fumiyau/python_comments_free_500000 · Datasets at Hugging Face

f71fa83d6989172da3743eb9fd560fc906515688

10,607

py

Python

tests/commandline/stubber_cli_test.py

Josverl/mipystubber

504814224b38208e9886661b181a57d2b9077be1

[ "MIT" ]

1

2019-03-26T16:03:04.000Z

tests/commandline/stubber_cli_test.py

Josverl/mipystubber

504814224b38208e9886661b181a57d2b9077be1

[ "MIT" ]

null

tests/commandline/stubber_cli_test.py

Josverl/mipystubber

504814224b38208e9886661b181a57d2b9077be1

[ "MIT" ]

null

from typing import List import pytest from pytest_mock import MockerFixture from mock import MagicMock from pathlib import Path from click.testing import CliRunner # module under test : import stubber.stubber as stubber def test_stubber_help(): # check basic commandline sanity check runner = CliRunner() result = runner.invoke(stubber.stubber_cli, ["--help"]) assert result.exit_code == 0 assert "Usage:" in result.output assert "Commands:" in result.output ########################################################################################## # clone ########################################################################################## def test_stubber_clone(mocker: MockerFixture, tmp_path: Path): runner = CliRunner() mock_clone: MagicMock = mocker.patch("stubber.stubber.git.clone", autospec=True, return_value=0) mock_fetch: MagicMock = mocker.patch("stubber.stubber.git.fetch", autospec=True, return_value=0) result = runner.invoke(stubber.stubber_cli, ["clone"]) assert result.exit_code == 0 # either clone or fetch assert mock_clone.call_count + mock_fetch.call_count == 2 if mock_clone.call_count > 0: mock_clone.assert_any_call(remote_repo="https://github.com/micropython/micropython.git", path=Path("repos/micropython")) mock_clone.assert_any_call(remote_repo="https://github.com/micropython/micropython-lib.git", path=Path("repos/micropython-lib")) else: mock_fetch.assert_any_call(Path("repos/micropython")) mock_fetch.assert_any_call(Path("repos/micropython-lib")) def test_stubber_clone_path(mocker: MockerFixture, tmp_path: Path): runner = CliRunner() mock_clone: MagicMock = mocker.MagicMock(return_value=0) mocker.patch("stubber.stubber.git.clone", mock_clone) m_tag = mocker.patch("stubber.stubber.git.get_tag", autospec=True) m_dir = mocker.patch("stubber.stubber.os.mkdir", autospec=True) # now test with path specified result = runner.invoke(stubber.stubber_cli, ["clone", "--path", "foobar"]) assert result.exit_code == 0 assert mock_clone.call_count >= 2 mock_clone.assert_any_call(remote_repo="https://github.com/micropython/micropython.git", path=Path("foobar/micropython")) mock_clone.assert_any_call(remote_repo="https://github.com/micropython/micropython-lib.git", path=Path("foobar/micropython-lib")) assert m_tag.call_count >= 2 ########################################################################################## # switch ########################################################################################## @pytest.mark.parametrize( "params", [ ["switch", "--version", "latest", "--path", "foobar"], ["switch", "--version", "v1.10", "--path", "foobar"], ], ) def test_stubber_switch(mocker: MockerFixture, params: List[str]): runner = CliRunner() # mock_clone: MagicMock = mocker.patch("stubber.stubber.git.clone", autospec=True, return_value=0) # Mock Path.exists m_fetch: MagicMock = mocker.patch("stubber.stubber.git.fetch", autospec=True, return_value=0) m_switch: MagicMock = mocker.patch("stubber.stubber.git.switch_branch", autospec=True, return_value=0) m_checkout: MagicMock = mocker.patch("stubber.stubber.git.checkout_tag", autospec=True, return_value=0) m_get_tag: MagicMock = mocker.patch("stubber.stubber.git.get_tag", autospec=True, return_value="v1.42") m_match = mocker.patch("stubber.stubber.get_mpy.match_lib_with_mpy", autospec=True) m_exists = mocker.patch("stubber.stubber.Path.exists", return_value=True) result = runner.invoke(stubber.stubber_cli, params) assert result.exit_code == 0 # fetch latest assert m_fetch.call_count == 2 # "foobar" from params is used as the path m_fetch.assert_any_call(Path("foobar/micropython")) m_fetch.assert_any_call(Path("foobar/micropython-lib")) # core m_match.assert_called_once() if "latest" in params: m_switch.assert_called_once() m_checkout.assert_not_called() else: m_switch.assert_not_called() m_checkout.assert_called_once() ########################################################################################## # minify ########################################################################################## def test_stubber_minify(mocker: MockerFixture): # check basic commandline sanity check runner = CliRunner() mock_minify: MagicMock = mocker.MagicMock(return_value=0) mocker.patch("stubber.stubber.minify", mock_minify) result = runner.invoke(stubber.stubber_cli, ["minify"]) assert result.exit_code == 0 mock_minify.assert_called_once_with("board/createstubs.py", "./minified", True, False, False) def test_stubber_minify_all(mocker: MockerFixture): # check basic commandline sanity check runner = CliRunner() mock_minify: MagicMock = mocker.MagicMock(return_value=0) mocker.patch("stubber.stubber.minify", mock_minify) result = runner.invoke(stubber.stubber_cli, ["minify", "--all"]) assert result.exit_code == 0 assert mock_minify.call_count == 3 mock_minify.assert_any_call("board/createstubs.py", "./minified", True, False, False) mock_minify.assert_any_call("board/createstubs_db.py", "./minified", True, False, False) mock_minify.assert_any_call("board/createstubs_mem.py", "./minified", True, False, False) ########################################################################################## # stub ########################################################################################## def test_stubber_stub(mocker: MockerFixture): # check basic commandline sanity check runner = CliRunner() # mock: MagicMock = mocker.MagicMock(return_value=True) mock: MagicMock = mocker.patch("stubber.stubber.utils.generate_pyi_files", autospec=True, return_value=True) # fake run on current folder result = runner.invoke(stubber.stubber_cli, ["stub", "--source", "."]) mock.assert_called_once_with(Path(".")) assert result.exit_code == 0 ########################################################################################## # get-frozen ########################################################################################## def test_stubber_get_frozen(mocker: MockerFixture, tmp_path: Path): # check basic commandline sanity check runner = CliRunner() mock_version: MagicMock = mocker.patch("stubber.stubber.git.get_tag", autospec=True, return_value="v1.42") mock: MagicMock = mocker.patch("stubber.stubber.get_mpy.get_frozen", autospec=True) mock_post: MagicMock = mocker.patch("stubber.stubber.utils.do_post_processing", autospec=True) # fake run - need to ensure that there is a destination folder result = runner.invoke(stubber.stubber_cli, ["get-frozen", "--stub-folder", tmp_path.as_posix()]) assert result.exit_code == 0 # FIXME : test failes in CI mock.assert_called_once() mock_version.assert_called_once() mock_post.assert_called_once_with([tmp_path / "micropython-v1_42-frozen"], True, True) ########################################################################################## # get-lobo ########################################################################################## def test_stubber_get_lobo(mocker: MockerFixture, tmp_path: Path): # check basic commandline sanity check runner = CliRunner() mock: MagicMock = mocker.patch("stubber.stubber.get_lobo.get_frozen", autospec=True) mock_post: MagicMock = mocker.patch("stubber.stubber.utils.do_post_processing", autospec=True) # fake run result = runner.invoke(stubber.stubber_cli, ["get-lobo", "--stub-folder", tmp_path.as_posix()]) mock.assert_called_once() mock_post.assert_called_once() mock_post.assert_called_once_with([tmp_path / "loboris-v3_2_24-frozen"], True, True) assert result.exit_code == 0 ########################################################################################## # get-core ########################################################################################## def test_stubber_get_core(mocker: MockerFixture, tmp_path: Path): # check basic commandline sanity check runner = CliRunner() mock: MagicMock = mocker.patch("stubber.stubber.get_cpython.get_core", autospec=True) mock_post: MagicMock = mocker.patch("stubber.stubber.utils.do_post_processing", autospec=True) # fake run result = runner.invoke(stubber.stubber_cli, ["get-core", "--stub-folder", tmp_path.as_posix()]) assert result.exit_code == 0 # process is called twice assert mock.call_count == 2 # post is called one mock_post.assert_called_with([tmp_path / "cpython_core-pycopy", tmp_path / "cpython_core-micropython"], True, True) ########################################################################################## # get-docstubs ########################################################################################## def test_stubber_get_docstubs(mocker: MockerFixture, tmp_path: Path): # check basic commandline sanity check runner = CliRunner() mock_version: MagicMock = mocker.patch("stubber.stubber.git.get_tag", autospec=True, return_value="v1.42") mock: MagicMock = mocker.patch("stubber.stubber.generate_from_rst", autospec=True) mock_post: MagicMock = mocker.patch("stubber.stubber.utils.do_post_processing", autospec=True) # fake run result = runner.invoke(stubber.stubber_cli, ["get-docstubs", "--stub-folder", tmp_path.as_posix()]) assert result.exit_code == 0 # process is called twice assert mock.call_count == 1 mock.assert_called_once() assert mock_version.call_count >= 1 # post is called one mock_post.assert_called_with([tmp_path / "micropython-v1_42-docstubs"], False, True) ########################################################################################## # get-lobo ########################################################################################## def test_stubber_fallback(mocker: MockerFixture, tmp_path: Path): # check basic commandline sanity check runner = CliRunner() mock: MagicMock = mocker.patch("stubber.stubber.update_fallback", autospec=True) # mock2: MagicMock = mocker.patch("stubber.update_fallback.update_fallback", autospec=True) # from .update_fallback import update_fallback, # fake run result = runner.invoke(stubber.stubber_cli, ["update-fallback", "--stub-folder", tmp_path.as_posix()]) mock.assert_called_once() assert result.exit_code == 0

42.258964

136

0.614217

from typing import List import pytest from pytest_mock import MockerFixture from mock import MagicMock from pathlib import Path from click.testing import CliRunner import stubber.stubber as stubber def test_stubber_help(): runner = CliRunner() result = runner.invoke(stubber.stubber_cli, ["--help"]) assert result.exit_code == 0 assert "Usage:" in result.output assert "Commands:" in result.output

true

f71fa8aed93c8b08ae1ae6669c787edf3afcae3d

12,641

py

Python

python/ccxt/async_support/base/exchange.py

gabvladov/ccxt

c26ba54afe1617d7314bf6714427a4db6d0c6381

[ "MIT" ]

null

python/ccxt/async_support/base/exchange.py

gabvladov/ccxt

c26ba54afe1617d7314bf6714427a4db6d0c6381

[ "MIT" ]

null

python/ccxt/async_support/base/exchange.py

gabvladov/ccxt

c26ba54afe1617d7314bf6714427a4db6d0c6381

[ "MIT" ]

null

# -*- coding: utf-8 -*- # ----------------------------------------------------------------------------- __version__ = '1.20.94' # ----------------------------------------------------------------------------- import asyncio import concurrent import socket import time import math import random import certifi import aiohttp import ssl import sys import yarl # ----------------------------------------------------------------------------- from ccxt.async_support.base.throttle import throttle # ----------------------------------------------------------------------------- from ccxt.base.errors import ExchangeError from ccxt.base.errors import ExchangeNotAvailable from ccxt.base.errors import RequestTimeout from ccxt.base.errors import NotSupported # ----------------------------------------------------------------------------- from ccxt.base.exchange import Exchange as BaseExchange # ----------------------------------------------------------------------------- __all__ = [ 'BaseExchange', 'Exchange', ] # ----------------------------------------------------------------------------- class Exchange(BaseExchange): def __init__(self, config={}): if 'asyncio_loop' in config: self.asyncio_loop = config['asyncio_loop'] self.asyncio_loop = self.asyncio_loop or asyncio.get_event_loop() self.aiohttp_trust_env = config.get('aiohttp_trust_env', self.aiohttp_trust_env) self.verify = config.get('verify', self.verify) self.own_session = 'session' not in config self.cafile = config.get('cafile', certifi.where()) super(Exchange, self).__init__(config) self.init_rest_rate_limiter() def init_rest_rate_limiter(self): self.throttle = throttle(self.extend({ 'loop': self.asyncio_loop, }, self.tokenBucket)) def __del__(self): if self.session is not None: self.logger.warning(self.id + " requires to release all resources with an explicit call to the .close() coroutine. If you are using the exchange instance with async coroutines, add exchange.close() to your code into a place when you're done with the exchange and don't need the exchange instance anymore (at the end of your async coroutine).") if sys.version_info >= (3, 5): async def __aenter__(self): self.open() return self async def __aexit__(self, exc_type, exc, tb): await self.close() def open(self): if self.own_session and self.session is None: # Create our SSL context object with our CA cert file context = ssl.create_default_context(cafile=self.cafile) if self.verify else self.verify # Pass this SSL context to aiohttp and create a TCPConnector connector = aiohttp.TCPConnector(ssl=context, loop=self.asyncio_loop) self.session = aiohttp.ClientSession(loop=self.asyncio_loop, connector=connector, trust_env=self.aiohttp_trust_env) async def close(self): if self.session is not None: if self.own_session: await self.session.close() self.session = None async def wait_for_token(self): while self.rateLimitTokens <= 1: # if self.verbose: # print('Waiting for tokens: Exchange: {0}'.format(self.id)) self.add_new_tokens() seconds_delays = [0.001, 0.005, 0.022, 0.106, 0.5] delay = random.choice(seconds_delays) await asyncio.sleep(delay) self.rateLimitTokens -= 1 def add_new_tokens(self): # if self.verbose: # print('Adding new tokens: Exchange: {0}'.format(self.id)) now = time.monotonic() time_since_update = now - self.rateLimitUpdateTime new_tokens = math.floor((0.8 * 1000.0 * time_since_update) / self.rateLimit) if new_tokens > 1: self.rateLimitTokens = min(self.rateLimitTokens + new_tokens, self.rateLimitMaxTokens) self.rateLimitUpdateTime = now async def fetch2(self, path, api='public', method='GET', params={}, headers=None, body=None): """A better wrapper over request for deferred signing""" if self.enableRateLimit: await self.throttle() self.lastRestRequestTimestamp = self.milliseconds() request = self.sign(path, api, method, params, headers, body) return await self.fetch(request['url'], request['method'], request['headers'], request['body']) async def fetch(self, url, method='GET', headers=None, body=None): """Perform a HTTP request and return decoded JSON data""" request_headers = self.prepare_request_headers(headers) url = self.proxy + url if self.verbose: print("\nRequest:", method, url, headers, body) self.logger.debug("%s %s, Request: %s %s", method, url, headers, body) request_body = body encoded_body = body.encode() if body else None self.open() session_method = getattr(self.session, method.lower()) http_response = None http_status_code = None http_status_text = None json_response = None try: async with session_method(yarl.URL(url, encoded=True), data=encoded_body, headers=request_headers, timeout=(self.timeout / 1000), proxy=self.aiohttp_proxy) as response: http_response = await response.text() http_status_code = response.status http_status_text = response.reason json_response = self.parse_json(http_response) headers = response.headers if self.enableLastHttpResponse: self.last_http_response = http_response if self.enableLastResponseHeaders: self.last_response_headers = headers if self.enableLastJsonResponse: self.last_json_response = json_response if self.verbose: print("\nResponse:", method, url, http_status_code, headers, http_response) self.logger.debug("%s %s, Response: %s %s %s", method, url, http_status_code, headers, http_response) except socket.gaierror as e: raise ExchangeNotAvailable(method + ' ' + url) except concurrent.futures._base.TimeoutError as e: raise RequestTimeout(method + ' ' + url) except aiohttp.client_exceptions.ClientConnectionError as e: raise ExchangeNotAvailable(method + ' ' + url) except aiohttp.client_exceptions.ClientError as e: # base exception class raise ExchangeError(method + ' ' + url) self.handle_errors(http_status_code, http_status_text, url, method, headers, http_response, json_response, request_headers, request_body) self.handle_rest_errors(http_status_code, http_status_text, http_response, url, method) self.handle_rest_response(http_response, json_response, url, method) if json_response is not None: return json_response if self.is_text_response(headers): return http_response return response.content async def load_markets(self, reload=False, params={}): if not reload: if self.markets: if not self.markets_by_id: return self.set_markets(self.markets) return self.markets currencies = None if self.has['fetchCurrencies']: currencies = await self.fetch_currencies() markets = await self.fetch_markets(params) return self.set_markets(markets, currencies) async def fetch_fees(self): trading = {} funding = {} if self.has['fetchTradingFees']: trading = await self.fetch_trading_fees() if self.has['fetchFundingFees']: funding = await self.fetch_funding_fees() return { 'trading': trading, 'funding': funding, } async def load_fees(self, reload=False): if not reload: if self.loaded_fees != Exchange.loaded_fees: return self.loaded_fees self.loaded_fees = self.deep_extend(self.loaded_fees, await self.fetch_fees()) return self.loaded_fees async def fetch_markets(self, params={}): # markets are returned as a list # currencies are returned as a dict # this is for historical reasons # and may be changed for consistency later return self.to_array(self.markets) async def fetch_currencies(self, params={}): # markets are returned as a list # currencies are returned as a dict # this is for historical reasons # and may be changed for consistency later return self.currencies async def fetch_status(self, params={}): if self.has['fetchTime']: updated = await self.fetch_time(params) self.status['updated'] = updated return self.status async def fetch_order_status(self, id, symbol=None, params={}): order = await self.fetch_order(id, symbol, params) return order['status'] async def fetch_partial_balance(self, part, params={}): balance = await self.fetch_balance(params) return balance[part] async def fetch_l2_order_book(self, symbol, limit=None, params={}): orderbook = await self.fetch_order_book(symbol, limit, params) return self.extend(orderbook, { 'bids': self.sort_by(self.aggregate(orderbook['bids']), 0, True), 'asks': self.sort_by(self.aggregate(orderbook['asks']), 0), }) async def perform_order_book_request(self, market, limit=None, params={}): raise NotSupported(self.id + ' performOrderBookRequest not supported yet') async def fetch_order_book(self, symbol, limit=None, params={}): await self.load_markets() market = self.market(symbol) orderbook = await self.perform_order_book_request(market, limit, params) return self.parse_order_book(orderbook, market, limit, params) async def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): if not self.has['fetchTrades']: raise NotSupported('fetch_ohlcv() not implemented yet') await self.load_markets() trades = await self.fetch_trades(symbol, since, limit, params) return self.build_ohlcv(trades, timeframe, since, limit) async def fetchOHLCV(self, symbol, timeframe='1m', since=None, limit=None, params={}): return await self.fetch_ohlcv(symbol, timeframe, since, limit, params) async def fetch_full_tickers(self, symbols=None, params={}): return await self.fetch_tickers(symbols, params) async def edit_order(self, id, symbol, *args): if not self.enableRateLimit: raise ExchangeError('updateOrder() requires enableRateLimit = true') await self.cancel_order(id, symbol) return await self.create_order(symbol, *args) async def fetch_trading_fees(self, params={}): raise NotSupported('fetch_trading_fees() not supported yet') async def fetch_trading_fee(self, symbol, params={}): if not self.has['fetchTradingFees']: raise NotSupported('fetch_trading_fee() not supported yet') return await self.fetch_trading_fees(params) async def load_trading_limits(self, symbols=None, reload=False, params={}): if self.has['fetchTradingLimits']: if reload or not('limitsLoaded' in list(self.options.keys())): response = await self.fetch_trading_limits(symbols) for i in range(0, len(symbols)): symbol = symbols[i] self.markets[symbol] = self.deep_extend(self.markets[symbol], response[symbol]) self.options['limitsLoaded'] = self.milliseconds() return self.markets async def load_accounts(self, reload=False, params={}): if reload: self.accounts = await self.fetch_accounts(params) else: if self.accounts: return self.accounts else: self.accounts = await self.fetch_accounts(params) self.accountsById = self.index_by(self.accounts, 'id') return self.accounts async def fetch_ticker(self, symbol, params={}): raise NotSupported('fetch_ticker() not supported yet')

41.719472

355

0.610316

__version__ = '1.20.94' import asyncio import concurrent import socket import time import math import random import certifi import aiohttp import ssl import sys import yarl from ccxt.async_support.base.throttle import throttle from ccxt.base.errors import ExchangeError from ccxt.base.errors import ExchangeNotAvailable from ccxt.base.errors import RequestTimeout from ccxt.base.errors import NotSupported from ccxt.base.exchange import Exchange as BaseExchange __all__ = [ 'BaseExchange', 'Exchange', ] class Exchange(BaseExchange): def __init__(self, config={}): if 'asyncio_loop' in config: self.asyncio_loop = config['asyncio_loop'] self.asyncio_loop = self.asyncio_loop or asyncio.get_event_loop() self.aiohttp_trust_env = config.get('aiohttp_trust_env', self.aiohttp_trust_env) self.verify = config.get('verify', self.verify) self.own_session = 'session' not in config self.cafile = config.get('cafile', certifi.where()) super(Exchange, self).__init__(config) self.init_rest_rate_limiter() def init_rest_rate_limiter(self): self.throttle = throttle(self.extend({ 'loop': self.asyncio_loop, }, self.tokenBucket)) def __del__(self): if self.session is not None: self.logger.warning(self.id + " requires to release all resources with an explicit call to the .close() coroutine. If you are using the exchange instance with async coroutines, add exchange.close() to your code into a place when you're done with the exchange and don't need the exchange instance anymore (at the end of your async coroutine).") if sys.version_info >= (3, 5): async def __aenter__(self): self.open() return self async def __aexit__(self, exc_type, exc, tb): await self.close() def open(self): if self.own_session and self.session is None: context = ssl.create_default_context(cafile=self.cafile) if self.verify else self.verify connector = aiohttp.TCPConnector(ssl=context, loop=self.asyncio_loop) self.session = aiohttp.ClientSession(loop=self.asyncio_loop, connector=connector, trust_env=self.aiohttp_trust_env) async def close(self): if self.session is not None: if self.own_session: await self.session.close() self.session = None async def wait_for_token(self): while self.rateLimitTokens <= 1: self.add_new_tokens() seconds_delays = [0.001, 0.005, 0.022, 0.106, 0.5] delay = random.choice(seconds_delays) await asyncio.sleep(delay) self.rateLimitTokens -= 1 def add_new_tokens(self): now = time.monotonic() time_since_update = now - self.rateLimitUpdateTime new_tokens = math.floor((0.8 * 1000.0 * time_since_update) / self.rateLimit) if new_tokens > 1: self.rateLimitTokens = min(self.rateLimitTokens + new_tokens, self.rateLimitMaxTokens) self.rateLimitUpdateTime = now async def fetch2(self, path, api='public', method='GET', params={}, headers=None, body=None): if self.enableRateLimit: await self.throttle() self.lastRestRequestTimestamp = self.milliseconds() request = self.sign(path, api, method, params, headers, body) return await self.fetch(request['url'], request['method'], request['headers'], request['body']) async def fetch(self, url, method='GET', headers=None, body=None): request_headers = self.prepare_request_headers(headers) url = self.proxy + url if self.verbose: print("\nRequest:", method, url, headers, body) self.logger.debug("%s %s, Request: %s %s", method, url, headers, body) request_body = body encoded_body = body.encode() if body else None self.open() session_method = getattr(self.session, method.lower()) http_response = None http_status_code = None http_status_text = None json_response = None try: async with session_method(yarl.URL(url, encoded=True), data=encoded_body, headers=request_headers, timeout=(self.timeout / 1000), proxy=self.aiohttp_proxy) as response: http_response = await response.text() http_status_code = response.status http_status_text = response.reason json_response = self.parse_json(http_response) headers = response.headers if self.enableLastHttpResponse: self.last_http_response = http_response if self.enableLastResponseHeaders: self.last_response_headers = headers if self.enableLastJsonResponse: self.last_json_response = json_response if self.verbose: print("\nResponse:", method, url, http_status_code, headers, http_response) self.logger.debug("%s %s, Response: %s %s %s", method, url, http_status_code, headers, http_response) except socket.gaierror as e: raise ExchangeNotAvailable(method + ' ' + url) except concurrent.futures._base.TimeoutError as e: raise RequestTimeout(method + ' ' + url) except aiohttp.client_exceptions.ClientConnectionError as e: raise ExchangeNotAvailable(method + ' ' + url) except aiohttp.client_exceptions.ClientError as e: raise ExchangeError(method + ' ' + url) self.handle_errors(http_status_code, http_status_text, url, method, headers, http_response, json_response, request_headers, request_body) self.handle_rest_errors(http_status_code, http_status_text, http_response, url, method) self.handle_rest_response(http_response, json_response, url, method) if json_response is not None: return json_response if self.is_text_response(headers): return http_response return response.content async def load_markets(self, reload=False, params={}): if not reload: if self.markets: if not self.markets_by_id: return self.set_markets(self.markets) return self.markets currencies = None if self.has['fetchCurrencies']: currencies = await self.fetch_currencies() markets = await self.fetch_markets(params) return self.set_markets(markets, currencies) async def fetch_fees(self): trading = {} funding = {} if self.has['fetchTradingFees']: trading = await self.fetch_trading_fees() if self.has['fetchFundingFees']: funding = await self.fetch_funding_fees() return { 'trading': trading, 'funding': funding, } async def load_fees(self, reload=False): if not reload: if self.loaded_fees != Exchange.loaded_fees: return self.loaded_fees self.loaded_fees = self.deep_extend(self.loaded_fees, await self.fetch_fees()) return self.loaded_fees async def fetch_markets(self, params={}): return self.to_array(self.markets) async def fetch_currencies(self, params={}): return self.currencies async def fetch_status(self, params={}): if self.has['fetchTime']: updated = await self.fetch_time(params) self.status['updated'] = updated return self.status async def fetch_order_status(self, id, symbol=None, params={}): order = await self.fetch_order(id, symbol, params) return order['status'] async def fetch_partial_balance(self, part, params={}): balance = await self.fetch_balance(params) return balance[part] async def fetch_l2_order_book(self, symbol, limit=None, params={}): orderbook = await self.fetch_order_book(symbol, limit, params) return self.extend(orderbook, { 'bids': self.sort_by(self.aggregate(orderbook['bids']), 0, True), 'asks': self.sort_by(self.aggregate(orderbook['asks']), 0), }) async def perform_order_book_request(self, market, limit=None, params={}): raise NotSupported(self.id + ' performOrderBookRequest not supported yet') async def fetch_order_book(self, symbol, limit=None, params={}): await self.load_markets() market = self.market(symbol) orderbook = await self.perform_order_book_request(market, limit, params) return self.parse_order_book(orderbook, market, limit, params) async def fetch_ohlcv(self, symbol, timeframe='1m', since=None, limit=None, params={}): if not self.has['fetchTrades']: raise NotSupported('fetch_ohlcv() not implemented yet') await self.load_markets() trades = await self.fetch_trades(symbol, since, limit, params) return self.build_ohlcv(trades, timeframe, since, limit) async def fetchOHLCV(self, symbol, timeframe='1m', since=None, limit=None, params={}): return await self.fetch_ohlcv(symbol, timeframe, since, limit, params) async def fetch_full_tickers(self, symbols=None, params={}): return await self.fetch_tickers(symbols, params) async def edit_order(self, id, symbol, *args): if not self.enableRateLimit: raise ExchangeError('updateOrder() requires enableRateLimit = true') await self.cancel_order(id, symbol) return await self.create_order(symbol, *args) async def fetch_trading_fees(self, params={}): raise NotSupported('fetch_trading_fees() not supported yet') async def fetch_trading_fee(self, symbol, params={}): if not self.has['fetchTradingFees']: raise NotSupported('fetch_trading_fee() not supported yet') return await self.fetch_trading_fees(params) async def load_trading_limits(self, symbols=None, reload=False, params={}): if self.has['fetchTradingLimits']: if reload or not('limitsLoaded' in list(self.options.keys())): response = await self.fetch_trading_limits(symbols) for i in range(0, len(symbols)): symbol = symbols[i] self.markets[symbol] = self.deep_extend(self.markets[symbol], response[symbol]) self.options['limitsLoaded'] = self.milliseconds() return self.markets async def load_accounts(self, reload=False, params={}): if reload: self.accounts = await self.fetch_accounts(params) else: if self.accounts: return self.accounts else: self.accounts = await self.fetch_accounts(params) self.accountsById = self.index_by(self.accounts, 'id') return self.accounts async def fetch_ticker(self, symbol, params={}): raise NotSupported('fetch_ticker() not supported yet')

true

f71fa96f8684f8b86d25128c15599561e0aa97b2

11,711

py

Python

fs_patches_of_hybrid_cloud/cherry_for_B038/nova_cascaded/nova/virt/vmwareapi/vmware_images.py

Hybrid-Cloud/badam

390ad3a6fc03948008f7c04ed2f9fcc8514cc1eb

[ "Apache-2.0" ]

2

2015-06-15T02:16:33.000Z

2022-02-23T07:10:38.000Z

patches_tool/vcloud_patch/code/nova/virt/vmwareapi/vmware_images.py

Hybrid-Cloud/badam

390ad3a6fc03948008f7c04ed2f9fcc8514cc1eb

[ "Apache-2.0" ]

7

2016-05-13T06:39:45.000Z

2016-05-20T02:55:31.000Z

fs_patches_of_hybrid_cloud/cherry_for_B038/nova_cascaded/nova/virt/vmwareapi/vmware_images.py

Hybrid-Cloud/badam

390ad3a6fc03948008f7c04ed2f9fcc8514cc1eb

[ "Apache-2.0" ]

4

2015-11-02T04:02:50.000Z

2021-05-13T17:06:00.000Z

# Copyright (c) 2012 VMware, Inc. # Copyright (c) 2011 Citrix Systems, Inc. # Copyright 2011 OpenStack Foundation # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """ Utility functions for Image transfer and manipulation. """ import os from oslo.config import cfg from nova import exception from nova import image from nova.openstack.common import log as logging from nova.openstack.common import strutils from nova.openstack.common import units from nova.virt.vmwareapi import constants from nova.virt.vmwareapi import io_util from nova.virt.vmwareapi import read_write_util # NOTE(mdbooth): We use use_linked_clone below, but don't have to import it # because nova.virt.vmwareapi.driver is imported first. In fact, it is not # possible to import it here, as nova.virt.vmwareapi.driver calls # CONF.register_opts() after the import chain which imports this module. This # is not a problem as long as the import order doesn't change. CONF = cfg.CONF LOG = logging.getLogger(__name__) IMAGE_API = image.API() QUEUE_BUFFER_SIZE = 10 LINKED_CLONE_PROPERTY = 'vmware_linked_clone' class VMwareImage(object): def __init__(self, image_id, file_size=0, os_type=constants.DEFAULT_OS_TYPE, adapter_type=constants.DEFAULT_ADAPTER_TYPE, disk_type=constants.DEFAULT_DISK_TYPE, file_type=constants.DEFAULT_DISK_FORMAT, linked_clone=None, vif_model=constants.DEFAULT_VIF_MODEL): """VMwareImage holds values for use in building VMs. image_id (str): uuid of the image file_size (int): size of file in bytes os_type (str): name of guest os (use vSphere names only) adapter_type (str): name of the adapter's type disk_type (str): type of disk in thin, thick, etc file_type (str): vmdk or iso linked_clone(bool): use linked clone, or don't """ self.image_id = image_id self.file_size = file_size self.os_type = os_type self.adapter_type = adapter_type self.disk_type = disk_type self.file_type = file_type # NOTE(vui): This should be removed when we restore the # descriptor-based validation. if (self.file_type is not None and self.file_type not in constants.DISK_FORMATS_ALL): raise exception.InvalidDiskFormat(disk_format=self.file_type) if linked_clone is not None: self.linked_clone = linked_clone else: self.linked_clone = CONF.vmware.use_linked_clone self.vif_model = vif_model @property def file_size_in_kb(self): return self.file_size / units.Ki @property def file_size_in_gb(self): return self.file_size / units.Gi @property def is_sparse(self): return self.disk_type == constants.DISK_TYPE_SPARSE @property def is_iso(self): return self.file_type == constants.DISK_FORMAT_ISO @classmethod def from_image(cls, image_id, image_meta=None): """Returns VMwareImage, the subset of properties the driver uses. :param image_id - image id of image :param image_meta - image metadata we are working with :return: vmware image object :rtype: nova.virt.vmwareapi.vmware_images.VmwareImage """ if image_meta is None: image_meta = {} properties = image_meta.get("properties", {}) # calculate linked_clone flag, allow image properties to override the # global property set in the configurations. image_linked_clone = properties.get(LINKED_CLONE_PROPERTY, CONF.vmware.use_linked_clone) # catch any string values that need to be interpreted as boolean values linked_clone = strutils.bool_from_string(image_linked_clone) props = { 'image_id': image_id, 'linked_clone': linked_clone } if 'size' in image_meta: props['file_size'] = image_meta['size'] if 'disk_format' in image_meta: props['file_type'] = image_meta['disk_format'] props_map = { 'vmware_ostype': 'os_type', 'vmware_adaptertype': 'adapter_type', 'vmware_disktype': 'disk_type', 'hw_vif_model': 'vif_model' } for k, v in props_map.iteritems(): if k in properties: props[v] = properties[k] return cls(**props) def start_transfer(context, read_file_handle, data_size, write_file_handle=None, image_id=None, image_meta=None): """Start the data transfer from the reader to the writer. Reader writes to the pipe and the writer reads from the pipe. This means that the total transfer time boils down to the slower of the read/write and not the addition of the two times. """ if not image_meta: image_meta = {} # The pipe that acts as an intermediate store of data for reader to write # to and writer to grab from. thread_safe_pipe = io_util.ThreadSafePipe(QUEUE_BUFFER_SIZE, data_size) # The read thread. In case of glance it is the instance of the # GlanceFileRead class. The glance client read returns an iterator # and this class wraps that iterator to provide datachunks in calls # to read. read_thread = io_util.IOThread(read_file_handle, thread_safe_pipe) # In case of Glance - VMware transfer, we just need a handle to the # HTTP Connection that is to send transfer data to the VMware datastore. if write_file_handle: write_thread = io_util.IOThread(thread_safe_pipe, write_file_handle) # In case of VMware - Glance transfer, we relinquish VMware HTTP file read # handle to Glance Client instance, but to be sure of the transfer we need # to be sure of the status of the image on glance changing to active. # The GlanceWriteThread handles the same for us. elif image_id: write_thread = io_util.GlanceWriteThread(context, thread_safe_pipe, image_id, image_meta) # Start the read and write threads. read_event = read_thread.start() write_event = write_thread.start() try: # Wait on the read and write events to signal their end read_event.wait() write_event.wait() except Exception as exc: # In case of any of the reads or writes raising an exception, # stop the threads so that we un-necessarily don't keep the other one # waiting. read_thread.stop() write_thread.stop() # Log and raise the exception. LOG.exception(exc) raise exception.NovaException(exc) finally: # No matter what, try closing the read and write handles, if it so # applies. read_file_handle.close() if write_file_handle: write_file_handle.close() def upload_iso_to_datastore(iso_path, instance, **kwargs): LOG.debug("Uploading iso %s to datastore", iso_path, instance=instance) with open(iso_path, 'r') as iso_file: write_file_handle = read_write_util.VMwareHTTPWriteFile( kwargs.get("host"), kwargs.get("data_center_name"), kwargs.get("datastore_name"), kwargs.get("cookies"), kwargs.get("file_path"), os.fstat(iso_file.fileno()).st_size) LOG.debug("Uploading iso of size : %s ", os.fstat(iso_file.fileno()).st_size) block_size = 0x10000 data = iso_file.read(block_size) while len(data) > 0: write_file_handle.write(data) data = iso_file.read(block_size) write_file_handle.close() LOG.debug("Uploaded iso %s to datastore", iso_path, instance=instance) def fetch_image(context, instance, host, dc_name, ds_name, file_path, cookies=None): """Download image from the glance image server.""" image_ref = instance['image_ref'] LOG.debug("Downloading image file data %(image_ref)s to the " "data store %(data_store_name)s", {'image_ref': image_ref, 'data_store_name': ds_name}, instance=instance) metadata = IMAGE_API.get(context, image_ref) file_size = int(metadata['size']) read_iter = IMAGE_API.download(context, image_ref) read_file_handle = read_write_util.GlanceFileRead(read_iter) write_file_handle = read_write_util.VMwareHTTPWriteFile( host, dc_name, ds_name, cookies, file_path, file_size) start_transfer(context, read_file_handle, file_size, write_file_handle=write_file_handle) LOG.debug("Downloaded image file data %(image_ref)s to " "%(upload_name)s on the data store " "%(data_store_name)s", {'image_ref': image_ref, 'upload_name': 'n/a' if file_path is None else file_path, 'data_store_name': 'n/a' if ds_name is None else ds_name}, instance=instance) def upload_image(context, image, instance, **kwargs): """Upload the snapshotted vm disk file to Glance image server.""" LOG.debug("Uploading image %s to the Glance image server", image, instance=instance) read_file_handle = read_write_util.VMwareHTTPReadFile( kwargs.get("host"), kwargs.get("data_center_name"), kwargs.get("datastore_name"), kwargs.get("cookies"), kwargs.get("file_path")) file_size = read_file_handle.get_size() metadata = IMAGE_API.get(context, image) # The properties and other fields that we need to set for the image. image_metadata = {"disk_format": "vmdk", "is_public": "false", "name": metadata['name'], "status": "active", "container_format": "bare", "size": file_size, "properties": {"vmware_adaptertype": kwargs.get("adapter_type"), "vmware_disktype": kwargs.get("disk_type"), "vmware_ostype": kwargs.get("os_type"), "vmware_image_version": kwargs.get("image_version"), "owner_id": instance['project_id']}} start_transfer(context, read_file_handle, file_size, image_id=metadata['id'], image_meta=image_metadata) LOG.debug("Uploaded image %s to the Glance image server", image, instance=instance)

40.663194

80

0.61566

import os from oslo.config import cfg from nova import exception from nova import image from nova.openstack.common import log as logging from nova.openstack.common import strutils from nova.openstack.common import units from nova.virt.vmwareapi import constants from nova.virt.vmwareapi import io_util from nova.virt.vmwareapi import read_write_util # because nova.virt.vmwareapi.driver is imported first. In fact, it is not # possible to import it here, as nova.virt.vmwareapi.driver calls # CONF.register_opts() after the import chain which imports this module. This # is not a problem as long as the import order doesn't change. CONF = cfg.CONF LOG = logging.getLogger(__name__) IMAGE_API = image.API() QUEUE_BUFFER_SIZE = 10 LINKED_CLONE_PROPERTY = 'vmware_linked_clone' class VMwareImage(object): def __init__(self, image_id, file_size=0, os_type=constants.DEFAULT_OS_TYPE, adapter_type=constants.DEFAULT_ADAPTER_TYPE, disk_type=constants.DEFAULT_DISK_TYPE, file_type=constants.DEFAULT_DISK_FORMAT, linked_clone=None, vif_model=constants.DEFAULT_VIF_MODEL): self.image_id = image_id self.file_size = file_size self.os_type = os_type self.adapter_type = adapter_type self.disk_type = disk_type self.file_type = file_type if (self.file_type is not None and self.file_type not in constants.DISK_FORMATS_ALL): raise exception.InvalidDiskFormat(disk_format=self.file_type) if linked_clone is not None: self.linked_clone = linked_clone else: self.linked_clone = CONF.vmware.use_linked_clone self.vif_model = vif_model @property def file_size_in_kb(self): return self.file_size / units.Ki @property def file_size_in_gb(self): return self.file_size / units.Gi @property def is_sparse(self): return self.disk_type == constants.DISK_TYPE_SPARSE @property def is_iso(self): return self.file_type == constants.DISK_FORMAT_ISO @classmethod def from_image(cls, image_id, image_meta=None): if image_meta is None: image_meta = {} properties = image_meta.get("properties", {}) image_linked_clone = properties.get(LINKED_CLONE_PROPERTY, CONF.vmware.use_linked_clone) linked_clone = strutils.bool_from_string(image_linked_clone) props = { 'image_id': image_id, 'linked_clone': linked_clone } if 'size' in image_meta: props['file_size'] = image_meta['size'] if 'disk_format' in image_meta: props['file_type'] = image_meta['disk_format'] props_map = { 'vmware_ostype': 'os_type', 'vmware_adaptertype': 'adapter_type', 'vmware_disktype': 'disk_type', 'hw_vif_model': 'vif_model' } for k, v in props_map.iteritems(): if k in properties: props[v] = properties[k] return cls(**props) def start_transfer(context, read_file_handle, data_size, write_file_handle=None, image_id=None, image_meta=None): if not image_meta: image_meta = {} thread_safe_pipe = io_util.ThreadSafePipe(QUEUE_BUFFER_SIZE, data_size) read_thread = io_util.IOThread(read_file_handle, thread_safe_pipe) if write_file_handle: write_thread = io_util.IOThread(thread_safe_pipe, write_file_handle) elif image_id: write_thread = io_util.GlanceWriteThread(context, thread_safe_pipe, image_id, image_meta) read_event = read_thread.start() write_event = write_thread.start() try: read_event.wait() write_event.wait() except Exception as exc: # waiting. read_thread.stop() write_thread.stop() # Log and raise the exception. LOG.exception(exc) raise exception.NovaException(exc) finally: # No matter what, try closing the read and write handles, if it so # applies. read_file_handle.close() if write_file_handle: write_file_handle.close() def upload_iso_to_datastore(iso_path, instance, **kwargs): LOG.debug("Uploading iso %s to datastore", iso_path, instance=instance) with open(iso_path, 'r') as iso_file: write_file_handle = read_write_util.VMwareHTTPWriteFile( kwargs.get("host"), kwargs.get("data_center_name"), kwargs.get("datastore_name"), kwargs.get("cookies"), kwargs.get("file_path"), os.fstat(iso_file.fileno()).st_size) LOG.debug("Uploading iso of size : %s ", os.fstat(iso_file.fileno()).st_size) block_size = 0x10000 data = iso_file.read(block_size) while len(data) > 0: write_file_handle.write(data) data = iso_file.read(block_size) write_file_handle.close() LOG.debug("Uploaded iso %s to datastore", iso_path, instance=instance) def fetch_image(context, instance, host, dc_name, ds_name, file_path, cookies=None): image_ref = instance['image_ref'] LOG.debug("Downloading image file data %(image_ref)s to the " "data store %(data_store_name)s", {'image_ref': image_ref, 'data_store_name': ds_name}, instance=instance) metadata = IMAGE_API.get(context, image_ref) file_size = int(metadata['size']) read_iter = IMAGE_API.download(context, image_ref) read_file_handle = read_write_util.GlanceFileRead(read_iter) write_file_handle = read_write_util.VMwareHTTPWriteFile( host, dc_name, ds_name, cookies, file_path, file_size) start_transfer(context, read_file_handle, file_size, write_file_handle=write_file_handle) LOG.debug("Downloaded image file data %(image_ref)s to " "%(upload_name)s on the data store " "%(data_store_name)s", {'image_ref': image_ref, 'upload_name': 'n/a' if file_path is None else file_path, 'data_store_name': 'n/a' if ds_name is None else ds_name}, instance=instance) def upload_image(context, image, instance, **kwargs): LOG.debug("Uploading image %s to the Glance image server", image, instance=instance) read_file_handle = read_write_util.VMwareHTTPReadFile( kwargs.get("host"), kwargs.get("data_center_name"), kwargs.get("datastore_name"), kwargs.get("cookies"), kwargs.get("file_path")) file_size = read_file_handle.get_size() metadata = IMAGE_API.get(context, image) # The properties and other fields that we need to set for the image. image_metadata = {"disk_format": "vmdk", "is_public": "false", "name": metadata['name'], "status": "active", "container_format": "bare", "size": file_size, "properties": {"vmware_adaptertype": kwargs.get("adapter_type"), "vmware_disktype": kwargs.get("disk_type"), "vmware_ostype": kwargs.get("os_type"), "vmware_image_version": kwargs.get("image_version"), "owner_id": instance['project_id']}} start_transfer(context, read_file_handle, file_size, image_id=metadata['id'], image_meta=image_metadata) LOG.debug("Uploaded image %s to the Glance image server", image, instance=instance)

true

f71fa9e010cfc60587fdfbd98f199c76f3a648ad

30,587

py

Python

mypyc/irbuild/expression.py

sileht/mypy

334876a0cdb80d76333e4976238fd7f42fbaabf2

[ "PSF-2.0" ]

1

2021-09-25T16:12:01.000Z

mypyc/irbuild/expression.py

sileht/mypy

334876a0cdb80d76333e4976238fd7f42fbaabf2

[ "PSF-2.0" ]

1

2021-08-21T07:40:45.000Z

mypyc/irbuild/expression.py

sileht/mypy

334876a0cdb80d76333e4976238fd7f42fbaabf2

[ "PSF-2.0" ]

1

2021-08-21T07:39:57.000Z

"""Transform mypy expression ASTs to mypyc IR (Intermediate Representation). The top-level AST transformation logic is implemented in mypyc.irbuild.visitor and mypyc.irbuild.builder. """ from typing import List, Optional, Union, Callable, cast from mypy.nodes import ( Expression, NameExpr, MemberExpr, SuperExpr, CallExpr, UnaryExpr, OpExpr, IndexExpr, ConditionalExpr, ComparisonExpr, IntExpr, FloatExpr, ComplexExpr, StrExpr, BytesExpr, EllipsisExpr, ListExpr, TupleExpr, DictExpr, SetExpr, ListComprehension, SetComprehension, DictionaryComprehension, SliceExpr, GeneratorExpr, CastExpr, StarExpr, AssignmentExpr, Var, RefExpr, MypyFile, TypeInfo, TypeApplication, LDEF, ARG_POS ) from mypy.types import TupleType, Instance, TypeType, ProperType, get_proper_type from mypyc.common import MAX_SHORT_INT from mypyc.ir.ops import ( Value, Register, TupleGet, TupleSet, BasicBlock, Assign, LoadAddress, RaiseStandardError ) from mypyc.ir.rtypes import ( RTuple, object_rprimitive, is_none_rprimitive, int_rprimitive, is_int_rprimitive ) from mypyc.ir.func_ir import FUNC_CLASSMETHOD, FUNC_STATICMETHOD from mypyc.irbuild.format_str_tokenizer import ( tokenizer_printf_style, join_formatted_strings, convert_expr ) from mypyc.primitives.bytes_ops import bytes_slice_op from mypyc.primitives.registry import CFunctionDescription, builtin_names, binary_ops from mypyc.primitives.generic_ops import iter_op from mypyc.primitives.misc_ops import new_slice_op, ellipsis_op, type_op, get_module_dict_op from mypyc.primitives.list_ops import list_append_op, list_extend_op, list_slice_op from mypyc.primitives.tuple_ops import list_tuple_op, tuple_slice_op from mypyc.primitives.dict_ops import dict_new_op, dict_set_item_op, dict_get_item_op from mypyc.primitives.set_ops import set_add_op, set_update_op from mypyc.primitives.str_ops import str_slice_op from mypyc.primitives.int_ops import int_comparison_op_mapping from mypyc.irbuild.specialize import specializers from mypyc.irbuild.builder import IRBuilder from mypyc.irbuild.for_helpers import ( translate_list_comprehension, translate_set_comprehension, comprehension_helper ) # Name and attribute references def transform_name_expr(builder: IRBuilder, expr: NameExpr) -> Value: if expr.node is None: builder.add(RaiseStandardError(RaiseStandardError.RUNTIME_ERROR, "mypyc internal error: should be unreachable", expr.line)) return builder.none() fullname = expr.node.fullname if fullname in builtin_names: typ, src = builtin_names[fullname] return builder.add(LoadAddress(typ, src, expr.line)) # special cases if fullname == 'builtins.None': return builder.none() if fullname == 'builtins.True': return builder.true() if fullname == 'builtins.False': return builder.false() if isinstance(expr.node, Var) and expr.node.is_final: value = builder.emit_load_final( expr.node, fullname, expr.name, builder.is_native_ref_expr(expr), builder.types[expr], expr.line, ) if value is not None: return value if isinstance(expr.node, MypyFile) and expr.node.fullname in builder.imports: return builder.load_module(expr.node.fullname) # If the expression is locally defined, then read the result from the corresponding # assignment target and return it. Otherwise if the expression is a global, load it from # the globals dictionary. # Except for imports, that currently always happens in the global namespace. if expr.kind == LDEF and not (isinstance(expr.node, Var) and expr.node.is_suppressed_import): # Try to detect and error when we hit the irritating mypy bug # where a local variable is cast to None. (#5423) if (isinstance(expr.node, Var) and is_none_rprimitive(builder.node_type(expr)) and expr.node.is_inferred): builder.error( 'Local variable "{}" has inferred type None; add an annotation'.format( expr.node.name), expr.node.line) # TODO: Behavior currently only defined for Var, FuncDef and MypyFile node types. if isinstance(expr.node, MypyFile): # Load reference to a module imported inside function from # the modules dictionary. It would be closer to Python # semantics to access modules imported inside functions # via local variables, but this is tricky since the mypy # AST doesn't include a Var node for the module. We # instead load the module separately on each access. mod_dict = builder.call_c(get_module_dict_op, [], expr.line) obj = builder.call_c(dict_get_item_op, [mod_dict, builder.load_str(expr.node.fullname)], expr.line) return obj else: return builder.read(builder.get_assignment_target(expr), expr.line) return builder.load_global(expr) def transform_member_expr(builder: IRBuilder, expr: MemberExpr) -> Value: # First check if this is maybe a final attribute. final = builder.get_final_ref(expr) if final is not None: fullname, final_var, native = final value = builder.emit_load_final(final_var, fullname, final_var.name, native, builder.types[expr], expr.line) if value is not None: return value if isinstance(expr.node, MypyFile) and expr.node.fullname in builder.imports: return builder.load_module(expr.node.fullname) obj = builder.accept(expr.expr) rtype = builder.node_type(expr) # Special case: for named tuples transform attribute access to faster index access. typ = get_proper_type(builder.types.get(expr.expr)) if isinstance(typ, TupleType) and typ.partial_fallback.type.is_named_tuple: fields = typ.partial_fallback.type.metadata['namedtuple']['fields'] if expr.name in fields: index = builder.builder.load_int(fields.index(expr.name)) return builder.gen_method_call(obj, '__getitem__', [index], rtype, expr.line) check_instance_attribute_access_through_class(builder, expr, typ) return builder.builder.get_attr(obj, expr.name, rtype, expr.line) def check_instance_attribute_access_through_class(builder: IRBuilder, expr: MemberExpr, typ: Optional[ProperType]) -> None: """Report error if accessing an instance attribute through class object.""" if isinstance(expr.expr, RefExpr): node = expr.expr.node if isinstance(typ, TypeType) and isinstance(typ.item, Instance): # TODO: Handle other item types node = typ.item.type if isinstance(node, TypeInfo): class_ir = builder.mapper.type_to_ir.get(node) if class_ir is not None and class_ir.is_ext_class: sym = node.get(expr.name) if (sym is not None and isinstance(sym.node, Var) and not sym.node.is_classvar and not sym.node.is_final): builder.error( 'Cannot access instance attribute "{}" through class object'.format( expr.name), expr.line ) builder.note( '(Hint: Use "x: Final = ..." or "x: ClassVar = ..." to define ' 'a class attribute)', expr.line ) def transform_super_expr(builder: IRBuilder, o: SuperExpr) -> Value: # warning(builder, 'can not optimize super() expression', o.line) sup_val = builder.load_module_attr_by_fullname('builtins.super', o.line) if o.call.args: args = [builder.accept(arg) for arg in o.call.args] else: assert o.info is not None typ = builder.load_native_type_object(o.info.fullname) ir = builder.mapper.type_to_ir[o.info] iter_env = iter(builder.builder.args) # Grab first argument vself: Value = next(iter_env) if builder.fn_info.is_generator: # grab sixth argument (see comment in translate_super_method_call) self_targ = list(builder.symtables[-1].values())[6] vself = builder.read(self_targ, builder.fn_info.fitem.line) elif not ir.is_ext_class: vself = next(iter_env) # second argument is self if non_extension class args = [typ, vself] res = builder.py_call(sup_val, args, o.line) return builder.py_get_attr(res, o.name, o.line) # Calls def transform_call_expr(builder: IRBuilder, expr: CallExpr) -> Value: if isinstance(expr.analyzed, CastExpr): return translate_cast_expr(builder, expr.analyzed) callee = expr.callee if isinstance(callee, IndexExpr) and isinstance(callee.analyzed, TypeApplication): callee = callee.analyzed.expr # Unwrap type application if isinstance(callee, MemberExpr): return translate_method_call(builder, expr, callee) elif isinstance(callee, SuperExpr): return translate_super_method_call(builder, expr, callee) else: return translate_call(builder, expr, callee) def translate_call(builder: IRBuilder, expr: CallExpr, callee: Expression) -> Value: # The common case of calls is refexprs if isinstance(callee, RefExpr): return translate_refexpr_call(builder, expr, callee) function = builder.accept(callee) args = [builder.accept(arg) for arg in expr.args] return builder.py_call(function, args, expr.line, arg_kinds=expr.arg_kinds, arg_names=expr.arg_names) def translate_refexpr_call(builder: IRBuilder, expr: CallExpr, callee: RefExpr) -> Value: """Translate a non-method call.""" # TODO: Allow special cases to have default args or named args. Currently they don't since # they check that everything in arg_kinds is ARG_POS. # If there is a specializer for this function, try calling it. # We would return the first successful one. if callee.fullname and (callee.fullname, None) in specializers: for specializer in specializers[callee.fullname, None]: val = specializer(builder, expr, callee) if val is not None: return val # Gen the argument values arg_values = [builder.accept(arg) for arg in expr.args] return builder.call_refexpr_with_args(expr, callee, arg_values) def translate_method_call(builder: IRBuilder, expr: CallExpr, callee: MemberExpr) -> Value: """Generate IR for an arbitrary call of form e.m(...). This can also deal with calls to module-level functions. """ if builder.is_native_ref_expr(callee): # Call to module-level native function or such return translate_call(builder, expr, callee) elif ( isinstance(callee.expr, RefExpr) and isinstance(callee.expr.node, TypeInfo) and callee.expr.node in builder.mapper.type_to_ir and builder.mapper.type_to_ir[callee.expr.node].has_method(callee.name) ): # Call a method via the *class* assert isinstance(callee.expr.node, TypeInfo) ir = builder.mapper.type_to_ir[callee.expr.node] decl = ir.method_decl(callee.name) args = [] arg_kinds, arg_names = expr.arg_kinds[:], expr.arg_names[:] # Add the class argument for class methods in extension classes if decl.kind == FUNC_CLASSMETHOD and ir.is_ext_class: args.append(builder.load_native_type_object(callee.expr.node.fullname)) arg_kinds.insert(0, ARG_POS) arg_names.insert(0, None) args += [builder.accept(arg) for arg in expr.args] if ir.is_ext_class: return builder.builder.call(decl, args, arg_kinds, arg_names, expr.line) else: obj = builder.accept(callee.expr) return builder.gen_method_call(obj, callee.name, args, builder.node_type(expr), expr.line, expr.arg_kinds, expr.arg_names) elif builder.is_module_member_expr(callee): # Fall back to a PyCall for non-native module calls function = builder.accept(callee) args = [builder.accept(arg) for arg in expr.args] return builder.py_call(function, args, expr.line, arg_kinds=expr.arg_kinds, arg_names=expr.arg_names) else: receiver_typ = builder.node_type(callee.expr) # If there is a specializer for this method name/type, try calling it. # We would return the first successful one. if (callee.name, receiver_typ) in specializers: for specializer in specializers[callee.name, receiver_typ]: val = specializer(builder, expr, callee) if val is not None: return val obj = builder.accept(callee.expr) args = [builder.accept(arg) for arg in expr.args] return builder.gen_method_call(obj, callee.name, args, builder.node_type(expr), expr.line, expr.arg_kinds, expr.arg_names) def translate_super_method_call(builder: IRBuilder, expr: CallExpr, callee: SuperExpr) -> Value: if callee.info is None or (len(callee.call.args) != 0 and len(callee.call.args) != 2): return translate_call(builder, expr, callee) # We support two-argument super but only when it is super(CurrentClass, self) # TODO: We could support it when it is a parent class in many cases? if len(callee.call.args) == 2: self_arg = callee.call.args[1] if ( not isinstance(self_arg, NameExpr) or not isinstance(self_arg.node, Var) or not self_arg.node.is_self ): return translate_call(builder, expr, callee) typ_arg = callee.call.args[0] if ( not isinstance(typ_arg, NameExpr) or not isinstance(typ_arg.node, TypeInfo) or callee.info is not typ_arg.node ): return translate_call(builder, expr, callee) ir = builder.mapper.type_to_ir[callee.info] # Search for the method in the mro, skipping ourselves. for base in ir.mro[1:]: if callee.name in base.method_decls: break else: return translate_call(builder, expr, callee) decl = base.method_decl(callee.name) arg_values = [builder.accept(arg) for arg in expr.args] arg_kinds, arg_names = expr.arg_kinds[:], expr.arg_names[:] if decl.kind != FUNC_STATICMETHOD: # Grab first argument vself: Value = builder.self() if decl.kind == FUNC_CLASSMETHOD: vself = builder.call_c(type_op, [vself], expr.line) elif builder.fn_info.is_generator: # For generator classes, the self target is the 6th value # in the symbol table (which is an ordered dict). This is sort # of ugly, but we can't search by name since the 'self' parameter # could be named anything, and it doesn't get added to the # environment indexes. self_targ = list(builder.symtables[-1].values())[6] vself = builder.read(self_targ, builder.fn_info.fitem.line) arg_values.insert(0, vself) arg_kinds.insert(0, ARG_POS) arg_names.insert(0, None) return builder.builder.call(decl, arg_values, arg_kinds, arg_names, expr.line) def translate_cast_expr(builder: IRBuilder, expr: CastExpr) -> Value: src = builder.accept(expr.expr) target_type = builder.type_to_rtype(expr.type) return builder.coerce(src, target_type, expr.line) # Operators def transform_unary_expr(builder: IRBuilder, expr: UnaryExpr) -> Value: return builder.unary_op(builder.accept(expr.expr), expr.op, expr.line) def transform_op_expr(builder: IRBuilder, expr: OpExpr) -> Value: if expr.op in ('and', 'or'): return builder.shortcircuit_expr(expr) # Special case for string formatting if expr.op == '%' and isinstance(expr.left, StrExpr): return translate_str_format_percent_sign(builder, expr.left, expr.right) return builder.binary_op( builder.accept(expr.left), builder.accept(expr.right), expr.op, expr.line ) def transform_index_expr(builder: IRBuilder, expr: IndexExpr) -> Value: base = builder.accept(expr.base) index = expr.index if isinstance(base.type, RTuple) and isinstance(index, IntExpr): return builder.add(TupleGet(base, index.value, expr.line)) if isinstance(index, SliceExpr): value = try_gen_slice_op(builder, base, index) if value: return value index_reg = builder.accept(expr.index) return builder.gen_method_call( base, '__getitem__', [index_reg], builder.node_type(expr), expr.line) def try_gen_slice_op(builder: IRBuilder, base: Value, index: SliceExpr) -> Optional[Value]: """Generate specialized slice op for some index expressions. Return None if a specialized op isn't available. This supports obj[x:y], obj[:x], and obj[x:] for a few types. """ if index.stride: # We can only handle the default stride of 1. return None if index.begin_index: begin_type = builder.node_type(index.begin_index) else: begin_type = int_rprimitive if index.end_index: end_type = builder.node_type(index.end_index) else: end_type = int_rprimitive # Both begin and end index must be int (or missing). if is_int_rprimitive(begin_type) and is_int_rprimitive(end_type): if index.begin_index: begin = builder.accept(index.begin_index) else: begin = builder.load_int(0) if index.end_index: end = builder.accept(index.end_index) else: # Replace missing end index with the largest short integer # (a sequence can't be longer). end = builder.load_int(MAX_SHORT_INT) candidates = [list_slice_op, tuple_slice_op, str_slice_op, bytes_slice_op] return builder.builder.matching_call_c(candidates, [base, begin, end], index.line) return None def transform_conditional_expr(builder: IRBuilder, expr: ConditionalExpr) -> Value: if_body, else_body, next = BasicBlock(), BasicBlock(), BasicBlock() builder.process_conditional(expr.cond, if_body, else_body) expr_type = builder.node_type(expr) # Having actual Phi nodes would be really nice here! target = Register(expr_type) builder.activate_block(if_body) true_value = builder.accept(expr.if_expr) true_value = builder.coerce(true_value, expr_type, expr.line) builder.add(Assign(target, true_value)) builder.goto(next) builder.activate_block(else_body) false_value = builder.accept(expr.else_expr) false_value = builder.coerce(false_value, expr_type, expr.line) builder.add(Assign(target, false_value)) builder.goto(next) builder.activate_block(next) return target def transform_comparison_expr(builder: IRBuilder, e: ComparisonExpr) -> Value: # x in (...)/[...] # x not in (...)/[...] if (e.operators[0] in ['in', 'not in'] and len(e.operators) == 1 and isinstance(e.operands[1], (TupleExpr, ListExpr))): items = e.operands[1].items n_items = len(items) # x in y -> x == y[0] or ... or x == y[n] # x not in y -> x != y[0] and ... and x != y[n] # 16 is arbitrarily chosen to limit code size if 1 < n_items < 16: if e.operators[0] == 'in': bin_op = 'or' cmp_op = '==' else: bin_op = 'and' cmp_op = '!=' lhs = e.operands[0] mypy_file = builder.graph['builtins'].tree assert mypy_file is not None bool_type = Instance(cast(TypeInfo, mypy_file.names['bool'].node), []) exprs = [] for item in items: expr = ComparisonExpr([cmp_op], [lhs, item]) builder.types[expr] = bool_type exprs.append(expr) or_expr: Expression = exprs.pop(0) for expr in exprs: or_expr = OpExpr(bin_op, or_expr, expr) builder.types[or_expr] = bool_type return builder.accept(or_expr) # x in [y]/(y) -> x == y # x not in [y]/(y) -> x != y elif n_items == 1: if e.operators[0] == 'in': cmp_op = '==' else: cmp_op = '!=' e.operators = [cmp_op] e.operands[1] = items[0] # x in []/() -> False # x not in []/() -> True elif n_items == 0: if e.operators[0] == 'in': return builder.false() else: return builder.true() # TODO: Don't produce an expression when used in conditional context # All of the trickiness here is due to support for chained conditionals # (`e1 < e2 > e3`, etc). `e1 < e2 > e3` is approximately equivalent to # `e1 < e2 and e2 > e3` except that `e2` is only evaluated once. expr_type = builder.node_type(e) # go(i, prev) generates code for `ei opi e{i+1} op{i+1} ... en`, # assuming that prev contains the value of `ei`. def go(i: int, prev: Value) -> Value: if i == len(e.operators) - 1: return transform_basic_comparison(builder, e.operators[i], prev, builder.accept(e.operands[i + 1]), e.line) next = builder.accept(e.operands[i + 1]) return builder.builder.shortcircuit_helper( 'and', expr_type, lambda: transform_basic_comparison(builder, e.operators[i], prev, next, e.line), lambda: go(i + 1, next), e.line) return go(0, builder.accept(e.operands[0])) def transform_basic_comparison(builder: IRBuilder, op: str, left: Value, right: Value, line: int) -> Value: if (is_int_rprimitive(left.type) and is_int_rprimitive(right.type) and op in int_comparison_op_mapping.keys()): return builder.compare_tagged(left, right, op, line) negate = False if op == 'is not': op, negate = 'is', True elif op == 'not in': op, negate = 'in', True target = builder.binary_op(left, right, op, line) if negate: target = builder.unary_op(target, 'not', line) return target def translate_str_format_percent_sign(builder: IRBuilder, format_expr: StrExpr, rhs: Expression) -> Value: tokens = tokenizer_printf_style(format_expr.value) if tokens is not None: literals, format_ops = tokens exprs = [] if isinstance(rhs, TupleExpr): exprs = rhs.items elif isinstance(rhs, Expression): exprs.append(rhs) substitutions = convert_expr(builder, format_ops, exprs, format_expr.line) if substitutions is not None: return join_formatted_strings(builder, literals, substitutions, format_expr.line) call_c_ops_candidates = binary_ops.get('%', []) ret = builder.builder.matching_call_c(call_c_ops_candidates, [builder.accept(format_expr), builder.accept(rhs)], format_expr.line) assert ret is not None, 'Cannot use binary op % at line {}'.format(format_expr.line) return ret # Literals def transform_int_expr(builder: IRBuilder, expr: IntExpr) -> Value: return builder.builder.load_int(expr.value) def transform_float_expr(builder: IRBuilder, expr: FloatExpr) -> Value: return builder.builder.load_float(expr.value) def transform_complex_expr(builder: IRBuilder, expr: ComplexExpr) -> Value: return builder.builder.load_complex(expr.value) def transform_str_expr(builder: IRBuilder, expr: StrExpr) -> Value: return builder.load_str(expr.value) def transform_bytes_expr(builder: IRBuilder, expr: BytesExpr) -> Value: value = bytes(expr.value, 'utf8').decode('unicode-escape').encode('raw-unicode-escape') return builder.builder.load_bytes(value) def transform_ellipsis(builder: IRBuilder, o: EllipsisExpr) -> Value: return builder.add(LoadAddress(ellipsis_op.type, ellipsis_op.src, o.line)) # Display expressions def transform_list_expr(builder: IRBuilder, expr: ListExpr) -> Value: return _visit_list_display(builder, expr.items, expr.line) def _visit_list_display(builder: IRBuilder, items: List[Expression], line: int) -> Value: return _visit_display( builder, items, builder.new_list_op, list_append_op, list_extend_op, line, True ) def transform_tuple_expr(builder: IRBuilder, expr: TupleExpr) -> Value: if any(isinstance(item, StarExpr) for item in expr.items): # create a tuple of unknown length return _visit_tuple_display(builder, expr) # create a tuple of fixed length (RTuple) tuple_type = builder.node_type(expr) # When handling NamedTuple et. al we might not have proper type info, # so make some up if we need it. types = (tuple_type.types if isinstance(tuple_type, RTuple) else [object_rprimitive] * len(expr.items)) items = [] for item_expr, item_type in zip(expr.items, types): reg = builder.accept(item_expr) items.append(builder.coerce(reg, item_type, item_expr.line)) return builder.add(TupleSet(items, expr.line)) def _visit_tuple_display(builder: IRBuilder, expr: TupleExpr) -> Value: """Create a list, then turn it into a tuple.""" val_as_list = _visit_list_display(builder, expr.items, expr.line) return builder.call_c(list_tuple_op, [val_as_list], expr.line) def transform_dict_expr(builder: IRBuilder, expr: DictExpr) -> Value: """First accepts all keys and values, then makes a dict out of them.""" key_value_pairs = [] for key_expr, value_expr in expr.items: key = builder.accept(key_expr) if key_expr is not None else None value = builder.accept(value_expr) key_value_pairs.append((key, value)) return builder.builder.make_dict(key_value_pairs, expr.line) def transform_set_expr(builder: IRBuilder, expr: SetExpr) -> Value: return _visit_display( builder, expr.items, builder.new_set_op, set_add_op, set_update_op, expr.line, False ) def _visit_display(builder: IRBuilder, items: List[Expression], constructor_op: Callable[[List[Value], int], Value], append_op: CFunctionDescription, extend_op: CFunctionDescription, line: int, is_list: bool ) -> Value: accepted_items = [] for item in items: if isinstance(item, StarExpr): accepted_items.append((True, builder.accept(item.expr))) else: accepted_items.append((False, builder.accept(item))) result: Union[Value, None] = None initial_items = [] for starred, value in accepted_items: if result is None and not starred and is_list: initial_items.append(value) continue if result is None: result = constructor_op(initial_items, line) builder.call_c(extend_op if starred else append_op, [result, value], line) if result is None: result = constructor_op(initial_items, line) return result # Comprehensions def transform_list_comprehension(builder: IRBuilder, o: ListComprehension) -> Value: if any(o.generator.is_async): builder.error('async comprehensions are unimplemented', o.line) return translate_list_comprehension(builder, o.generator) def transform_set_comprehension(builder: IRBuilder, o: SetComprehension) -> Value: if any(o.generator.is_async): builder.error('async comprehensions are unimplemented', o.line) return translate_set_comprehension(builder, o.generator) def transform_dictionary_comprehension(builder: IRBuilder, o: DictionaryComprehension) -> Value: if any(o.is_async): builder.error('async comprehensions are unimplemented', o.line) d = builder.call_c(dict_new_op, [], o.line) loop_params = list(zip(o.indices, o.sequences, o.condlists)) def gen_inner_stmts() -> None: k = builder.accept(o.key) v = builder.accept(o.value) builder.call_c(dict_set_item_op, [d, k, v], o.line) comprehension_helper(builder, loop_params, gen_inner_stmts, o.line) return d # Misc def transform_slice_expr(builder: IRBuilder, expr: SliceExpr) -> Value: def get_arg(arg: Optional[Expression]) -> Value: if arg is None: return builder.none_object() else: return builder.accept(arg) args = [get_arg(expr.begin_index), get_arg(expr.end_index), get_arg(expr.stride)] return builder.call_c(new_slice_op, args, expr.line) def transform_generator_expr(builder: IRBuilder, o: GeneratorExpr) -> Value: if any(o.is_async): builder.error('async comprehensions are unimplemented', o.line) builder.warning('Treating generator comprehension as list', o.line) return builder.call_c( iter_op, [translate_list_comprehension(builder, o)], o.line ) def transform_assignment_expr(builder: IRBuilder, o: AssignmentExpr) -> Value: value = builder.accept(o.value) target = builder.get_assignment_target(o.target) builder.assign(target, value, o.line) return value

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96

0.639193

from typing import List, Optional, Union, Callable, cast from mypy.nodes import ( Expression, NameExpr, MemberExpr, SuperExpr, CallExpr, UnaryExpr, OpExpr, IndexExpr, ConditionalExpr, ComparisonExpr, IntExpr, FloatExpr, ComplexExpr, StrExpr, BytesExpr, EllipsisExpr, ListExpr, TupleExpr, DictExpr, SetExpr, ListComprehension, SetComprehension, DictionaryComprehension, SliceExpr, GeneratorExpr, CastExpr, StarExpr, AssignmentExpr, Var, RefExpr, MypyFile, TypeInfo, TypeApplication, LDEF, ARG_POS ) from mypy.types import TupleType, Instance, TypeType, ProperType, get_proper_type from mypyc.common import MAX_SHORT_INT from mypyc.ir.ops import ( Value, Register, TupleGet, TupleSet, BasicBlock, Assign, LoadAddress, RaiseStandardError ) from mypyc.ir.rtypes import ( RTuple, object_rprimitive, is_none_rprimitive, int_rprimitive, is_int_rprimitive ) from mypyc.ir.func_ir import FUNC_CLASSMETHOD, FUNC_STATICMETHOD from mypyc.irbuild.format_str_tokenizer import ( tokenizer_printf_style, join_formatted_strings, convert_expr ) from mypyc.primitives.bytes_ops import bytes_slice_op from mypyc.primitives.registry import CFunctionDescription, builtin_names, binary_ops from mypyc.primitives.generic_ops import iter_op from mypyc.primitives.misc_ops import new_slice_op, ellipsis_op, type_op, get_module_dict_op from mypyc.primitives.list_ops import list_append_op, list_extend_op, list_slice_op from mypyc.primitives.tuple_ops import list_tuple_op, tuple_slice_op from mypyc.primitives.dict_ops import dict_new_op, dict_set_item_op, dict_get_item_op from mypyc.primitives.set_ops import set_add_op, set_update_op from mypyc.primitives.str_ops import str_slice_op from mypyc.primitives.int_ops import int_comparison_op_mapping from mypyc.irbuild.specialize import specializers from mypyc.irbuild.builder import IRBuilder from mypyc.irbuild.for_helpers import ( translate_list_comprehension, translate_set_comprehension, comprehension_helper ) def transform_name_expr(builder: IRBuilder, expr: NameExpr) -> Value: if expr.node is None: builder.add(RaiseStandardError(RaiseStandardError.RUNTIME_ERROR, "mypyc internal error: should be unreachable", expr.line)) return builder.none() fullname = expr.node.fullname if fullname in builtin_names: typ, src = builtin_names[fullname] return builder.add(LoadAddress(typ, src, expr.line)) if fullname == 'builtins.None': return builder.none() if fullname == 'builtins.True': return builder.true() if fullname == 'builtins.False': return builder.false() if isinstance(expr.node, Var) and expr.node.is_final: value = builder.emit_load_final( expr.node, fullname, expr.name, builder.is_native_ref_expr(expr), builder.types[expr], expr.line, ) if value is not None: return value if isinstance(expr.node, MypyFile) and expr.node.fullname in builder.imports: return builder.load_module(expr.node.fullname) if expr.kind == LDEF and not (isinstance(expr.node, Var) and expr.node.is_suppressed_import): if (isinstance(expr.node, Var) and is_none_rprimitive(builder.node_type(expr)) and expr.node.is_inferred): builder.error( 'Local variable "{}" has inferred type None; add an annotation'.format( expr.node.name), expr.node.line) if isinstance(expr.node, MypyFile): # instead load the module separately on each access. mod_dict = builder.call_c(get_module_dict_op, [], expr.line) obj = builder.call_c(dict_get_item_op, [mod_dict, builder.load_str(expr.node.fullname)], expr.line) return obj else: return builder.read(builder.get_assignment_target(expr), expr.line) return builder.load_global(expr) def transform_member_expr(builder: IRBuilder, expr: MemberExpr) -> Value: # First check if this is maybe a final attribute. final = builder.get_final_ref(expr) if final is not None: fullname, final_var, native = final value = builder.emit_load_final(final_var, fullname, final_var.name, native, builder.types[expr], expr.line) if value is not None: return value if isinstance(expr.node, MypyFile) and expr.node.fullname in builder.imports: return builder.load_module(expr.node.fullname) obj = builder.accept(expr.expr) rtype = builder.node_type(expr) # Special case: for named tuples transform attribute access to faster index access. typ = get_proper_type(builder.types.get(expr.expr)) if isinstance(typ, TupleType) and typ.partial_fallback.type.is_named_tuple: fields = typ.partial_fallback.type.metadata['namedtuple']['fields'] if expr.name in fields: index = builder.builder.load_int(fields.index(expr.name)) return builder.gen_method_call(obj, '__getitem__', [index], rtype, expr.line) check_instance_attribute_access_through_class(builder, expr, typ) return builder.builder.get_attr(obj, expr.name, rtype, expr.line) def check_instance_attribute_access_through_class(builder: IRBuilder, expr: MemberExpr, typ: Optional[ProperType]) -> None: if isinstance(expr.expr, RefExpr): node = expr.expr.node if isinstance(typ, TypeType) and isinstance(typ.item, Instance): # TODO: Handle other item types node = typ.item.type if isinstance(node, TypeInfo): class_ir = builder.mapper.type_to_ir.get(node) if class_ir is not None and class_ir.is_ext_class: sym = node.get(expr.name) if (sym is not None and isinstance(sym.node, Var) and not sym.node.is_classvar and not sym.node.is_final): builder.error( 'Cannot access instance attribute "{}" through class object'.format( expr.name), expr.line ) builder.note( '(Hint: Use "x: Final = ..." or "x: ClassVar = ..." to define ' 'a class attribute)', expr.line ) def transform_super_expr(builder: IRBuilder, o: SuperExpr) -> Value: # warning(builder, 'can not optimize super() expression', o.line) sup_val = builder.load_module_attr_by_fullname('builtins.super', o.line) if o.call.args: args = [builder.accept(arg) for arg in o.call.args] else: assert o.info is not None typ = builder.load_native_type_object(o.info.fullname) ir = builder.mapper.type_to_ir[o.info] iter_env = iter(builder.builder.args) # Grab first argument vself: Value = next(iter_env) if builder.fn_info.is_generator: # grab sixth argument (see comment in translate_super_method_call) self_targ = list(builder.symtables[-1].values())[6] vself = builder.read(self_targ, builder.fn_info.fitem.line) elif not ir.is_ext_class: vself = next(iter_env) # second argument is self if non_extension class args = [typ, vself] res = builder.py_call(sup_val, args, o.line) return builder.py_get_attr(res, o.name, o.line) # Calls def transform_call_expr(builder: IRBuilder, expr: CallExpr) -> Value: if isinstance(expr.analyzed, CastExpr): return translate_cast_expr(builder, expr.analyzed) callee = expr.callee if isinstance(callee, IndexExpr) and isinstance(callee.analyzed, TypeApplication): callee = callee.analyzed.expr # Unwrap type application if isinstance(callee, MemberExpr): return translate_method_call(builder, expr, callee) elif isinstance(callee, SuperExpr): return translate_super_method_call(builder, expr, callee) else: return translate_call(builder, expr, callee) def translate_call(builder: IRBuilder, expr: CallExpr, callee: Expression) -> Value: # The common case of calls is refexprs if isinstance(callee, RefExpr): return translate_refexpr_call(builder, expr, callee) function = builder.accept(callee) args = [builder.accept(arg) for arg in expr.args] return builder.py_call(function, args, expr.line, arg_kinds=expr.arg_kinds, arg_names=expr.arg_names) def translate_refexpr_call(builder: IRBuilder, expr: CallExpr, callee: RefExpr) -> Value: # TODO: Allow special cases to have default args or named args. Currently they don't since if callee.fullname and (callee.fullname, None) in specializers: for specializer in specializers[callee.fullname, None]: val = specializer(builder, expr, callee) if val is not None: return val arg_values = [builder.accept(arg) for arg in expr.args] return builder.call_refexpr_with_args(expr, callee, arg_values) def translate_method_call(builder: IRBuilder, expr: CallExpr, callee: MemberExpr) -> Value: if builder.is_native_ref_expr(callee): return translate_call(builder, expr, callee) elif ( isinstance(callee.expr, RefExpr) and isinstance(callee.expr.node, TypeInfo) and callee.expr.node in builder.mapper.type_to_ir and builder.mapper.type_to_ir[callee.expr.node].has_method(callee.name) ): assert isinstance(callee.expr.node, TypeInfo) ir = builder.mapper.type_to_ir[callee.expr.node] decl = ir.method_decl(callee.name) args = [] arg_kinds, arg_names = expr.arg_kinds[:], expr.arg_names[:] if decl.kind == FUNC_CLASSMETHOD and ir.is_ext_class: args.append(builder.load_native_type_object(callee.expr.node.fullname)) arg_kinds.insert(0, ARG_POS) arg_names.insert(0, None) args += [builder.accept(arg) for arg in expr.args] if ir.is_ext_class: return builder.builder.call(decl, args, arg_kinds, arg_names, expr.line) else: obj = builder.accept(callee.expr) return builder.gen_method_call(obj, callee.name, args, builder.node_type(expr), expr.line, expr.arg_kinds, expr.arg_names) elif builder.is_module_member_expr(callee): function = builder.accept(callee) args = [builder.accept(arg) for arg in expr.args] return builder.py_call(function, args, expr.line, arg_kinds=expr.arg_kinds, arg_names=expr.arg_names) else: receiver_typ = builder.node_type(callee.expr) if (callee.name, receiver_typ) in specializers: for specializer in specializers[callee.name, receiver_typ]: val = specializer(builder, expr, callee) if val is not None: return val obj = builder.accept(callee.expr) args = [builder.accept(arg) for arg in expr.args] return builder.gen_method_call(obj, callee.name, args, builder.node_type(expr), expr.line, expr.arg_kinds, expr.arg_names) def translate_super_method_call(builder: IRBuilder, expr: CallExpr, callee: SuperExpr) -> Value: if callee.info is None or (len(callee.call.args) != 0 and len(callee.call.args) != 2): return translate_call(builder, expr, callee) if len(callee.call.args) == 2: self_arg = callee.call.args[1] if ( not isinstance(self_arg, NameExpr) or not isinstance(self_arg.node, Var) or not self_arg.node.is_self ): return translate_call(builder, expr, callee) typ_arg = callee.call.args[0] if ( not isinstance(typ_arg, NameExpr) or not isinstance(typ_arg.node, TypeInfo) or callee.info is not typ_arg.node ): return translate_call(builder, expr, callee) ir = builder.mapper.type_to_ir[callee.info] for base in ir.mro[1:]: if callee.name in base.method_decls: break else: return translate_call(builder, expr, callee) decl = base.method_decl(callee.name) arg_values = [builder.accept(arg) for arg in expr.args] arg_kinds, arg_names = expr.arg_kinds[:], expr.arg_names[:] if decl.kind != FUNC_STATICMETHOD: vself: Value = builder.self() if decl.kind == FUNC_CLASSMETHOD: vself = builder.call_c(type_op, [vself], expr.line) elif builder.fn_info.is_generator: # could be named anything, and it doesn't get added to the self_targ = list(builder.symtables[-1].values())[6] vself = builder.read(self_targ, builder.fn_info.fitem.line) arg_values.insert(0, vself) arg_kinds.insert(0, ARG_POS) arg_names.insert(0, None) return builder.builder.call(decl, arg_values, arg_kinds, arg_names, expr.line) def translate_cast_expr(builder: IRBuilder, expr: CastExpr) -> Value: src = builder.accept(expr.expr) target_type = builder.type_to_rtype(expr.type) return builder.coerce(src, target_type, expr.line) def transform_unary_expr(builder: IRBuilder, expr: UnaryExpr) -> Value: return builder.unary_op(builder.accept(expr.expr), expr.op, expr.line) def transform_op_expr(builder: IRBuilder, expr: OpExpr) -> Value: if expr.op in ('and', 'or'): return builder.shortcircuit_expr(expr) if expr.op == '%' and isinstance(expr.left, StrExpr): return translate_str_format_percent_sign(builder, expr.left, expr.right) return builder.binary_op( builder.accept(expr.left), builder.accept(expr.right), expr.op, expr.line ) def transform_index_expr(builder: IRBuilder, expr: IndexExpr) -> Value: base = builder.accept(expr.base) index = expr.index if isinstance(base.type, RTuple) and isinstance(index, IntExpr): return builder.add(TupleGet(base, index.value, expr.line)) if isinstance(index, SliceExpr): value = try_gen_slice_op(builder, base, index) if value: return value index_reg = builder.accept(expr.index) return builder.gen_method_call( base, '__getitem__', [index_reg], builder.node_type(expr), expr.line) def try_gen_slice_op(builder: IRBuilder, base: Value, index: SliceExpr) -> Optional[Value]: if index.stride: return None if index.begin_index: begin_type = builder.node_type(index.begin_index) else: begin_type = int_rprimitive if index.end_index: end_type = builder.node_type(index.end_index) else: end_type = int_rprimitive if is_int_rprimitive(begin_type) and is_int_rprimitive(end_type): if index.begin_index: begin = builder.accept(index.begin_index) else: begin = builder.load_int(0) if index.end_index: end = builder.accept(index.end_index) else: end = builder.load_int(MAX_SHORT_INT) candidates = [list_slice_op, tuple_slice_op, str_slice_op, bytes_slice_op] return builder.builder.matching_call_c(candidates, [base, begin, end], index.line) return None def transform_conditional_expr(builder: IRBuilder, expr: ConditionalExpr) -> Value: if_body, else_body, next = BasicBlock(), BasicBlock(), BasicBlock() builder.process_conditional(expr.cond, if_body, else_body) expr_type = builder.node_type(expr) # Having actual Phi nodes would be really nice here! target = Register(expr_type) builder.activate_block(if_body) true_value = builder.accept(expr.if_expr) true_value = builder.coerce(true_value, expr_type, expr.line) builder.add(Assign(target, true_value)) builder.goto(next) builder.activate_block(else_body) false_value = builder.accept(expr.else_expr) false_value = builder.coerce(false_value, expr_type, expr.line) builder.add(Assign(target, false_value)) builder.goto(next) builder.activate_block(next) return target def transform_comparison_expr(builder: IRBuilder, e: ComparisonExpr) -> Value: # x in (...)/[...] # x not in (...)/[...] if (e.operators[0] in ['in', 'not in'] and len(e.operators) == 1 and isinstance(e.operands[1], (TupleExpr, ListExpr))): items = e.operands[1].items n_items = len(items) # x in y -> x == y[0] or ... or x == y[n] # x not in y -> x != y[0] and ... and x != y[n] # 16 is arbitrarily chosen to limit code size if 1 < n_items < 16: if e.operators[0] == 'in': bin_op = 'or' cmp_op = '==' else: bin_op = 'and' cmp_op = '!=' lhs = e.operands[0] mypy_file = builder.graph['builtins'].tree assert mypy_file is not None bool_type = Instance(cast(TypeInfo, mypy_file.names['bool'].node), []) exprs = [] for item in items: expr = ComparisonExpr([cmp_op], [lhs, item]) builder.types[expr] = bool_type exprs.append(expr) or_expr: Expression = exprs.pop(0) for expr in exprs: or_expr = OpExpr(bin_op, or_expr, expr) builder.types[or_expr] = bool_type return builder.accept(or_expr) # x in [y]/(y) -> x == y # x not in [y]/(y) -> x != y elif n_items == 1: if e.operators[0] == 'in': cmp_op = '==' else: cmp_op = '!=' e.operators = [cmp_op] e.operands[1] = items[0] # x in []/() -> False # x not in []/() -> True elif n_items == 0: if e.operators[0] == 'in': return builder.false() else: return builder.true() # TODO: Don't produce an expression when used in conditional context expr_type = builder.node_type(e) def go(i: int, prev: Value) -> Value: if i == len(e.operators) - 1: return transform_basic_comparison(builder, e.operators[i], prev, builder.accept(e.operands[i + 1]), e.line) next = builder.accept(e.operands[i + 1]) return builder.builder.shortcircuit_helper( 'and', expr_type, lambda: transform_basic_comparison(builder, e.operators[i], prev, next, e.line), lambda: go(i + 1, next), e.line) return go(0, builder.accept(e.operands[0])) def transform_basic_comparison(builder: IRBuilder, op: str, left: Value, right: Value, line: int) -> Value: if (is_int_rprimitive(left.type) and is_int_rprimitive(right.type) and op in int_comparison_op_mapping.keys()): return builder.compare_tagged(left, right, op, line) negate = False if op == 'is not': op, negate = 'is', True elif op == 'not in': op, negate = 'in', True target = builder.binary_op(left, right, op, line) if negate: target = builder.unary_op(target, 'not', line) return target def translate_str_format_percent_sign(builder: IRBuilder, format_expr: StrExpr, rhs: Expression) -> Value: tokens = tokenizer_printf_style(format_expr.value) if tokens is not None: literals, format_ops = tokens exprs = [] if isinstance(rhs, TupleExpr): exprs = rhs.items elif isinstance(rhs, Expression): exprs.append(rhs) substitutions = convert_expr(builder, format_ops, exprs, format_expr.line) if substitutions is not None: return join_formatted_strings(builder, literals, substitutions, format_expr.line) call_c_ops_candidates = binary_ops.get('%', []) ret = builder.builder.matching_call_c(call_c_ops_candidates, [builder.accept(format_expr), builder.accept(rhs)], format_expr.line) assert ret is not None, 'Cannot use binary op % at line {}'.format(format_expr.line) return ret def transform_int_expr(builder: IRBuilder, expr: IntExpr) -> Value: return builder.builder.load_int(expr.value) def transform_float_expr(builder: IRBuilder, expr: FloatExpr) -> Value: return builder.builder.load_float(expr.value) def transform_complex_expr(builder: IRBuilder, expr: ComplexExpr) -> Value: return builder.builder.load_complex(expr.value) def transform_str_expr(builder: IRBuilder, expr: StrExpr) -> Value: return builder.load_str(expr.value) def transform_bytes_expr(builder: IRBuilder, expr: BytesExpr) -> Value: value = bytes(expr.value, 'utf8').decode('unicode-escape').encode('raw-unicode-escape') return builder.builder.load_bytes(value) def transform_ellipsis(builder: IRBuilder, o: EllipsisExpr) -> Value: return builder.add(LoadAddress(ellipsis_op.type, ellipsis_op.src, o.line)) def transform_list_expr(builder: IRBuilder, expr: ListExpr) -> Value: return _visit_list_display(builder, expr.items, expr.line) def _visit_list_display(builder: IRBuilder, items: List[Expression], line: int) -> Value: return _visit_display( builder, items, builder.new_list_op, list_append_op, list_extend_op, line, True ) def transform_tuple_expr(builder: IRBuilder, expr: TupleExpr) -> Value: if any(isinstance(item, StarExpr) for item in expr.items): return _visit_tuple_display(builder, expr) tuple_type = builder.node_type(expr) types = (tuple_type.types if isinstance(tuple_type, RTuple) else [object_rprimitive] * len(expr.items)) items = [] for item_expr, item_type in zip(expr.items, types): reg = builder.accept(item_expr) items.append(builder.coerce(reg, item_type, item_expr.line)) return builder.add(TupleSet(items, expr.line)) def _visit_tuple_display(builder: IRBuilder, expr: TupleExpr) -> Value: val_as_list = _visit_list_display(builder, expr.items, expr.line) return builder.call_c(list_tuple_op, [val_as_list], expr.line) def transform_dict_expr(builder: IRBuilder, expr: DictExpr) -> Value: key_value_pairs = [] for key_expr, value_expr in expr.items: key = builder.accept(key_expr) if key_expr is not None else None value = builder.accept(value_expr) key_value_pairs.append((key, value)) return builder.builder.make_dict(key_value_pairs, expr.line) def transform_set_expr(builder: IRBuilder, expr: SetExpr) -> Value: return _visit_display( builder, expr.items, builder.new_set_op, set_add_op, set_update_op, expr.line, False ) def _visit_display(builder: IRBuilder, items: List[Expression], constructor_op: Callable[[List[Value], int], Value], append_op: CFunctionDescription, extend_op: CFunctionDescription, line: int, is_list: bool ) -> Value: accepted_items = [] for item in items: if isinstance(item, StarExpr): accepted_items.append((True, builder.accept(item.expr))) else: accepted_items.append((False, builder.accept(item))) result: Union[Value, None] = None initial_items = [] for starred, value in accepted_items: if result is None and not starred and is_list: initial_items.append(value) continue if result is None: result = constructor_op(initial_items, line) builder.call_c(extend_op if starred else append_op, [result, value], line) if result is None: result = constructor_op(initial_items, line) return result def transform_list_comprehension(builder: IRBuilder, o: ListComprehension) -> Value: if any(o.generator.is_async): builder.error('async comprehensions are unimplemented', o.line) return translate_list_comprehension(builder, o.generator) def transform_set_comprehension(builder: IRBuilder, o: SetComprehension) -> Value: if any(o.generator.is_async): builder.error('async comprehensions are unimplemented', o.line) return translate_set_comprehension(builder, o.generator) def transform_dictionary_comprehension(builder: IRBuilder, o: DictionaryComprehension) -> Value: if any(o.is_async): builder.error('async comprehensions are unimplemented', o.line) d = builder.call_c(dict_new_op, [], o.line) loop_params = list(zip(o.indices, o.sequences, o.condlists)) def gen_inner_stmts() -> None: k = builder.accept(o.key) v = builder.accept(o.value) builder.call_c(dict_set_item_op, [d, k, v], o.line) comprehension_helper(builder, loop_params, gen_inner_stmts, o.line) return d def transform_slice_expr(builder: IRBuilder, expr: SliceExpr) -> Value: def get_arg(arg: Optional[Expression]) -> Value: if arg is None: return builder.none_object() else: return builder.accept(arg) args = [get_arg(expr.begin_index), get_arg(expr.end_index), get_arg(expr.stride)] return builder.call_c(new_slice_op, args, expr.line) def transform_generator_expr(builder: IRBuilder, o: GeneratorExpr) -> Value: if any(o.is_async): builder.error('async comprehensions are unimplemented', o.line) builder.warning('Treating generator comprehension as list', o.line) return builder.call_c( iter_op, [translate_list_comprehension(builder, o)], o.line ) def transform_assignment_expr(builder: IRBuilder, o: AssignmentExpr) -> Value: value = builder.accept(o.value) target = builder.get_assignment_target(o.target) builder.assign(target, value, o.line) return value

true

f71fac3e2c7d6447e6fb71445d88074908c05f79

507

py

Python

neighborapp/migrations/0005_auto_20220104_1254.py

Kips-alih/neighborhood

216d81b352c0f7f61812280f3aa816f8450a61bc

[ "MIT" ]

null

neighborapp/migrations/0005_auto_20220104_1254.py

Kips-alih/neighborhood

216d81b352c0f7f61812280f3aa816f8450a61bc

[ "MIT" ]

null

neighborapp/migrations/0005_auto_20220104_1254.py

Kips-alih/neighborhood

216d81b352c0f7f61812280f3aa816f8450a61bc

[ "MIT" ]

null

# Generated by Django 2.2.24 on 2022-01-04 09:54 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('neighborapp', '0004_neighborhood_description'), ] operations = [ migrations.AlterField( model_name='neighborhood', name='location', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='neighborapp.Location'), ), ]

25.35

119

0.658777

from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('neighborapp', '0004_neighborhood_description'), ] operations = [ migrations.AlterField( model_name='neighborhood', name='location', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.CASCADE, to='neighborapp.Location'), ), ]

true

f71fada5cf3ee5e227b5f46f44161960eda0e4a2

4,783

py

Python

main.py

PiotrBosowski/prl-browser

eabfcc9307e0ff27d490841b80f9d1cdc06f022f

[ "MIT" ]

null

main.py

PiotrBosowski/prl-browser

eabfcc9307e0ff27d490841b80f9d1cdc06f022f

[ "MIT" ]

null

main.py

PiotrBosowski/prl-browser

eabfcc9307e0ff27d490841b80f9d1cdc06f022f

[ "MIT" ]

null

from bottle import * import os import settings from domain.training_model import Training from training_utils.combined_outputs import overview_csv @get(f'/<session_name>/<model_name>/refresh') def invalidate_cache(session_name, model_name): global global_models global_models = Training.load_all() return redirect(f"/{session_name}/{model_name}") @post(f'/<session_name>/<model_name>/delete') def delete_model(session_name, model_name): deleted = Training.delete_model(global_models, session_name, model_name) if deleted: return redirect( f"/{request.forms.next_session}/{request.forms.next_model}") else: return redirect('/') @route(f'/<session>/<model>/<filename>') def send_image(session, model, filename): """ Sends an image. :param session: :param model: model name :param filename: image name :return: static file of the requested image """ model_path = os.path.join(settings.models_dir, session, model) return static_file(filename, root=model_path, mimetype='image/png') @route(f'/<session>/<model>/<report>/<filename>') def send_report_image(session, model, report, filename): """ Sends an image. :param session: :param model: model name :param filename: image name :return: static file of the requested image """ model_path = os.path.join(settings.models_dir, session, model, report) return static_file(filename, root=model_path, mimetype='image/png') @get(f'/<session_name>/<model_name>') @view('model_template') def model_page(session_name, model_name): """ Returns model view page. :param session_name: :param model_name: model name :return: model view generated from model_template """ Training.refresh_models(global_models) current_model = global_models[session_name][model_name] test_id = request.query.test if test_id and current_model.reports: current_test = current_model.reports[int(test_id)] current_test_url = os.path.basename(current_test.path) elif current_model.reports: current_test = current_model.get_last_report() current_test_url = os.path.basename(current_test.path) else: current_test = None current_test_url = "" models = Training.models_flat(global_models) models = Training.models_select(models, recent, filter, sortby) # datasets_structures = {name: json.dumps(dataset['sources'], indent=2) # for name, dataset in current_model.datasets.items()} datasets = current_model.datasets if current_model in models: index = models.index(current_model) return template('browser/model_template', models=models, model=current_model, datasets=datasets, # datasets_structures=datasets_structures, validation=current_model.history[-1].report.confusion, previous=models[index - 1], following=Training.get_next_model(index, models), current_test=current_test, current_test_url=current_test_url, settings=settings) else: return redirect('/') @route('/favicon.ico', method='GET') def get_favicon(): """ Browsers for no reason keep asking for favicon, so there you go. :return: favicon """ return static_file('favicon.ico', root='browser') @route('/style.css') def send_style(): """ Sends style.css. :return: style.css """ return static_file('style.css', root='browser') @route('/navigation.js') def send_js(): return static_file('navigation.js', root='browser') @route('/jquery-3.5.1.min.js') def send_js(): return static_file('jquery-3.5.1.min.js', root='browser') @get('/overview.csv') def generate_csv(): filename = overview_csv(global_models) return static_file(filename, root=settings.models_dir) @route('/') @view('report_template') def index(): """ Returns main page of the server. :return: """ Training.refresh_models(global_models) models = Training.models_flat(global_models) models = Training.models_select(models, recent, filter, sortby) if models: return redirect(models[0].url()) else: return "no models to show" def browse_results(): """ Launches the server at localhost:8080. """ run(host='localhost', port=8080) if __name__ == "__main__": recent = 0 filter = "" sortby = "accuracy" reverse_order = True global_models = Training.load_all(skip_raw_outputs=True, skip_wrong_preds=True) browse_results()

29.708075

81

0.655237

from bottle import * import os import settings from domain.training_model import Training from training_utils.combined_outputs import overview_csv @get(f'/<session_name>/<model_name>/refresh') def invalidate_cache(session_name, model_name): global global_models global_models = Training.load_all() return redirect(f"/{session_name}/{model_name}") @post(f'/<session_name>/<model_name>/delete') def delete_model(session_name, model_name): deleted = Training.delete_model(global_models, session_name, model_name) if deleted: return redirect( f"/{request.forms.next_session}/{request.forms.next_model}") else: return redirect('/') @route(f'/<session>/<model>/<filename>') def send_image(session, model, filename): model_path = os.path.join(settings.models_dir, session, model) return static_file(filename, root=model_path, mimetype='image/png') @route(f'/<session>/<model>/<report>/<filename>') def send_report_image(session, model, report, filename): model_path = os.path.join(settings.models_dir, session, model, report) return static_file(filename, root=model_path, mimetype='image/png') @get(f'/<session_name>/<model_name>') @view('model_template') def model_page(session_name, model_name): Training.refresh_models(global_models) current_model = global_models[session_name][model_name] test_id = request.query.test if test_id and current_model.reports: current_test = current_model.reports[int(test_id)] current_test_url = os.path.basename(current_test.path) elif current_model.reports: current_test = current_model.get_last_report() current_test_url = os.path.basename(current_test.path) else: current_test = None current_test_url = "" models = Training.models_flat(global_models) models = Training.models_select(models, recent, filter, sortby) datasets = current_model.datasets if current_model in models: index = models.index(current_model) return template('browser/model_template', models=models, model=current_model, datasets=datasets, validation=current_model.history[-1].report.confusion, previous=models[index - 1], following=Training.get_next_model(index, models), current_test=current_test, current_test_url=current_test_url, settings=settings) else: return redirect('/') @route('/favicon.ico', method='GET') def get_favicon(): return static_file('favicon.ico', root='browser') @route('/style.css') def send_style(): return static_file('style.css', root='browser') @route('/navigation.js') def send_js(): return static_file('navigation.js', root='browser') @route('/jquery-3.5.1.min.js') def send_js(): return static_file('jquery-3.5.1.min.js', root='browser') @get('/overview.csv') def generate_csv(): filename = overview_csv(global_models) return static_file(filename, root=settings.models_dir) @route('/') @view('report_template') def index(): Training.refresh_models(global_models) models = Training.models_flat(global_models) models = Training.models_select(models, recent, filter, sortby) if models: return redirect(models[0].url()) else: return "no models to show" def browse_results(): run(host='localhost', port=8080) if __name__ == "__main__": recent = 0 filter = "" sortby = "accuracy" reverse_order = True global_models = Training.load_all(skip_raw_outputs=True, skip_wrong_preds=True) browse_results()

true

f71fada6cd8a268a05f3827d0723a58a4d32aa28

9,366

py

Python

MDSimsEval/pca_analysis.py

MikeXydas/MDSimsEval

6c32bd8b74e421120beca18d18c3e58fc8f85247

[ "MIT" ]

1

2020-06-30T12:56:41.000Z

MDSimsEval/pca_analysis.py

MikeXydas/MDSimsEval

6c32bd8b74e421120beca18d18c3e58fc8f85247

[ "MIT" ]

2

2021-06-08T21:53:33.000Z

2021-12-13T20:43:42.000Z

MDSimsEval/pca_analysis.py

MikeXydas/MDSimsEval

6c32bd8b74e421120beca18d18c3e58fc8f85247

[ "MIT" ]

null

import math import matplotlib.pyplot as plt import seaborn as sns import numpy as np import pandas as pd from tqdm import tqdm def scree_plot(analysis_actors_dict, dir_path, pcs_on_scree_plot=50, variance_ratio_line=0.75): """ Creates a plot with the scree plots for each ligand and saves it on the specified ``dir_path``. With blue color is class 1 and with orange color class 2. Args: analysis_actors_dict: ``{ "Agonists": List[AnalysisActor.class], "Antagonists": List[AnalysisActor.class] }`` dir_path (str): The path of the directory the plot will be saved (must end with a ``/``) pcs_on_scree_plot(int): The number of the first PCs that will be used on the scree plots variance_ratio_line(float): Float from 0.0 to 1.0 which specifies the variance ratio that a vertical line will be plotted """ # Get the dimensions of the final plot plot_cols = 3 plot_rows = math.ceil(len(analysis_actors_dict['Agonists']) + len(analysis_actors_dict['Antagonists']) / plot_cols) fig = plt.figure(figsize=(18, 6 * plot_rows)) plot_index = 1 # Agonists Iteration for which_ligand in analysis_actors_dict['Agonists']: ax = fig.add_subplot(plot_rows, plot_cols, plot_index) plt.axvline(x=np.where(np.cumsum(which_ligand.pca_res.explained_variance_ratio_) > variance_ratio_line)[0][0], ls='--', c='grey', label=f"Reached {int(variance_ratio_line * 100)}% variance") plt.plot(np.arange(len(which_ligand.pca_res.explained_variance_[:pcs_on_scree_plot])), which_ligand.pca_res.explained_variance_[:pcs_on_scree_plot], label="Variance Ratio") plt.ylabel("Variance") plt.xlabel("#PC") plt.title(which_ligand.drug_name) plt.legend() plot_index += 1 # Antagonists Iteration for which_ligand in analysis_actors_dict['Antagonists']: ax = fig.add_subplot(plot_rows, plot_cols, plot_index) plt.axvline(x=np.where(np.cumsum(which_ligand.pca_res.explained_variance_ratio_) > variance_ratio_line)[0][0], ls='--', c='grey', label=f"Reached {int(variance_ratio_line * 100)}% variance") plt.plot(np.arange(len(which_ligand.pca_res.explained_variance_[:pcs_on_scree_plot])), which_ligand.pca_res.explained_variance_[:pcs_on_scree_plot], label="Variance", color='orange') plt.ylabel("Variance") plt.xlabel("#PC") plt.title(which_ligand.drug_name) plt.legend() plot_index += 1 fig.suptitle('PCA Scree Plots\nAgonists: Blue\nAntagonists: Orange', fontsize=26, y=0.93) plt.savefig(f'{dir_path}pca_scree_plots.png', format='png') def populate_variance_showcase_df(analysis_actors_dict, drug_type): """ Creates a DataFrame having for each drug the number of PCs needed in order to have 50%, 75% and 95% variance Args: analysis_actors_dict: ``{ "Agonists": List[AnalysisActor.class], "Antagonists": List[AnalysisActor.class] }`` drug_type (str): The class name ('Agonists' or 'Antagonists') Returns: pd.DataFrame: A DataFrame with columns ``['Drug Name', 'Type', '50% Variance', '75% Variance', '95% Variance']`` """ inp_df = pd.DataFrame(columns=['Drug Name', 'Type', '50% Variance', '75% Variance', '95% Variance']) for which_ligand in analysis_actors_dict[drug_type]: pca_var_row = pd.DataFrame([[ which_ligand.drug_name, drug_type, np.where(np.cumsum(which_ligand.pca_res.explained_variance_ratio_) > 0.5)[0][0] + 1, # We +1 since the np.where will return np.where(np.cumsum(which_ligand.pca_res.explained_variance_ratio_) > 0.75)[0][0] + 1, # the 0 based index of the PC np.where(np.cumsum(which_ligand.pca_res.explained_variance_ratio_) > 0.95)[0][0] + 1] ], columns=['Drug Name', 'Type', '50% Variance', '75% Variance', '95% Variance']) inp_df = inp_df.append(pca_var_row, ignore_index=True) return inp_df def project_pca_on_2d(analysis_actors_dict, drug_type, dir_path): """ Plots the 2d projection on the first two PCs of the atom space. The colorbar expresses the progression of the frames (color0 -> frame0, color1 -> last_frame). The plot is shown inside the function but if need can be easily be changed to return it. Args: analysis_actors_dict: ``{ "Agonists": List[AnalysisActor.class], "Antagonists": List[AnalysisActor.class] }`` drug_type (str): 'Agonists' or 'Antagonists' dir_path (str): The path of the directory the plot will be saved (must end with a ``/``) """ cols = 3 rows = math.ceil(len(analysis_actors_dict[drug_type]) / cols) fig = plt.figure(figsize=(18, 25)) plot_index = 1 for which_ligand in tqdm(analysis_actors_dict[drug_type], desc="Projecting " + drug_type): pca_space_2D = which_ligand.pca_res.transform( which_ligand.pca_xyz) # Transform on the atom selection that PCA was fitted step = 1 # Frames we are skipping for computational reasons (if step == 1 then no frame is skipped) # Scatter Plotting ax = fig.add_subplot(rows, cols, plot_index) plt.scatter(pca_space_2D[::step, 0], pca_space_2D[::step, 1], c=np.arange(len(pca_space_2D) / step) / (len(pca_space_2D) / step), marker='o') plt.xlabel('PC1') plt.ylabel('PC2') explained_variance_2PC = which_ligand.pca_res.explained_variance_ratio_[0] + \ which_ligand.pca_res.explained_variance_ratio_[1] plt.title(f'{which_ligand.drug_name} | Structural Motion Variance: {explained_variance_2PC}') plt.colorbar() # Add the colorbar which goes from color0 to color1 as frames progress plot_index += 1 fig.suptitle(f'PCA 2D Projection of {drug_type} as frames progress', fontsize=26, y=1.03) plt.tight_layout() plt.savefig(f'{dir_path}pca_{drug_type}_2d_projection.png', format='png') return None def sort_residues_by_loadings(ligand, variance_explained=0.5): """ Having as an input **a ligand** find the loadings of each residue and return them in descending order. The method combines first k PCs where k is defined by the variance_explained argument. Args: ligand(AnalysisActor.class): An AnalysisActor object in which PCA is calculated variance_explained (float): Defines which PCs will be combined to calcualte the final loadings Returns: pd.DataFrame where ResidueId is the index and each row contains the loadings of the residue """ pca_res = ligand.get_pca() # How many pcs we need to cover variance_explained pcs_numb = np.where(np.cumsum(pca_res.explained_variance_ratio_) > variance_explained)[0][0] + 1 # Calculate loadings using loadings = eigenvectors @ sqrt(eigenvalues) loadings = np.abs(pca_res.components_[:pcs_numb, :]).T @ np.sqrt(pca_res.explained_variance_[:pcs_numb]) # Go from 3 * #residues columns to #residues columns, combining the 3 axes residue_loading = np.add.reduceat(loadings, range(0, len(loadings), 3)) return pd.DataFrame(enumerate(residue_loading), columns=['ResidueId', ligand.drug_name]).set_index('ResidueId') def loadings_heatmap(analysis_actors_dict, dir_path, explained_variance=0.75): """ | Creates a heatmap of the loadings of the residues for all the ligands. The blue line separates Class 1 fromClass 2 | .. figure:: ../_static/pca_loadings_heatmap.png :width: 550 :align: center :height: 500px :alt: pca loadings heatmap missing PCA Loadings Heatmap, click for higher resolution. Args: analysis_actors_dict: ``{ "Agonists": List[AnalysisActor.class], "Antagonists": List[AnalysisActor.class] }`` dir_path (str): The path of the directory the plot will be saved (must end with a ``/``) explained_variance(float 0.0 - 1.0): Defines the number of PCs that will be used for the loadings calculation """ loadings_df = sort_residues_by_loadings(analysis_actors_dict['Agonists'][0], explained_variance) # Join all the loadings of each ligand for which_ligand in analysis_actors_dict['Agonists'][1:]: loadings_df = loadings_df.join(sort_residues_by_loadings(which_ligand, explained_variance)) for which_ligand in analysis_actors_dict['Antagonists'][1:]: loadings_df = loadings_df.join(sort_residues_by_loadings(which_ligand, explained_variance)) fig, ax = plt.subplots(figsize=(20, 15)) sns.heatmap(loadings_df) # Seaborn heatmap of the loadings plt.axvline(len(analysis_actors_dict['Agonists'])) # Vertical line spearating agonists from antagonists ax.axis('tight') ax.set(xticks=np.arange(len(loadings_df.columns)), xticklabels=loadings_df.columns, yticks=np.arange(0, len(loadings_df.index), 10), yticklabels=np.arange(0, len(loadings_df.index), 10)) plt.xticks(rotation=45) plt.xlabel('Ligand', fontsize=18) plt.ylabel('Residue Id', fontsize=18) plt.title(f"Heatmap of Loadings of each ligand | Explained Variance: {int(explained_variance * 100)}%", fontsize=18) plt.tight_layout() plt.savefig(f'{dir_path}pca_loadings_heatmap.png', format='png') return None

46.366337

120

0.69069

import math import matplotlib.pyplot as plt import seaborn as sns import numpy as np import pandas as pd from tqdm import tqdm def scree_plot(analysis_actors_dict, dir_path, pcs_on_scree_plot=50, variance_ratio_line=0.75): plot_cols = 3 plot_rows = math.ceil(len(analysis_actors_dict['Agonists']) + len(analysis_actors_dict['Antagonists']) / plot_cols) fig = plt.figure(figsize=(18, 6 * plot_rows)) plot_index = 1 for which_ligand in analysis_actors_dict['Agonists']: ax = fig.add_subplot(plot_rows, plot_cols, plot_index) plt.axvline(x=np.where(np.cumsum(which_ligand.pca_res.explained_variance_ratio_) > variance_ratio_line)[0][0], ls='--', c='grey', label=f"Reached {int(variance_ratio_line * 100)}% variance") plt.plot(np.arange(len(which_ligand.pca_res.explained_variance_[:pcs_on_scree_plot])), which_ligand.pca_res.explained_variance_[:pcs_on_scree_plot], label="Variance Ratio") plt.ylabel("Variance") plt.xlabel("#PC") plt.title(which_ligand.drug_name) plt.legend() plot_index += 1 for which_ligand in analysis_actors_dict['Antagonists']: ax = fig.add_subplot(plot_rows, plot_cols, plot_index) plt.axvline(x=np.where(np.cumsum(which_ligand.pca_res.explained_variance_ratio_) > variance_ratio_line)[0][0], ls='--', c='grey', label=f"Reached {int(variance_ratio_line * 100)}% variance") plt.plot(np.arange(len(which_ligand.pca_res.explained_variance_[:pcs_on_scree_plot])), which_ligand.pca_res.explained_variance_[:pcs_on_scree_plot], label="Variance", color='orange') plt.ylabel("Variance") plt.xlabel("#PC") plt.title(which_ligand.drug_name) plt.legend() plot_index += 1 fig.suptitle('PCA Scree Plots\nAgonists: Blue\nAntagonists: Orange', fontsize=26, y=0.93) plt.savefig(f'{dir_path}pca_scree_plots.png', format='png') def populate_variance_showcase_df(analysis_actors_dict, drug_type): inp_df = pd.DataFrame(columns=['Drug Name', 'Type', '50% Variance', '75% Variance', '95% Variance']) for which_ligand in analysis_actors_dict[drug_type]: pca_var_row = pd.DataFrame([[ which_ligand.drug_name, drug_type, np.where(np.cumsum(which_ligand.pca_res.explained_variance_ratio_) > 0.5)[0][0] + 1, np.where(np.cumsum(which_ligand.pca_res.explained_variance_ratio_) > 0.75)[0][0] + 1, np.where(np.cumsum(which_ligand.pca_res.explained_variance_ratio_) > 0.95)[0][0] + 1] ], columns=['Drug Name', 'Type', '50% Variance', '75% Variance', '95% Variance']) inp_df = inp_df.append(pca_var_row, ignore_index=True) return inp_df def project_pca_on_2d(analysis_actors_dict, drug_type, dir_path): cols = 3 rows = math.ceil(len(analysis_actors_dict[drug_type]) / cols) fig = plt.figure(figsize=(18, 25)) plot_index = 1 for which_ligand in tqdm(analysis_actors_dict[drug_type], desc="Projecting " + drug_type): pca_space_2D = which_ligand.pca_res.transform( which_ligand.pca_xyz) step = 1 ax = fig.add_subplot(rows, cols, plot_index) plt.scatter(pca_space_2D[::step, 0], pca_space_2D[::step, 1], c=np.arange(len(pca_space_2D) / step) / (len(pca_space_2D) / step), marker='o') plt.xlabel('PC1') plt.ylabel('PC2') explained_variance_2PC = which_ligand.pca_res.explained_variance_ratio_[0] + \ which_ligand.pca_res.explained_variance_ratio_[1] plt.title(f'{which_ligand.drug_name} | Structural Motion Variance: {explained_variance_2PC}') plt.colorbar() plot_index += 1 fig.suptitle(f'PCA 2D Projection of {drug_type} as frames progress', fontsize=26, y=1.03) plt.tight_layout() plt.savefig(f'{dir_path}pca_{drug_type}_2d_projection.png', format='png') return None def sort_residues_by_loadings(ligand, variance_explained=0.5): pca_res = ligand.get_pca() pcs_numb = np.where(np.cumsum(pca_res.explained_variance_ratio_) > variance_explained)[0][0] + 1 loadings = np.abs(pca_res.components_[:pcs_numb, :]).T @ np.sqrt(pca_res.explained_variance_[:pcs_numb]) me(enumerate(residue_loading), columns=['ResidueId', ligand.drug_name]).set_index('ResidueId') def loadings_heatmap(analysis_actors_dict, dir_path, explained_variance=0.75): loadings_df = sort_residues_by_loadings(analysis_actors_dict['Agonists'][0], explained_variance) for which_ligand in analysis_actors_dict['Agonists'][1:]: loadings_df = loadings_df.join(sort_residues_by_loadings(which_ligand, explained_variance)) for which_ligand in analysis_actors_dict['Antagonists'][1:]: loadings_df = loadings_df.join(sort_residues_by_loadings(which_ligand, explained_variance)) fig, ax = plt.subplots(figsize=(20, 15)) sns.heatmap(loadings_df) plt.axvline(len(analysis_actors_dict['Agonists'])) ax.axis('tight') ax.set(xticks=np.arange(len(loadings_df.columns)), xticklabels=loadings_df.columns, yticks=np.arange(0, len(loadings_df.index), 10), yticklabels=np.arange(0, len(loadings_df.index), 10)) plt.xticks(rotation=45) plt.xlabel('Ligand', fontsize=18) plt.ylabel('Residue Id', fontsize=18) plt.title(f"Heatmap of Loadings of each ligand | Explained Variance: {int(explained_variance * 100)}%", fontsize=18) plt.tight_layout() plt.savefig(f'{dir_path}pca_loadings_heatmap.png', format='png') return None

true

f71faed40fe9843e23daeb4a3ae28c21eb2bec96

1,395

py

Python

tests/test_rand_spatial_crop_samples.py

sudohainguyen/MONAI

89f8a39a1c0bc6f480522c443ee7813cea21df47

[ "Apache-2.0" ]

2

2020-06-23T16:03:45.000Z

2020-06-25T05:30:45.000Z

tests/test_rand_spatial_crop_samples.py

Scitator/MONAI

a42b563acf0c7504cee18ee84c8af2eff6e948a7

[ "Apache-2.0" ]

null

tests/test_rand_spatial_crop_samples.py

Scitator/MONAI

a42b563acf0c7504cee18ee84c8af2eff6e948a7

[ "Apache-2.0" ]

1

2020-09-14T13:16:01.000Z

# Copyright 2020 MONAI Consortium # 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 unittest import numpy as np from parameterized import parameterized from monai.transforms import RandSpatialCropSamples TEST_CASE_1 = [ {"roi_size": [3, 3, 3], "num_samples": 4, "random_center": True}, np.random.randint(0, 2, size=[3, 3, 3, 3]), (3, 3, 3, 3), ] TEST_CASE_2 = [ {"roi_size": [3, 3, 3], "num_samples": 8, "random_center": False}, np.random.randint(0, 2, size=[3, 3, 3, 3]), (3, 3, 3, 3), ] class TestRandSpatialCropSamples(unittest.TestCase): @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_shape(self, input_param, input_data, expected_shape): result = RandSpatialCropSamples(**input_param)(input_data) for item in result: self.assertTupleEqual(item.shape, expected_shape) if __name__ == "__main__": unittest.main()

34.875

74

0.712545

import unittest import numpy as np from parameterized import parameterized from monai.transforms import RandSpatialCropSamples TEST_CASE_1 = [ {"roi_size": [3, 3, 3], "num_samples": 4, "random_center": True}, np.random.randint(0, 2, size=[3, 3, 3, 3]), (3, 3, 3, 3), ] TEST_CASE_2 = [ {"roi_size": [3, 3, 3], "num_samples": 8, "random_center": False}, np.random.randint(0, 2, size=[3, 3, 3, 3]), (3, 3, 3, 3), ] class TestRandSpatialCropSamples(unittest.TestCase): @parameterized.expand([TEST_CASE_1, TEST_CASE_2]) def test_shape(self, input_param, input_data, expected_shape): result = RandSpatialCropSamples(**input_param)(input_data) for item in result: self.assertTupleEqual(item.shape, expected_shape) if __name__ == "__main__": unittest.main()

true

f71fafdcdc00789924a0f65e4fdb20825b916e5f

52,274

py

Python

pymilvus/orm/collection.py

jingkl/pymilvus

f74b4741b7480d4e1740e1ea2d120c96f01bb56a

[ "Apache-2.0" ]

null

pymilvus/orm/collection.py

jingkl/pymilvus

f74b4741b7480d4e1740e1ea2d120c96f01bb56a

[ "Apache-2.0" ]

null

pymilvus/orm/collection.py

jingkl/pymilvus

f74b4741b7480d4e1740e1ea2d120c96f01bb56a

[ "Apache-2.0" ]

null

# Copyright (C) 2019-2021 Zilliz. 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 copy import json import pandas from .connections import get_connection from .schema import ( CollectionSchema, FieldSchema, parse_fields_from_data, infer_dtype_bydata, ) from .prepare import Prepare from .partition import Partition from .index import Index from .search import SearchResult from .mutation import MutationResult from .types import DataType from .exceptions import ( SchemaNotReadyException, DataTypeNotMatchException, DataNotMatchException, ConnectionNotExistException, PartitionAlreadyExistException, PartitionNotExistException, IndexNotExistException, AutoIDException, ExceptionsMessage, ) from .future import SearchFuture, MutationFuture def _check_schema(schema): if schema is None: raise SchemaNotReadyException(0, ExceptionsMessage.NoSchema) if len(schema.fields) < 1: raise SchemaNotReadyException(0, ExceptionsMessage.EmptySchema) vector_fields = [] for field in schema.fields: if field.dtype == DataType.FLOAT_VECTOR or field.dtype == DataType.BINARY_VECTOR: vector_fields.append(field.name) if len(vector_fields) < 1: raise SchemaNotReadyException(0, ExceptionsMessage.NoVector) def _check_data_schema(fields, data): if isinstance(data, pandas.DataFrame): for i, field in enumerate(fields): for j, _ in enumerate(data[field.name]): tmp_type = infer_dtype_bydata(data[field.name].iloc[j]) if tmp_type != field.dtype: raise DataNotMatchException(0, ExceptionsMessage.DataTypeInconsistent) else: for i, field in enumerate(fields): for j, _ in enumerate(data[i]): tmp_type = infer_dtype_bydata(data[i][j]) if tmp_type != field.dtype: raise DataNotMatchException(0, ExceptionsMessage.DataTypeInconsistent) class Collection: """ This is a class corresponding to collection in milvus. """ def __init__(self, name, schema=None, using="default", shards_num=2, **kwargs): """ Constructs a collection by name, schema and other parameters. Connection information is contained in kwargs. :param name: the name of collection :type name: str :param schema: the schema of collection :type schema: class `schema.CollectionSchema` :param using: Milvus link of create collection :type using: str :param shards_num: How wide to scale collection. Corresponds to how many active datanodes can be used on insert. :type shards_num: int :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() <pymilvus.client.stub.Milvus object at 0x7f9a190ca898> >>> fields = [ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=128) ... ] >>> description="This is a new collection description." >>> schema = CollectionSchema(fields=fields, description=description) >>> collection = Collection(name="test_collection_init", schema=schema) >>> collection.name 'test_collection_init' >>> collection.description 'This is a new collection description.' >>> collection.is_empty True >>> collection.num_entities 0 """ self._name = name self._using = using self._shards_num = shards_num self._kwargs = kwargs conn = self._get_connection() has = conn.has_collection(self._name) if has: resp = conn.describe_collection(self._name) server_schema = CollectionSchema.construct_from_dict(resp) if schema is None: self._schema = server_schema else: if not isinstance(schema, CollectionSchema): raise SchemaNotReadyException(0, ExceptionsMessage.SchemaType) if server_schema != schema: raise SchemaNotReadyException(0, ExceptionsMessage.SchemaInconsistent) self._schema = schema else: if schema is None: raise SchemaNotReadyException(0, ExceptionsMessage.CollectionNotExistNoSchema % name) if isinstance(schema, CollectionSchema): _check_schema(schema) conn.create_collection(self._name, fields=schema.to_dict(), shards_num=self._shards_num) self._schema = schema else: raise SchemaNotReadyException(0, ExceptionsMessage.SchemaType) def __repr__(self): return json.dumps({ 'name': self.name, 'schema': self._schema.to_dict(), 'partitions': [json.loads(p.__repr__()) for p in self.partitions], 'description': self.description, }) def _get_connection(self): conn = get_connection(self._using) if conn is None: raise ConnectionNotExistException(0, ExceptionsMessage.ConnectFirst) return conn def _check_insert_data_schema(self, data): """ Checks whether the data type matches the schema. """ if self._schema is None: return False if self._schema.auto_id: if isinstance(data, pandas.DataFrame): if self._schema.primary_field.name in data: if not data[self._schema.primary_field.name].isnull().all(): raise DataNotMatchException(0, ExceptionsMessage.AutoIDWithData) data = data.drop(self._schema.primary_field.name, axis=1) infer_fields = parse_fields_from_data(data) tmp_fields = copy.deepcopy(self._schema.fields) for i, field in enumerate(self._schema.fields): if field.is_primary and field.auto_id: tmp_fields.pop(i) if len(infer_fields) != len(tmp_fields): raise DataTypeNotMatchException(0, ExceptionsMessage.FieldsNumInconsistent) _check_data_schema(infer_fields, data) for x, y in zip(infer_fields, tmp_fields): if x.dtype != y.dtype: return False if isinstance(data, pandas.DataFrame): if x.name != y.name: return False # todo check dim return True def _check_schema(self): if self._schema is None: raise SchemaNotReadyException(0, ExceptionsMessage.NoSchema) def _get_vector_field(self) -> str: for field in self._schema.fields: if field.dtype == DataType.FLOAT_VECTOR or field.dtype == DataType.BINARY_VECTOR: return field.name raise SchemaNotReadyException(0, ExceptionsMessage.NoVector) @classmethod def construct_from_dataframe(cls, name, dataframe, **kwargs): if dataframe is None: raise SchemaNotReadyException(0, ExceptionsMessage.NoneDataFrame) if not isinstance(dataframe, pandas.DataFrame): raise SchemaNotReadyException(0, ExceptionsMessage.DataFrameType) primary_field = kwargs.pop("primary_field", None) if primary_field is None: raise SchemaNotReadyException(0, ExceptionsMessage.NoPrimaryKey) pk_index = -1 for i, field in enumerate(dataframe): if field == primary_field: pk_index = i if pk_index == -1: raise SchemaNotReadyException(0, ExceptionsMessage.PrimaryKeyNotExist) if "auto_id" in kwargs: if not isinstance(kwargs.get("auto_id", None), bool): raise AutoIDException(0, ExceptionsMessage.AutoIDType) auto_id = kwargs.pop("auto_id", False) if auto_id: if dataframe[primary_field].isnull().all(): dataframe = dataframe.drop(primary_field, axis=1) else: raise SchemaNotReadyException(0, ExceptionsMessage.AutoIDWithData) fields = parse_fields_from_data(dataframe) _check_data_schema(fields, dataframe) if auto_id: fields.insert(pk_index, FieldSchema(name=primary_field, dtype=DataType.INT64, is_primary=True, auto_id=True, **kwargs)) else: for field in fields: if field.name == primary_field: field.is_primary = True field.auto_id = False schema = CollectionSchema(fields=fields) _check_schema(schema) collection = cls(name, schema, **kwargs) res = collection.insert(data=dataframe) return collection, res @property def schema(self) -> CollectionSchema: """ Returns the schema of the collection. :return schema.CollectionSchema: Schema of the collection. """ return self._schema @property def description(self) -> str: """ Returns a text description of the collection. :return str: Collection description text, returned when the operation succeeds. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> fields = [ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=128) ... ] >>> description="This is an example text description." >>> schema = CollectionSchema(fields=fields, description=description) >>> collection = Collection(name="test_collection_description", schema=schema) >>> collection.description 'This is an example text description.' """ return self._schema.description @property def name(self) -> str: """ Returns the collection name. :return str: The collection name, returned when the operation succeeds. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> fields = [ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=128) ... ] >>> schema = CollectionSchema(fields) >>> collection = Collection("test_collection_name", schema) >>> collection.name 'test_collection_name' """ return self._name @property def is_empty(self) -> bool: """ Whether the collection is empty. This method need to call `num_entities <#pymilvus.Collection.num_entities>`_. :return bool: * True: The collection is empty. * False: The collection is gfghnot empty. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_is_empty", schema) >>> collection.is_empty True >>> collection.insert([[1], [[1.0, 2.0]]]) <pymilvus.search.MutationResult object at 0x7fabaf3e5d50> >>> collection.is_empty False """ return self.num_entities == 0 # read-only @property def num_entities(self) -> int: """ Returns the number of entities in the collection. :return int: Number of entities in the collection. :raises CollectionNotExistException: If the collection does not exist. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_num_entities", schema) >>> collection.num_entities 0 >>> collection.insert([[1, 2], [[1.0, 2.0], [3.0, 4.0]]]) >>> collection.num_entities 2 """ conn = self._get_connection() conn.flush([self._name]) status = conn.get_collection_stats(db_name="", collection_name=self._name) return status["row_count"] @property def primary_field(self) -> FieldSchema: """ Returns the primary field of the collection. :return schema.FieldSchema: The primary field of the collection. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("film_length", DataType.INT64, description="length in miniute"), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_primary_field", schema) >>> collection.primary_field.name 'film_id' """ return self._schema.primary_field def drop(self, timeout=None, **kwargs): """ Drops the collection together with its index files. :param timeout: * *timeout* (``float``) -- An optional duration of time in seconds to allow for the RPC. If timeout is set to None, the client keeps waiting until the server responds or an error occurs. :raises CollectionNotExistException: If the collection does not exist. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_drop", schema) >>> utility.has_collection("test_collection_drop") True >>> collection.drop() >>> utility.has_collection("test_collection_drop") False """ conn = self._get_connection() indexes = self.indexes for index in indexes: index.drop(timeout=timeout, **kwargs) conn.drop_collection(self._name, timeout=timeout, **kwargs) def load(self, partition_names=None, timeout=None, **kwargs): """ Loads the collection from disk to memory. :param partition_names: The specified partitions to load. :type partition_names: list[str] :param timeout:An optional duration of time in seconds to allow for the RPC. If timeout is set to None, the client keeps waiting until the server responds or error occurs. :type timeout: float :param kwargs: * *_async* (``bool``) -- Indicate if invoke asynchronously. :raises CollectionNotExistException: If the collection does not exist. :raises ParamError: If the parameters are invalid. :raises BaseException: If the specified field, index or partition does not exist. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_load", schema) >>> collection.insert([[1, 2], [[1.0, 2.0], [3.0, 4.0]]]) <pymilvus.search.MutationResult object at 0x7fabaf3e5d50> >>> collection.load() >>> collection.num_entities 2 """ conn = self._get_connection() if partition_names is not None: conn.load_partitions(self._name, partition_names, timeout=timeout, **kwargs) else: conn.load_collection(self._name, timeout=timeout, **kwargs) def release(self, timeout=None, **kwargs): """ Releases the collection from memory. :param timeout: * *timeout* (``float``) -- An optional duration of time in seconds to allow for the RPC. If timeout is set to None, the client keeps waiting until the server responds or an error occurs. :raises CollectionNotExistException: If collection does not exist. :raises BaseException: If collection has not been loaded to memory. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_release", schema) >>> collection.insert([[1, 2], [[1.0, 2.0], [3.0, 4.0]]]) <pymilvus.search.MutationResult object at 0x7fabaf3e5d50> >>> collection.load() >>> collection.num_entities 2 >>> collection.release() # release the collection from memory """ conn = self._get_connection() conn.release_collection(self._name, timeout=timeout, **kwargs) def insert(self, data, partition_name=None, timeout=None, **kwargs): """ Insert data into the collection. :param data: The specified data to insert, the dimension of data needs to align with column number :type data: list-like(list, tuple) object or pandas.DataFrame :param partition_name: The partition name which the data will be inserted to, if partition name is not passed, then the data will be inserted to "_default" partition :type partition_name: str :param timeout: * *timeout* (``float``) -- An optional duration of time in seconds to allow for the RPC. If timeout is set to None, the client keeps waiting until the server responds or an error occurs. :raises CollectionNotExistException: If the specified collection does not exist. :raises ParamError: If input parameters are invalid. :raises BaseException: If the specified partition does not exist. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> import random >>> connections.connect() <pymilvus.client.stub.Milvus object at 0x7f8579002dc0> >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_insert", schema) >>> data = [ ... [random.randint(1, 100) for _ in range(10)], ... [[random.random() for _ in range(2)] for _ in range(10)], ... ] >>> collection.insert(data) >>> collection.num_entities 10 """ if data is None: return MutationResult(data) if not self._check_insert_data_schema(data): raise SchemaNotReadyException(0, ExceptionsMessage.TypeOfDataAndSchemaInconsistent) conn = self._get_connection() entities = Prepare.prepare_insert_data(data, self._schema) res = conn.insert(collection_name=self._name, entities=entities, ids=None, partition_name=partition_name, timeout=timeout, **kwargs) if kwargs.get("_async", False): return MutationFuture(res) return MutationResult(res) def delete(self, expr, partition_name=None, timeout=None, **kwargs): """ Delete entities with an expression condition. And return results to show which primary key is deleted successfully :param expr: The expression to specify entities to be deleted :type expr: str :param partition_name: Name of partitions that contain entities :type partition_name: str :param timeout: An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur :type timeout: float :return: list of ids of the deleted vectors. :rtype: list :raises: RpcError: If gRPC encounter an error ParamError: If parameters are invalid BaseException: If the return result from server is not ok :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> import random >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("film_date", DataType.INT64), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_query", schema) >>> # insert >>> data = [ ... [i for i in range(10)], ... [i + 2000 for i in range(10)], ... [[random.random() for _ in range(2)] for _ in range(10)], ... ] >>> collection.insert(data) >>> collection.num_entities >>> expr = "film_id in [ 0, 1 ]" >>> res = collection.delete(expr) >>> assert len(res) == 2 >>> print(f"- Deleted entities: {res}") - Delete results: [0, 1] """ conn = self._get_connection() res = conn.delete(collection_name=self._name, expr=expr, partition_name=partition_name, timeout=timeout, **kwargs) if kwargs.get("_async", False): return MutationFuture(res) return MutationResult(res) def search(self, data, anns_field, param, limit, expr=None, partition_names=None, output_fields=None, timeout=None, round_decimal=-1, **kwargs): """ Conducts a vector similarity search with an optional boolean expression as filter. :param data: The vectors of search data, the length of data is number of query (nq), the dim of every vector in data must be equal to vector field's of collection. :type data: list[list[float]] :param anns_field: The vector field used to search of collection. :type anns_field: str :param param: The parameters of search, such as ``nprobe``. :type param: dict :param limit: The max number of returned record, also known as ``topk``. :type limit: int :param expr: The boolean expression used to filter attribute. :type expr: str :param partition_names: The names of partitions to search. :type partition_names: list[str] :param output_fields: The fields to return in the search result, not supported now. :type output_fields: list[str] :param timeout: An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur. :type timeout: float :param round_decimal: The specified number of decimal places of returned distance :type round_decimal: int :param kwargs: * *_async* (``bool``) -- Indicate if invoke asynchronously. When value is true, method returns a SearchFuture object; otherwise, method returns results from server directly. * *_callback* (``function``) -- The callback function which is invoked after server response successfully. It functions only if _async is set to True. * *guarantee_timestamp* (``int``) -- This function instructs Milvus to see all operations performed before a provided timestamp. If no such timestamp is provided, then Milvus will search all operations performed to date. :return: SearchResult: SearchResult is iterable and is a 2d-array-like class, the first dimension is the number of vectors to query (nq), the second dimension is the number of limit(topk). :rtype: SearchResult :raises RpcError: If gRPC encounter an error. :raises ParamError: If parameters are invalid. :raises DataTypeNotMatchException: If wrong type of param is passed. :raises BaseException: If the return result from server is not ok. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> import random >>> connections.connect() <pymilvus.client.stub.Milvus object at 0x7f8579002dc0> >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_search", schema) >>> # insert >>> data = [ ... [i for i in range(10)], ... [[random.random() for _ in range(2)] for _ in range(10)], ... ] >>> collection.insert(data) >>> collection.num_entities 10 >>> collection.load() >>> # search >>> search_param = { ... "data": [[1.0, 1.0]], ... "anns_field": "films", ... "param": {"metric_type": "L2"}, ... "limit": 2, ... "expr": "film_id > 0", ... } >>> res = collection.search(**search_param) >>> assert len(res) == 1 >>> hits = res[0] >>> assert len(hits) == 2 >>> print(f"- Total hits: {len(hits)}, hits ids: {hits.ids} ") - Total hits: 2, hits ids: [8, 5] >>> print(f"- Top1 hit id: {hits[0].id}, distance: {hits[0].distance}, score: {hits[0].score} ") - Top1 hit id: 8, distance: 0.10143111646175385, score: 0.10143111646175385 """ if expr is not None and not isinstance(expr, str): raise DataTypeNotMatchException(0, ExceptionsMessage.ExprType % type(expr)) conn = self._get_connection() res = conn.search(self._name, data, anns_field, param, limit, expr, partition_names, output_fields, timeout, round_decimal, **kwargs) if kwargs.get("_async", False): return SearchFuture(res) return SearchResult(res) def query(self, expr, output_fields=None, partition_names=None, timeout=None): """ Query with a set of criteria, and results in a list of records that match the query exactly. :param expr: The query expression :type expr: str :param output_fields: A list of fields to return :type output_fields: list[str] :param partition_names: Name of partitions that contain entities :type partition_names: list[str] :param timeout: An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur :type timeout: float :return: A list that contains all results :rtype: list :raises: RpcError: If gRPC encounter an error ParamError: If parameters are invalid DataTypeNotMatchException: If wrong type of param is passed BaseException: If the return result from server is not ok :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> import random >>> connections.connect() <pymilvus.client.stub.Milvus object at 0x7f8579002dc0> >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("film_date", DataType.INT64), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_query", schema) >>> # insert >>> data = [ ... [i for i in range(10)], ... [i + 2000 for i in range(10)], ... [[random.random() for _ in range(2)] for _ in range(10)], ... ] >>> collection.insert(data) >>> collection.num_entities 10 >>> collection.load() >>> # query >>> expr = "film_id in [ 0, 1 ]" >>> res = collection.query(expr, output_fields=["film_date"]) >>> assert len(res) == 2 >>> print(f"- Query results: {res}") - Query results: [{'film_id': 0, 'film_date': 2000}, {'film_id': 1, 'film_date': 2001}] """ if not isinstance(expr, str): raise DataTypeNotMatchException(0, ExceptionsMessage.ExprType % type(expr)) conn = self._get_connection() res = conn.query(self._name, expr, output_fields, partition_names, timeout) return res @property def partitions(self) -> list: """ Return all partitions of the collection. :return list[Partition]: List of Partition object, return when operation is successful. :raises CollectionNotExistException: If collection doesn't exist. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() <pymilvus.client.stub.Milvus object at 0x7f8579002dc0> >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_partitions", schema) >>> collection.partitions [{"name": "_default", "description": "", "num_entities": 0}] """ conn = self._get_connection() partition_strs = conn.list_partitions(self._name) partitions = [] for partition in partition_strs: partitions.append(Partition(self, partition, construct_only=True)) return partitions def partition(self, partition_name) -> Partition: """ Return the partition corresponding to name. Return None if not existed. :param partition_name: The name of the partition to get. :type partition_name: str :return Partition: Partition object corresponding to partition_name. :raises CollectionNotExistException: If collection doesn't exist. :raises BaseException: If partition doesn't exist. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() <pymilvus.client.stub.Milvus object at 0x7f8579002dc0> >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_partition", schema) >>> collection.partition("_default") {"name": "_default", "description": "", "num_entities": 0} >>> collection.partition("partition") """ if self.has_partition(partition_name) is False: return None return Partition(self, partition_name, construct_only=True) def create_partition(self, partition_name, description=""): """ Create the partition corresponding to name if not existed. :param partition_name: The name of the partition to create. :type partition_name: str :param description: The description of the partition corresponding to name. :type description: str :return Partition: Partition object corresponding to partition_name. :raises CollectionNotExistException: If collection doesn't exist. :raises BaseException: If partition doesn't exist. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_create_partition", schema) >>> collection.create_partition("comedy", description="comedy films") {"name": "comedy", "description": "comedy films", "num_entities": 0} >>> collection.partition("comedy") {"name": "partition", "description": "comedy films", "num_entities": 0} """ if self.has_partition(partition_name) is True: raise PartitionAlreadyExistException(0, ExceptionsMessage.PartitionAlreadyExist) return Partition(self, partition_name, description=description) def has_partition(self, partition_name, timeout=None) -> bool: """ Checks if a specified partition exists. :param partition_name: The name of the partition to check :type partition_name: str :param timeout: An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur :type timeout: float :return bool: Whether a specified partition exists. :raises CollectionNotExistException: If collection doesn't exist. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_has_partition", schema) >>> collection.create_partition("comedy", description="comedy films") {"name": "comedy", "description": "comedy films", "num_entities": 0} >>> collection.has_partition("comedy") True >>> collection.has_partition("science_fiction") False """ conn = self._get_connection() return conn.has_partition(self._name, partition_name, timeout=timeout) def drop_partition(self, partition_name, timeout=None, **kwargs): """ Drop the partition and its corresponding index files. :param partition_name: The name of the partition to drop. :type partition_name: str :param timeout: * *timeout* (``float``) -- An optional duration of time in seconds to allow for the RPC. If timeout is set to None, the client keeps waiting until the server responds or an error occurs. :raises CollectionNotExistException: If collection doesn't exist. :raises BaseException: If partition doesn't exist. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_drop_partition", schema) >>> collection.create_partition("comedy", description="comedy films") {"name": "comedy", "description": "comedy films", "num_entities": 0} >>> collection.has_partition("comedy") True >>> collection.drop_partition("comedy") >>> collection.has_partition("comedy") False """ if self.has_partition(partition_name) is False: raise PartitionNotExistException(0, ExceptionsMessage.PartitionNotExist) conn = self._get_connection() return conn.drop_partition(self._name, partition_name, timeout=timeout, **kwargs) # The server side not yet finished to return aliases by the describe_collection api. # Disable this property until the work is done. # @property # def aliases(self) -> list: # """ # Returns alias list of the collection. # # :return list of str: # The collection aliases, returned when the operation succeeds. # # :example: # >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType # >>> connections.connect() # >>> fields = [ # ... FieldSchema("film_id", DataType.INT64, is_primary=True), # ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=128) # ... ] # >>> schema = CollectionSchema(fields) # >>> collection = Collection("test_collection_name", schema) # >>> collection.create_alias("tom") # >>> collection.alias # ['tom'] # """ # conn = self._get_connection() # has = conn.has_collection(self._name) # aliases = [] # if has: # resp = conn.describe_collection(self._name) # aliases = resp['aliases'] # return aliases @property def indexes(self) -> list: """ Returns all indexes of the collection. :return list[Index]: List of Index objects, returned when this operation is successful. :raises CollectionNotExistException: If the collection does not exist. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_indexes", schema) >>> collection.indexes [] """ conn = self._get_connection() indexes = [] tmp_index = conn.describe_index(self._name) if tmp_index is not None: field_name = tmp_index.pop("field_name", None) indexes.append(Index(self, field_name, tmp_index, construct_only=True)) return indexes def index(self) -> Index: """ Fetches the index object of the of the specified name. :return Index: Index object corresponding to index_name. :raises CollectionNotExistException: If the collection does not exist. :raises BaseException: If the specified index does not exist. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_index", schema) >>> index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"} >>> collection.create_index("films", index) Status(code=0, message='') >>> collection.indexes [<pymilvus.index.Index object at 0x7f4435587e20>] >>> collection.index() <pymilvus.index.Index object at 0x7f44355a1460> """ conn = self._get_connection() tmp_index = conn.describe_index(self._name) if tmp_index is not None: field_name = tmp_index.pop("field_name", None) return Index(self, field_name, tmp_index, construct_only=True) raise IndexNotExistException(0, ExceptionsMessage.IndexNotExist) def create_index(self, field_name, index_params, timeout=None, **kwargs) -> Index: """ Creates index for a specified field. Return Index Object. :param field_name: The name of the field to create an index for. :type field_name: str :param index_params: The indexing parameters. :type index_params: dict :param timeout: An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur :type timeout: float :raises CollectionNotExistException: If the collection does not exist. :raises ParamError: If the index parameters are invalid. :raises BaseException: If field does not exist. :raises BaseException: If the index has been created. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_create_index", schema) >>> index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"} >>> collection.create_index("films", index) Status(code=0, message='') >>> collection.index() <pymilvus.index.Index object at 0x7f44355a1460> """ conn = self._get_connection() return conn.create_index(self._name, field_name, index_params, timeout=timeout, **kwargs) def has_index(self, timeout=None) -> bool: """ Checks whether a specified index exists. :param timeout: An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur :type timeout: float :return bool: Whether the specified index exists. :raises CollectionNotExistException: If the collection does not exist. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_has_index", schema) >>> index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"} >>> collection.create_index("films", index) >>> collection.has_index() True """ conn = self._get_connection() # TODO(yukun): Need field name, but provide index name if conn.describe_index(self._name, "", timeout=timeout) is None: return False return True def drop_index(self, timeout=None, **kwargs): """ Drop index and its corresponding index files. :param timeout: * *timeout* (``float``) -- An optional duration of time in seconds to allow for the RPC. If timeout is set to None, the client keeps waiting until the server responds or an error occurs. Optional. A duration of time in seconds. :raises CollectionNotExistException: If the collection does not exist. :raises BaseException: If the index does not exist or has been dropped. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_has_index", schema) >>> index = {"index_type": "IVF_FLAT", "params": {"nlist": 128}, "metric_type": "L2"} >>> collection.create_index("films", index) >>> collection.has_index() True >>> collection.drop_index() >>> collection.has_index() False """ if self.has_index() is False: raise IndexNotExistException(0, ExceptionsMessage.IndexNotExist) conn = self._get_connection() tmp_index = conn.describe_index(self._name, "") if tmp_index is not None: index = Index(self, tmp_index['field_name'], tmp_index, construct_only=True) index.drop(timeout=timeout, **kwargs) def create_alias(self, alias, timeout=None, **kwargs): """ Specify alias for a collection. Alias cannot be duplicated, you can't assign same alias to different collections. But you can specify multiple aliases for a collection, for example: before create_alias("collection_1", "bob"): collection_1's aliases = ["tom"] after create_alias("collection_1", "bob"): collection_1's aliases = ["tom", "bob"] :param alias: The alias of the collection. :type alias: str. :param timeout: An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur :type timeout: float :raises CollectionNotExistException: If the collection does not exist. :raises BaseException: If the alias failed to create. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_create_index", schema) >>> collection.create_alias("alias") Status(code=0, message='') """ conn = self._get_connection() conn.create_alias(self._name, alias, timeout=timeout, **kwargs) def drop_alias(self, alias, timeout=None, **kwargs): """ Delete an alias. This api no need to specify collection name because the milvus server knows which collection it belongs. For example: before drop_alias("bob"): collection_1's aliases = ["tom", "bob"] after drop_alias("bob"): collection_1's aliases = ["tom"] :param alias: The alias of the collection. :type alias: str. :param timeout: An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur :type timeout: float :raises CollectionNotExistException: If the collection does not exist. :raises BaseException: If the alias doesn't exist. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_create_index", schema) >>> collection.create_alias("alias") >>> collection.drop_alias("alias") Status(code=0, message='') """ conn = self._get_connection() conn.drop_alias(alias, timeout=timeout, **kwargs) def alter_alias(self, alias, timeout=None, **kwargs): """ Change alias of a collection to another collection. If the alias doesn't exist, the api will return error. Alias cannot be duplicated, you can't assign same alias to different collections. This api can change alias owner collection, for example: before alter_alias("collection_2", "bob"): collection_1's aliases = ["bob"] collection_2's aliases = [] after alter_alias("collection_2", "bob"): collection_1's aliases = [] collection_2's aliases = ["bob"] :param alias: The alias of the collection. :type alias: str. :param timeout: An optional duration of time in seconds to allow for the RPC. When timeout is set to None, client waits until server response or error occur :type timeout: float :raises CollectionNotExistException: If the collection does not exist. :raises BaseException: If the alias failed to alter. :example: >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType >>> connections.connect() >>> schema = CollectionSchema([ ... FieldSchema("film_id", DataType.INT64, is_primary=True), ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=2) ... ]) >>> collection = Collection("test_collection_create_index", schema) >>> collection.alter_alias("alias") if the alias exists, return Status(code=0, message='') otherwise return Status(code=1, message='alias does not exist') """ conn = self._get_connection() conn.alter_alias(self._name, alias, timeout=timeout, **kwargs)

43.309031

120

0.600318

import copy import json import pandas from .connections import get_connection from .schema import ( CollectionSchema, FieldSchema, parse_fields_from_data, infer_dtype_bydata, ) from .prepare import Prepare from .partition import Partition from .index import Index from .search import SearchResult from .mutation import MutationResult from .types import DataType from .exceptions import ( SchemaNotReadyException, DataTypeNotMatchException, DataNotMatchException, ConnectionNotExistException, PartitionAlreadyExistException, PartitionNotExistException, IndexNotExistException, AutoIDException, ExceptionsMessage, ) from .future import SearchFuture, MutationFuture def _check_schema(schema): if schema is None: raise SchemaNotReadyException(0, ExceptionsMessage.NoSchema) if len(schema.fields) < 1: raise SchemaNotReadyException(0, ExceptionsMessage.EmptySchema) vector_fields = [] for field in schema.fields: if field.dtype == DataType.FLOAT_VECTOR or field.dtype == DataType.BINARY_VECTOR: vector_fields.append(field.name) if len(vector_fields) < 1: raise SchemaNotReadyException(0, ExceptionsMessage.NoVector) def _check_data_schema(fields, data): if isinstance(data, pandas.DataFrame): for i, field in enumerate(fields): for j, _ in enumerate(data[field.name]): tmp_type = infer_dtype_bydata(data[field.name].iloc[j]) if tmp_type != field.dtype: raise DataNotMatchException(0, ExceptionsMessage.DataTypeInconsistent) else: for i, field in enumerate(fields): for j, _ in enumerate(data[i]): tmp_type = infer_dtype_bydata(data[i][j]) if tmp_type != field.dtype: raise DataNotMatchException(0, ExceptionsMessage.DataTypeInconsistent) class Collection: def __init__(self, name, schema=None, using="default", shards_num=2, **kwargs): self._name = name self._using = using self._shards_num = shards_num self._kwargs = kwargs conn = self._get_connection() has = conn.has_collection(self._name) if has: resp = conn.describe_collection(self._name) server_schema = CollectionSchema.construct_from_dict(resp) if schema is None: self._schema = server_schema else: if not isinstance(schema, CollectionSchema): raise SchemaNotReadyException(0, ExceptionsMessage.SchemaType) if server_schema != schema: raise SchemaNotReadyException(0, ExceptionsMessage.SchemaInconsistent) self._schema = schema else: if schema is None: raise SchemaNotReadyException(0, ExceptionsMessage.CollectionNotExistNoSchema % name) if isinstance(schema, CollectionSchema): _check_schema(schema) conn.create_collection(self._name, fields=schema.to_dict(), shards_num=self._shards_num) self._schema = schema else: raise SchemaNotReadyException(0, ExceptionsMessage.SchemaType) def __repr__(self): return json.dumps({ 'name': self.name, 'schema': self._schema.to_dict(), 'partitions': [json.loads(p.__repr__()) for p in self.partitions], 'description': self.description, }) def _get_connection(self): conn = get_connection(self._using) if conn is None: raise ConnectionNotExistException(0, ExceptionsMessage.ConnectFirst) return conn def _check_insert_data_schema(self, data): if self._schema is None: return False if self._schema.auto_id: if isinstance(data, pandas.DataFrame): if self._schema.primary_field.name in data: if not data[self._schema.primary_field.name].isnull().all(): raise DataNotMatchException(0, ExceptionsMessage.AutoIDWithData) data = data.drop(self._schema.primary_field.name, axis=1) infer_fields = parse_fields_from_data(data) tmp_fields = copy.deepcopy(self._schema.fields) for i, field in enumerate(self._schema.fields): if field.is_primary and field.auto_id: tmp_fields.pop(i) if len(infer_fields) != len(tmp_fields): raise DataTypeNotMatchException(0, ExceptionsMessage.FieldsNumInconsistent) _check_data_schema(infer_fields, data) for x, y in zip(infer_fields, tmp_fields): if x.dtype != y.dtype: return False if isinstance(data, pandas.DataFrame): if x.name != y.name: return False return True def _check_schema(self): if self._schema is None: raise SchemaNotReadyException(0, ExceptionsMessage.NoSchema) def _get_vector_field(self) -> str: for field in self._schema.fields: if field.dtype == DataType.FLOAT_VECTOR or field.dtype == DataType.BINARY_VECTOR: return field.name raise SchemaNotReadyException(0, ExceptionsMessage.NoVector) @classmethod def construct_from_dataframe(cls, name, dataframe, **kwargs): if dataframe is None: raise SchemaNotReadyException(0, ExceptionsMessage.NoneDataFrame) if not isinstance(dataframe, pandas.DataFrame): raise SchemaNotReadyException(0, ExceptionsMessage.DataFrameType) primary_field = kwargs.pop("primary_field", None) if primary_field is None: raise SchemaNotReadyException(0, ExceptionsMessage.NoPrimaryKey) pk_index = -1 for i, field in enumerate(dataframe): if field == primary_field: pk_index = i if pk_index == -1: raise SchemaNotReadyException(0, ExceptionsMessage.PrimaryKeyNotExist) if "auto_id" in kwargs: if not isinstance(kwargs.get("auto_id", None), bool): raise AutoIDException(0, ExceptionsMessage.AutoIDType) auto_id = kwargs.pop("auto_id", False) if auto_id: if dataframe[primary_field].isnull().all(): dataframe = dataframe.drop(primary_field, axis=1) else: raise SchemaNotReadyException(0, ExceptionsMessage.AutoIDWithData) fields = parse_fields_from_data(dataframe) _check_data_schema(fields, dataframe) if auto_id: fields.insert(pk_index, FieldSchema(name=primary_field, dtype=DataType.INT64, is_primary=True, auto_id=True, **kwargs)) else: for field in fields: if field.name == primary_field: field.is_primary = True field.auto_id = False schema = CollectionSchema(fields=fields) _check_schema(schema) collection = cls(name, schema, **kwargs) res = collection.insert(data=dataframe) return collection, res @property def schema(self) -> CollectionSchema: return self._schema @property def description(self) -> str: return self._schema.description @property def name(self) -> str: return self._name @property def is_empty(self) -> bool: return self.num_entities == 0 @property def num_entities(self) -> int: conn = self._get_connection() conn.flush([self._name]) status = conn.get_collection_stats(db_name="", collection_name=self._name) return status["row_count"] @property def primary_field(self) -> FieldSchema: return self._schema.primary_field def drop(self, timeout=None, **kwargs): conn = self._get_connection() indexes = self.indexes for index in indexes: index.drop(timeout=timeout, **kwargs) conn.drop_collection(self._name, timeout=timeout, **kwargs) def load(self, partition_names=None, timeout=None, **kwargs): conn = self._get_connection() if partition_names is not None: conn.load_partitions(self._name, partition_names, timeout=timeout, **kwargs) else: conn.load_collection(self._name, timeout=timeout, **kwargs) def release(self, timeout=None, **kwargs): conn = self._get_connection() conn.release_collection(self._name, timeout=timeout, **kwargs) def insert(self, data, partition_name=None, timeout=None, **kwargs): if data is None: return MutationResult(data) if not self._check_insert_data_schema(data): raise SchemaNotReadyException(0, ExceptionsMessage.TypeOfDataAndSchemaInconsistent) conn = self._get_connection() entities = Prepare.prepare_insert_data(data, self._schema) res = conn.insert(collection_name=self._name, entities=entities, ids=None, partition_name=partition_name, timeout=timeout, **kwargs) if kwargs.get("_async", False): return MutationFuture(res) return MutationResult(res) def delete(self, expr, partition_name=None, timeout=None, **kwargs): conn = self._get_connection() res = conn.delete(collection_name=self._name, expr=expr, partition_name=partition_name, timeout=timeout, **kwargs) if kwargs.get("_async", False): return MutationFuture(res) return MutationResult(res) def search(self, data, anns_field, param, limit, expr=None, partition_names=None, output_fields=None, timeout=None, round_decimal=-1, **kwargs): if expr is not None and not isinstance(expr, str): raise DataTypeNotMatchException(0, ExceptionsMessage.ExprType % type(expr)) conn = self._get_connection() res = conn.search(self._name, data, anns_field, param, limit, expr, partition_names, output_fields, timeout, round_decimal, **kwargs) if kwargs.get("_async", False): return SearchFuture(res) return SearchResult(res) def query(self, expr, output_fields=None, partition_names=None, timeout=None): if not isinstance(expr, str): raise DataTypeNotMatchException(0, ExceptionsMessage.ExprType % type(expr)) conn = self._get_connection() res = conn.query(self._name, expr, output_fields, partition_names, timeout) return res @property def partitions(self) -> list: conn = self._get_connection() partition_strs = conn.list_partitions(self._name) partitions = [] for partition in partition_strs: partitions.append(Partition(self, partition, construct_only=True)) return partitions def partition(self, partition_name) -> Partition: if self.has_partition(partition_name) is False: return None return Partition(self, partition_name, construct_only=True) def create_partition(self, partition_name, description=""): if self.has_partition(partition_name) is True: raise PartitionAlreadyExistException(0, ExceptionsMessage.PartitionAlreadyExist) return Partition(self, partition_name, description=description) def has_partition(self, partition_name, timeout=None) -> bool: conn = self._get_connection() return conn.has_partition(self._name, partition_name, timeout=timeout) def drop_partition(self, partition_name, timeout=None, **kwargs): if self.has_partition(partition_name) is False: raise PartitionNotExistException(0, ExceptionsMessage.PartitionNotExist) conn = self._get_connection() return conn.drop_partition(self._name, partition_name, timeout=timeout, **kwargs) # Returns alias list of the collection. # # :return list of str: # The collection aliases, returned when the operation succeeds. # # :example: # >>> from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType # >>> connections.connect() # >>> fields = [ # ... FieldSchema("film_id", DataType.INT64, is_primary=True), # ... FieldSchema("films", dtype=DataType.FLOAT_VECTOR, dim=128) # ... ] # >>> schema = CollectionSchema(fields) # >>> collection = Collection("test_collection_name", schema) # >>> collection.create_alias("tom") # >>> collection.alias # ['tom'] # """ @property def indexes(self) -> list: conn = self._get_connection() indexes = [] tmp_index = conn.describe_index(self._name) if tmp_index is not None: field_name = tmp_index.pop("field_name", None) indexes.append(Index(self, field_name, tmp_index, construct_only=True)) return indexes def index(self) -> Index: conn = self._get_connection() tmp_index = conn.describe_index(self._name) if tmp_index is not None: field_name = tmp_index.pop("field_name", None) return Index(self, field_name, tmp_index, construct_only=True) raise IndexNotExistException(0, ExceptionsMessage.IndexNotExist) def create_index(self, field_name, index_params, timeout=None, **kwargs) -> Index: conn = self._get_connection() return conn.create_index(self._name, field_name, index_params, timeout=timeout, **kwargs) def has_index(self, timeout=None) -> bool: conn = self._get_connection() if conn.describe_index(self._name, "", timeout=timeout) is None: return False return True def drop_index(self, timeout=None, **kwargs): if self.has_index() is False: raise IndexNotExistException(0, ExceptionsMessage.IndexNotExist) conn = self._get_connection() tmp_index = conn.describe_index(self._name, "") if tmp_index is not None: index = Index(self, tmp_index['field_name'], tmp_index, construct_only=True) index.drop(timeout=timeout, **kwargs) def create_alias(self, alias, timeout=None, **kwargs): conn = self._get_connection() conn.create_alias(self._name, alias, timeout=timeout, **kwargs) def drop_alias(self, alias, timeout=None, **kwargs): conn = self._get_connection() conn.drop_alias(alias, timeout=timeout, **kwargs) def alter_alias(self, alias, timeout=None, **kwargs): conn = self._get_connection() conn.alter_alias(self._name, alias, timeout=timeout, **kwargs)

true

f71fb01cbdb1f124478ac2b092b7ac4885231833

119

py

Python

examples/test_error.py

ak1ra24/pytest-md-report

9d861a9237176e9dd1e6872c197f5bb5985ee049

[ "MIT" ]

9

2020-05-06T20:54:29.000Z

2022-03-27T04:11:38.000Z

examples/test_error.py

solisa986/pytest-md-report

a6cdeda92ef8f1ab64c346a86a085ce9e1585880

[ "MIT" ]

null

examples/test_error.py

solisa986/pytest-md-report

a6cdeda92ef8f1ab64c346a86a085ce9e1585880

[ "MIT" ]

3

2021-05-05T19:58:33.000Z

2021-08-12T07:14:52.000Z

def test_error(invalid_fixture): pass class Test: def test_error(self, invalid_fixture): assert True

14.875

42

0.697479

def test_error(invalid_fixture): pass class Test: def test_error(self, invalid_fixture): assert True

true

f71fb03c0051a7dd823c621bfe4bd61238f148c4

2,121

py

Python

ciphers/rabin_miller.py

joeyzhou85/python

9c0cbe33076a570a3c02825b7c6d9866a760e777

[ "MIT" ]

1,568

2019-04-25T11:54:45.000Z

2022-03-31T23:35:23.000Z

ciphers/rabin_miller.py

joeyzhou85/python

9c0cbe33076a570a3c02825b7c6d9866a760e777

[ "MIT" ]

58

2019-02-20T10:45:50.000Z

2020-09-30T12:18:45.000Z

ciphers/rabin_miller.py

joeyzhou85/python

9c0cbe33076a570a3c02825b7c6d9866a760e777

[ "MIT" ]

464

2019-04-17T04:57:16.000Z

2022-03-31T04:12:57.000Z

from __future__ import print_function # Primality Testing with the Rabin-Miller Algorithm import random def rabinMiller(num): s = num - 1 t = 0 while s % 2 == 0: s = s // 2 t += 1 for trials in range(5): a = random.randrange(2, num - 1) v = pow(a, s, num) if v != 1: i = 0 while v != (num - 1): if i == t - 1: return False else: i = i + 1 v = (v ** 2) % num return True def isPrime(num): if (num < 2): return False lowPrimes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661, 673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809, 811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991, 997] if num in lowPrimes: return True for prime in lowPrimes: if (num % prime) == 0: return False return rabinMiller(num) def generateLargePrime(keysize = 1024): while True: num = random.randrange(2 ** (keysize - 1), 2 ** (keysize)) if isPrime(num): return num if __name__ == '__main__': num = generateLargePrime() print(('Prime number:', num)) print(('isPrime:', isPrime(num)))

32.630769

80

0.474305

from __future__ import print_function import random def rabinMiller(num): s = num - 1 t = 0 while s % 2 == 0: s = s // 2 t += 1 for trials in range(5): a = random.randrange(2, num - 1) v = pow(a, s, num) if v != 1: i = 0 while v != (num - 1): if i == t - 1: return False else: i = i + 1 v = (v ** 2) % num return True def isPrime(num): if (num < 2): return False lowPrimes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229, 233, 239, 241, 251, 257, 263, 269, 271, 277, 281, 283, 293, 307, 311, 313, 317, 331, 337, 347, 349, 353, 359, 367, 373, 379, 383, 389, 397, 401, 409, 419, 421, 431, 433, 439, 443, 449, 457, 461, 463, 467, 479, 487, 491, 499, 503, 509, 521, 523, 541, 547, 557, 563, 569, 571, 577, 587, 593, 599, 601, 607, 613, 617, 619, 631, 641, 643, 647, 653, 659, 661, 673, 677, 683, 691, 701, 709, 719, 727, 733, 739, 743, 751, 757, 761, 769, 773, 787, 797, 809, 811, 821, 823, 827, 829, 839, 853, 857, 859, 863, 877, 881, 883, 887, 907, 911, 919, 929, 937, 941, 947, 953, 967, 971, 977, 983, 991, 997] if num in lowPrimes: return True for prime in lowPrimes: if (num % prime) == 0: return False return rabinMiller(num) def generateLargePrime(keysize = 1024): while True: num = random.randrange(2 ** (keysize - 1), 2 ** (keysize)) if isPrime(num): return num if __name__ == '__main__': num = generateLargePrime() print(('Prime number:', num)) print(('isPrime:', isPrime(num)))

true

f71fb110925949ff47a2cad996420b5abb79125c

4,468

py

Python

dimod/generators/chimera.py

pau557/dimod

d3c6d3abf23182b035e1100c46f7c947202edefb

[ "Apache-2.0" ]

null

dimod/generators/chimera.py

pau557/dimod

d3c6d3abf23182b035e1100c46f7c947202edefb

[ "Apache-2.0" ]

24

2021-07-09T08:19:47.000Z

2022-03-08T08:15:48.000Z

dimod/generators/chimera.py

pau557/dimod

d3c6d3abf23182b035e1100c46f7c947202edefb

[ "Apache-2.0" ]

null

# Copyright 2018 D-Wave Systems Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # ============================================================================= from __future__ import absolute_import import numpy as np import numpy.random from dimod.binary_quadratic_model import BinaryQuadraticModel from dimod.decorators import graph_argument from dimod.vartypes import SPIN __all__ = ['chimera_anticluster'] @graph_argument('subgraph', allow_None=True) def chimera_anticluster(m, n=None, t=4, multiplier=3.0, cls=BinaryQuadraticModel, subgraph=None, seed=None): """Generate an anticluster problem on a Chimera lattice. An anticluster problem has weak interactions within a tile and strong interactions between tiles. Args: m (int): Number of rows in the Chimera lattice. n (int, optional, default=m): Number of columns in the Chimera lattice. t (int, optional, default=t): Size of the shore within each Chimera tile. multiplier (number, optional, default=3.0): Strength of the intertile edges. cls (type, optional): Binary quadratic model class to build from. Default is :class:`.BinaryQuadraticModel`. subgraph (int/tuple[nodes, edges]/list[edge]/:obj:`~networkx.Graph`): A subgraph of a Chimera(m, n, t) graph to build the anticluster problem on. seed (int, optional, default=None): Random seed. Returns: :obj:`.BinaryQuadraticModel`: spin-valued binary quadratic model. """ if seed is None: seed = numpy.random.randint(2**32, dtype=np.uint32) r = numpy.random.RandomState(seed) m = int(m) if n is None: n = m else: n = int(n) t = int(t) ldata = np.zeros(m*n*t*2) # number of nodes if m and n and t: inrow, incol = zip(*_iter_chimera_tile_edges(m, n, t)) if m > 1 or n > 1: outrow, outcol = zip(*_iter_chimera_intertile_edges(m, n, t)) else: outrow = outcol = tuple() qdata = r.choice((-1., 1.), size=len(inrow)+len(outrow)) qdata[len(inrow):] *= multiplier irow = inrow + outrow icol = incol + outcol else: irow = icol = qdata = tuple() bqm = cls.from_numpy_vectors(ldata, (irow, icol, qdata), 0.0, SPIN) if subgraph is not None: nodes, edges = subgraph subbqm = cls.empty(SPIN) try: subbqm.add_variables_from((v, bqm.linear[v]) for v in nodes) except KeyError: msg = "given 'subgraph' contains nodes not in Chimera({}, {}, {})".format(m, n, t) raise ValueError(msg) try: subbqm.add_interactions_from((u, v, bqm.adj[u][v]) for u, v in edges) except KeyError: msg = "given 'subgraph' contains edges not in Chimera({}, {}, {})".format(m, n, t) raise ValueError(msg) bqm = subbqm return bqm def _iter_chimera_tile_edges(m, n, t): hoff = 2 * t voff = n * hoff mi = m * voff ni = n * hoff # tile edges for edge in ((k0, k1) for i in range(0, ni, hoff) for j in range(i, mi, voff) for k0 in range(j, j + t) for k1 in range(j + t, j + 2 * t)): yield edge def _iter_chimera_intertile_edges(m, n, t): hoff = 2 * t voff = n * hoff mi = m * voff ni = n * hoff # horizontal edges for edge in ((k, k + hoff) for i in range(t, 2 * t) for j in range(i, ni - hoff, hoff) for k in range(j, mi, voff)): yield edge # vertical edges for edge in ((k, k + voff) for i in range(t) for j in range(i, ni, hoff) for k in range(j, mi - voff, voff)): yield edge

29.012987

94

0.574754

from __future__ import absolute_import import numpy as np import numpy.random from dimod.binary_quadratic_model import BinaryQuadraticModel from dimod.decorators import graph_argument from dimod.vartypes import SPIN __all__ = ['chimera_anticluster'] @graph_argument('subgraph', allow_None=True) def chimera_anticluster(m, n=None, t=4, multiplier=3.0, cls=BinaryQuadraticModel, subgraph=None, seed=None): if seed is None: seed = numpy.random.randint(2**32, dtype=np.uint32) r = numpy.random.RandomState(seed) m = int(m) if n is None: n = m else: n = int(n) t = int(t) ldata = np.zeros(m*n*t*2) if m and n and t: inrow, incol = zip(*_iter_chimera_tile_edges(m, n, t)) if m > 1 or n > 1: outrow, outcol = zip(*_iter_chimera_intertile_edges(m, n, t)) else: outrow = outcol = tuple() qdata = r.choice((-1., 1.), size=len(inrow)+len(outrow)) qdata[len(inrow):] *= multiplier irow = inrow + outrow icol = incol + outcol else: irow = icol = qdata = tuple() bqm = cls.from_numpy_vectors(ldata, (irow, icol, qdata), 0.0, SPIN) if subgraph is not None: nodes, edges = subgraph subbqm = cls.empty(SPIN) try: subbqm.add_variables_from((v, bqm.linear[v]) for v in nodes) except KeyError: msg = "given 'subgraph' contains nodes not in Chimera({}, {}, {})".format(m, n, t) raise ValueError(msg) try: subbqm.add_interactions_from((u, v, bqm.adj[u][v]) for u, v in edges) except KeyError: msg = "given 'subgraph' contains edges not in Chimera({}, {}, {})".format(m, n, t) raise ValueError(msg) bqm = subbqm return bqm def _iter_chimera_tile_edges(m, n, t): hoff = 2 * t voff = n * hoff mi = m * voff ni = n * hoff for edge in ((k0, k1) for i in range(0, ni, hoff) for j in range(i, mi, voff) for k0 in range(j, j + t) for k1 in range(j + t, j + 2 * t)): yield edge def _iter_chimera_intertile_edges(m, n, t): hoff = 2 * t voff = n * hoff mi = m * voff ni = n * hoff for edge in ((k, k + hoff) for i in range(t, 2 * t) for j in range(i, ni - hoff, hoff) for k in range(j, mi, voff)): yield edge for edge in ((k, k + voff) for i in range(t) for j in range(i, ni, hoff) for k in range(j, mi - voff, voff)): yield edge

true

f71fb1734f8db11d01bd46d0696b2f6a7c2a050c

1,845

py

Python

mediagoblin/plugins/subtitles/models.py

mtlynch/mediagoblin

b5ee42aed44052de114c6e45edb56856d2868858

[ "CC0-1.0" ]

7

2020-05-27T03:57:21.000Z

2021-04-21T02:17:39.000Z

mediagoblin/plugins/subtitles/models.py

jgarte/mediagoblin-mirror

c4599508b02f2e61df3a97ff314766a62a3e5934

[ "CC0-1.0" ]

null

mediagoblin/plugins/subtitles/models.py

jgarte/mediagoblin-mirror

c4599508b02f2e61df3a97ff314766a62a3e5934

[ "CC0-1.0" ]

2

2019-05-13T14:42:34.000Z

2021-08-28T10:36:46.000Z

# GNU MediaGoblin -- federated, autonomous media hosting # Copyright (C) 2016 MediaGoblin contributors. See AUTHORS. # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Affero General Public License for more details. # # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see <http://www.gnu.org/licenses/>. from sqlalchemy import Column, Integer, Unicode, ForeignKey from sqlalchemy.orm import relationship from mediagoblin.db.models import User from mediagoblin.db.base import Base,MediaEntry class MediaSubtitleFile(Base): __tablename__ = "core__subtitle_files" id = Column(Integer, primary_key=True) media_entry = Column( Integer, ForeignKey(MediaEntry.id), nullable=False) name = Column(Unicode, nullable=False) filepath = Column(PathTupleWithSlashes) created = Column(DateTime, nullable=False, default=datetime.datetime.utcnow) @property def dict_view(self): """A dict like view on this object""" return DictReadAttrProxy(self) subtitle_files_helper = relationship("MediaSubtitleFile", cascade="all, delete-orphan", order_by="MediaSubtitleFile.created" ) subtitle_files = association_proxy("subtitle_files_helper", "dict_view", creator=lambda v: MediaSubtitleFile( name=v["name"], filepath=v["filepath"]) ) MODELS = [ MediaSubtitleFile ]

36.9

80

0.732249

from sqlalchemy import Column, Integer, Unicode, ForeignKey from sqlalchemy.orm import relationship from mediagoblin.db.models import User from mediagoblin.db.base import Base,MediaEntry class MediaSubtitleFile(Base): __tablename__ = "core__subtitle_files" id = Column(Integer, primary_key=True) media_entry = Column( Integer, ForeignKey(MediaEntry.id), nullable=False) name = Column(Unicode, nullable=False) filepath = Column(PathTupleWithSlashes) created = Column(DateTime, nullable=False, default=datetime.datetime.utcnow) @property def dict_view(self): return DictReadAttrProxy(self) subtitle_files_helper = relationship("MediaSubtitleFile", cascade="all, delete-orphan", order_by="MediaSubtitleFile.created" ) subtitle_files = association_proxy("subtitle_files_helper", "dict_view", creator=lambda v: MediaSubtitleFile( name=v["name"], filepath=v["filepath"]) ) MODELS = [ MediaSubtitleFile ]

true

f71fb1cc1129767d19d13b370609bf72cca258f1

1,830

py

Python

scripts/agregar_empresas_gentor.py

edgarvalli/gentor_backend

115cadfc802cb5130b62aba5c9b6050cb5f0a466

[ "MIT" ]

null

scripts/agregar_empresas_gentor.py

edgarvalli/gentor_backend

115cadfc802cb5130b62aba5c9b6050cb5f0a466

[ "MIT" ]

null

scripts/agregar_empresas_gentor.py

edgarvalli/gentor_backend

115cadfc802cb5130b62aba5c9b6050cb5f0a466

[ "MIT" ]

null

from set_root_path import set_root_path set_root_path() import db.sql_server as db empresas = [ { "RazonSocial": "GENTOR, S.A. DE C.V.", "RFC": "GEN760712EM0" }, { "RazonSocial": "SERVICIOS CORPORATIVOS GENTOR, S.A.", "RFC": "SCG931026LW1" }, { "RazonSocial": "GENTOR SERVICIOS, S.A. DE C.V.", "RFC": "GSE9212163I9" }, { "RazonSocial": "SISTEMAS DE ENERGIA INTERNACIONAL, S.A. DE C.V.", "RFC": "SEI920618TC5" }, { "RazonSocial": "BIOENERGIA DE NUEVO LEON, S.A. DE C.V.", "RFC": "BNL020412HB8" }, { "RazonSocial": "SEISA SERVICIOS Y TECNOLOGIA, S.A. DE C.V.", "RFC": "SST951003FL8" }, { "RazonSocial": "ASTRA LYRA S.A. DE C.V.", "RFC": "ALY200814LZ4" }, { "RazonSocial": "ENVIRONMENT & SOCIETY S.A. DE C.V.", "RFC": "EAS200814BU1" }, { "RazonSocial": "LAND OPERATION S.A. DE C.V.", "RFC": "LOP191204CD6" }, { "RazonSocial": "CHP SOLUTIONS, S.A. DE C.V.", "RFC": "CSO200716G27" }, { "RazonSocial": "DOMOS TELECOMUNICACIONES, S.A. DE C.V.", "RFC": "DTE940421L42" }, { "RazonSocial": "RECOLECCION Y DISPOSICION DE DESECHOS AMBIENTALES SA DE CV", "RFC": "RDD101105Q91" }, { "RazonSocial": "SERVICIOS CORPORATIVOS DMS, S.A DE C.V.", "RFC": "SCD9609068X7" }, { "RazonSocial": "INGENIERIA Y MEJORAMIENTO AMBIENTAL, S.A. DE C.V.", "RFC": "IMA960906DW2" }, { "RazonSocial": "PROTECCION INTEGRAL ESPECIALIZADA, S.A. DE C.V.", "RFC": "PIE950316LU6" }, { "RazonSocial": "ODALTA S.A. DE C.V.", "RFC": "ODA200122KH6" } ] db.insertmany("Empresas", empresas)

24.4

84

0.519672

from set_root_path import set_root_path set_root_path() import db.sql_server as db empresas = [ { "RazonSocial": "GENTOR, S.A. DE C.V.", "RFC": "GEN760712EM0" }, { "RazonSocial": "SERVICIOS CORPORATIVOS GENTOR, S.A.", "RFC": "SCG931026LW1" }, { "RazonSocial": "GENTOR SERVICIOS, S.A. DE C.V.", "RFC": "GSE9212163I9" }, { "RazonSocial": "SISTEMAS DE ENERGIA INTERNACIONAL, S.A. DE C.V.", "RFC": "SEI920618TC5" }, { "RazonSocial": "BIOENERGIA DE NUEVO LEON, S.A. DE C.V.", "RFC": "BNL020412HB8" }, { "RazonSocial": "SEISA SERVICIOS Y TECNOLOGIA, S.A. DE C.V.", "RFC": "SST951003FL8" }, { "RazonSocial": "ASTRA LYRA S.A. DE C.V.", "RFC": "ALY200814LZ4" }, { "RazonSocial": "ENVIRONMENT & SOCIETY S.A. DE C.V.", "RFC": "EAS200814BU1" }, { "RazonSocial": "LAND OPERATION S.A. DE C.V.", "RFC": "LOP191204CD6" }, { "RazonSocial": "CHP SOLUTIONS, S.A. DE C.V.", "RFC": "CSO200716G27" }, { "RazonSocial": "DOMOS TELECOMUNICACIONES, S.A. DE C.V.", "RFC": "DTE940421L42" }, { "RazonSocial": "RECOLECCION Y DISPOSICION DE DESECHOS AMBIENTALES SA DE CV", "RFC": "RDD101105Q91" }, { "RazonSocial": "SERVICIOS CORPORATIVOS DMS, S.A DE C.V.", "RFC": "SCD9609068X7" }, { "RazonSocial": "INGENIERIA Y MEJORAMIENTO AMBIENTAL, S.A. DE C.V.", "RFC": "IMA960906DW2" }, { "RazonSocial": "PROTECCION INTEGRAL ESPECIALIZADA, S.A. DE C.V.", "RFC": "PIE950316LU6" }, { "RazonSocial": "ODALTA S.A. DE C.V.", "RFC": "ODA200122KH6" } ] db.insertmany("Empresas", empresas)

true

f71fb21ddb1fedaa154d0f69fe5a945c4b945b9c

13,241

py

Python

scrapy/core/engine.py

lizhaoxing1/scrapy-comment-zh

17c6279c63d9733598539589091c5a9551f341f6

[ "BSD-3-Clause" ]

null

scrapy/core/engine.py

lizhaoxing1/scrapy-comment-zh

17c6279c63d9733598539589091c5a9551f341f6

[ "BSD-3-Clause" ]

null

scrapy/core/engine.py

lizhaoxing1/scrapy-comment-zh

17c6279c63d9733598539589091c5a9551f341f6

[ "BSD-3-Clause" ]

null

""" This is the Scrapy engine which controls the Scheduler, Downloader and Spiders. For more information see docs/topics/architecture.rst """ import logging from time import time from twisted.internet import defer, task from twisted.python.failure import Failure from scrapy import signals from scrapy.core.scraper import Scraper from scrapy.exceptions import DontCloseSpider from scrapy.http import Response, Request from scrapy.utils.misc import load_object from scrapy.utils.reactor import CallLaterOnce from scrapy.utils.log import logformatter_adapter, failure_to_exc_info logger = logging.getLogger(__name__) class Slot(object): def __init__(self, start_requests, close_if_idle, nextcall, scheduler): self.closing = False self.inprogress = set() # requests in progress self.start_requests = iter(start_requests) self.close_if_idle = close_if_idle self.nextcall = nextcall self.scheduler = scheduler self.heartbeat = task.LoopingCall(nextcall.schedule) def add_request(self, request): self.inprogress.add(request) def remove_request(self, request): self.inprogress.remove(request) self._maybe_fire_closing() def close(self): self.closing = defer.Deferred() self._maybe_fire_closing() return self.closing def _maybe_fire_closing(self): if self.closing and not self.inprogress: if self.nextcall: self.nextcall.cancel() if self.heartbeat.running: self.heartbeat.stop() self.closing.callback(None) class ExecutionEngine(object): # __init__ 对爬虫的核心组件进行了初始化. def __init__(self, crawler, spider_closed_callback): self.crawler = crawler self.settings = crawler.settings self.signals = crawler.signals self.logformatter = crawler.logformatter self.slot = None self.spider = None self.running = False self.paused = False self.scheduler_cls = load_object(self.settings['SCHEDULER']) downloader_cls = load_object(self.settings['DOWNLOADER']) self.downloader = downloader_cls(crawler) self.scraper = Scraper(crawler) self._spider_closed_callback = spider_closed_callback @defer.inlineCallbacks def start(self): """Start the execution engine""" assert not self.running, "Engine already running" self.start_time = time() yield self.signals.send_catch_log_deferred(signal=signals.engine_started) self.running = True self._closewait = defer.Deferred() yield self._closewait def stop(self): """Stop the execution engine gracefully""" assert self.running, "Engine not running" self.running = False dfd = self._close_all_spiders() return dfd.addBoth(lambda _: self._finish_stopping_engine()) def close(self): """Close the execution engine gracefully. If it has already been started, stop it. In all cases, close all spiders and the downloader. """ if self.running: # Will also close spiders and downloader return self.stop() elif self.open_spiders: # Will also close downloader return self._close_all_spiders() else: return defer.succeed(self.downloader.close()) def pause(self): """Pause the execution engine""" self.paused = True def unpause(self): """Resume the execution engine""" self.paused = False def _next_request(self, spider): slot = self.slot if not slot: return if self.paused: return while not self._needs_backout(spider): if not self._next_request_from_scheduler(spider): break if slot.start_requests and not self._needs_backout(spider): try: request = next(slot.start_requests) except StopIteration: slot.start_requests = None except Exception: slot.start_requests = None logger.error('Error while obtaining start requests', exc_info=True, extra={'spider': spider}) else: self.crawl(request, spider) if self.spider_is_idle(spider) and slot.close_if_idle: self._spider_idle(spider) def _needs_backout(self, spider): slot = self.slot return not self.running \ or slot.closing \ or self.downloader.needs_backout() \ or self.scraper.slot.needs_backout() def _next_request_from_scheduler(self, spider): slot = self.slot request = slot.scheduler.next_request() if not request: return d = self._download(request, spider) d.addBoth(self._handle_downloader_output, request, spider) d.addErrback(lambda f: logger.info('Error while handling downloader output', exc_info=failure_to_exc_info(f), extra={'spider': spider})) d.addBoth(lambda _: slot.remove_request(request)) d.addErrback(lambda f: logger.info('Error while removing request from slot', exc_info=failure_to_exc_info(f), extra={'spider': spider})) d.addBoth(lambda _: slot.nextcall.schedule()) d.addErrback(lambda f: logger.info('Error while scheduling new request', exc_info=failure_to_exc_info(f), extra={'spider': spider})) return d def _handle_downloader_output(self, response, request, spider): assert isinstance(response, (Request, Response, Failure)), response # downloader middleware can return requests (for example, redirects) if isinstance(response, Request): self.crawl(response, spider) return # response is a Response or Failure d = self.scraper.enqueue_scrape(response, request, spider) d.addErrback(lambda f: logger.error('Error while enqueuing downloader output', exc_info=failure_to_exc_info(f), extra={'spider': spider})) return d def spider_is_idle(self, spider): if not self.scraper.slot.is_idle(): # scraper is not idle return False if self.downloader.active: # downloader has pending requests return False if self.slot.start_requests is not None: # not all start requests are handled return False if self.slot.scheduler.has_pending_requests(): # scheduler has pending requests return False return True @property def open_spiders(self): return [self.spider] if self.spider else [] def has_capacity(self): """Does the engine have capacity to handle more spiders""" return not bool(self.slot) def crawl(self, request, spider): assert spider in self.open_spiders, \ "Spider %r not opened when crawling: %s" % (spider.name, request) self.schedule(request, spider) self.slot.nextcall.schedule() def schedule(self, request, spider): self.signals.send_catch_log(signal=signals.request_scheduled, request=request, spider=spider) if not self.slot.scheduler.enqueue_request(request): self.signals.send_catch_log(signal=signals.request_dropped, request=request, spider=spider) def download(self, request, spider): d = self._download(request, spider) d.addBoth(self._downloaded, self.slot, request, spider) return d def _downloaded(self, response, slot, request, spider): slot.remove_request(request) return self.download(response, spider) \ if isinstance(response, Request) else response def _download(self, request, spider): slot = self.slot slot.add_request(request) def _on_success(response): assert isinstance(response, (Response, Request)) if isinstance(response, Response): response.request = request # tie request to response received logkws = self.logformatter.crawled(request, response, spider) logger.log(*logformatter_adapter(logkws), extra={'spider': spider}) self.signals.send_catch_log(signal=signals.response_received, \ response=response, request=request, spider=spider) return response def _on_complete(_): slot.nextcall.schedule() return _ dwld = self.downloader.fetch(request, spider) dwld.addCallbacks(_on_success) dwld.addBoth(_on_complete) return dwld @defer.inlineCallbacks def open_spider(self, spider, start_requests=(), close_if_idle=True): # 函数实例了调度器, 如何爬取,爬取过滤方法等等. assert self.has_capacity(), "No free spider slot when opening %r" % \ spider.name logger.info("Spider opened", extra={'spider': spider}) nextcall = CallLaterOnce(self._next_request, spider) # 这是给异步的循环调用用的东西. scheduler = self.scheduler_cls.from_crawler(self.crawler) # 调度器的实例化 start_requests = yield self.scraper.spidermw.process_start_requests(start_requests, spider) # 这里调用spdiermw 会读取你配置文件中中间件并处理 slot = Slot(start_requests, close_if_idle, nextcall, scheduler) self.slot = slot self.spider = spider yield scheduler.open(spider) yield self.scraper.open_spider(spider) self.crawler.stats.open_spider(spider) yield self.signals.send_catch_log_deferred(signals.spider_opened, spider=spider) slot.nextcall.schedule() slot.heartbeat.start(5) def _spider_idle(self, spider): """Called when a spider gets idle. This function is called when there are no remaining pages to download or schedule. It can be called multiple times. If some extension raises a DontCloseSpider exception (in the spider_idle signal handler) the spider is not closed until the next loop and this function is guaranteed to be called (at least) once again for this spider. """ res = self.signals.send_catch_log(signal=signals.spider_idle, \ spider=spider, dont_log=DontCloseSpider) if any(isinstance(x, Failure) and isinstance(x.value, DontCloseSpider) \ for _, x in res): return if self.spider_is_idle(spider): self.close_spider(spider, reason='finished') def close_spider(self, spider, reason='cancelled'): """Close (cancel) spider and clear all its outstanding requests""" slot = self.slot if slot.closing: return slot.closing logger.info("Closing spider (%(reason)s)", {'reason': reason}, extra={'spider': spider}) dfd = slot.close() def log_failure(msg): def errback(failure): logger.error( msg, exc_info=failure_to_exc_info(failure), extra={'spider': spider} ) return errback dfd.addBoth(lambda _: self.downloader.close()) dfd.addErrback(log_failure('Downloader close failure')) dfd.addBoth(lambda _: self.scraper.close_spider(spider)) dfd.addErrback(log_failure('Scraper close failure')) dfd.addBoth(lambda _: slot.scheduler.close(reason)) dfd.addErrback(log_failure('Scheduler close failure')) dfd.addBoth(lambda _: self.signals.send_catch_log_deferred( signal=signals.spider_closed, spider=spider, reason=reason)) dfd.addErrback(log_failure('Error while sending spider_close signal')) dfd.addBoth(lambda _: self.crawler.stats.close_spider(spider, reason=reason)) dfd.addErrback(log_failure('Stats close failure')) dfd.addBoth(lambda _: logger.info("Spider closed (%(reason)s)", {'reason': reason}, extra={'spider': spider})) dfd.addBoth(lambda _: setattr(self, 'slot', None)) dfd.addErrback(log_failure('Error while unassigning slot')) dfd.addBoth(lambda _: setattr(self, 'spider', None)) dfd.addErrback(log_failure('Error while unassigning spider')) dfd.addBoth(lambda _: self._spider_closed_callback(spider)) return dfd def _close_all_spiders(self): dfds = [self.close_spider(s, reason='shutdown') for s in self.open_spiders] dlist = defer.DeferredList(dfds) return dlist @defer.inlineCallbacks def _finish_stopping_engine(self): yield self.signals.send_catch_log_deferred(signal=signals.engine_stopped) self._closewait.callback(None)

37.939828

130

0.624726

import logging from time import time from twisted.internet import defer, task from twisted.python.failure import Failure from scrapy import signals from scrapy.core.scraper import Scraper from scrapy.exceptions import DontCloseSpider from scrapy.http import Response, Request from scrapy.utils.misc import load_object from scrapy.utils.reactor import CallLaterOnce from scrapy.utils.log import logformatter_adapter, failure_to_exc_info logger = logging.getLogger(__name__) class Slot(object): def __init__(self, start_requests, close_if_idle, nextcall, scheduler): self.closing = False self.inprogress = set() self.start_requests = iter(start_requests) self.close_if_idle = close_if_idle self.nextcall = nextcall self.scheduler = scheduler self.heartbeat = task.LoopingCall(nextcall.schedule) def add_request(self, request): self.inprogress.add(request) def remove_request(self, request): self.inprogress.remove(request) self._maybe_fire_closing() def close(self): self.closing = defer.Deferred() self._maybe_fire_closing() return self.closing def _maybe_fire_closing(self): if self.closing and not self.inprogress: if self.nextcall: self.nextcall.cancel() if self.heartbeat.running: self.heartbeat.stop() self.closing.callback(None) class ExecutionEngine(object): def __init__(self, crawler, spider_closed_callback): self.crawler = crawler self.settings = crawler.settings self.signals = crawler.signals self.logformatter = crawler.logformatter self.slot = None self.spider = None self.running = False self.paused = False self.scheduler_cls = load_object(self.settings['SCHEDULER']) downloader_cls = load_object(self.settings['DOWNLOADER']) self.downloader = downloader_cls(crawler) self.scraper = Scraper(crawler) self._spider_closed_callback = spider_closed_callback @defer.inlineCallbacks def start(self): assert not self.running, "Engine already running" self.start_time = time() yield self.signals.send_catch_log_deferred(signal=signals.engine_started) self.running = True self._closewait = defer.Deferred() yield self._closewait def stop(self): assert self.running, "Engine not running" self.running = False dfd = self._close_all_spiders() return dfd.addBoth(lambda _: self._finish_stopping_engine()) def close(self): if self.running: return self.stop() elif self.open_spiders: return self._close_all_spiders() else: return defer.succeed(self.downloader.close()) def pause(self): self.paused = True def unpause(self): self.paused = False def _next_request(self, spider): slot = self.slot if not slot: return if self.paused: return while not self._needs_backout(spider): if not self._next_request_from_scheduler(spider): break if slot.start_requests and not self._needs_backout(spider): try: request = next(slot.start_requests) except StopIteration: slot.start_requests = None except Exception: slot.start_requests = None logger.error('Error while obtaining start requests', exc_info=True, extra={'spider': spider}) else: self.crawl(request, spider) if self.spider_is_idle(spider) and slot.close_if_idle: self._spider_idle(spider) def _needs_backout(self, spider): slot = self.slot return not self.running \ or slot.closing \ or self.downloader.needs_backout() \ or self.scraper.slot.needs_backout() def _next_request_from_scheduler(self, spider): slot = self.slot request = slot.scheduler.next_request() if not request: return d = self._download(request, spider) d.addBoth(self._handle_downloader_output, request, spider) d.addErrback(lambda f: logger.info('Error while handling downloader output', exc_info=failure_to_exc_info(f), extra={'spider': spider})) d.addBoth(lambda _: slot.remove_request(request)) d.addErrback(lambda f: logger.info('Error while removing request from slot', exc_info=failure_to_exc_info(f), extra={'spider': spider})) d.addBoth(lambda _: slot.nextcall.schedule()) d.addErrback(lambda f: logger.info('Error while scheduling new request', exc_info=failure_to_exc_info(f), extra={'spider': spider})) return d def _handle_downloader_output(self, response, request, spider): assert isinstance(response, (Request, Response, Failure)), response if isinstance(response, Request): self.crawl(response, spider) return d = self.scraper.enqueue_scrape(response, request, spider) d.addErrback(lambda f: logger.error('Error while enqueuing downloader output', exc_info=failure_to_exc_info(f), extra={'spider': spider})) return d def spider_is_idle(self, spider): if not self.scraper.slot.is_idle(): return False if self.downloader.active: return False if self.slot.start_requests is not None: return False if self.slot.scheduler.has_pending_requests(): return False return True @property def open_spiders(self): return [self.spider] if self.spider else [] def has_capacity(self): return not bool(self.slot) def crawl(self, request, spider): assert spider in self.open_spiders, \ "Spider %r not opened when crawling: %s" % (spider.name, request) self.schedule(request, spider) self.slot.nextcall.schedule() def schedule(self, request, spider): self.signals.send_catch_log(signal=signals.request_scheduled, request=request, spider=spider) if not self.slot.scheduler.enqueue_request(request): self.signals.send_catch_log(signal=signals.request_dropped, request=request, spider=spider) def download(self, request, spider): d = self._download(request, spider) d.addBoth(self._downloaded, self.slot, request, spider) return d def _downloaded(self, response, slot, request, spider): slot.remove_request(request) return self.download(response, spider) \ if isinstance(response, Request) else response def _download(self, request, spider): slot = self.slot slot.add_request(request) def _on_success(response): assert isinstance(response, (Response, Request)) if isinstance(response, Response): response.request = request logkws = self.logformatter.crawled(request, response, spider) logger.log(*logformatter_adapter(logkws), extra={'spider': spider}) self.signals.send_catch_log(signal=signals.response_received, \ response=response, request=request, spider=spider) return response def _on_complete(_): slot.nextcall.schedule() return _ dwld = self.downloader.fetch(request, spider) dwld.addCallbacks(_on_success) dwld.addBoth(_on_complete) return dwld @defer.inlineCallbacks def open_spider(self, spider, start_requests=(), close_if_idle=True): assert self.has_capacity(), "No free spider slot when opening %r" % \ spider.name logger.info("Spider opened", extra={'spider': spider}) nextcall = CallLaterOnce(self._next_request, spider) scheduler = self.scheduler_cls.from_crawler(self.crawler) start_requests = yield self.scraper.spidermw.process_start_requests(start_requests, spider) slot = Slot(start_requests, close_if_idle, nextcall, scheduler) self.slot = slot self.spider = spider yield scheduler.open(spider) yield self.scraper.open_spider(spider) self.crawler.stats.open_spider(spider) yield self.signals.send_catch_log_deferred(signals.spider_opened, spider=spider) slot.nextcall.schedule() slot.heartbeat.start(5) def _spider_idle(self, spider): res = self.signals.send_catch_log(signal=signals.spider_idle, \ spider=spider, dont_log=DontCloseSpider) if any(isinstance(x, Failure) and isinstance(x.value, DontCloseSpider) \ for _, x in res): return if self.spider_is_idle(spider): self.close_spider(spider, reason='finished') def close_spider(self, spider, reason='cancelled'): slot = self.slot if slot.closing: return slot.closing logger.info("Closing spider (%(reason)s)", {'reason': reason}, extra={'spider': spider}) dfd = slot.close() def log_failure(msg): def errback(failure): logger.error( msg, exc_info=failure_to_exc_info(failure), extra={'spider': spider} ) return errback dfd.addBoth(lambda _: self.downloader.close()) dfd.addErrback(log_failure('Downloader close failure')) dfd.addBoth(lambda _: self.scraper.close_spider(spider)) dfd.addErrback(log_failure('Scraper close failure')) dfd.addBoth(lambda _: slot.scheduler.close(reason)) dfd.addErrback(log_failure('Scheduler close failure')) dfd.addBoth(lambda _: self.signals.send_catch_log_deferred( signal=signals.spider_closed, spider=spider, reason=reason)) dfd.addErrback(log_failure('Error while sending spider_close signal')) dfd.addBoth(lambda _: self.crawler.stats.close_spider(spider, reason=reason)) dfd.addErrback(log_failure('Stats close failure')) dfd.addBoth(lambda _: logger.info("Spider closed (%(reason)s)", {'reason': reason}, extra={'spider': spider})) dfd.addBoth(lambda _: setattr(self, 'slot', None)) dfd.addErrback(log_failure('Error while unassigning slot')) dfd.addBoth(lambda _: setattr(self, 'spider', None)) dfd.addErrback(log_failure('Error while unassigning spider')) dfd.addBoth(lambda _: self._spider_closed_callback(spider)) return dfd def _close_all_spiders(self): dfds = [self.close_spider(s, reason='shutdown') for s in self.open_spiders] dlist = defer.DeferredList(dfds) return dlist @defer.inlineCallbacks def _finish_stopping_engine(self): yield self.signals.send_catch_log_deferred(signal=signals.engine_stopped) self._closewait.callback(None)

true

f71fb24b1ca2ef3817592da8e3c5f8b5ac48df99

780

py

Python

nicos_mlz/kws2/setups/uvspectro.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

12

2019-11-06T15:40:36.000Z

2022-01-01T16:23:00.000Z

nicos_mlz/kws2/setups/uvspectro.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

91

2020-08-18T09:20:26.000Z

2022-02-01T11:07:14.000Z

nicos_mlz/kws2/setups/uvspectro.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

6

2020-01-11T10:52:30.000Z

2022-02-25T12:35:23.000Z

# -*- coding: utf-8 -*- description = 'controlling the UV-vis spectrometer and LEDs' group = 'optional' tango_base = 'tango://phys.kws2.frm2:10000/kws2/' devices = dict( OceanView = device('nicos.devices.entangle.DigitalOutput', description = 'spectrometer trigger interval (0 to switch off)', tangodevice = tango_base + 'uvspectro/plc_trigger', ), LEDdelay = device('nicos.devices.entangle.DigitalOutput', description = 'delay for LEDs switching on', tangodevice = tango_base + 'uvspectro/plc_leddelay', ), LEDswitch = device('nicos.devices.entangle.NamedDigitalOutput', description = 'LED switcher', tangodevice = tango_base + 'uvspectro/plc_led', mapping = {'off': 0, 'uv': 1, 'blue': 2}, ), )

33.913043

72

0.65641

description = 'controlling the UV-vis spectrometer and LEDs' group = 'optional' tango_base = 'tango://phys.kws2.frm2:10000/kws2/' devices = dict( OceanView = device('nicos.devices.entangle.DigitalOutput', description = 'spectrometer trigger interval (0 to switch off)', tangodevice = tango_base + 'uvspectro/plc_trigger', ), LEDdelay = device('nicos.devices.entangle.DigitalOutput', description = 'delay for LEDs switching on', tangodevice = tango_base + 'uvspectro/plc_leddelay', ), LEDswitch = device('nicos.devices.entangle.NamedDigitalOutput', description = 'LED switcher', tangodevice = tango_base + 'uvspectro/plc_led', mapping = {'off': 0, 'uv': 1, 'blue': 2}, ), )

true

f71fb300004e91ff987107bb558165bb8d7b340e

14,538

py

Python

chatto_transform/datastores/sqlalchemy_datastore.py

chatto-hub-test2/Spaceboy2

7b6b91baf06290e6b047ae75e7ea61cee4846b3a

[ "Unlicense", "MIT" ]

null

chatto_transform/datastores/sqlalchemy_datastore.py

chatto-hub-test2/Spaceboy2

7b6b91baf06290e6b047ae75e7ea61cee4846b3a

[ "Unlicense", "MIT" ]

null

chatto_transform/datastores/sqlalchemy_datastore.py

chatto-hub-test2/Spaceboy2

7b6b91baf06290e6b047ae75e7ea61cee4846b3a

[ "Unlicense", "MIT" ]

null

import pandas from ..schema.schema_base import * from .datastore_base import DataStore from .odo_datastore import OdoDataStore from ..config import config from functools import lru_cache, partial from sqlalchemy import Table, MetaData, select from sqlalchemy.orm import sessionmaker from sqlalchemy.ext.automap import automap_base from sqlalchemy import create_engine from sqlalchemy.ext.compiler import compiles from sqlalchemy.sql.expression import Select, and_ from sqlalchemy import sql import io import tempfile import time import os import datetime import ciso8601 import odo metadatas = {} def get_engine_metadata(engine): if engine in metadatas: return metadatas[engine] else: metadata = MetaData() metadata.bind = engine metadatas[engine] = metadata return metadata def get_reflected_metadata(engine, schema_name=None): metadata = MetaData() metadata.reflect(bind=engine, schema=schema_name) metadata.bind = engine return metadata ######################################################################## for col_type in [dt, delta, num, bool_]: col_type._storage_target_registry['sqlalchemy'] = col_type._storage_target_registry['pandas'].copy() @cat.register_check('sqlalchemy') def _(col): return col.dtype == 'object' @cat.register_transform('sqlalchemy') def _(col): return col.astype('object') @id_.register_check('sqlalchemy') def _(col): return col.dtype == 'object' @id_.register_transform('sqlalchemy') def _(col): return col.astype('object') ######################################################################## @cat.register_metadata('sqlalchemy') def _(self): return sql.schema.Column(self.name, sql.sqltypes.Text, nullable=True) @id_.register_metadata('sqlalchemy') def _(self): return sql.schema.Column(self.name, sql.sqltypes.Integer, nullable=True) @dt.register_metadata('sqlalchemy') def _(self): return sql.schema.Column(self.name, sql.sqltypes.DateTime(timezone=True), nullable=True) @delta.register_metadata('sqlalchemy') def _(self): return sql.schema.Column(self.name, sql.sqltypes.Interval, nullable=True) @big_dt.register_metadata('sqlalchemy') def _(self): return sql.schema.Column(self.name, sql.sqltypes.DateTime(timezone=True), nullable=True) @num.register_metadata('sqlalchemy') def _(self): return sql.schema.Column(self.name, sql.sqltypes.Float, nullable=True) @bool_.register_metadata('sqlalchemy') def _(self): return sql.schema.Column(self.name, sql.sqltypes.Boolean, nullable=True) ######################################################################## @lru_cache() def schema_as_table(schema, engine): if schema.options.get('temporary', False): prefixes = ['TEMPORARY'] else: prefixes = [] db_schema = schema.options.get('db_schema', None) metadata = get_engine_metadata(engine) return Table(schema.name, metadata, *[col.metadata('sqlalchemy') for col in schema.cols], schema=db_schema, prefixes=prefixes) sa_type_2_col_type = { sql.sqltypes.Integer: num, sql.sqltypes.String: cat, sql.sqltypes.Date: dt, sql.sqltypes.DateTime: dt, sql.sqltypes.Interval: delta, sql.sqltypes.Numeric: num, sql.sqltypes.Boolean: bool_ } def table_as_schema(table): schema_cols = [] for sa_col in table.c: for sa_type, col_type in sa_type_2_col_type.items(): if isinstance(sa_col.type, sa_type): if isinstance(sa_col.type, sql.sqltypes.Integer) and (sa_col.primary_key or sa_col.foreign_keys): schema_cols.append(id_(sa_col.name)) else: schema_cols.append(col_type(sa_col.name)) break options = {} if table.schema is not None: options['db_schema'] = table.schema s = Schema(table.name, schema_cols, options=options) return s ######################################################################## def fast_sql_to_df(table, schema): engine = table.bind if engine.dialect.name == 'mysql': return fast_mysql_to_df(table, schema) elif engine.dialect.name == 'postgresql': return fast_postgresql_to_df(table, schema) ods = OdoDataStore(schema, table) df = ods.load() df = df[schema.col_names()] return df def fast_mysql_to_df(table, schema): f = tempfile.NamedTemporaryFile('w', suffix='.csv', dir=config.data_dir+'tmp') try: f.close() table_name = str(table) if not isinstance(table, Table): table_name = '({})'.format(table_name) # converting to csv sql = """SELECT {cols} FROM {table} INTO OUTFILE '{filename}' FIELDS TERMINATED BY ',' OPTIONALLY ENCLOSED BY '"' ESCAPED BY '\\\\' LINES TERMINATED BY '\n'""".format( cols=', '.join('`'+colname+'`' for colname in schema.col_names()), filename=f.name, table=table_name) table.bind.execute(sql) # reading csv df = pandas.read_csv(f.name, header=None, names=schema.col_names(), na_values=['\\N']) finally: os.remove(f.name) for col in schema.cols: if isinstance(col, dt): # converting datetime column df[col.name] = pandas.to_datetime(df[col.name], format="%Y-%m-%d %H:%M:%S", coerce=True) if isinstance(col, big_dt): # converting big_dt column strptime = datetime.datetime.strptime parse_func = (lambda x: strptime(x, "%Y-%m-%d %H:%M:%S")) df[col.name] = df[col.name].map(parse_func, na_action='ignore') return df def fast_postgresql_to_df(table, schema): engine = table.bind conn = engine.raw_connection() with conn.cursor() as cur: with io.StringIO() as f: table_name = str(table) if not isinstance(table, Table): table_name = '({})'.format(table_name) sql = "COPY {table_name} TO STDOUT WITH (FORMAT CSV, HEADER TRUE)".format( table_name=table_name) cur.copy_expert(sql, f) f.seek(0) df = pandas.read_csv(f) for col in schema.cols: if isinstance(col, dt): # converting datetime column df[col.name] = pandas.to_datetime(df[col.name], format="%Y-%m-%d %H:%M:%S", coerce=True) if isinstance(col, big_dt): # converting big_dt column strptime = datetime.datetime.strptime parse_func = (lambda x: strptime(x, "%Y-%m-%d %H:%M:%S")) df[col.name] = df[col.name].map(parse_func, na_action='ignore') return df def fast_postgresql_to_csv(table, file_path): engine = table.bind conn = engine.raw_connection() with conn.cursor() as cur: with open(file_path, 'w') as f: table_name = str(table) if not isinstance(table, Table): table_name = '({})'.format(table_name) sql = "COPY {table_name} TO STDOUT WITH (FORMAT CSV, HEADER TRUE)".format( table_name=table_name) cur.copy_expert(sql, f) def fast_df_to_sql(df, table, schema): ods = OdoDataStore(schema, table, storage_target_type='sqlalchemy') ods.store(df) class SATableDataStore(DataStore): def __init__(self, schema, engine, where_clauses=None): super().__init__(schema) self.engine = engine self.table = schema_as_table(self.schema, self.engine) self.where_clauses = where_clauses def storage_target(self): return 'sqlalchemy' def _load(self): query = self.table if self.where_clauses is not None: query = query.select() for where_clause in self.where_clauses: query = query.where(where_clause) df = fast_sql_to_df(query, self.schema) return df def to_csv(self, file_path): if self.engine.dialect.name != 'postgresql': raise NotImplementedError('converting directly to csv not supported for non-postgres databases') query = self.table if self.where_clauses is not None: query = query.select() for where_clause in self.where_clauses: query = query.where(where_clause) fast_postgresql_to_csv(query, file_path) def _store(self, df): if self.where_clauses is not None: raise NotImplementedError('Cannot store to a query (where_clauses must be left blank)') df = df.copy() fast_df_to_sql(self.table, self.schema) def _update(self, df): if self.where_clauses is not None: raise NotImplementedError('Cannot update to a query (where_clauses must be left blank)') df = df.copy() with self.engine.connect() as conn: temp_schema = Schema.rename(self.schema, 'temp_'+self.schema.name) temp_schema.options['temporary'] = True temp_table = schema_as_table(temp_schema, self.engine) print('storing new df in temp table') fast_df_to_sql(df, temp_table, temp_schema) print('updating table from matching rows') index = self.schema.options['index'] update = self.table.update( values={ col_name: temp_table.c[col_name] for col_name in self.schema.col_names() }, whereclause=self.table.c[index] == temp_table.c[index] ) update_res = conn.execute(update) print('inserting new rows into table') exists_query = self.table.select().where(self.table.c[index] == temp_table.c[index]).exists() insert = self.table.insert().from_select( temp_schema.col_names(), temp_table.select().where(~exists_query)) ins_res = conn.execute(insert) def delete(self): if self.where_clauses is not None: raise NotImplementedError('Cannot delete a query (where_clauses must be left blank)') self.table.drop(self.engine) class SAJoinDataStore(DataStore): def __init__(self, root_schema, engine, has_schemas=None, belongs_to_schemas=None, root_conditions=None, where_clauses=None): self.engine = engine self.root_schema = root_schema self.root_table = schema_as_table(self.root_schema, self.engine) self.has_schemas, self.has_join_conditions = self._parse_schema_list(has_schemas) self.has_tables = [schema_as_table(h_schema, self.engine) for h_schema in self.has_schemas] self.belongs_to_schemas, self.belongs_to_join_conditions = self._parse_schema_list(belongs_to_schemas) self.belongs_to_tables = [schema_as_table(b_schema, self.engine) for b_schema in self.belongs_to_schemas] self.root_conditions = root_conditions self.where_clauses = where_clauses schema = Schema.union([self.root_schema] + self.has_schemas + self.belongs_to_schemas, with_prefix=True, schema_name=self.root_schema.name+'_join') super().__init__(schema) def _parse_schema_list(self, schema_list=None): if schema_list is None: schema_list = [] schemas = [] join_conditions = {} for schema in schema_list: if isinstance(schema, tuple): schema, j_c = schema join_conditions[schema] = j_c schemas.append(schema) return schemas, join_conditions def storage_target(self): return 'sqlalchemy' def _load(self): root = self.root_table if self.root_conditions is not None: root = root.select().where(and_(*self.root_conditions)).alias() join_clause = root select_clause = [] root_col_prefix = self.root_schema.options['prefix'] for col in root.c: select_clause.append(col.label("{}.{}".format(root_col_prefix, col.name))) for h_table, h_schema in zip(self.has_tables, self.has_schemas): col_prefix = h_schema.options['prefix'] h_join_conditions = [root.c.id == h_table.c['{}_id'.format(root_col_prefix)]] for join_condition in self.has_join_conditions.get(h_schema, []): h_join_conditions.append(join_condition) join_clause = join_clause.outerjoin(h_table, and_(*h_join_conditions)) for col in h_table.c: select_clause.append(col.label("{}.{}".format(col_prefix, col.name))) for b_table, b_schema in zip(self.belongs_to_tables, self.belongs_to_schemas): col_prefix = b_schema.options['prefix'] b_join_conditions = [root.c['{}_id'.format(col_prefix)] == b_table.c.id] for join_condition in self.belongs_to_join_conditions.get(b_schema, []): b_join_conditions.append(join_condition) join_clause = join_clause.outerjoin(b_table, and_(*b_join_conditions)) for col in b_table.c: select_clause.append(col.label("{}.{}".format(col_prefix, col.name))) temp_schema = Schema.rename(self.schema, 'temp_'+self.schema.name) temp_table = schema_as_table(temp_schema, self.engine) try: temp_table.create(self.engine) query = select(select_clause).select_from(join_clause) if self.where_clauses is not None: query = query.where(and_(*self.where_clauses)) insert = temp_table.insert().from_select(temp_schema.col_names(), query) start = time.time() print('executing join into temp table') self.engine.execute(insert) joined = time.time() print('loading rows from temp table') df = fast_sql_to_df(temp_table, temp_schema) loaded = time.time() finally: temp_table.drop(self.engine) print('type checking and sorting') print('took', joined - start, 'seconds to perform the join') print('took', loaded - joined, 'seconds to load the results') return df class SAQueryDataStore(DataStore): def __init__(self, schema, engine, query): self.engine = engine self.query = query self.schema = schema def _load(self): df = pandas.read_sql(self.query, self.engine) return df

35.896296

155

0.623882

import pandas from ..schema.schema_base import * from .datastore_base import DataStore from .odo_datastore import OdoDataStore from ..config import config from functools import lru_cache, partial from sqlalchemy import Table, MetaData, select from sqlalchemy.orm import sessionmaker from sqlalchemy.ext.automap import automap_base from sqlalchemy import create_engine from sqlalchemy.ext.compiler import compiles from sqlalchemy.sql.expression import Select, and_ from sqlalchemy import sql import io import tempfile import time import os import datetime import ciso8601 import odo metadatas = {} def get_engine_metadata(engine): if engine in metadatas: return metadatas[engine] else: metadata = MetaData() metadata.bind = engine metadatas[engine] = metadata return metadata def get_reflected_metadata(engine, schema_name=None): metadata = MetaData() metadata.reflect(bind=engine, schema=schema_name) metadata.bind = engine return metadata ) return schemas, join_conditions def storage_target(self): return 'sqlalchemy' def _load(self): root = self.root_table if self.root_conditions is not None: root = root.select().where(and_(*self.root_conditions)).alias() join_clause = root select_clause = [] root_col_prefix = self.root_schema.options['prefix'] for col in root.c: select_clause.append(col.label("{}.{}".format(root_col_prefix, col.name))) for h_table, h_schema in zip(self.has_tables, self.has_schemas): col_prefix = h_schema.options['prefix'] h_join_conditions = [root.c.id == h_table.c['{}_id'.format(root_col_prefix)]] for join_condition in self.has_join_conditions.get(h_schema, []): h_join_conditions.append(join_condition) join_clause = join_clause.outerjoin(h_table, and_(*h_join_conditions)) for col in h_table.c: select_clause.append(col.label("{}.{}".format(col_prefix, col.name))) for b_table, b_schema in zip(self.belongs_to_tables, self.belongs_to_schemas): col_prefix = b_schema.options['prefix'] b_join_conditions = [root.c['{}_id'.format(col_prefix)] == b_table.c.id] for join_condition in self.belongs_to_join_conditions.get(b_schema, []): b_join_conditions.append(join_condition) join_clause = join_clause.outerjoin(b_table, and_(*b_join_conditions)) for col in b_table.c: select_clause.append(col.label("{}.{}".format(col_prefix, col.name))) temp_schema = Schema.rename(self.schema, 'temp_'+self.schema.name) temp_table = schema_as_table(temp_schema, self.engine) try: temp_table.create(self.engine) query = select(select_clause).select_from(join_clause) if self.where_clauses is not None: query = query.where(and_(*self.where_clauses)) insert = temp_table.insert().from_select(temp_schema.col_names(), query) start = time.time() print('executing join into temp table') self.engine.execute(insert) joined = time.time() print('loading rows from temp table') df = fast_sql_to_df(temp_table, temp_schema) loaded = time.time() finally: temp_table.drop(self.engine) print('type checking and sorting') print('took', joined - start, 'seconds to perform the join') print('took', loaded - joined, 'seconds to load the results') return df class SAQueryDataStore(DataStore): def __init__(self, schema, engine, query): self.engine = engine self.query = query self.schema = schema def _load(self): df = pandas.read_sql(self.query, self.engine) return df

true

f71fb3203ce46c39849c5a3bac229726738a23a1

4,139

py

Python

src/core/network/llnms-scan-network.py

marvins/LLNMS

ebc15418e1a5dddafdb3e55cea4e8cb71f619b2d

[ "MIT" ]

null

src/core/network/llnms-scan-network.py

marvins/LLNMS

ebc15418e1a5dddafdb3e55cea4e8cb71f619b2d

[ "MIT" ]

null

src/core/network/llnms-scan-network.py

marvins/LLNMS

ebc15418e1a5dddafdb3e55cea4e8cb71f619b2d

[ "MIT" ]

1

2020-12-16T09:28:26.000Z

#!/usr/bin/env python # # File: llnms-scan-network.py # Author: Marvin Smith # Date: 6/13/2015 # # Purpose: Scan LLNMS networks # __author__ = 'Marvin Smith' # Python Libraries import argparse, os, sys # LLNMS Libraries if os.environ['LLNMS_HOME'] is not None: sys.path.append(os.environ['LLNMS_HOME'] + '/lib') import llnms # --------------------------------------------- # # - Parse Command-Line Arguments - # # --------------------------------------------- # def Parse_Command_Line(): # Create parser parser = argparse.ArgumentParser( description='Scan an LLNMS network.' ) # Version parser.add_argument('-v', '--version', action='version', version='%(prog)s ' + llnms.info.Get_Version_String(), help='Print the version information.') # Verbose Mode parser.add_argument('--verbose', dest='verbose_flag', required=False, default=False, action='store_true', help='Print with verbose output.') # Quiet Mode parser.add_argument('--quiet', required=False, default=False, action='store_true', help='Do not print output.') # Network Name parser.add_argument('-n', '--network', required=True, dest='network_input', help='ID of the network to scan.') # Scanner Name parser.add_argument('-s', '--scanner', required=True, dest='scanner_input', help='ID of the scanner to use.') # Print only passes parser.add_argument('-om', '--output-mode', required=False, dest='output_mode', default=None, help='Output mode. Supported options are xml and stdout. If xml provided, then user must provide filename.') # Return the parser return parser.parse_args() # ---------------------------- # # - Main Function - # # ---------------------------- # def Main(): # Grab LLNMS HOME llnms_home=None if os.environ['LLNMS_HOME'] is not None: llnms_home=os.environ['LLNMS_HOME'] # Parse Command-Line Arguments options = Parse_Command_Line() # Load the network definition network = llnms.Network.find_network( network_name=options.network_input, llnms_home=llnms_home) # Make sure we found a network if network is None: raise Exception('No network found matching name ' + options.network_input) # Print the Network if Verbose if options.verbose_flag is True: print(network.To_Debug_String()) # Load the scanner definition scanner = llnms.Scanner.find_scanner( scanner_id=options.scanner_input, llnms_home=llnms_home ) # Make sure we found a scanner if scanner is None: raise Exception('No scanner found matching name ' + options.scanner_input) # Print scanner if verbose if options.verbose_flag is True: print(scanner.To_Debug_String()) # Validate the scanner is registered within the network if network.Has_Scanner( scanner_id=scanner.id ) is False: raise Exception("Network does not have a scanner registered with id=" + scanner.id ) # Run scan on network results = scanner.Run_Scan_Range(endpoint_list=network.Get_Network_Range(), arg_list=network.Get_Scanner_Args(scanner.id), num_threads=4) # print results addresses = network.Get_Network_Range() for x in xrange(0, len(results)): print(addresses[x] + ' - ' + str(results[x])) # ----------------------------- # # - Run Main Script - # # ----------------------------- # if __name__ == '__main__': Main()

32.590551

132

0.530563

__author__ = 'Marvin Smith' import argparse, os, sys if os.environ['LLNMS_HOME'] is not None: sys.path.append(os.environ['LLNMS_HOME'] + '/lib') import llnms def Parse_Command_Line(): parser = argparse.ArgumentParser( description='Scan an LLNMS network.' ) parser.add_argument('-v', '--version', action='version', version='%(prog)s ' + llnms.info.Get_Version_String(), help='Print the version information.') parser.add_argument('--verbose', dest='verbose_flag', required=False, default=False, action='store_true', help='Print with verbose output.') parser.add_argument('--quiet', required=False, default=False, action='store_true', help='Do not print output.') parser.add_argument('-n', '--network', required=True, dest='network_input', help='ID of the network to scan.') parser.add_argument('-s', '--scanner', required=True, dest='scanner_input', help='ID of the scanner to use.') parser.add_argument('-om', '--output-mode', required=False, dest='output_mode', default=None, help='Output mode. Supported options are xml and stdout. If xml provided, then user must provide filename.') return parser.parse_args() def Main(): llnms_home=None if os.environ['LLNMS_HOME'] is not None: llnms_home=os.environ['LLNMS_HOME'] options = Parse_Command_Line() network = llnms.Network.find_network( network_name=options.network_input, llnms_home=llnms_home) if network is None: raise Exception('No network found matching name ' + options.network_input) if options.verbose_flag is True: print(network.To_Debug_String()) scanner = llnms.Scanner.find_scanner( scanner_id=options.scanner_input, llnms_home=llnms_home ) if scanner is None: raise Exception('No scanner found matching name ' + options.scanner_input) if options.verbose_flag is True: print(scanner.To_Debug_String()) if network.Has_Scanner( scanner_id=scanner.id ) is False: raise Exception("Network does not have a scanner registered with id=" + scanner.id ) results = scanner.Run_Scan_Range(endpoint_list=network.Get_Network_Range(), arg_list=network.Get_Scanner_Args(scanner.id), num_threads=4) addresses = network.Get_Network_Range() for x in xrange(0, len(results)): print(addresses[x] + ' - ' + str(results[x])) if __name__ == '__main__': Main()

true

f71fb3d05a7fffde16b9485af0a723ccfc10ba6f

16,760

py

Python

maven_proj_graph/pkg1/mvnsortmod1.py

lg-alabris/swagger-ui

fdb06ad6dc3dd9c416b08c8f7909c37cfcf1ece4

[ "Apache-2.0" ]

null

maven_proj_graph/pkg1/mvnsortmod1.py

lg-alabris/swagger-ui

fdb06ad6dc3dd9c416b08c8f7909c37cfcf1ece4

[ "Apache-2.0" ]

null

maven_proj_graph/pkg1/mvnsortmod1.py

lg-alabris/swagger-ui

fdb06ad6dc3dd9c416b08c8f7909c37cfcf1ece4

[ "Apache-2.0" ]

null

''' ====================================================================== Created on Jan 14, 2018 PURPOSE: this module provides classes to read Maven projects from git or other repos specifically intended to create the graph of multiple project dependencies ROADMAP: TODO - 1. review how properties are distributed and could break things 2. review subproject dependencies on top level, are props declared? 2. review parent POM, are props declared? 3. are external property files used? @author: Larry ====================================================================== ''' import os import subprocess #import json #import xml.etree.ElementTree as ET #import urllib2 #import csv import xml.etree.cElementTree as ET import re import urllib.request #======================================================================= # static functions and constants class Util(object): mvn_pom_ns = {"mvn":"http://maven.apache.org/POM/4.0.0"} def __init__(self): pass @staticmethod def get_tag_value(name, section): s = ('mvn:%s' % name) elem = section.find(s, Util.mvn_pom_ns) if elem ==None: return'' return elem.text @staticmethod def get_path(dirs): path = '' for d in dirs: path += d + '/' return path[:len(path) -1] # if hasattr(a, 'property'): @staticmethod def run_process_2(cmd_args): #result = subprocess.run(['dir', '../*.*'], stdout=subprocess.PIPE) #result = subprocess.run(['C:/apps/maven352/bin/mvn', 'help:effective-pom'], stdout=subprocess.PIPE) result = subprocess.run(['cd', '..'], stdout=subprocess.PIPE, shell=True) result = subprocess.run(cmd_args, stdout=subprocess.PIPE, shell=True) print(result.stdout.decode('utf-8')) @staticmethod def run_process(cmd_args, args_in): cmd = subprocess.Popen(cmd_args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, shell=True) if (args_in): cmd.stdin.write(args_in.encode('utf-8')) cmd.stdin.flush() # Must include this to ensure data is passed to child process result = cmd.stdout.read() print(args_in.encode('utf-8')) print(result) #.stdout.decode('utf-8')) ''' cmdline = ["cmd", "/q", "/k", "echo off"] batch = b"""\ rem vsinstr -coverage helloclass.exe /exclude:std::* vsperfcmd /start:coverage /output:run.coverage helloclass vsperfcmd /shutdown exit """ ''' def test_map_update(self): A = {'a':1, 'b':2, 'c': 3} B = {'c':99, 'd':4, 'e':5} A.update(B) print(A) #======================================================================= # identifies Maven coordinates for a project or dependnecy class MavenCoords(object): def __init__(self, element, props): if (not element): self.groupid ='' self.artifactid = '' self.version = '' self.scope = '' self.relative_path = '' self.key ='' return self.groupid = Util.get_tag_value('groupId', element) self.artifactid = Util.get_tag_value('artifactId', element) self.version = Util.get_tag_value('version', element) self.relative_path = Util.get_tag_value('relativePath', element) self.scope = Util.get_tag_value('scope', element) self.refresh_key(props) def refresh_key(self, props): if (props and self.version in props): self.version = props[self.version] self.key = '%s|%s|%s' % (self.groupid, self.artifactid, self.version) #======================================================================= # a maven project POM complete with properties and dependencies class MavenProject(object): def __init__(self, pom_url, project_map): #dirs = pom_url.split('/') self.pom_url = pom_url; self.project_map = project_map self.pom_file = self.get_pom_file(self.pom_url) self.name = Util.get_tag_value('name', self.pom_file) self.packaging = Util.get_tag_value('packaging', self.pom_file) self.init_from_parent() self.properties.update(self.get_properties(self.pom_file)) self.coord = MavenCoords(self.pom_file, self.properties) self.dependencies.update(self.get_dependencies(self.pom_file)) self.project_map[self.coord.key] = self self.get_sub_modules(self.pom_file) self.history = [] self.consumers = [] #if self.packaging =='pom': # parent pom's will always be pre-existent to child pom's. they will be looked by coord key from # the global graph / project list def init_from_parent(self): parent_section = self.pom_file.findall('mvn:parent', Util.mvn_pom_ns) if (parent_section): self.parent_coord = MavenCoords(parent_section[0], None) parent = self.project_map[self.parent_coord.key] if (parent): self.properties = parent.properties.copy() self.dependencies = parent.dependencies.copy() else: print('Error: POM {} has unresolved parent POM reference {}'.format(self.name, parent.key)) else: self.dependencies = {} self.properties = {} self.coord = MavenCoords(None, None) dirs = self.pom_url.split('/') print(dirs) print (Util.get_path(dirs)) def get_sub_modules(self, pom_file): section = pom_file.findall('mvn:modules', Util.mvn_pom_ns) self.modules = {} if (not section): return for elem in section[0].findall('*'): sub_proj = self.get_sub_module(elem.text) self.modules[sub_proj.coord.key] = sub_proj self.project_map[sub_proj.coord.key] = sub_proj def get_sub_module(self, sub_dir): dirs = self.pom_url.split('/') x = len(dirs) dirs[x-1] = 'pom.xml' dirs.insert(x-1, sub_dir) path = Util.get_path(dirs) module = MavenProject(path, self.project_map) return module def get_properties(self, pom): section = pom.findall('mvn:properties', Util.mvn_pom_ns) props = {} if (len(section)==0): return props for elem in section[0].findall('*'): k = re.sub('{.*?}', '', elem.tag) k = '${%s}' % k props[k] = elem.text return props def get_dependencies(self, pom): section = pom.findall('mvn:dependencies', Util.mvn_pom_ns) deps_map = {} if (len(section)==0): return deps_map for dep_section in section[0].findall('mvn:dependency', Util.mvn_pom_ns): obj = MavenCoords(dep_section, self.properties) deps_map[obj.key] = obj return deps_map @staticmethod def get_pom_file(pomfile): if pomfile.find("http://") >=0 or pomfile.find("https://") >=0: opener = urllib.request.build_opener() pom = ET.parse( opener.open(pomfile) ).getroot() else: pom = ET.parse(pomfile).getroot() return pom def logx(self, level): print() print('---------Maven Project---------') #print('key: %s * Group: %s * Id: %s * Ver: %s' % (self.coord.key, self.coord.groupid, self.coord.artifactid, self.coord.version)) print('key: {0} * Name: {1} * Group: {2} * Id: {3} * Ver: {4}'.format(self.coord.key, self.name, self.coord.groupid, self.coord.artifactid, self.coord.version)) print() if level ==0: return print(' dependencies') for k, v in self.dependencies.items(): print(' key: %s * Group: %s * Id: %s * Ver: %s' % (k, v.groupid, v.artifactid, v.version)) print() print(' properties: ', self.properties) print (' consumers') for proj in self.consumers: print(' ', proj.coord.key) class DAGerror(Exception): def __init__(self, arg): self.arg = arg #======================================================================= # class MavenProjectGraph(object): def __init__(self, pom_url_list): self.pom_url_list = pom_url_list self.proj_list = [] self.proj_map = {} #self.validation = {} def generate_pom_list(self): for pom_url in self.pom_url_list: MavenProject(pom_url, self.proj_map) #self.proj_list.append(proj) #self.proj_map[proj.coord.key] = proj self.proj_list = list(self.proj_map.values()) for proj in self.proj_list: proj.logx(1) #$$ print() def set_options(self): pass # PURPOSE: sort the list in DAG dependency order and capture each project consumers # # def resolve_graph(self): self.resolve_dependencies() self.resolve_consumers() # PURPOSE: reorder the project list such that each projects dependencies appear before that project # # NOTE #1: iterate thru the list looking fwd in the list for each project's dependencies # for each dependency found, move it behind that project # # NOTE #2: the DAG is complete when the list is scanned and no dependencies exist fwd of each project # # NOTE #3: a history of each dependency relocation is maintained for each project # a circular reference will be detected if that # def resolve_dependencies(self): try: while True: for p in self.proj_list: print(p.name) i = 0 #dependency_found = False while i < len(self.proj_list): dependency_found = False proj_base = self.proj_list[i] print('loop i={}, base={}'.format(i, proj_base.name)) j = i + 1 while j < len(self.proj_list): print(' loop j {}'.format(j)) proj_scan = self.proj_list[j] # a forward project dependency is found for the base project, move it behind the base project if proj_scan.coord.key in proj_base.dependencies: # dejavu - a repeated reorder indicates circular dependency if proj_scan.coord.key in proj_base.history: raise DAGerror("Error: base project - {} - encountered duplicate reorder for dependency - {} -".format ( proj_base.name, proj_scan.name)) # remove the fwd item first to avoid order issues del self.proj_list[j] #self.proj_list.remove(j) # insert behind the base project self.proj_list.insert(i, proj_scan) print(' reorded scan {} from j={} to i={}'.format( proj_scan.name, j, i)) for p in self.proj_list: print(p.name) proj_base.history.append(proj_scan.coord.key) dependency_found = True i = i -1 break j =j+1 # while j i=i+1 # while i # repeat outer loop until nothing is reordered if not dependency_found: break else: i = 0 except DAGerror as e: print(e) # PURPOSE: for each project in the list, discover the set of consuming projects # # NOTE #1: call this method AFTER the dependency graph has been properly resolved # consuming projects will be forward in the list # def resolve_consumers(self): for i in range(len(self.proj_list)): proj_base = self.proj_list[i] j = i while j < len(self.proj_list)-1: j = j+1 proj_scan = self.proj_list[j] if (proj_base.coord.key in proj_scan.dependencies): proj_base.consumers.append(proj_scan) def list_projects(self): for proj in self.proj_list: proj.logx(1) #========================================================================== def main(): pom_files = ['D:\\devspaces\\wks4\\py1\\snipits2.xml', 'https://raw.githubusercontent.com/LeonardoZ/java-concurrency-patterns/master/pom.xml'] pom_files = ['D:\\devspaces\\wks4\\py1\\pom-A.xml', 'D:\\devspaces\\wks4\\py1\\pom-B.xml', 'D:\\devspaces\\wks4\\py1\\pom-C.xml', 'D:\\devspaces\\wks4\\py1\\pom-D.xml', ] pom_files = ['C:/Users/Larry/Dropbox/gitcode/gh/maven_proj_graph/pom-A.xml', 'C:/Users/Larry/Dropbox/gitcode/gh/maven_proj_graph/pom-B.xml', 'C:/Users/Larry/Dropbox/gitcode/gh/maven_proj_graph/pom-C.xml', 'C:/Users/Larry/Dropbox/gitcode/gh/maven_proj_graph/pom-D.xml', ] # C:\Users\Larry\Dropbox\gitcode\gh\maven_proj_graph s = ['dir', '*'] s = ['C:/apps/maven352/bin/mvn', 'help:effective-pom'] s2 = ['C:\\apps\\maven352\\bin\\mvn', 'help:effective-pom'] #Util.run_process(['cd', '..'], 'C:\\apps\\maven352\\bin\\mvn help:effective-pom') #Util.run_process('C:\\apps\\maven352\\bin\\mvn help:effective-pom', '') #Util.test_map_update(None) #return() graph = MavenProjectGraph(pom_files) graph.generate_pom_list() graph.resolve_graph() graph.list_projects() #========================================================================== # see this article for opening remote xml files # https://stackoverflow.com/questions/28238713/python-xml-parsing-lxml-urllib-request def main2(): cwd = os.getcwd() cwd = 'D:\\devspaces\\wks4\\py1\\' pom_file = cwd + 'snipits2.xml' pom_file = 'D:\\devspaces\\wks4\\py1\\snipits2.xml' pom = ET.parse(pom_file).getroot() # https://github.com/LeonardoZ/java-concurrency-patterns.git # this is the correct patttern for reading single files from github # https://raw.githubusercontent.com/user/repository/branch/filename # this is the web page containing the file # 'https://github.com/LeonardoZ/java-concurrency-patterns/blob/master/pom.xml' pom_file_url = 'https://raw.githubusercontent.com/LeonardoZ/java-concurrency-patterns/master/pom.xml' opener = urllib.request.build_opener() f = opener.open(pom_file_url) # ng, file=urllib.urlopen(file=urllib.urlopen()) #parser = ET.HTMLParser() #with urlopen('https://pypi.python.org/simple') as f: #tree = ET.parse(f, parser) #pom_file = urllib.request.urlopen(pom_file) pom = ET.parse(opener.open(pom_file_url)).getroot() project = MavenProject(pom) project.logx() if __name__ == '__main__': main() #main() ''' ===================================================================== notes: alternatives - use maven to get equiv pom > mvn help:effective-pom https://stackoverflow.com/questions/4760215/running-shell-command-from-python-and-capturing-the-output '''

36.514161

178

0.515752

import os import subprocess import xml.etree.cElementTree as ET import re import urllib.request class Util(object): mvn_pom_ns = {"mvn":"http://maven.apache.org/POM/4.0.0"} def __init__(self): pass @staticmethod def get_tag_value(name, section): s = ('mvn:%s' % name) elem = section.find(s, Util.mvn_pom_ns) if elem ==None: return'' return elem.text @staticmethod def get_path(dirs): path = '' for d in dirs: path += d + '/' return path[:len(path) -1] @staticmethod def run_process_2(cmd_args): result = subprocess.run(['cd', '..'], stdout=subprocess.PIPE, shell=True) result = subprocess.run(cmd_args, stdout=subprocess.PIPE, shell=True) print(result.stdout.decode('utf-8')) @staticmethod def run_process(cmd_args, args_in): cmd = subprocess.Popen(cmd_args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, shell=True) if (args_in): cmd.stdin.write(args_in.encode('utf-8')) cmd.stdin.flush() result = cmd.stdout.read() print(args_in.encode('utf-8')) print(result) def test_map_update(self): A = {'a':1, 'b':2, 'c': 3} B = {'c':99, 'd':4, 'e':5} A.update(B) print(A) class MavenCoords(object): def __init__(self, element, props): if (not element): self.groupid ='' self.artifactid = '' self.version = '' self.scope = '' self.relative_path = '' self.key ='' return self.groupid = Util.get_tag_value('groupId', element) self.artifactid = Util.get_tag_value('artifactId', element) self.version = Util.get_tag_value('version', element) self.relative_path = Util.get_tag_value('relativePath', element) self.scope = Util.get_tag_value('scope', element) self.refresh_key(props) def refresh_key(self, props): if (props and self.version in props): self.version = props[self.version] self.key = '%s|%s|%s' % (self.groupid, self.artifactid, self.version) class MavenProject(object): def __init__(self, pom_url, project_map): self.pom_url = pom_url; self.project_map = project_map self.pom_file = self.get_pom_file(self.pom_url) self.name = Util.get_tag_value('name', self.pom_file) self.packaging = Util.get_tag_value('packaging', self.pom_file) self.init_from_parent() self.properties.update(self.get_properties(self.pom_file)) self.coord = MavenCoords(self.pom_file, self.properties) self.dependencies.update(self.get_dependencies(self.pom_file)) self.project_map[self.coord.key] = self self.get_sub_modules(self.pom_file) self.history = [] self.consumers = [] def init_from_parent(self): parent_section = self.pom_file.findall('mvn:parent', Util.mvn_pom_ns) if (parent_section): self.parent_coord = MavenCoords(parent_section[0], None) parent = self.project_map[self.parent_coord.key] if (parent): self.properties = parent.properties.copy() self.dependencies = parent.dependencies.copy() else: print('Error: POM {} has unresolved parent POM reference {}'.format(self.name, parent.key)) else: self.dependencies = {} self.properties = {} self.coord = MavenCoords(None, None) dirs = self.pom_url.split('/') print(dirs) print (Util.get_path(dirs)) def get_sub_modules(self, pom_file): section = pom_file.findall('mvn:modules', Util.mvn_pom_ns) self.modules = {} if (not section): return for elem in section[0].findall('*'): sub_proj = self.get_sub_module(elem.text) self.modules[sub_proj.coord.key] = sub_proj self.project_map[sub_proj.coord.key] = sub_proj def get_sub_module(self, sub_dir): dirs = self.pom_url.split('/') x = len(dirs) dirs[x-1] = 'pom.xml' dirs.insert(x-1, sub_dir) path = Util.get_path(dirs) module = MavenProject(path, self.project_map) return module def get_properties(self, pom): section = pom.findall('mvn:properties', Util.mvn_pom_ns) props = {} if (len(section)==0): return props for elem in section[0].findall('*'): k = re.sub('{.*?}', '', elem.tag) k = '${%s}' % k props[k] = elem.text return props def get_dependencies(self, pom): section = pom.findall('mvn:dependencies', Util.mvn_pom_ns) deps_map = {} if (len(section)==0): return deps_map for dep_section in section[0].findall('mvn:dependency', Util.mvn_pom_ns): obj = MavenCoords(dep_section, self.properties) deps_map[obj.key] = obj return deps_map @staticmethod def get_pom_file(pomfile): if pomfile.find("http://") >=0 or pomfile.find("https://") >=0: opener = urllib.request.build_opener() pom = ET.parse( opener.open(pomfile) ).getroot() else: pom = ET.parse(pomfile).getroot() return pom def logx(self, level): print() print('---------Maven Project---------') print('key: {0} * Name: {1} * Group: {2} * Id: {3} * Ver: {4}'.format(self.coord.key, self.name, self.coord.groupid, self.coord.artifactid, self.coord.version)) print() if level ==0: return print(' dependencies') for k, v in self.dependencies.items(): print(' key: %s * Group: %s * Id: %s * Ver: %s' % (k, v.groupid, v.artifactid, v.version)) print() print(' properties: ', self.properties) print (' consumers') for proj in self.consumers: print(' ', proj.coord.key) class DAGerror(Exception): def __init__(self, arg): self.arg = arg class MavenProjectGraph(object): def __init__(self, pom_url_list): self.pom_url_list = pom_url_list self.proj_list = [] self.proj_map = {} def generate_pom_list(self): for pom_url in self.pom_url_list: MavenProject(pom_url, self.proj_map) self.proj_list = list(self.proj_map.values()) for proj in self.proj_list: proj.logx(1) print() def set_options(self): pass def resolve_graph(self): self.resolve_dependencies() self.resolve_consumers() # NOTE #2: the DAG is complete when the list is scanned and no dependencies exist fwd of each project # # NOTE #3: a history of each dependency relocation is maintained for each project # a circular reference will be detected if that # def resolve_dependencies(self): try: while True: for p in self.proj_list: print(p.name) i = 0 #dependency_found = False while i < len(self.proj_list): dependency_found = False proj_base = self.proj_list[i] print('loop i={}, base={}'.format(i, proj_base.name)) j = i + 1 while j < len(self.proj_list): print(' loop j {}'.format(j)) proj_scan = self.proj_list[j] # a forward project dependency is found for the base project, move it behind the base project if proj_scan.coord.key in proj_base.dependencies: # dejavu - a repeated reorder indicates circular dependency if proj_scan.coord.key in proj_base.history: raise DAGerror("Error: base project - {} - encountered duplicate reorder for dependency - {} -".format ( proj_base.name, proj_scan.name)) # remove the fwd item first to avoid order issues del self.proj_list[j] #self.proj_list.remove(j) # insert behind the base project self.proj_list.insert(i, proj_scan) print(' reorded scan {} from j={} to i={}'.format( proj_scan.name, j, i)) for p in self.proj_list: print(p.name) proj_base.history.append(proj_scan.coord.key) dependency_found = True i = i -1 break j =j+1 # while j i=i+1 # while i # repeat outer loop until nothing is reordered if not dependency_found: break else: i = 0 except DAGerror as e: print(e) # PURPOSE: for each project in the list, discover the set of consuming projects # # NOTE #1: call this method AFTER the dependency graph has been properly resolved # consuming projects will be forward in the list # def resolve_consumers(self): for i in range(len(self.proj_list)): proj_base = self.proj_list[i] j = i while j < len(self.proj_list)-1: j = j+1 proj_scan = self.proj_list[j] if (proj_base.coord.key in proj_scan.dependencies): proj_base.consumers.append(proj_scan) def list_projects(self): for proj in self.proj_list: proj.logx(1) #========================================================================== def main(): pom_files = ['D:\\devspaces\\wks4\\py1\\snipits2.xml', 'https://raw.githubusercontent.com/LeonardoZ/java-concurrency-patterns/master/pom.xml'] pom_files = ['D:\\devspaces\\wks4\\py1\\pom-A.xml', 'D:\\devspaces\\wks4\\py1\\pom-B.xml', 'D:\\devspaces\\wks4\\py1\\pom-C.xml', 'D:\\devspaces\\wks4\\py1\\pom-D.xml', ] pom_files = ['C:/Users/Larry/Dropbox/gitcode/gh/maven_proj_graph/pom-A.xml', 'C:/Users/Larry/Dropbox/gitcode/gh/maven_proj_graph/pom-B.xml', 'C:/Users/Larry/Dropbox/gitcode/gh/maven_proj_graph/pom-C.xml', 'C:/Users/Larry/Dropbox/gitcode/gh/maven_proj_graph/pom-D.xml', ] # C:\Users\Larry\Dropbox\gitcode\gh\maven_proj_graph s = ['dir', '*'] s = ['C:/apps/maven352/bin/mvn', 'help:effective-pom'] s2 = ['C:\\apps\\maven352\\bin\\mvn', 'help:effective-pom'] #Util.run_process(['cd', '..'], 'C:\\apps\\maven352\\bin\\mvn help:effective-pom') #Util.run_process('C:\\apps\\maven352\\bin\\mvn help:effective-pom', '') #Util.test_map_update(None) #return() graph = MavenProjectGraph(pom_files) graph.generate_pom_list() graph.resolve_graph() graph.list_projects() #========================================================================== # see this article for opening remote xml files # https://stackoverflow.com/questions/28238713/python-xml-parsing-lxml-urllib-request def main2(): cwd = os.getcwd() cwd = 'D:\\devspaces\\wks4\\py1\\' pom_file = cwd + 'snipits2.xml' pom_file = 'D:\\devspaces\\wks4\\py1\\snipits2.xml' pom = ET.parse(pom_file).getroot() # https://github.com/LeonardoZ/java-concurrency-patterns.git # this is the correct patttern for reading single files from github # https://raw.githubusercontent.com/user/repository/branch/filename # this is the web page containing the file # 'https://github.com/LeonardoZ/java-concurrency-patterns/blob/master/pom.xml' pom_file_url = 'https://raw.githubusercontent.com/LeonardoZ/java-concurrency-patterns/master/pom.xml' opener = urllib.request.build_opener() f = opener.open(pom_file_url) # ng, file=urllib.urlopen(file=urllib.urlopen()) #parser = ET.HTMLParser() #with urlopen('https://pypi.python.org/simple') as f: #tree = ET.parse(f, parser) #pom_file = urllib.request.urlopen(pom_file) pom = ET.parse(opener.open(pom_file_url)).getroot() project = MavenProject(pom) project.logx() if __name__ == '__main__': main() #main()

true

f71fb4b548decad7d92f6c012d1d10217c8e029e

2,063

py

Python

Union_Find/1070.Accounts Merge/Solution.py

Zhenye-Na/LxxxCode

afd79d790d0a7495d75e6650f80adaa99bd0ff07

[ "MIT" ]

12

2019-05-04T04:21:27.000Z

2022-03-02T07:06:57.000Z

Union_Find/1070.Accounts Merge/Solution.py

Zhenye-Na/LxxxCode

afd79d790d0a7495d75e6650f80adaa99bd0ff07

[ "MIT" ]

1

2019-07-24T18:43:53.000Z

Union_Find/1070.Accounts Merge/Solution.py

Zhenye-Na/LxxxCode

afd79d790d0a7495d75e6650f80adaa99bd0ff07

[ "MIT" ]

10

2019-07-01T04:03:04.000Z

2022-03-09T03:57:37.000Z

from collections import defaultdict class Solution: """ @param accounts: List[List[str]] @return: return a List[List[str]] """ def accountsMerge(self, accounts): # write your code here merged = [] if not accounts or len(accounts) == 0: return merged self.forward_index = self.create_forward_index(accounts) self.inverted_index = self.create_inverted_index(accounts) self.parents = {i : i for i in range(len(accounts)) if len(accounts[i]) >= 1} for email, people in self.inverted_index.items(): if len(people) > 1: p1 = people[0] for i in range(1, len(people)): self.connect(p1, people[i]) curr = None for people, email in self.forward_index.items(): if len(email) > 0: curr = [] curr.append(accounts[people][0]) curr.extend(sorted(list(set(email)))) merged.append(curr) return merged def create_forward_index(self, accounts): forward_index = defaultdict(list) for idx, account in enumerate(accounts): forward_index[idx].extend(account[1:]) return forward_index def create_inverted_index(self, accounts): inverted_index = defaultdict(list) for idx, account in enumerate(accounts): name = account[0] for email in account[1:]: inverted_index[email].append(idx) return inverted_index def connect(self, p1, p2): parent1 = self.find(p1) parent2 = self.find(p2) if parent2 != parent1: self.parents[parent1] = parent2 self.forward_index[parent2].extend(self.forward_index[parent1]) self.forward_index[parent1] = [] def find(self, p): path = [] while p != self.parents[p]: path.append(p) p = self.parents[p] for ppl in path: self.parents[ppl] = p return p

27.506667

85

0.561318

from collections import defaultdict class Solution: def accountsMerge(self, accounts): merged = [] if not accounts or len(accounts) == 0: return merged self.forward_index = self.create_forward_index(accounts) self.inverted_index = self.create_inverted_index(accounts) self.parents = {i : i for i in range(len(accounts)) if len(accounts[i]) >= 1} for email, people in self.inverted_index.items(): if len(people) > 1: p1 = people[0] for i in range(1, len(people)): self.connect(p1, people[i]) curr = None for people, email in self.forward_index.items(): if len(email) > 0: curr = [] curr.append(accounts[people][0]) curr.extend(sorted(list(set(email)))) merged.append(curr) return merged def create_forward_index(self, accounts): forward_index = defaultdict(list) for idx, account in enumerate(accounts): forward_index[idx].extend(account[1:]) return forward_index def create_inverted_index(self, accounts): inverted_index = defaultdict(list) for idx, account in enumerate(accounts): name = account[0] for email in account[1:]: inverted_index[email].append(idx) return inverted_index def connect(self, p1, p2): parent1 = self.find(p1) parent2 = self.find(p2) if parent2 != parent1: self.parents[parent1] = parent2 self.forward_index[parent2].extend(self.forward_index[parent1]) self.forward_index[parent1] = [] def find(self, p): path = [] while p != self.parents[p]: path.append(p) p = self.parents[p] for ppl in path: self.parents[ppl] = p return p

true

f71fb575e9c0c22da60dd6194084df2483a9ba88

3,979

py

Python

tests/test_detect_score.py

pgftennis/tennis_analysis_tool

9f43545fa2b502930ec27a4de634ebc45e65cb19

[ "MIT" ]

1

2022-01-14T10:35:00.000Z

tests/test_detect_score.py

pgftennis/tennis_analysis_tool

9f43545fa2b502930ec27a4de634ebc45e65cb19

[ "MIT" ]

null

tests/test_detect_score.py

pgftennis/tennis_analysis_tool

9f43545fa2b502930ec27a4de634ebc45e65cb19

[ "MIT" ]

null

import unittest import sys from pathlib import Path sys.path.append(str(Path(__file__).parent.parent)) sys.path.append(str(Path(__file__).parent.parent / "src/predict")) import src.predict.detect_score as detect_score class TestDetectScore(unittest.TestCase): def setUp(self):#設定 self.ds=detect_score.DetectScore() def test_fix_text(self): #bugfix text="3 6 10 6 3 4 15"#10を1 0 に分解したい text=self.ds.fix_text(text) self.assertEqual("3 6 1 0 6 3 4 15",text) def test_fix_in_ad(self): print("text_fix_in_ad") text_array=['3','6','Ad'] text_array=self.ds.fix_in_ad(text_array) self.assertEqual(['3', '40','6','Ad'],text_array) text_array=['3','Ad','6'] text_array=self.ds.fix_in_ad(text_array) self.assertEqual(['3','Ad','6', '40'],text_array) text_array=['3', '6', '1', '6', '3', '4', 'Ad'] text_array=self.ds.fix_in_ad(text_array) self.assertEqual(['3', '6', '1', '40','6', '3', '4', 'Ad'],text_array) text_array=['3', '6', '1', 'Ad','6', '3', '4'] text_array=self.ds.fix_in_ad(text_array) self.assertEqual(['3', '6', '1', 'Ad','6', '3', '4', '40'],text_array) def test_text2score(self): text="A 40" set_num = self.ds.get_set_text_num(text) self.assertEqual(2,set_num) text="4 1 15\n6 1 15" set_num = self.ds.get_set_text_num(text) self.assertEqual(6,set_num) text="1 15 \n0\n0" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("1",game_a) self.assertEqual("0",game_b) self.assertEqual("15",score_a) self.assertEqual("0",score_b) text="1 A \n5\n40" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("1",game_a) self.assertEqual("5",game_b) self.assertEqual("A",score_a) self.assertEqual("40",score_b) text="30 15" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("0",game_a) self.assertEqual("0",game_b) self.assertEqual("30",score_a) self.assertEqual("15",score_b) text="A 40" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("0",game_a) self.assertEqual("0",game_b) self.assertEqual("A",score_a) self.assertEqual("40",score_b) text="15 " game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("0",game_a) self.assertEqual("0",game_b) self.assertEqual("15",score_a) self.assertEqual("",score_b) text="" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("",game_a) self.assertEqual("",game_b) self.assertEqual("",score_a) self.assertEqual("",score_b) text="4 1 15\n6 2 30" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("1",game_a) self.assertEqual("15",score_a) self.assertEqual("2",game_b) self.assertEqual("30",score_b) text="4 6 4 15\n6 2 2 30" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("4",game_a) self.assertEqual("15",score_a) self.assertEqual("2",game_b) self.assertEqual("30",score_b) text="6 4 6 4 15\n4 6 2 2 30" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("4",game_a) self.assertEqual("15",score_a) self.assertEqual("2",game_b) self.assertEqual("30",score_b) text="5 6 4 6 4 15\n7 4 6 2 2 30" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("4",game_a) self.assertEqual("15",score_a) self.assertEqual("2",game_b) self.assertEqual("30",score_b) # if __name__ == "__main__": # unittest.main()

32.08871

78

0.60191

import unittest import sys from pathlib import Path sys.path.append(str(Path(__file__).parent.parent)) sys.path.append(str(Path(__file__).parent.parent / "src/predict")) import src.predict.detect_score as detect_score class TestDetectScore(unittest.TestCase): def setUp(self): self.ds=detect_score.DetectScore() def test_fix_text(self): text="3 6 10 6 3 4 15" text=self.ds.fix_text(text) self.assertEqual("3 6 1 0 6 3 4 15",text) def test_fix_in_ad(self): print("text_fix_in_ad") text_array=['3','6','Ad'] text_array=self.ds.fix_in_ad(text_array) self.assertEqual(['3', '40','6','Ad'],text_array) text_array=['3','Ad','6'] text_array=self.ds.fix_in_ad(text_array) self.assertEqual(['3','Ad','6', '40'],text_array) text_array=['3', '6', '1', '6', '3', '4', 'Ad'] text_array=self.ds.fix_in_ad(text_array) self.assertEqual(['3', '6', '1', '40','6', '3', '4', 'Ad'],text_array) text_array=['3', '6', '1', 'Ad','6', '3', '4'] text_array=self.ds.fix_in_ad(text_array) self.assertEqual(['3', '6', '1', 'Ad','6', '3', '4', '40'],text_array) def test_text2score(self): text="A 40" set_num = self.ds.get_set_text_num(text) self.assertEqual(2,set_num) text="4 1 15\n6 1 15" set_num = self.ds.get_set_text_num(text) self.assertEqual(6,set_num) text="1 15 \n0\n0" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("1",game_a) self.assertEqual("0",game_b) self.assertEqual("15",score_a) self.assertEqual("0",score_b) text="1 A \n5\n40" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("1",game_a) self.assertEqual("5",game_b) self.assertEqual("A",score_a) self.assertEqual("40",score_b) text="30 15" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("0",game_a) self.assertEqual("0",game_b) self.assertEqual("30",score_a) self.assertEqual("15",score_b) text="A 40" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("0",game_a) self.assertEqual("0",game_b) self.assertEqual("A",score_a) self.assertEqual("40",score_b) text="15 " game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("0",game_a) self.assertEqual("0",game_b) self.assertEqual("15",score_a) self.assertEqual("",score_b) text="" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("",game_a) self.assertEqual("",game_b) self.assertEqual("",score_a) self.assertEqual("",score_b) text="4 1 15\n6 2 30" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("1",game_a) self.assertEqual("15",score_a) self.assertEqual("2",game_b) self.assertEqual("30",score_b) text="4 6 4 15\n6 2 2 30" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("4",game_a) self.assertEqual("15",score_a) self.assertEqual("2",game_b) self.assertEqual("30",score_b) text="6 4 6 4 15\n4 6 2 2 30" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("4",game_a) self.assertEqual("15",score_a) self.assertEqual("2",game_b) self.assertEqual("30",score_b) text="5 6 4 6 4 15\n7 4 6 2 2 30" game_a,game_b,score_a,score_b=self.ds.text2score(text) self.assertEqual("4",game_a) self.assertEqual("15",score_a) self.assertEqual("2",game_b) self.assertEqual("30",score_b)

true

f71fb5d233f60d4940a1f40506e107449b9cb848

837

py

Python

aql/connector.py

ryansb/aql

bc9f11aaf06caabe15981fb33b5ef37a60ce700a

[ "MIT" ]

24

2020-07-16T11:47:28.000Z

2021-12-02T20:38:52.000Z

aql/connector.py

ryansb/aql

bc9f11aaf06caabe15981fb33b5ef37a60ce700a

[ "MIT" ]

37

2020-10-17T14:04:05.000Z

2022-02-05T05:01:08.000Z

aql/connector.py

ryansb/aql

bc9f11aaf06caabe15981fb33b5ef37a60ce700a

[ "MIT" ]

1

2019-10-26T03:45:16.000Z

# Copyright 2020 John Reese # Licensed under the MIT license import re from typing import Any, Pattern, Union from .engines.base import Connection from .errors import InvalidURI from .types import Location _uri_regex: Pattern = re.compile(r"(?P<engine>\w+)://(?P<location>.+)") def connect(location: Union[str, Location], *args: Any, **kwargs: Any) -> Connection: """Connect to the specified database.""" if isinstance(location, str): match = _uri_regex.match(location) if match: engine, database = match.groups() location = Location(engine, database=database) else: raise InvalidURI(f"Invalid database connection URI {location}") connector, engine_kls = Connection.get_connector(location.engine) return connector(engine_kls(), location, *args, **kwargs)

32.192308

85

0.688172

import re from typing import Any, Pattern, Union from .engines.base import Connection from .errors import InvalidURI from .types import Location _uri_regex: Pattern = re.compile(r"(?P<engine>\w+)://(?P<location>.+)") def connect(location: Union[str, Location], *args: Any, **kwargs: Any) -> Connection: if isinstance(location, str): match = _uri_regex.match(location) if match: engine, database = match.groups() location = Location(engine, database=database) else: raise InvalidURI(f"Invalid database connection URI {location}") connector, engine_kls = Connection.get_connector(location.engine) return connector(engine_kls(), location, *args, **kwargs)

true

f71fb6028cbb2b09f79e06a91c06d14c015af377

2,712

py

Python

utils.py

TNLC/pycalc

a60e996c5e4e1b6fdae5da124864cdf9a7178d19

[ "Apache-2.0" ]

null

utils.py

TNLC/pycalc

a60e996c5e4e1b6fdae5da124864cdf9a7178d19

[ "Apache-2.0" ]

null

utils.py

TNLC/pycalc

a60e996c5e4e1b6fdae5da124864cdf9a7178d19

[ "Apache-2.0" ]

null

import math from rply import LexerGenerator, ParserGenerator def build_lexer(): # LEXERGENERATOR INSTANZIEREN lexer_generator = LexerGenerator() # WHITESPACES IGNORIEREN lexer_generator.ignore(r'\s+') # ZAHLEN ERKENNEN # -? => ENTWEDER MINUS ODER NICHT # \.? => ENTWEDER EIN PUNKT ODER NICHT # [0-9]* BELIEBIG OFT 0-9 (MINDESTENS 0 MAL) # [0-9]+ BELIEBIG OFT 0-9 (MINDESTENS 1 MAL) # 'NUM' => NUMBER lexer_generator.add('NUM', r'-?[0-9]*\.?[0-9]+') # OPERATOREN lexer_generator.add('ADD', r'\+') # 'ADD' => ADD lexer_generator.add('SUB', r'-') # 'SUB' => SUBTRACT lexer_generator.add('MUL', r'\*') # 'MUL' => MULTIPLY lexer_generator.add('DIV', r'/') # 'DIV' => DIVIDE lexer_generator.add('MOD', r'%') # 'MOD' => MODULO lexer_generator.add('EXP', r'^|\*\*') # 'EXP' => EXPONENTIATE lexer_generator.add('BR_O', r'$') # 'BR_O' => BRACKET OPEN lexer_generator.add('BR_C', r'$') # 'BR_C' => BRACKET CLOSE lexer_generator.add('ABS_P', r'\|') # 'ABS_P' => ABSOLUTE PART # LEXER ERSTELLEN UND ZURÜCKGEBEN return lexer_generator.build() def build_parser(): # TOKENS FÜR PARSER FESTLEGEN parser_generator = ParserGenerator([ 'NUM', 'ADD', 'SUB', 'MUL', 'DIV', 'MOD', 'EXP', 'ABS_P', 'BR_O', 'BR_C' ]) # REGELN FÜR PARSER FESTLEGEN @parser_generator.production('main : expr') def main(x): return x[0] @parser_generator.production('expr : factor') def term_zahl(x): return x[0] @parser_generator.production('expr : expr SUB factor') def term_zahl(x): return x[0] - x[2] @parser_generator.production('expr : expr ADD factor') def term_zahl(x): return x[0] + x[2] # STANDARD RECHENOPERATIONEN @parser_generator.production('factor : term') def term_zahl(x): return x[0] @parser_generator.production('factor : factor EXP term') def term_zahl(x): return x[0] ** x[2] @parser_generator.production('factor : factor DIV term') def term_zahl(x): return x[0] / x[2] @parser_generator.production('factor : factor MOD term') def term_zahl(x): return x[0] % x[2] @parser_generator.production('factor : factor MUL term') def term_zahl(x): return x[0] * x[2] @parser_generator.production('term : NUM') def term_zahl(x): return float(x[0].getstr()) # KLAMMERN @parser_generator.production('term : BR_O expr BR_C') def term_zahl(x): return x[1] # BETRAG @parser_generator.production('term : ABS_P expr ABS_P') def term_zahl(x): return x[0] if x[0] >= 0 else x[0] * -1 return parser_generator.build() lexer = build_lexer() parser = build_parser()

32.674699

68

0.625369

import math from rply import LexerGenerator, ParserGenerator def build_lexer(): lexer_generator = LexerGenerator() lexer_generator.ignore(r'\s+') lexer_generator.add('NUM', r'-?[0-9]*\.?[0-9]+') lexer_generator.add('ADD', r'\+') lexer_generator.add('SUB', r'-') lexer_generator.add('MUL', r'\*') lexer_generator.add('DIV', r'/') lexer_generator.add('MOD', r'%') lexer_generator.add('EXP', r'^|\*\*') lexer_generator.add('BR_O', r'$') lexer_generator.add('BR_C', r'$') lexer_generator.add('ABS_P', r'\|') return lexer_generator.build() def build_parser(): parser_generator = ParserGenerator([ 'NUM', 'ADD', 'SUB', 'MUL', 'DIV', 'MOD', 'EXP', 'ABS_P', 'BR_O', 'BR_C' ]) @parser_generator.production('main : expr') def main(x): return x[0] @parser_generator.production('expr : factor') def term_zahl(x): return x[0] @parser_generator.production('expr : expr SUB factor') def term_zahl(x): return x[0] - x[2] @parser_generator.production('expr : expr ADD factor') def term_zahl(x): return x[0] + x[2] @parser_generator.production('factor : term') def term_zahl(x): return x[0] @parser_generator.production('factor : factor EXP term') def term_zahl(x): return x[0] ** x[2] @parser_generator.production('factor : factor DIV term') def term_zahl(x): return x[0] / x[2] @parser_generator.production('factor : factor MOD term') def term_zahl(x): return x[0] % x[2] @parser_generator.production('factor : factor MUL term') def term_zahl(x): return x[0] * x[2] @parser_generator.production('term : NUM') def term_zahl(x): return float(x[0].getstr()) @parser_generator.production('term : BR_O expr BR_C') def term_zahl(x): return x[1] @parser_generator.production('term : ABS_P expr ABS_P') def term_zahl(x): return x[0] if x[0] >= 0 else x[0] * -1 return parser_generator.build() lexer = build_lexer() parser = build_parser()

true

f71fb63636601da0239ffd402fc4be7612c8b4ab

4,587

py

Python

ca_municipalities/people.py

dogooderapp/scrapers-ca

5e852eea93f05f3f397eec318d3094a3b1b0b458

[ "MIT" ]

19

2015-05-26T03:18:50.000Z

2022-01-31T03:27:41.000Z

ca_municipalities/people.py

dogooderapp/scrapers-ca

5e852eea93f05f3f397eec318d3094a3b1b0b458

[ "MIT" ]

119

2015-01-09T06:09:35.000Z

2022-01-20T23:05:05.000Z

ca_municipalities/people.py

dogooderapp/scrapers-ca

5e852eea93f05f3f397eec318d3094a3b1b0b458

[ "MIT" ]

17

2015-11-23T05:00:10.000Z

2021-09-15T16:03:33.000Z

from utils import CSVScraper, CanadianPerson as Person from pupa.scrape import Organization, Post from collections import defaultdict import re class CanadaMunicipalitiesPersonScraper(CSVScraper): csv_url = 'https://docs.google.com/spreadsheets/d/e/2PACX-1vRrGXQy8qk16OhuTjlccoGB4jL5e8X1CEqRbg896ufLdh67DQk9nuGm-oufIT0HRMPEnwePw2HDx1Vj/pub?gid=0&single=true&output=csv' encoding = 'utf-8' """ Returns whether the row should be imported. """ def is_valid_row(self, row): return super().is_valid_row(row) and row['organization'] def scrape(self): organizations = {} seat_numbers = defaultdict(lambda: defaultdict(int)) reader = self.csv_reader(self.csv_url, delimiter=self.delimiter, header=True, encoding=self.encoding, skip_rows=self.skip_rows) reader.fieldnames = [self.header_converter(field) for field in reader.fieldnames] for row in reader: try: if self.is_valid_row(row): for key, corrections in self.corrections.items(): if not isinstance(corrections, dict): row[key] = corrections(row[key]) elif row[key] in corrections: row[key] = corrections[row[key]] organization_classification = 'legislature' organization_name = row['organization'] organization_key = organization_name.lower() if organization_key in organizations: organization = organizations[organization_key] else: organization = Organization(organization_name, classification=organization_classification) organization.add_source(self.csv_url) yield organization organizations[organization_key] = organization if not row['primary role']: row['primary role'] = 'Councillor' role = row['primary role'] post = Post(role=role, label=organization_name, organization_id=organization._id) yield post name = row['name'].strip(' .,') district = row['district name'] if self.many_posts_per_area and role not in self.unique_roles: seat_numbers[role][district] += 1 district = '{} (seat {})'.format(district, seat_numbers[role][district]) p = Person(primary_org=organization_classification, name=name, district=district, role=role, party=row.get('party name')) p.add_source(self.csv_url) if row.get('gender'): p.gender = row['gender'] if row.get('photo url'): p.image = row['photo url'] if row.get('source url'): p.add_source(row['source url'].strip(' .,')) if row.get('website'): p.add_link(row['website'], note='web site') if row.get('facebook'): p.add_link(re.sub(r'[#?].+', '', row['facebook'])) if row.get('twitter'): p.add_link(row['twitter']) if row['email']: p.add_contact('email', row['email'].strip(' .,')) if row['address']: p.add_contact('address', row['address'], 'legislature') if row.get('phone'): p.add_contact('voice', row['phone'], 'legislature') if row.get('fax'): p.add_contact('fax', row['fax'], 'legislature') if row.get('cell'): p.add_contact('cell', row['cell'], 'legislature') if row.get('birth date'): p.birth_date = row['birth date'] if row.get('incumbent'): p.extras['incumbent'] = row['incumbent'] if name in self.other_names: for other_name in self.other_names[name]: p.add_name(other_name) # Validate person entity so that we can catch the exception if needed. p.validate() yield p except Exception as e: print(repr(e)) continue

42.869159

176

0.514716

from utils import CSVScraper, CanadianPerson as Person from pupa.scrape import Organization, Post from collections import defaultdict import re class CanadaMunicipalitiesPersonScraper(CSVScraper): csv_url = 'https://docs.google.com/spreadsheets/d/e/2PACX-1vRrGXQy8qk16OhuTjlccoGB4jL5e8X1CEqRbg896ufLdh67DQk9nuGm-oufIT0HRMPEnwePw2HDx1Vj/pub?gid=0&single=true&output=csv' encoding = 'utf-8' def is_valid_row(self, row): return super().is_valid_row(row) and row['organization'] def scrape(self): organizations = {} seat_numbers = defaultdict(lambda: defaultdict(int)) reader = self.csv_reader(self.csv_url, delimiter=self.delimiter, header=True, encoding=self.encoding, skip_rows=self.skip_rows) reader.fieldnames = [self.header_converter(field) for field in reader.fieldnames] for row in reader: try: if self.is_valid_row(row): for key, corrections in self.corrections.items(): if not isinstance(corrections, dict): row[key] = corrections(row[key]) elif row[key] in corrections: row[key] = corrections[row[key]] organization_classification = 'legislature' organization_name = row['organization'] organization_key = organization_name.lower() if organization_key in organizations: organization = organizations[organization_key] else: organization = Organization(organization_name, classification=organization_classification) organization.add_source(self.csv_url) yield organization organizations[organization_key] = organization if not row['primary role']: row['primary role'] = 'Councillor' role = row['primary role'] post = Post(role=role, label=organization_name, organization_id=organization._id) yield post name = row['name'].strip(' .,') district = row['district name'] if self.many_posts_per_area and role not in self.unique_roles: seat_numbers[role][district] += 1 district = '{} (seat {})'.format(district, seat_numbers[role][district]) p = Person(primary_org=organization_classification, name=name, district=district, role=role, party=row.get('party name')) p.add_source(self.csv_url) if row.get('gender'): p.gender = row['gender'] if row.get('photo url'): p.image = row['photo url'] if row.get('source url'): p.add_source(row['source url'].strip(' .,')) if row.get('website'): p.add_link(row['website'], note='web site') if row.get('facebook'): p.add_link(re.sub(r'[#?].+', '', row['facebook'])) if row.get('twitter'): p.add_link(row['twitter']) if row['email']: p.add_contact('email', row['email'].strip(' .,')) if row['address']: p.add_contact('address', row['address'], 'legislature') if row.get('phone'): p.add_contact('voice', row['phone'], 'legislature') if row.get('fax'): p.add_contact('fax', row['fax'], 'legislature') if row.get('cell'): p.add_contact('cell', row['cell'], 'legislature') if row.get('birth date'): p.birth_date = row['birth date'] if row.get('incumbent'): p.extras['incumbent'] = row['incumbent'] if name in self.other_names: for other_name in self.other_names[name]: p.add_name(other_name) p.validate() yield p except Exception as e: print(repr(e)) continue

true

f71fb66f5197af2f7a2dd9fb62e51560772987ee

398

py

Python

tests/connection/test_cursor.py

coverwallet/pysoni

49d3a8acb101436ad0724749572be2ad9d86f3ae

[ "MIT" ]

5

2019-07-08T15:38:06.000Z

2022-03-24T20:36:19.000Z

tests/connection/test_cursor.py

coverwallet/pysoni

49d3a8acb101436ad0724749572be2ad9d86f3ae

[ "MIT" ]

2

2019-07-07T23:26:32.000Z

2020-06-04T07:43:24.000Z

tests/connection/test_cursor.py

coverwallet/pysoni

49d3a8acb101436ad0724749572be2ad9d86f3ae

[ "MIT" ]

1

2019-05-31T09:11:22.000Z

def test_cursor_triggers_cursor_in_the_connection(open_connection): open_connection.cursor() open_connection._connection_handler.cursor.assert_called_once() def test_cursor_returns_a_cursor_in_the_handler(open_connection, mocker): cursor_mock = mocker.Mock() open_connection._connection_handler.cursor.return_value = cursor_mock assert open_connection.cursor() == cursor_mock

39.8

73

0.831658

def test_cursor_triggers_cursor_in_the_connection(open_connection): open_connection.cursor() open_connection._connection_handler.cursor.assert_called_once() def test_cursor_returns_a_cursor_in_the_handler(open_connection, mocker): cursor_mock = mocker.Mock() open_connection._connection_handler.cursor.return_value = cursor_mock assert open_connection.cursor() == cursor_mock

true

f71fb695ceaa12f53778fae43e8d0268e9cde5f9

3,235

py

Python

server/config/settings.py

sudosubin/playground-gunicorn

770b2db062446e47a92b37fd3488f0e657157293

[ "MIT" ]

null

server/config/settings.py

sudosubin/playground-gunicorn

770b2db062446e47a92b37fd3488f0e657157293

[ "MIT" ]

null

server/config/settings.py

sudosubin/playground-gunicorn

770b2db062446e47a92b37fd3488f0e657157293

[ "MIT" ]

null

""" Django settings for config project. Generated by 'django-admin startproject' using Django 4.0.2. For more information on this file, see https://docs.djangoproject.com/en/4.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/4.0/ref/settings/ """ from pathlib import Path # Build paths inside the project like this: BASE_DIR / 'subdir'. BASE_DIR = Path(__file__).resolve().parent.parent # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/4.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'django-insecure-n1vl4be=11s&5oo0^453rw&9(g3v0pjb6=t4ze@d_3j4i3)y+y' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'server.config.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'server.config.wsgi.application' # Database # https://docs.djangoproject.com/en/4.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/4.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/4.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/4.0/howto/static-files/ STATIC_URL = 'static/' # Default primary key field type # https://docs.djangoproject.com/en/4.0/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'

26.08871

91

0.703246

from pathlib import Path BASE_DIR = Path(__file__).resolve().parent.parent SECRET_KEY = 'django-insecure-n1vl4be=11s&5oo0^453rw&9(g3v0pjb6=t4ze@d_3j4i3)y+y' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'server.config.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'server.config.wsgi.application' # Database # https://docs.djangoproject.com/en/4.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': BASE_DIR / 'db.sqlite3', } } # Password validation # https://docs.djangoproject.com/en/4.0/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/4.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/4.0/howto/static-files/ STATIC_URL = 'static/' # Default primary key field type # https://docs.djangoproject.com/en/4.0/ref/settings/#default-auto-field DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'

true

f71fb6ffafd818eedc205dc12f215cb79fa5ad0e

680

py

Python

sa/profiles/Vyatta/Vyatta/get_capabilities.py

xUndero/noc

9fb34627721149fcf7064860bd63887e38849131

[ "BSD-3-Clause" ]

1

2019-09-20T09:36:48.000Z

sa/profiles/Vyatta/Vyatta/get_capabilities.py

ewwwcha/noc

aba08dc328296bb0e8e181c2ac9a766e1ec2a0bb

[ "BSD-3-Clause" ]

null

sa/profiles/Vyatta/Vyatta/get_capabilities.py

ewwwcha/noc

aba08dc328296bb0e8e181c2ac9a766e1ec2a0bb

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- # --------------------------------------------------------------------- # Vyatta.Vyatta.get_capabilities # --------------------------------------------------------------------- # Copyright (C) 2007-2015 The NOC Project # See LICENSE for details # --------------------------------------------------------------------- # NOC modules from noc.sa.profiles.Generic.get_capabilities import Script as BaseScript class Script(BaseScript): name = "Vyatta.Vyatta.get_capabilities" def has_lldp_cli(self): """ Check box has lldp enabled """ r = self.cli("show lldp neighbors") return "LLDP not configured" not in r

30.909091

73

0.457353

from noc.sa.profiles.Generic.get_capabilities import Script as BaseScript class Script(BaseScript): name = "Vyatta.Vyatta.get_capabilities" def has_lldp_cli(self): r = self.cli("show lldp neighbors") return "LLDP not configured" not in r

true

f71fb8631c4b145396e2bba66374e05637da08a5

130

py

Python

db_utils.py

bizmarcin/thefridge

13bde29a57aea09fecf5ec2f28ce013adf6c4d08

[ "MIT" ]

1

2019-07-01T13:04:02.000Z

db_utils.py

bizmarcin/thefridge

13bde29a57aea09fecf5ec2f28ce013adf6c4d08

[ "MIT" ]

2

2020-07-17T09:05:59.000Z

2021-05-09T06:42:06.000Z

db_utils.py

bizmarcin/thefridge

13bde29a57aea09fecf5ec2f28ce013adf6c4d08

[ "MIT" ]

null

import sqlite3 def get_connection(): conn = sqlite3.connect('fridge.db') conn.row_factory = sqlite3.Row return conn

16.25

39

0.7

import sqlite3 def get_connection(): conn = sqlite3.connect('fridge.db') conn.row_factory = sqlite3.Row return conn

true

f71fb9c25aa2d31e0378e8ac7911871707f58f10

6,328

py

Python

tests/components/switch/test_command_line.py

beschouten/home-assistant

f50c30bbbad4d92e342c8547630c63c0c7882803

[ "MIT" ]

1

2016-07-14T05:20:54.000Z

tests/components/switch/test_command_line.py

beschouten/home-assistant

f50c30bbbad4d92e342c8547630c63c0c7882803

[ "MIT" ]

null

tests/components/switch/test_command_line.py

beschouten/home-assistant

f50c30bbbad4d92e342c8547630c63c0c7882803

[ "MIT" ]

1

2018-11-22T13:55:23.000Z

"""the tests for the Command line switch platform.""" import json import os import tempfile import unittest from homeassistant.const import STATE_ON, STATE_OFF import homeassistant.components.switch as switch import homeassistant.components.switch.command_line as command_line from tests.common import get_test_home_assistant class TestCommandSwitch(unittest.TestCase): """Test the command switch.""" def setUp(self): # pylint: disable=invalid-name """Setup things to be run when tests are started.""" self.hass = get_test_home_assistant() def tearDown(self): # pylint: disable=invalid-name """Stop everything that was started.""" self.hass.stop() def test_state_none(self): """Test with none state.""" with tempfile.TemporaryDirectory() as tempdirname: path = os.path.join(tempdirname, 'switch_status') test_switch = { 'oncmd': 'echo 1 > {}'.format(path), 'offcmd': 'echo 0 > {}'.format(path), } self.assertTrue(switch.setup(self.hass, { 'switch': { 'platform': 'command_line', 'switches': { 'test': test_switch } } })) state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) switch.turn_on(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_ON, state.state) switch.turn_off(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) def test_state_value(self): """Test with state value.""" with tempfile.TemporaryDirectory() as tempdirname: path = os.path.join(tempdirname, 'switch_status') test_switch = { 'statecmd': 'cat {}'.format(path), 'oncmd': 'echo 1 > {}'.format(path), 'offcmd': 'echo 0 > {}'.format(path), 'value_template': '{{ value=="1" }}' } self.assertTrue(switch.setup(self.hass, { 'switch': { 'platform': 'command_line', 'switches': { 'test': test_switch } } })) state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) switch.turn_on(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_ON, state.state) switch.turn_off(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) def test_state_json_value(self): """Test with state JSON value.""" with tempfile.TemporaryDirectory() as tempdirname: path = os.path.join(tempdirname, 'switch_status') oncmd = json.dumps({'status': 'ok'}) offcmd = json.dumps({'status': 'nope'}) test_switch = { 'statecmd': 'cat {}'.format(path), 'oncmd': 'echo \'{}\' > {}'.format(oncmd, path), 'offcmd': 'echo \'{}\' > {}'.format(offcmd, path), 'value_template': '{{ value_json.status=="ok" }}' } self.assertTrue(switch.setup(self.hass, { 'switch': { 'platform': 'command_line', 'switches': { 'test': test_switch } } })) state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) switch.turn_on(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_ON, state.state) switch.turn_off(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) def test_state_code(self): """Test with state code.""" with tempfile.TemporaryDirectory() as tempdirname: path = os.path.join(tempdirname, 'switch_status') test_switch = { 'statecmd': 'cat {}'.format(path), 'oncmd': 'echo 1 > {}'.format(path), 'offcmd': 'echo 0 > {}'.format(path), } self.assertTrue(switch.setup(self.hass, { 'switch': { 'platform': 'command_line', 'switches': { 'test': test_switch } } })) state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) switch.turn_on(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_ON, state.state) switch.turn_off(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_ON, state.state) def test_assumed_state_should_be_true_if_command_state_is_false(self): """Test with state value.""" self.hass = get_test_home_assistant() # Set state command to false statecmd = False no_state_device = command_line.CommandSwitch(self.hass, "Test", "echo", "echo", statecmd, None) self.assertTrue(no_state_device.assumed_state) # Set state command statecmd = 'cat {}' state_device = command_line.CommandSwitch(self.hass, "Test", "echo", "echo", statecmd, None) self.assertFalse(state_device.assumed_state)

35.751412

79

0.533028

import json import os import tempfile import unittest from homeassistant.const import STATE_ON, STATE_OFF import homeassistant.components.switch as switch import homeassistant.components.switch.command_line as command_line from tests.common import get_test_home_assistant class TestCommandSwitch(unittest.TestCase): def setUp(self): self.hass = get_test_home_assistant() def tearDown(self): self.hass.stop() def test_state_none(self): with tempfile.TemporaryDirectory() as tempdirname: path = os.path.join(tempdirname, 'switch_status') test_switch = { 'oncmd': 'echo 1 > {}'.format(path), 'offcmd': 'echo 0 > {}'.format(path), } self.assertTrue(switch.setup(self.hass, { 'switch': { 'platform': 'command_line', 'switches': { 'test': test_switch } } })) state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) switch.turn_on(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_ON, state.state) switch.turn_off(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) def test_state_value(self): with tempfile.TemporaryDirectory() as tempdirname: path = os.path.join(tempdirname, 'switch_status') test_switch = { 'statecmd': 'cat {}'.format(path), 'oncmd': 'echo 1 > {}'.format(path), 'offcmd': 'echo 0 > {}'.format(path), 'value_template': '{{ value=="1" }}' } self.assertTrue(switch.setup(self.hass, { 'switch': { 'platform': 'command_line', 'switches': { 'test': test_switch } } })) state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) switch.turn_on(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_ON, state.state) switch.turn_off(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) def test_state_json_value(self): with tempfile.TemporaryDirectory() as tempdirname: path = os.path.join(tempdirname, 'switch_status') oncmd = json.dumps({'status': 'ok'}) offcmd = json.dumps({'status': 'nope'}) test_switch = { 'statecmd': 'cat {}'.format(path), 'oncmd': 'echo \'{}\' > {}'.format(oncmd, path), 'offcmd': 'echo \'{}\' > {}'.format(offcmd, path), 'value_template': '{{ value_json.status=="ok" }}' } self.assertTrue(switch.setup(self.hass, { 'switch': { 'platform': 'command_line', 'switches': { 'test': test_switch } } })) state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) switch.turn_on(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_ON, state.state) switch.turn_off(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) def test_state_code(self): with tempfile.TemporaryDirectory() as tempdirname: path = os.path.join(tempdirname, 'switch_status') test_switch = { 'statecmd': 'cat {}'.format(path), 'oncmd': 'echo 1 > {}'.format(path), 'offcmd': 'echo 0 > {}'.format(path), } self.assertTrue(switch.setup(self.hass, { 'switch': { 'platform': 'command_line', 'switches': { 'test': test_switch } } })) state = self.hass.states.get('switch.test') self.assertEqual(STATE_OFF, state.state) switch.turn_on(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_ON, state.state) switch.turn_off(self.hass, 'switch.test') self.hass.pool.block_till_done() state = self.hass.states.get('switch.test') self.assertEqual(STATE_ON, state.state) def test_assumed_state_should_be_true_if_command_state_is_false(self): self.hass = get_test_home_assistant() statecmd = False no_state_device = command_line.CommandSwitch(self.hass, "Test", "echo", "echo", statecmd, None) self.assertTrue(no_state_device.assumed_state) statecmd = 'cat {}' state_device = command_line.CommandSwitch(self.hass, "Test", "echo", "echo", statecmd, None) self.assertFalse(state_device.assumed_state)

true

f71fb9e8ec48b2f9258997378e50488a34fc3cb5

2,033

py

Python

scripts/utils/connection.py

CostaDiego/product-complaint-classification

42d44210553577616dcf8ac4bf616b587fa02e8c

[ "MIT" ]

null

scripts/utils/connection.py

CostaDiego/product-complaint-classification

42d44210553577616dcf8ac4bf616b587fa02e8c

[ "MIT" ]

null

scripts/utils/connection.py

CostaDiego/product-complaint-classification

42d44210553577616dcf8ac4bf616b587fa02e8c

[ "MIT" ]

null

import psycopg2 from getpass import getpass class DatabaseConection(object): """ """ def __init__(self, host: str, database: str, user: str): self._con = None self._host = host self._database = database self._user = user self.connected = False try: self._con = psycopg2.connect( host= self._host, database= self._database, user= self._user, password = getpass( prompt= 'Input the password:\n', stream= None )) self.connected = True print('\tConnection established!') except: print('\tFailed to establish connection!') def send(self, sql: str): try: cursor = self._con.cursor() cursor.execute(str(sql)) self._con.commit() return True except: return False def request(self, sql: str): try: cursor = self._con.cursor() cursor.execute(str(sql)) request = cursor.fetchall() return request except: return None def closeConnection(self): self._con.close() self.connected = False def connect(self, host = None, database = None, user = None): if host: self._host = host if database: self._database = database if user: self._user = user try: self._con = psycopg2.connect( host= self._host, database= self._database, user= self._user, password = getpass( prompt= 'Input the password:\n', stream= None )) self.connected = True print('\tConnection established!') except: self.connected = False print('\tFailed to establish connection!')

26.064103

65

0.484506

import psycopg2 from getpass import getpass class DatabaseConection(object): def __init__(self, host: str, database: str, user: str): self._con = None self._host = host self._database = database self._user = user self.connected = False try: self._con = psycopg2.connect( host= self._host, database= self._database, user= self._user, password = getpass( prompt= 'Input the password:\n', stream= None )) self.connected = True print('\tConnection established!') except: print('\tFailed to establish connection!') def send(self, sql: str): try: cursor = self._con.cursor() cursor.execute(str(sql)) self._con.commit() return True except: return False def request(self, sql: str): try: cursor = self._con.cursor() cursor.execute(str(sql)) request = cursor.fetchall() return request except: return None def closeConnection(self): self._con.close() self.connected = False def connect(self, host = None, database = None, user = None): if host: self._host = host if database: self._database = database if user: self._user = user try: self._con = psycopg2.connect( host= self._host, database= self._database, user= self._user, password = getpass( prompt= 'Input the password:\n', stream= None )) self.connected = True print('\tConnection established!') except: self.connected = False print('\tFailed to establish connection!')

true

f71fbb8b5e0dc57ce6f0bd235b70ff2b45cd4410

4,913

py

Python

ilustrado/util.py

ml-evs/ilustrado

3121ecaff9cb517f3946b2283bf50dce499caad9

[ "MIT" ]

3

2019-10-31T20:54:55.000Z

2022-01-05T16:39:43.000Z

ilustrado/util.py

ml-evs/ilustrado

3121ecaff9cb517f3946b2283bf50dce499caad9

[ "MIT" ]

null

ilustrado/util.py

ml-evs/ilustrado

3121ecaff9cb517f3946b2283bf50dce499caad9

[ "MIT" ]

2

2019-11-29T11:34:11.000Z

2020-08-12T12:31:48.000Z

# coding: utf-8 """ Catch-all file for utility functions. """ import sys import logging import numpy as np from matador.compute import ComputeTask from matador.utils.cell_utils import cart2frac, cart2abc LOG = logging.getLogger("ilustrado") LOG.setLevel(logging.DEBUG) def strip_useless(doc, to_run=False): """ Strip useless information from a matador doc. Parameters: doc (dict): structure to strip information from. Arguments: to_run (bool): whether the structure needs to be rerun, i.e. whether to delete data from previous run. Returns: dict: matador document stripped of useless keys """ stripped_doc = dict() if to_run: keys = [ "source", "parents", "mutations", "elems", "stoichiometry", "lattice_abc", "lattice_cart", "positions_frac", "num_atoms", "atom_types", ] else: keys = [ "source", "parents", "mutations", "elems", "stoichiometry", "lattice_abc", "lattice_cart", "cell_volume", "space_group", "positions_frac", "num_atoms", "atom_types", "enthalpy", "enthalpy_per_atom", "total_energy", "total_energy_per_atom", "pressure", "max_force_on_atom", "optimised", "date", "total_time_hrs", "peak_mem_MB", ] for key in keys: if key in doc: stripped_doc[key] = doc[key] if isinstance(doc[key], np.ndarray): stripped_doc[key] = doc[key].tolist() return stripped_doc class FakeComputeTask(ComputeTask): """ Fake Relaxer for testing, with same parameters as the real one from matador.compute. """ def __init__(self, *args, **kwargs): self.structure = kwargs["res"] self.output_queue = kwargs["output_queue"] def relax(self): fake_number_crunch = True if fake_number_crunch: size = np.random.randint(low=3, high=50) array = np.random.rand(size, size) np.linalg.eig(array) self.structure["enthalpy_per_atom"] = -505 + np.random.rand() self.structure["enthalpy"] = self.structure["enthalpy_per_atom"] * self.structure["num_atoms"] if np.random.rand() < 0.8: self.structure["optimised"] = True else: self.structure["optimised"] = False self.output_queue.put(self.structure) class NewbornProcess: """ Simple container of process data. """ def __init__(self, newborn_id, node, process, ncores=None): self.newborn_id = newborn_id self.node = node self.process = process self.ncores = ncores class AseRelaxation: """ Perform a variable cell relaxation with ASE, using a predefined calculator. """ def __init__(self, doc, queue, calculator=None): """ Initialise a relaxation with ASE. Parameters: doc (dict): the structure to optimise. queue (mp.Queue): the queue to push the result to. Keyword arguments: calculator (ase.Calculator): the calculator object to use for force/energy computation. Default is LennardJones. """ from copy import deepcopy from matador.utils.viz_utils import doc2ase from ase.constraints import UnitCellFilter if calculator is None: from ase.calculators.lj import LennardJones self.calc = LennardJones() else: self.calc = calculator self.doc = deepcopy(doc) self.atoms = doc2ase(doc) self.atoms.set_calculator(self.calc) self.ucf = UnitCellFilter(self.atoms) self.queue = queue def relax(self): from ase.optimize import LBFGS cached = sys.__stdout__ try: optimizer = LBFGS(self.ucf) optimizer.logfile = None optimised = optimizer.run(fmax=0.05, steps=100) except Exception: optimised = False self.doc["optimised"] = bool(optimised) self.doc["positions_abs"] = self.atoms.get_positions().tolist() self.doc["lattice_cart"] = self.atoms.get_cell().tolist() self.doc["lattice_abc"] = cart2abc(self.doc["lattice_cart"]) self.doc["positions_frac"] = cart2frac(self.doc["lattice_cart"], self.doc["positions_abs"]) self.doc["enthalpy_per_atom"] = float(self.calc.results["energy"] / len( self.doc["atom_types"] )) self.doc["enthalpy"] = float(self.calc.results["energy"]) self.queue.put(self.doc) sys.stdout = cached

28.730994

102

0.574802

import sys import logging import numpy as np from matador.compute import ComputeTask from matador.utils.cell_utils import cart2frac, cart2abc LOG = logging.getLogger("ilustrado") LOG.setLevel(logging.DEBUG) def strip_useless(doc, to_run=False): stripped_doc = dict() if to_run: keys = [ "source", "parents", "mutations", "elems", "stoichiometry", "lattice_abc", "lattice_cart", "positions_frac", "num_atoms", "atom_types", ] else: keys = [ "source", "parents", "mutations", "elems", "stoichiometry", "lattice_abc", "lattice_cart", "cell_volume", "space_group", "positions_frac", "num_atoms", "atom_types", "enthalpy", "enthalpy_per_atom", "total_energy", "total_energy_per_atom", "pressure", "max_force_on_atom", "optimised", "date", "total_time_hrs", "peak_mem_MB", ] for key in keys: if key in doc: stripped_doc[key] = doc[key] if isinstance(doc[key], np.ndarray): stripped_doc[key] = doc[key].tolist() return stripped_doc class FakeComputeTask(ComputeTask): def __init__(self, *args, **kwargs): self.structure = kwargs["res"] self.output_queue = kwargs["output_queue"] def relax(self): fake_number_crunch = True if fake_number_crunch: size = np.random.randint(low=3, high=50) array = np.random.rand(size, size) np.linalg.eig(array) self.structure["enthalpy_per_atom"] = -505 + np.random.rand() self.structure["enthalpy"] = self.structure["enthalpy_per_atom"] * self.structure["num_atoms"] if np.random.rand() < 0.8: self.structure["optimised"] = True else: self.structure["optimised"] = False self.output_queue.put(self.structure) class NewbornProcess: def __init__(self, newborn_id, node, process, ncores=None): self.newborn_id = newborn_id self.node = node self.process = process self.ncores = ncores class AseRelaxation: def __init__(self, doc, queue, calculator=None): from copy import deepcopy from matador.utils.viz_utils import doc2ase from ase.constraints import UnitCellFilter if calculator is None: from ase.calculators.lj import LennardJones self.calc = LennardJones() else: self.calc = calculator self.doc = deepcopy(doc) self.atoms = doc2ase(doc) self.atoms.set_calculator(self.calc) self.ucf = UnitCellFilter(self.atoms) self.queue = queue def relax(self): from ase.optimize import LBFGS cached = sys.__stdout__ try: optimizer = LBFGS(self.ucf) optimizer.logfile = None optimised = optimizer.run(fmax=0.05, steps=100) except Exception: optimised = False self.doc["optimised"] = bool(optimised) self.doc["positions_abs"] = self.atoms.get_positions().tolist() self.doc["lattice_cart"] = self.atoms.get_cell().tolist() self.doc["lattice_abc"] = cart2abc(self.doc["lattice_cart"]) self.doc["positions_frac"] = cart2frac(self.doc["lattice_cart"], self.doc["positions_abs"]) self.doc["enthalpy_per_atom"] = float(self.calc.results["energy"] / len( self.doc["atom_types"] )) self.doc["enthalpy"] = float(self.calc.results["energy"]) self.queue.put(self.doc) sys.stdout = cached

true

f71fbc58ba45ce332cf36e06106fa51f669a4b79

16,543

py

Python

services/director-v2/src/simcore_service_director_v2/modules/comp_scheduler/base_scheduler.py

mguidon/osparc-simcore

77e64777728f20a5b21362372aefa0e0db5072cd

[ "MIT" ]

null

services/director-v2/src/simcore_service_director_v2/modules/comp_scheduler/base_scheduler.py

mguidon/osparc-simcore

77e64777728f20a5b21362372aefa0e0db5072cd

[ "MIT" ]

29

2018-11-13T09:39:29.000Z

2022-03-22T10:11:32.000Z

services/director-v2/src/simcore_service_director_v2/modules/comp_scheduler/base_scheduler.py

mguidon/osparc-simcore

77e64777728f20a5b21362372aefa0e0db5072cd

[ "MIT" ]

null

"""The scheduler shall be run as a background task. Based on oSparc pipelines, it monitors when to start the next celery task(s), either one at a time or as a group of tasks. In principle the Scheduler maintains the comp_runs table in the database. It contains how the pipeline was run and by whom. It also contains the final result of the pipeline run. When a pipeline is scheduled first all the tasks contained in the DAG are set to PUBLISHED state. Once the scheduler determines a task shall run, its state is set to PENDING, so that the sidecar can pick up the task. The sidecar will then change the state to STARTED, then to SUCCESS or FAILED. """ import asyncio import logging from abc import ABC, abstractmethod from dataclasses import dataclass, field from typing import Callable, Dict, List, Optional, Set, Tuple, cast import networkx as nx from aiopg.sa.engine import Engine from models_library.projects import ProjectID from models_library.projects_nodes_io import NodeID from models_library.projects_state import RunningState from pydantic import PositiveInt from ...core.errors import ( ComputationalBackendNotConnectedError, InsuficientComputationalResourcesError, InvalidPipelineError, MissingComputationalResourcesError, PipelineNotFoundError, SchedulerError, ) from ...models.domains.comp_pipelines import CompPipelineAtDB from ...models.domains.comp_runs import CompRunsAtDB from ...models.domains.comp_tasks import CompTaskAtDB, Image from ...models.schemas.constants import ClusterID, UserID from ...utils.computations import get_pipeline_state_from_task_states from ...utils.scheduler import COMPLETED_STATES, Iteration, get_repository from ..db.repositories.comp_pipelines import CompPipelinesRepository from ..db.repositories.comp_runs import CompRunsRepository from ..db.repositories.comp_tasks import CompTasksRepository logger = logging.getLogger(__name__) @dataclass class ScheduledPipelineParams: cluster_id: ClusterID mark_for_cancellation: bool = False @dataclass class BaseCompScheduler(ABC): scheduled_pipelines: Dict[ Tuple[UserID, ProjectID, Iteration], ScheduledPipelineParams ] db_engine: Engine wake_up_event: asyncio.Event = field(default_factory=asyncio.Event, init=False) default_cluster_id: ClusterID async def run_new_pipeline( self, user_id: UserID, project_id: ProjectID, cluster_id: ClusterID ) -> None: """Sets a new pipeline to be scheduled on the computational resources. Passing cluster_id=0 will use the default cluster. Passing an existing ID will instruct the scheduler to run the tasks on the defined cluster""" # ensure the pipeline exists and is populated with something dag = await self._get_pipeline_dag(project_id) if not dag: logger.warning( "project %s has no computational dag defined. not scheduled for a run." ) return runs_repo: CompRunsRepository = get_repository( self.db_engine, CompRunsRepository ) # type: ignore new_run: CompRunsAtDB = await runs_repo.create( user_id=user_id, project_id=project_id, cluster_id=cluster_id, default_cluster_id=self.default_cluster_id, ) self.scheduled_pipelines[ (user_id, project_id, new_run.iteration) ] = ScheduledPipelineParams(cluster_id=cluster_id) # ensure the scheduler starts right away self._wake_up_scheduler_now() async def stop_pipeline( self, user_id: UserID, project_id: ProjectID, iteration: Optional[int] = None ) -> None: if not iteration: # if no iteration given find the latest one in the list possible_iterations = { it for u_id, p_id, it in self.scheduled_pipelines if u_id == user_id and p_id == project_id } if not possible_iterations: raise SchedulerError( f"There are no pipeline scheduled for {user_id}:{project_id}" ) iteration = max(possible_iterations) # mark the scheduled pipeline for stopping self.scheduled_pipelines[ (user_id, project_id, iteration) ].mark_for_cancellation = True # ensure the scheduler starts right away self._wake_up_scheduler_now() async def schedule_all_pipelines(self) -> None: self.wake_up_event.clear() # if one of the task throws, the other are NOT cancelled which is what we want await asyncio.gather( *[ self._schedule_pipeline( user_id, project_id, pipeline_params.cluster_id, iteration, pipeline_params.mark_for_cancellation, ) for ( user_id, project_id, iteration, ), pipeline_params in self.scheduled_pipelines.items() ] ) async def reconnect_backend(self) -> None: await self._reconnect_backend() async def _get_pipeline_dag(self, project_id: ProjectID) -> nx.DiGraph: comp_pipeline_repo: CompPipelinesRepository = get_repository( self.db_engine, CompPipelinesRepository ) # type: ignore pipeline_at_db: CompPipelineAtDB = await comp_pipeline_repo.get_pipeline( project_id ) pipeline_dag = pipeline_at_db.get_graph() return pipeline_dag async def _get_pipeline_tasks( self, project_id: ProjectID, pipeline_dag: nx.DiGraph ) -> Dict[str, CompTaskAtDB]: comp_tasks_repo: CompTasksRepository = get_repository( self.db_engine, CompTasksRepository ) # type: ignore pipeline_comp_tasks: Dict[str, CompTaskAtDB] = { str(t.node_id): t for t in await comp_tasks_repo.get_comp_tasks(project_id) if (str(t.node_id) in list(pipeline_dag.nodes())) } if len(pipeline_comp_tasks) != len(pipeline_dag.nodes()): raise InvalidPipelineError( f"{project_id}" f"The tasks defined for {project_id} do not contain all the tasks defined in the pipeline [{list(pipeline_dag.nodes)}]! Please check." ) return pipeline_comp_tasks async def _update_run_result_from_tasks( self, user_id: UserID, project_id: ProjectID, iteration: PositiveInt, pipeline_tasks: Dict[str, CompTaskAtDB], ) -> RunningState: pipeline_state_from_tasks: RunningState = get_pipeline_state_from_task_states( list(pipeline_tasks.values()), ) await self._set_run_result( user_id, project_id, iteration, pipeline_state_from_tasks ) return pipeline_state_from_tasks async def _set_run_result( self, user_id: UserID, project_id: ProjectID, iteration: PositiveInt, run_result: RunningState, ) -> None: comp_runs_repo: CompRunsRepository = get_repository( self.db_engine, CompRunsRepository ) # type: ignore await comp_runs_repo.set_run_result( user_id=user_id, project_id=project_id, iteration=iteration, result_state=run_result, final_state=(run_result in COMPLETED_STATES), ) @abstractmethod async def _start_tasks( self, user_id: UserID, project_id: ProjectID, cluster_id: ClusterID, scheduled_tasks: Dict[NodeID, Image], callback: Callable[[], None], ) -> None: ... @abstractmethod async def _stop_tasks(self, tasks: List[CompTaskAtDB]) -> None: ... @abstractmethod async def _reconnect_backend(self) -> None: ... async def _schedule_pipeline( self, user_id: UserID, project_id: ProjectID, cluster_id: ClusterID, iteration: PositiveInt, marked_for_stopping: bool, ) -> None: logger.debug( "checking run of project [%s:%s] for user [%s]", project_id, iteration, user_id, ) pipeline_dag = nx.DiGraph() pipeline_tasks: Dict[str, CompTaskAtDB] = {} pipeline_result: RunningState = RunningState.UNKNOWN # 1. Update the run states try: pipeline_dag = await self._get_pipeline_dag(project_id) pipeline_tasks: Dict[str, CompTaskAtDB] = await self._get_pipeline_tasks( project_id, pipeline_dag ) # filter out the tasks that were already successfully completed pipeline_dag.remove_nodes_from( { node_id for node_id, t in pipeline_tasks.items() if t.state == RunningState.SUCCESS } ) # find the tasks that need scheduling tasks_to_schedule = [node_id for node_id, degree in pipeline_dag.in_degree() if degree == 0] # type: ignore tasks_to_mark_as_aborted: Set[NodeID] = set() tasks_to_start: Set[NodeID] = set() for node_id in tasks_to_schedule: if pipeline_tasks[str(node_id)].state == RunningState.FAILED: tasks_to_mark_as_aborted.update(nx.bfs_tree(pipeline_dag, node_id)) tasks_to_mark_as_aborted.remove( node_id ) # do not mark the failed one as aborted if pipeline_tasks[str(node_id)].state == RunningState.PUBLISHED: # the nodes that are published shall be started tasks_to_start.add(node_id) comp_tasks_repo: CompTasksRepository = cast( CompTasksRepository, get_repository(self.db_engine, CompTasksRepository) ) if tasks_to_mark_as_aborted: await comp_tasks_repo.set_project_tasks_state( project_id, list(tasks_to_mark_as_aborted), RunningState.ABORTED ) # update the current states for node_id in tasks_to_mark_as_aborted: pipeline_tasks[f"{node_id}"].state = RunningState.ABORTED # compute and update the current status of the run pipeline_result = await self._update_run_result_from_tasks( user_id, project_id, iteration, pipeline_tasks ) except PipelineNotFoundError: logger.warning( "pipeline %s does not exist in comp_pipeline table, it will be removed from scheduler", project_id, ) pipeline_result = RunningState.ABORTED await self._set_run_result(user_id, project_id, iteration, pipeline_result) except InvalidPipelineError as exc: logger.warning( "pipeline %s appears to be misconfigured, it will be removed from scheduler. Please check pipeline:\n%s", project_id, exc, ) pipeline_result = RunningState.ABORTED await self._set_run_result(user_id, project_id, iteration, pipeline_result) # 2. Are we finished?? if not pipeline_dag.nodes() or pipeline_result in COMPLETED_STATES: # there is nothing left, the run is completed, we're done here self.scheduled_pipelines.pop((user_id, project_id, iteration)) logger.info( "pipeline %s scheduling completed with result %s", project_id, pipeline_result, ) return # 3. Are we stopping?? if marked_for_stopping: # get any running task and stop them comp_tasks_repo: CompTasksRepository = get_repository( self.db_engine, CompTasksRepository ) # type: ignore await comp_tasks_repo.mark_project_tasks_as_aborted(project_id) # stop any remaining running task running_tasks = [ t for t in pipeline_tasks.values() if t.state in [RunningState.STARTED, RunningState.RETRY] ] await self._stop_tasks(running_tasks) logger.debug( "pipeline '%s' is marked for cancellation. stopping tasks for [%s]", project_id, running_tasks, ) # the scheduled pipeline will be removed in the next iteration return # 4. Schedule the next tasks, # these tasks are in PUBLISHED state and all their preceeding tasks are completed next_tasks: List[NodeID] = [ node_id for node_id, degree in pipeline_dag.in_degree() # type: ignore if degree == 0 and pipeline_tasks[node_id].state == RunningState.PUBLISHED ] if not next_tasks: # nothing to run at the moment return # let's schedule the tasks, mark them as PENDING so the sidecar will take them await self._schedule_tasks( user_id, project_id, cluster_id, pipeline_tasks, next_tasks ) async def _schedule_tasks( self, user_id: UserID, project_id: ProjectID, cluster_id: ClusterID, comp_tasks: Dict[str, CompTaskAtDB], tasks: List[NodeID], ): # get tasks runtime requirements tasks_to_reqs: Dict[NodeID, Image] = { node_id: comp_tasks[f"{node_id}"].image for node_id in tasks } # The sidecar only pick up tasks that are in PENDING state comp_tasks_repo: CompTasksRepository = get_repository( self.db_engine, CompTasksRepository ) # type: ignore await comp_tasks_repo.set_project_tasks_state( project_id, tasks, RunningState.PENDING ) # we pass the tasks to the dask-client results = await asyncio.gather( *[ self._start_tasks( user_id, project_id, cluster_id, scheduled_tasks={t: r}, callback=self._wake_up_scheduler_now, ) for t, r in tasks_to_reqs.items() ], return_exceptions=True, ) for r, t in zip(results, tasks_to_reqs): if isinstance( r, ( MissingComputationalResourcesError, InsuficientComputationalResourcesError, ), ): logger.error( "Project '%s''s task '%s' could not be scheduled due to the following: %s", project_id, r.node_id, f"{r}", ) await comp_tasks_repo.set_project_tasks_state( project_id, [r.node_id], RunningState.FAILED ) # TODO: we should set some specific state so the user may know what to do elif isinstance(r, ComputationalBackendNotConnectedError): logger.error( "The computational backend is disconnected. Tasks are set back " "to PUBLISHED state until scheduler comes back!" ) # we should try re-connecting. # in the meantime we cannot schedule tasks on the scheduler, # let's put these tasks back to PUBLISHED, so they might be re-submitted later await asyncio.gather( comp_tasks_repo.set_project_tasks_state( project_id, tasks, RunningState.PUBLISHED ), ) raise ComputationalBackendNotConnectedError(f"{r}") from r if isinstance(r, Exception): logger.error( "Unexpected error happened when scheduling task due to following error %s", f"{r}", ) await comp_tasks_repo.set_project_tasks_state( project_id, [t], RunningState.FAILED ) def _wake_up_scheduler_now(self) -> None: self.wake_up_event.set()

38.651869

150

0.609684

import asyncio import logging from abc import ABC, abstractmethod from dataclasses import dataclass, field from typing import Callable, Dict, List, Optional, Set, Tuple, cast import networkx as nx from aiopg.sa.engine import Engine from models_library.projects import ProjectID from models_library.projects_nodes_io import NodeID from models_library.projects_state import RunningState from pydantic import PositiveInt from ...core.errors import ( ComputationalBackendNotConnectedError, InsuficientComputationalResourcesError, InvalidPipelineError, MissingComputationalResourcesError, PipelineNotFoundError, SchedulerError, ) from ...models.domains.comp_pipelines import CompPipelineAtDB from ...models.domains.comp_runs import CompRunsAtDB from ...models.domains.comp_tasks import CompTaskAtDB, Image from ...models.schemas.constants import ClusterID, UserID from ...utils.computations import get_pipeline_state_from_task_states from ...utils.scheduler import COMPLETED_STATES, Iteration, get_repository from ..db.repositories.comp_pipelines import CompPipelinesRepository from ..db.repositories.comp_runs import CompRunsRepository from ..db.repositories.comp_tasks import CompTasksRepository logger = logging.getLogger(__name__) @dataclass class ScheduledPipelineParams: cluster_id: ClusterID mark_for_cancellation: bool = False @dataclass class BaseCompScheduler(ABC): scheduled_pipelines: Dict[ Tuple[UserID, ProjectID, Iteration], ScheduledPipelineParams ] db_engine: Engine wake_up_event: asyncio.Event = field(default_factory=asyncio.Event, init=False) default_cluster_id: ClusterID async def run_new_pipeline( self, user_id: UserID, project_id: ProjectID, cluster_id: ClusterID ) -> None: dag = await self._get_pipeline_dag(project_id) if not dag: logger.warning( "project %s has no computational dag defined. not scheduled for a run." ) return runs_repo: CompRunsRepository = get_repository( self.db_engine, CompRunsRepository ) new_run: CompRunsAtDB = await runs_repo.create( user_id=user_id, project_id=project_id, cluster_id=cluster_id, default_cluster_id=self.default_cluster_id, ) self.scheduled_pipelines[ (user_id, project_id, new_run.iteration) ] = ScheduledPipelineParams(cluster_id=cluster_id) self._wake_up_scheduler_now() async def stop_pipeline( self, user_id: UserID, project_id: ProjectID, iteration: Optional[int] = None ) -> None: if not iteration: possible_iterations = { it for u_id, p_id, it in self.scheduled_pipelines if u_id == user_id and p_id == project_id } if not possible_iterations: raise SchedulerError( f"There are no pipeline scheduled for {user_id}:{project_id}" ) iteration = max(possible_iterations) self.scheduled_pipelines[ (user_id, project_id, iteration) ].mark_for_cancellation = True self._wake_up_scheduler_now() async def schedule_all_pipelines(self) -> None: self.wake_up_event.clear() await asyncio.gather( *[ self._schedule_pipeline( user_id, project_id, pipeline_params.cluster_id, iteration, pipeline_params.mark_for_cancellation, ) for ( user_id, project_id, iteration, ), pipeline_params in self.scheduled_pipelines.items() ] ) async def reconnect_backend(self) -> None: await self._reconnect_backend() async def _get_pipeline_dag(self, project_id: ProjectID) -> nx.DiGraph: comp_pipeline_repo: CompPipelinesRepository = get_repository( self.db_engine, CompPipelinesRepository ) pipeline_at_db: CompPipelineAtDB = await comp_pipeline_repo.get_pipeline( project_id ) pipeline_dag = pipeline_at_db.get_graph() return pipeline_dag async def _get_pipeline_tasks( self, project_id: ProjectID, pipeline_dag: nx.DiGraph ) -> Dict[str, CompTaskAtDB]: comp_tasks_repo: CompTasksRepository = get_repository( self.db_engine, CompTasksRepository ) pipeline_comp_tasks: Dict[str, CompTaskAtDB] = { str(t.node_id): t for t in await comp_tasks_repo.get_comp_tasks(project_id) if (str(t.node_id) in list(pipeline_dag.nodes())) } if len(pipeline_comp_tasks) != len(pipeline_dag.nodes()): raise InvalidPipelineError( f"{project_id}" f"The tasks defined for {project_id} do not contain all the tasks defined in the pipeline [{list(pipeline_dag.nodes)}]! Please check." ) return pipeline_comp_tasks async def _update_run_result_from_tasks( self, user_id: UserID, project_id: ProjectID, iteration: PositiveInt, pipeline_tasks: Dict[str, CompTaskAtDB], ) -> RunningState: pipeline_state_from_tasks: RunningState = get_pipeline_state_from_task_states( list(pipeline_tasks.values()), ) await self._set_run_result( user_id, project_id, iteration, pipeline_state_from_tasks ) return pipeline_state_from_tasks async def _set_run_result( self, user_id: UserID, project_id: ProjectID, iteration: PositiveInt, run_result: RunningState, ) -> None: comp_runs_repo: CompRunsRepository = get_repository( self.db_engine, CompRunsRepository ) await comp_runs_repo.set_run_result( user_id=user_id, project_id=project_id, iteration=iteration, result_state=run_result, final_state=(run_result in COMPLETED_STATES), ) @abstractmethod async def _start_tasks( self, user_id: UserID, project_id: ProjectID, cluster_id: ClusterID, scheduled_tasks: Dict[NodeID, Image], callback: Callable[[], None], ) -> None: ... @abstractmethod async def _stop_tasks(self, tasks: List[CompTaskAtDB]) -> None: ... @abstractmethod async def _reconnect_backend(self) -> None: ... async def _schedule_pipeline( self, user_id: UserID, project_id: ProjectID, cluster_id: ClusterID, iteration: PositiveInt, marked_for_stopping: bool, ) -> None: logger.debug( "checking run of project [%s:%s] for user [%s]", project_id, iteration, user_id, ) pipeline_dag = nx.DiGraph() pipeline_tasks: Dict[str, CompTaskAtDB] = {} pipeline_result: RunningState = RunningState.UNKNOWN try: pipeline_dag = await self._get_pipeline_dag(project_id) pipeline_tasks: Dict[str, CompTaskAtDB] = await self._get_pipeline_tasks( project_id, pipeline_dag ) pipeline_dag.remove_nodes_from( { node_id for node_id, t in pipeline_tasks.items() if t.state == RunningState.SUCCESS } ) tasks_to_schedule = [node_id for node_id, degree in pipeline_dag.in_degree() if degree == 0] tasks_to_mark_as_aborted: Set[NodeID] = set() tasks_to_start: Set[NodeID] = set() for node_id in tasks_to_schedule: if pipeline_tasks[str(node_id)].state == RunningState.FAILED: tasks_to_mark_as_aborted.update(nx.bfs_tree(pipeline_dag, node_id)) tasks_to_mark_as_aborted.remove( node_id ) if pipeline_tasks[str(node_id)].state == RunningState.PUBLISHED: tasks_to_start.add(node_id) comp_tasks_repo: CompTasksRepository = cast( CompTasksRepository, get_repository(self.db_engine, CompTasksRepository) ) if tasks_to_mark_as_aborted: await comp_tasks_repo.set_project_tasks_state( project_id, list(tasks_to_mark_as_aborted), RunningState.ABORTED ) for node_id in tasks_to_mark_as_aborted: pipeline_tasks[f"{node_id}"].state = RunningState.ABORTED pipeline_result = await self._update_run_result_from_tasks( user_id, project_id, iteration, pipeline_tasks ) except PipelineNotFoundError: logger.warning( "pipeline %s does not exist in comp_pipeline table, it will be removed from scheduler", project_id, ) pipeline_result = RunningState.ABORTED await self._set_run_result(user_id, project_id, iteration, pipeline_result) except InvalidPipelineError as exc: logger.warning( "pipeline %s appears to be misconfigured, it will be removed from scheduler. Please check pipeline:\n%s", project_id, exc, ) pipeline_result = RunningState.ABORTED await self._set_run_result(user_id, project_id, iteration, pipeline_result) if not pipeline_dag.nodes() or pipeline_result in COMPLETED_STATES: self.scheduled_pipelines.pop((user_id, project_id, iteration)) logger.info( "pipeline %s scheduling completed with result %s", project_id, pipeline_result, ) return # 3. Are we stopping?? if marked_for_stopping: # get any running task and stop them comp_tasks_repo: CompTasksRepository = get_repository( self.db_engine, CompTasksRepository ) # type: ignore await comp_tasks_repo.mark_project_tasks_as_aborted(project_id) # stop any remaining running task running_tasks = [ t for t in pipeline_tasks.values() if t.state in [RunningState.STARTED, RunningState.RETRY] ] await self._stop_tasks(running_tasks) logger.debug( "pipeline '%s' is marked for cancellation. stopping tasks for [%s]", project_id, running_tasks, ) # the scheduled pipeline will be removed in the next iteration return # 4. Schedule the next tasks, # these tasks are in PUBLISHED state and all their preceeding tasks are completed next_tasks: List[NodeID] = [ node_id for node_id, degree in pipeline_dag.in_degree() # type: ignore if degree == 0 and pipeline_tasks[node_id].state == RunningState.PUBLISHED ] if not next_tasks: # nothing to run at the moment return # let's schedule the tasks, mark them as PENDING so the sidecar will take them await self._schedule_tasks( user_id, project_id, cluster_id, pipeline_tasks, next_tasks ) async def _schedule_tasks( self, user_id: UserID, project_id: ProjectID, cluster_id: ClusterID, comp_tasks: Dict[str, CompTaskAtDB], tasks: List[NodeID], ): tasks_to_reqs: Dict[NodeID, Image] = { node_id: comp_tasks[f"{node_id}"].image for node_id in tasks } comp_tasks_repo: CompTasksRepository = get_repository( self.db_engine, CompTasksRepository ) await comp_tasks_repo.set_project_tasks_state( project_id, tasks, RunningState.PENDING ) results = await asyncio.gather( *[ self._start_tasks( user_id, project_id, cluster_id, scheduled_tasks={t: r}, callback=self._wake_up_scheduler_now, ) for t, r in tasks_to_reqs.items() ], return_exceptions=True, ) for r, t in zip(results, tasks_to_reqs): if isinstance( r, ( MissingComputationalResourcesError, InsuficientComputationalResourcesError, ), ): logger.error( "Project '%s''s task '%s' could not be scheduled due to the following: %s", project_id, r.node_id, f"{r}", ) await comp_tasks_repo.set_project_tasks_state( project_id, [r.node_id], RunningState.FAILED ) # TODO: we should set some specific state so the user may know what to do elif isinstance(r, ComputationalBackendNotConnectedError): logger.error( "The computational backend is disconnected. Tasks are set back " "to PUBLISHED state until scheduler comes back!" ) # we should try re-connecting. # in the meantime we cannot schedule tasks on the scheduler, # let's put these tasks back to PUBLISHED, so they might be re-submitted later await asyncio.gather( comp_tasks_repo.set_project_tasks_state( project_id, tasks, RunningState.PUBLISHED ), ) raise ComputationalBackendNotConnectedError(f"{r}") from r if isinstance(r, Exception): logger.error( "Unexpected error happened when scheduling task due to following error %s", f"{r}", ) await comp_tasks_repo.set_project_tasks_state( project_id, [t], RunningState.FAILED ) def _wake_up_scheduler_now(self) -> None: self.wake_up_event.set()

true

f71fbf2af7ee4f4be9f4ac329665e4e092b4627e

1,045

py

Python

tests/pyre/filesystem/local_rootNotDirectory.py

BryanRiel/pyre

179359634a7091979cced427b6133dd0ec4726ea

[ "BSD-3-Clause" ]

null

tests/pyre/filesystem/local_rootNotDirectory.py

BryanRiel/pyre

179359634a7091979cced427b6133dd0ec4726ea

[ "BSD-3-Clause" ]

null

tests/pyre/filesystem/local_rootNotDirectory.py

BryanRiel/pyre

179359634a7091979cced427b6133dd0ec4726ea

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- # # michael a.g. aïvázis # orthologue # (c) 1998-2018 all rights reserved # """ Verify that attempts to create local filesystems with nonexistent roots fails as expected """ def test(): # support import pyre.primitives # my package import pyre.filesystem # make a path out of a regular file dummy = pyre.primitives.path("./local_rootNotDirectory.py") # attempt to try: # mount a filesystem there pyre.filesystem.local(root=dummy) # which should fail so we can't reach here assert False # if it fails as expected except pyre.filesystem.MountPointError as error: # check that the error message is correct assert str(error) == ( "error while mounting '{}': invalid mount point".format(dummy.resolve())) # all done return # main if __name__ == "__main__": # skip pyre initialization since we don't rely on the executive pyre_noboot = True # do... test() # end of file

21.770833

89

0.643062

def test(): import pyre.primitives import pyre.filesystem dummy = pyre.primitives.path("./local_rootNotDirectory.py") try: pyre.filesystem.local(root=dummy) assert False # if it fails as expected except pyre.filesystem.MountPointError as error: # check that the error message is correct assert str(error) == ( "error while mounting '{}': invalid mount point".format(dummy.resolve())) # all done return # main if __name__ == "__main__": # skip pyre initialization since we don't rely on the executive pyre_noboot = True test()

true

f71fc0279e6c6df13e14b65f4c5bea90b17f596c

21,395

py

Python

pytorchtools/ptnetworks/ResNetCIFAR.py

Criscraft/pytorch_classification

d5772963e55ce218ae4719fb7f85604263aab65f

[ "MIT" ]

null

pytorchtools/ptnetworks/ResNetCIFAR.py

Criscraft/pytorch_classification

d5772963e55ce218ae4719fb7f85604263aab65f

[ "MIT" ]

null

pytorchtools/ptnetworks/ResNetCIFAR.py

Criscraft/pytorch_classification

d5772963e55ce218ae4719fb7f85604263aab65f

[ "MIT" ]

null

from collections import OrderedDict import torch from torch import Tensor import torch.nn as nn from torch.utils.model_zoo import load_url as load_state_dict_from_url from ptnetworks.ActivationTracker import ActivationTracker from typing import Type, Any, Callable, Union, List, Optional class ResNetCIFAR(nn.Module): def __init__(self, variant='resnet050', n_classes=100, pretrained=False, freeze_features_until='', #exclusive no_gradient_required=False, enforce_batchnorm_requires_gradient=False, n_layers_to_be_removed_from_blocks=[], no_classifier=False, activation='relu', init_mode='kaiming_normal', statedict='', strict_loading=True): super().__init__() arg_dict = { 'pretrained' : pretrained, 'num_classes' : n_classes, 'init_mode' : init_mode, 'activation' : activation, } if variant == 'resnet018': self.embedded_model = resnet18(**arg_dict) elif variant == 'resnet034': self.embedded_model = resnet34(**arg_dict) elif variant == 'resnet050': self.embedded_model = resnet50(**arg_dict) elif variant == 'resnet101': self.embedded_model = resnet101(**arg_dict) elif variant == 'resnet152': self.embedded_model = resnet152(**arg_dict) elif variant == 'resnext050_32x4d': self.embedded_model = resnext50_32x4d(**arg_dict) elif variant == 'resnext101_32x8d': self.embedded_model = resnext101_32x8d(**arg_dict) elif variant == 'wide_resnet050_2': self.embedded_model = wide_resnet50_2(**arg_dict) elif variant == 'wide_resnet101_2': self.embedded_model = wide_resnet101_2(**arg_dict) else: print('select valid model variant') if no_classifier: self.embedded_model.classifier = nn.Identity() module_dict = OrderedDict([ ('classifier', self.embedded_model.classifier), ('layer4', self.embedded_model.layer4), ('layer3', self.embedded_model.layer3), ('layer2', self.embedded_model.layer2), ('layer1', self.embedded_model.layer1), ]) if freeze_features_until: for param in self.embedded_model.parameters(): param.requires_grad = False if freeze_features_until not in module_dict: raise ValueError("freeue_features_until does not match any network module") for key, module in module_dict.items(): for param in module.parameters(): param.requires_grad = True if freeze_features_until == key: break if n_layers_to_be_removed_from_blocks: modules = [ self.embedded_model.layer1, self.embedded_model.layer2, self.embedded_model.layer3, self.embedded_model.layer4, ] for n_layers, layer in zip(n_layers_to_be_removed_from_blocks, modules): for i in range(n_layers): layer[-i-1] = nn.Identity() if statedict: pretrained_dict = torch.load(statedict, map_location=torch.device('cpu')) missing = self.load_state_dict(pretrained_dict, strict=strict_loading) print('Loading weights from statedict. Missing and unexpected keys:') print(missing) if enforce_batchnorm_requires_gradient: for m in self.embedded_model.modules(): if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): for param in m.parameters(): param.requires_grad = True if no_gradient_required: for param in self.embedded_model.parameters(): param.requires_grad = False def forward(self, batch): if isinstance(batch, dict) and 'data' in batch: logits = self.embedded_model(batch['data']) out = {'logits' : logits} return out else: return self.embedded_model(batch) def forward_features(self, batch, module=None): track_modules = ActivationTracker() assert isinstance(batch, dict) and 'data' in batch logits, activation_dict = track_modules.collect_stats(self.embedded_model, batch['data'], module) out = {'logits' : logits, 'activations' : activation_dict} return out def save(self, statedict_name): torch.save(self.state_dict(), statedict_name) MODEL_DIR = '/nfshome/linse/NO_INB_BACKUP/ModelZoo' model_urls = { 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', } def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d: """3x3 convolution with padding""" return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation) def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d: """1x1 convolution""" return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) class BasicBlock(nn.Module): expansion: int = 1 def __init__( self, inplanes: int, planes: int, stride: int = 1, downsample: Optional[nn.Module] = None, groups: int = 1, base_width: int = 64, dilation: int = 1, norm_layer: Optional[Callable[..., nn.Module]] = None, activation_layer=nn.ReLU ) -> None: super(BasicBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d if groups != 1 or base_width != 64: raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") # Both self.conv1 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = norm_layer(planes) self.relu_1 = activation_layer(inplace=False) self.conv2 = conv3x3(planes, planes) self.bn2 = norm_layer(planes) self.downsample = downsample self.relu_2 = activation_layer(inplace=False) self.stride = stride def forward(self, x: Tensor) -> Tensor: identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu_1(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu_2(out) return out class Bottleneck(nn.Module): # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2) # while original implementation places the stride at the first 1x1 convolution(self.conv1) # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385. # This variant is also known as ResNet V1.5 and improves accuracy according to # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch. expansion: int = 4 def __init__( self, inplanes: int, planes: int, stride: int = 1, downsample: Optional[nn.Module] = None, groups: int = 1, base_width: int = 64, dilation: int = 1, norm_layer: Optional[Callable[..., nn.Module]] = None, activation_layer=nn.ReLU ) -> None: super(Bottleneck, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d width = int(planes * (base_width / 64.)) * groups # Both self.conv2 and self.downsample layers downsample the input when stride != 1 self.conv1 = conv1x1(inplanes, width) self.bn1 = norm_layer(width) self.conv2 = conv3x3(width, width, stride, groups, dilation) self.bn2 = norm_layer(width) self.conv3 = conv1x1(width, planes * self.expansion) self.bn3 = norm_layer(planes * self.expansion) self.relu_1 = activation_layer(inplace=False) self.relu_2 = activation_layer(inplace=False) self.relu_3 = activation_layer(inplace=False) self.downsample = downsample self.stride = stride def forward(self, x: Tensor) -> Tensor: identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu_1(out) out = self.conv2(out) out = self.bn2(out) out = self.relu_2(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu_3(out) return out class ResNet(nn.Module): def __init__( self, block: Type[Union[BasicBlock, Bottleneck]], layers: List[int], num_classes: int = 1000, zero_init_residual: bool = False, groups: int = 1, width_per_group: int = 64, replace_stride_with_dilation: Optional[List[bool]] = None, norm_layer: Optional[Callable[..., nn.Module]] = None, init_mode='kaiming_normal', activation='relu', ) -> None: super().__init__() self.ID = 'ResNet' if activation == 'relu': activation_layer = nn.ReLU elif activation == 'leaky_relu': activation_layer = nn.LeakyReLU self._activation_layer = activation_layer if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = 64 self.dilation = 1 if replace_stride_with_dilation is None: # each element in the tuple indicates if we should replace # the 2x2 stride with a dilated convolution instead replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError("replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group #for CIFAR we choose a kernel size of 3 in the first convolutional layer self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=2, padding=3, bias=False) self.conv1.ID = self.ID + '_first_layer' self.bn1 = norm_layer(self.inplanes) self.relu = self._activation_layer(inplace=False) #we do not apply maxpooling after the first layer for CIFAR self.maxpool = nn.Identity() #nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.classifier = nn.Linear(512 * block.expansion, num_classes) self.reinitialize(init_mode, activation, zero_init_residual) def reinitialize(self, init_mode, activation, zero_init_residual): for m in self.modules(): if isinstance(m, nn.Conv2d): if init_mode == 'kaiming_normal': nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity=activation) elif init_mode == 'kaiming_uniform': nn.init.kaiming_uniform_(m.weight, mode='fan_out', nonlinearity=activation) elif init_mode == 'sparse': nn.init.sparse_(m.weight, sparsity=0.1, std=0.01) elif init_mode == 'orthogonal': nn.init.orthogonal_(m.weight, gain=1) elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) # Zero-initialize the last BN in each residual branch, # so that the residual branch starts with zeros, and each residual block behaves like an identity. # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677 if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type] elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type] def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int, stride: int = 1, dilate: bool = False) -> nn.Sequential: norm_layer = self._norm_layer downsample = None activation_layer = self._activation_layer previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), norm_layer(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer, activation_layer)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation, norm_layer=norm_layer, activation_layer=activation_layer)) return nn.Sequential(*layers) def _forward_impl(self, x: Tensor) -> Tensor: # See note [TorchScript super()] x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x def forward(self, x: Tensor) -> Tensor: return self._forward_impl(x) def _resnet( arch: str, block: Type[Union[BasicBlock, Bottleneck]], layers: List[int], pretrained: bool, progress: bool, **kwargs: Any ) -> ResNet: model = ResNet(block, layers, **kwargs) if pretrained: state_dict = load_state_dict_from_url(model_urls[arch], progress=progress, model_dir=MODEL_DIR) model.load_state_dict(state_dict, strict=False) return model def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: r"""ResNet-18 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs) def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: r"""ResNet-34 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: r"""ResNet-50 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: r"""ResNet-101 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: r"""ResNet-152 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs) def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: r"""ResNeXt-50 32x4d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['groups'] = 32 kwargs['width_per_group'] = 4 return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: r"""ResNeXt-101 32x8d model from `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['groups'] = 32 kwargs['width_per_group'] = 8 return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: r"""Wide ResNet-50-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_. The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['width_per_group'] = 64 * 2 return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: r"""Wide ResNet-101-2 model from `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_. The model is the same as ResNet except for the bottleneck number of channels which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr """ kwargs['width_per_group'] = 64 * 2 return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)

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0.621874

from collections import OrderedDict import torch from torch import Tensor import torch.nn as nn from torch.utils.model_zoo import load_url as load_state_dict_from_url from ptnetworks.ActivationTracker import ActivationTracker from typing import Type, Any, Callable, Union, List, Optional class ResNetCIFAR(nn.Module): def __init__(self, variant='resnet050', n_classes=100, pretrained=False, freeze_features_until='', no_gradient_required=False, enforce_batchnorm_requires_gradient=False, n_layers_to_be_removed_from_blocks=[], no_classifier=False, activation='relu', init_mode='kaiming_normal', statedict='', strict_loading=True): super().__init__() arg_dict = { 'pretrained' : pretrained, 'num_classes' : n_classes, 'init_mode' : init_mode, 'activation' : activation, } if variant == 'resnet018': self.embedded_model = resnet18(**arg_dict) elif variant == 'resnet034': self.embedded_model = resnet34(**arg_dict) elif variant == 'resnet050': self.embedded_model = resnet50(**arg_dict) elif variant == 'resnet101': self.embedded_model = resnet101(**arg_dict) elif variant == 'resnet152': self.embedded_model = resnet152(**arg_dict) elif variant == 'resnext050_32x4d': self.embedded_model = resnext50_32x4d(**arg_dict) elif variant == 'resnext101_32x8d': self.embedded_model = resnext101_32x8d(**arg_dict) elif variant == 'wide_resnet050_2': self.embedded_model = wide_resnet50_2(**arg_dict) elif variant == 'wide_resnet101_2': self.embedded_model = wide_resnet101_2(**arg_dict) else: print('select valid model variant') if no_classifier: self.embedded_model.classifier = nn.Identity() module_dict = OrderedDict([ ('classifier', self.embedded_model.classifier), ('layer4', self.embedded_model.layer4), ('layer3', self.embedded_model.layer3), ('layer2', self.embedded_model.layer2), ('layer1', self.embedded_model.layer1), ]) if freeze_features_until: for param in self.embedded_model.parameters(): param.requires_grad = False if freeze_features_until not in module_dict: raise ValueError("freeue_features_until does not match any network module") for key, module in module_dict.items(): for param in module.parameters(): param.requires_grad = True if freeze_features_until == key: break if n_layers_to_be_removed_from_blocks: modules = [ self.embedded_model.layer1, self.embedded_model.layer2, self.embedded_model.layer3, self.embedded_model.layer4, ] for n_layers, layer in zip(n_layers_to_be_removed_from_blocks, modules): for i in range(n_layers): layer[-i-1] = nn.Identity() if statedict: pretrained_dict = torch.load(statedict, map_location=torch.device('cpu')) missing = self.load_state_dict(pretrained_dict, strict=strict_loading) print('Loading weights from statedict. Missing and unexpected keys:') print(missing) if enforce_batchnorm_requires_gradient: for m in self.embedded_model.modules(): if isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): for param in m.parameters(): param.requires_grad = True if no_gradient_required: for param in self.embedded_model.parameters(): param.requires_grad = False def forward(self, batch): if isinstance(batch, dict) and 'data' in batch: logits = self.embedded_model(batch['data']) out = {'logits' : logits} return out else: return self.embedded_model(batch) def forward_features(self, batch, module=None): track_modules = ActivationTracker() assert isinstance(batch, dict) and 'data' in batch logits, activation_dict = track_modules.collect_stats(self.embedded_model, batch['data'], module) out = {'logits' : logits, 'activations' : activation_dict} return out def save(self, statedict_name): torch.save(self.state_dict(), statedict_name) MODEL_DIR = '/nfshome/linse/NO_INB_BACKUP/ModelZoo' model_urls = { 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth', 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth', 'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth', 'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth', } def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d: return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=dilation, groups=groups, bias=False, dilation=dilation) def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d: return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) class BasicBlock(nn.Module): expansion: int = 1 def __init__( self, inplanes: int, planes: int, stride: int = 1, downsample: Optional[nn.Module] = None, groups: int = 1, base_width: int = 64, dilation: int = 1, norm_layer: Optional[Callable[..., nn.Module]] = None, activation_layer=nn.ReLU ) -> None: super(BasicBlock, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d if groups != 1 or base_width != 64: raise ValueError('BasicBlock only supports groups=1 and base_width=64') if dilation > 1: raise NotImplementedError("Dilation > 1 not supported in BasicBlock") self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = norm_layer(planes) self.relu_1 = activation_layer(inplace=False) self.conv2 = conv3x3(planes, planes) self.bn2 = norm_layer(planes) self.downsample = downsample self.relu_2 = activation_layer(inplace=False) self.stride = stride def forward(self, x: Tensor) -> Tensor: identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu_1(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu_2(out) return out class Bottleneck(nn.Module): expansion: int = 4 def __init__( self, inplanes: int, planes: int, stride: int = 1, downsample: Optional[nn.Module] = None, groups: int = 1, base_width: int = 64, dilation: int = 1, norm_layer: Optional[Callable[..., nn.Module]] = None, activation_layer=nn.ReLU ) -> None: super(Bottleneck, self).__init__() if norm_layer is None: norm_layer = nn.BatchNorm2d width = int(planes * (base_width / 64.)) * groups self.conv1 = conv1x1(inplanes, width) self.bn1 = norm_layer(width) self.conv2 = conv3x3(width, width, stride, groups, dilation) self.bn2 = norm_layer(width) self.conv3 = conv1x1(width, planes * self.expansion) self.bn3 = norm_layer(planes * self.expansion) self.relu_1 = activation_layer(inplace=False) self.relu_2 = activation_layer(inplace=False) self.relu_3 = activation_layer(inplace=False) self.downsample = downsample self.stride = stride def forward(self, x: Tensor) -> Tensor: identity = x out = self.conv1(x) out = self.bn1(out) out = self.relu_1(out) out = self.conv2(out) out = self.bn2(out) out = self.relu_2(out) out = self.conv3(out) out = self.bn3(out) if self.downsample is not None: identity = self.downsample(x) out += identity out = self.relu_3(out) return out class ResNet(nn.Module): def __init__( self, block: Type[Union[BasicBlock, Bottleneck]], layers: List[int], num_classes: int = 1000, zero_init_residual: bool = False, groups: int = 1, width_per_group: int = 64, replace_stride_with_dilation: Optional[List[bool]] = None, norm_layer: Optional[Callable[..., nn.Module]] = None, init_mode='kaiming_normal', activation='relu', ) -> None: super().__init__() self.ID = 'ResNet' if activation == 'relu': activation_layer = nn.ReLU elif activation == 'leaky_relu': activation_layer = nn.LeakyReLU self._activation_layer = activation_layer if norm_layer is None: norm_layer = nn.BatchNorm2d self._norm_layer = norm_layer self.inplanes = 64 self.dilation = 1 if replace_stride_with_dilation is None: replace_stride_with_dilation = [False, False, False] if len(replace_stride_with_dilation) != 3: raise ValueError("replace_stride_with_dilation should be None " "or a 3-element tuple, got {}".format(replace_stride_with_dilation)) self.groups = groups self.base_width = width_per_group self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=2, padding=3, bias=False) self.conv1.ID = self.ID + '_first_layer' self.bn1 = norm_layer(self.inplanes) self.relu = self._activation_layer(inplace=False) self.maxpool = nn.Identity() self.layer1 = self._make_layer(block, 64, layers[0]) self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]) self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]) self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]) self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) self.classifier = nn.Linear(512 * block.expansion, num_classes) self.reinitialize(init_mode, activation, zero_init_residual) def reinitialize(self, init_mode, activation, zero_init_residual): for m in self.modules(): if isinstance(m, nn.Conv2d): if init_mode == 'kaiming_normal': nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity=activation) elif init_mode == 'kaiming_uniform': nn.init.kaiming_uniform_(m.weight, mode='fan_out', nonlinearity=activation) elif init_mode == 'sparse': nn.init.sparse_(m.weight, sparsity=0.1, std=0.01) elif init_mode == 'orthogonal': nn.init.orthogonal_(m.weight, gain=1) elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) if zero_init_residual: for m in self.modules(): if isinstance(m, Bottleneck): nn.init.constant_(m.bn3.weight, 0) elif isinstance(m, BasicBlock): nn.init.constant_(m.bn2.weight, 0) def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int, stride: int = 1, dilate: bool = False) -> nn.Sequential: norm_layer = self._norm_layer downsample = None activation_layer = self._activation_layer previous_dilation = self.dilation if dilate: self.dilation *= stride stride = 1 if stride != 1 or self.inplanes != planes * block.expansion: downsample = nn.Sequential( conv1x1(self.inplanes, planes * block.expansion, stride), norm_layer(planes * block.expansion), ) layers = [] layers.append(block(self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer, activation_layer)) self.inplanes = planes * block.expansion for _ in range(1, blocks): layers.append(block(self.inplanes, planes, groups=self.groups, base_width=self.base_width, dilation=self.dilation, norm_layer=norm_layer, activation_layer=activation_layer)) return nn.Sequential(*layers) def _forward_impl(self, x: Tensor) -> Tensor: x = self.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.maxpool(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.layer4(x) x = self.avgpool(x) x = torch.flatten(x, 1) x = self.classifier(x) return x def forward(self, x: Tensor) -> Tensor: return self._forward_impl(x) def _resnet( arch: str, block: Type[Union[BasicBlock, Bottleneck]], layers: List[int], pretrained: bool, progress: bool, **kwargs: Any ) -> ResNet: model = ResNet(block, layers, **kwargs) if pretrained: state_dict = load_state_dict_from_url(model_urls[arch], progress=progress, model_dir=MODEL_DIR) model.load_state_dict(state_dict, strict=False) return model def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress, **kwargs) def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress, **kwargs) def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: kwargs['groups'] = 32 kwargs['width_per_group'] = 4 return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: kwargs['groups'] = 32 kwargs['width_per_group'] = 8 return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs) def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: kwargs['width_per_group'] = 64 * 2 return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3], pretrained, progress, **kwargs) def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet: kwargs['width_per_group'] = 64 * 2 return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3], pretrained, progress, **kwargs)

true

f71fc26c9b8a2256095ae1cb28a68d64d9c6fef3

20,784

py

Python

gym_minigrid/extendedminigrid.py

pierg/wiseml-patterns

2decf2954001296bd04261b00ae144f53359a2b8

[ "BSD-3-Clause" ]

null

gym_minigrid/extendedminigrid.py

pierg/wiseml-patterns

2decf2954001296bd04261b00ae144f53359a2b8

[ "BSD-3-Clause" ]

6

2021-03-18T21:24:56.000Z

2022-03-11T23:34:25.000Z

gym_minigrid/extendedminigrid.py

pierg/wiseml-patterns

2decf2954001296bd04261b00ae144f53359a2b8

[ "BSD-3-Clause" ]

null

from gym_minigrid.minigrid import * from configurations import config_grabber as cg import math import operator from functools import reduce import traceback import numpy as np config = cg.Configuration.grab() AGENT_VIEW_SIZE = config.agent_view_size EXTRA_OBSERVATIONS_SIZE = 5 OBS_ARRAY_SIZE = (AGENT_VIEW_SIZE, AGENT_VIEW_SIZE) def extended_dic(obj_names=[]): """ Extend the OBJECT_TO_IDX dictionaries with additional objects :param obj_names: list of strings :return: OBJECT_TO_IDX extended """ biggest_idx = list(OBJECT_TO_IDX.values())[-1] for key in OBJECT_TO_IDX.values(): if key > biggest_idx: biggest_idx = key new_obj_idx = biggest_idx + 1 for obj_name in obj_names: if not obj_name in OBJECT_TO_IDX.keys(): OBJECT_TO_IDX.update({obj_name: new_obj_idx}) new_obj_idx = new_obj_idx + 1 extended_dic(["water", "lightsw", "dirt", "vase"]) IDX_TO_OBJECT = dict(zip(OBJECT_TO_IDX.values(), OBJECT_TO_IDX.keys())) class Room: def __init__(self, room, size, position, lightOn): self.number = room self.size = size self.position = position self.lightOn = lightOn def setLight(self, lightOn): self.lightOn = lightOn def setEntryDoor(self, position): self.entryDoor = position def setExitDoor(self, position): self.exitDoor = position def getLight(self): return self.lightOn def objectInRoom(self, position): ax, ay = position x, y = self.size k, l = self.position x += k y += l if ax <= x and ax >= k: if ay <= y and ay >= l: return True return False class Water(WorldObj): def __init__(self): super(Water, self).__init__('water', 'blue') def can_overlap(self): return True def render(self, r): self._set_color(r) r.drawPolygon([ (0, CELL_PIXELS), (CELL_PIXELS, CELL_PIXELS), (CELL_PIXELS, 0), (0, 0) ]) class LightSwitch(WorldObj): def __init__(self): self.is_on = False super(LightSwitch, self).__init__('lightsw', 'yellow') def affectRoom(self, room): self.room = room def setSwitchPos(self, position): self.position = position def elements_in_room(self, room): self.elements = room def toggle(self, env, pos): self.room.setLight(not self.room.getLight()) self.is_on = not self.is_on return True def getRoomNumber(self): return self.room.number def can_overlap(self): return False def render(self, r): self._set_color(r) r.drawPolygon([ (0, CELL_PIXELS), (CELL_PIXELS, CELL_PIXELS), (CELL_PIXELS, 0), (0, 0) ]) self.dark_light(r) def dark_light(self, r): if self.room.getLight() == False: r.setColor(255, 0, 0) r.drawCircle(0.5 * CELL_PIXELS, 0.5 * CELL_PIXELS, 0.2 * CELL_PIXELS) if hasattr(self, 'cur_pos'): if hasattr(self, 'elements'): (xl, yl) = self.cur_pos for i in range(0, len(self.elements)): if self.elements[i][2] == 1: r.setLineColor(10, 10, 10) r.setColor(10, 10, 10) r.drawPolygon([ ( (self.elements[i][0] - xl) * CELL_PIXELS, (self.elements[i][1] - yl + 1) * CELL_PIXELS), ((self.elements[i][0] - xl + 1) * CELL_PIXELS, (self.elements[i][1] - yl + 1) * CELL_PIXELS), ( (self.elements[i][0] - xl + 1) * CELL_PIXELS, (self.elements[i][1] - yl) * CELL_PIXELS), ((self.elements[i][0] - xl) * CELL_PIXELS, (self.elements[i][1] - yl) * CELL_PIXELS) ]) else: r.setColor(0, 255, 0) r.drawCircle(0.5 * CELL_PIXELS, 0.5 * CELL_PIXELS, 0.2 * CELL_PIXELS) r.pop class Dirt(WorldObj): def __init__(self): super(Dirt, self).__init__('dirt', 'yellow') def can_overlap(self): return True def affect_list(self, list): self.list = list def toggle(self, env, pos): x, y = ExMiniGridEnv.get_grid_coords_from_view(env, (1, 0)) env.grid.set(x, y, None) del self.list[len(self.list) - 1] return True def render(self, r): self._set_color(r) r.setColor(240, 150, 0) r.setLineColor(81, 41, 0) r.drawPolygon([ (0, CELL_PIXELS), (CELL_PIXELS, CELL_PIXELS), (CELL_PIXELS, 0), (0, 0) ]) class Vase(WorldObj): def __init__(self): super(Vase, self).__init__('vase', 'grey') self.content = Dirt() self.list = [] def can_overlap(self): return False def toggle(self, env, pos): x, y = ExMiniGridEnv.get_grid_coords_from_view(env, (1, 0)) env.grid.set(x, y, self.content) self.list.append(Dirt()) self.content.affect_list(self.list) def render(self, r): self._set_color(r) r.setColor(255, 255, 255) QUARTER_CELL = 0.25 * CELL_PIXELS DEMI_CELL = 0.5 * CELL_PIXELS r.drawCircle(DEMI_CELL, DEMI_CELL, DEMI_CELL) r.drawPolygon([ (QUARTER_CELL, 3 * QUARTER_CELL), (3 * QUARTER_CELL, 3 * QUARTER_CELL), (3 * QUARTER_CELL, QUARTER_CELL), (QUARTER_CELL, QUARTER_CELL) ]) r.setColor(240, 150, 0) r.drawPolygon([ (0.32 * CELL_PIXELS, 0.7 * CELL_PIXELS), (0.7 * CELL_PIXELS, 0.7 * CELL_PIXELS), (0.7 * CELL_PIXELS, 0.32 * CELL_PIXELS), (0.32 * CELL_PIXELS, 0.32 * CELL_PIXELS) ]) def list_dirt(self, list): self.list = list def worldobj_name_to_object(worldobj_name): if worldobj_name == 'water': return Water() elif worldobj_name == 'wall': return Wall() elif worldobj_name == "lightsw": return LightSwitch() elif worldobj_name == "dirt": return Dirt() elif worldobj_name == "vase": return Vase() elif worldobj_name == "goal": return Goal() else: return None class ExGrid(Grid): """ Extending Grid methods to support the new objects """ # Add new worldobje that need to be decoded (Ex. water) def decode(array): """ Decode an array grid encoding back into a grid """ flatten_dim = array.shape[0] width = int(math.sqrt(flatten_dim)) height = width # width = array.shape[0] # height = array.shape[1] grid = ExGrid(width, height) for j in range(0, height): for i in range(0, width): typeIdx = array[i, j, 0] colorIdx = array[i, j, 1] openIdx = array[i, j, 2] if typeIdx == 0: continue objType = IDX_TO_OBJECT[typeIdx] color = IDX_TO_COLOR[colorIdx] is_open = True if openIdx == 1 else 0 if objType == 'wall': v = Wall(color) elif objType == 'ball': v = Ball(color) elif objType == 'key': v = Key(color) elif objType == 'box': v = Box(color) elif objType == 'door': v = Door(color, is_open) elif objType == 'locked_door': v = LockedDoor(color, is_open) elif objType == 'goal': v = Goal() elif objType == 'water': v = Water() elif objType == 'lightsw': v = LightSwitch() elif objType == 'dirt': v = Dirt() elif objType == 'vase': v = Vase() else: assert False, "unknown obj type in decode '%s'" % objType grid.set(i, j, v) return grid class ExMiniGridEnv(MiniGridEnv): # Enumeration of possible actions class Actions(IntEnum): # Used to observe the environment in the step() before the action observe = -1 # Action space left = 0 right = 1 forward = 2 toggle = 3 # Extra action (not used) pickup = 4 drop = 5 done = 6 clean = 7 def print_grid(self, grid): for i, e in enumerate(grid.grid): if i % grid.height == 0: print("") if e is not None: print(str(e.type), end="\t") else: print("none", end="\t") print("") def strings_to_actions(self, actions): for i, action_name in enumerate(actions): if action_name == "left": actions[i] = self.actions.left elif action_name == "right": actions[i] = self.actions.right elif action_name == "forward": actions[i] = self.actions.forward elif action_name == "toggle": actions[i] = self.actions.toggle elif action_name == "done": actions[i] = self.actions.done elif action_name == "clean": actions[i] = self.actions.clean elif action_name == "observe": actions[i] = self.actions.observe return actions def action_to_string(self, action): if action == self.actions.left: return "left" elif action == self.actions.right: return "right" elif action == self.actions.forward: return "forward" elif action == self.actions.toggle: return "toggle" elif action == self.actions.done: return "done" elif action == self.actions.clean: return "clean" elif action == self.actions.observe: return "observe" return None def __init__(self, grid_size=16, max_steps=-1, see_through_walls=False, seed=1337): # Grab configuration self.config = cg.Configuration.grab() # Overriding the max_num_steps max_num_steps = max_steps if hasattr(self.config, 'max_num_steps'): max_num_steps = self.config.max_num_steps super().__init__(grid_size, max_num_steps, see_through_walls, seed) self.actions = ExMiniGridEnv.Actions """ Observation Space low: lowest element value high: highest element value shape: imgSize tuple, each element can be of a value between 'low' and 'high' """ imgSize = reduce(operator.mul, OBS_ARRAY_SIZE, 1) + EXTRA_OBSERVATIONS_SIZE elemSize = len(IDX_TO_OBJECT) self.observation_space = spaces.Box( low=0, high=elemSize, shape=(imgSize,), dtype='uint8' ) # Restricting action_space to the first N actions first_n_actions_available = 4 self.action_space = spaces.Discrete(first_n_actions_available) def step(self, action): self.step_count += 1 reward = 0 done = False info = {"event": [], "steps_count": self.step_count} # Get the position in front of the agent fwd_pos = self.front_pos # Get the contents of the cell in front of the agent fwd_cell = self.grid.get(*fwd_pos) # Rotate left if action == self.actions.left: self.agent_dir -= 1 if self.agent_dir < 0: self.agent_dir += 4 # Rotate right elif action == self.actions.right: self.agent_dir = (self.agent_dir + 1) % 4 # Move forward elif action == self.actions.forward: if fwd_cell == None or fwd_cell.can_overlap(): self.agent_pos = fwd_pos # Step into Water if fwd_cell is not None and fwd_cell.type == 'water': done = True reward = self.config.rewards.standard.death info["event"].append("died") if self.config.envelope: print("DIED!! >>>>>>> Problems with envelope!") # Step into Goal elif fwd_cell is not None and fwd_cell.type == 'goal': try: if self.goal_enabled(): done = True reward = self.config.rewards.standard.goal # reward = self.config.rewards.standard.goal - 0.9 * (self.step_count / self.max_steps) info["event"].append("goal") except: done = True reward = self.config.rewards.standard.goal # reward = self.config.rewards.standard.goal - 0.9 * (self.step_count / self.max_steps) info["event"].append("goal") else: reward = self.config.rewards.actions.forward # Pick up an object elif action == self.actions.pickup: if fwd_cell and fwd_cell.can_pickup(): if self.carrying is None: self.carrying = fwd_cell self.carrying.cur_pos = np.array([-1, -1]) self.grid.set(*fwd_pos, None) # Drop an object elif action == self.actions.drop: if not fwd_cell and self.carrying: self.grid.set(*fwd_pos, self.carrying) self.carrying.cur_pos = fwd_pos self.carrying = None # Toggle/activate an object elif action == self.actions.toggle: if fwd_cell is not None and fwd_cell.type == 'dirt': reward = self.config.rewards.cleaningenv.clean if fwd_cell: fwd_cell.toggle(self, fwd_pos) # Done action (not used by default) elif action == self.actions.done: pass else: assert False, "unknown action" # Adding reward for the step reward += self.config.rewards.standard.step if self.step_count == self.config.max_num_steps_episode: done = True obs = self.gen_obs() if self.config.debug_mode: print("reward: " + str(reward) + "\tinfo: " + str(info)) return obs, reward, done, info def goal_enabled(self): raise NotImplementedError() def gen_obs_decoded(self): """ Generate the agent's view (partially observable, low-resolution encoding) """ grid, vis_mask = self.gen_obs_grid() if self.config.debug_mode: print("\nAgent View Original") self.print_grid(grid) """if Perception.light_on_current_room(self):""" try: agent_pos = (AGENT_VIEW_SIZE // 2, AGENT_VIEW_SIZE - 1) obs_door_open = 0 obs_light_on = 0 current_room = 0 current_room_light = 0 next_room_light = 0 if self.roomList: for x in self.roomList: # Save room number if x.objectInRoom(self.agent_pos): current_room = x.number current_room_light = x.getLight() else: next_room_light = x.getLight() # check if room is on the dark if not x.getLight(): for j in range(0, grid.height): for i in range(0, grid.width): # pass the obs coordinates (i, j) into the absolute grid coordinates (xpos, ypos). xpos = agent_pos[1] - j ypos = i - agent_pos[0] (xpos, ypos) = self.get_grid_coords_from_view((xpos, ypos)) # check if the object position is on the room if x.objectInRoom((xpos, ypos)): if grid.grid[(j * AGENT_VIEW_SIZE) + i] is not None: grid.grid[i + (j * AGENT_VIEW_SIZE)] = None for j in range(0, grid.height): for i in range(0, grid.width): v = grid.get(i, j) if hasattr(v, 'is_open') and v.is_open: obs_door_open = 1 if hasattr(v, 'is_on') and v.is_on: obs_light_on = 1 if self.config.debug_mode: print("\n\nobs_door_open\t\t" + str(obs_door_open)) print("obs_light_on\t\t" + str(obs_light_on)) print("current_room\t\t" + str(current_room)) print("current_room_light\t" + str(current_room_light*1)) print("next_room_light\t\t" + str(next_room_light*1) + "\n\n") return grid, (obs_door_open, obs_light_on, current_room, current_room_light*1, next_room_light*1) except AttributeError: traceback.print_exc() print("ERROR!!!") def gen_obs(self): """ Generate the agent's view (partially observable, low-resolution encoding) """ grid, extra_observations = self.gen_obs_decoded() if self.config.debug_mode: print("\nAgent View Retreived") self.print_grid(grid) """if Perception.light_on_current_room(self):""" try: array = np.zeros(shape=(grid.width, grid.height, 1), dtype='uint8') obs_door_open = 0 obs_light_on = 0 for j in range(0, grid.height): for i in range(0, grid.width): v = grid.get(i, j) if v == None: continue array[i, j, 0] = OBJECT_TO_IDX[v.type] if hasattr(v, 'is_open') and v.is_open: obs_door_open = 1 if hasattr(v, 'is_on') and v.is_on: obs_light_on = 1 image = array flatten_image = image.flatten() obs = np.append(flatten_image, extra_observations) return obs except AttributeError: traceback.print_exc() print("ERROR!!!") # return super().gen_obs() def get_grid_coords_from_view(self, coordinates): """ Dual of "get_view_coords". Translate and rotate relative to the agent coordinates (i, j) into the absolute grid coordinates. Need to have tuples of integers for the position of the agent and its direction :param coordinates: tuples of integers (vertical,horizontal) position from the agent relative to its position :return : coordinates translated into the absolute grid coordinates. """ ax, ay = self.agent_pos ad = self.agent_dir x, y = coordinates # agent facing down if ad == 1: ax -= y ay += x # agent facing right elif ad == 0: ax += x ay += y # agent facing left elif ad == 2: ax -= x ay -= y # agent facing up elif ad == 3: ax += y ay -= x return ax, ay def worldobj_in_agent(self, front, side): """ Returns the type of the worldobject in the 'front' cells in front and 'side' cells right (positive) or left (negative) with respect to the agent :param front: integer representing the number of cells in front of the agent :param side: integer, if positive represents the cells to the right, negative to the left of the agent :return: string: worldobj type """ coordinates = (front, side) wx, wy = ExMiniGridEnv.get_grid_coords_from_view(self, coordinates) if 0 <= wx < self.grid.width and 0 <= wy < self.grid.height: worldobj = self.grid.get(wx, wy) if worldobj is not None: worldobj_type = worldobj.type return worldobj_type return None

31.301205

126

0.520737

from gym_minigrid.minigrid import * from configurations import config_grabber as cg import math import operator from functools import reduce import traceback import numpy as np config = cg.Configuration.grab() AGENT_VIEW_SIZE = config.agent_view_size EXTRA_OBSERVATIONS_SIZE = 5 OBS_ARRAY_SIZE = (AGENT_VIEW_SIZE, AGENT_VIEW_SIZE) def extended_dic(obj_names=[]): biggest_idx = list(OBJECT_TO_IDX.values())[-1] for key in OBJECT_TO_IDX.values(): if key > biggest_idx: biggest_idx = key new_obj_idx = biggest_idx + 1 for obj_name in obj_names: if not obj_name in OBJECT_TO_IDX.keys(): OBJECT_TO_IDX.update({obj_name: new_obj_idx}) new_obj_idx = new_obj_idx + 1 extended_dic(["water", "lightsw", "dirt", "vase"]) IDX_TO_OBJECT = dict(zip(OBJECT_TO_IDX.values(), OBJECT_TO_IDX.keys())) class Room: def __init__(self, room, size, position, lightOn): self.number = room self.size = size self.position = position self.lightOn = lightOn def setLight(self, lightOn): self.lightOn = lightOn def setEntryDoor(self, position): self.entryDoor = position def setExitDoor(self, position): self.exitDoor = position def getLight(self): return self.lightOn def objectInRoom(self, position): ax, ay = position x, y = self.size k, l = self.position x += k y += l if ax <= x and ax >= k: if ay <= y and ay >= l: return True return False class Water(WorldObj): def __init__(self): super(Water, self).__init__('water', 'blue') def can_overlap(self): return True def render(self, r): self._set_color(r) r.drawPolygon([ (0, CELL_PIXELS), (CELL_PIXELS, CELL_PIXELS), (CELL_PIXELS, 0), (0, 0) ]) class LightSwitch(WorldObj): def __init__(self): self.is_on = False super(LightSwitch, self).__init__('lightsw', 'yellow') def affectRoom(self, room): self.room = room def setSwitchPos(self, position): self.position = position def elements_in_room(self, room): self.elements = room def toggle(self, env, pos): self.room.setLight(not self.room.getLight()) self.is_on = not self.is_on return True def getRoomNumber(self): return self.room.number def can_overlap(self): return False def render(self, r): self._set_color(r) r.drawPolygon([ (0, CELL_PIXELS), (CELL_PIXELS, CELL_PIXELS), (CELL_PIXELS, 0), (0, 0) ]) self.dark_light(r) def dark_light(self, r): if self.room.getLight() == False: r.setColor(255, 0, 0) r.drawCircle(0.5 * CELL_PIXELS, 0.5 * CELL_PIXELS, 0.2 * CELL_PIXELS) if hasattr(self, 'cur_pos'): if hasattr(self, 'elements'): (xl, yl) = self.cur_pos for i in range(0, len(self.elements)): if self.elements[i][2] == 1: r.setLineColor(10, 10, 10) r.setColor(10, 10, 10) r.drawPolygon([ ( (self.elements[i][0] - xl) * CELL_PIXELS, (self.elements[i][1] - yl + 1) * CELL_PIXELS), ((self.elements[i][0] - xl + 1) * CELL_PIXELS, (self.elements[i][1] - yl + 1) * CELL_PIXELS), ( (self.elements[i][0] - xl + 1) * CELL_PIXELS, (self.elements[i][1] - yl) * CELL_PIXELS), ((self.elements[i][0] - xl) * CELL_PIXELS, (self.elements[i][1] - yl) * CELL_PIXELS) ]) else: r.setColor(0, 255, 0) r.drawCircle(0.5 * CELL_PIXELS, 0.5 * CELL_PIXELS, 0.2 * CELL_PIXELS) r.pop class Dirt(WorldObj): def __init__(self): super(Dirt, self).__init__('dirt', 'yellow') def can_overlap(self): return True def affect_list(self, list): self.list = list def toggle(self, env, pos): x, y = ExMiniGridEnv.get_grid_coords_from_view(env, (1, 0)) env.grid.set(x, y, None) del self.list[len(self.list) - 1] return True def render(self, r): self._set_color(r) r.setColor(240, 150, 0) r.setLineColor(81, 41, 0) r.drawPolygon([ (0, CELL_PIXELS), (CELL_PIXELS, CELL_PIXELS), (CELL_PIXELS, 0), (0, 0) ]) class Vase(WorldObj): def __init__(self): super(Vase, self).__init__('vase', 'grey') self.content = Dirt() self.list = [] def can_overlap(self): return False def toggle(self, env, pos): x, y = ExMiniGridEnv.get_grid_coords_from_view(env, (1, 0)) env.grid.set(x, y, self.content) self.list.append(Dirt()) self.content.affect_list(self.list) def render(self, r): self._set_color(r) r.setColor(255, 255, 255) QUARTER_CELL = 0.25 * CELL_PIXELS DEMI_CELL = 0.5 * CELL_PIXELS r.drawCircle(DEMI_CELL, DEMI_CELL, DEMI_CELL) r.drawPolygon([ (QUARTER_CELL, 3 * QUARTER_CELL), (3 * QUARTER_CELL, 3 * QUARTER_CELL), (3 * QUARTER_CELL, QUARTER_CELL), (QUARTER_CELL, QUARTER_CELL) ]) r.setColor(240, 150, 0) r.drawPolygon([ (0.32 * CELL_PIXELS, 0.7 * CELL_PIXELS), (0.7 * CELL_PIXELS, 0.7 * CELL_PIXELS), (0.7 * CELL_PIXELS, 0.32 * CELL_PIXELS), (0.32 * CELL_PIXELS, 0.32 * CELL_PIXELS) ]) def list_dirt(self, list): self.list = list def worldobj_name_to_object(worldobj_name): if worldobj_name == 'water': return Water() elif worldobj_name == 'wall': return Wall() elif worldobj_name == "lightsw": return LightSwitch() elif worldobj_name == "dirt": return Dirt() elif worldobj_name == "vase": return Vase() elif worldobj_name == "goal": return Goal() else: return None class ExGrid(Grid): def decode(array): flatten_dim = array.shape[0] width = int(math.sqrt(flatten_dim)) height = width grid = ExGrid(width, height) for j in range(0, height): for i in range(0, width): typeIdx = array[i, j, 0] colorIdx = array[i, j, 1] openIdx = array[i, j, 2] if typeIdx == 0: continue objType = IDX_TO_OBJECT[typeIdx] color = IDX_TO_COLOR[colorIdx] is_open = True if openIdx == 1 else 0 if objType == 'wall': v = Wall(color) elif objType == 'ball': v = Ball(color) elif objType == 'key': v = Key(color) elif objType == 'box': v = Box(color) elif objType == 'door': v = Door(color, is_open) elif objType == 'locked_door': v = LockedDoor(color, is_open) elif objType == 'goal': v = Goal() elif objType == 'water': v = Water() elif objType == 'lightsw': v = LightSwitch() elif objType == 'dirt': v = Dirt() elif objType == 'vase': v = Vase() else: assert False, "unknown obj type in decode '%s'" % objType grid.set(i, j, v) return grid class ExMiniGridEnv(MiniGridEnv): class Actions(IntEnum): observe = -1 left = 0 right = 1 forward = 2 toggle = 3 pickup = 4 drop = 5 done = 6 clean = 7 def print_grid(self, grid): for i, e in enumerate(grid.grid): if i % grid.height == 0: print("") if e is not None: print(str(e.type), end="\t") else: print("none", end="\t") print("") def strings_to_actions(self, actions): for i, action_name in enumerate(actions): if action_name == "left": actions[i] = self.actions.left elif action_name == "right": actions[i] = self.actions.right elif action_name == "forward": actions[i] = self.actions.forward elif action_name == "toggle": actions[i] = self.actions.toggle elif action_name == "done": actions[i] = self.actions.done elif action_name == "clean": actions[i] = self.actions.clean elif action_name == "observe": actions[i] = self.actions.observe return actions def action_to_string(self, action): if action == self.actions.left: return "left" elif action == self.actions.right: return "right" elif action == self.actions.forward: return "forward" elif action == self.actions.toggle: return "toggle" elif action == self.actions.done: return "done" elif action == self.actions.clean: return "clean" elif action == self.actions.observe: return "observe" return None def __init__(self, grid_size=16, max_steps=-1, see_through_walls=False, seed=1337): self.config = cg.Configuration.grab() max_num_steps = max_steps if hasattr(self.config, 'max_num_steps'): max_num_steps = self.config.max_num_steps super().__init__(grid_size, max_num_steps, see_through_walls, seed) self.actions = ExMiniGridEnv.Actions imgSize = reduce(operator.mul, OBS_ARRAY_SIZE, 1) + EXTRA_OBSERVATIONS_SIZE elemSize = len(IDX_TO_OBJECT) self.observation_space = spaces.Box( low=0, high=elemSize, shape=(imgSize,), dtype='uint8' ) first_n_actions_available = 4 self.action_space = spaces.Discrete(first_n_actions_available) def step(self, action): self.step_count += 1 reward = 0 done = False info = {"event": [], "steps_count": self.step_count} fwd_pos = self.front_pos fwd_cell = self.grid.get(*fwd_pos) if action == self.actions.left: self.agent_dir -= 1 if self.agent_dir < 0: self.agent_dir += 4 elif action == self.actions.right: self.agent_dir = (self.agent_dir + 1) % 4 elif action == self.actions.forward: if fwd_cell == None or fwd_cell.can_overlap(): self.agent_pos = fwd_pos if fwd_cell is not None and fwd_cell.type == 'water': done = True reward = self.config.rewards.standard.death info["event"].append("died") if self.config.envelope: print("DIED!! >>>>>>> Problems with envelope!") elif fwd_cell is not None and fwd_cell.type == 'goal': try: if self.goal_enabled(): done = True reward = self.config.rewards.standard.goal info["event"].append("goal") except: done = True reward = self.config.rewards.standard.goal info["event"].append("goal") else: reward = self.config.rewards.actions.forward elif action == self.actions.pickup: if fwd_cell and fwd_cell.can_pickup(): if self.carrying is None: self.carrying = fwd_cell self.carrying.cur_pos = np.array([-1, -1]) self.grid.set(*fwd_pos, None) elif action == self.actions.drop: if not fwd_cell and self.carrying: self.grid.set(*fwd_pos, self.carrying) self.carrying.cur_pos = fwd_pos self.carrying = None elif action == self.actions.toggle: if fwd_cell is not None and fwd_cell.type == 'dirt': reward = self.config.rewards.cleaningenv.clean if fwd_cell: fwd_cell.toggle(self, fwd_pos) elif action == self.actions.done: pass else: assert False, "unknown action" reward += self.config.rewards.standard.step if self.step_count == self.config.max_num_steps_episode: done = True obs = self.gen_obs() if self.config.debug_mode: print("reward: " + str(reward) + "\tinfo: " + str(info)) return obs, reward, done, info def goal_enabled(self): raise NotImplementedError() def gen_obs_decoded(self): grid, vis_mask = self.gen_obs_grid() if self.config.debug_mode: print("\nAgent View Original") self.print_grid(grid) try: agent_pos = (AGENT_VIEW_SIZE // 2, AGENT_VIEW_SIZE - 1) obs_door_open = 0 obs_light_on = 0 current_room = 0 current_room_light = 0 next_room_light = 0 if self.roomList: for x in self.roomList: if x.objectInRoom(self.agent_pos): current_room = x.number current_room_light = x.getLight() else: next_room_light = x.getLight() if not x.getLight(): for j in range(0, grid.height): for i in range(0, grid.width): xpos = agent_pos[1] - j ypos = i - agent_pos[0] (xpos, ypos) = self.get_grid_coords_from_view((xpos, ypos)) if x.objectInRoom((xpos, ypos)): if grid.grid[(j * AGENT_VIEW_SIZE) + i] is not None: grid.grid[i + (j * AGENT_VIEW_SIZE)] = None for j in range(0, grid.height): for i in range(0, grid.width): v = grid.get(i, j) if hasattr(v, 'is_open') and v.is_open: obs_door_open = 1 if hasattr(v, 'is_on') and v.is_on: obs_light_on = 1 if self.config.debug_mode: print("\n\nobs_door_open\t\t" + str(obs_door_open)) print("obs_light_on\t\t" + str(obs_light_on)) print("current_room\t\t" + str(current_room)) print("current_room_light\t" + str(current_room_light*1)) print("next_room_light\t\t" + str(next_room_light*1) + "\n\n") return grid, (obs_door_open, obs_light_on, current_room, current_room_light*1, next_room_light*1) except AttributeError: traceback.print_exc() print("ERROR!!!") def gen_obs(self): grid, extra_observations = self.gen_obs_decoded() if self.config.debug_mode: print("\nAgent View Retreived") self.print_grid(grid) try: array = np.zeros(shape=(grid.width, grid.height, 1), dtype='uint8') obs_door_open = 0 obs_light_on = 0 for j in range(0, grid.height): for i in range(0, grid.width): v = grid.get(i, j) if v == None: continue array[i, j, 0] = OBJECT_TO_IDX[v.type] if hasattr(v, 'is_open') and v.is_open: obs_door_open = 1 if hasattr(v, 'is_on') and v.is_on: obs_light_on = 1 image = array flatten_image = image.flatten() obs = np.append(flatten_image, extra_observations) return obs except AttributeError: traceback.print_exc() print("ERROR!!!") def get_grid_coords_from_view(self, coordinates): ax, ay = self.agent_pos ad = self.agent_dir x, y = coordinates if ad == 1: ax -= y ay += x elif ad == 0: ax += x ay += y elif ad == 2: ax -= x ay -= y elif ad == 3: ax += y ay -= x return ax, ay def worldobj_in_agent(self, front, side): coordinates = (front, side) wx, wy = ExMiniGridEnv.get_grid_coords_from_view(self, coordinates) if 0 <= wx < self.grid.width and 0 <= wy < self.grid.height: worldobj = self.grid.get(wx, wy) if worldobj is not None: worldobj_type = worldobj.type return worldobj_type return None

true

f71fc2a75dfc0689982cf24fdf8bcd85ccd1cfdc

3,121

py

Python

sponsors/notifications.py

geofft/pythondotorg

4e6747acaffad21ba22d4611b58dccbf04a4ccac

[ "Apache-2.0" ]

2

2021-04-06T16:22:51.000Z

2021-05-04T13:48:42.000Z

sponsors/notifications.py

vishalsingha/pythondotorg

af59bc03f63cdea16b0f2bd98aae2dcec713c4c1

[ "Apache-2.0" ]

6

2021-03-19T15:57:15.000Z

2021-12-13T20:50:11.000Z

sponsors/notifications.py

vishalsingha/pythondotorg

af59bc03f63cdea16b0f2bd98aae2dcec713c4c1

[ "Apache-2.0" ]

1

2021-08-21T10:36:44.000Z

from django.core.mail import send_mail from django.template.loader import render_to_string from django.conf import settings class BaseEmailSponsorshipNotification: subject_template = None message_template = None email_context_keys = None def get_subject(self, context): return render_to_string(self.subject_template, context).strip() def get_message(self, context): return render_to_string(self.message_template, context).strip() def get_recipient_list(self, context): raise NotImplementedError def notify(self, **kwargs): context = {k: kwargs.get(k) for k in self.email_context_keys} send_mail( subject=self.get_subject(context), message=self.get_message(context), recipient_list=self.get_recipient_list(context), from_email=settings.SPONSORSHIP_NOTIFICATION_FROM_EMAIL, ) class AppliedSponsorshipNotificationToPSF(BaseEmailSponsorshipNotification): subject_template = "sponsors/email/psf_new_application_subject.txt" message_template = "sponsors/email/psf_new_application.txt" email_context_keys = ["request", "sponsorship"] def get_recipient_list(self, context): return [settings.SPONSORSHIP_NOTIFICATION_TO_EMAIL] class AppliedSponsorshipNotificationToSponsors(BaseEmailSponsorshipNotification): subject_template = "sponsors/email/sponsor_new_application_subject.txt" message_template = "sponsors/email/sponsor_new_application.txt" email_context_keys = ["sponsorship"] def get_recipient_list(self, context): return context["sponsorship"].verified_emails class RejectedSponsorshipNotificationToPSF(BaseEmailSponsorshipNotification): subject_template = "sponsors/email/psf_rejected_sponsorship_subject.txt" message_template = "sponsors/email/psf_rejected_sponsorship.txt" email_context_keys = ["sponsorship"] def get_recipient_list(self, context): return [settings.SPONSORSHIP_NOTIFICATION_TO_EMAIL] class RejectedSponsorshipNotificationToSponsors(BaseEmailSponsorshipNotification): subject_template = "sponsors/email/sponsor_rejected_sponsorship_subject.txt" message_template = "sponsors/email/sponsor_rejected_sponsorship.txt" email_context_keys = ["sponsorship"] def get_recipient_list(self, context): return context["sponsorship"].verified_emails class StatementOfWorkNotificationToPSF(BaseEmailSponsorshipNotification): subject_template = "sponsors/email/psf_statement_of_work_subject.txt" message_template = "sponsors/email/psf_statement_of_work.txt" email_context_keys = ["sponsorship"] def get_recipient_list(self, context): return [settings.SPONSORSHIP_NOTIFICATION_TO_EMAIL] class StatementOfWorkNotificationToSponsors(BaseEmailSponsorshipNotification): subject_template = "sponsors/email/sponsor_statement_of_work_subject.txt" message_template = "sponsors/email/sponsor_statement_of_work.txt" email_context_keys = ["sponsorship"] def get_recipient_list(self, context): return context["sponsorship"].verified_emails

37.60241

82

0.779878

from django.core.mail import send_mail from django.template.loader import render_to_string from django.conf import settings class BaseEmailSponsorshipNotification: subject_template = None message_template = None email_context_keys = None def get_subject(self, context): return render_to_string(self.subject_template, context).strip() def get_message(self, context): return render_to_string(self.message_template, context).strip() def get_recipient_list(self, context): raise NotImplementedError def notify(self, **kwargs): context = {k: kwargs.get(k) for k in self.email_context_keys} send_mail( subject=self.get_subject(context), message=self.get_message(context), recipient_list=self.get_recipient_list(context), from_email=settings.SPONSORSHIP_NOTIFICATION_FROM_EMAIL, ) class AppliedSponsorshipNotificationToPSF(BaseEmailSponsorshipNotification): subject_template = "sponsors/email/psf_new_application_subject.txt" message_template = "sponsors/email/psf_new_application.txt" email_context_keys = ["request", "sponsorship"] def get_recipient_list(self, context): return [settings.SPONSORSHIP_NOTIFICATION_TO_EMAIL] class AppliedSponsorshipNotificationToSponsors(BaseEmailSponsorshipNotification): subject_template = "sponsors/email/sponsor_new_application_subject.txt" message_template = "sponsors/email/sponsor_new_application.txt" email_context_keys = ["sponsorship"] def get_recipient_list(self, context): return context["sponsorship"].verified_emails class RejectedSponsorshipNotificationToPSF(BaseEmailSponsorshipNotification): subject_template = "sponsors/email/psf_rejected_sponsorship_subject.txt" message_template = "sponsors/email/psf_rejected_sponsorship.txt" email_context_keys = ["sponsorship"] def get_recipient_list(self, context): return [settings.SPONSORSHIP_NOTIFICATION_TO_EMAIL] class RejectedSponsorshipNotificationToSponsors(BaseEmailSponsorshipNotification): subject_template = "sponsors/email/sponsor_rejected_sponsorship_subject.txt" message_template = "sponsors/email/sponsor_rejected_sponsorship.txt" email_context_keys = ["sponsorship"] def get_recipient_list(self, context): return context["sponsorship"].verified_emails class StatementOfWorkNotificationToPSF(BaseEmailSponsorshipNotification): subject_template = "sponsors/email/psf_statement_of_work_subject.txt" message_template = "sponsors/email/psf_statement_of_work.txt" email_context_keys = ["sponsorship"] def get_recipient_list(self, context): return [settings.SPONSORSHIP_NOTIFICATION_TO_EMAIL] class StatementOfWorkNotificationToSponsors(BaseEmailSponsorshipNotification): subject_template = "sponsors/email/sponsor_statement_of_work_subject.txt" message_template = "sponsors/email/sponsor_statement_of_work.txt" email_context_keys = ["sponsorship"] def get_recipient_list(self, context): return context["sponsorship"].verified_emails

true

f71fc31f870e9d876d456cf459dc6a6019bc3ab0

336

py

Python

py_001/py_contextmanager.py

shawn0915/python-study

4f6d59ed93cb63295f6e67e661860e1f6a4b18c2

[ "MIT" ]

null

py_001/py_contextmanager.py

shawn0915/python-study

4f6d59ed93cb63295f6e67e661860e1f6a4b18c2

[ "MIT" ]

null

py_001/py_contextmanager.py

shawn0915/python-study

4f6d59ed93cb63295f6e67e661860e1f6a4b18c2

[ "MIT" ]

null

#!/usr/bin/python # -*- coding: UTF-8 -*- import codecs from contextlib import contextmanager @contextmanager def Open(filename, mode, encoding='utf-8'): fp = codecs.open(filename, mode, encoding) try: yield fp finally: fp.close() data = u"context汉字测试" with Open('data.txt', 'w') as f: f.write(data)

17.684211

46

0.636905

import codecs from contextlib import contextmanager @contextmanager def Open(filename, mode, encoding='utf-8'): fp = codecs.open(filename, mode, encoding) try: yield fp finally: fp.close() data = u"context汉字测试" with Open('data.txt', 'w') as f: f.write(data)

true

f71fc3e734e5af8996dc6c80f55020bcea9a755a

2,085

py

Python

simpleAPI/api/v1/serializers.py

Gleb-Stasuyk/simpleAPI

9b30202915a4f144921b9bd5204f7bfac8b1201f

[ "MIT" ]

null

simpleAPI/api/v1/serializers.py

Gleb-Stasuyk/simpleAPI

9b30202915a4f144921b9bd5204f7bfac8b1201f

[ "MIT" ]

null

simpleAPI/api/v1/serializers.py

Gleb-Stasuyk/simpleAPI

9b30202915a4f144921b9bd5204f7bfac8b1201f

[ "MIT" ]

null

from django.contrib.auth import get_user_model from rest_framework import serializers from companys.models import Company, News from users.models import Profile User = get_user_model() class NewsSerializer(serializers.ModelSerializer): class Meta: model = News fields = '__all__' class CompanySerializer(serializers.ModelSerializer): company_news = NewsSerializer(many=True, required=False) class Meta: model = Company exclude = ['id'] class CompanySerializerNotAuth(serializers.ModelSerializer): class Meta: model = Company exclude = ['id', 'company_news'] class ProfileSerializer(serializers.ModelSerializer): company = serializers.StringRelatedField() class Meta: model = Profile exclude = ['user'] class UserSerializer(serializers.ModelSerializer): profile = ProfileSerializer() class Meta: model = User fields = ['id', 'profile', 'username', 'first_name', 'last_name', 'date_joined'] def create(self, validated_data): profile_data = validated_data.pop('profile') user = User.objects.create(**validated_data) Profile.objects.create(user=user, **profile_data) return user def update(self, instance, validated_data): profile_data = validated_data.pop('profile') profile = instance.profile # * User Info instance.first_name = validated_data.get( 'first_name', instance.first_name) instance.last_name = validated_data.get( 'last_name', instance.last_name) # * AccountProfile Info profile.company = profile_data.get( 'company', profile.company) profile.bio = profile_data.get( 'bio', profile.bio) profile.location = profile_data.get( 'location', profile.location) profile.birth_date = profile_data.get( 'birth_date', profile.birth_date) profile.role = profile_data.get( 'role', profile.role) profile.save() return instance

26.730769

88

0.655635

from django.contrib.auth import get_user_model from rest_framework import serializers from companys.models import Company, News from users.models import Profile User = get_user_model() class NewsSerializer(serializers.ModelSerializer): class Meta: model = News fields = '__all__' class CompanySerializer(serializers.ModelSerializer): company_news = NewsSerializer(many=True, required=False) class Meta: model = Company exclude = ['id'] class CompanySerializerNotAuth(serializers.ModelSerializer): class Meta: model = Company exclude = ['id', 'company_news'] class ProfileSerializer(serializers.ModelSerializer): company = serializers.StringRelatedField() class Meta: model = Profile exclude = ['user'] class UserSerializer(serializers.ModelSerializer): profile = ProfileSerializer() class Meta: model = User fields = ['id', 'profile', 'username', 'first_name', 'last_name', 'date_joined'] def create(self, validated_data): profile_data = validated_data.pop('profile') user = User.objects.create(**validated_data) Profile.objects.create(user=user, **profile_data) return user def update(self, instance, validated_data): profile_data = validated_data.pop('profile') profile = instance.profile instance.first_name = validated_data.get( 'first_name', instance.first_name) instance.last_name = validated_data.get( 'last_name', instance.last_name) profile.company = profile_data.get( 'company', profile.company) profile.bio = profile_data.get( 'bio', profile.bio) profile.location = profile_data.get( 'location', profile.location) profile.birth_date = profile_data.get( 'birth_date', profile.birth_date) profile.role = profile_data.get( 'role', profile.role) profile.save() return instance

true

f71fc5a556957ba4ad43afcc5e2c620d83d7aa7c

749

py

Python

Server/tests/queries/test_leaderboards.py

Team-SeeTo/SeeTo-Backend

19990cd6f4895e773eaa504f7b7a07ddbb5856e5

[ "Apache-2.0" ]

4

2018-06-18T06:50:12.000Z

2018-11-15T00:08:24.000Z

Server/tests/queries/test_leaderboards.py

Team-SeeTo/SeeTo-Backend

19990cd6f4895e773eaa504f7b7a07ddbb5856e5

[ "Apache-2.0" ]

null

Server/tests/queries/test_leaderboards.py

Team-SeeTo/SeeTo-Backend

19990cd6f4895e773eaa504f7b7a07ddbb5856e5

[ "Apache-2.0" ]

null

from tests import BasicTestCase class TestLeaderboards(BasicTestCase): def test_leaderboards(self): response = self.request(type="query", call='leaderboards(token :"{0}")'.format(self.access_token), body=''' ... on LeaderboardsField{ rank name point } ''') for user in response['leaderboards']: self.assertEqual(type(user['rank']), int) self.assertEqual(type(user['name']), str) self.assertEqual(type(user['point']), int)

37.45

92

0.419226

from tests import BasicTestCase class TestLeaderboards(BasicTestCase): def test_leaderboards(self): response = self.request(type="query", call='leaderboards(token :"{0}")'.format(self.access_token), body=''' ... on LeaderboardsField{ rank name point } ''') for user in response['leaderboards']: self.assertEqual(type(user['rank']), int) self.assertEqual(type(user['name']), str) self.assertEqual(type(user['point']), int)

true

f71fc6aac913f65673fbaf691c47f217c5bc9c25

536

py

Python

imglatex/cli.py

odarbelaeze/imglatex

8463531ae48dd4c2b4937ef4d27dbf74d6f732e3

[ "MIT" ]

2

2018-02-17T20:26:56.000Z

2022-02-10T13:23:55.000Z

imglatex/cli.py

odarbelaeze/imglatex

8463531ae48dd4c2b4937ef4d27dbf74d6f732e3

[ "MIT" ]

325

2018-03-18T15:28:48.000Z

2022-03-28T04:19:18.000Z

imglatex/cli.py

odarbelaeze/imglatex

8463531ae48dd4c2b4937ef4d27dbf74d6f732e3

[ "MIT" ]

null

"""Console script for imglatex.""" import click from imglatex.imglatex import find_images, Image, Document @click.command() @click.argument('path', type=click.Path(exists=True)) @click.option('--prefix', default='.', help='Prefix for the image paths') def main(path: click.Path, prefix: str): """Console script for imglatex.""" document = Document( list(Image(i, prefix) for i in find_images(path)) ) click.echo(document.latex()) return 0 if __name__ == "__main__": import sys sys.exit(main())

23.304348

73

0.671642

import click from imglatex.imglatex import find_images, Image, Document @click.command() @click.argument('path', type=click.Path(exists=True)) @click.option('--prefix', default='.', help='Prefix for the image paths') def main(path: click.Path, prefix: str): document = Document( list(Image(i, prefix) for i in find_images(path)) ) click.echo(document.latex()) return 0 if __name__ == "__main__": import sys sys.exit(main())

true

f71fc728dba3bae392f6f5d4e5b6e05cb75586f8

4,525

py

Python

knox/auth.py

liradb2000/django-rest-knox

2120bdb44173db121611387b9e1a2e8e358b0123

[ "MIT" ]

null

knox/auth.py

liradb2000/django-rest-knox

2120bdb44173db121611387b9e1a2e8e358b0123

[ "MIT" ]

null

knox/auth.py

liradb2000/django-rest-knox

2120bdb44173db121611387b9e1a2e8e358b0123

[ "MIT" ]

null

try: from hmac import compare_digest except ImportError: def compare_digest(a, b): return a == b import binascii from django.utils import timezone from django.utils.translation import gettext_lazy as _ from rest_framework import exceptions from rest_framework.authentication import ( BaseAuthentication, get_authorization_header, ) from knox.crypto import hash_token from knox.models import AuthToken from knox.settings import CONSTANTS, knox_settings from knox.signals import token_expired class TokenAuthentication(BaseAuthentication): ''' This authentication scheme uses Knox AuthTokens for authentication. Similar to DRF's TokenAuthentication, it overrides a large amount of that authentication scheme to cope with the fact that Tokens are not stored in plaintext in the database If successful - `request.user` will be a django `User` instance - `request.auth` will be an `AuthToken` instance ''' model = AuthToken def authenticate(self, request): auth = get_authorization_header(request).split() prefix = knox_settings.AUTH_HEADER_PREFIX.encode() if not auth: return None if auth[0].lower() != prefix.lower(): # Authorization header is possibly for another backend return None if len(auth) == 1: msg = _('Invalid token header. No credentials provided.') raise exceptions.AuthenticationFailed(msg) elif len(auth) > 2: msg = _('Invalid token header. ' 'Token string should not contain spaces.') raise exceptions.AuthenticationFailed(msg) if knox_settings.USE_COOKIE: auth[1] = b''.join([auth[1], request.COOKIES.get(knox_settings.COOKIE_SETTINGS['NAME'],'').encode()]) user, auth_token = self.authenticate_credentials(auth[1]) return (user, auth_token) def authenticate_credentials(self, token): ''' Due to the random nature of hashing a value, this must inspect each auth_token individually to find the correct one. Tokens that have expired will be deleted and skipped ''' msg = _('Invalid token.') token = token.decode("utf-8") for auth_token in AuthToken.objects.select_related('user').filter( token_key=token[:CONSTANTS.TOKEN_KEY_LENGTH]): if self._cleanup_token(auth_token): continue try: digest = hash_token(token) except (TypeError, binascii.Error): raise exceptions.AuthenticationFailed(msg) if compare_digest(digest, auth_token.digest): if knox_settings.AUTO_REFRESH and auth_token.expiry: self.renew_token(auth_token) return self.validate_user(auth_token) raise exceptions.AuthenticationFailed(msg) def renew_token(self, auth_token): current_expiry = auth_token.expiry new_expiry = timezone.now() + knox_settings.TOKEN_TTL auth_token.expiry = new_expiry # Throttle refreshing of token to avoid db writes delta = (new_expiry - current_expiry).total_seconds() if delta > knox_settings.MIN_REFRESH_INTERVAL: auth_token.save(update_fields=('expiry',)) def validate_user(self, auth_token): if not auth_token.user.is_active: raise exceptions.AuthenticationFailed( _('User inactive or deleted.')) return (auth_token.user, auth_token) def authenticate_header(self, request): return knox_settings.AUTH_HEADER_PREFIX def _cleanup_token(self, auth_token): # for other_token in auth_token.user.auth_token_set.all(): # if other_token.digest != auth_token.digest and other_token.expiry: # if other_token.expiry < timezone.now(): # other_token.delete() # username = other_token.user.get_username() # token_expired.send(sender=self.__class__, # username=username, source="other_token") if auth_token.expiry is not None: if auth_token.expiry < timezone.now(): username = auth_token.user.get_username() auth_token.delete() token_expired.send(sender=self.__class__, username=username, source="auth_token") return True return False

39.008621

113

0.643757

try: from hmac import compare_digest except ImportError: def compare_digest(a, b): return a == b import binascii from django.utils import timezone from django.utils.translation import gettext_lazy as _ from rest_framework import exceptions from rest_framework.authentication import ( BaseAuthentication, get_authorization_header, ) from knox.crypto import hash_token from knox.models import AuthToken from knox.settings import CONSTANTS, knox_settings from knox.signals import token_expired class TokenAuthentication(BaseAuthentication): model = AuthToken def authenticate(self, request): auth = get_authorization_header(request).split() prefix = knox_settings.AUTH_HEADER_PREFIX.encode() if not auth: return None if auth[0].lower() != prefix.lower(): return None if len(auth) == 1: msg = _('Invalid token header. No credentials provided.') raise exceptions.AuthenticationFailed(msg) elif len(auth) > 2: msg = _('Invalid token header. ' 'Token string should not contain spaces.') raise exceptions.AuthenticationFailed(msg) if knox_settings.USE_COOKIE: auth[1] = b''.join([auth[1], request.COOKIES.get(knox_settings.COOKIE_SETTINGS['NAME'],'').encode()]) user, auth_token = self.authenticate_credentials(auth[1]) return (user, auth_token) def authenticate_credentials(self, token): msg = _('Invalid token.') token = token.decode("utf-8") for auth_token in AuthToken.objects.select_related('user').filter( token_key=token[:CONSTANTS.TOKEN_KEY_LENGTH]): if self._cleanup_token(auth_token): continue try: digest = hash_token(token) except (TypeError, binascii.Error): raise exceptions.AuthenticationFailed(msg) if compare_digest(digest, auth_token.digest): if knox_settings.AUTO_REFRESH and auth_token.expiry: self.renew_token(auth_token) return self.validate_user(auth_token) raise exceptions.AuthenticationFailed(msg) def renew_token(self, auth_token): current_expiry = auth_token.expiry new_expiry = timezone.now() + knox_settings.TOKEN_TTL auth_token.expiry = new_expiry delta = (new_expiry - current_expiry).total_seconds() if delta > knox_settings.MIN_REFRESH_INTERVAL: auth_token.save(update_fields=('expiry',)) def validate_user(self, auth_token): if not auth_token.user.is_active: raise exceptions.AuthenticationFailed( _('User inactive or deleted.')) return (auth_token.user, auth_token) def authenticate_header(self, request): return knox_settings.AUTH_HEADER_PREFIX def _cleanup_token(self, auth_token): if auth_token.expiry is not None: if auth_token.expiry < timezone.now(): username = auth_token.user.get_username() auth_token.delete() token_expired.send(sender=self.__class__, username=username, source="auth_token") return True return False

true

f71fc7ba9f38450681a76a58e0a61f43125749ab

10,698

py

Python

codenames/players/codemaster_glove_lookahead.py

gohyun14/Game

39e6e192590059daade40c95cc177acb0f3a581b

[ "MIT" ]

1

2022-02-25T17:44:02.000Z

codenames/players/codemaster_glove_lookahead.py

gohyun14/Game

39e6e192590059daade40c95cc177acb0f3a581b

[ "MIT" ]

null

codenames/players/codemaster_glove_lookahead.py

gohyun14/Game

39e6e192590059daade40c95cc177acb0f3a581b

[ "MIT" ]

null

import scipy.spatial.distance from nltk.stem import WordNetLemmatizer from nltk.stem.lancaster import LancasterStemmer from math import ceil import numpy as np import copy import itertools from players.codemaster import Codemaster THRESHOLD = np.inf class AICodemaster(Codemaster): def __init__(self, brown_ic=None, glove_vecs=None, word_vectors=None): super().__init__() self.brown_ic = brown_ic self.glove_vecs = glove_vecs self.word_vectors = word_vectors self.wordnet_lemmatizer = WordNetLemmatizer() self.lancaster_stemmer = LancasterStemmer() self.cm_wordlist = [] with open('players/cm_wordlist.txt') as infile: for line in infile: self.cm_wordlist.append(line.rstrip()) self.root = None self.turn_number = 0 def set_game_state(self, words, maps): if self.turn_number == 0: self.original_words = copy.copy(words) print(f"original words: {self.original_words}") self.words = words self.maps = maps self.update_board() self.init_dists() self.turn_number += 1 def update_board(self): self.red_words = set() self.bad_words = set() self.words_guessed = set() for i in range(25): if self.words[i][0] == '*': self.words_guessed.add(self.original_words[i].lower()) elif self.maps[i] == "Assassin" or self.maps[i] == "Blue" or self.maps[i] == "Civilian": self.bad_words.add(self.words[i].lower()) if self.maps[i] == "Assassin": self.black_word = self.words[i] else: self.red_words.add(self.words[i].lower()) def init_dists(self): cos_dist = scipy.spatial.distance.cosine all_vectors = (self.glove_vecs,) self.bad_word_dists = {} for word in self.bad_words: self.bad_word_dists[word] = {} for val in self.cm_wordlist: b_dist = cos_dist(self.concatenate(val, all_vectors), self.concatenate(word, all_vectors)) self.bad_word_dists[word][val] = b_dist self.red_word_dists = {} for word in self.red_words: self.red_word_dists[word] = {} for val in self.cm_wordlist: b_dist = cos_dist(self.concatenate(val, all_vectors), self.concatenate(word, all_vectors)) self.red_word_dists[word][val] = b_dist def get_clue(self): #self.all_guesses = set() if self.root is None or self.root.words_guessed != self.words_guessed: if self.root: print("board mismatch: initializing new root") print(f"game's words guessed: {self.words_guessed} nodes' words guessed: {self.root.words_guessed}") self.root = Node(self, copy.copy(self.words_guessed), None, depth = self.turn_number-1) self.root.get_val() best_clue = self.root.best_clue print('chosen_clue is:', best_clue[0]) self.root = self.root.best_child return best_clue def arr_not_in_word(self, word, arr): if word in arr: return False lemm = self.wordnet_lemmatizer.lemmatize(word) lancas = self.lancaster_stemmer.stem(word) for i in arr: if i == lemm or i == lancas: return False if i.find(word) != -1: return False if word.find(i) != -1: return False return True def combine(self, words, wordvecs): factor = 1.0 / float(len(words)) new_word = self.concatenate(words[0], wordvecs) * factor for word in words[1:]: new_word += self.concatenate(word, wordvecs) * factor return new_word def concatenate(self, word, wordvecs): concatenated = wordvecs[0][word] for vec in wordvecs[1:]: concatenated = np.hstack((concatenated, vec[word])) return concatenated class Node: def __init__(self, codemaster, words_guessed, parent, depth = 0, best=np.inf): self.codemaster = codemaster self.words_guessed = words_guessed self.parent = parent self.depth = depth self.best_clue = None self.best_child = None self.val = np.inf self.terminal = False self.best = best def get_best_clues(self): bests = {} possible = {} cm = self.codemaster red_words = cm.red_words.difference(self.words_guessed) bad_words = cm.bad_words.difference(self.words_guessed) print(f"calculating best clues") #print(f"red word dists: {self.red_word_dists}") for clue_num in range(1, 3 + 1): best_per_dist = np.inf best_per = '' best_red_word = '' for red_word in list(itertools.combinations(red_words, clue_num)): best_word = '' best_dist = np.inf for word in cm.cm_wordlist: if not cm.arr_not_in_word(word, red_words.union(bad_words)): continue bad_dist = np.inf worst_bad = '' for bad_word in bad_words: if cm.bad_word_dists[bad_word][word] < bad_dist: bad_dist = cm.bad_word_dists[bad_word][word] worst_bad = bad_word worst_red = 0 for red in red_word: dist = cm.red_word_dists[red][word] if dist > worst_red: worst_red = dist if worst_red < best_dist and worst_red < bad_dist: best_dist = worst_red best_word = word # print(worst_red,red_word,word) if best_dist < best_per_dist: best_per_dist = best_dist best_per = best_word best_red_word = red_word if best_dist < THRESHOLD or clue_num == 1: possible[(best_word, clue_num)] = (red_word, best_dist) bests[clue_num] = (best_red_word, best_per, best_per_dist) print(f"length of possibilities: {len(possible)}") return possible def add_children(self): cos_dist = scipy.spatial.distance.cosine cm = self.codemaster all_vectors = (cm.glove_vecs,) print(f"at depth {self.depth}") bests = self.get_best_clues() for clue, clue_info in bests.items(): combined_clue, clue_num = clue best_red_word, combined_score = clue_info worst = -np.inf for word in best_red_word: dist = cos_dist(cm.concatenate(word, all_vectors), cm.concatenate(combined_clue, all_vectors)) if dist > worst: worst = dist if worst < 0.7 and worst != -np.inf or clue_num == 1: print(f"adding clue: {clue}") self.add_child(clue, best_red_word) def check_board(self): cm = self.codemaster self.black_guessed = cm.black_word in self.words_guessed red_words = cm.red_words.difference(self.words_guessed) red_count = len(red_words) if self.black_guessed: self.val = np.inf self.terminal = True elif red_count == 0: self.val = self.depth self.terminal = True print(f"Terminal Node: depth: {self.depth}") else: self.val = 25 def new_child(self, expected_words_chosen): new_words_guessed = copy.copy(self.words_guessed) for word in expected_words_chosen: new_words_guessed.add(word) return Node(self.codemaster, new_words_guessed, self, self.depth + 1, self.best) def get_val(self, depth=np.inf): # if self.words_guessed in self.codemaster.all_guesses: # print("Board State already explored") # return self.val # self.codemaster.all_guesses.add(self.words_guessed) self.check_board() if self.not_possible(): print("Skipped") return self.val if self.terminal: if self.val < self.best: self.best = self.val return self.val if self.best_clue is not None: return self.val best_val = np.inf possible = self.get_best_clues() for clue, clue_info in sorted(possible.items(), key = lambda x: (x[0][1],-x[1][1]), reverse=True): combined_clue, clue_num = clue best_red_word, combined_score = clue_info if self.check_clue_feasible(clue_num, combined_score): print(f"Exploring child, depth: {self.depth+1}, clue: {clue}, dist: {combined_score}") child = self.new_child(best_red_word) child_val = child.get_val(depth) if child_val < best_val: best_val = child_val self.best_clue = clue self.best_child = child if child.best < self.best: print(f"Found new best, prev: {self.best} new: {child.best}") self.best = child.best self.val = best_val return self.val # def best_child(self): # best_clue = self.best_clue # for child_key in self.children.keys(): # if child_key == best_clue: # best_child = self.children[child_key] # best_child.reset_depth() # return best_child def not_possible(self): red_words = self.codemaster.red_words.difference(self.words_guessed) best_possible = self.depth + ceil(len(red_words)/3) print(f"BEST POSSIBLE: {best_possible}") return self.best <= best_possible or self.depth >= self.best or (not self.terminal and self.depth == self.best - 1) def check_clue_feasible(self, clue_num, combined_score): return clue_num == 1 or combined_score < THRESHOLD # cos_dist = scipy.spatial.distance.cosine # cm = self.codemaster # all_vectors = (cm.glove_vecs,) # worst = -np.inf # for word in best_red_word: # dist = cos_dist(cm.concatenate(word, all_vectors), cm.concatenate(combined_clue, all_vectors)) # if dist > worst: # worst = dist # return worst < 0.7 and worst != -np.inf or clue_num == 1

39.476015

123

0.570667

import scipy.spatial.distance from nltk.stem import WordNetLemmatizer from nltk.stem.lancaster import LancasterStemmer from math import ceil import numpy as np import copy import itertools from players.codemaster import Codemaster THRESHOLD = np.inf class AICodemaster(Codemaster): def __init__(self, brown_ic=None, glove_vecs=None, word_vectors=None): super().__init__() self.brown_ic = brown_ic self.glove_vecs = glove_vecs self.word_vectors = word_vectors self.wordnet_lemmatizer = WordNetLemmatizer() self.lancaster_stemmer = LancasterStemmer() self.cm_wordlist = [] with open('players/cm_wordlist.txt') as infile: for line in infile: self.cm_wordlist.append(line.rstrip()) self.root = None self.turn_number = 0 def set_game_state(self, words, maps): if self.turn_number == 0: self.original_words = copy.copy(words) print(f"original words: {self.original_words}") self.words = words self.maps = maps self.update_board() self.init_dists() self.turn_number += 1 def update_board(self): self.red_words = set() self.bad_words = set() self.words_guessed = set() for i in range(25): if self.words[i][0] == '*': self.words_guessed.add(self.original_words[i].lower()) elif self.maps[i] == "Assassin" or self.maps[i] == "Blue" or self.maps[i] == "Civilian": self.bad_words.add(self.words[i].lower()) if self.maps[i] == "Assassin": self.black_word = self.words[i] else: self.red_words.add(self.words[i].lower()) def init_dists(self): cos_dist = scipy.spatial.distance.cosine all_vectors = (self.glove_vecs,) self.bad_word_dists = {} for word in self.bad_words: self.bad_word_dists[word] = {} for val in self.cm_wordlist: b_dist = cos_dist(self.concatenate(val, all_vectors), self.concatenate(word, all_vectors)) self.bad_word_dists[word][val] = b_dist self.red_word_dists = {} for word in self.red_words: self.red_word_dists[word] = {} for val in self.cm_wordlist: b_dist = cos_dist(self.concatenate(val, all_vectors), self.concatenate(word, all_vectors)) self.red_word_dists[word][val] = b_dist def get_clue(self): if self.root is None or self.root.words_guessed != self.words_guessed: if self.root: print("board mismatch: initializing new root") print(f"game's words guessed: {self.words_guessed} nodes' words guessed: {self.root.words_guessed}") self.root = Node(self, copy.copy(self.words_guessed), None, depth = self.turn_number-1) self.root.get_val() best_clue = self.root.best_clue print('chosen_clue is:', best_clue[0]) self.root = self.root.best_child return best_clue def arr_not_in_word(self, word, arr): if word in arr: return False lemm = self.wordnet_lemmatizer.lemmatize(word) lancas = self.lancaster_stemmer.stem(word) for i in arr: if i == lemm or i == lancas: return False if i.find(word) != -1: return False if word.find(i) != -1: return False return True def combine(self, words, wordvecs): factor = 1.0 / float(len(words)) new_word = self.concatenate(words[0], wordvecs) * factor for word in words[1:]: new_word += self.concatenate(word, wordvecs) * factor return new_word def concatenate(self, word, wordvecs): concatenated = wordvecs[0][word] for vec in wordvecs[1:]: concatenated = np.hstack((concatenated, vec[word])) return concatenated class Node: def __init__(self, codemaster, words_guessed, parent, depth = 0, best=np.inf): self.codemaster = codemaster self.words_guessed = words_guessed self.parent = parent self.depth = depth self.best_clue = None self.best_child = None self.val = np.inf self.terminal = False self.best = best def get_best_clues(self): bests = {} possible = {} cm = self.codemaster red_words = cm.red_words.difference(self.words_guessed) bad_words = cm.bad_words.difference(self.words_guessed) print(f"calculating best clues") for clue_num in range(1, 3 + 1): best_per_dist = np.inf best_per = '' best_red_word = '' for red_word in list(itertools.combinations(red_words, clue_num)): best_word = '' best_dist = np.inf for word in cm.cm_wordlist: if not cm.arr_not_in_word(word, red_words.union(bad_words)): continue bad_dist = np.inf worst_bad = '' for bad_word in bad_words: if cm.bad_word_dists[bad_word][word] < bad_dist: bad_dist = cm.bad_word_dists[bad_word][word] worst_bad = bad_word worst_red = 0 for red in red_word: dist = cm.red_word_dists[red][word] if dist > worst_red: worst_red = dist if worst_red < best_dist and worst_red < bad_dist: best_dist = worst_red best_word = word if best_dist < best_per_dist: best_per_dist = best_dist best_per = best_word best_red_word = red_word if best_dist < THRESHOLD or clue_num == 1: possible[(best_word, clue_num)] = (red_word, best_dist) bests[clue_num] = (best_red_word, best_per, best_per_dist) print(f"length of possibilities: {len(possible)}") return possible def add_children(self): cos_dist = scipy.spatial.distance.cosine cm = self.codemaster all_vectors = (cm.glove_vecs,) print(f"at depth {self.depth}") bests = self.get_best_clues() for clue, clue_info in bests.items(): combined_clue, clue_num = clue best_red_word, combined_score = clue_info worst = -np.inf for word in best_red_word: dist = cos_dist(cm.concatenate(word, all_vectors), cm.concatenate(combined_clue, all_vectors)) if dist > worst: worst = dist if worst < 0.7 and worst != -np.inf or clue_num == 1: print(f"adding clue: {clue}") self.add_child(clue, best_red_word) def check_board(self): cm = self.codemaster self.black_guessed = cm.black_word in self.words_guessed red_words = cm.red_words.difference(self.words_guessed) red_count = len(red_words) if self.black_guessed: self.val = np.inf self.terminal = True elif red_count == 0: self.val = self.depth self.terminal = True print(f"Terminal Node: depth: {self.depth}") else: self.val = 25 def new_child(self, expected_words_chosen): new_words_guessed = copy.copy(self.words_guessed) for word in expected_words_chosen: new_words_guessed.add(word) return Node(self.codemaster, new_words_guessed, self, self.depth + 1, self.best) def get_val(self, depth=np.inf): self.check_board() if self.not_possible(): print("Skipped") return self.val if self.terminal: if self.val < self.best: self.best = self.val return self.val if self.best_clue is not None: return self.val best_val = np.inf possible = self.get_best_clues() for clue, clue_info in sorted(possible.items(), key = lambda x: (x[0][1],-x[1][1]), reverse=True): combined_clue, clue_num = clue best_red_word, combined_score = clue_info if self.check_clue_feasible(clue_num, combined_score): print(f"Exploring child, depth: {self.depth+1}, clue: {clue}, dist: {combined_score}") child = self.new_child(best_red_word) child_val = child.get_val(depth) if child_val < best_val: best_val = child_val self.best_clue = clue self.best_child = child if child.best < self.best: print(f"Found new best, prev: {self.best} new: {child.best}") self.best = child.best self.val = best_val return self.val def not_possible(self): red_words = self.codemaster.red_words.difference(self.words_guessed) best_possible = self.depth + ceil(len(red_words)/3) print(f"BEST POSSIBLE: {best_possible}") return self.best <= best_possible or self.depth >= self.best or (not self.terminal and self.depth == self.best - 1) def check_clue_feasible(self, clue_num, combined_score): return clue_num == 1 or combined_score < THRESHOLD

true

f71fc8515d160a9327f631beba72fefeffeddf90

1,049

py

Python

misc/pytorch_toolkit/machine_translation/core/dataset/text_container.py

dqawami/openvino_training_extensions

dddda1dfd651eaae2d59cecda84275b1b03bd0ad

[ "Apache-2.0" ]

256

2020-09-09T03:27:57.000Z

2022-03-30T10:06:06.000Z

misc/pytorch_toolkit/machine_translation/core/dataset/text_container.py

dqawami/openvino_training_extensions

dddda1dfd651eaae2d59cecda84275b1b03bd0ad

[ "Apache-2.0" ]

604

2020-09-08T12:29:49.000Z

2022-03-31T21:51:08.000Z

misc/pytorch_toolkit/machine_translation/core/dataset/text_container.py

dqawami/openvino_training_extensions

dddda1dfd651eaae2d59cecda84275b1b03bd0ad

[ "Apache-2.0" ]

160

2020-09-09T14:06:07.000Z

2022-03-30T14:50:48.000Z

""" Copyright (c) 2020 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import io from torch.utils.data import Dataset from tqdm import tqdm class TextContainer(Dataset): def __init__(self, corpus): self.data = [] with io.open(corpus, mode='r', encoding='utf-8') as f: for line in tqdm(f): self.data.append(line.strip()) def __len__(self): return len(self.data) def __getitem__(self, idx): return { "text": self.data[idx], "key": idx }

32.78125

73

0.675882

import io from torch.utils.data import Dataset from tqdm import tqdm class TextContainer(Dataset): def __init__(self, corpus): self.data = [] with io.open(corpus, mode='r', encoding='utf-8') as f: for line in tqdm(f): self.data.append(line.strip()) def __len__(self): return len(self.data) def __getitem__(self, idx): return { "text": self.data[idx], "key": idx }

true

f71fc8b40616224f0c26320e75087d6d233ed41a

3,180

py

Python

wurst/searching.py

pjamesjoyce/wurst

95b37e72eaa18b33bdd83cd4a51d37d9eb4ae7ba

[ "BSD-2-Clause" ]

1

2022-03-29T14:59:13.000Z

wurst/searching.py

pjamesjoyce/wurst

95b37e72eaa18b33bdd83cd4a51d37d9eb4ae7ba

[ "BSD-2-Clause" ]

null

wurst/searching.py

pjamesjoyce/wurst

95b37e72eaa18b33bdd83cd4a51d37d9eb4ae7ba

[ "BSD-2-Clause" ]

null

from .errors import MultipleResults, NoResults def equals(field, value): """Return function where input ``field`` value is equal to ``value``""" return lambda x: x.get(field) == value def contains(field, value): return lambda x: value in x.get(field) def startswith(field, value): return lambda x: x.get(field, '').startswith(value) def either(*funcs): """Return ``True`` is any of the function evaluate true""" return lambda x: any(f(x) for f in funcs) def exclude(func): """Return the opposite of ``func`` (i.e. ``False`` instead of ``True``)""" return lambda x: not func(x) def doesnt_contain_any(field, values): """Exclude all dataset whose ``field`` contains any of ``values``""" return lambda x: all(exclude(contains(field, value))(x) for value in values) def get_many(data, *funcs): """Apply all filter functions ``funcs`` to ``data``""" for fltr in funcs: data = filter(fltr, data) return data def get_one(data, *funcs): """Apply filter functions ``funcs`` to ``data``, and return exactly one result. Raises ``wurst.errors.NoResults`` or ``wurst.errors.MultipleResults`` if zero or multiple results are returned. """ results = list(get_many(data, *funcs)) if not results: raise NoResults if not len(results) == 1: raise MultipleResults return results[0] def _exchanges(ds, kind, *funcs): if funcs == [None]: funcs = [] return get_many( filter(lambda x: x['type'] == kind, ds['exchanges']), *funcs ) def technosphere(ds, *funcs): """Get all technosphere exchanges in ``ds`` that pass filtering functions ``funcs``""" return _exchanges(ds, 'technosphere', *funcs) def biosphere(ds, *funcs): """Get all biosphere exchanges in ``ds`` that pass filtering functions ``funcs``""" return _exchanges(ds, 'biosphere', *funcs) def production(ds, *funcs): """Get all production exchanges in ``ds`` that pass filtering functions ``funcs``""" return _exchanges(ds, 'production', *funcs) def reference_product(ds): """Get single reference product exchange from a dataset. Raises ``wurst.errors.NoResults`` or ``wurst.errors.MultipleResults`` if zero or multiple results are returned.""" excs = [exc for exc in ds['exchanges'] if exc['amount'] and exc['type'] == 'production'] if not excs: raise NoResults("No suitable production exchanges founds") elif len(excs) > 1: raise MultipleResults("Multiple production exchanges found") return excs[0] def best_geo_match(possibles, ordered_locations): """Pick the dataset from ``possibles`` whose location is first in ``ordered_locations``. ``possibles`` is an interable with the field ``location``. ``ordered_locations`` is a list of locations in sorting order. Returns an element from ``possibles``, or ``None``. """ weights = {y: x for x, y in enumerate(ordered_locations)} filtered = (obj for obj in possibles if obj['location'] in weights) ordered = sorted(filtered, key=lambda x: weights[x['location']]) if ordered: return ordered[0]

30.576923

118

0.654088

from .errors import MultipleResults, NoResults def equals(field, value): return lambda x: x.get(field) == value def contains(field, value): return lambda x: value in x.get(field) def startswith(field, value): return lambda x: x.get(field, '').startswith(value) def either(*funcs): return lambda x: any(f(x) for f in funcs) def exclude(func): return lambda x: not func(x) def doesnt_contain_any(field, values): return lambda x: all(exclude(contains(field, value))(x) for value in values) def get_many(data, *funcs): for fltr in funcs: data = filter(fltr, data) return data def get_one(data, *funcs): results = list(get_many(data, *funcs)) if not results: raise NoResults if not len(results) == 1: raise MultipleResults return results[0] def _exchanges(ds, kind, *funcs): if funcs == [None]: funcs = [] return get_many( filter(lambda x: x['type'] == kind, ds['exchanges']), *funcs ) def technosphere(ds, *funcs): return _exchanges(ds, 'technosphere', *funcs) def biosphere(ds, *funcs): return _exchanges(ds, 'biosphere', *funcs) def production(ds, *funcs): return _exchanges(ds, 'production', *funcs) def reference_product(ds): excs = [exc for exc in ds['exchanges'] if exc['amount'] and exc['type'] == 'production'] if not excs: raise NoResults("No suitable production exchanges founds") elif len(excs) > 1: raise MultipleResults("Multiple production exchanges found") return excs[0] def best_geo_match(possibles, ordered_locations): weights = {y: x for x, y in enumerate(ordered_locations)} filtered = (obj for obj in possibles if obj['location'] in weights) ordered = sorted(filtered, key=lambda x: weights[x['location']]) if ordered: return ordered[0]

true

f71fca16be9fa3da257d719ec7efdae5f61d8f68

4,100

py

Python

sandbox/jorvis/generate_gff3_feature_identifiers.py

senjoro/biocode

6697c17570126d99fb1cbeabf5b8322db006643d

[ "MIT" ]

355

2015-01-15T18:11:20.000Z

2022-03-26T19:23:30.000Z

sandbox/jorvis/generate_gff3_feature_identifiers.py

senjoro/biocode

6697c17570126d99fb1cbeabf5b8322db006643d

[ "MIT" ]

43

2015-03-20T08:40:14.000Z

2022-03-09T22:37:38.000Z

sandbox/jorvis/generate_gff3_feature_identifiers.py

senjoro/biocode

6697c17570126d99fb1cbeabf5b8322db006643d

[ "MIT" ]

217

2015-01-29T08:40:33.000Z

2022-03-26T19:23:45.000Z

#!/usr/bin/env python3 """ This script is used to take any GFF3 file and re-generate feature identifiers within it to match the convention used at IGS. This is: $prefix.$type.$id.$version The mode here defines what the identifier. For example, if using --mode=sequential for an organism (--prefix) of b_microti, the ID for the 100th gene might be: The mode here defines what the identifier. Examples: --mode=sequential (the default) b_microti.gene.100.1 --mode=uuid (UUID, as specified in RFC 4122, using uuid4()) b_microti.gene.8d8f9231-262e-48e7-b066-a84b6a939746.1 --mode=hex8 b_microti.gene.c08ca446.1 --mode=hex12 b_microti.gene.191ccac20a56.1 The only values that are replaced are in the ID and Parent attributes in the 9th column. """ import argparse import os import sys from binascii import hexlify from collections import defaultdict from uuid import uuid4 from biocode import gff ## constants next_ids_sequential = defaultdict(lambda: 1) def main(): parser = argparse.ArgumentParser( description='Generates new identifiers in GFF3 files following the IGS identifier convention.') ## output file to be written parser.add_argument('-i', '--input_file', type=str, required=True, help='TA file of source molecules' ) parser.add_argument('-o', '--output_file', type=str, required=False, help='Optional output file path (else STDOUT)' ) parser.add_argument('-p', '--prefix', type=str, required=True, help='The prefix portion of IDs to be generated') parser.add_argument('-m', '--mode', type=str, required=False, default='sequential', help='ID modes (see embedded documentation): sequential, uuid, hex8, hex12') args = parser.parse_args() check_arguments(args) id_map = dict() ## output will either be a file or STDOUT fout = sys.stdout if args.output_file is not None: fout = open(args.output_file, 'wt') for line in open(args.input_file): line = line.rstrip() cols = line.split("\t") if len(cols) != 9: fout.write(line + "\n") continue # grab the ID column if any id = gff.column_9_value(cols[8], 'ID') parent = gff.column_9_value(cols[8], 'Parent') new_id = None new_parent = None type = cols[2] if id is not None: if id in id_map: new_id = id_map[id] else: new_id = get_new_id(args.prefix, type, args.mode) id_map[id] = new_id cols[8] = cols[8].replace("ID={0}".format(id), "ID={0}".format(new_id)) if parent is not None: if parent in id_map: new_parent = id_map[parent] else: raise Exception("ERROR: parent ({0}) referenced before it was used as an ID".format(parent)) cols[8] = cols[8].replace("Parent={0}".format(parent), "Parent={0}".format(new_parent)) #print("DEBUG: old_id:{0} - old_parent:{1}, new_id:{2} - new_parent:{3}".format(id, parent, new_id, new_parent)) fout.write("\t".join(cols) + "\n") #>>> binascii.hexlify(os.urandom(4)) #b'c08ca446' #>>> uuid.uuid4() #UUID('37cd0fbf-bdc3-49bc-8351-a7ebc5a93ea5') def get_new_id(prefix, type, mode): new_id = "{0}.{1}.".format(prefix, type) if mode == 'sequential': new_id += str(next_ids_sequential[type]) next_ids_sequential[type] += 1 elif mode == 'uuid': new_id += str(uuid4()) elif mode == 'hex8': new_id += hexlify(os.urandom(4)).decode('ascii') elif mode == 'hex12': new_id += hexlify(os.urandom(6)).decode('ascii') new_id += '.1' return new_id def check_arguments( args ): # Check the acceptable values for format mode_options = ('sequential', 'uuid', 'hex8', 'hex12') if args.mode not in mode_options: raise Exception("ERROR: The --mode provided ({0}) isn't supported. Please check the documentation again.".format(args.mode)) if __name__ == '__main__': main()

29.496403

164

0.629268

import argparse import os import sys from binascii import hexlify from collections import defaultdict from uuid import uuid4 from biocode import gff equential = defaultdict(lambda: 1) def main(): parser = argparse.ArgumentParser( description='Generates new identifiers in GFF3 files following the IGS identifier convention.') i', '--input_file', type=str, required=True, help='TA file of source molecules' ) parser.add_argument('-o', '--output_file', type=str, required=False, help='Optional output file path (else STDOUT)' ) parser.add_argument('-p', '--prefix', type=str, required=True, help='The prefix portion of IDs to be generated') parser.add_argument('-m', '--mode', type=str, required=False, default='sequential', help='ID modes (see embedded documentation): sequential, uuid, hex8, hex12') args = parser.parse_args() check_arguments(args) id_map = dict() t_file is not None: fout = open(args.output_file, 'wt') for line in open(args.input_file): line = line.rstrip() cols = line.split("\t") if len(cols) != 9: fout.write(line + "\n") continue id = gff.column_9_value(cols[8], 'ID') parent = gff.column_9_value(cols[8], 'Parent') new_id = None new_parent = None type = cols[2] if id is not None: if id in id_map: new_id = id_map[id] else: new_id = get_new_id(args.prefix, type, args.mode) id_map[id] = new_id cols[8] = cols[8].replace("ID={0}".format(id), "ID={0}".format(new_id)) if parent is not None: if parent in id_map: new_parent = id_map[parent] else: raise Exception("ERROR: parent ({0}) referenced before it was used as an ID".format(parent)) cols[8] = cols[8].replace("Parent={0}".format(parent), "Parent={0}".format(new_parent)) fout.write("\t".join(cols) + "\n") def get_new_id(prefix, type, mode): new_id = "{0}.{1}.".format(prefix, type) if mode == 'sequential': new_id += str(next_ids_sequential[type]) next_ids_sequential[type] += 1 elif mode == 'uuid': new_id += str(uuid4()) elif mode == 'hex8': new_id += hexlify(os.urandom(4)).decode('ascii') elif mode == 'hex12': new_id += hexlify(os.urandom(6)).decode('ascii') new_id += '.1' return new_id def check_arguments( args ): mode_options = ('sequential', 'uuid', 'hex8', 'hex12') if args.mode not in mode_options: raise Exception("ERROR: The --mode provided ({0}) isn't supported. Please check the documentation again.".format(args.mode)) if __name__ == '__main__': main()

true

f71fca6c7b717f1271683d16d11ce61370e99869

2,628

py

Python

Apps/phforescoutcounteract/forescoutcounteract_consts.py

ryanbsaunders/phantom-apps

1befda793a08d366fbd443894f993efb1baf9635

[ "Apache-2.0" ]

74

2019-10-22T02:00:53.000Z

2022-03-15T12:56:13.000Z

Apps/phforescoutcounteract/forescoutcounteract_consts.py

ryanbsaunders/phantom-apps

1befda793a08d366fbd443894f993efb1baf9635

[ "Apache-2.0" ]

375

2019-10-22T20:53:50.000Z

2021-11-09T21:28:43.000Z

Apps/phforescoutcounteract/forescoutcounteract_consts.py

ryanbsaunders/phantom-apps

1befda793a08d366fbd443894f993efb1baf9635

[ "Apache-2.0" ]

175

2019-10-23T15:30:42.000Z

2021-11-05T21:33:31.000Z

# File: forescoutcounteract_consts.py # Copyright (c) 2018-2021 Splunk Inc. # # Licensed under Apache 2.0 (https://www.apache.org/licenses/LICENSE-2.0.txt) # # -- # Define your constants here FS_DEX_HOST_ENDPOINT = '/fsapi/niCore/Hosts' FS_DEX_LIST_ENDPOINT = '/fsapi/niCore/Lists' FS_DEX_TEST_CONNECTIVITY = \ """<?xml version="1.0" encoding="UTF-8"?> <FSAPI TYPE="request" API_VERSION="1.0"> <TRANSACTION TYPE="update"> <OPTIONS CREATE_NEW_HOST="true"/> <HOST_KEY NAME="ip" VALUE="{host_key_value}"/> <PROPERTIES></PROPERTIES> </TRANSACTION> </FSAPI>""" FS_DEX_UPDATE_SIMPLE_PROPERTY = \ """<?xml version='1.0' encoding='utf-8'?> <FSAPI TYPE="request" API_VERSION="1.0"> <TRANSACTION TYPE="update"> <OPTIONS CREATE_NEW_HOST="{create_host}"/> <HOST_KEY NAME="{host_key_name}" VALUE="{host_key_value}"/> <PROPERTIES> <PROPERTY NAME="{property_name}"> <VALUE>{property_value}</VALUE> </PROPERTY> </PROPERTIES> </TRANSACTION> </FSAPI>""" FS_DEX_DELETE_SIMPLE_PROPERTY = \ """<?xml version='1.0' encoding='utf-8'?> <FSAPI TYPE="request" API_VERSION="1.0"> <TRANSACTION TYPE="delete"> <HOST_KEY NAME="{host_key_name}" VALUE="{host_key_value}"/> <PROPERTIES> <PROPERTY NAME="{property_name}" /> </PROPERTIES> </TRANSACTION> </FSAPI>""" FS_DEX_UPDATE_LIST_PROPERTY = \ """<?xml version="1.0" encoding="UTF-8"?> <FSAPI TYPE="request" API_VERSION="2.0"> <TRANSACTION TYPE="{transaction_type}"> <LISTS> {list_body} </LISTS> </TRANSACTION> </FSAPI>""" FS_WEB_LOGIN = '/api/login' FS_WEB_HOSTS = '/api/hosts' FS_WEB_HOSTFIELDS = '/api/hostfields' FS_WEB_POLICIES = '/api/policies' # Error message constants FS_ERR_CODE_MSG = "Error code unavailable" FS_ERR_MSG_UNAVAILABLE = "Error message unavailable. Please check the asset configuration and|or action parameters" FS_PARSE_ERR_MSG = "Unable to parse the error message. Please check the asset configuration and|or action parameters" # validate integer ERR_VALID_INT_MSG = "Please provide a valid integer value in the {}" ERR_NON_NEG_INT_MSG = "Please provide a valid non-negative integer value in the {}" ERR_POSITIVE_INTEGER_MSG = "Please provide a valid non-zero positive integer value in the {}" HOST_ID_INT_PARAM = "'host_id' action parameter"

36.5

117

0.619102

FS_DEX_HOST_ENDPOINT = '/fsapi/niCore/Hosts' FS_DEX_LIST_ENDPOINT = '/fsapi/niCore/Lists' FS_DEX_TEST_CONNECTIVITY = \ """<?xml version="1.0" encoding="UTF-8"?> <FSAPI TYPE="request" API_VERSION="1.0"> <TRANSACTION TYPE="update"> <OPTIONS CREATE_NEW_HOST="true"/> <HOST_KEY NAME="ip" VALUE="{host_key_value}"/> <PROPERTIES></PROPERTIES> </TRANSACTION> </FSAPI>""" FS_DEX_UPDATE_SIMPLE_PROPERTY = \ """<?xml version='1.0' encoding='utf-8'?> <FSAPI TYPE="request" API_VERSION="1.0"> <TRANSACTION TYPE="update"> <OPTIONS CREATE_NEW_HOST="{create_host}"/> <HOST_KEY NAME="{host_key_name}" VALUE="{host_key_value}"/> <PROPERTIES> <PROPERTY NAME="{property_name}"> <VALUE>{property_value}</VALUE> </PROPERTY> </PROPERTIES> </TRANSACTION> </FSAPI>""" FS_DEX_DELETE_SIMPLE_PROPERTY = \ """<?xml version='1.0' encoding='utf-8'?> <FSAPI TYPE="request" API_VERSION="1.0"> <TRANSACTION TYPE="delete"> <HOST_KEY NAME="{host_key_name}" VALUE="{host_key_value}"/> <PROPERTIES> <PROPERTY NAME="{property_name}" /> </PROPERTIES> </TRANSACTION> </FSAPI>""" FS_DEX_UPDATE_LIST_PROPERTY = \ """<?xml version="1.0" encoding="UTF-8"?> <FSAPI TYPE="request" API_VERSION="2.0"> <TRANSACTION TYPE="{transaction_type}"> <LISTS> {list_body} </LISTS> </TRANSACTION> </FSAPI>""" FS_WEB_LOGIN = '/api/login' FS_WEB_HOSTS = '/api/hosts' FS_WEB_HOSTFIELDS = '/api/hostfields' FS_WEB_POLICIES = '/api/policies' FS_ERR_CODE_MSG = "Error code unavailable" FS_ERR_MSG_UNAVAILABLE = "Error message unavailable. Please check the asset configuration and|or action parameters" FS_PARSE_ERR_MSG = "Unable to parse the error message. Please check the asset configuration and|or action parameters" ERR_VALID_INT_MSG = "Please provide a valid integer value in the {}" ERR_NON_NEG_INT_MSG = "Please provide a valid non-negative integer value in the {}" ERR_POSITIVE_INTEGER_MSG = "Please provide a valid non-zero positive integer value in the {}" HOST_ID_INT_PARAM = "'host_id' action parameter"

true

f71fcd6b02e4155fea4b2f52039a9ce2c32ad6f6

7,009

py

Python

nmeta2dpae/tc_policy_dpae.py

mattjhayes/nmeta2-dpae

f441d78f0cdbd63495d4f96cddb462b801d82925

[ "Apache-2.0" ]

1

2016-03-19T20:42:49.000Z

nmeta2dpae/tc_policy_dpae.py

mattjhayes/nmeta2-dpae

f441d78f0cdbd63495d4f96cddb462b801d82925

[ "Apache-2.0" ]

2

2016-04-09T01:17:39.000Z

2016-04-09T01:30:58.000Z

nmeta2dpae/tc_policy_dpae.py

mattjhayes/nmeta2-dpae

f441d78f0cdbd63495d4f96cddb462b801d82925

[ "Apache-2.0" ]

4

2016-05-04T08:42:29.000Z

2021-07-16T02:11:40.000Z

# 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. """ This module is part of nmeta Data Plane Auxiliary Engine (DPAE) . It is used to contain the Traffic Classification (TC) policy and provide methods and direct variables to access it . Version 2.x Toulouse Code """ #*** Logging imports: import logging import logging.handlers import coloredlogs import sys #*** YAML for config and policy file parsing: import yaml #*** Keys that must exist under 'identity' in the policy: IDENTITY_KEYS = ('arp', 'lldp', 'dns', 'dhcp') class TCPolicy(object): """ This class is instantiated by nmeta2.py and provides methods to ingest the policy file main_policy.yaml and validate that it is correctly structured """ def __init__(self, _config): #*** Get logging config values from config class: _logging_level_s = _config.get_value \ ('tc_policy_dpae_logging_level_s') _logging_level_c = _config.get_value \ ('tc_policy_dpae_logging_level_c') _syslog_enabled = _config.get_value('syslog_enabled') _loghost = _config.get_value('loghost') _logport = _config.get_value('logport') _logfacility = _config.get_value('logfacility') _syslog_format = _config.get_value('syslog_format') _console_log_enabled = _config.get_value('console_log_enabled') _coloredlogs_enabled = _config.get_value('coloredlogs_enabled') _console_format = _config.get_value('console_format') #*** Set up Logging: self.logger = logging.getLogger(__name__) self.logger.setLevel(logging.DEBUG) self.logger.propagate = False #*** Syslog: if _syslog_enabled: #*** Log to syslog on host specified in config.yaml: self.syslog_handler = logging.handlers.SysLogHandler(address=( _loghost, _logport), facility=_logfacility) syslog_formatter = logging.Formatter(_syslog_format) self.syslog_handler.setFormatter(syslog_formatter) self.syslog_handler.setLevel(_logging_level_s) #*** Add syslog log handler to logger: self.logger.addHandler(self.syslog_handler) #*** Console logging: if _console_log_enabled: #*** Log to the console: if _coloredlogs_enabled: #*** Colourise the logs to make them easier to understand: coloredlogs.install(level=_logging_level_c, logger=self.logger, fmt=_console_format, datefmt='%H:%M:%S') else: #*** Add console log handler to logger: self.console_handler = logging.StreamHandler() console_formatter = logging.Formatter(_console_format) self.console_handler.setFormatter(console_formatter) self.console_handler.setLevel(_logging_level_c) self.logger.addHandler(self.console_handler) #*** Object to hold Controller main policies per interface in YAML: self.main_policy = dict() #*** Object to hold Controller optimised TC rules per iface in YAML: self.opt_rules = dict() def ingest_main_policy(self, main_policy_text, if_name): """ Turn a plain text main policy file object into a YAML object and store it as a class variable """ #*** Ingest the policy file: try: self.main_policy[if_name] = yaml.load(main_policy_text) except: exc_type, exc_value, exc_traceback = sys.exc_info() self.logger.error("Failed to convert main policy to YAML " "%s, %s, %s", exc_type, exc_value, exc_traceback) return 0 self.logger.debug("Successfully ingested main policy into YAML") return 1 def ingest_optimised_rules(self, opt_rules_text, if_name): """ Turn a plain optimised TC rules file object into a YAML object and store it as a class variable """ #*** Ingest the policy file: try: self.opt_rules[if_name] = yaml.load(opt_rules_text) except: exc_type, exc_value, exc_traceback = sys.exc_info() self.logger.error("Failed to convert optimised TC rules to YAML " "%s, %s, %s", exc_type, exc_value, exc_traceback) return 0 self.logger.debug("Successfully ingested optimised TC rules into YAML") return 1 def get_id_flag(self, if_name, id_key): """ Get a value for an Identity Indicator harvesting flag """ if not id_key in IDENTITY_KEYS: self.logger.error("The key %s is not valid", id_key) return 0 return self.main_policy[if_name]['identity'][id_key] def get_tc_classifiers(self, if_name): """ Return a list of traffic classifiers that should be run against ingress packets on a sniff interface. Each entry is a tuple of type (statistical or payload) and classifier name, example: [('statistical', 'statistical_qos_bandwidth_1')] """ classifiers = [] for idx, fe_match_list in enumerate(self.opt_rules[if_name]): self.logger.info("Optimised fe_match_list %s is %s", idx, fe_match_list) if not 'install_type' in fe_match_list: self.logger.error("no install_type key") continue if fe_match_list['install_type'] == 'to_dpae': self.logger.debug("Matched a DPAE TC condition...") classifiers.append((fe_match_list['type'], fe_match_list['value'])) return classifiers def tc_mode(self, if_name): """ Return the tc mode for the policy (active or passive) """ _tc_policies = self.main_policy[if_name]['tc_policies'] _tc_policies_keys = list(_tc_policies.keys()) _tc_policy_name = _tc_policies_keys[0] tc_mode = _tc_policies[_tc_policy_name]['mode'] if tc_mode: return tc_mode else: self.logger.error("Could not find tc mode in policy") return 'passive'

40.75

79

0.612356

import logging import logging.handlers import coloredlogs import sys import yaml IDENTITY_KEYS = ('arp', 'lldp', 'dns', 'dhcp') class TCPolicy(object): def __init__(self, _config): _logging_level_s = _config.get_value \ ('tc_policy_dpae_logging_level_s') _logging_level_c = _config.get_value \ ('tc_policy_dpae_logging_level_c') _syslog_enabled = _config.get_value('syslog_enabled') _loghost = _config.get_value('loghost') _logport = _config.get_value('logport') _logfacility = _config.get_value('logfacility') _syslog_format = _config.get_value('syslog_format') _console_log_enabled = _config.get_value('console_log_enabled') _coloredlogs_enabled = _config.get_value('coloredlogs_enabled') _console_format = _config.get_value('console_format') self.logger = logging.getLogger(__name__) self.logger.setLevel(logging.DEBUG) self.logger.propagate = False if _syslog_enabled: self.syslog_handler = logging.handlers.SysLogHandler(address=( _loghost, _logport), facility=_logfacility) syslog_formatter = logging.Formatter(_syslog_format) self.syslog_handler.setFormatter(syslog_formatter) self.syslog_handler.setLevel(_logging_level_s) self.logger.addHandler(self.syslog_handler) if _console_log_enabled: if _coloredlogs_enabled: coloredlogs.install(level=_logging_level_c, logger=self.logger, fmt=_console_format, datefmt='%H:%M:%S') else: self.console_handler = logging.StreamHandler() console_formatter = logging.Formatter(_console_format) self.console_handler.setFormatter(console_formatter) self.console_handler.setLevel(_logging_level_c) self.logger.addHandler(self.console_handler) self.main_policy = dict() self.opt_rules = dict() def ingest_main_policy(self, main_policy_text, if_name): try: self.main_policy[if_name] = yaml.load(main_policy_text) except: exc_type, exc_value, exc_traceback = sys.exc_info() self.logger.error("Failed to convert main policy to YAML " "%s, %s, %s", exc_type, exc_value, exc_traceback) return 0 self.logger.debug("Successfully ingested main policy into YAML") return 1 def ingest_optimised_rules(self, opt_rules_text, if_name): try: self.opt_rules[if_name] = yaml.load(opt_rules_text) except: exc_type, exc_value, exc_traceback = sys.exc_info() self.logger.error("Failed to convert optimised TC rules to YAML " "%s, %s, %s", exc_type, exc_value, exc_traceback) return 0 self.logger.debug("Successfully ingested optimised TC rules into YAML") return 1 def get_id_flag(self, if_name, id_key): if not id_key in IDENTITY_KEYS: self.logger.error("The key %s is not valid", id_key) return 0 return self.main_policy[if_name]['identity'][id_key] def get_tc_classifiers(self, if_name): classifiers = [] for idx, fe_match_list in enumerate(self.opt_rules[if_name]): self.logger.info("Optimised fe_match_list %s is %s", idx, fe_match_list) if not 'install_type' in fe_match_list: self.logger.error("no install_type key") continue if fe_match_list['install_type'] == 'to_dpae': self.logger.debug("Matched a DPAE TC condition...") classifiers.append((fe_match_list['type'], fe_match_list['value'])) return classifiers def tc_mode(self, if_name): _tc_policies = self.main_policy[if_name]['tc_policies'] _tc_policies_keys = list(_tc_policies.keys()) _tc_policy_name = _tc_policies_keys[0] tc_mode = _tc_policies[_tc_policy_name]['mode'] if tc_mode: return tc_mode else: self.logger.error("Could not find tc mode in policy") return 'passive'

true

f71fcd8293089c972b431387d1197b53dd7b564d

516

py

Python

src/event_representations.py

ATTPC/VAE-event-classification

aae331d44bffffec2ca8a6cdef71208899db0052

[ "MIT" ]

null

src/event_representations.py

ATTPC/VAE-event-classification

aae331d44bffffec2ca8a6cdef71208899db0052

[ "MIT" ]

2

2018-12-20T20:10:52.000Z

2019-02-04T17:44:01.000Z

src/event_representations.py

ATTPC/VAE-event-classification

aae331d44bffffec2ca8a6cdef71208899db0052

[ "MIT" ]

null

import numpy as np def make_histograms(x, bins=40, interval=[1e-1, 1]): intervals = np.linspace(interval[0], interval[1], bins) flat_x = x.reshape((x.shape[0], -1)) hist_x = np.zeros((x.shape[0], bins)) for i in range(1, bins): mask = flat_x <= intervals[i] mask = np.logical_and(mask, flat_x > intervals[i-1]) hist_x[:, i] = mask.sum(1) return hist_x def make_net_count(x, **kwargs): flat_x = x.reshape((x.shape[0], -1)) sum_x = flat_x.sum(1) return sum_x

28.666667

60

0.604651

import numpy as np def make_histograms(x, bins=40, interval=[1e-1, 1]): intervals = np.linspace(interval[0], interval[1], bins) flat_x = x.reshape((x.shape[0], -1)) hist_x = np.zeros((x.shape[0], bins)) for i in range(1, bins): mask = flat_x <= intervals[i] mask = np.logical_and(mask, flat_x > intervals[i-1]) hist_x[:, i] = mask.sum(1) return hist_x def make_net_count(x, **kwargs): flat_x = x.reshape((x.shape[0], -1)) sum_x = flat_x.sum(1) return sum_x

true

f71fce257398e75ef7291d3d8d8cd6d620bff9ed

122

py

Python

zpy/classes/bases/utility/pretty.py

yu-ichiro/zpy

65e5ae7616d6e7fce91a03f20f663caa4af834b5

[ "MIT" ]

null

zpy/classes/bases/utility/pretty.py

yu-ichiro/zpy

65e5ae7616d6e7fce91a03f20f663caa4af834b5

[ "MIT" ]

null

zpy/classes/bases/utility/pretty.py

yu-ichiro/zpy

65e5ae7616d6e7fce91a03f20f663caa4af834b5

[ "MIT" ]

null

from abc import ABC, abstractmethod class Pretty(ABC): @abstractmethod def __pretty__(self) -> str: ...

15.25

35

0.639344

from abc import ABC, abstractmethod class Pretty(ABC): @abstractmethod def __pretty__(self) -> str: ...

true

f71fcef13051a8d05b65c648d775148a97470b27

167,246

py

Python

tensorflow/python/ops/nn_ops.py

handongke/tensorflow

c6bb5cd0447a0af2764c195fb14d218df8ae6471

[ "Apache-2.0" ]

5

2019-01-13T16:15:25.000Z

2019-07-07T16:17:32.000Z

tensorflow/python/ops/nn_ops.py

handongke/tensorflow

c6bb5cd0447a0af2764c195fb14d218df8ae6471

[ "Apache-2.0" ]

null

tensorflow/python/ops/nn_ops.py

handongke/tensorflow

c6bb5cd0447a0af2764c195fb14d218df8ae6471

[ "Apache-2.0" ]

null

# Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Wrappers for primitive Neural Net (NN) Operations.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import numbers import numpy as np from tensorflow.python.compat import compat from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import graph_util from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops # go/tf-wildcard-import # pylint: disable=wildcard-import from tensorflow.python.ops.gen_nn_ops import * # pylint: enable=wildcard-import from tensorflow.python.util import deprecation from tensorflow.python.util.deprecation import deprecated_args from tensorflow.python.util.deprecation import deprecated_argument_lookup from tensorflow.python.util.tf_export import tf_export # Aliases for some automatically-generated names. local_response_normalization = gen_nn_ops.lrn # pylint: disable=protected-access def _non_atrous_convolution( input, # pylint: disable=redefined-builtin filter, # pylint: disable=redefined-builtin padding, data_format=None, # pylint: disable=redefined-builtin strides=None, name=None): """Computes sums of N-D convolutions (actually cross correlation). It is required that 1 <= N <= 3. This is used to implement the more generic `convolution` function, which extends the interface of this function with a `dilation_rate` parameter. Args: input: Rank N+2 tensor of type T of shape `[batch_size] + input_spatial_shape + [in_channels]` if `data_format` does not start with `"NC"`, or `[batch_size, in_channels] + input_spatial_shape` if `data_format` starts with `"NC"`. filter: Rank N+2 tensor of type T of shape `filter_spatial_shape + [in_channels, out_channels]`. Rank of either `input` or `filter` must be known. padding: Padding method to use, must be either "VALID" or "SAME". data_format: A string or None. Specifies whether the channel dimension of the `input` and output is the last dimension (default, or if `data_format` does not start with "NC"), or the second dimension (if `data_format` starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW". strides: Sequence of N positive integers, defaults to `[1] * N`. name: Name prefix to use. Returns: Rank N+2 tensor of type T of shape `[batch_size] + output_spatial_shape + [out_channels]`, where if padding == "SAME": output_spatial_shape = input_spatial_shape if padding == "VALID": output_spatial_shape = input_spatial_shape - filter_spatial_shape + 1. Raises: ValueError: if ranks are incompatible. """ with ops.name_scope(name, "non_atrous_convolution", [input, filter]) as scope: input = ops.convert_to_tensor(input, name="input") # pylint: disable=redefined-builtin input_shape = input.get_shape() filter = ops.convert_to_tensor(filter, name="filter") # pylint: disable=redefined-builtin filter_shape = filter.get_shape() op = _NonAtrousConvolution( input_shape, filter_shape=filter_shape, padding=padding, data_format=data_format, strides=strides, name=scope) return op(input, filter) class _NonAtrousConvolution(object): """Helper class for _non_atrous_convolution. Note that this class assumes that shapes of input and filter passed to __call__ are compatible with input_shape and filter_shape passed to the constructor. Arguments: input_shape: static input shape, i.e. input.get_shape(). filter_shape: static filter shape, i.e. filter.get_shape(). padding: see _non_atrous_convolution. data_format: see _non_atrous_convolution. strides: see _non_atrous_convolution. name: see _non_atrous_convolution. """ def __init__( self, input_shape, filter_shape, # pylint: disable=redefined-builtin padding, data_format=None, strides=None, name=None): filter_shape = filter_shape.with_rank(input_shape.ndims) self.padding = padding self.name = name input_shape = input_shape.with_rank(filter_shape.ndims) if input_shape.ndims is None: raise ValueError("Rank of convolution must be known") if input_shape.ndims < 3 or input_shape.ndims > 5: raise ValueError( "`input` and `filter` must have rank at least 3 and at most 5") conv_dims = input_shape.ndims - 2 if strides is None: strides = [1] * conv_dims elif len(strides) != conv_dims: raise ValueError("len(strides)=%d, but should be %d" % (len(strides), conv_dims)) if conv_dims == 1: # conv1d uses the 2-d data format names if data_format is None: data_format = "NWC" elif data_format not in {"NCW", "NWC", "NCHW", "NHWC"}: raise ValueError("data_format must be \"NWC\" or \"NCW\".") self.strides = strides[0] self.data_format = data_format self.conv_op = self._conv1d elif conv_dims == 2: if data_format is None or data_format == "NHWC": data_format = "NHWC" strides = [1] + list(strides) + [1] elif data_format == "NCHW": strides = [1, 1] + list(strides) else: raise ValueError("data_format must be \"NHWC\" or \"NCHW\".") self.strides = strides self.data_format = data_format self.conv_op = conv2d elif conv_dims == 3: if data_format is None or data_format == "NDHWC": strides = [1] + list(strides) + [1] elif data_format == "NCDHW": strides = [1, 1] + list(strides) else: raise ValueError("data_format must be \"NDHWC\" or \"NCDHW\". Have: %s" % data_format) self.strides = strides self.data_format = data_format self.conv_op = gen_nn_ops.conv3d # Note that we need this adapter since argument names for conv1d don't match # those for gen_nn_ops.conv2d and gen_nn_ops.conv3d. # pylint: disable=redefined-builtin def _conv1d(self, input, filter, strides, padding, data_format, name): return conv1d( value=input, filters=filter, stride=strides, padding=padding, data_format=data_format, name=name) # pylint: enable=redefined-builtin def __call__(self, inp, filter): # pylint: disable=redefined-builtin return self.conv_op( input=inp, filter=filter, strides=self.strides, padding=self.padding, data_format=self.data_format, name=self.name) @tf_export("nn.dilation2d", v1=[]) def dilation2d_v2( input, # pylint: disable=redefined-builtin filters, # pylint: disable=redefined-builtin strides, padding, data_format, dilations, name=None): """Computes the grayscale dilation of 4-D `input` and 3-D `filters` tensors. The `input` tensor has shape `[batch, in_height, in_width, depth]` and the `filters` tensor has shape `[filter_height, filter_width, depth]`, i.e., each input channel is processed independently of the others with its own structuring function. The `output` tensor has shape `[batch, out_height, out_width, depth]`. The spatial dimensions of the output tensor depend on the `padding` algorithm. We currently only support the default "NHWC" `data_format`. In detail, the grayscale morphological 2-D dilation is the max-sum correlation (for consistency with `conv2d`, we use unmirrored filters): output[b, y, x, c] = max_{dy, dx} input[b, strides[1] * y + rates[1] * dy, strides[2] * x + rates[2] * dx, c] + filters[dy, dx, c] Max-pooling is a special case when the filter has size equal to the pooling kernel size and contains all zeros. Note on duality: The dilation of `input` by the `filters` is equal to the negation of the erosion of `-input` by the reflected `filters`. Args: input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. 4-D with shape `[batch, in_height, in_width, depth]`. filters: A `Tensor`. Must have the same type as `input`. 3-D with shape `[filter_height, filter_width, depth]`. strides: A list of `ints` that has length `>= 4`. The stride of the sliding window for each dimension of the input tensor. Must be: `[1, stride_height, stride_width, 1]`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: A `string`, only `"NCHW"` is currently supported. dilations: A list of `ints` that has length `>= 4`. The input stride for atrous morphological dilation. Must be: `[1, rate_height, rate_width, 1]`. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. """ if data_format != "NCHW": raise ValueError("Data formats other than NCHW are not yet supported") return gen_nn_ops.dilation2d(input=input, filter=filters, strides=strides, rates=dilations, padding=padding, name=name) @tf_export("nn.with_space_to_batch") def with_space_to_batch( input, # pylint: disable=redefined-builtin dilation_rate, padding, op, filter_shape=None, spatial_dims=None, data_format=None): """Performs `op` on the space-to-batch representation of `input`. This has the effect of transforming sliding window operations into the corresponding "atrous" operation in which the input is sampled at the specified `dilation_rate`. In the special case that `dilation_rate` is uniformly 1, this simply returns: op(input, num_spatial_dims, padding) Otherwise, it returns: batch_to_space_nd( op(space_to_batch_nd(input, adjusted_dilation_rate, adjusted_paddings), num_spatial_dims, "VALID") adjusted_dilation_rate, adjusted_crops), where: adjusted_dilation_rate is an int64 tensor of shape [max(spatial_dims)], adjusted_{paddings,crops} are int64 tensors of shape [max(spatial_dims), 2] defined as follows: We first define two int64 tensors `paddings` and `crops` of shape `[num_spatial_dims, 2]` based on the value of `padding` and the spatial dimensions of the `input`: If `padding = "VALID"`, then: paddings, crops = required_space_to_batch_paddings( input_shape[spatial_dims], dilation_rate) If `padding = "SAME"`, then: dilated_filter_shape = filter_shape + (filter_shape - 1) * (dilation_rate - 1) paddings, crops = required_space_to_batch_paddings( input_shape[spatial_dims], dilation_rate, [(dilated_filter_shape - 1) // 2, dilated_filter_shape - 1 - (dilated_filter_shape - 1) // 2]) Because `space_to_batch_nd` and `batch_to_space_nd` assume that the spatial dimensions are contiguous starting at the second dimension, but the specified `spatial_dims` may not be, we must adjust `dilation_rate`, `paddings` and `crops` in order to be usable with these operations. For a given dimension, if the block size is 1, and both the starting and ending padding and crop amounts are 0, then space_to_batch_nd effectively leaves that dimension alone, which is what is needed for dimensions not part of `spatial_dims`. Furthermore, `space_to_batch_nd` and `batch_to_space_nd` handle this case efficiently for any number of leading and trailing dimensions. For 0 <= i < len(spatial_dims), we assign: adjusted_dilation_rate[spatial_dims[i] - 1] = dilation_rate[i] adjusted_paddings[spatial_dims[i] - 1, :] = paddings[i, :] adjusted_crops[spatial_dims[i] - 1, :] = crops[i, :] All unassigned values of `adjusted_dilation_rate` default to 1, while all unassigned values of `adjusted_paddings` and `adjusted_crops` default to 0. Note in the case that `dilation_rate` is not uniformly 1, specifying "VALID" padding is equivalent to specifying `padding = "SAME"` with a filter_shape of `[1]*N`. Advanced usage. Note the following optimization: A sequence of `with_space_to_batch` operations with identical (not uniformly 1) `dilation_rate` parameters and "VALID" padding net = with_space_to_batch(net, dilation_rate, "VALID", op_1) ... net = with_space_to_batch(net, dilation_rate, "VALID", op_k) can be combined into a single `with_space_to_batch` operation as follows: def combined_op(converted_input, num_spatial_dims, _): result = op_1(converted_input, num_spatial_dims, "VALID") ... result = op_k(result, num_spatial_dims, "VALID") net = with_space_to_batch(net, dilation_rate, "VALID", combined_op) This eliminates the overhead of `k-1` calls to `space_to_batch_nd` and `batch_to_space_nd`. Similarly, a sequence of `with_space_to_batch` operations with identical (not uniformly 1) `dilation_rate` parameters, "SAME" padding, and odd filter dimensions net = with_space_to_batch(net, dilation_rate, "SAME", op_1, filter_shape_1) ... net = with_space_to_batch(net, dilation_rate, "SAME", op_k, filter_shape_k) can be combined into a single `with_space_to_batch` operation as follows: def combined_op(converted_input, num_spatial_dims, _): result = op_1(converted_input, num_spatial_dims, "SAME") ... result = op_k(result, num_spatial_dims, "SAME") net = with_space_to_batch(net, dilation_rate, "VALID", combined_op) Args: input: Tensor of rank > max(spatial_dims). dilation_rate: int32 Tensor of *known* shape [num_spatial_dims]. padding: str constant equal to "VALID" or "SAME" op: Function that maps (input, num_spatial_dims, padding) -> output filter_shape: If padding = "SAME", specifies the shape of the convolution kernel/pooling window as an integer Tensor of shape [>=num_spatial_dims]. If padding = "VALID", filter_shape is ignored and need not be specified. spatial_dims: Monotonically increasing sequence of `num_spatial_dims` integers (which are >= 1) specifying the spatial dimensions of `input` and output. Defaults to: `range(1, num_spatial_dims+1)`. data_format: A string or None. Specifies whether the channel dimension of the `input` and output is the last dimension (default, or if `data_format` does not start with "NC"), or the second dimension (if `data_format` starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW". Returns: The output Tensor as described above, dimensions will vary based on the op provided. Raises: ValueError: if `padding` is invalid or the arguments are incompatible. ValueError: if `spatial_dims` are invalid. """ input = ops.convert_to_tensor(input, name="input") # pylint: disable=redefined-builtin input_shape = input.get_shape() def build_op(num_spatial_dims, padding): return lambda inp, _: op(inp, num_spatial_dims, padding) new_op = _WithSpaceToBatch( input_shape, dilation_rate, padding, build_op, filter_shape=filter_shape, spatial_dims=spatial_dims, data_format=data_format) return new_op(input, None) class _WithSpaceToBatch(object): """Helper class for with_space_to_batch. Note that this class assumes that shapes of input and filter passed to __call__ are compatible with input_shape and filter_shape passed to the constructor. Arguments input_shape: static shape of input. i.e. input.get_shape(). dilation_rate: see with_space_to_batch padding: see with_space_to_batch build_op: Function that maps (num_spatial_dims, paddings) -> (function that maps (input, filter) -> output). filter_shape: see with_space_to_batch spatial_dims: see with_space_to_batch data_format: see with_space_to_batch """ def __init__(self, input_shape, dilation_rate, padding, build_op, filter_shape=None, spatial_dims=None, data_format=None): """Helper class for _with_space_to_batch.""" dilation_rate = ops.convert_to_tensor( dilation_rate, dtypes.int32, name="dilation_rate") try: rate_shape = dilation_rate.get_shape().with_rank(1) except ValueError: raise ValueError("rate must be rank 1") if not dilation_rate.get_shape().is_fully_defined(): raise ValueError("rate must have known shape") num_spatial_dims = rate_shape.dims[0].value if data_format is not None and data_format.startswith("NC"): starting_spatial_dim = 2 else: starting_spatial_dim = 1 if spatial_dims is None: spatial_dims = range(starting_spatial_dim, num_spatial_dims + starting_spatial_dim) orig_spatial_dims = list(spatial_dims) spatial_dims = sorted(set(int(x) for x in orig_spatial_dims)) if spatial_dims != orig_spatial_dims or any(x < 1 for x in spatial_dims): raise ValueError( "spatial_dims must be a montonically increasing sequence of positive " "integers") # pylint: disable=line-too-long if data_format is not None and data_format.startswith("NC"): expected_input_rank = spatial_dims[-1] else: expected_input_rank = spatial_dims[-1] + 1 try: input_shape.with_rank_at_least(expected_input_rank) except ValueError: raise ValueError( "input tensor must have rank %d at least" % (expected_input_rank)) const_rate = tensor_util.constant_value(dilation_rate) rate_or_const_rate = dilation_rate if const_rate is not None: rate_or_const_rate = const_rate if np.any(const_rate < 1): raise ValueError("dilation_rate must be positive") if np.all(const_rate == 1): self.call = build_op(num_spatial_dims, padding) return # We have two padding contributions. The first is used for converting "SAME" # to "VALID". The second is required so that the height and width of the # zero-padded value tensor are multiples of rate. # Padding required to reduce to "VALID" convolution if padding == "SAME": if filter_shape is None: raise ValueError("filter_shape must be specified for SAME padding") filter_shape = ops.convert_to_tensor(filter_shape, name="filter_shape") const_filter_shape = tensor_util.constant_value(filter_shape) if const_filter_shape is not None: filter_shape = const_filter_shape self.base_paddings = _with_space_to_batch_base_paddings( const_filter_shape, num_spatial_dims, rate_or_const_rate) else: self.num_spatial_dims = num_spatial_dims self.rate_or_const_rate = rate_or_const_rate self.base_paddings = None elif padding == "VALID": self.base_paddings = np.zeros([num_spatial_dims, 2], np.int32) else: raise ValueError("Invalid padding method %r" % padding) self.input_shape = input_shape self.spatial_dims = spatial_dims self.dilation_rate = dilation_rate self.data_format = data_format self.op = build_op(num_spatial_dims, "VALID") self.call = self._with_space_to_batch_call def _with_space_to_batch_call(self, inp, filter): # pylint: disable=redefined-builtin """Call functionality for with_space_to_batch.""" # Handle input whose shape is unknown during graph creation. input_spatial_shape = None input_shape = self.input_shape spatial_dims = self.spatial_dims if input_shape.ndims is not None: input_shape_list = input_shape.as_list() input_spatial_shape = [input_shape_list[i] for i in spatial_dims] if input_spatial_shape is None or None in input_spatial_shape: input_shape_tensor = array_ops.shape(inp) input_spatial_shape = array_ops.stack( [input_shape_tensor[i] for i in spatial_dims]) base_paddings = self.base_paddings if base_paddings is None: # base_paddings could not be computed at build time since static filter # shape was not fully defined. filter_shape = array_ops.shape(filter) base_paddings = _with_space_to_batch_base_paddings( filter_shape, self.num_spatial_dims, self.rate_or_const_rate) paddings, crops = array_ops.required_space_to_batch_paddings( input_shape=input_spatial_shape, base_paddings=base_paddings, block_shape=self.dilation_rate) dilation_rate = _with_space_to_batch_adjust(self.dilation_rate, 1, spatial_dims) paddings = _with_space_to_batch_adjust(paddings, 0, spatial_dims) crops = _with_space_to_batch_adjust(crops, 0, spatial_dims) input_converted = array_ops.space_to_batch_nd( input=inp, block_shape=dilation_rate, paddings=paddings) result = self.op(input_converted, filter) result_converted = array_ops.batch_to_space_nd( input=result, block_shape=dilation_rate, crops=crops) # Recover channel information for output shape if channels are not last. if self.data_format is not None and self.data_format.startswith("NC"): if not result_converted.shape.dims[1].value and filter is not None: output_shape = result_converted.shape.as_list() output_shape[1] = filter.shape[-1] result_converted.set_shape(output_shape) return result_converted def __call__(self, inp, filter): # pylint: disable=redefined-builtin return self.call(inp, filter) def _with_space_to_batch_base_paddings(filter_shape, num_spatial_dims, rate_or_const_rate): """Helper function to compute base_paddings.""" # Spatial dimensions of the filters and the upsampled filters in which we # introduce (rate - 1) zeros between consecutive filter values. filter_spatial_shape = filter_shape[:num_spatial_dims] dilated_filter_spatial_shape = ( filter_spatial_shape + (filter_spatial_shape - 1) * (rate_or_const_rate - 1)) pad_extra_shape = dilated_filter_spatial_shape - 1 # When full_padding_shape is odd, we pad more at end, following the same # convention as conv2d. pad_extra_start = pad_extra_shape // 2 pad_extra_end = pad_extra_shape - pad_extra_start base_paddings = array_ops.stack( [[pad_extra_start[i], pad_extra_end[i]] for i in range(num_spatial_dims)]) return base_paddings def _with_space_to_batch_adjust(orig, fill_value, spatial_dims): """Returns an `adjusted` version of `orig` based on `spatial_dims`. Tensor of the same type as `orig` and with shape `[max(spatial_dims), ...]` where: adjusted[spatial_dims[i] - 1, ...] = orig[i, ...] for 0 <= i < len(spatial_dims), and adjusted[j, ...] = fill_value for j != spatial_dims[i] - 1 for some i. If `orig` is a constant value, then the result will be a constant value. Args: orig: Tensor of rank > max(spatial_dims). fill_value: Numpy scalar (of same data type as `orig) specifying the fill value for non-spatial dimensions. spatial_dims: See with_space_to_batch. Returns: `adjusted` tensor. """ fill_dims = orig.get_shape().as_list()[1:] dtype = orig.dtype.as_numpy_dtype parts = [] const_orig = tensor_util.constant_value(orig) const_or_orig = const_orig if const_orig is not None else orig prev_spatial_dim = 0 i = 0 while i < len(spatial_dims): start_i = i start_spatial_dim = spatial_dims[i] if start_spatial_dim > 1: # Fill in any gap from the previous spatial dimension (or dimension 1 if # this is the first spatial dimension) with `fill_value`. parts.append( np.full( [start_spatial_dim - 1 - prev_spatial_dim] + fill_dims, fill_value, dtype=dtype)) # Find the largest value of i such that: # [spatial_dims[start_i], ..., spatial_dims[i]] # == [start_spatial_dim, ..., start_spatial_dim + i - start_i], # i.e. the end of a contiguous group of spatial dimensions. while (i + 1 < len(spatial_dims) and spatial_dims[i + 1] == spatial_dims[i] + 1): i += 1 parts.append(const_or_orig[start_i:i + 1]) prev_spatial_dim = spatial_dims[i] i += 1 if const_orig is not None: return np.concatenate(parts) else: return array_ops.concat(parts, 0) def _get_strides_and_dilation_rate(num_spatial_dims, strides, dilation_rate): """Helper function for verifying strides and dilation_rate arguments. This is used by `convolution` and `pool`. Args: num_spatial_dims: int strides: Optional. List of N ints >= 1. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1. dilation_rate: Optional. List of N ints >= 1. Defaults to [1]*N. If any value of dilation_rate is > 1, then all values of strides must be 1. Returns: Normalized (strides, dilation_rate) as int32 numpy arrays of shape [num_spatial_dims]. Raises: ValueError: if the parameters are invalid. """ if dilation_rate is None: dilation_rate = [1] * num_spatial_dims elif len(dilation_rate) != num_spatial_dims: raise ValueError("len(dilation_rate)=%d but should be %d" % (len(dilation_rate), num_spatial_dims)) dilation_rate = np.array(dilation_rate, dtype=np.int32) if np.any(dilation_rate < 1): raise ValueError("all values of dilation_rate must be positive") if strides is None: strides = [1] * num_spatial_dims elif len(strides) != num_spatial_dims: raise ValueError("len(strides)=%d but should be %d" % (len(strides), num_spatial_dims)) strides = np.array(strides, dtype=np.int32) if np.any(strides < 1): raise ValueError("all values of strides must be positive") if np.any(strides > 1) and np.any(dilation_rate > 1): raise ValueError( "strides > 1 not supported in conjunction with dilation_rate > 1") return strides, dilation_rate @tf_export(v1=["nn.convolution"]) def convolution( input, # pylint: disable=redefined-builtin filter, # pylint: disable=redefined-builtin padding, strides=None, dilation_rate=None, name=None, data_format=None): # pylint: disable=line-too-long """Computes sums of N-D convolutions (actually cross-correlation). This also supports either output striding via the optional `strides` parameter or atrous convolution (also known as convolution with holes or dilated convolution, based on the French word "trous" meaning holes in English) via the optional `dilation_rate` parameter. Currently, however, output striding is not supported for atrous convolutions. Specifically, in the case that `data_format` does not start with "NC", given a rank (N+2) `input` Tensor of shape [num_batches, input_spatial_shape[0], ..., input_spatial_shape[N-1], num_input_channels], a rank (N+2) `filter` Tensor of shape [spatial_filter_shape[0], ..., spatial_filter_shape[N-1], num_input_channels, num_output_channels], an optional `dilation_rate` tensor of shape [N] (defaulting to [1]*N) specifying the filter upsampling/input downsampling rate, and an optional list of N `strides` (defaulting [1]*N), this computes for each N-D spatial output position (x[0], ..., x[N-1]): ``` output[b, x[0], ..., x[N-1], k] = sum_{z[0], ..., z[N-1], q} filter[z[0], ..., z[N-1], q, k] * padded_input[b, x[0]*strides[0] + dilation_rate[0]*z[0], ..., x[N-1]*strides[N-1] + dilation_rate[N-1]*z[N-1], q] ``` where b is the index into the batch, k is the output channel number, q is the input channel number, and z is the N-D spatial offset within the filter. Here, `padded_input` is obtained by zero padding the input using an effective spatial filter shape of `(spatial_filter_shape-1) * dilation_rate + 1` and output striding `strides` as described in the [comment here](https://tensorflow.org/api_guides/python/nn#Convolution). In the case that `data_format` does start with `"NC"`, the `input` and output (but not the `filter`) are simply transposed as follows: convolution(input, data_format, **kwargs) = tf.transpose(convolution(tf.transpose(input, [0] + range(2,N+2) + [1]), **kwargs), [0, N+1] + range(1, N+1)) It is required that 1 <= N <= 3. Args: input: An (N+2)-D `Tensor` of type `T`, of shape `[batch_size] + input_spatial_shape + [in_channels]` if data_format does not start with "NC" (default), or `[batch_size, in_channels] + input_spatial_shape` if data_format starts with "NC". filter: An (N+2)-D `Tensor` with the same type as `input` and shape `spatial_filter_shape + [in_channels, out_channels]`. padding: A string, either `"VALID"` or `"SAME"`. The padding algorithm. strides: Optional. Sequence of N ints >= 1. Specifies the output stride. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1. dilation_rate: Optional. Sequence of N ints >= 1. Specifies the filter upsampling/input downsampling rate. In the literature, the same parameter is sometimes called `input stride` or `dilation`. The effective filter size used for the convolution will be `spatial_filter_shape + (spatial_filter_shape - 1) * (rate - 1)`, obtained by inserting (dilation_rate[i]-1) zeros between consecutive elements of the original filter in each spatial dimension i. If any value of dilation_rate is > 1, then all values of strides must be 1. name: Optional name for the returned tensor. data_format: A string or None. Specifies whether the channel dimension of the `input` and output is the last dimension (default, or if `data_format` does not start with "NC"), or the second dimension (if `data_format` starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW". Returns: A `Tensor` with the same type as `input` of shape `[batch_size] + output_spatial_shape + [out_channels]` if data_format is None or does not start with "NC", or `[batch_size, out_channels] + output_spatial_shape` if data_format starts with "NC", where `output_spatial_shape` depends on the value of `padding`. If padding == "SAME": output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i]) If padding == "VALID": output_spatial_shape[i] = ceil((input_spatial_shape[i] - (spatial_filter_shape[i]-1) * dilation_rate[i]) / strides[i]). Raises: ValueError: If input/output depth does not match `filter` shape, if padding is other than `"VALID"` or `"SAME"`, or if data_format is invalid. """ # pylint: enable=line-too-long with ops.name_scope(name, "convolution", [input, filter]) as name: input = ops.convert_to_tensor(input, name="input") # pylint: disable=redefined-builtin input_shape = input.get_shape() filter = ops.convert_to_tensor(filter, name="filter") # pylint: disable=redefined-builtin filter_shape = filter.get_shape() op = Convolution( input_shape, filter_shape, padding, strides=strides, dilation_rate=dilation_rate, name=name, data_format=data_format) return op(input, filter) @tf_export("nn.convolution", v1=[]) def convolution_v2( input, # pylint: disable=redefined-builtin filters, strides=None, padding="VALID", data_format=None, dilations=None, name=None): return convolution( input, # pylint: disable=redefined-builtin filters, padding=padding, strides=strides, dilation_rate=dilations, name=name, data_format=data_format) convolution_v2.__doc__ = deprecation.rewrite_argument_docstring( deprecation.rewrite_argument_docstring( convolution.__doc__, "dilation_rate", "dilations"), "filter", "filters") class Convolution(object): """Helper class for convolution. Note that this class assumes that shapes of input and filter passed to __call__ are compatible with input_shape and filter_shape passed to the constructor. Arguments input_shape: static shape of input. i.e. input.get_shape(). filter_shape: static shape of the filter. i.e. filter.get_shape(). padding: see convolution. strides: see convolution. dilation_rate: see convolution. name: see convolution. data_format: see convolution. """ def __init__(self, input_shape, filter_shape, padding, strides=None, dilation_rate=None, name=None, data_format=None): """Helper function for convolution.""" num_total_dims = filter_shape.ndims if num_total_dims is None: num_total_dims = input_shape.ndims if num_total_dims is None: raise ValueError("rank of input or filter must be known") num_spatial_dims = num_total_dims - 2 try: input_shape.with_rank(num_spatial_dims + 2) except ValueError: raise ValueError( "input tensor must have rank %d" % (num_spatial_dims + 2)) try: filter_shape.with_rank(num_spatial_dims + 2) except ValueError: raise ValueError( "filter tensor must have rank %d" % (num_spatial_dims + 2)) if data_format is None or not data_format.startswith("NC"): input_channels_dim = tensor_shape.dimension_at_index( input_shape, num_spatial_dims + 1) spatial_dims = range(1, num_spatial_dims + 1) else: input_channels_dim = tensor_shape.dimension_at_index(input_shape, 1) spatial_dims = range(2, num_spatial_dims + 2) if not input_channels_dim.is_compatible_with( filter_shape[num_spatial_dims]): raise ValueError( "number of input channels does not match corresponding dimension of " "filter, {} != {}".format(input_channels_dim, filter_shape[num_spatial_dims])) strides, dilation_rate = _get_strides_and_dilation_rate( num_spatial_dims, strides, dilation_rate) self.input_shape = input_shape self.filter_shape = filter_shape self.data_format = data_format self.strides = strides self.name = name self.conv_op = _WithSpaceToBatch( input_shape, dilation_rate=dilation_rate, padding=padding, build_op=self._build_op, filter_shape=filter_shape, spatial_dims=spatial_dims, data_format=data_format) def _build_op(self, _, padding): return _NonAtrousConvolution( self.input_shape, filter_shape=self.filter_shape, padding=padding, data_format=self.data_format, strides=self.strides, name=self.name) def __call__(self, inp, filter): # pylint: disable=redefined-builtin return self.conv_op(inp, filter) @tf_export(v1=["nn.pool"]) def pool( input, # pylint: disable=redefined-builtin window_shape, pooling_type, padding, dilation_rate=None, strides=None, name=None, data_format=None): # pylint: disable=line-too-long """Performs an N-D pooling operation. In the case that `data_format` does not start with "NC", computes for 0 <= b < batch_size, 0 <= x[i] < output_spatial_shape[i], 0 <= c < num_channels: ``` output[b, x[0], ..., x[N-1], c] = REDUCE_{z[0], ..., z[N-1]} input[b, x[0] * strides[0] - pad_before[0] + dilation_rate[0]*z[0], ... x[N-1]*strides[N-1] - pad_before[N-1] + dilation_rate[N-1]*z[N-1], c], ``` where the reduction function REDUCE depends on the value of `pooling_type`, and pad_before is defined based on the value of `padding` as described in the "returns" section of `tf.nn.convolution` for details. The reduction never includes out-of-bounds positions. In the case that `data_format` starts with `"NC"`, the `input` and output are simply transposed as follows: ``` pool(input, data_format, **kwargs) = tf.transpose(pool(tf.transpose(input, [0] + range(2,N+2) + [1]), **kwargs), [0, N+1] + range(1, N+1)) ``` Args: input: Tensor of rank N+2, of shape `[batch_size] + input_spatial_shape + [num_channels]` if data_format does not start with "NC" (default), or `[batch_size, num_channels] + input_spatial_shape` if data_format starts with "NC". Pooling happens over the spatial dimensions only. window_shape: Sequence of N ints >= 1. pooling_type: Specifies pooling operation, must be "AVG" or "MAX". padding: The padding algorithm, must be "SAME" or "VALID". See the "returns" section of `tf.nn.convolution` for details. dilation_rate: Optional. Dilation rate. List of N ints >= 1. Defaults to [1]*N. If any value of dilation_rate is > 1, then all values of strides must be 1. strides: Optional. Sequence of N ints >= 1. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1. name: Optional. Name of the op. data_format: A string or None. Specifies whether the channel dimension of the `input` and output is the last dimension (default, or if `data_format` does not start with "NC"), or the second dimension (if `data_format` starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW". Returns: Tensor of rank N+2, of shape [batch_size] + output_spatial_shape + [num_channels] if data_format is None or does not start with "NC", or [batch_size, num_channels] + output_spatial_shape if data_format starts with "NC", where `output_spatial_shape` depends on the value of padding: If padding = "SAME": output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i]) If padding = "VALID": output_spatial_shape[i] = ceil((input_spatial_shape[i] - (window_shape[i] - 1) * dilation_rate[i]) / strides[i]). Raises: ValueError: if arguments are invalid. """ # pylint: enable=line-too-long with ops.name_scope(name, "%s_pool" % (pooling_type.lower()), [input]) as scope: input = ops.convert_to_tensor(input, name="input") # pylint: disable=redefined-builtin num_spatial_dims = len(window_shape) if num_spatial_dims < 1 or num_spatial_dims > 3: raise ValueError("It is required that 1 <= num_spatial_dims <= 3.") input.get_shape().with_rank(num_spatial_dims + 2) strides, dilation_rate = _get_strides_and_dilation_rate( num_spatial_dims, strides, dilation_rate) if padding == "SAME" and np.any(dilation_rate > 1): raise ValueError( "pooling with SAME padding is not implemented for dilation_rate > 1") if np.any(strides > window_shape): raise ValueError( "strides > window_shape not supported due to inconsistency between " "CPU and GPU implementations") pooling_ops = { ("MAX", 1): max_pool, ("MAX", 2): max_pool, ("MAX", 3): max_pool3d, # pylint: disable=undefined-variable ("AVG", 1): avg_pool, ("AVG", 2): avg_pool, ("AVG", 3): avg_pool3d, # pylint: disable=undefined-variable } op_key = (pooling_type, num_spatial_dims) if op_key not in pooling_ops: raise ValueError("%d-D %s pooling is not supported." % (op_key[1], op_key[0])) if data_format is None or not data_format.startswith("NC"): adjusted_window_shape = [1] + list(window_shape) + [1] adjusted_strides = [1] + list(strides) + [1] spatial_dims = range(1, num_spatial_dims + 1) else: adjusted_window_shape = [1, 1] + list(window_shape) adjusted_strides = [1, 1] + list(strides) spatial_dims = range(2, num_spatial_dims + 2) if num_spatial_dims == 1: if data_format is None or data_format == "NWC": data_format_kwargs = dict(data_format="NHWC") elif data_format == "NCW": data_format_kwargs = dict(data_format="NCHW") else: raise ValueError("data_format must be either \"NWC\" or \"NCW\".") adjusted_window_shape = [1] + adjusted_window_shape adjusted_strides = [1] + adjusted_strides else: data_format_kwargs = dict(data_format=data_format) def op(converted_input, _, converted_padding): # pylint: disable=missing-docstring if num_spatial_dims == 1: converted_input = array_ops.expand_dims(converted_input, spatial_dims[0]) result = pooling_ops[op_key]( converted_input, adjusted_window_shape, adjusted_strides, converted_padding, name=scope, **data_format_kwargs) if num_spatial_dims == 1: result = array_ops.squeeze(result, [spatial_dims[0]]) return result return with_space_to_batch( input=input, dilation_rate=dilation_rate, padding=padding, op=op, spatial_dims=spatial_dims, filter_shape=window_shape) @tf_export("nn.pool", v1=[]) def pool_v2( input, # pylint: disable=redefined-builtin window_shape, pooling_type, strides=None, padding="VALID", data_format=None, dilations=None, name=None): # pylint: disable=line-too-long """Performs an N-D pooling operation. In the case that `data_format` does not start with "NC", computes for 0 <= b < batch_size, 0 <= x[i] < output_spatial_shape[i], 0 <= c < num_channels: ``` output[b, x[0], ..., x[N-1], c] = REDUCE_{z[0], ..., z[N-1]} input[b, x[0] * strides[0] - pad_before[0] + dilation_rate[0]*z[0], ... x[N-1]*strides[N-1] - pad_before[N-1] + dilation_rate[N-1]*z[N-1], c], ``` where the reduction function REDUCE depends on the value of `pooling_type`, and pad_before is defined based on the value of `padding` as described in the "returns" section of `tf.nn.convolution` for details. The reduction never includes out-of-bounds positions. In the case that `data_format` starts with `"NC"`, the `input` and output are simply transposed as follows: ``` pool(input, data_format, **kwargs) = tf.transpose(pool(tf.transpose(input, [0] + range(2,N+2) + [1]), **kwargs), [0, N+1] + range(1, N+1)) ``` Args: input: Tensor of rank N+2, of shape `[batch_size] + input_spatial_shape + [num_channels]` if data_format does not start with "NC" (default), or `[batch_size, num_channels] + input_spatial_shape` if data_format starts with "NC". Pooling happens over the spatial dimensions only. window_shape: Sequence of N ints >= 1. pooling_type: Specifies pooling operation, must be "AVG" or "MAX". strides: Optional. Sequence of N ints >= 1. Defaults to [1]*N. If any value of strides is > 1, then all values of dilation_rate must be 1. padding: The padding algorithm, must be "SAME" or "VALID". Defaults to "SAME". See the "returns" section of `tf.nn.convolution` for details. data_format: A string or None. Specifies whether the channel dimension of the `input` and output is the last dimension (default, or if `data_format` does not start with "NC"), or the second dimension (if `data_format` starts with "NC"). For N=1, the valid values are "NWC" (default) and "NCW". For N=2, the valid values are "NHWC" (default) and "NCHW". For N=3, the valid values are "NDHWC" (default) and "NCDHW". dilations: Optional. Dilation rate. List of N ints >= 1. Defaults to [1]*N. If any value of dilation_rate is > 1, then all values of strides must be 1. name: Optional. Name of the op. Returns: Tensor of rank N+2, of shape [batch_size] + output_spatial_shape + [num_channels] if data_format is None or does not start with "NC", or [batch_size, num_channels] + output_spatial_shape if data_format starts with "NC", where `output_spatial_shape` depends on the value of padding: If padding = "SAME": output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i]) If padding = "VALID": output_spatial_shape[i] = ceil((input_spatial_shape[i] - (window_shape[i] - 1) * dilation_rate[i]) / strides[i]). Raises: ValueError: if arguments are invalid. """ return pool( input=input, window_shape=window_shape, pooling_type=pooling_type, padding=padding, dilation_rate=dilations, strides=strides, name=name, data_format=data_format) @tf_export("nn.atrous_conv2d") def atrous_conv2d(value, filters, rate, padding, name=None): """Atrous convolution (a.k.a. convolution with holes or dilated convolution). This function is a simpler wrapper around the more general `tf.nn.convolution`, and exists only for backwards compatibility. You can use `tf.nn.convolution` to perform 1-D, 2-D, or 3-D atrous convolution. Computes a 2-D atrous convolution, also known as convolution with holes or dilated convolution, given 4-D `value` and `filters` tensors. If the `rate` parameter is equal to one, it performs regular 2-D convolution. If the `rate` parameter is greater than one, it performs convolution with holes, sampling the input values every `rate` pixels in the `height` and `width` dimensions. This is equivalent to convolving the input with a set of upsampled filters, produced by inserting `rate - 1` zeros between two consecutive values of the filters along the `height` and `width` dimensions, hence the name atrous convolution or convolution with holes (the French word trous means holes in English). More specifically: ``` output[batch, height, width, out_channel] = sum_{dheight, dwidth, in_channel} ( filters[dheight, dwidth, in_channel, out_channel] * value[batch, height + rate*dheight, width + rate*dwidth, in_channel] ) ``` Atrous convolution allows us to explicitly control how densely to compute feature responses in fully convolutional networks. Used in conjunction with bilinear interpolation, it offers an alternative to `conv2d_transpose` in dense prediction tasks such as semantic image segmentation, optical flow computation, or depth estimation. It also allows us to effectively enlarge the field of view of filters without increasing the number of parameters or the amount of computation. For a description of atrous convolution and how it can be used for dense feature extraction, please see: [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](http://arxiv.org/abs/1412.7062). The same operation is investigated further in [Multi-Scale Context Aggregation by Dilated Convolutions](http://arxiv.org/abs/1511.07122). Previous works that effectively use atrous convolution in different ways are, among others, [OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks](http://arxiv.org/abs/1312.6229) and [Fast Image Scanning with Deep Max-Pooling Convolutional Neural Networks](http://arxiv.org/abs/1302.1700). Atrous convolution is also closely related to the so-called noble identities in multi-rate signal processing. There are many different ways to implement atrous convolution (see the refs above). The implementation here reduces ```python atrous_conv2d(value, filters, rate, padding=padding) ``` to the following three operations: ```python paddings = ... net = space_to_batch(value, paddings, block_size=rate) net = conv2d(net, filters, strides=[1, 1, 1, 1], padding="VALID") crops = ... net = batch_to_space(net, crops, block_size=rate) ``` Advanced usage. Note the following optimization: A sequence of `atrous_conv2d` operations with identical `rate` parameters, 'SAME' `padding`, and filters with odd heights/ widths: ```python net = atrous_conv2d(net, filters1, rate, padding="SAME") net = atrous_conv2d(net, filters2, rate, padding="SAME") ... net = atrous_conv2d(net, filtersK, rate, padding="SAME") ``` can be equivalently performed cheaper in terms of computation and memory as: ```python pad = ... # padding so that the input dims are multiples of rate net = space_to_batch(net, paddings=pad, block_size=rate) net = conv2d(net, filters1, strides=[1, 1, 1, 1], padding="SAME") net = conv2d(net, filters2, strides=[1, 1, 1, 1], padding="SAME") ... net = conv2d(net, filtersK, strides=[1, 1, 1, 1], padding="SAME") net = batch_to_space(net, crops=pad, block_size=rate) ``` because a pair of consecutive `space_to_batch` and `batch_to_space` ops with the same `block_size` cancel out when their respective `paddings` and `crops` inputs are identical. Args: value: A 4-D `Tensor` of type `float`. It needs to be in the default "NHWC" format. Its shape is `[batch, in_height, in_width, in_channels]`. filters: A 4-D `Tensor` with the same type as `value` and shape `[filter_height, filter_width, in_channels, out_channels]`. `filters`' `in_channels` dimension must match that of `value`. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height `filter_height + (filter_height - 1) * (rate - 1)` and effective width `filter_width + (filter_width - 1) * (rate - 1)`, produced by inserting `rate - 1` zeros along consecutive elements across the `filters`' spatial dimensions. rate: A positive int32. The stride with which we sample input values across the `height` and `width` dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the `height` and `width` dimensions. In the literature, the same parameter is sometimes called `input stride` or `dilation`. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. name: Optional name for the returned tensor. Returns: A `Tensor` with the same type as `value`. Output shape with `'VALID'` padding is: [batch, height - 2 * (filter_width - 1), width - 2 * (filter_height - 1), out_channels]. Output shape with `'SAME'` padding is: [batch, height, width, out_channels]. Raises: ValueError: If input/output depth does not match `filters`' shape, or if padding is other than `'VALID'` or `'SAME'`. """ return convolution( input=value, filter=filters, padding=padding, dilation_rate=np.broadcast_to(rate, (2,)), name=name) def _convert_padding(padding): """Converts Python padding to C++ padding for ops which take EXPLICIT padding. Args: padding: the `padding` argument for a Python op which supports EXPLICIT padding. Returns: (padding, explicit_paddings) pair, which should be passed as attributes to a C++ op. Raises: ValueError: If padding is invalid. """ explicit_paddings = [] if padding == "EXPLICIT": # Give a better error message if EXPLICIT is passed. raise ValueError('"EXPLICIT" is not a valid value for the padding ' "parameter. To use explicit padding, the padding " "parameter must be a list.") if isinstance(padding, (list, tuple)): for i, dim_paddings in enumerate(padding): if not isinstance(dim_paddings, (list, tuple)): raise ValueError("When padding is a list, each element of padding must " "be a list/tuple of size 2. Element with index %d of " "padding is not a list/tuple" % i) if len(dim_paddings) != 2: raise ValueError("When padding is a list, each element of padding must " "be a list/tuple of size 2. Element with index %d of " "padding has size %d" % (i, len(dim_paddings))) explicit_paddings.extend(dim_paddings) if len(padding) != 4: raise ValueError("When padding is a list, it must be of size 4. Got " "padding of size: %d" % len(padding)) padding = "EXPLICIT" return padding, explicit_paddings @tf_export("nn.conv2d", v1=[]) def conv2d_v2(input, # pylint: disable=redefined-builtin filters, strides, padding, data_format="NHWC", dilations=None, name=None): # pylint: disable=line-too-long r"""Computes a 2-D convolution given 4-D `input` and `filters` tensors. Given an input tensor of shape `[batch, in_height, in_width, in_channels]` and a filter / kernel tensor of shape `[filter_height, filter_width, in_channels, out_channels]`, this op performs the following: 1. Flattens the filter to a 2-D matrix with shape `[filter_height * filter_width * in_channels, output_channels]`. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape `[batch, out_height, out_width, filter_height * filter_width * in_channels]`. 3. For each patch, right-multiplies the filter matrix and the image patch vector. In detail, with the default NHWC format, output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k] Must have `strides[0] = strides[3] = 1`. For the most common case of the same horizontal and vertices strides, `strides = [1, stride, stride, 1]`. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. A 4-D tensor. The dimension order is interpreted according to the value of `data_format`, see below for details. filters: A `Tensor`. Must have the same type as `input`. A 4-D tensor of shape `[filter_height, filter_width, in_channels, out_channels]` strides: A list of `ints`. 1-D tensor of length 4. The stride of the sliding window for each dimension of `input`. The dimension order is determined by the value of `data_format`, see below for details. padding: Either the `string `"SAME"` or `"VALID"` indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]`. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. """ # pylint: enable=line-too-long if dilations is None: dilations = [1, 1, 1, 1] return conv2d(input, # pylint: disable=redefined-builtin filters, strides, padding, use_cudnn_on_gpu=True, data_format=data_format, dilations=dilations, name=name) @tf_export(v1=["nn.conv2d"]) def conv2d( # pylint: disable=redefined-builtin,dangerous-default-value input, filter, strides, padding, use_cudnn_on_gpu=True, data_format="NHWC", dilations=[1, 1, 1, 1], name=None): r"""Computes a 2-D convolution given 4-D `input` and `filter` tensors. Given an input tensor of shape `[batch, in_height, in_width, in_channels]` and a filter / kernel tensor of shape `[filter_height, filter_width, in_channels, out_channels]`, this op performs the following: 1. Flattens the filter to a 2-D matrix with shape `[filter_height * filter_width * in_channels, output_channels]`. 2. Extracts image patches from the input tensor to form a *virtual* tensor of shape `[batch, out_height, out_width, filter_height * filter_width * in_channels]`. 3. For each patch, right-multiplies the filter matrix and the image patch vector. In detail, with the default NHWC format, output[b, i, j, k] = sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] * filter[di, dj, q, k] Must have `strides[0] = strides[3] = 1`. For the most common case of the same horizontal and vertices strides, `strides = [1, stride, stride, 1]`. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. A 4-D tensor. The dimension order is interpreted according to the value of `data_format`, see below for details. filter: A `Tensor`. Must have the same type as `input`. A 4-D tensor of shape `[filter_height, filter_width, in_channels, out_channels]` strides: A list of `ints`. 1-D tensor of length 4. The stride of the sliding window for each dimension of `input`. The dimension order is determined by the value of `data_format`, see below for details. padding: Either the `string `"SAME"` or `"VALID"` indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]`. use_cudnn_on_gpu: An optional `bool`. Defaults to `True`. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. """ padding, explicit_paddings = _convert_padding(padding) return gen_nn_ops.conv2d(input, # pylint: disable=redefined-builtin filter, strides, padding, use_cudnn_on_gpu=use_cudnn_on_gpu, explicit_paddings=explicit_paddings, data_format=data_format, dilations=dilations, name=name) @tf_export("nn.conv2d_backprop_filter", v1=[]) def conv2d_backprop_filter_v2(input, # pylint: disable=redefined-builtin filter_sizes, out_backprop, strides, padding, data_format="NHWC", dilations=None, name=None): r"""Computes the gradients of convolution with respect to the filter. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. 4-D with shape `[batch, in_height, in_width, in_channels]`. filter_sizes: A `Tensor` of type `int32`. An integer vector representing the tensor shape of `filter`, where `filter` is a 4-D `[filter_height, filter_width, in_channels, out_channels]` tensor. out_backprop: A `Tensor`. Must have the same type as `input`. 4-D with shape `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. strides: A list of `ints`. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format. padding: Either the `string `"SAME"` or `"VALID"` indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]`. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. """ if dilations is None: dilations = [1, 1, 1, 1] return conv2d_backprop_filter(input, # pylint: disable=redefined-builtin filter_sizes, out_backprop, strides, padding, use_cudnn_on_gpu=True, data_format=data_format, dilations=dilations, name=name) @tf_export(v1=["nn.conv2d_backprop_filter"]) def conv2d_backprop_filter( # pylint: disable=redefined-builtin,dangerous-default-value input, filter_sizes, out_backprop, strides, padding, use_cudnn_on_gpu=True, data_format="NHWC", dilations=[1, 1, 1, 1], name=None): r"""Computes the gradients of convolution with respect to the filter. Args: input: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. 4-D with shape `[batch, in_height, in_width, in_channels]`. filter_sizes: A `Tensor` of type `int32`. An integer vector representing the tensor shape of `filter`, where `filter` is a 4-D `[filter_height, filter_width, in_channels, out_channels]` tensor. out_backprop: A `Tensor`. Must have the same type as `input`. 4-D with shape `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. strides: A list of `ints`. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format. padding: Either the `string `"SAME"` or `"VALID"` indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]`. use_cudnn_on_gpu: An optional `bool`. Defaults to `True`. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. """ padding, explicit_paddings = _convert_padding(padding) return gen_nn_ops.conv2d_backprop_filter( input, filter_sizes, out_backprop, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name) @tf_export("nn.conv2d_backprop_input", v1=[]) def conv2d_backprop_input_v2(input_sizes, filters, out_backprop, strides, padding, data_format="NHWC", dilations=None, name=None): r"""Computes the gradients of convolution with respect to the input. Args: input_sizes: A `Tensor` of type `int32`. An integer vector representing the shape of `input`, where `input` is a 4-D `[batch, height, width, channels]` tensor. filters: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`. out_backprop: A `Tensor`. Must have the same type as `filters`. 4-D with shape `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. strides: A list of `ints`. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format. padding: Either the `string `"SAME"` or `"VALID"` indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]`. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `filters`. """ if dilations is None: dilations = [1, 1, 1, 1] return conv2d_backprop_input(input_sizes, filters, out_backprop, strides, padding, use_cudnn_on_gpu=True, data_format=data_format, dilations=dilations, name=name) @tf_export(v1=["nn.conv2d_backprop_input"]) def conv2d_backprop_input( # pylint: disable=redefined-builtin,dangerous-default-value input_sizes, filter, out_backprop, strides, padding, use_cudnn_on_gpu=True, data_format="NHWC", dilations=[1, 1, 1, 1], name=None): r"""Computes the gradients of convolution with respect to the input. Args: input_sizes: A `Tensor` of type `int32`. An integer vector representing the shape of `input`, where `input` is a 4-D `[batch, height, width, channels]` tensor. filter: A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. 4-D with shape `[filter_height, filter_width, in_channels, out_channels]`. out_backprop: A `Tensor`. Must have the same type as `filter`. 4-D with shape `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. strides: A list of `ints`. The stride of the sliding window for each dimension of the input of the convolution. Must be in the same order as the dimension specified with format. padding: Either the `string `"SAME"` or `"VALID"` indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. When explicit padding is used and data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]`. use_cudnn_on_gpu: An optional `bool`. Defaults to `True`. data_format: An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width]. dilations: An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions must be 1. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `filter`. """ padding, explicit_paddings = _convert_padding(padding) return gen_nn_ops.conv2d_backprop_input( input_sizes, filter, out_backprop, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name) @tf_export(v1=["nn.conv2d_transpose"]) def conv2d_transpose( value, filter, # pylint: disable=redefined-builtin output_shape, strides, padding="SAME", data_format="NHWC", name=None): """The transpose of `conv2d`. This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of `conv2d` rather than an actual deconvolution. Args: value: A 4-D `Tensor` of type `float` and shape `[batch, height, width, in_channels]` for `NHWC` data format or `[batch, in_channels, height, width]` for `NCHW` data format. filter: A 4-D `Tensor` with the same type as `value` and shape `[height, width, output_channels, in_channels]`. `filter`'s `in_channels` dimension must match that of `value`. output_shape: A 1-D `Tensor` representing the output shape of the deconvolution op. strides: A list of ints. The stride of the sliding window for each dimension of the input tensor. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See the "returns" section of `tf.nn.convolution` for details. data_format: A string. 'NHWC' and 'NCHW' are supported. name: Optional name for the returned tensor. Returns: A `Tensor` with the same type as `value`. Raises: ValueError: If input/output depth does not match `filter`'s shape, or if padding is other than `'VALID'` or `'SAME'`. """ with ops.name_scope(name, "conv2d_transpose", [value, filter, output_shape]) as name: if data_format not in ("NCHW", "NHWC"): raise ValueError("data_format has to be either NCHW or NHWC.") value = ops.convert_to_tensor(value, name="value") filter = ops.convert_to_tensor(filter, name="filter") # pylint: disable=redefined-builtin axis = 3 if data_format == "NHWC" else 1 if not value.get_shape().dims[axis].is_compatible_with( filter.get_shape()[3]): raise ValueError("input channels does not match filter's input channels, " "{} != {}".format(value.get_shape()[axis], filter.get_shape()[3])) output_shape_ = ops.convert_to_tensor(output_shape, name="output_shape") if not output_shape_.get_shape().is_compatible_with(tensor_shape.vector(4)): raise ValueError("output_shape must have shape (4,), got {}".format( output_shape_.get_shape())) if isinstance(output_shape, (list, np.ndarray)): # output_shape's shape should be == [4] if reached this point. if not filter.get_shape().dims[2].is_compatible_with( output_shape[axis]): raise ValueError( "output_shape does not match filter's output channels, " "{} != {}".format(output_shape[axis], filter.get_shape()[2])) if padding != "VALID" and padding != "SAME": raise ValueError("padding must be either VALID or SAME:" " {}".format(padding)) return gen_nn_ops.conv2d_backprop_input( input_sizes=output_shape_, filter=filter, out_backprop=value, strides=strides, padding=padding, data_format=data_format, name=name) # pylint: disable=redefined-builtin @tf_export("nn.conv2d_transpose", v1=[]) def conv2d_transpose_v2( input, filters, # pylint: disable=redefined-builtin output_shape, strides, padding="SAME", data_format="NHWC", name=None): return conv2d_transpose( input, filters, output_shape, strides, padding=padding, data_format=data_format, name=name) # pylint: enable=redefined-builtin conv2d_transpose_v2.__doc__ = deprecation.rewrite_argument_docstring( deprecation.rewrite_argument_docstring( conv2d_transpose.__doc__, "filter", "filters"), "value", "input") @tf_export("nn.atrous_conv2d_transpose") def atrous_conv2d_transpose(value, filters, output_shape, rate, padding, name=None): """The transpose of `atrous_conv2d`. This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of `atrous_conv2d` rather than an actual deconvolution. Args: value: A 4-D `Tensor` of type `float`. It needs to be in the default `NHWC` format. Its shape is `[batch, in_height, in_width, in_channels]`. filters: A 4-D `Tensor` with the same type as `value` and shape `[filter_height, filter_width, out_channels, in_channels]`. `filters`' `in_channels` dimension must match that of `value`. Atrous convolution is equivalent to standard convolution with upsampled filters with effective height `filter_height + (filter_height - 1) * (rate - 1)` and effective width `filter_width + (filter_width - 1) * (rate - 1)`, produced by inserting `rate - 1` zeros along consecutive elements across the `filters`' spatial dimensions. output_shape: A 1-D `Tensor` of shape representing the output shape of the deconvolution op. rate: A positive int32. The stride with which we sample input values across the `height` and `width` dimensions. Equivalently, the rate by which we upsample the filter values by inserting zeros across the `height` and `width` dimensions. In the literature, the same parameter is sometimes called `input stride` or `dilation`. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. name: Optional name for the returned tensor. Returns: A `Tensor` with the same type as `value`. Raises: ValueError: If input/output depth does not match `filters`' shape, or if padding is other than `'VALID'` or `'SAME'`, or if the `rate` is less than one, or if the output_shape is not a tensor with 4 elements. """ with ops.name_scope(name, "atrous_conv2d_transpose", [value, filters, output_shape]) as name: value = ops.convert_to_tensor(value, name="value") filters = ops.convert_to_tensor(filters, name="filters") if not value.get_shape().dims[3].is_compatible_with(filters.get_shape()[3]): raise ValueError( "value's input channels does not match filters' input channels, " "{} != {}".format(value.get_shape()[3], filters.get_shape()[3])) if rate < 1: raise ValueError("rate {} cannot be less than one".format(rate)) if rate == 1: return conv2d_transpose( value, filters, output_shape, strides=[1, 1, 1, 1], padding=padding, data_format="NHWC") output_shape_ = ops.convert_to_tensor(output_shape, name="output_shape") if not output_shape_.get_shape().is_compatible_with(tensor_shape.vector(4)): raise ValueError("output_shape must have shape (4,), got {}".format( output_shape_.get_shape())) if isinstance(output_shape, (list, np.ndarray)): # output_shape's shape should be == [4] if reached this point. if not filters.get_shape().dims[2].is_compatible_with(output_shape[3]): raise ValueError( "output_shape does not match filter's output channels, " "{} != {}".format(output_shape[3], filters.get_shape()[2])) # We have two padding contributions. The first is used for converting "SAME" # to "VALID". The second is required so that the height and width of the # zero-padded value tensor are multiples of rate. # Padding required to reduce to "VALID" convolution if padding == "SAME": # Handle filters whose shape is unknown during graph creation. if filters.get_shape().is_fully_defined(): filter_shape = filters.get_shape().as_list() else: filter_shape = array_ops.shape(filters) filter_height, filter_width = filter_shape[0], filter_shape[1] # Spatial dimensions of the filters and the upsampled filters in which we # introduce (rate - 1) zeros between consecutive filter values. filter_height_up = filter_height + (filter_height - 1) * (rate - 1) filter_width_up = filter_width + (filter_width - 1) * (rate - 1) pad_height = filter_height_up - 1 pad_width = filter_width_up - 1 # When pad_height (pad_width) is odd, we pad more to bottom (right), # following the same convention as conv2d(). pad_top = pad_height // 2 pad_bottom = pad_height - pad_top pad_left = pad_width // 2 pad_right = pad_width - pad_left elif padding == "VALID": pad_top = 0 pad_bottom = 0 pad_left = 0 pad_right = 0 else: raise ValueError("padding must be either VALID or SAME:" " {}".format(padding)) in_height = output_shape[1] + pad_top + pad_bottom in_width = output_shape[2] + pad_left + pad_right # More padding so that rate divides the height and width of the input. pad_bottom_extra = (rate - in_height % rate) % rate pad_right_extra = (rate - in_width % rate) % rate # The paddings argument to space_to_batch is just the extra padding # component. space_to_batch_pad = [[0, pad_bottom_extra], [0, pad_right_extra]] value = array_ops.space_to_batch( input=value, paddings=space_to_batch_pad, block_size=rate) input_sizes = [ rate * rate * output_shape[0], (in_height + pad_bottom_extra) // rate, (in_width + pad_right_extra) // rate, output_shape[3] ] value = gen_nn_ops.conv2d_backprop_input( input_sizes=input_sizes, filter=filters, out_backprop=value, strides=[1, 1, 1, 1], padding="VALID", data_format="NHWC") # The crops argument to batch_to_space includes both padding components. batch_to_space_crop = [[pad_top, pad_bottom + pad_bottom_extra], [pad_left, pad_right + pad_right_extra]] return array_ops.batch_to_space( input=value, crops=batch_to_space_crop, block_size=rate) @tf_export("nn.conv3d", v1=[]) def conv3d_v2(input, # pylint: disable=redefined-builtin,missing-docstring filters, strides, padding, data_format="NDHWC", dilations=None, name=None): if dilations is None: dilations = [1, 1, 1, 1, 1] return gen_nn_ops.conv3d(input, # pylint: disable=redefined-builtin filters, strides, padding, data_format=data_format, dilations=dilations, name=name) tf_export(v1=["nn.conv3d"])(gen_nn_ops.conv3d) conv3d_v2.__doc__ = deprecation.rewrite_argument_docstring( gen_nn_ops.conv3d.__doc__, "filter", "filters") @tf_export(v1=["nn.conv3d_transpose"]) def conv3d_transpose( value, filter, # pylint: disable=redefined-builtin output_shape, strides, padding="SAME", data_format="NDHWC", name=None): """The transpose of `conv3d`. This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of `conv3d` rather than an actual deconvolution. Args: value: A 5-D `Tensor` of type `float` and shape `[batch, depth, height, width, in_channels]`. filter: A 5-D `Tensor` with the same type as `value` and shape `[depth, height, width, output_channels, in_channels]`. `filter`'s `in_channels` dimension must match that of `value`. output_shape: A 1-D `Tensor` representing the output shape of the deconvolution op. strides: A list of ints. The stride of the sliding window for each dimension of the input tensor. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See the "returns" section of `tf.nn.convolution` for details. data_format: A string, either `'NDHWC'` or `'NCDHW`' specifying the layout of the input and output tensors. Defaults to `'NDHWC'`. name: Optional name for the returned tensor. Returns: A `Tensor` with the same type as `value`. Raises: ValueError: If input/output depth does not match `filter`'s shape, or if padding is other than `'VALID'` or `'SAME'`. """ with ops.name_scope(name, "conv3d_transpose", [value, filter, output_shape]) as name: value = ops.convert_to_tensor(value, name="value") filter = ops.convert_to_tensor(filter, name="filter") # pylint: disable=redefined-builtin axis = 1 if data_format == "NCDHW" else 4 if not value.get_shape().dims[axis].is_compatible_with( filter.get_shape()[4]): raise ValueError("input channels does not match filter's input channels, " "{} != {}".format(value.get_shape()[axis], filter.get_shape()[4])) output_shape_ = ops.convert_to_tensor(output_shape, name="output_shape") if not output_shape_.get_shape().is_compatible_with(tensor_shape.vector(5)): raise ValueError("output_shape must have shape (5,), got {}".format( output_shape_.get_shape())) if isinstance(output_shape, (list, np.ndarray)): # output_shape's shape should be == [5] if reached this point. if not filter.get_shape().dims[3].is_compatible_with( output_shape[axis]): raise ValueError( "output_shape does not match filter's output channels, " "{} != {}".format(output_shape[axis], filter.get_shape()[3])) if padding != "VALID" and padding != "SAME": raise ValueError("padding must be either VALID or SAME:" " {}".format(padding)) return gen_nn_ops.conv3d_backprop_input_v2( input_sizes=output_shape_, filter=filter, out_backprop=value, strides=strides, padding=padding, data_format=data_format, name=name) # pylint: disable=redefined-builtin @tf_export("nn.conv3d_transpose", v1=[]) def conv3d_transpose_v2( input, filters, output_shape, strides, padding="SAME", data_format="NDHWC", name=None): return conv3d_transpose( input, filters, output_shape, strides, padding=padding, data_format=data_format, name=name) # pylint: enable=redefined-builtin conv3d_transpose_v2.__doc__ = deprecation.rewrite_argument_docstring( deprecation.rewrite_argument_docstring( conv3d_transpose.__doc__, "filter", "filters"), "value", "input") @tf_export("nn.bias_add") def bias_add(value, bias, data_format=None, name=None): """Adds `bias` to `value`. This is (mostly) a special case of `tf.add` where `bias` is restricted to 1-D. Broadcasting is supported, so `value` may have any number of dimensions. Unlike `tf.add`, the type of `bias` is allowed to differ from `value` in the case where both types are quantized. Args: value: A `Tensor` with type `float`, `double`, `int64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, or `complex128`. bias: A 1-D `Tensor` with size matching the last dimension of `value`. Must be the same type as `value` unless `value` is a quantized type, in which case a different quantized type may be used. data_format: A string. 'NHWC' and 'NCHW' are supported. name: A name for the operation (optional). Returns: A `Tensor` with the same type as `value`. """ with ops.name_scope(name, "BiasAdd", [value, bias]) as name: if not context.executing_eagerly(): value = ops.convert_to_tensor(value, name="input") bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias") return gen_nn_ops.bias_add(value, bias, data_format=data_format, name=name) def bias_add_v1(value, bias, name=None): """Adds `bias` to `value`. This is a deprecated version of bias_add and will soon to be removed. This is (mostly) a special case of `tf.add` where `bias` is restricted to 1-D. Broadcasting is supported, so `value` may have any number of dimensions. Unlike `tf.add`, the type of `bias` is allowed to differ from `value` in the case where both types are quantized. Args: value: A `Tensor` with type `float`, `double`, `int64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, or `complex128`. bias: A 1-D `Tensor` with size matching the last dimension of `value`. Must be the same type as `value` unless `value` is a quantized type, in which case a different quantized type may be used. name: A name for the operation (optional). Returns: A `Tensor` with the same type as `value`. """ with ops.name_scope(name, "BiasAddV1", [value, bias]) as name: value = ops.convert_to_tensor(value, name="input") bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias") return gen_nn_ops.bias_add_v1(value, bias, name=name) @tf_export(v1=["nn.crelu"]) def crelu(features, name=None, axis=-1): """Computes Concatenated ReLU. Concatenates a ReLU which selects only the positive part of the activation with a ReLU which selects only the *negative* part of the activation. Note that as a result this non-linearity doubles the depth of the activations. Source: [Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units. W. Shang, et al.](https://arxiv.org/abs/1603.05201) Args: features: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`, `int16`, or `int8`. name: A name for the operation (optional). axis: The axis that the output values are concatenated along. Default is -1. Returns: A `Tensor` with the same type as `features`. """ with ops.name_scope(name, "CRelu", [features]) as name: features = ops.convert_to_tensor(features, name="features") c = array_ops.concat([features, -features], axis, name=name) return gen_nn_ops.relu(c) @tf_export("nn.crelu", v1=[]) def crelu_v2(features, axis=-1, name=None): return crelu(features, name=name, axis=axis) crelu_v2.__doc__ = crelu.__doc__ @tf_export("nn.relu6") def relu6(features, name=None): """Computes Rectified Linear 6: `min(max(features, 0), 6)`. Source: [Convolutional Deep Belief Networks on CIFAR-10. A. Krizhevsky](http://www.cs.utoronto.ca/~kriz/conv-cifar10-aug2010.pdf) Args: features: A `Tensor` with type `float`, `double`, `int32`, `int64`, `uint8`, `int16`, or `int8`. name: A name for the operation (optional). Returns: A `Tensor` with the same type as `features`. """ with ops.name_scope(name, "Relu6", [features]) as name: features = ops.convert_to_tensor(features, name="features") return gen_nn_ops.relu6(features, name=name) @tf_export("nn.leaky_relu") def leaky_relu(features, alpha=0.2, name=None): """Compute the Leaky ReLU activation function. "Rectifier Nonlinearities Improve Neural Network Acoustic Models" AL Maas, AY Hannun, AY Ng - Proc. ICML, 2013 https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf Args: features: A `Tensor` representing preactivation values. Must be one of the following types: `float16`, `float32`, `float64`, `int32`, `int64`. alpha: Slope of the activation function at x < 0. name: A name for the operation (optional). Returns: The activation value. """ with ops.name_scope(name, "LeakyRelu", [features, alpha]) as name: features = ops.convert_to_tensor(features, name="features") if features.dtype.is_integer: features = math_ops.to_float(features) if compat.forward_compatible(2018, 11, 1): if isinstance(alpha, np.ndarray): alpha = np.asscalar(alpha) return gen_nn_ops.leaky_relu(features, alpha=alpha, name=name) alpha = ops.convert_to_tensor(alpha, dtype=features.dtype, name="alpha") return math_ops.maximum(alpha * features, features, name=name) def _flatten_outer_dims(logits): """Flattens logits' outer dimensions and keep its last dimension.""" rank = array_ops.rank(logits) last_dim_size = array_ops.slice( array_ops.shape(logits), [math_ops.subtract(rank, 1)], [1]) output = array_ops.reshape(logits, array_ops.concat([[-1], last_dim_size], 0)) # Set output shape if known. if not context.executing_eagerly(): shape = logits.get_shape() if shape is not None and shape.dims is not None: shape = shape.as_list() product = 1 product_valid = True for d in shape[:-1]: if d is None: product_valid = False break else: product *= d if product_valid: output_shape = [product, shape[-1]] output.set_shape(output_shape) return output def _softmax(logits, compute_op, dim=-1, name=None): """Helper function for softmax and log_softmax. It reshapes and transposes the input logits into a 2-D Tensor and then invokes the tf.nn._softmax or tf.nn._log_softmax function. The output would be transposed and reshaped back. Args: logits: A non-empty `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. compute_op: Either gen_nn_ops.softmax or gen_nn_ops.log_softmax dim: The dimension softmax would be performed on. The default is -1 which indicates the last dimension. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `logits`. Same shape as `logits`. Raises: InvalidArgumentError: if `logits` is empty or `dim` is beyond the last dimension of `logits`. """ def _swap_axis(logits, dim_index, last_index, name=None): """Swaps logits's dim_index and last_index.""" return array_ops.transpose( logits, array_ops.concat([ math_ops.range(dim_index), [last_index], math_ops.range(dim_index + 1, last_index), [dim_index] ], 0), name=name) logits = ops.convert_to_tensor(logits) # We need its original shape for shape inference. shape = logits.get_shape() is_last_dim = (dim is -1) or (dim == shape.ndims - 1) if is_last_dim: return compute_op(logits, name=name) dim_val = dim if isinstance(dim, ops.Tensor): dim_val = tensor_util.constant_value(dim) if dim_val is not None and (dim_val < -shape.ndims or dim_val >= shape.ndims): raise errors_impl.InvalidArgumentError( None, None, "Dimension (%d) must be in the range [%d, %d) where %d is the number of" " dimensions in the input." % (dim_val, -shape.ndims, shape.ndims, shape.ndims)) # If dim is not the last dimension, we have to do a transpose so that we can # still perform softmax on its last dimension. # In case dim is negative (and is not last dimension -1), add shape.ndims ndims = array_ops.rank(logits) if not isinstance(dim, ops.Tensor): if dim < 0: dim += ndims else: dim = array_ops.where(math_ops.less(dim, 0), dim + ndims, dim) # Swap logits' dimension of dim and its last dimension. input_rank = array_ops.rank(logits) dim_axis = dim % shape.ndims logits = _swap_axis(logits, dim_axis, math_ops.subtract(input_rank, 1)) # Do the actual softmax on its last dimension. output = compute_op(logits) output = _swap_axis( output, dim_axis, math_ops.subtract(input_rank, 1), name=name) # Make shape inference work since transpose may erase its static shape. output.set_shape(shape) return output @tf_export(v1=["nn.softmax", "math.softmax"]) @deprecation.deprecated_args(None, "dim is deprecated, use axis instead", "dim") def softmax(logits, axis=None, name=None, dim=None): """Computes softmax activations. This function performs the equivalent of softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), axis) Args: logits: A non-empty `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. axis: The dimension softmax would be performed on. The default is -1 which indicates the last dimension. name: A name for the operation (optional). dim: Deprecated alias for `axis`. Returns: A `Tensor`. Has the same type and shape as `logits`. Raises: InvalidArgumentError: if `logits` is empty or `axis` is beyond the last dimension of `logits`. """ axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim) if axis is None: axis = -1 return _softmax(logits, gen_nn_ops.softmax, axis, name) @tf_export("nn.softmax", "math.softmax", v1=[]) def softmax_v2(logits, axis=None, name=None): """Computes softmax activations. This function performs the equivalent of softmax = tf.exp(logits) / tf.reduce_sum(tf.exp(logits), axis) Args: logits: A non-empty `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. axis: The dimension softmax would be performed on. The default is -1 which indicates the last dimension. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type and shape as `logits`. Raises: InvalidArgumentError: if `logits` is empty or `axis` is beyond the last dimension of `logits`. """ if axis is None: axis = -1 return _softmax(logits, gen_nn_ops.softmax, axis, name) @tf_export(v1=["nn.log_softmax", "math.log_softmax"]) @deprecation.deprecated_args(None, "dim is deprecated, use axis instead", "dim") def log_softmax(logits, axis=None, name=None, dim=None): """Computes log softmax activations. For each batch `i` and class `j` we have logsoftmax = logits - log(reduce_sum(exp(logits), axis)) Args: logits: A non-empty `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. axis: The dimension softmax would be performed on. The default is -1 which indicates the last dimension. name: A name for the operation (optional). dim: Deprecated alias for `axis`. Returns: A `Tensor`. Has the same type as `logits`. Same shape as `logits`. Raises: InvalidArgumentError: if `logits` is empty or `axis` is beyond the last dimension of `logits`. """ axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim) if axis is None: axis = -1 return _softmax(logits, gen_nn_ops.log_softmax, axis, name) @tf_export("nn.log_softmax", "math.log_softmax", v1=[]) def log_softmax_v2(logits, axis=None, name=None): """Computes log softmax activations. For each batch `i` and class `j` we have logsoftmax = logits - log(reduce_sum(exp(logits), axis)) Args: logits: A non-empty `Tensor`. Must be one of the following types: `half`, `float32`, `float64`. axis: The dimension softmax would be performed on. The default is -1 which indicates the last dimension. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `logits`. Same shape as `logits`. Raises: InvalidArgumentError: if `logits` is empty or `axis` is beyond the last dimension of `logits`. """ if axis is None: axis = -1 return _softmax(logits, gen_nn_ops.log_softmax, axis, name) def _ensure_xent_args(name, sentinel, labels, logits): # Make sure that all arguments were passed as named arguments. if sentinel is not None: raise ValueError("Only call `%s` with " "named arguments (labels=..., logits=..., ...)" % name) if labels is None or logits is None: raise ValueError("Both labels and logits must be provided.") @tf_export("nn.softmax_cross_entropy_with_logits", v1=[]) def softmax_cross_entropy_with_logits_v2(labels, logits, axis=-1, name=None): """Computes softmax cross entropy between `logits` and `labels`. Measures the probability error in discrete classification tasks in which the classes are mutually exclusive (each entry is in exactly one class). For example, each CIFAR-10 image is labeled with one and only one label: an image can be a dog or a truck, but not both. **NOTE:** While the classes are mutually exclusive, their probabilities need not be. All that is required is that each row of `labels` is a valid probability distribution. If they are not, the computation of the gradient will be incorrect. If using exclusive `labels` (wherein one and only one class is true at a time), see `sparse_softmax_cross_entropy_with_logits`. **WARNING:** This op expects unscaled logits, since it performs a `softmax` on `logits` internally for efficiency. Do not call this op with the output of `softmax`, as it will produce incorrect results. A common use case is to have logits and labels of shape `[batch_size, num_classes]`, but higher dimensions are supported, with the `axis` argument specifying the class dimension. `logits` and `labels` must have the same dtype (either `float16`, `float32`, or `float64`). Backpropagation will happen into both `logits` and `labels`. To disallow backpropagation into `labels`, pass label tensors through `tf.stop_gradient` before feeding it to this function. **Note that to avoid confusion, it is required to pass only named arguments to this function.** Args: labels: Each vector along the class dimension should hold a valid probability distribution e.g. for the case in which labels are of shape `[batch_size, num_classes]`, each row of `labels[i]` must be a valid probability distribution. logits: Unscaled log probabilities. axis: The class dimension. Defaulted to -1 which is the last dimension. name: A name for the operation (optional). Returns: A `Tensor` that contains the softmax cross entropy loss. Its type is the same as `logits` and its shape is the same as `labels` except that it does not have the last dimension of `labels`. """ return softmax_cross_entropy_with_logits_v2_helper( labels=labels, logits=logits, axis=axis, name=name) @tf_export(v1=["nn.softmax_cross_entropy_with_logits_v2"]) @deprecated_args(None, "dim is deprecated, use axis instead", "dim") def softmax_cross_entropy_with_logits_v2_helper( labels, logits, axis=None, name=None, dim=None): """Computes softmax cross entropy between `logits` and `labels`. Measures the probability error in discrete classification tasks in which the classes are mutually exclusive (each entry is in exactly one class). For example, each CIFAR-10 image is labeled with one and only one label: an image can be a dog or a truck, but not both. **NOTE:** While the classes are mutually exclusive, their probabilities need not be. All that is required is that each row of `labels` is a valid probability distribution. If they are not, the computation of the gradient will be incorrect. If using exclusive `labels` (wherein one and only one class is true at a time), see `sparse_softmax_cross_entropy_with_logits`. **WARNING:** This op expects unscaled logits, since it performs a `softmax` on `logits` internally for efficiency. Do not call this op with the output of `softmax`, as it will produce incorrect results. A common use case is to have logits and labels of shape `[batch_size, num_classes]`, but higher dimensions are supported, with the `axis` argument specifying the class dimension. `logits` and `labels` must have the same dtype (either `float16`, `float32`, or `float64`). Backpropagation will happen into both `logits` and `labels`. To disallow backpropagation into `labels`, pass label tensors through `tf.stop_gradient` before feeding it to this function. **Note that to avoid confusion, it is required to pass only named arguments to this function.** Args: labels: Each vector along the class dimension should hold a valid probability distribution e.g. for the case in which labels are of shape `[batch_size, num_classes]`, each row of `labels[i]` must be a valid probability distribution. logits: Unscaled log probabilities. axis: The class dimension. Defaulted to -1 which is the last dimension. name: A name for the operation (optional). dim: Deprecated alias for axis. Returns: A `Tensor` that contains the softmax cross entropy loss. Its type is the same as `logits` and its shape is the same as `labels` except that it does not have the last dimension of `labels`. """ # TODO(pcmurray) Raise an error when the labels do not sum to 1. Note: This # could break users who call this with bad labels, but disregard the bad # results. axis = deprecated_argument_lookup("axis", axis, "dim", dim) del dim if axis is None: axis = -1 with ops.name_scope(name, "softmax_cross_entropy_with_logits", [logits, labels]) as name: logits = ops.convert_to_tensor(logits, name="logits") labels = ops.convert_to_tensor(labels, name="labels") convert_to_float32 = ( logits.dtype == dtypes.float16 or logits.dtype == dtypes.bfloat16) precise_logits = math_ops.cast( logits, dtypes.float32) if convert_to_float32 else logits # labels and logits must be of the same type labels = math_ops.cast(labels, precise_logits.dtype) input_rank = array_ops.rank(precise_logits) # For shape inference. shape = logits.get_shape() # Move the dim to the end if dim is not the last dimension. if axis != -1: def _move_dim_to_end(tensor, dim_index, rank): return array_ops.transpose( tensor, array_ops.concat([ math_ops.range(dim_index), math_ops.range(dim_index + 1, rank), [dim_index] ], 0)) precise_logits = _move_dim_to_end(precise_logits, axis, input_rank) labels = _move_dim_to_end(labels, axis, input_rank) input_shape = array_ops.shape(precise_logits) # Make precise_logits and labels into matrices. precise_logits = _flatten_outer_dims(precise_logits) labels = _flatten_outer_dims(labels) # Do the actual op computation. # The second output tensor contains the gradients. We use it in # _CrossEntropyGrad() in nn_grad but not here. cost, unused_backprop = gen_nn_ops.softmax_cross_entropy_with_logits( precise_logits, labels, name=name) # The output cost shape should be the input minus axis. output_shape = array_ops.slice(input_shape, [0], [math_ops.subtract(input_rank, 1)]) cost = array_ops.reshape(cost, output_shape) # Make shape inference work since reshape and transpose may erase its static # shape. if not context.executing_eagerly( ) and shape is not None and shape.dims is not None: shape = shape.as_list() del shape[axis] cost.set_shape(shape) if convert_to_float32: return math_ops.cast(cost, logits.dtype) else: return cost _XENT_DEPRECATION = """ Future major versions of TensorFlow will allow gradients to flow into the labels input on backprop by default. See `tf.nn.softmax_cross_entropy_with_logits_v2`. """ @tf_export(v1=["nn.softmax_cross_entropy_with_logits"]) @deprecation.deprecated(date=None, instructions=_XENT_DEPRECATION) def softmax_cross_entropy_with_logits( _sentinel=None, # pylint: disable=invalid-name labels=None, logits=None, dim=-1, name=None): """Computes softmax cross entropy between `logits` and `labels`. Measures the probability error in discrete classification tasks in which the classes are mutually exclusive (each entry is in exactly one class). For example, each CIFAR-10 image is labeled with one and only one label: an image can be a dog or a truck, but not both. **NOTE:** While the classes are mutually exclusive, their probabilities need not be. All that is required is that each row of `labels` is a valid probability distribution. If they are not, the computation of the gradient will be incorrect. If using exclusive `labels` (wherein one and only one class is true at a time), see `sparse_softmax_cross_entropy_with_logits`. **WARNING:** This op expects unscaled logits, since it performs a `softmax` on `logits` internally for efficiency. Do not call this op with the output of `softmax`, as it will produce incorrect results. A common use case is to have logits and labels of shape `[batch_size, num_classes]`, but higher dimensions are supported, with the `dim` argument specifying the class dimension. Backpropagation will happen only into `logits`. To calculate a cross entropy loss that allows backpropagation into both `logits` and `labels`, see `tf.nn.softmax_cross_entropy_with_logits_v2`. **Note that to avoid confusion, it is required to pass only named arguments to this function.** Args: _sentinel: Used to prevent positional parameters. Internal, do not use. labels: Each vector along the class dimension should hold a valid probability distribution e.g. for the case in which labels are of shape `[batch_size, num_classes]`, each row of `labels[i]` must be a valid probability distribution. logits: Unscaled log probabilities. dim: The class dimension. Defaulted to -1 which is the last dimension. name: A name for the operation (optional). Returns: A `Tensor` that contains the softmax cross entropy loss. Its type is the same as `logits` and its shape is the same as `labels` except that it does not have the last dimension of `labels`. """ _ensure_xent_args("softmax_cross_entropy_with_logits", _sentinel, labels, logits) with ops.name_scope(name, "softmax_cross_entropy_with_logits_sg", [logits, labels]) as name: labels = array_ops.stop_gradient(labels, name="labels_stop_gradient") return softmax_cross_entropy_with_logits_v2( labels=labels, logits=logits, axis=dim, name=name) @tf_export("nn.sparse_softmax_cross_entropy_with_logits") def sparse_softmax_cross_entropy_with_logits( _sentinel=None, # pylint: disable=invalid-name labels=None, logits=None, name=None): """Computes sparse softmax cross entropy between `logits` and `labels`. Measures the probability error in discrete classification tasks in which the classes are mutually exclusive (each entry is in exactly one class). For example, each CIFAR-10 image is labeled with one and only one label: an image can be a dog or a truck, but not both. **NOTE:** For this operation, the probability of a given label is considered exclusive. That is, soft classes are not allowed, and the `labels` vector must provide a single specific index for the true class for each row of `logits` (each minibatch entry). For soft softmax classification with a probability distribution for each entry, see `softmax_cross_entropy_with_logits_v2`. **WARNING:** This op expects unscaled logits, since it performs a `softmax` on `logits` internally for efficiency. Do not call this op with the output of `softmax`, as it will produce incorrect results. A common use case is to have logits of shape `[batch_size, num_classes]` and have labels of shape `[batch_size]`, but higher dimensions are supported, in which case the `dim`-th dimension is assumed to be of size `num_classes`. `logits` must have the dtype of `float16`, `float32`, or `float64`, and `labels` must have the dtype of `int32` or `int64`. **Note that to avoid confusion, it is required to pass only named arguments to this function.** Args: _sentinel: Used to prevent positional parameters. Internal, do not use. labels: `Tensor` of shape `[d_0, d_1, ..., d_{r-1}]` (where `r` is rank of `labels` and result) and dtype `int32` or `int64`. Each entry in `labels` must be an index in `[0, num_classes)`. Other values will raise an exception when this op is run on CPU, and return `NaN` for corresponding loss and gradient rows on GPU. logits: Unscaled log probabilities of shape `[d_0, d_1, ..., d_{r-1}, num_classes]` and dtype `float16`, `float32`, or `float64`. name: A name for the operation (optional). Returns: A `Tensor` of the same shape as `labels` and of the same type as `logits` with the softmax cross entropy loss. Raises: ValueError: If logits are scalars (need to have rank >= 1) or if the rank of the labels is not equal to the rank of the logits minus one. """ _ensure_xent_args("sparse_softmax_cross_entropy_with_logits", _sentinel, labels, logits) # TODO(pcmurray) Raise an error when the label is not an index in # [0, num_classes). Note: This could break users who call this with bad # labels, but disregard the bad results. # Reshape logits and labels to rank 2. with ops.name_scope(name, "SparseSoftmaxCrossEntropyWithLogits", [labels, logits]): labels = ops.convert_to_tensor(labels) logits = ops.convert_to_tensor(logits) precise_logits = math_ops.cast(logits, dtypes.float32) if (dtypes.as_dtype( logits.dtype) == dtypes.float16) else logits # Store label shape for result later. labels_static_shape = labels.get_shape() labels_shape = array_ops.shape(labels) static_shapes_fully_defined = ( labels_static_shape.is_fully_defined() and logits.get_shape()[:-1].is_fully_defined()) if logits.get_shape().ndims is not None and logits.get_shape().ndims == 0: raise ValueError( "Logits cannot be scalars - received shape %s." % logits.get_shape()) if logits.get_shape().ndims is not None and ( labels_static_shape.ndims is not None and labels_static_shape.ndims != logits.get_shape().ndims - 1): raise ValueError("Rank mismatch: Rank of labels (received %s) should " "equal rank of logits minus 1 (received %s)." % (labels_static_shape.ndims, logits.get_shape().ndims)) if (static_shapes_fully_defined and labels_static_shape != logits.get_shape()[:-1]): raise ValueError("Shape mismatch: The shape of labels (received %s) " "should equal the shape of logits except for the last " "dimension (received %s)." % (labels_static_shape, logits.get_shape())) # Check if no reshapes are required. if logits.get_shape().ndims == 2: cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits( precise_logits, labels, name=name) if logits.dtype == dtypes.float16: return math_ops.cast(cost, dtypes.float16) else: return cost # Perform a check of the dynamic shapes if the static shapes are not fully # defined. shape_checks = [] if not static_shapes_fully_defined: shape_checks.append( check_ops.assert_equal( array_ops.shape(labels), array_ops.shape(logits)[:-1])) with ops.control_dependencies(shape_checks): # Reshape logits to 2 dim, labels to 1 dim. num_classes = array_ops.shape(logits)[array_ops.rank(logits) - 1] precise_logits = array_ops.reshape(precise_logits, [-1, num_classes]) labels = array_ops.reshape(labels, [-1]) # The second output tensor contains the gradients. We use it in # _CrossEntropyGrad() in nn_grad but not here. cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits( precise_logits, labels, name=name) cost = array_ops.reshape(cost, labels_shape) cost.set_shape(labels_static_shape) if logits.dtype == dtypes.float16: return math_ops.cast(cost, dtypes.float16) else: return cost @tf_export("nn.avg_pool") def avg_pool(value, ksize, strides, padding, data_format="NHWC", name=None): """Performs the average pooling on the input. Each entry in `output` is the mean of the corresponding size `ksize` window in `value`. Args: value: A 4-D `Tensor` of shape `[batch, height, width, channels]` and type `float32`, `float64`, `qint8`, `quint8`, or `qint32`. ksize: A list or tuple of 4 ints. The size of the window for each dimension of the input tensor. strides: A list or tuple of 4 ints. The stride of the sliding window for each dimension of the input tensor. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See the "returns" section of `tf.nn.convolution` for details. data_format: A string. 'NHWC' and 'NCHW' are supported. name: Optional name for the operation. Returns: A `Tensor` with the same type as `value`. The average pooled output tensor. """ with ops.name_scope(name, "AvgPool", [value]) as name: value = ops.convert_to_tensor(value, name="input") return gen_nn_ops.avg_pool( value, ksize=ksize, strides=strides, padding=padding, data_format=data_format, name=name) @tf_export("nn.max_pool") def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None): """Performs the max pooling on the input. Args: value: A 4-D `Tensor` of the format specified by `data_format`. ksize: A list or tuple of 4 ints. The size of the window for each dimension of the input tensor. strides: A list or tuple of 4 ints. The stride of the sliding window for each dimension of the input tensor. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See the "returns" section of `tf.nn.convolution` for details. data_format: A string. 'NHWC', 'NCHW' and 'NCHW_VECT_C' are supported. name: Optional name for the operation. Returns: A `Tensor` of format specified by `data_format`. The max pooled output tensor. """ with ops.name_scope(name, "MaxPool", [value]) as name: value = ops.convert_to_tensor(value, name="input") return gen_nn_ops.max_pool( value, ksize=ksize, strides=strides, padding=padding, data_format=data_format, name=name) # pylint: disable=redefined-builtin @tf_export("nn.max_pool_with_argmax", v1=[]) def max_pool_with_argmax_v2(input, ksize, strides, padding, data_format="NHWC", output_dtype=dtypes.int64, name=None): """Performs max pooling on the input and outputs both max values and indices. The indices in `argmax` are flattened, so that a maximum value at position `[b, y, x, c]` becomes flattened index `((b * height + y) * width + x) * channels + c`. The indices returned are always in `[0, height) x [0, width)` before flattening, even if padding is involved and the mathematically correct answer is outside (either negative or too large). This is a bug, but fixing it is difficult to do in a safe backwards compatible way, especially due to flattening. Args: input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`. 4-D with shape `[batch, height, width, channels]`. Input to pool over. ksize: A list of `ints` that has length `>= 4`. The size of the window for each dimension of the input tensor. strides: A list of `ints` that has length `>= 4`. The stride of the sliding window for each dimension of the input tensor. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: An optional `string`, must be set to `"NHWC"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. output_dtype: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int64`. The dtype of the returned argmax tensor. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (output, argmax). output: A `Tensor`. Has the same type as `input`. argmax: A `Tensor` of type `output_dtype`. """ if data_format != "NHWC": raise ValueError("Data formats other than 'NHWC' are not yet supported") return gen_nn_ops.max_pool_with_argmax(input=input, ksize=ksize, strides=strides, padding=padding, Targmax=output_dtype, name=name) # pylint: enable=redefined-builtin @ops.RegisterStatistics("Conv2D", "flops") def _calc_conv_flops(graph, node): """Calculates the compute resources needed for Conv2D.""" input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0]) input_shape.assert_is_fully_defined() filter_shape = graph_util.tensor_shape_from_node_def_name( graph, node.input[1]) filter_shape.assert_is_fully_defined() output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name) output_shape.assert_is_fully_defined() filter_height = int(filter_shape[0]) filter_width = int(filter_shape[1]) filter_in_depth = int(filter_shape[2]) output_count = np.prod(output_shape.as_list(), dtype=np.int64) return ops.OpStats( "flops", (output_count * filter_in_depth * filter_height * filter_width * 2)) @ops.RegisterStatistics("DepthwiseConv2dNative", "flops") def _calc_depthwise_conv_flops(graph, node): """Calculates the compute resources needed for DepthwiseConv2dNative.""" input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0]) input_shape.assert_is_fully_defined() filter_shape = graph_util.tensor_shape_from_node_def_name( graph, node.input[1]) filter_shape.assert_is_fully_defined() output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name) output_shape.assert_is_fully_defined() filter_height = int(filter_shape[0]) filter_width = int(filter_shape[1]) output_count = np.prod(output_shape.as_list(), dtype=np.int64) return ops.OpStats("flops", (output_count * filter_height * filter_width * 2)) @ops.RegisterStatistics("BiasAdd", "flops") def _calc_bias_add_flops(graph, node): """Calculates the computing needed for BiasAdd.""" input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0]) input_shape.assert_is_fully_defined() input_count = np.prod(input_shape.as_list()) return ops.OpStats("flops", input_count) @tf_export(v1=["nn.xw_plus_b"]) def xw_plus_b(x, weights, biases, name=None): # pylint: disable=invalid-name """Computes matmul(x, weights) + biases. Args: x: a 2D tensor. Dimensions typically: batch, in_units weights: a 2D tensor. Dimensions typically: in_units, out_units biases: a 1D tensor. Dimensions: out_units name: A name for the operation (optional). If not specified "xw_plus_b" is used. Returns: A 2-D Tensor computing matmul(x, weights) + biases. Dimensions typically: batch, out_units. """ with ops.name_scope(name, "xw_plus_b", [x, weights, biases]) as name: x = ops.convert_to_tensor(x, name="x") weights = ops.convert_to_tensor(weights, name="weights") biases = ops.convert_to_tensor(biases, name="biases") mm = math_ops.matmul(x, weights) return bias_add(mm, biases, name=name) def xw_plus_b_v1(x, weights, biases, name=None): # pylint: disable=invalid-name """Computes matmul(x, weights) + biases. This is a deprecated version of that will soon be removed. Args: x: a 2D tensor. Dimensions typically: batch, in_units weights: a 2D tensor. Dimensions typically: in_units, out_units biases: a 1D tensor. Dimensions: out_units name: A name for the operation (optional). If not specified "xw_plus_b_v1" is used. Returns: A 2-D Tensor computing matmul(x, weights) + biases. Dimensions typically: batch, out_units. """ with ops.name_scope(name, "xw_plus_b_v1", [x, weights, biases]) as name: x = ops.convert_to_tensor(x, name="x") weights = ops.convert_to_tensor(weights, name="weights") biases = ops.convert_to_tensor(biases, name="biases") mm = math_ops.matmul(x, weights) return bias_add_v1(mm, biases, name=name) def _get_noise_shape(x, noise_shape): # If noise_shape is none return immediately. if noise_shape is None: return array_ops.shape(x) try: # Best effort to figure out the intended shape. # If not possible, let the op to handle it. # In eager mode exception will show up. noise_shape_ = tensor_shape.as_shape(noise_shape) except (TypeError, ValueError): return noise_shape if x.shape.dims is not None and len(x.shape.dims) == len(noise_shape_.dims): new_dims = [] for i, dim in enumerate(x.shape.dims): if noise_shape_.dims[i].value is None and dim.value is not None: new_dims.append(dim.value) else: new_dims.append(noise_shape_.dims[i].value) return tensor_shape.TensorShape(new_dims) return noise_shape @tf_export(v1=["nn.dropout"]) @deprecation.deprecated_args(None, "Please use `rate` instead of `keep_prob`. " "Rate should be set to `rate = 1 - keep_prob`.", "keep_prob") def dropout(x, keep_prob=None, noise_shape=None, seed=None, name=None, rate=None): # pylint: disable=invalid-name """Computes dropout. For each element of `x`, with probability `rate`, outputs `0`, and otherwise scales up the input by `1 / (1-rate)`. The scaling is such that the expected sum is unchanged. By default, each element is kept or dropped independently. If `noise_shape` is specified, it must be [broadcastable](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) to the shape of `x`, and only dimensions with `noise_shape[i] == shape(x)[i]` will make independent decisions. For example, if `shape(x) = [k, l, m, n]` and `noise_shape = [k, 1, 1, n]`, each batch and channel component will be kept independently and each row and column will be kept or not kept together. Args: x: A floating point tensor. keep_prob: (deprecated) A deprecated alias for `(1-rate)`. noise_shape: A 1-D `Tensor` of type `int32`, representing the shape for randomly generated keep/drop flags. seed: A Python integer. Used to create random seeds. See `tf.set_random_seed` for behavior. name: A name for this operation (optional). rate: A scalar `Tensor` with the same type as `x`. The probability that each element of `x` is discarded. Returns: A Tensor of the same shape of `x`. Raises: ValueError: If `rate` is not in `[0, 1)` or if `x` is not a floating point tensor. """ try: keep = 1. - keep_prob if keep_prob is not None else None except TypeError: raise ValueError("keep_prob must be a floating point number or Tensor " "(got %r)" % keep_prob) rate = deprecation.deprecated_argument_lookup( "rate", rate, "keep_prob", keep) if rate is None: raise ValueError("You must provide a rate to dropout.") return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name) @tf_export("nn.dropout", v1=[]) def dropout_v2(x, rate, noise_shape=None, seed=None, name=None): # pylint: disable=invalid-name """Computes dropout. With probability `rate`, drops elements of `x`. Input that are kept are scaled up by `1 / (1 - rate)`, otherwise outputs `0`. The scaling is so that the expected sum is unchanged. By default, each element is kept or dropped independently. If `noise_shape` is specified, it must be [broadcastable](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) to the shape of `x`, and only dimensions with `noise_shape[i] == shape(x)[i]` will make independent decisions. For example, if `shape(x) = [k, l, m, n]` and `noise_shape = [k, 1, 1, n]`, each batch and channel component will be kept independently and each row and column will be kept or not kept together. Args: x: A floating point tensor. rate: A scalar `Tensor` with the same type as x. The probability that each element is dropped. For example, setting rate=0.1 would drop 10% of input elements. noise_shape: A 1-D `Tensor` of type `int32`, representing the shape for randomly generated keep/drop flags. seed: A Python integer. Used to create random seeds. See `tf.set_random_seed` for behavior. name: A name for this operation (optional). Returns: A Tensor of the same shape of `x`. Raises: ValueError: If `keep_prob` is not in `(0, 1]` or if `x` is not a floating point tensor. """ with ops.name_scope(name, "dropout", [x]) as name: x = ops.convert_to_tensor(x, name="x") if not x.dtype.is_floating: raise ValueError("x has to be a floating point tensor since it's going to" " be scaled. Got a %s tensor instead." % x.dtype) if isinstance(rate, numbers.Real) and not (rate >= 0 and rate < 1): raise ValueError("rate must be a scalar tensor or a float in the " "range [0, 1), got %g" % rate) # Early return if nothing needs to be dropped. if isinstance(rate, numbers.Real) and rate == 0: return x if context.executing_eagerly(): if isinstance(rate, ops.EagerTensor): if rate.numpy() == 0: return x else: rate = ops.convert_to_tensor( rate, dtype=x.dtype, name="rate") rate.get_shape().assert_is_compatible_with(tensor_shape.scalar()) # Do nothing if we know rate == 0 if tensor_util.constant_value(rate) == 0: return x noise_shape = _get_noise_shape(x, noise_shape) keep_prob = 1 - rate # uniform [keep_prob, 1.0 + keep_prob) random_tensor = keep_prob random_tensor += random_ops.random_uniform( noise_shape, seed=seed, dtype=x.dtype) # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob) binary_tensor = math_ops.floor(random_tensor) ret = math_ops.divide(x, keep_prob) * binary_tensor if not context.executing_eagerly(): ret.set_shape(x.get_shape()) return ret @tf_export("math.top_k", "nn.top_k") def top_k(input, k=1, sorted=True, name=None): # pylint: disable=redefined-builtin """Finds values and indices of the `k` largest entries for the last dimension. If the input is a vector (rank=1), finds the `k` largest entries in the vector and outputs their values and indices as vectors. Thus `values[j]` is the `j`-th largest entry in `input`, and its index is `indices[j]`. For matrices (resp. higher rank input), computes the top `k` entries in each row (resp. vector along the last dimension). Thus, values.shape = indices.shape = input.shape[:-1] + [k] If two elements are equal, the lower-index element appears first. Args: input: 1-D or higher `Tensor` with last dimension at least `k`. k: 0-D `int32` `Tensor`. Number of top elements to look for along the last dimension (along each row for matrices). sorted: If true the resulting `k` elements will be sorted by the values in descending order. name: Optional name for the operation. Returns: values: The `k` largest elements along each last dimensional slice. indices: The indices of `values` within the last dimension of `input`. """ return gen_nn_ops.top_kv2(input, k=k, sorted=sorted, name=name) def nth_element(input, n, reverse=False, name=None): # pylint: disable=redefined-builtin r"""Finds values of the `n`-th order statistic for the last dmension. If the input is a vector (rank-1), finds the entries which is the nth-smallest value in the vector and outputs their values as scalar tensor. For matrices (resp. higher rank input), computes the entries which is the nth-smallest value in each row (resp. vector along the last dimension). Thus, values.shape = input.shape[:-1] Args: input: 1-D or higher `Tensor` with last dimension at least `n+1`. n: A `Tensor` of type `int32`. 0-D. Position of sorted vector to select along the last dimension (along each row for matrices). Valid range of n is `[0, input.shape[:-1])` reverse: An optional `bool`. Defaults to `False`. When set to True, find the nth-largest value in the vector and vice versa. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as `input`. The `n`-th order statistic along each last dimensional slice. """ return gen_nn_ops.nth_element(input, n, reverse=reverse, name=name) @tf_export(v1=["nn.fractional_max_pool"]) @deprecation.deprecated(date=None, instructions="`seed2` and `deterministic` " "args are deprecated. Use fractional_max_pool_v2.") def fractional_max_pool(value, pooling_ratio, pseudo_random=False, overlapping=False, deterministic=False, seed=0, seed2=0, name=None): # pylint: disable=redefined-builtin r"""Performs fractional max pooling on the input. This is a deprecated version of `fractional_max_pool`. Fractional max pooling is slightly different than regular max pooling. In regular max pooling, you downsize an input set by taking the maximum value of smaller N x N subsections of the set (often 2x2), and try to reduce the set by a factor of N, where N is an integer. Fractional max pooling, as you might expect from the word "fractional", means that the overall reduction ratio N does not have to be an integer. The sizes of the pooling regions are generated randomly but are fairly uniform. For example, let's look at the height dimension, and the constraints on the list of rows that will be pool boundaries. First we define the following: 1. input_row_length : the number of rows from the input set 2. output_row_length : which will be smaller than the input 3. alpha = input_row_length / output_row_length : our reduction ratio 4. K = floor(alpha) 5. row_pooling_sequence : this is the result list of pool boundary rows Then, row_pooling_sequence should satisfy: 1. a[0] = 0 : the first value of the sequence is 0 2. a[end] = input_row_length : the last value of the sequence is the size 3. K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size 4. length(row_pooling_sequence) = output_row_length+1 For more details on fractional max pooling, see this paper: [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) Args: value: A `Tensor`. 4-D with shape `[batch, height, width, channels]`. pooling_ratio: A list of `floats` that has length >= 4. Pooling ratio for each dimension of `value`, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively. pseudo_random: An optional `bool`. Defaults to `False`. When set to `True`, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for difference between pseudorandom and random. overlapping: An optional `bool`. Defaults to `False`. When set to `True`, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: `index 0 1 2 3 4` `value 20 5 16 3 7` If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [20, 16] for fractional max pooling. deterministic: An optional `bool`. Deprecated; use `fractional_max_pool_v2` instead. seed: An optional `int`. Defaults to `0`. If set to be non-zero, the random number generator is seeded by the given seed. Otherwise it is seeded by a random seed. seed2: An optional `int`. Deprecated; use `fractional_max_pool_v2` instead. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (`output`, `row_pooling_sequence`, `col_pooling_sequence`). output: Output `Tensor` after fractional max pooling. Has the same type as `value`. row_pooling_sequence: A `Tensor` of type `int64`. col_pooling_sequence: A `Tensor` of type `int64`. """ return gen_nn_ops.fractional_max_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic, seed, seed2, name) @tf_export("nn.fractional_max_pool", v1=[]) def fractional_max_pool_v2(value, pooling_ratio, pseudo_random=False, overlapping=False, seed=0, name=None): # pylint: disable=redefined-builtin r"""Performs fractional max pooling on the input. Fractional max pooling is slightly different than regular max pooling. In regular max pooling, you downsize an input set by taking the maximum value of smaller N x N subsections of the set (often 2x2), and try to reduce the set by a factor of N, where N is an integer. Fractional max pooling, as you might expect from the word "fractional", means that the overall reduction ratio N does not have to be an integer. The sizes of the pooling regions are generated randomly but are fairly uniform. For example, let's look at the height dimension, and the constraints on the list of rows that will be pool boundaries. First we define the following: 1. input_row_length : the number of rows from the input set 2. output_row_length : which will be smaller than the input 3. alpha = input_row_length / output_row_length : our reduction ratio 4. K = floor(alpha) 5. row_pooling_sequence : this is the result list of pool boundary rows Then, row_pooling_sequence should satisfy: 1. a[0] = 0 : the first value of the sequence is 0 2. a[end] = input_row_length : the last value of the sequence is the size 3. K <= (a[i+1] - a[i]) <= K+1 : all intervals are K or K+1 size 4. length(row_pooling_sequence) = output_row_length+1 For more details on fractional max pooling, see this paper: [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) Args: value: A `Tensor`. 4-D with shape `[batch, height, width, channels]`. pooling_ratio: A list of `floats` that has length >= 4. Pooling ratio for each dimension of `value`, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively. pseudo_random: An optional `bool`. Defaults to `False`. When set to `True`, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for difference between pseudorandom and random. overlapping: An optional `bool`. Defaults to `False`. When set to `True`, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: `index 0 1 2 3 4` `value 20 5 16 3 7` If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [20, 16] for fractional max pooling. seed: An optional `int`. Defaults to `0`. If set to be non-zero, the random number generator is seeded by the given seed. Otherwise it is seeded by a random seed. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (`output`, `row_pooling_sequence`, `col_pooling_sequence`). output: Output `Tensor` after fractional max pooling. Has the same type as `value`. row_pooling_sequence: A `Tensor` of type `int64`. col_pooling_sequence: A `Tensor` of type `int64`. """ if seed == 0: return gen_nn_ops.fractional_max_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic=False, seed=0, seed2=0, name=name) else: seed1, seed2 = random_seed.get_seed(seed) return gen_nn_ops.fractional_max_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic=True, seed=seed1, seed2=seed2, name=name) @tf_export(v1=["nn.fractional_avg_pool"]) @deprecation.deprecated(date=None, instructions="`seed2` and `deterministic` " "args are deprecated. Use fractional_avg_pool_v2.") def fractional_avg_pool(value, pooling_ratio, pseudo_random=False, overlapping=False, deterministic=False, seed=0, seed2=0, name=None): # pylint: disable=redefined-builtin r"""Performs fractional average pooling on the input. This is a deprecated version of `fractional_avg_pool`. Fractional average pooling is similar to Fractional max pooling in the pooling region generation step. The only difference is that after pooling regions are generated, a mean operation is performed instead of a max operation in each pooling region. Args: value: A `Tensor`. 4-D with shape `[batch, height, width, channels]`. pooling_ratio: A list of `floats` that has length >= 4. Pooling ratio for each dimension of `value`, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively. pseudo_random: An optional `bool`. Defaults to `False`. When set to `True`, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for difference between pseudorandom and random. overlapping: An optional `bool`. Defaults to `False`. When set to `True`, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: `index 0 1 2 3 4` `value 20 5 16 3 7` If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [20, 16] for fractional avg pooling. deterministic: An optional `bool`. Deprecated; use `fractional_avg_pool_v2` instead. seed: An optional `int`. Defaults to `0`. If set to be non-zero, the random number generator is seeded by the given seed. Otherwise it is seeded by a random seed. seed2: An optional `int`. Deprecated; use `fractional_avg_pool_v2` instead. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (`output`, `row_pooling_sequence`, `col_pooling_sequence`). output: Output `Tensor` after fractional avg pooling. Has the same type as `value`. row_pooling_sequence: A `Tensor` of type `int64`. col_pooling_sequence: A `Tensor` of type `int64`. """ return gen_nn_ops.fractional_avg_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic, seed, seed2, name=name) @tf_export("nn.fractional_avg_pool", v1=[]) def fractional_avg_pool_v2(value, pooling_ratio, pseudo_random=False, overlapping=False, seed=0, name=None): # pylint: disable=redefined-builtin r"""Performs fractional average pooling on the input. Fractional average pooling is similar to Fractional max pooling in the pooling region generation step. The only difference is that after pooling regions are generated, a mean operation is performed instead of a max operation in each pooling region. Args: value: A `Tensor`. 4-D with shape `[batch, height, width, channels]`. pooling_ratio: A list of `floats` that has length >= 4. Pooling ratio for each dimension of `value`, currently only supports row and col dimension and should be >= 1.0. For example, a valid pooling ratio looks like [1.0, 1.44, 1.73, 1.0]. The first and last elements must be 1.0 because we don't allow pooling on batch and channels dimensions. 1.44 and 1.73 are pooling ratio on height and width dimensions respectively. pseudo_random: An optional `bool`. Defaults to `False`. When set to `True`, generates the pooling sequence in a pseudorandom fashion, otherwise, in a random fashion. Check paper [Benjamin Graham, Fractional Max-Pooling](http://arxiv.org/abs/1412.6071) for difference between pseudorandom and random. overlapping: An optional `bool`. Defaults to `False`. When set to `True`, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: `index 0 1 2 3 4` `value 20 5 16 3 7` If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [20, 16] for fractional avg pooling. seed: An optional `int`. Defaults to `0`. If set to be non-zero, the random number generator is seeded by the given seed. Otherwise it is seeded by a random seed. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (`output`, `row_pooling_sequence`, `col_pooling_sequence`). output: Output `Tensor` after fractional avg pooling. Has the same type as `value`. row_pooling_sequence: A `Tensor` of type `int64`. col_pooling_sequence: A `Tensor` of type `int64`. """ if seed == 0: return gen_nn_ops.fractional_avg_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic=False, seed=0, seed2=0, name=name) else: seed1, seed2 = random_seed.get_seed(seed) return gen_nn_ops.fractional_avg_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic=True, seed=seed1, seed2=seed2, name=name) @tf_export(v1=["nn.conv1d"]) @deprecation.deprecated_arg_values( None, "`NCHW` for data_format is deprecated, use `NCW` instead", warn_once=True, data_format="NCHW") @deprecation.deprecated_arg_values( None, "`NHWC` for data_format is deprecated, use `NWC` instead", warn_once=True, data_format="NHWC") def conv1d(value, filters, stride, padding, use_cudnn_on_gpu=None, data_format=None, name=None): r"""Computes a 1-D convolution given 3-D input and filter tensors. Given an input tensor of shape [batch, in_width, in_channels] if data_format is "NWC", or [batch, in_channels, in_width] if data_format is "NCW", and a filter / kernel tensor of shape [filter_width, in_channels, out_channels], this op reshapes the arguments to pass them to conv2d to perform the equivalent convolution operation. Internally, this op reshapes the input tensors and invokes `tf.nn.conv2d`. For example, if `data_format` does not start with "NC", a tensor of shape [batch, in_width, in_channels] is reshaped to [batch, 1, in_width, in_channels], and the filter is reshaped to [1, filter_width, in_channels, out_channels]. The result is then reshaped back to [batch, out_width, out_channels] $where out_width is a function of the stride and padding as in conv2d$ and returned to the caller. Args: value: A 3D `Tensor`. Must be of type `float16`, `float32`, or `float64`. filters: A 3D `Tensor`. Must have the same type as `value`. stride: An `integer`. The number of entries by which the filter is moved right at each step. padding: 'SAME' or 'VALID' use_cudnn_on_gpu: An optional `bool`. Defaults to `True`. data_format: An optional `string` from `"NWC", "NCW"`. Defaults to `"NWC"`, the data is stored in the order of [batch, in_width, in_channels]. The `"NCW"` format stores data as [batch, in_channels, in_width]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as input. Raises: ValueError: if `data_format` is invalid. """ with ops.name_scope(name, "conv1d", [value, filters]) as name: # Reshape the input tensor to [batch, 1, in_width, in_channels] if data_format is None or data_format == "NHWC" or data_format == "NWC": data_format = "NHWC" spatial_start_dim = 1 strides = [1, 1, stride, 1] elif data_format == "NCHW" or data_format == "NCW": data_format = "NCHW" spatial_start_dim = 2 strides = [1, 1, 1, stride] else: raise ValueError("data_format must be \"NWC\" or \"NCW\".") value = array_ops.expand_dims(value, spatial_start_dim) filters = array_ops.expand_dims(filters, 0) result = gen_nn_ops.conv2d( value, filters, strides, padding, use_cudnn_on_gpu=use_cudnn_on_gpu, data_format=data_format) return array_ops.squeeze(result, [spatial_start_dim]) @tf_export("nn.conv1d", v1=[]) def conv1d_v2(input, # pylint: disable=redefined-builtin filters, stride, padding, data_format=None, name=None): r"""Computes a 1-D convolution given 3-D input and filter tensors. Given an input tensor of shape [batch, in_width, in_channels] if data_format is "NWC", or [batch, in_channels, in_width] if data_format is "NCW", and a filter / kernel tensor of shape [filter_width, in_channels, out_channels], this op reshapes the arguments to pass them to conv2d to perform the equivalent convolution operation. Internally, this op reshapes the input tensors and invokes `tf.nn.conv2d`. For example, if `data_format` does not start with "NC", a tensor of shape [batch, in_width, in_channels] is reshaped to [batch, 1, in_width, in_channels], and the filter is reshaped to [1, filter_width, in_channels, out_channels]. The result is then reshaped back to [batch, out_width, out_channels] $where out_width is a function of the stride and padding as in conv2d$ and returned to the caller. Args: input: A 3D `Tensor`. Must be of type `float16`, `float32`, or `float64`. filters: A 3D `Tensor`. Must have the same type as `input`. stride: An `integer`. The number of entries by which the filter is moved right at each step. padding: 'SAME' or 'VALID' data_format: An optional `string` from `"NWC", "NCW"`. Defaults to `"NWC"`, the data is stored in the order of [batch, in_width, in_channels]. The `"NCW"` format stores data as [batch, in_channels, in_width]. name: A name for the operation (optional). Returns: A `Tensor`. Has the same type as input. Raises: ValueError: if `data_format` is invalid. """ return conv1d(input, # pylint: disable=redefined-builtin filters, stride, padding, use_cudnn_on_gpu=True, data_format=data_format, name=name) def conv1d_transpose( value, filter, # pylint: disable=redefined-builtin output_shape, stride, padding="SAME", data_format="NWC", name=None): """The transpose of `conv1d`. This operation is sometimes called "deconvolution" after [Deconvolutional Networks](http://www.matthewzeiler.com/pubs/cvpr2010/cvpr2010.pdf), but is actually the transpose (gradient) of `conv1d` rather than an actual deconvolution. Args: value: A 3-D `Tensor` of type `float` and shape `[batch, in_width, in_channels]` for `NWC` data format or `[batch, in_channels, in_width]` for `NCW` data format. filter: A 3-D `Tensor` with the same type as `value` and shape `[filter_width, output_channels, in_channels]`. `filter`'s `in_channels` dimension must match that of `value`. output_shape: A 1-D `Tensor` representing the output shape of the deconvolution op. stride: An `integer`. The number of entries by which the filter is moved right at each step. padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm. See the "returns" section of `tf.nn.convolution` for details. data_format: A string. 'NHWC' and 'NCHW' are supported. name: Optional name for the returned tensor. Returns: A `Tensor` with the same type as `value`. Raises: ValueError: If input/output depth does not match `filter`'s shape, or if padding is other than `'VALID'` or `'SAME'`. """ with ops.name_scope(name, "conv1d_transpose", [value, filter, output_shape]) as name: output_shape_ = ops.convert_to_tensor(output_shape, name="output_shape") if not output_shape_.get_shape().is_compatible_with(tensor_shape.vector(3)): raise ValueError("output_shape must have shape (3,), got {}".format( output_shape_.get_shape())) # The format could be either NWC or NCW, map to NHWC or NCHW if data_format is None or data_format == "NWC": data_format_2d = "NHWC" axis = 2 elif data_format == "NCW": data_format_2d = "NCHW" axis = 1 else: raise ValueError("data_format must be \"NWC\" or \"NCW\".") if not value.get_shape().dims[axis].is_compatible_with( filter.get_shape()[2]): raise ValueError("input channels does not match filter's input channels, " "{} != {}".format(value.get_shape()[axis], filter.get_shape()[2])) if isinstance(output_shape, (list, np.ndarray)): # output_shape's shape should be == [3] if reached this point. if not filter.get_shape().dims[1].is_compatible_with( output_shape[axis]): raise ValueError( "output_shape does not match filter's output channels, " "{} != {}".format(output_shape[axis], filter.get_shape()[1])) if padding != "VALID" and padding != "SAME": raise ValueError("padding must be either VALID or SAME:" " {}".format(padding)) # Reshape the input tensor to [batch, 1, in_width, in_channels] if data_format_2d == "NHWC": output_shape_ = array_ops.concat( [output_shape_[:1], [1], output_shape_[1:]], axis=0) spatial_start_dim = 1 strides = [1, 1, stride, 1] else: output_shape_ = array_ops.concat( [output_shape_[:2], [1], output_shape_[2:]], axis=0) spatial_start_dim = 2 strides = [1, 1, 1, stride] value = array_ops.expand_dims(value, spatial_start_dim) filter = array_ops.expand_dims(filter, 0) # pylint: disable=redefined-builtin result = gen_nn_ops.conv2d_backprop_input( input_sizes=output_shape_, filter=filter, out_backprop=value, strides=strides, padding=padding, data_format=data_format_2d, name=name) return array_ops.squeeze(result, [spatial_start_dim]) @ops.RegisterStatistics("Dilation2D", "flops") def _calc_dilation2d_flops(graph, node): """Calculates the compute resources needed for Dilation2D.""" input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0]) input_shape.assert_is_fully_defined() filter_shape = graph_util.tensor_shape_from_node_def_name( graph, node.input[1]) filter_shape.assert_is_fully_defined() output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name) output_shape.assert_is_fully_defined() filter_height = int(filter_shape[0]) filter_width = int(filter_shape[1]) output_count = np.prod(output_shape.as_list(), dtype=np.int64) return ops.OpStats("flops", (output_count * filter_height * filter_width * 2)) @tf_export(v1=["nn.erosion2d"]) def erosion2d(value, kernel, strides, rates, padding, name=None): """Computes the grayscale erosion of 4-D `value` and 3-D `kernel` tensors. The `value` tensor has shape `[batch, in_height, in_width, depth]` and the `kernel` tensor has shape `[kernel_height, kernel_width, depth]`, i.e., each input channel is processed independently of the others with its own structuring function. The `output` tensor has shape `[batch, out_height, out_width, depth]`. The spatial dimensions of the output tensor depend on the `padding` algorithm. We currently only support the default "NHWC" `data_format`. In detail, the grayscale morphological 2-D erosion is given by: output[b, y, x, c] = min_{dy, dx} value[b, strides[1] * y - rates[1] * dy, strides[2] * x - rates[2] * dx, c] - kernel[dy, dx, c] Duality: The erosion of `value` by the `kernel` is equal to the negation of the dilation of `-value` by the reflected `kernel`. Args: value: A `Tensor`. 4-D with shape `[batch, in_height, in_width, depth]`. kernel: A `Tensor`. Must have the same type as `value`. 3-D with shape `[kernel_height, kernel_width, depth]`. strides: A list of `ints` that has length `>= 4`. 1-D of length 4. The stride of the sliding window for each dimension of the input tensor. Must be: `[1, stride_height, stride_width, 1]`. rates: A list of `ints` that has length `>= 4`. 1-D of length 4. The input stride for atrous morphological dilation. Must be: `[1, rate_height, rate_width, 1]`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. name: A name for the operation (optional). If not specified "erosion2d" is used. Returns: A `Tensor`. Has the same type as `value`. 4-D with shape `[batch, out_height, out_width, depth]`. Raises: ValueError: If the `value` depth does not match `kernel`' shape, or if padding is other than `'VALID'` or `'SAME'`. """ with ops.name_scope(name, "erosion2d", [value, kernel]) as name: # Reduce erosion to dilation by duality. return math_ops.negative( gen_nn_ops.dilation2d( input=math_ops.negative(value), filter=array_ops.reverse_v2(kernel, [0, 1]), strides=strides, rates=rates, padding=padding, name=name)) @tf_export("nn.erosion2d", v1=[]) def erosion2d_v2(value, filters, strides, padding, data_format, dilations, name=None): """Computes the grayscale erosion of 4-D `value` and 3-D `filters` tensors. The `value` tensor has shape `[batch, in_height, in_width, depth]` and the `filters` tensor has shape `[filters_height, filters_width, depth]`, i.e., each input channel is processed independently of the others with its own structuring function. The `output` tensor has shape `[batch, out_height, out_width, depth]`. The spatial dimensions of the output tensor depend on the `padding` algorithm. We currently only support the default "NHWC" `data_format`. In detail, the grayscale morphological 2-D erosion is given by: output[b, y, x, c] = min_{dy, dx} value[b, strides[1] * y - dilations[1] * dy, strides[2] * x - dilations[2] * dx, c] - filters[dy, dx, c] Duality: The erosion of `value` by the `filters` is equal to the negation of the dilation of `-value` by the reflected `filters`. Args: value: A `Tensor`. 4-D with shape `[batch, in_height, in_width, depth]`. filters: A `Tensor`. Must have the same type as `value`. 3-D with shape `[filters_height, filters_width, depth]`. strides: A list of `ints` that has length `>= 4`. 1-D of length 4. The stride of the sliding window for each dimension of the input tensor. Must be: `[1, stride_height, stride_width, 1]`. padding: A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use. data_format: A `string`, only `"NHWC"` is currently supported. dilations: A list of `ints` that has length `>= 4`. 1-D of length 4. The input stride for atrous morphological dilation. Must be: `[1, rate_height, rate_width, 1]`. name: A name for the operation (optional). If not specified "erosion2d" is used. Returns: A `Tensor`. Has the same type as `value`. 4-D with shape `[batch, out_height, out_width, depth]`. Raises: ValueError: If the `value` depth does not match `filters`' shape, or if padding is other than `'VALID'` or `'SAME'`. """ if data_format != "NHWC": raise ValueError("Data formats other than NHWC are not yet supported") with ops.name_scope(name, "erosion2d", [value, filters]) as name: # Reduce erosion to dilation by duality. return math_ops.negative( gen_nn_ops.dilation2d( input=math_ops.negative(value), filter=array_ops.reverse_v2(filters, [0, 1]), strides=strides, rates=dilations, padding=padding, name=name)) @tf_export(v1=["math.in_top_k", "nn.in_top_k"]) def in_top_k(predictions, targets, k, name=None): r"""Says whether the targets are in the top `K` predictions. This outputs a `batch_size` bool array, an entry `out[i]` is `true` if the prediction for the target class is among the top `k` predictions among all predictions for example `i`. Note that the behavior of `InTopK` differs from the `TopK` op in its handling of ties; if multiple classes have the same prediction value and straddle the top-`k` boundary, all of those classes are considered to be in the top `k`. More formally, let \$predictions_i\$ be the predictions for all classes for example `i`, \$targets_i\$ be the target class for example `i`, \$out_i\$ be the output for example `i`, $$out_i = predictions_{i, targets_i} \in TopKIncludingTies(predictions_i)$$ Args: predictions: A `Tensor` of type `float32`. A `batch_size` x `classes` tensor. targets: A `Tensor`. Must be one of the following types: `int32`, `int64`. A `batch_size` vector of class ids. k: An `int`. Number of top elements to look at for computing precision. name: A name for the operation (optional). Returns: A `Tensor` of type `bool`. Computed Precision at `k` as a `bool Tensor`. """ with ops.name_scope(name, "in_top_k"): return gen_nn_ops.in_top_kv2(predictions, targets, k, name=name) @tf_export("math.in_top_k", "nn.in_top_k", v1=[]) def in_top_k_v2(targets, predictions, k, name=None): return in_top_k(predictions, targets, k, name) in_top_k_v2.__doc__ = in_top_k.__doc__ tf_export(v1=["nn.quantized_avg_pool"])(gen_nn_ops.quantized_avg_pool) tf_export(v1=["nn.quantized_conv2d"])(gen_nn_ops.quantized_conv2d) tf_export(v1=["nn.quantized_relu_x"])(gen_nn_ops.quantized_relu_x) tf_export(v1=["nn.quantized_max_pool"])(gen_nn_ops.quantized_max_pool)

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from __future__ import absolute_import from __future__ import division from __future__ import print_function import numbers import numpy as np from tensorflow.python.compat import compat from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors_impl from tensorflow.python.framework import graph_util from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import check_ops from tensorflow.python.ops import gen_nn_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import random_ops from tensorflow.python.ops.gen_nn_ops import * from tensorflow.python.util import deprecation from tensorflow.python.util.deprecation import deprecated_args from tensorflow.python.util.deprecation import deprecated_argument_lookup from tensorflow.python.util.tf_export import tf_export local_response_normalization = gen_nn_ops.lrn def _non_atrous_convolution( input, filter, padding, data_format=None, strides=None, name=None): with ops.name_scope(name, "non_atrous_convolution", [input, filter]) as scope: input = ops.convert_to_tensor(input, name="input") input_shape = input.get_shape() filter = ops.convert_to_tensor(filter, name="filter") filter_shape = filter.get_shape() op = _NonAtrousConvolution( input_shape, filter_shape=filter_shape, padding=padding, data_format=data_format, strides=strides, name=scope) return op(input, filter) class _NonAtrousConvolution(object): def __init__( self, input_shape, filter_shape, padding, data_format=None, strides=None, name=None): filter_shape = filter_shape.with_rank(input_shape.ndims) self.padding = padding self.name = name input_shape = input_shape.with_rank(filter_shape.ndims) if input_shape.ndims is None: raise ValueError("Rank of convolution must be known") if input_shape.ndims < 3 or input_shape.ndims > 5: raise ValueError( "`input` and `filter` must have rank at least 3 and at most 5") conv_dims = input_shape.ndims - 2 if strides is None: strides = [1] * conv_dims elif len(strides) != conv_dims: raise ValueError("len(strides)=%d, but should be %d" % (len(strides), conv_dims)) if conv_dims == 1: if data_format is None: data_format = "NWC" elif data_format not in {"NCW", "NWC", "NCHW", "NHWC"}: raise ValueError("data_format must be \"NWC\" or \"NCW\".") self.strides = strides[0] self.data_format = data_format self.conv_op = self._conv1d elif conv_dims == 2: if data_format is None or data_format == "NHWC": data_format = "NHWC" strides = [1] + list(strides) + [1] elif data_format == "NCHW": strides = [1, 1] + list(strides) else: raise ValueError("data_format must be \"NHWC\" or \"NCHW\".") self.strides = strides self.data_format = data_format self.conv_op = conv2d elif conv_dims == 3: if data_format is None or data_format == "NDHWC": strides = [1] + list(strides) + [1] elif data_format == "NCDHW": strides = [1, 1] + list(strides) else: raise ValueError("data_format must be \"NDHWC\" or \"NCDHW\". Have: %s" % data_format) self.strides = strides self.data_format = data_format self.conv_op = gen_nn_ops.conv3d # those for gen_nn_ops.conv2d and gen_nn_ops.conv3d. # pylint: disable=redefined-builtin def _conv1d(self, input, filter, strides, padding, data_format, name): return conv1d( value=input, filters=filter, stride=strides, padding=padding, data_format=data_format, name=name) # pylint: enable=redefined-builtin def __call__(self, inp, filter): # pylint: disable=redefined-builtin return self.conv_op( input=inp, filter=filter, strides=self.strides, padding=self.padding, data_format=self.data_format, name=self.name) @tf_export("nn.dilation2d", v1=[]) def dilation2d_v2( input, # pylint: disable=redefined-builtin filters, # pylint: disable=redefined-builtin strides, padding, data_format, dilations, name=None): if data_format != "NCHW": raise ValueError("Data formats other than NCHW are not yet supported") return gen_nn_ops.dilation2d(input=input, filter=filters, strides=strides, rates=dilations, padding=padding, name=name) @tf_export("nn.with_space_to_batch") def with_space_to_batch( input, # pylint: disable=redefined-builtin dilation_rate, padding, op, filter_shape=None, spatial_dims=None, data_format=None): input = ops.convert_to_tensor(input, name="input") # pylint: disable=redefined-builtin input_shape = input.get_shape() def build_op(num_spatial_dims, padding): return lambda inp, _: op(inp, num_spatial_dims, padding) new_op = _WithSpaceToBatch( input_shape, dilation_rate, padding, build_op, filter_shape=filter_shape, spatial_dims=spatial_dims, data_format=data_format) return new_op(input, None) class _WithSpaceToBatch(object): def __init__(self, input_shape, dilation_rate, padding, build_op, filter_shape=None, spatial_dims=None, data_format=None): dilation_rate = ops.convert_to_tensor( dilation_rate, dtypes.int32, name="dilation_rate") try: rate_shape = dilation_rate.get_shape().with_rank(1) except ValueError: raise ValueError("rate must be rank 1") if not dilation_rate.get_shape().is_fully_defined(): raise ValueError("rate must have known shape") num_spatial_dims = rate_shape.dims[0].value if data_format is not None and data_format.startswith("NC"): starting_spatial_dim = 2 else: starting_spatial_dim = 1 if spatial_dims is None: spatial_dims = range(starting_spatial_dim, num_spatial_dims + starting_spatial_dim) orig_spatial_dims = list(spatial_dims) spatial_dims = sorted(set(int(x) for x in orig_spatial_dims)) if spatial_dims != orig_spatial_dims or any(x < 1 for x in spatial_dims): raise ValueError( "spatial_dims must be a montonically increasing sequence of positive " "integers") # pylint: disable=line-too-long if data_format is not None and data_format.startswith("NC"): expected_input_rank = spatial_dims[-1] else: expected_input_rank = spatial_dims[-1] + 1 try: input_shape.with_rank_at_least(expected_input_rank) except ValueError: raise ValueError( "input tensor must have rank %d at least" % (expected_input_rank)) const_rate = tensor_util.constant_value(dilation_rate) rate_or_const_rate = dilation_rate if const_rate is not None: rate_or_const_rate = const_rate if np.any(const_rate < 1): raise ValueError("dilation_rate must be positive") if np.all(const_rate == 1): self.call = build_op(num_spatial_dims, padding) return # We have two padding contributions. The first is used for converting "SAME" # to "VALID". The second is required so that the height and width of the # zero-padded value tensor are multiples of rate. # Padding required to reduce to "VALID" convolution if padding == "SAME": if filter_shape is None: raise ValueError("filter_shape must be specified for SAME padding") filter_shape = ops.convert_to_tensor(filter_shape, name="filter_shape") const_filter_shape = tensor_util.constant_value(filter_shape) if const_filter_shape is not None: filter_shape = const_filter_shape self.base_paddings = _with_space_to_batch_base_paddings( const_filter_shape, num_spatial_dims, rate_or_const_rate) else: self.num_spatial_dims = num_spatial_dims self.rate_or_const_rate = rate_or_const_rate self.base_paddings = None elif padding == "VALID": self.base_paddings = np.zeros([num_spatial_dims, 2], np.int32) else: raise ValueError("Invalid padding method %r" % padding) self.input_shape = input_shape self.spatial_dims = spatial_dims self.dilation_rate = dilation_rate self.data_format = data_format self.op = build_op(num_spatial_dims, "VALID") self.call = self._with_space_to_batch_call def _with_space_to_batch_call(self, inp, filter): # pylint: disable=redefined-builtin # Handle input whose shape is unknown during graph creation. input_spatial_shape = None input_shape = self.input_shape spatial_dims = self.spatial_dims if input_shape.ndims is not None: input_shape_list = input_shape.as_list() input_spatial_shape = [input_shape_list[i] for i in spatial_dims] if input_spatial_shape is None or None in input_spatial_shape: input_shape_tensor = array_ops.shape(inp) input_spatial_shape = array_ops.stack( [input_shape_tensor[i] for i in spatial_dims]) base_paddings = self.base_paddings if base_paddings is None: # base_paddings could not be computed at build time since static filter # shape was not fully defined. filter_shape = array_ops.shape(filter) base_paddings = _with_space_to_batch_base_paddings( filter_shape, self.num_spatial_dims, self.rate_or_const_rate) paddings, crops = array_ops.required_space_to_batch_paddings( input_shape=input_spatial_shape, base_paddings=base_paddings, block_shape=self.dilation_rate) dilation_rate = _with_space_to_batch_adjust(self.dilation_rate, 1, spatial_dims) paddings = _with_space_to_batch_adjust(paddings, 0, spatial_dims) crops = _with_space_to_batch_adjust(crops, 0, spatial_dims) input_converted = array_ops.space_to_batch_nd( input=inp, block_shape=dilation_rate, paddings=paddings) result = self.op(input_converted, filter) result_converted = array_ops.batch_to_space_nd( input=result, block_shape=dilation_rate, crops=crops) # Recover channel information for output shape if channels are not last. if self.data_format is not None and self.data_format.startswith("NC"): if not result_converted.shape.dims[1].value and filter is not None: output_shape = result_converted.shape.as_list() output_shape[1] = filter.shape[-1] result_converted.set_shape(output_shape) return result_converted def __call__(self, inp, filter): # pylint: disable=redefined-builtin return self.call(inp, filter) def _with_space_to_batch_base_paddings(filter_shape, num_spatial_dims, rate_or_const_rate): # Spatial dimensions of the filters and the upsampled filters in which we # introduce (rate - 1) zeros between consecutive filter values. filter_spatial_shape = filter_shape[:num_spatial_dims] dilated_filter_spatial_shape = ( filter_spatial_shape + (filter_spatial_shape - 1) * (rate_or_const_rate - 1)) pad_extra_shape = dilated_filter_spatial_shape - 1 # When full_padding_shape is odd, we pad more at end, following the same # convention as conv2d. pad_extra_start = pad_extra_shape // 2 pad_extra_end = pad_extra_shape - pad_extra_start base_paddings = array_ops.stack( [[pad_extra_start[i], pad_extra_end[i]] for i in range(num_spatial_dims)]) return base_paddings def _with_space_to_batch_adjust(orig, fill_value, spatial_dims): fill_dims = orig.get_shape().as_list()[1:] dtype = orig.dtype.as_numpy_dtype parts = [] const_orig = tensor_util.constant_value(orig) const_or_orig = const_orig if const_orig is not None else orig prev_spatial_dim = 0 i = 0 while i < len(spatial_dims): start_i = i start_spatial_dim = spatial_dims[i] if start_spatial_dim > 1: # Fill in any gap from the previous spatial dimension (or dimension 1 if # this is the first spatial dimension) with `fill_value`. parts.append( np.full( [start_spatial_dim - 1 - prev_spatial_dim] + fill_dims, fill_value, dtype=dtype)) # Find the largest value of i such that: # [spatial_dims[start_i], ..., spatial_dims[i]] # == [start_spatial_dim, ..., start_spatial_dim + i - start_i], # i.e. the end of a contiguous group of spatial dimensions. while (i + 1 < len(spatial_dims) and spatial_dims[i + 1] == spatial_dims[i] + 1): i += 1 parts.append(const_or_orig[start_i:i + 1]) prev_spatial_dim = spatial_dims[i] i += 1 if const_orig is not None: return np.concatenate(parts) else: return array_ops.concat(parts, 0) def _get_strides_and_dilation_rate(num_spatial_dims, strides, dilation_rate): if dilation_rate is None: dilation_rate = [1] * num_spatial_dims elif len(dilation_rate) != num_spatial_dims: raise ValueError("len(dilation_rate)=%d but should be %d" % (len(dilation_rate), num_spatial_dims)) dilation_rate = np.array(dilation_rate, dtype=np.int32) if np.any(dilation_rate < 1): raise ValueError("all values of dilation_rate must be positive") if strides is None: strides = [1] * num_spatial_dims elif len(strides) != num_spatial_dims: raise ValueError("len(strides)=%d but should be %d" % (len(strides), num_spatial_dims)) strides = np.array(strides, dtype=np.int32) if np.any(strides < 1): raise ValueError("all values of strides must be positive") if np.any(strides > 1) and np.any(dilation_rate > 1): raise ValueError( "strides > 1 not supported in conjunction with dilation_rate > 1") return strides, dilation_rate @tf_export(v1=["nn.convolution"]) def convolution( input, # pylint: disable=redefined-builtin filter, # pylint: disable=redefined-builtin padding, strides=None, dilation_rate=None, name=None, data_format=None): # pylint: disable=line-too-long # pylint: enable=line-too-long with ops.name_scope(name, "convolution", [input, filter]) as name: input = ops.convert_to_tensor(input, name="input") # pylint: disable=redefined-builtin input_shape = input.get_shape() filter = ops.convert_to_tensor(filter, name="filter") # pylint: disable=redefined-builtin filter_shape = filter.get_shape() op = Convolution( input_shape, filter_shape, padding, strides=strides, dilation_rate=dilation_rate, name=name, data_format=data_format) return op(input, filter) @tf_export("nn.convolution", v1=[]) def convolution_v2( input, # pylint: disable=redefined-builtin filters, strides=None, padding="VALID", data_format=None, dilations=None, name=None): return convolution( input, # pylint: disable=redefined-builtin filters, padding=padding, strides=strides, dilation_rate=dilations, name=name, data_format=data_format) convolution_v2.__doc__ = deprecation.rewrite_argument_docstring( deprecation.rewrite_argument_docstring( convolution.__doc__, "dilation_rate", "dilations"), "filter", "filters") class Convolution(object): def __init__(self, input_shape, filter_shape, padding, strides=None, dilation_rate=None, name=None, data_format=None): num_total_dims = filter_shape.ndims if num_total_dims is None: num_total_dims = input_shape.ndims if num_total_dims is None: raise ValueError("rank of input or filter must be known") num_spatial_dims = num_total_dims - 2 try: input_shape.with_rank(num_spatial_dims + 2) except ValueError: raise ValueError( "input tensor must have rank %d" % (num_spatial_dims + 2)) try: filter_shape.with_rank(num_spatial_dims + 2) except ValueError: raise ValueError( "filter tensor must have rank %d" % (num_spatial_dims + 2)) if data_format is None or not data_format.startswith("NC"): input_channels_dim = tensor_shape.dimension_at_index( input_shape, num_spatial_dims + 1) spatial_dims = range(1, num_spatial_dims + 1) else: input_channels_dim = tensor_shape.dimension_at_index(input_shape, 1) spatial_dims = range(2, num_spatial_dims + 2) if not input_channels_dim.is_compatible_with( filter_shape[num_spatial_dims]): raise ValueError( "number of input channels does not match corresponding dimension of " "filter, {} != {}".format(input_channels_dim, filter_shape[num_spatial_dims])) strides, dilation_rate = _get_strides_and_dilation_rate( num_spatial_dims, strides, dilation_rate) self.input_shape = input_shape self.filter_shape = filter_shape self.data_format = data_format self.strides = strides self.name = name self.conv_op = _WithSpaceToBatch( input_shape, dilation_rate=dilation_rate, padding=padding, build_op=self._build_op, filter_shape=filter_shape, spatial_dims=spatial_dims, data_format=data_format) def _build_op(self, _, padding): return _NonAtrousConvolution( self.input_shape, filter_shape=self.filter_shape, padding=padding, data_format=self.data_format, strides=self.strides, name=self.name) def __call__(self, inp, filter): # pylint: disable=redefined-builtin return self.conv_op(inp, filter) @tf_export(v1=["nn.pool"]) def pool( input, # pylint: disable=redefined-builtin window_shape, pooling_type, padding, dilation_rate=None, strides=None, name=None, data_format=None): # pylint: disable=line-too-long # pylint: enable=line-too-long with ops.name_scope(name, "%s_pool" % (pooling_type.lower()), [input]) as scope: input = ops.convert_to_tensor(input, name="input") # pylint: disable=redefined-builtin num_spatial_dims = len(window_shape) if num_spatial_dims < 1 or num_spatial_dims > 3: raise ValueError("It is required that 1 <= num_spatial_dims <= 3.") input.get_shape().with_rank(num_spatial_dims + 2) strides, dilation_rate = _get_strides_and_dilation_rate( num_spatial_dims, strides, dilation_rate) if padding == "SAME" and np.any(dilation_rate > 1): raise ValueError( "pooling with SAME padding is not implemented for dilation_rate > 1") if np.any(strides > window_shape): raise ValueError( "strides > window_shape not supported due to inconsistency between " "CPU and GPU implementations") pooling_ops = { ("MAX", 1): max_pool, ("MAX", 2): max_pool, ("MAX", 3): max_pool3d, # pylint: disable=undefined-variable ("AVG", 1): avg_pool, ("AVG", 2): avg_pool, ("AVG", 3): avg_pool3d, # pylint: disable=undefined-variable } op_key = (pooling_type, num_spatial_dims) if op_key not in pooling_ops: raise ValueError("%d-D %s pooling is not supported." % (op_key[1], op_key[0])) if data_format is None or not data_format.startswith("NC"): adjusted_window_shape = [1] + list(window_shape) + [1] adjusted_strides = [1] + list(strides) + [1] spatial_dims = range(1, num_spatial_dims + 1) else: adjusted_window_shape = [1, 1] + list(window_shape) adjusted_strides = [1, 1] + list(strides) spatial_dims = range(2, num_spatial_dims + 2) if num_spatial_dims == 1: if data_format is None or data_format == "NWC": data_format_kwargs = dict(data_format="NHWC") elif data_format == "NCW": data_format_kwargs = dict(data_format="NCHW") else: raise ValueError("data_format must be either \"NWC\" or \"NCW\".") adjusted_window_shape = [1] + adjusted_window_shape adjusted_strides = [1] + adjusted_strides else: data_format_kwargs = dict(data_format=data_format) def op(converted_input, _, converted_padding): # pylint: disable=missing-docstring if num_spatial_dims == 1: converted_input = array_ops.expand_dims(converted_input, spatial_dims[0]) result = pooling_ops[op_key]( converted_input, adjusted_window_shape, adjusted_strides, converted_padding, name=scope, **data_format_kwargs) if num_spatial_dims == 1: result = array_ops.squeeze(result, [spatial_dims[0]]) return result return with_space_to_batch( input=input, dilation_rate=dilation_rate, padding=padding, op=op, spatial_dims=spatial_dims, filter_shape=window_shape) @tf_export("nn.pool", v1=[]) def pool_v2( input, # pylint: disable=redefined-builtin window_shape, pooling_type, strides=None, padding="VALID", data_format=None, dilations=None, name=None): # pylint: disable=line-too-long return pool( input=input, window_shape=window_shape, pooling_type=pooling_type, padding=padding, dilation_rate=dilations, strides=strides, name=name, data_format=data_format) @tf_export("nn.atrous_conv2d") def atrous_conv2d(value, filters, rate, padding, name=None): return convolution( input=value, filter=filters, padding=padding, dilation_rate=np.broadcast_to(rate, (2,)), name=name) def _convert_padding(padding): explicit_paddings = [] if padding == "EXPLICIT": # Give a better error message if EXPLICIT is passed. raise ValueError('"EXPLICIT" is not a valid value for the padding ' "parameter. To use explicit padding, the padding " "parameter must be a list.") if isinstance(padding, (list, tuple)): for i, dim_paddings in enumerate(padding): if not isinstance(dim_paddings, (list, tuple)): raise ValueError("When padding is a list, each element of padding must " "be a list/tuple of size 2. Element with index %d of " "padding is not a list/tuple" % i) if len(dim_paddings) != 2: raise ValueError("When padding is a list, each element of padding must " "be a list/tuple of size 2. Element with index %d of " "padding has size %d" % (i, len(dim_paddings))) explicit_paddings.extend(dim_paddings) if len(padding) != 4: raise ValueError("When padding is a list, it must be of size 4. Got " "padding of size: %d" % len(padding)) padding = "EXPLICIT" return padding, explicit_paddings @tf_export("nn.conv2d", v1=[]) def conv2d_v2(input, # pylint: disable=redefined-builtin filters, strides, padding, data_format="NHWC", dilations=None, name=None): # pylint: disable=line-too-long # pylint: enable=line-too-long if dilations is None: dilations = [1, 1, 1, 1] return conv2d(input, # pylint: disable=redefined-builtin filters, strides, padding, use_cudnn_on_gpu=True, data_format=data_format, dilations=dilations, name=name) @tf_export(v1=["nn.conv2d"]) def conv2d( # pylint: disable=redefined-builtin,dangerous-default-value input, filter, strides, padding, use_cudnn_on_gpu=True, data_format="NHWC", dilations=[1, 1, 1, 1], name=None): padding, explicit_paddings = _convert_padding(padding) return gen_nn_ops.conv2d(input, # pylint: disable=redefined-builtin filter, strides, padding, use_cudnn_on_gpu=use_cudnn_on_gpu, explicit_paddings=explicit_paddings, data_format=data_format, dilations=dilations, name=name) @tf_export("nn.conv2d_backprop_filter", v1=[]) def conv2d_backprop_filter_v2(input, # pylint: disable=redefined-builtin filter_sizes, out_backprop, strides, padding, data_format="NHWC", dilations=None, name=None): if dilations is None: dilations = [1, 1, 1, 1] return conv2d_backprop_filter(input, # pylint: disable=redefined-builtin filter_sizes, out_backprop, strides, padding, use_cudnn_on_gpu=True, data_format=data_format, dilations=dilations, name=name) @tf_export(v1=["nn.conv2d_backprop_filter"]) def conv2d_backprop_filter( # pylint: disable=redefined-builtin,dangerous-default-value input, filter_sizes, out_backprop, strides, padding, use_cudnn_on_gpu=True, data_format="NHWC", dilations=[1, 1, 1, 1], name=None): padding, explicit_paddings = _convert_padding(padding) return gen_nn_ops.conv2d_backprop_filter( input, filter_sizes, out_backprop, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name) @tf_export("nn.conv2d_backprop_input", v1=[]) def conv2d_backprop_input_v2(input_sizes, filters, out_backprop, strides, padding, data_format="NHWC", dilations=None, name=None): if dilations is None: dilations = [1, 1, 1, 1] return conv2d_backprop_input(input_sizes, filters, out_backprop, strides, padding, use_cudnn_on_gpu=True, data_format=data_format, dilations=dilations, name=name) @tf_export(v1=["nn.conv2d_backprop_input"]) def conv2d_backprop_input( # pylint: disable=redefined-builtin,dangerous-default-value input_sizes, filter, out_backprop, strides, padding, use_cudnn_on_gpu=True, data_format="NHWC", dilations=[1, 1, 1, 1], name=None): padding, explicit_paddings = _convert_padding(padding) return gen_nn_ops.conv2d_backprop_input( input_sizes, filter, out_backprop, strides, padding, use_cudnn_on_gpu, explicit_paddings, data_format, dilations, name) @tf_export(v1=["nn.conv2d_transpose"]) def conv2d_transpose( value, filter, # pylint: disable=redefined-builtin output_shape, strides, padding="SAME", data_format="NHWC", name=None): with ops.name_scope(name, "conv2d_transpose", [value, filter, output_shape]) as name: if data_format not in ("NCHW", "NHWC"): raise ValueError("data_format has to be either NCHW or NHWC.") value = ops.convert_to_tensor(value, name="value") filter = ops.convert_to_tensor(filter, name="filter") # pylint: disable=redefined-builtin axis = 3 if data_format == "NHWC" else 1 if not value.get_shape().dims[axis].is_compatible_with( filter.get_shape()[3]): raise ValueError("input channels does not match filter's input channels, " "{} != {}".format(value.get_shape()[axis], filter.get_shape()[3])) output_shape_ = ops.convert_to_tensor(output_shape, name="output_shape") if not output_shape_.get_shape().is_compatible_with(tensor_shape.vector(4)): raise ValueError("output_shape must have shape (4,), got {}".format( output_shape_.get_shape())) if isinstance(output_shape, (list, np.ndarray)): if not filter.get_shape().dims[2].is_compatible_with( output_shape[axis]): raise ValueError( "output_shape does not match filter's output channels, " "{} != {}".format(output_shape[axis], filter.get_shape()[2])) if padding != "VALID" and padding != "SAME": raise ValueError("padding must be either VALID or SAME:" " {}".format(padding)) return gen_nn_ops.conv2d_backprop_input( input_sizes=output_shape_, filter=filter, out_backprop=value, strides=strides, padding=padding, data_format=data_format, name=name) @tf_export("nn.conv2d_transpose", v1=[]) def conv2d_transpose_v2( input, filters, output_shape, strides, padding="SAME", data_format="NHWC", name=None): return conv2d_transpose( input, filters, output_shape, strides, padding=padding, data_format=data_format, name=name) conv2d_transpose_v2.__doc__ = deprecation.rewrite_argument_docstring( deprecation.rewrite_argument_docstring( conv2d_transpose.__doc__, "filter", "filters"), "value", "input") @tf_export("nn.atrous_conv2d_transpose") def atrous_conv2d_transpose(value, filters, output_shape, rate, padding, name=None): with ops.name_scope(name, "atrous_conv2d_transpose", [value, filters, output_shape]) as name: value = ops.convert_to_tensor(value, name="value") filters = ops.convert_to_tensor(filters, name="filters") if not value.get_shape().dims[3].is_compatible_with(filters.get_shape()[3]): raise ValueError( "value's input channels does not match filters' input channels, " "{} != {}".format(value.get_shape()[3], filters.get_shape()[3])) if rate < 1: raise ValueError("rate {} cannot be less than one".format(rate)) if rate == 1: return conv2d_transpose( value, filters, output_shape, strides=[1, 1, 1, 1], padding=padding, data_format="NHWC") output_shape_ = ops.convert_to_tensor(output_shape, name="output_shape") if not output_shape_.get_shape().is_compatible_with(tensor_shape.vector(4)): raise ValueError("output_shape must have shape (4,), got {}".format( output_shape_.get_shape())) if isinstance(output_shape, (list, np.ndarray)): if not filters.get_shape().dims[2].is_compatible_with(output_shape[3]): raise ValueError( "output_shape does not match filter's output channels, " "{} != {}".format(output_shape[3], filters.get_shape()[2])) if padding == "SAME": if filters.get_shape().is_fully_defined(): filter_shape = filters.get_shape().as_list() else: filter_shape = array_ops.shape(filters) filter_height, filter_width = filter_shape[0], filter_shape[1] filter_height_up = filter_height + (filter_height - 1) * (rate - 1) filter_width_up = filter_width + (filter_width - 1) * (rate - 1) pad_height = filter_height_up - 1 pad_width = filter_width_up - 1 pad_top = pad_height // 2 pad_bottom = pad_height - pad_top pad_left = pad_width // 2 pad_right = pad_width - pad_left elif padding == "VALID": pad_top = 0 pad_bottom = 0 pad_left = 0 pad_right = 0 else: raise ValueError("padding must be either VALID or SAME:" " {}".format(padding)) in_height = output_shape[1] + pad_top + pad_bottom in_width = output_shape[2] + pad_left + pad_right pad_bottom_extra = (rate - in_height % rate) % rate pad_right_extra = (rate - in_width % rate) % rate space_to_batch_pad = [[0, pad_bottom_extra], [0, pad_right_extra]] value = array_ops.space_to_batch( input=value, paddings=space_to_batch_pad, block_size=rate) input_sizes = [ rate * rate * output_shape[0], (in_height + pad_bottom_extra) // rate, (in_width + pad_right_extra) // rate, output_shape[3] ] value = gen_nn_ops.conv2d_backprop_input( input_sizes=input_sizes, filter=filters, out_backprop=value, strides=[1, 1, 1, 1], padding="VALID", data_format="NHWC") batch_to_space_crop = [[pad_top, pad_bottom + pad_bottom_extra], [pad_left, pad_right + pad_right_extra]] return array_ops.batch_to_space( input=value, crops=batch_to_space_crop, block_size=rate) @tf_export("nn.conv3d", v1=[]) def conv3d_v2(input, filters, strides, padding, data_format="NDHWC", dilations=None, name=None): if dilations is None: dilations = [1, 1, 1, 1, 1] return gen_nn_ops.conv3d(input, filters, strides, padding, data_format=data_format, dilations=dilations, name=name) tf_export(v1=["nn.conv3d"])(gen_nn_ops.conv3d) conv3d_v2.__doc__ = deprecation.rewrite_argument_docstring( gen_nn_ops.conv3d.__doc__, "filter", "filters") @tf_export(v1=["nn.conv3d_transpose"]) def conv3d_transpose( value, filter, output_shape, strides, padding="SAME", data_format="NDHWC", name=None): with ops.name_scope(name, "conv3d_transpose", [value, filter, output_shape]) as name: value = ops.convert_to_tensor(value, name="value") filter = ops.convert_to_tensor(filter, name="filter") axis = 1 if data_format == "NCDHW" else 4 if not value.get_shape().dims[axis].is_compatible_with( filter.get_shape()[4]): raise ValueError("input channels does not match filter's input channels, " "{} != {}".format(value.get_shape()[axis], filter.get_shape()[4])) output_shape_ = ops.convert_to_tensor(output_shape, name="output_shape") if not output_shape_.get_shape().is_compatible_with(tensor_shape.vector(5)): raise ValueError("output_shape must have shape (5,), got {}".format( output_shape_.get_shape())) if isinstance(output_shape, (list, np.ndarray)): # output_shape's shape should be == [5] if reached this point. if not filter.get_shape().dims[3].is_compatible_with( output_shape[axis]): raise ValueError( "output_shape does not match filter's output channels, " "{} != {}".format(output_shape[axis], filter.get_shape()[3])) if padding != "VALID" and padding != "SAME": raise ValueError("padding must be either VALID or SAME:" " {}".format(padding)) return gen_nn_ops.conv3d_backprop_input_v2( input_sizes=output_shape_, filter=filter, out_backprop=value, strides=strides, padding=padding, data_format=data_format, name=name) # pylint: disable=redefined-builtin @tf_export("nn.conv3d_transpose", v1=[]) def conv3d_transpose_v2( input, filters, output_shape, strides, padding="SAME", data_format="NDHWC", name=None): return conv3d_transpose( input, filters, output_shape, strides, padding=padding, data_format=data_format, name=name) # pylint: enable=redefined-builtin conv3d_transpose_v2.__doc__ = deprecation.rewrite_argument_docstring( deprecation.rewrite_argument_docstring( conv3d_transpose.__doc__, "filter", "filters"), "value", "input") @tf_export("nn.bias_add") def bias_add(value, bias, data_format=None, name=None): with ops.name_scope(name, "BiasAdd", [value, bias]) as name: if not context.executing_eagerly(): value = ops.convert_to_tensor(value, name="input") bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias") return gen_nn_ops.bias_add(value, bias, data_format=data_format, name=name) def bias_add_v1(value, bias, name=None): with ops.name_scope(name, "BiasAddV1", [value, bias]) as name: value = ops.convert_to_tensor(value, name="input") bias = ops.convert_to_tensor(bias, dtype=value.dtype, name="bias") return gen_nn_ops.bias_add_v1(value, bias, name=name) @tf_export(v1=["nn.crelu"]) def crelu(features, name=None, axis=-1): with ops.name_scope(name, "CRelu", [features]) as name: features = ops.convert_to_tensor(features, name="features") c = array_ops.concat([features, -features], axis, name=name) return gen_nn_ops.relu(c) @tf_export("nn.crelu", v1=[]) def crelu_v2(features, axis=-1, name=None): return crelu(features, name=name, axis=axis) crelu_v2.__doc__ = crelu.__doc__ @tf_export("nn.relu6") def relu6(features, name=None): with ops.name_scope(name, "Relu6", [features]) as name: features = ops.convert_to_tensor(features, name="features") return gen_nn_ops.relu6(features, name=name) @tf_export("nn.leaky_relu") def leaky_relu(features, alpha=0.2, name=None): with ops.name_scope(name, "LeakyRelu", [features, alpha]) as name: features = ops.convert_to_tensor(features, name="features") if features.dtype.is_integer: features = math_ops.to_float(features) if compat.forward_compatible(2018, 11, 1): if isinstance(alpha, np.ndarray): alpha = np.asscalar(alpha) return gen_nn_ops.leaky_relu(features, alpha=alpha, name=name) alpha = ops.convert_to_tensor(alpha, dtype=features.dtype, name="alpha") return math_ops.maximum(alpha * features, features, name=name) def _flatten_outer_dims(logits): rank = array_ops.rank(logits) last_dim_size = array_ops.slice( array_ops.shape(logits), [math_ops.subtract(rank, 1)], [1]) output = array_ops.reshape(logits, array_ops.concat([[-1], last_dim_size], 0)) # Set output shape if known. if not context.executing_eagerly(): shape = logits.get_shape() if shape is not None and shape.dims is not None: shape = shape.as_list() product = 1 product_valid = True for d in shape[:-1]: if d is None: product_valid = False break else: product *= d if product_valid: output_shape = [product, shape[-1]] output.set_shape(output_shape) return output def _softmax(logits, compute_op, dim=-1, name=None): def _swap_axis(logits, dim_index, last_index, name=None): return array_ops.transpose( logits, array_ops.concat([ math_ops.range(dim_index), [last_index], math_ops.range(dim_index + 1, last_index), [dim_index] ], 0), name=name) logits = ops.convert_to_tensor(logits) # We need its original shape for shape inference. shape = logits.get_shape() is_last_dim = (dim is -1) or (dim == shape.ndims - 1) if is_last_dim: return compute_op(logits, name=name) dim_val = dim if isinstance(dim, ops.Tensor): dim_val = tensor_util.constant_value(dim) if dim_val is not None and (dim_val < -shape.ndims or dim_val >= shape.ndims): raise errors_impl.InvalidArgumentError( None, None, "Dimension (%d) must be in the range [%d, %d) where %d is the number of" " dimensions in the input." % (dim_val, -shape.ndims, shape.ndims, shape.ndims)) # If dim is not the last dimension, we have to do a transpose so that we can # still perform softmax on its last dimension. # In case dim is negative (and is not last dimension -1), add shape.ndims ndims = array_ops.rank(logits) if not isinstance(dim, ops.Tensor): if dim < 0: dim += ndims else: dim = array_ops.where(math_ops.less(dim, 0), dim + ndims, dim) # Swap logits' dimension of dim and its last dimension. input_rank = array_ops.rank(logits) dim_axis = dim % shape.ndims logits = _swap_axis(logits, dim_axis, math_ops.subtract(input_rank, 1)) output = compute_op(logits) output = _swap_axis( output, dim_axis, math_ops.subtract(input_rank, 1), name=name) output.set_shape(shape) return output @tf_export(v1=["nn.softmax", "math.softmax"]) @deprecation.deprecated_args(None, "dim is deprecated, use axis instead", "dim") def softmax(logits, axis=None, name=None, dim=None): axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim) if axis is None: axis = -1 return _softmax(logits, gen_nn_ops.softmax, axis, name) @tf_export("nn.softmax", "math.softmax", v1=[]) def softmax_v2(logits, axis=None, name=None): if axis is None: axis = -1 return _softmax(logits, gen_nn_ops.softmax, axis, name) @tf_export(v1=["nn.log_softmax", "math.log_softmax"]) @deprecation.deprecated_args(None, "dim is deprecated, use axis instead", "dim") def log_softmax(logits, axis=None, name=None, dim=None): axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim) if axis is None: axis = -1 return _softmax(logits, gen_nn_ops.log_softmax, axis, name) @tf_export("nn.log_softmax", "math.log_softmax", v1=[]) def log_softmax_v2(logits, axis=None, name=None): if axis is None: axis = -1 return _softmax(logits, gen_nn_ops.log_softmax, axis, name) def _ensure_xent_args(name, sentinel, labels, logits): if sentinel is not None: raise ValueError("Only call `%s` with " "named arguments (labels=..., logits=..., ...)" % name) if labels is None or logits is None: raise ValueError("Both labels and logits must be provided.") @tf_export("nn.softmax_cross_entropy_with_logits", v1=[]) def softmax_cross_entropy_with_logits_v2(labels, logits, axis=-1, name=None): return softmax_cross_entropy_with_logits_v2_helper( labels=labels, logits=logits, axis=axis, name=name) @tf_export(v1=["nn.softmax_cross_entropy_with_logits_v2"]) @deprecated_args(None, "dim is deprecated, use axis instead", "dim") def softmax_cross_entropy_with_logits_v2_helper( labels, logits, axis=None, name=None, dim=None): axis = deprecated_argument_lookup("axis", axis, "dim", dim) del dim if axis is None: axis = -1 with ops.name_scope(name, "softmax_cross_entropy_with_logits", [logits, labels]) as name: logits = ops.convert_to_tensor(logits, name="logits") labels = ops.convert_to_tensor(labels, name="labels") convert_to_float32 = ( logits.dtype == dtypes.float16 or logits.dtype == dtypes.bfloat16) precise_logits = math_ops.cast( logits, dtypes.float32) if convert_to_float32 else logits labels = math_ops.cast(labels, precise_logits.dtype) input_rank = array_ops.rank(precise_logits) shape = logits.get_shape() if axis != -1: def _move_dim_to_end(tensor, dim_index, rank): return array_ops.transpose( tensor, array_ops.concat([ math_ops.range(dim_index), math_ops.range(dim_index + 1, rank), [dim_index] ], 0)) precise_logits = _move_dim_to_end(precise_logits, axis, input_rank) labels = _move_dim_to_end(labels, axis, input_rank) input_shape = array_ops.shape(precise_logits) precise_logits = _flatten_outer_dims(precise_logits) labels = _flatten_outer_dims(labels) cost, unused_backprop = gen_nn_ops.softmax_cross_entropy_with_logits( precise_logits, labels, name=name) output_shape = array_ops.slice(input_shape, [0], [math_ops.subtract(input_rank, 1)]) cost = array_ops.reshape(cost, output_shape) if not context.executing_eagerly( ) and shape is not None and shape.dims is not None: shape = shape.as_list() del shape[axis] cost.set_shape(shape) if convert_to_float32: return math_ops.cast(cost, logits.dtype) else: return cost _XENT_DEPRECATION = """ Future major versions of TensorFlow will allow gradients to flow into the labels input on backprop by default. See `tf.nn.softmax_cross_entropy_with_logits_v2`. """ @tf_export(v1=["nn.softmax_cross_entropy_with_logits"]) @deprecation.deprecated(date=None, instructions=_XENT_DEPRECATION) def softmax_cross_entropy_with_logits( _sentinel=None, labels=None, logits=None, dim=-1, name=None): _ensure_xent_args("softmax_cross_entropy_with_logits", _sentinel, labels, logits) with ops.name_scope(name, "softmax_cross_entropy_with_logits_sg", [logits, labels]) as name: labels = array_ops.stop_gradient(labels, name="labels_stop_gradient") return softmax_cross_entropy_with_logits_v2( labels=labels, logits=logits, axis=dim, name=name) @tf_export("nn.sparse_softmax_cross_entropy_with_logits") def sparse_softmax_cross_entropy_with_logits( _sentinel=None, labels=None, logits=None, name=None): _ensure_xent_args("sparse_softmax_cross_entropy_with_logits", _sentinel, labels, logits) with ops.name_scope(name, "SparseSoftmaxCrossEntropyWithLogits", [labels, logits]): labels = ops.convert_to_tensor(labels) logits = ops.convert_to_tensor(logits) precise_logits = math_ops.cast(logits, dtypes.float32) if (dtypes.as_dtype( logits.dtype) == dtypes.float16) else logits labels_static_shape = labels.get_shape() labels_shape = array_ops.shape(labels) static_shapes_fully_defined = ( labels_static_shape.is_fully_defined() and logits.get_shape()[:-1].is_fully_defined()) if logits.get_shape().ndims is not None and logits.get_shape().ndims == 0: raise ValueError( "Logits cannot be scalars - received shape %s." % logits.get_shape()) if logits.get_shape().ndims is not None and ( labels_static_shape.ndims is not None and labels_static_shape.ndims != logits.get_shape().ndims - 1): raise ValueError("Rank mismatch: Rank of labels (received %s) should " "equal rank of logits minus 1 (received %s)." % (labels_static_shape.ndims, logits.get_shape().ndims)) if (static_shapes_fully_defined and labels_static_shape != logits.get_shape()[:-1]): raise ValueError("Shape mismatch: The shape of labels (received %s) " "should equal the shape of logits except for the last " "dimension (received %s)." % (labels_static_shape, logits.get_shape())) if logits.get_shape().ndims == 2: cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits( precise_logits, labels, name=name) if logits.dtype == dtypes.float16: return math_ops.cast(cost, dtypes.float16) else: return cost shape_checks = [] if not static_shapes_fully_defined: shape_checks.append( check_ops.assert_equal( array_ops.shape(labels), array_ops.shape(logits)[:-1])) with ops.control_dependencies(shape_checks): num_classes = array_ops.shape(logits)[array_ops.rank(logits) - 1] precise_logits = array_ops.reshape(precise_logits, [-1, num_classes]) labels = array_ops.reshape(labels, [-1]) cost, _ = gen_nn_ops.sparse_softmax_cross_entropy_with_logits( precise_logits, labels, name=name) cost = array_ops.reshape(cost, labels_shape) cost.set_shape(labels_static_shape) if logits.dtype == dtypes.float16: return math_ops.cast(cost, dtypes.float16) else: return cost @tf_export("nn.avg_pool") def avg_pool(value, ksize, strides, padding, data_format="NHWC", name=None): with ops.name_scope(name, "AvgPool", [value]) as name: value = ops.convert_to_tensor(value, name="input") return gen_nn_ops.avg_pool( value, ksize=ksize, strides=strides, padding=padding, data_format=data_format, name=name) @tf_export("nn.max_pool") def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None): with ops.name_scope(name, "MaxPool", [value]) as name: value = ops.convert_to_tensor(value, name="input") return gen_nn_ops.max_pool( value, ksize=ksize, strides=strides, padding=padding, data_format=data_format, name=name) @tf_export("nn.max_pool_with_argmax", v1=[]) def max_pool_with_argmax_v2(input, ksize, strides, padding, data_format="NHWC", output_dtype=dtypes.int64, name=None): if data_format != "NHWC": raise ValueError("Data formats other than 'NHWC' are not yet supported") return gen_nn_ops.max_pool_with_argmax(input=input, ksize=ksize, strides=strides, padding=padding, Targmax=output_dtype, name=name) @ops.RegisterStatistics("Conv2D", "flops") def _calc_conv_flops(graph, node): input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0]) input_shape.assert_is_fully_defined() filter_shape = graph_util.tensor_shape_from_node_def_name( graph, node.input[1]) filter_shape.assert_is_fully_defined() output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name) output_shape.assert_is_fully_defined() filter_height = int(filter_shape[0]) filter_width = int(filter_shape[1]) filter_in_depth = int(filter_shape[2]) output_count = np.prod(output_shape.as_list(), dtype=np.int64) return ops.OpStats( "flops", (output_count * filter_in_depth * filter_height * filter_width * 2)) @ops.RegisterStatistics("DepthwiseConv2dNative", "flops") def _calc_depthwise_conv_flops(graph, node): input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0]) input_shape.assert_is_fully_defined() filter_shape = graph_util.tensor_shape_from_node_def_name( graph, node.input[1]) filter_shape.assert_is_fully_defined() output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name) output_shape.assert_is_fully_defined() filter_height = int(filter_shape[0]) filter_width = int(filter_shape[1]) output_count = np.prod(output_shape.as_list(), dtype=np.int64) return ops.OpStats("flops", (output_count * filter_height * filter_width * 2)) @ops.RegisterStatistics("BiasAdd", "flops") def _calc_bias_add_flops(graph, node): input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0]) input_shape.assert_is_fully_defined() input_count = np.prod(input_shape.as_list()) return ops.OpStats("flops", input_count) @tf_export(v1=["nn.xw_plus_b"]) def xw_plus_b(x, weights, biases, name=None): with ops.name_scope(name, "xw_plus_b", [x, weights, biases]) as name: x = ops.convert_to_tensor(x, name="x") weights = ops.convert_to_tensor(weights, name="weights") biases = ops.convert_to_tensor(biases, name="biases") mm = math_ops.matmul(x, weights) return bias_add(mm, biases, name=name) def xw_plus_b_v1(x, weights, biases, name=None): with ops.name_scope(name, "xw_plus_b_v1", [x, weights, biases]) as name: x = ops.convert_to_tensor(x, name="x") weights = ops.convert_to_tensor(weights, name="weights") biases = ops.convert_to_tensor(biases, name="biases") mm = math_ops.matmul(x, weights) return bias_add_v1(mm, biases, name=name) def _get_noise_shape(x, noise_shape): if noise_shape is None: return array_ops.shape(x) try: noise_shape_ = tensor_shape.as_shape(noise_shape) except (TypeError, ValueError): return noise_shape if x.shape.dims is not None and len(x.shape.dims) == len(noise_shape_.dims): new_dims = [] for i, dim in enumerate(x.shape.dims): if noise_shape_.dims[i].value is None and dim.value is not None: new_dims.append(dim.value) else: new_dims.append(noise_shape_.dims[i].value) return tensor_shape.TensorShape(new_dims) return noise_shape @tf_export(v1=["nn.dropout"]) @deprecation.deprecated_args(None, "Please use `rate` instead of `keep_prob`. " "Rate should be set to `rate = 1 - keep_prob`.", "keep_prob") def dropout(x, keep_prob=None, noise_shape=None, seed=None, name=None, rate=None): try: keep = 1. - keep_prob if keep_prob is not None else None except TypeError: raise ValueError("keep_prob must be a floating point number or Tensor " "(got %r)" % keep_prob) rate = deprecation.deprecated_argument_lookup( "rate", rate, "keep_prob", keep) if rate is None: raise ValueError("You must provide a rate to dropout.") return dropout_v2(x, rate, noise_shape=noise_shape, seed=seed, name=name) @tf_export("nn.dropout", v1=[]) def dropout_v2(x, rate, noise_shape=None, seed=None, name=None): with ops.name_scope(name, "dropout", [x]) as name: x = ops.convert_to_tensor(x, name="x") if not x.dtype.is_floating: raise ValueError("x has to be a floating point tensor since it's going to" " be scaled. Got a %s tensor instead." % x.dtype) if isinstance(rate, numbers.Real) and not (rate >= 0 and rate < 1): raise ValueError("rate must be a scalar tensor or a float in the " "range [0, 1), got %g" % rate) # Early return if nothing needs to be dropped. if isinstance(rate, numbers.Real) and rate == 0: return x if context.executing_eagerly(): if isinstance(rate, ops.EagerTensor): if rate.numpy() == 0: return x else: rate = ops.convert_to_tensor( rate, dtype=x.dtype, name="rate") rate.get_shape().assert_is_compatible_with(tensor_shape.scalar()) # Do nothing if we know rate == 0 if tensor_util.constant_value(rate) == 0: return x noise_shape = _get_noise_shape(x, noise_shape) keep_prob = 1 - rate # uniform [keep_prob, 1.0 + keep_prob) random_tensor = keep_prob random_tensor += random_ops.random_uniform( noise_shape, seed=seed, dtype=x.dtype) # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob) binary_tensor = math_ops.floor(random_tensor) ret = math_ops.divide(x, keep_prob) * binary_tensor if not context.executing_eagerly(): ret.set_shape(x.get_shape()) return ret @tf_export("math.top_k", "nn.top_k") def top_k(input, k=1, sorted=True, name=None): # pylint: disable=redefined-builtin return gen_nn_ops.top_kv2(input, k=k, sorted=sorted, name=name) def nth_element(input, n, reverse=False, name=None): # pylint: disable=redefined-builtin return gen_nn_ops.nth_element(input, n, reverse=reverse, name=name) @tf_export(v1=["nn.fractional_max_pool"]) @deprecation.deprecated(date=None, instructions="`seed2` and `deterministic` " "args are deprecated. Use fractional_max_pool_v2.") def fractional_max_pool(value, pooling_ratio, pseudo_random=False, overlapping=False, deterministic=False, seed=0, seed2=0, name=None): # pylint: disable=redefined-builtin return gen_nn_ops.fractional_max_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic, seed, seed2, name) @tf_export("nn.fractional_max_pool", v1=[]) def fractional_max_pool_v2(value, pooling_ratio, pseudo_random=False, overlapping=False, seed=0, name=None): # pylint: disable=redefined-builtin if seed == 0: return gen_nn_ops.fractional_max_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic=False, seed=0, seed2=0, name=name) else: seed1, seed2 = random_seed.get_seed(seed) return gen_nn_ops.fractional_max_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic=True, seed=seed1, seed2=seed2, name=name) @tf_export(v1=["nn.fractional_avg_pool"]) @deprecation.deprecated(date=None, instructions="`seed2` and `deterministic` " "args are deprecated. Use fractional_avg_pool_v2.") def fractional_avg_pool(value, pooling_ratio, pseudo_random=False, overlapping=False, deterministic=False, seed=0, seed2=0, name=None): # pylint: disable=redefined-builtin return gen_nn_ops.fractional_avg_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic, seed, seed2, name=name) @tf_export("nn.fractional_avg_pool", v1=[]) def fractional_avg_pool_v2(value, pooling_ratio, pseudo_random=False, overlapping=False, seed=0, name=None): # pylint: disable=redefined-builtin if seed == 0: return gen_nn_ops.fractional_avg_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic=False, seed=0, seed2=0, name=name) else: seed1, seed2 = random_seed.get_seed(seed) return gen_nn_ops.fractional_avg_pool(value, pooling_ratio, pseudo_random, overlapping, deterministic=True, seed=seed1, seed2=seed2, name=name) @tf_export(v1=["nn.conv1d"]) @deprecation.deprecated_arg_values( None, "`NCHW` for data_format is deprecated, use `NCW` instead", warn_once=True, data_format="NCHW") @deprecation.deprecated_arg_values( None, "`NHWC` for data_format is deprecated, use `NWC` instead", warn_once=True, data_format="NHWC") def conv1d(value, filters, stride, padding, use_cudnn_on_gpu=None, data_format=None, name=None): with ops.name_scope(name, "conv1d", [value, filters]) as name: # Reshape the input tensor to [batch, 1, in_width, in_channels] if data_format is None or data_format == "NHWC" or data_format == "NWC": data_format = "NHWC" spatial_start_dim = 1 strides = [1, 1, stride, 1] elif data_format == "NCHW" or data_format == "NCW": data_format = "NCHW" spatial_start_dim = 2 strides = [1, 1, 1, stride] else: raise ValueError("data_format must be \"NWC\" or \"NCW\".") value = array_ops.expand_dims(value, spatial_start_dim) filters = array_ops.expand_dims(filters, 0) result = gen_nn_ops.conv2d( value, filters, strides, padding, use_cudnn_on_gpu=use_cudnn_on_gpu, data_format=data_format) return array_ops.squeeze(result, [spatial_start_dim]) @tf_export("nn.conv1d", v1=[]) def conv1d_v2(input, # pylint: disable=redefined-builtin filters, stride, padding, data_format=None, name=None): return conv1d(input, # pylint: disable=redefined-builtin filters, stride, padding, use_cudnn_on_gpu=True, data_format=data_format, name=name) def conv1d_transpose( value, filter, # pylint: disable=redefined-builtin output_shape, stride, padding="SAME", data_format="NWC", name=None): with ops.name_scope(name, "conv1d_transpose", [value, filter, output_shape]) as name: output_shape_ = ops.convert_to_tensor(output_shape, name="output_shape") if not output_shape_.get_shape().is_compatible_with(tensor_shape.vector(3)): raise ValueError("output_shape must have shape (3,), got {}".format( output_shape_.get_shape())) # The format could be either NWC or NCW, map to NHWC or NCHW if data_format is None or data_format == "NWC": data_format_2d = "NHWC" axis = 2 elif data_format == "NCW": data_format_2d = "NCHW" axis = 1 else: raise ValueError("data_format must be \"NWC\" or \"NCW\".") if not value.get_shape().dims[axis].is_compatible_with( filter.get_shape()[2]): raise ValueError("input channels does not match filter's input channels, " "{} != {}".format(value.get_shape()[axis], filter.get_shape()[2])) if isinstance(output_shape, (list, np.ndarray)): if not filter.get_shape().dims[1].is_compatible_with( output_shape[axis]): raise ValueError( "output_shape does not match filter's output channels, " "{} != {}".format(output_shape[axis], filter.get_shape()[1])) if padding != "VALID" and padding != "SAME": raise ValueError("padding must be either VALID or SAME:" " {}".format(padding)) if data_format_2d == "NHWC": output_shape_ = array_ops.concat( [output_shape_[:1], [1], output_shape_[1:]], axis=0) spatial_start_dim = 1 strides = [1, 1, stride, 1] else: output_shape_ = array_ops.concat( [output_shape_[:2], [1], output_shape_[2:]], axis=0) spatial_start_dim = 2 strides = [1, 1, 1, stride] value = array_ops.expand_dims(value, spatial_start_dim) filter = array_ops.expand_dims(filter, 0) result = gen_nn_ops.conv2d_backprop_input( input_sizes=output_shape_, filter=filter, out_backprop=value, strides=strides, padding=padding, data_format=data_format_2d, name=name) return array_ops.squeeze(result, [spatial_start_dim]) @ops.RegisterStatistics("Dilation2D", "flops") def _calc_dilation2d_flops(graph, node): input_shape = graph_util.tensor_shape_from_node_def_name(graph, node.input[0]) input_shape.assert_is_fully_defined() filter_shape = graph_util.tensor_shape_from_node_def_name( graph, node.input[1]) filter_shape.assert_is_fully_defined() output_shape = graph_util.tensor_shape_from_node_def_name(graph, node.name) output_shape.assert_is_fully_defined() filter_height = int(filter_shape[0]) filter_width = int(filter_shape[1]) output_count = np.prod(output_shape.as_list(), dtype=np.int64) return ops.OpStats("flops", (output_count * filter_height * filter_width * 2)) @tf_export(v1=["nn.erosion2d"]) def erosion2d(value, kernel, strides, rates, padding, name=None): with ops.name_scope(name, "erosion2d", [value, kernel]) as name: return math_ops.negative( gen_nn_ops.dilation2d( input=math_ops.negative(value), filter=array_ops.reverse_v2(kernel, [0, 1]), strides=strides, rates=rates, padding=padding, name=name)) @tf_export("nn.erosion2d", v1=[]) def erosion2d_v2(value, filters, strides, padding, data_format, dilations, name=None): if data_format != "NHWC": raise ValueError("Data formats other than NHWC are not yet supported") with ops.name_scope(name, "erosion2d", [value, filters]) as name: return math_ops.negative( gen_nn_ops.dilation2d( input=math_ops.negative(value), filter=array_ops.reverse_v2(filters, [0, 1]), strides=strides, rates=dilations, padding=padding, name=name)) @tf_export(v1=["math.in_top_k", "nn.in_top_k"]) def in_top_k(predictions, targets, k, name=None): with ops.name_scope(name, "in_top_k"): return gen_nn_ops.in_top_kv2(predictions, targets, k, name=name) @tf_export("math.in_top_k", "nn.in_top_k", v1=[]) def in_top_k_v2(targets, predictions, k, name=None): return in_top_k(predictions, targets, k, name) in_top_k_v2.__doc__ = in_top_k.__doc__ tf_export(v1=["nn.quantized_avg_pool"])(gen_nn_ops.quantized_avg_pool) tf_export(v1=["nn.quantized_conv2d"])(gen_nn_ops.quantized_conv2d) tf_export(v1=["nn.quantized_relu_x"])(gen_nn_ops.quantized_relu_x) tf_export(v1=["nn.quantized_max_pool"])(gen_nn_ops.quantized_max_pool)

true

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Python

alipay/aop/api/request/ZhimaCreditEpProductCodeQueryRequest.py

snowxmas/alipay-sdk-python-all

96870ced60facd96c5bce18d19371720cbda3317

[ "Apache-2.0" ]

213

2018-08-27T16:49:32.000Z

2021-12-29T04:34:12.000Z

alipay/aop/api/request/ZhimaCreditEpProductCodeQueryRequest.py

snowxmas/alipay-sdk-python-all

96870ced60facd96c5bce18d19371720cbda3317

[ "Apache-2.0" ]

29

2018-09-29T06:43:00.000Z

2021-09-02T03:27:32.000Z

alipay/aop/api/request/ZhimaCreditEpProductCodeQueryRequest.py

snowxmas/alipay-sdk-python-all

96870ced60facd96c5bce18d19371720cbda3317

[ "Apache-2.0" ]

59

2018-08-27T16:59:26.000Z

2022-03-25T10:08:15.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.FileItem import FileItem from alipay.aop.api.constant.ParamConstants import * from alipay.aop.api.domain.ZhimaCreditEpProductCodeQueryModel import ZhimaCreditEpProductCodeQueryModel class ZhimaCreditEpProductCodeQueryRequest(object): def __init__(self, biz_model=None): self._biz_model = biz_model self._biz_content = 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 biz_content(self): return self._biz_content @biz_content.setter def biz_content(self, value): if isinstance(value, ZhimaCreditEpProductCodeQueryModel): self._biz_content = value else: self._biz_content = ZhimaCreditEpProductCodeQueryModel.from_alipay_dict(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] = 'zhima.credit.ep.product.code.query' params[P_VERSION] = self.version if self.biz_model: params[P_BIZ_CONTENT] = json.dumps(obj=self.biz_model.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':')) if self.biz_content: if hasattr(self.biz_content, 'to_alipay_dict'): params['biz_content'] = json.dumps(obj=self.biz_content.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['biz_content'] = self.biz_content 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() return multipart_params

27.406897

148

0.64469

import json from alipay.aop.api.FileItem import FileItem from alipay.aop.api.constant.ParamConstants import * from alipay.aop.api.domain.ZhimaCreditEpProductCodeQueryModel import ZhimaCreditEpProductCodeQueryModel class ZhimaCreditEpProductCodeQueryRequest(object): def __init__(self, biz_model=None): self._biz_model = biz_model self._biz_content = 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 biz_content(self): return self._biz_content @biz_content.setter def biz_content(self, value): if isinstance(value, ZhimaCreditEpProductCodeQueryModel): self._biz_content = value else: self._biz_content = ZhimaCreditEpProductCodeQueryModel.from_alipay_dict(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] = 'zhima.credit.ep.product.code.query' params[P_VERSION] = self.version if self.biz_model: params[P_BIZ_CONTENT] = json.dumps(obj=self.biz_model.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':')) if self.biz_content: if hasattr(self.biz_content, 'to_alipay_dict'): params['biz_content'] = json.dumps(obj=self.biz_content.to_alipay_dict(), ensure_ascii=False, sort_keys=True, separators=(',', ':')) else: params['biz_content'] = self.biz_content 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() return multipart_params

true

f71fd0d406c540386851f557a8dd36be11fad94d

37,269

py

Python

pandas/core/indexes/datetimes.py

cgangwar11/pandas

972f491cb7fdcc3c1c2cb9f05644128f13457f87

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

1

2020-09-05T07:09:39.000Z

pandas/core/indexes/datetimes.py

cgangwar11/pandas

972f491cb7fdcc3c1c2cb9f05644128f13457f87

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

null

pandas/core/indexes/datetimes.py

cgangwar11/pandas

972f491cb7fdcc3c1c2cb9f05644128f13457f87

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

null

from datetime import date, datetime, time, timedelta, tzinfo import operator from typing import Optional import warnings import numpy as np from pandas._libs import NaT, Period, Timestamp, index as libindex, lib from pandas._libs.tslibs import ( Resolution, ints_to_pydatetime, parsing, timezones, to_offset, ) from pandas._libs.tslibs.offsets import prefix_mapping from pandas._typing import DtypeObj, Label from pandas.errors import InvalidIndexError from pandas.util._decorators import cache_readonly, doc from pandas.core.dtypes.common import ( DT64NS_DTYPE, is_datetime64_any_dtype, is_datetime64_dtype, is_datetime64tz_dtype, is_float, is_integer, is_scalar, ) from pandas.core.dtypes.missing import is_valid_nat_for_dtype from pandas.core.arrays.datetimes import DatetimeArray, tz_to_dtype import pandas.core.common as com from pandas.core.indexes.base import Index, maybe_extract_name from pandas.core.indexes.datetimelike import DatetimeTimedeltaMixin from pandas.core.indexes.extension import inherit_names from pandas.core.tools.times import to_time def _new_DatetimeIndex(cls, d): """ This is called upon unpickling, rather than the default which doesn't have arguments and breaks __new__ """ if "data" in d and not isinstance(d["data"], DatetimeIndex): # Avoid need to verify integrity by calling simple_new directly data = d.pop("data") if not isinstance(data, DatetimeArray): # For backward compat with older pickles, we may need to construct # a DatetimeArray to adapt to the newer _simple_new signature tz = d.pop("tz") freq = d.pop("freq") dta = DatetimeArray._simple_new(data, dtype=tz_to_dtype(tz), freq=freq) else: dta = data for key in ["tz", "freq"]: # These are already stored in our DatetimeArray; if they are # also in the pickle and don't match, we have a problem. if key in d: assert d.pop(key) == getattr(dta, key) result = cls._simple_new(dta, **d) else: with warnings.catch_warnings(): # TODO: If we knew what was going in to **d, we might be able to # go through _simple_new instead warnings.simplefilter("ignore") result = cls.__new__(cls, **d) return result @inherit_names( ["to_perioddelta", "to_julian_date", "strftime", "isocalendar"] + DatetimeArray._field_ops + [ method for method in DatetimeArray._datetimelike_methods if method not in ("tz_localize",) ], DatetimeArray, wrap=True, ) @inherit_names(["is_normalized", "_resolution_obj"], DatetimeArray, cache=True) @inherit_names( [ "_bool_ops", "_object_ops", "_field_ops", "_datetimelike_ops", "_datetimelike_methods", "tz", "tzinfo", "dtype", "to_pydatetime", "_has_same_tz", "_format_native_types", "date", "time", "timetz", ] + DatetimeArray._bool_ops, DatetimeArray, ) class DatetimeIndex(DatetimeTimedeltaMixin): """ Immutable ndarray-like of datetime64 data. Represented internally as int64, and which can be boxed to Timestamp objects that are subclasses of datetime and carry metadata. Parameters ---------- data : array-like (1-dimensional), optional Optional datetime-like data to construct index with. freq : str or pandas offset object, optional One of pandas date offset strings or corresponding objects. The string 'infer' can be passed in order to set the frequency of the index as the inferred frequency upon creation. tz : pytz.timezone or dateutil.tz.tzfile or datetime.tzinfo or str Set the Timezone of the data. normalize : bool, default False Normalize start/end dates to midnight before generating date range. closed : {'left', 'right'}, optional Set whether to include `start` and `end` that are on the boundary. The default includes boundary points on either end. ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise' When clocks moved backward due to DST, ambiguous times may arise. For example in Central European Time (UTC+01), when going from 03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at 00:30:00 UTC and at 01:30:00 UTC. In such a situation, the `ambiguous` parameter dictates how ambiguous times should be handled. - 'infer' will attempt to infer fall dst-transition hours based on order - bool-ndarray where True signifies a DST time, False signifies a non-DST time (note that this flag is only applicable for ambiguous times) - 'NaT' will return NaT where there are ambiguous times - 'raise' will raise an AmbiguousTimeError if there are ambiguous times. dayfirst : bool, default False If True, parse dates in `data` with the day first order. yearfirst : bool, default False If True parse dates in `data` with the year first order. dtype : numpy.dtype or DatetimeTZDtype or str, default None Note that the only NumPy dtype allowed is ‘datetime64[ns]’. copy : bool, default False Make a copy of input ndarray. name : label, default None Name to be stored in the index. Attributes ---------- year month day hour minute second microsecond nanosecond date time timetz dayofyear weekofyear week dayofweek weekday quarter tz freq freqstr is_month_start is_month_end is_quarter_start is_quarter_end is_year_start is_year_end is_leap_year inferred_freq Methods ------- normalize strftime snap tz_convert tz_localize round floor ceil to_period to_perioddelta to_pydatetime to_series to_frame month_name day_name mean See Also -------- Index : The base pandas Index type. TimedeltaIndex : Index of timedelta64 data. PeriodIndex : Index of Period data. to_datetime : Convert argument to datetime. date_range : Create a fixed-frequency DatetimeIndex. Notes ----- To learn more about the frequency strings, please see `this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__. """ _typ = "datetimeindex" _engine_type = libindex.DatetimeEngine _supports_partial_string_indexing = True _comparables = ["name", "freqstr", "tz"] _attributes = ["name", "tz", "freq"] _is_numeric_dtype = False _data: DatetimeArray tz: Optional[tzinfo] # -------------------------------------------------------------------- # methods that dispatch to array and wrap result in DatetimeIndex @doc(DatetimeArray.tz_localize) def tz_localize( self, tz, ambiguous="raise", nonexistent="raise" ) -> "DatetimeIndex": arr = self._data.tz_localize(tz, ambiguous, nonexistent) return type(self)._simple_new(arr, name=self.name) @doc(DatetimeArray.to_period) def to_period(self, freq=None) -> "DatetimeIndex": arr = self._data.to_period(freq) return type(self)._simple_new(arr, name=self.name) # -------------------------------------------------------------------- # Constructors def __new__( cls, data=None, freq=lib.no_default, tz=None, normalize=False, closed=None, ambiguous="raise", dayfirst=False, yearfirst=False, dtype=None, copy=False, name=None, ): if is_scalar(data): raise TypeError( f"{cls.__name__}() must be called with a " f"collection of some kind, {repr(data)} was passed" ) # - Cases checked above all return/raise before reaching here - # name = maybe_extract_name(name, data, cls) dtarr = DatetimeArray._from_sequence( data, dtype=dtype, copy=copy, tz=tz, freq=freq, dayfirst=dayfirst, yearfirst=yearfirst, ambiguous=ambiguous, ) subarr = cls._simple_new(dtarr, name=name) return subarr @classmethod def _simple_new(cls, values: DatetimeArray, name: Label = None): assert isinstance(values, DatetimeArray), type(values) result = object.__new__(cls) result._data = values result.name = name result._cache = {} result._no_setting_name = False # For groupby perf. See note in indexes/base about _index_data result._index_data = values._data result._reset_identity() return result # -------------------------------------------------------------------- @cache_readonly def _is_dates_only(self) -> bool: """ Return a boolean if we are only dates (and don't have a timezone) Returns ------- bool """ from pandas.io.formats.format import _is_dates_only return self.tz is None and _is_dates_only(self._values) def __reduce__(self): # we use a special reduce here because we need # to simply set the .tz (and not reinterpret it) d = dict(data=self._data) d.update(self._get_attributes_dict()) return _new_DatetimeIndex, (type(self), d), None def _convert_for_op(self, value): """ Convert value to be insertable to ndarray. """ if self._has_same_tz(value): return Timestamp(value).asm8 raise ValueError("Passed item and index have different timezone") def _is_comparable_dtype(self, dtype: DtypeObj) -> bool: """ Can we compare values of the given dtype to our own? """ if not is_datetime64_any_dtype(dtype): return False if self.tz is not None: # If we have tz, we can compare to tzaware return is_datetime64tz_dtype(dtype) # if we dont have tz, we can only compare to tznaive return is_datetime64_dtype(dtype) # -------------------------------------------------------------------- # Rendering Methods def _mpl_repr(self): # how to represent ourselves to matplotlib return ints_to_pydatetime(self.asi8, self.tz) @property def _formatter_func(self): from pandas.io.formats.format import _get_format_datetime64 formatter = _get_format_datetime64(is_dates_only=self._is_dates_only) return lambda x: f"'{formatter(x, tz=self.tz)}'" # -------------------------------------------------------------------- # Set Operation Methods def union_many(self, others): """ A bit of a hack to accelerate unioning a collection of indexes. """ this = self for other in others: if not isinstance(this, DatetimeIndex): this = Index.union(this, other) continue if not isinstance(other, DatetimeIndex): try: other = DatetimeIndex(other) except TypeError: pass this, other = this._maybe_utc_convert(other) if this._can_fast_union(other): this = this._fast_union(other) else: this = Index.union(this, other) return this # -------------------------------------------------------------------- def _get_time_micros(self): """ Return the number of microseconds since midnight. Returns ------- ndarray[int64_t] """ values = self.asi8 if self.tz is not None and not timezones.is_utc(self.tz): values = self._data._local_timestamps() nanos = values % (24 * 3600 * 1_000_000_000) micros = nanos // 1000 micros[self._isnan] = -1 return micros def to_series(self, keep_tz=lib.no_default, index=None, name=None): """ Create a Series with both index and values equal to the index keys useful with map for returning an indexer based on an index. Parameters ---------- keep_tz : optional, defaults True Return the data keeping the timezone. If keep_tz is True: If the timezone is not set, the resulting Series will have a datetime64[ns] dtype. Otherwise the Series will have an datetime64[ns, tz] dtype; the tz will be preserved. If keep_tz is False: Series will have a datetime64[ns] dtype. TZ aware objects will have the tz removed. .. versionchanged:: 1.0.0 The default value is now True. In a future version, this keyword will be removed entirely. Stop passing the argument to obtain the future behavior and silence the warning. index : Index, optional Index of resulting Series. If None, defaults to original index. name : str, optional Name of resulting Series. If None, defaults to name of original index. Returns ------- Series """ from pandas import Series if index is None: index = self._shallow_copy() if name is None: name = self.name if keep_tz is not lib.no_default: if keep_tz: warnings.warn( "The 'keep_tz' keyword in DatetimeIndex.to_series " "is deprecated and will be removed in a future version. " "You can stop passing 'keep_tz' to silence this warning.", FutureWarning, stacklevel=2, ) else: warnings.warn( "Specifying 'keep_tz=False' is deprecated and this " "option will be removed in a future release. If " "you want to remove the timezone information, you " "can do 'idx.tz_convert(None)' before calling " "'to_series'.", FutureWarning, stacklevel=2, ) else: keep_tz = True if keep_tz and self.tz is not None: # preserve the tz & copy values = self.copy(deep=True) else: values = self._values.view("M8[ns]").copy() return Series(values, index=index, name=name) def snap(self, freq="S"): """ Snap time stamps to nearest occurring frequency. Returns ------- DatetimeIndex """ # Superdumb, punting on any optimizing freq = to_offset(freq) snapped = np.empty(len(self), dtype=DT64NS_DTYPE) for i, v in enumerate(self): s = v if not freq.is_on_offset(s): t0 = freq.rollback(s) t1 = freq.rollforward(s) if abs(s - t0) < abs(t1 - s): s = t0 else: s = t1 snapped[i] = s dta = DatetimeArray(snapped, dtype=self.dtype) return DatetimeIndex._simple_new(dta, name=self.name) def _parsed_string_to_bounds(self, reso: Resolution, parsed: datetime): """ Calculate datetime bounds for parsed time string and its resolution. Parameters ---------- reso : str Resolution provided by parsed string. parsed : datetime Datetime from parsed string. Returns ------- lower, upper: pd.Timestamp """ assert isinstance(reso, Resolution), (type(reso), reso) valid_resos = { "year", "month", "quarter", "day", "hour", "minute", "second", "minute", "second", "microsecond", } if reso.attrname not in valid_resos: raise KeyError grp = reso.freq_group per = Period(parsed, freq=grp) start, end = per.start_time, per.end_time # GH 24076 # If an incoming date string contained a UTC offset, need to localize # the parsed date to this offset first before aligning with the index's # timezone if parsed.tzinfo is not None: if self.tz is None: raise ValueError( "The index must be timezone aware when indexing " "with a date string with a UTC offset" ) start = start.tz_localize(parsed.tzinfo).tz_convert(self.tz) end = end.tz_localize(parsed.tzinfo).tz_convert(self.tz) elif self.tz is not None: start = start.tz_localize(self.tz) end = end.tz_localize(self.tz) return start, end def _validate_partial_date_slice(self, reso: Resolution): assert isinstance(reso, Resolution), (type(reso), reso) if ( self.is_monotonic and reso.attrname in ["day", "hour", "minute", "second"] and self._resolution_obj >= reso ): # These resolution/monotonicity validations came from GH3931, # GH3452 and GH2369. # See also GH14826 raise KeyError if reso == "microsecond": # _partial_date_slice doesn't allow microsecond resolution, but # _parsed_string_to_bounds allows it. raise KeyError def get_loc(self, key, method=None, tolerance=None): """ Get integer location for requested label Returns ------- loc : int """ if not is_scalar(key): raise InvalidIndexError(key) orig_key = key if is_valid_nat_for_dtype(key, self.dtype): key = NaT if isinstance(key, self._data._recognized_scalars): # needed to localize naive datetimes key = self._maybe_cast_for_get_loc(key) elif isinstance(key, str): try: return self._get_string_slice(key) except (TypeError, KeyError, ValueError, OverflowError): pass try: key = self._maybe_cast_for_get_loc(key) except ValueError as err: raise KeyError(key) from err elif isinstance(key, timedelta): # GH#20464 raise TypeError( f"Cannot index {type(self).__name__} with {type(key).__name__}" ) elif isinstance(key, time): if method is not None: raise NotImplementedError( "cannot yet lookup inexact labels when key is a time object" ) return self.indexer_at_time(key) else: # unrecognized type raise KeyError(key) try: return Index.get_loc(self, key, method, tolerance) except KeyError as err: raise KeyError(orig_key) from err def _maybe_cast_for_get_loc(self, key) -> Timestamp: # needed to localize naive datetimes key = Timestamp(key) if key.tzinfo is None: key = key.tz_localize(self.tz) else: key = key.tz_convert(self.tz) return key def _maybe_cast_slice_bound(self, label, side: str, kind): """ If label is a string, cast it to datetime according to resolution. Parameters ---------- label : object side : {'left', 'right'} kind : {'loc', 'getitem'} or None Returns ------- label : object Notes ----- Value of `side` parameter should be validated in caller. """ assert kind in ["loc", "getitem", None] if is_float(label) or isinstance(label, time) or is_integer(label): self._invalid_indexer("slice", label) if isinstance(label, str): freq = getattr(self, "freqstr", getattr(self, "inferred_freq", None)) parsed, reso = parsing.parse_time_string(label, freq) reso = Resolution.from_attrname(reso) lower, upper = self._parsed_string_to_bounds(reso, parsed) # lower, upper form the half-open interval: # [parsed, parsed + 1 freq) # because label may be passed to searchsorted # the bounds need swapped if index is reverse sorted and has a # length > 1 (is_monotonic_decreasing gives True for empty # and length 1 index) if self._is_strictly_monotonic_decreasing and len(self) > 1: return upper if side == "left" else lower return lower if side == "left" else upper else: return label def _get_string_slice(self, key: str, use_lhs: bool = True, use_rhs: bool = True): freq = getattr(self, "freqstr", getattr(self, "inferred_freq", None)) parsed, reso = parsing.parse_time_string(key, freq) reso = Resolution.from_attrname(reso) loc = self._partial_date_slice(reso, parsed, use_lhs=use_lhs, use_rhs=use_rhs) return loc def slice_indexer(self, start=None, end=None, step=None, kind=None): """ Return indexer for specified label slice. Index.slice_indexer, customized to handle time slicing. In addition to functionality provided by Index.slice_indexer, does the following: - if both `start` and `end` are instances of `datetime.time`, it invokes `indexer_between_time` - if `start` and `end` are both either string or None perform value-based selection in non-monotonic cases. """ # For historical reasons DatetimeIndex supports slices between two # instances of datetime.time as if it were applying a slice mask to # an array of (self.hour, self.minute, self.seconds, self.microsecond). if isinstance(start, time) and isinstance(end, time): if step is not None and step != 1: raise ValueError("Must have step size of 1 with time slices") return self.indexer_between_time(start, end) if isinstance(start, time) or isinstance(end, time): raise KeyError("Cannot mix time and non-time slice keys") # Pandas supports slicing with dates, treated as datetimes at midnight. # https://github.com/pandas-dev/pandas/issues/31501 if isinstance(start, date) and not isinstance(start, datetime): start = datetime.combine(start, time(0, 0)) if isinstance(end, date) and not isinstance(end, datetime): end = datetime.combine(end, time(0, 0)) try: return Index.slice_indexer(self, start, end, step, kind=kind) except KeyError: # For historical reasons DatetimeIndex by default supports # value-based partial (aka string) slices on non-monotonic arrays, # let's try that. if (start is None or isinstance(start, str)) and ( end is None or isinstance(end, str) ): mask = True if start is not None: start_casted = self._maybe_cast_slice_bound(start, "left", kind) mask = start_casted <= self if end is not None: end_casted = self._maybe_cast_slice_bound(end, "right", kind) mask = (self <= end_casted) & mask indexer = mask.nonzero()[0][::step] if len(indexer) == len(self): return slice(None) else: return indexer else: raise # -------------------------------------------------------------------- def is_type_compatible(self, typ) -> bool: return typ == self.inferred_type or typ == "datetime" @property def inferred_type(self) -> str: # b/c datetime is represented as microseconds since the epoch, make # sure we can't have ambiguous indexing return "datetime64" def indexer_at_time(self, time, asof=False): """ Return index locations of values at particular time of day (e.g. 9:30AM). Parameters ---------- time : datetime.time or str Time passed in either as object (datetime.time) or as string in appropriate format ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p", "%I%M%S%p"). Returns ------- values_at_time : array of integers See Also -------- indexer_between_time : Get index locations of values between particular times of day. DataFrame.at_time : Select values at particular time of day. """ if asof: raise NotImplementedError("'asof' argument is not supported") if isinstance(time, str): from dateutil.parser import parse time = parse(time).time() if time.tzinfo: if self.tz is None: raise ValueError("Index must be timezone aware.") time_micros = self.tz_convert(time.tzinfo)._get_time_micros() else: time_micros = self._get_time_micros() micros = _time_to_micros(time) return (micros == time_micros).nonzero()[0] def indexer_between_time( self, start_time, end_time, include_start=True, include_end=True ): """ Return index locations of values between particular times of day (e.g., 9:00-9:30AM). Parameters ---------- start_time, end_time : datetime.time, str Time passed either as object (datetime.time) or as string in appropriate format ("%H:%M", "%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p","%I%M%S%p"). include_start : bool, default True include_end : bool, default True Returns ------- values_between_time : array of integers See Also -------- indexer_at_time : Get index locations of values at particular time of day. DataFrame.between_time : Select values between particular times of day. """ start_time = to_time(start_time) end_time = to_time(end_time) time_micros = self._get_time_micros() start_micros = _time_to_micros(start_time) end_micros = _time_to_micros(end_time) if include_start and include_end: lop = rop = operator.le elif include_start: lop = operator.le rop = operator.lt elif include_end: lop = operator.lt rop = operator.le else: lop = rop = operator.lt if start_time <= end_time: join_op = operator.and_ else: join_op = operator.or_ mask = join_op(lop(start_micros, time_micros), rop(time_micros, end_micros)) return mask.nonzero()[0] DatetimeIndex._add_logical_methods_disabled() def date_range( start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, **kwargs, ) -> DatetimeIndex: """ Return a fixed frequency DatetimeIndex. Parameters ---------- start : str or datetime-like, optional Left bound for generating dates. end : str or datetime-like, optional Right bound for generating dates. periods : int, optional Number of periods to generate. freq : str or DateOffset, default 'D' Frequency strings can have multiples, e.g. '5H'. See :ref:`here <timeseries.offset_aliases>` for a list of frequency aliases. tz : str or tzinfo, optional Time zone name for returning localized DatetimeIndex, for example 'Asia/Hong_Kong'. By default, the resulting DatetimeIndex is timezone-naive. normalize : bool, default False Normalize start/end dates to midnight before generating date range. name : str, default None Name of the resulting DatetimeIndex. closed : {None, 'left', 'right'}, optional Make the interval closed with respect to the given frequency to the 'left', 'right', or both sides (None, the default). **kwargs For compatibility. Has no effect on the result. Returns ------- rng : DatetimeIndex See Also -------- DatetimeIndex : An immutable container for datetimes. timedelta_range : Return a fixed frequency TimedeltaIndex. period_range : Return a fixed frequency PeriodIndex. interval_range : Return a fixed frequency IntervalIndex. Notes ----- Of the four parameters ``start``, ``end``, ``periods``, and ``freq``, exactly three must be specified. If ``freq`` is omitted, the resulting ``DatetimeIndex`` will have ``periods`` linearly spaced elements between ``start`` and ``end`` (closed on both sides). To learn more about the frequency strings, please see `this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__. Examples -------- **Specifying the values** The next four examples generate the same `DatetimeIndex`, but vary the combination of `start`, `end` and `periods`. Specify `start` and `end`, with the default daily frequency. >>> pd.date_range(start='1/1/2018', end='1/08/2018') DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'], dtype='datetime64[ns]', freq='D') Specify `start` and `periods`, the number of periods (days). >>> pd.date_range(start='1/1/2018', periods=8) DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-06', '2018-01-07', '2018-01-08'], dtype='datetime64[ns]', freq='D') Specify `end` and `periods`, the number of periods (days). >>> pd.date_range(end='1/1/2018', periods=8) DatetimeIndex(['2017-12-25', '2017-12-26', '2017-12-27', '2017-12-28', '2017-12-29', '2017-12-30', '2017-12-31', '2018-01-01'], dtype='datetime64[ns]', freq='D') Specify `start`, `end`, and `periods`; the frequency is generated automatically (linearly spaced). >>> pd.date_range(start='2018-04-24', end='2018-04-27', periods=3) DatetimeIndex(['2018-04-24 00:00:00', '2018-04-25 12:00:00', '2018-04-27 00:00:00'], dtype='datetime64[ns]', freq=None) **Other Parameters** Changed the `freq` (frequency) to ``'M'`` (month end frequency). >>> pd.date_range(start='1/1/2018', periods=5, freq='M') DatetimeIndex(['2018-01-31', '2018-02-28', '2018-03-31', '2018-04-30', '2018-05-31'], dtype='datetime64[ns]', freq='M') Multiples are allowed >>> pd.date_range(start='1/1/2018', periods=5, freq='3M') DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31', '2019-01-31'], dtype='datetime64[ns]', freq='3M') `freq` can also be specified as an Offset object. >>> pd.date_range(start='1/1/2018', periods=5, freq=pd.offsets.MonthEnd(3)) DatetimeIndex(['2018-01-31', '2018-04-30', '2018-07-31', '2018-10-31', '2019-01-31'], dtype='datetime64[ns]', freq='3M') Specify `tz` to set the timezone. >>> pd.date_range(start='1/1/2018', periods=5, tz='Asia/Tokyo') DatetimeIndex(['2018-01-01 00:00:00+09:00', '2018-01-02 00:00:00+09:00', '2018-01-03 00:00:00+09:00', '2018-01-04 00:00:00+09:00', '2018-01-05 00:00:00+09:00'], dtype='datetime64[ns, Asia/Tokyo]', freq='D') `closed` controls whether to include `start` and `end` that are on the boundary. The default includes boundary points on either end. >>> pd.date_range(start='2017-01-01', end='2017-01-04', closed=None) DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D') Use ``closed='left'`` to exclude `end` if it falls on the boundary. >>> pd.date_range(start='2017-01-01', end='2017-01-04', closed='left') DatetimeIndex(['2017-01-01', '2017-01-02', '2017-01-03'], dtype='datetime64[ns]', freq='D') Use ``closed='right'`` to exclude `start` if it falls on the boundary. >>> pd.date_range(start='2017-01-01', end='2017-01-04', closed='right') DatetimeIndex(['2017-01-02', '2017-01-03', '2017-01-04'], dtype='datetime64[ns]', freq='D') """ if freq is None and com.any_none(periods, start, end): freq = "D" dtarr = DatetimeArray._generate_range( start=start, end=end, periods=periods, freq=freq, tz=tz, normalize=normalize, closed=closed, **kwargs, ) return DatetimeIndex._simple_new(dtarr, name=name) def bdate_range( start=None, end=None, periods=None, freq="B", tz=None, normalize=True, name=None, weekmask=None, holidays=None, closed=None, **kwargs, ) -> DatetimeIndex: """ Return a fixed frequency DatetimeIndex, with business day as the default frequency. Parameters ---------- start : str or datetime-like, default None Left bound for generating dates. end : str or datetime-like, default None Right bound for generating dates. periods : int, default None Number of periods to generate. freq : str or DateOffset, default 'B' (business daily) Frequency strings can have multiples, e.g. '5H'. tz : str or None Time zone name for returning localized DatetimeIndex, for example Asia/Beijing. normalize : bool, default False Normalize start/end dates to midnight before generating date range. name : str, default None Name of the resulting DatetimeIndex. weekmask : str or None, default None Weekmask of valid business days, passed to ``numpy.busdaycalendar``, only used when custom frequency strings are passed. The default value None is equivalent to 'Mon Tue Wed Thu Fri'. holidays : list-like or None, default None Dates to exclude from the set of valid business days, passed to ``numpy.busdaycalendar``, only used when custom frequency strings are passed. closed : str, default None Make the interval closed with respect to the given frequency to the 'left', 'right', or both sides (None). **kwargs For compatibility. Has no effect on the result. Returns ------- DatetimeIndex Notes ----- Of the four parameters: ``start``, ``end``, ``periods``, and ``freq``, exactly three must be specified. Specifying ``freq`` is a requirement for ``bdate_range``. Use ``date_range`` if specifying ``freq`` is not desired. To learn more about the frequency strings, please see `this link <https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__. Examples -------- Note how the two weekend days are skipped in the result. >>> pd.bdate_range(start='1/1/2018', end='1/08/2018') DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05', '2018-01-08'], dtype='datetime64[ns]', freq='B') """ if freq is None: msg = "freq must be specified for bdate_range; use date_range instead" raise TypeError(msg) if isinstance(freq, str) and freq.startswith("C"): try: weekmask = weekmask or "Mon Tue Wed Thu Fri" freq = prefix_mapping[freq](holidays=holidays, weekmask=weekmask) except (KeyError, TypeError) as err: msg = f"invalid custom frequency string: {freq}" raise ValueError(msg) from err elif holidays or weekmask: msg = ( "a custom frequency string is required when holidays or " f"weekmask are passed, got frequency {freq}" ) raise ValueError(msg) return date_range( start=start, end=end, periods=periods, freq=freq, tz=tz, normalize=normalize, name=name, closed=closed, **kwargs, ) def _time_to_micros(time_obj: time) -> int: seconds = time_obj.hour * 60 * 60 + 60 * time_obj.minute + time_obj.second return 1_000_000 * seconds + time_obj.microsecond

33.395161

96

0.584937

from datetime import date, datetime, time, timedelta, tzinfo import operator from typing import Optional import warnings import numpy as np from pandas._libs import NaT, Period, Timestamp, index as libindex, lib from pandas._libs.tslibs import ( Resolution, ints_to_pydatetime, parsing, timezones, to_offset, ) from pandas._libs.tslibs.offsets import prefix_mapping from pandas._typing import DtypeObj, Label from pandas.errors import InvalidIndexError from pandas.util._decorators import cache_readonly, doc from pandas.core.dtypes.common import ( DT64NS_DTYPE, is_datetime64_any_dtype, is_datetime64_dtype, is_datetime64tz_dtype, is_float, is_integer, is_scalar, ) from pandas.core.dtypes.missing import is_valid_nat_for_dtype from pandas.core.arrays.datetimes import DatetimeArray, tz_to_dtype import pandas.core.common as com from pandas.core.indexes.base import Index, maybe_extract_name from pandas.core.indexes.datetimelike import DatetimeTimedeltaMixin from pandas.core.indexes.extension import inherit_names from pandas.core.tools.times import to_time def _new_DatetimeIndex(cls, d): if "data" in d and not isinstance(d["data"], DatetimeIndex): data = d.pop("data") if not isinstance(data, DatetimeArray): tz = d.pop("tz") freq = d.pop("freq") dta = DatetimeArray._simple_new(data, dtype=tz_to_dtype(tz), freq=freq) else: dta = data for key in ["tz", "freq"]: if key in d: assert d.pop(key) == getattr(dta, key) result = cls._simple_new(dta, **d) else: with warnings.catch_warnings(): # TODO: If we knew what was going in to **d, we might be able to # go through _simple_new instead warnings.simplefilter("ignore") result = cls.__new__(cls, **d) return result @inherit_names( ["to_perioddelta", "to_julian_date", "strftime", "isocalendar"] + DatetimeArray._field_ops + [ method for method in DatetimeArray._datetimelike_methods if method not in ("tz_localize",) ], DatetimeArray, wrap=True, ) @inherit_names(["is_normalized", "_resolution_obj"], DatetimeArray, cache=True) @inherit_names( [ "_bool_ops", "_object_ops", "_field_ops", "_datetimelike_ops", "_datetimelike_methods", "tz", "tzinfo", "dtype", "to_pydatetime", "_has_same_tz", "_format_native_types", "date", "time", "timetz", ] + DatetimeArray._bool_ops, DatetimeArray, ) class DatetimeIndex(DatetimeTimedeltaMixin): _typ = "datetimeindex" _engine_type = libindex.DatetimeEngine _supports_partial_string_indexing = True _comparables = ["name", "freqstr", "tz"] _attributes = ["name", "tz", "freq"] _is_numeric_dtype = False _data: DatetimeArray tz: Optional[tzinfo] # -------------------------------------------------------------------- # methods that dispatch to array and wrap result in DatetimeIndex @doc(DatetimeArray.tz_localize) def tz_localize( self, tz, ambiguous="raise", nonexistent="raise" ) -> "DatetimeIndex": arr = self._data.tz_localize(tz, ambiguous, nonexistent) return type(self)._simple_new(arr, name=self.name) @doc(DatetimeArray.to_period) def to_period(self, freq=None) -> "DatetimeIndex": arr = self._data.to_period(freq) return type(self)._simple_new(arr, name=self.name) # -------------------------------------------------------------------- # Constructors def __new__( cls, data=None, freq=lib.no_default, tz=None, normalize=False, closed=None, ambiguous="raise", dayfirst=False, yearfirst=False, dtype=None, copy=False, name=None, ): if is_scalar(data): raise TypeError( f"{cls.__name__}() must be called with a " f"collection of some kind, {repr(data)} was passed" ) # - Cases checked above all return/raise before reaching here - # name = maybe_extract_name(name, data, cls) dtarr = DatetimeArray._from_sequence( data, dtype=dtype, copy=copy, tz=tz, freq=freq, dayfirst=dayfirst, yearfirst=yearfirst, ambiguous=ambiguous, ) subarr = cls._simple_new(dtarr, name=name) return subarr @classmethod def _simple_new(cls, values: DatetimeArray, name: Label = None): assert isinstance(values, DatetimeArray), type(values) result = object.__new__(cls) result._data = values result.name = name result._cache = {} result._no_setting_name = False # For groupby perf. See note in indexes/base about _index_data result._index_data = values._data result._reset_identity() return result # -------------------------------------------------------------------- @cache_readonly def _is_dates_only(self) -> bool: from pandas.io.formats.format import _is_dates_only return self.tz is None and _is_dates_only(self._values) def __reduce__(self): # we use a special reduce here because we need # to simply set the .tz (and not reinterpret it) d = dict(data=self._data) d.update(self._get_attributes_dict()) return _new_DatetimeIndex, (type(self), d), None def _convert_for_op(self, value): if self._has_same_tz(value): return Timestamp(value).asm8 raise ValueError("Passed item and index have different timezone") def _is_comparable_dtype(self, dtype: DtypeObj) -> bool: if not is_datetime64_any_dtype(dtype): return False if self.tz is not None: # If we have tz, we can compare to tzaware return is_datetime64tz_dtype(dtype) # if we dont have tz, we can only compare to tznaive return is_datetime64_dtype(dtype) # -------------------------------------------------------------------- # Rendering Methods def _mpl_repr(self): # how to represent ourselves to matplotlib return ints_to_pydatetime(self.asi8, self.tz) @property def _formatter_func(self): from pandas.io.formats.format import _get_format_datetime64 formatter = _get_format_datetime64(is_dates_only=self._is_dates_only) return lambda x: f"'{formatter(x, tz=self.tz)}'" # -------------------------------------------------------------------- # Set Operation Methods def union_many(self, others): this = self for other in others: if not isinstance(this, DatetimeIndex): this = Index.union(this, other) continue if not isinstance(other, DatetimeIndex): try: other = DatetimeIndex(other) except TypeError: pass this, other = this._maybe_utc_convert(other) if this._can_fast_union(other): this = this._fast_union(other) else: this = Index.union(this, other) return this # -------------------------------------------------------------------- def _get_time_micros(self): values = self.asi8 if self.tz is not None and not timezones.is_utc(self.tz): values = self._data._local_timestamps() nanos = values % (24 * 3600 * 1_000_000_000) micros = nanos // 1000 micros[self._isnan] = -1 return micros def to_series(self, keep_tz=lib.no_default, index=None, name=None): from pandas import Series if index is None: index = self._shallow_copy() if name is None: name = self.name if keep_tz is not lib.no_default: if keep_tz: warnings.warn( "The 'keep_tz' keyword in DatetimeIndex.to_series " "is deprecated and will be removed in a future version. " "You can stop passing 'keep_tz' to silence this warning.", FutureWarning, stacklevel=2, ) else: warnings.warn( "Specifying 'keep_tz=False' is deprecated and this " "option will be removed in a future release. If " "you want to remove the timezone information, you " "can do 'idx.tz_convert(None)' before calling " "'to_series'.", FutureWarning, stacklevel=2, ) else: keep_tz = True if keep_tz and self.tz is not None: # preserve the tz & copy values = self.copy(deep=True) else: values = self._values.view("M8[ns]").copy() return Series(values, index=index, name=name) def snap(self, freq="S"): # Superdumb, punting on any optimizing freq = to_offset(freq) snapped = np.empty(len(self), dtype=DT64NS_DTYPE) for i, v in enumerate(self): s = v if not freq.is_on_offset(s): t0 = freq.rollback(s) t1 = freq.rollforward(s) if abs(s - t0) < abs(t1 - s): s = t0 else: s = t1 snapped[i] = s dta = DatetimeArray(snapped, dtype=self.dtype) return DatetimeIndex._simple_new(dta, name=self.name) def _parsed_string_to_bounds(self, reso: Resolution, parsed: datetime): assert isinstance(reso, Resolution), (type(reso), reso) valid_resos = { "year", "month", "quarter", "day", "hour", "minute", "second", "minute", "second", "microsecond", } if reso.attrname not in valid_resos: raise KeyError grp = reso.freq_group per = Period(parsed, freq=grp) start, end = per.start_time, per.end_time # GH 24076 # If an incoming date string contained a UTC offset, need to localize # the parsed date to this offset first before aligning with the index's if parsed.tzinfo is not None: if self.tz is None: raise ValueError( "The index must be timezone aware when indexing " "with a date string with a UTC offset" ) start = start.tz_localize(parsed.tzinfo).tz_convert(self.tz) end = end.tz_localize(parsed.tzinfo).tz_convert(self.tz) elif self.tz is not None: start = start.tz_localize(self.tz) end = end.tz_localize(self.tz) return start, end def _validate_partial_date_slice(self, reso: Resolution): assert isinstance(reso, Resolution), (type(reso), reso) if ( self.is_monotonic and reso.attrname in ["day", "hour", "minute", "second"] and self._resolution_obj >= reso ): raise KeyError if reso == "microsecond": # _parsed_string_to_bounds allows it. raise KeyError def get_loc(self, key, method=None, tolerance=None): if not is_scalar(key): raise InvalidIndexError(key) orig_key = key if is_valid_nat_for_dtype(key, self.dtype): key = NaT if isinstance(key, self._data._recognized_scalars): # needed to localize naive datetimes key = self._maybe_cast_for_get_loc(key) elif isinstance(key, str): try: return self._get_string_slice(key) except (TypeError, KeyError, ValueError, OverflowError): pass try: key = self._maybe_cast_for_get_loc(key) except ValueError as err: raise KeyError(key) from err elif isinstance(key, timedelta): # GH#20464 raise TypeError( f"Cannot index {type(self).__name__} with {type(key).__name__}" ) elif isinstance(key, time): if method is not None: raise NotImplementedError( "cannot yet lookup inexact labels when key is a time object" ) return self.indexer_at_time(key) else: # unrecognized type raise KeyError(key) try: return Index.get_loc(self, key, method, tolerance) except KeyError as err: raise KeyError(orig_key) from err def _maybe_cast_for_get_loc(self, key) -> Timestamp: # needed to localize naive datetimes key = Timestamp(key) if key.tzinfo is None: key = key.tz_localize(self.tz) else: key = key.tz_convert(self.tz) return key def _maybe_cast_slice_bound(self, label, side: str, kind): assert kind in ["loc", "getitem", None] if is_float(label) or isinstance(label, time) or is_integer(label): self._invalid_indexer("slice", label) if isinstance(label, str): freq = getattr(self, "freqstr", getattr(self, "inferred_freq", None)) parsed, reso = parsing.parse_time_string(label, freq) reso = Resolution.from_attrname(reso) lower, upper = self._parsed_string_to_bounds(reso, parsed) # lower, upper form the half-open interval: # [parsed, parsed + 1 freq) # because label may be passed to searchsorted # the bounds need swapped if index is reverse sorted and has a # length > 1 (is_monotonic_decreasing gives True for empty # and length 1 index) if self._is_strictly_monotonic_decreasing and len(self) > 1: return upper if side == "left" else lower return lower if side == "left" else upper else: return label def _get_string_slice(self, key: str, use_lhs: bool = True, use_rhs: bool = True): freq = getattr(self, "freqstr", getattr(self, "inferred_freq", None)) parsed, reso = parsing.parse_time_string(key, freq) reso = Resolution.from_attrname(reso) loc = self._partial_date_slice(reso, parsed, use_lhs=use_lhs, use_rhs=use_rhs) return loc def slice_indexer(self, start=None, end=None, step=None, kind=None): # For historical reasons DatetimeIndex supports slices between two # instances of datetime.time as if it were applying a slice mask to # an array of (self.hour, self.minute, self.seconds, self.microsecond). if isinstance(start, time) and isinstance(end, time): if step is not None and step != 1: raise ValueError("Must have step size of 1 with time slices") return self.indexer_between_time(start, end) if isinstance(start, time) or isinstance(end, time): raise KeyError("Cannot mix time and non-time slice keys") # Pandas supports slicing with dates, treated as datetimes at midnight. # https://github.com/pandas-dev/pandas/issues/31501 if isinstance(start, date) and not isinstance(start, datetime): start = datetime.combine(start, time(0, 0)) if isinstance(end, date) and not isinstance(end, datetime): end = datetime.combine(end, time(0, 0)) try: return Index.slice_indexer(self, start, end, step, kind=kind) except KeyError: # For historical reasons DatetimeIndex by default supports # value-based partial (aka string) slices on non-monotonic arrays, # let's try that. if (start is None or isinstance(start, str)) and ( end is None or isinstance(end, str) ): mask = True if start is not None: start_casted = self._maybe_cast_slice_bound(start, "left", kind) mask = start_casted <= self if end is not None: end_casted = self._maybe_cast_slice_bound(end, "right", kind) mask = (self <= end_casted) & mask indexer = mask.nonzero()[0][::step] if len(indexer) == len(self): return slice(None) else: return indexer else: raise def is_type_compatible(self, typ) -> bool: return typ == self.inferred_type or typ == "datetime" @property def inferred_type(self) -> str: return "datetime64" def indexer_at_time(self, time, asof=False): if asof: raise NotImplementedError("'asof' argument is not supported") if isinstance(time, str): from dateutil.parser import parse time = parse(time).time() if time.tzinfo: if self.tz is None: raise ValueError("Index must be timezone aware.") time_micros = self.tz_convert(time.tzinfo)._get_time_micros() else: time_micros = self._get_time_micros() micros = _time_to_micros(time) return (micros == time_micros).nonzero()[0] def indexer_between_time( self, start_time, end_time, include_start=True, include_end=True ): start_time = to_time(start_time) end_time = to_time(end_time) time_micros = self._get_time_micros() start_micros = _time_to_micros(start_time) end_micros = _time_to_micros(end_time) if include_start and include_end: lop = rop = operator.le elif include_start: lop = operator.le rop = operator.lt elif include_end: lop = operator.lt rop = operator.le else: lop = rop = operator.lt if start_time <= end_time: join_op = operator.and_ else: join_op = operator.or_ mask = join_op(lop(start_micros, time_micros), rop(time_micros, end_micros)) return mask.nonzero()[0] DatetimeIndex._add_logical_methods_disabled() def date_range( start=None, end=None, periods=None, freq=None, tz=None, normalize=False, name=None, closed=None, **kwargs, ) -> DatetimeIndex: if freq is None and com.any_none(periods, start, end): freq = "D" dtarr = DatetimeArray._generate_range( start=start, end=end, periods=periods, freq=freq, tz=tz, normalize=normalize, closed=closed, **kwargs, ) return DatetimeIndex._simple_new(dtarr, name=name) def bdate_range( start=None, end=None, periods=None, freq="B", tz=None, normalize=True, name=None, weekmask=None, holidays=None, closed=None, **kwargs, ) -> DatetimeIndex: if freq is None: msg = "freq must be specified for bdate_range; use date_range instead" raise TypeError(msg) if isinstance(freq, str) and freq.startswith("C"): try: weekmask = weekmask or "Mon Tue Wed Thu Fri" freq = prefix_mapping[freq](holidays=holidays, weekmask=weekmask) except (KeyError, TypeError) as err: msg = f"invalid custom frequency string: {freq}" raise ValueError(msg) from err elif holidays or weekmask: msg = ( "a custom frequency string is required when holidays or " f"weekmask are passed, got frequency {freq}" ) raise ValueError(msg) return date_range( start=start, end=end, periods=periods, freq=freq, tz=tz, normalize=normalize, name=name, closed=closed, **kwargs, ) def _time_to_micros(time_obj: time) -> int: seconds = time_obj.hour * 60 * 60 + 60 * time_obj.minute + time_obj.second return 1_000_000 * seconds + time_obj.microsecond

true

f71fd14cad832cbba2759947ed66a936ca0786bb

5,432

py

Python

src/python/sim_doc.py

csiu/kick

0ebc9166074b702fc8b5835685ad102957ab349c

[ "MIT" ]

null

src/python/sim_doc.py

csiu/kick

0ebc9166074b702fc8b5835685ad102957ab349c

[ "MIT" ]

null

src/python/sim_doc.py

csiu/kick

0ebc9166074b702fc8b5835685ad102957ab349c

[ "MIT" ]

null

import sys sys.path.append("/Users/csiu/repo/kick/src/python") import argparse import custom import pandas as pd import numpy as np import re import os from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.utils.extmath import randomized_svd from sklearn.metrics import pairwise_distances usage = """ For finding similar documents """ def get_args(): parser = argparse.ArgumentParser(description=usage) parser.add_argument('-s', '--num_singular_values', default=100, type=int, help="Number of singular values to use from SVD") parser.add_argument('-n', '--num_results', default=None, type=int, help="Number of similar documents to print in the results") parser.add_argument('-w', '--term_weight', default="tfidf", choices=["tfidf", "raw"], help="How should terms in document be weighted? 'tfidf' or 'raw' counts") parser.add_argument('-d', '--distance', default="cosine", help="Metric for calculating the distance between documents.") parser.add_argument('-i', '--document0_id', default=None, type=int, help="Kickstarter ID of query document") parser.add_argument('-c', '--cache_dir', default=".", help="Specify cache dir") parser.add_argument('-v', '--verbose', action='store_true') args = parser.parse_args() return(args) def get_data(): """ Output dataframe w/ 2 columns: "id", "document" """ # Get data dk = custom.DatabaseKick() cur = dk.connect() cur.execute("SELECT id, concat_ws(name, blurb) FROM info") rows = cur.fetchall() df = pd.DataFrame(rows, columns=["id", "document"]) dk.disconnect() return(df) def preprocess_data(df): """ Preprocess 'document' of dataframe by - to lowercase - remove nonletters - tokenize - remove stopwords - stem Dataframe will contain additional 'doc_processed' column and df['doc_processed'] will be returned """ def join_output(func): """ Decorator function to join list output to string """ def func_wrapper(text, *arg, **karg): return ' '.join(func(text, *arg, **karg)) return func_wrapper def doc_to_string(doc): """ Replace None -> empty string, and text newlines (\n, \r) -> whitespace """ if doc == None: return("") else: return(re.sub("[\n\r]", "", doc)) df['document'] = df['document'].apply( lambda x: doc_to_string(x)) text_processing = join_output(custom.text_processing) df['doc_processed'] = df['document'].apply( lambda x: text_processing(x, method="stem")) return(df['doc_processed']) def compute_distance(U, i=None, sort=False, top_n=None, metric='euclidean'): """ Compute distance of document U[i] with all documents in U """ if i != None: index_document0 = df[df["id"] == i].index.tolist() else: index_document0 = 0 document0 = np.asmatrix(U[index_document0]) dist = pairwise_distances(document0, U, metric=metric) df_dist = pd.DataFrame(np.transpose(dist), columns=["dist"]) if sort: df_dist.sort_values(by="dist", inplace=True) if top_n != None: assert type(top_n) is int df_dist = df_dist.head(top_n) return(df_dist) if __name__ == '__main__': args = get_args() num_singular_values = args.num_singular_values document0_id = args.document0_id num_results = args.num_results cache_dir = args.cache_dir verbose = args.verbose term_weight = args.term_weight distance_metric = args.distance preprocess_file = os.path.join(os.path.abspath(cache_dir), "preprocessed.pkl") msg = "# Getting and preprocessing data..." if os.path.isfile(preprocess_file): if verbose: print(msg, "from cache...") df = pd.read_pickle(preprocess_file) else: if verbose: print(msg) df = get_data() _ = preprocess_data(df) df.to_pickle(preprocess_file) if term_weight == "raw": if verbose: print("# Making count matrix...") cv = CountVectorizer() X = cv.fit_transform(df['doc_processed']) else: if verbose: print("# Making TF-IDF matrix...") vectorizer = TfidfVectorizer() X = vectorizer.fit_transform(df['doc_processed']) if verbose: print("# Computing SVD for %s singular values..." % num_singular_values) U, s, Vh = randomized_svd(X, n_components=num_singular_values, n_iter=5, random_state=5) if verbose: print("# Computing distances (%s)..." % distance_metric) top_n = compute_distance(U, i=document0_id, sort=True, top_n=num_results, metric=distance_metric) if verbose: print("# Printing results...") results = [] counter = 0 for index, row in df.iloc[top_n.index].iterrows(): row["dist"] = top_n.iloc[counter]["dist"] results.append(row) counter += 1 print('>> %s | %s' % (row['id'], row['doc_processed']), row['document'], "\n", sep="\n")

30.01105

97

0.60475

import sys sys.path.append("/Users/csiu/repo/kick/src/python") import argparse import custom import pandas as pd import numpy as np import re import os from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.utils.extmath import randomized_svd from sklearn.metrics import pairwise_distances usage = """ For finding similar documents """ def get_args(): parser = argparse.ArgumentParser(description=usage) parser.add_argument('-s', '--num_singular_values', default=100, type=int, help="Number of singular values to use from SVD") parser.add_argument('-n', '--num_results', default=None, type=int, help="Number of similar documents to print in the results") parser.add_argument('-w', '--term_weight', default="tfidf", choices=["tfidf", "raw"], help="How should terms in document be weighted? 'tfidf' or 'raw' counts") parser.add_argument('-d', '--distance', default="cosine", help="Metric for calculating the distance between documents.") parser.add_argument('-i', '--document0_id', default=None, type=int, help="Kickstarter ID of query document") parser.add_argument('-c', '--cache_dir', default=".", help="Specify cache dir") parser.add_argument('-v', '--verbose', action='store_true') args = parser.parse_args() return(args) def get_data(): dk = custom.DatabaseKick() cur = dk.connect() cur.execute("SELECT id, concat_ws(name, blurb) FROM info") rows = cur.fetchall() df = pd.DataFrame(rows, columns=["id", "document"]) dk.disconnect() return(df) def preprocess_data(df): def join_output(func): def func_wrapper(text, *arg, **karg): return ' '.join(func(text, *arg, **karg)) return func_wrapper def doc_to_string(doc): if doc == None: return("") else: return(re.sub("[\n\r]", "", doc)) df['document'] = df['document'].apply( lambda x: doc_to_string(x)) text_processing = join_output(custom.text_processing) df['doc_processed'] = df['document'].apply( lambda x: text_processing(x, method="stem")) return(df['doc_processed']) def compute_distance(U, i=None, sort=False, top_n=None, metric='euclidean'): if i != None: index_document0 = df[df["id"] == i].index.tolist() else: index_document0 = 0 document0 = np.asmatrix(U[index_document0]) dist = pairwise_distances(document0, U, metric=metric) df_dist = pd.DataFrame(np.transpose(dist), columns=["dist"]) if sort: df_dist.sort_values(by="dist", inplace=True) if top_n != None: assert type(top_n) is int df_dist = df_dist.head(top_n) return(df_dist) if __name__ == '__main__': args = get_args() num_singular_values = args.num_singular_values document0_id = args.document0_id num_results = args.num_results cache_dir = args.cache_dir verbose = args.verbose term_weight = args.term_weight distance_metric = args.distance preprocess_file = os.path.join(os.path.abspath(cache_dir), "preprocessed.pkl") msg = "# Getting and preprocessing data..." if os.path.isfile(preprocess_file): if verbose: print(msg, "from cache...") df = pd.read_pickle(preprocess_file) else: if verbose: print(msg) df = get_data() _ = preprocess_data(df) df.to_pickle(preprocess_file) if term_weight == "raw": if verbose: print("# Making count matrix...") cv = CountVectorizer() X = cv.fit_transform(df['doc_processed']) else: if verbose: print("# Making TF-IDF matrix...") vectorizer = TfidfVectorizer() X = vectorizer.fit_transform(df['doc_processed']) if verbose: print("# Computing SVD for %s singular values..." % num_singular_values) U, s, Vh = randomized_svd(X, n_components=num_singular_values, n_iter=5, random_state=5) if verbose: print("# Computing distances (%s)..." % distance_metric) top_n = compute_distance(U, i=document0_id, sort=True, top_n=num_results, metric=distance_metric) if verbose: print("# Printing results...") results = [] counter = 0 for index, row in df.iloc[top_n.index].iterrows(): row["dist"] = top_n.iloc[counter]["dist"] results.append(row) counter += 1 print('>> %s | %s' % (row['id'], row['doc_processed']), row['document'], "\n", sep="\n")

true

f71fd21c199e5a31cb8e95fea4d6ad447b4eb6cf

2,082

py

Python

adsputils/tests/test_init.py

adsabs/ADSPipelineUtils

eb8cc988f57c19a256ebc8802cc2a812d5279d12

[ "MIT" ]

null

adsputils/tests/test_init.py

adsabs/ADSPipelineUtils

eb8cc988f57c19a256ebc8802cc2a812d5279d12

[ "MIT" ]

36

2017-06-23T20:29:22.000Z

2020-03-18T15:04:27.000Z

adsputils/tests/test_init.py

adsabs/ADSPipelineUtils

eb8cc988f57c19a256ebc8802cc2a812d5279d12

[ "MIT" ]

12

2017-06-21T18:10:57.000Z

2021-11-01T19:13:32.000Z

# -*- coding: utf-8 -*- import adsputils import unittest import os import json import time from inspect import currentframe, getframeinfo from adsputils.exceptions import UnicodeHandlerError def _read_file(fpath): with open(fpath, 'r') as fi: return fi.read() class TestInit(unittest.TestCase): def test_logging(self): logdir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../logs')) foo_log = logdir + '/foo.bar.log' if os.path.exists(foo_log): os.remove(foo_log) logger = adsputils.setup_logging('foo.bar') logger.warning('first') frameinfo = getframeinfo(currentframe()) #print foo_log self.assertTrue(os.path.exists(foo_log)) c = _read_file(foo_log) j = json.loads(c) self.assertEqual(j['message'], 'first') self.assertTrue('hostname' in j) # verify warning has filename and linenumber self.assertEqual(os.path.basename(frameinfo.filename), j['filename']) self.assertEqual(j['lineno'], frameinfo.lineno - 1) time.sleep(0.01) # now multiline message logger.warning(u'second\nthird') logger.warning('last') c = _read_file(foo_log) found = False msecs = False for x in c.strip().split('\n'): j = json.loads(x) self.assertTrue(j) if j['message'] == u'second\nthird': found = True t = adsputils.get_date(j['asctime']) if t.microsecond > 0: msecs = True self.assertTrue(found) self.assertTrue(msecs) def test_u2asc(self): input1 = 'benìtez, n' input2 = u'izzet, sakallı' output1 = adsputils.u2asc(input1) output2 = adsputils.u2asc(input2) self.assertEqual(output1,'benitez, n') self.assertEqual(output2,u'izzet, sakalli') input3 = input2.encode('utf16') self.assertRaises(UnicodeHandlerError, adsputils.u2asc, input3) if __name__ == '__main__': unittest.main()

28.135135

87

0.604707

import adsputils import unittest import os import json import time from inspect import currentframe, getframeinfo from adsputils.exceptions import UnicodeHandlerError def _read_file(fpath): with open(fpath, 'r') as fi: return fi.read() class TestInit(unittest.TestCase): def test_logging(self): logdir = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../logs')) foo_log = logdir + '/foo.bar.log' if os.path.exists(foo_log): os.remove(foo_log) logger = adsputils.setup_logging('foo.bar') logger.warning('first') frameinfo = getframeinfo(currentframe()) self.assertTrue(os.path.exists(foo_log)) c = _read_file(foo_log) j = json.loads(c) self.assertEqual(j['message'], 'first') self.assertTrue('hostname' in j) self.assertEqual(os.path.basename(frameinfo.filename), j['filename']) self.assertEqual(j['lineno'], frameinfo.lineno - 1) time.sleep(0.01) logger.warning(u'second\nthird') logger.warning('last') c = _read_file(foo_log) found = False msecs = False for x in c.strip().split('\n'): j = json.loads(x) self.assertTrue(j) if j['message'] == u'second\nthird': found = True t = adsputils.get_date(j['asctime']) if t.microsecond > 0: msecs = True self.assertTrue(found) self.assertTrue(msecs) def test_u2asc(self): input1 = 'benìtez, n' input2 = u'izzet, sakallı' output1 = adsputils.u2asc(input1) output2 = adsputils.u2asc(input2) self.assertEqual(output1,'benitez, n') self.assertEqual(output2,u'izzet, sakalli') input3 = input2.encode('utf16') self.assertRaises(UnicodeHandlerError, adsputils.u2asc, input3) if __name__ == '__main__': unittest.main()

true

f71fd223ff855bd602d59319796b96fc483982ca

20,550

py

Python

log_complete_bcl2/model_76.py

LoLab-VU/Bayesian_Inference_of_Network_Dynamics

54a5ef7e868be34289836bbbb024a2963c0c9c86

[ "MIT" ]

null

log_complete_bcl2/model_76.py

LoLab-VU/Bayesian_Inference_of_Network_Dynamics

54a5ef7e868be34289836bbbb024a2963c0c9c86

[ "MIT" ]

null

log_complete_bcl2/model_76.py

LoLab-VU/Bayesian_Inference_of_Network_Dynamics

54a5ef7e868be34289836bbbb024a2963c0c9c86

[ "MIT" ]

null

# exported from PySB model 'model' from pysb import Model, Monomer, Parameter, Expression, Compartment, Rule, Observable, Initial, MatchOnce, Annotation, ANY, WILD Model() Monomer('Ligand', ['Receptor']) Monomer('ParpU', ['C3A']) Monomer('C8A', ['BidU', 'C3pro']) Monomer('SmacM', ['BaxA']) Monomer('BaxM', ['BidM', 'BaxA']) Monomer('Apop', ['C3pro', 'Xiap']) Monomer('Fadd', ['Receptor', 'C8pro']) Monomer('SmacC', ['Xiap']) Monomer('ParpC') Monomer('Xiap', ['SmacC', 'Apop', 'C3A']) Monomer('C9') Monomer('C3ub') Monomer('C8pro', ['Fadd', 'C6A']) Monomer('Bcl2', ['BidM', 'BaxA']) Monomer('C3pro', ['Apop', 'C8A']) Monomer('CytoCM', ['BaxA']) Monomer('CytoCC') Monomer('BaxA', ['BaxM', 'Bcl2', 'BaxA_1', 'BaxA_2', 'SmacM', 'CytoCM']) Monomer('ApafI') Monomer('BidU', ['C8A']) Monomer('BidT') Monomer('C3A', ['Xiap', 'ParpU', 'C6pro']) Monomer('ApafA') Monomer('BidM', ['BaxM', 'Bcl2']) Monomer('Receptor', ['Ligand', 'Fadd']) Monomer('C6A', ['C8pro']) Monomer('C6pro', ['C3A']) Parameter('bind_0_Ligand_binder_Receptor_binder_target_2kf', 1.0) Parameter('bind_0_Ligand_binder_Receptor_binder_target_1kr', 1.0) Parameter('bind_0_Receptor_binder_Fadd_binder_target_2kf', 1.0) Parameter('bind_0_Receptor_binder_Fadd_binder_target_1kr', 1.0) Parameter('substrate_binding_0_Fadd_catalyzer_C8pro_substrate_2kf', 1.0) Parameter('substrate_binding_0_Fadd_catalyzer_C8pro_substrate_1kr', 1.0) Parameter('catalytic_step_0_Fadd_catalyzer_C8pro_substrate_C8A_product_1kc', 1.0) Parameter('catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_2kf', 1.0) Parameter('catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_1kr', 1.0) Parameter('catalysis_1_C8A_catalyzer_BidU_substrate_BidT_product_1kc', 1.0) Parameter('conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_2kf', 1.0) Parameter('conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_1kr', 1.0) Parameter('inhibition_0_SmacC_inhibitor_Xiap_inh_target_2kf', 1.0) Parameter('inhibition_0_SmacC_inhibitor_Xiap_inh_target_1kr', 1.0) Parameter('conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_2kf', 1.0) Parameter('conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_1kr', 1.0) Parameter('catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_2kf', 1.0) Parameter('catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_1kr', 1.0) Parameter('catalysis_1_Apop_catalyzer_C3pro_substrate_C3A_product_1kc', 1.0) Parameter('inhibition_0_Xiap_inhibitor_Apop_inh_target_2kf', 1.0) Parameter('inhibition_0_Xiap_inhibitor_Apop_inh_target_1kr', 1.0) Parameter('catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_2kf', 1.0) Parameter('catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_1kr', 1.0) Parameter('catalysis_1_Xiap_catalyzer_C3A_substrate_C3ub_product_1kc', 1.0) Parameter('catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_2kf', 1.0) Parameter('catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_1kr', 1.0) Parameter('catalysis_1_C3A_catalyzer_ParpU_substrate_ParpC_product_1kc', 1.0) Parameter('equilibration_0_BidT_equil_a_BidM_equil_b_1kf', 1.0) Parameter('equilibration_0_BidT_equil_a_BidM_equil_b_1kr', 1.0) Parameter('catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_2kf', 1.0) Parameter('catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_1kr', 1.0) Parameter('catalysis_1_BidM_catalyzer_BaxM_substrate_BaxA_product_1kc', 1.0) Parameter('self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_2kf', 1.0) Parameter('self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_1kr', 1.0) Parameter('self_catalyze_1_BaxA_self_catalyzer_BaxM_self_substrate_1kc', 1.0) Parameter('inhibition_0_Bcl2_inhibitor_BidM_inh_target_2kf', 1.0) Parameter('inhibition_0_Bcl2_inhibitor_BidM_inh_target_1kr', 1.0) Parameter('inhibition_0_Bcl2_inhibitor_BaxA_inh_target_2kf', 1.0) Parameter('inhibition_0_Bcl2_inhibitor_BaxA_inh_target_1kr', 1.0) Parameter('pore_formation_0_BaxA_pore_2kf', 1.0) Parameter('pore_formation_0_BaxA_pore_1kr', 1.0) Parameter('pore_formation_1_BaxA_pore_2kf', 1.0) Parameter('pore_formation_1_BaxA_pore_1kr', 1.0) Parameter('pore_formation_2_BaxA_pore_2kf', 1.0) Parameter('pore_formation_2_BaxA_pore_1kr', 1.0) Parameter('transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_2kf', 1.0) Parameter('transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kr', 1.0) Parameter('transport_1_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kc', 1.0) Parameter('transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_2kf', 1.0) Parameter('transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kr', 1.0) Parameter('transport_1_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kc', 1.0) Parameter('catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_2kf', 1.0) Parameter('catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_1kr', 1.0) Parameter('catalysis_1_C8A_catalyzer_C3pro_substrate_C3A_product_1kc', 1.0) Parameter('catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_2kf', 1.0) Parameter('catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_1kr', 1.0) Parameter('catalysis_1_C3A_catalyzer_C6pro_substrate_C6A_product_1kc', 1.0) Parameter('catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_2kf', 1.0) Parameter('catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_1kr', 1.0) Parameter('catalysis_1_C6A_catalyzer_C8pro_substrate_C8A_product_1kc', 1.0) Parameter('Ligand_0', 1000.0) Parameter('ParpU_0', 1000000.0) Parameter('C8A_0', 0.0) Parameter('SmacM_0', 100000.0) Parameter('BaxM_0', 40000.0) Parameter('Apop_0', 0.0) Parameter('Fadd_0', 130000.0) Parameter('SmacC_0', 0.0) Parameter('ParpC_0', 0.0) Parameter('Xiap_0', 19000.0) Parameter('C9_0', 100000.0) Parameter('C3ub_0', 0.0) Parameter('C8pro_0', 130000.0) Parameter('Bcl2_0', 328000.0) Parameter('C3pro_0', 21000.0) Parameter('CytoCM_0', 500000.0) Parameter('CytoCC_0', 0.0) Parameter('BaxA_0', 0.0) Parameter('ApafI_0', 100000.0) Parameter('BidU_0', 171000.0) Parameter('BidT_0', 0.0) Parameter('C3A_0', 0.0) Parameter('ApafA_0', 0.0) Parameter('BidM_0', 0.0) Parameter('Receptor_0', 100.0) Parameter('C6A_0', 0.0) Parameter('C6pro_0', 100.0) Observable('Ligand_obs', Ligand()) Observable('ParpU_obs', ParpU()) Observable('C8A_obs', C8A()) Observable('SmacM_obs', SmacM()) Observable('BaxM_obs', BaxM()) Observable('Apop_obs', Apop()) Observable('Fadd_obs', Fadd()) Observable('SmacC_obs', SmacC()) Observable('ParpC_obs', ParpC()) Observable('Xiap_obs', Xiap()) Observable('C9_obs', C9()) Observable('C3ub_obs', C3ub()) Observable('C8pro_obs', C8pro()) Observable('Bcl2_obs', Bcl2()) Observable('C3pro_obs', C3pro()) Observable('CytoCM_obs', CytoCM()) Observable('CytoCC_obs', CytoCC()) Observable('BaxA_obs', BaxA()) Observable('ApafI_obs', ApafI()) Observable('BidU_obs', BidU()) Observable('BidT_obs', BidT()) Observable('C3A_obs', C3A()) Observable('ApafA_obs', ApafA()) Observable('BidM_obs', BidM()) Observable('Receptor_obs', Receptor()) Observable('C6A_obs', C6A()) Observable('C6pro_obs', C6pro()) Rule('bind_0_Ligand_binder_Receptor_binder_target', Ligand(Receptor=None) + Receptor(Ligand=None, Fadd=None) | Ligand(Receptor=1) % Receptor(Ligand=1, Fadd=None), bind_0_Ligand_binder_Receptor_binder_target_2kf, bind_0_Ligand_binder_Receptor_binder_target_1kr) Rule('bind_0_Receptor_binder_Fadd_binder_target', Receptor(Ligand=ANY, Fadd=None) + Fadd(Receptor=None, C8pro=None) | Receptor(Ligand=ANY, Fadd=1) % Fadd(Receptor=1, C8pro=None), bind_0_Receptor_binder_Fadd_binder_target_2kf, bind_0_Receptor_binder_Fadd_binder_target_1kr) Rule('substrate_binding_0_Fadd_catalyzer_C8pro_substrate', Fadd(Receptor=ANY, C8pro=None) + C8pro(Fadd=None, C6A=None) | Fadd(Receptor=ANY, C8pro=1) % C8pro(Fadd=1, C6A=None), substrate_binding_0_Fadd_catalyzer_C8pro_substrate_2kf, substrate_binding_0_Fadd_catalyzer_C8pro_substrate_1kr) Rule('catalytic_step_0_Fadd_catalyzer_C8pro_substrate_C8A_product', Fadd(Receptor=ANY, C8pro=1) % C8pro(Fadd=1, C6A=None) >> Fadd(Receptor=ANY, C8pro=None) + C8A(BidU=None, C3pro=None), catalytic_step_0_Fadd_catalyzer_C8pro_substrate_C8A_product_1kc) Rule('catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product', C8A(BidU=None, C3pro=None) + BidU(C8A=None) | C8A(BidU=1, C3pro=None) % BidU(C8A=1), catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_2kf, catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_1kr) Rule('catalysis_1_C8A_catalyzer_BidU_substrate_BidT_product', C8A(BidU=1, C3pro=None) % BidU(C8A=1) >> C8A(BidU=None, C3pro=None) + BidT(), catalysis_1_C8A_catalyzer_BidU_substrate_BidT_product_1kc) Rule('conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex', ApafI() + CytoCC() | ApafA(), conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_2kf, conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_1kr) Rule('inhibition_0_SmacC_inhibitor_Xiap_inh_target', SmacC(Xiap=None) + Xiap(SmacC=None, Apop=None, C3A=None) | SmacC(Xiap=1) % Xiap(SmacC=1, Apop=None, C3A=None), inhibition_0_SmacC_inhibitor_Xiap_inh_target_2kf, inhibition_0_SmacC_inhibitor_Xiap_inh_target_1kr) Rule('conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex', ApafA() + C9() | Apop(C3pro=None, Xiap=None), conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_2kf, conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_1kr) Rule('catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product', Apop(C3pro=None, Xiap=None) + C3pro(Apop=None, C8A=None) | Apop(C3pro=1, Xiap=None) % C3pro(Apop=1, C8A=None), catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_2kf, catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_1kr) Rule('catalysis_1_Apop_catalyzer_C3pro_substrate_C3A_product', Apop(C3pro=1, Xiap=None) % C3pro(Apop=1, C8A=None) >> Apop(C3pro=None, Xiap=None) + C3A(Xiap=None, ParpU=None, C6pro=None), catalysis_1_Apop_catalyzer_C3pro_substrate_C3A_product_1kc) Rule('inhibition_0_Xiap_inhibitor_Apop_inh_target', Xiap(SmacC=None, Apop=None, C3A=None) + Apop(C3pro=None, Xiap=None) | Xiap(SmacC=None, Apop=1, C3A=None) % Apop(C3pro=None, Xiap=1), inhibition_0_Xiap_inhibitor_Apop_inh_target_2kf, inhibition_0_Xiap_inhibitor_Apop_inh_target_1kr) Rule('catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product', Xiap(SmacC=None, Apop=None, C3A=None) + C3A(Xiap=None, ParpU=None, C6pro=None) | Xiap(SmacC=None, Apop=None, C3A=1) % C3A(Xiap=1, ParpU=None, C6pro=None), catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_2kf, catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_1kr) Rule('catalysis_1_Xiap_catalyzer_C3A_substrate_C3ub_product', Xiap(SmacC=None, Apop=None, C3A=1) % C3A(Xiap=1, ParpU=None, C6pro=None) >> Xiap(SmacC=None, Apop=None, C3A=None) + C3ub(), catalysis_1_Xiap_catalyzer_C3A_substrate_C3ub_product_1kc) Rule('catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product', C3A(Xiap=None, ParpU=None, C6pro=None) + ParpU(C3A=None) | C3A(Xiap=None, ParpU=1, C6pro=None) % ParpU(C3A=1), catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_2kf, catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_1kr) Rule('catalysis_1_C3A_catalyzer_ParpU_substrate_ParpC_product', C3A(Xiap=None, ParpU=1, C6pro=None) % ParpU(C3A=1) >> C3A(Xiap=None, ParpU=None, C6pro=None) + ParpC(), catalysis_1_C3A_catalyzer_ParpU_substrate_ParpC_product_1kc) Rule('equilibration_0_BidT_equil_a_BidM_equil_b', BidT() | BidM(BaxM=None, Bcl2=None), equilibration_0_BidT_equil_a_BidM_equil_b_1kf, equilibration_0_BidT_equil_a_BidM_equil_b_1kr) Rule('catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product', BidM(BaxM=None, Bcl2=None) + BaxM(BidM=None, BaxA=None) | BidM(BaxM=1, Bcl2=None) % BaxM(BidM=1, BaxA=None), catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_2kf, catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_1kr) Rule('catalysis_1_BidM_catalyzer_BaxM_substrate_BaxA_product', BidM(BaxM=1, Bcl2=None) % BaxM(BidM=1, BaxA=None) >> BidM(BaxM=None, Bcl2=None) + BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), catalysis_1_BidM_catalyzer_BaxM_substrate_BaxA_product_1kc) Rule('self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate', BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxM(BidM=None, BaxA=None) | BaxA(BaxM=1, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) % BaxM(BidM=None, BaxA=1), self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_2kf, self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_1kr) Rule('self_catalyze_1_BaxA_self_catalyzer_BaxM_self_substrate', BaxA(BaxM=1, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) % BaxM(BidM=None, BaxA=1) >> BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), self_catalyze_1_BaxA_self_catalyzer_BaxM_self_substrate_1kc) Rule('inhibition_0_Bcl2_inhibitor_BidM_inh_target', Bcl2(BidM=None, BaxA=None) + BidM(BaxM=None, Bcl2=None) | Bcl2(BidM=1, BaxA=None) % BidM(BaxM=None, Bcl2=1), inhibition_0_Bcl2_inhibitor_BidM_inh_target_2kf, inhibition_0_Bcl2_inhibitor_BidM_inh_target_1kr) Rule('inhibition_0_Bcl2_inhibitor_BaxA_inh_target', Bcl2(BidM=None, BaxA=None) + BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) | Bcl2(BidM=None, BaxA=1) % BaxA(BaxM=None, Bcl2=1, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), inhibition_0_Bcl2_inhibitor_BaxA_inh_target_2kf, inhibition_0_Bcl2_inhibitor_BaxA_inh_target_1kr) Rule('pore_formation_0_BaxA_pore', BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) | BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=None, SmacM=None, CytoCM=None), pore_formation_0_BaxA_pore_2kf, pore_formation_0_BaxA_pore_1kr) Rule('pore_formation_1_BaxA_pore', BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=None, SmacM=None, CytoCM=None) | BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None), pore_formation_1_BaxA_pore_2kf, pore_formation_1_BaxA_pore_1kr) Rule('pore_formation_2_BaxA_pore', BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) | BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None), pore_formation_2_BaxA_pore_2kf, pore_formation_2_BaxA_pore_1kr) Rule('transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C', BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + SmacM(BaxA=None) | BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=5, CytoCM=None) % SmacM(BaxA=5), transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_2kf, transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kr) Rule('transport_1_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C', BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=5, CytoCM=None) % SmacM(BaxA=5) >> BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + SmacC(Xiap=None), transport_1_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kc) Rule('transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C', BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + CytoCM(BaxA=None) | BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=5) % CytoCM(BaxA=5), transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_2kf, transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kr) Rule('transport_1_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C', BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=5) % CytoCM(BaxA=5) >> BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + CytoCC(), transport_1_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kc) Rule('catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product', C8A(BidU=None, C3pro=None) + C3pro(Apop=None, C8A=None) | C8A(BidU=None, C3pro=1) % C3pro(Apop=None, C8A=1), catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_2kf, catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_1kr) Rule('catalysis_1_C8A_catalyzer_C3pro_substrate_C3A_product', C8A(BidU=None, C3pro=1) % C3pro(Apop=None, C8A=1) >> C8A(BidU=None, C3pro=None) + C3A(Xiap=None, ParpU=None, C6pro=None), catalysis_1_C8A_catalyzer_C3pro_substrate_C3A_product_1kc) Rule('catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product', C3A(Xiap=None, ParpU=None, C6pro=None) + C6pro(C3A=None) | C3A(Xiap=None, ParpU=None, C6pro=1) % C6pro(C3A=1), catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_2kf, catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_1kr) Rule('catalysis_1_C3A_catalyzer_C6pro_substrate_C6A_product', C3A(Xiap=None, ParpU=None, C6pro=1) % C6pro(C3A=1) >> C3A(Xiap=None, ParpU=None, C6pro=None) + C6A(C8pro=None), catalysis_1_C3A_catalyzer_C6pro_substrate_C6A_product_1kc) Rule('catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product', C6A(C8pro=None) + C8pro(Fadd=None, C6A=None) | C6A(C8pro=1) % C8pro(Fadd=None, C6A=1), catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_2kf, catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_1kr) Rule('catalysis_1_C6A_catalyzer_C8pro_substrate_C8A_product', C6A(C8pro=1) % C8pro(Fadd=None, C6A=1) >> C6A(C8pro=None) + C8A(BidU=None, C3pro=None), catalysis_1_C6A_catalyzer_C8pro_substrate_C8A_product_1kc) Initial(Ligand(Receptor=None), Ligand_0) Initial(ParpU(C3A=None), ParpU_0) Initial(C8A(BidU=None, C3pro=None), C8A_0) Initial(SmacM(BaxA=None), SmacM_0) Initial(BaxM(BidM=None, BaxA=None), BaxM_0) Initial(Apop(C3pro=None, Xiap=None), Apop_0) Initial(Fadd(Receptor=None, C8pro=None), Fadd_0) Initial(SmacC(Xiap=None), SmacC_0) Initial(ParpC(), ParpC_0) Initial(Xiap(SmacC=None, Apop=None, C3A=None), Xiap_0) Initial(C9(), C9_0) Initial(C3ub(), C3ub_0) Initial(C8pro(Fadd=None, C6A=None), C8pro_0) Initial(Bcl2(BidM=None, BaxA=None), Bcl2_0) Initial(C3pro(Apop=None, C8A=None), C3pro_0) Initial(CytoCM(BaxA=None), CytoCM_0) Initial(CytoCC(), CytoCC_0) Initial(BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), BaxA_0) Initial(ApafI(), ApafI_0) Initial(BidU(C8A=None), BidU_0) Initial(BidT(), BidT_0) Initial(C3A(Xiap=None, ParpU=None, C6pro=None), C3A_0) Initial(ApafA(), ApafA_0) Initial(BidM(BaxM=None, Bcl2=None), BidM_0) Initial(Receptor(Ligand=None, Fadd=None), Receptor_0) Initial(C6A(C8pro=None), C6A_0) Initial(C6pro(C3A=None), C6pro_0)

95.138889

798

0.804136

from pysb import Model, Monomer, Parameter, Expression, Compartment, Rule, Observable, Initial, MatchOnce, Annotation, ANY, WILD Model() Monomer('Ligand', ['Receptor']) Monomer('ParpU', ['C3A']) Monomer('C8A', ['BidU', 'C3pro']) Monomer('SmacM', ['BaxA']) Monomer('BaxM', ['BidM', 'BaxA']) Monomer('Apop', ['C3pro', 'Xiap']) Monomer('Fadd', ['Receptor', 'C8pro']) Monomer('SmacC', ['Xiap']) Monomer('ParpC') Monomer('Xiap', ['SmacC', 'Apop', 'C3A']) Monomer('C9') Monomer('C3ub') Monomer('C8pro', ['Fadd', 'C6A']) Monomer('Bcl2', ['BidM', 'BaxA']) Monomer('C3pro', ['Apop', 'C8A']) Monomer('CytoCM', ['BaxA']) Monomer('CytoCC') Monomer('BaxA', ['BaxM', 'Bcl2', 'BaxA_1', 'BaxA_2', 'SmacM', 'CytoCM']) Monomer('ApafI') Monomer('BidU', ['C8A']) Monomer('BidT') Monomer('C3A', ['Xiap', 'ParpU', 'C6pro']) Monomer('ApafA') Monomer('BidM', ['BaxM', 'Bcl2']) Monomer('Receptor', ['Ligand', 'Fadd']) Monomer('C6A', ['C8pro']) Monomer('C6pro', ['C3A']) Parameter('bind_0_Ligand_binder_Receptor_binder_target_2kf', 1.0) Parameter('bind_0_Ligand_binder_Receptor_binder_target_1kr', 1.0) Parameter('bind_0_Receptor_binder_Fadd_binder_target_2kf', 1.0) Parameter('bind_0_Receptor_binder_Fadd_binder_target_1kr', 1.0) Parameter('substrate_binding_0_Fadd_catalyzer_C8pro_substrate_2kf', 1.0) Parameter('substrate_binding_0_Fadd_catalyzer_C8pro_substrate_1kr', 1.0) Parameter('catalytic_step_0_Fadd_catalyzer_C8pro_substrate_C8A_product_1kc', 1.0) Parameter('catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_2kf', 1.0) Parameter('catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_1kr', 1.0) Parameter('catalysis_1_C8A_catalyzer_BidU_substrate_BidT_product_1kc', 1.0) Parameter('conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_2kf', 1.0) Parameter('conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_1kr', 1.0) Parameter('inhibition_0_SmacC_inhibitor_Xiap_inh_target_2kf', 1.0) Parameter('inhibition_0_SmacC_inhibitor_Xiap_inh_target_1kr', 1.0) Parameter('conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_2kf', 1.0) Parameter('conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_1kr', 1.0) Parameter('catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_2kf', 1.0) Parameter('catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_1kr', 1.0) Parameter('catalysis_1_Apop_catalyzer_C3pro_substrate_C3A_product_1kc', 1.0) Parameter('inhibition_0_Xiap_inhibitor_Apop_inh_target_2kf', 1.0) Parameter('inhibition_0_Xiap_inhibitor_Apop_inh_target_1kr', 1.0) Parameter('catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_2kf', 1.0) Parameter('catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_1kr', 1.0) Parameter('catalysis_1_Xiap_catalyzer_C3A_substrate_C3ub_product_1kc', 1.0) Parameter('catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_2kf', 1.0) Parameter('catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_1kr', 1.0) Parameter('catalysis_1_C3A_catalyzer_ParpU_substrate_ParpC_product_1kc', 1.0) Parameter('equilibration_0_BidT_equil_a_BidM_equil_b_1kf', 1.0) Parameter('equilibration_0_BidT_equil_a_BidM_equil_b_1kr', 1.0) Parameter('catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_2kf', 1.0) Parameter('catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_1kr', 1.0) Parameter('catalysis_1_BidM_catalyzer_BaxM_substrate_BaxA_product_1kc', 1.0) Parameter('self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_2kf', 1.0) Parameter('self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_1kr', 1.0) Parameter('self_catalyze_1_BaxA_self_catalyzer_BaxM_self_substrate_1kc', 1.0) Parameter('inhibition_0_Bcl2_inhibitor_BidM_inh_target_2kf', 1.0) Parameter('inhibition_0_Bcl2_inhibitor_BidM_inh_target_1kr', 1.0) Parameter('inhibition_0_Bcl2_inhibitor_BaxA_inh_target_2kf', 1.0) Parameter('inhibition_0_Bcl2_inhibitor_BaxA_inh_target_1kr', 1.0) Parameter('pore_formation_0_BaxA_pore_2kf', 1.0) Parameter('pore_formation_0_BaxA_pore_1kr', 1.0) Parameter('pore_formation_1_BaxA_pore_2kf', 1.0) Parameter('pore_formation_1_BaxA_pore_1kr', 1.0) Parameter('pore_formation_2_BaxA_pore_2kf', 1.0) Parameter('pore_formation_2_BaxA_pore_1kr', 1.0) Parameter('transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_2kf', 1.0) Parameter('transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kr', 1.0) Parameter('transport_1_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kc', 1.0) Parameter('transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_2kf', 1.0) Parameter('transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kr', 1.0) Parameter('transport_1_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kc', 1.0) Parameter('catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_2kf', 1.0) Parameter('catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_1kr', 1.0) Parameter('catalysis_1_C8A_catalyzer_C3pro_substrate_C3A_product_1kc', 1.0) Parameter('catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_2kf', 1.0) Parameter('catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_1kr', 1.0) Parameter('catalysis_1_C3A_catalyzer_C6pro_substrate_C6A_product_1kc', 1.0) Parameter('catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_2kf', 1.0) Parameter('catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_1kr', 1.0) Parameter('catalysis_1_C6A_catalyzer_C8pro_substrate_C8A_product_1kc', 1.0) Parameter('Ligand_0', 1000.0) Parameter('ParpU_0', 1000000.0) Parameter('C8A_0', 0.0) Parameter('SmacM_0', 100000.0) Parameter('BaxM_0', 40000.0) Parameter('Apop_0', 0.0) Parameter('Fadd_0', 130000.0) Parameter('SmacC_0', 0.0) Parameter('ParpC_0', 0.0) Parameter('Xiap_0', 19000.0) Parameter('C9_0', 100000.0) Parameter('C3ub_0', 0.0) Parameter('C8pro_0', 130000.0) Parameter('Bcl2_0', 328000.0) Parameter('C3pro_0', 21000.0) Parameter('CytoCM_0', 500000.0) Parameter('CytoCC_0', 0.0) Parameter('BaxA_0', 0.0) Parameter('ApafI_0', 100000.0) Parameter('BidU_0', 171000.0) Parameter('BidT_0', 0.0) Parameter('C3A_0', 0.0) Parameter('ApafA_0', 0.0) Parameter('BidM_0', 0.0) Parameter('Receptor_0', 100.0) Parameter('C6A_0', 0.0) Parameter('C6pro_0', 100.0) Observable('Ligand_obs', Ligand()) Observable('ParpU_obs', ParpU()) Observable('C8A_obs', C8A()) Observable('SmacM_obs', SmacM()) Observable('BaxM_obs', BaxM()) Observable('Apop_obs', Apop()) Observable('Fadd_obs', Fadd()) Observable('SmacC_obs', SmacC()) Observable('ParpC_obs', ParpC()) Observable('Xiap_obs', Xiap()) Observable('C9_obs', C9()) Observable('C3ub_obs', C3ub()) Observable('C8pro_obs', C8pro()) Observable('Bcl2_obs', Bcl2()) Observable('C3pro_obs', C3pro()) Observable('CytoCM_obs', CytoCM()) Observable('CytoCC_obs', CytoCC()) Observable('BaxA_obs', BaxA()) Observable('ApafI_obs', ApafI()) Observable('BidU_obs', BidU()) Observable('BidT_obs', BidT()) Observable('C3A_obs', C3A()) Observable('ApafA_obs', ApafA()) Observable('BidM_obs', BidM()) Observable('Receptor_obs', Receptor()) Observable('C6A_obs', C6A()) Observable('C6pro_obs', C6pro()) Rule('bind_0_Ligand_binder_Receptor_binder_target', Ligand(Receptor=None) + Receptor(Ligand=None, Fadd=None) | Ligand(Receptor=1) % Receptor(Ligand=1, Fadd=None), bind_0_Ligand_binder_Receptor_binder_target_2kf, bind_0_Ligand_binder_Receptor_binder_target_1kr) Rule('bind_0_Receptor_binder_Fadd_binder_target', Receptor(Ligand=ANY, Fadd=None) + Fadd(Receptor=None, C8pro=None) | Receptor(Ligand=ANY, Fadd=1) % Fadd(Receptor=1, C8pro=None), bind_0_Receptor_binder_Fadd_binder_target_2kf, bind_0_Receptor_binder_Fadd_binder_target_1kr) Rule('substrate_binding_0_Fadd_catalyzer_C8pro_substrate', Fadd(Receptor=ANY, C8pro=None) + C8pro(Fadd=None, C6A=None) | Fadd(Receptor=ANY, C8pro=1) % C8pro(Fadd=1, C6A=None), substrate_binding_0_Fadd_catalyzer_C8pro_substrate_2kf, substrate_binding_0_Fadd_catalyzer_C8pro_substrate_1kr) Rule('catalytic_step_0_Fadd_catalyzer_C8pro_substrate_C8A_product', Fadd(Receptor=ANY, C8pro=1) % C8pro(Fadd=1, C6A=None) >> Fadd(Receptor=ANY, C8pro=None) + C8A(BidU=None, C3pro=None), catalytic_step_0_Fadd_catalyzer_C8pro_substrate_C8A_product_1kc) Rule('catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product', C8A(BidU=None, C3pro=None) + BidU(C8A=None) | C8A(BidU=1, C3pro=None) % BidU(C8A=1), catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_2kf, catalysis_0_C8A_catalyzer_BidU_substrate_BidT_product_1kr) Rule('catalysis_1_C8A_catalyzer_BidU_substrate_BidT_product', C8A(BidU=1, C3pro=None) % BidU(C8A=1) >> C8A(BidU=None, C3pro=None) + BidT(), catalysis_1_C8A_catalyzer_BidU_substrate_BidT_product_1kc) Rule('conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex', ApafI() + CytoCC() | ApafA(), conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_2kf, conversion_0_CytoCC_subunit_d_ApafI_subunit_c_ApafA_complex_1kr) Rule('inhibition_0_SmacC_inhibitor_Xiap_inh_target', SmacC(Xiap=None) + Xiap(SmacC=None, Apop=None, C3A=None) | SmacC(Xiap=1) % Xiap(SmacC=1, Apop=None, C3A=None), inhibition_0_SmacC_inhibitor_Xiap_inh_target_2kf, inhibition_0_SmacC_inhibitor_Xiap_inh_target_1kr) Rule('conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex', ApafA() + C9() | Apop(C3pro=None, Xiap=None), conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_2kf, conversion_0_C9_subunit_d_ApafA_subunit_c_Apop_complex_1kr) Rule('catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product', Apop(C3pro=None, Xiap=None) + C3pro(Apop=None, C8A=None) | Apop(C3pro=1, Xiap=None) % C3pro(Apop=1, C8A=None), catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_2kf, catalysis_0_Apop_catalyzer_C3pro_substrate_C3A_product_1kr) Rule('catalysis_1_Apop_catalyzer_C3pro_substrate_C3A_product', Apop(C3pro=1, Xiap=None) % C3pro(Apop=1, C8A=None) >> Apop(C3pro=None, Xiap=None) + C3A(Xiap=None, ParpU=None, C6pro=None), catalysis_1_Apop_catalyzer_C3pro_substrate_C3A_product_1kc) Rule('inhibition_0_Xiap_inhibitor_Apop_inh_target', Xiap(SmacC=None, Apop=None, C3A=None) + Apop(C3pro=None, Xiap=None) | Xiap(SmacC=None, Apop=1, C3A=None) % Apop(C3pro=None, Xiap=1), inhibition_0_Xiap_inhibitor_Apop_inh_target_2kf, inhibition_0_Xiap_inhibitor_Apop_inh_target_1kr) Rule('catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product', Xiap(SmacC=None, Apop=None, C3A=None) + C3A(Xiap=None, ParpU=None, C6pro=None) | Xiap(SmacC=None, Apop=None, C3A=1) % C3A(Xiap=1, ParpU=None, C6pro=None), catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_2kf, catalysis_0_Xiap_catalyzer_C3A_substrate_C3ub_product_1kr) Rule('catalysis_1_Xiap_catalyzer_C3A_substrate_C3ub_product', Xiap(SmacC=None, Apop=None, C3A=1) % C3A(Xiap=1, ParpU=None, C6pro=None) >> Xiap(SmacC=None, Apop=None, C3A=None) + C3ub(), catalysis_1_Xiap_catalyzer_C3A_substrate_C3ub_product_1kc) Rule('catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product', C3A(Xiap=None, ParpU=None, C6pro=None) + ParpU(C3A=None) | C3A(Xiap=None, ParpU=1, C6pro=None) % ParpU(C3A=1), catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_2kf, catalysis_0_C3A_catalyzer_ParpU_substrate_ParpC_product_1kr) Rule('catalysis_1_C3A_catalyzer_ParpU_substrate_ParpC_product', C3A(Xiap=None, ParpU=1, C6pro=None) % ParpU(C3A=1) >> C3A(Xiap=None, ParpU=None, C6pro=None) + ParpC(), catalysis_1_C3A_catalyzer_ParpU_substrate_ParpC_product_1kc) Rule('equilibration_0_BidT_equil_a_BidM_equil_b', BidT() | BidM(BaxM=None, Bcl2=None), equilibration_0_BidT_equil_a_BidM_equil_b_1kf, equilibration_0_BidT_equil_a_BidM_equil_b_1kr) Rule('catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product', BidM(BaxM=None, Bcl2=None) + BaxM(BidM=None, BaxA=None) | BidM(BaxM=1, Bcl2=None) % BaxM(BidM=1, BaxA=None), catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_2kf, catalysis_0_BidM_catalyzer_BaxM_substrate_BaxA_product_1kr) Rule('catalysis_1_BidM_catalyzer_BaxM_substrate_BaxA_product', BidM(BaxM=1, Bcl2=None) % BaxM(BidM=1, BaxA=None) >> BidM(BaxM=None, Bcl2=None) + BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), catalysis_1_BidM_catalyzer_BaxM_substrate_BaxA_product_1kc) Rule('self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate', BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxM(BidM=None, BaxA=None) | BaxA(BaxM=1, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) % BaxM(BidM=None, BaxA=1), self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_2kf, self_catalyze_0_BaxA_self_catalyzer_BaxM_self_substrate_1kr) Rule('self_catalyze_1_BaxA_self_catalyzer_BaxM_self_substrate', BaxA(BaxM=1, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) % BaxM(BidM=None, BaxA=1) >> BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), self_catalyze_1_BaxA_self_catalyzer_BaxM_self_substrate_1kc) Rule('inhibition_0_Bcl2_inhibitor_BidM_inh_target', Bcl2(BidM=None, BaxA=None) + BidM(BaxM=None, Bcl2=None) | Bcl2(BidM=1, BaxA=None) % BidM(BaxM=None, Bcl2=1), inhibition_0_Bcl2_inhibitor_BidM_inh_target_2kf, inhibition_0_Bcl2_inhibitor_BidM_inh_target_1kr) Rule('inhibition_0_Bcl2_inhibitor_BaxA_inh_target', Bcl2(BidM=None, BaxA=None) + BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) | Bcl2(BidM=None, BaxA=1) % BaxA(BaxM=None, Bcl2=1, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), inhibition_0_Bcl2_inhibitor_BaxA_inh_target_2kf, inhibition_0_Bcl2_inhibitor_BaxA_inh_target_1kr) Rule('pore_formation_0_BaxA_pore', BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) | BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=None, SmacM=None, CytoCM=None), pore_formation_0_BaxA_pore_2kf, pore_formation_0_BaxA_pore_1kr) Rule('pore_formation_1_BaxA_pore', BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=None, SmacM=None, CytoCM=None) | BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None), pore_formation_1_BaxA_pore_2kf, pore_formation_1_BaxA_pore_1kr) Rule('pore_formation_2_BaxA_pore', BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None) + BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) | BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None), pore_formation_2_BaxA_pore_2kf, pore_formation_2_BaxA_pore_1kr) Rule('transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C', BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + SmacM(BaxA=None) | BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=5, CytoCM=None) % SmacM(BaxA=5), transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_2kf, transport_0_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kr) Rule('transport_1_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C', BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=5, CytoCM=None) % SmacM(BaxA=5) >> BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + SmacC(Xiap=None), transport_1_BaxA_pore_SmacM_cargo_M_SmacC_cargo_C_1kc) Rule('transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C', BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + CytoCM(BaxA=None) | BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=5) % CytoCM(BaxA=5), transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_2kf, transport_0_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kr) Rule('transport_1_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C', BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=5) % CytoCM(BaxA=5) >> BaxA(BaxM=None, Bcl2=None, BaxA_1=4, BaxA_2=1, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=1, BaxA_2=2, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=2, BaxA_2=3, SmacM=None, CytoCM=None) % BaxA(BaxM=None, Bcl2=None, BaxA_1=3, BaxA_2=4, SmacM=None, CytoCM=None) + CytoCC(), transport_1_BaxA_pore_CytoCM_cargo_M_CytoCC_cargo_C_1kc) Rule('catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product', C8A(BidU=None, C3pro=None) + C3pro(Apop=None, C8A=None) | C8A(BidU=None, C3pro=1) % C3pro(Apop=None, C8A=1), catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_2kf, catalysis_0_C8A_catalyzer_C3pro_substrate_C3A_product_1kr) Rule('catalysis_1_C8A_catalyzer_C3pro_substrate_C3A_product', C8A(BidU=None, C3pro=1) % C3pro(Apop=None, C8A=1) >> C8A(BidU=None, C3pro=None) + C3A(Xiap=None, ParpU=None, C6pro=None), catalysis_1_C8A_catalyzer_C3pro_substrate_C3A_product_1kc) Rule('catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product', C3A(Xiap=None, ParpU=None, C6pro=None) + C6pro(C3A=None) | C3A(Xiap=None, ParpU=None, C6pro=1) % C6pro(C3A=1), catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_2kf, catalysis_0_C3A_catalyzer_C6pro_substrate_C6A_product_1kr) Rule('catalysis_1_C3A_catalyzer_C6pro_substrate_C6A_product', C3A(Xiap=None, ParpU=None, C6pro=1) % C6pro(C3A=1) >> C3A(Xiap=None, ParpU=None, C6pro=None) + C6A(C8pro=None), catalysis_1_C3A_catalyzer_C6pro_substrate_C6A_product_1kc) Rule('catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product', C6A(C8pro=None) + C8pro(Fadd=None, C6A=None) | C6A(C8pro=1) % C8pro(Fadd=None, C6A=1), catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_2kf, catalysis_0_C6A_catalyzer_C8pro_substrate_C8A_product_1kr) Rule('catalysis_1_C6A_catalyzer_C8pro_substrate_C8A_product', C6A(C8pro=1) % C8pro(Fadd=None, C6A=1) >> C6A(C8pro=None) + C8A(BidU=None, C3pro=None), catalysis_1_C6A_catalyzer_C8pro_substrate_C8A_product_1kc) Initial(Ligand(Receptor=None), Ligand_0) Initial(ParpU(C3A=None), ParpU_0) Initial(C8A(BidU=None, C3pro=None), C8A_0) Initial(SmacM(BaxA=None), SmacM_0) Initial(BaxM(BidM=None, BaxA=None), BaxM_0) Initial(Apop(C3pro=None, Xiap=None), Apop_0) Initial(Fadd(Receptor=None, C8pro=None), Fadd_0) Initial(SmacC(Xiap=None), SmacC_0) Initial(ParpC(), ParpC_0) Initial(Xiap(SmacC=None, Apop=None, C3A=None), Xiap_0) Initial(C9(), C9_0) Initial(C3ub(), C3ub_0) Initial(C8pro(Fadd=None, C6A=None), C8pro_0) Initial(Bcl2(BidM=None, BaxA=None), Bcl2_0) Initial(C3pro(Apop=None, C8A=None), C3pro_0) Initial(CytoCM(BaxA=None), CytoCM_0) Initial(CytoCC(), CytoCC_0) Initial(BaxA(BaxM=None, Bcl2=None, BaxA_1=None, BaxA_2=None, SmacM=None, CytoCM=None), BaxA_0) Initial(ApafI(), ApafI_0) Initial(BidU(C8A=None), BidU_0) Initial(BidT(), BidT_0) Initial(C3A(Xiap=None, ParpU=None, C6pro=None), C3A_0) Initial(ApafA(), ApafA_0) Initial(BidM(BaxM=None, Bcl2=None), BidM_0) Initial(Receptor(Ligand=None, Fadd=None), Receptor_0) Initial(C6A(C8pro=None), C6A_0) Initial(C6pro(C3A=None), C6pro_0)

true

f71fd3f3075081a6f7b219e41391ee28001ad25c

2,073

py

Python

codes/poop/fizzbuzz.py

cassiobotaro/go_slides

1e9bedff22acc78a39cc054c0de432ef80c2df7b

[ "MIT" ]

2

2019-02-27T14:45:39.000Z

2021-09-27T03:46:20.000Z

codes/poop/fizzbuzz.py

cassiobotaro/go_slides

1e9bedff22acc78a39cc054c0de432ef80c2df7b

[ "MIT" ]

null

codes/poop/fizzbuzz.py

cassiobotaro/go_slides

1e9bedff22acc78a39cc054c0de432ef80c2df7b

[ "MIT" ]

2

2017-04-13T14:42:31.000Z

2021-09-27T03:46:22.000Z

# smalltalk infected fizzbuzz version from forbiddenfruit import curse from collections import deque def if_true(self, block): # simulate blocks using functions self and block() # close circuit when object is truthy return self def if_false(self, block): # simulate blocks using functions not self and block() # close circuit when object is falsy return self def println(self): """Prints the values to a stream, or to sys.stdout by default. >>> "Fizz".print() Fizz >>> "FizzBuzz".print() FizzBuzz """ print(self) def do(self, block): """Evaluate the receiver for each element in aBlock. >>> range(1, 11).do(lambda number: number.print()) """ deque(map(block, self), maxlen=0) return self curse(bool, "if_true", if_true) curse(bool, "if_false", if_false) curse(str, "print", println) curse(int, "print", println) curse(range, "do", do) # lambdas are used to simulate blocks """Summary We add a do methd on range objects that evaluates a block for each element on interval. This block will receive a number, that evaluated in the expression "number % 15 == 0", This will result in a boolean object, to which we will send two messages, one with a block to be evaluated if the expression is true and another for if it is false. If true, we will send a print message to a "FizzBuzz" object. If it is false, we will use the same numeric object to evaluate the expression number% 5 == 0. And so we repeat the cycle, until at last a message is sent to the number printed. """ range(1, 101).do( lambda number: (number % 15 == 0) .if_true("FizzBuzz".print) .if_false( lambda: (number % 5 == 0) .if_true("Buzz".print) .if_false( lambda: (number % 3 == 0) .if_true("Fizz".print) .if_false(number.print) ) ) ) """ Notes: - A message is sent to an object for printing - Lambdas are used to simulate a block - Add method do for a range, evaluating a block on each number on interval - Objects and messages """

25.280488

75

0.670043

from forbiddenfruit import curse from collections import deque def if_true(self, block): self and block() return self def if_false(self, block): not self and block() return self def println(self): print(self) def do(self, block): deque(map(block, self), maxlen=0) return self curse(bool, "if_true", if_true) curse(bool, "if_false", if_false) curse(str, "print", println) curse(int, "print", println) curse(range, "do", do) range(1, 101).do( lambda number: (number % 15 == 0) .if_true("FizzBuzz".print) .if_false( lambda: (number % 5 == 0) .if_true("Buzz".print) .if_false( lambda: (number % 3 == 0) .if_true("Fizz".print) .if_false(number.print) ) ) )

true

f71fd4a1f731db04f57e93f442976ca80b5b4b5d

8,349

py

Python

s2e_env/tui/tui.py

michaelbrownuc/s2e-env

4bd6a45bf1ec9456ed5acf5047b6aac3fcd19683

[ "BSD-3-Clause" ]

null

s2e_env/tui/tui.py

michaelbrownuc/s2e-env

4bd6a45bf1ec9456ed5acf5047b6aac3fcd19683

[ "BSD-3-Clause" ]

null

s2e_env/tui/tui.py

michaelbrownuc/s2e-env

4bd6a45bf1ec9456ed5acf5047b6aac3fcd19683

[ "BSD-3-Clause" ]

null

""" Copyright (c) 2017 Cyberhaven 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 curses import time _s_screen = None # TODO: this module requires clean up # pylint: disable=too-many-instance-attributes # pylint: disable=too-many-arguments # pylint: disable=no-self-use class Form: def __init__(self, parent, x, y, w=None, h=None): self._children = [] self._parent = parent self._x = x self._y = y self._h = h self._w = w self._vcenter, self._hcenter = False, False self.set_size(w, h) ax, ay = self.get_screen_coords(0, 0) self._wnd = curses.newwin(self._h, self._w, ay, ax) if parent is not None: parent._children.append(self) @property def x(self): return self._x @property def y(self): return self._y @property def width(self): return self._w @width.setter def width(self, value): self._w = value @property def height(self): return self._h @height.setter def height(self, value): self._h = value @property def window(self): return self._wnd def get_screen_coords(self, x, y): form = self ax, ay = x, y while form is not None: ax, ay = ax + form.x, ay + form.y form = form.get_parent() return ax, ay def set_pos(self, x, y): self._x, self._y = x, y def set_centering(self, hcenter, vcenter): if self._parent is None: raise Exception('Form must have a parent') self._vcenter = vcenter self._hcenter = hcenter def set_size(self, w=None, h=None): """ Width and Height can be set to None to expand the window to the size of the parent container. """ if w is None or h is None: form = self.get_parent() if form is None: mh, mw = _s_screen.getmaxyx() else: mh, mw = form.height, form.width if w is None: w = mw if h is None: h = mh self._w, self._h = w, h def get_parent(self): return self._parent def get_draw_coords(self, ax, ay): x, y = self.x, self.y # Center the form in the parent window if needed if self._hcenter: x = (self._parent._w - self._w) // 2 if self._vcenter: y = (self._parent._h - self._h) // 2 x += ax y += ay return x, y def draw(self, ax, ay): x, y = self.get_draw_coords(ax, ay) # TODO: clipping self.do_draw(x, y) for child in self._children: child.draw(x, y) def do_draw(self, ax, ay): self._wnd.mvwin(ay, ax) self._wnd.resize(self._h, self._w) self._wnd.border() self._wnd.refresh() class Label(Form): def __init__(self, parent, x, y, text): super().__init__(parent, x, y, len(text) + 2, 1) self._text = f' {text}' def do_draw(self, ax, ay): self._wnd.mvwin(ay, ax) self._wnd.resize(self._h, self._w) self._wnd.addstr(0, 0, self._text) self._wnd.refresh() class Table(Form): def __init__(self, parent, x, y, data, legend, layout): self._data = data self._legend = legend self._layout = layout self.set_data(data, legend, layout) w, h = self._get_dimensions() super().__init__(parent, x, y, w, h) def _get_dimensions(self): lw, dw, h = self._compute_data_size() w, h = self._compute_bounding_box(lw, dw, h) return w, h def _update_dimensions(self): w, h = self._get_dimensions() self.width = w self.height = h def set_data(self, data, legend, layout): self._data = data self._legend = legend self._layout = layout self._update_dimensions() def _compute_bounding_box(self, lw, dw, h): return lw + dw + 5, h def _compute_data_size(self): max_legend_width = 0 max_data_width = 0 max_height = len(self._layout) for k, v in self._data.items(): l = self._legend[k] max_legend_width = max(max_legend_width, len(l)) max_data_width = max(max_data_width, len(str(v))) return max_legend_width, max_data_width, max_height def do_draw(self, ax, ay): y = 0 lw, dw, h = self._compute_data_size() w, h = self._compute_bounding_box(lw, dw, h) self._wnd.clear() self._wnd.resize(h, w) self._wnd.mvwin(ay, ax) # self._wnd.border() for k in self._layout: l = self._legend[k] if not k in self._data: continue v = self._data[k] self._wnd.addstr(y, 0, l + ':') self._wnd.addstr(y, lw + 3, str(v)) y += 1 self._wnd.refresh() class Tui: def __init__(self): self._updated = True self._data = {} self._legend = {} self._layout = {} self._desktop = None self._stats = None self._title = None self._exitmsg = None self._table = None def _create_desktop(self): global _s_screen _s_screen = curses.initscr() curses.noecho() curses.curs_set(0) curses.start_color() self._desktop = Form(None, 0, 0) self._stats = Form(self._desktop, 0, 0, 70, 20) self._stats.set_centering(True, True) self._title = Label(self._stats, 0, 0, 'S2E') self._title.set_centering(True, False) self._exitmsg = Label(self._stats, 0, 17, 'Press q to exit') self._exitmsg.set_centering(True, False) self._table = Table(self._stats, 2, 2, self._data, self._legend, self._layout) self._table.set_centering(True, True) def _cleanup(self): curses.nocbreak() _s_screen.keypad(0) curses.echo() curses.endwin() def _redraw(self): self._desktop.window.clear() self._desktop.set_size() self._desktop.draw(0, 0) def set_content(self, data, legend, layout): self._data = data self._legend = legend self._layout = layout self._updated = True self._table.set_data(data, legend, layout) def _run(self, callback): self._create_desktop() if not callback(self): return self._redraw() self._desktop.window.nodelay(True) while True: c = self._desktop.window.getch() if c == curses.ERR: if not callback(self): return time.sleep(1) elif c == ord('q'): break elif c == curses.KEY_RESIZE: self._updated = True if self._updated: self._redraw() self._updated = False def run(self, callback): try: self._run(callback) except Exception: self._cleanup() # Print message only after screen is restored, otherwise we might # get unreadable garbage. raise finally: self._cleanup()

26.674121

78

0.573602

import curses import time _s_screen = None class Form: def __init__(self, parent, x, y, w=None, h=None): self._children = [] self._parent = parent self._x = x self._y = y self._h = h self._w = w self._vcenter, self._hcenter = False, False self.set_size(w, h) ax, ay = self.get_screen_coords(0, 0) self._wnd = curses.newwin(self._h, self._w, ay, ax) if parent is not None: parent._children.append(self) @property def x(self): return self._x @property def y(self): return self._y @property def width(self): return self._w @width.setter def width(self, value): self._w = value @property def height(self): return self._h @height.setter def height(self, value): self._h = value @property def window(self): return self._wnd def get_screen_coords(self, x, y): form = self ax, ay = x, y while form is not None: ax, ay = ax + form.x, ay + form.y form = form.get_parent() return ax, ay def set_pos(self, x, y): self._x, self._y = x, y def set_centering(self, hcenter, vcenter): if self._parent is None: raise Exception('Form must have a parent') self._vcenter = vcenter self._hcenter = hcenter def set_size(self, w=None, h=None): if w is None or h is None: form = self.get_parent() if form is None: mh, mw = _s_screen.getmaxyx() else: mh, mw = form.height, form.width if w is None: w = mw if h is None: h = mh self._w, self._h = w, h def get_parent(self): return self._parent def get_draw_coords(self, ax, ay): x, y = self.x, self.y if self._hcenter: x = (self._parent._w - self._w) // 2 if self._vcenter: y = (self._parent._h - self._h) // 2 x += ax y += ay return x, y def draw(self, ax, ay): x, y = self.get_draw_coords(ax, ay) self.do_draw(x, y) for child in self._children: child.draw(x, y) def do_draw(self, ax, ay): self._wnd.mvwin(ay, ax) self._wnd.resize(self._h, self._w) self._wnd.border() self._wnd.refresh() class Label(Form): def __init__(self, parent, x, y, text): super().__init__(parent, x, y, len(text) + 2, 1) self._text = f' {text}' def do_draw(self, ax, ay): self._wnd.mvwin(ay, ax) self._wnd.resize(self._h, self._w) self._wnd.addstr(0, 0, self._text) self._wnd.refresh() class Table(Form): def __init__(self, parent, x, y, data, legend, layout): self._data = data self._legend = legend self._layout = layout self.set_data(data, legend, layout) w, h = self._get_dimensions() super().__init__(parent, x, y, w, h) def _get_dimensions(self): lw, dw, h = self._compute_data_size() w, h = self._compute_bounding_box(lw, dw, h) return w, h def _update_dimensions(self): w, h = self._get_dimensions() self.width = w self.height = h def set_data(self, data, legend, layout): self._data = data self._legend = legend self._layout = layout self._update_dimensions() def _compute_bounding_box(self, lw, dw, h): return lw + dw + 5, h def _compute_data_size(self): max_legend_width = 0 max_data_width = 0 max_height = len(self._layout) for k, v in self._data.items(): l = self._legend[k] max_legend_width = max(max_legend_width, len(l)) max_data_width = max(max_data_width, len(str(v))) return max_legend_width, max_data_width, max_height def do_draw(self, ax, ay): y = 0 lw, dw, h = self._compute_data_size() w, h = self._compute_bounding_box(lw, dw, h) self._wnd.clear() self._wnd.resize(h, w) self._wnd.mvwin(ay, ax) for k in self._layout: l = self._legend[k] if not k in self._data: continue v = self._data[k] self._wnd.addstr(y, 0, l + ':') self._wnd.addstr(y, lw + 3, str(v)) y += 1 self._wnd.refresh() class Tui: def __init__(self): self._updated = True self._data = {} self._legend = {} self._layout = {} self._desktop = None self._stats = None self._title = None self._exitmsg = None self._table = None def _create_desktop(self): global _s_screen _s_screen = curses.initscr() curses.noecho() curses.curs_set(0) curses.start_color() self._desktop = Form(None, 0, 0) self._stats = Form(self._desktop, 0, 0, 70, 20) self._stats.set_centering(True, True) self._title = Label(self._stats, 0, 0, 'S2E') self._title.set_centering(True, False) self._exitmsg = Label(self._stats, 0, 17, 'Press q to exit') self._exitmsg.set_centering(True, False) self._table = Table(self._stats, 2, 2, self._data, self._legend, self._layout) self._table.set_centering(True, True) def _cleanup(self): curses.nocbreak() _s_screen.keypad(0) curses.echo() curses.endwin() def _redraw(self): self._desktop.window.clear() self._desktop.set_size() self._desktop.draw(0, 0) def set_content(self, data, legend, layout): self._data = data self._legend = legend self._layout = layout self._updated = True self._table.set_data(data, legend, layout) def _run(self, callback): self._create_desktop() if not callback(self): return self._redraw() self._desktop.window.nodelay(True) while True: c = self._desktop.window.getch() if c == curses.ERR: if not callback(self): return time.sleep(1) elif c == ord('q'): break elif c == curses.KEY_RESIZE: self._updated = True if self._updated: self._redraw() self._updated = False def run(self, callback): try: self._run(callback) except Exception: self._cleanup() raise finally: self._cleanup()

true

f71fd745dc747e5cd621bba11088fb4afbc2acb3

1,092

py

Python

jmatcher/users/migrations/0004_auto_20170303_2141.py

jamesaud/se1-group4

5280b13dff33e72ce717318a8dd78a06cd6effb3

[ "MIT" ]

1

2021-09-09T15:43:09.000Z

jmatcher/users/migrations/0004_auto_20170303_2141.py

jamesaud/se1-group4

5280b13dff33e72ce717318a8dd78a06cd6effb3

[ "MIT" ]

null

jmatcher/users/migrations/0004_auto_20170303_2141.py

jamesaud/se1-group4

5280b13dff33e72ce717318a8dd78a06cd6effb3

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.10.5 on 2017-03-03 21:41 from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('users', '0003_remove_student_student_name'), ] operations = [ migrations.CreateModel( name='Skill', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('skill', models.CharField(choices=[('Django', 'Django'), ('Python', 'Python'), ('Java', 'Java'), ('Ruby', 'Ruby')], max_length=255)), ], ), migrations.RemoveField( model_name='student', name='user', ), migrations.AddField( model_name='user', name='connections', field=models.ManyToManyField(related_name='_user_connections_+', to=settings.AUTH_USER_MODEL), ), migrations.DeleteModel( name='Student', ), ]

30.333333

150

0.57967

from __future__ import unicode_literals from django.conf import settings from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('users', '0003_remove_student_student_name'), ] operations = [ migrations.CreateModel( name='Skill', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('skill', models.CharField(choices=[('Django', 'Django'), ('Python', 'Python'), ('Java', 'Java'), ('Ruby', 'Ruby')], max_length=255)), ], ), migrations.RemoveField( model_name='student', name='user', ), migrations.AddField( model_name='user', name='connections', field=models.ManyToManyField(related_name='_user_connections_+', to=settings.AUTH_USER_MODEL), ), migrations.DeleteModel( name='Student', ), ]

true

f71fd8d00f312300adb54ea7c3dae8e0ed739a61

9,839

py

Python

datasets/preprocess/mpi_inf_3dhp.py

virgile-hernicot/SPIN

21871e3d333ef37866402ae21498b331aa771b2d

[ "BSD-3-Clause" ]

555

2019-09-30T01:03:23.000Z

2022-03-30T03:56:09.000Z

datasets/preprocess/mpi_inf_3dhp.py

virgile-hernicot/SPIN

21871e3d333ef37866402ae21498b331aa771b2d

[ "BSD-3-Clause" ]

110

2019-10-01T05:51:07.000Z

2022-03-23T13:51:05.000Z

datasets/preprocess/mpi_inf_3dhp.py

virgile-hernicot/SPIN

21871e3d333ef37866402ae21498b331aa771b2d

[ "BSD-3-Clause" ]

158

2019-09-30T07:06:48.000Z

2022-03-22T02:32:03.000Z

import os import sys import cv2 import glob import h5py import json import numpy as np import scipy.io as sio import scipy.misc from .read_openpose import read_openpose def read_calibration(calib_file, vid_list): Ks, Rs, Ts = [], [], [] file = open(calib_file, 'r') content = file.readlines() for vid_i in vid_list: K = np.array([float(s) for s in content[vid_i*7+5][11:-2].split()]) K = np.reshape(K, (4, 4)) RT = np.array([float(s) for s in content[vid_i*7+6][11:-2].split()]) RT = np.reshape(RT, (4, 4)) R = RT[:3,:3] T = RT[:3,3]/1000 Ks.append(K) Rs.append(R) Ts.append(T) return Ks, Rs, Ts def train_data(dataset_path, openpose_path, out_path, joints_idx, scaleFactor, extract_img=False, fits_3d=None): joints17_idx = [4, 18, 19, 20, 23, 24, 25, 3, 5, 6, 7, 9, 10, 11, 14, 15, 16] h, w = 2048, 2048 imgnames_, scales_, centers_ = [], [], [] parts_, Ss_, openposes_ = [], [], [] # training data user_list = range(1,9) seq_list = range(1,3) vid_list = list(range(3)) + list(range(4,9)) counter = 0 for user_i in user_list: for seq_i in seq_list: seq_path = os.path.join(dataset_path, 'S' + str(user_i), 'Seq' + str(seq_i)) # mat file with annotations annot_file = os.path.join(seq_path, 'annot.mat') annot2 = sio.loadmat(annot_file)['annot2'] annot3 = sio.loadmat(annot_file)['annot3'] # calibration file and camera parameters calib_file = os.path.join(seq_path, 'camera.calibration') Ks, Rs, Ts = read_calibration(calib_file, vid_list) for j, vid_i in enumerate(vid_list): # image folder imgs_path = os.path.join(seq_path, 'imageFrames', 'video_' + str(vid_i)) # extract frames from video file if extract_img: # if doesn't exist if not os.path.isdir(imgs_path): os.makedirs(imgs_path) # video file vid_file = os.path.join(seq_path, 'imageSequence', 'video_' + str(vid_i) + '.avi') vidcap = cv2.VideoCapture(vid_file) # process video frame = 0 while 1: # extract all frames success, image = vidcap.read() if not success: break frame += 1 # image name imgname = os.path.join(imgs_path, 'frame_%06d.jpg' % frame) # save image cv2.imwrite(imgname, image) # per frame cam_aa = cv2.Rodrigues(Rs[j])[0].T[0] pattern = os.path.join(imgs_path, '*.jpg') img_list = glob.glob(pattern) for i, img_i in enumerate(img_list): # for each image we store the relevant annotations img_name = img_i.split('/')[-1] img_view = os.path.join('S' + str(user_i), 'Seq' + str(seq_i), 'imageFrames', 'video_' + str(vid_i), img_name) joints = np.reshape(annot2[vid_i][0][i], (28, 2))[joints17_idx] S17 = np.reshape(annot3[vid_i][0][i], (28, 3))/1000 S17 = S17[joints17_idx] - S17[4] # 4 is the root bbox = [min(joints[:,0]), min(joints[:,1]), max(joints[:,0]), max(joints[:,1])] center = [(bbox[2]+bbox[0])/2, (bbox[3]+bbox[1])/2] scale = scaleFactor*max(bbox[2]-bbox[0], bbox[3]-bbox[1])/200 # check that all joints are visible x_in = np.logical_and(joints[:, 0] < w, joints[:, 0] >= 0) y_in = np.logical_and(joints[:, 1] < h, joints[:, 1] >= 0) ok_pts = np.logical_and(x_in, y_in) if np.sum(ok_pts) < len(joints_idx): continue part = np.zeros([24,3]) part[joints_idx] = np.hstack([joints, np.ones([17,1])]) json_file = os.path.join(openpose_path, 'mpi_inf_3dhp', img_view.replace('.jpg', '_keypoints.json')) openpose = read_openpose(json_file, part, 'mpi_inf_3dhp') S = np.zeros([24,4]) S[joints_idx] = np.hstack([S17, np.ones([17,1])]) # because of the dataset size, we only keep every 10th frame counter += 1 if counter % 10 != 1: continue # store the data imgnames_.append(img_view) centers_.append(center) scales_.append(scale) parts_.append(part) Ss_.append(S) openposes_.append(openpose) # store the data struct if not os.path.isdir(out_path): os.makedirs(out_path) out_file = os.path.join(out_path, 'mpi_inf_3dhp_train.npz') if fits_3d is not None: fits_3d = np.load(fits_3d) np.savez(out_file, imgname=imgnames_, center=centers_, scale=scales_, part=parts_, pose=fits_3d['pose'], shape=fits_3d['shape'], has_smpl=fits_3d['has_smpl'], S=Ss_, openpose=openposes_) else: np.savez(out_file, imgname=imgnames_, center=centers_, scale=scales_, part=parts_, S=Ss_, openpose=openposes_) def test_data(dataset_path, out_path, joints_idx, scaleFactor): joints17_idx = [14, 11, 12, 13, 8, 9, 10, 15, 1, 16, 0, 5, 6, 7, 2, 3, 4] imgnames_, scales_, centers_, parts_, Ss_ = [], [], [], [], [] # training data user_list = range(1,7) for user_i in user_list: seq_path = os.path.join(dataset_path, 'mpi_inf_3dhp_test_set', 'TS' + str(user_i)) # mat file with annotations annot_file = os.path.join(seq_path, 'annot_data.mat') mat_as_h5 = h5py.File(annot_file, 'r') annot2 = np.array(mat_as_h5['annot2']) annot3 = np.array(mat_as_h5['univ_annot3']) valid = np.array(mat_as_h5['valid_frame']) for frame_i, valid_i in enumerate(valid): if valid_i == 0: continue img_name = os.path.join('mpi_inf_3dhp_test_set', 'TS' + str(user_i), 'imageSequence', 'img_' + str(frame_i+1).zfill(6) + '.jpg') joints = annot2[frame_i,0,joints17_idx,:] S17 = annot3[frame_i,0,joints17_idx,:]/1000 S17 = S17 - S17[0] bbox = [min(joints[:,0]), min(joints[:,1]), max(joints[:,0]), max(joints[:,1])] center = [(bbox[2]+bbox[0])/2, (bbox[3]+bbox[1])/2] scale = scaleFactor*max(bbox[2]-bbox[0], bbox[3]-bbox[1])/200 # check that all joints are visible img_file = os.path.join(dataset_path, img_name) I = scipy.misc.imread(img_file) h, w, _ = I.shape x_in = np.logical_and(joints[:, 0] < w, joints[:, 0] >= 0) y_in = np.logical_and(joints[:, 1] < h, joints[:, 1] >= 0) ok_pts = np.logical_and(x_in, y_in) if np.sum(ok_pts) < len(joints_idx): continue part = np.zeros([24,3]) part[joints_idx] = np.hstack([joints, np.ones([17,1])]) S = np.zeros([24,4]) S[joints_idx] = np.hstack([S17, np.ones([17,1])]) # store the data imgnames_.append(img_name) centers_.append(center) scales_.append(scale) parts_.append(part) Ss_.append(S) # store the data struct if not os.path.isdir(out_path): os.makedirs(out_path) out_file = os.path.join(out_path, 'mpi_inf_3dhp_test.npz') np.savez(out_file, imgname=imgnames_, center=centers_, scale=scales_, part=parts_, S=Ss_) def mpi_inf_3dhp_extract(dataset_path, openpose_path, out_path, mode, extract_img=False, static_fits=None): scaleFactor = 1.2 joints_idx = [14, 3, 4, 5, 2, 1, 0, 16, 12, 17, 18, 9, 10, 11, 8, 7, 6] if static_fits is not None: fits_3d = os.path.join(static_fits, 'mpi-inf-3dhp_mview_fits.npz') else: fits_3d = None if mode == 'train': train_data(dataset_path, openpose_path, out_path, joints_idx, scaleFactor, extract_img=extract_img, fits_3d=fits_3d) elif mode == 'test': test_data(dataset_path, out_path, joints_idx, scaleFactor)

39.514056

112

0.468035

import os import sys import cv2 import glob import h5py import json import numpy as np import scipy.io as sio import scipy.misc from .read_openpose import read_openpose def read_calibration(calib_file, vid_list): Ks, Rs, Ts = [], [], [] file = open(calib_file, 'r') content = file.readlines() for vid_i in vid_list: K = np.array([float(s) for s in content[vid_i*7+5][11:-2].split()]) K = np.reshape(K, (4, 4)) RT = np.array([float(s) for s in content[vid_i*7+6][11:-2].split()]) RT = np.reshape(RT, (4, 4)) R = RT[:3,:3] T = RT[:3,3]/1000 Ks.append(K) Rs.append(R) Ts.append(T) return Ks, Rs, Ts def train_data(dataset_path, openpose_path, out_path, joints_idx, scaleFactor, extract_img=False, fits_3d=None): joints17_idx = [4, 18, 19, 20, 23, 24, 25, 3, 5, 6, 7, 9, 10, 11, 14, 15, 16] h, w = 2048, 2048 imgnames_, scales_, centers_ = [], [], [] parts_, Ss_, openposes_ = [], [], [] user_list = range(1,9) seq_list = range(1,3) vid_list = list(range(3)) + list(range(4,9)) counter = 0 for user_i in user_list: for seq_i in seq_list: seq_path = os.path.join(dataset_path, 'S' + str(user_i), 'Seq' + str(seq_i)) annot_file = os.path.join(seq_path, 'annot.mat') annot2 = sio.loadmat(annot_file)['annot2'] annot3 = sio.loadmat(annot_file)['annot3'] calib_file = os.path.join(seq_path, 'camera.calibration') Ks, Rs, Ts = read_calibration(calib_file, vid_list) for j, vid_i in enumerate(vid_list): imgs_path = os.path.join(seq_path, 'imageFrames', 'video_' + str(vid_i)) if extract_img: if not os.path.isdir(imgs_path): os.makedirs(imgs_path) # video file vid_file = os.path.join(seq_path, 'imageSequence', 'video_' + str(vid_i) + '.avi') vidcap = cv2.VideoCapture(vid_file) # process video frame = 0 while 1: # extract all frames success, image = vidcap.read() if not success: break frame += 1 # image name imgname = os.path.join(imgs_path, 'frame_%06d.jpg' % frame) # save image cv2.imwrite(imgname, image) # per frame cam_aa = cv2.Rodrigues(Rs[j])[0].T[0] pattern = os.path.join(imgs_path, '*.jpg') img_list = glob.glob(pattern) for i, img_i in enumerate(img_list): # for each image we store the relevant annotations img_name = img_i.split('/')[-1] img_view = os.path.join('S' + str(user_i), 'Seq' + str(seq_i), 'imageFrames', 'video_' + str(vid_i), img_name) joints = np.reshape(annot2[vid_i][0][i], (28, 2))[joints17_idx] S17 = np.reshape(annot3[vid_i][0][i], (28, 3))/1000 S17 = S17[joints17_idx] - S17[4] # 4 is the root bbox = [min(joints[:,0]), min(joints[:,1]), max(joints[:,0]), max(joints[:,1])] center = [(bbox[2]+bbox[0])/2, (bbox[3]+bbox[1])/2] scale = scaleFactor*max(bbox[2]-bbox[0], bbox[3]-bbox[1])/200 # check that all joints are visible x_in = np.logical_and(joints[:, 0] < w, joints[:, 0] >= 0) y_in = np.logical_and(joints[:, 1] < h, joints[:, 1] >= 0) ok_pts = np.logical_and(x_in, y_in) if np.sum(ok_pts) < len(joints_idx): continue part = np.zeros([24,3]) part[joints_idx] = np.hstack([joints, np.ones([17,1])]) json_file = os.path.join(openpose_path, 'mpi_inf_3dhp', img_view.replace('.jpg', '_keypoints.json')) openpose = read_openpose(json_file, part, 'mpi_inf_3dhp') S = np.zeros([24,4]) S[joints_idx] = np.hstack([S17, np.ones([17,1])]) # because of the dataset size, we only keep every 10th frame counter += 1 if counter % 10 != 1: continue # store the data imgnames_.append(img_view) centers_.append(center) scales_.append(scale) parts_.append(part) Ss_.append(S) openposes_.append(openpose) # store the data struct if not os.path.isdir(out_path): os.makedirs(out_path) out_file = os.path.join(out_path, 'mpi_inf_3dhp_train.npz') if fits_3d is not None: fits_3d = np.load(fits_3d) np.savez(out_file, imgname=imgnames_, center=centers_, scale=scales_, part=parts_, pose=fits_3d['pose'], shape=fits_3d['shape'], has_smpl=fits_3d['has_smpl'], S=Ss_, openpose=openposes_) else: np.savez(out_file, imgname=imgnames_, center=centers_, scale=scales_, part=parts_, S=Ss_, openpose=openposes_) def test_data(dataset_path, out_path, joints_idx, scaleFactor): joints17_idx = [14, 11, 12, 13, 8, 9, 10, 15, 1, 16, 0, 5, 6, 7, 2, 3, 4] imgnames_, scales_, centers_, parts_, Ss_ = [], [], [], [], [] # training data user_list = range(1,7) for user_i in user_list: seq_path = os.path.join(dataset_path, 'mpi_inf_3dhp_test_set', 'TS' + str(user_i)) # mat file with annotations annot_file = os.path.join(seq_path, 'annot_data.mat') mat_as_h5 = h5py.File(annot_file, 'r') annot2 = np.array(mat_as_h5['annot2']) annot3 = np.array(mat_as_h5['univ_annot3']) valid = np.array(mat_as_h5['valid_frame']) for frame_i, valid_i in enumerate(valid): if valid_i == 0: continue img_name = os.path.join('mpi_inf_3dhp_test_set', 'TS' + str(user_i), 'imageSequence', 'img_' + str(frame_i+1).zfill(6) + '.jpg') joints = annot2[frame_i,0,joints17_idx,:] S17 = annot3[frame_i,0,joints17_idx,:]/1000 S17 = S17 - S17[0] bbox = [min(joints[:,0]), min(joints[:,1]), max(joints[:,0]), max(joints[:,1])] center = [(bbox[2]+bbox[0])/2, (bbox[3]+bbox[1])/2] scale = scaleFactor*max(bbox[2]-bbox[0], bbox[3]-bbox[1])/200 # check that all joints are visible img_file = os.path.join(dataset_path, img_name) I = scipy.misc.imread(img_file) h, w, _ = I.shape x_in = np.logical_and(joints[:, 0] < w, joints[:, 0] >= 0) y_in = np.logical_and(joints[:, 1] < h, joints[:, 1] >= 0) ok_pts = np.logical_and(x_in, y_in) if np.sum(ok_pts) < len(joints_idx): continue part = np.zeros([24,3]) part[joints_idx] = np.hstack([joints, np.ones([17,1])]) S = np.zeros([24,4]) S[joints_idx] = np.hstack([S17, np.ones([17,1])]) # store the data imgnames_.append(img_name) centers_.append(center) scales_.append(scale) parts_.append(part) Ss_.append(S) # store the data struct if not os.path.isdir(out_path): os.makedirs(out_path) out_file = os.path.join(out_path, 'mpi_inf_3dhp_test.npz') np.savez(out_file, imgname=imgnames_, center=centers_, scale=scales_, part=parts_, S=Ss_) def mpi_inf_3dhp_extract(dataset_path, openpose_path, out_path, mode, extract_img=False, static_fits=None): scaleFactor = 1.2 joints_idx = [14, 3, 4, 5, 2, 1, 0, 16, 12, 17, 18, 9, 10, 11, 8, 7, 6] if static_fits is not None: fits_3d = os.path.join(static_fits, 'mpi-inf-3dhp_mview_fits.npz') else: fits_3d = None if mode == 'train': train_data(dataset_path, openpose_path, out_path, joints_idx, scaleFactor, extract_img=extract_img, fits_3d=fits_3d) elif mode == 'test': test_data(dataset_path, out_path, joints_idx, scaleFactor)

true

f71fd8ed4a60f1f0cb0713800b0c028bb7bc4489

16,140

py

Python

desktop/core/ext-py/pysaml2-4.4.0/src/saml2/cert.py

kokosing/hue

2307f5379a35aae9be871e836432e6f45138b3d9

[ "Apache-2.0" ]

3

2018-01-29T14:16:02.000Z

2019-02-05T21:33:05.000Z

desktop/core/ext-py/pysaml2-4.4.0/src/saml2/cert.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

4

2021-03-11T04:02:00.000Z

2022-03-27T08:31:56.000Z

desktop/core/ext-py/pysaml2-4.4.0/src/saml2/cert.py

zks888/hue

93a8c370713e70b216c428caa2f75185ef809deb

[ "Apache-2.0" ]

2

2019-06-17T11:51:56.000Z

2020-07-25T08:29:56.000Z

__author__ = 'haho0032' import base64 import datetime import dateutil.parser import pytz import six from OpenSSL import crypto from os.path import join from os import remove from Cryptodome.Util import asn1 class WrongInput(Exception): pass class CertificateError(Exception): pass class PayloadError(Exception): pass class OpenSSLWrapper(object): def __init__(self): pass def create_certificate(self, cert_info, request=False, valid_from=0, valid_to=315360000, sn=1, key_length=1024, hash_alg="sha256", write_to_file=False, cert_dir="", cipher_passphrase=None): """ Can create certificate requests, to be signed later by another certificate with the method create_cert_signed_certificate. If request is True. Can also create self signed root certificates if request is False. This is default behaviour. :param cert_info: Contains information about the certificate. Is a dictionary that must contain the keys: cn = Common name. This part must match the host being authenticated country_code = Two letter description of the country. state = State city = City organization = Organization, can be a company name. organization_unit = A unit at the organization, can be a department. Example: cert_info_ca = { "cn": "company.com", "country_code": "se", "state": "AC", "city": "Dorotea", "organization": "Company", "organization_unit": "Sales" } :param request: True if this is a request for certificate, that should be signed. False if this is a self signed certificate, root certificate. :param valid_from: When the certificate starts to be valid. Amount of seconds from when the certificate is generated. :param valid_to: How long the certificate will be valid from when it is generated. The value is in seconds. Default is 315360000 seconds, a.k.a 10 years. :param sn: Serial number for the certificate. Default is 1. :param key_length: Length of the key to be generated. Defaults to 1024. :param hash_alg: Hash algorithm to use for the key. Default is sha256. :param write_to_file: True if you want to write the certificate to a file. The method will then return a tuple with path to certificate file and path to key file. False if you want to get the result as strings. The method will then return a tuple with the certificate string and the key as string. WILL OVERWRITE ALL EXISTING FILES WITHOUT ASKING! :param cert_dir: Where to save the files if write_to_file is true. :param cipher_passphrase A dictionary with cipher and passphrase. Example:: {"cipher": "blowfish", "passphrase": "qwerty"} :return: string representation of certificate, string representation of private key if write_to_file parameter is False otherwise path to certificate file, path to private key file """ cn = cert_info["cn"] c_f = None k_f = None if write_to_file: cert_file = "%s.crt" % cn key_file = "%s.key" % cn try: remove(cert_file) except: pass try: remove(key_file) except: pass c_f = join(cert_dir, cert_file) k_f = join(cert_dir, key_file) # create a key pair k = crypto.PKey() k.generate_key(crypto.TYPE_RSA, key_length) # create a self-signed cert cert = crypto.X509() if request: cert = crypto.X509Req() if (len(cert_info["country_code"]) != 2): raise WrongInput("Country code must be two letters!") cert.get_subject().C = cert_info["country_code"] cert.get_subject().ST = cert_info["state"] cert.get_subject().L = cert_info["city"] cert.get_subject().O = cert_info["organization"] cert.get_subject().OU = cert_info["organization_unit"] cert.get_subject().CN = cn if not request: cert.set_serial_number(sn) cert.gmtime_adj_notBefore(valid_from) #Valid before present time cert.gmtime_adj_notAfter(valid_to) #3 650 days cert.set_issuer(cert.get_subject()) cert.set_pubkey(k) cert.sign(k, hash_alg) filesCreated = False try: if request: tmp_cert = crypto.dump_certificate_request(crypto.FILETYPE_PEM, cert) else: tmp_cert = crypto.dump_certificate(crypto.FILETYPE_PEM, cert) tmp_key = None if cipher_passphrase is not None: passphrase = cipher_passphrase["passphrase"] if isinstance(cipher_passphrase["passphrase"], six.string_types): passphrase = passphrase.encode('utf-8') tmp_key = crypto.dump_privatekey(crypto.FILETYPE_PEM, k, cipher_passphrase["cipher"], passphrase) else: tmp_key = crypto.dump_privatekey(crypto.FILETYPE_PEM, k) if write_to_file: fc = open(c_f, "wt") fk = open(k_f, "wt") if request: fc.write(tmp_cert.decode('utf-8')) else: fc.write(tmp_cert.decode('utf-8')) fk.write(tmp_key.decode('utf-8')) filesCreated = True try: fc.close() except: pass try: fk.close() except: pass return c_f, k_f return tmp_cert, tmp_key except Exception as ex: raise CertificateError("Certificate cannot be generated.", ex) def write_str_to_file(self, file, str_data): f = open(file, "wt") f.write(str_data) f.close() def read_str_from_file(self, file, type="pem"): f = open(file, 'rt') str_data = f.read() f.close() if type == "pem": return str_data if type in ["der", "cer", "crt"]: return base64.b64encode(str(str_data)) def create_cert_signed_certificate(self, sign_cert_str, sign_key_str, request_cert_str, hash_alg="sha256", valid_from=0, valid_to=315360000, sn=1, passphrase=None): """ Will sign a certificate request with a give certificate. :param sign_cert_str: This certificate will be used to sign with. Must be a string representation of the certificate. If you only have a file use the method read_str_from_file to get a string representation. :param sign_key_str: This is the key for the ca_cert_str represented as a string. If you only have a file use the method read_str_from_file to get a string representation. :param request_cert_str: This is the prepared certificate to be signed. Must be a string representation of the requested certificate. If you only have a file use the method read_str_from_file to get a string representation. :param hash_alg: Hash algorithm to use for the key. Default is sha256. :param valid_from: When the certificate starts to be valid. Amount of seconds from when the certificate is generated. :param valid_to: How long the certificate will be valid from when it is generated. The value is in seconds. Default is 315360000 seconds, a.k.a 10 years. :param sn: Serial number for the certificate. Default is 1. :param passphrase: Password for the private key in sign_key_str. :return: String representation of the signed certificate. """ ca_cert = crypto.load_certificate(crypto.FILETYPE_PEM, sign_cert_str) ca_key = None if passphrase is not None: ca_key = crypto.load_privatekey(crypto.FILETYPE_PEM, sign_key_str, passphrase) else: ca_key = crypto.load_privatekey(crypto.FILETYPE_PEM, sign_key_str) req_cert = crypto.load_certificate_request(crypto.FILETYPE_PEM, request_cert_str) cert = crypto.X509() cert.set_subject(req_cert.get_subject()) cert.set_serial_number(sn) cert.gmtime_adj_notBefore(valid_from) cert.gmtime_adj_notAfter(valid_to) cert.set_issuer(ca_cert.get_subject()) cert.set_pubkey(req_cert.get_pubkey()) cert.sign(ca_key, hash_alg) cert_dump = crypto.dump_certificate(crypto.FILETYPE_PEM, cert) if isinstance(cert_dump, six.string_types): return cert_dump return cert_dump.decode('utf-8') def verify_chain(self, cert_chain_str_list, cert_str): """ :param cert_chain_str_list: Must be a list of certificate strings, where the first certificate to be validate is in the beginning and the root certificate is last. :param cert_str: The certificate to be validated. :return: """ for tmp_cert_str in cert_chain_str_list: valid, message = self.verify(tmp_cert_str, cert_str) if not valid: return False, message else: cert_str = tmp_cert_str return (True, "Signed certificate is valid and correctly signed by CA " "certificate.") def certificate_not_valid_yet(self, cert): starts_to_be_valid = dateutil.parser.parse(cert.get_notBefore()) now = pytz.UTC.localize(datetime.datetime.utcnow()) if starts_to_be_valid < now: return False return True def verify(self, signing_cert_str, cert_str): """ Verifies if a certificate is valid and signed by a given certificate. :param signing_cert_str: This certificate will be used to verify the signature. Must be a string representation of the certificate. If you only have a file use the method read_str_from_file to get a string representation. :param cert_str: This certificate will be verified if it is correct. Must be a string representation of the certificate. If you only have a file use the method read_str_from_file to get a string representation. :return: Valid, Message Valid = True if the certificate is valid, otherwise false. Message = Why the validation failed. """ try: ca_cert = crypto.load_certificate(crypto.FILETYPE_PEM, signing_cert_str) cert = crypto.load_certificate(crypto.FILETYPE_PEM, cert_str) if self.certificate_not_valid_yet(ca_cert): return False, "CA certificate is not valid yet." if ca_cert.has_expired() == 1: return False, "CA certificate is expired." if cert.has_expired() == 1: return False, "The signed certificate is expired." if self.certificate_not_valid_yet(cert): return False, "The signed certificate is not valid yet." if ca_cert.get_subject().CN == cert.get_subject().CN: return False, ("CN may not be equal for CA certificate and the " "signed certificate.") cert_algorithm = cert.get_signature_algorithm() if six.PY3: cert_algorithm = cert_algorithm.decode('ascii') cert_asn1 = crypto.dump_certificate(crypto.FILETYPE_ASN1, cert) der_seq = asn1.DerSequence() der_seq.decode(cert_asn1) cert_certificate = der_seq[0] #cert_signature_algorithm=der_seq[1] cert_signature = der_seq[2] cert_signature_decoded = asn1.DerObject() cert_signature_decoded.decode(cert_signature) signature_payload = cert_signature_decoded.payload sig_pay0 = signature_payload[0] if ((isinstance(sig_pay0, int) and sig_pay0 != 0) or (isinstance(sig_pay0, str) and sig_pay0 != '\x00')): return (False, "The certificate should not contain any unused bits.") signature = signature_payload[1:] try: crypto.verify(ca_cert, signature, cert_certificate, cert_algorithm) return True, "Signed certificate is valid and correctly signed by CA certificate." except crypto.Error as e: return False, "Certificate is incorrectly signed." except Exception as e: return False, "Certificate is not valid for an unknown reason. %s" % str(e)

43.621622

98

0.495291

__author__ = 'haho0032' import base64 import datetime import dateutil.parser import pytz import six from OpenSSL import crypto from os.path import join from os import remove from Cryptodome.Util import asn1 class WrongInput(Exception): pass class CertificateError(Exception): pass class PayloadError(Exception): pass class OpenSSLWrapper(object): def __init__(self): pass def create_certificate(self, cert_info, request=False, valid_from=0, valid_to=315360000, sn=1, key_length=1024, hash_alg="sha256", write_to_file=False, cert_dir="", cipher_passphrase=None): cn = cert_info["cn"] c_f = None k_f = None if write_to_file: cert_file = "%s.crt" % cn key_file = "%s.key" % cn try: remove(cert_file) except: pass try: remove(key_file) except: pass c_f = join(cert_dir, cert_file) k_f = join(cert_dir, key_file) k = crypto.PKey() k.generate_key(crypto.TYPE_RSA, key_length) cert = crypto.X509() if request: cert = crypto.X509Req() if (len(cert_info["country_code"]) != 2): raise WrongInput("Country code must be two letters!") cert.get_subject().C = cert_info["country_code"] cert.get_subject().ST = cert_info["state"] cert.get_subject().L = cert_info["city"] cert.get_subject().O = cert_info["organization"] cert.get_subject().OU = cert_info["organization_unit"] cert.get_subject().CN = cn if not request: cert.set_serial_number(sn) cert.gmtime_adj_notBefore(valid_from) cert.gmtime_adj_notAfter(valid_to) cert.set_issuer(cert.get_subject()) cert.set_pubkey(k) cert.sign(k, hash_alg) filesCreated = False try: if request: tmp_cert = crypto.dump_certificate_request(crypto.FILETYPE_PEM, cert) else: tmp_cert = crypto.dump_certificate(crypto.FILETYPE_PEM, cert) tmp_key = None if cipher_passphrase is not None: passphrase = cipher_passphrase["passphrase"] if isinstance(cipher_passphrase["passphrase"], six.string_types): passphrase = passphrase.encode('utf-8') tmp_key = crypto.dump_privatekey(crypto.FILETYPE_PEM, k, cipher_passphrase["cipher"], passphrase) else: tmp_key = crypto.dump_privatekey(crypto.FILETYPE_PEM, k) if write_to_file: fc = open(c_f, "wt") fk = open(k_f, "wt") if request: fc.write(tmp_cert.decode('utf-8')) else: fc.write(tmp_cert.decode('utf-8')) fk.write(tmp_key.decode('utf-8')) filesCreated = True try: fc.close() except: pass try: fk.close() except: pass return c_f, k_f return tmp_cert, tmp_key except Exception as ex: raise CertificateError("Certificate cannot be generated.", ex) def write_str_to_file(self, file, str_data): f = open(file, "wt") f.write(str_data) f.close() def read_str_from_file(self, file, type="pem"): f = open(file, 'rt') str_data = f.read() f.close() if type == "pem": return str_data if type in ["der", "cer", "crt"]: return base64.b64encode(str(str_data)) def create_cert_signed_certificate(self, sign_cert_str, sign_key_str, request_cert_str, hash_alg="sha256", valid_from=0, valid_to=315360000, sn=1, passphrase=None): ca_cert = crypto.load_certificate(crypto.FILETYPE_PEM, sign_cert_str) ca_key = None if passphrase is not None: ca_key = crypto.load_privatekey(crypto.FILETYPE_PEM, sign_key_str, passphrase) else: ca_key = crypto.load_privatekey(crypto.FILETYPE_PEM, sign_key_str) req_cert = crypto.load_certificate_request(crypto.FILETYPE_PEM, request_cert_str) cert = crypto.X509() cert.set_subject(req_cert.get_subject()) cert.set_serial_number(sn) cert.gmtime_adj_notBefore(valid_from) cert.gmtime_adj_notAfter(valid_to) cert.set_issuer(ca_cert.get_subject()) cert.set_pubkey(req_cert.get_pubkey()) cert.sign(ca_key, hash_alg) cert_dump = crypto.dump_certificate(crypto.FILETYPE_PEM, cert) if isinstance(cert_dump, six.string_types): return cert_dump return cert_dump.decode('utf-8') def verify_chain(self, cert_chain_str_list, cert_str): for tmp_cert_str in cert_chain_str_list: valid, message = self.verify(tmp_cert_str, cert_str) if not valid: return False, message else: cert_str = tmp_cert_str return (True, "Signed certificate is valid and correctly signed by CA " "certificate.") def certificate_not_valid_yet(self, cert): starts_to_be_valid = dateutil.parser.parse(cert.get_notBefore()) now = pytz.UTC.localize(datetime.datetime.utcnow()) if starts_to_be_valid < now: return False return True def verify(self, signing_cert_str, cert_str): try: ca_cert = crypto.load_certificate(crypto.FILETYPE_PEM, signing_cert_str) cert = crypto.load_certificate(crypto.FILETYPE_PEM, cert_str) if self.certificate_not_valid_yet(ca_cert): return False, "CA certificate is not valid yet." if ca_cert.has_expired() == 1: return False, "CA certificate is expired." if cert.has_expired() == 1: return False, "The signed certificate is expired." if self.certificate_not_valid_yet(cert): return False, "The signed certificate is not valid yet." if ca_cert.get_subject().CN == cert.get_subject().CN: return False, ("CN may not be equal for CA certificate and the " "signed certificate.") cert_algorithm = cert.get_signature_algorithm() if six.PY3: cert_algorithm = cert_algorithm.decode('ascii') cert_asn1 = crypto.dump_certificate(crypto.FILETYPE_ASN1, cert) der_seq = asn1.DerSequence() der_seq.decode(cert_asn1) cert_certificate = der_seq[0] cert_signature = der_seq[2] cert_signature_decoded = asn1.DerObject() cert_signature_decoded.decode(cert_signature) signature_payload = cert_signature_decoded.payload sig_pay0 = signature_payload[0] if ((isinstance(sig_pay0, int) and sig_pay0 != 0) or (isinstance(sig_pay0, str) and sig_pay0 != '\x00')): return (False, "The certificate should not contain any unused bits.") signature = signature_payload[1:] try: crypto.verify(ca_cert, signature, cert_certificate, cert_algorithm) return True, "Signed certificate is valid and correctly signed by CA certificate." except crypto.Error as e: return False, "Certificate is incorrectly signed." except Exception as e: return False, "Certificate is not valid for an unknown reason. %s" % str(e)

true

f71fd916fcc0b6fd5407ff0c3b8ac492320273d7

26,028

py

Python

tf_rl_tutorial/models.py

4k4xs4pH1r3/tf_rl_tutorial

c58d10c60cfd79b2e0661b4a49cccae8d4584c57

[ "Apache-2.0" ]

40

2016-03-09T03:03:08.000Z

2021-09-11T21:44:12.000Z

tf_rl_tutorial/models.py

4k4xs4pH1r3/tf_rl_tutorial

c58d10c60cfd79b2e0661b4a49cccae8d4584c57

[ "Apache-2.0" ]

1

2018-05-08T13:45:43.000Z

tf_rl_tutorial/models.py

4k4xs4pH1r3/tf_rl_tutorial

c58d10c60cfd79b2e0661b4a49cccae8d4584c57

[ "Apache-2.0" ]

17

2016-03-17T14:57:11.000Z

2021-06-04T16:24:48.000Z

# Copyright 2016 Mandiant, A FireEye Company # Authors: Brian Jones # License: Apache 2.0 ''' Model classes for "Relational Learning with TensorFlow" tutorial ''' import numpy as np import tensorflow as tf from .util import ContrastiveTrainingProvider def least_squares_objective(output, target, add_bias=True): ''' Creates final model output and loss for least squares objective Args: output: Model output target: Training target placeholder add_bias: If True, a bias Variable will be added to the output Returns: tuple (final output, loss) ''' y = output if add_bias: bias = tf.Variable([0.0]) y = output + bias loss = tf.reduce_sum(tf.square(y - target)) return y, loss def logistic_objective(output, target, add_bias=True): ''' Creates final model output and loss for logistic objective Args: output: Model output target: Training target placeholder add_bias: If True, a bias Variable will be added to the output Returns: tuple (final output, loss) ''' y = output if add_bias: bias = tf.Variable([0.0]) y = output + bias sig_y = tf.clip_by_value(tf.sigmoid(y), 0.001, 0.999) # avoid NaNs loss = -tf.reduce_sum(target*tf.log(sig_y) + (1-target)*tf.log(1-sig_y)) return sig_y, loss def ranking_margin_objective(output, margin=1.0): ''' Create final model output and loss for pairwise ranking margin objective Loss for single pair (f(p), f(n)) = [margin - f(p) + f(n)]+ This only works when given model output on alternating positive/negative pairs: [pos,neg,pos,neg,...]. TODO: check target placeholder at runtime to make sure this is the case? Args: output: Model output margin: The margin value for the pairwise hinge loss Returns: tuple (final output, loss) ''' y_pairs = tf.reshape(output, [-1,2]) # fold: 1 x n -> [n/2 x 2] pos_scores, neg_scores = tf.split(1, 2, y_pairs) # separate pairs hinge_losses = tf.nn.relu(margin - pos_scores + neg_scores) total_hinge_loss = tf.reduce_sum(hinge_losses) return output, total_hinge_loss def sparse_maxnorm_update(var_matrix, indices, maxnorm=1.0): '''Sparse update operation that ensures selected rows in var_matrix do not have a Euclidean norm greater than maxnorm. Rows that exceed it are scaled to length. Args: var_matrix: 2D mutable tensor (Variable) to operate on indices: 1D tensor with the row indices to constrain maxnorm: the maximum Euclidean norm Returns: An operation that will update var_matrix when run in a Session ''' selected_rows = tf.nn.embedding_lookup(var_matrix, indices) row_norms = tf.sqrt(tf.reduce_sum(tf.square(selected_rows), 1)) scaling = maxnorm / tf.maximum(row_norms, maxnorm) scaled = selected_rows * tf.expand_dims(scaling, 1) return tf.scatter_update(var_matrix, indices, scaled) def dense_maxnorm_update(var_matrix, maxnorm=1.0): '''Dense update operation that ensures all rows in var_matrix do not have a Euclidean norm greater than maxnorm. Rows that exceed it are scaled to length. Args: var_matrix: 2D mutable tensor (Variable) to operate on maxnorm: the maximum Euclidean norm Returns: An operation that will update var_matrix when run in a Session ''' row_norms = tf.sqrt(tf.reduce_sum(tf.square(var_matrix), 1)) scaling = maxnorm / tf.maximum(row_norms, maxnorm) scaled = var_matrix * tf.expand_dims(scaling, 1) return tf.assign(var_matrix, scaled) def dense_maxnorm(var_matrix, maxnorm=1.0): '''Similar to dense_maxnorm_update(), except this returns a new Tensor instead of an operation that modifies var_matrix. Args: var_matrix: 2D tensor (Variable) maxnorm: the maximum Euclidean norm Returns: A new tensor where all rows have been scaled as necessary ''' axis_norms = tf.sqrt(tf.reduce_sum(tf.square(var_matrix), 1)) scaling = maxnorm / tf.maximum(axis_norms, maxnorm) return var_matrix * tf.expand_dims(scaling, 1) class BaseModel(object): ''' Base class for embedding-based relational learning models that use maxnorm regularization. Subclasses must implement _create_model() and populate self.train_step, and can optionally populate self.post_step for post-processing. Note: When model_type is 'ranking_margin', the mini-batch provider returned by _create_batch_provider() must provide instances in alternating pos/neg pairs: [pos, neg, pos, neg, ...]. This is satisfied when using ContrastiveTrainingProvider; be careful if you use a different one. Args: embedding_size: Embedding vector length maxnorm: Maximum Euclidean norm for embedding vectors batch_pos_cnt: Number of positive examples to use in each mini-batch max_iter: Maximum number of optimization iterations to perform model_type: Possible values: 'least_squares': squared loss on 0/1 targets 'logistic': sigmoid link function, crossent loss on 0/1 targets 'ranking_margin': ranking margin on pos/neg pairs add_bias: If True, a bias Variable will be added to the output for least_squares and logistic models. opt: An optimizer object to use. If None, the default optimizer is tf.train.AdagradOptimizer(1.0) TODO: add support for other regularizers like L2 ''' def __init__(self, embedding_size, maxnorm=1.0, batch_pos_cnt=100, max_iter=1000, model_type='least_squares', add_bias=True, opt=None): self.embedding_size = embedding_size self.maxnorm = maxnorm self.batch_pos_cnt = batch_pos_cnt self.max_iter = max_iter self.model_type = model_type self.add_bias = add_bias if opt is None: opt = tf.train.AdagradOptimizer(1.0) self.opt = opt self.sess = None self.train_step = None self.post_step = None self.graph = tf.Graph() with self.graph.as_default(): self.head_input = tf.placeholder(tf.int32, shape=[None]) self.rel_input = tf.placeholder(tf.int32, shape=[None]) self.tail_input = tf.placeholder(tf.int32, shape=[None]) self.target = tf.placeholder(tf.float32, shape=[None]) def _create_model(self, train_triples): ''' Subclasses must build Graph and set self.train_step ''' raise Exception('subclass must implement') def _create_batch_provider(self, train_triples): ''' Default implementation ''' return ContrastiveTrainingProvider(train_triples, self.batch_pos_cnt) def _create_output_and_loss(self, raw_output): if self.model_type == 'least_squares': return least_squares_objective(raw_output, self.target, self.add_bias) elif self.model_type == 'logistic': return logistic_objective(raw_output, self.target, self.add_bias) elif self.model_type == 'ranking_margin': return ranking_margin_objective(raw_output, 1.0) else: raise Exception('Unknown model_type') def _norm_constraint_op(self, var_matrix, row_indices, maxnorm): ''' Args: var_matrix: A 2D Tensor holding the vectors to constrain (in rows) row_indices: The rows in var_tensor that are being considered for constraint application (typically embedding vectors for entities observed for a minibatch of training data). These will be used for a sparse variable update operation if the chosen optimizer only modified these entries. Otherwise a dense operation is used and row_indices are ignored. maxnorm: The maximum Euclidean norm for the rows in var_tensor Returns: An operation which will apply the constraints when run in a Session ''' # Currently, TF optimizers do not update variables with zero gradient # except AdamOptimizer if isinstance(self.opt, tf.train.AdamOptimizer): return dense_maxnorm_update(var_matrix, maxnorm) else: return sparse_maxnorm_update(var_matrix, row_indices, maxnorm) def embeddings(self): ''' Subclass should override this if it uses different embedding variables Returns: A list of pairs: [(embedding name, embedding 2D Tensor)] ''' return [('entity', self.entity_embedding_vars), ('rel', self.rel_embedding_vars)] def create_feed_dict(self, triples, labels=None, training=False): ''' Create a TensorFlow feed dict for relationship triples Args: triples: A numpy integer array of relationship triples, where each row contains [head idx, relationship idx, tail idx] labels: (optional) A label array for triples training: (optional) A flag indicating whether the feed dict is for training or test purposes. Useful for things like dropout where a dropout_probability variable is set differently in the two contexts. ''' feed_dict = {self.head_input: triples[:, 0], self.rel_input: triples[:, 1], self.tail_input: triples[:, 2]} if labels is not None: feed_dict[self.target] = labels return feed_dict def close(self): ''' Closes the TensorFlow Session object ''' self.sess.close(); def fit(self, train_triples, step_callback=None): ''' Trains the model on relationship triples Args: train_triples: A numpy integer array of relationship triples, where each row of contains [head idx, relationship idx, tail idx] step_callback: (optional) A function that will be called before each optimization step, step_callback(iteration, feed_dict) ''' if self.sess is not None: self.sess.close() self.sess = tf.Session(graph=self.graph) with self.graph.as_default(): self._create_model(train_triples) self.sess.run(tf.initialize_all_variables()) batch_provider = self._create_batch_provider(train_triples) for i in range(self.max_iter): batch_triples, batch_labels = batch_provider.next_batch() feed_dict = self.create_feed_dict(batch_triples, batch_labels, training=True) if step_callback: keep_going = step_callback(i, feed_dict) if not keep_going: break self.sess.run(self.train_step, feed_dict) if self.post_step is not None: self.sess.run(self.post_step, feed_dict) def predict(self, triples): ''' Runs a trained model on the supplied relationship triples. fit() must be called before calling this function. Args: triples: A numpy integer array of relationship triples, where each row of contains [head idx, relationship idx, tail idx] ''' feed_dict = self.create_feed_dict(triples, training=False) return self.sess.run(self.output, feed_dict=feed_dict) class Contrastive_CP(BaseModel): ''' Model with a scoring function based on CANDECOMP/PARAFAC tensor decomposition. Optimization differs, however, in the use of maxnorm regularization and contrastive negative sampling. Score for (head i, rel k, tail j) triple is: h_i^T * diag(r_k) * t_j, where h_i and t_j are embedding vectors for the head and tail entities, and r_k is an embedding vector for the relationship type. Args: embedding_size: Embedding vector length maxnorm: Maximum Euclidean norm for embedding vectors batch_pos_cnt: Number of positive examples to use in each mini-batch max_iter: Maximum number of optimization iterations to perform model_type: Possible values: 'least_squares': squared loss on 0/1 targets 'logistic': sigmoid link function, crossent loss on 0/1 targets 'ranking_margin': ranking margin on pos/neg pairs add_bias: If True, a bias Variable will be added to the output for least_squares and logistic models. opt: An optimizer object to use. If None, the default optimizer is tf.train.AdagradOptimizer(1.0) References: Kolda, Tamara G., and Brett W. Bader. "Tensor decompositions and applications." SIAM review 51.3 (2009): 455-500. ''' def _create_model(self, train_triples): # Count unique items to determine embedding matrix sizes head_cnt = len(set(train_triples[:,0])) rel_cnt = len(set(train_triples[:,1])) tail_cnt = len(set(train_triples[:,2])) init_sd = 1.0 / np.sqrt(self.embedding_size) # Embedding matrices for entities and relationship types head_init = tf.truncated_normal([head_cnt, self.embedding_size], stddev=init_sd) rel_init = tf.truncated_normal([rel_cnt, self.embedding_size], stddev=init_sd) tail_init = tf.truncated_normal([tail_cnt, self.embedding_size], stddev=init_sd) if self.maxnorm is not None: # Ensure maxnorm constraints are initially satisfied head_init = dense_maxnorm(head_init, self.maxnorm) rel_init = dense_maxnorm(rel_init, self.maxnorm) tail_init = dense_maxnorm(tail_init, self.maxnorm) self.head_embedding_vars = tf.Variable(head_init) self.rel_embedding_vars = tf.Variable(rel_init) self.tail_embedding_vars = tf.Variable(tail_init) # Embedding layer for each (head, rel, tail) triple being fed in as input head_embed = tf.nn.embedding_lookup(self.head_embedding_vars, self.head_input) rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input) tail_embed = tf.nn.embedding_lookup(self.tail_embedding_vars, self.tail_input) # Model output raw_output = tf.reduce_sum(tf.mul(tf.mul(head_embed, rel_embed), tail_embed), 1) self.output, self.loss = self._create_output_and_loss(raw_output) # Optimization self.train_step = self.opt.minimize(self.loss) if self.maxnorm is not None: # Post-processing to limit embedding vars to L2 ball head_constraint = self._norm_constraint_op(self.head_embedding_vars, tf.unique(self.head_input)[0], self.maxnorm) rel_constraint = self._norm_constraint_op(self.rel_embedding_vars, tf.unique(self.rel_input)[0], self.maxnorm) tail_constraint = self._norm_constraint_op(self.tail_embedding_vars, tf.unique(self.tail_input)[0], self.maxnorm) self.post_step = [head_constraint, rel_constraint, tail_constraint] def _create_batch_provider(self, train): # CP treats head and tail entities separately return ContrastiveTrainingProvider(train, self.batch_pos_cnt, separate_head_tail=True) def embeddings(self): ''' Returns: A list of pairs: [(embedding name, embedding 2D Tensor)] ''' return [('head', self.head_embedding_vars), ('tail', self.head_embedding_vars), ('rel', self.rel_embedding_vars)] class Bilinear(BaseModel): ''' Model with a scoring function based on the bilinear formulation of RESCAL. Optimization differs, however, in the use of maxnorm regularization and contrastive negative sampling. Score for (head i, rel k, tail j) triple is: e_i^T * R_k * e_j where e_i and e_j are D-dimensional embedding vectors for the head and tail entities, and R_k is a (D x D) matrix for the relationship type acting as a bilinear operator. Args: embedding_size: Embedding vector length maxnorm: Maximum Euclidean norm for embedding vectors rel_maxnorm_mult: Multiplier for the maxnorm threshold used for relationship embeddings. Example: If maxnorm=2.0 and rel_maxnorm_mult=4.0, then the maxnorm constrain for relationships will be 2.0 * 4.0 = 8.0. batch_pos_cnt: Number of positive examples to use in each mini-batch max_iter: Maximum number of optimization iterations to perform model_type: Possible values: 'least_squares': squared loss on 0/1 targets 'logistic': sigmoid link function, crossent loss on 0/1 targets 'ranking_margin': ranking margin on pos/neg pairs add_bias: If True, a bias Variable will be added to the output for least_squares and logistic models. opt: An optimizer object to use. If None, the default optimizer is tf.train.AdagradOptimizer(1.0) References: Nickel, Maximilian, Volker Tresp, and Hans-Peter Kriegel. "A three-way model for collective learning on multi-relational data." Proceedings of the 28th international conference on machine learning (ICML-11). 2011. ''' def __init__(self, embedding_size, maxnorm=1.0, rel_maxnorm_mult=3.0, batch_pos_cnt=100, max_iter=1000, model_type='least_squares', add_bias=True, opt=None): super(Bilinear, self).__init__( embedding_size=embedding_size, maxnorm=maxnorm, batch_pos_cnt=batch_pos_cnt, max_iter=max_iter, model_type=model_type, opt=opt) self.rel_maxnorm_mult = rel_maxnorm_mult def _create_model(self, train_triples): # Count unique items to determine embedding matrix sizes entity_cnt = len(set(train_triples[:,0]).union(train_triples[:,2])) rel_cnt = len(set(train_triples[:,1])) init_sd = 1.0 / np.sqrt(self.embedding_size) # Embedding variables for all entities and relationship types entity_embedding_shape = [entity_cnt, self.embedding_size] # Relationship embeddings will be stored in flattened format to make # applying maxnorm constraints easier rel_embedding_shape = [rel_cnt, self.embedding_size * self.embedding_size] entity_init = tf.truncated_normal(entity_embedding_shape, stddev=init_sd) rel_init = tf.truncated_normal(rel_embedding_shape, stddev=init_sd) if self.maxnorm is not None: # Ensure maxnorm constraints are initially satisfied entity_init = dense_maxnorm(entity_init, self.maxnorm) rel_init = dense_maxnorm(rel_init, self.maxnorm) self.entity_embedding_vars = tf.Variable(entity_init) self.rel_embedding_vars = tf.Variable(rel_init) # Embedding layer for each (head, rel, tail) triple being fed in as input head_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.head_input) tail_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.tail_input) rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input) # Reshape rel_embed into square D x D matrices rel_embed_square = tf.reshape(rel_embed, (-1, self.embedding_size, self.embedding_size)) # Reshape head_embed and tail_embed to be suitable for the matrix multiplication head_embed_row = tf.expand_dims(head_embed, 1) # embeddings as row vectors tail_embed_col = tf.expand_dims(tail_embed, 2) # embeddings as column vectors head_rel_mult = tf.batch_matmul(head_embed_row, rel_embed_square) # Output needs a squeeze into a 1d vector raw_output = tf.squeeze(tf.batch_matmul(head_rel_mult, tail_embed_col)) self.output, self.loss = self._create_output_and_loss(raw_output) # Optimization self.train_step = self.opt.minimize(self.loss) if self.maxnorm is not None: # Post-processing to limit embedding vars to L2 ball rel_maxnorm = self.maxnorm * self.rel_maxnorm_mult unique_ent_indices = tf.unique(tf.concat(0, [self.head_input, self.tail_input]))[0] unique_rel_indices = tf.unique(self.rel_input)[0] entity_constraint = self._norm_constraint_op(self.entity_embedding_vars, unique_ent_indices, self.maxnorm) rel_constraint = self._norm_constraint_op(self.rel_embedding_vars, unique_rel_indices, rel_maxnorm) self.post_step = [entity_constraint, rel_constraint] class TransE(BaseModel): ''' TransE: Translational Embeddings Model Score for (head i, rel k, tail j) triple is: d(e_i + t_k, e_i) where e_i and e_j are D-dimensional embedding vectors for the head and tail entities, t_k is a another D-dimensional vector acting as a translation, and d() is a dissimilarity function like Euclidean distance. Optimization is performed uing SGD on ranking margin loss between contrastive training pairs. Entity embeddings are contrained to lie within the unit L2 ball, relationship vectors are left unconstrained. Args: embedding_size: Embedding vector length batch_pos_cnt: Number of positive examples to use in each mini-batch max_iter: Maximum number of optimization iterations to perform dist: Distance function used in loss: 'euclidean': sqrt(sum((x - y)^2)) 'sqeuclidean': squared Euclidean, sum((x - y)^2) 'manhattan': sum of absolute differences, sum(|x - y|) margin: Margin parameter for parwise ranking hinge loss opt: An optimizer object to use. If None, the default optimizer is tf.train.AdagradOptimizer(1.0) References: Bordes, Antoine, et al. "Translating embeddings for modeling multi-relational data." Advances in Neural Information Processing Systems. 2013. ''' def __init__(self, embedding_size, batch_pos_cnt=100, max_iter=1000, dist='euclidean', margin=1.0, opt=None): super(TransE, self).__init__(embedding_size=embedding_size, maxnorm=1.0, batch_pos_cnt=batch_pos_cnt, max_iter=max_iter, model_type='ranking_margin', opt=opt) self.dist = dist self.margin = margin self.EPS = 1e-3 # for sqrt gradient when dist='euclidean' def _create_model(self, train_triples): # Count unique items to determine embedding matrix sizes entity_cnt = len(set(train_triples[:,0]).union(train_triples[:,2])) rel_cnt = len(set(train_triples[:,1])) init_sd = 1.0 / np.sqrt(self.embedding_size) # Embedding variables entity_var_shape = [entity_cnt, self.embedding_size] rel_var_shape = [rel_cnt, self.embedding_size] entity_init = tf.truncated_normal(entity_var_shape, stddev=init_sd) rel_init = tf.truncated_normal(rel_var_shape, stddev=init_sd) # Ensure maxnorm constraints are initially satisfied entity_init = dense_maxnorm(entity_init, self.maxnorm) self.entity_embedding_vars = tf.Variable(entity_init) self.rel_embedding_vars = tf.Variable(rel_init) # Embedding layer for each (head, rel, tail) triple being fed in as input head_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.head_input) tail_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.tail_input) rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input) # Relationship vector acts as a translation in entity embedding space diff_vec = tail_embed - (head_embed + rel_embed) # negative dist so higher scores are better (important for pairwise loss) if self.dist == 'manhattan': raw_output = -tf.reduce_sum(tf.abs(diff_vec), 1) elif self.dist == 'euclidean': # +eps because gradients can misbehave for small values in sqrt raw_output = -tf.sqrt(tf.reduce_sum(tf.square(diff_vec), 1) + self.EPS) elif self.dist == 'sqeuclidean': raw_output = -tf.reduce_sum(tf.square(diff_vec), 1) else: raise Exception('Unknown distance type') # Model output self.output, self.loss = ranking_margin_objective(raw_output, self.margin) # Optimization with postprocessing to limit embedding vars to L2 ball self.train_step = self.opt.minimize(self.loss) unique_ent_indices = tf.unique(tf.concat(0, [self.head_input, self.tail_input]))[0] self.post_step = self._norm_constraint_op(self.entity_embedding_vars, unique_ent_indices, self.maxnorm)

47.67033

96

0.643807

import numpy as np import tensorflow as tf from .util import ContrastiveTrainingProvider def least_squares_objective(output, target, add_bias=True): y = output if add_bias: bias = tf.Variable([0.0]) y = output + bias loss = tf.reduce_sum(tf.square(y - target)) return y, loss def logistic_objective(output, target, add_bias=True): y = output if add_bias: bias = tf.Variable([0.0]) y = output + bias sig_y = tf.clip_by_value(tf.sigmoid(y), 0.001, 0.999) loss = -tf.reduce_sum(target*tf.log(sig_y) + (1-target)*tf.log(1-sig_y)) return sig_y, loss def ranking_margin_objective(output, margin=1.0): y_pairs = tf.reshape(output, [-1,2]) pos_scores, neg_scores = tf.split(1, 2, y_pairs) hinge_losses = tf.nn.relu(margin - pos_scores + neg_scores) total_hinge_loss = tf.reduce_sum(hinge_losses) return output, total_hinge_loss def sparse_maxnorm_update(var_matrix, indices, maxnorm=1.0): selected_rows = tf.nn.embedding_lookup(var_matrix, indices) row_norms = tf.sqrt(tf.reduce_sum(tf.square(selected_rows), 1)) scaling = maxnorm / tf.maximum(row_norms, maxnorm) scaled = selected_rows * tf.expand_dims(scaling, 1) return tf.scatter_update(var_matrix, indices, scaled) def dense_maxnorm_update(var_matrix, maxnorm=1.0): row_norms = tf.sqrt(tf.reduce_sum(tf.square(var_matrix), 1)) scaling = maxnorm / tf.maximum(row_norms, maxnorm) scaled = var_matrix * tf.expand_dims(scaling, 1) return tf.assign(var_matrix, scaled) def dense_maxnorm(var_matrix, maxnorm=1.0): axis_norms = tf.sqrt(tf.reduce_sum(tf.square(var_matrix), 1)) scaling = maxnorm / tf.maximum(axis_norms, maxnorm) return var_matrix * tf.expand_dims(scaling, 1) class BaseModel(object): def __init__(self, embedding_size, maxnorm=1.0, batch_pos_cnt=100, max_iter=1000, model_type='least_squares', add_bias=True, opt=None): self.embedding_size = embedding_size self.maxnorm = maxnorm self.batch_pos_cnt = batch_pos_cnt self.max_iter = max_iter self.model_type = model_type self.add_bias = add_bias if opt is None: opt = tf.train.AdagradOptimizer(1.0) self.opt = opt self.sess = None self.train_step = None self.post_step = None self.graph = tf.Graph() with self.graph.as_default(): self.head_input = tf.placeholder(tf.int32, shape=[None]) self.rel_input = tf.placeholder(tf.int32, shape=[None]) self.tail_input = tf.placeholder(tf.int32, shape=[None]) self.target = tf.placeholder(tf.float32, shape=[None]) def _create_model(self, train_triples): raise Exception('subclass must implement') def _create_batch_provider(self, train_triples): return ContrastiveTrainingProvider(train_triples, self.batch_pos_cnt) def _create_output_and_loss(self, raw_output): if self.model_type == 'least_squares': return least_squares_objective(raw_output, self.target, self.add_bias) elif self.model_type == 'logistic': return logistic_objective(raw_output, self.target, self.add_bias) elif self.model_type == 'ranking_margin': return ranking_margin_objective(raw_output, 1.0) else: raise Exception('Unknown model_type') def _norm_constraint_op(self, var_matrix, row_indices, maxnorm): if isinstance(self.opt, tf.train.AdamOptimizer): return dense_maxnorm_update(var_matrix, maxnorm) else: return sparse_maxnorm_update(var_matrix, row_indices, maxnorm) def embeddings(self): return [('entity', self.entity_embedding_vars), ('rel', self.rel_embedding_vars)] def create_feed_dict(self, triples, labels=None, training=False): feed_dict = {self.head_input: triples[:, 0], self.rel_input: triples[:, 1], self.tail_input: triples[:, 2]} if labels is not None: feed_dict[self.target] = labels return feed_dict def close(self): self.sess.close(); def fit(self, train_triples, step_callback=None): if self.sess is not None: self.sess.close() self.sess = tf.Session(graph=self.graph) with self.graph.as_default(): self._create_model(train_triples) self.sess.run(tf.initialize_all_variables()) batch_provider = self._create_batch_provider(train_triples) for i in range(self.max_iter): batch_triples, batch_labels = batch_provider.next_batch() feed_dict = self.create_feed_dict(batch_triples, batch_labels, training=True) if step_callback: keep_going = step_callback(i, feed_dict) if not keep_going: break self.sess.run(self.train_step, feed_dict) if self.post_step is not None: self.sess.run(self.post_step, feed_dict) def predict(self, triples): feed_dict = self.create_feed_dict(triples, training=False) return self.sess.run(self.output, feed_dict=feed_dict) class Contrastive_CP(BaseModel): def _create_model(self, train_triples): head_cnt = len(set(train_triples[:,0])) rel_cnt = len(set(train_triples[:,1])) tail_cnt = len(set(train_triples[:,2])) init_sd = 1.0 / np.sqrt(self.embedding_size) head_init = tf.truncated_normal([head_cnt, self.embedding_size], stddev=init_sd) rel_init = tf.truncated_normal([rel_cnt, self.embedding_size], stddev=init_sd) tail_init = tf.truncated_normal([tail_cnt, self.embedding_size], stddev=init_sd) if self.maxnorm is not None: head_init = dense_maxnorm(head_init, self.maxnorm) rel_init = dense_maxnorm(rel_init, self.maxnorm) tail_init = dense_maxnorm(tail_init, self.maxnorm) self.head_embedding_vars = tf.Variable(head_init) self.rel_embedding_vars = tf.Variable(rel_init) self.tail_embedding_vars = tf.Variable(tail_init) head_embed = tf.nn.embedding_lookup(self.head_embedding_vars, self.head_input) rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input) tail_embed = tf.nn.embedding_lookup(self.tail_embedding_vars, self.tail_input) raw_output = tf.reduce_sum(tf.mul(tf.mul(head_embed, rel_embed), tail_embed), 1) self.output, self.loss = self._create_output_and_loss(raw_output) self.train_step = self.opt.minimize(self.loss) if self.maxnorm is not None: head_constraint = self._norm_constraint_op(self.head_embedding_vars, tf.unique(self.head_input)[0], self.maxnorm) rel_constraint = self._norm_constraint_op(self.rel_embedding_vars, tf.unique(self.rel_input)[0], self.maxnorm) tail_constraint = self._norm_constraint_op(self.tail_embedding_vars, tf.unique(self.tail_input)[0], self.maxnorm) self.post_step = [head_constraint, rel_constraint, tail_constraint] def _create_batch_provider(self, train): return ContrastiveTrainingProvider(train, self.batch_pos_cnt, separate_head_tail=True) def embeddings(self): return [('head', self.head_embedding_vars), ('tail', self.head_embedding_vars), ('rel', self.rel_embedding_vars)] class Bilinear(BaseModel): def __init__(self, embedding_size, maxnorm=1.0, rel_maxnorm_mult=3.0, batch_pos_cnt=100, max_iter=1000, model_type='least_squares', add_bias=True, opt=None): super(Bilinear, self).__init__( embedding_size=embedding_size, maxnorm=maxnorm, batch_pos_cnt=batch_pos_cnt, max_iter=max_iter, model_type=model_type, opt=opt) self.rel_maxnorm_mult = rel_maxnorm_mult def _create_model(self, train_triples): entity_cnt = len(set(train_triples[:,0]).union(train_triples[:,2])) rel_cnt = len(set(train_triples[:,1])) init_sd = 1.0 / np.sqrt(self.embedding_size) entity_embedding_shape = [entity_cnt, self.embedding_size] rel_embedding_shape = [rel_cnt, self.embedding_size * self.embedding_size] entity_init = tf.truncated_normal(entity_embedding_shape, stddev=init_sd) rel_init = tf.truncated_normal(rel_embedding_shape, stddev=init_sd) if self.maxnorm is not None: entity_init = dense_maxnorm(entity_init, self.maxnorm) rel_init = dense_maxnorm(rel_init, self.maxnorm) self.entity_embedding_vars = tf.Variable(entity_init) self.rel_embedding_vars = tf.Variable(rel_init) head_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.head_input) tail_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.tail_input) rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input) rel_embed_square = tf.reshape(rel_embed, (-1, self.embedding_size, self.embedding_size)) head_embed_row = tf.expand_dims(head_embed, 1) tail_embed_col = tf.expand_dims(tail_embed, 2) head_rel_mult = tf.batch_matmul(head_embed_row, rel_embed_square) raw_output = tf.squeeze(tf.batch_matmul(head_rel_mult, tail_embed_col)) self.output, self.loss = self._create_output_and_loss(raw_output) self.train_step = self.opt.minimize(self.loss) if self.maxnorm is not None: rel_maxnorm = self.maxnorm * self.rel_maxnorm_mult unique_ent_indices = tf.unique(tf.concat(0, [self.head_input, self.tail_input]))[0] unique_rel_indices = tf.unique(self.rel_input)[0] entity_constraint = self._norm_constraint_op(self.entity_embedding_vars, unique_ent_indices, self.maxnorm) rel_constraint = self._norm_constraint_op(self.rel_embedding_vars, unique_rel_indices, rel_maxnorm) self.post_step = [entity_constraint, rel_constraint] class TransE(BaseModel): def __init__(self, embedding_size, batch_pos_cnt=100, max_iter=1000, dist='euclidean', margin=1.0, opt=None): super(TransE, self).__init__(embedding_size=embedding_size, maxnorm=1.0, batch_pos_cnt=batch_pos_cnt, max_iter=max_iter, model_type='ranking_margin', opt=opt) self.dist = dist self.margin = margin self.EPS = 1e-3 def _create_model(self, train_triples): entity_cnt = len(set(train_triples[:,0]).union(train_triples[:,2])) rel_cnt = len(set(train_triples[:,1])) init_sd = 1.0 / np.sqrt(self.embedding_size) entity_var_shape = [entity_cnt, self.embedding_size] rel_var_shape = [rel_cnt, self.embedding_size] entity_init = tf.truncated_normal(entity_var_shape, stddev=init_sd) rel_init = tf.truncated_normal(rel_var_shape, stddev=init_sd) entity_init = dense_maxnorm(entity_init, self.maxnorm) self.entity_embedding_vars = tf.Variable(entity_init) self.rel_embedding_vars = tf.Variable(rel_init) head_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.head_input) tail_embed = tf.nn.embedding_lookup(self.entity_embedding_vars, self.tail_input) rel_embed = tf.nn.embedding_lookup(self.rel_embedding_vars, self.rel_input) diff_vec = tail_embed - (head_embed + rel_embed) if self.dist == 'manhattan': raw_output = -tf.reduce_sum(tf.abs(diff_vec), 1) elif self.dist == 'euclidean': raw_output = -tf.sqrt(tf.reduce_sum(tf.square(diff_vec), 1) + self.EPS) elif self.dist == 'sqeuclidean': raw_output = -tf.reduce_sum(tf.square(diff_vec), 1) else: raise Exception('Unknown distance type') self.output, self.loss = ranking_margin_objective(raw_output, self.margin) self.train_step = self.opt.minimize(self.loss) unique_ent_indices = tf.unique(tf.concat(0, [self.head_input, self.tail_input]))[0] self.post_step = self._norm_constraint_op(self.entity_embedding_vars, unique_ent_indices, self.maxnorm)

true

f71fd969d3ac6dc91ff8442595549e245b3a9430

1,059

py

Python

salvia/wallet/payment.py

Salvia-Network/salvia-blockchain

b0ce4b9f75c2fc354941b45eb468ffcf917ead30

[ "Apache-2.0" ]

6

2021-09-13T17:20:49.000Z

2022-02-09T04:31:47.000Z

salvia/wallet/payment.py

Salvia-Network/salvia-blockchain

b0ce4b9f75c2fc354941b45eb468ffcf917ead30

[ "Apache-2.0" ]

21

2021-09-20T00:56:54.000Z

2022-03-22T01:12:12.000Z

salvia/wallet/payment.py

Salvia-Network/salvia-blockchain

b0ce4b9f75c2fc354941b45eb468ffcf917ead30

[ "Apache-2.0" ]

9

2021-09-13T17:54:04.000Z

2022-03-15T08:38:35.000Z

from dataclasses import dataclass from typing import List from salvia.types.blockchain_format.sized_bytes import bytes32 from salvia.types.blockchain_format.program import Program from salvia.util.ints import uint64 # This class is supposed to correspond to a CREATE_COIN condition @dataclass(frozen=True) class Payment: puzzle_hash: bytes32 amount: uint64 memos: List[bytes] def as_condition_args(self) -> List: return [self.puzzle_hash, self.amount, self.memos] def as_condition(self) -> Program: return Program.to([51, *self.as_condition_args()]) def name(self) -> bytes32: return self.as_condition().get_tree_hash() @classmethod def from_condition(cls, condition: Program) -> "Payment": python_condition: List = condition.as_python() puzzle_hash, amount = python_condition[1:3] memos: List[bytes] = [] if len(python_condition) > 3: memos = python_condition[3] return cls(bytes32(puzzle_hash), uint64(int.from_bytes(amount, "big")), memos)

31.147059

86

0.705382

from dataclasses import dataclass from typing import List from salvia.types.blockchain_format.sized_bytes import bytes32 from salvia.types.blockchain_format.program import Program from salvia.util.ints import uint64 @dataclass(frozen=True) class Payment: puzzle_hash: bytes32 amount: uint64 memos: List[bytes] def as_condition_args(self) -> List: return [self.puzzle_hash, self.amount, self.memos] def as_condition(self) -> Program: return Program.to([51, *self.as_condition_args()]) def name(self) -> bytes32: return self.as_condition().get_tree_hash() @classmethod def from_condition(cls, condition: Program) -> "Payment": python_condition: List = condition.as_python() puzzle_hash, amount = python_condition[1:3] memos: List[bytes] = [] if len(python_condition) > 3: memos = python_condition[3] return cls(bytes32(puzzle_hash), uint64(int.from_bytes(amount, "big")), memos)

true

f71fd999a54a3748a94533f9d632879d0495dbcd

1,271

py

Python

checklink/parse/__init__.py

zombie110year/find_dead_link

565ec99c0fcbecaa4f7d82006bc9d58d0c05fa06

[ "MIT" ]

null

checklink/parse/__init__.py

zombie110year/find_dead_link

565ec99c0fcbecaa4f7d82006bc9d58d0c05fa06

[ "MIT" ]

null

checklink/parse/__init__.py

zombie110year/find_dead_link

565ec99c0fcbecaa4f7d82006bc9d58d0c05fa06

[ "MIT" ]

null

""" Text Parsers to find url from content. Every url item should contain: - url - location(`filepath:row:column`) """ from abc import abstractmethod from typing import List class Link: def __init__(self, url: str, path: str, row: int, column: int): """init link object :param str url: link's href :param str path: where found this link, file path :param int row: where found this link, line number :param int column: where found this link, chars after line beginning """ self.__url = url self.__path = path self.__row = row self.__column = column @property def url(self) -> str: return self.__url @property def path(self) -> str: return self.__path @property def row(self) -> int: return self.__row @property def column(self) -> int: return self.__column @property def location(self) -> str: return f"{self.path}:{self.row}:{self.column}" @path.setter def path(self, other: str): self.__path = other class Parser: @abstractmethod def parse(self, text: str) -> List[Link]: pass @abstractmethod def parse_file(self, path: str) -> List[Link]: pass

21.183333

76

0.601888

from abc import abstractmethod from typing import List class Link: def __init__(self, url: str, path: str, row: int, column: int): self.__url = url self.__path = path self.__row = row self.__column = column @property def url(self) -> str: return self.__url @property def path(self) -> str: return self.__path @property def row(self) -> int: return self.__row @property def column(self) -> int: return self.__column @property def location(self) -> str: return f"{self.path}:{self.row}:{self.column}" @path.setter def path(self, other: str): self.__path = other class Parser: @abstractmethod def parse(self, text: str) -> List[Link]: pass @abstractmethod def parse_file(self, path: str) -> List[Link]: pass

true

f71fd9b490090c7c03e0f828032d2a989edaca88

3,415

py

Python

components/studio/projects/models.py

MuhammadNaumanAbid/stackn

484501efda19f8f9c9c088bcf6095060c925d3b1

[ "Apache-2.0" ]

null

components/studio/projects/models.py

MuhammadNaumanAbid/stackn

484501efda19f8f9c9c088bcf6095060c925d3b1

[ "Apache-2.0" ]

null

components/studio/projects/models.py

MuhammadNaumanAbid/stackn

484501efda19f8f9c9c088bcf6095060c925d3b1

[ "Apache-2.0" ]

null

import base64 from django.db import models from django.contrib.auth.models import User from django.utils.text import slugify import string import random DEFAULT_ENVIRONMENT_ID = 1 class Flavor(models.Model): name = models.CharField(max_length=512) slug = models.CharField(max_length=512) cpu = models.TextField(blank=True, null=True) mem = models.TextField(blank=True, null=True) gpu = models.TextField(blank=True, null=True) updated_at = models.DateTimeField(auto_now=True) created_at = models.DateTimeField(auto_now_add=True) def __str__(self): return str(self.name) class Environment(models.Model): name = models.CharField(max_length=512) slug = models.CharField(max_length=512, blank=True, null=True) image = models.CharField(max_length=512) dockerfile = models.TextField(default='FROM jupyter/base-notebook') startup = models.TextField(null=True, blank=True) teardown = models.TextField(null=True, blank=True) updated_at = models.DateTimeField(auto_now=True) created_at = models.DateTimeField(auto_now_add=True) def __str__(self): return str(self.name) class ProjectManager(models.Manager): def generate_passkey(self, length=20): import secrets import string alphabet = string.ascii_letters + string.digits password = ''.join(secrets.choice(alphabet) for _ in range(length)) # Encrypt the key password = password.encode('ascii') base64_bytes = base64.b64encode(password) password = base64_bytes.decode('ascii') return password def create_project(self, name, owner, description, repository): letters = string.ascii_lowercase slug = name.replace(" ","-").replace("_","-") from .helpers import urlify slug = urlify(slug) slug_extension = ''.join(random.choice(letters) for i in range(3)) slug = '{}-{}'.format(slugify(slug), slug_extension) key = self.generate_passkey() secret = self.generate_passkey(40) project = self.create(name=name, owner=owner, slug=slug, project_key=key, project_secret=secret, description=description, repository=repository, repository_imported=False) return project class Project(models.Model): objects = ProjectManager() name = models.CharField(max_length=512, unique=True) description = models.TextField(null=True, blank=True) slug = models.CharField(max_length=512, unique=True) owner = models.ForeignKey(User, on_delete=models.DO_NOTHING, related_name='owner') authorized = models.ManyToManyField(User, blank=True) image = models.CharField(max_length=2048, blank=True, null=True) project_key = models.CharField(max_length=512) project_secret = models.CharField(max_length=512) updated_at = models.DateTimeField(auto_now=True) created_at = models.DateTimeField(auto_now_add=True) repository = models.CharField(max_length=512, null=True, blank=True) repository_imported = models.BooleanField(default=False) def __str__(self): return "Name: {} Description: {}".format(self.name, self.description) environment = models.ForeignKey('projects.Environment', on_delete=models.DO_NOTHING, default=DEFAULT_ENVIRONMENT_ID) clone_url = models.CharField(max_length=512, null=True, blank=True)

34.846939

120

0.703075

import base64 from django.db import models from django.contrib.auth.models import User from django.utils.text import slugify import string import random DEFAULT_ENVIRONMENT_ID = 1 class Flavor(models.Model): name = models.CharField(max_length=512) slug = models.CharField(max_length=512) cpu = models.TextField(blank=True, null=True) mem = models.TextField(blank=True, null=True) gpu = models.TextField(blank=True, null=True) updated_at = models.DateTimeField(auto_now=True) created_at = models.DateTimeField(auto_now_add=True) def __str__(self): return str(self.name) class Environment(models.Model): name = models.CharField(max_length=512) slug = models.CharField(max_length=512, blank=True, null=True) image = models.CharField(max_length=512) dockerfile = models.TextField(default='FROM jupyter/base-notebook') startup = models.TextField(null=True, blank=True) teardown = models.TextField(null=True, blank=True) updated_at = models.DateTimeField(auto_now=True) created_at = models.DateTimeField(auto_now_add=True) def __str__(self): return str(self.name) class ProjectManager(models.Manager): def generate_passkey(self, length=20): import secrets import string alphabet = string.ascii_letters + string.digits password = ''.join(secrets.choice(alphabet) for _ in range(length)) password = password.encode('ascii') base64_bytes = base64.b64encode(password) password = base64_bytes.decode('ascii') return password def create_project(self, name, owner, description, repository): letters = string.ascii_lowercase slug = name.replace(" ","-").replace("_","-") from .helpers import urlify slug = urlify(slug) slug_extension = ''.join(random.choice(letters) for i in range(3)) slug = '{}-{}'.format(slugify(slug), slug_extension) key = self.generate_passkey() secret = self.generate_passkey(40) project = self.create(name=name, owner=owner, slug=slug, project_key=key, project_secret=secret, description=description, repository=repository, repository_imported=False) return project class Project(models.Model): objects = ProjectManager() name = models.CharField(max_length=512, unique=True) description = models.TextField(null=True, blank=True) slug = models.CharField(max_length=512, unique=True) owner = models.ForeignKey(User, on_delete=models.DO_NOTHING, related_name='owner') authorized = models.ManyToManyField(User, blank=True) image = models.CharField(max_length=2048, blank=True, null=True) project_key = models.CharField(max_length=512) project_secret = models.CharField(max_length=512) updated_at = models.DateTimeField(auto_now=True) created_at = models.DateTimeField(auto_now_add=True) repository = models.CharField(max_length=512, null=True, blank=True) repository_imported = models.BooleanField(default=False) def __str__(self): return "Name: {} Description: {}".format(self.name, self.description) environment = models.ForeignKey('projects.Environment', on_delete=models.DO_NOTHING, default=DEFAULT_ENVIRONMENT_ID) clone_url = models.CharField(max_length=512, null=True, blank=True)

true

f71fdbd179d815f56f9c409701685cd66a7005c3

23,154

py

Python

yuu/ext/abematv.py

soltia48/yuu

30d2fcf9427cbbea930d01baef337b64ad7fb05b

[ "BSD-3-Clause" ]

null

yuu/ext/abematv.py

soltia48/yuu

30d2fcf9427cbbea930d01baef337b64ad7fb05b

[ "BSD-3-Clause" ]

null

yuu/ext/abematv.py

soltia48/yuu

30d2fcf9427cbbea930d01baef337b64ad7fb05b

[ "BSD-3-Clause" ]

null

import hashlib import hmac import json import logging import os import re import struct import tempfile import time import uuid from base64 import urlsafe_b64encode from binascii import unhexlify import m3u8 from Crypto.Cipher import AES from tqdm import tqdm def is_channel(url): url = re.findall('(slot)', url) if url: return True return False yuu_log = logging.getLogger('yuu.abematv') class AbemaTVDownloader: def __init__(self, url, session): self.key = None self.iv = None self.url = url self.session = session self.merge = True if os.name == "nt": self.yuu_folder = os.path.join(os.getenv('LOCALAPPDATA'), 'yuu_data') sffx = '\\' else: self.yuu_folder = os.path.join(os.getenv('HOME'), '.yuu_data') sffx = '/' if not os.path.isdir(self.yuu_folder): os.mkdir(self.yuu_folder) self.temporary_folder = tempfile.mkdtemp(dir=self.yuu_folder) self.temporary_folder = self.temporary_folder + sffx self._aes = None def setup_decryptor(self): self.iv = unhexlify(self.iv) self._aes = AES.new(self.key, AES.MODE_CBC, IV=self.iv) def download_chunk(self, files, key, iv): if iv.startswith('0x'): self.iv = iv[2:] else: self.iv = iv self.key = key self.downloaded_files = [] self.setup_decryptor() # Initialize a new decryptor try: with tqdm(total=len(files), desc='Downloading', ascii=True, unit='file') as pbar: for tsf in files: outputtemp = self.temporary_folder + os.path.basename(tsf) if outputtemp.find('?tver') != -1: outputtemp = outputtemp[:outputtemp.find('?tver')] with open(outputtemp, 'wb') as outf: try: vid = self.session.get(tsf) vid = self._aes.decrypt(vid.content) outf.write(vid) except Exception as err: yuu_log.error('Problem occured\nreason: {}'.format(err)) return None pbar.update() self.downloaded_files.append(outputtemp) except KeyboardInterrupt: yuu_log.warn('User pressed CTRL+C, cleaning up...') return None return self.downloaded_files class AbemaTV: def __init__(self, url, session): self.session = session self.type = 'AbemaTV' self.yuu_logger = logging.getLogger('yuu.abematv.AbemaTV') self.url = url self.m3u8_url = None self.resolution = None self.resolution_o = None self.device_id = None self.is_m3u8 = False self.est_filesize = None # In MiB self.resolution_data = { "1080p": ["4000kb/s", "AAC 192kb/s 2ch"], "720p": ["2000kb/s", "AAC 160kb/s 2ch"], "480p": ["900kb/s", "AAC 128kb/s 2ch"], "360p": ["550kb/s", "AAC 128kb/s 2ch"], "240p": ["240kb/s", "AAC 64kb/s 1ch"], "180p": ["120kb/s", "AAC 64kb/s 1ch"] } self.bitrate_calculation = { "1080p": 5175, "720p": 2373, "480p": 1367, "360p": 878, "240p": 292, "180p": 179 } self.authorization_required = False self.authorized = False # Ignore for now #self.authorize = True # Ignore for now self.resumable = True self._STRTABLE = "123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz" self._HKEY = b"3AF0298C219469522A313570E8583005A642E73EDD58E3EA2FB7339D3DF1597E" self._KEYPARAMS = { "osName": "android", "osVersion": "6.0.1", "osLand": "ja_JP", "osTimezone": "Asia/Tokyo", "appId": "tv.abema", "appVersion": "3.27.1" } self._MEDIATOKEN_API = "https://api.abema.io/v1/media/token" self._LICENSE_API = "https://license.abema.io/abematv-hls" self._USERAPI = "https://api.abema.io/v1/users" self._PROGRAMAPI = 'https://api.abema.io/v1/video/programs/' self._CHANNELAPI = 'https://api.abema.io/v1/media/slots/' self._SERIESAPI = "https://api.abema.io/v1/video/series/" # Use Chrome UA self.session.headers.update({'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36'}) def __repr__(self): return '<yuu.AbemaTV: URL={}, Resolution={}, Device ID={}, m3u8 URL={}>'.format(self.url, self.resolution, self.device_id, self.m3u8_url) def get_downloader(self): """ Return a :class: of the Downloader """ return AbemaTVDownloader(self.url, self.session) def resume_prepare(self): """ Add support for resuming files, this function will prepare everything to start resuming download. """ return None def authorize(self, username, password): if not self.device_id: self.yuu_logger.info('{}: Fetching temporary token'.format(self.type)) res, reas = self.get_token() # Abema needs authorization header before authenticating if not res: return res, reas _ENDPOINT_MAIL = 'https://api.abema.io/v1/auth/user/email' _ENDPOINT_OTP = 'https://api.abema.io/v1/auth/oneTimePassword' mail_regex = r'^\w+([\.-]?\w+)*@\w+([\.-]?\w+)*(\.\w{2,3})+$' if re.search(mail_regex, username): _ENDPOINT_USE = _ENDPOINT_MAIL _USERNAME_METHOD = 'email' else: _ENDPOINT_USE = _ENDPOINT_OTP _USERNAME_METHOD = 'userId' auth_ = { _USERNAME_METHOD: username, "password": password } res = self.session.post(_ENDPOINT_USE, json=auth_) if res.status_code > 299: res_j = res.json() self.yuu_logger.debug('Abema Response: {}'.format(res_j['message'])) return False, 'Wrong {} and password combination'.format(_USERNAME_METHOD) res_j = res.json() self.yuu_logger.debug('Authentication Token: {}'.format(res_j['token'])) self.session.headers.update({'Authorization': 'bearer ' + res_j['token']}) self.authorized = True return True, 'Authorized' def get_token(self): def key_secret(devid): SECRETKEY = (b"v+Gjs=25Aw5erR!J8ZuvRrCx*rGswhB&qdHd_SYerEWdU&a?3DzN9B" b"Rbp5KwY4hEmcj5#fykMjJ=AuWz5GSMY-d@H7DMEh3M@9n2G552Us$$" b"k9cD=3TxwWe86!x#Zyhe") deviceid = devid.encode("utf-8") ts_1hour = (int(time.time()) + 60 * 60) // 3600 * 3600 time_struct = time.gmtime(ts_1hour) ts_1hour_str = str(ts_1hour).encode("utf-8") h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(SECRETKEY) tmp = h.digest() for _ in range(time_struct.tm_mon): h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(tmp) tmp = h.digest() h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(urlsafe_b64encode(tmp).rstrip(b"=") + deviceid) tmp = h.digest() for _ in range(time_struct.tm_mday % 5): h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(tmp) tmp = h.digest() h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(urlsafe_b64encode(tmp).rstrip(b"=") + ts_1hour_str) tmp = h.digest() for _ in range(time_struct.tm_hour % 5): # utc hour h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(tmp) tmp = h.digest() finalize = urlsafe_b64encode(tmp).rstrip(b"=").decode("utf-8") self.yuu_logger.debug('Secret Key: {}'.format(finalize)) return finalize if self.authorized: # Ignore this if already login return True, 'Success' deviceid = str(uuid.uuid4()) self.yuu_logger.debug('Generated Device UUID: {}'.format(deviceid)) json_data = {"deviceId": deviceid, "applicationKeySecret": key_secret(deviceid)} self.yuu_logger.debug('Generated applicationKeySecret: {}'.format(json_data['applicationKeySecret'])) self.yuu_logger.debug('Sending json data') res = self.session.post(self._USERAPI, json=json_data).json() try: self.yuu_logger.debug('Data sent, getting token') token = res['token'] self.yuu_logger.debug('User token: {}'.format(token)) except: return None, 'Failed to get user token.' self.device_id = deviceid self.session.headers.update({'Authorization': 'bearer ' + token}) return 'Success', 'Success' def parse(self, resolution=None, check_only=False): """ Function to parse abema url """ res_list = [ '180p', '240p', '360p', '480p', '720p', '1080p', 'best', 'worst' ] if resolution not in res_list: if not check_only: return None, 'Unknown resolution: {}. (Check it with `-R`)'.format(resolution) if resolution == 'best': resolution = '1080p' self.resolution_o = 'best' if resolution == 'worst': resolution = '180p' # https://abema.tv/video/title/26-55 (series/playlists) # https://api.abema.io/v1/video/series/26-55 # https://api.abema.io/v1/video/series/26-55/programs?seriesVersion=1577436473958778090&seasonId=26-55_s1&offset=0&order=seq&limit=40 series = re.search(r"(?P<series>title)/(?P<video_id>.*[^-_])", self.url) if series: video_id = series.group(2) self.yuu_logger.info('Series url format detected, fetching all links...') self.yuu_logger.debug('Requesting data to Abema API.') req = self.session.get(self._SERIESAPI + video_id) if req.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json results...') m3u8_url_list = [] output_list = [] jsdata = req.json() to_be_requested = "{api}{vid}/programs?seriesVersion={sv}&seasonId={si}&offset=0&order={od}" season_data = jsdata['seasons'] if not season_data: season_data = [{'id': ''}] # Assume film or some shit version = jsdata['version'] prog_order = jsdata['programOrder'] for ns, season in enumerate(season_data, 1): self.yuu_logger.info('Processing season ' + str(ns)) self.yuu_logger.debug('Requesting data to Abema API.') req_season = self.session.get(to_be_requested.format(api=self._SERIESAPI, vid=video_id, sv=version, si=season['id'], od=prog_order)) if req_season.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req_season.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req_season.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json results...') season_jsdata = req_season.json() self.yuu_logger.debug('Processing total of {ep} episode for season {se}'.format(ep=len(season_jsdata['programs']), se=ns)) for nep, episode in enumerate(season_jsdata['programs'], 1): free_episode = False if 'label' in episode: if 'free' in episode['label']: free_episode = True elif 'freeEndAt' in episode: free_episode = True if 'episode' in episode: try: episode_name = episode['episode']['title'] if not episode_name: episode_name = episode_name['title']['number'] except KeyError: episode_name = episode_name['title']['number'] else: episode_name = nep if not free_episode and not self.authorized: self.yuu_logger.warn('Skipping episode {} (Not authorized and premium video)'.format(episode_name)) continue self.yuu_logger.info('Processing episode {}'.format(episode_name)) req_ep = self.session.get(self._PROGRAMAPI + episode['id']) if req_ep.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req_ep.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req_ep.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json API') ep_json = req_ep.json() title = ep_json['series']['title'] epnum = ep_json['episode']['title'] hls = ep_json['playback']['hls'] output_name = title + ' - ' + epnum m3u8_url = '{x}/{r}/playlist.m3u8'.format(x=hls[:hls.rfind('/')], r=resolution[:-1]) self.yuu_logger.debug('M3U8 Link: {}'.format(m3u8_url)) self.yuu_logger.debug('Video title: {}'.format(title)) m3u8_url_list.append(m3u8_url) output_list.append(output_name) self.resolution = resolution self.m3u8_url = m3u8_url_list if not output_list: err_msg = "All video are for premium only, please provide login details." else: err_msg = "Success" return output_list, err_msg if '.m3u8' in self.url[-5:]: reg = re.compile(r'(program|slot)\/[\w+-]+') self.url = re.search(reg, m3u8)[0] self.is_m3u8 = True ep_link = self.url[self.url.rfind('/')+1:] self.yuu_logger.debug('Requesting data to Abema API') if is_channel(self.url): req = self.session.get(self._CHANNELAPI + ep_link) if req.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json API') jsdata = req.json() output_name = jsdata['slot']['title'] if 'playback' in jsdata['slot']: hls = jsdata['slot']['playback']['hls'] else: hls = jsdata['slot']['chasePlayback']['hls'] # Compat m3u8_url = '{x}/{r}/playlist.m3u8'.format(x=hls[:hls.rfind('/')], r=resolution[:-1]) if self.is_m3u8: m3u8_url = self.url self.yuu_logger.debug('M3U8 Link: {}'.format(m3u8_url)) self.yuu_logger.debug('Title: {}'.format(output_name)) else: req = self.session.get(self._PROGRAMAPI + ep_link) if req.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json API') jsdata = req.json() if jsdata['mediaStatus']: if 'drm' in jsdata['mediaStatus']: if jsdata['mediaStatus']['drm']: return None, 'This video has a different DRM method and cannot be decrypted by yuu for now' title = jsdata['series']['title'] epnum = jsdata['episode']['title'] hls = jsdata['playback']['hls'] output_name = title + ' - ' + epnum m3u8_url = '{x}/{r}/playlist.m3u8'.format(x=hls[:hls.rfind('/')], r=resolution[:-1]) if self.is_m3u8: m3u8_url = self.url self.yuu_logger.debug('M3U8 Link: {}'.format(m3u8_url)) self.yuu_logger.debug('Video title: {}'.format(title)) self.yuu_logger.debug('Episode number: {}'.format(epnum)) self.resolution = resolution self.m3u8_url = m3u8_url return output_name, 'Success' def parse_m3u8(self, m3u8_url): self.yuu_logger.debug('Requesting m3u8') r = self.session.get(m3u8_url) self.yuu_logger.debug('Data requested') if 'timeshift forbidden' in r.text: return None, None, None, 'This video can\'t be downloaded for now.' if r.status_code == 403: return None, None, None, 'This video is geo-locked for Japan only.' self.yuu_logger.debug('Parsing m3u8') x = m3u8.loads(r.text) files = x.files[1:] if not files[0]: files = files[1:] if 'tsda' in files[5]: # Assume DRMed return None, None, None, 'This video has a different DRM method and cannot be decrypted by yuu for now' resgex = re.findall(r'(\d*)(?:\/\w+.ts)', files[0])[0] keys_data = x.keys[0] iv = x.keys[0].iv ticket = x.keys[0].uri[18:] parsed_files = [] for f in files: if f.startswith('/tsvpg') or f.startswith('/tspg'): f = 'https://ds-vod-abematv.akamaized.net' + f parsed_files.append(f) if self.resolution[:-1] != resgex: if not self.resolution_o: self.yuu_logger.warn('Changing resolution, from {} to {}p'.format(self.resolution, resgex)) self.resolution = resgex + 'p' self.yuu_logger.debug('Total files: {}'.format(len(files))) self.yuu_logger.debug('IV: {}'.format(iv)) self.yuu_logger.debug('Ticket key: {}'.format(ticket)) n = 0.0 for seg in x.segments: n += seg.duration self.est_filesize = round((round(n) * self.bitrate_calculation[self.resolution]) / 1024 / 6, 2) return parsed_files, iv[2:], ticket, 'Success' def get_video_key(self, ticket): self.yuu_logger.debug('Sending parameter to API') restoken = self.session.get(self._MEDIATOKEN_API, params=self._KEYPARAMS).json() mediatoken = restoken['token'] self.yuu_logger.debug('Media token: {}'.format(mediatoken)) self.yuu_logger.debug('Sending ticket and media token to License API') rgl = self.session.post(self._LICENSE_API, params={"t": mediatoken}, json={"kv": "a", "lt": ticket}) if rgl.status_code == 403: return None, 'Access to this video are not allowed\nProbably a premium video or geo-locked.' gl = rgl.json() cid = gl['cid'] k = gl['k'] self.yuu_logger.debug('CID: {}'.format(cid)) self.yuu_logger.debug('K: {}'.format(k)) self.yuu_logger.debug('Summing up data with STRTABLE') res = sum([self._STRTABLE.find(k[i]) * (58 ** (len(k) - 1 - i)) for i in range(len(k))]) self.yuu_logger.debug('Result: {}'.format(res)) self.yuu_logger.debug('Intepreting data') encvk = struct.pack('>QQ', res >> 64, res & 0xffffffffffffffff) self.yuu_logger.debug('Encoded video key: {}'.format(encvk)) self.yuu_logger.debug('Hashing data') h = hmac.new(unhexlify(self._HKEY), (cid + self.device_id).encode("utf-8"), digestmod=hashlib.sha256) enckey = h.digest() self.yuu_logger.debug('Second Encoded video key: {}'.format(enckey)) self.yuu_logger.debug('Decrypting result') aes = AES.new(enckey, AES.MODE_ECB) vkey = aes.decrypt(encvk) self.yuu_logger.debug('Decrypted, Result: {}'.format(vkey)) return vkey, 'Success getting video key' def resolutions(self, m3u8_uri): self.yuu_logger.debug('Requesting data to API') m3u8_ = m3u8_uri[:m3u8_uri.rfind('/')] base_url = m3u8_[:m3u8_.rfind('/')] + '/' m3u8_1080 = m3u8_[:m3u8_.rfind('/')] + '/1080/playlist.m3u8' m3u8_720 = m3u8_[:m3u8_.rfind('/')] + '/720/playlist.m3u8' m3u8_480 = m3u8_[:m3u8_.rfind('/')] + '/480/playlist.m3u8' m3u8_360 = m3u8_[:m3u8_.rfind('/')] + '/360/playlist.m3u8' m3u8_240 = m3u8_[:m3u8_.rfind('/')] + '/240/playlist.m3u8' m3u8_180 = m3u8_[:m3u8_.rfind('/')] + '/180/playlist.m3u8' rr_all = self.session.get(base_url + 'playlist.m3u8') if 'timeshift forbidden' in rr_all.text: return None, 'This video can\'t be downloaded for now.' r_all = m3u8.loads(rr_all.text) play_res = [] for r_p in r_all.playlists: temp = [] temp.append(r_p.stream_info.resolution) temp.append(base_url + r_p.uri) play_res.append(temp) resgex = re.compile(r'(\d*)(?:\/\w+.ts)') ava_reso = [] for resdata in play_res: reswh, m3u8_uri = resdata resw, resh = reswh self.yuu_logger.debug('Validating {}p resolution'.format(resh)) rres = m3u8.loads(self.session.get(m3u8_uri).text) m3f = rres.files[1:] if not m3f: return None, 'This video can\'t be downloaded for now.' self.yuu_logger.debug('Sample link: ' + m3f[5]) if 'tsda' in files[5]: # Assume DRMed return None, 'This video has a different DRM method and cannot be decrypted by yuu for now' if str(resh) in re.findall(resgex, m3f[5]): ava_reso.append( [ '{h}p'.format(h=resh), '{w}x{h}'.format(w=resw, h=resh) ] ) if ava_reso: reso = [r[0] for r in ava_reso] self.yuu_logger.debug('Resolution list: {}'.format(', '.join(reso))) return ava_reso, 'Success' def check_output(self, output=None, output_name=None): if output: fn_, ext_ = os.path.splitext(output) if ext_ != 'ts': output = fn_ + '.ts' else: output = '{x} ({m} {r}).ts'.format(x=output_name, m=self.type, r=self.resolution) return output

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import hashlib import hmac import json import logging import os import re import struct import tempfile import time import uuid from base64 import urlsafe_b64encode from binascii import unhexlify import m3u8 from Crypto.Cipher import AES from tqdm import tqdm def is_channel(url): url = re.findall('(slot)', url) if url: return True return False yuu_log = logging.getLogger('yuu.abematv') class AbemaTVDownloader: def __init__(self, url, session): self.key = None self.iv = None self.url = url self.session = session self.merge = True if os.name == "nt": self.yuu_folder = os.path.join(os.getenv('LOCALAPPDATA'), 'yuu_data') sffx = '\\' else: self.yuu_folder = os.path.join(os.getenv('HOME'), '.yuu_data') sffx = '/' if not os.path.isdir(self.yuu_folder): os.mkdir(self.yuu_folder) self.temporary_folder = tempfile.mkdtemp(dir=self.yuu_folder) self.temporary_folder = self.temporary_folder + sffx self._aes = None def setup_decryptor(self): self.iv = unhexlify(self.iv) self._aes = AES.new(self.key, AES.MODE_CBC, IV=self.iv) def download_chunk(self, files, key, iv): if iv.startswith('0x'): self.iv = iv[2:] else: self.iv = iv self.key = key self.downloaded_files = [] self.setup_decryptor() try: with tqdm(total=len(files), desc='Downloading', ascii=True, unit='file') as pbar: for tsf in files: outputtemp = self.temporary_folder + os.path.basename(tsf) if outputtemp.find('?tver') != -1: outputtemp = outputtemp[:outputtemp.find('?tver')] with open(outputtemp, 'wb') as outf: try: vid = self.session.get(tsf) vid = self._aes.decrypt(vid.content) outf.write(vid) except Exception as err: yuu_log.error('Problem occured\nreason: {}'.format(err)) return None pbar.update() self.downloaded_files.append(outputtemp) except KeyboardInterrupt: yuu_log.warn('User pressed CTRL+C, cleaning up...') return None return self.downloaded_files class AbemaTV: def __init__(self, url, session): self.session = session self.type = 'AbemaTV' self.yuu_logger = logging.getLogger('yuu.abematv.AbemaTV') self.url = url self.m3u8_url = None self.resolution = None self.resolution_o = None self.device_id = None self.is_m3u8 = False self.est_filesize = None self.resolution_data = { "1080p": ["4000kb/s", "AAC 192kb/s 2ch"], "720p": ["2000kb/s", "AAC 160kb/s 2ch"], "480p": ["900kb/s", "AAC 128kb/s 2ch"], "360p": ["550kb/s", "AAC 128kb/s 2ch"], "240p": ["240kb/s", "AAC 64kb/s 1ch"], "180p": ["120kb/s", "AAC 64kb/s 1ch"] } self.bitrate_calculation = { "1080p": 5175, "720p": 2373, "480p": 1367, "360p": 878, "240p": 292, "180p": 179 } self.authorization_required = False self.authorized = False esumable = True self._STRTABLE = "123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz" self._HKEY = b"3AF0298C219469522A313570E8583005A642E73EDD58E3EA2FB7339D3DF1597E" self._KEYPARAMS = { "osName": "android", "osVersion": "6.0.1", "osLand": "ja_JP", "osTimezone": "Asia/Tokyo", "appId": "tv.abema", "appVersion": "3.27.1" } self._MEDIATOKEN_API = "https://api.abema.io/v1/media/token" self._LICENSE_API = "https://license.abema.io/abematv-hls" self._USERAPI = "https://api.abema.io/v1/users" self._PROGRAMAPI = 'https://api.abema.io/v1/video/programs/' self._CHANNELAPI = 'https://api.abema.io/v1/media/slots/' self._SERIESAPI = "https://api.abema.io/v1/video/series/" self.session.headers.update({'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/75.0.3770.100 Safari/537.36'}) def __repr__(self): return '<yuu.AbemaTV: URL={}, Resolution={}, Device ID={}, m3u8 URL={}>'.format(self.url, self.resolution, self.device_id, self.m3u8_url) def get_downloader(self): return AbemaTVDownloader(self.url, self.session) def resume_prepare(self): return None def authorize(self, username, password): if not self.device_id: self.yuu_logger.info('{}: Fetching temporary token'.format(self.type)) res, reas = self.get_token() if not res: return res, reas _ENDPOINT_MAIL = 'https://api.abema.io/v1/auth/user/email' _ENDPOINT_OTP = 'https://api.abema.io/v1/auth/oneTimePassword' mail_regex = r'^\w+([\.-]?\w+)*@\w+([\.-]?\w+)*(\.\w{2,3})+$' if re.search(mail_regex, username): _ENDPOINT_USE = _ENDPOINT_MAIL _USERNAME_METHOD = 'email' else: _ENDPOINT_USE = _ENDPOINT_OTP _USERNAME_METHOD = 'userId' auth_ = { _USERNAME_METHOD: username, "password": password } res = self.session.post(_ENDPOINT_USE, json=auth_) if res.status_code > 299: res_j = res.json() self.yuu_logger.debug('Abema Response: {}'.format(res_j['message'])) return False, 'Wrong {} and password combination'.format(_USERNAME_METHOD) res_j = res.json() self.yuu_logger.debug('Authentication Token: {}'.format(res_j['token'])) self.session.headers.update({'Authorization': 'bearer ' + res_j['token']}) self.authorized = True return True, 'Authorized' def get_token(self): def key_secret(devid): SECRETKEY = (b"v+Gjs=25Aw5erR!J8ZuvRrCx*rGswhB&qdHd_SYerEWdU&a?3DzN9B" b"Rbp5KwY4hEmcj5#fykMjJ=AuWz5GSMY-d@H7DMEh3M@9n2G552Us$$" b"k9cD=3TxwWe86!x#Zyhe") deviceid = devid.encode("utf-8") ts_1hour = (int(time.time()) + 60 * 60) // 3600 * 3600 time_struct = time.gmtime(ts_1hour) ts_1hour_str = str(ts_1hour).encode("utf-8") h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(SECRETKEY) tmp = h.digest() for _ in range(time_struct.tm_mon): h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(tmp) tmp = h.digest() h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(urlsafe_b64encode(tmp).rstrip(b"=") + deviceid) tmp = h.digest() for _ in range(time_struct.tm_mday % 5): h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(tmp) tmp = h.digest() h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(urlsafe_b64encode(tmp).rstrip(b"=") + ts_1hour_str) tmp = h.digest() for _ in range(time_struct.tm_hour % 5): h = hmac.new(SECRETKEY, digestmod=hashlib.sha256) h.update(tmp) tmp = h.digest() finalize = urlsafe_b64encode(tmp).rstrip(b"=").decode("utf-8") self.yuu_logger.debug('Secret Key: {}'.format(finalize)) return finalize if self.authorized: return True, 'Success' deviceid = str(uuid.uuid4()) self.yuu_logger.debug('Generated Device UUID: {}'.format(deviceid)) json_data = {"deviceId": deviceid, "applicationKeySecret": key_secret(deviceid)} self.yuu_logger.debug('Generated applicationKeySecret: {}'.format(json_data['applicationKeySecret'])) self.yuu_logger.debug('Sending json data') res = self.session.post(self._USERAPI, json=json_data).json() try: self.yuu_logger.debug('Data sent, getting token') token = res['token'] self.yuu_logger.debug('User token: {}'.format(token)) except: return None, 'Failed to get user token.' self.device_id = deviceid self.session.headers.update({'Authorization': 'bearer ' + token}) return 'Success', 'Success' def parse(self, resolution=None, check_only=False): res_list = [ '180p', '240p', '360p', '480p', '720p', '1080p', 'best', 'worst' ] if resolution not in res_list: if not check_only: return None, 'Unknown resolution: {}. (Check it with `-R`)'.format(resolution) if resolution == 'best': resolution = '1080p' self.resolution_o = 'best' if resolution == 'worst': resolution = '180p' series = re.search(r"(?P<series>title)/(?P<video_id>.*[^-_])", self.url) if series: video_id = series.group(2) self.yuu_logger.info('Series url format detected, fetching all links...') self.yuu_logger.debug('Requesting data to Abema API.') req = self.session.get(self._SERIESAPI + video_id) if req.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json results...') m3u8_url_list = [] output_list = [] jsdata = req.json() to_be_requested = "{api}{vid}/programs?seriesVersion={sv}&seasonId={si}&offset=0&order={od}" season_data = jsdata['seasons'] if not season_data: season_data = [{'id': ''}] version = jsdata['version'] prog_order = jsdata['programOrder'] for ns, season in enumerate(season_data, 1): self.yuu_logger.info('Processing season ' + str(ns)) self.yuu_logger.debug('Requesting data to Abema API.') req_season = self.session.get(to_be_requested.format(api=self._SERIESAPI, vid=video_id, sv=version, si=season['id'], od=prog_order)) if req_season.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req_season.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req_season.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json results...') season_jsdata = req_season.json() self.yuu_logger.debug('Processing total of {ep} episode for season {se}'.format(ep=len(season_jsdata['programs']), se=ns)) for nep, episode in enumerate(season_jsdata['programs'], 1): free_episode = False if 'label' in episode: if 'free' in episode['label']: free_episode = True elif 'freeEndAt' in episode: free_episode = True if 'episode' in episode: try: episode_name = episode['episode']['title'] if not episode_name: episode_name = episode_name['title']['number'] except KeyError: episode_name = episode_name['title']['number'] else: episode_name = nep if not free_episode and not self.authorized: self.yuu_logger.warn('Skipping episode {} (Not authorized and premium video)'.format(episode_name)) continue self.yuu_logger.info('Processing episode {}'.format(episode_name)) req_ep = self.session.get(self._PROGRAMAPI + episode['id']) if req_ep.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req_ep.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req_ep.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json API') ep_json = req_ep.json() title = ep_json['series']['title'] epnum = ep_json['episode']['title'] hls = ep_json['playback']['hls'] output_name = title + ' - ' + epnum m3u8_url = '{x}/{r}/playlist.m3u8'.format(x=hls[:hls.rfind('/')], r=resolution[:-1]) self.yuu_logger.debug('M3U8 Link: {}'.format(m3u8_url)) self.yuu_logger.debug('Video title: {}'.format(title)) m3u8_url_list.append(m3u8_url) output_list.append(output_name) self.resolution = resolution self.m3u8_url = m3u8_url_list if not output_list: err_msg = "All video are for premium only, please provide login details." else: err_msg = "Success" return output_list, err_msg if '.m3u8' in self.url[-5:]: reg = re.compile(r'(program|slot)\/[\w+-]+') self.url = re.search(reg, m3u8)[0] self.is_m3u8 = True ep_link = self.url[self.url.rfind('/')+1:] self.yuu_logger.debug('Requesting data to Abema API') if is_channel(self.url): req = self.session.get(self._CHANNELAPI + ep_link) if req.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json API') jsdata = req.json() output_name = jsdata['slot']['title'] if 'playback' in jsdata['slot']: hls = jsdata['slot']['playback']['hls'] else: hls = jsdata['slot']['chasePlayback']['hls'] m3u8_url = '{x}/{r}/playlist.m3u8'.format(x=hls[:hls.rfind('/')], r=resolution[:-1]) if self.is_m3u8: m3u8_url = self.url self.yuu_logger.debug('M3U8 Link: {}'.format(m3u8_url)) self.yuu_logger.debug('Title: {}'.format(output_name)) else: req = self.session.get(self._PROGRAMAPI + ep_link) if req.status_code != 200: self.yuu_logger.log(40, 'Abema Response: ' + req.text) return None, 'Error occured when communicating with Abema (Response: {})'.format(req.status_code) self.yuu_logger.debug('Data requested') self.yuu_logger.debug('Parsing json API') jsdata = req.json() if jsdata['mediaStatus']: if 'drm' in jsdata['mediaStatus']: if jsdata['mediaStatus']['drm']: return None, 'This video has a different DRM method and cannot be decrypted by yuu for now' title = jsdata['series']['title'] epnum = jsdata['episode']['title'] hls = jsdata['playback']['hls'] output_name = title + ' - ' + epnum m3u8_url = '{x}/{r}/playlist.m3u8'.format(x=hls[:hls.rfind('/')], r=resolution[:-1]) if self.is_m3u8: m3u8_url = self.url self.yuu_logger.debug('M3U8 Link: {}'.format(m3u8_url)) self.yuu_logger.debug('Video title: {}'.format(title)) self.yuu_logger.debug('Episode number: {}'.format(epnum)) self.resolution = resolution self.m3u8_url = m3u8_url return output_name, 'Success' def parse_m3u8(self, m3u8_url): self.yuu_logger.debug('Requesting m3u8') r = self.session.get(m3u8_url) self.yuu_logger.debug('Data requested') if 'timeshift forbidden' in r.text: return None, None, None, 'This video can\'t be downloaded for now.' if r.status_code == 403: return None, None, None, 'This video is geo-locked for Japan only.' self.yuu_logger.debug('Parsing m3u8') x = m3u8.loads(r.text) files = x.files[1:] if not files[0]: files = files[1:] if 'tsda' in files[5]: # Assume DRMed return None, None, None, 'This video has a different DRM method and cannot be decrypted by yuu for now' resgex = re.findall(r'(\d*)(?:\/\w+.ts)', files[0])[0] keys_data = x.keys[0] iv = x.keys[0].iv ticket = x.keys[0].uri[18:] parsed_files = [] for f in files: if f.startswith('/tsvpg') or f.startswith('/tspg'): f = 'https://ds-vod-abematv.akamaized.net' + f parsed_files.append(f) if self.resolution[:-1] != resgex: if not self.resolution_o: self.yuu_logger.warn('Changing resolution, from {} to {}p'.format(self.resolution, resgex)) self.resolution = resgex + 'p' self.yuu_logger.debug('Total files: {}'.format(len(files))) self.yuu_logger.debug('IV: {}'.format(iv)) self.yuu_logger.debug('Ticket key: {}'.format(ticket)) n = 0.0 for seg in x.segments: n += seg.duration self.est_filesize = round((round(n) * self.bitrate_calculation[self.resolution]) / 1024 / 6, 2) return parsed_files, iv[2:], ticket, 'Success' def get_video_key(self, ticket): self.yuu_logger.debug('Sending parameter to API') restoken = self.session.get(self._MEDIATOKEN_API, params=self._KEYPARAMS).json() mediatoken = restoken['token'] self.yuu_logger.debug('Media token: {}'.format(mediatoken)) self.yuu_logger.debug('Sending ticket and media token to License API') rgl = self.session.post(self._LICENSE_API, params={"t": mediatoken}, json={"kv": "a", "lt": ticket}) if rgl.status_code == 403: return None, 'Access to this video are not allowed\nProbably a premium video or geo-locked.' gl = rgl.json() cid = gl['cid'] k = gl['k'] self.yuu_logger.debug('CID: {}'.format(cid)) self.yuu_logger.debug('K: {}'.format(k)) self.yuu_logger.debug('Summing up data with STRTABLE') res = sum([self._STRTABLE.find(k[i]) * (58 ** (len(k) - 1 - i)) for i in range(len(k))]) self.yuu_logger.debug('Result: {}'.format(res)) self.yuu_logger.debug('Intepreting data') encvk = struct.pack('>QQ', res >> 64, res & 0xffffffffffffffff) self.yuu_logger.debug('Encoded video key: {}'.format(encvk)) self.yuu_logger.debug('Hashing data') h = hmac.new(unhexlify(self._HKEY), (cid + self.device_id).encode("utf-8"), digestmod=hashlib.sha256) enckey = h.digest() self.yuu_logger.debug('Second Encoded video key: {}'.format(enckey)) self.yuu_logger.debug('Decrypting result') aes = AES.new(enckey, AES.MODE_ECB) vkey = aes.decrypt(encvk) self.yuu_logger.debug('Decrypted, Result: {}'.format(vkey)) return vkey, 'Success getting video key' def resolutions(self, m3u8_uri): self.yuu_logger.debug('Requesting data to API') m3u8_ = m3u8_uri[:m3u8_uri.rfind('/')] base_url = m3u8_[:m3u8_.rfind('/')] + '/' m3u8_1080 = m3u8_[:m3u8_.rfind('/')] + '/1080/playlist.m3u8' m3u8_720 = m3u8_[:m3u8_.rfind('/')] + '/720/playlist.m3u8' m3u8_480 = m3u8_[:m3u8_.rfind('/')] + '/480/playlist.m3u8' m3u8_360 = m3u8_[:m3u8_.rfind('/')] + '/360/playlist.m3u8' m3u8_240 = m3u8_[:m3u8_.rfind('/')] + '/240/playlist.m3u8' m3u8_180 = m3u8_[:m3u8_.rfind('/')] + '/180/playlist.m3u8' rr_all = self.session.get(base_url + 'playlist.m3u8') if 'timeshift forbidden' in rr_all.text: return None, 'This video can\'t be downloaded for now.' r_all = m3u8.loads(rr_all.text) play_res = [] for r_p in r_all.playlists: temp = [] temp.append(r_p.stream_info.resolution) temp.append(base_url + r_p.uri) play_res.append(temp) resgex = re.compile(r'(\d*)(?:\/\w+.ts)') ava_reso = [] for resdata in play_res: reswh, m3u8_uri = resdata resw, resh = reswh self.yuu_logger.debug('Validating {}p resolution'.format(resh)) rres = m3u8.loads(self.session.get(m3u8_uri).text) m3f = rres.files[1:] if not m3f: return None, 'This video can\'t be downloaded for now.' self.yuu_logger.debug('Sample link: ' + m3f[5]) if 'tsda' in files[5]: # Assume DRMed return None, 'This video has a different DRM method and cannot be decrypted by yuu for now' if str(resh) in re.findall(resgex, m3f[5]): ava_reso.append( [ '{h}p'.format(h=resh), '{w}x{h}'.format(w=resw, h=resh) ] ) if ava_reso: reso = [r[0] for r in ava_reso] self.yuu_logger.debug('Resolution list: {}'.format(', '.join(reso))) return ava_reso, 'Success' def check_output(self, output=None, output_name=None): if output: fn_, ext_ = os.path.splitext(output) if ext_ != 'ts': output = fn_ + '.ts' else: output = '{x} ({m} {r}).ts'.format(x=output_name, m=self.type, r=self.resolution) return output

true

f71fde1bd02fcc1f714d372b9d638e5ed8bbe7be

4,468

py

Python

data_steward/cdr_cleaner/cleaning_rules/deid/dateshift.py

lrwb-aou/curation

e80447e56d269dc2c9c8bc79e78218d4b0dc504c

[ "MIT" ]

16

2017-06-30T20:05:05.000Z

2022-03-08T21:03:19.000Z

data_steward/cdr_cleaner/cleaning_rules/deid/dateshift.py

lrwb-aou/curation

e80447e56d269dc2c9c8bc79e78218d4b0dc504c

[ "MIT" ]

342

2017-06-23T21:37:40.000Z

2022-03-30T16:44:16.000Z

data_steward/cdr_cleaner/cleaning_rules/deid/dateshift.py

lrwb-aou/curation

e80447e56d269dc2c9c8bc79e78218d4b0dc504c

[ "MIT" ]

33

2017-07-01T00:12:20.000Z

2022-01-26T18:06:53.000Z

""" The basic date shifting rule.. Original Issue: DC-1005 This is an abstract class and cannot be directly instantiated. It must be extended to be used. """ # Python Imports import logging from abc import abstractmethod # Project imports from cdr_cleaner.cleaning_rules.base_cleaning_rule import BaseCleaningRule from common import JINJA_ENV LOGGER = logging.getLogger(__name__) SHIFT_EXP = JINJA_ENV.from_string(""" {{field_type}}_SUB( CAST({{field}} AS {{field_type}}), INTERVAL ( SELECT shift FROM `{{project}}.{{mapping_dataset_id}}.{{mapping_table_id}}` AS map WHERE map.research_id = remodel.person_id) DAY) AS {{field}} """) SELECT_STATEMENT = JINJA_ENV.from_string(""" CREATE OR REPLACE TABLE `{{project}}.{{dataset}}.{{table}}` AS ( SELECT {{fields}} FROM `{{project}}.{{dataset}}.{{table}}` AS remodel) """) class DateShiftRule(BaseCleaningRule): """ Date shift fields from 1 - 365 days in the past. Performs a "day" shift for any field in the provided table names and schemas. Uses the field type to determine the shift function to use. Currently works for the DATE, DATETIME, and TIMESTAMP type fields. """ def __init__(self, project_id, dataset_id, sandbox_dataset_id, issue_numbers, description, affected_datasets, affected_tables, mapping_dataset_id, mapping_table_id, depends_on=None): """ Initialize the class. Set the issue numbers, description and affected datasets. As other tickets may affect this SQL, append them to the list of Jira Issues. DO NOT REMOVE ORIGINAL JIRA ISSUE NUMBERS! """ if depends_on is None: depends_on = [] desc = (f'Date shift date and timestamp fields by the date shift ' f'calculated in the static mapping table.') self.mapping_dataset_id = mapping_dataset_id self.mapping_table_id = mapping_table_id super().__init__(issue_numbers=issue_numbers, description=description, affected_datasets=affected_datasets, project_id=project_id, dataset_id=dataset_id, sandbox_dataset_id=sandbox_dataset_id, affected_tables=affected_tables, depends_on=depends_on) @abstractmethod def get_tables_and_schemas(self): """ Provide dictionary of table names and schemas. :returns: a dictionary whose key, value patterns are in the form of {"tablename": "json schema",}. """ pass def get_query_specs(self): """ Return a list of dictionary query specifications. :return: A list of dictionaries. Each dictionary contains a single query and a specification for how to execute that query. The specifications are optional but the query is required. """ date_shift_queries = [] for table, schema in self.get_tables_and_schemas().items(): LOGGER.info(f"Building Date Shifting query for {self.dataset_id}." f"{table}") fields = [] for field in schema: field_type = field.get('type').lower() field_name = field.get('name') if field_type in ['date', 'datetime', 'timestamp']: shift_string = SHIFT_EXP.render( project=self.project_id, mapping_dataset_id=self.mapping_dataset_id, mapping_table_id=self.mapping_table_id, field_type=field_type.upper(), field=field_name, table=table) fields.append(shift_string) else: fields.append(field_name) fields_string = ',\n'.join(fields) query = SELECT_STATEMENT.render(project=self.project_id, dataset=self.dataset_id, table=table, fields=fields_string) date_shift_queries.append({'query': query}) return date_shift_queries

34.90625

78

0.576321

import logging from abc import abstractmethod from cdr_cleaner.cleaning_rules.base_cleaning_rule import BaseCleaningRule from common import JINJA_ENV LOGGER = logging.getLogger(__name__) SHIFT_EXP = JINJA_ENV.from_string(""" {{field_type}}_SUB( CAST({{field}} AS {{field_type}}), INTERVAL ( SELECT shift FROM `{{project}}.{{mapping_dataset_id}}.{{mapping_table_id}}` AS map WHERE map.research_id = remodel.person_id) DAY) AS {{field}} """) SELECT_STATEMENT = JINJA_ENV.from_string(""" CREATE OR REPLACE TABLE `{{project}}.{{dataset}}.{{table}}` AS ( SELECT {{fields}} FROM `{{project}}.{{dataset}}.{{table}}` AS remodel) """) class DateShiftRule(BaseCleaningRule): def __init__(self, project_id, dataset_id, sandbox_dataset_id, issue_numbers, description, affected_datasets, affected_tables, mapping_dataset_id, mapping_table_id, depends_on=None): if depends_on is None: depends_on = [] desc = (f'Date shift date and timestamp fields by the date shift ' f'calculated in the static mapping table.') self.mapping_dataset_id = mapping_dataset_id self.mapping_table_id = mapping_table_id super().__init__(issue_numbers=issue_numbers, description=description, affected_datasets=affected_datasets, project_id=project_id, dataset_id=dataset_id, sandbox_dataset_id=sandbox_dataset_id, affected_tables=affected_tables, depends_on=depends_on) @abstractmethod def get_tables_and_schemas(self): pass def get_query_specs(self): date_shift_queries = [] for table, schema in self.get_tables_and_schemas().items(): LOGGER.info(f"Building Date Shifting query for {self.dataset_id}." f"{table}") fields = [] for field in schema: field_type = field.get('type').lower() field_name = field.get('name') if field_type in ['date', 'datetime', 'timestamp']: shift_string = SHIFT_EXP.render( project=self.project_id, mapping_dataset_id=self.mapping_dataset_id, mapping_table_id=self.mapping_table_id, field_type=field_type.upper(), field=field_name, table=table) fields.append(shift_string) else: fields.append(field_name) fields_string = ',\n'.join(fields) query = SELECT_STATEMENT.render(project=self.project_id, dataset=self.dataset_id, table=table, fields=fields_string) date_shift_queries.append({'query': query}) return date_shift_queries

true

f71fde73faca108579365cdbb13033f096a89b4b

37,146

py

Python

conf_selection_and_DFT/PL_dft_library_201027.py

aspuru-guzik-group/kraken

4eaad505c1343e6083032b4a3fda47e004e19734

[ "MIT" ]

3

2022-01-13T12:39:54.000Z

2022-03-30T00:10:52.000Z

conf_selection_and_DFT/PL_dft_library_201027.py

aspuru-guzik-group/kraken

4eaad505c1343e6083032b4a3fda47e004e19734

[ "MIT" ]

null

conf_selection_and_DFT/PL_dft_library_201027.py

aspuru-guzik-group/kraken

4eaad505c1343e6083032b4a3fda47e004e19734

[ "MIT" ]

null

# 201005: rename/restructure .yml files for consistency with xtb-level data # 201006: in read_conformer() fix error message when log files are missing import os,re,itertools,time #import pybel #from openbabel import pybel import numpy as np import pandas as pd import pathlib as pl cwd = pl.Path.cwd() import yaml from yaml import CLoader as Loader from yaml import CDumper as Dumper from rdkit import Chem,Geometry from rdkit.Chem import rdmolfiles, AllChem, rdMolAlign,rdmolops from multiprocessing import Pool import morfeus # Kjell Jorner from PL_split_logs_201006 import split_log # TG from PL_conformer_selection_200411 import mirror_mol, delete_element_from_rdkitmol, delete_haloalkane_halides # TG #changed from PL_conformer_selection_201019 5/17/21 by EP import PL_gaussian_properties_201021 as gp # TG import vmin4 as vmin # TG/Iris Guo import P_int_200916 as P_int # Robert Pollice (,TG(,ML)) # import PL_visvol as visvol # Ellyn Peters # covalent radii, from Pyykko and Atsumi, Chem. Eur. J. 15, 2009, 188-197 # values for metals decreased by 10% according to Robert Paton's Sterimol implementation rcov = { "H": 0.32,"He": 0.46,"Li": 1.2,"Be": 0.94,"B": 0.77,"C": 0.75,"N": 0.71,"O": 0.63,"F": 0.64,"Ne": 0.67,"Na": 1.4,"Mg": 1.25,"Al": 1.13,"Si": 1.04,"P": 1.1,"S": 1.02,"Cl": 0.99,"Ar": 0.96,"K": 1.76,"Ca": 1.54,"Sc": 1.33,"Ti": 1.22,"V": 1.21,"Cr": 1.1,"Mn": 1.07,"Fe": 1.04,"Co": 1.0,"Ni": 0.99,"Cu": 1.01,"Zn": 1.09,"Ga": 1.12,"Ge": 1.09,"As": 1.15,"Se": 1.1,"Br": 1.14,"Kr": 1.17,"Rb": 1.89,"Sr": 1.67,"Y": 1.47,"Zr": 1.39,"Nb": 1.32,"Mo": 1.24,"Tc": 1.15,"Ru": 1.13,"Rh": 1.13,"Pd": 1.08,"Ag": 1.15,"Cd": 1.23,"In": 1.28,"Sn": 1.26,"Sb": 1.26,"Te": 1.23,"I": 1.32,"Xe": 1.31,"Cs": 2.09,"Ba": 1.76,"La": 1.62,"Ce": 1.47,"Pr": 1.58,"Nd": 1.57,"Pm": 1.56,"Sm": 1.55,"Eu": 1.51,"Gd": 1.52,"Tb": 1.51,"Dy": 1.5,"Ho": 1.49,"Er": 1.49,"Tm": 1.48,"Yb": 1.53,"Lu": 1.46,"Hf": 1.37,"Ta": 1.31,"W": 1.23,"Re": 1.18,"Os": 1.16,"Ir": 1.11,"Pt": 1.12,"Au": 1.13,"Hg": 1.32,"Tl": 1.3,"Pb": 1.3,"Bi": 1.36,"Po": 1.31,"At": 1.38,"Rn": 1.42,"Fr": 2.01,"Ra": 1.81,"Ac": 1.67,"Th": 1.58,"Pa": 1.52,"U": 1.53,"Np": 1.54,"Pu": 1.55 } # some constants R = 0.0019872036 #kcal mol^-1 K^-1 T = 298.15 #K hartree_kcalmol = 627.50947 periodictable = ["Bq","H","He","Li","Be","B","C","N","O","F","Ne","Na","Mg","Al","Si","P","S","Cl","Ar","K","Ca","Sc","Ti","V","Cr","Mn","Fe","Co","Ni","Cu","Zn","Ga","Ge","As","Se","Br","Kr","Rb","Sr","Y","Zr","Nb","Mo","Tc","Ru","Rh","Pd","Ag","Cd","In","Sn","Sb","Te","I","Xe","Cs","Ba","La","Ce","Pr","Nd","Pm","Sm","Eu","Gd","Tb","Dy","Ho","Er","Tm","Yb","Lu","Hf","Ta","W","Re","Os","Ir","Pt","Au","Hg","Tl","Pb","Bi","Po","At","Rn","Fr","Ra","Ac","Th","Pa","U","Np","Pu","Am","Cm","Bk","Cf","Es","Fm","Md","No","Lr","Rf","Db","Sg","Bh","Hs","Mt","Ds","Rg","Uub","Uut","Uuq","Uup","Uuh","Uus","Uuo","X"] def get_conmat(elements, coords): # partially based on code from Robert Paton's Sterimol script, which based this part on Grimme's D3 code # elements is a list of strings, coords is a numpy array or nested list of shape N_atoms x 3 if type(coords) == list: coords = np.asarray(coords) natom = len(elements) #max_elem = 94 k1 = 16.0 k2 = 4.0/3.0 conmat = np.zeros((natom,natom)) for i in range(0,natom): if elements[i] not in rcov.keys(): continue for iat in range(0,natom): if elements[iat] not in rcov.keys(): continue if iat != i: dxyz = coords[iat]-coords[i] r = np.linalg.norm(dxyz) rco = rcov[elements[i]]+rcov[elements[iat]] rco = rco*k2 rr=rco/r damp=1.0/(1.0+np.math.exp(-k1*(rr-1.0))) if damp > 0.85: #check if threshold is good enough for general purpose conmat[i,iat],conmat[iat,i] = 1,1 return(conmat) def add_valence(elements,coords,conmat,base_idx,add_element="Pd"): # Adds a valence to base so that the angle to the previous substituents is maximized and reorders the coordinate output for convenience # add_element: add any of the following elements: distpx = {"O":1.5,"Se":2.12,"Pd":2.28,"X":1.8} # typical bond distances to P if type(coords) == list: coords = np.asarray(coords) num_atoms = len(elements) coord_base = coords[base_idx] base_element = elements[base_idx] vec = np.array([0.0,0.0,0.0]) bonded = [] for atom in range(num_atoms): if conmat[base_idx][atom]: bonded.append(atom) vec += coord_base - coords[atom] coordox = distpx[add_element]*vec/np.linalg.norm(vec) + coord_base atoms = [x for x in range(num_atoms+1)] coords_temp = np.vstack((coords,coordox)) if sum(get_conmat(elements+[add_element],coords_temp)[-1]) != 1.0: print(" Warning: possible collision!") # sort coordinates so that base is first, add_element is second, and the other atoms bonded to base are next elements_new = [base_element,add_element]+[elements[a] for a in bonded] + [a for i,a in enumerate(elements) if i not in [base_idx]+bonded] coords_new = np.vstack((coord_base, coordox, coords[bonded], coords[[i for i,a in enumerate(elements) if i not in [base_idx]+bonded]])) return(elements_new, coords_new) def write_xyz(elements,coords,filename): with open(filename,"w") as f: f.write(f"{len(elements)}\n\n") for i,a in enumerate(elements): f.write(f"{a.title():>3} " + " ".join([f"{coords[i][j]:15f}" for j in range(3)]) + "\n") def rmsd_matrix(conformers): molobjects = [rdmolfiles.MolFromMolFile(str(cwd/conformer/f"{conformer}_opt.sdf"),removeHs=False,strictParsing=False) for conformer in conformers] molobjects = [Chem.RemoveHs(mol) for mol in molobjects] # Remove all H: optional but speeds up RMSD calculation molobjects = [delete_haloalkane_halides(mol) for mol in molobjects] # Remove halides in perhaloalkyl moieties. Improves RMSD matching and timing molobjects_inv = [mirror_mol(mol) for mol in molobjects] # create mirror images of each conformer rmsd_mat = np.zeros((len(conformers),len(conformers))) for i,j in itertools.product(range(len(conformers)),range(len(conformers))): if i<j: continue if i==j: rmsd_mat[i,j] = 1 else: rmsd_mat[i,j] = min((rdMolAlign.GetBestRMS(molobjects[i],molobjects[j]),rdMolAlign.GetBestRMS(molobjects[i],molobjects_inv[j]))) rmsd_mat[j,i] = rmsd_mat[i,j] return(rmsd_mat) def dict_key_rmsd(candidate_pair): return float(rmsd_matrix(candidate_pair)[0,1]) # which energies to read from which log-file energylogs = { "e_dz":"freq", "e_tz_gas":"nbo", "e_tz_gas":"sp", "e_tz_solv":"solv", "e_tz_ra":"ra", "e_tz_rc":"rc", } # which properties to read from which log-file proplogs = { "freq":["nimag","g","t"], "sp" :["dipole","homo","qpole","t"], "ra" :["homo","nbo","t"], "rc" :["homo","nbo","t"], "nbo" :["nbo","nborbsP","t"], "nmr" :["nmr","t"], "efg" :["efg","nuesp","t"], "solv":["ecds","t"], } # assign names to each descriptor propoutput = { "freq_g": ["","g"], "freq_nimag": ["nimag"], "sp_dipole": ["dipolemoment",], "sp_homo": ["fmo_e_homo","fmo_e_lumo","fmo_mu","fmo_eta","fmo_omega"], "ra_homo":["somo_ra","","","",""], "rc_homo":["somo_rc","","","",""], "sp_qpole": ["qpole_amp","qpoletens_xx","qpoletens_yy","qpoletens_zz"], "nbo_nbo": ["nbo_P"], "ra_nbo": ["nbo_P_ra","spindens_P_ra"], "rc_nbo": ["nbo_P_rc","spindens_P_rc"], "nmr_nmr": ["nmr_P","nmrtens_sxx_P","nmrtens_syy_P","nmrtens_szz_P",], "efg_efg": ["efg_amp_P","efgtens_xx_P","efgtens_yy_P","efgtens_zz_P"], "efg_nuesp": ["nuesp_P",], "solv_ecds": ["E_solv_cds"], "nbo_dipole": ["dipolemoment",], "nbo_homo": ["fmo_e_homo","fmo_e_lumo","fmo_mu","fmo_eta","fmo_omega"], "nbo_qpole": ["qpole_amp","qpoletens_xx","qpoletens_yy","qpoletens_zz"], } boltzproperties = ['vmin_vmin','vmin_r','dipolemoment', 'fmo_e_homo', 'fmo_e_lumo', 'fmo_mu', 'fmo_eta', 'fmo_omega', 'somo_ra', 'somo_rc', 'qpole_amp', 'qpoletens_xx', 'qpoletens_yy', 'qpoletens_zz', 'nbo_P', 'nbo_P_ra', 'spindens_P_ra', 'nbo_P_rc', 'spindens_P_rc', 'nmr_P', 'nmrtens_sxx_P', 'nmrtens_syy_P', 'nmrtens_szz_P', 'efg_amp_P', 'efgtens_xx_P', 'efgtens_yy_P', 'efgtens_zz_P', 'nuesp_P', 'E_solv_cds', 'nbo_lp_P_percent_s', 'nbo_lp_P_occ', 'nbo_lp_P_e', 'nbo_bd_e_max', 'nbo_bd_e_avg', 'nbo_bds_e_min', 'nbo_bds_e_avg', 'nbo_bd_occ_min', 'nbo_bd_occ_avg', 'nbo_bds_occ_max', 'nbo_bds_occ_avg', 'E_solv_total', 'E_solv_elstat', 'E_oxidation', 'E_reduction', 'fukui_p', 'fukui_m', 'pyr_P', 'pyr_alpha', 'vbur_vbur', 'vbur_vtot', 'vbur_ratio_vbur_vtot', 'vbur_qvbur_min', 'vbur_qvbur_max', 'vbur_qvtot_min', 'vbur_qvtot_max', 'vbur_max_delta_qvbur', 'vbur_max_delta_qvtot', 'vbur_ovbur_min', 'vbur_ovbur_max', 'vbur_ovtot_min', 'vbur_ovtot_max', 'vbur_near_vbur', 'vbur_far_vbur', 'vbur_near_vtot', 'vbur_far_vtot', 'sterimol_B1', 'sterimol_B5', 'sterimol_L', 'sterimol_burB1', 'sterimol_burB5', 'sterimol_burL',"Pint_P_int","Pint_dP","Pint_P_min","Pint_P_max","volume","surface_area","sphericity"] # "vv_total_visible_volume","vv_proximal_visible_volume","vv_distal_visible_volume","vv_ratio_visible_total","vv_ratio_proxvis_total", mmproperties = ['dipolemoment', 'qpole_amp', 'qpoletens_xx', 'qpoletens_yy', 'qpoletens_zz', 'pyr_P', 'pyr_alpha', 'vbur_vbur', 'vbur_vtot', 'vbur_qvbur_min', 'vbur_qvbur_max', 'vbur_qvtot_min', 'vbur_qvtot_max', 'vbur_max_delta_qvbur', 'vbur_max_delta_qvtot', 'vbur_ovbur_min', 'vbur_ovbur_max', 'vbur_ovtot_min', 'vbur_ovtot_max', 'vbur_near_vbur', 'vbur_far_vbur', 'vbur_near_vtot', 'vbur_far_vtot', 'sterimol_B1', 'sterimol_B5', 'sterimol_L', 'sterimol_burB1', 'sterimol_burB5', 'sterimol_burL'] # ,"vv_total_visible_volume","vv_proximal_visible_volume","vv_distal_visible_volume","vv_ratio_visible_total","vv_ratio_proxvis_total", Pintresults = ["Pint_P_int","Pint_dP","Pint_P_min","Pint_P_max","volume","surface_area","sphericity"] def morfeus_properties(elements,coordinates,confdata): # Morfeus: Sterimol, Vbur, pyr morfdict = {} if "pyr_P" not in confdata.keys() and confdata["p_val"] == 3: # Pyramidalization - two equivalent measurments P and alpha pyr = morfeus.Pyramidalization(elements=elements,coordinates=coordinates,atom_index=1,excluded_atoms=[2]) # remove Pd morfdict["pyr_P"] = float(pyr.P) morfdict["pyr_alpha"] = float(pyr.alpha) if "vbur_vbur" not in confdata.keys(): #Buried volume - get quadrant volumes and distal volume # iterate through P-substituents, aligning the quadrants paralell to each once (= xz_plane definition) # Metal/point of reference should be 2.28 A away from P # z_axis_atoms: P # xz_plane_atoms: each of the substituents once # keep lowest and highest quadrant and octant volume across all three orientations of the coordinate system # keep highest difference of any neighboring quadrant volume # keep volume in each of the two hemispheres qvbur_all = np.array([]) qvdist_all = np.array([]) qvtot_all = np.array([]) max_delta_qvbur_all = [] max_delta_qvtot_all = [] ovbur_all = np.array([]) ovtot_all = np.array([]) for i in range(3):#confdata["p_val"]): bv = morfeus.BuriedVolume(elements,coordinates,2,excluded_atoms=[2],z_axis_atoms=[1],xz_plane_atoms=[3+i]) bv.octant_analysis() bv.compute_distal_volume(method="buried_volume",octants=True) vbur = bv.buried_volume # these are identical for each iteration vdist = bv.distal_volume # vtot = vbur + vdist # qvbur = np.asarray(list(bv.quadrants["buried_volume"].values())) qvdist = np.asarray(list(bv.quadrants["distal_volume"].values())) qvtot = qvbur + qvdist qvbur_all = np.append(qvbur_all,qvbur) qvtot_all = np.append(qvtot_all,qvtot) max_delta_qvbur_all.append(max([abs(qvbur[j]-qvbur[j-1]) for j in range(4)])) max_delta_qvtot_all.append(max([abs(qvtot[j]-qvtot[j-1]) for j in range(4)])) ovbur = np.asarray(list(bv.octants["buried_volume"].values())) ovdist = np.asarray(list(bv.octants["distal_volume"].values())) ovtot = ovbur + ovdist ovbur_all = np.append(ovbur_all,ovbur) ovtot_all = np.append(ovtot_all,ovtot) near_vbur = ovbur[4:].sum() # these are identical for each iteration far_vbur = ovbur[:4].sum() # near_vtot = ovtot[4:].sum() # far_vtot = ovtot[:4].sum() # morfdict["vbur_vbur"] = vbur morfdict["vbur_vtot"] = float(vtot) morfdict["vbur_ratio_vbur_vtot"] = float(vbur/vtot) morfdict["vbur_qvbur_min"] = float(min(qvbur_all)) morfdict["vbur_qvbur_max"] = float(max(qvbur_all)) morfdict["vbur_qvtot_min"] = float(min(qvtot_all)) morfdict["vbur_qvtot_max"] = float(max(qvtot_all)) morfdict["vbur_max_delta_qvbur"] = float(max(max_delta_qvbur_all)) morfdict["vbur_max_delta_qvtot"] = float(max(max_delta_qvtot_all)) morfdict["vbur_ovbur_min"] = float(min(ovbur_all)) morfdict["vbur_ovbur_max"] = float(max(ovbur_all)) morfdict["vbur_ovtot_min"] = float(min(ovtot_all)) morfdict["vbur_ovtot_max"] = float(max(ovtot_all)) morfdict["vbur_near_vbur"] = float(near_vbur) morfdict["vbur_far_vbur"] = float(far_vbur) morfdict["vbur_near_vtot"] = float(near_vtot) morfdict["vbur_far_vtot"] = float(far_vtot) if "sterimol_B1" not in confdata.keys(): # Sterimol # for Sterimol values matching Rob Paton's implementation: patonradii = morfeus.helpers.get_radii(elements, radii_type="bondi") patonradii = np.array(patonradii) patonradii[patonradii == 1.2] = 1.09 sterimol = morfeus.Sterimol(elements, coordinates, 2, 1, radii=patonradii, n_rot_vectors=3600) morfdict["sterimol_B1"] = float(sterimol.B_1_value) morfdict["sterimol_B5"] = float(sterimol.B_5_value) morfdict["sterimol_L"] = float(sterimol.L_value) # buried Sterimol sterimol_bur = morfeus.Sterimol(elements, coordinates, 2, 1,calculate=False,radii=patonradii, n_rot_vectors=3600) sterimol_bur.bury(sphere_radius=5.5,method="delete",radii_scale=0.5) # sterimol.bury(sphere_radius=4.5,method="delete",radii_scale=1) morfdict["sterimol_burB1"] = float(sterimol_bur.B_1_value) morfdict["sterimol_burB5"] = float(sterimol_bur.B_5_value) morfdict["sterimol_burL"] = float(sterimol_bur.L_value) return(morfdict) def gp_properties(ligand,conformer,p_idx): # reads gaussian log files gpdict = {} gpdict["properties"] = {} contents = { "streams":{}, "filecont":{}, } # read energies for e,log in energylogs.items(): contents["streams"][log] = gp.get_outstreams(cwd/conformer/f"{conformer}_{log}.log") if contents["streams"][log] == "failed or incomplete job": return({"error":True}) else: gpdict[e] = gp.get_e_hf(contents["streams"][log]) gpdict["error"] = False # going through each log file, get the relevant properties for log in proplogs.keys(): contents["filecont"][log] = gp.get_filecont(cwd/conformer/f"{conformer}_{log}.log") for prop in proplogs[log]: gpresults = gp.jobtypes[prop][0](contents[gp.jobtypes[prop][1]][log],p_idx) if prop == "nborbsP": # NBO orbital analysis returns a dictionary with the proper labels gpdict["properties"].update(gpresults) elif prop == "t": # subjob time gpdict[f"{log}_t"] = gpresults elif prop in ["e_dz","g","e_tz_gas","e_tz_solv","e_tz_ra","e_tz_rc","nimag"]: gpdict.update({propoutput[f"{log}_{prop}"][i]: float(gpresults[i]) for i in range(len(gpresults))}) else: # all other functions return a list. This is assigned into a dict with proper names here gpdict["properties"].update({propoutput[f"{log}_{prop}"][i]: float(gpresults[i]) for i in range(len(gpresults))}) gpdict["g_tz_gas"] = gpdict["g"] - gpdict["e_dz"] + gpdict["e_tz_gas"] # in Hartree gpdict["g_tz_solv"] = gpdict["g"] - gpdict["e_dz"] + gpdict["e_tz_solv"] # in Hartree gpdict["properties"]["E_solv_total"] = (gpdict["e_tz_solv"] - gpdict["e_tz_gas"]) * hartree_kcalmol # in kcal/mol gpdict["properties"]["E_solv_elstat"] = gpdict["properties"]["E_solv_total"] - gpdict["properties"]["E_solv_cds"] # in kcal/mol gpdict["properties"]["E_oxidation"] = gpdict["e_tz_rc"] - gpdict["e_tz_gas"] # in Hartree gpdict["properties"]["E_reduction"] = gpdict["e_tz_ra"] - gpdict["e_tz_gas"] # in Hartree gpdict["properties"]["fukui_p"] = gpdict["properties"]["nbo_P"]-gpdict["properties"]["nbo_P_ra"] # fukui electrophilicity gpdict["properties"]["fukui_m"] = gpdict["properties"]["nbo_P_rc"]-gpdict["properties"]["nbo_P"] # fukui nucleophilicity gpdict["t_total"] = sum([gpdict[f"{log}_t"] for log in proplogs.keys()]) if "" in gpdict.keys(): del gpdict[""] if "" in gpdict["properties"].keys(): del gpdict["properties"][""] return(gpdict) def read_conformer(cwd, ligand, conformer): # cwd: pathlib path of current working directory. ligand: 0-digit ligand ID. conformer: full name of the conformer (including the ID at the beginnig) confdata = {} errors = [] checklogs = [cwd/conformer/f"{conformer}_{l}.log" for l in proplogs.keys() if not (cwd/conformer/f"{conformer}_{l}.log").exists()] if len(checklogs) != 0: #! log this as a conformer-level error err = f"Missing Gaussian log files, flagged in read_conformer: {','.join([chkl.name for chkl in checklogs])}" errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") confdata["error"] = True return(confdata,errors) if "elements_pd" not in confdata.keys(): # mol = next(pybel.readfile("g09",str(cwd/conformer/f"{conformer}_nbo.log"))) #mol = next(pybel.readfile("g09",str(cwd/conformer/f"{conformer}_opt.log"))) #elements = [periodictable[a.atomicnum] for a in mol.atoms] #coordinates = [list(a.coords) for a in mol.atoms] #coordinates_a = np.array([a.coords for a in mol.atoms]) def read_gaussian_logfile(fn): time0=time.time() read=False for line in open(fn,"r"): if read: if "---" in line and len(elements)>0: read=False if read: if "X" not in line and "---" not in line: atomnum = int(line.split()[1]) #print(line.replace("\n","")) #print(atomnum) el = periodictable[atomnum] elements.append(el) coordinates.append([float(line.split()[3]),float(line.split()[4]), float(line.split()[5])]) if "Coordinates (Angstroms)" in line: coordinates, elements = [], [] read=True time1=time.time() print("gaussian log parser done in %.2f seconds"%(time1-time0)) return(coordinates, elements) coordinates, elements = read_gaussian_logfile(str(cwd/conformer/f"{conformer}_opt.log")) coordinates_a = np.array(coordinates) conmat = get_conmat(elements,coordinates_a) p_idx = [i for i in range(len(elements)) if elements[i] == "P" and sum(conmat[i]) <= 3][0] # this removes quaternary P (phosphonium, phosphate etc) but allows for P with 2 substituents (phosphabenzene, phosphaimine etc). Can we be sure that we never have more than one non-quaternary P(III)? elements_pd, coordinates_pd = add_valence(elements,coordinates,conmat,p_idx,add_element="Pd") # Add "Pd" at the reference position in the P-lone pair region if not (cwd/conformer/f"{conformer}_opt_Pd.xyz").exists(): #out = pybel.Outputfile("xyz",str(cwd/conformer/f"{conformer}_opt.xyz")) #out.write(mol) #out.close() write_xyz(elements, coordinates, cwd/conformer/f"{conformer}_opt.xyz") #out = pybel.Outputfile("sdf",str(cwd/conformer/f"{conformer}_opt.sdf")) #out.write(mol) #out.close() os.system("obabel -ixyz %s -osdf >> %s"%(str(cwd/conformer/f"{conformer}_opt.xyz"), str(cwd/conformer/f"{conformer}_opt.sdf"))) write_xyz(elements_pd,coordinates_pd,cwd/conformer/f"{conformer}_opt_Pd.xyz") confdata["coords"] = coordinates confdata["coords_pd"] = coordinates_pd.tolist() confdata["elements"] = elements confdata["elements_pd"] = elements_pd confdata["conmat"] = conmat.tolist() confdata["p_idx"] = p_idx confdata["p_val"] = int(sum(conmat[p_idx])) # how many substituents at P confdata["properties"] = {} ## get properties # gp_properties: everything that can be read from the Gaussian log files (most electronic properties) confdata.update(gp_properties(ligand,conformer,confdata["p_idx"])) if confdata["error"]: #! log this as a conformer-level error err = "Error in the Gaussian computations, flagged in read_conformer, please check log files." errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") with open(cwd/conformer/f"{conformer}_data.yml","w") as f: yaml.dump(confdata,f,Dumper=Dumper) return(confdata,errors) if confdata["nimag"] != 0: #! log this as a conformer-level error err = f"Number of imaginary frequencies: {confdata['nimag']}." errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") with open(cwd/conformer/f"{conformer}_data.yml","w") as f: yaml.dump(confdata,f,Dumper=Dumper) confdata["error"] = True return(confdata,errors) # morfeus: properties that use the geometry/steric properties confdata["properties"].update(morfeus_properties(confdata["elements_pd"],confdata["coords_pd"],confdata)) # # P_int # if "Pint_P_int" not in confdata.keys(): # confdata.update(P_int.P_int_main(name=conformer,directory=cwd/conformer)) # read results disp = "d3" pint_read = P_int.read_dedout(cwd/conformer,conformer,disp)+P_int.read_multiwfnout(cwd/conformer,conformer)+P_int.read_disp(cwd/conformer,conformer,disp) confdata["properties"].update({Pintresults[i]:float(pint_read[i]) for i in range(7)}) # V_min try: if "vmin_vmin" not in confdata.keys(): vminob = vmin.get_vmin(f"{conformer}.fchk",str(cwd/conformer)+"/",True) confdata["properties"]["vmin_vmin"] = float(vminob.v_min) confdata["properties"]["vmin_r"] = float(vminob.r_min) except: err = f"Vmin FileNotFoundError." errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") confdata["error"] = True # visvol # if "vv_total_visible_volume" not in confdata.keys(): # confdata.update(visvol.get_vis_vol(cwd/conformer/f"{conformer}_opt_Pd.xyz",radii_type = 'rcov',prox_cutoff = 3.5,ignore_H = 0,write_results = 1, plot = 0)) with open(cwd/conformer/f"{conformer}_data.yml","w") as f: yaml.dump(confdata,f,Dumper=Dumper) return(confdata,errors) def read_ligand(cwd, ligand, conformers, liganddata = {}): # cwd is the ligand-level directory status = {"ligandlevel": [],} if len(liganddata.keys()) == 0: if (cwd/f"{ligand}_data.yml").exists(): with open(cwd/f"{ligand}_data.yml","r") as f: liganddata = yaml.load(f,Loader=Loader) if (cwd/f"{ligand}_confdata.yml").exists(): with open(cwd/f"{ligand}_confdata.yml","r") as f: liganddata["confdata"] = yaml.load(f,Loader=Loader) else: liganddata = { "conformers_all": conformers, "conformers": conformers.copy(), # Duplicates and computations with errors (including nimag=1) will be removed from this list "number_of_conformers": len(conformers), "removed_duplicates": [], "confdata": {},#{c:{} for c in conformers}, "boltzmann_averaged_data": {}, "min_data": {}, "max_data": {}, "delta_data": {}, "vburminconf_data": {}, } newconfs = 0 for conformer in conformers: if conformer in liganddata["removed_duplicates"]: continue print(conformer) if conformer in liganddata["confdata"].keys(): pass elif (cwd/conformer/f"{conformer}_data.yml").exists(): with open(cwd/conformer/f"{conformer}_data.yml","r") as f: liganddata["confdata"][conformer] = yaml.load(f,Loader=Loader) newconfs += 1 else: print("read conformer data") liganddata["confdata"][conformer],status[conformer] = read_conformer(cwd, ligand, conformer) # returns the dictionary with the conformer data and a list with errors newconfs += 1 if newconfs > 0: # error, NIMAG removal liganddata["conformers_w_error"] = [conformer for conformer in liganddata["conformers"] if liganddata["confdata"][conformer]["error"]] liganddata["conformers"] = [c for c in liganddata["conformers"] if c not in liganddata["conformers_w_error"]] liganddata["number_of_conformers"] = len(liganddata["conformers"]) energies = ["e_dz","g","e_tz_gas","g_tz_gas","e_tz_solv","g_tz_solv"] liganddata["energies"] = {} liganddata["relative_energies"] = {} for e in energies: liganddata["energies"][e] = {conformer: liganddata["confdata"][conformer][e] for conformer in liganddata["conformers"]} liganddata[e+"_min"] = min(liganddata["energies"][e].values()) liganddata[e+"_minconf"] = list(liganddata["energies"][e].keys())[np.argmin(list(liganddata["energies"][e].values()))] liganddata["relative_energies"][e+"_rel"] = {conformer: (liganddata["energies"][e][conformer]-liganddata[e+"_min"])*hartree_kcalmol for conformer in liganddata["conformers"]} # erel_df = pd.DataFrame(np.array([list(liganddata[e+"_rel"].values()) for e in energies]).T ,columns=energies,index=liganddata["conformers"] ) erel_df = pd.DataFrame([liganddata["relative_energies"][e+"_rel"] for e in energies],index=energies).T #liganddata["relative_energies_df"] = erel_df liganddata["relative_energies_dict"] = erel_df.to_dict() # Find duplicates: # 1) find pairs of conformers that are within E_rel < 0.1 kcal/mol (relative energies seem to be much more reliable than relative free energies) # 2) check these pairs to also have RMSD < 0.2 A # 3) Remove the conformer with higher relative free energy duplicates_candidates = [(i,j) for i,j in itertools.combinations(liganddata["conformers"],2) if abs(erel_df["e_dz"].loc[i] - erel_df["e_dz"].loc[j]) < 0.1] try: # Throw a name error here if you wanna only run the except cores = max(os.cpu_count() - 2, 1) with Pool(cores) as p: values = p.map(dict_key_rmsd, duplicates_candidates) liganddata["rmsd_candidates"] = {key: value for key, value in zip(duplicates_candidates, values)} # The less cool, non-parallel way #liganddata["rmsd_candidates"] = {candidate_pair: float(rmsd_matrix(candidate_pair)[0,1]) for candidate_pair in duplicates_candidates} # keep all RMSD for potential debugging liganddata["duplicates"] = [candidate_pair for candidate_pair in liganddata["rmsd_candidates"] if liganddata["rmsd_candidates"][candidate_pair] < 0.2] except: # RDkit failed to generate Mol objects and thus could not compute RMSD, or some of the internal structures in those mol files are different despite actually being the same. Default to duplicate detection based on dipole moment and chemical shift similarity #! log this on ligand level for double-checking err = "Warning: RDKit error at duplicate RMSD testing. Please double check." status["ligandlevel"].append(err) print(f"{ligand};ligandlevel;{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};ligandlevel;{err}\n") dipole_candidates = set([(i,j) for i,j in duplicates_candidates if abs(liganddata["confdata"][i]["properties"]["dipolemoment"] - liganddata["confdata"][j]["properties"]["dipolemoment"]) < 0.025]) nmr_candidates = set([(i,j) for i,j in duplicates_candidates if abs(liganddata["confdata"][i]["properties"]["nmr_P"] - liganddata["confdata"][j]["properties"]["nmr_P"]) < 0.1]) liganddata["duplicates"] = sorted(dipole_candidates & nmr_candidates) liganddata["removed_duplicates"] = [erel_df.loc[list(pair)]["g_tz_gas"].idxmax() for pair in liganddata["duplicates"]] liganddata["conformers"] = [c for c in liganddata["conformers"] if c not in liganddata["removed_duplicates"]] liganddata["number_of_conformers"] = len(liganddata["conformers"]) # Boltzmann averaging #boltzfacs = {conformer: np.exp(-liganddata["relative_energies_df"]["g_tz_gas"].loc[conformer]/(R*T)) for conformer in liganddata["conformers"]} boltzfacs = {conformer: np.exp(-erel_df["g_tz_gas"].loc[conformer]/(R*T)) for conformer in liganddata["conformers"]} Q = sum(boltzfacs.values()) liganddata["boltzmann_weights"] = {conformer: float(boltzfacs[conformer]/Q) for conformer in liganddata["conformers"] } # probability for prop in boltzproperties: confsmissingprop = [conf for conf in liganddata["conformers"] if prop not in liganddata["confdata"][conf]["properties"].keys()] if len(confsmissingprop) == 0: liganddata["boltzmann_averaged_data"][prop] = sum([liganddata["boltzmann_weights"][conf] * liganddata["confdata"][conf]["properties"][prop] for conf in liganddata["conformers"]]) else: # if a single conformer is missing a property value, set Boltzmann-average to None #! log this as a ligand-level error with prop and confsmissingprop err = f"Warning: {len(confsmissingprop)}/{len(liganddata['conformers'])} conformers missing values for property {prop}: {','.join(confsmissingprop)}." status["ligandlevel"].append(err) print(f"{ligand};ligandlevel;{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};ligandlevel;{err}\n") liganddata["boltzmann_averaged_data"][prop] = None continue # "Condensed" properties liganddata["vburminconf"] = liganddata["conformers"][np.argmin([liganddata["confdata"][conf]["properties"]["vbur_vbur"] for conf in liganddata["conformers"]])] for prop in mmproperties: proplist = [liganddata["confdata"][conf]["properties"][prop] for conf in liganddata["conformers"] if prop in liganddata["confdata"][conf]["properties"].keys()] # if a single conformer is missing a property value, still perform min/max analysis (Boltzmann-average will be None to indicate missing value(s)) # if all confs are missing this prop, set min/max/delta to None if len(proplist) == 0: liganddata["min_data"][prop] = None liganddata["max_data"][prop] = None liganddata["delta_data"][prop] = None liganddata["vburminconf_data"][prop] = None else: liganddata["min_data"][prop] = min(proplist) liganddata["max_data"][prop] = max(proplist) liganddata["delta_data"][prop] = liganddata["max_data"][prop] - liganddata["min_data"][prop] liganddata["vburminconf_data"][prop] = liganddata["confdata"][liganddata["vburminconf"]]["properties"][prop] liganddata["time_all"] = sum([liganddata["confdata"][conf]["t_total"] for conf in liganddata["conformers_all"] if "t_total" in liganddata["confdata"][conf].keys()]) with open(cwd/f"{ligand}_data.yml","w") as f: yaml.dump({k:v for k,v in liganddata.items() if k != "confdata"},f,Dumper=Dumper) with open(cwd/f"{ligand}_confdata.yml","w") as f: yaml.dump(liganddata["confdata"],f,Dumper=Dumper) erel_df.to_csv(cwd/f"{ligand}_relative_energies.csv",sep=";") return(liganddata,status) def main_split_logs(cwd, ligand): if not (cwd/"ERR").exists(): (cwd/"ERR").mkdir() # if not (cwd/"done").exists(): # (cwd/"done").mkdir() conformers = [i.name for i in (cwd/ligand).iterdir() if i.is_dir()] conformers_good = [] for conformer in conformers: logs = [i.name for i in (cwd/ligand/conformer).rglob("*.log")] if f"{conformer}.log" in logs and f"{conformer}_opt.log" not in logs: status = split_log(ligand, conformer) if status != "Error": #(cwd/ligand/conformer/f"{conformer}.log").rename(cwd/f"done/{conformer}.log") conformers_good.append(conformer) return(conformers_good) if __name__ == '__main__': starttime_all = time.time() ligname = re.compile("[0-9]{8}") ligands = sorted([i.name for i in cwd.iterdir() if (ligname.match(i.name) and i.is_dir())]) conformers = {ligand: [i.name for i in (cwd/ligand).iterdir() if i.is_dir()] for ligand in ligands} if not (cwd/"ERR").exists(): (cwd/"ERR").mkdir() if not (cwd/"done").exists(): (cwd/"done").mkdir() for ligand in ligands: for conformer in conformers[ligand]: logs = [i.name for i in (cwd/ligand/conformer).rglob("*.log")] if f"{conformer}.log" in logs and f"{conformer}_opt.log" not in logs: status = split_log(ligand,conformer) if status != "Error": (cwd/ligand/conformer/f"{conformer}.log").rename(cwd/f"done/{conformer}.log") if (cwd/"allligands_data.yml").exists(): with open(cwd/"allligands_data.yml","r") as f: allliganddata = yaml.load(f,Loader=Loader) else: allliganddata = {} for ligand in ligands: print(ligand) print(conformers[ligand]) if ligand in allliganddata.keys(): allliganddata[ligand],status = read_ligand(cwd,ligand,conformers[ligand],allliganddata[ligand]) else: allliganddata[ligand],status = read_ligand(cwd,ligand,conformers[ligand]) with open(cwd/"allligands_data.yml","w") as f: yaml.dump(allliganddata,f,Dumper=Dumper) variants = ["boltz","min","max","delta","vburminconf"] columns = [i+"_boltz" for i in boltzproperties if i not in mmproperties] + [f"{i}_{j}" for i,j in itertools.product(mmproperties,variants)]# + ["t_total","number_of_conformers"] df = pd.DataFrame(columns = columns,index = ligands) for l in ligands: for c in columns: print(allliganddata[l]["properties"]) exit() df.loc[l][c] = allliganddata[l]["properties"][c] df["t_total"] = [allliganddata[l]["t_total"] for l in ligands] df["number_of_conformers"] = [allliganddata[l]["number_of_conformers"] for l in ligands] df.to_csv("allligands_data.csv",sep=";") print(f"All done. Total time: {round((time.time()-starttime_all),2)} sec")

57.324074

1,343

0.619959

import os,re,itertools,time import numpy as np import pandas as pd import pathlib as pl cwd = pl.Path.cwd() import yaml from yaml import CLoader as Loader from yaml import CDumper as Dumper from rdkit import Chem,Geometry from rdkit.Chem import rdmolfiles, AllChem, rdMolAlign,rdmolops from multiprocessing import Pool import morfeus from PL_split_logs_201006 import split_log from PL_conformer_selection_200411 import mirror_mol, delete_element_from_rdkitmol, delete_haloalkane_halides as vmin import P_int_200916 as P_int "H": 0.32,"He": 0.46,"Li": 1.2,"Be": 0.94,"B": 0.77,"C": 0.75,"N": 0.71,"O": 0.63,"F": 0.64,"Ne": 0.67,"Na": 1.4,"Mg": 1.25,"Al": 1.13,"Si": 1.04,"P": 1.1,"S": 1.02,"Cl": 0.99,"Ar": 0.96,"K": 1.76,"Ca": 1.54,"Sc": 1.33,"Ti": 1.22,"V": 1.21,"Cr": 1.1,"Mn": 1.07,"Fe": 1.04,"Co": 1.0,"Ni": 0.99,"Cu": 1.01,"Zn": 1.09,"Ga": 1.12,"Ge": 1.09,"As": 1.15,"Se": 1.1,"Br": 1.14,"Kr": 1.17,"Rb": 1.89,"Sr": 1.67,"Y": 1.47,"Zr": 1.39,"Nb": 1.32,"Mo": 1.24,"Tc": 1.15,"Ru": 1.13,"Rh": 1.13,"Pd": 1.08,"Ag": 1.15,"Cd": 1.23,"In": 1.28,"Sn": 1.26,"Sb": 1.26,"Te": 1.23,"I": 1.32,"Xe": 1.31,"Cs": 2.09,"Ba": 1.76,"La": 1.62,"Ce": 1.47,"Pr": 1.58,"Nd": 1.57,"Pm": 1.56,"Sm": 1.55,"Eu": 1.51,"Gd": 1.52,"Tb": 1.51,"Dy": 1.5,"Ho": 1.49,"Er": 1.49,"Tm": 1.48,"Yb": 1.53,"Lu": 1.46,"Hf": 1.37,"Ta": 1.31,"W": 1.23,"Re": 1.18,"Os": 1.16,"Ir": 1.11,"Pt": 1.12,"Au": 1.13,"Hg": 1.32,"Tl": 1.3,"Pb": 1.3,"Bi": 1.36,"Po": 1.31,"At": 1.38,"Rn": 1.42,"Fr": 2.01,"Ra": 1.81,"Ac": 1.67,"Th": 1.58,"Pa": 1.52,"U": 1.53,"Np": 1.54,"Pu": 1.55 } # some constants R = 0.0019872036 #kcal mol^-1 K^-1 T = 298.15 #K hartree_kcalmol = 627.50947 periodictable = ["Bq","H","He","Li","Be","B","C","N","O","F","Ne","Na","Mg","Al","Si","P","S","Cl","Ar","K","Ca","Sc","Ti","V","Cr","Mn","Fe","Co","Ni","Cu","Zn","Ga","Ge","As","Se","Br","Kr","Rb","Sr","Y","Zr","Nb","Mo","Tc","Ru","Rh","Pd","Ag","Cd","In","Sn","Sb","Te","I","Xe","Cs","Ba","La","Ce","Pr","Nd","Pm","Sm","Eu","Gd","Tb","Dy","Ho","Er","Tm","Yb","Lu","Hf","Ta","W","Re","Os","Ir","Pt","Au","Hg","Tl","Pb","Bi","Po","At","Rn","Fr","Ra","Ac","Th","Pa","U","Np","Pu","Am","Cm","Bk","Cf","Es","Fm","Md","No","Lr","Rf","Db","Sg","Bh","Hs","Mt","Ds","Rg","Uub","Uut","Uuq","Uup","Uuh","Uus","Uuo","X"] def get_conmat(elements, coords): # partially based on code from Robert Paton's Sterimol script, which based this part on Grimme's D3 code # elements is a list of strings, coords is a numpy array or nested list of shape N_atoms x 3 if type(coords) == list: coords = np.asarray(coords) natom = len(elements) #max_elem = 94 k1 = 16.0 k2 = 4.0/3.0 conmat = np.zeros((natom,natom)) for i in range(0,natom): if elements[i] not in rcov.keys(): continue for iat in range(0,natom): if elements[iat] not in rcov.keys(): continue if iat != i: dxyz = coords[iat]-coords[i] r = np.linalg.norm(dxyz) rco = rcov[elements[i]]+rcov[elements[iat]] rco = rco*k2 rr=rco/r damp=1.0/(1.0+np.math.exp(-k1*(rr-1.0))) if damp > 0.85: #check if threshold is good enough for general purpose conmat[i,iat],conmat[iat,i] = 1,1 return(conmat) def add_valence(elements,coords,conmat,base_idx,add_element="Pd"): # Adds a valence to base so that the angle to the previous substituents is maximized and reorders the coordinate output for convenience # add_element: add any of the following elements: distpx = {"O":1.5,"Se":2.12,"Pd":2.28,"X":1.8} # typical bond distances to P if type(coords) == list: coords = np.asarray(coords) num_atoms = len(elements) coord_base = coords[base_idx] base_element = elements[base_idx] vec = np.array([0.0,0.0,0.0]) bonded = [] for atom in range(num_atoms): if conmat[base_idx][atom]: bonded.append(atom) vec += coord_base - coords[atom] coordox = distpx[add_element]*vec/np.linalg.norm(vec) + coord_base atoms = [x for x in range(num_atoms+1)] coords_temp = np.vstack((coords,coordox)) if sum(get_conmat(elements+[add_element],coords_temp)[-1]) != 1.0: print(" Warning: possible collision!") # sort coordinates so that base is first, add_element is second, and the other atoms bonded to base are next elements_new = [base_element,add_element]+[elements[a] for a in bonded] + [a for i,a in enumerate(elements) if i not in [base_idx]+bonded] coords_new = np.vstack((coord_base, coordox, coords[bonded], coords[[i for i,a in enumerate(elements) if i not in [base_idx]+bonded]])) return(elements_new, coords_new) def write_xyz(elements,coords,filename): with open(filename,"w") as f: f.write(f"{len(elements)}\n\n") for i,a in enumerate(elements): f.write(f"{a.title():>3} " + " ".join([f"{coords[i][j]:15f}" for j in range(3)]) + "\n") def rmsd_matrix(conformers): molobjects = [rdmolfiles.MolFromMolFile(str(cwd/conformer/f"{conformer}_opt.sdf"),removeHs=False,strictParsing=False) for conformer in conformers] molobjects = [Chem.RemoveHs(mol) for mol in molobjects] # Remove all H: optional but speeds up RMSD calculation molobjects = [delete_haloalkane_halides(mol) for mol in molobjects] # Remove halides in perhaloalkyl moieties. Improves RMSD matching and timing molobjects_inv = [mirror_mol(mol) for mol in molobjects] # create mirror images of each conformer rmsd_mat = np.zeros((len(conformers),len(conformers))) for i,j in itertools.product(range(len(conformers)),range(len(conformers))): if i<j: continue if i==j: rmsd_mat[i,j] = 1 else: rmsd_mat[i,j] = min((rdMolAlign.GetBestRMS(molobjects[i],molobjects[j]),rdMolAlign.GetBestRMS(molobjects[i],molobjects_inv[j]))) rmsd_mat[j,i] = rmsd_mat[i,j] return(rmsd_mat) def dict_key_rmsd(candidate_pair): return float(rmsd_matrix(candidate_pair)[0,1]) # which energies to read from which log-file energylogs = { "e_dz":"freq", "e_tz_gas":"nbo", "e_tz_gas":"sp", "e_tz_solv":"solv", "e_tz_ra":"ra", "e_tz_rc":"rc", } # which properties to read from which log-file proplogs = { "freq":["nimag","g","t"], "sp" :["dipole","homo","qpole","t"], "ra" :["homo","nbo","t"], "rc" :["homo","nbo","t"], "nbo" :["nbo","nborbsP","t"], "nmr" :["nmr","t"], "efg" :["efg","nuesp","t"], "solv":["ecds","t"], } # assign names to each descriptor propoutput = { "freq_g": ["","g"], "freq_nimag": ["nimag"], "sp_dipole": ["dipolemoment",], "sp_homo": ["fmo_e_homo","fmo_e_lumo","fmo_mu","fmo_eta","fmo_omega"], "ra_homo":["somo_ra","","","",""], "rc_homo":["somo_rc","","","",""], "sp_qpole": ["qpole_amp","qpoletens_xx","qpoletens_yy","qpoletens_zz"], "nbo_nbo": ["nbo_P"], "ra_nbo": ["nbo_P_ra","spindens_P_ra"], "rc_nbo": ["nbo_P_rc","spindens_P_rc"], "nmr_nmr": ["nmr_P","nmrtens_sxx_P","nmrtens_syy_P","nmrtens_szz_P",], "efg_efg": ["efg_amp_P","efgtens_xx_P","efgtens_yy_P","efgtens_zz_P"], "efg_nuesp": ["nuesp_P",], "solv_ecds": ["E_solv_cds"], "nbo_dipole": ["dipolemoment",], "nbo_homo": ["fmo_e_homo","fmo_e_lumo","fmo_mu","fmo_eta","fmo_omega"], "nbo_qpole": ["qpole_amp","qpoletens_xx","qpoletens_yy","qpoletens_zz"], } boltzproperties = ['vmin_vmin','vmin_r','dipolemoment', 'fmo_e_homo', 'fmo_e_lumo', 'fmo_mu', 'fmo_eta', 'fmo_omega', 'somo_ra', 'somo_rc', 'qpole_amp', 'qpoletens_xx', 'qpoletens_yy', 'qpoletens_zz', 'nbo_P', 'nbo_P_ra', 'spindens_P_ra', 'nbo_P_rc', 'spindens_P_rc', 'nmr_P', 'nmrtens_sxx_P', 'nmrtens_syy_P', 'nmrtens_szz_P', 'efg_amp_P', 'efgtens_xx_P', 'efgtens_yy_P', 'efgtens_zz_P', 'nuesp_P', 'E_solv_cds', 'nbo_lp_P_percent_s', 'nbo_lp_P_occ', 'nbo_lp_P_e', 'nbo_bd_e_max', 'nbo_bd_e_avg', 'nbo_bds_e_min', 'nbo_bds_e_avg', 'nbo_bd_occ_min', 'nbo_bd_occ_avg', 'nbo_bds_occ_max', 'nbo_bds_occ_avg', 'E_solv_total', 'E_solv_elstat', 'E_oxidation', 'E_reduction', 'fukui_p', 'fukui_m', 'pyr_P', 'pyr_alpha', 'vbur_vbur', 'vbur_vtot', 'vbur_ratio_vbur_vtot', 'vbur_qvbur_min', 'vbur_qvbur_max', 'vbur_qvtot_min', 'vbur_qvtot_max', 'vbur_max_delta_qvbur', 'vbur_max_delta_qvtot', 'vbur_ovbur_min', 'vbur_ovbur_max', 'vbur_ovtot_min', 'vbur_ovtot_max', 'vbur_near_vbur', 'vbur_far_vbur', 'vbur_near_vtot', 'vbur_far_vtot', 'sterimol_B1', 'sterimol_B5', 'sterimol_L', 'sterimol_burB1', 'sterimol_burB5', 'sterimol_burL',"Pint_P_int","Pint_dP","Pint_P_min","Pint_P_max","volume","surface_area","sphericity"] # "vv_total_visible_volume","vv_proximal_visible_volume","vv_distal_visible_volume","vv_ratio_visible_total","vv_ratio_proxvis_total", mmproperties = ['dipolemoment', 'qpole_amp', 'qpoletens_xx', 'qpoletens_yy', 'qpoletens_zz', 'pyr_P', 'pyr_alpha', 'vbur_vbur', 'vbur_vtot', 'vbur_qvbur_min', 'vbur_qvbur_max', 'vbur_qvtot_min', 'vbur_qvtot_max', 'vbur_max_delta_qvbur', 'vbur_max_delta_qvtot', 'vbur_ovbur_min', 'vbur_ovbur_max', 'vbur_ovtot_min', 'vbur_ovtot_max', 'vbur_near_vbur', 'vbur_far_vbur', 'vbur_near_vtot', 'vbur_far_vtot', 'sterimol_B1', 'sterimol_B5', 'sterimol_L', 'sterimol_burB1', 'sterimol_burB5', 'sterimol_burL'] # ,"vv_total_visible_volume","vv_proximal_visible_volume","vv_distal_visible_volume","vv_ratio_visible_total","vv_ratio_proxvis_total", Pintresults = ["Pint_P_int","Pint_dP","Pint_P_min","Pint_P_max","volume","surface_area","sphericity"] def morfeus_properties(elements,coordinates,confdata): # Morfeus: Sterimol, Vbur, pyr morfdict = {} if "pyr_P" not in confdata.keys() and confdata["p_val"] == 3: # Pyramidalization - two equivalent measurments P and alpha pyr = morfeus.Pyramidalization(elements=elements,coordinates=coordinates,atom_index=1,excluded_atoms=[2]) # remove Pd morfdict["pyr_P"] = float(pyr.P) morfdict["pyr_alpha"] = float(pyr.alpha) if "vbur_vbur" not in confdata.keys(): #Buried volume - get quadrant volumes and distal volume # iterate through P-substituents, aligning the quadrants paralell to each once (= xz_plane definition) # Metal/point of reference should be 2.28 A away from P # z_axis_atoms: P # xz_plane_atoms: each of the substituents once # keep lowest and highest quadrant and octant volume across all three orientations of the coordinate system # keep highest difference of any neighboring quadrant volume # keep volume in each of the two hemispheres qvbur_all = np.array([]) qvdist_all = np.array([]) qvtot_all = np.array([]) max_delta_qvbur_all = [] max_delta_qvtot_all = [] ovbur_all = np.array([]) ovtot_all = np.array([]) for i in range(3):#confdata["p_val"]): bv = morfeus.BuriedVolume(elements,coordinates,2,excluded_atoms=[2],z_axis_atoms=[1],xz_plane_atoms=[3+i]) bv.octant_analysis() bv.compute_distal_volume(method="buried_volume",octants=True) vbur = bv.buried_volume # these are identical for each iteration vdist = bv.distal_volume # vtot = vbur + vdist # qvbur = np.asarray(list(bv.quadrants["buried_volume"].values())) qvdist = np.asarray(list(bv.quadrants["distal_volume"].values())) qvtot = qvbur + qvdist qvbur_all = np.append(qvbur_all,qvbur) qvtot_all = np.append(qvtot_all,qvtot) max_delta_qvbur_all.append(max([abs(qvbur[j]-qvbur[j-1]) for j in range(4)])) max_delta_qvtot_all.append(max([abs(qvtot[j]-qvtot[j-1]) for j in range(4)])) ovbur = np.asarray(list(bv.octants["buried_volume"].values())) ovdist = np.asarray(list(bv.octants["distal_volume"].values())) ovtot = ovbur + ovdist ovbur_all = np.append(ovbur_all,ovbur) ovtot_all = np.append(ovtot_all,ovtot) near_vbur = ovbur[4:].sum() # these are identical for each iteration far_vbur = ovbur[:4].sum() # near_vtot = ovtot[4:].sum() # far_vtot = ovtot[:4].sum() # morfdict["vbur_vbur"] = vbur morfdict["vbur_vtot"] = float(vtot) morfdict["vbur_ratio_vbur_vtot"] = float(vbur/vtot) morfdict["vbur_qvbur_min"] = float(min(qvbur_all)) morfdict["vbur_qvbur_max"] = float(max(qvbur_all)) morfdict["vbur_qvtot_min"] = float(min(qvtot_all)) morfdict["vbur_qvtot_max"] = float(max(qvtot_all)) morfdict["vbur_max_delta_qvbur"] = float(max(max_delta_qvbur_all)) morfdict["vbur_max_delta_qvtot"] = float(max(max_delta_qvtot_all)) morfdict["vbur_ovbur_min"] = float(min(ovbur_all)) morfdict["vbur_ovbur_max"] = float(max(ovbur_all)) morfdict["vbur_ovtot_min"] = float(min(ovtot_all)) morfdict["vbur_ovtot_max"] = float(max(ovtot_all)) morfdict["vbur_near_vbur"] = float(near_vbur) morfdict["vbur_far_vbur"] = float(far_vbur) morfdict["vbur_near_vtot"] = float(near_vtot) morfdict["vbur_far_vtot"] = float(far_vtot) if "sterimol_B1" not in confdata.keys(): # Sterimol # for Sterimol values matching Rob Paton's implementation: patonradii = morfeus.helpers.get_radii(elements, radii_type="bondi") patonradii = np.array(patonradii) patonradii[patonradii == 1.2] = 1.09 sterimol = morfeus.Sterimol(elements, coordinates, 2, 1, radii=patonradii, n_rot_vectors=3600) morfdict["sterimol_B1"] = float(sterimol.B_1_value) morfdict["sterimol_B5"] = float(sterimol.B_5_value) morfdict["sterimol_L"] = float(sterimol.L_value) sterimol_bur = morfeus.Sterimol(elements, coordinates, 2, 1,calculate=False,radii=patonradii, n_rot_vectors=3600) sterimol_bur.bury(sphere_radius=5.5,method="delete",radii_scale=0.5) morfdict["sterimol_burB1"] = float(sterimol_bur.B_1_value) morfdict["sterimol_burB5"] = float(sterimol_bur.B_5_value) morfdict["sterimol_burL"] = float(sterimol_bur.L_value) return(morfdict) def gp_properties(ligand,conformer,p_idx): gpdict = {} gpdict["properties"] = {} contents = { "streams":{}, "filecont":{}, } for e,log in energylogs.items(): contents["streams"][log] = gp.get_outstreams(cwd/conformer/f"{conformer}_{log}.log") if contents["streams"][log] == "failed or incomplete job": return({"error":True}) else: gpdict[e] = gp.get_e_hf(contents["streams"][log]) gpdict["error"] = False for log in proplogs.keys(): contents["filecont"][log] = gp.get_filecont(cwd/conformer/f"{conformer}_{log}.log") for prop in proplogs[log]: gpresults = gp.jobtypes[prop][0](contents[gp.jobtypes[prop][1]][log],p_idx) if prop == "nborbsP": gpdict["properties"].update(gpresults) elif prop == "t": gpdict[f"{log}_t"] = gpresults elif prop in ["e_dz","g","e_tz_gas","e_tz_solv","e_tz_ra","e_tz_rc","nimag"]: gpdict.update({propoutput[f"{log}_{prop}"][i]: float(gpresults[i]) for i in range(len(gpresults))}) else: gpdict["properties"].update({propoutput[f"{log}_{prop}"][i]: float(gpresults[i]) for i in range(len(gpresults))}) gpdict["g_tz_gas"] = gpdict["g"] - gpdict["e_dz"] + gpdict["e_tz_gas"] gpdict["g_tz_solv"] = gpdict["g"] - gpdict["e_dz"] + gpdict["e_tz_solv"] gpdict["properties"]["E_solv_total"] = (gpdict["e_tz_solv"] - gpdict["e_tz_gas"]) * hartree_kcalmol gpdict["properties"]["E_solv_elstat"] = gpdict["properties"]["E_solv_total"] - gpdict["properties"]["E_solv_cds"] gpdict["properties"]["E_oxidation"] = gpdict["e_tz_rc"] - gpdict["e_tz_gas"] gpdict["properties"]["E_reduction"] = gpdict["e_tz_ra"] - gpdict["e_tz_gas"] gpdict["properties"]["fukui_p"] = gpdict["properties"]["nbo_P"]-gpdict["properties"]["nbo_P_ra"] gpdict["properties"]["fukui_m"] = gpdict["properties"]["nbo_P_rc"]-gpdict["properties"]["nbo_P"] gpdict["t_total"] = sum([gpdict[f"{log}_t"] for log in proplogs.keys()]) if "" in gpdict.keys(): del gpdict[""] if "" in gpdict["properties"].keys(): del gpdict["properties"][""] return(gpdict) def read_conformer(cwd, ligand, conformer): confdata = {} errors = [] checklogs = [cwd/conformer/f"{conformer}_{l}.log" for l in proplogs.keys() if not (cwd/conformer/f"{conformer}_{l}.log").exists()] if len(checklogs) != 0: err = f"Missing Gaussian log files, flagged in read_conformer: {','.join([chkl.name for chkl in checklogs])}" errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") confdata["error"] = True return(confdata,errors) if "elements_pd" not in confdata.keys(): def read_gaussian_logfile(fn): time0=time.time() read=False for line in open(fn,"r"): if read: if "---" in line and len(elements)>0: read=False if read: if "X" not in line and "---" not in line: atomnum = int(line.split()[1]) el = periodictable[atomnum] elements.append(el) coordinates.append([float(line.split()[3]),float(line.split()[4]), float(line.split()[5])]) if "Coordinates (Angstroms)" in line: coordinates, elements = [], [] read=True time1=time.time() print("gaussian log parser done in %.2f seconds"%(time1-time0)) return(coordinates, elements) coordinates, elements = read_gaussian_logfile(str(cwd/conformer/f"{conformer}_opt.log")) coordinates_a = np.array(coordinates) conmat = get_conmat(elements,coordinates_a) p_idx = [i for i in range(len(elements)) if elements[i] == "P" and sum(conmat[i]) <= 3][0] elements_pd, coordinates_pd = add_valence(elements,coordinates,conmat,p_idx,add_element="Pd") if not (cwd/conformer/f"{conformer}_opt_Pd.xyz").exists(): write_xyz(elements, coordinates, cwd/conformer/f"{conformer}_opt.xyz") os.system("obabel -ixyz %s -osdf >> %s"%(str(cwd/conformer/f"{conformer}_opt.xyz"), str(cwd/conformer/f"{conformer}_opt.sdf"))) write_xyz(elements_pd,coordinates_pd,cwd/conformer/f"{conformer}_opt_Pd.xyz") confdata["coords"] = coordinates confdata["coords_pd"] = coordinates_pd.tolist() confdata["elements"] = elements confdata["elements_pd"] = elements_pd confdata["conmat"] = conmat.tolist() confdata["p_idx"] = p_idx confdata["p_val"] = int(sum(conmat[p_idx])) confdata["properties"] = {} a.update(gp_properties(ligand,conformer,confdata["p_idx"])) if confdata["error"]: err = "Error in the Gaussian computations, flagged in read_conformer, please check log files." errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") with open(cwd/conformer/f"{conformer}_data.yml","w") as f: yaml.dump(confdata,f,Dumper=Dumper) return(confdata,errors) if confdata["nimag"] != 0: err = f"Number of imaginary frequencies: {confdata['nimag']}." errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") with open(cwd/conformer/f"{conformer}_data.yml","w") as f: yaml.dump(confdata,f,Dumper=Dumper) confdata["error"] = True return(confdata,errors) confdata["properties"].update(morfeus_properties(confdata["elements_pd"],confdata["coords_pd"],confdata)) disp = "d3" pint_read = P_int.read_dedout(cwd/conformer,conformer,disp)+P_int.read_multiwfnout(cwd/conformer,conformer)+P_int.read_disp(cwd/conformer,conformer,disp) confdata["properties"].update({Pintresults[i]:float(pint_read[i]) for i in range(7)}) try: if "vmin_vmin" not in confdata.keys(): vminob = vmin.get_vmin(f"{conformer}.fchk",str(cwd/conformer)+"/",True) confdata["properties"]["vmin_vmin"] = float(vminob.v_min) confdata["properties"]["vmin_r"] = float(vminob.r_min) except: err = f"Vmin FileNotFoundError." errors.append(err) print(f"{ligand};{conformer};{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};{conformer};{err}\n") confdata["error"] = True with open(cwd/conformer/f"{conformer}_data.yml","w") as f: yaml.dump(confdata,f,Dumper=Dumper) return(confdata,errors) def read_ligand(cwd, ligand, conformers, liganddata = {}): status = {"ligandlevel": [],} if len(liganddata.keys()) == 0: if (cwd/f"{ligand}_data.yml").exists(): with open(cwd/f"{ligand}_data.yml","r") as f: liganddata = yaml.load(f,Loader=Loader) if (cwd/f"{ligand}_confdata.yml").exists(): with open(cwd/f"{ligand}_confdata.yml","r") as f: liganddata["confdata"] = yaml.load(f,Loader=Loader) else: liganddata = { "conformers_all": conformers, "conformers": conformers.copy(), "number_of_conformers": len(conformers), "removed_duplicates": [], "confdata": {}, "boltzmann_averaged_data": {}, "min_data": {}, "max_data": {}, "delta_data": {}, "vburminconf_data": {}, } newconfs = 0 for conformer in conformers: if conformer in liganddata["removed_duplicates"]: continue print(conformer) if conformer in liganddata["confdata"].keys(): pass elif (cwd/conformer/f"{conformer}_data.yml").exists(): with open(cwd/conformer/f"{conformer}_data.yml","r") as f: liganddata["confdata"][conformer] = yaml.load(f,Loader=Loader) newconfs += 1 else: print("read conformer data") liganddata["confdata"][conformer],status[conformer] = read_conformer(cwd, ligand, conformer) newconfs += 1 if newconfs > 0: liganddata["conformers_w_error"] = [conformer for conformer in liganddata["conformers"] if liganddata["confdata"][conformer]["error"]] liganddata["conformers"] = [c for c in liganddata["conformers"] if c not in liganddata["conformers_w_error"]] liganddata["number_of_conformers"] = len(liganddata["conformers"]) energies = ["e_dz","g","e_tz_gas","g_tz_gas","e_tz_solv","g_tz_solv"] liganddata["energies"] = {} liganddata["relative_energies"] = {} for e in energies: liganddata["energies"][e] = {conformer: liganddata["confdata"][conformer][e] for conformer in liganddata["conformers"]} liganddata[e+"_min"] = min(liganddata["energies"][e].values()) liganddata[e+"_minconf"] = list(liganddata["energies"][e].keys())[np.argmin(list(liganddata["energies"][e].values()))] liganddata["relative_energies"][e+"_rel"] = {conformer: (liganddata["energies"][e][conformer]-liganddata[e+"_min"])*hartree_kcalmol for conformer in liganddata["conformers"]} erel_df = pd.DataFrame([liganddata["relative_energies"][e+"_rel"] for e in energies],index=energies).T liganddata["relative_energies_dict"] = erel_df.to_dict() duplicates_candidates = [(i,j) for i,j in itertools.combinations(liganddata["conformers"],2) if abs(erel_df["e_dz"].loc[i] - erel_df["e_dz"].loc[j]) < 0.1] try: cores = max(os.cpu_count() - 2, 1) with Pool(cores) as p: values = p.map(dict_key_rmsd, duplicates_candidates) liganddata["rmsd_candidates"] = {key: value for key, value in zip(duplicates_candidates, values)} [candidate_pair for candidate_pair in liganddata["rmsd_candidates"] if liganddata["rmsd_candidates"][candidate_pair] < 0.2] except: err = "Warning: RDKit error at duplicate RMSD testing. Please double check." status["ligandlevel"].append(err) print(f"{ligand};ligandlevel;{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};ligandlevel;{err}\n") dipole_candidates = set([(i,j) for i,j in duplicates_candidates if abs(liganddata["confdata"][i]["properties"]["dipolemoment"] - liganddata["confdata"][j]["properties"]["dipolemoment"]) < 0.025]) nmr_candidates = set([(i,j) for i,j in duplicates_candidates if abs(liganddata["confdata"][i]["properties"]["nmr_P"] - liganddata["confdata"][j]["properties"]["nmr_P"]) < 0.1]) liganddata["duplicates"] = sorted(dipole_candidates & nmr_candidates) liganddata["removed_duplicates"] = [erel_df.loc[list(pair)]["g_tz_gas"].idxmax() for pair in liganddata["duplicates"]] liganddata["conformers"] = [c for c in liganddata["conformers"] if c not in liganddata["removed_duplicates"]] liganddata["number_of_conformers"] = len(liganddata["conformers"]) boltzfacs = {conformer: np.exp(-erel_df["g_tz_gas"].loc[conformer]/(R*T)) for conformer in liganddata["conformers"]} Q = sum(boltzfacs.values()) liganddata["boltzmann_weights"] = {conformer: float(boltzfacs[conformer]/Q) for conformer in liganddata["conformers"] } for prop in boltzproperties: confsmissingprop = [conf for conf in liganddata["conformers"] if prop not in liganddata["confdata"][conf]["properties"].keys()] if len(confsmissingprop) == 0: liganddata["boltzmann_averaged_data"][prop] = sum([liganddata["boltzmann_weights"][conf] * liganddata["confdata"][conf]["properties"][prop] for conf in liganddata["conformers"]]) else: err = f"Warning: {len(confsmissingprop)}/{len(liganddata['conformers'])} conformers missing values for property {prop}: {','.join(confsmissingprop)}." status["ligandlevel"].append(err) print(f"{ligand};ligandlevel;{err}") with open(cwd/f"{ligand}_errors.txt","a") as f: f.write(f"{ligand};ligandlevel;{err}\n") liganddata["boltzmann_averaged_data"][prop] = None continue liganddata["vburminconf"] = liganddata["conformers"][np.argmin([liganddata["confdata"][conf]["properties"]["vbur_vbur"] for conf in liganddata["conformers"]])] for prop in mmproperties: proplist = [liganddata["confdata"][conf]["properties"][prop] for conf in liganddata["conformers"] if prop in liganddata["confdata"][conf]["properties"].keys()] if len(proplist) == 0: liganddata["min_data"][prop] = None liganddata["max_data"][prop] = None liganddata["delta_data"][prop] = None liganddata["vburminconf_data"][prop] = None else: liganddata["min_data"][prop] = min(proplist) liganddata["max_data"][prop] = max(proplist) liganddata["delta_data"][prop] = liganddata["max_data"][prop] - liganddata["min_data"][prop] liganddata["vburminconf_data"][prop] = liganddata["confdata"][liganddata["vburminconf"]]["properties"][prop] liganddata["time_all"] = sum([liganddata["confdata"][conf]["t_total"] for conf in liganddata["conformers_all"] if "t_total" in liganddata["confdata"][conf].keys()]) with open(cwd/f"{ligand}_data.yml","w") as f: yaml.dump({k:v for k,v in liganddata.items() if k != "confdata"},f,Dumper=Dumper) with open(cwd/f"{ligand}_confdata.yml","w") as f: yaml.dump(liganddata["confdata"],f,Dumper=Dumper) erel_df.to_csv(cwd/f"{ligand}_relative_energies.csv",sep=";") return(liganddata,status) def main_split_logs(cwd, ligand): if not (cwd/"ERR").exists(): (cwd/"ERR").mkdir() conformers = [i.name for i in (cwd/ligand).iterdir() if i.is_dir()] conformers_good = [] for conformer in conformers: logs = [i.name for i in (cwd/ligand/conformer).rglob("*.log")] if f"{conformer}.log" in logs and f"{conformer}_opt.log" not in logs: status = split_log(ligand, conformer) if status != "Error": conformers_good.append(conformer) return(conformers_good) if __name__ == '__main__': starttime_all = time.time() ligname = re.compile("[0-9]{8}") ligands = sorted([i.name for i in cwd.iterdir() if (ligname.match(i.name) and i.is_dir())]) conformers = {ligand: [i.name for i in (cwd/ligand).iterdir() if i.is_dir()] for ligand in ligands} if not (cwd/"ERR").exists(): (cwd/"ERR").mkdir() if not (cwd/"done").exists(): (cwd/"done").mkdir() for ligand in ligands: for conformer in conformers[ligand]: logs = [i.name for i in (cwd/ligand/conformer).rglob("*.log")] if f"{conformer}.log" in logs and f"{conformer}_opt.log" not in logs: status = split_log(ligand,conformer) if status != "Error": (cwd/ligand/conformer/f"{conformer}.log").rename(cwd/f"done/{conformer}.log") if (cwd/"allligands_data.yml").exists(): with open(cwd/"allligands_data.yml","r") as f: allliganddata = yaml.load(f,Loader=Loader) else: allliganddata = {} for ligand in ligands: print(ligand) print(conformers[ligand]) if ligand in allliganddata.keys(): allliganddata[ligand],status = read_ligand(cwd,ligand,conformers[ligand],allliganddata[ligand]) else: allliganddata[ligand],status = read_ligand(cwd,ligand,conformers[ligand]) with open(cwd/"allligands_data.yml","w") as f: yaml.dump(allliganddata,f,Dumper=Dumper) variants = ["boltz","min","max","delta","vburminconf"] columns = [i+"_boltz" for i in boltzproperties if i not in mmproperties] + [f"{i}_{j}" for i,j in itertools.product(mmproperties,variants)] df = pd.DataFrame(columns = columns,index = ligands) for l in ligands: for c in columns: print(allliganddata[l]["properties"]) exit() df.loc[l][c] = allliganddata[l]["properties"][c] df["t_total"] = [allliganddata[l]["t_total"] for l in ligands] df["number_of_conformers"] = [allliganddata[l]["number_of_conformers"] for l in ligands] df.to_csv("allligands_data.csv",sep=";") print(f"All done. Total time: {round((time.time()-starttime_all),2)} sec")

true

f71fdf25a6bbbf3c2ecdf90eda58463aa369ca88

52,543

py

Python

t5/seqio/dataset_providers.py

dptam/text-to-text-transfer-transformer

3662823b126ebf39d9d8ed147a8af0c6973f0ba9

[ "Apache-2.0" ]

null

t5/seqio/dataset_providers.py

dptam/text-to-text-transfer-transformer

3662823b126ebf39d9d8ed147a8af0c6973f0ba9

[ "Apache-2.0" ]

null

t5/seqio/dataset_providers.py

dptam/text-to-text-transfer-transformer

3662823b126ebf39d9d8ed147a8af0c6973f0ba9

[ "Apache-2.0" ]

null

# Copyright 2021 The T5 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. # Lint as: python3 """Classes for data loading and processing. Defines Tasks, TaskRegistry, Mixture, and MixtureRegistry """ import abc import collections import inspect import json import os import re from typing import Any, Callable, Iterable, Mapping, MutableMapping, Optional, Sequence, Tuple, Type, Union from absl import logging import dataclasses import numpy as np from packaging import version from t5.seqio import utils from t5.seqio.feature_converters import FeatureConverter from t5.seqio.vocabularies import Vocabulary import tensorflow.compat.v2 as tf import tensorflow_datasets as tfds import typing_extensions _DEFAULT_FEATURE_KEYS = ["inputs", "targets"] _VALID_TASK_NAME_REGEX = re.compile(r"^[\w\d\._]+$") _MAX_EXAMPLES_TO_MEM_CACHE = 10000 SHUFFLE_BUFFER_SIZE = 1000 @dataclasses.dataclass(frozen=True) class Feature: """A container for attributes of output features of data providers.""" vocabulary: Vocabulary add_eos: bool = True required: bool = True dtype: tf.DType = tf.int32 @dataclasses.dataclass(frozen=True) class ShardInfo: """A container for specifying sharding info.""" index: int num_shards: int class DatasetProviderBase(metaclass=abc.ABCMeta): """Abstract base for classes that provide a tf.data.Dataset.""" @abc.abstractproperty def output_features(self) -> Mapping[str, Feature]: raise NotImplementedError @abc.abstractproperty def splits(self) -> Sequence[str]: raise NotImplementedError @abc.abstractmethod def get_dataset( self, sequence_length: int, split: str, use_cached: bool = False, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None, num_epochs: int = 1 ) -> tf.data.Dataset: """Returns the requested tf.data.Dataset.""" raise NotImplementedError @abc.abstractmethod def num_input_examples(self, split: str) -> int: raise NotImplementedError class DatasetProviderRegistry(object): """Base for registry of data providers. Subclasses must wrap `get` method to override the return type for pytype. TODO(adarob): Remove the need to override `get`. """ # Class variables must be defined in subclasses. _REGISTRY: MutableMapping[str, DatasetProviderBase] _PROVIDER_TYPE: Type[DatasetProviderBase] @classmethod def add_provider(cls, name: str, provider): """Adds a data provider instance to the registry.""" if name in cls._REGISTRY: raise ValueError("Attempting to register duplicate provider: %s" % name) if not isinstance(provider, cls._PROVIDER_TYPE): raise ValueError( "Attempting to register a class not of an invalid type. " "Expecting instance of %s, got %s" % (cls._PROVIDER_TYPE, type(provider).__name__)) cls._REGISTRY[name] = provider @classmethod def add( cls, name: str, provider_cls, *provider_args, **provider_kwargs ): """Instantiates and adds provider to the registry.""" if not issubclass(provider_cls, cls._PROVIDER_TYPE): raise ValueError( "Attempting to register a class not of an invalid type. " "Expecting instance of %s, got %s" % (cls._PROVIDER_TYPE, provider_cls)) provider = provider_cls(*provider_args, **provider_kwargs) cls.add_provider(name, provider) return provider @classmethod def remove(cls, name): """Remove provider from the registry, if it exists.""" if name in cls._REGISTRY: del cls._REGISTRY[name] @classmethod def get(cls, name): """Returns provider from the registry.""" if name not in cls._REGISTRY: raise ValueError("Provider name not registered: %s" % name) return cls._REGISTRY[name] @classmethod def names(cls): """Returns all provider names in registry.""" return cls._REGISTRY.keys() @classmethod def reset(cls): """Removes all of the registered tasks.""" cls._REGISTRY = {} @classmethod def get_dataset( cls, name, sequence_length, split, use_cached=False, shuffle=True, seed=None, shard_info=None, num_epochs=1): """Returns the requested tf.data.Dataset.""" return cls.get(name).get_dataset( sequence_length=sequence_length, split=split, use_cached=use_cached, shuffle=shuffle, seed=seed, shard_info=shard_info, num_epochs=num_epochs) # =============================== DataSources ================================== class DataSource(DatasetProviderBase): """A `DatasetProvider` that provides raw data from an input source. Inherits all abstract methods and properties of `DatasetProviderBase` except those overidden below. """ def __init__( self, splits: Iterable[str], num_input_examples: Optional[Mapping[str, int]] = None): self._splits = tuple(splits) self._num_input_examples = ( dict(num_input_examples) if num_input_examples is not None else None) @property def splits(self) -> Sequence[str]: return self._splits @property def output_features(self) -> Mapping[str, Feature]: """Override unused property of `DatasetProviderBase`.""" raise NotImplementedError @abc.abstractmethod def list_shards(self, split: str) -> Sequence[str]: """Returns string identifiers of input shards.""" raise NotImplementedError @abc.abstractmethod def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: """Overrides base class to add shard identifier and remove use_cached. Args: split: string, the split to return. shuffle: bool, whether to shuffle the input source. seed: tf.int64 scalar tf.Tensor (or None) for shuffling input source. shard_info: optional specification for loading a shard of the split. """ raise NotImplementedError def num_input_examples(self, split: str) -> Optional[int]: if self._num_input_examples is None: return None return self._num_input_examples[split] def _validate_args(fn, expected_pos_args): """Ensure function has exactly expected positional args.""" argspec = inspect.getfullargspec(fn) expected_pos_args = tuple(expected_pos_args) actual_args = tuple(argspec.args) if actual_args[:len(expected_pos_args)] != expected_pos_args: raise ValueError( "'%s' must have positional args %s, got: %s" % ( fn.__name__, expected_pos_args, actual_args)) actual_pos_args = tuple( argspec.args[:-len(argspec.defaults)] if argspec.defaults else argspec.args) if actual_pos_args != expected_pos_args[:len(actual_pos_args)]: raise ValueError( "'%s' may only have positional args %s, got: %s" % ( fn.__name__, expected_pos_args, actual_pos_args)) class DatasetFnCallable(typing_extensions.Protocol): def __call__(self, split: str, shuffle_files: bool, seed: Optional[int] = None) -> tf.data.Dataset: ... class FunctionDataSource(DataSource): """A `DataSource` that uses a function to provide the input data.""" def __init__( self, dataset_fn: DatasetFnCallable, splits: Iterable[str], num_input_examples: Optional[Mapping[str, int]] = None ): """FunctionDataSource constructor. Args: dataset_fn: a function with the signature `dataset_fn(split, shuffle_files)' (and optionally the variable `seed`) that returns a `tf.data.Dataset`. splits: an iterable of applicable string split names. num_input_examples: dict or None, an optional dictionary mapping split to its size in number of input examples (before preprocessing). The `num_input_examples` method will return None if not provided. """ _validate_args(dataset_fn, ["split", "shuffle_files"]) self._dataset_fn = dataset_fn super().__init__(splits=splits, num_input_examples=num_input_examples) def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: if shard_info and shard_info.num_shards > 1: raise ValueError( "`FunctionDataSource` does not support low-level sharding. Use " "tf.data.Dataset.shard instead.") if seed is None: ds = self._dataset_fn(split=split, shuffle_files=shuffle) else: _validate_args(self._dataset_fn, ["split", "shuffle_files", "seed"]) ds = self._dataset_fn(split=split, shuffle_files=shuffle, seed=seed) return ds def list_shards(self, split: str) -> Sequence[str]: return [split] class TfdsDataSource(DataSource): """A `DataSource` that uses TensorFlow Datasets to provide the input data.""" def __init__( self, tfds_name: str, tfds_data_dir: Optional[str] = None, splits: Optional[Union[Iterable[str], Mapping[str, str]]] = None ): """TfdsTask constructor. Args: tfds_name: string, the name and version number of a TFDS dataset, optionally with a config. tfds_data_dir: string, an optional path to a specific TFDS data directory to use. splits: an iterable of allowable string split names, a dict mapping allowable canonical splits (e.g., 'validation') to TFDS splits or slices (e.g., 'train[':1%']), or None. The default, None, uses all available splits from the TFDS dataset info. """ if ":" not in tfds_name: raise ValueError("TFDS name must contain a version number, got: %s" % tfds_name) self._tfds_dataset = utils.LazyTfdsLoader( tfds_name, data_dir=tfds_data_dir, split_map=splits if isinstance(splits, dict) else None) # If splits are not provided, we pass an empty tuple and use the lazy # lookup in the `splits` property. super().__init__(splits=splits or ()) @property def splits(self): """Overrides since we can't call `info.splits` until after init.""" return self._splits or self._tfds_dataset.info.splits @property def tfds_dataset(self): return self._tfds_dataset def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: return self.tfds_dataset.load( split, shuffle_files=shuffle, seed=seed, shard_info=shard_info) def num_input_examples(self, split: str) -> int: """Overrides since we can't call `info.splits` until after init.""" return self.tfds_dataset.size(split) def list_shards(self, split: str) -> Sequence[str]: return self.tfds_dataset.files(split) class FileDataSource(DataSource): """A `DataSource` that reads a file to provide the input dataset.""" def __init__( self, read_file_fn: Callable[[tf.data.Dataset], tf.data.Dataset], split_to_filepattern: Mapping[str, Union[str, Iterable[str]]], num_input_examples: Optional[Mapping[str, int]] = None, ): """FileDataSource constructor. Args: read_file_fn: a callable for creating a `tf.data.Dataset` from a `tf.data.Dataset` of file paths, e.g., `tf.data.TFRecordDataset`. split_to_filepattern: a mapping from split names to filepatterns to be expanded with glob. num_input_examples: dict or None, an optional dictionary mapping split to its size in number of input examples (before preprocessing). The `num_input_examples` method will return None if not provided. """ self._split_to_filepattern = split_to_filepattern self._reader = read_file_fn super().__init__( splits=split_to_filepattern.keys(), num_input_examples=num_input_examples) def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: files = self.list_shards(split) if not files: raise ValueError( "No file is found for the file pattern: " f"{self._split_to_filepattern[split]}." ) files_ds = tf.data.Dataset.from_tensor_slices(np.array(files, dtype=np.str)) if shard_info: if len(files) < shard_info.num_shards: raise ValueError( f"Dataset has too few files to shard. {len(files)} files vs " f"{shard_info.num_shards} shards requested.") files_ds = files_ds.shard(shard_info.num_shards, shard_info.index) if shuffle: files_ds = files_ds.shuffle(buffer_size=16, seed=seed) return files_ds.interleave( self._reader, cycle_length=16, block_length=16, num_parallel_calls=tf.data.experimental.AUTOTUNE) def list_shards(self, split: str) -> Sequence[str]: return tf.io.gfile.glob(self._split_to_filepattern[split]) class TextLineDataSource(FileDataSource): """A `FileDataSource` that reads lines of text from a file as input.""" def __init__( self, split_to_filepattern: Mapping[str, Union[str, Iterable[str]]], skip_header_lines: int = 0, num_input_examples: Optional[Mapping[str, int]] = None, ): """TextLineDataSource constructor. Args: split_to_filepattern: a mapping from split names to filepatterns to be expanded with glob. skip_header_lines: int, number of header lines to skip in each source file. num_input_examples: dict or None, an optional dictionary mapping split to its size in number of input examples (before preprocessing). The `num_input_examples` method will return None if not provided. """ # Used during caching. self._skip_header_lines = skip_header_lines def read_file_fn(filepattern): return tf.data.TextLineDataset(filepattern).skip(skip_header_lines) super().__init__( read_file_fn=read_file_fn, split_to_filepattern=split_to_filepattern, num_input_examples=num_input_examples) class TFExampleDataSource(FileDataSource): """A `FileDataSource` that reads files of tf.train.Example protos as input.""" def __init__( self, split_to_filepattern: Mapping[str, Union[str, Iterable[str]]], feature_description: Mapping[str, Union[tf.io.FixedLenFeature, tf.io.VarLenFeature]], reader_cls: Type[tf.data.Dataset] = tf.data.TFRecordDataset, num_input_examples: Optional[Mapping[str, int]] = None, ): """TFExampleDataSource constructor. Args: split_to_filepattern: dict of string (split name) to either string (filename or filepattern) or list of strings (filenames or filepatterns). feature_description: dict, a mapping of string feature keys to `tf.io.FixedLenFeature` or `tf.io.VarLenFeature` values. reader_cls: `tf.data.Dataset`, a dataset class to read the input files. num_input_examples: dict or None, an optional dictionary mapping split to its size in number of input examples (before preprocessing). The `num_input_examples` method will return None if not provided. """ def read_file_fn(filepattern): return reader_cls(filepattern).map( lambda pb: tf.io.parse_single_example(pb, feature_description), num_parallel_calls=tf.data.experimental.AUTOTUNE) super().__init__( read_file_fn=read_file_fn, split_to_filepattern=split_to_filepattern, num_input_examples=num_input_examples) # ========================== Offline Caching Helpers =========================== def _rename_plaintext_to_pretokenized( dataset: tf.data.Dataset) -> tf.data.Dataset: """Rename cached _plaintext features to new _pretokenized standard.""" def _rename(inputs): outputs = {} for k, v in inputs.items(): if k.endswith("_plaintext"): k = k[:-len("plaintext")] + "pretokenized" outputs[k] = v return outputs return dataset.map( _rename, num_parallel_calls=tf.data.experimental.AUTOTUNE) class _CachedDataSource(FileDataSource): """A `FileDataSource` for reading datasets cached offline.""" def __init__(self, cache_dir: str, split: str): with tf.io.gfile.GFile(utils.get_cached_info_path(cache_dir, split)) as f: split_info = json.load(f) features = split_info["features"] with tf.io.gfile.GFile(utils.get_cached_stats_path(cache_dir, split)) as f: stats = json.load(f) version_when_cached = version.Version( split_info.get("seqio_version", "0.pre")) version_with_true_dtypes = version.Version("0.0.0") if version_when_cached < version_with_true_dtypes: # Assume that all int64 features are really int32. for name, feat in features.items(): if feat["dtype"] == "int64": logging.info("Casting cached '%s' to int32.", name) feat["dtype"] = "int32" # Use `FixedLenSequenceFeature` for sequences with variable length. def _feature_config(shape, dtype): if dtype in ("int32", "bool"): # int32 and bool are stored as int64 in the tf.train.Example protobuf. # TODO(adarob): Support other conversions. dtype = "int64" if shape and shape[0] is None: return tf.io.FixedLenSequenceFeature( shape[1:], dtype, allow_missing=True) return tf.io.FixedLenFeature(shape, dtype) feature_description = { feat: _feature_config(**desc) for feat, desc in features.items() } def read_file_fn(filepattern): ds = tf.data.TFRecordDataset(filepattern) ds = ds.map( lambda pb: tf.io.parse_single_example(pb, feature_description), num_parallel_calls=tf.data.experimental.AUTOTUNE) # Cast features back to the types from the info JSON since some features # must be cast for storage (e.g., in32 is stored as int64). ds = ds.map( lambda x: {k: tf.cast(v, features[k]["dtype"]) for k, v in x.items()}, num_parallel_calls=tf.data.experimental.AUTOTUNE) # Legacy cached datasets may use old "_plaintext" suffix. Rename to # "_pretokenized". ds = _rename_plaintext_to_pretokenized(ds) return ds split_to_filepattern = { split: "%s-*-of-*%d" % ( utils.get_cached_tfrecord_prefix(cache_dir, split), split_info["num_shards"]) } super().__init__( read_file_fn=read_file_fn, split_to_filepattern=split_to_filepattern, num_input_examples={split: stats["examples"]} ) class CacheDatasetPlaceholder(object): """A placeholder to signal when in the pipeline offline caching will occur.""" def __init__(self, required=False): """CacheDatasetPlaceholder constructor. Args: required: whether the dataset must be accessed in its cached form, and on-the-fly preprocessing is disallowed. """ self._required = required @property def required(self): return self._required def __call__(self, dataset): raise RuntimeError("`CacheDatasetPlaceholder` should never be called.") # ================================ Tasks ======================================= MetricFnCallable = Callable[..., Mapping[str, float]] class Task(DatasetProviderBase): """A class to manage a dataset and its related metrics.""" def __init__( self, name: str, source: DataSource, output_features: Mapping[str, Feature], preprocessors: Optional[Sequence[Callable[..., tf.data.Dataset]]] = None, postprocess_fn: Optional[Callable[..., Any]] = None, metric_fns: Optional[Sequence[MetricFnCallable]] = None, shuffle_buffer_size: Optional[int] = SHUFFLE_BUFFER_SIZE): """Task constructor. Args: name: a unique name for the Task. source: a `DataSource` that provides a raw `tf.data.Dataset`. output_features: dict(str, Feature), output features of the Task to be passed to the model. After preprocessing, examples will be validated to ensure they include features that match this specification. Note that additional features may be included (e.g., for evaluation), but they will not be passed to the model. preprocessors: list(callable), an optional list of functions that receive a tf.data.Dataset and return a tf.data.Dataset. These will be executed sequentually and the final dataset must include features matching `output_features`. postprocess_fn: callable, an optional function that receives decoded model outputs and converts them to a form that is ready for evaluation using the metric functions in `metric_fns`. metric_fns: list(callable), an optional list of metric functions with the signature `metric_fn(targets, predictions)` to use during evaluation. If undefined or empty, no evaluation will occur on the task. shuffle_buffer_size: an optional integer to set the shuffle buffer size. If None, shuffling will be disallowed. """ if not _VALID_TASK_NAME_REGEX.match(name): raise ValueError( "Task name '%s' contains invalid characters. Must match regex: %s" % ( name, _VALID_TASK_NAME_REGEX.pattern)) metric_fns = metric_fns or [] self._predict_metric_fns = [] self._score_metric_fns = [] for metric_fn in metric_fns: pos_args = tuple( key for key, param in inspect.signature(metric_fn).parameters.items() if param.default == inspect.Parameter.empty ) if pos_args == ("targets", "scores"): self._score_metric_fns.append(metric_fn) elif pos_args == ("targets", "predictions"): self._predict_metric_fns.append(metric_fn) else: raise ValueError( "Metric functions must have positional arguments matching either " "('targets', 'predictions') or ('targets', 'scores'). " f"Got: {pos_args}") self._name = name self._source = source # Find optional CacheDatasetPlaceholder. preprocessors = tuple(preprocessors or []) cache_step_idxs = [ i for i, p in enumerate(preprocessors) if isinstance(p, CacheDatasetPlaceholder) ] if len(cache_step_idxs) > 1: raise ValueError( "`CacheDatasetPlaceholder` can appear at most once in the " f"preprocessing pipeline. Found {len(cache_step_idxs)} in '{name}'.") cache_step_idx = cache_step_idxs[0] if cache_step_idxs else None if cache_step_idx is not None: for prep in preprocessors[:cache_step_idx]: prep_args = inspect.signature(prep).parameters.keys() if "sequence_length" in prep_args: raise ValueError( f"'{prep.__name__}' has a `sequence_length` argument but occurs " f"before `CacheDatasetPlaceholder` in '{name}'. This is not " "allowed since the sequence length is specified at run time.") if "seed" in prep_args or "seeds" in prep_args: raise logging.warning( # pylint:disable=logging-format-interpolation f"'{prep.__name__}' has a `seed(s)` argument but occurs before " f"`CacheDatasetPlaceholder` in '{name}'. This is not recommended " "since the same samples will be used each epoch when reading " "from the cache.") self._cache_step_idx = cache_step_idx self._preprocessors = preprocessors self._metric_fns = tuple(metric_fns) self._postprocess_fn = postprocess_fn self._cache_dir = None self._stats = {} self._shuffle_buffer_size = shuffle_buffer_size self._output_features = collections.OrderedDict( sorted(list(output_features.items())) ) @property def name(self) -> str: return self._name @property def metric_fns(self) -> Sequence[MetricFnCallable]: """List of all metric functions.""" return self._predict_metric_fns + self._score_metric_fns @property def score_metric_fns(self) -> Sequence[MetricFnCallable]: """List of metric functions that use log likelihood scores.""" return self._score_metric_fns @property def predict_metric_fns(self) -> Sequence[MetricFnCallable]: """List of metric functions that use model predictions.""" return self._predict_metric_fns @property def output_features(self) -> Mapping[str, Feature]: return self._output_features @property def splits(self) -> Sequence[str]: s = self.source.splits if not s: raise ValueError(f"Task {self.name} has no splits") return s @property def source(self) -> DataSource: return self._source @property def preprocessors(self) -> Sequence[Callable[..., tf.data.Dataset]]: return self._preprocessors def num_input_examples(self, split: str) -> Optional[int]: return self.source.num_input_examples(split) def _preprocess_dataset( self, dataset: tf.data.Dataset, preprocessors: Sequence[Callable[..., tf.data.Dataset]], sequence_length: Optional[Mapping[str, int]] = None) -> tf.data.Dataset: """Sequentially applies preprocessors.""" for prep_fn in preprocessors: # prep_fn must not rely on variable length keyword args such as **kwargs. fn_args = set(inspect.signature(prep_fn).parameters.keys()) kwargs = {} if "sequence_length" in fn_args: kwargs["sequence_length"] = sequence_length if "output_features" in fn_args: kwargs["output_features"] = self.output_features dataset = prep_fn(dataset, **kwargs) return dataset def _validate_preprocessing( self, dataset: tf.data.Dataset ) -> tf.data.Dataset: """Validates preprocessed dataset, raising Exceptions if needed. Args: dataset: a tf.data.Dataset to validate. Returns: a validated tf.data.Dataset. """ actual_specs = dataset.element_spec for feat, feat_spec in self.output_features.items(): if feat not in actual_specs: if feat_spec.required: raise ValueError( "Task dataset is missing expected output feature after " f"preprocessing: {feat}") else: # It's ok that this feature does not exist. continue actual_spec = actual_specs[feat] if feat_spec.dtype != actual_spec.dtype: raise ValueError( f"Task dataset has incorrect type for feature '{feat}' after " f"preprocessing: Got {actual_spec.dtype.name}, expected " f"{feat_spec.dtype.name}") if actual_spec.shape.rank != 1: raise ValueError( f"Task dataset has incorrect rank for feature '{feat}' after " f"preprocessing: Got {actual_spec.shape.rank}, expected 1") return dataset def _trim_output_features( self, dataset: tf.data.Dataset, sequence_length: Optional[Mapping[str, int]] ) -> tf.data.Dataset: """Trim output features to sequence length.""" def _trim(k: str, v: tf.Tensor) -> tf.Tensor: if k not in self.output_features or not sequence_length: return v return v[:sequence_length[k]] return dataset.map( lambda ex: {k: _trim(k, v) for k, v in ex.items()}, num_parallel_calls=tf.data.experimental.AUTOTUNE) def preprocess_precache( self, dataset: tf.data.Dataset, seed: Optional[int] = None ) -> tf.data.Dataset: """Runs preprocessing steps before the optional CacheDatasetPlaceholder.""" if not self.supports_caching: return dataset with utils.map_seed_manager(seed): return self._preprocess_dataset( dataset, self._preprocessors[:self._cache_step_idx], ) def preprocess_postcache( self, dataset: tf.data.Dataset, sequence_length: Optional[Mapping[str, int]], seed: Optional[int] = None ) -> tf.data.Dataset: """Runs preprocessing steps after the optional CacheDatasetPlaceholder. Args: dataset: a tf.data.Dataset sequence_length: dict mapping feature key to int length for that feature. If None, the features will not be truncated. seed: an optional random seed for deterministic preprocessing. Returns: a tf.data.Dataset """ start_idx = 0 if self.supports_caching: # Skip a sufficient number of seeds to avoid duplicating any from # pre-cache preprocessing. seed = None if seed is None else seed + 42 * self._cache_step_idx start_idx = self._cache_step_idx + 1 with utils.map_seed_manager(seed): dataset = self._preprocess_dataset( dataset, self._preprocessors[start_idx:], sequence_length=sequence_length, ) return dataset @property def cache_dir(self) -> Optional[str]: """Returns the cache directory (or None), initializing if needed.""" if not self._cache_dir: # See if cached data exists in any of the cache directories. potential_cache_dirs = [ os.path.join(d, self.name) for d in utils.get_global_cache_dirs()] for cache_dir in potential_cache_dirs: try: if tf.io.gfile.exists(os.path.join(cache_dir, "COMPLETED")): self._cache_dir = cache_dir logging.info("'%s' is cached at %s.", self.name, self.cache_dir) break except tf.errors.PermissionDeniedError: logging.warning( "Permission denied for global cache folder: %s", cache_dir) if not self._cache_dir: logging.info( "'%s' does not exist in any task cache directories (searched %s).", self.name, potential_cache_dirs, ) return self._cache_dir @property def supports_caching(self) -> bool: """Whether or not this task supports offline caching.""" return self._cache_step_idx is not None @property def requires_caching(self) -> bool: """Whether or not this task requires offline caching.""" return (self._cache_step_idx is not None and self.preprocessors[self._cache_step_idx].required) def assert_cached(self) -> None: """Raises an assertion error if cached dataset does not exist.""" assert self.cache_dir, ( f"'{self.name}' does not exist in any of the task cache directories.") def get_cached_stats(self, split: str = tfds.Split.TRAIN ) -> Mapping[str, Union[int, float]]: """Returns basic statistics for cached dataset.""" self.assert_cached() if split not in self._stats: stats_path = utils.get_cached_stats_path(self.cache_dir, split) if not tf.io.gfile.exists(stats_path): raise ValueError( "Stats do not exist for '%s' split: %s" % (self.name, split)) with tf.io.gfile.GFile(stats_path) as f: self._stats[split] = json.load(f) return self._stats[split] def get_dataset( self, sequence_length: Optional[Mapping[str, int]], split: str = tfds.Split.TRAIN, use_cached: bool = False, shuffle: bool = True, shuffle_buffer_size: Optional[int] = None, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None, num_epochs: Optional[int] = 1 ) -> tf.data.Dataset: """Returns a tf.data.Dataset from cache or generated on the fly. Args: sequence_length: dict mapping feature key to maximum int length for that feature. If longer after preprocessing, the feature will be truncated. May be set to None to avoid truncation. split: string, the split to return. use_cached: bool, whether to use the cached dataset instead of processing it on the fly. Defaults to False. shuffle: bool, whether to shuffle the dataset. Only used when generating on the fly (use_cached=False). shuffle_buffer_size: an integer or None to use task-specific buffer size. seed: tf.int64 scalar tf.Tensor (or None) for shuffling tf.data. shard_info: optional specification for loading a shard of the split. If the Task's DataSource contains at least the number of shards in the specification, it will be passed the shard info to avoid loading the full source dataset. Otherwise, the full source dataset will be loaded and sharded at the individual examples. num_epochs: the number of times to iterate through the dataset, or `None` to repeat indefinitely. Note that the repeat occurs in the pipeline after offline caching, but before applying potentially stochastic post-cache preprocessors and is therefore typically preferred to calling `repeat()` on the returned dataset. Defaults to `1`. Returns: A tf.data.Dataset. """ if use_cached and not self.supports_caching: logging.warning( "Task '%s' does not support caching. Switching to on-the-fly " "preprocessing.", self.name) use_cached = False elif self.requires_caching and not use_cached: raise ValueError( f"Task '{self.name}' requires caching, but was called with " "`use_cached=False`.") if shard_info: # Whether we should shard at source or on the examples from the source. shard_data_source = ( len(self.source.list_shards(split=split)) >= shard_info.num_shards) logging.info("Sharding at the %s: %d of %d", "data source" if shard_data_source else "examples", shard_info.index, shard_info.num_shards) else: # No sharding. shard_data_source = False shard_info = ShardInfo(0, 1) if use_cached: source = self._get_cached_source(split) else: source = self.source if shard_data_source: ds = source.get_dataset( split=split, shuffle=shuffle, seed=seed, shard_info=shard_info) else: ds = source.get_dataset(split=split, shuffle=shuffle, seed=seed) ds = ds.shard(shard_info.num_shards, shard_info.index) if ((use_cached and self.get_cached_stats(split)["examples"] < _MAX_EXAMPLES_TO_MEM_CACHE) or (self.num_input_examples(split) and self.num_input_examples(split) < _MAX_EXAMPLES_TO_MEM_CACHE)): logging.info( "Automatically caching small dataset in memory: '%s:%s'", self.name, split) ds = ds.cache() if not use_cached: ds = self.preprocess_precache(ds, seed=seed) ds = ds.prefetch(tf.data.experimental.AUTOTUNE) # We repeat before calling any (potentially) stochastic post-cache # preprocessing in order to take new samples each epoch. ds = ds.repeat(num_epochs) # Post cache processing. ds = self.preprocess_postcache( ds, sequence_length=sequence_length, seed=seed) ds = self._validate_preprocessing(ds) ds = self._trim_output_features(ds, sequence_length=sequence_length) if shuffle: if self._shuffle_buffer_size is None: raise ValueError( f"Shuffling is disallowed for Task '{self.name}' since its " "`shuffle_buffer_size` was set to `None` on construction.") shuffle_buffer_size = shuffle_buffer_size or self._shuffle_buffer_size # Shuffle before mixing since preprocessor can output multiple # (correlated) examples per input. ds = ds.shuffle(shuffle_buffer_size, seed=seed) return ds.prefetch(tf.data.experimental.AUTOTUNE) def _get_cached_source(self, split) -> _CachedDataSource: """Returns a DataSource to read cached files for split.""" self.assert_cached() return _CachedDataSource(self.cache_dir, split) def postprocess_fn(self, decoded_model_output: Any, **postprocess_kwargs) -> Any: """Returns the model output after applying the postprocess function.""" if self._postprocess_fn: return self._postprocess_fn(decoded_model_output, **postprocess_kwargs) return decoded_model_output class TaskRegistry(DatasetProviderRegistry): """Registry of Tasks.""" _REGISTRY = {} _PROVIDER_TYPE = Task @classmethod def add( cls, name: str, source: DataSource, output_features: Mapping[str, Feature], preprocessors: Optional[Sequence[Callable[..., tf.data.Dataset]]] = None, postprocess_fn: Optional[Callable[..., Any]] = None, metric_fns: Optional[Sequence[Callable[..., Mapping[str, float]]]] = None, **kwargs) -> Task: return super().add(name, Task, name, source, output_features, preprocessors, postprocess_fn, metric_fns, **kwargs) @classmethod def get(cls, name) -> Task: return super().get(name) # ================================ Mixtures ==================================== class Mixture(DatasetProviderBase): """Class for mixing multiple tasks.""" def __init__(self, name: str, tasks: Union[Sequence[str], Sequence[Tuple[str, Union[int, float, Callable[[Task], float]]]]], default_rate: Union[float, Callable[[Task], float]] = None): """Mixture constructor. A mixture specifies a set of tasks with associated mixing rates. Mixing happens on preprocessed tokenized examples. The mixing rates represent relative numbers of examples to use from their associated tasks. Setting the mixing rates to be equal to the numbers of examples in the tasks will result in each task going through an epoch in about the same amount of time - i.e. all examples are sampled equally across all tasks. Rates can be expressed either as absolute numbers or as functions that receive the Task as an argument. Args: name: string, a unique name for the Mixture. tasks: a list where each element is either a string (task name) or a pair whose first element is the task name and whose second element is either a float (rate) or a function from Task to float. default_rate: a float or a function from Task to float. This specifies the default rate if rates are not provided in the `tasks` argument. """ self._task_to_rate = {} self._tasks = [] self._sub_mixtures = [] self._name = name for t in tasks: if isinstance(t, str): task_name = t rate = default_rate if default_rate is None: raise ValueError("need a rate for each task") else: task_name, rate = t if task_name in TaskRegistry.names(): self._tasks.append(TaskRegistry.get(task_name)) self._task_to_rate[task_name] = rate else: self._sub_mixtures.append(MixtureRegistry.get(task_name)) # pytype:disable=name-error self._task_to_rate[task_name] = rate if len(set(tuple(t.output_features) for t in self.tasks)) != 1: raise ValueError( "All Tasks in a Mixture must have the same output features." ) @property def name(self) -> str: return self._name @property def tasks(self) -> Sequence[Task]: sub_tasks = (mix.tasks for mix in self._sub_mixtures) return list(sorted(set(sum(sub_tasks, self._tasks)), key=lambda t: t.name)) @property def total_rate(self) -> float: return sum(float(rate(TaskRegistry.get(name)) if callable(rate) else rate) for name, rate in self._task_to_rate.items()) def get_rate(self, task: Task) -> float: """Computes the mixing rate for the given task.""" value = 0.0 for mix in self._sub_mixtures: if task in mix.tasks: rate = self._task_to_rate[mix.name] value += rate * mix.get_rate(task) / mix.total_rate if task.name in self._task_to_rate: rate = self._task_to_rate[task.name] value += float(rate(task) if callable(rate) else rate) return value def num_input_examples(self, split: str) -> int: return sum(t.num_input_examples(split) for t in self.tasks) @property def splits(self) -> Sequence[str]: splits = set() for task in self.tasks: splits.update(task.splits) return tuple(splits) @property def output_features(self) -> Mapping[str, Feature]: # We require all tasks to have the same output_features in __init__ # so we can just get the output_features for the 0th task return self.tasks[0].output_features def _check_compatible_features(self) -> None: """Throw Exception if features across tasks have different vocabs or dtypes. """ for name, feature in self.tasks[0].output_features.items(): for task in self.tasks[1:]: if task.output_features[name].vocabulary != feature.vocabulary: raise ValueError( "Features across tasks in a mixture must use the same vocabulary." ) if task.output_features[name].dtype != feature.dtype: raise ValueError( "Features across tasks in a mixture must use the same dtype." ) def get_dataset( self, sequence_length: Optional[Mapping[str, int]], split: str = tfds.Split.TRAIN, use_cached: bool = False, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None, num_epochs: Optional[int] = None, copy_pretokenized: bool = False, compute_stats_empirically: bool = False, ) -> tf.data.Dataset: """Returns the dataset of mixed tasks using the object-specified rates. Args: sequence_length: dict mapping feature key to maximum int length for that feature. If longer after preprocessing, the feature will be truncated. May be set to None to avoid truncation. split: string, the split to return for all tasks. use_cached: bool, whether to use the cached dataset instead of processing it on the fly. Defaults to False. shuffle: bool, whether to shuffle the dataset. Only used when generating on the fly (use_cached=False). seed: tf.int64 scalar tf.Tensor (or None) for shuffling tf.data. shard_info: optional specification for loading a shard of the split. num_epochs: the number of times to iterate through the dataset, or `None` to repeat indefinitely. Note that the repeat occurs in the pipeline after offline caching, but before applying potentially stochastic post-cache preprocessors and is therefore typically preferred to calling `repeat()` on the returned dataset. Defaults to `None`. copy_pretokenized: bool, whether to pass through copies of pretokenized features a "_pretokenized" suffix added to the key. compute_stats_empirically: a boolean - does not work on TPU """ self._check_compatible_features() tasks = [] for task in self.tasks: if split not in task.splits: logging.warning( "Task %s has no '%s' split, skipping.", task.name, split ) continue tasks.append(task) if not tasks: raise ValueError("No datasets have a '{}' split".format(split)) output_feature_keys = set(self.output_features.keys()) if copy_pretokenized: output_feature_keys.update( {f + "_pretokenized" for f in output_feature_keys}) def filter_features(ex): return {k: v for k, v in ex.items() if k in output_feature_keys} datasets = [ task.get_dataset( # pylint:disable=g-complex-comprehension sequence_length, split=split, use_cached=use_cached, shuffle=shuffle, seed=seed, shard_info=shard_info, num_epochs=num_epochs) .map(filter_features, num_parallel_calls=tf.data.experimental.AUTOTUNE) for task in tasks] rates = [self.get_rate(task) for task in tasks] # Sample from the dataset with the rates rates if seed is not None: sample_seed = seed elif shuffle: sample_seed = None else: sample_seed = 42 dataset = tf.data.experimental.sample_from_datasets( datasets, rates, sample_seed) if (split == "train" and use_cached and all(t.supports_caching for t in tasks)): _log_mixing_proportions(tasks, datasets, rates, dataset, sequence_length, compute_stats_empirically) return dataset def _log_padding_fractions(dataset, sequence_length, num_examples=100): """Empirically compute the fraction of padding - log the results. Args: dataset: a tf.data.Dataset sequence_length: dict from string to int (packed lengths) num_examples: an integer """ logging.info("computing padding fractions") keys = sequence_length.keys() padding_frac = {k: 0 for k in keys} for ex in tfds.as_numpy(dataset.take(num_examples)): for k in keys: padding_frac[k] += 1 - (sequence_length[k] / len(ex[k])) for k in keys: logging.info("%s padding fraction = %g", k, padding_frac[k]) def _log_mixing_proportions( tasks, datasets, rates, mixed_dataset, sequence_length, compute_stats_empirically): """Log information about the mixing proportions. Called from Mixture.get_dataset. Args: tasks: a list of Task datasets: a list of tf.data.Dataset rates: a list of floats mixed_dataset: a tf.data.Dataset sequence_length: dict from string to int (packed lengths) compute_stats_empirically: a boolean - does not work on TPU """ def _normalize(l): denom = sum(l) if not denom: return l return [x / denom for x in l] # compute some stats about the mixture examples_fraction = _normalize(rates) if compute_stats_empirically: stats_examples = 100 mean_inputs_length = [] mean_targets_length = [] for dataset in datasets: inputs_sum = 0 targets_sum = 0 for ex in tfds.as_numpy(dataset.take(stats_examples)): # Some tasks, like LMs, don't have inputs. if "inputs" in ex: inputs_sum += ex["inputs"].size targets_sum += ex["targets"].size mean_inputs_length.append(inputs_sum / float(stats_examples)) mean_targets_length.append(targets_sum / float(stats_examples)) else: def _estimated_mean_length(task, key): if key not in sequence_length: return 0 if (task.supports_caching and task._cache_step_idx < len(task._preprocessors) - 1): # pylint:disable=protected-access # There is processing after caching, so we can't rely on the stats. return sequence_length[key] # Some tasks, like LMs, don't have inputs. if key + "_tokens" in task.get_cached_stats("train"): return min(sequence_length[key], (task.get_cached_stats("train")[key + "_tokens"] / task.get_cached_stats("train")["examples"])) else: return 0 mean_inputs_length = [_estimated_mean_length(task, "inputs") for task in tasks] mean_targets_length = [_estimated_mean_length(task, "targets") for task in tasks] inputs_fraction = _normalize( [l * r for l, r in zip(mean_inputs_length, rates)]) targets_fraction = _normalize( [l * r for l, r in zip(mean_targets_length, rates)]) logging.info("%12s %12s %12s %12s %12s %12s %s", "rate", "ex.frac.", "inp.frac.", "tgt.frac.", "inp.len.", "tgt.len", "task") for i in range(len(rates)): logging.info("%12g %12g %12g %12g %12g %12g %s", rates[i], examples_fraction[i], inputs_fraction[i], targets_fraction[i], mean_inputs_length[i], mean_targets_length[i], tasks[i].name) if compute_stats_empirically: _log_padding_fractions(mixed_dataset, sequence_length) class MixtureRegistry(DatasetProviderRegistry): """Registry of Mixtures.""" _REGISTRY = {} _PROVIDER_TYPE = Mixture @classmethod def add(cls, name, tasks, default_rate=None) -> Mixture: return super().add(name, Mixture, name, tasks, default_rate) @classmethod def get(cls, name) -> Mixture: return super().get(name) def get_mixture_or_task(task_or_mixture_name): """Return the Task or Mixture from the appropriate registry.""" mixtures = MixtureRegistry.names() tasks = TaskRegistry.names() if task_or_mixture_name in mixtures: if task_or_mixture_name in tasks: logging.warning("%s is both a Task and a Mixture, returning Mixture", task_or_mixture_name) return MixtureRegistry.get(task_or_mixture_name) if task_or_mixture_name in tasks: return TaskRegistry.get(task_or_mixture_name) else: raise ValueError("No Task or Mixture found with name: %s" % task_or_mixture_name) def get_subtasks(task_or_mixture): """Returns all the Tasks in a Mixture as a list or the Task itself.""" if isinstance(task_or_mixture, Task): return [task_or_mixture] else: return task_or_mixture.tasks def get_dataset( mixture_or_task_name: str, task_feature_lengths: Mapping[str, int], feature_converter: FeatureConverter, dataset_split: str = "train", use_cached: bool = False, shuffle: bool = False, num_epochs: Optional[int] = 1, shard_info: ShardInfo = None, verbose: bool = True, seed: Optional[int] = None ) -> tf.data.Dataset: """Get processed dataset with the model features. In order to use options specific to a feature converter, e.g., packing, `feature_converter` instance should be instantiated with those options before being pased to this function. Getting sharded datasets is supported. To use this feature, pass in `shard_info`, with shard_index and num_shards information. Sharding is done before the feature converter stage. Therefore, if packing is used it will be done on the sharded dataset. Args: mixture_or_task_name: mixture or task name for the Task API. task_feature_lengths: dict mapping task feature key to its sequence length. This specifies the sequence length of the dataset from the Task API. feature_converter: a feature converter object to use to convert the task features to model features. Must be a subclass of FeatureConverter. dataset_split: the split to use. use_cached: whether to use the cached dataset instead of processing it on the fly. shuffle: whether to shuffle the dataset. num_epochs: the number of times to iterate through the dataset, or `None` to repeat indefinitely. Note that the repeat occurs in the pipeline after offline caching, but before applying potentially stochastic post-cache preprocessors and is therefore typically preferred to calling `repeat()` on the returned dataset. Defaults to `1`. shard_info: number of shards and shard index information. verbose: if true, log the feature shapes. seed: a random seed to for shuffling tf.data. Returns: ds: the processed dataset. """ if not isinstance(feature_converter, FeatureConverter): raise TypeError( "feature_converter should be an instance of FeatureConverter.") mixture_or_task = get_mixture_or_task(mixture_or_task_name) ds = mixture_or_task.get_dataset( task_feature_lengths, split=dataset_split, use_cached=use_cached, shuffle=shuffle, seed=seed, shard_info=shard_info, num_epochs=num_epochs) ds = feature_converter(ds, task_feature_lengths=task_feature_lengths) if verbose: logging.info( "The output dataset from seqio.get_dataset has the following features") for feature_name, tensor_spec in ds.element_spec.items(): logging.info("feature: %s \t shape: %s \t dtype: %s", feature_name, tensor_spec.shape.as_list(), tensor_spec.dtype.name) return ds

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import abc import collections import inspect import json import os import re from typing import Any, Callable, Iterable, Mapping, MutableMapping, Optional, Sequence, Tuple, Type, Union from absl import logging import dataclasses import numpy as np from packaging import version from t5.seqio import utils from t5.seqio.feature_converters import FeatureConverter from t5.seqio.vocabularies import Vocabulary import tensorflow.compat.v2 as tf import tensorflow_datasets as tfds import typing_extensions _DEFAULT_FEATURE_KEYS = ["inputs", "targets"] _VALID_TASK_NAME_REGEX = re.compile(r"^[\w\d\._]+$") _MAX_EXAMPLES_TO_MEM_CACHE = 10000 SHUFFLE_BUFFER_SIZE = 1000 @dataclasses.dataclass(frozen=True) class Feature: vocabulary: Vocabulary add_eos: bool = True required: bool = True dtype: tf.DType = tf.int32 @dataclasses.dataclass(frozen=True) class ShardInfo: index: int num_shards: int class DatasetProviderBase(metaclass=abc.ABCMeta): @abc.abstractproperty def output_features(self) -> Mapping[str, Feature]: raise NotImplementedError @abc.abstractproperty def splits(self) -> Sequence[str]: raise NotImplementedError @abc.abstractmethod def get_dataset( self, sequence_length: int, split: str, use_cached: bool = False, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None, num_epochs: int = 1 ) -> tf.data.Dataset: raise NotImplementedError @abc.abstractmethod def num_input_examples(self, split: str) -> int: raise NotImplementedError class DatasetProviderRegistry(object): _REGISTRY: MutableMapping[str, DatasetProviderBase] _PROVIDER_TYPE: Type[DatasetProviderBase] @classmethod def add_provider(cls, name: str, provider): if name in cls._REGISTRY: raise ValueError("Attempting to register duplicate provider: %s" % name) if not isinstance(provider, cls._PROVIDER_TYPE): raise ValueError( "Attempting to register a class not of an invalid type. " "Expecting instance of %s, got %s" % (cls._PROVIDER_TYPE, type(provider).__name__)) cls._REGISTRY[name] = provider @classmethod def add( cls, name: str, provider_cls, *provider_args, **provider_kwargs ): if not issubclass(provider_cls, cls._PROVIDER_TYPE): raise ValueError( "Attempting to register a class not of an invalid type. " "Expecting instance of %s, got %s" % (cls._PROVIDER_TYPE, provider_cls)) provider = provider_cls(*provider_args, **provider_kwargs) cls.add_provider(name, provider) return provider @classmethod def remove(cls, name): if name in cls._REGISTRY: del cls._REGISTRY[name] @classmethod def get(cls, name): if name not in cls._REGISTRY: raise ValueError("Provider name not registered: %s" % name) return cls._REGISTRY[name] @classmethod def names(cls): return cls._REGISTRY.keys() @classmethod def reset(cls): cls._REGISTRY = {} @classmethod def get_dataset( cls, name, sequence_length, split, use_cached=False, shuffle=True, seed=None, shard_info=None, num_epochs=1): return cls.get(name).get_dataset( sequence_length=sequence_length, split=split, use_cached=use_cached, shuffle=shuffle, seed=seed, shard_info=shard_info, num_epochs=num_epochs) class DataSource(DatasetProviderBase): def __init__( self, splits: Iterable[str], num_input_examples: Optional[Mapping[str, int]] = None): self._splits = tuple(splits) self._num_input_examples = ( dict(num_input_examples) if num_input_examples is not None else None) @property def splits(self) -> Sequence[str]: return self._splits @property def output_features(self) -> Mapping[str, Feature]: raise NotImplementedError @abc.abstractmethod def list_shards(self, split: str) -> Sequence[str]: raise NotImplementedError @abc.abstractmethod def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: raise NotImplementedError def num_input_examples(self, split: str) -> Optional[int]: if self._num_input_examples is None: return None return self._num_input_examples[split] def _validate_args(fn, expected_pos_args): argspec = inspect.getfullargspec(fn) expected_pos_args = tuple(expected_pos_args) actual_args = tuple(argspec.args) if actual_args[:len(expected_pos_args)] != expected_pos_args: raise ValueError( "'%s' must have positional args %s, got: %s" % ( fn.__name__, expected_pos_args, actual_args)) actual_pos_args = tuple( argspec.args[:-len(argspec.defaults)] if argspec.defaults else argspec.args) if actual_pos_args != expected_pos_args[:len(actual_pos_args)]: raise ValueError( "'%s' may only have positional args %s, got: %s" % ( fn.__name__, expected_pos_args, actual_pos_args)) class DatasetFnCallable(typing_extensions.Protocol): def __call__(self, split: str, shuffle_files: bool, seed: Optional[int] = None) -> tf.data.Dataset: ... class FunctionDataSource(DataSource): def __init__( self, dataset_fn: DatasetFnCallable, splits: Iterable[str], num_input_examples: Optional[Mapping[str, int]] = None ): _validate_args(dataset_fn, ["split", "shuffle_files"]) self._dataset_fn = dataset_fn super().__init__(splits=splits, num_input_examples=num_input_examples) def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: if shard_info and shard_info.num_shards > 1: raise ValueError( "`FunctionDataSource` does not support low-level sharding. Use " "tf.data.Dataset.shard instead.") if seed is None: ds = self._dataset_fn(split=split, shuffle_files=shuffle) else: _validate_args(self._dataset_fn, ["split", "shuffle_files", "seed"]) ds = self._dataset_fn(split=split, shuffle_files=shuffle, seed=seed) return ds def list_shards(self, split: str) -> Sequence[str]: return [split] class TfdsDataSource(DataSource): def __init__( self, tfds_name: str, tfds_data_dir: Optional[str] = None, splits: Optional[Union[Iterable[str], Mapping[str, str]]] = None ): if ":" not in tfds_name: raise ValueError("TFDS name must contain a version number, got: %s" % tfds_name) self._tfds_dataset = utils.LazyTfdsLoader( tfds_name, data_dir=tfds_data_dir, split_map=splits if isinstance(splits, dict) else None) super().__init__(splits=splits or ()) @property def splits(self): return self._splits or self._tfds_dataset.info.splits @property def tfds_dataset(self): return self._tfds_dataset def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: return self.tfds_dataset.load( split, shuffle_files=shuffle, seed=seed, shard_info=shard_info) def num_input_examples(self, split: str) -> int: return self.tfds_dataset.size(split) def list_shards(self, split: str) -> Sequence[str]: return self.tfds_dataset.files(split) class FileDataSource(DataSource): def __init__( self, read_file_fn: Callable[[tf.data.Dataset], tf.data.Dataset], split_to_filepattern: Mapping[str, Union[str, Iterable[str]]], num_input_examples: Optional[Mapping[str, int]] = None, ): self._split_to_filepattern = split_to_filepattern self._reader = read_file_fn super().__init__( splits=split_to_filepattern.keys(), num_input_examples=num_input_examples) def get_dataset( self, split: str, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None ) -> tf.data.Dataset: files = self.list_shards(split) if not files: raise ValueError( "No file is found for the file pattern: " f"{self._split_to_filepattern[split]}." ) files_ds = tf.data.Dataset.from_tensor_slices(np.array(files, dtype=np.str)) if shard_info: if len(files) < shard_info.num_shards: raise ValueError( f"Dataset has too few files to shard. {len(files)} files vs " f"{shard_info.num_shards} shards requested.") files_ds = files_ds.shard(shard_info.num_shards, shard_info.index) if shuffle: files_ds = files_ds.shuffle(buffer_size=16, seed=seed) return files_ds.interleave( self._reader, cycle_length=16, block_length=16, num_parallel_calls=tf.data.experimental.AUTOTUNE) def list_shards(self, split: str) -> Sequence[str]: return tf.io.gfile.glob(self._split_to_filepattern[split]) class TextLineDataSource(FileDataSource): def __init__( self, split_to_filepattern: Mapping[str, Union[str, Iterable[str]]], skip_header_lines: int = 0, num_input_examples: Optional[Mapping[str, int]] = None, ): self._skip_header_lines = skip_header_lines def read_file_fn(filepattern): return tf.data.TextLineDataset(filepattern).skip(skip_header_lines) super().__init__( read_file_fn=read_file_fn, split_to_filepattern=split_to_filepattern, num_input_examples=num_input_examples) class TFExampleDataSource(FileDataSource): def __init__( self, split_to_filepattern: Mapping[str, Union[str, Iterable[str]]], feature_description: Mapping[str, Union[tf.io.FixedLenFeature, tf.io.VarLenFeature]], reader_cls: Type[tf.data.Dataset] = tf.data.TFRecordDataset, num_input_examples: Optional[Mapping[str, int]] = None, ): def read_file_fn(filepattern): return reader_cls(filepattern).map( lambda pb: tf.io.parse_single_example(pb, feature_description), num_parallel_calls=tf.data.experimental.AUTOTUNE) super().__init__( read_file_fn=read_file_fn, split_to_filepattern=split_to_filepattern, num_input_examples=num_input_examples) def _rename_plaintext_to_pretokenized( dataset: tf.data.Dataset) -> tf.data.Dataset: def _rename(inputs): outputs = {} for k, v in inputs.items(): if k.endswith("_plaintext"): k = k[:-len("plaintext")] + "pretokenized" outputs[k] = v return outputs return dataset.map( _rename, num_parallel_calls=tf.data.experimental.AUTOTUNE) class _CachedDataSource(FileDataSource): def __init__(self, cache_dir: str, split: str): with tf.io.gfile.GFile(utils.get_cached_info_path(cache_dir, split)) as f: split_info = json.load(f) features = split_info["features"] with tf.io.gfile.GFile(utils.get_cached_stats_path(cache_dir, split)) as f: stats = json.load(f) version_when_cached = version.Version( split_info.get("seqio_version", "0.pre")) version_with_true_dtypes = version.Version("0.0.0") if version_when_cached < version_with_true_dtypes: for name, feat in features.items(): if feat["dtype"] == "int64": logging.info("Casting cached '%s' to int32.", name) feat["dtype"] = "int32" def _feature_config(shape, dtype): if dtype in ("int32", "bool"): dtype = "int64" if shape and shape[0] is None: return tf.io.FixedLenSequenceFeature( shape[1:], dtype, allow_missing=True) return tf.io.FixedLenFeature(shape, dtype) feature_description = { feat: _feature_config(**desc) for feat, desc in features.items() } def read_file_fn(filepattern): ds = tf.data.TFRecordDataset(filepattern) ds = ds.map( lambda pb: tf.io.parse_single_example(pb, feature_description), num_parallel_calls=tf.data.experimental.AUTOTUNE) ds = ds.map( lambda x: {k: tf.cast(v, features[k]["dtype"]) for k, v in x.items()}, num_parallel_calls=tf.data.experimental.AUTOTUNE) ds = _rename_plaintext_to_pretokenized(ds) return ds split_to_filepattern = { split: "%s-*-of-*%d" % ( utils.get_cached_tfrecord_prefix(cache_dir, split), split_info["num_shards"]) } super().__init__( read_file_fn=read_file_fn, split_to_filepattern=split_to_filepattern, num_input_examples={split: stats["examples"]} ) class CacheDatasetPlaceholder(object): def __init__(self, required=False): self._required = required @property def required(self): return self._required def __call__(self, dataset): raise RuntimeError("`CacheDatasetPlaceholder` should never be called.") MetricFnCallable = Callable[..., Mapping[str, float]] class Task(DatasetProviderBase): def __init__( self, name: str, source: DataSource, output_features: Mapping[str, Feature], preprocessors: Optional[Sequence[Callable[..., tf.data.Dataset]]] = None, postprocess_fn: Optional[Callable[..., Any]] = None, metric_fns: Optional[Sequence[MetricFnCallable]] = None, shuffle_buffer_size: Optional[int] = SHUFFLE_BUFFER_SIZE): if not _VALID_TASK_NAME_REGEX.match(name): raise ValueError( "Task name '%s' contains invalid characters. Must match regex: %s" % ( name, _VALID_TASK_NAME_REGEX.pattern)) metric_fns = metric_fns or [] self._predict_metric_fns = [] self._score_metric_fns = [] for metric_fn in metric_fns: pos_args = tuple( key for key, param in inspect.signature(metric_fn).parameters.items() if param.default == inspect.Parameter.empty ) if pos_args == ("targets", "scores"): self._score_metric_fns.append(metric_fn) elif pos_args == ("targets", "predictions"): self._predict_metric_fns.append(metric_fn) else: raise ValueError( "Metric functions must have positional arguments matching either " "('targets', 'predictions') or ('targets', 'scores'). " f"Got: {pos_args}") self._name = name self._source = source preprocessors = tuple(preprocessors or []) cache_step_idxs = [ i for i, p in enumerate(preprocessors) if isinstance(p, CacheDatasetPlaceholder) ] if len(cache_step_idxs) > 1: raise ValueError( "`CacheDatasetPlaceholder` can appear at most once in the " f"preprocessing pipeline. Found {len(cache_step_idxs)} in '{name}'.") cache_step_idx = cache_step_idxs[0] if cache_step_idxs else None if cache_step_idx is not None: for prep in preprocessors[:cache_step_idx]: prep_args = inspect.signature(prep).parameters.keys() if "sequence_length" in prep_args: raise ValueError( f"'{prep.__name__}' has a `sequence_length` argument but occurs " f"before `CacheDatasetPlaceholder` in '{name}'. This is not " "allowed since the sequence length is specified at run time.") if "seed" in prep_args or "seeds" in prep_args: raise logging.warning( f"'{prep.__name__}' has a `seed(s)` argument but occurs before " f"`CacheDatasetPlaceholder` in '{name}'. This is not recommended " "since the same samples will be used each epoch when reading " "from the cache.") self._cache_step_idx = cache_step_idx self._preprocessors = preprocessors self._metric_fns = tuple(metric_fns) self._postprocess_fn = postprocess_fn self._cache_dir = None self._stats = {} self._shuffle_buffer_size = shuffle_buffer_size self._output_features = collections.OrderedDict( sorted(list(output_features.items())) ) @property def name(self) -> str: return self._name @property def metric_fns(self) -> Sequence[MetricFnCallable]: return self._predict_metric_fns + self._score_metric_fns @property def score_metric_fns(self) -> Sequence[MetricFnCallable]: return self._score_metric_fns @property def predict_metric_fns(self) -> Sequence[MetricFnCallable]: return self._predict_metric_fns @property def output_features(self) -> Mapping[str, Feature]: return self._output_features @property def splits(self) -> Sequence[str]: s = self.source.splits if not s: raise ValueError(f"Task {self.name} has no splits") return s @property def source(self) -> DataSource: return self._source @property def preprocessors(self) -> Sequence[Callable[..., tf.data.Dataset]]: return self._preprocessors def num_input_examples(self, split: str) -> Optional[int]: return self.source.num_input_examples(split) def _preprocess_dataset( self, dataset: tf.data.Dataset, preprocessors: Sequence[Callable[..., tf.data.Dataset]], sequence_length: Optional[Mapping[str, int]] = None) -> tf.data.Dataset: for prep_fn in preprocessors: fn_args = set(inspect.signature(prep_fn).parameters.keys()) kwargs = {} if "sequence_length" in fn_args: kwargs["sequence_length"] = sequence_length if "output_features" in fn_args: kwargs["output_features"] = self.output_features dataset = prep_fn(dataset, **kwargs) return dataset def _validate_preprocessing( self, dataset: tf.data.Dataset ) -> tf.data.Dataset: actual_specs = dataset.element_spec for feat, feat_spec in self.output_features.items(): if feat not in actual_specs: if feat_spec.required: raise ValueError( "Task dataset is missing expected output feature after " f"preprocessing: {feat}") else: continue actual_spec = actual_specs[feat] if feat_spec.dtype != actual_spec.dtype: raise ValueError( f"Task dataset has incorrect type for feature '{feat}' after " f"preprocessing: Got {actual_spec.dtype.name}, expected " f"{feat_spec.dtype.name}") if actual_spec.shape.rank != 1: raise ValueError( f"Task dataset has incorrect rank for feature '{feat}' after " f"preprocessing: Got {actual_spec.shape.rank}, expected 1") return dataset def _trim_output_features( self, dataset: tf.data.Dataset, sequence_length: Optional[Mapping[str, int]] ) -> tf.data.Dataset: def _trim(k: str, v: tf.Tensor) -> tf.Tensor: if k not in self.output_features or not sequence_length: return v return v[:sequence_length[k]] return dataset.map( lambda ex: {k: _trim(k, v) for k, v in ex.items()}, num_parallel_calls=tf.data.experimental.AUTOTUNE) def preprocess_precache( self, dataset: tf.data.Dataset, seed: Optional[int] = None ) -> tf.data.Dataset: if not self.supports_caching: return dataset with utils.map_seed_manager(seed): return self._preprocess_dataset( dataset, self._preprocessors[:self._cache_step_idx], ) def preprocess_postcache( self, dataset: tf.data.Dataset, sequence_length: Optional[Mapping[str, int]], seed: Optional[int] = None ) -> tf.data.Dataset: start_idx = 0 if self.supports_caching: # Skip a sufficient number of seeds to avoid duplicating any from # pre-cache preprocessing. seed = None if seed is None else seed + 42 * self._cache_step_idx start_idx = self._cache_step_idx + 1 with utils.map_seed_manager(seed): dataset = self._preprocess_dataset( dataset, self._preprocessors[start_idx:], sequence_length=sequence_length, ) return dataset @property def cache_dir(self) -> Optional[str]: if not self._cache_dir: # See if cached data exists in any of the cache directories. potential_cache_dirs = [ os.path.join(d, self.name) for d in utils.get_global_cache_dirs()] for cache_dir in potential_cache_dirs: try: if tf.io.gfile.exists(os.path.join(cache_dir, "COMPLETED")): self._cache_dir = cache_dir logging.info("'%s' is cached at %s.", self.name, self.cache_dir) break except tf.errors.PermissionDeniedError: logging.warning( "Permission denied for global cache folder: %s", cache_dir) if not self._cache_dir: logging.info( "'%s' does not exist in any task cache directories (searched %s).", self.name, potential_cache_dirs, ) return self._cache_dir @property def supports_caching(self) -> bool: return self._cache_step_idx is not None @property def requires_caching(self) -> bool: return (self._cache_step_idx is not None and self.preprocessors[self._cache_step_idx].required) def assert_cached(self) -> None: assert self.cache_dir, ( f"'{self.name}' does not exist in any of the task cache directories.") def get_cached_stats(self, split: str = tfds.Split.TRAIN ) -> Mapping[str, Union[int, float]]: self.assert_cached() if split not in self._stats: stats_path = utils.get_cached_stats_path(self.cache_dir, split) if not tf.io.gfile.exists(stats_path): raise ValueError( "Stats do not exist for '%s' split: %s" % (self.name, split)) with tf.io.gfile.GFile(stats_path) as f: self._stats[split] = json.load(f) return self._stats[split] def get_dataset( self, sequence_length: Optional[Mapping[str, int]], split: str = tfds.Split.TRAIN, use_cached: bool = False, shuffle: bool = True, shuffle_buffer_size: Optional[int] = None, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None, num_epochs: Optional[int] = 1 ) -> tf.data.Dataset: if use_cached and not self.supports_caching: logging.warning( "Task '%s' does not support caching. Switching to on-the-fly " "preprocessing.", self.name) use_cached = False elif self.requires_caching and not use_cached: raise ValueError( f"Task '{self.name}' requires caching, but was called with " "`use_cached=False`.") if shard_info: # Whether we should shard at source or on the examples from the source. shard_data_source = ( len(self.source.list_shards(split=split)) >= shard_info.num_shards) logging.info("Sharding at the %s: %d of %d", "data source" if shard_data_source else "examples", shard_info.index, shard_info.num_shards) else: # No sharding. shard_data_source = False shard_info = ShardInfo(0, 1) if use_cached: source = self._get_cached_source(split) else: source = self.source if shard_data_source: ds = source.get_dataset( split=split, shuffle=shuffle, seed=seed, shard_info=shard_info) else: ds = source.get_dataset(split=split, shuffle=shuffle, seed=seed) ds = ds.shard(shard_info.num_shards, shard_info.index) if ((use_cached and self.get_cached_stats(split)["examples"] < _MAX_EXAMPLES_TO_MEM_CACHE) or (self.num_input_examples(split) and self.num_input_examples(split) < _MAX_EXAMPLES_TO_MEM_CACHE)): logging.info( "Automatically caching small dataset in memory: '%s:%s'", self.name, split) ds = ds.cache() if not use_cached: ds = self.preprocess_precache(ds, seed=seed) ds = ds.prefetch(tf.data.experimental.AUTOTUNE) # We repeat before calling any (potentially) stochastic post-cache # preprocessing in order to take new samples each epoch. ds = ds.repeat(num_epochs) # Post cache processing. ds = self.preprocess_postcache( ds, sequence_length=sequence_length, seed=seed) ds = self._validate_preprocessing(ds) ds = self._trim_output_features(ds, sequence_length=sequence_length) if shuffle: if self._shuffle_buffer_size is None: raise ValueError( f"Shuffling is disallowed for Task '{self.name}' since its " "`shuffle_buffer_size` was set to `None` on construction.") shuffle_buffer_size = shuffle_buffer_size or self._shuffle_buffer_size # Shuffle before mixing since preprocessor can output multiple # (correlated) examples per input. ds = ds.shuffle(shuffle_buffer_size, seed=seed) return ds.prefetch(tf.data.experimental.AUTOTUNE) def _get_cached_source(self, split) -> _CachedDataSource: self.assert_cached() return _CachedDataSource(self.cache_dir, split) def postprocess_fn(self, decoded_model_output: Any, **postprocess_kwargs) -> Any: if self._postprocess_fn: return self._postprocess_fn(decoded_model_output, **postprocess_kwargs) return decoded_model_output class TaskRegistry(DatasetProviderRegistry): _REGISTRY = {} _PROVIDER_TYPE = Task @classmethod def add( cls, name: str, source: DataSource, output_features: Mapping[str, Feature], preprocessors: Optional[Sequence[Callable[..., tf.data.Dataset]]] = None, postprocess_fn: Optional[Callable[..., Any]] = None, metric_fns: Optional[Sequence[Callable[..., Mapping[str, float]]]] = None, **kwargs) -> Task: return super().add(name, Task, name, source, output_features, preprocessors, postprocess_fn, metric_fns, **kwargs) @classmethod def get(cls, name) -> Task: return super().get(name) # ================================ Mixtures ==================================== class Mixture(DatasetProviderBase): def __init__(self, name: str, tasks: Union[Sequence[str], Sequence[Tuple[str, Union[int, float, Callable[[Task], float]]]]], default_rate: Union[float, Callable[[Task], float]] = None): self._task_to_rate = {} self._tasks = [] self._sub_mixtures = [] self._name = name for t in tasks: if isinstance(t, str): task_name = t rate = default_rate if default_rate is None: raise ValueError("need a rate for each task") else: task_name, rate = t if task_name in TaskRegistry.names(): self._tasks.append(TaskRegistry.get(task_name)) self._task_to_rate[task_name] = rate else: self._sub_mixtures.append(MixtureRegistry.get(task_name)) # pytype:disable=name-error self._task_to_rate[task_name] = rate if len(set(tuple(t.output_features) for t in self.tasks)) != 1: raise ValueError( "All Tasks in a Mixture must have the same output features." ) @property def name(self) -> str: return self._name @property def tasks(self) -> Sequence[Task]: sub_tasks = (mix.tasks for mix in self._sub_mixtures) return list(sorted(set(sum(sub_tasks, self._tasks)), key=lambda t: t.name)) @property def total_rate(self) -> float: return sum(float(rate(TaskRegistry.get(name)) if callable(rate) else rate) for name, rate in self._task_to_rate.items()) def get_rate(self, task: Task) -> float: value = 0.0 for mix in self._sub_mixtures: if task in mix.tasks: rate = self._task_to_rate[mix.name] value += rate * mix.get_rate(task) / mix.total_rate if task.name in self._task_to_rate: rate = self._task_to_rate[task.name] value += float(rate(task) if callable(rate) else rate) return value def num_input_examples(self, split: str) -> int: return sum(t.num_input_examples(split) for t in self.tasks) @property def splits(self) -> Sequence[str]: splits = set() for task in self.tasks: splits.update(task.splits) return tuple(splits) @property def output_features(self) -> Mapping[str, Feature]: # We require all tasks to have the same output_features in __init__ # so we can just get the output_features for the 0th task return self.tasks[0].output_features def _check_compatible_features(self) -> None: for name, feature in self.tasks[0].output_features.items(): for task in self.tasks[1:]: if task.output_features[name].vocabulary != feature.vocabulary: raise ValueError( "Features across tasks in a mixture must use the same vocabulary." ) if task.output_features[name].dtype != feature.dtype: raise ValueError( "Features across tasks in a mixture must use the same dtype." ) def get_dataset( self, sequence_length: Optional[Mapping[str, int]], split: str = tfds.Split.TRAIN, use_cached: bool = False, shuffle: bool = True, seed: Optional[int] = None, shard_info: Optional[ShardInfo] = None, num_epochs: Optional[int] = None, copy_pretokenized: bool = False, compute_stats_empirically: bool = False, ) -> tf.data.Dataset: self._check_compatible_features() tasks = [] for task in self.tasks: if split not in task.splits: logging.warning( "Task %s has no '%s' split, skipping.", task.name, split ) continue tasks.append(task) if not tasks: raise ValueError("No datasets have a '{}' split".format(split)) output_feature_keys = set(self.output_features.keys()) if copy_pretokenized: output_feature_keys.update( {f + "_pretokenized" for f in output_feature_keys}) def filter_features(ex): return {k: v for k, v in ex.items() if k in output_feature_keys} datasets = [ task.get_dataset( # pylint:disable=g-complex-comprehension sequence_length, split=split, use_cached=use_cached, shuffle=shuffle, seed=seed, shard_info=shard_info, num_epochs=num_epochs) .map(filter_features, num_parallel_calls=tf.data.experimental.AUTOTUNE) for task in tasks] rates = [self.get_rate(task) for task in tasks] # Sample from the dataset with the rates rates if seed is not None: sample_seed = seed elif shuffle: sample_seed = None else: sample_seed = 42 dataset = tf.data.experimental.sample_from_datasets( datasets, rates, sample_seed) if (split == "train" and use_cached and all(t.supports_caching for t in tasks)): _log_mixing_proportions(tasks, datasets, rates, dataset, sequence_length, compute_stats_empirically) return dataset def _log_padding_fractions(dataset, sequence_length, num_examples=100): logging.info("computing padding fractions") keys = sequence_length.keys() padding_frac = {k: 0 for k in keys} for ex in tfds.as_numpy(dataset.take(num_examples)): for k in keys: padding_frac[k] += 1 - (sequence_length[k] / len(ex[k])) for k in keys: logging.info("%s padding fraction = %g", k, padding_frac[k]) def _log_mixing_proportions( tasks, datasets, rates, mixed_dataset, sequence_length, compute_stats_empirically): def _normalize(l): denom = sum(l) if not denom: return l return [x / denom for x in l] # compute some stats about the mixture examples_fraction = _normalize(rates) if compute_stats_empirically: stats_examples = 100 mean_inputs_length = [] mean_targets_length = [] for dataset in datasets: inputs_sum = 0 targets_sum = 0 for ex in tfds.as_numpy(dataset.take(stats_examples)): # Some tasks, like LMs, don't have inputs. if "inputs" in ex: inputs_sum += ex["inputs"].size targets_sum += ex["targets"].size mean_inputs_length.append(inputs_sum / float(stats_examples)) mean_targets_length.append(targets_sum / float(stats_examples)) else: def _estimated_mean_length(task, key): if key not in sequence_length: return 0 if (task.supports_caching and task._cache_step_idx < len(task._preprocessors) - 1): return sequence_length[key] # Some tasks, like LMs, don't have inputs. if key + "_tokens" in task.get_cached_stats("train"): return min(sequence_length[key], (task.get_cached_stats("train")[key + "_tokens"] / task.get_cached_stats("train")["examples"])) else: return 0 mean_inputs_length = [_estimated_mean_length(task, "inputs") for task in tasks] mean_targets_length = [_estimated_mean_length(task, "targets") for task in tasks] inputs_fraction = _normalize( [l * r for l, r in zip(mean_inputs_length, rates)]) targets_fraction = _normalize( [l * r for l, r in zip(mean_targets_length, rates)]) logging.info("%12s %12s %12s %12s %12s %12s %s", "rate", "ex.frac.", "inp.frac.", "tgt.frac.", "inp.len.", "tgt.len", "task") for i in range(len(rates)): logging.info("%12g %12g %12g %12g %12g %12g %s", rates[i], examples_fraction[i], inputs_fraction[i], targets_fraction[i], mean_inputs_length[i], mean_targets_length[i], tasks[i].name) if compute_stats_empirically: _log_padding_fractions(mixed_dataset, sequence_length) class MixtureRegistry(DatasetProviderRegistry): _REGISTRY = {} _PROVIDER_TYPE = Mixture @classmethod def add(cls, name, tasks, default_rate=None) -> Mixture: return super().add(name, Mixture, name, tasks, default_rate) @classmethod def get(cls, name) -> Mixture: return super().get(name) def get_mixture_or_task(task_or_mixture_name): mixtures = MixtureRegistry.names() tasks = TaskRegistry.names() if task_or_mixture_name in mixtures: if task_or_mixture_name in tasks: logging.warning("%s is both a Task and a Mixture, returning Mixture", task_or_mixture_name) return MixtureRegistry.get(task_or_mixture_name) if task_or_mixture_name in tasks: return TaskRegistry.get(task_or_mixture_name) else: raise ValueError("No Task or Mixture found with name: %s" % task_or_mixture_name) def get_subtasks(task_or_mixture): if isinstance(task_or_mixture, Task): return [task_or_mixture] else: return task_or_mixture.tasks def get_dataset( mixture_or_task_name: str, task_feature_lengths: Mapping[str, int], feature_converter: FeatureConverter, dataset_split: str = "train", use_cached: bool = False, shuffle: bool = False, num_epochs: Optional[int] = 1, shard_info: ShardInfo = None, verbose: bool = True, seed: Optional[int] = None ) -> tf.data.Dataset: if not isinstance(feature_converter, FeatureConverter): raise TypeError( "feature_converter should be an instance of FeatureConverter.") mixture_or_task = get_mixture_or_task(mixture_or_task_name) ds = mixture_or_task.get_dataset( task_feature_lengths, split=dataset_split, use_cached=use_cached, shuffle=shuffle, seed=seed, shard_info=shard_info, num_epochs=num_epochs) ds = feature_converter(ds, task_feature_lengths=task_feature_lengths) if verbose: logging.info( "The output dataset from seqio.get_dataset has the following features") for feature_name, tensor_spec in ds.element_spec.items(): logging.info("feature: %s \t shape: %s \t dtype: %s", feature_name, tensor_spec.shape.as_list(), tensor_spec.dtype.name) return ds

true

f71fdf38821c8803ae681fd71c4c71d7da8b1c90

2,037

py

Python

airflow/migrations/versions/0090_30867afad44a_rename_concurrency_column_in_dag_table_.py

npodewitz/airflow

511ea702d5f732582d018dad79754b54d5e53f9d

[ "Apache-2.0" ]

8,092

2016-04-27T20:32:29.000Z

2019-01-05T07:39:33.000Z

airflow/migrations/versions/0090_30867afad44a_rename_concurrency_column_in_dag_table_.py

npodewitz/airflow

511ea702d5f732582d018dad79754b54d5e53f9d

[ "Apache-2.0" ]

2,961

2016-05-05T07:16:16.000Z

2019-01-05T08:47:59.000Z

airflow/migrations/versions/0090_30867afad44a_rename_concurrency_column_in_dag_table_.py

npodewitz/airflow

511ea702d5f732582d018dad79754b54d5e53f9d

[ "Apache-2.0" ]

3,546

2016-05-04T20:33:16.000Z

2019-01-05T05:14:26.000Z

# # Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. """Rename ``concurrency`` column in ``dag`` table to`` max_active_tasks`` Revision ID: 30867afad44a Revises: e9304a3141f0 Create Date: 2021-06-04 22:11:19.849981 """ import sqlalchemy as sa from alembic import op # revision identifiers, used by Alembic. revision = '30867afad44a' down_revision = 'e9304a3141f0' branch_labels = None depends_on = None airflow_version = '2.2.0' def upgrade(): """Apply Rename ``concurrency`` column in ``dag`` table to`` max_active_tasks``""" conn = op.get_bind() is_sqlite = bool(conn.dialect.name == "sqlite") if is_sqlite: op.execute("PRAGMA foreign_keys=off") with op.batch_alter_table('dag') as batch_op: batch_op.alter_column( 'concurrency', new_column_name='max_active_tasks', type_=sa.Integer(), nullable=False, ) if is_sqlite: op.execute("PRAGMA foreign_keys=on") def downgrade(): """Unapply Rename ``concurrency`` column in ``dag`` table to`` max_active_tasks``""" with op.batch_alter_table('dag') as batch_op: batch_op.alter_column( 'max_active_tasks', new_column_name='concurrency', type_=sa.Integer(), nullable=False, )

31.338462

88

0.691703

import sqlalchemy as sa from alembic import op revision = '30867afad44a' down_revision = 'e9304a3141f0' branch_labels = None depends_on = None airflow_version = '2.2.0' def upgrade(): conn = op.get_bind() is_sqlite = bool(conn.dialect.name == "sqlite") if is_sqlite: op.execute("PRAGMA foreign_keys=off") with op.batch_alter_table('dag') as batch_op: batch_op.alter_column( 'concurrency', new_column_name='max_active_tasks', type_=sa.Integer(), nullable=False, ) if is_sqlite: op.execute("PRAGMA foreign_keys=on") def downgrade(): with op.batch_alter_table('dag') as batch_op: batch_op.alter_column( 'max_active_tasks', new_column_name='concurrency', type_=sa.Integer(), nullable=False, )

true

f71fdf687002e4d434788ab435b395447a70728b

228

py

Python

dstlib/node.py

Algebra7/dstlib

3f891fb48c8c00caf89255c45a1b41f76331d252

[ "MIT" ]

1

2021-07-27T08:22:29.000Z

dstlib/node.py

Algebra7/dstlib

3f891fb48c8c00caf89255c45a1b41f76331d252

[ "MIT" ]

null

dstlib/node.py

Algebra7/dstlib

3f891fb48c8c00caf89255c45a1b41f76331d252

[ "MIT" ]

null

class Node: """Class for storing linked list node.""" def __init__(self, element, next_pointer, prev_pointer=None): self._element = element self._next = next_pointer self._prev = prev_pointer

32.571429

65

0.653509

class Node: def __init__(self, element, next_pointer, prev_pointer=None): self._element = element self._next = next_pointer self._prev = prev_pointer

true

f71fdf8b98012c19be34962342155bc04ff638eb

25,034

py

Python

garnets.py

seanth/garnets

3ff37bcbf095df14586cccb39a52bcf7b221c8ee

[ "MIT" ]

1

2022-02-25T14:32:34.000Z

garnets.py

seanth/garnets

3ff37bcbf095df14586cccb39a52bcf7b221c8ee

[ "MIT" ]

null

garnets.py

seanth/garnets

3ff37bcbf095df14586cccb39a52bcf7b221c8ee

[ "MIT" ]

2

2020-09-02T17:18:33.000Z

2022-02-25T14:32:48.000Z

import logging import random from math import sqrt, log from stellar_system import Star from stellar_system import Planetesimal from stellar_system import Protoplanet from stellar_system import Protomoon from stellar_system import Planet from stellar_system import Orbit from accrete import CircumstellarDisk from constants import ECCENTRICITY_COEFF, PROTOPLANET_MASS from constants import SUN_MASS_IN_EARTH_MASSES from constants import EARTH_ALBEDO, GAS_GIANT_ALBEDO, FREEZING_POINT_OF_WATER, KM_PER_AU, EARTH_AVERAGE_KELVIN, EARTH_EXOSPHERE_TEMP from constants import MOL_NITROGEN, MOL_HYDROGEN, HELIUM from constants import ASTEROID_MASS_LIMIT from constants import MILLIBARS_PER_BAR from enviroment import kothari_radius, gas_life, rms_vel, est_temp, period, day_length, acceleration, gravity, min_molec_weight, orb_zone, volume_radius, volume_density, grnhouse, boiling_point, escape_vel, empirical_density, inclination, iterate_surface_temp, pressure, vol_inventory from enviroment import PlanetType from math import exp from math import inf as INCREDIBLY_LARGE_NUMBER # TODO(woursler): Just use inf from util import about, random_number, random_eccentricity from chemtable import gases logging.getLogger().setLevel(logging.INFO) def random_star(): # TODO: Add seed? # Sources # exoplanets.co/exoplanet-correlations/host-star-mass-distribution.html # en.wikipedia.org/wiki/Main_sequence#mediaviewer/File:HRDiagram.png # TODO: Code up generation. age = random.randrange(1*10**9, 6*10**9) return Star(age=age, mass_ratio=1) def generate_stellar_system(star, do_gases=True, do_moons=True): protoplanets = generate_planetary_masses( star, 0.0, star.stellar_dust_limit, do_moons=do_moons ) star.planets = [ generate_planet( p, star, do_gases=do_gases, do_moons=do_moons ) for p in protoplanets ] return star # Create protoplanets. def random_planetesimal(disk): a = random.uniform(disk.planet_inner_bound, disk.planet_outer_bound) e = 1.0 - (random.uniform(0.0, 1.0) ** ECCENTRICITY_COEFF) if e > .99: e = .99 return Planetesimal( disk=disk, orbit=Orbit( a=a, e=e, ), dust_mass=PROTOPLANET_MASS, gas_mass=0, ) def generate_planetary_masses(star, inner_dust, outer_dust, do_moons=True): disk = CircumstellarDisk(star) planets = [] sequential_failures = 0 while disk.dust_left and sequential_failures < 10**3: canidate = random_planetesimal(disk) iel = canidate.inner_effect_limit oel = canidate.outer_effect_limit if disk.dust_available(iel, oel) > 0: sequential_failures = 0 logging.info("Injecting planetesimal at " + str(canidate.orbit.a) + " AU ...") disk.accrete_dust(canidate) if canidate.mass > PROTOPLANET_MASS: coalesce_planetesimals(disk, planets, canidate, do_moons) logging.info("\tsuccess.\n") else: logging.info("\tfailed due to large neighbor.\n") else: sequential_failures += 1 return planets def convert_planetesimal_to_protoplanet(planetesimal): return Protoplanet( star=planetesimal.disk.star, orbit=planetesimal.orbit, dust_mass=planetesimal.dust_mass, gas_mass=planetesimal.gas_mass ) def convert_planetesimal_to_protomoon(planetesimal, planet): print(" Capturing a protomoon.") return Protomoon( protoplanet=planet, orbit=Orbit( a=None, e=None, ), dust_mass=planetesimal.dust_mass, gas_mass=planetesimal.gas_mass, ) def coalesce_planetesimals(disk, planets, canidate, do_moons): finished = False # First we try to find an existing planet with an over-lapping orbit. for planet in planets: #print("Out of order", planet, canidate) diff = planet.orbit.a - canidate.orbit.a if diff > 0.0: dist1 = canidate.orbit.apoapsis * (1.0 + canidate.reduced_mass) - canidate.orbit.a # x aphelion dist2 = planet.orbit.a - (planet.orbit.periapsis * (1.0 - planet.reduced_mass)) else: dist1 = canidate.orbit.a - (canidate.orbit.periapsis * (1.0 - canidate.reduced_mass)) # x perihelion dist2 = (planet.orbit.apoapsis * (1.0 + planet.reduced_mass)) - planet.orbit.a if abs(diff) <= abs(dist1) or abs(diff) <= abs(dist2): # Figure out the new orbit. a = (planet.mass + canidate.mass) / \ ((planet.mass / planet.orbit.a) + (canidate.mass / canidate.orbit.a)) temp = planet.mass * sqrt(planet.orbit.a) * sqrt(1.0 - (planet.orbit.e ** 2.0)) temp = temp + (canidate.mass * sqrt(canidate.orbit.a) * sqrt(sqrt(1.0 - (canidate.orbit.e ** 2.0)))) temp = temp / ((planet.mass + canidate.mass) * sqrt(canidate.orbit.a)) temp = 1.0 - (temp ** 2.0) if temp < 0.0 or temp >= 1.0: temp = 0.0 e = sqrt(temp) if do_moons: if canidate.mass < canidate.critical_mass: if canidate.mass * SUN_MASS_IN_EARTH_MASSES < 2.5 \ and canidate.mass * SUN_MASS_IN_EARTH_MASSES > .0001 \ and planet.mass_of_moons < planet.mass * .05 \ and planet.mass > canidate.mass: # TODO: Remove planet.mass > canidate.mass distinction, just switch the canidate and planet! planet.add_moon( convert_planetesimal_to_protomoon(canidate, planet)) logging.info("Moon captured at " + str(planet.orbit.a) + " AU. Planet Mass: " + str(planet.mass * SUN_MASS_IN_EARTH_MASSES) + " earth masses Moon Mass: " + str(canidate.mass * SUN_MASS_IN_EARTH_MASSES) + " earth masses.") finished = True break else: # TODO: Reasons. logging.info("Did not capture potential moon at " + str(planet.orbit.a) + " AU. Collision imminent.") logging.info( "Collision between two planetesimals! Computing new orbit and accumulating additional mass.") # Accrete MORE DUST! TODO: Refactor to this. disk.accrete_dust(planet) planet.orbit = Orbit(a=a, e=e) ##### planet.orbit_a = a planet.orbit_e = e planet.dust_mass = planet.dust_mass + canidate.dust_mass # + new_dust planet.gas_mass = planet.gas_mass + canidate.gas_mass # + new_gas finished = True logging.info( "Conglomerate is now " + str(planet.mass * SUN_MASS_IN_EARTH_MASSES) + " earth masses at " + str(planet.orbit.a) + " AU." ) if not finished: # TODO: Extra info. logging.info("New Protoplanet at " + str(canidate.orbit.a) + "AU.") planets.append(convert_planetesimal_to_protoplanet(canidate)) def calculate_gases(star, planet, planet_id): if planet.surf_pressure > 0: amount = [0 for _ in range(len(gases))] totamount = 0 pressure = planet.surf_pressure/MILLIBARS_PER_BAR n = 0 for i in range(len(gases)): yp = gases[i].boil / \ (373. * ((log((pressure) + 0.001) / -5050.5) + (1.0 / 373.))) if ((yp >= 0 and yp < planet.low_temp) and (gases[i].weight >= planet.molec_weight)): vrms = rms_vel(gases[i].weight, planet.exospheric_temp) pvrms = pow(1 / (1 + vrms / planet.esc_velocity), star.age / 1e9) abund = gases[i].abunds # gases[i].abunde react = 1.0 fract = 1.0 pres2 = 1.0 if gases[i].symbol == "Ar": react = .15 * star.age/4e9 elif gases[i].symbol == "He": abund = abund * (0.001 + (planet.gas_mass / planet.mass)) pres2 = (0.75 + pressure) react = pow(1 / (1 + gases[i].reactivity), star.age/2e9 * pres2) elif (gases[i].symbol == "O" or gases[i].symbol == "O2") and star.age > 2e9 and planet.surf_temp > 270 and planet.surf_temp < 400: pres2 = (0.89 + pressure/4) react = pow( 1 / (1 + gases[i].reactivity), pow(star.age/2e9, 0.25) * pres2) elif gases[i].symbol == "CO2" and star.age > 2e9 and planet.surf_temp > 270 and planet.surf_temp < 400: pres2 = (0.75 + pressure) react = pow( 1 / (1 + gases[i].reactivity), pow(star.age/2e9, 0.5) * pres2) react *= 1.5 else: pres2 = 0.75 + pressure react = pow( 1 / (1 + gases[i].reactivity), star.age/2e9 * pres2) fract = (1 - (planet.molec_weight / gases[i].weight)) amount[i] = abund * pvrms * react * fract '''if ((flag_verbose & 0x4000) and (strcmp(gases[i].symbol, "O") == 0 or strcmp(gases[i].symbol, "N") == 0 or strcmp(gases[i].symbol, "Ar") == 0 or strcmp(gases[i].symbol, "He") == 0 or strcmp(gases[i].symbol, "CO2") == 0)) fprintf (stderr, "%-5.2Lf %-3.3s, %-5.2Lf = a %-5.2Lf * p %-5.2Lf * r %-5.2Lf * p2 %-5.2Lf * f %-5.2Lf\t(%.3Lf%%)\n", planet.mass * SUN_MASS_IN_EARTH_MASSES, gases[i].symbol, amount[i], abund, pvrms, react, pres2, fract, 100.0 * (planet.gas_mass / planet.mass) )''' totamount += amount[i] if (amount[i] > 0.0): n += 1 else: amount[i] = 0.0 if n > 0: planet.gases = n planet.atmosphere = [] for i in range(len(gases)): if amount[i] > 0.0: planet.atmosphere.append((gases[i], planet.surf_pressure * amount[i] / totamount)) '''if (flag_verbose & 0x2000) if ((planet.atmosphere[n].num == AN_O) and inspired_partial_pressure (planet.surf_pressure, planet.atmosphere[n].surf_pressure) > gases[i].max_ipp) fprintf (stderr, "%s\t Poisoned by O2\n", planet_id)''' n += 1 # TODO(woursler): sort planet.atmosphere '''if (flag_verbose & 0x0010): fprintf (stderr, "\n%s (%5.1Lf AU) gases:\n", planet_id, planet.orbit.a) for (i = 0; i < planet.gases; i++) fprintf (stderr, "%3d: %6.1Lf, %11.7Lf%%\n", planet.atmosphere[i].num, planet.atmosphere[i].surf_pressure, 100. * (planet.atmosphere[i].surf_pressure / planet.surf_pressure) )''' def roche_limit(planet, moon): return 2.44 * planet.radius * pow((planet.density / moon.density), (1.0 / 3.0)) def hill_sphere(planet, star): return planet.orbit.a * KM_PER_AU * pow((planet.mass / (3.0 * star.mass_ratio)), (1.0 / 3.0)) def generate_planet(protoplanet, star, random_tilt=0, planet_id=None, do_gases=True, do_moons=True, is_moon=False): planet = Planet( sun=star, orbit=protoplanet.orbit, dust_mass=protoplanet.dust_mass, gas_mass=protoplanet.gas_mass, mass=protoplanet.mass, axial_tilt=inclination(protoplanet.orbit.a) if random_tilt else 0, atmosphere=None, surf_temp=0, high_temp=0, low_temp=0, max_temp=0, min_temp=0, greenhs_rise=0, resonant_period=False, orbit_zone=orb_zone(star.luminosity_ratio, protoplanet.orbit.a), orb_period=period(protoplanet.orbit.a, protoplanet.mass, star.mass_ratio) ) planet.exospheric_temp = EARTH_EXOSPHERE_TEMP / \ ((planet.orbit.a / star.r_ecosphere) ** 2) planet.rms_velocity = rms_vel(MOL_NITROGEN, planet.exospheric_temp) planet.core_radius = kothari_radius( planet.dust_mass, False, planet.orbit_zone) # Calculate the radius as a gas giant, to verify it will retain gas. # Then if mass > Earth, it's at least 5% gas and retains He, it's # some flavor of gas giant. planet.density = empirical_density( planet.mass, planet.orbit.a, star.r_ecosphere, True) planet.radius = volume_radius(planet.mass, planet.density) planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) planet.molec_weight = min_molec_weight(planet) if (((planet.mass * SUN_MASS_IN_EARTH_MASSES) > 1.0) and ((planet.gas_mass / planet.mass) > 0.05) and (min_molec_weight(planet) <= 4.0)): if ((planet.gas_mass / planet.mass) < 0.20): planet.type = PlanetType.SUB_SUB_GAS_GIANT elif ((planet.mass * SUN_MASS_IN_EARTH_MASSES) < 20.0): planet.type = PlanetType.SUB_GAS_GIANT else: planet.type = PlanetType.GAS_GIANT else: # If not, it's rocky. planet.radius = kothari_radius(planet.mass, False, planet.orbit_zone) planet.density = volume_density(planet.mass, planet.radius) planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) if ((planet.gas_mass / planet.mass) > 0.000001): h2_mass = planet.gas_mass * 0.85 he_mass = (planet.gas_mass - h2_mass) * 0.999 h2_loss = 0.0 he_loss = 0.0 h2_life = gas_life(MOL_HYDROGEN, planet) he_life = gas_life(HELIUM, planet) if (h2_life < star.age): #math.exp with a value above 709 results in a math range error #this is a dumb fix. STH 2021-0131 if (star.age / h2_life)>709: h2_loss = ((1.0 - (1.0 / exp(709.0))) * h2_mass) else: h2_loss = ((1.0 - (1.0 / exp(star.age / h2_life))) * h2_mass) planet.gas_mass -= h2_loss planet.mass -= h2_loss planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) if (he_life < star.age): he_loss = ((1.0 - (1.0 / exp(star.age / he_life))) * he_mass) planet.gas_mass -= he_loss planet.mass -= he_loss planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) '''if (((h2_loss + he_loss) > .000001) and (flag_verbose & 0x0080)): fprintf(stderr, "%s\tLosing gas: H2: %5.3Lf EM, He: %5.3Lf EM\n", planet_id, h2_loss * SUN_MASS_IN_EARTH_MASSES, he_loss * SUN_MASS_IN_EARTH_MASSES)''' planet.day = day_length(planet) # Modifies planet.resonant_period planet.esc_velocity = escape_vel(planet.mass, planet.radius) if planet.type == PlanetType.GAS_GIANT or planet.type == PlanetType.SUB_GAS_GIANT or planet.type == PlanetType.SUB_SUB_GAS_GIANT: planet.greenhouse_effect = False planet.volatile_gas_inventory = INCREDIBLY_LARGE_NUMBER planet.surf_pressure = INCREDIBLY_LARGE_NUMBER planet.boil_point = INCREDIBLY_LARGE_NUMBER planet.surf_temp = INCREDIBLY_LARGE_NUMBER planet.greenhs_rise = 0 planet.albedo = about(GAS_GIANT_ALBEDO, 0.1) planet.hydrosphere = 1.0 planet.cloud_cover = 1.0 planet.ice_cover = 0.0 planet.surf_grav = gravity(planet.surf_accel) planet.molec_weight = min_molec_weight(planet) planet.surf_grav = INCREDIBLY_LARGE_NUMBER planet.estimated_temp = est_temp( star.r_ecosphere, planet.orbit.a, planet.albedo) planet.estimated_terr_temp = est_temp( star.r_ecosphere, planet.orbit.a, EARTH_ALBEDO) temp = planet.estimated_terr_temp if (temp >= FREEZING_POINT_OF_WATER) and (temp <= EARTH_AVERAGE_KELVIN + 10.) and (star.age > 2.0E9): pass '''if (flag_verbose & 0x8000): fprintf (stderr, "%s\t%s (%4.2LfEM %5.3Lf By)%s with earth-like temperature (%.1Lf C, %.1Lf F, %+.1Lf C Earth).\n", planet_id, planet.type == PlanetType.GAS_GIANT ? "Jovian" : planet.type == PlanetType.SUB_GAS_GIANT ? "Sub-Jovian" : planet.type == PlanetType.SUB_SUB_GAS_GIANT ? "Gas Dwarf" : "Big", planet.mass * SUN_MASS_IN_EARTH_MASSES, star.age /1.0E9, planet.first_moon == NULL ? "" : " WITH MOON", temp - FREEZING_POINT_OF_WATER, 32 + ((temp - FREEZING_POINT_OF_WATER) * 1.8), temp - EARTH_AVERAGE_KELVIN)''' else: planet.estimated_temp = est_temp( star.r_ecosphere, planet.orbit.a, EARTH_ALBEDO) planet.estimated_terr_temp = est_temp( star.r_ecosphere, planet.orbit.a, EARTH_ALBEDO) planet.surf_grav = gravity(planet.surf_accel) planet.molec_weight = min_molec_weight(planet) planet.greenhouse_effect = grnhouse(star.r_ecosphere, planet.orbit.a) planet.volatile_gas_inventory = vol_inventory(planet.mass, planet.esc_velocity, planet.rms_velocity, star.mass_ratio, planet.orbit_zone, planet.greenhouse_effect, (planet.gas_mass / planet.mass) > 0.000001) planet.surf_pressure = pressure(planet.volatile_gas_inventory, planet.radius, planet.surf_grav) if ((planet.surf_pressure == 0.0)): planet.boil_point = 0.0 else: planet.boil_point = boiling_point(planet.surf_pressure) # Sets: # planet.surf_temp # planet.greenhs_rise # planet.albedo # planet.hydrosphere # planet.cloud_cover # planet.ice_cover iterate_surface_temp(planet) if (do_gases and (planet.max_temp >= FREEZING_POINT_OF_WATER) and (planet.min_temp <= planet.boil_point)): calculate_gases(star, planet, planet_id) # Next we assign a type to the planet. if (planet.surf_pressure < 1.0): if (not is_moon) and ((planet.mass * SUN_MASS_IN_EARTH_MASSES) < ASTEROID_MASS_LIMIT): planet.type = PlanetType.ASTEROIDS else: planet.type = PlanetType.ROCK elif (planet.surf_pressure > 6000.0) and (planet.molec_weight <= 2.0): # Retains Hydrogen planet.type = PlanetType.SUB_SUB_GAS_GIANT planet.gases = 0 planet.atmosphere = None else: # Atmospheres: if (int(planet.day) == int(planet.orb_period * 24.0)) or planet.resonant_period: planet.type = PlanetType.ONE_FACE elif (planet.hydrosphere >= 0.95): planet.type = PlanetType.WATER # >95% water elif (planet.ice_cover >= 0.95): planet.type = PlanetType.ICE # >95% ice elif (planet.hydrosphere > 0.05): planet.type = PlanetType.TERRESTRIAL # Terrestrial # else <5% water elif (planet.max_temp > planet.boil_point): planet.type = PlanetType.VENUSIAN # Hot = Venusian elif ((planet.gas_mass / planet.mass) > 0.0001): # Accreted gas planet.type = PlanetType.ICE # But no Greenhouse planet.ice_cover = 1.0 # or liquid water # Make it an Ice World elif (planet.surf_pressure <= 250.0): # Thin air = Martian planet.type = PlanetType.MARTIAN elif (planet.surf_temp < FREEZING_POINT_OF_WATER): planet.type = PlanetType.ICE else: planet.type = PlanetType.UNKNOWN # TODO(woursler): Consider throwing an error here. '''if (flag_verbose & 0x0001) fprintf (stderr, "%12s\tp=%4.2Lf\tm=%4.2Lf\tg=%4.2Lf\tt=%+.1Lf\t%s\t Unknown %s\n", type_string (planet.type), planet.surf_pressure, planet.mass * SUN_MASS_IN_EARTH_MASSES, planet.surf_grav, planet.surf_temp - EARTH_AVERAGE_KELVIN, planet_id, ((int)planet.day == (int)(planet.orb_period * 24.0) or (planet.resonant_period)) ? "(1-Face)" : "" )''' if do_moons and not is_moon: for protomoon in protoplanet.moons: if protomoon.mass * SUN_MASS_IN_EARTH_MASSES > .000001: protomoon.orbit = planet.orbit # Note: adjusts density, which is used in computing the roche limit. moon = generate_planet( protoplanet=protomoon, star=star, random_tilt=random_tilt, do_gases=do_gases, do_moons=do_moons, is_moon=True ) # TODO(woursler): these should be their own subroutines. roche_limit_r = roche_limit(planet, moon) hill_sphere_r = hill_sphere(planet, star) if (roche_limit_r * 3.0) < hill_sphere_r: moon_a = random_number( roche_limit_r * 1.5, hill_sphere_r / 2.0) / KM_PER_AU moon_e = random_eccentricity() moon.orbit = Orbit(a=moon_a, e=moon_e) else: moon.orbit = Orbit(a=0, e=0) planet.moons.append(moon) ''' if (flag_verbose & 0x40000): fprintf (stderr, " Roche limit: R = %4.2Lg, rM = %4.2Lg, rm = %4.2Lg . %.0Lf km\n" " Hill Sphere: a = %4.2Lg, m = %4.2Lg, M = %4.2Lg . %.0Lf km\n" "%s Moon orbit: a = %.0Lf km, e = %.0Lg\n", planet.radius, planet.density, ptr.density, roche_limit, planet.orbit.a * KM_PER_AU, planet.mass * SOLAR_MASS_IN_KILOGRAMS, star.mass_ratio * SOLAR_MASS_IN_KILOGRAMS, hill_sphere, moon_id, ptr.moon_a * KM_PER_AU, ptr.moon_e ) if (flag_verbose & 0x1000): fprintf (stderr, " %s: (%7.2LfEM) %d %4.2LgEM\n", planet_id, planet.mass * SUN_MASS_IN_EARTH_MASSES, n, ptr.mass * SUN_MASS_IN_EARTH_MASSES)''' return planet ### # Smoke Test ### if __name__ == '__main__': random.seed('earth') print(generate_stellar_system(random_star()))

40.247588

284

0.533874

import logging import random from math import sqrt, log from stellar_system import Star from stellar_system import Planetesimal from stellar_system import Protoplanet from stellar_system import Protomoon from stellar_system import Planet from stellar_system import Orbit from accrete import CircumstellarDisk from constants import ECCENTRICITY_COEFF, PROTOPLANET_MASS from constants import SUN_MASS_IN_EARTH_MASSES from constants import EARTH_ALBEDO, GAS_GIANT_ALBEDO, FREEZING_POINT_OF_WATER, KM_PER_AU, EARTH_AVERAGE_KELVIN, EARTH_EXOSPHERE_TEMP from constants import MOL_NITROGEN, MOL_HYDROGEN, HELIUM from constants import ASTEROID_MASS_LIMIT from constants import MILLIBARS_PER_BAR from enviroment import kothari_radius, gas_life, rms_vel, est_temp, period, day_length, acceleration, gravity, min_molec_weight, orb_zone, volume_radius, volume_density, grnhouse, boiling_point, escape_vel, empirical_density, inclination, iterate_surface_temp, pressure, vol_inventory from enviroment import PlanetType from math import exp from math import inf as INCREDIBLY_LARGE_NUMBER from util import about, random_number, random_eccentricity from chemtable import gases logging.getLogger().setLevel(logging.INFO) def random_star(): e(1*10**9, 6*10**9) return Star(age=age, mass_ratio=1) def generate_stellar_system(star, do_gases=True, do_moons=True): protoplanets = generate_planetary_masses( star, 0.0, star.stellar_dust_limit, do_moons=do_moons ) star.planets = [ generate_planet( p, star, do_gases=do_gases, do_moons=do_moons ) for p in protoplanets ] return star def random_planetesimal(disk): a = random.uniform(disk.planet_inner_bound, disk.planet_outer_bound) e = 1.0 - (random.uniform(0.0, 1.0) ** ECCENTRICITY_COEFF) if e > .99: e = .99 return Planetesimal( disk=disk, orbit=Orbit( a=a, e=e, ), dust_mass=PROTOPLANET_MASS, gas_mass=0, ) def generate_planetary_masses(star, inner_dust, outer_dust, do_moons=True): disk = CircumstellarDisk(star) planets = [] sequential_failures = 0 while disk.dust_left and sequential_failures < 10**3: canidate = random_planetesimal(disk) iel = canidate.inner_effect_limit oel = canidate.outer_effect_limit if disk.dust_available(iel, oel) > 0: sequential_failures = 0 logging.info("Injecting planetesimal at " + str(canidate.orbit.a) + " AU ...") disk.accrete_dust(canidate) if canidate.mass > PROTOPLANET_MASS: coalesce_planetesimals(disk, planets, canidate, do_moons) logging.info("\tsuccess.\n") else: logging.info("\tfailed due to large neighbor.\n") else: sequential_failures += 1 return planets def convert_planetesimal_to_protoplanet(planetesimal): return Protoplanet( star=planetesimal.disk.star, orbit=planetesimal.orbit, dust_mass=planetesimal.dust_mass, gas_mass=planetesimal.gas_mass ) def convert_planetesimal_to_protomoon(planetesimal, planet): print(" Capturing a protomoon.") return Protomoon( protoplanet=planet, orbit=Orbit( a=None, e=None, ), dust_mass=planetesimal.dust_mass, gas_mass=planetesimal.gas_mass, ) def coalesce_planetesimals(disk, planets, canidate, do_moons): finished = False for planet in planets: diff = planet.orbit.a - canidate.orbit.a if diff > 0.0: dist1 = canidate.orbit.apoapsis * (1.0 + canidate.reduced_mass) - canidate.orbit.a dist2 = planet.orbit.a - (planet.orbit.periapsis * (1.0 - planet.reduced_mass)) else: dist1 = canidate.orbit.a - (canidate.orbit.periapsis * (1.0 - canidate.reduced_mass)) dist2 = (planet.orbit.apoapsis * (1.0 + planet.reduced_mass)) - planet.orbit.a if abs(diff) <= abs(dist1) or abs(diff) <= abs(dist2): a = (planet.mass + canidate.mass) / \ ((planet.mass / planet.orbit.a) + (canidate.mass / canidate.orbit.a)) temp = planet.mass * sqrt(planet.orbit.a) * sqrt(1.0 - (planet.orbit.e ** 2.0)) temp = temp + (canidate.mass * sqrt(canidate.orbit.a) * sqrt(sqrt(1.0 - (canidate.orbit.e ** 2.0)))) temp = temp / ((planet.mass + canidate.mass) * sqrt(canidate.orbit.a)) temp = 1.0 - (temp ** 2.0) if temp < 0.0 or temp >= 1.0: temp = 0.0 e = sqrt(temp) if do_moons: if canidate.mass < canidate.critical_mass: if canidate.mass * SUN_MASS_IN_EARTH_MASSES < 2.5 \ and canidate.mass * SUN_MASS_IN_EARTH_MASSES > .0001 \ and planet.mass_of_moons < planet.mass * .05 \ and planet.mass > canidate.mass: planet.add_moon( convert_planetesimal_to_protomoon(canidate, planet)) logging.info("Moon captured at " + str(planet.orbit.a) + " AU. Planet Mass: " + str(planet.mass * SUN_MASS_IN_EARTH_MASSES) + " earth masses Moon Mass: " + str(canidate.mass * SUN_MASS_IN_EARTH_MASSES) + " earth masses.") finished = True break else: logging.info("Did not capture potential moon at " + str(planet.orbit.a) + " AU. Collision imminent.") logging.info( "Collision between two planetesimals! Computing new orbit and accumulating additional mass.") disk.accrete_dust(planet) planet.orbit = Orbit(a=a, e=e) planet.orbit_a = a planet.orbit_e = e planet.dust_mass = planet.dust_mass + canidate.dust_mass planet.gas_mass = planet.gas_mass + canidate.gas_mass finished = True logging.info( "Conglomerate is now " + str(planet.mass * SUN_MASS_IN_EARTH_MASSES) + " earth masses at " + str(planet.orbit.a) + " AU." ) if not finished: logging.info("New Protoplanet at " + str(canidate.orbit.a) + "AU.") planets.append(convert_planetesimal_to_protoplanet(canidate)) def calculate_gases(star, planet, planet_id): if planet.surf_pressure > 0: amount = [0 for _ in range(len(gases))] totamount = 0 pressure = planet.surf_pressure/MILLIBARS_PER_BAR n = 0 for i in range(len(gases)): yp = gases[i].boil / \ (373. * ((log((pressure) + 0.001) / -5050.5) + (1.0 / 373.))) if ((yp >= 0 and yp < planet.low_temp) and (gases[i].weight >= planet.molec_weight)): vrms = rms_vel(gases[i].weight, planet.exospheric_temp) pvrms = pow(1 / (1 + vrms / planet.esc_velocity), star.age / 1e9) abund = gases[i].abunds react = 1.0 fract = 1.0 pres2 = 1.0 if gases[i].symbol == "Ar": react = .15 * star.age/4e9 elif gases[i].symbol == "He": abund = abund * (0.001 + (planet.gas_mass / planet.mass)) pres2 = (0.75 + pressure) react = pow(1 / (1 + gases[i].reactivity), star.age/2e9 * pres2) elif (gases[i].symbol == "O" or gases[i].symbol == "O2") and star.age > 2e9 and planet.surf_temp > 270 and planet.surf_temp < 400: pres2 = (0.89 + pressure/4) react = pow( 1 / (1 + gases[i].reactivity), pow(star.age/2e9, 0.25) * pres2) elif gases[i].symbol == "CO2" and star.age > 2e9 and planet.surf_temp > 270 and planet.surf_temp < 400: pres2 = (0.75 + pressure) react = pow( 1 / (1 + gases[i].reactivity), pow(star.age/2e9, 0.5) * pres2) react *= 1.5 else: pres2 = 0.75 + pressure react = pow( 1 / (1 + gases[i].reactivity), star.age/2e9 * pres2) fract = (1 - (planet.molec_weight / gases[i].weight)) amount[i] = abund * pvrms * react * fract totamount += amount[i] if (amount[i] > 0.0): n += 1 else: amount[i] = 0.0 if n > 0: planet.gases = n planet.atmosphere = [] for i in range(len(gases)): if amount[i] > 0.0: planet.atmosphere.append((gases[i], planet.surf_pressure * amount[i] / totamount)) n += 1 def roche_limit(planet, moon): return 2.44 * planet.radius * pow((planet.density / moon.density), (1.0 / 3.0)) def hill_sphere(planet, star): return planet.orbit.a * KM_PER_AU * pow((planet.mass / (3.0 * star.mass_ratio)), (1.0 / 3.0)) def generate_planet(protoplanet, star, random_tilt=0, planet_id=None, do_gases=True, do_moons=True, is_moon=False): planet = Planet( sun=star, orbit=protoplanet.orbit, dust_mass=protoplanet.dust_mass, gas_mass=protoplanet.gas_mass, mass=protoplanet.mass, axial_tilt=inclination(protoplanet.orbit.a) if random_tilt else 0, atmosphere=None, surf_temp=0, high_temp=0, low_temp=0, max_temp=0, min_temp=0, greenhs_rise=0, resonant_period=False, orbit_zone=orb_zone(star.luminosity_ratio, protoplanet.orbit.a), orb_period=period(protoplanet.orbit.a, protoplanet.mass, star.mass_ratio) ) planet.exospheric_temp = EARTH_EXOSPHERE_TEMP / \ ((planet.orbit.a / star.r_ecosphere) ** 2) planet.rms_velocity = rms_vel(MOL_NITROGEN, planet.exospheric_temp) planet.core_radius = kothari_radius( planet.dust_mass, False, planet.orbit_zone) planet.density = empirical_density( planet.mass, planet.orbit.a, star.r_ecosphere, True) planet.radius = volume_radius(planet.mass, planet.density) planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) planet.molec_weight = min_molec_weight(planet) if (((planet.mass * SUN_MASS_IN_EARTH_MASSES) > 1.0) and ((planet.gas_mass / planet.mass) > 0.05) and (min_molec_weight(planet) <= 4.0)): if ((planet.gas_mass / planet.mass) < 0.20): planet.type = PlanetType.SUB_SUB_GAS_GIANT elif ((planet.mass * SUN_MASS_IN_EARTH_MASSES) < 20.0): planet.type = PlanetType.SUB_GAS_GIANT else: planet.type = PlanetType.GAS_GIANT else: planet.radius = kothari_radius(planet.mass, False, planet.orbit_zone) planet.density = volume_density(planet.mass, planet.radius) planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) if ((planet.gas_mass / planet.mass) > 0.000001): h2_mass = planet.gas_mass * 0.85 he_mass = (planet.gas_mass - h2_mass) * 0.999 h2_loss = 0.0 he_loss = 0.0 h2_life = gas_life(MOL_HYDROGEN, planet) he_life = gas_life(HELIUM, planet) if (h2_life < star.age): #math.exp with a value above 709 results in a math range error #this is a dumb fix. STH 2021-0131 if (star.age / h2_life)>709: h2_loss = ((1.0 - (1.0 / exp(709.0))) * h2_mass) else: h2_loss = ((1.0 - (1.0 / exp(star.age / h2_life))) * h2_mass) planet.gas_mass -= h2_loss planet.mass -= h2_loss planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) if (he_life < star.age): he_loss = ((1.0 - (1.0 / exp(star.age / he_life))) * he_mass) planet.gas_mass -= he_loss planet.mass -= he_loss planet.surf_accel = acceleration(planet.mass, planet.radius) planet.surf_grav = gravity(planet.surf_accel) '''if (((h2_loss + he_loss) > .000001) and (flag_verbose & 0x0080)): fprintf(stderr, "%s\tLosing gas: H2: %5.3Lf EM, He: %5.3Lf EM\n", planet_id, h2_loss * SUN_MASS_IN_EARTH_MASSES, he_loss * SUN_MASS_IN_EARTH_MASSES)''' planet.day = day_length(planet) # Modifies planet.resonant_period planet.esc_velocity = escape_vel(planet.mass, planet.radius) if planet.type == PlanetType.GAS_GIANT or planet.type == PlanetType.SUB_GAS_GIANT or planet.type == PlanetType.SUB_SUB_GAS_GIANT: planet.greenhouse_effect = False planet.volatile_gas_inventory = INCREDIBLY_LARGE_NUMBER planet.surf_pressure = INCREDIBLY_LARGE_NUMBER planet.boil_point = INCREDIBLY_LARGE_NUMBER planet.surf_temp = INCREDIBLY_LARGE_NUMBER planet.greenhs_rise = 0 planet.albedo = about(GAS_GIANT_ALBEDO, 0.1) planet.hydrosphere = 1.0 planet.cloud_cover = 1.0 planet.ice_cover = 0.0 planet.surf_grav = gravity(planet.surf_accel) planet.molec_weight = min_molec_weight(planet) planet.surf_grav = INCREDIBLY_LARGE_NUMBER planet.estimated_temp = est_temp( star.r_ecosphere, planet.orbit.a, planet.albedo) planet.estimated_terr_temp = est_temp( star.r_ecosphere, planet.orbit.a, EARTH_ALBEDO) temp = planet.estimated_terr_temp if (temp >= FREEZING_POINT_OF_WATER) and (temp <= EARTH_AVERAGE_KELVIN + 10.) and (star.age > 2.0E9): pass else: planet.estimated_temp = est_temp( star.r_ecosphere, planet.orbit.a, EARTH_ALBEDO) planet.estimated_terr_temp = est_temp( star.r_ecosphere, planet.orbit.a, EARTH_ALBEDO) planet.surf_grav = gravity(planet.surf_accel) planet.molec_weight = min_molec_weight(planet) planet.greenhouse_effect = grnhouse(star.r_ecosphere, planet.orbit.a) planet.volatile_gas_inventory = vol_inventory(planet.mass, planet.esc_velocity, planet.rms_velocity, star.mass_ratio, planet.orbit_zone, planet.greenhouse_effect, (planet.gas_mass / planet.mass) > 0.000001) planet.surf_pressure = pressure(planet.volatile_gas_inventory, planet.radius, planet.surf_grav) if ((planet.surf_pressure == 0.0)): planet.boil_point = 0.0 else: planet.boil_point = boiling_point(planet.surf_pressure) # Sets: # planet.surf_temp # planet.greenhs_rise # planet.albedo # planet.hydrosphere # planet.cloud_cover # planet.ice_cover iterate_surface_temp(planet) if (do_gases and (planet.max_temp >= FREEZING_POINT_OF_WATER) and (planet.min_temp <= planet.boil_point)): calculate_gases(star, planet, planet_id) # Next we assign a type to the planet. if (planet.surf_pressure < 1.0): if (not is_moon) and ((planet.mass * SUN_MASS_IN_EARTH_MASSES) < ASTEROID_MASS_LIMIT): planet.type = PlanetType.ASTEROIDS else: planet.type = PlanetType.ROCK elif (planet.surf_pressure > 6000.0) and (planet.molec_weight <= 2.0): # Retains Hydrogen planet.type = PlanetType.SUB_SUB_GAS_GIANT planet.gases = 0 planet.atmosphere = None else: # Atmospheres: if (int(planet.day) == int(planet.orb_period * 24.0)) or planet.resonant_period: planet.type = PlanetType.ONE_FACE elif (planet.hydrosphere >= 0.95): planet.type = PlanetType.WATER # >95% water elif (planet.ice_cover >= 0.95): planet.type = PlanetType.ICE # >95% ice elif (planet.hydrosphere > 0.05): planet.type = PlanetType.TERRESTRIAL # Terrestrial # else <5% water elif (planet.max_temp > planet.boil_point): planet.type = PlanetType.VENUSIAN # Hot = Venusian elif ((planet.gas_mass / planet.mass) > 0.0001): # Accreted gas planet.type = PlanetType.ICE # But no Greenhouse planet.ice_cover = 1.0 # or liquid water # Make it an Ice World elif (planet.surf_pressure <= 250.0): # Thin air = Martian planet.type = PlanetType.MARTIAN elif (planet.surf_temp < FREEZING_POINT_OF_WATER): planet.type = PlanetType.ICE else: planet.type = PlanetType.UNKNOWN # TODO(woursler): Consider throwing an error here. '''if (flag_verbose & 0x0001) fprintf (stderr, "%12s\tp=%4.2Lf\tm=%4.2Lf\tg=%4.2Lf\tt=%+.1Lf\t%s\t Unknown %s\n", type_string (planet.type), planet.surf_pressure, planet.mass * SUN_MASS_IN_EARTH_MASSES, planet.surf_grav, planet.surf_temp - EARTH_AVERAGE_KELVIN, planet_id, ((int)planet.day == (int)(planet.orb_period * 24.0) or (planet.resonant_period)) ? "(1-Face)" : "" )''' if do_moons and not is_moon: for protomoon in protoplanet.moons: if protomoon.mass * SUN_MASS_IN_EARTH_MASSES > .000001: protomoon.orbit = planet.orbit # Note: adjusts density, which is used in computing the roche limit. moon = generate_planet( protoplanet=protomoon, star=star, random_tilt=random_tilt, do_gases=do_gases, do_moons=do_moons, is_moon=True ) # TODO(woursler): these should be their own subroutines. roche_limit_r = roche_limit(planet, moon) hill_sphere_r = hill_sphere(planet, star) if (roche_limit_r * 3.0) < hill_sphere_r: moon_a = random_number( roche_limit_r * 1.5, hill_sphere_r / 2.0) / KM_PER_AU moon_e = random_eccentricity() moon.orbit = Orbit(a=moon_a, e=moon_e) else: moon.orbit = Orbit(a=0, e=0) planet.moons.append(moon) return planet ### # Smoke Test ### if __name__ == '__main__': random.seed('earth') print(generate_stellar_system(random_star()))

true

f71fe0dc7aac7d9afad4c1f08c6e9b94fde74e57

4,160

py

Python

vivisect/extensions/example_gui_extension.py

bat-serjo/vivisect

f60934a2c8c51c7acdba52a65756e717a108a440

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

null

vivisect/extensions/example_gui_extension.py

bat-serjo/vivisect

f60934a2c8c51c7acdba52a65756e717a108a440

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

null

vivisect/extensions/example_gui_extension.py

bat-serjo/vivisect

f60934a2c8c51c7acdba52a65756e717a108a440

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

null

try: from PyQt5.QtWidgets import QToolBar, QLabel, QPushButton, QTextEdit, QWidget, QInputDialog from PyQt5 import QtCore except: from PyQt4.QtGui import QToolBar, QLabel, QPushButton, QTextEdit, QWidget, QInputDialog from PyQt4 import QtCore from vqt.main import idlethread from vqt.basics import VBox from vqt.common import ACT ''' This is an example of a vivisect GUI extension module. Set the environment variable VIV_EXT_PATH to point at a directory full of python modules such as this to extend and implement your own vivisect features. The extension should be a python module, either in the form of a .py file or a directory with a __init__.py file. Either way, the module will be loaded into memory and the "vivExtension" function called. ''' from PyQt5.QtWidgets import QToolBar, QLabel, QPushButton, QTextEdit from vqt.main import idlethread from vqt.basics import VBox class ExampleToolbar(QToolBar): def __init__(self, vw, vwgui): self.vw = vw self.vwgui = vwgui QToolBar.__init__(self, parent=vwgui) # Add a label to the toolbar self.addWidget( QLabel('Example Toolbar:', parent=self) ) # Add an action button to the toolbar self.addAction('ONE', self.doOne) def doOne(self): self.vw.vprint('did one!') class ExampleWindow(QWidget): def __init__(self, vw, vwgui): self.vw = vw self.vwgui = vwgui QWidget.__init__(self, parent=vwgui) # Set the window title self.setWindowTitle('Example Window!') # Add a Button and a Text Edit object in a basic VBox layout button = QPushButton('My Button!', parent=self) textedit = QTextEdit('WOOT! Some text!', parent=self) self.setLayout( VBox(button, textedit) ) def vprint(vw, s, *args, **kwargs): vw.vprint(s % args) print(s % args) def ctxMenuHook(vw, va, expr, menu, parent, nav): ''' Context Menu handler (adds options as we wish) ''' try: if va == 0x41414141: menu.addAction('WAT?', ACT(vw.vprint, "We're at AAAA!")) menu.addAction('bookmark (B)', ACT(vw.getVivGui().addBookmark, va)) menu.addAction('YEEE HAH', ACT(vw.vprint, "YEE HAH %x %r %r %r %r" % (va, expr, menu, parent, nav))) menu.addAction('YEEE HAH1', ACT(vprint, vw, "YEE HAH %x %r %r %r %r", va, expr, menu, parent, nav)) except Exception as e: import traceback traceback.print_exc() class Crap: ''' This is a helpful class for storing vw and vwgui and "doing the thing" Currently Vivisect's Hot Keys are tied to the many gui widgets, so vw and vwgui are not available when the "thing" is called. ''' def __init__(self, vw, vwgui): self.vw = vw self.vwgui = vwgui def thing(self): vprint(self.vw, "Blah Blah Blah") def printUserInput(self): # ok is whether the "OK" button was pressed, utext is the user text utext, ok = QInputDialog.getText(self.vwgui, 'Enter...', 'User Text') vprint(self.vw, '%r: %r', ok, utext) @idlethread def vivExtension(vw, vwgui): # Create a toolbar and add it to the GUI toolbar = ExampleToolbar(vw, vwgui) vwgui.addToolBar(QtCore.Qt.TopToolBarArea, toolbar) # Create a new Vivisect Dock Window (based on a QWidget) window = ExampleWindow(vw, vwgui) d = vwgui.vqDockWidget(window, floating=True) d.resize(300,200) # Add a menu item vwgui.vqAddMenuField('&Example.&FooBar.&PrintDiscoveredStats', vw.printDiscoveredStats, ()) # hook context menu vw.addCtxMenuHook('example', ctxMenuHook) # add HotKeyTargets and HotKeys tempmod = Crap(vw, vwgui) vwgui.addHotKey('ctrl+p', 'file:hackme') vwgui.addHotKeyTarget('file:hackme', tempmod.thing) # Popups/Dialogs - add a menu entry to ask for input and print the output vwgui.vqAddMenuField("&Example.&FooBar.&PrintUserInput", tempmod.printUserInput, ()) # get Dock Windows by name for w, vqDW in vwgui.vqGetDockWidgetsByName('viv'): vprint(vw, "Window: %r DockWidget: %r (%r)", w, vqDW, w.getEnviNavName())

31.755725

111

0.665625

try: from PyQt5.QtWidgets import QToolBar, QLabel, QPushButton, QTextEdit, QWidget, QInputDialog from PyQt5 import QtCore except: from PyQt4.QtGui import QToolBar, QLabel, QPushButton, QTextEdit, QWidget, QInputDialog from PyQt4 import QtCore from vqt.main import idlethread from vqt.basics import VBox from vqt.common import ACT from PyQt5.QtWidgets import QToolBar, QLabel, QPushButton, QTextEdit from vqt.main import idlethread from vqt.basics import VBox class ExampleToolbar(QToolBar): def __init__(self, vw, vwgui): self.vw = vw self.vwgui = vwgui QToolBar.__init__(self, parent=vwgui) self.addWidget( QLabel('Example Toolbar:', parent=self) ) self.addAction('ONE', self.doOne) def doOne(self): self.vw.vprint('did one!') class ExampleWindow(QWidget): def __init__(self, vw, vwgui): self.vw = vw self.vwgui = vwgui QWidget.__init__(self, parent=vwgui) self.setWindowTitle('Example Window!') button = QPushButton('My Button!', parent=self) textedit = QTextEdit('WOOT! Some text!', parent=self) self.setLayout( VBox(button, textedit) ) def vprint(vw, s, *args, **kwargs): vw.vprint(s % args) print(s % args) def ctxMenuHook(vw, va, expr, menu, parent, nav): try: if va == 0x41414141: menu.addAction('WAT?', ACT(vw.vprint, "We're at AAAA!")) menu.addAction('bookmark (B)', ACT(vw.getVivGui().addBookmark, va)) menu.addAction('YEEE HAH', ACT(vw.vprint, "YEE HAH %x %r %r %r %r" % (va, expr, menu, parent, nav))) menu.addAction('YEEE HAH1', ACT(vprint, vw, "YEE HAH %x %r %r %r %r", va, expr, menu, parent, nav)) except Exception as e: import traceback traceback.print_exc() class Crap: def __init__(self, vw, vwgui): self.vw = vw self.vwgui = vwgui def thing(self): vprint(self.vw, "Blah Blah Blah") def printUserInput(self): # ok is whether the "OK" button was pressed, utext is the user text utext, ok = QInputDialog.getText(self.vwgui, 'Enter...', 'User Text') vprint(self.vw, '%r: %r', ok, utext) @idlethread def vivExtension(vw, vwgui): # Create a toolbar and add it to the GUI toolbar = ExampleToolbar(vw, vwgui) vwgui.addToolBar(QtCore.Qt.TopToolBarArea, toolbar) # Create a new Vivisect Dock Window (based on a QWidget) window = ExampleWindow(vw, vwgui) d = vwgui.vqDockWidget(window, floating=True) d.resize(300,200) # Add a menu item vwgui.vqAddMenuField('&Example.&FooBar.&PrintDiscoveredStats', vw.printDiscoveredStats, ()) # hook context menu vw.addCtxMenuHook('example', ctxMenuHook) # add HotKeyTargets and HotKeys tempmod = Crap(vw, vwgui) vwgui.addHotKey('ctrl+p', 'file:hackme') vwgui.addHotKeyTarget('file:hackme', tempmod.thing) # Popups/Dialogs - add a menu entry to ask for input and print the output vwgui.vqAddMenuField("&Example.&FooBar.&PrintUserInput", tempmod.printUserInput, ()) # get Dock Windows by name for w, vqDW in vwgui.vqGetDockWidgetsByName('viv'): vprint(vw, "Window: %r DockWidget: %r (%r)", w, vqDW, w.getEnviNavName())

true

f71fe1a680e38a876089f6c92424e564085015cd

728

py

Python

tests/test_bit.py

robertchase/aiomysql

80236fca02c70cd693cb02112646ca14f2c7e2be

[ "MIT" ]

null

tests/test_bit.py

robertchase/aiomysql

80236fca02c70cd693cb02112646ca14f2c7e2be

[ "MIT" ]

null

tests/test_bit.py

robertchase/aiomysql

80236fca02c70cd693cb02112646ca14f2c7e2be

[ "MIT" ]

1

2021-04-30T14:11:42.000Z

"""test Bit operations""" import pytest from aiomysql.bit import Bit @pytest.mark.parametrize( 'length, value, expected', ( (10, None, ValueError), (10, 1, 1), (10, '123', TypeError), (10, '0', 0), (10, '1', 1), (10, '010', 2), (10, '1010', 10), (10, '01000000000', ValueError), ), ) def test_bit(length, value, expected): """test different inputs""" bit = Bit(length) if expected in (ValueError, TypeError): with pytest.raises(expected): bit(value) else: assert bit(value).value == expected def test_as_binary(): """verify binary conversion""" bit = Bit(5)(10) assert bit.as_binary() == '1010'

22.060606

43

0.54533

import pytest from aiomysql.bit import Bit @pytest.mark.parametrize( 'length, value, expected', ( (10, None, ValueError), (10, 1, 1), (10, '123', TypeError), (10, '0', 0), (10, '1', 1), (10, '010', 2), (10, '1010', 10), (10, '01000000000', ValueError), ), ) def test_bit(length, value, expected): bit = Bit(length) if expected in (ValueError, TypeError): with pytest.raises(expected): bit(value) else: assert bit(value).value == expected def test_as_binary(): bit = Bit(5)(10) assert bit.as_binary() == '1010'

true

f71fe27393e1a3a75142186240ffddd9c0d963a4

15,812

py

Python

contrib/experimental/input/osx.py

bitcraft/pyglet

144257c365ca85528c6a4c5bed8141e683d7a9b6

[ "BSD-3-Clause" ]

15

2015-01-21T12:29:01.000Z

2018-12-09T09:17:33.000Z

contrib/experimental/input/osx.py

bitcraft/pyglet

144257c365ca85528c6a4c5bed8141e683d7a9b6

[ "BSD-3-Clause" ]

null

contrib/experimental/input/osx.py

bitcraft/pyglet

144257c365ca85528c6a4c5bed8141e683d7a9b6

[ "BSD-3-Clause" ]

9

2015-12-12T09:12:46.000Z

2021-12-26T13:29:14.000Z

#!/usr/bin/env python """ """ __docformat__ = 'restructuredtext' __version__ = '$Id: $' import ctypes import pyglet from pyglet.libs.darwin import carbon, _oscheck, create_cfstring from pyglet.libs.darwin.constants import * import input import usage # non-broken c_void_p void_p = ctypes.POINTER(ctypes.c_int) class CFUUIDBytes(ctypes.Structure): _fields_ = [('byte%d' % i, ctypes.c_uint8) for i in range(16)] mach_port_t = void_p io_iterator_t = void_p kern_return_t = ctypes.c_int IOReturn = ctypes.c_uint CFDictionaryRef = void_p CFMutableDictionaryRef = void_p CFArrayRef = void_p CFUUIDRef = ctypes.POINTER(CFUUIDBytes) AbsoluteTime = ctypes.c_double HRESULT = ctypes.c_int REFIID = CFUUIDBytes IOHIDElementType = ctypes.c_int kIOHIDElementTypeInput_Misc = 1 kIOHIDElementTypeInput_Button = 2 kIOHIDElementTypeInput_Axis = 3 kIOHIDElementTypeInput_ScanCodes = 4 kIOHIDElementTypeOutput = 129 kIOHIDElementTypeFeature = 257 kIOHIDElementTypeCollection = 513 IOHIDElementCookie = ctypes.c_void_p MACH_PORT_NULL = 0 kIOHIDDeviceKey = "IOHIDDevice" kIOServicePlane = "IOService" kIOHIDProductIDKey = "ProductID" kCFNumberIntType = 9 kIOHIDOptionsTypeSeizeDevice = 1 kIOReturnExclusiveAccess = 0xe00002c5 carbon.CFUUIDGetConstantUUIDWithBytes.restype = CFUUIDRef kIOHIDDeviceUserClientTypeID = carbon.CFUUIDGetConstantUUIDWithBytes(None, 0xFA, 0x12, 0xFA, 0x38, 0x6F, 0x1A, 0x11, 0xD4, 0xBA, 0x0C, 0x00, 0x05, 0x02, 0x8F, 0x18, 0xD5) kIOCFPlugInInterfaceID = carbon.CFUUIDGetConstantUUIDWithBytes(None, 0xC2, 0x44, 0xE8, 0x58, 0x10, 0x9C, 0x11, 0xD4, 0x91, 0xD4, 0x00, 0x50, 0xE4, 0xC6, 0x42, 0x6F) kIOHIDDeviceInterfaceID = carbon.CFUUIDGetConstantUUIDWithBytes(None, 0x78, 0xBD, 0x42, 0x0C, 0x6F, 0x14, 0x11, 0xD4, 0x94, 0x74, 0x00, 0x05, 0x02, 0x8F, 0x18, 0xD5) class IOHIDEventStruct(ctypes.Structure): _fields_ = ( ('type', IOHIDElementType), ('elementCookie', IOHIDElementCookie), ('value', ctypes.c_int32), ('timestamp', AbsoluteTime), ('longValueSize', ctypes.c_uint32), ('longValue', ctypes.c_void_p) ) Self = ctypes.c_void_p class IUnknown(ctypes.Structure): _fields_ = ( ('_reserved', ctypes.c_void_p), ('QueryInterface', ctypes.CFUNCTYPE(HRESULT, Self, REFIID, ctypes.c_void_p)), ('AddRef', ctypes.CFUNCTYPE(ctypes.c_ulong, Self)), ('Release', ctypes.CFUNCTYPE(ctypes.c_ulong, Self)), ) # Most of these function prototypes are not filled in yet because I haven't # bothered. class IOHIDQueueInterface(ctypes.Structure): _fields_ = IUnknown._fields_ + ( ('createAsyncEventSource', ctypes.c_void_p), ('getAsyncEventSource', ctypes.c_void_p), ('createAsyncPort', ctypes.c_void_p), ('getAsyncPort', ctypes.c_void_p), ('create', ctypes.CFUNCTYPE(IOReturn, Self, ctypes.c_uint32, ctypes.c_uint32)), ('dispose', ctypes.CFUNCTYPE(IOReturn, Self)), ('addElement', ctypes.CFUNCTYPE(IOReturn, Self, IOHIDElementCookie)), ('removeElement', ctypes.c_void_p), ('hasElement', ctypes.c_void_p), ('start', ctypes.CFUNCTYPE(IOReturn, Self)), ('stop', ctypes.CFUNCTYPE(IOReturn, Self)), ('getNextEvent', ctypes.CFUNCTYPE(IOReturn, Self, ctypes.POINTER(IOHIDEventStruct), AbsoluteTime, ctypes.c_uint32)), ('setEventCallout', ctypes.c_void_p), ('getEventCallout', ctypes.c_void_p), ) class IOHIDDeviceInterface(ctypes.Structure): _fields_ = IUnknown._fields_ + ( ('createAsyncEventSource', ctypes.c_void_p), ('getAsyncEventSource', ctypes.c_void_p), ('createAsyncPort', ctypes.c_void_p), ('getAsyncPort', ctypes.c_void_p), ('open', ctypes.CFUNCTYPE(IOReturn, Self, ctypes.c_uint32)), ('close', ctypes.CFUNCTYPE(IOReturn, Self)), ('setRemovalCallback', ctypes.c_void_p), ('getElementValue', ctypes.CFUNCTYPE(IOReturn, Self, IOHIDElementCookie, ctypes.POINTER(IOHIDEventStruct))), ('setElementValue', ctypes.c_void_p), ('queryElementValue', ctypes.c_void_p), ('startAllQueues', ctypes.c_void_p), ('stopAllQueues', ctypes.c_void_p), ('allocQueue', ctypes.CFUNCTYPE( ctypes.POINTER(ctypes.POINTER(IOHIDQueueInterface)), Self)), ('allocOutputTransaction', ctypes.c_void_p), # 1.2.1 (10.2.3) ('setReport', ctypes.c_void_p), ('getReport', ctypes.c_void_p), # 1.2.2 (10.3) ('copyMatchingElements', ctypes.CFUNCTYPE(IOReturn, Self, CFDictionaryRef, ctypes.POINTER(CFArrayRef))), ('setInterruptReportHandlerCallback', ctypes.c_void_p), ) def get_master_port(): master_port = mach_port_t() _oscheck( carbon.IOMasterPort(MACH_PORT_NULL, ctypes.byref(master_port)) ) return master_port def get_matching_dictionary(): carbon.IOServiceMatching.restype = CFMutableDictionaryRef matching_dictionary = carbon.IOServiceMatching(kIOHIDDeviceKey) return matching_dictionary def get_existing_devices(master_port, matching_dictionary): # Consumes reference to matching_dictionary iterator = io_iterator_t() _oscheck( carbon.IOServiceGetMatchingServices(master_port, matching_dictionary, ctypes.byref(iterator)) ) devices = list() while carbon.IOIteratorIsValid(iterator): device = carbon.IOIteratorNext(iterator) if not device: break devices.append(Device(device)) carbon.IOObjectRelease(iterator) return devices def cfstring_to_string(value_string): value_length = carbon.CFStringGetLength(value_string) buffer_length = carbon.CFStringGetMaximumSizeForEncoding( value_length, kCFStringEncodingUTF8) buffer = ctypes.c_buffer(buffer_length + 1) result = carbon.CFStringGetCString(value_string, buffer, len(buffer), kCFStringEncodingUTF8) if not result: return return buffer.value def cfnumber_to_int(value): result = ctypes.c_int() carbon.CFNumberGetValue(value, kCFNumberIntType, ctypes.byref(result)) return result.value def cfvalue_to_value(value): if not value: return None value_type = carbon.CFGetTypeID(value) if value_type == carbon.CFStringGetTypeID(): return cfstring_to_string(value) elif value_type == carbon.CFNumberGetTypeID(): return cfnumber_to_int(value) else: return None def get_property_value(properties, key): key_string = create_cfstring(key) value = ctypes.c_void_p() present = carbon.CFDictionaryGetValueIfPresent(properties, key_string, ctypes.byref(value)) carbon.CFRelease(key_string) if not present: return None return value def get_property(properties, key): return cfvalue_to_value(get_property_value(properties, key)) def dump_properties(properties): def func(key, value, context): print('%s = %s' % (cfstring_to_string(key), cfvalue_to_value(value))) CFDictionaryApplierFunction = ctypes.CFUNCTYPE(None, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p) carbon.CFDictionaryApplyFunction(properties, CFDictionaryApplierFunction(func), None) class Device: """ :IVariables: `name` : str `manufacturer` : str """ def __init__(self, generic_device): self._init_properties(generic_device) self._device = self._get_device_interface(generic_device) self.elements = self._get_elements() self._open = False self._queue = None self._queue_depth = 8 # Number of events queue can buffer def _init_properties(self, generic_device): properties = CFMutableDictionaryRef() _oscheck( carbon.IORegistryEntryCreateCFProperties(generic_device, ctypes.byref(properties), None, 0) ) self.name = get_property(properties, "Product") self.manufacturer = get_property(properties, "Manufacturer") carbon.CFRelease(properties) def _get_device_interface(self, generic_device): plug_in_interface = \ ctypes.POINTER(ctypes.POINTER(IUnknown))() score = ctypes.c_int32() _oscheck( carbon.IOCreatePlugInInterfaceForService( generic_device, kIOHIDDeviceUserClientTypeID, kIOCFPlugInInterfaceID, ctypes.byref(plug_in_interface), ctypes.byref(score)) ) carbon.CFUUIDGetUUIDBytes.restype = CFUUIDBytes hid_device_interface = \ ctypes.POINTER(ctypes.POINTER(IOHIDDeviceInterface))() _oscheck( plug_in_interface.contents.contents.QueryInterface( plug_in_interface, carbon.CFUUIDGetUUIDBytes(kIOHIDDeviceInterfaceID), ctypes.byref(hid_device_interface)) ) plug_in_interface.contents.contents.Release(plug_in_interface) return hid_device_interface def _get_elements(self): elements_array = CFArrayRef() _oscheck( self._device.contents.contents.copyMatchingElements(self._device, None, ctypes.byref( elements_array)) ) self._element_cookies = dict() elements = list() n_elements = carbon.CFArrayGetCount(elements_array) for i in range(n_elements): properties = carbon.CFArrayGetValueAtIndex(elements_array, i) element = DeviceElement(self, properties) elements.append(element) self._element_cookies[element._cookie] = element carbon.CFRelease(elements_array) return elements def __repr__(self): return '%s(name=%r, manufacturer=%r)' % ( self.__class__.__name__, self.product, self.manufacturer) def open(self, exclusive=False): flags = 0 if exclusive: flags |= kIOHIDOptionsTypeSeizeDevice result = self._device.contents.contents.open(self._device, flags) if result == 0: self._open = True elif result == kIOReturnExclusiveAccess: raise input.InputDeviceExclusiveException() # Create event queue self._queue = self._device.contents.contents.allocQueue(self._device) _oscheck( self._queue.contents.contents.create(self._queue, 0, self._queue_depth) ) # Add all elements into queue # TODO: only "interesting/known" elements? for element in self.elements: r = self._queue.contents.contents.addElement(self._queue, element._cookie, 0) if r != 0: print('error adding %r' % element) _oscheck( self._queue.contents.contents.start(self._queue) ) # HACK TODO: pyglet.clock.schedule(self.dispatch_events) def close(self): if not self._open: return # HACK TODO: pyglet.clock.unschedule(self.dispatch_events) _oscheck( self._queue.contents.contents.stop(self._queue) ) _oscheck( self._queue.contents.contents.dispose(self._queue) ) self._queue.contents.contents.Release(self._queue) self._queue = None _oscheck( self._device.contents.contents.close(self._device) ) self._open = False # TODO: TEMP/HACK def dispatch_events(self, dt=None): if not self._open: return event = IOHIDEventStruct() r = self._queue.contents.contents.getNextEvent(self._queue, ctypes.byref(event), 0, 0) if r != 0: # Undocumented behaviour? returns 3758097127L when no events are # in queue (is documented to block) return try: element = self._element_cookies[event.elementCookie] element.value = event.value except KeyError: pass class DeviceElement: def __init__(self, device, properties): self.device = device self._cookie = get_property(properties, 'ElementCookie') _usage = get_property(properties, 'Usage') usage_page = get_property(properties, 'UsagePage') self.name = usage.get_element_usage_name(usage_page, _usage) self.known = usage.get_element_usage_known(usage_page, _usage) self.value = None def get_value(self): return self.value """ def get_value(self): event = IOHIDEventStruct() self.device._device.contents.contents.getElementValue( self.device._device, self._cookie, ctypes.byref(event)) return event.value """ def get_devices(): return get_existing_devices(get_master_port(), get_matching_dictionary())

34.982301

84

0.544397

__docformat__ = 'restructuredtext' __version__ = '$Id: $' import ctypes import pyglet from pyglet.libs.darwin import carbon, _oscheck, create_cfstring from pyglet.libs.darwin.constants import * import input import usage void_p = ctypes.POINTER(ctypes.c_int) class CFUUIDBytes(ctypes.Structure): _fields_ = [('byte%d' % i, ctypes.c_uint8) for i in range(16)] mach_port_t = void_p io_iterator_t = void_p kern_return_t = ctypes.c_int IOReturn = ctypes.c_uint CFDictionaryRef = void_p CFMutableDictionaryRef = void_p CFArrayRef = void_p CFUUIDRef = ctypes.POINTER(CFUUIDBytes) AbsoluteTime = ctypes.c_double HRESULT = ctypes.c_int REFIID = CFUUIDBytes IOHIDElementType = ctypes.c_int kIOHIDElementTypeInput_Misc = 1 kIOHIDElementTypeInput_Button = 2 kIOHIDElementTypeInput_Axis = 3 kIOHIDElementTypeInput_ScanCodes = 4 kIOHIDElementTypeOutput = 129 kIOHIDElementTypeFeature = 257 kIOHIDElementTypeCollection = 513 IOHIDElementCookie = ctypes.c_void_p MACH_PORT_NULL = 0 kIOHIDDeviceKey = "IOHIDDevice" kIOServicePlane = "IOService" kIOHIDProductIDKey = "ProductID" kCFNumberIntType = 9 kIOHIDOptionsTypeSeizeDevice = 1 kIOReturnExclusiveAccess = 0xe00002c5 carbon.CFUUIDGetConstantUUIDWithBytes.restype = CFUUIDRef kIOHIDDeviceUserClientTypeID = carbon.CFUUIDGetConstantUUIDWithBytes(None, 0xFA, 0x12, 0xFA, 0x38, 0x6F, 0x1A, 0x11, 0xD4, 0xBA, 0x0C, 0x00, 0x05, 0x02, 0x8F, 0x18, 0xD5) kIOCFPlugInInterfaceID = carbon.CFUUIDGetConstantUUIDWithBytes(None, 0xC2, 0x44, 0xE8, 0x58, 0x10, 0x9C, 0x11, 0xD4, 0x91, 0xD4, 0x00, 0x50, 0xE4, 0xC6, 0x42, 0x6F) kIOHIDDeviceInterfaceID = carbon.CFUUIDGetConstantUUIDWithBytes(None, 0x78, 0xBD, 0x42, 0x0C, 0x6F, 0x14, 0x11, 0xD4, 0x94, 0x74, 0x00, 0x05, 0x02, 0x8F, 0x18, 0xD5) class IOHIDEventStruct(ctypes.Structure): _fields_ = ( ('type', IOHIDElementType), ('elementCookie', IOHIDElementCookie), ('value', ctypes.c_int32), ('timestamp', AbsoluteTime), ('longValueSize', ctypes.c_uint32), ('longValue', ctypes.c_void_p) ) Self = ctypes.c_void_p class IUnknown(ctypes.Structure): _fields_ = ( ('_reserved', ctypes.c_void_p), ('QueryInterface', ctypes.CFUNCTYPE(HRESULT, Self, REFIID, ctypes.c_void_p)), ('AddRef', ctypes.CFUNCTYPE(ctypes.c_ulong, Self)), ('Release', ctypes.CFUNCTYPE(ctypes.c_ulong, Self)), ) # bothered. class IOHIDQueueInterface(ctypes.Structure): _fields_ = IUnknown._fields_ + ( ('createAsyncEventSource', ctypes.c_void_p), ('getAsyncEventSource', ctypes.c_void_p), ('createAsyncPort', ctypes.c_void_p), ('getAsyncPort', ctypes.c_void_p), ('create', ctypes.CFUNCTYPE(IOReturn, Self, ctypes.c_uint32, ctypes.c_uint32)), ('dispose', ctypes.CFUNCTYPE(IOReturn, Self)), ('addElement', ctypes.CFUNCTYPE(IOReturn, Self, IOHIDElementCookie)), ('removeElement', ctypes.c_void_p), ('hasElement', ctypes.c_void_p), ('start', ctypes.CFUNCTYPE(IOReturn, Self)), ('stop', ctypes.CFUNCTYPE(IOReturn, Self)), ('getNextEvent', ctypes.CFUNCTYPE(IOReturn, Self, ctypes.POINTER(IOHIDEventStruct), AbsoluteTime, ctypes.c_uint32)), ('setEventCallout', ctypes.c_void_p), ('getEventCallout', ctypes.c_void_p), ) class IOHIDDeviceInterface(ctypes.Structure): _fields_ = IUnknown._fields_ + ( ('createAsyncEventSource', ctypes.c_void_p), ('getAsyncEventSource', ctypes.c_void_p), ('createAsyncPort', ctypes.c_void_p), ('getAsyncPort', ctypes.c_void_p), ('open', ctypes.CFUNCTYPE(IOReturn, Self, ctypes.c_uint32)), ('close', ctypes.CFUNCTYPE(IOReturn, Self)), ('setRemovalCallback', ctypes.c_void_p), ('getElementValue', ctypes.CFUNCTYPE(IOReturn, Self, IOHIDElementCookie, ctypes.POINTER(IOHIDEventStruct))), ('setElementValue', ctypes.c_void_p), ('queryElementValue', ctypes.c_void_p), ('startAllQueues', ctypes.c_void_p), ('stopAllQueues', ctypes.c_void_p), ('allocQueue', ctypes.CFUNCTYPE( ctypes.POINTER(ctypes.POINTER(IOHIDQueueInterface)), Self)), ('allocOutputTransaction', ctypes.c_void_p), # 1.2.1 (10.2.3) ('setReport', ctypes.c_void_p), ('getReport', ctypes.c_void_p), # 1.2.2 (10.3) ('copyMatchingElements', ctypes.CFUNCTYPE(IOReturn, Self, CFDictionaryRef, ctypes.POINTER(CFArrayRef))), ('setInterruptReportHandlerCallback', ctypes.c_void_p), ) def get_master_port(): master_port = mach_port_t() _oscheck( carbon.IOMasterPort(MACH_PORT_NULL, ctypes.byref(master_port)) ) return master_port def get_matching_dictionary(): carbon.IOServiceMatching.restype = CFMutableDictionaryRef matching_dictionary = carbon.IOServiceMatching(kIOHIDDeviceKey) return matching_dictionary def get_existing_devices(master_port, matching_dictionary): # Consumes reference to matching_dictionary iterator = io_iterator_t() _oscheck( carbon.IOServiceGetMatchingServices(master_port, matching_dictionary, ctypes.byref(iterator)) ) devices = list() while carbon.IOIteratorIsValid(iterator): device = carbon.IOIteratorNext(iterator) if not device: break devices.append(Device(device)) carbon.IOObjectRelease(iterator) return devices def cfstring_to_string(value_string): value_length = carbon.CFStringGetLength(value_string) buffer_length = carbon.CFStringGetMaximumSizeForEncoding( value_length, kCFStringEncodingUTF8) buffer = ctypes.c_buffer(buffer_length + 1) result = carbon.CFStringGetCString(value_string, buffer, len(buffer), kCFStringEncodingUTF8) if not result: return return buffer.value def cfnumber_to_int(value): result = ctypes.c_int() carbon.CFNumberGetValue(value, kCFNumberIntType, ctypes.byref(result)) return result.value def cfvalue_to_value(value): if not value: return None value_type = carbon.CFGetTypeID(value) if value_type == carbon.CFStringGetTypeID(): return cfstring_to_string(value) elif value_type == carbon.CFNumberGetTypeID(): return cfnumber_to_int(value) else: return None def get_property_value(properties, key): key_string = create_cfstring(key) value = ctypes.c_void_p() present = carbon.CFDictionaryGetValueIfPresent(properties, key_string, ctypes.byref(value)) carbon.CFRelease(key_string) if not present: return None return value def get_property(properties, key): return cfvalue_to_value(get_property_value(properties, key)) def dump_properties(properties): def func(key, value, context): print('%s = %s' % (cfstring_to_string(key), cfvalue_to_value(value))) CFDictionaryApplierFunction = ctypes.CFUNCTYPE(None, ctypes.c_void_p, ctypes.c_void_p, ctypes.c_void_p) carbon.CFDictionaryApplyFunction(properties, CFDictionaryApplierFunction(func), None) class Device: def __init__(self, generic_device): self._init_properties(generic_device) self._device = self._get_device_interface(generic_device) self.elements = self._get_elements() self._open = False self._queue = None self._queue_depth = 8 # Number of events queue can buffer def _init_properties(self, generic_device): properties = CFMutableDictionaryRef() _oscheck( carbon.IORegistryEntryCreateCFProperties(generic_device, ctypes.byref(properties), None, 0) ) self.name = get_property(properties, "Product") self.manufacturer = get_property(properties, "Manufacturer") carbon.CFRelease(properties) def _get_device_interface(self, generic_device): plug_in_interface = \ ctypes.POINTER(ctypes.POINTER(IUnknown))() score = ctypes.c_int32() _oscheck( carbon.IOCreatePlugInInterfaceForService( generic_device, kIOHIDDeviceUserClientTypeID, kIOCFPlugInInterfaceID, ctypes.byref(plug_in_interface), ctypes.byref(score)) ) carbon.CFUUIDGetUUIDBytes.restype = CFUUIDBytes hid_device_interface = \ ctypes.POINTER(ctypes.POINTER(IOHIDDeviceInterface))() _oscheck( plug_in_interface.contents.contents.QueryInterface( plug_in_interface, carbon.CFUUIDGetUUIDBytes(kIOHIDDeviceInterfaceID), ctypes.byref(hid_device_interface)) ) plug_in_interface.contents.contents.Release(plug_in_interface) return hid_device_interface def _get_elements(self): elements_array = CFArrayRef() _oscheck( self._device.contents.contents.copyMatchingElements(self._device, None, ctypes.byref( elements_array)) ) self._element_cookies = dict() elements = list() n_elements = carbon.CFArrayGetCount(elements_array) for i in range(n_elements): properties = carbon.CFArrayGetValueAtIndex(elements_array, i) element = DeviceElement(self, properties) elements.append(element) self._element_cookies[element._cookie] = element carbon.CFRelease(elements_array) return elements def __repr__(self): return '%s(name=%r, manufacturer=%r)' % ( self.__class__.__name__, self.product, self.manufacturer) def open(self, exclusive=False): flags = 0 if exclusive: flags |= kIOHIDOptionsTypeSeizeDevice result = self._device.contents.contents.open(self._device, flags) if result == 0: self._open = True elif result == kIOReturnExclusiveAccess: raise input.InputDeviceExclusiveException() # Create event queue self._queue = self._device.contents.contents.allocQueue(self._device) _oscheck( self._queue.contents.contents.create(self._queue, 0, self._queue_depth) ) # Add all elements into queue # TODO: only "interesting/known" elements? for element in self.elements: r = self._queue.contents.contents.addElement(self._queue, element._cookie, 0) if r != 0: print('error adding %r' % element) _oscheck( self._queue.contents.contents.start(self._queue) ) # HACK TODO: pyglet.clock.schedule(self.dispatch_events) def close(self): if not self._open: return # HACK TODO: pyglet.clock.unschedule(self.dispatch_events) _oscheck( self._queue.contents.contents.stop(self._queue) ) _oscheck( self._queue.contents.contents.dispose(self._queue) ) self._queue.contents.contents.Release(self._queue) self._queue = None _oscheck( self._device.contents.contents.close(self._device) ) self._open = False # TODO: TEMP/HACK def dispatch_events(self, dt=None): if not self._open: return event = IOHIDEventStruct() r = self._queue.contents.contents.getNextEvent(self._queue, ctypes.byref(event), 0, 0) if r != 0: # Undocumented behaviour? returns 3758097127L when no events are # in queue (is documented to block) return try: element = self._element_cookies[event.elementCookie] element.value = event.value except KeyError: pass class DeviceElement: def __init__(self, device, properties): self.device = device self._cookie = get_property(properties, 'ElementCookie') _usage = get_property(properties, 'Usage') usage_page = get_property(properties, 'UsagePage') self.name = usage.get_element_usage_name(usage_page, _usage) self.known = usage.get_element_usage_known(usage_page, _usage) self.value = None def get_value(self): return self.value def get_devices(): return get_existing_devices(get_master_port(), get_matching_dictionary())

true

f71fe39b002b6987bd56fdc8f822aa4a1ab3f554

548

py

Python

config_parser.py

benkelaci/qrcode_medicinedispenser

41cee011dc0e9ab5d1ef0738efd5e1ea11c13d0a

[ "MIT" ]

null

config_parser.py

benkelaci/qrcode_medicinedispenser

41cee011dc0e9ab5d1ef0738efd5e1ea11c13d0a

[ "MIT" ]

null

config_parser.py

benkelaci/qrcode_medicinedispenser

41cee011dc0e9ab5d1ef0738efd5e1ea11c13d0a

[ "MIT" ]

null

import json class Struct(object): def __init__(self, data): for name, value in data.items(): setattr(self, name, self._wrap(value)) def _wrap(self, value): if isinstance(value, (tuple, list, set, frozenset)): return type(value)([self._wrap(v) for v in value]) else: return Struct(value) if isinstance(value, dict) else value config_path = "/home/pi/qrcode_detect/cfg.json" with open(config_path) as config: cfg = json.load(config, object_hook=Struct)

27.4

71

0.616788

import json class Struct(object): def __init__(self, data): for name, value in data.items(): setattr(self, name, self._wrap(value)) def _wrap(self, value): if isinstance(value, (tuple, list, set, frozenset)): return type(value)([self._wrap(v) for v in value]) else: return Struct(value) if isinstance(value, dict) else value config_path = "/home/pi/qrcode_detect/cfg.json" with open(config_path) as config: cfg = json.load(config, object_hook=Struct)

true

f71fe39bd8323e42c4bdd0221f1966de94ab7729

904

py

Python

examples/sawyer/moveit_planning.py

gujralsanyam22/pyrobot

a0448714857b684d8b280f710e9304988524d2e0

[ "MIT" ]

2,150

2019-06-12T20:55:41.000Z

2022-03-21T07:14:51.000Z

examples/sawyer/moveit_planning.py

gujralsanyam22/pyrobot

a0448714857b684d8b280f710e9304988524d2e0

[ "MIT" ]

124

2019-06-22T17:12:27.000Z

2022-02-26T11:43:13.000Z

examples/sawyer/moveit_planning.py

gujralsanyam22/pyrobot

a0448714857b684d8b280f710e9304988524d2e0

[ "MIT" ]

329

2019-06-13T03:03:54.000Z

2022-03-30T07:04:55.000Z

# 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. """ Example for commanding robot with position control using moveit planner """ import time from pyrobot import Robot def main(): target_joints = [ [0.704, -0.455, -0.159, 1.395, -1.240, 1.069, 2.477], [-0.341, -0.384, -0.018, 1.533, -0.977, -1.492, -1.084], ] config = dict(moveit_planner_type="ESTkConfigDefault") bot = Robot( "sawyer", use_arm=True, use_base=False, use_camera=False, use_gripper=True, arm_config=config, ) bot.arm.go_home() time.sleep(1) for joint in target_joints: bot.arm.set_joint_positions(joint, plan=True) time.sleep(1) bot.arm.go_home() if __name__ == "__main__": main()

21.52381

71

0.622788

import time from pyrobot import Robot def main(): target_joints = [ [0.704, -0.455, -0.159, 1.395, -1.240, 1.069, 2.477], [-0.341, -0.384, -0.018, 1.533, -0.977, -1.492, -1.084], ] config = dict(moveit_planner_type="ESTkConfigDefault") bot = Robot( "sawyer", use_arm=True, use_base=False, use_camera=False, use_gripper=True, arm_config=config, ) bot.arm.go_home() time.sleep(1) for joint in target_joints: bot.arm.set_joint_positions(joint, plan=True) time.sleep(1) bot.arm.go_home() if __name__ == "__main__": main()

true

f71fe3a7ec4b148032db722f5c53b2c067b9a249

1,863

py

Python

random_colors.py

electric-blue-green/trinket

82e1e265934252c0cf3b2fa72f9bc1d60a35ac93

[ "Unlicense" ]

1

2021-06-05T03:12:36.000Z

random_colors.py

aejb/trinket

82e1e265934252c0cf3b2fa72f9bc1d60a35ac93

[ "Unlicense" ]

1

2018-02-26T11:22:50.000Z

random_colors.py

electric-blue-green/trinket

82e1e265934252c0cf3b2fa72f9bc1d60a35ac93

[ "Unlicense" ]

null

import board import busio import time import random dotstar = busio.SPI(board.APA102_SCK, board.APA102_MOSI) #colors = [1, 128, 244] # set colors all to 1 colors = [random.randint(3, 240), random.randint(3, 240), random.randint(3, 240), ] # selects random start color in "safe zone" steps = [1, 3, 4] # set wavelength steps = [random.randint(1, 5), random.randint(1, 5), random.randint(1, 5)] # selects random step beteween 1 and 5 print("INIT") ## REPL def getColor(index, colors, steps): if colors[index] >= 255 or colors[index] <= 0: # flip the sign of the step at the max/min steps[index] *= -1 colors[index] += steps[index] # increment the value if colors[index] > 255: colors[index] = 255 # accounting for stepping over 255 if colors[index] < 0: colors[index] = 0 # accounting for stepping under 0 return (colors[index], colors, steps) # returns colors for index def setPixel(red, green, blue): # call setpixel if not dotstar.try_lock(): # see if clock is locked return #print("setting pixel to: %d %d %d" % (red, green, blue)) # debug dotstar.write(bytearray([0x00, 0x00, 0x00, 0x00, 0xff, blue, green, red, 0xff, 0xff, 0xff, 0xff])) dotstar.unlock() # pass new color while True: r, colors, steps = getColor(0, colors, steps) # gets red g, colors, steps = getColor(1, colors, steps) # gets green b, colors, steps = getColor(2, colors, steps) # gets blue print("STEP = ", steps, "COLOR = ", colors) # REPL debug print setPixel(r, g, b) # calls setPixel time.sleep(random.random()) # random wait time between 0 and 1

56.454545

131

0.574342

import board import busio import time import random dotstar = busio.SPI(board.APA102_SCK, board.APA102_MOSI) dint(3, 240), random.randint(3, 240), random.randint(3, 240), ] steps = [1, 3, 4] steps = [random.randint(1, 5), random.randint(1, 5), random.randint(1, 5)] print("INIT") etColor(index, colors, steps): if colors[index] >= 255 or colors[index] <= 0: steps[index] *= -1 colors[index] += steps[index] if colors[index] > 255: colors[index] = 255 if colors[index] < 0: colors[index] = 0 return (colors[index], colors, steps) def setPixel(red, green, blue): if not dotstar.try_lock(): return tstar.write(bytearray([0x00, 0x00, 0x00, 0x00, 0xff, blue, green, red, 0xff, 0xff, 0xff, 0xff])) dotstar.unlock() while True: r, colors, steps = getColor(0, colors, steps) g, colors, steps = getColor(1, colors, steps) b, colors, steps = getColor(2, colors, steps) print("STEP = ", steps, "COLOR = ", colors) setPixel(r, g, b) time.sleep(random.random())

true

f71fe485cdc1d845da328b9bf8355e5b4665fa3d

393

py

Python

project-euler/py/e6.py

aaycee/aaycee.github.io

b609a869c5c9d02f7cbc1798b643ec083475f741

[ "MIT" ]

null

project-euler/py/e6.py

aaycee/aaycee.github.io

b609a869c5c9d02f7cbc1798b643ec083475f741

[ "MIT" ]

null

project-euler/py/e6.py

aaycee/aaycee.github.io

b609a869c5c9d02f7cbc1798b643ec083475f741

[ "MIT" ]

null

# Akachukwu Obi, 2018 # Project Euler #6 # see .js file for build up def diffOfSumOfSquares(max): sumOfNumbers = max * (max + 1) / 2 # sum of n natural numbers is n(n + 1)/2 sumOfSquares = (max / 6.0) * (2 * max + 1) * (max + 1) # I used 6.0 to avoid getting a math.floor situation in puthon2.7 return sumOfNumbers * sumOfNumbers - sumOfSquares print(diffOfSumOfSquares(100)) #25164150.0

35.727273

121

0.689567

def diffOfSumOfSquares(max): sumOfNumbers = max * (max + 1) / 2 sumOfSquares = (max / 6.0) * (2 * max + 1) * (max + 1) return sumOfNumbers * sumOfNumbers - sumOfSquares print(diffOfSumOfSquares(100))

true

f71fe4adf7dd0cb5bea1c0b3a038207a017240b9

250

py

Python

experiments_approximate/experiments/create_dico_alphacsc.py

bmalezieux/unrolled_dl

5854a6991e44db025a99a9f0d38be6b1e669aa83

[ "MIT" ]

null

experiments_approximate/experiments/create_dico_alphacsc.py

bmalezieux/unrolled_dl

5854a6991e44db025a99a9f0d38be6b1e669aa83

[ "MIT" ]

null

experiments_approximate/experiments/create_dico_alphacsc.py

bmalezieux/unrolled_dl

5854a6991e44db025a99a9f0d38be6b1e669aa83

[ "MIT" ]

null

import numpy as np atoms_to_save = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 12, 15, 18]) u_cdl = np.load("u_cdl.npy") v_cdl = np.load("v_cdl.npy") np.save("u_cdl_modified.npy", u_cdl[atoms_to_save]) np.save("v_cdl_modified.npy", v_cdl[atoms_to_save])

25

65

0.68

import numpy as np atoms_to_save = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 12, 15, 18]) u_cdl = np.load("u_cdl.npy") v_cdl = np.load("v_cdl.npy") np.save("u_cdl_modified.npy", u_cdl[atoms_to_save]) np.save("v_cdl_modified.npy", v_cdl[atoms_to_save])

true

f71fead294ee942753789629c9a72d7384c394a0

2,087

py

Python

main.py

KevHg/reddit-sentiment

383407105957b8a582a524fa29b9f21d7b2cbd23

[ "MIT" ]

3

2020-12-22T09:03:15.000Z

2021-05-13T18:17:44.000Z

main.py

KevHg/reddit-sentiment

383407105957b8a582a524fa29b9f21d7b2cbd23

[ "MIT" ]

3

2020-11-11T15:33:13.000Z

2021-12-13T20:18:41.000Z

main.py

KevHg/reddit-sentiment

383407105957b8a582a524fa29b9f21d7b2cbd23

[ "MIT" ]

1

2021-02-18T19:56:09.000Z

import os from scrapy.crawler import CrawlerProcess import pandas as pd import logging import nltk import json_reader from sentiment_score import clean_text, calculate_sentiment_score from reddit_scraper.reddit_scraper.spiders.reddit_post_scraper import RedditPostCrawler if __name__ == '__main__': # Initial setup: Disable scrapy logs and download NLTK files logging.getLogger('scrapy').propagate = False nltk.download('averaged_perceptron_tagger', quiet=True) nltk.download('wordnet', quiet=True) # Ask for user query subreddit = input('Subreddit: ') term = input('Search term: ') term = term.replace(' ', '+') # Start crawler process print('[LOG] Crawling Reddit, this will take a little time...') process = CrawlerProcess(settings={ 'FEED_FORMAT': 'jl', 'FEED_URI': 'data.jl' }) process.crawl(RedditPostCrawler, domain=f'https://old.reddit.com/r/{subreddit}/search?q={term}&restrict_sr=on&sort=relevance&t=all') process.start() # Convert data file to class print('[LOG] Creating DataFrame table...') reddit_posts = json_reader.convert_json('data.jl') all_comments = [] all_upvotes = [] for post in reddit_posts: for comment in post.comments: all_comments.append(clean_text(comment.text)) # Convert upvote text to float, e.g. '15.3k upvotes' -> 15300 upvote = comment.upvotes.split(' ')[0] if 'k' in upvote: upvote = upvote[:-1] upvote = float(upvote) * 1000 all_upvotes.append(float(upvote)) df = pd.DataFrame({'comment': all_comments, 'upvotes': all_upvotes}) df = df[df.upvotes >= 1] print('[LOG] Calculating sentiment score, this may take a longer time...') df = calculate_sentiment_score(df) # df.to_csv('results.csv') normalized_result = df.sentiment.mean() print('[LOG] Completed!\n') print('Average sentiment:', normalized_result) print('where +1 is most positive and -1 is most negative') os.remove('data.jl')

33.66129

117

0.661236

import os from scrapy.crawler import CrawlerProcess import pandas as pd import logging import nltk import json_reader from sentiment_score import clean_text, calculate_sentiment_score from reddit_scraper.reddit_scraper.spiders.reddit_post_scraper import RedditPostCrawler if __name__ == '__main__': logging.getLogger('scrapy').propagate = False nltk.download('averaged_perceptron_tagger', quiet=True) nltk.download('wordnet', quiet=True) subreddit = input('Subreddit: ') term = input('Search term: ') term = term.replace(' ', '+') print('[LOG] Crawling Reddit, this will take a little time...') process = CrawlerProcess(settings={ 'FEED_FORMAT': 'jl', 'FEED_URI': 'data.jl' }) process.crawl(RedditPostCrawler, domain=f'https://old.reddit.com/r/{subreddit}/search?q={term}&restrict_sr=on&sort=relevance&t=all') process.start() print('[LOG] Creating DataFrame table...') reddit_posts = json_reader.convert_json('data.jl') all_comments = [] all_upvotes = [] for post in reddit_posts: for comment in post.comments: all_comments.append(clean_text(comment.text)) upvote = comment.upvotes.split(' ')[0] if 'k' in upvote: upvote = upvote[:-1] upvote = float(upvote) * 1000 all_upvotes.append(float(upvote)) df = pd.DataFrame({'comment': all_comments, 'upvotes': all_upvotes}) df = df[df.upvotes >= 1] print('[LOG] Calculating sentiment score, this may take a longer time...') df = calculate_sentiment_score(df) normalized_result = df.sentiment.mean() print('[LOG] Completed!\n') print('Average sentiment:', normalized_result) print('where +1 is most positive and -1 is most negative') os.remove('data.jl')

true

f71feb945e7c962481692be8e2384e367a3c4bbd

3,443

py

Python

pollbot/models/reference.py

shubham-king/poll

677e870bea36dffbf27f24e4cdeec892b40f7128

[ "MIT" ]

112

2019-06-11T17:52:57.000Z

2022-03-18T00:05:21.000Z

pollbot/models/reference.py

shubham-king/poll

677e870bea36dffbf27f24e4cdeec892b40f7128

[ "MIT" ]

91

2019-05-28T11:33:40.000Z

2022-02-27T12:12:07.000Z

pollbot/models/reference.py

shubham-king/poll

677e870bea36dffbf27f24e4cdeec892b40f7128

[ "MIT" ]

69

2019-07-10T16:58:06.000Z

2022-03-30T22:09:44.000Z

"""The sqlalchemy model for a polloption.""" from __future__ import annotations from sqlalchemy import Column, ForeignKey, Index, func from sqlalchemy.orm import relationship from sqlalchemy.types import BigInteger, DateTime, Integer, String from pollbot.db import base from pollbot.enums import ReferenceType class Reference(base): """The model for a Reference.""" __tablename__ = "reference" __mapper_args__ = {"confirm_deleted_rows": False} id = Column(Integer, primary_key=True) type = Column(String) bot_inline_message_id = Column(String) message_id = Column(BigInteger) # Keep those for now, in case we migrate to mtproto message_dc_id = Column(BigInteger) message_access_hash = Column(BigInteger) user_id = Column( BigInteger, ForeignKey("user.id", ondelete="cascade", name="user_fk"), nullable=True, index=True, ) user = relationship("User", foreign_keys="Reference.user_id") created_at = Column(DateTime, server_default=func.now(), nullable=False) updated_at = Column( DateTime, server_default=func.now(), onupdate=func.now(), nullable=False ) # ManyToOne poll_id = Column( Integer, ForeignKey("poll.id", ondelete="cascade", name="reference_poll"), nullable=False, index=True, ) poll = relationship("Poll", back_populates="references") def __init__( self, poll, reference_type, user=None, message_id=None, inline_message_id=None, ): """Create a new poll.""" self.poll = poll self.type = reference_type # There are three types of references # 1. Messages in private chat: # - Admin interface # - Private vote if ( user is not None and message_id is not None and reference_type in [ReferenceType.admin.name, ReferenceType.private_vote.name] ): self.user = user self.message_id = message_id # 2. Messages shared via inline query elif ( inline_message_id is not None and reference_type == ReferenceType.inline.name ): self.bot_inline_message_id = inline_message_id else: raise Exception( "Tried to create Reference with wrong type or missing parameters" ) def __repr__(self): """Print as string.""" if self.type == ReferenceType.inline.name: message = f"Reference {self.id}: message_id {self.message_id}" elif self.type == ReferenceType.admin.name: message = f"Reference {self.id}: message_id {self.message_id}, admin: {self.user.id}" else: message = f"Reference {self.id}: message_id {self.message_id}, user: {self.user.id}" return message Index( "ix_unique_admin_reference", Reference.poll_id, Reference.user_id, Reference.message_id, unique=True, postgresql_where=Reference.type == "admin", ) Index( "ix_unique_private_vote_reference", Reference.poll_id, Reference.user_id, Reference.message_id, unique=True, postgresql_where=Reference.type == "private_vote", ) Index( "ix_unique_inline_share", Reference.poll_id, Reference.bot_inline_message_id, unique=True, postgresql_where=Reference.type == "inline", )

27.99187

97

0.637816

from __future__ import annotations from sqlalchemy import Column, ForeignKey, Index, func from sqlalchemy.orm import relationship from sqlalchemy.types import BigInteger, DateTime, Integer, String from pollbot.db import base from pollbot.enums import ReferenceType class Reference(base): __tablename__ = "reference" __mapper_args__ = {"confirm_deleted_rows": False} id = Column(Integer, primary_key=True) type = Column(String) bot_inline_message_id = Column(String) message_id = Column(BigInteger) message_dc_id = Column(BigInteger) message_access_hash = Column(BigInteger) user_id = Column( BigInteger, ForeignKey("user.id", ondelete="cascade", name="user_fk"), nullable=True, index=True, ) user = relationship("User", foreign_keys="Reference.user_id") created_at = Column(DateTime, server_default=func.now(), nullable=False) updated_at = Column( DateTime, server_default=func.now(), onupdate=func.now(), nullable=False ) poll_id = Column( Integer, ForeignKey("poll.id", ondelete="cascade", name="reference_poll"), nullable=False, index=True, ) poll = relationship("Poll", back_populates="references") def __init__( self, poll, reference_type, user=None, message_id=None, inline_message_id=None, ): self.poll = poll self.type = reference_type if ( user is not None and message_id is not None and reference_type in [ReferenceType.admin.name, ReferenceType.private_vote.name] ): self.user = user self.message_id = message_id elif ( inline_message_id is not None and reference_type == ReferenceType.inline.name ): self.bot_inline_message_id = inline_message_id else: raise Exception( "Tried to create Reference with wrong type or missing parameters" ) def __repr__(self): if self.type == ReferenceType.inline.name: message = f"Reference {self.id}: message_id {self.message_id}" elif self.type == ReferenceType.admin.name: message = f"Reference {self.id}: message_id {self.message_id}, admin: {self.user.id}" else: message = f"Reference {self.id}: message_id {self.message_id}, user: {self.user.id}" return message Index( "ix_unique_admin_reference", Reference.poll_id, Reference.user_id, Reference.message_id, unique=True, postgresql_where=Reference.type == "admin", ) Index( "ix_unique_private_vote_reference", Reference.poll_id, Reference.user_id, Reference.message_id, unique=True, postgresql_where=Reference.type == "private_vote", ) Index( "ix_unique_inline_share", Reference.poll_id, Reference.bot_inline_message_id, unique=True, postgresql_where=Reference.type == "inline", )

true

f71fec1a43d05877719e3969203eaab05fae4883

10,145

py

Python

metadl/core/scoring/scoring.py

mikehuisman/metadl

61ece0364b08e67412ab87da4a41425b2e88a562

[ "Apache-2.0" ]

26

2020-09-23T13:04:52.000Z

2022-03-03T03:07:49.000Z

metadl/core/scoring/scoring.py

mikehuisman/metadl

61ece0364b08e67412ab87da4a41425b2e88a562

[ "Apache-2.0" ]

5

2020-11-04T13:26:09.000Z

2021-09-17T07:42:01.000Z

metadl/core/scoring/scoring.py

mikehuisman/metadl

61ece0364b08e67412ab87da4a41425b2e88a562

[ "Apache-2.0" ]

12

2020-11-03T12:01:35.000Z

2021-12-19T03:58:50.000Z

""" Runs the scoring procedure for the challenge. It assumes that there exists a ./model_dir folder containing both the submission code and the saved learner. It will create a folder named ./scoring_output (default) in which a txt file will contain the average score over 600 episodes. You can change the folder name via the score_dir flag. Usage example executed from the metadl/ repository : python -m metadl.core.scoring.scoring --meta_test_dir=<path_dataset.meta_test> """ import os from sys import path import scipy.stats import gin import numpy as np from absl import app from absl import flags from absl import logging import tensorflow as tf from metadl.data.dataset import DataGenerator from metadl.core.ingestion.ingestion import get_gin_path, show_dir FLAGS = flags.FLAGS flags.DEFINE_string('meta_test_dir', '/Users/adrian/GitInria/meta-dataset/records/', ('Directory of the meta-test dataset. This directory ' + 'should contain records and a json spec file.')) flags.DEFINE_string('saved_model_dir', './model_dir', ('Directory path that contains the participant\'s code ' + 'along with the serialized learner from meta-fit.')) flags.DEFINE_string('score_dir', './scoring_output', 'Path to the score directory.') flags.DEFINE_string('evaltype', 'test', 'Data type on which to perform evaluation. [train, val, test]') tf.random.set_seed(1234) def NwayKshot_accuracy(predictions, ground_truth, metric): """ N-way, K-shot accuracy which corresponds to the accuracy in a multi-classification context with N classes. Args: predictions : tensors, sparse tensors corresponding to the predicted labels. ground_truth : tensors, sparse tensors corresponding the ground truth labels. metric : keras.metrics , the metric we use to evaluate the classification performance of the meta-learning algorithm. We use the SparseCategoricalAccuracy in this challenge. Retruns: score : Float, the resulting performance using the given metric. """ ground_truth = tf.expand_dims(ground_truth, axis = 1) predictions = tf.expand_dims(predictions, axis = 1) logging.debug('Predictions shape : {} - Ground truth shape : {}'.format( predictions.shape, ground_truth.shape)) metric.update_state(ground_truth, predictions) score = metric.result() logging.debug('An episode score: {}'.format(score)) metric.reset_states() return score def is_one_hot_vector(x, axis=None, keepdims=False): """Check if a vector 'x' is one-hot (i.e. one entry is 1 and others 0).""" norm_1 = np.linalg.norm(x, ord=1, axis=axis, keepdims=keepdims) norm_inf = np.linalg.norm(x, ord=np.inf, axis=axis, keepdims=keepdims) return np.logical_and(norm_1 == 1, norm_inf == 1) def write_score(score, conf_int, file_score, duration=-1): """Write score of the k-th task in the given file_score.""" file_score.write('set1_score: {:.6f}\n'.format(float(score))) file_score.write('conf_int: {:.3f}\n'.format(float(conf_int))) file_score.write('Duration: {:.6f}\n'.format(float(duration))) def extract_elapsed_time(saved_model_dir): """ Extracts elapsed time from the metadata file. It corresponds to the meta-training time, the duration of the ingestion process. """ if not os.path.isdir(saved_model_dir): raise ValueError('Saved model directory does not exists.') if os.path.isfile(os.path.join(saved_model_dir, 'metadata')): with open(os.path.join(saved_model_dir, 'metadata'), 'r') as f : lines = f.readlines() for line in lines : splitted_line = line.split(' ') for k, word in enumerate(splitted_line): if 'elapsed' in splitted_line[k]: elapsed_time = float(splitted_line[k+1]) return elapsed_time return -1 def process_task(task): """We are using the meta-dataset code to generate episodes from a dataset. Generated episodes have a specific format. Each is processed such that the the support and query sets are ready to be used by the participants. Each set is returned as a tf.data.Dataset object. The que_labs are kept hidden. Returns : support_dataset : tf.data.Dataset containing the support examples and labels. query_dataset : tf.data.Dataset containing the query examples que_labs : tuple (query_batch_size, 1), the query examples labels i.e. the ground truth labels. """ sup_set = tf.data.Dataset.from_tensor_slices(\ (task[0][1], task[0][0])) dim = task[0][4].shape[1] arr = np.arange(dim) np.random.shuffle(arr) # shuffling arr query_labs = task[0][4] query_imgs = task[0][3] query_labs_s = tf.gather(query_labs, arr, axis=1) query_imgs_s = tf.gather(query_imgs, arr, axis=1) que_set = tf.data.Dataset.from_tensor_slices( (query_labs_s, query_imgs_s) ) new_ds = tf.data.Dataset.zip((sup_set, que_set)) for ((supp_labs, supp_img), (que_labs, que_img)) \ in new_ds : logging.debug('Supp labs : {}'.format(supp_labs)) logging.debug('Query labs : {}'.format(que_labs)) support_set = tf.data.Dataset.from_tensor_slices(\ (supp_img, supp_labs)) query_set = tf.data.Dataset.from_tensor_slices(\ (que_img,)) support_set = support_set.batch(5) query_set = query_set.batch(95) return support_set, query_set, que_labs def scoring(argv): """ For each task, load and fit the Learner with the support set and evaluate the submission performance with the query set. A directory 'scoring_output' is created and contains a txt file that contains the submission score and duration. Note that the former is the time elapsed during the ingestion program and hence the meta-fit() duration. The metric considered here is the Sparse Categorical Accuracy for a 5 classes image classification problem. """ del argv saved_model_dir = FLAGS.saved_model_dir meta_test_dir = FLAGS.meta_test_dir eval_type = FLAGS.evaltype # Making eval type compatible with DataGenerator specs if eval_type == 'train' or eval_type == 'val': data_generator_eval_type = 'train' elif eval_type == 'test': data_generator_eval_type = 'test' # Use CodaLab's path `run/input/ref` in parallel with `run/input/res` if not os.path.isdir(meta_test_dir): meta_test_dir = os.path.join(saved_model_dir, os.pardir, 'ref') # Evaluation type scenario: if meta_test is specified -> act as normal # scoring on meta_test data if (eval_type == 'train' or eval_type == 'val') and 'meta_test' in meta_test_dir: raise ValueError('Cannot perform train/val evaluation on meta-test data!') #if 'meta_test' not in meta_test_dir: # if eval_type == 'test': # meta_test_dir = os.path.join(meta_test_dir, 'meta_test') # else: # meta_test_dir = os.path.join(meta_test_dir, 'meta_train') code_dir = os.path.join(saved_model_dir, 'code_dir') score_dir = FLAGS.score_dir path.append(code_dir) from model import MyLearner if(os.path.exists(os.path.join(code_dir, 'model.gin'))): gin.parse_config_file(os.path.join(code_dir, 'model.gin')) logging.info('Ingestion done! Starting scoring process ... ') logging.info('Creating the meta-test episode generator ... \n ') generator = DataGenerator(path_to_records=meta_test_dir, batch_config=None, episode_config=[28, 5, 1, 19], pool= data_generator_eval_type, mode='episode') if eval_type == 'test': meta_test_dataset = generator.meta_test_pipeline elif eval_type == 'train': meta_test_dataset = generator.meta_train_pipeline elif eval_type == 'val': meta_test_dataset = generator.meta_valid_pipeline else: raise ValueError('Wrong eval_type : {}'.format(eval_type)) logging.info('Evaluating performance on episodes ... ') meta_test_dataset = meta_test_dataset.batch(1) meta_test_dataset = meta_test_dataset.prefetch(5) learner = MyLearner() if (not os.path.isdir(score_dir)): os.mkdir(score_dir) score_file = os.path.join(score_dir, 'scores.txt') results = [] metric = tf.metrics.SparseCategoricalAccuracy() nbr_episodes = 600 for k , task in enumerate(meta_test_dataset) : support_set, query_set, ground_truth = process_task(task) learner.load(saved_model_dir) predictor = learner.fit(support_set) predictions = predictor.predict(query_set) score = NwayKshot_accuracy(predictions, ground_truth, metric) results.append(score) logging.debug('Score on {} : {}'.format(k, score)) logging.debug('Results : {}'.format(results[:20])) if(k > nbr_episodes): break def mean_confidence_interval(data, confidence=0.95): a = 1.0 * np.array(data) n = len(a) m, se = np.mean(a), scipy.stats.sem(a) h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1) return m, h m, conf_int = mean_confidence_interval(results) with open(score_file, 'w') as f : write_score(m, conf_int, f, extract_elapsed_time(saved_model_dir)) logging.info(('Scoring done! The average score over {} ' + 'episodes is : {:.3%}').format(nbr_episodes, sum(results)/len(results)) ) if __name__ == '__main__': np.random.seed(seed=1234) tf.get_logger().setLevel('ERROR') app.run(scoring)

38.869732

99

0.648398

import os from sys import path import scipy.stats import gin import numpy as np from absl import app from absl import flags from absl import logging import tensorflow as tf from metadl.data.dataset import DataGenerator from metadl.core.ingestion.ingestion import get_gin_path, show_dir FLAGS = flags.FLAGS flags.DEFINE_string('meta_test_dir', '/Users/adrian/GitInria/meta-dataset/records/', ('Directory of the meta-test dataset. This directory ' + 'should contain records and a json spec file.')) flags.DEFINE_string('saved_model_dir', './model_dir', ('Directory path that contains the participant\'s code ' + 'along with the serialized learner from meta-fit.')) flags.DEFINE_string('score_dir', './scoring_output', 'Path to the score directory.') flags.DEFINE_string('evaltype', 'test', 'Data type on which to perform evaluation. [train, val, test]') tf.random.set_seed(1234) def NwayKshot_accuracy(predictions, ground_truth, metric): ground_truth = tf.expand_dims(ground_truth, axis = 1) predictions = tf.expand_dims(predictions, axis = 1) logging.debug('Predictions shape : {} - Ground truth shape : {}'.format( predictions.shape, ground_truth.shape)) metric.update_state(ground_truth, predictions) score = metric.result() logging.debug('An episode score: {}'.format(score)) metric.reset_states() return score def is_one_hot_vector(x, axis=None, keepdims=False): norm_1 = np.linalg.norm(x, ord=1, axis=axis, keepdims=keepdims) norm_inf = np.linalg.norm(x, ord=np.inf, axis=axis, keepdims=keepdims) return np.logical_and(norm_1 == 1, norm_inf == 1) def write_score(score, conf_int, file_score, duration=-1): file_score.write('set1_score: {:.6f}\n'.format(float(score))) file_score.write('conf_int: {:.3f}\n'.format(float(conf_int))) file_score.write('Duration: {:.6f}\n'.format(float(duration))) def extract_elapsed_time(saved_model_dir): if not os.path.isdir(saved_model_dir): raise ValueError('Saved model directory does not exists.') if os.path.isfile(os.path.join(saved_model_dir, 'metadata')): with open(os.path.join(saved_model_dir, 'metadata'), 'r') as f : lines = f.readlines() for line in lines : splitted_line = line.split(' ') for k, word in enumerate(splitted_line): if 'elapsed' in splitted_line[k]: elapsed_time = float(splitted_line[k+1]) return elapsed_time return -1 def process_task(task): sup_set = tf.data.Dataset.from_tensor_slices(\ (task[0][1], task[0][0])) dim = task[0][4].shape[1] arr = np.arange(dim) np.random.shuffle(arr) # shuffling arr query_labs = task[0][4] query_imgs = task[0][3] query_labs_s = tf.gather(query_labs, arr, axis=1) query_imgs_s = tf.gather(query_imgs, arr, axis=1) que_set = tf.data.Dataset.from_tensor_slices( (query_labs_s, query_imgs_s) ) new_ds = tf.data.Dataset.zip((sup_set, que_set)) for ((supp_labs, supp_img), (que_labs, que_img)) \ in new_ds : logging.debug('Supp labs : {}'.format(supp_labs)) logging.debug('Query labs : {}'.format(que_labs)) support_set = tf.data.Dataset.from_tensor_slices(\ (supp_img, supp_labs)) query_set = tf.data.Dataset.from_tensor_slices(\ (que_img,)) support_set = support_set.batch(5) query_set = query_set.batch(95) return support_set, query_set, que_labs def scoring(argv): del argv saved_model_dir = FLAGS.saved_model_dir meta_test_dir = FLAGS.meta_test_dir eval_type = FLAGS.evaltype # Making eval type compatible with DataGenerator specs if eval_type == 'train' or eval_type == 'val': data_generator_eval_type = 'train' elif eval_type == 'test': data_generator_eval_type = 'test' # Use CodaLab's path `run/input/ref` in parallel with `run/input/res` if not os.path.isdir(meta_test_dir): meta_test_dir = os.path.join(saved_model_dir, os.pardir, 'ref') if (eval_type == 'train' or eval_type == 'val') and 'meta_test' in meta_test_dir: raise ValueError('Cannot perform train/val evaluation on meta-test data!') code_dir = os.path.join(saved_model_dir, 'code_dir') score_dir = FLAGS.score_dir path.append(code_dir) from model import MyLearner if(os.path.exists(os.path.join(code_dir, 'model.gin'))): gin.parse_config_file(os.path.join(code_dir, 'model.gin')) logging.info('Ingestion done! Starting scoring process ... ') logging.info('Creating the meta-test episode generator ... \n ') generator = DataGenerator(path_to_records=meta_test_dir, batch_config=None, episode_config=[28, 5, 1, 19], pool= data_generator_eval_type, mode='episode') if eval_type == 'test': meta_test_dataset = generator.meta_test_pipeline elif eval_type == 'train': meta_test_dataset = generator.meta_train_pipeline elif eval_type == 'val': meta_test_dataset = generator.meta_valid_pipeline else: raise ValueError('Wrong eval_type : {}'.format(eval_type)) logging.info('Evaluating performance on episodes ... ') meta_test_dataset = meta_test_dataset.batch(1) meta_test_dataset = meta_test_dataset.prefetch(5) learner = MyLearner() if (not os.path.isdir(score_dir)): os.mkdir(score_dir) score_file = os.path.join(score_dir, 'scores.txt') results = [] metric = tf.metrics.SparseCategoricalAccuracy() nbr_episodes = 600 for k , task in enumerate(meta_test_dataset) : support_set, query_set, ground_truth = process_task(task) learner.load(saved_model_dir) predictor = learner.fit(support_set) predictions = predictor.predict(query_set) score = NwayKshot_accuracy(predictions, ground_truth, metric) results.append(score) logging.debug('Score on {} : {}'.format(k, score)) logging.debug('Results : {}'.format(results[:20])) if(k > nbr_episodes): break def mean_confidence_interval(data, confidence=0.95): a = 1.0 * np.array(data) n = len(a) m, se = np.mean(a), scipy.stats.sem(a) h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1) return m, h m, conf_int = mean_confidence_interval(results) with open(score_file, 'w') as f : write_score(m, conf_int, f, extract_elapsed_time(saved_model_dir)) logging.info(('Scoring done! The average score over {} ' + 'episodes is : {:.3%}').format(nbr_episodes, sum(results)/len(results)) ) if __name__ == '__main__': np.random.seed(seed=1234) tf.get_logger().setLevel('ERROR') app.run(scoring)

true

f71fed6c463f4fb9305f4215a3d3f237674e9c98

6,399

py

Python

Graph-based/processor/recognition.py

EnTimeMent/Group-Behavior-Recognition

d6606e9e7bef836a9ccc5b4ada66933a4770171c

[ "MIT" ]

3

2020-12-29T04:07:58.000Z

2022-01-11T14:47:16.000Z

Graph-based/processor/recognition.py

EnTimeMent/Group-Behavior-Recognition

d6606e9e7bef836a9ccc5b4ada66933a4770171c

[ "MIT" ]

1

2021-01-02T10:28:07.000Z

2021-01-04T18:01:42.000Z

Graph-based/processor/recognition.py

EnTimeMent/Group-Behavior-Recognition

d6606e9e7bef836a9ccc5b4ada66933a4770171c

[ "MIT" ]

1

2022-01-09T12:55:41.000Z

#!/usr/bin/env python # pylint: disable=W0201 import sys import argparse import yaml import numpy as np # torch import torch import torch.nn as nn import torch.optim as optim # torchlight import torchlight from torchlight import str2bool from torchlight import DictAction from torchlight import import_class from .processor import Processor from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv1d') != -1: m.weight.data.normal_(0.0, 0.02) if m.bias is not None: m.bias.data.fill_(0) elif classname.find('Conv2d') != -1: m.weight.data.normal_(0.0, 0.02) if m.bias is not None: m.bias.data.fill_(0) elif classname.find('BatchNorm') != -1: m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) class REC_Processor(Processor): """ Processor for Skeleton-based Action Recgnition """ def load_model(self): # print("load model") self.model = self.io.load_model(self.arg.model, **(self.arg.model_args)) self.model.apply(weights_init) self.loss = nn.CrossEntropyLoss() # self.loss = nn.BCEWithLogitsLoss() def load_optimizer(self): if self.arg.optimizer == 'SGD': self.optimizer = optim.SGD( self.model.parameters(), lr=self.arg.base_lr, momentum=0.9, nesterov=self.arg.nesterov, weight_decay=self.arg.weight_decay) elif self.arg.optimizer == 'Adam': self.optimizer = optim.Adam( self.model.parameters(), lr=self.arg.base_lr, weight_decay=self.arg.weight_decay) else: raise ValueError() def adjust_lr(self): if self.arg.optimizer == 'SGD' and self.arg.step: lr = self.arg.base_lr * ( 0.1**np.sum(self.meta_info['epoch'] >= np.array(self.arg.step))) for param_group in self.optimizer.param_groups: param_group['lr'] = lr self.lr = lr else: self.lr = self.arg.base_lr def show_topk(self, k): rank = self.result.argsort() hit_top_k = [l in rank[i, -k:] for i, l in enumerate(self.label)] accuracy = sum(hit_top_k) * 1.0 / len(hit_top_k) self.io.print_log('\tTop{}: {:.2f}%'.format(k, 100 * accuracy)) def train(self): self.model.train() self.adjust_lr() loader = self.data_loader['train'] loss_value = [] result_frag = [] label_frag = [] # print("train") for data, label in loader: # get data data = data.float().to(self.dev) label = label.long().to(self.dev) # forward output = self.model(data) result_frag.extend( output.data.cpu().numpy().argmax(axis=1)) label_frag.extend(label.data.cpu().numpy()) # print(output) loss = self.loss(output, label) # print(label) # backward self.optimizer.zero_grad() loss.backward() self.optimizer.step() # statistics self.iter_info['loss'] = loss.data.item() self.iter_info['lr'] = '{:.6f}'.format(self.lr) loss_value.append(self.iter_info['loss']) self.show_iter_info() self.meta_info['iter'] += 1 ac = accuracy_score(label_frag, result_frag) # print(result_frag) # print(label_frag) print("train acc: {}".format(ac)) self.epoch_info['mean_loss'] = np.mean(loss_value) self.show_epoch_info() # self.io.print_timer() def test(self, evaluation=True): self.model.eval() loader = self.data_loader['test'] loss_value = [] result_frag = [] label_frag = [] for data, label in loader: # get data data = data.float().to(self.dev) label = label.long().to(self.dev) # inference with torch.no_grad(): output = self.model(data) result_frag.append(output.data.cpu().numpy()) # get loss if evaluation: loss = self.loss(output, label) loss_value.append(loss.item()) label_frag.append(label.data.cpu().numpy()) self.result = np.concatenate(result_frag) # print(self.result) if evaluation: self.label = np.concatenate(label_frag) self.epoch_info['mean_loss'] = np.mean(loss_value) self.show_epoch_info() # show top-k accuracy for k in self.arg.show_topk: self.show_topk(k) top = self.result.argmax(axis=1) print(top) print(self.label) cm = confusion_matrix(self.label, top) print(cm) @staticmethod def get_parser(add_help=False): # parameter priority: command line > config > default parent_parser = Processor.get_parser(add_help=False) parser = argparse.ArgumentParser( add_help=add_help, parents=[parent_parser], description='Spatial Temporal Graph Convolution Network') # region arguments yapf: disable # evaluation parser.add_argument('--show_topk', type=int, default=[1], nargs='+', help='which Top K accuracy will be shown') # optim parser.add_argument('--base_lr', type=float, default=0.01, help='initial learning rate') parser.add_argument('--step', type=int, default=[], nargs='+', help='the epoch where optimizer reduce the learning rate') parser.add_argument('--optimizer', default='SGD', help='type of optimizer') parser.add_argument('--nesterov', type=str2bool, default=True, help='use nesterov or not') parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay for optimizer') # endregion yapf: enable return parser

32.482234

94

0.556493

import sys import argparse import yaml import numpy as np import torch import torch.nn as nn import torch.optim as optim import torchlight from torchlight import str2bool from torchlight import DictAction from torchlight import import_class from .processor import Processor from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score def weights_init(m): classname = m.__class__.__name__ if classname.find('Conv1d') != -1: m.weight.data.normal_(0.0, 0.02) if m.bias is not None: m.bias.data.fill_(0) elif classname.find('Conv2d') != -1: m.weight.data.normal_(0.0, 0.02) if m.bias is not None: m.bias.data.fill_(0) elif classname.find('BatchNorm') != -1: m.weight.data.normal_(1.0, 0.02) m.bias.data.fill_(0) class REC_Processor(Processor): def load_model(self): self.model = self.io.load_model(self.arg.model, **(self.arg.model_args)) self.model.apply(weights_init) self.loss = nn.CrossEntropyLoss() def load_optimizer(self): if self.arg.optimizer == 'SGD': self.optimizer = optim.SGD( self.model.parameters(), lr=self.arg.base_lr, momentum=0.9, nesterov=self.arg.nesterov, weight_decay=self.arg.weight_decay) elif self.arg.optimizer == 'Adam': self.optimizer = optim.Adam( self.model.parameters(), lr=self.arg.base_lr, weight_decay=self.arg.weight_decay) else: raise ValueError() def adjust_lr(self): if self.arg.optimizer == 'SGD' and self.arg.step: lr = self.arg.base_lr * ( 0.1**np.sum(self.meta_info['epoch'] >= np.array(self.arg.step))) for param_group in self.optimizer.param_groups: param_group['lr'] = lr self.lr = lr else: self.lr = self.arg.base_lr def show_topk(self, k): rank = self.result.argsort() hit_top_k = [l in rank[i, -k:] for i, l in enumerate(self.label)] accuracy = sum(hit_top_k) * 1.0 / len(hit_top_k) self.io.print_log('\tTop{}: {:.2f}%'.format(k, 100 * accuracy)) def train(self): self.model.train() self.adjust_lr() loader = self.data_loader['train'] loss_value = [] result_frag = [] label_frag = [] for data, label in loader: data = data.float().to(self.dev) label = label.long().to(self.dev) output = self.model(data) result_frag.extend( output.data.cpu().numpy().argmax(axis=1)) label_frag.extend(label.data.cpu().numpy()) loss = self.loss(output, label) self.optimizer.zero_grad() loss.backward() self.optimizer.step() self.iter_info['loss'] = loss.data.item() self.iter_info['lr'] = '{:.6f}'.format(self.lr) loss_value.append(self.iter_info['loss']) self.show_iter_info() self.meta_info['iter'] += 1 ac = accuracy_score(label_frag, result_frag) print("train acc: {}".format(ac)) self.epoch_info['mean_loss'] = np.mean(loss_value) self.show_epoch_info() def test(self, evaluation=True): self.model.eval() loader = self.data_loader['test'] loss_value = [] result_frag = [] label_frag = [] for data, label in loader: data = data.float().to(self.dev) label = label.long().to(self.dev) with torch.no_grad(): output = self.model(data) result_frag.append(output.data.cpu().numpy()) if evaluation: loss = self.loss(output, label) loss_value.append(loss.item()) label_frag.append(label.data.cpu().numpy()) self.result = np.concatenate(result_frag) if evaluation: self.label = np.concatenate(label_frag) self.epoch_info['mean_loss'] = np.mean(loss_value) self.show_epoch_info() for k in self.arg.show_topk: self.show_topk(k) top = self.result.argmax(axis=1) print(top) print(self.label) cm = confusion_matrix(self.label, top) print(cm) @staticmethod def get_parser(add_help=False): parent_parser = Processor.get_parser(add_help=False) parser = argparse.ArgumentParser( add_help=add_help, parents=[parent_parser], description='Spatial Temporal Graph Convolution Network') parser.add_argument('--show_topk', type=int, default=[1], nargs='+', help='which Top K accuracy will be shown') parser.add_argument('--base_lr', type=float, default=0.01, help='initial learning rate') parser.add_argument('--step', type=int, default=[], nargs='+', help='the epoch where optimizer reduce the learning rate') parser.add_argument('--optimizer', default='SGD', help='type of optimizer') parser.add_argument('--nesterov', type=str2bool, default=True, help='use nesterov or not') parser.add_argument('--weight_decay', type=float, default=0.0001, help='weight decay for optimizer') return parser

true

f71fedf23526603a8b5b482439d51773bdec5bd3

3,347

py

Python

medseer/migrations/0001_initial.py

noureldin-eg/medseer

8a68cd92a757ab3141081547d322c0c6b2056d66

[ "Apache-2.0" ]

null

medseer/migrations/0001_initial.py

noureldin-eg/medseer

8a68cd92a757ab3141081547d322c0c6b2056d66

[ "Apache-2.0" ]

null

medseer/migrations/0001_initial.py

noureldin-eg/medseer

8a68cd92a757ab3141081547d322c0c6b2056d66

[ "Apache-2.0" ]

null

# Generated by Django 3.2.10 on 2022-01-25 22:13 from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Author', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('forename', models.CharField(max_length=100)), ('surname', models.CharField(max_length=100)), ('email', models.EmailField(blank=True, max_length=254, null=True, unique=True)), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='Journal', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=200, unique=True)), ('rank', models.PositiveSmallIntegerField()), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='Organization', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=200, unique=True)), ('rank', models.PositiveSmallIntegerField()), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='Paper', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('pdf', models.FileField(null=True, unique=True, upload_to='pdfs/%Y/%m/%d/')), ('tei', models.FileField(null=True, unique=True, upload_to='xmls/%Y/%m/%d/')), ('title', models.TextField(blank=True, null=True, unique=True)), ('abstract', models.TextField(blank=True, null=True, unique=True)), ('doi', models.CharField(blank=True, max_length=100, null=True, unique=True)), ('url', models.URLField(blank=True, null=True, unique=True)), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ('authors', models.ManyToManyField(to='medseer.Author')), ('journal', models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, to='medseer.journal')), ], ), migrations.AddField( model_name='author', name='organization', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, to='medseer.organization'), ), migrations.AddConstraint( model_name='author', constraint=models.UniqueConstraint(fields=('forename', 'surname'), name='unique_author_name'), ), ]

46.486111

125

0.582611

from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Author', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('forename', models.CharField(max_length=100)), ('surname', models.CharField(max_length=100)), ('email', models.EmailField(blank=True, max_length=254, null=True, unique=True)), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='Journal', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=200, unique=True)), ('rank', models.PositiveSmallIntegerField()), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='Organization', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=200, unique=True)), ('rank', models.PositiveSmallIntegerField()), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='Paper', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('pdf', models.FileField(null=True, unique=True, upload_to='pdfs/%Y/%m/%d/')), ('tei', models.FileField(null=True, unique=True, upload_to='xmls/%Y/%m/%d/')), ('title', models.TextField(blank=True, null=True, unique=True)), ('abstract', models.TextField(blank=True, null=True, unique=True)), ('doi', models.CharField(blank=True, max_length=100, null=True, unique=True)), ('url', models.URLField(blank=True, null=True, unique=True)), ('created_at', models.DateTimeField(auto_now_add=True)), ('modified_at', models.DateTimeField(auto_now=True)), ('authors', models.ManyToManyField(to='medseer.Author')), ('journal', models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, to='medseer.journal')), ], ), migrations.AddField( model_name='author', name='organization', field=models.ForeignKey(null=True, on_delete=django.db.models.deletion.PROTECT, to='medseer.organization'), ), migrations.AddConstraint( model_name='author', constraint=models.UniqueConstraint(fields=('forename', 'surname'), name='unique_author_name'), ), ]

true

f71fee147634858badedf4ea69e3f4bc26bb7e78

2,052

py

Python

tests/parsers/plist_plugins/ipod.py

jeppetrost/plaso

b48008c6ea79950eeeef3a05b3a859086c8704b6

[ "Apache-2.0" ]

null

tests/parsers/plist_plugins/ipod.py

jeppetrost/plaso

b48008c6ea79950eeeef3a05b3a859086c8704b6

[ "Apache-2.0" ]

null

tests/parsers/plist_plugins/ipod.py

jeppetrost/plaso

b48008c6ea79950eeeef3a05b3a859086c8704b6

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """Tests for the iPod plist plugin.""" from __future__ import unicode_literals import unittest from plaso.formatters import ipod as _ # pylint: disable=unused-import from plaso.lib import definitions from plaso.parsers.plist_plugins import ipod from tests import test_lib as shared_test_lib from tests.parsers.plist_plugins import test_lib class TestIPodPlugin(test_lib.PlistPluginTestCase): """Tests for the iPod plist plugin.""" @shared_test_lib.skipUnlessHasTestFile(['com.apple.iPod.plist']) def testProcess(self): """Tests the Process function.""" plist_name = 'com.apple.iPod.plist' plugin = ipod.IPodPlugin() storage_writer = self._ParsePlistFileWithPlugin( plugin, [plist_name], plist_name) self.assertEqual(storage_writer.number_of_warnings, 0) self.assertEqual(storage_writer.number_of_events, 4) # The order in which PlistParser generates events is nondeterministic # hence we sort the events. events = list(storage_writer.GetSortedEvents()) event = events[0] self.CheckTimestamp(event.timestamp, '1995-11-22 18:25:07.000000') self.assertEqual(event.device_id, '0000A11300000000') event = events[2] self.CheckTimestamp(event.timestamp, '2013-10-09 19:27:54.000000') expected_message = ( 'Device ID: 4C6F6F6E65000000 ' 'Type: iPhone [10016] ' 'Connected 1 times ' 'Serial nr: 526F676572 ' 'IMEI [012345678901234]') expected_short_message = '{0:s}...'.format(expected_message[:77]) self._TestGetMessageStrings(event, expected_message, expected_short_message) self.assertEqual( event.timestamp_desc, definitions.TIME_DESCRIPTION_LAST_CONNECTED) self.assertEqual(event.device_class, 'iPhone') self.assertEqual(event.device_id, '4C6F6F6E65000000') self.assertEqual(event.firmware_version, 256) self.assertEqual(event.imei, '012345678901234') self.assertEqual(event.use_count, 1) if __name__ == '__main__': unittest.main()

30.176471

80

0.729532

from __future__ import unicode_literals import unittest from plaso.formatters import ipod as _ from plaso.lib import definitions from plaso.parsers.plist_plugins import ipod from tests import test_lib as shared_test_lib from tests.parsers.plist_plugins import test_lib class TestIPodPlugin(test_lib.PlistPluginTestCase): @shared_test_lib.skipUnlessHasTestFile(['com.apple.iPod.plist']) def testProcess(self): plist_name = 'com.apple.iPod.plist' plugin = ipod.IPodPlugin() storage_writer = self._ParsePlistFileWithPlugin( plugin, [plist_name], plist_name) self.assertEqual(storage_writer.number_of_warnings, 0) self.assertEqual(storage_writer.number_of_events, 4) events = list(storage_writer.GetSortedEvents()) event = events[0] self.CheckTimestamp(event.timestamp, '1995-11-22 18:25:07.000000') self.assertEqual(event.device_id, '0000A11300000000') event = events[2] self.CheckTimestamp(event.timestamp, '2013-10-09 19:27:54.000000') expected_message = ( 'Device ID: 4C6F6F6E65000000 ' 'Type: iPhone [10016] ' 'Connected 1 times ' 'Serial nr: 526F676572 ' 'IMEI [012345678901234]') expected_short_message = '{0:s}...'.format(expected_message[:77]) self._TestGetMessageStrings(event, expected_message, expected_short_message) self.assertEqual( event.timestamp_desc, definitions.TIME_DESCRIPTION_LAST_CONNECTED) self.assertEqual(event.device_class, 'iPhone') self.assertEqual(event.device_id, '4C6F6F6E65000000') self.assertEqual(event.firmware_version, 256) self.assertEqual(event.imei, '012345678901234') self.assertEqual(event.use_count, 1) if __name__ == '__main__': unittest.main()

true

f71fee22e27eb7d42dc3efe0a61407b797d283a4

63,809

py

Python

pandas/core/groupby/groupby.py

paritoshmittal09/pandas

862d2d89b8fe0a93ec8e714315175e2eba1fa6e5

[ "BSD-3-Clause" ]

null

pandas/core/groupby/groupby.py

paritoshmittal09/pandas

862d2d89b8fe0a93ec8e714315175e2eba1fa6e5

[ "BSD-3-Clause" ]

null

pandas/core/groupby/groupby.py

paritoshmittal09/pandas

862d2d89b8fe0a93ec8e714315175e2eba1fa6e5

[ "BSD-3-Clause" ]

null

""" Provide the groupby split-apply-combine paradigm. Define the GroupBy class providing the base-class of operations. The SeriesGroupBy and DataFrameGroupBy sub-class (defined in pandas.core.groupby.generic) expose these user-facing objects to provide specific functionailty. """ import types from functools import wraps, partial import datetime import collections import warnings from contextlib import contextmanager import numpy as np from pandas._libs import groupby as libgroupby, Timestamp from pandas.util._validators import validate_kwargs from pandas.util._decorators import ( cache_readonly, Substitution, Appender) from pandas import compat from pandas.compat import zip, range, callable, set_function_name from pandas.compat.numpy import function as nv from pandas.core.dtypes.common import ( is_numeric_dtype, is_scalar, ensure_float) from pandas.core.dtypes.cast import maybe_downcast_to_dtype from pandas.core.dtypes.missing import isna, notna from pandas.core.groupby import base from pandas.core.base import (PandasObject, SelectionMixin, GroupByError, DataError, SpecificationError) from pandas.core.index import Index, MultiIndex from pandas.core.generic import NDFrame from pandas.core.frame import DataFrame from pandas.core.series import Series from pandas.core.sorting import get_group_index_sorter import pandas.core.common as com import pandas.core.algorithms as algorithms from pandas.core.config import option_context _doc_template = """ See also -------- pandas.Series.%(name)s pandas.DataFrame.%(name)s pandas.Panel.%(name)s """ _apply_docs = dict( template=""" Apply function `func` group-wise and combine the results together. The function passed to `apply` must take a {input} as its first argument and return a DataFrame, Series or scalar. `apply` will then take care of combining the results back together into a single dataframe or series. `apply` is therefore a highly flexible grouping method. While `apply` is a very flexible method, its downside is that using it can be quite a bit slower than using more specific methods like `agg` or `transform`. Pandas offers a wide range of method that will be much faster than using `apply` for their specific purposes, so try to use them before reaching for `apply`. Parameters ---------- func : callable A callable that takes a {input} as its first argument, and returns a dataframe, a series or a scalar. In addition the callable may take positional and keyword arguments. args, kwargs : tuple and dict Optional positional and keyword arguments to pass to `func`. Returns ------- applied : Series or DataFrame Notes ----- In the current implementation `apply` calls `func` twice on the first group to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if `func` has side-effects, as they will take effect twice for the first group. Examples -------- {examples} See also -------- pipe : Apply function to the full GroupBy object instead of to each group. aggregate : Apply aggregate function to the GroupBy object. transform : Apply function column-by-column to the GroupBy object. Series.apply : Apply a function to a Series. DataFrame.apply : Apply a function to each row or column of a DataFrame. """, dataframe_examples=""" >>> df = pd.DataFrame({'A': 'a a b'.split(), 'B': [1,2,3], 'C': [4,6, 5]}) >>> g = df.groupby('A') Notice that ``g`` has two groups, ``a`` and ``b``. Calling `apply` in various ways, we can get different grouping results: Example 1: below the function passed to `apply` takes a DataFrame as its argument and returns a DataFrame. `apply` combines the result for each group together into a new DataFrame: >>> g[['B', 'C']].apply(lambda x: x / x.sum()) B C 0 0.333333 0.4 1 0.666667 0.6 2 1.000000 1.0 Example 2: The function passed to `apply` takes a DataFrame as its argument and returns a Series. `apply` combines the result for each group together into a new DataFrame: >>> g[['B', 'C']].apply(lambda x: x.max() - x.min()) B C A a 1 2 b 0 0 Example 3: The function passed to `apply` takes a DataFrame as its argument and returns a scalar. `apply` combines the result for each group together into a Series, including setting the index as appropriate: >>> g.apply(lambda x: x.C.max() - x.B.min()) A a 5 b 2 dtype: int64 """, series_examples=""" >>> s = pd.Series([0, 1, 2], index='a a b'.split()) >>> g = s.groupby(s.index) From ``s`` above we can see that ``g`` has two groups, ``a`` and ``b``. Calling `apply` in various ways, we can get different grouping results: Example 1: The function passed to `apply` takes a Series as its argument and returns a Series. `apply` combines the result for each group together into a new Series: >>> g.apply(lambda x: x*2 if x.name == 'b' else x/2) 0 0.0 1 0.5 2 4.0 dtype: float64 Example 2: The function passed to `apply` takes a Series as its argument and returns a scalar. `apply` combines the result for each group together into a Series, including setting the index as appropriate: >>> g.apply(lambda x: x.max() - x.min()) a 1 b 0 dtype: int64 """) _pipe_template = """\ Apply a function `func` with arguments to this %(klass)s object and return the function's result. %(versionadded)s Use `.pipe` when you want to improve readability by chaining together functions that expect Series, DataFrames, GroupBy or Resampler objects. Instead of writing >>> h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c) You can write >>> (df.groupby('group') ... .pipe(f) ... .pipe(g, arg1=a) ... .pipe(h, arg2=b, arg3=c)) which is much more readable. Parameters ---------- func : callable or tuple of (callable, string) Function to apply to this %(klass)s object or, alternatively, a `(callable, data_keyword)` tuple where `data_keyword` is a string indicating the keyword of `callable` that expects the %(klass)s object. args : iterable, optional positional arguments passed into `func`. kwargs : dict, optional a dictionary of keyword arguments passed into `func`. Returns ------- object : the return type of `func`. Notes ----- See more `here <http://pandas.pydata.org/pandas-docs/stable/groupby.html#piping-function-calls>`_ Examples -------- %(examples)s See Also -------- pandas.Series.pipe : Apply a function with arguments to a series pandas.DataFrame.pipe: Apply a function with arguments to a dataframe apply : Apply function to each group instead of to the full %(klass)s object. """ _transform_template = """ Call function producing a like-indexed %(klass)s on each group and return a %(klass)s having the same indexes as the original object filled with the transformed values Parameters ---------- f : function Function to apply to each group Notes ----- Each group is endowed the attribute 'name' in case you need to know which group you are working on. The current implementation imposes three requirements on f: * f must return a value that either has the same shape as the input subframe or can be broadcast to the shape of the input subframe. For example, f returns a scalar it will be broadcast to have the same shape as the input subframe. * if this is a DataFrame, f must support application column-by-column in the subframe. If f also supports application to the entire subframe, then a fast path is used starting from the second chunk. * f must not mutate groups. Mutation is not supported and may produce unexpected results. Returns ------- %(klass)s See also -------- aggregate, transform Examples -------- # Same shape >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', ... 'foo', 'bar'], ... 'B' : ['one', 'one', 'two', 'three', ... 'two', 'two'], ... 'C' : [1, 5, 5, 2, 5, 5], ... 'D' : [2.0, 5., 8., 1., 2., 9.]}) >>> grouped = df.groupby('A') >>> grouped.transform(lambda x: (x - x.mean()) / x.std()) C D 0 -1.154701 -0.577350 1 0.577350 0.000000 2 0.577350 1.154701 3 -1.154701 -1.000000 4 0.577350 -0.577350 5 0.577350 1.000000 # Broadcastable >>> grouped.transform(lambda x: x.max() - x.min()) C D 0 4 6.0 1 3 8.0 2 4 6.0 3 3 8.0 4 4 6.0 5 3 8.0 """ class GroupByPlot(PandasObject): """ Class implementing the .plot attribute for groupby objects """ def __init__(self, groupby): self._groupby = groupby def __call__(self, *args, **kwargs): def f(self): return self.plot(*args, **kwargs) f.__name__ = 'plot' return self._groupby.apply(f) def __getattr__(self, name): def attr(*args, **kwargs): def f(self): return getattr(self.plot, name)(*args, **kwargs) return self._groupby.apply(f) return attr @contextmanager def _group_selection_context(groupby): """ set / reset the _group_selection_context """ groupby._set_group_selection() yield groupby groupby._reset_group_selection() class _GroupBy(PandasObject, SelectionMixin): _group_selection = None _apply_whitelist = frozenset([]) def __init__(self, obj, keys=None, axis=0, level=None, grouper=None, exclusions=None, selection=None, as_index=True, sort=True, group_keys=True, squeeze=False, observed=False, **kwargs): self._selection = selection if isinstance(obj, NDFrame): obj._consolidate_inplace() self.level = level if not as_index: if not isinstance(obj, DataFrame): raise TypeError('as_index=False only valid with DataFrame') if axis != 0: raise ValueError('as_index=False only valid for axis=0') self.as_index = as_index self.keys = keys self.sort = sort self.group_keys = group_keys self.squeeze = squeeze self.observed = observed self.mutated = kwargs.pop('mutated', False) if grouper is None: from pandas.core.groupby.grouper import _get_grouper grouper, exclusions, obj = _get_grouper(obj, keys, axis=axis, level=level, sort=sort, observed=observed, mutated=self.mutated) self.obj = obj self.axis = obj._get_axis_number(axis) self.grouper = grouper self.exclusions = set(exclusions) if exclusions else set() # we accept no other args validate_kwargs('group', kwargs, {}) def __len__(self): return len(self.groups) def __unicode__(self): # TODO: Better unicode/repr for GroupBy object return object.__repr__(self) def _assure_grouper(self): """ we create the grouper on instantiation sub-classes may have a different policy """ pass @property def groups(self): """ dict {group name -> group labels} """ self._assure_grouper() return self.grouper.groups @property def ngroups(self): self._assure_grouper() return self.grouper.ngroups @property def indices(self): """ dict {group name -> group indices} """ self._assure_grouper() return self.grouper.indices def _get_indices(self, names): """ safe get multiple indices, translate keys for datelike to underlying repr """ def get_converter(s): # possibly convert to the actual key types # in the indices, could be a Timestamp or a np.datetime64 if isinstance(s, (Timestamp, datetime.datetime)): return lambda key: Timestamp(key) elif isinstance(s, np.datetime64): return lambda key: Timestamp(key).asm8 else: return lambda key: key if len(names) == 0: return [] if len(self.indices) > 0: index_sample = next(iter(self.indices)) else: index_sample = None # Dummy sample name_sample = names[0] if isinstance(index_sample, tuple): if not isinstance(name_sample, tuple): msg = ("must supply a tuple to get_group with multiple" " grouping keys") raise ValueError(msg) if not len(name_sample) == len(index_sample): try: # If the original grouper was a tuple return [self.indices[name] for name in names] except KeyError: # turns out it wasn't a tuple msg = ("must supply a a same-length tuple to get_group" " with multiple grouping keys") raise ValueError(msg) converters = [get_converter(s) for s in index_sample] names = [tuple(f(n) for f, n in zip(converters, name)) for name in names] else: converter = get_converter(index_sample) names = [converter(name) for name in names] return [self.indices.get(name, []) for name in names] def _get_index(self, name): """ safe get index, translate keys for datelike to underlying repr """ return self._get_indices([name])[0] @cache_readonly def _selected_obj(self): if self._selection is None or isinstance(self.obj, Series): if self._group_selection is not None: return self.obj[self._group_selection] return self.obj else: return self.obj[self._selection] def _reset_group_selection(self): """ Clear group based selection. Used for methods needing to return info on each group regardless of whether a group selection was previously set. """ if self._group_selection is not None: # GH12839 clear cached selection too when changing group selection self._group_selection = None self._reset_cache('_selected_obj') def _set_group_selection(self): """ Create group based selection. Used when selection is not passed directly but instead via a grouper. NOTE: this should be paired with a call to _reset_group_selection """ grp = self.grouper if not (self.as_index and getattr(grp, 'groupings', None) is not None and self.obj.ndim > 1 and self._group_selection is None): return ax = self.obj._info_axis groupers = [g.name for g in grp.groupings if g.level is None and g.in_axis] if len(groupers): # GH12839 clear selected obj cache when group selection changes self._group_selection = ax.difference(Index(groupers)).tolist() self._reset_cache('_selected_obj') def _set_result_index_ordered(self, result): # set the result index on the passed values object and # return the new object, xref 8046 # the values/counts are repeated according to the group index # shortcut if we have an already ordered grouper if not self.grouper.is_monotonic: index = Index(np.concatenate( self._get_indices(self.grouper.result_index))) result.set_axis(index, axis=self.axis, inplace=True) result = result.sort_index(axis=self.axis) result.set_axis(self.obj._get_axis(self.axis), axis=self.axis, inplace=True) return result def _dir_additions(self): return self.obj._dir_additions() | self._apply_whitelist def __getattr__(self, attr): if attr in self._internal_names_set: return object.__getattribute__(self, attr) if attr in self.obj: return self[attr] if hasattr(self.obj, attr): return self._make_wrapper(attr) raise AttributeError("%r object has no attribute %r" % (type(self).__name__, attr)) @Substitution(klass='GroupBy', versionadded='.. versionadded:: 0.21.0', examples="""\ >>> df = pd.DataFrame({'A': 'a b a b'.split(), 'B': [1, 2, 3, 4]}) >>> df A B 0 a 1 1 b 2 2 a 3 3 b 4 To get the difference between each groups maximum and minimum value in one pass, you can do >>> df.groupby('A').pipe(lambda x: x.max() - x.min()) B A a 2 b 2""") @Appender(_pipe_template) def pipe(self, func, *args, **kwargs): return com._pipe(self, func, *args, **kwargs) plot = property(GroupByPlot) def _make_wrapper(self, name): if name not in self._apply_whitelist: is_callable = callable(getattr(self._selected_obj, name, None)) kind = ' callable ' if is_callable else ' ' msg = ("Cannot access{0}attribute {1!r} of {2!r} objects, try " "using the 'apply' method".format(kind, name, type(self).__name__)) raise AttributeError(msg) self._set_group_selection() # need to setup the selection # as are not passed directly but in the grouper f = getattr(self._selected_obj, name) if not isinstance(f, types.MethodType): return self.apply(lambda self: getattr(self, name)) f = getattr(type(self._selected_obj), name) def wrapper(*args, **kwargs): # a little trickery for aggregation functions that need an axis # argument kwargs_with_axis = kwargs.copy() if 'axis' not in kwargs_with_axis or \ kwargs_with_axis['axis'] is None: kwargs_with_axis['axis'] = self.axis def curried_with_axis(x): return f(x, *args, **kwargs_with_axis) def curried(x): return f(x, *args, **kwargs) # preserve the name so we can detect it when calling plot methods, # to avoid duplicates curried.__name__ = curried_with_axis.__name__ = name # special case otherwise extra plots are created when catching the # exception below if name in base.plotting_methods: return self.apply(curried) try: return self.apply(curried_with_axis) except Exception: try: return self.apply(curried) except Exception: # related to : GH3688 # try item-by-item # this can be called recursively, so need to raise # ValueError # if we don't have this method to indicated to aggregate to # mark this column as an error try: return self._aggregate_item_by_item(name, *args, **kwargs) except (AttributeError): raise ValueError return wrapper def get_group(self, name, obj=None): """ Constructs NDFrame from group with provided name Parameters ---------- name : object the name of the group to get as a DataFrame obj : NDFrame, default None the NDFrame to take the DataFrame out of. If it is None, the object groupby was called on will be used Returns ------- group : same type as obj """ if obj is None: obj = self._selected_obj inds = self._get_index(name) if not len(inds): raise KeyError(name) return obj._take(inds, axis=self.axis) def __iter__(self): """ Groupby iterator Returns ------- Generator yielding sequence of (name, subsetted object) for each group """ return self.grouper.get_iterator(self.obj, axis=self.axis) @Appender(_apply_docs['template'] .format(input="dataframe", examples=_apply_docs['dataframe_examples'])) def apply(self, func, *args, **kwargs): func = self._is_builtin_func(func) # this is needed so we don't try and wrap strings. If we could # resolve functions to their callable functions prior, this # wouldn't be needed if args or kwargs: if callable(func): @wraps(func) def f(g): with np.errstate(all='ignore'): return func(g, *args, **kwargs) else: raise ValueError('func must be a callable if args or ' 'kwargs are supplied') else: f = func # ignore SettingWithCopy here in case the user mutates with option_context('mode.chained_assignment', None): try: result = self._python_apply_general(f) except Exception: # gh-20949 # try again, with .apply acting as a filtering # operation, by excluding the grouping column # This would normally not be triggered # except if the udf is trying an operation that # fails on *some* columns, e.g. a numeric operation # on a string grouper column with _group_selection_context(self): return self._python_apply_general(f) return result def _python_apply_general(self, f): keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis) return self._wrap_applied_output( keys, values, not_indexed_same=mutated or self.mutated) def _iterate_slices(self): yield self._selection_name, self._selected_obj def transform(self, func, *args, **kwargs): raise com.AbstractMethodError(self) def _cumcount_array(self, ascending=True): """ Parameters ---------- ascending : bool, default True If False, number in reverse, from length of group - 1 to 0. Notes ----- this is currently implementing sort=False (though the default is sort=True) for groupby in general """ ids, _, ngroups = self.grouper.group_info sorter = get_group_index_sorter(ids, ngroups) ids, count = ids[sorter], len(ids) if count == 0: return np.empty(0, dtype=np.int64) run = np.r_[True, ids[:-1] != ids[1:]] rep = np.diff(np.r_[np.nonzero(run)[0], count]) out = (~run).cumsum() if ascending: out -= np.repeat(out[run], rep) else: out = np.repeat(out[np.r_[run[1:], True]], rep) - out rev = np.empty(count, dtype=np.intp) rev[sorter] = np.arange(count, dtype=np.intp) return out[rev].astype(np.int64, copy=False) def _try_cast(self, result, obj, numeric_only=False): """ try to cast the result to our obj original type, we may have roundtripped thru object in the mean-time if numeric_only is True, then only try to cast numerics and not datetimelikes """ if obj.ndim > 1: dtype = obj.values.dtype else: dtype = obj.dtype if not is_scalar(result): if numeric_only and is_numeric_dtype(dtype) or not numeric_only: result = maybe_downcast_to_dtype(result, dtype) return result def _transform_should_cast(self, func_nm): """ Parameters: ----------- func_nm: str The name of the aggregation function being performed Returns: -------- bool Whether transform should attempt to cast the result of aggregation """ return (self.size().fillna(0) > 0).any() and ( func_nm not in base.cython_cast_blacklist) def _cython_transform(self, how, numeric_only=True, **kwargs): output = collections.OrderedDict() for name, obj in self._iterate_slices(): is_numeric = is_numeric_dtype(obj.dtype) if numeric_only and not is_numeric: continue try: result, names = self.grouper.transform(obj.values, how, **kwargs) except NotImplementedError: continue except AssertionError as e: raise GroupByError(str(e)) if self._transform_should_cast(how): output[name] = self._try_cast(result, obj) else: output[name] = result if len(output) == 0: raise DataError('No numeric types to aggregate') return self._wrap_transformed_output(output, names) def _cython_agg_general(self, how, alt=None, numeric_only=True, min_count=-1): output = {} for name, obj in self._iterate_slices(): is_numeric = is_numeric_dtype(obj.dtype) if numeric_only and not is_numeric: continue try: result, names = self.grouper.aggregate(obj.values, how, min_count=min_count) except AssertionError as e: raise GroupByError(str(e)) output[name] = self._try_cast(result, obj) if len(output) == 0: raise DataError('No numeric types to aggregate') return self._wrap_aggregated_output(output, names) def _python_agg_general(self, func, *args, **kwargs): func = self._is_builtin_func(func) f = lambda x: func(x, *args, **kwargs) # iterate through "columns" ex exclusions to populate output dict output = {} for name, obj in self._iterate_slices(): try: result, counts = self.grouper.agg_series(obj, f) output[name] = self._try_cast(result, obj, numeric_only=True) except TypeError: continue if len(output) == 0: return self._python_apply_general(f) if self.grouper._filter_empty_groups: mask = counts.ravel() > 0 for name, result in compat.iteritems(output): # since we are masking, make sure that we have a float object values = result if is_numeric_dtype(values.dtype): values = ensure_float(values) output[name] = self._try_cast(values[mask], result) return self._wrap_aggregated_output(output) def _wrap_applied_output(self, *args, **kwargs): raise com.AbstractMethodError(self) def _concat_objects(self, keys, values, not_indexed_same=False): from pandas.core.reshape.concat import concat def reset_identity(values): # reset the identities of the components # of the values to prevent aliasing for v in com._not_none(*values): ax = v._get_axis(self.axis) ax._reset_identity() return values if not not_indexed_same: result = concat(values, axis=self.axis) ax = self._selected_obj._get_axis(self.axis) if isinstance(result, Series): result = result.reindex(ax) else: # this is a very unfortunate situation # we have a multi-index that is NOT lexsorted # and we have a result which is duplicated # we can't reindex, so we resort to this # GH 14776 if isinstance(ax, MultiIndex) and not ax.is_unique: indexer = algorithms.unique1d( result.index.get_indexer_for(ax.values)) result = result.take(indexer, axis=self.axis) else: result = result.reindex(ax, axis=self.axis) elif self.group_keys: values = reset_identity(values) if self.as_index: # possible MI return case group_keys = keys group_levels = self.grouper.levels group_names = self.grouper.names result = concat(values, axis=self.axis, keys=group_keys, levels=group_levels, names=group_names, sort=False) else: # GH5610, returns a MI, with the first level being a # range index keys = list(range(len(values))) result = concat(values, axis=self.axis, keys=keys) else: values = reset_identity(values) result = concat(values, axis=self.axis) if (isinstance(result, Series) and getattr(self, '_selection_name', None) is not None): result.name = self._selection_name return result def _apply_filter(self, indices, dropna): if len(indices) == 0: indices = np.array([], dtype='int64') else: indices = np.sort(np.concatenate(indices)) if dropna: filtered = self._selected_obj.take(indices, axis=self.axis) else: mask = np.empty(len(self._selected_obj.index), dtype=bool) mask.fill(False) mask[indices.astype(int)] = True # mask fails to broadcast when passed to where; broadcast manually. mask = np.tile(mask, list(self._selected_obj.shape[1:]) + [1]).T filtered = self._selected_obj.where(mask) # Fill with NaNs. return filtered class GroupBy(_GroupBy): """ Class for grouping and aggregating relational data. See aggregate, transform, and apply functions on this object. It's easiest to use obj.groupby(...) to use GroupBy, but you can also do: :: grouped = groupby(obj, ...) Parameters ---------- obj : pandas object axis : int, default 0 level : int, default None Level of MultiIndex groupings : list of Grouping objects Most users should ignore this exclusions : array-like, optional List of columns to exclude name : string Most users should ignore this Notes ----- After grouping, see aggregate, apply, and transform functions. Here are some other brief notes about usage. When grouping by multiple groups, the result index will be a MultiIndex (hierarchical) by default. Iteration produces (key, group) tuples, i.e. chunking the data by group. So you can write code like: :: grouped = obj.groupby(keys, axis=axis) for key, group in grouped: # do something with the data Function calls on GroupBy, if not specially implemented, "dispatch" to the grouped data. So if you group a DataFrame and wish to invoke the std() method on each group, you can simply do: :: df.groupby(mapper).std() rather than :: df.groupby(mapper).aggregate(np.std) You can pass arguments to these "wrapped" functions, too. See the online documentation for full exposition on these topics and much more Returns ------- **Attributes** groups : dict {group name -> group labels} len(grouped) : int Number of groups """ def _bool_agg(self, val_test, skipna): """Shared func to call any / all Cython GroupBy implementations""" def objs_to_bool(vals): try: vals = vals.astype(np.bool) except ValueError: # for objects vals = np.array([bool(x) for x in vals]) return vals.view(np.uint8) def result_to_bool(result): return result.astype(np.bool, copy=False) return self._get_cythonized_result('group_any_all', self.grouper, aggregate=True, cython_dtype=np.uint8, needs_values=True, needs_mask=True, pre_processing=objs_to_bool, post_processing=result_to_bool, val_test=val_test, skipna=skipna) @Substitution(name='groupby') @Appender(_doc_template) def any(self, skipna=True): """ Returns True if any value in the group is truthful, else False Parameters ---------- skipna : bool, default True Flag to ignore nan values during truth testing """ return self._bool_agg('any', skipna) @Substitution(name='groupby') @Appender(_doc_template) def all(self, skipna=True): """Returns True if all values in the group are truthful, else False Parameters ---------- skipna : bool, default True Flag to ignore nan values during truth testing """ return self._bool_agg('all', skipna) @Substitution(name='groupby') @Appender(_doc_template) def count(self): """Compute count of group, excluding missing values""" # defined here for API doc raise NotImplementedError @Substitution(name='groupby') @Appender(_doc_template) def mean(self, *args, **kwargs): """ Compute mean of groups, excluding missing values. Returns ------- pandas.Series or pandas.DataFrame Examples -------- >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2], ... 'B': [np.nan, 2, 3, 4, 5], ... 'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C']) Groupby one column and return the mean of the remaining columns in each group. >>> df.groupby('A').mean() >>> B C A 1 3.0 1.333333 2 4.0 1.500000 Groupby two columns and return the mean of the remaining column. >>> df.groupby(['A', 'B']).mean() >>> C A B 1 2.0 2 4.0 1 2 3.0 1 5.0 2 Groupby one column and return the mean of only particular column in the group. >>> df.groupby('A')['B'].mean() >>> A 1 3.0 2 4.0 Name: B, dtype: float64 """ nv.validate_groupby_func('mean', args, kwargs, ['numeric_only']) try: return self._cython_agg_general('mean', **kwargs) except GroupByError: raise except Exception: # pragma: no cover with _group_selection_context(self): f = lambda x: x.mean(axis=self.axis, **kwargs) return self._python_agg_general(f) @Substitution(name='groupby') @Appender(_doc_template) def median(self, **kwargs): """ Compute median of groups, excluding missing values For multiple groupings, the result index will be a MultiIndex """ try: return self._cython_agg_general('median', **kwargs) except GroupByError: raise except Exception: # pragma: no cover def f(x): if isinstance(x, np.ndarray): x = Series(x) return x.median(axis=self.axis, **kwargs) with _group_selection_context(self): return self._python_agg_general(f) @Substitution(name='groupby') @Appender(_doc_template) def std(self, ddof=1, *args, **kwargs): """ Compute standard deviation of groups, excluding missing values For multiple groupings, the result index will be a MultiIndex Parameters ---------- ddof : integer, default 1 degrees of freedom """ # TODO: implement at Cython level? nv.validate_groupby_func('std', args, kwargs) return np.sqrt(self.var(ddof=ddof, **kwargs)) @Substitution(name='groupby') @Appender(_doc_template) def var(self, ddof=1, *args, **kwargs): """ Compute variance of groups, excluding missing values For multiple groupings, the result index will be a MultiIndex Parameters ---------- ddof : integer, default 1 degrees of freedom """ nv.validate_groupby_func('var', args, kwargs) if ddof == 1: try: return self._cython_agg_general('var', **kwargs) except Exception: f = lambda x: x.var(ddof=ddof, **kwargs) with _group_selection_context(self): return self._python_agg_general(f) else: f = lambda x: x.var(ddof=ddof, **kwargs) with _group_selection_context(self): return self._python_agg_general(f) @Substitution(name='groupby') @Appender(_doc_template) def sem(self, ddof=1): """ Compute standard error of the mean of groups, excluding missing values For multiple groupings, the result index will be a MultiIndex Parameters ---------- ddof : integer, default 1 degrees of freedom """ return self.std(ddof=ddof) / np.sqrt(self.count()) @Substitution(name='groupby') @Appender(_doc_template) def size(self): """Compute group sizes""" result = self.grouper.size() if isinstance(self.obj, Series): result.name = getattr(self.obj, 'name', None) return result @classmethod def _add_numeric_operations(cls): """ add numeric operations to the GroupBy generically """ def groupby_function(name, alias, npfunc, numeric_only=True, _convert=False, min_count=-1): _local_template = "Compute %(f)s of group values" @Substitution(name='groupby', f=name) @Appender(_doc_template) @Appender(_local_template) def f(self, **kwargs): if 'numeric_only' not in kwargs: kwargs['numeric_only'] = numeric_only if 'min_count' not in kwargs: kwargs['min_count'] = min_count self._set_group_selection() try: return self._cython_agg_general( alias, alt=npfunc, **kwargs) except AssertionError as e: raise SpecificationError(str(e)) except Exception: result = self.aggregate( lambda x: npfunc(x, axis=self.axis)) if _convert: result = result._convert(datetime=True) return result set_function_name(f, name, cls) return f def first_compat(x, axis=0): def first(x): x = np.asarray(x) x = x[notna(x)] if len(x) == 0: return np.nan return x[0] if isinstance(x, DataFrame): return x.apply(first, axis=axis) else: return first(x) def last_compat(x, axis=0): def last(x): x = np.asarray(x) x = x[notna(x)] if len(x) == 0: return np.nan return x[-1] if isinstance(x, DataFrame): return x.apply(last, axis=axis) else: return last(x) cls.sum = groupby_function('sum', 'add', np.sum, min_count=0) cls.prod = groupby_function('prod', 'prod', np.prod, min_count=0) cls.min = groupby_function('min', 'min', np.min, numeric_only=False) cls.max = groupby_function('max', 'max', np.max, numeric_only=False) cls.first = groupby_function('first', 'first', first_compat, numeric_only=False) cls.last = groupby_function('last', 'last', last_compat, numeric_only=False) @Substitution(name='groupby') @Appender(_doc_template) def ohlc(self): """ Compute sum of values, excluding missing values For multiple groupings, the result index will be a MultiIndex """ return self._apply_to_column_groupbys( lambda x: x._cython_agg_general('ohlc')) @Appender(DataFrame.describe.__doc__) def describe(self, **kwargs): with _group_selection_context(self): result = self.apply(lambda x: x.describe(**kwargs)) if self.axis == 1: return result.T return result.unstack() @Substitution(name='groupby') @Appender(_doc_template) def resample(self, rule, *args, **kwargs): """ Provide resampling when using a TimeGrouper Return a new grouper with our resampler appended """ from pandas.core.resample import get_resampler_for_grouping return get_resampler_for_grouping(self, rule, *args, **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def rolling(self, *args, **kwargs): """ Return a rolling grouper, providing rolling functionality per group """ from pandas.core.window import RollingGroupby return RollingGroupby(self, *args, **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def expanding(self, *args, **kwargs): """ Return an expanding grouper, providing expanding functionality per group """ from pandas.core.window import ExpandingGroupby return ExpandingGroupby(self, *args, **kwargs) def _fill(self, direction, limit=None): """Shared function for `pad` and `backfill` to call Cython method Parameters ---------- direction : {'ffill', 'bfill'} Direction passed to underlying Cython function. `bfill` will cause values to be filled backwards. `ffill` and any other values will default to a forward fill limit : int, default None Maximum number of consecutive values to fill. If `None`, this method will convert to -1 prior to passing to Cython Returns ------- `Series` or `DataFrame` with filled values See Also -------- pad backfill """ # Need int value for Cython if limit is None: limit = -1 return self._get_cythonized_result('group_fillna_indexer', self.grouper, needs_mask=True, cython_dtype=np.int64, result_is_index=True, direction=direction, limit=limit) @Substitution(name='groupby') def pad(self, limit=None): """ Forward fill the values Parameters ---------- limit : integer, optional limit of how many values to fill See Also -------- Series.pad DataFrame.pad Series.fillna DataFrame.fillna """ return self._fill('ffill', limit=limit) ffill = pad @Substitution(name='groupby') def backfill(self, limit=None): """ Backward fill the values Parameters ---------- limit : integer, optional limit of how many values to fill See Also -------- Series.backfill DataFrame.backfill Series.fillna DataFrame.fillna """ return self._fill('bfill', limit=limit) bfill = backfill @Substitution(name='groupby') @Appender(_doc_template) def nth(self, n, dropna=None): """ Take the nth row from each group if n is an int, or a subset of rows if n is a list of ints. If dropna, will take the nth non-null row, dropna is either Truthy (if a Series) or 'all', 'any' (if a DataFrame); this is equivalent to calling dropna(how=dropna) before the groupby. Parameters ---------- n : int or list of ints a single nth value for the row or a list of nth values dropna : None or str, optional apply the specified dropna operation before counting which row is the nth row. Needs to be None, 'any' or 'all' Examples -------- >>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2], ... 'B': [np.nan, 2, 3, 4, 5]}, columns=['A', 'B']) >>> g = df.groupby('A') >>> g.nth(0) B A 1 NaN 2 3.0 >>> g.nth(1) B A 1 2.0 2 5.0 >>> g.nth(-1) B A 1 4.0 2 5.0 >>> g.nth([0, 1]) B A 1 NaN 1 2.0 2 3.0 2 5.0 Specifying `dropna` allows count ignoring ``NaN`` >>> g.nth(0, dropna='any') B A 1 2.0 2 3.0 NaNs denote group exhausted when using dropna >>> g.nth(3, dropna='any') B A 1 NaN 2 NaN Specifying `as_index=False` in `groupby` keeps the original index. >>> df.groupby('A', as_index=False).nth(1) A B 1 1 2.0 4 2 5.0 """ if isinstance(n, int): nth_values = [n] elif isinstance(n, (set, list, tuple)): nth_values = list(set(n)) if dropna is not None: raise ValueError( "dropna option with a list of nth values is not supported") else: raise TypeError("n needs to be an int or a list/set/tuple of ints") nth_values = np.array(nth_values, dtype=np.intp) self._set_group_selection() if not dropna: mask = np.in1d(self._cumcount_array(), nth_values) | \ np.in1d(self._cumcount_array(ascending=False) + 1, -nth_values) out = self._selected_obj[mask] if not self.as_index: return out ids, _, _ = self.grouper.group_info out.index = self.grouper.result_index[ids[mask]] return out.sort_index() if self.sort else out if dropna not in ['any', 'all']: if isinstance(self._selected_obj, Series) and dropna is True: warnings.warn("the dropna={dropna} keyword is deprecated," "use dropna='all' instead. " "For a Series groupby, dropna must be " "either None, 'any' or 'all'.".format( dropna=dropna), FutureWarning, stacklevel=2) dropna = 'all' else: # Note: when agg-ing picker doesn't raise this, # just returns NaN raise ValueError("For a DataFrame groupby, dropna must be " "either None, 'any' or 'all', " "(was passed %s)." % (dropna),) # old behaviour, but with all and any support for DataFrames. # modified in GH 7559 to have better perf max_len = n if n >= 0 else - 1 - n dropped = self.obj.dropna(how=dropna, axis=self.axis) # get a new grouper for our dropped obj if self.keys is None and self.level is None: # we don't have the grouper info available # (e.g. we have selected out # a column that is not in the current object) axis = self.grouper.axis grouper = axis[axis.isin(dropped.index)] else: # create a grouper with the original parameters, but on the dropped # object from pandas.core.groupby.grouper import _get_grouper grouper, _, _ = _get_grouper(dropped, key=self.keys, axis=self.axis, level=self.level, sort=self.sort, mutated=self.mutated) grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort) sizes, result = grb.size(), grb.nth(n) mask = (sizes < max_len).values # set the results which don't meet the criteria if len(result) and mask.any(): result.loc[mask] = np.nan # reset/reindex to the original groups if len(self.obj) == len(dropped) or \ len(result) == len(self.grouper.result_index): result.index = self.grouper.result_index else: result = result.reindex(self.grouper.result_index) return result @Substitution(name='groupby') def ngroup(self, ascending=True): """ Number each group from 0 to the number of groups - 1. This is the enumerative complement of cumcount. Note that the numbers given to the groups match the order in which the groups would be seen when iterating over the groupby object, not the order they are first observed. .. versionadded:: 0.20.2 Parameters ---------- ascending : bool, default True If False, number in reverse, from number of group - 1 to 0. Examples -------- >>> df = pd.DataFrame({"A": list("aaabba")}) >>> df A 0 a 1 a 2 a 3 b 4 b 5 a >>> df.groupby('A').ngroup() 0 0 1 0 2 0 3 1 4 1 5 0 dtype: int64 >>> df.groupby('A').ngroup(ascending=False) 0 1 1 1 2 1 3 0 4 0 5 1 dtype: int64 >>> df.groupby(["A", [1,1,2,3,2,1]]).ngroup() 0 0 1 0 2 1 3 3 4 2 5 0 dtype: int64 See also -------- .cumcount : Number the rows in each group. """ with _group_selection_context(self): index = self._selected_obj.index result = Series(self.grouper.group_info[0], index) if not ascending: result = self.ngroups - 1 - result return result @Substitution(name='groupby') def cumcount(self, ascending=True): """ Number each item in each group from 0 to the length of that group - 1. Essentially this is equivalent to >>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index)) Parameters ---------- ascending : bool, default True If False, number in reverse, from length of group - 1 to 0. Examples -------- >>> df = pd.DataFrame([['a'], ['a'], ['a'], ['b'], ['b'], ['a']], ... columns=['A']) >>> df A 0 a 1 a 2 a 3 b 4 b 5 a >>> df.groupby('A').cumcount() 0 0 1 1 2 2 3 0 4 1 5 3 dtype: int64 >>> df.groupby('A').cumcount(ascending=False) 0 3 1 2 2 1 3 1 4 0 5 0 dtype: int64 See also -------- .ngroup : Number the groups themselves. """ with _group_selection_context(self): index = self._selected_obj.index cumcounts = self._cumcount_array(ascending=ascending) return Series(cumcounts, index) @Substitution(name='groupby') @Appender(_doc_template) def rank(self, method='average', ascending=True, na_option='keep', pct=False, axis=0): """ Provides the rank of values within each group. Parameters ---------- method : {'average', 'min', 'max', 'first', 'dense'}, default 'average' * average: average rank of group * min: lowest rank in group * max: highest rank in group * first: ranks assigned in order they appear in the array * dense: like 'min', but rank always increases by 1 between groups ascending : boolean, default True False for ranks by high (1) to low (N) na_option : {'keep', 'top', 'bottom'}, default 'keep' * keep: leave NA values where they are * top: smallest rank if ascending * bottom: smallest rank if descending pct : boolean, default False Compute percentage rank of data within each group axis : int, default 0 The axis of the object over which to compute the rank. Returns ----- DataFrame with ranking of values within each group """ if na_option not in {'keep', 'top', 'bottom'}: msg = "na_option must be one of 'keep', 'top', or 'bottom'" raise ValueError(msg) return self._cython_transform('rank', numeric_only=False, ties_method=method, ascending=ascending, na_option=na_option, pct=pct, axis=axis) @Substitution(name='groupby') @Appender(_doc_template) def cumprod(self, axis=0, *args, **kwargs): """Cumulative product for each group""" nv.validate_groupby_func('cumprod', args, kwargs, ['numeric_only', 'skipna']) if axis != 0: return self.apply(lambda x: x.cumprod(axis=axis, **kwargs)) return self._cython_transform('cumprod', **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def cumsum(self, axis=0, *args, **kwargs): """Cumulative sum for each group""" nv.validate_groupby_func('cumsum', args, kwargs, ['numeric_only', 'skipna']) if axis != 0: return self.apply(lambda x: x.cumsum(axis=axis, **kwargs)) return self._cython_transform('cumsum', **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def cummin(self, axis=0, **kwargs): """Cumulative min for each group""" if axis != 0: return self.apply(lambda x: np.minimum.accumulate(x, axis)) return self._cython_transform('cummin', numeric_only=False) @Substitution(name='groupby') @Appender(_doc_template) def cummax(self, axis=0, **kwargs): """Cumulative max for each group""" if axis != 0: return self.apply(lambda x: np.maximum.accumulate(x, axis)) return self._cython_transform('cummax', numeric_only=False) def _get_cythonized_result(self, how, grouper, aggregate=False, cython_dtype=None, needs_values=False, needs_mask=False, needs_ngroups=False, result_is_index=False, pre_processing=None, post_processing=None, **kwargs): """Get result for Cythonized functions Parameters ---------- how : str, Cythonized function name to be called grouper : Grouper object containing pertinent group info aggregate : bool, default False Whether the result should be aggregated to match the number of groups cython_dtype : default None Type of the array that will be modified by the Cython call. If `None`, the type will be inferred from the values of each slice needs_values : bool, default False Whether the values should be a part of the Cython call signature needs_mask : bool, default False Whether boolean mask needs to be part of the Cython call signature needs_ngroups : bool, default False Whether number of groups is part of the Cython call signature result_is_index : bool, default False Whether the result of the Cython operation is an index of values to be retrieved, instead of the actual values themselves pre_processing : function, default None Function to be applied to `values` prior to passing to Cython Raises if `needs_values` is False post_processing : function, default None Function to be applied to result of Cython function **kwargs : dict Extra arguments to be passed back to Cython funcs Returns ------- `Series` or `DataFrame` with filled values """ if result_is_index and aggregate: raise ValueError("'result_is_index' and 'aggregate' cannot both " "be True!") if post_processing: if not callable(pre_processing): raise ValueError("'post_processing' must be a callable!") if pre_processing: if not callable(pre_processing): raise ValueError("'pre_processing' must be a callable!") if not needs_values: raise ValueError("Cannot use 'pre_processing' without " "specifying 'needs_values'!") labels, _, ngroups = grouper.group_info output = collections.OrderedDict() base_func = getattr(libgroupby, how) for name, obj in self._iterate_slices(): if aggregate: result_sz = ngroups else: result_sz = len(obj.values) if not cython_dtype: cython_dtype = obj.values.dtype result = np.zeros(result_sz, dtype=cython_dtype) func = partial(base_func, result, labels) if needs_values: vals = obj.values if pre_processing: vals = pre_processing(vals) func = partial(func, vals) if needs_mask: mask = isna(obj.values).view(np.uint8) func = partial(func, mask) if needs_ngroups: func = partial(func, ngroups) func(**kwargs) # Call func to modify indexer values in place if result_is_index: result = algorithms.take_nd(obj.values, result) if post_processing: result = post_processing(result) output[name] = result if aggregate: return self._wrap_aggregated_output(output) else: return self._wrap_transformed_output(output) @Substitution(name='groupby') @Appender(_doc_template) def shift(self, periods=1, freq=None, axis=0): """ Shift each group by periods observations Parameters ---------- periods : integer, default 1 number of periods to shift freq : frequency string axis : axis to shift, default 0 """ if freq is not None or axis != 0: return self.apply(lambda x: x.shift(periods, freq, axis)) return self._get_cythonized_result('group_shift_indexer', self.grouper, cython_dtype=np.int64, needs_ngroups=True, result_is_index=True, periods=periods) @Substitution(name='groupby') @Appender(_doc_template) def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None, axis=0): """Calculate pct_change of each value to previous entry in group""" if freq is not None or axis != 0: return self.apply(lambda x: x.pct_change(periods=periods, fill_method=fill_method, limit=limit, freq=freq, axis=axis)) filled = getattr(self, fill_method)(limit=limit).drop( self.grouper.names, axis=1) shifted = filled.shift(periods=periods, freq=freq) return (filled / shifted) - 1 @Substitution(name='groupby') @Appender(_doc_template) def head(self, n=5): """ Returns first n rows of each group. Essentially equivalent to ``.apply(lambda x: x.head(n))``, except ignores as_index flag. Examples -------- >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B']) >>> df.groupby('A', as_index=False).head(1) A B 0 1 2 2 5 6 >>> df.groupby('A').head(1) A B 0 1 2 2 5 6 """ self._reset_group_selection() mask = self._cumcount_array() < n return self._selected_obj[mask].dropna(subset=[self.keys]) @Substitution(name='groupby') @Appender(_doc_template) def tail(self, n=5): """ Returns last n rows of each group Essentially equivalent to ``.apply(lambda x: x.tail(n))``, except ignores as_index flag. Examples -------- >>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]], columns=['A', 'B']) >>> df.groupby('A').tail(1) A B 1 a 2 3 b 2 >>> df.groupby('A').head(1) A B 0 a 1 2 b 1 """ self._reset_group_selection() mask = self._cumcount_array(ascending=False) < n return self._selected_obj[mask].dropna(subset=[self.keys]) GroupBy._add_numeric_operations() @Appender(GroupBy.__doc__) def groupby(obj, by, **kwds): if isinstance(obj, Series): from pandas.core.groupby.generic import SeriesGroupBy klass = SeriesGroupBy elif isinstance(obj, DataFrame): from pandas.core.groupby.generic import DataFrameGroupBy klass = DataFrameGroupBy else: # pragma: no cover raise TypeError('invalid type: %s' % type(obj)) return klass(obj, by, **kwds)

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import types from functools import wraps, partial import datetime import collections import warnings from contextlib import contextmanager import numpy as np from pandas._libs import groupby as libgroupby, Timestamp from pandas.util._validators import validate_kwargs from pandas.util._decorators import ( cache_readonly, Substitution, Appender) from pandas import compat from pandas.compat import zip, range, callable, set_function_name from pandas.compat.numpy import function as nv from pandas.core.dtypes.common import ( is_numeric_dtype, is_scalar, ensure_float) from pandas.core.dtypes.cast import maybe_downcast_to_dtype from pandas.core.dtypes.missing import isna, notna from pandas.core.groupby import base from pandas.core.base import (PandasObject, SelectionMixin, GroupByError, DataError, SpecificationError) from pandas.core.index import Index, MultiIndex from pandas.core.generic import NDFrame from pandas.core.frame import DataFrame from pandas.core.series import Series from pandas.core.sorting import get_group_index_sorter import pandas.core.common as com import pandas.core.algorithms as algorithms from pandas.core.config import option_context _doc_template = """ See also -------- pandas.Series.%(name)s pandas.DataFrame.%(name)s pandas.Panel.%(name)s """ _apply_docs = dict( template=""" Apply function `func` group-wise and combine the results together. The function passed to `apply` must take a {input} as its first argument and return a DataFrame, Series or scalar. `apply` will then take care of combining the results back together into a single dataframe or series. `apply` is therefore a highly flexible grouping method. While `apply` is a very flexible method, its downside is that using it can be quite a bit slower than using more specific methods like `agg` or `transform`. Pandas offers a wide range of method that will be much faster than using `apply` for their specific purposes, so try to use them before reaching for `apply`. Parameters ---------- func : callable A callable that takes a {input} as its first argument, and returns a dataframe, a series or a scalar. In addition the callable may take positional and keyword arguments. args, kwargs : tuple and dict Optional positional and keyword arguments to pass to `func`. Returns ------- applied : Series or DataFrame Notes ----- In the current implementation `apply` calls `func` twice on the first group to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if `func` has side-effects, as they will take effect twice for the first group. Examples -------- {examples} See also -------- pipe : Apply function to the full GroupBy object instead of to each group. aggregate : Apply aggregate function to the GroupBy object. transform : Apply function column-by-column to the GroupBy object. Series.apply : Apply a function to a Series. DataFrame.apply : Apply a function to each row or column of a DataFrame. """, dataframe_examples=""" >>> df = pd.DataFrame({'A': 'a a b'.split(), 'B': [1,2,3], 'C': [4,6, 5]}) >>> g = df.groupby('A') Notice that ``g`` has two groups, ``a`` and ``b``. Calling `apply` in various ways, we can get different grouping results: Example 1: below the function passed to `apply` takes a DataFrame as its argument and returns a DataFrame. `apply` combines the result for each group together into a new DataFrame: >>> g[['B', 'C']].apply(lambda x: x / x.sum()) B C 0 0.333333 0.4 1 0.666667 0.6 2 1.000000 1.0 Example 2: The function passed to `apply` takes a DataFrame as its argument and returns a Series. `apply` combines the result for each group together into a new DataFrame: >>> g[['B', 'C']].apply(lambda x: x.max() - x.min()) B C A a 1 2 b 0 0 Example 3: The function passed to `apply` takes a DataFrame as its argument and returns a scalar. `apply` combines the result for each group together into a Series, including setting the index as appropriate: >>> g.apply(lambda x: x.C.max() - x.B.min()) A a 5 b 2 dtype: int64 """, series_examples=""" >>> s = pd.Series([0, 1, 2], index='a a b'.split()) >>> g = s.groupby(s.index) From ``s`` above we can see that ``g`` has two groups, ``a`` and ``b``. Calling `apply` in various ways, we can get different grouping results: Example 1: The function passed to `apply` takes a Series as its argument and returns a Series. `apply` combines the result for each group together into a new Series: >>> g.apply(lambda x: x*2 if x.name == 'b' else x/2) 0 0.0 1 0.5 2 4.0 dtype: float64 Example 2: The function passed to `apply` takes a Series as its argument and returns a scalar. `apply` combines the result for each group together into a Series, including setting the index as appropriate: >>> g.apply(lambda x: x.max() - x.min()) a 1 b 0 dtype: int64 """) _pipe_template = """\ Apply a function `func` with arguments to this %(klass)s object and return the function's result. %(versionadded)s Use `.pipe` when you want to improve readability by chaining together functions that expect Series, DataFrames, GroupBy or Resampler objects. Instead of writing >>> h(g(f(df.groupby('group')), arg1=a), arg2=b, arg3=c) You can write >>> (df.groupby('group') ... .pipe(f) ... .pipe(g, arg1=a) ... .pipe(h, arg2=b, arg3=c)) which is much more readable. Parameters ---------- func : callable or tuple of (callable, string) Function to apply to this %(klass)s object or, alternatively, a `(callable, data_keyword)` tuple where `data_keyword` is a string indicating the keyword of `callable` that expects the %(klass)s object. args : iterable, optional positional arguments passed into `func`. kwargs : dict, optional a dictionary of keyword arguments passed into `func`. Returns ------- object : the return type of `func`. Notes ----- See more `here <http://pandas.pydata.org/pandas-docs/stable/groupby.html#piping-function-calls>`_ Examples -------- %(examples)s See Also -------- pandas.Series.pipe : Apply a function with arguments to a series pandas.DataFrame.pipe: Apply a function with arguments to a dataframe apply : Apply function to each group instead of to the full %(klass)s object. """ _transform_template = """ Call function producing a like-indexed %(klass)s on each group and return a %(klass)s having the same indexes as the original object filled with the transformed values Parameters ---------- f : function Function to apply to each group Notes ----- Each group is endowed the attribute 'name' in case you need to know which group you are working on. The current implementation imposes three requirements on f: * f must return a value that either has the same shape as the input subframe or can be broadcast to the shape of the input subframe. For example, f returns a scalar it will be broadcast to have the same shape as the input subframe. * if this is a DataFrame, f must support application column-by-column in the subframe. If f also supports application to the entire subframe, then a fast path is used starting from the second chunk. * f must not mutate groups. Mutation is not supported and may produce unexpected results. Returns ------- %(klass)s See also -------- aggregate, transform Examples -------- # Same shape >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', ... 'foo', 'bar'], ... 'B' : ['one', 'one', 'two', 'three', ... 'two', 'two'], ... 'C' : [1, 5, 5, 2, 5, 5], ... 'D' : [2.0, 5., 8., 1., 2., 9.]}) >>> grouped = df.groupby('A') >>> grouped.transform(lambda x: (x - x.mean()) / x.std()) C D 0 -1.154701 -0.577350 1 0.577350 0.000000 2 0.577350 1.154701 3 -1.154701 -1.000000 4 0.577350 -0.577350 5 0.577350 1.000000 # Broadcastable >>> grouped.transform(lambda x: x.max() - x.min()) C D 0 4 6.0 1 3 8.0 2 4 6.0 3 3 8.0 4 4 6.0 5 3 8.0 """ class GroupByPlot(PandasObject): def __init__(self, groupby): self._groupby = groupby def __call__(self, *args, **kwargs): def f(self): return self.plot(*args, **kwargs) f.__name__ = 'plot' return self._groupby.apply(f) def __getattr__(self, name): def attr(*args, **kwargs): def f(self): return getattr(self.plot, name)(*args, **kwargs) return self._groupby.apply(f) return attr @contextmanager def _group_selection_context(groupby): groupby._set_group_selection() yield groupby groupby._reset_group_selection() class _GroupBy(PandasObject, SelectionMixin): _group_selection = None _apply_whitelist = frozenset([]) def __init__(self, obj, keys=None, axis=0, level=None, grouper=None, exclusions=None, selection=None, as_index=True, sort=True, group_keys=True, squeeze=False, observed=False, **kwargs): self._selection = selection if isinstance(obj, NDFrame): obj._consolidate_inplace() self.level = level if not as_index: if not isinstance(obj, DataFrame): raise TypeError('as_index=False only valid with DataFrame') if axis != 0: raise ValueError('as_index=False only valid for axis=0') self.as_index = as_index self.keys = keys self.sort = sort self.group_keys = group_keys self.squeeze = squeeze self.observed = observed self.mutated = kwargs.pop('mutated', False) if grouper is None: from pandas.core.groupby.grouper import _get_grouper grouper, exclusions, obj = _get_grouper(obj, keys, axis=axis, level=level, sort=sort, observed=observed, mutated=self.mutated) self.obj = obj self.axis = obj._get_axis_number(axis) self.grouper = grouper self.exclusions = set(exclusions) if exclusions else set() # we accept no other args validate_kwargs('group', kwargs, {}) def __len__(self): return len(self.groups) def __unicode__(self): # TODO: Better unicode/repr for GroupBy object return object.__repr__(self) def _assure_grouper(self): pass @property def groups(self): self._assure_grouper() return self.grouper.groups @property def ngroups(self): self._assure_grouper() return self.grouper.ngroups @property def indices(self): self._assure_grouper() return self.grouper.indices def _get_indices(self, names): def get_converter(s): # possibly convert to the actual key types # in the indices, could be a Timestamp or a np.datetime64 if isinstance(s, (Timestamp, datetime.datetime)): return lambda key: Timestamp(key) elif isinstance(s, np.datetime64): return lambda key: Timestamp(key).asm8 else: return lambda key: key if len(names) == 0: return [] if len(self.indices) > 0: index_sample = next(iter(self.indices)) else: index_sample = None # Dummy sample name_sample = names[0] if isinstance(index_sample, tuple): if not isinstance(name_sample, tuple): msg = ("must supply a tuple to get_group with multiple" " grouping keys") raise ValueError(msg) if not len(name_sample) == len(index_sample): try: # If the original grouper was a tuple return [self.indices[name] for name in names] except KeyError: # turns out it wasn't a tuple msg = ("must supply a a same-length tuple to get_group" " with multiple grouping keys") raise ValueError(msg) converters = [get_converter(s) for s in index_sample] names = [tuple(f(n) for f, n in zip(converters, name)) for name in names] else: converter = get_converter(index_sample) names = [converter(name) for name in names] return [self.indices.get(name, []) for name in names] def _get_index(self, name): return self._get_indices([name])[0] @cache_readonly def _selected_obj(self): if self._selection is None or isinstance(self.obj, Series): if self._group_selection is not None: return self.obj[self._group_selection] return self.obj else: return self.obj[self._selection] def _reset_group_selection(self): if self._group_selection is not None: self._group_selection = None self._reset_cache('_selected_obj') def _set_group_selection(self): grp = self.grouper if not (self.as_index and getattr(grp, 'groupings', None) is not None and self.obj.ndim > 1 and self._group_selection is None): return ax = self.obj._info_axis groupers = [g.name for g in grp.groupings if g.level is None and g.in_axis] if len(groupers): self._group_selection = ax.difference(Index(groupers)).tolist() self._reset_cache('_selected_obj') def _set_result_index_ordered(self, result): if not self.grouper.is_monotonic: index = Index(np.concatenate( self._get_indices(self.grouper.result_index))) result.set_axis(index, axis=self.axis, inplace=True) result = result.sort_index(axis=self.axis) result.set_axis(self.obj._get_axis(self.axis), axis=self.axis, inplace=True) return result def _dir_additions(self): return self.obj._dir_additions() | self._apply_whitelist def __getattr__(self, attr): if attr in self._internal_names_set: return object.__getattribute__(self, attr) if attr in self.obj: return self[attr] if hasattr(self.obj, attr): return self._make_wrapper(attr) raise AttributeError("%r object has no attribute %r" % (type(self).__name__, attr)) @Substitution(klass='GroupBy', versionadded='.. versionadded:: 0.21.0', examples="""\ >>> df = pd.DataFrame({'A': 'a b a b'.split(), 'B': [1, 2, 3, 4]}) >>> df A B 0 a 1 1 b 2 2 a 3 3 b 4 To get the difference between each groups maximum and minimum value in one pass, you can do >>> df.groupby('A').pipe(lambda x: x.max() - x.min()) B A a 2 b 2""") @Appender(_pipe_template) def pipe(self, func, *args, **kwargs): return com._pipe(self, func, *args, **kwargs) plot = property(GroupByPlot) def _make_wrapper(self, name): if name not in self._apply_whitelist: is_callable = callable(getattr(self._selected_obj, name, None)) kind = ' callable ' if is_callable else ' ' msg = ("Cannot access{0}attribute {1!r} of {2!r} objects, try " "using the 'apply' method".format(kind, name, type(self).__name__)) raise AttributeError(msg) self._set_group_selection() f = getattr(self._selected_obj, name) if not isinstance(f, types.MethodType): return self.apply(lambda self: getattr(self, name)) f = getattr(type(self._selected_obj), name) def wrapper(*args, **kwargs): kwargs_with_axis = kwargs.copy() if 'axis' not in kwargs_with_axis or \ kwargs_with_axis['axis'] is None: kwargs_with_axis['axis'] = self.axis def curried_with_axis(x): return f(x, *args, **kwargs_with_axis) def curried(x): return f(x, *args, **kwargs) curried.__name__ = curried_with_axis.__name__ = name if name in base.plotting_methods: return self.apply(curried) try: return self.apply(curried_with_axis) except Exception: try: return self.apply(curried) except Exception: # mark this column as an error try: return self._aggregate_item_by_item(name, *args, **kwargs) except (AttributeError): raise ValueError return wrapper def get_group(self, name, obj=None): if obj is None: obj = self._selected_obj inds = self._get_index(name) if not len(inds): raise KeyError(name) return obj._take(inds, axis=self.axis) def __iter__(self): return self.grouper.get_iterator(self.obj, axis=self.axis) @Appender(_apply_docs['template'] .format(input="dataframe", examples=_apply_docs['dataframe_examples'])) def apply(self, func, *args, **kwargs): func = self._is_builtin_func(func) # this is needed so we don't try and wrap strings. If we could if args or kwargs: if callable(func): @wraps(func) def f(g): with np.errstate(all='ignore'): return func(g, *args, **kwargs) else: raise ValueError('func must be a callable if args or ' 'kwargs are supplied') else: f = func # ignore SettingWithCopy here in case the user mutates with option_context('mode.chained_assignment', None): try: result = self._python_apply_general(f) except Exception: # gh-20949 # try again, with .apply acting as a filtering # operation, by excluding the grouping column # This would normally not be triggered # except if the udf is trying an operation that # fails on *some* columns, e.g. a numeric operation # on a string grouper column with _group_selection_context(self): return self._python_apply_general(f) return result def _python_apply_general(self, f): keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis) return self._wrap_applied_output( keys, values, not_indexed_same=mutated or self.mutated) def _iterate_slices(self): yield self._selection_name, self._selected_obj def transform(self, func, *args, **kwargs): raise com.AbstractMethodError(self) def _cumcount_array(self, ascending=True): ids, _, ngroups = self.grouper.group_info sorter = get_group_index_sorter(ids, ngroups) ids, count = ids[sorter], len(ids) if count == 0: return np.empty(0, dtype=np.int64) run = np.r_[True, ids[:-1] != ids[1:]] rep = np.diff(np.r_[np.nonzero(run)[0], count]) out = (~run).cumsum() if ascending: out -= np.repeat(out[run], rep) else: out = np.repeat(out[np.r_[run[1:], True]], rep) - out rev = np.empty(count, dtype=np.intp) rev[sorter] = np.arange(count, dtype=np.intp) return out[rev].astype(np.int64, copy=False) def _try_cast(self, result, obj, numeric_only=False): if obj.ndim > 1: dtype = obj.values.dtype else: dtype = obj.dtype if not is_scalar(result): if numeric_only and is_numeric_dtype(dtype) or not numeric_only: result = maybe_downcast_to_dtype(result, dtype) return result def _transform_should_cast(self, func_nm): return (self.size().fillna(0) > 0).any() and ( func_nm not in base.cython_cast_blacklist) def _cython_transform(self, how, numeric_only=True, **kwargs): output = collections.OrderedDict() for name, obj in self._iterate_slices(): is_numeric = is_numeric_dtype(obj.dtype) if numeric_only and not is_numeric: continue try: result, names = self.grouper.transform(obj.values, how, **kwargs) except NotImplementedError: continue except AssertionError as e: raise GroupByError(str(e)) if self._transform_should_cast(how): output[name] = self._try_cast(result, obj) else: output[name] = result if len(output) == 0: raise DataError('No numeric types to aggregate') return self._wrap_transformed_output(output, names) def _cython_agg_general(self, how, alt=None, numeric_only=True, min_count=-1): output = {} for name, obj in self._iterate_slices(): is_numeric = is_numeric_dtype(obj.dtype) if numeric_only and not is_numeric: continue try: result, names = self.grouper.aggregate(obj.values, how, min_count=min_count) except AssertionError as e: raise GroupByError(str(e)) output[name] = self._try_cast(result, obj) if len(output) == 0: raise DataError('No numeric types to aggregate') return self._wrap_aggregated_output(output, names) def _python_agg_general(self, func, *args, **kwargs): func = self._is_builtin_func(func) f = lambda x: func(x, *args, **kwargs) # iterate through "columns" ex exclusions to populate output dict output = {} for name, obj in self._iterate_slices(): try: result, counts = self.grouper.agg_series(obj, f) output[name] = self._try_cast(result, obj, numeric_only=True) except TypeError: continue if len(output) == 0: return self._python_apply_general(f) if self.grouper._filter_empty_groups: mask = counts.ravel() > 0 for name, result in compat.iteritems(output): # since we are masking, make sure that we have a float object values = result if is_numeric_dtype(values.dtype): values = ensure_float(values) output[name] = self._try_cast(values[mask], result) return self._wrap_aggregated_output(output) def _wrap_applied_output(self, *args, **kwargs): raise com.AbstractMethodError(self) def _concat_objects(self, keys, values, not_indexed_same=False): from pandas.core.reshape.concat import concat def reset_identity(values): # reset the identities of the components # of the values to prevent aliasing for v in com._not_none(*values): ax = v._get_axis(self.axis) ax._reset_identity() return values if not not_indexed_same: result = concat(values, axis=self.axis) ax = self._selected_obj._get_axis(self.axis) if isinstance(result, Series): result = result.reindex(ax) else: # this is a very unfortunate situation # we have a multi-index that is NOT lexsorted # and we have a result which is duplicated # we can't reindex, so we resort to this if isinstance(ax, MultiIndex) and not ax.is_unique: indexer = algorithms.unique1d( result.index.get_indexer_for(ax.values)) result = result.take(indexer, axis=self.axis) else: result = result.reindex(ax, axis=self.axis) elif self.group_keys: values = reset_identity(values) if self.as_index: group_keys = keys group_levels = self.grouper.levels group_names = self.grouper.names result = concat(values, axis=self.axis, keys=group_keys, levels=group_levels, names=group_names, sort=False) else: keys = list(range(len(values))) result = concat(values, axis=self.axis, keys=keys) else: values = reset_identity(values) result = concat(values, axis=self.axis) if (isinstance(result, Series) and getattr(self, '_selection_name', None) is not None): result.name = self._selection_name return result def _apply_filter(self, indices, dropna): if len(indices) == 0: indices = np.array([], dtype='int64') else: indices = np.sort(np.concatenate(indices)) if dropna: filtered = self._selected_obj.take(indices, axis=self.axis) else: mask = np.empty(len(self._selected_obj.index), dtype=bool) mask.fill(False) mask[indices.astype(int)] = True mask = np.tile(mask, list(self._selected_obj.shape[1:]) + [1]).T filtered = self._selected_obj.where(mask) return filtered class GroupBy(_GroupBy): def _bool_agg(self, val_test, skipna): def objs_to_bool(vals): try: vals = vals.astype(np.bool) except ValueError: vals = np.array([bool(x) for x in vals]) return vals.view(np.uint8) def result_to_bool(result): return result.astype(np.bool, copy=False) return self._get_cythonized_result('group_any_all', self.grouper, aggregate=True, cython_dtype=np.uint8, needs_values=True, needs_mask=True, pre_processing=objs_to_bool, post_processing=result_to_bool, val_test=val_test, skipna=skipna) @Substitution(name='groupby') @Appender(_doc_template) def any(self, skipna=True): return self._bool_agg('any', skipna) @Substitution(name='groupby') @Appender(_doc_template) def all(self, skipna=True): return self._bool_agg('all', skipna) @Substitution(name='groupby') @Appender(_doc_template) def count(self): raise NotImplementedError @Substitution(name='groupby') @Appender(_doc_template) def mean(self, *args, **kwargs): nv.validate_groupby_func('mean', args, kwargs, ['numeric_only']) try: return self._cython_agg_general('mean', **kwargs) except GroupByError: raise except Exception: with _group_selection_context(self): f = lambda x: x.mean(axis=self.axis, **kwargs) return self._python_agg_general(f) @Substitution(name='groupby') @Appender(_doc_template) def median(self, **kwargs): try: return self._cython_agg_general('median', **kwargs) except GroupByError: raise except Exception: def f(x): if isinstance(x, np.ndarray): x = Series(x) return x.median(axis=self.axis, **kwargs) with _group_selection_context(self): return self._python_agg_general(f) @Substitution(name='groupby') @Appender(_doc_template) def std(self, ddof=1, *args, **kwargs): nv.validate_groupby_func('std', args, kwargs) return np.sqrt(self.var(ddof=ddof, **kwargs)) @Substitution(name='groupby') @Appender(_doc_template) def var(self, ddof=1, *args, **kwargs): nv.validate_groupby_func('var', args, kwargs) if ddof == 1: try: return self._cython_agg_general('var', **kwargs) except Exception: f = lambda x: x.var(ddof=ddof, **kwargs) with _group_selection_context(self): return self._python_agg_general(f) else: f = lambda x: x.var(ddof=ddof, **kwargs) with _group_selection_context(self): return self._python_agg_general(f) @Substitution(name='groupby') @Appender(_doc_template) def sem(self, ddof=1): return self.std(ddof=ddof) / np.sqrt(self.count()) @Substitution(name='groupby') @Appender(_doc_template) def size(self): result = self.grouper.size() if isinstance(self.obj, Series): result.name = getattr(self.obj, 'name', None) return result @classmethod def _add_numeric_operations(cls): def groupby_function(name, alias, npfunc, numeric_only=True, _convert=False, min_count=-1): _local_template = "Compute %(f)s of group values" @Substitution(name='groupby', f=name) @Appender(_doc_template) @Appender(_local_template) def f(self, **kwargs): if 'numeric_only' not in kwargs: kwargs['numeric_only'] = numeric_only if 'min_count' not in kwargs: kwargs['min_count'] = min_count self._set_group_selection() try: return self._cython_agg_general( alias, alt=npfunc, **kwargs) except AssertionError as e: raise SpecificationError(str(e)) except Exception: result = self.aggregate( lambda x: npfunc(x, axis=self.axis)) if _convert: result = result._convert(datetime=True) return result set_function_name(f, name, cls) return f def first_compat(x, axis=0): def first(x): x = np.asarray(x) x = x[notna(x)] if len(x) == 0: return np.nan return x[0] if isinstance(x, DataFrame): return x.apply(first, axis=axis) else: return first(x) def last_compat(x, axis=0): def last(x): x = np.asarray(x) x = x[notna(x)] if len(x) == 0: return np.nan return x[-1] if isinstance(x, DataFrame): return x.apply(last, axis=axis) else: return last(x) cls.sum = groupby_function('sum', 'add', np.sum, min_count=0) cls.prod = groupby_function('prod', 'prod', np.prod, min_count=0) cls.min = groupby_function('min', 'min', np.min, numeric_only=False) cls.max = groupby_function('max', 'max', np.max, numeric_only=False) cls.first = groupby_function('first', 'first', first_compat, numeric_only=False) cls.last = groupby_function('last', 'last', last_compat, numeric_only=False) @Substitution(name='groupby') @Appender(_doc_template) def ohlc(self): return self._apply_to_column_groupbys( lambda x: x._cython_agg_general('ohlc')) @Appender(DataFrame.describe.__doc__) def describe(self, **kwargs): with _group_selection_context(self): result = self.apply(lambda x: x.describe(**kwargs)) if self.axis == 1: return result.T return result.unstack() @Substitution(name='groupby') @Appender(_doc_template) def resample(self, rule, *args, **kwargs): from pandas.core.resample import get_resampler_for_grouping return get_resampler_for_grouping(self, rule, *args, **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def rolling(self, *args, **kwargs): from pandas.core.window import RollingGroupby return RollingGroupby(self, *args, **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def expanding(self, *args, **kwargs): from pandas.core.window import ExpandingGroupby return ExpandingGroupby(self, *args, **kwargs) def _fill(self, direction, limit=None): if limit is None: limit = -1 return self._get_cythonized_result('group_fillna_indexer', self.grouper, needs_mask=True, cython_dtype=np.int64, result_is_index=True, direction=direction, limit=limit) @Substitution(name='groupby') def pad(self, limit=None): return self._fill('ffill', limit=limit) ffill = pad @Substitution(name='groupby') def backfill(self, limit=None): return self._fill('bfill', limit=limit) bfill = backfill @Substitution(name='groupby') @Appender(_doc_template) def nth(self, n, dropna=None): if isinstance(n, int): nth_values = [n] elif isinstance(n, (set, list, tuple)): nth_values = list(set(n)) if dropna is not None: raise ValueError( "dropna option with a list of nth values is not supported") else: raise TypeError("n needs to be an int or a list/set/tuple of ints") nth_values = np.array(nth_values, dtype=np.intp) self._set_group_selection() if not dropna: mask = np.in1d(self._cumcount_array(), nth_values) | \ np.in1d(self._cumcount_array(ascending=False) + 1, -nth_values) out = self._selected_obj[mask] if not self.as_index: return out ids, _, _ = self.grouper.group_info out.index = self.grouper.result_index[ids[mask]] return out.sort_index() if self.sort else out if dropna not in ['any', 'all']: if isinstance(self._selected_obj, Series) and dropna is True: warnings.warn("the dropna={dropna} keyword is deprecated," "use dropna='all' instead. " "For a Series groupby, dropna must be " "either None, 'any' or 'all'.".format( dropna=dropna), FutureWarning, stacklevel=2) dropna = 'all' else: # just returns NaN raise ValueError("For a DataFrame groupby, dropna must be " "either None, 'any' or 'all', " "(was passed %s)." % (dropna),) # old behaviour, but with all and any support for DataFrames. # modified in GH 7559 to have better perf max_len = n if n >= 0 else - 1 - n dropped = self.obj.dropna(how=dropna, axis=self.axis) # get a new grouper for our dropped obj if self.keys is None and self.level is None: # we don't have the grouper info available axis = self.grouper.axis grouper = axis[axis.isin(dropped.index)] else: from pandas.core.groupby.grouper import _get_grouper grouper, _, _ = _get_grouper(dropped, key=self.keys, axis=self.axis, level=self.level, sort=self.sort, mutated=self.mutated) grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort) sizes, result = grb.size(), grb.nth(n) mask = (sizes < max_len).values if len(result) and mask.any(): result.loc[mask] = np.nan # reset/reindex to the original groups if len(self.obj) == len(dropped) or \ len(result) == len(self.grouper.result_index): result.index = self.grouper.result_index else: result = result.reindex(self.grouper.result_index) return result @Substitution(name='groupby') def ngroup(self, ascending=True): with _group_selection_context(self): index = self._selected_obj.index result = Series(self.grouper.group_info[0], index) if not ascending: result = self.ngroups - 1 - result return result @Substitution(name='groupby') def cumcount(self, ascending=True): with _group_selection_context(self): index = self._selected_obj.index cumcounts = self._cumcount_array(ascending=ascending) return Series(cumcounts, index) @Substitution(name='groupby') @Appender(_doc_template) def rank(self, method='average', ascending=True, na_option='keep', pct=False, axis=0): if na_option not in {'keep', 'top', 'bottom'}: msg = "na_option must be one of 'keep', 'top', or 'bottom'" raise ValueError(msg) return self._cython_transform('rank', numeric_only=False, ties_method=method, ascending=ascending, na_option=na_option, pct=pct, axis=axis) @Substitution(name='groupby') @Appender(_doc_template) def cumprod(self, axis=0, *args, **kwargs): nv.validate_groupby_func('cumprod', args, kwargs, ['numeric_only', 'skipna']) if axis != 0: return self.apply(lambda x: x.cumprod(axis=axis, **kwargs)) return self._cython_transform('cumprod', **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def cumsum(self, axis=0, *args, **kwargs): nv.validate_groupby_func('cumsum', args, kwargs, ['numeric_only', 'skipna']) if axis != 0: return self.apply(lambda x: x.cumsum(axis=axis, **kwargs)) return self._cython_transform('cumsum', **kwargs) @Substitution(name='groupby') @Appender(_doc_template) def cummin(self, axis=0, **kwargs): if axis != 0: return self.apply(lambda x: np.minimum.accumulate(x, axis)) return self._cython_transform('cummin', numeric_only=False) @Substitution(name='groupby') @Appender(_doc_template) def cummax(self, axis=0, **kwargs): if axis != 0: return self.apply(lambda x: np.maximum.accumulate(x, axis)) return self._cython_transform('cummax', numeric_only=False) def _get_cythonized_result(self, how, grouper, aggregate=False, cython_dtype=None, needs_values=False, needs_mask=False, needs_ngroups=False, result_is_index=False, pre_processing=None, post_processing=None, **kwargs): if result_is_index and aggregate: raise ValueError("'result_is_index' and 'aggregate' cannot both " "be True!") if post_processing: if not callable(pre_processing): raise ValueError("'post_processing' must be a callable!") if pre_processing: if not callable(pre_processing): raise ValueError("'pre_processing' must be a callable!") if not needs_values: raise ValueError("Cannot use 'pre_processing' without " "specifying 'needs_values'!") labels, _, ngroups = grouper.group_info output = collections.OrderedDict() base_func = getattr(libgroupby, how) for name, obj in self._iterate_slices(): if aggregate: result_sz = ngroups else: result_sz = len(obj.values) if not cython_dtype: cython_dtype = obj.values.dtype result = np.zeros(result_sz, dtype=cython_dtype) func = partial(base_func, result, labels) if needs_values: vals = obj.values if pre_processing: vals = pre_processing(vals) func = partial(func, vals) if needs_mask: mask = isna(obj.values).view(np.uint8) func = partial(func, mask) if needs_ngroups: func = partial(func, ngroups) func(**kwargs) # Call func to modify indexer values in place if result_is_index: result = algorithms.take_nd(obj.values, result) if post_processing: result = post_processing(result) output[name] = result if aggregate: return self._wrap_aggregated_output(output) else: return self._wrap_transformed_output(output) @Substitution(name='groupby') @Appender(_doc_template) def shift(self, periods=1, freq=None, axis=0): if freq is not None or axis != 0: return self.apply(lambda x: x.shift(periods, freq, axis)) return self._get_cythonized_result('group_shift_indexer', self.grouper, cython_dtype=np.int64, needs_ngroups=True, result_is_index=True, periods=periods) @Substitution(name='groupby') @Appender(_doc_template) def pct_change(self, periods=1, fill_method='pad', limit=None, freq=None, axis=0): if freq is not None or axis != 0: return self.apply(lambda x: x.pct_change(periods=periods, fill_method=fill_method, limit=limit, freq=freq, axis=axis)) filled = getattr(self, fill_method)(limit=limit).drop( self.grouper.names, axis=1) shifted = filled.shift(periods=periods, freq=freq) return (filled / shifted) - 1 @Substitution(name='groupby') @Appender(_doc_template) def head(self, n=5): self._reset_group_selection() mask = self._cumcount_array() < n return self._selected_obj[mask].dropna(subset=[self.keys]) @Substitution(name='groupby') @Appender(_doc_template) def tail(self, n=5): self._reset_group_selection() mask = self._cumcount_array(ascending=False) < n return self._selected_obj[mask].dropna(subset=[self.keys]) GroupBy._add_numeric_operations() @Appender(GroupBy.__doc__) def groupby(obj, by, **kwds): if isinstance(obj, Series): from pandas.core.groupby.generic import SeriesGroupBy klass = SeriesGroupBy elif isinstance(obj, DataFrame): from pandas.core.groupby.generic import DataFrameGroupBy klass = DataFrameGroupBy else: # pragma: no cover raise TypeError('invalid type: %s' % type(obj)) return klass(obj, by, **kwds)

true

f71fef0c42afe5737fff68a898b0a1503169b16b

303

py

Python

mundo 3/des085.py

Pedroluis1/python

d949fa2646c049aa51a41a32dc62de7b14eae90f

[ "MIT" ]

null

mundo 3/des085.py

Pedroluis1/python

d949fa2646c049aa51a41a32dc62de7b14eae90f

[ "MIT" ]

null

mundo 3/des085.py

Pedroluis1/python

d949fa2646c049aa51a41a32dc62de7b14eae90f

[ "MIT" ]

null

valores = [[], []] val = 0 for c in range(1, 8): val = int(input(f'Digite o {c}° valor: ')) if val % 2 == 0: valores[0].append(val) else: valores[1].append(val) valores[0].sort() valores[1].sort() print(f'valores impares: {valores[1]}') print(f'valores pares: {valores[0]}')

23.307692

46

0.567657

valores = [[], []] val = 0 for c in range(1, 8): val = int(input(f'Digite o {c}° valor: ')) if val % 2 == 0: valores[0].append(val) else: valores[1].append(val) valores[0].sort() valores[1].sort() print(f'valores impares: {valores[1]}') print(f'valores pares: {valores[0]}')

true

f71fefeee470de4abd81815f4b20fd0e5aa1ae84

6,228

py

Python

pypy/translator/platform/posix.py

woodrow/pyoac

b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7

[ "MIT" ]

1

2019-05-27T00:58:46.000Z

pypy/translator/platform/posix.py

woodrow/pyoac

b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7

[ "MIT" ]

null

pypy/translator/platform/posix.py

woodrow/pyoac

b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7

[ "MIT" ]

null

""" Base class for all posixish platforms """ from pypy.translator.platform import Platform, log, _run_subprocess from pypy.tool import autopath import py, os class BasePosix(Platform): exe_ext = '' def __init__(self, cc=None): if cc is None: cc = 'gcc' self.cc = cc def _libs(self, libraries): return ['-l%s' % (lib,) for lib in libraries] def _libdirs(self, library_dirs): return ['-L%s' % (ldir,) for ldir in library_dirs] def _includedirs(self, include_dirs): return ['-I%s' % (idir,) for idir in include_dirs] def _linkfiles(self, link_files): return list(link_files) def _compile_c_file(self, cc, cfile, compile_args): oname = cfile.new(ext='o') args = ['-c'] + compile_args + [str(cfile), '-o', str(oname)] self._execute_c_compiler(cc, args, oname) return oname def _link(self, cc, ofiles, link_args, standalone, exe_name): args = [str(ofile) for ofile in ofiles] + link_args args += ['-o', str(exe_name)] if not standalone: args = self._args_for_shared(args) self._execute_c_compiler(cc, args, exe_name) return exe_name def _preprocess_dirs(self, include_dirs): # hook for maemo return include_dirs def gen_makefile(self, cfiles, eci, exe_name=None, path=None): cfiles = [py.path.local(f) for f in cfiles] cfiles += [py.path.local(f) for f in eci.separate_module_files] if path is None: path = cfiles[0].dirpath() pypypath = py.path.local(autopath.pypydir) if exe_name is None: exe_name = cfiles[0].new(ext=self.exe_ext) m = GnuMakefile(path) m.exe_name = exe_name m.eci = eci def pypyrel(fpath): rel = py.path.local(fpath).relto(pypypath) if rel: return os.path.join('$(PYPYDIR)', rel) else: return fpath rel_cfiles = [m.pathrel(cfile) for cfile in cfiles] rel_ofiles = [rel_cfile[:-2]+'.o' for rel_cfile in rel_cfiles] m.cfiles = rel_cfiles rel_includedirs = [pypyrel(incldir) for incldir in self._preprocess_dirs(eci.include_dirs)] m.comment('automatically generated makefile') definitions = [ ('PYPYDIR', autopath.pypydir), ('TARGET', exe_name.basename), ('DEFAULT_TARGET', '$(TARGET)'), ('SOURCES', rel_cfiles), ('OBJECTS', rel_ofiles), ('LIBS', self._libs(eci.libraries)), ('LIBDIRS', self._libdirs(eci.library_dirs)), ('INCLUDEDIRS', self._includedirs(rel_includedirs)), ('CFLAGS', self.cflags + list(eci.compile_extra)), ('LDFLAGS', self.link_flags + list(eci.link_extra)), ('CC', self.cc) ] for args in definitions: m.definition(*args) rules = [ ('all', '$(DEFAULT_TARGET)', []), ('$(TARGET)', '$(OBJECTS)', '$(CC) $(LDFLAGS) -o $@ $(OBJECTS) $(LIBDIRS) $(LIBS)'), ('%.o', '%.c', '$(CC) $(CFLAGS) -o $@ -c $< $(INCLUDEDIRS)'), ] for rule in rules: m.rule(*rule) return m def execute_makefile(self, path_to_makefile): if isinstance(path_to_makefile, GnuMakefile): path = path_to_makefile.makefile_dir else: path = path_to_makefile log.execute('make in %s' % (path,)) returncode, stdout, stderr = _run_subprocess('make', ['-C', str(path)]) self._handle_error(returncode, stdout, stderr, path.join('make')) class Definition(object): def __init__(self, name, value): self.name = name self.value = value def write(self, f): def write_list(prefix, lst): for i, fn in enumerate(lst): print >> f, prefix, fn, if i < len(lst)-1: print >> f, '\\' else: print >> f prefix = ' ' * len(prefix) name, value = self.name, self.value if isinstance(value, str): f.write('%s = %s\n' % (name, value)) else: write_list('%s =' % (name,), value) if value: f.write('\n') class Rule(object): def __init__(self, target, deps, body): self.target = target self.deps = deps self.body = body def write(self, f): target, deps, body = self.target, self.deps, self.body if isinstance(deps, str): dep_s = deps else: dep_s = ' '.join(deps) f.write('%s: %s\n' % (target, dep_s)) if isinstance(body, str): f.write('\t%s\n' % body) elif body: f.write('\t%s\n' % '\n\t'.join(body)) f.write('\n') class Comment(object): def __init__(self, body): self.body = body def write(self, f): f.write('# %s\n' % (self.body,)) class GnuMakefile(object): def __init__(self, path=None): self.defs = {} self.lines = [] self.makefile_dir = py.path.local(path) def pathrel(self, fpath): if fpath.dirpath() == self.makefile_dir: return fpath.basename elif fpath.dirpath().dirpath() == self.makefile_dir.dirpath(): return '../' + fpath.relto(self.makefile_dir.dirpath()) else: return str(fpath) def definition(self, name, value): defs = self.defs defn = Definition(name, value) if name in defs: self.lines[defs[name]] = defn else: defs[name] = len(self.lines) self.lines.append(defn) def rule(self, target, deps, body): self.lines.append(Rule(target, deps, body)) def comment(self, body): self.lines.append(Comment(body)) def write(self, out=None): if out is None: f = self.makefile_dir.join('Makefile').open('w') else: f = out for line in self.lines: line.write(f) f.flush() if out is None: f.close()

30.985075

96

0.538536

from pypy.translator.platform import Platform, log, _run_subprocess from pypy.tool import autopath import py, os class BasePosix(Platform): exe_ext = '' def __init__(self, cc=None): if cc is None: cc = 'gcc' self.cc = cc def _libs(self, libraries): return ['-l%s' % (lib,) for lib in libraries] def _libdirs(self, library_dirs): return ['-L%s' % (ldir,) for ldir in library_dirs] def _includedirs(self, include_dirs): return ['-I%s' % (idir,) for idir in include_dirs] def _linkfiles(self, link_files): return list(link_files) def _compile_c_file(self, cc, cfile, compile_args): oname = cfile.new(ext='o') args = ['-c'] + compile_args + [str(cfile), '-o', str(oname)] self._execute_c_compiler(cc, args, oname) return oname def _link(self, cc, ofiles, link_args, standalone, exe_name): args = [str(ofile) for ofile in ofiles] + link_args args += ['-o', str(exe_name)] if not standalone: args = self._args_for_shared(args) self._execute_c_compiler(cc, args, exe_name) return exe_name def _preprocess_dirs(self, include_dirs): return include_dirs def gen_makefile(self, cfiles, eci, exe_name=None, path=None): cfiles = [py.path.local(f) for f in cfiles] cfiles += [py.path.local(f) for f in eci.separate_module_files] if path is None: path = cfiles[0].dirpath() pypypath = py.path.local(autopath.pypydir) if exe_name is None: exe_name = cfiles[0].new(ext=self.exe_ext) m = GnuMakefile(path) m.exe_name = exe_name m.eci = eci def pypyrel(fpath): rel = py.path.local(fpath).relto(pypypath) if rel: return os.path.join('$(PYPYDIR)', rel) else: return fpath rel_cfiles = [m.pathrel(cfile) for cfile in cfiles] rel_ofiles = [rel_cfile[:-2]+'.o' for rel_cfile in rel_cfiles] m.cfiles = rel_cfiles rel_includedirs = [pypyrel(incldir) for incldir in self._preprocess_dirs(eci.include_dirs)] m.comment('automatically generated makefile') definitions = [ ('PYPYDIR', autopath.pypydir), ('TARGET', exe_name.basename), ('DEFAULT_TARGET', '$(TARGET)'), ('SOURCES', rel_cfiles), ('OBJECTS', rel_ofiles), ('LIBS', self._libs(eci.libraries)), ('LIBDIRS', self._libdirs(eci.library_dirs)), ('INCLUDEDIRS', self._includedirs(rel_includedirs)), ('CFLAGS', self.cflags + list(eci.compile_extra)), ('LDFLAGS', self.link_flags + list(eci.link_extra)), ('CC', self.cc) ] for args in definitions: m.definition(*args) rules = [ ('all', '$(DEFAULT_TARGET)', []), ('$(TARGET)', '$(OBJECTS)', '$(CC) $(LDFLAGS) -o $@ $(OBJECTS) $(LIBDIRS) $(LIBS)'), ('%.o', '%.c', '$(CC) $(CFLAGS) -o $@ -c $< $(INCLUDEDIRS)'), ] for rule in rules: m.rule(*rule) return m def execute_makefile(self, path_to_makefile): if isinstance(path_to_makefile, GnuMakefile): path = path_to_makefile.makefile_dir else: path = path_to_makefile log.execute('make in %s' % (path,)) returncode, stdout, stderr = _run_subprocess('make', ['-C', str(path)]) self._handle_error(returncode, stdout, stderr, path.join('make')) class Definition(object): def __init__(self, name, value): self.name = name self.value = value def write(self, f): def write_list(prefix, lst): for i, fn in enumerate(lst): print >> f, prefix, fn, if i < len(lst)-1: print >> f, '\\' else: print >> f prefix = ' ' * len(prefix) name, value = self.name, self.value if isinstance(value, str): f.write('%s = %s\n' % (name, value)) else: write_list('%s =' % (name,), value) if value: f.write('\n') class Rule(object): def __init__(self, target, deps, body): self.target = target self.deps = deps self.body = body def write(self, f): target, deps, body = self.target, self.deps, self.body if isinstance(deps, str): dep_s = deps else: dep_s = ' '.join(deps) f.write('%s: %s\n' % (target, dep_s)) if isinstance(body, str): f.write('\t%s\n' % body) elif body: f.write('\t%s\n' % '\n\t'.join(body)) f.write('\n') class Comment(object): def __init__(self, body): self.body = body def write(self, f): f.write('# %s\n' % (self.body,)) class GnuMakefile(object): def __init__(self, path=None): self.defs = {} self.lines = [] self.makefile_dir = py.path.local(path) def pathrel(self, fpath): if fpath.dirpath() == self.makefile_dir: return fpath.basename elif fpath.dirpath().dirpath() == self.makefile_dir.dirpath(): return '../' + fpath.relto(self.makefile_dir.dirpath()) else: return str(fpath) def definition(self, name, value): defs = self.defs defn = Definition(name, value) if name in defs: self.lines[defs[name]] = defn else: defs[name] = len(self.lines) self.lines.append(defn) def rule(self, target, deps, body): self.lines.append(Rule(target, deps, body)) def comment(self, body): self.lines.append(Comment(body)) def write(self, out=None): if out is None: f = self.makefile_dir.join('Makefile').open('w') else: f = out for line in self.lines: line.write(f) f.flush() if out is None: f.close()

true

f71ff11e0a47dc49e9286ac8954a588df8c45b2e

64

py

Python

test/core/metaflow_custom/toplevel/__init__.py

saikonen/metaflow

48e37bea3ea4e83ddab8227869bbe56b52d9957d

[ "Apache-2.0" ]

7

2020-07-24T17:07:58.000Z

2021-05-19T21:47:12.000Z

test/core/metaflow_custom/toplevel/__init__.py

saikonen/metaflow

48e37bea3ea4e83ddab8227869bbe56b52d9957d

[ "Apache-2.0" ]

55

2020-07-20T16:56:27.000Z

2022-03-28T12:51:15.000Z

test/core/metaflow_custom/toplevel/__init__.py

saikonen/metaflow

48e37bea3ea4e83ddab8227869bbe56b52d9957d

[ "Apache-2.0" ]

6

2020-10-15T18:38:35.000Z

2021-06-20T03:05:43.000Z

__mf_customization__ = 'test' tl_value = 42 __version__ = None

12.8

29

0.765625

__mf_customization__ = 'test' tl_value = 42 __version__ = None

true

f71ff1359f7c6ddeef12a05d12e6963d96fa007e

2,569

py

Python

jspp_imageutils/annotations/convert.py

jspaezp/jspp_imageutils

6376e274a1b0675622a7979c181b9effc125aa09

[ "Apache-2.0" ]

null

jspp_imageutils/annotations/convert.py

jspaezp/jspp_imageutils

6376e274a1b0675622a7979c181b9effc125aa09

[ "Apache-2.0" ]

null

jspp_imageutils/annotations/convert.py

jspaezp/jspp_imageutils

6376e274a1b0675622a7979c181b9effc125aa09

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # modified from: # https://gist.github.com/rotemtam/88d9a4efae243fc77ed4a0f9917c8f6c import os import glob import click import pandas as pd import xml.etree.ElementTree as ET def xml_to_csv(path: str) -> pd.DataFrame: xml_list = [] for xml_file in glob.glob(path): tree = ET.parse(xml_file) root = tree.getroot() for member in root.findall('object'): bbx = member.find('bndbox') xmin = int(bbx.find('xmin').text) ymin = int(bbx.find('ymin').text) xmax = int(bbx.find('xmax').text) ymax = int(bbx.find('ymax').text) label = member.find('name').text # The columns are organized as the csv required by keras-retinanet # https://github.com/fizyr/keras-retinanet#csv-datasets # path/to/image.jpg,x1,y1,x2,y2,class_name value = (root.find('filename').text, # int(root.find('size')[0].text), # int(root.find('size')[1].text), xmin, ymin, xmax, ymax, label) xml_list.append(value) column_name = ['filename', # 'width', # 'height', 'xmin', 'ymin', 'xmax', 'ymax', 'class'] xml_df = pd.DataFrame(xml_list, columns=column_name) return xml_df def xml_to_csv_file(infile: str, outfile: str): xml_df = xml_to_csv(infile) print(xml_df) xml_df.to_csv(outfile, index=None) @click.group(help='Converts a pascal xml to csv') def cli(): pass @cli.command() @click.option('--dir', type=str, help='Name of source directory,' + ' will convert all xml files in it') @click.option('--out_dir', type=str, help='Name of the destination directory') def directory(dir, out_dir): files_convert = [x for x in os.listdir(dir) if x.endswith("xml")] for xml_file in files_convert: base = os.path.basename(xml_file) filename = os.path.splitext(base)[0] out_filename = filename + ".csv" out_path = os.path.join(out_dir, out_filename) xml_to_csv_file(os.path.join(dir, xml_file), out_path) @cli.command() @click.option('--file', type=str, help='File to be converted to csv') @click.option('--out', type=str, help='Name of the destination file') def xml(file, out): xml_to_csv_file(file, out) if __name__ == '__main__': cli()

29.528736

79

0.570261

import os import glob import click import pandas as pd import xml.etree.ElementTree as ET def xml_to_csv(path: str) -> pd.DataFrame: xml_list = [] for xml_file in glob.glob(path): tree = ET.parse(xml_file) root = tree.getroot() for member in root.findall('object'): bbx = member.find('bndbox') xmin = int(bbx.find('xmin').text) ymin = int(bbx.find('ymin').text) xmax = int(bbx.find('xmax').text) ymax = int(bbx.find('ymax').text) label = member.find('name').text value = (root.find('filename').text, xmin, ymin, xmax, ymax, label) xml_list.append(value) column_name = ['filename', 'xmin', 'ymin', 'xmax', 'ymax', 'class'] xml_df = pd.DataFrame(xml_list, columns=column_name) return xml_df def xml_to_csv_file(infile: str, outfile: str): xml_df = xml_to_csv(infile) print(xml_df) xml_df.to_csv(outfile, index=None) @click.group(help='Converts a pascal xml to csv') def cli(): pass @cli.command() @click.option('--dir', type=str, help='Name of source directory,' + ' will convert all xml files in it') @click.option('--out_dir', type=str, help='Name of the destination directory') def directory(dir, out_dir): files_convert = [x for x in os.listdir(dir) if x.endswith("xml")] for xml_file in files_convert: base = os.path.basename(xml_file) filename = os.path.splitext(base)[0] out_filename = filename + ".csv" out_path = os.path.join(out_dir, out_filename) xml_to_csv_file(os.path.join(dir, xml_file), out_path) @cli.command() @click.option('--file', type=str, help='File to be converted to csv') @click.option('--out', type=str, help='Name of the destination file') def xml(file, out): xml_to_csv_file(file, out) if __name__ == '__main__': cli()

true

f71ff15ecd2f2844f2c2b918043ff8217bac2c9b

3,796

py

Python

Week6/AdvML_Week6_ex2.py

mikkokotola/AdvancedMachineLearning

574e82d4104ac04f1cb9889beb5be7d122bd0d01

[ "MIT" ]

1

2020-03-18T08:51:44.000Z

Week6/AdvML_Week6_ex2.py

mikkokotola/AdvancedMachineLearning

574e82d4104ac04f1cb9889beb5be7d122bd0d01

[ "MIT" ]

null

Week6/AdvML_Week6_ex2.py

mikkokotola/AdvancedMachineLearning

574e82d4104ac04f1cb9889beb5be7d122bd0d01

[ "MIT" ]

null

#!/usr/bin/env python # coding: utf-8 # In[8]: ## Advanced Course in Machine Learning ## Week 6 ## Exercise 2 / Random forest import numpy as np import scipy import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import matplotlib.animation as animation from numpy import linalg as LA from sklearn import decomposition from sklearn.preprocessing import StandardScaler from sklearn.metrics import pairwise_distances from sklearn.manifold import TSNE import math import sys import mnist from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import cross_val_score sns.set_style("darkgrid") # In[4]: x_train, t_train, x_test, t_test = mnist.load() # In[48]: x_train = x_train[0:50000,:] t_train = t_train[0:50000] # In[49]: print(x_train.shape) print(t_train.shape) print(x_test.shape) print(t_test.shape) # In[69]: startTestIx = 0 endTestIx = 100 # clf.classes_ # clf.feature_importances_ # print(clf.max_features_) # print(clf.n_classes_) # print(clf.n_features_) # print(clf.n_outputs_) # #clf.tree_ # In[70]: # Randomly select the samples and features for the tree def sample(n, k, x_train, t_train): idx = np.random.randint(x_train.shape[0], size=n) fidx = np.random.randint(x_train.shape[1], size=k) x = x_train[idx, :] x = x[:, fidx] y = t_train[idx] return x, y, idx, fidx #print("Rows: ", idx, ", features ", fidx) #print(x.shape) #print(y.shape) # In[71]: def trainTree(x_train, t_train): clf = DecisionTreeClassifier(random_state=0) clf = clf.fit(x_train, t_train) return clf #cross_val_score(clf, x_train, t_train, cv=10) # In[72]: def ensureAllClasses(newPred, clf): for i in range(10): if i not in clf.classes_: newPred = np.insert(newPred, i, 0, axis=1) return newPred # In[75]: # Main loop def main(M, n, k): pred = np.zeros(shape = (endTestIx - startTestIx, 10), dtype = 'float32') for m in range(M): x, y, idx, fidx = sample(n, k, x_train, t_train) clf = trainTree(x, y) newPred = clf.predict_proba(x_test[startTestIx:endTestIx,fidx]) newPred = ensureAllClasses(newPred, clf) pred = np.add(pred, newPred) pred_classes = np.argmax(pred, axis=1) correct = pred_classes == t_test[startTestIx:endTestIx] acc = sum(correct)/len(correct) #print(pred_classes) #print (acc) return acc # In[85]: Mmax = 100 n = 1000 k = 20 accs = list() for m in range(1, Mmax): accs.append(main(m, n, k)) plt.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k') sns.lineplot(range(1,Mmax), accs) plt.xlabel('Number of trees (M)') plt.ylabel('Accuracy of predictions (%)') plt.title('Number of trees vs. accuracy, n = {0}, k = {1}'.format(n, k)) plt.show() # In[80]: M = 100 n = 1000 kmax = 200 accs = list() for k in range(1, kmax, 10): accs.append(main(M, n, k)) plt.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k') sns.lineplot(range(1,kmax,10), accs) plt.xlabel('Number of features per tree (k)') plt.ylabel('Accuracy of predictions (%)') plt.title('Number of features per tree vs. accuracy, M = {0}, n = {1}'.format(M, n)) plt.show() # In[81]: M = 100 nmax = 5000 k = 50 accs = list() for n in range(1, nmax, 100): accs.append(main(M, n, k)) plt.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k') sns.lineplot(range(1, nmax, 100), accs) plt.xlabel('Number of samples per tree (n)') plt.ylabel('Accuracy of predictions (%)') plt.title('Number of samples per tree vs. accuracy, M = {0}, k = {1}'.format(M, k)) plt.show() # In[84]: M = 100 n = 1000 k = 50 repeats = 50 accs = list() for i in range(50): accs.append(main(M, n, k)) avAcc = sum(accs)/len(accs) print(avAcc)

19.171717

84

0.663593

sns import matplotlib.pyplot as plt import matplotlib.animation as animation from numpy import linalg as LA from sklearn import decomposition from sklearn.preprocessing import StandardScaler from sklearn.metrics import pairwise_distances from sklearn.manifold import TSNE import math import sys import mnist from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import cross_val_score sns.set_style("darkgrid") x_train, t_train, x_test, t_test = mnist.load() x_train = x_train[0:50000,:] t_train = t_train[0:50000] print(x_train.shape) print(t_train.shape) print(x_test.shape) print(t_test.shape) startTestIx = 0 endTestIx = 100 sample(n, k, x_train, t_train): idx = np.random.randint(x_train.shape[0], size=n) fidx = np.random.randint(x_train.shape[1], size=k) x = x_train[idx, :] x = x[:, fidx] y = t_train[idx] return x, y, idx, fidx def trainTree(x_train, t_train): clf = DecisionTreeClassifier(random_state=0) clf = clf.fit(x_train, t_train) return clf def ensureAllClasses(newPred, clf): for i in range(10): if i not in clf.classes_: newPred = np.insert(newPred, i, 0, axis=1) return newPred def main(M, n, k): pred = np.zeros(shape = (endTestIx - startTestIx, 10), dtype = 'float32') for m in range(M): x, y, idx, fidx = sample(n, k, x_train, t_train) clf = trainTree(x, y) newPred = clf.predict_proba(x_test[startTestIx:endTestIx,fidx]) newPred = ensureAllClasses(newPred, clf) pred = np.add(pred, newPred) pred_classes = np.argmax(pred, axis=1) correct = pred_classes == t_test[startTestIx:endTestIx] acc = sum(correct)/len(correct) return acc Mmax = 100 n = 1000 k = 20 accs = list() for m in range(1, Mmax): accs.append(main(m, n, k)) plt.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k') sns.lineplot(range(1,Mmax), accs) plt.xlabel('Number of trees (M)') plt.ylabel('Accuracy of predictions (%)') plt.title('Number of trees vs. accuracy, n = {0}, k = {1}'.format(n, k)) plt.show() M = 100 n = 1000 kmax = 200 accs = list() for k in range(1, kmax, 10): accs.append(main(M, n, k)) plt.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k') sns.lineplot(range(1,kmax,10), accs) plt.xlabel('Number of features per tree (k)') plt.ylabel('Accuracy of predictions (%)') plt.title('Number of features per tree vs. accuracy, M = {0}, n = {1}'.format(M, n)) plt.show() M = 100 nmax = 5000 k = 50 accs = list() for n in range(1, nmax, 100): accs.append(main(M, n, k)) plt.figure(num=None, figsize=(8, 6), dpi=100, facecolor='w', edgecolor='k') sns.lineplot(range(1, nmax, 100), accs) plt.xlabel('Number of samples per tree (n)') plt.ylabel('Accuracy of predictions (%)') plt.title('Number of samples per tree vs. accuracy, M = {0}, k = {1}'.format(M, k)) plt.show() M = 100 n = 1000 k = 50 repeats = 50 accs = list() for i in range(50): accs.append(main(M, n, k)) avAcc = sum(accs)/len(accs) print(avAcc)

true

f71ff26a2521e900d339ce88a0f2dc11b89982ac

8,979

py

Python

models/layers/mesh_pool.py

yamaguchi1024/MeshCNN

197530eab2aa4c2419511c1854dcbc662377f340

[ "MIT" ]

null

models/layers/mesh_pool.py

yamaguchi1024/MeshCNN

197530eab2aa4c2419511c1854dcbc662377f340

[ "MIT" ]

null

models/layers/mesh_pool.py

yamaguchi1024/MeshCNN

197530eab2aa4c2419511c1854dcbc662377f340

[ "MIT" ]

1

2020-10-10T23:31:50.000Z

import torch import torch.nn as nn from threading import Thread from models.layers.mesh_union import MeshUnion import numpy as np from heapq import heappop, heapify class MeshPool(nn.Module): def __init__(self, target, multi_thread=False): super(MeshPool, self).__init__() self.__out_target = target self.__multi_thread = multi_thread self.__fe = None self.__updated_fe = None self.__meshes = None self.__merge_edges = [-1, -1] def __call__(self, fe, meshes): return self.forward(fe, meshes) def forward(self, fe, meshes): self.__updated_fe = [[] for _ in range(len(meshes))] pool_threads = [] self.__fe = fe self.__meshes = meshes # iterate over batch for mesh_index in range(len(meshes)): if self.__multi_thread: pool_threads.append(Thread(target=self.__pool_main, args=(mesh_index,))) pool_threads[-1].start() else: self.__pool_main(mesh_index) if self.__multi_thread: for mesh_index in range(len(meshes)): pool_threads[mesh_index].join() out_features = torch.cat(self.__updated_fe).view(len(meshes), -1, self.__out_target) return out_features def __pool_main(self, mesh_index): mesh = self.__meshes[mesh_index] queue = self.__build_queue(self.__fe[mesh_index, :, :mesh.edges_count], mesh.edges_count) # recycle = [] # last_queue_len = len(queue) last_count = mesh.edges_count + 1 mask = np.ones(mesh.edges_count, dtype=np.uint8) edge_groups = MeshUnion(mesh.edges_count, self.__fe.device) while mesh.edges_count > self.__out_target: value, edge_id = heappop(queue) edge_id = int(edge_id) if mask[edge_id]: self.__pool_edge(mesh, edge_id, mask, edge_groups) mesh.clean(mask, edge_groups) fe = edge_groups.rebuild_features(self.__fe[mesh_index], mask, self.__out_target) self.__updated_fe[mesh_index] = fe def __pool_edge(self, mesh, edge_id, mask, edge_groups): if self.has_boundaries(mesh, edge_id): return False elif self.__clean_side(mesh, edge_id, mask, edge_groups, 0)\ and self.__clean_side(mesh, edge_id, mask, edge_groups, 2) \ and self.__is_one_ring_valid(mesh, edge_id): self.__merge_edges[0] = self.__pool_side(mesh, edge_id, mask, edge_groups, 0) self.__merge_edges[1] = self.__pool_side(mesh, edge_id, mask, edge_groups, 2) mesh.merge_vertices(edge_id) mask[edge_id] = False MeshPool.__remove_group(mesh, edge_groups, edge_id) mesh.edges_count -= 1 return True else: return False def __clean_side(self, mesh, edge_id, mask, edge_groups, side): if mesh.edges_count <= self.__out_target: return False invalid_edges = MeshPool.__get_invalids(mesh, edge_id, edge_groups, side) while len(invalid_edges) != 0 and mesh.edges_count > self.__out_target: self.__remove_triplete(mesh, mask, edge_groups, invalid_edges) if mesh.edges_count <= self.__out_target: return False if self.has_boundaries(mesh, edge_id): return False invalid_edges = self.__get_invalids(mesh, edge_id, edge_groups, side) return True @staticmethod def has_boundaries(mesh, edge_id): for edge in mesh.gemm_edges[edge_id]: if edge == -1 or -1 in mesh.gemm_edges[edge]: return True return False @staticmethod def __is_one_ring_valid(mesh, edge_id): v_a = set(mesh.edges[mesh.ve[mesh.edges[edge_id, 0]]].reshape(-1)) v_b = set(mesh.edges[mesh.ve[mesh.edges[edge_id, 1]]].reshape(-1)) shared = v_a & v_b - set(mesh.edges[edge_id]) return len(shared) == 2 def __pool_side(self, mesh, edge_id, mask, edge_groups, side): info = MeshPool.__get_face_info(mesh, edge_id, side) key_a, key_b, side_a, side_b, _, other_side_b, _, other_keys_b = info self.__redirect_edges(mesh, key_a, side_a - side_a % 2, other_keys_b[0], mesh.sides[key_b, other_side_b]) self.__redirect_edges(mesh, key_a, side_a - side_a % 2 + 1, other_keys_b[1], mesh.sides[key_b, other_side_b + 1]) MeshPool.__union_groups(mesh, edge_groups, key_b, key_a) MeshPool.__union_groups(mesh, edge_groups, edge_id, key_a) mask[key_b] = False MeshPool.__remove_group(mesh, edge_groups, key_b) mesh.remove_edge(key_b) mesh.edges_count -= 1 return key_a @staticmethod def __get_invalids(mesh, edge_id, edge_groups, side): info = MeshPool.__get_face_info(mesh, edge_id, side) key_a, key_b, side_a, side_b, other_side_a, other_side_b, other_keys_a, other_keys_b = info shared_items = MeshPool.__get_shared_items(other_keys_a, other_keys_b) if len(shared_items) == 0: return [] else: assert (len(shared_items) == 2) middle_edge = other_keys_a[shared_items[0]] update_key_a = other_keys_a[1 - shared_items[0]] update_key_b = other_keys_b[1 - shared_items[1]] update_side_a = mesh.sides[key_a, other_side_a + 1 - shared_items[0]] update_side_b = mesh.sides[key_b, other_side_b + 1 - shared_items[1]] MeshPool.__redirect_edges(mesh, edge_id, side, update_key_a, update_side_a) MeshPool.__redirect_edges(mesh, edge_id, side + 1, update_key_b, update_side_b) MeshPool.__redirect_edges(mesh, update_key_a, MeshPool.__get_other_side(update_side_a), update_key_b, MeshPool.__get_other_side(update_side_b)) MeshPool.__union_groups(mesh, edge_groups, key_a, edge_id) MeshPool.__union_groups(mesh, edge_groups, key_b, edge_id) MeshPool.__union_groups(mesh, edge_groups, key_a, update_key_a) MeshPool.__union_groups(mesh, edge_groups, middle_edge, update_key_a) MeshPool.__union_groups(mesh, edge_groups, key_b, update_key_b) MeshPool.__union_groups(mesh, edge_groups, middle_edge, update_key_b) return [key_a, key_b, middle_edge] @staticmethod def __redirect_edges(mesh, edge_a_key, side_a, edge_b_key, side_b): mesh.gemm_edges[edge_a_key, side_a] = edge_b_key mesh.gemm_edges[edge_b_key, side_b] = edge_a_key mesh.sides[edge_a_key, side_a] = side_b mesh.sides[edge_b_key, side_b] = side_a @staticmethod def __get_shared_items(list_a, list_b): shared_items = [] for i in range(len(list_a)): for j in range(len(list_b)): if list_a[i] == list_b[j]: shared_items.extend([i, j]) return shared_items @staticmethod def __get_other_side(side): return side + 1 - 2 * (side % 2) @staticmethod def __get_face_info(mesh, edge_id, side): key_a = mesh.gemm_edges[edge_id, side] key_b = mesh.gemm_edges[edge_id, side + 1] side_a = mesh.sides[edge_id, side] side_b = mesh.sides[edge_id, side + 1] other_side_a = (side_a - (side_a % 2) + 2) % 4 other_side_b = (side_b - (side_b % 2) + 2) % 4 other_keys_a = [mesh.gemm_edges[key_a, other_side_a], mesh.gemm_edges[key_a, other_side_a + 1]] other_keys_b = [mesh.gemm_edges[key_b, other_side_b], mesh.gemm_edges[key_b, other_side_b + 1]] return key_a, key_b, side_a, side_b, other_side_a, other_side_b, other_keys_a, other_keys_b @staticmethod def __remove_triplete(mesh, mask, edge_groups, invalid_edges): vertex = set(mesh.edges[invalid_edges[0]]) for edge_key in invalid_edges: vertex &= set(mesh.edges[edge_key]) mask[edge_key] = False MeshPool.__remove_group(mesh, edge_groups, edge_key) mesh.edges_count -= 3 vertex = list(vertex) assert(len(vertex) == 1) mesh.remove_vertex(vertex[0]) def __build_queue(self, features, edges_count): # delete edges with smallest norm squared_magnitude = torch.sum(features * features, 0) if squared_magnitude.shape[-1] != 1: squared_magnitude = squared_magnitude.unsqueeze(-1) edge_ids = torch.arange(edges_count, device=squared_magnitude.device, dtype=torch.float32).unsqueeze(-1) heap = torch.cat((squared_magnitude, edge_ids), dim=-1).tolist() heapify(heap) return heap @staticmethod def __union_groups(mesh, edge_groups, source, target): edge_groups.union(source, target) mesh.union_groups(source, target) @staticmethod def __remove_group(mesh, edge_groups, index): edge_groups.remove_group(index) mesh.remove_group(index)

44.014706

155

0.646397

import torch import torch.nn as nn from threading import Thread from models.layers.mesh_union import MeshUnion import numpy as np from heapq import heappop, heapify class MeshPool(nn.Module): def __init__(self, target, multi_thread=False): super(MeshPool, self).__init__() self.__out_target = target self.__multi_thread = multi_thread self.__fe = None self.__updated_fe = None self.__meshes = None self.__merge_edges = [-1, -1] def __call__(self, fe, meshes): return self.forward(fe, meshes) def forward(self, fe, meshes): self.__updated_fe = [[] for _ in range(len(meshes))] pool_threads = [] self.__fe = fe self.__meshes = meshes for mesh_index in range(len(meshes)): if self.__multi_thread: pool_threads.append(Thread(target=self.__pool_main, args=(mesh_index,))) pool_threads[-1].start() else: self.__pool_main(mesh_index) if self.__multi_thread: for mesh_index in range(len(meshes)): pool_threads[mesh_index].join() out_features = torch.cat(self.__updated_fe).view(len(meshes), -1, self.__out_target) return out_features def __pool_main(self, mesh_index): mesh = self.__meshes[mesh_index] queue = self.__build_queue(self.__fe[mesh_index, :, :mesh.edges_count], mesh.edges_count) last_count = mesh.edges_count + 1 mask = np.ones(mesh.edges_count, dtype=np.uint8) edge_groups = MeshUnion(mesh.edges_count, self.__fe.device) while mesh.edges_count > self.__out_target: value, edge_id = heappop(queue) edge_id = int(edge_id) if mask[edge_id]: self.__pool_edge(mesh, edge_id, mask, edge_groups) mesh.clean(mask, edge_groups) fe = edge_groups.rebuild_features(self.__fe[mesh_index], mask, self.__out_target) self.__updated_fe[mesh_index] = fe def __pool_edge(self, mesh, edge_id, mask, edge_groups): if self.has_boundaries(mesh, edge_id): return False elif self.__clean_side(mesh, edge_id, mask, edge_groups, 0)\ and self.__clean_side(mesh, edge_id, mask, edge_groups, 2) \ and self.__is_one_ring_valid(mesh, edge_id): self.__merge_edges[0] = self.__pool_side(mesh, edge_id, mask, edge_groups, 0) self.__merge_edges[1] = self.__pool_side(mesh, edge_id, mask, edge_groups, 2) mesh.merge_vertices(edge_id) mask[edge_id] = False MeshPool.__remove_group(mesh, edge_groups, edge_id) mesh.edges_count -= 1 return True else: return False def __clean_side(self, mesh, edge_id, mask, edge_groups, side): if mesh.edges_count <= self.__out_target: return False invalid_edges = MeshPool.__get_invalids(mesh, edge_id, edge_groups, side) while len(invalid_edges) != 0 and mesh.edges_count > self.__out_target: self.__remove_triplete(mesh, mask, edge_groups, invalid_edges) if mesh.edges_count <= self.__out_target: return False if self.has_boundaries(mesh, edge_id): return False invalid_edges = self.__get_invalids(mesh, edge_id, edge_groups, side) return True @staticmethod def has_boundaries(mesh, edge_id): for edge in mesh.gemm_edges[edge_id]: if edge == -1 or -1 in mesh.gemm_edges[edge]: return True return False @staticmethod def __is_one_ring_valid(mesh, edge_id): v_a = set(mesh.edges[mesh.ve[mesh.edges[edge_id, 0]]].reshape(-1)) v_b = set(mesh.edges[mesh.ve[mesh.edges[edge_id, 1]]].reshape(-1)) shared = v_a & v_b - set(mesh.edges[edge_id]) return len(shared) == 2 def __pool_side(self, mesh, edge_id, mask, edge_groups, side): info = MeshPool.__get_face_info(mesh, edge_id, side) key_a, key_b, side_a, side_b, _, other_side_b, _, other_keys_b = info self.__redirect_edges(mesh, key_a, side_a - side_a % 2, other_keys_b[0], mesh.sides[key_b, other_side_b]) self.__redirect_edges(mesh, key_a, side_a - side_a % 2 + 1, other_keys_b[1], mesh.sides[key_b, other_side_b + 1]) MeshPool.__union_groups(mesh, edge_groups, key_b, key_a) MeshPool.__union_groups(mesh, edge_groups, edge_id, key_a) mask[key_b] = False MeshPool.__remove_group(mesh, edge_groups, key_b) mesh.remove_edge(key_b) mesh.edges_count -= 1 return key_a @staticmethod def __get_invalids(mesh, edge_id, edge_groups, side): info = MeshPool.__get_face_info(mesh, edge_id, side) key_a, key_b, side_a, side_b, other_side_a, other_side_b, other_keys_a, other_keys_b = info shared_items = MeshPool.__get_shared_items(other_keys_a, other_keys_b) if len(shared_items) == 0: return [] else: assert (len(shared_items) == 2) middle_edge = other_keys_a[shared_items[0]] update_key_a = other_keys_a[1 - shared_items[0]] update_key_b = other_keys_b[1 - shared_items[1]] update_side_a = mesh.sides[key_a, other_side_a + 1 - shared_items[0]] update_side_b = mesh.sides[key_b, other_side_b + 1 - shared_items[1]] MeshPool.__redirect_edges(mesh, edge_id, side, update_key_a, update_side_a) MeshPool.__redirect_edges(mesh, edge_id, side + 1, update_key_b, update_side_b) MeshPool.__redirect_edges(mesh, update_key_a, MeshPool.__get_other_side(update_side_a), update_key_b, MeshPool.__get_other_side(update_side_b)) MeshPool.__union_groups(mesh, edge_groups, key_a, edge_id) MeshPool.__union_groups(mesh, edge_groups, key_b, edge_id) MeshPool.__union_groups(mesh, edge_groups, key_a, update_key_a) MeshPool.__union_groups(mesh, edge_groups, middle_edge, update_key_a) MeshPool.__union_groups(mesh, edge_groups, key_b, update_key_b) MeshPool.__union_groups(mesh, edge_groups, middle_edge, update_key_b) return [key_a, key_b, middle_edge] @staticmethod def __redirect_edges(mesh, edge_a_key, side_a, edge_b_key, side_b): mesh.gemm_edges[edge_a_key, side_a] = edge_b_key mesh.gemm_edges[edge_b_key, side_b] = edge_a_key mesh.sides[edge_a_key, side_a] = side_b mesh.sides[edge_b_key, side_b] = side_a @staticmethod def __get_shared_items(list_a, list_b): shared_items = [] for i in range(len(list_a)): for j in range(len(list_b)): if list_a[i] == list_b[j]: shared_items.extend([i, j]) return shared_items @staticmethod def __get_other_side(side): return side + 1 - 2 * (side % 2) @staticmethod def __get_face_info(mesh, edge_id, side): key_a = mesh.gemm_edges[edge_id, side] key_b = mesh.gemm_edges[edge_id, side + 1] side_a = mesh.sides[edge_id, side] side_b = mesh.sides[edge_id, side + 1] other_side_a = (side_a - (side_a % 2) + 2) % 4 other_side_b = (side_b - (side_b % 2) + 2) % 4 other_keys_a = [mesh.gemm_edges[key_a, other_side_a], mesh.gemm_edges[key_a, other_side_a + 1]] other_keys_b = [mesh.gemm_edges[key_b, other_side_b], mesh.gemm_edges[key_b, other_side_b + 1]] return key_a, key_b, side_a, side_b, other_side_a, other_side_b, other_keys_a, other_keys_b @staticmethod def __remove_triplete(mesh, mask, edge_groups, invalid_edges): vertex = set(mesh.edges[invalid_edges[0]]) for edge_key in invalid_edges: vertex &= set(mesh.edges[edge_key]) mask[edge_key] = False MeshPool.__remove_group(mesh, edge_groups, edge_key) mesh.edges_count -= 3 vertex = list(vertex) assert(len(vertex) == 1) mesh.remove_vertex(vertex[0]) def __build_queue(self, features, edges_count): squared_magnitude = torch.sum(features * features, 0) if squared_magnitude.shape[-1] != 1: squared_magnitude = squared_magnitude.unsqueeze(-1) edge_ids = torch.arange(edges_count, device=squared_magnitude.device, dtype=torch.float32).unsqueeze(-1) heap = torch.cat((squared_magnitude, edge_ids), dim=-1).tolist() heapify(heap) return heap @staticmethod def __union_groups(mesh, edge_groups, source, target): edge_groups.union(source, target) mesh.union_groups(source, target) @staticmethod def __remove_group(mesh, edge_groups, index): edge_groups.remove_group(index) mesh.remove_group(index)

true

f71ff2713f88d105f4975f0cee61ae6ef8e14fed

896

py

Python

nn_dataflow/nns/mlp_s.py

joeshow79/nn_dataflow

279440452148ebf327992bd178a37cd5fd5330c5

[ "BSD-3-Clause" ]

null

nn_dataflow/nns/mlp_s.py

joeshow79/nn_dataflow

279440452148ebf327992bd178a37cd5fd5330c5

[ "BSD-3-Clause" ]

null

nn_dataflow/nns/mlp_s.py

joeshow79/nn_dataflow

279440452148ebf327992bd178a37cd5fd5330c5

[ "BSD-3-Clause" ]

null

""" $lic$ Copyright (C) 2016-2019 by The Board of Trustees of Stanford University This program is free software: you can redistribute it and/or modify it under the terms of the Modified BSD-3 License as published by the Open Source Initiative. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the BSD-3 License for more details. You should have received a copy of the Modified BSD-3 License along with this program. If not, see <https://opensource.org/licenses/BSD-3-Clause>. """ from nn_dataflow.core import Network from nn_dataflow.core import InputLayer, FCLayer ''' MLP-S PRIME, 2016 ''' NN = Network('MLP-S') NN.set_input_layer(InputLayer(784, 1)) NN.add('fc1', FCLayer(784, 500)) NN.add('fc2', FCLayer(500, 250)) NN.add('fc3', FCLayer(250, 10))

27.151515

79

0.756696

from nn_dataflow.core import Network from nn_dataflow.core import InputLayer, FCLayer NN = Network('MLP-S') NN.set_input_layer(InputLayer(784, 1)) NN.add('fc1', FCLayer(784, 500)) NN.add('fc2', FCLayer(500, 250)) NN.add('fc3', FCLayer(250, 10))

true

f71ff2d8ade82949986924ccc275d50947e1700f

3,753

py

Python

nexuscasc/config/k8s_config_handler.py

vjda/nexus3-casc-cli

6d33503c19f75b73b656293141911e083331faf0

[ "MIT" ]

7

2020-06-30T08:15:53.000Z

2022-03-22T10:28:09.000Z

nexuscasc/config/k8s_config_handler.py

vjda/nexus3-casc-cli

6d33503c19f75b73b656293141911e083331faf0

[ "MIT" ]

1

2021-09-27T03:24:46.000Z

nexuscasc/config/k8s_config_handler.py

vjda/nexus3-casc-cli

6d33503c19f75b73b656293141911e083331faf0

[ "MIT" ]

1

2020-12-07T14:55:30.000Z

import base64 import re from dataclasses import dataclass from enum import Enum from typing import Union, List from kubernetes import client, config from kubernetes.client import V1ConfigMapList, V1SecretList, CoreV1Api, V1Secret, V1ConfigMap from nexuscasc.logger import Logger class ResourceType(Enum): SECRET, CONFIGMAP = range(2) @dataclass class WatchedResource: name: str version: str type: ResourceType class K8sConfigHandler: v1: CoreV1Api = None watch_list: List[WatchedResource] = list() def __init__(self, local: bool = False): if local: config.load_kube_config() else: config.load_incluster_config() self.v1 = client.CoreV1Api() @staticmethod def filter_resources( resources: Union[V1ConfigMapList, V1SecretList], label_value: str = None ) -> List[Union[V1ConfigMap, V1Secret]]: matches = list() for res in resources.items: if label_value is None: matches.append(res) elif len(list(filter(lambda x: res.metadata.labels[x] == label_value, res.metadata.labels.keys()))) > 0: matches.append(res) return matches def find_config_maps(self, namespace: str, label: str, label_value: str = None) -> List[V1ConfigMap]: config_maps = self.v1.list_namespaced_config_map(namespace=namespace, label_selector=label) return self.filter_resources(config_maps, label_value) def find_secrets(self, namespace: str, label: str, label_value: str = None) -> List[V1Secret]: secrets = self.v1.list_namespaced_secret(namespace=namespace, label_selector=label) return self.filter_resources(secrets, label_value) @staticmethod def extract_yaml_strings_from_resources(resources: List[Union[V1ConfigMap, V1Secret]]) -> List[str]: yaml_str = list() for res in resources: for k in filter(lambda key: re.search("\\.yml|\\.yaml$", key), res.data.keys()): if type(res) == V1Secret: Logger.debug(f"Found yaml in key '{k}' for secret '{res.metadata.name}'") yaml_str.append(base64.b64decode(res.data[k]).decode()) else: Logger.debug(f"Found yaml in key '{k}' for configmap '{res.metadata.name}'") yaml_str.append(res.data[k]) return yaml_str def any_resource_has_changed(self, resources: List[Union[V1ConfigMap, V1Secret]]) -> bool: has_changed = False if len(self.watch_list) == 0: has_changed = True for res in resources: self.watch_resource(res) else: for res in resources: r_name = res.metadata.name r_type = ResourceType.SECRET if type(res) == V1Secret else ResourceType.CONFIGMAP watched_resource = next(filter(lambda r: r_name == r.name and r_type == r.type, self.watch_list), None) if watched_resource is None: self.watch_resource(res) has_changed = True break elif watched_resource.version != res.metadata.resource_version: watched_resource.version = res.metadata.resource_version has_changed = True break return has_changed def watch_resource(self, resource: Union[V1ConfigMap, V1Secret]): self.watch_list.append( WatchedResource( name=resource.metadata.name, version=resource.metadata.resource_version, type=ResourceType.SECRET if type(resource) == V1Secret else ResourceType.CONFIGMAP ))

38.295918

119

0.625633

import base64 import re from dataclasses import dataclass from enum import Enum from typing import Union, List from kubernetes import client, config from kubernetes.client import V1ConfigMapList, V1SecretList, CoreV1Api, V1Secret, V1ConfigMap from nexuscasc.logger import Logger class ResourceType(Enum): SECRET, CONFIGMAP = range(2) @dataclass class WatchedResource: name: str version: str type: ResourceType class K8sConfigHandler: v1: CoreV1Api = None watch_list: List[WatchedResource] = list() def __init__(self, local: bool = False): if local: config.load_kube_config() else: config.load_incluster_config() self.v1 = client.CoreV1Api() @staticmethod def filter_resources( resources: Union[V1ConfigMapList, V1SecretList], label_value: str = None ) -> List[Union[V1ConfigMap, V1Secret]]: matches = list() for res in resources.items: if label_value is None: matches.append(res) elif len(list(filter(lambda x: res.metadata.labels[x] == label_value, res.metadata.labels.keys()))) > 0: matches.append(res) return matches def find_config_maps(self, namespace: str, label: str, label_value: str = None) -> List[V1ConfigMap]: config_maps = self.v1.list_namespaced_config_map(namespace=namespace, label_selector=label) return self.filter_resources(config_maps, label_value) def find_secrets(self, namespace: str, label: str, label_value: str = None) -> List[V1Secret]: secrets = self.v1.list_namespaced_secret(namespace=namespace, label_selector=label) return self.filter_resources(secrets, label_value) @staticmethod def extract_yaml_strings_from_resources(resources: List[Union[V1ConfigMap, V1Secret]]) -> List[str]: yaml_str = list() for res in resources: for k in filter(lambda key: re.search("\\.yml|\\.yaml$", key), res.data.keys()): if type(res) == V1Secret: Logger.debug(f"Found yaml in key '{k}' for secret '{res.metadata.name}'") yaml_str.append(base64.b64decode(res.data[k]).decode()) else: Logger.debug(f"Found yaml in key '{k}' for configmap '{res.metadata.name}'") yaml_str.append(res.data[k]) return yaml_str def any_resource_has_changed(self, resources: List[Union[V1ConfigMap, V1Secret]]) -> bool: has_changed = False if len(self.watch_list) == 0: has_changed = True for res in resources: self.watch_resource(res) else: for res in resources: r_name = res.metadata.name r_type = ResourceType.SECRET if type(res) == V1Secret else ResourceType.CONFIGMAP watched_resource = next(filter(lambda r: r_name == r.name and r_type == r.type, self.watch_list), None) if watched_resource is None: self.watch_resource(res) has_changed = True break elif watched_resource.version != res.metadata.resource_version: watched_resource.version = res.metadata.resource_version has_changed = True break return has_changed def watch_resource(self, resource: Union[V1ConfigMap, V1Secret]): self.watch_list.append( WatchedResource( name=resource.metadata.name, version=resource.metadata.resource_version, type=ResourceType.SECRET if type(resource) == V1Secret else ResourceType.CONFIGMAP ))

true

f71ff2dfd267b7ca272fcc6a2a50017e19cd8ff1

3,423

py

Python

tests/commands/spot/test_limit_order_cmd.py

mpetrinidev/bnb-cli

d10fc36b6a1e2dd34597d9f31ea143019ba6f4b9

[ "MIT" ]

5

2021-04-02T20:49:19.000Z

2021-06-11T06:22:24.000Z

tests/commands/spot/test_limit_order_cmd.py

mpetrinidev/bnb-cli

d10fc36b6a1e2dd34597d9f31ea143019ba6f4b9

[ "MIT" ]

2

2021-06-13T20:22:09.000Z

2021-06-14T23:09:14.000Z

tests/commands/spot/test_limit_order_cmd.py

mpetrinidev/bnc-cli

d10fc36b6a1e2dd34597d9f31ea143019ba6f4b9

[ "MIT" ]

null

import datetime import os from unittest.mock import Mock from bnc.cli import cli from bnc.utils.utils import json_to_str from tests.commands.common import read_json_test_file, get_headers from tests.commands.common_fixtures import * def get_json_filename(): return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'res', 'new_order.json') @pytest.fixture(scope='session') def data(): return read_json_test_file(get_json_filename()) @pytest.mark.parametrize("params", [ ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621], ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-ncoid', "custom_id"], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--new_client_order_id', "custom_id"], ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-iq', 0.20], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--iceberg_qty', 0.20] ]) def test_new_order_limit_return_full_resp(runner, params, mock_default_deps, data): mock_response = Mock(status_code=200, elapsed=datetime.datetime.now(), headers=get_headers()) mock_response.json.return_value = data['limit_full'] mock_default_deps.patch('bnc.builder.requests.post', return_value=mock_response) result = runner.invoke(cli, params) assert result.exit_code == 0 assert result.output == json_to_str(data['limit_full']) + '\n' @pytest.mark.parametrize("params", [ ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-nort', 'ACK'], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--new_order_resp_type', 'ACK'] ]) def test_new_order_limit_return_ack_resp(runner, params, mock_default_deps, data): mock_response = Mock(status_code=200, elapsed=datetime.datetime.now(), headers=get_headers()) mock_response.json.return_value = data['limit_ack'] mock_default_deps.patch('bnc.builder.requests.post', return_value=mock_response) result = runner.invoke(cli, params) assert result.exit_code == 0 assert result.output == json_to_str(data['limit_ack']) + '\n' @pytest.mark.parametrize("params", [ ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-nort', 'RESULT'], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--new_order_resp_type', 'RESULT'] ]) def test_new_order_limit_return_ack_resp(runner, params, mock_default_deps, data): mock_response = Mock(status_code=200, elapsed=datetime.datetime.now(), headers=get_headers()) mock_response.json.return_value = data['limit_result'] mock_default_deps.patch('bnc.builder.requests.post', return_value=mock_response) result = runner.invoke(cli, params) assert result.exit_code == 0 assert result.output == json_to_str(data['limit_result']) + '\n'

48.211268

138

0.650891

import datetime import os from unittest.mock import Mock from bnc.cli import cli from bnc.utils.utils import json_to_str from tests.commands.common import read_json_test_file, get_headers from tests.commands.common_fixtures import * def get_json_filename(): return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'res', 'new_order.json') @pytest.fixture(scope='session') def data(): return read_json_test_file(get_json_filename()) @pytest.mark.parametrize("params", [ ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621], ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-ncoid', "custom_id"], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--new_client_order_id', "custom_id"], ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-iq', 0.20], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--iceberg_qty', 0.20] ]) def test_new_order_limit_return_full_resp(runner, params, mock_default_deps, data): mock_response = Mock(status_code=200, elapsed=datetime.datetime.now(), headers=get_headers()) mock_response.json.return_value = data['limit_full'] mock_default_deps.patch('bnc.builder.requests.post', return_value=mock_response) result = runner.invoke(cli, params) assert result.exit_code == 0 assert result.output == json_to_str(data['limit_full']) + '\n' @pytest.mark.parametrize("params", [ ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-nort', 'ACK'], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--new_order_resp_type', 'ACK'] ]) def test_new_order_limit_return_ack_resp(runner, params, mock_default_deps, data): mock_response = Mock(status_code=200, elapsed=datetime.datetime.now(), headers=get_headers()) mock_response.json.return_value = data['limit_ack'] mock_default_deps.patch('bnc.builder.requests.post', return_value=mock_response) result = runner.invoke(cli, params) assert result.exit_code == 0 assert result.output == json_to_str(data['limit_ack']) + '\n' @pytest.mark.parametrize("params", [ ['spot', 'new_order', 'limit', '-sy', 'LTCBTC', '-si', 'BUY', '-tif', 'GTC', '-q', 1, '-p', 0.003621, '-nort', 'RESULT'], ['spot', 'new_order', 'limit', '--symbol', 'LTCBTC', '--side', 'BUY', '--time_in_force', 'GTC', '--quantity', 1, '--price', 0.003621, '--new_order_resp_type', 'RESULT'] ]) def test_new_order_limit_return_ack_resp(runner, params, mock_default_deps, data): mock_response = Mock(status_code=200, elapsed=datetime.datetime.now(), headers=get_headers()) mock_response.json.return_value = data['limit_result'] mock_default_deps.patch('bnc.builder.requests.post', return_value=mock_response) result = runner.invoke(cli, params) assert result.exit_code == 0 assert result.output == json_to_str(data['limit_result']) + '\n'

true

f71ff3f9334377468b2b24b73aa343bc3c717efb

2,218

py

Python

src/odontology/core/migrations/0009_chapter_tariff.py

nanomolina/JP

248a47bced4dac850f85d28968ddf279cd123400

[ "Apache-2.0" ]

2

2016-06-23T15:35:29.000Z

2022-01-11T00:55:21.000Z

src/odontology/core/migrations/0009_chapter_tariff.py

nanomolina/JP

248a47bced4dac850f85d28968ddf279cd123400

[ "Apache-2.0" ]

27

2016-06-24T12:28:01.000Z

2022-01-13T00:37:25.000Z

src/odontology/core/migrations/0009_chapter_tariff.py

nanomolina/JP

248a47bced4dac850f85d28968ddf279cd123400

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.9.2 on 2016-09-02 20:22 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('core', '0008_auto_20160801_1937'), ] operations = [ migrations.CreateModel( name='Chapter', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=250)), ('number', models.PositiveSmallIntegerField()), ('date', models.DateField(blank=True, null=True)), ('date_created', models.DateField(auto_now_add=True)), ('date_modified', models.DateField(auto_now=True)), ], ), migrations.CreateModel( name='Tariff', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('index', models.PositiveSmallIntegerField(blank=True, null=True)), ('sub_index', models.PositiveSmallIntegerField(blank=True, null=True)), ('name', models.CharField(blank=True, max_length=250)), ('variable_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo variable')), ('fixed_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo fijo')), ('workshop_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo taller')), ('total_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo total')), ('fees', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Honorarios')), ('total_tariff', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Total arancel')), ('chapter', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core.Chapter')), ], ), ]

50.409091

131

0.625338

from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ ('core', '0008_auto_20160801_1937'), ] operations = [ migrations.CreateModel( name='Chapter', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(max_length=250)), ('number', models.PositiveSmallIntegerField()), ('date', models.DateField(blank=True, null=True)), ('date_created', models.DateField(auto_now_add=True)), ('date_modified', models.DateField(auto_now=True)), ], ), migrations.CreateModel( name='Tariff', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('index', models.PositiveSmallIntegerField(blank=True, null=True)), ('sub_index', models.PositiveSmallIntegerField(blank=True, null=True)), ('name', models.CharField(blank=True, max_length=250)), ('variable_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo variable')), ('fixed_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo fijo')), ('workshop_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo taller')), ('total_cost', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Costo total')), ('fees', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Honorarios')), ('total_tariff', models.DecimalField(decimal_places=2, default=0, max_digits=12, verbose_name=b'Total arancel')), ('chapter', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='core.Chapter')), ], ), ]

true

f71ff457dfbaab5f3f5847da75668942d7052f7a

3,330

py

Python

dockerdjango/dockerdjango/settings.py

ducanhvna/DockerDjango

ab5dcb801691fa1ef15a38f05e75fe58066c61ae

[ "MIT" ]

null

dockerdjango/dockerdjango/settings.py

ducanhvna/DockerDjango

ab5dcb801691fa1ef15a38f05e75fe58066c61ae

[ "MIT" ]

9

2019-12-04T23:29:22.000Z

2022-02-10T12:19:23.000Z

dockerdjango/dockerdjango/settings.py

ducanhvna/DockerDjango

ab5dcb801691fa1ef15a38f05e75fe58066c61ae

[ "MIT" ]

null

""" Django settings for dockerdjango project. Generated by 'django-admin startproject' using Django 2.2.2. For more information on this file, see https://docs.djangoproject.com/en/2.2/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.2/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.2/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'gc(_1u30_&as3g*xqy8k@u$aj22*@_#d$ylw@s8cjfjmz%ukq(' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'dockerdjango.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'dockerdjango.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases # DATABASES = { # 'default': { # 'ENGINE': 'django.db.backends.sqlite3', # 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), # } # } DATABASES = { 'default':{ 'ENGINE': 'django.db.backends.mysql', 'NAME': 'my-app-db', 'USER': 'root', 'PASSWORD': 'password', 'HOST': 'db', 'PORT': 3306 } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/'

25.419847

91

0.678378

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = 'gc(_1u30_&as3g*xqy8k@u$aj22*@_#d$ylw@s8cjfjmz%ukq(' DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'dockerdjango.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'dockerdjango.wsgi.application' # Database # https://docs.djangoproject.com/en/2.2/ref/settings/#databases # DATABASES = { # 'default': { # 'ENGINE': 'django.db.backends.sqlite3', # 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), # } # } DATABASES = { 'default':{ 'ENGINE': 'django.db.backends.mysql', 'NAME': 'my-app-db', 'USER': 'root', 'PASSWORD': 'password', 'HOST': 'db', 'PORT': 3306 } } # Password validation # https://docs.djangoproject.com/en/2.2/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.2/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.2/howto/static-files/ STATIC_URL = '/static/'

true

f71ff4f192b3057091ebc889bf78714a2e21a9d6

2,455

py

Python

examples/pybullet/examples/quadruped_setup_playback.py

felipeek/bullet3

6a59241074720e9df119f2f86bc01765917feb1e

[ "Zlib" ]

9,136

2015-01-02T00:41:45.000Z

2022-03-31T15:30:02.000Z

examples/pybullet/examples/quadruped_setup_playback.py

felipeek/bullet3

6a59241074720e9df119f2f86bc01765917feb1e

[ "Zlib" ]

2,424

2015-01-05T08:55:58.000Z

2022-03-30T19:34:55.000Z

examples/pybullet/examples/quadruped_setup_playback.py

felipeek/bullet3

6a59241074720e9df119f2f86bc01765917feb1e

[ "Zlib" ]

2,921

2015-01-02T10:19:30.000Z

2022-03-31T02:48:42.000Z

import pybullet as p import pybullet_data p.connect(p.SHARED_MEMORY) p.setAdditionalSearchPath(pybullet_data.getDataPath()) objects = [ p.loadURDF("plane.urdf", 0.000000, 0.000000, -.300000, 0.000000, 0.000000, 0.000000, 1.000000) ] objects = [ p.loadURDF("quadruped/minitaur.urdf", [-0.000046, -0.000068, 0.200774], [-0.000701, 0.000387, -0.000252, 1.000000], useFixedBase=False) ] ob = objects[0] jointPositions = [ 0.000000, 1.531256, 0.000000, -2.240112, 1.527979, 0.000000, -2.240646, 1.533105, 0.000000, -2.238254, 1.530335, 0.000000, -2.238298, 0.000000, -1.528038, 0.000000, 2.242656, -1.525193, 0.000000, 2.244008, -1.530011, 0.000000, 2.240683, -1.528687, 0.000000, 2.240517 ] for ji in range(p.getNumJoints(ob)): p.resetJointState(ob, ji, jointPositions[ji]) p.setJointMotorControl2(bodyIndex=ob, jointIndex=ji, controlMode=p.VELOCITY_CONTROL, force=0) cid0 = p.createConstraint(1, 3, 1, 6, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid0, maxForce=500.000000) cid1 = p.createConstraint(1, 16, 1, 19, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid1, maxForce=500.000000) cid2 = p.createConstraint(1, 9, 1, 12, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid2, maxForce=500.000000) cid3 = p.createConstraint(1, 22, 1, 25, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid3, maxForce=500.000000) p.setGravity(0.000000, 0.000000, 0.000000) p.stepSimulation() p.disconnect()

51.145833

98

0.608147

import pybullet as p import pybullet_data p.connect(p.SHARED_MEMORY) p.setAdditionalSearchPath(pybullet_data.getDataPath()) objects = [ p.loadURDF("plane.urdf", 0.000000, 0.000000, -.300000, 0.000000, 0.000000, 0.000000, 1.000000) ] objects = [ p.loadURDF("quadruped/minitaur.urdf", [-0.000046, -0.000068, 0.200774], [-0.000701, 0.000387, -0.000252, 1.000000], useFixedBase=False) ] ob = objects[0] jointPositions = [ 0.000000, 1.531256, 0.000000, -2.240112, 1.527979, 0.000000, -2.240646, 1.533105, 0.000000, -2.238254, 1.530335, 0.000000, -2.238298, 0.000000, -1.528038, 0.000000, 2.242656, -1.525193, 0.000000, 2.244008, -1.530011, 0.000000, 2.240683, -1.528687, 0.000000, 2.240517 ] for ji in range(p.getNumJoints(ob)): p.resetJointState(ob, ji, jointPositions[ji]) p.setJointMotorControl2(bodyIndex=ob, jointIndex=ji, controlMode=p.VELOCITY_CONTROL, force=0) cid0 = p.createConstraint(1, 3, 1, 6, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid0, maxForce=500.000000) cid1 = p.createConstraint(1, 16, 1, 19, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid1, maxForce=500.000000) cid2 = p.createConstraint(1, 9, 1, 12, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid2, maxForce=500.000000) cid3 = p.createConstraint(1, 22, 1, 25, p.JOINT_POINT2POINT, [0.000000, 0.000000, 0.000000], [0.000000, 0.005000, 0.200000], [0.000000, 0.010000, 0.200000], [0.000000, 0.000000, 0.000000, 1.000000], [0.000000, 0.000000, 0.000000, 1.000000]) p.changeConstraint(cid3, maxForce=500.000000) p.setGravity(0.000000, 0.000000, 0.000000) p.stepSimulation() p.disconnect()

true

f71ff582921b55fc70764b796e2441625d14a39b

3,212

py

Python

quati/dataset/corpora/imdb.py

onenoc/quati

ba372b2ad14076294af62cbcbc27e1b3ca8421c1

[ "MIT" ]

2

2021-01-30T21:20:36.000Z

2021-01-30T22:15:07.000Z

quati/dataset/corpora/imdb.py

onenoc/quati

ba372b2ad14076294af62cbcbc27e1b3ca8421c1

[ "MIT" ]

null

quati/dataset/corpora/imdb.py

onenoc/quati

ba372b2ad14076294af62cbcbc27e1b3ca8421c1

[ "MIT" ]

1

2021-01-18T23:12:18.000Z

from itertools import chain from pathlib import Path import nltk import torchtext from quati.dataset.fields.words import WordsField from quati.dataset.fields.tags import TagsField from quati.dataset.corpora.corpus import Corpus def create_single_file_for_pos_and_neg(corpus_path): new_file_path = Path(corpus_path, 'data.txt') # do not create this file again if it is already there if not new_file_path.exists(): neg_files = sorted(Path(corpus_path, 'neg').glob('*.txt')) pos_files = sorted(Path(corpus_path, 'pos').glob('*.txt')) paths = chain(neg_files, pos_files) new_file = new_file_path.open('w', encoding='utf8') for file_path in paths: content = file_path.read_text().strip() content = content.replace(' ', ' ') content = content.replace(' ', ' ') content = content.replace(' ', ' ') content = content.replace(' ', ' ') label = '1' if 'pos' in str(file_path) else '0' new_file.write(label + ' ' + content + '\n') new_file.seek(0) new_file.close() return new_file_path class IMDBCorpus(Corpus): task = 'doc' @staticmethod def create_fields_tuples(): # if you choose tokenizer='spacy', please install the en package: # python3 -m spacy download en tokenizer = nltk.WordPunctTokenizer() # tokenizer = nltk.TreebankWordTokenizer() fields_tuples = [ ('words', WordsField(tokenize=tokenizer.tokenize)), ('target', TagsField()) ] return fields_tuples def read(self, corpus_path): """ First, read the positive and negative examples, which are located in different folders: `pos/` and `neg/`. Second, split the ` ` tags from other tokens. Third, save a new file called `data.txt` in the root directory, with the following structure: label_0 text_0 label_1 text_1 ... label_M text_M Args: corpus_path: path to the root directory where `pos/` and `neg/` are located. """ new_file_path = create_single_file_for_pos_and_neg(corpus_path) self.corpus_path = str(new_file_path) self.open(self.corpus_path) if self.lazy is True: return self else: return list(self) def _read(self, file): for line in file: line = line.strip().split() if line: label = line[0] text = ' '.join(line[1:]) yield self.make_torchtext_example(text, label) def make_torchtext_example(self, text, label=None): ex = {'words': text, 'target': label} if 'target' not in self.fields_dict.keys(): del ex['target'] assert ex.keys() == self.fields_dict.keys() return torchtext.data.Example.fromdict(ex, self.fields_dict) if __name__ == '__main__': from quati.dataset.corpora.test_corpus import quick_test quick_test( IMDBCorpus, '../../../data/corpus/imdb/test/', lazy=True, )

33.113402

76

0.59589

from itertools import chain from pathlib import Path import nltk import torchtext from quati.dataset.fields.words import WordsField from quati.dataset.fields.tags import TagsField from quati.dataset.corpora.corpus import Corpus def create_single_file_for_pos_and_neg(corpus_path): new_file_path = Path(corpus_path, 'data.txt') if not new_file_path.exists(): neg_files = sorted(Path(corpus_path, 'neg').glob('*.txt')) pos_files = sorted(Path(corpus_path, 'pos').glob('*.txt')) paths = chain(neg_files, pos_files) new_file = new_file_path.open('w', encoding='utf8') for file_path in paths: content = file_path.read_text().strip() content = content.replace(' ', ' ') content = content.replace(' ', ' ') content = content.replace(' ', ' ') content = content.replace(' ', ' ') label = '1' if 'pos' in str(file_path) else '0' new_file.write(label + ' ' + content + '\n') new_file.seek(0) new_file.close() return new_file_path class IMDBCorpus(Corpus): task = 'doc' @staticmethod def create_fields_tuples(): tokenizer = nltk.WordPunctTokenizer() fields_tuples = [ ('words', WordsField(tokenize=tokenizer.tokenize)), ('target', TagsField()) ] return fields_tuples def read(self, corpus_path): new_file_path = create_single_file_for_pos_and_neg(corpus_path) self.corpus_path = str(new_file_path) self.open(self.corpus_path) if self.lazy is True: return self else: return list(self) def _read(self, file): for line in file: line = line.strip().split() if line: label = line[0] text = ' '.join(line[1:]) yield self.make_torchtext_example(text, label) def make_torchtext_example(self, text, label=None): ex = {'words': text, 'target': label} if 'target' not in self.fields_dict.keys(): del ex['target'] assert ex.keys() == self.fields_dict.keys() return torchtext.data.Example.fromdict(ex, self.fields_dict) if __name__ == '__main__': from quati.dataset.corpora.test_corpus import quick_test quick_test( IMDBCorpus, '../../../data/corpus/imdb/test/', lazy=True, )

true

f71ff58b38aa884ffd4cc5997958ad2274a7a77b

491

py

Python

tests/system/test_base.py

aalmazanarbs/memstatsbeat

554be8f67c385f4e9aeccfdc09b838075c8e1714

[ "Apache-2.0" ]

null

tests/system/test_base.py

aalmazanarbs/memstatsbeat

554be8f67c385f4e9aeccfdc09b838075c8e1714

[ "Apache-2.0" ]

null

tests/system/test_base.py

aalmazanarbs/memstatsbeat

554be8f67c385f4e9aeccfdc09b838075c8e1714

[ "Apache-2.0" ]

null

from memstatsbeat import BaseTest import os class Test(BaseTest): def test_base(self): """ Basic test with exiting Memstatsbeat normally """ self.render_config_template( path=os.path.abspath(self.working_dir) + "/log/*" ) memstatsbeat_proc = self.start_beat() self.wait_until(lambda: self.log_contains("memstatsbeat is running")) exit_code = memstatsbeat_proc.kill_and_wait() assert exit_code == 0

24.55

77

0.641548

from memstatsbeat import BaseTest import os class Test(BaseTest): def test_base(self): self.render_config_template( path=os.path.abspath(self.working_dir) + "/log/*" ) memstatsbeat_proc = self.start_beat() self.wait_until(lambda: self.log_contains("memstatsbeat is running")) exit_code = memstatsbeat_proc.kill_and_wait() assert exit_code == 0

true