = share_volume['size'] else: share_size = self._get_mounted_share_size( mount_path, share_server['backend_details']) export_locations = helper.get_exports_for_share( server_details, old_export_location) return {'size': share_size, 'export_locations': export_locations} def manage_existing_snapshot(self, snapshot, driver_options): model_update = {} volume_snapshot = None snapshot_size = snapshot.get('share_size', 0) provider_location = snapshot.get('provider_location') try: volume_snapshot = self.volume_api.get_snapshot( self.admin_context, provider_location) except exception.VolumeSnapshotNotFound as e: raise exception.ManageInvalidShareSnapshot( reason=six.text_type(e)) if volume_snapshot: snapshot_size = volume_snapshot['size'] self.private_storage.update( snapshot['id'], {'volume_snapshot_id': volume_snapshot['id']}) model_update['provider_location'] = volume_snapshot['id'] model_update['size'] = snapshot_size return model_update def unmanage_snapshot(self, snapshot): self.private_storage.delete(snapshot['id']) def _get_mount_stats_by_index(self, mount_path, server_details, index, block_size='G'): share_size_cmd = ['df', '-PB%s' % block_size, mount_path] output, __ = self._ssh_exec(server_details, share_size_cmd) lines = output.split('\n') return int(lines[1].split()[index][:-1]) def _get_mounted_share_size(self, mount_path, server_details): try: size = self._get_mount_stats_by_index( mount_path, server_details, BLOCK_DEVICE_SIZE_INDEX) except Exception as e: msg = _("Cannot calculate size of share %(path)s : %(error)s") % { 'path': mount_path, 'error': six.text_type(e) } raise exception.ManageInvalidShare(reason=msg) return size def _get_consumed_space(self, mount_path, server_details): try: size = self._get_mount_stats_by_index( mount_path, server_details, USED_SPACE_INDEX, block_size='M') size /= float(units.Ki) except Exception as e: msg = _("Cannot calculate consumed space on share " "%(path)s : %(error)s") % { 'path': mount_path, 'error': six.text_type(e) } raise exception.InvalidShare(reason=msg) return size

true

f7177eeda341fbfbb4601d6a8a82e1d73d1f95ba

17,754

py

Python

acg/custom_widgets/selection_widgets.py

david-fischer/Anki_CardGen

909d088ed4e98b97f65a2c896dc607941b00e4da

[ "MIT" ]

2

2021-01-11T08:59:57.000Z

2021-02-01T12:15:30.000Z

acg/custom_widgets/selection_widgets.py

david-fischer/Anki_CardGen

909d088ed4e98b97f65a2c896dc607941b00e4da

[ "MIT" ]

null

acg/custom_widgets/selection_widgets.py

david-fischer/Anki_CardGen

909d088ed4e98b97f65a2c896dc607941b00e4da

[ "MIT" ]

null

"""Implements various elements to get user selection.""" from functools import partial from kivy.animation import Animation from kivy.factory import Factory from kivy.lang import Builder from kivy.properties import ( BooleanProperty, ListProperty, NumericProperty, ObjectProperty, OptionProperty, StringProperty, ) from kivy.uix.behaviors import ButtonBehavior from kivy.uix.boxlayout import BoxLayout from kivy.uix.floatlayout import FloatLayout from kivy.uix.image import AsyncImage from kivy.uix.modalview import ModalView from kivy.uix.stacklayout import StackLayout from kivymd.app import MDApp from kivymd.theming import ThemableBehavior from kivymd.uix.behaviors import CircularRippleBehavior, RectangularRippleBehavior from kivymd.uix.card import MDCard from kivymd.uix.imagelist import SmartTile from .behaviors import ( CheckBehavior, ChildrenFromDataBehavior, LongPressBehavior, ThemableColorChangeBehavior, TranslationOnCheckBehavior, ) class SeparatorWithHeading(FloatLayout): r"""Two :class:`MDSeparator`\ s with a heading in between.""" heading = StringProperty("") """:class:`~kivy.properties.StringProperty` with string used as heading.""" class CheckContainer(ChildrenFromDataBehavior): """Container for widgets with :class:`~custom_widgets.behaviors.CheckBehavior`.""" check_one = BooleanProperty(False) """:class:`~kivy.properties.BooleanProperty` defaults to ``False``. If ``True`` only one child can be selected.""" def __init__(self, **kwargs): super().__init__(**kwargs) self.child_bindings["current_state"] = self.conditional_uncheck def conditional_uncheck(self, instance, value): """Uncheck other widgets if :attr:`check_one` is ``True``.""" if self.check_one: for check_element in [ others for others in self.children if others != instance and value ]: check_element.current_state = False def get_checked(self, attribute_name=None): """ Return current selection. Args: attribute_name: Name of attribute to return. Defaults to ``None``. Returns: :* If ``attribute_name`` is None: List of selected children * Else: List of attribute values """ checked_elements = [ element for element in self.children[::-1] if element.current_state ] if attribute_name is None: return checked_elements return [ getattr(element, attribute_name) for element in checked_elements if element ] class CheckCard(ThemableColorChangeBehavior, MDCard): """Selectable :~kivymd.uix.card.MDCard`. Select by click. Changes color on selection.""" text = StringProperty("test " * 15) """:class:`~kivy.properties.StringProperty`.""" def on_press(self): """Change boolean value of :attr:`self.current_state`.""" self.current_state = ( # pylint: disable=attribute-defined-outside-init not self.current_state ) class CheckChip( CircularRippleBehavior, ButtonBehavior, ThemableColorChangeBehavior, BoxLayout, ): """Selectable Chip. Select by click. Change color on selection.""" icon = StringProperty("") """:class:`~kivy.properties.StringProperty` defaults to "".""" text = StringProperty("") """:class:`~kivy.properties.StringProperty` defaults to "".""" def on_press(self): """Change boolean value of :attr:`current_state`.""" self.current_state = ( # pylint: disable=attribute-defined-outside-init not self.current_state ) class TransChip(TranslationOnCheckBehavior, CheckChip): """Selectable Chip. Select by click. Change color and text on selection.""" class CheckChipContainer(CheckContainer, ThemableBehavior, StackLayout): r"""Container for :class:`CheckChip`\ s. Use :attr:`child_dict` to populate.""" child_class_name = "CheckChip" draw_box = BooleanProperty(False) class CheckImageTile(CheckBehavior, SmartTile): """ Selectable :class:`~kivymd.uix.imagelist.SmartTile`. Select by click. Changes :attr:`opacity` and :attr:`boarder_width` on selection. """ border_width = NumericProperty(0.01) """:class:`~kivy.properties.NumericProperty` describing boarder-width of image tile.""" def __init__(self, **kwargs): self.state_dicts = { True: {"opacity": 1, "border_width": 3}, False: {"opacity": 0.8, "border_width": 0.01}, } super().__init__(**kwargs) def on_press(self): """Change boolean value of current state on press.""" self.current_state = ( # pylint: disable=attribute-defined-outside-init not self.current_state ) class TransCard(LongPressBehavior, MDCard, RectangularRippleBehavior): """Displays :attr:`text_orig` and :attr:`text_trans`, separated by a line.""" text_orig = StringProperty("") """:class:`~kivy.properties.StringProperty` first text.""" text_trans = StringProperty("") """:class:`~kivy.properties.StringProperty` second text.""" orientation = OptionProperty("vertical", options=["vertical", "horizontal"]) """:class:`~kivy.properties.OptionProperty` possible values ["vertical", "horizontal"] defaults to "vertical".""" class LongPressImage(LongPressBehavior, AsyncImage): """:class:`~kivy.uix.image.AsyncImage` with additional "on_press" and "on_long_press" event.""" Factory.register("LongPressImage", LongPressImage) Factory.register("TransCard", TransCard) class MyCarousel(FloatLayout, ChildrenFromDataBehavior): """ Carousel that constructs contents from :attr:`data`. On click, opens a modal with list of content. """ carousel = ObjectProperty() """:class:`~kivy.properties.ObjectProperty`""" modal_layout_name = StringProperty() """:class:`~kivy.properties.StringProperty`""" modal_data_cls_name = StringProperty() """:class:`~kivy.properties.StringProperty`""" modal = ModalView() def __init__(self, **kwargs): super().__init__(**kwargs) self.child_bindings = { "height": self.update_height, "on_press": self.open_menu, } self.on_data() def update_num_children(self): """Add/remove children until correct number is reached.""" diff = len(self.data) - len(self.root_for_children.children) + 1 for _ in range(abs(diff)): if diff > 0: self.add_child() else: self.remove_child() def on_data(self, *_): """Override :meth:`behaviors.ChildrenFromDataBehavior.on_data` with correct list of children. The children are in ``carousel.slides`` as opposed to ``carousel.children``. """ if self.child_class_name: self.update_num_children() self.carousel.index = 1 for i, child_dict in enumerate(self.data, start=1): for key, val in child_dict.items(): setattr(self.carousel.slides[i], key, val) def remove_child(self): """Override :meth:`behaviors.ChildrenFromDataBehavior.remove_child` with correct list of children. The children are in ``carousel.slides`` as opposed to ``carousel.children``. """ last_slide = self.carousel.slides[-1] self.carousel.remove_widget(last_slide) def before_add_child(self, child): """Bind :meth:`set_child_width` to change of :attr:`width`.""" self.bind(width=lambda *_: self.set_child_width(child)) def after_add_child(self, child): """Call :meth:`set_child_width` after adding child.""" self.set_child_width(child) def set_child_width(self, child, *_): """Set width of child to :attr:`width` - width of left and right-icon.""" width = self.width - self.ids.left_icon.width - self.ids.right_icon.width setattr(child, "width", width) def update_height(self, *_): """Implement in sub class. Placeholder.""" def get_modal_content(self, size_hint=(1, None)): """Return root widget to display on the modal.""" def set_carousel_index(i, *_): self.carousel.index = i self.modal.dismiss() data_dicts = [ {"size_hint": size_hint, "on_press": partial(set_carousel_index, 0)} ] + [ {**dict, "size_hint": size_hint, "on_press": partial(set_carousel_index, i)} for i, dict in enumerate(self.data, start=1) ] recycle_view_cls = Factory.get(self.modal_layout_name) recycle_view = recycle_view_cls() recycle_view.child_class_name = self.modal_data_cls_name recycle_view.data = data_dicts return recycle_view def get_checked(self, attribute_name=None): """If ``attribute_name`` is ``None``, return currently selected widget, else return a property thereof.""" checked_elements = [self.carousel.current_slide] if attribute_name is None: return checked_elements return [ getattr(element, attribute_name) for element in checked_elements if element ] def open_menu(self, *_): """Open :class:`kivy.uix.modalview.ModalView` with content given by :meth:`get_modal_content`.""" self.modal = ModalView() modal_content = self.get_modal_content() self.modal.add_widget(modal_content) self.modal.open() class ImageCarousel(MyCarousel): """Carousel of images.""" def __init__(self, **kwargs): super().__init__(**kwargs) self.child_bindings["on_error"] = lambda *_: self.dispatch("on_error", *_) self.register_event_type("on_error") self.on_data() def get_modal_content(self, size_hint=(1, 1)): """Call :meth:`MyCarousel.get_modal_content` with ``size_hint=(1,1)``.""" return super().get_modal_content(size_hint=size_hint) def on_error(self, *_): """Placeholder-function.""" class CardCarousel(MyCarousel): """ Carousel of :class:`TransCard`. To use it with different objects, change :attr:`viewclass` and :attr:`modal_data_cls_name`. """ def __init__(self, **kwargs): super().__init__(**kwargs) del self.child_bindings["on_press"] def update_height(self, *_): """Update height via animation, so that Widget has height of currently displayed card.""" if self.carousel.current_slide: new_height = self.carousel.current_slide.height + 24 if self.height != new_height: anim = Animation(height=new_height, duration=0.2) anim.start(self) class RecycleCarousel(FloatLayout): """ Wrapper class for a :class:`~kivy.uix.carousel.Carousel` that uses only 3 slides to update content dynamically. The :attr:`index` is updated according to the change of the carousel index and each time one of the slides is updated with data from :attr:`data`. The content of the slides is constructed as instances of :attr:`viewclass`. """ carousel = ObjectProperty() """:class:`kivy.properties.ObjectProperty` defaults to ``None``.""" viewclass = StringProperty("TransCard") """:class:`kivy.properties.StringProperty` defaults to ``"TransCard"``. Class name of the widgets that are added to the carousel.""" data = ListProperty() """:class:`kivy.properties.ListProperty` defaults to ``None``. List of dictionaries from which the content is generated.""" slide_width = NumericProperty() """:class:`kivy.properties.NumericProperty` defaults to ``None``. Width that the content of the slides should have.""" dynamic_height = BooleanProperty(False) """:class:`kivy.properties.BooleanProperty` defaults to ``False``. If ``True`` updates the height of the root widget to the height of the object on the current slide + 24. Only possible if size_hint_y of the widget on the slide is not set.""" index = NumericProperty(0) """:class:`kivy.properties.NumericProperty` defaults to ``0``. Current (virtual) index.""" last_carousel_index = NumericProperty(0) """:class:`kivy.properties.NumericProperty` defaults to ``0``. Last index that the :attr:`carousel` had. Used to determine whether the user did slide right or left.""" current_slide = ObjectProperty() """:class:`kivy.properties.ObjectProperty`. Reference to :attr:`carousel`.current_slide.""" modal_layout_name = StringProperty() """:class:`kivy.properties.StringProperty` defaults to ``None``. Class name for root widget of :attr:`modal`.""" modal_data_cls_name = StringProperty() """:class:`kivy.properties.StringProperty` defaults to ``None``. Class name for children of :attr:`modal`.""" modal = ObjectProperty(ModalView()) """:class:`kivy.properties.ObjectProperty` defaults to ``ModalView()``.""" default_modal_size_hint = ListProperty([1, None]) def update_height(self, *_): """Update height via animation, so that Widget has height of currently displayed card.""" if self.dynamic_height: new_height = self.carousel.current_slide.height + 24 if self.height != new_height: anim = Animation(height=new_height, duration=0.3) anim.start(self) def setup_modal(self): """Return root widget to display on the modal.""" self.modal = ModalView() modal_root_cls = Factory.get(self.modal_layout_name) modal_root = modal_root_cls() self.modal.add_widget(modal_root) def _modal_child_callback(self, i, *_): self.set_index(i) self.modal.dismiss() def update_modal_content(self): """Update content of modal.""" data_dicts = [ { **dict, "size_hint": self.default_modal_size_hint, "on_press": partial(self._modal_child_callback, i), } for i, dict in enumerate(self.data) ] self.modal.children[0].child_class_name = self.modal_data_cls_name self.modal.children[0].data = data_dicts def get_checked(self, attribute_name=None): """If ``attribute_name`` is ``None``, return currently selected widget, else return a property thereof.""" checked_elements = [self.carousel.current_slide] if attribute_name is None: return checked_elements return [ getattr(element, attribute_name) for element in checked_elements if element ] def open_menu(self, *_): """Open :class:`kivy.uix.modalview.ModalView` with content given by :meth:`setup_modal`.""" if not self.modal.children: self.setup_modal() self.update_modal_content() self.modal.open() def on_data(self, *_): """Set up :attr:`carousel` by initializing 3 widgets, adding them and binding some Properties.""" self.carousel.clear_widgets() if len(self.data) >= 3: for i in [0, 1, -1]: widget = Factory.get(self.viewclass)(**self.data[i]) self.carousel.add_widget(widget) self.bind(slide_width=widget.setter("width")) widget.bind(on_press=self.open_menu) widget.width = self.slide_width self.carousel.register_event_type("on_index") self.carousel.bind(index=self.update_index) self.carousel.bind(current_slide=self.update_height) self.carousel.current_slide.bind(height=self.update_height) print("RecylceCarousel needs at least 3 elements to be displayed correctly.") def update_index(self, _, carousel_index): """Change :attr:`index` according to change in ``carousel_index`` and update one of the three slides.""" diff = carousel_index - self.last_carousel_index diff = -1 if diff == 2 else 1 if diff == -2 else diff self.last_carousel_index = carousel_index self.index = (self.index + diff) % len(self.data) self.update_slide(carousel_index + diff, self.index + diff) def update_slide(self, carousel_index, index): """ Update slide with index ``carousel_index`` by content from :attr:`data` [index]. Modulo function applied to indices guarantees values to be in the correct range. """ carousel_index %= 3 index %= len(self.data) for name, val in self.data[index].items(): setattr(self.carousel.slides[carousel_index], name, val) def set_index(self, index): """Set :attr:`index` to ``index`` and updates carousel accordingly.""" self.index = index self.update_height() for i in [0, 1, -1]: self.update_slide((self.last_carousel_index + i) % 3, self.index + i) # pylint: disable = W,C,R,I if __name__ == "__main__": CARD_CAROUSEL_STRING = ( "CardCarousel:\n" ' data: [{"text_orig":str(i)*50*i,"text_trans":"Trans"} for i in range(10)]' ) RECYCLE_CAROUSEL_STRING = ( "RecycleCardCarousel:\n" # some comment ' data: [{"text_orig":str(i)*50*i,"text_trans":"Trans"} for i in range(10)]' ) IMAGE_CAROUSEL_STRING = ( "ImageCarousel:\n" ' data: [{"source":"../assets/AnkiCardGen.png"} for _ in range(5)]' ) class _TestApp(MDApp): def build(self): self.theme_cls.primary_palette = "Red" # "Purple", "Red" self.theme_cls.theme_style = "Light" # "Purple", "Red" return Builder.load_string(RECYCLE_CAROUSEL_STRING) _TestApp().run()

37.694268

118

0.652529

from functools import partial from kivy.animation import Animation from kivy.factory import Factory from kivy.lang import Builder from kivy.properties import ( BooleanProperty, ListProperty, NumericProperty, ObjectProperty, OptionProperty, StringProperty, ) from kivy.uix.behaviors import ButtonBehavior from kivy.uix.boxlayout import BoxLayout from kivy.uix.floatlayout import FloatLayout from kivy.uix.image import AsyncImage from kivy.uix.modalview import ModalView from kivy.uix.stacklayout import StackLayout from kivymd.app import MDApp from kivymd.theming import ThemableBehavior from kivymd.uix.behaviors import CircularRippleBehavior, RectangularRippleBehavior from kivymd.uix.card import MDCard from kivymd.uix.imagelist import SmartTile from .behaviors import ( CheckBehavior, ChildrenFromDataBehavior, LongPressBehavior, ThemableColorChangeBehavior, TranslationOnCheckBehavior, ) class SeparatorWithHeading(FloatLayout): heading = StringProperty("") class CheckContainer(ChildrenFromDataBehavior): check_one = BooleanProperty(False) def __init__(self, **kwargs): super().__init__(**kwargs) self.child_bindings["current_state"] = self.conditional_uncheck def conditional_uncheck(self, instance, value): if self.check_one: for check_element in [ others for others in self.children if others != instance and value ]: check_element.current_state = False def get_checked(self, attribute_name=None): checked_elements = [ element for element in self.children[::-1] if element.current_state ] if attribute_name is None: return checked_elements return [ getattr(element, attribute_name) for element in checked_elements if element ] class CheckCard(ThemableColorChangeBehavior, MDCard): text = StringProperty("test " * 15) def on_press(self): self.current_state = ( not self.current_state ) class CheckChip( CircularRippleBehavior, ButtonBehavior, ThemableColorChangeBehavior, BoxLayout, ): icon = StringProperty("") text = StringProperty("") def on_press(self): self.current_state = ( not self.current_state ) class TransChip(TranslationOnCheckBehavior, CheckChip): class CheckChipContainer(CheckContainer, ThemableBehavior, StackLayout): child_class_name = "CheckChip" draw_box = BooleanProperty(False) class CheckImageTile(CheckBehavior, SmartTile): border_width = NumericProperty(0.01) def __init__(self, **kwargs): self.state_dicts = { True: {"opacity": 1, "border_width": 3}, False: {"opacity": 0.8, "border_width": 0.01}, } super().__init__(**kwargs) def on_press(self): self.current_state = ( not self.current_state ) class TransCard(LongPressBehavior, MDCard, RectangularRippleBehavior): text_orig = StringProperty("") text_trans = StringProperty("") orientation = OptionProperty("vertical", options=["vertical", "horizontal"]) class LongPressImage(LongPressBehavior, AsyncImage): Factory.register("LongPressImage", LongPressImage) Factory.register("TransCard", TransCard) class MyCarousel(FloatLayout, ChildrenFromDataBehavior): carousel = ObjectProperty() modal_layout_name = StringProperty() modal_data_cls_name = StringProperty() modal = ModalView() def __init__(self, **kwargs): super().__init__(**kwargs) self.child_bindings = { "height": self.update_height, "on_press": self.open_menu, } self.on_data() def update_num_children(self): diff = len(self.data) - len(self.root_for_children.children) + 1 for _ in range(abs(diff)): if diff > 0: self.add_child() else: self.remove_child() def on_data(self, *_): if self.child_class_name: self.update_num_children() self.carousel.index = 1 for i, child_dict in enumerate(self.data, start=1): for key, val in child_dict.items(): setattr(self.carousel.slides[i], key, val) def remove_child(self): last_slide = self.carousel.slides[-1] self.carousel.remove_widget(last_slide) def before_add_child(self, child): self.bind(width=lambda *_: self.set_child_width(child)) def after_add_child(self, child): self.set_child_width(child) def set_child_width(self, child, *_): width = self.width - self.ids.left_icon.width - self.ids.right_icon.width setattr(child, "width", width) def update_height(self, *_): def get_modal_content(self, size_hint=(1, None)): def set_carousel_index(i, *_): self.carousel.index = i self.modal.dismiss() data_dicts = [ {"size_hint": size_hint, "on_press": partial(set_carousel_index, 0)} ] + [ {**dict, "size_hint": size_hint, "on_press": partial(set_carousel_index, i)} for i, dict in enumerate(self.data, start=1) ] recycle_view_cls = Factory.get(self.modal_layout_name) recycle_view = recycle_view_cls() recycle_view.child_class_name = self.modal_data_cls_name recycle_view.data = data_dicts return recycle_view def get_checked(self, attribute_name=None): checked_elements = [self.carousel.current_slide] if attribute_name is None: return checked_elements return [ getattr(element, attribute_name) for element in checked_elements if element ] def open_menu(self, *_): self.modal = ModalView() modal_content = self.get_modal_content() self.modal.add_widget(modal_content) self.modal.open() class ImageCarousel(MyCarousel): def __init__(self, **kwargs): super().__init__(**kwargs) self.child_bindings["on_error"] = lambda *_: self.dispatch("on_error", *_) self.register_event_type("on_error") self.on_data() def get_modal_content(self, size_hint=(1, 1)): return super().get_modal_content(size_hint=size_hint) def on_error(self, *_): class CardCarousel(MyCarousel): def __init__(self, **kwargs): super().__init__(**kwargs) del self.child_bindings["on_press"] def update_height(self, *_): if self.carousel.current_slide: new_height = self.carousel.current_slide.height + 24 if self.height != new_height: anim = Animation(height=new_height, duration=0.2) anim.start(self) class RecycleCarousel(FloatLayout): carousel = ObjectProperty() viewclass = StringProperty("TransCard") data = ListProperty() slide_width = NumericProperty() dynamic_height = BooleanProperty(False) index = NumericProperty(0) last_carousel_index = NumericProperty(0) current_slide = ObjectProperty() modal_layout_name = StringProperty() modal_data_cls_name = StringProperty() modal = ObjectProperty(ModalView()) default_modal_size_hint = ListProperty([1, None]) def update_height(self, *_): if self.dynamic_height: new_height = self.carousel.current_slide.height + 24 if self.height != new_height: anim = Animation(height=new_height, duration=0.3) anim.start(self) def setup_modal(self): self.modal = ModalView() modal_root_cls = Factory.get(self.modal_layout_name) modal_root = modal_root_cls() self.modal.add_widget(modal_root) def _modal_child_callback(self, i, *_): self.set_index(i) self.modal.dismiss() def update_modal_content(self): data_dicts = [ { **dict, "size_hint": self.default_modal_size_hint, "on_press": partial(self._modal_child_callback, i), } for i, dict in enumerate(self.data) ] self.modal.children[0].child_class_name = self.modal_data_cls_name self.modal.children[0].data = data_dicts def get_checked(self, attribute_name=None): checked_elements = [self.carousel.current_slide] if attribute_name is None: return checked_elements return [ getattr(element, attribute_name) for element in checked_elements if element ] def open_menu(self, *_): if not self.modal.children: self.setup_modal() self.update_modal_content() self.modal.open() def on_data(self, *_): self.carousel.clear_widgets() if len(self.data) >= 3: for i in [0, 1, -1]: widget = Factory.get(self.viewclass)(**self.data[i]) self.carousel.add_widget(widget) self.bind(slide_width=widget.setter("width")) widget.bind(on_press=self.open_menu) widget.width = self.slide_width self.carousel.register_event_type("on_index") self.carousel.bind(index=self.update_index) self.carousel.bind(current_slide=self.update_height) self.carousel.current_slide.bind(height=self.update_height) print("RecylceCarousel needs at least 3 elements to be displayed correctly.") def update_index(self, _, carousel_index): diff = carousel_index - self.last_carousel_index diff = -1 if diff == 2 else 1 if diff == -2 else diff self.last_carousel_index = carousel_index self.index = (self.index + diff) % len(self.data) self.update_slide(carousel_index + diff, self.index + diff) def update_slide(self, carousel_index, index): carousel_index %= 3 index %= len(self.data) for name, val in self.data[index].items(): setattr(self.carousel.slides[carousel_index], name, val) def set_index(self, index): self.index = index self.update_height() for i in [0, 1, -1]: self.update_slide((self.last_carousel_index + i) % 3, self.index + i) if __name__ == "__main__": CARD_CAROUSEL_STRING = ( "CardCarousel:\n" ' data: [{"text_orig":str(i)*50*i,"text_trans":"Trans"} for i in range(10)]' ) RECYCLE_CAROUSEL_STRING = ( "RecycleCardCarousel:\n" ' data: [{"text_orig":str(i)*50*i,"text_trans":"Trans"} for i in range(10)]' ) IMAGE_CAROUSEL_STRING = ( "ImageCarousel:\n" ' data: [{"source":"../assets/AnkiCardGen.png"} for _ in range(5)]' ) class _TestApp(MDApp): def build(self): self.theme_cls.primary_palette = "Red" self.theme_cls.theme_style = "Light" return Builder.load_string(RECYCLE_CAROUSEL_STRING) _TestApp().run()

true

f7177f17985f47452311b910b6f0dc8fb2631393

276

py

Python

packaging/setup/plugins/ovirt-engine-setup/ovirt_imageio/__init__.py

jihwahn1018/ovirt-engine

5c8a3d9a9637eefb28e4accc3cbd2b7f530d5ec9

[ "Apache-2.0" ]

347

2015-01-20T14:13:21.000Z

2022-03-31T17:53:11.000Z

packaging/setup/plugins/ovirt-engine-setup/ovirt_imageio/__init__.py

jihwahn1018/ovirt-engine

5c8a3d9a9637eefb28e4accc3cbd2b7f530d5ec9

[ "Apache-2.0" ]

128

2015-05-22T19:14:32.000Z

2022-03-31T08:11:18.000Z

packaging/setup/plugins/ovirt-engine-setup/ovirt_imageio/__init__.py

jihwahn1018/ovirt-engine

5c8a3d9a9637eefb28e4accc3cbd2b7f530d5ec9

[ "Apache-2.0" ]

202

2015-01-04T06:20:49.000Z

2022-03-08T15:30:08.000Z

# # ovirt-engine-setup -- ovirt engine setup # # Copyright oVirt Authors # SPDX-License-Identifier: Apache-2.0 # # """ovirt-imageio setup plugin.""" from otopi import util from . import config @util.export def createPlugins(context): config.Plugin(context=context)

13.142857

42

0.721014

from otopi import util from . import config @util.export def createPlugins(context): config.Plugin(context=context)

true

f71780030f07f5124673cea42eacb5d831327dea

543

py

Python

manage.py

arpitmisraw/Effervescence18-Website

3c510f066986af80aaced566b32c5040310d3107

[ "MIT" ]

2

2018-06-27T20:46:16.000Z

2018-08-02T11:02:26.000Z

manage.py

arpitmisraw/Effervescence18-Website

3c510f066986af80aaced566b32c5040310d3107

[ "MIT" ]

3

2020-06-05T18:15:37.000Z

2021-06-10T20:20:29.000Z

manage.py

arpitmisraw/Effervescence18-Website

3c510f066986af80aaced566b32c5040310d3107

[ "MIT" ]

1

2019-08-01T12:06:35.000Z

#!/usr/bin/env python import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "effe_portal.settings") try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv)

33.9375

75

0.688766

import os import sys if __name__ == "__main__": os.environ.setdefault("DJANGO_SETTINGS_MODULE", "effe_portal.settings") try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv)

true

f71780c4b08f54dd66b5a1991c6159621e4cec1f

18,071

py

Python

test/cpython/test_audioop.py

aisk/pyston

ac69cfef0621dbc8901175e84fa2b5cb5781a646

[ "BSD-2-Clause", "Apache-2.0" ]

1

2020-02-06T14:28:45.000Z

test/cpython/test_audioop.py

aisk/pyston

ac69cfef0621dbc8901175e84fa2b5cb5781a646

[ "BSD-2-Clause", "Apache-2.0" ]

null

test/cpython/test_audioop.py

aisk/pyston

ac69cfef0621dbc8901175e84fa2b5cb5781a646

[ "BSD-2-Clause", "Apache-2.0" ]

1

2020-02-06T14:29:00.000Z

# expected: fail import audioop import sys import unittest import struct from test.test_support import run_unittest formats = { 1: 'b', 2: 'h', 4: 'i', } def pack(width, data): return struct.pack('=%d%s' % (len(data), formats[width]), *data) packs = { 1: lambda *data: pack(1, data), 2: lambda *data: pack(2, data), 4: lambda *data: pack(4, data), } maxvalues = {w: (1 << (8 * w - 1)) - 1 for w in (1, 2, 4)} minvalues = {w: -1 << (8 * w - 1) for w in (1, 2, 4)} datas = { 1: b'\x00\x12\x45\xbb\x7f\x80\xff', 2: packs[2](0, 0x1234, 0x4567, -0x4567, 0x7fff, -0x8000, -1), 4: packs[4](0, 0x12345678, 0x456789ab, -0x456789ab, 0x7fffffff, -0x80000000, -1), } INVALID_DATA = [ (b'abc', 0), (b'abc', 2), (b'abc', 4), ] class TestAudioop(unittest.TestCase): def test_max(self): for w in 1, 2, 4: self.assertEqual(audioop.max(b'', w), 0) p = packs[w] self.assertEqual(audioop.max(p(5), w), 5) self.assertEqual(audioop.max(p(5, -8, -1), w), 8) self.assertEqual(audioop.max(p(maxvalues[w]), w), maxvalues[w]) self.assertEqual(audioop.max(p(minvalues[w]), w), -minvalues[w]) self.assertEqual(audioop.max(datas[w], w), -minvalues[w]) def test_minmax(self): for w in 1, 2, 4: self.assertEqual(audioop.minmax(b'', w), (0x7fffffff, -0x80000000)) p = packs[w] self.assertEqual(audioop.minmax(p(5), w), (5, 5)) self.assertEqual(audioop.minmax(p(5, -8, -1), w), (-8, 5)) self.assertEqual(audioop.minmax(p(maxvalues[w]), w), (maxvalues[w], maxvalues[w])) self.assertEqual(audioop.minmax(p(minvalues[w]), w), (minvalues[w], minvalues[w])) self.assertEqual(audioop.minmax(datas[w], w), (minvalues[w], maxvalues[w])) def test_maxpp(self): for w in 1, 2, 4: self.assertEqual(audioop.maxpp(b'', w), 0) self.assertEqual(audioop.maxpp(packs[w](*range(100)), w), 0) self.assertEqual(audioop.maxpp(packs[w](9, 10, 5, 5, 0, 1), w), 10) self.assertEqual(audioop.maxpp(datas[w], w), maxvalues[w] - minvalues[w]) def test_avg(self): for w in 1, 2, 4: self.assertEqual(audioop.avg(b'', w), 0) p = packs[w] self.assertEqual(audioop.avg(p(5), w), 5) self .assertEqual(audioop.avg(p(5, 8), w), 6) self.assertEqual(audioop.avg(p(5, -8), w), -2) self.assertEqual(audioop.avg(p(maxvalues[w], maxvalues[w]), w), maxvalues[w]) self.assertEqual(audioop.avg(p(minvalues[w], minvalues[w]), w), minvalues[w]) self.assertEqual(audioop.avg(packs[4](0x50000000, 0x70000000), 4), 0x60000000) self.assertEqual(audioop.avg(packs[4](-0x50000000, -0x70000000), 4), -0x60000000) def test_avgpp(self): for w in 1, 2, 4: self.assertEqual(audioop.avgpp(b'', w), 0) self.assertEqual(audioop.avgpp(packs[w](*range(100)), w), 0) self.assertEqual(audioop.avgpp(packs[w](9, 10, 5, 5, 0, 1), w), 10) self.assertEqual(audioop.avgpp(datas[1], 1), 196) self.assertEqual(audioop.avgpp(datas[2], 2), 50534) self.assertEqual(audioop.avgpp(datas[4], 4), 3311897002) def test_rms(self): for w in 1, 2, 4: self.assertEqual(audioop.rms(b'', w), 0) p = packs[w] self.assertEqual(audioop.rms(p(*range(100)), w), 57) self.assertAlmostEqual(audioop.rms(p(maxvalues[w]) * 5, w), maxvalues[w], delta=1) self.assertAlmostEqual(audioop.rms(p(minvalues[w]) * 5, w), -minvalues[w], delta=1) self.assertEqual(audioop.rms(datas[1], 1), 77) self.assertEqual(audioop.rms(datas[2], 2), 20001) self.assertEqual(audioop.rms(datas[4], 4), 1310854152) def test_cross(self): for w in 1, 2, 4: self.assertEqual(audioop.cross(b'', w), -1) p = packs[w] self.assertEqual(audioop.cross(p(0, 1, 2), w), 0) self.assertEqual(audioop.cross(p(1, 2, -3, -4), w), 1) self.assertEqual(audioop.cross(p(-1, -2, 3, 4), w), 1) self.assertEqual(audioop.cross(p(0, minvalues[w]), w), 1) self.assertEqual(audioop.cross(p(minvalues[w], maxvalues[w]), w), 1) def test_add(self): for w in 1, 2, 4: self.assertEqual(audioop.add(b'', b'', w), b'') self.assertEqual(audioop.add(datas[w], b'\0' * len(datas[w]), w), datas[w]) self.assertEqual(audioop.add(datas[1], datas[1], 1), b'\x00\x24\x7f\x80\x7f\x80\xfe') self.assertEqual(audioop.add(datas[2], datas[2], 2), packs[2](0, 0x2468, 0x7fff, -0x8000, 0x7fff, -0x8000, -2)) self.assertEqual(audioop.add(datas[4], datas[4], 4), packs[4](0, 0x2468acf0, 0x7fffffff, -0x80000000, 0x7fffffff, -0x80000000, -2)) def test_bias(self): for w in 1, 2, 4: for bias in 0, 1, -1, 127, -128, 0x7fffffff, -0x80000000: self.assertEqual(audioop.bias(b'', w, bias), b'') self.assertEqual(audioop.bias(datas[1], 1, 1), b'\x01\x13\x46\xbc\x80\x81\x00') self.assertEqual(audioop.bias(datas[1], 1, -1), b'\xff\x11\x44\xba\x7e\x7f\xfe') self.assertEqual(audioop.bias(datas[1], 1, 0x7fffffff), b'\xff\x11\x44\xba\x7e\x7f\xfe') self.assertEqual(audioop.bias(datas[1], 1, -0x80000000), datas[1]) self.assertEqual(audioop.bias(datas[2], 2, 1), packs[2](1, 0x1235, 0x4568, -0x4566, -0x8000, -0x7fff, 0)) self.assertEqual(audioop.bias(datas[2], 2, -1), packs[2](-1, 0x1233, 0x4566, -0x4568, 0x7ffe, 0x7fff, -2)) self.assertEqual(audioop.bias(datas[2], 2, 0x7fffffff), packs[2](-1, 0x1233, 0x4566, -0x4568, 0x7ffe, 0x7fff, -2)) self.assertEqual(audioop.bias(datas[2], 2, -0x80000000), datas[2]) self.assertEqual(audioop.bias(datas[4], 4, 1), packs[4](1, 0x12345679, 0x456789ac, -0x456789aa, -0x80000000, -0x7fffffff, 0)) self.assertEqual(audioop.bias(datas[4], 4, -1), packs[4](-1, 0x12345677, 0x456789aa, -0x456789ac, 0x7ffffffe, 0x7fffffff, -2)) self.assertEqual(audioop.bias(datas[4], 4, 0x7fffffff), packs[4](0x7fffffff, -0x6dcba989, -0x3a987656, 0x3a987654, -2, -1, 0x7ffffffe)) self.assertEqual(audioop.bias(datas[4], 4, -0x80000000), packs[4](-0x80000000, -0x6dcba988, -0x3a987655, 0x3a987655, -1, 0, 0x7fffffff)) def test_lin2lin(self): for w in 1, 2, 4: self.assertEqual(audioop.lin2lin(datas[w], w, w), datas[w]) self.assertEqual(audioop.lin2lin(datas[1], 1, 2), packs[2](0, 0x1200, 0x4500, -0x4500, 0x7f00, -0x8000, -0x100)) self.assertEqual(audioop.lin2lin(datas[1], 1, 4), packs[4](0, 0x12000000, 0x45000000, -0x45000000, 0x7f000000, -0x80000000, -0x1000000)) self.assertEqual(audioop.lin2lin(datas[2], 2, 1), b'\x00\x12\x45\xba\x7f\x80\xff') self.assertEqual(audioop.lin2lin(datas[2], 2, 4), packs[4](0, 0x12340000, 0x45670000, -0x45670000, 0x7fff0000, -0x80000000, -0x10000)) self.assertEqual(audioop.lin2lin(datas[4], 4, 1), b'\x00\x12\x45\xba\x7f\x80\xff') self.assertEqual(audioop.lin2lin(datas[4], 4, 2), packs[2](0, 0x1234, 0x4567, -0x4568, 0x7fff, -0x8000, -1)) def test_adpcm2lin(self): self.assertEqual(audioop.adpcm2lin(b'\x07\x7f\x7f', 1, None), (b'\x00\x00\x00\xff\x00\xff', (-179, 40))) self.assertEqual(audioop.adpcm2lin(b'\x07\x7f\x7f', 2, None), (packs[2](0, 0xb, 0x29, -0x16, 0x72, -0xb3), (-179, 40))) self.assertEqual(audioop.adpcm2lin(b'\x07\x7f\x7f', 4, None), (packs[4](0, 0xb0000, 0x290000, -0x160000, 0x720000, -0xb30000), (-179, 40))) # Very cursory test for w in 1, 2, 4: self.assertEqual(audioop.adpcm2lin(b'\0' * 5, w, None), (b'\0' * w * 10, (0, 0))) def test_lin2adpcm(self): self.assertEqual(audioop.lin2adpcm(datas[1], 1, None), (b'\x07\x7f\x7f', (-221, 39))) self.assertEqual(audioop.lin2adpcm(datas[2], 2, None), (b'\x07\x7f\x7f', (31, 39))) self.assertEqual(audioop.lin2adpcm(datas[4], 4, None), (b'\x07\x7f\x7f', (31, 39))) # Very cursory test for w in 1, 2, 4: self.assertEqual(audioop.lin2adpcm(b'\0' * w * 10, w, None), (b'\0' * 5, (0, 0))) def test_lin2alaw(self): self.assertEqual(audioop.lin2alaw(datas[1], 1), b'\xd5\x87\xa4\x24\xaa\x2a\x5a') self.assertEqual(audioop.lin2alaw(datas[2], 2), b'\xd5\x87\xa4\x24\xaa\x2a\x55') self.assertEqual(audioop.lin2alaw(datas[4], 4), b'\xd5\x87\xa4\x24\xaa\x2a\x55') def test_alaw2lin(self): encoded = b'\x00\x03\x24\x2a\x51\x54\x55\x58\x6b\x71\x7f'\ b'\x80\x83\xa4\xaa\xd1\xd4\xd5\xd8\xeb\xf1\xff' src = [-688, -720, -2240, -4032, -9, -3, -1, -27, -244, -82, -106, 688, 720, 2240, 4032, 9, 3, 1, 27, 244, 82, 106] for w in 1, 2, 4: self.assertEqual(audioop.alaw2lin(encoded, w), packs[w](*(x << (w * 8) >> 13 for x in src))) encoded = ''.join(chr(x) for x in xrange(256)) for w in 2, 4: decoded = audioop.alaw2lin(encoded, w) self.assertEqual(audioop.lin2alaw(decoded, w), encoded) def test_lin2ulaw(self): self.assertEqual(audioop.lin2ulaw(datas[1], 1), b'\xff\xad\x8e\x0e\x80\x00\x67') self.assertEqual(audioop.lin2ulaw(datas[2], 2), b'\xff\xad\x8e\x0e\x80\x00\x7e') self.assertEqual(audioop.lin2ulaw(datas[4], 4), b'\xff\xad\x8e\x0e\x80\x00\x7e') def test_ulaw2lin(self): encoded = b'\x00\x0e\x28\x3f\x57\x6a\x76\x7c\x7e\x7f'\ b'\x80\x8e\xa8\xbf\xd7\xea\xf6\xfc\xfe\xff' src = [-8031, -4447, -1471, -495, -163, -53, -18, -6, -2, 0, 8031, 4447, 1471, 495, 163, 53, 18, 6, 2, 0] for w in 1, 2, 4: self.assertEqual(audioop.ulaw2lin(encoded, w), packs[w](*(x << (w * 8) >> 14 for x in src))) # Current u-law implementation has two codes fo 0: 0x7f and 0xff. encoded = ''.join(chr(x) for x in range(127) + range(128, 256)) for w in 2, 4: decoded = audioop.ulaw2lin(encoded, w) self.assertEqual(audioop.lin2ulaw(decoded, w), encoded) def test_mul(self): for w in 1, 2, 4: self.assertEqual(audioop.mul(b'', w, 2), b'') self.assertEqual(audioop.mul(datas[w], w, 0), b'\0' * len(datas[w])) self.assertEqual(audioop.mul(datas[w], w, 1), datas[w]) self.assertEqual(audioop.mul(datas[1], 1, 2), b'\x00\x24\x7f\x80\x7f\x80\xfe') self.assertEqual(audioop.mul(datas[2], 2, 2), packs[2](0, 0x2468, 0x7fff, -0x8000, 0x7fff, -0x8000, -2)) self.assertEqual(audioop.mul(datas[4], 4, 2), packs[4](0, 0x2468acf0, 0x7fffffff, -0x80000000, 0x7fffffff, -0x80000000, -2)) def test_ratecv(self): for w in 1, 2, 4: self.assertEqual(audioop.ratecv(b'', w, 1, 8000, 8000, None), (b'', (-1, ((0, 0),)))) self.assertEqual(audioop.ratecv(b'', w, 5, 8000, 8000, None), (b'', (-1, ((0, 0),) * 5))) self.assertEqual(audioop.ratecv(b'', w, 1, 8000, 16000, None), (b'', (-2, ((0, 0),)))) self.assertEqual(audioop.ratecv(datas[w], w, 1, 8000, 8000, None)[0], datas[w]) state = None d1, state = audioop.ratecv(b'\x00\x01\x02', 1, 1, 8000, 16000, state) d2, state = audioop.ratecv(b'\x00\x01\x02', 1, 1, 8000, 16000, state) self.assertEqual(d1 + d2, b'\000\000\001\001\002\001\000\000\001\001\002') for w in 1, 2, 4: d0, state0 = audioop.ratecv(datas[w], w, 1, 8000, 16000, None) d, state = b'', None for i in range(0, len(datas[w]), w): d1, state = audioop.ratecv(datas[w][i:i + w], w, 1, 8000, 16000, state) d += d1 self.assertEqual(d, d0) self.assertEqual(state, state0) def test_reverse(self): for w in 1, 2, 4: self.assertEqual(audioop.reverse(b'', w), b'') self.assertEqual(audioop.reverse(packs[w](0, 1, 2), w), packs[w](2, 1, 0)) def test_tomono(self): for w in 1, 2, 4: data1 = datas[w] data2 = bytearray(2 * len(data1)) for k in range(w): data2[k::2*w] = data1[k::w] self.assertEqual(audioop.tomono(str(data2), w, 1, 0), data1) self.assertEqual(audioop.tomono(str(data2), w, 0, 1), b'\0' * len(data1)) for k in range(w): data2[k+w::2*w] = data1[k::w] self.assertEqual(audioop.tomono(str(data2), w, 0.5, 0.5), data1) def test_tostereo(self): for w in 1, 2, 4: data1 = datas[w] data2 = bytearray(2 * len(data1)) for k in range(w): data2[k::2*w] = data1[k::w] self.assertEqual(audioop.tostereo(data1, w, 1, 0), data2) self.assertEqual(audioop.tostereo(data1, w, 0, 0), b'\0' * len(data2)) for k in range(w): data2[k+w::2*w] = data1[k::w] self.assertEqual(audioop.tostereo(data1, w, 1, 1), data2) def test_findfactor(self): self.assertEqual(audioop.findfactor(datas[2], datas[2]), 1.0) self.assertEqual(audioop.findfactor(b'\0' * len(datas[2]), datas[2]), 0.0) def test_findfit(self): self.assertEqual(audioop.findfit(datas[2], datas[2]), (0, 1.0)) self.assertEqual(audioop.findfit(datas[2], packs[2](1, 2, 0)), (1, 8038.8)) self.assertEqual(audioop.findfit(datas[2][:-2] * 5 + datas[2], datas[2]), (30, 1.0)) def test_findmax(self): self.assertEqual(audioop.findmax(datas[2], 1), 5) def test_getsample(self): for w in 1, 2, 4: data = packs[w](0, 1, -1, maxvalues[w], minvalues[w]) self.assertEqual(audioop.getsample(data, w, 0), 0) self.assertEqual(audioop.getsample(data, w, 1), 1) self.assertEqual(audioop.getsample(data, w, 2), -1) self.assertEqual(audioop.getsample(data, w, 3), maxvalues[w]) self.assertEqual(audioop.getsample(data, w, 4), minvalues[w]) def test_negativelen(self): # from issue 3306, previously it segfaulted self.assertRaises(audioop.error, audioop.findmax, ''.join( chr(x) for x in xrange(256)), -2392392) def test_issue7673(self): state = None for data, size in INVALID_DATA: size2 = size self.assertRaises(audioop.error, audioop.getsample, data, size, 0) self.assertRaises(audioop.error, audioop.max, data, size) self.assertRaises(audioop.error, audioop.minmax, data, size) self.assertRaises(audioop.error, audioop.avg, data, size) self.assertRaises(audioop.error, audioop.rms, data, size) self.assertRaises(audioop.error, audioop.avgpp, data, size) self.assertRaises(audioop.error, audioop.maxpp, data, size) self.assertRaises(audioop.error, audioop.cross, data, size) self.assertRaises(audioop.error, audioop.mul, data, size, 1.0) self.assertRaises(audioop.error, audioop.tomono, data, size, 0.5, 0.5) self.assertRaises(audioop.error, audioop.tostereo, data, size, 0.5, 0.5) self.assertRaises(audioop.error, audioop.add, data, data, size) self.assertRaises(audioop.error, audioop.bias, data, size, 0) self.assertRaises(audioop.error, audioop.reverse, data, size) self.assertRaises(audioop.error, audioop.lin2lin, data, size, size2) self.assertRaises(audioop.error, audioop.ratecv, data, size, 1, 1, 1, state) self.assertRaises(audioop.error, audioop.lin2ulaw, data, size) self.assertRaises(audioop.error, audioop.lin2alaw, data, size) self.assertRaises(audioop.error, audioop.lin2adpcm, data, size, state) def test_wrongsize(self): data = b'abcdefgh' state = None for size in (-1, 0, 3, 5, 1024): self.assertRaises(audioop.error, audioop.ulaw2lin, data, size) self.assertRaises(audioop.error, audioop.alaw2lin, data, size) self.assertRaises(audioop.error, audioop.adpcm2lin, data, size, state) def test_main(): run_unittest(TestAudioop) if __name__ == '__main__': test_main()

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0.531791

import audioop import sys import unittest import struct from test.test_support import run_unittest formats = { 1: 'b', 2: 'h', 4: 'i', } def pack(width, data): return struct.pack('=%d%s' % (len(data), formats[width]), *data) packs = { 1: lambda *data: pack(1, data), 2: lambda *data: pack(2, data), 4: lambda *data: pack(4, data), } maxvalues = {w: (1 << (8 * w - 1)) - 1 for w in (1, 2, 4)} minvalues = {w: -1 << (8 * w - 1) for w in (1, 2, 4)} datas = { 1: b'\x00\x12\x45\xbb\x7f\x80\xff', 2: packs[2](0, 0x1234, 0x4567, -0x4567, 0x7fff, -0x8000, -1), 4: packs[4](0, 0x12345678, 0x456789ab, -0x456789ab, 0x7fffffff, -0x80000000, -1), } INVALID_DATA = [ (b'abc', 0), (b'abc', 2), (b'abc', 4), ] class TestAudioop(unittest.TestCase): def test_max(self): for w in 1, 2, 4: self.assertEqual(audioop.max(b'', w), 0) p = packs[w] self.assertEqual(audioop.max(p(5), w), 5) self.assertEqual(audioop.max(p(5, -8, -1), w), 8) self.assertEqual(audioop.max(p(maxvalues[w]), w), maxvalues[w]) self.assertEqual(audioop.max(p(minvalues[w]), w), -minvalues[w]) self.assertEqual(audioop.max(datas[w], w), -minvalues[w]) def test_minmax(self): for w in 1, 2, 4: self.assertEqual(audioop.minmax(b'', w), (0x7fffffff, -0x80000000)) p = packs[w] self.assertEqual(audioop.minmax(p(5), w), (5, 5)) self.assertEqual(audioop.minmax(p(5, -8, -1), w), (-8, 5)) self.assertEqual(audioop.minmax(p(maxvalues[w]), w), (maxvalues[w], maxvalues[w])) self.assertEqual(audioop.minmax(p(minvalues[w]), w), (minvalues[w], minvalues[w])) self.assertEqual(audioop.minmax(datas[w], w), (minvalues[w], maxvalues[w])) def test_maxpp(self): for w in 1, 2, 4: self.assertEqual(audioop.maxpp(b'', w), 0) self.assertEqual(audioop.maxpp(packs[w](*range(100)), w), 0) self.assertEqual(audioop.maxpp(packs[w](9, 10, 5, 5, 0, 1), w), 10) self.assertEqual(audioop.maxpp(datas[w], w), maxvalues[w] - minvalues[w]) def test_avg(self): for w in 1, 2, 4: self.assertEqual(audioop.avg(b'', w), 0) p = packs[w] self.assertEqual(audioop.avg(p(5), w), 5) self .assertEqual(audioop.avg(p(5, 8), w), 6) self.assertEqual(audioop.avg(p(5, -8), w), -2) self.assertEqual(audioop.avg(p(maxvalues[w], maxvalues[w]), w), maxvalues[w]) self.assertEqual(audioop.avg(p(minvalues[w], minvalues[w]), w), minvalues[w]) self.assertEqual(audioop.avg(packs[4](0x50000000, 0x70000000), 4), 0x60000000) self.assertEqual(audioop.avg(packs[4](-0x50000000, -0x70000000), 4), -0x60000000) def test_avgpp(self): for w in 1, 2, 4: self.assertEqual(audioop.avgpp(b'', w), 0) self.assertEqual(audioop.avgpp(packs[w](*range(100)), w), 0) self.assertEqual(audioop.avgpp(packs[w](9, 10, 5, 5, 0, 1), w), 10) self.assertEqual(audioop.avgpp(datas[1], 1), 196) self.assertEqual(audioop.avgpp(datas[2], 2), 50534) self.assertEqual(audioop.avgpp(datas[4], 4), 3311897002) def test_rms(self): for w in 1, 2, 4: self.assertEqual(audioop.rms(b'', w), 0) p = packs[w] self.assertEqual(audioop.rms(p(*range(100)), w), 57) self.assertAlmostEqual(audioop.rms(p(maxvalues[w]) * 5, w), maxvalues[w], delta=1) self.assertAlmostEqual(audioop.rms(p(minvalues[w]) * 5, w), -minvalues[w], delta=1) self.assertEqual(audioop.rms(datas[1], 1), 77) self.assertEqual(audioop.rms(datas[2], 2), 20001) self.assertEqual(audioop.rms(datas[4], 4), 1310854152) def test_cross(self): for w in 1, 2, 4: self.assertEqual(audioop.cross(b'', w), -1) p = packs[w] self.assertEqual(audioop.cross(p(0, 1, 2), w), 0) self.assertEqual(audioop.cross(p(1, 2, -3, -4), w), 1) self.assertEqual(audioop.cross(p(-1, -2, 3, 4), w), 1) self.assertEqual(audioop.cross(p(0, minvalues[w]), w), 1) self.assertEqual(audioop.cross(p(minvalues[w], maxvalues[w]), w), 1) def test_add(self): for w in 1, 2, 4: self.assertEqual(audioop.add(b'', b'', w), b'') self.assertEqual(audioop.add(datas[w], b'\0' * len(datas[w]), w), datas[w]) self.assertEqual(audioop.add(datas[1], datas[1], 1), b'\x00\x24\x7f\x80\x7f\x80\xfe') self.assertEqual(audioop.add(datas[2], datas[2], 2), packs[2](0, 0x2468, 0x7fff, -0x8000, 0x7fff, -0x8000, -2)) self.assertEqual(audioop.add(datas[4], datas[4], 4), packs[4](0, 0x2468acf0, 0x7fffffff, -0x80000000, 0x7fffffff, -0x80000000, -2)) def test_bias(self): for w in 1, 2, 4: for bias in 0, 1, -1, 127, -128, 0x7fffffff, -0x80000000: self.assertEqual(audioop.bias(b'', w, bias), b'') self.assertEqual(audioop.bias(datas[1], 1, 1), b'\x01\x13\x46\xbc\x80\x81\x00') self.assertEqual(audioop.bias(datas[1], 1, -1), b'\xff\x11\x44\xba\x7e\x7f\xfe') self.assertEqual(audioop.bias(datas[1], 1, 0x7fffffff), b'\xff\x11\x44\xba\x7e\x7f\xfe') self.assertEqual(audioop.bias(datas[1], 1, -0x80000000), datas[1]) self.assertEqual(audioop.bias(datas[2], 2, 1), packs[2](1, 0x1235, 0x4568, -0x4566, -0x8000, -0x7fff, 0)) self.assertEqual(audioop.bias(datas[2], 2, -1), packs[2](-1, 0x1233, 0x4566, -0x4568, 0x7ffe, 0x7fff, -2)) self.assertEqual(audioop.bias(datas[2], 2, 0x7fffffff), packs[2](-1, 0x1233, 0x4566, -0x4568, 0x7ffe, 0x7fff, -2)) self.assertEqual(audioop.bias(datas[2], 2, -0x80000000), datas[2]) self.assertEqual(audioop.bias(datas[4], 4, 1), packs[4](1, 0x12345679, 0x456789ac, -0x456789aa, -0x80000000, -0x7fffffff, 0)) self.assertEqual(audioop.bias(datas[4], 4, -1), packs[4](-1, 0x12345677, 0x456789aa, -0x456789ac, 0x7ffffffe, 0x7fffffff, -2)) self.assertEqual(audioop.bias(datas[4], 4, 0x7fffffff), packs[4](0x7fffffff, -0x6dcba989, -0x3a987656, 0x3a987654, -2, -1, 0x7ffffffe)) self.assertEqual(audioop.bias(datas[4], 4, -0x80000000), packs[4](-0x80000000, -0x6dcba988, -0x3a987655, 0x3a987655, -1, 0, 0x7fffffff)) def test_lin2lin(self): for w in 1, 2, 4: self.assertEqual(audioop.lin2lin(datas[w], w, w), datas[w]) self.assertEqual(audioop.lin2lin(datas[1], 1, 2), packs[2](0, 0x1200, 0x4500, -0x4500, 0x7f00, -0x8000, -0x100)) self.assertEqual(audioop.lin2lin(datas[1], 1, 4), packs[4](0, 0x12000000, 0x45000000, -0x45000000, 0x7f000000, -0x80000000, -0x1000000)) self.assertEqual(audioop.lin2lin(datas[2], 2, 1), b'\x00\x12\x45\xba\x7f\x80\xff') self.assertEqual(audioop.lin2lin(datas[2], 2, 4), packs[4](0, 0x12340000, 0x45670000, -0x45670000, 0x7fff0000, -0x80000000, -0x10000)) self.assertEqual(audioop.lin2lin(datas[4], 4, 1), b'\x00\x12\x45\xba\x7f\x80\xff') self.assertEqual(audioop.lin2lin(datas[4], 4, 2), packs[2](0, 0x1234, 0x4567, -0x4568, 0x7fff, -0x8000, -1)) def test_adpcm2lin(self): self.assertEqual(audioop.adpcm2lin(b'\x07\x7f\x7f', 1, None), (b'\x00\x00\x00\xff\x00\xff', (-179, 40))) self.assertEqual(audioop.adpcm2lin(b'\x07\x7f\x7f', 2, None), (packs[2](0, 0xb, 0x29, -0x16, 0x72, -0xb3), (-179, 40))) self.assertEqual(audioop.adpcm2lin(b'\x07\x7f\x7f', 4, None), (packs[4](0, 0xb0000, 0x290000, -0x160000, 0x720000, -0xb30000), (-179, 40))) for w in 1, 2, 4: self.assertEqual(audioop.adpcm2lin(b'\0' * 5, w, None), (b'\0' * w * 10, (0, 0))) def test_lin2adpcm(self): self.assertEqual(audioop.lin2adpcm(datas[1], 1, None), (b'\x07\x7f\x7f', (-221, 39))) self.assertEqual(audioop.lin2adpcm(datas[2], 2, None), (b'\x07\x7f\x7f', (31, 39))) self.assertEqual(audioop.lin2adpcm(datas[4], 4, None), (b'\x07\x7f\x7f', (31, 39))) for w in 1, 2, 4: self.assertEqual(audioop.lin2adpcm(b'\0' * w * 10, w, None), (b'\0' * 5, (0, 0))) def test_lin2alaw(self): self.assertEqual(audioop.lin2alaw(datas[1], 1), b'\xd5\x87\xa4\x24\xaa\x2a\x5a') self.assertEqual(audioop.lin2alaw(datas[2], 2), b'\xd5\x87\xa4\x24\xaa\x2a\x55') self.assertEqual(audioop.lin2alaw(datas[4], 4), b'\xd5\x87\xa4\x24\xaa\x2a\x55') def test_alaw2lin(self): encoded = b'\x00\x03\x24\x2a\x51\x54\x55\x58\x6b\x71\x7f'\ b'\x80\x83\xa4\xaa\xd1\xd4\xd5\xd8\xeb\xf1\xff' src = [-688, -720, -2240, -4032, -9, -3, -1, -27, -244, -82, -106, 688, 720, 2240, 4032, 9, 3, 1, 27, 244, 82, 106] for w in 1, 2, 4: self.assertEqual(audioop.alaw2lin(encoded, w), packs[w](*(x << (w * 8) >> 13 for x in src))) encoded = ''.join(chr(x) for x in xrange(256)) for w in 2, 4: decoded = audioop.alaw2lin(encoded, w) self.assertEqual(audioop.lin2alaw(decoded, w), encoded) def test_lin2ulaw(self): self.assertEqual(audioop.lin2ulaw(datas[1], 1), b'\xff\xad\x8e\x0e\x80\x00\x67') self.assertEqual(audioop.lin2ulaw(datas[2], 2), b'\xff\xad\x8e\x0e\x80\x00\x7e') self.assertEqual(audioop.lin2ulaw(datas[4], 4), b'\xff\xad\x8e\x0e\x80\x00\x7e') def test_ulaw2lin(self): encoded = b'\x00\x0e\x28\x3f\x57\x6a\x76\x7c\x7e\x7f'\ b'\x80\x8e\xa8\xbf\xd7\xea\xf6\xfc\xfe\xff' src = [-8031, -4447, -1471, -495, -163, -53, -18, -6, -2, 0, 8031, 4447, 1471, 495, 163, 53, 18, 6, 2, 0] for w in 1, 2, 4: self.assertEqual(audioop.ulaw2lin(encoded, w), packs[w](*(x << (w * 8) >> 14 for x in src))) encoded = ''.join(chr(x) for x in range(127) + range(128, 256)) for w in 2, 4: decoded = audioop.ulaw2lin(encoded, w) self.assertEqual(audioop.lin2ulaw(decoded, w), encoded) def test_mul(self): for w in 1, 2, 4: self.assertEqual(audioop.mul(b'', w, 2), b'') self.assertEqual(audioop.mul(datas[w], w, 0), b'\0' * len(datas[w])) self.assertEqual(audioop.mul(datas[w], w, 1), datas[w]) self.assertEqual(audioop.mul(datas[1], 1, 2), b'\x00\x24\x7f\x80\x7f\x80\xfe') self.assertEqual(audioop.mul(datas[2], 2, 2), packs[2](0, 0x2468, 0x7fff, -0x8000, 0x7fff, -0x8000, -2)) self.assertEqual(audioop.mul(datas[4], 4, 2), packs[4](0, 0x2468acf0, 0x7fffffff, -0x80000000, 0x7fffffff, -0x80000000, -2)) def test_ratecv(self): for w in 1, 2, 4: self.assertEqual(audioop.ratecv(b'', w, 1, 8000, 8000, None), (b'', (-1, ((0, 0),)))) self.assertEqual(audioop.ratecv(b'', w, 5, 8000, 8000, None), (b'', (-1, ((0, 0),) * 5))) self.assertEqual(audioop.ratecv(b'', w, 1, 8000, 16000, None), (b'', (-2, ((0, 0),)))) self.assertEqual(audioop.ratecv(datas[w], w, 1, 8000, 8000, None)[0], datas[w]) state = None d1, state = audioop.ratecv(b'\x00\x01\x02', 1, 1, 8000, 16000, state) d2, state = audioop.ratecv(b'\x00\x01\x02', 1, 1, 8000, 16000, state) self.assertEqual(d1 + d2, b'\000\000\001\001\002\001\000\000\001\001\002') for w in 1, 2, 4: d0, state0 = audioop.ratecv(datas[w], w, 1, 8000, 16000, None) d, state = b'', None for i in range(0, len(datas[w]), w): d1, state = audioop.ratecv(datas[w][i:i + w], w, 1, 8000, 16000, state) d += d1 self.assertEqual(d, d0) self.assertEqual(state, state0) def test_reverse(self): for w in 1, 2, 4: self.assertEqual(audioop.reverse(b'', w), b'') self.assertEqual(audioop.reverse(packs[w](0, 1, 2), w), packs[w](2, 1, 0)) def test_tomono(self): for w in 1, 2, 4: data1 = datas[w] data2 = bytearray(2 * len(data1)) for k in range(w): data2[k::2*w] = data1[k::w] self.assertEqual(audioop.tomono(str(data2), w, 1, 0), data1) self.assertEqual(audioop.tomono(str(data2), w, 0, 1), b'\0' * len(data1)) for k in range(w): data2[k+w::2*w] = data1[k::w] self.assertEqual(audioop.tomono(str(data2), w, 0.5, 0.5), data1) def test_tostereo(self): for w in 1, 2, 4: data1 = datas[w] data2 = bytearray(2 * len(data1)) for k in range(w): data2[k::2*w] = data1[k::w] self.assertEqual(audioop.tostereo(data1, w, 1, 0), data2) self.assertEqual(audioop.tostereo(data1, w, 0, 0), b'\0' * len(data2)) for k in range(w): data2[k+w::2*w] = data1[k::w] self.assertEqual(audioop.tostereo(data1, w, 1, 1), data2) def test_findfactor(self): self.assertEqual(audioop.findfactor(datas[2], datas[2]), 1.0) self.assertEqual(audioop.findfactor(b'\0' * len(datas[2]), datas[2]), 0.0) def test_findfit(self): self.assertEqual(audioop.findfit(datas[2], datas[2]), (0, 1.0)) self.assertEqual(audioop.findfit(datas[2], packs[2](1, 2, 0)), (1, 8038.8)) self.assertEqual(audioop.findfit(datas[2][:-2] * 5 + datas[2], datas[2]), (30, 1.0)) def test_findmax(self): self.assertEqual(audioop.findmax(datas[2], 1), 5) def test_getsample(self): for w in 1, 2, 4: data = packs[w](0, 1, -1, maxvalues[w], minvalues[w]) self.assertEqual(audioop.getsample(data, w, 0), 0) self.assertEqual(audioop.getsample(data, w, 1), 1) self.assertEqual(audioop.getsample(data, w, 2), -1) self.assertEqual(audioop.getsample(data, w, 3), maxvalues[w]) self.assertEqual(audioop.getsample(data, w, 4), minvalues[w]) def test_negativelen(self): self.assertRaises(audioop.error, audioop.findmax, ''.join( chr(x) for x in xrange(256)), -2392392) def test_issue7673(self): state = None for data, size in INVALID_DATA: size2 = size self.assertRaises(audioop.error, audioop.getsample, data, size, 0) self.assertRaises(audioop.error, audioop.max, data, size) self.assertRaises(audioop.error, audioop.minmax, data, size) self.assertRaises(audioop.error, audioop.avg, data, size) self.assertRaises(audioop.error, audioop.rms, data, size) self.assertRaises(audioop.error, audioop.avgpp, data, size) self.assertRaises(audioop.error, audioop.maxpp, data, size) self.assertRaises(audioop.error, audioop.cross, data, size) self.assertRaises(audioop.error, audioop.mul, data, size, 1.0) self.assertRaises(audioop.error, audioop.tomono, data, size, 0.5, 0.5) self.assertRaises(audioop.error, audioop.tostereo, data, size, 0.5, 0.5) self.assertRaises(audioop.error, audioop.add, data, data, size) self.assertRaises(audioop.error, audioop.bias, data, size, 0) self.assertRaises(audioop.error, audioop.reverse, data, size) self.assertRaises(audioop.error, audioop.lin2lin, data, size, size2) self.assertRaises(audioop.error, audioop.ratecv, data, size, 1, 1, 1, state) self.assertRaises(audioop.error, audioop.lin2ulaw, data, size) self.assertRaises(audioop.error, audioop.lin2alaw, data, size) self.assertRaises(audioop.error, audioop.lin2adpcm, data, size, state) def test_wrongsize(self): data = b'abcdefgh' state = None for size in (-1, 0, 3, 5, 1024): self.assertRaises(audioop.error, audioop.ulaw2lin, data, size) self.assertRaises(audioop.error, audioop.alaw2lin, data, size) self.assertRaises(audioop.error, audioop.adpcm2lin, data, size, state) def test_main(): run_unittest(TestAudioop) if __name__ == '__main__': test_main()

true

f7178158b29d68a175b0ebeab0012d4e31f2f3e6

2,610

py

Python

boofuzz/requests/http_post.py

youngcraft/boofuzz-modbus

bfeb48345b56797b48079e0620e7b06b27085789

[ "Apache-2.0" ]

23

2018-08-11T12:12:33.000Z

2022-01-28T10:22:49.000Z

boofuzz/requests/http_post.py

ctf-fuzzer/boofuzz-modbus

bfeb48345b56797b48079e0620e7b06b27085789

[ "Apache-2.0" ]

2

2018-07-24T15:15:40.000Z

2020-07-12T13:06:56.000Z

boofuzz/requests/http_post.py

ctf-fuzzer/boofuzz-modbus

bfeb48345b56797b48079e0620e7b06b27085789

[ "Apache-2.0" ]

10

2018-04-02T13:21:36.000Z

2022-01-17T09:20:27.000Z

from boofuzz import * # All POST mimetypes that I could think of/find # List of all blocks defined here (for easy copy/paste) """ sess.connect(s_get("HTTP VERBS POST")) sess.connect(s_get("HTTP VERBS POST ALL")) sess.connect(s_get("HTTP VERBS POST REQ")) """ # Fuzz POST requests with most MIMETypes known s_initialize("HTTP VERBS POST ALL") s_static("POST / HTTP/1.1\r\n") s_static("Content-Type: ") s_group("mimetypes", values=[ "audio/basic", "audio/x-mpeg", "drawing/x-dwf", "graphics/x-inventor", "image/x-portable-bitmap", "message/external-body", "message/http", "message/news", "message/partial", "message/rfc822", "multipart/alternative", "multipart/appledouble", "multipart/digest", "multipart/form-data", "multipart/header-set", "multipart/mixed", "multipart/parallel", "multipart/related", "multipart/report", "multipart/voice-message", "multipart/x-mixed-replace", "text/css", "text/enriched", "text/html", "text/javascript", "text/plain", "text/richtext", "text/sgml", "text/tab-separated-values", "text/vbscript", "video/x-msvideo", "video/x-sgi-movie", "workbook/formulaone", "x-conference/x-cooltalk", "x-form/x-openscape", "x-music/x-midi", "x-script/x-wfxclient", "x-world/x-3dmf" ]) if s_block_start("mime", group="mimetypes"): s_static("\r\n") s_static("Content-Length: ") s_size("post blob", output_format="ascii", signed=True, fuzzable=True) s_static("\r\n\r\n") s_block_end() if s_block_start("post blob"): s_string("A" * 100 + "=" + "B" * 100) s_block_end() s_static("\r\n\r\n") # Basic fuzz of post payloads s_initialize("HTTP VERBS POST") s_static("POST / HTTP/1.1\r\n") s_static("Content-Type: ") s_string("application/x-www-form-urlencoded") s_static("\r\n") s_static("Content-Length: ") s_size("post blob", output_format="ascii", signed=True, fuzzable=True) s_static("\r\n") if s_block_start("post blob"): s_string("A" * 100 + "=" + "B" * 100) s_block_end() s_static("\r\n\r\n") # Fuzz POST request MIMETypes s_initialize("HTTP VERBS POST REQ") s_static("POST / HTTP/1.1\r\n") s_static("Content-Type: ") s_string("application") s_delim("/") s_string("x") s_delim("-") s_string("www") s_delim("-") s_string("form") s_delim("-") s_string("urlencoded") s_static("\r\n") s_static("Content-Length: ") s_size("post blob", output_format="ascii", signed=True, fuzzable=True) s_static("\r\n") if s_block_start("post blob"): s_string("A" * 100 + "=" + "B" * 100) s_block_end() s_static("\r\n\r\n")

23.944954

74

0.650958

from boofuzz import * s_initialize("HTTP VERBS POST ALL") s_static("POST / HTTP/1.1\r\n") s_static("Content-Type: ") s_group("mimetypes", values=[ "audio/basic", "audio/x-mpeg", "drawing/x-dwf", "graphics/x-inventor", "image/x-portable-bitmap", "message/external-body", "message/http", "message/news", "message/partial", "message/rfc822", "multipart/alternative", "multipart/appledouble", "multipart/digest", "multipart/form-data", "multipart/header-set", "multipart/mixed", "multipart/parallel", "multipart/related", "multipart/report", "multipart/voice-message", "multipart/x-mixed-replace", "text/css", "text/enriched", "text/html", "text/javascript", "text/plain", "text/richtext", "text/sgml", "text/tab-separated-values", "text/vbscript", "video/x-msvideo", "video/x-sgi-movie", "workbook/formulaone", "x-conference/x-cooltalk", "x-form/x-openscape", "x-music/x-midi", "x-script/x-wfxclient", "x-world/x-3dmf" ]) if s_block_start("mime", group="mimetypes"): s_static("\r\n") s_static("Content-Length: ") s_size("post blob", output_format="ascii", signed=True, fuzzable=True) s_static("\r\n\r\n") s_block_end() if s_block_start("post blob"): s_string("A" * 100 + "=" + "B" * 100) s_block_end() s_static("\r\n\r\n") s_initialize("HTTP VERBS POST") s_static("POST / HTTP/1.1\r\n") s_static("Content-Type: ") s_string("application/x-www-form-urlencoded") s_static("\r\n") s_static("Content-Length: ") s_size("post blob", output_format="ascii", signed=True, fuzzable=True) s_static("\r\n") if s_block_start("post blob"): s_string("A" * 100 + "=" + "B" * 100) s_block_end() s_static("\r\n\r\n") s_initialize("HTTP VERBS POST REQ") s_static("POST / HTTP/1.1\r\n") s_static("Content-Type: ") s_string("application") s_delim("/") s_string("x") s_delim("-") s_string("www") s_delim("-") s_string("form") s_delim("-") s_string("urlencoded") s_static("\r\n") s_static("Content-Length: ") s_size("post blob", output_format="ascii", signed=True, fuzzable=True) s_static("\r\n") if s_block_start("post blob"): s_string("A" * 100 + "=" + "B" * 100) s_block_end() s_static("\r\n\r\n")

true

f71781cb54b95efe9a515ff1e8acbb559ba8adb6

70,600

py

Python

pyspeckit/spectrum/readers/read_class.py

FaceThePirate/pyspeckit

734b9f81d440ca3a6db9bf68e9409dbddb52d08b

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

null

pyspeckit/spectrum/readers/read_class.py

FaceThePirate/pyspeckit

734b9f81d440ca3a6db9bf68e9409dbddb52d08b

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

null

pyspeckit/spectrum/readers/read_class.py

FaceThePirate/pyspeckit

734b9f81d440ca3a6db9bf68e9409dbddb52d08b

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

null

""" ------------------------ GILDAS CLASS file reader ------------------------ Read a CLASS file into an :class:`pyspeckit.spectrum.ObsBlock` """ from __future__ import print_function from six.moves import xrange from six import iteritems import six import astropy.io.fits as pyfits import numpy import numpy as np from numpy import pi from astropy import log # from astropy.time import Time from astropy import units as u import pyspeckit import sys import re try: from astropy.utils.console import ProgressBar except ImportError: ProgressBar = lambda x: None ProgressBar.update = lambda x: None import struct import time # 'range' is needed as a keyword irange = range def print_timing(func): """ Prints execution time of decorated function. Included here because CLASS files can take a little while to read; this should probably be replaced with a progressbar """ def wrapper(*arg,**kwargs): t1 = time.time() res = func(*arg,**kwargs) t2 = time.time() log.info('%s took %0.5g s' % (func.__name__, (t2-t1))) return res wrapper.__doc__ = func.__doc__ return wrapper def ensure_bytes(string): """ Ensure a given string is in byte form """ if six.PY3: return bytes(string, 'utf-8') else: return str(string) """ Specification: http://iram.fr/IRAMFR/GILDAS/doc/html/class-html/node58.html """ filetype_dict = {'1A ':'Multiple_IEEE', '1 ':'Multiple_Vax', '1B ':'Multiple_EEEI', '2A ':'v2', '2 ':'v2', '2B ':'v2', '9A ':'Single_IEEE', '9 ':'Single_Vax', '9B ':'Single_EEEI'} for key in list(filetype_dict.keys()): filetype_dict[ensure_bytes(key)] = filetype_dict[key] fileversion_dict = {'1A ':'v1', '2A ':'v2', '9A ':'v1', # untested } for key in list(fileversion_dict.keys()): fileversion_dict[ensure_bytes(key)] = fileversion_dict[key] record_lengths = {'1A': 512, '2A': 1024*4} header_id_numbers = {0: 'USER CODE', -1: 'COMMENT', -2: 'GENERAL', -3: 'POSITION', -4: 'SPECTRO', -5: 'BASELINE', -6: 'HISTORY', -7: 'UNKNOWN-APEX', # -8: 'SWITCH', -9: 'GAUSSFIT', # "private"; see class-interfaces-private.f90 -10: 'DRIFT', -11: 'BEAMSWITCH', # "private"; see class-interfaces-private.f90 -12: 'SHELLFIT', # "private"; see class-interfaces-private.f90 -13: 'NH3FIT', # "private"; see class-interfaces-private.f90 -14: 'CALIBRATION', -18: 'ABSFIT', # "private"; see class-interfaces-private.f90 } header_id_lengths = {-2: 9, # may really be 10? -3: 17, -4: 17, -5: None, # variable length -6: 3, # variable length -14: 25, } # from packages/classic/lib/classic_mod.f90 filedescv2_nw1=14 """ GENERAL integer(kind=obsnum_length) :: num ! [ ] Observation number integer(kind=4) :: ver ! [ ] Version number integer(kind=4) :: teles(3) ! [ ] Telescope name integer(kind=4) :: dobs ! [MJD-60549] Date of observation integer(kind=4) :: dred ! [MJD-60549] Date of reduction integer(kind=4) :: typec ! [ code] Type of coordinates integer(kind=4) :: kind ! [ code] Type of data integer(kind=4) :: qual ! [ code] Quality of data integer(kind=4) :: subscan ! [ ] Subscan number integer(kind=obsnum_length) :: scan ! [ ] Scan number ! Written in the entry real(kind=8) :: ut ! 1-2 [ rad] UT of observation real(kind=8) :: st ! 3-4 [ rad] LST of observation real(kind=4) :: az ! 5 [ rad] Azimuth real(kind=4) :: el ! 6 [ rad] Elevation real(kind=4) :: tau ! 7 [neper] Opacity real(kind=4) :: tsys ! 8 [ K] System temperature real(kind=4) :: time ! 9 [ s] Integration time ! Not in this section in file integer(kind=4) :: xunit ! [ code] X unit (if X coordinates section is present) ! NOT in data --- character(len=12) :: cdobs ! [string] Duplicate of dobs character(len=12) :: cdred ! [string] Duplicate of dred """ keys_lengths = { 'unknown': [ #('NUM' ,1,'int32'), # Observation number ('VER' ,1,'int32'), # Version number ('TELES' ,3,'|S12') , # Telescope name ('DOBS' ,1,'int32'), # Date of observation ('DRED' ,1,'int32'), # Date of reduction ('TYPEC' ,1,'int32'), # Type of coordinates ('KIND' ,1,'int32'), # Type of data ('QUAL' ,1,'int32'), # Quality of data ('SCAN' ,1,'int32'), # Scan number ('SUBSCAN' ,1,'int32'), # Subscan number ], 'COMMENT': [ # -1 ('LTEXT',1,'int32'), # integer(kind=4) :: ltext ! Length of comment ('CTEXT',1024//4,'|S1024'), # character ctext*1024 ! Comment string ], 'GENERAL': [ # -2 ('UT' ,2,'float64'), # rad UT of observation ('ST' ,2,'float64'), # rad LST of observation ('AZ' ,1,'float32'), # rad Azimuth ('EL' ,1,'float32'), # rad Elevation ('TAU' ,1,'float32'), # neper Opacity ('TSYS' ,1,'float32'), # K System temperature ('TIME' ,1,'float32'), # s Integration time # XUNIT should not be there? #( 'XUNIT' ,1,'int32'), # code X unit (if xcoord_sec is present) ] , 'POSITION': [ # -3 ('SOURC',3,'|S12') , # [ ] Source name ('EPOCH',1,'float32'), # [ ] Epoch of coordinates ('LAM' ,2,'float64'), #[rad] Lambda ('BET' ,2,'float64'), #[rad] Beta ('LAMOF',1,'float32'), # [rad] Offset in Lambda ('BETOF',1,'float32'), # [rad] Offset in Beta ('PROJ' ,1,'int32') , # [rad] Projection system ('SL0P' ,1,'float64'), # lambda of descriptive system # MAY NOT EXIST IN OLD CLASS ('SB0P' ,1,'float64'), # beta of descriptive system # MAY NOT EXIST IN OLD CLASS ('SK0P' ,1,'float64'), # angle of descriptive system # MAY NOT EXIST IN OLD CLASS ], 'SPECTRO': [ # -4 #('align' ,1,'int32'), # [ ] Alignment padding ('LINE' ,3,'|S12'), # [ ] Line name ('RESTF' ,2,'float64'), # [ MHz] Rest frequency ('NCHAN' ,1,'int32'), # [ ] Number of channels ('RCHAN' ,1,'float32'), # [ ] Reference channels ('FRES' ,1,'float32'), # [ MHz] Frequency resolution ('FOFF' ,1,'float32'), # [ MHz] Frequency offset ('VRES' ,1,'float32'), # [km/s] Velocity resolution ('VOFF' ,1,'float32'), # [km/s] Velocity at reference channel ('BAD' ,1,'float32'), # [ ] Blanking value #('ALIGN_1',1,'int32'), # [ ] Alignment padding ('IMAGE' ,2,'float64'), # [ MHz] Image frequency #('ALIGN_2',1,'int32'), # [ ] Alignment padding ('VTYPE' ,1,'int32'), # [code] Type of velocity ('DOPPLER',2,'float64'), # [ ] Doppler factor = -V/c (CLASS convention) ], 'CALIBRATION': [ # -14 ('ALIGN',1,'int32'), # BUFFER (it's a zero - it is not declared in the docs!!!!) ('BEEFF',1,'float32'), # [ ] Beam efficiency ('FOEFF',1,'float32'), # [ ] Forward efficiency ('GAINI',1,'float32'), # [ ] Image/Signal gain ratio ('H2OMM',1,'float32'), # [ mm] Water vapor content ('PAMB',1,'float32'), # [ hPa] Ambient pressure ('TAMB',1,'float32'), # [ K] Ambient temperature ('TATMS',1,'float32'), # [ K] Atmosphere temp. in signal band ('TCHOP',1,'float32'), # [ K] Chopper temperature ('TCOLD',1,'float32'), # [ K] Cold load temperature ('TAUS',1,'float32'), # [neper] Opacity in signal band ('TAUI',1,'float32'), # [neper] Opacity in image band ('TATMI',1,'float32'), # [ K] Atmosphere temp. in image band ('TREC',1,'float32'), # [ K] Receiver temperature ('CMODE',1,'int32'), # [ code] Calibration mode ('ATFAC',1,'float32'), # [ ] Applied calibration factor ('ALTI',1,'float32'), # [ m] Site elevation ('COUNT',3,'3float32'), # [count] Power of Atm., Chopp., Cold ('LCALOF',1,'float32'), # [ rad] Longitude offset for sky measurement ('BCALOF',1,'float32'), # [ rad] Latitude offset for sky measurement ('GEOLONG',1,'float64'), # [ rad] Geographic longitude of observatory # MAY NOT EXIST IN OLD CLASS ('GEOLAT',1,'float64'), # [ rad] Geographic latitude of observatory # MAY NOT EXIST IN OLD CLASS ], 'BASELINE':[ ('DEG',1,'int32'), #! [ ] Degree of last baseline ('SIGFI',1,'float32'), #! [Int. unit] Sigma ('AIRE',1,'float32'), #! [Int. unit] Area under windows ('NWIND',1,'int32'), #! [ ] Number of line windows # WARNING: These should probably have 'n', the second digit, = NWIND # The docs are really unclear about this, they say "W1(MWIND)" ('W1MWIND',1,'float32'), #! [km/s] Lower limits of windows ('W2MWIND',1,'float32'), #! [km/s] Upper limits of windows ('SINUS',3,'float32'), #![] Sinus baseline results ], 'DRIFT':[ # 16? ('FREQ',1,'float64') , #! [ MHz] Rest frequency real(kind=8) :: ('WIDTH',1,'float32'), #! [ MHz] Bandwidth real(kind=4) :: ('NPOIN',1,'int32') , #! [ ] Number of data points integer(kind=4) :: ('RPOIN',1,'float32'), #! [ ] Reference point real(kind=4) :: ('TREF',1,'float32') , #! [ ?] Time at reference real(kind=4) :: ('AREF',1,'float32') , #! [ rad] Angular offset at ref. real(kind=4) :: ('APOS',1,'float32') , #! [ rad] Position angle of drift real(kind=4) :: ('TRES',1,'float32') , #! [ ?] Time resolution real(kind=4) :: ('ARES',1,'float32') , #! [ rad] Angular resolution real(kind=4) :: ('BAD',1,'float32') , #! [ ] Blanking value real(kind=4) :: ('CTYPE',1,'int32') , #! [code] Type of offsets integer(kind=4) :: ('CIMAG',1,'float64'), #! [ MHz] Image frequency real(kind=8) :: ('COLLA',1,'float32'), #! [ ?] Collimation error Az real(kind=4) :: ('COLLE',1,'float32'), #! [ ?] Collimation error El real(kind=4) :: ], } def _read_bytes(f, n): '''Read the next `n` bytes (from idlsave)''' return f.read(n) """ Warning: UNCLEAR what endianness should be! Numpy seemed to get it right, and I think numpy assumes NATIVE endianness """ def _read_byte(f): '''Read a single byte (from idlsave)''' return numpy.uint8(struct.unpack('=B', f.read(4)[:1])[0]) def _read_int16(f): '''Read a signed 16-bit integer (from idlsave)''' return numpy.int16(struct.unpack('=h', f.read(4)[2:4])[0]) def _read_int32(f): '''Read a signed 32-bit integer (from idlsave)''' return numpy.int32(struct.unpack('=i', f.read(4))[0]) def _read_int64(f): '''Read a signed 64-bit integer ''' return numpy.int64(struct.unpack('=q', f.read(8))[0]) def _read_float32(f): '''Read a 32-bit float (from idlsave)''' return numpy.float32(struct.unpack('=f', f.read(4))[0]) def _align_32(f): '''Align to the next 32-bit position in a file (from idlsave)''' pos = f.tell() if pos % 4 != 0: f.seek(pos + 4 - pos % 4) return def _read_word(f,length): if length > 0: chars = _read_bytes(f, length) _align_32(f) else: chars = None return chars def _read_int(f): return struct.unpack('i',f.read(4)) def is_ascii(s): """Check if there are non-ascii characters in Unicode string Parameters ---------- s : str The string to be checked Returns ------- is_ascii : bool Returns True if all characters in the string are ascii. False otherwise. """ return len(s) == len(s.decode('ascii').encode('utf-8')) def is_all_null(s): return all(x=='\x00' for x in s) or all(x==b'\x00' for x in s) """ from clic_file.f90: v1, v2 integer(kind=4) :: bloc ! 1 : observation address [records] integer(kind=8) :: bloc ! 1- 2: observation address [records] integer(kind=4) :: bloc ! 1 : block read from index integer(kind=4) :: num ! 2 : observation number integer(kind=4) :: word ! 3 : address offset [4-bytes] integer(kind=4) :: num ! 2 : number read integer(kind=4) :: ver ! 3 : observation version integer(kind=4) :: ver ! 4 : observation version integer(kind=4) :: ver ! 3 : version read from index integer(kind=4) :: sourc(3) ! 4- 6: source name integer(kind=8) :: num ! 5- 6: observation number character(len=12) :: csour ! 4- 6: source read from index integer(kind=4) :: line(3) ! 7- 9: line name integer(kind=4) :: sourc(3) ! 7- 9: source name character(len=12) :: cline ! 7- 9: line read from index integer(kind=4) :: teles(3) ! 10-12: telescope name integer(kind=4) :: line(3) ! 10-12: line name character(len=12) :: ctele ! 10-12: telescope read from index integer(kind=4) :: dobs ! 13 : observation date [class_date] integer(kind=4) :: teles(3) ! 13-15: telescope name integer(kind=4) :: dobs ! 13 : date obs. read from index integer(kind=4) :: dred ! 14 : reduction date [class_date] integer(kind=4) :: dobs ! 16 : observation date [class_date] integer(kind=4) :: dred ! 14 : date red. read from index real(kind=4) :: off1 ! 15 : lambda offset [radian] integer(kind=4) :: dred ! 17 : reduction date [class_date] real(kind=4) :: off1 ! 15 : read offset 1 real(kind=4) :: off2 ! 16 : beta offset [radian] real(kind=4) :: off1 ! 18 : lambda offset [radian] real(kind=4) :: off2 ! 16 : read offset 2 integer(kind=4) :: typec ! 17 : coordinates types real(kind=4) :: off2 ! 19 : beta offset [radian] integer(kind=4) :: type ! 17 : type of read offsets integer(kind=4) :: kind ! 18 : data kind integer(kind=4) :: typec ! 20 : coordinates types integer(kind=4) :: kind ! 18 : type of observation integer(kind=4) :: qual ! 19 : data quality integer(kind=4) :: kind ! 21 : data kind integer(kind=4) :: qual ! 19 : Quality read from index integer(kind=4) :: scan ! 20 : scan number integer(kind=4) :: qual ! 22 : data quality integer(kind=4) :: scan ! 20 : Scan number read from index integer(kind=4) :: proc ! 21 : procedure type integer(kind=4) :: scan ! 23 : scan number real(kind=4) :: posa ! 21 : Position angle integer(kind=4) :: itype ! 22 : observation type integer(kind=4) :: proc ! 24 : procedure type integer(kind=4) :: subscan ! 22 : Subscan number real(kind=4) :: houra ! 23 : hour angle [radian] integer(kind=4) :: itype ! 25 : observation type integer(kind=4) :: pad(10) ! 23-32: Pad to 32 words integer(kind=4) :: project ! 24 : project name real(kind=4) :: houra ! 26 : hour angle [radian] integer(kind=4) :: pad1 ! 25 : unused word integer(kind=4) :: project(2) ! 27 : project name integer(kind=4) :: bpc ! 26 : baseline bandpass cal status integer(kind=4) :: bpc ! 29 : baseline bandpass cal status integer(kind=4) :: ic ! 27 : instrumental cal status integer(kind=4) :: ic ! 30 : instrumental cal status integer(kind=4) :: recei ! 28 : receiver number integer(kind=4) :: recei ! 31 : receiver number real(kind=4) :: ut ! 29 : UT [s] real(kind=4) :: ut ! 32 : UT [s] integer(kind=4) :: pad2(3) ! 30-32: padding to 32 4-bytes word equivalently integer(kind=obsnum_length) :: num ! [ ] Observation number integer(kind=4) :: ver ! [ ] Version number integer(kind=4) :: teles(3) ! [ ] Telescope name integer(kind=4) :: dobs ! [MJD-60549] Date of observation integer(kind=4) :: dred ! [MJD-60549] Date of reduction integer(kind=4) :: typec ! [ code] Type of coordinates integer(kind=4) :: kind ! [ code] Type of data integer(kind=4) :: qual ! [ code] Quality of data integer(kind=4) :: subscan ! [ ] Subscan number integer(kind=obsnum_length) :: scan ! [ ] Scan number """ """ index.f90: call conv%read%i8(data(1), indl%bloc, 1) ! bloc call conv%read%i4(data(3), indl%word, 1) ! word call conv%read%i8(data(4), indl%num, 1) ! num call conv%read%i4(data(6), indl%ver, 1) ! ver call conv%read%cc(data(7), indl%csour, 3) ! csour call conv%read%cc(data(10),indl%cline, 3) ! cline call conv%read%cc(data(13),indl%ctele, 3) ! ctele call conv%read%i4(data(16),indl%dobs, 1) ! dobs call conv%read%i4(data(17),indl%dred, 1) ! dred call conv%read%r4(data(18),indl%off1, 1) ! off1 call conv%read%r4(data(19),indl%off2, 1) ! off2 call conv%read%i4(data(20),indl%type, 1) ! type call conv%read%i4(data(21),indl%kind, 1) ! kind call conv%read%i4(data(22),indl%qual, 1) ! qual call conv%read%r4(data(23),indl%posa, 1) ! posa call conv%read%i8(data(24),indl%scan, 1) ! scan call conv%read%i4(data(26),indl%subscan,1) ! subscan if (isv3) then call conv%read%r8(data(27),indl%ut, 1) ! ut else """ def _read_indices(f, file_description): #if file_description['version'] in (1,2): # extension_positions = (file_description['aex']-1)*file_description['reclen']*4 # all_indices = {extension: # [_read_index(f, # filetype=file_description['version'], # entry=ii, # #position=position, # ) # for ii in range(file_description['lex1'])] # for extension,position in enumerate(extension_positions) # if position > 0 # } #elif file_description['version'] == 1: extension_positions = ((file_description['aex'].astype('int64')-1) *file_description['reclen']*4) all_indices = [_read_index(f, filetype=file_description['version'], # 1-indexed files entry_number=ii+1, file_description=file_description, ) for ii in range(file_description['xnext']-1)] #else: # raise ValueError("Invalid file version {0}".format(file_description['version'])) return all_indices def _find_index(entry_number, file_description, return_position=False): if file_description['gex'] == 10: kex=(entry_number-1)//file_description['lex1'] + 1 else: # exponential growth: #kex = gi8_dicho(file_description['nex'], file_description['lexn'], entry_number) - 1 kex = len([xx for xx in file_description['lexn'] if xx<entry_number]) ken = entry_number - file_description['lexn'][kex-1] #! Find ken (relative entry number in the extension, starts from 1) #ken = entry_num - file%desc%lexn(kex-1) kb = ((ken-1)*file_description['lind'])//file_description['reclen'] #kb = ((ken-1)*file%desc%lind)/file%desc%reclen ! In the extension, the # ! relative record position (as an offset, starts from 0) where the # ! Entry Index starts. NB: there can be a non-integer number of Entry # ! Indexes per record # Subtract 1: 'aex' is 1-indexed kbl = (file_description['aex'][kex-1]+kb)-1 # kbl = file%desc%aex(kex)+kb ! The absolute record number where the Entry Index goes k = ((ken-1)*file_description['lind']) % file_description['reclen'] #k = mod((ken-1)*file%desc%lind,file%desc%reclen)+1 ! = in the record, the # ! first word of the Entry Index of the entry number 'entry_num' if return_position: return (kbl*file_description['reclen']+k)*4 else: return kbl,k def _read_index(f, filetype='v1', DEBUG=False, clic=False, position=None, entry_number=None, file_description=None): if position is not None: f.seek(position) if entry_number is not None: indpos = _find_index(entry_number, file_description, return_position=True) f.seek(indpos) x0 = f.tell() if filetype in ('1A ','v1', 1): log.debug('Index filetype 1A') index = { "XBLOC":_read_int32(f), "XNUM":_read_int32(f), "XVER":_read_int32(f), "XSOURC":_read_word(f,12), "XLINE":_read_word(f,12), "XTEL":_read_word(f,12), "XDOBS":_read_int32(f), "XDRED":_read_int32(f), "XOFF1":_read_float32(f),# first offset (real, radians) "XOFF2":_read_float32(f),# second offset (real, radians) "XTYPE":_read_int32(f),# coordinate system ('EQ'', 'GA', 'HO') "XKIND":_read_int32(f),# Kind of observation (0: spectral, 1: continuum, ) "XQUAL":_read_int32(f),# Quality (0-9) "XSCAN":_read_int32(f),# Scan number } index['BLOC'] = index['XBLOC'] # v2 compatibility index['WORD'] = 1 # v2 compatibility index['SOURC'] = index['CSOUR'] = index['XSOURC'] index['DOBS'] = index['CDOBS'] = index['XDOBS'] index['CTELE'] = index['XTEL'] index['LINE'] = index['XLINE'] index['OFF1'] = index['XOFF1'] index['OFF2'] = index['XOFF2'] index['QUAL'] = index['XQUAL'] index['SCAN'] = index['XSCAN'] index['KIND'] = index['XKIND'] if clic: # use header set up in clic nextchunk = { "XPROC":_read_int32(f),# "procedure type" "XITYPE":_read_int32(f),# "XHOURANG":_read_float32(f),# "XPROJNAME":_read_int32(f),# "XPAD1":_read_int32(f), "XBPC" :_read_int32(f), "XIC" :_read_int32(f), "XRECEI" :_read_int32(f), "XUT":_read_float32(f), "XPAD2":numpy.fromfile(f,count=3,dtype='int32') # BLANK is NOT ALLOWED!!! It is a special KW } else: nextchunk = {"XPOSA":_read_float32(f), "XSUBSCAN":_read_int32(f), 'XPAD2': numpy.fromfile(f,count=10,dtype='int32'), } nextchunk['SUBSCAN'] = nextchunk['XSUBSCAN'] nextchunk['POSA'] = nextchunk['XPOSA'] index.update(nextchunk) if (f.tell() - x0 != 128): missed_bits = (f.tell()-x0) X = f.read(128-missed_bits) if DEBUG: print("read_index missed %i bits: %s" % (128-missed_bits,X)) #raise IndexError("read_index did not successfully read 128 bytes at %i. Read %i bytes." % (x0,f.tell()-x0)) if any(not is_ascii(index[x]) for x in ('XSOURC','XLINE','XTEL')): raise ValueError("Invalid index read from {0}.".format(x0)) elif filetype in ('2A ','v2', 2): log.debug('Index filetype 2A') index = { "BLOC" : _read_int64(f) , #(data(1), 1) ! bloc "WORD" : _read_int32(f) , #(data(3), 1) ! word "NUM" : _read_int64(f) , #(data(4), 1) ! num "VER" : _read_int32(f) , #(data(6), 1) ! ver "CSOUR" : _read_word(f,12), #(data(7), 3) ! csour "CLINE" : _read_word(f,12), #(data(10), 3) ! cline "CTELE" : _read_word(f,12), #(data(13), 3) ! ctele "DOBS" : _read_int32(f) , #(data(16), 1) ! dobs "DRED" : _read_int32(f) , #(data(17), 1) ! dred "OFF1" : _read_float32(f), #(data(18), 1) ! off1 "OFF2" : _read_float32(f), #(data(19), 1) ! off2 "TYPE" : _read_int32(f) , #(data(20), 1) ! type "KIND" : _read_int32(f) , #(data(21), 1) ! kind "QUAL" : _read_int32(f) , #(data(22), 1) ! qual "POSA" : _read_float32(f), #(data(23), 1) ! posa "SCAN" : _read_int64(f) , #(data(24), 1) ! scan "SUBSCAN": _read_int32(f) , #(data(26), 1) ! subscan } #last24bits = f.read(24) #log.debug("Read 24 bits: '{0}'".format(last24bits)) if any((is_all_null(index[x]) or not is_ascii(index[x])) for x in ('CSOUR','CLINE','CTELE')): raise ValueError("Invalid index read from {0}.".format(x0)) index['SOURC'] = index['XSOURC'] = index['CSOUR'] index['LINE'] = index['XLINE'] = index['CLINE'] index['XKIND'] = index['KIND'] try: index['DOBS'] = index['XDOBS'] = index['CDOBS'] except KeyError: index['CDOBS'] = index['XDOBS'] = index['DOBS'] else: raise NotImplementedError("Filetype {0} not implemented.".format(filetype)) # from kernel/lib/gsys/date.f90: gag_julda index['MJD'] = index['DOBS'] + 60549 class_dobs = index['DOBS'] index['DOBS'] = ((class_dobs + 365*2025)/365.2425 + 1) # SLOW #index['DATEOBS'] = Time(index['DOBS'], format='jyear') #index['DATEOBSS'] = index['DATEOBS'].iso log.debug("Indexing finished at {0}".format(f.tell())) return index def _read_header(f, type=0, position=None): """ Read a header entry from a CLASS file (helper function) """ if position is not None: f.seek(position) if type in keys_lengths: hdrsec = [(x[0],numpy.fromfile(f,count=1,dtype=x[2])[0]) for x in keys_lengths[type]] return dict(hdrsec) else: return {} raise ValueError("Unrecognized type {0}".format(type)) def _read_first_record(f): f.seek(0) filetype = f.read(4) if fileversion_dict[filetype] == 'v1': return _read_first_record_v1(f) elif fileversion_dict[filetype] == 'v2': return _read_first_record_v2(f) else: raise ValueError("Unrecognized filetype {0}".format(filetype)) def _read_first_record_v1(f, record_length_words=128): r""" Position & Parameter & Fortran Kind & Purpose \\ \hline 1 & {\tt code} & Character*4 & File code \\ 2 & {\tt next} & Integer*4 & Next free record \\ 3 & {\tt lex} & Integer*4 & Length of first extension (number of entries) \\ 4 & {\tt nex} & Integer*4 & Number of extensions \\ 5 & {\tt xnext} & Integer*4 & Next available entry number \\ 6:2*{\tt reclen} & {\tt ex(:)} & Integer*4 & Array of extension addresses from classic_mod.f90: integer(kind=4) :: code ! 1 File code integer(kind=4) :: next ! 2 Next free record integer(kind=4) :: lex ! 3 Extension length (number of entries) integer(kind=4) :: nex ! 4 Number of extensions integer(kind=4) :: xnext ! 5 Next available entry number integer(kind=4) :: aex(mex_v1) ! 6:256 Extension addresses from old (<dec2013) class, file.f90: read(ilun,rec=1,err=11,iostat=ier) ibx%code,ibx%next, & & ibx%ilex,ibx%imex,ibx%xnext also uses filedesc_v1tov2 from classic/lib/file.f90 """ # OLD NOTES # hdr = header # hdr.update(obshead) # re-overwrite things # hdr.update({'OBSNUM':obsnum,'RECNUM':spcount}) # hdr.update({'RA':hdr['LAM']/pi*180,'DEC':hdr['BET']/pi*180}) # hdr.update({'RAoff':hdr['LAMOF']/pi*180,'DECoff':hdr['BETOF']/pi*180}) # hdr.update({'OBJECT':hdr['SOURC'].strip()}) # hdr.update({'BUNIT':'Tastar'}) # hdr.update({'EXPOSURE':hdr['TIME']}) f.seek(0) file_description = { 'code': f.read(4), 'next': _read_int32(f), 'lex': _read_int32(f), 'nex': _read_int32(f), 'xnext': _read_int32(f), 'gex': 10., 'vind': 1, # classic_vind_v1 packages/classic/lib/classic_mod.f90 'version': 1, 'nextrec': 3, 'nextword': 1, 'lind': 32, #classic_lind_v1 packages/classic/lib/classic_mod.f90 'kind': 'unknown', 'flags': 0, } file_description['reclen'] = record_length_words # should be 128w = 512 bytes ex = np.fromfile(f, count=(record_length_words*2-5), dtype='int32') file_description['ex'] = ex[ex!=0] file_description['nextrec'] = file_description['next'] # this can't be... file_description['lex1'] = file_description['lex'] # number of entries file_description['lexn'] = (np.arange(file_description['nex']+1) * file_description['lex1']) file_description['nentries'] = np.sum(file_description['lexn']) file_description['aex'] = file_description['ex'][:file_description['nex']] #file_description['version'] = fileversion_dict[file_description['code']] assert f.tell() == 1024 # Something is not quite right with the 'ex' parsing #assert len(file_description['ex']) == file_description['nex'] return file_description def _read_first_record_v2(f): r""" packages/classic/lib/file.f90 Position & Parameter & Fortran Kind & Purpose & Unit \\ \hline 1 & {\tt code} & Character*4 & File code & - \\ 2 & {\tt reclen} & Integer*4 & Record length & words \\ 3 & {\tt kind} & Integer*4 & File kind & - \\ 4 & {\tt vind} & Integer*4 & Index version & - \\ 5 & {\tt lind} & Integer*4 & Index length & words \\ 6 & {\tt flags} & Integer*4 & Bit flags. \#1: single or multiple, & - \\ & & & \#2-32: provision (0-filled) & \\ \hline 7:8 & {\tt xnext} & Integer*8 & Next available entry number & - \\ 9:10 & {\tt nextrec} & Integer*8 & Next record which contains free space & record \\ 11 & {\tt nextword} & Integer*4 & Next free word in this record & word \\ \hline 12 & {\tt lex1} & Integer*4 & Length of first extension index & entries \\ 13 & {\tt nex} & Integer*4 & Number of extensions & - \\ 14 & {\tt gex} & Integer*4 & Extension growth rule & - \\ 15:{\tt reclen} & {\tt aex(:)} & Integer*8 & Array of extension addresses & record """ f.seek(0) file_description = { 'code': f.read(4), 'reclen': _read_int32(f), 'kind': _read_int32(f), 'vind': _read_int32(f), 'lind': _read_int32(f), 'flags': _read_int32(f), 'xnext': _read_int64(f), 'nextrec': _read_int64(f), 'nextword': _read_int32(f), 'lex1': _read_int32(f), 'nex': _read_int32(f), 'gex': _read_int32(f), } file_description['lexn'] = [0] if file_description['gex'] == 10: for ii in range(1, file_description['nex']+1): file_description['lexn'].append(file_description['lexn'][-1]+file_description['lex1']) else: #! Exponential growth. Only growth with mantissa 2.0 is supported for ii in range(1, file_description['nex']): # I don't know what the fortran does here!!! # ahh, maybe 2_8 means int(2, dtype='int64') nent = int(file_description['lex1'] * 2**(ii-1)) #nent = int(file%desc%lex1,kind=8) * 2_8**(iex-1) file_description['lexn'].append(file_description['lexn'][-1]+nent) #file%desc%lexn(iex) = file%desc%lexn(iex-1) + nent file_description['nentries'] = np.sum(file_description['lexn']) record_length_words = file_description['reclen'] aex = numpy.fromfile(f, count=(record_length_words-15)//2, dtype='int64') file_description['aex'] = aex[aex!=0] assert len(file_description['aex']) == file_description['nex'] file_description['version'] = 2 return file_description def gi8_dicho(ninp,lexn,xval,ceil=True): """ ! @ public ! Find ival such as ! X(ival-1) < xval <= X(ival) (ceiling mode) ! or ! X(ival) <= xval < X(ival+1) (floor mode) ! for input data ordered. Use a dichotomic search for that. call gi8_dicho(nex,file%desc%lexn,entry_num,.true.,kex,error) """ #integer(kind=size_length), intent(in) :: np ! Number of input points #integer(kind=8), intent(in) :: x(np) ! Input ordered Values #integer(kind=8), intent(in) :: xval ! The value we search for #logical, intent(in) :: ceil ! Ceiling or floor mode? #integer(kind=size_length), intent(out) :: ival ! Position in the array #logical, intent(inout) :: error ! Logical error flag iinf = 1 isup = ninp #! Ceiling mode while isup > (iinf+1): imid = int(np.floor((isup + iinf)/2.)) if (lexn[imid-1] < xval): iinf = imid else: isup = imid ival = isup return ival def _read_obshead(f, file_description, position=None, verbose=False): if file_description['version'] == 1: return _read_obshead_v1(f, position=position, verbose=verbose) if file_description['version'] == 2: return _read_obshead_v2(f, position=position) else: raise ValueError("Invalid file version {0}.". format(file_description['version'])) def _read_obshead_v2(f, position=None): """ ! Version 2 (public) integer(kind=4), parameter :: entrydescv2_nw1=11 ! Number of words, in 1st part integer(kind=4), parameter :: entrydescv2_nw2=5 ! Number of words for 1 section in 2nd part type classic_entrydesc_t sequence integer(kind=4) :: code ! 1 : code observation icode integer(kind=4) :: version ! 2 : observation version integer(kind=4) :: nsec ! 3 : number of sections integer(kind=4) :: pad1 ! - : memory padding (not in data) integer(kind=8) :: nword ! 4- 5: number of words integer(kind=8) :: adata ! 6- 7: data address integer(kind=8) :: ldata ! 8- 9: data length integer(kind=8) :: xnum ! 10-11: entry number ! Out of the 'sequence' block: integer(kind=4) :: msec ! Not in data: maximum number of sections the ! Observation Index can hold integer(kind=4) :: pad2 ! Memory padding for 8 bytes alignment integer(kind=4) :: seciden(classic_maxsec) ! Section Numbers (on disk: 1 to ed%nsec) integer(kind=8) :: secleng(classic_maxsec) ! Section Lengths (on disk: 1 to ed%nsec) integer(kind=8) :: secaddr(classic_maxsec) ! Section Addresses (on disk: 1 to ed%nsec) end type classic_entrydesc_t """ if position is not None: f.seek(position) else: position = f.tell() IDcode = f.read(4) if IDcode.strip() != b'2': raise IndexError("Observation Header reading failure at {0}. " "Record does not appear to be an observation header.". format(position)) f.seek(position) entrydescv2_nw1 = 11 entrydescv2_nw2 = 5 obshead = { 'CODE': f.read(4), 'VERSION': _read_int32(f), 'NSEC': _read_int32(f), #'_blank': _read_int32(f), 'NWORD': _read_int64(f), 'ADATA': _read_int64(f), 'LDATA': _read_int64(f), 'XNUM': _read_int64(f), #'MSEC': _read_int32(f), #'_blank2': _read_int32(f), } section_numbers = np.fromfile(f, count=obshead['NSEC'], dtype='int32') section_lengths = np.fromfile(f, count=obshead['NSEC'], dtype='int64') section_addresses = np.fromfile(f, count=obshead['NSEC'], dtype='int64') return obshead['XNUM'],obshead,dict(zip(section_numbers,section_addresses)) def _read_obshead_v1(f, position=None, verbose=False): """ Read the observation header of a CLASS file (helper function for read_class; should not be used independently) """ if position is not None: f.seek(position) IDcode = f.read(4) if IDcode.strip() != b'2': raise IndexError("Observation Header reading failure at {0}. " "Record does not appear to be an observation header.". format(f.tell() - 4)) (nblocks, nbyteob, data_address, nheaders, data_length, obindex, nsec, obsnum) = numpy.fromfile(f, count=8, dtype='int32') if verbose: print("nblocks,nbyteob,data_address,data_length,nheaders,obindex,nsec,obsnum",nblocks,nbyteob,data_address,data_length,nheaders,obindex,nsec,obsnum) print("DATA_LENGTH: ",data_length) seccodes = numpy.fromfile(f,count=nsec,dtype='int32') # Documentation says addresses then length: It is apparently wrong seclen = numpy.fromfile(f,count=nsec,dtype='int32') secaddr = numpy.fromfile(f,count=nsec,dtype='int32') if verbose: print("Section codes, addresses, lengths: ",seccodes,secaddr,seclen) hdr = {'NBLOCKS':nblocks, 'NBYTEOB':nbyteob, 'DATAADDR':data_address, 'DATALEN':data_length, 'NHEADERS':nheaders, 'OBINDEX':obindex, 'NSEC':nsec, 'OBSNUM':obsnum} #return obsnum,seccodes return obsnum,hdr,dict(zip(seccodes,secaddr)) # THIS IS IN READ_OBSHEAD!!! # def _read_preheader(f): # """ # Not entirely clear what this is, but it is stuff that precedes the actual data # # Looks something like this: # array([ 1, -2, -3, -4, -14, # 9, 17, 18, 25, 55, # 64, 81, 99, -1179344801, 979657591, # # -2, -3, -4, -14 indicate the 4 header types # 9,17,18,25 *MAY* indicate the number of bytes in each # # # HOW is it indicated how many entries there are? # """ # # 13 comes from counting 1, -2,....99 above # numbers = np.fromfile(f, count=13, dtype='int32') # sections = [n for n in numbers if n in header_id_numbers] # return sections def downsample_1d(myarr,factor,estimator=np.mean, weight=None): """ Downsample a 1D array by averaging over *factor* pixels. Crops right side if the shape is not a multiple of factor. This code is pure numpy and should be fast. keywords: estimator - default to mean. You can downsample by summing or something else if you want a different estimator (e.g., downsampling error: you want to sum & divide by sqrt(n)) weight: np.ndarray An array of weights to use for the downsampling. If None, assumes uniform 1 """ if myarr.ndim != 1: raise ValueError("Only works on 1d data. Says so in the title.") xs = myarr.size crarr = myarr[:xs-(xs % int(factor))] if weight is None: dsarr = estimator(np.concatenate([[crarr[i::factor] for i in range(factor)]]),axis=0) else: dsarr = estimator(np.concatenate([[crarr[i::factor]*weight[i::factor] for i in range(factor)]]),axis=0) warr = estimator(np.concatenate([[weight[i::factor] for i in range(factor)]]),axis=0) dsarr = dsarr/warr return dsarr # unit test def test_downsample1d(): data = np.arange(10) weight = np.ones(10) weight[5]=0 assert np.all(downsample_1d(data, 2, weight=weight, estimator=np.mean) == np.array([0.5, 2.5, 4.0, 6.5, 8.5])) def read_observation(f, obsid, file_description=None, indices=None, my_memmap=None, memmap=True, verbose=False): if isinstance(f, str): f = open(f,'rb') opened = True if memmap: my_memmap = numpy.memmap(f, offset=0, dtype='float32', mode='r') else: my_memmap = None elif my_memmap is None and memmap: raise ValueError("Must pass in a memmap object if passing in a file object.") else: opened = False if file_description is None: file_description = _read_first_record(f) if indices is None: indices = _read_indices(f, file_description) index = indices[obsid] obs_position = (index['BLOC']-1)*file_description['reclen']*4 + (index['WORD']-1)*4 log.debug("Reading observation at position {0}".format(obs_position)) obsnum,obshead,sections = _read_obshead(f, file_description, position=obs_position, verbose=verbose) header = obshead datastart = 0 for section_id,section_address in iteritems(sections): # Section addresses are 1-indexed byte addresses # in the current "block" sec_position = obs_position + (section_address-1)*4 temp_hdr = _read_header(f, type=header_id_numbers[section_id], position=sec_position) header.update(temp_hdr) datastart = max(datastart,f.tell()) hdr = header hdr.update(obshead) # re-overwrite things hdr.update({'OBSNUM':obsnum,'RECNUM':obsid}) hdr.update({'RA':hdr['LAM']/pi*180,'DEC':hdr['BET']/pi*180}) hdr.update({'RAoff':hdr['LAMOF']/pi*180,'DECoff':hdr['BETOF']/pi*180}) hdr.update({'OBJECT':hdr['SOURC'].strip()}) hdr.update({'BUNIT':'Tastar'}) hdr.update({'EXPOSURE':float(hdr['TIME'])}) hdr['HDRSTART'] = obs_position hdr['DATASTART'] = datastart hdr.update(indices[obsid]) # Define MJD as mid-exposure time in MJD hdr.update({'OBSDATE': hdr['MJD'] + hdr['UT']/2./pi}) # Apparently the data are still valid in this case? #if hdr['XNUM'] != obsid+1: # log.error("The spectrum read was {0} but {1} was requested.". # format(hdr['XNUM']-1, obsid)) if hdr['KIND'] == 1: # continuum nchan = hdr['NPOIN'] elif 'NCHAN' in hdr: nchan = hdr['NCHAN'] else: log.error("No NCHAN in header. This is not a spectrum.") import ipdb; ipdb.set_trace() # There may be a 1-channel offset? CHECK!!! # (changed by 1 pixel - October 14, 2014) # (changed back - October 21, 2014 - I think the ends are just bad, but not # zero.) f.seek(datastart-1) spec = _read_spectrum(f, position=datastart-1, nchan=nchan, memmap=memmap, my_memmap=my_memmap) if opened: f.close() return spec, hdr def _read_spectrum(f, position, nchan, my_memmap=None, memmap=True): if position != f.tell(): log.warning("Reading data from {0}, but the file is wound " "to {1}.".format(position, f.tell())) if memmap: here = position #spectrum = numpy.memmap(filename, offset=here, dtype='float32', # mode='r', shape=(nchan,)) spectrum = my_memmap[here//4:here//4+nchan] f.seek(here+nchan*4) else: f.seek(position) spectrum = numpy.fromfile(f,count=nchan,dtype='float32') return spectrum def _spectrum_from_header(fileobj, header, memmap=None): return _read_spectrum(fileobj, position=header['DATASTART'], nchan=header['NCHAN'] if 'NCHAN' in hdr else hdr['NPOIN'], my_memmap=memmap) def clean_header(header): newheader = {} for k in header: if not isinstance(header[k], (int, float, str)): if isinstance(header[k], np.ndarray) and header[k].size > 1: if header[k].size > 10: raise ValueError("Large array being put in header. That's no good. key={0}".format(k)) for ii,val in enumerate(header[k]): newheader[k[:7]+str(ii)] = val else: newheader[k[:8]] = str(header[k]) else: newheader[k[:8]] = header[k] return newheader class ClassObject(object): def __init__(self, filename, verbose=False): t0 = time.time() self._file = open(filename, 'rb') self.file_description = _read_first_record(self._file) self.allind = _read_indices(self._file, self.file_description) self._data = np.memmap(self._file, dtype='float32', mode='r') if verbose: log.info("Setting _spectra") self._spectra = LazyItem(self) t1 = time.time() if verbose: log.info("Setting posang. t={0}".format(t1-t0)) self.set_posang() t2 = time.time() if verbose: log.info("Identifying otf scans. t={0}".format(t2-t1)) self._identify_otf_scans(verbose=verbose) t3 = time.time() #self._load_all_spectra() if verbose: log.info("Loaded CLASS object with {3} indices. Time breakdown:" " {0}s for indices, " "{1}s for posang, and {2}s for OTF scan identification" .format(t1-t0, t2-t1, t3-t2, len(self.allind))) def __repr__(self): s = "\n".join(["{k}: {v}".format(k=k,v=v) for k,v in iteritems(self.getinfo())]) return "ClassObject({id}) with {nspec} entries\n".format(id=id(self), nspec=len(self.allind)) + s def getinfo(self, allsources=False): info = dict( tels = self.tels, lines = self.lines, scans = self.scans, sources = self.sources if allsources else self.sci_sources, ) return info def set_posang(self): h0 = self.headers[0] for h in self.headers: dx = h['OFF1'] - h0['OFF1'] dy = h['OFF2'] - h0['OFF2'] h['COMPPOSA'] = np.arctan2(dy,dx)*180/np.pi h0 = h def _identify_otf_scans(self, verbose=False): h0 = self.allind[0] st = 0 otfscan = 0 posangs = [h['COMPPOSA'] for h in self.allind] if verbose: pb = ProgressBar(len(self.allind)) for ii,h in enumerate(self.allind): if (h['SCAN'] != h0['SCAN'] or h['SOURC'] != h0['SOURC']): h0['FIRSTSCAN'] = st cpa = np.median(posangs[st:ii]) for hh in self.allind[st:ii]: hh['SCANPOSA'] = cpa % 180 st = ii if h['SCAN'] == h0['SCAN']: h0['OTFSCAN'] = otfscan otfscan += 1 h['OTFSCAN'] = otfscan else: otfscan = 0 h['OTFSCAN'] = otfscan else: h['OTFSCAN'] = otfscan if verbose: pb.update(ii) def listscans(self, source=None, telescope=None, out=sys.stdout): minid=0 scan = -1 sourc = "" #tel = '' minoff1,maxoff1 = np.inf,-np.inf minoff2,maxoff2 = np.inf,-np.inf ttlangle,nangle = 0.0,0 print("{entries:15s} {SOURC:12s} {XTEL:12s} {SCAN:>8s} {SUBSCAN:>8s} " "[ {RAmin:>12s}, {RAmax:>12s} ] " "[ {DECmin:>12s}, {DECmax:>12s} ] " "{angle:>12s} {SCANPOSA:>12s} {OTFSCAN:>8s} {TSYS:>8s} {UTD:>12s}" .format(entries='Scans', SOURC='Source', XTEL='Telescope', SCAN='Scan', SUBSCAN='Subscan', RAmin='min(RA)', RAmax='max(RA)', DECmin='min(DEC)', DECmax='max(DEC)', SCANPOSA='Scan PA', angle='Angle', OTFSCAN='OTFscan', TSYS='TSYS', UTD='UTD'), file=out) data_rows = [] for ii,row in enumerate(self.headers): if (row['SCAN'] == scan and row['SOURC'] == sourc #and row['XTEL'] == tel ): minoff1 = min(minoff1, row['OFF1']) maxoff1 = max(maxoff1, row['OFF1']) minoff2 = min(minoff2, row['OFF2']) maxoff2 = max(maxoff2, row['OFF2']) ttlangle += np.arctan2(row['OFF2'] - prevrow['OFF2'], row['OFF1'] - prevrow['OFF1'])%np.pi nangle += 1 prevrow = row else: if scan == -1: scan = row['SCAN'] sourc = row['SOURC'] #tel = row['XTEL'] prevrow = row continue ok = True if source is not None: if isinstance(source, (list,tuple)): ok = ok and any(re.search((s), prevrow['SOURC']) for s in source) else: ok = ok and re.search((source), prevrow['SOURC']) if telescope is not None: ok = ok and re.search((telescope), prevrow['XTEL']) if ok: data = dict(RAmin=minoff1*180/np.pi*3600, RAmax=maxoff1*180/np.pi*3600, DECmin=minoff2*180/np.pi*3600, DECmax=maxoff2*180/np.pi*3600, angle=(ttlangle/nangle)*180/np.pi if nangle>0 else 0, e0=minid, e1=ii-1, #TSYS=row['TSYS'] if 'TSYS' in row else '--', UTD=row['DOBS']+row['UT'] if 'UT' in row else -99, **prevrow) print("{e0:7d}-{e1:7d} {SOURC:12s} {XTEL:12s} {SCAN:8d} {SUBSCAN:8d} " "[ {RAmin:12f}, {RAmax:12f} ] " "[ {DECmin:12f}, {DECmax:12f} ] " "{angle:12.1f} {SCANPOSA:12.1f} {OTFSCAN:8d}" " {TSYS:>8.1f} {UTD:12f}". format(**data), file=out) data_rows.append(data) minoff1,maxoff1 = np.inf,-np.inf minoff2,maxoff2 = np.inf,-np.inf ttlangle,nangle = 0.0,0 scan = row['SCAN'] sourc = row['SOURC'] #tel = row['XTEL'] minid = ii return data @property def tels(self): if hasattr(self,'_tels'): return self._tels else: self._tels = set([h['CTELE'] for h in self.allind]) #testing if CTELE even works return self._tels @property def sources(self): if hasattr(self,'_source'): return self._source else: self._source = set([h['SOURC'] for h in self.allind]) return self._source @property def scans(self): if hasattr(self,'_scan'): return self._scan else: self._scan = set([h['SCAN'] for h in self.allind]) return self._scan @property def sci_sources(self): return set([s for s in self.sources if s[:4] not in ('SKY-', 'TSYS', 'TCAL', 'TREC', 'HOT-', 'COLD')]) @property def lines(self): if hasattr(self,'_lines'): return self._lines else: self._lines = set([h['LINE'] for h in self.allind]) return self._lines def _load_all_spectra(self, indices=None): if indices is None: indices = range(self.file_description['xnext']-1) if hasattr(self, '_loaded_indices'): indices_set = set(indices) indices_to_load = (indices_set.difference(self._loaded_indices)) self._loaded_indices = self._loaded_indices.union(indices_set) if any(indices_to_load): pb = ProgressBar(len(indices_to_load)) for ii,k in enumerate(xrange(indices_to_load)): self._spectra[k] pb.update(ii) else: self._loaded_indices = set(indices) self._spectra.load_all() @property def spectra(self): return [x[0] for x in self._spectra] @property def headers(self): return [self._spectra[ii][1] if ii in self._spectra else x for ii,x in enumerate(self.allind)] def select_spectra(self, all=None, line=None, linere=None, linereflags=re.IGNORECASE, number=None, scan=None, offset=None, source=None, sourcere=None, sourcereflags=re.IGNORECASE, range=None, quality=None, telescope=None, telescopere=None, telescopereflags=re.IGNORECASE, subscan=None, entry=None, posang=None, #observed=None, #reduced=None, frequency=None, section=None, user=None, include_old_versions=False, ): """ Parameters ---------- include_old_versions: bool Include spectra with XVER numbers <0? These are CLASS spectra that have been "overwritten" (re-reduced?) """ if entry is not None and len(entry)==2: return irange(entry[0], entry[1]) if frequency is not None: self._load_all_spectra() sel = [(re.search(re.escape(ensure_bytes(line)), h['LINE'], re.IGNORECASE) if line is not None else True) and (re.search(ensure_bytes(linere), h['LINE'], linereflags) if linere is not None else True) and (h['SCAN'] == scan if scan is not None else True) and ((h['OFF1'] == offset or h['OFF2'] == offset) if offset is not None else True) and (re.search(re.escape(ensure_bytes(source)), h['CSOUR'], re.IGNORECASE) if source is not None else True) and (re.search(ensure_bytes(sourcere), h['CSOUR'], sourcereflags) if sourcere is not None else True) and (h['OFF1']>range[0] and h['OFF1'] < range[1] and h['OFF2']>range[2] and h['OFF2'] < range[3] if range is not None and len(range)==4 else True) and (h['QUAL'] == quality if quality is not None else True) and (re.search(re.escape(ensure_bytes(telescope)), h['CTELE'], re.IGNORECASE) if telescope is not None else True) and (re.search(ensure_bytes(telescopere), h['CTELE'], telescopereflags) if telescopere is not None else True) and (h['SUBSCAN']==subscan if subscan is not None else True) and ('RESTF' in h and # Need to check that it IS a spectrum: continuum data can't be accessed this way h['RESTF'] > frequency[0] and h['RESTF'] < frequency[1] if frequency is not None and len(frequency)==2 else True) and (h['COMPPOSA']%180 > posang[0] and h['COMPPOSA']%180 < posang[1] if posang is not None and len(posang)==2 else True) and # 1A uses XVER, 2A uses VER. If neither are present, it's # probably not a valid spectrum? (h.get('XVER', h.get('VER', -999)) > 0 if not include_old_versions else True) for h in self.headers ] return [ii for ii,k in enumerate(sel) if k] def get_spectra(self, progressbar=True, **kwargs): selected_indices = self.select_spectra(**kwargs) if not any(selected_indices): raise ValueError("Selection yielded empty.") self._spectra.load(selected_indices, progressbar=progressbar) return [self._spectra[ii] for ii in selected_indices] def get_pyspeckit_spectra(self, progressbar=True, **kwargs): spdata = self.get_spectra(progressbar=progressbar, **kwargs) spectra = [pyspeckit.Spectrum(data=data, xarr=make_axis(header), header=clean_header(header)) for data,header in spdata] return spectra def read_observations(self, observation_indices, progressbar=True): self._spectra.load(observation_indices, progressbar=progressbar) return [self._spectra[ii] for ii in observation_indices] @print_timing def read_class(filename, downsample_factor=None, sourcename=None, telescope=None, line=None, posang=None, verbose=False, flag_array=None): """ Read a binary class file. Based on the `GILDAS CLASS file type Specification <http://iram.fr/IRAMFR/GILDAS/doc/html/class-html/node58.html>`_ Parameters ---------- filename: str downsample_factor: None or int Factor by which to downsample data by averaging. Useful for overresolved data. sourcename: str or list of str Source names to match to the data (uses regex) telescope: str or list of str 'XTEL' or 'TELE' parameters: the telescope & instrument line: str or list of str The line name posang: tuple of 2 floats The first float is the minimum value for the position angle. The second float is the maximum value for the position angle. verbose: bool Log messages with severity INFO flag_array: np.ndarray An array with the same shape as the data used to flag out (remove) data when downsampling. True = flag out """ classobj = ClassObject(filename) if not isinstance(sourcename, (list,tuple)): sourcename = [sourcename] if not isinstance(telescope, (list,tuple)): telescope = [telescope] if not isinstance(line, (list,tuple)): line = [line] spectra,headers = [],[] if verbose: log.info("Reading...") selection = [ii for source in sourcename for tel in telescope for li in line for ii in classobj.select_spectra(sourcere=source, telescope=tel, line=li, posang=posang)] sphdr = classobj.read_observations(selection) if len(sphdr) == 0: return None spec,hdr = zip(*sphdr) spectra += spec headers += hdr indexes = headers weight = ~flag_array if flag_array is not None else None if downsample_factor is not None: if verbose: log.info("Downsampling...") spectra = [downsample_1d(spec, downsample_factor, weight=weight) for spec in ProgressBar(spectra)] headers = [downsample_header(h, downsample_factor) for h in ProgressBar(headers)] for hdr in headers: stringify_header(hdr) return spectra,headers,indexes def stringify_header(header): from six import string_types, integer_types import string FITS_allowed_types = (string_types + integer_types + (float, complex, bool, np.floating, np.integer, np.complexfloating, np.bool_)) bad_chars = string.printable[96:] badcharre = re.compile("[{0}]".format(bad_chars)) for key, value in header.items(): if isinstance(value, bytes): header[key] = value.decode() elif not isinstance(value, FITS_allowed_types): header[key] = badcharre.sub("", str(header[key])) def downsample_header(hdr, downsample_factor): for k in ('NCHAN','NPOIN','DATALEN'): if k in hdr: hdr[k] = int((hdr[k] / downsample_factor)) # maybe wrong? h['RCHAN'] = (h['RCHAN']-1) / downsample_factor + 1 scalefactor = 1./downsample_factor hdr['RCHAN'] = (hdr['RCHAN']-1)*scalefactor + 0.5 + scalefactor/2. for kw in ['FRES','VRES']: if kw in hdr: hdr[kw] *= downsample_factor return hdr def make_axis(header,imagfreq=False): """ Create a :class:`pyspeckit.spectrum.units.SpectroscopicAxis` from the CLASS "header" """ from .. import units rest_frequency = header.get('RESTF') xunits = 'MHz' nchan = header.get('NCHAN') voff = header.get('VOFF') foff = header.get('FOFF') doppler = header.get('DOPPLER') fres = header.get('FRES') refchan = header.get('RCHAN') imfreq = header.get('IMAGE') if foff in (None, 0.0) and voff not in (None, 0.0): # Radio convention foff = -voff/2.997924580e5 * rest_frequency if not imagfreq: xarr = rest_frequency + foff + (numpy.arange(1, nchan+1) - refchan) * fres XAxis = units.SpectroscopicAxis(xarr,unit='MHz',refX=rest_frequency*u.MHz) else: xarr = imfreq - (numpy.arange(1, nchan+1) - refchan) * fres XAxis = units.SpectroscopicAxis(xarr,unit='MHz',refX=imfreq*u.MHz) return XAxis @print_timing def class_to_obsblocks(filename, telescope, line, datatuple=None, source=None, imagfreq=False, DEBUG=False, **kwargs): """ Load an entire CLASS observing session into a list of ObsBlocks based on matches to the 'telescope', 'line' and 'source' names Parameters ---------- filename : string The Gildas CLASS data file to read the spectra from. telescope : list List of telescope names to be matched. line : list List of line names to be matched. source : list (optional) List of source names to be matched. Defaults to None. imagfreq : bool Create a SpectroscopicAxis with the image frequency. """ if datatuple is None: spectra,header,indexes = read_class(filename, **kwargs) else: spectra,header,indexes = datatuple obslist = [] lastscannum = -1 spectrumlist = None for sp,hdr,ind in zip(spectra,header,indexes): hdr.update(ind) # this is slow but necessary... H = pyfits.Header() for k,v in iteritems(hdr): if hasattr(v,"__len__") and not isinstance(v,str): # make an array of header entries, but this # supports only up to 10 of them... if len(v) > 1: if len(v) < 10: for ii,vv in enumerate(v): newkey = k[:7]+str(ii) H[newkey] = vv elif len(v) < 100: for ii,vv in enumerate(v): newkey = k[:6]+str(ii) H[newkey] = vv else: raise ValueError("Too many entries for {0}".format(k)) else: H[k] = v[0] #elif not any(x in str(v).lower() for x in ('comment', 'end', 'history')): # # do not try to add comments... # This commented out block used to attempt to reject comments # using a private regex in the old pyfits which no longer exists. # I don't know if it was necessary. else: H[k] = v scannum = hdr['SCAN'] if 'XTEL' in hdr and hdr['XTEL'].strip() not in telescope: continue if hdr['LINE'].strip() not in line: continue if (source is not None) and (hdr['SOURC'].strip() not in source): continue hdr['RESTFREQ'] = hdr.get('RESTF') H['RESTFREQ'] = hdr.get('RESTF') #print "Did not skip %s,%s. Scannum, last: %i,%i" % (hdr['XTEL'],hdr['LINE'],scannum,lastscannum) if scannum != lastscannum: lastscannum = scannum if spectrumlist is not None: obslist.append(pyspeckit.ObsBlock(spectrumlist)) xarr = make_axis(hdr,imagfreq=imagfreq) spectrumlist = [( pyspeckit.Spectrum(xarr=xarr, header=H, data=sp))] else: spectrumlist.append( pyspeckit.Spectrum(xarr=xarr, header=H, data=sp)) return obslist class LazyItem(object): """ Simple lazy spectrum-retriever wrapper """ def __init__(self, parent): self.parent = parent self.sphdr = {} self.nind = len(self.parent.allind) self.nloaded = 0 def __repr__(self): return ("Set of {0} spectra & headers, {1} loaded" " ({2:0.2f}%)".format(self.nind, self.nloaded, (float(self.nloaded)/self.nind)*100)) def load_all(self, progressbar=True): self.load(range(self.nind)) def load(self, indices, progressbar=True): pb = ProgressBar(len(indices)) counter = 0 for k in indices: self[k] counter += 1 pb.update(counter) def __getitem__(self, key): if key in self.sphdr: return self.sphdr[key] elif isinstance(key, slice): return [self[k] for k in xrange(key.start or 0, key.end or len(self.parent.allind), key.step or 1)] else: sphd = read_observation(self.parent._file, key, file_description=self.parent.file_description, indices=self.parent.allind, my_memmap=self.parent._data) # Update the header with OTFSCAN and POSANG info sphd[1].update(self.parent.allind[key]) self.sphdr[key] = sphd self.nloaded += 1 return sphd def __iter__(self): return self.next() def __next__(self): for k in self.spheader: yield self.spheader[k] def __contains__(self, key): return key in self.sphdr @print_timing def class_to_spectra(filename, datatuple=None, **kwargs): """ Load each individual spectrum within a CLASS file into a list of Spectrum objects """ if datatuple is None: spectra,header,indexes = read_class(filename, **kwargs) else: spectra,header,indexes = datatuple spectrumlist = [] for sp,hdr,ind in zip(spectra,header,indexes): hdr.update(ind) xarr = make_axis(hdr) spectrumlist.append( pyspeckit.Spectrum(xarr=xarr, header=hdr, data=sp)) return pyspeckit.Spectra(spectrumlist) def tests(): """ Tests are specific to the machine on which this code was developed. """ fn1 = '/Users/adam/work/bolocam/hht/class_003.smt' #fn1 = '/Users/adam/work/bolocam/hht/class_001.smt' #fn1 = '/Users/adam/work/bolocam/hht/test_SMT-F1M-VU-20824-073.cls' #fn2 = '/Users/adam/work/bolocam/hht/test_SMT-F1M-VU-79472+203.cls' #F1 = read_class(fn1)#,DEBUG=True) #F2 = read_class(fn2) n2hp = class_to_obsblocks(fn1,telescope=['SMT-F1M-HU','SMT-F1M-VU'],line=['N2HP(3-2)','N2H+(3-2)']) hcop = class_to_obsblocks(fn1,telescope=['SMT-F1M-HL','SMT-F1M-VL'],line=['HCOP(3-2)','HCO+(3-2)'])

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from __future__ import print_function from six.moves import xrange from six import iteritems import six import astropy.io.fits as pyfits import numpy import numpy as np from numpy import pi from astropy import log from astropy import units as u import pyspeckit import sys import re try: from astropy.utils.console import ProgressBar except ImportError: ProgressBar = lambda x: None ProgressBar.update = lambda x: None import struct import time irange = range def print_timing(func): def wrapper(*arg,**kwargs): t1 = time.time() res = func(*arg,**kwargs) t2 = time.time() log.info('%s took %0.5g s' % (func.__name__, (t2-t1))) return res wrapper.__doc__ = func.__doc__ return wrapper def ensure_bytes(string): if six.PY3: return bytes(string, 'utf-8') else: return str(string) filetype_dict = {'1A ':'Multiple_IEEE', '1 ':'Multiple_Vax', '1B ':'Multiple_EEEI', '2A ':'v2', '2 ':'v2', '2B ':'v2', '9A ':'Single_IEEE', '9 ':'Single_Vax', '9B ':'Single_EEEI'} for key in list(filetype_dict.keys()): filetype_dict[ensure_bytes(key)] = filetype_dict[key] fileversion_dict = {'1A ':'v1', '2A ':'v2', '9A ':'v1', } for key in list(fileversion_dict.keys()): fileversion_dict[ensure_bytes(key)] = fileversion_dict[key] record_lengths = {'1A': 512, '2A': 1024*4} header_id_numbers = {0: 'USER CODE', -1: 'COMMENT', -2: 'GENERAL', -3: 'POSITION', -4: 'SPECTRO', -5: 'BASELINE', -6: 'HISTORY', -7: 'UNKNOWN-APEX', -9: 'GAUSSFIT', -10: 'DRIFT', -11: 'BEAMSWITCH', -12: 'SHELLFIT', -13: 'NH3FIT', -14: 'CALIBRATION', -18: 'ABSFIT', } header_id_lengths = {-2: 9, -3: 17, -4: 17, -5: None, -6: 3, -14: 25, } filedescv2_nw1=14 keys_lengths = { 'unknown': [ 'int32'), ('TELES' ,3,'|S12') , ('DOBS' ,1,'int32'), ('DRED' ,1,'int32'), ('TYPEC' ,1,'int32'), ('KIND' ,1,'int32'), ('QUAL' ,1,'int32'), ('SCAN' ,1,'int32'), ('SUBSCAN' ,1,'int32'), ], 'COMMENT': [ ('LTEXT',1,'int32'), ('CTEXT',1024//4,'|S1024'), ], 'GENERAL': [ ('UT' ,2,'float64'), ('ST' ,2,'float64'), ('AZ' ,1,'float32'), ('EL' ,1,'float32'), ('TAU' ,1,'float32'), ('TSYS' ,1,'float32'), ('TIME' ,1,'float32'), C',3,'|S12') , ('EPOCH',1,'float32'), ('LAM' ,2,'float64'), ('BET' ,2,'float64'), ('LAMOF',1,'float32'), ('BETOF',1,'float32'), ('PROJ' ,1,'int32') , ('SL0P' ,1,'float64'), ('RESTF' ,2,'float64'), ('NCHAN' ,1,'int32'), ('RCHAN' ,1,'float32'), ('FRES' ,1,'float32'), ('FOFF' ,1,'float32'), ('VRES' ,1,'float32'), ('VOFF' ,1,'float32'), ('BAD' ,1,'float32'), '), , ('DOPPLER',2,'float64'), ], 'CALIBRATION': [ ('ALIGN',1,'int32'), ('BEEFF',1,'float32'), # [ ] Beam efficiency ('FOEFF',1,'float32'), # [ ] Forward efficiency ('GAINI',1,'float32'), # [ ] Image/Signal gain ratio ('H2OMM',1,'float32'), # [ mm] Water vapor content ('PAMB',1,'float32'), # [ hPa] Ambient pressure ('TAMB',1,'float32'), # [ K] Ambient temperature ('TATMS',1,'float32'), # [ K] Atmosphere temp. in signal band ('TCHOP',1,'float32'), # [ K] Chopper temperature ('TCOLD',1,'float32'), # [ K] Cold load temperature ('TAUS',1,'float32'), # [neper] Opacity in signal band ('TAUI',1,'float32'), # [neper] Opacity in image band ('TATMI',1,'float32'), # [ K] Atmosphere temp. in image band ('TREC',1,'float32'), # [ K] Receiver temperature ('CMODE',1,'int32'), # [ code] Calibration mode ('ATFAC',1,'float32'), # [ ] Applied calibration factor ('ALTI',1,'float32'), # [ m] Site elevation ('COUNT',3,'3float32'), # [count] Power of Atm., Chopp., Cold ('LCALOF',1,'float32'), # [ rad] Longitude offset for sky measurement ('BCALOF',1,'float32'), # [ rad] Latitude offset for sky measurement ('GEOLONG',1,'float64'), # [ rad] Geographic longitude of observatory # MAY NOT EXIST IN OLD CLASS ('GEOLAT',1,'float64'), # [ rad] Geographic latitude of observatory # MAY NOT EXIST IN OLD CLASS ], 'BASELINE':[ ('DEG',1,'int32'), #! [ ] Degree of last baseline ('SIGFI',1,'float32'), #! [Int. unit] Sigma ('AIRE',1,'float32'), #! [Int. unit] Area under windows ('NWIND',1,'int32'), #! [ ] Number of line windows # WARNING: These should probably have 'n', the second digit, = NWIND # The docs are really unclear about this, they say "W1(MWIND)" ('W1MWIND',1,'float32'), #! [km/s] Lower limits of windows ('W2MWIND',1,'float32'), #! [km/s] Upper limits of windows ('SINUS',3,'float32'), #![] Sinus baseline results ], 'DRIFT':[ # 16? ('FREQ',1,'float64') , #! [ MHz] Rest frequency real(kind=8) :: ('WIDTH',1,'float32'), #! [ MHz] Bandwidth real(kind=4) :: ('NPOIN',1,'int32') , #! [ ] Number of data points integer(kind=4) :: ('RPOIN',1,'float32'), #! [ ] Reference point real(kind=4) :: ('TREF',1,'float32') , #! [ ?] Time at reference real(kind=4) :: ('AREF',1,'float32') , #! [ rad] Angular offset at ref. real(kind=4) :: ('APOS',1,'float32') , #! [ rad] Position angle of drift real(kind=4) :: ('TRES',1,'float32') , #! [ ?] Time resolution real(kind=4) :: ('ARES',1,'float32') , #! [ rad] Angular resolution real(kind=4) :: ('BAD',1,'float32') , #! [ ] Blanking value real(kind=4) :: ('CTYPE',1,'int32') , #! [code] Type of offsets integer(kind=4) :: ('CIMAG',1,'float64'), #! [ MHz] Image frequency real(kind=8) :: ('COLLA',1,'float32'), #! [ ?] Collimation error Az real(kind=4) :: ('COLLE',1,'float32'), #! [ ?] Collimation error El real(kind=4) :: ], } def _read_bytes(f, n): return f.read(n) def _read_byte(f): return numpy.uint8(struct.unpack('=B', f.read(4)[:1])[0]) def _read_int16(f): return numpy.int16(struct.unpack('=h', f.read(4)[2:4])[0]) def _read_int32(f): return numpy.int32(struct.unpack('=i', f.read(4))[0]) def _read_int64(f): return numpy.int64(struct.unpack('=q', f.read(8))[0]) def _read_float32(f): return numpy.float32(struct.unpack('=f', f.read(4))[0]) def _align_32(f): pos = f.tell() if pos % 4 != 0: f.seek(pos + 4 - pos % 4) return def _read_word(f,length): if length > 0: chars = _read_bytes(f, length) _align_32(f) else: chars = None return chars def _read_int(f): return struct.unpack('i',f.read(4)) def is_ascii(s): return len(s) == len(s.decode('ascii').encode('utf-8')) def is_all_null(s): return all(x=='\x00' for x in s) or all(x==b'\x00' for x in s) def _read_indices(f, file_description): #if file_description['version'] in (1,2): # extension_positions = (file_description['aex']-1)*file_description['reclen']*4 # all_indices = {extension: # [_read_index(f, # filetype=file_description['version'], # entry=ii, # #position=position, # ) # for ii in range(file_description['lex1'])] # for extension,position in enumerate(extension_positions) # if position > 0 # } #elif file_description['version'] == 1: extension_positions = ((file_description['aex'].astype('int64')-1) *file_description['reclen']*4) all_indices = [_read_index(f, filetype=file_description['version'], # 1-indexed files entry_number=ii+1, file_description=file_description, ) for ii in range(file_description['xnext']-1)] #else: # raise ValueError("Invalid file version {0}".format(file_description['version'])) return all_indices def _find_index(entry_number, file_description, return_position=False): if file_description['gex'] == 10: kex=(entry_number-1)//file_description['lex1'] + 1 else: # exponential growth: #kex = gi8_dicho(file_description['nex'], file_description['lexn'], entry_number) - 1 kex = len([xx for xx in file_description['lexn'] if xx<entry_number]) ken = entry_number - file_description['lexn'][kex-1] #! Find ken (relative entry number in the extension, starts from 1) #ken = entry_num - file%desc%lexn(kex-1) kb = ((ken-1)*file_description['lind'])//file_description['reclen'] #kb = ((ken-1)*file%desc%lind)/file%desc%reclen ! In the extension, the # ! relative record position (as an offset, starts from 0) where the # ! Entry Index starts. NB: there can be a non-integer number of Entry # ! Indexes per record # Subtract 1: 'aex' is 1-indexed kbl = (file_description['aex'][kex-1]+kb)-1 # kbl = file%desc%aex(kex)+kb ! The absolute record number where the Entry Index goes k = ((ken-1)*file_description['lind']) % file_description['reclen'] #k = mod((ken-1)*file%desc%lind,file%desc%reclen)+1 ! = in the record, the # ! first word of the Entry Index of the entry number 'entry_num' if return_position: return (kbl*file_description['reclen']+k)*4 else: return kbl,k def _read_index(f, filetype='v1', DEBUG=False, clic=False, position=None, entry_number=None, file_description=None): if position is not None: f.seek(position) if entry_number is not None: indpos = _find_index(entry_number, file_description, return_position=True) f.seek(indpos) x0 = f.tell() if filetype in ('1A ','v1', 1): log.debug('Index filetype 1A') index = { "XBLOC":_read_int32(f), "XNUM":_read_int32(f), "XVER":_read_int32(f), "XSOURC":_read_word(f,12), "XLINE":_read_word(f,12), "XTEL":_read_word(f,12), "XDOBS":_read_int32(f), "XDRED":_read_int32(f), "XOFF1":_read_float32(f),# first offset (real, radians) "XOFF2":_read_float32(f),# second offset (real, radians) "XTYPE":_read_int32(f),# coordinate system ('EQ'', 'GA', 'HO') "XKIND":_read_int32(f), "XQUAL":_read_int32(f), "XSCAN":_read_int32(f), } index['BLOC'] = index['XBLOC'] index['WORD'] = 1 index['SOURC'] = index['CSOUR'] = index['XSOURC'] index['DOBS'] = index['CDOBS'] = index['XDOBS'] index['CTELE'] = index['XTEL'] index['LINE'] = index['XLINE'] index['OFF1'] = index['XOFF1'] index['OFF2'] = index['XOFF2'] index['QUAL'] = index['XQUAL'] index['SCAN'] = index['XSCAN'] index['KIND'] = index['XKIND'] if clic: nextchunk = { "XPROC":_read_int32(f), "XITYPE":_read_int32(f), "XHOURANG":_read_float32(f), "XPROJNAME":_read_int32(f), "XPAD1":_read_int32(f), "XBPC" :_read_int32(f), "XIC" :_read_int32(f), "XRECEI" :_read_int32(f), "XUT":_read_float32(f), "XPAD2":numpy.fromfile(f,count=3,dtype='int32') } else: nextchunk = {"XPOSA":_read_float32(f), "XSUBSCAN":_read_int32(f), 'XPAD2': numpy.fromfile(f,count=10,dtype='int32'), } nextchunk['SUBSCAN'] = nextchunk['XSUBSCAN'] nextchunk['POSA'] = nextchunk['XPOSA'] index.update(nextchunk) if (f.tell() - x0 != 128): missed_bits = (f.tell()-x0) X = f.read(128-missed_bits) if DEBUG: print("read_index missed %i bits: %s" % (128-missed_bits,X)) if any(not is_ascii(index[x]) for x in ('XSOURC','XLINE','XTEL')): raise ValueError("Invalid index read from {0}.".format(x0)) elif filetype in ('2A ','v2', 2): log.debug('Index filetype 2A') index = { "BLOC" : _read_int64(f) , "WORD" : _read_int32(f) , "NUM" : _read_int64(f) , "VER" : _read_int32(f) , "CSOUR" : _read_word(f,12), "CLINE" : _read_word(f,12), "CTELE" : _read_word(f,12), "DOBS" : _read_int32(f) , "DRED" : _read_int32(f) , "OFF1" : _read_float32(f), "OFF2" : _read_float32(f), "TYPE" : _read_int32(f) , "KIND" : _read_int32(f) , "QUAL" : _read_int32(f) , "POSA" : _read_float32(f), "SCAN" : _read_int64(f) , "SUBSCAN": _read_int32(f) , } if any((is_all_null(index[x]) or not is_ascii(index[x])) for x in ('CSOUR','CLINE','CTELE')): raise ValueError("Invalid index read from {0}.".format(x0)) index['SOURC'] = index['XSOURC'] = index['CSOUR'] index['LINE'] = index['XLINE'] = index['CLINE'] index['XKIND'] = index['KIND'] try: index['DOBS'] = index['XDOBS'] = index['CDOBS'] except KeyError: index['CDOBS'] = index['XDOBS'] = index['DOBS'] else: raise NotImplementedError("Filetype {0} not implemented.".format(filetype)) index['MJD'] = index['DOBS'] + 60549 class_dobs = index['DOBS'] index['DOBS'] = ((class_dobs + 365*2025)/365.2425 + 1) log.debug("Indexing finished at {0}".format(f.tell())) return index def _read_header(f, type=0, position=None): if position is not None: f.seek(position) if type in keys_lengths: hdrsec = [(x[0],numpy.fromfile(f,count=1,dtype=x[2])[0]) for x in keys_lengths[type]] return dict(hdrsec) else: return {} raise ValueError("Unrecognized type {0}".format(type)) def _read_first_record(f): f.seek(0) filetype = f.read(4) if fileversion_dict[filetype] == 'v1': return _read_first_record_v1(f) elif fileversion_dict[filetype] == 'v2': return _read_first_record_v2(f) else: raise ValueError("Unrecognized filetype {0}".format(filetype)) def _read_first_record_v1(f, record_length_words=128): ) file_description = { 'code': f.read(4), 'next': _read_int32(f), 'lex': _read_int32(f), 'nex': _read_int32(f), 'xnext': _read_int32(f), 'gex': 10., 'vind': 1, 'version': 1, 'nextrec': 3, 'nextword': 1, 'lind': 32, 'kind': 'unknown', 'flags': 0, } file_description['reclen'] = record_length_words ex = np.fromfile(f, count=(record_length_words*2-5), dtype='int32') file_description['ex'] = ex[ex!=0] file_description['nextrec'] = file_description['next'] file_description['lex1'] = file_description['lex'] # number of entries file_description['lexn'] = (np.arange(file_description['nex']+1) * file_description['lex1']) file_description['nentries'] = np.sum(file_description['lexn']) file_description['aex'] = file_description['ex'][:file_description['nex']] #file_description['version'] = fileversion_dict[file_description['code']] assert f.tell() == 1024 # Something is not quite right with the 'ex' parsing #assert len(file_description['ex']) == file_description['nex'] return file_description def _read_first_record_v2(f): f.seek(0) file_description = { 'code': f.read(4), 'reclen': _read_int32(f), 'kind': _read_int32(f), 'vind': _read_int32(f), 'lind': _read_int32(f), 'flags': _read_int32(f), 'xnext': _read_int64(f), 'nextrec': _read_int64(f), 'nextword': _read_int32(f), 'lex1': _read_int32(f), 'nex': _read_int32(f), 'gex': _read_int32(f), } file_description['lexn'] = [0] if file_description['gex'] == 10: for ii in range(1, file_description['nex']+1): file_description['lexn'].append(file_description['lexn'][-1]+file_description['lex1']) else: #! Exponential growth. Only growth with mantissa 2.0 is supported for ii in range(1, file_description['nex']): # I don't know what the fortran does here!!! nent = int(file_description['lex1'] * 2**(ii-1)) file_description['lexn'].append(file_description['lexn'][-1]+nent) file_description['nentries'] = np.sum(file_description['lexn']) record_length_words = file_description['reclen'] aex = numpy.fromfile(f, count=(record_length_words-15)//2, dtype='int64') file_description['aex'] = aex[aex!=0] assert len(file_description['aex']) == file_description['nex'] file_description['version'] = 2 return file_description def gi8_dicho(ninp,lexn,xval,ceil=True): iinf = 1 isup = ninp while isup > (iinf+1): imid = int(np.floor((isup + iinf)/2.)) if (lexn[imid-1] < xval): iinf = imid else: isup = imid ival = isup return ival def _read_obshead(f, file_description, position=None, verbose=False): if file_description['version'] == 1: return _read_obshead_v1(f, position=position, verbose=verbose) if file_description['version'] == 2: return _read_obshead_v2(f, position=position) else: raise ValueError("Invalid file version {0}.". format(file_description['version'])) def _read_obshead_v2(f, position=None): if position is not None: f.seek(position) else: position = f.tell() IDcode = f.read(4) if IDcode.strip() != b'2': raise IndexError("Observation Header reading failure at {0}. " "Record does not appear to be an observation header.". format(position)) f.seek(position) entrydescv2_nw1 = 11 entrydescv2_nw2 = 5 obshead = { 'CODE': f.read(4), 'VERSION': _read_int32(f), 'NSEC': _read_int32(f), 'NWORD': _read_int64(f), 'ADATA': _read_int64(f), 'LDATA': _read_int64(f), 'XNUM': _read_int64(f), } section_numbers = np.fromfile(f, count=obshead['NSEC'], dtype='int32') section_lengths = np.fromfile(f, count=obshead['NSEC'], dtype='int64') section_addresses = np.fromfile(f, count=obshead['NSEC'], dtype='int64') return obshead['XNUM'],obshead,dict(zip(section_numbers,section_addresses)) def _read_obshead_v1(f, position=None, verbose=False): if position is not None: f.seek(position) IDcode = f.read(4) if IDcode.strip() != b'2': raise IndexError("Observation Header reading failure at {0}. " "Record does not appear to be an observation header.". format(f.tell() - 4)) (nblocks, nbyteob, data_address, nheaders, data_length, obindex, nsec, obsnum) = numpy.fromfile(f, count=8, dtype='int32') if verbose: print("nblocks,nbyteob,data_address,data_length,nheaders,obindex,nsec,obsnum",nblocks,nbyteob,data_address,data_length,nheaders,obindex,nsec,obsnum) print("DATA_LENGTH: ",data_length) seccodes = numpy.fromfile(f,count=nsec,dtype='int32') seclen = numpy.fromfile(f,count=nsec,dtype='int32') secaddr = numpy.fromfile(f,count=nsec,dtype='int32') if verbose: print("Section codes, addresses, lengths: ",seccodes,secaddr,seclen) hdr = {'NBLOCKS':nblocks, 'NBYTEOB':nbyteob, 'DATAADDR':data_address, 'DATALEN':data_length, 'NHEADERS':nheaders, 'OBINDEX':obindex, 'NSEC':nsec, 'OBSNUM':obsnum} return obsnum,hdr,dict(zip(seccodes,secaddr)) # Not entirely clear what this is, but it is stuff that precedes the actual data # # Looks something like this: # array([ 1, -2, -3, -4, -14, # 9, 17, 18, 25, 55, # 64, 81, 99, -1179344801, 979657591, # # -2, -3, -4, -14 indicate the 4 header types # 9,17,18,25 *MAY* indicate the number of bytes in each # # # HOW is it indicated how many entries there are? # """ or=np.mean, weight=None): if myarr.ndim != 1: raise ValueError("Only works on 1d data. Says so in the title.") xs = myarr.size crarr = myarr[:xs-(xs % int(factor))] if weight is None: dsarr = estimator(np.concatenate([[crarr[i::factor] for i in range(factor)]]),axis=0) else: dsarr = estimator(np.concatenate([[crarr[i::factor]*weight[i::factor] for i in range(factor)]]),axis=0) warr = estimator(np.concatenate([[weight[i::factor] for i in range(factor)]]),axis=0) dsarr = dsarr/warr return dsarr def test_downsample1d(): data = np.arange(10) weight = np.ones(10) weight[5]=0 assert np.all(downsample_1d(data, 2, weight=weight, estimator=np.mean) == np.array([0.5, 2.5, 4.0, 6.5, 8.5])) def read_observation(f, obsid, file_description=None, indices=None, my_memmap=None, memmap=True, verbose=False): if isinstance(f, str): f = open(f,'rb') opened = True if memmap: my_memmap = numpy.memmap(f, offset=0, dtype='float32', mode='r') else: my_memmap = None elif my_memmap is None and memmap: raise ValueError("Must pass in a memmap object if passing in a file object.") else: opened = False if file_description is None: file_description = _read_first_record(f) if indices is None: indices = _read_indices(f, file_description) index = indices[obsid] obs_position = (index['BLOC']-1)*file_description['reclen']*4 + (index['WORD']-1)*4 log.debug("Reading observation at position {0}".format(obs_position)) obsnum,obshead,sections = _read_obshead(f, file_description, position=obs_position, verbose=verbose) header = obshead datastart = 0 for section_id,section_address in iteritems(sections): sec_position = obs_position + (section_address-1)*4 temp_hdr = _read_header(f, type=header_id_numbers[section_id], position=sec_position) header.update(temp_hdr) datastart = max(datastart,f.tell()) hdr = header hdr.update(obshead) hdr.update({'OBSNUM':obsnum,'RECNUM':obsid}) hdr.update({'RA':hdr['LAM']/pi*180,'DEC':hdr['BET']/pi*180}) hdr.update({'RAoff':hdr['LAMOF']/pi*180,'DECoff':hdr['BETOF']/pi*180}) hdr.update({'OBJECT':hdr['SOURC'].strip()}) hdr.update({'BUNIT':'Tastar'}) hdr.update({'EXPOSURE':float(hdr['TIME'])}) hdr['HDRSTART'] = obs_position hdr['DATASTART'] = datastart hdr.update(indices[obsid]) hdr.update({'OBSDATE': hdr['MJD'] + hdr['UT']/2./pi}) if hdr['KIND'] == 1: nchan = hdr['NPOIN'] elif 'NCHAN' in hdr: nchan = hdr['NCHAN'] else: log.error("No NCHAN in header. This is not a spectrum.") import ipdb; ipdb.set_trace() f.seek(datastart-1) spec = _read_spectrum(f, position=datastart-1, nchan=nchan, memmap=memmap, my_memmap=my_memmap) if opened: f.close() return spec, hdr def _read_spectrum(f, position, nchan, my_memmap=None, memmap=True): if position != f.tell(): log.warning("Reading data from {0}, but the file is wound " "to {1}.".format(position, f.tell())) if memmap: here = position spectrum = my_memmap[here//4:here//4+nchan] f.seek(here+nchan*4) else: f.seek(position) spectrum = numpy.fromfile(f,count=nchan,dtype='float32') return spectrum def _spectrum_from_header(fileobj, header, memmap=None): return _read_spectrum(fileobj, position=header['DATASTART'], nchan=header['NCHAN'] if 'NCHAN' in hdr else hdr['NPOIN'], my_memmap=memmap) def clean_header(header): newheader = {} for k in header: if not isinstance(header[k], (int, float, str)): if isinstance(header[k], np.ndarray) and header[k].size > 1: if header[k].size > 10: raise ValueError("Large array being put in header. That's no good. key={0}".format(k)) for ii,val in enumerate(header[k]): newheader[k[:7]+str(ii)] = val else: newheader[k[:8]] = str(header[k]) else: newheader[k[:8]] = header[k] return newheader class ClassObject(object): def __init__(self, filename, verbose=False): t0 = time.time() self._file = open(filename, 'rb') self.file_description = _read_first_record(self._file) self.allind = _read_indices(self._file, self.file_description) self._data = np.memmap(self._file, dtype='float32', mode='r') if verbose: log.info("Setting _spectra") self._spectra = LazyItem(self) t1 = time.time() if verbose: log.info("Setting posang. t={0}".format(t1-t0)) self.set_posang() t2 = time.time() if verbose: log.info("Identifying otf scans. t={0}".format(t2-t1)) self._identify_otf_scans(verbose=verbose) t3 = time.time() #self._load_all_spectra() if verbose: log.info("Loaded CLASS object with {3} indices. Time breakdown:" " {0}s for indices, " "{1}s for posang, and {2}s for OTF scan identification" .format(t1-t0, t2-t1, t3-t2, len(self.allind))) def __repr__(self): s = "\n".join(["{k}: {v}".format(k=k,v=v) for k,v in iteritems(self.getinfo())]) return "ClassObject({id}) with {nspec} entries\n".format(id=id(self), nspec=len(self.allind)) + s def getinfo(self, allsources=False): info = dict( tels = self.tels, lines = self.lines, scans = self.scans, sources = self.sources if allsources else self.sci_sources, ) return info def set_posang(self): h0 = self.headers[0] for h in self.headers: dx = h['OFF1'] - h0['OFF1'] dy = h['OFF2'] - h0['OFF2'] h['COMPPOSA'] = np.arctan2(dy,dx)*180/np.pi h0 = h def _identify_otf_scans(self, verbose=False): h0 = self.allind[0] st = 0 otfscan = 0 posangs = [h['COMPPOSA'] for h in self.allind] if verbose: pb = ProgressBar(len(self.allind)) for ii,h in enumerate(self.allind): if (h['SCAN'] != h0['SCAN'] or h['SOURC'] != h0['SOURC']): h0['FIRSTSCAN'] = st cpa = np.median(posangs[st:ii]) for hh in self.allind[st:ii]: hh['SCANPOSA'] = cpa % 180 st = ii if h['SCAN'] == h0['SCAN']: h0['OTFSCAN'] = otfscan otfscan += 1 h['OTFSCAN'] = otfscan else: otfscan = 0 h['OTFSCAN'] = otfscan else: h['OTFSCAN'] = otfscan if verbose: pb.update(ii) def listscans(self, source=None, telescope=None, out=sys.stdout): minid=0 scan = -1 sourc = "" #tel = '' minoff1,maxoff1 = np.inf,-np.inf minoff2,maxoff2 = np.inf,-np.inf ttlangle,nangle = 0.0,0 print("{entries:15s} {SOURC:12s} {XTEL:12s} {SCAN:>8s} {SUBSCAN:>8s} " "[ {RAmin:>12s}, {RAmax:>12s} ] " "[ {DECmin:>12s}, {DECmax:>12s} ] " "{angle:>12s} {SCANPOSA:>12s} {OTFSCAN:>8s} {TSYS:>8s} {UTD:>12s}" .format(entries='Scans', SOURC='Source', XTEL='Telescope', SCAN='Scan', SUBSCAN='Subscan', RAmin='min(RA)', RAmax='max(RA)', DECmin='min(DEC)', DECmax='max(DEC)', SCANPOSA='Scan PA', angle='Angle', OTFSCAN='OTFscan', TSYS='TSYS', UTD='UTD'), file=out) data_rows = [] for ii,row in enumerate(self.headers): if (row['SCAN'] == scan and row['SOURC'] == sourc #and row['XTEL'] == tel ): minoff1 = min(minoff1, row['OFF1']) maxoff1 = max(maxoff1, row['OFF1']) minoff2 = min(minoff2, row['OFF2']) maxoff2 = max(maxoff2, row['OFF2']) ttlangle += np.arctan2(row['OFF2'] - prevrow['OFF2'], row['OFF1'] - prevrow['OFF1'])%np.pi nangle += 1 prevrow = row else: if scan == -1: scan = row['SCAN'] sourc = row['SOURC'] #tel = row['XTEL'] prevrow = row continue ok = True if source is not None: if isinstance(source, (list,tuple)): ok = ok and any(re.search((s), prevrow['SOURC']) for s in source) else: ok = ok and re.search((source), prevrow['SOURC']) if telescope is not None: ok = ok and re.search((telescope), prevrow['XTEL']) if ok: data = dict(RAmin=minoff1*180/np.pi*3600, RAmax=maxoff1*180/np.pi*3600, DECmin=minoff2*180/np.pi*3600, DECmax=maxoff2*180/np.pi*3600, angle=(ttlangle/nangle)*180/np.pi if nangle>0 else 0, e0=minid, e1=ii-1, #TSYS=row['TSYS'] if 'TSYS' in row else '--', UTD=row['DOBS']+row['UT'] if 'UT' in row else -99, **prevrow) print("{e0:7d}-{e1:7d} {SOURC:12s} {XTEL:12s} {SCAN:8d} {SUBSCAN:8d} " "[ {RAmin:12f}, {RAmax:12f} ] " "[ {DECmin:12f}, {DECmax:12f} ] " "{angle:12.1f} {SCANPOSA:12.1f} {OTFSCAN:8d}" " {TSYS:>8.1f} {UTD:12f}". format(**data), file=out) data_rows.append(data) minoff1,maxoff1 = np.inf,-np.inf minoff2,maxoff2 = np.inf,-np.inf ttlangle,nangle = 0.0,0 scan = row['SCAN'] sourc = row['SOURC'] #tel = row['XTEL'] minid = ii return data @property def tels(self): if hasattr(self,'_tels'): return self._tels else: self._tels = set([h['CTELE'] for h in self.allind]) #testing if CTELE even works return self._tels @property def sources(self): if hasattr(self,'_source'): return self._source else: self._source = set([h['SOURC'] for h in self.allind]) return self._source @property def scans(self): if hasattr(self,'_scan'): return self._scan else: self._scan = set([h['SCAN'] for h in self.allind]) return self._scan @property def sci_sources(self): return set([s for s in self.sources if s[:4] not in ('SKY-', 'TSYS', 'TCAL', 'TREC', 'HOT-', 'COLD')]) @property def lines(self): if hasattr(self,'_lines'): return self._lines else: self._lines = set([h['LINE'] for h in self.allind]) return self._lines def _load_all_spectra(self, indices=None): if indices is None: indices = range(self.file_description['xnext']-1) if hasattr(self, '_loaded_indices'): indices_set = set(indices) indices_to_load = (indices_set.difference(self._loaded_indices)) self._loaded_indices = self._loaded_indices.union(indices_set) if any(indices_to_load): pb = ProgressBar(len(indices_to_load)) for ii,k in enumerate(xrange(indices_to_load)): self._spectra[k] pb.update(ii) else: self._loaded_indices = set(indices) self._spectra.load_all() @property def spectra(self): return [x[0] for x in self._spectra] @property def headers(self): return [self._spectra[ii][1] if ii in self._spectra else x for ii,x in enumerate(self.allind)] def select_spectra(self, all=None, line=None, linere=None, linereflags=re.IGNORECASE, number=None, scan=None, offset=None, source=None, sourcere=None, sourcereflags=re.IGNORECASE, range=None, quality=None, telescope=None, telescopere=None, telescopereflags=re.IGNORECASE, subscan=None, entry=None, posang=None, #observed=None, #reduced=None, frequency=None, section=None, user=None, include_old_versions=False, ): if entry is not None and len(entry)==2: return irange(entry[0], entry[1]) if frequency is not None: self._load_all_spectra() sel = [(re.search(re.escape(ensure_bytes(line)), h['LINE'], re.IGNORECASE) if line is not None else True) and (re.search(ensure_bytes(linere), h['LINE'], linereflags) if linere is not None else True) and (h['SCAN'] == scan if scan is not None else True) and ((h['OFF1'] == offset or h['OFF2'] == offset) if offset is not None else True) and (re.search(re.escape(ensure_bytes(source)), h['CSOUR'], re.IGNORECASE) if source is not None else True) and (re.search(ensure_bytes(sourcere), h['CSOUR'], sourcereflags) if sourcere is not None else True) and (h['OFF1']>range[0] and h['OFF1'] < range[1] and h['OFF2']>range[2] and h['OFF2'] < range[3] if range is not None and len(range)==4 else True) and (h['QUAL'] == quality if quality is not None else True) and (re.search(re.escape(ensure_bytes(telescope)), h['CTELE'], re.IGNORECASE) if telescope is not None else True) and (re.search(ensure_bytes(telescopere), h['CTELE'], telescopereflags) if telescopere is not None else True) and (h['SUBSCAN']==subscan if subscan is not None else True) and ('RESTF' in h and # Need to check that it IS a spectrum: continuum data can't be accessed this way h['RESTF'] > frequency[0] and h['RESTF'] < frequency[1] if frequency is not None and len(frequency)==2 else True) and (h['COMPPOSA']%180 > posang[0] and h['COMPPOSA']%180 < posang[1] if posang is not None and len(posang)==2 else True) and # probably not a valid spectrum? (h.get('XVER', h.get('VER', -999)) > 0 if not include_old_versions else True) for h in self.headers ] return [ii for ii,k in enumerate(sel) if k] def get_spectra(self, progressbar=True, **kwargs): selected_indices = self.select_spectra(**kwargs) if not any(selected_indices): raise ValueError("Selection yielded empty.") self._spectra.load(selected_indices, progressbar=progressbar) return [self._spectra[ii] for ii in selected_indices] def get_pyspeckit_spectra(self, progressbar=True, **kwargs): spdata = self.get_spectra(progressbar=progressbar, **kwargs) spectra = [pyspeckit.Spectrum(data=data, xarr=make_axis(header), header=clean_header(header)) for data,header in spdata] return spectra def read_observations(self, observation_indices, progressbar=True): self._spectra.load(observation_indices, progressbar=progressbar) return [self._spectra[ii] for ii in observation_indices] @print_timing def read_class(filename, downsample_factor=None, sourcename=None, telescope=None, line=None, posang=None, verbose=False, flag_array=None): classobj = ClassObject(filename) if not isinstance(sourcename, (list,tuple)): sourcename = [sourcename] if not isinstance(telescope, (list,tuple)): telescope = [telescope] if not isinstance(line, (list,tuple)): line = [line] spectra,headers = [],[] if verbose: log.info("Reading...") selection = [ii for source in sourcename for tel in telescope for li in line for ii in classobj.select_spectra(sourcere=source, telescope=tel, line=li, posang=posang)] sphdr = classobj.read_observations(selection) if len(sphdr) == 0: return None spec,hdr = zip(*sphdr) spectra += spec headers += hdr indexes = headers weight = ~flag_array if flag_array is not None else None if downsample_factor is not None: if verbose: log.info("Downsampling...") spectra = [downsample_1d(spec, downsample_factor, weight=weight) for spec in ProgressBar(spectra)] headers = [downsample_header(h, downsample_factor) for h in ProgressBar(headers)] for hdr in headers: stringify_header(hdr) return spectra,headers,indexes def stringify_header(header): from six import string_types, integer_types import string FITS_allowed_types = (string_types + integer_types + (float, complex, bool, np.floating, np.integer, np.complexfloating, np.bool_)) bad_chars = string.printable[96:] badcharre = re.compile("[{0}]".format(bad_chars)) for key, value in header.items(): if isinstance(value, bytes): header[key] = value.decode() elif not isinstance(value, FITS_allowed_types): header[key] = badcharre.sub("", str(header[key])) def downsample_header(hdr, downsample_factor): for k in ('NCHAN','NPOIN','DATALEN'): if k in hdr: hdr[k] = int((hdr[k] / downsample_factor)) # maybe wrong? h['RCHAN'] = (h['RCHAN']-1) / downsample_factor + 1 scalefactor = 1./downsample_factor hdr['RCHAN'] = (hdr['RCHAN']-1)*scalefactor + 0.5 + scalefactor/2. for kw in ['FRES','VRES']: if kw in hdr: hdr[kw] *= downsample_factor return hdr def make_axis(header,imagfreq=False): from .. import units rest_frequency = header.get('RESTF') xunits = 'MHz' nchan = header.get('NCHAN') voff = header.get('VOFF') foff = header.get('FOFF') doppler = header.get('DOPPLER') fres = header.get('FRES') refchan = header.get('RCHAN') imfreq = header.get('IMAGE') if foff in (None, 0.0) and voff not in (None, 0.0): # Radio convention foff = -voff/2.997924580e5 * rest_frequency if not imagfreq: xarr = rest_frequency + foff + (numpy.arange(1, nchan+1) - refchan) * fres XAxis = units.SpectroscopicAxis(xarr,unit='MHz',refX=rest_frequency*u.MHz) else: xarr = imfreq - (numpy.arange(1, nchan+1) - refchan) * fres XAxis = units.SpectroscopicAxis(xarr,unit='MHz',refX=imfreq*u.MHz) return XAxis @print_timing def class_to_obsblocks(filename, telescope, line, datatuple=None, source=None, imagfreq=False, DEBUG=False, **kwargs): if datatuple is None: spectra,header,indexes = read_class(filename, **kwargs) else: spectra,header,indexes = datatuple obslist = [] lastscannum = -1 spectrumlist = None for sp,hdr,ind in zip(spectra,header,indexes): hdr.update(ind) # this is slow but necessary... H = pyfits.Header() for k,v in iteritems(hdr): if hasattr(v,"__len__") and not isinstance(v,str): # make an array of header entries, but this # supports only up to 10 of them... if len(v) > 1: if len(v) < 10: for ii,vv in enumerate(v): newkey = k[:7]+str(ii) H[newkey] = vv elif len(v) < 100: for ii,vv in enumerate(v): newkey = k[:6]+str(ii) H[newkey] = vv else: raise ValueError("Too many entries for {0}".format(k)) else: H[k] = v[0] #elif not any(x in str(v).lower() for x in ('comment', 'end', 'history')): # # do not try to add comments... # This commented out block used to attempt to reject comments # using a private regex in the old pyfits which no longer exists. # I don't know if it was necessary. else: H[k] = v scannum = hdr['SCAN'] if 'XTEL' in hdr and hdr['XTEL'].strip() not in telescope: continue if hdr['LINE'].strip() not in line: continue if (source is not None) and (hdr['SOURC'].strip() not in source): continue hdr['RESTFREQ'] = hdr.get('RESTF') H['RESTFREQ'] = hdr.get('RESTF') if scannum != lastscannum: lastscannum = scannum if spectrumlist is not None: obslist.append(pyspeckit.ObsBlock(spectrumlist)) xarr = make_axis(hdr,imagfreq=imagfreq) spectrumlist = [( pyspeckit.Spectrum(xarr=xarr, header=H, data=sp))] else: spectrumlist.append( pyspeckit.Spectrum(xarr=xarr, header=H, data=sp)) return obslist class LazyItem(object): def __init__(self, parent): self.parent = parent self.sphdr = {} self.nind = len(self.parent.allind) self.nloaded = 0 def __repr__(self): return ("Set of {0} spectra & headers, {1} loaded" " ({2:0.2f}%)".format(self.nind, self.nloaded, (float(self.nloaded)/self.nind)*100)) def load_all(self, progressbar=True): self.load(range(self.nind)) def load(self, indices, progressbar=True): pb = ProgressBar(len(indices)) counter = 0 for k in indices: self[k] counter += 1 pb.update(counter) def __getitem__(self, key): if key in self.sphdr: return self.sphdr[key] elif isinstance(key, slice): return [self[k] for k in xrange(key.start or 0, key.end or len(self.parent.allind), key.step or 1)] else: sphd = read_observation(self.parent._file, key, file_description=self.parent.file_description, indices=self.parent.allind, my_memmap=self.parent._data) sphd[1].update(self.parent.allind[key]) self.sphdr[key] = sphd self.nloaded += 1 return sphd def __iter__(self): return self.next() def __next__(self): for k in self.spheader: yield self.spheader[k] def __contains__(self, key): return key in self.sphdr @print_timing def class_to_spectra(filename, datatuple=None, **kwargs): if datatuple is None: spectra,header,indexes = read_class(filename, **kwargs) else: spectra,header,indexes = datatuple spectrumlist = [] for sp,hdr,ind in zip(spectra,header,indexes): hdr.update(ind) xarr = make_axis(hdr) spectrumlist.append( pyspeckit.Spectrum(xarr=xarr, header=hdr, data=sp)) return pyspeckit.Spectra(spectrumlist) def tests(): fn1 = '/Users/adam/work/bolocam/hht/class_003.smt' p = class_to_obsblocks(fn1,telescope=['SMT-F1M-HU','SMT-F1M-VU'],line=['N2HP(3-2)','N2H+(3-2)']) hcop = class_to_obsblocks(fn1,telescope=['SMT-F1M-HL','SMT-F1M-VL'],line=['HCOP(3-2)','HCO+(3-2)'])

true

f71781d481d127f72294f8baec04d9d74461c11a

121

py

Python

okta/models/usergroup/__init__.py

rkhleics/oktasdk-python

da8183444704c6d16831d1edd619390e9120dd70

[ "Apache-2.0" ]

1

2020-09-09T12:59:19.000Z

okta/models/usergroup/__init__.py

torchbox/oktasdk-python

da8183444704c6d16831d1edd619390e9120dd70

[ "Apache-2.0" ]

null

okta/models/usergroup/__init__.py

torchbox/oktasdk-python

da8183444704c6d16831d1edd619390e9120dd70

[ "Apache-2.0" ]

2

2017-11-02T22:12:57.000Z

2019-09-16T08:02:23.000Z

from .UserGroup import UserGroup from .UserGroupProfile import UserGroupProfile from .UserGroupRule import UserGroupRule

30.25

46

0.876033

from .UserGroup import UserGroup from .UserGroupProfile import UserGroupProfile from .UserGroupRule import UserGroupRule

true

f717822d090647eb7a44dad23a51405caa178759

1,679

py

Python

stockbot/ticker/sinotrade/session.py

tanlin2013/stockbot

08322ed4d847ea9e58b091985cef5c128a694b12

[ "Apache-2.0" ]

1

2021-07-12T23:55:20.000Z

stockbot/ticker/sinotrade/session.py

ajmal017/stockbot-7

08322ed4d847ea9e58b091985cef5c128a694b12

[ "Apache-2.0" ]

null

stockbot/ticker/sinotrade/session.py

ajmal017/stockbot-7

08322ed4d847ea9e58b091985cef5c128a694b12

[ "Apache-2.0" ]

1

2021-07-12T23:55:12.000Z

import os import logging import pandas as pd from datetime import date from shioaji import Shioaji class Session(Shioaji): def __init__(self, simulation: bool = False, timeout: int = 10000) -> None: """ Args: simulation: timeout: Notes: The ID of test account ranging from `PAPIUSER01` to `PAPIUSER08`, with password `2222`. """ _person_id = f"PAPIUSER05" \ if simulation else os.environ['SINOTRADE_ID'] _passwd = "2222" \ if simulation else os.environ['SINOTRADE_PASSWD'] super(Session, self).__init__(simulation=simulation) self.login( person_id=_person_id, passwd=_passwd, contracts_cb=lambda security_type: logging.info(f"{repr(security_type)} fetch done."), contracts_timeout=timeout ) def __del__(self) -> None: self.logout() logging.info("session closed.") @property def positions(self) -> pd.DataFrame: return pd.DataFrame( self.list_positions(self.stock_account) ) def profit_loss(self, begin_date: date, end_date: date) -> pd.DataFrame: return pd.DataFrame(self.list_profit_loss( self.stock_account, begin_date=begin_date.strftime('%Y-%m-%d'), end_date=end_date.strftime('%Y-%m-%d') )) @property def settlements(self) -> pd.DataFrame: return pd.DataFrame( self.list_settlements(self.stock_account) ) @property def balance(self) -> pd.DataFrame: return pd.DataFrame( self.account_balance() )

27.983333

98

0.596784

import os import logging import pandas as pd from datetime import date from shioaji import Shioaji class Session(Shioaji): def __init__(self, simulation: bool = False, timeout: int = 10000) -> None: _person_id = f"PAPIUSER05" \ if simulation else os.environ['SINOTRADE_ID'] _passwd = "2222" \ if simulation else os.environ['SINOTRADE_PASSWD'] super(Session, self).__init__(simulation=simulation) self.login( person_id=_person_id, passwd=_passwd, contracts_cb=lambda security_type: logging.info(f"{repr(security_type)} fetch done."), contracts_timeout=timeout ) def __del__(self) -> None: self.logout() logging.info("session closed.") @property def positions(self) -> pd.DataFrame: return pd.DataFrame( self.list_positions(self.stock_account) ) def profit_loss(self, begin_date: date, end_date: date) -> pd.DataFrame: return pd.DataFrame(self.list_profit_loss( self.stock_account, begin_date=begin_date.strftime('%Y-%m-%d'), end_date=end_date.strftime('%Y-%m-%d') )) @property def settlements(self) -> pd.DataFrame: return pd.DataFrame( self.list_settlements(self.stock_account) ) @property def balance(self) -> pd.DataFrame: return pd.DataFrame( self.account_balance() )

true

f71782577c5c2b9953da5e9ae6c0a019748c14b7

3,361

py

Python

src/application-insights/azext_applicationinsights/vendored_sdks/applicationinsights/models/events_trace_result.py

Mannan2812/azure-cli-extensions

e2b34efe23795f6db9c59100534a40f0813c3d95

[ "MIT" ]

2,728

2015-01-09T10:19:32.000Z

2022-03-31T14:50:33.000Z

src/application-insights/azext_applicationinsights/vendored_sdks/applicationinsights/models/events_trace_result.py

Mannan2812/azure-cli-extensions

e2b34efe23795f6db9c59100534a40f0813c3d95

[ "MIT" ]

17,773

2015-01-05T15:57:17.000Z

2022-03-31T23:50:25.000Z

src/application-insights/azext_applicationinsights/vendored_sdks/applicationinsights/models/events_trace_result.py

Mannan2812/azure-cli-extensions

e2b34efe23795f6db9c59100534a40f0813c3d95

[ "MIT" ]

1,916

2015-01-19T05:05:41.000Z

2022-03-31T19:36:44.000Z

# 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 .events_result_data import EventsResultData class EventsTraceResult(EventsResultData): """A trace result. All required parameters must be populated in order to send to Azure. :param id: The unique ID for this event. :type id: str :param count: Count of the event :type count: long :param timestamp: Timestamp of the event :type timestamp: datetime :param custom_dimensions: Custom dimensions of the event :type custom_dimensions: ~azure.applicationinsights.models.EventsResultDataCustomDimensions :param custom_measurements: Custom measurements of the event :type custom_measurements: ~azure.applicationinsights.models.EventsResultDataCustomMeasurements :param operation: Operation info of the event :type operation: ~azure.applicationinsights.models.EventsOperationInfo :param session: Session info of the event :type session: ~azure.applicationinsights.models.EventsSessionInfo :param user: User info of the event :type user: ~azure.applicationinsights.models.EventsUserInfo :param cloud: Cloud info of the event :type cloud: ~azure.applicationinsights.models.EventsCloudInfo :param ai: AI info of the event :type ai: ~azure.applicationinsights.models.EventsAiInfo :param application: Application info of the event :type application: ~azure.applicationinsights.models.EventsApplicationInfo :param client: Client info of the event :type client: ~azure.applicationinsights.models.EventsClientInfo :param type: Required. Constant filled by server. :type type: str :param trace: :type trace: ~azure.applicationinsights.models.EventsTraceInfo """ _validation = { 'type': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'count': {'key': 'count', 'type': 'long'}, 'timestamp': {'key': 'timestamp', 'type': 'iso-8601'}, 'custom_dimensions': {'key': 'customDimensions', 'type': 'EventsResultDataCustomDimensions'}, 'custom_measurements': {'key': 'customMeasurements', 'type': 'EventsResultDataCustomMeasurements'}, 'operation': {'key': 'operation', 'type': 'EventsOperationInfo'}, 'session': {'key': 'session', 'type': 'EventsSessionInfo'}, 'user': {'key': 'user', 'type': 'EventsUserInfo'}, 'cloud': {'key': 'cloud', 'type': 'EventsCloudInfo'}, 'ai': {'key': 'ai', 'type': 'EventsAiInfo'}, 'application': {'key': 'application', 'type': 'EventsApplicationInfo'}, 'client': {'key': 'client', 'type': 'EventsClientInfo'}, 'type': {'key': 'type', 'type': 'str'}, 'trace': {'key': 'trace', 'type': 'EventsTraceInfo'}, } def __init__(self, **kwargs): super(EventsTraceResult, self).__init__(**kwargs) self.trace = kwargs.get('trace', None) self.type = 'trace'

43.649351

107

0.650997

from .events_result_data import EventsResultData class EventsTraceResult(EventsResultData): _validation = { 'type': {'required': True}, } _attribute_map = { 'id': {'key': 'id', 'type': 'str'}, 'count': {'key': 'count', 'type': 'long'}, 'timestamp': {'key': 'timestamp', 'type': 'iso-8601'}, 'custom_dimensions': {'key': 'customDimensions', 'type': 'EventsResultDataCustomDimensions'}, 'custom_measurements': {'key': 'customMeasurements', 'type': 'EventsResultDataCustomMeasurements'}, 'operation': {'key': 'operation', 'type': 'EventsOperationInfo'}, 'session': {'key': 'session', 'type': 'EventsSessionInfo'}, 'user': {'key': 'user', 'type': 'EventsUserInfo'}, 'cloud': {'key': 'cloud', 'type': 'EventsCloudInfo'}, 'ai': {'key': 'ai', 'type': 'EventsAiInfo'}, 'application': {'key': 'application', 'type': 'EventsApplicationInfo'}, 'client': {'key': 'client', 'type': 'EventsClientInfo'}, 'type': {'key': 'type', 'type': 'str'}, 'trace': {'key': 'trace', 'type': 'EventsTraceInfo'}, } def __init__(self, **kwargs): super(EventsTraceResult, self).__init__(**kwargs) self.trace = kwargs.get('trace', None) self.type = 'trace'

true

f71782e87705531559d4a97ca72db46a973a03f6

30,692

py

Python

examples/ner/run_ner_strain.py

Tarpelite/BERT_self_training

f50ff015f0d3669b5d927a6d28d8a08201c101b6

[ "MIT" ]

null

examples/ner/run_ner_strain.py

Tarpelite/BERT_self_training

f50ff015f0d3669b5d927a6d28d8a08201c101b6

[ "MIT" ]

null

examples/ner/run_ner_strain.py

Tarpelite/BERT_self_training

f50ff015f0d3669b5d927a6d28d8a08201c101b6

[ "MIT" ]

null

# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # Copyright (c) 2018, NVIDIA CORPORATION. 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. """ Fine-tuning the library models for named entity recognition on CoNLL-2003 (Bert or Roberta). """ import argparse import glob import logging import os import random import numpy as np import torch from seqeval.metrics import f1_score, precision_score, recall_score from torch.nn import CrossEntropyLoss from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange import pickle # from pudb import set_trace # set_trace() from transformers import ( MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, WEIGHTS_NAME, AdamW, AutoConfig, AutoModelForTokenClassification, AutoTokenizer, get_linear_schedule_with_warmup, ) from utils_ner import convert_examples_to_features, get_labels, read_examples_from_file try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter logger = logging.getLogger(__name__) MODEL_CONFIG_CLASSES = list(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in MODEL_CONFIG_CLASSES), ()) TOKENIZER_ARGS = ["do_lower_case", "strip_accents", "keep_accents", "use_fast"] def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id): """ Train the model """ if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs if args.warmup_ratio > 0: args.warmup_steps = int(t_total * args.warmup_ratio) # Prepare optimizer and schedule (linear warmup and decay) no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0}, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total ) # Check if saved optimizer or scheduler states exist if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile( os.path.join(args.model_name_or_path, "scheduler.pt") ): # Load in optimizer and scheduler states optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt"))) scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt"))) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) # multi-gpu training (should be after apex fp16 initialization) if args.n_gpu > 1: model = torch.nn.DataParallel(model) # Distributed training (should be after apex fp16 initialization) if args.local_rank != -1: model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True ) # Train! logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), ) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) global_step = 0 epochs_trained = 0 steps_trained_in_current_epoch = 0 # Check if continuing training from a checkpoint if os.path.exists(args.model_name_or_path): # set global_step to gobal_step of last saved checkpoint from model path try: global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0]) except ValueError: global_step = 0 epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps) logger.info(" Continuing training from checkpoint, will skip to saved global_step") logger.info(" Continuing training from epoch %d", epochs_trained) logger.info(" Continuing training from global step %d", global_step) logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch) tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange( epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0] ) set_seed(args) # Added here for reproductibility for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iter(loss=X.XXX, lr=X.XXXXXXXX)", disable=args.local_rank not in [-1, 0]) for step, batch in enumerate(epoch_iterator): # Skip past any already trained steps if resuming training if steps_trained_in_current_epoch > 0: steps_trained_in_current_epoch -= 1 continue model.train() batch = tuple(t.to(args.device) for t in batch) inputs = {"input_ids": batch[0], "attention_mask": batch[1], "soft_labels": batch[3]} if args.model_type != "distilbert": inputs["token_type_ids"] = ( batch[2] if args.model_type in ["bert", "xlnet"] else None ) # XLM and RoBERTa don"t use segment_ids outputs = model(**inputs) loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc) if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: epoch_iterator.set_description('Iter (loss=%5.3f) lr=%9.7f' % (loss.item(), scheduler.get_lr()[0])) if args.fp16: torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: # Log metrics if ( args.local_rank == -1 and args.evaluate_during_training ): # Only evaluate when single GPU otherwise metrics may not average well results, _ = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="dev") for key, value in results.items(): tb_writer.add_scalar("eval_{}".format(key), value, global_step) tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step) tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step) logging_loss = tr_loss if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) logger.info("Saving optimizer and scheduler states to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""): eval_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode=mode) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu evaluate if args.n_gpu > 1: model = torch.nn.DataParallel(model) # Eval! logger.info("***** Running evaluation %s *****", prefix) logger.info(" Num examples = %d", len(eval_dataset)) logger.info(" Batch size = %d", args.eval_batch_size) eval_loss = 0.0 nb_eval_steps = 0 preds = None out_label_ids = None model.eval() for batch in tqdm(eval_dataloader, desc="Evaluating"): batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]} if args.model_type != "distilbert": inputs["token_type_ids"] = ( batch[2] if args.model_type in ["bert", "xlnet"] else None ) # XLM and RoBERTa don"t use segment_ids outputs = model(**inputs) tmp_eval_loss, logits = outputs[:2] if args.n_gpu > 1: tmp_eval_loss = tmp_eval_loss.mean() # mean() to average on multi-gpu parallel evaluating eval_loss += tmp_eval_loss.item() nb_eval_steps += 1 if preds is None: preds = logits.detach().cpu().numpy() out_label_ids = inputs["labels"].detach().cpu().numpy() else: preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0) eval_loss = eval_loss / nb_eval_steps preds = np.argmax(preds, axis=2) label_map = {i: label for i, label in enumerate(labels)} out_label_list = [[] for _ in range(out_label_ids.shape[0])] preds_list = [[] for _ in range(out_label_ids.shape[0])] for i in range(out_label_ids.shape[0]): for j in range(out_label_ids.shape[1]): if out_label_ids[i, j] != pad_token_label_id: out_label_list[i].append(label_map[out_label_ids[i][j]]) preds_list[i].append(label_map[preds[i][j]]) print("preds:", preds_list[0]) print("labels:", out_label_list[0]) results = { "loss": eval_loss, "precision": precision_score(out_label_list, preds_list), "recall": recall_score(out_label_list, preds_list), "f1": f1_score(out_label_list, preds_list), } logger.info("***** Eval results %s *****", prefix) for key in sorted(results.keys()): logger.info(" %s = %s", key, str(results[key])) return results, preds_list def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode): if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Load data features from cache or dataset file logger.info("Creating features from dataset file at %s", args.data_dir) examples = read_examples_from_file(args.eval_file, mode) features = convert_examples_to_features( examples, labels, args.max_seq_length, tokenizer, cls_token_at_end=bool(args.model_type in ["xlnet"]), # xlnet has a cls token at the end cls_token=tokenizer.cls_token, cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0, sep_token=tokenizer.sep_token, sep_token_extra=bool(args.model_type in ["roberta"]), # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805 pad_on_left=bool(args.model_type in ["xlnet"]), # pad on the left for xlnet pad_token=tokenizer.pad_token_id, pad_token_segment_id=tokenizer.pad_token_type_id, pad_token_label_id=pad_token_label_id, ) if args.local_rank == 0 and not evaluate: torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache # Convert to Tensors and build dataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long) dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) return dataset def main(): parser = argparse.ArgumentParser() # Required parameters parser.add_argument( "--data_dir", default=None, type=str, help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.", ) parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_TYPES), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS), ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.", ) # Other parameters parser.add_argument( "--labels", default="", type=str, help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.", ) parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name" ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from s3", ) parser.add_argument( "--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded.", ) parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument("--do_predict", action="store_true", help="Whether to run predictions on the test set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Whether to run evaluation during training at each logging step.", ) parser.add_argument( "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model." ) parser.add_argument( "--keep_accents", action="store_const", const=True, help="Set this flag if model is trained with accents." ) parser.add_argument( "--strip_accents", action="store_const", const=True, help="Set this flag if model is trained without accents." ) parser.add_argument("--use_fast", action="store_const", const=True, help="Set this flag to use fast tokenization.") parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument( "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation." ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform." ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.") parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.") parser.add_argument("--logits_file", type=str, default="") parser.add_argument("--eval_file", type=str, default="") parser.add_argument("--warmup_ratio", type=float, default=0.1) parser.add_argument( "--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available") parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory" ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." "See details at https://nvidia.github.io/apex/amp.html", ) parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.") parser.add_argument("--server_port", type=str, default="", help="For distant debugging.") args = parser.parse_args() if ( os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir ): raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( args.output_dir ) ) # Setup distant debugging if needed if args.server_ip and args.server_port: # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script import ptvsd print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() # Setup CUDA, GPU & distributed training if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device # Setup logging logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) # Set seed set_seed(args) # Prepare CONLL-2003 task labels = get_labels(args.labels) num_labels = len(labels) # Use cross entropy ignore index as padding label id so that only real label ids contribute to the loss later pad_token_label_id = CrossEntropyLoss().ignore_index # Load pretrained model and tokenizer if args.local_rank not in [-1, 0]: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab args.model_type = args.model_type.lower() config = AutoConfig.from_pretrained( args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, id2label={str(i): label for i, label in enumerate(labels)}, label2id={label: i for i, label in enumerate(labels)}, cache_dir=args.cache_dir if args.cache_dir else None, ) tokenizer_args = {k: v for k, v in vars(args).items() if v is not None and k in TOKENIZER_ARGS} logger.info("Tokenizer arguments: %s", tokenizer_args) tokenizer = AutoTokenizer.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, cache_dir=args.cache_dir if args.cache_dir else None, **tokenizer_args, ) model = AutoModelForTokenClassification.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None, ) if args.local_rank == 0: torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab model.to(args.device) logger.info("Training/evaluation parameters %s", args) # Training if args.do_train: # train_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode="train") with open(args.logits_file, "rb") as f: datasets = pickle.load(f) all_input_ids = torch.tensor(datasets[0], dtype=torch.long) all_input_mask = torch.tensor(datasets[1], dtype=torch.long) all_segment_ids = torch.tensor(datasets[2], dtype=torch.long) all_ner_logits = torch.tensor(datasets[3], dtype=torch.float) train_dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_ner_logits) global_step, tr_loss = train(args, train_dataset, model, tokenizer, labels, pad_token_label_id) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained() if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): # Create output directory if needed if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) logger.info("Saving model checkpoint to %s", args.output_dir) # Save a trained model, configuration and tokenizer using `save_pretrained()`. # They can then be reloaded using `from_pretrained()` model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) # Good practice: save your training arguments together with the trained model torch.save(args, os.path.join(args.output_dir, "training_args.bin")) # Evaluation results = {} if args.do_eval and args.local_rank in [-1, 0]: tokenizer = AutoTokenizer.from_pretrained(args.output_dir, **tokenizer_args) checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = list( os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) ) logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" model = AutoModelForTokenClassification.from_pretrained(checkpoint) model.to(args.device) result, _ = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="dev", prefix=global_step) if global_step: result = {"{}_{}".format(global_step, k): v for k, v in result.items()} results.update(result) output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: for key in sorted(results.keys()): writer.write("{} = {}\n".format(key, str(results[key]))) if args.do_predict and args.local_rank in [-1, 0]: tokenizer = AutoTokenizer.from_pretrained(args.output_dir, **tokenizer_args) model = AutoModelForTokenClassification.from_pretrained(args.output_dir) model.to(args.device) result, predictions = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="test") # Save results output_test_results_file = os.path.join(args.output_dir, "test_results.txt") with open(output_test_results_file, "w") as writer: for key in sorted(result.keys()): writer.write("{} = {}\n".format(key, str(result[key]))) # Save predictions output_test_predictions_file = os.path.join(args.output_dir, "test_predictions.txt") with open(output_test_predictions_file, "w") as writer: with open(os.path.join(args.data_dir, "test.txt"), "r") as f: example_id = 0 for line in f: if line.startswith("-DOCSTART-") or line == "" or line == "\n": writer.write(line) if not predictions[example_id]: example_id += 1 elif predictions[example_id]: output_line = line.split()[0] + " " + predictions[example_id].pop(0) + "\n" writer.write(output_line) else: logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]) return results if __name__ == "__main__": main()

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import argparse import glob import logging import os import random import numpy as np import torch from seqeval.metrics import f1_score, precision_score, recall_score from torch.nn import CrossEntropyLoss from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset from torch.utils.data.distributed import DistributedSampler from tqdm import tqdm, trange import pickle from transformers import ( MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, WEIGHTS_NAME, AdamW, AutoConfig, AutoModelForTokenClassification, AutoTokenizer, get_linear_schedule_with_warmup, ) from utils_ner import convert_examples_to_features, get_labels, read_examples_from_file try: from torch.utils.tensorboard import SummaryWriter except ImportError: from tensorboardX import SummaryWriter logger = logging.getLogger(__name__) MODEL_CONFIG_CLASSES = list(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in MODEL_CONFIG_CLASSES), ()) TOKENIZER_ARGS = ["do_lower_case", "strip_accents", "keep_accents", "use_fast"] def set_seed(args): random.seed(args.seed) np.random.seed(args.seed) torch.manual_seed(args.seed) if args.n_gpu > 0: torch.cuda.manual_seed_all(args.seed) def train(args, train_dataset, model, tokenizer, labels, pad_token_label_id): if args.local_rank in [-1, 0]: tb_writer = SummaryWriter() args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu) train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset) train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size) if args.max_steps > 0: t_total = args.max_steps args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1 else: t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs if args.warmup_ratio > 0: args.warmup_steps = int(t_total * args.warmup_ratio) no_decay = ["bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], "weight_decay": args.weight_decay, }, {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], "weight_decay": 0.0}, ] optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon) scheduler = get_linear_schedule_with_warmup( optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total ) if os.path.isfile(os.path.join(args.model_name_or_path, "optimizer.pt")) and os.path.isfile( os.path.join(args.model_name_or_path, "scheduler.pt") ): optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "optimizer.pt"))) scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, "scheduler.pt"))) if args.fp16: try: from apex import amp except ImportError: raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.") model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level) if args.n_gpu > 1: model = torch.nn.DataParallel(model) if args.local_rank != -1: model = torch.nn.parallel.DistributedDataParallel( model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True ) logger.info("***** Running training *****") logger.info(" Num examples = %d", len(train_dataset)) logger.info(" Num Epochs = %d", args.num_train_epochs) logger.info(" Instantaneous batch size per GPU = %d", args.per_gpu_train_batch_size) logger.info( " Total train batch size (w. parallel, distributed & accumulation) = %d", args.train_batch_size * args.gradient_accumulation_steps * (torch.distributed.get_world_size() if args.local_rank != -1 else 1), ) logger.info(" Gradient Accumulation steps = %d", args.gradient_accumulation_steps) logger.info(" Total optimization steps = %d", t_total) global_step = 0 epochs_trained = 0 steps_trained_in_current_epoch = 0 if os.path.exists(args.model_name_or_path): try: global_step = int(args.model_name_or_path.split("-")[-1].split("/")[0]) except ValueError: global_step = 0 epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps) steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps) logger.info(" Continuing training from checkpoint, will skip to saved global_step") logger.info(" Continuing training from epoch %d", epochs_trained) logger.info(" Continuing training from global step %d", global_step) logger.info(" Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch) tr_loss, logging_loss = 0.0, 0.0 model.zero_grad() train_iterator = trange( epochs_trained, int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0] ) set_seed(args) for _ in train_iterator: epoch_iterator = tqdm(train_dataloader, desc="Iter(loss=X.XXX, lr=X.XXXXXXXX)", disable=args.local_rank not in [-1, 0]) for step, batch in enumerate(epoch_iterator): if steps_trained_in_current_epoch > 0: steps_trained_in_current_epoch -= 1 continue model.train() batch = tuple(t.to(args.device) for t in batch) inputs = {"input_ids": batch[0], "attention_mask": batch[1], "soft_labels": batch[3]} if args.model_type != "distilbert": inputs["token_type_ids"] = ( batch[2] if args.model_type in ["bert", "xlnet"] else None ) outputs = model(**inputs) loss = outputs[0] # model outputs are always tuple in pytorch-transformers (see doc) if args.n_gpu > 1: loss = loss.mean() # mean() to average on multi-gpu parallel training if args.gradient_accumulation_steps > 1: loss = loss / args.gradient_accumulation_steps if args.fp16: with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() else: loss.backward() tr_loss += loss.item() if (step + 1) % args.gradient_accumulation_steps == 0: epoch_iterator.set_description('Iter (loss=%5.3f) lr=%9.7f' % (loss.item(), scheduler.get_lr()[0])) if args.fp16: torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm) else: torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm) optimizer.step() scheduler.step() # Update learning rate schedule model.zero_grad() global_step += 1 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0: # Log metrics if ( args.local_rank == -1 and args.evaluate_during_training ): # Only evaluate when single GPU otherwise metrics may not average well results, _ = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="dev") for key, value in results.items(): tb_writer.add_scalar("eval_{}".format(key), value, global_step) tb_writer.add_scalar("lr", scheduler.get_lr()[0], global_step) tb_writer.add_scalar("loss", (tr_loss - logging_loss) / args.logging_steps, global_step) logging_loss = tr_loss if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0: # Save model checkpoint output_dir = os.path.join(args.output_dir, "checkpoint-{}".format(global_step)) if not os.path.exists(output_dir): os.makedirs(output_dir) model_to_save = ( model.module if hasattr(model, "module") else model ) # Take care of distributed/parallel training model_to_save.save_pretrained(output_dir) tokenizer.save_pretrained(output_dir) torch.save(args, os.path.join(output_dir, "training_args.bin")) logger.info("Saving model checkpoint to %s", output_dir) torch.save(optimizer.state_dict(), os.path.join(output_dir, "optimizer.pt")) torch.save(scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt")) logger.info("Saving optimizer and scheduler states to %s", output_dir) if args.max_steps > 0 and global_step > args.max_steps: epoch_iterator.close() break if args.max_steps > 0 and global_step > args.max_steps: train_iterator.close() break if args.local_rank in [-1, 0]: tb_writer.close() return global_step, tr_loss / global_step def evaluate(args, model, tokenizer, labels, pad_token_label_id, mode, prefix=""): eval_dataset = load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode=mode) args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu) # Note that DistributedSampler samples randomly eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset) eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size) # multi-gpu evaluate if args.n_gpu > 1: model = torch.nn.DataParallel(model) # Eval! logger.info("***** Running evaluation %s *****", prefix) logger.info(" Num examples = %d", len(eval_dataset)) logger.info(" Batch size = %d", args.eval_batch_size) eval_loss = 0.0 nb_eval_steps = 0 preds = None out_label_ids = None model.eval() for batch in tqdm(eval_dataloader, desc="Evaluating"): batch = tuple(t.to(args.device) for t in batch) with torch.no_grad(): inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]} if args.model_type != "distilbert": inputs["token_type_ids"] = ( batch[2] if args.model_type in ["bert", "xlnet"] else None ) # XLM and RoBERTa don"t use segment_ids outputs = model(**inputs) tmp_eval_loss, logits = outputs[:2] if args.n_gpu > 1: tmp_eval_loss = tmp_eval_loss.mean() eval_loss += tmp_eval_loss.item() nb_eval_steps += 1 if preds is None: preds = logits.detach().cpu().numpy() out_label_ids = inputs["labels"].detach().cpu().numpy() else: preds = np.append(preds, logits.detach().cpu().numpy(), axis=0) out_label_ids = np.append(out_label_ids, inputs["labels"].detach().cpu().numpy(), axis=0) eval_loss = eval_loss / nb_eval_steps preds = np.argmax(preds, axis=2) label_map = {i: label for i, label in enumerate(labels)} out_label_list = [[] for _ in range(out_label_ids.shape[0])] preds_list = [[] for _ in range(out_label_ids.shape[0])] for i in range(out_label_ids.shape[0]): for j in range(out_label_ids.shape[1]): if out_label_ids[i, j] != pad_token_label_id: out_label_list[i].append(label_map[out_label_ids[i][j]]) preds_list[i].append(label_map[preds[i][j]]) print("preds:", preds_list[0]) print("labels:", out_label_list[0]) results = { "loss": eval_loss, "precision": precision_score(out_label_list, preds_list), "recall": recall_score(out_label_list, preds_list), "f1": f1_score(out_label_list, preds_list), } logger.info("***** Eval results %s *****", prefix) for key in sorted(results.keys()): logger.info(" %s = %s", key, str(results[key])) return results, preds_list def load_and_cache_examples(args, tokenizer, labels, pad_token_label_id, mode): if args.local_rank not in [-1, 0] and not evaluate: torch.distributed.barrier() logger.info("Creating features from dataset file at %s", args.data_dir) examples = read_examples_from_file(args.eval_file, mode) features = convert_examples_to_features( examples, labels, args.max_seq_length, tokenizer, cls_token_at_end=bool(args.model_type in ["xlnet"]), cls_token=tokenizer.cls_token, cls_token_segment_id=2 if args.model_type in ["xlnet"] else 0, sep_token=tokenizer.sep_token, sep_token_extra=bool(args.model_type in ["roberta"]), pad_on_left=bool(args.model_type in ["xlnet"]), pad_token=tokenizer.pad_token_id, pad_token_segment_id=tokenizer.pad_token_type_id, pad_token_label_id=pad_token_label_id, ) if args.local_rank == 0 and not evaluate: torch.distributed.barrier() all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long) all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long) all_label_ids = torch.tensor([f.label_ids for f in features], dtype=torch.long) dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids) return dataset def main(): parser = argparse.ArgumentParser() parser.add_argument( "--data_dir", default=None, type=str, help="The input data dir. Should contain the training files for the CoNLL-2003 NER task.", ) parser.add_argument( "--model_type", default=None, type=str, required=True, help="Model type selected in the list: " + ", ".join(MODEL_TYPES), ) parser.add_argument( "--model_name_or_path", default=None, type=str, required=True, help="Path to pre-trained model or shortcut name selected in the list: " + ", ".join(ALL_MODELS), ) parser.add_argument( "--output_dir", default=None, type=str, required=True, help="The output directory where the model predictions and checkpoints will be written.", ) parser.add_argument( "--labels", default="", type=str, help="Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.", ) parser.add_argument( "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name" ) parser.add_argument( "--tokenizer_name", default="", type=str, help="Pretrained tokenizer name or path if not the same as model_name", ) parser.add_argument( "--cache_dir", default="", type=str, help="Where do you want to store the pre-trained models downloaded from s3", ) parser.add_argument( "--max_seq_length", default=128, type=int, help="The maximum total input sequence length after tokenization. Sequences longer " "than this will be truncated, sequences shorter will be padded.", ) parser.add_argument("--do_train", action="store_true", help="Whether to run training.") parser.add_argument("--do_eval", action="store_true", help="Whether to run eval on the dev set.") parser.add_argument("--do_predict", action="store_true", help="Whether to run predictions on the test set.") parser.add_argument( "--evaluate_during_training", action="store_true", help="Whether to run evaluation during training at each logging step.", ) parser.add_argument( "--do_lower_case", action="store_true", help="Set this flag if you are using an uncased model." ) parser.add_argument( "--keep_accents", action="store_const", const=True, help="Set this flag if model is trained with accents." ) parser.add_argument( "--strip_accents", action="store_const", const=True, help="Set this flag if model is trained without accents." ) parser.add_argument("--use_fast", action="store_const", const=True, help="Set this flag to use fast tokenization.") parser.add_argument("--per_gpu_train_batch_size", default=8, type=int, help="Batch size per GPU/CPU for training.") parser.add_argument( "--per_gpu_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation." ) parser.add_argument( "--gradient_accumulation_steps", type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.", ) parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.") parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.") parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") parser.add_argument( "--num_train_epochs", default=3.0, type=float, help="Total number of training epochs to perform." ) parser.add_argument( "--max_steps", default=-1, type=int, help="If > 0: set total number of training steps to perform. Override num_train_epochs.", ) parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.") parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.") parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.") parser.add_argument("--logits_file", type=str, default="") parser.add_argument("--eval_file", type=str, default="") parser.add_argument("--warmup_ratio", type=float, default=0.1) parser.add_argument( "--eval_all_checkpoints", action="store_true", help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number", ) parser.add_argument("--no_cuda", action="store_true", help="Avoid using CUDA when available") parser.add_argument( "--overwrite_output_dir", action="store_true", help="Overwrite the content of the output directory" ) parser.add_argument( "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets" ) parser.add_argument("--seed", type=int, default=42, help="random seed for initialization") parser.add_argument( "--fp16", action="store_true", help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit", ) parser.add_argument( "--fp16_opt_level", type=str, default="O1", help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']." "See details at https://nvidia.github.io/apex/amp.html", ) parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank") parser.add_argument("--server_ip", type=str, default="", help="For distant debugging.") parser.add_argument("--server_port", type=str, default="", help="For distant debugging.") args = parser.parse_args() if ( os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir ): raise ValueError( "Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.".format( args.output_dir ) ) if args.server_ip and args.server_port: print("Waiting for debugger attach") ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True) ptvsd.wait_for_attach() if args.local_rank == -1 or args.no_cuda: device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu") args.n_gpu = 0 if args.no_cuda else torch.cuda.device_count() else: torch.cuda.set_device(args.local_rank) device = torch.device("cuda", args.local_rank) torch.distributed.init_process_group(backend="nccl") args.n_gpu = 1 args.device = device logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", args.local_rank, device, args.n_gpu, bool(args.local_rank != -1), args.fp16, ) set_seed(args) labels = get_labels(args.labels) num_labels = len(labels) pad_token_label_id = CrossEntropyLoss().ignore_index if args.local_rank not in [-1, 0]: torch.distributed.barrier() args.model_type = args.model_type.lower() config = AutoConfig.from_pretrained( args.config_name if args.config_name else args.model_name_or_path, num_labels=num_labels, id2label={str(i): label for i, label in enumerate(labels)}, label2id={label: i for i, label in enumerate(labels)}, cache_dir=args.cache_dir if args.cache_dir else None, ) tokenizer_args = {k: v for k, v in vars(args).items() if v is not None and k in TOKENIZER_ARGS} logger.info("Tokenizer arguments: %s", tokenizer_args) tokenizer = AutoTokenizer.from_pretrained( args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, cache_dir=args.cache_dir if args.cache_dir else None, **tokenizer_args, ) model = AutoModelForTokenClassification.from_pretrained( args.model_name_or_path, from_tf=bool(".ckpt" in args.model_name_or_path), config=config, cache_dir=args.cache_dir if args.cache_dir else None, ) if args.local_rank == 0: torch.distributed.barrier() model.to(args.device) logger.info("Training/evaluation parameters %s", args) if args.do_train: with open(args.logits_file, "rb") as f: datasets = pickle.load(f) all_input_ids = torch.tensor(datasets[0], dtype=torch.long) all_input_mask = torch.tensor(datasets[1], dtype=torch.long) all_segment_ids = torch.tensor(datasets[2], dtype=torch.long) all_ner_logits = torch.tensor(datasets[3], dtype=torch.float) train_dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_ner_logits) global_step, tr_loss = train(args, train_dataset, model, tokenizer, labels, pad_token_label_id) logger.info(" global_step = %s, average loss = %s", global_step, tr_loss) if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0): if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]: os.makedirs(args.output_dir) logger.info("Saving model checkpoint to %s", args.output_dir) model_to_save = ( model.module if hasattr(model, "module") else model ) model_to_save.save_pretrained(args.output_dir) tokenizer.save_pretrained(args.output_dir) torch.save(args, os.path.join(args.output_dir, "training_args.bin")) results = {} if args.do_eval and args.local_rank in [-1, 0]: tokenizer = AutoTokenizer.from_pretrained(args.output_dir, **tokenizer_args) checkpoints = [args.output_dir] if args.eval_all_checkpoints: checkpoints = list( os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + "/**/" + WEIGHTS_NAME, recursive=True)) ) logging.getLogger("pytorch_transformers.modeling_utils").setLevel(logging.WARN) logger.info("Evaluate the following checkpoints: %s", checkpoints) for checkpoint in checkpoints: global_step = checkpoint.split("-")[-1] if len(checkpoints) > 1 else "" model = AutoModelForTokenClassification.from_pretrained(checkpoint) model.to(args.device) result, _ = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="dev", prefix=global_step) if global_step: result = {"{}_{}".format(global_step, k): v for k, v in result.items()} results.update(result) output_eval_file = os.path.join(args.output_dir, "eval_results.txt") with open(output_eval_file, "w") as writer: for key in sorted(results.keys()): writer.write("{} = {}\n".format(key, str(results[key]))) if args.do_predict and args.local_rank in [-1, 0]: tokenizer = AutoTokenizer.from_pretrained(args.output_dir, **tokenizer_args) model = AutoModelForTokenClassification.from_pretrained(args.output_dir) model.to(args.device) result, predictions = evaluate(args, model, tokenizer, labels, pad_token_label_id, mode="test") output_test_results_file = os.path.join(args.output_dir, "test_results.txt") with open(output_test_results_file, "w") as writer: for key in sorted(result.keys()): writer.write("{} = {}\n".format(key, str(result[key]))) output_test_predictions_file = os.path.join(args.output_dir, "test_predictions.txt") with open(output_test_predictions_file, "w") as writer: with open(os.path.join(args.data_dir, "test.txt"), "r") as f: example_id = 0 for line in f: if line.startswith("-DOCSTART-") or line == "" or line == "\n": writer.write(line) if not predictions[example_id]: example_id += 1 elif predictions[example_id]: output_line = line.split()[0] + " " + predictions[example_id].pop(0) + "\n" writer.write(output_line) else: logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0]) return results if __name__ == "__main__": main()

true

f717830652b40b5662e7d5601be70f7d7dfd33fa

629

py

Python

code/LTI/Demos/Tex_matplotlib.py

chipmuenk/acoustics

c85ac95a10c09d7fa15d63b2bdb24acab89fec60

[ "MIT" ]

null

code/LTI/Demos/Tex_matplotlib.py

chipmuenk/acoustics

c85ac95a10c09d7fa15d63b2bdb24acab89fec60

[ "MIT" ]

null

code/LTI/Demos/Tex_matplotlib.py

chipmuenk/acoustics

c85ac95a10c09d7fa15d63b2bdb24acab89fec60

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Mon May 14 14:15:52 2012 Plot mit TeX-Formatierung der Labels (LaTeX muss auf dem Rechner installiert sein) """ import numpy as np from matplotlib import rc import matplotlib.pyplot as plt rc('text', usetex=True) plt.figure(1) ax = plt.axes([0.1, 0.1, 0.8, 0.7]) t = np.arange(0.0, 1.0+0.01, 0.01) s = np.cos(2*2*np.pi*t)+2 plt.plot(t, s) plt.xlabel(r'\textbf{Time (s)}') plt.ylabel(r'\textit{Voltage} (mV)',fontsize=16) plt.title(r"\TeX\ is Number $\displaystyle\sum_{n=1}^\infty\frac{-e^{i\pi}}{2^n}$!", fontsize=16, color='r') plt.grid(True) plt.savefig('tex_demo') plt.show()

23.296296

84

0.659777

import numpy as np from matplotlib import rc import matplotlib.pyplot as plt rc('text', usetex=True) plt.figure(1) ax = plt.axes([0.1, 0.1, 0.8, 0.7]) t = np.arange(0.0, 1.0+0.01, 0.01) s = np.cos(2*2*np.pi*t)+2 plt.plot(t, s) plt.xlabel(r'\textbf{Time (s)}') plt.ylabel(r'\textit{Voltage} (mV)',fontsize=16) plt.title(r"\TeX\ is Number $\displaystyle\sum_{n=1}^\infty\frac{-e^{i\pi}}{2^n}$!", fontsize=16, color='r') plt.grid(True) plt.savefig('tex_demo') plt.show()

true

f717831ab43bf5e60f20fa256ffdf13e2b588a99

6,871

py

Python

env/Lib/site-packages/pyttsx/drivers/dummy.py

TrinhAnBinh/covid_vir_assistant_ver_0.0.2

b4471f4894c1bc203980f06b811f63e8e8f6b3ab

[ "MIT" ]

160

2016-10-04T22:45:36.000Z

2022-02-10T06:41:56.000Z

env/Lib/site-packages/pyttsx/drivers/dummy.py

TrinhAnBinh/covid_vir_assistant_ver_0.0.2

b4471f4894c1bc203980f06b811f63e8e8f6b3ab

[ "MIT" ]

27

2016-10-04T02:45:18.000Z

2022-03-09T15:15:54.000Z

env/Lib/site-packages/pyttsx/drivers/dummy.py

TrinhAnBinh/covid_vir_assistant_ver_0.0.2

b4471f4894c1bc203980f06b811f63e8e8f6b3ab

[ "MIT" ]

58

2016-10-06T16:53:43.000Z

2021-10-21T22:17:35.000Z

''' Dummy driver that produces no output but gives all expected callbacks. Useful for testing and as a model for real drivers. Copyright (c) 2009, 2013 Peter Parente Permission to use, copy, modify, and distribute this software for any purpose with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies. THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE. ''' from ..voice import Voice import time def buildDriver(proxy): ''' Builds a new instance of a driver and returns it for use by the driver proxy. @param proxy: Proxy creating the driver @type proxy: L{driver.DriverProxy} ''' return DummyDriver(proxy) class DummyDriver(object): ''' Dummy speech engine implementation. Documents the interface, notifications, properties, and sequencing responsibilities of a driver implementation. @ivar _proxy: Driver proxy that manages this instance @type _proxy: L{driver.DriverProxy} @ivar _config: Dummy configuration @type _config: dict @ivar _looping: True when in the dummy event loop, False when not @ivar _looping: bool ''' def __init__(self, proxy): ''' Constructs the driver. @param proxy: Proxy creating the driver @type proxy: L{driver.DriverProxy} ''' self._proxy = proxy self._looping = False # hold config values as if we had a real tts implementation that # supported them voices = [ Voice('dummy.voice1', 'John Doe', ['en-US', 'en-GB'], 'male', 'adult'), Voice('dummy.voice2', 'Jane Doe', ['en-US', 'en-GB'], 'female', 'adult'), Voice('dummy.voice3', 'Jimmy Doe', ['en-US', 'en-GB'], 'male', 10) ] self._config = { 'rate' : 200, 'volume' : 1.0, 'voice' : voices[0], 'voices' : voices } def destroy(self): ''' Optional method that will be called when the driver proxy is being destroyed. Can cleanup any resources to make sure the engine terminates properly. ''' pass def startLoop(self): ''' Starts a blocking run loop in which driver callbacks are properly invoked. @precondition: There was no previous successful call to L{startLoop} without an intervening call to L{stopLoop}. ''' first = True self._looping = True while self._looping: if first: self._proxy.setBusy(False) first = False time.sleep(0.5) def endLoop(self): ''' Stops a previously started run loop. @precondition: A previous call to L{startLoop} suceeded and there was no intervening call to L{endLoop}. ''' self._looping = False def iterate(self): ''' Iterates from within an external run loop. ''' self._proxy.setBusy(False) yield def say(self, text): ''' Speaks the given text. Generates the following notifications during output: started-utterance: When speech output has started started-word: When a word is about to be spoken. Includes the character "location" of the start of the word in the original utterance text and the "length" of the word in characters. finished-utterance: When speech output has finished. Includes a flag indicating if the entire utterance was "completed" or not. The proxy automatically adds any "name" associated with the utterance to the notifications on behalf of the driver. When starting to output an utterance, the driver must inform its proxy that it is busy by invoking L{driver.DriverProxy.setBusy} with a flag of True. When the utterance completes or is interrupted, the driver inform the proxy that it is no longer busy by invoking L{driver.DriverProxy.setBusy} with a flag of False. @param text: Unicode text to speak @type text: unicode ''' self._proxy.setBusy(True) self._proxy.notify('started-utterance') i = 0 for word in text.split(' '): self._proxy.notify('started-word', location=i, length=len(word)) try: i = text.index(' ', i+1)+1 except Exception: pass self._proxy.notify('finished-utterance', completed=True) self._proxy.setBusy(False) def stop(self): ''' Stops any current output. If an utterance was being spoken, the driver is still responsible for sending the closing finished-utterance notification documented above and resetting the busy state of the proxy. ''' pass def getProperty(self, name): ''' Gets a property value of the speech engine. The suppoted properties and their values are: voices: List of L{voice.Voice} objects supported by the driver voice: String ID of the current voice rate: Integer speech rate in words per minute volume: Floating point volume of speech in the range [0.0, 1.0] @param name: Property name @type name: str @raise KeyError: When the property name is unknown ''' try: return self._config[name] except KeyError: raise KeyError('unknown property %s' % name) def setProperty(self, name, value): ''' Sets one of the supported property values of the speech engine listed above. If a value is invalid, attempts to clip it / coerce so it is valid before giving up and firing an exception. @param name: Property name @type name: str @param value: Property value @type value: object @raise KeyError: When the property name is unknown @raise ValueError: When the value cannot be coerced to fit the property ''' if name == 'voice': v = [v for v in self._config['voices'] if v.id == value] self._config['voice'] = v[0] elif name == 'rate': self._config['rate'] = value elif name == 'volume': self._config['volume'] = value else: raise KeyError('unknown property %s' % name)

35.601036

85

0.625382

from ..voice import Voice import time def buildDriver(proxy): return DummyDriver(proxy) class DummyDriver(object): def __init__(self, proxy): self._proxy = proxy self._looping = False voices = [ Voice('dummy.voice1', 'John Doe', ['en-US', 'en-GB'], 'male', 'adult'), Voice('dummy.voice2', 'Jane Doe', ['en-US', 'en-GB'], 'female', 'adult'), Voice('dummy.voice3', 'Jimmy Doe', ['en-US', 'en-GB'], 'male', 10) ] self._config = { 'rate' : 200, 'volume' : 1.0, 'voice' : voices[0], 'voices' : voices } def destroy(self): pass def startLoop(self): first = True self._looping = True while self._looping: if first: self._proxy.setBusy(False) first = False time.sleep(0.5) def endLoop(self): self._looping = False def iterate(self): self._proxy.setBusy(False) yield def say(self, text): self._proxy.setBusy(True) self._proxy.notify('started-utterance') i = 0 for word in text.split(' '): self._proxy.notify('started-word', location=i, length=len(word)) try: i = text.index(' ', i+1)+1 except Exception: pass self._proxy.notify('finished-utterance', completed=True) self._proxy.setBusy(False) def stop(self): pass def getProperty(self, name): try: return self._config[name] except KeyError: raise KeyError('unknown property %s' % name) def setProperty(self, name, value): if name == 'voice': v = [v for v in self._config['voices'] if v.id == value] self._config['voice'] = v[0] elif name == 'rate': self._config['rate'] = value elif name == 'volume': self._config['volume'] = value else: raise KeyError('unknown property %s' % name)

true

f71785323c845db207650f7917b7bc72e98e1b96

26,982

py

Python

manila/tests/share/drivers/quobyte/test_quobyte.py

deiter/manila

ba94d20e823d2edad7e9bd01546cf1642b17d212

[ "Apache-2.0" ]

1

2019-05-06T10:33:38.000Z

manila/tests/share/drivers/quobyte/test_quobyte.py

deiter/manila

ba94d20e823d2edad7e9bd01546cf1642b17d212

[ "Apache-2.0" ]

4

2019-05-06T11:45:17.000Z

2019-05-09T14:23:28.000Z

manila/tests/share/drivers/quobyte/test_quobyte.py

deiter/manila

ba94d20e823d2edad7e9bd01546cf1642b17d212

[ "Apache-2.0" ]

3

2019-05-03T12:32:47.000Z

2021-01-30T20:26:19.000Z

# Copyright (c) 2015 Quobyte, Inc. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import mock from oslo_config import cfg import six from manila import context from manila import exception from manila.share import configuration as config from manila.share import driver from manila.share.drivers.quobyte import jsonrpc from manila.share.drivers.quobyte import quobyte from manila import test from manila.tests import fake_share CONF = cfg.CONF def fake_rpc_handler(name, *args): if name == 'resolveVolumeName': return None elif name == 'createVolume': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': 'fake_location', 'nfs_export_path': '/fake_share'} elif name == 'getConfiguration': return { "tenant_configuration": [{ "domain_name": "fake_domain_name", "volume_access": [ {"volume_uuid": "fake_id_1", "restrict_to_network": "10.0.0.1", "read_only": False}, {"volume_uuid": "fake_id_1", "restrict_to_network": "10.0.0.2", "read_only": False}, {"volume_uuid": "fake_id_2", "restrict_to_network": "10.0.0.3", "read_only": False} ]}, {"domain_name": "fake_domain_name_2", "volume_access": [ {"volume_uuid": "fake_id_3", "restrict_to_network": "10.0.0.4", "read_only": False}, {"volume_uuid": "fake_id_3", "restrict_to_network": "10.0.0.5", "read_only": True}, {"volume_uuid": "fake_id_4", "restrict_to_network": "10.0.0.6", "read_only": False} ]} ] } else: return "Unknown fake rpc handler call" def create_fake_access(access_adr, access_id='fake_access_id', access_type='ip', access_level='rw'): return { 'access_id': access_id, 'access_type': access_type, 'access_to': access_adr, 'access_level': access_level } class QuobyteShareDriverTestCase(test.TestCase): """Tests QuobyteShareDriver.""" def setUp(self): super(QuobyteShareDriverTestCase, self).setUp() self._context = context.get_admin_context() CONF.set_default('driver_handles_share_servers', False) self.fake_conf = config.Configuration(None) self._driver = quobyte.QuobyteShareDriver(configuration=self.fake_conf) self._driver.rpc = mock.Mock() self.share = fake_share.fake_share(share_proto='NFS') self.access = fake_share.fake_access() @mock.patch('manila.share.drivers.quobyte.jsonrpc.JsonRpc', mock.Mock()) def test_do_setup_success(self): self._driver.rpc.call = mock.Mock(return_value=None) self._driver.do_setup(self._context) self._driver.rpc.call.assert_called_with('getInformation', {}) @mock.patch('manila.share.drivers.quobyte.jsonrpc.JsonRpc.__init__', mock.Mock(return_value=None)) @mock.patch.object(jsonrpc.JsonRpc, 'call', side_effect=exception.QBRpcException) def test_do_setup_failure(self, mock_call): self.assertRaises(exception.QBException, self._driver.do_setup, self._context) def test_create_share_new_volume(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) result = self._driver.create_share(self._context, self.share) self.assertEqual(self.share['export_location'], result) self._driver.rpc.call.assert_has_calls([ mock.call('createVolume', dict( name=self.share['name'], tenant_domain=self.share['project_id'], root_user_id=self.fake_conf.quobyte_default_volume_user, root_group_id=self.fake_conf.quobyte_default_volume_group, configuration_name=self.fake_conf.quobyte_volume_configuration )), mock.call('exportVolume', dict(protocol='NFS', volume_uuid='voluuid'))]) def test_create_share_existing_volume(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self._driver.create_share(self._context, self.share) self._driver.rpc.call.assert_called_with( 'exportVolume', dict(protocol='NFS', volume_uuid='voluuid')) def test_create_share_wrong_protocol(self): share = {'share_proto': 'WRONG_PROTOCOL'} self.assertRaises(exception.QBException, self._driver.create_share, context=None, share=share) def test_delete_share_existing_volume(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {} self._driver.configuration.quobyte_delete_shares = True self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.delete_share(self._context, self.share) self._driver.rpc.call.assert_has_calls([ mock.call('resolveVolumeName', {'volume_name': 'fakename', 'tenant_domain': 'fake_project_uuid'}), mock.call('deleteVolume', {'volume_uuid': 'voluuid'}), mock.call('exportVolume', {'volume_uuid': 'voluuid', 'remove_export': True})]) def test_delete_share_existing_volume_disabled(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {} CONF.set_default('quobyte_delete_shares', False) self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.delete_share(self._context, self.share) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'remove_export': True}) @mock.patch.object(quobyte.LOG, 'warning') def test_delete_share_nonexisting_volume(self, mock_warning): def rpc_handler(name, *args): if name == 'resolveVolumeName': return None self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.delete_share(self._context, self.share) mock_warning.assert_called_with( 'No volume found for share fake_project_uuid/fakename') def test_allow_access(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': '10.10.1.1', 'nfs_export_path': '/voluuid'} self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver._allow_access(self._context, self.share, self.access) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'read_only': False, 'add_allow_ip': '10.0.0.1'}) def test_allow_ro_access(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': '10.10.1.1', 'nfs_export_path': '/voluuid'} self._driver.rpc.call = mock.Mock(wraps=rpc_handler) ro_access = fake_share.fake_access(access_level='ro') self._driver._allow_access(self._context, self.share, ro_access) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'read_only': True, 'add_allow_ip': '10.0.0.1'}) def test_allow_access_nonip(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self.access = fake_share.fake_access(**{"access_type": "non_existant_access_type"}) self.assertRaises(exception.InvalidShareAccess, self._driver._allow_access, self._context, self.share, self.access) def test_deny_access(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': '10.10.1.1', 'nfs_export_path': '/voluuid'} self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver._deny_access(self._context, self.share, self.access) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'remove_allow_ip': '10.0.0.1'}) @mock.patch.object(quobyte.LOG, 'debug') def test_deny_access_nonip(self, mock_debug): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self.access = fake_share.fake_access( access_type="non_existant_access_type") self._driver._deny_access(self._context, self.share, self.access) mock_debug.assert_called_with( 'Quobyte driver only supports ip access control. ' 'Ignoring deny access call for %s , %s', 'fakename', 'fake_project_uuid') def test_resolve_volume_name(self): self._driver.rpc.call = mock.Mock( return_value={'volume_uuid': 'fake_uuid'}) self._driver._resolve_volume_name('fake_vol_name', 'fake_domain_name') self._driver.rpc.call.assert_called_with( 'resolveVolumeName', {'volume_name': 'fake_vol_name', 'tenant_domain': 'fake_domain_name'}) def test_resolve_volume_name_NOENT(self): self._driver.rpc.call = mock.Mock( return_value=None) self.assertIsNone( self._driver._resolve_volume_name('fake_vol_name', 'fake_domain_name')) def test_resolve_volume_name_other_error(self): self._driver.rpc.call = mock.Mock( side_effect=exception.QBRpcException( result='fubar', qbcode=666)) self.assertRaises(exception.QBRpcException, self._driver._resolve_volume_name, volume_name='fake_vol_name', tenant_domain='fake_domain_name') @mock.patch.object(driver.ShareDriver, '_update_share_stats') def test_update_share_stats(self, mock_uss): self._driver._get_capacities = mock.Mock(return_value=[42, 23]) self._driver._update_share_stats() mock_uss.assert_called_once_with( dict(storage_protocol='NFS', vendor_name='Quobyte', share_backend_name=self._driver.backend_name, driver_version=self._driver.DRIVER_VERSION, total_capacity_gb=42, free_capacity_gb=23, reserved_percentage=0)) def test_get_capacities_gb(self): capval = 42115548133 useval = 19695128917 self._driver.rpc.call = mock.Mock( return_value={'total_logical_capacity': six.text_type(capval), 'total_logical_usage': six.text_type(useval)}) self.assertEqual((39.223160718, 20.880642548), self._driver._get_capacities()) @mock.patch.object(quobyte.QuobyteShareDriver, "_resolve_volume_name", return_value="fake_uuid") def test_ensure_share(self, mock_qb_resolve_volname): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) result = self._driver.ensure_share(self._context, self.share, None) self.assertEqual(self.share["export_location"], result) (mock_qb_resolve_volname. assert_called_once_with(self.share['name'], self.share['project_id'])) self._driver.rpc.call.assert_has_calls([ mock.call('exportVolume', dict( volume_uuid="fake_uuid", protocol='NFS' ))]) @mock.patch.object(quobyte.QuobyteShareDriver, "_resolve_volume_name", return_value=None) def test_ensure_deleted_share(self, mock_qb_resolve_volname): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self.assertRaises(exception.ShareResourceNotFound, self._driver.ensure_share, self._context, self.share, None) (mock_qb_resolve_volname. assert_called_once_with(self.share['name'], self.share['project_id'])) @mock.patch.object(quobyte.QuobyteShareDriver, "_resize_share") def test_extend_share(self, mock_qsd_resize_share): self._driver.extend_share(ext_share=self.share, ext_size=2, share_server=None) mock_qsd_resize_share.assert_called_once_with(share=self.share, new_size=2) def test_resize_share(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self._driver._resize_share(share=self.share, new_size=7) self._driver.rpc.call.assert_has_calls([ mock.call('setQuota', {"consumer": {"type": 3, "identifier": self.share["name"]}, "limits": {"type": 5, "value": 7}})]) @mock.patch.object(quobyte.QuobyteShareDriver, "_resolve_volume_name", return_value="fake_id_3") def test_fetch_existing_access(self, mock_qb_resolve_volname): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) old_access_1 = create_fake_access(access_id="old_1", access_adr="10.0.0.4") old_access_2 = create_fake_access(access_id="old_2", access_adr="10.0.0.5") exist_list = self._driver._fetch_existing_access(context=self._context, share=self.share) # assert expected result here self.assertEqual([old_access_1['access_to'], old_access_2['access_to']], [e.get('access_to') for e in exist_list]) (mock_qb_resolve_volname. assert_called_once_with(self.share['name'], self.share['project_id'])) @mock.patch.object(quobyte.QuobyteShareDriver, "_resize_share") def test_shrink_share(self, mock_qsd_resize_share): self._driver.shrink_share(shrink_share=self.share, shrink_size=3, share_server=None) mock_qsd_resize_share.assert_called_once_with(share=self.share, new_size=3) def test_subtract_access_lists(self): access_1 = create_fake_access(access_id="new_1", access_adr="10.0.0.5", access_type="rw",) access_2 = create_fake_access(access_id="old_1", access_adr="10.0.0.1", access_type="rw") access_3 = create_fake_access(access_id="old_2", access_adr="10.0.0.3", access_type="ro") access_4 = create_fake_access(access_id="new_2", access_adr="10.0.0.6", access_type="rw") access_5 = create_fake_access(access_id="old_3", access_adr="10.0.0.4", access_type="rw") min_list = [access_1, access_2, access_3, access_4] sub_list = [access_5, access_3, access_2] self.assertEqual([access_1, access_4], self._driver._subtract_access_lists(min_list, sub_list)) def test_subtract_access_lists_level(self): access_1 = create_fake_access(access_id="new_1", access_adr="10.0.0.5", access_level="rw") access_2 = create_fake_access(access_id="old_1", access_adr="10.0.0.1", access_level="rw") access_3 = create_fake_access(access_id="old_2", access_adr="10.0.0.3", access_level="rw") access_4 = create_fake_access(access_id="new_2", access_adr="10.0.0.6", access_level="rw") access_5 = create_fake_access(access_id="old_2_ro", access_adr="10.0.0.3", access_level="ro") min_list = [access_1, access_2, access_3, access_4] sub_list = [access_5, access_2] self.assertEqual([access_1, access_3, access_4], self._driver._subtract_access_lists(min_list, sub_list)) def test_subtract_access_lists_type(self): access_1 = create_fake_access(access_id="new_1", access_adr="10.0.0.5", access_type="ip") access_2 = create_fake_access(access_id="old_1", access_adr="10.0.0.1", access_type="ip") access_3 = create_fake_access(access_id="old_2", access_adr="10.0.0.3", access_type="ip") access_4 = create_fake_access(access_id="new_2", access_adr="10.0.0.6", access_type="ip") access_5 = create_fake_access(access_id="old_2_ro", access_adr="10.0.0.3", access_type="other") min_list = [access_1, access_2, access_3, access_4] sub_list = [access_5, access_2] self.assertEqual([access_1, access_3, access_4], self._driver._subtract_access_lists(min_list, sub_list)) @mock.patch.object(quobyte.QuobyteShareDriver, "_allow_access") @mock.patch.object(quobyte.QuobyteShareDriver, "_deny_access") def test_update_access_add_delete(self, qb_deny_mock, qb_allow_mock): access_1 = create_fake_access(access_id="new_1", access_adr="10.0.0.5", access_level="rw") access_2 = create_fake_access(access_id="old_1", access_adr="10.0.0.1", access_level="rw") access_3 = create_fake_access(access_id="old_2", access_adr="10.0.0.3", access_level="rw") self._driver.update_access(self._context, self.share, access_rules=None, add_rules=[access_1], delete_rules=[access_2, access_3]) qb_allow_mock.assert_called_once_with(self._context, self.share, access_1) deny_calls = [mock.call(self._context, self.share, access_2), mock.call(self._context, self.share, access_3)] qb_deny_mock.assert_has_calls(deny_calls) @mock.patch.object(quobyte.LOG, "warning") def test_update_access_no_rules(self, qb_log_mock): self._driver.update_access(context=None, share=None, access_rules=[], add_rules=[], delete_rules=[]) qb_log_mock.assert_has_calls([mock.ANY]) @mock.patch.object(quobyte.QuobyteShareDriver, "_subtract_access_lists") @mock.patch.object(quobyte.QuobyteShareDriver, "_fetch_existing_access") @mock.patch.object(quobyte.QuobyteShareDriver, "_allow_access") def test_update_access_recovery_additionals(self, qb_allow_mock, qb_exist_mock, qb_subtr_mock): new_access_1 = create_fake_access(access_id="new_1", access_adr="10.0.0.2") old_access = create_fake_access(access_id="fake_access_id", access_adr="10.0.0.1") new_access_2 = create_fake_access(access_id="new_2", access_adr="10.0.0.3") add_access_rules = [new_access_1, old_access, new_access_2] qb_exist_mock.return_value = [old_access] qb_subtr_mock.side_effect = [[new_access_1, new_access_2], []] self._driver.update_access(self._context, self.share, access_rules=add_access_rules, add_rules=[], delete_rules=[]) assert_calls = [mock.call(self._context, self.share, new_access_1), mock.call(self._context, self.share, new_access_2)] qb_allow_mock.assert_has_calls(assert_calls, any_order=True) qb_exist_mock.assert_called_once_with(self._context, self.share) @mock.patch.object(quobyte.QuobyteShareDriver, "_subtract_access_lists") @mock.patch.object(quobyte.QuobyteShareDriver, "_fetch_existing_access") @mock.patch.object(quobyte.QuobyteShareDriver, "_deny_access") def test_update_access_recovery_superfluous(self, qb_deny_mock, qb_exist_mock, qb_subtr_mock): old_access_1 = create_fake_access(access_id="old_1", access_adr="10.0.0.1") missing_access_1 = create_fake_access(access_id="mis_1", access_adr="10.0.0.2") old_access_2 = create_fake_access(access_id="old_2", access_adr="10.0.0.3") qb_exist_mock.side_effect = [[old_access_1, old_access_2]] qb_subtr_mock.side_effect = [[], [missing_access_1]] old_access_rules = [old_access_1, old_access_2] self._driver.update_access(self._context, self.share, access_rules=old_access_rules, add_rules=[], delete_rules=[]) qb_deny_mock.assert_called_once_with(self._context, self.share, (missing_access_1)) qb_exist_mock.assert_called_once_with(self._context, self.share) @mock.patch.object(quobyte.QuobyteShareDriver, "_subtract_access_lists") @mock.patch.object(quobyte.QuobyteShareDriver, "_fetch_existing_access") @mock.patch.object(quobyte.QuobyteShareDriver, "_deny_access") @mock.patch.object(quobyte.QuobyteShareDriver, "_allow_access") def test_update_access_recovery_add_superfluous(self, qb_allow_mock, qb_deny_mock, qb_exist_mock, qb_subtr_mock): new_access_1 = create_fake_access(access_id="new_1", access_adr="10.0.0.5") old_access_1 = create_fake_access(access_id="old_1", access_adr="10.0.0.1") old_access_2 = create_fake_access(access_id="old_2", access_adr="10.0.0.3") old_access_3 = create_fake_access(access_id="old_3", access_adr="10.0.0.4") miss_access_1 = create_fake_access(access_id="old_3", access_adr="10.0.0.4") new_access_2 = create_fake_access(access_id="new_2", access_adr="10.0.0.3", access_level="ro") new_access_rules = [new_access_1, old_access_1, old_access_2, old_access_3, new_access_2] qb_exist_mock.return_value = [old_access_1, old_access_2, old_access_3, miss_access_1] qb_subtr_mock.side_effect = [[new_access_1, new_access_2], [miss_access_1, old_access_2]] self._driver.update_access(self._context, self.share, new_access_rules, add_rules=[], delete_rules=[]) a_calls = [mock.call(self._context, self.share, new_access_1), mock.call(self._context, self.share, new_access_2)] qb_allow_mock.assert_has_calls(a_calls) b_calls = [mock.call(self._context, self.share, miss_access_1), mock.call(self._context, self.share, old_access_2)] qb_deny_mock.assert_has_calls(b_calls) qb_exist_mock.assert_called_once_with(self._context, self.share)

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import mock from oslo_config import cfg import six from manila import context from manila import exception from manila.share import configuration as config from manila.share import driver from manila.share.drivers.quobyte import jsonrpc from manila.share.drivers.quobyte import quobyte from manila import test from manila.tests import fake_share CONF = cfg.CONF def fake_rpc_handler(name, *args): if name == 'resolveVolumeName': return None elif name == 'createVolume': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': 'fake_location', 'nfs_export_path': '/fake_share'} elif name == 'getConfiguration': return { "tenant_configuration": [{ "domain_name": "fake_domain_name", "volume_access": [ {"volume_uuid": "fake_id_1", "restrict_to_network": "10.0.0.1", "read_only": False}, {"volume_uuid": "fake_id_1", "restrict_to_network": "10.0.0.2", "read_only": False}, {"volume_uuid": "fake_id_2", "restrict_to_network": "10.0.0.3", "read_only": False} ]}, {"domain_name": "fake_domain_name_2", "volume_access": [ {"volume_uuid": "fake_id_3", "restrict_to_network": "10.0.0.4", "read_only": False}, {"volume_uuid": "fake_id_3", "restrict_to_network": "10.0.0.5", "read_only": True}, {"volume_uuid": "fake_id_4", "restrict_to_network": "10.0.0.6", "read_only": False} ]} ] } else: return "Unknown fake rpc handler call" def create_fake_access(access_adr, access_id='fake_access_id', access_type='ip', access_level='rw'): return { 'access_id': access_id, 'access_type': access_type, 'access_to': access_adr, 'access_level': access_level } class QuobyteShareDriverTestCase(test.TestCase): def setUp(self): super(QuobyteShareDriverTestCase, self).setUp() self._context = context.get_admin_context() CONF.set_default('driver_handles_share_servers', False) self.fake_conf = config.Configuration(None) self._driver = quobyte.QuobyteShareDriver(configuration=self.fake_conf) self._driver.rpc = mock.Mock() self.share = fake_share.fake_share(share_proto='NFS') self.access = fake_share.fake_access() @mock.patch('manila.share.drivers.quobyte.jsonrpc.JsonRpc', mock.Mock()) def test_do_setup_success(self): self._driver.rpc.call = mock.Mock(return_value=None) self._driver.do_setup(self._context) self._driver.rpc.call.assert_called_with('getInformation', {}) @mock.patch('manila.share.drivers.quobyte.jsonrpc.JsonRpc.__init__', mock.Mock(return_value=None)) @mock.patch.object(jsonrpc.JsonRpc, 'call', side_effect=exception.QBRpcException) def test_do_setup_failure(self, mock_call): self.assertRaises(exception.QBException, self._driver.do_setup, self._context) def test_create_share_new_volume(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) result = self._driver.create_share(self._context, self.share) self.assertEqual(self.share['export_location'], result) self._driver.rpc.call.assert_has_calls([ mock.call('createVolume', dict( name=self.share['name'], tenant_domain=self.share['project_id'], root_user_id=self.fake_conf.quobyte_default_volume_user, root_group_id=self.fake_conf.quobyte_default_volume_group, configuration_name=self.fake_conf.quobyte_volume_configuration )), mock.call('exportVolume', dict(protocol='NFS', volume_uuid='voluuid'))]) def test_create_share_existing_volume(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self._driver.create_share(self._context, self.share) self._driver.rpc.call.assert_called_with( 'exportVolume', dict(protocol='NFS', volume_uuid='voluuid')) def test_create_share_wrong_protocol(self): share = {'share_proto': 'WRONG_PROTOCOL'} self.assertRaises(exception.QBException, self._driver.create_share, context=None, share=share) def test_delete_share_existing_volume(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {} self._driver.configuration.quobyte_delete_shares = True self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.delete_share(self._context, self.share) self._driver.rpc.call.assert_has_calls([ mock.call('resolveVolumeName', {'volume_name': 'fakename', 'tenant_domain': 'fake_project_uuid'}), mock.call('deleteVolume', {'volume_uuid': 'voluuid'}), mock.call('exportVolume', {'volume_uuid': 'voluuid', 'remove_export': True})]) def test_delete_share_existing_volume_disabled(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {} CONF.set_default('quobyte_delete_shares', False) self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.delete_share(self._context, self.share) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'remove_export': True}) @mock.patch.object(quobyte.LOG, 'warning') def test_delete_share_nonexisting_volume(self, mock_warning): def rpc_handler(name, *args): if name == 'resolveVolumeName': return None self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver.delete_share(self._context, self.share) mock_warning.assert_called_with( 'No volume found for share fake_project_uuid/fakename') def test_allow_access(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': '10.10.1.1', 'nfs_export_path': '/voluuid'} self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver._allow_access(self._context, self.share, self.access) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'read_only': False, 'add_allow_ip': '10.0.0.1'}) def test_allow_ro_access(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': '10.10.1.1', 'nfs_export_path': '/voluuid'} self._driver.rpc.call = mock.Mock(wraps=rpc_handler) ro_access = fake_share.fake_access(access_level='ro') self._driver._allow_access(self._context, self.share, ro_access) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'read_only': True, 'add_allow_ip': '10.0.0.1'}) def test_allow_access_nonip(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self.access = fake_share.fake_access(**{"access_type": "non_existant_access_type"}) self.assertRaises(exception.InvalidShareAccess, self._driver._allow_access, self._context, self.share, self.access) def test_deny_access(self): def rpc_handler(name, *args): if name == 'resolveVolumeName': return {'volume_uuid': 'voluuid'} elif name == 'exportVolume': return {'nfs_server_ip': '10.10.1.1', 'nfs_export_path': '/voluuid'} self._driver.rpc.call = mock.Mock(wraps=rpc_handler) self._driver._deny_access(self._context, self.share, self.access) self._driver.rpc.call.assert_called_with( 'exportVolume', {'volume_uuid': 'voluuid', 'remove_allow_ip': '10.0.0.1'}) @mock.patch.object(quobyte.LOG, 'debug') def test_deny_access_nonip(self, mock_debug): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self.access = fake_share.fake_access( access_type="non_existant_access_type") self._driver._deny_access(self._context, self.share, self.access) mock_debug.assert_called_with( 'Quobyte driver only supports ip access control. ' 'Ignoring deny access call for %s , %s', 'fakename', 'fake_project_uuid') def test_resolve_volume_name(self): self._driver.rpc.call = mock.Mock( return_value={'volume_uuid': 'fake_uuid'}) self._driver._resolve_volume_name('fake_vol_name', 'fake_domain_name') self._driver.rpc.call.assert_called_with( 'resolveVolumeName', {'volume_name': 'fake_vol_name', 'tenant_domain': 'fake_domain_name'}) def test_resolve_volume_name_NOENT(self): self._driver.rpc.call = mock.Mock( return_value=None) self.assertIsNone( self._driver._resolve_volume_name('fake_vol_name', 'fake_domain_name')) def test_resolve_volume_name_other_error(self): self._driver.rpc.call = mock.Mock( side_effect=exception.QBRpcException( result='fubar', qbcode=666)) self.assertRaises(exception.QBRpcException, self._driver._resolve_volume_name, volume_name='fake_vol_name', tenant_domain='fake_domain_name') @mock.patch.object(driver.ShareDriver, '_update_share_stats') def test_update_share_stats(self, mock_uss): self._driver._get_capacities = mock.Mock(return_value=[42, 23]) self._driver._update_share_stats() mock_uss.assert_called_once_with( dict(storage_protocol='NFS', vendor_name='Quobyte', share_backend_name=self._driver.backend_name, driver_version=self._driver.DRIVER_VERSION, total_capacity_gb=42, free_capacity_gb=23, reserved_percentage=0)) def test_get_capacities_gb(self): capval = 42115548133 useval = 19695128917 self._driver.rpc.call = mock.Mock( return_value={'total_logical_capacity': six.text_type(capval), 'total_logical_usage': six.text_type(useval)}) self.assertEqual((39.223160718, 20.880642548), self._driver._get_capacities()) @mock.patch.object(quobyte.QuobyteShareDriver, "_resolve_volume_name", return_value="fake_uuid") def test_ensure_share(self, mock_qb_resolve_volname): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) result = self._driver.ensure_share(self._context, self.share, None) self.assertEqual(self.share["export_location"], result) (mock_qb_resolve_volname. assert_called_once_with(self.share['name'], self.share['project_id'])) self._driver.rpc.call.assert_has_calls([ mock.call('exportVolume', dict( volume_uuid="fake_uuid", protocol='NFS' ))]) @mock.patch.object(quobyte.QuobyteShareDriver, "_resolve_volume_name", return_value=None) def test_ensure_deleted_share(self, mock_qb_resolve_volname): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self.assertRaises(exception.ShareResourceNotFound, self._driver.ensure_share, self._context, self.share, None) (mock_qb_resolve_volname. assert_called_once_with(self.share['name'], self.share['project_id'])) @mock.patch.object(quobyte.QuobyteShareDriver, "_resize_share") def test_extend_share(self, mock_qsd_resize_share): self._driver.extend_share(ext_share=self.share, ext_size=2, share_server=None) mock_qsd_resize_share.assert_called_once_with(share=self.share, new_size=2) def test_resize_share(self): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) self._driver._resize_share(share=self.share, new_size=7) self._driver.rpc.call.assert_has_calls([ mock.call('setQuota', {"consumer": {"type": 3, "identifier": self.share["name"]}, "limits": {"type": 5, "value": 7}})]) @mock.patch.object(quobyte.QuobyteShareDriver, "_resolve_volume_name", return_value="fake_id_3") def test_fetch_existing_access(self, mock_qb_resolve_volname): self._driver.rpc.call = mock.Mock(wraps=fake_rpc_handler) old_access_1 = create_fake_access(access_id="old_1", access_adr="10.0.0.4") old_access_2 = create_fake_access(access_id="old_2", access_adr="10.0.0.5") exist_list = self._driver._fetch_existing_access(context=self._context, share=self.share) self.assertEqual([old_access_1['access_to'], old_access_2['access_to']], [e.get('access_to') for e in exist_list]) (mock_qb_resolve_volname. assert_called_once_with(self.share['name'], self.share['project_id'])) @mock.patch.object(quobyte.QuobyteShareDriver, "_resize_share") def test_shrink_share(self, mock_qsd_resize_share): self._driver.shrink_share(shrink_share=self.share, shrink_size=3, share_server=None) mock_qsd_resize_share.assert_called_once_with(share=self.share, new_size=3) def test_subtract_access_lists(self): access_1 = create_fake_access(access_id="new_1", access_adr="10.0.0.5", access_type="rw",) access_2 = create_fake_access(access_id="old_1", access_adr="10.0.0.1", access_type="rw") access_3 = create_fake_access(access_id="old_2", access_adr="10.0.0.3", access_type="ro") access_4 = create_fake_access(access_id="new_2", access_adr="10.0.0.6", access_type="rw") access_5 = create_fake_access(access_id="old_3", access_adr="10.0.0.4", access_type="rw") min_list = [access_1, access_2, access_3, access_4] sub_list = [access_5, access_3, access_2] self.assertEqual([access_1, access_4], self._driver._subtract_access_lists(min_list, sub_list)) def test_subtract_access_lists_level(self): access_1 = create_fake_access(access_id="new_1", access_adr="10.0.0.5", access_level="rw") access_2 = create_fake_access(access_id="old_1", access_adr="10.0.0.1", access_level="rw") access_3 = create_fake_access(access_id="old_2", access_adr="10.0.0.3", access_level="rw") access_4 = create_fake_access(access_id="new_2", access_adr="10.0.0.6", access_level="rw") access_5 = create_fake_access(access_id="old_2_ro", access_adr="10.0.0.3", access_level="ro") min_list = [access_1, access_2, access_3, access_4] sub_list = [access_5, access_2] self.assertEqual([access_1, access_3, access_4], self._driver._subtract_access_lists(min_list, sub_list)) def test_subtract_access_lists_type(self): access_1 = create_fake_access(access_id="new_1", access_adr="10.0.0.5", access_type="ip") access_2 = create_fake_access(access_id="old_1", access_adr="10.0.0.1", access_type="ip") access_3 = create_fake_access(access_id="old_2", access_adr="10.0.0.3", access_type="ip") access_4 = create_fake_access(access_id="new_2", access_adr="10.0.0.6", access_type="ip") access_5 = create_fake_access(access_id="old_2_ro", access_adr="10.0.0.3", access_type="other") min_list = [access_1, access_2, access_3, access_4] sub_list = [access_5, access_2] self.assertEqual([access_1, access_3, access_4], self._driver._subtract_access_lists(min_list, sub_list)) @mock.patch.object(quobyte.QuobyteShareDriver, "_allow_access") @mock.patch.object(quobyte.QuobyteShareDriver, "_deny_access") def test_update_access_add_delete(self, qb_deny_mock, qb_allow_mock): access_1 = create_fake_access(access_id="new_1", access_adr="10.0.0.5", access_level="rw") access_2 = create_fake_access(access_id="old_1", access_adr="10.0.0.1", access_level="rw") access_3 = create_fake_access(access_id="old_2", access_adr="10.0.0.3", access_level="rw") self._driver.update_access(self._context, self.share, access_rules=None, add_rules=[access_1], delete_rules=[access_2, access_3]) qb_allow_mock.assert_called_once_with(self._context, self.share, access_1) deny_calls = [mock.call(self._context, self.share, access_2), mock.call(self._context, self.share, access_3)] qb_deny_mock.assert_has_calls(deny_calls) @mock.patch.object(quobyte.LOG, "warning") def test_update_access_no_rules(self, qb_log_mock): self._driver.update_access(context=None, share=None, access_rules=[], add_rules=[], delete_rules=[]) qb_log_mock.assert_has_calls([mock.ANY]) @mock.patch.object(quobyte.QuobyteShareDriver, "_subtract_access_lists") @mock.patch.object(quobyte.QuobyteShareDriver, "_fetch_existing_access") @mock.patch.object(quobyte.QuobyteShareDriver, "_allow_access") def test_update_access_recovery_additionals(self, qb_allow_mock, qb_exist_mock, qb_subtr_mock): new_access_1 = create_fake_access(access_id="new_1", access_adr="10.0.0.2") old_access = create_fake_access(access_id="fake_access_id", access_adr="10.0.0.1") new_access_2 = create_fake_access(access_id="new_2", access_adr="10.0.0.3") add_access_rules = [new_access_1, old_access, new_access_2] qb_exist_mock.return_value = [old_access] qb_subtr_mock.side_effect = [[new_access_1, new_access_2], []] self._driver.update_access(self._context, self.share, access_rules=add_access_rules, add_rules=[], delete_rules=[]) assert_calls = [mock.call(self._context, self.share, new_access_1), mock.call(self._context, self.share, new_access_2)] qb_allow_mock.assert_has_calls(assert_calls, any_order=True) qb_exist_mock.assert_called_once_with(self._context, self.share) @mock.patch.object(quobyte.QuobyteShareDriver, "_subtract_access_lists") @mock.patch.object(quobyte.QuobyteShareDriver, "_fetch_existing_access") @mock.patch.object(quobyte.QuobyteShareDriver, "_deny_access") def test_update_access_recovery_superfluous(self, qb_deny_mock, qb_exist_mock, qb_subtr_mock): old_access_1 = create_fake_access(access_id="old_1", access_adr="10.0.0.1") missing_access_1 = create_fake_access(access_id="mis_1", access_adr="10.0.0.2") old_access_2 = create_fake_access(access_id="old_2", access_adr="10.0.0.3") qb_exist_mock.side_effect = [[old_access_1, old_access_2]] qb_subtr_mock.side_effect = [[], [missing_access_1]] old_access_rules = [old_access_1, old_access_2] self._driver.update_access(self._context, self.share, access_rules=old_access_rules, add_rules=[], delete_rules=[]) qb_deny_mock.assert_called_once_with(self._context, self.share, (missing_access_1)) qb_exist_mock.assert_called_once_with(self._context, self.share) @mock.patch.object(quobyte.QuobyteShareDriver, "_subtract_access_lists") @mock.patch.object(quobyte.QuobyteShareDriver, "_fetch_existing_access") @mock.patch.object(quobyte.QuobyteShareDriver, "_deny_access") @mock.patch.object(quobyte.QuobyteShareDriver, "_allow_access") def test_update_access_recovery_add_superfluous(self, qb_allow_mock, qb_deny_mock, qb_exist_mock, qb_subtr_mock): new_access_1 = create_fake_access(access_id="new_1", access_adr="10.0.0.5") old_access_1 = create_fake_access(access_id="old_1", access_adr="10.0.0.1") old_access_2 = create_fake_access(access_id="old_2", access_adr="10.0.0.3") old_access_3 = create_fake_access(access_id="old_3", access_adr="10.0.0.4") miss_access_1 = create_fake_access(access_id="old_3", access_adr="10.0.0.4") new_access_2 = create_fake_access(access_id="new_2", access_adr="10.0.0.3", access_level="ro") new_access_rules = [new_access_1, old_access_1, old_access_2, old_access_3, new_access_2] qb_exist_mock.return_value = [old_access_1, old_access_2, old_access_3, miss_access_1] qb_subtr_mock.side_effect = [[new_access_1, new_access_2], [miss_access_1, old_access_2]] self._driver.update_access(self._context, self.share, new_access_rules, add_rules=[], delete_rules=[]) a_calls = [mock.call(self._context, self.share, new_access_1), mock.call(self._context, self.share, new_access_2)] qb_allow_mock.assert_has_calls(a_calls) b_calls = [mock.call(self._context, self.share, miss_access_1), mock.call(self._context, self.share, old_access_2)] qb_deny_mock.assert_has_calls(b_calls) qb_exist_mock.assert_called_once_with(self._context, self.share)

true

f7178564a143b7e9c9ec5547d04715becbaafd35

1,183

py

Python

scripts/pyqtgraph-develop/examples/MultiPlotWidget.py

kuldeepaman/tf-pose

8050912c52a7b4f3c8a2656f267d47ba21d093f6

[ "Apache-2.0" ]

null

scripts/pyqtgraph-develop/examples/MultiPlotWidget.py

kuldeepaman/tf-pose

8050912c52a7b4f3c8a2656f267d47ba21d093f6

[ "Apache-2.0" ]

null

scripts/pyqtgraph-develop/examples/MultiPlotWidget.py

kuldeepaman/tf-pose

8050912c52a7b4f3c8a2656f267d47ba21d093f6

[ "Apache-2.0" ]

null

#!/usr/bin/python # -*- coding: utf-8 -*- ## Add path to library (just for examples; you do not need this) import initExample from scipy import random from numpy import linspace from pyqtgraph.Qt import QtGui, QtCore import pyqtgraph as pg from pyqtgraph import MultiPlotWidget try: from pyqtgraph.metaarray import * except: print("MultiPlot is only used with MetaArray for now (and you do not have the metaarray package)") exit() app = QtGui.QApplication([]) mw = QtGui.QMainWindow() mw.resize(800,800) pw = MultiPlotWidget() mw.setCentralWidget(pw) mw.show() data = random.normal(size=(3, 1000)) * np.array([[0.1], [1e-5], [1]]) ma = MetaArray(data, info=[ {'name': 'Signal', 'cols': [ {'name': 'Col1', 'units': 'V'}, {'name': 'Col2', 'units': 'A'}, {'name': 'Col3'}, ]}, {'name': 'Time', 'values': linspace(0., 1., 1000), 'units': 's'} ]) pw.plot(ma) ## Start Qt event loop unless running in interactive mode. if __name__ == '__main__': import sys if (sys.flags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'): QtGui.QApplication.instance().exec_()

28.166667

103

0.618766

port linspace from pyqtgraph.Qt import QtGui, QtCore import pyqtgraph as pg from pyqtgraph import MultiPlotWidget try: from pyqtgraph.metaarray import * except: print("MultiPlot is only used with MetaArray for now (and you do not have the metaarray package)") exit() app = QtGui.QApplication([]) mw = QtGui.QMainWindow() mw.resize(800,800) pw = MultiPlotWidget() mw.setCentralWidget(pw) mw.show() data = random.normal(size=(3, 1000)) * np.array([[0.1], [1e-5], [1]]) ma = MetaArray(data, info=[ {'name': 'Signal', 'cols': [ {'name': 'Col1', 'units': 'V'}, {'name': 'Col2', 'units': 'A'}, {'name': 'Col3'}, ]}, {'name': 'Time', 'values': linspace(0., 1., 1000), 'units': 's'} ]) pw.plot(ma) lags.interactive != 1) or not hasattr(QtCore, 'PYQT_VERSION'): QtGui.QApplication.instance().exec_()

true

f71786f5f39627c1fac7f1b3ad0367fe2a1feb53

1,888

py

Python

silk-src/src/pysilk/python/_netsa_silk.py

mjschultz/netsa-pkg

07bf4ff29a73ebc0f58e4aa27d3ad6b1dee7fc83

[ "Apache-2.0" ]

3

2018-06-01T06:55:14.000Z

2021-11-14T22:51:04.000Z

silk-src/src/pysilk/python/_netsa_silk.py

mjschultz/netsa-pkg

07bf4ff29a73ebc0f58e4aa27d3ad6b1dee7fc83

[ "Apache-2.0" ]

3

2017-07-02T17:03:34.000Z

2021-09-09T17:05:31.000Z

silk-src/src/pysilk/python/_netsa_silk.py

mjschultz/netsa-pkg

07bf4ff29a73ebc0f58e4aa27d3ad6b1dee7fc83

[ "Apache-2.0" ]

4

2017-08-14T15:42:31.000Z

2022-01-24T16:24:27.000Z

####################################################################### # Copyright (C) 2011-2020 by Carnegie Mellon University. # # @OPENSOURCE_LICENSE_START@ # See license information in ../../../LICENSE.txt # @OPENSOURCE_LICENSE_END@ # ####################################################################### ####################################################################### # $SiLK: _netsa_silk.py ef14e54179be 2020-04-14 21:57:45Z mthomas $ ####################################################################### """ The netsa_silk module contains a shared API for working with common Internet data in both netsa-python and PySiLK. If netsa-python is installed but PySiLK is not, the less efficient but more portable pure-Python version of this functionality that is included in netsa-python is used. If PySiLK is installed, then the high-performance C version of this functionality that is included in PySiLK is used. """ # This module provides the symbols exported by PySiLK for the # netsa_silk API. It exists to rename PySiLK symbols that have a # different name from the netsa_silk symbols, and to constrain the set # of PySiLK symbols that are exported. If a new symbol is added (to # provide a new feature), it need only be added here and it will # automatically be exported by netsa_silk. from silk import ( ipv6_enabled as has_IPv6Addr, IPAddr, IPv4Addr, IPv6Addr, IPSet as ip_set, IPWildcard, TCPFlags, TCP_FIN, TCP_SYN, TCP_RST, TCP_PSH, TCP_ACK, TCP_URG, TCP_ECE, TCP_CWR, silk_version ) # PySiLK API version __version__ = "1.0" # Implementation version __impl_version__ = " ".join(["SiLK", silk_version()]) __all__ = """ has_IPv6Addr IPAddr IPv4Addr IPv6Addr ip_set IPWildcard TCPFlags TCP_FIN TCP_SYN TCP_RST TCP_PSH TCP_ACK TCP_URG TCP_ECE TCP_CWR __version__ __impl_version__ """.split()

31.466667

75

0.632945

true

f717870bb59f4f607b6c5181895c66f109cd7bcf

523

py

Python

juriscraper/opinions/united_states/state/calctapp_1st.py

EvandoBlanco/juriscraper

3d16af258620d4ba1b4827f66ef69e8a2c5a0484

[ "BSD-2-Clause" ]

228

2015-01-23T04:41:39.000Z

2022-03-30T09:52:20.000Z

juriscraper/opinions/united_states/state/calctapp_1st.py

EvandoBlanco/juriscraper

3d16af258620d4ba1b4827f66ef69e8a2c5a0484

[ "BSD-2-Clause" ]

331

2015-01-05T18:53:40.000Z

2022-03-29T23:43:30.000Z

juriscraper/opinions/united_states/state/calctapp_1st.py

EvandoBlanco/juriscraper

3d16af258620d4ba1b4827f66ef69e8a2c5a0484

[ "BSD-2-Clause" ]

84

2015-01-03T01:19:21.000Z

2022-03-01T08:09:32.000Z

# Scraper for California's First District Court of Appeal # CourtID: calctapp_1st # Court Short Name: Cal. Ct. App. from juriscraper.opinions.united_states.state import cal class Site(cal.Site): def __init__(self, *args, **kwargs): super(Site, self).__init__(*args, **kwargs) self.court_id = self.__module__ self.court_code = "A" self.division = "1st App. Dist." self.url = self.build_url() def _get_divisions(self): return [self.division] * len(self.case_names)

29.055556

57

0.667304

# CourtID: calctapp_1st # Court Short Name: Cal. Ct. App. from juriscraper.opinions.united_states.state import cal class Site(cal.Site): def __init__(self, *args, **kwargs): super(Site, self).__init__(*args, **kwargs) self.court_id = self.__module__ self.court_code = "A" self.division = "1st App. Dist." self.url = self.build_url() def _get_divisions(self): return [self.division] * len(self.case_names)

true

f71787192e7a235fa2e02fbfdcda490a79b3300e

12,258

py

Python

source/deepsecurity/models/anti_malware_computer_extension.py

felipecosta09/cloudone-workload-controltower-lifecycle

7927c84d164058b034fc872701b5ee117641f4d1

[ "Apache-2.0" ]

1

2021-10-30T16:40:09.000Z

source/deepsecurity/models/anti_malware_computer_extension.py

felipecosta09/cloudone-workload-controltower-lifecycle

7927c84d164058b034fc872701b5ee117641f4d1

[ "Apache-2.0" ]

1

2021-07-28T20:19:03.000Z

source/deepsecurity/models/anti_malware_computer_extension.py

felipecosta09/cloudone-workload-controltower-lifecycle

7927c84d164058b034fc872701b5ee117641f4d1

[ "Apache-2.0" ]

1

2021-10-30T16:40:02.000Z

# coding: utf-8 """ Trend Micro Deep Security API Copyright 2018 - 2020 Trend Micro Incorporated.<br/>Get protected, stay secured, and keep informed with Trend Micro Deep Security's new RESTful API. Access system data and manage security configurations to automate your security workflows and integrate Deep Security into your CI/CD pipeline. # noqa: E501 OpenAPI spec version: 12.5.841 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six from deepsecurity.models.computer_module_status import ComputerModuleStatus # noqa: F401,E501 class AntiMalwareComputerExtension(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'state': 'str', 'module_status': 'ComputerModuleStatus', 'real_time_scan_configuration_id': 'int', 'real_time_scan_schedule_id': 'int', 'manual_scan_configuration_id': 'int', 'scheduled_scan_configuration_id': 'int', 'last_manual_scan': 'int', 'last_scheduled_scan': 'int' } attribute_map = { 'state': 'state', 'module_status': 'moduleStatus', 'real_time_scan_configuration_id': 'realTimeScanConfigurationID', 'real_time_scan_schedule_id': 'realTimeScanScheduleID', 'manual_scan_configuration_id': 'manualScanConfigurationID', 'scheduled_scan_configuration_id': 'scheduledScanConfigurationID', 'last_manual_scan': 'lastManualScan', 'last_scheduled_scan': 'lastScheduledScan' } def __init__(self, state=None, module_status=None, real_time_scan_configuration_id=None, real_time_scan_schedule_id=None, manual_scan_configuration_id=None, scheduled_scan_configuration_id=None, last_manual_scan=None, last_scheduled_scan=None): # noqa: E501 """AntiMalwareComputerExtension - a model defined in Swagger""" # noqa: E501 self._state = None self._module_status = None self._real_time_scan_configuration_id = None self._real_time_scan_schedule_id = None self._manual_scan_configuration_id = None self._scheduled_scan_configuration_id = None self._last_manual_scan = None self._last_scheduled_scan = None self.discriminator = None if state is not None: self.state = state if module_status is not None: self.module_status = module_status if real_time_scan_configuration_id is not None: self.real_time_scan_configuration_id = real_time_scan_configuration_id if real_time_scan_schedule_id is not None: self.real_time_scan_schedule_id = real_time_scan_schedule_id if manual_scan_configuration_id is not None: self.manual_scan_configuration_id = manual_scan_configuration_id if scheduled_scan_configuration_id is not None: self.scheduled_scan_configuration_id = scheduled_scan_configuration_id if last_manual_scan is not None: self.last_manual_scan = last_manual_scan if last_scheduled_scan is not None: self.last_scheduled_scan = last_scheduled_scan @property def state(self): """Gets the state of this AntiMalwareComputerExtension. # noqa: E501 Module state. # noqa: E501 :return: The state of this AntiMalwareComputerExtension. # noqa: E501 :rtype: str """ return self._state @state.setter def state(self, state): """Sets the state of this AntiMalwareComputerExtension. Module state. # noqa: E501 :param state: The state of this AntiMalwareComputerExtension. # noqa: E501 :type: str """ allowed_values = ["inherited", "on", "off"] # noqa: E501 if state not in allowed_values: raise ValueError( "Invalid value for `state` ({0}), must be one of {1}" # noqa: E501 .format(state, allowed_values) ) self._state = state @property def module_status(self): """Gets the module_status of this AntiMalwareComputerExtension. # noqa: E501 :return: The module_status of this AntiMalwareComputerExtension. # noqa: E501 :rtype: ComputerModuleStatus """ return self._module_status @module_status.setter def module_status(self, module_status): """Sets the module_status of this AntiMalwareComputerExtension. :param module_status: The module_status of this AntiMalwareComputerExtension. # noqa: E501 :type: ComputerModuleStatus """ self._module_status = module_status @property def real_time_scan_configuration_id(self): """Gets the real_time_scan_configuration_id of this AntiMalwareComputerExtension. # noqa: E501 ID of the Real Time Scan Configuration. # noqa: E501 :return: The real_time_scan_configuration_id of this AntiMalwareComputerExtension. # noqa: E501 :rtype: int """ return self._real_time_scan_configuration_id @real_time_scan_configuration_id.setter def real_time_scan_configuration_id(self, real_time_scan_configuration_id): """Sets the real_time_scan_configuration_id of this AntiMalwareComputerExtension. ID of the Real Time Scan Configuration. # noqa: E501 :param real_time_scan_configuration_id: The real_time_scan_configuration_id of this AntiMalwareComputerExtension. # noqa: E501 :type: int """ self._real_time_scan_configuration_id = real_time_scan_configuration_id @property def real_time_scan_schedule_id(self): """Gets the real_time_scan_schedule_id of this AntiMalwareComputerExtension. # noqa: E501 ID of the Real Time Schedule. # noqa: E501 :return: The real_time_scan_schedule_id of this AntiMalwareComputerExtension. # noqa: E501 :rtype: int """ return self._real_time_scan_schedule_id @real_time_scan_schedule_id.setter def real_time_scan_schedule_id(self, real_time_scan_schedule_id): """Sets the real_time_scan_schedule_id of this AntiMalwareComputerExtension. ID of the Real Time Schedule. # noqa: E501 :param real_time_scan_schedule_id: The real_time_scan_schedule_id of this AntiMalwareComputerExtension. # noqa: E501 :type: int """ self._real_time_scan_schedule_id = real_time_scan_schedule_id @property def manual_scan_configuration_id(self): """Gets the manual_scan_configuration_id of this AntiMalwareComputerExtension. # noqa: E501 ID of the Manual Scan Configuration. # noqa: E501 :return: The manual_scan_configuration_id of this AntiMalwareComputerExtension. # noqa: E501 :rtype: int """ return self._manual_scan_configuration_id @manual_scan_configuration_id.setter def manual_scan_configuration_id(self, manual_scan_configuration_id): """Sets the manual_scan_configuration_id of this AntiMalwareComputerExtension. ID of the Manual Scan Configuration. # noqa: E501 :param manual_scan_configuration_id: The manual_scan_configuration_id of this AntiMalwareComputerExtension. # noqa: E501 :type: int """ self._manual_scan_configuration_id = manual_scan_configuration_id @property def scheduled_scan_configuration_id(self): """Gets the scheduled_scan_configuration_id of this AntiMalwareComputerExtension. # noqa: E501 ID of the Scheduled Scan Configuration. # noqa: E501 :return: The scheduled_scan_configuration_id of this AntiMalwareComputerExtension. # noqa: E501 :rtype: int """ return self._scheduled_scan_configuration_id @scheduled_scan_configuration_id.setter def scheduled_scan_configuration_id(self, scheduled_scan_configuration_id): """Sets the scheduled_scan_configuration_id of this AntiMalwareComputerExtension. ID of the Scheduled Scan Configuration. # noqa: E501 :param scheduled_scan_configuration_id: The scheduled_scan_configuration_id of this AntiMalwareComputerExtension. # noqa: E501 :type: int """ self._scheduled_scan_configuration_id = scheduled_scan_configuration_id @property def last_manual_scan(self): """Gets the last_manual_scan of this AntiMalwareComputerExtension. # noqa: E501 Timestamp of the last manual scan for malware, in milliseconds since epoch. # noqa: E501 :return: The last_manual_scan of this AntiMalwareComputerExtension. # noqa: E501 :rtype: int """ return self._last_manual_scan @last_manual_scan.setter def last_manual_scan(self, last_manual_scan): """Sets the last_manual_scan of this AntiMalwareComputerExtension. Timestamp of the last manual scan for malware, in milliseconds since epoch. # noqa: E501 :param last_manual_scan: The last_manual_scan of this AntiMalwareComputerExtension. # noqa: E501 :type: int """ self._last_manual_scan = last_manual_scan @property def last_scheduled_scan(self): """Gets the last_scheduled_scan of this AntiMalwareComputerExtension. # noqa: E501 Timestamp of the last scheduled scan for malware, in milliseconds since epoch. # noqa: E501 :return: The last_scheduled_scan of this AntiMalwareComputerExtension. # noqa: E501 :rtype: int """ return self._last_scheduled_scan @last_scheduled_scan.setter def last_scheduled_scan(self, last_scheduled_scan): """Sets the last_scheduled_scan of this AntiMalwareComputerExtension. Timestamp of the last scheduled scan for malware, in milliseconds since epoch. # noqa: E501 :param last_scheduled_scan: The last_scheduled_scan of this AntiMalwareComputerExtension. # noqa: E501 :type: int """ self._last_scheduled_scan = last_scheduled_scan def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.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 if issubclass(AntiMalwareComputerExtension, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.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""" if not isinstance(other, AntiMalwareComputerExtension): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

38.186916

311

0.660548

import pprint import re import six from deepsecurity.models.computer_module_status import ComputerModuleStatus class AntiMalwareComputerExtension(object): swagger_types = { 'state': 'str', 'module_status': 'ComputerModuleStatus', 'real_time_scan_configuration_id': 'int', 'real_time_scan_schedule_id': 'int', 'manual_scan_configuration_id': 'int', 'scheduled_scan_configuration_id': 'int', 'last_manual_scan': 'int', 'last_scheduled_scan': 'int' } attribute_map = { 'state': 'state', 'module_status': 'moduleStatus', 'real_time_scan_configuration_id': 'realTimeScanConfigurationID', 'real_time_scan_schedule_id': 'realTimeScanScheduleID', 'manual_scan_configuration_id': 'manualScanConfigurationID', 'scheduled_scan_configuration_id': 'scheduledScanConfigurationID', 'last_manual_scan': 'lastManualScan', 'last_scheduled_scan': 'lastScheduledScan' } def __init__(self, state=None, module_status=None, real_time_scan_configuration_id=None, real_time_scan_schedule_id=None, manual_scan_configuration_id=None, scheduled_scan_configuration_id=None, last_manual_scan=None, last_scheduled_scan=None): self._state = None self._module_status = None self._real_time_scan_configuration_id = None self._real_time_scan_schedule_id = None self._manual_scan_configuration_id = None self._scheduled_scan_configuration_id = None self._last_manual_scan = None self._last_scheduled_scan = None self.discriminator = None if state is not None: self.state = state if module_status is not None: self.module_status = module_status if real_time_scan_configuration_id is not None: self.real_time_scan_configuration_id = real_time_scan_configuration_id if real_time_scan_schedule_id is not None: self.real_time_scan_schedule_id = real_time_scan_schedule_id if manual_scan_configuration_id is not None: self.manual_scan_configuration_id = manual_scan_configuration_id if scheduled_scan_configuration_id is not None: self.scheduled_scan_configuration_id = scheduled_scan_configuration_id if last_manual_scan is not None: self.last_manual_scan = last_manual_scan if last_scheduled_scan is not None: self.last_scheduled_scan = last_scheduled_scan @property def state(self): return self._state @state.setter def state(self, state): allowed_values = ["inherited", "on", "off"] if state not in allowed_values: raise ValueError( "Invalid value for `state` ({0}), must be one of {1}" .format(state, allowed_values) ) self._state = state @property def module_status(self): return self._module_status @module_status.setter def module_status(self, module_status): self._module_status = module_status @property def real_time_scan_configuration_id(self): return self._real_time_scan_configuration_id @real_time_scan_configuration_id.setter def real_time_scan_configuration_id(self, real_time_scan_configuration_id): self._real_time_scan_configuration_id = real_time_scan_configuration_id @property def real_time_scan_schedule_id(self): return self._real_time_scan_schedule_id @real_time_scan_schedule_id.setter def real_time_scan_schedule_id(self, real_time_scan_schedule_id): self._real_time_scan_schedule_id = real_time_scan_schedule_id @property def manual_scan_configuration_id(self): return self._manual_scan_configuration_id @manual_scan_configuration_id.setter def manual_scan_configuration_id(self, manual_scan_configuration_id): self._manual_scan_configuration_id = manual_scan_configuration_id @property def scheduled_scan_configuration_id(self): return self._scheduled_scan_configuration_id @scheduled_scan_configuration_id.setter def scheduled_scan_configuration_id(self, scheduled_scan_configuration_id): self._scheduled_scan_configuration_id = scheduled_scan_configuration_id @property def last_manual_scan(self): return self._last_manual_scan @last_manual_scan.setter def last_manual_scan(self, last_manual_scan): self._last_manual_scan = last_manual_scan @property def last_scheduled_scan(self): return self._last_scheduled_scan @last_scheduled_scan.setter def last_scheduled_scan(self, last_scheduled_scan): self._last_scheduled_scan = last_scheduled_scan def to_dict(self): result = {} for attr, _ in six.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 if issubclass(AntiMalwareComputerExtension, dict): for key, value in self.items(): result[key] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, AntiMalwareComputerExtension): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

f71787b41422d7fb8b33c9454081d2bd60c5fe8f

1,150

py

Python

cirq/contrib/svg/svg_test.py

lilies/Cirq

519b8b70ba4d2d92d1c034c398161ebdbd23e2e7

[ "Apache-2.0" ]

3

2020-09-26T03:56:28.000Z

2020-09-27T13:21:04.000Z

cirq/contrib/svg/svg_test.py

lilies/Cirq

519b8b70ba4d2d92d1c034c398161ebdbd23e2e7

[ "Apache-2.0" ]

1

2020-08-11T15:45:17.000Z

cirq/contrib/svg/svg_test.py

lilies/Cirq

519b8b70ba4d2d92d1c034c398161ebdbd23e2e7

[ "Apache-2.0" ]

1

2020-04-14T15:29:29.000Z

import pytest import numpy as np import cirq from cirq.contrib.svg import circuit_to_svg def test_svg(): a, b, c = cirq.LineQubit.range(3) svg_text = circuit_to_svg( cirq.Circuit( cirq.CNOT(a, b), cirq.CZ(b, c), cirq.SWAP(a, c), cirq.PhasedXPowGate(exponent=0.123, phase_exponent=0.456).on(c), cirq.Z(a), cirq.measure(a, b, c, key='z'), cirq.MatrixGate(np.eye(2)).on(a), )) assert '<svg' in svg_text assert '</svg>' in svg_text def test_svg_noise(): noise_model = cirq.ConstantQubitNoiseModel(cirq.DepolarizingChannel(p=1e-3)) q = cirq.LineQubit(0) circuit = cirq.Circuit(cirq.X(q)) circuit = cirq.Circuit(noise_model.noisy_moments(circuit.moments, [q])) svg = circuit_to_svg(circuit) assert '>D(0.001)</text>' in svg def test_validation(): with pytest.raises(ValueError): circuit_to_svg(cirq.Circuit()) q0 = cirq.LineQubit(0) with pytest.raises(ValueError): circuit_to_svg( cirq.Circuit([cirq.Moment([cirq.X(q0)]), cirq.Moment([])]))

26.744186

80

0.6

import pytest import numpy as np import cirq from cirq.contrib.svg import circuit_to_svg def test_svg(): a, b, c = cirq.LineQubit.range(3) svg_text = circuit_to_svg( cirq.Circuit( cirq.CNOT(a, b), cirq.CZ(b, c), cirq.SWAP(a, c), cirq.PhasedXPowGate(exponent=0.123, phase_exponent=0.456).on(c), cirq.Z(a), cirq.measure(a, b, c, key='z'), cirq.MatrixGate(np.eye(2)).on(a), )) assert '<svg' in svg_text assert '</svg>' in svg_text def test_svg_noise(): noise_model = cirq.ConstantQubitNoiseModel(cirq.DepolarizingChannel(p=1e-3)) q = cirq.LineQubit(0) circuit = cirq.Circuit(cirq.X(q)) circuit = cirq.Circuit(noise_model.noisy_moments(circuit.moments, [q])) svg = circuit_to_svg(circuit) assert '>D(0.001)</text>' in svg def test_validation(): with pytest.raises(ValueError): circuit_to_svg(cirq.Circuit()) q0 = cirq.LineQubit(0) with pytest.raises(ValueError): circuit_to_svg( cirq.Circuit([cirq.Moment([cirq.X(q0)]), cirq.Moment([])]))

true

f71788ff490545bb580c7e52b9c71e363cbb8d15

6,165

py

Python

recipes/libtool/all/test_package/conanfile.py

dpronin/conan-center-index

5c6e41a618097d04e731c9831118a51dcb39ab3f

[ "MIT" ]

1

2021-11-11T03:07:13.000Z

recipes/libtool/all/test_package/conanfile.py

dpronin/conan-center-index

5c6e41a618097d04e731c9831118a51dcb39ab3f

[ "MIT" ]

1

2021-11-22T13:54:48.000Z

2021-11-22T14:09:45.000Z

recipes/libtool/all/test_package/conanfile.py

dpronin/conan-center-index

5c6e41a618097d04e731c9831118a51dcb39ab3f

[ "MIT" ]

null

from conans import AutoToolsBuildEnvironment, CMake, ConanFile, tools from contextlib import contextmanager import glob import os import shutil class TestPackageConan(ConanFile): settings = "os", "compiler", "build_type", "arch" generators = "cmake" test_type = "explicit" short_paths = True @property def _settings_build(self): return getattr(self, "settings_build", self.settings) def requirements(self): self.requires(self.tested_reference_str) def build_requirements(self): self.build_requires(self.tested_reference_str) if self._settings_build.os == "Windows" and not tools.get_env("CONAN_BASH_PATH"): self.build_requires("msys2/cci.latest") @contextmanager def _build_context(self): if self.settings.compiler == "Visual Studio": with tools.vcvars(self.settings): with tools.environment_append({ "CC": "{} cl -nologo".format(tools.unix_path(self.deps_user_info["automake"].compile)), "CXX": "{} cl -nologo".format(tools.unix_path(self.deps_user_info["automake"].compile)), "AR": "{} lib".format(tools.unix_path(self.deps_user_info["automake"].ar_lib)), "LD": "link", }): yield else: yield @property def _package_folder(self): return os.path.join(self.build_folder, "package") def _build_autotools(self): """ Test autotools integration """ # Copy autotools directory to build folder shutil.copytree(os.path.join(self.source_folder, "autotools"), os.path.join(self.build_folder, "autotools")) with tools.chdir("autotools"): self.run("{} --install --verbose -Wall".format(os.environ["AUTORECONF"]), win_bash=tools.os_info.is_windows) tools.mkdir(self._package_folder) conf_args = [ "--prefix={}".format(tools.unix_path(self._package_folder)), "--enable-shared", "--enable-static", ] os.mkdir("bin_autotools") with tools.chdir("bin_autotools"): with self._build_context(): autotools = AutoToolsBuildEnvironment(self, win_bash=tools.os_info.is_windows) autotools.libs = [] autotools.configure(args=conf_args, configure_dir=os.path.join(self.build_folder, "autotools")) autotools.make(args=["V=1"]) autotools.install() def _test_autotools(self): assert os.path.isdir(os.path.join(self._package_folder, "bin")) assert os.path.isfile(os.path.join(self._package_folder, "include", "lib.h")) assert os.path.isdir(os.path.join(self._package_folder, "lib")) if not tools.cross_building(self): self.run(os.path.join(self._package_folder, "bin", "test_package"), run_environment=True) def _build_ltdl(self): """ Build library using ltdl library """ cmake = CMake(self) cmake.configure(source_folder="ltdl") cmake.build() def _test_ltdl(self): """ Test library using ltdl library""" lib_suffix = { "Linux": "so", "FreeBSD": "so", "Macos": "dylib", "Windows": "dll", }[str(self.settings.os)] if not tools.cross_building(self): bin_path = os.path.join("bin", "test_package") libdir = "bin" if self.settings.os == "Windows" else "lib" lib_path = os.path.join(libdir, "liba.{}".format(lib_suffix)) self.run("{} {}".format(bin_path, lib_path), run_environment=True) def _build_static_lib_in_shared(self): """ Build shared library using libtool (while linking to a static library) """ # Copy static-in-shared directory to build folder autotools_folder = os.path.join(self.build_folder, "sis") shutil.copytree(os.path.join(self.source_folder, "sis"), autotools_folder) install_prefix = os.path.join(autotools_folder, "prefix") # Build static library using CMake cmake = CMake(self) cmake.definitions["CMAKE_INSTALL_PREFIX"] = install_prefix cmake.configure(source_folder=autotools_folder, build_folder=os.path.join(autotools_folder, "cmake_build")) cmake.build() cmake.install() # Copy autotools directory to build folder with tools.chdir(autotools_folder): self.run("{} -ifv -Wall".format(os.environ["AUTORECONF"]), win_bash=tools.os_info.is_windows) with tools.chdir(autotools_folder): conf_args = [ "--enable-shared", "--disable-static", "--prefix={}".format(tools.unix_path(os.path.join(install_prefix))), ] with self._build_context(): autotools = AutoToolsBuildEnvironment(self, win_bash=tools.os_info.is_windows) autotools.libs = [] autotools.link_flags.append("-L{}".format(tools.unix_path(os.path.join(install_prefix, "lib")))) autotools.configure(args=conf_args, configure_dir=autotools_folder) autotools.make(args=["V=1"]) autotools.install() def _test_static_lib_in_shared(self): """ Test existence of shared library """ install_prefix = os.path.join(self.build_folder, "sis", "prefix") with tools.chdir(install_prefix): if self.settings.os == "Windows": assert len(list(glob.glob(os.path.join("bin", "*.dll")))) > 0 elif tools.is_apple_os(self.settings.os): assert len(list(glob.glob(os.path.join("lib", "*.dylib")))) > 0 else: assert len(list(glob.glob(os.path.join("lib", "*.so")))) > 0 def build(self): self._build_ltdl() if not tools.cross_building(self): self._build_autotools() self._build_static_lib_in_shared() def test(self): self._test_ltdl() if not tools.cross_building(self): self._test_autotools() self._test_static_lib_in_shared()

40.559211

120

0.610868

from conans import AutoToolsBuildEnvironment, CMake, ConanFile, tools from contextlib import contextmanager import glob import os import shutil class TestPackageConan(ConanFile): settings = "os", "compiler", "build_type", "arch" generators = "cmake" test_type = "explicit" short_paths = True @property def _settings_build(self): return getattr(self, "settings_build", self.settings) def requirements(self): self.requires(self.tested_reference_str) def build_requirements(self): self.build_requires(self.tested_reference_str) if self._settings_build.os == "Windows" and not tools.get_env("CONAN_BASH_PATH"): self.build_requires("msys2/cci.latest") @contextmanager def _build_context(self): if self.settings.compiler == "Visual Studio": with tools.vcvars(self.settings): with tools.environment_append({ "CC": "{} cl -nologo".format(tools.unix_path(self.deps_user_info["automake"].compile)), "CXX": "{} cl -nologo".format(tools.unix_path(self.deps_user_info["automake"].compile)), "AR": "{} lib".format(tools.unix_path(self.deps_user_info["automake"].ar_lib)), "LD": "link", }): yield else: yield @property def _package_folder(self): return os.path.join(self.build_folder, "package") def _build_autotools(self): shutil.copytree(os.path.join(self.source_folder, "autotools"), os.path.join(self.build_folder, "autotools")) with tools.chdir("autotools"): self.run("{} --install --verbose -Wall".format(os.environ["AUTORECONF"]), win_bash=tools.os_info.is_windows) tools.mkdir(self._package_folder) conf_args = [ "--prefix={}".format(tools.unix_path(self._package_folder)), "--enable-shared", "--enable-static", ] os.mkdir("bin_autotools") with tools.chdir("bin_autotools"): with self._build_context(): autotools = AutoToolsBuildEnvironment(self, win_bash=tools.os_info.is_windows) autotools.libs = [] autotools.configure(args=conf_args, configure_dir=os.path.join(self.build_folder, "autotools")) autotools.make(args=["V=1"]) autotools.install() def _test_autotools(self): assert os.path.isdir(os.path.join(self._package_folder, "bin")) assert os.path.isfile(os.path.join(self._package_folder, "include", "lib.h")) assert os.path.isdir(os.path.join(self._package_folder, "lib")) if not tools.cross_building(self): self.run(os.path.join(self._package_folder, "bin", "test_package"), run_environment=True) def _build_ltdl(self): cmake = CMake(self) cmake.configure(source_folder="ltdl") cmake.build() def _test_ltdl(self): lib_suffix = { "Linux": "so", "FreeBSD": "so", "Macos": "dylib", "Windows": "dll", }[str(self.settings.os)] if not tools.cross_building(self): bin_path = os.path.join("bin", "test_package") libdir = "bin" if self.settings.os == "Windows" else "lib" lib_path = os.path.join(libdir, "liba.{}".format(lib_suffix)) self.run("{} {}".format(bin_path, lib_path), run_environment=True) def _build_static_lib_in_shared(self): autotools_folder = os.path.join(self.build_folder, "sis") shutil.copytree(os.path.join(self.source_folder, "sis"), autotools_folder) install_prefix = os.path.join(autotools_folder, "prefix") cmake = CMake(self) cmake.definitions["CMAKE_INSTALL_PREFIX"] = install_prefix cmake.configure(source_folder=autotools_folder, build_folder=os.path.join(autotools_folder, "cmake_build")) cmake.build() cmake.install() with tools.chdir(autotools_folder): self.run("{} -ifv -Wall".format(os.environ["AUTORECONF"]), win_bash=tools.os_info.is_windows) with tools.chdir(autotools_folder): conf_args = [ "--enable-shared", "--disable-static", "--prefix={}".format(tools.unix_path(os.path.join(install_prefix))), ] with self._build_context(): autotools = AutoToolsBuildEnvironment(self, win_bash=tools.os_info.is_windows) autotools.libs = [] autotools.link_flags.append("-L{}".format(tools.unix_path(os.path.join(install_prefix, "lib")))) autotools.configure(args=conf_args, configure_dir=autotools_folder) autotools.make(args=["V=1"]) autotools.install() def _test_static_lib_in_shared(self): install_prefix = os.path.join(self.build_folder, "sis", "prefix") with tools.chdir(install_prefix): if self.settings.os == "Windows": assert len(list(glob.glob(os.path.join("bin", "*.dll")))) > 0 elif tools.is_apple_os(self.settings.os): assert len(list(glob.glob(os.path.join("lib", "*.dylib")))) > 0 else: assert len(list(glob.glob(os.path.join("lib", "*.so")))) > 0 def build(self): self._build_ltdl() if not tools.cross_building(self): self._build_autotools() self._build_static_lib_in_shared() def test(self): self._test_ltdl() if not tools.cross_building(self): self._test_autotools() self._test_static_lib_in_shared()

true

f71789c0f44a5dca7010d4a66926c97673dff301

4,401

py

Python

models/backbone.py

playerkk/HoiTransformer

b710216d6b338863ebe9d40a96765ab52780cefa

[ "Apache-2.0" ]

107

2021-03-03T13:31:32.000Z

2022-03-31T10:59:45.000Z

models/backbone.py

playerkk/HoiTransformer

b710216d6b338863ebe9d40a96765ab52780cefa

[ "Apache-2.0" ]

37

2021-03-10T11:36:49.000Z

2022-02-22T03:58:12.000Z

models/backbone.py

playerkk/HoiTransformer

b710216d6b338863ebe9d40a96765ab52780cefa

[ "Apache-2.0" ]

19

2021-03-17T13:21:03.000Z

2022-02-09T09:48:58.000Z

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved """ Backbone modules. """ import torch import torch.nn.functional as F import torchvision from torch import nn from torchvision.models._utils import IntermediateLayerGetter from typing import Dict, List from util.misc import NestedTensor, is_main_process from .position_encoding import build_position_encoding class FrozenBatchNorm2d(torch.nn.Module): """ BatchNorm2d where the batch statistics and the affine parameters are fixed. Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than torchvision.models.resnet[18,34,50,101] produce nans. """ def __init__(self, n): super(FrozenBatchNorm2d, self).__init__() self.register_buffer("weight", torch.ones(n)) self.register_buffer("bias", torch.zeros(n)) self.register_buffer("running_mean", torch.zeros(n)) self.register_buffer("running_var", torch.ones(n)) def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): num_batches_tracked_key = prefix + 'num_batches_tracked' if num_batches_tracked_key in state_dict: del state_dict[num_batches_tracked_key] super(FrozenBatchNorm2d, self)._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) def forward(self, x): # move reshapes to the beginning # to make it fuser-friendly w = self.weight.reshape(1, -1, 1, 1) b = self.bias.reshape(1, -1, 1, 1) rv = self.running_var.reshape(1, -1, 1, 1) rm = self.running_mean.reshape(1, -1, 1, 1) eps = 1e-5 scale = w * (rv + eps).rsqrt() bias = b - rm * scale return x * scale + bias class BackboneBase(nn.Module): def __init__(self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool): super().__init__() for name, parameter in backbone.named_parameters(): if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name: parameter.requires_grad_(False) if return_interm_layers: return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} else: return_layers = {'layer4': "0"} self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) self.num_channels = num_channels def forward(self, tensor_list: NestedTensor): xs = self.body(tensor_list.tensors) out: Dict[str, NestedTensor] = {} for name, x in xs.items(): m = tensor_list.mask assert m is not None mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] out[name] = NestedTensor(x, mask) return out class Backbone(BackboneBase): """ResNet backbone with frozen BatchNorm.""" def __init__(self, name: str, train_backbone: bool, return_interm_layers: bool, dilation: bool): backbone = getattr(torchvision.models, name)( replace_stride_with_dilation=[False, False, dilation], pretrained=is_main_process(), norm_layer=FrozenBatchNorm2d) num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 super().__init__(backbone, train_backbone, num_channels, return_interm_layers) class Joiner(nn.Sequential): def __init__(self, backbone, position_embedding): super().__init__(backbone, position_embedding) def forward(self, tensor_list: NestedTensor): xs = self[0](tensor_list) out: List[NestedTensor] = [] pos = [] for name, x in xs.items(): out.append(x) # position encoding pos.append(self[1](x).to(x.tensors.dtype)) return out, pos def build_backbone(args): position_embedding = build_position_encoding(args) train_backbone = args.lr_backbone > 0 return_interm_layers = False # args.masks backbone = Backbone(args.backbone, train_backbone, return_interm_layers, False) model = Joiner(backbone, position_embedding) model.num_channels = backbone.num_channels return model

37.29661

113

0.656669

import torch import torch.nn.functional as F import torchvision from torch import nn from torchvision.models._utils import IntermediateLayerGetter from typing import Dict, List from util.misc import NestedTensor, is_main_process from .position_encoding import build_position_encoding class FrozenBatchNorm2d(torch.nn.Module): def __init__(self, n): super(FrozenBatchNorm2d, self).__init__() self.register_buffer("weight", torch.ones(n)) self.register_buffer("bias", torch.zeros(n)) self.register_buffer("running_mean", torch.zeros(n)) self.register_buffer("running_var", torch.ones(n)) def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): num_batches_tracked_key = prefix + 'num_batches_tracked' if num_batches_tracked_key in state_dict: del state_dict[num_batches_tracked_key] super(FrozenBatchNorm2d, self)._load_from_state_dict( state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs) def forward(self, x): w = self.weight.reshape(1, -1, 1, 1) b = self.bias.reshape(1, -1, 1, 1) rv = self.running_var.reshape(1, -1, 1, 1) rm = self.running_mean.reshape(1, -1, 1, 1) eps = 1e-5 scale = w * (rv + eps).rsqrt() bias = b - rm * scale return x * scale + bias class BackboneBase(nn.Module): def __init__(self, backbone: nn.Module, train_backbone: bool, num_channels: int, return_interm_layers: bool): super().__init__() for name, parameter in backbone.named_parameters(): if not train_backbone or 'layer2' not in name and 'layer3' not in name and 'layer4' not in name: parameter.requires_grad_(False) if return_interm_layers: return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"} else: return_layers = {'layer4': "0"} self.body = IntermediateLayerGetter(backbone, return_layers=return_layers) self.num_channels = num_channels def forward(self, tensor_list: NestedTensor): xs = self.body(tensor_list.tensors) out: Dict[str, NestedTensor] = {} for name, x in xs.items(): m = tensor_list.mask assert m is not None mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0] out[name] = NestedTensor(x, mask) return out class Backbone(BackboneBase): def __init__(self, name: str, train_backbone: bool, return_interm_layers: bool, dilation: bool): backbone = getattr(torchvision.models, name)( replace_stride_with_dilation=[False, False, dilation], pretrained=is_main_process(), norm_layer=FrozenBatchNorm2d) num_channels = 512 if name in ('resnet18', 'resnet34') else 2048 super().__init__(backbone, train_backbone, num_channels, return_interm_layers) class Joiner(nn.Sequential): def __init__(self, backbone, position_embedding): super().__init__(backbone, position_embedding) def forward(self, tensor_list: NestedTensor): xs = self[0](tensor_list) out: List[NestedTensor] = [] pos = [] for name, x in xs.items(): out.append(x) pos.append(self[1](x).to(x.tensors.dtype)) return out, pos def build_backbone(args): position_embedding = build_position_encoding(args) train_backbone = args.lr_backbone > 0 return_interm_layers = False backbone = Backbone(args.backbone, train_backbone, return_interm_layers, False) model = Joiner(backbone, position_embedding) model.num_channels = backbone.num_channels return model

true

f7178a5f0eaaff23bb2efef24ecd4ae204c5ee9e

638

py

Python

src/django_backend_api/manage.py

Adityaraj1711/django-backend-architecture

7f3c270af0cb5dd2ebc097c7436a4958cd48ff7c

[ "MIT" ]

25

2020-04-28T19:25:28.000Z

2021-07-04T17:24:35.000Z

src/django_backend_api/manage.py

Adityaraj1711/django-backend-architecture

7f3c270af0cb5dd2ebc097c7436a4958cd48ff7c

[ "MIT" ]

13

2020-08-05T22:40:37.000Z

2022-03-12T00:24:36.000Z

src/django_backend_api/manage.py

Adityaraj1711/django-backend-architecture

7f3c270af0cb5dd2ebc097c7436a4958cd48ff7c

[ "MIT" ]

2

2020-10-29T13:10:01.000Z

2021-11-22T01:55:14.000Z

#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'django_backend_api.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

29

82

0.688088

import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'django_backend_api.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

true

f7178a80bd5a4eb44808ea505c773373aa6ce545

102

py

Python

example.py

CJSmekens/code-refinery-bq-1

12cf67033e82176da665156037204eb405a0f6a7

[ "Apache-2.0" ]

null

example.py

CJSmekens/code-refinery-bq-1

12cf67033e82176da665156037204eb405a0f6a7

[ "Apache-2.0" ]

2

2021-06-07T10:01:32.000Z

2021-06-07T10:10:18.000Z

example.py

CJSmekens/code-refinery-bq-1

12cf67033e82176da665156037204eb405a0f6a7

[ "Apache-2.0" ]

1

2021-06-07T09:53:19.000Z

def add(a,b): return a + b def subtract(a,b): return a - b def product(a,b): return a * b

11.333333

18

0.558824

def add(a,b): return a + b def subtract(a,b): return a - b def product(a,b): return a * b

true

f7178af25c554b1c64de2dc78db065ca48b7edd5

10,521

py

Python

micro_app_sdk/model/ops_automation/job_tasks_pb2.py

easyopsapis/easyops-api-python

adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0

[ "Apache-2.0" ]

5

2019-07-31T04:11:05.000Z

2021-01-07T03:23:20.000Z

micro_app_sdk/model/ops_automation/job_tasks_pb2.py

easyopsapis/easyops-api-python

adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0

[ "Apache-2.0" ]

null

micro_app_sdk/model/ops_automation/job_tasks_pb2.py

easyopsapis/easyops-api-python

adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: job_tasks.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) 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 micro_app_sdk.model.ops_automation import mail_info_pb2 as micro__app__sdk_dot_model_dot_ops__automation_dot_mail__info__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='job_tasks.proto', package='ops_automation', syntax='proto3', serialized_options=_b('ZHgo.easyops.local/contracts/protorepo-models/easyops/model/ops_automation'), serialized_pb=_b('\n\x0fjob_tasks.proto\x12\x0eops_automation\x1a\x32micro_app_sdk/model/ops_automation/mail_info.proto\"\x82\x03\n\x08JobTasks\x12\n\n\x02id\x18\x01 \x01(\t\x12\r\n\x05jobId\x18\x02 \x01(\t\x12\x0f\n\x07jobName\x18\x03 \x01(\t\x12\x10\n\x08menuName\x18\x04 \x01(\t\x12\x0e\n\x06\x65xecId\x18\x05 \x01(\t\x12\x14\n\x0cresourceType\x18\x06 \x01(\t\x12\x12\n\nresourceId\x18\x07 \x01(\t\x12\x13\n\x0bresourceVId\x18\x08 \x01(\t\x12\x15\n\rresourceVName\x18\t \x01(\t\x12\x0f\n\x07trigger\x18\n \x01(\t\x12\x10\n\x08\x65xecUser\x18\x0b \x01(\t\x12\r\n\x05hosts\x18\x0c \x03(\t\x12\x0e\n\x06status\x18\r \x01(\t\x12&\n\x04mail\x18\x0e \x01(\x0b\x32\x18.ops_automation.MailInfo\x12\x13\n\x0bsuccessRate\x18\x0f \x01(\x02\x12\r\n\x05\x65rror\x18\x10 \x01(\t\x12\x12\n\ncreateTime\x18\x11 \x01(\t\x12\x12\n\nupdateTime\x18\x12 \x01(\t\x12\x0f\n\x07\x63reator\x18\x13 \x01(\t\x12\x0b\n\x03org\x18\x14 \x01(\x05\x42JZHgo.easyops.local/contracts/protorepo-models/easyops/model/ops_automationb\x06proto3') , dependencies=[micro__app__sdk_dot_model_dot_ops__automation_dot_mail__info__pb2.DESCRIPTOR,]) _JOBTASKS = _descriptor.Descriptor( name='JobTasks', full_name='ops_automation.JobTasks', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='id', full_name='ops_automation.JobTasks.id', 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), _descriptor.FieldDescriptor( name='jobId', full_name='ops_automation.JobTasks.jobId', index=1, number=2, 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), _descriptor.FieldDescriptor( name='jobName', full_name='ops_automation.JobTasks.jobName', index=2, number=3, 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), _descriptor.FieldDescriptor( name='menuName', full_name='ops_automation.JobTasks.menuName', index=3, number=4, 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), _descriptor.FieldDescriptor( name='execId', full_name='ops_automation.JobTasks.execId', index=4, number=5, 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), _descriptor.FieldDescriptor( name='resourceType', full_name='ops_automation.JobTasks.resourceType', index=5, number=6, 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), _descriptor.FieldDescriptor( name='resourceId', full_name='ops_automation.JobTasks.resourceId', index=6, number=7, 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), _descriptor.FieldDescriptor( name='resourceVId', full_name='ops_automation.JobTasks.resourceVId', index=7, number=8, 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), _descriptor.FieldDescriptor( name='resourceVName', full_name='ops_automation.JobTasks.resourceVName', index=8, number=9, 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), _descriptor.FieldDescriptor( name='trigger', full_name='ops_automation.JobTasks.trigger', index=9, number=10, 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), _descriptor.FieldDescriptor( name='execUser', full_name='ops_automation.JobTasks.execUser', index=10, number=11, 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), _descriptor.FieldDescriptor( name='hosts', full_name='ops_automation.JobTasks.hosts', index=11, number=12, 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=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='status', full_name='ops_automation.JobTasks.status', index=12, number=13, 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), _descriptor.FieldDescriptor( name='mail', full_name='ops_automation.JobTasks.mail', index=13, number=14, 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), _descriptor.FieldDescriptor( name='successRate', full_name='ops_automation.JobTasks.successRate', index=14, number=15, type=2, cpp_type=6, 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), _descriptor.FieldDescriptor( name='error', full_name='ops_automation.JobTasks.error', index=15, number=16, 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), _descriptor.FieldDescriptor( name='createTime', full_name='ops_automation.JobTasks.createTime', index=16, number=17, 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), _descriptor.FieldDescriptor( name='updateTime', full_name='ops_automation.JobTasks.updateTime', index=17, number=18, 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), _descriptor.FieldDescriptor( name='creator', full_name='ops_automation.JobTasks.creator', index=18, number=19, 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), _descriptor.FieldDescriptor( name='org', full_name='ops_automation.JobTasks.org', index=19, number=20, 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), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=88, serialized_end=474, ) _JOBTASKS.fields_by_name['mail'].message_type = micro__app__sdk_dot_model_dot_ops__automation_dot_mail__info__pb2._MAILINFO DESCRIPTOR.message_types_by_name['JobTasks'] = _JOBTASKS _sym_db.RegisterFileDescriptor(DESCRIPTOR) JobTasks = _reflection.GeneratedProtocolMessageType('JobTasks', (_message.Message,), { 'DESCRIPTOR' : _JOBTASKS, '__module__' : 'job_tasks_pb2' # @@protoc_insertion_point(class_scope:ops_automation.JobTasks) }) _sym_db.RegisterMessage(JobTasks) DESCRIPTOR._options = None # @@protoc_insertion_point(module_scope)

50.581731

1,012

0.737763

import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) 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 micro_app_sdk.model.ops_automation import mail_info_pb2 as micro__app__sdk_dot_model_dot_ops__automation_dot_mail__info__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='job_tasks.proto', package='ops_automation', syntax='proto3', serialized_options=_b('ZHgo.easyops.local/contracts/protorepo-models/easyops/model/ops_automation'), serialized_pb=_b('\n\x0fjob_tasks.proto\x12\x0eops_automation\x1a\x32micro_app_sdk/model/ops_automation/mail_info.proto\"\x82\x03\n\x08JobTasks\x12\n\n\x02id\x18\x01 \x01(\t\x12\r\n\x05jobId\x18\x02 \x01(\t\x12\x0f\n\x07jobName\x18\x03 \x01(\t\x12\x10\n\x08menuName\x18\x04 \x01(\t\x12\x0e\n\x06\x65xecId\x18\x05 \x01(\t\x12\x14\n\x0cresourceType\x18\x06 \x01(\t\x12\x12\n\nresourceId\x18\x07 \x01(\t\x12\x13\n\x0bresourceVId\x18\x08 \x01(\t\x12\x15\n\rresourceVName\x18\t \x01(\t\x12\x0f\n\x07trigger\x18\n \x01(\t\x12\x10\n\x08\x65xecUser\x18\x0b \x01(\t\x12\r\n\x05hosts\x18\x0c \x03(\t\x12\x0e\n\x06status\x18\r \x01(\t\x12&\n\x04mail\x18\x0e \x01(\x0b\x32\x18.ops_automation.MailInfo\x12\x13\n\x0bsuccessRate\x18\x0f \x01(\x02\x12\r\n\x05\x65rror\x18\x10 \x01(\t\x12\x12\n\ncreateTime\x18\x11 \x01(\t\x12\x12\n\nupdateTime\x18\x12 \x01(\t\x12\x0f\n\x07\x63reator\x18\x13 \x01(\t\x12\x0b\n\x03org\x18\x14 \x01(\x05\x42JZHgo.easyops.local/contracts/protorepo-models/easyops/model/ops_automationb\x06proto3') , dependencies=[micro__app__sdk_dot_model_dot_ops__automation_dot_mail__info__pb2.DESCRIPTOR,]) _JOBTASKS = _descriptor.Descriptor( name='JobTasks', full_name='ops_automation.JobTasks', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='id', full_name='ops_automation.JobTasks.id', 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), _descriptor.FieldDescriptor( name='jobId', full_name='ops_automation.JobTasks.jobId', index=1, number=2, 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), _descriptor.FieldDescriptor( name='jobName', full_name='ops_automation.JobTasks.jobName', index=2, number=3, 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), _descriptor.FieldDescriptor( name='menuName', full_name='ops_automation.JobTasks.menuName', index=3, number=4, 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), _descriptor.FieldDescriptor( name='execId', full_name='ops_automation.JobTasks.execId', index=4, number=5, 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), _descriptor.FieldDescriptor( name='resourceType', full_name='ops_automation.JobTasks.resourceType', index=5, number=6, 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), _descriptor.FieldDescriptor( name='resourceId', full_name='ops_automation.JobTasks.resourceId', index=6, number=7, 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), _descriptor.FieldDescriptor( name='resourceVId', full_name='ops_automation.JobTasks.resourceVId', index=7, number=8, 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), _descriptor.FieldDescriptor( name='resourceVName', full_name='ops_automation.JobTasks.resourceVName', index=8, number=9, 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), _descriptor.FieldDescriptor( name='trigger', full_name='ops_automation.JobTasks.trigger', index=9, number=10, 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), _descriptor.FieldDescriptor( name='execUser', full_name='ops_automation.JobTasks.execUser', index=10, number=11, 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), _descriptor.FieldDescriptor( name='hosts', full_name='ops_automation.JobTasks.hosts', index=11, number=12, 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=None, file=DESCRIPTOR), _descriptor.FieldDescriptor( name='status', full_name='ops_automation.JobTasks.status', index=12, number=13, 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), _descriptor.FieldDescriptor( name='mail', full_name='ops_automation.JobTasks.mail', index=13, number=14, 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), _descriptor.FieldDescriptor( name='successRate', full_name='ops_automation.JobTasks.successRate', index=14, number=15, type=2, cpp_type=6, 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), _descriptor.FieldDescriptor( name='error', full_name='ops_automation.JobTasks.error', index=15, number=16, 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), _descriptor.FieldDescriptor( name='createTime', full_name='ops_automation.JobTasks.createTime', index=16, number=17, 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), _descriptor.FieldDescriptor( name='updateTime', full_name='ops_automation.JobTasks.updateTime', index=17, number=18, 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), _descriptor.FieldDescriptor( name='creator', full_name='ops_automation.JobTasks.creator', index=18, number=19, 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), _descriptor.FieldDescriptor( name='org', full_name='ops_automation.JobTasks.org', index=19, number=20, 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), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=88, serialized_end=474, ) _JOBTASKS.fields_by_name['mail'].message_type = micro__app__sdk_dot_model_dot_ops__automation_dot_mail__info__pb2._MAILINFO DESCRIPTOR.message_types_by_name['JobTasks'] = _JOBTASKS _sym_db.RegisterFileDescriptor(DESCRIPTOR) JobTasks = _reflection.GeneratedProtocolMessageType('JobTasks', (_message.Message,), { 'DESCRIPTOR' : _JOBTASKS, '__module__' : 'job_tasks_pb2' # @@protoc_insertion_point(class_scope:ops_automation.JobTasks) }) _sym_db.RegisterMessage(JobTasks) DESCRIPTOR._options = None # @@protoc_insertion_point(module_scope)

true

f7178bb0eb777f6aea029f4307dd32ff4cc85674

2,500

py

Python

mobilecoind/strategies/balances.py

mccobr/mobilecoin

cd7753a0aed838097b456d230151fb34e8cff034

[ "Apache-2.0" ]

2

2021-03-13T04:41:04.000Z

2021-03-13T04:42:37.000Z

mobilecoind/strategies/balances.py

mccobr/mobilecoin

cd7753a0aed838097b456d230151fb34e8cff034

[ "Apache-2.0" ]

292

2020-10-22T00:34:35.000Z

2022-03-29T09:29:14.000Z

mobilecoind/strategies/balances.py

mccobr/mobilecoin

cd7753a0aed838097b456d230151fb34e8cff034

[ "Apache-2.0" ]

1

2022-03-26T20:34:00.000Z

#!/usr/bin/env python3 # Copyright (c) 2018-2021 The MobileCoin Foundation """ The purpose of this script is to print the balances for all keys in a given account directory. Example setup and usage: ``` python3 balances.py --key-dir ../../../target/sample_data/master/keys/ ``` """ import argparse import grpc import mobilecoind_api_pb2 import mobilecoind_api_pb2_grpc import os from accounts import connect, load_key_and_register from google.protobuf.empty_pb2 import Empty def parse_args() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() parser.add_argument("--mobilecoind-host", default="localhost", type=str, help="Mobilecoind host") parser.add_argument("--mobilecoind-port", default="4444", type=str, help="Mobilecoind port") parser.add_argument("--key-dir", required=True, type=str, help="Path to account key dir") parser.add_argument("--prune", action="store_true", help="Prune key files for accounts with 0 balance") return parser.parse_args() if __name__ == '__main__': args = parse_args() print(args) stub = connect(args.mobilecoind_host, args.mobilecoind_port) block_count = stub.GetLedgerInfo(Empty()).block_count total = 0 for keyfile in sorted( filter(lambda x: x.endswith(".json"), os.listdir(args.key_dir))): print(keyfile) account_data = load_key_and_register( os.path.join(args.key_dir, keyfile), stub) # Get starting balance request = mobilecoind_api_pb2.GetMonitorStatusRequest(monitor_id=account_data.monitor_id) monitor_block = stub.GetMonitorStatus(request).status.next_block if block_count != monitor_block: print(f"\tAccount not synced.") else: resp = stub.GetBalance( mobilecoind_api_pb2.GetBalanceRequest(monitor_id=account_data.monitor_id)) balance = resp.balance total += balance print(f"\tBalance: {resp.balance:,}") # Remove balances of 0 FIXME: MC-367 also from mobilecoind wallet if int(balance) == 0 and args.prune: os.remove(os.path.join(args.key_dir, keyfile)) print(f"Total balance of key collection: {total:,} PicoMob")

34.246575

97

0.6156

import argparse import grpc import mobilecoind_api_pb2 import mobilecoind_api_pb2_grpc import os from accounts import connect, load_key_and_register from google.protobuf.empty_pb2 import Empty def parse_args() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() parser.add_argument("--mobilecoind-host", default="localhost", type=str, help="Mobilecoind host") parser.add_argument("--mobilecoind-port", default="4444", type=str, help="Mobilecoind port") parser.add_argument("--key-dir", required=True, type=str, help="Path to account key dir") parser.add_argument("--prune", action="store_true", help="Prune key files for accounts with 0 balance") return parser.parse_args() if __name__ == '__main__': args = parse_args() print(args) stub = connect(args.mobilecoind_host, args.mobilecoind_port) block_count = stub.GetLedgerInfo(Empty()).block_count total = 0 for keyfile in sorted( filter(lambda x: x.endswith(".json"), os.listdir(args.key_dir))): print(keyfile) account_data = load_key_and_register( os.path.join(args.key_dir, keyfile), stub) request = mobilecoind_api_pb2.GetMonitorStatusRequest(monitor_id=account_data.monitor_id) monitor_block = stub.GetMonitorStatus(request).status.next_block if block_count != monitor_block: print(f"\tAccount not synced.") else: resp = stub.GetBalance( mobilecoind_api_pb2.GetBalanceRequest(monitor_id=account_data.monitor_id)) balance = resp.balance total += balance print(f"\tBalance: {resp.balance:,}") if int(balance) == 0 and args.prune: os.remove(os.path.join(args.key_dir, keyfile)) print(f"Total balance of key collection: {total:,} PicoMob")

true

f7178bf35043b3dda373e2b1334ac17197d12419

52,295

py

Python

Lib/test/test_posix.py

sireliah/polish-python

605df4944c2d3bc25f8bf6964b274c0a0d297cc3

[ "PSF-2.0" ]

1

2018-06-21T18:21:24.000Z

Lib/test/test_posix.py

sireliah/polish-python

605df4944c2d3bc25f8bf6964b274c0a0d297cc3

[ "PSF-2.0" ]

null

Lib/test/test_posix.py

sireliah/polish-python

605df4944c2d3bc25f8bf6964b274c0a0d297cc3

[ "PSF-2.0" ]

null

"Test posix functions" z test zaimportuj support # Skip these tests jeżeli there jest no posix module. posix = support.import_module('posix') zaimportuj errno zaimportuj sys zaimportuj time zaimportuj os zaimportuj platform zaimportuj pwd zaimportuj shutil zaimportuj stat zaimportuj tempfile zaimportuj unittest zaimportuj warnings _DUMMY_SYMLINK = os.path.join(tempfile.gettempdir(), support.TESTFN + '-dummy-symlink') klasa PosixTester(unittest.TestCase): def setUp(self): # create empty file fp = open(support.TESTFN, 'w+') fp.close() self.teardown_files = [ support.TESTFN ] self._warnings_manager = support.check_warnings() self._warnings_manager.__enter__() warnings.filterwarnings('ignore', '.* potential security risk .*', RuntimeWarning) def tearDown(self): dla teardown_file w self.teardown_files: support.unlink(teardown_file) self._warnings_manager.__exit__(Nic, Nic, Nic) def testNoArgFunctions(self): # test posix functions which take no arguments oraz have # no side-effects which we need to cleanup (e.g., fork, wait, abort) NO_ARG_FUNCTIONS = [ "ctermid", "getcwd", "getcwdb", "uname", "times", "getloadavg", "getegid", "geteuid", "getgid", "getgroups", "getpid", "getpgrp", "getppid", "getuid", "sync", ] dla name w NO_ARG_FUNCTIONS: posix_func = getattr(posix, name, Nic) jeżeli posix_func jest nie Nic: posix_func() self.assertRaises(TypeError, posix_func, 1) @unittest.skipUnless(hasattr(posix, 'getresuid'), 'test needs posix.getresuid()') def test_getresuid(self): user_ids = posix.getresuid() self.assertEqual(len(user_ids), 3) dla val w user_ids: self.assertGreaterEqual(val, 0) @unittest.skipUnless(hasattr(posix, 'getresgid'), 'test needs posix.getresgid()') def test_getresgid(self): group_ids = posix.getresgid() self.assertEqual(len(group_ids), 3) dla val w group_ids: self.assertGreaterEqual(val, 0) @unittest.skipUnless(hasattr(posix, 'setresuid'), 'test needs posix.setresuid()') def test_setresuid(self): current_user_ids = posix.getresuid() self.assertIsNic(posix.setresuid(*current_user_ids)) # -1 means don't change that value. self.assertIsNic(posix.setresuid(-1, -1, -1)) @unittest.skipUnless(hasattr(posix, 'setresuid'), 'test needs posix.setresuid()') def test_setresuid_exception(self): # Don't do this test jeżeli someone jest silly enough to run us jako root. current_user_ids = posix.getresuid() jeżeli 0 nie w current_user_ids: new_user_ids = (current_user_ids[0]+1, -1, -1) self.assertRaises(OSError, posix.setresuid, *new_user_ids) @unittest.skipUnless(hasattr(posix, 'setresgid'), 'test needs posix.setresgid()') def test_setresgid(self): current_group_ids = posix.getresgid() self.assertIsNic(posix.setresgid(*current_group_ids)) # -1 means don't change that value. self.assertIsNic(posix.setresgid(-1, -1, -1)) @unittest.skipUnless(hasattr(posix, 'setresgid'), 'test needs posix.setresgid()') def test_setresgid_exception(self): # Don't do this test jeżeli someone jest silly enough to run us jako root. current_group_ids = posix.getresgid() jeżeli 0 nie w current_group_ids: new_group_ids = (current_group_ids[0]+1, -1, -1) self.assertRaises(OSError, posix.setresgid, *new_group_ids) @unittest.skipUnless(hasattr(posix, 'initgroups'), "test needs os.initgroups()") def test_initgroups(self): # It takes a string oraz an integer; check that it podnieśs a TypeError # dla other argument lists. self.assertRaises(TypeError, posix.initgroups) self.assertRaises(TypeError, posix.initgroups, Nic) self.assertRaises(TypeError, posix.initgroups, 3, "foo") self.assertRaises(TypeError, posix.initgroups, "foo", 3, object()) # If a non-privileged user invokes it, it should fail przy OSError # EPERM. jeżeli os.getuid() != 0: spróbuj: name = pwd.getpwuid(posix.getuid()).pw_name wyjąwszy KeyError: # the current UID may nie have a pwd entry podnieś unittest.SkipTest("need a pwd entry") spróbuj: posix.initgroups(name, 13) wyjąwszy OSError jako e: self.assertEqual(e.errno, errno.EPERM) inaczej: self.fail("Expected OSError to be podnieśd by initgroups") @unittest.skipUnless(hasattr(posix, 'statvfs'), 'test needs posix.statvfs()') def test_statvfs(self): self.assertPrawda(posix.statvfs(os.curdir)) @unittest.skipUnless(hasattr(posix, 'fstatvfs'), 'test needs posix.fstatvfs()') def test_fstatvfs(self): fp = open(support.TESTFN) spróbuj: self.assertPrawda(posix.fstatvfs(fp.fileno())) self.assertPrawda(posix.statvfs(fp.fileno())) w_końcu: fp.close() @unittest.skipUnless(hasattr(posix, 'ftruncate'), 'test needs posix.ftruncate()') def test_ftruncate(self): fp = open(support.TESTFN, 'w+') spróbuj: # we need to have some data to truncate fp.write('test') fp.flush() posix.ftruncate(fp.fileno(), 0) w_końcu: fp.close() @unittest.skipUnless(hasattr(posix, 'truncate'), "test needs posix.truncate()") def test_truncate(self): przy open(support.TESTFN, 'w') jako fp: fp.write('test') fp.flush() posix.truncate(support.TESTFN, 0) @unittest.skipUnless(getattr(os, 'execve', Nic) w os.supports_fd, "test needs execve() to support the fd parameter") @unittest.skipUnless(hasattr(os, 'fork'), "test needs os.fork()") @unittest.skipUnless(hasattr(os, 'waitpid'), "test needs os.waitpid()") def test_fexecve(self): fp = os.open(sys.executable, os.O_RDONLY) spróbuj: pid = os.fork() jeżeli pid == 0: os.chdir(os.path.split(sys.executable)[0]) posix.execve(fp, [sys.executable, '-c', 'pass'], os.environ) inaczej: self.assertEqual(os.waitpid(pid, 0), (pid, 0)) w_końcu: os.close(fp) @unittest.skipUnless(hasattr(posix, 'waitid'), "test needs posix.waitid()") @unittest.skipUnless(hasattr(os, 'fork'), "test needs os.fork()") def test_waitid(self): pid = os.fork() jeżeli pid == 0: os.chdir(os.path.split(sys.executable)[0]) posix.execve(sys.executable, [sys.executable, '-c', 'pass'], os.environ) inaczej: res = posix.waitid(posix.P_PID, pid, posix.WEXITED) self.assertEqual(pid, res.si_pid) @unittest.skipUnless(hasattr(posix, 'lockf'), "test needs posix.lockf()") def test_lockf(self): fd = os.open(support.TESTFN, os.O_WRONLY | os.O_CREAT) spróbuj: os.write(fd, b'test') os.lseek(fd, 0, os.SEEK_SET) posix.lockf(fd, posix.F_LOCK, 4) # section jest locked posix.lockf(fd, posix.F_ULOCK, 4) w_końcu: os.close(fd) @unittest.skipUnless(hasattr(posix, 'pread'), "test needs posix.pread()") def test_pread(self): fd = os.open(support.TESTFN, os.O_RDWR | os.O_CREAT) spróbuj: os.write(fd, b'test') os.lseek(fd, 0, os.SEEK_SET) self.assertEqual(b'es', posix.pread(fd, 2, 1)) # the first pread() shouldn't disturb the file offset self.assertEqual(b'te', posix.read(fd, 2)) w_końcu: os.close(fd) @unittest.skipUnless(hasattr(posix, 'pwrite'), "test needs posix.pwrite()") def test_pwrite(self): fd = os.open(support.TESTFN, os.O_RDWR | os.O_CREAT) spróbuj: os.write(fd, b'test') os.lseek(fd, 0, os.SEEK_SET) posix.pwrite(fd, b'xx', 1) self.assertEqual(b'txxt', posix.read(fd, 4)) w_końcu: os.close(fd) @unittest.skipUnless(hasattr(posix, 'posix_fallocate'), "test needs posix.posix_fallocate()") def test_posix_fallocate(self): fd = os.open(support.TESTFN, os.O_WRONLY | os.O_CREAT) spróbuj: posix.posix_fallocate(fd, 0, 10) wyjąwszy OSError jako inst: # issue10812, ZFS doesn't appear to support posix_fallocate, # so skip Solaris-based since they are likely to have ZFS. jeżeli inst.errno != errno.EINVAL albo nie sys.platform.startswith("sunos"): podnieś w_końcu: os.close(fd) @unittest.skipUnless(hasattr(posix, 'posix_fadvise'), "test needs posix.posix_fadvise()") def test_posix_fadvise(self): fd = os.open(support.TESTFN, os.O_RDONLY) spróbuj: posix.posix_fadvise(fd, 0, 0, posix.POSIX_FADV_WILLNEED) w_końcu: os.close(fd) @unittest.skipUnless(os.utime w os.supports_fd, "test needs fd support w os.utime") def test_utime_with_fd(self): now = time.time() fd = os.open(support.TESTFN, os.O_RDONLY) spróbuj: posix.utime(fd) posix.utime(fd, Nic) self.assertRaises(TypeError, posix.utime, fd, (Nic, Nic)) self.assertRaises(TypeError, posix.utime, fd, (now, Nic)) self.assertRaises(TypeError, posix.utime, fd, (Nic, now)) posix.utime(fd, (int(now), int(now))) posix.utime(fd, (now, now)) self.assertRaises(ValueError, posix.utime, fd, (now, now), ns=(now, now)) self.assertRaises(ValueError, posix.utime, fd, (now, 0), ns=(Nic, Nic)) self.assertRaises(ValueError, posix.utime, fd, (Nic, Nic), ns=(now, 0)) posix.utime(fd, (int(now), int((now - int(now)) * 1e9))) posix.utime(fd, ns=(int(now), int((now - int(now)) * 1e9))) w_końcu: os.close(fd) @unittest.skipUnless(os.utime w os.supports_follow_symlinks, "test needs follow_symlinks support w os.utime") def test_utime_nofollow_symlinks(self): now = time.time() posix.utime(support.TESTFN, Nic, follow_symlinks=Nieprawda) self.assertRaises(TypeError, posix.utime, support.TESTFN, (Nic, Nic), follow_symlinks=Nieprawda) self.assertRaises(TypeError, posix.utime, support.TESTFN, (now, Nic), follow_symlinks=Nieprawda) self.assertRaises(TypeError, posix.utime, support.TESTFN, (Nic, now), follow_symlinks=Nieprawda) posix.utime(support.TESTFN, (int(now), int(now)), follow_symlinks=Nieprawda) posix.utime(support.TESTFN, (now, now), follow_symlinks=Nieprawda) posix.utime(support.TESTFN, follow_symlinks=Nieprawda) @unittest.skipUnless(hasattr(posix, 'writev'), "test needs posix.writev()") def test_writev(self): fd = os.open(support.TESTFN, os.O_RDWR | os.O_CREAT) spróbuj: n = os.writev(fd, (b'test1', b'tt2', b't3')) self.assertEqual(n, 10) os.lseek(fd, 0, os.SEEK_SET) self.assertEqual(b'test1tt2t3', posix.read(fd, 10)) # Issue #20113: empty list of buffers should nie crash spróbuj: size = posix.writev(fd, []) wyjąwszy OSError: # writev(fd, []) podnieśs OSError(22, "Invalid argument") # on OpenIndiana dalej inaczej: self.assertEqual(size, 0) w_końcu: os.close(fd) @unittest.skipUnless(hasattr(posix, 'readv'), "test needs posix.readv()") def test_readv(self): fd = os.open(support.TESTFN, os.O_RDWR | os.O_CREAT) spróbuj: os.write(fd, b'test1tt2t3') os.lseek(fd, 0, os.SEEK_SET) buf = [bytearray(i) dla i w [5, 3, 2]] self.assertEqual(posix.readv(fd, buf), 10) self.assertEqual([b'test1', b'tt2', b't3'], [bytes(i) dla i w buf]) # Issue #20113: empty list of buffers should nie crash spróbuj: size = posix.readv(fd, []) wyjąwszy OSError: # readv(fd, []) podnieśs OSError(22, "Invalid argument") # on OpenIndiana dalej inaczej: self.assertEqual(size, 0) w_końcu: os.close(fd) @unittest.skipUnless(hasattr(posix, 'dup'), 'test needs posix.dup()') def test_dup(self): fp = open(support.TESTFN) spróbuj: fd = posix.dup(fp.fileno()) self.assertIsInstance(fd, int) os.close(fd) w_końcu: fp.close() @unittest.skipUnless(hasattr(posix, 'confstr'), 'test needs posix.confstr()') def test_confstr(self): self.assertRaises(ValueError, posix.confstr, "CS_garbage") self.assertEqual(len(posix.confstr("CS_PATH")) > 0, Prawda) @unittest.skipUnless(hasattr(posix, 'dup2'), 'test needs posix.dup2()') def test_dup2(self): fp1 = open(support.TESTFN) fp2 = open(support.TESTFN) spróbuj: posix.dup2(fp1.fileno(), fp2.fileno()) w_końcu: fp1.close() fp2.close() @unittest.skipUnless(hasattr(os, 'O_CLOEXEC'), "needs os.O_CLOEXEC") @support.requires_linux_version(2, 6, 23) def test_oscloexec(self): fd = os.open(support.TESTFN, os.O_RDONLY|os.O_CLOEXEC) self.addCleanup(os.close, fd) self.assertNieprawda(os.get_inheritable(fd)) @unittest.skipUnless(hasattr(posix, 'O_EXLOCK'), 'test needs posix.O_EXLOCK') def test_osexlock(self): fd = os.open(support.TESTFN, os.O_WRONLY|os.O_EXLOCK|os.O_CREAT) self.assertRaises(OSError, os.open, support.TESTFN, os.O_WRONLY|os.O_EXLOCK|os.O_NONBLOCK) os.close(fd) jeżeli hasattr(posix, "O_SHLOCK"): fd = os.open(support.TESTFN, os.O_WRONLY|os.O_SHLOCK|os.O_CREAT) self.assertRaises(OSError, os.open, support.TESTFN, os.O_WRONLY|os.O_EXLOCK|os.O_NONBLOCK) os.close(fd) @unittest.skipUnless(hasattr(posix, 'O_SHLOCK'), 'test needs posix.O_SHLOCK') def test_osshlock(self): fd1 = os.open(support.TESTFN, os.O_WRONLY|os.O_SHLOCK|os.O_CREAT) fd2 = os.open(support.TESTFN, os.O_WRONLY|os.O_SHLOCK|os.O_CREAT) os.close(fd2) os.close(fd1) jeżeli hasattr(posix, "O_EXLOCK"): fd = os.open(support.TESTFN, os.O_WRONLY|os.O_SHLOCK|os.O_CREAT) self.assertRaises(OSError, os.open, support.TESTFN, os.O_RDONLY|os.O_EXLOCK|os.O_NONBLOCK) os.close(fd) @unittest.skipUnless(hasattr(posix, 'fstat'), 'test needs posix.fstat()') def test_fstat(self): fp = open(support.TESTFN) spróbuj: self.assertPrawda(posix.fstat(fp.fileno())) self.assertPrawda(posix.stat(fp.fileno())) self.assertRaisesRegex(TypeError, 'should be string, bytes albo integer, not', posix.stat, float(fp.fileno())) w_końcu: fp.close() @unittest.skipUnless(hasattr(posix, 'stat'), 'test needs posix.stat()') def test_stat(self): self.assertPrawda(posix.stat(support.TESTFN)) self.assertPrawda(posix.stat(os.fsencode(support.TESTFN))) self.assertPrawda(posix.stat(bytearray(os.fsencode(support.TESTFN)))) self.assertRaisesRegex(TypeError, 'can\'t specify Nic dla path argument', posix.stat, Nic) self.assertRaisesRegex(TypeError, 'should be string, bytes albo integer, not', posix.stat, list(support.TESTFN)) self.assertRaisesRegex(TypeError, 'should be string, bytes albo integer, not', posix.stat, list(os.fsencode(support.TESTFN))) @unittest.skipUnless(hasattr(posix, 'mkfifo'), "don't have mkfifo()") def test_mkfifo(self): support.unlink(support.TESTFN) posix.mkfifo(support.TESTFN, stat.S_IRUSR | stat.S_IWUSR) self.assertPrawda(stat.S_ISFIFO(posix.stat(support.TESTFN).st_mode)) @unittest.skipUnless(hasattr(posix, 'mknod') oraz hasattr(stat, 'S_IFIFO'), "don't have mknod()/S_IFIFO") def test_mknod(self): # Test using mknod() to create a FIFO (the only use specified # by POSIX). support.unlink(support.TESTFN) mode = stat.S_IFIFO | stat.S_IRUSR | stat.S_IWUSR spróbuj: posix.mknod(support.TESTFN, mode, 0) wyjąwszy OSError jako e: # Some old systems don't allow unprivileged users to use # mknod(), albo only support creating device nodes. self.assertIn(e.errno, (errno.EPERM, errno.EINVAL)) inaczej: self.assertPrawda(stat.S_ISFIFO(posix.stat(support.TESTFN).st_mode)) @unittest.skipUnless(hasattr(posix, 'stat'), 'test needs posix.stat()') @unittest.skipUnless(hasattr(posix, 'makedev'), 'test needs posix.makedev()') def test_makedev(self): st = posix.stat(support.TESTFN) dev = st.st_dev self.assertIsInstance(dev, int) self.assertGreaterEqual(dev, 0) major = posix.major(dev) self.assertIsInstance(major, int) self.assertGreaterEqual(major, 0) self.assertEqual(posix.major(dev), major) self.assertRaises(TypeError, posix.major, float(dev)) self.assertRaises(TypeError, posix.major) self.assertRaises((ValueError, OverflowError), posix.major, -1) minor = posix.minor(dev) self.assertIsInstance(minor, int) self.assertGreaterEqual(minor, 0) self.assertEqual(posix.minor(dev), minor) self.assertRaises(TypeError, posix.minor, float(dev)) self.assertRaises(TypeError, posix.minor) self.assertRaises((ValueError, OverflowError), posix.minor, -1) self.assertEqual(posix.makedev(major, minor), dev) self.assertRaises(TypeError, posix.makedev, float(major), minor) self.assertRaises(TypeError, posix.makedev, major, float(minor)) self.assertRaises(TypeError, posix.makedev, major) self.assertRaises(TypeError, posix.makedev) def _test_all_chown_common(self, chown_func, first_param, stat_func): """Common code dla chown, fchown oraz lchown tests.""" def check_stat(uid, gid): jeżeli stat_func jest nie Nic: stat = stat_func(first_param) self.assertEqual(stat.st_uid, uid) self.assertEqual(stat.st_gid, gid) uid = os.getuid() gid = os.getgid() # test a successful chown call chown_func(first_param, uid, gid) check_stat(uid, gid) chown_func(first_param, -1, gid) check_stat(uid, gid) chown_func(first_param, uid, -1) check_stat(uid, gid) jeżeli uid == 0: # Try an amusingly large uid/gid to make sure we handle # large unsigned values. (chown lets you use any # uid/gid you like, even jeżeli they aren't defined.) # # This problem keeps coming up: # http://bugs.python.org/issue1747858 # http://bugs.python.org/issue4591 # http://bugs.python.org/issue15301 # Hopefully the fix w 4591 fixes it dla good! # # This part of the test only runs when run jako root. # Only scary people run their tests jako root. big_value = 2**31 chown_func(first_param, big_value, big_value) check_stat(big_value, big_value) chown_func(first_param, -1, -1) check_stat(big_value, big_value) chown_func(first_param, uid, gid) check_stat(uid, gid) albo_inaczej platform.system() w ('HP-UX', 'SunOS'): # HP-UX oraz Solaris can allow a non-root user to chown() to root # (issue #5113) podnieś unittest.SkipTest("Skipping because of non-standard chown() " "behavior") inaczej: # non-root cannot chown to root, podnieśs OSError self.assertRaises(OSError, chown_func, first_param, 0, 0) check_stat(uid, gid) self.assertRaises(OSError, chown_func, first_param, 0, -1) check_stat(uid, gid) jeżeli 0 nie w os.getgroups(): self.assertRaises(OSError, chown_func, first_param, -1, 0) check_stat(uid, gid) # test illegal types dla t w str, float: self.assertRaises(TypeError, chown_func, first_param, t(uid), gid) check_stat(uid, gid) self.assertRaises(TypeError, chown_func, first_param, uid, t(gid)) check_stat(uid, gid) @unittest.skipUnless(hasattr(posix, 'chown'), "test needs os.chown()") def test_chown(self): # podnieś an OSError jeżeli the file does nie exist os.unlink(support.TESTFN) self.assertRaises(OSError, posix.chown, support.TESTFN, -1, -1) # re-create the file support.create_empty_file(support.TESTFN) self._test_all_chown_common(posix.chown, support.TESTFN, getattr(posix, 'stat', Nic)) @unittest.skipUnless(hasattr(posix, 'fchown'), "test needs os.fchown()") def test_fchown(self): os.unlink(support.TESTFN) # re-create the file test_file = open(support.TESTFN, 'w') spróbuj: fd = test_file.fileno() self._test_all_chown_common(posix.fchown, fd, getattr(posix, 'fstat', Nic)) w_końcu: test_file.close() @unittest.skipUnless(hasattr(posix, 'lchown'), "test needs os.lchown()") def test_lchown(self): os.unlink(support.TESTFN) # create a symlink os.symlink(_DUMMY_SYMLINK, support.TESTFN) self._test_all_chown_common(posix.lchown, support.TESTFN, getattr(posix, 'lstat', Nic)) @unittest.skipUnless(hasattr(posix, 'chdir'), 'test needs posix.chdir()') def test_chdir(self): posix.chdir(os.curdir) self.assertRaises(OSError, posix.chdir, support.TESTFN) def test_listdir(self): self.assertPrawda(support.TESTFN w posix.listdir(os.curdir)) def test_listdir_default(self): # When listdir jest called without argument, # it's the same jako listdir(os.curdir). self.assertPrawda(support.TESTFN w posix.listdir()) def test_listdir_bytes(self): # When listdir jest called przy a bytes object, # the returned strings are of type bytes. self.assertPrawda(os.fsencode(support.TESTFN) w posix.listdir(b'.')) @unittest.skipUnless(posix.listdir w os.supports_fd, "test needs fd support dla posix.listdir()") def test_listdir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) self.addCleanup(posix.close, f) self.assertEqual( sorted(posix.listdir('.')), sorted(posix.listdir(f)) ) # Check that the fd offset was reset (issue #13739) self.assertEqual( sorted(posix.listdir('.')), sorted(posix.listdir(f)) ) @unittest.skipUnless(hasattr(posix, 'access'), 'test needs posix.access()') def test_access(self): self.assertPrawda(posix.access(support.TESTFN, os.R_OK)) @unittest.skipUnless(hasattr(posix, 'umask'), 'test needs posix.umask()') def test_umask(self): old_mask = posix.umask(0) self.assertIsInstance(old_mask, int) posix.umask(old_mask) @unittest.skipUnless(hasattr(posix, 'strerror'), 'test needs posix.strerror()') def test_strerror(self): self.assertPrawda(posix.strerror(0)) @unittest.skipUnless(hasattr(posix, 'pipe'), 'test needs posix.pipe()') def test_pipe(self): reader, writer = posix.pipe() os.close(reader) os.close(writer) @unittest.skipUnless(hasattr(os, 'pipe2'), "test needs os.pipe2()") @support.requires_linux_version(2, 6, 27) def test_pipe2(self): self.assertRaises(TypeError, os.pipe2, 'DEADBEEF') self.assertRaises(TypeError, os.pipe2, 0, 0) # try calling przy flags = 0, like os.pipe() r, w = os.pipe2(0) os.close(r) os.close(w) # test flags r, w = os.pipe2(os.O_CLOEXEC|os.O_NONBLOCK) self.addCleanup(os.close, r) self.addCleanup(os.close, w) self.assertNieprawda(os.get_inheritable(r)) self.assertNieprawda(os.get_inheritable(w)) self.assertNieprawda(os.get_blocking(r)) self.assertNieprawda(os.get_blocking(w)) # try reading z an empty pipe: this should fail, nie block self.assertRaises(OSError, os.read, r, 1) # try a write big enough to fill-up the pipe: this should either # fail albo perform a partial write, nie block spróbuj: os.write(w, b'x' * support.PIPE_MAX_SIZE) wyjąwszy OSError: dalej @support.cpython_only @unittest.skipUnless(hasattr(os, 'pipe2'), "test needs os.pipe2()") @support.requires_linux_version(2, 6, 27) def test_pipe2_c_limits(self): # Issue 15989 zaimportuj _testcapi self.assertRaises(OverflowError, os.pipe2, _testcapi.INT_MAX + 1) self.assertRaises(OverflowError, os.pipe2, _testcapi.UINT_MAX + 1) @unittest.skipUnless(hasattr(posix, 'utime'), 'test needs posix.utime()') def test_utime(self): now = time.time() posix.utime(support.TESTFN, Nic) self.assertRaises(TypeError, posix.utime, support.TESTFN, (Nic, Nic)) self.assertRaises(TypeError, posix.utime, support.TESTFN, (now, Nic)) self.assertRaises(TypeError, posix.utime, support.TESTFN, (Nic, now)) posix.utime(support.TESTFN, (int(now), int(now))) posix.utime(support.TESTFN, (now, now)) def _test_chflags_regular_file(self, chflags_func, target_file, **kwargs): st = os.stat(target_file) self.assertPrawda(hasattr(st, 'st_flags')) # ZFS returns EOPNOTSUPP when attempting to set flag UF_IMMUTABLE. flags = st.st_flags | stat.UF_IMMUTABLE spróbuj: chflags_func(target_file, flags, **kwargs) wyjąwszy OSError jako err: jeżeli err.errno != errno.EOPNOTSUPP: podnieś msg = 'chflag UF_IMMUTABLE nie supported by underlying fs' self.skipTest(msg) spróbuj: new_st = os.stat(target_file) self.assertEqual(st.st_flags | stat.UF_IMMUTABLE, new_st.st_flags) spróbuj: fd = open(target_file, 'w+') wyjąwszy OSError jako e: self.assertEqual(e.errno, errno.EPERM) w_końcu: posix.chflags(target_file, st.st_flags) @unittest.skipUnless(hasattr(posix, 'chflags'), 'test needs os.chflags()') def test_chflags(self): self._test_chflags_regular_file(posix.chflags, support.TESTFN) @unittest.skipUnless(hasattr(posix, 'lchflags'), 'test needs os.lchflags()') def test_lchflags_regular_file(self): self._test_chflags_regular_file(posix.lchflags, support.TESTFN) self._test_chflags_regular_file(posix.chflags, support.TESTFN, follow_symlinks=Nieprawda) @unittest.skipUnless(hasattr(posix, 'lchflags'), 'test needs os.lchflags()') def test_lchflags_symlink(self): testfn_st = os.stat(support.TESTFN) self.assertPrawda(hasattr(testfn_st, 'st_flags')) os.symlink(support.TESTFN, _DUMMY_SYMLINK) self.teardown_files.append(_DUMMY_SYMLINK) dummy_symlink_st = os.lstat(_DUMMY_SYMLINK) def chflags_nofollow(path, flags): zwróć posix.chflags(path, flags, follow_symlinks=Nieprawda) dla fn w (posix.lchflags, chflags_nofollow): # ZFS returns EOPNOTSUPP when attempting to set flag UF_IMMUTABLE. flags = dummy_symlink_st.st_flags | stat.UF_IMMUTABLE spróbuj: fn(_DUMMY_SYMLINK, flags) wyjąwszy OSError jako err: jeżeli err.errno != errno.EOPNOTSUPP: podnieś msg = 'chflag UF_IMMUTABLE nie supported by underlying fs' self.skipTest(msg) spróbuj: new_testfn_st = os.stat(support.TESTFN) new_dummy_symlink_st = os.lstat(_DUMMY_SYMLINK) self.assertEqual(testfn_st.st_flags, new_testfn_st.st_flags) self.assertEqual(dummy_symlink_st.st_flags | stat.UF_IMMUTABLE, new_dummy_symlink_st.st_flags) w_końcu: fn(_DUMMY_SYMLINK, dummy_symlink_st.st_flags) def test_environ(self): jeżeli os.name == "nt": item_type = str inaczej: item_type = bytes dla k, v w posix.environ.items(): self.assertEqual(type(k), item_type) self.assertEqual(type(v), item_type) @unittest.skipUnless(hasattr(posix, 'getcwd'), 'test needs posix.getcwd()') def test_getcwd_long_pathnames(self): dirname = 'getcwd-test-directory-0123456789abcdef-01234567890abcdef' curdir = os.getcwd() base_path = os.path.abspath(support.TESTFN) + '.getcwd' spróbuj: os.mkdir(base_path) os.chdir(base_path) wyjąwszy: # Just returning nothing instead of the SkipTest exception, because # the test results w Error w that case. Is that ok? # podnieś unittest.SkipTest("cannot create directory dla testing") zwróć def _create_and_do_getcwd(dirname, current_path_length = 0): spróbuj: os.mkdir(dirname) wyjąwszy: podnieś unittest.SkipTest("mkdir cannot create directory sufficiently deep dla getcwd test") os.chdir(dirname) spróbuj: os.getcwd() jeżeli current_path_length < 1027: _create_and_do_getcwd(dirname, current_path_length + len(dirname) + 1) w_końcu: os.chdir('..') os.rmdir(dirname) _create_and_do_getcwd(dirname) w_końcu: os.chdir(curdir) support.rmtree(base_path) @unittest.skipUnless(hasattr(posix, 'getgrouplist'), "test needs posix.getgrouplist()") @unittest.skipUnless(hasattr(pwd, 'getpwuid'), "test needs pwd.getpwuid()") @unittest.skipUnless(hasattr(os, 'getuid'), "test needs os.getuid()") def test_getgrouplist(self): user = pwd.getpwuid(os.getuid())[0] group = pwd.getpwuid(os.getuid())[3] self.assertIn(group, posix.getgrouplist(user, group)) @unittest.skipUnless(hasattr(os, 'getegid'), "test needs os.getegid()") def test_getgroups(self): przy os.popen('id -G 2>/dev/null') jako idg: groups = idg.read().strip() ret = idg.close() jeżeli ret jest nie Nic albo nie groups: podnieś unittest.SkipTest("need working 'id -G'") # Issues 16698: OS X ABIs prior to 10.6 have limits on getgroups() jeżeli sys.platform == 'darwin': zaimportuj sysconfig dt = sysconfig.get_config_var('MACOSX_DEPLOYMENT_TARGET') albo '10.0' jeżeli tuple(int(n) dla n w dt.split('.')[0:2]) < (10, 6): podnieś unittest.SkipTest("getgroups(2) jest broken prior to 10.6") # 'id -G' oraz 'os.getgroups()' should zwróć the same # groups, ignoring order oraz duplicates. # #10822 - it jest implementation defined whether posix.getgroups() # includes the effective gid so we include it anyway, since id -G does self.assertEqual( set([int(x) dla x w groups.split()]), set(posix.getgroups() + [posix.getegid()])) # tests dla the posix *at functions follow @unittest.skipUnless(os.access w os.supports_dir_fd, "test needs dir_fd support dla os.access()") def test_access_dir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: self.assertPrawda(posix.access(support.TESTFN, os.R_OK, dir_fd=f)) w_końcu: posix.close(f) @unittest.skipUnless(os.chmod w os.supports_dir_fd, "test needs dir_fd support w os.chmod()") def test_chmod_dir_fd(self): os.chmod(support.TESTFN, stat.S_IRUSR) f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.chmod(support.TESTFN, stat.S_IRUSR | stat.S_IWUSR, dir_fd=f) s = posix.stat(support.TESTFN) self.assertEqual(s[0] & stat.S_IRWXU, stat.S_IRUSR | stat.S_IWUSR) w_końcu: posix.close(f) @unittest.skipUnless(os.chown w os.supports_dir_fd, "test needs dir_fd support w os.chown()") def test_chown_dir_fd(self): support.unlink(support.TESTFN) support.create_empty_file(support.TESTFN) f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.chown(support.TESTFN, os.getuid(), os.getgid(), dir_fd=f) w_końcu: posix.close(f) @unittest.skipUnless(os.stat w os.supports_dir_fd, "test needs dir_fd support w os.stat()") def test_stat_dir_fd(self): support.unlink(support.TESTFN) przy open(support.TESTFN, 'w') jako outfile: outfile.write("testline\n") f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: s1 = posix.stat(support.TESTFN) s2 = posix.stat(support.TESTFN, dir_fd=f) self.assertEqual(s1, s2) s2 = posix.stat(support.TESTFN, dir_fd=Nic) self.assertEqual(s1, s2) self.assertRaisesRegex(TypeError, 'should be integer, not', posix.stat, support.TESTFN, dir_fd=posix.getcwd()) self.assertRaisesRegex(TypeError, 'should be integer, not', posix.stat, support.TESTFN, dir_fd=float(f)) self.assertRaises(OverflowError, posix.stat, support.TESTFN, dir_fd=10**20) w_końcu: posix.close(f) @unittest.skipUnless(os.utime w os.supports_dir_fd, "test needs dir_fd support w os.utime()") def test_utime_dir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: now = time.time() posix.utime(support.TESTFN, Nic, dir_fd=f) posix.utime(support.TESTFN, dir_fd=f) self.assertRaises(TypeError, posix.utime, support.TESTFN, now, dir_fd=f) self.assertRaises(TypeError, posix.utime, support.TESTFN, (Nic, Nic), dir_fd=f) self.assertRaises(TypeError, posix.utime, support.TESTFN, (now, Nic), dir_fd=f) self.assertRaises(TypeError, posix.utime, support.TESTFN, (Nic, now), dir_fd=f) self.assertRaises(TypeError, posix.utime, support.TESTFN, (now, "x"), dir_fd=f) posix.utime(support.TESTFN, (int(now), int(now)), dir_fd=f) posix.utime(support.TESTFN, (now, now), dir_fd=f) posix.utime(support.TESTFN, (int(now), int((now - int(now)) * 1e9)), dir_fd=f) posix.utime(support.TESTFN, dir_fd=f, times=(int(now), int((now - int(now)) * 1e9))) # try dir_fd oraz follow_symlinks together jeżeli os.utime w os.supports_follow_symlinks: spróbuj: posix.utime(support.TESTFN, follow_symlinks=Nieprawda, dir_fd=f) wyjąwszy ValueError: # whoops! using both together nie supported on this platform. dalej w_końcu: posix.close(f) @unittest.skipUnless(os.link w os.supports_dir_fd, "test needs dir_fd support w os.link()") def test_link_dir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.link(support.TESTFN, support.TESTFN + 'link', src_dir_fd=f, dst_dir_fd=f) # should have same inodes self.assertEqual(posix.stat(support.TESTFN)[1], posix.stat(support.TESTFN + 'link')[1]) w_końcu: posix.close(f) support.unlink(support.TESTFN + 'link') @unittest.skipUnless(os.mkdir w os.supports_dir_fd, "test needs dir_fd support w os.mkdir()") def test_mkdir_dir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.mkdir(support.TESTFN + 'dir', dir_fd=f) posix.stat(support.TESTFN + 'dir') # should nie podnieś exception w_końcu: posix.close(f) support.rmtree(support.TESTFN + 'dir') @unittest.skipUnless((os.mknod w os.supports_dir_fd) oraz hasattr(stat, 'S_IFIFO'), "test requires both stat.S_IFIFO oraz dir_fd support dla os.mknod()") def test_mknod_dir_fd(self): # Test using mknodat() to create a FIFO (the only use specified # by POSIX). support.unlink(support.TESTFN) mode = stat.S_IFIFO | stat.S_IRUSR | stat.S_IWUSR f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.mknod(support.TESTFN, mode, 0, dir_fd=f) wyjąwszy OSError jako e: # Some old systems don't allow unprivileged users to use # mknod(), albo only support creating device nodes. self.assertIn(e.errno, (errno.EPERM, errno.EINVAL)) inaczej: self.assertPrawda(stat.S_ISFIFO(posix.stat(support.TESTFN).st_mode)) w_końcu: posix.close(f) @unittest.skipUnless(os.open w os.supports_dir_fd, "test needs dir_fd support w os.open()") def test_open_dir_fd(self): support.unlink(support.TESTFN) przy open(support.TESTFN, 'w') jako outfile: outfile.write("testline\n") a = posix.open(posix.getcwd(), posix.O_RDONLY) b = posix.open(support.TESTFN, posix.O_RDONLY, dir_fd=a) spróbuj: res = posix.read(b, 9).decode(encoding="utf-8") self.assertEqual("testline\n", res) w_końcu: posix.close(a) posix.close(b) @unittest.skipUnless(os.readlink w os.supports_dir_fd, "test needs dir_fd support w os.readlink()") def test_readlink_dir_fd(self): os.symlink(support.TESTFN, support.TESTFN + 'link') f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: self.assertEqual(posix.readlink(support.TESTFN + 'link'), posix.readlink(support.TESTFN + 'link', dir_fd=f)) w_końcu: support.unlink(support.TESTFN + 'link') posix.close(f) @unittest.skipUnless(os.rename w os.supports_dir_fd, "test needs dir_fd support w os.rename()") def test_rename_dir_fd(self): support.unlink(support.TESTFN) support.create_empty_file(support.TESTFN + 'ren') f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.rename(support.TESTFN + 'ren', support.TESTFN, src_dir_fd=f, dst_dir_fd=f) wyjąwszy: posix.rename(support.TESTFN + 'ren', support.TESTFN) podnieś inaczej: posix.stat(support.TESTFN) # should nie podnieś exception w_końcu: posix.close(f) @unittest.skipUnless(os.symlink w os.supports_dir_fd, "test needs dir_fd support w os.symlink()") def test_symlink_dir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.symlink(support.TESTFN, support.TESTFN + 'link', dir_fd=f) self.assertEqual(posix.readlink(support.TESTFN + 'link'), support.TESTFN) w_końcu: posix.close(f) support.unlink(support.TESTFN + 'link') @unittest.skipUnless(os.unlink w os.supports_dir_fd, "test needs dir_fd support w os.unlink()") def test_unlink_dir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) support.create_empty_file(support.TESTFN + 'del') posix.stat(support.TESTFN + 'del') # should nie podnieś exception spróbuj: posix.unlink(support.TESTFN + 'del', dir_fd=f) wyjąwszy: support.unlink(support.TESTFN + 'del') podnieś inaczej: self.assertRaises(OSError, posix.stat, support.TESTFN + 'link') w_końcu: posix.close(f) @unittest.skipUnless(os.mkfifo w os.supports_dir_fd, "test needs dir_fd support w os.mkfifo()") def test_mkfifo_dir_fd(self): support.unlink(support.TESTFN) f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.mkfifo(support.TESTFN, stat.S_IRUSR | stat.S_IWUSR, dir_fd=f) self.assertPrawda(stat.S_ISFIFO(posix.stat(support.TESTFN).st_mode)) w_końcu: posix.close(f) requires_sched_h = unittest.skipUnless(hasattr(posix, 'sched_uzyskaj'), "don't have scheduling support") requires_sched_affinity = unittest.skipUnless(hasattr(posix, 'sched_setaffinity'), "don't have sched affinity support") @requires_sched_h def test_sched_uzyskaj(self): # This has no error conditions (at least on Linux). posix.sched_uzyskaj() @requires_sched_h @unittest.skipUnless(hasattr(posix, 'sched_get_priority_max'), "requires sched_get_priority_max()") def test_sched_priority(self): # Round-robin usually has interesting priorities. pol = posix.SCHED_RR lo = posix.sched_get_priority_min(pol) hi = posix.sched_get_priority_max(pol) self.assertIsInstance(lo, int) self.assertIsInstance(hi, int) self.assertGreaterEqual(hi, lo) # OSX evidently just returns 15 without checking the argument. jeżeli sys.platform != "darwin": self.assertRaises(OSError, posix.sched_get_priority_min, -23) self.assertRaises(OSError, posix.sched_get_priority_max, -23) @unittest.skipUnless(hasattr(posix, 'sched_setscheduler'), "can't change scheduler") def test_get_and_set_scheduler_and_param(self): possible_schedulers = [sched dla name, sched w posix.__dict__.items() jeżeli name.startswith("SCHED_")] mine = posix.sched_getscheduler(0) self.assertIn(mine, possible_schedulers) spróbuj: parent = posix.sched_getscheduler(os.getppid()) wyjąwszy OSError jako e: jeżeli e.errno != errno.EPERM: podnieś inaczej: self.assertIn(parent, possible_schedulers) self.assertRaises(OSError, posix.sched_getscheduler, -1) self.assertRaises(OSError, posix.sched_getparam, -1) param = posix.sched_getparam(0) self.assertIsInstance(param.sched_priority, int) # POSIX states that calling sched_setparam() albo sched_setscheduler() on # a process przy a scheduling policy other than SCHED_FIFO albo SCHED_RR # jest implementation-defined: NetBSD oraz FreeBSD can zwróć EINVAL. jeżeli nie sys.platform.startswith(('freebsd', 'netbsd')): spróbuj: posix.sched_setscheduler(0, mine, param) posix.sched_setparam(0, param) wyjąwszy OSError jako e: jeżeli e.errno != errno.EPERM: podnieś self.assertRaises(OSError, posix.sched_setparam, -1, param) self.assertRaises(OSError, posix.sched_setscheduler, -1, mine, param) self.assertRaises(TypeError, posix.sched_setscheduler, 0, mine, Nic) self.assertRaises(TypeError, posix.sched_setparam, 0, 43) param = posix.sched_param(Nic) self.assertRaises(TypeError, posix.sched_setparam, 0, param) large = 214748364700 param = posix.sched_param(large) self.assertRaises(OverflowError, posix.sched_setparam, 0, param) param = posix.sched_param(sched_priority=-large) self.assertRaises(OverflowError, posix.sched_setparam, 0, param) @unittest.skipUnless(hasattr(posix, "sched_rr_get_interval"), "no function") def test_sched_rr_get_interval(self): spróbuj: interval = posix.sched_rr_get_interval(0) wyjąwszy OSError jako e: # This likely means that sched_rr_get_interval jest only valid for # processes przy the SCHED_RR scheduler w effect. jeżeli e.errno != errno.EINVAL: podnieś self.skipTest("only works on SCHED_RR processes") self.assertIsInstance(interval, float) # Reasonable constraints, I think. self.assertGreaterEqual(interval, 0.) self.assertLess(interval, 1.) @requires_sched_affinity def test_sched_getaffinity(self): mask = posix.sched_getaffinity(0) self.assertIsInstance(mask, set) self.assertGreaterEqual(len(mask), 1) self.assertRaises(OSError, posix.sched_getaffinity, -1) dla cpu w mask: self.assertIsInstance(cpu, int) self.assertGreaterEqual(cpu, 0) self.assertLess(cpu, 1 << 32) @requires_sched_affinity def test_sched_setaffinity(self): mask = posix.sched_getaffinity(0) jeżeli len(mask) > 1: # Empty masks are forbidden mask.pop() posix.sched_setaffinity(0, mask) self.assertEqual(posix.sched_getaffinity(0), mask) self.assertRaises(OSError, posix.sched_setaffinity, 0, []) self.assertRaises(ValueError, posix.sched_setaffinity, 0, [-10]) self.assertRaises(OverflowError, posix.sched_setaffinity, 0, [1<<128]) self.assertRaises(OSError, posix.sched_setaffinity, -1, mask) def test_rtld_constants(self): # check presence of major RTLD_* constants posix.RTLD_LAZY posix.RTLD_NOW posix.RTLD_GLOBAL posix.RTLD_LOCAL @unittest.skipUnless(hasattr(os, 'SEEK_HOLE'), "test needs an OS that reports file holes") def test_fs_holes(self): # Even jeżeli the filesystem doesn't report holes, # jeżeli the OS supports it the SEEK_* constants # will be defined oraz will have a consistent # behaviour: # os.SEEK_DATA = current position # os.SEEK_HOLE = end of file position przy open(support.TESTFN, 'r+b') jako fp: fp.write(b"hello") fp.flush() size = fp.tell() fno = fp.fileno() try : dla i w range(size): self.assertEqual(i, os.lseek(fno, i, os.SEEK_DATA)) self.assertLessEqual(size, os.lseek(fno, i, os.SEEK_HOLE)) self.assertRaises(OSError, os.lseek, fno, size, os.SEEK_DATA) self.assertRaises(OSError, os.lseek, fno, size, os.SEEK_HOLE) wyjąwszy OSError : # Some OSs claim to support SEEK_HOLE/SEEK_DATA # but it jest nie true. # For instance: # http://lists.freebsd.org/pipermail/freebsd-amd64/2012-January/014332.html podnieś unittest.SkipTest("OSError podnieśd!") def test_path_error2(self): """ Test functions that call path_error2(), providing two filenames w their exceptions. """ dla name w ("rename", "replace", "link"): function = getattr(os, name, Nic) jeżeli function jest Nic: kontynuuj dla dst w ("noodly2", support.TESTFN): spróbuj: function('doesnotexistfilename', dst) wyjąwszy OSError jako e: self.assertIn("'doesnotexistfilename' -> '{}'".format(dst), str(e)) przerwij inaczej: self.fail("No valid path_error2() test dla os." + name) def test_path_with_null_character(self): fn = support.TESTFN fn_with_NUL = fn + '\0' self.addCleanup(support.unlink, fn) support.unlink(fn) fd = Nic spróbuj: przy self.assertRaises(ValueError): fd = os.open(fn_with_NUL, os.O_WRONLY | os.O_CREAT) # podnieśs w_końcu: jeżeli fd jest nie Nic: os.close(fd) self.assertNieprawda(os.path.exists(fn)) self.assertRaises(ValueError, os.mkdir, fn_with_NUL) self.assertNieprawda(os.path.exists(fn)) open(fn, 'wb').close() self.assertRaises(ValueError, os.stat, fn_with_NUL) def test_path_with_null_byte(self): fn = os.fsencode(support.TESTFN) fn_with_NUL = fn + b'\0' self.addCleanup(support.unlink, fn) support.unlink(fn) fd = Nic spróbuj: przy self.assertRaises(ValueError): fd = os.open(fn_with_NUL, os.O_WRONLY | os.O_CREAT) # podnieśs w_końcu: jeżeli fd jest nie Nic: os.close(fd) self.assertNieprawda(os.path.exists(fn)) self.assertRaises(ValueError, os.mkdir, fn_with_NUL) self.assertNieprawda(os.path.exists(fn)) open(fn, 'wb').close() self.assertRaises(ValueError, os.stat, fn_with_NUL) klasa PosixGroupsTester(unittest.TestCase): def setUp(self): jeżeli posix.getuid() != 0: podnieś unittest.SkipTest("not enough privileges") jeżeli nie hasattr(posix, 'getgroups'): podnieś unittest.SkipTest("need posix.getgroups") jeżeli sys.platform == 'darwin': podnieś unittest.SkipTest("getgroups(2) jest broken on OSX") self.saved_groups = posix.getgroups() def tearDown(self): jeżeli hasattr(posix, 'setgroups'): posix.setgroups(self.saved_groups) albo_inaczej hasattr(posix, 'initgroups'): name = pwd.getpwuid(posix.getuid()).pw_name posix.initgroups(name, self.saved_groups[0]) @unittest.skipUnless(hasattr(posix, 'initgroups'), "test needs posix.initgroups()") def test_initgroups(self): # find missing group g = max(self.saved_groups albo [0]) + 1 name = pwd.getpwuid(posix.getuid()).pw_name posix.initgroups(name, g) self.assertIn(g, posix.getgroups()) @unittest.skipUnless(hasattr(posix, 'setgroups'), "test needs posix.setgroups()") def test_setgroups(self): dla groups w [[0], list(range(16))]: posix.setgroups(groups) self.assertListEqual(groups, posix.getgroups()) def test_main(): spróbuj: support.run_unittest(PosixTester, PosixGroupsTester) w_końcu: support.reap_children() jeżeli __name__ == '__main__': test_main()

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"Test posix functions" z test zaimportuj support posix = support.import_module('posix') zaimportuj errno zaimportuj sys zaimportuj time zaimportuj os zaimportuj platform zaimportuj pwd zaimportuj shutil zaimportuj stat zaimportuj tempfile zaimportuj unittest zaimportuj warnings _DUMMY_SYMLINK = os.path.join(tempfile.gettempdir(), support.TESTFN + '-dummy-symlink') klasa PosixTester(unittest.TestCase): def setUp(self): fp = open(support.TESTFN, 'w+') fp.close() self.teardown_files = [ support.TESTFN ] self._warnings_manager = support.check_warnings() self._warnings_manager.__enter__() warnings.filterwarnings('ignore', '.* potential security risk .*', RuntimeWarning) def tearDown(self): dla teardown_file w self.teardown_files: support.unlink(teardown_file) self._warnings_manager.__exit__(Nic, Nic, Nic) def testNoArgFunctions(self): NO_ARG_FUNCTIONS = [ "ctermid", "getcwd", "getcwdb", "uname", "times", "getloadavg", "getegid", "geteuid", "getgid", "getgroups", "getpid", "getpgrp", "getppid", "getuid", "sync", ] dla name w NO_ARG_FUNCTIONS: posix_func = getattr(posix, name, Nic) jeżeli posix_func jest nie Nic: posix_func() self.assertRaises(TypeError, posix_func, 1) @unittest.skipUnless(hasattr(posix, 'getresuid'), 'test needs posix.getresuid()') def test_getresuid(self): user_ids = posix.getresuid() self.assertEqual(len(user_ids), 3) dla val w user_ids: self.assertGreaterEqual(val, 0) @unittest.skipUnless(hasattr(posix, 'getresgid'), 'test needs posix.getresgid()') def test_getresgid(self): group_ids = posix.getresgid() self.assertEqual(len(group_ids), 3) dla val w group_ids: self.assertGreaterEqual(val, 0) @unittest.skipUnless(hasattr(posix, 'setresuid'), 'test needs posix.setresuid()') def test_setresuid(self): current_user_ids = posix.getresuid() self.assertIsNic(posix.setresuid(*current_user_ids)) self.assertIsNic(posix.setresuid(-1, -1, -1)) @unittest.skipUnless(hasattr(posix, 'setresuid'), 'test needs posix.setresuid()') def test_setresuid_exception(self): # Don't do this test jeżeli someone jest silly enough to run us jako root. current_user_ids = posix.getresuid() jeżeli 0 nie w current_user_ids: new_user_ids = (current_user_ids[0]+1, -1, -1) self.assertRaises(OSError, posix.setresuid, *new_user_ids) @unittest.skipUnless(hasattr(posix, 'setresgid'), 'test needs posix.setresgid()') def test_setresgid(self): current_group_ids = posix.getresgid() self.assertIsNic(posix.setresgid(*current_group_ids)) self.assertIsNic(posix.setresgid(-1, -1, -1)) @unittest.skipUnless(hasattr(posix, 'setresgid'), 'test needs posix.setresgid()') def test_setresgid_exception(self): # Don't do this test jeżeli someone jest silly enough to run us jako root. current_group_ids = posix.getresgid() jeżeli 0 nie w current_group_ids: new_group_ids = (current_group_ids[0]+1, -1, -1) self.assertRaises(OSError, posix.setresgid, *new_group_ids) @unittest.skipUnless(hasattr(posix, 'initgroups'), "test needs os.initgroups()") def test_initgroups(self): self.assertRaises(TypeError, posix.initgroups) self.assertRaises(TypeError, posix.initgroups, Nic) self.assertRaises(TypeError, posix.initgroups, 3, "foo") self.assertRaises(TypeError, posix.initgroups, "foo", 3, object()) jeżeli os.getuid() != 0: spróbuj: name = pwd.getpwuid(posix.getuid()).pw_name wyjąwszy KeyError: podnieś unittest.SkipTest("need a pwd entry") spróbuj: posix.initgroups(name, 13) wyjąwszy OSError jako e: self.assertEqual(e.errno, errno.EPERM) inaczej: self.fail("Expected OSError to be podnieśd by initgroups") @unittest.skipUnless(hasattr(posix, 'statvfs'), 'test needs posix.statvfs()') def test_statvfs(self): self.assertPrawda(posix.statvfs(os.curdir)) @unittest.skipUnless(hasattr(posix, 'fstatvfs'), 'test needs posix.fstatvfs()') def test_fstatvfs(self): fp = open(support.TESTFN) spróbuj: self.assertPrawda(posix.fstatvfs(fp.fileno())) self.assertPrawda(posix.statvfs(fp.fileno())) w_końcu: fp.close() @unittest.skipUnless(hasattr(posix, 'ftruncate'), 'test needs posix.ftruncate()') def test_ftruncate(self): fp = open(support.TESTFN, 'w+') spróbuj: fp.write('test') fp.flush() posix.ftruncate(fp.fileno(), 0) w_końcu: fp.close() @unittest.skipUnless(hasattr(posix, 'truncate'), "test needs posix.truncate()") def test_truncate(self): przy open(support.TESTFN, 'w') jako fp: fp.write('test') fp.flush() posix.truncate(support.TESTFN, 0) @unittest.skipUnless(getattr(os, 'execve', Nic) w os.supports_fd, "test needs execve() to support the fd parameter") @unittest.skipUnless(hasattr(os, 'fork'), "test needs os.fork()") @unittest.skipUnless(hasattr(os, 'waitpid'), "test needs os.waitpid()") def test_fexecve(self): fp = os.open(sys.executable, os.O_RDONLY) spróbuj: pid = os.fork() jeżeli pid == 0: os.chdir(os.path.split(sys.executable)[0]) posix.execve(fp, [sys.executable, '-c', 'pass'], os.environ) inaczej: self.assertEqual(os.waitpid(pid, 0), (pid, 0)) w_końcu: os.close(fp) @unittest.skipUnless(hasattr(posix, 'waitid'), "test needs posix.waitid()") @unittest.skipUnless(hasattr(os, 'fork'), "test needs os.fork()") def test_waitid(self): pid = os.fork() jeżeli pid == 0: os.chdir(os.path.split(sys.executable)[0]) posix.execve(sys.executable, [sys.executable, '-c', 'pass'], os.environ) inaczej: res = posix.waitid(posix.P_PID, pid, posix.WEXITED) self.assertEqual(pid, res.si_pid) @unittest.skipUnless(hasattr(posix, 'lockf'), "test needs posix.lockf()") def test_lockf(self): fd = os.open(support.TESTFN, os.O_WRONLY | os.O_CREAT) spróbuj: os.write(fd, b'test') os.lseek(fd, 0, os.SEEK_SET) posix.lockf(fd, posix.F_LOCK, 4) posix.lockf(fd, posix.F_ULOCK, 4) w_końcu: os.close(fd) @unittest.skipUnless(hasattr(posix, 'pread'), "test needs posix.pread()") def test_pread(self): fd = os.open(support.TESTFN, os.O_RDWR | os.O_CREAT) spróbuj: os.write(fd, b'test') os.lseek(fd, 0, os.SEEK_SET) self.assertEqual(b'es', posix.pread(fd, 2, 1)) self.assertEqual(b'te', posix.read(fd, 2)) w_końcu: os.close(fd) @unittest.skipUnless(hasattr(posix, 'pwrite'), "test needs posix.pwrite()") def test_pwrite(self): fd = os.open(support.TESTFN, os.O_RDWR | os.O_CREAT) spróbuj: os.write(fd, b'test') os.lseek(fd, 0, os.SEEK_SET) posix.pwrite(fd, b'xx', 1) self.assertEqual(b'txxt', posix.read(fd, 4)) w_końcu: os.close(fd) @unittest.skipUnless(hasattr(posix, 'posix_fallocate'), "test needs posix.posix_fallocate()") def test_posix_fallocate(self): fd = os.open(support.TESTFN, os.O_WRONLY | os.O_CREAT) spróbuj: posix.posix_fallocate(fd, 0, 10) wyjąwszy OSError jako inst: # issue10812, ZFS doesn't appear to support posix_fallocate, jeżeli inst.errno != errno.EINVAL albo nie sys.platform.startswith("sunos"): podnieś w_końcu: os.close(fd) @unittest.skipUnless(hasattr(posix, 'posix_fadvise'), "test needs posix.posix_fadvise()") def test_posix_fadvise(self): fd = os.open(support.TESTFN, os.O_RDONLY) spróbuj: posix.posix_fadvise(fd, 0, 0, posix.POSIX_FADV_WILLNEED) w_końcu: os.close(fd) @unittest.skipUnless(os.utime w os.supports_fd, "test needs fd support w os.utime") def test_utime_with_fd(self): now = time.time() fd = os.open(support.TESTFN, os.O_RDONLY) spróbuj: posix.utime(fd) posix.utime(fd, Nic) self.assertRaises(TypeError, posix.utime, fd, (Nic, Nic)) self.assertRaises(TypeError, posix.utime, fd, (now, Nic)) self.assertRaises(TypeError, posix.utime, fd, (Nic, now)) posix.utime(fd, (int(now), int(now))) posix.utime(fd, (now, now)) self.assertRaises(ValueError, posix.utime, fd, (now, now), ns=(now, now)) self.assertRaises(ValueError, posix.utime, fd, (now, 0), ns=(Nic, Nic)) self.assertRaises(ValueError, posix.utime, fd, (Nic, Nic), ns=(now, 0)) posix.utime(fd, (int(now), int((now - int(now)) * 1e9))) posix.utime(fd, ns=(int(now), int((now - int(now)) * 1e9))) w_końcu: os.close(fd) @unittest.skipUnless(os.utime w os.supports_follow_symlinks, "test needs follow_symlinks support w os.utime") def test_utime_nofollow_symlinks(self): now = time.time() posix.utime(support.TESTFN, Nic, follow_symlinks=Nieprawda) self.assertRaises(TypeError, posix.utime, support.TESTFN, (Nic, Nic), follow_symlinks=Nieprawda) self.assertRaises(TypeError, posix.utime, support.TESTFN, (now, Nic), follow_symlinks=Nieprawda) self.assertRaises(TypeError, posix.utime, support.TESTFN, (Nic, now), follow_symlinks=Nieprawda) posix.utime(support.TESTFN, (int(now), int(now)), follow_symlinks=Nieprawda) posix.utime(support.TESTFN, (now, now), follow_symlinks=Nieprawda) posix.utime(support.TESTFN, follow_symlinks=Nieprawda) @unittest.skipUnless(hasattr(posix, 'writev'), "test needs posix.writev()") def test_writev(self): fd = os.open(support.TESTFN, os.O_RDWR | os.O_CREAT) spróbuj: n = os.writev(fd, (b'test1', b'tt2', b't3')) self.assertEqual(n, 10) os.lseek(fd, 0, os.SEEK_SET) self.assertEqual(b'test1tt2t3', posix.read(fd, 10)) osix.writev(fd, []) wyjąwszy OSError: dalej inaczej: self.assertEqual(size, 0) w_końcu: os.close(fd) @unittest.skipUnless(hasattr(posix, 'readv'), "test needs posix.readv()") def test_readv(self): fd = os.open(support.TESTFN, os.O_RDWR | os.O_CREAT) spróbuj: os.write(fd, b'test1tt2t3') os.lseek(fd, 0, os.SEEK_SET) buf = [bytearray(i) dla i w [5, 3, 2]] self.assertEqual(posix.readv(fd, buf), 10) self.assertEqual([b'test1', b'tt2', b't3'], [bytes(i) dla i w buf]) osix.readv(fd, []) wyjąwszy OSError: dalej inaczej: self.assertEqual(size, 0) w_końcu: os.close(fd) @unittest.skipUnless(hasattr(posix, 'dup'), 'test needs posix.dup()') def test_dup(self): fp = open(support.TESTFN) spróbuj: fd = posix.dup(fp.fileno()) self.assertIsInstance(fd, int) os.close(fd) w_końcu: fp.close() @unittest.skipUnless(hasattr(posix, 'confstr'), 'test needs posix.confstr()') def test_confstr(self): self.assertRaises(ValueError, posix.confstr, "CS_garbage") self.assertEqual(len(posix.confstr("CS_PATH")) > 0, Prawda) @unittest.skipUnless(hasattr(posix, 'dup2'), 'test needs posix.dup2()') def test_dup2(self): fp1 = open(support.TESTFN) fp2 = open(support.TESTFN) spróbuj: posix.dup2(fp1.fileno(), fp2.fileno()) w_końcu: fp1.close() fp2.close() @unittest.skipUnless(hasattr(os, 'O_CLOEXEC'), "needs os.O_CLOEXEC") @support.requires_linux_version(2, 6, 23) def test_oscloexec(self): fd = os.open(support.TESTFN, os.O_RDONLY|os.O_CLOEXEC) self.addCleanup(os.close, fd) self.assertNieprawda(os.get_inheritable(fd)) @unittest.skipUnless(hasattr(posix, 'O_EXLOCK'), 'test needs posix.O_EXLOCK') def test_osexlock(self): fd = os.open(support.TESTFN, os.O_WRONLY|os.O_EXLOCK|os.O_CREAT) self.assertRaises(OSError, os.open, support.TESTFN, os.O_WRONLY|os.O_EXLOCK|os.O_NONBLOCK) os.close(fd) jeżeli hasattr(posix, "O_SHLOCK"): fd = os.open(support.TESTFN, os.O_WRONLY|os.O_SHLOCK|os.O_CREAT) self.assertRaises(OSError, os.open, support.TESTFN, os.O_WRONLY|os.O_EXLOCK|os.O_NONBLOCK) os.close(fd) @unittest.skipUnless(hasattr(posix, 'O_SHLOCK'), 'test needs posix.O_SHLOCK') def test_osshlock(self): fd1 = os.open(support.TESTFN, os.O_WRONLY|os.O_SHLOCK|os.O_CREAT) fd2 = os.open(support.TESTFN, os.O_WRONLY|os.O_SHLOCK|os.O_CREAT) os.close(fd2) os.close(fd1) jeżeli hasattr(posix, "O_EXLOCK"): fd = os.open(support.TESTFN, os.O_WRONLY|os.O_SHLOCK|os.O_CREAT) self.assertRaises(OSError, os.open, support.TESTFN, os.O_RDONLY|os.O_EXLOCK|os.O_NONBLOCK) os.close(fd) @unittest.skipUnless(hasattr(posix, 'fstat'), 'test needs posix.fstat()') def test_fstat(self): fp = open(support.TESTFN) spróbuj: self.assertPrawda(posix.fstat(fp.fileno())) self.assertPrawda(posix.stat(fp.fileno())) self.assertRaisesRegex(TypeError, 'should be string, bytes albo integer, not', posix.stat, float(fp.fileno())) w_końcu: fp.close() @unittest.skipUnless(hasattr(posix, 'stat'), 'test needs posix.stat()') def test_stat(self): self.assertPrawda(posix.stat(support.TESTFN)) self.assertPrawda(posix.stat(os.fsencode(support.TESTFN))) self.assertPrawda(posix.stat(bytearray(os.fsencode(support.TESTFN)))) self.assertRaisesRegex(TypeError, 'can\'t specify Nic dla path argument', posix.stat, Nic) self.assertRaisesRegex(TypeError, 'should be string, bytes albo integer, not', posix.stat, list(support.TESTFN)) self.assertRaisesRegex(TypeError, 'should be string, bytes albo integer, not', posix.stat, list(os.fsencode(support.TESTFN))) @unittest.skipUnless(hasattr(posix, 'mkfifo'), "don't have mkfifo()") def test_mkfifo(self): support.unlink(support.TESTFN) posix.mkfifo(support.TESTFN, stat.S_IRUSR | stat.S_IWUSR) self.assertPrawda(stat.S_ISFIFO(posix.stat(support.TESTFN).st_mode)) @unittest.skipUnless(hasattr(posix, 'mknod') oraz hasattr(stat, 'S_IFIFO'), "don't have mknod()/S_IFIFO") def test_mknod(self): # Test using mknod() to create a FIFO (the only use specified # by POSIX). support.unlink(support.TESTFN) mode = stat.S_IFIFO | stat.S_IRUSR | stat.S_IWUSR spróbuj: posix.mknod(support.TESTFN, mode, 0) wyjąwszy OSError jako e: # Some old systems don't allow unprivileged users to use self.assertIn(e.errno, (errno.EPERM, errno.EINVAL)) inaczej: self.assertPrawda(stat.S_ISFIFO(posix.stat(support.TESTFN).st_mode)) @unittest.skipUnless(hasattr(posix, 'stat'), 'test needs posix.stat()') @unittest.skipUnless(hasattr(posix, 'makedev'), 'test needs posix.makedev()') def test_makedev(self): st = posix.stat(support.TESTFN) dev = st.st_dev self.assertIsInstance(dev, int) self.assertGreaterEqual(dev, 0) major = posix.major(dev) self.assertIsInstance(major, int) self.assertGreaterEqual(major, 0) self.assertEqual(posix.major(dev), major) self.assertRaises(TypeError, posix.major, float(dev)) self.assertRaises(TypeError, posix.major) self.assertRaises((ValueError, OverflowError), posix.major, -1) minor = posix.minor(dev) self.assertIsInstance(minor, int) self.assertGreaterEqual(minor, 0) self.assertEqual(posix.minor(dev), minor) self.assertRaises(TypeError, posix.minor, float(dev)) self.assertRaises(TypeError, posix.minor) self.assertRaises((ValueError, OverflowError), posix.minor, -1) self.assertEqual(posix.makedev(major, minor), dev) self.assertRaises(TypeError, posix.makedev, float(major), minor) self.assertRaises(TypeError, posix.makedev, major, float(minor)) self.assertRaises(TypeError, posix.makedev, major) self.assertRaises(TypeError, posix.makedev) def _test_all_chown_common(self, chown_func, first_param, stat_func): """Common code dla chown, fchown oraz lchown tests.""" def check_stat(uid, gid): jeżeli stat_func jest nie Nic: stat = stat_func(first_param) self.assertEqual(stat.st_uid, uid) self.assertEqual(stat.st_gid, gid) uid = os.getuid() gid = os.getgid() chown_func(first_param, uid, gid) check_stat(uid, gid) chown_func(first_param, -1, gid) check_stat(uid, gid) chown_func(first_param, uid, -1) check_stat(uid, gid) jeżeli uid == 0: # # This problem keeps coming up: # http://bugs.python.org/issue1747858 # http://bugs.python.org/issue4591 # http://bugs.python.org/issue15301 # Hopefully the fix w 4591 fixes it dla good! # # This part of the test only runs when run jako root. # Only scary people run their tests jako root. big_value = 2**31 chown_func(first_param, big_value, big_value) check_stat(big_value, big_value) chown_func(first_param, -1, -1) check_stat(big_value, big_value) chown_func(first_param, uid, gid) check_stat(uid, gid) albo_inaczej platform.system() w ('HP-UX', 'SunOS'): # HP-UX oraz Solaris can allow a non-root user to chown() to root # (issue #5113) podnieś unittest.SkipTest("Skipping because of non-standard chown() " "behavior") inaczej: # non-root cannot chown to root, podnieśs OSError self.assertRaises(OSError, chown_func, first_param, 0, 0) check_stat(uid, gid) self.assertRaises(OSError, chown_func, first_param, 0, -1) check_stat(uid, gid) jeżeli 0 nie w os.getgroups(): self.assertRaises(OSError, chown_func, first_param, -1, 0) check_stat(uid, gid) # test illegal types dla t w str, float: self.assertRaises(TypeError, chown_func, first_param, t(uid), gid) check_stat(uid, gid) self.assertRaises(TypeError, chown_func, first_param, uid, t(gid)) check_stat(uid, gid) @unittest.skipUnless(hasattr(posix, 'chown'), "test needs os.chown()") def test_chown(self): # podnieś an OSError jeżeli the file does nie exist os.unlink(support.TESTFN) self.assertRaises(OSError, posix.chown, support.TESTFN, -1, -1) # re-create the file support.create_empty_file(support.TESTFN) self._test_all_chown_common(posix.chown, support.TESTFN, getattr(posix, 'stat', Nic)) @unittest.skipUnless(hasattr(posix, 'fchown'), "test needs os.fchown()") def test_fchown(self): os.unlink(support.TESTFN) # re-create the file test_file = open(support.TESTFN, 'w') spróbuj: fd = test_file.fileno() self._test_all_chown_common(posix.fchown, fd, getattr(posix, 'fstat', Nic)) w_końcu: test_file.close() @unittest.skipUnless(hasattr(posix, 'lchown'), "test needs os.lchown()") def test_lchown(self): os.unlink(support.TESTFN) # create a symlink os.symlink(_DUMMY_SYMLINK, support.TESTFN) self._test_all_chown_common(posix.lchown, support.TESTFN, getattr(posix, 'lstat', Nic)) @unittest.skipUnless(hasattr(posix, 'chdir'), 'test needs posix.chdir()') def test_chdir(self): posix.chdir(os.curdir) self.assertRaises(OSError, posix.chdir, support.TESTFN) def test_listdir(self): self.assertPrawda(support.TESTFN w posix.listdir(os.curdir)) def test_listdir_default(self): # When listdir jest called without argument, # it's the same jako listdir(os.curdir). self.assertPrawda(support.TESTFN w posix.listdir()) def test_listdir_bytes(self): self.assertPrawda(os.fsencode(support.TESTFN) w posix.listdir(b'.')) @unittest.skipUnless(posix.listdir w os.supports_fd, "test needs fd support dla posix.listdir()") def test_listdir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) self.addCleanup(posix.close, f) self.assertEqual( sorted(posix.listdir('.')), sorted(posix.listdir(f)) ) self.assertEqual( sorted(posix.listdir('.')), sorted(posix.listdir(f)) ) @unittest.skipUnless(hasattr(posix, 'access'), 'test needs posix.access()') def test_access(self): self.assertPrawda(posix.access(support.TESTFN, os.R_OK)) @unittest.skipUnless(hasattr(posix, 'umask'), 'test needs posix.umask()') def test_umask(self): old_mask = posix.umask(0) self.assertIsInstance(old_mask, int) posix.umask(old_mask) @unittest.skipUnless(hasattr(posix, 'strerror'), 'test needs posix.strerror()') def test_strerror(self): self.assertPrawda(posix.strerror(0)) @unittest.skipUnless(hasattr(posix, 'pipe'), 'test needs posix.pipe()') def test_pipe(self): reader, writer = posix.pipe() os.close(reader) os.close(writer) @unittest.skipUnless(hasattr(os, 'pipe2'), "test needs os.pipe2()") @support.requires_linux_version(2, 6, 27) def test_pipe2(self): self.assertRaises(TypeError, os.pipe2, 'DEADBEEF') self.assertRaises(TypeError, os.pipe2, 0, 0) r, w = os.pipe2(0) os.close(r) os.close(w) r, w = os.pipe2(os.O_CLOEXEC|os.O_NONBLOCK) self.addCleanup(os.close, r) self.addCleanup(os.close, w) self.assertNieprawda(os.get_inheritable(r)) self.assertNieprawda(os.get_inheritable(w)) self.assertNieprawda(os.get_blocking(r)) self.assertNieprawda(os.get_blocking(w)) self.assertRaises(OSError, os.read, r, 1) spróbuj: os.write(w, b'x' * support.PIPE_MAX_SIZE) wyjąwszy OSError: dalej @support.cpython_only @unittest.skipUnless(hasattr(os, 'pipe2'), "test needs os.pipe2()") @support.requires_linux_version(2, 6, 27) def test_pipe2_c_limits(self): zaimportuj _testcapi self.assertRaises(OverflowError, os.pipe2, _testcapi.INT_MAX + 1) self.assertRaises(OverflowError, os.pipe2, _testcapi.UINT_MAX + 1) @unittest.skipUnless(hasattr(posix, 'utime'), 'test needs posix.utime()') def test_utime(self): now = time.time() posix.utime(support.TESTFN, Nic) self.assertRaises(TypeError, posix.utime, support.TESTFN, (Nic, Nic)) self.assertRaises(TypeError, posix.utime, support.TESTFN, (now, Nic)) self.assertRaises(TypeError, posix.utime, support.TESTFN, (Nic, now)) posix.utime(support.TESTFN, (int(now), int(now))) posix.utime(support.TESTFN, (now, now)) def _test_chflags_regular_file(self, chflags_func, target_file, **kwargs): st = os.stat(target_file) self.assertPrawda(hasattr(st, 'st_flags')) flags = st.st_flags | stat.UF_IMMUTABLE spróbuj: chflags_func(target_file, flags, **kwargs) wyjąwszy OSError jako err: jeżeli err.errno != errno.EOPNOTSUPP: podnieś msg = 'chflag UF_IMMUTABLE nie supported by underlying fs' self.skipTest(msg) spróbuj: new_st = os.stat(target_file) self.assertEqual(st.st_flags | stat.UF_IMMUTABLE, new_st.st_flags) spróbuj: fd = open(target_file, 'w+') wyjąwszy OSError jako e: self.assertEqual(e.errno, errno.EPERM) w_końcu: posix.chflags(target_file, st.st_flags) @unittest.skipUnless(hasattr(posix, 'chflags'), 'test needs os.chflags()') def test_chflags(self): self._test_chflags_regular_file(posix.chflags, support.TESTFN) @unittest.skipUnless(hasattr(posix, 'lchflags'), 'test needs os.lchflags()') def test_lchflags_regular_file(self): self._test_chflags_regular_file(posix.lchflags, support.TESTFN) self._test_chflags_regular_file(posix.chflags, support.TESTFN, follow_symlinks=Nieprawda) @unittest.skipUnless(hasattr(posix, 'lchflags'), 'test needs os.lchflags()') def test_lchflags_symlink(self): testfn_st = os.stat(support.TESTFN) self.assertPrawda(hasattr(testfn_st, 'st_flags')) os.symlink(support.TESTFN, _DUMMY_SYMLINK) self.teardown_files.append(_DUMMY_SYMLINK) dummy_symlink_st = os.lstat(_DUMMY_SYMLINK) def chflags_nofollow(path, flags): zwróć posix.chflags(path, flags, follow_symlinks=Nieprawda) dla fn w (posix.lchflags, chflags_nofollow): flags = dummy_symlink_st.st_flags | stat.UF_IMMUTABLE spróbuj: fn(_DUMMY_SYMLINK, flags) wyjąwszy OSError jako err: jeżeli err.errno != errno.EOPNOTSUPP: podnieś msg = 'chflag UF_IMMUTABLE nie supported by underlying fs' self.skipTest(msg) spróbuj: new_testfn_st = os.stat(support.TESTFN) new_dummy_symlink_st = os.lstat(_DUMMY_SYMLINK) self.assertEqual(testfn_st.st_flags, new_testfn_st.st_flags) self.assertEqual(dummy_symlink_st.st_flags | stat.UF_IMMUTABLE, new_dummy_symlink_st.st_flags) w_końcu: fn(_DUMMY_SYMLINK, dummy_symlink_st.st_flags) def test_environ(self): jeżeli os.name == "nt": item_type = str inaczej: item_type = bytes dla k, v w posix.environ.items(): self.assertEqual(type(k), item_type) self.assertEqual(type(v), item_type) @unittest.skipUnless(hasattr(posix, 'getcwd'), 'test needs posix.getcwd()') def test_getcwd_long_pathnames(self): dirname = 'getcwd-test-directory-0123456789abcdef-01234567890abcdef' curdir = os.getcwd() base_path = os.path.abspath(support.TESTFN) + '.getcwd' spróbuj: os.mkdir(base_path) os.chdir(base_path) wyjąwszy: zwróć def _create_and_do_getcwd(dirname, current_path_length = 0): spróbuj: os.mkdir(dirname) wyjąwszy: podnieś unittest.SkipTest("mkdir cannot create directory sufficiently deep dla getcwd test") os.chdir(dirname) spróbuj: os.getcwd() jeżeli current_path_length < 1027: _create_and_do_getcwd(dirname, current_path_length + len(dirname) + 1) w_końcu: os.chdir('..') os.rmdir(dirname) _create_and_do_getcwd(dirname) w_końcu: os.chdir(curdir) support.rmtree(base_path) @unittest.skipUnless(hasattr(posix, 'getgrouplist'), "test needs posix.getgrouplist()") @unittest.skipUnless(hasattr(pwd, 'getpwuid'), "test needs pwd.getpwuid()") @unittest.skipUnless(hasattr(os, 'getuid'), "test needs os.getuid()") def test_getgrouplist(self): user = pwd.getpwuid(os.getuid())[0] group = pwd.getpwuid(os.getuid())[3] self.assertIn(group, posix.getgrouplist(user, group)) @unittest.skipUnless(hasattr(os, 'getegid'), "test needs os.getegid()") def test_getgroups(self): przy os.popen('id -G 2>/dev/null') jako idg: groups = idg.read().strip() ret = idg.close() jeżeli ret jest nie Nic albo nie groups: podnieś unittest.SkipTest("need working 'id -G'") jeżeli sys.platform == 'darwin': zaimportuj sysconfig dt = sysconfig.get_config_var('MACOSX_DEPLOYMENT_TARGET') albo '10.0' jeżeli tuple(int(n) dla n w dt.split('.')[0:2]) < (10, 6): podnieś unittest.SkipTest("getgroups(2) jest broken prior to 10.6") la x w groups.split()]), set(posix.getgroups() + [posix.getegid()])) @unittest.skipUnless(os.access w os.supports_dir_fd, "test needs dir_fd support dla os.access()") def test_access_dir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: self.assertPrawda(posix.access(support.TESTFN, os.R_OK, dir_fd=f)) w_końcu: posix.close(f) @unittest.skipUnless(os.chmod w os.supports_dir_fd, "test needs dir_fd support w os.chmod()") def test_chmod_dir_fd(self): os.chmod(support.TESTFN, stat.S_IRUSR) f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.chmod(support.TESTFN, stat.S_IRUSR | stat.S_IWUSR, dir_fd=f) s = posix.stat(support.TESTFN) self.assertEqual(s[0] & stat.S_IRWXU, stat.S_IRUSR | stat.S_IWUSR) w_końcu: posix.close(f) @unittest.skipUnless(os.chown w os.supports_dir_fd, "test needs dir_fd support w os.chown()") def test_chown_dir_fd(self): support.unlink(support.TESTFN) support.create_empty_file(support.TESTFN) f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.chown(support.TESTFN, os.getuid(), os.getgid(), dir_fd=f) w_końcu: posix.close(f) @unittest.skipUnless(os.stat w os.supports_dir_fd, "test needs dir_fd support w os.stat()") def test_stat_dir_fd(self): support.unlink(support.TESTFN) przy open(support.TESTFN, 'w') jako outfile: outfile.write("testline\n") f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: s1 = posix.stat(support.TESTFN) s2 = posix.stat(support.TESTFN, dir_fd=f) self.assertEqual(s1, s2) s2 = posix.stat(support.TESTFN, dir_fd=Nic) self.assertEqual(s1, s2) self.assertRaisesRegex(TypeError, 'should be integer, not', posix.stat, support.TESTFN, dir_fd=posix.getcwd()) self.assertRaisesRegex(TypeError, 'should be integer, not', posix.stat, support.TESTFN, dir_fd=float(f)) self.assertRaises(OverflowError, posix.stat, support.TESTFN, dir_fd=10**20) w_końcu: posix.close(f) @unittest.skipUnless(os.utime w os.supports_dir_fd, "test needs dir_fd support w os.utime()") def test_utime_dir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: now = time.time() posix.utime(support.TESTFN, Nic, dir_fd=f) posix.utime(support.TESTFN, dir_fd=f) self.assertRaises(TypeError, posix.utime, support.TESTFN, now, dir_fd=f) self.assertRaises(TypeError, posix.utime, support.TESTFN, (Nic, Nic), dir_fd=f) self.assertRaises(TypeError, posix.utime, support.TESTFN, (now, Nic), dir_fd=f) self.assertRaises(TypeError, posix.utime, support.TESTFN, (Nic, now), dir_fd=f) self.assertRaises(TypeError, posix.utime, support.TESTFN, (now, "x"), dir_fd=f) posix.utime(support.TESTFN, (int(now), int(now)), dir_fd=f) posix.utime(support.TESTFN, (now, now), dir_fd=f) posix.utime(support.TESTFN, (int(now), int((now - int(now)) * 1e9)), dir_fd=f) posix.utime(support.TESTFN, dir_fd=f, times=(int(now), int((now - int(now)) * 1e9))) jeżeli os.utime w os.supports_follow_symlinks: spróbuj: posix.utime(support.TESTFN, follow_symlinks=Nieprawda, dir_fd=f) wyjąwszy ValueError: dalej w_końcu: posix.close(f) @unittest.skipUnless(os.link w os.supports_dir_fd, "test needs dir_fd support w os.link()") def test_link_dir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.link(support.TESTFN, support.TESTFN + 'link', src_dir_fd=f, dst_dir_fd=f) self.assertEqual(posix.stat(support.TESTFN)[1], posix.stat(support.TESTFN + 'link')[1]) w_końcu: posix.close(f) support.unlink(support.TESTFN + 'link') @unittest.skipUnless(os.mkdir w os.supports_dir_fd, "test needs dir_fd support w os.mkdir()") def test_mkdir_dir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.mkdir(support.TESTFN + 'dir', dir_fd=f) posix.stat(support.TESTFN + 'dir') w_końcu: posix.close(f) support.rmtree(support.TESTFN + 'dir') @unittest.skipUnless((os.mknod w os.supports_dir_fd) oraz hasattr(stat, 'S_IFIFO'), "test requires both stat.S_IFIFO oraz dir_fd support dla os.mknod()") def test_mknod_dir_fd(self): support.unlink(support.TESTFN) mode = stat.S_IFIFO | stat.S_IRUSR | stat.S_IWUSR f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.mknod(support.TESTFN, mode, 0, dir_fd=f) wyjąwszy OSError jako e: # mknod(), albo only support creating device nodes. self.assertIn(e.errno, (errno.EPERM, errno.EINVAL)) inaczej: self.assertPrawda(stat.S_ISFIFO(posix.stat(support.TESTFN).st_mode)) w_końcu: posix.close(f) @unittest.skipUnless(os.open w os.supports_dir_fd, "test needs dir_fd support w os.open()") def test_open_dir_fd(self): support.unlink(support.TESTFN) przy open(support.TESTFN, 'w') jako outfile: outfile.write("testline\n") a = posix.open(posix.getcwd(), posix.O_RDONLY) b = posix.open(support.TESTFN, posix.O_RDONLY, dir_fd=a) spróbuj: res = posix.read(b, 9).decode(encoding="utf-8") self.assertEqual("testline\n", res) w_końcu: posix.close(a) posix.close(b) @unittest.skipUnless(os.readlink w os.supports_dir_fd, "test needs dir_fd support w os.readlink()") def test_readlink_dir_fd(self): os.symlink(support.TESTFN, support.TESTFN + 'link') f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: self.assertEqual(posix.readlink(support.TESTFN + 'link'), posix.readlink(support.TESTFN + 'link', dir_fd=f)) w_końcu: support.unlink(support.TESTFN + 'link') posix.close(f) @unittest.skipUnless(os.rename w os.supports_dir_fd, "test needs dir_fd support w os.rename()") def test_rename_dir_fd(self): support.unlink(support.TESTFN) support.create_empty_file(support.TESTFN + 'ren') f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.rename(support.TESTFN + 'ren', support.TESTFN, src_dir_fd=f, dst_dir_fd=f) wyjąwszy: posix.rename(support.TESTFN + 'ren', support.TESTFN) podnieś inaczej: posix.stat(support.TESTFN) # should nie podnieś exception w_końcu: posix.close(f) @unittest.skipUnless(os.symlink w os.supports_dir_fd, "test needs dir_fd support w os.symlink()") def test_symlink_dir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.symlink(support.TESTFN, support.TESTFN + 'link', dir_fd=f) self.assertEqual(posix.readlink(support.TESTFN + 'link'), support.TESTFN) w_końcu: posix.close(f) support.unlink(support.TESTFN + 'link') @unittest.skipUnless(os.unlink w os.supports_dir_fd, "test needs dir_fd support w os.unlink()") def test_unlink_dir_fd(self): f = posix.open(posix.getcwd(), posix.O_RDONLY) support.create_empty_file(support.TESTFN + 'del') posix.stat(support.TESTFN + 'del') # should nie podnieś exception spróbuj: posix.unlink(support.TESTFN + 'del', dir_fd=f) wyjąwszy: support.unlink(support.TESTFN + 'del') podnieś inaczej: self.assertRaises(OSError, posix.stat, support.TESTFN + 'link') w_końcu: posix.close(f) @unittest.skipUnless(os.mkfifo w os.supports_dir_fd, "test needs dir_fd support w os.mkfifo()") def test_mkfifo_dir_fd(self): support.unlink(support.TESTFN) f = posix.open(posix.getcwd(), posix.O_RDONLY) spróbuj: posix.mkfifo(support.TESTFN, stat.S_IRUSR | stat.S_IWUSR, dir_fd=f) self.assertPrawda(stat.S_ISFIFO(posix.stat(support.TESTFN).st_mode)) w_końcu: posix.close(f) requires_sched_h = unittest.skipUnless(hasattr(posix, 'sched_uzyskaj'), "don't have scheduling support") requires_sched_affinity = unittest.skipUnless(hasattr(posix, 'sched_setaffinity'), "don't have sched affinity support") @requires_sched_h def test_sched_uzyskaj(self): # This has no error conditions (at least on Linux). posix.sched_uzyskaj() @requires_sched_h @unittest.skipUnless(hasattr(posix, 'sched_get_priority_max'), "requires sched_get_priority_max()") def test_sched_priority(self): # Round-robin usually has interesting priorities. pol = posix.SCHED_RR lo = posix.sched_get_priority_min(pol) hi = posix.sched_get_priority_max(pol) self.assertIsInstance(lo, int) self.assertIsInstance(hi, int) self.assertGreaterEqual(hi, lo) # OSX evidently just returns 15 without checking the argument. jeżeli sys.platform != "darwin": self.assertRaises(OSError, posix.sched_get_priority_min, -23) self.assertRaises(OSError, posix.sched_get_priority_max, -23) @unittest.skipUnless(hasattr(posix, 'sched_setscheduler'), "can't change scheduler") def test_get_and_set_scheduler_and_param(self): possible_schedulers = [sched dla name, sched w posix.__dict__.items() jeżeli name.startswith("SCHED_")] mine = posix.sched_getscheduler(0) self.assertIn(mine, possible_schedulers) spróbuj: parent = posix.sched_getscheduler(os.getppid()) wyjąwszy OSError jako e: jeżeli e.errno != errno.EPERM: podnieś inaczej: self.assertIn(parent, possible_schedulers) self.assertRaises(OSError, posix.sched_getscheduler, -1) self.assertRaises(OSError, posix.sched_getparam, -1) param = posix.sched_getparam(0) self.assertIsInstance(param.sched_priority, int) jeżeli nie sys.platform.startswith(('freebsd', 'netbsd')): spróbuj: posix.sched_setscheduler(0, mine, param) posix.sched_setparam(0, param) wyjąwszy OSError jako e: jeżeli e.errno != errno.EPERM: podnieś self.assertRaises(OSError, posix.sched_setparam, -1, param) self.assertRaises(OSError, posix.sched_setscheduler, -1, mine, param) self.assertRaises(TypeError, posix.sched_setscheduler, 0, mine, Nic) self.assertRaises(TypeError, posix.sched_setparam, 0, 43) param = posix.sched_param(Nic) self.assertRaises(TypeError, posix.sched_setparam, 0, param) large = 214748364700 param = posix.sched_param(large) self.assertRaises(OverflowError, posix.sched_setparam, 0, param) param = posix.sched_param(sched_priority=-large) self.assertRaises(OverflowError, posix.sched_setparam, 0, param) @unittest.skipUnless(hasattr(posix, "sched_rr_get_interval"), "no function") def test_sched_rr_get_interval(self): spróbuj: interval = posix.sched_rr_get_interval(0) wyjąwszy OSError jako e: jeżeli e.errno != errno.EINVAL: podnieś self.skipTest("only works on SCHED_RR processes") self.assertIsInstance(interval, float) self.assertGreaterEqual(interval, 0.) self.assertLess(interval, 1.) @requires_sched_affinity def test_sched_getaffinity(self): mask = posix.sched_getaffinity(0) self.assertIsInstance(mask, set) self.assertGreaterEqual(len(mask), 1) self.assertRaises(OSError, posix.sched_getaffinity, -1) dla cpu w mask: self.assertIsInstance(cpu, int) self.assertGreaterEqual(cpu, 0) self.assertLess(cpu, 1 << 32) @requires_sched_affinity def test_sched_setaffinity(self): mask = posix.sched_getaffinity(0) jeżeli len(mask) > 1: mask.pop() posix.sched_setaffinity(0, mask) self.assertEqual(posix.sched_getaffinity(0), mask) self.assertRaises(OSError, posix.sched_setaffinity, 0, []) self.assertRaises(ValueError, posix.sched_setaffinity, 0, [-10]) self.assertRaises(OverflowError, posix.sched_setaffinity, 0, [1<<128]) self.assertRaises(OSError, posix.sched_setaffinity, -1, mask) def test_rtld_constants(self): posix.RTLD_LAZY posix.RTLD_NOW posix.RTLD_GLOBAL posix.RTLD_LOCAL @unittest.skipUnless(hasattr(os, 'SEEK_HOLE'), "test needs an OS that reports file holes") def test_fs_holes(self): # jeżeli the OS supports it the SEEK_* constants # will be defined oraz will have a consistent # behaviour: # os.SEEK_DATA = current position # os.SEEK_HOLE = end of file position przy open(support.TESTFN, 'r+b') jako fp: fp.write(b"hello") fp.flush() size = fp.tell() fno = fp.fileno() try : dla i w range(size): self.assertEqual(i, os.lseek(fno, i, os.SEEK_DATA)) self.assertLessEqual(size, os.lseek(fno, i, os.SEEK_HOLE)) self.assertRaises(OSError, os.lseek, fno, size, os.SEEK_DATA) self.assertRaises(OSError, os.lseek, fno, size, os.SEEK_HOLE) wyjąwszy OSError : # Some OSs claim to support SEEK_HOLE/SEEK_DATA # but it jest nie true. # For instance: # http://lists.freebsd.org/pipermail/freebsd-amd64/2012-January/014332.html podnieś unittest.SkipTest("OSError podnieśd!") def test_path_error2(self): """ Test functions that call path_error2(), providing two filenames w their exceptions. """ dla name w ("rename", "replace", "link"): function = getattr(os, name, Nic) jeżeli function jest Nic: kontynuuj dla dst w ("noodly2", support.TESTFN): spróbuj: function('doesnotexistfilename', dst) wyjąwszy OSError jako e: self.assertIn("'doesnotexistfilename' -> '{}'".format(dst), str(e)) przerwij inaczej: self.fail("No valid path_error2() test dla os." + name) def test_path_with_null_character(self): fn = support.TESTFN fn_with_NUL = fn + '\0' self.addCleanup(support.unlink, fn) support.unlink(fn) fd = Nic spróbuj: przy self.assertRaises(ValueError): fd = os.open(fn_with_NUL, os.O_WRONLY | os.O_CREAT) # podnieśs w_końcu: jeżeli fd jest nie Nic: os.close(fd) self.assertNieprawda(os.path.exists(fn)) self.assertRaises(ValueError, os.mkdir, fn_with_NUL) self.assertNieprawda(os.path.exists(fn)) open(fn, 'wb').close() self.assertRaises(ValueError, os.stat, fn_with_NUL) def test_path_with_null_byte(self): fn = os.fsencode(support.TESTFN) fn_with_NUL = fn + b'\0' self.addCleanup(support.unlink, fn) support.unlink(fn) fd = Nic spróbuj: przy self.assertRaises(ValueError): fd = os.open(fn_with_NUL, os.O_WRONLY | os.O_CREAT) # podnieśs w_końcu: jeżeli fd jest nie Nic: os.close(fd) self.assertNieprawda(os.path.exists(fn)) self.assertRaises(ValueError, os.mkdir, fn_with_NUL) self.assertNieprawda(os.path.exists(fn)) open(fn, 'wb').close() self.assertRaises(ValueError, os.stat, fn_with_NUL) klasa PosixGroupsTester(unittest.TestCase): def setUp(self): jeżeli posix.getuid() != 0: podnieś unittest.SkipTest("not enough privileges") jeżeli nie hasattr(posix, 'getgroups'): podnieś unittest.SkipTest("need posix.getgroups") jeżeli sys.platform == 'darwin': podnieś unittest.SkipTest("getgroups(2) jest broken on OSX") self.saved_groups = posix.getgroups() def tearDown(self): jeżeli hasattr(posix, 'setgroups'): posix.setgroups(self.saved_groups) albo_inaczej hasattr(posix, 'initgroups'): name = pwd.getpwuid(posix.getuid()).pw_name posix.initgroups(name, self.saved_groups[0]) @unittest.skipUnless(hasattr(posix, 'initgroups'), "test needs posix.initgroups()") def test_initgroups(self): # find missing group g = max(self.saved_groups albo [0]) + 1 name = pwd.getpwuid(posix.getuid()).pw_name posix.initgroups(name, g) self.assertIn(g, posix.getgroups()) @unittest.skipUnless(hasattr(posix, 'setgroups'), "test needs posix.setgroups()") def test_setgroups(self): dla groups w [[0], list(range(16))]: posix.setgroups(groups) self.assertListEqual(groups, posix.getgroups()) def test_main(): spróbuj: support.run_unittest(PosixTester, PosixGroupsTester) w_końcu: support.reap_children() jeżeli __name__ == '__main__': test_main()

false

true

f7178ca40551efa85c6e00fccc36031532a0273f

2,864

py

Python

DataManagement.py

DonCammne/OpenSeesPyAssistant

f380f0f2a2f3d1336320bd8d26fa5efe00a12134

[ "MIT" ]

null

DataManagement.py

DonCammne/OpenSeesPyAssistant

f380f0f2a2f3d1336320bd8d26fa5efe00a12134

[ "MIT" ]

null

DataManagement.py

DonCammne/OpenSeesPyAssistant

f380f0f2a2f3d1336320bd8d26fa5efe00a12134

[ "MIT" ]

null

""" Module with the parent abstract class DataManagement. \n Carmine Schipani, 2021 """ from abc import ABC, abstractmethod from OpenSeesPyAssistant.ErrorHandling import * import numpy as np class DataManagement(ABC): """ Abstract parent class for data management. Using the associated MATLAB class \n LOAD_CLASS.m \n for the postprocessing in MATLAB, allowing for simpler and more reliable data management because the parameters from the OpenSeesPy analysis are imported automatically. """ def SaveData(self, f): """ Function that lists in the command window and saves in a opened file text "f" the data from the "self" class that calls it. Example: call this function after this line: \n with open(FileName, 'w') as f: @param f (io.TextIOWrapper): Opened file to write into @exception WrongDimension: The number of lists in the list self.data needs to be 2 """ if len(self.data[0]) != 2: raise WrongDimension() delimiter = "##############################" # 30 times # col_delimiter = "\t" # tab for data_line in self.data: f.write('\n') for col in data_line: if type(col) == np.ndarray: tmp_str = np.array_str(col, max_line_width = np.inf) else: tmp_str = str(col) f.write(tmp_str) f.write(col_delimiter) f.write('\n') f.write('NEW INFO SECTION DELIMITER \t') f.write(delimiter) @abstractmethod def ShowInfo(self): """ Abstract method that shows the data stored in the class in the command window. In some cases, it's possible to plot some information (for example the curve of the material model). """ pass @abstractmethod def ReInit(self): """ Abstract method that computes the value of the parameters with respect of the arguments. \n Use after changing the value of argument inside the class (to update the values accordingly). \n This function can be very useful in combination with the function "deepcopy()" from the module "copy". \n Be careful that the parameter self.Initialized is also copied, thus it is safer to copy the class before the method that calls the actual OpenSees commands (and initialise the object). """ pass @abstractmethod def UpdateStoredData(self): """ Abstract method used to define and update the self.data member variable. \n This member variable (self.data) is a list of lists with 2 entries (info_name and info_value) and for each list is stored a different member variable of the class. \n Useful to debug the model, export data, copy object. """ pass

39.232877

192

0.631634

from abc import ABC, abstractmethod from OpenSeesPyAssistant.ErrorHandling import * import numpy as np class DataManagement(ABC): def SaveData(self, f): if len(self.data[0]) != 2: raise WrongDimension() delimiter = "##############################" col_delimiter = "\t" for data_line in self.data: f.write('\n') for col in data_line: if type(col) == np.ndarray: tmp_str = np.array_str(col, max_line_width = np.inf) else: tmp_str = str(col) f.write(tmp_str) f.write(col_delimiter) f.write('\n') f.write('NEW INFO SECTION DELIMITER \t') f.write(delimiter) @abstractmethod def ShowInfo(self): pass @abstractmethod def ReInit(self): pass @abstractmethod def UpdateStoredData(self): pass

true

f7178e22c6a7b86147f3ad4e697f29a6c67dce4f

826

py

Python

final exam 2/World Tour.py

DiyanKalaydzhiev23/fundamentals---python

7fa032d9a3270648ffa383bb00dad8e51613189d

[ "MIT" ]

null

final exam 2/World Tour.py

DiyanKalaydzhiev23/fundamentals---python

7fa032d9a3270648ffa383bb00dad8e51613189d

[ "MIT" ]

null

final exam 2/World Tour.py

DiyanKalaydzhiev23/fundamentals---python

7fa032d9a3270648ffa383bb00dad8e51613189d

[ "MIT" ]

null

stops = list(input()) command = input().split(":") while command[0] != "Travel": if command[0] == "Add Stop": if 0 <= int(command[1]) < len(stops): index = int(command[1]) for letter in command[2]: stops.insert(index, letter) index += 1 elif command[0] == "Remove Stop": if 0 <= int(command[1]) < len(stops) and 0 <= int(command[2]) < len(stops): [stops.pop(int(command[1])) for i in range(int(command[1]), int(command[2])+1)] elif command[0] == "Switch": stops = ''.join(stops) if command[1] in stops: stops = stops.replace(command[1], command[2]) stops = list(stops) print(''.join(stops)) command = input().split(":") print(f"Ready for world tour! Planned stops: {''.join(stops)}")

35.913043

91

0.53632

stops = list(input()) command = input().split(":") while command[0] != "Travel": if command[0] == "Add Stop": if 0 <= int(command[1]) < len(stops): index = int(command[1]) for letter in command[2]: stops.insert(index, letter) index += 1 elif command[0] == "Remove Stop": if 0 <= int(command[1]) < len(stops) and 0 <= int(command[2]) < len(stops): [stops.pop(int(command[1])) for i in range(int(command[1]), int(command[2])+1)] elif command[0] == "Switch": stops = ''.join(stops) if command[1] in stops: stops = stops.replace(command[1], command[2]) stops = list(stops) print(''.join(stops)) command = input().split(":") print(f"Ready for world tour! Planned stops: {''.join(stops)}")

true

f7178e9db3fd873fbe00c6932d476c7f06e20608

3,456

py

Python

hydrus/core/HydrusExceptions.py

ReAnzu/hydrus

069f77e1941d13b3bdd969aeeffd7ae003fcb71e

[ "WTFPL" ]

1

2021-02-24T22:12:30.000Z

hydrus/core/HydrusExceptions.py

ReAnzu/hydrus

069f77e1941d13b3bdd969aeeffd7ae003fcb71e

[ "WTFPL" ]

null

hydrus/core/HydrusExceptions.py

ReAnzu/hydrus

069f77e1941d13b3bdd969aeeffd7ae003fcb71e

[ "WTFPL" ]

null

import collections.abc import os class HydrusException( Exception ): def __str__( self ): if isinstance( self.args, collections.abc.Iterable ): s = [] for arg in self.args: try: s.append( str( arg ) ) except: s.append( repr( arg ) ) else: s = [ repr( self.args ) ] return os.linesep.join( s ) class CantRenderWithCVException( HydrusException ): pass class DataMissing( HydrusException ): pass class DBException( HydrusException ): def __init__( self, e, first_line, db_traceback ): self.db_e = e HydrusException.__init__( self, first_line, db_traceback ) class DBAccessException( HydrusException ): pass class DBCredentialsException( HydrusException ): pass class FileMissingException( HydrusException ): pass class DirectoryMissingException( HydrusException ): pass class SerialisationException( HydrusException ): pass class NameException( HydrusException ): pass class ShutdownException( HydrusException ): pass class QtDeadWindowException(HydrusException): pass class VetoException( HydrusException ): pass class CancelledException( VetoException ): pass class UnsupportedFileException( VetoException ): pass class DamagedOrUnusualFileException( UnsupportedFileException ): pass class FileSizeException( UnsupportedFileException ): pass class DecompressionBombException( FileSizeException ): pass class TagSizeException( VetoException ): pass class ParseException( HydrusException ): pass class StringConvertException( ParseException ): pass class StringMatchException( ParseException ): pass class StringSplitterException( ParseException ): pass class URLClassException( ParseException ): pass class GUGException( ParseException ): pass class NetworkException( HydrusException ): pass class NetworkInfrastructureException( NetworkException ): pass class ConnectionException( NetworkInfrastructureException ): pass class FirewallException( NetworkInfrastructureException ): pass class RouterException( NetworkInfrastructureException ): pass class CloudFlareException( NetworkInfrastructureException ): pass class BandwidthException( NetworkInfrastructureException ): pass class ServerException( NetworkInfrastructureException ): pass class ServerBusyException( NetworkInfrastructureException ): pass class StreamTimeoutException( NetworkException ): pass class NetworkVersionException( NetworkException ): pass class NoContentException( NetworkException ): pass class NotFoundException( NetworkException ): pass class NotModifiedException( NetworkException ): pass class BadRequestException( NetworkException ): pass class ConflictException( NetworkException ): pass class MissingCredentialsException( NetworkException ): pass class DoesNotSupportCORSException( NetworkException ): pass class InsufficientCredentialsException( NetworkException ): pass class RedirectionException( NetworkException ): pass class SessionException( NetworkException ): pass class WrongServiceTypeException( NetworkException ): pass class ValidationException( NetworkException ): pass class ShouldReattemptNetworkException( NetworkException ): pass

35.265306

69

0.732928

import collections.abc import os class HydrusException( Exception ): def __str__( self ): if isinstance( self.args, collections.abc.Iterable ): s = [] for arg in self.args: try: s.append( str( arg ) ) except: s.append( repr( arg ) ) else: s = [ repr( self.args ) ] return os.linesep.join( s ) class CantRenderWithCVException( HydrusException ): pass class DataMissing( HydrusException ): pass class DBException( HydrusException ): def __init__( self, e, first_line, db_traceback ): self.db_e = e HydrusException.__init__( self, first_line, db_traceback ) class DBAccessException( HydrusException ): pass class DBCredentialsException( HydrusException ): pass class FileMissingException( HydrusException ): pass class DirectoryMissingException( HydrusException ): pass class SerialisationException( HydrusException ): pass class NameException( HydrusException ): pass class ShutdownException( HydrusException ): pass class QtDeadWindowException(HydrusException): pass class VetoException( HydrusException ): pass class CancelledException( VetoException ): pass class UnsupportedFileException( VetoException ): pass class DamagedOrUnusualFileException( UnsupportedFileException ): pass class FileSizeException( UnsupportedFileException ): pass class DecompressionBombException( FileSizeException ): pass class TagSizeException( VetoException ): pass class ParseException( HydrusException ): pass class StringConvertException( ParseException ): pass class StringMatchException( ParseException ): pass class StringSplitterException( ParseException ): pass class URLClassException( ParseException ): pass class GUGException( ParseException ): pass class NetworkException( HydrusException ): pass class NetworkInfrastructureException( NetworkException ): pass class ConnectionException( NetworkInfrastructureException ): pass class FirewallException( NetworkInfrastructureException ): pass class RouterException( NetworkInfrastructureException ): pass class CloudFlareException( NetworkInfrastructureException ): pass class BandwidthException( NetworkInfrastructureException ): pass class ServerException( NetworkInfrastructureException ): pass class ServerBusyException( NetworkInfrastructureException ): pass class StreamTimeoutException( NetworkException ): pass class NetworkVersionException( NetworkException ): pass class NoContentException( NetworkException ): pass class NotFoundException( NetworkException ): pass class NotModifiedException( NetworkException ): pass class BadRequestException( NetworkException ): pass class ConflictException( NetworkException ): pass class MissingCredentialsException( NetworkException ): pass class DoesNotSupportCORSException( NetworkException ): pass class InsufficientCredentialsException( NetworkException ): pass class RedirectionException( NetworkException ): pass class SessionException( NetworkException ): pass class WrongServiceTypeException( NetworkException ): pass class ValidationException( NetworkException ): pass class ShouldReattemptNetworkException( NetworkException ): pass

true

f7178ea0d3c97e41fedd16e6e263162427023912

8,139

py

Python

synapse/appservice/__init__.py

mweinelt/synapse

42a9ea37e4c6ff9d91b530c40d366446b9fc2234

[ "Apache-2.0" ]

null

synapse/appservice/__init__.py

mweinelt/synapse

42a9ea37e4c6ff9d91b530c40d366446b9fc2234

[ "Apache-2.0" ]

null

synapse/appservice/__init__.py

mweinelt/synapse

42a9ea37e4c6ff9d91b530c40d366446b9fc2234

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Copyright 2015, 2016 OpenMarket Ltd # # 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 synapse.api.constants import EventTypes import logging import re logger = logging.getLogger(__name__) class ApplicationServiceState(object): DOWN = "down" UP = "up" class AppServiceTransaction(object): """Represents an application service transaction.""" def __init__(self, service, id, events): self.service = service self.id = id self.events = events def send(self, as_api): """Sends this transaction using the provided AS API interface. Args: as_api(ApplicationServiceApi): The API to use to send. Returns: A Deferred which resolves to True if the transaction was sent. """ return as_api.push_bulk( service=self.service, events=self.events, txn_id=self.id ) def complete(self, store): """Completes this transaction as successful. Marks this transaction ID on the application service and removes the transaction contents from the database. Args: store: The database store to operate on. Returns: A Deferred which resolves to True if the transaction was completed. """ return store.complete_appservice_txn( service=self.service, txn_id=self.id ) class ApplicationService(object): """Defines an application service. This definition is mostly what is provided to the /register AS API. Provides methods to check if this service is "interested" in events. """ NS_USERS = "users" NS_ALIASES = "aliases" NS_ROOMS = "rooms" # The ordering here is important as it is used to map database values (which # are stored as ints representing the position in this list) to namespace # values. NS_LIST = [NS_USERS, NS_ALIASES, NS_ROOMS] def __init__(self, token, url=None, namespaces=None, hs_token=None, sender=None, id=None): self.token = token self.url = url self.hs_token = hs_token self.sender = sender self.namespaces = self._check_namespaces(namespaces) self.id = id def _check_namespaces(self, namespaces): # Sanity check that it is of the form: # { # users: [ {regex: "[A-z]+.*", exclusive: true}, ...], # aliases: [ {regex: "[A-z]+.*", exclusive: true}, ...], # rooms: [ {regex: "[A-z]+.*", exclusive: true}, ...], # } if not namespaces: namespaces = {} for ns in ApplicationService.NS_LIST: if ns not in namespaces: namespaces[ns] = [] continue if type(namespaces[ns]) != list: raise ValueError("Bad namespace value for '%s'" % ns) for regex_obj in namespaces[ns]: if not isinstance(regex_obj, dict): raise ValueError("Expected dict regex for ns '%s'" % ns) if not isinstance(regex_obj.get("exclusive"), bool): raise ValueError( "Expected bool for 'exclusive' in ns '%s'" % ns ) if not isinstance(regex_obj.get("regex"), basestring): raise ValueError( "Expected string for 'regex' in ns '%s'" % ns ) return namespaces def _matches_regex(self, test_string, namespace_key, return_obj=False): if not isinstance(test_string, basestring): logger.error( "Expected a string to test regex against, but got %s", test_string ) return False for regex_obj in self.namespaces[namespace_key]: if re.match(regex_obj["regex"], test_string): if return_obj: return regex_obj return True return False def _is_exclusive(self, ns_key, test_string): regex_obj = self._matches_regex(test_string, ns_key, return_obj=True) if regex_obj: return regex_obj["exclusive"] return False def _matches_user(self, event, member_list): if (hasattr(event, "sender") and self.is_interested_in_user(event.sender)): return True # also check m.room.member state key if (hasattr(event, "type") and event.type == EventTypes.Member and hasattr(event, "state_key") and self.is_interested_in_user(event.state_key)): return True # check joined member events for user_id in member_list: if self.is_interested_in_user(user_id): return True return False def _matches_room_id(self, event): if hasattr(event, "room_id"): return self.is_interested_in_room(event.room_id) return False def _matches_aliases(self, event, alias_list): for alias in alias_list: if self.is_interested_in_alias(alias): return True return False def is_interested(self, event, restrict_to=None, aliases_for_event=None, member_list=None): """Check if this service is interested in this event. Args: event(Event): The event to check. restrict_to(str): The namespace to restrict regex tests to. aliases_for_event(list): A list of all the known room aliases for this event. member_list(list): A list of all joined user_ids in this room. Returns: bool: True if this service would like to know about this event. """ if aliases_for_event is None: aliases_for_event = [] if member_list is None: member_list = [] if restrict_to and restrict_to not in ApplicationService.NS_LIST: # this is a programming error, so fail early and raise a general # exception raise Exception("Unexpected restrict_to value: %s". restrict_to) if not restrict_to: return (self._matches_user(event, member_list) or self._matches_aliases(event, aliases_for_event) or self._matches_room_id(event)) elif restrict_to == ApplicationService.NS_ALIASES: return self._matches_aliases(event, aliases_for_event) elif restrict_to == ApplicationService.NS_ROOMS: return self._matches_room_id(event) elif restrict_to == ApplicationService.NS_USERS: return self._matches_user(event, member_list) def is_interested_in_user(self, user_id): return ( self._matches_regex(user_id, ApplicationService.NS_USERS) or user_id == self.sender ) def is_interested_in_alias(self, alias): return self._matches_regex(alias, ApplicationService.NS_ALIASES) def is_interested_in_room(self, room_id): return self._matches_regex(room_id, ApplicationService.NS_ROOMS) def is_exclusive_user(self, user_id): return ( self._is_exclusive(ApplicationService.NS_USERS, user_id) or user_id == self.sender ) def is_exclusive_alias(self, alias): return self._is_exclusive(ApplicationService.NS_ALIASES, alias) def is_exclusive_room(self, room_id): return self._is_exclusive(ApplicationService.NS_ROOMS, room_id) def __str__(self): return "ApplicationService: %s" % (self.__dict__,)

35.854626

80

0.6158

from synapse.api.constants import EventTypes import logging import re logger = logging.getLogger(__name__) class ApplicationServiceState(object): DOWN = "down" UP = "up" class AppServiceTransaction(object): def __init__(self, service, id, events): self.service = service self.id = id self.events = events def send(self, as_api): return as_api.push_bulk( service=self.service, events=self.events, txn_id=self.id ) def complete(self, store): return store.complete_appservice_txn( service=self.service, txn_id=self.id ) class ApplicationService(object): NS_USERS = "users" NS_ALIASES = "aliases" NS_ROOMS = "rooms" NS_LIST = [NS_USERS, NS_ALIASES, NS_ROOMS] def __init__(self, token, url=None, namespaces=None, hs_token=None, sender=None, id=None): self.token = token self.url = url self.hs_token = hs_token self.sender = sender self.namespaces = self._check_namespaces(namespaces) self.id = id def _check_namespaces(self, namespaces): if not namespaces: namespaces = {} for ns in ApplicationService.NS_LIST: if ns not in namespaces: namespaces[ns] = [] continue if type(namespaces[ns]) != list: raise ValueError("Bad namespace value for '%s'" % ns) for regex_obj in namespaces[ns]: if not isinstance(regex_obj, dict): raise ValueError("Expected dict regex for ns '%s'" % ns) if not isinstance(regex_obj.get("exclusive"), bool): raise ValueError( "Expected bool for 'exclusive' in ns '%s'" % ns ) if not isinstance(regex_obj.get("regex"), basestring): raise ValueError( "Expected string for 'regex' in ns '%s'" % ns ) return namespaces def _matches_regex(self, test_string, namespace_key, return_obj=False): if not isinstance(test_string, basestring): logger.error( "Expected a string to test regex against, but got %s", test_string ) return False for regex_obj in self.namespaces[namespace_key]: if re.match(regex_obj["regex"], test_string): if return_obj: return regex_obj return True return False def _is_exclusive(self, ns_key, test_string): regex_obj = self._matches_regex(test_string, ns_key, return_obj=True) if regex_obj: return regex_obj["exclusive"] return False def _matches_user(self, event, member_list): if (hasattr(event, "sender") and self.is_interested_in_user(event.sender)): return True if (hasattr(event, "type") and event.type == EventTypes.Member and hasattr(event, "state_key") and self.is_interested_in_user(event.state_key)): return True for user_id in member_list: if self.is_interested_in_user(user_id): return True return False def _matches_room_id(self, event): if hasattr(event, "room_id"): return self.is_interested_in_room(event.room_id) return False def _matches_aliases(self, event, alias_list): for alias in alias_list: if self.is_interested_in_alias(alias): return True return False def is_interested(self, event, restrict_to=None, aliases_for_event=None, member_list=None): if aliases_for_event is None: aliases_for_event = [] if member_list is None: member_list = [] if restrict_to and restrict_to not in ApplicationService.NS_LIST: raise Exception("Unexpected restrict_to value: %s". restrict_to) if not restrict_to: return (self._matches_user(event, member_list) or self._matches_aliases(event, aliases_for_event) or self._matches_room_id(event)) elif restrict_to == ApplicationService.NS_ALIASES: return self._matches_aliases(event, aliases_for_event) elif restrict_to == ApplicationService.NS_ROOMS: return self._matches_room_id(event) elif restrict_to == ApplicationService.NS_USERS: return self._matches_user(event, member_list) def is_interested_in_user(self, user_id): return ( self._matches_regex(user_id, ApplicationService.NS_USERS) or user_id == self.sender ) def is_interested_in_alias(self, alias): return self._matches_regex(alias, ApplicationService.NS_ALIASES) def is_interested_in_room(self, room_id): return self._matches_regex(room_id, ApplicationService.NS_ROOMS) def is_exclusive_user(self, user_id): return ( self._is_exclusive(ApplicationService.NS_USERS, user_id) or user_id == self.sender ) def is_exclusive_alias(self, alias): return self._is_exclusive(ApplicationService.NS_ALIASES, alias) def is_exclusive_room(self, room_id): return self._is_exclusive(ApplicationService.NS_ROOMS, room_id) def __str__(self): return "ApplicationService: %s" % (self.__dict__,)

true

f717904cee8dca9e6d25f6ea498b85acd973ab00

8,645

py

Python

src/extractor/make_bb_info_mats.py

lonelu/Metalprot_learning

8edb2c3e4f6ba129a409d75fd4d15ceb3a9e307b

[ "MIT" ]

null

src/extractor/make_bb_info_mats.py

lonelu/Metalprot_learning

8edb2c3e4f6ba129a409d75fd4d15ceb3a9e307b

[ "MIT" ]

null

src/extractor/make_bb_info_mats.py

lonelu/Metalprot_learning

8edb2c3e4f6ba129a409d75fd4d15ceb3a9e307b

[ "MIT" ]

null

from numpy.core.numeric import full from numpy.lib.function_base import append import prody as pr import os import numpy import matplotlib as mpl import pylab from itertools import combinations, combinations_with_replacement from docopt import docopt import itertools import pickle import sys from scipy.linalg.basic import matrix_balance from scipy.spatial.distance import cdist from . import ligand_database as ld from . import features_pdb2dihe as fpdh metal_sel = 'ion or name NI MN ZN CO CU MG FE' #TO DO: create artificial aa in the 4th aa. def get_atg(full_pdb): ''' prody atomgroup will be used to calc bb info. If the contact aa is at terminal, then the shape of the dist matrix will be < 12. So contact aa will be copied and added. ''' metal = full_pdb.select(metal_sel)[0] contact_aas = full_pdb.select('protein and not carbon and not hydrogen and within 2.83 of resindex ' + str(metal.getResindex())) contact_aa_resinds = numpy.unique(contact_aas.getResindices()) extention = 1 coords = [] resnames = [] names = [] resnums = [] resn = 1 for resind in contact_aa_resinds: ext_inds = ld.extend_res_indices([resind], full_pdb, extend =extention) #In some cases, the contact aa is at terminal. We can add more aa to match the shape. if len(ext_inds) == 2: if ext_inds[0] == resind: ext_inds.insert(0, resind) else: ext_inds.append(resind) if len(ext_inds) == 1: ext_inds.append(resind) ext_inds.append(resind) for ind in ext_inds: aa = full_pdb.select('resindex ' + str(ind)) coords.extend(aa.getCoords()) resnames.extend(aa.getResnames()) names.extend(aa.getNames()) resnums.extend([resn for _i in range(len(aa))]) resn += 1 if len(contact_aa_resinds) == 3: coords.extend([]) resnames.extend([]) names.extend([]) resnums.extend([]) #ag = pr.AtomGroup('-'.join([str(p) for p in per])) ag = pr.AtomGroup('0-1-2-3') ag.setCoords(coords) ag.setResnums(resnums) ag.setResnames(resnames) ag.setNames(names) return ag def get_atgs(full_pdb, contain_metal = True): ''' prody atomgroup will be used to calc bb info. If the contact aa is at terminal, then the shape of the dist matrix will be < 12. So contact aa will be copied and added. ''' if contain_metal: metal = full_pdb.select(metal_sel)[0] contact_aas = full_pdb.select('protein and not carbon and not hydrogen and within 2.83 of resindex ' + str(metal.getResindex())) else: #TO DO: it is not quite right here if the pdb happened to have more HIS-CYS-GLU-ASP. Skip now. contact_aas = full_pdb.select('resname HIS CYS GLU ASP') if not contact_aas and len(numpy.unique(contact_aas.getResindices())) > 4: return [] contact_aa_resinds = numpy.unique(contact_aas.getResindices()) extention = 1 # TO DO: If the len of contact_ass is not 4... ags = [] #for per in itertools.permutations(range(len(contact_aa_resinds))): for per in [range(len(contact_aa_resinds))]: print(per) coords = [] resnames = [] names = [] resnums = [] resn = 1 for idx in per: resind = contact_aa_resinds[idx] ext_inds = ld.extend_res_indices([resind], full_pdb, extend =extention) #In some cases, the contact aa is at terminal. We can add more aa to match the shape. if len(ext_inds) == 2: if ext_inds[0] == resind: ext_inds.insert(0, resind) else: ext_inds.append(resind) if len(ext_inds) == 1: ext_inds.append(resind) ext_inds.append(resind) for ind in ext_inds: aa = full_pdb.select('resindex ' + str(ind)) coords.extend(aa.getCoords()) resnames.extend(aa.getResnames()) names.extend(aa.getNames()) resnums.extend([resn for _i in range(len(aa))]) resn += 1 ag = pr.AtomGroup('-'.join([str(p) for p in per])) ag.setCoords(coords) ag.setResnums(resnums) ag.setResnames(resnames) ag.setNames(names) ags.append(ag) return ags def get_bb_dist_seq(core): ''' If we know N CA C, The coords of CB could be calcualted. So we may not need CB coords. ''' n_coords = core.select('name N').getCoords() c_coords = core.select('name C').getCoords() ca_coords = core.select('name CA').getCoords() n_n = cdist(n_coords, n_coords) c_c = cdist(c_coords, c_coords) ca_ca = cdist(ca_coords, ca_coords) cb_coords = [] for i in range(len(n_coords)): Ca = ca_coords[i] C = c_coords[i] N = n_coords[i] b = Ca - N c = C - Ca a = numpy.cross(b, c) Cb = -0.58273431*a + 0.56802827*b - 0.54067466*c + Ca cb_coords.append(Cb) cb_coords = core.select('name CB').getCoords() cb_cb = cdist(cb_coords, cb_coords) return n_n, c_c, ca_ca, cb_cb def get_dihe(ag): ''' Please check features_pdb2dihe.py. Only the contact aa will be extracted. ''' nres = len(ag.select('name CA')) print(nres) dist, _omega, _theta_asym, _phi_asym = fpdh.get_neighbors(ag, nres, 20.0) #TO DO: extract info, only the contact aa matters?! omega = numpy.zeros((nres, nres)) theta_asym = numpy.zeros((nres, nres)) phi_asym = numpy.zeros((nres, nres)) for i in range(1, nres, 3): for j in range(1, nres, 3): omega[i, j] = _omega[i, j] theta_asym[i, j] = _theta_asym[i, j] phi_asym[i, j] = _phi_asym[i, j] return omega, theta_asym, phi_asym def get_seq_mat(ag, matrix_size = 12): seq = ag.select('name CA').getResnames() threelettercodes = ['ALA', 'ARG', 'ASN', 'ASP', 'CYS', 'GLU', 'GLN', 'GLY', 'HIS', 'ILE', 'LEU', 'LYS', 'MET',\ 'PHE', 'PRO', 'SER', 'THR', 'TRP', 'TYR', 'VAL'] seq_channels = numpy.zeros([40, matrix_size, matrix_size], dtype=int) for i in range(len(seq)): aa = seq[i] try: idx = threelettercodes.index(aa) except: print('Resname of following atom not found: {}'.format(aa)) continue for j in range(len(seq)): seq_channels[idx][i][j] = 1 # horizontal rows of 1's in first 20 channels seq_channels[idx+20][j][i] = 1 # vertical columns of 1's in next 20 channels return seq_channels def mk_full_mats(ag, matrix_size = 12): nres = len(ag.select('name CA')) n_n, c_c, ca_ca, cb_cb = get_bb_dist_seq(ag) omega, theta_asym, phi_asym = get_dihe(ag) seq_mats = get_seq_mat(ag, matrix_size) full_mat = numpy.zeros((47, matrix_size, matrix_size)) # Make sure the shape of each matrix is smaller than the matrix_size. full_mat[0,0:n_n.shape[0], 0:n_n.shape[1]] = n_n full_mat[1,0:c_c.shape[0], 0:c_c.shape[1]] = c_c full_mat[2,0:ca_ca.shape[0], 0:ca_ca.shape[1]] = ca_ca full_mat[3,0:cb_cb.shape[0], 0:cb_cb.shape[1]] = cb_cb full_mat[4,0:omega.shape[0], 0:omega.shape[1]] = omega full_mat[5,0:theta_asym.shape[0], 0:theta_asym.shape[1]] = theta_asym full_mat[6,0:phi_asym.shape[0], 0:phi_asym.shape[1]] = phi_asym for i in range(7, 47): full_mat[i, :, :] = seq_mats[i - 7] return full_mat def write_pickle_file(full_mat, pdb, ag, out_folder, tag = ''): """ Writes a pickle file containing the input numpy array into the current permutation's folder. Currently using this only to save the full matrix (all 46 channels). """ numpy.set_printoptions(threshold=numpy.inf) pdb_name = pdb.split('.')[0] pkl_file = out_folder + pdb_name + '_full_mat_' + ag.getTitle() + tag + '.pkl' with open(pkl_file, 'wb') as f: print(pkl_file) pickle.dump(full_mat, f) return def write_dist_mat_file(mat, pdb, ag, out_folder, tag = ''): """ Writes out a file containing the distance matrix """ # output_folder = 'core_contact_maps/dist_mat_txt_folder/' numpy.set_printoptions(threshold=numpy.inf) dist_mat_file = pdb.split('.')[0] dist_mat_file = out_folder + dist_mat_file + '_full_mat_' + ag.getTitle() + tag + '.txt' with open(dist_mat_file, 'w') as open_file: for i in mat: open_file.write(str(i) + '\n') return def run_mk_bb_info_mats(workdir, out_path, mat_size = 12, top = 1000, contain_metal = True, opts = None): os.makedirs(out_path, exist_ok=True) count = 0 errors = '' for pdb_name in os.listdir(workdir): if count >= top: break if '.pdb' not in pdb_name: continue pdb_file = workdir + pdb_name pdb = pr.parsePDB(pdb_file) ags = get_atgs(pdb, contain_metal) for ag in ags: try: #TO DO: currently, only consider 3 or 4 aa binding. if len(ag.select('name CA'))> 12 or len(ag.select('name CA')) < 7: print(pdb_name + ' not used. ') continue full_mat = mk_full_mats(ag, mat_size) write_dist_mat_file(full_mat, pdb_name, ag, out_path) write_pickle_file(full_mat, pdb_name, ag, out_path) count += 1 except: print('error: ' + pdb_name) errors += pdb_name + '\n' if count >= top: break with open(out_path + '_error.txt', 'w') as f: f.write(errors) return

25.501475

131

0.682244

from numpy.core.numeric import full from numpy.lib.function_base import append import prody as pr import os import numpy import matplotlib as mpl import pylab from itertools import combinations, combinations_with_replacement from docopt import docopt import itertools import pickle import sys from scipy.linalg.basic import matrix_balance from scipy.spatial.distance import cdist from . import ligand_database as ld from . import features_pdb2dihe as fpdh metal_sel = 'ion or name NI MN ZN CO CU MG FE' def get_atg(full_pdb): metal = full_pdb.select(metal_sel)[0] contact_aas = full_pdb.select('protein and not carbon and not hydrogen and within 2.83 of resindex ' + str(metal.getResindex())) contact_aa_resinds = numpy.unique(contact_aas.getResindices()) extention = 1 coords = [] resnames = [] names = [] resnums = [] resn = 1 for resind in contact_aa_resinds: ext_inds = ld.extend_res_indices([resind], full_pdb, extend =extention) if len(ext_inds) == 2: if ext_inds[0] == resind: ext_inds.insert(0, resind) else: ext_inds.append(resind) if len(ext_inds) == 1: ext_inds.append(resind) ext_inds.append(resind) for ind in ext_inds: aa = full_pdb.select('resindex ' + str(ind)) coords.extend(aa.getCoords()) resnames.extend(aa.getResnames()) names.extend(aa.getNames()) resnums.extend([resn for _i in range(len(aa))]) resn += 1 if len(contact_aa_resinds) == 3: coords.extend([]) resnames.extend([]) names.extend([]) resnums.extend([]) ag = pr.AtomGroup('0-1-2-3') ag.setCoords(coords) ag.setResnums(resnums) ag.setResnames(resnames) ag.setNames(names) return ag def get_atgs(full_pdb, contain_metal = True): if contain_metal: metal = full_pdb.select(metal_sel)[0] contact_aas = full_pdb.select('protein and not carbon and not hydrogen and within 2.83 of resindex ' + str(metal.getResindex())) else: contact_aas = full_pdb.select('resname HIS CYS GLU ASP') if not contact_aas and len(numpy.unique(contact_aas.getResindices())) > 4: return [] contact_aa_resinds = numpy.unique(contact_aas.getResindices()) extention = 1 ags = [] for per in [range(len(contact_aa_resinds))]: print(per) coords = [] resnames = [] names = [] resnums = [] resn = 1 for idx in per: resind = contact_aa_resinds[idx] ext_inds = ld.extend_res_indices([resind], full_pdb, extend =extention) if len(ext_inds) == 2: if ext_inds[0] == resind: ext_inds.insert(0, resind) else: ext_inds.append(resind) if len(ext_inds) == 1: ext_inds.append(resind) ext_inds.append(resind) for ind in ext_inds: aa = full_pdb.select('resindex ' + str(ind)) coords.extend(aa.getCoords()) resnames.extend(aa.getResnames()) names.extend(aa.getNames()) resnums.extend([resn for _i in range(len(aa))]) resn += 1 ag = pr.AtomGroup('-'.join([str(p) for p in per])) ag.setCoords(coords) ag.setResnums(resnums) ag.setResnames(resnames) ag.setNames(names) ags.append(ag) return ags def get_bb_dist_seq(core): n_coords = core.select('name N').getCoords() c_coords = core.select('name C').getCoords() ca_coords = core.select('name CA').getCoords() n_n = cdist(n_coords, n_coords) c_c = cdist(c_coords, c_coords) ca_ca = cdist(ca_coords, ca_coords) cb_coords = [] for i in range(len(n_coords)): Ca = ca_coords[i] C = c_coords[i] N = n_coords[i] b = Ca - N c = C - Ca a = numpy.cross(b, c) Cb = -0.58273431*a + 0.56802827*b - 0.54067466*c + Ca cb_coords.append(Cb) cb_coords = core.select('name CB').getCoords() cb_cb = cdist(cb_coords, cb_coords) return n_n, c_c, ca_ca, cb_cb def get_dihe(ag): nres = len(ag.select('name CA')) print(nres) dist, _omega, _theta_asym, _phi_asym = fpdh.get_neighbors(ag, nres, 20.0) omega = numpy.zeros((nres, nres)) theta_asym = numpy.zeros((nres, nres)) phi_asym = numpy.zeros((nres, nres)) for i in range(1, nres, 3): for j in range(1, nres, 3): omega[i, j] = _omega[i, j] theta_asym[i, j] = _theta_asym[i, j] phi_asym[i, j] = _phi_asym[i, j] return omega, theta_asym, phi_asym def get_seq_mat(ag, matrix_size = 12): seq = ag.select('name CA').getResnames() threelettercodes = ['ALA', 'ARG', 'ASN', 'ASP', 'CYS', 'GLU', 'GLN', 'GLY', 'HIS', 'ILE', 'LEU', 'LYS', 'MET',\ 'PHE', 'PRO', 'SER', 'THR', 'TRP', 'TYR', 'VAL'] seq_channels = numpy.zeros([40, matrix_size, matrix_size], dtype=int) for i in range(len(seq)): aa = seq[i] try: idx = threelettercodes.index(aa) except: print('Resname of following atom not found: {}'.format(aa)) continue for j in range(len(seq)): seq_channels[idx][i][j] = 1 seq_channels[idx+20][j][i] = 1 # vertical columns of 1's in next 20 channels return seq_channels def mk_full_mats(ag, matrix_size = 12): nres = len(ag.select('name CA')) n_n, c_c, ca_ca, cb_cb = get_bb_dist_seq(ag) omega, theta_asym, phi_asym = get_dihe(ag) seq_mats = get_seq_mat(ag, matrix_size) full_mat = numpy.zeros((47, matrix_size, matrix_size)) full_mat[0,0:n_n.shape[0], 0:n_n.shape[1]] = n_n full_mat[1,0:c_c.shape[0], 0:c_c.shape[1]] = c_c full_mat[2,0:ca_ca.shape[0], 0:ca_ca.shape[1]] = ca_ca full_mat[3,0:cb_cb.shape[0], 0:cb_cb.shape[1]] = cb_cb full_mat[4,0:omega.shape[0], 0:omega.shape[1]] = omega full_mat[5,0:theta_asym.shape[0], 0:theta_asym.shape[1]] = theta_asym full_mat[6,0:phi_asym.shape[0], 0:phi_asym.shape[1]] = phi_asym for i in range(7, 47): full_mat[i, :, :] = seq_mats[i - 7] return full_mat def write_pickle_file(full_mat, pdb, ag, out_folder, tag = ''): numpy.set_printoptions(threshold=numpy.inf) pdb_name = pdb.split('.')[0] pkl_file = out_folder + pdb_name + '_full_mat_' + ag.getTitle() + tag + '.pkl' with open(pkl_file, 'wb') as f: print(pkl_file) pickle.dump(full_mat, f) return def write_dist_mat_file(mat, pdb, ag, out_folder, tag = ''): numpy.set_printoptions(threshold=numpy.inf) dist_mat_file = pdb.split('.')[0] dist_mat_file = out_folder + dist_mat_file + '_full_mat_' + ag.getTitle() + tag + '.txt' with open(dist_mat_file, 'w') as open_file: for i in mat: open_file.write(str(i) + '\n') return def run_mk_bb_info_mats(workdir, out_path, mat_size = 12, top = 1000, contain_metal = True, opts = None): os.makedirs(out_path, exist_ok=True) count = 0 errors = '' for pdb_name in os.listdir(workdir): if count >= top: break if '.pdb' not in pdb_name: continue pdb_file = workdir + pdb_name pdb = pr.parsePDB(pdb_file) ags = get_atgs(pdb, contain_metal) for ag in ags: try: if len(ag.select('name CA'))> 12 or len(ag.select('name CA')) < 7: print(pdb_name + ' not used. ') continue full_mat = mk_full_mats(ag, mat_size) write_dist_mat_file(full_mat, pdb_name, ag, out_path) write_pickle_file(full_mat, pdb_name, ag, out_path) count += 1 except: print('error: ' + pdb_name) errors += pdb_name + '\n' if count >= top: break with open(out_path + '_error.txt', 'w') as f: f.write(errors) return

true

f71791c60cc7fc628325ca20e013c1f715b14cbf

1,168

py

Python

openamundsen/__init__.py

openamundsen/openamundsen

2ac09eb34b0c72c84c421a0dac08d114a05b7b1c

[ "MIT" ]

3

2021-05-28T06:46:36.000Z

2021-06-14T13:39:25.000Z

openamundsen/__init__.py

openamundsen/openamundsen

2ac09eb34b0c72c84c421a0dac08d114a05b7b1c

[ "MIT" ]

22

2021-04-28T12:31:58.000Z

2022-03-09T18:29:12.000Z

openamundsen/__init__.py

openamundsen/openamundsen

2ac09eb34b0c72c84c421a0dac08d114a05b7b1c

[ "MIT" ]

1

2021-06-01T12:48:54.000Z

from .conf import Configuration, parse_config, read_config from .model import OpenAmundsen, Model from . import constants, errors, terrain # Get version (method as used by matplotlib: https://github.com/matplotlib/matplotlib/blob/bcc1ce8461f5b6e874baaaa02ef776d0243a4abe/lib/matplotlib/__init__.py#L133-L151) def __getattr__(name): if name == '__version__': from pathlib import Path import setuptools_scm global __version__ root = Path(__file__).resolve().parents[1] if (root / '.git').exists() and not (root / '.git/shallow').exists(): __version__ = setuptools_scm.get_version( root=root, version_scheme='post-release', fallback_version='0.0.0+UNKNOWN', ) else: try: from . import _version __version__ = _version.version except ImportError: __version__ = '0.0.0+UNKNOWN' return __version__ raise AttributeError(f'module {__name__!r} has no attribute {name!r}') __all__ = [ 'OpenAmundsen', 'Configuration', 'parse_config', 'read_config', ]

33.371429

169

0.626712

from .conf import Configuration, parse_config, read_config from .model import OpenAmundsen, Model from . import constants, errors, terrain ttr__(name): if name == '__version__': from pathlib import Path import setuptools_scm global __version__ root = Path(__file__).resolve().parents[1] if (root / '.git').exists() and not (root / '.git/shallow').exists(): __version__ = setuptools_scm.get_version( root=root, version_scheme='post-release', fallback_version='0.0.0+UNKNOWN', ) else: try: from . import _version __version__ = _version.version except ImportError: __version__ = '0.0.0+UNKNOWN' return __version__ raise AttributeError(f'module {__name__!r} has no attribute {name!r}') __all__ = [ 'OpenAmundsen', 'Configuration', 'parse_config', 'read_config', ]

true

f71791d678307a538466ef5f55e3423ce32202b3

2,431

py

Python

h2o-py/tests/testdir_jira/pyunit_NOPASS_hex_1897_glm_offset.py

ChristosChristofidis/h2o-3

2a926c0950a98eff5a4c06aeaf0373e17176ecd8

[ "Apache-2.0" ]

null

h2o-py/tests/testdir_jira/pyunit_NOPASS_hex_1897_glm_offset.py

ChristosChristofidis/h2o-3

2a926c0950a98eff5a4c06aeaf0373e17176ecd8

[ "Apache-2.0" ]

null

h2o-py/tests/testdir_jira/pyunit_NOPASS_hex_1897_glm_offset.py

ChristosChristofidis/h2o-3

2a926c0950a98eff5a4c06aeaf0373e17176ecd8

[ "Apache-2.0" ]

1

2020-12-18T19:20:02.000Z

import sys sys.path.insert(1, "../../") import h2o def offset_1897(ip, port): h2o.init(ip, port) print 'Checking binomial models for GLM with and without offset' print 'Import prostate dataset into H2O and R...' prostate_hex = h2o.import_frame(h2o.locate("smalldata/prostate/prostate.csv")) print "Checking binomial model without offset..." prostate_glm_h2o = h2o.glm(x=prostate_hex["RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"], y=prostate_hex["CAPSULE"], training_frame=prostate_hex, family="binomial", standardize=False) print "h2o residual: {0}".format(prostate_glm_h2o.residual_deviance()) print "r residual: {0}".format(379.053509501537) assert abs(379.053509501537 - prostate_glm_h2o.residual_deviance()) < 0.1 print "Checking binomial model with offset..." prostate_glm_h2o = h2o.glm(x=prostate_hex["RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON", "AGE"], y=prostate_hex["CAPSULE"], training_frame=prostate_hex, family="binomial", offset_column = "AGE", standardize = False) print "h2o residual: {0}".format(prostate_glm_h2o.residual_deviance()) print "r residual: {0}".format(1515.91815848623) assert abs(1515.91815848623 - prostate_glm_h2o.residual_deviance()) < 0.1 print "Checking binomial model without offset..." prostate_glm_h2o = h2o.glm(x=prostate_hex["RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"], y=prostate_hex["CAPSULE"], training_frame=prostate_hex, family="poisson", standardize=False) print "h2o residual: {0}".format(prostate_glm_h2o.residual_deviance()) print "r residual: {0}".format(216.339989007507) assert abs(216.339989007507 - prostate_glm_h2o.residual_deviance()) < 0.1 print "Checking binomial model with offset..." prostate_glm_h2o = h2o.glm(x=prostate_hex["RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON", "AGE"], y=prostate_hex["CAPSULE"], training_frame=prostate_hex, family="poisson", offset_column = "AGE", standardize = False) print "h2o residual: {0}".format(prostate_glm_h2o.residual_deviance()) print "r residual: {0}".format(2761.76218461138) assert abs(2761.76218461138 - prostate_glm_h2o.residual_deviance()) < 0.1 if __name__ == "__main__": h2o.run_test(sys.argv, offset_1897)

55.25

124

0.661456

import sys sys.path.insert(1, "../../") import h2o def offset_1897(ip, port): h2o.init(ip, port) print 'Checking binomial models for GLM with and without offset' print 'Import prostate dataset into H2O and R...' prostate_hex = h2o.import_frame(h2o.locate("smalldata/prostate/prostate.csv")) print "Checking binomial model without offset..." prostate_glm_h2o = h2o.glm(x=prostate_hex["RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"], y=prostate_hex["CAPSULE"], training_frame=prostate_hex, family="binomial", standardize=False) print "h2o residual: {0}".format(prostate_glm_h2o.residual_deviance()) print "r residual: {0}".format(379.053509501537) assert abs(379.053509501537 - prostate_glm_h2o.residual_deviance()) < 0.1 print "Checking binomial model with offset..." prostate_glm_h2o = h2o.glm(x=prostate_hex["RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON", "AGE"], y=prostate_hex["CAPSULE"], training_frame=prostate_hex, family="binomial", offset_column = "AGE", standardize = False) print "h2o residual: {0}".format(prostate_glm_h2o.residual_deviance()) print "r residual: {0}".format(1515.91815848623) assert abs(1515.91815848623 - prostate_glm_h2o.residual_deviance()) < 0.1 print "Checking binomial model without offset..." prostate_glm_h2o = h2o.glm(x=prostate_hex["RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON"], y=prostate_hex["CAPSULE"], training_frame=prostate_hex, family="poisson", standardize=False) print "h2o residual: {0}".format(prostate_glm_h2o.residual_deviance()) print "r residual: {0}".format(216.339989007507) assert abs(216.339989007507 - prostate_glm_h2o.residual_deviance()) < 0.1 print "Checking binomial model with offset..." prostate_glm_h2o = h2o.glm(x=prostate_hex["RACE", "DPROS", "DCAPS", "PSA", "VOL", "GLEASON", "AGE"], y=prostate_hex["CAPSULE"], training_frame=prostate_hex, family="poisson", offset_column = "AGE", standardize = False) print "h2o residual: {0}".format(prostate_glm_h2o.residual_deviance()) print "r residual: {0}".format(2761.76218461138) assert abs(2761.76218461138 - prostate_glm_h2o.residual_deviance()) < 0.1 if __name__ == "__main__": h2o.run_test(sys.argv, offset_1897)

false

true

f71791e019fc01edfa394b5acc7c6d6563b6ea45

35,147

py

Python

tests/components/mqtt/test_light_json.py

jlvaillant/core

ae37f9a1d9c5067957854b3c25dcc73fe9a10bee

[ "Apache-2.0" ]

2

2019-11-20T20:56:59.000Z

2021-01-03T08:52:18.000Z

tests/components/mqtt/test_light_json.py

jlvaillant/core

ae37f9a1d9c5067957854b3c25dcc73fe9a10bee

[ "Apache-2.0" ]

2

2021-06-08T21:54:21.000Z

2022-03-12T00:37:59.000Z

tests/components/mqtt/test_light_json.py

sampou/homeassistant

6c1a5d9e5a72eed3a582457142b1db7867b6ff7b

[ "Apache-2.0" ]

1

2021-04-18T19:36:34.000Z

"""The tests for the MQTT JSON light platform. Configuration with RGB, brightness, color temp, effect, white value and XY: light: platform: mqtt_json name: mqtt_json_light_1 state_topic: "home/rgb1" command_topic: "home/rgb1/set" brightness: true color_temp: true effect: true rgb: true white_value: true xy: true Configuration with RGB, brightness, color temp, effect, white value: light: platform: mqtt_json name: mqtt_json_light_1 state_topic: "home/rgb1" command_topic: "home/rgb1/set" brightness: true color_temp: true effect: true rgb: true white_value: true Configuration with RGB, brightness, color temp and effect: light: platform: mqtt_json name: mqtt_json_light_1 state_topic: "home/rgb1" command_topic: "home/rgb1/set" brightness: true color_temp: true effect: true rgb: true Configuration with RGB, brightness and color temp: light: platform: mqtt_json name: mqtt_json_light_1 state_topic: "home/rgb1" command_topic: "home/rgb1/set" brightness: true rgb: true color_temp: true Configuration with RGB, brightness: light: platform: mqtt_json name: mqtt_json_light_1 state_topic: "home/rgb1" command_topic: "home/rgb1/set" brightness: true rgb: true Config without RGB: light: platform: mqtt_json name: mqtt_json_light_1 state_topic: "home/rgb1" command_topic: "home/rgb1/set" brightness: true Config without RGB and brightness: light: platform: mqtt_json name: mqtt_json_light_1 state_topic: "home/rgb1" command_topic: "home/rgb1/set" Config with brightness and scale: light: platform: mqtt_json name: test state_topic: "mqtt_json_light_1" command_topic: "mqtt_json_light_1/set" brightness: true brightness_scale: 99 """ import json from unittest import mock from unittest.mock import patch from homeassistant.components import light from homeassistant.const import ( ATTR_ASSUMED_STATE, ATTR_SUPPORTED_FEATURES, STATE_OFF, STATE_ON, ) import homeassistant.core as ha from homeassistant.setup import async_setup_component from .test_common import ( help_test_availability_without_topic, help_test_custom_availability_payload, help_test_default_availability_payload, help_test_discovery_broken, help_test_discovery_removal, help_test_discovery_update, help_test_discovery_update_attr, help_test_entity_device_info_remove, help_test_entity_device_info_update, help_test_entity_device_info_with_connection, help_test_entity_device_info_with_identifier, help_test_entity_id_update_discovery_update, help_test_entity_id_update_subscriptions, help_test_setting_attribute_via_mqtt_json_message, help_test_setting_attribute_with_template, help_test_unique_id, help_test_update_with_json_attrs_bad_JSON, help_test_update_with_json_attrs_not_dict, ) from tests.common import async_fire_mqtt_message, mock_coro from tests.components.light import common DEFAULT_CONFIG = { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test-topic", } } class JsonValidator(object): """Helper to compare JSON.""" def __init__(self, jsondata): """Initialize JSON validator.""" self.jsondata = jsondata def __eq__(self, other): """Compare JSON data.""" return json.loads(self.jsondata) == json.loads(other) async def test_fail_setup_if_no_command_topic(hass, mqtt_mock): """Test if setup fails with no command topic.""" assert await async_setup_component( hass, light.DOMAIN, {light.DOMAIN: {"platform": "mqtt", "schema": "json", "name": "test"}}, ) assert hass.states.get("light.test") is None async def test_no_color_brightness_color_temp_white_val_if_no_topics(hass, mqtt_mock): """Test for no RGB, brightness, color temp, effect, white val or XY.""" assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "state_topic": "test_light_rgb", "command_topic": "test_light_rgb/set", } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 40 assert state.attributes.get("rgb_color") is None assert state.attributes.get("brightness") is None assert state.attributes.get("color_temp") is None assert state.attributes.get("effect") is None assert state.attributes.get("white_value") is None assert state.attributes.get("xy_color") is None assert state.attributes.get("hs_color") is None async_fire_mqtt_message(hass, "test_light_rgb", '{"state":"ON"}') state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("rgb_color") is None assert state.attributes.get("brightness") is None assert state.attributes.get("color_temp") is None assert state.attributes.get("effect") is None assert state.attributes.get("white_value") is None assert state.attributes.get("xy_color") is None assert state.attributes.get("hs_color") is None async def test_controlling_state_via_topic(hass, mqtt_mock): """Test the controlling of the state via topic.""" assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "state_topic": "test_light_rgb", "command_topic": "test_light_rgb/set", "brightness": True, "color_temp": True, "effect": True, "rgb": True, "white_value": True, "xy": True, "hs": True, "qos": "0", } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 191 assert state.attributes.get("rgb_color") is None assert state.attributes.get("brightness") is None assert state.attributes.get("color_temp") is None assert state.attributes.get("effect") is None assert state.attributes.get("white_value") is None assert state.attributes.get("xy_color") is None assert state.attributes.get("hs_color") is None assert not state.attributes.get(ATTR_ASSUMED_STATE) # Turn on the light, full white async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"color":{"r":255,"g":255,"b":255},' '"brightness":255,' '"color_temp":155,' '"effect":"colorloop",' '"white_value":150}', ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("rgb_color") == (255, 255, 255) assert state.attributes.get("brightness") == 255 assert state.attributes.get("color_temp") == 155 assert state.attributes.get("effect") == "colorloop" assert state.attributes.get("white_value") == 150 assert state.attributes.get("xy_color") == (0.323, 0.329) assert state.attributes.get("hs_color") == (0.0, 0.0) # Turn the light off async_fire_mqtt_message(hass, "test_light_rgb", '{"state":"OFF"}') state = hass.states.get("light.test") assert state.state == STATE_OFF async_fire_mqtt_message(hass, "test_light_rgb", '{"state":"ON", "brightness":100}') light_state = hass.states.get("light.test") assert light_state.attributes["brightness"] == 100 async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON", ' '"color":{"r":125,"g":125,"b":125}}' ) light_state = hass.states.get("light.test") assert light_state.attributes.get("rgb_color") == (255, 255, 255) async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON", "color":{"x":0.135,"y":0.135}}' ) light_state = hass.states.get("light.test") assert light_state.attributes.get("xy_color") == (0.141, 0.14) async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON", "color":{"h":180,"s":50}}' ) light_state = hass.states.get("light.test") assert light_state.attributes.get("hs_color") == (180.0, 50.0) async_fire_mqtt_message(hass, "test_light_rgb", '{"state":"ON", "color_temp":155}') light_state = hass.states.get("light.test") assert light_state.attributes.get("color_temp") == 155 async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON", "effect":"colorloop"}' ) light_state = hass.states.get("light.test") assert light_state.attributes.get("effect") == "colorloop" async_fire_mqtt_message(hass, "test_light_rgb", '{"state":"ON", "white_value":155}') light_state = hass.states.get("light.test") assert light_state.attributes.get("white_value") == 155 async def test_sending_mqtt_commands_and_optimistic(hass, mqtt_mock): """Test the sending of command in optimistic mode.""" fake_state = ha.State( "light.test", "on", { "brightness": 95, "hs_color": [100, 100], "effect": "random", "color_temp": 100, "white_value": 50, }, ) with patch( "homeassistant.helpers.restore_state.RestoreEntity.async_get_last_state", return_value=mock_coro(fake_state), ): assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "brightness": True, "color_temp": True, "effect": True, "hs": True, "rgb": True, "xy": True, "white_value": True, "qos": 2, } }, ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("brightness") == 95 assert state.attributes.get("hs_color") == (100, 100) assert state.attributes.get("effect") == "random" assert state.attributes.get("color_temp") == 100 assert state.attributes.get("white_value") == 50 assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 191 assert state.attributes.get(ATTR_ASSUMED_STATE) await common.async_turn_on(hass, "light.test") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", '{"state": "ON"}', 2, False ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON await common.async_turn_on(hass, "light.test", color_temp=90) mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON", "color_temp": 90}'), 2, False, ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON await common.async_turn_off(hass, "light.test") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", '{"state": "OFF"}', 2, False ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_OFF mqtt_mock.reset_mock() await common.async_turn_on( hass, "light.test", brightness=50, xy_color=[0.123, 0.123] ) await common.async_turn_on(hass, "light.test", brightness=50, hs_color=[359, 78]) await common.async_turn_on( hass, "light.test", rgb_color=[255, 128, 0], white_value=80 ) mqtt_mock.async_publish.assert_has_calls( [ mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"r": 0, "g": 123, "b": 255,' ' "x": 0.14, "y": 0.131, "h": 210.824, "s": 100.0},' ' "brightness": 50}' ), 2, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"r": 255, "g": 56, "b": 59,' ' "x": 0.654, "y": 0.301, "h": 359.0, "s": 78.0},' ' "brightness": 50}' ), 2, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"r": 255, "g": 128, "b": 0,' ' "x": 0.611, "y": 0.375, "h": 30.118, "s": 100.0},' ' "white_value": 80}' ), 2, False, ), ], any_order=True, ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes["rgb_color"] == (255, 128, 0) assert state.attributes["brightness"] == 50 assert state.attributes["hs_color"] == (30.118, 100) assert state.attributes["white_value"] == 80 assert state.attributes["xy_color"] == (0.611, 0.375) async def test_sending_hs_color(hass, mqtt_mock): """Test light.turn_on with hs color sends hs color parameters.""" assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "brightness": True, "hs": True, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF mqtt_mock.reset_mock() await common.async_turn_on( hass, "light.test", brightness=50, xy_color=[0.123, 0.123] ) await common.async_turn_on(hass, "light.test", brightness=50, hs_color=[359, 78]) await common.async_turn_on( hass, "light.test", rgb_color=[255, 128, 0], white_value=80 ) mqtt_mock.async_publish.assert_has_calls( [ mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"h": 210.824, "s": 100.0},' ' "brightness": 50}' ), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"h": 359.0, "s": 78.0},' ' "brightness": 50}' ), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"h": 30.118, "s": 100.0},' ' "white_value": 80}' ), 0, False, ), ], any_order=True, ) async def test_sending_rgb_color_no_brightness(hass, mqtt_mock): """Test light.turn_on with hs color sends rgb color parameters.""" assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "rgb": True, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF await common.async_turn_on( hass, "light.test", brightness=50, xy_color=[0.123, 0.123] ) await common.async_turn_on(hass, "light.test", brightness=50, hs_color=[359, 78]) await common.async_turn_on( hass, "light.test", rgb_color=[255, 128, 0], brightness=255 ) mqtt_mock.async_publish.assert_has_calls( [ mock.call( "test_light_rgb/set", JsonValidator('{"state": "ON", "color": {"r": 0, "g": 24, "b": 50}}'), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator('{"state": "ON", "color": {"r": 50, "g": 11, "b": 11}}'), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator('{"state": "ON", "color": {"r": 255, "g": 128, "b": 0}}'), 0, False, ), ], any_order=True, ) async def test_sending_rgb_color_with_brightness(hass, mqtt_mock): """Test light.turn_on with hs color sends rgb color parameters.""" assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "brightness": True, "rgb": True, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF await common.async_turn_on( hass, "light.test", brightness=50, xy_color=[0.123, 0.123] ) await common.async_turn_on(hass, "light.test", brightness=50, hs_color=[359, 78]) await common.async_turn_on( hass, "light.test", rgb_color=[255, 128, 0], white_value=80 ) mqtt_mock.async_publish.assert_has_calls( [ mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"r": 0, "g": 123, "b": 255},' ' "brightness": 50}' ), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"r": 255, "g": 56, "b": 59},' ' "brightness": 50}' ), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"r": 255, "g": 128, "b": 0},' ' "white_value": 80}' ), 0, False, ), ], any_order=True, ) async def test_sending_xy_color(hass, mqtt_mock): """Test light.turn_on with hs color sends xy color parameters.""" assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "brightness": True, "xy": True, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF await common.async_turn_on( hass, "light.test", brightness=50, xy_color=[0.123, 0.123] ) await common.async_turn_on(hass, "light.test", brightness=50, hs_color=[359, 78]) await common.async_turn_on( hass, "light.test", rgb_color=[255, 128, 0], white_value=80 ) mqtt_mock.async_publish.assert_has_calls( [ mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"x": 0.14, "y": 0.131},' ' "brightness": 50}' ), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"x": 0.654, "y": 0.301},' ' "brightness": 50}' ), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"x": 0.611, "y": 0.375},' ' "white_value": 80}' ), 0, False, ), ], any_order=True, ) async def test_effect(hass, mqtt_mock): """Test for effect being sent when included.""" assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "effect": True, "qos": 0, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 44 await common.async_turn_on(hass, "light.test") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON"}'), 0, False ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("effect") == "none" await common.async_turn_on(hass, "light.test", effect="rainbow") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON", "effect": "rainbow"}'), 0, False, ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("effect") == "rainbow" await common.async_turn_on(hass, "light.test", effect="colorloop") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON", "effect": "colorloop"}'), 0, False, ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("effect") == "colorloop" async def test_flash_short_and_long(hass, mqtt_mock): """Test for flash length being sent when included.""" assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "flash_time_short": 5, "flash_time_long": 15, "qos": 0, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 40 await common.async_turn_on(hass, "light.test", flash="short") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON", "flash": 5}'), 0, False ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON await common.async_turn_on(hass, "light.test", flash="long") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON", "flash": 15}'), 0, False ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON async def test_transition(hass, mqtt_mock): """Test for transition time being sent when included.""" assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "qos": 0, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 40 await common.async_turn_on(hass, "light.test", transition=15) mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON", "transition": 15}'), 0, False, ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON await common.async_turn_off(hass, "light.test", transition=30) mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "OFF", "transition": 30}'), 0, False, ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_OFF async def test_brightness_scale(hass, mqtt_mock): """Test for brightness scaling.""" assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "state_topic": "test_light_bright_scale", "command_topic": "test_light_bright_scale/set", "brightness": True, "brightness_scale": 99, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get("brightness") is None assert not state.attributes.get(ATTR_ASSUMED_STATE) # Turn on the light async_fire_mqtt_message(hass, "test_light_bright_scale", '{"state":"ON"}') state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("brightness") == 255 # Turn on the light with brightness async_fire_mqtt_message( hass, "test_light_bright_scale", '{"state":"ON", "brightness": 99}' ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("brightness") == 255 async def test_invalid_values(hass, mqtt_mock): """Test that invalid color/brightness/white/etc. values are ignored.""" assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "state_topic": "test_light_rgb", "command_topic": "test_light_rgb/set", "brightness": True, "color_temp": True, "rgb": True, "white_value": True, "qos": "0", } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 187 assert state.attributes.get("rgb_color") is None assert state.attributes.get("brightness") is None assert state.attributes.get("white_value") is None assert state.attributes.get("color_temp") is None assert not state.attributes.get(ATTR_ASSUMED_STATE) # Turn on the light async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"color":{"r":255,"g":255,"b":255},' '"brightness": 255,' '"white_value": 255,' '"color_temp": 100,' '"effect": "rainbow"}', ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("rgb_color") == (255, 255, 255) assert state.attributes.get("brightness") == 255 assert state.attributes.get("white_value") == 255 assert state.attributes.get("color_temp") == 100 # Bad HS color values async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"color":{"h":"bad","s":"val"}}', ) # Color should not have changed state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("rgb_color") == (255, 255, 255) # Bad RGB color values async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"color":{"r":"bad","g":"val","b":"test"}}', ) # Color should not have changed state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("rgb_color") == (255, 255, 255) # Bad XY color values async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"color":{"x":"bad","y":"val"}}', ) # Color should not have changed state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("rgb_color") == (255, 255, 255) # Bad brightness values async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"brightness": "badValue"}' ) # Brightness should not have changed state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("brightness") == 255 # Bad white value async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"white_value": "badValue"}' ) # White value should not have changed state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("white_value") == 255 # Bad color temperature async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"color_temp": "badValue"}' ) # Color temperature should not have changed state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("color_temp") == 100 async def test_availability_without_topic(hass, mqtt_mock): """Test availability without defined availability topic.""" await help_test_availability_without_topic( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_default_availability_payload(hass, mqtt_mock): """Test availability by default payload with defined topic.""" await help_test_default_availability_payload( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_custom_availability_payload(hass, mqtt_mock): """Test availability by custom payload with defined topic.""" await help_test_custom_availability_payload( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_setting_attribute_via_mqtt_json_message(hass, mqtt_mock): """Test the setting of attribute via MQTT with JSON payload.""" await help_test_setting_attribute_via_mqtt_json_message( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_setting_attribute_with_template(hass, mqtt_mock): """Test the setting of attribute via MQTT with JSON payload.""" await help_test_setting_attribute_with_template( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_update_with_json_attrs_not_dict(hass, mqtt_mock, caplog): """Test attributes get extracted from a JSON result.""" await help_test_update_with_json_attrs_not_dict( hass, mqtt_mock, caplog, light.DOMAIN, DEFAULT_CONFIG ) async def test_update_with_json_attrs_bad_JSON(hass, mqtt_mock, caplog): """Test attributes get extracted from a JSON result.""" await help_test_update_with_json_attrs_bad_JSON( hass, mqtt_mock, caplog, light.DOMAIN, DEFAULT_CONFIG ) async def test_discovery_update_attr(hass, mqtt_mock, caplog): """Test update of discovered MQTTAttributes.""" await help_test_discovery_update_attr( hass, mqtt_mock, caplog, light.DOMAIN, DEFAULT_CONFIG ) async def test_unique_id(hass): """Test unique id option only creates one light per unique_id.""" config = { light.DOMAIN: [ { "platform": "mqtt", "name": "Test 1", "schema": "json", "state_topic": "test-topic", "command_topic": "test_topic", "unique_id": "TOTALLY_UNIQUE", }, { "platform": "mqtt", "name": "Test 2", "schema": "json", "state_topic": "test-topic", "command_topic": "test_topic", "unique_id": "TOTALLY_UNIQUE", }, ] } await help_test_unique_id(hass, light.DOMAIN, config) async def test_discovery_removal(hass, mqtt_mock, caplog): """Test removal of discovered mqtt_json lights.""" data = '{ "name": "test",' ' "schema": "json",' ' "command_topic": "test_topic" }' await help_test_discovery_removal(hass, mqtt_mock, caplog, light.DOMAIN, data) async def test_discovery_update_light(hass, mqtt_mock, caplog): """Test update of discovered light.""" data1 = ( '{ "name": "Beer",' ' "schema": "json",' ' "state_topic": "test_topic",' ' "command_topic": "test_topic" }' ) data2 = ( '{ "name": "Milk",' ' "schema": "json",' ' "state_topic": "test_topic",' ' "command_topic": "test_topic" }' ) await help_test_discovery_update( hass, mqtt_mock, caplog, light.DOMAIN, data1, data2 ) async def test_discovery_broken(hass, mqtt_mock, caplog): """Test handling of bad discovery message.""" data1 = '{ "name": "Beer" }' data2 = ( '{ "name": "Milk",' ' "schema": "json",' ' "state_topic": "test_topic",' ' "command_topic": "test_topic" }' ) await help_test_discovery_broken( hass, mqtt_mock, caplog, light.DOMAIN, data1, data2 ) async def test_entity_device_info_with_connection(hass, mqtt_mock): """Test MQTT light device registry integration.""" await help_test_entity_device_info_with_connection( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_entity_device_info_with_identifier(hass, mqtt_mock): """Test MQTT light device registry integration.""" await help_test_entity_device_info_with_identifier( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_entity_device_info_update(hass, mqtt_mock): """Test device registry update.""" await help_test_entity_device_info_update( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_entity_device_info_remove(hass, mqtt_mock): """Test device registry remove.""" await help_test_entity_device_info_remove( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_entity_id_update_subscriptions(hass, mqtt_mock): """Test MQTT subscriptions are managed when entity_id is updated.""" await help_test_entity_id_update_subscriptions( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_entity_id_update_discovery_update(hass, mqtt_mock): """Test MQTT discovery update when entity_id is updated.""" await help_test_entity_id_update_discovery_update( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG )

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import json from unittest import mock from unittest.mock import patch from homeassistant.components import light from homeassistant.const import ( ATTR_ASSUMED_STATE, ATTR_SUPPORTED_FEATURES, STATE_OFF, STATE_ON, ) import homeassistant.core as ha from homeassistant.setup import async_setup_component from .test_common import ( help_test_availability_without_topic, help_test_custom_availability_payload, help_test_default_availability_payload, help_test_discovery_broken, help_test_discovery_removal, help_test_discovery_update, help_test_discovery_update_attr, help_test_entity_device_info_remove, help_test_entity_device_info_update, help_test_entity_device_info_with_connection, help_test_entity_device_info_with_identifier, help_test_entity_id_update_discovery_update, help_test_entity_id_update_subscriptions, help_test_setting_attribute_via_mqtt_json_message, help_test_setting_attribute_with_template, help_test_unique_id, help_test_update_with_json_attrs_bad_JSON, help_test_update_with_json_attrs_not_dict, ) from tests.common import async_fire_mqtt_message, mock_coro from tests.components.light import common DEFAULT_CONFIG = { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test-topic", } } class JsonValidator(object): def __init__(self, jsondata): self.jsondata = jsondata def __eq__(self, other): return json.loads(self.jsondata) == json.loads(other) async def test_fail_setup_if_no_command_topic(hass, mqtt_mock): assert await async_setup_component( hass, light.DOMAIN, {light.DOMAIN: {"platform": "mqtt", "schema": "json", "name": "test"}}, ) assert hass.states.get("light.test") is None async def test_no_color_brightness_color_temp_white_val_if_no_topics(hass, mqtt_mock): assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "state_topic": "test_light_rgb", "command_topic": "test_light_rgb/set", } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 40 assert state.attributes.get("rgb_color") is None assert state.attributes.get("brightness") is None assert state.attributes.get("color_temp") is None assert state.attributes.get("effect") is None assert state.attributes.get("white_value") is None assert state.attributes.get("xy_color") is None assert state.attributes.get("hs_color") is None async_fire_mqtt_message(hass, "test_light_rgb", '{"state":"ON"}') state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("rgb_color") is None assert state.attributes.get("brightness") is None assert state.attributes.get("color_temp") is None assert state.attributes.get("effect") is None assert state.attributes.get("white_value") is None assert state.attributes.get("xy_color") is None assert state.attributes.get("hs_color") is None async def test_controlling_state_via_topic(hass, mqtt_mock): assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "state_topic": "test_light_rgb", "command_topic": "test_light_rgb/set", "brightness": True, "color_temp": True, "effect": True, "rgb": True, "white_value": True, "xy": True, "hs": True, "qos": "0", } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 191 assert state.attributes.get("rgb_color") is None assert state.attributes.get("brightness") is None assert state.attributes.get("color_temp") is None assert state.attributes.get("effect") is None assert state.attributes.get("white_value") is None assert state.attributes.get("xy_color") is None assert state.attributes.get("hs_color") is None assert not state.attributes.get(ATTR_ASSUMED_STATE) async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"color":{"r":255,"g":255,"b":255},' '"brightness":255,' '"color_temp":155,' '"effect":"colorloop",' '"white_value":150}', ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("rgb_color") == (255, 255, 255) assert state.attributes.get("brightness") == 255 assert state.attributes.get("color_temp") == 155 assert state.attributes.get("effect") == "colorloop" assert state.attributes.get("white_value") == 150 assert state.attributes.get("xy_color") == (0.323, 0.329) assert state.attributes.get("hs_color") == (0.0, 0.0) async_fire_mqtt_message(hass, "test_light_rgb", '{"state":"OFF"}') state = hass.states.get("light.test") assert state.state == STATE_OFF async_fire_mqtt_message(hass, "test_light_rgb", '{"state":"ON", "brightness":100}') light_state = hass.states.get("light.test") assert light_state.attributes["brightness"] == 100 async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON", ' '"color":{"r":125,"g":125,"b":125}}' ) light_state = hass.states.get("light.test") assert light_state.attributes.get("rgb_color") == (255, 255, 255) async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON", "color":{"x":0.135,"y":0.135}}' ) light_state = hass.states.get("light.test") assert light_state.attributes.get("xy_color") == (0.141, 0.14) async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON", "color":{"h":180,"s":50}}' ) light_state = hass.states.get("light.test") assert light_state.attributes.get("hs_color") == (180.0, 50.0) async_fire_mqtt_message(hass, "test_light_rgb", '{"state":"ON", "color_temp":155}') light_state = hass.states.get("light.test") assert light_state.attributes.get("color_temp") == 155 async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON", "effect":"colorloop"}' ) light_state = hass.states.get("light.test") assert light_state.attributes.get("effect") == "colorloop" async_fire_mqtt_message(hass, "test_light_rgb", '{"state":"ON", "white_value":155}') light_state = hass.states.get("light.test") assert light_state.attributes.get("white_value") == 155 async def test_sending_mqtt_commands_and_optimistic(hass, mqtt_mock): fake_state = ha.State( "light.test", "on", { "brightness": 95, "hs_color": [100, 100], "effect": "random", "color_temp": 100, "white_value": 50, }, ) with patch( "homeassistant.helpers.restore_state.RestoreEntity.async_get_last_state", return_value=mock_coro(fake_state), ): assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "brightness": True, "color_temp": True, "effect": True, "hs": True, "rgb": True, "xy": True, "white_value": True, "qos": 2, } }, ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("brightness") == 95 assert state.attributes.get("hs_color") == (100, 100) assert state.attributes.get("effect") == "random" assert state.attributes.get("color_temp") == 100 assert state.attributes.get("white_value") == 50 assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 191 assert state.attributes.get(ATTR_ASSUMED_STATE) await common.async_turn_on(hass, "light.test") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", '{"state": "ON"}', 2, False ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON await common.async_turn_on(hass, "light.test", color_temp=90) mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON", "color_temp": 90}'), 2, False, ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON await common.async_turn_off(hass, "light.test") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", '{"state": "OFF"}', 2, False ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_OFF mqtt_mock.reset_mock() await common.async_turn_on( hass, "light.test", brightness=50, xy_color=[0.123, 0.123] ) await common.async_turn_on(hass, "light.test", brightness=50, hs_color=[359, 78]) await common.async_turn_on( hass, "light.test", rgb_color=[255, 128, 0], white_value=80 ) mqtt_mock.async_publish.assert_has_calls( [ mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"r": 0, "g": 123, "b": 255,' ' "x": 0.14, "y": 0.131, "h": 210.824, "s": 100.0},' ' "brightness": 50}' ), 2, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"r": 255, "g": 56, "b": 59,' ' "x": 0.654, "y": 0.301, "h": 359.0, "s": 78.0},' ' "brightness": 50}' ), 2, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"r": 255, "g": 128, "b": 0,' ' "x": 0.611, "y": 0.375, "h": 30.118, "s": 100.0},' ' "white_value": 80}' ), 2, False, ), ], any_order=True, ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes["rgb_color"] == (255, 128, 0) assert state.attributes["brightness"] == 50 assert state.attributes["hs_color"] == (30.118, 100) assert state.attributes["white_value"] == 80 assert state.attributes["xy_color"] == (0.611, 0.375) async def test_sending_hs_color(hass, mqtt_mock): assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "brightness": True, "hs": True, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF mqtt_mock.reset_mock() await common.async_turn_on( hass, "light.test", brightness=50, xy_color=[0.123, 0.123] ) await common.async_turn_on(hass, "light.test", brightness=50, hs_color=[359, 78]) await common.async_turn_on( hass, "light.test", rgb_color=[255, 128, 0], white_value=80 ) mqtt_mock.async_publish.assert_has_calls( [ mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"h": 210.824, "s": 100.0},' ' "brightness": 50}' ), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"h": 359.0, "s": 78.0},' ' "brightness": 50}' ), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"h": 30.118, "s": 100.0},' ' "white_value": 80}' ), 0, False, ), ], any_order=True, ) async def test_sending_rgb_color_no_brightness(hass, mqtt_mock): assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "rgb": True, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF await common.async_turn_on( hass, "light.test", brightness=50, xy_color=[0.123, 0.123] ) await common.async_turn_on(hass, "light.test", brightness=50, hs_color=[359, 78]) await common.async_turn_on( hass, "light.test", rgb_color=[255, 128, 0], brightness=255 ) mqtt_mock.async_publish.assert_has_calls( [ mock.call( "test_light_rgb/set", JsonValidator('{"state": "ON", "color": {"r": 0, "g": 24, "b": 50}}'), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator('{"state": "ON", "color": {"r": 50, "g": 11, "b": 11}}'), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator('{"state": "ON", "color": {"r": 255, "g": 128, "b": 0}}'), 0, False, ), ], any_order=True, ) async def test_sending_rgb_color_with_brightness(hass, mqtt_mock): assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "brightness": True, "rgb": True, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF await common.async_turn_on( hass, "light.test", brightness=50, xy_color=[0.123, 0.123] ) await common.async_turn_on(hass, "light.test", brightness=50, hs_color=[359, 78]) await common.async_turn_on( hass, "light.test", rgb_color=[255, 128, 0], white_value=80 ) mqtt_mock.async_publish.assert_has_calls( [ mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"r": 0, "g": 123, "b": 255},' ' "brightness": 50}' ), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"r": 255, "g": 56, "b": 59},' ' "brightness": 50}' ), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"r": 255, "g": 128, "b": 0},' ' "white_value": 80}' ), 0, False, ), ], any_order=True, ) async def test_sending_xy_color(hass, mqtt_mock): assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "brightness": True, "xy": True, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF await common.async_turn_on( hass, "light.test", brightness=50, xy_color=[0.123, 0.123] ) await common.async_turn_on(hass, "light.test", brightness=50, hs_color=[359, 78]) await common.async_turn_on( hass, "light.test", rgb_color=[255, 128, 0], white_value=80 ) mqtt_mock.async_publish.assert_has_calls( [ mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"x": 0.14, "y": 0.131},' ' "brightness": 50}' ), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"x": 0.654, "y": 0.301},' ' "brightness": 50}' ), 0, False, ), mock.call( "test_light_rgb/set", JsonValidator( '{"state": "ON", "color": {"x": 0.611, "y": 0.375},' ' "white_value": 80}' ), 0, False, ), ], any_order=True, ) async def test_effect(hass, mqtt_mock): assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "effect": True, "qos": 0, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 44 await common.async_turn_on(hass, "light.test") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON"}'), 0, False ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("effect") == "none" await common.async_turn_on(hass, "light.test", effect="rainbow") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON", "effect": "rainbow"}'), 0, False, ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("effect") == "rainbow" await common.async_turn_on(hass, "light.test", effect="colorloop") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON", "effect": "colorloop"}'), 0, False, ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("effect") == "colorloop" async def test_flash_short_and_long(hass, mqtt_mock): assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "flash_time_short": 5, "flash_time_long": 15, "qos": 0, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 40 await common.async_turn_on(hass, "light.test", flash="short") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON", "flash": 5}'), 0, False ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON await common.async_turn_on(hass, "light.test", flash="long") mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON", "flash": 15}'), 0, False ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON async def test_transition(hass, mqtt_mock): assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "command_topic": "test_light_rgb/set", "qos": 0, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 40 await common.async_turn_on(hass, "light.test", transition=15) mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "ON", "transition": 15}'), 0, False, ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_ON await common.async_turn_off(hass, "light.test", transition=30) mqtt_mock.async_publish.assert_called_once_with( "test_light_rgb/set", JsonValidator('{"state": "OFF", "transition": 30}'), 0, False, ) mqtt_mock.async_publish.reset_mock() state = hass.states.get("light.test") assert state.state == STATE_OFF async def test_brightness_scale(hass, mqtt_mock): assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "state_topic": "test_light_bright_scale", "command_topic": "test_light_bright_scale/set", "brightness": True, "brightness_scale": 99, } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get("brightness") is None assert not state.attributes.get(ATTR_ASSUMED_STATE) async_fire_mqtt_message(hass, "test_light_bright_scale", '{"state":"ON"}') state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("brightness") == 255 async_fire_mqtt_message( hass, "test_light_bright_scale", '{"state":"ON", "brightness": 99}' ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("brightness") == 255 async def test_invalid_values(hass, mqtt_mock): assert await async_setup_component( hass, light.DOMAIN, { light.DOMAIN: { "platform": "mqtt", "schema": "json", "name": "test", "state_topic": "test_light_rgb", "command_topic": "test_light_rgb/set", "brightness": True, "color_temp": True, "rgb": True, "white_value": True, "qos": "0", } }, ) state = hass.states.get("light.test") assert state.state == STATE_OFF assert state.attributes.get(ATTR_SUPPORTED_FEATURES) == 187 assert state.attributes.get("rgb_color") is None assert state.attributes.get("brightness") is None assert state.attributes.get("white_value") is None assert state.attributes.get("color_temp") is None assert not state.attributes.get(ATTR_ASSUMED_STATE) async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"color":{"r":255,"g":255,"b":255},' '"brightness": 255,' '"white_value": 255,' '"color_temp": 100,' '"effect": "rainbow"}', ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("rgb_color") == (255, 255, 255) assert state.attributes.get("brightness") == 255 assert state.attributes.get("white_value") == 255 assert state.attributes.get("color_temp") == 100 async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"color":{"h":"bad","s":"val"}}', ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("rgb_color") == (255, 255, 255) async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"color":{"r":"bad","g":"val","b":"test"}}', ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("rgb_color") == (255, 255, 255) async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"color":{"x":"bad","y":"val"}}', ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("rgb_color") == (255, 255, 255) async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"brightness": "badValue"}' ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("brightness") == 255 async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"white_value": "badValue"}' ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("white_value") == 255 async_fire_mqtt_message( hass, "test_light_rgb", '{"state":"ON",' '"color_temp": "badValue"}' ) state = hass.states.get("light.test") assert state.state == STATE_ON assert state.attributes.get("color_temp") == 100 async def test_availability_without_topic(hass, mqtt_mock): await help_test_availability_without_topic( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_default_availability_payload(hass, mqtt_mock): await help_test_default_availability_payload( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_custom_availability_payload(hass, mqtt_mock): await help_test_custom_availability_payload( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_setting_attribute_via_mqtt_json_message(hass, mqtt_mock): await help_test_setting_attribute_via_mqtt_json_message( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_setting_attribute_with_template(hass, mqtt_mock): await help_test_setting_attribute_with_template( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_update_with_json_attrs_not_dict(hass, mqtt_mock, caplog): await help_test_update_with_json_attrs_not_dict( hass, mqtt_mock, caplog, light.DOMAIN, DEFAULT_CONFIG ) async def test_update_with_json_attrs_bad_JSON(hass, mqtt_mock, caplog): await help_test_update_with_json_attrs_bad_JSON( hass, mqtt_mock, caplog, light.DOMAIN, DEFAULT_CONFIG ) async def test_discovery_update_attr(hass, mqtt_mock, caplog): await help_test_discovery_update_attr( hass, mqtt_mock, caplog, light.DOMAIN, DEFAULT_CONFIG ) async def test_unique_id(hass): config = { light.DOMAIN: [ { "platform": "mqtt", "name": "Test 1", "schema": "json", "state_topic": "test-topic", "command_topic": "test_topic", "unique_id": "TOTALLY_UNIQUE", }, { "platform": "mqtt", "name": "Test 2", "schema": "json", "state_topic": "test-topic", "command_topic": "test_topic", "unique_id": "TOTALLY_UNIQUE", }, ] } await help_test_unique_id(hass, light.DOMAIN, config) async def test_discovery_removal(hass, mqtt_mock, caplog): data = '{ "name": "test",' ' "schema": "json",' ' "command_topic": "test_topic" }' await help_test_discovery_removal(hass, mqtt_mock, caplog, light.DOMAIN, data) async def test_discovery_update_light(hass, mqtt_mock, caplog): data1 = ( '{ "name": "Beer",' ' "schema": "json",' ' "state_topic": "test_topic",' ' "command_topic": "test_topic" }' ) data2 = ( '{ "name": "Milk",' ' "schema": "json",' ' "state_topic": "test_topic",' ' "command_topic": "test_topic" }' ) await help_test_discovery_update( hass, mqtt_mock, caplog, light.DOMAIN, data1, data2 ) async def test_discovery_broken(hass, mqtt_mock, caplog): data1 = '{ "name": "Beer" }' data2 = ( '{ "name": "Milk",' ' "schema": "json",' ' "state_topic": "test_topic",' ' "command_topic": "test_topic" }' ) await help_test_discovery_broken( hass, mqtt_mock, caplog, light.DOMAIN, data1, data2 ) async def test_entity_device_info_with_connection(hass, mqtt_mock): await help_test_entity_device_info_with_connection( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_entity_device_info_with_identifier(hass, mqtt_mock): await help_test_entity_device_info_with_identifier( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_entity_device_info_update(hass, mqtt_mock): await help_test_entity_device_info_update( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_entity_device_info_remove(hass, mqtt_mock): await help_test_entity_device_info_remove( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_entity_id_update_subscriptions(hass, mqtt_mock): await help_test_entity_id_update_subscriptions( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG ) async def test_entity_id_update_discovery_update(hass, mqtt_mock): await help_test_entity_id_update_discovery_update( hass, mqtt_mock, light.DOMAIN, DEFAULT_CONFIG )

true

f717922d65b80aab39f063f12af29d0dc7bc2b8a

4,431

py

Python

tests/test_settings.py

lulle2007200/EasyClangComplete

55d2c47d9f1b9f2bc85e6cc6de8b92457d9bbeb6

[ "MIT" ]

648

2016-04-18T16:10:47.000Z

2022-03-30T01:48:53.000Z

tests/test_settings.py

lulle2007200/EasyClangComplete

55d2c47d9f1b9f2bc85e6cc6de8b92457d9bbeb6

[ "MIT" ]

672

2016-04-24T13:55:35.000Z

2022-03-23T06:38:42.000Z

tests/test_settings.py

lulle2007200/EasyClangComplete

55d2c47d9f1b9f2bc85e6cc6de8b92457d9bbeb6

[ "MIT" ]

130

2016-05-26T19:27:00.000Z

2022-01-15T10:24:17.000Z

"""Tests for settings.""" import sublime import imp from os import path from EasyClangComplete.tests.gui_test_wrapper import GuiTestWrapper from EasyClangComplete.plugin.settings import settings_manager from EasyClangComplete.plugin.settings import settings_storage from EasyClangComplete.plugin.utils import flag imp.reload(settings_manager) imp.reload(settings_storage) imp.reload(flag) SettingsManager = settings_manager.SettingsManager SettingsStorage = settings_storage.SettingsStorage Flag = flag.Flag class test_settings(GuiTestWrapper): """Test settings.""" def test_setup_view(self): """Test that setup view correctly sets up the view.""" file_name = path.join(path.dirname(__file__), 'test_files', 'test.cpp') self.check_view(file_name) def test_init(self): """Test that settings are correctly initialized.""" manager = SettingsManager() settings = manager.user_settings() self.assertIsNotNone(settings.verbose) self.assertIsNotNone(settings.triggers) self.assertIsNotNone(settings.common_flags) self.assertIsNotNone(settings.clang_binary) self.assertIsNotNone(settings.flags_sources) self.assertIsNotNone(settings.show_errors) self.assertIsNotNone(settings.valid_lang_syntaxes) def test_valid(self): """Test validity.""" manager = SettingsManager() settings = manager.user_settings() valid, _ = settings.is_valid() self.assertTrue(valid) def test_parse_cmake_flags(self): """Testing that we can parse cmake flags.""" file_name = path.join(path.dirname(__file__), 'test_files', 'test_wrong_triggers.cpp') self.set_up_view(file_name) current_folder = path.dirname(__file__) flags_sources = [ { "file": "CMakeLists.txt", "flags": [ "-DBLAH={}/*".format(current_folder), "-DSMTH=ON", "-D XXX=1", "-D FLAG=word" ] } ] self.view.settings().set("flags_sources", flags_sources) settings = SettingsManager().user_settings() settings.update_from_view(self.view, project_specific=False) valid, _ = settings.is_valid() self.assertTrue(valid) self.assertEquals(len(settings.flags_sources), 1) entry = settings.flags_sources[0] self.assertIn('flags', entry) flags = entry['flags'] self.assertEquals(len(flags), 4) self.assertIn('-DSMTH=ON', flags) self.assertIn('-D FLAG=word', flags) self.assertIn('-D XXX=1', flags) import glob all_files = glob.glob(path.join(current_folder, "*")) for file in all_files: self.assertIn(file, flags[0]) def test_populate_flags(self): """Testing include population.""" # open any existing file file_name = path.join(path.dirname(__file__), 'test_files', 'test_wrong_triggers.cpp') self.set_up_view(file_name) # now test the things manager = SettingsManager() settings = manager.user_settings() valid, _ = settings.is_valid() self.assertTrue(valid) p = path.join(sublime.packages_path(), "User", "EasyClangComplete.sublime-settings") if path.exists(p): user = sublime.load_resource( "Packages/User/EasyClangComplete.sublime-settings") if "common_flags" in user: # The user modified the default common flags, just skip the # next few tests. return initial_common_flags = list(settings.common_flags) settings = manager.settings_for_view(self.view) dirs = settings.common_flags self.assertTrue(len(initial_common_flags) <= len(dirs)) reference_flag_0 = Flag.Builder().from_unparsed_string( initial_common_flags[0]).build() self.assertIn(reference_flag_0, dirs) reference_flag_1 = Flag.Builder().from_unparsed_string( initial_common_flags[1]).build() self.assertNotIn(reference_flag_1, dirs)

35.448

75

0.612051

import sublime import imp from os import path from EasyClangComplete.tests.gui_test_wrapper import GuiTestWrapper from EasyClangComplete.plugin.settings import settings_manager from EasyClangComplete.plugin.settings import settings_storage from EasyClangComplete.plugin.utils import flag imp.reload(settings_manager) imp.reload(settings_storage) imp.reload(flag) SettingsManager = settings_manager.SettingsManager SettingsStorage = settings_storage.SettingsStorage Flag = flag.Flag class test_settings(GuiTestWrapper): def test_setup_view(self): file_name = path.join(path.dirname(__file__), 'test_files', 'test.cpp') self.check_view(file_name) def test_init(self): manager = SettingsManager() settings = manager.user_settings() self.assertIsNotNone(settings.verbose) self.assertIsNotNone(settings.triggers) self.assertIsNotNone(settings.common_flags) self.assertIsNotNone(settings.clang_binary) self.assertIsNotNone(settings.flags_sources) self.assertIsNotNone(settings.show_errors) self.assertIsNotNone(settings.valid_lang_syntaxes) def test_valid(self): manager = SettingsManager() settings = manager.user_settings() valid, _ = settings.is_valid() self.assertTrue(valid) def test_parse_cmake_flags(self): file_name = path.join(path.dirname(__file__), 'test_files', 'test_wrong_triggers.cpp') self.set_up_view(file_name) current_folder = path.dirname(__file__) flags_sources = [ { "file": "CMakeLists.txt", "flags": [ "-DBLAH={}/*".format(current_folder), "-DSMTH=ON", "-D XXX=1", "-D FLAG=word" ] } ] self.view.settings().set("flags_sources", flags_sources) settings = SettingsManager().user_settings() settings.update_from_view(self.view, project_specific=False) valid, _ = settings.is_valid() self.assertTrue(valid) self.assertEquals(len(settings.flags_sources), 1) entry = settings.flags_sources[0] self.assertIn('flags', entry) flags = entry['flags'] self.assertEquals(len(flags), 4) self.assertIn('-DSMTH=ON', flags) self.assertIn('-D FLAG=word', flags) self.assertIn('-D XXX=1', flags) import glob all_files = glob.glob(path.join(current_folder, "*")) for file in all_files: self.assertIn(file, flags[0]) def test_populate_flags(self): file_name = path.join(path.dirname(__file__), 'test_files', 'test_wrong_triggers.cpp') self.set_up_view(file_name) manager = SettingsManager() settings = manager.user_settings() valid, _ = settings.is_valid() self.assertTrue(valid) p = path.join(sublime.packages_path(), "User", "EasyClangComplete.sublime-settings") if path.exists(p): user = sublime.load_resource( "Packages/User/EasyClangComplete.sublime-settings") if "common_flags" in user: return initial_common_flags = list(settings.common_flags) settings = manager.settings_for_view(self.view) dirs = settings.common_flags self.assertTrue(len(initial_common_flags) <= len(dirs)) reference_flag_0 = Flag.Builder().from_unparsed_string( initial_common_flags[0]).build() self.assertIn(reference_flag_0, dirs) reference_flag_1 = Flag.Builder().from_unparsed_string( initial_common_flags[1]).build() self.assertNotIn(reference_flag_1, dirs)

true

f71792385df7b999e78480b6b8bb47ec29d5d377

922

py

Python

arrays/countNumberOfPairsWithAbsoluteDifferenceK.py

kushvr7/High-On-DSA

d424bd48e35ebd1a588d96c1c0dcb5a9c47a0ef1

[ "MIT" ]

76

2021-12-12T08:42:20.000Z

2022-03-31T19:48:46.000Z

arrays/countNumberOfPairsWithAbsoluteDifferenceK.py

kushvr7/High-On-DSA

d424bd48e35ebd1a588d96c1c0dcb5a9c47a0ef1

[ "MIT" ]

4

2022-01-04T09:58:39.000Z

2022-03-30T17:00:39.000Z

arrays/countNumberOfPairsWithAbsoluteDifferenceK.py

kushvr7/High-On-DSA

d424bd48e35ebd1a588d96c1c0dcb5a9c47a0ef1

[ "MIT" ]

13

2021-12-12T14:44:41.000Z

2022-03-10T14:08:20.000Z

# https://leetcode.com/problems/count-number-of-pairs-with-absolute-difference-k/ from collections import Counter class Solution(object): # Brute Force Approach # TC : O(N # SC : O(N) def countKDifference(self, nums, k): """ :type nums: List[int] :type k: int :rtype: int """ count =0 for i in range(len(nums)): for j in range(i, len(nums)): if abs(nums[i]-nums[j])==k: count +=1 return count def countKDifference(self, nums, k): """ :type nums: List[int] :type k: int :rtype: int """ dictionary =Counter() count=0 for i in nums: dictionary[i]+=1 count += dictionary[i-k] + dictionary[i+k] return count

23.05

81

0.452278

from collections import Counter class Solution(object): def countKDifference(self, nums, k): count =0 for i in range(len(nums)): for j in range(i, len(nums)): if abs(nums[i]-nums[j])==k: count +=1 return count def countKDifference(self, nums, k): dictionary =Counter() count=0 for i in nums: dictionary[i]+=1 count += dictionary[i-k] + dictionary[i+k] return count

true

f71792d21101ca8f9fdb2b92a64c021d5e816fea

28,771

py

Python

pyspark/bigdl/transform/vision/image.py

twicoder/BigDL

f065db372e1c682fa4a7903e287bba21d5f46750

[ "Apache-2.0" ]

55

2018-01-12T01:43:29.000Z

2021-03-09T02:35:56.000Z

pyspark/bigdl/transform/vision/image.py

jason-hzw/BigDL

ef4f4137965147e2bc59e41f40c4acbb50eeda97

[ "Apache-2.0" ]

4

2018-01-15T07:34:41.000Z

2018-01-16T05:46:12.000Z

pyspark/bigdl/transform/vision/image.py

jason-hzw/BigDL

ef4f4137965147e2bc59e41f40c4acbb50eeda97

[ "Apache-2.0" ]

22

2018-01-15T14:18:15.000Z

2019-12-16T18:51:33.000Z

# # Copyright 2016 The BigDL 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. # import sys from bigdl.util.common import JavaValue from bigdl.util.common import callBigDlFunc from bigdl.util.common import * if sys.version >= '3': long = int unicode = str class FeatureTransformer(JavaValue): """ FeatureTransformer is a transformer that transform ImageFeature """ def __init__(self, bigdl_type="float", *args): self.value = callBigDlFunc( bigdl_type, JavaValue.jvm_class_constructor(self), *args) def transform(self, image_feature, bigdl_type="float"): """ transform ImageFeature """ callBigDlFunc(bigdl_type, "transformImageFeature", self.value, image_feature) return image_feature def __call__(self, image_frame, bigdl_type="float"): """ transform ImageFrame """ jframe = callBigDlFunc(bigdl_type, "transformImageFrame", self.value, image_frame) return ImageFrame(jvalue=jframe) class Pipeline(FeatureTransformer): """ Pipeline of FeatureTransformer """ def __init__(self, transformers, bigdl_type="float"): for transfomer in transformers: assert transfomer.__class__.__bases__[0].__name__ == "FeatureTransformer", "the transformer should be " \ "subclass of FeatureTransformer" super(Pipeline, self).__init__(bigdl_type, transformers) class ImageFeature(JavaValue): """ Each ImageFeature keeps information about single image, it can include various status of an image, e.g. original bytes read from image file, an opencv mat, pixels in float array, image label, meta data and so on. it uses HashMap to store all these data, the key is string that identify the corresponding value """ def __init__(self, image=None, label=None, path=None, bigdl_type="float"): image_tensor = JTensor.from_ndarray(image) if image is not None else None label_tensor = JTensor.from_ndarray(label) if label is not None else None self.bigdl_type = bigdl_type self.value = callBigDlFunc( bigdl_type, JavaValue.jvm_class_constructor(self), image_tensor, label_tensor, path) def get_image(self, float_key="floats", to_chw=True): """ get image as ndarray from ImageFeature """ tensor = callBigDlFunc(self.bigdl_type, "imageFeatureToImageTensor", self.value, float_key, to_chw) return tensor.to_ndarray() def get_label(self): """ get label as ndarray from ImageFeature """ label = callBigDlFunc(self.bigdl_type, "imageFeatureToLabelTensor", self.value) return label.to_ndarray() def keys(self): """ get key set from ImageFeature """ return callBigDlFunc(self.bigdl_type, "imageFeatureGetKeys", self.value) class ImageFrame(JavaValue): """ ImageFrame wraps a set of ImageFeature """ def __init__(self, jvalue, bigdl_type="float"): self.value = jvalue self.bigdl_type = bigdl_type if self.is_local(): self.image_frame = LocalImageFrame(jvalue=self.value) else: self.image_frame = DistributedImageFrame(jvalue=self.value) @classmethod def read(cls, path, sc=None, min_partitions=1, bigdl_type="float"): """ Read images as Image Frame if sc is defined, Read image as DistributedImageFrame from local file system or HDFS if sc is null, Read image as LocalImageFrame from local file system :param path path to read images if sc is defined, path can be local or HDFS. Wildcard character are supported. if sc is null, path is local directory/image file/image file with wildcard character :param sc SparkContext :param min_partitions A suggestion value of the minimal splitting number for input data. :return ImageFrame """ return ImageFrame(jvalue=callBigDlFunc(bigdl_type, "read", path, sc, min_partitions)) @classmethod def read_parquet(cls, path, sc, bigdl_type="float"): """ Read parquet file as DistributedImageFrame """ return DistributedImageFrame(jvalue=callBigDlFunc(bigdl_type, "readParquet", path, sc)) @classmethod def write_parquet(cls, path, output, sc, partition_num = 1, bigdl_type="float"): """ write ImageFrame as parquet file """ return callBigDlFunc(bigdl_type, "writeParquet", path, output, sc, partition_num) def is_local(self): """ whether this is a LocalImageFrame """ return callBigDlFunc(self.bigdl_type, "isLocal", self.value) def is_distributed(self): """ whether this is a DistributedImageFrame """ return callBigDlFunc(self.bigdl_type, "isDistributed", self.value) def transform(self, transformer, bigdl_type="float"): """ transformImageFrame """ self.value = callBigDlFunc(bigdl_type, "transformImageFrame", transformer, self.value) return self def get_image(self, float_key="floats", to_chw=True): """ get image from ImageFrame """ return self.image_frame.get_image(float_key, to_chw) def get_label(self): """ get label from ImageFrame """ return self.image_frame.get_label() def get_predict(self, key="predict"): """ get prediction from ImageFrame """ return self.image_frame.get_predict(key) def get_sample(self): """ get sample from ImageFrame """ return self.image_frame.get_sample() def get_uri(self): """ get uri from imageframe """ return self.image_frame.get_uri() def set_label(self, label, bigdl_type="float"): """ set label for imageframe """ return callBigDlFunc(bigdl_type, "setLabel", label, self.value) def random_split(self, weights): """ Random split imageframes according to weights :param weights: weights for each ImageFrame :return: """ jvalues = self.image_frame.random_split(weights) return [ImageFrame(jvalue) for jvalue in jvalues] class LocalImageFrame(ImageFrame): """ LocalImageFrame wraps a list of ImageFeature """ def __init__(self, image_list=None, label_list=None, jvalue=None, bigdl_type="float"): assert jvalue or image_list, "jvalue and image_list cannot be None in the same time" if jvalue: self.value = jvalue else: # init from image ndarray list and label rdd(optional) image_tensor_list = map(lambda image: JTensor.from_ndarray(image), image_list) label_tensor_list = map(lambda label: JTensor.from_ndarray(label), label_list) if label_list else None self.value = callBigDlFunc(bigdl_type, JavaValue.jvm_class_constructor(self), image_tensor_list, label_tensor_list) self.bigdl_type = bigdl_type def get_image(self, float_key="floats", to_chw=True): """ get image list from ImageFrame """ tensors = callBigDlFunc(self.bigdl_type, "localImageFrameToImageTensor", self.value, float_key, to_chw) return map(lambda tensor: tensor.to_ndarray(), tensors) def get_label(self): """ get label list from ImageFrame """ labels = callBigDlFunc(self.bigdl_type, "localImageFrameToLabelTensor", self.value) return map(lambda tensor: tensor.to_ndarray(), labels) def get_predict(self, key="predict"): """ get prediction list from ImageFrame """ predicts = callBigDlFunc(self.bigdl_type, "localImageFrameToPredict", self.value, key) return map(lambda predict: (predict[0], predict[1].to_ndarray()) if predict[1] else (predict[0], None), predicts) def get_sample(self, key="sample"): return callBigDlFunc(self.bigdl_type, "localImageFrameToSample", self.value, key) def get_uri(self, key = "uri"): return callBigDlFunc(self.bigdl_type, "localImageFrameToUri", self.value, key) def random_split(self, weights): raise "random split not supported in LocalImageFrame" class DistributedImageFrame(ImageFrame): """ DistributedImageFrame wraps an RDD of ImageFeature """ def __init__(self, image_rdd=None, label_rdd=None, jvalue=None, bigdl_type="float"): assert jvalue or image_rdd, "jvalue and image_rdd cannot be None in the same time" if jvalue: self.value = jvalue else: # init from image ndarray rdd and label rdd(optional) image_tensor_rdd = image_rdd.map(lambda image: JTensor.from_ndarray(image)) label_tensor_rdd = label_rdd.map(lambda label: JTensor.from_ndarray(label)) if label_rdd else None self.value = callBigDlFunc(bigdl_type, JavaValue.jvm_class_constructor(self), image_tensor_rdd, label_tensor_rdd) self.bigdl_type = bigdl_type def get_image(self, float_key="floats", to_chw=True): """ get image rdd from ImageFrame """ tensor_rdd = callBigDlFunc(self.bigdl_type, "distributedImageFrameToImageTensorRdd", self.value, float_key, to_chw) return tensor_rdd.map(lambda tensor: tensor.to_ndarray()) def get_label(self): """ get label rdd from ImageFrame """ tensor_rdd = callBigDlFunc(self.bigdl_type, "distributedImageFrameToLabelTensorRdd", self.value) return tensor_rdd.map(lambda tensor: tensor.to_ndarray()) def get_predict(self, key="predict"): """ get prediction rdd from ImageFrame """ predicts = callBigDlFunc(self.bigdl_type, "distributedImageFrameToPredict", self.value, key) return predicts.map(lambda predict: (predict[0], predict[1].to_ndarray()) if predict[1] else (predict[0], None)) def get_sample(self, key="sample"): return callBigDlFunc(self.bigdl_type, "distributedImageFrameToSample", self.value, key) def get_uri(self, key = "uri"): return callBigDlFunc(self.bigdl_type, "distributedImageFrameToUri", self.value, key) def random_split(self, weights): return callBigDlFunc(self.bigdl_type, "distributedImageFrameRandomSplit", self.value, weights) class HFlip(FeatureTransformer): """ Flip the image horizontally """ def __init__(self, bigdl_type="float"): super(HFlip, self).__init__(bigdl_type) class Resize(FeatureTransformer): """ Resize image :param resize_h height after resize :param resize_w width after resize :param resize_mode if resizeMode = -1, random select a mode from (Imgproc.INTER_LINEAR, Imgproc.INTER_CUBIC, Imgproc.INTER_AREA, Imgproc.INTER_NEAREST, Imgproc.INTER_LANCZOS4) :param use_scale_factor if true, scale factor fx and fy is used, fx = fy = 0 note that the result of the following are different Imgproc.resize(mat, mat, new Size(resizeWH, resizeWH), 0, 0, Imgproc.INTER_LINEAR) Imgproc.resize(mat, mat, new Size(resizeWH, resizeWH)) """ def __init__(self, resize_h, resize_w, resize_mode = 1, use_scale_factor=True, bigdl_type="float"): super(Resize, self).__init__(bigdl_type, resize_h, resize_w, resize_mode, use_scale_factor) class Brightness(FeatureTransformer): """ adjust the image brightness :param deltaLow brightness parameter: low bound :param deltaHigh brightness parameter: high bound """ def __init__(self, delta_low, delta_high, bigdl_type="float"): super(Brightness, self).__init__(bigdl_type, delta_low, delta_high) class ChannelOrder(FeatureTransformer): """ random change the channel of an image """ def __init__(self, bigdl_type="float"): super(ChannelOrder, self).__init__(bigdl_type) class Contrast(FeatureTransformer): """ Adjust the image contrast :param delta_low contrast parameter low bound :param delta_high contrast parameter high bound """ def __init__(self, delta_low, delta_high, bigdl_type="float"): super(Contrast, self).__init__(bigdl_type, delta_low, delta_high) class Saturation(FeatureTransformer): """ Adjust image saturation """ def __init__(self, delta_low, delta_high, bigdl_type="float"): super(Saturation, self).__init__(bigdl_type, delta_low, delta_high) class Hue(FeatureTransformer): """ Adjust image hue :param delta_low hue parameter: low bound :param delta_high hue parameter: high bound """ def __init__(self, delta_low, delta_high, bigdl_type="float"): super(Hue, self).__init__(bigdl_type, delta_low, delta_high) class ChannelNormalize(FeatureTransformer): """ image channel normalize :param mean_r mean value in R channel :param mean_g mean value in G channel :param meanB_b mean value in B channel :param std_r std value in R channel :param std_g std value in G channel :param std_b std value in B channel """ def __init__(self, mean_r, mean_g, mean_b, std_r=1.0, std_g=1.0, std_b=1.0, bigdl_type="float"): super(ChannelNormalize, self).__init__(bigdl_type, mean_r, mean_g, mean_b, std_r, std_g, std_b) class PixelNormalize(FeatureTransformer): """ Pixel level normalizer, data(i) = data(i) - mean(i) :param means pixel level mean, following H * W * C order """ def __init__(self, means, bigdl_type="float"): super(PixelNormalize, self).__init__(bigdl_type, means) class RandomCrop(FeatureTransformer): """ Random crop a `cropWidth` x `cropHeight` patch from an image. The patch size should be less than the image size. :param crop_width width after crop :param crop_height height after crop :param is_clip whether to clip the roi to image boundaries """ def __init__(self, crop_width, crop_height, is_clip=True, bigdl_type="float"): super(RandomCrop, self).__init__(bigdl_type, crop_width, crop_height, is_clip) class CenterCrop(FeatureTransformer): """ Crop a `cropWidth` x `cropHeight` patch from center of image. The patch size should be less than the image size. :param crop_width width after crop :param crop_height height after crop :param is_clip clip cropping box boundary """ def __init__(self, crop_width, crop_height, is_clip=True, bigdl_type="float"): super(CenterCrop, self).__init__(bigdl_type, crop_width, crop_height, is_clip) class FixedCrop(FeatureTransformer): """ Crop a fixed area of image :param x1 start in width :param y1 start in height :param x2 end in width :param y2 end in height :param normalized whether args are normalized, i.e. in range [0, 1] :param is_clip whether to clip the roi to image boundaries """ def __init__(self, x1, y1, x2, y2, normalized=True, is_clip=True, bigdl_type="float"): super(FixedCrop, self).__init__(bigdl_type, x1, y1, x2, y2, normalized, is_clip) class DetectionCrop(FeatureTransformer): """ Crop from object detections, each image should has a tensor detection, which is stored in ImageFeature :param roi_key key that map a tensor detection :param normalized whether is detection is normalized, i.e. in range [0, 1] """ def __init__(self, roi_key, normalized=True, bigdl_type="float"): super(DetectionCrop, self).__init__(bigdl_type, roi_key, normalized) class Expand(FeatureTransformer): """ expand image, fill the blank part with the meanR, meanG, meanB :param means_r means in R channel :param means_g means in G channel :param means_b means in B channel :param min_expand_ratio min expand ratio :param max_expand_ratio max expand ratio """ def __init__(self, means_r=123, means_g=117, means_b=104, min_expand_ratio=1.0, max_expand_ratio=4.0, bigdl_type="float"): super(Expand, self).__init__(bigdl_type, means_r, means_g, means_b, min_expand_ratio, max_expand_ratio) class Filler(FeatureTransformer): """ Fill part of image with certain pixel value :param start_x start x ratio :param start_y start y ratio :param end_x end x ratio :param end_y end y ratio :param value filling value """ def __init__(self, start_x, start_y, end_x, end_y, value = 255, bigdl_type="float"): super(Filler, self).__init__(bigdl_type, start_x, start_y, end_x, end_y, value) class RandomTransformer(FeatureTransformer): """ It is a wrapper for transformers to control the transform probability :param transformer transformer to apply randomness :param prob max prob """ def __init__(self, transformer, prob, bigdl_type="float"): super(RandomTransformer, self).__init__(bigdl_type, transformer, prob) class ColorJitter(FeatureTransformer): """ Random adjust brightness, contrast, hue, saturation :param brightness_prob probability to adjust brightness :param brightness_delta brightness parameter :param contrast_prob probability to adjust contrast :param contrast_lower contrast lower parameter :param contrast_upper contrast upper parameter :param hue_prob probability to adjust hue :param hue_delta hue parameter :param saturation_prob probability to adjust saturation :param saturation_lower saturation lower parameter :param saturation_upper saturation upper parameter :param random_order_prob random order for different operation :param shuffle shuffle the transformers """ def __init__(self, brightness_prob = 0.5, brightness_delta = 32.0, contrast_prob = 0.5, contrast_lower = 0.5, contrast_upper = 1.5, hue_prob = 0.5, hue_delta = 18.0, saturation_prob = 0.5, saturation_lower = 0.5, saturation_upper = 1.5, random_order_prob = 0.0, shuffle = False, bigdl_type="float"): super(ColorJitter, self).__init__(bigdl_type, brightness_prob, brightness_delta, contrast_prob, contrast_lower, contrast_upper, hue_prob, hue_delta, saturation_prob, saturation_lower, saturation_upper, random_order_prob, shuffle) class RandomSampler(FeatureTransformer): """ Random sample a bounding box given some constraints and crop the image This is used in SSD training augmentation """ def __init__(self): super(RandomSampler, self).__init__(bigdl_type) class RoiProject(FeatureTransformer): """ Project gt boxes onto the coordinate system defined by image boundary :param need_meet_center_constraint whether need to meet center constraint, i.e., the center of gt box need be within image boundary """ def __init__(self, need_meet_center_constraint, bigdl_type="float"): super(RoiProject, self).__init__(bigdl_type, need_meet_center_constraint) class RoiHFlip(FeatureTransformer): """ horizontally flip the roi :param normalized whether the roi is normalized, i.e. in range [0, 1] """ def __init__(self, normalized=True, bigdl_type="float"): super(RoiHFlip, self).__init__(bigdl_type, normalized) class RoiResize(FeatureTransformer): """ resize the roi according to scale :param normalized whether the roi is normalized, i.e. in range [0, 1] """ def __init__(self, normalized=True, bigdl_type="float"): super(RoiResize, self).__init__(bigdl_type, normalized) class RoiNormalize(FeatureTransformer): """ Normalize Roi to [0, 1] """ def __init__(self, bigdl_type="float"): super(RoiNormalize, self).__init__(bigdl_type) class MatToFloats(FeatureTransformer): """ Transform OpenCVMat to float array, note that in this transformer, the mat is released :param valid_height valid height in case the mat is invalid :param valid_width valid width in case the mat is invalid :param valid_channel valid channel in case the mat is invalid :param out_key key to store float array :param share_buffer share buffer of output """ def __init__(self, valid_height=300, valid_width=300, valid_channel=300, out_key = "floats", share_buffer=True, bigdl_type="float"): super(MatToFloats, self).__init__(bigdl_type, valid_height, valid_width, valid_channel, out_key, share_buffer) class MatToTensor(FeatureTransformer): """ transform opencv mat to tensor :param to_rgb BGR to RGB (default is BGR) :param tensor_key key to store transformed tensor """ def __init__(self, to_rgb=False, tensor_key="imageTensor", bigdl_type="float"): super(MatToTensor, self).__init__(bigdl_type, to_rgb, tensor_key) class AspectScale(FeatureTransformer): """ Resize the image, keep the aspect ratio. scale according to the short edge :param min_size scale size, apply to short edge :param scale_multiple_of make the scaled size multiple of some value :param max_size max size after scale :param resize_mode if resizeMode = -1, random select a mode from (Imgproc.INTER_LINEAR, Imgproc.INTER_CUBIC, Imgproc.INTER_AREA, Imgproc.INTER_NEAREST, Imgproc.INTER_LANCZOS4) :param use_scale_factor if true, scale factor fx and fy is used, fx = fy = 0 :aram min_scale control the minimum scale up for image """ def __init__(self, min_size, scale_multiple_of = 1, max_size = 1000, resize_mode = 1, use_scale_factor=True, min_scale=-1.0, bigdl_type="float"): super(AspectScale, self).__init__(bigdl_type, min_size, scale_multiple_of, max_size, resize_mode, use_scale_factor, min_scale) class RandomAspectScale(FeatureTransformer): """ resize the image by randomly choosing a scale :param scales array of scale options that for random choice :param scaleMultipleOf Resize test images so that its width and height are multiples of :param maxSize Max pixel size of the longest side of a scaled input image """ def __init__(self, scales, scale_multiple_of = 1, max_size = 1000, bigdl_type="float"): super(RandomAspectScale, self).__init__(bigdl_type, scales, scale_multiple_of, max_size) class BytesToMat(FeatureTransformer): """ Transform byte array(original image file in byte) to OpenCVMat :param byte_key key that maps byte array """ def __init__(self, byte_key = "bytes", bigdl_type="float"): super(BytesToMat, self).__init__(bigdl_type, byte_key) class ImageFrameToSample(FeatureTransformer): """ transform imageframe to samples :param input_keys keys that maps inputs (each input should be a tensor) :param target_keys keys that maps targets (each target should be a tensor) :param sample_key key to store sample """ def __init__(self, input_keys=["imageTensor"], target_keys=None, sample_key="sample", bigdl_type="float"): super(ImageFrameToSample, self).__init__(bigdl_type, input_keys, target_keys, sample_key) class PixelBytesToMat(FeatureTransformer): """ Transform byte array(pixels in byte) to OpenCVMat :param byte_key key that maps byte array """ def __init__(self, byte_key = "bytes", bigdl_type="float"): super(PixelBytesToMat, self).__init__(bigdl_type, byte_key) class FixExpand(FeatureTransformer): """ Expand image with given expandHeight and expandWidth, put the original image to the center of expanded image :param expand_height height expand to :param expand_width width expand to """ def __init__(self, expand_height, expand_width, bigdl_type="float"): super(FixExpand, self).__init__(bigdl_type, expand_height, expand_width) class ChannelScaledNormalizer(FeatureTransformer): """ Scaled image at channel level with offset and scale :param mean_r : offset for R channel :param mean_g : offset for G channel :param mean_b: offset for B channel :param scale: scaling factor for all channels """ def __init__(self, mean_r, mean_g, mean_b, scale, bigdl_type="float"): super(ChannelScaledNormalizer, self).__init__(bigdl_type, mean_r, mean_g, mean_b, scale) class RandomAlterAspect(FeatureTransformer): """ Apply random crop based on area ratio and resize to cropLenth size :param min_area_ratio min area ratio :param max_area_ratio max area ratio :param min_aspect_ratio_change factor applied to ratio area :param interp_mode interp mode applied in resize :param crop_length final size resized to """ def __init__(self, min_area_ratio, max_area_ratio, min_aspect_ratio_change, interp_mode, crop_length, bigdl_type="float"): super(RandomAlterAspect, self).__init__(bigdl_type, min_area_ratio, max_area_ratio, min_aspect_ratio_change, interp_mode, crop_length) class RandomCropper(FeatureTransformer): """ Random cropper on uniform distribution with fixed height & width :param crop_w width cropped to :param crop_h height cropped to :param mirror whether mirror :param cropper_method crop method :param channels total channels """ def __init__(self, crop_w, crop_h, mirror, cropper_method, channels, bigdl_type="float"): super(RandomCropper, self).__init__(bigdl_type, crop_w, crop_h, mirror, cropper_method, channels) class RandomResize(FeatureTransformer): """ Random resize between minSize and maxSize and scale height and width to each other :param min_size min size to resize to :param max_size max size to resize to """ def __init__(self, min_size, max_size, bigdl_type="float"): super(RandomResize, self).__init__(bigdl_type, min_size, max_size) class SeqFileFolder(JavaValue): @classmethod def files_to_image_frame(cls, url, sc, class_num, partition_num=-1, bigdl_type="float"): """ Extract hadoop sequence files from an HDFS path as ImageFrame :param url: sequence files folder path :param sc: spark context :param class_num: class number of data :param partition_num: partition number, default: Engine.nodeNumber() * Engine.coreNumber() """ jvalue = callBigDlFunc(bigdl_type, "seqFilesToImageFrame", url, sc, class_num, partition_num) return ImageFrame(jvalue=jvalue)

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import sys from bigdl.util.common import JavaValue from bigdl.util.common import callBigDlFunc from bigdl.util.common import * if sys.version >= '3': long = int unicode = str class FeatureTransformer(JavaValue): def __init__(self, bigdl_type="float", *args): self.value = callBigDlFunc( bigdl_type, JavaValue.jvm_class_constructor(self), *args) def transform(self, image_feature, bigdl_type="float"): callBigDlFunc(bigdl_type, "transformImageFeature", self.value, image_feature) return image_feature def __call__(self, image_frame, bigdl_type="float"): jframe = callBigDlFunc(bigdl_type, "transformImageFrame", self.value, image_frame) return ImageFrame(jvalue=jframe) class Pipeline(FeatureTransformer): def __init__(self, transformers, bigdl_type="float"): for transfomer in transformers: assert transfomer.__class__.__bases__[0].__name__ == "FeatureTransformer", "the transformer should be " \ "subclass of FeatureTransformer" super(Pipeline, self).__init__(bigdl_type, transformers) class ImageFeature(JavaValue): def __init__(self, image=None, label=None, path=None, bigdl_type="float"): image_tensor = JTensor.from_ndarray(image) if image is not None else None label_tensor = JTensor.from_ndarray(label) if label is not None else None self.bigdl_type = bigdl_type self.value = callBigDlFunc( bigdl_type, JavaValue.jvm_class_constructor(self), image_tensor, label_tensor, path) def get_image(self, float_key="floats", to_chw=True): tensor = callBigDlFunc(self.bigdl_type, "imageFeatureToImageTensor", self.value, float_key, to_chw) return tensor.to_ndarray() def get_label(self): label = callBigDlFunc(self.bigdl_type, "imageFeatureToLabelTensor", self.value) return label.to_ndarray() def keys(self): return callBigDlFunc(self.bigdl_type, "imageFeatureGetKeys", self.value) class ImageFrame(JavaValue): def __init__(self, jvalue, bigdl_type="float"): self.value = jvalue self.bigdl_type = bigdl_type if self.is_local(): self.image_frame = LocalImageFrame(jvalue=self.value) else: self.image_frame = DistributedImageFrame(jvalue=self.value) @classmethod def read(cls, path, sc=None, min_partitions=1, bigdl_type="float"): return ImageFrame(jvalue=callBigDlFunc(bigdl_type, "read", path, sc, min_partitions)) @classmethod def read_parquet(cls, path, sc, bigdl_type="float"): return DistributedImageFrame(jvalue=callBigDlFunc(bigdl_type, "readParquet", path, sc)) @classmethod def write_parquet(cls, path, output, sc, partition_num = 1, bigdl_type="float"): return callBigDlFunc(bigdl_type, "writeParquet", path, output, sc, partition_num) def is_local(self): return callBigDlFunc(self.bigdl_type, "isLocal", self.value) def is_distributed(self): return callBigDlFunc(self.bigdl_type, "isDistributed", self.value) def transform(self, transformer, bigdl_type="float"): self.value = callBigDlFunc(bigdl_type, "transformImageFrame", transformer, self.value) return self def get_image(self, float_key="floats", to_chw=True): return self.image_frame.get_image(float_key, to_chw) def get_label(self): return self.image_frame.get_label() def get_predict(self, key="predict"): return self.image_frame.get_predict(key) def get_sample(self): return self.image_frame.get_sample() def get_uri(self): return self.image_frame.get_uri() def set_label(self, label, bigdl_type="float"): return callBigDlFunc(bigdl_type, "setLabel", label, self.value) def random_split(self, weights): jvalues = self.image_frame.random_split(weights) return [ImageFrame(jvalue) for jvalue in jvalues] class LocalImageFrame(ImageFrame): def __init__(self, image_list=None, label_list=None, jvalue=None, bigdl_type="float"): assert jvalue or image_list, "jvalue and image_list cannot be None in the same time" if jvalue: self.value = jvalue else: image_tensor_list = map(lambda image: JTensor.from_ndarray(image), image_list) label_tensor_list = map(lambda label: JTensor.from_ndarray(label), label_list) if label_list else None self.value = callBigDlFunc(bigdl_type, JavaValue.jvm_class_constructor(self), image_tensor_list, label_tensor_list) self.bigdl_type = bigdl_type def get_image(self, float_key="floats", to_chw=True): tensors = callBigDlFunc(self.bigdl_type, "localImageFrameToImageTensor", self.value, float_key, to_chw) return map(lambda tensor: tensor.to_ndarray(), tensors) def get_label(self): labels = callBigDlFunc(self.bigdl_type, "localImageFrameToLabelTensor", self.value) return map(lambda tensor: tensor.to_ndarray(), labels) def get_predict(self, key="predict"): predicts = callBigDlFunc(self.bigdl_type, "localImageFrameToPredict", self.value, key) return map(lambda predict: (predict[0], predict[1].to_ndarray()) if predict[1] else (predict[0], None), predicts) def get_sample(self, key="sample"): return callBigDlFunc(self.bigdl_type, "localImageFrameToSample", self.value, key) def get_uri(self, key = "uri"): return callBigDlFunc(self.bigdl_type, "localImageFrameToUri", self.value, key) def random_split(self, weights): raise "random split not supported in LocalImageFrame" class DistributedImageFrame(ImageFrame): def __init__(self, image_rdd=None, label_rdd=None, jvalue=None, bigdl_type="float"): assert jvalue or image_rdd, "jvalue and image_rdd cannot be None in the same time" if jvalue: self.value = jvalue else: image_tensor_rdd = image_rdd.map(lambda image: JTensor.from_ndarray(image)) label_tensor_rdd = label_rdd.map(lambda label: JTensor.from_ndarray(label)) if label_rdd else None self.value = callBigDlFunc(bigdl_type, JavaValue.jvm_class_constructor(self), image_tensor_rdd, label_tensor_rdd) self.bigdl_type = bigdl_type def get_image(self, float_key="floats", to_chw=True): tensor_rdd = callBigDlFunc(self.bigdl_type, "distributedImageFrameToImageTensorRdd", self.value, float_key, to_chw) return tensor_rdd.map(lambda tensor: tensor.to_ndarray()) def get_label(self): tensor_rdd = callBigDlFunc(self.bigdl_type, "distributedImageFrameToLabelTensorRdd", self.value) return tensor_rdd.map(lambda tensor: tensor.to_ndarray()) def get_predict(self, key="predict"): predicts = callBigDlFunc(self.bigdl_type, "distributedImageFrameToPredict", self.value, key) return predicts.map(lambda predict: (predict[0], predict[1].to_ndarray()) if predict[1] else (predict[0], None)) def get_sample(self, key="sample"): return callBigDlFunc(self.bigdl_type, "distributedImageFrameToSample", self.value, key) def get_uri(self, key = "uri"): return callBigDlFunc(self.bigdl_type, "distributedImageFrameToUri", self.value, key) def random_split(self, weights): return callBigDlFunc(self.bigdl_type, "distributedImageFrameRandomSplit", self.value, weights) class HFlip(FeatureTransformer): def __init__(self, bigdl_type="float"): super(HFlip, self).__init__(bigdl_type) class Resize(FeatureTransformer): def __init__(self, resize_h, resize_w, resize_mode = 1, use_scale_factor=True, bigdl_type="float"): super(Resize, self).__init__(bigdl_type, resize_h, resize_w, resize_mode, use_scale_factor) class Brightness(FeatureTransformer): def __init__(self, delta_low, delta_high, bigdl_type="float"): super(Brightness, self).__init__(bigdl_type, delta_low, delta_high) class ChannelOrder(FeatureTransformer): def __init__(self, bigdl_type="float"): super(ChannelOrder, self).__init__(bigdl_type) class Contrast(FeatureTransformer): def __init__(self, delta_low, delta_high, bigdl_type="float"): super(Contrast, self).__init__(bigdl_type, delta_low, delta_high) class Saturation(FeatureTransformer): def __init__(self, delta_low, delta_high, bigdl_type="float"): super(Saturation, self).__init__(bigdl_type, delta_low, delta_high) class Hue(FeatureTransformer): def __init__(self, delta_low, delta_high, bigdl_type="float"): super(Hue, self).__init__(bigdl_type, delta_low, delta_high) class ChannelNormalize(FeatureTransformer): def __init__(self, mean_r, mean_g, mean_b, std_r=1.0, std_g=1.0, std_b=1.0, bigdl_type="float"): super(ChannelNormalize, self).__init__(bigdl_type, mean_r, mean_g, mean_b, std_r, std_g, std_b) class PixelNormalize(FeatureTransformer): def __init__(self, means, bigdl_type="float"): super(PixelNormalize, self).__init__(bigdl_type, means) class RandomCrop(FeatureTransformer): def __init__(self, crop_width, crop_height, is_clip=True, bigdl_type="float"): super(RandomCrop, self).__init__(bigdl_type, crop_width, crop_height, is_clip) class CenterCrop(FeatureTransformer): def __init__(self, crop_width, crop_height, is_clip=True, bigdl_type="float"): super(CenterCrop, self).__init__(bigdl_type, crop_width, crop_height, is_clip) class FixedCrop(FeatureTransformer): def __init__(self, x1, y1, x2, y2, normalized=True, is_clip=True, bigdl_type="float"): super(FixedCrop, self).__init__(bigdl_type, x1, y1, x2, y2, normalized, is_clip) class DetectionCrop(FeatureTransformer): def __init__(self, roi_key, normalized=True, bigdl_type="float"): super(DetectionCrop, self).__init__(bigdl_type, roi_key, normalized) class Expand(FeatureTransformer): def __init__(self, means_r=123, means_g=117, means_b=104, min_expand_ratio=1.0, max_expand_ratio=4.0, bigdl_type="float"): super(Expand, self).__init__(bigdl_type, means_r, means_g, means_b, min_expand_ratio, max_expand_ratio) class Filler(FeatureTransformer): def __init__(self, start_x, start_y, end_x, end_y, value = 255, bigdl_type="float"): super(Filler, self).__init__(bigdl_type, start_x, start_y, end_x, end_y, value) class RandomTransformer(FeatureTransformer): def __init__(self, transformer, prob, bigdl_type="float"): super(RandomTransformer, self).__init__(bigdl_type, transformer, prob) class ColorJitter(FeatureTransformer): def __init__(self, brightness_prob = 0.5, brightness_delta = 32.0, contrast_prob = 0.5, contrast_lower = 0.5, contrast_upper = 1.5, hue_prob = 0.5, hue_delta = 18.0, saturation_prob = 0.5, saturation_lower = 0.5, saturation_upper = 1.5, random_order_prob = 0.0, shuffle = False, bigdl_type="float"): super(ColorJitter, self).__init__(bigdl_type, brightness_prob, brightness_delta, contrast_prob, contrast_lower, contrast_upper, hue_prob, hue_delta, saturation_prob, saturation_lower, saturation_upper, random_order_prob, shuffle) class RandomSampler(FeatureTransformer): def __init__(self): super(RandomSampler, self).__init__(bigdl_type) class RoiProject(FeatureTransformer): def __init__(self, need_meet_center_constraint, bigdl_type="float"): super(RoiProject, self).__init__(bigdl_type, need_meet_center_constraint) class RoiHFlip(FeatureTransformer): def __init__(self, normalized=True, bigdl_type="float"): super(RoiHFlip, self).__init__(bigdl_type, normalized) class RoiResize(FeatureTransformer): def __init__(self, normalized=True, bigdl_type="float"): super(RoiResize, self).__init__(bigdl_type, normalized) class RoiNormalize(FeatureTransformer): def __init__(self, bigdl_type="float"): super(RoiNormalize, self).__init__(bigdl_type) class MatToFloats(FeatureTransformer): def __init__(self, valid_height=300, valid_width=300, valid_channel=300, out_key = "floats", share_buffer=True, bigdl_type="float"): super(MatToFloats, self).__init__(bigdl_type, valid_height, valid_width, valid_channel, out_key, share_buffer) class MatToTensor(FeatureTransformer): def __init__(self, to_rgb=False, tensor_key="imageTensor", bigdl_type="float"): super(MatToTensor, self).__init__(bigdl_type, to_rgb, tensor_key) class AspectScale(FeatureTransformer): def __init__(self, min_size, scale_multiple_of = 1, max_size = 1000, resize_mode = 1, use_scale_factor=True, min_scale=-1.0, bigdl_type="float"): super(AspectScale, self).__init__(bigdl_type, min_size, scale_multiple_of, max_size, resize_mode, use_scale_factor, min_scale) class RandomAspectScale(FeatureTransformer): def __init__(self, scales, scale_multiple_of = 1, max_size = 1000, bigdl_type="float"): super(RandomAspectScale, self).__init__(bigdl_type, scales, scale_multiple_of, max_size) class BytesToMat(FeatureTransformer): def __init__(self, byte_key = "bytes", bigdl_type="float"): super(BytesToMat, self).__init__(bigdl_type, byte_key) class ImageFrameToSample(FeatureTransformer): def __init__(self, input_keys=["imageTensor"], target_keys=None, sample_key="sample", bigdl_type="float"): super(ImageFrameToSample, self).__init__(bigdl_type, input_keys, target_keys, sample_key) class PixelBytesToMat(FeatureTransformer): def __init__(self, byte_key = "bytes", bigdl_type="float"): super(PixelBytesToMat, self).__init__(bigdl_type, byte_key) class FixExpand(FeatureTransformer): def __init__(self, expand_height, expand_width, bigdl_type="float"): super(FixExpand, self).__init__(bigdl_type, expand_height, expand_width) class ChannelScaledNormalizer(FeatureTransformer): def __init__(self, mean_r, mean_g, mean_b, scale, bigdl_type="float"): super(ChannelScaledNormalizer, self).__init__(bigdl_type, mean_r, mean_g, mean_b, scale) class RandomAlterAspect(FeatureTransformer): def __init__(self, min_area_ratio, max_area_ratio, min_aspect_ratio_change, interp_mode, crop_length, bigdl_type="float"): super(RandomAlterAspect, self).__init__(bigdl_type, min_area_ratio, max_area_ratio, min_aspect_ratio_change, interp_mode, crop_length) class RandomCropper(FeatureTransformer): def __init__(self, crop_w, crop_h, mirror, cropper_method, channels, bigdl_type="float"): super(RandomCropper, self).__init__(bigdl_type, crop_w, crop_h, mirror, cropper_method, channels) class RandomResize(FeatureTransformer): def __init__(self, min_size, max_size, bigdl_type="float"): super(RandomResize, self).__init__(bigdl_type, min_size, max_size) class SeqFileFolder(JavaValue): @classmethod def files_to_image_frame(cls, url, sc, class_num, partition_num=-1, bigdl_type="float"): jvalue = callBigDlFunc(bigdl_type, "seqFilesToImageFrame", url, sc, class_num, partition_num) return ImageFrame(jvalue=jvalue)

true

f71793b555d90d054bf1905d45ea0235b077f075

2,312

py

Python

pyramid_debugtoolbar/panels/traceback.py

rollbar/pyramid_debugtoolbar

dab4278eb68b801b1d3e9679cf1308096c3f849f

[ "Apache-2.0" ]

null

pyramid_debugtoolbar/panels/traceback.py

rollbar/pyramid_debugtoolbar

dab4278eb68b801b1d3e9679cf1308096c3f849f

[ "Apache-2.0" ]

null

pyramid_debugtoolbar/panels/traceback.py

rollbar/pyramid_debugtoolbar

dab4278eb68b801b1d3e9679cf1308096c3f849f

[ "Apache-2.0" ]

1

2021-02-21T12:18:04.000Z

import re from pyramid_debugtoolbar.tbtools import Traceback from pyramid_debugtoolbar.panels import DebugPanel from pyramid_debugtoolbar.utils import escape from pyramid_debugtoolbar.utils import STATIC_PATH from pyramid_debugtoolbar.utils import ROOT_ROUTE_NAME from pyramid_debugtoolbar.utils import EXC_ROUTE_NAME _ = lambda x: x class TracebackPanel(DebugPanel): name = 'Traceback' template = 'pyramid_debugtoolbar.panels:templates/traceback.dbtmako' def __init__(self, request): self.request = request self.exc_history = request.exc_history def nav_title(self): return _("Traceback") def nav_subtitle(self): return "" def title(self): return _("Traceback") def url(self): return "" @property def has_content(self): if hasattr(self.request, 'pdbt_tb'): return True else: return False def process_response(self, response): if self.has_content: traceback = self.request.pdbt_tb exc = escape(traceback.exception) summary = Traceback.render_summary(traceback, include_title=False, request=self.request) token = self.request.registry.pdtb_token url = '' # self.request.route_url(EXC_ROUTE_NAME, _query=qs) evalex = self.exc_history.eval_exc self.data = { 'evalex': evalex and 'true' or 'false', 'console': 'false', 'lodgeit_url': None, 'title': exc, 'exception': exc, 'exception_type': escape(traceback.exception_type), 'summary': summary, 'plaintext': traceback.plaintext, 'plaintext_cs': re.sub('-{2,}', '-', traceback.plaintext), 'traceback_id': traceback.id, 'token': token, 'url': url, } def render_content(self, request): return super(TracebackPanel, self).render_content(request) def render_vars(self, request): return { 'static_path': request.static_url(STATIC_PATH), 'root_path': request.route_url(ROOT_ROUTE_NAME) }

32.111111

100

0.588668

import re from pyramid_debugtoolbar.tbtools import Traceback from pyramid_debugtoolbar.panels import DebugPanel from pyramid_debugtoolbar.utils import escape from pyramid_debugtoolbar.utils import STATIC_PATH from pyramid_debugtoolbar.utils import ROOT_ROUTE_NAME from pyramid_debugtoolbar.utils import EXC_ROUTE_NAME _ = lambda x: x class TracebackPanel(DebugPanel): name = 'Traceback' template = 'pyramid_debugtoolbar.panels:templates/traceback.dbtmako' def __init__(self, request): self.request = request self.exc_history = request.exc_history def nav_title(self): return _("Traceback") def nav_subtitle(self): return "" def title(self): return _("Traceback") def url(self): return "" @property def has_content(self): if hasattr(self.request, 'pdbt_tb'): return True else: return False def process_response(self, response): if self.has_content: traceback = self.request.pdbt_tb exc = escape(traceback.exception) summary = Traceback.render_summary(traceback, include_title=False, request=self.request) token = self.request.registry.pdtb_token url = '' evalex = self.exc_history.eval_exc self.data = { 'evalex': evalex and 'true' or 'false', 'console': 'false', 'lodgeit_url': None, 'title': exc, 'exception': exc, 'exception_type': escape(traceback.exception_type), 'summary': summary, 'plaintext': traceback.plaintext, 'plaintext_cs': re.sub('-{2,}', '-', traceback.plaintext), 'traceback_id': traceback.id, 'token': token, 'url': url, } def render_content(self, request): return super(TracebackPanel, self).render_content(request) def render_vars(self, request): return { 'static_path': request.static_url(STATIC_PATH), 'root_path': request.route_url(ROOT_ROUTE_NAME) }

true

f717942ddd2886f2dcfba64b6e7601b1de639677

571

py

Python

test/api_test.py

kuro2a/kiku

d4e6500970a20d1955f1773e0e2cfb8e2db819ba

[ "MIT" ]

2

2019-08-14T14:32:36.000Z

2019-08-15T08:28:15.000Z

test/api_test.py

kuro2a/kiku

d4e6500970a20d1955f1773e0e2cfb8e2db819ba

[ "MIT" ]

1

2019-10-02T16:35:05.000Z

test/api_test.py

kuro2a/kiku

d4e6500970a20d1955f1773e0e2cfb8e2db819ba

[ "MIT" ]

1

2019-08-14T14:33:01.000Z

#!/usr/bin/python3 import sys import pathlib from datetime import datetime import pytest from falcon import testing sys.path.append( str(pathlib.Path(__file__).resolve().parent) + '/../' ) import main @pytest.fixture() def client(): return testing.TestClient(main.create_service()) def test_api_version(client): doc = { u'meta': { u'status': u'OK', u'message': u'OK', u'timestamp': datetime.now() }, u'data': None } result = client.simulate_get('/api/v1/version') assert result.json == doc

18.419355

72

0.623468

import sys import pathlib from datetime import datetime import pytest from falcon import testing sys.path.append( str(pathlib.Path(__file__).resolve().parent) + '/../' ) import main @pytest.fixture() def client(): return testing.TestClient(main.create_service()) def test_api_version(client): doc = { u'meta': { u'status': u'OK', u'message': u'OK', u'timestamp': datetime.now() }, u'data': None } result = client.simulate_get('/api/v1/version') assert result.json == doc

true

f7179491275351836a0592d841bc9b9fe7d43c7d

151

py

Python

src/GracefulKiller/__init__.py

MaxMaxoff/GracefulKiller

dab06ecc7573211ae7acf90e5f889e37d48a88d2

[ "MIT" ]

1

2021-10-04T09:09:12.000Z

src/GracefulKiller/__init__.py

MaxMaxoff/GracefulKiller

dab06ecc7573211ae7acf90e5f889e37d48a88d2

[ "MIT" ]

null

src/GracefulKiller/__init__.py

MaxMaxoff/GracefulKiller

dab06ecc7573211ae7acf90e5f889e37d48a88d2

[ "MIT" ]

null

try: from GracefulKiller.GracefulKiller import GracefulKiller, Loop except: from src.GracefulKiller.GracefulKiller import GracefulKiller, Loop

30.2

70

0.821192

try: from GracefulKiller.GracefulKiller import GracefulKiller, Loop except: from src.GracefulKiller.GracefulKiller import GracefulKiller, Loop

true

f71794ea091735736fa90a59266f6bb2161ac032

1,897

py

Python

tests/test_test.py

yehzhang/RapidTest

2302fc10ddafba1d16ef1d7448d46c66f5a05da2

[ "MIT" ]

null

tests/test_test.py

yehzhang/RapidTest

2302fc10ddafba1d16ef1d7448d46c66f5a05da2

[ "MIT" ]

null

tests/test_test.py

yehzhang/RapidTest

2302fc10ddafba1d16ef1d7448d46c66f5a05da2

[ "MIT" ]

null

from unittest import TestCase from rapidtest import Result, Test, Case, TreeNode class TestTest(TestCase): def test_check_result(self): t = Test(list, operation=True) t.add_case(Case('append', [1], 'pop', Result(1), 'append', [2], 'append', [3], 'pop', 'pop', Result(2))) t.add_case(Case('append', [1], 'pop', 'append', [2], 'append', [3], 'pop', 'pop', result=list)) t.run() # Test if output difference is detected # t.add_case(Case('append', [1], # 'pop', Result(2))) # t.add_case(Case('append', [1], # 'pop', Result(1))) # with self.assertRaisesRegexp(ValueError, r'differ'): # t.run() # t.run() def test_summary(self): def assert_sum_code(c): code, _ = t.summary() self.assertEqual(code, c) t = Test(list, operation=True) assert_sum_code(t.EXIT_EMPTY) t.add_case(Case('append', [1], 'pop', Result(1))) t.run() assert_sum_code(t.EXIT_PASS) t.add_case(Case('pop', Result(None))) # assert_sum_code(t.EXIT_PENDING) with self.assertRaises(IndexError): t.run() assert_sum_code(t.EXIT_FAIL) t.add_case(Case('append', [1], Result(None))) # assert_sum_code(t.EXIT_PENDING) t.run() assert_sum_code(t.EXIT_FAIL) def f(i): if i == 0: return Case('append', [1], Result(None)) raise ValueError t.add_func(f) with self.assertRaises(ValueError): t.run() assert_sum_code(t.EXIT_GEN_ERR)

29.640625

62

0.470216

from unittest import TestCase from rapidtest import Result, Test, Case, TreeNode class TestTest(TestCase): def test_check_result(self): t = Test(list, operation=True) t.add_case(Case('append', [1], 'pop', Result(1), 'append', [2], 'append', [3], 'pop', 'pop', Result(2))) t.add_case(Case('append', [1], 'pop', 'append', [2], 'append', [3], 'pop', 'pop', result=list)) t.run() def test_summary(self): def assert_sum_code(c): code, _ = t.summary() self.assertEqual(code, c) t = Test(list, operation=True) assert_sum_code(t.EXIT_EMPTY) t.add_case(Case('append', [1], 'pop', Result(1))) t.run() assert_sum_code(t.EXIT_PASS) t.add_case(Case('pop', Result(None))) with self.assertRaises(IndexError): t.run() assert_sum_code(t.EXIT_FAIL) t.add_case(Case('append', [1], Result(None))) t.run() assert_sum_code(t.EXIT_FAIL) def f(i): if i == 0: return Case('append', [1], Result(None)) raise ValueError t.add_func(f) with self.assertRaises(ValueError): t.run() assert_sum_code(t.EXIT_GEN_ERR)

true

f7179508810234674ea1f3cf934a800af733803f

794

py

Python

gaphor/RAAML/modelinglanguage.py

mrmonkington/gaphor

f0fcd4deb90d24b14723840a689fac901f645a43

[ "Apache-2.0" ]

867

2018-01-09T00:19:09.000Z

2022-03-31T02:49:23.000Z

gaphor/RAAML/modelinglanguage.py

mrmonkington/gaphor

f0fcd4deb90d24b14723840a689fac901f645a43

[ "Apache-2.0" ]

790

2018-01-13T23:47:07.000Z

2022-03-31T16:04:27.000Z

gaphor/RAAML/modelinglanguage.py

sitedata/gaphor

c83eff0bd595d1a8e766a157f0268e5206eed22c

[ "Apache-2.0" ]

117

2018-01-09T02:24:49.000Z

2022-03-23T08:07:42.000Z

"""The RAAML Modeling Language module is the entrypoint for RAAML related assets.""" import gaphor.SysML.propertypages # noqa from gaphor.abc import ModelingLanguage from gaphor.core import gettext from gaphor.diagram.diagramtoolbox import ToolboxDefinition from gaphor.RAAML import diagramitems, raaml from gaphor.RAAML.toolbox import raaml_toolbox_actions class RAAMLModelingLanguage(ModelingLanguage): @property def name(self) -> str: return gettext("RAAML") @property def toolbox_definition(self) -> ToolboxDefinition: return raaml_toolbox_actions def lookup_element(self, name): element_type = getattr(raaml, name, None) if not element_type: element_type = getattr(diagramitems, name, None) return element_type

30.538462

73

0.746851

import gaphor.SysML.propertypages from gaphor.abc import ModelingLanguage from gaphor.core import gettext from gaphor.diagram.diagramtoolbox import ToolboxDefinition from gaphor.RAAML import diagramitems, raaml from gaphor.RAAML.toolbox import raaml_toolbox_actions class RAAMLModelingLanguage(ModelingLanguage): @property def name(self) -> str: return gettext("RAAML") @property def toolbox_definition(self) -> ToolboxDefinition: return raaml_toolbox_actions def lookup_element(self, name): element_type = getattr(raaml, name, None) if not element_type: element_type = getattr(diagramitems, name, None) return element_type

true