fumiyau/python_no_comments_test100000 · Datasets at Hugging Face

f71e77b8f44d22273b61f23147bbe79a416e5b87

1,166

py

Python

aoc_2021/day03.py

guido-weber/AoC_2021

f5d41ea0600f702857b2c479a67f4f9578afb52b

[ "Unlicense" ]

null

aoc_2021/day03.py

guido-weber/AoC_2021

f5d41ea0600f702857b2c479a67f4f9578afb52b

[ "Unlicense" ]

null

aoc_2021/day03.py

guido-weber/AoC_2021

f5d41ea0600f702857b2c479a67f4f9578afb52b

[ "Unlicense" ]

null

import io from collections import Counter from typing import Iterable def read_input(): with io.open("input/day03") as f: return f.read() def most_common(bits: Iterable[str]): c = Counter(bits) return "0" if c["0"] > c["1"] else "1" def least_common(bits: Iterable[str]): c = Counter(bits) return "1" if c["1"] < c["0"] else "0" def invert(bits: str): return "".join("0" if b == "1" else "1" for b in bits) def day03_1(input: str): lines = input.splitlines() gamma = "".join(most_common(bits) for bits in zip(*lines)) epsilon = invert(gamma) return int(gamma, 2) * int(epsilon, 2) def select_line(lines: list[str], crit): prefix = "" while len(lines) > 1: pl = len(prefix) prefix += crit(line[pl] for line in lines) lines = [line for line in lines if line.startswith(prefix)] return int(lines[0], 2) def day03_2(input: str): lines = input.splitlines() oxy = select_line(lines, most_common) co2 = select_line(lines, least_common) return oxy * co2 if __name__ == "__main__": input = read_input() print(day03_1(input)) print(day03_2(input))

22.423077

67

0.621784

import io from collections import Counter from typing import Iterable def read_input(): with io.open("input/day03") as f: return f.read() def most_common(bits: Iterable[str]): c = Counter(bits) return "0" if c["0"] > c["1"] else "1" def least_common(bits: Iterable[str]): c = Counter(bits) return "1" if c["1"] < c["0"] else "0" def invert(bits: str): return "".join("0" if b == "1" else "1" for b in bits) def day03_1(input: str): lines = input.splitlines() gamma = "".join(most_common(bits) for bits in zip(*lines)) epsilon = invert(gamma) return int(gamma, 2) * int(epsilon, 2) def select_line(lines: list[str], crit): prefix = "" while len(lines) > 1: pl = len(prefix) prefix += crit(line[pl] for line in lines) lines = [line for line in lines if line.startswith(prefix)] return int(lines[0], 2) def day03_2(input: str): lines = input.splitlines() oxy = select_line(lines, most_common) co2 = select_line(lines, least_common) return oxy * co2 if __name__ == "__main__": input = read_input() print(day03_1(input)) print(day03_2(input))

true

f71e77d479a5c19a10183f4785ab075fdd327612

380

py

Python

vfio_isolate/action/action.py

spheenik/vfio-isolate

6d6a1f0d5e5d84a5ad9911c635a81b86710d12d5

[ "MIT" ]

44

2020-05-03T15:03:32.000Z

2022-03-23T19:03:23.000Z

vfio_isolate/action/action.py

darkguy2008/vfio-isolate

6c16cf363a627f02202586a17df58522e097ef10

[ "MIT" ]

7

2020-08-18T10:17:14.000Z

2022-01-14T14:18:47.000Z

vfio_isolate/action/action.py

darkguy2008/vfio-isolate

6c16cf363a627f02202586a17df58522e097ef10

[ "MIT" ]

6

2020-06-02T05:29:34.000Z

2022-02-04T17:12:40.000Z

from dataclasses import dataclass from typing import Generator @dataclass class Execution: action: type params: object class Action: @classmethod def can_execute(cls, p): return True @classmethod def execute(cls, p): pass @classmethod def record_undo(cls, p) -> Generator[Execution, None, None]: return yield

15.2

64

0.644737

from dataclasses import dataclass from typing import Generator @dataclass class Execution: action: type params: object class Action: @classmethod def can_execute(cls, p): return True @classmethod def execute(cls, p): pass @classmethod def record_undo(cls, p) -> Generator[Execution, None, None]: return yield

true

f71e781a9cac3e602bc48bcfc5f1e148c85a0985

1,536

py

Python

upy/__init__.py

transforma-digital/upy

b70b65ea3f8b8c47a64d54567289280fd78877fe

[ "BSD-3-Clause" ]

null

upy/__init__.py

transforma-digital/upy

b70b65ea3f8b8c47a64d54567289280fd78877fe

[ "BSD-3-Clause" ]

5

2021-08-04T01:30:48.000Z

2021-08-06T17:42:08.000Z

upy/__init__.py

transforma-digital/upy

b70b65ea3f8b8c47a64d54567289280fd78877fe

[ "BSD-3-Clause" ]

null

# Copyright 2021 sinek-dev # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # 1. Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS # IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED # TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A # PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED # TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR # PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

52.965517

75

0.775391

# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED

true

f71e781ca6c1fe1d224c75e75b6f5fc9def42831

8,411

py

Python

lib/win32popen.py

manuvaldi/viewvc-wiki

a8627d695cc80425ed201ded9ab7030438d67a03

[ "BSD-2-Clause" ]

null

lib/win32popen.py

manuvaldi/viewvc-wiki

a8627d695cc80425ed201ded9ab7030438d67a03

[ "BSD-2-Clause" ]

null

lib/win32popen.py

manuvaldi/viewvc-wiki

a8627d695cc80425ed201ded9ab7030438d67a03

[ "BSD-2-Clause" ]

null

# -*-python-*- # # Copyright (C) 1999-2015 The ViewCVS Group. All Rights Reserved. # # By using this file, you agree to the terms and conditions set forth in # the LICENSE.html file which can be found at the top level of the ViewVC # distribution or at http://viewvc.org/license-1.html. # # For more information, visit http://viewvc.org/ # # ----------------------------------------------------------------------- # # Utilities for controlling processes and pipes on win32 # # ----------------------------------------------------------------------- import os, sys, traceback, string, thread try: import win32api except ImportError, e: raise ImportError, str(e) + """ Did you install the Python for Windows Extensions? http://sourceforge.net/projects/pywin32/ """ import win32process, win32pipe, win32con import win32event, win32file, winerror import pywintypes, msvcrt # Buffer size for spooling SPOOL_BYTES = 4096 # File object to write error messages SPOOL_ERROR = sys.stderr #SPOOL_ERROR = open("m:/temp/error.txt", "wt") def CommandLine(command, args): """Convert an executable path and a sequence of arguments into a command line that can be passed to CreateProcess""" cmd = "\"" + string.replace(command, "\"", "\"\"") + "\"" for arg in args: cmd = cmd + " \"" + string.replace(arg, "\"", "\"\"") + "\"" return cmd def CreateProcess(cmd, hStdInput, hStdOutput, hStdError): """Creates a new process which uses the specified handles for its standard input, output, and error. The handles must be inheritable. 0 can be passed as a special handle indicating that the process should inherit the current process's input, output, or error streams, and None can be passed to discard the child process's output or to prevent it from reading any input.""" # initialize new process's startup info si = win32process.STARTUPINFO() si.dwFlags = win32process.STARTF_USESTDHANDLES if hStdInput == 0: si.hStdInput = win32api.GetStdHandle(win32api.STD_INPUT_HANDLE) else: si.hStdInput = hStdInput if hStdOutput == 0: si.hStdOutput = win32api.GetStdHandle(win32api.STD_OUTPUT_HANDLE) else: si.hStdOutput = hStdOutput if hStdError == 0: si.hStdError = win32api.GetStdHandle(win32api.STD_ERROR_HANDLE) else: si.hStdError = hStdError # create the process phandle, pid, thandle, tid = win32process.CreateProcess \ ( None, # appName cmd, # commandLine None, # processAttributes None, # threadAttributes 1, # bInheritHandles win32con.NORMAL_PRIORITY_CLASS, # dwCreationFlags None, # newEnvironment None, # currentDirectory si # startupinfo ) if hStdInput and hasattr(hStdInput, 'Close'): hStdInput.Close() if hStdOutput and hasattr(hStdOutput, 'Close'): hStdOutput.Close() if hStdError and hasattr(hStdError, 'Close'): hStdError.Close() return phandle, pid, thandle, tid def CreatePipe(readInheritable, writeInheritable): """Create a new pipe specifying whether the read and write ends are inheritable and whether they should be created for blocking or nonblocking I/O.""" r, w = win32pipe.CreatePipe(None, SPOOL_BYTES) if readInheritable: r = MakeInheritedHandle(r) if writeInheritable: w = MakeInheritedHandle(w) return r, w def File2FileObject(pipe, mode): """Make a C stdio file object out of a win32 file handle""" if string.find(mode, 'r') >= 0: wmode = os.O_RDONLY elif string.find(mode, 'w') >= 0: wmode = os.O_WRONLY if string.find(mode, 'b') >= 0: wmode = wmode | os.O_BINARY if string.find(mode, 't') >= 0: wmode = wmode | os.O_TEXT return os.fdopen(msvcrt.open_osfhandle(pipe.Detach(),wmode),mode) def FileObject2File(fileObject): """Get the win32 file handle from a C stdio file object""" return win32file._get_osfhandle(fileObject.fileno()) def DuplicateHandle(handle): """Duplicates a win32 handle.""" proc = win32api.GetCurrentProcess() return win32api.DuplicateHandle(proc,handle,proc,0,0,win32con.DUPLICATE_SAME_ACCESS) def MakePrivateHandle(handle, replace = 1): """Turn an inherited handle into a non inherited one. This avoids the handle duplication that occurs on CreateProcess calls which can create uncloseable pipes.""" ### Could change implementation to use SetHandleInformation()... flags = win32con.DUPLICATE_SAME_ACCESS proc = win32api.GetCurrentProcess() if replace: flags = flags | win32con.DUPLICATE_CLOSE_SOURCE newhandle = win32api.DuplicateHandle(proc,handle,proc,0,0,flags) if replace: handle.Detach() # handle was already deleted by the last call return newhandle def MakeInheritedHandle(handle, replace = 1): """Turn a private handle into an inherited one.""" ### Could change implementation to use SetHandleInformation()... flags = win32con.DUPLICATE_SAME_ACCESS proc = win32api.GetCurrentProcess() if replace: flags = flags | win32con.DUPLICATE_CLOSE_SOURCE newhandle = win32api.DuplicateHandle(proc,handle,proc,0,1,flags) if replace: handle.Detach() # handle was deleted by the last call return newhandle def MakeSpyPipe(readInheritable, writeInheritable, outFiles = None, doneEvent = None): """Return read and write handles to a pipe that asynchronously writes all of its input to the files in the outFiles sequence. doneEvent can be None, or a a win32 event handle that will be set when the write end of pipe is closed. """ if outFiles is None: return CreatePipe(readInheritable, writeInheritable) r, writeHandle = CreatePipe(0, writeInheritable) if readInheritable is None: readHandle, w = None, None else: readHandle, w = CreatePipe(readInheritable, 0) thread.start_new_thread(SpoolWorker, (r, w, outFiles, doneEvent)) return readHandle, writeHandle def SpoolWorker(srcHandle, destHandle, outFiles, doneEvent): """Thread entry point for implementation of MakeSpyPipe""" try: buffer = win32file.AllocateReadBuffer(SPOOL_BYTES) while 1: try: #print >> SPOOL_ERROR, "Calling ReadFile..."; SPOOL_ERROR.flush() hr, data = win32file.ReadFile(srcHandle, buffer) #print >> SPOOL_ERROR, "ReadFile returned '%s', '%s'" % (str(hr), str(data)); SPOOL_ERROR.flush() if hr != 0: raise "win32file.ReadFile returned %i, '%s'" % (hr, data) elif len(data) == 0: break except pywintypes.error, e: #print >> SPOOL_ERROR, "ReadFile threw '%s'" % str(e); SPOOL_ERROR.flush() if e.args[0] == winerror.ERROR_BROKEN_PIPE: break else: raise e #print >> SPOOL_ERROR, "Writing to %i file objects..." % len(outFiles); SPOOL_ERROR.flush() for f in outFiles: f.write(data) #print >> SPOOL_ERROR, "Done writing to file objects."; SPOOL_ERROR.flush() #print >> SPOOL_ERROR, "Writing to destination %s" % str(destHandle); SPOOL_ERROR.flush() if destHandle: #print >> SPOOL_ERROR, "Calling WriteFile..."; SPOOL_ERROR.flush() hr, bytes = win32file.WriteFile(destHandle, data) #print >> SPOOL_ERROR, "WriteFile() passed %i bytes and returned %i, %i" % (len(data), hr, bytes); SPOOL_ERROR.flush() if hr != 0 or bytes != len(data): raise "win32file.WriteFile() passed %i bytes and returned %i, %i" % (len(data), hr, bytes) srcHandle.Close() if doneEvent: win32event.SetEvent(doneEvent) if destHandle: destHandle.Close() except: info = sys.exc_info() SPOOL_ERROR.writelines(apply(traceback.format_exception, info), '') SPOOL_ERROR.flush() del info def NullFile(inheritable): """Create a null file handle.""" if inheritable: sa = pywintypes.SECURITY_ATTRIBUTES() sa.bInheritHandle = 1 else: sa = None return win32file.CreateFile("nul", win32file.GENERIC_READ | win32file.GENERIC_WRITE, win32file.FILE_SHARE_READ | win32file.FILE_SHARE_WRITE, sa, win32file.OPEN_EXISTING, 0, None)

35.639831

127

0.653311

import os, sys, traceback, string, thread try: import win32api except ImportError, e: raise ImportError, str(e) + """ Did you install the Python for Windows Extensions? http://sourceforge.net/projects/pywin32/ """ import win32process, win32pipe, win32con import win32event, win32file, winerror import pywintypes, msvcrt SPOOL_BYTES = 4096 SPOOL_ERROR = sys.stderr def CommandLine(command, args): """Convert an executable path and a sequence of arguments into a command line that can be passed to CreateProcess""" cmd = "\"" + string.replace(command, "\"", "\"\"") + "\"" for arg in args: cmd = cmd + " \"" + string.replace(arg, "\"", "\"\"") + "\"" return cmd def CreateProcess(cmd, hStdInput, hStdOutput, hStdError): """Creates a new process which uses the specified handles for its standard input, output, and error. The handles must be inheritable. 0 can be passed as a special handle indicating that the process should inherit the current process's input, output, or error streams, and None can be passed to discard the child process's output or to prevent it from reading any input.""" si = win32process.STARTUPINFO() si.dwFlags = win32process.STARTF_USESTDHANDLES if hStdInput == 0: si.hStdInput = win32api.GetStdHandle(win32api.STD_INPUT_HANDLE) else: si.hStdInput = hStdInput if hStdOutput == 0: si.hStdOutput = win32api.GetStdHandle(win32api.STD_OUTPUT_HANDLE) else: si.hStdOutput = hStdOutput if hStdError == 0: si.hStdError = win32api.GetStdHandle(win32api.STD_ERROR_HANDLE) else: si.hStdError = hStdError # create the process phandle, pid, thandle, tid = win32process.CreateProcess \ ( None, # appName cmd, # commandLine None, # processAttributes None, # threadAttributes 1, # bInheritHandles win32con.NORMAL_PRIORITY_CLASS, # dwCreationFlags None, # newEnvironment None, # currentDirectory si # startupinfo ) if hStdInput and hasattr(hStdInput, 'Close'): hStdInput.Close() if hStdOutput and hasattr(hStdOutput, 'Close'): hStdOutput.Close() if hStdError and hasattr(hStdError, 'Close'): hStdError.Close() return phandle, pid, thandle, tid def CreatePipe(readInheritable, writeInheritable): """Create a new pipe specifying whether the read and write ends are inheritable and whether they should be created for blocking or nonblocking I/O.""" r, w = win32pipe.CreatePipe(None, SPOOL_BYTES) if readInheritable: r = MakeInheritedHandle(r) if writeInheritable: w = MakeInheritedHandle(w) return r, w def File2FileObject(pipe, mode): """Make a C stdio file object out of a win32 file handle""" if string.find(mode, 'r') >= 0: wmode = os.O_RDONLY elif string.find(mode, 'w') >= 0: wmode = os.O_WRONLY if string.find(mode, 'b') >= 0: wmode = wmode | os.O_BINARY if string.find(mode, 't') >= 0: wmode = wmode | os.O_TEXT return os.fdopen(msvcrt.open_osfhandle(pipe.Detach(),wmode),mode) def FileObject2File(fileObject): """Get the win32 file handle from a C stdio file object""" return win32file._get_osfhandle(fileObject.fileno()) def DuplicateHandle(handle): """Duplicates a win32 handle.""" proc = win32api.GetCurrentProcess() return win32api.DuplicateHandle(proc,handle,proc,0,0,win32con.DUPLICATE_SAME_ACCESS) def MakePrivateHandle(handle, replace = 1): """Turn an inherited handle into a non inherited one. This avoids the handle duplication that occurs on CreateProcess calls which can create uncloseable pipes.""" ### Could change implementation to use SetHandleInformation()... flags = win32con.DUPLICATE_SAME_ACCESS proc = win32api.GetCurrentProcess() if replace: flags = flags | win32con.DUPLICATE_CLOSE_SOURCE newhandle = win32api.DuplicateHandle(proc,handle,proc,0,0,flags) if replace: handle.Detach() # handle was already deleted by the last call return newhandle def MakeInheritedHandle(handle, replace = 1): """Turn a private handle into an inherited one.""" ### Could change implementation to use SetHandleInformation()... flags = win32con.DUPLICATE_SAME_ACCESS proc = win32api.GetCurrentProcess() if replace: flags = flags | win32con.DUPLICATE_CLOSE_SOURCE newhandle = win32api.DuplicateHandle(proc,handle,proc,0,1,flags) if replace: handle.Detach() # handle was deleted by the last call return newhandle def MakeSpyPipe(readInheritable, writeInheritable, outFiles = None, doneEvent = None): """Return read and write handles to a pipe that asynchronously writes all of its input to the files in the outFiles sequence. doneEvent can be None, or a a win32 event handle that will be set when the write end of pipe is closed. """ if outFiles is None: return CreatePipe(readInheritable, writeInheritable) r, writeHandle = CreatePipe(0, writeInheritable) if readInheritable is None: readHandle, w = None, None else: readHandle, w = CreatePipe(readInheritable, 0) thread.start_new_thread(SpoolWorker, (r, w, outFiles, doneEvent)) return readHandle, writeHandle def SpoolWorker(srcHandle, destHandle, outFiles, doneEvent): """Thread entry point for implementation of MakeSpyPipe""" try: buffer = win32file.AllocateReadBuffer(SPOOL_BYTES) while 1: try: #print >> SPOOL_ERROR, "Calling ReadFile..."; SPOOL_ERROR.flush() hr, data = win32file.ReadFile(srcHandle, buffer) #print >> SPOOL_ERROR, "ReadFile returned '%s', '%s'" % (str(hr), str(data)); SPOOL_ERROR.flush() if hr != 0: raise "win32file.ReadFile returned %i, '%s'" % (hr, data) elif len(data) == 0: break except pywintypes.error, e: #print >> SPOOL_ERROR, "ReadFile threw '%s'" % str(e); SPOOL_ERROR.flush() if e.args[0] == winerror.ERROR_BROKEN_PIPE: break else: raise e #print >> SPOOL_ERROR, "Writing to %i file objects..." % len(outFiles); SPOOL_ERROR.flush() for f in outFiles: f.write(data) #print >> SPOOL_ERROR, "Done writing to file objects."; SPOOL_ERROR.flush() #print >> SPOOL_ERROR, "Writing to destination %s" % str(destHandle); SPOOL_ERROR.flush() if destHandle: #print >> SPOOL_ERROR, "Calling WriteFile..."; SPOOL_ERROR.flush() hr, bytes = win32file.WriteFile(destHandle, data) #print >> SPOOL_ERROR, "WriteFile() passed %i bytes and returned %i, %i" % (len(data), hr, bytes); SPOOL_ERROR.flush() if hr != 0 or bytes != len(data): raise "win32file.WriteFile() passed %i bytes and returned %i, %i" % (len(data), hr, bytes) srcHandle.Close() if doneEvent: win32event.SetEvent(doneEvent) if destHandle: destHandle.Close() except: info = sys.exc_info() SPOOL_ERROR.writelines(apply(traceback.format_exception, info), '') SPOOL_ERROR.flush() del info def NullFile(inheritable): """Create a null file handle.""" if inheritable: sa = pywintypes.SECURITY_ATTRIBUTES() sa.bInheritHandle = 1 else: sa = None return win32file.CreateFile("nul", win32file.GENERIC_READ | win32file.GENERIC_WRITE, win32file.FILE_SHARE_READ | win32file.FILE_SHARE_WRITE, sa, win32file.OPEN_EXISTING, 0, None)

false

true

f71e798522edd2d2ed86a48c227d9550e9392a77

19,740

py

Python

tests/wallet/did_wallet/test_did.py

MintNetwork/mint-blockchain

65ec05a015a07664ed25f83efa736065a17f7d7a

[ "Apache-2.0" ]

12

2021-08-18T20:53:31.000Z

2022-03-15T21:45:13.000Z

tests/wallet/did_wallet/test_did.py

MintNetwork/mint-blockchain

65ec05a015a07664ed25f83efa736065a17f7d7a

[ "Apache-2.0" ]

34

2021-08-18T19:12:11.000Z

2022-01-06T17:15:34.000Z

tests/wallet/did_wallet/test_did.py

MintNetwork/mint-blockchain

65ec05a015a07664ed25f83efa736065a17f7d7a

[ "Apache-2.0" ]

7

2021-08-18T20:53:34.000Z

2022-03-15T08:37:40.000Z

import asyncio import pytest from mint.simulator.simulator_protocol import FarmNewBlockProtocol from mint.types.peer_info import PeerInfo from mint.util.ints import uint16, uint32, uint64 from tests.setup_nodes import setup_simulators_and_wallets from mint.wallet.did_wallet.did_wallet import DIDWallet from mint.types.blockchain_format.program import Program from blspy import AugSchemeMPL from mint.types.spend_bundle import SpendBundle from mint.consensus.block_rewards import calculate_pool_reward, calculate_base_farmer_reward from tests.time_out_assert import time_out_assert @pytest.fixture(scope="module") def event_loop(): loop = asyncio.get_event_loop() yield loop class TestDIDWallet: @pytest.fixture(scope="function") async def wallet_node(self): async for _ in setup_simulators_and_wallets(1, 1, {}): yield _ @pytest.fixture(scope="function") async def two_wallet_nodes(self): async for _ in setup_simulators_and_wallets(1, 2, {}): yield _ @pytest.fixture(scope="function") async def three_wallet_nodes(self): async for _ in setup_simulators_and_wallets(1, 3, {}): yield _ @pytest.fixture(scope="function") async def two_wallet_nodes_five_freeze(self): async for _ in setup_simulators_and_wallets(1, 2, {}): yield _ @pytest.fixture(scope="function") async def three_sim_two_wallets(self): async for _ in setup_simulators_and_wallets(3, 2, {}): yield _ @pytest.mark.asyncio async def test_creation_from_backup_file(self, three_wallet_nodes): num_blocks = 5 full_nodes, wallets = three_wallet_nodes full_node_api = full_nodes[0] full_node_server = full_node_api.server wallet_node_0, server_0 = wallets[0] wallet_node_1, server_1 = wallets[1] wallet_node_2, server_2 = wallets[2] wallet_0 = wallet_node_0.wallet_state_manager.main_wallet wallet_1 = wallet_node_1.wallet_state_manager.main_wallet wallet_2 = wallet_node_2.wallet_state_manager.main_wallet ph = await wallet_0.get_new_puzzlehash() ph1 = await wallet_1.get_new_puzzlehash() ph2 = await wallet_2.get_new_puzzlehash() await server_0.start_client(PeerInfo("localhost", uint16(full_node_server._port)), None) await server_1.start_client(PeerInfo("localhost", uint16(full_node_server._port)), None) await server_2.start_client(PeerInfo("localhost", uint16(full_node_server._port)), None) for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph)) funds = sum( [ calculate_pool_reward(uint32(i)) + calculate_base_farmer_reward(uint32(i)) for i in range(1, num_blocks - 1) ] ) await time_out_assert(10, wallet_0.get_unconfirmed_balance, funds) await time_out_assert(10, wallet_0.get_confirmed_balance, funds) for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph1)) for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) # Wallet1 sets up DIDWallet1 without any backup set async with wallet_node_0.wallet_state_manager.lock: did_wallet_0: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node_0.wallet_state_manager, wallet_0, uint64(101) ) for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet_0.get_confirmed_balance, 101) await time_out_assert(15, did_wallet_0.get_unconfirmed_balance, 101) await time_out_assert(15, did_wallet_0.get_pending_change_balance, 0) # Wallet1 sets up DIDWallet_1 with DIDWallet_0 as backup backup_ids = [bytes.fromhex(did_wallet_0.get_my_DID())] async with wallet_node_1.wallet_state_manager.lock: did_wallet_1: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node_1.wallet_state_manager, wallet_1, uint64(201), backup_ids ) for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet_1.get_confirmed_balance, 201) await time_out_assert(15, did_wallet_1.get_unconfirmed_balance, 201) await time_out_assert(15, did_wallet_1.get_pending_change_balance, 0) filename = "test.backup" did_wallet_1.create_backup(filename) # Wallet2 recovers DIDWallet2 to a new set of keys async with wallet_node_2.wallet_state_manager.lock: did_wallet_2 = await DIDWallet.create_new_did_wallet_from_recovery( wallet_node_2.wallet_state_manager, wallet_2, filename ) coins = await did_wallet_1.select_coins(1) coin = coins.copy().pop() assert did_wallet_2.did_info.temp_coin == coin newpuzhash = await did_wallet_2.get_new_inner_hash() pubkey = bytes( (await did_wallet_2.wallet_state_manager.get_unused_derivation_record(did_wallet_2.wallet_info.id)).pubkey ) message_spend_bundle = await did_wallet_0.create_attestment( did_wallet_2.did_info.temp_coin.name(), newpuzhash, pubkey, "test.attest" ) print(f"pubkey: {pubkey}") for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph)) ( test_info_list, test_message_spend_bundle, ) = await did_wallet_2.load_attest_files_for_recovery_spend(["test.attest"]) assert message_spend_bundle == test_message_spend_bundle await did_wallet_2.recovery_spend( did_wallet_2.did_info.temp_coin, newpuzhash, test_info_list, pubkey, test_message_spend_bundle, ) print(f"pubkey: {did_wallet_2}") for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(45, did_wallet_2.get_confirmed_balance, 201) await time_out_assert(45, did_wallet_2.get_unconfirmed_balance, 201) some_ph = 32 * b"\2" await did_wallet_2.create_exit_spend(some_ph) for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph)) async def get_coins_with_ph(): coins = await full_node_api.full_node.coin_store.get_coin_records_by_puzzle_hash(True, some_ph) if len(coins) == 1: return True return False await time_out_assert(15, get_coins_with_ph, True) await time_out_assert(45, did_wallet_2.get_confirmed_balance, 0) await time_out_assert(45, did_wallet_2.get_unconfirmed_balance, 0) @pytest.mark.asyncio async def test_did_recovery_with_multiple_backup_dids(self, two_wallet_nodes): num_blocks = 5 full_nodes, wallets = two_wallet_nodes full_node_1 = full_nodes[0] server_1 = full_node_1.server wallet_node, server_2 = wallets[0] wallet_node_2, server_3 = wallets[1] wallet = wallet_node.wallet_state_manager.main_wallet wallet2 = wallet_node_2.wallet_state_manager.main_wallet ph = await wallet.get_new_puzzlehash() await server_2.start_client(PeerInfo("localhost", uint16(server_1._port)), None) await server_3.start_client(PeerInfo("localhost", uint16(server_1._port)), None) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) funds = sum( [ calculate_pool_reward(uint32(i)) + calculate_base_farmer_reward(uint32(i)) for i in range(1, num_blocks - 1) ] ) await time_out_assert(15, wallet.get_confirmed_balance, funds) async with wallet_node.wallet_state_manager.lock: did_wallet: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node.wallet_state_manager, wallet, uint64(101) ) ph = await wallet2.get_new_puzzlehash() for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet.get_confirmed_balance, 101) await time_out_assert(15, did_wallet.get_unconfirmed_balance, 101) recovery_list = [bytes.fromhex(did_wallet.get_my_DID())] async with wallet_node_2.wallet_state_manager.lock: did_wallet_2: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node_2.wallet_state_manager, wallet2, uint64(101), recovery_list ) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet_2.get_confirmed_balance, 101) await time_out_assert(15, did_wallet_2.get_unconfirmed_balance, 101) assert did_wallet_2.did_info.backup_ids == recovery_list recovery_list.append(bytes.fromhex(did_wallet_2.get_my_DID())) async with wallet_node_2.wallet_state_manager.lock: did_wallet_3: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node_2.wallet_state_manager, wallet2, uint64(201), recovery_list ) ph2 = await wallet.get_new_puzzlehash() for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) assert did_wallet_3.did_info.backup_ids == recovery_list await time_out_assert(15, did_wallet_3.get_confirmed_balance, 201) await time_out_assert(15, did_wallet_3.get_unconfirmed_balance, 201) coins = await did_wallet_3.select_coins(1) coin = coins.pop() filename = "test.backup" did_wallet_3.create_backup(filename) async with wallet_node.wallet_state_manager.lock: did_wallet_4 = await DIDWallet.create_new_did_wallet_from_recovery( wallet_node.wallet_state_manager, wallet, filename, ) pubkey = ( await did_wallet_4.wallet_state_manager.get_unused_derivation_record(did_wallet_2.wallet_info.id) ).pubkey new_ph = await did_wallet_4.get_new_inner_hash() message_spend_bundle = await did_wallet.create_attestment(coin.name(), new_ph, pubkey, "test1.attest") message_spend_bundle2 = await did_wallet_2.create_attestment(coin.name(), new_ph, pubkey, "test2.attest") message_spend_bundle = message_spend_bundle.aggregate([message_spend_bundle, message_spend_bundle2]) ( test_info_list, test_message_spend_bundle, ) = await did_wallet_4.load_attest_files_for_recovery_spend(["test1.attest", "test2.attest"]) assert message_spend_bundle == test_message_spend_bundle for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) await did_wallet_4.recovery_spend(coin, new_ph, test_info_list, pubkey, message_spend_bundle) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) await time_out_assert(15, did_wallet_4.get_confirmed_balance, 201) await time_out_assert(15, did_wallet_4.get_unconfirmed_balance, 201) await time_out_assert(15, did_wallet_3.get_confirmed_balance, 0) await time_out_assert(15, did_wallet_3.get_unconfirmed_balance, 0) @pytest.mark.asyncio async def test_did_recovery_with_empty_set(self, two_wallet_nodes): num_blocks = 5 full_nodes, wallets = two_wallet_nodes full_node_1 = full_nodes[0] server_1 = full_node_1.server wallet_node, server_2 = wallets[0] wallet_node_2, server_3 = wallets[1] wallet = wallet_node.wallet_state_manager.main_wallet ph = await wallet.get_new_puzzlehash() await server_2.start_client(PeerInfo("localhost", uint16(server_1._port)), None) await server_3.start_client(PeerInfo("localhost", uint16(server_1._port)), None) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) funds = sum( [ calculate_pool_reward(uint32(i)) + calculate_base_farmer_reward(uint32(i)) for i in range(1, num_blocks - 1) ] ) await time_out_assert(15, wallet.get_confirmed_balance, funds) async with wallet_node.wallet_state_manager.lock: did_wallet: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node.wallet_state_manager, wallet, uint64(101) ) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet.get_confirmed_balance, 101) await time_out_assert(15, did_wallet.get_unconfirmed_balance, 101) coins = await did_wallet.select_coins(1) coin = coins.pop() info = Program.to([]) pubkey = (await did_wallet.wallet_state_manager.get_unused_derivation_record(did_wallet.wallet_info.id)).pubkey spend_bundle = await did_wallet.recovery_spend( coin, ph, info, pubkey, SpendBundle([], AugSchemeMPL.aggregate([])) ) additions = spend_bundle.additions() assert additions == [] @pytest.mark.asyncio async def test_did_attest_after_recovery(self, two_wallet_nodes): num_blocks = 5 full_nodes, wallets = two_wallet_nodes full_node_1 = full_nodes[0] server_1 = full_node_1.server wallet_node, server_2 = wallets[0] wallet_node_2, server_3 = wallets[1] wallet = wallet_node.wallet_state_manager.main_wallet wallet2 = wallet_node_2.wallet_state_manager.main_wallet ph = await wallet.get_new_puzzlehash() await server_2.start_client(PeerInfo("localhost", uint16(server_1._port)), None) await server_3.start_client(PeerInfo("localhost", uint16(server_1._port)), None) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) funds = sum( [ calculate_pool_reward(uint32(i)) + calculate_base_farmer_reward(uint32(i)) for i in range(1, num_blocks - 1) ] ) await time_out_assert(15, wallet.get_confirmed_balance, funds) async with wallet_node.wallet_state_manager.lock: did_wallet: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node.wallet_state_manager, wallet, uint64(101) ) ph2 = await wallet2.get_new_puzzlehash() for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) await time_out_assert(15, did_wallet.get_confirmed_balance, 101) await time_out_assert(15, did_wallet.get_unconfirmed_balance, 101) recovery_list = [bytes.fromhex(did_wallet.get_my_DID())] async with wallet_node_2.wallet_state_manager.lock: did_wallet_2: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node_2.wallet_state_manager, wallet2, uint64(101), recovery_list ) ph = await wallet.get_new_puzzlehash() for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet_2.get_confirmed_balance, 101) await time_out_assert(15, did_wallet_2.get_unconfirmed_balance, 101) assert did_wallet_2.did_info.backup_ids == recovery_list # Update coin with new ID info recovery_list = [bytes.fromhex(did_wallet_2.get_my_DID())] await did_wallet.update_recovery_list(recovery_list, uint64(1)) assert did_wallet.did_info.backup_ids == recovery_list await did_wallet.create_update_spend() for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) await time_out_assert(15, did_wallet.get_confirmed_balance, 101) await time_out_assert(15, did_wallet.get_unconfirmed_balance, 101) # DID Wallet 2 recovers into DID Wallet 3 with new innerpuz filename = "test.backup" did_wallet_2.create_backup(filename) async with wallet_node.wallet_state_manager.lock: did_wallet_3 = await DIDWallet.create_new_did_wallet_from_recovery( wallet_node.wallet_state_manager, wallet, filename, ) new_ph = await did_wallet_3.get_new_inner_hash() coins = await did_wallet_2.select_coins(1) coin = coins.pop() pubkey = ( await did_wallet_3.wallet_state_manager.get_unused_derivation_record(did_wallet_3.wallet_info.id) ).pubkey message_spend_bundle = await did_wallet.create_attestment(coin.name(), new_ph, pubkey, "test.attest") for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) ( info, message_spend_bundle, ) = await did_wallet_3.load_attest_files_for_recovery_spend(["test.attest"]) await did_wallet_3.recovery_spend(coin, new_ph, info, pubkey, message_spend_bundle) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet_3.get_confirmed_balance, 101) await time_out_assert(15, did_wallet_3.get_unconfirmed_balance, 101) # DID Wallet 1 recovery spends into DID Wallet 4 filename = "test.backup" did_wallet.create_backup(filename) async with wallet_node_2.wallet_state_manager.lock: did_wallet_4 = await DIDWallet.create_new_did_wallet_from_recovery( wallet_node_2.wallet_state_manager, wallet2, filename, ) coins = await did_wallet.select_coins(1) coin = coins.pop() new_ph = await did_wallet_4.get_new_inner_hash() pubkey = ( await did_wallet_4.wallet_state_manager.get_unused_derivation_record(did_wallet_4.wallet_info.id) ).pubkey await did_wallet_3.create_attestment(coin.name(), new_ph, pubkey, "test.attest") for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) ( test_info_list, test_message_spend_bundle, ) = await did_wallet_4.load_attest_files_for_recovery_spend(["test.attest"]) await did_wallet_4.recovery_spend(coin, new_ph, test_info_list, pubkey, test_message_spend_bundle) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet_4.get_confirmed_balance, 101) await time_out_assert(15, did_wallet_4.get_unconfirmed_balance, 101) await time_out_assert(15, did_wallet.get_confirmed_balance, 0) await time_out_assert(15, did_wallet.get_unconfirmed_balance, 0)

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import asyncio import pytest from mint.simulator.simulator_protocol import FarmNewBlockProtocol from mint.types.peer_info import PeerInfo from mint.util.ints import uint16, uint32, uint64 from tests.setup_nodes import setup_simulators_and_wallets from mint.wallet.did_wallet.did_wallet import DIDWallet from mint.types.blockchain_format.program import Program from blspy import AugSchemeMPL from mint.types.spend_bundle import SpendBundle from mint.consensus.block_rewards import calculate_pool_reward, calculate_base_farmer_reward from tests.time_out_assert import time_out_assert @pytest.fixture(scope="module") def event_loop(): loop = asyncio.get_event_loop() yield loop class TestDIDWallet: @pytest.fixture(scope="function") async def wallet_node(self): async for _ in setup_simulators_and_wallets(1, 1, {}): yield _ @pytest.fixture(scope="function") async def two_wallet_nodes(self): async for _ in setup_simulators_and_wallets(1, 2, {}): yield _ @pytest.fixture(scope="function") async def three_wallet_nodes(self): async for _ in setup_simulators_and_wallets(1, 3, {}): yield _ @pytest.fixture(scope="function") async def two_wallet_nodes_five_freeze(self): async for _ in setup_simulators_and_wallets(1, 2, {}): yield _ @pytest.fixture(scope="function") async def three_sim_two_wallets(self): async for _ in setup_simulators_and_wallets(3, 2, {}): yield _ @pytest.mark.asyncio async def test_creation_from_backup_file(self, three_wallet_nodes): num_blocks = 5 full_nodes, wallets = three_wallet_nodes full_node_api = full_nodes[0] full_node_server = full_node_api.server wallet_node_0, server_0 = wallets[0] wallet_node_1, server_1 = wallets[1] wallet_node_2, server_2 = wallets[2] wallet_0 = wallet_node_0.wallet_state_manager.main_wallet wallet_1 = wallet_node_1.wallet_state_manager.main_wallet wallet_2 = wallet_node_2.wallet_state_manager.main_wallet ph = await wallet_0.get_new_puzzlehash() ph1 = await wallet_1.get_new_puzzlehash() ph2 = await wallet_2.get_new_puzzlehash() await server_0.start_client(PeerInfo("localhost", uint16(full_node_server._port)), None) await server_1.start_client(PeerInfo("localhost", uint16(full_node_server._port)), None) await server_2.start_client(PeerInfo("localhost", uint16(full_node_server._port)), None) for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph)) funds = sum( [ calculate_pool_reward(uint32(i)) + calculate_base_farmer_reward(uint32(i)) for i in range(1, num_blocks - 1) ] ) await time_out_assert(10, wallet_0.get_unconfirmed_balance, funds) await time_out_assert(10, wallet_0.get_confirmed_balance, funds) for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph1)) for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) async with wallet_node_0.wallet_state_manager.lock: did_wallet_0: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node_0.wallet_state_manager, wallet_0, uint64(101) ) for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet_0.get_confirmed_balance, 101) await time_out_assert(15, did_wallet_0.get_unconfirmed_balance, 101) await time_out_assert(15, did_wallet_0.get_pending_change_balance, 0) backup_ids = [bytes.fromhex(did_wallet_0.get_my_DID())] async with wallet_node_1.wallet_state_manager.lock: did_wallet_1: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node_1.wallet_state_manager, wallet_1, uint64(201), backup_ids ) for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet_1.get_confirmed_balance, 201) await time_out_assert(15, did_wallet_1.get_unconfirmed_balance, 201) await time_out_assert(15, did_wallet_1.get_pending_change_balance, 0) filename = "test.backup" did_wallet_1.create_backup(filename) async with wallet_node_2.wallet_state_manager.lock: did_wallet_2 = await DIDWallet.create_new_did_wallet_from_recovery( wallet_node_2.wallet_state_manager, wallet_2, filename ) coins = await did_wallet_1.select_coins(1) coin = coins.copy().pop() assert did_wallet_2.did_info.temp_coin == coin newpuzhash = await did_wallet_2.get_new_inner_hash() pubkey = bytes( (await did_wallet_2.wallet_state_manager.get_unused_derivation_record(did_wallet_2.wallet_info.id)).pubkey ) message_spend_bundle = await did_wallet_0.create_attestment( did_wallet_2.did_info.temp_coin.name(), newpuzhash, pubkey, "test.attest" ) print(f"pubkey: {pubkey}") for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph)) ( test_info_list, test_message_spend_bundle, ) = await did_wallet_2.load_attest_files_for_recovery_spend(["test.attest"]) assert message_spend_bundle == test_message_spend_bundle await did_wallet_2.recovery_spend( did_wallet_2.did_info.temp_coin, newpuzhash, test_info_list, pubkey, test_message_spend_bundle, ) print(f"pubkey: {did_wallet_2}") for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(45, did_wallet_2.get_confirmed_balance, 201) await time_out_assert(45, did_wallet_2.get_unconfirmed_balance, 201) some_ph = 32 * b"\2" await did_wallet_2.create_exit_spend(some_ph) for i in range(1, num_blocks): await full_node_api.farm_new_transaction_block(FarmNewBlockProtocol(ph)) async def get_coins_with_ph(): coins = await full_node_api.full_node.coin_store.get_coin_records_by_puzzle_hash(True, some_ph) if len(coins) == 1: return True return False await time_out_assert(15, get_coins_with_ph, True) await time_out_assert(45, did_wallet_2.get_confirmed_balance, 0) await time_out_assert(45, did_wallet_2.get_unconfirmed_balance, 0) @pytest.mark.asyncio async def test_did_recovery_with_multiple_backup_dids(self, two_wallet_nodes): num_blocks = 5 full_nodes, wallets = two_wallet_nodes full_node_1 = full_nodes[0] server_1 = full_node_1.server wallet_node, server_2 = wallets[0] wallet_node_2, server_3 = wallets[1] wallet = wallet_node.wallet_state_manager.main_wallet wallet2 = wallet_node_2.wallet_state_manager.main_wallet ph = await wallet.get_new_puzzlehash() await server_2.start_client(PeerInfo("localhost", uint16(server_1._port)), None) await server_3.start_client(PeerInfo("localhost", uint16(server_1._port)), None) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) funds = sum( [ calculate_pool_reward(uint32(i)) + calculate_base_farmer_reward(uint32(i)) for i in range(1, num_blocks - 1) ] ) await time_out_assert(15, wallet.get_confirmed_balance, funds) async with wallet_node.wallet_state_manager.lock: did_wallet: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node.wallet_state_manager, wallet, uint64(101) ) ph = await wallet2.get_new_puzzlehash() for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet.get_confirmed_balance, 101) await time_out_assert(15, did_wallet.get_unconfirmed_balance, 101) recovery_list = [bytes.fromhex(did_wallet.get_my_DID())] async with wallet_node_2.wallet_state_manager.lock: did_wallet_2: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node_2.wallet_state_manager, wallet2, uint64(101), recovery_list ) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet_2.get_confirmed_balance, 101) await time_out_assert(15, did_wallet_2.get_unconfirmed_balance, 101) assert did_wallet_2.did_info.backup_ids == recovery_list recovery_list.append(bytes.fromhex(did_wallet_2.get_my_DID())) async with wallet_node_2.wallet_state_manager.lock: did_wallet_3: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node_2.wallet_state_manager, wallet2, uint64(201), recovery_list ) ph2 = await wallet.get_new_puzzlehash() for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) assert did_wallet_3.did_info.backup_ids == recovery_list await time_out_assert(15, did_wallet_3.get_confirmed_balance, 201) await time_out_assert(15, did_wallet_3.get_unconfirmed_balance, 201) coins = await did_wallet_3.select_coins(1) coin = coins.pop() filename = "test.backup" did_wallet_3.create_backup(filename) async with wallet_node.wallet_state_manager.lock: did_wallet_4 = await DIDWallet.create_new_did_wallet_from_recovery( wallet_node.wallet_state_manager, wallet, filename, ) pubkey = ( await did_wallet_4.wallet_state_manager.get_unused_derivation_record(did_wallet_2.wallet_info.id) ).pubkey new_ph = await did_wallet_4.get_new_inner_hash() message_spend_bundle = await did_wallet.create_attestment(coin.name(), new_ph, pubkey, "test1.attest") message_spend_bundle2 = await did_wallet_2.create_attestment(coin.name(), new_ph, pubkey, "test2.attest") message_spend_bundle = message_spend_bundle.aggregate([message_spend_bundle, message_spend_bundle2]) ( test_info_list, test_message_spend_bundle, ) = await did_wallet_4.load_attest_files_for_recovery_spend(["test1.attest", "test2.attest"]) assert message_spend_bundle == test_message_spend_bundle for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) await did_wallet_4.recovery_spend(coin, new_ph, test_info_list, pubkey, message_spend_bundle) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) await time_out_assert(15, did_wallet_4.get_confirmed_balance, 201) await time_out_assert(15, did_wallet_4.get_unconfirmed_balance, 201) await time_out_assert(15, did_wallet_3.get_confirmed_balance, 0) await time_out_assert(15, did_wallet_3.get_unconfirmed_balance, 0) @pytest.mark.asyncio async def test_did_recovery_with_empty_set(self, two_wallet_nodes): num_blocks = 5 full_nodes, wallets = two_wallet_nodes full_node_1 = full_nodes[0] server_1 = full_node_1.server wallet_node, server_2 = wallets[0] wallet_node_2, server_3 = wallets[1] wallet = wallet_node.wallet_state_manager.main_wallet ph = await wallet.get_new_puzzlehash() await server_2.start_client(PeerInfo("localhost", uint16(server_1._port)), None) await server_3.start_client(PeerInfo("localhost", uint16(server_1._port)), None) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) funds = sum( [ calculate_pool_reward(uint32(i)) + calculate_base_farmer_reward(uint32(i)) for i in range(1, num_blocks - 1) ] ) await time_out_assert(15, wallet.get_confirmed_balance, funds) async with wallet_node.wallet_state_manager.lock: did_wallet: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node.wallet_state_manager, wallet, uint64(101) ) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet.get_confirmed_balance, 101) await time_out_assert(15, did_wallet.get_unconfirmed_balance, 101) coins = await did_wallet.select_coins(1) coin = coins.pop() info = Program.to([]) pubkey = (await did_wallet.wallet_state_manager.get_unused_derivation_record(did_wallet.wallet_info.id)).pubkey spend_bundle = await did_wallet.recovery_spend( coin, ph, info, pubkey, SpendBundle([], AugSchemeMPL.aggregate([])) ) additions = spend_bundle.additions() assert additions == [] @pytest.mark.asyncio async def test_did_attest_after_recovery(self, two_wallet_nodes): num_blocks = 5 full_nodes, wallets = two_wallet_nodes full_node_1 = full_nodes[0] server_1 = full_node_1.server wallet_node, server_2 = wallets[0] wallet_node_2, server_3 = wallets[1] wallet = wallet_node.wallet_state_manager.main_wallet wallet2 = wallet_node_2.wallet_state_manager.main_wallet ph = await wallet.get_new_puzzlehash() await server_2.start_client(PeerInfo("localhost", uint16(server_1._port)), None) await server_3.start_client(PeerInfo("localhost", uint16(server_1._port)), None) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) funds = sum( [ calculate_pool_reward(uint32(i)) + calculate_base_farmer_reward(uint32(i)) for i in range(1, num_blocks - 1) ] ) await time_out_assert(15, wallet.get_confirmed_balance, funds) async with wallet_node.wallet_state_manager.lock: did_wallet: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node.wallet_state_manager, wallet, uint64(101) ) ph2 = await wallet2.get_new_puzzlehash() for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) await time_out_assert(15, did_wallet.get_confirmed_balance, 101) await time_out_assert(15, did_wallet.get_unconfirmed_balance, 101) recovery_list = [bytes.fromhex(did_wallet.get_my_DID())] async with wallet_node_2.wallet_state_manager.lock: did_wallet_2: DIDWallet = await DIDWallet.create_new_did_wallet( wallet_node_2.wallet_state_manager, wallet2, uint64(101), recovery_list ) ph = await wallet.get_new_puzzlehash() for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet_2.get_confirmed_balance, 101) await time_out_assert(15, did_wallet_2.get_unconfirmed_balance, 101) assert did_wallet_2.did_info.backup_ids == recovery_list recovery_list = [bytes.fromhex(did_wallet_2.get_my_DID())] await did_wallet.update_recovery_list(recovery_list, uint64(1)) assert did_wallet.did_info.backup_ids == recovery_list await did_wallet.create_update_spend() for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) await time_out_assert(15, did_wallet.get_confirmed_balance, 101) await time_out_assert(15, did_wallet.get_unconfirmed_balance, 101) filename = "test.backup" did_wallet_2.create_backup(filename) async with wallet_node.wallet_state_manager.lock: did_wallet_3 = await DIDWallet.create_new_did_wallet_from_recovery( wallet_node.wallet_state_manager, wallet, filename, ) new_ph = await did_wallet_3.get_new_inner_hash() coins = await did_wallet_2.select_coins(1) coin = coins.pop() pubkey = ( await did_wallet_3.wallet_state_manager.get_unused_derivation_record(did_wallet_3.wallet_info.id) ).pubkey message_spend_bundle = await did_wallet.create_attestment(coin.name(), new_ph, pubkey, "test.attest") for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) ( info, message_spend_bundle, ) = await did_wallet_3.load_attest_files_for_recovery_spend(["test.attest"]) await did_wallet_3.recovery_spend(coin, new_ph, info, pubkey, message_spend_bundle) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet_3.get_confirmed_balance, 101) await time_out_assert(15, did_wallet_3.get_unconfirmed_balance, 101) filename = "test.backup" did_wallet.create_backup(filename) async with wallet_node_2.wallet_state_manager.lock: did_wallet_4 = await DIDWallet.create_new_did_wallet_from_recovery( wallet_node_2.wallet_state_manager, wallet2, filename, ) coins = await did_wallet.select_coins(1) coin = coins.pop() new_ph = await did_wallet_4.get_new_inner_hash() pubkey = ( await did_wallet_4.wallet_state_manager.get_unused_derivation_record(did_wallet_4.wallet_info.id) ).pubkey await did_wallet_3.create_attestment(coin.name(), new_ph, pubkey, "test.attest") for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph2)) ( test_info_list, test_message_spend_bundle, ) = await did_wallet_4.load_attest_files_for_recovery_spend(["test.attest"]) await did_wallet_4.recovery_spend(coin, new_ph, test_info_list, pubkey, test_message_spend_bundle) for i in range(1, num_blocks): await full_node_1.farm_new_transaction_block(FarmNewBlockProtocol(ph)) await time_out_assert(15, did_wallet_4.get_confirmed_balance, 101) await time_out_assert(15, did_wallet_4.get_unconfirmed_balance, 101) await time_out_assert(15, did_wallet.get_confirmed_balance, 0) await time_out_assert(15, did_wallet.get_unconfirmed_balance, 0)

true

f71e799f520b151af3e8ef5669e04ee4d51fa841

1,587

py

Python

amulog/external/tpl_match.py

cpflat/amulog

b7a8c7478d2e5253158f0bce3a7f7109d23e40cb

[ "BSD-3-Clause" ]

5

2019-07-03T09:57:30.000Z

2021-02-13T13:15:47.000Z

amulog/external/tpl_match.py

cpflat/amulog

b7a8c7478d2e5253158f0bce3a7f7109d23e40cb

[ "BSD-3-Clause" ]

null

amulog/external/tpl_match.py

cpflat/amulog

b7a8c7478d2e5253158f0bce3a7f7109d23e40cb

[ "BSD-3-Clause" ]

1

2021-09-09T02:21:42.000Z

#!/usr/bin/env python # coding: utf-8 """Matching raw log messages and its templates that is generated by external tools.""" import re from collections import defaultdict # shortest match REPLACER_REGEX_ESCAPED = re.compile(r"\\\*[A-Z]*?\\\*") def add_esc_external(tpl): """Add escape sequence for imported external templates. It fails if the template has some statement that cannot be distinguished with variable replacers (e.g., *****). In that case, use option log_template_import.import_format_ext_esc and add escape sequences manually (or with another way). """ from amulog import strutil from amulog import lt_common l_wild = lt_common.REPLACER_REGEX.findall(tpl) l_others = [strutil.add_esc(tmp) for tmp in lt_common.REPLACER_REGEX.split(tpl)] return "".join([other + wild for other, wild in zip(l_others, l_wild + [""])]) def generate_regex(tpl): d_name = defaultdict(list) def _replace_wildcard(matchobj): name = matchobj.group(0).strip("\\*") v = len(d_name[name]) vname = "v" + name + str(v) d_name[name].append(vname) # shortest match regexstr = r"(?P<" + vname + r">[^*]*)" return regexstr regex_base = r"^" + re.escape(tpl) + r"$" tmp = REPLACER_REGEX_ESCAPED.sub(_replace_wildcard, regex_base, count=0) return re.compile(tmp) def match_line(parsed_line, l_regex): for rid, regex in enumerate(l_regex): m = regex.match(parsed_line["message"]) if m: return rid, m else: return None

29.388889

82

0.653434

import re from collections import defaultdict REPLACER_REGEX_ESCAPED = re.compile(r"\\\*[A-Z]*?\\\*") def add_esc_external(tpl): from amulog import strutil from amulog import lt_common l_wild = lt_common.REPLACER_REGEX.findall(tpl) l_others = [strutil.add_esc(tmp) for tmp in lt_common.REPLACER_REGEX.split(tpl)] return "".join([other + wild for other, wild in zip(l_others, l_wild + [""])]) def generate_regex(tpl): d_name = defaultdict(list) def _replace_wildcard(matchobj): name = matchobj.group(0).strip("\\*") v = len(d_name[name]) vname = "v" + name + str(v) d_name[name].append(vname) regexstr = r"(?P<" + vname + r">[^*]*)" return regexstr regex_base = r"^" + re.escape(tpl) + r"$" tmp = REPLACER_REGEX_ESCAPED.sub(_replace_wildcard, regex_base, count=0) return re.compile(tmp) def match_line(parsed_line, l_regex): for rid, regex in enumerate(l_regex): m = regex.match(parsed_line["message"]) if m: return rid, m else: return None

true

f71e79a12d7b5249926962e2bc9b26fef30bcffc

4,889

py

Python

PiSnapND/s2a_fm/Snap!Files/Snap!Mobile/arduino/scratch_http_server.py

rasplay/PiSnap-

657b97d2349604ee5d67dd8f055a1070ba57a676

[ "MIT" ]

null

PiSnapND/s2a_fm/Snap!Files/Snap!Mobile/arduino/scratch_http_server.py

rasplay/PiSnap-

657b97d2349604ee5d67dd8f055a1070ba57a676

[ "MIT" ]

null

PiSnapND/s2a_fm/Snap!Files/Snap!Mobile/arduino/scratch_http_server.py

rasplay/PiSnap-

657b97d2349604ee5d67dd8f055a1070ba57a676

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Mon Nov 25 14:45:49 2013 @author: Alan Yorinks Copyright (c) 2013-14 Alan Yorinks All right reserved. This program is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library 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 Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA """ import logging from BaseHTTPServer import BaseHTTPRequestHandler from BaseHTTPServer import HTTPServer from string import split class GetHandler(BaseHTTPRequestHandler): """ This class contains the HTTP server that Scratch2 communicates with Scratch sends HTTP GET requests and this class processes the requests. HTTP GET requests are accepted and the appropriate command handler is called to process the command. """ firmata = None # tcp server port - must match that in the .s2e descriptor file port = 50209 # instance handle for the scratch command handler scratch_command_handler = None #indicator so that we can tell user Scratch is ready to go waiting_for_first_scratch_poll = True # this is a 'classmethod' because we need to set data before starting # the HTTP server. #noinspection PyMethodParameters @classmethod def set_items(self, firmata, scratch_command_handler): """ This method stores the input parameters for later use. It is a class method, because these values need to established prior to instantiating the class """ # instance variable for PyMata #noinspection PyAttributeOutsideInit self.firmata = firmata # instance variable for scratch command handler #noinspection PyAttributeOutsideInit self.command_handler = scratch_command_handler #noinspection PyPep8Naming def do_GET(self): """ Scratch2 only sends HTTP GET commands. This method processes them. It differentiates between a "normal" command request and a request to send policy information to keep Flash happy on Scratch. (This may change when Scratch is converted to HTML 5 """ # skip over the / in the command cmd = self.path[1:] # create a list containing the command and all of its parameters cmd_list = split(cmd, '/') # get the command handler method for the command and call the handler # cmd_list[0] contains the command. look up the command method s = self.command_handler.do_command(cmd_list) self.send_resp(s) # we can't use the standard send_response since we don't conform to its # standards, so we craft our own response handler here def send_resp(self, response): """ This method sends Scratch an HTTP response to an HTTP GET command. """ crlf = "\r\n" # http_response = str(response + crlf) http_response = "HTTP/1.1 200 OK" + crlf http_response += "Content-Type: text/html; charset=ISO-8859-1" + crlf http_response += "Content-Length" + str(len(response)) + crlf http_response += "Access-Control-Allow-Origin: *" + crlf http_response += crlf #add the response to the nonsense above if response != 'okay': http_response += str(response + crlf) # send it out the door to Scratch self.wfile.write(http_response) def start_server(firmata, command_handler): """ This function populates class variables with essential data and instantiates the HTTP Server """ GetHandler.set_items(firmata, command_handler) try: server = HTTPServer(('192.168.2.189', 50209), GetHandler) print 'Starting HTTP Server!' print 'Use <Ctrl-C> to exit the extension\n' print 'Please start Scratch or Snap!' except Exception: logging.debug('Exception in scratch_http_server.py: HTTP Socket may already be in use - restart Scratch') print 'HTTP Socket may already be in use - restart Scratch' raise try: #start the server server.serve_forever() except KeyboardInterrupt: logging.info('scratch_http_server.py: keyboard interrupt exception') print "Goodbye !" raise KeyboardInterrupt except Exception: logging.debug('scratch_http_server.py: Exception %s' % str(Exception)) raise

36.759398

113

0.689712

""" Created on Mon Nov 25 14:45:49 2013 @author: Alan Yorinks Copyright (c) 2013-14 Alan Yorinks All right reserved. This program is free software; you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation; either version 2.1 of the License, or (at your option) any later version. This library 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 Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA """ import logging from BaseHTTPServer import BaseHTTPRequestHandler from BaseHTTPServer import HTTPServer from string import split class GetHandler(BaseHTTPRequestHandler): """ This class contains the HTTP server that Scratch2 communicates with Scratch sends HTTP GET requests and this class processes the requests. HTTP GET requests are accepted and the appropriate command handler is called to process the command. """ firmata = None port = 50209 scratch_command_handler = None waiting_for_first_scratch_poll = True @classmethod def set_items(self, firmata, scratch_command_handler): """ This method stores the input parameters for later use. It is a class method, because these values need to established prior to instantiating the class """ self.firmata = firmata self.command_handler = scratch_command_handler def do_GET(self): """ Scratch2 only sends HTTP GET commands. This method processes them. It differentiates between a "normal" command request and a request to send policy information to keep Flash happy on Scratch. (This may change when Scratch is converted to HTML 5 """ cmd = self.path[1:] cmd_list = split(cmd, '/') s = self.command_handler.do_command(cmd_list) self.send_resp(s) def send_resp(self, response): """ This method sends Scratch an HTTP response to an HTTP GET command. """ crlf = "\r\n" http_response = "HTTP/1.1 200 OK" + crlf http_response += "Content-Type: text/html; charset=ISO-8859-1" + crlf http_response += "Content-Length" + str(len(response)) + crlf http_response += "Access-Control-Allow-Origin: *" + crlf http_response += crlf if response != 'okay': http_response += str(response + crlf) self.wfile.write(http_response) def start_server(firmata, command_handler): """ This function populates class variables with essential data and instantiates the HTTP Server """ GetHandler.set_items(firmata, command_handler) try: server = HTTPServer(('192.168.2.189', 50209), GetHandler) print 'Starting HTTP Server!' print 'Use <Ctrl-C> to exit the extension\n' print 'Please start Scratch or Snap!' except Exception: logging.debug('Exception in scratch_http_server.py: HTTP Socket may already be in use - restart Scratch') print 'HTTP Socket may already be in use - restart Scratch' raise try: server.serve_forever() except KeyboardInterrupt: logging.info('scratch_http_server.py: keyboard interrupt exception') print "Goodbye !" raise KeyboardInterrupt except Exception: logging.debug('scratch_http_server.py: Exception %s' % str(Exception)) raise

false

true

f71e79a6e2049d202d18bd4eb9b2e1332868a805

15,551

py

Python

tests/test_autointerface.py

sphinx-contrib/zopeext

b749d0023f4fb8b8eea3a8f3216f63397c6272de

[ "BSD-2-Clause" ]

1

2020-03-16T07:20:58.000Z

tests/test_autointerface.py

sphinx-contrib/zopeext

b749d0023f4fb8b8eea3a8f3216f63397c6272de

[ "BSD-2-Clause" ]

3

2021-12-19T09:39:45.000Z

2022-01-06T05:05:03.000Z

tests/test_autointerface.py

sphinx-contrib/zopeext

b749d0023f4fb8b8eea3a8f3216f63397c6272de

[ "BSD-2-Clause" ]

null

# -*- coding: utf-8 -*- import os import sys import pytest # Add current directory to path so we can import the example.py file. sys.path.insert(0, os.path.abspath(__file__)) pytest_plugins = "sphinx.testing.fixtures" @pytest.mark.sphinx( "html", srcdir=os.path.join(os.path.dirname(__file__), "examples"), ) @pytest.mark.skip(reason="Test needs updating.") def test_sphinx_build(app, status, warning): app.build() html = (app.outdir / "index.html").read_text() for _n, _E in enumerate(_EXPECTED): assert _E.strip() in html _EXPECTED = [ """ <script> $(document).ready(function() { $('.interface').addClass('class'); }); </script> """, """ <div class="section" id="the-example-module"> <h1>The <a class="reference internal" href="#module-example" title="example"><code class="xref py py-mod docutils literal notranslate">example</code></a> Module<a class="headerlink" href="#the-example-module" title="Permalink to this headline">¶</a></h1> Here is a reference to the Interface: <a class="reference internal" href="#example.IMyInterface" title="example.IMyInterface"><code class="xref py py-interface docutils literal notranslate">example.IMyInterface</code></a>, and to the implementation: <a class="reference internal" href="#example.MyImplementation" title="example.MyImplementation"><code class="xref py py-class docutils literal notranslate">example.MyImplementation</code></a>. <table class="longtable docutils align-default"> <colgroup> <col style="width: 10%" /> <col style="width: 90%" /> </colgroup> <tbody> <tr class="row-odd"><td><a class="reference internal" href="#module-example" title="example"><code class="xref py py-obj docutils literal notranslate">example</code></a></td> <td>Example module using <code class="xref py py-mod docutils literal notranslate">zope.interface</code>.</td> </tr> <tr class="row-even"><td><a class="reference internal" href="#example.IMyInterface" title="example.IMyInterface"><code class="xref py py-obj docutils literal notranslate">example.IMyInterface</code></a>(x)</td> <td>This is an example of an interface.</td> </tr> <tr class="row-odd"><td><a class="reference internal" href="#example.MyImplementation" title="example.MyImplementation"><code class="xref py py-obj docutils literal notranslate">example.MyImplementation</code></a>(x[, y])</td> <td>Example</td> </tr> </tbody> </table> Example module using <code class="xref py py-mod docutils literal notranslate">zope.interface</code>. Here we define an interface <a class="reference internal" href="#example.IMyInterface" title="example.IMyInterface"><code class="xref py py-interface docutils literal notranslate">IMyInterface</code></a> and an implementation <a class="reference internal" href="#example.MyImplementation" title="example.MyImplementation"><code class="xref py py-class docutils literal notranslate">MyImplementation</code></a>. """, """ <dl class="py interface"> <dt class="sig sig-object py" id="example.IMyInterface"> interface example.IMyInterface(x)<a class="headerlink" href="#example.IMyInterface" title="Permalink to this definition">¶</a></dt> <dd>This is an example of an interface. <dl class="py attribute"> <dt class="sig sig-object py" id="example.IMyInterface.x"> x<a class="headerlink" href="#example.IMyInterface.x" title="Permalink to this definition">¶</a></dt> <dd>A required attribute of the interface </dd></dl> <dl class="py method"> <dt class="sig sig-object py" id="example.IMyInterface.equals"> equals(x)<a class="headerlink" href="#example.IMyInterface.equals" title="Permalink to this definition">¶</a></dt> <dd>A required method of the interface. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> </dl> The argument <cite>self</cite> is not specified as part of the interface and should be omitted, even though it is required in the implementation. </dd></dl> </dd></dl>""", """ <dl class="py interface"> <dt class="sig sig-object py" id="example.IMySecondInterface"> interface example.IMySecondInterface(x)<a class="headerlink" href="#example.IMySecondInterface" title="Permalink to this definition">¶</a></dt> <dd>A refinement of the previous interface. <dl class="py attribute"> <dt class="sig sig-object py" id="example.IMySecondInterface.y"> y<a class="headerlink" href="#example.IMySecondInterface.y" title="Permalink to this definition">¶</a></dt> <dd>A new required attribute </dd></dl> </dd></dl>""", """ <dl class="py class"> <dt class="sig sig-object py" id="example.MyImplementation"> class example.MyImplementation(x, y=3.0)<a class="headerlink" href="#example.MyImplementation" title="Permalink to this definition">¶</a></dt> <dd>Example <div class="doctest highlight-default notranslate"><div class="highlight"><pre>>>> a = MyImplementation(x=2.0) >>> a.equals(2.0) True </pre></div> </div> <dl class="py method"> <dt class="sig sig-object py" id="example.MyImplementation.equals"> equals(x)<a class="headerlink" href="#example.MyImplementation.equals" title="Permalink to this definition">¶</a></dt> <dd>A required method of the interface. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> </dl> </dd></dl> </dd></dl>""", """ <li>Here is an explicit example of <cite>autointerface</cite> <dl class="py interface"> <dt class="sig sig-object py"> interface example.IMyInterface(x)</dt> <dd>This is an example of an interface. <dl class="py attribute"> <dt class="sig sig-object py"> x</dt> <dd>A required attribute of the interface </dd></dl> <dl class="py method"> <dt class="sig sig-object py"> equals(x)</dt> <dd>A required method of the interface. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> </dl> The argument <cite>self</cite> is not specified as part of the interface and should be omitted, even though it is required in the implementation. </dd></dl> </dd></dl>""", """ <dl class="py interface"> <dt class="sig sig-object py"> interface example.IMySecondInterface(x)</dt> <dd>Bases: <a class="reference internal" href="#example.IMyInterface" title="example.IMyInterface"><code class="xref py py-class docutils literal notranslate">example.IMyInterface</code></a> A refinement of the previous interface. <dl class="py attribute"> <dt class="sig sig-object py"> y</dt> <dd>A new required attribute </dd></dl> </dd></dl>""", """ <li>Now the interface with explicit members. <dl class="py interface"> <dt class="sig sig-object py"> interface example.IMyInterface(x)</dt> <dd>This is an example of an interface. <dl class="py method"> <dt class="sig sig-object py" id="example.IMyInterface.__init__"> __init__(**kw)<a class="headerlink" href="#example.IMyInterface.__init__" title="Permalink to this definition">¶</a></dt> <dd>The constructor should set the attribute <cite>x</cite>. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> </dl> </dd></dl> <dl class="py attribute"> <dt class="sig sig-object py"> _a</dt> <dd>A private required attribute of the interface </dd></dl> <dl class="py method"> <dt class="sig sig-object py"> equals(x)</dt> <dd>A required method of the interface. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> </dl> The argument <cite>self</cite> is not specified as part of the interface and should be omitted, even though it is required in the implementation. </dd></dl> </dd></dl> </li>""", """ <li>Now the class with explicit members. <dl class="py class"> <dt class="sig sig-object py"> class example.MyImplementation(x, y=3.0)</dt> <dd>Example <div class="doctest highlight-default notranslate"><div class="highlight"><pre>>>> a = MyImplementation(x=2.0) >>> a.equals(2.0) True </pre></div> </div> <dl class="py method"> <dt class="sig sig-object py" id="example.MyImplementation.__init__"> __init__(x, y=3.0)<a class="headerlink" href="#example.MyImplementation.__init__" title="Permalink to this definition">¶</a></dt> <dd>Constructor. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> <dt>yfloat, optional</dt><dd>An additional parameter <cite>y</cite> that is not part of the interface, but which has a default value (3.0) and so does not violate the interface definition. </dd> </dl> </dd></dl> <dl class="py method"> <dt class="sig sig-object py"> equals(x)</dt> <dd>A required method of the interface. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> </dl> </dd></dl> <dl class="py attribute"> <dt class="sig sig-object py"> x = None</dt> <dd></dd></dl> <dl class="py attribute"> <dt class="sig sig-object py"> y = None</dt> <dd></dd></dl> </dd></dl> </li>""", ]

64.795833

672

0.67957

import os import sys import pytest sys.path.insert(0, os.path.abspath(__file__)) pytest_plugins = "sphinx.testing.fixtures" @pytest.mark.sphinx( "html", srcdir=os.path.join(os.path.dirname(__file__), "examples"), ) @pytest.mark.skip(reason="Test needs updating.") def test_sphinx_build(app, status, warning): app.build() html = (app.outdir / "index.html").read_text() for _n, _E in enumerate(_EXPECTED): assert _E.strip() in html _EXPECTED = [ """ <script> $(document).ready(function() { $('.interface').addClass('class'); }); </script> """, """ <div class="section" id="the-example-module"> <h1>The <a class="reference internal" href="#module-example" title="example"><code class="xref py py-mod docutils literal notranslate">example</code></a> Module<a class="headerlink" href="#the-example-module" title="Permalink to this headline">¶</a></h1> Here is a reference to the Interface: <a class="reference internal" href="#example.IMyInterface" title="example.IMyInterface"><code class="xref py py-interface docutils literal notranslate">example.IMyInterface</code></a>, and to the implementation: <a class="reference internal" href="#example.MyImplementation" title="example.MyImplementation"><code class="xref py py-class docutils literal notranslate">example.MyImplementation</code></a>. <table class="longtable docutils align-default"> <colgroup> <col style="width: 10%" /> <col style="width: 90%" /> </colgroup> <tbody> <tr class="row-odd"><td><a class="reference internal" href="#module-example" title="example"><code class="xref py py-obj docutils literal notranslate">example</code></a></td> <td>Example module using <code class="xref py py-mod docutils literal notranslate">zope.interface</code>.</td> </tr> <tr class="row-even"><td><a class="reference internal" href="#example.IMyInterface" title="example.IMyInterface"><code class="xref py py-obj docutils literal notranslate">example.IMyInterface</code></a>(x)</td> <td>This is an example of an interface.</td> </tr> <tr class="row-odd"><td><a class="reference internal" href="#example.MyImplementation" title="example.MyImplementation"><code class="xref py py-obj docutils literal notranslate">example.MyImplementation</code></a>(x[, y])</td> <td>Example</td> </tr> </tbody> </table> Example module using <code class="xref py py-mod docutils literal notranslate">zope.interface</code>. Here we define an interface <a class="reference internal" href="#example.IMyInterface" title="example.IMyInterface"><code class="xref py py-interface docutils literal notranslate">IMyInterface</code></a> and an implementation <a class="reference internal" href="#example.MyImplementation" title="example.MyImplementation"><code class="xref py py-class docutils literal notranslate">MyImplementation</code></a>. """, """ <dl class="py interface"> <dt class="sig sig-object py" id="example.IMyInterface"> interface example.IMyInterface(x)<a class="headerlink" href="#example.IMyInterface" title="Permalink to this definition">¶</a></dt> <dd>This is an example of an interface. <dl class="py attribute"> <dt class="sig sig-object py" id="example.IMyInterface.x"> x<a class="headerlink" href="#example.IMyInterface.x" title="Permalink to this definition">¶</a></dt> <dd>A required attribute of the interface </dd></dl> <dl class="py method"> <dt class="sig sig-object py" id="example.IMyInterface.equals"> equals(x)<a class="headerlink" href="#example.IMyInterface.equals" title="Permalink to this definition">¶</a></dt> <dd>A required method of the interface. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> </dl> The argument <cite>self</cite> is not specified as part of the interface and should be omitted, even though it is required in the implementation. </dd></dl> </dd></dl>""", """ <dl class="py interface"> <dt class="sig sig-object py" id="example.IMySecondInterface"> interface example.IMySecondInterface(x)<a class="headerlink" href="#example.IMySecondInterface" title="Permalink to this definition">¶</a></dt> <dd>A refinement of the previous interface. <dl class="py attribute"> <dt class="sig sig-object py" id="example.IMySecondInterface.y"> y<a class="headerlink" href="#example.IMySecondInterface.y" title="Permalink to this definition">¶</a></dt> <dd>A new required attribute </dd></dl> </dd></dl>""", """ <dl class="py class"> <dt class="sig sig-object py" id="example.MyImplementation"> class example.MyImplementation(x, y=3.0)<a class="headerlink" href="#example.MyImplementation" title="Permalink to this definition">¶</a></dt> <dd>Example <div class="doctest highlight-default notranslate"><div class="highlight"><pre>>>> a = MyImplementation(x=2.0) >>> a.equals(2.0) True </pre></div> </div> <dl class="py method"> <dt class="sig sig-object py" id="example.MyImplementation.equals"> equals(x)<a class="headerlink" href="#example.MyImplementation.equals" title="Permalink to this definition">¶</a></dt> <dd>A required method of the interface. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> </dl> </dd></dl> </dd></dl>""", """ <li>Here is an explicit example of <cite>autointerface</cite> <dl class="py interface"> <dt class="sig sig-object py"> interface example.IMyInterface(x)</dt> <dd>This is an example of an interface. <dl class="py attribute"> <dt class="sig sig-object py"> x</dt> <dd>A required attribute of the interface </dd></dl> <dl class="py method"> <dt class="sig sig-object py"> equals(x)</dt> <dd>A required method of the interface. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> </dl> The argument <cite>self</cite> is not specified as part of the interface and should be omitted, even though it is required in the implementation. </dd></dl> </dd></dl>""", """ <dl class="py interface"> <dt class="sig sig-object py"> interface example.IMySecondInterface(x)</dt> <dd>Bases: <a class="reference internal" href="#example.IMyInterface" title="example.IMyInterface"><code class="xref py py-class docutils literal notranslate">example.IMyInterface</code></a> A refinement of the previous interface. <dl class="py attribute"> <dt class="sig sig-object py"> y</dt> <dd>A new required attribute </dd></dl> </dd></dl>""", """ <li>Now the interface with explicit members. <dl class="py interface"> <dt class="sig sig-object py"> interface example.IMyInterface(x)</dt> <dd>This is an example of an interface. <dl class="py method"> <dt class="sig sig-object py" id="example.IMyInterface.__init__"> __init__(**kw)<a class="headerlink" href="#example.IMyInterface.__init__" title="Permalink to this definition">¶</a></dt> <dd>The constructor should set the attribute <cite>x</cite>. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> </dl> </dd></dl> <dl class="py attribute"> <dt class="sig sig-object py"> _a</dt> <dd>A private required attribute of the interface </dd></dl> <dl class="py method"> <dt class="sig sig-object py"> equals(x)</dt> <dd>A required method of the interface. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> </dl> The argument <cite>self</cite> is not specified as part of the interface and should be omitted, even though it is required in the implementation. </dd></dl> </dd></dl> </li>""", """ <li>Now the class with explicit members. <dl class="py class"> <dt class="sig sig-object py"> class example.MyImplementation(x, y=3.0)</dt> <dd>Example <div class="doctest highlight-default notranslate"><div class="highlight"><pre>>>> a = MyImplementation(x=2.0) >>> a.equals(2.0) True </pre></div> </div> <dl class="py method"> <dt class="sig sig-object py" id="example.MyImplementation.__init__"> __init__(x, y=3.0)<a class="headerlink" href="#example.MyImplementation.__init__" title="Permalink to this definition">¶</a></dt> <dd>Constructor. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> <dt>yfloat, optional</dt><dd>An additional parameter <cite>y</cite> that is not part of the interface, but which has a default value (3.0) and so does not violate the interface definition. </dd> </dl> </dd></dl> <dl class="py method"> <dt class="sig sig-object py"> equals(x)</dt> <dd>A required method of the interface. <dl class="simple"> <dt>xfloat</dt><dd>The parameter <cite>x</cite>. </dd> </dl> </dd></dl> <dl class="py attribute"> <dt class="sig sig-object py"> x = None</dt> <dd></dd></dl> <dl class="py attribute"> <dt class="sig sig-object py"> y = None</dt> <dd></dd></dl> </dd></dl> </li>""", ]

true

f71e7ab3cb5bcd17f3735b3ac4b491ebc205bce5

1,134

py

Python

Modules/Filtering/Smoothing/wrapping/test/MedianImageFilterFunctionalDocumentationTest.py

HongdaZ/ITK

f5d004fa3607b8e11edc30f1ba299df35af8aff8

[ "Apache-2.0" ]

1

2021-01-10T14:19:08.000Z

Modules/Filtering/Smoothing/wrapping/test/MedianImageFilterFunctionalDocumentationTest.py

HongdaZ/ITK

f5d004fa3607b8e11edc30f1ba299df35af8aff8

[ "Apache-2.0" ]

1

2017-03-19T12:56:50.000Z

2018-10-24T10:40:21.000Z

Modules/Filtering/Smoothing/wrapping/test/MedianImageFilterFunctionalDocumentationTest.py

HongdaZ/ITK

f5d004fa3607b8e11edc30f1ba299df35af8aff8

[ "Apache-2.0" ]

1

2020-07-24T22:58:19.000Z

#========================================================================== # # Copyright NumFOCUS # # 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.txt # # 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. # #==========================================================================*/ # # Example on the use of the MedianImageFilter # import itk # Test that docstring in snake_case function is replaced by # docstring from corresponding object. # Not the default docstring. assert "Procedural interface for" not in itk.median_image_filter.__doc__ # But the actual filter docstring. assert "Applies a median filter to an image" in itk.median_image_filter.__doc__

35.4375

79

0.652557

import itk assert "Procedural interface for" not in itk.median_image_filter.__doc__ assert "Applies a median filter to an image" in itk.median_image_filter.__doc__

true

f71e7e5849fed1929fa7be9aa2c73ed76e795347

3,026

py

Python

statsmodels/graphics/tests/test_factorplots.py

aliavni/statsmodels

ef5d57a8d45de76a895e9401705280d558d688ad

[ "BSD-3-Clause" ]

1

2022-01-24T15:17:37.000Z

statsmodels/graphics/tests/test_factorplots.py

aliavni/statsmodels

ef5d57a8d45de76a895e9401705280d558d688ad

[ "BSD-3-Clause" ]

null

statsmodels/graphics/tests/test_factorplots.py

aliavni/statsmodels

ef5d57a8d45de76a895e9401705280d558d688ad

[ "BSD-3-Clause" ]

null

import numpy as np from numpy.testing import assert_equal, assert_raises from pandas import Series import pytest from statsmodels.graphics.factorplots import _recode, interaction_plot try: import matplotlib.pyplot as plt except ImportError: pass class TestInteractionPlot: @classmethod def setup_class(cls): np.random.seed(12345) cls.weight = np.random.randint(1,4,size=60) cls.duration = np.random.randint(1,3,size=60) cls.days = np.log(np.random.randint(1,30, size=60)) @pytest.mark.matplotlib def test_plot_both(self, close_figures): fig = interaction_plot(self.weight, self.duration, self.days, colors=['red','blue'], markers=['D','^'], ms=10) @pytest.mark.matplotlib def test_plot_rainbow(self, close_figures): fig = interaction_plot(self.weight, self.duration, self.days, markers=['D','^'], ms=10) @pytest.mark.matplotlib @pytest.mark.parametrize('astype', ['str', 'int']) def test_plot_pandas(self, astype, close_figures): weight = Series(self.weight, name='Weight').astype(astype) duration = Series(self.duration, name='Duration') days = Series(self.days, name='Days') fig = interaction_plot(weight, duration, days, markers=['D', '^'], ms=10) ax = fig.axes[0] trace = ax.get_legend().get_title().get_text() assert_equal(trace, 'Duration') assert_equal(ax.get_ylabel(), 'mean of Days') assert_equal(ax.get_xlabel(), 'Weight') @pytest.mark.matplotlib def test_formatting(self, close_figures): fig = interaction_plot(self.weight, self.duration, self.days, colors=['r','g'], linestyles=['--','-.']) assert_equal(isinstance(fig, plt.Figure), True) @pytest.mark.matplotlib def test_formatting_errors(self, close_figures): assert_raises(ValueError, interaction_plot, self.weight, self.duration, self.days, markers=['D']) assert_raises(ValueError, interaction_plot, self.weight, self.duration, self.days, colors=['b','r','g']) assert_raises(ValueError, interaction_plot, self.weight, self.duration, self.days, linestyles=['--','-.',':']) @pytest.mark.matplotlib def test_plottype(self, close_figures): fig = interaction_plot(self.weight, self.duration, self.days, plottype='line') assert_equal(isinstance(fig, plt.Figure), True) fig = interaction_plot(self.weight, self.duration, self.days, plottype='scatter') assert_equal(isinstance(fig, plt.Figure), True) assert_raises(ValueError, interaction_plot, self.weight, self.duration, self.days, plottype='unknown') def test_recode_series(self): series = Series(['a', 'b'] * 10, index=np.arange(0, 40, 2), name='index_test') series_ = _recode(series, {'a': 0, 'b': 1}) assert_equal(series_.index.values, series.index.values, err_msg='_recode changed the index')

42.027778

118

0.653668

import numpy as np from numpy.testing import assert_equal, assert_raises from pandas import Series import pytest from statsmodels.graphics.factorplots import _recode, interaction_plot try: import matplotlib.pyplot as plt except ImportError: pass class TestInteractionPlot: @classmethod def setup_class(cls): np.random.seed(12345) cls.weight = np.random.randint(1,4,size=60) cls.duration = np.random.randint(1,3,size=60) cls.days = np.log(np.random.randint(1,30, size=60)) @pytest.mark.matplotlib def test_plot_both(self, close_figures): fig = interaction_plot(self.weight, self.duration, self.days, colors=['red','blue'], markers=['D','^'], ms=10) @pytest.mark.matplotlib def test_plot_rainbow(self, close_figures): fig = interaction_plot(self.weight, self.duration, self.days, markers=['D','^'], ms=10) @pytest.mark.matplotlib @pytest.mark.parametrize('astype', ['str', 'int']) def test_plot_pandas(self, astype, close_figures): weight = Series(self.weight, name='Weight').astype(astype) duration = Series(self.duration, name='Duration') days = Series(self.days, name='Days') fig = interaction_plot(weight, duration, days, markers=['D', '^'], ms=10) ax = fig.axes[0] trace = ax.get_legend().get_title().get_text() assert_equal(trace, 'Duration') assert_equal(ax.get_ylabel(), 'mean of Days') assert_equal(ax.get_xlabel(), 'Weight') @pytest.mark.matplotlib def test_formatting(self, close_figures): fig = interaction_plot(self.weight, self.duration, self.days, colors=['r','g'], linestyles=['--','-.']) assert_equal(isinstance(fig, plt.Figure), True) @pytest.mark.matplotlib def test_formatting_errors(self, close_figures): assert_raises(ValueError, interaction_plot, self.weight, self.duration, self.days, markers=['D']) assert_raises(ValueError, interaction_plot, self.weight, self.duration, self.days, colors=['b','r','g']) assert_raises(ValueError, interaction_plot, self.weight, self.duration, self.days, linestyles=['--','-.',':']) @pytest.mark.matplotlib def test_plottype(self, close_figures): fig = interaction_plot(self.weight, self.duration, self.days, plottype='line') assert_equal(isinstance(fig, plt.Figure), True) fig = interaction_plot(self.weight, self.duration, self.days, plottype='scatter') assert_equal(isinstance(fig, plt.Figure), True) assert_raises(ValueError, interaction_plot, self.weight, self.duration, self.days, plottype='unknown') def test_recode_series(self): series = Series(['a', 'b'] * 10, index=np.arange(0, 40, 2), name='index_test') series_ = _recode(series, {'a': 0, 'b': 1}) assert_equal(series_.index.values, series.index.values, err_msg='_recode changed the index')

true

f71e7e75013346712238ce0f9ab6dfad2b41203f

2,066

py

Python

src/draw_pictures.py

mpeychev/disentangled-autoencoders

2d1f18fe198486f29c74ba5606ffcadaff7055cf

[ "MIT" ]

8

2017-11-24T22:26:50.000Z

2018-10-15T07:12:51.000Z

src/draw_pictures.py

mpeychev/disentangled-autoencoders

2d1f18fe198486f29c74ba5606ffcadaff7055cf

[ "MIT" ]

1

2018-01-10T03:44:37.000Z

2018-01-10T19:59:39.000Z

src/draw_pictures.py

mpeychev/disentangled-autoencoders

2d1f18fe198486f29c74ba5606ffcadaff7055cf

[ "MIT" ]

3

2017-12-22T01:07:14.000Z

2019-08-08T09:45:30.000Z

from PIL import Image import os import util import numpy as np import matplotlib import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec def show_images(images, save_name, hard=False): print images.shape dim = images.shape[0] if hard: images = np.array( map(lambda image: map(lambda pixel: 0.0 if pixel < 0.5 else 1.0, image), images)) n_image_rows = images.shape[0] / 10 n_image_cols = 10 gs = gridspec.GridSpec(n_image_rows, n_image_cols, hspace=0., wspace=0.) for i in range(n_image_rows): for j in range(n_image_cols): ax = plt.subplot(gs[i * n_image_cols + j]) ax.imshow(images[i * n_image_cols + j].reshape((64, 64))) ax.set_xticks([]) ax.set_yticks([]) if i == 0 and j == 0: ax.set_title('scale') if i == 0 and j == 3: ax.set_title('y') if i == 0 and j == 4: ax.set_title('x') if i == 0 and j == 7: ax.set_title('rotation') if j == 0 and i == 0: ax.set_ylabel('+1.0') if j == 0 and i == 1: ax.set_ylabel('+0.5') if j == 0 and i == 2: ax.set_ylabel('base') if j == 0 and i == 3: ax.set_ylabel('-0.5') if j == 0 and i == 4: ax.set_ylabel('-1.0') if i == n_image_rows - 1: ax.set_xlabel('z{0}'.format(j)) ax.set_aspect('equal') plt.subplots_adjust(wspace=None, hspace=None) plt.tight_layout() plt.subplots_adjust(top=0.94) plt.savefig(save_name + '_vis.png') results_dir = util.get_results_dir() images = np.load(os.path.join(results_dir, 'pictures_4.npy')) indexes = [] SHIFT_RANGE = len(images) / 10 for shift in range(SHIFT_RANGE): for i in range(10): indexes.append(i * SHIFT_RANGE + shift) new_images = [] for index in indexes: new_images.append(images[index]) show_images(np.array(new_images), 'all')

32.793651

93

0.548403

from PIL import Image import os import util import numpy as np import matplotlib import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec def show_images(images, save_name, hard=False): print images.shape dim = images.shape[0] if hard: images = np.array( map(lambda image: map(lambda pixel: 0.0 if pixel < 0.5 else 1.0, image), images)) n_image_rows = images.shape[0] / 10 n_image_cols = 10 gs = gridspec.GridSpec(n_image_rows, n_image_cols, hspace=0., wspace=0.) for i in range(n_image_rows): for j in range(n_image_cols): ax = plt.subplot(gs[i * n_image_cols + j]) ax.imshow(images[i * n_image_cols + j].reshape((64, 64))) ax.set_xticks([]) ax.set_yticks([]) if i == 0 and j == 0: ax.set_title('scale') if i == 0 and j == 3: ax.set_title('y') if i == 0 and j == 4: ax.set_title('x') if i == 0 and j == 7: ax.set_title('rotation') if j == 0 and i == 0: ax.set_ylabel('+1.0') if j == 0 and i == 1: ax.set_ylabel('+0.5') if j == 0 and i == 2: ax.set_ylabel('base') if j == 0 and i == 3: ax.set_ylabel('-0.5') if j == 0 and i == 4: ax.set_ylabel('-1.0') if i == n_image_rows - 1: ax.set_xlabel('z{0}'.format(j)) ax.set_aspect('equal') plt.subplots_adjust(wspace=None, hspace=None) plt.tight_layout() plt.subplots_adjust(top=0.94) plt.savefig(save_name + '_vis.png') results_dir = util.get_results_dir() images = np.load(os.path.join(results_dir, 'pictures_4.npy')) indexes = [] SHIFT_RANGE = len(images) / 10 for shift in range(SHIFT_RANGE): for i in range(10): indexes.append(i * SHIFT_RANGE + shift) new_images = [] for index in indexes: new_images.append(images[index]) show_images(np.array(new_images), 'all')

false

true

f71e7e82c3f619f7d9bc39e1ced2dcf72b788c44

1,977

py

Python

erpnext_furniture_to_go/erpnext_furniture_to_go/doctype/furniture_to_go_settings/furniture_to_go_settings.py

artykbasar/erpnext_furniture_to_go

c93894b2cc23bf64ff49ffb4485a30b5be38bfc1

[ "MIT" ]

null

erpnext_furniture_to_go/erpnext_furniture_to_go/doctype/furniture_to_go_settings/furniture_to_go_settings.py

artykbasar/erpnext_furniture_to_go

c93894b2cc23bf64ff49ffb4485a30b5be38bfc1

[ "MIT" ]

null

erpnext_furniture_to_go/erpnext_furniture_to_go/doctype/furniture_to_go_settings/furniture_to_go_settings.py

artykbasar/erpnext_furniture_to_go

c93894b2cc23bf64ff49ffb4485a30b5be38bfc1

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Copyright (c) 2021, Artyk Basarov and contributors # For license information, please see license.txt from __future__ import unicode_literals import frappe # import erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods as f2g from frappe.model.document import Document class FurnitureToGoSettings(Document): @frappe.whitelist() def find_new_products(self): if self.enable == 1: frappe.enqueue('erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods.find_new_products', timeout=3000) @frappe.whitelist() def find_product_group(self): if self.enable == 1: frappe.enqueue('erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods.product_group_finder', timeout=3000) @frappe.whitelist() def find_product_range(self): if self.enable == 1: frappe.enqueue('erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods.product_range_finder', timeout=3000) @frappe.whitelist() def sync_products_to_items(self): if self.enable == 1: frappe.enqueue('erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods.f2g_to_item', timeout=30000) @frappe.whitelist() def auto_fill_defaults(self): if self.enable == 1: from erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods import default_f2g_values default_f2g_values() self.reload() @frappe.whitelist() def tester(self): if self.enable == 1: from erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods import default_f2g_values default_f2g_values() # frappe.enqueue('erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods.tester', timeout=7200) self.reload()

42.978261

160

0.828022

from __future__ import unicode_literals import frappe from frappe.model.document import Document class FurnitureToGoSettings(Document): @frappe.whitelist() def find_new_products(self): if self.enable == 1: frappe.enqueue('erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods.find_new_products', timeout=3000) @frappe.whitelist() def find_product_group(self): if self.enable == 1: frappe.enqueue('erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods.product_group_finder', timeout=3000) @frappe.whitelist() def find_product_range(self): if self.enable == 1: frappe.enqueue('erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods.product_range_finder', timeout=3000) @frappe.whitelist() def sync_products_to_items(self): if self.enable == 1: frappe.enqueue('erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods.f2g_to_item', timeout=30000) @frappe.whitelist() def auto_fill_defaults(self): if self.enable == 1: from erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods import default_f2g_values default_f2g_values() self.reload() @frappe.whitelist() def tester(self): if self.enable == 1: from erpnext_furniture_to_go.erpnext_furniture_to_go.doctype.furniture_to_go_settings.furniture_to_go_methods import default_f2g_values default_f2g_values() self.reload()

true

f71e7ee02f11010b14d10aee34612e2db23ec030

83,913

py

Python

pycoalescence/tests/test_coalescence_tree.py

thompsonsed/pycoalescence

eddce52ad7b3584e1fb208532d6851751b27dd4a

[ "MIT" ]

null

pycoalescence/tests/test_coalescence_tree.py

thompsonsed/pycoalescence

eddce52ad7b3584e1fb208532d6851751b27dd4a

[ "MIT" ]

null

pycoalescence/tests/test_coalescence_tree.py

thompsonsed/pycoalescence

eddce52ad7b3584e1fb208532d6851751b27dd4a

[ "MIT" ]

null

""" Tests the coalescence tree object. """ import os import random import shutil import sqlite3 import sys import unittest import numpy as np import pandas as pd from pandas.testing import assert_frame_equal from setup_tests import setUpAll, tearDownAll, skipLongTest from pycoalescence import Simulation from pycoalescence.coalescence_tree import CoalescenceTree, get_parameter_description from pycoalescence.sqlite_connection import check_sql_table_exist def setUpModule(): """ Creates the output directory and moves logging files """ setUpAll() t = CoalescenceTree("sample/sample.db") t.clear_calculations() def tearDownModule(): """ Removes the output directory """ tearDownAll() class TestNullSimulationErrors(unittest.TestCase): """ Tests that simulations that are not linked raise the correct error. """ def testRaisesError(self): """ Tests that a null simulation will raise an error when any operation is performed. """ t = CoalescenceTree() with self.assertRaises(RuntimeError): t.get_species_richness() with self.assertRaises(RuntimeError): t.calculate_fragment_richness() with self.assertRaises(RuntimeError): t.calculate_alpha_diversity() with self.assertRaises(RuntimeError): t.calculate_beta_diversity() with self.assertRaises(RuntimeError): t.calculate_fragment_abundances() with self.assertRaises(RuntimeError): t.calculate_fragment_octaves() with self.assertRaises(RuntimeError): t.calculate_octaves() with self.assertRaises(RuntimeError): t.get_fragment_list() with self.assertRaises(RuntimeError): t.get_alpha_diversity() with self.assertRaises(RuntimeError): t.get_beta_diversity() with self.assertRaises(RuntimeError): t.get_community_references() with self.assertRaises(RuntimeError): t.get_metacommunity_references() with self.assertRaises(RuntimeError): t.get_species_locations() with self.assertRaises(RuntimeError): t.get_species_abundances() with self.assertRaises(RuntimeError): t.get_species_list() with self.assertRaises(RuntimeError): _ = t.get_simulation_parameters() with self.assertRaises(RuntimeError): t.get_fragment_abundances("null", 1) with self.assertRaises(RuntimeError): t.get_species_richness() with self.assertRaises(RuntimeError): t.get_octaves(1) class TestParameterDescriptions(unittest.TestCase): """ Tests that program correctly reads from the parameter_descriptions.json dictionary. """ def testReadsCorrectly(self): """ Tests that the dictionary is read correctly. """ tmp_dict = { "habitat_change_rate": "the rate of change from present density maps to historic density maps", "sample_file": "the sample area map for spatially selective sampling. Can be null to sample all " "cells", "sample_x": "the sample map x dimension", "sample_y": "the sample map y dimension", "sample_x_offset": "the sample x map offset from the grid", "sample_y_offset": "the sample y map offset from the grid", "output_dir": "the output directory for the simulation database", "seed": "the random seed to start the simulation, for repeatability", "coarse_map_x": "the coarse density map x dimension", "fine_map_file": "the density map file location at the finer resolution, covering a smaller area", "tau": "the tau dispersal value for fat-tailed dispersal", "grid_y": "the simulated grid y dimension", "dispersal_relative_cost": "the relative rate of moving through non-habitat compared to habitat", "fine_map_y_offset": "the number of cells the fine map is offset from the sample map in the y " "dimension, at the fine resolution", "gen_since_historical": "the number of generations that occur before the historical, or historic," " state is reached", "dispersal_method": "the dispersal method used. Can be one of 'normal', 'norm-uniform' or " "'fat-tail'.", "historical_fine_map": "the historical, or historic, coarse density map file location", "coarse_map_scale": "the scale of the coarse density map compared to the fine density map. 1 " "means equal density", "grid_x": "the simulated grid x dimension", "coarse_map_file": "the density map file location at the coarser resolution, covering a larger " "area", "min_num_species": "the minimum number of species known to exist (currently has no effect)", "historical_coarse_map": "the historical, or historic, coarse density map file location", "m_probability": "the probability of choosing from the uniform dispersal kernel in normal-uniform" " dispersal", "sigma": "the sigma dispersal value for normal, fat-tailed and normal-uniform dispersals", "deme": "the number of individuals inhabiting a cell at a map density of 1", "time_config_file": "will be 'set' if temporal sampling is used, 'null' otherwise", "coarse_map_y": "the coarse density map y dimension", "fine_map_x": "the fine density map x dimension", "coarse_map_y_offset": "the number of cells the coarse map is offset from the fine map in the y " "dimension, at the fine resolution", "cutoff": "the maximal dispersal distance possible, for normal-uniform dispersal", "fine_map_y": "the fine density map y dimension", "sample_size": "the proportion of individuals to sample from each cell (0-1)", "fine_map_x_offset": "the number of cells the fine map is offset from the sample map in the x " "dimension, at the fine resolution", "speciation_rate": "the minimum speciation rate the simulation was run with", "task": "the job or task reference number given to this simulation", "coarse_map_x_offset": "the number of cells the coarse map is offset from the fine map in the x " "dimension, at the fine resolution", "landscape_type": "if false, landscapes have hard boundaries. Otherwise, can be infinite, " "with 1s everywhere, or tiled_coarse or tiled_fine for repeated units of tiled " "maps", "max_time": "the maximum simulation time to run for (in seconds)", "sim_complete": "set to true upon simulation completion, false for incomplete simulations", "protracted": "if true, the simulation was run with protracted speciation.", "min_speciation_gen": "the minimum number of generations required before speciation can occur", "max_speciation_gen": "the maximum number of generations a lineage can exist before it is " "speciated", "dispersal_map": "a tif file where rows represent cumulative dispersal probability to every other " "cell, using the row number = x + (y * x_max)", } t = CoalescenceTree("sample/sample.db") sim_output = t.get_simulation_parameters() for key in sim_output.keys(): self.assertIn(key, get_parameter_description().keys()) self.assertEqual(get_parameter_description(key), t.get_parameter_description(key)) for key in get_parameter_description().keys(): self.assertIn(key, sim_output.keys()) for key in tmp_dict.keys(): self.assertEqual(tmp_dict[key], get_parameter_description(key)) self.assertDictEqual(tmp_dict, get_parameter_description()) with self.assertRaises(KeyError): get_parameter_description(key="notakey") dispersal_parameters = t.dispersal_parameters() expected_disp_dict = { "dispersal_method": "normal", "sigma": 3.55, "tau": 0.470149, "m_probability": 0, "cutoff": 0, } for key in dispersal_parameters.keys(): self.assertIn(key, tmp_dict.keys()) self.assertIn(key, expected_disp_dict.keys()) for key, val in expected_disp_dict.items(): self.assertIn(key, dispersal_parameters.keys()) if isinstance(val, float): self.assertAlmostEqual(val, dispersal_parameters[key]) else: self.assertEqual(val, dispersal_parameters[key]) class TestCoalescenceTreeSettingSpeciationParameters(unittest.TestCase): """Tests that the correct errors are raised when speciation parameters are supplied incorrectly.""" @classmethod def setUpClass(cls): """Generates the temporary databases to attempt analysis on.""" src = [os.path.join("sample", "sample{}.db".format(x)) for x in [2, 3]] cls.dst = [os.path.join("output", "sample{}.db".format(x)) for x in [2, 3]] for tmp_src, tmp_dst in zip(src, cls.dst): if os.path.exists(tmp_dst): os.remove(tmp_dst) shutil.copy(tmp_src, tmp_dst) def testSetSpeciationRates(self): """Tests setting speciation rates works as intended and raises appropriate errors""" ct = CoalescenceTree(self.dst[0]) for attempt in ["a string", ["a", "string"], [["list", "list2"], 0.2, 0.1], [None]]: with self.assertRaises(TypeError): ct._set_speciation_rates(attempt) with self.assertRaises(RuntimeError): ct._set_speciation_rates(None) for attempt in [-10, -2.0, 1.1, 100, [-1, 0.1, 0.2], [0.2, 0.8, 1.1]]: with self.assertRaises(ValueError): ct._set_speciation_rates(attempt) expected_list = [0.1, 0.2, 0.3] ct._set_speciation_rates(expected_list) self.assertEqual(expected_list, ct.applied_speciation_rates_list) ct._set_speciation_rates(0.2) self.assertEqual([0.2], ct.applied_speciation_rates_list) def testSetRecordFragments(self): """Tests that setting the record_fragments flag works as expected.""" ct = CoalescenceTree(self.dst[0]) ct._set_record_fragments(True) self.assertEqual("null", ct.record_fragments) ct._set_record_fragments(False) self.assertEqual("F", ct.record_fragments) for each in ["PlotBiodiversityMetrics.db", "doesntexist.csv"]: config_path = os.path.join("sample", each) with self.assertRaises(IOError): ct._set_record_fragments(config_path) expected = os.path.join("sample", "FragmentsTest.csv") ct._set_record_fragments(expected) self.assertEqual(expected, ct.record_fragments) def testSetRecordSpatial(self): """Tests that the setting the record_spatial flag works as expected""" ct = CoalescenceTree(self.dst[0]) ct._set_record_spatial("T") self.assertTrue(ct.record_spatial) ct._set_record_spatial("F") self.assertFalse(ct.record_spatial) with self.assertRaises(TypeError): ct._set_record_spatial("nota bool") ct._set_record_spatial(True) self.assertTrue(ct.record_spatial) def testSetMetacommunityParameters(self): """Tests that setting the metacommunity parameters works as expected.""" ct = CoalescenceTree(self.dst[0]) for size, spec in [[-10, 0.1], [10, -0.1], [10, 1.1]]: with self.assertRaises(ValueError): ct.fragments = "F" ct._set_record_fragments(False) ct._set_record_spatial(False) ct.times = [0.0] ct._set_metacommunity_parameters(size, spec) ct._set_metacommunity_parameters() self.assertEqual(0.0, ct.metacommunity_size) self.assertEqual(0.0, ct.metacommunity_speciation_rate) ct._set_metacommunity_parameters(10, 0.1, "simulated") self.assertEqual(10, ct.metacommunity_size) self.assertEqual(0.1, ct.metacommunity_speciation_rate) def testSetProtractedParameters(self): """Tests that setting the protracted parameters works as expected.""" ct = CoalescenceTree(self.dst[0]) with self.assertRaises(ValueError): ct._set_protracted_parameters(0.1, 100) ct = CoalescenceTree(self.dst[1]) ct._set_protracted_parameters(10, 100) self.assertEqual((10.0, 100.0), ct.protracted_parameters[0]) ct.protracted_parameters = [] for min_proc, max_proc in [[200, 5000], [80, 50], [200, 11000]]: with self.assertRaises(ValueError): ct._check_protracted_parameters(min_proc, max_proc) with self.assertRaises(ValueError): ct._set_protracted_parameters(min_proc, max_proc) with self.assertRaises(ValueError): ct.add_protracted_parameters(min_proc, max_proc) ct._set_protracted_parameters(50, 5000) self.assertEqual((50.0, 5000.0), ct.protracted_parameters[0]) ct.protracted_parameters = [] ct._set_protracted_parameters() self.assertEqual((0.0, 0.0), ct.protracted_parameters[0]) def testSetSampleFile(self): """Tests that the sample file is correctly set.""" ct = CoalescenceTree(self.dst[0]) for file in ["notafile.tif", os.path.join("sample", "sample.db")]: with self.assertRaises(IOError): ct._set_sample_file(file) ct._set_sample_file() self.assertEqual("null", ct.sample_file) expected_file = os.path.join("sample", "SA_sample_coarse.tif") ct._set_sample_file(expected_file) self.assertEqual(expected_file, ct.sample_file) def testSetTimes(self): """Tests that times are correctly set.""" ct = CoalescenceTree(self.dst[0]) ct._set_times(None) self.assertEqual(0.0, ct.times[0]) with self.assertRaises(TypeError): ct.add_times(0.5) with self.assertRaises(TypeError): ct.add_times([0.2, 0.5, "string"]) ct.times = None ct.add_times([0.2, 0.5, 10]) self.assertEqual([0.0, 0.2, 0.5, 10.0], ct.times) ct.times = None ct._set_times(0.2) self.assertEqual([0.0, 0.2], ct.times) ct.times = None ct._set_times([0.1, 0.5, 10.0]) self.assertEqual([0.0, 0.1, 0.5, 10.0], ct.times) class TestCoalescenceTreeParameters(unittest.TestCase): """Tests that parameters are correctly obtained from the databases and the relevant errors are raised.""" def testCommunityParameters1(self): """Tests the community parameters make sense in a very simple community.""" shutil.copyfile(os.path.join("sample", "sample3.db"), os.path.join("output", "temp_sample3.db")) t = CoalescenceTree(os.path.join("output", "temp_sample3.db"), logging_level=50) self.assertEqual([], t.get_metacommunity_references()) self.assertEqual([1], t.get_community_references()) params = t.get_community_parameters(1) expected_dict = { "speciation_rate": 0.001, "time": 0.0, "fragments": 0, "metacommunity_reference": 0, "min_speciation_gen": 100.0, "max_speciation_gen": 10000.0, } self.assertEqual(expected_dict, params) with self.assertRaises(sqlite3.Error): t.get_metacommunity_parameters(1) with self.assertRaises(KeyError): t.get_community_parameters(2) with self.assertRaises(KeyError): t.get_community_reference(0.1, 0.0, 0, 0, 0.0, min_speciation_gen=100.0, max_speciation_gen=10000.0) with self.assertRaises(KeyError): _ = t.get_community_reference(speciation_rate=0.001, time=0.0, fragments=False) ref = t.get_community_reference( speciation_rate=0.001, time=0.0, fragments=False, min_speciation_gen=100.0, max_speciation_gen=10000.0 ) self.assertEqual(1, ref) self.assertEqual(expected_dict, t.get_community_parameters(ref)) t.wipe_data() with self.assertRaises(IOError): t.get_community_parameters_pd() def testCommunityParameters2(self): """Tests the community parameters make sense in a very simple community.""" t = CoalescenceTree(os.path.join("sample", "sample4.db")) self.assertEqual([1, 2, 3, 4, 5], t.get_community_references()) expected_params1 = {"speciation_rate": 0.1, "time": 0.0, "fragments": 0, "metacommunity_reference": 0} expected_params2 = {"speciation_rate": 0.1, "time": 0.0, "fragments": 0, "metacommunity_reference": 1} expected_params3 = {"speciation_rate": 0.2, "time": 0.0, "fragments": 0, "metacommunity_reference": 1} expected_params4 = {"speciation_rate": 0.1, "time": 0.0, "fragments": 0, "metacommunity_reference": 2} expected_params5 = {"speciation_rate": 0.2, "time": 0.0, "fragments": 0, "metacommunity_reference": 2} expected_meta_params1 = { "speciation_rate": 0.001, "metacommunity_size": 10000.0, "option": "simulated", "external_reference": 0, } expected_meta_params2 = { "speciation_rate": 0.001, "metacommunity_size": 10000.0, "option": "analytical", "external_reference": 0, } params1 = t.get_community_parameters(1) params2 = t.get_community_parameters(2) params3 = t.get_community_parameters(3) params4 = t.get_community_parameters(4) params5 = t.get_community_parameters(5) params6 = t.get_metacommunity_parameters(1) params7 = t.get_metacommunity_parameters(2) self.assertEqual([1, 2], t.get_metacommunity_references()) self.assertEqual(expected_params1, params1) self.assertEqual(expected_params2, params2) self.assertEqual(expected_params3, params3) self.assertEqual(expected_params4, params4) self.assertEqual(expected_params5, params5) self.assertEqual(expected_meta_params1, params6) self.assertEqual(expected_meta_params2, params7) with self.assertRaises(KeyError): t.get_community_parameters(6) with self.assertRaises(KeyError): t.get_metacommunity_parameters(3) ref1 = t.get_community_reference(speciation_rate=0.1, time=0.0, fragments=False) with self.assertRaises(KeyError): t.get_community_reference( speciation_rate=0.1, time=0.0, fragments=False, min_speciation_gen=0.1, max_speciation_gen=10000.0 ) ref2 = t.get_community_reference( speciation_rate=0.1, time=0.0, fragments=False, metacommunity_size=10000.0, metacommunity_speciation_rate=0.001, metacommunity_option="simulated", ) with self.assertRaises(KeyError): t.get_community_reference( speciation_rate=0.1, time=0.0, fragments=False, metacommunity_size=10000.0, metacommunity_speciation_rate=0.01, metacommunity_option="simulated", ) ref3 = t.get_community_reference( speciation_rate=0.2, time=0.0, fragments=False, metacommunity_size=10000.0, metacommunity_speciation_rate=0.001, metacommunity_option="simulated", ) ref4 = t.get_community_reference( speciation_rate=0.1, time=0.0, fragments=False, metacommunity_size=10000.0, metacommunity_speciation_rate=0.001, metacommunity_option="analytical", ) ref5 = t.get_community_reference( speciation_rate=0.2, time=0.0, fragments=False, metacommunity_size=10000.0, metacommunity_speciation_rate=0.001, metacommunity_option="analytical", ) self.assertEqual(1, ref1) self.assertEqual(2, ref2) self.assertEqual(3, ref3) self.assertEqual(4, ref4) self.assertEqual(5, ref5) expected_community_params_list = [] for reference in t.get_community_references(): params = t.get_community_parameters(reference) params["reference"] = reference expected_community_params_list.append(params) expected_community_params = pd.DataFrame(expected_community_params_list) actual_output = t.get_community_parameters_pd() assert_frame_equal(expected_community_params, actual_output, check_like=True) def testIsComplete(self): """Tests sims are correctly identified as complete.""" t = CoalescenceTree(os.path.join("sample", "sample4.db")) self.assertTrue(t.is_complete) class TestCoalescenceTreeAnalysis(unittest.TestCase): """Tests analysis is performed correctly""" @classmethod def setUpClass(cls): """Sets up the Coalescence object test case.""" dst1 = os.path.join("output", "sampledb0.db") for i in range(0, 11): dst = os.path.join("output", "sampledb{}.db".format(i)) if os.path.exists(dst): os.remove(dst) shutil.copyfile(os.path.join("sample", "sample.db"), dst) shutil.copyfile(os.path.join("sample", "nse_reference.db"), os.path.join("output", "nse_reference1.db")) random.seed(2) cls.test = CoalescenceTree(dst1, logging_level=50) cls.test.clear_calculations() cls.test.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) cls.test.calculate_fragment_richness() cls.test.calculate_fragment_octaves() cls.test.calculate_octaves_error() cls.test.calculate_alpha_diversity() cls.test.calculate_beta_diversity() cls.test2 = CoalescenceTree() cls.test2.set_database(os.path.join("sample", "sample_nofrag.db")) dstx = os.path.join("output", "sampledbx.db") shutil.copyfile(dst1, dstx) c = CoalescenceTree(dstx) c.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) c.calculate_goodness_of_fit() @classmethod def tearDownClass(cls): """ Removes the files from output." """ cls.test.clear_calculations() def testComparisonDataNoExistError(self): c = CoalescenceTree(os.path.join("sample", "sample.db")) with self.assertRaises(IOError): c.import_comparison_data(os.path.join("sample", "doesnotexist.db")) def testFragmentOctaves(self): num = self.test.cursor.execute( "SELECT richness FROM FRAGMENT_OCTAVES WHERE fragment == 'P09' AND octave == 0" " AND community_reference == 1" ).fetchall()[0][0] self.assertEqual(num, 7, msg="Fragment octaves not correctly calculated.") num = self.test.cursor.execute( "SELECT richness FROM FRAGMENT_OCTAVES WHERE fragment == 'P09' AND octave == 0 " " AND community_reference == 2" ).fetchall()[0][0] self.assertEqual(num, 7, msg="Fragment octaves not correctly calculated.") num = self.test.cursor.execute( "SELECT richness FROM FRAGMENT_OCTAVES WHERE fragment == 'cerrogalera' AND octave == 1 " " AND community_reference == 1" ).fetchall()[0][0] self.assertEqual(num, 3, msg="Fragment octaves not correctly calculated.") num = self.test.cursor.execute( "SELECT richness FROM FRAGMENT_OCTAVES WHERE fragment == 'whole' AND octave == 1 " " AND community_reference == 2" ).fetchall()[0][0] self.assertEqual(num, 221, msg="Fragment octaves not correctly calculated.") def testFragmentAbundances(self): """ Tests that fragment abundances are produced properly by the fragment detection functions. """ num = self.test.cursor.execute( "SELECT COUNT(fragment) FROM FRAGMENT_ABUNDANCES WHERE fragment == 'P09' " " AND community_reference == 1" ).fetchall()[0][0] self.assertEqual(num, 9, msg="Fragment abundances not correctly calculated.") num = self.test.cursor.execute( "SELECT COUNT(fragment) FROM FRAGMENT_ABUNDANCES WHERE fragment == 'P09' " " AND community_reference == 2" ).fetchall()[0][0] self.assertEqual(num, 9, msg="Fragment abundances not correctly calculated.") num = self.test.cursor.execute( "SELECT COUNT(fragment) FROM FRAGMENT_ABUNDANCES WHERE fragment == 'cerrogalera' " " AND community_reference == 1" ).fetchall()[0][0] self.assertEqual(num, 9, msg="Fragment abundances not correctly calculated.") def testSpeciesAbundances(self): """Tests that the produced species abundances are correct by comparing species richness.""" num = self.test.cursor.execute( "SELECT COUNT(species_id) FROM SPECIES_ABUNDANCES WHERE community_reference == 2" ).fetchall()[0][0] self.assertEqual(num, 1029, msg="Species abundances not correctly calculated.") num = self.test.cursor.execute( "SELECT COUNT(species_id) FROM SPECIES_ABUNDANCES WHERE community_reference == 1" ).fetchall()[0][0] self.assertEqual(num, 884, msg="Species abundances not correctly calculated.") def testGetOctaves(self): """Tests getting the octaves.""" c = CoalescenceTree(os.path.join("output", "sampledb4.db")) c.clear_calculations() c.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) c.calculate_richness() self.assertEqual([[0, 585], [1, 231], [2, 59], [3, 5]], c.get_octaves(1)) c = CoalescenceTree(os.path.join("output", "sampledb4.db")) c.clear_calculations() c.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) c.calculate_richness() actual = c.get_octaves_pd().head() expected = pd.DataFrame( [[1, 0, 585], [1, 1, 231], [1, 2, 59], [1, 3, 5], [2, 0, 760]], columns=["community_reference", "octave", "richness"], ) assert_frame_equal(actual, expected, check_like=True) def testSpeciesLocations(self): """ Tests that species locations have been correctly assigned. """ num = self.test.cursor.execute( "SELECT species_id FROM SPECIES_LOCATIONS WHERE x==1662 AND y==4359 " " AND community_reference == 1" ).fetchall() self.assertEqual(len(set(num)), 2, msg="Species locations not correctly assigned") all_list = self.test.get_species_locations() select_list = self.test.get_species_locations(community_reference=1) self.assertListEqual([1, 1662, 4359, 1], all_list[0]) self.assertListEqual([1, 1662, 4359], select_list[0]) def testAlphaDiversity(self): """ Tests that alpha diversity is correctly calculated and fetched for each parameter reference """ c = CoalescenceTree(os.path.join("sample", "sample.db")) with self.assertRaises(IOError): c.get_alpha_diversity_pd() self.assertEqual(9, self.test.get_alpha_diversity(1)) self.assertEqual(10, self.test.get_alpha_diversity(2)) expected_alphas_list = [] for reference in self.test.get_community_references(): expected_alphas_list.append( {"community_reference": reference, "alpha_diversity": self.test.get_alpha_diversity(reference)} ) expected_alphas = pd.DataFrame(expected_alphas_list).reset_index(drop=True) actual_alphas = self.test.get_alpha_diversity_pd().reset_index(drop=True) assert_frame_equal(expected_alphas, actual_alphas, check_like=True) def testBetaDiversity(self): """ Tests that beta diversity is correctly calculated and fetched for the reference """ c = CoalescenceTree(os.path.join("sample", "sample.db")) with self.assertRaises(IOError): c.get_beta_diversity_pd() self.assertAlmostEqual(98.111111111, self.test.get_beta_diversity(1), places=5) self.assertAlmostEqual(102.8, self.test.get_beta_diversity(2), places=5) expected_betas_list = [] for reference in self.test.get_community_references(): expected_betas_list.append( {"community_reference": reference, "beta_diversity": self.test.get_beta_diversity(reference)} ) expected_betas = pd.DataFrame(expected_betas_list).reset_index(drop=True) actual_betas = self.test.get_beta_diversity_pd().reset_index(drop=True) assert_frame_equal(expected_betas, actual_betas, check_like=True) def testGetNumberIndividuals(self): """Tests that the number of individuals is obtained correctly.""" c = CoalescenceTree(os.path.join("output", "sampledb7.db")) self.assertEqual(1504, c.get_number_individuals(community_reference=1)) self.assertEqual(12, c.get_number_individuals(fragment="P09", community_reference=1)) c.wipe_data() c.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) with self.assertRaises(IOError): c.get_number_individuals(fragment="none") with self.assertRaises(IOError): c.get_number_individuals() def testGetFragmentAbundances(self): """Tests that fragment abundances are correctly obtained.""" c = CoalescenceTree(os.path.join("sample", "sample3.db")) with self.assertRaises(IOError): c.get_fragment_abundances(fragment="P09", reference=1) with self.assertRaises(IOError): c.get_fragment_abundances_pd() abundances = self.test.get_fragment_abundances(fragment="P09", reference=1) expected_abundances = [[302, 1], [303, 1], [304, 1], [305, 1], [306, 1], [307, 1], [546, 2], [693, 1], [732, 3]] self.assertEqual(expected_abundances, abundances[:10]) all_abundances = self.test.get_all_fragment_abundances() expected_abundances2 = [ [1, "P09", 302, 1], [1, "P09", 303, 1], [1, "P09", 304, 1], [1, "P09", 305, 1], [1, "P09", 306, 1], [1, "P09", 307, 1], [1, "P09", 546, 2], [1, "P09", 693, 1], [1, "P09", 732, 3], [1, "cerrogalera", 416, 1], ] self.assertEqual(expected_abundances2, all_abundances[:10]) df = pd.DataFrame( expected_abundances2, columns=["community_reference", "fragment", "species_id", "no_individuals"] ) actual_df = self.test.get_fragment_abundances_pd().head(n=10) assert_frame_equal(df, actual_df, check_like=True) def testGetFragmentListErrors(self): """Tests the error is raised when obtaining fragment list.""" c = CoalescenceTree(os.path.join("output", "sampledb8.db")) c.wipe_data() with self.assertRaises(IOError): c.get_fragment_list() def testClearGoodnessFit(self): """Tests that goodness of fit are correctly cleared.""" c = CoalescenceTree(os.path.join("output", "sampledbx.db")) exec_command = "SELECT * FROM BIODIVERSITY_METRICS WHERE metric LIKE 'goodness_%'" self.assertTrue(len(c.cursor.execute(exec_command).fetchall()) >= 1) c._clear_goodness_of_fit() self.assertFalse(len(c.cursor.execute(exec_command).fetchall()) >= 1) def testGetBiodiversityMetrics(self): """Tests that biodiversity metrics are correctly obtained from the database.""" c1 = CoalescenceTree(os.path.join("sample", "sample.db")) with self.assertRaises(IOError): c1.get_biodiversity_metrics() c2 = CoalescenceTree(os.path.join("sample", "sample2.db")) expected_biodiversity_metrics = pd.DataFrame( [ [1, "fragment_richness", "fragment2", 129.0, np.NaN, np.NaN], [2, "fragment_richness", "fragment2", 130.0, np.NAN, np.NaN], [1, "fragment_richness", "fragment1", 174.0, np.NaN, np.NaN], [2, "fragment_richness", "fragment1", 175.0, np.NaN, np.NaN], [1, "fragment_richness", "whole", 1163.0, np.NaN, np.NaN], [2, "fragment_richness", "whole", 1170.0, np.NaN, np.NaN], ], columns=["community_reference", "metric", "fragment", "value", "simulated", "actual"], ).reset_index(drop=True) actual_biodiversity_metrics = c2.get_biodiversity_metrics().reset_index(drop=True).fillna(value=np.nan) assert_frame_equal(expected_biodiversity_metrics, actual_biodiversity_metrics) def testRaisesErrorNoFragmentsAlpha(self): """ Tests that an error is raised when alpha diversity is calculated without any fragment abundance data """ with self.assertRaises(IOError): self.test2.calculate_alpha_diversity() def testRaisesErrorNoFragmentsBeta(self): """ Tests that an error is raised when alpha diversity is calculated without any fragment abundance data """ with self.assertRaises(IOError): self.test2.calculate_beta_diversity() def testRaisesErrorNoFragmentsRichness(self): """ Tests that an error is raised when fragment richness is calculated without any fragment abundance data """ with self.assertRaises(IOError): self.test2.calculate_fragment_richness() def testRaisesErrorNoFragmentsOctaves(self): """ Tests that an error is raised when fragment richness is calculated without any fragment abundance data """ with self.assertRaises(IOError): self.test2.calculate_fragment_octaves() @unittest.skipIf(sys.version[0] != "3", "Skipping Python 3.x tests") def testModelFitting2(self): """ Tests that the goodness-of-fit calculations are correctly performed. """ random.seed(2) self.test.calculate_goodness_of_fit() self.assertAlmostEqual(self.test.get_goodness_of_fit(), 0.30140801329929373, places=6) self.assertAlmostEqual(self.test.get_goodness_of_fit_fragment_octaves(), 0.0680205429120108, places=6) self.assertAlmostEqual(self.test.get_goodness_of_fit_fragment_richness(), 0.9244977999898334, places=6) @unittest.skipIf(sys.version[0] == "3", "Skipping Python 2.x tests") def testModelFitting3(self): """ Tests that the goodness-of-fit calculations are correctly performed. """ random.seed(2) self.test.calculate_goodness_of_fit() self.assertAlmostEqual(self.test.get_goodness_of_fit(), 0.30140801329929373, places=6) self.assertAlmostEqual(self.test.get_goodness_of_fit_fragment_octaves(), 0.0680205429120108, places=6) self.assertAlmostEqual(self.test.get_goodness_of_fit_fragment_richness(), 0.9244977999898334, places=6) def testErrorIfNotApplied(self): """Tests that an error is raised if outputting is attempted without applying any community parameters.""" c = CoalescenceTree(os.path.join("sample", "sample.db")) with self.assertRaises(RuntimeError): c.output() def testFragmentNumbersMatching(self): """Checks behaviour when matching fragment numbers.""" test = CoalescenceTree(os.path.join("output", "sampledb1.db"), logging_level=50) test.clear_calculations() with self.assertRaises(RuntimeError): test._check_fragment_numbers_match() with self.assertRaises(ValueError): test.calculate_fragment_abundances() test._check_fragment_numbers_match() test.comparison_file = os.path.join("sample", "PlotBiodiversityMetrics.db") self.assertTrue(test._check_fragment_numbers_match()) test.fragment_abundances.pop(0) self.assertFalse(test._check_fragment_numbers_match()) def testFragmentNumbersEqualisation(self): """Checks behaviour when equalising fragment numbers.""" test = CoalescenceTree(os.path.join("output", "sampledb2.db"), logging_level=50) test.clear_calculations() test.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) test.calculate_fragment_richness() self.test._equalise_fragment_number("notafrag", 1) test.fragment_abundances[0][2] += 1000 test._equalise_fragment_number("P09", 1) self.assertTrue(test._check_fragment_numbers_match()) def testFragmentNumbersErrors(self): """Checks behaviour when equalising fragment numbers.""" test = CoalescenceTree(os.path.join("output", "sampledb3.db"), logging_level=50) test.clear_calculations() test.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) test.comparison_abundances = None with self.assertRaises(ValueError): test._equalise_all_fragment_numbers() def testAdjustBiodiversityMetrics(self): """Checks that biodiversity metrics are correctly adjusted.""" test = CoalescenceTree(os.path.join("output", "sampledb5.db"), logging_level=50) test.clear_calculations() test.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) test.adjust_data() def testComparisonOctavesModification(self): """Tests that the comparison database is modified.""" test = CoalescenceTree(os.path.join("output", "sampledb6.db"), logging_level=50) dst = os.path.join("output", "PlotBiodiversityMetricsNoAlpha2.db") shutil.copy(os.path.join("sample", "PlotBiodiversityMetricsNoAlpha.db"), dst) test.import_comparison_data(dst) test.calculate_comparison_octaves(store=True) self.assertTrue(os.path.exists(dst)) @unittest.skipIf(sys.version[0] == "2", "Skipping Python 3.x tests") def testDownsamplingAndRevert(self): """Tests that downsampling works as intended and can be reverted.""" c = CoalescenceTree(os.path.join("output", "sampledb9.db")) random.seed(a=10, version=3) original_individuals = c.get_number_individuals() original_richness = c.get_species_richness_pd() c.wipe_data() with self.assertRaises(ValueError): c.downsample(sample_proportion=2.0) c.downsample(sample_proportion=0.1) c.set_speciation_parameters([0.1, 0.2]) c.apply() new_individuals = c.get_number_individuals() self.assertEqual(1452, new_individuals) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST")) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST_ORIGINAL")) c = CoalescenceTree(os.path.join("output", "sampledb9.db")) c.revert_downsample() c.wipe_data() c.set_speciation_parameters([0.1, 0.2]) c.apply() final_individuals = c.get_number_individuals() assert_frame_equal(original_richness, c.get_species_richness_pd()) self.assertEqual(original_individuals, final_individuals) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST")) self.assertFalse(check_sql_table_exist(c.database, "SPECIES_LIST_ORIGINAL")) # Now test with NSE sim to ensure correct sampling c = CoalescenceTree(os.path.join("output", "nse_reference1.db")) nse_richness = c.get_species_richness_pd() nse_no_individuals = c.get_number_individuals() c.wipe_data() c.downsample(sample_proportion=0.1) c.set_speciation_parameters([0.000001, 0.999999]) c.apply() new_no_individuals = c.get_number_individuals() self.assertAlmostEqual(new_no_individuals / nse_no_individuals, 0.1, 5) self.assertEqual(1000, c.get_species_richness(reference=2)) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST")) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST_ORIGINAL")) c = CoalescenceTree(os.path.join("output", "nse_reference1.db")) c.revert_downsample() c.wipe_data() c.set_speciation_parameters([0.000001, 0.999999]) c.apply_incremental() c.set_speciation_parameters([0.5]) c.apply() actual_richness = c.get_species_richness_pd() assert_frame_equal(nse_richness, actual_richness) self.assertEqual(nse_no_individuals, c.get_number_individuals()) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST")) self.assertFalse(check_sql_table_exist(c.database, "SPECIES_LIST_ORIGINAL")) with self.assertRaises(IOError): c.revert_downsample() @unittest.skipIf(sys.version[0] == "2", "Skipping Python 3.x tests") def testDownsamplingByLocationAndRevert(self): """Tests that downsampling works as intended and can be reverted.""" c = CoalescenceTree(os.path.join("output", "sampledb10.db")) random.seed(a=10, version=3) original_individuals = c.get_number_individuals() original_richness = c.get_species_richness_pd() c.wipe_data() with self.assertRaises(ValueError): c.downsample_at_locations(fragment_csv=os.path.join("sample", "FragmentsTestFail1.csv")) with self.assertRaises(IOError): c.downsample_at_locations(fragment_csv="not_a_file.csv") c.downsample_at_locations(fragment_csv=os.path.join("sample", "FragmentsTest3.csv")) c.set_speciation_parameters([0.1, 0.2]) c.apply() new_individuals = c.get_number_individuals() self.assertEqual(2, new_individuals) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST")) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST_ORIGINAL")) c = CoalescenceTree(os.path.join("output", "sampledb10.db")) c.revert_downsample() c.wipe_data() c.set_speciation_parameters([0.1, 0.2]) c.apply() final_individuals = c.get_number_individuals() assert_frame_equal(original_richness, c.get_species_richness_pd()) self.assertEqual(original_individuals, final_individuals) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST")) self.assertFalse(check_sql_table_exist(c.database, "SPECIES_LIST_ORIGINAL")) c = CoalescenceTree(os.path.join("output", "sampledb10.db")) c.wipe_data() c.downsample_at_locations(fragment_csv=os.path.join("sample", "FragmentsTest4.csv"), ignore_errors=True) c.set_speciation_parameters([0.1, 0.2]) c.apply() new_individuals = c.get_number_individuals() self.assertEqual(3, new_individuals) class TestCoalescenceTreeWriteCsvs(unittest.TestCase): """Tests that csvs are correctly outputted.""" @classmethod def setUpClass(cls): """Creates the CoalescenceTree object.""" cls.c = CoalescenceTree(os.path.join("sample", "nse_reference.db")) def testWriteCommunityParameterToCsv(self): """Tests that community parameters are correctly written to a csv.""" output_csv = os.path.join("output", "community_parameters1.csv") self.c.write_to_csv(output_csv, "COMMUNITY_PARAMETERS") self.assertTrue(os.path.exists(output_csv)) import csv if sys.version_info[0] < 3: # pragma: no cover infile = open(output_csv, "rb") else: infile = open(output_csv, "r") expected_output = [ ["reference", "speciation_rate", "time", "fragments", "metacommunity_reference"], ["1", "1e-06", "0.0", "0", "0"], ["2", "0.99999", "0.0", "0", "0"], ["3", "0.5", "0.0", "0", "0"], ] actual_output = [] with infile as csv_file: csv_reader = csv.reader(csv_file) for row in csv_reader: actual_output.append(row) self.assertEqual(expected_output, actual_output) with self.assertRaises(IOError): self.c.write_to_csv(output_csv, "COMMUNITY_PARAMETERS") with self.assertRaises(KeyError): self.c.write_to_csv("notacsv.csv", "NOTATABLE") def testWritesAllCsvs(self): """Tests that all csvs write to the output correctly.""" output_dir = os.path.join("output", "csvdir") if os.path.exists(output_dir): os.remove(output_dir) self.c.write_all_to_csvs(output_dir, "out1") expected_tables = ["COMMUNITY_PARAMETERS", "SIMULATION_PARAMETERS", "SPECIES_ABUNDANCES", "SPECIES_LIST"] for table in expected_tables: self.assertTrue(os.path.exists(os.path.join(output_dir, "out1_{}.csv".format(table)))) for file in os.listdir(output_dir): if ".csv" in file: self.assertIn(file, ["out1_{}.csv".format(x) for x in expected_tables]) self.c.write_all_to_csvs(output_dir, "out2.csv") for table in expected_tables: self.assertTrue(os.path.exists(os.path.join(output_dir, "out2_{}.csv".format(table)))) self.c.write_all_to_csvs(output_dir, "out3.") for table in expected_tables: self.assertTrue(os.path.exists(os.path.join(output_dir, "out3_{}.csv".format(table)))) class TestCoalescenceTreeSpeciesDistances(unittest.TestCase): """Tests analysis is performed correctly.""" @classmethod def setUpClass(cls): """ Sets up the Coalescence object test case. """ dst = os.path.join("output", "sampledb1.db") if os.path.exists(dst): os.remove(dst) shutil.copyfile(os.path.join("sample", "sample.db"), dst) cls.test = CoalescenceTree(dst) cls.test.clear_calculations() cls.test.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) cls.test.calculate_species_distance_similarity() def testSpeciesDistanceSimilarity(self): """ Tests that the species distance similarity function works as intended. """ mean = self.test.cursor.execute( "SELECT value FROM BIODIVERSITY_METRICS WHERE community_reference == 1 AND " "metric == 'mean_distance_between_individuals'" ).fetchone()[0] self.assertAlmostEqual(mean, 5.423769507803121, places=5) species_distances = self.test.get_species_distance_similarity(community_reference=1) # for distance, similar in species_distances: # self.assertLessEqual(similar, dissimilar) self.assertListEqual(species_distances[0], [0, 11]) self.assertListEqual(species_distances[1], [1, 274]) self.assertListEqual(species_distances[2], [2, 289]) class TestCoalescenceTreeAnalyseIncorrectComparison(unittest.TestCase): """ Tests errors are raised correctly for incorrect comparison data. """ @classmethod def setUpClass(cls): """ Sets up the Coalescence object test case. """ random.seed(10) dst = os.path.join("output", "sampledb2.db") if os.path.exists(dst): os.remove(dst) shutil.copyfile(os.path.join("sample", "sample.db"), dst) cls.test = CoalescenceTree(logging_level=40) cls.test.set_database(dst) cls.test.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetricsNoAlpha.db")) cls.test.calculate_comparison_octaves(False) cls.test.clear_calculations() cls.test.calculate_fragment_richness() cls.test.calculate_fragment_octaves() cls.test.calculate_octaves_error() cls.test.calculate_alpha_diversity() cls.test.calculate_alpha_diversity() cls.test.calculate_beta_diversity() cls.test2 = CoalescenceTree() cls.test2.set_database(os.path.join("sample", "sample_nofrag.db")) @classmethod def tearDownClass(cls): """ Removes the files from output." """ cls.test.clear_calculations() def testRaisesErrorMismatchParameters(self): """ Tests that an error is raised when there is a parameter mismatch """ with self.assertRaises(ValueError): self.test.calculate_goodness_of_fit() class TestSimulationAnalysisTemporal(unittest.TestCase): """Tests that applying multiple times works as expected.""" @classmethod def setUpClass(cls): """Generates the analysis object.""" src = os.path.join("sample", "sample2.db") dst = os.path.join("output", "sample2.db") if not os.path.exists(dst): shutil.copy(src, dst) cls.tree = CoalescenceTree() cls.tree.set_database(dst) cls.tree.wipe_data() def testTimesWrongFormatError(self): """Tests that an error is raised when the times are in the wrong format.""" with self.assertRaises(TypeError): self.tree.set_speciation_parameters([0.4, 0.6], times=[0.1, 0.2, "notafloat"]) with self.assertRaises(TypeError): # noinspection PyTypeChecker self.tree.set_speciation_parameters([0.4, 0.6], times="notafloat") self.tree.times = [] self.tree.set_speciation_parameters([0.4, 0.6], times=[0, 1, 10]) self.assertEqual([0.0, 1.0, 10.0], self.tree.times) class TestSimulationAnalysis(unittest.TestCase): """ Tests that the simulation can perform all required analyses, and that the correct errors are thrown if the object does not exist. """ @classmethod def setUpClass(cls): """Copies the sample databases and applies a basic set of community parameters.""" src = os.path.join("sample", "sample2.db") dst = os.path.join("output", "sample2.db") if os.path.exists(dst): os.remove(dst) shutil.copy(src, dst) cls.tree = CoalescenceTree(logging_level=50) cls.tree.set_database(dst) cls.tree.wipe_data() cls.tree.set_speciation_parameters( speciation_rates=[0.5, 0.7], record_spatial="T", record_fragments=os.path.join("sample", "FragmentsTest.csv"), sample_file=os.path.join("sample", "SA_samplemaskINT.tif"), ) cls.tree.apply() cls.tree.calculate_fragment_richness() cls.tree.calculate_fragment_octaves() np.random.seed(100) def testSetDatabaseErrors(self): """Tests that the set database errors are correctly raised.""" sim = Simulation() c = CoalescenceTree() with self.assertRaises(RuntimeError): c.set_database(sim) c = CoalescenceTree() with self.assertRaises(IOError): c.set_database(os.path.join("sample", "failsampledoesntexist.db")) def testFragmentConfigNoExistError(self): """Tests that an error is raised if the fragment config file does not exist.""" tree = CoalescenceTree(self.tree.file) with self.assertRaises(IOError): tree.set_speciation_parameters( speciation_rates=[0.5, 0.7], record_spatial="T", record_fragments=os.path.join("sample", "notafragmentconfig.csv"), sample_file=os.path.join("sample", "SA_samplemaskINT.tif"), ) with self.assertRaises(IOError): tree.set_speciation_parameters( speciation_rates=[0.5, 0.7], record_spatial="T", record_fragments=os.path.join("sample", "example_historical_fine.tif"), sample_file=os.path.join("sample", "SA_samplemaskINT.tif"), ) def testReadsFragmentsRichness(self): """ Tests that the fragment richness can be read correctly """ sim_params = self.tree.get_simulation_parameters() expected_params = dict( seed=9, task=1, output_dir="output", speciation_rate=0.5, sigma=2.828427, tau=2.0, deme=1, sample_size=0.1, max_time=2.0, dispersal_relative_cost=1.0, min_num_species=1, habitat_change_rate=0.0, gen_since_historical=200.0, time_config_file="null", coarse_map_file="sample/SA_sample_coarse.tif", coarse_map_x=35, coarse_map_y=41, coarse_map_x_offset=11, coarse_map_y_offset=14, coarse_map_scale=1.0, fine_map_file="sample/SA_sample_fine.tif", fine_map_x=13, fine_map_y=13, fine_map_x_offset=0, fine_map_y_offset=0, sample_file="sample/SA_samplemaskINT.tif", grid_x=13, grid_y=13, sample_x=13, sample_y=13, sample_x_offset=0, sample_y_offset=0, historical_coarse_map="none", historical_fine_map="none", sim_complete=1, dispersal_method="normal", m_probability=0.0, cutoff=0.0, landscape_type="closed", protracted=0, min_speciation_gen=0.0, max_speciation_gen=0.0, dispersal_map="none", ) for key in sim_params.keys(): self.assertEqual( sim_params[key], expected_params[key], msg="Error in {}: {} != {}".format(key, sim_params[key], expected_params[key]), ) fragment2_richness = ["fragment2", 1, 129] self.assertEqual(self.tree.get_fragment_richness(fragment="fragment2", reference=1), 129) self.assertEqual(self.tree.get_fragment_richness(fragment="fragment1", reference=2), 175) octaves = self.tree.get_fragment_richness() self.assertListEqual(fragment2_richness, [list(x) for x in octaves if x[0] == "fragment2" and x[1] == 1][0]) expected_fragment_richness = [] for reference in self.tree.get_community_references(): for fragment in self.tree.get_fragment_list(reference): fragment_richness = self.tree.get_fragment_richness(fragment=fragment, reference=reference) expected_fragment_richness.append( {"fragment": fragment, "community_reference": reference, "fragment_richness": fragment_richness} ) expected_fragment_richness_df = ( pd.DataFrame(expected_fragment_richness) .sort_values(by=["fragment", "community_reference"]) .reset_index(drop=True) ) actual_fragment_richness = self.tree.get_fragment_richness_pd().reset_index(drop=True) assert_frame_equal(expected_fragment_richness_df, actual_fragment_richness, check_like=True) def testGetsFragmentList(self): """ Tests that fetching the list of fragments from FRAGMENT_ABUNDANCES is as expected """ fragment_list = self.tree.get_fragment_list() expected_list = ["fragment1", "fragment2"] self.assertListEqual(expected_list, fragment_list) def testReadsFragmentAbundances(self): """ Tests that the fragment abundances are correctly read """ expected_abundances = [ [610, 1], [611, 1], [612, 1], [613, 1], [614, 1], [615, 1], [616, 1], [617, 1], [618, 1], [619, 1], ] actual_abundances = self.tree.get_species_abundances(fragment="fragment2", reference=1) for i, each in enumerate(expected_abundances): self.assertListEqual(actual_abundances[i], each) with self.assertRaises(ValueError): self.tree.get_species_abundances(fragment="fragment2") expected_fragment_abundances_list = [] for reference in self.tree.get_community_references(): for fragment in self.tree.get_fragment_list(reference): fragment_abundances = self.tree.get_fragment_abundances(fragment=fragment, reference=reference) for species_id, abundance in fragment_abundances: expected_fragment_abundances_list.append( { "fragment": fragment, "community_reference": reference, "species_id": species_id, "no_individuals": abundance, } ) expected_fragment_abundances = ( pd.DataFrame(expected_fragment_abundances_list) .sort_values(by=["fragment", "community_reference", "species_id"]) .reset_index(drop=True) ) actual_fragment_abundances = ( self.tree.get_fragment_abundances_pd() .sort_values(by=["fragment", "community_reference", "species_id"]) .reset_index(drop=True) ) assert_frame_equal(expected_fragment_abundances, actual_fragment_abundances, check_like=True) def testFragmentRichnessRaiseError(self): """ Tests that the correct errors are raised when no fragment exists with that name, or with the specified speciation rate, or time. Also checks SyntaxErrors and sqlite3.Errors when no FRAGMENT_RICHNESS table exists. """ failtree = CoalescenceTree() failtree.set_database(os.path.join("sample", "failsample.db")) with self.assertRaises(IOError): failtree.get_fragment_richness() with self.assertRaises(IOError): failtree.get_fragment_richness_pd() with self.assertRaises(IOError): self.tree.get_fragment_richness(fragment="fragment4", reference=1) with self.assertRaises(SyntaxError): self.tree.get_fragment_richness(fragment="fragment4") with self.assertRaises(SyntaxError): self.tree.get_fragment_richness(reference=1) def testReadsFragmentOctaves(self): """ Tests that the fragment octaves can be read correctly. """ octaves = self.tree.get_fragment_octaves(fragment="fragment2", reference=1) octaves2 = self.tree.get_fragment_octaves(fragment="fragment1", reference=1) all_octaves = self.tree.get_fragment_octaves() desired = ["fragment1", 1, 0, 173] self.assertListEqual([0, 128], octaves[0]) self.assertListEqual([0, 173], octaves2[0]) self.assertListEqual(desired, [x for x in all_octaves if x[0] == "fragment1" and x[1] == 1 and x[2] == 0][0]) expected_fragment_octaves_list = [] for reference in self.tree.get_community_references(): fragment_list = self.tree.get_fragment_list(reference) fragment_list.append("whole") for fragment in fragment_list: try: octaves = self.tree.get_fragment_octaves(fragment=fragment, reference=reference) for octave, richness in octaves: expected_fragment_octaves_list.append( { "fragment": fragment, "community_reference": reference, "octave": octave, "richness": richness, } ) except RuntimeError: continue expected_fragment_octaves = ( pd.DataFrame(expected_fragment_octaves_list) .sort_values(["fragment", "community_reference", "octave"], axis=0) .reset_index(drop=True) ) actual_fragment_octaves = ( self.tree.get_fragment_octaves_pd() .sort_values(["fragment", "community_reference", "octave"], axis=0) .reset_index(drop=True) ) assert_frame_equal(expected_fragment_octaves, actual_fragment_octaves, check_like=True) def testFragmentOctavesRaiseError(self): """ Tests that the correct errors are raised for different situations for reading fragment octaves """ failtree = CoalescenceTree() try: failtree.set_database("sample/failsample.db") except sqlite3.Error: pass with self.assertRaises(sqlite3.Error): failtree.get_fragment_octaves(fragment="fragment4", reference=100) with self.assertRaises(RuntimeError): self.tree.get_fragment_octaves(fragment="fragment4", reference=100) with self.assertRaises(SyntaxError): self.tree.get_fragment_octaves(fragment="fragment4") with self.assertRaises(SyntaxError): self.tree.get_fragment_octaves(reference=100) def testFragmentSampling(self): """ Tests that sampling from fragments is accurate. """ self.assertEqual( 10, self.tree.sample_fragment_richness( fragment="fragment1", number_of_individuals=10, n=1, community_reference=2 ), ) self.assertEqual( 10, self.tree.sample_fragment_richness( fragment="fragment2", number_of_individuals=10, n=10, community_reference=2 ), ) def testLandscapeSampling(self): """Tests that the sampling from the landscape works as intended.""" number_dict = {"fragment1": 3, "fragment2": 10} np.random.seed(100) self.assertEqual( 13, self.tree.sample_landscape_richness(number_of_individuals=number_dict, n=1, community_reference=2) ) self.assertAlmostEqual( 99.9, self.tree.sample_landscape_richness(number_of_individuals=100, n=10, community_reference=1), places=3 ) def testRaisesSamplingErrors(self): """Tests that sampling errors are correctly raised""" number_dict = {"fragment1": 3000000, "fragment2": 10} with self.assertRaises(KeyError): self.assertEqual( 13, self.tree.sample_landscape_richness(number_of_individuals=number_dict, n=1, community_reference=2) ) number_dict2 = {"fragment": 10, "fragment2": 10} with self.assertRaises(KeyError): self.assertEqual( 13, self.tree.sample_landscape_richness(number_of_individuals=number_dict2, n=1, community_reference=2) ) def testSpeciesRichness(self): """Tests that the simulation species richness is read correctly.""" actual_species_richness = ( self.tree.get_species_richness_pd().sort_values(by=["community_reference"]).reset_index(drop=True) ) expected_species_richness_list = [] for reference in self.tree.get_community_references(): expected_species_richness_list.append( {"community_reference": reference, "richness": self.tree.get_species_richness(reference=reference)} ) expected_species_richness = pd.DataFrame(expected_species_richness_list) assert_frame_equal(actual_species_richness, expected_species_richness, check_like=True) def testOctaves(self): """Tests that the simulation octave classes are correctly calculated.""" actual_species_octaves = ( self.tree.get_octaves_pd().sort_values(by=["community_reference", "octave"]).reset_index(drop=True) ) expected_species_octaves_list = [] for reference in self.tree.get_community_references(): for octave, richness in self.tree.get_octaves(reference): expected_species_octaves_list.append( {"community_reference": reference, "octave": octave, "richness": richness} ) expected_species_octaves = pd.DataFrame(expected_species_octaves_list) assert_frame_equal(actual_species_octaves, expected_species_octaves, check_like=True) class TestMetacommunityApplication(unittest.TestCase): """ Tests that a metacommunity can be applied correctly under the three different scenarios. Note that this does not test edge cases, just that the parameters are correctly stored and the different application methods work as intended. """ @classmethod def setUpClass(cls): """Initialises the three database files to use.""" src = os.path.join("sample", "sample.db") for i in range(6): dst = os.path.join("output", "sample_{}.db".format(i)) if os.path.exists(dst): os.remove(dst) shutil.copy2(src, dst) def testMetacommunityAddingInvalidParameters(self): """Tests that adding invalid parameter for a metacommunity raises the appropriate errors.""" tree = CoalescenceTree(os.path.join("output", "sample_0.db")) tree.wipe_data() with self.assertRaises(IOError): tree.get_metacommunity_parameters_pd() tree.set_speciation_parameters([0.1, 0.2]) for size, spec, opt, ref in [ [0, 0.1, "simulated", None], [10, 0.0, "analytical", None], [None, None, "analytical", None], [10, 0.0, "path/to/file", None], [0, 0.0, "path/to/file", None], [0, 0.0, "path/to/not/a/file.db", 1], ]: with self.assertRaises(ValueError): tree.add_metacommunity_parameters( metacommunity_size=size, metacommunity_speciation_rate=spec, metacommunity_option=opt, metacommunity_reference=ref, ) with self.assertRaises(IOError): tree.add_metacommunity_parameters(metacommunity_option="not/a/file/db.db", metacommunity_reference=1) def testMetacommunitySimulation(self): """Tests that a simulated metacommunity works as intended.""" tree = CoalescenceTree(os.path.join("output", "sample_1.db")) tree.wipe_data() tree.set_speciation_parameters( [0.1, 0.2], metacommunity_size=10000, metacommunity_speciation_rate=0.001, metacommunity_option="simulated" ) tree.add_metacommunity_parameters( metacommunity_size=15000, metacommunity_speciation_rate=0.1, metacommunity_option="simulated" ) tree.add_metacommunity_parameters( metacommunity_size=100000, metacommunity_speciation_rate=0.001, metacommunity_option="simulated" ) tree.apply() params_1 = tree.get_metacommunity_parameters(1) params_2 = tree.get_metacommunity_parameters(2) params_3 = tree.get_metacommunity_parameters(3) self.assertEqual(10000, params_1["metacommunity_size"]) self.assertEqual(0.001, params_1["speciation_rate"]) self.assertEqual("simulated", params_1["option"]) self.assertEqual(0, params_1["external_reference"]) self.assertEqual(15000, params_2["metacommunity_size"]) self.assertEqual(0.1, params_2["speciation_rate"]) self.assertEqual("simulated", params_2["option"]) self.assertEqual(0, params_2["external_reference"]) self.assertEqual(100000, params_3["metacommunity_size"]) self.assertEqual(0.001, params_3["speciation_rate"]) self.assertEqual("simulated", params_3["option"]) self.assertEqual(0, params_3["external_reference"]) self.assertEqual(51, tree.get_species_richness(1)) self.assertEqual(47, tree.get_species_richness(2)) self.assertEqual(681, tree.get_species_richness(3)) self.assertEqual(783, tree.get_species_richness(4)) self.assertEqual(247, tree.get_species_richness(5)) self.assertEqual(241, tree.get_species_richness(6)) expected_metacommunity_parameters_list = [] for reference in tree.get_community_references(): try: params = tree.get_metacommunity_parameters(reference) params["reference"] = reference expected_metacommunity_parameters_list.append(params) except KeyError: continue expected_metacommunity_parameters = pd.DataFrame(expected_metacommunity_parameters_list).sort_values( ["reference"] ) actual_metacommunity_parameters = tree.get_metacommunity_parameters_pd().sort_values(["reference"]) assert_frame_equal(expected_metacommunity_parameters, actual_metacommunity_parameters, check_like=True) def testMetacommunityAnalytical(self): """Tests that an analytical metacommunity works as intended.""" tree = CoalescenceTree(os.path.join("output", "sample_2.db")) tree.wipe_data() tree.set_speciation_parameters( [0.1, 0.2], metacommunity_size=10000, metacommunity_speciation_rate=0.001, metacommunity_option="analytical" ) tree.add_metacommunity_parameters( metacommunity_size=15000, metacommunity_speciation_rate=0.1, metacommunity_option="analytical" ) tree.add_metacommunity_parameters( metacommunity_size=100000, metacommunity_speciation_rate=0.001, metacommunity_option="analytical" ) tree.apply() params_1 = tree.get_metacommunity_parameters(1) params_2 = tree.get_metacommunity_parameters(2) params_3 = tree.get_metacommunity_parameters(3) self.assertEqual(10000, params_1["metacommunity_size"]) self.assertEqual(0.001, params_1["speciation_rate"]) self.assertEqual("analytical", params_1["option"]) self.assertEqual(0, params_1["external_reference"]) self.assertEqual(15000, params_2["metacommunity_size"]) self.assertEqual(0.1, params_2["speciation_rate"]) self.assertEqual("analytical", params_2["option"]) self.assertEqual(0, params_2["external_reference"]) self.assertEqual(100000, params_3["metacommunity_size"]) self.assertEqual(0.001, params_3["speciation_rate"]) self.assertEqual("analytical", params_3["option"]) self.assertEqual(0, params_3["external_reference"]) self.assertEqual(51, tree.get_species_richness(1)) self.assertEqual(57, tree.get_species_richness(2)) self.assertEqual(694, tree.get_species_richness(3)) self.assertEqual(760, tree.get_species_richness(4)) self.assertEqual(222, tree.get_species_richness(5)) self.assertEqual(234, tree.get_species_richness(6)) def testMetacommunityExternal(self): """Tests that an external metacommunity works as intended.""" tree = CoalescenceTree(os.path.join("output", "sample_3.db")) tree.wipe_data() tree.set_speciation_parameters([0.1, 0.2], metacommunity_option=os.path.join("sample", "nse_reference.db")) tree.add_metacommunity_parameters( metacommunity_option=os.path.join("sample", "nse_reference.db"), metacommunity_reference=2 ) tree.apply() params_1 = tree.get_metacommunity_parameters(1) params_2 = tree.get_metacommunity_parameters(2) self.assertEqual(0, params_1["metacommunity_size"]) self.assertEqual(0.0, params_1["speciation_rate"]) self.assertEqual(os.path.join("sample", "nse_reference.db"), params_1["option"]) self.assertEqual(1, params_1["external_reference"]) self.assertEqual(0, params_2["metacommunity_size"]) self.assertEqual(0.0, params_2["speciation_rate"]) self.assertEqual(os.path.join("sample", "nse_reference.db"), params_2["option"]) self.assertEqual(2, params_2["external_reference"]) self.assertEqual(1, tree.get_species_richness(1)) self.assertEqual(1, tree.get_species_richness(2)) self.assertEqual(850, tree.get_species_richness(3)) self.assertEqual(975, tree.get_species_richness(4)) def testMetacommunityAnalyticalMethodDetection(self): """Tests that the analytical method detection works correctly.""" tree = CoalescenceTree(os.path.join("output", "sample_4.db")) tree.wipe_data() tree.set_speciation_parameters( [0.1, 0.2], metacommunity_size=110000, metacommunity_speciation_rate=0.5, metacommunity_option="none" ) tree.add_metacommunity_parameters( metacommunity_speciation_rate=0.5, metacommunity_size=120000, metacommunity_option="none" ) tree.apply() params_1 = tree.get_metacommunity_parameters(1) params_2 = tree.get_metacommunity_parameters(2) self.assertEqual(110000, params_1["metacommunity_size"]) self.assertEqual(0.5, params_1["speciation_rate"]) self.assertEqual("analytical", params_1["option"]) self.assertEqual(120000, params_2["metacommunity_size"]) self.assertEqual(0.5, params_2["speciation_rate"]) self.assertEqual("analytical", params_2["option"]) def testMetacommunitySimulatedMethodDetection(self): """Tests that the simulated method detection works correctly.""" tree = CoalescenceTree(os.path.join("output", "sample_5.db")) tree.wipe_data() tree.set_speciation_parameters( [0.1, 0.2], metacommunity_size=1000, metacommunity_speciation_rate=0.5, metacommunity_option="none" ) tree.add_metacommunity_parameters( metacommunity_speciation_rate=0.5, metacommunity_size=2000, metacommunity_option="none" ) tree.apply() params_1 = tree.get_metacommunity_parameters(1) params_2 = tree.get_metacommunity_parameters(2) self.assertEqual(1000, params_1["metacommunity_size"]) self.assertEqual(0.5, params_1["speciation_rate"]) self.assertEqual("simulated", params_1["option"]) self.assertEqual(2000, params_2["metacommunity_size"]) self.assertEqual(0.5, params_2["speciation_rate"]) self.assertEqual("simulated", params_2["option"]) @skipLongTest class TestMetacommunityApplicationSpeciesAbundances(unittest.TestCase): """Tests that the metacommunity application produces the expected species abundance distribution.""" @classmethod def setUpClass(cls): """Run a non-spatial sim and apply a metacommunity.""" cls.sim = Simulation() cls.sim.set_simulation_parameters( seed=11, task=110, output_directory="output", min_speciation_rate=0.1, spatial=False, deme=20541 ) cls.sim.run() cls.ct = CoalescenceTree(cls.sim) cls.ct.wipe_data() cls.ct.set_speciation_parameters(speciation_rates=0.1) cls.ct.add_metacommunity_parameters( metacommunity_option="analytical", metacommunity_size=1000000, metacommunity_speciation_rate=0.00005 ) cls.ct.add_metacommunity_parameters( metacommunity_option="simulated", metacommunity_size=1000000, metacommunity_speciation_rate=0.00005 ) # This just tests that it doesn't take forever and produces a sensible output cls.ct.add_metacommunity_parameters( metacommunity_option="analytical", metacommunity_size=1000000000, metacommunity_speciation_rate=0.1 ) cls.ct.apply() def testRichnessMatchness(self): """Tests that the species richness is roughly equivalent between the two methods.""" self.assertAlmostEqual(244, self.ct.get_species_richness(2), delta=10) self.assertAlmostEqual(self.ct.get_species_richness(1), self.ct.get_species_richness(2), delta=30) self.assertEqual(5212, self.ct.get_species_richness(3)) def testSpeciesAbundances(self): """Tests the species abundance distribution is roughly equivalent between the two methods.""" sad_1 = [x[1] for x in self.ct.get_species_abundances(reference=1)] sad_2 = [x[1] for x in self.ct.get_species_abundances(reference=2)] mean_1 = sum(sad_1) / len(sad_1) mean_2 = sum(sad_2) / len(sad_2) # Check the mean abundance is roughly equivalent self.assertAlmostEqual(mean_1, mean_2, delta=10) # Check that the variances are roughly equivalent var_list_1 = [abs(x - mean_1) for x in sad_1] var_list_2 = [abs(x - mean_2) for x in sad_2] var_1 = sum(var_list_1) / len(var_list_1) var_2 = sum(var_list_2) / len(var_list_2) self.assertAlmostEqual(var_1, var_2, delta=5) expected_abundances_list = [] for reference in self.ct.get_community_references(): for species_id, abundance in self.ct.get_species_abundances(reference=reference): expected_abundances_list.append( {"community_reference": reference, "species_id": species_id, "no_individuals": abundance} ) expected_abundances = pd.DataFrame(expected_abundances_list) actual_abundances = self.ct.get_species_abundances_pd() assert_frame_equal(actual_abundances, expected_abundances, check_like=True) class TestMetacommunityApplicationOrdering(unittest.TestCase): """Tests that the ordering of adding parameters to the metacommunity does not matter.""" @classmethod def setUpClass(cls): """Generates the test databases.""" src = os.path.join("sample", "sample3.db") for i in [1, 2]: dst = os.path.join("output", "sample_order_{}.db".format(i)) if os.path.exists(dst): os.remove(dst) shutil.copy(src, dst) src = os.path.join("sample", "sample5.db") for i in range(3, 6): dst = os.path.join("output", "sample_order_{}.db".format(i)) if os.path.exists(dst): os.remove(dst) shutil.copy(src, dst) cls.c1 = CoalescenceTree(os.path.join("output", "sample_order_1.db")) cls.c2 = CoalescenceTree(os.path.join("output", "sample_order_2.db")) cls.proc1 = CoalescenceTree(os.path.join("output", "sample_order_3.db")) cls.proc2 = CoalescenceTree(os.path.join("output", "sample_order_4.db")) cls.proc3 = CoalescenceTree(os.path.join("output", "sample_order_5.db")) cls.c1.set_speciation_parameters( [0.1, 0.5, 0.9], metacommunity_speciation_rate=0.001, metacommunity_option="simulated", metacommunity_size=10000, ) cls.c1.apply() cls.c2.set_speciation_parameters([0.1, 0.5, 0.9]) cls.c2.add_metacommunity_parameters( metacommunity_size=10000, metacommunity_speciation_rate=0.001, metacommunity_option="simulated" ) cls.c2.apply() cls.proc1.set_speciation_parameters( [0.1, 0.5, 0.9], protracted_speciation_min=5, protracted_speciation_max=1000, metacommunity_option="simulated", metacommunity_speciation_rate=0.001, metacommunity_size=10000, ) cls.proc1.apply() cls.proc2.set_speciation_parameters([0.1, 0.5, 0.9]) cls.proc2.add_metacommunity_parameters( metacommunity_size=10000, metacommunity_speciation_rate=0.001, metacommunity_option="simulated" ) cls.proc2.add_protracted_parameters(min_speciation_gen=5, max_speciation_gen=1000) cls.proc2.apply() cls.proc3.set_speciation_parameters([0.1, 0.5, 0.9]) cls.proc3.add_protracted_parameters(min_speciation_gen=5, max_speciation_gen=1000) cls.proc3.add_metacommunity_parameters( metacommunity_size=10000, metacommunity_speciation_rate=0.001, metacommunity_option="simulated" ) cls.proc3.apply() def testEquivalentMethodsMatch(self): """Tests that equivalent methods of applying metacommunities produce equivalent results.""" for i in range(1, 4): self.assertEqual(self.c1.get_species_richness(i), self.c2.get_species_richness(i)) self.assertEqual(self.proc1.get_species_richness(i), self.proc2.get_species_richness(i)) self.assertEqual(self.proc2.get_species_richness(i), self.proc3.get_species_richness(i)) def testMultipleProtractedError(self): """Tests that adding multiple protracted speciation parameters raises the correct error.""" with self.assertRaises(ValueError): self.proc2.add_multiple_protracted_parameters() class TestProtractedSpeciationEquality(unittest.TestCase): """Tests that analysis performs as expected when protracted speciation parameters match the minimums.""" @classmethod def setUpClass(cls): """Copy the sample database.""" dst = os.path.join("output", "sample_protracted3.db") shutil.copy(os.path.join("sample", "sample3.db"), dst) cls.ct = CoalescenceTree(dst) cls.ct.wipe_data() def testApplyEqualParameters(self): """Tests that equal protracted parameters can be applied""" self.ct.set_speciation_parameters( [0.001, 0.1], protracted_speciation_min=100.0, protracted_speciation_max=10000.0 ) self.ct.apply() self.assertEqual(1, self.ct.get_species_richness(1)) self.assertEqual(3, self.ct.get_species_richness(2)) class TestSpeciesAgesCalculations(unittest.TestCase): """Tests that operations associated with the species ages operate as expected""" @classmethod def setUpClass(cls): """Copies the sample databases and applies a basic set of community parameters.""" src = os.path.join("sample", "sample6.db") dst = os.path.join("output", "sample6.db") if os.path.exists(dst): os.remove(dst) shutil.copy(src, dst) cls.dst_file = dst def testSmallSimulation(self): tree = CoalescenceTree(logging_level=50) tree.set_database(self.dst_file) with self.assertRaises(IOError): _ = tree.get_species_ages() with self.assertRaises(IOError): _ = tree.get_species_ages_pd() tree.wipe_data() with self.assertRaises(IOError): _ = tree.get_species_ages() with self.assertRaises(IOError): _ = tree.get_species_ages_pd() tree.set_speciation_parameters( speciation_rates=[0.000001, 0.0001], record_spatial=False, record_ages=True, ) tree.apply() self.assertTrue(check_sql_table_exist(tree.database, "SPECIES_AGES")) expected_df = pd.read_csv(os.path.join("sample", "expected_species_ages.csv")) actual_df = tree.get_species_ages_pd().reset_index(drop=True) assert_frame_equal(expected_df, actual_df) for community_ref, group in expected_df.groupby(["community_reference"]): actual_output = sorted(tree.get_species_ages(community_ref), key=lambda x: x[0]) expected_output = group.drop(columns=["community_reference"]).sort_values(by=["species_id"]).values.tolist() for ex, act in zip(expected_output, actual_output): self.assertEqual(ex[0], act[0]) self.assertAlmostEqual(ex[1], act[1], delta=0.0000001)

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import os import random import shutil import sqlite3 import sys import unittest import numpy as np import pandas as pd from pandas.testing import assert_frame_equal from setup_tests import setUpAll, tearDownAll, skipLongTest from pycoalescence import Simulation from pycoalescence.coalescence_tree import CoalescenceTree, get_parameter_description from pycoalescence.sqlite_connection import check_sql_table_exist def setUpModule(): setUpAll() t = CoalescenceTree("sample/sample.db") t.clear_calculations() def tearDownModule(): tearDownAll() class TestNullSimulationErrors(unittest.TestCase): def testRaisesError(self): t = CoalescenceTree() with self.assertRaises(RuntimeError): t.get_species_richness() with self.assertRaises(RuntimeError): t.calculate_fragment_richness() with self.assertRaises(RuntimeError): t.calculate_alpha_diversity() with self.assertRaises(RuntimeError): t.calculate_beta_diversity() with self.assertRaises(RuntimeError): t.calculate_fragment_abundances() with self.assertRaises(RuntimeError): t.calculate_fragment_octaves() with self.assertRaises(RuntimeError): t.calculate_octaves() with self.assertRaises(RuntimeError): t.get_fragment_list() with self.assertRaises(RuntimeError): t.get_alpha_diversity() with self.assertRaises(RuntimeError): t.get_beta_diversity() with self.assertRaises(RuntimeError): t.get_community_references() with self.assertRaises(RuntimeError): t.get_metacommunity_references() with self.assertRaises(RuntimeError): t.get_species_locations() with self.assertRaises(RuntimeError): t.get_species_abundances() with self.assertRaises(RuntimeError): t.get_species_list() with self.assertRaises(RuntimeError): _ = t.get_simulation_parameters() with self.assertRaises(RuntimeError): t.get_fragment_abundances("null", 1) with self.assertRaises(RuntimeError): t.get_species_richness() with self.assertRaises(RuntimeError): t.get_octaves(1) class TestParameterDescriptions(unittest.TestCase): def testReadsCorrectly(self): tmp_dict = { "habitat_change_rate": "the rate of change from present density maps to historic density maps", "sample_file": "the sample area map for spatially selective sampling. Can be null to sample all " "cells", "sample_x": "the sample map x dimension", "sample_y": "the sample map y dimension", "sample_x_offset": "the sample x map offset from the grid", "sample_y_offset": "the sample y map offset from the grid", "output_dir": "the output directory for the simulation database", "seed": "the random seed to start the simulation, for repeatability", "coarse_map_x": "the coarse density map x dimension", "fine_map_file": "the density map file location at the finer resolution, covering a smaller area", "tau": "the tau dispersal value for fat-tailed dispersal", "grid_y": "the simulated grid y dimension", "dispersal_relative_cost": "the relative rate of moving through non-habitat compared to habitat", "fine_map_y_offset": "the number of cells the fine map is offset from the sample map in the y " "dimension, at the fine resolution", "gen_since_historical": "the number of generations that occur before the historical, or historic," " state is reached", "dispersal_method": "the dispersal method used. Can be one of 'normal', 'norm-uniform' or " "'fat-tail'.", "historical_fine_map": "the historical, or historic, coarse density map file location", "coarse_map_scale": "the scale of the coarse density map compared to the fine density map. 1 " "means equal density", "grid_x": "the simulated grid x dimension", "coarse_map_file": "the density map file location at the coarser resolution, covering a larger " "area", "min_num_species": "the minimum number of species known to exist (currently has no effect)", "historical_coarse_map": "the historical, or historic, coarse density map file location", "m_probability": "the probability of choosing from the uniform dispersal kernel in normal-uniform" " dispersal", "sigma": "the sigma dispersal value for normal, fat-tailed and normal-uniform dispersals", "deme": "the number of individuals inhabiting a cell at a map density of 1", "time_config_file": "will be 'set' if temporal sampling is used, 'null' otherwise", "coarse_map_y": "the coarse density map y dimension", "fine_map_x": "the fine density map x dimension", "coarse_map_y_offset": "the number of cells the coarse map is offset from the fine map in the y " "dimension, at the fine resolution", "cutoff": "the maximal dispersal distance possible, for normal-uniform dispersal", "fine_map_y": "the fine density map y dimension", "sample_size": "the proportion of individuals to sample from each cell (0-1)", "fine_map_x_offset": "the number of cells the fine map is offset from the sample map in the x " "dimension, at the fine resolution", "speciation_rate": "the minimum speciation rate the simulation was run with", "task": "the job or task reference number given to this simulation", "coarse_map_x_offset": "the number of cells the coarse map is offset from the fine map in the x " "dimension, at the fine resolution", "landscape_type": "if false, landscapes have hard boundaries. Otherwise, can be infinite, " "with 1s everywhere, or tiled_coarse or tiled_fine for repeated units of tiled " "maps", "max_time": "the maximum simulation time to run for (in seconds)", "sim_complete": "set to true upon simulation completion, false for incomplete simulations", "protracted": "if true, the simulation was run with protracted speciation.", "min_speciation_gen": "the minimum number of generations required before speciation can occur", "max_speciation_gen": "the maximum number of generations a lineage can exist before it is " "speciated", "dispersal_map": "a tif file where rows represent cumulative dispersal probability to every other " "cell, using the row number = x + (y * x_max)", } t = CoalescenceTree("sample/sample.db") sim_output = t.get_simulation_parameters() for key in sim_output.keys(): self.assertIn(key, get_parameter_description().keys()) self.assertEqual(get_parameter_description(key), t.get_parameter_description(key)) for key in get_parameter_description().keys(): self.assertIn(key, sim_output.keys()) for key in tmp_dict.keys(): self.assertEqual(tmp_dict[key], get_parameter_description(key)) self.assertDictEqual(tmp_dict, get_parameter_description()) with self.assertRaises(KeyError): get_parameter_description(key="notakey") dispersal_parameters = t.dispersal_parameters() expected_disp_dict = { "dispersal_method": "normal", "sigma": 3.55, "tau": 0.470149, "m_probability": 0, "cutoff": 0, } for key in dispersal_parameters.keys(): self.assertIn(key, tmp_dict.keys()) self.assertIn(key, expected_disp_dict.keys()) for key, val in expected_disp_dict.items(): self.assertIn(key, dispersal_parameters.keys()) if isinstance(val, float): self.assertAlmostEqual(val, dispersal_parameters[key]) else: self.assertEqual(val, dispersal_parameters[key]) class TestCoalescenceTreeSettingSpeciationParameters(unittest.TestCase): @classmethod def setUpClass(cls): src = [os.path.join("sample", "sample{}.db".format(x)) for x in [2, 3]] cls.dst = [os.path.join("output", "sample{}.db".format(x)) for x in [2, 3]] for tmp_src, tmp_dst in zip(src, cls.dst): if os.path.exists(tmp_dst): os.remove(tmp_dst) shutil.copy(tmp_src, tmp_dst) def testSetSpeciationRates(self): ct = CoalescenceTree(self.dst[0]) for attempt in ["a string", ["a", "string"], [["list", "list2"], 0.2, 0.1], [None]]: with self.assertRaises(TypeError): ct._set_speciation_rates(attempt) with self.assertRaises(RuntimeError): ct._set_speciation_rates(None) for attempt in [-10, -2.0, 1.1, 100, [-1, 0.1, 0.2], [0.2, 0.8, 1.1]]: with self.assertRaises(ValueError): ct._set_speciation_rates(attempt) expected_list = [0.1, 0.2, 0.3] ct._set_speciation_rates(expected_list) self.assertEqual(expected_list, ct.applied_speciation_rates_list) ct._set_speciation_rates(0.2) self.assertEqual([0.2], ct.applied_speciation_rates_list) def testSetRecordFragments(self): ct = CoalescenceTree(self.dst[0]) ct._set_record_fragments(True) self.assertEqual("null", ct.record_fragments) ct._set_record_fragments(False) self.assertEqual("F", ct.record_fragments) for each in ["PlotBiodiversityMetrics.db", "doesntexist.csv"]: config_path = os.path.join("sample", each) with self.assertRaises(IOError): ct._set_record_fragments(config_path) expected = os.path.join("sample", "FragmentsTest.csv") ct._set_record_fragments(expected) self.assertEqual(expected, ct.record_fragments) def testSetRecordSpatial(self): ct = CoalescenceTree(self.dst[0]) ct._set_record_spatial("T") self.assertTrue(ct.record_spatial) ct._set_record_spatial("F") self.assertFalse(ct.record_spatial) with self.assertRaises(TypeError): ct._set_record_spatial("nota bool") ct._set_record_spatial(True) self.assertTrue(ct.record_spatial) def testSetMetacommunityParameters(self): ct = CoalescenceTree(self.dst[0]) for size, spec in [[-10, 0.1], [10, -0.1], [10, 1.1]]: with self.assertRaises(ValueError): ct.fragments = "F" ct._set_record_fragments(False) ct._set_record_spatial(False) ct.times = [0.0] ct._set_metacommunity_parameters(size, spec) ct._set_metacommunity_parameters() self.assertEqual(0.0, ct.metacommunity_size) self.assertEqual(0.0, ct.metacommunity_speciation_rate) ct._set_metacommunity_parameters(10, 0.1, "simulated") self.assertEqual(10, ct.metacommunity_size) self.assertEqual(0.1, ct.metacommunity_speciation_rate) def testSetProtractedParameters(self): ct = CoalescenceTree(self.dst[0]) with self.assertRaises(ValueError): ct._set_protracted_parameters(0.1, 100) ct = CoalescenceTree(self.dst[1]) ct._set_protracted_parameters(10, 100) self.assertEqual((10.0, 100.0), ct.protracted_parameters[0]) ct.protracted_parameters = [] for min_proc, max_proc in [[200, 5000], [80, 50], [200, 11000]]: with self.assertRaises(ValueError): ct._check_protracted_parameters(min_proc, max_proc) with self.assertRaises(ValueError): ct._set_protracted_parameters(min_proc, max_proc) with self.assertRaises(ValueError): ct.add_protracted_parameters(min_proc, max_proc) ct._set_protracted_parameters(50, 5000) self.assertEqual((50.0, 5000.0), ct.protracted_parameters[0]) ct.protracted_parameters = [] ct._set_protracted_parameters() self.assertEqual((0.0, 0.0), ct.protracted_parameters[0]) def testSetSampleFile(self): ct = CoalescenceTree(self.dst[0]) for file in ["notafile.tif", os.path.join("sample", "sample.db")]: with self.assertRaises(IOError): ct._set_sample_file(file) ct._set_sample_file() self.assertEqual("null", ct.sample_file) expected_file = os.path.join("sample", "SA_sample_coarse.tif") ct._set_sample_file(expected_file) self.assertEqual(expected_file, ct.sample_file) def testSetTimes(self): ct = CoalescenceTree(self.dst[0]) ct._set_times(None) self.assertEqual(0.0, ct.times[0]) with self.assertRaises(TypeError): ct.add_times(0.5) with self.assertRaises(TypeError): ct.add_times([0.2, 0.5, "string"]) ct.times = None ct.add_times([0.2, 0.5, 10]) self.assertEqual([0.0, 0.2, 0.5, 10.0], ct.times) ct.times = None ct._set_times(0.2) self.assertEqual([0.0, 0.2], ct.times) ct.times = None ct._set_times([0.1, 0.5, 10.0]) self.assertEqual([0.0, 0.1, 0.5, 10.0], ct.times) class TestCoalescenceTreeParameters(unittest.TestCase): def testCommunityParameters1(self): shutil.copyfile(os.path.join("sample", "sample3.db"), os.path.join("output", "temp_sample3.db")) t = CoalescenceTree(os.path.join("output", "temp_sample3.db"), logging_level=50) self.assertEqual([], t.get_metacommunity_references()) self.assertEqual([1], t.get_community_references()) params = t.get_community_parameters(1) expected_dict = { "speciation_rate": 0.001, "time": 0.0, "fragments": 0, "metacommunity_reference": 0, "min_speciation_gen": 100.0, "max_speciation_gen": 10000.0, } self.assertEqual(expected_dict, params) with self.assertRaises(sqlite3.Error): t.get_metacommunity_parameters(1) with self.assertRaises(KeyError): t.get_community_parameters(2) with self.assertRaises(KeyError): t.get_community_reference(0.1, 0.0, 0, 0, 0.0, min_speciation_gen=100.0, max_speciation_gen=10000.0) with self.assertRaises(KeyError): _ = t.get_community_reference(speciation_rate=0.001, time=0.0, fragments=False) ref = t.get_community_reference( speciation_rate=0.001, time=0.0, fragments=False, min_speciation_gen=100.0, max_speciation_gen=10000.0 ) self.assertEqual(1, ref) self.assertEqual(expected_dict, t.get_community_parameters(ref)) t.wipe_data() with self.assertRaises(IOError): t.get_community_parameters_pd() def testCommunityParameters2(self): t = CoalescenceTree(os.path.join("sample", "sample4.db")) self.assertEqual([1, 2, 3, 4, 5], t.get_community_references()) expected_params1 = {"speciation_rate": 0.1, "time": 0.0, "fragments": 0, "metacommunity_reference": 0} expected_params2 = {"speciation_rate": 0.1, "time": 0.0, "fragments": 0, "metacommunity_reference": 1} expected_params3 = {"speciation_rate": 0.2, "time": 0.0, "fragments": 0, "metacommunity_reference": 1} expected_params4 = {"speciation_rate": 0.1, "time": 0.0, "fragments": 0, "metacommunity_reference": 2} expected_params5 = {"speciation_rate": 0.2, "time": 0.0, "fragments": 0, "metacommunity_reference": 2} expected_meta_params1 = { "speciation_rate": 0.001, "metacommunity_size": 10000.0, "option": "simulated", "external_reference": 0, } expected_meta_params2 = { "speciation_rate": 0.001, "metacommunity_size": 10000.0, "option": "analytical", "external_reference": 0, } params1 = t.get_community_parameters(1) params2 = t.get_community_parameters(2) params3 = t.get_community_parameters(3) params4 = t.get_community_parameters(4) params5 = t.get_community_parameters(5) params6 = t.get_metacommunity_parameters(1) params7 = t.get_metacommunity_parameters(2) self.assertEqual([1, 2], t.get_metacommunity_references()) self.assertEqual(expected_params1, params1) self.assertEqual(expected_params2, params2) self.assertEqual(expected_params3, params3) self.assertEqual(expected_params4, params4) self.assertEqual(expected_params5, params5) self.assertEqual(expected_meta_params1, params6) self.assertEqual(expected_meta_params2, params7) with self.assertRaises(KeyError): t.get_community_parameters(6) with self.assertRaises(KeyError): t.get_metacommunity_parameters(3) ref1 = t.get_community_reference(speciation_rate=0.1, time=0.0, fragments=False) with self.assertRaises(KeyError): t.get_community_reference( speciation_rate=0.1, time=0.0, fragments=False, min_speciation_gen=0.1, max_speciation_gen=10000.0 ) ref2 = t.get_community_reference( speciation_rate=0.1, time=0.0, fragments=False, metacommunity_size=10000.0, metacommunity_speciation_rate=0.001, metacommunity_option="simulated", ) with self.assertRaises(KeyError): t.get_community_reference( speciation_rate=0.1, time=0.0, fragments=False, metacommunity_size=10000.0, metacommunity_speciation_rate=0.01, metacommunity_option="simulated", ) ref3 = t.get_community_reference( speciation_rate=0.2, time=0.0, fragments=False, metacommunity_size=10000.0, metacommunity_speciation_rate=0.001, metacommunity_option="simulated", ) ref4 = t.get_community_reference( speciation_rate=0.1, time=0.0, fragments=False, metacommunity_size=10000.0, metacommunity_speciation_rate=0.001, metacommunity_option="analytical", ) ref5 = t.get_community_reference( speciation_rate=0.2, time=0.0, fragments=False, metacommunity_size=10000.0, metacommunity_speciation_rate=0.001, metacommunity_option="analytical", ) self.assertEqual(1, ref1) self.assertEqual(2, ref2) self.assertEqual(3, ref3) self.assertEqual(4, ref4) self.assertEqual(5, ref5) expected_community_params_list = [] for reference in t.get_community_references(): params = t.get_community_parameters(reference) params["reference"] = reference expected_community_params_list.append(params) expected_community_params = pd.DataFrame(expected_community_params_list) actual_output = t.get_community_parameters_pd() assert_frame_equal(expected_community_params, actual_output, check_like=True) def testIsComplete(self): t = CoalescenceTree(os.path.join("sample", "sample4.db")) self.assertTrue(t.is_complete) class TestCoalescenceTreeAnalysis(unittest.TestCase): @classmethod def setUpClass(cls): dst1 = os.path.join("output", "sampledb0.db") for i in range(0, 11): dst = os.path.join("output", "sampledb{}.db".format(i)) if os.path.exists(dst): os.remove(dst) shutil.copyfile(os.path.join("sample", "sample.db"), dst) shutil.copyfile(os.path.join("sample", "nse_reference.db"), os.path.join("output", "nse_reference1.db")) random.seed(2) cls.test = CoalescenceTree(dst1, logging_level=50) cls.test.clear_calculations() cls.test.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) cls.test.calculate_fragment_richness() cls.test.calculate_fragment_octaves() cls.test.calculate_octaves_error() cls.test.calculate_alpha_diversity() cls.test.calculate_beta_diversity() cls.test2 = CoalescenceTree() cls.test2.set_database(os.path.join("sample", "sample_nofrag.db")) dstx = os.path.join("output", "sampledbx.db") shutil.copyfile(dst1, dstx) c = CoalescenceTree(dstx) c.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) c.calculate_goodness_of_fit() @classmethod def tearDownClass(cls): cls.test.clear_calculations() def testComparisonDataNoExistError(self): c = CoalescenceTree(os.path.join("sample", "sample.db")) with self.assertRaises(IOError): c.import_comparison_data(os.path.join("sample", "doesnotexist.db")) def testFragmentOctaves(self): num = self.test.cursor.execute( "SELECT richness FROM FRAGMENT_OCTAVES WHERE fragment == 'P09' AND octave == 0" " AND community_reference == 1" ).fetchall()[0][0] self.assertEqual(num, 7, msg="Fragment octaves not correctly calculated.") num = self.test.cursor.execute( "SELECT richness FROM FRAGMENT_OCTAVES WHERE fragment == 'P09' AND octave == 0 " " AND community_reference == 2" ).fetchall()[0][0] self.assertEqual(num, 7, msg="Fragment octaves not correctly calculated.") num = self.test.cursor.execute( "SELECT richness FROM FRAGMENT_OCTAVES WHERE fragment == 'cerrogalera' AND octave == 1 " " AND community_reference == 1" ).fetchall()[0][0] self.assertEqual(num, 3, msg="Fragment octaves not correctly calculated.") num = self.test.cursor.execute( "SELECT richness FROM FRAGMENT_OCTAVES WHERE fragment == 'whole' AND octave == 1 " " AND community_reference == 2" ).fetchall()[0][0] self.assertEqual(num, 221, msg="Fragment octaves not correctly calculated.") def testFragmentAbundances(self): num = self.test.cursor.execute( "SELECT COUNT(fragment) FROM FRAGMENT_ABUNDANCES WHERE fragment == 'P09' " " AND community_reference == 1" ).fetchall()[0][0] self.assertEqual(num, 9, msg="Fragment abundances not correctly calculated.") num = self.test.cursor.execute( "SELECT COUNT(fragment) FROM FRAGMENT_ABUNDANCES WHERE fragment == 'P09' " " AND community_reference == 2" ).fetchall()[0][0] self.assertEqual(num, 9, msg="Fragment abundances not correctly calculated.") num = self.test.cursor.execute( "SELECT COUNT(fragment) FROM FRAGMENT_ABUNDANCES WHERE fragment == 'cerrogalera' " " AND community_reference == 1" ).fetchall()[0][0] self.assertEqual(num, 9, msg="Fragment abundances not correctly calculated.") def testSpeciesAbundances(self): num = self.test.cursor.execute( "SELECT COUNT(species_id) FROM SPECIES_ABUNDANCES WHERE community_reference == 2" ).fetchall()[0][0] self.assertEqual(num, 1029, msg="Species abundances not correctly calculated.") num = self.test.cursor.execute( "SELECT COUNT(species_id) FROM SPECIES_ABUNDANCES WHERE community_reference == 1" ).fetchall()[0][0] self.assertEqual(num, 884, msg="Species abundances not correctly calculated.") def testGetOctaves(self): c = CoalescenceTree(os.path.join("output", "sampledb4.db")) c.clear_calculations() c.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) c.calculate_richness() self.assertEqual([[0, 585], [1, 231], [2, 59], [3, 5]], c.get_octaves(1)) c = CoalescenceTree(os.path.join("output", "sampledb4.db")) c.clear_calculations() c.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) c.calculate_richness() actual = c.get_octaves_pd().head() expected = pd.DataFrame( [[1, 0, 585], [1, 1, 231], [1, 2, 59], [1, 3, 5], [2, 0, 760]], columns=["community_reference", "octave", "richness"], ) assert_frame_equal(actual, expected, check_like=True) def testSpeciesLocations(self): num = self.test.cursor.execute( "SELECT species_id FROM SPECIES_LOCATIONS WHERE x==1662 AND y==4359 " " AND community_reference == 1" ).fetchall() self.assertEqual(len(set(num)), 2, msg="Species locations not correctly assigned") all_list = self.test.get_species_locations() select_list = self.test.get_species_locations(community_reference=1) self.assertListEqual([1, 1662, 4359, 1], all_list[0]) self.assertListEqual([1, 1662, 4359], select_list[0]) def testAlphaDiversity(self): c = CoalescenceTree(os.path.join("sample", "sample.db")) with self.assertRaises(IOError): c.get_alpha_diversity_pd() self.assertEqual(9, self.test.get_alpha_diversity(1)) self.assertEqual(10, self.test.get_alpha_diversity(2)) expected_alphas_list = [] for reference in self.test.get_community_references(): expected_alphas_list.append( {"community_reference": reference, "alpha_diversity": self.test.get_alpha_diversity(reference)} ) expected_alphas = pd.DataFrame(expected_alphas_list).reset_index(drop=True) actual_alphas = self.test.get_alpha_diversity_pd().reset_index(drop=True) assert_frame_equal(expected_alphas, actual_alphas, check_like=True) def testBetaDiversity(self): c = CoalescenceTree(os.path.join("sample", "sample.db")) with self.assertRaises(IOError): c.get_beta_diversity_pd() self.assertAlmostEqual(98.111111111, self.test.get_beta_diversity(1), places=5) self.assertAlmostEqual(102.8, self.test.get_beta_diversity(2), places=5) expected_betas_list = [] for reference in self.test.get_community_references(): expected_betas_list.append( {"community_reference": reference, "beta_diversity": self.test.get_beta_diversity(reference)} ) expected_betas = pd.DataFrame(expected_betas_list).reset_index(drop=True) actual_betas = self.test.get_beta_diversity_pd().reset_index(drop=True) assert_frame_equal(expected_betas, actual_betas, check_like=True) def testGetNumberIndividuals(self): c = CoalescenceTree(os.path.join("output", "sampledb7.db")) self.assertEqual(1504, c.get_number_individuals(community_reference=1)) self.assertEqual(12, c.get_number_individuals(fragment="P09", community_reference=1)) c.wipe_data() c.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) with self.assertRaises(IOError): c.get_number_individuals(fragment="none") with self.assertRaises(IOError): c.get_number_individuals() def testGetFragmentAbundances(self): c = CoalescenceTree(os.path.join("sample", "sample3.db")) with self.assertRaises(IOError): c.get_fragment_abundances(fragment="P09", reference=1) with self.assertRaises(IOError): c.get_fragment_abundances_pd() abundances = self.test.get_fragment_abundances(fragment="P09", reference=1) expected_abundances = [[302, 1], [303, 1], [304, 1], [305, 1], [306, 1], [307, 1], [546, 2], [693, 1], [732, 3]] self.assertEqual(expected_abundances, abundances[:10]) all_abundances = self.test.get_all_fragment_abundances() expected_abundances2 = [ [1, "P09", 302, 1], [1, "P09", 303, 1], [1, "P09", 304, 1], [1, "P09", 305, 1], [1, "P09", 306, 1], [1, "P09", 307, 1], [1, "P09", 546, 2], [1, "P09", 693, 1], [1, "P09", 732, 3], [1, "cerrogalera", 416, 1], ] self.assertEqual(expected_abundances2, all_abundances[:10]) df = pd.DataFrame( expected_abundances2, columns=["community_reference", "fragment", "species_id", "no_individuals"] ) actual_df = self.test.get_fragment_abundances_pd().head(n=10) assert_frame_equal(df, actual_df, check_like=True) def testGetFragmentListErrors(self): c = CoalescenceTree(os.path.join("output", "sampledb8.db")) c.wipe_data() with self.assertRaises(IOError): c.get_fragment_list() def testClearGoodnessFit(self): c = CoalescenceTree(os.path.join("output", "sampledbx.db")) exec_command = "SELECT * FROM BIODIVERSITY_METRICS WHERE metric LIKE 'goodness_%'" self.assertTrue(len(c.cursor.execute(exec_command).fetchall()) >= 1) c._clear_goodness_of_fit() self.assertFalse(len(c.cursor.execute(exec_command).fetchall()) >= 1) def testGetBiodiversityMetrics(self): c1 = CoalescenceTree(os.path.join("sample", "sample.db")) with self.assertRaises(IOError): c1.get_biodiversity_metrics() c2 = CoalescenceTree(os.path.join("sample", "sample2.db")) expected_biodiversity_metrics = pd.DataFrame( [ [1, "fragment_richness", "fragment2", 129.0, np.NaN, np.NaN], [2, "fragment_richness", "fragment2", 130.0, np.NAN, np.NaN], [1, "fragment_richness", "fragment1", 174.0, np.NaN, np.NaN], [2, "fragment_richness", "fragment1", 175.0, np.NaN, np.NaN], [1, "fragment_richness", "whole", 1163.0, np.NaN, np.NaN], [2, "fragment_richness", "whole", 1170.0, np.NaN, np.NaN], ], columns=["community_reference", "metric", "fragment", "value", "simulated", "actual"], ).reset_index(drop=True) actual_biodiversity_metrics = c2.get_biodiversity_metrics().reset_index(drop=True).fillna(value=np.nan) assert_frame_equal(expected_biodiversity_metrics, actual_biodiversity_metrics) def testRaisesErrorNoFragmentsAlpha(self): with self.assertRaises(IOError): self.test2.calculate_alpha_diversity() def testRaisesErrorNoFragmentsBeta(self): with self.assertRaises(IOError): self.test2.calculate_beta_diversity() def testRaisesErrorNoFragmentsRichness(self): with self.assertRaises(IOError): self.test2.calculate_fragment_richness() def testRaisesErrorNoFragmentsOctaves(self): with self.assertRaises(IOError): self.test2.calculate_fragment_octaves() @unittest.skipIf(sys.version[0] != "3", "Skipping Python 3.x tests") def testModelFitting2(self): random.seed(2) self.test.calculate_goodness_of_fit() self.assertAlmostEqual(self.test.get_goodness_of_fit(), 0.30140801329929373, places=6) self.assertAlmostEqual(self.test.get_goodness_of_fit_fragment_octaves(), 0.0680205429120108, places=6) self.assertAlmostEqual(self.test.get_goodness_of_fit_fragment_richness(), 0.9244977999898334, places=6) @unittest.skipIf(sys.version[0] == "3", "Skipping Python 2.x tests") def testModelFitting3(self): random.seed(2) self.test.calculate_goodness_of_fit() self.assertAlmostEqual(self.test.get_goodness_of_fit(), 0.30140801329929373, places=6) self.assertAlmostEqual(self.test.get_goodness_of_fit_fragment_octaves(), 0.0680205429120108, places=6) self.assertAlmostEqual(self.test.get_goodness_of_fit_fragment_richness(), 0.9244977999898334, places=6) def testErrorIfNotApplied(self): c = CoalescenceTree(os.path.join("sample", "sample.db")) with self.assertRaises(RuntimeError): c.output() def testFragmentNumbersMatching(self): test = CoalescenceTree(os.path.join("output", "sampledb1.db"), logging_level=50) test.clear_calculations() with self.assertRaises(RuntimeError): test._check_fragment_numbers_match() with self.assertRaises(ValueError): test.calculate_fragment_abundances() test._check_fragment_numbers_match() test.comparison_file = os.path.join("sample", "PlotBiodiversityMetrics.db") self.assertTrue(test._check_fragment_numbers_match()) test.fragment_abundances.pop(0) self.assertFalse(test._check_fragment_numbers_match()) def testFragmentNumbersEqualisation(self): test = CoalescenceTree(os.path.join("output", "sampledb2.db"), logging_level=50) test.clear_calculations() test.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) test.calculate_fragment_richness() self.test._equalise_fragment_number("notafrag", 1) test.fragment_abundances[0][2] += 1000 test._equalise_fragment_number("P09", 1) self.assertTrue(test._check_fragment_numbers_match()) def testFragmentNumbersErrors(self): test = CoalescenceTree(os.path.join("output", "sampledb3.db"), logging_level=50) test.clear_calculations() test.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) test.comparison_abundances = None with self.assertRaises(ValueError): test._equalise_all_fragment_numbers() def testAdjustBiodiversityMetrics(self): test = CoalescenceTree(os.path.join("output", "sampledb5.db"), logging_level=50) test.clear_calculations() test.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) test.adjust_data() def testComparisonOctavesModification(self): test = CoalescenceTree(os.path.join("output", "sampledb6.db"), logging_level=50) dst = os.path.join("output", "PlotBiodiversityMetricsNoAlpha2.db") shutil.copy(os.path.join("sample", "PlotBiodiversityMetricsNoAlpha.db"), dst) test.import_comparison_data(dst) test.calculate_comparison_octaves(store=True) self.assertTrue(os.path.exists(dst)) @unittest.skipIf(sys.version[0] == "2", "Skipping Python 3.x tests") def testDownsamplingAndRevert(self): c = CoalescenceTree(os.path.join("output", "sampledb9.db")) random.seed(a=10, version=3) original_individuals = c.get_number_individuals() original_richness = c.get_species_richness_pd() c.wipe_data() with self.assertRaises(ValueError): c.downsample(sample_proportion=2.0) c.downsample(sample_proportion=0.1) c.set_speciation_parameters([0.1, 0.2]) c.apply() new_individuals = c.get_number_individuals() self.assertEqual(1452, new_individuals) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST")) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST_ORIGINAL")) c = CoalescenceTree(os.path.join("output", "sampledb9.db")) c.revert_downsample() c.wipe_data() c.set_speciation_parameters([0.1, 0.2]) c.apply() final_individuals = c.get_number_individuals() assert_frame_equal(original_richness, c.get_species_richness_pd()) self.assertEqual(original_individuals, final_individuals) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST")) self.assertFalse(check_sql_table_exist(c.database, "SPECIES_LIST_ORIGINAL")) c = CoalescenceTree(os.path.join("output", "nse_reference1.db")) nse_richness = c.get_species_richness_pd() nse_no_individuals = c.get_number_individuals() c.wipe_data() c.downsample(sample_proportion=0.1) c.set_speciation_parameters([0.000001, 0.999999]) c.apply() new_no_individuals = c.get_number_individuals() self.assertAlmostEqual(new_no_individuals / nse_no_individuals, 0.1, 5) self.assertEqual(1000, c.get_species_richness(reference=2)) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST")) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST_ORIGINAL")) c = CoalescenceTree(os.path.join("output", "nse_reference1.db")) c.revert_downsample() c.wipe_data() c.set_speciation_parameters([0.000001, 0.999999]) c.apply_incremental() c.set_speciation_parameters([0.5]) c.apply() actual_richness = c.get_species_richness_pd() assert_frame_equal(nse_richness, actual_richness) self.assertEqual(nse_no_individuals, c.get_number_individuals()) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST")) self.assertFalse(check_sql_table_exist(c.database, "SPECIES_LIST_ORIGINAL")) with self.assertRaises(IOError): c.revert_downsample() @unittest.skipIf(sys.version[0] == "2", "Skipping Python 3.x tests") def testDownsamplingByLocationAndRevert(self): c = CoalescenceTree(os.path.join("output", "sampledb10.db")) random.seed(a=10, version=3) original_individuals = c.get_number_individuals() original_richness = c.get_species_richness_pd() c.wipe_data() with self.assertRaises(ValueError): c.downsample_at_locations(fragment_csv=os.path.join("sample", "FragmentsTestFail1.csv")) with self.assertRaises(IOError): c.downsample_at_locations(fragment_csv="not_a_file.csv") c.downsample_at_locations(fragment_csv=os.path.join("sample", "FragmentsTest3.csv")) c.set_speciation_parameters([0.1, 0.2]) c.apply() new_individuals = c.get_number_individuals() self.assertEqual(2, new_individuals) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST")) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST_ORIGINAL")) c = CoalescenceTree(os.path.join("output", "sampledb10.db")) c.revert_downsample() c.wipe_data() c.set_speciation_parameters([0.1, 0.2]) c.apply() final_individuals = c.get_number_individuals() assert_frame_equal(original_richness, c.get_species_richness_pd()) self.assertEqual(original_individuals, final_individuals) self.assertTrue(check_sql_table_exist(c.database, "SPECIES_LIST")) self.assertFalse(check_sql_table_exist(c.database, "SPECIES_LIST_ORIGINAL")) c = CoalescenceTree(os.path.join("output", "sampledb10.db")) c.wipe_data() c.downsample_at_locations(fragment_csv=os.path.join("sample", "FragmentsTest4.csv"), ignore_errors=True) c.set_speciation_parameters([0.1, 0.2]) c.apply() new_individuals = c.get_number_individuals() self.assertEqual(3, new_individuals) class TestCoalescenceTreeWriteCsvs(unittest.TestCase): @classmethod def setUpClass(cls): cls.c = CoalescenceTree(os.path.join("sample", "nse_reference.db")) def testWriteCommunityParameterToCsv(self): output_csv = os.path.join("output", "community_parameters1.csv") self.c.write_to_csv(output_csv, "COMMUNITY_PARAMETERS") self.assertTrue(os.path.exists(output_csv)) import csv if sys.version_info[0] < 3: infile = open(output_csv, "rb") else: infile = open(output_csv, "r") expected_output = [ ["reference", "speciation_rate", "time", "fragments", "metacommunity_reference"], ["1", "1e-06", "0.0", "0", "0"], ["2", "0.99999", "0.0", "0", "0"], ["3", "0.5", "0.0", "0", "0"], ] actual_output = [] with infile as csv_file: csv_reader = csv.reader(csv_file) for row in csv_reader: actual_output.append(row) self.assertEqual(expected_output, actual_output) with self.assertRaises(IOError): self.c.write_to_csv(output_csv, "COMMUNITY_PARAMETERS") with self.assertRaises(KeyError): self.c.write_to_csv("notacsv.csv", "NOTATABLE") def testWritesAllCsvs(self): output_dir = os.path.join("output", "csvdir") if os.path.exists(output_dir): os.remove(output_dir) self.c.write_all_to_csvs(output_dir, "out1") expected_tables = ["COMMUNITY_PARAMETERS", "SIMULATION_PARAMETERS", "SPECIES_ABUNDANCES", "SPECIES_LIST"] for table in expected_tables: self.assertTrue(os.path.exists(os.path.join(output_dir, "out1_{}.csv".format(table)))) for file in os.listdir(output_dir): if ".csv" in file: self.assertIn(file, ["out1_{}.csv".format(x) for x in expected_tables]) self.c.write_all_to_csvs(output_dir, "out2.csv") for table in expected_tables: self.assertTrue(os.path.exists(os.path.join(output_dir, "out2_{}.csv".format(table)))) self.c.write_all_to_csvs(output_dir, "out3.") for table in expected_tables: self.assertTrue(os.path.exists(os.path.join(output_dir, "out3_{}.csv".format(table)))) class TestCoalescenceTreeSpeciesDistances(unittest.TestCase): @classmethod def setUpClass(cls): dst = os.path.join("output", "sampledb1.db") if os.path.exists(dst): os.remove(dst) shutil.copyfile(os.path.join("sample", "sample.db"), dst) cls.test = CoalescenceTree(dst) cls.test.clear_calculations() cls.test.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetrics.db")) cls.test.calculate_species_distance_similarity() def testSpeciesDistanceSimilarity(self): mean = self.test.cursor.execute( "SELECT value FROM BIODIVERSITY_METRICS WHERE community_reference == 1 AND " "metric == 'mean_distance_between_individuals'" ).fetchone()[0] self.assertAlmostEqual(mean, 5.423769507803121, places=5) species_distances = self.test.get_species_distance_similarity(community_reference=1) self.assertListEqual(species_distances[0], [0, 11]) self.assertListEqual(species_distances[1], [1, 274]) self.assertListEqual(species_distances[2], [2, 289]) class TestCoalescenceTreeAnalyseIncorrectComparison(unittest.TestCase): @classmethod def setUpClass(cls): random.seed(10) dst = os.path.join("output", "sampledb2.db") if os.path.exists(dst): os.remove(dst) shutil.copyfile(os.path.join("sample", "sample.db"), dst) cls.test = CoalescenceTree(logging_level=40) cls.test.set_database(dst) cls.test.import_comparison_data(os.path.join("sample", "PlotBiodiversityMetricsNoAlpha.db")) cls.test.calculate_comparison_octaves(False) cls.test.clear_calculations() cls.test.calculate_fragment_richness() cls.test.calculate_fragment_octaves() cls.test.calculate_octaves_error() cls.test.calculate_alpha_diversity() cls.test.calculate_alpha_diversity() cls.test.calculate_beta_diversity() cls.test2 = CoalescenceTree() cls.test2.set_database(os.path.join("sample", "sample_nofrag.db")) @classmethod def tearDownClass(cls): cls.test.clear_calculations() def testRaisesErrorMismatchParameters(self): with self.assertRaises(ValueError): self.test.calculate_goodness_of_fit() class TestSimulationAnalysisTemporal(unittest.TestCase): @classmethod def setUpClass(cls): src = os.path.join("sample", "sample2.db") dst = os.path.join("output", "sample2.db") if not os.path.exists(dst): shutil.copy(src, dst) cls.tree = CoalescenceTree() cls.tree.set_database(dst) cls.tree.wipe_data() def testTimesWrongFormatError(self): with self.assertRaises(TypeError): self.tree.set_speciation_parameters([0.4, 0.6], times=[0.1, 0.2, "notafloat"]) with self.assertRaises(TypeError): self.tree.set_speciation_parameters([0.4, 0.6], times="notafloat") self.tree.times = [] self.tree.set_speciation_parameters([0.4, 0.6], times=[0, 1, 10]) self.assertEqual([0.0, 1.0, 10.0], self.tree.times) class TestSimulationAnalysis(unittest.TestCase): @classmethod def setUpClass(cls): src = os.path.join("sample", "sample2.db") dst = os.path.join("output", "sample2.db") if os.path.exists(dst): os.remove(dst) shutil.copy(src, dst) cls.tree = CoalescenceTree(logging_level=50) cls.tree.set_database(dst) cls.tree.wipe_data() cls.tree.set_speciation_parameters( speciation_rates=[0.5, 0.7], record_spatial="T", record_fragments=os.path.join("sample", "FragmentsTest.csv"), sample_file=os.path.join("sample", "SA_samplemaskINT.tif"), ) cls.tree.apply() cls.tree.calculate_fragment_richness() cls.tree.calculate_fragment_octaves() np.random.seed(100) def testSetDatabaseErrors(self): sim = Simulation() c = CoalescenceTree() with self.assertRaises(RuntimeError): c.set_database(sim) c = CoalescenceTree() with self.assertRaises(IOError): c.set_database(os.path.join("sample", "failsampledoesntexist.db")) def testFragmentConfigNoExistError(self): tree = CoalescenceTree(self.tree.file) with self.assertRaises(IOError): tree.set_speciation_parameters( speciation_rates=[0.5, 0.7], record_spatial="T", record_fragments=os.path.join("sample", "notafragmentconfig.csv"), sample_file=os.path.join("sample", "SA_samplemaskINT.tif"), ) with self.assertRaises(IOError): tree.set_speciation_parameters( speciation_rates=[0.5, 0.7], record_spatial="T", record_fragments=os.path.join("sample", "example_historical_fine.tif"), sample_file=os.path.join("sample", "SA_samplemaskINT.tif"), ) def testReadsFragmentsRichness(self): sim_params = self.tree.get_simulation_parameters() expected_params = dict( seed=9, task=1, output_dir="output", speciation_rate=0.5, sigma=2.828427, tau=2.0, deme=1, sample_size=0.1, max_time=2.0, dispersal_relative_cost=1.0, min_num_species=1, habitat_change_rate=0.0, gen_since_historical=200.0, time_config_file="null", coarse_map_file="sample/SA_sample_coarse.tif", coarse_map_x=35, coarse_map_y=41, coarse_map_x_offset=11, coarse_map_y_offset=14, coarse_map_scale=1.0, fine_map_file="sample/SA_sample_fine.tif", fine_map_x=13, fine_map_y=13, fine_map_x_offset=0, fine_map_y_offset=0, sample_file="sample/SA_samplemaskINT.tif", grid_x=13, grid_y=13, sample_x=13, sample_y=13, sample_x_offset=0, sample_y_offset=0, historical_coarse_map="none", historical_fine_map="none", sim_complete=1, dispersal_method="normal", m_probability=0.0, cutoff=0.0, landscape_type="closed", protracted=0, min_speciation_gen=0.0, max_speciation_gen=0.0, dispersal_map="none", ) for key in sim_params.keys(): self.assertEqual( sim_params[key], expected_params[key], msg="Error in {}: {} != {}".format(key, sim_params[key], expected_params[key]), ) fragment2_richness = ["fragment2", 1, 129] self.assertEqual(self.tree.get_fragment_richness(fragment="fragment2", reference=1), 129) self.assertEqual(self.tree.get_fragment_richness(fragment="fragment1", reference=2), 175) octaves = self.tree.get_fragment_richness() self.assertListEqual(fragment2_richness, [list(x) for x in octaves if x[0] == "fragment2" and x[1] == 1][0]) expected_fragment_richness = [] for reference in self.tree.get_community_references(): for fragment in self.tree.get_fragment_list(reference): fragment_richness = self.tree.get_fragment_richness(fragment=fragment, reference=reference) expected_fragment_richness.append( {"fragment": fragment, "community_reference": reference, "fragment_richness": fragment_richness} ) expected_fragment_richness_df = ( pd.DataFrame(expected_fragment_richness) .sort_values(by=["fragment", "community_reference"]) .reset_index(drop=True) ) actual_fragment_richness = self.tree.get_fragment_richness_pd().reset_index(drop=True) assert_frame_equal(expected_fragment_richness_df, actual_fragment_richness, check_like=True) def testGetsFragmentList(self): fragment_list = self.tree.get_fragment_list() expected_list = ["fragment1", "fragment2"] self.assertListEqual(expected_list, fragment_list) def testReadsFragmentAbundances(self): expected_abundances = [ [610, 1], [611, 1], [612, 1], [613, 1], [614, 1], [615, 1], [616, 1], [617, 1], [618, 1], [619, 1], ] actual_abundances = self.tree.get_species_abundances(fragment="fragment2", reference=1) for i, each in enumerate(expected_abundances): self.assertListEqual(actual_abundances[i], each) with self.assertRaises(ValueError): self.tree.get_species_abundances(fragment="fragment2") expected_fragment_abundances_list = [] for reference in self.tree.get_community_references(): for fragment in self.tree.get_fragment_list(reference): fragment_abundances = self.tree.get_fragment_abundances(fragment=fragment, reference=reference) for species_id, abundance in fragment_abundances: expected_fragment_abundances_list.append( { "fragment": fragment, "community_reference": reference, "species_id": species_id, "no_individuals": abundance, } ) expected_fragment_abundances = ( pd.DataFrame(expected_fragment_abundances_list) .sort_values(by=["fragment", "community_reference", "species_id"]) .reset_index(drop=True) ) actual_fragment_abundances = ( self.tree.get_fragment_abundances_pd() .sort_values(by=["fragment", "community_reference", "species_id"]) .reset_index(drop=True) ) assert_frame_equal(expected_fragment_abundances, actual_fragment_abundances, check_like=True) def testFragmentRichnessRaiseError(self): failtree = CoalescenceTree() failtree.set_database(os.path.join("sample", "failsample.db")) with self.assertRaises(IOError): failtree.get_fragment_richness() with self.assertRaises(IOError): failtree.get_fragment_richness_pd() with self.assertRaises(IOError): self.tree.get_fragment_richness(fragment="fragment4", reference=1) with self.assertRaises(SyntaxError): self.tree.get_fragment_richness(fragment="fragment4") with self.assertRaises(SyntaxError): self.tree.get_fragment_richness(reference=1) def testReadsFragmentOctaves(self): octaves = self.tree.get_fragment_octaves(fragment="fragment2", reference=1) octaves2 = self.tree.get_fragment_octaves(fragment="fragment1", reference=1) all_octaves = self.tree.get_fragment_octaves() desired = ["fragment1", 1, 0, 173] self.assertListEqual([0, 128], octaves[0]) self.assertListEqual([0, 173], octaves2[0]) self.assertListEqual(desired, [x for x in all_octaves if x[0] == "fragment1" and x[1] == 1 and x[2] == 0][0]) expected_fragment_octaves_list = [] for reference in self.tree.get_community_references(): fragment_list = self.tree.get_fragment_list(reference) fragment_list.append("whole") for fragment in fragment_list: try: octaves = self.tree.get_fragment_octaves(fragment=fragment, reference=reference) for octave, richness in octaves: expected_fragment_octaves_list.append( { "fragment": fragment, "community_reference": reference, "octave": octave, "richness": richness, } ) except RuntimeError: continue expected_fragment_octaves = ( pd.DataFrame(expected_fragment_octaves_list) .sort_values(["fragment", "community_reference", "octave"], axis=0) .reset_index(drop=True) ) actual_fragment_octaves = ( self.tree.get_fragment_octaves_pd() .sort_values(["fragment", "community_reference", "octave"], axis=0) .reset_index(drop=True) ) assert_frame_equal(expected_fragment_octaves, actual_fragment_octaves, check_like=True) def testFragmentOctavesRaiseError(self): failtree = CoalescenceTree() try: failtree.set_database("sample/failsample.db") except sqlite3.Error: pass with self.assertRaises(sqlite3.Error): failtree.get_fragment_octaves(fragment="fragment4", reference=100) with self.assertRaises(RuntimeError): self.tree.get_fragment_octaves(fragment="fragment4", reference=100) with self.assertRaises(SyntaxError): self.tree.get_fragment_octaves(fragment="fragment4") with self.assertRaises(SyntaxError): self.tree.get_fragment_octaves(reference=100) def testFragmentSampling(self): self.assertEqual( 10, self.tree.sample_fragment_richness( fragment="fragment1", number_of_individuals=10, n=1, community_reference=2 ), ) self.assertEqual( 10, self.tree.sample_fragment_richness( fragment="fragment2", number_of_individuals=10, n=10, community_reference=2 ), ) def testLandscapeSampling(self): number_dict = {"fragment1": 3, "fragment2": 10} np.random.seed(100) self.assertEqual( 13, self.tree.sample_landscape_richness(number_of_individuals=number_dict, n=1, community_reference=2) ) self.assertAlmostEqual( 99.9, self.tree.sample_landscape_richness(number_of_individuals=100, n=10, community_reference=1), places=3 ) def testRaisesSamplingErrors(self): number_dict = {"fragment1": 3000000, "fragment2": 10} with self.assertRaises(KeyError): self.assertEqual( 13, self.tree.sample_landscape_richness(number_of_individuals=number_dict, n=1, community_reference=2) ) number_dict2 = {"fragment": 10, "fragment2": 10} with self.assertRaises(KeyError): self.assertEqual( 13, self.tree.sample_landscape_richness(number_of_individuals=number_dict2, n=1, community_reference=2) ) def testSpeciesRichness(self): actual_species_richness = ( self.tree.get_species_richness_pd().sort_values(by=["community_reference"]).reset_index(drop=True) ) expected_species_richness_list = [] for reference in self.tree.get_community_references(): expected_species_richness_list.append( {"community_reference": reference, "richness": self.tree.get_species_richness(reference=reference)} ) expected_species_richness = pd.DataFrame(expected_species_richness_list) assert_frame_equal(actual_species_richness, expected_species_richness, check_like=True) def testOctaves(self): actual_species_octaves = ( self.tree.get_octaves_pd().sort_values(by=["community_reference", "octave"]).reset_index(drop=True) ) expected_species_octaves_list = [] for reference in self.tree.get_community_references(): for octave, richness in self.tree.get_octaves(reference): expected_species_octaves_list.append( {"community_reference": reference, "octave": octave, "richness": richness} ) expected_species_octaves = pd.DataFrame(expected_species_octaves_list) assert_frame_equal(actual_species_octaves, expected_species_octaves, check_like=True) class TestMetacommunityApplication(unittest.TestCase): @classmethod def setUpClass(cls): src = os.path.join("sample", "sample.db") for i in range(6): dst = os.path.join("output", "sample_{}.db".format(i)) if os.path.exists(dst): os.remove(dst) shutil.copy2(src, dst) def testMetacommunityAddingInvalidParameters(self): tree = CoalescenceTree(os.path.join("output", "sample_0.db")) tree.wipe_data() with self.assertRaises(IOError): tree.get_metacommunity_parameters_pd() tree.set_speciation_parameters([0.1, 0.2]) for size, spec, opt, ref in [ [0, 0.1, "simulated", None], [10, 0.0, "analytical", None], [None, None, "analytical", None], [10, 0.0, "path/to/file", None], [0, 0.0, "path/to/file", None], [0, 0.0, "path/to/not/a/file.db", 1], ]: with self.assertRaises(ValueError): tree.add_metacommunity_parameters( metacommunity_size=size, metacommunity_speciation_rate=spec, metacommunity_option=opt, metacommunity_reference=ref, ) with self.assertRaises(IOError): tree.add_metacommunity_parameters(metacommunity_option="not/a/file/db.db", metacommunity_reference=1) def testMetacommunitySimulation(self): tree = CoalescenceTree(os.path.join("output", "sample_1.db")) tree.wipe_data() tree.set_speciation_parameters( [0.1, 0.2], metacommunity_size=10000, metacommunity_speciation_rate=0.001, metacommunity_option="simulated" ) tree.add_metacommunity_parameters( metacommunity_size=15000, metacommunity_speciation_rate=0.1, metacommunity_option="simulated" ) tree.add_metacommunity_parameters( metacommunity_size=100000, metacommunity_speciation_rate=0.001, metacommunity_option="simulated" ) tree.apply() params_1 = tree.get_metacommunity_parameters(1) params_2 = tree.get_metacommunity_parameters(2) params_3 = tree.get_metacommunity_parameters(3) self.assertEqual(10000, params_1["metacommunity_size"]) self.assertEqual(0.001, params_1["speciation_rate"]) self.assertEqual("simulated", params_1["option"]) self.assertEqual(0, params_1["external_reference"]) self.assertEqual(15000, params_2["metacommunity_size"]) self.assertEqual(0.1, params_2["speciation_rate"]) self.assertEqual("simulated", params_2["option"]) self.assertEqual(0, params_2["external_reference"]) self.assertEqual(100000, params_3["metacommunity_size"]) self.assertEqual(0.001, params_3["speciation_rate"]) self.assertEqual("simulated", params_3["option"]) self.assertEqual(0, params_3["external_reference"]) self.assertEqual(51, tree.get_species_richness(1)) self.assertEqual(47, tree.get_species_richness(2)) self.assertEqual(681, tree.get_species_richness(3)) self.assertEqual(783, tree.get_species_richness(4)) self.assertEqual(247, tree.get_species_richness(5)) self.assertEqual(241, tree.get_species_richness(6)) expected_metacommunity_parameters_list = [] for reference in tree.get_community_references(): try: params = tree.get_metacommunity_parameters(reference) params["reference"] = reference expected_metacommunity_parameters_list.append(params) except KeyError: continue expected_metacommunity_parameters = pd.DataFrame(expected_metacommunity_parameters_list).sort_values( ["reference"] ) actual_metacommunity_parameters = tree.get_metacommunity_parameters_pd().sort_values(["reference"]) assert_frame_equal(expected_metacommunity_parameters, actual_metacommunity_parameters, check_like=True) def testMetacommunityAnalytical(self): tree = CoalescenceTree(os.path.join("output", "sample_2.db")) tree.wipe_data() tree.set_speciation_parameters( [0.1, 0.2], metacommunity_size=10000, metacommunity_speciation_rate=0.001, metacommunity_option="analytical" ) tree.add_metacommunity_parameters( metacommunity_size=15000, metacommunity_speciation_rate=0.1, metacommunity_option="analytical" ) tree.add_metacommunity_parameters( metacommunity_size=100000, metacommunity_speciation_rate=0.001, metacommunity_option="analytical" ) tree.apply() params_1 = tree.get_metacommunity_parameters(1) params_2 = tree.get_metacommunity_parameters(2) params_3 = tree.get_metacommunity_parameters(3) self.assertEqual(10000, params_1["metacommunity_size"]) self.assertEqual(0.001, params_1["speciation_rate"]) self.assertEqual("analytical", params_1["option"]) self.assertEqual(0, params_1["external_reference"]) self.assertEqual(15000, params_2["metacommunity_size"]) self.assertEqual(0.1, params_2["speciation_rate"]) self.assertEqual("analytical", params_2["option"]) self.assertEqual(0, params_2["external_reference"]) self.assertEqual(100000, params_3["metacommunity_size"]) self.assertEqual(0.001, params_3["speciation_rate"]) self.assertEqual("analytical", params_3["option"]) self.assertEqual(0, params_3["external_reference"]) self.assertEqual(51, tree.get_species_richness(1)) self.assertEqual(57, tree.get_species_richness(2)) self.assertEqual(694, tree.get_species_richness(3)) self.assertEqual(760, tree.get_species_richness(4)) self.assertEqual(222, tree.get_species_richness(5)) self.assertEqual(234, tree.get_species_richness(6)) def testMetacommunityExternal(self): tree = CoalescenceTree(os.path.join("output", "sample_3.db")) tree.wipe_data() tree.set_speciation_parameters([0.1, 0.2], metacommunity_option=os.path.join("sample", "nse_reference.db")) tree.add_metacommunity_parameters( metacommunity_option=os.path.join("sample", "nse_reference.db"), metacommunity_reference=2 ) tree.apply() params_1 = tree.get_metacommunity_parameters(1) params_2 = tree.get_metacommunity_parameters(2) self.assertEqual(0, params_1["metacommunity_size"]) self.assertEqual(0.0, params_1["speciation_rate"]) self.assertEqual(os.path.join("sample", "nse_reference.db"), params_1["option"]) self.assertEqual(1, params_1["external_reference"]) self.assertEqual(0, params_2["metacommunity_size"]) self.assertEqual(0.0, params_2["speciation_rate"]) self.assertEqual(os.path.join("sample", "nse_reference.db"), params_2["option"]) self.assertEqual(2, params_2["external_reference"]) self.assertEqual(1, tree.get_species_richness(1)) self.assertEqual(1, tree.get_species_richness(2)) self.assertEqual(850, tree.get_species_richness(3)) self.assertEqual(975, tree.get_species_richness(4)) def testMetacommunityAnalyticalMethodDetection(self): tree = CoalescenceTree(os.path.join("output", "sample_4.db")) tree.wipe_data() tree.set_speciation_parameters( [0.1, 0.2], metacommunity_size=110000, metacommunity_speciation_rate=0.5, metacommunity_option="none" ) tree.add_metacommunity_parameters( metacommunity_speciation_rate=0.5, metacommunity_size=120000, metacommunity_option="none" ) tree.apply() params_1 = tree.get_metacommunity_parameters(1) params_2 = tree.get_metacommunity_parameters(2) self.assertEqual(110000, params_1["metacommunity_size"]) self.assertEqual(0.5, params_1["speciation_rate"]) self.assertEqual("analytical", params_1["option"]) self.assertEqual(120000, params_2["metacommunity_size"]) self.assertEqual(0.5, params_2["speciation_rate"]) self.assertEqual("analytical", params_2["option"]) def testMetacommunitySimulatedMethodDetection(self): tree = CoalescenceTree(os.path.join("output", "sample_5.db")) tree.wipe_data() tree.set_speciation_parameters( [0.1, 0.2], metacommunity_size=1000, metacommunity_speciation_rate=0.5, metacommunity_option="none" ) tree.add_metacommunity_parameters( metacommunity_speciation_rate=0.5, metacommunity_size=2000, metacommunity_option="none" ) tree.apply() params_1 = tree.get_metacommunity_parameters(1) params_2 = tree.get_metacommunity_parameters(2) self.assertEqual(1000, params_1["metacommunity_size"]) self.assertEqual(0.5, params_1["speciation_rate"]) self.assertEqual("simulated", params_1["option"]) self.assertEqual(2000, params_2["metacommunity_size"]) self.assertEqual(0.5, params_2["speciation_rate"]) self.assertEqual("simulated", params_2["option"]) @skipLongTest class TestMetacommunityApplicationSpeciesAbundances(unittest.TestCase): @classmethod def setUpClass(cls): cls.sim = Simulation() cls.sim.set_simulation_parameters( seed=11, task=110, output_directory="output", min_speciation_rate=0.1, spatial=False, deme=20541 ) cls.sim.run() cls.ct = CoalescenceTree(cls.sim) cls.ct.wipe_data() cls.ct.set_speciation_parameters(speciation_rates=0.1) cls.ct.add_metacommunity_parameters( metacommunity_option="analytical", metacommunity_size=1000000, metacommunity_speciation_rate=0.00005 ) cls.ct.add_metacommunity_parameters( metacommunity_option="simulated", metacommunity_size=1000000, metacommunity_speciation_rate=0.00005 ) cls.ct.add_metacommunity_parameters( metacommunity_option="analytical", metacommunity_size=1000000000, metacommunity_speciation_rate=0.1 ) cls.ct.apply() def testRichnessMatchness(self): self.assertAlmostEqual(244, self.ct.get_species_richness(2), delta=10) self.assertAlmostEqual(self.ct.get_species_richness(1), self.ct.get_species_richness(2), delta=30) self.assertEqual(5212, self.ct.get_species_richness(3)) def testSpeciesAbundances(self): sad_1 = [x[1] for x in self.ct.get_species_abundances(reference=1)] sad_2 = [x[1] for x in self.ct.get_species_abundances(reference=2)] mean_1 = sum(sad_1) / len(sad_1) mean_2 = sum(sad_2) / len(sad_2) # Check the mean abundance is roughly equivalent self.assertAlmostEqual(mean_1, mean_2, delta=10) # Check that the variances are roughly equivalent var_list_1 = [abs(x - mean_1) for x in sad_1] var_list_2 = [abs(x - mean_2) for x in sad_2] var_1 = sum(var_list_1) / len(var_list_1) var_2 = sum(var_list_2) / len(var_list_2) self.assertAlmostEqual(var_1, var_2, delta=5) expected_abundances_list = [] for reference in self.ct.get_community_references(): for species_id, abundance in self.ct.get_species_abundances(reference=reference): expected_abundances_list.append( {"community_reference": reference, "species_id": species_id, "no_individuals": abundance} ) expected_abundances = pd.DataFrame(expected_abundances_list) actual_abundances = self.ct.get_species_abundances_pd() assert_frame_equal(actual_abundances, expected_abundances, check_like=True) class TestMetacommunityApplicationOrdering(unittest.TestCase): @classmethod def setUpClass(cls): src = os.path.join("sample", "sample3.db") for i in [1, 2]: dst = os.path.join("output", "sample_order_{}.db".format(i)) if os.path.exists(dst): os.remove(dst) shutil.copy(src, dst) src = os.path.join("sample", "sample5.db") for i in range(3, 6): dst = os.path.join("output", "sample_order_{}.db".format(i)) if os.path.exists(dst): os.remove(dst) shutil.copy(src, dst) cls.c1 = CoalescenceTree(os.path.join("output", "sample_order_1.db")) cls.c2 = CoalescenceTree(os.path.join("output", "sample_order_2.db")) cls.proc1 = CoalescenceTree(os.path.join("output", "sample_order_3.db")) cls.proc2 = CoalescenceTree(os.path.join("output", "sample_order_4.db")) cls.proc3 = CoalescenceTree(os.path.join("output", "sample_order_5.db")) cls.c1.set_speciation_parameters( [0.1, 0.5, 0.9], metacommunity_speciation_rate=0.001, metacommunity_option="simulated", metacommunity_size=10000, ) cls.c1.apply() cls.c2.set_speciation_parameters([0.1, 0.5, 0.9]) cls.c2.add_metacommunity_parameters( metacommunity_size=10000, metacommunity_speciation_rate=0.001, metacommunity_option="simulated" ) cls.c2.apply() cls.proc1.set_speciation_parameters( [0.1, 0.5, 0.9], protracted_speciation_min=5, protracted_speciation_max=1000, metacommunity_option="simulated", metacommunity_speciation_rate=0.001, metacommunity_size=10000, ) cls.proc1.apply() cls.proc2.set_speciation_parameters([0.1, 0.5, 0.9]) cls.proc2.add_metacommunity_parameters( metacommunity_size=10000, metacommunity_speciation_rate=0.001, metacommunity_option="simulated" ) cls.proc2.add_protracted_parameters(min_speciation_gen=5, max_speciation_gen=1000) cls.proc2.apply() cls.proc3.set_speciation_parameters([0.1, 0.5, 0.9]) cls.proc3.add_protracted_parameters(min_speciation_gen=5, max_speciation_gen=1000) cls.proc3.add_metacommunity_parameters( metacommunity_size=10000, metacommunity_speciation_rate=0.001, metacommunity_option="simulated" ) cls.proc3.apply() def testEquivalentMethodsMatch(self): for i in range(1, 4): self.assertEqual(self.c1.get_species_richness(i), self.c2.get_species_richness(i)) self.assertEqual(self.proc1.get_species_richness(i), self.proc2.get_species_richness(i)) self.assertEqual(self.proc2.get_species_richness(i), self.proc3.get_species_richness(i)) def testMultipleProtractedError(self): with self.assertRaises(ValueError): self.proc2.add_multiple_protracted_parameters() class TestProtractedSpeciationEquality(unittest.TestCase): @classmethod def setUpClass(cls): dst = os.path.join("output", "sample_protracted3.db") shutil.copy(os.path.join("sample", "sample3.db"), dst) cls.ct = CoalescenceTree(dst) cls.ct.wipe_data() def testApplyEqualParameters(self): self.ct.set_speciation_parameters( [0.001, 0.1], protracted_speciation_min=100.0, protracted_speciation_max=10000.0 ) self.ct.apply() self.assertEqual(1, self.ct.get_species_richness(1)) self.assertEqual(3, self.ct.get_species_richness(2)) class TestSpeciesAgesCalculations(unittest.TestCase): @classmethod def setUpClass(cls): src = os.path.join("sample", "sample6.db") dst = os.path.join("output", "sample6.db") if os.path.exists(dst): os.remove(dst) shutil.copy(src, dst) cls.dst_file = dst def testSmallSimulation(self): tree = CoalescenceTree(logging_level=50) tree.set_database(self.dst_file) with self.assertRaises(IOError): _ = tree.get_species_ages() with self.assertRaises(IOError): _ = tree.get_species_ages_pd() tree.wipe_data() with self.assertRaises(IOError): _ = tree.get_species_ages() with self.assertRaises(IOError): _ = tree.get_species_ages_pd() tree.set_speciation_parameters( speciation_rates=[0.000001, 0.0001], record_spatial=False, record_ages=True, ) tree.apply() self.assertTrue(check_sql_table_exist(tree.database, "SPECIES_AGES")) expected_df = pd.read_csv(os.path.join("sample", "expected_species_ages.csv")) actual_df = tree.get_species_ages_pd().reset_index(drop=True) assert_frame_equal(expected_df, actual_df) for community_ref, group in expected_df.groupby(["community_reference"]): actual_output = sorted(tree.get_species_ages(community_ref), key=lambda x: x[0]) expected_output = group.drop(columns=["community_reference"]).sort_values(by=["species_id"]).values.tolist() for ex, act in zip(expected_output, actual_output): self.assertEqual(ex[0], act[0]) self.assertAlmostEqual(ex[1], act[1], delta=0.0000001)

true

f71e7fc0b5d8bec62882115f024b707c4da34b3b

10,111

py

Python

xapp-image-base/swagger/swagger_client/models/detailed_gear.py

martinsallandm/hw-xapp-python-lenovo

2123289d3a5ea7122607dea8e8f0d03a348d131b

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

null

xapp-image-base/swagger/swagger_client/models/detailed_gear.py

martinsallandm/hw-xapp-python-lenovo

2123289d3a5ea7122607dea8e8f0d03a348d131b

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

null

xapp-image-base/swagger/swagger_client/models/detailed_gear.py

martinsallandm/hw-xapp-python-lenovo

2123289d3a5ea7122607dea8e8f0d03a348d131b

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

null

# coding: utf-8 """ Strava API v3 The [Swagger Playground](https://developers.strava.com/playground) is the easiest way to familiarize yourself with the Strava API by submitting HTTP requests and observing the responses before you write any client code. It will show what a response will look like with different endpoints depending on the authorization scope you receive from your athletes. To use the Playground, go to https://www.strava.com/settings/api and change your “Authorization Callback Domain” to developers.strava.com. Please note, we only support Swagger 2.0. There is a known issue where you can only select one scope at a time. For more information, please check the section “client code” at https://developers.strava.com/docs. # noqa: E501 OpenAPI spec version: 3.0.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ import pprint import re # noqa: F401 import six class DetailedGear(object): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ """ Attributes: swagger_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ swagger_types = { 'id': 'str', 'resource_state': 'int', 'primary': 'bool', 'name': 'str', 'distance': 'float', 'brand_name': 'str', 'model_name': 'str', 'frame_type': 'int', 'description': 'str' } attribute_map = { 'id': 'id', 'resource_state': 'resource_state', 'primary': 'primary', 'name': 'name', 'distance': 'distance', 'brand_name': 'brand_name', 'model_name': 'model_name', 'frame_type': 'frame_type', 'description': 'description' } def __init__(self, id=None, resource_state=None, primary=None, name=None, distance=None, brand_name=None, model_name=None, frame_type=None, description=None): # noqa: E501 """DetailedGear - a model defined in Swagger""" # noqa: E501 self._id = None self._resource_state = None self._primary = None self._name = None self._distance = None self._brand_name = None self._model_name = None self._frame_type = None self._description = None self.discriminator = None if id is not None: self.id = id if resource_state is not None: self.resource_state = resource_state if primary is not None: self.primary = primary if name is not None: self.name = name if distance is not None: self.distance = distance if brand_name is not None: self.brand_name = brand_name if model_name is not None: self.model_name = model_name if frame_type is not None: self.frame_type = frame_type if description is not None: self.description = description @property def id(self): """Gets the id of this DetailedGear. # noqa: E501 The gear's unique identifier. # noqa: E501 :return: The id of this DetailedGear. # noqa: E501 :rtype: str """ return self._id @id.setter def id(self, id): """Sets the id of this DetailedGear. The gear's unique identifier. # noqa: E501 :param id: The id of this DetailedGear. # noqa: E501 :type: str """ self._id = id @property def resource_state(self): """Gets the resource_state of this DetailedGear. # noqa: E501 Resource state, indicates level of detail. Possible values: 2 -> \"summary\", 3 -> \"detail\" # noqa: E501 :return: The resource_state of this DetailedGear. # noqa: E501 :rtype: int """ return self._resource_state @resource_state.setter def resource_state(self, resource_state): """Sets the resource_state of this DetailedGear. Resource state, indicates level of detail. Possible values: 2 -> \"summary\", 3 -> \"detail\" # noqa: E501 :param resource_state: The resource_state of this DetailedGear. # noqa: E501 :type: int """ self._resource_state = resource_state @property def primary(self): """Gets the primary of this DetailedGear. # noqa: E501 Whether this gear's is the owner's default one. # noqa: E501 :return: The primary of this DetailedGear. # noqa: E501 :rtype: bool """ return self._primary @primary.setter def primary(self, primary): """Sets the primary of this DetailedGear. Whether this gear's is the owner's default one. # noqa: E501 :param primary: The primary of this DetailedGear. # noqa: E501 :type: bool """ self._primary = primary @property def name(self): """Gets the name of this DetailedGear. # noqa: E501 The gear's name. # noqa: E501 :return: The name of this DetailedGear. # noqa: E501 :rtype: str """ return self._name @name.setter def name(self, name): """Sets the name of this DetailedGear. The gear's name. # noqa: E501 :param name: The name of this DetailedGear. # noqa: E501 :type: str """ self._name = name @property def distance(self): """Gets the distance of this DetailedGear. # noqa: E501 The distance logged with this gear. # noqa: E501 :return: The distance of this DetailedGear. # noqa: E501 :rtype: float """ return self._distance @distance.setter def distance(self, distance): """Sets the distance of this DetailedGear. The distance logged with this gear. # noqa: E501 :param distance: The distance of this DetailedGear. # noqa: E501 :type: float """ self._distance = distance @property def brand_name(self): """Gets the brand_name of this DetailedGear. # noqa: E501 The gear's brand name. # noqa: E501 :return: The brand_name of this DetailedGear. # noqa: E501 :rtype: str """ return self._brand_name @brand_name.setter def brand_name(self, brand_name): """Sets the brand_name of this DetailedGear. The gear's brand name. # noqa: E501 :param brand_name: The brand_name of this DetailedGear. # noqa: E501 :type: str """ self._brand_name = brand_name @property def model_name(self): """Gets the model_name of this DetailedGear. # noqa: E501 The gear's model name. # noqa: E501 :return: The model_name of this DetailedGear. # noqa: E501 :rtype: str """ return self._model_name @model_name.setter def model_name(self, model_name): """Sets the model_name of this DetailedGear. The gear's model name. # noqa: E501 :param model_name: The model_name of this DetailedGear. # noqa: E501 :type: str """ self._model_name = model_name @property def frame_type(self): """Gets the frame_type of this DetailedGear. # noqa: E501 The gear's frame type (bike only). # noqa: E501 :return: The frame_type of this DetailedGear. # noqa: E501 :rtype: int """ return self._frame_type @frame_type.setter def frame_type(self, frame_type): """Sets the frame_type of this DetailedGear. The gear's frame type (bike only). # noqa: E501 :param frame_type: The frame_type of this DetailedGear. # noqa: E501 :type: int """ self._frame_type = frame_type @property def description(self): """Gets the description of this DetailedGear. # noqa: E501 The gear's description. # noqa: E501 :return: The description of this DetailedGear. # noqa: E501 :rtype: str """ return self._description @description.setter def description(self, description): """Sets the description of this DetailedGear. The gear's description. # noqa: E501 :param description: The description of this DetailedGear. # noqa: E501 :type: str """ self._description = description def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(DetailedGear, dict): for key, value in self.items(): result[key] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, DetailedGear): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

29.564327

726

0.588666

import pprint import re import six class DetailedGear(object): swagger_types = { 'id': 'str', 'resource_state': 'int', 'primary': 'bool', 'name': 'str', 'distance': 'float', 'brand_name': 'str', 'model_name': 'str', 'frame_type': 'int', 'description': 'str' } attribute_map = { 'id': 'id', 'resource_state': 'resource_state', 'primary': 'primary', 'name': 'name', 'distance': 'distance', 'brand_name': 'brand_name', 'model_name': 'model_name', 'frame_type': 'frame_type', 'description': 'description' } def __init__(self, id=None, resource_state=None, primary=None, name=None, distance=None, brand_name=None, model_name=None, frame_type=None, description=None): self._id = None self._resource_state = None self._primary = None self._name = None self._distance = None self._brand_name = None self._model_name = None self._frame_type = None self._description = None self.discriminator = None if id is not None: self.id = id if resource_state is not None: self.resource_state = resource_state if primary is not None: self.primary = primary if name is not None: self.name = name if distance is not None: self.distance = distance if brand_name is not None: self.brand_name = brand_name if model_name is not None: self.model_name = model_name if frame_type is not None: self.frame_type = frame_type if description is not None: self.description = description @property def id(self): return self._id @id.setter def id(self, id): self._id = id @property def resource_state(self): return self._resource_state @resource_state.setter def resource_state(self, resource_state): self._resource_state = resource_state @property def primary(self): return self._primary @primary.setter def primary(self, primary): self._primary = primary @property def name(self): return self._name @name.setter def name(self, name): self._name = name @property def distance(self): return self._distance @distance.setter def distance(self, distance): self._distance = distance @property def brand_name(self): return self._brand_name @brand_name.setter def brand_name(self, brand_name): self._brand_name = brand_name @property def model_name(self): return self._model_name @model_name.setter def model_name(self, model_name): self._model_name = model_name @property def frame_type(self): return self._frame_type @frame_type.setter def frame_type(self, frame_type): self._frame_type = frame_type @property def description(self): return self._description @description.setter def description(self, description): self._description = description def to_dict(self): result = {} for attr, _ in six.iteritems(self.swagger_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value if issubclass(DetailedGear, dict): for key, value in self.items(): result[key] = value return result def to_str(self): return pprint.pformat(self.to_dict()) def __repr__(self): return self.to_str() def __eq__(self, other): if not isinstance(other, DetailedGear): return False return self.__dict__ == other.__dict__ def __ne__(self, other): return not self == other

true

f71e805336c27b6f2b7f452d5fbab1f6282e8202

2,315

py

Python

aliyun-python-sdk-bssopenapi/aliyunsdkbssopenapi/request/v20171214/QueryResourcePackageInstancesRequest.py

liumihust/aliyun-openapi-python-sdk

c7b5dd4befae4b9c59181654289f9272531207ef

[ "Apache-2.0" ]

null

aliyun-python-sdk-bssopenapi/aliyunsdkbssopenapi/request/v20171214/QueryResourcePackageInstancesRequest.py

liumihust/aliyun-openapi-python-sdk

c7b5dd4befae4b9c59181654289f9272531207ef

[ "Apache-2.0" ]

null

aliyun-python-sdk-bssopenapi/aliyunsdkbssopenapi/request/v20171214/QueryResourcePackageInstancesRequest.py

liumihust/aliyun-openapi-python-sdk

c7b5dd4befae4b9c59181654289f9272531207ef

[ "Apache-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # # http://www.apache.org/licenses/LICENSE-2.0 # # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from aliyunsdkcore.request import RpcRequest from aliyunsdkbssopenapi.endpoint import endpoint_data class QueryResourcePackageInstancesRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'BssOpenApi', '2017-12-14', 'QueryResourcePackageInstances') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_ExpiryTimeEnd(self): return self.get_query_params().get('ExpiryTimeEnd') def set_ExpiryTimeEnd(self,ExpiryTimeEnd): self.add_query_param('ExpiryTimeEnd',ExpiryTimeEnd) def get_ProductCode(self): return self.get_query_params().get('ProductCode') def set_ProductCode(self,ProductCode): self.add_query_param('ProductCode',ProductCode) def get_OwnerId(self): return self.get_query_params().get('OwnerId') def set_OwnerId(self,OwnerId): self.add_query_param('OwnerId',OwnerId) def get_ExpiryTimeStart(self): return self.get_query_params().get('ExpiryTimeStart') def set_ExpiryTimeStart(self,ExpiryTimeStart): self.add_query_param('ExpiryTimeStart',ExpiryTimeStart) def get_PageNum(self): return self.get_query_params().get('PageNum') def set_PageNum(self,PageNum): self.add_query_param('PageNum',PageNum) def get_PageSize(self): return self.get_query_params().get('PageSize') def set_PageSize(self,PageSize): self.add_query_param('PageSize',PageSize)

34.552239

88

0.770194

from aliyunsdkcore.request import RpcRequest from aliyunsdkbssopenapi.endpoint import endpoint_data class QueryResourcePackageInstancesRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'BssOpenApi', '2017-12-14', 'QueryResourcePackageInstances') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_ExpiryTimeEnd(self): return self.get_query_params().get('ExpiryTimeEnd') def set_ExpiryTimeEnd(self,ExpiryTimeEnd): self.add_query_param('ExpiryTimeEnd',ExpiryTimeEnd) def get_ProductCode(self): return self.get_query_params().get('ProductCode') def set_ProductCode(self,ProductCode): self.add_query_param('ProductCode',ProductCode) def get_OwnerId(self): return self.get_query_params().get('OwnerId') def set_OwnerId(self,OwnerId): self.add_query_param('OwnerId',OwnerId) def get_ExpiryTimeStart(self): return self.get_query_params().get('ExpiryTimeStart') def set_ExpiryTimeStart(self,ExpiryTimeStart): self.add_query_param('ExpiryTimeStart',ExpiryTimeStart) def get_PageNum(self): return self.get_query_params().get('PageNum') def set_PageNum(self,PageNum): self.add_query_param('PageNum',PageNum) def get_PageSize(self): return self.get_query_params().get('PageSize') def set_PageSize(self,PageSize): self.add_query_param('PageSize',PageSize)

true

f71e80a48abdef12de18705d859ad1449bdec6da

7,884

py

Python

xAIbenchmark.py

cmougan/OODBenchmark

e5d7b9540840afe64f6a00139cbc41a44ed01a80

[ "MIT" ]

null

xAIbenchmark.py

cmougan/OODBenchmark

e5d7b9540840afe64f6a00139cbc41a44ed01a80

[ "MIT" ]

null

xAIbenchmark.py

cmougan/OODBenchmark

e5d7b9540840afe64f6a00139cbc41a44ed01a80

[ "MIT" ]

null

# %% from pmlb import fetch_data from sklearn.preprocessing import StandardScaler from sklearn.model_selection import cross_val_predict, KFold from sklearn.metrics import mean_squared_error, roc_auc_score from tqdm import tqdm import pandas as pd import numpy as np from collections import defaultdict import warnings import re import traceback from pmlb import classification_dataset_names, regression_dataset_names from benchmark import benchmark_experiment from sklearn.linear_model import Lasso, LinearRegression from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.tree import DecisionTreeRegressor import warnings from fairtools.xaiUtils import ShapEstimator import xgboost warnings.filterwarnings("ignore") # %% def benchmark_experiment(datasets: list, model, classification: str = "classification"): assert classification in [ "classification", "regression", "explainableAI", ], "Classification type introduced --{}-- does not match: classification,regression,explainableAI".format( classification ) if classification == "classification": extension = "_clas" elif classification == "regression": extension = "_reg" elif classification == "explainableAI": extension = "_explain" else: raise "Classification type not contained" results = defaultdict() for i, dataset in enumerate(datasets): try: # Initialise the scaler standard_scaler = StandardScaler() # Load the dataset and split it X, y = fetch_data(dataset, return_X_y=True, local_cache_dir="data/") # Scale the dataset X = standard_scaler.fit_transform(X) if classification == False: y = standard_scaler.fit_transform(y.reshape(-1, 1)) # Back to dataframe X = pd.DataFrame(X, columns=["Var %d" % (i + 1) for i in range(X.shape[1])]) data = X.copy() data["target"] = y # Min and max data limits for the experiment if X.shape[0] < 100: continue if X.shape[0] > 100_000: continue # Train test splitting points fracc = 0.33 oneThird = int(data.shape[0] * fracc) twoThird = data.shape[0] - int(data.shape[0] * fracc) for idx, col in tqdm(enumerate(X.columns), total=len(X.columns)): # Sort data on the column data = data.sort_values(col).reset_index(drop=True).copy() # Train Test Split data_sub = data.iloc[:oneThird] data_train = data.iloc[oneThird:twoThird] data_up = data.iloc[twoThird:] X_tot = data.drop(columns="target") X_tr = data_train.drop(columns="target") X_sub = data_sub.drop(columns="target") X_up = data_up.drop(columns="target") y_tot = data[["target"]].target.values y_tr = data_train[["target"]].target.values y_sub = data_sub[["target"]].target.values y_up = data_up[["target"]].target.values # Error Calculation if classification == "classification": ## Test predictions pred_test = cross_val_predict( estimator=model, X=X_tr, y=y_tr, cv=KFold(n_splits=5, shuffle=True, random_state=0), method="predict_proba", )[:, 1] ## Train model.fit(X_tr, y_tr) pred_train = model.predict_proba(X_tr)[:, 1] ## OOD X_ood = X_sub.append(X_up) y_ood = np.concatenate((y_sub, y_up)) pred_ood = model.predict_proba(X_ood)[:, 1] train_error = roc_auc_score(y_tr, pred_train) test_error = roc_auc_score(y_tr, pred_test) ood_error = roc_auc_score(y_ood, pred_ood) generalizationError = test_error - train_error ood_performance = ood_error - test_error elif classification == "regression": ## Test predictions pred_test = cross_val_predict( estimator=model, X=X_tr, y=y_tr, cv=KFold(n_splits=5, shuffle=True, random_state=0), ) ## Train model.fit(X_tr, y_tr) pred_train = model.predict(X_tr) ## OOD X_ood = X_sub.append(X_up) y_ood = np.concatenate((y_sub, y_up)) pred_ood = model.predict(X_ood) train_error = mean_squared_error(pred_train, y_tr) test_error = mean_squared_error(pred_test, y_tr) ood_error = mean_squared_error(pred_ood, y_ood) generalizationError = test_error - train_error ood_performance = ood_error - test_error elif classification == "explainableAI": # Explainer predictor se = ShapEstimator(model=xgboost.XGBRegressor()) shap_pred_tr = cross_val_predict(se, X_tr, y_tr, cv=3) ## Test predictions pred_test = cross_val_predict( estimator=model, X=shap_pred_tr, y=y_tr, cv=KFold(n_splits=5, shuffle=True, random_state=0), ) ## Train se.fit(X_tr, y_tr) model.fit(shap_pred_tr, y_tr) pred_train = model.predict(shap_pred_tr) ## Generate OOD Shap data X_ood = X_sub.append(X_up) y_ood = np.concatenate((y_sub, y_up)) shap_pred_ood = se.predict(X_ood) ## OOD pred_ood = model.predict(shap_pred_ood) train_error = mean_squared_error(pred_train, y_tr) test_error = mean_squared_error(pred_test, y_tr) ood_error = mean_squared_error(pred_ood, y_ood) generalizationError = test_error - train_error ood_performance = ood_error - test_error # Append Results model_name = str(type(model)).split(".")[-1] model_name = re.sub("[^A-Za-z0-9]+", "", model_name) name = dataset + "_column_" + col results[name] = [ train_error, test_error, ood_error, generalizationError, ood_performance, model_name, ] except Exception: print(traceback.format_exc()) print("Not Working:", dataset) print("Dataset shape:", len(dataset)) pass df = pd.DataFrame(data=results).T df.columns = [ "trainError", "testError", "oodError", "generalizationError", "oodPerformance", "model", ] df.to_csv("results/" + model_name + extension + ".csv") # %% regression_dataset_names_sample = regression_dataset_names[:10] # %% modelitos = [ GradientBoostingRegressor(), ] for m in modelitos: benchmark_experiment( datasets=regression_dataset_names_sample, model=m, classification="explainableAI", ) # %%

35.513514

110

0.535769

from pmlb import fetch_data from sklearn.preprocessing import StandardScaler from sklearn.model_selection import cross_val_predict, KFold from sklearn.metrics import mean_squared_error, roc_auc_score from tqdm import tqdm import pandas as pd import numpy as np from collections import defaultdict import warnings import re import traceback from pmlb import classification_dataset_names, regression_dataset_names from benchmark import benchmark_experiment from sklearn.linear_model import Lasso, LinearRegression from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor from sklearn.tree import DecisionTreeRegressor import warnings from fairtools.xaiUtils import ShapEstimator import xgboost warnings.filterwarnings("ignore") def benchmark_experiment(datasets: list, model, classification: str = "classification"): assert classification in [ "classification", "regression", "explainableAI", ], "Classification type introduced --{}-- does not match: classification,regression,explainableAI".format( classification ) if classification == "classification": extension = "_clas" elif classification == "regression": extension = "_reg" elif classification == "explainableAI": extension = "_explain" else: raise "Classification type not contained" results = defaultdict() for i, dataset in enumerate(datasets): try: standard_scaler = StandardScaler() X, y = fetch_data(dataset, return_X_y=True, local_cache_dir="data/") X = standard_scaler.fit_transform(X) if classification == False: y = standard_scaler.fit_transform(y.reshape(-1, 1)) X = pd.DataFrame(X, columns=["Var %d" % (i + 1) for i in range(X.shape[1])]) data = X.copy() data["target"] = y if X.shape[0] < 100: continue if X.shape[0] > 100_000: continue fracc = 0.33 oneThird = int(data.shape[0] * fracc) twoThird = data.shape[0] - int(data.shape[0] * fracc) for idx, col in tqdm(enumerate(X.columns), total=len(X.columns)): data = data.sort_values(col).reset_index(drop=True).copy() data_sub = data.iloc[:oneThird] data_train = data.iloc[oneThird:twoThird] data_up = data.iloc[twoThird:] X_tot = data.drop(columns="target") X_tr = data_train.drop(columns="target") X_sub = data_sub.drop(columns="target") X_up = data_up.drop(columns="target") y_tot = data[["target"]].target.values y_tr = data_train[["target"]].target.values y_sub = data_sub[["target"]].target.values y_up = data_up[["target"]].target.values if classification == "classification": pred_test = cross_val_predict( estimator=model, X=X_tr, y=y_tr, cv=KFold(n_splits=5, shuffle=True, random_state=0), method="predict_proba", )[:, 1] model.fit(X_tr, y_tr) pred_train = model.predict_proba(X_tr)[:, 1] X_ood = X_sub.append(X_up) y_ood = np.concatenate((y_sub, y_up)) pred_ood = model.predict_proba(X_ood)[:, 1] train_error = roc_auc_score(y_tr, pred_train) test_error = roc_auc_score(y_tr, pred_test) ood_error = roc_auc_score(y_ood, pred_ood) generalizationError = test_error - train_error ood_performance = ood_error - test_error elif classification == "regression": pred_test = cross_val_predict( estimator=model, X=X_tr, y=y_tr, cv=KFold(n_splits=5, shuffle=True, random_state=0), ) model.fit(X_tr, y_tr) pred_train = model.predict(X_tr) X_ood = X_sub.append(X_up) y_ood = np.concatenate((y_sub, y_up)) pred_ood = model.predict(X_ood) train_error = mean_squared_error(pred_train, y_tr) test_error = mean_squared_error(pred_test, y_tr) ood_error = mean_squared_error(pred_ood, y_ood) generalizationError = test_error - train_error ood_performance = ood_error - test_error elif classification == "explainableAI": se = ShapEstimator(model=xgboost.XGBRegressor()) shap_pred_tr = cross_val_predict(se, X_tr, y_tr, cv=3) pred_test = cross_val_predict( estimator=model, X=shap_pred_tr, y=y_tr, cv=KFold(n_splits=5, shuffle=True, random_state=0), ) se.fit(X_tr, y_tr) model.fit(shap_pred_tr, y_tr) pred_train = model.predict(shap_pred_tr) od = X_sub.append(X_up) y_ood = np.concatenate((y_sub, y_up)) shap_pred_ood = se.predict(X_ood) pred_ood = model.predict(shap_pred_ood) train_error = mean_squared_error(pred_train, y_tr) test_error = mean_squared_error(pred_test, y_tr) ood_error = mean_squared_error(pred_ood, y_ood) generalizationError = test_error - train_error ood_performance = ood_error - test_error model_name = str(type(model)).split(".")[-1] model_name = re.sub("[^A-Za-z0-9]+", "", model_name) name = dataset + "_column_" + col results[name] = [ train_error, test_error, ood_error, generalizationError, ood_performance, model_name, ] except Exception: print(traceback.format_exc()) print("Not Working:", dataset) print("Dataset shape:", len(dataset)) pass df = pd.DataFrame(data=results).T df.columns = [ "trainError", "testError", "oodError", "generalizationError", "oodPerformance", "model", ] df.to_csv("results/" + model_name + extension + ".csv") regression_dataset_names_sample = regression_dataset_names[:10] modelitos = [ GradientBoostingRegressor(), ] for m in modelitos: benchmark_experiment( datasets=regression_dataset_names_sample, model=m, classification="explainableAI", )

true

f71e80df92838cc46c6391213b7e32330e5f7bba

2,216

py

Python

count_calls/count_calls.py

chimicus/addons

0fa1110df999fc9a8622a12e00453fc67b62fce1

[ "BSD-3-Clause" ]

null

count_calls/count_calls.py

chimicus/addons

0fa1110df999fc9a8622a12e00453fc67b62fce1

[ "BSD-3-Clause" ]

6

2019-08-23T15:53:05.000Z

2021-07-14T08:24:06.000Z

count_calls/count_calls.py

chimicus/addons

0fa1110df999fc9a8622a12e00453fc67b62fce1

[ "BSD-3-Clause" ]

3

2019-11-04T12:02:11.000Z

2020-03-05T13:57:11.000Z

#! /usr/bin/env udb-automate import sys import textwrap from undodb.udb_launcher import ( REDIRECTION_COLLECT, UdbLauncher, ) def main(argv): # Get the arguments from the command line. try: recording, func_name = argv[1:] except ValueError: # Wrong number of arguments. print("{} RECORDING_FILE FUNCTION_NAME".format(sys.argv[0])) raise SystemExit(1) # Prepare for launching UDB. launcher = UdbLauncher() # Make UDB run with our recording. launcher.recording_file = recording # Make UDB load the count_calls_extension.py file from the current # directory. launcher.add_extension("count_calls_extension") # Tell the extension which function name it needs to check. # The run_data attribute is a dictionary in which arbitrary data can be # stored and passed to the extension (as long as it can be serialised using # the Python pickle module). launcher.run_data["func_name"] = func_name # Finally, launch UDB! # We collect the output as, in normal conditions, we don't want to show it # to the user but, in case of errors, we want to display it. res = launcher.run_debugger(redirect_debugger_output=REDIRECTION_COLLECT) if res.exit_code == 0: # All good as UDB exited with exit code 0 (i.e. no errors). print( 'The recording hit "{}" {} time(s).'.format( func_name, # The result_data attribute is analogous to UdbLauncher.run_data but # it's used to pass information the opposite way, from the extension # to this script. res.result_data["hit-count"], ) ) else: # Something went wrong! Print a useful message. print( textwrap.dedent( """\ Error! UDB exited with code {res.exit_code}. The output was: {res.output} """ ).format(res=res), file=sys.stderr, ) # Exit this script with the same error code as UDB. raise SystemExit(res.exit_code) if __name__ == "__main__": main(sys.argv)

31.657143

84

0.610108

import sys import textwrap from undodb.udb_launcher import ( REDIRECTION_COLLECT, UdbLauncher, ) def main(argv): try: recording, func_name = argv[1:] except ValueError: print("{} RECORDING_FILE FUNCTION_NAME".format(sys.argv[0])) raise SystemExit(1) launcher = UdbLauncher() launcher.recording_file = recording launcher.add_extension("count_calls_extension") launcher.run_data["func_name"] = func_name # to the user but, in case of errors, we want to display it. res = launcher.run_debugger(redirect_debugger_output=REDIRECTION_COLLECT) if res.exit_code == 0: # All good as UDB exited with exit code 0 (i.e. no errors). print( 'The recording hit "{}" {} time(s).'.format( func_name, # The result_data attribute is analogous to UdbLauncher.run_data but # it's used to pass information the opposite way, from the extension res.result_data["hit-count"], ) ) else: print( textwrap.dedent( """\ Error! UDB exited with code {res.exit_code}. The output was: {res.output} """ ).format(res=res), file=sys.stderr, ) raise SystemExit(res.exit_code) if __name__ == "__main__": main(sys.argv)

true

f71e8181713f3cb6ee3858ac80a6422e514e559e

17,192

py

Python

obswebsocket/events.py

SirCleric/obs-websocket-py

f04104ab1db7f9164d0d9fe9842232450fc72048

[ "MIT" ]

null

obswebsocket/events.py

SirCleric/obs-websocket-py

f04104ab1db7f9164d0d9fe9842232450fc72048

[ "MIT" ]

null

obswebsocket/events.py

SirCleric/obs-websocket-py

f04104ab1db7f9164d0d9fe9842232450fc72048

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- ### THIS FILE WAS GENERATED BY generate_classes.py - DO NOT EDIT ### ### (Generated on 2018-11-01 23:48:48.307368) ### from . import base_classes class SourceOrderChanged(base_classes.Baseevents): """Scene items have been reordered. :Returns: *name* type: String Name of the scene where items have been reordered. *sources* type: Array Array of sources. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SourceOrderChanged" self.datain["name"] = None self.datain["sources"] = None def getName(self): return self.datain["name"] def getSources(self): return self.datain["sources"] class SceneItemTransformChanged(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SceneItemTransformChanged" self.datain["scene-name"] = None self.datain["item-name"] = None self.datain["item-id"] = None self.datain["transform"] = None def getSceneName(self): return self.datain["scene-name"] def getItemName(self): return self.datain["item-name"] def getItemId(self): return self.datain["item-id"] def getItemTransform(self): return self.datain["transform"] class SceneItemAdded(base_classes.Baseevents): """An item has been added to the current scene. :Returns: *scene_name* type: String Name of the scene. *item_name* type: String Name of the item added to the scene. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SceneItemAdded" self.datain["scene-name"] = None self.datain["item-name"] = None def getSceneName(self): return self.datain["scene-name"] def getItemName(self): return self.datain["item-name"] class SceneItemRemoved(base_classes.Baseevents): """An item has been removed from the current scene. :Returns: *scene_name* type: String Name of the scene. *item_name* type: String Name of the item removed from the scene. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SceneItemRemoved" self.datain["scene-name"] = None self.datain["item-name"] = None def getSceneName(self): return self.datain["scene-name"] def getItemName(self): return self.datain["item-name"] class SceneItemVisibilityChanged(base_classes.Baseevents): """An item's visibility has been toggled. :Returns: *scene_name* type: String Name of the scene. *item_name* type: String Name of the item in the scene. *item_visible* type: boolean New visibility state of the item. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SceneItemVisibilityChanged" self.datain["scene-name"] = None self.datain["item-name"] = None self.datain["item-visible"] = None def getSceneName(self): return self.datain["scene-name"] def getItemName(self): return self.datain["item-name"] def getItemVisible(self): return self.datain["item-visible"] class PreviewSceneChanged(base_classes.Baseevents): """The selected preview scene has changed (only available in Studio Mode). :Returns: *scene_name* type: String Name of the scene being previewed. *sources* type: Source|Array List of sources composing the scene. Same specification as [`GetCurrentScene`](#getcurrentscene). """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "PreviewSceneChanged" self.datain["scene-name"] = None self.datain["sources"] = None def getSceneName(self): return self.datain["scene-name"] def getSources(self): return self.datain["sources"] class StudioModeSwitched(base_classes.Baseevents): """Studio Mode has been enabled or disabled. :Returns: *new_state* type: boolean The new enabled state of Studio Mode. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "StudioModeSwitched" self.datain["new-state"] = None def getNewState(self): return self.datain["new-state"] class ReplayStarting(base_classes.Baseevents): """A request to start the replay buffer has been issued. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ReplayStarting" class ReplayStarted(base_classes.Baseevents): """Replay Buffer started successfully """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ReplayStarted" class ReplayStopping(base_classes.Baseevents): """A request to stop the replay buffer has been issued. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ReplayStopping" class ReplayStopped(base_classes.Baseevents): """Replay Buffer stopped successfully """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ReplayStopped" class SwitchScenes(base_classes.Baseevents): """Indicates a scene change. :Returns: *scene_name* type: String The new scene. *sources* type: Array List of sources in the new scene. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SwitchScenes" self.datain["scene-name"] = None self.datain["sources"] = None def getSceneName(self): return self.datain["scene-name"] def getSources(self): return self.datain["sources"] class ScenesChanged(base_classes.Baseevents): """The scene list has been modified. Scenes have been added, removed, or renamed. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ScenesChanged" class SceneCollectionChanged(base_classes.Baseevents): """Triggered when switching to another scene collection or when renaming the current scene collection. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SceneCollectionChanged" class SceneCollectionListChanged(base_classes.Baseevents): """Triggered when a scene collection is created, added, renamed, or removed. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SceneCollectionListChanged" class ProfileChanged(base_classes.Baseevents): """Triggered when switching to another profile or when renaming the current profile. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ProfileChanged" class ProfileListChanged(base_classes.Baseevents): """Triggered when a profile is created, added, renamed, or removed. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ProfileListChanged" class Heartbeat(base_classes.Baseevents): """Emitted every 2 seconds after enabling it by calling SetHeartbeat. :Returns: *pulse* type: boolean Toggles between every JSON message as an "I am alive" indicator. *current_profile* type: string (optional) Current active profile. *current_scene* type: string (optional) Current active scene. *streaming* type: boolean (optional) Current streaming state. *total_stream_time* type: int (optional) Total time (in seconds) since the stream started. *total_stream_bytes* type: int (optional) Total bytes sent since the stream started. *total_stream_frames* type: int (optional) Total frames streamed since the stream started. *recording* type: boolean (optional) Current recording state. *total_record_time* type: int (optional) Total time (in seconds) since recording started. *total_record_bytes* type: int (optional) Total bytes recorded since the recording started. *total_record_frames* type: int (optional) Total frames recorded since the recording started. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "Heartbeat" self.datain["pulse"] = None self.datain["current-profile"] = None self.datain["current-scene"] = None self.datain["streaming"] = None self.datain["total-stream-time"] = None self.datain["total-stream-bytes"] = None self.datain["total-stream-frames"] = None self.datain["recording"] = None self.datain["total-record-time"] = None self.datain["total-record-bytes"] = None self.datain["total-record-frames"] = None def getPulse(self): return self.datain["pulse"] def getCurrentProfile(self): return self.datain["current-profile"] def getCurrentScene(self): return self.datain["current-scene"] def getStreaming(self): return self.datain["streaming"] def getTotalStreamTime(self): return self.datain["total-stream-time"] def getTotalStreamBytes(self): return self.datain["total-stream-bytes"] def getTotalStreamFrames(self): return self.datain["total-stream-frames"] def getRecording(self): return self.datain["recording"] def getTotalRecordTime(self): return self.datain["total-record-time"] def getTotalRecordBytes(self): return self.datain["total-record-bytes"] def getTotalRecordFrames(self): return self.datain["total-record-frames"] class RecordingStarting(base_classes.Baseevents): """A request to start recording has been issued. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "RecordingStarting" class RecordingStarted(base_classes.Baseevents): """Recording started successfully. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "RecordingStarted" class RecordingStopping(base_classes.Baseevents): """A request to stop recording has been issued. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "RecordingStopping" class RecordingStopped(base_classes.Baseevents): """Recording stopped successfully. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "RecordingStopped" class StreamStarting(base_classes.Baseevents): """A request to start streaming has been issued. :Returns: *preview_only* type: boolean Always false (retrocompatibility). """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "StreamStarting" self.datain["preview-only"] = None def getPreviewOnly(self): return self.datain["preview-only"] class StreamStarted(base_classes.Baseevents): """Streaming started successfully. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "StreamStarted" class StreamStopping(base_classes.Baseevents): """A request to stop streaming has been issued. :Returns: *preview_only* type: boolean Always false (retrocompatibility). """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "StreamStopping" self.datain["preview-only"] = None def getPreviewOnly(self): return self.datain["preview-only"] class StreamStopped(base_classes.Baseevents): """Streaming stopped successfully. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "StreamStopped" class StreamStatus(base_classes.Baseevents): """Emit every 2 seconds. :Returns: *streaming* type: boolean Current streaming state. *recording* type: boolean Current recording state. *preview_only* type: boolean Always false (retrocompatibility). *bytes_per_sec* type: int Amount of data per second (in bytes) transmitted by the stream encoder. *kbits_per_sec* type: int Amount of data per second (in kilobits) transmitted by the stream encoder. *strain* type: double Percentage of dropped frames. *total_stream_time* type: int Total time (in seconds) since the stream started. *num_total_frames* type: int Total number of frames transmitted since the stream started. *num_dropped_frames* type: int Number of frames dropped by the encoder since the stream started. *fps* type: double Current framerate. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "StreamStatus" self.datain["streaming"] = None self.datain["recording"] = None self.datain["preview-only"] = None self.datain["bytes-per-sec"] = None self.datain["kbits-per-sec"] = None self.datain["strain"] = None self.datain["total-stream-time"] = None self.datain["num-total-frames"] = None self.datain["num-dropped-frames"] = None self.datain["fps"] = None def getStreaming(self): return self.datain["streaming"] def getRecording(self): return self.datain["recording"] def getPreviewOnly(self): return self.datain["preview-only"] def getBytesPerSec(self): return self.datain["bytes-per-sec"] def getKbitsPerSec(self): return self.datain["kbits-per-sec"] def getStrain(self): return self.datain["strain"] def getTotalStreamTime(self): return self.datain["total-stream-time"] def getNumTotalFrames(self): return self.datain["num-total-frames"] def getNumDroppedFrames(self): return self.datain["num-dropped-frames"] def getFps(self): return self.datain["fps"] class Exiting(base_classes.Baseevents): """OBS is exiting. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "Exiting" class SwitchTransition(base_classes.Baseevents): """The active transition has been changed. :Returns: *transition_name* type: String The name of the new active transition. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SwitchTransition" self.datain["transition-name"] = None def getTransitionName(self): return self.datain["transition-name"] class TransitionListChanged(base_classes.Baseevents): """The list of available transitions has been modified. Transitions have been added, removed, or renamed. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "TransitionListChanged" class TransitionDurationChanged(base_classes.Baseevents): """The active transition duration has been changed. :Returns: *new_duration* type: int New transition duration. """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "TransitionDurationChanged" self.datain["new-duration"] = None def getNewDuration(self): return self.datain["new-duration"] class TransitionBegin(base_classes.Baseevents): """A transition (other than "cut") has begun. :Returns: *name* type: String Transition name. *duration* type: int Transition duration (in milliseconds). *from_scene* type: String Source scene of the transition *to_scene* type: String Destination scene of the transition """ def __init__(self): base_classes.Baseevents.__init__(self) self.name = "TransitionBegin" self.datain["name"] = None self.datain["duration"] = None self.datain["from-scene"] = None self.datain["to-scene"] = None def getName(self): return self.datain["name"] def getDuration(self): return self.datain["duration"] def getFromScene(self): return self.datain["from-scene"] def getToScene(self): return self.datain["to-scene"]

27.463259

109

0.627966

f.datain["sources"] = None def getName(self): return self.datain["name"] def getSources(self): return self.datain["sources"] class SceneItemTransformChanged(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SceneItemTransformChanged" self.datain["scene-name"] = None self.datain["item-name"] = None self.datain["item-id"] = None self.datain["transform"] = None def getSceneName(self): return self.datain["scene-name"] def getItemName(self): return self.datain["item-name"] def getItemId(self): return self.datain["item-id"] def getItemTransform(self): return self.datain["transform"] class SceneItemAdded(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SceneItemAdded" self.datain["scene-name"] = None self.datain["item-name"] = None def getSceneName(self): return self.datain["scene-name"] def getItemName(self): return self.datain["item-name"] class SceneItemRemoved(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SceneItemRemoved" self.datain["scene-name"] = None self.datain["item-name"] = None def getSceneName(self): return self.datain["scene-name"] def getItemName(self): return self.datain["item-name"] class SceneItemVisibilityChanged(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SceneItemVisibilityChanged" self.datain["scene-name"] = None self.datain["item-name"] = None self.datain["item-visible"] = None def getSceneName(self): return self.datain["scene-name"] def getItemName(self): return self.datain["item-name"] def getItemVisible(self): return self.datain["item-visible"] class PreviewSceneChanged(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "PreviewSceneChanged" self.datain["scene-name"] = None self.datain["sources"] = None def getSceneName(self): return self.datain["scene-name"] def getSources(self): return self.datain["sources"] class StudioModeSwitched(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "StudioModeSwitched" self.datain["new-state"] = None def getNewState(self): return self.datain["new-state"] class ReplayStarting(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ReplayStarting" class ReplayStarted(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ReplayStarted" class ReplayStopping(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ReplayStopping" class ReplayStopped(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ReplayStopped" class SwitchScenes(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SwitchScenes" self.datain["scene-name"] = None self.datain["sources"] = None def getSceneName(self): return self.datain["scene-name"] def getSources(self): return self.datain["sources"] class ScenesChanged(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ScenesChanged" class SceneCollectionChanged(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SceneCollectionChanged" class SceneCollectionListChanged(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SceneCollectionListChanged" class ProfileChanged(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ProfileChanged" class ProfileListChanged(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "ProfileListChanged" class Heartbeat(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "Heartbeat" self.datain["pulse"] = None self.datain["current-profile"] = None self.datain["current-scene"] = None self.datain["streaming"] = None self.datain["total-stream-time"] = None self.datain["total-stream-bytes"] = None self.datain["total-stream-frames"] = None self.datain["recording"] = None self.datain["total-record-time"] = None self.datain["total-record-bytes"] = None self.datain["total-record-frames"] = None def getPulse(self): return self.datain["pulse"] def getCurrentProfile(self): return self.datain["current-profile"] def getCurrentScene(self): return self.datain["current-scene"] def getStreaming(self): return self.datain["streaming"] def getTotalStreamTime(self): return self.datain["total-stream-time"] def getTotalStreamBytes(self): return self.datain["total-stream-bytes"] def getTotalStreamFrames(self): return self.datain["total-stream-frames"] def getRecording(self): return self.datain["recording"] def getTotalRecordTime(self): return self.datain["total-record-time"] def getTotalRecordBytes(self): return self.datain["total-record-bytes"] def getTotalRecordFrames(self): return self.datain["total-record-frames"] class RecordingStarting(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "RecordingStarting" class RecordingStarted(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "RecordingStarted" class RecordingStopping(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "RecordingStopping" class RecordingStopped(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "RecordingStopped" class StreamStarting(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "StreamStarting" self.datain["preview-only"] = None def getPreviewOnly(self): return self.datain["preview-only"] class StreamStarted(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "StreamStarted" class StreamStopping(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "StreamStopping" self.datain["preview-only"] = None def getPreviewOnly(self): return self.datain["preview-only"] class StreamStopped(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "StreamStopped" class StreamStatus(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "StreamStatus" self.datain["streaming"] = None self.datain["recording"] = None self.datain["preview-only"] = None self.datain["bytes-per-sec"] = None self.datain["kbits-per-sec"] = None self.datain["strain"] = None self.datain["total-stream-time"] = None self.datain["num-total-frames"] = None self.datain["num-dropped-frames"] = None self.datain["fps"] = None def getStreaming(self): return self.datain["streaming"] def getRecording(self): return self.datain["recording"] def getPreviewOnly(self): return self.datain["preview-only"] def getBytesPerSec(self): return self.datain["bytes-per-sec"] def getKbitsPerSec(self): return self.datain["kbits-per-sec"] def getStrain(self): return self.datain["strain"] def getTotalStreamTime(self): return self.datain["total-stream-time"] def getNumTotalFrames(self): return self.datain["num-total-frames"] def getNumDroppedFrames(self): return self.datain["num-dropped-frames"] def getFps(self): return self.datain["fps"] class Exiting(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "Exiting" class SwitchTransition(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "SwitchTransition" self.datain["transition-name"] = None def getTransitionName(self): return self.datain["transition-name"] class TransitionListChanged(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "TransitionListChanged" class TransitionDurationChanged(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "TransitionDurationChanged" self.datain["new-duration"] = None def getNewDuration(self): return self.datain["new-duration"] class TransitionBegin(base_classes.Baseevents): def __init__(self): base_classes.Baseevents.__init__(self) self.name = "TransitionBegin" self.datain["name"] = None self.datain["duration"] = None self.datain["from-scene"] = None self.datain["to-scene"] = None def getName(self): return self.datain["name"] def getDuration(self): return self.datain["duration"] def getFromScene(self): return self.datain["from-scene"] def getToScene(self): return self.datain["to-scene"]

true

f71e81879e0d08f2be5ab349bd6798f8f04829fe

346

py

Python

AllRoutesLeadToRome/app.py

kkhan01/softdev

60c94919e8a5aba3db3d91849878057b8426cb4c

[ "MIT" ]

1

2020-05-02T01:41:06.000Z

AllRoutesLeadToRome/app.py

kkhan01/softdev

60c94919e8a5aba3db3d91849878057b8426cb4c

[ "MIT" ]

null

AllRoutesLeadToRome/app.py

kkhan01/softdev

60c94919e8a5aba3db3d91849878057b8426cb4c

[ "MIT" ]

null

from flask import Flask app = Flask(__name__) @app.route('/') def display00(): return 'Heya! This is the first page! Others are at: /01 /02' @app.route('/01') def display01(): return 'And now: The second page!' @app.route('/02') def display02(): return 'Woah! The last page!' if __name__ == '__main__': app.run()

18.210526

77

0.627168

from flask import Flask app = Flask(__name__) @app.route('/') def display00(): return 'Heya! This is the first page! Others are at: /01 /02' @app.route('/01') def display01(): return 'And now: The second page!' @app.route('/02') def display02(): return 'Woah! The last page!' if __name__ == '__main__': app.run()

true

f71e822e91707a7d824f5756df17632036e10f8a

2,936

py

Python

Browser/keywords/promises.py

emanlove/robotframework-browser

8d9dae4301fe263bc0f7682de58a6bf299211382

[ "Apache-2.0" ]

null

Browser/keywords/promises.py

emanlove/robotframework-browser

8d9dae4301fe263bc0f7682de58a6bf299211382

[ "Apache-2.0" ]

null

Browser/keywords/promises.py

emanlove/robotframework-browser

8d9dae4301fe263bc0f7682de58a6bf299211382

[ "Apache-2.0" ]

null

# Copyright 2020- Robot Framework Foundation # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from concurrent.futures import Future, ThreadPoolExecutor from robot.api.deco import keyword # type: ignore from robot.libraries.BuiltIn import EXECUTION_CONTEXTS # type: ignore from ..base import LibraryComponent class Promises(LibraryComponent): def __init__(self, library): LibraryComponent.__init__(self, library) self._executor = ThreadPoolExecutor(max_workers=256) @keyword(tags=["Wait"]) def promise_to(self, kw: str, *args): """ Wrap a Browser library keyword and make it a promise. Returns that promise and executes the keyword on background. ``kw`` <str> Keyword that will work async on background. Example: | ${promise}= Promise To Wait For Response matcher= timeout=3 | Click \\#delayed_request | ${body}= Wait For ${promise} """ browser_lib = EXECUTION_CONTEXTS.current.namespace._kw_store.get_library( self.library ) handler = browser_lib.handlers[kw] positional, named = handler.resolve_arguments( args, EXECUTION_CONTEXTS.current.variables ) named = dict(named) promise = self._executor.submit(handler.current_handler(), *positional, **named) self.unresolved_promises.add(promise) return promise @keyword(tags=["Wait"]) def wait_for(self, *promises: Future): """ Waits for promises to finish and returns results from them. Returns one result if one promise waited. Otherwise returns an array of results. If one fails, then this keyword will fail. ``promises`` *Work in progress* Example: | ${promise}= Promise To Wait For Response matcher= timeout=3 | Click \\#delayed_request | ${body}= Wait For ${promise} """ self.unresolved_promises -= {*promises} if len(promises) == 1: return promises[0].result() return [promise.result() for promise in promises] @keyword(tags=["Wait"]) def wait_for_all_promises(self): """ Waits for all promises to finish. If one fails, then this keyword will fail. """ self.wait_for(*self.unresolved_promises)

36.7

92

0.641008

from concurrent.futures import Future, ThreadPoolExecutor from robot.api.deco import keyword from robot.libraries.BuiltIn import EXECUTION_CONTEXTS from ..base import LibraryComponent class Promises(LibraryComponent): def __init__(self, library): LibraryComponent.__init__(self, library) self._executor = ThreadPoolExecutor(max_workers=256) @keyword(tags=["Wait"]) def promise_to(self, kw: str, *args): browser_lib = EXECUTION_CONTEXTS.current.namespace._kw_store.get_library( self.library ) handler = browser_lib.handlers[kw] positional, named = handler.resolve_arguments( args, EXECUTION_CONTEXTS.current.variables ) named = dict(named) promise = self._executor.submit(handler.current_handler(), *positional, **named) self.unresolved_promises.add(promise) return promise @keyword(tags=["Wait"]) def wait_for(self, *promises: Future): self.unresolved_promises -= {*promises} if len(promises) == 1: return promises[0].result() return [promise.result() for promise in promises] @keyword(tags=["Wait"]) def wait_for_all_promises(self): self.wait_for(*self.unresolved_promises)

true

f71e828d2972790e9832de08ed3694172916c5fe

6,556

py

Python

andres@programo.ual.es/bayesian_pca_DR.py

andresmasegosa/PRML-CoreSets

fb768debb15e3ff6f5b65b7224915a41c1493f3d

[ "MIT" ]

null

andres@programo.ual.es/bayesian_pca_DR.py

andresmasegosa/PRML-CoreSets

fb768debb15e3ff6f5b65b7224915a41c1493f3d

[ "MIT" ]

null

andres@programo.ual.es/bayesian_pca_DR.py

andresmasegosa/PRML-CoreSets

fb768debb15e3ff6f5b65b7224915a41c1493f3d

[ "MIT" ]

null

import numpy as np from prml.feature_extractions.pca import PCA from sklearn.cluster import KMeans, MiniBatchKMeans from sklearn.preprocessing import StandardScaler class BayesianPCA_DR(PCA): def _clusteringError(self, X, kmeans): sum = 0 for i in range(0, kmeans.cluster_centers_.shape[0]): a = X[kmeans.labels_ == i, :] - kmeans.cluster_centers_[i, :] sum += np.sqrt((a * a).sum(axis=1)).sum(axis=0) return sum def _random(self, X, n_clusters): centers_X = X[np.random.choice(X.shape[0], n_clusters, replace=False),:] centers_XX = centers_X**2 weights = np.repeat(X.shape[0]/n_clusters,n_clusters) self.X_dr = {'X': centers_X, 'XX': centers_XX, 'W': weights} def _clusterSS(self, X, n_clusters): XX = X ** 2 XJoin = np.concatenate((X, XX), axis=1) self.kmeans = MiniBatchKMeans(n_clusters=n_clusters).fit(XJoin) weights = np.asarray([sum(self.kmeans.labels_ == x) for x in range(0, n_clusters)]) D=X.shape[1] self.X_dr = {'X': self.kmeans.cluster_centers_[:, 0:D], 'XX': self.kmeans.cluster_centers_[:, D:2 * D], 'W': weights} self.clusterError = self._clusteringError(XJoin,self.kmeans) def _cluster(self, X, n_clusters): self.kmeans = MiniBatchKMeans(n_clusters=n_clusters).fit(X) weights = np.asarray([sum(self.kmeans.labels_ == x) for x in range(0, n_clusters)]) self.X_dr = {'X': self.kmeans.cluster_centers_, 'XX': self.kmeans.cluster_centers_ ** 2, 'W': weights} # def _clusterSS(self, X, n_clusters): # scaler = StandardScaler() # XX = X ** 2 # XJoin = np.concatenate((X, XX), axis=1) # self.kmeans = MiniBatchKMeans(n_clusters=n_clusters).fit(scaler.fit_transform(XJoin)) # weights = np.asarray([sum(self.kmeans.labels_ == x) for x in range(0, n_clusters)]) # D=X.shape[1] # self.kmeans.cluster_centers_=scaler.inverse_transform(self.kmeans.cluster_centers_) # self.X_dr = {'X': self.kmeans.cluster_centers_[:, 0:D], 'XX': self.kmeans.cluster_centers_[:, D:2 * D], 'W': weights} # # def _cluster(self, X, n_clusters): # scaler = StandardScaler() # self.kmeans = MiniBatchKMeans(n_clusters=n_clusters).fit(scaler.fit_transform(X)) # weights = np.asarray([sum(self.kmeans.labels_ == x) for x in range(0, n_clusters)]) # self.kmeans.cluster_centers_=scaler.inverse_transform(self.kmeans.cluster_centers_) # self.X_dr = {'X': self.kmeans.cluster_centers_, 'XX': self.kmeans.cluster_centers_ ** 2, 'W': weights} def eigen(self, X_dr, *arg): sample_size = np.sum(X_dr['W']) X = self.X_dr['W'][:,None]*self.X_dr['X'] n_features = X.shape[1] if sample_size >= n_features: cov = np.cov(X, rowvar=False) values, vectors = np.linalg.eigh(cov) index = n_features - self.n_components else: cov = np.cov(X) values, vectors = np.linalg.eigh(cov) vectors = (X.T @ vectors) / np.sqrt(sample_size * values) index = sample_size - self.n_components self.I = np.eye(self.n_components) if index == 0: self.var = 0 else: self.var = np.mean(values[:index]) self.W = vectors[:, index:].dot(np.sqrt(np.diag(values[index:]) - self.var * self.I)) self.__M = self.W.T @ self.W + self.var * self.I self.C = self.W @ self.W.T + self.var * np.eye(n_features) if index == 0: self.Cinv = np.linalg.inv(self.C) else: self.Cinv = np.eye(n_features) / np.sqrt(self.var) - self.W @ np.linalg.inv(self.__M) @ self.W.T / self.var def fit(self, X, iter_max=100, initial="random", n_clusters=10, cluster_method="SS"): """ empirical bayes estimation of pca parameters Parameters ---------- X : (sample_size, n_features) ndarray input data iter_max : int maximum number of em steps Returns ------- mean : (n_features,) ndarray sample mean fo the input data W : (n_features, n_components) ndarray projection matrix var : float variance of observation noise """ if cluster_method== "SS": self._clusterSS(X,n_clusters) elif cluster_method== "NoSS": self._cluster(X,n_clusters) elif cluster_method == "random": self._random(X,n_clusters) initial_list = ["random", "eigen"] self.mean = np.sum(self.X_dr['W'][:,None]*self.X_dr['X'], axis=0)/sum(self.X_dr['W']) self.I = np.eye(self.n_components) if initial not in initial_list: print("availabel initializations are {}".format(initial_list)) if initial == "random": self.W = np.eye(np.size(self.X_dr['X'], 1), self.n_components) self.var = 1. elif initial == "eigen": self.eigen(self.X_dr) self.alpha = len(self.mean) / np.sum(self.W ** 2, axis=0).clip(min=1e-10) for i in range(iter_max): W = np.copy(self.W) Ez, Ezz = self._expectation(self.X_dr['X']-self.mean) self._maximization(self.X_dr, Ez, Ezz) #self.alpha = len(self.mean) / np.sum(self.W ** 2, axis=0).clip(min=1e-10) if np.allclose(W, self.W): break self.n_iter = i + 1 self.C = self.W @ self.W.T + self.var * np.eye(np.size(self.X_dr['X'], 1)) self.Cinv = np.linalg.inv(self.C) def _maximization(self, X_dr, Ez, Ezz): X_mean = (X_dr['X']-self.mean) self.W = (X_mean*X_dr['W'][:,None]).T @ Ez @ np.linalg.inv(np.sum(Ezz*X_dr['W'][:,None,None], axis=0) + self.var * np.diag(self.alpha)) self.var = np.sum( (np.mean((X_dr['XX'] - 2*X_dr['X']*self.mean + self.mean ** 2), axis=-1) #(np.mean((X_mean** 2), axis=-1) - 2 * np.mean(Ez @ self.W.T * X_mean, axis=-1) + np.trace((Ezz @ self.W.T @ self.W).T)/ len(self.mean))*X_dr['W'])/sum(X_dr['W']) self.var=max(self.var,0.000001) def maximize(self, D, Ez, Ezz): self.W = D.T.dot(Ez).dot(np.linalg.inv(np.sum(Ezz, axis=0) + self.var * np.diag(self.alpha))) self.var = np.mean( np.mean(D ** 2, axis=-1) - 2 * np.mean(Ez.dot(self.W.T) * D, axis=-1) + np.trace(Ezz.dot(self.W.T).dot(self.W).T) / self.ndim)

43.706667

143

0.575046

import numpy as np from prml.feature_extractions.pca import PCA from sklearn.cluster import KMeans, MiniBatchKMeans from sklearn.preprocessing import StandardScaler class BayesianPCA_DR(PCA): def _clusteringError(self, X, kmeans): sum = 0 for i in range(0, kmeans.cluster_centers_.shape[0]): a = X[kmeans.labels_ == i, :] - kmeans.cluster_centers_[i, :] sum += np.sqrt((a * a).sum(axis=1)).sum(axis=0) return sum def _random(self, X, n_clusters): centers_X = X[np.random.choice(X.shape[0], n_clusters, replace=False),:] centers_XX = centers_X**2 weights = np.repeat(X.shape[0]/n_clusters,n_clusters) self.X_dr = {'X': centers_X, 'XX': centers_XX, 'W': weights} def _clusterSS(self, X, n_clusters): XX = X ** 2 XJoin = np.concatenate((X, XX), axis=1) self.kmeans = MiniBatchKMeans(n_clusters=n_clusters).fit(XJoin) weights = np.asarray([sum(self.kmeans.labels_ == x) for x in range(0, n_clusters)]) D=X.shape[1] self.X_dr = {'X': self.kmeans.cluster_centers_[:, 0:D], 'XX': self.kmeans.cluster_centers_[:, D:2 * D], 'W': weights} self.clusterError = self._clusteringError(XJoin,self.kmeans) def _cluster(self, X, n_clusters): self.kmeans = MiniBatchKMeans(n_clusters=n_clusters).fit(X) weights = np.asarray([sum(self.kmeans.labels_ == x) for x in range(0, n_clusters)]) self.X_dr = {'X': self.kmeans.cluster_centers_, 'XX': self.kmeans.cluster_centers_ ** 2, 'W': weights} def eigen(self, X_dr, *arg): sample_size = np.sum(X_dr['W']) X = self.X_dr['W'][:,None]*self.X_dr['X'] n_features = X.shape[1] if sample_size >= n_features: cov = np.cov(X, rowvar=False) values, vectors = np.linalg.eigh(cov) index = n_features - self.n_components else: cov = np.cov(X) values, vectors = np.linalg.eigh(cov) vectors = (X.T @ vectors) / np.sqrt(sample_size * values) index = sample_size - self.n_components self.I = np.eye(self.n_components) if index == 0: self.var = 0 else: self.var = np.mean(values[:index]) self.W = vectors[:, index:].dot(np.sqrt(np.diag(values[index:]) - self.var * self.I)) self.__M = self.W.T @ self.W + self.var * self.I self.C = self.W @ self.W.T + self.var * np.eye(n_features) if index == 0: self.Cinv = np.linalg.inv(self.C) else: self.Cinv = np.eye(n_features) / np.sqrt(self.var) - self.W @ np.linalg.inv(self.__M) @ self.W.T / self.var def fit(self, X, iter_max=100, initial="random", n_clusters=10, cluster_method="SS"): if cluster_method== "SS": self._clusterSS(X,n_clusters) elif cluster_method== "NoSS": self._cluster(X,n_clusters) elif cluster_method == "random": self._random(X,n_clusters) initial_list = ["random", "eigen"] self.mean = np.sum(self.X_dr['W'][:,None]*self.X_dr['X'], axis=0)/sum(self.X_dr['W']) self.I = np.eye(self.n_components) if initial not in initial_list: print("availabel initializations are {}".format(initial_list)) if initial == "random": self.W = np.eye(np.size(self.X_dr['X'], 1), self.n_components) self.var = 1. elif initial == "eigen": self.eigen(self.X_dr) self.alpha = len(self.mean) / np.sum(self.W ** 2, axis=0).clip(min=1e-10) for i in range(iter_max): W = np.copy(self.W) Ez, Ezz = self._expectation(self.X_dr['X']-self.mean) self._maximization(self.X_dr, Ez, Ezz) if np.allclose(W, self.W): break self.n_iter = i + 1 self.C = self.W @ self.W.T + self.var * np.eye(np.size(self.X_dr['X'], 1)) self.Cinv = np.linalg.inv(self.C) def _maximization(self, X_dr, Ez, Ezz): X_mean = (X_dr['X']-self.mean) self.W = (X_mean*X_dr['W'][:,None]).T @ Ez @ np.linalg.inv(np.sum(Ezz*X_dr['W'][:,None,None], axis=0) + self.var * np.diag(self.alpha)) self.var = np.sum( (np.mean((X_dr['XX'] - 2*X_dr['X']*self.mean + self.mean ** 2), axis=-1) - 2 * np.mean(Ez @ self.W.T * X_mean, axis=-1) + np.trace((Ezz @ self.W.T @ self.W).T)/ len(self.mean))*X_dr['W'])/sum(X_dr['W']) self.var=max(self.var,0.000001) def maximize(self, D, Ez, Ezz): self.W = D.T.dot(Ez).dot(np.linalg.inv(np.sum(Ezz, axis=0) + self.var * np.diag(self.alpha))) self.var = np.mean( np.mean(D ** 2, axis=-1) - 2 * np.mean(Ez.dot(self.W.T) * D, axis=-1) + np.trace(Ezz.dot(self.W.T).dot(self.W).T) / self.ndim)

true

f71e836c03860d0845b884fe67b551b2e44b4a7b

2,858

py

Python

pycfmodel/model/resources/properties/security_group_ingress_prop.py

donatoaz/pycfmodel

1586e290b67d2347493dd4a77d2b0c8ee6c0936b

[ "Apache-2.0" ]

null

pycfmodel/model/resources/properties/security_group_ingress_prop.py

donatoaz/pycfmodel

1586e290b67d2347493dd4a77d2b0c8ee6c0936b

[ "Apache-2.0" ]

null

pycfmodel/model/resources/properties/security_group_ingress_prop.py

donatoaz/pycfmodel

1586e290b67d2347493dd4a77d2b0c8ee6c0936b

[ "Apache-2.0" ]

null

from ipaddress import IPv4Network, IPv6Network from typing import Optional from pydantic import validator from pycfmodel.constants import IPV4_ZERO_VALUE, IPV6_ZERO_VALUE from pycfmodel.model.resources.properties.property import Property from pycfmodel.model.types import ( ResolvableInt, ResolvableIntOrStr, ResolvableIPv4Network, ResolvableIPv6Network, ResolvableStr, ) class SecurityGroupIngressProp(Property): """ An inbound rule permits instances to receive traffic from the specified IPv4 or IPv6 CIDR address range, or from the instances associated with the specified security group. Properties: - CidrIp: The IPv4 ranges. - CidrIpv6: The IPv6 ranges. - Description: The description of an egress (outbound) security group rule. - FromPort: The start of port range for the TCP and UDP protocols. - IpProtocol: The IP protocol name (tcp, udp, icmp, icmpv6) or number ([see Protocol Numbers](http://www.iana.org/assignments/protocol-numbers/protocol-numbers.xhtml)). - SourcePrefixListId: The prefix list IDs for an AWS service. - SourceSecurityGroupId: The ID of the security group. - SourceSecurityGroupName: The name of the source security group. - SourceSecurityGroupOwnerId: The AWS account ID for the source security group. - ToPort: The end of port range for the TCP and UDP protocols. More info at [AWS Docs](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-ec2-security-group-ingress.html) """ CidrIp: Optional[ResolvableIPv4Network] = None CidrIpv6: Optional[ResolvableIPv6Network] = None Description: Optional[ResolvableStr] = None FromPort: Optional[ResolvableInt] = None IpProtocol: ResolvableIntOrStr SourcePrefixListId: Optional[ResolvableStr] = None SourceSecurityGroupId: Optional[ResolvableStr] = None SourceSecurityGroupName: Optional[ResolvableStr] = None SourceSecurityGroupOwnerId: Optional[ResolvableStr] = None ToPort: Optional[ResolvableInt] = None @validator("CidrIp", pre=True) def set_CidrIp(cls, v): return IPv4Network(v, strict=False) @validator("CidrIpv6", pre=True) def set_CidrIpv6(cls, v): return IPv6Network(v, strict=False) def ipv4_slash_zero(self) -> bool: """Returns True if `CidrIp` matches `0.0.0.0/0`, otherwise False.""" # Remove after this is fixed https://bugs.python.org/issue38655 if not self.CidrIp: return False return self.CidrIp == IPv4Network(IPV4_ZERO_VALUE) def ipv6_slash_zero(self) -> bool: """Returns True if `CidrIpv6` matches `::/0`, otherwise False.""" # Remove after this is fixed https://bugs.python.org/issue38655 if not self.CidrIpv6: return False return self.CidrIpv6 == IPv6Network(IPV6_ZERO_VALUE)

41.42029

176

0.728132

from ipaddress import IPv4Network, IPv6Network from typing import Optional from pydantic import validator from pycfmodel.constants import IPV4_ZERO_VALUE, IPV6_ZERO_VALUE from pycfmodel.model.resources.properties.property import Property from pycfmodel.model.types import ( ResolvableInt, ResolvableIntOrStr, ResolvableIPv4Network, ResolvableIPv6Network, ResolvableStr, ) class SecurityGroupIngressProp(Property): CidrIp: Optional[ResolvableIPv4Network] = None CidrIpv6: Optional[ResolvableIPv6Network] = None Description: Optional[ResolvableStr] = None FromPort: Optional[ResolvableInt] = None IpProtocol: ResolvableIntOrStr SourcePrefixListId: Optional[ResolvableStr] = None SourceSecurityGroupId: Optional[ResolvableStr] = None SourceSecurityGroupName: Optional[ResolvableStr] = None SourceSecurityGroupOwnerId: Optional[ResolvableStr] = None ToPort: Optional[ResolvableInt] = None @validator("CidrIp", pre=True) def set_CidrIp(cls, v): return IPv4Network(v, strict=False) @validator("CidrIpv6", pre=True) def set_CidrIpv6(cls, v): return IPv6Network(v, strict=False) def ipv4_slash_zero(self) -> bool: if not self.CidrIp: return False return self.CidrIp == IPv4Network(IPV4_ZERO_VALUE) def ipv6_slash_zero(self) -> bool: if not self.CidrIpv6: return False return self.CidrIpv6 == IPv6Network(IPV6_ZERO_VALUE)

true

f71e85137e6e9b5019198c6010110150cbe49a78

34

py

Python

pycl/Code/readfilec.py

dcavar/dcavar.github.io

bf96820f41563bab73ba35a98142da4ab5ad50a1

[ "Apache-2.0" ]

4

2018-01-11T22:14:11.000Z

2019-06-13T09:56:18.000Z

pycl/Code/readfilec.py

dcavar/dcavar.github.io

bf96820f41563bab73ba35a98142da4ab5ad50a1

[ "Apache-2.0" ]

null

pycl/Code/readfilec.py

dcavar/dcavar.github.io

bf96820f41563bab73ba35a98142da4ab5ad50a1

[ "Apache-2.0" ]

1

2020-01-25T02:16:38.000Z

print open("readfilec.py").read()

17

33

0.705882

print open("readfilec.py").read()

false

true

f71e8601ba5f31a1c9d2c21ca11533def0e76aa3

1,022

py

Python

nostradamus/apps/description_assessment/serializers.py

exactpro/nostradamus

80df847a012374ad2b702cc9f9c9cb46c1153ee7

[ "Apache-2.0" ]

25

2019-12-18T05:32:41.000Z

2022-03-23T12:16:49.000Z

nostradamus/apps/description_assessment/serializers.py

Exactpro/nostradamus

80df847a012374ad2b702cc9f9c9cb46c1153ee7

[ "Apache-2.0" ]

12

2018-12-24T14:56:50.000Z

2019-11-29T16:53:49.000Z

nostradamus/apps/description_assessment/serializers.py

exactpro/nostradamus

80df847a012374ad2b702cc9f9c9cb46c1153ee7

[ "Apache-2.0" ]

7

2019-12-18T05:32:43.000Z

2021-08-18T05:27:04.000Z

from rest_framework import serializers class DescriptionAssessmentResponseSerializer(serializers.Serializer): priority = serializers.ListField(child=serializers.CharField()) resolution = serializers.ListField(child=serializers.CharField()) areas_of_testing = serializers.ListField(child=serializers.CharField()) class PredictorResponseSerializer(serializers.Serializer): priority = serializers.DictField(child=serializers.FloatField()) resolution = serializers.DictField(child=serializers.FloatField()) areas_of_testing = serializers.DictField(child=serializers.FloatField()) time_to_resolve = serializers.DictField(child=serializers.FloatField()) class HighlightingResponseSerializer(serializers.ListSerializer): child = serializers.CharField() class PredictorRequestSerializer(serializers.Serializer): description = serializers.CharField() class HighlightingRequestSerializer(serializers.Serializer): metric = serializers.CharField() value = serializers.CharField()

36.5

76

0.811155

from rest_framework import serializers class DescriptionAssessmentResponseSerializer(serializers.Serializer): priority = serializers.ListField(child=serializers.CharField()) resolution = serializers.ListField(child=serializers.CharField()) areas_of_testing = serializers.ListField(child=serializers.CharField()) class PredictorResponseSerializer(serializers.Serializer): priority = serializers.DictField(child=serializers.FloatField()) resolution = serializers.DictField(child=serializers.FloatField()) areas_of_testing = serializers.DictField(child=serializers.FloatField()) time_to_resolve = serializers.DictField(child=serializers.FloatField()) class HighlightingResponseSerializer(serializers.ListSerializer): child = serializers.CharField() class PredictorRequestSerializer(serializers.Serializer): description = serializers.CharField() class HighlightingRequestSerializer(serializers.Serializer): metric = serializers.CharField() value = serializers.CharField()

true

f71e8612627c373ae43be44f6304eef90f1f77b7

3,431

py

Python

diarization/toys/gen_xvec_lbl.py

theScrabi/kaldi_voxceleb_pytorch

bce3f8c5506df0128dd87f6aff60f9924806f5b6

[ "MIT" ]

3

2020-04-06T06:33:19.000Z

2020-04-08T06:24:15.000Z

diarization/toys/gen_xvec_lbl.py

theScrabi/kaldi_voxceleb_pytorch

bce3f8c5506df0128dd87f6aff60f9924806f5b6

[ "MIT" ]

null

diarization/toys/gen_xvec_lbl.py

theScrabi/kaldi_voxceleb_pytorch

bce3f8c5506df0128dd87f6aff60f9924806f5b6

[ "MIT" ]

null

#!/usr/bin/python3 import sys import numpy as np from math import floor if len(sys.argv) <= 3: print("gen_xvec_lbl.py <segments_file> <frame_size> <stride>") print("You need to enter the segments file") print("generated by generate_segments.py") print("Second and third parameter need to be") print("length and strade of the frame used") print("to generate xvectors over.") print("The length needs to be enterd in 10ms units") print("A frame of 150ms therefore has needs to be entered as 15.") print("Stride can be float values") exit(1) segments_file_name = sys.argv[1] frame_size = int(sys.argv[2]) half_frame = frame_size/2 stride = float(sys.argv[3]) def read_segments(file_name): convs = {} segments = [] current_conv = "" mfcc_pos = 0 for seg in open(file_name).readlines(): seg = seg.replace("\n", "").split(" ") cid = seg[0] sid = seg[1] start = int(seg[2]) stop = int(seg[3]) # save last count of segments if not current_conv == cid and not current_conv == "": convs[current_conv] = segments segments = [] mfcc_pos = 0 current_conv = cid seg_dur = stop - start segments.append({"sid":sid, "mfcc_pos":mfcc_pos, "time_pos":start, "dur":seg_dur}) mfcc_pos += seg_dur if len(segments) > 0: convs[current_conv] = segments return convs def get_mfcc_count_of_segments(segments): count = 0 for seg in segments: count += seg["dur"] return count def get_count_of_frames(mfcc_count): return int((mfcc_count - frame_size) / stride) + 1 def get_speaker_of_segments(segments): speaker = {} i = 1 for s in segments: if not s["sid"] in speaker: speaker[s["sid"]] = i i += 1 return speaker def get_touching_segments(segments, start, stop): touching_segments = [] for seg in segments: if seg["mfcc_pos"] < stop and seg["mfcc_pos"] + seg["dur"] >= start: touch_start = seg["time_pos"] if seg["mfcc_pos"] >= start else (seg["time_pos"] - seg["mfcc_pos"] + start) touch_end = (seg["time_pos"] + seg["dur"]) if seg["mfcc_pos"] + seg["dur"] <= stop else (seg["time_pos"] - seg["mfcc_pos"] + stop) touching_segments.append((seg, touch_start, touch_end)) return touching_segments def label_frames_by_segments(conv_id, segments): frames = np.zeros(get_count_of_frames(get_mfcc_count_of_segments(segments))) speaker = get_speaker_of_segments(segments) for i in range(0, frames.shape[0]): frame_center = i * stride + half_frame sids_of_frame = [] touch_starts_of_frame = [] touch_ends_of_frame = [] for seg in get_touching_segments(segments, frame_center - half_frame, frame_center + half_frame): sids_of_frame.append(seg[0]["sid"]) touch_starts_of_frame.append(str(int(seg[1]))) touch_ends_of_frame.append(str(int(seg[2]))) sids_of_frame = "-".join(sids_of_frame) touch_starts_of_frame = "-".join(touch_starts_of_frame) touch_ends_of_frame = "-".join(touch_ends_of_frame) print(f"{conv_id} {i} {sids_of_frame} {touch_starts_of_frame} {touch_ends_of_frame}") convs = read_segments(segments_file_name) for i in convs: label_frames_by_segments(i, convs[i])

31.477064

142

0.633926

import sys import numpy as np from math import floor if len(sys.argv) <= 3: print("gen_xvec_lbl.py <segments_file> <frame_size> <stride>") print("You need to enter the segments file") print("generated by generate_segments.py") print("Second and third parameter need to be") print("length and strade of the frame used") print("to generate xvectors over.") print("The length needs to be enterd in 10ms units") print("A frame of 150ms therefore has needs to be entered as 15.") print("Stride can be float values") exit(1) segments_file_name = sys.argv[1] frame_size = int(sys.argv[2]) half_frame = frame_size/2 stride = float(sys.argv[3]) def read_segments(file_name): convs = {} segments = [] current_conv = "" mfcc_pos = 0 for seg in open(file_name).readlines(): seg = seg.replace("\n", "").split(" ") cid = seg[0] sid = seg[1] start = int(seg[2]) stop = int(seg[3]) if not current_conv == cid and not current_conv == "": convs[current_conv] = segments segments = [] mfcc_pos = 0 current_conv = cid seg_dur = stop - start segments.append({"sid":sid, "mfcc_pos":mfcc_pos, "time_pos":start, "dur":seg_dur}) mfcc_pos += seg_dur if len(segments) > 0: convs[current_conv] = segments return convs def get_mfcc_count_of_segments(segments): count = 0 for seg in segments: count += seg["dur"] return count def get_count_of_frames(mfcc_count): return int((mfcc_count - frame_size) / stride) + 1 def get_speaker_of_segments(segments): speaker = {} i = 1 for s in segments: if not s["sid"] in speaker: speaker[s["sid"]] = i i += 1 return speaker def get_touching_segments(segments, start, stop): touching_segments = [] for seg in segments: if seg["mfcc_pos"] < stop and seg["mfcc_pos"] + seg["dur"] >= start: touch_start = seg["time_pos"] if seg["mfcc_pos"] >= start else (seg["time_pos"] - seg["mfcc_pos"] + start) touch_end = (seg["time_pos"] + seg["dur"]) if seg["mfcc_pos"] + seg["dur"] <= stop else (seg["time_pos"] - seg["mfcc_pos"] + stop) touching_segments.append((seg, touch_start, touch_end)) return touching_segments def label_frames_by_segments(conv_id, segments): frames = np.zeros(get_count_of_frames(get_mfcc_count_of_segments(segments))) speaker = get_speaker_of_segments(segments) for i in range(0, frames.shape[0]): frame_center = i * stride + half_frame sids_of_frame = [] touch_starts_of_frame = [] touch_ends_of_frame = [] for seg in get_touching_segments(segments, frame_center - half_frame, frame_center + half_frame): sids_of_frame.append(seg[0]["sid"]) touch_starts_of_frame.append(str(int(seg[1]))) touch_ends_of_frame.append(str(int(seg[2]))) sids_of_frame = "-".join(sids_of_frame) touch_starts_of_frame = "-".join(touch_starts_of_frame) touch_ends_of_frame = "-".join(touch_ends_of_frame) print(f"{conv_id} {i} {sids_of_frame} {touch_starts_of_frame} {touch_ends_of_frame}") convs = read_segments(segments_file_name) for i in convs: label_frames_by_segments(i, convs[i])

true

f71e8647524eb5aa1aee85b239a44b7b231aacd1

9,304

py

Python

tests/test_data_collator.py

WERimagin/transformers

cc7d14511c647f8147494df72f8b0575015e37ab

[ "Apache-2.0" ]

47

2021-04-16T22:29:25.000Z

2022-02-11T08:19:13.000Z

tests/test_data_collator.py

WERimagin/transformers

cc7d14511c647f8147494df72f8b0575015e37ab

[ "Apache-2.0" ]

12

2021-04-28T19:45:02.000Z

2021-08-31T13:56:02.000Z

tests/test_data_collator.py

WERimagin/transformers

cc7d14511c647f8147494df72f8b0575015e37ab

[ "Apache-2.0" ]

5

2021-04-28T21:54:15.000Z

2022-02-11T07:48:17.000Z

import unittest from transformers import AutoTokenizer, is_torch_available from transformers.testing_utils import require_torch, slow if is_torch_available(): import torch from transformers import ( DataCollatorForLanguageModeling, DataCollatorForNextSentencePrediction, DataCollatorForPermutationLanguageModeling, DataCollatorForSOP, GlueDataset, GlueDataTrainingArguments, LineByLineTextDataset, LineByLineWithSOPTextDataset, TextDataset, TextDatasetForNextSentencePrediction, default_data_collator, ) PATH_SAMPLE_TEXT = "./tests/fixtures/sample_text.txt" PATH_SAMPLE_TEXT_DIR = "./tests/fixtures/tests_samples/wiki_text" @require_torch class DataCollatorIntegrationTest(unittest.TestCase): def test_default_with_dict(self): features = [{"label": i, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)] batch = default_data_collator(features) self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8))))) self.assertEqual(batch["labels"].dtype, torch.long) self.assertEqual(batch["inputs"].shape, torch.Size([8, 6])) # With label_ids features = [{"label_ids": [0, 1, 2], "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)] batch = default_data_collator(features) self.assertTrue(batch["labels"].equal(torch.tensor([[0, 1, 2]] * 8))) self.assertEqual(batch["labels"].dtype, torch.long) self.assertEqual(batch["inputs"].shape, torch.Size([8, 6])) # Features can already be tensors features = [{"label": i, "inputs": torch.randint(10, [10])} for i in range(8)] batch = default_data_collator(features) self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8))))) self.assertEqual(batch["labels"].dtype, torch.long) self.assertEqual(batch["inputs"].shape, torch.Size([8, 10])) # Labels can already be tensors features = [{"label": torch.tensor(i), "inputs": torch.randint(10, [10])} for i in range(8)] batch = default_data_collator(features) self.assertEqual(batch["labels"].dtype, torch.long) self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8))))) self.assertEqual(batch["labels"].dtype, torch.long) self.assertEqual(batch["inputs"].shape, torch.Size([8, 10])) def test_default_with_no_labels(self): features = [{"label": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)] batch = default_data_collator(features) self.assertTrue("labels" not in batch) self.assertEqual(batch["inputs"].shape, torch.Size([8, 6])) # With label_ids features = [{"label_ids": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)] batch = default_data_collator(features) self.assertTrue("labels" not in batch) self.assertEqual(batch["inputs"].shape, torch.Size([8, 6])) @slow def test_default_classification(self): MODEL_ID = "bert-base-cased-finetuned-mrpc" tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) data_args = GlueDataTrainingArguments( task_name="mrpc", data_dir="./tests/fixtures/tests_samples/MRPC", overwrite_cache=True ) dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev") data_collator = default_data_collator batch = data_collator(dataset.features) self.assertEqual(batch["labels"].dtype, torch.long) @slow def test_default_regression(self): MODEL_ID = "distilroberta-base" tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) data_args = GlueDataTrainingArguments( task_name="sts-b", data_dir="./tests/fixtures/tests_samples/STS-B", overwrite_cache=True ) dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev") data_collator = default_data_collator batch = data_collator(dataset.features) self.assertEqual(batch["labels"].dtype, torch.float) @slow def test_lm_tokenizer_without_padding(self): tokenizer = AutoTokenizer.from_pretrained("gpt2") data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False) # ^ causal lm dataset = LineByLineTextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512) examples = [dataset[i] for i in range(len(dataset))] with self.assertRaises(ValueError): # Expect error due to padding token missing on gpt2: data_collator(examples) dataset = TextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512, overwrite_cache=True) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512))) self.assertEqual(batch["labels"].shape, torch.Size((2, 512))) @slow def test_lm_tokenizer_with_padding(self): tokenizer = AutoTokenizer.from_pretrained("distilroberta-base") data_collator = DataCollatorForLanguageModeling(tokenizer) # ^ masked lm dataset = LineByLineTextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) self.assertEqual(batch["input_ids"].shape, torch.Size((31, 107))) self.assertEqual(batch["labels"].shape, torch.Size((31, 107))) dataset = TextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512, overwrite_cache=True) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512))) self.assertEqual(batch["labels"].shape, torch.Size((2, 512))) @slow def test_plm(self): tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased") data_collator = DataCollatorForPermutationLanguageModeling(tokenizer) # ^ permutation lm dataset = LineByLineTextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) self.assertEqual(batch["input_ids"].shape, torch.Size((31, 112))) self.assertEqual(batch["perm_mask"].shape, torch.Size((31, 112, 112))) self.assertEqual(batch["target_mapping"].shape, torch.Size((31, 112, 112))) self.assertEqual(batch["labels"].shape, torch.Size((31, 112))) dataset = TextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512, overwrite_cache=True) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512))) self.assertEqual(batch["perm_mask"].shape, torch.Size((2, 512, 512))) self.assertEqual(batch["target_mapping"].shape, torch.Size((2, 512, 512))) self.assertEqual(batch["labels"].shape, torch.Size((2, 512))) example = [torch.randint(5, [5])] with self.assertRaises(ValueError): # Expect error due to odd sequence length data_collator(example) @slow def test_nsp(self): tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") data_collator = DataCollatorForNextSentencePrediction(tokenizer) dataset = TextDatasetForNextSentencePrediction(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) # Since there are randomly generated false samples, the total number of samples is not fixed. total_samples = batch["input_ids"].shape[0] self.assertEqual(batch["input_ids"].shape, torch.Size((total_samples, 512))) self.assertEqual(batch["token_type_ids"].shape, torch.Size((total_samples, 512))) self.assertEqual(batch["masked_lm_labels"].shape, torch.Size((total_samples, 512))) self.assertEqual(batch["next_sentence_label"].shape, torch.Size((total_samples,))) @slow def test_sop(self): tokenizer = AutoTokenizer.from_pretrained("albert-base-v2") data_collator = DataCollatorForSOP(tokenizer) dataset = LineByLineWithSOPTextDataset(tokenizer, file_dir=PATH_SAMPLE_TEXT_DIR, block_size=512) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) # Since there are randomly generated false samples, the total number of samples is not fixed. total_samples = batch["input_ids"].shape[0] self.assertEqual(batch["input_ids"].shape, torch.Size((total_samples, 512))) self.assertEqual(batch["token_type_ids"].shape, torch.Size((total_samples, 512))) self.assertEqual(batch["labels"].shape, torch.Size((total_samples, 512))) self.assertEqual(batch["sentence_order_label"].shape, torch.Size((total_samples,)))

47.228426

109

0.677343

import unittest from transformers import AutoTokenizer, is_torch_available from transformers.testing_utils import require_torch, slow if is_torch_available(): import torch from transformers import ( DataCollatorForLanguageModeling, DataCollatorForNextSentencePrediction, DataCollatorForPermutationLanguageModeling, DataCollatorForSOP, GlueDataset, GlueDataTrainingArguments, LineByLineTextDataset, LineByLineWithSOPTextDataset, TextDataset, TextDatasetForNextSentencePrediction, default_data_collator, ) PATH_SAMPLE_TEXT = "./tests/fixtures/sample_text.txt" PATH_SAMPLE_TEXT_DIR = "./tests/fixtures/tests_samples/wiki_text" @require_torch class DataCollatorIntegrationTest(unittest.TestCase): def test_default_with_dict(self): features = [{"label": i, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)] batch = default_data_collator(features) self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8))))) self.assertEqual(batch["labels"].dtype, torch.long) self.assertEqual(batch["inputs"].shape, torch.Size([8, 6])) features = [{"label_ids": [0, 1, 2], "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)] batch = default_data_collator(features) self.assertTrue(batch["labels"].equal(torch.tensor([[0, 1, 2]] * 8))) self.assertEqual(batch["labels"].dtype, torch.long) self.assertEqual(batch["inputs"].shape, torch.Size([8, 6])) features = [{"label": i, "inputs": torch.randint(10, [10])} for i in range(8)] batch = default_data_collator(features) self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8))))) self.assertEqual(batch["labels"].dtype, torch.long) self.assertEqual(batch["inputs"].shape, torch.Size([8, 10])) features = [{"label": torch.tensor(i), "inputs": torch.randint(10, [10])} for i in range(8)] batch = default_data_collator(features) self.assertEqual(batch["labels"].dtype, torch.long) self.assertTrue(batch["labels"].equal(torch.tensor(list(range(8))))) self.assertEqual(batch["labels"].dtype, torch.long) self.assertEqual(batch["inputs"].shape, torch.Size([8, 10])) def test_default_with_no_labels(self): features = [{"label": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)] batch = default_data_collator(features) self.assertTrue("labels" not in batch) self.assertEqual(batch["inputs"].shape, torch.Size([8, 6])) features = [{"label_ids": None, "inputs": [0, 1, 2, 3, 4, 5]} for i in range(8)] batch = default_data_collator(features) self.assertTrue("labels" not in batch) self.assertEqual(batch["inputs"].shape, torch.Size([8, 6])) @slow def test_default_classification(self): MODEL_ID = "bert-base-cased-finetuned-mrpc" tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) data_args = GlueDataTrainingArguments( task_name="mrpc", data_dir="./tests/fixtures/tests_samples/MRPC", overwrite_cache=True ) dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev") data_collator = default_data_collator batch = data_collator(dataset.features) self.assertEqual(batch["labels"].dtype, torch.long) @slow def test_default_regression(self): MODEL_ID = "distilroberta-base" tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) data_args = GlueDataTrainingArguments( task_name="sts-b", data_dir="./tests/fixtures/tests_samples/STS-B", overwrite_cache=True ) dataset = GlueDataset(data_args, tokenizer=tokenizer, mode="dev") data_collator = default_data_collator batch = data_collator(dataset.features) self.assertEqual(batch["labels"].dtype, torch.float) @slow def test_lm_tokenizer_without_padding(self): tokenizer = AutoTokenizer.from_pretrained("gpt2") data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False) dataset = LineByLineTextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512) examples = [dataset[i] for i in range(len(dataset))] with self.assertRaises(ValueError): data_collator(examples) dataset = TextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512, overwrite_cache=True) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512))) self.assertEqual(batch["labels"].shape, torch.Size((2, 512))) @slow def test_lm_tokenizer_with_padding(self): tokenizer = AutoTokenizer.from_pretrained("distilroberta-base") data_collator = DataCollatorForLanguageModeling(tokenizer) dataset = LineByLineTextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) self.assertEqual(batch["input_ids"].shape, torch.Size((31, 107))) self.assertEqual(batch["labels"].shape, torch.Size((31, 107))) dataset = TextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512, overwrite_cache=True) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512))) self.assertEqual(batch["labels"].shape, torch.Size((2, 512))) @slow def test_plm(self): tokenizer = AutoTokenizer.from_pretrained("xlnet-base-cased") data_collator = DataCollatorForPermutationLanguageModeling(tokenizer) dataset = LineByLineTextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) self.assertEqual(batch["input_ids"].shape, torch.Size((31, 112))) self.assertEqual(batch["perm_mask"].shape, torch.Size((31, 112, 112))) self.assertEqual(batch["target_mapping"].shape, torch.Size((31, 112, 112))) self.assertEqual(batch["labels"].shape, torch.Size((31, 112))) dataset = TextDataset(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512, overwrite_cache=True) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) self.assertEqual(batch["input_ids"].shape, torch.Size((2, 512))) self.assertEqual(batch["perm_mask"].shape, torch.Size((2, 512, 512))) self.assertEqual(batch["target_mapping"].shape, torch.Size((2, 512, 512))) self.assertEqual(batch["labels"].shape, torch.Size((2, 512))) example = [torch.randint(5, [5])] with self.assertRaises(ValueError): data_collator(example) @slow def test_nsp(self): tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") data_collator = DataCollatorForNextSentencePrediction(tokenizer) dataset = TextDatasetForNextSentencePrediction(tokenizer, file_path=PATH_SAMPLE_TEXT, block_size=512) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) total_samples = batch["input_ids"].shape[0] self.assertEqual(batch["input_ids"].shape, torch.Size((total_samples, 512))) self.assertEqual(batch["token_type_ids"].shape, torch.Size((total_samples, 512))) self.assertEqual(batch["masked_lm_labels"].shape, torch.Size((total_samples, 512))) self.assertEqual(batch["next_sentence_label"].shape, torch.Size((total_samples,))) @slow def test_sop(self): tokenizer = AutoTokenizer.from_pretrained("albert-base-v2") data_collator = DataCollatorForSOP(tokenizer) dataset = LineByLineWithSOPTextDataset(tokenizer, file_dir=PATH_SAMPLE_TEXT_DIR, block_size=512) examples = [dataset[i] for i in range(len(dataset))] batch = data_collator(examples) self.assertIsInstance(batch, dict) total_samples = batch["input_ids"].shape[0] self.assertEqual(batch["input_ids"].shape, torch.Size((total_samples, 512))) self.assertEqual(batch["token_type_ids"].shape, torch.Size((total_samples, 512))) self.assertEqual(batch["labels"].shape, torch.Size((total_samples, 512))) self.assertEqual(batch["sentence_order_label"].shape, torch.Size((total_samples,)))

true

f71e87941ec4d26a71c4d0edea7028a110dd3419

1,040

py

Python

examples/tensorflow_example.py

lucko515/cnn-raccoon

e1c46544372751d82cc0c0f9cb2218d881a21f70

[ "Apache-2.0" ]

30

2021-01-08T11:50:54.000Z

2021-08-01T07:31:54.000Z

examples/tensorflow_example.py

lucko515/cnn-raccoon

e1c46544372751d82cc0c0f9cb2218d881a21f70

[ "Apache-2.0" ]

1

2021-01-24T23:10:38.000Z

examples/tensorflow_example.py

lucko515/cnn-raccoon

e1c46544372751d82cc0c0f9cb2218d881a21f70

[ "Apache-2.0" ]

4

2021-01-08T11:21:30.000Z

2021-02-26T16:06:37.000Z

import tensorflow as tf model = tf.keras.models.Sequential([ # YOUR CODE HERE tf.keras.layers.BatchNormalization(input_shape=(32, 32, 3)), tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation="relu"), tf.keras.layers.MaxPool2D(2, 2), tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation="relu"), tf.keras.layers.MaxPool2D(2, 2), tf.keras.layers.Conv2D(filters=256, kernel_size=(3, 3), activation="relu"), tf.keras.layers.MaxPool2D(2, 2), tf.keras.layers.Flatten(), tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(units=128, activation="relu"), tf.keras.layers.Dense(10, activation="softmax") ]) model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["acc"]) from tensorflow.keras.datasets import cifar10 (X_train, y_train), (X_test, y_test) = cifar10.load_data() from cnn_raccoon import inspector inspector(model=model, images=X_train[:10], number_of_classes=10, engine="keras")

40

88

0.682692

import tensorflow as tf model = tf.keras.models.Sequential([ tf.keras.layers.BatchNormalization(input_shape=(32, 32, 3)), tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation="relu"), tf.keras.layers.MaxPool2D(2, 2), tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation="relu"), tf.keras.layers.MaxPool2D(2, 2), tf.keras.layers.Conv2D(filters=256, kernel_size=(3, 3), activation="relu"), tf.keras.layers.MaxPool2D(2, 2), tf.keras.layers.Flatten(), tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(units=128, activation="relu"), tf.keras.layers.Dense(10, activation="softmax") ]) model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["acc"]) from tensorflow.keras.datasets import cifar10 (X_train, y_train), (X_test, y_test) = cifar10.load_data() from cnn_raccoon import inspector inspector(model=model, images=X_train[:10], number_of_classes=10, engine="keras")

true

f71e879b585ccedee624c36d400debbe26d4a4ba

2,235

py

Python

chaospy/distributions/collection/f.py

krystophny/chaospy

e09f8e3f6dfc26145f15774edd5b03665140712f

[ "MIT" ]

1

2019-12-20T00:32:44.000Z

chaospy/distributions/collection/f.py

QianWanghhu/chaospy

18ff6c4fc56c632825e53fb24e17de51a7febd7d

[ "MIT" ]

null

chaospy/distributions/collection/f.py

QianWanghhu/chaospy

18ff6c4fc56c632825e53fb24e17de51a7febd7d

[ "MIT" ]

null

"""(Non-central) F distribution.""" import numpy from scipy import special from ..baseclass import Dist from ..operators.addition import Add class f(Dist): """F distribution.""" def __init__(self, dfn, dfd, nc): Dist.__init__(self, dfn=dfn, dfd=dfd, nc=nc) def _pdf(self, x, dfn, dfd, nc): n1, n2 = dfn, dfd term = -nc/2.+nc*n1*x/(2*(n2+n1*x)) + special.gammaln(n1/2.)+special.gammaln(1+n2/2.) term -= special.gammaln((n1+n2)/2.) Px = numpy.exp(term) Px *= n1**(n1/2.) * n2**(n2/2.) * x**(n1/2.-1) Px *= (n2+n1*x)**(-(n1+n2)/2.) Px *= special.assoc_laguerre(-nc*n1*x/(2.*(n2+n1*x)), n2/2., n1/2.-1) Px /= special.beta(n1/2., n2/2.) return Px def _cdf(self, x, dfn, dfd, nc): return special.ncfdtr(dfn, dfd, nc, x) def _ppf(self, q, dfn, dfd, nc): return special.ncfdtri(dfn, dfd, nc, q) def _bnd(self, x, dfn, dfd, nc): return 0.0, self._ppf(1-1e-10, dfn, dfd, nc) class F(Add): """ (Non-central) F or Fisher-Snedecor distribution. Args: n (float, Dist) : Degres of freedom for numerator m (float, Dist) : Degres of freedom for denominator scale (float, Dist) : Scaling parameter shift (float, Dist) : Location parameter nc (float, Dist) : Non-centrality parameter Examples: >>> distribution = chaospy.F(3, 3, 2, 1, 1) >>> print(distribution) F(m=3, n=3, nc=1, scale=2, shift=1) >>> q = numpy.linspace(0, 1, 6)[1:-1] >>> print(numpy.around(distribution.inv(q), 4)) [1.9336 2.9751 4.7028 8.8521] >>> print(numpy.around(distribution.fwd(distribution.inv(q)), 4)) [0.2 0.4 0.6 0.8] >>> print(numpy.around(distribution.pdf(distribution.inv(q)), 4)) [0.2277 0.1572 0.0837 0.027 ] >>> print(numpy.around(distribution.sample(4), 4)) [ 5.4212 1.5739 25.7656 3.5586] >>> print(distribution.mom(1) > 10**8) # undefined True """ def __init__(self, n=1, m=1, scale=1, shift=0, nc=0): self._repr = {"n": n, "m": m, "scale": scale, "shift": shift, "nc": nc} Add.__init__(self, left=f(n, m, nc)*scale, right=shift)

33.358209

93

0.555257

import numpy from scipy import special from ..baseclass import Dist from ..operators.addition import Add class f(Dist): def __init__(self, dfn, dfd, nc): Dist.__init__(self, dfn=dfn, dfd=dfd, nc=nc) def _pdf(self, x, dfn, dfd, nc): n1, n2 = dfn, dfd term = -nc/2.+nc*n1*x/(2*(n2+n1*x)) + special.gammaln(n1/2.)+special.gammaln(1+n2/2.) term -= special.gammaln((n1+n2)/2.) Px = numpy.exp(term) Px *= n1**(n1/2.) * n2**(n2/2.) * x**(n1/2.-1) Px *= (n2+n1*x)**(-(n1+n2)/2.) Px *= special.assoc_laguerre(-nc*n1*x/(2.*(n2+n1*x)), n2/2., n1/2.-1) Px /= special.beta(n1/2., n2/2.) return Px def _cdf(self, x, dfn, dfd, nc): return special.ncfdtr(dfn, dfd, nc, x) def _ppf(self, q, dfn, dfd, nc): return special.ncfdtri(dfn, dfd, nc, q) def _bnd(self, x, dfn, dfd, nc): return 0.0, self._ppf(1-1e-10, dfn, dfd, nc) class F(Add): def __init__(self, n=1, m=1, scale=1, shift=0, nc=0): self._repr = {"n": n, "m": m, "scale": scale, "shift": shift, "nc": nc} Add.__init__(self, left=f(n, m, nc)*scale, right=shift)

true

f71e88c0491436f3ee784fb6beab57efad7201e5

7,966

py

Python

third_party/WebKit/Tools/Scripts/webkitpy/layout_tests/port/mock_drt_unittest.py

google-ar/chromium

2441c86a5fd975f09a6c30cddb57dfb7fc239699

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

777

2017-08-29T15:15:32.000Z

2022-03-21T05:29:41.000Z

third_party/WebKit/Tools/Scripts/webkitpy/layout_tests/port/mock_drt_unittest.py

harrymarkovskiy/WebARonARCore

2441c86a5fd975f09a6c30cddb57dfb7fc239699

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

66

2017-08-30T18:31:18.000Z

2021-08-02T10:59:35.000Z

third_party/WebKit/Tools/Scripts/webkitpy/layout_tests/port/mock_drt_unittest.py

harrymarkovskiy/WebARonARCore

2441c86a5fd975f09a6c30cddb57dfb7fc239699

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

123

2017-08-30T01:19:34.000Z

2022-03-17T22:55:31.000Z

# Copyright (C) 2011 Google Inc. All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: # # * Redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer. # * Redistributions in binary form must reproduce the above # copyright notice, this list of conditions and the following disclaimer # in the documentation and/or other materials provided with the # distribution. # * Neither the name of Google Inc. nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """Unit tests for MockDRT.""" import io import optparse import unittest from webkitpy.common.system.system_host_mock import MockSystemHost from webkitpy.layout_tests.port import mock_drt from webkitpy.layout_tests.port import port_testcase from webkitpy.layout_tests.port import test from webkitpy.layout_tests.port.factory import PortFactory class MockDRTPortTest(port_testcase.PortTestCase): def make_port(self, host=None, options=optparse.Values({'configuration': 'Release'})): host = host or MockSystemHost() test.add_unit_tests_to_mock_filesystem(host.filesystem) return mock_drt.MockDRTPort(host, port_name='mock-mac', options=options) def test_port_name_in_constructor(self): self.assertTrue(mock_drt.MockDRTPort(MockSystemHost(), port_name='mock-test')) def test_check_sys_deps(self): pass def test_default_max_locked_shards(self): pass def test_diff_image(self): pass def test_diff_image_crashed(self): pass def test_uses_apache(self): pass def test_get_crash_log(self): pass def test_check_build(self): pass def test_virtual_test_suites(self): pass def test_path_to_apache_config_file(self): pass class MockDRTTest(unittest.TestCase): def input_line(self, port, test_name, pixel_tests, checksum=None): url = port.create_driver(0).test_to_uri(test_name) if url.startswith('file://'): url = url[len('file://'):] if pixel_tests: url += "'--pixel-test" if checksum: url += "'" + checksum return url + '\n' def make_drt(self, options, args, host, stdin, stdout, stderr): return mock_drt.MockDRT(options, args, host, stdin, stdout, stderr) def make_input_output(self, port, test_name, pixel_tests, expected_checksum, drt_output, drt_input=None, expected_text=None): if pixel_tests: if not expected_checksum: expected_checksum = port.expected_checksum(test_name) if not drt_input: drt_input = self.input_line(port, test_name, pixel_tests, expected_checksum) text_output = expected_text or port.expected_text(test_name) or '' if not drt_output: drt_output = self.expected_output(port, test_name, pixel_tests, text_output, expected_checksum) return (drt_input, drt_output) def expected_output(self, port, test_name, pixel_tests, text_output, expected_checksum): output = ['#READY\n', 'Content-Type: text/plain\n'] if text_output: output.append(text_output) output.append('#EOF\n') if pixel_tests and expected_checksum: output.extend(['\n', 'ActualHash: %s\n' % expected_checksum, 'ExpectedHash: %s\n' % expected_checksum]) output.append('#EOF\n') return output def assertTest(self, test_name, pixel_tests, expected_checksum=None, drt_output=None, host=None, expected_text=None): port_name = 'test' host = host or MockSystemHost() test.add_unit_tests_to_mock_filesystem(host.filesystem) port = PortFactory(host).get(port_name) drt_input, drt_output = self.make_input_output( port, test_name, pixel_tests, expected_checksum, drt_output, drt_input=None, expected_text=expected_text) args = ['--run-layout-test', '--platform', port_name, '-'] stdin = io.BytesIO(drt_input) stdout = io.BytesIO() stderr = io.BytesIO() options, args = mock_drt.parse_options(args) drt = self.make_drt(options, args, host, stdin, stdout, stderr) res = drt.run() self.assertEqual(res, 0) self.assertEqual(stdout.getvalue(), ''.join(drt_output)) self.assertEqual(stderr.getvalue(), '#EOF\n') def test_main(self): host = MockSystemHost() test.add_unit_tests_to_mock_filesystem(host.filesystem) stdin = io.BytesIO() stdout = io.BytesIO() stderr = io.BytesIO() res = mock_drt.main(['--run-layout-test', '--platform', 'test', '-'], host, stdin, stdout, stderr) self.assertEqual(res, 0) self.assertEqual(stdout.getvalue(), '#READY\n') self.assertEqual(stderr.getvalue(), '') self.assertEqual(host.filesystem.written_files, {}) def test_pixeltest_passes(self): # This also tests that we handle HTTP: test URLs properly. self.assertTest('http/tests/passes/text.html', True) def test_pixeltest__fails(self): self.assertTest('failures/expected/image_checksum.html', pixel_tests=True, expected_checksum='image_checksum-checksum', drt_output=[ '#READY\n', 'Content-Type: text/plain\n', 'image_checksum-txt', '#EOF\n', '\n', 'ActualHash: image_checksum-checksum\n', 'ExpectedHash: image_checksum-checksum\n', '#EOF\n', ]) def test_textonly(self): self.assertTest('passes/image.html', False) def test_checksum_in_png(self): self.assertTest('passes/checksum_in_image.html', True) def test_reftest_match(self): self.assertTest('passes/reftest.html', True, expected_checksum='mock-checksum', expected_text='reference text\n') def test_reftest_mismatch(self): self.assertTest('passes/mismatch.html', True, expected_checksum='mock-checksum', expected_text='reference text\n') def test_audio(self): self.assertTest('passes/audio.html', pixel_tests=True, drt_output=[ '#READY\n', 'Content-Type: audio/wav\n', 'Content-Transfer-Encoding: base64\n', 'YXVkaW8td2F2', '\n', '#EOF\n', '#EOF\n', ]) def test_virtual(self): self.assertTest('virtual/passes/text.html', True)

39.435644

122

0.63407

import io import optparse import unittest from webkitpy.common.system.system_host_mock import MockSystemHost from webkitpy.layout_tests.port import mock_drt from webkitpy.layout_tests.port import port_testcase from webkitpy.layout_tests.port import test from webkitpy.layout_tests.port.factory import PortFactory class MockDRTPortTest(port_testcase.PortTestCase): def make_port(self, host=None, options=optparse.Values({'configuration': 'Release'})): host = host or MockSystemHost() test.add_unit_tests_to_mock_filesystem(host.filesystem) return mock_drt.MockDRTPort(host, port_name='mock-mac', options=options) def test_port_name_in_constructor(self): self.assertTrue(mock_drt.MockDRTPort(MockSystemHost(), port_name='mock-test')) def test_check_sys_deps(self): pass def test_default_max_locked_shards(self): pass def test_diff_image(self): pass def test_diff_image_crashed(self): pass def test_uses_apache(self): pass def test_get_crash_log(self): pass def test_check_build(self): pass def test_virtual_test_suites(self): pass def test_path_to_apache_config_file(self): pass class MockDRTTest(unittest.TestCase): def input_line(self, port, test_name, pixel_tests, checksum=None): url = port.create_driver(0).test_to_uri(test_name) if url.startswith('file://'): url = url[len('file://'):] if pixel_tests: url += "'--pixel-test" if checksum: url += "'" + checksum return url + '\n' def make_drt(self, options, args, host, stdin, stdout, stderr): return mock_drt.MockDRT(options, args, host, stdin, stdout, stderr) def make_input_output(self, port, test_name, pixel_tests, expected_checksum, drt_output, drt_input=None, expected_text=None): if pixel_tests: if not expected_checksum: expected_checksum = port.expected_checksum(test_name) if not drt_input: drt_input = self.input_line(port, test_name, pixel_tests, expected_checksum) text_output = expected_text or port.expected_text(test_name) or '' if not drt_output: drt_output = self.expected_output(port, test_name, pixel_tests, text_output, expected_checksum) return (drt_input, drt_output) def expected_output(self, port, test_name, pixel_tests, text_output, expected_checksum): output = ['#READY\n', 'Content-Type: text/plain\n'] if text_output: output.append(text_output) output.append('#EOF\n') if pixel_tests and expected_checksum: output.extend(['\n', 'ActualHash: %s\n' % expected_checksum, 'ExpectedHash: %s\n' % expected_checksum]) output.append('#EOF\n') return output def assertTest(self, test_name, pixel_tests, expected_checksum=None, drt_output=None, host=None, expected_text=None): port_name = 'test' host = host or MockSystemHost() test.add_unit_tests_to_mock_filesystem(host.filesystem) port = PortFactory(host).get(port_name) drt_input, drt_output = self.make_input_output( port, test_name, pixel_tests, expected_checksum, drt_output, drt_input=None, expected_text=expected_text) args = ['--run-layout-test', '--platform', port_name, '-'] stdin = io.BytesIO(drt_input) stdout = io.BytesIO() stderr = io.BytesIO() options, args = mock_drt.parse_options(args) drt = self.make_drt(options, args, host, stdin, stdout, stderr) res = drt.run() self.assertEqual(res, 0) self.assertEqual(stdout.getvalue(), ''.join(drt_output)) self.assertEqual(stderr.getvalue(), '#EOF\n') def test_main(self): host = MockSystemHost() test.add_unit_tests_to_mock_filesystem(host.filesystem) stdin = io.BytesIO() stdout = io.BytesIO() stderr = io.BytesIO() res = mock_drt.main(['--run-layout-test', '--platform', 'test', '-'], host, stdin, stdout, stderr) self.assertEqual(res, 0) self.assertEqual(stdout.getvalue(), '#READY\n') self.assertEqual(stderr.getvalue(), '') self.assertEqual(host.filesystem.written_files, {}) def test_pixeltest_passes(self): self.assertTest('http/tests/passes/text.html', True) def test_pixeltest__fails(self): self.assertTest('failures/expected/image_checksum.html', pixel_tests=True, expected_checksum='image_checksum-checksum', drt_output=[ '#READY\n', 'Content-Type: text/plain\n', 'image_checksum-txt', '#EOF\n', '\n', 'ActualHash: image_checksum-checksum\n', 'ExpectedHash: image_checksum-checksum\n', '#EOF\n', ]) def test_textonly(self): self.assertTest('passes/image.html', False) def test_checksum_in_png(self): self.assertTest('passes/checksum_in_image.html', True) def test_reftest_match(self): self.assertTest('passes/reftest.html', True, expected_checksum='mock-checksum', expected_text='reference text\n') def test_reftest_mismatch(self): self.assertTest('passes/mismatch.html', True, expected_checksum='mock-checksum', expected_text='reference text\n') def test_audio(self): self.assertTest('passes/audio.html', pixel_tests=True, drt_output=[ '#READY\n', 'Content-Type: audio/wav\n', 'Content-Transfer-Encoding: base64\n', 'YXVkaW8td2F2', '\n', '#EOF\n', '#EOF\n', ]) def test_virtual(self): self.assertTest('virtual/passes/text.html', True)

true

f71e88e8cd933bb1d1bfa245c1fa64a7035fb1eb

32

py

Python

helpers/__init__.py

Rensselaer-AI-Leage/GeneralizedGameServer

e6e97371ca5697bb4842911dbf0f961058f09b9e

[ "MIT" ]

null

helpers/__init__.py

Rensselaer-AI-Leage/GeneralizedGameServer

e6e97371ca5697bb4842911dbf0f961058f09b9e

[ "MIT" ]

1

2016-03-29T22:49:44.000Z

2016-03-29T22:58:04.000Z

helpers/__init__.py

Rensselaer-AI-League/GeneralizedGameServer

e6e97371ca5697bb4842911dbf0f961058f09b9e

[ "MIT" ]

null

__all__ = ["config", "message"]

16

31

0.625

__all__ = ["config", "message"]

true

f71e89961dbf08ac30188dfd72f19f86d8c382f3

1,626

py

Python

pydocmd/preprocessors/smart.py

vemel/pydoc-markdown

7cd22c2ec8110df5a67205b7a641581914d0b45a

[ "MIT" ]

1

2021-02-16T10:01:34.000Z

pydocmd/preprocessors/smart.py

vemel/pydoc-markdown

7cd22c2ec8110df5a67205b7a641581914d0b45a

[ "MIT" ]

null

pydocmd/preprocessors/smart.py

vemel/pydoc-markdown

7cd22c2ec8110df5a67205b7a641581914d0b45a

[ "MIT" ]

null

from pydocmd.preprocessors.rst import Preprocessor as RSTPreprocessor from pydocmd.preprocessors.google import Preprocessor as GooglePreprocessor class Preprocessor(object): """ This class implements the preprocessor for restructured text and google. """ def __init__(self, config=None): self.config = config self._google_preprocessor = GooglePreprocessor(config) self._rst_preprocessor = RSTPreprocessor(config) def is_google_format(self, docstring): """ Check if `docstring` is written in Google docstring format https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html """ lines = [line.strip() for line in docstring.split('\n')] google_section_names = self._google_preprocessor.get_section_names() for section_name in google_section_names: if section_name in lines: return True return False def preprocess_section(self, section): """ Preprocessors a given section into it's components. """ if self.is_google_format(section.content): return self._google_preprocessor.preprocess_section(section) return self._rst_preprocessor.preprocess_section(section) @staticmethod def _append_section(lines, key, sections): section = sections.get(key) if not section: return if lines and lines[-1]: lines.append('') # add an extra line because of markdown syntax lines.extend(['**{}**:'.format(key), '']) lines.extend(section)

32.52

83

0.660517

from pydocmd.preprocessors.rst import Preprocessor as RSTPreprocessor from pydocmd.preprocessors.google import Preprocessor as GooglePreprocessor class Preprocessor(object): def __init__(self, config=None): self.config = config self._google_preprocessor = GooglePreprocessor(config) self._rst_preprocessor = RSTPreprocessor(config) def is_google_format(self, docstring): lines = [line.strip() for line in docstring.split('\n')] google_section_names = self._google_preprocessor.get_section_names() for section_name in google_section_names: if section_name in lines: return True return False def preprocess_section(self, section): if self.is_google_format(section.content): return self._google_preprocessor.preprocess_section(section) return self._rst_preprocessor.preprocess_section(section) @staticmethod def _append_section(lines, key, sections): section = sections.get(key) if not section: return if lines and lines[-1]: lines.append('') lines.extend(['**{}**:'.format(key), '']) lines.extend(section)

true

f71e89bcea253798193ff85e6610a0a39c8656d1

21,687

py

Python

ucsmsdk/mometa/sw/SwAccessDomain.py

Kego/ucsmsdk

244f283a5c295cf746110bb96686d079b19927ce

[ "Apache-2.0" ]

78

2015-11-30T14:10:05.000Z

2022-02-13T00:29:08.000Z

ucsmsdk/mometa/sw/SwAccessDomain.py

Kego/ucsmsdk

244f283a5c295cf746110bb96686d079b19927ce

[ "Apache-2.0" ]

113

2015-11-20T09:42:46.000Z

2022-03-16T16:53:29.000Z

ucsmsdk/mometa/sw/SwAccessDomain.py

Kego/ucsmsdk

244f283a5c295cf746110bb96686d079b19927ce

[ "Apache-2.0" ]

86

2015-12-12T08:22:18.000Z

2022-01-23T03:56:34.000Z

"""This module contains the general information for SwAccessDomain ManagedObject.""" from ...ucsmo import ManagedObject from ...ucscoremeta import MoPropertyMeta, MoMeta from ...ucsmeta import VersionMeta class SwAccessDomainConsts: FSM_PREV_DEPLOY_BEGIN = "DeployBegin" FSM_PREV_DEPLOY_FAIL = "DeployFail" FSM_PREV_DEPLOY_SUCCESS = "DeploySuccess" FSM_PREV_DEPLOY_UPDATE_CONNECTIVITY = "DeployUpdateConnectivity" FSM_PREV_NOP = "nop" FSM_RMT_INV_ERR_CODE_ERR_2FA_AUTH_RETRY = "ERR-2fa-auth-retry" FSM_RMT_INV_ERR_CODE_ERR_ACTIVATE_FAILED = "ERR-ACTIVATE-failed" FSM_RMT_INV_ERR_CODE_ERR_ACTIVATE_IN_PROGRESS = "ERR-ACTIVATE-in-progress" FSM_RMT_INV_ERR_CODE_ERR_ACTIVATE_RETRY = "ERR-ACTIVATE-retry" FSM_RMT_INV_ERR_CODE_ERR_BIOS_TOKENS_OLD_BIOS = "ERR-BIOS-TOKENS-OLD-BIOS" FSM_RMT_INV_ERR_CODE_ERR_BIOS_TOKENS_OLD_CIMC = "ERR-BIOS-TOKENS-OLD-CIMC" FSM_RMT_INV_ERR_CODE_ERR_BIOS_NETWORK_BOOT_ORDER_NOT_FOUND = "ERR-BIOS-network-boot-order-not-found" FSM_RMT_INV_ERR_CODE_ERR_BOARDCTRLUPDATE_IGNORE = "ERR-BOARDCTRLUPDATE-ignore" FSM_RMT_INV_ERR_CODE_ERR_DIAG_CANCELLED = "ERR-DIAG-cancelled" FSM_RMT_INV_ERR_CODE_ERR_DIAG_FSM_RESTARTED = "ERR-DIAG-fsm-restarted" FSM_RMT_INV_ERR_CODE_ERR_DIAG_TEST_FAILED = "ERR-DIAG-test-failed" FSM_RMT_INV_ERR_CODE_ERR_DNLD_AUTHENTICATION_FAILURE = "ERR-DNLD-authentication-failure" FSM_RMT_INV_ERR_CODE_ERR_DNLD_HOSTKEY_MISMATCH = "ERR-DNLD-hostkey-mismatch" FSM_RMT_INV_ERR_CODE_ERR_DNLD_INVALID_IMAGE = "ERR-DNLD-invalid-image" FSM_RMT_INV_ERR_CODE_ERR_DNLD_NO_FILE = "ERR-DNLD-no-file" FSM_RMT_INV_ERR_CODE_ERR_DNLD_NO_SPACE = "ERR-DNLD-no-space" FSM_RMT_INV_ERR_CODE_ERR_DNLD_USB_UNMOUNTED = "ERR-DNLD-usb-unmounted" FSM_RMT_INV_ERR_CODE_ERR_DNS_DELETE_ERROR = "ERR-DNS-delete-error" FSM_RMT_INV_ERR_CODE_ERR_DNS_GET_ERROR = "ERR-DNS-get-error" FSM_RMT_INV_ERR_CODE_ERR_DNS_SET_ERROR = "ERR-DNS-set-error" FSM_RMT_INV_ERR_CODE_ERR_DIAGNOSTICS_IN_PROGRESS = "ERR-Diagnostics-in-progress" FSM_RMT_INV_ERR_CODE_ERR_DIAGNOSTICS_MEMTEST_IN_PROGRESS = "ERR-Diagnostics-memtest-in-progress" FSM_RMT_INV_ERR_CODE_ERR_DIAGNOSTICS_NETWORK_IN_PROGRESS = "ERR-Diagnostics-network-in-progress" FSM_RMT_INV_ERR_CODE_ERR_FILTER_ILLEGAL_FORMAT = "ERR-FILTER-illegal-format" FSM_RMT_INV_ERR_CODE_ERR_FSM_NO_SUCH_STATE = "ERR-FSM-no-such-state" FSM_RMT_INV_ERR_CODE_ERR_HOST_FRU_IDENTITY_MISMATCH = "ERR-HOST-fru-identity-mismatch" FSM_RMT_INV_ERR_CODE_ERR_HTTP_SET_ERROR = "ERR-HTTP-set-error" FSM_RMT_INV_ERR_CODE_ERR_HTTPS_SET_ERROR = "ERR-HTTPS-set-error" FSM_RMT_INV_ERR_CODE_ERR_IBMC_ANALYZE_RESULTS = "ERR-IBMC-analyze-results" FSM_RMT_INV_ERR_CODE_ERR_IBMC_CONNECT_ERROR = "ERR-IBMC-connect-error" FSM_RMT_INV_ERR_CODE_ERR_IBMC_CONNECTOR_INFO_RETRIEVAL_ERROR = "ERR-IBMC-connector-info-retrieval-error" FSM_RMT_INV_ERR_CODE_ERR_IBMC_FRU_RETRIEVAL_ERROR = "ERR-IBMC-fru-retrieval-error" FSM_RMT_INV_ERR_CODE_ERR_IBMC_INVALID_END_POINT_CONFIG = "ERR-IBMC-invalid-end-point-config" FSM_RMT_INV_ERR_CODE_ERR_IBMC_RESULTS_NOT_READY = "ERR-IBMC-results-not-ready" FSM_RMT_INV_ERR_CODE_ERR_MAX_SUBSCRIPTIONS_ALLOWED_ERROR = "ERR-MAX-subscriptions-allowed-error" FSM_RMT_INV_ERR_CODE_ERR_MO_CONFIG_CHILD_OBJECT_CANT_BE_CONFIGURED = "ERR-MO-CONFIG-child-object-cant-be-configured" FSM_RMT_INV_ERR_CODE_ERR_MO_META_NO_SUCH_OBJECT_CLASS = "ERR-MO-META-no-such-object-class" FSM_RMT_INV_ERR_CODE_ERR_MO_PROPERTY_NO_SUCH_PROPERTY = "ERR-MO-PROPERTY-no-such-property" FSM_RMT_INV_ERR_CODE_ERR_MO_PROPERTY_VALUE_OUT_OF_RANGE = "ERR-MO-PROPERTY-value-out-of-range" FSM_RMT_INV_ERR_CODE_ERR_MO_ACCESS_DENIED = "ERR-MO-access-denied" FSM_RMT_INV_ERR_CODE_ERR_MO_DELETION_RULE_VIOLATION = "ERR-MO-deletion-rule-violation" FSM_RMT_INV_ERR_CODE_ERR_MO_DUPLICATE_OBJECT = "ERR-MO-duplicate-object" FSM_RMT_INV_ERR_CODE_ERR_MO_ILLEGAL_CONTAINMENT = "ERR-MO-illegal-containment" FSM_RMT_INV_ERR_CODE_ERR_MO_ILLEGAL_CREATION = "ERR-MO-illegal-creation" FSM_RMT_INV_ERR_CODE_ERR_MO_ILLEGAL_ITERATOR_STATE = "ERR-MO-illegal-iterator-state" FSM_RMT_INV_ERR_CODE_ERR_MO_ILLEGAL_OBJECT_LIFECYCLE_TRANSITION = "ERR-MO-illegal-object-lifecycle-transition" FSM_RMT_INV_ERR_CODE_ERR_MO_NAMING_RULE_VIOLATION = "ERR-MO-naming-rule-violation" FSM_RMT_INV_ERR_CODE_ERR_MO_OBJECT_NOT_FOUND = "ERR-MO-object-not-found" FSM_RMT_INV_ERR_CODE_ERR_MO_RESOURCE_ALLOCATION = "ERR-MO-resource-allocation" FSM_RMT_INV_ERR_CODE_ERR_NTP_DELETE_ERROR = "ERR-NTP-delete-error" FSM_RMT_INV_ERR_CODE_ERR_NTP_GET_ERROR = "ERR-NTP-get-error" FSM_RMT_INV_ERR_CODE_ERR_NTP_SET_ERROR = "ERR-NTP-set-error" FSM_RMT_INV_ERR_CODE_ERR_POWER_CAP_UNSUPPORTED = "ERR-POWER-CAP-UNSUPPORTED" FSM_RMT_INV_ERR_CODE_ERR_POWER_PROFILE_IN_PROGRESS = "ERR-POWER-PROFILE-IN-PROGRESS" FSM_RMT_INV_ERR_CODE_ERR_SERVER_MIS_CONNECT = "ERR-SERVER-mis-connect" FSM_RMT_INV_ERR_CODE_ERR_SWITCH_INVALID_IF_CONFIG = "ERR-SWITCH-invalid-if-config" FSM_RMT_INV_ERR_CODE_ERR_TOKEN_REQUEST_DENIED = "ERR-TOKEN-request-denied" FSM_RMT_INV_ERR_CODE_ERR_UNABLE_TO_FETCH_BIOS_SETTINGS = "ERR-UNABLE-TO-FETCH-BIOS-SETTINGS" FSM_RMT_INV_ERR_CODE_ERR_UPDATE_FAILED = "ERR-UPDATE-failed" FSM_RMT_INV_ERR_CODE_ERR_UPDATE_IN_PROGRESS = "ERR-UPDATE-in-progress" FSM_RMT_INV_ERR_CODE_ERR_UPDATE_RETRY = "ERR-UPDATE-retry" FSM_RMT_INV_ERR_CODE_ERR_AAA_CONFIG_MODIFY_ERROR = "ERR-aaa-config-modify-error" FSM_RMT_INV_ERR_CODE_ERR_ACCT_REALM_SET_ERROR = "ERR-acct-realm-set-error" FSM_RMT_INV_ERR_CODE_ERR_ADMIN_PASSWD_SET = "ERR-admin-passwd-set" FSM_RMT_INV_ERR_CODE_ERR_AUTH_ISSUE = "ERR-auth-issue" FSM_RMT_INV_ERR_CODE_ERR_AUTH_REALM_GET_ERROR = "ERR-auth-realm-get-error" FSM_RMT_INV_ERR_CODE_ERR_AUTH_REALM_SET_ERROR = "ERR-auth-realm-set-error" FSM_RMT_INV_ERR_CODE_ERR_AUTHENTICATION = "ERR-authentication" FSM_RMT_INV_ERR_CODE_ERR_AUTHORIZATION_REQUIRED = "ERR-authorization-required" FSM_RMT_INV_ERR_CODE_ERR_CLI_SESSION_LIMIT_REACHED = "ERR-cli-session-limit-reached" FSM_RMT_INV_ERR_CODE_ERR_CREATE_KEYRING = "ERR-create-keyring" FSM_RMT_INV_ERR_CODE_ERR_CREATE_LOCALE = "ERR-create-locale" FSM_RMT_INV_ERR_CODE_ERR_CREATE_ROLE = "ERR-create-role" FSM_RMT_INV_ERR_CODE_ERR_CREATE_TP = "ERR-create-tp" FSM_RMT_INV_ERR_CODE_ERR_CREATE_USER = "ERR-create-user" FSM_RMT_INV_ERR_CODE_ERR_DELETE_LOCALE = "ERR-delete-locale" FSM_RMT_INV_ERR_CODE_ERR_DELETE_ROLE = "ERR-delete-role" FSM_RMT_INV_ERR_CODE_ERR_DELETE_SESSION = "ERR-delete-session" FSM_RMT_INV_ERR_CODE_ERR_DELETE_USER = "ERR-delete-user" FSM_RMT_INV_ERR_CODE_ERR_DOWNGRADE_FAIL = "ERR-downgrade-fail" FSM_RMT_INV_ERR_CODE_ERR_EFI_DIAGNOSTICS_IN_PROGRESS = "ERR-efi-Diagnostics--in-progress" FSM_RMT_INV_ERR_CODE_ERR_ENABLE_MGMT_CONN = "ERR-enable-mgmt-conn" FSM_RMT_INV_ERR_CODE_ERR_EP_SET_ERROR = "ERR-ep-set-error" FSM_RMT_INV_ERR_CODE_ERR_GET_MAX_HTTP_USER_SESSIONS = "ERR-get-max-http-user-sessions" FSM_RMT_INV_ERR_CODE_ERR_HTTP_INITIALIZING = "ERR-http-initializing" FSM_RMT_INV_ERR_CODE_ERR_INSUFFICIENTLY_EQUIPPED = "ERR-insufficiently-equipped" FSM_RMT_INV_ERR_CODE_ERR_INTERNAL_ERROR = "ERR-internal-error" FSM_RMT_INV_ERR_CODE_ERR_LDAP_DELETE_ERROR = "ERR-ldap-delete-error" FSM_RMT_INV_ERR_CODE_ERR_LDAP_GET_ERROR = "ERR-ldap-get-error" FSM_RMT_INV_ERR_CODE_ERR_LDAP_GROUP_MODIFY_ERROR = "ERR-ldap-group-modify-error" FSM_RMT_INV_ERR_CODE_ERR_LDAP_GROUP_SET_ERROR = "ERR-ldap-group-set-error" FSM_RMT_INV_ERR_CODE_ERR_LDAP_SET_ERROR = "ERR-ldap-set-error" FSM_RMT_INV_ERR_CODE_ERR_LOCALE_SET_ERROR = "ERR-locale-set-error" FSM_RMT_INV_ERR_CODE_ERR_MAX_USERID_SESSIONS_REACHED = "ERR-max-userid-sessions-reached" FSM_RMT_INV_ERR_CODE_ERR_MISSING_METHOD = "ERR-missing-method" FSM_RMT_INV_ERR_CODE_ERR_MODIFY_LOCALE = "ERR-modify-locale" FSM_RMT_INV_ERR_CODE_ERR_MODIFY_ROLE = "ERR-modify-role" FSM_RMT_INV_ERR_CODE_ERR_MODIFY_USER = "ERR-modify-user" FSM_RMT_INV_ERR_CODE_ERR_MODIFY_USER_LOCALE = "ERR-modify-user-locale" FSM_RMT_INV_ERR_CODE_ERR_MODIFY_USER_ROLE = "ERR-modify-user-role" FSM_RMT_INV_ERR_CODE_ERR_PROVIDER_GROUP_MODIFY_ERROR = "ERR-provider-group-modify-error" FSM_RMT_INV_ERR_CODE_ERR_PROVIDER_GROUP_SET_ERROR = "ERR-provider-group-set-error" FSM_RMT_INV_ERR_CODE_ERR_RADIUS_GET_ERROR = "ERR-radius-get-error" FSM_RMT_INV_ERR_CODE_ERR_RADIUS_GLOBAL_SET_ERROR = "ERR-radius-global-set-error" FSM_RMT_INV_ERR_CODE_ERR_RADIUS_GROUP_SET_ERROR = "ERR-radius-group-set-error" FSM_RMT_INV_ERR_CODE_ERR_RADIUS_SET_ERROR = "ERR-radius-set-error" FSM_RMT_INV_ERR_CODE_ERR_REQUEST_TIMEOUT = "ERR-request-timeout" FSM_RMT_INV_ERR_CODE_ERR_RESET_ADAPTER = "ERR-reset-adapter" FSM_RMT_INV_ERR_CODE_ERR_ROLE_SET_ERROR = "ERR-role-set-error" FSM_RMT_INV_ERR_CODE_ERR_SECONDARY_NODE = "ERR-secondary-node" FSM_RMT_INV_ERR_CODE_ERR_SERVICE_NOT_READY = "ERR-service-not-ready" FSM_RMT_INV_ERR_CODE_ERR_SESSION_CACHE_FULL = "ERR-session-cache-full" FSM_RMT_INV_ERR_CODE_ERR_SESSION_NOT_FOUND = "ERR-session-not-found" FSM_RMT_INV_ERR_CODE_ERR_SET_KEY_CERT = "ERR-set-key-cert" FSM_RMT_INV_ERR_CODE_ERR_SET_LOGIN_PROFILE = "ERR-set-login-profile" FSM_RMT_INV_ERR_CODE_ERR_SET_MIN_PASSPHRASE_LENGTH = "ERR-set-min-passphrase-length" FSM_RMT_INV_ERR_CODE_ERR_SET_NETWORK = "ERR-set-network" FSM_RMT_INV_ERR_CODE_ERR_SET_PASSWORD_STRENGTH_CHECK = "ERR-set-password-strength-check" FSM_RMT_INV_ERR_CODE_ERR_SET_PORT_CHANNEL = "ERR-set-port-channel" FSM_RMT_INV_ERR_CODE_ERR_STORE_PRE_LOGIN_BANNER_MSG = "ERR-store-pre-login-banner-msg" FSM_RMT_INV_ERR_CODE_ERR_TACACS_ENABLE_ERROR = "ERR-tacacs-enable-error" FSM_RMT_INV_ERR_CODE_ERR_TACACS_GLOBAL_SET_ERROR = "ERR-tacacs-global-set-error" FSM_RMT_INV_ERR_CODE_ERR_TACACS_GROUP_SET_ERROR = "ERR-tacacs-group-set-error" FSM_RMT_INV_ERR_CODE_ERR_TACACS_PLUS_GET_ERROR = "ERR-tacacs-plus-get-error" FSM_RMT_INV_ERR_CODE_ERR_TACACS_SET_ERROR = "ERR-tacacs-set-error" FSM_RMT_INV_ERR_CODE_ERR_TEST_ERROR_1 = "ERR-test-error-1" FSM_RMT_INV_ERR_CODE_ERR_TEST_ERROR_2 = "ERR-test-error-2" FSM_RMT_INV_ERR_CODE_ERR_TIMEZONE_SET_ERROR = "ERR-timezone-set-error" FSM_RMT_INV_ERR_CODE_ERR_USER_ACCOUNT_EXPIRED = "ERR-user-account-expired" FSM_RMT_INV_ERR_CODE_ERR_USER_PASSWD_EXPIRED = "ERR-user-passwd-expired" FSM_RMT_INV_ERR_CODE_ERR_USER_SET_ERROR = "ERR-user-set-error" FSM_RMT_INV_ERR_CODE_ERR_XML_PARSE_ERROR = "ERR-xml-parse-error" FSM_RMT_INV_ERR_CODE_NONE = "none" FSM_STAMP_NEVER = "never" FSM_STATUS_DEPLOY_BEGIN = "DeployBegin" FSM_STATUS_DEPLOY_FAIL = "DeployFail" FSM_STATUS_DEPLOY_SUCCESS = "DeploySuccess" FSM_STATUS_DEPLOY_UPDATE_CONNECTIVITY = "DeployUpdateConnectivity" FSM_STATUS_NOP = "nop" SWITCH_ID_A = "A" SWITCH_ID_B = "B" SWITCH_ID_NONE = "NONE" class SwAccessDomain(ManagedObject): """This is SwAccessDomain class.""" consts = SwAccessDomainConsts() naming_props = set([]) mo_meta = MoMeta("SwAccessDomain", "swAccessDomain", "access-eth", VersionMeta.Version101e, "InputOutput", 0x7f, [], ["read-only"], ['networkElement'], ['eventInst', 'faultInst', 'swAccessDomainFsm', 'swAccessDomainFsmTask', 'swAccessEp', 'swSubGroup'], ["Get"]) prop_meta = { "child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, 0x2, None, None, r"""((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}""", [], []), "dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version101e, MoPropertyMeta.READ_ONLY, 0x4, 0, 256, None, [], []), "fsm_descr": MoPropertyMeta("fsm_descr", "fsmDescr", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), "fsm_prev": MoPropertyMeta("fsm_prev", "fsmPrev", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, ["DeployBegin", "DeployFail", "DeploySuccess", "DeployUpdateConnectivity", "nop"], []), "fsm_progr": MoPropertyMeta("fsm_progr", "fsmProgr", "byte", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, [], ["0-100"]), "fsm_rmt_inv_err_code": MoPropertyMeta("fsm_rmt_inv_err_code", "fsmRmtInvErrCode", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, ["ERR-2fa-auth-retry", "ERR-ACTIVATE-failed", "ERR-ACTIVATE-in-progress", "ERR-ACTIVATE-retry", "ERR-BIOS-TOKENS-OLD-BIOS", "ERR-BIOS-TOKENS-OLD-CIMC", "ERR-BIOS-network-boot-order-not-found", "ERR-BOARDCTRLUPDATE-ignore", "ERR-DIAG-cancelled", "ERR-DIAG-fsm-restarted", "ERR-DIAG-test-failed", "ERR-DNLD-authentication-failure", "ERR-DNLD-hostkey-mismatch", "ERR-DNLD-invalid-image", "ERR-DNLD-no-file", "ERR-DNLD-no-space", "ERR-DNLD-usb-unmounted", "ERR-DNS-delete-error", "ERR-DNS-get-error", "ERR-DNS-set-error", "ERR-Diagnostics-in-progress", "ERR-Diagnostics-memtest-in-progress", "ERR-Diagnostics-network-in-progress", "ERR-FILTER-illegal-format", "ERR-FSM-no-such-state", "ERR-HOST-fru-identity-mismatch", "ERR-HTTP-set-error", "ERR-HTTPS-set-error", "ERR-IBMC-analyze-results", "ERR-IBMC-connect-error", "ERR-IBMC-connector-info-retrieval-error", "ERR-IBMC-fru-retrieval-error", "ERR-IBMC-invalid-end-point-config", "ERR-IBMC-results-not-ready", "ERR-MAX-subscriptions-allowed-error", "ERR-MO-CONFIG-child-object-cant-be-configured", "ERR-MO-META-no-such-object-class", "ERR-MO-PROPERTY-no-such-property", "ERR-MO-PROPERTY-value-out-of-range", "ERR-MO-access-denied", "ERR-MO-deletion-rule-violation", "ERR-MO-duplicate-object", "ERR-MO-illegal-containment", "ERR-MO-illegal-creation", "ERR-MO-illegal-iterator-state", "ERR-MO-illegal-object-lifecycle-transition", "ERR-MO-naming-rule-violation", "ERR-MO-object-not-found", "ERR-MO-resource-allocation", "ERR-NTP-delete-error", "ERR-NTP-get-error", "ERR-NTP-set-error", "ERR-POWER-CAP-UNSUPPORTED", "ERR-POWER-PROFILE-IN-PROGRESS", "ERR-SERVER-mis-connect", "ERR-SWITCH-invalid-if-config", "ERR-TOKEN-request-denied", "ERR-UNABLE-TO-FETCH-BIOS-SETTINGS", "ERR-UPDATE-failed", "ERR-UPDATE-in-progress", "ERR-UPDATE-retry", "ERR-aaa-config-modify-error", "ERR-acct-realm-set-error", "ERR-admin-passwd-set", "ERR-auth-issue", "ERR-auth-realm-get-error", "ERR-auth-realm-set-error", "ERR-authentication", "ERR-authorization-required", "ERR-cli-session-limit-reached", "ERR-create-keyring", "ERR-create-locale", "ERR-create-role", "ERR-create-tp", "ERR-create-user", "ERR-delete-locale", "ERR-delete-role", "ERR-delete-session", "ERR-delete-user", "ERR-downgrade-fail", "ERR-efi-Diagnostics--in-progress", "ERR-enable-mgmt-conn", "ERR-ep-set-error", "ERR-get-max-http-user-sessions", "ERR-http-initializing", "ERR-insufficiently-equipped", "ERR-internal-error", "ERR-ldap-delete-error", "ERR-ldap-get-error", "ERR-ldap-group-modify-error", "ERR-ldap-group-set-error", "ERR-ldap-set-error", "ERR-locale-set-error", "ERR-max-userid-sessions-reached", "ERR-missing-method", "ERR-modify-locale", "ERR-modify-role", "ERR-modify-user", "ERR-modify-user-locale", "ERR-modify-user-role", "ERR-provider-group-modify-error", "ERR-provider-group-set-error", "ERR-radius-get-error", "ERR-radius-global-set-error", "ERR-radius-group-set-error", "ERR-radius-set-error", "ERR-request-timeout", "ERR-reset-adapter", "ERR-role-set-error", "ERR-secondary-node", "ERR-service-not-ready", "ERR-session-cache-full", "ERR-session-not-found", "ERR-set-key-cert", "ERR-set-login-profile", "ERR-set-min-passphrase-length", "ERR-set-network", "ERR-set-password-strength-check", "ERR-set-port-channel", "ERR-store-pre-login-banner-msg", "ERR-tacacs-enable-error", "ERR-tacacs-global-set-error", "ERR-tacacs-group-set-error", "ERR-tacacs-plus-get-error", "ERR-tacacs-set-error", "ERR-test-error-1", "ERR-test-error-2", "ERR-timezone-set-error", "ERR-user-account-expired", "ERR-user-passwd-expired", "ERR-user-set-error", "ERR-xml-parse-error", "none"], ["0-4294967295"]), "fsm_rmt_inv_err_descr": MoPropertyMeta("fsm_rmt_inv_err_descr", "fsmRmtInvErrDescr", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, 0, 510, None, [], []), "fsm_rmt_inv_rslt": MoPropertyMeta("fsm_rmt_inv_rslt", "fsmRmtInvRslt", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, r"""((defaultValue|not-applicable|resource-unavailable|service-unavailable|intermittent-error|sw-defect|service-not-implemented-ignore|extend-timeout|capability-not-implemented-failure|illegal-fru|end-point-unavailable|failure|resource-capacity-exceeded|service-protocol-error|fw-defect|service-not-implemented-fail|task-reset|unidentified-fail|capability-not-supported|end-point-failed|fru-state-indeterminate|resource-dependency|fru-identity-indeterminate|internal-error|hw-defect|service-not-supported|fru-not-supported|end-point-protocol-error|capability-unavailable|fru-not-ready|capability-not-implemented-ignore|fru-info-malformed|timeout),){0,32}(defaultValue|not-applicable|resource-unavailable|service-unavailable|intermittent-error|sw-defect|service-not-implemented-ignore|extend-timeout|capability-not-implemented-failure|illegal-fru|end-point-unavailable|failure|resource-capacity-exceeded|service-protocol-error|fw-defect|service-not-implemented-fail|task-reset|unidentified-fail|capability-not-supported|end-point-failed|fru-state-indeterminate|resource-dependency|fru-identity-indeterminate|internal-error|hw-defect|service-not-supported|fru-not-supported|end-point-protocol-error|capability-unavailable|fru-not-ready|capability-not-implemented-ignore|fru-info-malformed|timeout){0,1}""", [], []), "fsm_stage_descr": MoPropertyMeta("fsm_stage_descr", "fsmStageDescr", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), "fsm_stamp": MoPropertyMeta("fsm_stamp", "fsmStamp", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, r"""([0-9]){4}-([0-9]){2}-([0-9]){2}T([0-9]){2}:([0-9]){2}:([0-9]){2}((\.([0-9]){3})){0,1}""", ["never"], []), "fsm_status": MoPropertyMeta("fsm_status", "fsmStatus", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, ["DeployBegin", "DeployFail", "DeploySuccess", "DeployUpdateConnectivity", "nop"], []), "fsm_try": MoPropertyMeta("fsm_try", "fsmTry", "byte", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), "locale": MoPropertyMeta("locale", "locale", "string", VersionMeta.Version101e, MoPropertyMeta.READ_ONLY, None, None, None, r"""((defaultValue|unknown|server|chassis|internal|external),){0,5}(defaultValue|unknown|server|chassis|internal|external){0,1}""", [], []), "name": MoPropertyMeta("name", "name", "string", VersionMeta.Version101e, MoPropertyMeta.CREATE_ONLY, 0x8, None, None, r"""[\-\.:_a-zA-Z0-9]{0,16}""", [], []), "rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version101e, MoPropertyMeta.READ_ONLY, 0x10, 0, 256, None, [], []), "sacl": MoPropertyMeta("sacl", "sacl", "string", VersionMeta.Version302c, MoPropertyMeta.READ_ONLY, None, None, None, r"""((none|del|mod|addchild|cascade),){0,4}(none|del|mod|addchild|cascade){0,1}""", [], []), "status": MoPropertyMeta("status", "status", "string", VersionMeta.Version101e, MoPropertyMeta.READ_WRITE, 0x20, None, None, r"""((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}""", [], []), "switch_id": MoPropertyMeta("switch_id", "switchId", "string", VersionMeta.Version101e, MoPropertyMeta.READ_WRITE, 0x40, None, None, None, ["A", "B", "NONE"], []), "transport": MoPropertyMeta("transport", "transport", "string", VersionMeta.Version101e, MoPropertyMeta.READ_ONLY, None, None, None, r"""((defaultValue|unknown|ether|dce|fc),){0,4}(defaultValue|unknown|ether|dce|fc){0,1}""", [], []), "type": MoPropertyMeta("type", "type", "string", VersionMeta.Version101e, MoPropertyMeta.READ_ONLY, None, None, None, r"""((defaultValue|unknown|lan|san|ipc),){0,4}(defaultValue|unknown|lan|san|ipc){0,1}""", [], []), } prop_map = { "childAction": "child_action", "dn": "dn", "fsmDescr": "fsm_descr", "fsmPrev": "fsm_prev", "fsmProgr": "fsm_progr", "fsmRmtInvErrCode": "fsm_rmt_inv_err_code", "fsmRmtInvErrDescr": "fsm_rmt_inv_err_descr", "fsmRmtInvRslt": "fsm_rmt_inv_rslt", "fsmStageDescr": "fsm_stage_descr", "fsmStamp": "fsm_stamp", "fsmStatus": "fsm_status", "fsmTry": "fsm_try", "locale": "locale", "name": "name", "rn": "rn", "sacl": "sacl", "status": "status", "switchId": "switch_id", "transport": "transport", "type": "type", } def __init__(self, parent_mo_or_dn, **kwargs): self._dirty_mask = 0 self.child_action = None self.fsm_descr = None self.fsm_prev = None self.fsm_progr = None self.fsm_rmt_inv_err_code = None self.fsm_rmt_inv_err_descr = None self.fsm_rmt_inv_rslt = None self.fsm_stage_descr = None self.fsm_stamp = None self.fsm_status = None self.fsm_try = None self.locale = None self.name = None self.sacl = None self.status = None self.switch_id = None self.transport = None self.type = None ManagedObject.__init__(self, "SwAccessDomain", parent_mo_or_dn, **kwargs)

92.679487

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0.763729

from ...ucsmo import ManagedObject from ...ucscoremeta import MoPropertyMeta, MoMeta from ...ucsmeta import VersionMeta class SwAccessDomainConsts: FSM_PREV_DEPLOY_BEGIN = "DeployBegin" FSM_PREV_DEPLOY_FAIL = "DeployFail" FSM_PREV_DEPLOY_SUCCESS = "DeploySuccess" FSM_PREV_DEPLOY_UPDATE_CONNECTIVITY = "DeployUpdateConnectivity" FSM_PREV_NOP = "nop" FSM_RMT_INV_ERR_CODE_ERR_2FA_AUTH_RETRY = "ERR-2fa-auth-retry" FSM_RMT_INV_ERR_CODE_ERR_ACTIVATE_FAILED = "ERR-ACTIVATE-failed" FSM_RMT_INV_ERR_CODE_ERR_ACTIVATE_IN_PROGRESS = "ERR-ACTIVATE-in-progress" FSM_RMT_INV_ERR_CODE_ERR_ACTIVATE_RETRY = "ERR-ACTIVATE-retry" FSM_RMT_INV_ERR_CODE_ERR_BIOS_TOKENS_OLD_BIOS = "ERR-BIOS-TOKENS-OLD-BIOS" FSM_RMT_INV_ERR_CODE_ERR_BIOS_TOKENS_OLD_CIMC = "ERR-BIOS-TOKENS-OLD-CIMC" FSM_RMT_INV_ERR_CODE_ERR_BIOS_NETWORK_BOOT_ORDER_NOT_FOUND = "ERR-BIOS-network-boot-order-not-found" FSM_RMT_INV_ERR_CODE_ERR_BOARDCTRLUPDATE_IGNORE = "ERR-BOARDCTRLUPDATE-ignore" FSM_RMT_INV_ERR_CODE_ERR_DIAG_CANCELLED = "ERR-DIAG-cancelled" FSM_RMT_INV_ERR_CODE_ERR_DIAG_FSM_RESTARTED = "ERR-DIAG-fsm-restarted" FSM_RMT_INV_ERR_CODE_ERR_DIAG_TEST_FAILED = "ERR-DIAG-test-failed" FSM_RMT_INV_ERR_CODE_ERR_DNLD_AUTHENTICATION_FAILURE = "ERR-DNLD-authentication-failure" FSM_RMT_INV_ERR_CODE_ERR_DNLD_HOSTKEY_MISMATCH = "ERR-DNLD-hostkey-mismatch" FSM_RMT_INV_ERR_CODE_ERR_DNLD_INVALID_IMAGE = "ERR-DNLD-invalid-image" FSM_RMT_INV_ERR_CODE_ERR_DNLD_NO_FILE = "ERR-DNLD-no-file" FSM_RMT_INV_ERR_CODE_ERR_DNLD_NO_SPACE = "ERR-DNLD-no-space" FSM_RMT_INV_ERR_CODE_ERR_DNLD_USB_UNMOUNTED = "ERR-DNLD-usb-unmounted" FSM_RMT_INV_ERR_CODE_ERR_DNS_DELETE_ERROR = "ERR-DNS-delete-error" FSM_RMT_INV_ERR_CODE_ERR_DNS_GET_ERROR = "ERR-DNS-get-error" FSM_RMT_INV_ERR_CODE_ERR_DNS_SET_ERROR = "ERR-DNS-set-error" FSM_RMT_INV_ERR_CODE_ERR_DIAGNOSTICS_IN_PROGRESS = "ERR-Diagnostics-in-progress" FSM_RMT_INV_ERR_CODE_ERR_DIAGNOSTICS_MEMTEST_IN_PROGRESS = "ERR-Diagnostics-memtest-in-progress" FSM_RMT_INV_ERR_CODE_ERR_DIAGNOSTICS_NETWORK_IN_PROGRESS = "ERR-Diagnostics-network-in-progress" FSM_RMT_INV_ERR_CODE_ERR_FILTER_ILLEGAL_FORMAT = "ERR-FILTER-illegal-format" FSM_RMT_INV_ERR_CODE_ERR_FSM_NO_SUCH_STATE = "ERR-FSM-no-such-state" FSM_RMT_INV_ERR_CODE_ERR_HOST_FRU_IDENTITY_MISMATCH = "ERR-HOST-fru-identity-mismatch" FSM_RMT_INV_ERR_CODE_ERR_HTTP_SET_ERROR = "ERR-HTTP-set-error" FSM_RMT_INV_ERR_CODE_ERR_HTTPS_SET_ERROR = "ERR-HTTPS-set-error" FSM_RMT_INV_ERR_CODE_ERR_IBMC_ANALYZE_RESULTS = "ERR-IBMC-analyze-results" FSM_RMT_INV_ERR_CODE_ERR_IBMC_CONNECT_ERROR = "ERR-IBMC-connect-error" FSM_RMT_INV_ERR_CODE_ERR_IBMC_CONNECTOR_INFO_RETRIEVAL_ERROR = "ERR-IBMC-connector-info-retrieval-error" FSM_RMT_INV_ERR_CODE_ERR_IBMC_FRU_RETRIEVAL_ERROR = "ERR-IBMC-fru-retrieval-error" FSM_RMT_INV_ERR_CODE_ERR_IBMC_INVALID_END_POINT_CONFIG = "ERR-IBMC-invalid-end-point-config" FSM_RMT_INV_ERR_CODE_ERR_IBMC_RESULTS_NOT_READY = "ERR-IBMC-results-not-ready" FSM_RMT_INV_ERR_CODE_ERR_MAX_SUBSCRIPTIONS_ALLOWED_ERROR = "ERR-MAX-subscriptions-allowed-error" FSM_RMT_INV_ERR_CODE_ERR_MO_CONFIG_CHILD_OBJECT_CANT_BE_CONFIGURED = "ERR-MO-CONFIG-child-object-cant-be-configured" FSM_RMT_INV_ERR_CODE_ERR_MO_META_NO_SUCH_OBJECT_CLASS = "ERR-MO-META-no-such-object-class" FSM_RMT_INV_ERR_CODE_ERR_MO_PROPERTY_NO_SUCH_PROPERTY = "ERR-MO-PROPERTY-no-such-property" FSM_RMT_INV_ERR_CODE_ERR_MO_PROPERTY_VALUE_OUT_OF_RANGE = "ERR-MO-PROPERTY-value-out-of-range" FSM_RMT_INV_ERR_CODE_ERR_MO_ACCESS_DENIED = "ERR-MO-access-denied" FSM_RMT_INV_ERR_CODE_ERR_MO_DELETION_RULE_VIOLATION = "ERR-MO-deletion-rule-violation" FSM_RMT_INV_ERR_CODE_ERR_MO_DUPLICATE_OBJECT = "ERR-MO-duplicate-object" FSM_RMT_INV_ERR_CODE_ERR_MO_ILLEGAL_CONTAINMENT = "ERR-MO-illegal-containment" FSM_RMT_INV_ERR_CODE_ERR_MO_ILLEGAL_CREATION = "ERR-MO-illegal-creation" FSM_RMT_INV_ERR_CODE_ERR_MO_ILLEGAL_ITERATOR_STATE = "ERR-MO-illegal-iterator-state" FSM_RMT_INV_ERR_CODE_ERR_MO_ILLEGAL_OBJECT_LIFECYCLE_TRANSITION = "ERR-MO-illegal-object-lifecycle-transition" FSM_RMT_INV_ERR_CODE_ERR_MO_NAMING_RULE_VIOLATION = "ERR-MO-naming-rule-violation" FSM_RMT_INV_ERR_CODE_ERR_MO_OBJECT_NOT_FOUND = "ERR-MO-object-not-found" FSM_RMT_INV_ERR_CODE_ERR_MO_RESOURCE_ALLOCATION = "ERR-MO-resource-allocation" FSM_RMT_INV_ERR_CODE_ERR_NTP_DELETE_ERROR = "ERR-NTP-delete-error" FSM_RMT_INV_ERR_CODE_ERR_NTP_GET_ERROR = "ERR-NTP-get-error" FSM_RMT_INV_ERR_CODE_ERR_NTP_SET_ERROR = "ERR-NTP-set-error" FSM_RMT_INV_ERR_CODE_ERR_POWER_CAP_UNSUPPORTED = "ERR-POWER-CAP-UNSUPPORTED" FSM_RMT_INV_ERR_CODE_ERR_POWER_PROFILE_IN_PROGRESS = "ERR-POWER-PROFILE-IN-PROGRESS" FSM_RMT_INV_ERR_CODE_ERR_SERVER_MIS_CONNECT = "ERR-SERVER-mis-connect" FSM_RMT_INV_ERR_CODE_ERR_SWITCH_INVALID_IF_CONFIG = "ERR-SWITCH-invalid-if-config" FSM_RMT_INV_ERR_CODE_ERR_TOKEN_REQUEST_DENIED = "ERR-TOKEN-request-denied" FSM_RMT_INV_ERR_CODE_ERR_UNABLE_TO_FETCH_BIOS_SETTINGS = "ERR-UNABLE-TO-FETCH-BIOS-SETTINGS" FSM_RMT_INV_ERR_CODE_ERR_UPDATE_FAILED = "ERR-UPDATE-failed" FSM_RMT_INV_ERR_CODE_ERR_UPDATE_IN_PROGRESS = "ERR-UPDATE-in-progress" FSM_RMT_INV_ERR_CODE_ERR_UPDATE_RETRY = "ERR-UPDATE-retry" FSM_RMT_INV_ERR_CODE_ERR_AAA_CONFIG_MODIFY_ERROR = "ERR-aaa-config-modify-error" FSM_RMT_INV_ERR_CODE_ERR_ACCT_REALM_SET_ERROR = "ERR-acct-realm-set-error" FSM_RMT_INV_ERR_CODE_ERR_ADMIN_PASSWD_SET = "ERR-admin-passwd-set" FSM_RMT_INV_ERR_CODE_ERR_AUTH_ISSUE = "ERR-auth-issue" FSM_RMT_INV_ERR_CODE_ERR_AUTH_REALM_GET_ERROR = "ERR-auth-realm-get-error" FSM_RMT_INV_ERR_CODE_ERR_AUTH_REALM_SET_ERROR = "ERR-auth-realm-set-error" FSM_RMT_INV_ERR_CODE_ERR_AUTHENTICATION = "ERR-authentication" FSM_RMT_INV_ERR_CODE_ERR_AUTHORIZATION_REQUIRED = "ERR-authorization-required" FSM_RMT_INV_ERR_CODE_ERR_CLI_SESSION_LIMIT_REACHED = "ERR-cli-session-limit-reached" FSM_RMT_INV_ERR_CODE_ERR_CREATE_KEYRING = "ERR-create-keyring" FSM_RMT_INV_ERR_CODE_ERR_CREATE_LOCALE = "ERR-create-locale" FSM_RMT_INV_ERR_CODE_ERR_CREATE_ROLE = "ERR-create-role" FSM_RMT_INV_ERR_CODE_ERR_CREATE_TP = "ERR-create-tp" FSM_RMT_INV_ERR_CODE_ERR_CREATE_USER = "ERR-create-user" FSM_RMT_INV_ERR_CODE_ERR_DELETE_LOCALE = "ERR-delete-locale" FSM_RMT_INV_ERR_CODE_ERR_DELETE_ROLE = "ERR-delete-role" FSM_RMT_INV_ERR_CODE_ERR_DELETE_SESSION = "ERR-delete-session" FSM_RMT_INV_ERR_CODE_ERR_DELETE_USER = "ERR-delete-user" FSM_RMT_INV_ERR_CODE_ERR_DOWNGRADE_FAIL = "ERR-downgrade-fail" FSM_RMT_INV_ERR_CODE_ERR_EFI_DIAGNOSTICS_IN_PROGRESS = "ERR-efi-Diagnostics--in-progress" FSM_RMT_INV_ERR_CODE_ERR_ENABLE_MGMT_CONN = "ERR-enable-mgmt-conn" FSM_RMT_INV_ERR_CODE_ERR_EP_SET_ERROR = "ERR-ep-set-error" FSM_RMT_INV_ERR_CODE_ERR_GET_MAX_HTTP_USER_SESSIONS = "ERR-get-max-http-user-sessions" FSM_RMT_INV_ERR_CODE_ERR_HTTP_INITIALIZING = "ERR-http-initializing" FSM_RMT_INV_ERR_CODE_ERR_INSUFFICIENTLY_EQUIPPED = "ERR-insufficiently-equipped" FSM_RMT_INV_ERR_CODE_ERR_INTERNAL_ERROR = "ERR-internal-error" FSM_RMT_INV_ERR_CODE_ERR_LDAP_DELETE_ERROR = "ERR-ldap-delete-error" FSM_RMT_INV_ERR_CODE_ERR_LDAP_GET_ERROR = "ERR-ldap-get-error" FSM_RMT_INV_ERR_CODE_ERR_LDAP_GROUP_MODIFY_ERROR = "ERR-ldap-group-modify-error" FSM_RMT_INV_ERR_CODE_ERR_LDAP_GROUP_SET_ERROR = "ERR-ldap-group-set-error" FSM_RMT_INV_ERR_CODE_ERR_LDAP_SET_ERROR = "ERR-ldap-set-error" FSM_RMT_INV_ERR_CODE_ERR_LOCALE_SET_ERROR = "ERR-locale-set-error" FSM_RMT_INV_ERR_CODE_ERR_MAX_USERID_SESSIONS_REACHED = "ERR-max-userid-sessions-reached" FSM_RMT_INV_ERR_CODE_ERR_MISSING_METHOD = "ERR-missing-method" FSM_RMT_INV_ERR_CODE_ERR_MODIFY_LOCALE = "ERR-modify-locale" FSM_RMT_INV_ERR_CODE_ERR_MODIFY_ROLE = "ERR-modify-role" FSM_RMT_INV_ERR_CODE_ERR_MODIFY_USER = "ERR-modify-user" FSM_RMT_INV_ERR_CODE_ERR_MODIFY_USER_LOCALE = "ERR-modify-user-locale" FSM_RMT_INV_ERR_CODE_ERR_MODIFY_USER_ROLE = "ERR-modify-user-role" FSM_RMT_INV_ERR_CODE_ERR_PROVIDER_GROUP_MODIFY_ERROR = "ERR-provider-group-modify-error" FSM_RMT_INV_ERR_CODE_ERR_PROVIDER_GROUP_SET_ERROR = "ERR-provider-group-set-error" FSM_RMT_INV_ERR_CODE_ERR_RADIUS_GET_ERROR = "ERR-radius-get-error" FSM_RMT_INV_ERR_CODE_ERR_RADIUS_GLOBAL_SET_ERROR = "ERR-radius-global-set-error" FSM_RMT_INV_ERR_CODE_ERR_RADIUS_GROUP_SET_ERROR = "ERR-radius-group-set-error" FSM_RMT_INV_ERR_CODE_ERR_RADIUS_SET_ERROR = "ERR-radius-set-error" FSM_RMT_INV_ERR_CODE_ERR_REQUEST_TIMEOUT = "ERR-request-timeout" FSM_RMT_INV_ERR_CODE_ERR_RESET_ADAPTER = "ERR-reset-adapter" FSM_RMT_INV_ERR_CODE_ERR_ROLE_SET_ERROR = "ERR-role-set-error" FSM_RMT_INV_ERR_CODE_ERR_SECONDARY_NODE = "ERR-secondary-node" FSM_RMT_INV_ERR_CODE_ERR_SERVICE_NOT_READY = "ERR-service-not-ready" FSM_RMT_INV_ERR_CODE_ERR_SESSION_CACHE_FULL = "ERR-session-cache-full" FSM_RMT_INV_ERR_CODE_ERR_SESSION_NOT_FOUND = "ERR-session-not-found" FSM_RMT_INV_ERR_CODE_ERR_SET_KEY_CERT = "ERR-set-key-cert" FSM_RMT_INV_ERR_CODE_ERR_SET_LOGIN_PROFILE = "ERR-set-login-profile" FSM_RMT_INV_ERR_CODE_ERR_SET_MIN_PASSPHRASE_LENGTH = "ERR-set-min-passphrase-length" FSM_RMT_INV_ERR_CODE_ERR_SET_NETWORK = "ERR-set-network" FSM_RMT_INV_ERR_CODE_ERR_SET_PASSWORD_STRENGTH_CHECK = "ERR-set-password-strength-check" FSM_RMT_INV_ERR_CODE_ERR_SET_PORT_CHANNEL = "ERR-set-port-channel" FSM_RMT_INV_ERR_CODE_ERR_STORE_PRE_LOGIN_BANNER_MSG = "ERR-store-pre-login-banner-msg" FSM_RMT_INV_ERR_CODE_ERR_TACACS_ENABLE_ERROR = "ERR-tacacs-enable-error" FSM_RMT_INV_ERR_CODE_ERR_TACACS_GLOBAL_SET_ERROR = "ERR-tacacs-global-set-error" FSM_RMT_INV_ERR_CODE_ERR_TACACS_GROUP_SET_ERROR = "ERR-tacacs-group-set-error" FSM_RMT_INV_ERR_CODE_ERR_TACACS_PLUS_GET_ERROR = "ERR-tacacs-plus-get-error" FSM_RMT_INV_ERR_CODE_ERR_TACACS_SET_ERROR = "ERR-tacacs-set-error" FSM_RMT_INV_ERR_CODE_ERR_TEST_ERROR_1 = "ERR-test-error-1" FSM_RMT_INV_ERR_CODE_ERR_TEST_ERROR_2 = "ERR-test-error-2" FSM_RMT_INV_ERR_CODE_ERR_TIMEZONE_SET_ERROR = "ERR-timezone-set-error" FSM_RMT_INV_ERR_CODE_ERR_USER_ACCOUNT_EXPIRED = "ERR-user-account-expired" FSM_RMT_INV_ERR_CODE_ERR_USER_PASSWD_EXPIRED = "ERR-user-passwd-expired" FSM_RMT_INV_ERR_CODE_ERR_USER_SET_ERROR = "ERR-user-set-error" FSM_RMT_INV_ERR_CODE_ERR_XML_PARSE_ERROR = "ERR-xml-parse-error" FSM_RMT_INV_ERR_CODE_NONE = "none" FSM_STAMP_NEVER = "never" FSM_STATUS_DEPLOY_BEGIN = "DeployBegin" FSM_STATUS_DEPLOY_FAIL = "DeployFail" FSM_STATUS_DEPLOY_SUCCESS = "DeploySuccess" FSM_STATUS_DEPLOY_UPDATE_CONNECTIVITY = "DeployUpdateConnectivity" FSM_STATUS_NOP = "nop" SWITCH_ID_A = "A" SWITCH_ID_B = "B" SWITCH_ID_NONE = "NONE" class SwAccessDomain(ManagedObject): consts = SwAccessDomainConsts() naming_props = set([]) mo_meta = MoMeta("SwAccessDomain", "swAccessDomain", "access-eth", VersionMeta.Version101e, "InputOutput", 0x7f, [], ["read-only"], ['networkElement'], ['eventInst', 'faultInst', 'swAccessDomainFsm', 'swAccessDomainFsmTask', 'swAccessEp', 'swSubGroup'], ["Get"]) prop_meta = { "child_action": MoPropertyMeta("child_action", "childAction", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, 0x2, None, None, r"""((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}""", [], []), "dn": MoPropertyMeta("dn", "dn", "string", VersionMeta.Version101e, MoPropertyMeta.READ_ONLY, 0x4, 0, 256, None, [], []), "fsm_descr": MoPropertyMeta("fsm_descr", "fsmDescr", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), "fsm_prev": MoPropertyMeta("fsm_prev", "fsmPrev", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, ["DeployBegin", "DeployFail", "DeploySuccess", "DeployUpdateConnectivity", "nop"], []), "fsm_progr": MoPropertyMeta("fsm_progr", "fsmProgr", "byte", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, [], ["0-100"]), "fsm_rmt_inv_err_code": MoPropertyMeta("fsm_rmt_inv_err_code", "fsmRmtInvErrCode", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, ["ERR-2fa-auth-retry", "ERR-ACTIVATE-failed", "ERR-ACTIVATE-in-progress", "ERR-ACTIVATE-retry", "ERR-BIOS-TOKENS-OLD-BIOS", "ERR-BIOS-TOKENS-OLD-CIMC", "ERR-BIOS-network-boot-order-not-found", "ERR-BOARDCTRLUPDATE-ignore", "ERR-DIAG-cancelled", "ERR-DIAG-fsm-restarted", "ERR-DIAG-test-failed", "ERR-DNLD-authentication-failure", "ERR-DNLD-hostkey-mismatch", "ERR-DNLD-invalid-image", "ERR-DNLD-no-file", "ERR-DNLD-no-space", "ERR-DNLD-usb-unmounted", "ERR-DNS-delete-error", "ERR-DNS-get-error", "ERR-DNS-set-error", "ERR-Diagnostics-in-progress", "ERR-Diagnostics-memtest-in-progress", "ERR-Diagnostics-network-in-progress", "ERR-FILTER-illegal-format", "ERR-FSM-no-such-state", "ERR-HOST-fru-identity-mismatch", "ERR-HTTP-set-error", "ERR-HTTPS-set-error", "ERR-IBMC-analyze-results", "ERR-IBMC-connect-error", "ERR-IBMC-connector-info-retrieval-error", "ERR-IBMC-fru-retrieval-error", "ERR-IBMC-invalid-end-point-config", "ERR-IBMC-results-not-ready", "ERR-MAX-subscriptions-allowed-error", "ERR-MO-CONFIG-child-object-cant-be-configured", "ERR-MO-META-no-such-object-class", "ERR-MO-PROPERTY-no-such-property", "ERR-MO-PROPERTY-value-out-of-range", "ERR-MO-access-denied", "ERR-MO-deletion-rule-violation", "ERR-MO-duplicate-object", "ERR-MO-illegal-containment", "ERR-MO-illegal-creation", "ERR-MO-illegal-iterator-state", "ERR-MO-illegal-object-lifecycle-transition", "ERR-MO-naming-rule-violation", "ERR-MO-object-not-found", "ERR-MO-resource-allocation", "ERR-NTP-delete-error", "ERR-NTP-get-error", "ERR-NTP-set-error", "ERR-POWER-CAP-UNSUPPORTED", "ERR-POWER-PROFILE-IN-PROGRESS", "ERR-SERVER-mis-connect", "ERR-SWITCH-invalid-if-config", "ERR-TOKEN-request-denied", "ERR-UNABLE-TO-FETCH-BIOS-SETTINGS", "ERR-UPDATE-failed", "ERR-UPDATE-in-progress", "ERR-UPDATE-retry", "ERR-aaa-config-modify-error", "ERR-acct-realm-set-error", "ERR-admin-passwd-set", "ERR-auth-issue", "ERR-auth-realm-get-error", "ERR-auth-realm-set-error", "ERR-authentication", "ERR-authorization-required", "ERR-cli-session-limit-reached", "ERR-create-keyring", "ERR-create-locale", "ERR-create-role", "ERR-create-tp", "ERR-create-user", "ERR-delete-locale", "ERR-delete-role", "ERR-delete-session", "ERR-delete-user", "ERR-downgrade-fail", "ERR-efi-Diagnostics--in-progress", "ERR-enable-mgmt-conn", "ERR-ep-set-error", "ERR-get-max-http-user-sessions", "ERR-http-initializing", "ERR-insufficiently-equipped", "ERR-internal-error", "ERR-ldap-delete-error", "ERR-ldap-get-error", "ERR-ldap-group-modify-error", "ERR-ldap-group-set-error", "ERR-ldap-set-error", "ERR-locale-set-error", "ERR-max-userid-sessions-reached", "ERR-missing-method", "ERR-modify-locale", "ERR-modify-role", "ERR-modify-user", "ERR-modify-user-locale", "ERR-modify-user-role", "ERR-provider-group-modify-error", "ERR-provider-group-set-error", "ERR-radius-get-error", "ERR-radius-global-set-error", "ERR-radius-group-set-error", "ERR-radius-set-error", "ERR-request-timeout", "ERR-reset-adapter", "ERR-role-set-error", "ERR-secondary-node", "ERR-service-not-ready", "ERR-session-cache-full", "ERR-session-not-found", "ERR-set-key-cert", "ERR-set-login-profile", "ERR-set-min-passphrase-length", "ERR-set-network", "ERR-set-password-strength-check", "ERR-set-port-channel", "ERR-store-pre-login-banner-msg", "ERR-tacacs-enable-error", "ERR-tacacs-global-set-error", "ERR-tacacs-group-set-error", "ERR-tacacs-plus-get-error", "ERR-tacacs-set-error", "ERR-test-error-1", "ERR-test-error-2", "ERR-timezone-set-error", "ERR-user-account-expired", "ERR-user-passwd-expired", "ERR-user-set-error", "ERR-xml-parse-error", "none"], ["0-4294967295"]), "fsm_rmt_inv_err_descr": MoPropertyMeta("fsm_rmt_inv_err_descr", "fsmRmtInvErrDescr", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, 0, 510, None, [], []), "fsm_rmt_inv_rslt": MoPropertyMeta("fsm_rmt_inv_rslt", "fsmRmtInvRslt", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, r"""((defaultValue|not-applicable|resource-unavailable|service-unavailable|intermittent-error|sw-defect|service-not-implemented-ignore|extend-timeout|capability-not-implemented-failure|illegal-fru|end-point-unavailable|failure|resource-capacity-exceeded|service-protocol-error|fw-defect|service-not-implemented-fail|task-reset|unidentified-fail|capability-not-supported|end-point-failed|fru-state-indeterminate|resource-dependency|fru-identity-indeterminate|internal-error|hw-defect|service-not-supported|fru-not-supported|end-point-protocol-error|capability-unavailable|fru-not-ready|capability-not-implemented-ignore|fru-info-malformed|timeout),){0,32}(defaultValue|not-applicable|resource-unavailable|service-unavailable|intermittent-error|sw-defect|service-not-implemented-ignore|extend-timeout|capability-not-implemented-failure|illegal-fru|end-point-unavailable|failure|resource-capacity-exceeded|service-protocol-error|fw-defect|service-not-implemented-fail|task-reset|unidentified-fail|capability-not-supported|end-point-failed|fru-state-indeterminate|resource-dependency|fru-identity-indeterminate|internal-error|hw-defect|service-not-supported|fru-not-supported|end-point-protocol-error|capability-unavailable|fru-not-ready|capability-not-implemented-ignore|fru-info-malformed|timeout){0,1}""", [], []), "fsm_stage_descr": MoPropertyMeta("fsm_stage_descr", "fsmStageDescr", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), "fsm_stamp": MoPropertyMeta("fsm_stamp", "fsmStamp", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, r"""([0-9]){4}-([0-9]){2}-([0-9]){2}T([0-9]){2}:([0-9]){2}:([0-9]){2}((\.([0-9]){3})){0,1}""", ["never"], []), "fsm_status": MoPropertyMeta("fsm_status", "fsmStatus", "string", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, ["DeployBegin", "DeployFail", "DeploySuccess", "DeployUpdateConnectivity", "nop"], []), "fsm_try": MoPropertyMeta("fsm_try", "fsmTry", "byte", VersionMeta.Version101e, MoPropertyMeta.INTERNAL, None, None, None, None, [], []), "locale": MoPropertyMeta("locale", "locale", "string", VersionMeta.Version101e, MoPropertyMeta.READ_ONLY, None, None, None, r"""((defaultValue|unknown|server|chassis|internal|external),){0,5}(defaultValue|unknown|server|chassis|internal|external){0,1}""", [], []), "name": MoPropertyMeta("name", "name", "string", VersionMeta.Version101e, MoPropertyMeta.CREATE_ONLY, 0x8, None, None, r"""[\-\.:_a-zA-Z0-9]{0,16}""", [], []), "rn": MoPropertyMeta("rn", "rn", "string", VersionMeta.Version101e, MoPropertyMeta.READ_ONLY, 0x10, 0, 256, None, [], []), "sacl": MoPropertyMeta("sacl", "sacl", "string", VersionMeta.Version302c, MoPropertyMeta.READ_ONLY, None, None, None, r"""((none|del|mod|addchild|cascade),){0,4}(none|del|mod|addchild|cascade){0,1}""", [], []), "status": MoPropertyMeta("status", "status", "string", VersionMeta.Version101e, MoPropertyMeta.READ_WRITE, 0x20, None, None, r"""((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}""", [], []), "switch_id": MoPropertyMeta("switch_id", "switchId", "string", VersionMeta.Version101e, MoPropertyMeta.READ_WRITE, 0x40, None, None, None, ["A", "B", "NONE"], []), "transport": MoPropertyMeta("transport", "transport", "string", VersionMeta.Version101e, MoPropertyMeta.READ_ONLY, None, None, None, r"""((defaultValue|unknown|ether|dce|fc),){0,4}(defaultValue|unknown|ether|dce|fc){0,1}""", [], []), "type": MoPropertyMeta("type", "type", "string", VersionMeta.Version101e, MoPropertyMeta.READ_ONLY, None, None, None, r"""((defaultValue|unknown|lan|san|ipc),){0,4}(defaultValue|unknown|lan|san|ipc){0,1}""", [], []), } prop_map = { "childAction": "child_action", "dn": "dn", "fsmDescr": "fsm_descr", "fsmPrev": "fsm_prev", "fsmProgr": "fsm_progr", "fsmRmtInvErrCode": "fsm_rmt_inv_err_code", "fsmRmtInvErrDescr": "fsm_rmt_inv_err_descr", "fsmRmtInvRslt": "fsm_rmt_inv_rslt", "fsmStageDescr": "fsm_stage_descr", "fsmStamp": "fsm_stamp", "fsmStatus": "fsm_status", "fsmTry": "fsm_try", "locale": "locale", "name": "name", "rn": "rn", "sacl": "sacl", "status": "status", "switchId": "switch_id", "transport": "transport", "type": "type", } def __init__(self, parent_mo_or_dn, **kwargs): self._dirty_mask = 0 self.child_action = None self.fsm_descr = None self.fsm_prev = None self.fsm_progr = None self.fsm_rmt_inv_err_code = None self.fsm_rmt_inv_err_descr = None self.fsm_rmt_inv_rslt = None self.fsm_stage_descr = None self.fsm_stamp = None self.fsm_status = None self.fsm_try = None self.locale = None self.name = None self.sacl = None self.status = None self.switch_id = None self.transport = None self.type = None ManagedObject.__init__(self, "SwAccessDomain", parent_mo_or_dn, **kwargs)

true

f71e8a1667be90cf32f5b0d97e063dc0e58bd349

1,061

py

Python

venv/lib/python3.8/site-packages/vsts/project_analysis/v4_0/models/code_change_trend_item.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

null

venv/lib/python3.8/site-packages/vsts/project_analysis/v4_0/models/code_change_trend_item.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

null

venv/lib/python3.8/site-packages/vsts/project_analysis/v4_0/models/code_change_trend_item.py

amcclead7336/Enterprise_Data_Science_Final

ccdc0aa08d4726bf82d71c11a1cc0c63eb301a28

[ "Unlicense", "MIT" ]

2

2021-05-23T16:46:31.000Z

2021-05-26T23:51:09.000Z

# -------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License. See License.txt in the project root for license information. # -------------------------------------------------------------------------------------------- # Generated file, DO NOT EDIT # Changes may cause incorrect behavior and will be lost if the code is regenerated. # -------------------------------------------------------------------------------------------- from msrest.serialization import Model class CodeChangeTrendItem(Model): """CodeChangeTrendItem. :param time: :type time: datetime :param value: :type value: int """ _attribute_map = { 'time': {'key': 'time', 'type': 'iso-8601'}, 'value': {'key': 'value', 'type': 'int'} } def __init__(self, time=None, value=None): super(CodeChangeTrendItem, self).__init__() self.time = time self.value = value

35.366667

95

0.462771

from msrest.serialization import Model class CodeChangeTrendItem(Model): _attribute_map = { 'time': {'key': 'time', 'type': 'iso-8601'}, 'value': {'key': 'value', 'type': 'int'} } def __init__(self, time=None, value=None): super(CodeChangeTrendItem, self).__init__() self.time = time self.value = value

true

f71e8a720251ce2de0a066d3b179ea50b2fd4a45

4,594

py

Python

tencentcloud/ocr/v20181119/ocr_client.py

liangzhengkang/tencentcloud-sdk-python

c8f990b33f3701e04149a3d613538829a88269eb

[ "Apache-2.0" ]

null

tencentcloud/ocr/v20181119/ocr_client.py

liangzhengkang/tencentcloud-sdk-python

c8f990b33f3701e04149a3d613538829a88269eb

[ "Apache-2.0" ]

null

tencentcloud/ocr/v20181119/ocr_client.py

liangzhengkang/tencentcloud-sdk-python

c8f990b33f3701e04149a3d613538829a88269eb

[ "Apache-2.0" ]

1

2019-03-25T02:21:47.000Z

# -*- coding: utf8 -*- # Copyright (c) 2017-2018 THL A29 Limited, a Tencent company. 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 json from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException from tencentcloud.common.abstract_client import AbstractClient from tencentcloud.ocr.v20181119 import models class OcrClient(AbstractClient): _apiVersion = '2018-11-19' _endpoint = 'ocr.tencentcloudapi.com' def GeneralBasicOCR(self, request): """通用印刷体识别接口用于提供图像整体文字的检测和识别服务，返回文字框位置与文字内容。支持多场景、任意版面下整图文字的识别，以及中英文、字母、数字和日文、韩文的识别。应用场景包括：印刷文档识别、网络图片识别、广告图文字识别、街景店招识别、菜单识别、视频标题识别、头像文字识别等。 :param request: 调用GeneralBasicOCR所需参数的结构体。 :type request: :class:`tencentcloud.ocr.v20181119.models.GeneralBasicOCRRequest` :rtype: :class:`tencentcloud.ocr.v20181119.models.GeneralBasicOCRResponse` """ try: params = request._serialize() body = self.call("GeneralBasicOCR", params) response = json.loads(body) if "Error" not in response["Response"]: model = models.GeneralBasicOCRResponse() model._deserialize(response["Response"]) return model else: code = response["Response"]["Error"]["Code"] message = response["Response"]["Error"]["Message"] reqid = response["Response"]["RequestId"] raise TencentCloudSDKException(code, message, reqid) except Exception as e: if isinstance(e, TencentCloudSDKException): raise else: raise TencentCloudSDKException(e.message, e.message) def GeneralFastOCR(self, request): """通用印刷体识别（高速版）接口用于提供图像整体文字的检测和识别服务，返回文字框位置与文字内容。相比通用印刷体识别接口，识别速度更快、支持的QPS更高。 :param request: 调用GeneralFastOCR所需参数的结构体。 :type request: :class:`tencentcloud.ocr.v20181119.models.GeneralFastOCRRequest` :rtype: :class:`tencentcloud.ocr.v20181119.models.GeneralFastOCRResponse` """ try: params = request._serialize() body = self.call("GeneralFastOCR", params) response = json.loads(body) if "Error" not in response["Response"]: model = models.GeneralFastOCRResponse() model._deserialize(response["Response"]) return model else: code = response["Response"]["Error"]["Code"] message = response["Response"]["Error"]["Message"] reqid = response["Response"]["RequestId"] raise TencentCloudSDKException(code, message, reqid) except Exception as e: if isinstance(e, TencentCloudSDKException): raise else: raise TencentCloudSDKException(e.message, e.message) def IDCardOCR(self, request): """身份证识别接口支持二代身份证正反面所有字段的识别，包括姓名、性别、民族、出生日期、住址、公民身份证号、签发机关、有效期限；具备身份证照片、人像照片的裁剪功能和翻拍件、复印件的识别告警功能。应用场景包括：银行开户、用户注册、人脸核身等各种身份证信息有效性核验场景。 :param request: 调用IDCardOCR所需参数的结构体。 :type request: :class:`tencentcloud.ocr.v20181119.models.IDCardOCRRequest` :rtype: :class:`tencentcloud.ocr.v20181119.models.IDCardOCRResponse` """ try: params = request._serialize() body = self.call("IDCardOCR", params) response = json.loads(body) if "Error" not in response["Response"]: model = models.IDCardOCRResponse() model._deserialize(response["Response"]) return model else: code = response["Response"]["Error"]["Code"] message = response["Response"]["Error"]["Message"] reqid = response["Response"]["RequestId"] raise TencentCloudSDKException(code, message, reqid) except Exception as e: if isinstance(e, TencentCloudSDKException): raise else: raise TencentCloudSDKException(e.message, e.message)

42.146789

148

0.637135

import json from tencentcloud.common.exception.tencent_cloud_sdk_exception import TencentCloudSDKException from tencentcloud.common.abstract_client import AbstractClient from tencentcloud.ocr.v20181119 import models class OcrClient(AbstractClient): _apiVersion = '2018-11-19' _endpoint = 'ocr.tencentcloudapi.com' def GeneralBasicOCR(self, request): try: params = request._serialize() body = self.call("GeneralBasicOCR", params) response = json.loads(body) if "Error" not in response["Response"]: model = models.GeneralBasicOCRResponse() model._deserialize(response["Response"]) return model else: code = response["Response"]["Error"]["Code"] message = response["Response"]["Error"]["Message"] reqid = response["Response"]["RequestId"] raise TencentCloudSDKException(code, message, reqid) except Exception as e: if isinstance(e, TencentCloudSDKException): raise else: raise TencentCloudSDKException(e.message, e.message) def GeneralFastOCR(self, request): try: params = request._serialize() body = self.call("GeneralFastOCR", params) response = json.loads(body) if "Error" not in response["Response"]: model = models.GeneralFastOCRResponse() model._deserialize(response["Response"]) return model else: code = response["Response"]["Error"]["Code"] message = response["Response"]["Error"]["Message"] reqid = response["Response"]["RequestId"] raise TencentCloudSDKException(code, message, reqid) except Exception as e: if isinstance(e, TencentCloudSDKException): raise else: raise TencentCloudSDKException(e.message, e.message) def IDCardOCR(self, request): try: params = request._serialize() body = self.call("IDCardOCR", params) response = json.loads(body) if "Error" not in response["Response"]: model = models.IDCardOCRResponse() model._deserialize(response["Response"]) return model else: code = response["Response"]["Error"]["Code"] message = response["Response"]["Error"]["Message"] reqid = response["Response"]["RequestId"] raise TencentCloudSDKException(code, message, reqid) except Exception as e: if isinstance(e, TencentCloudSDKException): raise else: raise TencentCloudSDKException(e.message, e.message)

true

f71e8ab3c51338680d4b08a028ea1ec34d4be34f

2,675

py

Python

wxviews/core/tests/pipes_tests.py

eumis/wxviews

7b3adffb119e480807276ea3149d878c3879baaf

[ "MIT" ]

6

2018-04-12T20:30:57.000Z

2020-03-25T12:57:00.000Z

wxviews/core/tests/pipes_tests.py

eumis/wxviews

7b3adffb119e480807276ea3149d878c3879baaf

[ "MIT" ]

null

wxviews/core/tests/pipes_tests.py

eumis/wxviews

7b3adffb119e480807276ea3149d878c3879baaf

[ "MIT" ]

null

from unittest.mock import Mock, call, patch from pytest import fixture, mark, fail from pyviews.core import XmlAttr from wxviews.core import pipes, WxRenderingContext from wxviews.core.pipes import apply_attributes, add_to_sizer from wxviews.widgets import WxNode class TestControl: def __init__(self): self.node_key = None self.instance_key = None class TestNode(WxNode): def __init__(self, widget): super().__init__(widget, Mock()) self.node_key = None @fixture def apply_attribute_fixture(request): with patch(pipes.__name__ + '.apply_attribute') as apply_attribute_mock: request.cls.apply_attribute = apply_attribute_mock yield apply_attribute_mock @mark.usefixtures('apply_attribute_fixture') class ApplyAttributesTests: """apply_attributes() step tests""" @mark.parametrize('attr', [ XmlAttr('key', 'value', 'init') ]) def test_skip_special_attributes(self, attr): """should skip attributes with "init" and "sizer" namespaces""" self.apply_attribute.reset_mock() node = Mock(xml_node=Mock(attrs=[attr])) apply_attributes(node, WxRenderingContext()) assert not self.apply_attribute.called @mark.parametrize('attrs', [ [XmlAttr('key', 'value')], [XmlAttr('key', 'value', ''), XmlAttr('other_key', 'key', 'some namespace')] ]) def test_apply_attributes(self, attrs): """should apply passed attributes""" self.apply_attribute.reset_mock() node = Mock(xml_node=Mock(attrs=attrs)) apply_attributes(node, WxRenderingContext()) assert self.apply_attribute.call_args_list == [call(node, attr) for attr in attrs] class AddToSizerTests: """add_to_sizer() step tests""" @staticmethod def _get_mocks(sizer_args=None, node_globals=None): sizer_args = sizer_args if sizer_args else {} node = Mock(sizer_args=sizer_args, node_globals=node_globals, instace=Mock()) return node, Mock() @mark.parametrize('sizer_args', [ {}, {'key': 'value'}, {'key': 'value', 'one': 1} ]) def test_passes_attr_args(self, sizer_args): """should call sizer.Add with node.sizer_args""" node, sizer = self._get_mocks(sizer_args) add_to_sizer(node, WxRenderingContext({'sizer': sizer})) assert sizer.Add.call_args == call(node.instance, **sizer_args) def test_skips_if_sizer_missed(self): """should skip if sizer is missed""" node = self._get_mocks()[0] try: add_to_sizer(node, WxRenderingContext()) except BaseException: fail()

29.722222

90

0.662804

from unittest.mock import Mock, call, patch from pytest import fixture, mark, fail from pyviews.core import XmlAttr from wxviews.core import pipes, WxRenderingContext from wxviews.core.pipes import apply_attributes, add_to_sizer from wxviews.widgets import WxNode class TestControl: def __init__(self): self.node_key = None self.instance_key = None class TestNode(WxNode): def __init__(self, widget): super().__init__(widget, Mock()) self.node_key = None @fixture def apply_attribute_fixture(request): with patch(pipes.__name__ + '.apply_attribute') as apply_attribute_mock: request.cls.apply_attribute = apply_attribute_mock yield apply_attribute_mock @mark.usefixtures('apply_attribute_fixture') class ApplyAttributesTests: @mark.parametrize('attr', [ XmlAttr('key', 'value', 'init') ]) def test_skip_special_attributes(self, attr): self.apply_attribute.reset_mock() node = Mock(xml_node=Mock(attrs=[attr])) apply_attributes(node, WxRenderingContext()) assert not self.apply_attribute.called @mark.parametrize('attrs', [ [XmlAttr('key', 'value')], [XmlAttr('key', 'value', ''), XmlAttr('other_key', 'key', 'some namespace')] ]) def test_apply_attributes(self, attrs): self.apply_attribute.reset_mock() node = Mock(xml_node=Mock(attrs=attrs)) apply_attributes(node, WxRenderingContext()) assert self.apply_attribute.call_args_list == [call(node, attr) for attr in attrs] class AddToSizerTests: @staticmethod def _get_mocks(sizer_args=None, node_globals=None): sizer_args = sizer_args if sizer_args else {} node = Mock(sizer_args=sizer_args, node_globals=node_globals, instace=Mock()) return node, Mock() @mark.parametrize('sizer_args', [ {}, {'key': 'value'}, {'key': 'value', 'one': 1} ]) def test_passes_attr_args(self, sizer_args): node, sizer = self._get_mocks(sizer_args) add_to_sizer(node, WxRenderingContext({'sizer': sizer})) assert sizer.Add.call_args == call(node.instance, **sizer_args) def test_skips_if_sizer_missed(self): node = self._get_mocks()[0] try: add_to_sizer(node, WxRenderingContext()) except BaseException: fail()

true

f71e8b07ffd796c578661e541401ebb3b60cb3f3

4,489

py

Python

tests/test_estimators.py

amgrigoriev/daal4py

97fbe7a9181410dac348dc724178e8605492e3c4

[ "Apache-2.0" ]

null

tests/test_estimators.py

amgrigoriev/daal4py

97fbe7a9181410dac348dc724178e8605492e3c4

[ "Apache-2.0" ]

null

tests/test_estimators.py

amgrigoriev/daal4py

97fbe7a9181410dac348dc724178e8605492e3c4

[ "Apache-2.0" ]

null

#******************************************************************************* # Copyright 2014-2020 Intel Corporation # All Rights Reserved. # # This software is licensed under the Apache License, Version 2.0 (the # "License"), the following terms apply: # # 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 from sklearn.utils.estimator_checks import check_estimator import sklearn.utils.estimator_checks from daal4py import __daal_run_version__ daal_run_version = tuple(map(int, (__daal_run_version__[0:4], __daal_run_version__[4:8]))) from daal4py.sklearn.neighbors import KNeighborsClassifier from daal4py.sklearn.ensemble import RandomForestClassifier from daal4py.sklearn.ensemble import RandomForestRegressor from daal4py.sklearn.ensemble import GBTDAALClassifier from daal4py.sklearn.ensemble import GBTDAALRegressor from daal4py.sklearn.ensemble import AdaBoostClassifier from daal4py import __daal_link_version__ as dv daal_version = tuple(map(int, (dv[0:4], dv[4:8]))) def check_version(rule, target): if not isinstance(rule[0], type(target)): if rule > target: return False else: for rule_item in range(len(rule)): if rule[rule_item] > target: return False else: if rule[rule_item][0]==target[0]: break return True def _replace_and_save(md, fns, replacing_fn): """ Replaces functions in `fns` list in `md` module with `replacing_fn`. Returns the dictionary with functions that were replaced. """ saved = dict() for check_f in fns: try: fn = getattr(md, check_f) setattr(md, check_f, replacing_fn) saved[check_f] = fn except: pass return saved def _restore_from_saved(md, saved_dict): """ Restores functions in `md` that were replaced in the function above. """ for check_f in saved_dict: setattr(md, check_f, saved_dict[check_f]) class Test(unittest.TestCase): def test_KNeighborsClassifier(self): check_estimator(KNeighborsClassifier) @unittest.skipUnless(check_version(((2019,0),(2021, 107)), daal_version), "not supported in this library version") def test_RandomForestClassifier(self): # check_methods_subset_invariance fails. # Issue is created: # https://github.com/IntelPython/daal4py/issues/129 # Skip the test def dummy(*args, **kwargs): pass md = sklearn.utils.estimator_checks saved = _replace_and_save(md, ['check_methods_subset_invariance', 'check_dict_unchanged'], dummy) check_estimator(RandomForestClassifier) _restore_from_saved(md, saved) def test_RandomForestRegressor(self): # check_fit_idempotent is known to fail with DAAL's decision # forest regressor, due to different partitioning of data # between threads from run to run. # Hence skip that test def dummy(*args, **kwargs): pass md = sklearn.utils.estimator_checks saved = _replace_and_save(md, ['check_methods_subset_invariance', 'check_dict_unchanged'], dummy) check_estimator(RandomForestRegressor) _restore_from_saved(md, saved) def test_GBTDAALClassifier(self): check_estimator(GBTDAALClassifier) def test_GBTDAALRegressor(self): def dummy(*args, **kwargs): pass md = sklearn.utils.estimator_checks # got unexpected slightly different prediction result between two same calls in this test saved = _replace_and_save(md, ['check_estimators_data_not_an_array'], dummy) check_estimator(GBTDAALRegressor) _restore_from_saved(md, saved) @unittest.skipIf(daal_run_version < (2020, 0), "not supported in this library version") def test_AdaBoostClassifier(self): check_estimator(AdaBoostClassifier) if __name__ == '__main__': unittest.main()

35.626984

118

0.676543

import unittest from sklearn.utils.estimator_checks import check_estimator import sklearn.utils.estimator_checks from daal4py import __daal_run_version__ daal_run_version = tuple(map(int, (__daal_run_version__[0:4], __daal_run_version__[4:8]))) from daal4py.sklearn.neighbors import KNeighborsClassifier from daal4py.sklearn.ensemble import RandomForestClassifier from daal4py.sklearn.ensemble import RandomForestRegressor from daal4py.sklearn.ensemble import GBTDAALClassifier from daal4py.sklearn.ensemble import GBTDAALRegressor from daal4py.sklearn.ensemble import AdaBoostClassifier from daal4py import __daal_link_version__ as dv daal_version = tuple(map(int, (dv[0:4], dv[4:8]))) def check_version(rule, target): if not isinstance(rule[0], type(target)): if rule > target: return False else: for rule_item in range(len(rule)): if rule[rule_item] > target: return False else: if rule[rule_item][0]==target[0]: break return True def _replace_and_save(md, fns, replacing_fn): saved = dict() for check_f in fns: try: fn = getattr(md, check_f) setattr(md, check_f, replacing_fn) saved[check_f] = fn except: pass return saved def _restore_from_saved(md, saved_dict): for check_f in saved_dict: setattr(md, check_f, saved_dict[check_f]) class Test(unittest.TestCase): def test_KNeighborsClassifier(self): check_estimator(KNeighborsClassifier) @unittest.skipUnless(check_version(((2019,0),(2021, 107)), daal_version), "not supported in this library version") def test_RandomForestClassifier(self): def dummy(*args, **kwargs): pass md = sklearn.utils.estimator_checks saved = _replace_and_save(md, ['check_methods_subset_invariance', 'check_dict_unchanged'], dummy) check_estimator(RandomForestClassifier) _restore_from_saved(md, saved) def test_RandomForestRegressor(self): # forest regressor, due to different partitioning of data # between threads from run to run. # Hence skip that test def dummy(*args, **kwargs): pass md = sklearn.utils.estimator_checks saved = _replace_and_save(md, ['check_methods_subset_invariance', 'check_dict_unchanged'], dummy) check_estimator(RandomForestRegressor) _restore_from_saved(md, saved) def test_GBTDAALClassifier(self): check_estimator(GBTDAALClassifier) def test_GBTDAALRegressor(self): def dummy(*args, **kwargs): pass md = sklearn.utils.estimator_checks # got unexpected slightly different prediction result between two same calls in this test saved = _replace_and_save(md, ['check_estimators_data_not_an_array'], dummy) check_estimator(GBTDAALRegressor) _restore_from_saved(md, saved) @unittest.skipIf(daal_run_version < (2020, 0), "not supported in this library version") def test_AdaBoostClassifier(self): check_estimator(AdaBoostClassifier) if __name__ == '__main__': unittest.main()

true

f71e8b1a591ecfd26ed606bf4d5a7a7fd8179642

783

py

Python

glue_vispy_viewers/extern/vispy/io/__init__.py

jzuhone/glue-vispy-viewers

d940705f4ba95f8d7a9a74d37fb68c71080b490a

[ "BSD-2-Clause" ]

3

2018-05-09T17:55:53.000Z

2019-07-22T09:14:41.000Z

glue_vispy_viewers/extern/vispy/io/__init__.py

jzuhone/glue-vispy-viewers

d940705f4ba95f8d7a9a74d37fb68c71080b490a

[ "BSD-2-Clause" ]

9

2017-04-07T01:44:15.000Z

2018-12-16T20:47:08.000Z

graphViz/vispy/io/__init__.py

onecklam/ethereum-graphviz

6993accf0cb85e23013bf7ae6b04145724a6dbd2

[ "Apache-2.0" ]

1

2021-09-15T08:52:26.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2015, Vispy Development Team. # Distributed under the (new) BSD License. See LICENSE.txt for more info. """ Utilities related to data reading, writing, fetching, and generation. """ from os import path as _op from .datasets import (load_iris, load_crate, load_data_file, # noqa load_spatial_filters) # noqa from .mesh import read_mesh, write_mesh # noqa from .image import (read_png, write_png, imread, imsave, _make_png, # noqa _check_img_lib) # noqa _data_dir = _op.join(_op.dirname(__file__), '_data') __all__ = ['imread', 'imsave', 'load_iris', 'load_crate', 'load_spatial_filters', 'load_data_file', 'read_mesh', 'read_png', 'write_mesh', 'write_png']

34.043478

75

0.659004

from os import path as _op from .datasets import (load_iris, load_crate, load_data_file, load_spatial_filters) from .mesh import read_mesh, write_mesh from .image import (read_png, write_png, imread, imsave, _make_png, _check_img_lib) _data_dir = _op.join(_op.dirname(__file__), '_data') __all__ = ['imread', 'imsave', 'load_iris', 'load_crate', 'load_spatial_filters', 'load_data_file', 'read_mesh', 'read_png', 'write_mesh', 'write_png']

true

f71e8b9167da6ce9a96bc811cf78357b138536ca

2,654

py

Python

dfp/create_creatives.py

togetter/dfp-prebid-setup

a9d0b2c60558c9b561de430a4f0b191996c98da0

[ "MIT" ]

null

dfp/create_creatives.py

togetter/dfp-prebid-setup

a9d0b2c60558c9b561de430a4f0b191996c98da0

[ "MIT" ]

null

dfp/create_creatives.py

togetter/dfp-prebid-setup

a9d0b2c60558c9b561de430a4f0b191996c98da0

[ "MIT" ]

null

import logging import os import pprint from googleads import ad_manager from dfp.client import get_client logger = logging.getLogger(__name__) def create_creatives(creatives): """ Creates creatives in DFP. Args: creatives (arr): an array of objects, each a creative configuration Returns: an array: an array of created creative IDs """ dfp_client = get_client() creative_service = dfp_client.GetService('CreativeService', version='v201811') creatives = creative_service.createCreatives(creatives) # Return IDs of created line items. created_creative_ids = [] for creative in creatives: created_creative_ids.append(creative['id']) logger.info(u'Created creative with name "{name}".'.format(name=creative['name'])) return created_creative_ids def create_creative_config(name, advertiser_id): """ Creates a creative config object. Args: name (str): the name of the creative advertiser_id (int): the ID of the advertiser in DFP Returns: an object: the line item config """ snippet_file_path = os.path.join(os.path.dirname(__file__), 'creative_snippet.html') with open(snippet_file_path, 'r') as snippet_file: snippet = snippet_file.read() # https://developers.google.com/doubleclick-publishers/docs/reference/v201802/CreativeService.Creative config = { 'xsi_type': 'ThirdPartyCreative', 'name': name, 'advertiserId': advertiser_id, 'size': { 'width': '1', 'height': '1' }, 'snippet': snippet, 'isSafeFrameCompatible': True, } return config def build_creative_name(order_name, creative_num): """ Returns a name for a creative. Args: order_name (int): the name of the order in DFP creative_num (int): the num_creatives distinguising this creative from any duplicates Returns: a string """ return 'HB {order_name}, #{num}'.format( order_name=order_name, num=creative_num) def create_duplicate_creative_configs(order_name, advertiser_id, num_creatives=1): """ Returns an array of creative config object. Args: order_name (int): the name of the order in DFP advertiser_id (int): the ID of the advertiser in DFP num_creatives (int): how many creative configs to generate Returns: an array: an array of length `num_creatives`, each item a line item config """ creative_configs = [] for creative_num in range(1, num_creatives + 1): config = create_creative_config( name=build_creative_name(order_name, creative_num), advertiser_id=advertiser_id, ) creative_configs.append(config) return creative_configs

26.54

104

0.708365

import logging import os import pprint from googleads import ad_manager from dfp.client import get_client logger = logging.getLogger(__name__) def create_creatives(creatives): dfp_client = get_client() creative_service = dfp_client.GetService('CreativeService', version='v201811') creatives = creative_service.createCreatives(creatives) created_creative_ids = [] for creative in creatives: created_creative_ids.append(creative['id']) logger.info(u'Created creative with name "{name}".'.format(name=creative['name'])) return created_creative_ids def create_creative_config(name, advertiser_id): snippet_file_path = os.path.join(os.path.dirname(__file__), 'creative_snippet.html') with open(snippet_file_path, 'r') as snippet_file: snippet = snippet_file.read() config = { 'xsi_type': 'ThirdPartyCreative', 'name': name, 'advertiserId': advertiser_id, 'size': { 'width': '1', 'height': '1' }, 'snippet': snippet, 'isSafeFrameCompatible': True, } return config def build_creative_name(order_name, creative_num): return 'HB {order_name}, #{num}'.format( order_name=order_name, num=creative_num) def create_duplicate_creative_configs(order_name, advertiser_id, num_creatives=1): creative_configs = [] for creative_num in range(1, num_creatives + 1): config = create_creative_config( name=build_creative_name(order_name, creative_num), advertiser_id=advertiser_id, ) creative_configs.append(config) return creative_configs

true

f71e8d1c9a2ded59f56da7cb9494713a1bd65190

354

py

Python

pwgen.py

anokata/pythonPetProjects

245c3ff11ae560b17830970061d8d60013948fd7

[ "MIT" ]

3

2017-04-30T17:44:53.000Z

2018-02-03T06:02:11.000Z

pwgen.py

anokata/pythonPetProjects

245c3ff11ae560b17830970061d8d60013948fd7

[ "MIT" ]

10

2021-03-18T20:17:19.000Z

2022-03-11T23:14:19.000Z

pwgen.py

anokata/pythonPetProjects

245c3ff11ae560b17830970061d8d60013948fd7

[ "MIT" ]

null

for a in range(10): for b in range(10): for c in range(10): for d in range(10): for e in range(10): for f in range(10): for g in range(10): for h in range(10): print("{}{}{}{}{}{}{}{}".format(a,b,c,d,e,f,g,h))

35.4

81

0.347458

for a in range(10): for b in range(10): for c in range(10): for d in range(10): for e in range(10): for f in range(10): for g in range(10): for h in range(10): print("{}{}{}{}{}{}{}{}".format(a,b,c,d,e,f,g,h))

true

f71e8e3cc6255da0daeb05d09e65a982f946b838

521

py

Python

elements/python/11/1/soln.py

mmcloughlin/problems

6095842ffe007a12ec8c2093850515aa4e046616

[ "MIT" ]

11

2019-02-08T06:54:34.000Z

2021-08-07T18:57:39.000Z

elements/python/11/1/soln.py

mmcloughlin/problems

6095842ffe007a12ec8c2093850515aa4e046616

[ "MIT" ]

1

2019-05-21T08:14:10.000Z

elements/python/11/1/soln.py

mmcloughlin/problems

6095842ffe007a12ec8c2093850515aa4e046616

[ "MIT" ]

null

import heapq import random def merge(lists): heapq.heapify(lists) m = [] while len(lists) > 0: l = heapq.heappop(lists) if len(l) == 0: continue m.append(l.pop(0)) heapq.heappush(lists, l) return m def test(n, k): lists = [[] for _ in xrange(k)] for i in xrange(n): lists[random.randrange(k)].append(i) m = merge(lists) assert m == range(n) print 'pass' def main(): test(100000, 50) if __name__ == '__main__': main()

16.28125

44

0.539347

import heapq import random def merge(lists): heapq.heapify(lists) m = [] while len(lists) > 0: l = heapq.heappop(lists) if len(l) == 0: continue m.append(l.pop(0)) heapq.heappush(lists, l) return m def test(n, k): lists = [[] for _ in xrange(k)] for i in xrange(n): lists[random.randrange(k)].append(i) m = merge(lists) assert m == range(n) print 'pass' def main(): test(100000, 50) if __name__ == '__main__': main()

false

true

f71e8ea85b6b54c6670609e8e6c0a91688ec4952

87

py

Python

pyjswidgets/pyjamas/XMLDoc.py

takipsizad/pyjs

54db0ba6747aca744f9f3c3e985a17e913dfb951

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

739

2015-01-01T02:05:11.000Z

2022-03-30T15:26:16.000Z

pyjswidgets/pyjamas/XMLDoc.py

takipsizad/pyjs

54db0ba6747aca744f9f3c3e985a17e913dfb951

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

33

2015-03-25T23:17:04.000Z

2021-08-19T08:25:22.000Z

pyjswidgets/pyjamas/XMLDoc.py

takipsizad/pyjs

54db0ba6747aca744f9f3c3e985a17e913dfb951

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

167

2015-01-01T22:27:47.000Z

2022-03-17T13:29:19.000Z

from __pyjamas__ import get_main_frame, JS def create_xml_doc(text): return None

14.5

42

0.781609

from __pyjamas__ import get_main_frame, JS def create_xml_doc(text): return None

true

f71e8f3a720d0140da369b16a9db389ed78c68db

1,016

py

Python

mak/libs/ircc/ir_grammar/ir_opcodes/ir_vector.py

bugengine/BugEngine

1b3831d494ee06b0bd74a8227c939dd774b91226

[ "BSD-3-Clause" ]

4

2015-05-13T16:28:36.000Z

2017-05-24T15:34:14.000Z

mak/libs/ircc/ir_grammar/ir_opcodes/ir_vector.py

bugengine/BugEngine

1b3831d494ee06b0bd74a8227c939dd774b91226

[ "BSD-3-Clause" ]

null

mak/libs/ircc/ir_grammar/ir_opcodes/ir_vector.py

bugengine/BugEngine

1b3831d494ee06b0bd74a8227c939dd774b91226

[ "BSD-3-Clause" ]

1

2017-03-21T08:28:07.000Z

from ...ir_ast.instructions import IrInstExtractElement, IrInstInsertElement, IrInstShuffleVector from be_typing import TYPE_CHECKING def p_ir_opcode_vector_extract(p): # type: (YaccProduction) -> None """ ir-opcode : ir-instruction-assignment EXTRACTELEMENT ir-value COMMA ir-value ir-instruction-attachment-list """ p[0] = IrInstExtractElement(p[1], p[3], p[5], p[6]) def p_ir_opcode_vector_insert(p): # type: (YaccProduction) -> None """ ir-opcode : ir-instruction-assignment INSERTELEMENT ir-value COMMA ir-value COMMA ir-value ir-instruction-attachment-list """ p[0] = IrInstInsertElement(p[1], p[3], p[5], p[7], p[8]) def p_ir_opcode_vector_shuffle(p): # type: (YaccProduction) -> None """ ir-opcode : ir-instruction-assignment SHUFFLEVECTOR ir-value COMMA ir-value COMMA ir-value ir-instruction-attachment-list """ p[0] = IrInstShuffleVector(p[1], p[3], p[5], p[7], p[8]) if TYPE_CHECKING: from ply.yacc import YaccProduction

33.866667

129

0.694882

from ...ir_ast.instructions import IrInstExtractElement, IrInstInsertElement, IrInstShuffleVector from be_typing import TYPE_CHECKING def p_ir_opcode_vector_extract(p): p[0] = IrInstExtractElement(p[1], p[3], p[5], p[6]) def p_ir_opcode_vector_insert(p): p[0] = IrInstInsertElement(p[1], p[3], p[5], p[7], p[8]) def p_ir_opcode_vector_shuffle(p): p[0] = IrInstShuffleVector(p[1], p[3], p[5], p[7], p[8]) if TYPE_CHECKING: from ply.yacc import YaccProduction

true

f71e8f9b1530c92926a1479f24db743c1cf1dcfc

3,414

py

Python

modules/boost/simd/predicates/script/is_eqz.py

timblechmann/nt2

6c71f7063ca4e5975c9c019877e6b2fe07c9e4ce

[ "BSL-1.0" ]

2

2016-09-14T00:23:53.000Z

2018-01-14T12:51:18.000Z

modules/boost/simd/predicates/script/is_eqz.py

timblechmann/nt2

6c71f7063ca4e5975c9c019877e6b2fe07c9e4ce

[ "BSL-1.0" ]

null

modules/boost/simd/predicates/script/is_eqz.py

timblechmann/nt2

6c71f7063ca4e5975c9c019877e6b2fe07c9e4ce

[ "BSL-1.0" ]

null

[ ## this file was manually modified by jt { 'functor' : { 'description' : ['Returns True<result_type>() or False<result_type>() according a0 is zero or not.'], 'module' : 'boost', 'arity' : '1', 'call_types' : [], 'ret_arity' : '0', 'rturn' : { 'default' : 'typename boost::simd::meta::as_logical<T>::type', }, 'simd_types' : ['real_'], 'special' : ['predicate'], 'type_defs' : [], 'types' : ['real_', 'signed_int_', 'unsigned_int_'], }, 'info' : 'manually modified', 'unit' : { 'global_header' : { 'first_stamp' : 'created by jt the 21/02/2011', 'included' : ['#include <boost/simd/sdk/simd/logical.hpp>'], 'no_ulp' : 'True', 'notes' : [], 'stamp' : 'modified by jt the 21/02/2011', }, 'ranges' : { 'default' : [['T(-10000)', 'T(10000)']], }, 'specific_values' : { 'default' : { 'boost::simd::One<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Two<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Zero<T>()' : {'result' : 'boost::simd::True<r_t>()','ulp_thresh' : '0.5',}, }, 'real_' : { 'boost::simd::Mzero<T>()' : {'result' : 'boost::simd::True<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Half<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Inf<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Minf<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Mone<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Nan<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::One<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Quarter<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Two<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Zero<T>()' : {'result' : 'boost::simd::True<r_t>()','ulp_thresh' : '0.5',}, }, 'signed_int_' : { 'boost::simd::Mone<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::One<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Two<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Zero<T>()' : {'result' : 'boost::simd::True<r_t>()','ulp_thresh' : '0.5',}, }, }, 'verif_test' : { 'property_call' : { 'default' : ['boost::simd::is_eqz(a0)'], }, 'property_value' : { 'default' : ['a0==0'], }, 'simd' : { }, 'ulp_thresh' : { 'default' : ['0'], }, }, }, }, ]

48.771429

110

0.411541

[ cription' : ['Returns True<result_type>() or False<result_type>() according a0 is zero or not.'], 'module' : 'boost', 'arity' : '1', 'call_types' : [], 'ret_arity' : '0', 'rturn' : { 'default' : 'typename boost::simd::meta::as_logical<T>::type', }, 'simd_types' : ['real_'], 'special' : ['predicate'], 'type_defs' : [], 'types' : ['real_', 'signed_int_', 'unsigned_int_'], }, 'info' : 'manually modified', 'unit' : { 'global_header' : { 'first_stamp' : 'created by jt the 21/02/2011', 'included' : ['#include <boost/simd/sdk/simd/logical.hpp>'], 'no_ulp' : 'True', 'notes' : [], 'stamp' : 'modified by jt the 21/02/2011', }, 'ranges' : { 'default' : [['T(-10000)', 'T(10000)']], }, 'specific_values' : { 'default' : { 'boost::simd::One<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Two<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Zero<T>()' : {'result' : 'boost::simd::True<r_t>()','ulp_thresh' : '0.5',}, }, 'real_' : { 'boost::simd::Mzero<T>()' : {'result' : 'boost::simd::True<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Half<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Inf<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Minf<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Mone<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Nan<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::One<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Quarter<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Two<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Zero<T>()' : {'result' : 'boost::simd::True<r_t>()','ulp_thresh' : '0.5',}, }, 'signed_int_' : { 'boost::simd::Mone<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::One<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Two<T>()' : {'result' : 'boost::simd::False<r_t>()','ulp_thresh' : '0.5',}, 'boost::simd::Zero<T>()' : {'result' : 'boost::simd::True<r_t>()','ulp_thresh' : '0.5',}, }, }, 'verif_test' : { 'property_call' : { 'default' : ['boost::simd::is_eqz(a0)'], }, 'property_value' : { 'default' : ['a0==0'], }, 'simd' : { }, 'ulp_thresh' : { 'default' : ['0'], }, }, }, }, ]

true

f71e91cc662cdd088a323256c7ea4e2c01a5e589

4,175

py

Python

st2common/st2common/transport/utils.py

benmcbenben/st2

f067176640d86924b99bc035c2eb9aabe3b3a734

[ "Apache-2.0" ]

null

st2common/st2common/transport/utils.py

benmcbenben/st2

f067176640d86924b99bc035c2eb9aabe3b3a734

[ "Apache-2.0" ]

null

st2common/st2common/transport/utils.py

benmcbenben/st2

f067176640d86924b99bc035c2eb9aabe3b3a734

[ "Apache-2.0" ]

null

# Licensed to the StackStorm, Inc ('StackStorm') under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import import ssl as ssl_lib from oslo_config import cfg from kombu import Connection from st2common import log as logging __all__ = [ 'get_connection', 'get_messaging_urls' ] LOG = logging.getLogger(__name__) def get_messaging_urls(): ''' Determines the right messaging urls to supply. In case the `cluster_urls` config is specified then that is used. Else the single `url` property is used. :rtype: ``list`` ''' if cfg.CONF.messaging.cluster_urls: return cfg.CONF.messaging.cluster_urls return [cfg.CONF.messaging.url] def get_connection(urls=None, connection_kwargs=None): """ Retrieve kombu "Conection" class instance configured with all the correct options using values from the config and provided values. :param connection_kwargs: Any additional connection keyword arguments passed directly to the Connection class constructor. :type connection_kwargs: ``dict`` """ urls = urls or get_messaging_urls() connection_kwargs = connection_kwargs or {} kwargs = {} ssl_kwargs = _get_ssl_kwargs(ssl=cfg.CONF.messaging.ssl, ssl_keyfile=cfg.CONF.messaging.ssl_keyfile, ssl_certfile=cfg.CONF.messaging.ssl_certfile, ssl_cert_reqs=cfg.CONF.messaging.ssl_cert_reqs, ssl_ca_certs=cfg.CONF.messaging.ssl_ca_certs, login_method=cfg.CONF.messaging.login_method) # NOTE: "connection_kwargs" argument passed to this function has precedence over config values if len(ssl_kwargs) == 1 and ssl_kwargs['ssl'] is True: kwargs.update({'ssl': True}) elif len(ssl_kwargs) >= 2: ssl_kwargs.pop('ssl') kwargs.update({'ssl': ssl_kwargs}) kwargs['login_method'] = cfg.CONF.messaging.login_method kwargs.update(connection_kwargs) # NOTE: This line contains no secret values so it's OK to log it LOG.debug('Using SSL context for RabbitMQ connection: %s' % (ssl_kwargs)) connection = Connection(urls, **kwargs) return connection def _get_ssl_kwargs(ssl=False, ssl_keyfile=None, ssl_certfile=None, ssl_cert_reqs=None, ssl_ca_certs=None, login_method=None): """ Return SSL keyword arguments to be used with the kombu.Connection class. """ ssl_kwargs = {} # NOTE: If "ssl" is not set to True we don't pass "ssl=False" argument to the constructor # because user could still specify to use SSL by including "?ssl=true" query param at the # end of the connection URL string if ssl is True: ssl_kwargs['ssl'] = True if ssl_keyfile: ssl_kwargs['ssl'] = True ssl_kwargs['keyfile'] = ssl_keyfile if ssl_certfile: ssl_kwargs['ssl'] = True ssl_kwargs['certfile'] = ssl_certfile if ssl_cert_reqs: if ssl_cert_reqs == 'none': ssl_cert_reqs = ssl_lib.CERT_NONE elif ssl_cert_reqs == 'optional': ssl_cert_reqs = ssl_lib.CERT_OPTIONAL elif ssl_cert_reqs == 'required': ssl_cert_reqs = ssl_lib.CERT_REQUIRED ssl_kwargs['cert_reqs'] = ssl_cert_reqs if ssl_ca_certs: ssl_kwargs['ssl'] = True ssl_kwargs['ca_certs'] = ssl_ca_certs return ssl_kwargs

34.791667

98

0.679281

from __future__ import absolute_import import ssl as ssl_lib from oslo_config import cfg from kombu import Connection from st2common import log as logging __all__ = [ 'get_connection', 'get_messaging_urls' ] LOG = logging.getLogger(__name__) def get_messaging_urls(): if cfg.CONF.messaging.cluster_urls: return cfg.CONF.messaging.cluster_urls return [cfg.CONF.messaging.url] def get_connection(urls=None, connection_kwargs=None): urls = urls or get_messaging_urls() connection_kwargs = connection_kwargs or {} kwargs = {} ssl_kwargs = _get_ssl_kwargs(ssl=cfg.CONF.messaging.ssl, ssl_keyfile=cfg.CONF.messaging.ssl_keyfile, ssl_certfile=cfg.CONF.messaging.ssl_certfile, ssl_cert_reqs=cfg.CONF.messaging.ssl_cert_reqs, ssl_ca_certs=cfg.CONF.messaging.ssl_ca_certs, login_method=cfg.CONF.messaging.login_method) if len(ssl_kwargs) == 1 and ssl_kwargs['ssl'] is True: kwargs.update({'ssl': True}) elif len(ssl_kwargs) >= 2: ssl_kwargs.pop('ssl') kwargs.update({'ssl': ssl_kwargs}) kwargs['login_method'] = cfg.CONF.messaging.login_method kwargs.update(connection_kwargs) LOG.debug('Using SSL context for RabbitMQ connection: %s' % (ssl_kwargs)) connection = Connection(urls, **kwargs) return connection def _get_ssl_kwargs(ssl=False, ssl_keyfile=None, ssl_certfile=None, ssl_cert_reqs=None, ssl_ca_certs=None, login_method=None): ssl_kwargs = {} # NOTE: If "ssl" is not set to True we don't pass "ssl=False" argument to the constructor if ssl is True: ssl_kwargs['ssl'] = True if ssl_keyfile: ssl_kwargs['ssl'] = True ssl_kwargs['keyfile'] = ssl_keyfile if ssl_certfile: ssl_kwargs['ssl'] = True ssl_kwargs['certfile'] = ssl_certfile if ssl_cert_reqs: if ssl_cert_reqs == 'none': ssl_cert_reqs = ssl_lib.CERT_NONE elif ssl_cert_reqs == 'optional': ssl_cert_reqs = ssl_lib.CERT_OPTIONAL elif ssl_cert_reqs == 'required': ssl_cert_reqs = ssl_lib.CERT_REQUIRED ssl_kwargs['cert_reqs'] = ssl_cert_reqs if ssl_ca_certs: ssl_kwargs['ssl'] = True ssl_kwargs['ca_certs'] = ssl_ca_certs return ssl_kwargs

true

f71e9293b1b22dfe1dd09dc3be56a2df0b029670

2,578

py

Python

info/Modules/index/views.py

xihuaxone/NewsWeb

d6f0b7f854a9b21619c81d6c736c5f084c572dc2

[ "MIT" ]

null

info/Modules/index/views.py

xihuaxone/NewsWeb

d6f0b7f854a9b21619c81d6c736c5f084c572dc2

[ "MIT" ]

null

info/Modules/index/views.py

xihuaxone/NewsWeb

d6f0b7f854a9b21619c81d6c736c5f084c572dc2

[ "MIT" ]

null

# encoding=utf-8 from flask import Blueprint, make_response, render_template, current_app, session, jsonify, g from flask import session, request import math from info.utils.response_code import RET from info.models import * from info import constants from info.utils.common import LoginUser, RankList blue = Blueprint('blue1', __name__) @blue.route('/favicon.ico') def Favicon(): return current_app.send_static_file('news/favicon.ico') @blue.route('/') @LoginUser @RankList def index(): from info import db # 从redis查询login_user, 若不存在则user = None；存在则从数据库查询得到user相关信息，存储为字典； current_user_id = None current_user = g.login_user # 获取所有新闻类别的列表； try: category_list = Category.query.all() category_list = [item.to_dict() for item in category_list] except Exception as err: current_app.logger.error(err) return jsonify(errno=RET.DBERR, errmsg='数据库操作失败') datas = { 'login_user': current_user.to_dict() if current_user else None, 'click_rank': g.news_click_rank, 'category_list': category_list } ret = make_response(render_template('news/html/index.html', datas = datas)) return ret @blue.route('/news_list') def newsList(): list_infos = request.args # 请求新闻列表id； cid = list_infos.get('cid',1) # 请求页码； page = list_infos.get('page',1) # 每页信息数； per_page = list_infos.get('per_page', constants.HOME_PAGE_MAX_NEWS) news_list = list() request_infos = list() # 判断请求数据是否完整； if not all([cid, page, per_page]): return jsonify(errno=RET.NODATA, errmsg='请求数据不全') # 判断请求数据格式，防止非法注入； try: cid = int(cid) page = int(page) per_page = int(per_page) except Exception as err: current_app.logger.error(err) return jsonify(errno=RET.DATAERR, errmsg='html请求格式错误') # try 查询数据库，生成要求的数据； try: filter_op = [] if not cid == 1: filter_op.append(News.category_id == cid) request_infos = News.query.filter(News.status == 0, *filter_op).order_by(News.create_time.desc()).paginate(page = page, per_page = per_page) page_count = request_infos.pages except Exception as err: current_app.logger.error(err) return jsonify(errno=RET.DBERR, errmsg='数据库查询失败') # 把查询到的结果以前端要求的格式封装； if request_infos: for news in request_infos.items: news_list.append(news.to_basic_dict()) return jsonify(errno = RET.OK, errmsg = 'OK', cid = cid, currentPage = page, newsList = news_list, totalPage = page_count)

27.72043

148

0.669123

from flask import Blueprint, make_response, render_template, current_app, session, jsonify, g from flask import session, request import math from info.utils.response_code import RET from info.models import * from info import constants from info.utils.common import LoginUser, RankList blue = Blueprint('blue1', __name__) @blue.route('/favicon.ico') def Favicon(): return current_app.send_static_file('news/favicon.ico') @blue.route('/') @LoginUser @RankList def index(): from info import db current_user_id = None current_user = g.login_user try: category_list = Category.query.all() category_list = [item.to_dict() for item in category_list] except Exception as err: current_app.logger.error(err) return jsonify(errno=RET.DBERR, errmsg='数据库操作失败') datas = { 'login_user': current_user.to_dict() if current_user else None, 'click_rank': g.news_click_rank, 'category_list': category_list } ret = make_response(render_template('news/html/index.html', datas = datas)) return ret @blue.route('/news_list') def newsList(): list_infos = request.args cid = list_infos.get('cid',1) page = list_infos.get('page',1) per_page = list_infos.get('per_page', constants.HOME_PAGE_MAX_NEWS) news_list = list() request_infos = list() if not all([cid, page, per_page]): return jsonify(errno=RET.NODATA, errmsg='请求数据不全') try: cid = int(cid) page = int(page) per_page = int(per_page) except Exception as err: current_app.logger.error(err) return jsonify(errno=RET.DATAERR, errmsg='html请求格式错误') try: filter_op = [] if not cid == 1: filter_op.append(News.category_id == cid) request_infos = News.query.filter(News.status == 0, *filter_op).order_by(News.create_time.desc()).paginate(page = page, per_page = per_page) page_count = request_infos.pages except Exception as err: current_app.logger.error(err) return jsonify(errno=RET.DBERR, errmsg='数据库查询失败') if request_infos: for news in request_infos.items: news_list.append(news.to_basic_dict()) return jsonify(errno = RET.OK, errmsg = 'OK', cid = cid, currentPage = page, newsList = news_list, totalPage = page_count)

true

f71e9396e107a5d1eba85299cfd4f19d90e0a5d8

3,987

py

Python

tests/unit/test_gff3_parser.py

dcolligan/ga4gh-server

dd0b00a52de9684609b7f04a9d70946c36afa8a5

[ "Apache-2.0" ]

83

2015-01-05T22:21:11.000Z

2017-02-20T01:25:28.000Z

tests/unit/test_gff3_parser.py

dcolligan/ga4gh-server

dd0b00a52de9684609b7f04a9d70946c36afa8a5

[ "Apache-2.0" ]

1,508

2015-01-02T14:06:12.000Z

2017-03-08T19:49:18.000Z

tests/unit/test_gff3_parser.py

dcolligan/ga4gh-server

dd0b00a52de9684609b7f04a9d70946c36afa8a5

[ "Apache-2.0" ]

99

2015-01-14T20:48:56.000Z

2017-03-08T18:35:06.000Z

""" GFF3 parser unit tests. """ from __future__ import division from __future__ import print_function from __future__ import unicode_literals import ga4gh.server.gff3 as gff3 import unittest _testDataDir = "tests/data/datasets/dataset1/sequenceAnnotations/" class TestGff3ParserOnTypicalFile(unittest.TestCase): """ Data driven unit tests for the GFF3 parser """ def setUp(self): testDataFile = _testDataDir + "gencodeV21Set1.gff3" self.gff3Parser = gff3.Gff3Parser(testDataFile) self.gff3Data = self.gff3Parser.parse() def testFileParsedHasSomeRootFeatures(self): self.assertIsNotNone(self.gff3Data.roots, "No root features") self.assertNotEqual(len(self.gff3Data.roots), 0, "No root features") def testSomeFeatureIsWellFormed(self): featId = self.gff3Data.byFeatureName.keys()[0] feat = self.gff3Data.byFeatureName[featId][0] self.assertEqual(featId, feat.featureName, "featureName mismatch") self.assertIsNotNone(feat.seqname, "sequence name is not populated") self.assertGreaterEqual(feat.end, feat.start, "end less than start") self.assertIn(feat.strand, u"+-", "strand is neither + nor -") self.assertIsNotNone(feat.source, "source is unspecified") self.assertIsNotNone(feat.type, "feature type is unspecified") self.assertIsInstance(feat.parents, set, "parents not a set") self.assertIsInstance(feat.children, set, "children not a set") def testRootFeaturesHaveNoParents(self): for root in self.gff3Data.roots: self.assertEqual( len(root.parents), 0, "root feature has a parent") def testAllFeaturesContainAllRootFeatures(self): for root in self.gff3Data.roots: feat = self.gff3Data.byFeatureName[root.featureName] self.assertGreaterEqual( len(feat), 1, "root feature not in list of all features") def testInvalidFeatureNameKeyQueryFails(self): badFeatureName = "987654" badFeat = self.gff3Data.byFeatureName[badFeatureName] self.assertEqual( len(badFeat), 0, "invalid feature ID returned valid object") def testAllChildrenFeaturesArePresentInSet(self): for featList in self.gff3Data.byFeatureName.values(): for feat in featList: for child in feat.children: childLookup = self.gff3Data.byFeatureName[ child.featureName] self.assertGreaterEqual( len(childLookup), 1, "child feature not in set") class TestGff3ParserOnDiscontinuousFeatureFile(TestGff3ParserOnTypicalFile): """ Data driven parser test on file with discontinuous features. The tests here rely on specific data in the file being parsed. """ def setUp(self): testDataFile = _testDataDir + "discontinuous.gff3" self.gff3Parser = gff3.Gff3Parser(testDataFile) self.gff3Data = self.gff3Parser.parse() def testDiscontinuousFeature(self): feat = self.gff3Data.byFeatureName['apidb|cds_MAL13P1.103-1'] self.assertEqual( len(feat), 10, "not all parts of discontinuous feature parsed") class TestGff3ParserOnSacCerFile(TestGff3ParserOnTypicalFile): """ Data driven parser test on file from Saccharomyces cerevisiae S288C genome. """ def setUp(self): testDataFile = _testDataDir + "sacCerTest.gff3" self.gff3Parser = gff3.Gff3Parser(testDataFile) self.gff3Data = self.gff3Parser.parse() class TestGff3ParserOnSpecialCasesFile(TestGff3ParserOnTypicalFile): """ Data driven parser test on a GFF3 file representing edge cases. """ def setUp(self): testDataFile = _testDataDir + "specialCasesTest.gff3" self.gff3Parser = gff3.Gff3Parser(testDataFile) self.gff3Data = self.gff3Parser.parse()

38.336538

79

0.676198

from __future__ import division from __future__ import print_function from __future__ import unicode_literals import ga4gh.server.gff3 as gff3 import unittest _testDataDir = "tests/data/datasets/dataset1/sequenceAnnotations/" class TestGff3ParserOnTypicalFile(unittest.TestCase): def setUp(self): testDataFile = _testDataDir + "gencodeV21Set1.gff3" self.gff3Parser = gff3.Gff3Parser(testDataFile) self.gff3Data = self.gff3Parser.parse() def testFileParsedHasSomeRootFeatures(self): self.assertIsNotNone(self.gff3Data.roots, "No root features") self.assertNotEqual(len(self.gff3Data.roots), 0, "No root features") def testSomeFeatureIsWellFormed(self): featId = self.gff3Data.byFeatureName.keys()[0] feat = self.gff3Data.byFeatureName[featId][0] self.assertEqual(featId, feat.featureName, "featureName mismatch") self.assertIsNotNone(feat.seqname, "sequence name is not populated") self.assertGreaterEqual(feat.end, feat.start, "end less than start") self.assertIn(feat.strand, u"+-", "strand is neither + nor -") self.assertIsNotNone(feat.source, "source is unspecified") self.assertIsNotNone(feat.type, "feature type is unspecified") self.assertIsInstance(feat.parents, set, "parents not a set") self.assertIsInstance(feat.children, set, "children not a set") def testRootFeaturesHaveNoParents(self): for root in self.gff3Data.roots: self.assertEqual( len(root.parents), 0, "root feature has a parent") def testAllFeaturesContainAllRootFeatures(self): for root in self.gff3Data.roots: feat = self.gff3Data.byFeatureName[root.featureName] self.assertGreaterEqual( len(feat), 1, "root feature not in list of all features") def testInvalidFeatureNameKeyQueryFails(self): badFeatureName = "987654" badFeat = self.gff3Data.byFeatureName[badFeatureName] self.assertEqual( len(badFeat), 0, "invalid feature ID returned valid object") def testAllChildrenFeaturesArePresentInSet(self): for featList in self.gff3Data.byFeatureName.values(): for feat in featList: for child in feat.children: childLookup = self.gff3Data.byFeatureName[ child.featureName] self.assertGreaterEqual( len(childLookup), 1, "child feature not in set") class TestGff3ParserOnDiscontinuousFeatureFile(TestGff3ParserOnTypicalFile): def setUp(self): testDataFile = _testDataDir + "discontinuous.gff3" self.gff3Parser = gff3.Gff3Parser(testDataFile) self.gff3Data = self.gff3Parser.parse() def testDiscontinuousFeature(self): feat = self.gff3Data.byFeatureName['apidb|cds_MAL13P1.103-1'] self.assertEqual( len(feat), 10, "not all parts of discontinuous feature parsed") class TestGff3ParserOnSacCerFile(TestGff3ParserOnTypicalFile): def setUp(self): testDataFile = _testDataDir + "sacCerTest.gff3" self.gff3Parser = gff3.Gff3Parser(testDataFile) self.gff3Data = self.gff3Parser.parse() class TestGff3ParserOnSpecialCasesFile(TestGff3ParserOnTypicalFile): def setUp(self): testDataFile = _testDataDir + "specialCasesTest.gff3" self.gff3Parser = gff3.Gff3Parser(testDataFile) self.gff3Data = self.gff3Parser.parse()

true

f71e939873aac156dae8e715c3fca52635645354

13,575

py

Python

dlex/datasets/nlp/utils.py

dvtrung/dl-torch

b49e57d10d32bb223e2d7643f2579ccc32c63a9a

[ "MIT" ]

null

dlex/datasets/nlp/utils.py

dvtrung/dl-torch

b49e57d10d32bb223e2d7643f2579ccc32c63a9a

[ "MIT" ]

null

dlex/datasets/nlp/utils.py

dvtrung/dl-torch

b49e57d10d32bb223e2d7643f2579ccc32c63a9a

[ "MIT" ]

null

"""NLP Dataset""" import os import re from typing import List, Union, Dict, Tuple import nltk import unicodedata import numpy as np from dlex.configs import ModuleConfigs from dlex.utils.logging import logger # nltk.download('punkt') # Turn a Unicode string to plain ASCII, thanks to # https://stackoverflow.com/a/518232/2809427 def unicodeToAscii(s): return ''.join( c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn' ) def load_tkn_to_idx(filename): tkn_to_idx = {} fo = open(filename, encoding='utf-8') for line in fo: line = line.strip() if line == "": continue tkn_to_idx[line] = len(tkn_to_idx) fo.close() return tkn_to_idx def normalize_lower(sentence: str): return sentence.strip().lower() def normalize_lower_alphanumeric(sentence: str): s = sentence.strip().lower() s = re.sub("[^a-z0-9\uAC00-\uD7A3]+", " ", s) return s def normalize_string_ascii(sentence): """ :param str sentence: :return: normalized sentence, separated by space :rtype str """ # x = re.sub("[^ a-zA-Z0-9\uAC00-\uD7A3]+", " ", x) # x = re.sub("[\u3040-\u30FF]+", "\u3042", x) # convert Hiragana and Katakana to あ # x = re.sub("[\u4E00-\u9FFF]+", "\u6F22", x) # convert CJK unified ideographs to 漢 sent = unicodeToAscii(sentence.lower().strip()) sent = re.sub(r"([.!?,])", r" \1", sent) sent = re.sub(r"[^a-zA-Z.!?,]+", r" ", sent) sent = re.sub(r"\s+", " ", sent) sent = re.sub("^ | $", "", sent) words = sent.split(' ') ret = [] for word in words: ret.append(normalize_word(word)) return ' '.join(ret) def normalize_string(sentence): """ :param str sentence: :return: normalized sentence, separated by space :rtype str """ # x = re.sub("[^ a-zA-Z0-9\uAC00-\uD7A3]+", " ", x) # x = re.sub("[\u3040-\u30FF]+", "\u3042", x) # convert Hiragana and Katakana to あ # x = re.sub("[\u4E00-\u9FFF]+", "\u6F22", x) # convert CJK unified ideographs to 漢 sentence = re.sub(r"([\.!?,\";])\'", r" \1", sentence) # sent = re.sub(r"[^a-zA-Z.!?,]+", r" ", sent) sentence = re.sub(r"\s+", " ", sentence) sentence = re.sub("^ | $", "", sentence) words = sentence.split(' ') ret = [] for word in words: ret.append(normalize_word(word)) return ' '.join(ret) def normalize_word(word): punctuations = [',', '.', '-', '"', ':', '!', '(', ')', '...', '?'] if word in ',.!?': return word elif word in punctuations: return '<punc>' elif any('0' <= c <= '9' for c in word): return '<non-word>' else: return word.lower() def normalize_none(s): return s def nltk_tokenize(s): return nltk.word_tokenize(s) class Tokenizer: def __init__(self, normalize_fn=None, tokenize_fn=None): self.normalize_fn = normalize_fn self.tokenize_fn = tokenize_fn def process(self, s): s = self.normalize_fn(s) s = self.tokenize_fn(s) return s spacy_nlp = None def spacy_tokenize(s): import spacy from spacy.symbols import ORTH global spacy_nlp if spacy_nlp is None: # sputnik.install('spacy', spacy.about.__version__, 'en_default', data_path=ModuleConfigs.get_tmp_path()) spacy_nlp = spacy.load('en_core_web_sm', via=ModuleConfigs.get_tmp_path()) spacy_nlp.tokenizer.add_special_case('<eos>', [{ORTH: '<eos>'}]) spacy_nlp.tokenizer.add_special_case('<bos>', [{ORTH: '<bos>'}]) spacy_nlp.tokenizer.add_special_case('<unk>', [{ORTH: '<unk>'}]) return [_s.text for _s in spacy_nlp.tokenizer(s)] def normalize_char(char): return char.lower().replace(' ', '_') def space_tokenize(s): return s.split(' ') def char_tokenize(s: str): s = s.replace(" ", "_") return list(s) def mecab_tokenize(s): import MeCab wakati = MeCab.Tagger("-Owakati") return wakati.parse(s).split() def write_vocab( text: Union[str, List[str], List[List[str]]], output_path: str, tokenizer: Tokenizer = None, min_freq=0, specials=None): """ :param text: text or list of sentences :param output_path: :param tokenizer: if tokenizer is None, tokens are separated by space :param min_freq: :param specials: :return: """ if tokenizer is None: tokenizer = Tokenizer(normalize_none, space_tokenize) if specials is None: specials = ['<pad>', '<sos>', '<eos>', '<oov>'] word_freqs = {} if isinstance(text, str): text = [text] for sent in text: if isinstance(sent, str): # if normalize_fn is not None: # s = normalize_fn(sent.replace('_', ' ')) # else: # s = sent # ls = char_tokenize(s) if token == 'char' else space_tokenize(s) sent = tokenizer.process(sent) for word in sent: if word.strip() == '': continue if word in word_freqs: word_freqs[word] += 1 else: word_freqs[word] = 1 words = list([word for word in word_freqs if word_freqs[word] > min_freq]) words.sort(key=lambda word: word_freqs[word], reverse=True) with open(output_path, "w", encoding='utf-8') as fo: fo.write('\n'.join(specials) + '\n') fo.write("\n".join(words)) logger.info("Vocab written to %s (%d tokens)", output_path, len(specials) + len(words)) def get_token_id(vocab, word): """ :type vocab: Vocab :type word: str :rtype: int """ if word in vocab: return vocab[word] else: if '<oov>' in vocab: return vocab['<oov>'] elif '<unk>' in vocab: return vocab['<unk>'] else: raise Exception("No out-of-vocabulary token found.") class Vocab: def __init__(self, index2token: List[str] = None, token2index: Dict[str, int] = None): if index2token is None: self._token2index = {} self._index2token = [] else: self._index2token = index2token if token2index: self._token2index = token2index else: self._token2index = {token: idx for idx, token in enumerate(index2token)} self.embeddings = None self.embedding_dim = None @classmethod def from_file(cls, file_name): index2token = [] fo = open(file_name, encoding='utf-8') for line in fo: line = line.strip() if line == "": continue index2token.append(line) fo.close() return cls(index2token) def __getitem__(self, token: str) -> int: return self._token2index[token] if token in self._token2index else self.oov_token_idx def tolist(self) -> List[str]: return self._index2token def get_token_id(self, token): return self[token] or self.oov_token_idx def add_token(self, token: str): if token not in self._token2index: self._token2index[token] = len(self._token2index) self._index2token.append(token) def __len__(self): return len(self._token2index) def get_token(self, idx: int) -> str: return self._index2token[idx] def decode_idx_list(self, ls: List[int], ignore: List[int] = None, stop_at: int = None) -> List[str]: ret = [] for idx in ls: if stop_at and idx == stop_at: break elif ignore and idx in ignore: continue else: ret.append(self.get_token(idx)) return ret def encode_token_list(self, ls: List[str]) -> List[int]: return [self.get_token_id(token) for token in ls] @property def sos_token_idx(self) -> int: idx = self['<sos>'] or self['<s>'] assert idx is not None return idx @property def eos_token_idx(self) -> int: idx = self['<eos>'] or self['</s>'] assert idx is not None return idx @property def blank_token_idx(self): idx = self['<blank>'] or self['<pad>'] assert idx is not None return idx @property def oov_token_idx(self) -> int: if '<oov>' in self._token2index: return self._token2index['<oov>'] elif '<unk>' in self._token2index: return self._token2index['<unk>'] else: raise Exception("<oov> token not found.") def get_specials(self): return [token for token in self._index2token if token.startswith('<')] def init_pretrained_embeddings( self, pretrained: str, emb_name: str = None, dim: int = None) -> np.ndarray: if pretrained == 'glove': from torchtext.vocab import GloVe dim = dim or 300 vocab = GloVe( name=emb_name or '840B', dim=dim, cache=os.path.join(ModuleConfigs.get_tmp_path(), "torchtext")) elif pretrained == 'fasttext': from torchtext.vocab import FastText vocab = FastText() else: raise ValueError("Pre-trained embeddings not found.") vectors = vocab.vectors oovs = [] embeddings = np.zeros([len(self), dim]) for idx, t in enumerate(self._index2token): _t = t.lower() if _t in vocab.stoi: embeddings[idx, :] = vectors[vocab.stoi[_t]].cpu().numpy() if all(token in vocab.stoi for token in _t.split(' ')): embeddings[idx, :] = np.sum([vectors[vocab.stoi[token]].cpu().numpy() for token in _t.split(' ')]) else: oovs.append(_t) if oovs: logger.warning(f"{len(oovs)} tokens not found in pre-trained embeddings: {', '.join(oovs)}") logger.debug(f"Load embeddings: {pretrained} (no. embeddings: {len(self) - len(oovs):,})") self.embedding_dim = dim self.embeddings = embeddings def get_token_embedding(self, token: str) -> np.ndarray: if self.embeddings is None: raise ValueError('Embeddings are not initialized') return self.embeddings[self.get_token_id(token)] def embed_token_list(self, ls): emb = np.zeros(self.embedding_dim) for token in ls: emb += self.get_token_embedding(token) return emb def load_embeddings( pretrained: str, emb_name: str = None, dim: int = None, vocab_size: int = None, tokens: List[str] = None, specials: List[str] = None) -> Tuple[np.ndarray, Vocab]: """ Load pre-trained embedding defined in dataset.embeddings :param tokens: if specified, only load embeddings of these tokens :param specials: special tokens :return: """ if not pretrained: assert dim is not None assert vocab_size is not None return np.random.rand(vocab_size, dim), None elif pretrained.lower() in ["glove", "fasttext"]: if pretrained.lower() == 'glove': from torchtext.vocab import GloVe vocab = GloVe( name=emb_name, dim=dim, cache=os.path.join(ModuleConfigs.get_tmp_path(), "torchtext")) elif pretrained.lower() == 'fasttext': from torchtext.vocab import FastText vocab = FastText() else: raise ValueError("Pre-trained embeddings not found.") vectors = vocab.vectors index2token = vocab.itos token2index = None if tokens: # limit vocabulary to list of tokens num_oovs = 0 keep = [] index2token = [] token2index = {} for t in tokens: _t = t.lower() if _t in token2index: if t not in token2index: token2index[t] = token2index[_t] elif _t in vocab.stoi: keep.append(vocab.stoi[_t.lower()]) token2index[_t] = len(index2token) token2index[t] = len(index2token) index2token.append(_t) else: num_oovs += 1 vectors = vectors[keep] if num_oovs: logger.warning(f"{num_oovs} tokens not found in pre-trained embeddings") logger.debug(f"Load embeddings: {pretrained} (no. embeddings: {len(index2token):,})") if specials is not None: for s in specials: token2index[s] = len(index2token) index2token.append(s) index2token += specials vectors = torch.cat([vectors, torch.rand(len(specials), len(vectors[0]))]) # return nn.Embedding.from_pretrained(vectors, freeze=emb.freeze or True), Vocab(index2token, token2index) return vectors, Vocab(index2token, token2index) else: raise ValueError(f"{pretrained} is not supported.")

31.49652

115

0.550571

import os import re from typing import List, Union, Dict, Tuple import nltk import unicodedata import numpy as np from dlex.configs import ModuleConfigs from dlex.utils.logging import logger def unicodeToAscii(s): return ''.join( c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn' ) def load_tkn_to_idx(filename): tkn_to_idx = {} fo = open(filename, encoding='utf-8') for line in fo: line = line.strip() if line == "": continue tkn_to_idx[line] = len(tkn_to_idx) fo.close() return tkn_to_idx def normalize_lower(sentence: str): return sentence.strip().lower() def normalize_lower_alphanumeric(sentence: str): s = sentence.strip().lower() s = re.sub("[^a-z0-9\uAC00-\uD7A3]+", " ", s) return s def normalize_string_ascii(sentence): b(r"([.!?,])", r" \1", sent) sent = re.sub(r"[^a-zA-Z.!?,]+", r" ", sent) sent = re.sub(r"\s+", " ", sent) sent = re.sub("^ | $", "", sent) words = sent.split(' ') ret = [] for word in words: ret.append(normalize_word(word)) return ' '.join(ret) def normalize_string(sentence): sent = re.sub(r"[^a-zA-Z.!?,]+", r" ", sent) sentence = re.sub(r"\s+", " ", sentence) sentence = re.sub("^ | $", "", sentence) words = sentence.split(' ') ret = [] for word in words: ret.append(normalize_word(word)) return ' '.join(ret) def normalize_word(word): punctuations = [',', '.', '-', '"', ':', '!', '(', ')', '...', '?'] if word in ',.!?': return word elif word in punctuations: return '<punc>' elif any('0' <= c <= '9' for c in word): return '<non-word>' else: return word.lower() def normalize_none(s): return s def nltk_tokenize(s): return nltk.word_tokenize(s) class Tokenizer: def __init__(self, normalize_fn=None, tokenize_fn=None): self.normalize_fn = normalize_fn self.tokenize_fn = tokenize_fn def process(self, s): s = self.normalize_fn(s) s = self.tokenize_fn(s) return s spacy_nlp = None def spacy_tokenize(s): import spacy from spacy.symbols import ORTH global spacy_nlp if spacy_nlp is None: # sputnik.install('spacy', spacy.about.__version__, 'en_default', data_path=ModuleConfigs.get_tmp_path()) spacy_nlp = spacy.load('en_core_web_sm', via=ModuleConfigs.get_tmp_path()) spacy_nlp.tokenizer.add_special_case('<eos>', [{ORTH: '<eos>'}]) spacy_nlp.tokenizer.add_special_case('<bos>', [{ORTH: '<bos>'}]) spacy_nlp.tokenizer.add_special_case('<unk>', [{ORTH: '<unk>'}]) return [_s.text for _s in spacy_nlp.tokenizer(s)] def normalize_char(char): return char.lower().replace(' ', '_') def space_tokenize(s): return s.split(' ') def char_tokenize(s: str): s = s.replace(" ", "_") return list(s) def mecab_tokenize(s): import MeCab wakati = MeCab.Tagger("-Owakati") return wakati.parse(s).split() def write_vocab( text: Union[str, List[str], List[List[str]]], output_path: str, tokenizer: Tokenizer = None, min_freq=0, specials=None): if tokenizer is None: tokenizer = Tokenizer(normalize_none, space_tokenize) if specials is None: specials = ['<pad>', '<sos>', '<eos>', '<oov>'] word_freqs = {} if isinstance(text, str): text = [text] for sent in text: if isinstance(sent, str): # if normalize_fn is not None: # s = normalize_fn(sent.replace('_', ' ')) # else: # s = sent # ls = char_tokenize(s) if token == 'char' else space_tokenize(s) sent = tokenizer.process(sent) for word in sent: if word.strip() == '': continue if word in word_freqs: word_freqs[word] += 1 else: word_freqs[word] = 1 words = list([word for word in word_freqs if word_freqs[word] > min_freq]) words.sort(key=lambda word: word_freqs[word], reverse=True) with open(output_path, "w", encoding='utf-8') as fo: fo.write('\n'.join(specials) + '\n') fo.write("\n".join(words)) logger.info("Vocab written to %s (%d tokens)", output_path, len(specials) + len(words)) def get_token_id(vocab, word): if word in vocab: return vocab[word] else: if '<oov>' in vocab: return vocab['<oov>'] elif '<unk>' in vocab: return vocab['<unk>'] else: raise Exception("No out-of-vocabulary token found.") class Vocab: def __init__(self, index2token: List[str] = None, token2index: Dict[str, int] = None): if index2token is None: self._token2index = {} self._index2token = [] else: self._index2token = index2token if token2index: self._token2index = token2index else: self._token2index = {token: idx for idx, token in enumerate(index2token)} self.embeddings = None self.embedding_dim = None @classmethod def from_file(cls, file_name): index2token = [] fo = open(file_name, encoding='utf-8') for line in fo: line = line.strip() if line == "": continue index2token.append(line) fo.close() return cls(index2token) def __getitem__(self, token: str) -> int: return self._token2index[token] if token in self._token2index else self.oov_token_idx def tolist(self) -> List[str]: return self._index2token def get_token_id(self, token): return self[token] or self.oov_token_idx def add_token(self, token: str): if token not in self._token2index: self._token2index[token] = len(self._token2index) self._index2token.append(token) def __len__(self): return len(self._token2index) def get_token(self, idx: int) -> str: return self._index2token[idx] def decode_idx_list(self, ls: List[int], ignore: List[int] = None, stop_at: int = None) -> List[str]: ret = [] for idx in ls: if stop_at and idx == stop_at: break elif ignore and idx in ignore: continue else: ret.append(self.get_token(idx)) return ret def encode_token_list(self, ls: List[str]) -> List[int]: return [self.get_token_id(token) for token in ls] @property def sos_token_idx(self) -> int: idx = self['<sos>'] or self['<s>'] assert idx is not None return idx @property def eos_token_idx(self) -> int: idx = self['<eos>'] or self['</s>'] assert idx is not None return idx @property def blank_token_idx(self): idx = self['<blank>'] or self['<pad>'] assert idx is not None return idx @property def oov_token_idx(self) -> int: if '<oov>' in self._token2index: return self._token2index['<oov>'] elif '<unk>' in self._token2index: return self._token2index['<unk>'] else: raise Exception("<oov> token not found.") def get_specials(self): return [token for token in self._index2token if token.startswith('<')] def init_pretrained_embeddings( self, pretrained: str, emb_name: str = None, dim: int = None) -> np.ndarray: if pretrained == 'glove': from torchtext.vocab import GloVe dim = dim or 300 vocab = GloVe( name=emb_name or '840B', dim=dim, cache=os.path.join(ModuleConfigs.get_tmp_path(), "torchtext")) elif pretrained == 'fasttext': from torchtext.vocab import FastText vocab = FastText() else: raise ValueError("Pre-trained embeddings not found.") vectors = vocab.vectors oovs = [] embeddings = np.zeros([len(self), dim]) for idx, t in enumerate(self._index2token): _t = t.lower() if _t in vocab.stoi: embeddings[idx, :] = vectors[vocab.stoi[_t]].cpu().numpy() if all(token in vocab.stoi for token in _t.split(' ')): embeddings[idx, :] = np.sum([vectors[vocab.stoi[token]].cpu().numpy() for token in _t.split(' ')]) else: oovs.append(_t) if oovs: logger.warning(f"{len(oovs)} tokens not found in pre-trained embeddings: {', '.join(oovs)}") logger.debug(f"Load embeddings: {pretrained} (no. embeddings: {len(self) - len(oovs):,})") self.embedding_dim = dim self.embeddings = embeddings def get_token_embedding(self, token: str) -> np.ndarray: if self.embeddings is None: raise ValueError('Embeddings are not initialized') return self.embeddings[self.get_token_id(token)] def embed_token_list(self, ls): emb = np.zeros(self.embedding_dim) for token in ls: emb += self.get_token_embedding(token) return emb def load_embeddings( pretrained: str, emb_name: str = None, dim: int = None, vocab_size: int = None, tokens: List[str] = None, specials: List[str] = None) -> Tuple[np.ndarray, Vocab]: if not pretrained: assert dim is not None assert vocab_size is not None return np.random.rand(vocab_size, dim), None elif pretrained.lower() in ["glove", "fasttext"]: if pretrained.lower() == 'glove': from torchtext.vocab import GloVe vocab = GloVe( name=emb_name, dim=dim, cache=os.path.join(ModuleConfigs.get_tmp_path(), "torchtext")) elif pretrained.lower() == 'fasttext': from torchtext.vocab import FastText vocab = FastText() else: raise ValueError("Pre-trained embeddings not found.") vectors = vocab.vectors index2token = vocab.itos token2index = None if tokens: # limit vocabulary to list of tokens num_oovs = 0 keep = [] index2token = [] token2index = {} for t in tokens: _t = t.lower() if _t in token2index: if t not in token2index: token2index[t] = token2index[_t] elif _t in vocab.stoi: keep.append(vocab.stoi[_t.lower()]) token2index[_t] = len(index2token) token2index[t] = len(index2token) index2token.append(_t) else: num_oovs += 1 vectors = vectors[keep] if num_oovs: logger.warning(f"{num_oovs} tokens not found in pre-trained embeddings") logger.debug(f"Load embeddings: {pretrained} (no. embeddings: {len(index2token):,})") if specials is not None: for s in specials: token2index[s] = len(index2token) index2token.append(s) index2token += specials vectors = torch.cat([vectors, torch.rand(len(specials), len(vectors[0]))]) # return nn.Embedding.from_pretrained(vectors, freeze=emb.freeze or True), Vocab(index2token, token2index) return vectors, Vocab(index2token, token2index) else: raise ValueError(f"{pretrained} is not supported.")

true

f71e93dc1e8f76e4a04e77f4cc3875792895275e

2,360

py

Python

src/processData.py

mabelzunce/PETAtlases

438276ff06a8f2f61eb506e5f0e28a257c85d9aa

[ "MIT" ]

null

src/processData.py

mabelzunce/PETAtlases

438276ff06a8f2f61eb506e5f0e28a257c85d9aa

[ "MIT" ]

null

src/processData.py

mabelzunce/PETAtlases

438276ff06a8f2f61eb506e5f0e28a257c85d9aa

[ "MIT" ]

null

#! python3 from __future__ import print_function import SimpleITK as sitk import ImageRegistration as reg import numpy as np import sys import os outputPath = "D:\\Martin\\Personal\\UNSAM\\CursoNeuroimagenes\\TrabajosFinales\\NicolasFuentes\\ADNI\\002_S_5018\\RegisteredData\\" if not os.path.exists(outputPath): os.mkdir(outputPath) petImageFilename = "D:\\Martin\\Personal\\UNSAM\\CursoNeuroimagenes\\TrabajosFinales\\NicolasFuentes\\ADNI\\002_S_5018\\ADNI_Brain_PET__Raw_AV45\\2012-11-15_16_29_51.0\\I347148\\ADNI_002_S_5018_PT_ADNI_Brain_PET__Raw_AV45_br_raw_20121119110623877_305_S174962_I347148.nii" mriImageFilename = "D:\\Martin\\Personal\\UNSAM\\CursoNeuroimagenes\\TrabajosFinales\\NicolasFuentes\\ADNI\\002_S_5018\\ADNI_002_S_5018_MR_MPRAGE_br_raw_20121112145218294_127_S174291_I346242.nii" mni152Filename = "D:\\Martin\\Personal\\UNSAM\\CursoNeuroimagenes\\TrabajosFinales\\NicolasFuentes\\Atlas\\icbm_avg_152_t1_tal_nlin_symmetric_VI.mnc" petImage = sitk.Cast(sitk.ReadImage(petImageFilename), sitk.sitkFloat32) mriImage = sitk.Cast(sitk.ReadImage(mriImageFilename), sitk.sitkFloat32) mriMni152Image = sitk.Cast(sitk.ReadImage(mni152Filename), sitk.sitkFloat32) sitk.WriteImage(petImage, outputPath + "PET.nii") sitk.WriteImage(mriImage, outputPath + "MRI.nii") sitk.WriteImage(mriMni152Image, outputPath + "MNI152.nii") # Registration resultsRegistration = reg.RigidImageRegistration(petImage, sitk.Cast(mriImage, sitk.sitkFloat32), printLog = True) sitk.WriteImage(resultsRegistration["image"], outputPath + "regPET.nii") # Normalize MRI into MNI152. # Create a mask for MNI 152: otsuSegmentation = sitk.OtsuMultipleThresholds(mriMni152Image, 3, 0, 128, False) maskMNI152 = otsuSegmentation > 0 sitk.WriteImage(maskMNI152, outputPath + "maskMNI152.nii") # Two steps, first affine transform, then nonlinear: resultsFirstStepNormalization = reg.AffineImageRegistration(mriImage, mriMni152Image, printLog = True, fixedMask = maskMNI152) sitk.WriteImage(resultsFirstStepNormalization["image"], outputPath + "normalizedAffineMRI.nii") # Now the nonlinear registration: resultsNonlinearRegistration = reg.NonlinearImageRegistration(resultsFirstStepNormalization["image"], mriMni152Image, printLog = True) sitk.WriteImage(resultsNonlinearRegistration["image"], outputPath + "normalizedMRI.nii")

53.636364

272

0.806356

from __future__ import print_function import SimpleITK as sitk import ImageRegistration as reg import numpy as np import sys import os outputPath = "D:\\Martin\\Personal\\UNSAM\\CursoNeuroimagenes\\TrabajosFinales\\NicolasFuentes\\ADNI\\002_S_5018\\RegisteredData\\" if not os.path.exists(outputPath): os.mkdir(outputPath) petImageFilename = "D:\\Martin\\Personal\\UNSAM\\CursoNeuroimagenes\\TrabajosFinales\\NicolasFuentes\\ADNI\\002_S_5018\\ADNI_Brain_PET__Raw_AV45\\2012-11-15_16_29_51.0\\I347148\\ADNI_002_S_5018_PT_ADNI_Brain_PET__Raw_AV45_br_raw_20121119110623877_305_S174962_I347148.nii" mriImageFilename = "D:\\Martin\\Personal\\UNSAM\\CursoNeuroimagenes\\TrabajosFinales\\NicolasFuentes\\ADNI\\002_S_5018\\ADNI_002_S_5018_MR_MPRAGE_br_raw_20121112145218294_127_S174291_I346242.nii" mni152Filename = "D:\\Martin\\Personal\\UNSAM\\CursoNeuroimagenes\\TrabajosFinales\\NicolasFuentes\\Atlas\\icbm_avg_152_t1_tal_nlin_symmetric_VI.mnc" petImage = sitk.Cast(sitk.ReadImage(petImageFilename), sitk.sitkFloat32) mriImage = sitk.Cast(sitk.ReadImage(mriImageFilename), sitk.sitkFloat32) mriMni152Image = sitk.Cast(sitk.ReadImage(mni152Filename), sitk.sitkFloat32) sitk.WriteImage(petImage, outputPath + "PET.nii") sitk.WriteImage(mriImage, outputPath + "MRI.nii") sitk.WriteImage(mriMni152Image, outputPath + "MNI152.nii") resultsRegistration = reg.RigidImageRegistration(petImage, sitk.Cast(mriImage, sitk.sitkFloat32), printLog = True) sitk.WriteImage(resultsRegistration["image"], outputPath + "regPET.nii") otsuSegmentation = sitk.OtsuMultipleThresholds(mriMni152Image, 3, 0, 128, False) maskMNI152 = otsuSegmentation > 0 sitk.WriteImage(maskMNI152, outputPath + "maskMNI152.nii") resultsFirstStepNormalization = reg.AffineImageRegistration(mriImage, mriMni152Image, printLog = True, fixedMask = maskMNI152) sitk.WriteImage(resultsFirstStepNormalization["image"], outputPath + "normalizedAffineMRI.nii") resultsNonlinearRegistration = reg.NonlinearImageRegistration(resultsFirstStepNormalization["image"], mriMni152Image, printLog = True) sitk.WriteImage(resultsNonlinearRegistration["image"], outputPath + "normalizedMRI.nii")

true

f71e943cd279890286dde5c70ab1018c6adc2ce4

4,541

py

Python

allennlp/tests/data/fields/sequence_label_field_test.py

annaproxy/udify-metalearning

55206a3aac0aba74a3615a36192d03b6467cfd6f

[ "MIT" ]

65

2020-11-13T05:36:29.000Z

2022-03-26T22:45:46.000Z

allennlp/tests/data/fields/sequence_label_field_test.py

annaproxy/udify-metalearning

55206a3aac0aba74a3615a36192d03b6467cfd6f

[ "MIT" ]

11

2021-05-26T16:22:17.000Z

2022-03-02T04:03:18.000Z

allennlp/tests/data/fields/sequence_label_field_test.py

annaproxy/udify-metalearning

55206a3aac0aba74a3615a36192d03b6467cfd6f

[ "MIT" ]

10

2019-12-06T11:32:37.000Z

2022-01-06T15:39:09.000Z

# pylint: disable=no-self-use,invalid-name from collections import defaultdict import pytest import numpy from allennlp.common.checks import ConfigurationError from allennlp.common.testing import AllenNlpTestCase from allennlp.data import Token, Vocabulary from allennlp.data.fields import TextField, SequenceLabelField from allennlp.data.token_indexers import SingleIdTokenIndexer class TestSequenceLabelField(AllenNlpTestCase): def setUp(self): super(TestSequenceLabelField, self).setUp() self.text = TextField([Token(t) for t in ["here", "are", "some", "words", "."]], {"words": SingleIdTokenIndexer("words")}) def test_tag_length_mismatch_raises(self): with pytest.raises(ConfigurationError): wrong_tags = ["B", "O", "O"] _ = SequenceLabelField(wrong_tags, self.text) def test_count_vocab_items_correctly_indexes_tags(self): tags = ["B", "I", "O", "O", "O"] sequence_label_field = SequenceLabelField(tags, self.text, label_namespace="labels") counter = defaultdict(lambda: defaultdict(int)) sequence_label_field.count_vocab_items(counter) assert counter["labels"]["B"] == 1 assert counter["labels"]["I"] == 1 assert counter["labels"]["O"] == 3 assert set(counter.keys()) == {"labels"} def test_index_converts_field_correctly(self): vocab = Vocabulary() b_index = vocab.add_token_to_namespace("B", namespace='*labels') i_index = vocab.add_token_to_namespace("I", namespace='*labels') o_index = vocab.add_token_to_namespace("O", namespace='*labels') tags = ["B", "I", "O", "O", "O"] sequence_label_field = SequenceLabelField(tags, self.text, label_namespace="*labels") sequence_label_field.index(vocab) # pylint: disable=protected-access assert sequence_label_field._indexed_labels == [b_index, i_index, o_index, o_index, o_index] # pylint: enable=protected-access def test_as_tensor_produces_integer_targets(self): vocab = Vocabulary() vocab.add_token_to_namespace("B", namespace='*labels') vocab.add_token_to_namespace("I", namespace='*labels') vocab.add_token_to_namespace("O", namespace='*labels') tags = ["B", "I", "O", "O", "O"] sequence_label_field = SequenceLabelField(tags, self.text, label_namespace="*labels") sequence_label_field.index(vocab) padding_lengths = sequence_label_field.get_padding_lengths() tensor = sequence_label_field.as_tensor(padding_lengths).detach().cpu().numpy() numpy.testing.assert_array_almost_equal(tensor, numpy.array([0, 1, 2, 2, 2])) def test_sequence_label_field_raises_on_incorrect_type(self): with pytest.raises(ConfigurationError): _ = SequenceLabelField([[], [], [], [], []], self.text) def test_class_variables_for_namespace_warnings_work_correctly(self): # pylint: disable=protected-access tags = ["B", "I", "O", "O", "O"] assert "text" not in SequenceLabelField._already_warned_namespaces with self.assertLogs(logger="allennlp.data.fields.sequence_label_field", level="WARNING"): _ = SequenceLabelField(tags, self.text, label_namespace="text") # We've warned once, so we should have set the class variable to False. assert "text" in SequenceLabelField._already_warned_namespaces with pytest.raises(AssertionError): with self.assertLogs(logger="allennlp.data.fields.sequence_label_field", level="WARNING"): _ = SequenceLabelField(tags, self.text, label_namespace="text") # ... but a new namespace should still log a warning. assert "text2" not in SequenceLabelField._already_warned_namespaces with self.assertLogs(logger="allennlp.data.fields.sequence_label_field", level="WARNING"): _ = SequenceLabelField(tags, self.text, label_namespace="text2") def test_printing_doesnt_crash(self): tags = ["B", "I", "O", "O", "O"] sequence_label_field = SequenceLabelField(tags, self.text, label_namespace="labels") print(sequence_label_field) def test_sequence_methods(self): tags = ["B", "I", "O", "O", "O"] sequence_label_field = SequenceLabelField(tags, self.text, label_namespace="labels") assert len(sequence_label_field) == 5 assert sequence_label_field[1] == "I" assert [label for label in sequence_label_field] == tags

45.868687

102

0.679366

from collections import defaultdict import pytest import numpy from allennlp.common.checks import ConfigurationError from allennlp.common.testing import AllenNlpTestCase from allennlp.data import Token, Vocabulary from allennlp.data.fields import TextField, SequenceLabelField from allennlp.data.token_indexers import SingleIdTokenIndexer class TestSequenceLabelField(AllenNlpTestCase): def setUp(self): super(TestSequenceLabelField, self).setUp() self.text = TextField([Token(t) for t in ["here", "are", "some", "words", "."]], {"words": SingleIdTokenIndexer("words")}) def test_tag_length_mismatch_raises(self): with pytest.raises(ConfigurationError): wrong_tags = ["B", "O", "O"] _ = SequenceLabelField(wrong_tags, self.text) def test_count_vocab_items_correctly_indexes_tags(self): tags = ["B", "I", "O", "O", "O"] sequence_label_field = SequenceLabelField(tags, self.text, label_namespace="labels") counter = defaultdict(lambda: defaultdict(int)) sequence_label_field.count_vocab_items(counter) assert counter["labels"]["B"] == 1 assert counter["labels"]["I"] == 1 assert counter["labels"]["O"] == 3 assert set(counter.keys()) == {"labels"} def test_index_converts_field_correctly(self): vocab = Vocabulary() b_index = vocab.add_token_to_namespace("B", namespace='*labels') i_index = vocab.add_token_to_namespace("I", namespace='*labels') o_index = vocab.add_token_to_namespace("O", namespace='*labels') tags = ["B", "I", "O", "O", "O"] sequence_label_field = SequenceLabelField(tags, self.text, label_namespace="*labels") sequence_label_field.index(vocab) assert sequence_label_field._indexed_labels == [b_index, i_index, o_index, o_index, o_index] def test_as_tensor_produces_integer_targets(self): vocab = Vocabulary() vocab.add_token_to_namespace("B", namespace='*labels') vocab.add_token_to_namespace("I", namespace='*labels') vocab.add_token_to_namespace("O", namespace='*labels') tags = ["B", "I", "O", "O", "O"] sequence_label_field = SequenceLabelField(tags, self.text, label_namespace="*labels") sequence_label_field.index(vocab) padding_lengths = sequence_label_field.get_padding_lengths() tensor = sequence_label_field.as_tensor(padding_lengths).detach().cpu().numpy() numpy.testing.assert_array_almost_equal(tensor, numpy.array([0, 1, 2, 2, 2])) def test_sequence_label_field_raises_on_incorrect_type(self): with pytest.raises(ConfigurationError): _ = SequenceLabelField([[], [], [], [], []], self.text) def test_class_variables_for_namespace_warnings_work_correctly(self): tags = ["B", "I", "O", "O", "O"] assert "text" not in SequenceLabelField._already_warned_namespaces with self.assertLogs(logger="allennlp.data.fields.sequence_label_field", level="WARNING"): _ = SequenceLabelField(tags, self.text, label_namespace="text") assert "text" in SequenceLabelField._already_warned_namespaces with pytest.raises(AssertionError): with self.assertLogs(logger="allennlp.data.fields.sequence_label_field", level="WARNING"): _ = SequenceLabelField(tags, self.text, label_namespace="text") # ... but a new namespace should still log a warning. assert "text2" not in SequenceLabelField._already_warned_namespaces with self.assertLogs(logger="allennlp.data.fields.sequence_label_field", level="WARNING"): _ = SequenceLabelField(tags, self.text, label_namespace="text2") def test_printing_doesnt_crash(self): tags = ["B", "I", "O", "O", "O"] sequence_label_field = SequenceLabelField(tags, self.text, label_namespace="labels") print(sequence_label_field) def test_sequence_methods(self): tags = ["B", "I", "O", "O", "O"] sequence_label_field = SequenceLabelField(tags, self.text, label_namespace="labels") assert len(sequence_label_field) == 5 assert sequence_label_field[1] == "I" assert [label for label in sequence_label_field] == tags

true

f71e947b79357afd20224cfeefc521067a15de20

476

py

Python

setup.py

CampbellCrowley/bplot

b5e5080cdcdc9c4d3e5114c13702cbb2f49fbb8c

[ "BSD-3-Clause" ]

null

setup.py

CampbellCrowley/bplot

b5e5080cdcdc9c4d3e5114c13702cbb2f49fbb8c

[ "BSD-3-Clause" ]

null

setup.py

CampbellCrowley/bplot

b5e5080cdcdc9c4d3e5114c13702cbb2f49fbb8c

[ "BSD-3-Clause" ]

null

from setuptools import setup setup( name="bplot", version="0.2", description="Functional plotting.", url="http://github.com/roualdes/bplot", author="Edward A. Roualdes", author_email="eroualdes@csuchico.edu", license="BSD (3-clause)", install_requires=[ "matplotlib>=3.0.0", "numpy>=1.7,<2.0", "scipy>=0.19.1", "pandas>=0.25.0", ], packages=["bplot"], package_dir={"": "src"}, zip_safe=False, )

22.666667

43

0.573529

from setuptools import setup setup( name="bplot", version="0.2", description="Functional plotting.", url="http://github.com/roualdes/bplot", author="Edward A. Roualdes", author_email="eroualdes@csuchico.edu", license="BSD (3-clause)", install_requires=[ "matplotlib>=3.0.0", "numpy>=1.7,<2.0", "scipy>=0.19.1", "pandas>=0.25.0", ], packages=["bplot"], package_dir={"": "src"}, zip_safe=False, )

true

f71e949573678c6e993f1d29898b3f2046e7012c

1,912

py

Python

imap_tools/errors.py

unqx/imap_tools

7f8fd5e4f3976bbd2efa507843c577affa61d996

[ "Apache-2.0" ]

344

2017-05-31T09:45:41.000Z

2022-03-31T18:32:16.000Z

imap_tools/errors.py

unqx/imap_tools

7f8fd5e4f3976bbd2efa507843c577affa61d996

[ "Apache-2.0" ]

153

2017-07-26T07:49:06.000Z

2022-03-31T16:43:52.000Z

imap_tools/errors.py

unqx/imap_tools

7f8fd5e4f3976bbd2efa507843c577affa61d996

[ "Apache-2.0" ]

53

2018-12-06T05:49:14.000Z

2022-02-19T12:42:56.000Z

class ImapToolsError(Exception): """Base lib error""" class MailboxFolderStatusValueError(ImapToolsError): """Wrong folder status value error""" class UnexpectedCommandStatusError(ImapToolsError): """Unexpected status in IMAP command response""" def __init__(self, command_result: tuple, expected: str): """ :param command_result: imap command result :param expected: expected command status """ self.command_result = command_result self.expected = expected def __str__(self): return 'Response status "{exp}" expected, but "{typ}" received. Data: {data}'.format( exp=self.expected, typ=self.command_result[0], data=str(self.command_result[1])) class MailboxFolderSelectError(UnexpectedCommandStatusError): pass class MailboxFolderCreateError(UnexpectedCommandStatusError): pass class MailboxFolderRenameError(UnexpectedCommandStatusError): pass class MailboxFolderDeleteError(UnexpectedCommandStatusError): pass class MailboxFolderStatusError(UnexpectedCommandStatusError): pass class MailboxFolderSubscribeError(UnexpectedCommandStatusError): pass class MailboxLoginError(UnexpectedCommandStatusError): pass class MailboxLogoutError(UnexpectedCommandStatusError): pass class MailboxNumbersError(UnexpectedCommandStatusError): pass class MailboxUidsError(UnexpectedCommandStatusError): pass class MailboxStarttlsError(UnexpectedCommandStatusError): pass class MailboxFetchError(UnexpectedCommandStatusError): pass class MailboxExpungeError(UnexpectedCommandStatusError): pass class MailboxDeleteError(UnexpectedCommandStatusError): pass class MailboxCopyError(UnexpectedCommandStatusError): pass class MailboxFlagError(UnexpectedCommandStatusError): pass class MailboxAppendError(UnexpectedCommandStatusError): pass

21.010989

93

0.769874

class ImapToolsError(Exception): class MailboxFolderStatusValueError(ImapToolsError): class UnexpectedCommandStatusError(ImapToolsError): def __init__(self, command_result: tuple, expected: str): self.command_result = command_result self.expected = expected def __str__(self): return 'Response status "{exp}" expected, but "{typ}" received. Data: {data}'.format( exp=self.expected, typ=self.command_result[0], data=str(self.command_result[1])) class MailboxFolderSelectError(UnexpectedCommandStatusError): pass class MailboxFolderCreateError(UnexpectedCommandStatusError): pass class MailboxFolderRenameError(UnexpectedCommandStatusError): pass class MailboxFolderDeleteError(UnexpectedCommandStatusError): pass class MailboxFolderStatusError(UnexpectedCommandStatusError): pass class MailboxFolderSubscribeError(UnexpectedCommandStatusError): pass class MailboxLoginError(UnexpectedCommandStatusError): pass class MailboxLogoutError(UnexpectedCommandStatusError): pass class MailboxNumbersError(UnexpectedCommandStatusError): pass class MailboxUidsError(UnexpectedCommandStatusError): pass class MailboxStarttlsError(UnexpectedCommandStatusError): pass class MailboxFetchError(UnexpectedCommandStatusError): pass class MailboxExpungeError(UnexpectedCommandStatusError): pass class MailboxDeleteError(UnexpectedCommandStatusError): pass class MailboxCopyError(UnexpectedCommandStatusError): pass class MailboxFlagError(UnexpectedCommandStatusError): pass class MailboxAppendError(UnexpectedCommandStatusError): pass

true

f71e9497bcf482a547136061650f08ce8c27aa78

345

py

Python

app/__init__.py

gordinmitya/tgnotifierbot

200a27bc0ee63dcb74018f30cc5e855d8b30cda8

[ "MIT" ]

null

app/__init__.py

gordinmitya/tgnotifierbot

200a27bc0ee63dcb74018f30cc5e855d8b30cda8

[ "MIT" ]

null

app/__init__.py

gordinmitya/tgnotifierbot

200a27bc0ee63dcb74018f30cc5e855d8b30cda8

[ "MIT" ]

null

#!/usr/bin/env python3 def api(environ, start_response): """Simplest possible application object""" data = b'{"code": 200}\n' status = '200 OK' response_headers = [ ('Content-type', 'application/json'), ('Content-Length', str(len(data))) ] start_response(status, response_headers) return iter([data])

28.75

46

0.623188

def api(environ, start_response): data = b'{"code": 200}\n' status = '200 OK' response_headers = [ ('Content-type', 'application/json'), ('Content-Length', str(len(data))) ] start_response(status, response_headers) return iter([data])

true

f71e99704e778f9397e5fa8db226d45e87f41161

19,241

py

Python

cinder/zonemanager/drivers/cisco/cisco_fc_zone_client_cli.py

yanheven/cinder

89797971f30d547acbf715fea099c52d90966d1f

[ "Apache-2.0" ]

null

cinder/zonemanager/drivers/cisco/cisco_fc_zone_client_cli.py

yanheven/cinder

89797971f30d547acbf715fea099c52d90966d1f

[ "Apache-2.0" ]

null

cinder/zonemanager/drivers/cisco/cisco_fc_zone_client_cli.py

yanheven/cinder

89797971f30d547acbf715fea099c52d90966d1f

[ "Apache-2.0" ]

null

# (c) Copyright 2014 Cisco Systems Inc. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. # """ Script to push the zone configuration to Cisco SAN switches. """ import random import re from eventlet import greenthread from oslo_concurrency import processutils from oslo_log import log as logging from oslo_utils import excutils import six from cinder import exception from cinder.i18n import _, _LE, _LI from cinder import ssh_utils from cinder import utils import cinder.zonemanager.drivers.cisco.fc_zone_constants as ZoneConstant LOG = logging.getLogger(__name__) class CiscoFCZoneClientCLI(object): """Cisco FC zone client cli implementation. OpenStack Fibre Channel zone client cli connector to manage FC zoning in Cisco SAN fabrics. Version history: 1.0 - Initial Cisco FC zone client cli """ switch_ip = None switch_port = '22' switch_user = 'admin' switch_pwd = 'none' def __init__(self, ipaddress, username, password, port, vsan): """initializing the client.""" self.switch_ip = ipaddress self.switch_port = port self.switch_user = username self.switch_pwd = password self.fabric_vsan = vsan self.sshpool = None def get_active_zone_set(self): """Return the active zone configuration. Return active zoneset from fabric. When none of the configurations are active then it will return empty map. :returns: Map -- active zone set map in the following format { 'zones': {'openstack50060b0000c26604201900051ee8e329': ['50060b0000c26604', '201900051ee8e329'] }, 'active_zone_config': 'OpenStack_Cfg' } """ zone_set = {} zone = {} zone_member = None zone_name = None switch_data = None zone_set_name = None try: switch_data = self._get_switch_info( [ZoneConstant.GET_ACTIVE_ZONE_CFG, self.fabric_vsan, ' | no-more']) except exception.CiscoZoningCliException: with excutils.save_and_reraise_exception(): LOG.error(_LE("Failed getting active zone set " "from fabric %s"), self.switch_ip) try: for line in switch_data: # Split on non-word characters, line_split = re.split('[\s\[\]]+', line) if ZoneConstant.CFG_ZONESET in line_split: # zoneset name [name] vsan [vsan] zone_set_name = \ line_split[line_split.index(ZoneConstant.CFG_ZONESET) + 2] continue if ZoneConstant.CFG_ZONE in line_split: # zone name [name] vsan [vsan] zone_name = \ line_split[line_split.index(ZoneConstant.CFG_ZONE) + 2] zone[zone_name] = list() continue if ZoneConstant.CFG_ZONE_MEMBER in line_split: # Examples: # pwwn c0:50:76:05:15:9f:00:12 # * fcid 0x1e01c0 [pwwn 50:05:07:68:02:20:48:04] [V7K_N1P2] zone_member = \ line_split[ line_split.index(ZoneConstant.CFG_ZONE_MEMBER) + 1] zone_member_list = zone.get(zone_name) zone_member_list.append(zone_member) zone_set[ZoneConstant.CFG_ZONES] = zone zone_set[ZoneConstant.ACTIVE_ZONE_CONFIG] = zone_set_name except Exception as ex: # In case of parsing error here, it should be malformed cli output. msg = _("Malformed zone configuration: (switch=%(switch)s " "zone_config=%(zone_config)s)." ) % {'switch': self.switch_ip, 'zone_config': switch_data} LOG.error(msg) exc_msg = _("Exception: %s") % six.text_type(ex) LOG.exception(exc_msg) raise exception.FCZoneDriverException(reason=msg) return zone_set def add_zones(self, zones, activate, fabric_vsan, active_zone_set, zone_status): """Add zone configuration. This method will add the zone configuration passed by user. input params: zones - zone names mapped to members and VSANs. zone members are colon separated but case-insensitive { zonename1:[zonememeber1,zonemember2,...], zonename2:[zonemember1, zonemember2,...]...} e.g: {'openstack50060b0000c26604201900051ee8e329': ['50:06:0b:00:00:c2:66:04', '20:19:00:05:1e:e8:e3:29'] } activate - True/False """ LOG.debug("Add Zones - Zones passed: %s", zones) LOG.debug("Active zone set:%s", active_zone_set) zone_list = active_zone_set[ZoneConstant.CFG_ZONES] LOG.debug("zone list:%s", zone_list) LOG.debug("zone status:%s", zone_status) cfg_name = active_zone_set[ZoneConstant.ACTIVE_ZONE_CONFIG] zone_cmds = [['conf'], ['zoneset', 'name', cfg_name, 'vsan', fabric_vsan]] for zone in zones.keys(): # if zone exists, its an update. Delete & insert LOG.debug("Update call") if zone in zone_list: # Response from get_active_zone_set strips colons from WWPNs current_zone = set(zone_list[zone]) new_wwpns = map(lambda x: x.lower().replace(':', ''), zones[zone]) new_zone = set(new_wwpns) if current_zone != new_zone: try: self.delete_zones([zone], activate, fabric_vsan, active_zone_set, zone_status) except exception.CiscoZoningCliException: with excutils.save_and_reraise_exception(): LOG.error(_LE("Deleting zone failed %s"), zone) LOG.debug("Deleted Zone before insert : %s", zone) zone_cmds.append(['zone', 'name', zone]) for member in zones[zone]: zone_cmds.append(['member', 'pwwn', member]) zone_cmds.append(['end']) try: LOG.debug("Add zones: Config cmd to run:%s", zone_cmds) self._ssh_execute(zone_cmds, True, 1) if activate: self.activate_zoneset(cfg_name, fabric_vsan, zone_status) self._cfg_save() except Exception as e: msg = _("Creating and activating zone set failed: " "(Zone set=%(zoneset)s error=%(err)s)." ) % {'zoneset': cfg_name, 'err': six.text_type(e)} LOG.error(msg) raise exception.CiscoZoningCliException(reason=msg) def activate_zoneset(self, cfgname, fabric_vsan, zone_status): """Method to Activate the zone config. Param cfgname - ZonesetName.""" LOG.debug("zone status:%s", zone_status) cmd_list = [['conf'], ['zoneset', 'activate', 'name', cfgname, 'vsan', self.fabric_vsan]] if zone_status['mode'] == 'enhanced': cmd_list.append(['zone', 'commit', 'vsan', fabric_vsan]) cmd_list.append(['end']) return self._ssh_execute(cmd_list, True, 1) def get_zoning_status(self): """Return the zoning mode and session for a zoneset.""" zone_status = {} try: switch_data = self._get_switch_info( [ZoneConstant.GET_ZONE_STATUS, self.fabric_vsan]) except exception.CiscoZoningCliException: with excutils.save_and_reraise_exception(): LOG.error(_LE("Failed getting zone status " "from fabric %s"), self.switch_ip) try: for line in switch_data: # Split on non-word characters, line_split = re.split('[\s\[\]]+', line) if 'mode:' in line_split: # mode: <enhanced|basic> zone_status['mode'] = line_split[line_split.index('mode:') + 1] continue if 'session:' in line_split: # session: <none|a value other than none> zone_status['session'] = \ line_split[line_split.index('session:') + 1] continue except Exception as ex: # In case of parsing error here, it should be malformed cli output. msg = _("Malformed zone status: (switch=%(switch)s " "zone_config=%(zone_config)s)." ) % {'switch': self.switch_ip, 'zone_status': switch_data} LOG.error(msg) exc_msg = _("Exception: %s") % six.text_type(ex) LOG.exception(exc_msg) raise exception.FCZoneDriverException(reason=msg) return zone_status def delete_zones(self, zone_names, activate, fabric_vsan, active_zone_set, zone_status): """Delete zones from fabric. Method to delete the active zone config zones params zone_names: zoneNames separated by semicolon params activate: True/False """ LOG.debug("zone_names %s", zone_names) active_zoneset_name = active_zone_set[ZoneConstant.ACTIVE_ZONE_CONFIG] cmds = [['conf'], ['zoneset', 'name', active_zoneset_name, 'vsan', fabric_vsan]] try: for zone in set(zone_names.split(';')): cmds.append(['no', 'zone', 'name', zone]) cmds.append(['end']) LOG.debug("Delete zones: Config cmd to run:%s", cmds) self._ssh_execute(cmds, True, 1) if activate: self.activate_zoneset(active_zoneset_name, fabric_vsan, zone_status) self._cfg_save() except Exception as e: msg = _("Deleting zones failed: (command=%(cmd)s error=%(err)s)." ) % {'cmd': cmds, 'err': six.text_type(e)} LOG.error(msg) raise exception.CiscoZoningCliException(reason=msg) def get_nameserver_info(self): """Get name server data from fabric. This method will return the connected node port wwn list(local and remote) for the given switch fabric show fcns database """ cli_output = None return_list = [] try: cli_output = self._get_switch_info([ZoneConstant.FCNS_SHOW, self.fabric_vsan]) except exception.CiscoZoningCliException: with excutils.save_and_reraise_exception(): LOG.error(_LE("Failed collecting fcns database " "info for fabric %s"), self.switch_ip) if (cli_output): return_list = self._parse_ns_output(cli_output) LOG.info(_LI("Connector returning fcnsinfo-%s"), return_list) return return_list def _cfg_save(self): cmd = ['copy', 'running-config', 'startup-config'] self._run_ssh(cmd, True, 1) def _get_switch_info(self, cmd_list): stdout, stderr, sw_data = None, None, None try: stdout, stderr = self._run_ssh(cmd_list, True, 1) LOG.debug("CLI output from ssh - output:%s", stdout) if (stdout): sw_data = stdout.splitlines() return sw_data except processutils.ProcessExecutionError as e: msg = _("Error while getting data via ssh: (command=%(cmd)s " "error=%(err)s).") % {'cmd': cmd_list, 'err': six.text_type(e)} LOG.error(msg) raise exception.CiscoZoningCliException(reason=msg) def _parse_ns_output(self, switch_data): """Parses name server data. Parses nameserver raw data and adds the device port wwns to the list :returns: List -- list of device port wwn from ns info """ return_list = [] for line in switch_data: if not(" N " in line): continue linesplit = line.split() if len(linesplit) > 2: node_port_wwn = linesplit[2] return_list.append(node_port_wwn) else: msg = _("Malformed show fcns database string: %s") % line LOG.error(msg) raise exception.InvalidParameterValue(err=msg) return return_list def _run_ssh(self, cmd_list, check_exit_code=True, attempts=1): command = ' '.join(cmd_list) if not self.sshpool: self.sshpool = ssh_utils.SSHPool(self.switch_ip, self.switch_port, None, self.switch_user, self.switch_pwd, min_size=1, max_size=5) last_exception = None try: with self.sshpool.item() as ssh: while attempts > 0: attempts -= 1 try: return processutils.ssh_execute( ssh, command, check_exit_code=check_exit_code) except Exception as e: msg = _("Exception: %s") % six.text_type(e) LOG.error(msg) last_exception = e greenthread.sleep(random.randint(20, 500) / 100.0) try: raise processutils.ProcessExecutionError( exit_code=last_exception.exit_code, stdout=last_exception.stdout, stderr=last_exception.stderr, cmd=last_exception.cmd) except AttributeError: raise processutils.ProcessExecutionError( exit_code=-1, stdout="", stderr="Error running SSH command", cmd=command) except Exception: with excutils.save_and_reraise_exception(): LOG.error(_LE("Error running SSH command: %s") % command) def _ssh_execute(self, cmd_list, check_exit_code=True, attempts=1): """Execute cli with status update. Executes CLI commands where status return is expected. cmd_list is a list of commands, where each command is itself a list of parameters. We use utils.check_ssh_injection to check each command, but then join then with " ; " to form a single command. """ # Check that each command is secure for cmd in cmd_list: utils.check_ssh_injection(cmd) # Combine into a single command. command = ' ; '.join(map(lambda x: ' '.join(x), cmd_list)) if not self.sshpool: self.sshpool = ssh_utils.SSHPool(self.switch_ip, self.switch_port, None, self.switch_user, self.switch_pwd, min_size=1, max_size=5) stdin, stdout, stderr = None, None, None LOG.debug("Executing command via ssh: %s" % command) last_exception = None try: with self.sshpool.item() as ssh: while attempts > 0: attempts -= 1 try: stdin, stdout, stderr = ssh.exec_command(command) greenthread.sleep(random.randint(20, 500) / 100.0) channel = stdout.channel exit_status = channel.recv_exit_status() LOG.debug("Exit Status from ssh:%s", exit_status) # exit_status == -1 if no exit code was returned if exit_status != -1: LOG.debug('Result was %s' % exit_status) if check_exit_code and exit_status != 0: raise processutils.ProcessExecutionError( exit_code=exit_status, stdout=stdout, stderr=stderr, cmd=command) else: return True else: return True except Exception as e: msg = _("Exception: %s") % six.text_type(e) LOG.error(msg) last_exception = e greenthread.sleep(random.randint(20, 500) / 100.0) LOG.debug("Handling error case after SSH:%s", last_exception) try: raise processutils.ProcessExecutionError( exit_code=last_exception.exit_code, stdout=last_exception.stdout, stderr=last_exception.stderr, cmd=last_exception.cmd) except AttributeError: raise processutils.ProcessExecutionError( exit_code=-1, stdout="", stderr="Error running SSH command", cmd=command) except Exception as e: with excutils.save_and_reraise_exception(): msg = (_("Error executing command via ssh: %s") % six.text_type(e)) LOG.error(msg) finally: if stdin: stdin.flush() stdin.close() if stdout: stdout.close() if stderr: stderr.close() def cleanup(self): self.sshpool = None

39.754132

79

0.521958

import random import re from eventlet import greenthread from oslo_concurrency import processutils from oslo_log import log as logging from oslo_utils import excutils import six from cinder import exception from cinder.i18n import _, _LE, _LI from cinder import ssh_utils from cinder import utils import cinder.zonemanager.drivers.cisco.fc_zone_constants as ZoneConstant LOG = logging.getLogger(__name__) class CiscoFCZoneClientCLI(object): switch_ip = None switch_port = '22' switch_user = 'admin' switch_pwd = 'none' def __init__(self, ipaddress, username, password, port, vsan): self.switch_ip = ipaddress self.switch_port = port self.switch_user = username self.switch_pwd = password self.fabric_vsan = vsan self.sshpool = None def get_active_zone_set(self): zone_set = {} zone = {} zone_member = None zone_name = None switch_data = None zone_set_name = None try: switch_data = self._get_switch_info( [ZoneConstant.GET_ACTIVE_ZONE_CFG, self.fabric_vsan, ' | no-more']) except exception.CiscoZoningCliException: with excutils.save_and_reraise_exception(): LOG.error(_LE("Failed getting active zone set " "from fabric %s"), self.switch_ip) try: for line in switch_data: line_split = re.split('[\s\[\]]+', line) if ZoneConstant.CFG_ZONESET in line_split: zone_set_name = \ line_split[line_split.index(ZoneConstant.CFG_ZONESET) + 2] continue if ZoneConstant.CFG_ZONE in line_split: zone_name = \ line_split[line_split.index(ZoneConstant.CFG_ZONE) + 2] zone[zone_name] = list() continue if ZoneConstant.CFG_ZONE_MEMBER in line_split: zone_member = \ line_split[ line_split.index(ZoneConstant.CFG_ZONE_MEMBER) + 1] zone_member_list = zone.get(zone_name) zone_member_list.append(zone_member) zone_set[ZoneConstant.CFG_ZONES] = zone zone_set[ZoneConstant.ACTIVE_ZONE_CONFIG] = zone_set_name except Exception as ex: msg = _("Malformed zone configuration: (switch=%(switch)s " "zone_config=%(zone_config)s)." ) % {'switch': self.switch_ip, 'zone_config': switch_data} LOG.error(msg) exc_msg = _("Exception: %s") % six.text_type(ex) LOG.exception(exc_msg) raise exception.FCZoneDriverException(reason=msg) return zone_set def add_zones(self, zones, activate, fabric_vsan, active_zone_set, zone_status): LOG.debug("Add Zones - Zones passed: %s", zones) LOG.debug("Active zone set:%s", active_zone_set) zone_list = active_zone_set[ZoneConstant.CFG_ZONES] LOG.debug("zone list:%s", zone_list) LOG.debug("zone status:%s", zone_status) cfg_name = active_zone_set[ZoneConstant.ACTIVE_ZONE_CONFIG] zone_cmds = [['conf'], ['zoneset', 'name', cfg_name, 'vsan', fabric_vsan]] for zone in zones.keys(): LOG.debug("Update call") if zone in zone_list: current_zone = set(zone_list[zone]) new_wwpns = map(lambda x: x.lower().replace(':', ''), zones[zone]) new_zone = set(new_wwpns) if current_zone != new_zone: try: self.delete_zones([zone], activate, fabric_vsan, active_zone_set, zone_status) except exception.CiscoZoningCliException: with excutils.save_and_reraise_exception(): LOG.error(_LE("Deleting zone failed %s"), zone) LOG.debug("Deleted Zone before insert : %s", zone) zone_cmds.append(['zone', 'name', zone]) for member in zones[zone]: zone_cmds.append(['member', 'pwwn', member]) zone_cmds.append(['end']) try: LOG.debug("Add zones: Config cmd to run:%s", zone_cmds) self._ssh_execute(zone_cmds, True, 1) if activate: self.activate_zoneset(cfg_name, fabric_vsan, zone_status) self._cfg_save() except Exception as e: msg = _("Creating and activating zone set failed: " "(Zone set=%(zoneset)s error=%(err)s)." ) % {'zoneset': cfg_name, 'err': six.text_type(e)} LOG.error(msg) raise exception.CiscoZoningCliException(reason=msg) def activate_zoneset(self, cfgname, fabric_vsan, zone_status): LOG.debug("zone status:%s", zone_status) cmd_list = [['conf'], ['zoneset', 'activate', 'name', cfgname, 'vsan', self.fabric_vsan]] if zone_status['mode'] == 'enhanced': cmd_list.append(['zone', 'commit', 'vsan', fabric_vsan]) cmd_list.append(['end']) return self._ssh_execute(cmd_list, True, 1) def get_zoning_status(self): zone_status = {} try: switch_data = self._get_switch_info( [ZoneConstant.GET_ZONE_STATUS, self.fabric_vsan]) except exception.CiscoZoningCliException: with excutils.save_and_reraise_exception(): LOG.error(_LE("Failed getting zone status " "from fabric %s"), self.switch_ip) try: for line in switch_data: line_split = re.split('[\s\[\]]+', line) if 'mode:' in line_split: zone_status['mode'] = line_split[line_split.index('mode:') + 1] continue if 'session:' in line_split: zone_status['session'] = \ line_split[line_split.index('session:') + 1] continue except Exception as ex: msg = _("Malformed zone status: (switch=%(switch)s " "zone_config=%(zone_config)s)." ) % {'switch': self.switch_ip, 'zone_status': switch_data} LOG.error(msg) exc_msg = _("Exception: %s") % six.text_type(ex) LOG.exception(exc_msg) raise exception.FCZoneDriverException(reason=msg) return zone_status def delete_zones(self, zone_names, activate, fabric_vsan, active_zone_set, zone_status): LOG.debug("zone_names %s", zone_names) active_zoneset_name = active_zone_set[ZoneConstant.ACTIVE_ZONE_CONFIG] cmds = [['conf'], ['zoneset', 'name', active_zoneset_name, 'vsan', fabric_vsan]] try: for zone in set(zone_names.split(';')): cmds.append(['no', 'zone', 'name', zone]) cmds.append(['end']) LOG.debug("Delete zones: Config cmd to run:%s", cmds) self._ssh_execute(cmds, True, 1) if activate: self.activate_zoneset(active_zoneset_name, fabric_vsan, zone_status) self._cfg_save() except Exception as e: msg = _("Deleting zones failed: (command=%(cmd)s error=%(err)s)." ) % {'cmd': cmds, 'err': six.text_type(e)} LOG.error(msg) raise exception.CiscoZoningCliException(reason=msg) def get_nameserver_info(self): cli_output = None return_list = [] try: cli_output = self._get_switch_info([ZoneConstant.FCNS_SHOW, self.fabric_vsan]) except exception.CiscoZoningCliException: with excutils.save_and_reraise_exception(): LOG.error(_LE("Failed collecting fcns database " "info for fabric %s"), self.switch_ip) if (cli_output): return_list = self._parse_ns_output(cli_output) LOG.info(_LI("Connector returning fcnsinfo-%s"), return_list) return return_list def _cfg_save(self): cmd = ['copy', 'running-config', 'startup-config'] self._run_ssh(cmd, True, 1) def _get_switch_info(self, cmd_list): stdout, stderr, sw_data = None, None, None try: stdout, stderr = self._run_ssh(cmd_list, True, 1) LOG.debug("CLI output from ssh - output:%s", stdout) if (stdout): sw_data = stdout.splitlines() return sw_data except processutils.ProcessExecutionError as e: msg = _("Error while getting data via ssh: (command=%(cmd)s " "error=%(err)s).") % {'cmd': cmd_list, 'err': six.text_type(e)} LOG.error(msg) raise exception.CiscoZoningCliException(reason=msg) def _parse_ns_output(self, switch_data): return_list = [] for line in switch_data: if not(" N " in line): continue linesplit = line.split() if len(linesplit) > 2: node_port_wwn = linesplit[2] return_list.append(node_port_wwn) else: msg = _("Malformed show fcns database string: %s") % line LOG.error(msg) raise exception.InvalidParameterValue(err=msg) return return_list def _run_ssh(self, cmd_list, check_exit_code=True, attempts=1): command = ' '.join(cmd_list) if not self.sshpool: self.sshpool = ssh_utils.SSHPool(self.switch_ip, self.switch_port, None, self.switch_user, self.switch_pwd, min_size=1, max_size=5) last_exception = None try: with self.sshpool.item() as ssh: while attempts > 0: attempts -= 1 try: return processutils.ssh_execute( ssh, command, check_exit_code=check_exit_code) except Exception as e: msg = _("Exception: %s") % six.text_type(e) LOG.error(msg) last_exception = e greenthread.sleep(random.randint(20, 500) / 100.0) try: raise processutils.ProcessExecutionError( exit_code=last_exception.exit_code, stdout=last_exception.stdout, stderr=last_exception.stderr, cmd=last_exception.cmd) except AttributeError: raise processutils.ProcessExecutionError( exit_code=-1, stdout="", stderr="Error running SSH command", cmd=command) except Exception: with excutils.save_and_reraise_exception(): LOG.error(_LE("Error running SSH command: %s") % command) def _ssh_execute(self, cmd_list, check_exit_code=True, attempts=1): for cmd in cmd_list: utils.check_ssh_injection(cmd) command = ' ; '.join(map(lambda x: ' '.join(x), cmd_list)) if not self.sshpool: self.sshpool = ssh_utils.SSHPool(self.switch_ip, self.switch_port, None, self.switch_user, self.switch_pwd, min_size=1, max_size=5) stdin, stdout, stderr = None, None, None LOG.debug("Executing command via ssh: %s" % command) last_exception = None try: with self.sshpool.item() as ssh: while attempts > 0: attempts -= 1 try: stdin, stdout, stderr = ssh.exec_command(command) greenthread.sleep(random.randint(20, 500) / 100.0) channel = stdout.channel exit_status = channel.recv_exit_status() LOG.debug("Exit Status from ssh:%s", exit_status) if exit_status != -1: LOG.debug('Result was %s' % exit_status) if check_exit_code and exit_status != 0: raise processutils.ProcessExecutionError( exit_code=exit_status, stdout=stdout, stderr=stderr, cmd=command) else: return True else: return True except Exception as e: msg = _("Exception: %s") % six.text_type(e) LOG.error(msg) last_exception = e greenthread.sleep(random.randint(20, 500) / 100.0) LOG.debug("Handling error case after SSH:%s", last_exception) try: raise processutils.ProcessExecutionError( exit_code=last_exception.exit_code, stdout=last_exception.stdout, stderr=last_exception.stderr, cmd=last_exception.cmd) except AttributeError: raise processutils.ProcessExecutionError( exit_code=-1, stdout="", stderr="Error running SSH command", cmd=command) except Exception as e: with excutils.save_and_reraise_exception(): msg = (_("Error executing command via ssh: %s") % six.text_type(e)) LOG.error(msg) finally: if stdin: stdin.flush() stdin.close() if stdout: stdout.close() if stderr: stderr.close() def cleanup(self): self.sshpool = None

true

f71e99cda0f1de1255e911ccc3a8bdebb2c5f5b9

3,846

py

Python

plugins/modules/netbox_manufacturer.py

FragmentedPacket/netbox_modules

608b387eb0d3af8a29222905a4ff19515f006a88

[ "MIT" ]

38

2019-08-28T18:43:20.000Z

2020-01-09T15:51:34.000Z

plugins/modules/netbox_manufacturer.py

FragmentedPacket/netbox_modules

608b387eb0d3af8a29222905a4ff19515f006a88

[ "MIT" ]

24

2019-09-11T03:46:35.000Z

2019-12-17T06:25:20.000Z

plugins/modules/netbox_manufacturer.py

FragmentedPacket/netbox_modules

608b387eb0d3af8a29222905a4ff19515f006a88

[ "MIT" ]

9

2019-09-20T12:27:39.000Z

2020-01-09T03:12:27.000Z

#!/usr/bin/python # -*- coding: utf-8 -*- # Copyright: (c) 2018, Mikhail Yohman (@FragmentedPacket) <mikhail.yohman@gmail.com> # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type DOCUMENTATION = r""" --- module: netbox_manufacturer short_description: Create or delete manufacturers within NetBox description: - Creates or removes manufacturers from NetBox notes: - Tags should be defined as a YAML list - This should be ran with connection C(local) and hosts C(localhost) author: - Mikhail Yohman (@FragmentedPacket) requirements: - pynetbox version_added: '0.1.0' extends_documentation_fragment: - netbox.netbox.common options: data: type: dict description: - Defines the manufacturer configuration suboptions: name: description: - The name of the manufacturer required: true type: str slug: description: - The slugified version of the name or custom slug. - This is auto-generated following NetBox rules if not provided required: false type: str description: description: - The description of the manufacturer required: false type: str tags: description: - The tags to add/update required: false type: list elements: raw version_added: "3.6.0" custom_fields: description: - Must exist in NetBox required: false type: dict version_added: "3.6.0" required: true """ EXAMPLES = r""" - name: "Test NetBox modules" connection: local hosts: localhost gather_facts: False tasks: - name: Create manufacturer within NetBox with only required information netbox_manufacturer: netbox_url: http://netbox.local netbox_token: thisIsMyToken data: name: Test Manufacturer state: present - name: Delete manufacturer within netbox netbox_manufacturer: netbox_url: http://netbox.local netbox_token: thisIsMyToken data: name: Test Manufacturer state: absent """ RETURN = r""" manufacturer: description: Serialized object as created or already existent within NetBox returned: success (when I(state=present)) type: dict msg: description: Message indicating failure or info about what has been achieved returned: always type: str """ from ansible_collections.netbox.netbox.plugins.module_utils.netbox_utils import ( NetboxAnsibleModule, NETBOX_ARG_SPEC, ) from ansible_collections.netbox.netbox.plugins.module_utils.netbox_dcim import ( NetboxDcimModule, NB_MANUFACTURERS, ) from copy import deepcopy def main(): """ Main entry point for module execution """ argument_spec = deepcopy(NETBOX_ARG_SPEC) argument_spec.update( dict( data=dict( type="dict", required=True, options=dict( name=dict(required=True, type="str"), slug=dict(required=False, type="str"), description=dict(required=False, type="str"), tags=dict(required=False, type="list", elements="raw"), custom_fields=dict(required=False, type="dict"), ), ), ) ) required_if = [("state", "present", ["name"]), ("state", "absent", ["name"])] module = NetboxAnsibleModule( argument_spec=argument_spec, supports_check_mode=True, required_if=required_if ) netbox_manufacturer = NetboxDcimModule(module, NB_MANUFACTURERS) netbox_manufacturer.run() if __name__ == "__main__": # pragma: no cover main()

26.895105

92

0.642226

from __future__ import absolute_import, division, print_function __metaclass__ = type DOCUMENTATION = r""" --- module: netbox_manufacturer short_description: Create or delete manufacturers within NetBox description: - Creates or removes manufacturers from NetBox notes: - Tags should be defined as a YAML list - This should be ran with connection C(local) and hosts C(localhost) author: - Mikhail Yohman (@FragmentedPacket) requirements: - pynetbox version_added: '0.1.0' extends_documentation_fragment: - netbox.netbox.common options: data: type: dict description: - Defines the manufacturer configuration suboptions: name: description: - The name of the manufacturer required: true type: str slug: description: - The slugified version of the name or custom slug. - This is auto-generated following NetBox rules if not provided required: false type: str description: description: - The description of the manufacturer required: false type: str tags: description: - The tags to add/update required: false type: list elements: raw version_added: "3.6.0" custom_fields: description: - Must exist in NetBox required: false type: dict version_added: "3.6.0" required: true """ EXAMPLES = r""" - name: "Test NetBox modules" connection: local hosts: localhost gather_facts: False tasks: - name: Create manufacturer within NetBox with only required information netbox_manufacturer: netbox_url: http://netbox.local netbox_token: thisIsMyToken data: name: Test Manufacturer state: present - name: Delete manufacturer within netbox netbox_manufacturer: netbox_url: http://netbox.local netbox_token: thisIsMyToken data: name: Test Manufacturer state: absent """ RETURN = r""" manufacturer: description: Serialized object as created or already existent within NetBox returned: success (when I(state=present)) type: dict msg: description: Message indicating failure or info about what has been achieved returned: always type: str """ from ansible_collections.netbox.netbox.plugins.module_utils.netbox_utils import ( NetboxAnsibleModule, NETBOX_ARG_SPEC, ) from ansible_collections.netbox.netbox.plugins.module_utils.netbox_dcim import ( NetboxDcimModule, NB_MANUFACTURERS, ) from copy import deepcopy def main(): argument_spec = deepcopy(NETBOX_ARG_SPEC) argument_spec.update( dict( data=dict( type="dict", required=True, options=dict( name=dict(required=True, type="str"), slug=dict(required=False, type="str"), description=dict(required=False, type="str"), tags=dict(required=False, type="list", elements="raw"), custom_fields=dict(required=False, type="dict"), ), ), ) ) required_if = [("state", "present", ["name"]), ("state", "absent", ["name"])] module = NetboxAnsibleModule( argument_spec=argument_spec, supports_check_mode=True, required_if=required_if ) netbox_manufacturer = NetboxDcimModule(module, NB_MANUFACTURERS) netbox_manufacturer.run() if __name__ == "__main__": main()

true

f71e9b85f8f4af462b5dcd665e455ebdbab39bbd

7,950

py

Python

tensorpack/dataflow/dataset/ilsvrc.py

andrewliao11/Andrew_tensorpack

735a2672e3d93b5b612a303b5b6d222e9b2d4280

[ "Apache-2.0" ]

1

2018-03-23T16:26:23.000Z

tensorpack/dataflow/dataset/ilsvrc.py

andrewliao11/Andrew_tensorpack

735a2672e3d93b5b612a303b5b6d222e9b2d4280

[ "Apache-2.0" ]

null

tensorpack/dataflow/dataset/ilsvrc.py

andrewliao11/Andrew_tensorpack

735a2672e3d93b5b612a303b5b6d222e9b2d4280

[ "Apache-2.0" ]

2

2017-12-16T04:23:35.000Z

2021-03-04T23:44:13.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- # File: ilsvrc.py # Author: Yuxin Wu <ppwwyyxxc@gmail.com> import os import tarfile import cv2 import numpy as np from six.moves import range import xml.etree.ElementTree as ET from ...utils import logger, get_rng, get_dataset_path from ...utils.loadcaffe import get_caffe_pb from ...utils.fs import mkdir_p, download from ...utils.timer import timed_operation from ..base import RNGDataFlow __all__ = ['ILSVRCMeta', 'ILSVRC12'] CAFFE_ILSVRC12_URL = "http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz" class ILSVRCMeta(object): """ Some metadata for ILSVRC dataset. """ def __init__(self, dir=None): if dir is None: dir = get_dataset_path('ilsvrc_metadata') self.dir = dir mkdir_p(self.dir) self.caffepb = get_caffe_pb() f = os.path.join(self.dir, 'synsets.txt') if not os.path.isfile(f): self._download_caffe_meta() def get_synset_words_1000(self): """ :returns a dict of {cls_number: cls_name} """ fname = os.path.join(self.dir, 'synset_words.txt') assert os.path.isfile(fname) lines = [x.strip() for x in open(fname).readlines()] return dict(enumerate(lines)) def get_synset_1000(self): """ :returns a dict of {cls_number: synset_id} """ fname = os.path.join(self.dir, 'synsets.txt') assert os.path.isfile(fname) lines = [x.strip() for x in open(fname).readlines()] return dict(enumerate(lines)) def _download_caffe_meta(self): fpath = download(CAFFE_ILSVRC12_URL, self.dir) tarfile.open(fpath, 'r:gz').extractall(self.dir) def get_image_list(self, name): """ :param name: 'train' or 'val' or 'test' :returns: list of (image filename, cls) """ assert name in ['train', 'val', 'test'] fname = os.path.join(self.dir, name + '.txt') assert os.path.isfile(fname) with open(fname) as f: ret = [] for line in f.readlines(): name, cls = line.strip().split() ret.append((name, int(cls))) assert len(ret) return ret def get_per_pixel_mean(self, size=None): """ :param size: return image size in [h, w]. default to (256, 256) :returns: per-pixel mean as an array of shape (h, w, 3) in range [0, 255] """ obj = self.caffepb.BlobProto() mean_file = os.path.join(self.dir, 'imagenet_mean.binaryproto') with open(mean_file, 'rb') as f: obj.ParseFromString(f.read()) arr = np.array(obj.data).reshape((3, 256, 256)).astype('float32') arr = np.transpose(arr, [1,2,0]) if size is not None: arr = cv2.resize(arr, size[::-1]) return arr class ILSVRC12(RNGDataFlow): def __init__(self, dir, name, meta_dir=None, shuffle=True, dir_structure='original', include_bb=False): """ :param dir: A directory containing a subdir named `name`, where the original ILSVRC12_`name`.tar gets decompressed. :param name: 'train' or 'val' or 'test' :param dir_structure: The dir structure of 'val' and 'test'. If is 'original' then keep the original decompressed directory with list of image files (as below). If set to 'train', use the the same directory structure as 'train/', with class name as subdirectories. :param include_bb: Include the bounding box. Maybe useful in training. When `dir_structure=='original'`, `dir` should have the following structure: .. code-block:: none dir/ train/ n02134418/ n02134418_198.JPEG ... ... val/ ILSVRC2012_val_00000001.JPEG ... test/ ILSVRC2012_test_00000001.JPEG ... bbox/ n02134418/ n02134418_198.xml ... ... After decompress ILSVRC12_img_train.tar, you can use the following command to build the above structure for `train/`: .. code-block:: none tar xvf ILSVRC12_img_train.tar -C train && cd train find -type f -name '*.tar' | parallel -P 10 'echo {} && mkdir -p {/.} && tar xf {} -C {/.}' Or: for i in *.tar; do dir=${i%.tar}; echo $dir; mkdir -p $dir; tar xf $i -C $dir; done """ assert name in ['train', 'test', 'val'] self.full_dir = os.path.join(dir, name) self.name = name assert os.path.isdir(self.full_dir), self.full_dir self.shuffle = shuffle meta = ILSVRCMeta(meta_dir) self.imglist = meta.get_image_list(name) self.dir_structure = dir_structure self.synset = meta.get_synset_1000() if include_bb: bbdir = os.path.join(dir, 'bbox') if not \ isinstance(include_bb, six.string_types) else include_bb assert name == 'train', 'Bounding box only available for training' self.bblist = ILSVRC12.get_training_bbox(bbdir, self.imglist) self.include_bb = include_bb def size(self): return len(self.imglist) def get_data(self): """ Produce original images of shape [h, w, 3(BGR)], and label, and optionally a bbox of [xmin, ymin, xmax, ymax] """ idxs = np.arange(len(self.imglist)) add_label_to_fname = (self.name != 'train' and self.dir_structure != 'original') if self.shuffle: self.rng.shuffle(idxs) for k in idxs: fname, label = self.imglist[k] if add_label_to_fname: fname = os.path.join(self.full_dir, self.synset[label], fname) else: fname = os.path.join(self.full_dir, fname) im = cv2.imread(fname.strip(), cv2.IMREAD_COLOR) assert im is not None, fname if im.ndim == 2: im = np.expand_dims(im, 2).repeat(3,2) if self.include_bb: bb = self.bblist[k] if bb is None: bb = [0, 0, im.shape[1]-1, im.shape[0]-1] yield [im, label, bb] else: yield [im, label] @staticmethod def get_training_bbox(bbox_dir, imglist): ret = [] def parse_bbox(fname): root = ET.parse(fname).getroot() size = root.find('size').getchildren() size = map(int, [size[0].text, size[1].text]) box = root.find('object').find('bndbox').getchildren() box = map(lambda x: float(x.text), box) #box[0] /= size[0] #box[1] /= size[1] #box[2] /= size[0] #box[3] /= size[1] return np.asarray(box, dtype='float32') with timed_operation('Loading Bounding Boxes ...'): cnt = 0 import tqdm for k in tqdm.trange(len(imglist)): fname = imglist[k][0] fname = fname[:-4] + 'xml' fname = os.path.join(bbox_dir, fname) try: ret.append(parse_bbox(fname)) cnt += 1 except KeyboardInterrupt: raise except: ret.append(None) logger.info("{}/{} images have bounding box.".format(cnt, len(imglist))) return ret if __name__ == '__main__': meta = ILSVRCMeta() #print(meta.get_synset_words_1000()) ds = ILSVRC12('/home/wyx/data/fake_ilsvrc/', 'train', include_bb=True, shuffle=False) ds.reset_state() for k in ds.get_data(): from IPython import embed; embed() break

34.868421

103

0.550692

import os import tarfile import cv2 import numpy as np from six.moves import range import xml.etree.ElementTree as ET from ...utils import logger, get_rng, get_dataset_path from ...utils.loadcaffe import get_caffe_pb from ...utils.fs import mkdir_p, download from ...utils.timer import timed_operation from ..base import RNGDataFlow __all__ = ['ILSVRCMeta', 'ILSVRC12'] CAFFE_ILSVRC12_URL = "http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz" class ILSVRCMeta(object): def __init__(self, dir=None): if dir is None: dir = get_dataset_path('ilsvrc_metadata') self.dir = dir mkdir_p(self.dir) self.caffepb = get_caffe_pb() f = os.path.join(self.dir, 'synsets.txt') if not os.path.isfile(f): self._download_caffe_meta() def get_synset_words_1000(self): fname = os.path.join(self.dir, 'synset_words.txt') assert os.path.isfile(fname) lines = [x.strip() for x in open(fname).readlines()] return dict(enumerate(lines)) def get_synset_1000(self): fname = os.path.join(self.dir, 'synsets.txt') assert os.path.isfile(fname) lines = [x.strip() for x in open(fname).readlines()] return dict(enumerate(lines)) def _download_caffe_meta(self): fpath = download(CAFFE_ILSVRC12_URL, self.dir) tarfile.open(fpath, 'r:gz').extractall(self.dir) def get_image_list(self, name): assert name in ['train', 'val', 'test'] fname = os.path.join(self.dir, name + '.txt') assert os.path.isfile(fname) with open(fname) as f: ret = [] for line in f.readlines(): name, cls = line.strip().split() ret.append((name, int(cls))) assert len(ret) return ret def get_per_pixel_mean(self, size=None): obj = self.caffepb.BlobProto() mean_file = os.path.join(self.dir, 'imagenet_mean.binaryproto') with open(mean_file, 'rb') as f: obj.ParseFromString(f.read()) arr = np.array(obj.data).reshape((3, 256, 256)).astype('float32') arr = np.transpose(arr, [1,2,0]) if size is not None: arr = cv2.resize(arr, size[::-1]) return arr class ILSVRC12(RNGDataFlow): def __init__(self, dir, name, meta_dir=None, shuffle=True, dir_structure='original', include_bb=False): assert name in ['train', 'test', 'val'] self.full_dir = os.path.join(dir, name) self.name = name assert os.path.isdir(self.full_dir), self.full_dir self.shuffle = shuffle meta = ILSVRCMeta(meta_dir) self.imglist = meta.get_image_list(name) self.dir_structure = dir_structure self.synset = meta.get_synset_1000() if include_bb: bbdir = os.path.join(dir, 'bbox') if not \ isinstance(include_bb, six.string_types) else include_bb assert name == 'train', 'Bounding box only available for training' self.bblist = ILSVRC12.get_training_bbox(bbdir, self.imglist) self.include_bb = include_bb def size(self): return len(self.imglist) def get_data(self): idxs = np.arange(len(self.imglist)) add_label_to_fname = (self.name != 'train' and self.dir_structure != 'original') if self.shuffle: self.rng.shuffle(idxs) for k in idxs: fname, label = self.imglist[k] if add_label_to_fname: fname = os.path.join(self.full_dir, self.synset[label], fname) else: fname = os.path.join(self.full_dir, fname) im = cv2.imread(fname.strip(), cv2.IMREAD_COLOR) assert im is not None, fname if im.ndim == 2: im = np.expand_dims(im, 2).repeat(3,2) if self.include_bb: bb = self.bblist[k] if bb is None: bb = [0, 0, im.shape[1]-1, im.shape[0]-1] yield [im, label, bb] else: yield [im, label] @staticmethod def get_training_bbox(bbox_dir, imglist): ret = [] def parse_bbox(fname): root = ET.parse(fname).getroot() size = root.find('size').getchildren() size = map(int, [size[0].text, size[1].text]) box = root.find('object').find('bndbox').getchildren() box = map(lambda x: float(x.text), box) return np.asarray(box, dtype='float32') with timed_operation('Loading Bounding Boxes ...'): cnt = 0 import tqdm for k in tqdm.trange(len(imglist)): fname = imglist[k][0] fname = fname[:-4] + 'xml' fname = os.path.join(bbox_dir, fname) try: ret.append(parse_bbox(fname)) cnt += 1 except KeyboardInterrupt: raise except: ret.append(None) logger.info("{}/{} images have bounding box.".format(cnt, len(imglist))) return ret if __name__ == '__main__': meta = ILSVRCMeta() ds = ILSVRC12('/home/wyx/data/fake_ilsvrc/', 'train', include_bb=True, shuffle=False) ds.reset_state() for k in ds.get_data(): from IPython import embed; embed() break

true

f71e9c66cde7730f1b239e26a61bd195378915a1

8,835

py

Python

app/clean_test_app.py

droyston/spectralize

572770e7358acc3ec433470659759c17453409f2

[ "MIT" ]

null

app/clean_test_app.py

droyston/spectralize

572770e7358acc3ec433470659759c17453409f2

[ "MIT" ]

null

app/clean_test_app.py

droyston/spectralize

572770e7358acc3ec433470659759c17453409f2

[ "MIT" ]

null

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Jun 18 18:54:48 2020 @author: dylanroyston """ # -*- coding: utf-8 -*- # import packages #import dash_player import dash import dash_table import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output, State import psycopg2 import os import pandas as pd import numpy as np import plotly import plotly.express as px import plotly.graph_objects as go import librosa import librosa.display as ld import IPython.display as ipd import pylab as pl import boto3 #import matplotlib as mpl #import matplotlib.pyplot as plt #from matplotlib import cm #from colorspacious import cspace_converter #from collections import OrderedDict ###### # connect to PSQL and retrieve psql_usr = os.environ.get('PSQL_USR') psql_pw = os.environ.get('PSQL_PW') conn = psycopg2.connect(host = 'ec2-13-58-251-142.us-east-2.compute.amazonaws.com', dbname = 'spectralize', user='postgres', password=psql_pw) ##### read out metadata metadata = conn.cursor() metadata.execute("SELECT * FROM clean_metadata WHERE false;") cols = set(metadata.fetchall()) metadata.execute("SELECT * FROM clean_metadata;") md = set(metadata.fetchall()) cols = ["s3_key", "song_id", "album", "albumartist", "artist", "audio_offset", "bitrate", "channels", "comment", "composer", "disc", "disc_total", "duration", "filesize", "genre", "samplerate", "title", "track", "track_total", "year"] tag_df = pd.DataFrame(data=md, columns=cols) ##### s3 acess for playing audio files s3_bucket = 'mdp-spectralize-pal' number_of_files = 0 s3 = boto3.resource('s3') bucket = s3.Bucket(s3_bucket) # placeholders for callback initialization standin_fp = '/home/dylanroyston/Documents/GIT/spectralize/app/hello.wav' audio_sd_file = standin_fp #audio_rawfile, new_sr = librosa.load(standin_fp, sr=None) standin_data = np.array([[0,0],[0,0]]) standin_df = pd.DataFrame(standin_data, columns=['x','y']) #audio_fig = px.line(standin_df, x='x', y='y', title='audio data', render_mode='webgl') spec_fig = px.imshow(standin_df) def load_audio_data(selected_row): # read out audio data #curr_song_id = tag_df.iloc[selected_row]['song_id'] curr_song_id = selected_row # audiodata = conn.cursor() # qstring = 'SELECT intensity FROM clean_audio WHERE song_id=' + str(curr_song_id) # audiodata.execute(qstring) # ad = np.array(audiodata.fetchall()) # audio_df = pd.DataFrame(data=ad, columns=['I']) # audio_fig = px.line(audio_df, x=audio_df.index, y='I', title='audio data', render_mode='webgl') # audio_fig.update_layout( # height=250, # margin_r=0, # margin_l=0, # margin_t=0, # yaxis_title='', # yaxis_fixedrange=True) s3_key = tag_df.iloc[curr_song_id]['s3_key'] #this_row = tag_df.loc[tag_df['song_id'] == curr_song_id] #s3_key = tag_df.iloc[this_row]['s3_key'] ext = s3_key[-4:] audio_sd_file = '/home/dylanroyston/Documents/GIT/spectralize/app/audio_file' + ext bucket.download_file(s3_key, audio_sd_file) #audio_rawfile = librosa.load(audio_sd_file) return audio_sd_file#, audio_fig def load_spec_data(selected_row): curr_song_id = selected_row specdata = conn.cursor() qstring = 'SELECT * FROM clean_spec WHERE song_id=' + str(curr_song_id) specdata.execute(qstring) sd = np.array(specdata.fetchall()) spec_df = pd.DataFrame(data=sd) #currtitle = tag_df.iloc[curr_song_id]['title'] #currdur = tag_df.iloc[curr_song_id]['duration'] # numpts = len(sd) # interval = float(currdur) / numpts # timeline = np.linspace(0,float(currdur),numpts) # rt = timeline.round(0) trim_sd = spec_df.iloc[:,2:] spec_fig = px.imshow(trim_sd.transpose(), origin='lower', #title=currtitle, #x=timeline ) spec_fig.update_layout( height=250, margin_r=0, margin_l=0, margin_t=0, yaxis_title='Frequency', xaxis_title='Time', #colorbar.title='power', yaxis_fixedrange=True, #x=str(rt) #title=currtitle ) return spec_fig ##### # initialize Dash app external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) app.layout = html.Div(children=[ # header html.H1(children='Metadata'), # metadata table dash_table.DataTable( id = 'metadata_table', data=tag_df.to_dict('rows'), columns=[{'id': c, 'name': c} for c in tag_df.columns], style_cell={ 'overflowX': 'auto', 'overflow': 'hidden', 'textOverflow': 'ellipsis', 'maxWidth': 10, 'row_selectable': 'single', 'font_family': 'Arial', 'font_size': '1.5rem', 'padding': '.5rem', 'backgroundColor': '#f4f4f2' }, style_cell_conditional=[ {'textAlign': 'center'} ], style_header={ 'backgroundColor':'#f4f4f2', 'fontWeight': 'bold', 'overflowX': 'auto', 'textOverflow': 'ellipsis' }, style_table={ 'maxHeight':'500px', 'overflowX': 'scroll' }, tooltip_data=[ { column: {'value': str(value), 'type': 'markdown'} for column, value in row.items() } for row in tag_df.to_dict('rows') ], tooltip_duration=None, style_as_list_view=True, ),# end table # load audio button html.Br(), html.Div( [ dcc.Input(id='input_songnum', value='input song number', type='number'), html.Button('Load audio', id='submit-val', style={'display': 'inline-block'}, n_clicks=0), html.Div(id='song_input') ], ), html.Br(), # html.Audio(id="player", src=audio_sd_file, controls=True, style={ # "width": "100%" # }), # dash_player.DashPlayer( # id='player', # url='audio_sd_file', # controls=True # ), html.Br(), #dcc.Graph(id='waveform', figure=audio_fig), html.Br(), dcc.Graph(id='spect', figure=spec_fig) ]) ##### finish Dash layout ##### callbacks # load-audio button control # @app.callback( # Output('input_songnum', 'value'), # [Input('submit-val', 'n_clicks')] # ) # def retrieve_audio(value): # return load_audio_data(value) # @app.callback( # Output('waveform', 'figure'), # [Input('submit-val', 'n_clicks')] # ) # def update_A_figure(submit_val): # audio_fig = load_audio_data(submit_val) # return audio_fig ## update audio player # @app.callback( # Output('player', 'src'), # [Input('submit-val', 'n_clicks')] # ) # def update_player(submit_val): # audio_sd_file = load_audio_data(submit_val) # return audio_sd_file ## update spect figure on button click @app.callback( Output('spect', 'figure'), [Input('submit-val', 'n_clicks'), Input('input_songnum', 'value')] ) def update_S_figure(n_clicks, value): changed_id = [p['prop_id'] for p in dash.callback_context.triggered][0] if 'submit-val' in changed_id: spec_fig = load_spec_data(value) return spec_fig ## combined audiofile/spec update # @app.callback( # [Output('player', 'src'), # Output('spect', 'figure')], # [Input('submit-val', 'n_clicks')] # ) # def update_figures(submit_val): # audio_sd_file = load_audio_data(submit_val) # spec_fig = load_spec_data(submit_val) # return audio_sd_file, spec_fig # @app.callback( # Output('metadata_table', 'derived_virtual_selected_rows'), # [Input('submit-val', 'n_clicks'), # State('metadata_table', 'derived_virtual_selected_rows')] # ) # def update_audio(n_clicks, derived_virtual_selected_rows): # if derived_virtual_selected_rows is None: # derived_virtual_selected_rows = [] # return load_audio_data(derived_virtual_selected_rows) if __name__ == '__main__': #app.run_server(debug=True, port=8050, host='127.0.0.1') app.run_server(debug=True, port=8050, host='127.0.0.1') #app.run_server(debug=True, port=80, host='ec2-18-224-114-72.us-east-2.compute.amazonaws.com')

26.216617

101

0.603509

import dash import dash_table import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output, State import psycopg2 import os import pandas as pd import numpy as np import plotly import plotly.express as px import plotly.graph_objects as go import librosa import librosa.display as ld import IPython.display as ipd import pylab as pl import boto3 os.environ.get('PSQL_USR') psql_pw = os.environ.get('PSQL_PW') conn = psycopg2.connect(host = 'ec2-13-58-251-142.us-east-2.compute.amazonaws.com', dbname = 'spectralize', user='postgres', password=psql_pw) lse;") cols = set(metadata.fetchall()) metadata.execute("SELECT * FROM clean_metadata;") md = set(metadata.fetchall()) cols = ["s3_key", "song_id", "album", "albumartist", "artist", "audio_offset", "bitrate", "channels", "comment", "composer", "disc", "disc_total", "duration", "filesize", "genre", "samplerate", "title", "track", "track_total", "year"] tag_df = pd.DataFrame(data=md, columns=cols) royston/Documents/GIT/spectralize/app/hello.wav' audio_sd_file = standin_fp standin_data = np.array([[0,0],[0,0]]) standin_df = pd.DataFrame(standin_data, columns=['x','y']) spec_fig = px.imshow(standin_df) def load_audio_data(selected_row): curr_song_id = selected_row s3_key = tag_df.iloc[curr_song_id]['s3_key'] ext = s3_key[-4:] audio_sd_file = '/home/dylanroyston/Documents/GIT/spectralize/app/audio_file' + ext bucket.download_file(s3_key, audio_sd_file) return audio_sd_file def load_spec_data(selected_row): curr_song_id = selected_row specdata = conn.cursor() qstring = 'SELECT * FROM clean_spec WHERE song_id=' + str(curr_song_id) specdata.execute(qstring) sd = np.array(specdata.fetchall()) spec_df = pd.DataFrame(data=sd) trim_sd = spec_df.iloc[:,2:] spec_fig = px.imshow(trim_sd.transpose(), origin='lower', ) spec_fig.update_layout( height=250, margin_r=0, margin_l=0, margin_t=0, yaxis_title='Frequency', xaxis_title='Time', yaxis_fixedrange=True, ) return spec_fig _stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) app.layout = html.Div(children=[ html.H1(children='Metadata'), dash_table.DataTable( id = 'metadata_table', data=tag_df.to_dict('rows'), columns=[{'id': c, 'name': c} for c in tag_df.columns], style_cell={ 'overflowX': 'auto', 'overflow': 'hidden', 'textOverflow': 'ellipsis', 'maxWidth': 10, 'row_selectable': 'single', 'font_family': 'Arial', 'font_size': '1.5rem', 'padding': '.5rem', 'backgroundColor': '#f4f4f2' }, style_cell_conditional=[ {'textAlign': 'center'} ], style_header={ 'backgroundColor':'#f4f4f2', 'fontWeight': 'bold', 'overflowX': 'auto', 'textOverflow': 'ellipsis' }, style_table={ 'maxHeight':'500px', 'overflowX': 'scroll' }, tooltip_data=[ { column: {'value': str(value), 'type': 'markdown'} for column, value in row.items() } for row in tag_df.to_dict('rows') ], tooltip_duration=None, style_as_list_view=True, ), html.Br(), html.Div( [ dcc.Input(id='input_songnum', value='input song number', type='number'), html.Button('Load audio', id='submit-val', style={'display': 'inline-block'}, n_clicks=0), html.Div(id='song_input') ], ), html.Br(), html.Br(), html.Br(), dcc.Graph(id='spect', figure=spec_fig) ]) s, value): changed_id = [p['prop_id'] for p in dash.callback_context.triggered][0] if 'submit-val' in changed_id: spec_fig = load_spec_data(value) return spec_fig if __name__ == '__main__': app.run_server(debug=True, port=8050, host='127.0.0.1')

true

f71e9d55e1ce3fe397a614f51b15c10f303d5fcd

2,491

py

Python

cogs/idk.py

Mr-Owllers/owll

4753ec57429dbf06da0850a40ddd0ba7c8964bc6

[ "MIT" ]

1

2022-01-12T17:11:10.000Z

cogs/idk.py

Mr-Owllers/owll

4753ec57429dbf06da0850a40ddd0ba7c8964bc6

[ "MIT" ]

null

cogs/idk.py

Mr-Owllers/owll

4753ec57429dbf06da0850a40ddd0ba7c8964bc6

[ "MIT" ]

null

import discord from discord.ext import commands import aiohttp hug = ["https://c.tenor.com/bFZKN-tlQP4AAAAC/love-you-my-best-friend.gif", "https://c.tenor.com/KlkE8vt8gOIAAAAM/love-is-the-answer-to-everything-hug.gif", "https://c.tenor.com/OkpKo5iPu-8AAAAM/huge-hug.gif", "https://c.tenor.com/BW8ZMOHHrgMAAAAM/friends-joey-tribbiani.gif", "https://c.tenor.com/ut3cq1GezaoAAAAM/hug-hugs.gif", "https://media1.tenor.com/images/8ac5ada8524d767b77d3d54239773e48/tenor.gif?itemid=16334628", "https://c.tenor.com/0gz0aKX9vcQAAAAC/owl-hug-sweet.gif"] import random class general(commands.Cog): def __init__(self, client): self.client = client @commands.command(help="Invite me!", aliases=["inv", "i"]) async def invite(self, ctx): async with ctx.typing(): embed = discord.Embed( author="Owll", title="Invite me!", description="Invite me by pressing [here](https://dsc/owll)", footer="I love you" ) await ctx.message.reply(embed=embed) @commands.command(help="get a link to the support server", aliases=["xtrahelp", "extrahelp", "helpme"]) async def support(self, ctx): async with ctx.typing(): embed = discord.Embed( author="Owll", title="Support server", description="You may join our [support server](https://dsc.gg/goldwilde) :D" ) await ctx.message.reply(embed=embed) @commands.command(help="hug someone!", aliases=["hog"]) async def hug(self, ctx, members: commands.Greedy[discord.Member]): async with aiohttp.ClientSession() as cs: async with ctx.typing(): async with cs.get("https://some-random-api.ml/animu/hug") as r: js = await r.json() if not members: return await ctx.send("Please specify someone to hug.") if ctx.author in members: return await ctx.send("do you... need a hug?") e = discord.Embed(color=0xff0000, description=f"**{ctx.message.author.display_name}** hugs " + "**" + '**, **'.join(x.display_name for x in members) + "**") manual = hug manual.append(js['link']) image = random.choice(manual) e.set_image(url=image) await ctx.send(embed=e) def setup(client): client.add_cog(general(client))

44.482143

480

0.594942

import discord from discord.ext import commands import aiohttp hug = ["https://c.tenor.com/bFZKN-tlQP4AAAAC/love-you-my-best-friend.gif", "https://c.tenor.com/KlkE8vt8gOIAAAAM/love-is-the-answer-to-everything-hug.gif", "https://c.tenor.com/OkpKo5iPu-8AAAAM/huge-hug.gif", "https://c.tenor.com/BW8ZMOHHrgMAAAAM/friends-joey-tribbiani.gif", "https://c.tenor.com/ut3cq1GezaoAAAAM/hug-hugs.gif", "https://media1.tenor.com/images/8ac5ada8524d767b77d3d54239773e48/tenor.gif?itemid=16334628", "https://c.tenor.com/0gz0aKX9vcQAAAAC/owl-hug-sweet.gif"] import random class general(commands.Cog): def __init__(self, client): self.client = client @commands.command(help="Invite me!", aliases=["inv", "i"]) async def invite(self, ctx): async with ctx.typing(): embed = discord.Embed( author="Owll", title="Invite me!", description="Invite me by pressing [here](https://dsc/owll)", footer="I love you" ) await ctx.message.reply(embed=embed) @commands.command(help="get a link to the support server", aliases=["xtrahelp", "extrahelp", "helpme"]) async def support(self, ctx): async with ctx.typing(): embed = discord.Embed( author="Owll", title="Support server", description="You may join our [support server](https://dsc.gg/goldwilde) :D" ) await ctx.message.reply(embed=embed) @commands.command(help="hug someone!", aliases=["hog"]) async def hug(self, ctx, members: commands.Greedy[discord.Member]): async with aiohttp.ClientSession() as cs: async with ctx.typing(): async with cs.get("https://some-random-api.ml/animu/hug") as r: js = await r.json() if not members: return await ctx.send("Please specify someone to hug.") if ctx.author in members: return await ctx.send("do you... need a hug?") e = discord.Embed(color=0xff0000, description=f"**{ctx.message.author.display_name}** hugs " + "**" + '**, **'.join(x.display_name for x in members) + "**") manual = hug manual.append(js['link']) image = random.choice(manual) e.set_image(url=image) await ctx.send(embed=e) def setup(client): client.add_cog(general(client))

true

f71e9e447882fb2e25d1fc3c1cdbf309c949b7a8

7,334

py

Python

pychron/hardware/gauges/base_controller.py

ASUPychron/pychron

dfe551bdeb4ff8b8ba5cdea0edab336025e8cc76

[ "Apache-2.0" ]

31

2016-03-07T02:38:17.000Z

2022-02-14T18:23:43.000Z

pychron/hardware/gauges/base_controller.py

ASUPychron/pychron

dfe551bdeb4ff8b8ba5cdea0edab336025e8cc76

[ "Apache-2.0" ]

1,626

2015-01-07T04:52:35.000Z

2022-03-25T19:15:59.000Z

pychron/hardware/gauges/base_controller.py

UIllinoisHALPychron/pychron

f21b79f4592a9fb9dc9a4cb2e4e943a3885ededc

[ "Apache-2.0" ]

26

2015-05-23T00:10:06.000Z

2022-03-07T16:51:57.000Z

# =============================================================================== # Copyright 2017 ross # # 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 traits.api import HasTraits, List, Str, Float, Int from traitsui.api import View, HGroup, Item, Group, InstanceEditor, ListEditor from pychron.core.ui.color_map_bar_editor import BarGaugeEditor from pychron.graph.time_series_graph import TimeSeriesStreamStackedGraph class BaseGauge(HasTraits): name = Str pressure = Float display_name = Str low = 5e-10 high = 1e-8 color_scalar = 1 width = Int(100) channel = Str def traits_view(self): v = View( HGroup( Item( "display_name", show_label=False, style="readonly", width=-100, ), Item( "pressure", format_str="%0.2e", show_label=False, style="readonly" ), Item( "pressure", show_label=False, width=self.width, editor=BarGaugeEditor( low=self.low, high=self.high, scale="power", color_scalar=self.color_scalar, width=self.width, ), ), ) ) return v class BaseGaugeController(HasTraits): address = Str gauges = List display_name = Str gauge_klass = BaseGauge graph_klass = TimeSeriesStreamStackedGraph def initialize(self, *args, **kw): self.scan_func = "update_pressures" self.graph_y_title = "Pressure (torr)" return True def update_pressures(self, verbose=False): if verbose: self.debug("update pressures") resps = [self._update_pressure(g, verbose) for g in self.gauges] return tuple(resps) def get_gauge(self, name): return next( (gi for gi in self.gauges if gi.name == name or gi.display_name == name), None, ) def get_pressure(self, gauge, force=False, verbose=False): if isinstance(gauge, str): gauge = self.get_gauge(gauge) if gauge is not None: if force: self._update_pressure(gauge.name, verbose) return gauge.pressure def get_pressures(self, force=False, **kw): if force: self.update_pressures(**kw) return [g.pressure for g in self.gauges] def _pressure_change(self, obj, name, old, new): self.trait_set(**{"{}_pressure".format(obj.name): new}) def _read_pressure(self, *args, **kw): raise NotImplementedError def _set_gauge_pressure(self, gauge, v): if isinstance(gauge, str): gauge = self.get_gauge(gauge) if gauge is not None: try: gauge.pressure = float(v) return True except (TypeError, ValueError): pass def _get_pressure(self, name, verbose=False, force=False): if self._scanning and not force: attr = "{}_pressure".format(name) if hasattr(self, attr): return getattr(self, attr) return self._read_pressure(name, verbose) def _update_pressure(self, gauge, verbose=False): if isinstance(gauge, str): gauge = self.get_gauge(gauge) if verbose: self.debug("_update_pressure: {}".format(gauge)) if gauge: p = self._read_pressure(gauge, verbose) if self._set_gauge_pressure(gauge, p): return p def _load_gauges(self, config, *args, **kw): ns = self.config_get(config, "Gauges", "names") if ns: ans = self.config_get(config, "Gauges", "display_names", optional=True) if not ans: ans = ns lows = self.config_get( config, "Gauges", "lows", optional=True, default="1e-10, 1e-3, 1e-3" ) highs = self.config_get( config, "Gauges", "highs", optional=True, default="1e-6, 1, 1" ) cs = self.config_get( config, "Gauges", "color_scalars", optional=True, default="1, 1, 1" ) chs = self.config_get( config, "Gauges", "channels", optional=True, default="1, 2, 3" ) for gi in zip(*[x.split(",") for x in (ns, ans, lows, highs, cs, chs)]): # ni, ai, li, hi, ci, cn = list(map(str.strip, gi)) ni, ai, li, hi, ci, cn = [gg.strip() for gg in gi] g = self.gauge_klass(name=ni, display_name=ai, channel=cn) try: g.low = float(li) except ValueError as e: self.warning_dialog( "Invalid lows string. {}".format(e), title=self.config_path ) continue try: g.high = float(hi) except ValueError as e: self.warning_dialog( "Invalid highs string. {}".format(e), title=self.config_path ) continue try: g.color_scalar = int(ci) except ValueError as e: self.warning_dialog( "Invalid color_scalar string. {}".format(e), title=self.config_path, ) continue p = "{}_pressure".format(ni) self.add_trait(p, Float) g.on_trait_change(self._pressure_change, "pressure") self.gauges.append(g) def gauge_view(self): v = View( Group( Item( "gauges", style="custom", show_label=False, editor=ListEditor( mutable=False, style="custom", editor=InstanceEditor() ), ), show_border=True, label=self.display_name, ) ) return v def graph_builder(self, g, **kw): for i, gi in enumerate(self.gauges): g.new_plot(padding=[50, 5, 5, 35], zoom=True, pan=True) g.set_y_title(self.graph_ytitle, plotid=i) g.set_x_title("Time") g.new_series(plotid=i) g.set_series_label(gi.display_name, plotid=i) # ============= EOF =============================================

33.036036

86

0.504363

from traits.api import HasTraits, List, Str, Float, Int from traitsui.api import View, HGroup, Item, Group, InstanceEditor, ListEditor from pychron.core.ui.color_map_bar_editor import BarGaugeEditor from pychron.graph.time_series_graph import TimeSeriesStreamStackedGraph class BaseGauge(HasTraits): name = Str pressure = Float display_name = Str low = 5e-10 high = 1e-8 color_scalar = 1 width = Int(100) channel = Str def traits_view(self): v = View( HGroup( Item( "display_name", show_label=False, style="readonly", width=-100, ), Item( "pressure", format_str="%0.2e", show_label=False, style="readonly" ), Item( "pressure", show_label=False, width=self.width, editor=BarGaugeEditor( low=self.low, high=self.high, scale="power", color_scalar=self.color_scalar, width=self.width, ), ), ) ) return v class BaseGaugeController(HasTraits): address = Str gauges = List display_name = Str gauge_klass = BaseGauge graph_klass = TimeSeriesStreamStackedGraph def initialize(self, *args, **kw): self.scan_func = "update_pressures" self.graph_y_title = "Pressure (torr)" return True def update_pressures(self, verbose=False): if verbose: self.debug("update pressures") resps = [self._update_pressure(g, verbose) for g in self.gauges] return tuple(resps) def get_gauge(self, name): return next( (gi for gi in self.gauges if gi.name == name or gi.display_name == name), None, ) def get_pressure(self, gauge, force=False, verbose=False): if isinstance(gauge, str): gauge = self.get_gauge(gauge) if gauge is not None: if force: self._update_pressure(gauge.name, verbose) return gauge.pressure def get_pressures(self, force=False, **kw): if force: self.update_pressures(**kw) return [g.pressure for g in self.gauges] def _pressure_change(self, obj, name, old, new): self.trait_set(**{"{}_pressure".format(obj.name): new}) def _read_pressure(self, *args, **kw): raise NotImplementedError def _set_gauge_pressure(self, gauge, v): if isinstance(gauge, str): gauge = self.get_gauge(gauge) if gauge is not None: try: gauge.pressure = float(v) return True except (TypeError, ValueError): pass def _get_pressure(self, name, verbose=False, force=False): if self._scanning and not force: attr = "{}_pressure".format(name) if hasattr(self, attr): return getattr(self, attr) return self._read_pressure(name, verbose) def _update_pressure(self, gauge, verbose=False): if isinstance(gauge, str): gauge = self.get_gauge(gauge) if verbose: self.debug("_update_pressure: {}".format(gauge)) if gauge: p = self._read_pressure(gauge, verbose) if self._set_gauge_pressure(gauge, p): return p def _load_gauges(self, config, *args, **kw): ns = self.config_get(config, "Gauges", "names") if ns: ans = self.config_get(config, "Gauges", "display_names", optional=True) if not ans: ans = ns lows = self.config_get( config, "Gauges", "lows", optional=True, default="1e-10, 1e-3, 1e-3" ) highs = self.config_get( config, "Gauges", "highs", optional=True, default="1e-6, 1, 1" ) cs = self.config_get( config, "Gauges", "color_scalars", optional=True, default="1, 1, 1" ) chs = self.config_get( config, "Gauges", "channels", optional=True, default="1, 2, 3" ) for gi in zip(*[x.split(",") for x in (ns, ans, lows, highs, cs, chs)]): ni, ai, li, hi, ci, cn = [gg.strip() for gg in gi] g = self.gauge_klass(name=ni, display_name=ai, channel=cn) try: g.low = float(li) except ValueError as e: self.warning_dialog( "Invalid lows string. {}".format(e), title=self.config_path ) continue try: g.high = float(hi) except ValueError as e: self.warning_dialog( "Invalid highs string. {}".format(e), title=self.config_path ) continue try: g.color_scalar = int(ci) except ValueError as e: self.warning_dialog( "Invalid color_scalar string. {}".format(e), title=self.config_path, ) continue p = "{}_pressure".format(ni) self.add_trait(p, Float) g.on_trait_change(self._pressure_change, "pressure") self.gauges.append(g) def gauge_view(self): v = View( Group( Item( "gauges", style="custom", show_label=False, editor=ListEditor( mutable=False, style="custom", editor=InstanceEditor() ), ), show_border=True, label=self.display_name, ) ) return v def graph_builder(self, g, **kw): for i, gi in enumerate(self.gauges): g.new_plot(padding=[50, 5, 5, 35], zoom=True, pan=True) g.set_y_title(self.graph_ytitle, plotid=i) g.set_x_title("Time") g.new_series(plotid=i) g.set_series_label(gi.display_name, plotid=i)

true

f71e9ef1b07ffec34348f9fc349bdc12ee2c9d58

2,061

py

Python

tanuki/history/views.py

addisonmaupin/capstone2020

cf8c8e7336aa9866859349838e4f42bc6831679c

[ "MIT" ]

null

tanuki/history/views.py

addisonmaupin/capstone2020

cf8c8e7336aa9866859349838e4f42bc6831679c

[ "MIT" ]

9

2021-03-19T14:50:48.000Z

2022-03-12T00:47:25.000Z

tanuki/history/views.py

pabsromo/capstone2020

cf8c8e7336aa9866859349838e4f42bc6831679c

[ "MIT" ]

null

from django.shortcuts import render from django import template register = template.Library() from django.contrib.auth.decorators import login_required import json from django.core.serializers.json import DjangoJSONEncoder from django.forms.models import model_to_dict from overview.models import AddItem from overview.forms import AddItemForm # Create your views here. @login_required(login_url='login:index') def history(request): if request.method == 'POST': form = AddItemForm(request.POST, label_suffix=' ') if form.is_valid(): addItem = form.save(commit=False) addItem.itemType = form.cleaned_data['itemType'] addItem.user = request.user addItem.save() # save form after the user and itemType have been determined itemName = form.cleaned_data['itemName'] itemPrice = form.cleaned_data['itemPrice'] return redirect('overview:home') else: context = {'form': form} else: # only show objects for authenticated user items = AddItem.objects.filter(user=request.user) form = AddItemForm(label_suffix=' ') temp = [] if items.first() is None: form = [] items = [] items_json = [] else: print(items[0].itemName) for item in items: t = [] t.append(int(str(item.user.id))) t.append(str(item.user)) t.append(item.itemName) t.append(float(item.itemPrice)) t.append(item.itemType) # t.append(item.dateAdded) t.append(item.dateDisplayed.strftime('%m/%d/%Y')) temp.append(t) print(temp) # items_json = json.dumps(temp, cls=DjangoJSONEncoder) items_json = json.dumps(temp) context = { 'form': form, 'items': items, 'items_json': items_json, } return render(request, 'history.html', context)

34.932203

88

0.585153

from django.shortcuts import render from django import template register = template.Library() from django.contrib.auth.decorators import login_required import json from django.core.serializers.json import DjangoJSONEncoder from django.forms.models import model_to_dict from overview.models import AddItem from overview.forms import AddItemForm @login_required(login_url='login:index') def history(request): if request.method == 'POST': form = AddItemForm(request.POST, label_suffix=' ') if form.is_valid(): addItem = form.save(commit=False) addItem.itemType = form.cleaned_data['itemType'] addItem.user = request.user addItem.save() itemName = form.cleaned_data['itemName'] itemPrice = form.cleaned_data['itemPrice'] return redirect('overview:home') else: context = {'form': form} else: items = AddItem.objects.filter(user=request.user) form = AddItemForm(label_suffix=' ') temp = [] if items.first() is None: form = [] items = [] items_json = [] else: print(items[0].itemName) for item in items: t = [] t.append(int(str(item.user.id))) t.append(str(item.user)) t.append(item.itemName) t.append(float(item.itemPrice)) t.append(item.itemType) t.append(item.dateDisplayed.strftime('%m/%d/%Y')) temp.append(t) print(temp) items_json = json.dumps(temp) context = { 'form': form, 'items': items, 'items_json': items_json, } return render(request, 'history.html', context)

true

f71e9f4ae8aeaa716d941ad00f7fd5c17c49b75c

1,182

py

Python

test/test_files/pylops/examples/plot_imag.py

SoftwareUnderstanding/inspect4py

9c4d7252535082ad938b26baf281d93f3a27285e

[ "BSD-3-Clause" ]

2

2022-02-15T20:30:57.000Z

2022-03-17T00:50:37.000Z

test/test_files/pylops/examples/plot_imag.py

SoftwareUnderstanding/code_inspector

a820b5a7bb18f5df9c3e79346108d8280b20c39a

[ "BSD-3-Clause" ]

101

2021-06-09T14:19:59.000Z

2022-01-24T13:24:39.000Z

test/test_files/pylops/examples/plot_imag.py

SoftwareUnderstanding/inspect4py

9c4d7252535082ad938b26baf281d93f3a27285e

[ "BSD-3-Clause" ]

1

2021-09-22T06:59:32.000Z

""" Imag ==== This example shows how to use the :py:class:`pylops.basicoperators.Imag` operator. This operator returns the imaginary part of the data as a real value in forward mode, and the real part of the model as an imaginary value in adjoint mode (with zero real part). """ import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as pltgs import pylops plt.close('all') ############################################################################### # Let's define a Imag operator :math:`\mathbf{\Im}` to extract the imaginary # component of the input. M = 5 x = np.arange(M) + 1j * np.arange(M)[::-1] Rop = pylops.basicoperators.Imag(M, dtype='complex128') y = Rop*x xadj = Rop.H*y _, axs = plt.subplots(1, 3, figsize=(10, 4)) axs[0].plot(np.real(x), lw=2, label='Real') axs[0].plot(np.imag(x), lw=2, label='Imag') axs[0].legend() axs[0].set_title('Input') axs[1].plot(np.real(y), lw=2, label='Real') axs[1].plot(np.imag(y), lw=2, label='Imag') axs[1].legend() axs[1].set_title('Forward of Input') axs[2].plot(np.real(xadj), lw=2, label='Real') axs[2].plot(np.imag(xadj), lw=2, label='Imag') axs[2].legend() axs[2].set_title('Adjoint of Forward')

27.488372

79

0.643824

import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as pltgs import pylops plt.close('all')

true

f71e9fc678e62a608c02041f6e0914f9fcbec0c2

674

py

Python

test/test_tensorboard.py

ethan4335/pytorch-YOLOv4

44f67130d83fc2949efb50afe67337735836169b

[ "Apache-2.0" ]

null

test/test_tensorboard.py

ethan4335/pytorch-YOLOv4

44f67130d83fc2949efb50afe67337735836169b

[ "Apache-2.0" ]

null

test/test_tensorboard.py

ethan4335/pytorch-YOLOv4

44f67130d83fc2949efb50afe67337735836169b

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- """ __title__ = 'pytorch-YOLOv4' __author__ = 'deagle' __date__ = '11/23/2020 11:30' # code is far away from bugs with the god animal protecting I love animals. They taste delicious. """ import datetime from tensorboardX import SummaryWriter def main(): from tensorboardX import SummaryWriter writer = SummaryWriter() x = range(100) for i in x: writer.add_scalar('y=2x', i * 2, i) writer.close() if __name__ == '__main__': start_time = datetime.datetime.now() main() end_time = datetime.datetime.now() time_cost = end_time - start_time print(str(time_cost).split('.')[0])

23.241379

59

0.663205

import datetime from tensorboardX import SummaryWriter def main(): from tensorboardX import SummaryWriter writer = SummaryWriter() x = range(100) for i in x: writer.add_scalar('y=2x', i * 2, i) writer.close() if __name__ == '__main__': start_time = datetime.datetime.now() main() end_time = datetime.datetime.now() time_cost = end_time - start_time print(str(time_cost).split('.')[0])

true

f71e9fdd77eb43e16314dd553b822f51a7dab59b

3,213

py

Python

enCount/tests/gtfs.py

mstrazar/enCount

dcff565ce96afe37aa8a41995637d00cce02360d

[ "MIT" ]

null

enCount/tests/gtfs.py

mstrazar/enCount

dcff565ce96afe37aa8a41995637d00cce02360d

[ "MIT" ]

null

enCount/tests/gtfs.py

mstrazar/enCount

dcff565ce96afe37aa8a41995637d00cce02360d

[ "MIT" ]

null

# coding=utf-8 import os import enCount.gtfs as gtfs import enCount.db as db import enCount.queues as queue from enCount.config import genomes_root import datetime import unittest import time # Mock system calls from mock import Mock gtfs.rnastar.sp_call = Mock(return_value=0) gtfs.get_version_before = Mock(return_value="chM") class TestGtfs(unittest.TestCase): """ Test for the gtfs queue and DB. Assumes directory structure: /endata/genomes /endata/genomes/gtf /endata/genomes/gtf/minimal.gtf /endata/genomes/fasta /endata/genomes/index """ def setUp(self): db.gtfs.drop() self.gtf_ver = gtfs.get_version_before(datetime.datetime.min) self.in_gtf = gtfs.version_to_path(gtf_ver=self.gtf_ver) self.in_gff = gtfs.version_to_path(gtf_ver=self.gtf_ver, gff=True) self.genome_dir = gtfs.get_genome_index_dir(self.gtf_ver) if os.path.exists(self.in_gff): os.remove(self.in_gff) def test_gtf(self): """ Simple test of the genome index generation pipeline without using the queue mechanism. """ self.assertEqual(db.gtfs.find().count(), 1) self.assertTrue(os.path.exists(self.in_gtf)) # Generate a genome index and get path via DB in_genome_fasta_dir = os.path.join(genomes_root, "fasta", self.gtf_ver) self.assertTrue(os.path.isdir(in_genome_fasta_dir)) # Insert record into database self.assertTrue(os.path.isdir(self.genome_dir)) # The method gtfs.get_genome_index_dir is called within gtfs.generate_genome_index # and shall not be called elsewhere before a genome index is generated gtfs.generate_genome_index(self.in_gtf, in_genome_fasta_dir, self.genome_dir, self.in_gff) self.genome_dir = gtfs.get_genome_index_dir(self.gtf_ver) # Check mapping mappings = list(db.gtfs.find({"gtf_ver": self.gtf_ver, "status": "ready"})) self.assertEqual(len(mappings), 1) def test_process_queue(self): """ Simple test of the genome index generation pipeline using the process queue. """ self.assertEqual(db.gtfs.find().count(), 1) # Get latest genome version self.assertTrue(os.path.exists(self.in_gtf)) # Process gtf_version mappings = list(db.gtfs.find({"gtf_ver": self.gtf_ver})) self.assertEqual(len(mappings), 1) # Process outstanding requests; Mock submitted jobs explicitly empty = False while not empty: gtfs.process(mock=True) empty = queue.gtfs.is_empty() # Wait for database to refresh # TODO: is there a cleaner way to ensure transactions? mappings = list(db.gtfs.find({"gtf_ver": self.gtf_ver, "status": "ready"})) while not len(mappings): time.sleep(1) mappings = list(db.gtfs.find({"gtf_ver": self.gtf_ver, "status": "ready"})) # Make sure results exist self.genome_dir = gtfs.get_genome_index_dir(self.gtf_ver) self.assertTrue(os.path.isdir(self.genome_dir)) if __name__ == "__main__": unittest.main()

33.123711

98

0.659197

import os import enCount.gtfs as gtfs import enCount.db as db import enCount.queues as queue from enCount.config import genomes_root import datetime import unittest import time from mock import Mock gtfs.rnastar.sp_call = Mock(return_value=0) gtfs.get_version_before = Mock(return_value="chM") class TestGtfs(unittest.TestCase): def setUp(self): db.gtfs.drop() self.gtf_ver = gtfs.get_version_before(datetime.datetime.min) self.in_gtf = gtfs.version_to_path(gtf_ver=self.gtf_ver) self.in_gff = gtfs.version_to_path(gtf_ver=self.gtf_ver, gff=True) self.genome_dir = gtfs.get_genome_index_dir(self.gtf_ver) if os.path.exists(self.in_gff): os.remove(self.in_gff) def test_gtf(self): self.assertEqual(db.gtfs.find().count(), 1) self.assertTrue(os.path.exists(self.in_gtf)) in_genome_fasta_dir = os.path.join(genomes_root, "fasta", self.gtf_ver) self.assertTrue(os.path.isdir(in_genome_fasta_dir)) self.assertTrue(os.path.isdir(self.genome_dir)) gtfs.generate_genome_index(self.in_gtf, in_genome_fasta_dir, self.genome_dir, self.in_gff) self.genome_dir = gtfs.get_genome_index_dir(self.gtf_ver) mappings = list(db.gtfs.find({"gtf_ver": self.gtf_ver, "status": "ready"})) self.assertEqual(len(mappings), 1) def test_process_queue(self): self.assertEqual(db.gtfs.find().count(), 1) self.assertTrue(os.path.exists(self.in_gtf)) mappings = list(db.gtfs.find({"gtf_ver": self.gtf_ver})) self.assertEqual(len(mappings), 1) empty = False while not empty: gtfs.process(mock=True) empty = queue.gtfs.is_empty() mappings = list(db.gtfs.find({"gtf_ver": self.gtf_ver, "status": "ready"})) while not len(mappings): time.sleep(1) mappings = list(db.gtfs.find({"gtf_ver": self.gtf_ver, "status": "ready"})) self.genome_dir = gtfs.get_genome_index_dir(self.gtf_ver) self.assertTrue(os.path.isdir(self.genome_dir)) if __name__ == "__main__": unittest.main()

true

f71ea15a3576fb6c010db825bd3096593169e5a3

1,999

py

Python

apps/fithm-service/apps/model/models.py

sergio1221/flask-backend

11a9e0db5b5e664fcc820919d97039738176ac62

[ "BSD-3-Clause" ]

3

2022-03-04T03:05:55.000Z

2022-03-04T09:02:32.000Z

apps/fithm-service/apps/model/models.py

sergio1221/flask-backend

11a9e0db5b5e664fcc820919d97039738176ac62

[ "BSD-3-Clause" ]

null

apps/fithm-service/apps/model/models.py

sergio1221/flask-backend

11a9e0db5b5e664fcc820919d97039738176ac62

[ "BSD-3-Clause" ]

null

from sqlalchemy import ( Column, String, ForeignKey, Float, Integer, Boolean ) from sqlalchemy.orm import relationship from sqlalchemy.dialects import postgresql from libs.database import Base, Stateful class Model(Stateful): '''Model table''' __tablename__ = 'models' id = Column(Integer, primary_key=True) business_id = Column(Integer, ForeignKey('businesses.id'), nullable=False) name = Column(String) description = Column(String) keywords = Column("data", postgresql.ARRAY(String)) is_public = Column(Boolean, default=False, nullable=False) business = relationship("Business", back_populates="models") allocation = relationship( "ModelPosition", back_populates="model", cascade="all, delete, delete-orphan") portfolio = relationship("Portfolio", back_populates="model") def as_dict(self): result = { 'id': self.id, 'name': self.name, 'keywords': [], 'is_public': self.is_public, 'description': self.description } result['user_id'] = self.business.user_id if self.allocation: result['positions'] = [a.as_dict() for a in self.allocation] if self.keywords: result['keywords'] = [k for k in self.keywords] return result class ModelPosition(Base): __tablename__ = 'model_positions' id = Column(Integer, primary_key=True) model_id = Column(Integer, ForeignKey('models.id'), nullable=False) symbol = Column(String) weight = Column(Float) price = Column(Float) model = relationship("Model", back_populates="allocation") trade_prices = relationship( "Price", back_populates="model_position", cascade="all, delete, delete-orphan") def as_dict(self): result = {'model_id': self.model_id, 'symbol': self.symbol, 'weight': self.weight} return result

32.770492

88

0.626313

from sqlalchemy import ( Column, String, ForeignKey, Float, Integer, Boolean ) from sqlalchemy.orm import relationship from sqlalchemy.dialects import postgresql from libs.database import Base, Stateful class Model(Stateful): __tablename__ = 'models' id = Column(Integer, primary_key=True) business_id = Column(Integer, ForeignKey('businesses.id'), nullable=False) name = Column(String) description = Column(String) keywords = Column("data", postgresql.ARRAY(String)) is_public = Column(Boolean, default=False, nullable=False) business = relationship("Business", back_populates="models") allocation = relationship( "ModelPosition", back_populates="model", cascade="all, delete, delete-orphan") portfolio = relationship("Portfolio", back_populates="model") def as_dict(self): result = { 'id': self.id, 'name': self.name, 'keywords': [], 'is_public': self.is_public, 'description': self.description } result['user_id'] = self.business.user_id if self.allocation: result['positions'] = [a.as_dict() for a in self.allocation] if self.keywords: result['keywords'] = [k for k in self.keywords] return result class ModelPosition(Base): __tablename__ = 'model_positions' id = Column(Integer, primary_key=True) model_id = Column(Integer, ForeignKey('models.id'), nullable=False) symbol = Column(String) weight = Column(Float) price = Column(Float) model = relationship("Model", back_populates="allocation") trade_prices = relationship( "Price", back_populates="model_position", cascade="all, delete, delete-orphan") def as_dict(self): result = {'model_id': self.model_id, 'symbol': self.symbol, 'weight': self.weight} return result

true

f71ea1692f9195dcc858ae156af3624dfca9a2ef

1,661

py

Python

TA-linode/bin/ta_linode/aob_py3/splunktalib/file_monitor.py

jriddle-linode/splunk-addon-linode

5954acd12ef88ab991365ef51072db68aed46aa1

[ "Apache-2.0" ]

11

2020-01-23T11:32:26.000Z

2021-09-23T09:24:02.000Z

TA-linode/bin/ta_linode/aob_py3/splunktalib/file_monitor.py

jriddle-linode/splunk-addon-linode

5954acd12ef88ab991365ef51072db68aed46aa1

[ "Apache-2.0" ]

26

2019-07-15T02:38:22.000Z

2021-12-01T04:14:17.000Z

TA-linode/bin/ta_linode/aob_py3/splunktalib/file_monitor.py

jriddle-linode/splunk-addon-linode

5954acd12ef88ab991365ef51072db68aed46aa1

[ "Apache-2.0" ]

6

2019-07-14T17:44:06.000Z

2020-11-17T17:33:23.000Z

# SPDX-FileCopyrightText: 2020 Splunk Inc. # # SPDX-License-Identifier: Apache-2.0 from builtins import object import os.path as op import traceback from splunktalib.common import log class FileMonitor(object): def __init__(self, callback, files): """ :files: files to be monidtored with full path """ self._callback = callback self._files = files self.file_mtimes = {file_name: None for file_name in self._files} for k in self.file_mtimes: if not op.exists(k): continue try: if not op.exists(k): continue self.file_mtimes[k] = op.getmtime(k) except OSError: log.logger.error( "Getmtime for %s, failed: %s", k, traceback.format_exc() ) def __call__(self): return self.check_changes() def check_changes(self): log.logger.debug("Checking files=%s", self._files) file_mtimes = self.file_mtimes changed_files = [] for f, last_mtime in file_mtimes.items(): try: if not op.exists(f): continue current_mtime = op.getmtime(f) if current_mtime != last_mtime: file_mtimes[f] = current_mtime changed_files.append(f) log.logger.info("Detect %s has changed", f) except OSError: pass if changed_files: if self._callback: self._callback(changed_files) return True return False

27.683333

76

0.53823

from builtins import object import os.path as op import traceback from splunktalib.common import log class FileMonitor(object): def __init__(self, callback, files): self._callback = callback self._files = files self.file_mtimes = {file_name: None for file_name in self._files} for k in self.file_mtimes: if not op.exists(k): continue try: if not op.exists(k): continue self.file_mtimes[k] = op.getmtime(k) except OSError: log.logger.error( "Getmtime for %s, failed: %s", k, traceback.format_exc() ) def __call__(self): return self.check_changes() def check_changes(self): log.logger.debug("Checking files=%s", self._files) file_mtimes = self.file_mtimes changed_files = [] for f, last_mtime in file_mtimes.items(): try: if not op.exists(f): continue current_mtime = op.getmtime(f) if current_mtime != last_mtime: file_mtimes[f] = current_mtime changed_files.append(f) log.logger.info("Detect %s has changed", f) except OSError: pass if changed_files: if self._callback: self._callback(changed_files) return True return False

true

f71ea182be9148ef98e2c8611759cbd24edced2a

1,333

py

Python

copilot/views.py

Feudo-Laranja-ave-do-paraiso-DS-2021-2/copilot-api

3f8c64cc2fafab1902dcd37f624fcff93f9494aa

[ "MIT" ]

null

copilot/views.py

Feudo-Laranja-ave-do-paraiso-DS-2021-2/copilot-api

3f8c64cc2fafab1902dcd37f624fcff93f9494aa

[ "MIT" ]

3

2022-03-10T21:40:58.000Z

2022-03-15T02:14:50.000Z

copilot/views.py

Feudo-Laranja-ave-do-paraiso-DS-2021-2/copilot-api

3f8c64cc2fafab1902dcd37f624fcff93f9494aa

[ "MIT" ]

null

from rest_framework.viewsets import ModelViewSet from .models import Profile, Group from .serializers import ProfileSerializers, GroupSerializers from rest_framework.response import Response from rest_framework.decorators import action from itertools import chain class ProfileViewSet(ModelViewSet): serializer_class = ProfileSerializers queryset = Profile.objects.all() filterset_fields = ['id_dispositivo',] class GroupViewSet(ModelViewSet): serializer_class = GroupSerializers queryset = Group.objects.all() filterset_fields = ['token', ] @action(methods=['post'], detail=True) def adicionar_profile(self, request, pk): profiles = request.data['ids'] group = Group.objects.get(id=pk) old_profiles = group.profiles.all() all_profiles = chain(old_profiles, profiles) group.profiles.set(all_profiles) group.save() serializer = self.get_serializer(group) return Response(serializer.data) @action(methods=['delete'], detail=True) def retirar_profile(self, request, pk): profiles = request.data['ids'] group = Group.objects.get(id=pk) for id in profiles: group.profiles.remove(id) group.save() serializer = self.get_serializer(group) return Response(serializer.data)

34.179487

61

0.702926

from rest_framework.viewsets import ModelViewSet from .models import Profile, Group from .serializers import ProfileSerializers, GroupSerializers from rest_framework.response import Response from rest_framework.decorators import action from itertools import chain class ProfileViewSet(ModelViewSet): serializer_class = ProfileSerializers queryset = Profile.objects.all() filterset_fields = ['id_dispositivo',] class GroupViewSet(ModelViewSet): serializer_class = GroupSerializers queryset = Group.objects.all() filterset_fields = ['token', ] @action(methods=['post'], detail=True) def adicionar_profile(self, request, pk): profiles = request.data['ids'] group = Group.objects.get(id=pk) old_profiles = group.profiles.all() all_profiles = chain(old_profiles, profiles) group.profiles.set(all_profiles) group.save() serializer = self.get_serializer(group) return Response(serializer.data) @action(methods=['delete'], detail=True) def retirar_profile(self, request, pk): profiles = request.data['ids'] group = Group.objects.get(id=pk) for id in profiles: group.profiles.remove(id) group.save() serializer = self.get_serializer(group) return Response(serializer.data)

true

f71ea2c7e4a8e614b8fbb239298e8c5e740555c8

3,282

py

Python

aliyun-python-sdk-bssopenapi/aliyunsdkbssopenapi/request/v20171214/QuerySettleBillRequest.py

ankitdobhal/aliyun-openapi-python-sdk

991b1c2d91adc468480defc23ba790d4369cce7b

[ "Apache-2.0" ]

1

2021-03-08T02:59:17.000Z

aliyun-python-sdk-bssopenapi/aliyunsdkbssopenapi/request/v20171214/QuerySettleBillRequest.py

bricklayer-Liu/aliyun-openapi-python-sdk

20da2554de22679fc7c5462c483663e4d79512aa

[ "Apache-2.0" ]

null

aliyun-python-sdk-bssopenapi/aliyunsdkbssopenapi/request/v20171214/QuerySettleBillRequest.py

bricklayer-Liu/aliyun-openapi-python-sdk

20da2554de22679fc7c5462c483663e4d79512aa

[ "Apache-2.0" ]

null

# Licensed to the Apache Software Foundation (ASF) under one # or more contributor license agreements. See the NOTICE file # distributed with this work for additional information # regarding copyright ownership. The ASF licenses this file # to you under the Apache License, Version 2.0 (the # "License"); you may not use this file except in compliance # with the License. You may obtain a copy of the License at # # # http://www.apache.org/licenses/LICENSE-2.0 # # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. from aliyunsdkcore.request import RpcRequest from aliyunsdkbssopenapi.endpoint import endpoint_data class QuerySettleBillRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'BssOpenApi', '2017-12-14', 'QuerySettleBill') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_ProductCode(self): return self.get_query_params().get('ProductCode') def set_ProductCode(self,ProductCode): self.add_query_param('ProductCode',ProductCode) def get_IsHideZeroCharge(self): return self.get_query_params().get('IsHideZeroCharge') def set_IsHideZeroCharge(self,IsHideZeroCharge): self.add_query_param('IsHideZeroCharge',IsHideZeroCharge) def get_IsDisplayLocalCurrency(self): return self.get_query_params().get('IsDisplayLocalCurrency') def set_IsDisplayLocalCurrency(self,IsDisplayLocalCurrency): self.add_query_param('IsDisplayLocalCurrency',IsDisplayLocalCurrency) def get_SubscriptionType(self): return self.get_query_params().get('SubscriptionType') def set_SubscriptionType(self,SubscriptionType): self.add_query_param('SubscriptionType',SubscriptionType) def get_BillingCycle(self): return self.get_query_params().get('BillingCycle') def set_BillingCycle(self,BillingCycle): self.add_query_param('BillingCycle',BillingCycle) def get_Type(self): return self.get_query_params().get('Type') def set_Type(self,Type): self.add_query_param('Type',Type) def get_OwnerId(self): return self.get_query_params().get('OwnerId') def set_OwnerId(self,OwnerId): self.add_query_param('OwnerId',OwnerId) def get_BillOwnerId(self): return self.get_query_params().get('BillOwnerId') def set_BillOwnerId(self,BillOwnerId): self.add_query_param('BillOwnerId',BillOwnerId) def get_ProductType(self): return self.get_query_params().get('ProductType') def set_ProductType(self,ProductType): self.add_query_param('ProductType',ProductType) def get_NextToken(self): return self.get_query_params().get('NextToken') def set_NextToken(self,NextToken): self.add_query_param('NextToken',NextToken) def get_MaxResults(self): return self.get_query_params().get('MaxResults') def set_MaxResults(self,MaxResults): self.add_query_param('MaxResults',MaxResults)

33.489796

75

0.771176

from aliyunsdkcore.request import RpcRequest from aliyunsdkbssopenapi.endpoint import endpoint_data class QuerySettleBillRequest(RpcRequest): def __init__(self): RpcRequest.__init__(self, 'BssOpenApi', '2017-12-14', 'QuerySettleBill') self.set_method('POST') if hasattr(self, "endpoint_map"): setattr(self, "endpoint_map", endpoint_data.getEndpointMap()) if hasattr(self, "endpoint_regional"): setattr(self, "endpoint_regional", endpoint_data.getEndpointRegional()) def get_ProductCode(self): return self.get_query_params().get('ProductCode') def set_ProductCode(self,ProductCode): self.add_query_param('ProductCode',ProductCode) def get_IsHideZeroCharge(self): return self.get_query_params().get('IsHideZeroCharge') def set_IsHideZeroCharge(self,IsHideZeroCharge): self.add_query_param('IsHideZeroCharge',IsHideZeroCharge) def get_IsDisplayLocalCurrency(self): return self.get_query_params().get('IsDisplayLocalCurrency') def set_IsDisplayLocalCurrency(self,IsDisplayLocalCurrency): self.add_query_param('IsDisplayLocalCurrency',IsDisplayLocalCurrency) def get_SubscriptionType(self): return self.get_query_params().get('SubscriptionType') def set_SubscriptionType(self,SubscriptionType): self.add_query_param('SubscriptionType',SubscriptionType) def get_BillingCycle(self): return self.get_query_params().get('BillingCycle') def set_BillingCycle(self,BillingCycle): self.add_query_param('BillingCycle',BillingCycle) def get_Type(self): return self.get_query_params().get('Type') def set_Type(self,Type): self.add_query_param('Type',Type) def get_OwnerId(self): return self.get_query_params().get('OwnerId') def set_OwnerId(self,OwnerId): self.add_query_param('OwnerId',OwnerId) def get_BillOwnerId(self): return self.get_query_params().get('BillOwnerId') def set_BillOwnerId(self,BillOwnerId): self.add_query_param('BillOwnerId',BillOwnerId) def get_ProductType(self): return self.get_query_params().get('ProductType') def set_ProductType(self,ProductType): self.add_query_param('ProductType',ProductType) def get_NextToken(self): return self.get_query_params().get('NextToken') def set_NextToken(self,NextToken): self.add_query_param('NextToken',NextToken) def get_MaxResults(self): return self.get_query_params().get('MaxResults') def set_MaxResults(self,MaxResults): self.add_query_param('MaxResults',MaxResults)

true

f71ea31341a506b476c8ad73d75b95e72760cafc

48

py

Python

aiomatrix/dispatcher/storage/presence/engines/__init__.py

Forden/aiomatrix

d258076bae8eb776495b92be46ee9f4baec8d9a6

[ "MIT" ]

2

2021-10-29T18:07:08.000Z

2021-11-19T00:25:43.000Z

aiomatrix/dispatcher/storage/presence/engines/__init__.py

Forden/aiomatrix

d258076bae8eb776495b92be46ee9f4baec8d9a6

[ "MIT" ]

1

2022-03-06T11:17:43.000Z

aiomatrix/dispatcher/storage/presence/engines/__init__.py

Forden/aiomatrix

d258076bae8eb776495b92be46ee9f4baec8d9a6

[ "MIT" ]

null

from .sqlite import SqlitePresenceStorageEngine

24

47

0.895833

from .sqlite import SqlitePresenceStorageEngine

true

f71ea377dea2ef46a9377c4904dbec66d0fe8968

7,373

py

Python

tensorflow_datasets/summarization/summscreen/summscreen.py

shubhamkumaR630/datasets

fe9ee91849cefed0953141ea3588f73b7def78fd

[ "Apache-2.0" ]

2

2022-02-14T09:51:39.000Z

2022-02-14T13:27:49.000Z

tensorflow_datasets/summarization/summscreen/summscreen.py

shubhamkumaR630/datasets

fe9ee91849cefed0953141ea3588f73b7def78fd

[ "Apache-2.0" ]

null

tensorflow_datasets/summarization/summscreen/summscreen.py

shubhamkumaR630/datasets

fe9ee91849cefed0953141ea3588f73b7def78fd

[ "Apache-2.0" ]

1

2020-12-13T22:11:33.000Z

# coding=utf-8 # Copyright 2022 The TensorFlow Datasets 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. """SummScreen Summarization dataset, non-anonymized, non-tokenized version.""" import json import os import tensorflow as tf import tensorflow_datasets.public_api as tfds _DESCRIPTION = """ SummScreen Summarization dataset, non-anonymized, non-tokenized version. Train/val/test splits and filtering are based on the final tokenized dataset, but transcripts and recaps provided are based on the untokenized text. There are two features: - transcript: Full episode transcripts, each line of dialogue separated by newlines - recap: Recaps or summaries of episodes """ _CITATION = """\ @article{DBLP:journals/corr/abs-2104-07091, author = {Mingda Chen and Zewei Chu and Sam Wiseman and Kevin Gimpel}, title = {SummScreen: {A} Dataset for Abstractive Screenplay Summarization}, journal = {CoRR}, volume = {abs/2104.07091}, year = {2021}, url = {https://arxiv.org/abs/2104.07091}, archivePrefix = {arXiv}, eprint = {2104.07091}, timestamp = {Mon, 19 Apr 2021 16:45:47 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2104-07091.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ _DL_URLS = { # pylint: disable=line-too-long 'tokenized': 'https://drive.google.com/uc?export=download&id=1BvdIllGBo9d2-bzXQRzWuJXB04XPVmfF', 'untokenized': 'https://drive.google.com/uc?export=download&id=1tFpt32USOO2i1FWhtFTsyYyFzuRm2k36', # pylint: enable=line-too-long } _RECAP = 'recap' _TRANSCRIPT = 'transcript' _RECAP_SOURCE_FULL_NAMES = { 'fd': 'ForeverDreaming', 'tms': 'TVMegaSite', } _SPLITS = ['train', 'dev', 'test'] def _load_file(path): with tf.io.gfile.GFile(path, 'r') as f: return f.read() def _load_json(path): return json.loads(_load_file(path)) def _load_jsonl(path): return [json.loads(line) for line in _load_file(path).strip().splitlines()] def _get_filenames_dict(tokenized_path, recap_source: str): """Get dictionary of filenames for each split.""" filenames_dict = {} for split in _SPLITS: tokenized_data = _load_jsonl( os.path.join(tokenized_path, 'SummScreen', _RECAP_SOURCE_FULL_NAMES[recap_source], f'{recap_source}_{split}.json')) filenames_dict[split] = [row['filename'] for row in tokenized_data] return filenames_dict def _get_paths_dict(untokenized_path, recap_source, filenames_dict): """Get dictionary of example paths for each split.""" paths_dict = {} for split, filenames in filenames_dict.items(): paths_dict[split] = [ os.path.join(untokenized_path, 'SummScreen_raw', recap_source, filename) for filename in filenames ] return paths_dict class SummscreenConfig(tfds.core.BuilderConfig): """BuilderConfig for Summscreen.""" def __init__(self, *, recap_source=None, **kwargs): """BuilderConfig for Summscreen. Args: recap_source: str. The directory for the source of recaps to read. **kwargs: keyword arguments forwarded to super. """ super(SummscreenConfig, self).__init__(**kwargs) self.recap_source = recap_source class Summscreen(tfds.core.GeneratorBasedBuilder): """DatasetBuilder for non-tokenized, non-anonymized SummScreen dataset.""" VERSION = tfds.core.Version('1.0.0') RELEASE_NOTES = { '1.0.0': 'Initial release.', } BUILDER_CONFIGS = [ SummscreenConfig( name='fd', description='ForeverDreaming', recap_source='fd', ), SummscreenConfig( name='tms', description='TVMegaSite', recap_source='tms', ), ] def _info(self): # Should return a tfds.core.DatasetInfo object if self._builder_config.recap_source == 'fd': features = tfds.features.FeaturesDict({ _TRANSCRIPT: tfds.features.Text(), _RECAP: tfds.features.Text(), 'episode_number': tfds.features.Text(), 'episode_title': tfds.features.Text(), 'show_title': tfds.features.Text(), 'transcript_author': tfds.features.Text(), }) elif self._builder_config.recap_source == 'tms': features = tfds.features.FeaturesDict({ _TRANSCRIPT: tfds.features.Text(), _RECAP: tfds.features.Text(), 'episode_summary': tfds.features.Text(), 'show_title': tfds.features.Text(), 'transcript_author': tfds.features.Tensor(shape=(None,), dtype=tf.string), 'recap_author': tfds.features.Text(), }) else: raise KeyError( f'Unknown recap_source {self._builder_config.recap_source}') return tfds.core.DatasetInfo( builder=self, description=_DESCRIPTION, features=features, supervised_keys=(_TRANSCRIPT, _RECAP), homepage='https://github.com/mingdachen/SummScreen', citation=_CITATION, ) def _split_generators(self, dl_manager): dl_paths = dl_manager.download_and_extract(_DL_URLS) filenames_dict = _get_filenames_dict( tokenized_path=dl_paths['tokenized'], recap_source=self._builder_config.recap_source, ) paths_dict = _get_paths_dict( untokenized_path=dl_paths['untokenized'], recap_source=self._builder_config.recap_source, filenames_dict=filenames_dict, ) return { 'train': self._generate_examples(paths=paths_dict['train']), 'validation': self._generate_examples(paths=paths_dict['dev']), 'test': self._generate_examples(paths=paths_dict['test']), } def _generate_examples(self, paths): for path in paths: example = _load_json(path) fname = os.path.basename(path) if self._builder_config.recap_source == 'fd': yield fname, { _TRANSCRIPT: '\n'.join(example['Transcript']), _RECAP: '\n'.join(example['Recap']), 'episode_number': example['Episode Number'], 'episode_title': example['Episode Title'], 'show_title': example['Show Title'], 'transcript_author': example['Transcript Author'], } elif self._builder_config.recap_source == 'tms': yield fname, { _TRANSCRIPT: '\n'.join(example['Transcript']), _RECAP: '\n'.join(example['Recap']), 'episode_summary': '\n'.join(example['Episode Summary']), 'show_title': example['Show Title'], 'transcript_author': example['Transcript Author'], 'recap_author': example['Recap Author'], } else: raise KeyError( f'Unknown recap_source {self._builder_config.recap_source}')

32.623894

91

0.655907

import json import os import tensorflow as tf import tensorflow_datasets.public_api as tfds _DESCRIPTION = """ SummScreen Summarization dataset, non-anonymized, non-tokenized version. Train/val/test splits and filtering are based on the final tokenized dataset, but transcripts and recaps provided are based on the untokenized text. There are two features: - transcript: Full episode transcripts, each line of dialogue separated by newlines - recap: Recaps or summaries of episodes """ _CITATION = """\ @article{DBLP:journals/corr/abs-2104-07091, author = {Mingda Chen and Zewei Chu and Sam Wiseman and Kevin Gimpel}, title = {SummScreen: {A} Dataset for Abstractive Screenplay Summarization}, journal = {CoRR}, volume = {abs/2104.07091}, year = {2021}, url = {https://arxiv.org/abs/2104.07091}, archivePrefix = {arXiv}, eprint = {2104.07091}, timestamp = {Mon, 19 Apr 2021 16:45:47 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2104-07091.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } """ _DL_URLS = { 'tokenized': 'https://drive.google.com/uc?export=download&id=1BvdIllGBo9d2-bzXQRzWuJXB04XPVmfF', 'untokenized': 'https://drive.google.com/uc?export=download&id=1tFpt32USOO2i1FWhtFTsyYyFzuRm2k36', } _RECAP = 'recap' _TRANSCRIPT = 'transcript' _RECAP_SOURCE_FULL_NAMES = { 'fd': 'ForeverDreaming', 'tms': 'TVMegaSite', } _SPLITS = ['train', 'dev', 'test'] def _load_file(path): with tf.io.gfile.GFile(path, 'r') as f: return f.read() def _load_json(path): return json.loads(_load_file(path)) def _load_jsonl(path): return [json.loads(line) for line in _load_file(path).strip().splitlines()] def _get_filenames_dict(tokenized_path, recap_source: str): filenames_dict = {} for split in _SPLITS: tokenized_data = _load_jsonl( os.path.join(tokenized_path, 'SummScreen', _RECAP_SOURCE_FULL_NAMES[recap_source], f'{recap_source}_{split}.json')) filenames_dict[split] = [row['filename'] for row in tokenized_data] return filenames_dict def _get_paths_dict(untokenized_path, recap_source, filenames_dict): paths_dict = {} for split, filenames in filenames_dict.items(): paths_dict[split] = [ os.path.join(untokenized_path, 'SummScreen_raw', recap_source, filename) for filename in filenames ] return paths_dict class SummscreenConfig(tfds.core.BuilderConfig): def __init__(self, *, recap_source=None, **kwargs): super(SummscreenConfig, self).__init__(**kwargs) self.recap_source = recap_source class Summscreen(tfds.core.GeneratorBasedBuilder): VERSION = tfds.core.Version('1.0.0') RELEASE_NOTES = { '1.0.0': 'Initial release.', } BUILDER_CONFIGS = [ SummscreenConfig( name='fd', description='ForeverDreaming', recap_source='fd', ), SummscreenConfig( name='tms', description='TVMegaSite', recap_source='tms', ), ] def _info(self): if self._builder_config.recap_source == 'fd': features = tfds.features.FeaturesDict({ _TRANSCRIPT: tfds.features.Text(), _RECAP: tfds.features.Text(), 'episode_number': tfds.features.Text(), 'episode_title': tfds.features.Text(), 'show_title': tfds.features.Text(), 'transcript_author': tfds.features.Text(), }) elif self._builder_config.recap_source == 'tms': features = tfds.features.FeaturesDict({ _TRANSCRIPT: tfds.features.Text(), _RECAP: tfds.features.Text(), 'episode_summary': tfds.features.Text(), 'show_title': tfds.features.Text(), 'transcript_author': tfds.features.Tensor(shape=(None,), dtype=tf.string), 'recap_author': tfds.features.Text(), }) else: raise KeyError( f'Unknown recap_source {self._builder_config.recap_source}') return tfds.core.DatasetInfo( builder=self, description=_DESCRIPTION, features=features, supervised_keys=(_TRANSCRIPT, _RECAP), homepage='https://github.com/mingdachen/SummScreen', citation=_CITATION, ) def _split_generators(self, dl_manager): dl_paths = dl_manager.download_and_extract(_DL_URLS) filenames_dict = _get_filenames_dict( tokenized_path=dl_paths['tokenized'], recap_source=self._builder_config.recap_source, ) paths_dict = _get_paths_dict( untokenized_path=dl_paths['untokenized'], recap_source=self._builder_config.recap_source, filenames_dict=filenames_dict, ) return { 'train': self._generate_examples(paths=paths_dict['train']), 'validation': self._generate_examples(paths=paths_dict['dev']), 'test': self._generate_examples(paths=paths_dict['test']), } def _generate_examples(self, paths): for path in paths: example = _load_json(path) fname = os.path.basename(path) if self._builder_config.recap_source == 'fd': yield fname, { _TRANSCRIPT: '\n'.join(example['Transcript']), _RECAP: '\n'.join(example['Recap']), 'episode_number': example['Episode Number'], 'episode_title': example['Episode Title'], 'show_title': example['Show Title'], 'transcript_author': example['Transcript Author'], } elif self._builder_config.recap_source == 'tms': yield fname, { _TRANSCRIPT: '\n'.join(example['Transcript']), _RECAP: '\n'.join(example['Recap']), 'episode_summary': '\n'.join(example['Episode Summary']), 'show_title': example['Show Title'], 'transcript_author': example['Transcript Author'], 'recap_author': example['Recap Author'], } else: raise KeyError( f'Unknown recap_source {self._builder_config.recap_source}')

true

f71ea5d93843377ff6b080e7d44cc423b011871b

2,783

py

Python

components_library/cachehierarchies/abstract_cache_hierarchy.py

zinob15/gem5

fb2946e314ea9e63c7696ee8023150ed13956582

[ "BSD-3-Clause" ]

19

2018-07-20T15:08:50.000Z

2022-03-26T16:15:59.000Z

components_library/cachehierarchies/abstract_cache_hierarchy.py

zinob15/gem5

fb2946e314ea9e63c7696ee8023150ed13956582

[ "BSD-3-Clause" ]

148

2018-07-20T00:58:36.000Z

2021-11-16T01:52:33.000Z

components_library/cachehierarchies/abstract_cache_hierarchy.py

zinob15/gem5

fb2946e314ea9e63c7696ee8023150ed13956582

[ "BSD-3-Clause" ]

10

2019-01-10T03:01:30.000Z

2022-01-21T18:36:18.000Z

# Copyright (c) 2021 The Regents of the University of California # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are # met: redistributions of source code must retain the above copyright # notice, this list of conditions and the following disclaimer; # redistributions in binary form must reproduce the above copyright # notice, this list of conditions and the following disclaimer in the # documentation and/or other materials provided with the distribution; # neither the name of the copyright holders nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS # "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT # LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT # OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT # LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, # DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY # THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. from abc import ABCMeta, abstractmethod from ..boards.abstract_board import AbstractBoard from m5.objects import SubSystem class AbstractCacheHierarchy(SubSystem): __metaclass__ = ABCMeta def __init__(self): super(AbstractCacheHierarchy, self).__init__() """ A Cache Hierarchy incorporates any system components which manages communicaton between the processor and memory. E.g., Caches, the MemBus, MMU, and the MMU Cache. All Cache Hierarchies must have this as a base class. """ @abstractmethod def incorporate_cache(self, board: AbstractBoard) -> None: """ Incorporates the caches into a board. Each specific hierarchy needs to implement this function and will be unique for each setup. :param board: The board in which the cache heirarchy is to be incorporated. :type board: AbstractBoard """ raise NotImplementedError @abstractmethod def is_ruby(self) -> bool: """ Specifies whether this cache hierarchy is using the Ruby memory system or not. :returns: True if the cache hierarchy is ruby. Otherwise False. """ raise NotImplementedError

38.123288

78

0.743442

from abc import ABCMeta, abstractmethod from ..boards.abstract_board import AbstractBoard from m5.objects import SubSystem class AbstractCacheHierarchy(SubSystem): __metaclass__ = ABCMeta def __init__(self): super(AbstractCacheHierarchy, self).__init__() @abstractmethod def incorporate_cache(self, board: AbstractBoard) -> None: raise NotImplementedError @abstractmethod def is_ruby(self) -> bool: raise NotImplementedError

true

f71ea6560181038415ecc054dc22addd8cc08dd2

13,031

py

Python

mt3/datasets.py

AK391/mt3

e03242bdbb877c64677024adb3b9eb915d9929d6

[ "Apache-2.0" ]

1

2022-01-04T04:37:07.000Z

mt3/datasets.py

dogdogshit/mt3

d43c95ccbf9caa08d18e985ca2f2fc7e286a2f66

[ "Apache-2.0" ]

null

mt3/datasets.py

dogdogshit/mt3

d43c95ccbf9caa08d18e985ca2f2fc7e286a2f66

[ "Apache-2.0" ]

null

# Copyright 2021 The MT3 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. """Dataset configurations.""" import dataclasses from typing import Mapping, Sequence, Union from mt3 import note_sequences import tensorflow as tf @dataclasses.dataclass class InferEvalSplit: # key in dictionary containing all dataset splits name: str # task name suffix (each eval split is a separate task) suffix: str # whether or not to include in the mixture of all eval tasks include_in_mixture: bool = True @dataclasses.dataclass class DatasetConfig: """Configuration for a transcription dataset.""" # dataset name name: str # mapping from split name to path paths: Mapping[str, str] # mapping from feature name to feature features: Mapping[str, Union[tf.io.FixedLenFeature, tf.io.FixedLenSequenceFeature]] # training split name train_split: str # training eval split name train_eval_split: str # list of infer eval split specs infer_eval_splits: Sequence[InferEvalSplit] # list of track specs to be used for metrics track_specs: Sequence[note_sequences.TrackSpec] = dataclasses.field( default_factory=list) MAESTROV1_CONFIG = DatasetConfig( name='maestrov1', paths={ 'train': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_ns_wav_train.tfrecord-?????-of-00010', 'train_subset': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_ns_wav_train.tfrecord-00002-of-00010', 'validation': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_ns_wav_validation.tfrecord-?????-of-00010', 'validation_subset': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_ns_wav_validation.tfrecord-0000[06]-of-00010', 'test': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_ns_wav_test.tfrecord-?????-of-00010' }, features={ 'audio': tf.io.FixedLenFeature([], dtype=tf.string), 'sequence': tf.io.FixedLenFeature([], dtype=tf.string), 'id': tf.io.FixedLenFeature([], dtype=tf.string) }, train_split='train', train_eval_split='validation_subset', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train_full', include_in_mixture=False), InferEvalSplit(name='train_subset', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation_full', include_in_mixture=False), InferEvalSplit(name='validation_subset', suffix='validation'), InferEvalSplit(name='test', suffix='test', include_in_mixture=False) ]) MAESTROV3_CONFIG = DatasetConfig( name='maestrov3', paths={ 'train': 'gs://magentadata/datasets/maestro/v3.0.0/maestro-v3.0.0_ns_wav_train.tfrecord-?????-of-00025', 'train_subset': 'gs://magentadata/datasets/maestro/v3.0.0/maestro-v3.0.0_ns_wav_train.tfrecord-00004-of-00025', 'validation': 'gs://magentadata/datasets/maestro/v3.0.0/maestro-v3.0.0_ns_wav_validation.tfrecord-?????-of-00025', 'validation_subset': 'gs://magentadata/datasets/maestro/v3.0.0/maestro-v3.0.0_ns_wav_validation.tfrecord-0002?-of-00025', 'test': 'gs://magentadata/datasets/maestro/v3.0.0/maestro-v3.0.0_ns_wav_test.tfrecord-?????-of-00025' }, features={ 'audio': tf.io.FixedLenFeature([], dtype=tf.string), 'sequence': tf.io.FixedLenFeature([], dtype=tf.string), 'id': tf.io.FixedLenFeature([], dtype=tf.string) }, train_split='train', train_eval_split='validation_subset', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train_full', include_in_mixture=False), InferEvalSplit(name='train_subset', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation_full', include_in_mixture=False), InferEvalSplit(name='validation_subset', suffix='validation'), InferEvalSplit(name='test', suffix='test', include_in_mixture=False) ]) GUITARSET_CONFIG = DatasetConfig( name='guitarset', paths={ 'train': 'gs://mt3/data/datasets/guitarset/train.tfrecord-?????-of-00019', 'validation': 'gs://mt3/data/datasets/guitarset/validation.tfrecord-?????-of-00006', }, features={ 'sequence': tf.io.FixedLenFeature([], dtype=tf.string), 'audio': tf.io.FixedLenFeature([], dtype=tf.string), 'velocity_range': tf.io.FixedLenFeature([], dtype=tf.string), 'id': tf.io.FixedLenFeature([], dtype=tf.string), }, train_split='train', train_eval_split='validation', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation'), ]) URMP_CONFIG = DatasetConfig( name='urmp', paths={ 'train': 'gs://mt3/data/datasets/urmp/train.tfrecord', 'validation': 'gs://mt3/data/datasets/urmp/validation.tfrecord', }, features={ 'id': tf.io.FixedLenFeature([], dtype=tf.string), 'tracks': tf.io.FixedLenSequenceFeature( [], dtype=tf.int64, allow_missing=True), 'inst_names': tf.io.FixedLenSequenceFeature( [], dtype=tf.string, allow_missing=True), 'audio': tf.io.FixedLenFeature([], dtype=tf.string), 'sequence': tf.io.FixedLenFeature([], dtype=tf.string), 'instrument_sequences': tf.io.FixedLenSequenceFeature( [], dtype=tf.string, allow_missing=True), }, train_split='train', train_eval_split='validation', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation') ]) MUSICNET_CONFIG = DatasetConfig( name='musicnet', paths={ 'train': 'gs://mt3/data/datasets/musicnet/musicnet-train.tfrecord-?????-of-00036', 'validation': 'gs://mt3/data/datasets/musicnet/musicnet-validation.tfrecord-?????-of-00005', 'test': 'gs://mt3/data/datasets/musicnet/musicnet-test.tfrecord-?????-of-00003' }, features={ 'id': tf.io.FixedLenFeature([], dtype=tf.string), 'sample_rate': tf.io.FixedLenFeature([], dtype=tf.float32), 'audio': tf.io.FixedLenSequenceFeature( [], dtype=tf.float32, allow_missing=True), 'sequence': tf.io.FixedLenFeature([], dtype=tf.string) }, train_split='train', train_eval_split='validation', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation'), InferEvalSplit(name='test', suffix='test', include_in_mixture=False) ]) CERBERUS4_CONFIG = DatasetConfig( name='cerberus4', paths={ 'train': 'gs://mt3/data/datasets/cerberus4/slakh_multi_cerberus_train_bass:drums:guitar:piano.tfrecord-?????-of-00286', 'train_subset': 'gs://mt3/data/datasets/cerberus4/slakh_multi_cerberus_train_bass:drums:guitar:piano.tfrecord-00000-of-00286', 'validation': 'gs://mt3/data/datasets/cerberus4/slakh_multi_cerberus_validation_bass:drums:guitar:piano.tfrecord-?????-of-00212', 'validation_subset': 'gs://mt3/data/datasets/cerberus4/slakh_multi_cerberus_validation_bass:drums:guitar:piano.tfrecord-0000?-of-00212', 'test': 'gs://mt3/data/datasets/cerberus4/slakh_multi_cerberus_test_bass:drums:guitar:piano.tfrecord-?????-of-00106' }, features={ 'audio_sample_rate': tf.io.FixedLenFeature([], dtype=tf.int64), 'inst_names': tf.io.FixedLenSequenceFeature( [], dtype=tf.string, allow_missing=True), 'midi_class': tf.io.FixedLenSequenceFeature( [], dtype=tf.int64, allow_missing=True), 'mix': tf.io.FixedLenSequenceFeature( [], dtype=tf.float32, allow_missing=True), 'note_sequences': tf.io.FixedLenSequenceFeature( [], dtype=tf.string, allow_missing=True), 'plugin_name': tf.io.FixedLenSequenceFeature( [], dtype=tf.int64, allow_missing=True), 'program_num': tf.io.FixedLenSequenceFeature( [], dtype=tf.int64, allow_missing=True), 'slakh_class': tf.io.FixedLenSequenceFeature( [], dtype=tf.int64, allow_missing=True), 'src_ids': tf.io.FixedLenSequenceFeature( [], dtype=tf.string, allow_missing=True), 'stems': tf.io.FixedLenSequenceFeature( [], dtype=tf.float32, allow_missing=True), 'stems_shape': tf.io.FixedLenFeature([2], dtype=tf.int64), 'target_type': tf.io.FixedLenFeature([], dtype=tf.string), 'track_id': tf.io.FixedLenFeature([], dtype=tf.string), }, train_split='train', train_eval_split='validation_subset', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train_full', include_in_mixture=False), InferEvalSplit(name='train_subset', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation_full', include_in_mixture=False), InferEvalSplit(name='validation_subset', suffix='validation'), InferEvalSplit(name='test', suffix='test', include_in_mixture=False) ], track_specs=[ note_sequences.TrackSpec('bass', program=32), note_sequences.TrackSpec('drums', is_drum=True), note_sequences.TrackSpec('guitar', program=24), note_sequences.TrackSpec('piano', program=0) ]) SLAKH_CONFIG = DatasetConfig( name='slakh', paths={ 'train': 'gs://mt3/data/datasets/slakh/slakh_multi_full_subsets_10_train_all_inst.tfrecord-?????-of-02307', 'train_subset': 'gs://mt3/data/datasets/slakh/slakh_multi_full_subsets_10_train_all_inst.tfrecord-00000-of-02307', 'validation': 'gs://mt3/data/datasets/slakh/slakh_multi_full_validation_all_inst.tfrecord-?????-of-00168', 'validation_subset': 'gs://mt3/data/datasets/slakh/slakh_multi_full_validation_all_inst.tfrecord-0000?-of-00168', 'test': 'gs://mt3/data/datasets/slakh/slakh_multi_full_test_all_inst.tfrecord-?????-of-00109' }, features={ 'audio_sample_rate': tf.io.FixedLenFeature([], dtype=tf.int64), 'inst_names': tf.io.FixedLenSequenceFeature([], dtype=tf.string, allow_missing=True), 'midi_class': tf.io.FixedLenSequenceFeature([], dtype=tf.int64, allow_missing=True), 'mix': tf.io.FixedLenSequenceFeature([], dtype=tf.float32, allow_missing=True), 'note_sequences': tf.io.FixedLenSequenceFeature([], dtype=tf.string, allow_missing=True), 'plugin_name': tf.io.FixedLenSequenceFeature([], dtype=tf.int64, allow_missing=True), 'program_num': tf.io.FixedLenSequenceFeature([], dtype=tf.int64, allow_missing=True), 'slakh_class': tf.io.FixedLenSequenceFeature([], dtype=tf.int64, allow_missing=True), 'src_ids': tf.io.FixedLenSequenceFeature([], dtype=tf.string, allow_missing=True), 'stems': tf.io.FixedLenSequenceFeature([], dtype=tf.float32, allow_missing=True), 'stems_shape': tf.io.FixedLenFeature([2], dtype=tf.int64), 'target_type': tf.io.FixedLenFeature([], dtype=tf.string), 'track_id': tf.io.FixedLenFeature([], dtype=tf.string), }, train_split='train', train_eval_split='validation_subset', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train_full', include_in_mixture=False), InferEvalSplit(name='train_subset', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation_full', include_in_mixture=False), InferEvalSplit(name='validation_subset', suffix='validation'), InferEvalSplit(name='test', suffix='test', include_in_mixture=False) ])

43.875421

127

0.634027

import dataclasses from typing import Mapping, Sequence, Union from mt3 import note_sequences import tensorflow as tf @dataclasses.dataclass class InferEvalSplit: name: str suffix: str include_in_mixture: bool = True @dataclasses.dataclass class DatasetConfig: name: str paths: Mapping[str, str] features: Mapping[str, Union[tf.io.FixedLenFeature, tf.io.FixedLenSequenceFeature]] train_split: str train_eval_split: str infer_eval_splits: Sequence[InferEvalSplit] track_specs: Sequence[note_sequences.TrackSpec] = dataclasses.field( default_factory=list) MAESTROV1_CONFIG = DatasetConfig( name='maestrov1', paths={ 'train': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_ns_wav_train.tfrecord-?????-of-00010', 'train_subset': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_ns_wav_train.tfrecord-00002-of-00010', 'validation': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_ns_wav_validation.tfrecord-?????-of-00010', 'validation_subset': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_ns_wav_validation.tfrecord-0000[06]-of-00010', 'test': 'gs://magentadata/datasets/maestro/v1.0.0/maestro-v1.0.0_ns_wav_test.tfrecord-?????-of-00010' }, features={ 'audio': tf.io.FixedLenFeature([], dtype=tf.string), 'sequence': tf.io.FixedLenFeature([], dtype=tf.string), 'id': tf.io.FixedLenFeature([], dtype=tf.string) }, train_split='train', train_eval_split='validation_subset', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train_full', include_in_mixture=False), InferEvalSplit(name='train_subset', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation_full', include_in_mixture=False), InferEvalSplit(name='validation_subset', suffix='validation'), InferEvalSplit(name='test', suffix='test', include_in_mixture=False) ]) MAESTROV3_CONFIG = DatasetConfig( name='maestrov3', paths={ 'train': 'gs://magentadata/datasets/maestro/v3.0.0/maestro-v3.0.0_ns_wav_train.tfrecord-?????-of-00025', 'train_subset': 'gs://magentadata/datasets/maestro/v3.0.0/maestro-v3.0.0_ns_wav_train.tfrecord-00004-of-00025', 'validation': 'gs://magentadata/datasets/maestro/v3.0.0/maestro-v3.0.0_ns_wav_validation.tfrecord-?????-of-00025', 'validation_subset': 'gs://magentadata/datasets/maestro/v3.0.0/maestro-v3.0.0_ns_wav_validation.tfrecord-0002?-of-00025', 'test': 'gs://magentadata/datasets/maestro/v3.0.0/maestro-v3.0.0_ns_wav_test.tfrecord-?????-of-00025' }, features={ 'audio': tf.io.FixedLenFeature([], dtype=tf.string), 'sequence': tf.io.FixedLenFeature([], dtype=tf.string), 'id': tf.io.FixedLenFeature([], dtype=tf.string) }, train_split='train', train_eval_split='validation_subset', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train_full', include_in_mixture=False), InferEvalSplit(name='train_subset', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation_full', include_in_mixture=False), InferEvalSplit(name='validation_subset', suffix='validation'), InferEvalSplit(name='test', suffix='test', include_in_mixture=False) ]) GUITARSET_CONFIG = DatasetConfig( name='guitarset', paths={ 'train': 'gs://mt3/data/datasets/guitarset/train.tfrecord-?????-of-00019', 'validation': 'gs://mt3/data/datasets/guitarset/validation.tfrecord-?????-of-00006', }, features={ 'sequence': tf.io.FixedLenFeature([], dtype=tf.string), 'audio': tf.io.FixedLenFeature([], dtype=tf.string), 'velocity_range': tf.io.FixedLenFeature([], dtype=tf.string), 'id': tf.io.FixedLenFeature([], dtype=tf.string), }, train_split='train', train_eval_split='validation', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation'), ]) URMP_CONFIG = DatasetConfig( name='urmp', paths={ 'train': 'gs://mt3/data/datasets/urmp/train.tfrecord', 'validation': 'gs://mt3/data/datasets/urmp/validation.tfrecord', }, features={ 'id': tf.io.FixedLenFeature([], dtype=tf.string), 'tracks': tf.io.FixedLenSequenceFeature( [], dtype=tf.int64, allow_missing=True), 'inst_names': tf.io.FixedLenSequenceFeature( [], dtype=tf.string, allow_missing=True), 'audio': tf.io.FixedLenFeature([], dtype=tf.string), 'sequence': tf.io.FixedLenFeature([], dtype=tf.string), 'instrument_sequences': tf.io.FixedLenSequenceFeature( [], dtype=tf.string, allow_missing=True), }, train_split='train', train_eval_split='validation', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation') ]) MUSICNET_CONFIG = DatasetConfig( name='musicnet', paths={ 'train': 'gs://mt3/data/datasets/musicnet/musicnet-train.tfrecord-?????-of-00036', 'validation': 'gs://mt3/data/datasets/musicnet/musicnet-validation.tfrecord-?????-of-00005', 'test': 'gs://mt3/data/datasets/musicnet/musicnet-test.tfrecord-?????-of-00003' }, features={ 'id': tf.io.FixedLenFeature([], dtype=tf.string), 'sample_rate': tf.io.FixedLenFeature([], dtype=tf.float32), 'audio': tf.io.FixedLenSequenceFeature( [], dtype=tf.float32, allow_missing=True), 'sequence': tf.io.FixedLenFeature([], dtype=tf.string) }, train_split='train', train_eval_split='validation', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation'), InferEvalSplit(name='test', suffix='test', include_in_mixture=False) ]) CERBERUS4_CONFIG = DatasetConfig( name='cerberus4', paths={ 'train': 'gs://mt3/data/datasets/cerberus4/slakh_multi_cerberus_train_bass:drums:guitar:piano.tfrecord-?????-of-00286', 'train_subset': 'gs://mt3/data/datasets/cerberus4/slakh_multi_cerberus_train_bass:drums:guitar:piano.tfrecord-00000-of-00286', 'validation': 'gs://mt3/data/datasets/cerberus4/slakh_multi_cerberus_validation_bass:drums:guitar:piano.tfrecord-?????-of-00212', 'validation_subset': 'gs://mt3/data/datasets/cerberus4/slakh_multi_cerberus_validation_bass:drums:guitar:piano.tfrecord-0000?-of-00212', 'test': 'gs://mt3/data/datasets/cerberus4/slakh_multi_cerberus_test_bass:drums:guitar:piano.tfrecord-?????-of-00106' }, features={ 'audio_sample_rate': tf.io.FixedLenFeature([], dtype=tf.int64), 'inst_names': tf.io.FixedLenSequenceFeature( [], dtype=tf.string, allow_missing=True), 'midi_class': tf.io.FixedLenSequenceFeature( [], dtype=tf.int64, allow_missing=True), 'mix': tf.io.FixedLenSequenceFeature( [], dtype=tf.float32, allow_missing=True), 'note_sequences': tf.io.FixedLenSequenceFeature( [], dtype=tf.string, allow_missing=True), 'plugin_name': tf.io.FixedLenSequenceFeature( [], dtype=tf.int64, allow_missing=True), 'program_num': tf.io.FixedLenSequenceFeature( [], dtype=tf.int64, allow_missing=True), 'slakh_class': tf.io.FixedLenSequenceFeature( [], dtype=tf.int64, allow_missing=True), 'src_ids': tf.io.FixedLenSequenceFeature( [], dtype=tf.string, allow_missing=True), 'stems': tf.io.FixedLenSequenceFeature( [], dtype=tf.float32, allow_missing=True), 'stems_shape': tf.io.FixedLenFeature([2], dtype=tf.int64), 'target_type': tf.io.FixedLenFeature([], dtype=tf.string), 'track_id': tf.io.FixedLenFeature([], dtype=tf.string), }, train_split='train', train_eval_split='validation_subset', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train_full', include_in_mixture=False), InferEvalSplit(name='train_subset', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation_full', include_in_mixture=False), InferEvalSplit(name='validation_subset', suffix='validation'), InferEvalSplit(name='test', suffix='test', include_in_mixture=False) ], track_specs=[ note_sequences.TrackSpec('bass', program=32), note_sequences.TrackSpec('drums', is_drum=True), note_sequences.TrackSpec('guitar', program=24), note_sequences.TrackSpec('piano', program=0) ]) SLAKH_CONFIG = DatasetConfig( name='slakh', paths={ 'train': 'gs://mt3/data/datasets/slakh/slakh_multi_full_subsets_10_train_all_inst.tfrecord-?????-of-02307', 'train_subset': 'gs://mt3/data/datasets/slakh/slakh_multi_full_subsets_10_train_all_inst.tfrecord-00000-of-02307', 'validation': 'gs://mt3/data/datasets/slakh/slakh_multi_full_validation_all_inst.tfrecord-?????-of-00168', 'validation_subset': 'gs://mt3/data/datasets/slakh/slakh_multi_full_validation_all_inst.tfrecord-0000?-of-00168', 'test': 'gs://mt3/data/datasets/slakh/slakh_multi_full_test_all_inst.tfrecord-?????-of-00109' }, features={ 'audio_sample_rate': tf.io.FixedLenFeature([], dtype=tf.int64), 'inst_names': tf.io.FixedLenSequenceFeature([], dtype=tf.string, allow_missing=True), 'midi_class': tf.io.FixedLenSequenceFeature([], dtype=tf.int64, allow_missing=True), 'mix': tf.io.FixedLenSequenceFeature([], dtype=tf.float32, allow_missing=True), 'note_sequences': tf.io.FixedLenSequenceFeature([], dtype=tf.string, allow_missing=True), 'plugin_name': tf.io.FixedLenSequenceFeature([], dtype=tf.int64, allow_missing=True), 'program_num': tf.io.FixedLenSequenceFeature([], dtype=tf.int64, allow_missing=True), 'slakh_class': tf.io.FixedLenSequenceFeature([], dtype=tf.int64, allow_missing=True), 'src_ids': tf.io.FixedLenSequenceFeature([], dtype=tf.string, allow_missing=True), 'stems': tf.io.FixedLenSequenceFeature([], dtype=tf.float32, allow_missing=True), 'stems_shape': tf.io.FixedLenFeature([2], dtype=tf.int64), 'target_type': tf.io.FixedLenFeature([], dtype=tf.string), 'track_id': tf.io.FixedLenFeature([], dtype=tf.string), }, train_split='train', train_eval_split='validation_subset', infer_eval_splits=[ InferEvalSplit(name='train', suffix='eval_train_full', include_in_mixture=False), InferEvalSplit(name='train_subset', suffix='eval_train'), InferEvalSplit(name='validation', suffix='validation_full', include_in_mixture=False), InferEvalSplit(name='validation_subset', suffix='validation'), InferEvalSplit(name='test', suffix='test', include_in_mixture=False) ])

true

f71ea6594940687e4ec4ac3a81683903513d4887

6,303

py

Python

repo/script.module.liveresolver/lib/js2py/constructors/jsobject.py

Hades01/Addons

710da97ac850197498a3cd64be1811c593610add

[ "Apache-2.0" ]

3

2020-03-03T13:21:44.000Z

2021-07-21T09:53:31.000Z

repo/script.module.liveresolver/lib/js2py/constructors/jsobject.py

Hades01/Addons

710da97ac850197498a3cd64be1811c593610add

[ "Apache-2.0" ]

null

repo/script.module.liveresolver/lib/js2py/constructors/jsobject.py

Hades01/Addons

710da97ac850197498a3cd64be1811c593610add

[ "Apache-2.0" ]

2

2020-04-01T22:11:12.000Z

2020-05-07T23:54:52.000Z

from js2py.base import * #todo Double check everything is OK @Js def Object(): val = arguments.get('0') if val.is_null() or val.is_undefined(): return PyJsObject(prototype=ObjectPrototype) return val.to_object() @Js def object_constructor(): if len(arguments): val = arguments.get('0') if val.TYPE=='Object': #Implementation dependent, but my will simply return :) return val elif val.TYPE in ['Number', 'String', 'Boolean']: return val.to_object() return PyJsObject(prototype=ObjectPrototype) Object.create = object_constructor class ObjectMethods: def getPrototypeOf(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.getPrototypeOf called on non-object') return null if obj.prototype is None else obj.prototype def getOwnPropertyDescriptor (obj, prop): if not obj.is_object(): raise MakeError('TypeError', 'Object.getOwnPropertyDescriptor called on non-object') return obj.own.get(prop.to_string().value) # will return undefined if we dont have this prop def getOwnPropertyNames(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.getOwnPropertyDescriptor called on non-object') return obj.own.keys() def create(obj): if not (obj.is_object() or obj.is_null()): raise MakeError('TypeError', 'Object prototype may only be an Object or null') temp = PyJsObject(prototype=(None if obj.is_null() else obj)) if len(arguments)>1 and not arguments[1].is_undefined(): ObjectMethods.defineProperties.__func__(temp, arguments[1]) return temp def defineProperty(obj, prop, attrs): if not obj.is_object(): raise MakeError('TypeError', 'Object.defineProperty called on non-object') name = prop.to_string().value if not obj.define_own_property(name, ToPropertyDescriptor(attrs)): raise MakeError('TypeError', 'Cannot redefine property: %s' % name) return obj def defineProperties(obj, properties): if not obj.is_object(): raise MakeError('TypeError', 'Object.defineProperties called on non-object') props = properties.to_object() for name in props: desc = ToPropertyDescriptor(props.get(name.value)) if not obj.define_own_property(name.value, desc): raise MakeError('TypeError', 'Failed to define own property: %s'%name.value) return obj def seal(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.seal called on non-object') for desc in obj.own.values(): desc['configurable'] = False obj.extensible = False return obj def freeze(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.freeze called on non-object') for desc in obj.own.values(): desc['configurable'] = False if is_data_descriptor(desc): desc['writable'] = False obj.extensible = False return obj def preventExtensions(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.preventExtensions on non-object') obj.extensible = False return obj def isSealed(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.isSealed called on non-object') if obj.extensible: return False for desc in obj.own.values(): if desc['configurable']: return False return True def isFrozen(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.isFrozen called on non-object') if obj.extensible: return False for desc in obj.own.values(): if desc['configurable']: return False if is_data_descriptor(desc) and desc['writable']: return False return True def isExtensible(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.isExtensible called on non-object') return obj.extensible def keys(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.keys called on non-object') return [e for e,d in obj.own.iteritems() if d.get('enumerable')] # add methods attached to Object constructor fill_prototype(Object, ObjectMethods, default_attrs) # add constructor to prototype fill_in_props(ObjectPrototype, {'constructor':Object}, default_attrs) # add prototype property to the constructor. Object.define_own_property('prototype', {'value': ObjectPrototype, 'enumerable': False, 'writable': False, 'configurable': False}) # some utility functions: def ToPropertyDescriptor(obj): # page 38 (50 absolute) if obj.TYPE!='Object': raise MakeError('TypeError', 'Can\'t convert non-object to property descriptor') desc = {} if obj.has_property('enumerable'): desc['enumerable'] = obj.get('enumerable').to_boolean().value if obj.has_property('configurable'): desc['configurable'] = obj.get('configurable').to_boolean().value if obj.has_property('value'): desc['value'] = obj.get('value') if obj.has_property('writable'): desc['writable'] = obj.get('writable').to_boolean().value if obj.has_property('get'): cand = obj.get('get') if not (cand.is_undefined() or cand.is_callable()): raise MakeError('TypeError', 'Invalid getter (it has to be a function or undefined)') desc['get'] = cand if obj.has_property('set'): cand = obj.get('set') if not (cand.is_undefined() or cand.is_callable()): raise MakeError('TypeError', 'Invalid setter (it has to be a function or undefined)') desc['set'] = cand if ('get' in desc or 'set' in desc) and ('value' in desc or 'writable' in desc): raise MakeError('TypeError', 'Invalid property. A property cannot both have accessors and be writable or have a value.') return desc

37.517857

129

0.621609

from js2py.base import * @Js def Object(): val = arguments.get('0') if val.is_null() or val.is_undefined(): return PyJsObject(prototype=ObjectPrototype) return val.to_object() @Js def object_constructor(): if len(arguments): val = arguments.get('0') if val.TYPE=='Object': return val elif val.TYPE in ['Number', 'String', 'Boolean']: return val.to_object() return PyJsObject(prototype=ObjectPrototype) Object.create = object_constructor class ObjectMethods: def getPrototypeOf(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.getPrototypeOf called on non-object') return null if obj.prototype is None else obj.prototype def getOwnPropertyDescriptor (obj, prop): if not obj.is_object(): raise MakeError('TypeError', 'Object.getOwnPropertyDescriptor called on non-object') return obj.own.get(prop.to_string().value) def getOwnPropertyNames(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.getOwnPropertyDescriptor called on non-object') return obj.own.keys() def create(obj): if not (obj.is_object() or obj.is_null()): raise MakeError('TypeError', 'Object prototype may only be an Object or null') temp = PyJsObject(prototype=(None if obj.is_null() else obj)) if len(arguments)>1 and not arguments[1].is_undefined(): ObjectMethods.defineProperties.__func__(temp, arguments[1]) return temp def defineProperty(obj, prop, attrs): if not obj.is_object(): raise MakeError('TypeError', 'Object.defineProperty called on non-object') name = prop.to_string().value if not obj.define_own_property(name, ToPropertyDescriptor(attrs)): raise MakeError('TypeError', 'Cannot redefine property: %s' % name) return obj def defineProperties(obj, properties): if not obj.is_object(): raise MakeError('TypeError', 'Object.defineProperties called on non-object') props = properties.to_object() for name in props: desc = ToPropertyDescriptor(props.get(name.value)) if not obj.define_own_property(name.value, desc): raise MakeError('TypeError', 'Failed to define own property: %s'%name.value) return obj def seal(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.seal called on non-object') for desc in obj.own.values(): desc['configurable'] = False obj.extensible = False return obj def freeze(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.freeze called on non-object') for desc in obj.own.values(): desc['configurable'] = False if is_data_descriptor(desc): desc['writable'] = False obj.extensible = False return obj def preventExtensions(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.preventExtensions on non-object') obj.extensible = False return obj def isSealed(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.isSealed called on non-object') if obj.extensible: return False for desc in obj.own.values(): if desc['configurable']: return False return True def isFrozen(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.isFrozen called on non-object') if obj.extensible: return False for desc in obj.own.values(): if desc['configurable']: return False if is_data_descriptor(desc) and desc['writable']: return False return True def isExtensible(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.isExtensible called on non-object') return obj.extensible def keys(obj): if not obj.is_object(): raise MakeError('TypeError', 'Object.keys called on non-object') return [e for e,d in obj.own.iteritems() if d.get('enumerable')] fill_prototype(Object, ObjectMethods, default_attrs) fill_in_props(ObjectPrototype, {'constructor':Object}, default_attrs) Object.define_own_property('prototype', {'value': ObjectPrototype, 'enumerable': False, 'writable': False, 'configurable': False}) def ToPropertyDescriptor(obj): if obj.TYPE!='Object': raise MakeError('TypeError', 'Can\'t convert non-object to property descriptor') desc = {} if obj.has_property('enumerable'): desc['enumerable'] = obj.get('enumerable').to_boolean().value if obj.has_property('configurable'): desc['configurable'] = obj.get('configurable').to_boolean().value if obj.has_property('value'): desc['value'] = obj.get('value') if obj.has_property('writable'): desc['writable'] = obj.get('writable').to_boolean().value if obj.has_property('get'): cand = obj.get('get') if not (cand.is_undefined() or cand.is_callable()): raise MakeError('TypeError', 'Invalid getter (it has to be a function or undefined)') desc['get'] = cand if obj.has_property('set'): cand = obj.get('set') if not (cand.is_undefined() or cand.is_callable()): raise MakeError('TypeError', 'Invalid setter (it has to be a function or undefined)') desc['set'] = cand if ('get' in desc or 'set' in desc) and ('value' in desc or 'writable' in desc): raise MakeError('TypeError', 'Invalid property. A property cannot both have accessors and be writable or have a value.') return desc

true

f71ea68918003b20c8ce85f2c5cf70c422756b26

3,125

py

Python

fuji_server/models/license_output_inner.py

ignpelloz/fuji

5e6fe8333c1706d1b628a84108bff7a97fdf11a7

[ "MIT" ]

25

2020-09-22T08:28:45.000Z

2022-02-23T07:10:28.000Z

fuji_server/models/license_output_inner.py

ignpelloz/fuji

5e6fe8333c1706d1b628a84108bff7a97fdf11a7

[ "MIT" ]

188

2020-05-11T08:54:59.000Z

2022-03-31T12:28:15.000Z

fuji_server/models/license_output_inner.py

ignpelloz/fuji

5e6fe8333c1706d1b628a84108bff7a97fdf11a7

[ "MIT" ]

20

2020-05-04T13:56:26.000Z

2022-03-02T13:39:04.000Z

# -*- coding: utf-8 -*- from __future__ import absolute_import from datetime import date, datetime # noqa: F401 from typing import List, Dict # noqa: F401 from fuji_server.models.base_model_ import Model from fuji_server import util class LicenseOutputInner(Model): """NOTE: This class is auto generated by the swagger code generator program. Do not edit the class manually. """ def __init__(self, license: str = None, osi_approved: bool = False, details_url: str = None): # noqa: E501 """LicenseOutputInner - a model defined in Swagger :param license: The license of this LicenseOutputInner. # noqa: E501 :type license: str :param osi_approved: The osi_approved of this LicenseOutputInner. # noqa: E501 :type osi_approved: bool :param details_url: The details_url of this LicenseOutputInner. # noqa: E501 :type details_url: str """ self.swagger_types = {'license': str, 'osi_approved': bool, 'details_url': str} self.attribute_map = {'license': 'license', 'osi_approved': 'OSI_approved', 'details_url': 'details_url'} self._license = license self._osi_approved = osi_approved self._details_url = details_url @classmethod def from_dict(cls, dikt) -> 'LicenseOutputInner': """Returns the dict as a model :param dikt: A dict. :type: dict :return: The License_output_inner of this LicenseOutputInner. # noqa: E501 :rtype: LicenseOutputInner """ return util.deserialize_model(dikt, cls) @property def license(self) -> str: """Gets the license of this LicenseOutputInner. :return: The license of this LicenseOutputInner. :rtype: str """ return self._license @license.setter def license(self, license: str): """Sets the license of this LicenseOutputInner. :param license: The license of this LicenseOutputInner. :type license: str """ self._license = license @property def osi_approved(self) -> bool: """Gets the osi_approved of this LicenseOutputInner. :return: The osi_approved of this LicenseOutputInner. :rtype: bool """ return self._osi_approved @osi_approved.setter def osi_approved(self, osi_approved: bool): """Sets the osi_approved of this LicenseOutputInner. :param osi_approved: The osi_approved of this LicenseOutputInner. :type osi_approved: bool """ self._osi_approved = osi_approved @property def details_url(self) -> str: """Gets the details_url of this LicenseOutputInner. :return: The details_url of this LicenseOutputInner. :rtype: str """ return self._details_url @details_url.setter def details_url(self, details_url: str): """Sets the details_url of this LicenseOutputInner. :param details_url: The details_url of this LicenseOutputInner. :type details_url: str """ self._details_url = details_url

28.935185

113

0.65056

from __future__ import absolute_import from datetime import date, datetime from typing import List, Dict from fuji_server.models.base_model_ import Model from fuji_server import util class LicenseOutputInner(Model): def __init__(self, license: str = None, osi_approved: bool = False, details_url: str = None): self.swagger_types = {'license': str, 'osi_approved': bool, 'details_url': str} self.attribute_map = {'license': 'license', 'osi_approved': 'OSI_approved', 'details_url': 'details_url'} self._license = license self._osi_approved = osi_approved self._details_url = details_url @classmethod def from_dict(cls, dikt) -> 'LicenseOutputInner': return util.deserialize_model(dikt, cls) @property def license(self) -> str: return self._license @license.setter def license(self, license: str): self._license = license @property def osi_approved(self) -> bool: return self._osi_approved @osi_approved.setter def osi_approved(self, osi_approved: bool): self._osi_approved = osi_approved @property def details_url(self) -> str: return self._details_url @details_url.setter def details_url(self, details_url: str): self._details_url = details_url

true

f71ea6b4496eae33b99557e0515c9fe2901709df

244

py

Python

problems/303_range_sum_query_immutable.py

wasi0013/leet_code

c589c10f06043fa0ac7643e09ae3903d77c2f8e9

[ "MIT" ]

null

problems/303_range_sum_query_immutable.py

wasi0013/leet_code

c589c10f06043fa0ac7643e09ae3903d77c2f8e9

[ "MIT" ]

null

problems/303_range_sum_query_immutable.py

wasi0013/leet_code

c589c10f06043fa0ac7643e09ae3903d77c2f8e9

[ "MIT" ]

null

class NumArray: def __init__(self, nums: List[int]): self.n = list(accumulate(nums)) def sumRange(self, left: int, right: int) -> int: return self.n[right]- (self.n[left-1] if left>0 else 0)

22.181818

63

0.545082

class NumArray: def __init__(self, nums: List[int]): self.n = list(accumulate(nums)) def sumRange(self, left: int, right: int) -> int: return self.n[right]- (self.n[left-1] if left>0 else 0)

true

f71ea6bc8a22ebb8427b7204071db94474f6208a

5,847

py

Python

plugins/nxt_plugin/nxt/motcont.py

RodPy/Turtlebots.activity

f885d7d2e5d710c01294ae60da995dfb0eb36b21

[ "MIT" ]

null

plugins/nxt_plugin/nxt/motcont.py

RodPy/Turtlebots.activity

f885d7d2e5d710c01294ae60da995dfb0eb36b21

[ "MIT" ]

null

plugins/nxt_plugin/nxt/motcont.py

RodPy/Turtlebots.activity

f885d7d2e5d710c01294ae60da995dfb0eb36b21

[ "MIT" ]

1

2020-06-17T15:44:16.000Z

# nxt.motcont module -- Interface to Linus Atorf's MotorControl NXC # Copyright (C) 2011 Marcus Wanner # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU 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 General Public License for more details. import nxt import nxt.error import time from threading import Lock class MotorConError(nxt.error.ProtocolError): pass def _power(power): pw = abs(power) psign = int(power >= 0) * 2 - 1 if psign == -1: pw += 100 pw = str(pw) pw = '0'*(3-len(pw))+pw #pad front with 0s to make 3 chars return pw def _tacho(tacholimit): tacho = str(tacholimit) tacho = '0'*(6-len(tacho))+tacho #pad front with 0s to make 6 chars return tacho def interval(delay, lastrun): now = time.time() if lastrun+delay > now: diff = now - lastrun time.sleep(0.010 - diff) class MotCont(): ''' This class provides an interface to Linus Atorf's MotorControl NXC program. It is a wrapper which follows the documentation at http://www.mindstorms.rwth-aachen.de/trac/wiki/MotorControl and provides command strings and timing intervals as dictated there. To use this module, you will need to put MotorControl22.rxe on your NXT brick. This file and its corresponding source can be found at http://www.mindstorms.rwth-aachen.de/trac/browser/trunk/tools/MotorControl You can use nxt_push or any other nxt file manager to put the file on the NXT. Before using any of the functions here, use MotCont.start() to start the program. You can also start it manually my using the menu on the brick. When your script exits, it would be a good idea to do b.stop_program(). ''' def __init__(self, brick): self.brick = brick self.is_ready_lock = Lock() self.last_is_ready = time.time()-1 self.last_cmd = {} def cmd(self, port, power, tacholimit, speedreg=1, smoothstart=0, brake=0): ''' Sends a "CONTROLLED_MOTORCMD" to MotorControl. port is nxt.motor.PORT_[A-C], power is -100-100, tacholimit is 0-999999, speedreg is whether to try to maintain speeds under load, and brake is whether to enable active braking after the motor is in the specified place (DIFFERENT from the nxt.motor.turn() function's brake arg).''' interval(0.010, self.last_is_ready) if port in self.last_cmd: interval(0.015, self.last_cmd[port]) mode = str( 0x01*int(brake)+ 0x02*int(speedreg)+ 0x04*int(smoothstart) ) command = '1'+str(port)+_power(power)+_tacho(tacholimit)+mode self.brick.message_write(1, command) self.last_cmd[port] = time.time() def move_to(self, port, power, tachocount, speedreg=1, smoothstart=0, brake=0): ''' Same as cmd(), except that the tachocount is subtracted from the motor's current position and that value is used to turn the motor. Power is -100-100, but the sign is rewritten as needed.''' tacho = nxt.Motor(self.brick, port).get_tacho().block_tacho_count tacho = tachocount-tacho tsign = int(tacho >= 0) * 2 - 1 tacho = abs(tacho) power = abs(power)*tsign self.cmd(port, power, tacho, speedreg, smoothstart, brake) def reset_tacho(self, port): ''' Sends a "RESET_ERROR_CORRECTION" to MotorControl, which causes it to reset the current tacho count for that motor.''' interval(0.010, self.last_is_ready) self.brick.message_write(1, '2'+str(port)) self.last_cmd[port] = time.time() def is_ready(self, port): ''' Sends an "ISMOTORREADY" to MotorControl and returns the reply.''' interval(0.010, self.last_is_ready) with self.is_ready_lock: self.brick.message_write(1, '3'+str(port)) time.sleep(0.015) #10ms pause from the docs seems to not be adequate reply = self.brick.message_read(0, 1, 1)[1] if reply[0] != str(port): raise MotorConError, 'Wrong port returned from ISMOTORREADY' self.last_is_ready = time.time() return bool(int(reply[1])) def set_output_state(self, port, power, tacholimit, speedreg=1): ''' Sends a "CLASSIC_MOTORCMD" to MotorControl. Brick is a brick object, port is nxt.motor.PORT_[A-C], power is -100-100, tacholimit is 0-999999, speedreg is whether to try to maintain speeds under load, and brake is whether to enable active braking after the motor is in the specified place (DIFFERENT from the nxt.motor.turn() function's brake arg).''' interval(0.010, self.last_is_ready) if port in self.last_cmd: interval(0.015, self.last_cmd[port]) command = '4'+str(port)+_power(power)+_tacho(tacholimit)+str(speedreg) self.brick.message_write(1, command) self.last_cmd[port] = time.time() def start(self, version=22): ''' Starts the MotorControl program on the brick. It needs to already be present on the brick's flash and named MotorControlXX.rxc, where XX is the version number passed as the version arg (default is whatever is bundled with this version of nxt-python).''' try: self.brick.stop_program() except nxt.error.DirProtError: pass self.brick.start_program('MotorControl%d.rxe' % version) time.sleep(0.1) def stop(self): ''' Used to stop the MotorControl program. All this actually does is stop the currently running rxe.''' self.brick.stop_program()

39.77551

83

0.677612

# Copyright (C) 2011 Marcus Wanner # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU 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 General Public License for more details. import nxt import nxt.error import time from threading import Lock class MotorConError(nxt.error.ProtocolError): pass def _power(power): pw = abs(power) psign = int(power >= 0) * 2 - 1 if psign == -1: pw += 100 pw = str(pw) pw = '0'*(3-len(pw))+pw #pad front with 0s to make 3 chars return pw def _tacho(tacholimit): tacho = str(tacholimit) tacho = '0'*(6-len(tacho))+tacho #pad front with 0s to make 6 chars return tacho def interval(delay, lastrun): now = time.time() if lastrun+delay > now: diff = now - lastrun time.sleep(0.010 - diff) class MotCont(): ''' This class provides an interface to Linus Atorf's MotorControl NXC program. It is a wrapper which follows the documentation at http://www.mindstorms.rwth-aachen.de/trac/wiki/MotorControl and provides command strings and timing intervals as dictated there. To use this module, you will need to put MotorControl22.rxe on your NXT brick. This file and its corresponding source can be found at http://www.mindstorms.rwth-aachen.de/trac/browser/trunk/tools/MotorControl You can use nxt_push or any other nxt file manager to put the file on the NXT. Before using any of the functions here, use MotCont.start() to start the program. You can also start it manually my using the menu on the brick. When your script exits, it would be a good idea to do b.stop_program(). ''' def __init__(self, brick): self.brick = brick self.is_ready_lock = Lock() self.last_is_ready = time.time()-1 self.last_cmd = {} def cmd(self, port, power, tacholimit, speedreg=1, smoothstart=0, brake=0): ''' Sends a "CONTROLLED_MOTORCMD" to MotorControl. port is nxt.motor.PORT_[A-C], power is -100-100, tacholimit is 0-999999, speedreg is whether to try to maintain speeds under load, and brake is whether to enable active braking after the motor is in the specified place (DIFFERENT from the nxt.motor.turn() function's brake arg).''' interval(0.010, self.last_is_ready) if port in self.last_cmd: interval(0.015, self.last_cmd[port]) mode = str( 0x01*int(brake)+ 0x02*int(speedreg)+ 0x04*int(smoothstart) ) command = '1'+str(port)+_power(power)+_tacho(tacholimit)+mode self.brick.message_write(1, command) self.last_cmd[port] = time.time() def move_to(self, port, power, tachocount, speedreg=1, smoothstart=0, brake=0): ''' Same as cmd(), except that the tachocount is subtracted from the motor's current position and that value is used to turn the motor. Power is -100-100, but the sign is rewritten as needed.''' tacho = nxt.Motor(self.brick, port).get_tacho().block_tacho_count tacho = tachocount-tacho tsign = int(tacho >= 0) * 2 - 1 tacho = abs(tacho) power = abs(power)*tsign self.cmd(port, power, tacho, speedreg, smoothstart, brake) def reset_tacho(self, port): ''' Sends a "RESET_ERROR_CORRECTION" to MotorControl, which causes it to reset the current tacho count for that motor.''' interval(0.010, self.last_is_ready) self.brick.message_write(1, '2'+str(port)) self.last_cmd[port] = time.time() def is_ready(self, port): ''' Sends an "ISMOTORREADY" to MotorControl and returns the reply.''' interval(0.010, self.last_is_ready) with self.is_ready_lock: self.brick.message_write(1, '3'+str(port)) time.sleep(0.015) reply = self.brick.message_read(0, 1, 1)[1] if reply[0] != str(port): raise MotorConError, 'Wrong port returned from ISMOTORREADY' self.last_is_ready = time.time() return bool(int(reply[1])) def set_output_state(self, port, power, tacholimit, speedreg=1): ''' Sends a "CLASSIC_MOTORCMD" to MotorControl. Brick is a brick object, port is nxt.motor.PORT_[A-C], power is -100-100, tacholimit is 0-999999, speedreg is whether to try to maintain speeds under load, and brake is whether to enable active braking after the motor is in the specified place (DIFFERENT from the nxt.motor.turn() function's brake arg).''' interval(0.010, self.last_is_ready) if port in self.last_cmd: interval(0.015, self.last_cmd[port]) command = '4'+str(port)+_power(power)+_tacho(tacholimit)+str(speedreg) self.brick.message_write(1, command) self.last_cmd[port] = time.time() def start(self, version=22): ''' Starts the MotorControl program on the brick. It needs to already be present on the brick's flash and named MotorControlXX.rxc, where XX is the version number passed as the version arg (default is whatever is bundled with this version of nxt-python).''' try: self.brick.stop_program() except nxt.error.DirProtError: pass self.brick.start_program('MotorControl%d.rxe' % version) time.sleep(0.1) def stop(self): ''' Used to stop the MotorControl program. All this actually does is stop the currently running rxe.''' self.brick.stop_program()

false

true

f71ea7abd7acda31d3df346c7631db22cb58ddb5

13,002

py

Python

house_rocket_app.py

Leonardodsch/house-rocket-insights

dd8405b776e223ec5ff8392a027d4b0116fcd7ca

[ "MIT" ]

1

2021-12-24T13:40:09.000Z

house_rocket_app.py

Leonardodsch/house-rocket-insights

dd8405b776e223ec5ff8392a027d4b0116fcd7ca

[ "MIT" ]

null

house_rocket_app.py

Leonardodsch/house-rocket-insights

dd8405b776e223ec5ff8392a027d4b0116fcd7ca

[ "MIT" ]

null

import pandas as pd import numpy as np import streamlit as st import plotly.express as px import ipywidgets as widgets from ipywidgets import fixed import seaborn as sns import matplotlib.pyplot as plt sns.set_style('whitegrid') st.set_page_config(layout='wide') @st.cache(allow_output_mutation=True) def get_data(path): data = pd.read_csv(path) return data def barplot(a,b, aux): plot = sns.barplot(x=a, y=b, data=aux, edgecolor='k', palette='Blues') sns.despine() return plot # get data path = 'data/df_sugestions01.csv' path2 = 'data/df_sugestions02.csv' path3 = 'data/df_full.csv' data = get_data(path) df = get_data(path2) df1 = get_data(path3) st.sidebar.write() f_zipcode = st.sidebar.multiselect('Select Zipcode', data['zipcode'].unique()) f_condition = st.sidebar.multiselect('Select Condition', data['condition'].sort_values(ascending=True).unique()) f_buy = st.sidebar.multiselect('Select buy option', data['buy'].unique()) f_season = st.sidebar.multiselect('Select season', df['season'].unique()) min_price = int(df['price'].min()) max_price = int(df['price'].max()) median_price = int(df['price'].median()) st.title('House Rocket') st.write('A House Rocket é uma empresa focada na compra e venda de imóveis, buscando avaliar e encontrar bons negócios para constituir seu portfólio e oferecer também bons' ' negocios para seus clientes. Diante disso foi realizada uma análise onde diversos imóveis foram explorados e avaliados buscando o que poderia se tornar uma boa oportunidade para a empresa' ' e alguns insights interessantes foram descobertos, algo que se tornará de extremo valor caso seja bem utilizado.' 'Para detalhes mais técnicos e visualização do projeto completo acessar:' ' [GitHub](https://github.com/Leonardodsch/house-rocket-insights)') st.title('Business Questions') st.write('As tabelas são interativas e podem ser filtradas a partir das opções na barra lateral, permitindo assim que os imóveis' ' possam ser exibidos de acordo com a preferência.') st.header(' Quais são os imóveis que a House Rocket deveria comprar e por qual preço ?') st.write(' Na primeita tabela estão os imóveis agrupados por região (zipcode), com os preços médios de cada região. Estes são avaliados juntamente com o valor' ' da coluna condition de cada imóvel, para assim ser feita uma sugestão de compra ou não') st.header(' Uma vez a casa comprada, qual o melhor momento para vendê-las e por qual preço ?') st.write('Na segunda tabela é possivel filtrar os imóveis pela região mas também pela sazonalidade, o que permite ver as melhores opções de compra em cada estação do ano' ' e o valor da venda baseado nas premissas de assumidas no começo do projeto') if (f_zipcode != []) & (f_condition == []) & (f_buy == []) & (f_season == []): st.write(data.loc[data['zipcode'].isin(f_zipcode)]) st.write(df.loc[(df['zipcode'].isin(f_zipcode))]) elif (f_condition != []) & (f_zipcode != []) & (f_buy != []) & (f_season != []): st.write(data.loc[(data['condition'].isin(f_condition)) & (data['zipcode'].isin(f_zipcode)) & (data['buy'].isin(f_buy))]) st.write(df.loc[(df['season'].isin(f_season)) & (df['zipcode'].isin(f_zipcode))]) elif (f_condition != []) & (f_zipcode == []) & (f_buy == []) & (f_season == []): st.write(data.loc[data['condition'].isin(f_condition)]) st.dataframe(df) elif (f_buy != []) & (f_zipcode == []) & (f_condition == []) & (f_season == []): st.write(data.loc[data['buy'].isin(f_buy)]) st.dataframe(df) elif (f_condition != []) & (f_zipcode != []) & (f_buy == []) & (f_season != []): st.write(data.loc[(data['condition'].isin(f_condition)) & (data['zipcode'].isin(f_zipcode))]) st.write(df.loc[(df['season'].isin(f_season)) & (df['zipcode'].isin(f_zipcode))]) elif (f_condition == []) & (f_zipcode != []) & (f_buy != []) & (f_season == []): st.write(data.loc[(data['zipcode'].isin(f_zipcode)) & (data['buy'].isin(f_buy))]) st.write(df.loc[(df['zipcode'].isin(f_zipcode))]) elif (f_season != []) & (f_zipcode == []) & (f_buy == []) & (f_condition == []): st.dataframe(data, height=400, width=700) st.write(df.loc[(df['season'].isin(f_season))]) elif (f_season != []) & (f_zipcode == []) & (f_buy != []) & (f_condition == []): st.write(data.loc[data['buy'].isin(f_buy)]) st.write(df.loc[df['season'].isin(f_season)]) elif (f_season != []) & (f_zipcode == []) & (f_buy == []) & (f_condition != []): st.write(data.loc[data['condition'].isin(f_condition)]) st.write(df.loc[df['season'].isin(f_season)]) elif (f_season != []) & (f_zipcode == []) & (f_buy != []) & (f_condition != []): st.write(data.loc[data['condition'].isin(f_condition) & (data['buy'].isin(f_buy))]) st.write(df.loc[df['season'].isin(f_season)]) elif (f_zipcode != []) & (f_condition == []) & (f_buy == []) & (f_season != []): st.write(data.loc[data['zipcode'].isin(f_zipcode)]) st.write(df.loc[(df['season'].isin(f_season)) & (df['zipcode'].isin(f_zipcode))]) elif (f_condition == []) & (f_zipcode != []) & (f_buy != []) & (f_season != []): st.write(data.loc[(data['zipcode'].isin(f_zipcode)) & (data['buy'].isin(f_buy))]) st.write(df.loc[(df['season'].isin(f_season)) & (df['zipcode'].isin(f_zipcode))]) elif (f_condition != []) & (f_zipcode != []) & (f_buy == []) & (f_season == []): st.write(data.loc[(data['condition'].isin(f_condition)) & (data['zipcode'].isin(f_zipcode))]) st.write(df.loc[(df['zipcode'].isin(f_zipcode))]) elif (f_condition != []) & (f_zipcode != []) & (f_buy != []) & (f_season == []): st.write(data.loc[(data['condition'].isin(f_condition)) & (data['zipcode'].isin(f_zipcode)) & (data['buy'].isin(f_buy))]) st.write(df.loc[(df['zipcode'].isin(f_zipcode))]) else: data = data.copy() df = df.copy() st.dataframe(data, height=400, width=700) st.dataframe(df) st.header('Mapa com as indicações de compra') is_check = st.checkbox('Show Map') if is_check: selected_price_range = st.slider('Select the price range', min_price, max_price, median_price) buy_select = st.multiselect('Buy option', df1['buy'].unique()) if (buy_select != []): # select rows houses = df1[(df1['price'] < selected_price_range) & (df1['buy'].isin(buy_select))][['id','zipcode','price','median_price','condition', 'lat', 'long']] # draw map fig = px.scatter_mapbox( houses, lat='lat', lon='long', color="condition", size="price", color_continuous_scale=px.colors.cyclical.IceFire, size_max=15, zoom=10 ) fig.update_layout(mapbox_style="open-street-map") fig.update_layout(height=600, margin={"r":0,"t":0,"l":0,"b":0}) st.plotly_chart(fig) else: # select rows houses = df1[['id','zipcode','price','median_price','condition', 'lat', 'long']].copy() # draw map fig = px.scatter_mapbox( houses, lat='lat', lon='long', color="condition", size="price", color_continuous_scale=px.colors.cyclical.IceFire, size_max=15, zoom=10 ) fig.update_layout(mapbox_style="open-street-map") fig.update_layout(height=600, margin={"r":0,"t":0,"l":0,"b":0}) st.plotly_chart(fig) st.title('Business Hypothesis') # H1 st.header('H1: Imóveis que possuem vista para água, são 30% mais caros, na média') st.text('Falsa! Imóveis com vista para a agua são 200% mais caros na mádia') aux = df1[['price','waterfront']].groupby('waterfront').mean().reset_index() fig = plt.figure(figsize=(9,3)) barplot('waterfront','price',aux) st.pyplot(fig) #H2 st.header('H2: Imóveis com data de construção menor que 1955, são 50% mais baratos, na média') st.text('Falsa! Imóveis com data de construção menot do que 1955 são aproximadamente 1,6% mais baratos') aux2 = df1[['price','yr_built']].copy() aux2['yr_built'] = aux2['yr_built'].apply(lambda x: '<= 1955' if x <= 1955 else '> 1955') aux = aux2[['price','yr_built']].groupby('yr_built').mean().reset_index() fig2 = plt.figure(figsize=(9,3)) barplot('yr_built','price',aux) st.pyplot(fig2) # Evolution over the year st.header('Evolution over the years') aux = df1[['price','yr_built']].loc[df1['yr_built'] <= 1955].groupby('yr_built').mean().reset_index() aux2 = df1[['price','yr_built']].loc[df1['yr_built'] > 1955].groupby('yr_built').mean().reset_index() fig_ = plt.figure(figsize=(15,7)) plt.subplot(2,1,1) barplot('yr_built','price', aux) plt.xticks(rotation=60); plt.title('Yr_built <= 1955') plt.subplot(2,1,2) barplot('yr_built','price',aux2) plt.xticks(rotation=60); plt.title('Yr_built > 1955') plt.tight_layout() st.pyplot(fig_) #H3 st.header('H3: Imóveis sem porão possuem area total (sqrt_lot), são 50% maiores do que com porão') st.text('Falsa! Imóveis sem porão possuem uma area total 23% maior') aux = df1[['sqft_basement','sqft_lot']].copy() aux['sqft_basement'] = aux['sqft_basement'].apply(lambda x: 'yes' if x != 0 else 'no') aux1 = aux[['sqft_basement','sqft_lot']].groupby('sqft_basement').mean().reset_index() aux1.sort_values(by='sqft_lot', ascending=True, inplace=True) fig3 = plt.figure(figsize=(9,3)) barplot('sqft_basement','sqft_lot',aux1) st.pyplot(fig3) #4 st.header('H4: O crescimento do preço dos imóveis YoY ( Year over Year ) é de 10%') st.text('Falsa O crescimento do preço dos imoveis YoY é de 2%') aux = df1[['price','year']].loc[df1['month'] == 5].copy() aux1 = aux[['price','year']].groupby('year').mean().reset_index() fig4 = plt.figure(figsize=(9,3)) barplot('year','price',aux1) st.pyplot(fig4) #5 st.header('H5: Imóveis com 3 banheiros tem um crescimento MoM ( Month over Month ) de 15%') st.text('Falsa! Imóveis com 3 banheiros não possuem um crescimento MoM de 15%') aux = df1[['price','month']].loc[df1['bathrooms'] == 3].groupby(['month']).mean().reset_index() aux['growth'] = aux['price'].pct_change() fig5 = plt.figure(figsize=(9,3)) plt.subplot(2,1,1) plt.plot('month','price', data=aux) plt.ylabel('Price') plt.subplot(2,1,2) barplot('month','growth',aux) st.pyplot(fig5) #6 st.header('H6: Imóveis com 3 ou mais banheiros são 30% mais caros, na média') st.text('Falsa! Impoveis com 3 ou mais banheiros são 100% mais caros na média') aux = df1[['bathrooms','price']].copy() aux['bathrooms'] = aux['bathrooms'].apply(lambda x: '>= 3' if x >=3 else '< 3') aux1 = aux[['price','bathrooms']].groupby('bathrooms').mean().reset_index() fig6 = plt.figure(figsize=(9,3)) barplot('bathrooms','price',aux1) st.pyplot(fig6) #7 st.header('H7: Imóveis com condition igual ou maior do que 4 são 40% mais caros, na média') st.text('Falsa! Imóveis com condition igual ou maior do que 4 são 0,5% mais caros, na média') aux = df1[['price','condition']].copy() aux['condition'] = aux['condition'].apply(lambda x: '< 4' if x < 4 else '>= 4') aux1 = aux[['price','condition']].groupby('condition').mean().reset_index() fig7 = plt.figure(figsize=(9,3)) barplot('condition','price',aux1) st.pyplot(fig7) #8 st.header('H8: Imóveis vendidos no inverno são 30% mais baratos na média do que imóveis vendidos no verão') st.text('Falsa! Imóveis vendidos no inverno são 4% mais baratos na média do que imóveis vendidos no verão') aux = df1[['price','season']].loc[(df1['season'] == 'winter') | (df1['season'] == 'summer') ].copy() aux1 = aux[['price','season']].groupby('season').mean().reset_index() aux1.sort_values(by='price', ascending=True, inplace=True) fig8 = plt.figure(figsize=(9,3)) barplot('season','price',aux1) st.pyplot(fig8) #9 st.header('H9: Imóveis com mais de 400m2 (m2_living) são 50% mais caros na media') st.text('Falsa! Imóveis com mais de 400m2 são 230% mais caros na média') aux = df1[['price','m2_living']].copy() aux['m2_living'] = aux['m2_living'].apply(lambda x: '< 400' if x < 400 else '> 400') aux1= aux[['price','m2_living']].groupby('m2_living').mean().reset_index() fig9 = plt.figure(figsize=(9,3)) barplot('m2_living','price',aux1) st.pyplot(fig9) #10 st.header('H10: Imóveis com menos de 100m2 tem um crescimento Mom ( Month over Month ) de 20%') st.text('Falsa! Imóveis com menos de 100m2 não possuem um crescimento MoM de 20%') aux = df1[['price','month']].loc[df1['m2_living'] < 100 ].groupby('month').mean().reset_index() aux['growth'] = aux['price'].pct_change() fig10 = plt.figure(figsize=(9,3)) plt.subplot(2,1,1) plt.plot('month','price', data=aux) plt.ylabel('Price') plt.subplot(2,1,2) barplot('month','growth',aux) st.pyplot(fig10) #11 st.header('H11: Imóveis com 4 ou mais quartos são 50% mais caros, na média') st.text('Verdadeira! Imóveis com 4 ou mais quartos são 50% mais caros, na média') aux = df1[['bedrooms','price']].copy() aux['bedrooms'] = aux['bedrooms'].apply(lambda x: '< 4' if x < 4 else '>= 4') aux1= aux[['price','bedrooms']].groupby('bedrooms').mean().reset_index() fig11 = plt.figure(figsize=(9,3)) barplot('bedrooms','price',aux1) st.pyplot(fig11)

42.769737

199

0.659745

import pandas as pd import numpy as np import streamlit as st import plotly.express as px import ipywidgets as widgets from ipywidgets import fixed import seaborn as sns import matplotlib.pyplot as plt sns.set_style('whitegrid') st.set_page_config(layout='wide') @st.cache(allow_output_mutation=True) def get_data(path): data = pd.read_csv(path) return data def barplot(a,b, aux): plot = sns.barplot(x=a, y=b, data=aux, edgecolor='k', palette='Blues') sns.despine() return plot path = 'data/df_sugestions01.csv' path2 = 'data/df_sugestions02.csv' path3 = 'data/df_full.csv' data = get_data(path) df = get_data(path2) df1 = get_data(path3) st.sidebar.write() f_zipcode = st.sidebar.multiselect('Select Zipcode', data['zipcode'].unique()) f_condition = st.sidebar.multiselect('Select Condition', data['condition'].sort_values(ascending=True).unique()) f_buy = st.sidebar.multiselect('Select buy option', data['buy'].unique()) f_season = st.sidebar.multiselect('Select season', df['season'].unique()) min_price = int(df['price'].min()) max_price = int(df['price'].max()) median_price = int(df['price'].median()) st.title('House Rocket') st.write('A House Rocket é uma empresa focada na compra e venda de imóveis, buscando avaliar e encontrar bons negócios para constituir seu portfólio e oferecer também bons' ' negocios para seus clientes. Diante disso foi realizada uma análise onde diversos imóveis foram explorados e avaliados buscando o que poderia se tornar uma boa oportunidade para a empresa' ' e alguns insights interessantes foram descobertos, algo que se tornará de extremo valor caso seja bem utilizado.' 'Para detalhes mais técnicos e visualização do projeto completo acessar:' ' [GitHub](https://github.com/Leonardodsch/house-rocket-insights)') st.title('Business Questions') st.write('As tabelas são interativas e podem ser filtradas a partir das opções na barra lateral, permitindo assim que os imóveis' ' possam ser exibidos de acordo com a preferência.') st.header(' Quais são os imóveis que a House Rocket deveria comprar e por qual preço ?') st.write(' Na primeita tabela estão os imóveis agrupados por região (zipcode), com os preços médios de cada região. Estes são avaliados juntamente com o valor' ' da coluna condition de cada imóvel, para assim ser feita uma sugestão de compra ou não') st.header(' Uma vez a casa comprada, qual o melhor momento para vendê-las e por qual preço ?') st.write('Na segunda tabela é possivel filtrar os imóveis pela região mas também pela sazonalidade, o que permite ver as melhores opções de compra em cada estação do ano' ' e o valor da venda baseado nas premissas de assumidas no começo do projeto') if (f_zipcode != []) & (f_condition == []) & (f_buy == []) & (f_season == []): st.write(data.loc[data['zipcode'].isin(f_zipcode)]) st.write(df.loc[(df['zipcode'].isin(f_zipcode))]) elif (f_condition != []) & (f_zipcode != []) & (f_buy != []) & (f_season != []): st.write(data.loc[(data['condition'].isin(f_condition)) & (data['zipcode'].isin(f_zipcode)) & (data['buy'].isin(f_buy))]) st.write(df.loc[(df['season'].isin(f_season)) & (df['zipcode'].isin(f_zipcode))]) elif (f_condition != []) & (f_zipcode == []) & (f_buy == []) & (f_season == []): st.write(data.loc[data['condition'].isin(f_condition)]) st.dataframe(df) elif (f_buy != []) & (f_zipcode == []) & (f_condition == []) & (f_season == []): st.write(data.loc[data['buy'].isin(f_buy)]) st.dataframe(df) elif (f_condition != []) & (f_zipcode != []) & (f_buy == []) & (f_season != []): st.write(data.loc[(data['condition'].isin(f_condition)) & (data['zipcode'].isin(f_zipcode))]) st.write(df.loc[(df['season'].isin(f_season)) & (df['zipcode'].isin(f_zipcode))]) elif (f_condition == []) & (f_zipcode != []) & (f_buy != []) & (f_season == []): st.write(data.loc[(data['zipcode'].isin(f_zipcode)) & (data['buy'].isin(f_buy))]) st.write(df.loc[(df['zipcode'].isin(f_zipcode))]) elif (f_season != []) & (f_zipcode == []) & (f_buy == []) & (f_condition == []): st.dataframe(data, height=400, width=700) st.write(df.loc[(df['season'].isin(f_season))]) elif (f_season != []) & (f_zipcode == []) & (f_buy != []) & (f_condition == []): st.write(data.loc[data['buy'].isin(f_buy)]) st.write(df.loc[df['season'].isin(f_season)]) elif (f_season != []) & (f_zipcode == []) & (f_buy == []) & (f_condition != []): st.write(data.loc[data['condition'].isin(f_condition)]) st.write(df.loc[df['season'].isin(f_season)]) elif (f_season != []) & (f_zipcode == []) & (f_buy != []) & (f_condition != []): st.write(data.loc[data['condition'].isin(f_condition) & (data['buy'].isin(f_buy))]) st.write(df.loc[df['season'].isin(f_season)]) elif (f_zipcode != []) & (f_condition == []) & (f_buy == []) & (f_season != []): st.write(data.loc[data['zipcode'].isin(f_zipcode)]) st.write(df.loc[(df['season'].isin(f_season)) & (df['zipcode'].isin(f_zipcode))]) elif (f_condition == []) & (f_zipcode != []) & (f_buy != []) & (f_season != []): st.write(data.loc[(data['zipcode'].isin(f_zipcode)) & (data['buy'].isin(f_buy))]) st.write(df.loc[(df['season'].isin(f_season)) & (df['zipcode'].isin(f_zipcode))]) elif (f_condition != []) & (f_zipcode != []) & (f_buy == []) & (f_season == []): st.write(data.loc[(data['condition'].isin(f_condition)) & (data['zipcode'].isin(f_zipcode))]) st.write(df.loc[(df['zipcode'].isin(f_zipcode))]) elif (f_condition != []) & (f_zipcode != []) & (f_buy != []) & (f_season == []): st.write(data.loc[(data['condition'].isin(f_condition)) & (data['zipcode'].isin(f_zipcode)) & (data['buy'].isin(f_buy))]) st.write(df.loc[(df['zipcode'].isin(f_zipcode))]) else: data = data.copy() df = df.copy() st.dataframe(data, height=400, width=700) st.dataframe(df) st.header('Mapa com as indicações de compra') is_check = st.checkbox('Show Map') if is_check: selected_price_range = st.slider('Select the price range', min_price, max_price, median_price) buy_select = st.multiselect('Buy option', df1['buy'].unique()) if (buy_select != []): houses = df1[(df1['price'] < selected_price_range) & (df1['buy'].isin(buy_select))][['id','zipcode','price','median_price','condition', 'lat', 'long']] fig = px.scatter_mapbox( houses, lat='lat', lon='long', color="condition", size="price", color_continuous_scale=px.colors.cyclical.IceFire, size_max=15, zoom=10 ) fig.update_layout(mapbox_style="open-street-map") fig.update_layout(height=600, margin={"r":0,"t":0,"l":0,"b":0}) st.plotly_chart(fig) else: houses = df1[['id','zipcode','price','median_price','condition', 'lat', 'long']].copy() fig = px.scatter_mapbox( houses, lat='lat', lon='long', color="condition", size="price", color_continuous_scale=px.colors.cyclical.IceFire, size_max=15, zoom=10 ) fig.update_layout(mapbox_style="open-street-map") fig.update_layout(height=600, margin={"r":0,"t":0,"l":0,"b":0}) st.plotly_chart(fig) st.title('Business Hypothesis') st.header('H1: Imóveis que possuem vista para água, são 30% mais caros, na média') st.text('Falsa! Imóveis com vista para a agua são 200% mais caros na mádia') aux = df1[['price','waterfront']].groupby('waterfront').mean().reset_index() fig = plt.figure(figsize=(9,3)) barplot('waterfront','price',aux) st.pyplot(fig) st.header('H2: Imóveis com data de construção menor que 1955, são 50% mais baratos, na média') st.text('Falsa! Imóveis com data de construção menot do que 1955 são aproximadamente 1,6% mais baratos') aux2 = df1[['price','yr_built']].copy() aux2['yr_built'] = aux2['yr_built'].apply(lambda x: '<= 1955' if x <= 1955 else '> 1955') aux = aux2[['price','yr_built']].groupby('yr_built').mean().reset_index() fig2 = plt.figure(figsize=(9,3)) barplot('yr_built','price',aux) st.pyplot(fig2) st.header('Evolution over the years') aux = df1[['price','yr_built']].loc[df1['yr_built'] <= 1955].groupby('yr_built').mean().reset_index() aux2 = df1[['price','yr_built']].loc[df1['yr_built'] > 1955].groupby('yr_built').mean().reset_index() fig_ = plt.figure(figsize=(15,7)) plt.subplot(2,1,1) barplot('yr_built','price', aux) plt.xticks(rotation=60); plt.title('Yr_built <= 1955') plt.subplot(2,1,2) barplot('yr_built','price',aux2) plt.xticks(rotation=60); plt.title('Yr_built > 1955') plt.tight_layout() st.pyplot(fig_) st.header('H3: Imóveis sem porão possuem area total (sqrt_lot), são 50% maiores do que com porão') st.text('Falsa! Imóveis sem porão possuem uma area total 23% maior') aux = df1[['sqft_basement','sqft_lot']].copy() aux['sqft_basement'] = aux['sqft_basement'].apply(lambda x: 'yes' if x != 0 else 'no') aux1 = aux[['sqft_basement','sqft_lot']].groupby('sqft_basement').mean().reset_index() aux1.sort_values(by='sqft_lot', ascending=True, inplace=True) fig3 = plt.figure(figsize=(9,3)) barplot('sqft_basement','sqft_lot',aux1) st.pyplot(fig3) st.header('H4: O crescimento do preço dos imóveis YoY ( Year over Year ) é de 10%') st.text('Falsa O crescimento do preço dos imoveis YoY é de 2%') aux = df1[['price','year']].loc[df1['month'] == 5].copy() aux1 = aux[['price','year']].groupby('year').mean().reset_index() fig4 = plt.figure(figsize=(9,3)) barplot('year','price',aux1) st.pyplot(fig4) st.header('H5: Imóveis com 3 banheiros tem um crescimento MoM ( Month over Month ) de 15%') st.text('Falsa! Imóveis com 3 banheiros não possuem um crescimento MoM de 15%') aux = df1[['price','month']].loc[df1['bathrooms'] == 3].groupby(['month']).mean().reset_index() aux['growth'] = aux['price'].pct_change() fig5 = plt.figure(figsize=(9,3)) plt.subplot(2,1,1) plt.plot('month','price', data=aux) plt.ylabel('Price') plt.subplot(2,1,2) barplot('month','growth',aux) st.pyplot(fig5) st.header('H6: Imóveis com 3 ou mais banheiros são 30% mais caros, na média') st.text('Falsa! Impoveis com 3 ou mais banheiros são 100% mais caros na média') aux = df1[['bathrooms','price']].copy() aux['bathrooms'] = aux['bathrooms'].apply(lambda x: '>= 3' if x >=3 else '< 3') aux1 = aux[['price','bathrooms']].groupby('bathrooms').mean().reset_index() fig6 = plt.figure(figsize=(9,3)) barplot('bathrooms','price',aux1) st.pyplot(fig6) st.header('H7: Imóveis com condition igual ou maior do que 4 são 40% mais caros, na média') st.text('Falsa! Imóveis com condition igual ou maior do que 4 são 0,5% mais caros, na média') aux = df1[['price','condition']].copy() aux['condition'] = aux['condition'].apply(lambda x: '< 4' if x < 4 else '>= 4') aux1 = aux[['price','condition']].groupby('condition').mean().reset_index() fig7 = plt.figure(figsize=(9,3)) barplot('condition','price',aux1) st.pyplot(fig7) st.header('H8: Imóveis vendidos no inverno são 30% mais baratos na média do que imóveis vendidos no verão') st.text('Falsa! Imóveis vendidos no inverno são 4% mais baratos na média do que imóveis vendidos no verão') aux = df1[['price','season']].loc[(df1['season'] == 'winter') | (df1['season'] == 'summer') ].copy() aux1 = aux[['price','season']].groupby('season').mean().reset_index() aux1.sort_values(by='price', ascending=True, inplace=True) fig8 = plt.figure(figsize=(9,3)) barplot('season','price',aux1) st.pyplot(fig8) st.header('H9: Imóveis com mais de 400m2 (m2_living) são 50% mais caros na media') st.text('Falsa! Imóveis com mais de 400m2 são 230% mais caros na média') aux = df1[['price','m2_living']].copy() aux['m2_living'] = aux['m2_living'].apply(lambda x: '< 400' if x < 400 else '> 400') aux1= aux[['price','m2_living']].groupby('m2_living').mean().reset_index() fig9 = plt.figure(figsize=(9,3)) barplot('m2_living','price',aux1) st.pyplot(fig9) st.header('H10: Imóveis com menos de 100m2 tem um crescimento Mom ( Month over Month ) de 20%') st.text('Falsa! Imóveis com menos de 100m2 não possuem um crescimento MoM de 20%') aux = df1[['price','month']].loc[df1['m2_living'] < 100 ].groupby('month').mean().reset_index() aux['growth'] = aux['price'].pct_change() fig10 = plt.figure(figsize=(9,3)) plt.subplot(2,1,1) plt.plot('month','price', data=aux) plt.ylabel('Price') plt.subplot(2,1,2) barplot('month','growth',aux) st.pyplot(fig10) st.header('H11: Imóveis com 4 ou mais quartos são 50% mais caros, na média') st.text('Verdadeira! Imóveis com 4 ou mais quartos são 50% mais caros, na média') aux = df1[['bedrooms','price']].copy() aux['bedrooms'] = aux['bedrooms'].apply(lambda x: '< 4' if x < 4 else '>= 4') aux1= aux[['price','bedrooms']].groupby('bedrooms').mean().reset_index() fig11 = plt.figure(figsize=(9,3)) barplot('bedrooms','price',aux1) st.pyplot(fig11)

true

f71ea7c37ca8c9223d397b63be289fc1ae452dd6

10,645

py

Python

extractor.py

vivdiwakar/BambooHR

c1471d4b743aace11cb39efca42be6250d37dc6e

[ "BSD-3-Clause" ]

1

2019-05-15T07:25:01.000Z

extractor.py

vivdiwakar/BambooHR

c1471d4b743aace11cb39efca42be6250d37dc6e

[ "BSD-3-Clause" ]

null

extractor.py

vivdiwakar/BambooHR

c1471d4b743aace11cb39efca42be6250d37dc6e

[ "BSD-3-Clause" ]

1

2021-08-04T20:44:48.000Z

import argparse import datetime import sys import requests from os import makedirs from os.path import dirname, exists from re import search, sub, escape import xmltodict # Setup the CLI arguments parser parser = argparse.ArgumentParser() parser.add_argument('auth', help='User API auth key.', type=str) parser.add_argument('company', help='Company name within BambooHR.', type=str) parser.add_argument('dest', help='Full path to CSV and artifacts destination.', type=str) args = parser.parse_args() epochNow = datetime.datetime.today().strftime('%Y%m%d_%s') APIPrefix = 'https://api.bamboohr.com/api/gateway.php/' + args.company + '/v1' userTables = ['jobInfo', 'employmentStatus', 'emergencyContacts', 'compensation', 'customBankDetails', 'customRSADetails', 'employeedependents'] def fetchFromAPI(url, outform): try: results = requests.get(url, headers={'Accept': 'application/json'}, auth=(args.auth, ":x")) if results.status_code == 200: if outform == 'json': return results.json() elif outform == 'xml': return results.text else: sys.stderr.write('API Request error on "' + url + '"; exiting...' + "\n") exit(1) except (requests.ConnectionError, requests.exceptions.HTTPError, requests.exceptions.Timeout) as e: sys.stderr.write('ERROR: ' + str(e) + '; exiting...' + "\n") exit(1) def fetchBinaryFile(url, destination): try: binary = requests.get(url, headers={'Accept': 'application/json'}, auth=(args.auth, ":x")) directory = dirname(destination) if not exists(directory): makedirs(directory) with open(destination, 'wb') as f: f.write(binary.content) f.close() except (requests.ConnectionError, requests.exceptions.HTTPError, requests.exceptions.Timeout) as e: sys.stderr.write('ERROR: ' + str(e) + '; exiting...' + "\n") exit(1) def openFileHandler(fileName): try: fh = open(fileName, 'a') return fh except (PermissionError, OSError, IOError) as e: sys.stderr.write('ERROR: ' + str(e) + '; exiting...' + "\n") exit(1) def processAttrValue(String): if str(String) == "None" or str(String) == "": return '-,' else: if search("'", str(String)): return sub("'", '', str(String)) + ',' elif search(",", str(String)): return sub(r'(.*)', r'"\1"', str(String)) + ',' else: return str(String) + ',' def checkHeaderForAttribute(fileName, keyword): try: fh = open(fileName, 'r') firstLine = fh.readline() fh.close() if search(keyword, firstLine): return True else: return False except FileNotFoundError: return False except (PermissionError, OSError, IOError) as e: sys.stderr.write('ERROR: ' + str(e) + '; exiting...' + "\n") exit(1) def processAPIInfo(httpReturn, allKeys, subKeyList): csvOutput = '' if isinstance(httpReturn, dict): csvOutput = processAttrValue(employee) for key in allKeys: if key in subKeyList.keys(): for tag in subKeyList[key]: csvOutput += processAttrValue(httpReturn[key][tag]) else: csvOutput += processAttrValue(httpReturn[key]) else: index = -1 for index in range(len(httpReturn) - 1): csvOutput += processAPIInfo(httpReturn[index], allKeys, subKeyList) + '\n' csvOutput += processAPIInfo(httpReturn[(index + 1)], allKeys, subKeyList) return csvOutput def writeCSVToFile(fetchInfo, tableName, topKeyList, subKeyList): allKeys = topKeyList[:] for parKey in sorted(subKeyList.keys()): allKeys.append(parKey) fileName = args.dest + '/' + epochNow + '_' + tableName + '.csv' headerPresent = checkHeaderForAttribute(fileName, 'displayName') statusCSV = openFileHandler(fileName) if headerPresent == False: header = 'displayName,' + str(','.join(map(str, topKeyList))) for child in sorted(subKeyList.keys()): header += ',' + (str(','.join(map(str, subKeyList[child])))) statusCSV.write(header + "\n") statusCSV.write(processAPIInfo(fetchInfo, allKeys, subKeyList).rstrip(',') + "\n") statusCSV.close() def exec_jobInfo(tableName): jobInfoKeys = ['jobTitle', 'reportsTo', 'location', 'division', 'department', 'date'] fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/tables/' + tableName, 'json') if len(fetchInfo) > 0: writeCSVToFile(fetchInfo, tableName, jobInfoKeys, {}) def exec_employmentStatus(tableName): statusKeys = ['employmentStatus', 'employeeId', 'date'] fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/tables/' + tableName, 'json') if len(fetchInfo) > 0: writeCSVToFile(fetchInfo, tableName, statusKeys, {}) def exec_emergencyContacts(tableName): contactKeys = ['employeeId', 'name', 'relationship', 'homePhone', 'addressLine1', 'addressLine2', 'mobilePhone', 'email', 'zipcode', 'city', 'state', 'country', 'workPhone', 'workPhoneExtension'] fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/tables/' + tableName, 'json') if len(fetchInfo) > 0: writeCSVToFile(fetchInfo, tableName, contactKeys, {}) def exec_compensation(tableName): compKeys = ['type', 'payPeriod', 'employeeId', 'startDate'] subKeys = {'rate': ['currency', 'value']} fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/tables/' + tableName, 'json') if len(fetchInfo) > 0: writeCSVToFile(fetchInfo, tableName, compKeys, subKeys) def exec_customBankDetails(tableName): bankKeys = ['employeeId', 'customBankName', 'customAccountNumber'] fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/tables/' + tableName, 'json') if len(fetchInfo) > 0: writeCSVToFile(fetchInfo, tableName, bankKeys, {}) def exec_customRSADetails(tableName): rsaKeys = ['employeeId', 'customPFAName', 'customRSANumber'] fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/tables/' + tableName, 'json') if len(fetchInfo) > 0: writeCSVToFile(fetchInfo, tableName, rsaKeys, {}) def exec_employeedependents(tableName): depKeys = ['employeeId', 'firstName', 'middleName', 'lastName', 'relationship', 'gender', 'dateOfBirth', 'addressLine1', 'addressLine2', 'city', 'state', 'zipCode', 'homePhone', 'country', 'isUsCitizen', 'isStudent'] fetchInfo = fetchFromAPI(APIPrefix + '/' + tableName + '/?employeeid=' + str(employeeID), 'json') if len(fetchInfo['Employee Dependents']) > 0: writeCSVToFile(fetchInfo['Employee Dependents'], tableName, depKeys, {}) def processDict(arg, catName): spaces = [' '] return sub(u'(?u)[' + escape(''.join(spaces)) + ']', '_', str(args.dest + catName + '/' + arg['dateCreated'] + '_' + arg['name'])) def downloadDocuments(employeeID): fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/files/view', 'xml') obj = xmltodict.parse(fetchInfo) for i in range(len(obj['employee']['category'])): catName = obj['employee']['category'][i]['name'] try: if isinstance(obj['employee']['category'][i]['file'], list): for ind in range(len(obj['employee']['category'][i]['file'])): filename = processDict(obj['employee']['category'][i]['file'][ind], catName) fetchBinaryFile(APIPrefix + '/employees/' + str(employeeID) + '/files/' + str(obj['employee']['category'][i]['file'][ind]['@id']) + '/', filename) elif isinstance(obj['employee']['category'][i]['file'], dict): filename = processDict(obj['employee']['category'][i]['file'], catName) fetchBinaryFile(APIPrefix + '/employees/' + str(employeeID) + '/files/' + str(obj['employee']['category'][i]['file']['@id']) + '/', filename) else: print(type(obj['employee']['category'][i]['file'])) except KeyError: pass # Key sets userKeys = ['id', 'address1', 'address2', 'age', 'bestEmail', 'city', 'country', 'dateOfBirth', 'employeeNumber', 'employmentHistoryStatus', 'firstName', 'fullName1', 'fullName2', 'fullName3', 'fullName4', 'fullName5', 'gender', 'hireDate', 'homeEmail', 'homePhone', 'jobTitle', 'lastChanged', 'department', 'lastName', 'location', 'maritalStatus', 'middleName', 'mobilePhone', 'payChangeReason', 'payGroupId', 'payRate', 'payRateEffectiveDate', 'payType', 'paidPer', 'payPeriod', 'ssn', 'state', 'stateCode', 'supervisor', 'supervisorEId', 'terminationDate', 'workEmail', 'workPhone', 'workPhonePlusExtension', 'workPhoneExtension', 'zipcode', 'isPhotoUploaded', 'employmentStatus', 'nickname', 'photoUploaded', 'customBenefitDue', 'division', 'customBenefitDue', 'customCompany', 'customDateofConfirmation', 'customGrade1', 'customLagosGrade', 'customLevel', 'customNationalInsuranceNumber', 'customNationality', 'customNHFNumber', 'customNIC', 'customNigeriaMobilePhone', 'customNon-DomStatus', 'customPakistanMobilePhone', 'customRwandaMobilePhone', 'customStateofOrigin', 'customTaxIDNumber', 'customUKWorkPermit', 'supervisorId', 'displayName'] # Fetch the list of user IDs userIDs = [] userIDGet = fetchFromAPI(APIPrefix + '/employees/directory', 'json') for employee in userIDGet['employees']: userIDs.append(employee['id']) # for employeeID in userIDs: for employeeID in userIDs: # Do not run for ID 671 - Viv Diwakar if employeeID != 671: userInfoGet = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '?fields=' + ','.join(map(str, userKeys)), 'json') employee = userInfoGet['displayName'] writeCSVToFile(userInfoGet, 'employees', userKeys[:-1], {}) downloadDocuments(employeeID) userPicUploaded = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '?fields=isPhotoUploaded', 'json') if userPicUploaded['isPhotoUploaded'] == 'true': fetchBinaryFile(APIPrefix + '/employees/' + str(employeeID) + '/photo/small', sub(',', '', str(args.dest + '/photos/photo_employeeID_' + str(employeeID) + '_' + sub(' ', '_', employee) + '.jpg'))) for table in userTables: locals()[str('exec_' + table)](table)

42.242063

119

0.626867

import argparse import datetime import sys import requests from os import makedirs from os.path import dirname, exists from re import search, sub, escape import xmltodict parser = argparse.ArgumentParser() parser.add_argument('auth', help='User API auth key.', type=str) parser.add_argument('company', help='Company name within BambooHR.', type=str) parser.add_argument('dest', help='Full path to CSV and artifacts destination.', type=str) args = parser.parse_args() epochNow = datetime.datetime.today().strftime('%Y%m%d_%s') APIPrefix = 'https://api.bamboohr.com/api/gateway.php/' + args.company + '/v1' userTables = ['jobInfo', 'employmentStatus', 'emergencyContacts', 'compensation', 'customBankDetails', 'customRSADetails', 'employeedependents'] def fetchFromAPI(url, outform): try: results = requests.get(url, headers={'Accept': 'application/json'}, auth=(args.auth, ":x")) if results.status_code == 200: if outform == 'json': return results.json() elif outform == 'xml': return results.text else: sys.stderr.write('API Request error on "' + url + '"; exiting...' + "\n") exit(1) except (requests.ConnectionError, requests.exceptions.HTTPError, requests.exceptions.Timeout) as e: sys.stderr.write('ERROR: ' + str(e) + '; exiting...' + "\n") exit(1) def fetchBinaryFile(url, destination): try: binary = requests.get(url, headers={'Accept': 'application/json'}, auth=(args.auth, ":x")) directory = dirname(destination) if not exists(directory): makedirs(directory) with open(destination, 'wb') as f: f.write(binary.content) f.close() except (requests.ConnectionError, requests.exceptions.HTTPError, requests.exceptions.Timeout) as e: sys.stderr.write('ERROR: ' + str(e) + '; exiting...' + "\n") exit(1) def openFileHandler(fileName): try: fh = open(fileName, 'a') return fh except (PermissionError, OSError, IOError) as e: sys.stderr.write('ERROR: ' + str(e) + '; exiting...' + "\n") exit(1) def processAttrValue(String): if str(String) == "None" or str(String) == "": return '-,' else: if search("'", str(String)): return sub("'", '', str(String)) + ',' elif search(",", str(String)): return sub(r'(.*)', r'"\1"', str(String)) + ',' else: return str(String) + ',' def checkHeaderForAttribute(fileName, keyword): try: fh = open(fileName, 'r') firstLine = fh.readline() fh.close() if search(keyword, firstLine): return True else: return False except FileNotFoundError: return False except (PermissionError, OSError, IOError) as e: sys.stderr.write('ERROR: ' + str(e) + '; exiting...' + "\n") exit(1) def processAPIInfo(httpReturn, allKeys, subKeyList): csvOutput = '' if isinstance(httpReturn, dict): csvOutput = processAttrValue(employee) for key in allKeys: if key in subKeyList.keys(): for tag in subKeyList[key]: csvOutput += processAttrValue(httpReturn[key][tag]) else: csvOutput += processAttrValue(httpReturn[key]) else: index = -1 for index in range(len(httpReturn) - 1): csvOutput += processAPIInfo(httpReturn[index], allKeys, subKeyList) + '\n' csvOutput += processAPIInfo(httpReturn[(index + 1)], allKeys, subKeyList) return csvOutput def writeCSVToFile(fetchInfo, tableName, topKeyList, subKeyList): allKeys = topKeyList[:] for parKey in sorted(subKeyList.keys()): allKeys.append(parKey) fileName = args.dest + '/' + epochNow + '_' + tableName + '.csv' headerPresent = checkHeaderForAttribute(fileName, 'displayName') statusCSV = openFileHandler(fileName) if headerPresent == False: header = 'displayName,' + str(','.join(map(str, topKeyList))) for child in sorted(subKeyList.keys()): header += ',' + (str(','.join(map(str, subKeyList[child])))) statusCSV.write(header + "\n") statusCSV.write(processAPIInfo(fetchInfo, allKeys, subKeyList).rstrip(',') + "\n") statusCSV.close() def exec_jobInfo(tableName): jobInfoKeys = ['jobTitle', 'reportsTo', 'location', 'division', 'department', 'date'] fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/tables/' + tableName, 'json') if len(fetchInfo) > 0: writeCSVToFile(fetchInfo, tableName, jobInfoKeys, {}) def exec_employmentStatus(tableName): statusKeys = ['employmentStatus', 'employeeId', 'date'] fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/tables/' + tableName, 'json') if len(fetchInfo) > 0: writeCSVToFile(fetchInfo, tableName, statusKeys, {}) def exec_emergencyContacts(tableName): contactKeys = ['employeeId', 'name', 'relationship', 'homePhone', 'addressLine1', 'addressLine2', 'mobilePhone', 'email', 'zipcode', 'city', 'state', 'country', 'workPhone', 'workPhoneExtension'] fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/tables/' + tableName, 'json') if len(fetchInfo) > 0: writeCSVToFile(fetchInfo, tableName, contactKeys, {}) def exec_compensation(tableName): compKeys = ['type', 'payPeriod', 'employeeId', 'startDate'] subKeys = {'rate': ['currency', 'value']} fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/tables/' + tableName, 'json') if len(fetchInfo) > 0: writeCSVToFile(fetchInfo, tableName, compKeys, subKeys) def exec_customBankDetails(tableName): bankKeys = ['employeeId', 'customBankName', 'customAccountNumber'] fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/tables/' + tableName, 'json') if len(fetchInfo) > 0: writeCSVToFile(fetchInfo, tableName, bankKeys, {}) def exec_customRSADetails(tableName): rsaKeys = ['employeeId', 'customPFAName', 'customRSANumber'] fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/tables/' + tableName, 'json') if len(fetchInfo) > 0: writeCSVToFile(fetchInfo, tableName, rsaKeys, {}) def exec_employeedependents(tableName): depKeys = ['employeeId', 'firstName', 'middleName', 'lastName', 'relationship', 'gender', 'dateOfBirth', 'addressLine1', 'addressLine2', 'city', 'state', 'zipCode', 'homePhone', 'country', 'isUsCitizen', 'isStudent'] fetchInfo = fetchFromAPI(APIPrefix + '/' + tableName + '/?employeeid=' + str(employeeID), 'json') if len(fetchInfo['Employee Dependents']) > 0: writeCSVToFile(fetchInfo['Employee Dependents'], tableName, depKeys, {}) def processDict(arg, catName): spaces = [' '] return sub(u'(?u)[' + escape(''.join(spaces)) + ']', '_', str(args.dest + catName + '/' + arg['dateCreated'] + '_' + arg['name'])) def downloadDocuments(employeeID): fetchInfo = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '/files/view', 'xml') obj = xmltodict.parse(fetchInfo) for i in range(len(obj['employee']['category'])): catName = obj['employee']['category'][i]['name'] try: if isinstance(obj['employee']['category'][i]['file'], list): for ind in range(len(obj['employee']['category'][i]['file'])): filename = processDict(obj['employee']['category'][i]['file'][ind], catName) fetchBinaryFile(APIPrefix + '/employees/' + str(employeeID) + '/files/' + str(obj['employee']['category'][i]['file'][ind]['@id']) + '/', filename) elif isinstance(obj['employee']['category'][i]['file'], dict): filename = processDict(obj['employee']['category'][i]['file'], catName) fetchBinaryFile(APIPrefix + '/employees/' + str(employeeID) + '/files/' + str(obj['employee']['category'][i]['file']['@id']) + '/', filename) else: print(type(obj['employee']['category'][i]['file'])) except KeyError: pass userKeys = ['id', 'address1', 'address2', 'age', 'bestEmail', 'city', 'country', 'dateOfBirth', 'employeeNumber', 'employmentHistoryStatus', 'firstName', 'fullName1', 'fullName2', 'fullName3', 'fullName4', 'fullName5', 'gender', 'hireDate', 'homeEmail', 'homePhone', 'jobTitle', 'lastChanged', 'department', 'lastName', 'location', 'maritalStatus', 'middleName', 'mobilePhone', 'payChangeReason', 'payGroupId', 'payRate', 'payRateEffectiveDate', 'payType', 'paidPer', 'payPeriod', 'ssn', 'state', 'stateCode', 'supervisor', 'supervisorEId', 'terminationDate', 'workEmail', 'workPhone', 'workPhonePlusExtension', 'workPhoneExtension', 'zipcode', 'isPhotoUploaded', 'employmentStatus', 'nickname', 'photoUploaded', 'customBenefitDue', 'division', 'customBenefitDue', 'customCompany', 'customDateofConfirmation', 'customGrade1', 'customLagosGrade', 'customLevel', 'customNationalInsuranceNumber', 'customNationality', 'customNHFNumber', 'customNIC', 'customNigeriaMobilePhone', 'customNon-DomStatus', 'customPakistanMobilePhone', 'customRwandaMobilePhone', 'customStateofOrigin', 'customTaxIDNumber', 'customUKWorkPermit', 'supervisorId', 'displayName'] userIDs = [] userIDGet = fetchFromAPI(APIPrefix + '/employees/directory', 'json') for employee in userIDGet['employees']: userIDs.append(employee['id']) for employeeID in userIDs: if employeeID != 671: userInfoGet = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '?fields=' + ','.join(map(str, userKeys)), 'json') employee = userInfoGet['displayName'] writeCSVToFile(userInfoGet, 'employees', userKeys[:-1], {}) downloadDocuments(employeeID) userPicUploaded = fetchFromAPI(APIPrefix + '/employees/' + str(employeeID) + '?fields=isPhotoUploaded', 'json') if userPicUploaded['isPhotoUploaded'] == 'true': fetchBinaryFile(APIPrefix + '/employees/' + str(employeeID) + '/photo/small', sub(',', '', str(args.dest + '/photos/photo_employeeID_' + str(employeeID) + '_' + sub(' ', '_', employee) + '.jpg'))) for table in userTables: locals()[str('exec_' + table)](table)

true

f71ea8648cb914829cbd5e2b6998226a113ea3c8

2,831

py

Python

payment.py

trytonus/trytond-magento

f27e8d136e5e222fdf86b679d10d468de38262eb

[ "BSD-3-Clause" ]

3

2015-10-07T15:51:40.000Z

2016-04-06T09:00:57.000Z

payment.py

trytonus/trytond-magento

f27e8d136e5e222fdf86b679d10d468de38262eb

[ "BSD-3-Clause" ]

19

2015-07-28T14:24:24.000Z

2016-07-13T06:02:35.000Z

payment.py

trytonus/trytond-magento

f27e8d136e5e222fdf86b679d10d468de38262eb

[ "BSD-3-Clause" ]

15

2015-07-28T05:54:17.000Z

2016-05-27T12:23:29.000Z

# -*- coding: utf-8 -*- from trytond.pool import PoolMeta from trytond.model import fields, ModelSQL, ModelView from trytond.transaction import Transaction __metaclass__ = PoolMeta __all__ = ['MagentoPaymentGateway', 'Payment'] class MagentoPaymentGateway(ModelSQL, ModelView): """ This model maps the available payment gateways from magento to tryton. """ __name__ = 'magento.instance.payment_gateway' _rec_name = 'title' name = fields.Char("Name", required=True, select=True) title = fields.Char('Title', required=True, select=True) gateway = fields.Many2One( 'payment_gateway.gateway', 'Gateway', required=True, ondelete='RESTRICT', select=True, ) channel = fields.Many2One( 'sale.channel', 'Magento Channel', readonly=True, select=True, domain=[('source', '=', 'magento')] ) @classmethod def __setup__(cls): """ Setup the class before adding to pool """ super(MagentoPaymentGateway, cls).__setup__() cls._sql_constraints += [ ( 'name_channel_unique', 'unique(name, channel)', 'Payment gateway already exist for this channel' ) ] @classmethod def create_all_using_magento_data(cls, magento_data): """ Creates record for list of payment gateways sent by magento. It creates a new gateway only if one with the same name does not exist for this channel. """ gateways = [] for data in magento_data: gateway = cls.find_using_magento_data(data) if gateway: gateways.append(gateway) else: gateways.append(cls.create_using_magento_data(data)) return gateways @classmethod def create_using_magento_data(cls, gateway_data): """ Create record for gateway data sent by magento """ raise NotImplementedError @classmethod def find_using_magento_data(cls, gateway_data): """ Search for an existing gateway by matching name and channel. If found, return its active record else None """ try: gateway, = cls.search([ ('name', '=', gateway_data['name']), ('channel', '=', Transaction().context['current_channel']), ]) except ValueError: return None else: return gateway class Payment: __name__ = "sale.payment" magento_id = fields.Integer('Magento ID', readonly=True) @classmethod def __setup__(cls): """ Setup the class before adding to pool """ super(Payment, cls).__setup__() # TODO: Add validation to make sure payment magento id per channel # is unique!

30.117021

75

0.604733

from trytond.pool import PoolMeta from trytond.model import fields, ModelSQL, ModelView from trytond.transaction import Transaction __metaclass__ = PoolMeta __all__ = ['MagentoPaymentGateway', 'Payment'] class MagentoPaymentGateway(ModelSQL, ModelView): __name__ = 'magento.instance.payment_gateway' _rec_name = 'title' name = fields.Char("Name", required=True, select=True) title = fields.Char('Title', required=True, select=True) gateway = fields.Many2One( 'payment_gateway.gateway', 'Gateway', required=True, ondelete='RESTRICT', select=True, ) channel = fields.Many2One( 'sale.channel', 'Magento Channel', readonly=True, select=True, domain=[('source', '=', 'magento')] ) @classmethod def __setup__(cls): super(MagentoPaymentGateway, cls).__setup__() cls._sql_constraints += [ ( 'name_channel_unique', 'unique(name, channel)', 'Payment gateway already exist for this channel' ) ] @classmethod def create_all_using_magento_data(cls, magento_data): gateways = [] for data in magento_data: gateway = cls.find_using_magento_data(data) if gateway: gateways.append(gateway) else: gateways.append(cls.create_using_magento_data(data)) return gateways @classmethod def create_using_magento_data(cls, gateway_data): raise NotImplementedError @classmethod def find_using_magento_data(cls, gateway_data): try: gateway, = cls.search([ ('name', '=', gateway_data['name']), ('channel', '=', Transaction().context['current_channel']), ]) except ValueError: return None else: return gateway class Payment: __name__ = "sale.payment" magento_id = fields.Integer('Magento ID', readonly=True) @classmethod def __setup__(cls): super(Payment, cls).__setup__()

true

f71ea87e24c61c899af7fdfebf66e69cf9fcd73b

1,841

py

Python

neutron/db/migration/alembic_migrations/agent_init_ops.py

NeCTAR-RC/neutron

acf78cc3c88aff638180819419a65145a9a79695

[ "Apache-2.0" ]

1

2019-01-13T04:42:21.000Z

neutron/db/migration/alembic_migrations/agent_init_ops.py

NeCTAR-RC/neutron

acf78cc3c88aff638180819419a65145a9a79695

[ "Apache-2.0" ]

null

neutron/db/migration/alembic_migrations/agent_init_ops.py

NeCTAR-RC/neutron

acf78cc3c88aff638180819419a65145a9a79695

[ "Apache-2.0" ]

null

# Copyright 2014 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. # # Initial operations for agent management extension # This module only manages the 'agents' table. Binding tables are created # in the modules for relevant resources from alembic import op import sqlalchemy as sa def upgrade(): op.create_table( 'agents', sa.Column('id', sa.String(length=36), nullable=False), sa.Column('agent_type', sa.String(length=255), nullable=False), sa.Column('binary', sa.String(length=255), nullable=False), sa.Column('topic', sa.String(length=255), nullable=False), sa.Column('host', sa.String(length=255), nullable=False), sa.Column('admin_state_up', sa.Boolean(), nullable=False, server_default=sa.sql.true()), sa.Column('created_at', sa.DateTime(), nullable=False), sa.Column('started_at', sa.DateTime(), nullable=False), sa.Column('heartbeat_timestamp', sa.DateTime(), nullable=False), sa.Column('description', sa.String(length=255), nullable=True), sa.Column('configurations', sa.String(length=4095), nullable=False), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('agent_type', 'host', name='uniq_agents0agent_type0host'))

42.813953

78

0.681152

from alembic import op import sqlalchemy as sa def upgrade(): op.create_table( 'agents', sa.Column('id', sa.String(length=36), nullable=False), sa.Column('agent_type', sa.String(length=255), nullable=False), sa.Column('binary', sa.String(length=255), nullable=False), sa.Column('topic', sa.String(length=255), nullable=False), sa.Column('host', sa.String(length=255), nullable=False), sa.Column('admin_state_up', sa.Boolean(), nullable=False, server_default=sa.sql.true()), sa.Column('created_at', sa.DateTime(), nullable=False), sa.Column('started_at', sa.DateTime(), nullable=False), sa.Column('heartbeat_timestamp', sa.DateTime(), nullable=False), sa.Column('description', sa.String(length=255), nullable=True), sa.Column('configurations', sa.String(length=4095), nullable=False), sa.PrimaryKeyConstraint('id'), sa.UniqueConstraint('agent_type', 'host', name='uniq_agents0agent_type0host'))

true

f71ea8a17abddd1dcc7b6d5c987d32eb1bff55b7

11,642

py

Python

nidm/experiment/tests/test_query.py

tvanerp/PyNIDM

6a94875969c6bc5247b09d7d2793ed979b18ab3f

[ "Apache-2.0" ]

null

nidm/experiment/tests/test_query.py

tvanerp/PyNIDM

6a94875969c6bc5247b09d7d2793ed979b18ab3f

[ "Apache-2.0" ]

null

nidm/experiment/tests/test_query.py

tvanerp/PyNIDM

6a94875969c6bc5247b09d7d2793ed979b18ab3f

[ "Apache-2.0" ]

null

import pytest from nidm.experiment import Project, Session, AssessmentAcquisition, AssessmentObject, Acquisition, AcquisitionObject, Query from nidm.core import Constants from rdflib import Namespace,URIRef import prov.model as pm from os import remove import pprint from prov.model import ProvDocument, QualifiedName from prov.model import Namespace as provNamespace import json import urllib.request from pathlib import Path # when set to true, this will test example NIDM files downloaded from # the GitHub dbkeator/simple2_NIDM_examples repo # # DBK: this is a bit unsafe as the TTL files in the github repo above can change and the UUID will change since they are randomly # generated at this point. It's probably more robust to explicitly create these files for the time being and explicitly set the # UUID in the test file: # For example: kwargs={Constants.NIDM_PROJECT_NAME:"FBIRN_PhaseIII",Constants.NIDM_PROJECT_IDENTIFIER:1200,Constants.NIDM_PROJECT_DESCRIPTION:"Test investigation2"} # project = Project(uuid="_654321",attributes=kwargs) USE_GITHUB_DATA = False def test_GetProjectMetadata(): kwargs={Constants.NIDM_PROJECT_NAME:"FBIRN_PhaseII",Constants.NIDM_PROJECT_IDENTIFIER:9610,Constants.NIDM_PROJECT_DESCRIPTION:"Test investigation"} project = Project(uuid="_123456",attributes=kwargs) #save a turtle file with open("test.ttl",'w') as f: f.write(project.serializeTurtle()) kwargs={Constants.NIDM_PROJECT_NAME:"FBIRN_PhaseIII",Constants.NIDM_PROJECT_IDENTIFIER:1200,Constants.NIDM_PROJECT_DESCRIPTION:"Test investigation2"} project = Project(uuid="_654321",attributes=kwargs) #save a turtle file with open("test2.ttl",'w') as f: f.write(project.serializeTurtle()) #WIP test = Query.GetProjectMetadata(["test.ttl", "test2.ttl"]) #assert URIRef(Constants.NIDM + "_654321") in test #assert URIRef(Constants.NIDM + "_123456") in test #assert URIRef(Constants.NIDM_PROJECT_IDENTIFIER + "1200") in test #assert URIRef(Constants.NIDM_PROJECT_IDENTIFIER + "9610") in test #assert URIRef((Constants.NIDM_PROJECT_NAME + "FBIRN_PhaseII")) in test #assert URIRef((Constants.NIDM_PROJECT_NAME + "FBIRN_PhaseIII")) in test #assert URIRef((Constants.NIDM_PROJECT_DESCRIPTION + "Test investigation")) in test #assert URIRef((Constants.NIDM_PROJECT_DESCRIPTION + "Test investigation2")) in test remove("test2.ttl") def test_GetProjects(): kwargs={Constants.NIDM_PROJECT_NAME:"FBIRN_PhaseII",Constants.NIDM_PROJECT_IDENTIFIER:9610,Constants.NIDM_PROJECT_DESCRIPTION:"Test investigation"} project = Project(uuid="_123456",attributes=kwargs) #save a turtle file with open("test.ttl",'w') as f: f.write(project.serializeTurtle()) project_list = Query.GetProjectsUUID(["test.ttl"]) remove("test.ttl") assert URIRef(Constants.NIIRI + "_123456") in project_list def test_GetParticipantIDs(): kwargs={Constants.NIDM_PROJECT_NAME:"FBIRN_PhaseII",Constants.NIDM_PROJECT_IDENTIFIER:9610,Constants.NIDM_PROJECT_DESCRIPTION:"Test investigation"} project = Project(uuid="_123456",attributes=kwargs) session = Session(uuid="_13579",project=project) acq = Acquisition(uuid="_15793",session=session) acq2 = Acquisition(uuid="_15795",session=session) person=acq.add_person(attributes=({Constants.NIDM_SUBJECTID:"9999"})) acq.add_qualified_association(person=person,role=Constants.NIDM_PARTICIPANT) person2=acq2.add_person(attributes=({Constants.NIDM_SUBJECTID:"8888"})) acq2.add_qualified_association(person=person2,role=Constants.NIDM_PARTICIPANT) #save a turtle file with open("test.ttl",'w') as f: f.write(project.serializeTurtle()) participant_list = Query.GetParticipantIDs(["test.ttl"]) remove("test.ttl") assert (participant_list['ID'].str.contains('9999').any()) assert (participant_list['ID'].str.contains('8888').any()) def test_GetProjectInstruments(): kwargs={Constants.NIDM_PROJECT_NAME:"FBIRN_PhaseII",Constants.NIDM_PROJECT_IDENTIFIER:9610,Constants.NIDM_PROJECT_DESCRIPTION:"Test investigation"} project = Project(uuid="_123456",attributes=kwargs) session = Session(project) acq = AssessmentAcquisition(session) kwargs={pm.PROV_TYPE:pm.QualifiedName(pm.Namespace("nidm",Constants.NIDM),"NorthAmericanAdultReadingTest")} acq_obj = AssessmentObject(acq,attributes=kwargs) acq2 = AssessmentAcquisition(session) kwargs={pm.PROV_TYPE:pm.QualifiedName(pm.Namespace("nidm",Constants.NIDM),"PositiveAndNegativeSyndromeScale")} acq_obj2 = AssessmentObject(acq2,attributes=kwargs) #save a turtle file with open("test.ttl",'w') as f: f.write(project.serializeTurtle()) assessment_list = Query.GetProjectInstruments(["test.ttl"],"_123456") #remove("test.ttl") assert URIRef(Constants.NIDM + "NorthAmericanAdultReadingTest") in assessment_list['assessment_type'].to_list() assert URIRef(Constants.NIDM + "PositiveAndNegativeSyndromeScale") in assessment_list['assessment_type'].to_list() ''' The test data file could/should have the following project meta data. Taken from https://raw.githubusercontent.com/incf-nidash/nidm/master/nidm/nidm-experiment/terms/nidm-experiment.owl - descrption - fileName - license - source - title - hadNumericalValue ??? - BathSolution ??? - CellType - ChannelNumber - ElectrodeImpedance - GroupLabel - HollowElectrodeSolution - hadImageContrastType - hadImageUsageType - NumberOfChannels - AppliedFilter - SolutionFlowSpeed - RecordingLocation Returns the ''' def saveTestFile(file_name, data): project = Project(uuid="_123_" + file_name, attributes=data) return saveProject(file_name, project) def saveProject(file_name, project): # save a turtle file with open(file_name, 'w') as f: f.write(project.serializeTurtle()) return "nidm:_123_{}".format(file_name) def makeProjectTestFile(filename): DCTYPES = Namespace("http://purl.org/dc/dcmitype/") kwargs = {Constants.NIDM_PROJECT_NAME: "FBIRN_PhaseII", # this is the "title" Constants.NIDM_PROJECT_IDENTIFIER: 9610, Constants.NIDM_PROJECT_DESCRIPTION: "Test investigation", Constants.NIDM_FILENAME: "testfile.ttl", Constants.NIDM_PROJECT_LICENSE: "MIT Licence", Constants.NIDM_PROJECT_SOURCE: "Educational Source", Constants.NIDM_HAD_NUMERICAL_VALUE: "numval???", Constants.NIDM_BATH_SOLUTION: "bath", Constants.NIDM_CELL_TYPE: "ctype", Constants.NIDM_CHANNEL_NUMBER: "5", Constants.NIDM_ELECTRODE_IMPEDANCE: ".01", Constants.NIDM_GROUP_LABEL: "group 123", Constants.NIDM_HOLLOW_ELECTRODE_SOLUTION: "water", Constants.NIDM_HAD_IMAGE_CONTRACT_TYPE: "off", Constants.NIDM_HAD_IMAGE_USAGE_TYPE: "abcd", Constants.NIDM_NUBMER_OF_CHANNELS: "11", Constants.NIDM_APPLIED_FILTER: "on", Constants.NIDM_SOLUTION_FLOW_SPEED: "2.8", Constants.NIDM_RECORDING_LOCATION: "lab" } return saveTestFile(filename, kwargs) def makeProjectTestFile2(filename): DCTYPES = Namespace("http://purl.org/dc/dcmitype/") kwargs = {Constants.NIDM_PROJECT_NAME: "TEST B", # this is the "title" Constants.NIDM_PROJECT_IDENTIFIER: 1234, Constants.NIDM_PROJECT_DESCRIPTION: "More Scans", Constants.NIDM_FILENAME: "testfile2.ttl", Constants.NIDM_PROJECT_LICENSE: "Creative Commons", Constants.NIDM_PROJECT_SOURCE: "Other", Constants.NIDM_HAD_NUMERICAL_VALUE: "numval???", Constants.NIDM_BATH_SOLUTION: "bath", Constants.NIDM_CELL_TYPE: "ctype", Constants.NIDM_CHANNEL_NUMBER: "5", Constants.NIDM_ELECTRODE_IMPEDANCE: ".01", Constants.NIDM_GROUP_LABEL: "group 123", Constants.NIDM_HOLLOW_ELECTRODE_SOLUTION: "water", Constants.NIDM_HAD_IMAGE_CONTRACT_TYPE: "off", Constants.NIDM_HAD_IMAGE_USAGE_TYPE: "abcd", Constants.NIDM_NUBMER_OF_CHANNELS: "11", Constants.NIDM_APPLIED_FILTER: "on", Constants.NIDM_SOLUTION_FLOW_SPEED: "2.8", Constants.NIDM_RECORDING_LOCATION: "lab" } project = Project(uuid="_123_" + filename, attributes=kwargs) s1 = Session(project) a1 = AssessmentAcquisition(session=s1) # = s1.add_acquisition("a1", attributes={"http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#Age" : 22}) p1 = a1.add_person("p1", attributes={Constants.NIDM_GIVEN_NAME:"George", Constants.NIDM_AGE: 22}) a1.add_qualified_association(person=p1, role=Constants.NIDM_PARTICIPANT) return saveProject(filename, project) def test_GetProjectsMetadata(): p1 = makeProjectTestFile("testfile.ttl") p2 = makeProjectTestFile2("testfile2.ttl") files = ["testfile.ttl", "testfile2.ttl"] if USE_GITHUB_DATA and not Path('./cmu_a.nidm.ttl').is_file(): urllib.request.urlretrieve ( "https://raw.githubusercontent.com/dbkeator/simple2_NIDM_examples/master/datasets.datalad.org/abide/RawDataBIDS/CMU_a/nidm.ttl", "cmu_a.nidm.ttl" ) files.append("cmu_a.nidm.ttl") parsed = Query.GetProjectsMetadata(files) # assert parsed['projects'][p1][str(Constants.NIDM_PROJECT_DESCRIPTION)] == "Test investigation" # assert parsed['projects'][p2][str(Constants.NIDM_PROJECT_DESCRIPTION)] == "More Scans" # we shouldn't have the computed metadata in this result # assert parsed['projects'][p1].get (Query.matchPrefix(str(Constants.NIDM_NUMBER_OF_SUBJECTS)), -1) == -1 if USE_GITHUB_DATA: # find the project ID from the CMU file for project_id in parsed['projects']: if project_id != p1 and project_id != p2: p3 = project_id assert parsed['projects'][p3][str(Constants.NIDM_PROJECT_NAME)] == "ABIDE CMU_a Site" def test_GetProjectsComputedMetadata(): p1 = makeProjectTestFile("testfile.ttl") p2 = makeProjectTestFile2("testfile2.ttl") files = ["testfile.ttl", "testfile2.ttl"] if USE_GITHUB_DATA and not Path('./cmu_a.nidm.ttl').is_file(): urllib.request.urlretrieve ( "https://raw.githubusercontent.com/dbkeator/simple2_NIDM_examples/master/datasets.datalad.org/abide/RawDataBIDS/CMU_a/nidm.ttl", "cmu_a.nidm.ttl" ) files.append("cmu_a.nidm.ttl") parsed = Query.GetProjectsComputedMetadata(files) # assert parsed['projects'][p1][str(Constants.NIDM_PROJECT_DESCRIPTION)] == "Test investigation" # assert parsed['projects'][p2][str(Constants.NIDM_PROJECT_DESCRIPTION)] == "More Scans" # assert parsed['projects'][p2][Query.matchPrefix(str(Constants.NIDM_NUMBER_OF_SUBJECTS))] == 0 # assert parsed['projects'][p1][Query.matchPrefix(str(Constants.NIDM_NUMBER_OF_SUBJECTS))] == 0 if USE_GITHUB_DATA: for project_id in parsed['projects']: if project_id != p1 and project_id != p2: p3 = project_id assert parsed['projects'][p3][str(Constants.NIDM_PROJECT_NAME)] == "ABIDE CMU_a Site" assert parsed['projects'][p3][Query.matchPrefix(str(Constants.NIDM_NUMBER_OF_SUBJECTS))] == 14 assert parsed['projects'][p3]["age_min"] == 21 assert parsed['projects'][p3]["age_max"] == 33 assert parsed['projects'][p3][str(Constants.NIDM_GENDER)] == ['1', '2']

40.006873

165

0.707954

import pytest from nidm.experiment import Project, Session, AssessmentAcquisition, AssessmentObject, Acquisition, AcquisitionObject, Query from nidm.core import Constants from rdflib import Namespace,URIRef import prov.model as pm from os import remove import pprint from prov.model import ProvDocument, QualifiedName from prov.model import Namespace as provNamespace import json import urllib.request from pathlib import Path # UUID in the test file: # For example: kwargs={Constants.NIDM_PROJECT_NAME:"FBIRN_PhaseIII",Constants.NIDM_PROJECT_IDENTIFIER:1200,Constants.NIDM_PROJECT_DESCRIPTION:"Test investigation2"} # project = Project(uuid="_654321",attributes=kwargs) USE_GITHUB_DATA = False def test_GetProjectMetadata(): kwargs={Constants.NIDM_PROJECT_NAME:"FBIRN_PhaseII",Constants.NIDM_PROJECT_IDENTIFIER:9610,Constants.NIDM_PROJECT_DESCRIPTION:"Test investigation"} project = Project(uuid="_123456",attributes=kwargs) #save a turtle file with open("test.ttl",'w') as f: f.write(project.serializeTurtle()) kwargs={Constants.NIDM_PROJECT_NAME:"FBIRN_PhaseIII",Constants.NIDM_PROJECT_IDENTIFIER:1200,Constants.NIDM_PROJECT_DESCRIPTION:"Test investigation2"} project = Project(uuid="_654321",attributes=kwargs) #save a turtle file with open("test2.ttl",'w') as f: f.write(project.serializeTurtle()) #WIP test = Query.GetProjectMetadata(["test.ttl", "test2.ttl"]) #assert URIRef(Constants.NIDM + "_654321") in test #assert URIRef(Constants.NIDM + "_123456") in test #assert URIRef(Constants.NIDM_PROJECT_IDENTIFIER + "1200") in test #assert URIRef(Constants.NIDM_PROJECT_IDENTIFIER + "9610") in test #assert URIRef((Constants.NIDM_PROJECT_NAME + "FBIRN_PhaseII")) in test #assert URIRef((Constants.NIDM_PROJECT_NAME + "FBIRN_PhaseIII")) in test #assert URIRef((Constants.NIDM_PROJECT_DESCRIPTION + "Test investigation")) in test #assert URIRef((Constants.NIDM_PROJECT_DESCRIPTION + "Test investigation2")) in test remove("test2.ttl") def test_GetProjects(): kwargs={Constants.NIDM_PROJECT_NAME:"FBIRN_PhaseII",Constants.NIDM_PROJECT_IDENTIFIER:9610,Constants.NIDM_PROJECT_DESCRIPTION:"Test investigation"} project = Project(uuid="_123456",attributes=kwargs) #save a turtle file with open("test.ttl",'w') as f: f.write(project.serializeTurtle()) project_list = Query.GetProjectsUUID(["test.ttl"]) remove("test.ttl") assert URIRef(Constants.NIIRI + "_123456") in project_list def test_GetParticipantIDs(): kwargs={Constants.NIDM_PROJECT_NAME:"FBIRN_PhaseII",Constants.NIDM_PROJECT_IDENTIFIER:9610,Constants.NIDM_PROJECT_DESCRIPTION:"Test investigation"} project = Project(uuid="_123456",attributes=kwargs) session = Session(uuid="_13579",project=project) acq = Acquisition(uuid="_15793",session=session) acq2 = Acquisition(uuid="_15795",session=session) person=acq.add_person(attributes=({Constants.NIDM_SUBJECTID:"9999"})) acq.add_qualified_association(person=person,role=Constants.NIDM_PARTICIPANT) person2=acq2.add_person(attributes=({Constants.NIDM_SUBJECTID:"8888"})) acq2.add_qualified_association(person=person2,role=Constants.NIDM_PARTICIPANT) #save a turtle file with open("test.ttl",'w') as f: f.write(project.serializeTurtle()) participant_list = Query.GetParticipantIDs(["test.ttl"]) remove("test.ttl") assert (participant_list['ID'].str.contains('9999').any()) assert (participant_list['ID'].str.contains('8888').any()) def test_GetProjectInstruments(): kwargs={Constants.NIDM_PROJECT_NAME:"FBIRN_PhaseII",Constants.NIDM_PROJECT_IDENTIFIER:9610,Constants.NIDM_PROJECT_DESCRIPTION:"Test investigation"} project = Project(uuid="_123456",attributes=kwargs) session = Session(project) acq = AssessmentAcquisition(session) kwargs={pm.PROV_TYPE:pm.QualifiedName(pm.Namespace("nidm",Constants.NIDM),"NorthAmericanAdultReadingTest")} acq_obj = AssessmentObject(acq,attributes=kwargs) acq2 = AssessmentAcquisition(session) kwargs={pm.PROV_TYPE:pm.QualifiedName(pm.Namespace("nidm",Constants.NIDM),"PositiveAndNegativeSyndromeScale")} acq_obj2 = AssessmentObject(acq2,attributes=kwargs) #save a turtle file with open("test.ttl",'w') as f: f.write(project.serializeTurtle()) assessment_list = Query.GetProjectInstruments(["test.ttl"],"_123456") #remove("test.ttl") assert URIRef(Constants.NIDM + "NorthAmericanAdultReadingTest") in assessment_list['assessment_type'].to_list() assert URIRef(Constants.NIDM + "PositiveAndNegativeSyndromeScale") in assessment_list['assessment_type'].to_list() def saveTestFile(file_name, data): project = Project(uuid="_123_" + file_name, attributes=data) return saveProject(file_name, project) def saveProject(file_name, project): # save a turtle file with open(file_name, 'w') as f: f.write(project.serializeTurtle()) return "nidm:_123_{}".format(file_name) def makeProjectTestFile(filename): DCTYPES = Namespace("http://purl.org/dc/dcmitype/") kwargs = {Constants.NIDM_PROJECT_NAME: "FBIRN_PhaseII", # this is the "title" Constants.NIDM_PROJECT_IDENTIFIER: 9610, Constants.NIDM_PROJECT_DESCRIPTION: "Test investigation", Constants.NIDM_FILENAME: "testfile.ttl", Constants.NIDM_PROJECT_LICENSE: "MIT Licence", Constants.NIDM_PROJECT_SOURCE: "Educational Source", Constants.NIDM_HAD_NUMERICAL_VALUE: "numval???", Constants.NIDM_BATH_SOLUTION: "bath", Constants.NIDM_CELL_TYPE: "ctype", Constants.NIDM_CHANNEL_NUMBER: "5", Constants.NIDM_ELECTRODE_IMPEDANCE: ".01", Constants.NIDM_GROUP_LABEL: "group 123", Constants.NIDM_HOLLOW_ELECTRODE_SOLUTION: "water", Constants.NIDM_HAD_IMAGE_CONTRACT_TYPE: "off", Constants.NIDM_HAD_IMAGE_USAGE_TYPE: "abcd", Constants.NIDM_NUBMER_OF_CHANNELS: "11", Constants.NIDM_APPLIED_FILTER: "on", Constants.NIDM_SOLUTION_FLOW_SPEED: "2.8", Constants.NIDM_RECORDING_LOCATION: "lab" } return saveTestFile(filename, kwargs) def makeProjectTestFile2(filename): DCTYPES = Namespace("http://purl.org/dc/dcmitype/") kwargs = {Constants.NIDM_PROJECT_NAME: "TEST B", # this is the "title" Constants.NIDM_PROJECT_IDENTIFIER: 1234, Constants.NIDM_PROJECT_DESCRIPTION: "More Scans", Constants.NIDM_FILENAME: "testfile2.ttl", Constants.NIDM_PROJECT_LICENSE: "Creative Commons", Constants.NIDM_PROJECT_SOURCE: "Other", Constants.NIDM_HAD_NUMERICAL_VALUE: "numval???", Constants.NIDM_BATH_SOLUTION: "bath", Constants.NIDM_CELL_TYPE: "ctype", Constants.NIDM_CHANNEL_NUMBER: "5", Constants.NIDM_ELECTRODE_IMPEDANCE: ".01", Constants.NIDM_GROUP_LABEL: "group 123", Constants.NIDM_HOLLOW_ELECTRODE_SOLUTION: "water", Constants.NIDM_HAD_IMAGE_CONTRACT_TYPE: "off", Constants.NIDM_HAD_IMAGE_USAGE_TYPE: "abcd", Constants.NIDM_NUBMER_OF_CHANNELS: "11", Constants.NIDM_APPLIED_FILTER: "on", Constants.NIDM_SOLUTION_FLOW_SPEED: "2.8", Constants.NIDM_RECORDING_LOCATION: "lab" } project = Project(uuid="_123_" + filename, attributes=kwargs) s1 = Session(project) a1 = AssessmentAcquisition(session=s1) # = s1.add_acquisition("a1", attributes={"http://ncicb.nci.nih.gov/xml/owl/EVS/Thesaurus.owl#Age" : 22}) p1 = a1.add_person("p1", attributes={Constants.NIDM_GIVEN_NAME:"George", Constants.NIDM_AGE: 22}) a1.add_qualified_association(person=p1, role=Constants.NIDM_PARTICIPANT) return saveProject(filename, project) def test_GetProjectsMetadata(): p1 = makeProjectTestFile("testfile.ttl") p2 = makeProjectTestFile2("testfile2.ttl") files = ["testfile.ttl", "testfile2.ttl"] if USE_GITHUB_DATA and not Path('./cmu_a.nidm.ttl').is_file(): urllib.request.urlretrieve ( "https://raw.githubusercontent.com/dbkeator/simple2_NIDM_examples/master/datasets.datalad.org/abide/RawDataBIDS/CMU_a/nidm.ttl", "cmu_a.nidm.ttl" ) files.append("cmu_a.nidm.ttl") parsed = Query.GetProjectsMetadata(files) # assert parsed['projects'][p1][str(Constants.NIDM_PROJECT_DESCRIPTION)] == "Test investigation" # assert parsed['projects'][p2][str(Constants.NIDM_PROJECT_DESCRIPTION)] == "More Scans" # we shouldn't have the computed metadata in this result if USE_GITHUB_DATA: for project_id in parsed['projects']: if project_id != p1 and project_id != p2: p3 = project_id assert parsed['projects'][p3][str(Constants.NIDM_PROJECT_NAME)] == "ABIDE CMU_a Site" def test_GetProjectsComputedMetadata(): p1 = makeProjectTestFile("testfile.ttl") p2 = makeProjectTestFile2("testfile2.ttl") files = ["testfile.ttl", "testfile2.ttl"] if USE_GITHUB_DATA and not Path('./cmu_a.nidm.ttl').is_file(): urllib.request.urlretrieve ( "https://raw.githubusercontent.com/dbkeator/simple2_NIDM_examples/master/datasets.datalad.org/abide/RawDataBIDS/CMU_a/nidm.ttl", "cmu_a.nidm.ttl" ) files.append("cmu_a.nidm.ttl") parsed = Query.GetProjectsComputedMetadata(files) if USE_GITHUB_DATA: for project_id in parsed['projects']: if project_id != p1 and project_id != p2: p3 = project_id assert parsed['projects'][p3][str(Constants.NIDM_PROJECT_NAME)] == "ABIDE CMU_a Site" assert parsed['projects'][p3][Query.matchPrefix(str(Constants.NIDM_NUMBER_OF_SUBJECTS))] == 14 assert parsed['projects'][p3]["age_min"] == 21 assert parsed['projects'][p3]["age_max"] == 33 assert parsed['projects'][p3][str(Constants.NIDM_GENDER)] == ['1', '2']

true

f71eaa69788aff0d572f50f2d2f88af0daf622b4

624

py

Python

pizdyuk/test.py

DeathAdder1999/Pizdyuk

3fd7c71508c79b36e3cc801d78cd1a87eee5aa0b

[ "Apache-2.0" ]

1

2021-05-06T20:23:08.000Z

pizdyuk/test.py

aufdnb/Pizdyuk

75096ffa54df831eb05360d7b39f49000d466f80

[ "Apache-2.0" ]

null

pizdyuk/test.py

aufdnb/Pizdyuk

75096ffa54df831eb05360d7b39f49000d466f80

[ "Apache-2.0" ]

null

import csv import random from pzd_constants import DATE_FORMAT from datetime import datetime, timedelta date = None price = 0 with open('stock_data/aapl.csv', mode="r") as f: reader = csv.reader(f, delimiter=',') for row in reader: date = datetime.strptime(row[0], DATE_FORMAT) price = float(row[1]) with open('stock_data/aapl.csv', mode="w+") as f: writer = csv.writer(f, delimiter=",") for i in range(1, 360): date = date + timedelta(seconds=1) price = round(random.uniform(price - 1, price + 1), 2) writer.writerow([datetime.strftime(date, DATE_FORMAT), price])

29.714286

70

0.655449

import csv import random from pzd_constants import DATE_FORMAT from datetime import datetime, timedelta date = None price = 0 with open('stock_data/aapl.csv', mode="r") as f: reader = csv.reader(f, delimiter=',') for row in reader: date = datetime.strptime(row[0], DATE_FORMAT) price = float(row[1]) with open('stock_data/aapl.csv', mode="w+") as f: writer = csv.writer(f, delimiter=",") for i in range(1, 360): date = date + timedelta(seconds=1) price = round(random.uniform(price - 1, price + 1), 2) writer.writerow([datetime.strftime(date, DATE_FORMAT), price])

true

f71eaaf92d5fa9575d3e1b1f9dadd5a505d8934d

1,578

py

Python

pirates/minigame/Distributed7StudTable.py

itsyaboyrocket/pirates

6ca1e7d571c670b0d976f65e608235707b5737e3

[ "BSD-3-Clause" ]

3

2021-02-25T06:38:13.000Z

2022-03-22T07:00:15.000Z

pirates/minigame/Distributed7StudTable.py

itsyaboyrocket/pirates

6ca1e7d571c670b0d976f65e608235707b5737e3

[ "BSD-3-Clause" ]

null

pirates/minigame/Distributed7StudTable.py

itsyaboyrocket/pirates

6ca1e7d571c670b0d976f65e608235707b5737e3

[ "BSD-3-Clause" ]

1

2021-02-25T06:38:17.000Z

# uncompyle6 version 3.2.0 # Python bytecode 2.4 (62061) # Decompiled from: Python 2.7.14 (v2.7.14:84471935ed, Sep 16 2017, 20:19:30) [MSC v.1500 32 bit (Intel)] # Embedded file name: pirates.minigame.Distributed7StudTable from pirates.minigame import PlayingCardGlobals from pirates.minigame import DistributedPokerTable from direct.interval.IntervalGlobal import * from pandac.PandaModules import Point3, Vec3 from pirates.piratesbase import PLocalizer class Distributed7StudTable(DistributedPokerTable.DistributedPokerTable): __module__ = __name__ def __init__(self, cr): DistributedPokerTable.DistributedPokerTable.__init__(self, cr, '7stud', numRounds=6) self.maxCommunityCards = 0 self.maxHandCards = 7 self.gameType = 1 def getGameType(self): return PlayingCardGlobals.SevenStud def getInteractText(self): return PLocalizer.InteractTable7StudPoker def getSitDownText(self): return PLocalizer.PokerSitDown7StudPoker def dealerAnim(self, round): deals = Sequence() if round == 0: if self.isLocalAvatarSeated(): self.gui.disableAction() self.gui.clearTable() for card in self.PocketCards: card.hide() if round == 1: deals.append(self.dealPlayerCards(numCards=3)) if round in [2, 3, 4, 5]: deals.append(self.dealPlayerCards(numCards=1)) return deals def checkForVisiblePair(self): return self.sevenStudCheckForVisiblePair(self.playerHands)

35.066667

104

0.691381

from pirates.minigame import PlayingCardGlobals from pirates.minigame import DistributedPokerTable from direct.interval.IntervalGlobal import * from pandac.PandaModules import Point3, Vec3 from pirates.piratesbase import PLocalizer class Distributed7StudTable(DistributedPokerTable.DistributedPokerTable): __module__ = __name__ def __init__(self, cr): DistributedPokerTable.DistributedPokerTable.__init__(self, cr, '7stud', numRounds=6) self.maxCommunityCards = 0 self.maxHandCards = 7 self.gameType = 1 def getGameType(self): return PlayingCardGlobals.SevenStud def getInteractText(self): return PLocalizer.InteractTable7StudPoker def getSitDownText(self): return PLocalizer.PokerSitDown7StudPoker def dealerAnim(self, round): deals = Sequence() if round == 0: if self.isLocalAvatarSeated(): self.gui.disableAction() self.gui.clearTable() for card in self.PocketCards: card.hide() if round == 1: deals.append(self.dealPlayerCards(numCards=3)) if round in [2, 3, 4, 5]: deals.append(self.dealPlayerCards(numCards=1)) return deals def checkForVisiblePair(self): return self.sevenStudCheckForVisiblePair(self.playerHands)

true

f71eab8b25014501aa6d123e70fba4506c095cea

10,366

py

Python

youtube_dl/extractor/svt.py

NessDan/youtube-dl

62280188e6fa692f1dd1253eb21eb4b7a5e5fc20

[ "Unlicense" ]

24

2017-03-17T10:27:12.000Z

2022-02-16T05:55:50.000Z

youtube_dl/extractor/svt.py

NessDan/youtube-dl

62280188e6fa692f1dd1253eb21eb4b7a5e5fc20

[ "Unlicense" ]

7

2017-07-26T08:15:27.000Z

2018-09-20T12:56:53.000Z

youtube_dl/extractor/svt.py

NessDan/youtube-dl

62280188e6fa692f1dd1253eb21eb4b7a5e5fc20

[ "Unlicense" ]

3

2017-03-17T10:27:13.000Z

2019-01-28T01:19:17.000Z

# coding: utf-8 from __future__ import unicode_literals import re from .common import InfoExtractor from ..compat import ( compat_parse_qs, compat_urllib_parse_urlparse, ) from ..utils import ( determine_ext, dict_get, int_or_none, try_get, urljoin, compat_str, ) class SVTBaseIE(InfoExtractor): _GEO_COUNTRIES = ['SE'] def _extract_video(self, video_info, video_id): is_live = dict_get(video_info, ('live', 'simulcast'), default=False) m3u8_protocol = 'm3u8' if is_live else 'm3u8_native' formats = [] for vr in video_info['videoReferences']: player_type = vr.get('playerType') or vr.get('format') vurl = vr['url'] ext = determine_ext(vurl) if ext == 'm3u8': formats.extend(self._extract_m3u8_formats( vurl, video_id, ext='mp4', entry_protocol=m3u8_protocol, m3u8_id=player_type, fatal=False)) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( vurl + '?hdcore=3.3.0', video_id, f4m_id=player_type, fatal=False)) elif ext == 'mpd': if player_type == 'dashhbbtv': formats.extend(self._extract_mpd_formats( vurl, video_id, mpd_id=player_type, fatal=False)) else: formats.append({ 'format_id': player_type, 'url': vurl, }) if not formats and video_info.get('rights', {}).get('geoBlockedSweden'): self.raise_geo_restricted( 'This video is only available in Sweden', countries=self._GEO_COUNTRIES) self._sort_formats(formats) subtitles = {} subtitle_references = dict_get(video_info, ('subtitles', 'subtitleReferences')) if isinstance(subtitle_references, list): for sr in subtitle_references: subtitle_url = sr.get('url') subtitle_lang = sr.get('language', 'sv') if subtitle_url: if determine_ext(subtitle_url) == 'm3u8': # TODO(yan12125): handle WebVTT in m3u8 manifests continue subtitles.setdefault(subtitle_lang, []).append({'url': subtitle_url}) title = video_info.get('title') series = video_info.get('programTitle') season_number = int_or_none(video_info.get('season')) episode = video_info.get('episodeTitle') episode_number = int_or_none(video_info.get('episodeNumber')) duration = int_or_none(dict_get(video_info, ('materialLength', 'contentDuration'))) age_limit = None adult = dict_get( video_info, ('inappropriateForChildren', 'blockedForChildren'), skip_false_values=False) if adult is not None: age_limit = 18 if adult else 0 return { 'id': video_id, 'title': title, 'formats': formats, 'subtitles': subtitles, 'duration': duration, 'age_limit': age_limit, 'series': series, 'season_number': season_number, 'episode': episode, 'episode_number': episode_number, 'is_live': is_live, } class SVTIE(SVTBaseIE): _VALID_URL = r'https?://(?:www\.)?svt\.se/wd\?(?:.*?&)?widgetId=(?P<widget_id>\d+)&.*?\barticleId=(?P<id>\d+)' _TEST = { 'url': 'http://www.svt.se/wd?widgetId=23991&sectionId=541&articleId=2900353&type=embed&contextSectionId=123&autostart=false', 'md5': '33e9a5d8f646523ce0868ecfb0eed77d', 'info_dict': { 'id': '2900353', 'ext': 'mp4', 'title': 'Stjärnorna skojar till det - under SVT-intervjun', 'duration': 27, 'age_limit': 0, }, } @staticmethod def _extract_url(webpage): mobj = re.search( r'(?:<iframe src|href)="(?P<url>%s[^"]*)"' % SVTIE._VALID_URL, webpage) if mobj: return mobj.group('url') def _real_extract(self, url): mobj = re.match(self._VALID_URL, url) widget_id = mobj.group('widget_id') article_id = mobj.group('id') info = self._download_json( 'http://www.svt.se/wd?widgetId=%s&articleId=%s&format=json&type=embed&output=json' % (widget_id, article_id), article_id) info_dict = self._extract_video(info['video'], article_id) info_dict['title'] = info['context']['title'] return info_dict class SVTPlayBaseIE(SVTBaseIE): _SVTPLAY_RE = r'root\s*\[\s*(["\'])_*svtplay\1\s*\]\s*=\s*(?P<json>{.+?})\s*;\s*\n' class SVTPlayIE(SVTPlayBaseIE): IE_DESC = 'SVT Play and Öppet arkiv' _VALID_URL = r'https?://(?:www\.)?(?:svtplay|oppetarkiv)\.se/(?:video|klipp|kanaler)/(?P<id>[^/?#&]+)' _TESTS = [{ 'url': 'http://www.svtplay.se/video/5996901/flygplan-till-haile-selassie/flygplan-till-haile-selassie-2', 'md5': '2b6704fe4a28801e1a098bbf3c5ac611', 'info_dict': { 'id': '5996901', 'ext': 'mp4', 'title': 'Flygplan till Haile Selassie', 'duration': 3527, 'thumbnail': r're:^https?://.*[\.-]jpg$', 'age_limit': 0, 'subtitles': { 'sv': [{ 'ext': 'wsrt', }] }, }, }, { # geo restricted to Sweden 'url': 'http://www.oppetarkiv.se/video/5219710/trollflojten', 'only_matching': True, }, { 'url': 'http://www.svtplay.se/klipp/9023742/stopptid-om-bjorn-borg', 'only_matching': True, }, { 'url': 'https://www.svtplay.se/kanaler/svt1', 'only_matching': True, }] def _real_extract(self, url): video_id = self._match_id(url) webpage = self._download_webpage(url, video_id) data = self._parse_json( self._search_regex( self._SVTPLAY_RE, webpage, 'embedded data', default='{}', group='json'), video_id, fatal=False) thumbnail = self._og_search_thumbnail(webpage) def adjust_title(info): if info['is_live']: info['title'] = self._live_title(info['title']) if data: video_info = try_get( data, lambda x: x['context']['dispatcher']['stores']['VideoTitlePageStore']['data']['video'], dict) if video_info: info_dict = self._extract_video(video_info, video_id) info_dict.update({ 'title': data['context']['dispatcher']['stores']['MetaStore']['title'], 'thumbnail': thumbnail, }) adjust_title(info_dict) return info_dict video_id = self._search_regex( r'<video[^>]+data-video-id=["\']([\da-zA-Z-]+)', webpage, 'video id', default=None) if video_id: data = self._download_json( 'https://api.svt.se/videoplayer-api/video/%s' % video_id, video_id, headers=self.geo_verification_headers()) info_dict = self._extract_video(data, video_id) if not info_dict.get('title'): info_dict['title'] = re.sub( r'\s*\|\s*.+?$', '', info_dict.get('episode') or self._og_search_title(webpage)) adjust_title(info_dict) return info_dict class SVTSeriesIE(SVTPlayBaseIE): _VALID_URL = r'https?://(?:www\.)?svtplay\.se/(?P<id>[^/?&#]+)' _TESTS = [{ 'url': 'https://www.svtplay.se/rederiet', 'info_dict': { 'id': 'rederiet', 'title': 'Rederiet', 'description': 'md5:505d491a58f4fcf6eb418ecab947e69e', }, 'playlist_mincount': 318, }, { 'url': 'https://www.svtplay.se/rederiet?tab=sasong2', 'info_dict': { 'id': 'rederiet-sasong2', 'title': 'Rederiet - Säsong 2', 'description': 'md5:505d491a58f4fcf6eb418ecab947e69e', }, 'playlist_count': 12, }] @classmethod def suitable(cls, url): return False if SVTIE.suitable(url) or SVTPlayIE.suitable(url) else super(SVTSeriesIE, cls).suitable(url) def _real_extract(self, url): series_id = self._match_id(url) qs = compat_parse_qs(compat_urllib_parse_urlparse(url).query) season_slug = qs.get('tab', [None])[0] if season_slug: series_id += '-%s' % season_slug webpage = self._download_webpage( url, series_id, 'Downloading series page') root = self._parse_json( self._search_regex( self._SVTPLAY_RE, webpage, 'content', group='json'), series_id) season_name = None entries = [] for season in root['relatedVideoContent']['relatedVideosAccordion']: if not isinstance(season, dict): continue if season_slug: if season.get('slug') != season_slug: continue season_name = season.get('name') videos = season.get('videos') if not isinstance(videos, list): continue for video in videos: content_url = video.get('contentUrl') if not content_url or not isinstance(content_url, compat_str): continue entries.append( self.url_result( urljoin(url, content_url), ie=SVTPlayIE.ie_key(), video_title=video.get('title') )) metadata = root.get('metaData') if not isinstance(metadata, dict): metadata = {} title = metadata.get('title') season_name = season_name or season_slug if title and season_name: title = '%s - %s' % (title, season_name) elif season_slug: title = season_slug return self.playlist_result( entries, series_id, title, metadata.get('description'))

35.138983

133

0.54071

from __future__ import unicode_literals import re from .common import InfoExtractor from ..compat import ( compat_parse_qs, compat_urllib_parse_urlparse, ) from ..utils import ( determine_ext, dict_get, int_or_none, try_get, urljoin, compat_str, ) class SVTBaseIE(InfoExtractor): _GEO_COUNTRIES = ['SE'] def _extract_video(self, video_info, video_id): is_live = dict_get(video_info, ('live', 'simulcast'), default=False) m3u8_protocol = 'm3u8' if is_live else 'm3u8_native' formats = [] for vr in video_info['videoReferences']: player_type = vr.get('playerType') or vr.get('format') vurl = vr['url'] ext = determine_ext(vurl) if ext == 'm3u8': formats.extend(self._extract_m3u8_formats( vurl, video_id, ext='mp4', entry_protocol=m3u8_protocol, m3u8_id=player_type, fatal=False)) elif ext == 'f4m': formats.extend(self._extract_f4m_formats( vurl + '?hdcore=3.3.0', video_id, f4m_id=player_type, fatal=False)) elif ext == 'mpd': if player_type == 'dashhbbtv': formats.extend(self._extract_mpd_formats( vurl, video_id, mpd_id=player_type, fatal=False)) else: formats.append({ 'format_id': player_type, 'url': vurl, }) if not formats and video_info.get('rights', {}).get('geoBlockedSweden'): self.raise_geo_restricted( 'This video is only available in Sweden', countries=self._GEO_COUNTRIES) self._sort_formats(formats) subtitles = {} subtitle_references = dict_get(video_info, ('subtitles', 'subtitleReferences')) if isinstance(subtitle_references, list): for sr in subtitle_references: subtitle_url = sr.get('url') subtitle_lang = sr.get('language', 'sv') if subtitle_url: if determine_ext(subtitle_url) == 'm3u8': continue subtitles.setdefault(subtitle_lang, []).append({'url': subtitle_url}) title = video_info.get('title') series = video_info.get('programTitle') season_number = int_or_none(video_info.get('season')) episode = video_info.get('episodeTitle') episode_number = int_or_none(video_info.get('episodeNumber')) duration = int_or_none(dict_get(video_info, ('materialLength', 'contentDuration'))) age_limit = None adult = dict_get( video_info, ('inappropriateForChildren', 'blockedForChildren'), skip_false_values=False) if adult is not None: age_limit = 18 if adult else 0 return { 'id': video_id, 'title': title, 'formats': formats, 'subtitles': subtitles, 'duration': duration, 'age_limit': age_limit, 'series': series, 'season_number': season_number, 'episode': episode, 'episode_number': episode_number, 'is_live': is_live, } class SVTIE(SVTBaseIE): _VALID_URL = r'https?://(?:www\.)?svt\.se/wd\?(?:.*?&)?widgetId=(?P<widget_id>\d+)&.*?\barticleId=(?P<id>\d+)' _TEST = { 'url': 'http://www.svt.se/wd?widgetId=23991&sectionId=541&articleId=2900353&type=embed&contextSectionId=123&autostart=false', 'md5': '33e9a5d8f646523ce0868ecfb0eed77d', 'info_dict': { 'id': '2900353', 'ext': 'mp4', 'title': 'Stjärnorna skojar till det - under SVT-intervjun', 'duration': 27, 'age_limit': 0, }, } @staticmethod def _extract_url(webpage): mobj = re.search( r'(?:<iframe src|href)="(?P<url>%s[^"]*)"' % SVTIE._VALID_URL, webpage) if mobj: return mobj.group('url') def _real_extract(self, url): mobj = re.match(self._VALID_URL, url) widget_id = mobj.group('widget_id') article_id = mobj.group('id') info = self._download_json( 'http://www.svt.se/wd?widgetId=%s&articleId=%s&format=json&type=embed&output=json' % (widget_id, article_id), article_id) info_dict = self._extract_video(info['video'], article_id) info_dict['title'] = info['context']['title'] return info_dict class SVTPlayBaseIE(SVTBaseIE): _SVTPLAY_RE = r'root\s*\[\s*(["\'])_*svtplay\1\s*\]\s*=\s*(?P<json>{.+?})\s*;\s*\n' class SVTPlayIE(SVTPlayBaseIE): IE_DESC = 'SVT Play and Öppet arkiv' _VALID_URL = r'https?://(?:www\.)?(?:svtplay|oppetarkiv)\.se/(?:video|klipp|kanaler)/(?P<id>[^/? _TESTS = [{ 'url': 'http://www.svtplay.se/video/5996901/flygplan-till-haile-selassie/flygplan-till-haile-selassie-2', 'md5': '2b6704fe4a28801e1a098bbf3c5ac611', 'info_dict': { 'id': '5996901', 'ext': 'mp4', 'title': 'Flygplan till Haile Selassie', 'duration': 3527, 'thumbnail': r're:^https?://.*[\.-]jpg$', 'age_limit': 0, 'subtitles': { 'sv': [{ 'ext': 'wsrt', }] }, }, }, { # geo restricted to Sweden 'url': 'http://www.oppetarkiv.se/video/5219710/trollflojten', 'only_matching': True, }, { 'url': 'http://www.svtplay.se/klipp/9023742/stopptid-om-bjorn-borg', 'only_matching': True, }, { 'url': 'https://www.svtplay.se/kanaler/svt1', 'only_matching': True, }] def _real_extract(self, url): video_id = self._match_id(url) webpage = self._download_webpage(url, video_id) data = self._parse_json( self._search_regex( self._SVTPLAY_RE, webpage, 'embedded data', default='{}', group='json'), video_id, fatal=False) thumbnail = self._og_search_thumbnail(webpage) def adjust_title(info): if info['is_live']: info['title'] = self._live_title(info['title']) if data: video_info = try_get( data, lambda x: x['context']['dispatcher']['stores']['VideoTitlePageStore']['data']['video'], dict) if video_info: info_dict = self._extract_video(video_info, video_id) info_dict.update({ 'title': data['context']['dispatcher']['stores']['MetaStore']['title'], 'thumbnail': thumbnail, }) adjust_title(info_dict) return info_dict video_id = self._search_regex( r'<video[^>]+data-video-id=["\']([\da-zA-Z-]+)', webpage, 'video id', default=None) if video_id: data = self._download_json( 'https://api.svt.se/videoplayer-api/video/%s' % video_id, video_id, headers=self.geo_verification_headers()) info_dict = self._extract_video(data, video_id) if not info_dict.get('title'): info_dict['title'] = re.sub( r'\s*\|\s*.+?$', '', info_dict.get('episode') or self._og_search_title(webpage)) adjust_title(info_dict) return info_dict class SVTSeriesIE(SVTPlayBaseIE): _VALID_URL = r'https?://(?:www\.)?svtplay\.se/(?P<id>[^/?&#]+)' _TESTS = [{ 'url': 'https://www.svtplay.se/rederiet', 'info_dict': { 'id': 'rederiet', 'title': 'Rederiet', 'description': 'md5:505d491a58f4fcf6eb418ecab947e69e', }, 'playlist_mincount': 318, }, { 'url': 'https://www.svtplay.se/rederiet?tab=sasong2', 'info_dict': { 'id': 'rederiet-sasong2', 'title': 'Rederiet - Säsong 2', 'description': 'md5:505d491a58f4fcf6eb418ecab947e69e', }, 'playlist_count': 12, }] @classmethod def suitable(cls, url): return False if SVTIE.suitable(url) or SVTPlayIE.suitable(url) else super(SVTSeriesIE, cls).suitable(url) def _real_extract(self, url): series_id = self._match_id(url) qs = compat_parse_qs(compat_urllib_parse_urlparse(url).query) season_slug = qs.get('tab', [None])[0] if season_slug: series_id += '-%s' % season_slug webpage = self._download_webpage( url, series_id, 'Downloading series page') root = self._parse_json( self._search_regex( self._SVTPLAY_RE, webpage, 'content', group='json'), series_id) season_name = None entries = [] for season in root['relatedVideoContent']['relatedVideosAccordion']: if not isinstance(season, dict): continue if season_slug: if season.get('slug') != season_slug: continue season_name = season.get('name') videos = season.get('videos') if not isinstance(videos, list): continue for video in videos: content_url = video.get('contentUrl') if not content_url or not isinstance(content_url, compat_str): continue entries.append( self.url_result( urljoin(url, content_url), ie=SVTPlayIE.ie_key(), video_title=video.get('title') )) metadata = root.get('metaData') if not isinstance(metadata, dict): metadata = {} title = metadata.get('title') season_name = season_name or season_slug if title and season_name: title = '%s - %s' % (title, season_name) elif season_slug: title = season_slug return self.playlist_result( entries, series_id, title, metadata.get('description'))

true

f71eabb5fc9c2151ea1e3e4278d18a8e8b93c07f

1,032

py

Python

RecoLocalTracker/SubCollectionProducers/python/test/MCsplitStripsCustomize_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

852

2015-01-11T21:03:51.000Z

2022-03-25T21:14:00.000Z

RecoLocalTracker/SubCollectionProducers/python/test/MCsplitStripsCustomize_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

30,371

2015-01-02T00:14:40.000Z

2022-03-31T23:26:05.000Z

RecoLocalTracker/SubCollectionProducers/python/test/MCsplitStripsCustomize_cff.py

ckamtsikis/cmssw

ea19fe642bb7537cbf58451dcf73aa5fd1b66250

[ "Apache-2.0" ]

3,240

2015-01-02T05:53:18.000Z

2022-03-31T17:24:21.000Z

# # With this customization the ClusterMCsplitStrips module will be substituted # for the standard clusterizer. If a cluster is matched to more than one simTrack # it will be split into the corresponding true clusters. # import FWCore.ParameterSet.Config as cms def splitMCmerged(process): process.siStripClustersUnsplit = process.siStripClusters.clone() stripClusIndex = process.striptrackerlocalreco.index(process.siStripClusters) process.striptrackerlocalreco.remove(process.siStripClusters) del process.siStripClusters process.load('RecoLocalTracker.SubCollectionProducers.test.ClusterMCsplitStrips_cfi') process.siStripClustersMCsplit = cms.Sequence(process.siStripClustersUnsplit*process.siStripClusters) process.striptrackerlocalreco.insert(stripClusIndex,process.siStripClustersMCsplit) # Override the chargePerCM cut in stripCPE process.StripCPEfromTrackAngleESProducer.parameters.maxChgOneMIP = cms.double(-6000.) return(process)

44.869565

146

0.782946

import FWCore.ParameterSet.Config as cms def splitMCmerged(process): process.siStripClustersUnsplit = process.siStripClusters.clone() stripClusIndex = process.striptrackerlocalreco.index(process.siStripClusters) process.striptrackerlocalreco.remove(process.siStripClusters) del process.siStripClusters process.load('RecoLocalTracker.SubCollectionProducers.test.ClusterMCsplitStrips_cfi') process.siStripClustersMCsplit = cms.Sequence(process.siStripClustersUnsplit*process.siStripClusters) process.striptrackerlocalreco.insert(stripClusIndex,process.siStripClustersMCsplit) process.StripCPEfromTrackAngleESProducer.parameters.maxChgOneMIP = cms.double(-6000.) return(process)

true

f71eac7b3cc16b05e2e80303287fcdacd9ff87af

9,986

py

Python

src/cmudict_parser/SentenceToIPA.py

stefantaubert/cmudict-parser

8f5d1b191a41929f1ce8c7acf391c23c08d2be15

[ "MIT" ]

null

src/cmudict_parser/SentenceToIPA.py

stefantaubert/cmudict-parser

8f5d1b191a41929f1ce8c7acf391c23c08d2be15

[ "MIT" ]

14

2020-12-01T08:45:16.000Z

2021-06-01T08:00:39.000Z

src/cmudict_parser/SentenceToIPA.py

stefantaubert/cmudict-parser

8f5d1b191a41929f1ce8c7acf391c23c08d2be15

[ "MIT" ]

null

""" Remarks: https://github.com/cmusphinx/cmudict is newer than 0.7b! It has for example 'declarative' but is has unfortunately no MIT-license. """ import string from logging import getLogger from typing import Callable, Dict, List, Optional, Tuple, Union PUNCTUATION_AND_LINEBREAK = f"{string.punctuation}\n" IPA_CACHE: Dict[str, str] = {} def clear_cache() -> None: IPA_CACHE.clear() def sentence_to_ipa(dict: Dict[str, str], sentence: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]], use_caching: bool) -> str: words = sentence.split(" ") if use_caching: ipa_words = [get_ipa_of_word_in_sentence_cache( dict, word, replace_unknown_with) for word in words] else: ipa_words = [get_ipa_of_word_in_sentence(dict, word, replace_unknown_with) for word in words] res = " ".join(ipa_words) return res def get_ipa_of_word_in_sentence_cache(dict: Dict[str, str], word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: global IPA_CACHE if word in IPA_CACHE: return IPA_CACHE[word] else: ipa = get_ipa_of_word_in_sentence(dict, word, replace_unknown_with) IPA_CACHE[word] = ipa return ipa def get_ipa_of_word_in_sentence(dict: Dict[str, str], word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: global IPA_CACHE if any(char in PUNCTUATION_AND_LINEBREAK for char in word): ipa = get_ipa_of_word_with_punctuation(dict, word, replace_unknown_with) else: ipa = get_ipa_of_word_without_punctuation_or_unknown_words(dict, word, replace_unknown_with) return ipa def get_ipa_of_word_with_punctuation(dict: Dict[str, str], word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: word, punctuation_before_word = extract_punctuation_before_word(word) if word == "": return punctuation_before_word word_without_punctuation, punctuation_after_word = extract_punctuation_after_word_except_hyphen_or_apostrophe( word) return ipa_of_punctuation_and_words_combined(dict, punctuation_before_word, word_without_punctuation, punctuation_after_word, replace_unknown_with) def extract_punctuation_before_word(word: str) -> Tuple[str, str]: punctuation_before_word = "" while word != "" and (word[0] in PUNCTUATION_AND_LINEBREAK): punctuation_before_word += word[0] word = word[1:] return word, punctuation_before_word def extract_punctuation_after_word_except_hyphen_or_apostrophe(word: str) -> Tuple[str, str]: punctuation_after_word = word word_without_punctuation = "" while punctuation_after_word != "" and (punctuation_after_word[0].isalpha() or punctuation_after_word[0] in "'-"): word_without_punctuation += punctuation_after_word[0] punctuation_after_word = punctuation_after_word[1:] return word_without_punctuation, punctuation_after_word def ipa_of_punctuation_and_words_combined(dict: Dict[str, str], punctuation_before_word: str, word_without_punctuation: str, punctuation_after_word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: assert word_without_punctuation != "" and word_without_punctuation[0].isalpha() word_without_punctuation, char_at_end, word_with_apo_at_beginning, word_with_apo_at_end, word_with_apo_at_end_and_beginning = word_with_apo( word_without_punctuation) if punctuation_before_word != "" and punctuation_before_word[-1] == "'" and char_at_end == "'" and word_with_apo_at_end_and_beginning.upper() in dict: punctuation_before_word = punctuation_before_word[:-1] ipa_of_word_without_punct = get_ipa_of_word_without_punctuation_or_unknown_words( dict, word_with_apo_at_end_and_beginning, replace_unknown_with) elif punctuation_before_word != "" and word_with_apo_at_beginning.upper() in dict and punctuation_before_word[-1] == "'": punctuation_before_word = punctuation_before_word[:-1] ipa_of_word_without_punct = f"{get_ipa_of_word_without_punctuation_or_unknown_words(dict, word_with_apo_at_beginning, replace_unknown_with)}{char_at_end}" elif word_with_apo_at_end.upper() in dict and char_at_end == "'": ipa_of_word_without_punct = get_ipa_of_word_without_punctuation_or_unknown_words( dict, word_with_apo_at_end, replace_unknown_with) elif "-" in word_without_punctuation and not word_without_punctuation.upper() in dict: ipa_of_word_without_punct = f"{get_ipa_of_words_with_hyphen(dict, word_without_punctuation, replace_unknown_with)}{char_at_end}" else: ipa_of_word_without_punct = f"{get_ipa_of_word_without_punctuation_or_unknown_words(dict, word_without_punctuation, replace_unknown_with)}{char_at_end}" return value_depending_on_is_alphabetic_value_in_punctuation_after_word(dict, punctuation_before_word, ipa_of_word_without_punct, punctuation_after_word, replace_unknown_with) def word_with_apo(word_without_punctuation: str) -> Tuple[str, str, str, str, str]: if word_without_punctuation[-1] in "-'": return word_without_punctuation[:-1], word_without_punctuation[-1], f"'{word_without_punctuation[:-1]}", f"{word_without_punctuation[:-1]}'", f"'{word_without_punctuation[:-1]}'" return word_without_punctuation, "", f"'{word_without_punctuation}", f"{word_without_punctuation}'", f"'{word_without_punctuation}'" def value_depending_on_is_alphabetic_value_in_punctuation_after_word(dict: Dict[str, str], punctuation_before_word: str, ipa_of_word_without_punct: str, punctuation_after_word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: if any(char.isalpha() for char in punctuation_after_word): return f"{punctuation_before_word}{ipa_of_word_without_punct}{get_ipa_of_word_with_punctuation(dict, punctuation_after_word, replace_unknown_with)}" return f"{punctuation_before_word}{ipa_of_word_without_punct}{punctuation_after_word}" def get_ipa_of_words_with_hyphen(dict: Dict[str, str], word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: parts = word.split("-") ipa = "" for length_of_combination in range(len(parts), 0, -1): ipa = find_combination_of_certain_length_in_dict( dict, parts, length_of_combination, replace_unknown_with) if ipa is not None: break if ipa is None: unknown_list = [get_ipa_of_word_without_punctuation_or_unknown_words( dict, part, replace_unknown_with) for part in parts] ipa = "-".join(unknown_list) return ipa def find_combination_of_certain_length_in_dict(dict: Dict[str, str], parts: List[str], length_of_combination, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> Optional[str]: assert all_keys_are_upper(dict) for startword_pos in range(len(parts) - length_of_combination + 1): combination = recombine_word(parts, startword_pos, startword_pos + length_of_combination) word, apos_before, apos_after = strip_apos_at_beginning_and_end_if_they_do_not_belong_to_word( dict, combination) if word.upper() in dict: word_before, hyphen_before = word_and_hyphen_before_or_after(parts, 0, startword_pos) word_after, hyphen_after = word_and_hyphen_before_or_after( parts, startword_pos + length_of_combination, len(parts)) return f"{get_ipa_of_word_in_sentence(dict, word_before, replace_unknown_with)}{hyphen_before}{apos_before}{dict[word.upper()]}{apos_after}{hyphen_after}{get_ipa_of_word_in_sentence(dict, word_after, replace_unknown_with)}" return None def strip_apos_at_beginning_and_end_if_they_do_not_belong_to_word(dict: Dict[str, str], word: str) -> Tuple[str, str, str]: word, apos_before = strip_apos(word, 0) word, apos_after = strip_apos(word, -1) if f"{word}'".upper() in dict and apos_after != "": word = f"{word}'" apos_after = apos_after[:-1] if f"'{word}".upper() in dict and apos_before != "": word = f"'{word}" apos_before = apos_before[:-1] return word, apos_before, apos_after def strip_apos(word: str, pos: int) -> Tuple[str, str]: assert pos == 0 or pos == -1 apos = "" while word != "" and word[pos] == "'": apos += "'" word = word[1:] if pos == 0 else word[:-1] return word, apos def word_and_hyphen_before_or_after(parts: List[str], startpos: int, endpos: int) -> Tuple[str, str]: if endpos == 0 or startpos == len(parts): return "", "" return recombine_word(parts, startpos, endpos), "-" def recombine_word(parts: List[str], startpos: int, endpos: int) -> str: assert startpos >= 0 and startpos < endpos and endpos <= len(parts) parts = parts[startpos:endpos] word = "-".join(parts) return word def get_ipa_of_word_without_punctuation_or_unknown_words(dict: Dict[str, str], word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: assert all_keys_are_upper(dict) if word == "": return "" if word.upper() in dict: return dict[word.upper()] if word_is_really_upper(word): return big_letters_to_ipa(dict, word) if replace_unknown_with is None: return word if isinstance(replace_unknown_with, str): return replace_unknown_with_is_string(word, replace_unknown_with) return replace_unknown_with(word) def replace_unknown_with_is_string(word: str, replace_unknown_with: str) -> str: assert isinstance(replace_unknown_with, str) if len(replace_unknown_with) >= 2: raise ValueError("Parameter replace_unknown_with can only be 0 or 1 char.") res = len(word) * replace_unknown_with logger = getLogger(__name__) logger.warning(f"Replaced {word} with {res}") return res def word_is_really_upper(word: str) -> bool: return word.isupper() and word.isalpha() def big_letters_to_ipa(dict: Dict[str, str], word: str) -> str: assert all_keys_are_upper(dict) assert word_is_really_upper(word) or word == "" ipa = "" for char in word: assert char in dict ipa += dict[char] return ipa def all_keys_are_upper(dict: Dict[str, str]) -> bool: for key in dict.keys(): if not key.isupper(): return False return True

46.446512

255

0.763168

import string from logging import getLogger from typing import Callable, Dict, List, Optional, Tuple, Union PUNCTUATION_AND_LINEBREAK = f"{string.punctuation}\n" IPA_CACHE: Dict[str, str] = {} def clear_cache() -> None: IPA_CACHE.clear() def sentence_to_ipa(dict: Dict[str, str], sentence: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]], use_caching: bool) -> str: words = sentence.split(" ") if use_caching: ipa_words = [get_ipa_of_word_in_sentence_cache( dict, word, replace_unknown_with) for word in words] else: ipa_words = [get_ipa_of_word_in_sentence(dict, word, replace_unknown_with) for word in words] res = " ".join(ipa_words) return res def get_ipa_of_word_in_sentence_cache(dict: Dict[str, str], word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: global IPA_CACHE if word in IPA_CACHE: return IPA_CACHE[word] else: ipa = get_ipa_of_word_in_sentence(dict, word, replace_unknown_with) IPA_CACHE[word] = ipa return ipa def get_ipa_of_word_in_sentence(dict: Dict[str, str], word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: global IPA_CACHE if any(char in PUNCTUATION_AND_LINEBREAK for char in word): ipa = get_ipa_of_word_with_punctuation(dict, word, replace_unknown_with) else: ipa = get_ipa_of_word_without_punctuation_or_unknown_words(dict, word, replace_unknown_with) return ipa def get_ipa_of_word_with_punctuation(dict: Dict[str, str], word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: word, punctuation_before_word = extract_punctuation_before_word(word) if word == "": return punctuation_before_word word_without_punctuation, punctuation_after_word = extract_punctuation_after_word_except_hyphen_or_apostrophe( word) return ipa_of_punctuation_and_words_combined(dict, punctuation_before_word, word_without_punctuation, punctuation_after_word, replace_unknown_with) def extract_punctuation_before_word(word: str) -> Tuple[str, str]: punctuation_before_word = "" while word != "" and (word[0] in PUNCTUATION_AND_LINEBREAK): punctuation_before_word += word[0] word = word[1:] return word, punctuation_before_word def extract_punctuation_after_word_except_hyphen_or_apostrophe(word: str) -> Tuple[str, str]: punctuation_after_word = word word_without_punctuation = "" while punctuation_after_word != "" and (punctuation_after_word[0].isalpha() or punctuation_after_word[0] in "'-"): word_without_punctuation += punctuation_after_word[0] punctuation_after_word = punctuation_after_word[1:] return word_without_punctuation, punctuation_after_word def ipa_of_punctuation_and_words_combined(dict: Dict[str, str], punctuation_before_word: str, word_without_punctuation: str, punctuation_after_word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: assert word_without_punctuation != "" and word_without_punctuation[0].isalpha() word_without_punctuation, char_at_end, word_with_apo_at_beginning, word_with_apo_at_end, word_with_apo_at_end_and_beginning = word_with_apo( word_without_punctuation) if punctuation_before_word != "" and punctuation_before_word[-1] == "'" and char_at_end == "'" and word_with_apo_at_end_and_beginning.upper() in dict: punctuation_before_word = punctuation_before_word[:-1] ipa_of_word_without_punct = get_ipa_of_word_without_punctuation_or_unknown_words( dict, word_with_apo_at_end_and_beginning, replace_unknown_with) elif punctuation_before_word != "" and word_with_apo_at_beginning.upper() in dict and punctuation_before_word[-1] == "'": punctuation_before_word = punctuation_before_word[:-1] ipa_of_word_without_punct = f"{get_ipa_of_word_without_punctuation_or_unknown_words(dict, word_with_apo_at_beginning, replace_unknown_with)}{char_at_end}" elif word_with_apo_at_end.upper() in dict and char_at_end == "'": ipa_of_word_without_punct = get_ipa_of_word_without_punctuation_or_unknown_words( dict, word_with_apo_at_end, replace_unknown_with) elif "-" in word_without_punctuation and not word_without_punctuation.upper() in dict: ipa_of_word_without_punct = f"{get_ipa_of_words_with_hyphen(dict, word_without_punctuation, replace_unknown_with)}{char_at_end}" else: ipa_of_word_without_punct = f"{get_ipa_of_word_without_punctuation_or_unknown_words(dict, word_without_punctuation, replace_unknown_with)}{char_at_end}" return value_depending_on_is_alphabetic_value_in_punctuation_after_word(dict, punctuation_before_word, ipa_of_word_without_punct, punctuation_after_word, replace_unknown_with) def word_with_apo(word_without_punctuation: str) -> Tuple[str, str, str, str, str]: if word_without_punctuation[-1] in "-'": return word_without_punctuation[:-1], word_without_punctuation[-1], f"'{word_without_punctuation[:-1]}", f"{word_without_punctuation[:-1]}'", f"'{word_without_punctuation[:-1]}'" return word_without_punctuation, "", f"'{word_without_punctuation}", f"{word_without_punctuation}'", f"'{word_without_punctuation}'" def value_depending_on_is_alphabetic_value_in_punctuation_after_word(dict: Dict[str, str], punctuation_before_word: str, ipa_of_word_without_punct: str, punctuation_after_word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: if any(char.isalpha() for char in punctuation_after_word): return f"{punctuation_before_word}{ipa_of_word_without_punct}{get_ipa_of_word_with_punctuation(dict, punctuation_after_word, replace_unknown_with)}" return f"{punctuation_before_word}{ipa_of_word_without_punct}{punctuation_after_word}" def get_ipa_of_words_with_hyphen(dict: Dict[str, str], word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: parts = word.split("-") ipa = "" for length_of_combination in range(len(parts), 0, -1): ipa = find_combination_of_certain_length_in_dict( dict, parts, length_of_combination, replace_unknown_with) if ipa is not None: break if ipa is None: unknown_list = [get_ipa_of_word_without_punctuation_or_unknown_words( dict, part, replace_unknown_with) for part in parts] ipa = "-".join(unknown_list) return ipa def find_combination_of_certain_length_in_dict(dict: Dict[str, str], parts: List[str], length_of_combination, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> Optional[str]: assert all_keys_are_upper(dict) for startword_pos in range(len(parts) - length_of_combination + 1): combination = recombine_word(parts, startword_pos, startword_pos + length_of_combination) word, apos_before, apos_after = strip_apos_at_beginning_and_end_if_they_do_not_belong_to_word( dict, combination) if word.upper() in dict: word_before, hyphen_before = word_and_hyphen_before_or_after(parts, 0, startword_pos) word_after, hyphen_after = word_and_hyphen_before_or_after( parts, startword_pos + length_of_combination, len(parts)) return f"{get_ipa_of_word_in_sentence(dict, word_before, replace_unknown_with)}{hyphen_before}{apos_before}{dict[word.upper()]}{apos_after}{hyphen_after}{get_ipa_of_word_in_sentence(dict, word_after, replace_unknown_with)}" return None def strip_apos_at_beginning_and_end_if_they_do_not_belong_to_word(dict: Dict[str, str], word: str) -> Tuple[str, str, str]: word, apos_before = strip_apos(word, 0) word, apos_after = strip_apos(word, -1) if f"{word}'".upper() in dict and apos_after != "": word = f"{word}'" apos_after = apos_after[:-1] if f"'{word}".upper() in dict and apos_before != "": word = f"'{word}" apos_before = apos_before[:-1] return word, apos_before, apos_after def strip_apos(word: str, pos: int) -> Tuple[str, str]: assert pos == 0 or pos == -1 apos = "" while word != "" and word[pos] == "'": apos += "'" word = word[1:] if pos == 0 else word[:-1] return word, apos def word_and_hyphen_before_or_after(parts: List[str], startpos: int, endpos: int) -> Tuple[str, str]: if endpos == 0 or startpos == len(parts): return "", "" return recombine_word(parts, startpos, endpos), "-" def recombine_word(parts: List[str], startpos: int, endpos: int) -> str: assert startpos >= 0 and startpos < endpos and endpos <= len(parts) parts = parts[startpos:endpos] word = "-".join(parts) return word def get_ipa_of_word_without_punctuation_or_unknown_words(dict: Dict[str, str], word: str, replace_unknown_with: Optional[Union[str, Callable[[str], str]]]) -> str: assert all_keys_are_upper(dict) if word == "": return "" if word.upper() in dict: return dict[word.upper()] if word_is_really_upper(word): return big_letters_to_ipa(dict, word) if replace_unknown_with is None: return word if isinstance(replace_unknown_with, str): return replace_unknown_with_is_string(word, replace_unknown_with) return replace_unknown_with(word) def replace_unknown_with_is_string(word: str, replace_unknown_with: str) -> str: assert isinstance(replace_unknown_with, str) if len(replace_unknown_with) >= 2: raise ValueError("Parameter replace_unknown_with can only be 0 or 1 char.") res = len(word) * replace_unknown_with logger = getLogger(__name__) logger.warning(f"Replaced {word} with {res}") return res def word_is_really_upper(word: str) -> bool: return word.isupper() and word.isalpha() def big_letters_to_ipa(dict: Dict[str, str], word: str) -> str: assert all_keys_are_upper(dict) assert word_is_really_upper(word) or word == "" ipa = "" for char in word: assert char in dict ipa += dict[char] return ipa def all_keys_are_upper(dict: Dict[str, str]) -> bool: for key in dict.keys(): if not key.isupper(): return False return True

true

f71eac9488b5831e389ba288de7a4535c5b2afd7

1,304

py

Python

controlapp/monitorapp/src/serverapp/serversocket/reptilesserversocket.py

kuspen/reptiles-monitor

ccb4a96e5b0091228a10eaa0e6fbf1a72795ca91

[ "MIT" ]

null

controlapp/monitorapp/src/serverapp/serversocket/reptilesserversocket.py

kuspen/reptiles-monitor

ccb4a96e5b0091228a10eaa0e6fbf1a72795ca91

[ "MIT" ]

null

controlapp/monitorapp/src/serverapp/serversocket/reptilesserversocket.py

kuspen/reptiles-monitor

ccb4a96e5b0091228a10eaa0e6fbf1a72795ca91

[ "MIT" ]

null

import sys import socket sys.path.append('../') import common.define BUF_SIZE = 2048 class ReptilesServerSocket(): def __init__(self, sock=None): if sock is None: self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) else: self.sock = sock def setsocket(self): self.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) def bind(self): self.sock.bind((common.define.SERVER_IP_ADDR, common.define.SERVER_PORT)) def listen(self, num=1): self.sock.listen(num) def accept(self): self.conn, self.addr = self.sock.accept() def send(self, msg): totalsent = 0 msglen = len(msg) while totalsent < msglen: sent = self.conn.send(msg[totalsent:]) if sent == 0: raise RuntimeError("socket connection broken") totalsent = totalsent + sent def receive(self, msglen): chunks = [] bytes_recd = 0 while bytes_recd < msglen: chunk = self.conn.recv(min(msglen - bytes_recd, 2048)) if chunk == b'': raise RuntimeError("socket connection broken") chunks.append(chunk) bytes_recd = bytes_recd + len(chunk) return b''.join(chunks)

26.08

81

0.58819

import sys import socket sys.path.append('../') import common.define BUF_SIZE = 2048 class ReptilesServerSocket(): def __init__(self, sock=None): if sock is None: self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) else: self.sock = sock def setsocket(self): self.sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) def bind(self): self.sock.bind((common.define.SERVER_IP_ADDR, common.define.SERVER_PORT)) def listen(self, num=1): self.sock.listen(num) def accept(self): self.conn, self.addr = self.sock.accept() def send(self, msg): totalsent = 0 msglen = len(msg) while totalsent < msglen: sent = self.conn.send(msg[totalsent:]) if sent == 0: raise RuntimeError("socket connection broken") totalsent = totalsent + sent def receive(self, msglen): chunks = [] bytes_recd = 0 while bytes_recd < msglen: chunk = self.conn.recv(min(msglen - bytes_recd, 2048)) if chunk == b'': raise RuntimeError("socket connection broken") chunks.append(chunk) bytes_recd = bytes_recd + len(chunk) return b''.join(chunks)

true

f71eacb0cebaf99c989c8497d1bdf211436cdebe

774

py

Python

python_zabbix/client.py

zhenghuaHe/stu_python

e0937070248269527661ccf32e5bea048170ac17

[ "Apache-2.0" ]

null

python_zabbix/client.py

zhenghuaHe/stu_python

e0937070248269527661ccf32e5bea048170ac17

[ "Apache-2.0" ]

null

python_zabbix/client.py

zhenghuaHe/stu_python

e0937070248269527661ccf32e5bea048170ac17

[ "Apache-2.0" ]

null

# -*- coding=utf-8 -*- import socket import psutil import json # 创建链接 # 生成一个socket对象 sk = socket.socket(socket.AF_INET, socket.SOCK_STREAM) host = socket.gethostname() port = 8888 # 请求连接服务端 sk.connect((host, port)) #获取信息 #获取主机名 hostname = socket.getfqdn(socket.gethostname()) #获取主机IP地址 host_ip = socket.gethostbyname(hostname) #获取内存使用率 host_memory = str(psutil.virtual_memory().percent) #获取CPU的使用率 host_cpu = str(psutil.cpu_percent(0)) #本机登录用户 host_user = str(psutil.users()) #写入字典 info = {"主机名:": hostname,"主机IP地址:": host_ip,"内存使用率:": host_memory,"CPU使用率:": host_cpu,"登录用户详情:": host_user} result = json.dumps(info) #发送数据 # sk.send(bytes(dict)) sk.send(result.encode('utf8')) #接受信息 #接受小于1024字节的数据 msg = sk.recv(1024) print(msg.decode('utf-8')) #关闭连接 sk.close()

16.125

107

0.719638

import socket import psutil import json sk = socket.socket(socket.AF_INET, socket.SOCK_STREAM) host = socket.gethostname() port = 8888 sk.connect((host, port)) hostname = socket.getfqdn(socket.gethostname()) host_ip = socket.gethostbyname(hostname) host_memory = str(psutil.virtual_memory().percent) host_cpu = str(psutil.cpu_percent(0)) host_user = str(psutil.users()) info = {"主机名:": hostname,"主机IP地址:": host_ip,"内存使用率:": host_memory,"CPU使用率:": host_cpu,"登录用户详情:": host_user} result = json.dumps(info) sk.send(result.encode('utf8')) msg = sk.recv(1024) print(msg.decode('utf-8')) sk.close()

true

f71ead2ecdc6c76a593c5eab8b227fec6b66241f

3,111

py

Python

helloworld/helloworld/settings.py

yprateek136/complete_web_developement

e74faad9a9f0708a12df085d1c08170c4b6a7691

[ "MIT" ]

null

helloworld/helloworld/settings.py

yprateek136/complete_web_developement

e74faad9a9f0708a12df085d1c08170c4b6a7691

[ "MIT" ]

null

helloworld/helloworld/settings.py

yprateek136/complete_web_developement

e74faad9a9f0708a12df085d1c08170c4b6a7691

[ "MIT" ]

null

""" Django settings for helloworld project. Generated by 'django-admin startproject' using Django 3.0.8. For more information on this file, see https://docs.djangoproject.com/en/3.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/3.0/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/3.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'r%rqu$hl0#i4xpvy@9cvj_2(_y+f1q6n%d%klw3tihj($v+3n-' # 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 = 'helloworld.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': ['templates'], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'helloworld.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/3.0/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/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = '/static/'

25.710744

91

0.697525

import os BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) SECRET_KEY = 'r%rqu$hl0#i4xpvy@9cvj_2(_y+f1q6n%d%klw3tihj($v+3n-' 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 = 'helloworld.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': ['templates'], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, ] WSGI_APPLICATION = 'helloworld.wsgi.application' # Database # https://docs.djangoproject.com/en/3.0/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/3.0/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/3.0/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/3.0/howto/static-files/ STATIC_URL = '/static/'

true

f71ead8c9b58fad05373969af02b648b96bc2ab9

3,950

py

Python

tests20/python_client/load/test_workload.py

reyoung/milvus

7557616fea7ff7a8b093c85a5c17134112fa89f8

[ "Apache-2.0" ]

null

tests20/python_client/load/test_workload.py

reyoung/milvus

7557616fea7ff7a8b093c85a5c17134112fa89f8

[ "Apache-2.0" ]

null

tests20/python_client/load/test_workload.py

reyoung/milvus

7557616fea7ff7a8b093c85a5c17134112fa89f8

[ "Apache-2.0" ]

null

import datetime import pytest from base.client_base import TestcaseBase from common import common_func as cf from common import common_type as ct from common.common_type import CaseLabel from utils.util_log import test_log as log from pymilvus_orm import utility rounds = 100 per_nb = 100000 default_field_name = ct.default_float_vec_field_name default_index_params = {"index_type": "IVF_SQ8", "metric_type": "L2", "params": {"nlist": 64}} class TestLoad(TestcaseBase): """ Test case of end to end""" @pytest.mark.tags(CaseLabel.L3) def test_load_default(self): name = 'load_test_collection_1' name2 = 'load_test_collection_2' # create # collection_w = self.init_collection_wrap(name=name) # collection_w2 = self.init_collection_wrap(name=name2) # assert collection_w.name == name for i in range(50): name = f"load_collection2_{i}" self.init_collection_wrap(name=name) log.debug(f"total collections: {len(utility.list_collections())}") # # insert # data = cf.gen_default_list_data(per_nb) # log.debug(f"data len: {len(data[0])}") # for i in range(rounds): # t0 = datetime.datetime.now() # ins_res, res = collection_w.insert(data, timeout=180) # tt = datetime.datetime.now() - t0 # log.debug(f"round{i} insert: {len(ins_res.primary_keys)} entities in {tt}s") # assert res # and per_nb == len(ins_res.primary_keys) # # t0 = datetime.datetime.now() # ins_res2, res = collection_w2.insert(data, timeout=180) # tt = datetime.datetime.now() - t0 # log.debug(f"round{i} insert2: {len(ins_res2.primary_keys)} entities in {tt}s") # assert res # # # flush # t0 = datetime.datetime.now() # log.debug(f"current collection num_entities: {collection_w.num_entities}") # tt = datetime.datetime.now() - t0 # log.debug(f"round{i} flush in {tt}") # # t0 = datetime.datetime.now() # log.debug(f"current collection2 num_entities: {collection_w2.num_entities}") # tt = datetime.datetime.now() - t0 # log.debug(f"round{i} flush2 in {tt}") # index, res = collection_w.create_index(default_field_name, default_index_params, timeout=60) # assert res # # search # collection_w.load() # search_vectors = cf.gen_vectors(1, ct.default_dim) # t0 = datetime.datetime.now() # res_1, _ = collection_w.search(data=search_vectors, # anns_field=ct.default_float_vec_field_name, # param={"nprobe": 16}, limit=1) # tt = datetime.datetime.now() - t0 # log.debug(f"assert search: {tt}") # assert len(res_1) == 1 # # collection_w.release() # # # index # collection_w.insert(cf.gen_default_dataframe_data(nb=5000)) # assert collection_w.num_entities == len(data[0]) + 5000 # _index_params = {"index_type": "IVF_SQ8", "metric_type": "L2", "params": {"nlist": 64}} # t0 = datetime.datetime.now() # index, _ = collection_w.create_index(field_name=ct.default_float_vec_field_name, # index_params=_index_params, # name=cf.gen_unique_str()) # tt = datetime.datetime.now() - t0 # log.debug(f"assert index: {tt}") # assert len(collection_w.indexes) == 1 # # # query # term_expr = f'{ct.default_int64_field_name} in [3001,4001,4999,2999]' # t0 = datetime.datetime.now() # res, _ = collection_w.query(term_expr) # tt = datetime.datetime.now() - t0 # log.debug(f"assert query: {tt}") # assert len(res) == 4

41.578947

102

0.586076

import datetime import pytest from base.client_base import TestcaseBase from common import common_func as cf from common import common_type as ct from common.common_type import CaseLabel from utils.util_log import test_log as log from pymilvus_orm import utility rounds = 100 per_nb = 100000 default_field_name = ct.default_float_vec_field_name default_index_params = {"index_type": "IVF_SQ8", "metric_type": "L2", "params": {"nlist": 64}} class TestLoad(TestcaseBase): @pytest.mark.tags(CaseLabel.L3) def test_load_default(self): name = 'load_test_collection_1' name2 = 'load_test_collection_2' for i in range(50): name = f"load_collection2_{i}" self.init_collection_wrap(name=name) log.debug(f"total collections: {len(utility.list_collections())}")

true

f71ead9063e6e1999ccb1b30dc9112382068d484

293

py

Python

python exercicios/func/funct10.py

gabrielqoliveiraa/bomdia

b5e0fe6aa347a0e31b5960a69fbd6f32df352094

[ "MIT" ]

null

python exercicios/func/funct10.py

gabrielqoliveiraa/bomdia

b5e0fe6aa347a0e31b5960a69fbd6f32df352094

[ "MIT" ]

null

python exercicios/func/funct10.py

gabrielqoliveiraa/bomdia

b5e0fe6aa347a0e31b5960a69fbd6f32df352094

[ "MIT" ]

null

def leiaInt(msg): ok = False valor = 0 while True: n = str(input(msg)) if n.isnumeric(): valor = int(n) ok = True else: print('ERRO!') if ok: break return valor n = leiaInt('Digite um número: ')

18.3125

33

0.440273

def leiaInt(msg): ok = False valor = 0 while True: n = str(input(msg)) if n.isnumeric(): valor = int(n) ok = True else: print('ERRO!') if ok: break return valor n = leiaInt('Digite um número: ')

true

f71eadd98dbe3ba79d4203630eff5be2409e013c

3,410

py

Python

Bake_bot/migrations/0001_initial.py

annfike/Bake_Cake_bot

9407f99d1832d0bd5be409d1c02a6dfa8c3a4fff

[ "MIT" ]

null

Bake_bot/migrations/0001_initial.py

annfike/Bake_Cake_bot

9407f99d1832d0bd5be409d1c02a6dfa8c3a4fff

[ "MIT" ]

null

Bake_bot/migrations/0001_initial.py

annfike/Bake_Cake_bot

9407f99d1832d0bd5be409d1c02a6dfa8c3a4fff

[ "MIT" ]

null

# Generated by Django 3.2.7 on 2021-10-27 09:42 from django.db import migrations, models import django.db.models.deletion import phonenumber_field.modelfields class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Customer', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('external_id', models.PositiveIntegerField(unique=True, verbose_name='Внешний ID покупателя')), ('tg_username', models.CharField(blank=True, default='', max_length=50, verbose_name='Имя покупателя в Телеграме')), ('first_name', models.CharField(blank=True, default='', max_length=256, verbose_name='Имя')), ('last_name', models.CharField(blank=True, default='', max_length=256, verbose_name='Фамилия')), ('phone_number', phonenumber_field.modelfields.PhoneNumberField(max_length=128, region=None)), ('GDPR_status', models.BooleanField(default=False, null=True)), ('home_address', models.CharField(blank=True, default='', max_length=50, verbose_name='Домашний адрес')), ], options={ 'verbose_name': 'Покупатель', 'verbose_name_plural': 'Покупатели', }, ), migrations.CreateModel( name='Order', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('order_number', models.PositiveIntegerField(default=None, null=True, unique=True, verbose_name='Номер заказа')), ('order_price', models.PositiveIntegerField(verbose_name='Цена заказа')), ('order_date', models.DateTimeField()), ('order_status', models.CharField(choices=[('Заявка обрабатывается', 'Заявка обрабатывается'), ('Готовим ваш торт', 'Готовим ваш торт'), ('Продукт в пути', 'Продукт в пути'), ('Продукт у вас', 'Продукт у вас')], default='Заявка обрабатывается', max_length=256)), ], options={ 'verbose_name': 'Заказ', 'verbose_name_plural': 'Заказы', }, ), migrations.CreateModel( name='Product', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('product_name', models.CharField(max_length=256)), ], options={ 'verbose_name': 'Продукт', 'verbose_name_plural': 'Продукты', }, ), migrations.CreateModel( name='Product_parameters', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('parameter_name', models.CharField(max_length=256)), ('parameter_price', models.PositiveIntegerField(verbose_name='Цена')), ('product', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='Bake_bot.product')), ], options={ 'verbose_name': 'Параметры продукта', 'verbose_name_plural': 'Параметры продуктов', }, ), ]

47.361111

278

0.58827

from django.db import migrations, models import django.db.models.deletion import phonenumber_field.modelfields class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Customer', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('external_id', models.PositiveIntegerField(unique=True, verbose_name='Внешний ID покупателя')), ('tg_username', models.CharField(blank=True, default='', max_length=50, verbose_name='Имя покупателя в Телеграме')), ('first_name', models.CharField(blank=True, default='', max_length=256, verbose_name='Имя')), ('last_name', models.CharField(blank=True, default='', max_length=256, verbose_name='Фамилия')), ('phone_number', phonenumber_field.modelfields.PhoneNumberField(max_length=128, region=None)), ('GDPR_status', models.BooleanField(default=False, null=True)), ('home_address', models.CharField(blank=True, default='', max_length=50, verbose_name='Домашний адрес')), ], options={ 'verbose_name': 'Покупатель', 'verbose_name_plural': 'Покупатели', }, ), migrations.CreateModel( name='Order', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('order_number', models.PositiveIntegerField(default=None, null=True, unique=True, verbose_name='Номер заказа')), ('order_price', models.PositiveIntegerField(verbose_name='Цена заказа')), ('order_date', models.DateTimeField()), ('order_status', models.CharField(choices=[('Заявка обрабатывается', 'Заявка обрабатывается'), ('Готовим ваш торт', 'Готовим ваш торт'), ('Продукт в пути', 'Продукт в пути'), ('Продукт у вас', 'Продукт у вас')], default='Заявка обрабатывается', max_length=256)), ], options={ 'verbose_name': 'Заказ', 'verbose_name_plural': 'Заказы', }, ), migrations.CreateModel( name='Product', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('product_name', models.CharField(max_length=256)), ], options={ 'verbose_name': 'Продукт', 'verbose_name_plural': 'Продукты', }, ), migrations.CreateModel( name='Product_parameters', fields=[ ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('parameter_name', models.CharField(max_length=256)), ('parameter_price', models.PositiveIntegerField(verbose_name='Цена')), ('product', models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='Bake_bot.product')), ], options={ 'verbose_name': 'Параметры продукта', 'verbose_name_plural': 'Параметры продуктов', }, ), ]

true

f71eae5bad1890b78020354c67dece0739586089

1,359

py

Python

blog/migrations/0001_initial.py

elasyaf/djangblog

d064662ad5eb6642022d957c99d434f96fc9fb51

[ "Unlicense" ]

null

blog/migrations/0001_initial.py

elasyaf/djangblog

d064662ad5eb6642022d957c99d434f96fc9fb51

[ "Unlicense" ]

null

blog/migrations/0001_initial.py

elasyaf/djangblog

d064662ad5eb6642022d957c99d434f96fc9fb51

[ "Unlicense" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.9.2 on 2016-02-27 15:28 from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Blog', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=100, unique=True)), ('slug', models.SlugField(max_length=100, unique=True)), ('body', models.TextField()), ('posted', models.DateTimeField(auto_now_add=True, db_index=True)), ], ), migrations.CreateModel( name='Category', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(db_index=True, max_length=100)), ('slug', models.SlugField(max_length=100)), ], ), migrations.AddField( model_name='blog', name='category', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='blog.Category'), ), ]

33.146341

114

0.577631

from __future__ import unicode_literals from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='Blog', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(max_length=100, unique=True)), ('slug', models.SlugField(max_length=100, unique=True)), ('body', models.TextField()), ('posted', models.DateTimeField(auto_now_add=True, db_index=True)), ], ), migrations.CreateModel( name='Category', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('title', models.CharField(db_index=True, max_length=100)), ('slug', models.SlugField(max_length=100)), ], ), migrations.AddField( model_name='blog', name='category', field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='blog.Category'), ), ]

true

f71eaf00550df0365a2be759b57becb21a74722f

509

py

Python

university_dost/users/tests/test_tasks.py

dhavalsavalia/university_dost

ef6c78239dd648542b68b610528e0b9a23a94295

[ "MIT" ]

null

university_dost/users/tests/test_tasks.py

dhavalsavalia/university_dost

ef6c78239dd648542b68b610528e0b9a23a94295

[ "MIT" ]

null

university_dost/users/tests/test_tasks.py

dhavalsavalia/university_dost

ef6c78239dd648542b68b610528e0b9a23a94295

[ "MIT" ]

1

2020-06-05T09:29:09.000Z

import pytest from celery.result import EagerResult from university_dost.users.tasks import get_users_count from university_dost.users.tests.factories import UserFactory pytestmark = pytest.mark.django_db def test_user_count(settings): """A basic test to execute the get_users_count Celery task.""" UserFactory.create_batch(3) settings.CELERY_TASK_ALWAYS_EAGER = True task_result = get_users_count.delay() assert isinstance(task_result, EagerResult) assert task_result.result == 3

29.941176

66

0.795678

import pytest from celery.result import EagerResult from university_dost.users.tasks import get_users_count from university_dost.users.tests.factories import UserFactory pytestmark = pytest.mark.django_db def test_user_count(settings): UserFactory.create_batch(3) settings.CELERY_TASK_ALWAYS_EAGER = True task_result = get_users_count.delay() assert isinstance(task_result, EagerResult) assert task_result.result == 3

true

f71eafe0c6ec1bcc5a035105c312dffa9e35645e

158

py

Python

Concursera - USP/Semana 2/Conversordetemperatura.py

Pablopfrj/Cursos-de-Python

805f1fa42d41e842df66d24420fed0f5c0cdc740

[ "MIT" ]

null

Concursera - USP/Semana 2/Conversordetemperatura.py

Pablopfrj/Cursos-de-Python

805f1fa42d41e842df66d24420fed0f5c0cdc740

[ "MIT" ]

null

Concursera - USP/Semana 2/Conversordetemperatura.py

Pablopfrj/Cursos-de-Python

805f1fa42d41e842df66d24420fed0f5c0cdc740

[ "MIT" ]

null

temperaturaF = input ("Qual temperatura desejada? ") K = float(temperaturaF) temperarturaC = 5*(K - 32)/9 print ('A temepratura celsius é ',temperarturaC)

22.571429

53

0.721519

temperaturaF = input ("Qual temperatura desejada? ") K = float(temperaturaF) temperarturaC = 5*(K - 32)/9 print ('A temepratura celsius é ',temperarturaC)

true

f71eb1bcceacb04d1e517066e97c304ca359d409

444

py

Python

Set0/p0_3.py

izzy-el/mitbrazil-intro-python

193d552832393d193eb24d6881be0ab2a37b41d1

[ "MIT" ]

null

Set0/p0_3.py

izzy-el/mitbrazil-intro-python

193d552832393d193eb24d6881be0ab2a37b41d1

[ "MIT" ]

null

Set0/p0_3.py

izzy-el/mitbrazil-intro-python

193d552832393d193eb24d6881be0ab2a37b41d1

[ "MIT" ]

null

kwh_used = 1000 out = 0 if(kwh_used < 500): out += 500 * 0.45 elif(kwh_used >= 500 and kwh_used < 1500): out += 500 * 0.45 + ((kwh_used - 500) * 0.74) elif(kwh_used >= 1500 and kwh_used < 2500): out += 500 * 0.45 + ((kwh_used - 500) * 0.74) + ((kwh_used - 1500) * 1.25) elif(kwh_used >= 2500): out += 500 * 0.45 + ((kwh_used - 500) * 0.74) + ((kwh_used - 1500) * 1.25) + ((kwh_used - 2500) * 2) out += out * 0.2 print(out)

31.714286

104

0.547297

kwh_used = 1000 out = 0 if(kwh_used < 500): out += 500 * 0.45 elif(kwh_used >= 500 and kwh_used < 1500): out += 500 * 0.45 + ((kwh_used - 500) * 0.74) elif(kwh_used >= 1500 and kwh_used < 2500): out += 500 * 0.45 + ((kwh_used - 500) * 0.74) + ((kwh_used - 1500) * 1.25) elif(kwh_used >= 2500): out += 500 * 0.45 + ((kwh_used - 500) * 0.74) + ((kwh_used - 1500) * 1.25) + ((kwh_used - 2500) * 2) out += out * 0.2 print(out)

true

f71eb2893c929f8c7aacf04acd0eedb825e80d13

2,653

py

Python

PyStationB/docs/conf.py

BrunoKM/station-b-libraries

ea3591837e4a33f0bef789d905467754c27913b3

[ "MIT" ]

6

2021-09-29T15:46:55.000Z

2021-12-14T18:39:51.000Z

PyStationB/docs/conf.py

BrunoKM/station-b-libraries

ea3591837e4a33f0bef789d905467754c27913b3

[ "MIT" ]

null

PyStationB/docs/conf.py

BrunoKM/station-b-libraries

ea3591837e4a33f0bef789d905467754c27913b3

[ "MIT" ]

3

2021-09-27T10:35:20.000Z

2021-10-02T17:53:07.000Z

# Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # ------------------------------------------------------------------------------------------- # Copyright (c) Microsoft Corporation. All rights reserved. # Licensed under the MIT License (MIT). See LICENSE in the repo root for license information. # ------------------------------------------------------------------------------------------- # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # -- Project information ----------------------------------------------------- project = "PyStationB" copyright = "2021, Station B, Microsoft Research Cambridge" author = "Station B, Microsoft Research Cambridge" # The full version, including alpha/beta/rc tags release = "0.0.1" # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.todo", "sphinx.ext.mathjax", "sphinx.ext.napoleon", "sphinx_rtd_theme", # "sphinx.ext.autosectionlabel", ] napoleon_google_docstring = True napoleon_use_param = False napoleon_use_rtype = True napoleon_attr_annotations = True # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = "sphinx_rtd_theme" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = [] # type: ignore # Display Todos todo_include_todos = True # Module index will look better with this option modindex_common_prefix = ["abex."]

36.847222

93

0.640407

project = "PyStationB" copyright = "2021, Station B, Microsoft Research Cambridge" author = "Station B, Microsoft Research Cambridge" release = "0.0.1" extensions = [ "sphinx.ext.todo", "sphinx.ext.mathjax", "sphinx.ext.napoleon", "sphinx_rtd_theme", ] napoleon_google_docstring = True napoleon_use_param = False napoleon_use_rtype = True napoleon_attr_annotations = True templates_path = ["_templates"] exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"] html_theme = "sphinx_rtd_theme" html_static_path = [] todo_include_todos = True modindex_common_prefix = ["abex."]

true

f71eb38201529059e853f2ffea8fb7a836decd22

4,485

py

Python

arturia_midi.py

rjuang/flstudio-arturia-keylab-mk2

91fe800769bb724010122d5f6a67af7f51418682

[ "MIT" ]

19

2020-11-25T18:31:57.000Z

2022-03-09T14:45:18.000Z

arturia_midi.py

rjuang/flstudio-arturia-keylab-mk2

91fe800769bb724010122d5f6a67af7f51418682

[ "MIT" ]

1

2021-01-24T11:31:16.000Z

2021-06-05T21:39:10.000Z

arturia_midi.py

rjuang/flstudio-arturia-keylab-mk2

91fe800769bb724010122d5f6a67af7f51418682

[ "MIT" ]

3

2021-03-20T03:50:10.000Z

2021-12-24T00:27:49.000Z

import debug import device # Status command to use when sending commands between scripts INTER_SCRIPT_STATUS_BYTE = 0x00 INTER_SCRIPT_DATA1_BTN_DOWN_CMD = 0x01 # Data2 contains the id of the button INTER_SCRIPT_DATA1_BTN_UP_CMD = 0x02 # Data2 contains the id of the button INTER_SCRIPT_DATA1_UPDATE_STATE = 0x03 # Data2 contains the status of the update (INTER_SCRIPT_DATA2_STATE_...) INTER_SCRIPT_DATA1_BEGIN_PAYLOAD_CMD = 0xFE INTER_SCRIPT_DATA1_END_PAYLOAD_CMD = 0xFF PAYLOAD_STATUS_BYTE = 0x01 PLUGIN_PORT_NUM = 10 SYSEX_HEADER = [0xF0, 0x00, 0x20, 0x6B, 0x7F, 0x42] SYSEX_FOOTER = [0xF7] INTER_SCRIPT_DATA2_STATE_PAD_RECORD_STOP = 0x00 INTER_SCRIPT_DATA2_STATE_PAD_RECORD_START = 0x01 class MidiEventDispatcher: """ Dispatches a MIDI event after feeding it through a transform function. MIDI event dispatcher transforms the MIDI event into a value through a transform function provided at construction time. This value is then used as a key into a lookup table that provides a dispatcher and filter function. If the filter function returns true, then the event is sent to the dispatcher function. """ def __init__(self, transform_fn): self._transform_fn = transform_fn # Table contains a mapping of status code -> (callback_fn, filter_fn) self._dispatch_map = {} def SetHandler(self, key, callback_fn, filter_fn=None): """ Associate a handler function and optional filter predicate function to a key. If the transform of the midi event matches the key, then the event is dispatched to the callback function given that the filter predicate function also returns true. :param key: the result value of transform_fn(event) to match against. :param callback_fn: function that is called with the event in the event the transformed event matches. :param filter_fn: function that takes an event and returns true if the event should be dispatched. If false is returned, then the event is dropped and never passed to callback_fn. Not specifying means that callback_fn is always called if transform_fn matches the key. """ def _default_true_fn(_): return True if filter_fn is None: filter_fn = _default_true_fn self._dispatch_map[key] = (callback_fn, filter_fn) return self def SetHandlerForKeys(self, keys, callback_fn, filter_fn=None): """ Associate the same handler for a group of keys. See SetHandler for more details. """ for k in keys: self.SetHandler(k, callback_fn, filter_fn=filter_fn) return self def Dispatch(self, event): """ Dispatches a midi event to the appropriate listener. :param event: the event to dispatch. """ key = self._transform_fn(event) processed = False if key in self._dispatch_map: callback_fn, filter_fn = self._dispatch_map[key] if filter_fn(event): callback_fn(event) processed = True else: debug.log("DISPATCHER", "Event dropped by filter.", event=event) processed = True else: debug.log("DISPATCHER", "No handler found.", event=event) return processed def send_to_device(data): """Sends a data payload to Arturia device. """ # debug.log('CMD', 'Sending payload: ' + str(data)) # Reference regarding SysEx code : # https://forum.arturia.com/index.php?topic=90496.0 device.midiOutSysex(bytes(SYSEX_HEADER) + bytes(data) + bytes(SYSEX_FOOTER)) def dispatch_message_to_other_scripts(status, data1, data2, payload=None): """ Sends midi commands to other scripts scripts. """ for i in range(device.dispatchReceiverCount()): msg = status + (data1 << 8) + (data2 << 16) if payload is None: device.dispatch(i, msg) else: msg = INTER_SCRIPT_DATA1_BEGIN_PAYLOAD_CMD msg += (INTER_SCRIPT_DATA1_BEGIN_PAYLOAD_CMD << 8) device.dispatch(i, msg) # Send payload for j in range(0, len(payload), 2): msg = PAYLOAD_STATUS_BYTE + (payload[j] << 8) if j + 1 < len(payload): msg += (payload[j + 1] << 16) device.dispatch(i, msg) msg = INTER_SCRIPT_STATUS_BYTE msg += (INTER_SCRIPT_DATA1_END_PAYLOAD_CMD << 8) device.dispatch(i, msg)

41.915888

118

0.672464

import debug import device INTER_SCRIPT_STATUS_BYTE = 0x00 INTER_SCRIPT_DATA1_BTN_DOWN_CMD = 0x01 INTER_SCRIPT_DATA1_BTN_UP_CMD = 0x02 INTER_SCRIPT_DATA1_UPDATE_STATE = 0x03 INTER_SCRIPT_DATA1_BEGIN_PAYLOAD_CMD = 0xFE INTER_SCRIPT_DATA1_END_PAYLOAD_CMD = 0xFF PAYLOAD_STATUS_BYTE = 0x01 PLUGIN_PORT_NUM = 10 SYSEX_HEADER = [0xF0, 0x00, 0x20, 0x6B, 0x7F, 0x42] SYSEX_FOOTER = [0xF7] INTER_SCRIPT_DATA2_STATE_PAD_RECORD_STOP = 0x00 INTER_SCRIPT_DATA2_STATE_PAD_RECORD_START = 0x01 class MidiEventDispatcher: def __init__(self, transform_fn): self._transform_fn = transform_fn self._dispatch_map = {} def SetHandler(self, key, callback_fn, filter_fn=None): def _default_true_fn(_): return True if filter_fn is None: filter_fn = _default_true_fn self._dispatch_map[key] = (callback_fn, filter_fn) return self def SetHandlerForKeys(self, keys, callback_fn, filter_fn=None): for k in keys: self.SetHandler(k, callback_fn, filter_fn=filter_fn) return self def Dispatch(self, event): key = self._transform_fn(event) processed = False if key in self._dispatch_map: callback_fn, filter_fn = self._dispatch_map[key] if filter_fn(event): callback_fn(event) processed = True else: debug.log("DISPATCHER", "Event dropped by filter.", event=event) processed = True else: debug.log("DISPATCHER", "No handler found.", event=event) return processed def send_to_device(data): s(data) + bytes(SYSEX_FOOTER)) def dispatch_message_to_other_scripts(status, data1, data2, payload=None): for i in range(device.dispatchReceiverCount()): msg = status + (data1 << 8) + (data2 << 16) if payload is None: device.dispatch(i, msg) else: msg = INTER_SCRIPT_DATA1_BEGIN_PAYLOAD_CMD msg += (INTER_SCRIPT_DATA1_BEGIN_PAYLOAD_CMD << 8) device.dispatch(i, msg) for j in range(0, len(payload), 2): msg = PAYLOAD_STATUS_BYTE + (payload[j] << 8) if j + 1 < len(payload): msg += (payload[j + 1] << 16) device.dispatch(i, msg) msg = INTER_SCRIPT_STATUS_BYTE msg += (INTER_SCRIPT_DATA1_END_PAYLOAD_CMD << 8) device.dispatch(i, msg)

true

f71eb4e7d27b7bafa25c7ecc98bfe686ddc35042

6,389

py

Python

app/service/send_notification.py

cds-snc/notifier-api

90b385ec49efbaee7e607516fc7d9f08991af813

[ "MIT" ]

41

2019-11-28T16:58:41.000Z

2022-01-28T21:11:16.000Z

app/service/send_notification.py

cds-snc/notification-api

b1c1064f291eb860b494c3fa65ac256ad70bf47c

[ "MIT" ]

1,083

2019-07-08T12:57:24.000Z

2022-03-08T18:53:40.000Z

app/service/send_notification.py

cds-snc/notifier-api

90b385ec49efbaee7e607516fc7d9f08991af813

[ "MIT" ]

9

2020-01-24T19:56:43.000Z

2022-01-27T21:36:53.000Z

from flask import current_app from notifications_utils.s3 import S3ObjectNotFound from notifications_utils.s3 import s3download as utils_s3download from sqlalchemy.orm.exc import NoResultFound from app import create_random_identifier from app.dao.notifications_dao import _update_notification_status from app.dao.service_email_reply_to_dao import dao_get_reply_to_by_id from app.dao.service_sms_sender_dao import dao_get_service_sms_senders_by_id from app.dao.services_dao import dao_fetch_service_by_id from app.dao.templates_dao import ( dao_get_template_by_id_and_service_id, get_precompiled_letter_template, ) from app.dao.users_dao import get_user_by_id from app.letters.utils import ( get_letter_pdf_filename, get_page_count, move_uploaded_pdf_to_letters_bucket, ) from app.models import ( EMAIL_TYPE, KEY_TYPE_NORMAL, LETTER_TYPE, NOTIFICATION_DELIVERED, SMS_TYPE, UPLOAD_LETTERS, ) from app.notifications.process_notifications import ( persist_notification, send_notification_to_queue, ) from app.notifications.validators import ( check_service_has_permission, check_service_over_daily_message_limit, validate_and_format_recipient, validate_template, ) from app.v2.errors import BadRequestError def validate_created_by(service, created_by_id): user = get_user_by_id(created_by_id) if service not in user.services: message = 'Can’t create notification - {} is not part of the "{}" service'.format(user.name, service.name) raise BadRequestError(message=message) def create_one_off_reference(template_type): if template_type == LETTER_TYPE: return create_random_identifier() return None def send_one_off_notification(service_id, post_data): service = dao_fetch_service_by_id(service_id) template = dao_get_template_by_id_and_service_id(template_id=post_data["template_id"], service_id=service_id) personalisation = post_data.get("personalisation", None) validate_template(template.id, personalisation, service, template.template_type) check_service_over_daily_message_limit(KEY_TYPE_NORMAL, service) validate_and_format_recipient( send_to=post_data["to"], key_type=KEY_TYPE_NORMAL, service=service, notification_type=template.template_type, allow_safelisted_recipients=False, ) validate_created_by(service, post_data["created_by"]) sender_id = post_data.get("sender_id", None) reply_to = get_reply_to_text( notification_type=template.template_type, sender_id=sender_id, service=service, template=template, ) notification = persist_notification( template_id=template.id, template_version=template.version, template_postage=template.postage, recipient=post_data["to"], service=service, personalisation=personalisation, notification_type=template.template_type, api_key_id=None, key_type=KEY_TYPE_NORMAL, created_by_id=post_data["created_by"], reply_to_text=reply_to, reference=create_one_off_reference(template.template_type), ) if template.template_type == LETTER_TYPE and service.research_mode: _update_notification_status( notification, NOTIFICATION_DELIVERED, ) else: send_notification_to_queue( notification=notification, research_mode=service.research_mode, queue=template.queue_to_use(), ) return {"id": str(notification.id)} def get_reply_to_text(notification_type, sender_id, service, template): reply_to = None if sender_id: try: if notification_type == EMAIL_TYPE: message = "Reply to email address not found" reply_to = dao_get_reply_to_by_id(service.id, sender_id).email_address elif notification_type == SMS_TYPE: message = "SMS sender not found" reply_to = dao_get_service_sms_senders_by_id(service.id, sender_id).get_reply_to_text() except NoResultFound: raise BadRequestError(message=message) else: reply_to = template.get_reply_to_text() return reply_to def send_pdf_letter_notification(service_id, post_data): service = dao_fetch_service_by_id(service_id) check_service_has_permission(LETTER_TYPE, service.permissions) check_service_has_permission(UPLOAD_LETTERS, service.permissions) check_service_over_daily_message_limit(KEY_TYPE_NORMAL, service) validate_created_by(service, post_data["created_by"]) template = get_precompiled_letter_template(service.id) file_location = "service-{}/{}.pdf".format(service.id, post_data["file_id"]) try: letter = utils_s3download(current_app.config["TRANSIENT_UPLOADED_LETTERS"], file_location) except S3ObjectNotFound as e: current_app.logger.exception( "Letter {}.pdf not in transient {} bucket".format( post_data["file_id"], current_app.config["TRANSIENT_UPLOADED_LETTERS"] ) ) raise e # Getting the page count won't raise an error since admin has already checked the PDF is valid billable_units = get_page_count(letter.read()) personalisation = {"address_line_1": post_data["filename"]} # TODO: stop hard-coding postage as 'second' once we get postage from the admin notification = persist_notification( notification_id=post_data["file_id"], template_id=template.id, template_version=template.version, template_postage=template.postage, recipient=post_data["filename"], service=service, personalisation=personalisation, notification_type=LETTER_TYPE, api_key_id=None, key_type=KEY_TYPE_NORMAL, reference=create_one_off_reference(LETTER_TYPE), client_reference=post_data["filename"], created_by_id=post_data["created_by"], billable_units=billable_units, postage="second", ) upload_filename = get_letter_pdf_filename( notification.reference, notification.service.crown, is_scan_letter=False, postage=notification.postage, ) move_uploaded_pdf_to_letters_bucket(file_location, upload_filename) return {"id": str(notification.id)}

34.722826

114

0.728283

from flask import current_app from notifications_utils.s3 import S3ObjectNotFound from notifications_utils.s3 import s3download as utils_s3download from sqlalchemy.orm.exc import NoResultFound from app import create_random_identifier from app.dao.notifications_dao import _update_notification_status from app.dao.service_email_reply_to_dao import dao_get_reply_to_by_id from app.dao.service_sms_sender_dao import dao_get_service_sms_senders_by_id from app.dao.services_dao import dao_fetch_service_by_id from app.dao.templates_dao import ( dao_get_template_by_id_and_service_id, get_precompiled_letter_template, ) from app.dao.users_dao import get_user_by_id from app.letters.utils import ( get_letter_pdf_filename, get_page_count, move_uploaded_pdf_to_letters_bucket, ) from app.models import ( EMAIL_TYPE, KEY_TYPE_NORMAL, LETTER_TYPE, NOTIFICATION_DELIVERED, SMS_TYPE, UPLOAD_LETTERS, ) from app.notifications.process_notifications import ( persist_notification, send_notification_to_queue, ) from app.notifications.validators import ( check_service_has_permission, check_service_over_daily_message_limit, validate_and_format_recipient, validate_template, ) from app.v2.errors import BadRequestError def validate_created_by(service, created_by_id): user = get_user_by_id(created_by_id) if service not in user.services: message = 'Can’t create notification - {} is not part of the "{}" service'.format(user.name, service.name) raise BadRequestError(message=message) def create_one_off_reference(template_type): if template_type == LETTER_TYPE: return create_random_identifier() return None def send_one_off_notification(service_id, post_data): service = dao_fetch_service_by_id(service_id) template = dao_get_template_by_id_and_service_id(template_id=post_data["template_id"], service_id=service_id) personalisation = post_data.get("personalisation", None) validate_template(template.id, personalisation, service, template.template_type) check_service_over_daily_message_limit(KEY_TYPE_NORMAL, service) validate_and_format_recipient( send_to=post_data["to"], key_type=KEY_TYPE_NORMAL, service=service, notification_type=template.template_type, allow_safelisted_recipients=False, ) validate_created_by(service, post_data["created_by"]) sender_id = post_data.get("sender_id", None) reply_to = get_reply_to_text( notification_type=template.template_type, sender_id=sender_id, service=service, template=template, ) notification = persist_notification( template_id=template.id, template_version=template.version, template_postage=template.postage, recipient=post_data["to"], service=service, personalisation=personalisation, notification_type=template.template_type, api_key_id=None, key_type=KEY_TYPE_NORMAL, created_by_id=post_data["created_by"], reply_to_text=reply_to, reference=create_one_off_reference(template.template_type), ) if template.template_type == LETTER_TYPE and service.research_mode: _update_notification_status( notification, NOTIFICATION_DELIVERED, ) else: send_notification_to_queue( notification=notification, research_mode=service.research_mode, queue=template.queue_to_use(), ) return {"id": str(notification.id)} def get_reply_to_text(notification_type, sender_id, service, template): reply_to = None if sender_id: try: if notification_type == EMAIL_TYPE: message = "Reply to email address not found" reply_to = dao_get_reply_to_by_id(service.id, sender_id).email_address elif notification_type == SMS_TYPE: message = "SMS sender not found" reply_to = dao_get_service_sms_senders_by_id(service.id, sender_id).get_reply_to_text() except NoResultFound: raise BadRequestError(message=message) else: reply_to = template.get_reply_to_text() return reply_to def send_pdf_letter_notification(service_id, post_data): service = dao_fetch_service_by_id(service_id) check_service_has_permission(LETTER_TYPE, service.permissions) check_service_has_permission(UPLOAD_LETTERS, service.permissions) check_service_over_daily_message_limit(KEY_TYPE_NORMAL, service) validate_created_by(service, post_data["created_by"]) template = get_precompiled_letter_template(service.id) file_location = "service-{}/{}.pdf".format(service.id, post_data["file_id"]) try: letter = utils_s3download(current_app.config["TRANSIENT_UPLOADED_LETTERS"], file_location) except S3ObjectNotFound as e: current_app.logger.exception( "Letter {}.pdf not in transient {} bucket".format( post_data["file_id"], current_app.config["TRANSIENT_UPLOADED_LETTERS"] ) ) raise e billable_units = get_page_count(letter.read()) personalisation = {"address_line_1": post_data["filename"]} # TODO: stop hard-coding postage as 'second' once we get postage from the admin notification = persist_notification( notification_id=post_data["file_id"], template_id=template.id, template_version=template.version, template_postage=template.postage, recipient=post_data["filename"], service=service, personalisation=personalisation, notification_type=LETTER_TYPE, api_key_id=None, key_type=KEY_TYPE_NORMAL, reference=create_one_off_reference(LETTER_TYPE), client_reference=post_data["filename"], created_by_id=post_data["created_by"], billable_units=billable_units, postage="second", ) upload_filename = get_letter_pdf_filename( notification.reference, notification.service.crown, is_scan_letter=False, postage=notification.postage, ) move_uploaded_pdf_to_letters_bucket(file_location, upload_filename) return {"id": str(notification.id)}

true

f71eb58503fb1ccaf1b57d0371c42c1a0e1947b6

2,952

py

Python

artifact_sdk/model/msgsender/send_message_with_appendix_request_pb2.py

easyopsapis/easyops-api-python

adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0

[ "Apache-2.0" ]

5

2019-07-31T04:11:05.000Z

2021-01-07T03:23:20.000Z

artifact_sdk/model/msgsender/send_message_with_appendix_request_pb2.py

easyopsapis/easyops-api-python

adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0

[ "Apache-2.0" ]

null

artifact_sdk/model/msgsender/send_message_with_appendix_request_pb2.py

easyopsapis/easyops-api-python

adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: send_message_with_appendix_request.proto import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from artifact_sdk.model.msgsender import send_message_request_data_pb2 as artifact__sdk_dot_model_dot_msgsender_dot_send__message__request__data__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='send_message_with_appendix_request.proto', package='msgsender', syntax='proto3', serialized_options=_b('ZCgo.easyops.local/contracts/protorepo-models/easyops/model/msgsender'), serialized_pb=_b('\n(send_message_with_appendix_request.proto\x12\tmsgsender\x1a<artifact_sdk/model/msgsender/send_message_request_data.proto\"Q\n\x1eSendMessageWithAppendixRequest\x12/\n\x04\x64\x61ta\x18\x01 \x01(\x0b\x32!.msgsender.SendMessageRequestDataBEZCgo.easyops.local/contracts/protorepo-models/easyops/model/msgsenderb\x06proto3') , dependencies=[artifact__sdk_dot_model_dot_msgsender_dot_send__message__request__data__pb2.DESCRIPTOR,]) _SENDMESSAGEWITHAPPENDIXREQUEST = _descriptor.Descriptor( name='SendMessageWithAppendixRequest', full_name='msgsender.SendMessageWithAppendixRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='data', full_name='msgsender.SendMessageWithAppendixRequest.data', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=117, serialized_end=198, ) _SENDMESSAGEWITHAPPENDIXREQUEST.fields_by_name['data'].message_type = artifact__sdk_dot_model_dot_msgsender_dot_send__message__request__data__pb2._SENDMESSAGEREQUESTDATA DESCRIPTOR.message_types_by_name['SendMessageWithAppendixRequest'] = _SENDMESSAGEWITHAPPENDIXREQUEST _sym_db.RegisterFileDescriptor(DESCRIPTOR) SendMessageWithAppendixRequest = _reflection.GeneratedProtocolMessageType('SendMessageWithAppendixRequest', (_message.Message,), { 'DESCRIPTOR' : _SENDMESSAGEWITHAPPENDIXREQUEST, '__module__' : 'send_message_with_appendix_request_pb2' # @@protoc_insertion_point(class_scope:msgsender.SendMessageWithAppendixRequest) }) _sym_db.RegisterMessage(SendMessageWithAppendixRequest) DESCRIPTOR._options = None # @@protoc_insertion_point(module_scope)

39.36

343

0.817751

import sys _b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1')) from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database _sym_db = _symbol_database.Default() from artifact_sdk.model.msgsender import send_message_request_data_pb2 as artifact__sdk_dot_model_dot_msgsender_dot_send__message__request__data__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='send_message_with_appendix_request.proto', package='msgsender', syntax='proto3', serialized_options=_b('ZCgo.easyops.local/contracts/protorepo-models/easyops/model/msgsender'), serialized_pb=_b('\n(send_message_with_appendix_request.proto\x12\tmsgsender\x1a<artifact_sdk/model/msgsender/send_message_request_data.proto\"Q\n\x1eSendMessageWithAppendixRequest\x12/\n\x04\x64\x61ta\x18\x01 \x01(\x0b\x32!.msgsender.SendMessageRequestDataBEZCgo.easyops.local/contracts/protorepo-models/easyops/model/msgsenderb\x06proto3') , dependencies=[artifact__sdk_dot_model_dot_msgsender_dot_send__message__request__data__pb2.DESCRIPTOR,]) _SENDMESSAGEWITHAPPENDIXREQUEST = _descriptor.Descriptor( name='SendMessageWithAppendixRequest', full_name='msgsender.SendMessageWithAppendixRequest', filename=None, file=DESCRIPTOR, containing_type=None, fields=[ _descriptor.FieldDescriptor( name='data', full_name='msgsender.SendMessageWithAppendixRequest.data', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=117, serialized_end=198, ) _SENDMESSAGEWITHAPPENDIXREQUEST.fields_by_name['data'].message_type = artifact__sdk_dot_model_dot_msgsender_dot_send__message__request__data__pb2._SENDMESSAGEREQUESTDATA DESCRIPTOR.message_types_by_name['SendMessageWithAppendixRequest'] = _SENDMESSAGEWITHAPPENDIXREQUEST _sym_db.RegisterFileDescriptor(DESCRIPTOR) SendMessageWithAppendixRequest = _reflection.GeneratedProtocolMessageType('SendMessageWithAppendixRequest', (_message.Message,), { 'DESCRIPTOR' : _SENDMESSAGEWITHAPPENDIXREQUEST, '__module__' : 'send_message_with_appendix_request_pb2' # @@protoc_insertion_point(class_scope:msgsender.SendMessageWithAppendixRequest) }) _sym_db.RegisterMessage(SendMessageWithAppendixRequest) DESCRIPTOR._options = None # @@protoc_insertion_point(module_scope)

true

f71eb5ed63903bcf15e5dee58dfa773837782770

1,333

py

Python

lib/galaxy/datatypes/display_applications/util.py

emily101-gif/immport-galaxy

8f353d1f9b4e0d044e1a9d0b1f928b440df78b8c

[ "CC-BY-3.0" ]

4

2018-10-29T18:34:38.000Z

2021-09-29T23:30:42.000Z

lib/galaxy/datatypes/display_applications/util.py

emily101-gif/immport-galaxy

8f353d1f9b4e0d044e1a9d0b1f928b440df78b8c

[ "CC-BY-3.0" ]

30

2016-10-20T15:35:12.000Z

2018-10-02T15:59:54.000Z

lib/galaxy/datatypes/display_applications/util.py

emily101-gif/immport-galaxy

8f353d1f9b4e0d044e1a9d0b1f928b440df78b8c

[ "CC-BY-3.0" ]

7

2016-11-03T19:11:01.000Z

2020-05-11T14:23:52.000Z

from Crypto.Cipher import Blowfish def encode_dataset_user(trans, dataset, user): # encode dataset id as usual # encode user id using the dataset create time as the key dataset_hash = trans.security.encode_id(dataset.id) if user is None: user_hash = 'None' else: user_hash = str(user.id) # Pad to a multiple of 8 with leading "!" user_hash = ("!" * (8 - len(user_hash) % 8)) + user_hash cipher = Blowfish.new(str(dataset.create_time)) user_hash = cipher.encrypt(user_hash).encode('hex') return dataset_hash, user_hash def decode_dataset_user(trans, dataset_hash, user_hash): # decode dataset id as usual # decode user id using the dataset create time as the key dataset_id = trans.security.decode_id(dataset_hash) dataset = trans.sa_session.query(trans.app.model.HistoryDatasetAssociation).get(dataset_id) assert dataset, "Bad Dataset id provided to decode_dataset_user" if user_hash in [None, 'None']: user = None else: cipher = Blowfish.new(str(dataset.create_time)) user_id = cipher.decrypt(user_hash.decode('hex')).lstrip("!") user = trans.sa_session.query(trans.app.model.User).get(int(user_id)) assert user, "A Bad user id was passed to decode_dataset_user" return dataset, user

40.393939

95

0.687922

from Crypto.Cipher import Blowfish def encode_dataset_user(trans, dataset, user): dataset_hash = trans.security.encode_id(dataset.id) if user is None: user_hash = 'None' else: user_hash = str(user.id) user_hash = ("!" * (8 - len(user_hash) % 8)) + user_hash cipher = Blowfish.new(str(dataset.create_time)) user_hash = cipher.encrypt(user_hash).encode('hex') return dataset_hash, user_hash def decode_dataset_user(trans, dataset_hash, user_hash): dataset_id = trans.security.decode_id(dataset_hash) dataset = trans.sa_session.query(trans.app.model.HistoryDatasetAssociation).get(dataset_id) assert dataset, "Bad Dataset id provided to decode_dataset_user" if user_hash in [None, 'None']: user = None else: cipher = Blowfish.new(str(dataset.create_time)) user_id = cipher.decrypt(user_hash.decode('hex')).lstrip("!") user = trans.sa_session.query(trans.app.model.User).get(int(user_id)) assert user, "A Bad user id was passed to decode_dataset_user" return dataset, user

true

f71eb6e5d084bd30a401f2798f8959e21dc37208

2,157

py

Python

examples/phosim/phosim_pipeline.py

jchiang87/workflow_engine

983e0b1a07e4ca02719a22928028daea2a5fbff4

[ "BSD-3-Clause" ]

null

examples/phosim/phosim_pipeline.py

jchiang87/workflow_engine

983e0b1a07e4ca02719a22928028daea2a5fbff4

[ "BSD-3-Clause" ]

null

examples/phosim/phosim_pipeline.py

jchiang87/workflow_engine

983e0b1a07e4ca02719a22928028daea2a5fbff4

[ "BSD-3-Clause" ]

null

""" Script to generate xml for running phosim jobs with the SLAC workflow engine. """ from __future__ import absolute_import, print_function import os import desc.workflow_engine.workflow_engine as engine pipeline = engine.Pipeline('JC_phoSim_pipeline', '0.1') main_task = pipeline.main_task main_task.notation = 'PhoSim Execution Pipeline' main_task.set_variables() # Reset output and script directories at SLAC and NERSC. slac_root_dir = '/nfs/farm/g/lsst/u/jchiang/workflow_engine_tests/phosim_pipeline' slac_path = lambda x: os.path.join(slac_root_dir, x) nersc_root_dir = '/global/cscratch1/sd/jchiang8/workflow_engine_tests/phosim_pipeline' nersc_path = lambda x: os.path.join(nersc_root_dir, x) main_task.set_variable('SLAC_OUTPUT_DATA_DIR', slac_path('output')) main_task.set_variable('NERSC_OUTPUT_DATA_DIR', nersc_path('output')) main_task.set_variable('SLAC_SCRIPT_LOCATION', slac_path('scripts')) main_task.set_variable('NERSC_SCRIPT_LOCATION', nersc_path('scripts')) main_task.set_variable('SCRIPT_NAME', 'phosim_pipeline_workflow.py') setupVisits = main_task.create_process('setupVisits') setupPhosim = main_task.create_process('setupPhosim', job_type='script', requirements=[setupVisits]) singleVisitTask = engine.Task('singleVisitTask') smokeTest = singleVisitTask.create_process('smokeTest') runPhoSim = singleVisitTask.create_process('runPhoSim', requirements=[smokeTest]) phoSimReg = singleVisitTask.create_process('phoSimReg', requirements=[runPhoSim]) phoSimFinalize = singleVisitTask.create_process('phoSimFinalize', job_type='script', requirements=[phoSimReg]) setupPhosim.add_subtask(singleVisitTask) wrapUp = main_task.create_process('wrapUp', job_type='script', requirements=[phoSimFinalize]) with open('phosim_pipeline.xml', 'w') as output: output.write(pipeline.toxml() + '\n') pipeline.write_python_module(clobber=True) pipeline.write_process_scripts()

38.517857

86

0.713491

from __future__ import absolute_import, print_function import os import desc.workflow_engine.workflow_engine as engine pipeline = engine.Pipeline('JC_phoSim_pipeline', '0.1') main_task = pipeline.main_task main_task.notation = 'PhoSim Execution Pipeline' main_task.set_variables() slac_root_dir = '/nfs/farm/g/lsst/u/jchiang/workflow_engine_tests/phosim_pipeline' slac_path = lambda x: os.path.join(slac_root_dir, x) nersc_root_dir = '/global/cscratch1/sd/jchiang8/workflow_engine_tests/phosim_pipeline' nersc_path = lambda x: os.path.join(nersc_root_dir, x) main_task.set_variable('SLAC_OUTPUT_DATA_DIR', slac_path('output')) main_task.set_variable('NERSC_OUTPUT_DATA_DIR', nersc_path('output')) main_task.set_variable('SLAC_SCRIPT_LOCATION', slac_path('scripts')) main_task.set_variable('NERSC_SCRIPT_LOCATION', nersc_path('scripts')) main_task.set_variable('SCRIPT_NAME', 'phosim_pipeline_workflow.py') setupVisits = main_task.create_process('setupVisits') setupPhosim = main_task.create_process('setupPhosim', job_type='script', requirements=[setupVisits]) singleVisitTask = engine.Task('singleVisitTask') smokeTest = singleVisitTask.create_process('smokeTest') runPhoSim = singleVisitTask.create_process('runPhoSim', requirements=[smokeTest]) phoSimReg = singleVisitTask.create_process('phoSimReg', requirements=[runPhoSim]) phoSimFinalize = singleVisitTask.create_process('phoSimFinalize', job_type='script', requirements=[phoSimReg]) setupPhosim.add_subtask(singleVisitTask) wrapUp = main_task.create_process('wrapUp', job_type='script', requirements=[phoSimFinalize]) with open('phosim_pipeline.xml', 'w') as output: output.write(pipeline.toxml() + '\n') pipeline.write_python_module(clobber=True) pipeline.write_process_scripts()

true